From ab231e855d5609ef8479fc2634e4951581372d6e Mon Sep 17 00:00:00 2001
From: seaotterman <seaotterman@users.noreply.github.com>
Date: Mon, 22 May 2017 22:01:32 +0200
Subject: [PATCH 0001/1375] Update RELEASE.md (#10076)

typo correction
---
 RELEASE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RELEASE.md b/RELEASE.md
index b5398f2b3d..fd029f1565 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -24,7 +24,7 @@
 * We released a new open-source version of TensorBoard.
 
 ## Breaking Changes to the API
-* `org.tensorflow.contrib.android.TensorFlowInferenceInterface` now throws exceptions where possible and and has simplified method signatures.
+* `org.tensorflow.contrib.android.TensorFlowInferenceInterface` now throws exceptions where possible and has simplified method signatures.
 
 ## Changes to contrib APIs
 * Added `tf.contrib.util.create_example`.
-- 
GitLab


From fbe4af7271c971accb3e3d96a96b764cc647a934 Mon Sep 17 00:00:00 2001
From: Chi Zeng <chizeng@google.com>
Date: Mon, 22 May 2017 17:51:58 -0700
Subject: [PATCH 0002/1375] Update vulcanized output of TensorBoard to fix
 health pill console errors

The previous version of TensorBoard in release 1.2 had outputted console errors related to debugger health pills (a currently internal feature). This new vulcanized output fixes that bug in release 1.2 by adding guards.
---
 .../tensorboard/dist/tf-tensorboard.html      | 92 +++++++++----------
 1 file changed, 44 insertions(+), 48 deletions(-)

diff --git a/tensorflow/tensorboard/dist/tf-tensorboard.html b/tensorflow/tensorboard/dist/tf-tensorboard.html
index 9f13cdfb47..a49e15fbfc 100644
--- a/tensorflow/tensorboard/dist/tf-tensorboard.html
+++ b/tensorflow/tensorboard/dist/tf-tensorboard.html
@@ -1779,7 +1779,7 @@ var TF;
       :host:first-of-type {
         margin-top: 20px;
       }
-      
+
       :host:last-of-type {
         margin-bottom: 20px;
       }
@@ -2208,7 +2208,7 @@ svg.plottable {
 
 <dom-module id="tf-panes-helper" assetpath="../tf-dashboard-common/">
   <template>
-    <content></content> 
+    <content></content>
     <tf-no-data-warning data-type="[[dataType]]" show-warning="[[dataNotFound]]"></tf-no-data-warning>
 
     <template is="dom-repeat" items="[[categories]]" as="category">
@@ -2224,7 +2224,7 @@ svg.plottable {
                 </div>
                 <div class="card-content">
                   <tf-chart-scaffold tag="[[item.tag]]" data-provider="[[dataProvider]]" visible-series="[[item.runs]]" on-ready="_instantiateTemplate">
-                    
+
                   </tf-chart-scaffold>
                 </div>
                 <div class="card-bottom-row">
@@ -6281,7 +6281,7 @@ var VZ;
               <paper-button id="relative">relative</paper-button>
               <paper-button id="wall_time">wall</paper-button>
             </tf-option-selector>
-          
+
        </div>
       </tf-sidebar-helper></div>
 
@@ -14741,7 +14741,7 @@ Polymer({
 <svg id="svg">
   <defs>
 
-    
+
     <path id="ref-arrowhead-path" d="M 10,0 L 0,5 L 10,10 C 7,7 7,3 10,0"></path>
     <marker class="ref-arrowhead" id="ref-arrowhead-small" viewBox="0 0 10 10" markerWidth="10" markerHeight="10" refX="8" refY="5" orient="auto" markerUnits="userSpaceOnUse">
       <use xlink:href="#ref-arrowhead-path"></use>
@@ -14756,7 +14756,7 @@ Polymer({
       <use xlink:href="#ref-arrowhead-path"></use>
     </marker>
 
-    
+
     <marker id="annotation-arrowhead" markerWidth="5" markerHeight="5" refX="5" refY="2.5" orient="auto">
       <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"></path>
     </marker>
@@ -14769,23 +14769,23 @@ Polymer({
     <marker id="ref-annotation-arrowhead-faded" markerWidth="5" markerHeight="5" refX="0" refY="2.5" orient="auto">
       <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"></path>
     </marker>
-    
+
     <ellipse id="op-node-stamp" rx="7.5" ry="3" stroke="inherit" fill="inherit"></ellipse>
-    
+
     <ellipse id="op-node-annotation-stamp" rx="5" ry="2" stroke="inherit" fill="inherit"></ellipse>
-    
+
     <g id="op-series-vertical-stamp">
       <use xlink:href="#op-node-stamp" x="8" y="9"></use>
       <use xlink:href="#op-node-stamp" x="8" y="6"></use>
       <use xlink:href="#op-node-stamp" x="8" y="3"></use>
     </g>
-    
+
     <g id="op-series-horizontal-stamp">
       <use xlink:href="#op-node-stamp" x="16" y="4"></use>
       <use xlink:href="#op-node-stamp" x="12" y="4"></use>
       <use xlink:href="#op-node-stamp" x="8" y="4"></use>
     </g>
-    
+
     <g id="op-series-annotation-stamp">
       <use xlink:href="#op-node-annotation-stamp" x="9" y="2"></use>
       <use xlink:href="#op-node-annotation-stamp" x="7" y="2"></use>
@@ -14794,10 +14794,10 @@ Polymer({
     <svg id="summary-icon" fill="#848484" height="12" viewBox="0 0 24 24" width="12">
       <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"></path>
     </svg>
-    
+
     <g id="linearGradients"></g>
 
-    
+
     <pattern id="rectHatch" patternTransform="rotate(45 0 0)" width="5" height="5" patternUnits="userSpaceOnUse">
       <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"></line>
     </pattern>
@@ -14805,7 +14805,7 @@ Polymer({
       <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"></line>
     </pattern>
 
-    
+
     <filter id="health-pill-shadow" x="-40%" y="-40%" width="180%" height="180%">
       <feGaussianBlur in="SourceAlpha" stdDeviation="0.8"></feGaussianBlur>
       <feOffset dx="0" dy="0" result="offsetblur"></feOffset>
@@ -14817,7 +14817,7 @@ Polymer({
       </feMerge>
     </filter>
   </defs>
-  
+
   <rect fill="white" width="10000" height="10000"></rect>
   <g id="root"></g>
 </svg>
@@ -17424,7 +17424,7 @@ table.tf-graph-controls td.input-element-table-data {
       </div>
     </template>
   </div>
-  
+
   <template is="dom-if" if="[[!_isGradientColoring(stats, colorBy)]]">
     <div class="legend-holder">
       <table>
@@ -17831,7 +17831,7 @@ TF.Dashboard.TfGraphDashboard = Polymer({
     'node-toggle-expand': '_handleNodeToggleExpand',
   },
   observers: [
-    '_maybeFetchHealthPillsAtSpecificStep(allStepsModeEnabled, specificHealthPillStep)',
+    '_maybeFetchHealthPills(allStepsModeEnabled, specificHealthPillStep)',
     '_maybeInitializeDashboard(backend, _isAttached)',
   ],
   attached: function() {
@@ -17841,19 +17841,16 @@ TF.Dashboard.TfGraphDashboard = Polymer({
     this.set('_isAttached', false);
   },
   reload: function() {
-    if (!this.debuggerDataEnabled ||
-        !this.healthPillsToggledOn ||
-        !this._renderHierarchy ||
-        this._datasetsEmpty(this._datasets)) {
-      // Do not load debugger data if the feature is disabled, if the user toggled off the feature,
-      // or if the graph itself has not loaded yet. We need the graph to load so that we know which
-      // nodes to request health pills for.
-      return;
-    }
-
-    // Request debugger data on graph reloads, but do not re-request the graph itself. The graph
-    // would not change across reloads.
-    this._requestHealthPills();
+    this._maybeFetchHealthPills();
+  },
+  _shouldRequestHealthPills: function() {
+    // Do not load debugger data if the feature is disabled, if the user toggled off the feature,
+    // or if the graph itself has not loaded yet. We need the graph to load so that we know which
+    // nodes to request health pills for.
+    return this.debuggerDataEnabled &&
+        this.healthPillsToggledOn &&
+        this._renderHierarchy &&
+        !this._datasetsEmpty(this._datasets);
   },
   _maybeInitializeDashboard: function(backend, isAttached) {
     if (this._initialized || !backend || !isAttached) {
@@ -17953,7 +17950,7 @@ TF.Dashboard.TfGraphDashboard = Polymer({
   },
   _handleNodeToggleExpand: function() {
     // Nodes were toggled. We may need to request health pills for more nodes.
-    this._requestHealthPills();
+    this._maybeFetchHealthPills();
   },
   _healthPillsToggledOnChanged: function(healthPillsToggledOn) {
     if (healthPillsToggledOn) {
@@ -17965,9 +17962,8 @@ TF.Dashboard.TfGraphDashboard = Polymer({
     }
   },
   // Fetch health pills for a specific step if applicable.
-  _maybeFetchHealthPillsAtSpecificStep: function(allStepsModeEnabled, specificHealthPillStep) {
-    if (!this._renderHierarchy) {
-      // The graph is not ready yet.
+  _maybeFetchHealthPills: function() {
+    if (!this._shouldRequestHealthPills()) {
       return;
     }
 
@@ -18006,7 +18002,7 @@ TF.Dashboard.TfGraphDashboard = Polymer({
 </style>
 <dom-module id="tf-text-loader" assetpath="../tf-text-dashboard/">
 
-  
+
   <template>
     <style include="scrollbar-style"></style>
     <paper-material elevation="1" id="outer" class="container scrollbar">
@@ -18430,13 +18426,13 @@ paper-textarea {
       </paper-tooltip>
     </div>
     <div id="icon-container">
-      
+
       <paper-icon-button id="expand-more" icon="expand-less" on-tap="_expandMore"></paper-icon-button>
       <paper-icon-button id="expand-less" style="display: none" icon="expand-more" on-tap="_expandLess"></paper-icon-button>
     </div>
   </div>
   <iron-collapse id="panel">
-    
+
     <div id="state-section">
       <template is="dom-if" if="[[!savedStates.length]]">
         <p id="no-bookmarks">
@@ -18693,7 +18689,7 @@ paper-dropdown-menu paper-item {
 </style>
 <div class="title">DATA</div>
 <div class="container">
-  
+
   <template is="dom-if" if="[[_hasChoices(runNames)]]">
     <paper-dropdown-menu no-animations="" label="[[_getNumRunsLabel(runNames)]] found">
       <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedRun}}">
@@ -18707,7 +18703,7 @@ paper-dropdown-menu paper-item {
   </template>
 
   <template is="dom-if" if="[[tensorNames]]">
-    
+
     <paper-dropdown-menu no-animations="" label="[[_getNumTensorsLabel(tensorNames)]] found">
       <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedTensor}}">
         <template is="dom-repeat" items="[[tensorNames]]">
@@ -18721,7 +18717,7 @@ paper-dropdown-menu paper-item {
       </paper-listbox>
     </paper-dropdown-menu>
   </template>
-  
+
   <template is="dom-if" if="[[_hasChoices(labelOptions)]]">
     <paper-dropdown-menu no-animations="" label="Label by">
       <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedLabelOption}}">
@@ -18734,7 +18730,7 @@ paper-dropdown-menu paper-item {
     </paper-dropdown-menu>
   </template>
 
-  
+
   <div hidden$="[[!_hasChoices(colorOptions)]]" class="colorby-container">
     <paper-dropdown-menu id="colorby" no-animations="" label="Color by">
       <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedColorOptionName}}">
@@ -19367,7 +19363,7 @@ paper-dropdown-menu paper-item {
     </div>
   </div>
   <div class="container">
-    
+
     <div data-panel="tsne" class="ink-panel-content">
       <div class="slider">
         <label>Dimension</label>
@@ -19418,10 +19414,10 @@ paper-dropdown-menu paper-item {
         </a>
       </p>
     </div>
-    
+
     <div data-panel="pca" class="ink-panel-content">
       <div class="two-columns">
-        <div> 
+        <div>
           <paper-dropdown-menu class="pca-dropdown" vertical-align="bottom" no-animations="" label="X">
             <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{pcaX}}">
               <paper-item disabled="" class="dropdown-item header">
@@ -19451,7 +19447,7 @@ paper-dropdown-menu paper-item {
             </paper-listbox>
           </paper-dropdown-menu>
         </div>
-        <div> 
+        <div>
           <paper-dropdown-menu class="pca-dropdown" vertical-align="bottom" no-animations="" label="Y">
             <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{pcaY}}">
               <paper-item disabled="" class="dropdown-item header">
@@ -19478,7 +19474,7 @@ paper-dropdown-menu paper-item {
         For fast results, the data was sampled to [[getPcaSampleSizeText()]] points and randomly projected down to [[getPcaSampledDimText()]] dimensions.
       </paper-tooltip>
     </div>
-    
+
     <div data-panel="custom" class="ink-panel-content">
       <paper-dropdown-menu style="width: 100%" no-animations="" label="Search by">
         <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{customSelectedSearchByMetadataOption}}">
@@ -19806,7 +19802,7 @@ paper-dropdown-menu paper-item {
 </div>
 <paper-toast id="toast" always-on-top=""></paper-toast>
 
-</template> 
+</template>
 </dom-module>
 <dom-module id="vz-projector-dashboard" assetpath="../vz-projector/">
 <template>
@@ -20167,7 +20163,7 @@ var TF;
       },
     });
   </script>
-  
+
   <script>(function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
 "use strict";
 var AnalyticsLogger = (function () {
-- 
GitLab


From 6f15b738897f793115d64947f8152d7d348e722a Mon Sep 17 00:00:00 2001
From: Yifei Feng <fengyifei2026@gmail.com>
Date: Tue, 23 May 2017 12:45:40 -0700
Subject: [PATCH 0003/1375] Update RELEASE.md

---
 RELEASE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RELEASE.md b/RELEASE.md
index fd029f1565..6edde017a6 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -22,6 +22,7 @@
 * Support client-provided ClusterSpec's and propagate them to all workers to enable the creation of dynamic TensorFlow clusters.
 * TensorFlow C library now available for Windows.
 * We released a new open-source version of TensorBoard.
+* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/programmers_guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel
 
 ## Breaking Changes to the API
 * `org.tensorflow.contrib.android.TensorFlowInferenceInterface` now throws exceptions where possible and has simplified method signatures.
@@ -73,7 +74,6 @@
 * Add `tf.summary.text` for outputting text to TensorBoard.
 * The "run" command of tfdbg's command-line interface now supports filtering of tensors by node name, op type and tensor dtype.
 * `tf.string_to_number` now supports int64 and float64 outputs.
-* `SavedModel CLI` tool available to inspect and execute MetaGraph in SavedModel
 
 ## Thanks to our Contributors
 
-- 
GitLab


From 0fa3799d83933ebf66e0817abcccd9182a3bc6e0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 17 May 2017 17:09:01 -0700
Subject: [PATCH 0004/1375] Fix a bug in Scaffold's copy_from_scaffold argument
 because tf.Tensor cannot be converted to python bool

PiperOrigin-RevId: 156375789
---
 .../python/training/monitored_session.py      | 26 +++++++++++--------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index 7f737399ab..b184aae159 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -129,19 +129,23 @@ class Scaffold(object):
       copy_from_scaffold: Optional scaffold object to copy fields from. Its
         fields will be overwritten by the provided fields in this function.
     """
-    if copy_from_scaffold:
+    if copy_from_scaffold is not None:
       if not isinstance(copy_from_scaffold, Scaffold):
         raise TypeError('copy_from_scaffold is not a Scaffold instance.')
-      init_op = init_op or copy_from_scaffold.init_op
-      init_feed_dict = init_feed_dict or copy_from_scaffold.init_feed_dict
+      # We need _coalesce since Tensor is not converted to bool automatically,
+      # so the common idiom of (a or b) does not work.
+      coalesce = lambda a, b: a if a is not None else b
+      init_op = coalesce(init_op, copy_from_scaffold.init_op)
+      init_feed_dict = coalesce(init_feed_dict,
+                                copy_from_scaffold.init_feed_dict)
       # Use the original init_fn provided by the user to init the new Scaffold.
-      init_fn = init_fn or copy_from_scaffold._user_init_fn  # pylint: disable=protected-access
-      ready_op = ready_op or copy_from_scaffold.ready_op
-      ready_for_local_init_op = ready_for_local_init_op or (
-          copy_from_scaffold.ready_for_local_init_op)
-      local_init_op = local_init_op or copy_from_scaffold.local_init_op
-      summary_op = summary_op or copy_from_scaffold.summary_op
-      saver = saver or copy_from_scaffold.saver
+      init_fn = coalesce(init_fn, copy_from_scaffold._user_init_fn)  # pylint: disable=protected-access
+      ready_op = coalesce(ready_op, copy_from_scaffold.ready_op)
+      ready_for_local_init_op = coalesce(
+          ready_for_local_init_op, copy_from_scaffold.ready_for_local_init_op)
+      local_init_op = coalesce(local_init_op, copy_from_scaffold.local_init_op)
+      summary_op = coalesce(summary_op, copy_from_scaffold.summary_op)
+      saver = coalesce(saver, copy_from_scaffold.saver)
 
     # NOTE(touts): modifying the init function to be passed the scaffold is a
     # hack to make it easy to find the saver.  Is there a better way?
@@ -152,12 +156,12 @@ class Scaffold(object):
       self._init_fn = None
 
     self._init_op = init_op
+    self._init_feed_dict = init_feed_dict
     self._ready_op = ready_op
     self._ready_for_local_init_op = ready_for_local_init_op
     self._local_init_op = local_init_op
     self._summary_op = summary_op
     self._saver = saver
-    self._init_feed_dict = init_feed_dict
 
   def finalize(self):
     """Creates operations if needed and finalizes the graph."""
-- 
GitLab


From 02c5156722a5a0868fcc1949177de6954bd6dac1 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Thu, 18 May 2017 14:42:44 -0700
Subject: [PATCH 0005/1375] Support the 1-D categorical feature with
 categorical column family

PiperOrigin-RevId: 156481273
---
 tensorflow/python/feature_column/BUILD        |   1 +
 .../python/feature_column/feature_column.py   |  40 ++++-
 .../feature_column/feature_column_test.py     | 155 ++++++++++++++++++
 3 files changed, 193 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/feature_column/BUILD b/tensorflow/python/feature_column/BUILD
index e86e4dcab1..673111be9a 100644
--- a/tensorflow/python/feature_column/BUILD
+++ b/tensorflow/python/feature_column/BUILD
@@ -80,5 +80,6 @@ py_test(
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:training",
+        "//tensorflow/python/estimator:inputs",
     ],
 )
diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index 61c02a2fc6..fda9c3f23f 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -140,6 +140,7 @@ from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import lookup_ops
@@ -1443,9 +1444,7 @@ class _LazyBuilder(object):
       return self._feature_tensors[key]
 
     if key in self._features:
-      # FeatureColumn is a raw feature.
-      feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
-          self._features[key])
+      feature_tensor = self._maybe_expand_dims(self._features[key])
       self._feature_tensors[key] = feature_tensor
       return feature_tensor
 
@@ -1464,6 +1463,41 @@ class _LazyBuilder(object):
     self._feature_tensors[column] = transformed
     return transformed
 
+  def _maybe_expand_dims(self, raw_feature):
+    """Converts the `raw_feature` to (sparse) tensor and maybe expand dim.
+
+    For both `Tensor` and `SparseTensor`, the rank will be expanded (to 2) if
+    the rank is 1. This supports dynamic rank also.
+
+    Args:
+      raw_feature: The raw feature from FeatureColumn.
+
+    Returns:
+      A `Tensor` or `SparseTensor`.
+    """
+    feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
+        raw_feature)
+
+    rank = feature_tensor.get_shape().ndims
+    if (rank is not None) and rank != 1:
+      return feature_tensor
+
+    def expand_dims(input_tensor):
+      # Input_tensor has rank 1.
+      if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
+        return sparse_ops.sparse_reshape(
+            input_tensor, [array_ops.shape(input_tensor)[0], -1])
+      else:
+        return array_ops.expand_dims(input_tensor, -1)
+
+    if rank is None:
+      return control_flow_ops.cond(
+          math_ops.equal(1, array_ops.rank(feature_tensor)),
+          lambda: expand_dims(feature_tensor),
+          lambda: feature_tensor)
+    else:
+      return expand_dims(feature_tensor)
+
 
 # TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py
 def _shape_offsets(shape):
diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py
index 83250b5d1d..d394e923cc 100644
--- a/tensorflow/python/feature_column/feature_column_test.py
+++ b/tensorflow/python/feature_column/feature_column_test.py
@@ -26,6 +26,7 @@ import numpy as np
 from tensorflow.core.example import example_pb2
 from tensorflow.core.example import feature_pb2
 from tensorflow.python.client import session
+from tensorflow.python.estimator.inputs import numpy_io
 from tensorflow.python.feature_column import feature_column_lib as fc
 from tensorflow.python.feature_column.feature_column import _CategoricalColumn
 from tensorflow.python.feature_column.feature_column import _DenseColumn
@@ -43,6 +44,8 @@ from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
 from tensorflow.python.platform import test
+from tensorflow.python.training import coordinator
+from tensorflow.python.training import queue_runner_impl
 
 
 def _initialized_session():
@@ -1663,6 +1666,158 @@ class InputLayerTest(test.TestCase):
                 features['price2']: [[1.], [5.]],
             })
 
+  def test_with_numpy_input_fn(self):
+    embedding_values = (
+        (1., 2., 3., 4., 5.),  # id 0
+        (6., 7., 8., 9., 10.),  # id 1
+        (11., 12., 13., 14., 15.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      del shape, dtype, partition_info
+      return embedding_values
+
+    # price has 1 dimension in input_layer
+    price = fc.numeric_column('price')
+    body_style = fc.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    # one_hot_body_style has 3 dims in input_layer.
+    one_hot_body_style = fc.indicator_column(body_style)
+    # embedded_body_style has 5 dims in input_layer.
+    embedded_body_style = fc.embedding_column(body_style, dimension=5,
+                                              initializer=_initializer)
+
+    input_fn = numpy_io.numpy_input_fn(
+        x={
+            'price': np.array([11., 12., 13., 14.]),
+            'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']),
+        },
+        batch_size=2,
+        shuffle=False)
+    features = input_fn()
+    net = fc.input_layer(features,
+                         [price, one_hot_body_style, embedded_body_style])
+    self.assertEqual(1 + 3 + 5, net.shape[1])
+    with _initialized_session() as sess:
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(sess, coord=coord)
+
+      # Each row is formed by concatenating `embedded_body_style`,
+      # `one_hot_body_style`, and `price` in order.
+      self.assertAllEqual(
+          [[11., 12., 13., 14., 15., 0., 0., 1., 11.],
+           [1., 2., 3., 4., 5., 1., 0., 0., 12]],
+          sess.run(net))
+
+      coord.request_stop()
+      coord.join(threads)
+
+  def test_with_1d_sparse_tensor(self):
+    embedding_values = (
+        (1., 2., 3., 4., 5.),  # id 0
+        (6., 7., 8., 9., 10.),  # id 1
+        (11., 12., 13., 14., 15.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      del shape, dtype, partition_info
+      return embedding_values
+
+    # price has 1 dimension in input_layer
+    price = fc.numeric_column('price')
+
+    # one_hot_body_style has 3 dims in input_layer.
+    body_style = fc.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    one_hot_body_style = fc.indicator_column(body_style)
+
+    # embedded_body_style has 5 dims in input_layer.
+    country = fc.categorical_column_with_vocabulary_list(
+        'country', vocabulary_list=['US', 'JP', 'CA'])
+    embedded_country = fc.embedding_column(country, dimension=5,
+                                           initializer=_initializer)
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': constant_op.constant([11., 12.,]),
+        'body-style': sparse_tensor.SparseTensor(
+            indices=((0,), (1,)),
+            values=('sedan', 'hardtop'),
+            dense_shape=(2,)),
+        # This is dense tensor for the categorical_column.
+        'country': constant_op.constant(['CA', 'US']),
+    }
+    self.assertEqual(1, features['price'].shape.ndims)
+    self.assertEqual(1, features['body-style'].dense_shape.get_shape()[0])
+    self.assertEqual(1, features['country'].shape.ndims)
+
+    net = fc.input_layer(features,
+                         [price, one_hot_body_style, embedded_country])
+    self.assertEqual(1 + 3 + 5, net.shape[1])
+    with _initialized_session() as sess:
+
+      # Each row is formed by concatenating `embedded_body_style`,
+      # `one_hot_body_style`, and `price` in order.
+      self.assertAllEqual(
+          [[0., 0., 1., 11., 12., 13., 14., 15., 11.],
+           [1., 0., 0., 1., 2., 3., 4., 5., 12.]],
+          sess.run(net))
+
+  def test_with_1d_unknown_shape_sparse_tensor(self):
+    embedding_values = (
+        (1., 2., 3., 4., 5.),  # id 0
+        (6., 7., 8., 9., 10.),  # id 1
+        (11., 12., 13., 14., 15.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      del shape, dtype, partition_info
+      return embedding_values
+
+    # price has 1 dimension in input_layer
+    price = fc.numeric_column('price')
+
+    # one_hot_body_style has 3 dims in input_layer.
+    body_style = fc.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    one_hot_body_style = fc.indicator_column(body_style)
+
+    # embedded_body_style has 5 dims in input_layer.
+    country = fc.categorical_column_with_vocabulary_list(
+        'country', vocabulary_list=['US', 'JP', 'CA'])
+    embedded_country = fc.embedding_column(country, dimension=5,
+                                           initializer=_initializer)
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+        'body-style': array_ops.sparse_placeholder(dtypes.string),
+        # This is dense tensor for the categorical_column.
+        'country': array_ops.placeholder(dtypes.string),
+    }
+    self.assertIsNone(features['price'].shape.ndims)
+    self.assertIsNone(features['body-style'].get_shape().ndims)
+    self.assertIsNone(features['country'].shape.ndims)
+
+    price_data = np.array([11., 12.])
+    body_style_data = sparse_tensor.SparseTensorValue(
+        indices=((0,), (1,)),
+        values=('sedan', 'hardtop'),
+        dense_shape=(2,))
+
+    # Dense categorical_column with unknown shape is not allowed.
+    with self.assertRaisesRegexp(ValueError, 'Undefined input_tensor shape.'):
+      fc.input_layer(features, [price, one_hot_body_style, embedded_country])
+
+    net = fc.input_layer(features, [price, one_hot_body_style])
+    self.assertEqual(1 + 3, net.shape[1])
+    with _initialized_session() as sess:
+
+      # Each row is formed by concatenating `embedded_body_style`,
+      # `one_hot_body_style`, and `price` in order.
+      self.assertAllEqual(
+          [[0., 0., 1., 11.], [1., 0., 0., 12.]],
+          sess.run(net, feed_dict={
+              features['price']: price_data,
+              features['body-style']: body_style_data}))
+
 
 class MakeParseExampleSpecTest(test.TestCase):
 
-- 
GitLab


From c6da4c846fe490d3d9fd75f01bc9ce336a62f86f Mon Sep 17 00:00:00 2001
From: Andrew Harp <andrewharp@google.com>
Date: Fri, 19 May 2017 16:13:33 -0700
Subject: [PATCH 0006/1375] Android: Replace 1.2.0-preview specifier in AAR
 dependency with <LATEST_VERSION> so that the latest version of TF is always
 used.

PiperOrigin-RevId: 156609509
---
 tensorflow/contrib/android/README.md     | 9 ++++++++-
 tensorflow/examples/android/build.gradle | 2 +-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/android/README.md b/tensorflow/contrib/android/README.md
index 88960b48c9..f49e5857fe 100644
--- a/tensorflow/contrib/android/README.md
+++ b/tensorflow/contrib/android/README.md
@@ -27,10 +27,17 @@ allprojects {
 }
 
 dependencies {
-    compile 'org.tensorflow:tensorflow-android:1.2.0-preview'
+    compile 'org.tensorflow:tensorflow-android:+'
 }
 ```
 
+This will tell Gradle to use the
+[latest version](https://bintray.com/google/tensorflow/tensorflow-android/_latestVersion)
+of the TensorFlow AAR that has been released to
+[https://bintray.com/google/tensorflow/tensorflow-android](https://bintray.com/google/tensorflow/tensorflow-android).
+You may replace the `+` with an explicit version label if you wish to
+use a specific release of TensorFlow in your app.
+
 To build the libraries yourself (if, for example, you want to support custom
 TensorFlow operators), pick your preferred approach below:
 
diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle
index 5a173b129b..001d57f88a 100644
--- a/tensorflow/examples/android/build.gradle
+++ b/tensorflow/examples/android/build.gradle
@@ -186,6 +186,6 @@ apply from: "download-models.gradle"
 
 dependencies {
     if (nativeBuildSystem == 'cmake' || nativeBuildSystem == 'none') {
-        compile 'org.tensorflow:tensorflow-android:1.2.0-preview'
+        compile 'org.tensorflow:tensorflow-android:+'
     }
 }
-- 
GitLab


From 8304e197ea9eeb617f224a1ba0cc4068596098d1 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Mon, 22 May 2017 12:03:44 -0700
Subject: [PATCH 0007/1375] Java: Plug memory leak in Session.run()

(Kudos to @jpangburn for pointing it out).

PiperOrigin-RevId: 156767122
---
 tensorflow/java/src/main/native/session_jni.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/java/src/main/native/session_jni.cc b/tensorflow/java/src/main/native/session_jni.cc
index 048e0824a3..e26367ea00 100644
--- a/tensorflow/java/src/main/native/session_jni.cc
+++ b/tensorflow/java/src/main/native/session_jni.cc
@@ -211,6 +211,7 @@ JNIEXPORT jbyteArray JNICALL Java_org_tensorflow_Session_run(
   }
 
   if (!throwExceptionIfNotOK(env, status)) {
+    TF_DeleteStatus(status);
     return nullptr;
   }
   jlong* t = env->GetLongArrayElements(output_tensor_handles, nullptr);
@@ -226,5 +227,6 @@ JNIEXPORT jbyteArray JNICALL Java_org_tensorflow_Session_run(
     memcpy(elems, run_metadata->data, run_metadata->length);
     env->ReleaseByteArrayElements(ret, elems, JNI_COMMIT);
   }
+  TF_DeleteStatus(status);
   return ret;
 }
-- 
GitLab


From 0141bac1b0ebbdbf7e253b856319eaa21d869b4c Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Mon, 22 May 2017 12:44:19 -0700
Subject: [PATCH 0008/1375] Error out if the raw feature in FC has rank 0

PiperOrigin-RevId: 156772183
---
 .../python/feature_column/feature_column.py   |  40 +++--
 .../feature_column/feature_column_test.py     | 147 ++++++++++++++++++
 2 files changed, 174 insertions(+), 13 deletions(-)

diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index fda9c3f23f..b03953bf90 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -1444,7 +1444,7 @@ class _LazyBuilder(object):
       return self._feature_tensors[key]
 
     if key in self._features:
-      feature_tensor = self._maybe_expand_dims(self._features[key])
+      feature_tensor = self._get_raw_feature_as_tensor(key)
       self._feature_tensors[key] = feature_tensor
       return feature_tensor
 
@@ -1463,40 +1463,54 @@ class _LazyBuilder(object):
     self._feature_tensors[column] = transformed
     return transformed
 
-  def _maybe_expand_dims(self, raw_feature):
-    """Converts the `raw_feature` to (sparse) tensor and maybe expand dim.
+  def _get_raw_feature_as_tensor(self, key):
+    """Gets the raw_feature (keyed by `key`) as `tensor`.
+
+    The raw feature is converted to (sparse) tensor and maybe expand dim.
 
     For both `Tensor` and `SparseTensor`, the rank will be expanded (to 2) if
-    the rank is 1. This supports dynamic rank also.
+    the rank is 1. This supports dynamic rank also. For rank 0 raw feature, will
+    error out as it is not supported.
 
     Args:
-      raw_feature: The raw feature from FeatureColumn.
+      key: A `str` key to access the raw feature.
 
     Returns:
       A `Tensor` or `SparseTensor`.
+
+    Raises:
+      ValueError: if the raw feature has rank 0.
     """
+    raw_feature = self._features[key]
     feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
         raw_feature)
 
-    rank = feature_tensor.get_shape().ndims
-    if (rank is not None) and rank != 1:
-      return feature_tensor
-
     def expand_dims(input_tensor):
-      # Input_tensor has rank 1.
+      # Input_tensor must have rank 1.
       if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
         return sparse_ops.sparse_reshape(
             input_tensor, [array_ops.shape(input_tensor)[0], -1])
       else:
         return array_ops.expand_dims(input_tensor, -1)
 
-    if rank is None:
+    rank = feature_tensor.get_shape().ndims
+    if rank is not None:
+      if rank == 0:
+        raise ValueError(
+            'Feature (key: {}) cannot have rank 0. Give: {}'.format(
+                key, feature_tensor))
+      return feature_tensor if rank != 1 else expand_dims(feature_tensor)
+
+    # Handle dynamic rank.
+    with ops.control_dependencies([
+        check_ops.assert_positive(
+            array_ops.rank(feature_tensor),
+            message='Feature (key: {}) cannot have rank 0. Given: {}'.format(
+                key, feature_tensor))]):
       return control_flow_ops.cond(
           math_ops.equal(1, array_ops.rank(feature_tensor)),
           lambda: expand_dims(feature_tensor),
           lambda: feature_tensor)
-    else:
-      return expand_dims(feature_tensor)
 
 
 # TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py
diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py
index d394e923cc..40ff3b5b18 100644
--- a/tensorflow/python/feature_column/feature_column_test.py
+++ b/tensorflow/python/feature_column/feature_column_test.py
@@ -1507,6 +1507,131 @@ class LinearModelTest(test.TestCase):
                 features['price2']: [[1.], [5.]],
             })
 
+  def test_with_numpy_input_fn(self):
+    price = fc.numeric_column('price')
+    price_buckets = fc.bucketized_column(price, boundaries=[0., 10., 100.,])
+    body_style = fc.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+
+    input_fn = numpy_io.numpy_input_fn(
+        x={
+            'price': np.array([-1., 2., 13., 104.]),
+            'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']),
+        },
+        batch_size=2,
+        shuffle=False)
+    features = input_fn()
+    net = fc.linear_model(features, [price_buckets, body_style])
+    # self.assertEqual(1 + 3 + 5, net.shape[1])
+    with _initialized_session() as sess:
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(sess, coord=coord)
+
+      bias = get_linear_model_bias()
+      price_buckets_var = get_linear_model_column_var(price_buckets)
+      body_style_var = get_linear_model_column_var(body_style)
+
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [100 - 10 + 5.]], sess.run(net))
+
+      coord.request_stop()
+      coord.join(threads)
+
+  def test_with_1d_sparse_tensor(self):
+    price = fc.numeric_column('price')
+    price_buckets = fc.bucketized_column(price, boundaries=[0., 10., 100.,])
+    body_style = fc.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': constant_op.constant([-1., 12.,]),
+        'body-style': sparse_tensor.SparseTensor(
+            indices=((0,), (1,)),
+            values=('sedan', 'hardtop'),
+            dense_shape=(2,)),
+    }
+    self.assertEqual(1, features['price'].shape.ndims)
+    self.assertEqual(1, features['body-style'].dense_shape.get_shape()[0])
+
+    net = fc.linear_model(features, [price_buckets, body_style])
+    with _initialized_session() as sess:
+      bias = get_linear_model_bias()
+      price_buckets_var = get_linear_model_column_var(price_buckets)
+      body_style_var = get_linear_model_column_var(body_style)
+
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [1000 - 10 + 5.]], sess.run(net))
+
+  def test_with_1d_unknown_shape_sparse_tensor(self):
+    price = fc.numeric_column('price')
+    price_buckets = fc.bucketized_column(price, boundaries=[0., 10., 100.,])
+    body_style = fc.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    country = fc.categorical_column_with_vocabulary_list(
+        'country', vocabulary_list=['US', 'JP', 'CA'])
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+        'body-style': array_ops.sparse_placeholder(dtypes.string),
+        'country': array_ops.placeholder(dtypes.string),
+    }
+    self.assertIsNone(features['price'].shape.ndims)
+    self.assertIsNone(features['body-style'].get_shape().ndims)
+
+    price_data = np.array([-1., 12.])
+    body_style_data = sparse_tensor.SparseTensorValue(
+        indices=((0,), (1,)),
+        values=('sedan', 'hardtop'),
+        dense_shape=(2,))
+
+    net = fc.linear_model(features, [price_buckets, body_style])
+    bias = get_linear_model_bias()
+    price_buckets_var = get_linear_model_column_var(price_buckets)
+    body_style_var = get_linear_model_column_var(body_style)
+    with _initialized_session() as sess:
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose(
+          [[10 - 1000 + 5.], [1000 - 10 + 5.]],
+          sess.run(net, feed_dict={
+              features['price']: price_data,
+              features['body-style']: body_style_data}))
+
+    # Dense categorical_column with unknown shape is not allowed.
+    with self.assertRaisesRegexp(ValueError, 'Undefined input_tensor shape.'):
+      fc.linear_model(features, [price_buckets, body_style, country])
+
+  def test_with_rank_0_feature(self):
+    price = fc.numeric_column('price')
+    features = {
+        'price': constant_op.constant(0),
+    }
+    self.assertEqual(0, features['price'].shape.ndims)
+
+    # Static rank 0 should fail
+    with self.assertRaisesRegexp(ValueError, 'Feature .* cannot have rank 0'):
+      fc.linear_model(features, [price])
+
+    # Dynamic rank 0 should fail
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+    }
+    net = fc.linear_model(features, [price])
+    self.assertEqual(1, net.shape[1])
+    with _initialized_session() as sess:
+      with self.assertRaisesOpError('Feature .* cannot have rank 0'):
+        sess.run(net, feed_dict={features['price']: np.array(1)})
+
 
 class InputLayerTest(test.TestCase):
 
@@ -1818,6 +1943,28 @@ class InputLayerTest(test.TestCase):
               features['price']: price_data,
               features['body-style']: body_style_data}))
 
+  def test_with_rank_0_feature(self):
+    # price has 1 dimension in input_layer
+    price = fc.numeric_column('price')
+    features = {
+        'price': constant_op.constant(0),
+    }
+    self.assertEqual(0, features['price'].shape.ndims)
+
+    # Static rank 0 should fail
+    with self.assertRaisesRegexp(ValueError, 'Feature .* cannot have rank 0'):
+      fc.input_layer(features, [price])
+
+    # Dynamic rank 0 should fail
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+    }
+    net = fc.input_layer(features, [price])
+    self.assertEqual(1, net.shape[1])
+    with _initialized_session() as sess:
+      with self.assertRaisesOpError('Feature .* cannot have rank 0'):
+        sess.run(net, feed_dict={features['price']: np.array(1)})
+
 
 class MakeParseExampleSpecTest(test.TestCase):
 
-- 
GitLab


From 3e61d9fc094ce1136993bf6bdbed437c80aa7acd Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Mon, 22 May 2017 17:32:50 -0700
Subject: [PATCH 0009/1375] Move many of the "core" RNNCells and rnn functions
 back to TF core.

Unit test files will move in a followup PR.  This is the big API change.
The old behavior (using tf.contrib.rnn....) will continue to work for
backwards compatibility.

PiperOrigin-RevId: 156809677
---
 RELEASE.md                                    |   27 +-
 tensorflow/contrib/crf/python/ops/crf.py      |    4 +-
 .../kernel_tests/cudnn_rnn_ops_benchmark.py   |    8 +-
 .../python/kernel_tests/grid_rnn_test.py      |   12 +-
 .../estimators/dynamic_rnn_estimator_test.py  |   14 +-
 .../estimators/state_saving_rnn_estimator.py  |    4 +-
 .../python/learn/ops/seq2seq_ops_test.py      |    4 +-
 .../python/kernel_tests/seq2seq_test.py       |   95 +-
 .../legacy_seq2seq/python/ops/seq2seq.py      |   47 +-
 tensorflow/contrib/ndlstm/python/lstm1d.py    |    8 +-
 tensorflow/contrib/rnn/__init__.py            |   43 +-
 .../python/kernel_tests/core_rnn_cell_test.py |  196 ++--
 .../rnn/python/kernel_tests/core_rnn_test.py  |  157 ++-
 .../kernel_tests/fused_rnn_cell_test.py       |   23 +-
 .../rnn/python/kernel_tests/gru_ops_test.py   |   18 +-
 .../rnn/python/kernel_tests/lstm_ops_test.py  |   69 +-
 .../rnn/python/kernel_tests/rnn_cell_test.py  |  133 +--
 .../rnn/python/kernel_tests/rnn_test.py       |   16 +-
 tensorflow/contrib/rnn/python/ops/core_rnn.py |  357 ------
 .../contrib/rnn/python/ops/core_rnn_cell.py   |  232 +++-
 .../rnn/python/ops/core_rnn_cell_impl.py      | 1037 -----------------
 .../contrib/rnn/python/ops/fused_rnn_cell.py  |   14 +-
 tensorflow/contrib/rnn/python/ops/gru_ops.py  |    4 +-
 tensorflow/contrib/rnn/python/ops/lstm_ops.py |   15 +-
 tensorflow/contrib/rnn/python/ops/rnn.py      |    3 +-
 tensorflow/contrib/rnn/python/ops/rnn_cell.py |   66 +-
 .../kernel_tests/attention_wrapper_test.py    |    8 +-
 .../python/kernel_tests/basic_decoder_test.py |   26 +-
 .../kernel_tests/beam_search_decoder_test.py  |    4 +-
 .../python/kernel_tests/decoder_test.py       |    8 +-
 .../seq2seq/python/ops/attention_wrapper.py   |    3 +-
 .../tools/tfprof/model_analyzer_testlib.py    |    6 +-
 tensorflow/python/BUILD                       |    9 +
 .../python/debug/lib/session_debug_testlib.py |    2 +-
 tensorflow/python/kernel_tests/rnn_test.py    |   20 +-
 tensorflow/python/ops/nn.py                   |    9 +-
 tensorflow/python/ops/rnn.py                  |  358 +++++-
 tensorflow/python/ops/rnn_cell.py             |   51 +
 tensorflow/python/ops/rnn_cell_impl.py        |  834 ++++++++++++-
 .../tools/api/golden/tensorflow.nn.pbtxt      |   16 +
 ...flow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt |   95 ++
 ...orflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt |   95 ++
 ...nsorflow.nn.rnn_cell.-device-wrapper.pbtxt |   95 ++
 ...sorflow.nn.rnn_cell.-dropout-wrapper.pbtxt |   95 ++
 .../tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt  |   95 ++
 ...tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt |   95 ++
 ...low.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt |   27 +
 ...orflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt |   95 ++
 .../tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt  |   94 ++
 ...orflow.nn.rnn_cell.-residual-wrapper.pbtxt |   95 ++
 .../api/golden/tensorflow.nn.rnn_cell.pbtxt   |   43 +
 51 files changed, 2905 insertions(+), 1979 deletions(-)
 delete mode 100644 tensorflow/contrib/rnn/python/ops/core_rnn.py
 delete mode 100644 tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py
 create mode 100644 tensorflow/python/ops/rnn_cell.py
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.pbtxt

diff --git a/RELEASE.md b/RELEASE.md
index 6edde017a6..4af8f62498 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -3,9 +3,8 @@
 ## Major Features and Improvements
 * Added `tf.layers.conv3d_transpose` layer for spatio temporal deconvolution.
 * Added `tf.Session.make_callable()`, which provides a lower overhead means of running a similar step multiple times.
-* Added libverbs-based RDMA support to contrib (courtesy @junshi15 from Yahoo).
-* Bring `tf.feature_column.*` into the API. Non-deprecated functionality from `tf.contrib.layers.*` is moved to `tf.feature_column.*` with cosmetic changes.
-* `RNNCell` objects now subclass `tf.layers._Layer`.  The strictness described
+* Added ibverbs-based RDMA support to contrib (courtesy @junshi15 from Yahoo).
+* `RNNCell` objects now subclass `tf.layers.Layer`.  The strictness described
   in the TensorFlow 1.1 release is gone:  The first time an RNNCell is used,
   it caches its scope.  All future uses of the RNNCell will reuse variables from
   that same scope.  This is a breaking change from the behavior of RNNCells
@@ -23,6 +22,28 @@
 * TensorFlow C library now available for Windows.
 * We released a new open-source version of TensorBoard.
 * [`SavedModel CLI`](https://www.tensorflow.org/versions/master/programmers_guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel
+* RNNCells' variable names have been renamed for consistency with Keras layers.
+  Specifically, the previous variable names "weights" and "biases" have
+  been changed to "kernel" and "bias", respectively.
+  This may cause backward incompatibility with regard to your old
+  checkpoints containing such RNN cells, in which case you can use the tool
+  [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
+  to convert the variable names in your old checkpoints.
+* Many of the RNN functions and classes that were in the `tf.nn` namespace
+  before the 1.0 release and which were moved to `tf.contrib.rnn` have now
+  been moved back to the core namespace.  This includes
+  `RNNCell`, `LSTMCell`, `GRUCell`, and a number of other cells.  These
+  now reside in `tf.nn.rnn_cell` (with aliases in `tf.contrib.rnn` for backwards
+  compatibility).  The original `tf.nn.rnn` function is now `tf.nn.static_rnn`,
+  and the bidirectional static and state saving static rnn functions are also
+  now back in the `tf.nn` namespace.
+
+  Notable exceptions are the `EmbeddingWrapper`, `InputProjectionWrapper` and
+  `OutputProjectionWrapper`,  which will slowly be moved to deprecation
+  in `tf.contrib.rnn`.  These are inefficient wrappers that should often
+  be replaced by calling `embedding_lookup` or `layers.dense` as pre- or post-
+  processing of the rnn.  For RNN decoding, this functionality has been replaced
+  with an alternative API in `tf.contrib.seq2seq`.
 
 ## Breaking Changes to the API
 * `org.tensorflow.contrib.android.TensorFlowInferenceInterface` now throws exceptions where possible and has simplified method signatures.
diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py
index 4bcf93e78f..a19c70717a 100644
--- a/tensorflow/contrib/crf/python/ops/crf.py
+++ b/tensorflow/contrib/crf/python/ops/crf.py
@@ -41,11 +41,11 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope as vs
 
 __all__ = [
@@ -225,7 +225,7 @@ def crf_binary_score(tag_indices, sequence_lengths, transition_params):
   return binary_scores
 
 
-class CrfForwardRnnCell(core_rnn_cell.RNNCell):
+class CrfForwardRnnCell(rnn_cell.RNNCell):
   """Computes the alpha values in a linear-chain CRF.
 
   See http://www.cs.columbia.edu/~mcollins/fb.pdf for reference.
diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py
index 34b2d49d26..6ca38c2e47 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py
@@ -22,7 +22,6 @@ import time
 
 from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
 from tensorflow.contrib.rnn.python.ops import core_rnn
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.python.client import session
 from tensorflow.python.framework import dtypes
@@ -31,6 +30,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
@@ -131,9 +131,9 @@ class CudnnRNNBenchmark(test.Benchmark):
         ]
         initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127)
 
-        cell = core_rnn_cell_impl.LSTMCell(
+        cell = rnn_cell.LSTMCell(
             num_units=num_units, initializer=initializer, state_is_tuple=True)
-        multi_cell = core_rnn_cell_impl.MultiRNNCell(
+        multi_cell = rnn_cell.MultiRNNCell(
             [cell() for _ in range(num_layers)])
         outputs, final_state = core_rnn.static_rnn(
             multi_cell, inputs, dtype=dtypes.float32)
@@ -159,7 +159,7 @@ class CudnnRNNBenchmark(test.Benchmark):
         ]
         cell = lambda: lstm_ops.LSTMBlockCell(num_units=num_units)  # pylint: disable=cell-var-from-loop
 
-        multi_cell = core_rnn_cell_impl.MultiRNNCell(
+        multi_cell = rnn_cell.MultiRNNCell(
             [cell() for _ in range(num_layers)])
         outputs, final_state = core_rnn.static_rnn(
             multi_cell, inputs, dtype=dtypes.float32)
diff --git a/tensorflow/contrib/grid_rnn/python/kernel_tests/grid_rnn_test.py b/tensorflow/contrib/grid_rnn/python/kernel_tests/grid_rnn_test.py
index 280271a42d..fed8a771cc 100644
--- a/tensorflow/contrib/grid_rnn/python/kernel_tests/grid_rnn_test.py
+++ b/tensorflow/contrib/grid_rnn/python/kernel_tests/grid_rnn_test.py
@@ -21,11 +21,11 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib.grid_rnn.python.ops import grid_rnn_cell
-from tensorflow.contrib.rnn.python.ops import core_rnn
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import rnn
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -527,7 +527,7 @@ class GridRNNCellTest(test.TestCase):
               dtypes.float32, shape=(batch_size, input_size))
       ]
 
-      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
     self.assertEqual(len(outputs), len(inputs))
     self.assertEqual(state[0].c.get_shape(), (batch_size, 2))
@@ -569,7 +569,7 @@ class GridRNNCellTest(test.TestCase):
           array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
       ]
 
-      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
     self.assertEqual(len(outputs), len(inputs))
     self.assertEqual(state[0].c.get_shape(), (batch_size, 2))
@@ -609,7 +609,7 @@ class GridRNNCellTest(test.TestCase):
           array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
       ]
 
-      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
     self.assertEqual(len(outputs), len(inputs))
     self.assertEqual(state[0].c.get_shape(), (batch_size, 2))
@@ -652,7 +652,7 @@ class GridRNNCellTest(test.TestCase):
               dtypes.float32, shape=(batch_size, input_size))
       ] + (max_length - 1) * [array_ops.zeros([batch_size, input_size])])
 
-      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
     self.assertEqual(len(outputs), len(inputs))
     self.assertEqual(state[0].c.get_shape(), (batch_size, 2))
@@ -690,7 +690,7 @@ class GridRNNCellTest(test.TestCase):
           array_ops.placeholder(dtypes.float32, shape=(None, input_size))
       ]
 
-      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
     self.assertEqual(len(outputs), len(inputs))
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
index 6fc028ab70..d518e38fe0 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
@@ -31,7 +31,6 @@ from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_f
 from tensorflow.contrib.learn.python.learn.estimators import prediction_key
 from tensorflow.contrib.learn.python.learn.estimators import rnn_common
 from tensorflow.contrib.learn.python.learn.estimators import run_config
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -42,6 +41,7 @@ from tensorflow.python.ops import functional_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
@@ -107,7 +107,7 @@ class DynamicRnnEstimatorTest(test.TestCase):
 
   def setUp(self):
     super(DynamicRnnEstimatorTest, self).setUp()
-    self.rnn_cell = core_rnn_cell_impl.BasicRNNCell(self.NUM_RNN_CELL_UNITS)
+    self.rnn_cell = rnn_cell.BasicRNNCell(self.NUM_RNN_CELL_UNITS)
     self.mock_target_column = MockTargetColumn(
         num_label_columns=self.NUM_LABEL_COLUMNS)
 
@@ -312,19 +312,19 @@ class DynamicRnnEstimatorTest(test.TestCase):
     # A MultiRNNCell of LSTMCells is both a common choice and an interesting
     # test case, because it has two levels of nesting, with an inner class that
     # is not a plain tuple.
-    cell = core_rnn_cell_impl.MultiRNNCell(
-        [core_rnn_cell_impl.LSTMCell(i) for i in cell_sizes])
+    cell = rnn_cell.MultiRNNCell(
+        [rnn_cell.LSTMCell(i) for i in cell_sizes])
     state_dict = {
         dynamic_rnn_estimator._get_state_name(i):
         array_ops.expand_dims(math_ops.range(cell_size), 0)
         for i, cell_size in enumerate([5, 5, 3, 3, 7, 7])
     }
-    expected_state = (core_rnn_cell_impl.LSTMStateTuple(
+    expected_state = (rnn_cell.LSTMStateTuple(
         np.reshape(np.arange(5), [1, -1]), np.reshape(np.arange(5), [1, -1])),
-                      core_rnn_cell_impl.LSTMStateTuple(
+                      rnn_cell.LSTMStateTuple(
                           np.reshape(np.arange(3), [1, -1]),
                           np.reshape(np.arange(3), [1, -1])),
-                      core_rnn_cell_impl.LSTMStateTuple(
+                      rnn_cell.LSTMStateTuple(
                           np.reshape(np.arange(7), [1, -1]),
                           np.reshape(np.arange(7), [1, -1])))
     actual_state = dynamic_rnn_estimator.dict_to_state_tuple(state_dict, cell)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
index 9cb4c3515a..0cea35e219 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
@@ -26,13 +26,13 @@ from tensorflow.contrib.learn.python.learn.estimators import constants
 from tensorflow.contrib.learn.python.learn.estimators import estimator
 from tensorflow.contrib.learn.python.learn.estimators import model_fn
 from tensorflow.contrib.learn.python.learn.estimators import rnn_common
-from tensorflow.contrib.rnn.python.ops import core_rnn
 from tensorflow.contrib.training.python.training import sequence_queueing_state_saver as sqss
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import rnn
 from tensorflow.python.training import momentum as momentum_opt
 from tensorflow.python.util import nest
 
@@ -64,7 +64,7 @@ def construct_state_saving_rnn(cell,
     final_state: The final state output by the RNN
   """
   with ops.name_scope(scope):
-    rnn_outputs, final_state = core_rnn.static_state_saving_rnn(
+    rnn_outputs, final_state = rnn.static_state_saving_rnn(
         cell=cell,
         inputs=inputs,
         state_saver=state_saver,
diff --git a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py
index a9116c2d54..95aec61955 100644
--- a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py
+++ b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py
@@ -21,9 +21,9 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib.learn.python.learn import ops
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.platform import test
 
 
@@ -82,7 +82,7 @@ class Seq2SeqOpsTest(test.TestCase):
           array_ops.placeholder(dtypes.float32, [2, 2]) for _ in range(3)
       ]
       encoding = array_ops.placeholder(dtypes.float32, [2, 2])
-      cell = core_rnn_cell_impl.GRUCell(2)
+      cell = rnn_cell.GRUCell(2)
       outputs, states, sampling_outputs, sampling_states = (
           ops.rnn_decoder(decoder_inputs, encoding, cell))
       self.assertEqual(len(outputs), 3)
diff --git a/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py b/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py
index 2898935a47..4395138e20 100644
--- a/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py
+++ b/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py
@@ -25,8 +25,7 @@ import random
 import numpy as np
 
 from tensorflow.contrib.legacy_seq2seq.python.ops import seq2seq as seq2seq_lib
-from tensorflow.contrib.rnn.python.ops import core_rnn
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
+from tensorflow.contrib.rnn.python.ops import core_rnn_cell
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -37,6 +36,7 @@ from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import nn_impl
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
@@ -51,11 +51,10 @@ class Seq2SeqTest(test.TestCase):
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
-        _, enc_state = core_rnn.static_rnn(
-            core_rnn_cell_impl.GRUCell(2), inp, dtype=dtypes.float32)
+        _, enc_state = rnn.static_rnn(
+            rnn_cell.GRUCell(2), inp, dtype=dtypes.float32)
         dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3
-        cell = core_rnn_cell_impl.OutputProjectionWrapper(
-            core_rnn_cell_impl.GRUCell(2), 4)
+        cell = core_rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(2), 4)
         dec, mem = seq2seq_lib.rnn_decoder(dec_inp, enc_state, cell)
         sess.run([variables.global_variables_initializer()])
         res = sess.run(dec)
@@ -71,8 +70,7 @@ class Seq2SeqTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
         dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3
-        cell = core_rnn_cell_impl.OutputProjectionWrapper(
-            core_rnn_cell_impl.GRUCell(2), 4)
+        cell = core_rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(2), 4)
         dec, mem = seq2seq_lib.basic_rnn_seq2seq(inp, dec_inp, cell)
         sess.run([variables.global_variables_initializer()])
         res = sess.run(dec)
@@ -88,8 +86,7 @@ class Seq2SeqTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
         dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3
-        cell = core_rnn_cell_impl.OutputProjectionWrapper(
-            core_rnn_cell_impl.GRUCell(2), 4)
+        cell = core_rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(2), 4)
         dec, mem = seq2seq_lib.tied_rnn_seq2seq(inp, dec_inp, cell)
         sess.run([variables.global_variables_initializer()])
         res = sess.run(dec)
@@ -105,9 +102,9 @@ class Seq2SeqTest(test.TestCase):
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
-        cell_fn = lambda: core_rnn_cell_impl.BasicLSTMCell(2)
+        cell_fn = lambda: rnn_cell.BasicLSTMCell(2)
         cell = cell_fn()
-        _, enc_state = core_rnn.static_rnn(cell, inp, dtype=dtypes.float32)
+        _, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32)
         dec_inp = [
             constant_op.constant(
                 i, dtypes.int32, shape=[2]) for i in range(3)
@@ -138,7 +135,7 @@ class Seq2SeqTest(test.TestCase):
             constant_op.constant(
                 i, dtypes.int32, shape=[2]) for i in range(3)
         ]
-        cell_fn = lambda: core_rnn_cell_impl.BasicLSTMCell(2)
+        cell_fn = lambda: rnn_cell.BasicLSTMCell(2)
         cell = cell_fn()
         dec, mem = seq2seq_lib.embedding_rnn_seq2seq(
             enc_inp,
@@ -158,7 +155,7 @@ class Seq2SeqTest(test.TestCase):
 
         # Test with state_is_tuple=False.
         with variable_scope.variable_scope("no_tuple"):
-          cell_nt = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
+          cell_nt = rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
           dec, mem = seq2seq_lib.embedding_rnn_seq2seq(
               enc_inp,
               dec_inp,
@@ -242,9 +239,7 @@ class Seq2SeqTest(test.TestCase):
             constant_op.constant(
                 i, dtypes.int32, shape=[2]) for i in range(3)
         ]
-        cell = functools.partial(
-            core_rnn_cell_impl.BasicLSTMCell,
-            2, state_is_tuple=True)
+        cell = functools.partial(rnn_cell.BasicLSTMCell, 2, state_is_tuple=True)
         dec, mem = seq2seq_lib.embedding_tied_rnn_seq2seq(
             enc_inp, dec_inp, cell(), num_symbols=5, embedding_size=2)
         sess.run([variables.global_variables_initializer()])
@@ -324,11 +319,10 @@ class Seq2SeqTest(test.TestCase):
     with self.test_session() as sess:
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell_fn = lambda: core_rnn_cell_impl.GRUCell(2)
+        cell_fn = lambda: rnn_cell.GRUCell(2)
         cell = cell_fn()
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
-        enc_outputs, enc_state = core_rnn.static_rnn(
-            cell, inp, dtype=dtypes.float32)
+        enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32)
         attn_states = array_ops.concat([
             array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
         ], 1)
@@ -350,11 +344,10 @@ class Seq2SeqTest(test.TestCase):
     with self.test_session() as sess:
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell_fn = lambda: core_rnn_cell_impl.GRUCell(2)
+        cell_fn = lambda: rnn_cell.GRUCell(2)
         cell = cell_fn()
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
-        enc_outputs, enc_state = core_rnn.static_rnn(
-            cell, inp, dtype=dtypes.float32)
+        enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32)
         attn_states = array_ops.concat([
             array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
         ], 1)
@@ -377,7 +370,7 @@ class Seq2SeqTest(test.TestCase):
     with self.test_session() as sess:
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell_fn = lambda: core_rnn_cell_impl.GRUCell(2)
+        cell_fn = lambda: rnn_cell.GRUCell(2)
         cell = cell_fn()
         inp = constant_op.constant(0.5, shape=[2, 2, 2])
         enc_outputs, enc_state = rnn.dynamic_rnn(
@@ -401,7 +394,7 @@ class Seq2SeqTest(test.TestCase):
     with self.test_session() as sess:
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell_fn = lambda: core_rnn_cell_impl.GRUCell(2)
+        cell_fn = lambda: rnn_cell.GRUCell(2)
         cell = cell_fn()
         inp = constant_op.constant(0.5, shape=[2, 2, 2])
         enc_outputs, enc_state = rnn.dynamic_rnn(
@@ -426,14 +419,13 @@ class Seq2SeqTest(test.TestCase):
     with self.test_session() as sess:
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        single_cell = lambda: core_rnn_cell_impl.BasicLSTMCell(  # pylint: disable=g-long-lambda
+        single_cell = lambda: rnn_cell.BasicLSTMCell(  # pylint: disable=g-long-lambda
             2, state_is_tuple=True)
-        cell_fn = lambda: core_rnn_cell_impl.MultiRNNCell(  # pylint: disable=g-long-lambda
+        cell_fn = lambda: rnn_cell.MultiRNNCell(  # pylint: disable=g-long-lambda
             cells=[single_cell() for _ in range(2)], state_is_tuple=True)
         cell = cell_fn()
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
-        enc_outputs, enc_state = core_rnn.static_rnn(
-            cell, inp, dtype=dtypes.float32)
+        enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32)
         attn_states = array_ops.concat([
             array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
         ], 1)
@@ -459,12 +451,11 @@ class Seq2SeqTest(test.TestCase):
     with self.test_session() as sess:
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell_fn = lambda: core_rnn_cell_impl.MultiRNNCell(  # pylint: disable=g-long-lambda
-            cells=[core_rnn_cell_impl.BasicLSTMCell(2) for _ in range(2)])
+        cell_fn = lambda: rnn_cell.MultiRNNCell(  # pylint: disable=g-long-lambda
+            cells=[rnn_cell.BasicLSTMCell(2) for _ in range(2)])
         cell = cell_fn()
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
-        enc_outputs, enc_state = core_rnn.static_rnn(
-            cell, inp, dtype=dtypes.float32)
+        enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32)
         attn_states = array_ops.concat([
             array_ops.reshape(e, [-1, 1, cell.output_size])
             for e in enc_outputs
@@ -492,10 +483,9 @@ class Seq2SeqTest(test.TestCase):
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
         inp = [constant_op.constant(0.5, shape=[2, 2])] * 2
-        cell_fn = lambda: core_rnn_cell_impl.GRUCell(2)
+        cell_fn = lambda: rnn_cell.GRUCell(2)
         cell = cell_fn()
-        enc_outputs, enc_state = core_rnn.static_rnn(
-            cell, inp, dtype=dtypes.float32)
+        enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32)
         attn_states = array_ops.concat([
             array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
         ], 1)
@@ -534,7 +524,7 @@ class Seq2SeqTest(test.TestCase):
             constant_op.constant(
                 i, dtypes.int32, shape=[2]) for i in range(3)
         ]
-        cell_fn = lambda: core_rnn_cell_impl.BasicLSTMCell(2)
+        cell_fn = lambda: rnn_cell.BasicLSTMCell(2)
         cell = cell_fn()
         dec, mem = seq2seq_lib.embedding_attention_seq2seq(
             enc_inp,
@@ -555,8 +545,7 @@ class Seq2SeqTest(test.TestCase):
         # Test with state_is_tuple=False.
         with variable_scope.variable_scope("no_tuple"):
           cell_fn = functools.partial(
-              core_rnn_cell_impl.BasicLSTMCell,
-              2, state_is_tuple=False)
+              rnn_cell.BasicLSTMCell, 2, state_is_tuple=False)
           cell_nt = cell_fn()
           dec, mem = seq2seq_lib.embedding_attention_seq2seq(
               enc_inp,
@@ -651,11 +640,10 @@ class Seq2SeqTest(test.TestCase):
         ]
         dec_symbols_dict = {"0": 5, "1": 6}
         def EncCellFn():
-          return core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=True)
+          return rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
         def DecCellsFn():
-          return dict(
-              (k, core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=True))
-              for k in dec_symbols_dict)
+          return dict((k, rnn_cell.BasicLSTMCell(2, state_is_tuple=True))
+                      for k in dec_symbols_dict)
         outputs_dict, state_dict = (seq2seq_lib.one2many_rnn_seq2seq(
             enc_inp, dec_inp_dict, EncCellFn(), DecCellsFn(),
             2, dec_symbols_dict, embedding_size=2))
@@ -796,8 +784,8 @@ class Seq2SeqTest(test.TestCase):
   #       """Example sequence-to-sequence model that uses GRU cells."""
 
   #       def GRUSeq2Seq(enc_inp, dec_inp):
-  #         cell = core_rnn_cell_impl.MultiRNNCell(
-  #             [core_rnn_cell_impl.GRUCell(24) for _ in range(2)])
+  #         cell = rnn_cell.MultiRNNCell(
+  #             [rnn_cell.GRUCell(24) for _ in range(2)])
   #         return seq2seq_lib.embedding_attention_seq2seq(
   #             enc_inp,
   #             dec_inp,
@@ -862,9 +850,8 @@ class Seq2SeqTest(test.TestCase):
         """Example sequence-to-sequence model that uses GRU cells."""
 
         def GRUSeq2Seq(enc_inp, dec_inp):
-          cell = core_rnn_cell_impl.MultiRNNCell(
-              [core_rnn_cell_impl.GRUCell(24) for _ in range(2)],
-              state_is_tuple=True)
+          cell = rnn_cell.MultiRNNCell(
+              [rnn_cell.GRUCell(24) for _ in range(2)], state_is_tuple=True)
           return seq2seq_lib.embedding_attention_seq2seq(
               enc_inp,
               dec_inp,
@@ -1040,7 +1027,7 @@ class Seq2SeqTest(test.TestCase):
           self.assertAllClose(v_true.eval(), v_false.eval())
 
     def EmbeddingRNNSeq2SeqF(enc_inp, dec_inp, feed_previous):
-      cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=True)
+      cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
       return seq2seq_lib.embedding_rnn_seq2seq(
           enc_inp,
           dec_inp,
@@ -1051,7 +1038,7 @@ class Seq2SeqTest(test.TestCase):
           feed_previous=feed_previous)
 
     def EmbeddingRNNSeq2SeqNoTupleF(enc_inp, dec_inp, feed_previous):
-      cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
+      cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
       return seq2seq_lib.embedding_rnn_seq2seq(
           enc_inp,
           dec_inp,
@@ -1062,7 +1049,7 @@ class Seq2SeqTest(test.TestCase):
           feed_previous=feed_previous)
 
     def EmbeddingTiedRNNSeq2Seq(enc_inp, dec_inp, feed_previous):
-      cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=True)
+      cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
       return seq2seq_lib.embedding_tied_rnn_seq2seq(
           enc_inp,
           dec_inp,
@@ -1072,7 +1059,7 @@ class Seq2SeqTest(test.TestCase):
           feed_previous=feed_previous)
 
     def EmbeddingTiedRNNSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous):
-      cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
+      cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
       return seq2seq_lib.embedding_tied_rnn_seq2seq(
           enc_inp,
           dec_inp,
@@ -1082,7 +1069,7 @@ class Seq2SeqTest(test.TestCase):
           feed_previous=feed_previous)
 
     def EmbeddingAttentionSeq2Seq(enc_inp, dec_inp, feed_previous):
-      cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=True)
+      cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
       return seq2seq_lib.embedding_attention_seq2seq(
           enc_inp,
           dec_inp,
@@ -1093,7 +1080,7 @@ class Seq2SeqTest(test.TestCase):
           feed_previous=feed_previous)
 
     def EmbeddingAttentionSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous):
-      cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
+      cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
       return seq2seq_lib.embedding_attention_seq2seq(
           enc_inp,
           dec_inp,
diff --git a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
index a80b898156..23b4a73b23 100644
--- a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
+++ b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
@@ -62,9 +62,7 @@ import copy
 from six.moves import xrange  # pylint: disable=redefined-builtin
 from six.moves import zip  # pylint: disable=redefined-builtin
 
-from tensorflow.contrib.rnn.python.ops import core_rnn
 from tensorflow.contrib.rnn.python.ops import core_rnn_cell
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
@@ -72,11 +70,13 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.util import nest
 
 # TODO(ebrevdo): Remove once _linear is fully deprecated.
-linear = core_rnn_cell_impl._linear  # pylint: disable=protected-access
+linear = rnn_cell_impl._linear  # pylint: disable=protected-access
 
 
 def _extract_argmax_and_embed(embedding,
@@ -119,7 +119,7 @@ def rnn_decoder(decoder_inputs,
   Args:
     decoder_inputs: A list of 2D Tensors [batch_size x input_size].
     initial_state: 2D Tensor with shape [batch_size x cell.state_size].
-    cell: core_rnn_cell.RNNCell defining the cell function and size.
+    cell: rnn_cell.RNNCell defining the cell function and size.
     loop_function: If not None, this function will be applied to the i-th output
       in order to generate the i+1-st input, and decoder_inputs will be ignored,
       except for the first element ("GO" symbol). This can be used for decoding,
@@ -170,7 +170,7 @@ def basic_rnn_seq2seq(encoder_inputs,
   Args:
     encoder_inputs: A list of 2D Tensors [batch_size x input_size].
     decoder_inputs: A list of 2D Tensors [batch_size x input_size].
-    cell: core_rnn_cell.RNNCell defining the cell function and size.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function and size.
     dtype: The dtype of the initial state of the RNN cell (default: tf.float32).
     scope: VariableScope for the created subgraph; default: "basic_rnn_seq2seq".
 
@@ -183,7 +183,7 @@ def basic_rnn_seq2seq(encoder_inputs,
   """
   with variable_scope.variable_scope(scope or "basic_rnn_seq2seq"):
     enc_cell = copy.deepcopy(cell)
-    _, enc_state = core_rnn.static_rnn(enc_cell, encoder_inputs, dtype=dtype)
+    _, enc_state = rnn.static_rnn(enc_cell, encoder_inputs, dtype=dtype)
     return rnn_decoder(decoder_inputs, enc_state, cell)
 
 
@@ -202,7 +202,7 @@ def tied_rnn_seq2seq(encoder_inputs,
   Args:
     encoder_inputs: A list of 2D Tensors [batch_size x input_size].
     decoder_inputs: A list of 2D Tensors [batch_size x input_size].
-    cell: core_rnn_cell.RNNCell defining the cell function and size.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function and size.
     loop_function: If not None, this function will be applied to i-th output
       in order to generate i+1-th input, and decoder_inputs will be ignored,
       except for the first element ("GO" symbol), see rnn_decoder for details.
@@ -219,7 +219,7 @@ def tied_rnn_seq2seq(encoder_inputs,
   """
   with variable_scope.variable_scope("combined_tied_rnn_seq2seq"):
     scope = scope or "tied_rnn_seq2seq"
-    _, enc_state = core_rnn.static_rnn(
+    _, enc_state = rnn.static_rnn(
         cell, encoder_inputs, dtype=dtype, scope=scope)
     variable_scope.get_variable_scope().reuse_variables()
     return rnn_decoder(
@@ -244,7 +244,7 @@ def embedding_rnn_decoder(decoder_inputs,
   Args:
     decoder_inputs: A list of 1D batch-sized int32 Tensors (decoder inputs).
     initial_state: 2D Tensor [batch_size x cell.state_size].
-    cell: core_rnn_cell.RNNCell defining the cell function.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function.
     num_symbols: Integer, how many symbols come into the embedding.
     embedding_size: Integer, the length of the embedding vector for each symbol.
     output_projection: None or a pair (W, B) of output projection weights and
@@ -320,7 +320,7 @@ def embedding_rnn_seq2seq(encoder_inputs,
   Args:
     encoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
     decoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
-    cell: core_rnn_cell.RNNCell defining the cell function and size.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function and size.
     num_encoder_symbols: Integer; number of symbols on the encoder side.
     num_decoder_symbols: Integer; number of symbols on the decoder side.
     embedding_size: Integer, the length of the embedding vector for each symbol.
@@ -360,8 +360,7 @@ def embedding_rnn_seq2seq(encoder_inputs,
         encoder_cell,
         embedding_classes=num_encoder_symbols,
         embedding_size=embedding_size)
-    _, encoder_state = core_rnn.static_rnn(
-        encoder_cell, encoder_inputs, dtype=dtype)
+    _, encoder_state = rnn.static_rnn(encoder_cell, encoder_inputs, dtype=dtype)
 
     # Decoder.
     if output_projection is None:
@@ -431,7 +430,7 @@ def embedding_tied_rnn_seq2seq(encoder_inputs,
   Args:
     encoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
     decoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
-    cell: core_rnn_cell.RNNCell defining the cell function and size.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function and size.
     num_symbols: Integer; number of symbols for both encoder and decoder.
     embedding_size: Integer, the length of the embedding vector for each symbol.
     num_decoder_symbols: Integer; number of output symbols for decoder. If
@@ -560,7 +559,7 @@ def attention_decoder(decoder_inputs,
     decoder_inputs: A list of 2D Tensors [batch_size x input_size].
     initial_state: 2D Tensor [batch_size x cell.state_size].
     attention_states: 3D Tensor [batch_size x attn_length x attn_size].
-    cell: core_rnn_cell.RNNCell defining the cell function and size.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function and size.
     output_size: Size of the output vectors; if None, we use cell.output_size.
     num_heads: Number of attention heads that read from attention_states.
     loop_function: If not None, this function will be applied to i-th output
@@ -720,7 +719,7 @@ def embedding_attention_decoder(decoder_inputs,
     decoder_inputs: A list of 1D batch-sized int32 Tensors (decoder inputs).
     initial_state: 2D Tensor [batch_size x cell.state_size].
     attention_states: 3D Tensor [batch_size x attn_length x attn_size].
-    cell: core_rnn_cell.RNNCell defining the cell function.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function.
     num_symbols: Integer, how many symbols come into the embedding.
     embedding_size: Integer, the length of the embedding vector for each symbol.
     num_heads: Number of attention heads that read from attention_states.
@@ -814,7 +813,7 @@ def embedding_attention_seq2seq(encoder_inputs,
   Args:
     encoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
     decoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
-    cell: core_rnn_cell.RNNCell defining the cell function and size.
+    cell: tf.nn.rnn_cell.RNNCell defining the cell function and size.
     num_encoder_symbols: Integer; number of symbols on the encoder side.
     num_decoder_symbols: Integer; number of symbols on the decoder side.
     embedding_size: Integer, the length of the embedding vector for each symbol.
@@ -851,7 +850,7 @@ def embedding_attention_seq2seq(encoder_inputs,
         encoder_cell,
         embedding_classes=num_encoder_symbols,
         embedding_size=embedding_size)
-    encoder_outputs, encoder_state = core_rnn.static_rnn(
+    encoder_outputs, encoder_state = rnn.static_rnn(
         encoder_cell, encoder_inputs, dtype=dtype)
 
     # First calculate a concatenation of encoder outputs to put attention on.
@@ -937,9 +936,10 @@ def one2many_rnn_seq2seq(encoder_inputs,
       the corresponding decoder_inputs; each decoder_inputs is a list of 1D
       Tensors of shape [batch_size]; num_decoders is defined as
       len(decoder_inputs_dict).
-    enc_cell: core_rnn_cell.RNNCell defining the encoder cell function and size.
+    enc_cell: tf.nn.rnn_cell.RNNCell defining the encoder cell function and
+      size.
     dec_cells_dict: A dictionary mapping encoder name (string) to an
-      instance of core_rnn_cell.RNNCell.
+      instance of tf.nn.rnn_cell.RNNCell.
     num_encoder_symbols: Integer; number of symbols on the encoder side.
     num_decoder_symbols_dict: A dictionary mapping decoder name (string) to an
       integer specifying number of symbols for the corresponding decoder;
@@ -971,12 +971,12 @@ def one2many_rnn_seq2seq(encoder_inputs,
   outputs_dict = {}
   state_dict = {}
 
-  if not isinstance(enc_cell, core_rnn_cell.RNNCell):
+  if not isinstance(enc_cell, rnn_cell_impl.RNNCell):
     raise TypeError("enc_cell is not an RNNCell: %s" % type(enc_cell))
   if set(dec_cells_dict) != set(decoder_inputs_dict):
     raise ValueError("keys of dec_cells_dict != keys of decodre_inputs_dict")
   for dec_cell in dec_cells_dict.values():
-    if not isinstance(dec_cell, core_rnn_cell.RNNCell):
+    if not isinstance(dec_cell, rnn_cell_impl.RNNCell):
       raise TypeError("dec_cell is not an RNNCell: %s" % type(dec_cell))
 
   with variable_scope.variable_scope(
@@ -988,8 +988,7 @@ def one2many_rnn_seq2seq(encoder_inputs,
         enc_cell,
         embedding_classes=num_encoder_symbols,
         embedding_size=embedding_size)
-    _, encoder_state = core_rnn.static_rnn(
-        enc_cell, encoder_inputs, dtype=dtype)
+    _, encoder_state = rnn.static_rnn(enc_cell, encoder_inputs, dtype=dtype)
 
     # Decoder.
     for name, decoder_inputs in decoder_inputs_dict.items():
@@ -1153,7 +1152,7 @@ def model_with_buckets(encoder_inputs,
 
   The seq2seq argument is a function that defines a sequence-to-sequence model,
   e.g., seq2seq = lambda x, y: basic_rnn_seq2seq(
-      x, y, core_rnn_cell.GRUCell(24))
+      x, y, rnn_cell.GRUCell(24))
 
   Args:
     encoder_inputs: A list of Tensors to feed the encoder; first seq2seq input.
diff --git a/tensorflow/contrib/ndlstm/python/lstm1d.py b/tensorflow/contrib/ndlstm/python/lstm1d.py
index e4edff00a7..d3c3531f40 100644
--- a/tensorflow/contrib/ndlstm/python/lstm1d.py
+++ b/tensorflow/contrib/ndlstm/python/lstm1d.py
@@ -20,13 +20,13 @@ from __future__ import print_function
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
 from tensorflow.contrib.framework.python.ops import variables
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.python.framework import constant_op
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope
 
 
@@ -52,7 +52,7 @@ def ndlstm_base_unrolled(inputs, noutput, scope=None, reverse=False):
   """
   with variable_scope.variable_scope(scope, "SeqLstmUnrolled", [inputs]):
     length, batch_size, _ = _shape(inputs)
-    lstm_cell = core_rnn_cell_impl.BasicLSTMCell(noutput, state_is_tuple=False)
+    lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
     state = array_ops.zeros([batch_size, lstm_cell.state_size])
     output_u = []
     inputs_u = array_ops.unstack(inputs)
@@ -88,7 +88,7 @@ def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False):
     # TODO(tmb) make batch size, sequence_length dynamic
     # example: sequence_length = tf.shape(inputs)[0]
     _, batch_size, _ = _shape(inputs)
-    lstm_cell = core_rnn_cell_impl.BasicLSTMCell(noutput, state_is_tuple=False)
+    lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
     state = array_ops.zeros([batch_size, lstm_cell.state_size])
     sequence_length = int(inputs.get_shape()[0])
     sequence_lengths = math_ops.to_int64(
@@ -145,7 +145,7 @@ def sequence_to_final(inputs, noutput, scope=None, name=None, reverse=False):
   """
   with variable_scope.variable_scope(scope, "SequenceToFinal", [inputs]):
     length, batch_size, _ = _shape(inputs)
-    lstm = core_rnn_cell_impl.BasicLSTMCell(noutput, state_is_tuple=False)
+    lstm = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
     state = array_ops.zeros([batch_size, lstm.state_size])
     inputs_u = array_ops.unstack(inputs)
     if reverse:
diff --git a/tensorflow/contrib/rnn/__init__.py b/tensorflow/contrib/rnn/__init__.py
index 2420c3e179..d39c1f062a 100644
--- a/tensorflow/contrib/rnn/__init__.py
+++ b/tensorflow/contrib/rnn/__init__.py
@@ -16,21 +16,26 @@
 
 See @{$python/contrib.rnn} guide.
 
+# From core
 @@RNNCell
 @@BasicRNNCell
 @@BasicLSTMCell
 @@GRUCell
 @@LSTMCell
-@@LayerNormBasicLSTMCell
 @@LSTMStateTuple
-@@MultiRNNCell
-@@LSTMBlockWrapper
 @@DropoutWrapper
+@@MultiRNNCell
+@@DeviceWrapper
+@@ResidualWrapper
+
+# Used to be in core, but kept in contrib.
 @@EmbeddingWrapper
 @@InputProjectionWrapper
 @@OutputProjectionWrapper
-@@DeviceWrapper
-@@ResidualWrapper
+
+# Created in contrib, eventual plans to move to core.
+@@LayerNormBasicLSTMCell
+@@LSTMBlockWrapper
 @@LSTMBlockCell
 @@GRUBlockCell
 @@FusedRNNCell
@@ -48,9 +53,11 @@ See @{$python/contrib.rnn} guide.
 @@HighwayWrapper
 @@GLSTMCell
 
-### RNNCell wrappers
+# RNNCell wrappers
 @@AttentionCellWrapper
 @@CompiledWrapper
+
+# RNN functions
 @@static_rnn
 @@static_state_saving_rnn
 @@static_bidirectional_rnn
@@ -62,31 +69,23 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.rnn.python.ops.core_rnn import static_bidirectional_rnn
-from tensorflow.contrib.rnn.python.ops.core_rnn import static_rnn
-from tensorflow.contrib.rnn.python.ops.core_rnn import static_state_saving_rnn
-
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import BasicLSTMCell
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import BasicRNNCell
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import DeviceWrapper
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import DropoutWrapper
+# pylint: disable=unused-import,wildcard-import,line-too-long
 from tensorflow.contrib.rnn.python.ops.core_rnn_cell import EmbeddingWrapper
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import GRUCell
 from tensorflow.contrib.rnn.python.ops.core_rnn_cell import InputProjectionWrapper
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import LSTMCell
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import LSTMStateTuple
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import MultiRNNCell
 from tensorflow.contrib.rnn.python.ops.core_rnn_cell import OutputProjectionWrapper
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import ResidualWrapper
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import RNNCell
 
-# pylint: disable=unused-import,wildcard-import,line-too-long
 from tensorflow.contrib.rnn.python.ops.fused_rnn_cell import *
 from tensorflow.contrib.rnn.python.ops.gru_ops import *
 from tensorflow.contrib.rnn.python.ops.lstm_ops import *
 from tensorflow.contrib.rnn.python.ops.rnn import *
 from tensorflow.contrib.rnn.python.ops.rnn_cell import *
+
+from tensorflow.python.ops.rnn import static_bidirectional_rnn
+from tensorflow.python.ops.rnn import static_rnn
+from tensorflow.python.ops.rnn import static_state_saving_rnn
+
+from tensorflow.python.ops.rnn_cell import *
 # pylint: enable=unused-import,wildcard-import,line-too-long
 
 from tensorflow.python.util.all_util import remove_undocumented
-remove_undocumented(__name__, ['core_rnn_cell'])
+remove_undocumented(__name__)
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index 0f207b088d..06954f51d8 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -25,8 +25,7 @@ import numpy as np
 # TODO(ebrevdo): Remove once _linear is fully deprecated.
 # pylint: disable=protected-access
 
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl import _linear as linear
+from tensorflow.contrib import rnn as contrib_rnn
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -37,12 +36,14 @@ from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
 from tensorflow.python.platform import test
 
 
 # pylint: enable=protected-access
+linear = rnn_cell_impl._linear
 
 
 class RNNCellTest(test.TestCase):
@@ -74,14 +75,12 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 2])
         m = array_ops.zeros([1, 2])
-        cell = core_rnn_cell_impl.BasicRNNCell(2)
+        cell = rnn_cell_impl.BasicRNNCell(2)
         g, _ = cell(x, m)
-        self.assertEqual(
-            ["root/basic_rnn_cell/%s:0"
-             % core_rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
-             "root/basic_rnn_cell/%s:0"
-             % core_rnn_cell_impl._BIAS_VARIABLE_NAME],
-            [v.name for v in cell.trainable_variables])
+        self.assertEqual([
+            "root/basic_rnn_cell/%s:0" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
+            "root/basic_rnn_cell/%s:0" % rnn_cell_impl._BIAS_VARIABLE_NAME
+        ], [v.name for v in cell.trainable_variables])
         self.assertFalse(cell.non_trainable_variables)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(
@@ -100,15 +99,13 @@ class RNNCellTest(test.TestCase):
           custom_getter=not_trainable_getter):
         x = array_ops.zeros([1, 2])
         m = array_ops.zeros([1, 2])
-        cell = core_rnn_cell_impl.BasicRNNCell(2)
+        cell = rnn_cell_impl.BasicRNNCell(2)
         g, _ = cell(x, m)
         self.assertFalse(cell.trainable_variables)
-        self.assertEqual(
-            ["root/basic_rnn_cell/%s:0"
-             % core_rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
-             "root/basic_rnn_cell/%s:0"
-             % core_rnn_cell_impl._BIAS_VARIABLE_NAME],
-            [v.name for v in cell.non_trainable_variables])
+        self.assertEqual([
+            "root/basic_rnn_cell/%s:0" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
+            "root/basic_rnn_cell/%s:0" % rnn_cell_impl._BIAS_VARIABLE_NAME
+        ], [v.name for v in cell.non_trainable_variables])
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(
             [g], {x.name: np.array([[1., 1.]]),
@@ -121,7 +118,7 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 2])
         m = array_ops.zeros([1, 2])
-        g, _ = core_rnn_cell_impl.GRUCell(2)(x, m)
+        g, _ = rnn_cell_impl.GRUCell(2)(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(
             [g], {x.name: np.array([[1., 1.]]),
@@ -133,7 +130,7 @@ class RNNCellTest(test.TestCase):
         x = array_ops.zeros(
             [1, 3])  # Test GRUCell with input_size != num_units.
         m = array_ops.zeros([1, 2])
-        g, _ = core_rnn_cell_impl.GRUCell(2)(x, m)
+        g, _ = rnn_cell_impl.GRUCell(2)(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(
             [g],
@@ -148,20 +145,23 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 2])
         m = array_ops.zeros([1, 8])
-        cell = core_rnn_cell_impl.MultiRNNCell(
-            [core_rnn_cell_impl.BasicLSTMCell(
-                2, state_is_tuple=False) for _ in range(2)],
+        cell = rnn_cell_impl.MultiRNNCell(
+            [
+                rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
+                for _ in range(2)
+            ],
             state_is_tuple=False)
         g, out_m = cell(x, m)
         expected_variable_names = [
-            "root/multi_rnn_cell/cell_0/basic_lstm_cell/%s:0"
-            % core_rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
-            "root/multi_rnn_cell/cell_0/basic_lstm_cell/%s:0"
-            % core_rnn_cell_impl._BIAS_VARIABLE_NAME,
-            "root/multi_rnn_cell/cell_1/basic_lstm_cell/%s:0"
-            % core_rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
-            "root/multi_rnn_cell/cell_1/basic_lstm_cell/%s:0"
-            % core_rnn_cell_impl._BIAS_VARIABLE_NAME]
+            "root/multi_rnn_cell/cell_0/basic_lstm_cell/%s:0" %
+            rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
+            "root/multi_rnn_cell/cell_0/basic_lstm_cell/%s:0" %
+            rnn_cell_impl._BIAS_VARIABLE_NAME,
+            "root/multi_rnn_cell/cell_1/basic_lstm_cell/%s:0" %
+            rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
+            "root/multi_rnn_cell/cell_1/basic_lstm_cell/%s:0" %
+            rnn_cell_impl._BIAS_VARIABLE_NAME
+        ]
         self.assertEqual(
             expected_variable_names, [v.name for v in cell.trainable_variables])
         self.assertFalse(cell.non_trainable_variables)
@@ -185,8 +185,7 @@ class RNNCellTest(test.TestCase):
         x = array_ops.zeros(
             [1, 3])  # Test BasicLSTMCell with input_size != num_units.
         m = array_ops.zeros([1, 4])
-        g, out_m = core_rnn_cell_impl.BasicLSTMCell(
-            2, state_is_tuple=False)(x, m)
+        g, out_m = rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(
             [g, out_m],
@@ -206,7 +205,7 @@ class RNNCellTest(test.TestCase):
         x = array_ops.zeros([batch_size, input_size])
         m = array_ops.zeros([batch_size - 1, state_size])
         with self.assertRaises(ValueError):
-          g, out_m = core_rnn_cell_impl.BasicLSTMCell(
+          g, out_m = rnn_cell_impl.BasicLSTMCell(
               num_units, state_is_tuple=False)(x, m)
           sess.run([variables_lib.global_variables_initializer()])
           sess.run([g, out_m],
@@ -225,7 +224,7 @@ class RNNCellTest(test.TestCase):
         x = array_ops.zeros([batch_size, input_size])
         m = array_ops.zeros([batch_size, state_size])
         with self.assertRaises(ValueError):
-          g, out_m = core_rnn_cell_impl.BasicLSTMCell(
+          g, out_m = rnn_cell_impl.BasicLSTMCell(
               num_units, state_is_tuple=False)(x, m)
           sess.run([variables_lib.global_variables_initializer()])
           sess.run([g, out_m],
@@ -239,31 +238,29 @@ class RNNCellTest(test.TestCase):
         x = array_ops.zeros([1, 2])
         m0 = (array_ops.zeros([1, 2]),) * 2
         m1 = (array_ops.zeros([1, 2]),) * 2
-        cell = core_rnn_cell_impl.MultiRNNCell(
-            [core_rnn_cell_impl.BasicLSTMCell(2) for _ in range(2)],
+        cell = rnn_cell_impl.MultiRNNCell(
+            [rnn_cell_impl.BasicLSTMCell(2) for _ in range(2)],
             state_is_tuple=True)
         self.assertTrue(isinstance(cell.state_size, tuple))
         self.assertTrue(
-            isinstance(cell.state_size[0], core_rnn_cell_impl.LSTMStateTuple))
+            isinstance(cell.state_size[0], rnn_cell_impl.LSTMStateTuple))
         self.assertTrue(
-            isinstance(cell.state_size[1], core_rnn_cell_impl.LSTMStateTuple))
+            isinstance(cell.state_size[1], rnn_cell_impl.LSTMStateTuple))
 
         # Pass in regular tuples
         _, (out_m0, out_m1) = cell(x, (m0, m1))
-        self.assertTrue(isinstance(out_m0, core_rnn_cell_impl.LSTMStateTuple))
-        self.assertTrue(isinstance(out_m1, core_rnn_cell_impl.LSTMStateTuple))
+        self.assertTrue(isinstance(out_m0, rnn_cell_impl.LSTMStateTuple))
+        self.assertTrue(isinstance(out_m1, rnn_cell_impl.LSTMStateTuple))
 
         # Pass in LSTMStateTuples
         variable_scope.get_variable_scope().reuse_variables()
         zero_state = cell.zero_state(1, dtypes.float32)
         self.assertTrue(isinstance(zero_state, tuple))
-        self.assertTrue(
-            isinstance(zero_state[0], core_rnn_cell_impl.LSTMStateTuple))
-        self.assertTrue(
-            isinstance(zero_state[1], core_rnn_cell_impl.LSTMStateTuple))
+        self.assertTrue(isinstance(zero_state[0], rnn_cell_impl.LSTMStateTuple))
+        self.assertTrue(isinstance(zero_state[1], rnn_cell_impl.LSTMStateTuple))
         _, (out_m0, out_m1) = cell(x, zero_state)
-        self.assertTrue(isinstance(out_m0, core_rnn_cell_impl.LSTMStateTuple))
-        self.assertTrue(isinstance(out_m1, core_rnn_cell_impl.LSTMStateTuple))
+        self.assertTrue(isinstance(out_m0, rnn_cell_impl.LSTMStateTuple))
+        self.assertTrue(isinstance(out_m1, rnn_cell_impl.LSTMStateTuple))
 
   def testBasicLSTMCellWithStateTuple(self):
     with self.test_session() as sess:
@@ -272,9 +269,11 @@ class RNNCellTest(test.TestCase):
         x = array_ops.zeros([1, 2])
         m0 = array_ops.zeros([1, 4])
         m1 = array_ops.zeros([1, 4])
-        cell = core_rnn_cell_impl.MultiRNNCell(
-            [core_rnn_cell_impl.BasicLSTMCell(
-                2, state_is_tuple=False) for _ in range(2)],
+        cell = rnn_cell_impl.MultiRNNCell(
+            [
+                rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
+                for _ in range(2)
+            ],
             state_is_tuple=True)
         g, (out_m0, out_m1) = cell(x, (m0, m1))
         sess.run([variables_lib.global_variables_initializer()])
@@ -306,7 +305,7 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([batch_size, input_size])
         m = array_ops.zeros([batch_size, state_size])
-        cell = core_rnn_cell_impl.LSTMCell(
+        cell = rnn_cell_impl.LSTMCell(
             num_units=num_units,
             num_proj=num_proj,
             forget_bias=1.0,
@@ -340,7 +339,7 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([batch_size, input_size])
         m = array_ops.zeros([batch_size, state_size])
-        cell = core_rnn_cell_impl.LSTMCell(
+        cell = rnn_cell_impl.LSTMCell(
             num_units=num_units,
             num_proj=num_proj,
             forget_bias=1.0,
@@ -358,8 +357,7 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 3])
         m = array_ops.zeros([1, 3])
-        cell = core_rnn_cell_impl.OutputProjectionWrapper(
-            core_rnn_cell_impl.GRUCell(3), 2)
+        cell = contrib_rnn.OutputProjectionWrapper(rnn_cell_impl.GRUCell(3), 2)
         g, new_m = cell(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run([g, new_m], {
@@ -376,8 +374,8 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 2])
         m = array_ops.zeros([1, 3])
-        cell = core_rnn_cell_impl.InputProjectionWrapper(
-            core_rnn_cell_impl.GRUCell(3), num_proj=3)
+        cell = contrib_rnn.InputProjectionWrapper(
+            rnn_cell_impl.GRUCell(3), num_proj=3)
         g, new_m = cell(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(
@@ -394,10 +392,10 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 3])
         m = array_ops.zeros([1, 3])
-        base_cell = core_rnn_cell_impl.GRUCell(3)
+        base_cell = rnn_cell_impl.GRUCell(3)
         g, m_new = base_cell(x, m)
         variable_scope.get_variable_scope().reuse_variables()
-        g_res, m_new_res = core_rnn_cell_impl.ResidualWrapper(base_cell)(x, m)
+        g_res, m_new_res = rnn_cell_impl.ResidualWrapper(base_cell)(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run([g, g_res, m_new, m_new_res], {
             x: np.array([[1., 1., 1.]]),
@@ -413,8 +411,7 @@ class RNNCellTest(test.TestCase):
         "root", initializer=init_ops.constant_initializer(0.5)):
       x = array_ops.zeros([1, 3])
       m = array_ops.zeros([1, 3])
-      cell = core_rnn_cell_impl.DeviceWrapper(
-          core_rnn_cell_impl.GRUCell(3), "/cpu:14159")
+      cell = rnn_cell_impl.DeviceWrapper(rnn_cell_impl.GRUCell(3), "/cpu:14159")
       outputs, _ = cell(x, m)
       self.assertTrue("cpu:14159" in outputs.device.lower())
 
@@ -427,8 +424,7 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 1, 3])
-        cell = core_rnn_cell_impl.DeviceWrapper(
-            core_rnn_cell_impl.GRUCell(3), "/gpu:0")
+        cell = rnn_cell_impl.DeviceWrapper(rnn_cell_impl.GRUCell(3), "/gpu:0")
         with ops.device("/cpu:0"):
           outputs, _ = rnn.dynamic_rnn(
               cell=cell, inputs=x, dtype=dtypes.float32)
@@ -446,39 +442,14 @@ class RNNCellTest(test.TestCase):
       self.assertFalse([s for s in cpu_stats if "gru_cell" in s.node_name])
       self.assertTrue([s for s in gpu_stats if "gru_cell" in s.node_name])
 
-  # def testUsingSecondCellInScopeWithExistingVariablesFails(self):
-  #   # This test should go away when this behavior is no longer an
-  #   # error (Approx. May 2017)
-  #   cell1 = core_rnn_cell_impl.LSTMCell(3)
-  #   cell2 = core_rnn_cell_impl.LSTMCell(3)
-  #   x = array_ops.zeros([1, 3])
-  #   m = core_rnn_cell_impl.LSTMStateTuple(*[array_ops.zeros([1, 3])] * 2)
-  #   cell1(x, m)
-  #   with self.assertRaisesRegexp(ValueError, r"LSTMCell\(..., reuse=True\)"):
-  #     cell2(x, m)
-
-  # def testUsingCellInDifferentScopeFromFirstCallFails(self):
-  #   # This test should go away when this behavior is no longer an
-  #   # error (Approx. May 2017)
-  #   cell = core_rnn_cell_impl.LSTMCell(3)
-  #   x = array_ops.zeros([1, 3])
-  #   m = core_rnn_cell_impl.LSTMStateTuple(*[array_ops.zeros([1, 3])] * 2)
-  #   with variable_scope.variable_scope("scope1"):
-  #     cell(x, m)
-  #   with variable_scope.variable_scope("scope2"):
-  #     with self.assertRaisesRegexp(ValueError, r"Attempt to reuse RNNCell"):
-  #       cell(x, m)
-
   def testEmbeddingWrapper(self):
     with self.test_session() as sess:
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 1], dtype=dtypes.int32)
         m = array_ops.zeros([1, 2])
-        embedding_cell = core_rnn_cell_impl.EmbeddingWrapper(
-            core_rnn_cell_impl.GRUCell(2),
-            embedding_classes=3,
-            embedding_size=2)
+        embedding_cell = contrib_rnn.EmbeddingWrapper(
+            rnn_cell_impl.GRUCell(2), embedding_classes=3, embedding_size=2)
         self.assertEqual(embedding_cell.output_size, 2)
         g, new_m = embedding_cell(x, m)
         sess.run([variables_lib.global_variables_initializer()])
@@ -495,9 +466,8 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope("root"):
         inputs = ops.convert_to_tensor([[[0], [0]]], dtype=dtypes.int64)
         input_lengths = ops.convert_to_tensor([2], dtype=dtypes.int64)
-        embedding_cell = core_rnn_cell_impl.EmbeddingWrapper(
-            core_rnn_cell_impl.BasicLSTMCell(
-                1, state_is_tuple=True),
+        embedding_cell = contrib_rnn.EmbeddingWrapper(
+            rnn_cell_impl.BasicLSTMCell(1, state_is_tuple=True),
             embedding_classes=1,
             embedding_size=2)
         outputs, _ = rnn.dynamic_rnn(
@@ -515,9 +485,9 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 2])
         m = array_ops.zeros([1, 4])
-        _, ml = core_rnn_cell_impl.MultiRNNCell(
-            [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
-            state_is_tuple=False)(x, m)
+        _, ml = rnn_cell_impl.MultiRNNCell(
+            [rnn_cell_impl.GRUCell(2)
+             for _ in range(2)], state_is_tuple=False)(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(ml, {
             x.name: np.array([[1., 1.]]),
@@ -536,13 +506,13 @@ class RNNCellTest(test.TestCase):
 
         # Test incorrectness of state
         with self.assertRaisesRegexp(ValueError, "Expected state .* a tuple"):
-          core_rnn_cell_impl.MultiRNNCell(
-              [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
-              state_is_tuple=True)(x, m_bad)
+          rnn_cell_impl.MultiRNNCell(
+              [rnn_cell_impl.GRUCell(2)
+               for _ in range(2)], state_is_tuple=True)(x, m_bad)
 
-        _, ml = core_rnn_cell_impl.MultiRNNCell(
-            [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
-            state_is_tuple=True)(x, m_good)
+        _, ml = rnn_cell_impl.MultiRNNCell(
+            [rnn_cell_impl.GRUCell(2)
+             for _ in range(2)], state_is_tuple=True)(x, m_good)
 
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(ml, {
@@ -571,23 +541,23 @@ class DropoutWrapperTest(test.TestCase):
           time_steps = 2
           x = constant_op.constant(
               [[[2., 2., 2.]], [[1., 1., 1.]]], dtype=dtypes.float32)
-          m = core_rnn_cell_impl.LSTMStateTuple(
-              *[constant_op.constant([[0.1, 0.1, 0.1]],
-                                     dtype=dtypes.float32)] * 2)
+          m = rnn_cell_impl.LSTMStateTuple(
+              *[constant_op.constant([[0.1, 0.1, 0.1]], dtype=dtypes.float32)
+               ] * 2)
         else:
           x = constant_op.constant(
               np.random.randn(time_steps, batch_size, 3).astype(np.float32))
-          m = core_rnn_cell_impl.LSTMStateTuple(
-              *[constant_op.constant([[0.1, 0.1, 0.1]] * batch_size,
-                                     dtype=dtypes.float32)] * 2)
+          m = rnn_cell_impl.LSTMStateTuple(*[
+              constant_op.constant(
+                  [[0.1, 0.1, 0.1]] * batch_size, dtype=dtypes.float32)
+          ] * 2)
         outputs, final_state = rnn.dynamic_rnn(
-            cell=core_rnn_cell_impl.DropoutWrapper(
-                core_rnn_cell_impl.LSTMCell(3),
-                dtype=x.dtype,
-                **kwargs),
+            cell=rnn_cell_impl.DropoutWrapper(
+                rnn_cell_impl.LSTMCell(3), dtype=x.dtype, **kwargs),
             time_major=True,
             parallel_iterations=parallel_iterations,
-            inputs=x, initial_state=m)
+            inputs=x,
+            initial_state=m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run([outputs, final_state])
         self.assertEqual(res[0].shape, (time_steps, batch_size, 3))
@@ -775,7 +745,7 @@ class SlimRNNCellTest(test.TestCase):
         m = array_ops.zeros([1, 2])
         my_cell = functools.partial(basic_rnn_cell, num_units=2)
         # pylint: disable=protected-access
-        g, _ = core_rnn_cell_impl._SlimRNNCell(my_cell)(x, m)
+        g, _ = rnn_cell_impl._SlimRNNCell(my_cell)(x, m)
         # pylint: enable=protected-access
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run(
@@ -792,12 +762,12 @@ class SlimRNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         inputs = random_ops.random_uniform((batch_size, input_size))
         _, initial_state = basic_rnn_cell(inputs, None, num_units)
-        rnn_cell = core_rnn_cell_impl.BasicRNNCell(num_units)
+        rnn_cell = rnn_cell_impl.BasicRNNCell(num_units)
         outputs, state = rnn_cell(inputs, initial_state)
         variable_scope.get_variable_scope().reuse_variables()
         my_cell = functools.partial(basic_rnn_cell, num_units=num_units)
         # pylint: disable=protected-access
-        slim_cell = core_rnn_cell_impl._SlimRNNCell(my_cell)
+        slim_cell = rnn_cell_impl._SlimRNNCell(my_cell)
         # pylint: enable=protected-access
         slim_outputs, slim_state = slim_cell(inputs, initial_state)
         self.assertEqual(slim_outputs.get_shape(), outputs.get_shape())
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
index 54e3a0dadf..bf24347c43 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
@@ -24,9 +24,6 @@ import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
 from tensorflow.contrib import rnn as rnn_lib
-from tensorflow.contrib.rnn.python.ops import core_rnn
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -38,6 +35,7 @@ from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
@@ -153,7 +151,7 @@ class RNNTest(test.TestCase):
     cell = Plus1RNNCell()
     inputs = [array_ops.placeholder(dtypes.float32, shape=(3, 4))]
     with self.assertRaisesRegexp(ValueError, "must be a vector"):
-      core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32, sequence_length=4)
+      rnn.static_rnn(cell, inputs, dtype=dtypes.float32, sequence_length=4)
 
   def testRNN(self):
     cell = Plus1RNNCell()
@@ -164,7 +162,7 @@ class RNNTest(test.TestCase):
         array_ops.placeholder(
             dtypes.float32, shape=(batch_size, input_size))
     ]
-    outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+    outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
     self.assertEqual(len(outputs), len(inputs))
     for out, inp in zip(outputs, inputs):
       self.assertEqual(out.get_shape(), inp.get_shape())
@@ -186,7 +184,7 @@ class RNNTest(test.TestCase):
 
   def testDropout(self):
     cell = Plus1RNNCell()
-    full_dropout_cell = core_rnn_cell_impl.DropoutWrapper(
+    full_dropout_cell = rnn_cell.DropoutWrapper(
         cell, input_keep_prob=1e-12, seed=0)
     batch_size = 2
     input_size = 5
@@ -196,9 +194,9 @@ class RNNTest(test.TestCase):
             dtypes.float32, shape=(batch_size, input_size))
     ]
     with variable_scope.variable_scope("share_scope"):
-      outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
     with variable_scope.variable_scope("drop_scope"):
-      dropped_outputs, _ = core_rnn.static_rnn(
+      dropped_outputs, _ = rnn.static_rnn(
           full_dropout_cell, inputs, dtype=dtypes.float32)
     self.assertEqual(len(outputs), len(inputs))
     for out, inp in zip(outputs, inputs):
@@ -227,7 +225,7 @@ class RNNTest(test.TestCase):
             dtypes.float32, shape=(batch_size, input_size))
     ]
     with variable_scope.variable_scope("drop_scope"):
-      dynamic_outputs, dynamic_state = core_rnn.static_rnn(
+      dynamic_outputs, dynamic_state = rnn.static_rnn(
           cell, inputs, sequence_length=sequence_length, dtype=dtypes.float32)
     self.assertEqual(len(dynamic_outputs), len(inputs))
 
@@ -297,8 +295,7 @@ class RNNTest(test.TestCase):
           array_ops.placeholder(
               dtypes.float32, shape=(batch_size, input_size))
       ]
-      return core_rnn.static_rnn(
-          cell, inputs, dtype=dtypes.float32, scope=scope)
+      return rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope=scope)
 
     self._testScope(factory, use_outer_scope=True)
     self._testScope(factory, use_outer_scope=False)
@@ -319,13 +316,13 @@ class LSTMTest(test.TestCase):
     with self.test_session(use_gpu=use_gpu, graph=ops_lib.Graph()) as sess:
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=self._seed)
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units, initializer=initializer, state_is_tuple=False)
       inputs = max_length * [
           array_ops.placeholder(
               dtypes.float32, shape=(batch_size, input_size))
       ]
-      outputs, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
       self.assertEqual(len(outputs), len(inputs))
       for out in outputs:
         self.assertEqual(out.get_shape().as_list(), [batch_size, num_units])
@@ -342,7 +339,7 @@ class LSTMTest(test.TestCase):
     with self.test_session(use_gpu=use_gpu, graph=ops_lib.Graph()) as sess:
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=self._seed)
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           cell_clip=0.0,
@@ -352,7 +349,7 @@ class LSTMTest(test.TestCase):
           array_ops.placeholder(
               dtypes.float32, shape=(batch_size, input_size))
       ]
-      outputs, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
       self.assertEqual(len(outputs), len(inputs))
       for out in outputs:
         self.assertEqual(out.get_shape().as_list(), [batch_size, num_units])
@@ -374,7 +371,7 @@ class LSTMTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=self._seed)
       state_saver = TestStateSaver(batch_size, 2 * num_units)
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=False,
           initializer=initializer,
@@ -384,7 +381,7 @@ class LSTMTest(test.TestCase):
               dtypes.float32, shape=(batch_size, input_size))
       ]
       with variable_scope.variable_scope("share_scope"):
-        outputs, state = core_rnn.static_state_saving_rnn(
+        outputs, state = rnn.static_state_saving_rnn(
             cell, inputs, state_saver=state_saver, state_name="save_lstm")
       self.assertEqual(len(outputs), len(inputs))
       for out in outputs:
@@ -406,7 +403,7 @@ class LSTMTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=self._seed)
       state_saver = TestStateSaver(batch_size, num_units)
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=False,
           initializer=initializer,
@@ -416,7 +413,7 @@ class LSTMTest(test.TestCase):
               dtypes.float32, shape=(batch_size, input_size))
       ]
       with variable_scope.variable_scope("share_scope"):
-        outputs, state = core_rnn.static_state_saving_rnn(
+        outputs, state = rnn.static_state_saving_rnn(
             cell, inputs, state_saver=state_saver, state_name=("c", "m"))
       self.assertEqual(len(outputs), len(inputs))
       for out in outputs:
@@ -450,14 +447,14 @@ class LSTMTest(test.TestCase):
       })
 
       def _cell(i):
-        return core_rnn_cell_impl.LSTMCell(
+        return rnn_cell.LSTMCell(
             num_units + i,
             use_peepholes=False,
             initializer=initializer,
             state_is_tuple=True)
 
       # This creates a state tuple which has 4 sub-tuples of length 2 each.
-      cell = core_rnn_cell_impl.MultiRNNCell(
+      cell = rnn_cell.MultiRNNCell(
           [_cell(i) for i in range(4)], state_is_tuple=True)
 
       self.assertEqual(len(cell.state_size), 4)
@@ -471,7 +468,7 @@ class LSTMTest(test.TestCase):
 
       state_names = (("c0", "m0"), ("c1", "m1"), ("c2", "m2"), ("c3", "m3"))
       with variable_scope.variable_scope("share_scope"):
-        outputs, state = core_rnn.static_state_saving_rnn(
+        outputs, state = rnn.static_state_saving_rnn(
             cell, inputs, state_saver=state_saver, state_name=state_names)
       self.assertEqual(len(outputs), len(inputs))
 
@@ -508,13 +505,13 @@ class LSTMTest(test.TestCase):
           array_ops.placeholder(
               dtypes.float32, shape=(None, input_size))
       ]
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
           initializer=initializer,
           state_is_tuple=False)
-      outputs, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
       self.assertEqual(len(outputs), len(inputs))
 
       variables_lib.global_variables_initializer().run()
@@ -535,20 +532,20 @@ class LSTMTest(test.TestCase):
           array_ops.placeholder(
               dtypes.float32, shape=(None, input_size))
       ]
-      cell_notuple = core_rnn_cell_impl.LSTMCell(
+      cell_notuple = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
           initializer=initializer,
           state_is_tuple=False)
-      cell_tuple = core_rnn_cell_impl.LSTMCell(
+      cell_tuple = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
           initializer=initializer,
           state_is_tuple=True)
       with variable_scope.variable_scope("root") as scope:
-        outputs_notuple, state_notuple = core_rnn.static_rnn(
+        outputs_notuple, state_notuple = rnn.static_rnn(
             cell_notuple,
             inputs,
             dtype=dtypes.float32,
@@ -562,7 +559,7 @@ class LSTMTest(test.TestCase):
         # the parameters from different RNNCell instances.  Right now,
         # this seems an unrealistic use case except for testing.
         cell_tuple._scope = cell_notuple._scope  # pylint: disable=protected-access
-        outputs_tuple, state_tuple = core_rnn.static_rnn(
+        outputs_tuple, state_tuple = rnn.static_rnn(
             cell_tuple,
             inputs,
             dtype=dtypes.float32,
@@ -603,7 +600,7 @@ class LSTMTest(test.TestCase):
               dtypes.float32, shape=(None, input_size))
       ]
 
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
@@ -612,7 +609,7 @@ class LSTMTest(test.TestCase):
           initializer=initializer,
           state_is_tuple=False)
 
-      outputs, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+      outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
       self.assertEqual(len(outputs), len(inputs))
 
@@ -635,7 +632,7 @@ class LSTMTest(test.TestCase):
               dtypes.float64, shape=(None, input_size))
       ]
 
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
@@ -644,7 +641,7 @@ class LSTMTest(test.TestCase):
           initializer=initializer,
           state_is_tuple=False)
 
-      outputs, _ = core_rnn.static_rnn(
+      outputs, _ = rnn.static_rnn(
           cell,
           inputs,
           initial_state=cell.zero_state(batch_size, dtypes.float64))
@@ -672,7 +669,7 @@ class LSTMTest(test.TestCase):
       ]
       initializer = init_ops.constant_initializer(0.001)
 
-      cell_noshard = core_rnn_cell_impl.LSTMCell(
+      cell_noshard = rnn_cell.LSTMCell(
           num_units,
           num_proj=num_proj,
           use_peepholes=True,
@@ -681,7 +678,7 @@ class LSTMTest(test.TestCase):
           num_proj_shards=num_proj_shards,
           state_is_tuple=False)
 
-      cell_shard = core_rnn_cell_impl.LSTMCell(
+      cell_shard = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           initializer=initializer,
@@ -689,10 +686,10 @@ class LSTMTest(test.TestCase):
           state_is_tuple=False)
 
       with variable_scope.variable_scope("noshard_scope"):
-        outputs_noshard, state_noshard = core_rnn.static_rnn(
+        outputs_noshard, state_noshard = rnn.static_rnn(
             cell_noshard, inputs, dtype=dtypes.float32)
       with variable_scope.variable_scope("shard_scope"):
-        outputs_shard, state_shard = core_rnn.static_rnn(
+        outputs_shard, state_shard = rnn.static_rnn(
             cell_shard, inputs, dtype=dtypes.float32)
 
       self.assertEqual(len(outputs_noshard), len(inputs))
@@ -731,7 +728,7 @@ class LSTMTest(test.TestCase):
               dtypes.float64, shape=(None, input_size))
       ]
 
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
@@ -739,9 +736,9 @@ class LSTMTest(test.TestCase):
           num_proj_shards=num_proj_shards,
           initializer=initializer,
           state_is_tuple=False)
-      dropout_cell = core_rnn_cell_impl.DropoutWrapper(cell, 0.5, seed=0)
+      dropout_cell = rnn_cell.DropoutWrapper(cell, 0.5, seed=0)
 
-      outputs, state = core_rnn.static_rnn(
+      outputs, state = rnn.static_rnn(
           dropout_cell,
           inputs,
           sequence_length=sequence_length,
@@ -776,13 +773,13 @@ class LSTMTest(test.TestCase):
           array_ops.placeholder(
               dtypes.float32, shape=(None, input_size))
       ]
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
           initializer=initializer,
           state_is_tuple=False)
-      cell_d = core_rnn_cell_impl.LSTMCell(
+      cell_d = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
@@ -790,11 +787,11 @@ class LSTMTest(test.TestCase):
           state_is_tuple=False)
 
       with variable_scope.variable_scope("share_scope"):
-        outputs0, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+        outputs0, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
       with variable_scope.variable_scope("share_scope", reuse=True):
-        outputs1, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+        outputs1, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
       with variable_scope.variable_scope("diff_scope"):
-        outputs2, _ = core_rnn.static_rnn(cell_d, inputs, dtype=dtypes.float32)
+        outputs2, _ = rnn.static_rnn(cell_d, inputs, dtype=dtypes.float32)
 
       variables_lib.global_variables_initializer().run()
       input_value = np.random.randn(batch_size, input_size)
@@ -823,7 +820,7 @@ class LSTMTest(test.TestCase):
           array_ops.placeholder(
               dtypes.float32, shape=(None, input_size))
       ]
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
@@ -832,10 +829,10 @@ class LSTMTest(test.TestCase):
 
       with ops_lib.name_scope("scope0"):
         with variable_scope.variable_scope("share_scope"):
-          outputs0, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+          outputs0, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
       with ops_lib.name_scope("scope1"):
         with variable_scope.variable_scope("share_scope", reuse=True):
-          outputs1, _ = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+          outputs1, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
       variables_lib.global_variables_initializer().run()
       input_value = np.random.randn(batch_size, input_size)
@@ -881,7 +878,7 @@ class LSTMTest(test.TestCase):
 
   def testDynamicRNNAllowsUnknownTimeDimension(self):
     inputs = array_ops.placeholder(dtypes.float32, shape=[1, None, 20])
-    cell = core_rnn_cell.GRUCell(30)
+    cell = rnn_cell.GRUCell(30)
     # Smoke test, this should not raise an error
     rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32)
 
@@ -900,14 +897,14 @@ class LSTMTest(test.TestCase):
               dtypes.float32, shape=(None, input_size))
       ]
       inputs_c = array_ops.stack(inputs)
-      cell = core_rnn_cell.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           num_proj=num_proj,
           initializer=initializer,
           state_is_tuple=True)
       with variable_scope.variable_scope("root") as scope:
-        outputs_static, state_static = core_rnn.static_rnn(
+        outputs_static, state_static = rnn.static_rnn(
             cell,
             inputs,
             dtype=dtypes.float32,
@@ -921,8 +918,8 @@ class LSTMTest(test.TestCase):
             time_major=True,
             sequence_length=sequence_length,
             scope=scope)
-      self.assertTrue(isinstance(state_static, core_rnn_cell.LSTMStateTuple))
-      self.assertTrue(isinstance(state_dynamic, core_rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(state_static, rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(state_dynamic, rnn_cell.LSTMStateTuple))
       self.assertEqual(state_static[0], state_static.c)
       self.assertEqual(state_static[1], state_static.h)
       self.assertEqual(state_dynamic[0], state_dynamic.c)
@@ -960,7 +957,7 @@ class LSTMTest(test.TestCase):
       inputs_c = array_ops.stack(inputs)
 
       def _cell(i):
-        return core_rnn_cell.LSTMCell(
+        return rnn_cell.LSTMCell(
             num_units + i,
             use_peepholes=True,
             num_proj=num_proj + i,
@@ -968,7 +965,7 @@ class LSTMTest(test.TestCase):
             state_is_tuple=True)
 
       # This creates a state tuple which has 4 sub-tuples of length 2 each.
-      cell = core_rnn_cell.MultiRNNCell(
+      cell = rnn_cell.MultiRNNCell(
           [_cell(i) for i in range(4)], state_is_tuple=True)
 
       self.assertEqual(len(cell.state_size), 4)
@@ -982,7 +979,7 @@ class LSTMTest(test.TestCase):
         self.assertEqual(test_zero[i][1].get_shape()[1], cell.state_size[i][1])
 
       with variable_scope.variable_scope("root") as scope:
-        outputs_static, state_static = core_rnn.static_rnn(
+        outputs_static, state_static = rnn.static_rnn(
             cell,
             inputs,
             dtype=dtypes.float32,
@@ -1034,7 +1031,7 @@ class LSTMTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=self._seed)
 
-      cell = core_rnn_cell.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           initializer=initializer,
@@ -1042,7 +1039,7 @@ class LSTMTest(test.TestCase):
           state_is_tuple=False)
 
       with variable_scope.variable_scope("dynamic_scope"):
-        outputs_static, state_static = core_rnn.static_rnn(
+        outputs_static, state_static = rnn.static_rnn(
             cell, inputs, sequence_length=sequence_length, dtype=dtypes.float32)
 
       feeds = {concat_inputs: input_values}
@@ -1092,7 +1089,7 @@ class LSTMTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=self._seed)
 
-      cell = core_rnn_cell.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=True,
           initializer=initializer,
@@ -1205,16 +1202,16 @@ class BidirectionalRNNTest(test.TestCase):
         -0.01, 0.01, seed=self._seed)
     sequence_length = array_ops.placeholder(
         dtypes.int64) if use_sequence_length else None
-    cell_fw = core_rnn_cell_impl.LSTMCell(
+    cell_fw = rnn_cell.LSTMCell(
         num_units, input_size, initializer=initializer, state_is_tuple=False)
-    cell_bw = core_rnn_cell_impl.LSTMCell(
+    cell_bw = rnn_cell.LSTMCell(
         num_units, input_size, initializer=initializer, state_is_tuple=False)
     inputs = max_length * [
         array_ops.placeholder(
             dtypes.float32,
             shape=(batch_size, input_size) if use_shape else (None, input_size))
     ]
-    outputs, state_fw, state_bw = core_rnn.static_bidirectional_rnn(
+    outputs, state_fw, state_bw = rnn.static_bidirectional_rnn(
         cell_fw,
         cell_bw,
         inputs,
@@ -1337,9 +1334,9 @@ class BidirectionalRNNTest(test.TestCase):
         -0.01, 0.01, seed=self._seed)
     sequence_length = (
         array_ops.placeholder(dtypes.int64) if use_sequence_length else None)
-    cell_fw = core_rnn_cell.LSTMCell(
+    cell_fw = rnn_cell.LSTMCell(
         num_units, initializer=initializer, state_is_tuple=use_state_tuple)
-    cell_bw = core_rnn_cell.LSTMCell(
+    cell_bw = rnn_cell.LSTMCell(
         num_units, initializer=initializer, state_is_tuple=use_state_tuple)
     inputs = max_length * [
         array_ops.placeholder(
@@ -1530,7 +1527,7 @@ class MultiDimensionalLSTMTest(test.TestCase):
       # variables.
       cell = DummyMultiDimensionalLSTM(feature_dims)
       state_saver = TestStateSaver(batch_size, input_size)
-      outputs_static, state_static = core_rnn.static_rnn(
+      outputs_static, state_static = rnn.static_rnn(
           cell, inputs, dtype=dtypes.float32, sequence_length=sequence_length)
       outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
           cell,
@@ -1538,13 +1535,13 @@ class MultiDimensionalLSTMTest(test.TestCase):
           dtype=dtypes.float32,
           time_major=True,
           sequence_length=sequence_length)
-      outputs_bid, state_fw, state_bw = core_rnn.static_bidirectional_rnn(
+      outputs_bid, state_fw, state_bw = rnn.static_bidirectional_rnn(
           cell,
           cell,
           inputs_using_dim,
           dtype=dtypes.float32,
           sequence_length=sequence_length)
-      outputs_sav, state_sav = core_rnn.static_state_saving_rnn(
+      outputs_sav, state_sav = rnn.static_state_saving_rnn(
           cell,
           inputs_using_dim,
           sequence_length=sequence_length,
@@ -1634,15 +1631,15 @@ class NestedLSTMTest(test.TestCase):
           dtype=dtypes.float32,
           time_major=True,
           sequence_length=sequence_length)
-      outputs_static, state_static = core_rnn.static_rnn(
+      outputs_static, state_static = rnn.static_rnn(
           cell, inputs, dtype=dtypes.float32, sequence_length=sequence_length)
-      outputs_bid, state_fw, state_bw = core_rnn.static_bidirectional_rnn(
+      outputs_bid, state_fw, state_bw = rnn.static_bidirectional_rnn(
           cell,
           cell,
           inputs_using_dim,
           dtype=dtypes.float32,
           sequence_length=sequence_length)
-      outputs_sav, state_sav = core_rnn.static_state_saving_rnn(
+      outputs_sav, state_sav = rnn.static_state_saving_rnn(
           cell,
           inputs_using_dim,
           sequence_length=sequence_length,
@@ -1738,7 +1735,7 @@ class StateSaverRNNTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=self._seed)
       state_saver = TestStateSaver(batch_size, 2 * num_units)
-      cell = core_rnn_cell_impl.LSTMCell(
+      cell = rnn_cell.LSTMCell(
           num_units,
           use_peepholes=False,
           initializer=initializer,
@@ -1747,7 +1744,7 @@ class StateSaverRNNTest(test.TestCase):
           array_ops.placeholder(
               dtypes.float32, shape=(batch_size, input_size))
       ]
-      return core_rnn.static_state_saving_rnn(
+      return rnn.static_state_saving_rnn(
           cell,
           inputs,
           state_saver=state_saver,
@@ -1779,7 +1776,7 @@ class GRUTest(test.TestCase):
       concat_inputs = array_ops.placeholder(
           dtypes.float32, shape=(time_steps, batch_size, input_size))
 
-      cell = core_rnn_cell.GRUCell(num_units=num_units)
+      cell = rnn_cell.GRUCell(num_units=num_units)
 
       with variable_scope.variable_scope("dynamic_scope"):
         outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
@@ -1830,7 +1827,7 @@ class GRUTest(test.TestCase):
     def factory(scope):
       concat_inputs = array_ops.placeholder(
           dtypes.float32, shape=(time_steps, batch_size, input_size))
-      cell = core_rnn_cell.GRUCell(num_units=num_units)
+      cell = rnn_cell.GRUCell(num_units=num_units)
       return rnn.dynamic_rnn(
           cell,
           inputs=concat_inputs,
@@ -1864,7 +1861,7 @@ class RawRNNTest(test.TestCase):
           dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
       inputs_ta = inputs_ta.unstack(inputs)
 
-      cell = core_rnn_cell.LSTMCell(num_units, state_is_tuple=True)
+      cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
 
       def loop_fn(time_, cell_output, cell_state, unused_loop_state):
         emit_output = cell_output  # == None for time == 0
@@ -1965,7 +1962,7 @@ class RawRNNTest(test.TestCase):
           dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
       inputs_ta = inputs_ta.unstack(inputs)
 
-      cell = core_rnn_cell.LSTMCell(num_units, state_is_tuple=True)
+      cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
 
       def loop_fn(time_, cell_output, cell_state, loop_state):
         if cell_output is None:
@@ -2001,7 +1998,7 @@ class RawRNNTest(test.TestCase):
           dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
       inputs_ta = inputs_ta.unstack(inputs)
 
-      cell = core_rnn_cell.LSTMCell(num_units, state_is_tuple=True)
+      cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
 
       def loop_fn(time_, cell_output, cell_state, loop_state):
         if cell_output is None:
@@ -2044,7 +2041,7 @@ class RawRNNTest(test.TestCase):
           dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
       inputs_ta = inputs_ta.unstack(inputs)
 
-      cell = core_rnn_cell.LSTMCell(num_units, state_is_tuple=True)
+      cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
 
       def loop_fn(time_, cell_output, cell_state, _):
         if cell_output is None:
@@ -2113,7 +2110,7 @@ class RawRNNTest(test.TestCase):
           dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
       inputs_ta = inputs_ta.unstack(inputs)
 
-      cell = core_rnn_cell.LSTMCell(num_units, state_is_tuple=True)
+      cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
 
       def loop_fn(time_, cell_output, cell_state, unused_loop_state):
         emit_output = cell_output  # == None for time == 0
@@ -2138,7 +2135,7 @@ class RawRNNTest(test.TestCase):
     self._testScope(factory, prefix=None, use_outer_scope=False)
 
 
-class DeviceWrapperCell(core_rnn_cell.RNNCell):
+class DeviceWrapperCell(rnn_cell.RNNCell):
   """Class to ensure cell calculation happens on a specific device."""
 
   def __init__(self, cell, device):
@@ -2172,7 +2169,7 @@ class TensorArrayOnCorrectDeviceTest(test.TestCase):
     input_size = 5
     num_units = 10
 
-    cell = core_rnn_cell.LSTMCell(num_units, use_peepholes=True)
+    cell = rnn_cell.LSTMCell(num_units, use_peepholes=True)
     gpu_cell = DeviceWrapperCell(cell, cell_device)
     inputs = np.random.randn(batch_size, time_steps,
                              input_size).astype(np.float32)
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/fused_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/fused_rnn_cell_test.py
index a656831c02..f2a032e41e 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/fused_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/fused_rnn_cell_test.py
@@ -20,14 +20,14 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.rnn.python.ops import core_rnn
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.contrib.rnn.python.ops import fused_rnn_cell
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -41,7 +41,7 @@ class FusedRnnCellTest(test.TestCase):
     with self.test_session() as sess:
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=19890212)
-      cell = core_rnn_cell_impl.BasicRNNCell(10)
+      cell = rnn_cell.BasicRNNCell(10)
       batch_size = 5
       input_size = 20
       timelen = 15
@@ -49,7 +49,7 @@ class FusedRnnCellTest(test.TestCase):
           np.random.randn(timelen, batch_size, input_size))
       with variable_scope.variable_scope("basic", initializer=initializer):
         unpacked_inputs = array_ops.unstack(inputs)
-        outputs, state = core_rnn.static_rnn(
+        outputs, state = rnn.static_rnn(
             cell, unpacked_inputs, dtype=dtypes.float64)
         packed_outputs = array_ops.stack(outputs)
         basic_vars = [
@@ -65,7 +65,7 @@ class FusedRnnCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "fused_static", initializer=initializer):
         fused_cell = fused_rnn_cell.FusedRNNCellAdaptor(
-            core_rnn_cell_impl.BasicRNNCell(10))
+            rnn_cell.BasicRNNCell(10))
         outputs, state = fused_cell(inputs, dtype=dtypes.float64)
         fused_static_vars = [
             v for v in variables.trainable_variables()
@@ -86,7 +86,7 @@ class FusedRnnCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "fused_dynamic", initializer=initializer):
         fused_cell = fused_rnn_cell.FusedRNNCellAdaptor(
-            core_rnn_cell_impl.BasicRNNCell(10), use_dynamic_rnn=True)
+            rnn_cell.BasicRNNCell(10), use_dynamic_rnn=True)
         outputs, state = fused_cell(inputs, dtype=dtypes.float64)
         fused_dynamic_vars = [
             v for v in variables.trainable_variables()
@@ -109,8 +109,8 @@ class FusedRnnCellTest(test.TestCase):
     with self.test_session() as sess:
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=19890213)
-      fw_cell = core_rnn_cell_impl.BasicRNNCell(10)
-      bw_cell = core_rnn_cell_impl.BasicRNNCell(10)
+      fw_cell = rnn_cell.BasicRNNCell(10)
+      bw_cell = rnn_cell.BasicRNNCell(10)
       batch_size = 5
       input_size = 20
       timelen = 15
@@ -120,7 +120,7 @@ class FusedRnnCellTest(test.TestCase):
       # test bi-directional rnn
       with variable_scope.variable_scope("basic", initializer=initializer):
         unpacked_inputs = array_ops.unstack(inputs)
-        outputs, fw_state, bw_state = core_rnn.static_bidirectional_rnn(
+        outputs, fw_state, bw_state = rnn.static_bidirectional_rnn(
             fw_cell, bw_cell, unpacked_inputs, dtype=dtypes.float64)
         packed_outputs = array_ops.stack(outputs)
         basic_vars = [
@@ -136,10 +136,9 @@ class FusedRnnCellTest(test.TestCase):
 
       with variable_scope.variable_scope("fused", initializer=initializer):
         fused_cell = fused_rnn_cell.FusedRNNCellAdaptor(
-            core_rnn_cell_impl.BasicRNNCell(10))
+            rnn_cell.BasicRNNCell(10))
         fused_bw_cell = fused_rnn_cell.TimeReversedFusedRNN(
-            fused_rnn_cell.FusedRNNCellAdaptor(
-                core_rnn_cell_impl.BasicRNNCell(10)))
+            fused_rnn_cell.FusedRNNCellAdaptor(rnn_cell.BasicRNNCell(10)))
         fw_outputs, fw_state = fused_cell(
             inputs, dtype=dtypes.float64, scope="fw")
         bw_outputs, bw_state = fused_bw_cell(
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py b/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
index 4247aeb839..d2ec648537 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
@@ -22,7 +22,6 @@ import time
 
 import numpy as np
 
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.contrib.rnn.python.ops import gru_ops
 from tensorflow.python.client import session
 from tensorflow.python.framework import dtypes
@@ -33,6 +32,7 @@ from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -78,7 +78,7 @@ class GRUBlockCellTest(test.TestCase):
 
       # Output from the basic GRU cell implementation.
       with vs.variable_scope("basic", initializer=initializer):
-        output = core_rnn_cell_impl.GRUCell(cell_size)(x, h)
+        output = rnn_cell.GRUCell(cell_size)(x, h)
         sess.run([variables.global_variables_initializer()])
         basic_res = sess.run([output], {x: x_value, h: h_value})
 
@@ -128,7 +128,7 @@ class GRUBlockCellTest(test.TestCase):
 
       # Output from the basic GRU cell implementation.
       with vs.variable_scope("basic", initializer=initializer):
-        cell = core_rnn_cell_impl.GRUCell(cell_size)
+        cell = rnn_cell.GRUCell(cell_size)
         outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
             cell,
             inputs=concat_x,
@@ -192,7 +192,7 @@ class GRUBlockCellTest(test.TestCase):
 
       # Gradients from the basic GRU cell implementation.
       with vs.variable_scope("basic", initializer=initializer):
-        output = core_rnn_cell_impl.GRUCell(cell_size)(x, h)
+        output = rnn_cell.GRUCell(cell_size)(x, h)
         sess.run([variables.global_variables_initializer()])
 
         all_variables = variables.global_variables()[4:8]
@@ -258,7 +258,7 @@ class GRUBlockCellTest(test.TestCase):
 
       # Gradients from the basic GRU cell implementation.
       with vs.variable_scope("basic", initializer=initializer):
-        cell = core_rnn_cell_impl.GRUCell(cell_size)
+        cell = rnn_cell.GRUCell(cell_size)
 
         outputs_dynamic, _ = rnn.dynamic_rnn(
             cell,
@@ -377,7 +377,7 @@ def training_gru_block_vs_gru_cell(batch_size,
 
       # Output from the basic GRU cell implementation.
       with vs.variable_scope("basic", initializer=initializer):
-        cell = core_rnn_cell_impl.GRUCell(cell_size)
+        cell = rnn_cell.GRUCell(cell_size)
 
         outputs_dynamic, _ = rnn.dynamic_rnn(
             cell,
@@ -448,7 +448,7 @@ def inference_gru_block_vs_gru_cell(batch_size,
 
       # Output from the basic GRU cell implementation.
       with vs.variable_scope("basic", initializer=initializer):
-        cell = core_rnn_cell_impl.GRUCell(cell_size)
+        cell = rnn_cell.GRUCell(cell_size)
         outputs_dynamic, _ = rnn.dynamic_rnn(
             cell,
             inputs=concat_x,
@@ -497,8 +497,8 @@ def single_bprop_step_gru_block_vs_gru_cell(batch_size,
 
       # Output from the basic GRU cell implementation.
       with vs.variable_scope("basic", initializer=initializer):
-        output = core_rnn_cell_impl.GRUCell(cell_size)(array_ops.identity(x),
-                                                       array_ops.identity(h))
+        output = rnn_cell.GRUCell(cell_size)(array_ops.identity(x),
+                                             array_ops.identity(h))
         sess.run([variables.global_variables_initializer()])
         grad_output_wrt_input = gradients_impl.gradients([output], h)
         basic_time_bprop = time_taken_by_op(grad_output_wrt_input, sess, iters)
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/lstm_ops_test.py b/tensorflow/contrib/rnn/python/kernel_tests/lstm_ops_test.py
index 3a5cbf604d..1e6c44a115 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/lstm_ops_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/lstm_ops_test.py
@@ -20,8 +20,6 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.rnn.python.ops import core_rnn
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -30,6 +28,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -66,10 +65,9 @@ class LSTMBlockCellTest(test.TestCase):
         m1 = array_ops.zeros([1, 2])
         m2 = array_ops.zeros([1, 2])
         m3 = array_ops.zeros([1, 2])
-        g, ((out_m0, out_m1),
-            (out_m2, out_m3)) = core_rnn_cell_impl.MultiRNNCell(
-                [lstm_ops.LSTMBlockCell(2) for _ in range(2)],
-                state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
+        g, ((out_m0, out_m1), (out_m2, out_m3)) = rnn_cell.MultiRNNCell(
+            [lstm_ops.LSTMBlockCell(2)
+             for _ in range(2)], state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
         sess.run([variables.global_variables_initializer()])
         res = sess.run([g, out_m0, out_m1, out_m2, out_m3], {
             x.name: np.array([[1., 1.]]),
@@ -88,11 +86,11 @@ class LSTMBlockCellTest(test.TestCase):
 
   def testCompatibleNames(self):
     with self.test_session(use_gpu=self._use_gpu, graph=ops.Graph()):
-      cell = core_rnn_cell_impl.LSTMCell(10)
-      pcell = core_rnn_cell_impl.LSTMCell(10, use_peepholes=True)
+      cell = rnn_cell.LSTMCell(10)
+      pcell = rnn_cell.LSTMCell(10, use_peepholes=True)
       inputs = [array_ops.zeros([4, 5])] * 6
-      core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
-      core_rnn.static_rnn(pcell, inputs, dtype=dtypes.float32, scope="peephole")
+      rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
+      rnn.static_rnn(pcell, inputs, dtype=dtypes.float32, scope="peephole")
       basic_names = {
           v.name: v.get_shape()
           for v in variables.trainable_variables()
@@ -102,8 +100,8 @@ class LSTMBlockCellTest(test.TestCase):
       cell = lstm_ops.LSTMBlockCell(10)
       pcell = lstm_ops.LSTMBlockCell(10, use_peephole=True)
       inputs = [array_ops.zeros([4, 5])] * 6
-      core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
-      core_rnn.static_rnn(pcell, inputs, dtype=dtypes.float32, scope="peephole")
+      rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
+      rnn.static_rnn(pcell, inputs, dtype=dtypes.float32, scope="peephole")
       block_names = {
           v.name: v.get_shape()
           for v in variables.trainable_variables()
@@ -140,11 +138,9 @@ class LSTMBlockCellTest(test.TestCase):
         m1 = array_ops.zeros([1, 2])
         m2 = array_ops.zeros([1, 2])
         m3 = array_ops.zeros([1, 2])
-        g, ((out_m0, out_m1),
-            (out_m2, out_m3)) = core_rnn_cell_impl.MultiRNNCell(
-                [core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=True)
-                 for _ in range(2)],
-                state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
+        g, ((out_m0, out_m1), (out_m2, out_m3)) = rnn_cell.MultiRNNCell(
+            [rnn_cell.BasicLSTMCell(2, state_is_tuple=True) for _ in range(2)],
+            state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
         sess.run([variables.global_variables_initializer()])
         basic_res = sess.run([g, out_m0, out_m1, out_m2, out_m3], {
             x.name: x_values,
@@ -159,10 +155,9 @@ class LSTMBlockCellTest(test.TestCase):
         m1 = array_ops.zeros([1, 2])
         m2 = array_ops.zeros([1, 2])
         m3 = array_ops.zeros([1, 2])
-        g, ((out_m0, out_m1),
-            (out_m2, out_m3)) = core_rnn_cell_impl.MultiRNNCell(
-                [lstm_ops.LSTMBlockCell(2) for _ in range(2)],
-                state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
+        g, ((out_m0, out_m1), (out_m2, out_m3)) = rnn_cell.MultiRNNCell(
+            [lstm_ops.LSTMBlockCell(2)
+             for _ in range(2)], state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
         sess.run([variables.global_variables_initializer()])
         block_res = sess.run([g, out_m0, out_m1, out_m2, out_m3], {
             x.name: x_values,
@@ -193,12 +188,12 @@ class LSTMBlockCellTest(test.TestCase):
         m1 = array_ops.zeros([1, 2])
         m2 = array_ops.zeros([1, 2])
         m3 = array_ops.zeros([1, 2])
-        g, ((out_m0, out_m1),
-            (out_m2, out_m3)) = core_rnn_cell_impl.MultiRNNCell(
-                [core_rnn_cell_impl.LSTMCell(
-                    2, use_peepholes=True, state_is_tuple=True)
-                 for _ in range(2)],
-                state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
+        g, ((out_m0, out_m1), (out_m2, out_m3)) = rnn_cell.MultiRNNCell(
+            [
+                rnn_cell.LSTMCell(2, use_peepholes=True, state_is_tuple=True)
+                for _ in range(2)
+            ],
+            state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
         sess.run([variables.global_variables_initializer()])
         basic_res = sess.run([g, out_m0, out_m1, out_m2, out_m3], {
             x.name: x_values,
@@ -213,11 +208,9 @@ class LSTMBlockCellTest(test.TestCase):
         m1 = array_ops.zeros([1, 2])
         m2 = array_ops.zeros([1, 2])
         m3 = array_ops.zeros([1, 2])
-        g, ((out_m0, out_m1),
-            (out_m2, out_m3)) = core_rnn_cell_impl.MultiRNNCell(
-                [lstm_ops.LSTMBlockCell(2, use_peephole=True)
-                 for _ in range(2)],
-                state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
+        g, ((out_m0, out_m1), (out_m2, out_m3)) = rnn_cell.MultiRNNCell(
+            [lstm_ops.LSTMBlockCell(2, use_peephole=True) for _ in range(2)],
+            state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
         sess.run([variables.global_variables_initializer()])
         block_res = sess.run([g, out_m0, out_m1, out_m2, out_m3], {
             x.name: x_values,
@@ -247,8 +240,8 @@ class LSTMBlockCellTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=19890212)
       with variable_scope.variable_scope("basic", initializer=initializer):
-        cell = core_rnn_cell_impl.BasicLSTMCell(cell_size, state_is_tuple=True)
-        outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+        cell = rnn_cell.BasicLSTMCell(cell_size, state_is_tuple=True)
+        outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
         sess.run([variables.global_variables_initializer()])
         basic_outputs, basic_state = sess.run([outputs, state[0]])
@@ -321,9 +314,9 @@ class LSTMBlockCellTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=19890212)
       with variable_scope.variable_scope("basic", initializer=initializer):
-        cell = core_rnn_cell_impl.LSTMCell(
+        cell = rnn_cell.LSTMCell(
             cell_size, use_peepholes=True, state_is_tuple=True)
-        outputs, state = core_rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
+        outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
 
         sess.run([variables.global_variables_initializer()])
         basic_outputs, basic_state = sess.run([outputs, state[0]])
@@ -410,8 +403,8 @@ class LSTMBlockCellTest(test.TestCase):
       initializer = init_ops.random_uniform_initializer(
           -0.01, 0.01, seed=19890213)
       with variable_scope.variable_scope("basic", initializer=initializer):
-        cell = core_rnn_cell_impl.BasicLSTMCell(cell_size, state_is_tuple=True)
-        outputs, state = core_rnn.static_rnn(
+        cell = rnn_cell.BasicLSTMCell(cell_size, state_is_tuple=True)
+        outputs, state = rnn.static_rnn(
             cell, inputs, dtype=dtypes.float32, sequence_length=seq_lengths)
         sess.run([variables.global_variables_initializer()])
         basic_outputs, basic_state = sess.run([outputs, state[0]])
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
index 334baa5f9c..04b0c5876b 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
@@ -22,8 +22,7 @@ import itertools
 
 import numpy as np
 
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
-from tensorflow.contrib.rnn.python.ops import rnn_cell
+from tensorflow.contrib.rnn.python.ops import rnn_cell as contrib_rnn_cell
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
@@ -37,6 +36,7 @@ from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -65,7 +65,7 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([batch_size, input_size])
         m = array_ops.zeros([batch_size, state_size])
-        output, state = rnn_cell.CoupledInputForgetGateLSTMCell(
+        output, state = contrib_rnn_cell.CoupledInputForgetGateLSTMCell(
             num_units=num_units, forget_bias=1.0, state_is_tuple=False)(x, m)
         sess.run([variables.global_variables_initializer()])
         res = sess.run([output, state], {
@@ -94,7 +94,7 @@ class RNNCellTest(test.TestCase):
           "root", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([batch_size, input_size])
         m = array_ops.zeros([batch_size, state_size * num_shifts])
-        output, state = rnn_cell.TimeFreqLSTMCell(
+        output, state = contrib_rnn_cell.TimeFreqLSTMCell(
             num_units=num_units,
             feature_size=feature_size,
             frequency_skip=frequency_skip,
@@ -130,7 +130,7 @@ class RNNCellTest(test.TestCase):
       num_shifts = int((input_size - feature_size) / frequency_skip + 1)
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.GridLSTMCell(
+        cell = contrib_rnn_cell.GridLSTMCell(
             num_units=num_units,
             feature_size=feature_size,
             frequency_skip=frequency_skip,
@@ -181,7 +181,7 @@ class RNNCellTest(test.TestCase):
       end_freqindex_list = [2, 4]
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.GridLSTMCell(
+        cell = contrib_rnn_cell.GridLSTMCell(
             num_units=num_units,
             feature_size=feature_size,
             frequency_skip=frequency_skip,
@@ -249,7 +249,7 @@ class RNNCellTest(test.TestCase):
         with variable_scope.variable_scope(
             "state_is_tuple" + str(state_is_tuple),
             initializer=init_ops.constant_initializer(0.5)):
-          cell = rnn_cell.GridLSTMCell(
+          cell = contrib_rnn_cell.GridLSTMCell(
               num_units=num_units,
               feature_size=feature_size,
               frequency_skip=frequency_skip,
@@ -330,7 +330,7 @@ class RNNCellTest(test.TestCase):
           dtype=np.float32)
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.BidirectionalGridLSTMCell(
+        cell = contrib_rnn_cell.BidirectionalGridLSTMCell(
             num_units=num_units,
             feature_size=feature_size,
             share_time_frequency_weights=True,
@@ -403,7 +403,7 @@ class RNNCellTest(test.TestCase):
           dtype=np.float32)
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.BidirectionalGridLSTMCell(
+        cell = contrib_rnn_cell.BidirectionalGridLSTMCell(
             num_units=num_units,
             feature_size=feature_size,
             share_time_frequency_weights=True,
@@ -442,28 +442,28 @@ class RNNCellTest(test.TestCase):
   def testAttentionCellWrapperFailures(self):
     with self.assertRaisesRegexp(TypeError,
                                  "The parameter cell is not RNNCell."):
-      rnn_cell.AttentionCellWrapper(None, 0)
+      contrib_rnn_cell.AttentionCellWrapper(None, 0)
 
     num_units = 8
     for state_is_tuple in [False, True]:
       with ops.Graph().as_default():
-        lstm_cell = core_rnn_cell_impl.BasicLSTMCell(
+        lstm_cell = rnn_cell.BasicLSTMCell(
             num_units, state_is_tuple=state_is_tuple)
         with self.assertRaisesRegexp(
             ValueError, "attn_length should be greater than zero, got 0"):
-          rnn_cell.AttentionCellWrapper(
+          contrib_rnn_cell.AttentionCellWrapper(
               lstm_cell, 0, state_is_tuple=state_is_tuple)
         with self.assertRaisesRegexp(
             ValueError, "attn_length should be greater than zero, got -1"):
-          rnn_cell.AttentionCellWrapper(
+          contrib_rnn_cell.AttentionCellWrapper(
               lstm_cell, -1, state_is_tuple=state_is_tuple)
       with ops.Graph().as_default():
-        lstm_cell = core_rnn_cell_impl.BasicLSTMCell(
-            num_units, state_is_tuple=True)
+        lstm_cell = rnn_cell.BasicLSTMCell(num_units, state_is_tuple=True)
         with self.assertRaisesRegexp(
             ValueError, "Cell returns tuple of states, but the flag "
             "state_is_tuple is not set. State size is: *"):
-          rnn_cell.AttentionCellWrapper(lstm_cell, 4, state_is_tuple=False)
+          contrib_rnn_cell.AttentionCellWrapper(
+              lstm_cell, 4, state_is_tuple=False)
 
   def testAttentionCellWrapperZeros(self):
     num_units = 8
@@ -475,9 +475,9 @@ class RNNCellTest(test.TestCase):
         with self.test_session() as sess:
           with variable_scope.variable_scope("state_is_tuple_" + str(
               state_is_tuple)):
-            lstm_cell = core_rnn_cell_impl.BasicLSTMCell(
+            lstm_cell = rnn_cell.BasicLSTMCell(
                 num_units, state_is_tuple=state_is_tuple)
-            cell = rnn_cell.AttentionCellWrapper(
+            cell = contrib_rnn_cell.AttentionCellWrapper(
                 lstm_cell, attn_length, state_is_tuple=state_is_tuple)
             if state_is_tuple:
               zeros = array_ops.zeros([batch_size, num_units], dtype=np.float32)
@@ -526,9 +526,9 @@ class RNNCellTest(test.TestCase):
         with self.test_session() as sess:
           with variable_scope.variable_scope("state_is_tuple_" + str(
               state_is_tuple)):
-            lstm_cell = core_rnn_cell_impl.BasicLSTMCell(
+            lstm_cell = rnn_cell.BasicLSTMCell(
                 num_units, state_is_tuple=state_is_tuple)
-            cell = rnn_cell.AttentionCellWrapper(
+            cell = contrib_rnn_cell.AttentionCellWrapper(
                 lstm_cell, attn_length, state_is_tuple=state_is_tuple)
             if state_is_tuple:
               zeros = constant_op.constant(
@@ -603,9 +603,9 @@ class RNNCellTest(test.TestCase):
         with variable_scope.variable_scope(
             "state_is_tuple", reuse=state_is_tuple,
             initializer=init_ops.glorot_uniform_initializer()):
-          lstm_cell = core_rnn_cell_impl.BasicLSTMCell(
+          lstm_cell = rnn_cell.BasicLSTMCell(
               num_units, state_is_tuple=state_is_tuple)
-          cell = rnn_cell.AttentionCellWrapper(
+          cell = contrib_rnn_cell.AttentionCellWrapper(
               lstm_cell, attn_length, state_is_tuple=state_is_tuple)
           # This is legacy behavior to preserve the test.  Weight
           # sharing no longer works by creating a new RNNCell in the
@@ -665,8 +665,7 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "nas_test",
           initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.NASCell(
-            num_units=num_units)
+        cell = contrib_rnn_cell.NASCell(num_units=num_units)
         inputs = constant_op.constant(
             np.array([[1., 1., 1., 1.],
                       [2., 2., 2., 2.],
@@ -677,8 +676,7 @@ class RNNCellTest(test.TestCase):
             0.1 * np.ones(
                 (batch_size, num_units), dtype=np.float32),
             dtype=dtypes.float32)
-        init_state = core_rnn_cell_impl.LSTMStateTuple(state_value,
-                                                       state_value)
+        init_state = rnn_cell.LSTMStateTuple(state_value, state_value)
         output, state = cell(inputs, init_state)
         sess.run([variables.global_variables_initializer()])
         res = sess.run([output, state])
@@ -719,9 +717,7 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "nas_proj_test",
           initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.NASCell(
-            num_units=num_units,
-            num_proj=num_proj)
+        cell = contrib_rnn_cell.NASCell(num_units=num_units, num_proj=num_proj)
         inputs = constant_op.constant(
             np.array([[1., 1., 1., 1.],
                       [2., 2., 2., 2.],
@@ -736,8 +732,7 @@ class RNNCellTest(test.TestCase):
             0.1 * np.ones(
                 (batch_size, num_proj), dtype=np.float32),
             dtype=dtypes.float32)
-        init_state = core_rnn_cell_impl.LSTMStateTuple(state_value_c,
-                                                       state_value_h)
+        init_state = rnn_cell.LSTMStateTuple(state_value_c, state_value_h)
         output, state = cell(inputs, init_state)
         sess.run([variables.global_variables_initializer()])
         res = sess.run([output, state])
@@ -767,7 +762,7 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "ugrnn_cell_test",
           initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.UGRNNCell(num_units=num_units)
+        cell = contrib_rnn_cell.UGRNNCell(num_units=num_units)
         inputs = constant_op.constant(
             np.array([[1., 1., 1., 1.],
                       [2., 2., 2., 2.],
@@ -803,8 +798,8 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "intersection_rnn_cell_test",
           initializer=init_ops.constant_initializer(0.5)):
-        cell = rnn_cell.IntersectionRNNCell(num_units=num_units,
-                                            num_in_proj=num_units)
+        cell = contrib_rnn_cell.IntersectionRNNCell(
+            num_units=num_units, num_in_proj=num_units)
         inputs = constant_op.constant(
             np.array([[1., 1., 1., 1.],
                       [2., 2., 2., 2.],
@@ -826,7 +821,7 @@ class RNNCellTest(test.TestCase):
   def testIntersectionRNNCellFailure(self):
     num_units = 2
     batch_size = 3
-    cell = rnn_cell.IntersectionRNNCell(num_units=num_units)
+    cell = contrib_rnn_cell.IntersectionRNNCell(num_units=num_units)
     inputs = constant_op.constant(
         np.array([[1., 1., 1., 1.],
                   [2., 2., 2., 2.],
@@ -862,9 +857,9 @@ class RNNCellTest(test.TestCase):
         x = array_ops.zeros([batch_size, input_size])
         c0 = array_ops.zeros([batch_size, 2])
         h0 = array_ops.zeros([batch_size, 2])
-        state0 = core_rnn_cell_impl.LSTMStateTuple(c0, h0)
-        output, state = rnn_cell.PhasedLSTMCell(num_units=num_units)((t, x),
-                                                                     state0)
+        state0 = rnn_cell.LSTMStateTuple(c0, h0)
+        output, state = contrib_rnn_cell.PhasedLSTMCell(num_units=num_units)(
+            (t, x), state0)
         sess.run([variables.global_variables_initializer()])
         res = sess.run([output, state], {
             t.name:
@@ -886,12 +881,12 @@ class RNNCellTest(test.TestCase):
           "base_cell", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.zeros([1, 3])
         m = array_ops.zeros([1, 3])
-        base_cell = core_rnn_cell_impl.GRUCell(3)
+        base_cell = rnn_cell.GRUCell(3)
         g, m_new = base_cell(x, m)
       with variable_scope.variable_scope(
           "hw_cell", initializer=init_ops.constant_initializer(0.5)):
-        hw_cell = rnn_cell.HighwayWrapper(
-            core_rnn_cell_impl.GRUCell(3), carry_bias_init=-100.0)
+        hw_cell = contrib_rnn_cell.HighwayWrapper(
+            rnn_cell.GRUCell(3), carry_bias_init=-100.0)
         g_res, m_new_res = hw_cell(x, m)
         sess.run([variables.global_variables_initializer()])
       res = sess.run([g, g_res, m_new, m_new_res], {
@@ -915,9 +910,9 @@ class RNNCellTest(test.TestCase):
           "root1", initializer=init_ops.constant_initializer(0.5)):
         x = array_ops.ones([batch_size, num_units])
         # When number_of_groups = 1, G-LSTM is equivalent to regular LSTM
-        gcell = rnn_cell.GLSTMCell(num_units=num_units,
-                                   number_of_groups=number_of_groups)
-        cell = core_rnn_cell_impl.LSTMCell(num_units=num_units)
+        gcell = contrib_rnn_cell.GLSTMCell(
+            num_units=num_units, number_of_groups=number_of_groups)
+        cell = rnn_cell.LSTMCell(num_units=num_units)
         self.assertTrue(isinstance(gcell.state_size, tuple))
         zero_state = gcell.zero_state(batch_size=batch_size,
                                       dtype=dtypes.float32)
@@ -941,8 +936,8 @@ class RNNCellTest(test.TestCase):
           "root2", initializer=init_ops.constant_initializer(0.5)):
         # input for G-LSTM with 2 groups
         glstm_input = array_ops.ones([batch_size, num_units])
-        gcell = rnn_cell.GLSTMCell(num_units=num_units,
-                                   number_of_groups=number_of_groups)
+        gcell = contrib_rnn_cell.GLSTMCell(
+            num_units=num_units, number_of_groups=number_of_groups)
         gcell_zero_state = gcell.zero_state(batch_size=batch_size,
                                             dtype=dtypes.float32)
         gh, gs = gcell(glstm_input, gcell_zero_state)
@@ -950,8 +945,7 @@ class RNNCellTest(test.TestCase):
         # input for LSTM cell simulating single G-LSTM group
         lstm_input = array_ops.ones([batch_size, num_units / number_of_groups])
         # note division by number_of_groups. This cell one simulates G-LSTM group
-        cell = core_rnn_cell_impl.LSTMCell(num_units=
-                                           int(num_units / number_of_groups))
+        cell = rnn_cell.LSTMCell(num_units=int(num_units / number_of_groups))
         cell_zero_state = cell.zero_state(batch_size=batch_size,
                                           dtype=dtypes.float32)
         h, g = cell(lstm_input, cell_zero_state)
@@ -974,13 +968,13 @@ class LayerNormBasicLSTMCellTest(test.TestCase):
         x = array_ops.zeros([1, 2])
         c0 = array_ops.zeros([1, 2])
         h0 = array_ops.zeros([1, 2])
-        state0 = core_rnn_cell_impl.LSTMStateTuple(c0, h0)
+        state0 = rnn_cell.LSTMStateTuple(c0, h0)
         c1 = array_ops.zeros([1, 2])
         h1 = array_ops.zeros([1, 2])
-        state1 = core_rnn_cell_impl.LSTMStateTuple(c1, h1)
+        state1 = rnn_cell.LSTMStateTuple(c1, h1)
         state = (state0, state1)
-        single_cell = lambda: rnn_cell.LayerNormBasicLSTMCell(2)
-        cell = core_rnn_cell_impl.MultiRNNCell([single_cell() for _ in range(2)])
+        single_cell = lambda: contrib_rnn_cell.LayerNormBasicLSTMCell(2)
+        cell = rnn_cell.MultiRNNCell([single_cell() for _ in range(2)])
         g, out_m = cell(x, state)
         sess.run([variables.global_variables_initializer()])
         res = sess.run([g, out_m], {
@@ -1015,8 +1009,8 @@ class LayerNormBasicLSTMCellTest(test.TestCase):
             [1, 3])  # Test BasicLSTMCell with input_size != num_units.
         c = array_ops.zeros([1, 2])
         h = array_ops.zeros([1, 2])
-        state = core_rnn_cell_impl.LSTMStateTuple(c, h)
-        cell = rnn_cell.LayerNormBasicLSTMCell(2)
+        state = rnn_cell.LSTMStateTuple(c, h)
+        cell = contrib_rnn_cell.LayerNormBasicLSTMCell(2)
         g, out_m = cell(x, state)
         sess.run([variables.global_variables_initializer()])
         res = sess.run([g, out_m], {
@@ -1039,12 +1033,12 @@ class LayerNormBasicLSTMCellTest(test.TestCase):
         x = array_ops.zeros([1, 2])
         c0 = array_ops.zeros([1, 2])
         h0 = array_ops.zeros([1, 2])
-        state0 = core_rnn_cell_impl.LSTMStateTuple(c0, h0)
+        state0 = rnn_cell.LSTMStateTuple(c0, h0)
         c1 = array_ops.zeros([1, 2])
         h1 = array_ops.zeros([1, 2])
-        state1 = core_rnn_cell_impl.LSTMStateTuple(c1, h1)
-        cell = core_rnn_cell_impl.MultiRNNCell(
-            [rnn_cell.LayerNormBasicLSTMCell(2) for _ in range(2)])
+        state1 = rnn_cell.LSTMStateTuple(c1, h1)
+        cell = rnn_cell.MultiRNNCell(
+            [contrib_rnn_cell.LayerNormBasicLSTMCell(2) for _ in range(2)])
         h, (s0, s1) = cell(x, (state0, state1))
         sess.run([variables.global_variables_initializer()])
         res = sess.run([h, s0, s1], {
@@ -1094,8 +1088,8 @@ class LayerNormBasicLSTMCellTest(test.TestCase):
         x = array_ops.zeros([1, 5])
         c = array_ops.zeros([1, 5])
         h = array_ops.zeros([1, 5])
-        state = core_rnn_cell_impl.LSTMStateTuple(c, h)
-        cell = rnn_cell.LayerNormBasicLSTMCell(
+        state = rnn_cell.LSTMStateTuple(c, h)
+        cell = contrib_rnn_cell.LayerNormBasicLSTMCell(
             num_units, layer_norm=False, dropout_keep_prob=keep_prob)
 
         g, s = cell(x, state)
@@ -1138,10 +1132,9 @@ def _create_multi_lstm_cell_ops(batch_size, num_units, input_depth,
     inputs = variable_scope.get_variable(
         "inputs", initializer=random_ops.random_uniform(
             (max_time, batch_size, input_depth), seed=1))
-    maybe_xla = lambda c: rnn_cell.CompiledWrapper(c) if compiled else c
-    cell = core_rnn_cell_impl.MultiRNNCell(
-        [maybe_xla(core_rnn_cell_impl.LSTMCell(num_units))
-         for _ in range(num_layers)])
+    maybe_xla = lambda c: contrib_rnn_cell.CompiledWrapper(c) if compiled else c
+    cell = rnn_cell.MultiRNNCell(
+        [maybe_xla(rnn_cell.LSTMCell(num_units)) for _ in range(num_layers)])
     initial_state = cell.zero_state(
         batch_size=batch_size, dtype=dtypes.float32)
     outputs, final_state = rnn.dynamic_rnn(
@@ -1219,13 +1212,13 @@ class CompiledWrapperTest(test.TestCase):
 
         # Test incorrectness of state
         with self.assertRaisesRegexp(ValueError, "Expected state .* a tuple"):
-          core_rnn_cell_impl.MultiRNNCell(
-              [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
-              state_is_tuple=True)(x, m_bad)
+          rnn_cell.MultiRNNCell(
+              [rnn_cell.GRUCell(2)
+               for _ in range(2)], state_is_tuple=True)(x, m_bad)
 
-        _, ml = core_rnn_cell_impl.MultiRNNCell(
-            [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
-            state_is_tuple=True)(x, m_good)
+        _, ml = rnn_cell.MultiRNNCell(
+            [rnn_cell.GRUCell(2)
+             for _ in range(2)], state_is_tuple=True)(x, m_good)
 
         sess.run([variables.global_variables_initializer()])
         res = sess.run(ml, {
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_test.py
index 5f96c565e8..e0d063a1b6 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_test.py
@@ -22,12 +22,12 @@ import itertools
 
 import numpy as np
 
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
-from tensorflow.contrib.rnn.python.ops import rnn
+from tensorflow.contrib.rnn.python.ops import rnn as contrib_rnn
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -58,14 +58,14 @@ class StackBidirectionalRNNTest(test.TestCase):
         dtypes.int64) if use_sequence_length else None
 
     self.cells_fw = [
-        core_rnn_cell_impl.LSTMCell(
+        rnn_cell.LSTMCell(
             num_units,
             input_size,
             initializer=initializer,
             state_is_tuple=False) for num_units in self.layers
     ]
     self.cells_bw = [
-        core_rnn_cell_impl.LSTMCell(
+        rnn_cell.LSTMCell(
             num_units,
             input_size,
             initializer=initializer,
@@ -77,7 +77,7 @@ class StackBidirectionalRNNTest(test.TestCase):
             dtypes.float32,
             shape=(batch_size, input_size) if use_shape else (None, input_size))
     ]
-    outputs, state_fw, state_bw = rnn.stack_bidirectional_rnn(
+    outputs, state_fw, state_bw = contrib_rnn.stack_bidirectional_rnn(
         self.cells_fw,
         self.cells_bw,
         inputs,
@@ -237,14 +237,14 @@ class StackBidirectionalRNNTest(test.TestCase):
     sequence_length = array_ops.placeholder(dtypes.int64)
 
     self.cells_fw = [
-        core_rnn_cell_impl.LSTMCell(
+        rnn_cell.LSTMCell(
             num_units,
             input_size,
             initializer=initializer,
             state_is_tuple=False) for num_units in self.layers
     ]
     self.cells_bw = [
-        core_rnn_cell_impl.LSTMCell(
+        rnn_cell.LSTMCell(
             num_units,
             input_size,
             initializer=initializer,
@@ -258,7 +258,7 @@ class StackBidirectionalRNNTest(test.TestCase):
     ]
     inputs_c = array_ops.stack(inputs)
     inputs_c = array_ops.transpose(inputs_c, [1, 0, 2])
-    outputs, st_fw, st_bw = rnn.stack_bidirectional_dynamic_rnn(
+    outputs, st_fw, st_bw = contrib_rnn.stack_bidirectional_dynamic_rnn(
         self.cells_fw,
         self.cells_bw,
         inputs_c,
diff --git a/tensorflow/contrib/rnn/python/ops/core_rnn.py b/tensorflow/contrib/rnn/python/ops/core_rnn.py
deleted file mode 100644
index 3ce075ce9c..0000000000
--- a/tensorflow/contrib/rnn/python/ops/core_rnn.py
+++ /dev/null
@@ -1,357 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""RNN helpers for TensorFlow models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import rnn
-from tensorflow.python.ops import rnn_cell_impl
-from tensorflow.python.ops import variable_scope as vs
-from tensorflow.python.util import nest
-
-
-# pylint: disable=protected-access
-_concat = rnn_cell_impl._concat
-_like_rnncell = rnn_cell_impl._like_rnncell
-_infer_state_dtype = rnn._infer_state_dtype
-_reverse_seq = rnn._reverse_seq
-_rnn_step = rnn._rnn_step
-# pylint: enable=protected-access
-
-
-def static_rnn(cell, inputs, initial_state=None, dtype=None,
-               sequence_length=None, scope=None):
-  """Creates a recurrent neural network specified by RNNCell `cell`.
-
-  The simplest form of RNN network generated is:
-
-  ```python
-    state = cell.zero_state(...)
-    outputs = []
-    for input_ in inputs:
-      output, state = cell(input_, state)
-      outputs.append(output)
-    return (outputs, state)
-  ```
-  However, a few other options are available:
-
-  An initial state can be provided.
-  If the sequence_length vector is provided, dynamic calculation is performed.
-  This method of calculation does not compute the RNN steps past the maximum
-  sequence length of the minibatch (thus saving computational time),
-  and properly propagates the state at an example's sequence length
-  to the final state output.
-
-  The dynamic calculation performed is, at time `t` for batch row `b`,
-
-  ```python
-    (output, state)(b, t) =
-      (t >= sequence_length(b))
-        ? (zeros(cell.output_size), states(b, sequence_length(b) - 1))
-        : cell(input(b, t), state(b, t - 1))
-  ```
-
-  Args:
-    cell: An instance of RNNCell.
-    inputs: A length T list of inputs, each a `Tensor` of shape
-      `[batch_size, input_size]`, or a nested tuple of such elements.
-    initial_state: (optional) An initial state for the RNN.
-      If `cell.state_size` is an integer, this must be
-      a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.
-      If `cell.state_size` is a tuple, this should be a tuple of
-      tensors having shapes `[batch_size, s] for s in cell.state_size`.
-    dtype: (optional) The data type for the initial state and expected output.
-      Required if initial_state is not provided or RNN state has a heterogeneous
-      dtype.
-    sequence_length: Specifies the length of each sequence in inputs.
-      An int32 or int64 vector (tensor) size `[batch_size]`, values in `[0, T)`.
-    scope: VariableScope for the created subgraph; defaults to "rnn".
-
-  Returns:
-    A pair (outputs, state) where:
-
-    - outputs is a length T list of outputs (one for each input), or a nested
-      tuple of such elements.
-    - state is the final state
-
-  Raises:
-    TypeError: If `cell` is not an instance of RNNCell.
-    ValueError: If `inputs` is `None` or an empty list, or if the input depth
-      (column size) cannot be inferred from inputs via shape inference.
-  """
-
-  if not _like_rnncell(cell):
-    raise TypeError("cell must be an instance of RNNCell")
-  if not nest.is_sequence(inputs):
-    raise TypeError("inputs must be a sequence")
-  if not inputs:
-    raise ValueError("inputs must not be empty")
-
-  outputs = []
-  # Create a new scope in which the caching device is either
-  # determined by the parent scope, or is set to place the cached
-  # Variable using the same placement as for the rest of the RNN.
-  with vs.variable_scope(scope or "rnn") as varscope:
-    if varscope.caching_device is None:
-      varscope.set_caching_device(lambda op: op.device)
-
-    # Obtain the first sequence of the input
-    first_input = inputs
-    while nest.is_sequence(first_input):
-      first_input = first_input[0]
-
-    # Temporarily avoid EmbeddingWrapper and seq2seq badness
-    # TODO(lukaszkaiser): remove EmbeddingWrapper
-    if first_input.get_shape().ndims != 1:
-
-      input_shape = first_input.get_shape().with_rank_at_least(2)
-      fixed_batch_size = input_shape[0]
-
-      flat_inputs = nest.flatten(inputs)
-      for flat_input in flat_inputs:
-        input_shape = flat_input.get_shape().with_rank_at_least(2)
-        batch_size, input_size = input_shape[0], input_shape[1:]
-        fixed_batch_size.merge_with(batch_size)
-        for i, size in enumerate(input_size):
-          if size.value is None:
-            raise ValueError(
-                "Input size (dimension %d of inputs) must be accessible via "
-                "shape inference, but saw value None." % i)
-    else:
-      fixed_batch_size = first_input.get_shape().with_rank_at_least(1)[0]
-
-    if fixed_batch_size.value:
-      batch_size = fixed_batch_size.value
-    else:
-      batch_size = array_ops.shape(first_input)[0]
-    if initial_state is not None:
-      state = initial_state
-    else:
-      if not dtype:
-        raise ValueError("If no initial_state is provided, "
-                         "dtype must be specified")
-      state = cell.zero_state(batch_size, dtype)
-
-    if sequence_length is not None:  # Prepare variables
-      sequence_length = ops.convert_to_tensor(
-          sequence_length, name="sequence_length")
-      if sequence_length.get_shape().ndims not in (None, 1):
-        raise ValueError(
-            "sequence_length must be a vector of length batch_size")
-      def _create_zero_output(output_size):
-        # convert int to TensorShape if necessary
-        size = _concat(batch_size, output_size)
-        output = array_ops.zeros(
-            array_ops.stack(size), _infer_state_dtype(dtype, state))
-        shape = _concat(fixed_batch_size.value, output_size, static=True)
-        output.set_shape(tensor_shape.TensorShape(shape))
-        return output
-
-      output_size = cell.output_size
-      flat_output_size = nest.flatten(output_size)
-      flat_zero_output = tuple(
-          _create_zero_output(size) for size in flat_output_size)
-      zero_output = nest.pack_sequence_as(structure=output_size,
-                                          flat_sequence=flat_zero_output)
-
-      sequence_length = math_ops.to_int32(sequence_length)
-      min_sequence_length = math_ops.reduce_min(sequence_length)
-      max_sequence_length = math_ops.reduce_max(sequence_length)
-
-    for time, input_ in enumerate(inputs):
-      if time > 0: varscope.reuse_variables()
-      # pylint: disable=cell-var-from-loop
-      call_cell = lambda: cell(input_, state)
-      # pylint: enable=cell-var-from-loop
-      if sequence_length is not None:
-        (output, state) = _rnn_step(
-            time=time,
-            sequence_length=sequence_length,
-            min_sequence_length=min_sequence_length,
-            max_sequence_length=max_sequence_length,
-            zero_output=zero_output,
-            state=state,
-            call_cell=call_cell,
-            state_size=cell.state_size)
-      else:
-        (output, state) = call_cell()
-
-      outputs.append(output)
-
-    return (outputs, state)
-
-
-def static_state_saving_rnn(cell, inputs, state_saver, state_name,
-                            sequence_length=None, scope=None):
-  """RNN that accepts a state saver for time-truncated RNN calculation.
-
-  Args:
-    cell: An instance of `RNNCell`.
-    inputs: A length T list of inputs, each a `Tensor` of shape
-      `[batch_size, input_size]`.
-    state_saver: A state saver object with methods `state` and `save_state`.
-    state_name: Python string or tuple of strings.  The name to use with the
-      state_saver. If the cell returns tuples of states (i.e.,
-      `cell.state_size` is a tuple) then `state_name` should be a tuple of
-      strings having the same length as `cell.state_size`.  Otherwise it should
-      be a single string.
-    sequence_length: (optional) An int32/int64 vector size [batch_size].
-      See the documentation for rnn() for more details about sequence_length.
-    scope: VariableScope for the created subgraph; defaults to "rnn".
-
-  Returns:
-    A pair (outputs, state) where:
-      outputs is a length T list of outputs (one for each input)
-      states is the final state
-
-  Raises:
-    TypeError: If `cell` is not an instance of RNNCell.
-    ValueError: If `inputs` is `None` or an empty list, or if the arity and
-     type of `state_name` does not match that of `cell.state_size`.
-  """
-  state_size = cell.state_size
-  state_is_tuple = nest.is_sequence(state_size)
-  state_name_tuple = nest.is_sequence(state_name)
-
-  if state_is_tuple != state_name_tuple:
-    raise ValueError(
-        "state_name should be the same type as cell.state_size.  "
-        "state_name: %s, cell.state_size: %s"
-        % (str(state_name), str(state_size)))
-
-  if state_is_tuple:
-    state_name_flat = nest.flatten(state_name)
-    state_size_flat = nest.flatten(state_size)
-
-    if len(state_name_flat) != len(state_size_flat):
-      raise ValueError("#elems(state_name) != #elems(state_size): %d vs. %d"
-                       % (len(state_name_flat), len(state_size_flat)))
-
-    initial_state = nest.pack_sequence_as(
-        structure=state_size,
-        flat_sequence=[state_saver.state(s) for s in state_name_flat])
-  else:
-    initial_state = state_saver.state(state_name)
-
-  (outputs, state) = static_rnn(cell, inputs, initial_state=initial_state,
-                                sequence_length=sequence_length, scope=scope)
-
-  if state_is_tuple:
-    flat_state = nest.flatten(state)
-    state_name = nest.flatten(state_name)
-    save_state = [state_saver.save_state(name, substate)
-                  for name, substate in zip(state_name, flat_state)]
-  else:
-    save_state = [state_saver.save_state(state_name, state)]
-
-  with ops.control_dependencies(save_state):
-    last_output = outputs[-1]
-    flat_last_output = nest.flatten(last_output)
-    flat_last_output = [
-        array_ops.identity(output) for output in flat_last_output]
-    outputs[-1] = nest.pack_sequence_as(structure=last_output,
-                                        flat_sequence=flat_last_output)
-
-  return (outputs, state)
-
-
-def static_bidirectional_rnn(cell_fw, cell_bw, inputs,
-                             initial_state_fw=None, initial_state_bw=None,
-                             dtype=None, sequence_length=None, scope=None):
-  """Creates a bidirectional recurrent neural network.
-
-  Similar to the unidirectional case above (rnn) but takes input and builds
-  independent forward and backward RNNs with the final forward and backward
-  outputs depth-concatenated, such that the output will have the format
-  [time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
-  forward and backward cell must match. The initial state for both directions
-  is zero by default (but can be set optionally) and no intermediate states are
-  ever returned -- the network is fully unrolled for the given (passed in)
-  length(s) of the sequence(s) or completely unrolled if length(s) is not given.
-
-  Args:
-    cell_fw: An instance of RNNCell, to be used for forward direction.
-    cell_bw: An instance of RNNCell, to be used for backward direction.
-    inputs: A length T list of inputs, each a tensor of shape
-      [batch_size, input_size], or a nested tuple of such elements.
-    initial_state_fw: (optional) An initial state for the forward RNN.
-      This must be a tensor of appropriate type and shape
-      `[batch_size, cell_fw.state_size]`.
-      If `cell_fw.state_size` is a tuple, this should be a tuple of
-      tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
-    initial_state_bw: (optional) Same as for `initial_state_fw`, but using
-      the corresponding properties of `cell_bw`.
-    dtype: (optional) The data type for the initial state.  Required if
-      either of the initial states are not provided.
-    sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
-      containing the actual lengths for each of the sequences.
-    scope: VariableScope for the created subgraph; defaults to
-      "bidirectional_rnn"
-
-  Returns:
-    A tuple (outputs, output_state_fw, output_state_bw) where:
-      outputs is a length `T` list of outputs (one for each input), which
-        are depth-concatenated forward and backward outputs.
-      output_state_fw is the final state of the forward rnn.
-      output_state_bw is the final state of the backward rnn.
-
-  Raises:
-    TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
-    ValueError: If inputs is None or an empty list.
-  """
-
-  if not _like_rnncell(cell_fw):
-    raise TypeError("cell_fw must be an instance of RNNCell")
-  if not _like_rnncell(cell_bw):
-    raise TypeError("cell_bw must be an instance of RNNCell")
-  if not nest.is_sequence(inputs):
-    raise TypeError("inputs must be a sequence")
-  if not inputs:
-    raise ValueError("inputs must not be empty")
-
-  with vs.variable_scope(scope or "bidirectional_rnn"):
-    # Forward direction
-    with vs.variable_scope("fw") as fw_scope:
-      output_fw, output_state_fw = static_rnn(
-          cell_fw, inputs, initial_state_fw, dtype,
-          sequence_length, scope=fw_scope)
-
-    # Backward direction
-    with vs.variable_scope("bw") as bw_scope:
-      reversed_inputs = _reverse_seq(inputs, sequence_length)
-      tmp, output_state_bw = static_rnn(
-          cell_bw, reversed_inputs, initial_state_bw,
-          dtype, sequence_length, scope=bw_scope)
-
-  output_bw = _reverse_seq(tmp, sequence_length)
-  # Concat each of the forward/backward outputs
-  flat_output_fw = nest.flatten(output_fw)
-  flat_output_bw = nest.flatten(output_bw)
-
-  flat_outputs = tuple(
-      array_ops.concat([fw, bw], 1)
-      for fw, bw in zip(flat_output_fw, flat_output_bw))
-
-  outputs = nest.pack_sequence_as(structure=output_fw,
-                                  flat_sequence=flat_outputs)
-
-  return (outputs, output_state_fw, output_state_bw)
diff --git a/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py b/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py
index c101b68d92..6b6bd503ce 100644
--- a/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py
@@ -12,45 +12,219 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""Module implementing RNN Cells that used to be in core.
 
-"""Module for constructing RNN Cells.
+@@EmbeddingWrapper
+@@InputProjectionWrapper
+@@OutputProjectionWrapper
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 
-## Base interface for all RNN Cells
+import math
 
-@@RNNCell
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import embedding_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import rnn_cell_impl
+from tensorflow.python.ops import variable_scope as vs
+from tensorflow.python.platform import tf_logging as logging
 
-## RNN Cells for use with TensorFlow's core RNN methods
+RNNCell = rnn_cell_impl.RNNCell  # pylint: disable=invalid-name
+_linear = rnn_cell_impl._linear  # pylint: disable=invalid-name, protected-access
+_like_rnncell = rnn_cell_impl._like_rnncell  # pylint: disable=invalid-name, protected-access
 
-@@BasicRNNCell
-@@BasicLSTMCell
-@@GRUCell
-@@LSTMCell
 
-## Classes storing split `RNNCell` state
+class EmbeddingWrapper(RNNCell):
+  """Operator adding input embedding to the given cell.
 
-@@LSTMStateTuple
+  Note: in many cases it may be more efficient to not use this wrapper,
+  but instead concatenate the whole sequence of your inputs in time,
+  do the embedding on this batch-concatenated sequence, then split it and
+  feed into your RNN.
+  """
 
-## RNN Cell wrappers (RNNCells that wrap other RNNCells)
+  def __init__(self,
+               cell,
+               embedding_classes,
+               embedding_size,
+               initializer=None,
+               reuse=None):
+    """Create a cell with an added input embedding.
 
-@@MultiRNNCell
-@@DropoutWrapper
-@@EmbeddingWrapper
-@@InputProjectionWrapper
-@@OutputProjectionWrapper
-@@DeviceWrapper
-@@ResidualWrapper
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+    Args:
+      cell: an RNNCell, an embedding will be put before its inputs.
+      embedding_classes: integer, how many symbols will be embedded.
+      embedding_size: integer, the size of the vectors we embed into.
+      initializer: an initializer to use when creating the embedding;
+        if None, the initializer from variable scope or a default one is used.
+      reuse: (optional) Python boolean describing whether to reuse variables
+        in an existing scope.  If not `True`, and the existing scope already has
+        the given variables, an error is raised.
+
+    Raises:
+      TypeError: if cell is not an RNNCell.
+      ValueError: if embedding_classes is not positive.
+    """
+    super(EmbeddingWrapper, self).__init__(_reuse=reuse)
+    if not _like_rnncell(cell):
+      raise TypeError("The parameter cell is not RNNCell.")
+    if embedding_classes <= 0 or embedding_size <= 0:
+      raise ValueError("Both embedding_classes and embedding_size must be > 0: "
+                       "%d, %d." % (embedding_classes, embedding_size))
+    self._cell = cell
+    self._embedding_classes = embedding_classes
+    self._embedding_size = embedding_size
+    self._initializer = initializer
+
+  @property
+  def state_size(self):
+    return self._cell.state_size
+
+  @property
+  def output_size(self):
+    return self._cell.output_size
+
+  def zero_state(self, batch_size, dtype):
+    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      return self._cell.zero_state(batch_size, dtype)
+
+  def call(self, inputs, state):
+    """Run the cell on embedded inputs."""
+    with ops.device("/cpu:0"):
+      if self._initializer:
+        initializer = self._initializer
+      elif vs.get_variable_scope().initializer:
+        initializer = vs.get_variable_scope().initializer
+      else:
+        # Default initializer for embeddings should have variance=1.
+        sqrt3 = math.sqrt(3)  # Uniform(-sqrt(3), sqrt(3)) has variance=1.
+        initializer = init_ops.random_uniform_initializer(-sqrt3, sqrt3)
+
+      if isinstance(state, tuple):
+        data_type = state[0].dtype
+      else:
+        data_type = state.dtype
+
+      embedding = vs.get_variable(
+          "embedding", [self._embedding_classes, self._embedding_size],
+          initializer=initializer,
+          dtype=data_type)
+      embedded = embedding_ops.embedding_lookup(embedding,
+                                                array_ops.reshape(inputs, [-1]))
+
+      return self._cell(embedded, state)
+
+
+class InputProjectionWrapper(RNNCell):
+  """Operator adding an input projection to the given cell.
+
+  Note: in many cases it may be more efficient to not use this wrapper,
+  but instead concatenate the whole sequence of your inputs in time,
+  do the projection on this batch-concatenated sequence, then split it.
+  """
+
+  def __init__(self,
+               cell,
+               num_proj,
+               activation=None,
+               input_size=None,
+               reuse=None):
+    """Create a cell with input projection.
+
+    Args:
+      cell: an RNNCell, a projection of inputs is added before it.
+      num_proj: Python integer.  The dimension to project to.
+      activation: (optional) an optional activation function.
+      input_size: Deprecated and unused.
+      reuse: (optional) Python boolean describing whether to reuse variables
+        in an existing scope.  If not `True`, and the existing scope already has
+        the given variables, an error is raised.
+
+    Raises:
+      TypeError: if cell is not an RNNCell.
+    """
+    super(InputProjectionWrapper, self).__init__(_reuse=reuse)
+    if input_size is not None:
+      logging.warn("%s: The input_size parameter is deprecated.", self)
+    if not _like_rnncell(cell):
+      raise TypeError("The parameter cell is not RNNCell.")
+    self._cell = cell
+    self._num_proj = num_proj
+    self._activation = activation
+
+  @property
+  def state_size(self):
+    return self._cell.state_size
+
+  @property
+  def output_size(self):
+    return self._cell.output_size
+
+  def zero_state(self, batch_size, dtype):
+    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      return self._cell.zero_state(batch_size, dtype)
+
+  def call(self, inputs, state):
+    """Run the input projection and then the cell."""
+    # Default scope: "InputProjectionWrapper"
+    projected = _linear(inputs, self._num_proj, True)
+    if self._activation:
+      projected = self._activation(projected)
+    return self._cell(projected, state)
+
+
+class OutputProjectionWrapper(RNNCell):
+  """Operator adding an output projection to the given cell.
+
+  Note: in many cases it may be more efficient to not use this wrapper,
+  but instead concatenate the whole sequence of your outputs in time,
+  do the projection on this batch-concatenated sequence, then split it
+  if needed or directly feed into a softmax.
+  """
+
+  def __init__(self, cell, output_size, activation=None, reuse=None):
+    """Create a cell with output projection.
+
+    Args:
+      cell: an RNNCell, a projection to output_size is added to it.
+      output_size: integer, the size of the output after projection.
+      activation: (optional) an optional activation function.
+      reuse: (optional) Python boolean describing whether to reuse variables
+        in an existing scope.  If not `True`, and the existing scope already has
+        the given variables, an error is raised.
+
+    Raises:
+      TypeError: if cell is not an RNNCell.
+      ValueError: if output_size is not positive.
+    """
+    super(OutputProjectionWrapper, self).__init__(_reuse=reuse)
+    if not _like_rnncell(cell):
+      raise TypeError("The parameter cell is not RNNCell.")
+    if output_size < 1:
+      raise ValueError("Parameter output_size must be > 0: %d." % output_size)
+    self._cell = cell
+    self._output_size = output_size
+    self._activation = activation
 
-# go/tf-wildcard-import
-# pylint: disable=wildcard-import
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl import *
-# pylint: enable=wildcard-import
-from tensorflow.python.util.all_util import remove_undocumented
+  @property
+  def state_size(self):
+    return self._cell.state_size
 
+  @property
+  def output_size(self):
+    return self._output_size
 
-_allowed_symbols = []
+  def zero_state(self, batch_size, dtype):
+    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      return self._cell.zero_state(batch_size, dtype)
 
-remove_undocumented(__name__, _allowed_symbols)
+  def call(self, inputs, state):
+    """Run the cell and output projection on inputs, starting from state."""
+    output, res_state = self._cell(inputs, state)
+    projected = _linear(output, self._output_size, True)
+    if self._activation:
+      projected = self._activation(projected)
+    return projected, res_state
diff --git a/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py b/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py
deleted file mode 100644
index d56d1ba579..0000000000
--- a/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py
+++ /dev/null
@@ -1,1037 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Module implementing RNN Cells.
-
-This module provides a number of basic commonly used RNN cells, such as LSTM
-(Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of
-operators that allow adding dropouts, projections, or embeddings for inputs.
-Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by
-calling the `rnn` ops several times.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import hashlib
-import math
-import numbers
-
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import clip_ops
-from tensorflow.python.ops import embedding_ops
-from tensorflow.python.ops import init_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
-from tensorflow.python.ops import partitioned_variables
-from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import rnn_cell_impl
-from tensorflow.python.ops import variable_scope as vs
-
-from tensorflow.python.ops.math_ops import sigmoid
-from tensorflow.python.ops.math_ops import tanh
-
-from tensorflow.python.platform import tf_logging as logging
-from tensorflow.python.util import nest
-
-
-# pylint: disable=protected-access
-RNNCell = rnn_cell_impl._RNNCell  # pylint: disable=invalid-name
-_like_rnncell = rnn_cell_impl._like_rnncell
-# pylint: enable=protected-access
-
-_BIAS_VARIABLE_NAME = "bias"
-_WEIGHTS_VARIABLE_NAME = "kernel"
-
-
-class BasicRNNCell(RNNCell):
-  """The most basic RNN cell."""
-
-  def __init__(self, num_units, input_size=None, activation=tanh, reuse=None):
-    super(BasicRNNCell, self).__init__(_reuse=reuse)
-    if input_size is not None:
-      logging.warn("%s: The input_size parameter is deprecated.", self)
-    self._num_units = num_units
-    self._activation = activation
-
-  @property
-  def state_size(self):
-    return self._num_units
-
-  @property
-  def output_size(self):
-    return self._num_units
-
-  def call(self, inputs, state):
-    """Most basic RNN: output = new_state = act(W * input + U * state + B)."""
-    output = self._activation(_linear([inputs, state], self._num_units, True))
-    return output, output
-
-
-class GRUCell(RNNCell):
-  """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078)."""
-
-  def __init__(self, num_units, input_size=None, activation=tanh, reuse=None,
-               kernel_initializer=None, bias_initializer=None):
-    super(GRUCell, self).__init__(_reuse=reuse)
-    if input_size is not None:
-      logging.warn("%s: The input_size parameter is deprecated.", self)
-    self._num_units = num_units
-    self._activation = activation
-    self._kernel_initializer = kernel_initializer
-    self._bias_initializer = bias_initializer
-
-  @property
-  def state_size(self):
-    return self._num_units
-
-  @property
-  def output_size(self):
-    return self._num_units
-
-  def call(self, inputs, state):
-    """Gated recurrent unit (GRU) with nunits cells."""
-    with vs.variable_scope("gates"):  # Reset gate and update gate.
-      # We start with bias of 1.0 to not reset and not update.
-      bias_ones = self._bias_initializer
-      if self._bias_initializer is None:
-        dtype = [a.dtype for a in [inputs, state]][0]
-        bias_ones = init_ops.constant_initializer(1.0, dtype=dtype)
-      value = sigmoid(_linear([inputs, state], 2 * self._num_units, True,
-          bias_ones, self._kernel_initializer))
-      r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
-    with vs.variable_scope("candidate"):
-      c = self._activation(_linear([inputs, r * state], self._num_units, True,
-          self._bias_initializer, self._kernel_initializer))
-    new_h = u * state + (1 - u) * c
-    return new_h, new_h
-
-
-_LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h"))
-
-
-class LSTMStateTuple(_LSTMStateTuple):
-  """Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state.
-
-  Stores two elements: `(c, h)`, in that order.
-
-  Only used when `state_is_tuple=True`.
-  """
-  __slots__ = ()
-
-  @property
-  def dtype(self):
-    (c, h) = self
-    if not c.dtype == h.dtype:
-      raise TypeError("Inconsistent internal state: %s vs %s" %
-                      (str(c.dtype), str(h.dtype)))
-    return c.dtype
-
-
-class BasicLSTMCell(RNNCell):
-  """Basic LSTM recurrent network cell.
-
-  The implementation is based on: http://arxiv.org/abs/1409.2329.
-
-  We add forget_bias (default: 1) to the biases of the forget gate in order to
-  reduce the scale of forgetting in the beginning of the training.
-
-  It does not allow cell clipping, a projection layer, and does not
-  use peep-hole connections: it is the basic baseline.
-
-  For advanced models, please use the full LSTMCell that follows.
-  """
-
-  def __init__(self, num_units, forget_bias=1.0, input_size=None,
-               state_is_tuple=True, activation=tanh, reuse=None):
-    """Initialize the basic LSTM cell.
-
-    Args:
-      num_units: int, The number of units in the LSTM cell.
-      forget_bias: float, The bias added to forget gates (see above).
-      input_size: Deprecated and unused.
-      state_is_tuple: If True, accepted and returned states are 2-tuples of
-        the `c_state` and `m_state`.  If False, they are concatenated
-        along the column axis.  The latter behavior will soon be deprecated.
-      activation: Activation function of the inner states.
-      reuse: (optional) Python boolean describing whether to reuse variables
-        in an existing scope.  If not `True`, and the existing scope already has
-        the given variables, an error is raised.
-    """
-    super(BasicLSTMCell, self).__init__(_reuse=reuse)
-    if not state_is_tuple:
-      logging.warn("%s: Using a concatenated state is slower and will soon be "
-                   "deprecated.  Use state_is_tuple=True.", self)
-    if input_size is not None:
-      logging.warn("%s: The input_size parameter is deprecated.", self)
-    self._num_units = num_units
-    self._forget_bias = forget_bias
-    self._state_is_tuple = state_is_tuple
-    self._activation = activation
-
-  @property
-  def state_size(self):
-    return (LSTMStateTuple(self._num_units, self._num_units)
-            if self._state_is_tuple else 2 * self._num_units)
-
-  @property
-  def output_size(self):
-    return self._num_units
-
-  def call(self, inputs, state):
-    """Long short-term memory cell (LSTM)."""
-    # Parameters of gates are concatenated into one multiply for efficiency.
-    if self._state_is_tuple:
-      c, h = state
-    else:
-      c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
-
-    concat = _linear([inputs, h], 4 * self._num_units, True)
-
-    # i = input_gate, j = new_input, f = forget_gate, o = output_gate
-    i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
-
-    new_c = (
-        c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
-    new_h = self._activation(new_c) * sigmoid(o)
-
-    if self._state_is_tuple:
-      new_state = LSTMStateTuple(new_c, new_h)
-    else:
-      new_state = array_ops.concat([new_c, new_h], 1)
-    return new_h, new_state
-
-
-class LSTMCell(RNNCell):
-  """Long short-term memory unit (LSTM) recurrent network cell.
-
-  The default non-peephole implementation is based on:
-
-    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
-
-  S. Hochreiter and J. Schmidhuber.
-  "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
-
-  The peephole implementation is based on:
-
-    https://research.google.com/pubs/archive/43905.pdf
-
-  Hasim Sak, Andrew Senior, and Francoise Beaufays.
-  "Long short-term memory recurrent neural network architectures for
-   large scale acoustic modeling." INTERSPEECH, 2014.
-
-  The class uses optional peep-hole connections, optional cell clipping, and
-  an optional projection layer.
-  """
-
-  def __init__(self, num_units, input_size=None,
-               use_peepholes=False, cell_clip=None,
-               initializer=None, num_proj=None, proj_clip=None,
-               num_unit_shards=None, num_proj_shards=None,
-               forget_bias=1.0, state_is_tuple=True,
-               activation=tanh, reuse=None):
-    """Initialize the parameters for an LSTM cell.
-
-    Args:
-      num_units: int, The number of units in the LSTM cell
-      input_size: Deprecated and unused.
-      use_peepholes: bool, set True to enable diagonal/peephole connections.
-      cell_clip: (optional) A float value, if provided the cell state is clipped
-        by this value prior to the cell output activation.
-      initializer: (optional) The initializer to use for the weight and
-        projection matrices.
-      num_proj: (optional) int, The output dimensionality for the projection
-        matrices.  If None, no projection is performed.
-      proj_clip: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
-        provided, then the projected values are clipped elementwise to within
-        `[-proj_clip, proj_clip]`.
-      num_unit_shards: Deprecated, will be removed by Jan. 2017.
-        Use a variable_scope partitioner instead.
-      num_proj_shards: Deprecated, will be removed by Jan. 2017.
-        Use a variable_scope partitioner instead.
-      forget_bias: Biases of the forget gate are initialized by default to 1
-        in order to reduce the scale of forgetting at the beginning of
-        the training.
-      state_is_tuple: If True, accepted and returned states are 2-tuples of
-        the `c_state` and `m_state`.  If False, they are concatenated
-        along the column axis.  This latter behavior will soon be deprecated.
-      activation: Activation function of the inner states.
-      reuse: (optional) Python boolean describing whether to reuse variables
-        in an existing scope.  If not `True`, and the existing scope already has
-        the given variables, an error is raised.
-    """
-    super(LSTMCell, self).__init__(_reuse=reuse)
-    if not state_is_tuple:
-      logging.warn("%s: Using a concatenated state is slower and will soon be "
-                   "deprecated.  Use state_is_tuple=True.", self)
-    if input_size is not None:
-      logging.warn("%s: The input_size parameter is deprecated.", self)
-    if num_unit_shards is not None or num_proj_shards is not None:
-      logging.warn(
-          "%s: The num_unit_shards and proj_unit_shards parameters are "
-          "deprecated and will be removed in Jan 2017.  "
-          "Use a variable scope with a partitioner instead.", self)
-
-    self._num_units = num_units
-    self._use_peepholes = use_peepholes
-    self._cell_clip = cell_clip
-    self._initializer = initializer
-    self._num_proj = num_proj
-    self._proj_clip = proj_clip
-    self._num_unit_shards = num_unit_shards
-    self._num_proj_shards = num_proj_shards
-    self._forget_bias = forget_bias
-    self._state_is_tuple = state_is_tuple
-    self._activation = activation
-
-    if num_proj:
-      self._state_size = (
-          LSTMStateTuple(num_units, num_proj)
-          if state_is_tuple else num_units + num_proj)
-      self._output_size = num_proj
-    else:
-      self._state_size = (
-          LSTMStateTuple(num_units, num_units)
-          if state_is_tuple else 2 * num_units)
-      self._output_size = num_units
-
-  @property
-  def state_size(self):
-    return self._state_size
-
-  @property
-  def output_size(self):
-    return self._output_size
-
-  def call(self, inputs, state):
-    """Run one step of LSTM.
-
-    Args:
-      inputs: input Tensor, 2D, batch x num_units.
-      state: if `state_is_tuple` is False, this must be a state Tensor,
-        `2-D, batch x state_size`.  If `state_is_tuple` is True, this must be a
-        tuple of state Tensors, both `2-D`, with column sizes `c_state` and
-        `m_state`.
-
-    Returns:
-      A tuple containing:
-
-      - A `2-D, [batch x output_dim]`, Tensor representing the output of the
-        LSTM after reading `inputs` when previous state was `state`.
-        Here output_dim is:
-           num_proj if num_proj was set,
-           num_units otherwise.
-      - Tensor(s) representing the new state of LSTM after reading `inputs` when
-        the previous state was `state`.  Same type and shape(s) as `state`.
-
-    Raises:
-      ValueError: If input size cannot be inferred from inputs via
-        static shape inference.
-    """
-    num_proj = self._num_units if self._num_proj is None else self._num_proj
-
-    if self._state_is_tuple:
-      (c_prev, m_prev) = state
-    else:
-      c_prev = array_ops.slice(state, [0, 0], [-1, self._num_units])
-      m_prev = array_ops.slice(state, [0, self._num_units], [-1, num_proj])
-
-    dtype = inputs.dtype
-    input_size = inputs.get_shape().with_rank(2)[1]
-    if input_size.value is None:
-      raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
-    scope = vs.get_variable_scope()
-    with vs.variable_scope(scope, initializer=self._initializer) as unit_scope:
-      if self._num_unit_shards is not None:
-        unit_scope.set_partitioner(
-            partitioned_variables.fixed_size_partitioner(
-                self._num_unit_shards))
-      # i = input_gate, j = new_input, f = forget_gate, o = output_gate
-      lstm_matrix = _linear([inputs, m_prev], 4 * self._num_units, bias=True)
-      i, j, f, o = array_ops.split(
-          value=lstm_matrix, num_or_size_splits=4, axis=1)
-      # Diagonal connections
-      if self._use_peepholes:
-        with vs.variable_scope(unit_scope) as projection_scope:
-          if self._num_unit_shards is not None:
-            projection_scope.set_partitioner(None)
-          w_f_diag = vs.get_variable(
-              "w_f_diag", shape=[self._num_units], dtype=dtype)
-          w_i_diag = vs.get_variable(
-              "w_i_diag", shape=[self._num_units], dtype=dtype)
-          w_o_diag = vs.get_variable(
-              "w_o_diag", shape=[self._num_units], dtype=dtype)
-
-      if self._use_peepholes:
-        c = (sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev +
-             sigmoid(i + w_i_diag * c_prev) * self._activation(j))
-      else:
-        c = (sigmoid(f + self._forget_bias) * c_prev + sigmoid(i) *
-             self._activation(j))
-
-      if self._cell_clip is not None:
-        # pylint: disable=invalid-unary-operand-type
-        c = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip)
-        # pylint: enable=invalid-unary-operand-type
-      if self._use_peepholes:
-        m = sigmoid(o + w_o_diag * c) * self._activation(c)
-      else:
-        m = sigmoid(o) * self._activation(c)
-
-      if self._num_proj is not None:
-        with vs.variable_scope("projection") as proj_scope:
-          if self._num_proj_shards is not None:
-            proj_scope.set_partitioner(
-                partitioned_variables.fixed_size_partitioner(
-                    self._num_proj_shards))
-          m = _linear(m, self._num_proj, bias=False)
-
-        if self._proj_clip is not None:
-          # pylint: disable=invalid-unary-operand-type
-          m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip)
-          # pylint: enable=invalid-unary-operand-type
-
-    new_state = (LSTMStateTuple(c, m) if self._state_is_tuple else
-                 array_ops.concat([c, m], 1))
-    return m, new_state
-
-
-class OutputProjectionWrapper(RNNCell):
-  """Operator adding an output projection to the given cell.
-
-  Note: in many cases it may be more efficient to not use this wrapper,
-  but instead concatenate the whole sequence of your outputs in time,
-  do the projection on this batch-concatenated sequence, then split it
-  if needed or directly feed into a softmax.
-  """
-
-  def __init__(self, cell, output_size, activation=None, reuse=None):
-    """Create a cell with output projection.
-
-    Args:
-      cell: an RNNCell, a projection to output_size is added to it.
-      output_size: integer, the size of the output after projection.
-      activation: (optional) an optional activation function.
-      reuse: (optional) Python boolean describing whether to reuse variables
-        in an existing scope.  If not `True`, and the existing scope already has
-        the given variables, an error is raised.
-
-    Raises:
-      TypeError: if cell is not an RNNCell.
-      ValueError: if output_size is not positive.
-    """
-    super(OutputProjectionWrapper, self).__init__(_reuse=reuse)
-    if not _like_rnncell(cell):
-      raise TypeError("The parameter cell is not RNNCell.")
-    if output_size < 1:
-      raise ValueError("Parameter output_size must be > 0: %d." % output_size)
-    self._cell = cell
-    self._output_size = output_size
-    self._activation = activation
-
-  @property
-  def state_size(self):
-    return self._cell.state_size
-
-  @property
-  def output_size(self):
-    return self._output_size
-
-  def zero_state(self, batch_size, dtype):
-    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      return self._cell.zero_state(batch_size, dtype)
-
-  def call(self, inputs, state):
-    """Run the cell and output projection on inputs, starting from state."""
-    output, res_state = self._cell(inputs, state)
-    projected = _linear(output, self._output_size, True)
-    if self._activation:
-      projected = self._activation(projected)
-    return projected, res_state
-
-
-class InputProjectionWrapper(RNNCell):
-  """Operator adding an input projection to the given cell.
-
-  Note: in many cases it may be more efficient to not use this wrapper,
-  but instead concatenate the whole sequence of your inputs in time,
-  do the projection on this batch-concatenated sequence, then split it.
-  """
-
-  def __init__(self, cell, num_proj, activation=None, input_size=None,
-               reuse=None):
-    """Create a cell with input projection.
-
-    Args:
-      cell: an RNNCell, a projection of inputs is added before it.
-      num_proj: Python integer.  The dimension to project to.
-      activation: (optional) an optional activation function.
-      input_size: Deprecated and unused.
-      reuse: (optional) Python boolean describing whether to reuse variables
-        in an existing scope.  If not `True`, and the existing scope already has
-        the given variables, an error is raised.
-
-    Raises:
-      TypeError: if cell is not an RNNCell.
-    """
-    super(InputProjectionWrapper, self).__init__(_reuse=reuse)
-    if input_size is not None:
-      logging.warn("%s: The input_size parameter is deprecated.", self)
-    if not _like_rnncell(cell):
-      raise TypeError("The parameter cell is not RNNCell.")
-    self._cell = cell
-    self._num_proj = num_proj
-    self._activation = activation
-
-  @property
-  def state_size(self):
-    return self._cell.state_size
-
-  @property
-  def output_size(self):
-    return self._cell.output_size
-
-  def zero_state(self, batch_size, dtype):
-    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      return self._cell.zero_state(batch_size, dtype)
-
-  def call(self, inputs, state):
-    """Run the input projection and then the cell."""
-    # Default scope: "InputProjectionWrapper"
-    projected = _linear(inputs, self._num_proj, True)
-    if self._activation:
-      projected = self._activation(projected)
-    return self._cell(projected, state)
-
-
-def _enumerated_map_structure(map_fn, *args, **kwargs):
-  ix = [0]
-  def enumerated_fn(*inner_args, **inner_kwargs):
-    r = map_fn(ix[0], *inner_args, **inner_kwargs)
-    ix[0] += 1
-    return r
-  return nest.map_structure(enumerated_fn, *args, **kwargs)
-
-
-class DropoutWrapper(RNNCell):
-  """Operator adding dropout to inputs and outputs of the given cell."""
-
-  def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
-               state_keep_prob=1.0, variational_recurrent=False,
-               input_size=None, dtype=None, seed=None):
-    """Create a cell with added input, state, and/or output dropout.
-
-    If `variational_recurrent` is set to `True` (**NOT** the default behavior),
-    then the the same dropout mask is applied at every step, as described in:
-
-    Y. Gal, Z Ghahramani.  "A Theoretically Grounded Application of Dropout in
-    Recurrent Neural Networks".  https://arxiv.org/abs/1512.05287
-
-    Otherwise a different dropout mask is applied at every time step.
-
-    Args:
-      cell: an RNNCell, a projection to output_size is added to it.
-      input_keep_prob: unit Tensor or float between 0 and 1, input keep
-        probability; if it is constant and 1, no input dropout will be added.
-      output_keep_prob: unit Tensor or float between 0 and 1, output keep
-        probability; if it is constant and 1, no output dropout will be added.
-      state_keep_prob: unit Tensor or float between 0 and 1, output keep
-        probability; if it is constant and 1, no output dropout will be added.
-        State dropout is performed on the *output* states of the cell.
-      variational_recurrent: Python bool.  If `True`, then the same
-        dropout pattern is applied across all time steps per run call.
-        If this parameter is set, `input_size` **must** be provided.
-      input_size: (optional) (possibly nested tuple of) `TensorShape` objects
-        containing the depth(s) of the input tensors expected to be passed in to
-        the `DropoutWrapper`.  Required and used **iff**
-         `variational_recurrent = True` and `input_keep_prob < 1`.
-      dtype: (optional) The `dtype` of the input, state, and output tensors.
-        Required and used **iff** `variational_recurrent = True`.
-      seed: (optional) integer, the randomness seed.
-
-    Raises:
-      TypeError: if cell is not an RNNCell.
-      ValueError: if any of the keep_probs are not between 0 and 1.
-    """
-    if not _like_rnncell(cell):
-      raise TypeError("The parameter cell is not a RNNCell.")
-    with ops.name_scope("DropoutWrapperInit"):
-      def tensor_and_const_value(v):
-        tensor_value = ops.convert_to_tensor(v)
-        const_value = tensor_util.constant_value(tensor_value)
-        return (tensor_value, const_value)
-      for prob, attr in [(input_keep_prob, "input_keep_prob"),
-                         (state_keep_prob, "state_keep_prob"),
-                         (output_keep_prob, "output_keep_prob")]:
-        tensor_prob, const_prob = tensor_and_const_value(prob)
-        if const_prob is not None:
-          if const_prob < 0 or const_prob > 1:
-            raise ValueError("Parameter %s must be between 0 and 1: %d"
-                             % (attr, const_prob))
-          setattr(self, "_%s" % attr, float(const_prob))
-        else:
-          setattr(self, "_%s" % attr, tensor_prob)
-
-    # Set cell, variational_recurrent, seed before running the code below
-    self._cell = cell
-    self._variational_recurrent = variational_recurrent
-    self._seed = seed
-
-    self._recurrent_input_noise = None
-    self._recurrent_state_noise = None
-    self._recurrent_output_noise = None
-
-    if variational_recurrent:
-      if dtype is None:
-        raise ValueError(
-            "When variational_recurrent=True, dtype must be provided")
-
-      def convert_to_batch_shape(s):
-        # Prepend a 1 for the batch dimension; for recurrent
-        # variational dropout we use the same dropout mask for all
-        # batch elements.
-        return array_ops.concat(
-            ([1], tensor_shape.TensorShape(s).as_list()), 0)
-
-      def batch_noise(s, inner_seed):
-        shape = convert_to_batch_shape(s)
-        return random_ops.random_uniform(shape, seed=inner_seed, dtype=dtype)
-
-      if (not isinstance(self._input_keep_prob, numbers.Real) or
-          self._input_keep_prob < 1.0):
-        if input_size is None:
-          raise ValueError(
-              "When variational_recurrent=True and input_keep_prob < 1.0 or "
-              "is unknown, input_size must be provided")
-        self._recurrent_input_noise = _enumerated_map_structure(
-            lambda i, s: batch_noise(s, inner_seed=self._gen_seed("input", i)),
-            input_size)
-      self._recurrent_state_noise = _enumerated_map_structure(
-          lambda i, s: batch_noise(s, inner_seed=self._gen_seed("state", i)),
-          cell.state_size)
-      self._recurrent_output_noise = _enumerated_map_structure(
-          lambda i, s: batch_noise(s, inner_seed=self._gen_seed("output", i)),
-          cell.output_size)
-
-  def _gen_seed(self, salt_prefix, index):
-    if self._seed is None:
-      return None
-    salt = "%s_%d" % (salt_prefix, index)
-    string = (str(self._seed) + salt).encode("utf-8")
-    return int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
-
-  @property
-  def state_size(self):
-    return self._cell.state_size
-
-  @property
-  def output_size(self):
-    return self._cell.output_size
-
-  def zero_state(self, batch_size, dtype):
-    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      return self._cell.zero_state(batch_size, dtype)
-
-  def _variational_recurrent_dropout_value(
-      self, index, value, noise, keep_prob):
-    """Performs dropout given the pre-calculated noise tensor."""
-    # uniform [keep_prob, 1.0 + keep_prob)
-    random_tensor = keep_prob + noise
-
-    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
-    binary_tensor = math_ops.floor(random_tensor)
-    ret = math_ops.div(value, keep_prob) * binary_tensor
-    ret.set_shape(value.get_shape())
-    return ret
-
-  def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob):
-    """Decides whether to perform standard dropout or recurrent dropout."""
-    if not self._variational_recurrent:
-      def dropout(i, v):
-        return nn_ops.dropout(
-            v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i))
-      return _enumerated_map_structure(dropout, values)
-    else:
-      def dropout(i, v, n):
-        return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
-      return _enumerated_map_structure(dropout, values, recurrent_noise)
-
-  def __call__(self, inputs, state, scope=None):
-    """Run the cell with the declared dropouts."""
-    def _should_dropout(p):
-      return (not isinstance(p, float)) or p < 1
-
-    if _should_dropout(self._input_keep_prob):
-      inputs = self._dropout(inputs, "input",
-                             self._recurrent_input_noise,
-                             self._input_keep_prob)
-    output, new_state = self._cell(inputs, state, scope)
-    if _should_dropout(self._state_keep_prob):
-      new_state = self._dropout(new_state, "state",
-                                self._recurrent_state_noise,
-                                self._state_keep_prob)
-    if _should_dropout(self._output_keep_prob):
-      output = self._dropout(output, "output",
-                             self._recurrent_output_noise,
-                             self._output_keep_prob)
-    return output, new_state
-
-
-class ResidualWrapper(RNNCell):
-  """RNNCell wrapper that ensures cell inputs are added to the outputs."""
-
-  def __init__(self, cell):
-    """Constructs a `ResidualWrapper` for `cell`.
-
-    Args:
-      cell: An instance of `RNNCell`.
-    """
-    self._cell = cell
-
-  @property
-  def state_size(self):
-    return self._cell.state_size
-
-  @property
-  def output_size(self):
-    return self._cell.output_size
-
-  def zero_state(self, batch_size, dtype):
-    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      return self._cell.zero_state(batch_size, dtype)
-
-  def __call__(self, inputs, state, scope=None):
-    """Run the cell and add its inputs to its outputs.
-
-    Args:
-      inputs: cell inputs.
-      state: cell state.
-      scope: optional cell scope.
-
-    Returns:
-      Tuple of cell outputs and new state.
-
-    Raises:
-      TypeError: If cell inputs and outputs have different structure (type).
-      ValueError: If cell inputs and outputs have different structure (value).
-    """
-    outputs, new_state = self._cell(inputs, state, scope=scope)
-    nest.assert_same_structure(inputs, outputs)
-    # Ensure shapes match
-    def assert_shape_match(inp, out):
-      inp.get_shape().assert_is_compatible_with(out.get_shape())
-    nest.map_structure(assert_shape_match, inputs, outputs)
-    res_outputs = nest.map_structure(
-        lambda inp, out: inp + out, inputs, outputs)
-    return (res_outputs, new_state)
-
-
-class DeviceWrapper(RNNCell):
-  """Operator that ensures an RNNCell runs on a particular device."""
-
-  def __init__(self, cell, device):
-    """Construct a `DeviceWrapper` for `cell` with device `device`.
-
-    Ensures the wrapped `cell` is called with `tf.device(device)`.
-
-    Args:
-      cell: An instance of `RNNCell`.
-      device: A device string or function, for passing to `tf.device`.
-    """
-    self._cell = cell
-    self._device = device
-
-  @property
-  def state_size(self):
-    return self._cell.state_size
-
-  @property
-  def output_size(self):
-    return self._cell.output_size
-
-  def zero_state(self, batch_size, dtype):
-    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      with ops.device(self._device):
-        return self._cell.zero_state(batch_size, dtype)
-
-  def __call__(self, inputs, state, scope=None):
-    """Run the cell on specified device."""
-    with ops.device(self._device):
-      return self._cell(inputs, state, scope=scope)
-
-
-class EmbeddingWrapper(RNNCell):
-  """Operator adding input embedding to the given cell.
-
-  Note: in many cases it may be more efficient to not use this wrapper,
-  but instead concatenate the whole sequence of your inputs in time,
-  do the embedding on this batch-concatenated sequence, then split it and
-  feed into your RNN.
-  """
-
-  def __init__(self, cell, embedding_classes, embedding_size, initializer=None,
-               reuse=None):
-    """Create a cell with an added input embedding.
-
-    Args:
-      cell: an RNNCell, an embedding will be put before its inputs.
-      embedding_classes: integer, how many symbols will be embedded.
-      embedding_size: integer, the size of the vectors we embed into.
-      initializer: an initializer to use when creating the embedding;
-        if None, the initializer from variable scope or a default one is used.
-      reuse: (optional) Python boolean describing whether to reuse variables
-        in an existing scope.  If not `True`, and the existing scope already has
-        the given variables, an error is raised.
-
-    Raises:
-      TypeError: if cell is not an RNNCell.
-      ValueError: if embedding_classes is not positive.
-    """
-    super(EmbeddingWrapper, self).__init__(_reuse=reuse)
-    if not _like_rnncell(cell):
-      raise TypeError("The parameter cell is not RNNCell.")
-    if embedding_classes <= 0 or embedding_size <= 0:
-      raise ValueError("Both embedding_classes and embedding_size must be > 0: "
-                       "%d, %d." % (embedding_classes, embedding_size))
-    self._cell = cell
-    self._embedding_classes = embedding_classes
-    self._embedding_size = embedding_size
-    self._initializer = initializer
-
-  @property
-  def state_size(self):
-    return self._cell.state_size
-
-  @property
-  def output_size(self):
-    return self._cell.output_size
-
-  def zero_state(self, batch_size, dtype):
-    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      return self._cell.zero_state(batch_size, dtype)
-
-  def call(self, inputs, state):
-    """Run the cell on embedded inputs."""
-    with ops.device("/cpu:0"):
-      if self._initializer:
-        initializer = self._initializer
-      elif vs.get_variable_scope().initializer:
-        initializer = vs.get_variable_scope().initializer
-      else:
-        # Default initializer for embeddings should have variance=1.
-        sqrt3 = math.sqrt(3)  # Uniform(-sqrt(3), sqrt(3)) has variance=1.
-        initializer = init_ops.random_uniform_initializer(-sqrt3, sqrt3)
-
-      if type(state) is tuple:
-        data_type = state[0].dtype
-      else:
-        data_type = state.dtype
-
-      embedding = vs.get_variable(
-          "embedding", [self._embedding_classes, self._embedding_size],
-          initializer=initializer,
-          dtype=data_type)
-      embedded = embedding_ops.embedding_lookup(embedding,
-                                                array_ops.reshape(inputs, [-1]))
-
-      return self._cell(embedded, state)
-
-
-class MultiRNNCell(RNNCell):
-  """RNN cell composed sequentially of multiple simple cells."""
-
-  def __init__(self, cells, state_is_tuple=True):
-    """Create a RNN cell composed sequentially of a number of RNNCells.
-
-    Args:
-      cells: list of RNNCells that will be composed in this order.
-      state_is_tuple: If True, accepted and returned states are n-tuples, where
-        `n = len(cells)`.  If False, the states are all
-        concatenated along the column axis.  This latter behavior will soon be
-        deprecated.
-
-    Raises:
-      ValueError: if cells is empty (not allowed), or at least one of the cells
-        returns a state tuple but the flag `state_is_tuple` is `False`.
-    """
-    super(MultiRNNCell, self).__init__()
-    if not cells:
-      raise ValueError("Must specify at least one cell for MultiRNNCell.")
-    if not nest.is_sequence(cells):
-      raise TypeError(
-          "cells must be a list or tuple, but saw: %s." % cells)
-
-    self._cells = cells
-    self._state_is_tuple = state_is_tuple
-    if not state_is_tuple:
-      if any(nest.is_sequence(c.state_size) for c in self._cells):
-        raise ValueError("Some cells return tuples of states, but the flag "
-                         "state_is_tuple is not set.  State sizes are: %s"
-                         % str([c.state_size for c in self._cells]))
-
-  @property
-  def state_size(self):
-    if self._state_is_tuple:
-      return tuple(cell.state_size for cell in self._cells)
-    else:
-      return sum([cell.state_size for cell in self._cells])
-
-  @property
-  def output_size(self):
-    return self._cells[-1].output_size
-
-  def zero_state(self, batch_size, dtype):
-    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      if self._state_is_tuple:
-        return tuple(cell.zero_state(batch_size, dtype) for cell in self._cells)
-      else:
-        # We know here that state_size of each cell is not a tuple and
-        # presumably does not contain TensorArrays or anything else fancy
-        return super(MultiRNNCell, self).zero_state(batch_size, dtype)
-
-  def call(self, inputs, state):
-    """Run this multi-layer cell on inputs, starting from state."""
-    cur_state_pos = 0
-    cur_inp = inputs
-    new_states = []
-    for i, cell in enumerate(self._cells):
-      with vs.variable_scope("cell_%d" % i):
-        if self._state_is_tuple:
-          if not nest.is_sequence(state):
-            raise ValueError(
-                "Expected state to be a tuple of length %d, but received: %s" %
-                (len(self.state_size), state))
-          cur_state = state[i]
-        else:
-          cur_state = array_ops.slice(state, [0, cur_state_pos],
-                                      [-1, cell.state_size])
-          cur_state_pos += cell.state_size
-        cur_inp, new_state = cell(cur_inp, cur_state)
-        new_states.append(new_state)
-
-    new_states = (tuple(new_states) if self._state_is_tuple else
-                  array_ops.concat(new_states, 1))
-
-    return cur_inp, new_states
-
-
-class _SlimRNNCell(RNNCell):
-  """A simple wrapper for slim.rnn_cells."""
-
-  def __init__(self, cell_fn):
-    """Create a SlimRNNCell from a cell_fn.
-
-    Args:
-      cell_fn: a function which takes (inputs, state, scope) and produces the
-        outputs and the new_state. Additionally when called with inputs=None and
-        state=None it should return (initial_outputs, initial_state).
-
-    Raises:
-      TypeError: if cell_fn is not callable
-      ValueError: if cell_fn cannot produce a valid initial state.
-    """
-    if not callable(cell_fn):
-      raise TypeError("cell_fn %s needs to be callable", cell_fn)
-    self._cell_fn = cell_fn
-    self._cell_name = cell_fn.func.__name__
-    init_output, init_state = self._cell_fn(None, None)
-    output_shape = init_output.get_shape()
-    state_shape = init_state.get_shape()
-    self._output_size = output_shape.with_rank(2)[1].value
-    self._state_size = state_shape.with_rank(2)[1].value
-    if self._output_size is None:
-      raise ValueError("Initial output created by %s has invalid shape %s" %
-                       (self._cell_name, output_shape))
-    if self._state_size is None:
-      raise ValueError("Initial state created by %s has invalid shape %s" %
-                       (self._cell_name, state_shape))
-
-  @property
-  def state_size(self):
-    return self._state_size
-
-  @property
-  def output_size(self):
-    return self._output_size
-
-  def __call__(self, inputs, state, scope=None):
-    scope = scope or self._cell_name
-    output, state = self._cell_fn(inputs, state, scope=scope)
-    return output, state
-
-
-def _linear(args, output_size, bias, bias_initializer=None,
-            kernel_initializer=None):
-  """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
-
-  Args:
-    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
-    output_size: int, second dimension of W[i].
-    bias: boolean, whether to add a bias term or not.
-    bias_initializer: starting value to initialize the bias; None by default.
-    kernel_initializer: starting value to initialize the weight; None by default.
-
-  Returns:
-    A 2D Tensor with shape [batch x output_size] equal to
-    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
-
-  Raises:
-    ValueError: if some of the arguments has unspecified or wrong shape.
-  """
-  if args is None or (nest.is_sequence(args) and not args):
-    raise ValueError("`args` must be specified")
-  if not nest.is_sequence(args):
-    args = [args]
-
-  # Calculate the total size of arguments on dimension 1.
-  total_arg_size = 0
-  shapes = [a.get_shape() for a in args]
-  for shape in shapes:
-    if shape.ndims != 2:
-      raise ValueError("linear is expecting 2D arguments: %s" % shapes)
-    if shape[1].value is None:
-      raise ValueError("linear expects shape[1] to be provided for shape %s, "
-                       "but saw %s" % (shape, shape[1]))
-    else:
-      total_arg_size += shape[1].value
-
-  dtype = [a.dtype for a in args][0]
-
-  # Now the computation.
-  scope = vs.get_variable_scope()
-  with vs.variable_scope(scope) as outer_scope:
-    weights = vs.get_variable(
-        _WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size], dtype=dtype,
-        initializer=kernel_initializer)
-    if len(args) == 1:
-      res = math_ops.matmul(args[0], weights)
-    else:
-      res = math_ops.matmul(array_ops.concat(args, 1), weights)
-    if not bias:
-      return res
-    with vs.variable_scope(outer_scope) as inner_scope:
-      inner_scope.set_partitioner(None)
-      if bias_initializer is None:
-        bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
-      biases = vs.get_variable(
-          _BIAS_VARIABLE_NAME, [output_size],
-          dtype=dtype,
-          initializer=bias_initializer)
-    return nn_ops.bias_add(res, biases)
diff --git a/tensorflow/contrib/rnn/python/ops/fused_rnn_cell.py b/tensorflow/contrib/rnn/python/ops/fused_rnn_cell.py
index 65e8705d1e..b7393d8b98 100644
--- a/tensorflow/contrib/rnn/python/ops/fused_rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/fused_rnn_cell.py
@@ -20,7 +20,6 @@ from __future__ import print_function
 
 import abc
 
-from tensorflow.contrib.rnn.python.ops import core_rnn as contrib_rnn
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import rnn
 
@@ -116,12 +115,13 @@ class FusedRNNCellAdaptor(FusedRNNCell):
     else:  # non-dynamic rnn
       if not is_list:
         inputs = array_ops.unstack(inputs)
-      outputs, state = contrib_rnn.static_rnn(self._cell,
-                                              inputs,
-                                              initial_state=initial_state,
-                                              dtype=dtype,
-                                              sequence_length=sequence_length,
-                                              scope=scope)
+      outputs, state = rnn.static_rnn(
+          self._cell,
+          inputs,
+          initial_state=initial_state,
+          dtype=dtype,
+          sequence_length=sequence_length,
+          scope=scope)
       if not is_list:
         # Convert outputs back to tensor
         outputs = array_ops.stack(outputs)
diff --git a/tensorflow/contrib/rnn/python/ops/gru_ops.py b/tensorflow/contrib/rnn/python/ops/gru_ops.py
index 071a570e2d..de57e7d81e 100644
--- a/tensorflow/contrib/rnn/python/ops/gru_ops.py
+++ b/tensorflow/contrib/rnn/python/ops/gru_ops.py
@@ -18,13 +18,13 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.rnn.ops import gen_gru_ops
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell
 from tensorflow.contrib.util import loader
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.platform import resource_loader
 
@@ -94,7 +94,7 @@ def _GRUBlockCellGrad(op, *grad):
   return d_x, d_h_prev, d_w_ru, d_w_c, d_b_ru, d_b_c
 
 
-class GRUBlockCell(core_rnn_cell.RNNCell):
+class GRUBlockCell(rnn_cell_impl.RNNCell):
   r"""Block GRU cell implementation.
 
   The implementation is based on:  http://arxiv.org/abs/1406.1078
diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
index 0e70939cce..c41b5793fc 100644
--- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py
+++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
@@ -20,7 +20,6 @@ from __future__ import print_function
 import abc
 
 from tensorflow.contrib.rnn.ops import gen_lstm_ops
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell
 from tensorflow.contrib.rnn.python.ops import fused_rnn_cell
 from tensorflow.contrib.util import loader
 from tensorflow.python.framework import dtypes
@@ -29,6 +28,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.platform import resource_loader
 
@@ -325,7 +325,7 @@ def _BlockLSTMGrad(op, *grad):
           wcf_grad, b_grad]
 
 
-class LSTMBlockCell(core_rnn_cell.RNNCell):
+class LSTMBlockCell(rnn_cell_impl.RNNCell):
   """Basic LSTM recurrent network cell.
 
   The implementation is based on: http://arxiv.org/abs/1409.2329.
@@ -333,7 +333,7 @@ class LSTMBlockCell(core_rnn_cell.RNNCell):
   We add `forget_bias` (default: 1) to the biases of the forget gate in order to
   reduce the scale of forgetting in the beginning of the training.
 
-  Unlike `core_rnn_cell.LSTMCell`, this is a monolithic op and should be much
+  Unlike `rnn_cell_impl.LSTMCell`, this is a monolithic op and should be much
   faster.  The weight and bias matrices should be compatible as long as the
   variable scope matches.
   """
@@ -363,7 +363,7 @@ class LSTMBlockCell(core_rnn_cell.RNNCell):
 
   @property
   def state_size(self):
-    return core_rnn_cell.LSTMStateTuple(self._num_units, self._num_units)
+    return rnn_cell_impl.LSTMStateTuple(self._num_units, self._num_units)
 
   @property
   def output_size(self):
@@ -402,7 +402,7 @@ class LSTMBlockCell(core_rnn_cell.RNNCell):
           forget_bias=self._forget_bias,
           use_peephole=self._use_peephole)
 
-      new_state = core_rnn_cell.LSTMStateTuple(cs, h)
+      new_state = rnn_cell_impl.LSTMStateTuple(cs, h)
       return h, new_state
 
 
@@ -546,8 +546,7 @@ class LSTMBlockWrapper(fused_rnn_cell.FusedRNNCell):
         # Input was a list, so return a list
         outputs = array_ops.unstack(outputs)
 
-      final_state = core_rnn_cell.LSTMStateTuple(final_cell_state,
-                                                 final_output)
+      final_state = rnn_cell_impl.LSTMStateTuple(final_cell_state, final_output)
       return outputs, final_state
 
   def _gather_states(self, data, indices, batch_size):
@@ -569,7 +568,7 @@ class LSTMBlockFusedCell(LSTMBlockWrapper):
   We add forget_bias (default: 1) to the biases of the forget gate in order to
   reduce the scale of forgetting in the beginning of the training.
 
-  The variable naming is consistent with `core_rnn_cell.LSTMCell`.
+  The variable naming is consistent with `rnn_cell_impl.LSTMCell`.
   """
 
   def __init__(self,
diff --git a/tensorflow/contrib/rnn/python/ops/rnn.py b/tensorflow/contrib/rnn/python/ops/rnn.py
index 1757a2148c..676441b4fc 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn.py
@@ -17,7 +17,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.rnn.python.ops import core_rnn as contrib_rnn
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import rnn
 from tensorflow.python.ops import variable_scope as vs
@@ -106,7 +105,7 @@ def stack_bidirectional_rnn(cells_fw,
         initial_state_bw = initial_states_bw[i]
 
       with vs.variable_scope("cell_%d" % i) as cell_scope:
-        prev_layer, state_fw, state_bw = contrib_rnn.static_bidirectional_rnn(
+        prev_layer, state_fw, state_bw = rnn.static_bidirectional_rnn(
             cell_fw,
             cell_bw,
             prev_layer,
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index 0898e78837..3dc8abb8b8 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -23,8 +23,6 @@ import math
 
 from tensorflow.contrib.compiler import jit
 from tensorflow.contrib.layers.python.layers import layers
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import op_def_registry
 from tensorflow.python.framework import ops
@@ -76,7 +74,7 @@ def _get_sharded_variable(name, shape, dtype, num_shards):
   return shards
 
 
-class CoupledInputForgetGateLSTMCell(core_rnn_cell.RNNCell):
+class CoupledInputForgetGateLSTMCell(rnn_cell_impl.RNNCell):
   """Long short-term memory unit (LSTM) recurrent network cell.
 
   The default non-peephole implementation is based on:
@@ -154,14 +152,12 @@ class CoupledInputForgetGateLSTMCell(core_rnn_cell.RNNCell):
     self._reuse = reuse
 
     if num_proj:
-      self._state_size = (
-          core_rnn_cell.LSTMStateTuple(num_units, num_proj)
-          if state_is_tuple else num_units + num_proj)
+      self._state_size = (rnn_cell_impl.LSTMStateTuple(num_units, num_proj)
+                          if state_is_tuple else num_units + num_proj)
       self._output_size = num_proj
     else:
-      self._state_size = (
-          core_rnn_cell.LSTMStateTuple(num_units, num_units)
-          if state_is_tuple else 2 * num_units)
+      self._state_size = (rnn_cell_impl.LSTMStateTuple(num_units, num_units)
+                          if state_is_tuple else 2 * num_units)
       self._output_size = num_units
 
   @property
@@ -254,12 +250,12 @@ class CoupledInputForgetGateLSTMCell(core_rnn_cell.RNNCell):
         m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip)
         # pylint: enable=invalid-unary-operand-type
 
-    new_state = (core_rnn_cell.LSTMStateTuple(c, m) if self._state_is_tuple else
-                 array_ops.concat([c, m], 1))
+    new_state = (rnn_cell_impl.LSTMStateTuple(c, m)
+                 if self._state_is_tuple else array_ops.concat([c, m], 1))
     return m, new_state
 
 
-class TimeFreqLSTMCell(core_rnn_cell.RNNCell):
+class TimeFreqLSTMCell(rnn_cell_impl.RNNCell):
   """Time-Frequency Long short-term memory unit (LSTM) recurrent network cell.
 
   This implementation is based on:
@@ -427,7 +423,7 @@ class TimeFreqLSTMCell(core_rnn_cell.RNNCell):
     return freq_inputs
 
 
-class GridLSTMCell(core_rnn_cell.RNNCell):
+class GridLSTMCell(rnn_cell_impl.RNNCell):
   """Grid Long short-term memory unit (LSTM) recurrent network cell.
 
   The default is based on:
@@ -1020,11 +1016,11 @@ class BidirectionalGridLSTMCell(GridLSTMCell):
 
 
 # pylint: disable=protected-access
-_linear = core_rnn_cell_impl._linear
+_linear = rnn_cell_impl._linear
 # pylint: enable=protected-access
 
 
-class AttentionCellWrapper(core_rnn_cell.RNNCell):
+class AttentionCellWrapper(rnn_cell_impl.RNNCell):
   """Basic attention cell wrapper.
 
   Implementation based on https://arxiv.org/abs/1409.0473.
@@ -1155,7 +1151,7 @@ class AttentionCellWrapper(core_rnn_cell.RNNCell):
       return new_attns, new_attn_states
 
 
-class HighwayWrapper(core_rnn_cell.RNNCell):
+class HighwayWrapper(rnn_cell_impl.RNNCell):
   """RNNCell wrapper that adds highway connection on cell input and output.
 
   Based on:
@@ -1238,7 +1234,7 @@ class HighwayWrapper(core_rnn_cell.RNNCell):
     return (res_outputs, new_state)
 
 
-class LayerNormBasicLSTMCell(core_rnn_cell.RNNCell):
+class LayerNormBasicLSTMCell(rnn_cell_impl.RNNCell):
   """LSTM unit with layer normalization and recurrent dropout.
 
   This class adds layer normalization and recurrent dropout to a
@@ -1300,7 +1296,7 @@ class LayerNormBasicLSTMCell(core_rnn_cell.RNNCell):
 
   @property
   def state_size(self):
-    return core_rnn_cell.LSTMStateTuple(self._num_units, self._num_units)
+    return rnn_cell_impl.LSTMStateTuple(self._num_units, self._num_units)
 
   @property
   def output_size(self):
@@ -1350,11 +1346,11 @@ class LayerNormBasicLSTMCell(core_rnn_cell.RNNCell):
       new_c = self._norm(new_c, "state")
     new_h = self._activation(new_c) * math_ops.sigmoid(o)
 
-    new_state = core_rnn_cell.LSTMStateTuple(new_c, new_h)
+    new_state = rnn_cell_impl.LSTMStateTuple(new_c, new_h)
     return new_h, new_state
 
 
-class NASCell(core_rnn_cell.RNNCell):
+class NASCell(rnn_cell_impl.RNNCell):
   """Neural Architecture Search (NAS) recurrent network cell.
 
   This implements the recurrent cell from the paper:
@@ -1388,10 +1384,10 @@ class NASCell(core_rnn_cell.RNNCell):
     self._reuse = reuse
 
     if num_proj is not None:
-      self._state_size = core_rnn_cell.LSTMStateTuple(num_units, num_proj)
+      self._state_size = rnn_cell_impl.LSTMStateTuple(num_units, num_proj)
       self._output_size = num_proj
     else:
-      self._state_size = core_rnn_cell.LSTMStateTuple(num_units, num_units)
+      self._state_size = rnn_cell_impl.LSTMStateTuple(num_units, num_units)
       self._output_size = num_units
 
   @property
@@ -1498,11 +1494,11 @@ class NASCell(core_rnn_cell.RNNCell):
           dtype)
       new_m = math_ops.matmul(new_m, concat_w_proj)
 
-    new_state = core_rnn_cell.LSTMStateTuple(new_c, new_m)
+    new_state = rnn_cell_impl.LSTMStateTuple(new_c, new_m)
     return new_m, new_state
 
 
-class UGRNNCell(core_rnn_cell.RNNCell):
+class UGRNNCell(rnn_cell_impl.RNNCell):
   """Update Gate Recurrent Neural Network (UGRNN) cell.
 
   Compromise between a LSTM/GRU and a vanilla RNN.  There is only one
@@ -1589,7 +1585,7 @@ class UGRNNCell(core_rnn_cell.RNNCell):
     return new_output, new_state
 
 
-class IntersectionRNNCell(core_rnn_cell.RNNCell):
+class IntersectionRNNCell(rnn_cell_impl.RNNCell):
   """Intersection Recurrent Neural Network (+RNN) cell.
 
   Architecture with coupled recurrent gate as well as coupled depth
@@ -1712,7 +1708,7 @@ class IntersectionRNNCell(core_rnn_cell.RNNCell):
 _REGISTERED_OPS = None
 
 
-class CompiledWrapper(core_rnn_cell.RNNCell):
+class CompiledWrapper(rnn_cell_impl.RNNCell):
   """Wraps step execution in an XLA JIT scope."""
 
   def __init__(self, cell, compile_stateful=False):
@@ -1783,7 +1779,7 @@ def _random_exp_initializer(minval,
   return _initializer
 
 
-class PhasedLSTMCell(core_rnn_cell.RNNCell):
+class PhasedLSTMCell(rnn_cell_impl.RNNCell):
   """Phased LSTM recurrent network cell.
 
   https://arxiv.org/pdf/1610.09513v1.pdf
@@ -1831,7 +1827,7 @@ class PhasedLSTMCell(core_rnn_cell.RNNCell):
 
   @property
   def state_size(self):
-    return core_rnn_cell.LSTMStateTuple(self._num_units, self._num_units)
+    return rnn_cell_impl.LSTMStateTuple(self._num_units, self._num_units)
 
   @property
   def output_size(self):
@@ -1858,13 +1854,13 @@ class PhasedLSTMCell(core_rnn_cell.RNNCell):
          It stores the time.
          The second Tensor has shape [batch, features_size], and type float32.
          It stores the features.
-      state: core_rnn_cell.LSTMStateTuple, state from previous timestep.
+      state: rnn_cell_impl.LSTMStateTuple, state from previous timestep.
 
     Returns:
       A tuple containing:
       - A Tensor of float32, and shape [batch_size, num_units], representing the
         output of the cell.
-      - A core_rnn_cell.LSTMStateTuple, containing 2 Tensors of float32, shape
+      - A rnn_cell_impl.LSTMStateTuple, containing 2 Tensors of float32, shape
         [batch_size, num_units], representing the new state and the output.
     """
     (c_prev, h_prev) = state
@@ -1921,12 +1917,12 @@ class PhasedLSTMCell(core_rnn_cell.RNNCell):
     new_c = k * new_c + (1 - k) * c_prev
     new_h = k * new_h + (1 - k) * h_prev
 
-    new_state = core_rnn_cell.LSTMStateTuple(new_c, new_h)
+    new_state = rnn_cell_impl.LSTMStateTuple(new_c, new_h)
 
     return new_h, new_state
 
 
-class GLSTMCell(core_rnn_cell.RNNCell):
+class GLSTMCell(rnn_cell_impl.RNNCell):
   """Group LSTM cell (G-LSTM).
 
   The implementation is based on:
@@ -1982,10 +1978,10 @@ class GLSTMCell(core_rnn_cell.RNNCell):
                            int(self._num_units / self._number_of_groups)]
 
     if num_proj:
-      self._state_size = core_rnn_cell.LSTMStateTuple(num_units, num_proj)
+      self._state_size = rnn_cell_impl.LSTMStateTuple(num_units, num_proj)
       self._output_size = num_proj
     else:
-      self._state_size = core_rnn_cell.LSTMStateTuple(num_units, num_units)
+      self._state_size = rnn_cell_impl.LSTMStateTuple(num_units, num_units)
       self._output_size = num_units
 
   @property
@@ -2097,5 +2093,5 @@ class GLSTMCell(core_rnn_cell.RNNCell):
       with vs.variable_scope("projection"):
         m = _linear(m, self._num_proj, bias=False)
 
-    new_state = core_rnn_cell.LSTMStateTuple(c, m)
+    new_state = rnn_cell_impl.LSTMStateTuple(c, m)
     return m, new_state
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
index b8b420e10a..ea34333360 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
@@ -24,13 +24,13 @@ import functools
 
 import numpy as np
 
-from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.contrib.seq2seq.python.ops import decoder
 from tensorflow.contrib.seq2seq.python.ops import attention_wrapper as wrapper
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.contrib.seq2seq.python.ops import basic_decoder
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.platform import test
@@ -41,7 +41,7 @@ from tensorflow.python.util import nest
 
 # for testing
 AttentionWrapperState = wrapper.AttentionWrapperState  # pylint: disable=invalid-name
-LSTMStateTuple = core_rnn_cell.LSTMStateTuple  # pylint: disable=invalid-name
+LSTMStateTuple = rnn_cell.LSTMStateTuple  # pylint: disable=invalid-name
 BasicDecoderOutput = basic_decoder.BasicDecoderOutput  # pylint: disable=invalid-name
 float32 = np.float32
 int32 = np.int32
@@ -112,7 +112,7 @@ class AttentionWrapperTest(test.TestCase):
       with vs.variable_scope(
           'root',
           initializer=init_ops.random_normal_initializer(stddev=0.01, seed=3)):
-        cell = core_rnn_cell.LSTMCell(cell_depth)
+        cell = rnn_cell.LSTMCell(cell_depth)
         cell = wrapper.AttentionWrapper(
             cell,
             attention_mechanism,
@@ -133,7 +133,7 @@ class AttentionWrapperTest(test.TestCase):
       self.assertTrue(
           isinstance(final_state, wrapper.AttentionWrapperState))
       self.assertTrue(
-          isinstance(final_state.cell_state, core_rnn_cell.LSTMStateTuple))
+          isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))
 
       self.assertEqual((batch_size, None, attention_depth),
                        tuple(final_outputs.rnn_output.get_shape().as_list()))
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
index 8fc4ecfc82..600adea189 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
@@ -21,13 +21,13 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.contrib.seq2seq.python.ops import basic_decoder
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.layers import core as layers_core
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 # pylint: enable=g-import-not-at-top
@@ -46,7 +46,7 @@ class BasicDecoderTest(test.TestCase):
     with self.test_session(use_gpu=True) as sess:
       inputs = np.random.randn(batch_size, max_time,
                                input_depth).astype(np.float32)
-      cell = core_rnn_cell.LSTMCell(cell_depth)
+      cell = rnn_cell.LSTMCell(cell_depth)
       helper = helper_py.TrainingHelper(
           inputs, sequence_length, time_major=False)
       if use_output_layer:
@@ -77,8 +77,8 @@ class BasicDecoderTest(test.TestCase):
            constant_op.constant(0), first_inputs, first_state)
       batch_size_t = my_decoder.batch_size
 
-      self.assertTrue(isinstance(first_state, core_rnn_cell.LSTMStateTuple))
-      self.assertTrue(isinstance(step_state, core_rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
       self.assertTrue(
           isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
       self.assertEqual((batch_size, expected_output_depth),
@@ -130,7 +130,7 @@ class BasicDecoderTest(test.TestCase):
     with self.test_session(use_gpu=True) as sess:
       embeddings = np.random.randn(vocabulary_size,
                                    input_depth).astype(np.float32)
-      cell = core_rnn_cell.LSTMCell(vocabulary_size)
+      cell = rnn_cell.LSTMCell(vocabulary_size)
       helper = helper_py.GreedyEmbeddingHelper(embeddings, start_tokens,
                                                end_token)
       my_decoder = basic_decoder.BasicDecoder(
@@ -154,8 +154,8 @@ class BasicDecoderTest(test.TestCase):
            constant_op.constant(0), first_inputs, first_state)
       batch_size_t = my_decoder.batch_size
 
-      self.assertTrue(isinstance(first_state, core_rnn_cell.LSTMStateTuple))
-      self.assertTrue(isinstance(step_state, core_rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
       self.assertTrue(
           isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
       self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape())
@@ -202,7 +202,7 @@ class BasicDecoderTest(test.TestCase):
       embeddings = np.random.randn(
           vocabulary_size, input_depth).astype(np.float32)
       half = constant_op.constant(0.5)
-      cell = core_rnn_cell.LSTMCell(vocabulary_size)
+      cell = rnn_cell.LSTMCell(vocabulary_size)
       helper = helper_py.ScheduledEmbeddingTrainingHelper(
           inputs=inputs,
           sequence_length=sequence_length,
@@ -230,8 +230,8 @@ class BasicDecoderTest(test.TestCase):
            constant_op.constant(0), first_inputs, first_state)
       batch_size_t = my_decoder.batch_size
 
-      self.assertTrue(isinstance(first_state, core_rnn_cell.LSTMStateTuple))
-      self.assertTrue(isinstance(step_state, core_rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
       self.assertTrue(
           isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
       self.assertEqual((batch_size, vocabulary_size),
@@ -293,7 +293,7 @@ class BasicDecoderTest(test.TestCase):
     with self.test_session(use_gpu=True) as sess:
       inputs = np.random.randn(batch_size, max_time,
                                input_depth).astype(np.float32)
-      cell = core_rnn_cell.LSTMCell(cell_depth)
+      cell = rnn_cell.LSTMCell(cell_depth)
       sampling_probability = constant_op.constant(sampling_probability)
 
       next_input_layer = None
@@ -335,8 +335,8 @@ class BasicDecoderTest(test.TestCase):
 
       batch_size_t = my_decoder.batch_size
 
-      self.assertTrue(isinstance(first_state, core_rnn_cell.LSTMStateTuple))
-      self.assertTrue(isinstance(step_state, core_rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
       self.assertTrue(
           isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
       self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape())
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
index cb0cb4f8c3..873a39154f 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
@@ -21,7 +21,6 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.contrib.seq2seq.python.ops import attention_wrapper
 from tensorflow.contrib.seq2seq.python.ops import beam_search_decoder
 from tensorflow.contrib.seq2seq.python.ops import beam_search_ops
@@ -32,6 +31,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.layers import core as layers_core
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
@@ -241,7 +241,7 @@ class BeamSearchDecoderTest(test.TestCase):
 
     with self.test_session() as sess:
       embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32)
-      cell = core_rnn_cell.LSTMCell(cell_depth)
+      cell = rnn_cell.LSTMCell(cell_depth)
       if has_attention:
         inputs = np.random.randn(batch_size, decoder_max_time,
                                  input_depth).astype(np.float32)
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/decoder_test.py
index 96dc7b4bee..ac830ae98e 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/decoder_test.py
@@ -21,12 +21,12 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.contrib.seq2seq.python.ops import decoder
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.contrib.seq2seq.python.ops import basic_decoder
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.platform import test
@@ -51,7 +51,7 @@ class DynamicDecodeRNNTest(test.TestCase):
       else:
         inputs = np.random.randn(batch_size, max_time,
                                  input_depth).astype(np.float32)
-      cell = core_rnn_cell.LSTMCell(cell_depth)
+      cell = rnn_cell.LSTMCell(cell_depth)
       helper = helper_py.TrainingHelper(
           inputs, sequence_length, time_major=time_major)
       my_decoder = basic_decoder.BasicDecoder(
@@ -71,7 +71,7 @@ class DynamicDecodeRNNTest(test.TestCase):
 
       self.assertTrue(
           isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
-      self.assertTrue(isinstance(final_state, core_rnn_cell.LSTMStateTuple))
+      self.assertTrue(isinstance(final_state, rnn_cell.LSTMStateTuple))
 
       self.assertEqual(
           (batch_size,),
@@ -126,7 +126,7 @@ class DynamicDecodeRNNTest(test.TestCase):
       inputs = np.random.randn(batch_size, max_time,
                                input_depth).astype(np.float32)
 
-      cell = core_rnn_cell.LSTMCell(cell_depth)
+      cell = rnn_cell.LSTMCell(cell_depth)
       zero_state = cell.zero_state(dtype=dtypes.float32, batch_size=batch_size)
       helper = helper_py.TrainingHelper(inputs, sequence_length)
       my_decoder = basic_decoder.BasicDecoder(
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index fd76882d84..64ffb6ca40 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -21,7 +21,6 @@ from __future__ import print_function
 import collections
 import math
 
-from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -500,7 +499,7 @@ def hardmax(logits, name=None):
         math_ops.argmax(logits, -1), depth, dtype=logits.dtype)
 
 
-class AttentionWrapper(core_rnn_cell.RNNCell):
+class AttentionWrapper(rnn_cell_impl.RNNCell):
   """Wraps another `RNNCell` with attention.
   """
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
index ed26f001c2..1234b15199 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
@@ -17,13 +17,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import BasicRNNCell
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.training import gradient_descent
 
@@ -55,7 +55,7 @@ def BuildFullModel():
     with variable_scope.variable_scope('inp_%d' % i):
       seq.append(array_ops.reshape(BuildSmallModel(), [2, 1, -1]))
 
-  cell = BasicRNNCell(16, 48)
+  cell = rnn_cell.BasicRNNCell(16)
   out = rnn.dynamic_rnn(
       cell, array_ops.concat(seq, axis=1), dtype=dtypes.float32)[0]
 
@@ -63,5 +63,3 @@ def BuildFullModel():
   loss = nn_ops.l2_loss(math_ops.reduce_mean(target - out))
   sgd_op = gradient_descent.GradientDescentOptimizer(1e-2)
   return sgd_op.minimize(loss)
-
-
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index a20b86a235..97a1fab1ee 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -1709,14 +1709,23 @@ py_library(
 py_library(
     name = "rnn_cell",
     srcs = [
+        "ops/rnn_cell.py",
         "ops/rnn_cell_impl.py",
     ],
     srcs_version = "PY2AND3",
     deps = [
         ":array_ops",
+        ":clip_ops",
         ":framework_for_generated_wrappers",
+        ":init_ops",
         ":layers_base",
+        ":math_ops",
+        ":nn_ops",
+        ":partitioned_variables",
+        ":random_ops",
         ":util",
+        ":variable_scope",
+        ":variables",
     ],
 )
 
diff --git a/tensorflow/python/debug/lib/session_debug_testlib.py b/tensorflow/python/debug/lib/session_debug_testlib.py
index deb8249343..ca223ef895 100644
--- a/tensorflow/python/debug/lib/session_debug_testlib.py
+++ b/tensorflow/python/debug/lib/session_debug_testlib.py
@@ -53,7 +53,7 @@ from tensorflow.python.platform import test
 from tensorflow.python.training import gradient_descent
 
 
-class _RNNCellForTest(rnn_cell_impl._RNNCell):  # pylint: disable=protected-access
+class _RNNCellForTest(rnn_cell_impl.RNNCell):  # pylint: disable=protected-access
   """RNN cell for testing."""
 
   def __init__(self, input_output_size, state_size):
diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py
index 5f61c54c78..09160c41e4 100644
--- a/tensorflow/python/kernel_tests/rnn_test.py
+++ b/tensorflow/python/kernel_tests/rnn_test.py
@@ -42,7 +42,7 @@ import tensorflow.python.ops.tensor_array_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
 
 
-class Plus1RNNCell(rnn_cell_impl._RNNCell):
+class Plus1RNNCell(rnn_cell_impl.RNNCell):
   """RNN Cell generating (output, new_state) = (input + 1, state + 1)."""
 
   @property
@@ -57,6 +57,24 @@ class Plus1RNNCell(rnn_cell_impl._RNNCell):
     return (input_ + 1, state + 1)
 
 
+class ScalarStateRNNCell(rnn_cell_impl.RNNCell):
+  """RNN Cell generating (output, new_state) = (input + 1, state + 1)."""
+
+  @property
+  def output_size(self):
+    return 1
+
+  @property
+  def state_size(self):
+    return tensor_shape.TensorShape([])
+
+  def zero_state(self, batch_size, dtype):
+    return array_ops.zeros([], dtype=dtypes.int32)
+
+  def __call__(self, input_, state, scope=None):
+    return (input_, state + 1)
+
+
 class RNNTest(test.TestCase):
 
   def setUp(self):
diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py
index 7b6494e0c9..d05cba2e93 100644
--- a/tensorflow/python/ops/nn.py
+++ b/tensorflow/python/ops/nn.py
@@ -78,6 +78,9 @@ See the @{$python/nn} guide.
 @@dynamic_rnn
 @@bidirectional_dynamic_rnn
 @@raw_rnn
+@@static_rnn
+@@static_state_saving_rnn
+@@static_bidirectional_rnn
 @@ctc_loss
 @@ctc_greedy_decoder
 @@ctc_beam_search_decoder
@@ -113,14 +116,15 @@ from tensorflow.python.util.all_util import remove_undocumented
 
 # Bring more nn-associated functionality into this package.
 # go/tf-wildcard-import
-# pylint: disable=wildcard-import
+# pylint: disable=wildcard-import,unused-import
 from tensorflow.python.ops.ctc_ops import *
 from tensorflow.python.ops.nn_impl import *
 from tensorflow.python.ops.nn_ops import *
 from tensorflow.python.ops.candidate_sampling_ops import *
 from tensorflow.python.ops.embedding_ops import *
 from tensorflow.python.ops.rnn import *
-# pylint: enable=wildcard-import
+from tensorflow.python.ops import rnn_cell
+# pylint: enable=wildcard-import,unused-import
 
 
 # TODO(cwhipkey): sigmoid and tanh should not be exposed from tf.nn.
@@ -135,6 +139,7 @@ _allowed_symbols = [
     "lrn",  # Excluded in gen_docs_combined.
     "relu_layer",  # Excluded in gen_docs_combined.
     "xw_plus_b",  # Excluded in gen_docs_combined.
+    "rnn_cell",  # rnn_cell is a submodule of tf.nn.
 ]
 
 remove_undocumented(__name__, _allowed_symbols,
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py
index a6fba046da..fb30086679 100644
--- a/tensorflow/python/ops/rnn.py
+++ b/tensorflow/python/ops/rnn.py
@@ -13,8 +13,16 @@
 # limitations under the License.
 # ==============================================================================
 
-"""RNN helpers for TensorFlow models."""
+"""RNN helpers for TensorFlow models.
 
+
+@@bidirectional_dynamic_rnn
+@@dynamic_rnn
+@@raw_rnn
+@@static_rnn
+@@static_state_saving_rnn
+@@static_bidirectional_rnn
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -1038,3 +1046,351 @@ def raw_rnn(cell, loop_fn,
       final_loop_state = None
 
     return (emit_ta, final_state, final_loop_state)
+
+
+def static_rnn(cell,
+               inputs,
+               initial_state=None,
+               dtype=None,
+               sequence_length=None,
+               scope=None):
+  """Creates a recurrent neural network specified by RNNCell `cell`.
+
+  The simplest form of RNN network generated is:
+
+  ```python
+    state = cell.zero_state(...)
+    outputs = []
+    for input_ in inputs:
+      output, state = cell(input_, state)
+      outputs.append(output)
+    return (outputs, state)
+  ```
+  However, a few other options are available:
+
+  An initial state can be provided.
+  If the sequence_length vector is provided, dynamic calculation is performed.
+  This method of calculation does not compute the RNN steps past the maximum
+  sequence length of the minibatch (thus saving computational time),
+  and properly propagates the state at an example's sequence length
+  to the final state output.
+
+  The dynamic calculation performed is, at time `t` for batch row `b`,
+
+  ```python
+    (output, state)(b, t) =
+      (t >= sequence_length(b))
+        ? (zeros(cell.output_size), states(b, sequence_length(b) - 1))
+        : cell(input(b, t), state(b, t - 1))
+  ```
+
+  Args:
+    cell: An instance of RNNCell.
+    inputs: A length T list of inputs, each a `Tensor` of shape
+      `[batch_size, input_size]`, or a nested tuple of such elements.
+    initial_state: (optional) An initial state for the RNN.
+      If `cell.state_size` is an integer, this must be
+      a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.
+      If `cell.state_size` is a tuple, this should be a tuple of
+      tensors having shapes `[batch_size, s] for s in cell.state_size`.
+    dtype: (optional) The data type for the initial state and expected output.
+      Required if initial_state is not provided or RNN state has a heterogeneous
+      dtype.
+    sequence_length: Specifies the length of each sequence in inputs.
+      An int32 or int64 vector (tensor) size `[batch_size]`, values in `[0, T)`.
+    scope: VariableScope for the created subgraph; defaults to "rnn".
+
+  Returns:
+    A pair (outputs, state) where:
+
+    - outputs is a length T list of outputs (one for each input), or a nested
+      tuple of such elements.
+    - state is the final state
+
+  Raises:
+    TypeError: If `cell` is not an instance of RNNCell.
+    ValueError: If `inputs` is `None` or an empty list, or if the input depth
+      (column size) cannot be inferred from inputs via shape inference.
+  """
+
+  if not _like_rnncell(cell):
+    raise TypeError("cell must be an instance of RNNCell")
+  if not nest.is_sequence(inputs):
+    raise TypeError("inputs must be a sequence")
+  if not inputs:
+    raise ValueError("inputs must not be empty")
+
+  outputs = []
+  # Create a new scope in which the caching device is either
+  # determined by the parent scope, or is set to place the cached
+  # Variable using the same placement as for the rest of the RNN.
+  with vs.variable_scope(scope or "rnn") as varscope:
+    if varscope.caching_device is None:
+      varscope.set_caching_device(lambda op: op.device)
+
+    # Obtain the first sequence of the input
+    first_input = inputs
+    while nest.is_sequence(first_input):
+      first_input = first_input[0]
+
+    # Temporarily avoid EmbeddingWrapper and seq2seq badness
+    # TODO(lukaszkaiser): remove EmbeddingWrapper
+    if first_input.get_shape().ndims != 1:
+
+      input_shape = first_input.get_shape().with_rank_at_least(2)
+      fixed_batch_size = input_shape[0]
+
+      flat_inputs = nest.flatten(inputs)
+      for flat_input in flat_inputs:
+        input_shape = flat_input.get_shape().with_rank_at_least(2)
+        batch_size, input_size = input_shape[0], input_shape[1:]
+        fixed_batch_size.merge_with(batch_size)
+        for i, size in enumerate(input_size):
+          if size.value is None:
+            raise ValueError(
+                "Input size (dimension %d of inputs) must be accessible via "
+                "shape inference, but saw value None." % i)
+    else:
+      fixed_batch_size = first_input.get_shape().with_rank_at_least(1)[0]
+
+    if fixed_batch_size.value:
+      batch_size = fixed_batch_size.value
+    else:
+      batch_size = array_ops.shape(first_input)[0]
+    if initial_state is not None:
+      state = initial_state
+    else:
+      if not dtype:
+        raise ValueError("If no initial_state is provided, "
+                         "dtype must be specified")
+      state = cell.zero_state(batch_size, dtype)
+
+    if sequence_length is not None:  # Prepare variables
+      sequence_length = ops.convert_to_tensor(
+          sequence_length, name="sequence_length")
+      if sequence_length.get_shape().ndims not in (None, 1):
+        raise ValueError(
+            "sequence_length must be a vector of length batch_size")
+
+      def _create_zero_output(output_size):
+        # convert int to TensorShape if necessary
+        size = _concat(batch_size, output_size)
+        output = array_ops.zeros(
+            array_ops.stack(size), _infer_state_dtype(dtype, state))
+        shape = _concat(fixed_batch_size.value, output_size, static=True)
+        output.set_shape(tensor_shape.TensorShape(shape))
+        return output
+
+      output_size = cell.output_size
+      flat_output_size = nest.flatten(output_size)
+      flat_zero_output = tuple(
+          _create_zero_output(size) for size in flat_output_size)
+      zero_output = nest.pack_sequence_as(
+          structure=output_size, flat_sequence=flat_zero_output)
+
+      sequence_length = math_ops.to_int32(sequence_length)
+      min_sequence_length = math_ops.reduce_min(sequence_length)
+      max_sequence_length = math_ops.reduce_max(sequence_length)
+
+    for time, input_ in enumerate(inputs):
+      if time > 0:
+        varscope.reuse_variables()
+      # pylint: disable=cell-var-from-loop
+      call_cell = lambda: cell(input_, state)
+      # pylint: enable=cell-var-from-loop
+      if sequence_length is not None:
+        (output, state) = _rnn_step(
+            time=time,
+            sequence_length=sequence_length,
+            min_sequence_length=min_sequence_length,
+            max_sequence_length=max_sequence_length,
+            zero_output=zero_output,
+            state=state,
+            call_cell=call_cell,
+            state_size=cell.state_size)
+      else:
+        (output, state) = call_cell()
+
+      outputs.append(output)
+
+    return (outputs, state)
+
+
+def static_state_saving_rnn(cell,
+                            inputs,
+                            state_saver,
+                            state_name,
+                            sequence_length=None,
+                            scope=None):
+  """RNN that accepts a state saver for time-truncated RNN calculation.
+
+  Args:
+    cell: An instance of `RNNCell`.
+    inputs: A length T list of inputs, each a `Tensor` of shape
+      `[batch_size, input_size]`.
+    state_saver: A state saver object with methods `state` and `save_state`.
+    state_name: Python string or tuple of strings.  The name to use with the
+      state_saver. If the cell returns tuples of states (i.e.,
+      `cell.state_size` is a tuple) then `state_name` should be a tuple of
+      strings having the same length as `cell.state_size`.  Otherwise it should
+      be a single string.
+    sequence_length: (optional) An int32/int64 vector size [batch_size].
+      See the documentation for rnn() for more details about sequence_length.
+    scope: VariableScope for the created subgraph; defaults to "rnn".
+
+  Returns:
+    A pair (outputs, state) where:
+      outputs is a length T list of outputs (one for each input)
+      states is the final state
+
+  Raises:
+    TypeError: If `cell` is not an instance of RNNCell.
+    ValueError: If `inputs` is `None` or an empty list, or if the arity and
+     type of `state_name` does not match that of `cell.state_size`.
+  """
+  state_size = cell.state_size
+  state_is_tuple = nest.is_sequence(state_size)
+  state_name_tuple = nest.is_sequence(state_name)
+
+  if state_is_tuple != state_name_tuple:
+    raise ValueError("state_name should be the same type as cell.state_size.  "
+                     "state_name: %s, cell.state_size: %s" % (str(state_name),
+                                                              str(state_size)))
+
+  if state_is_tuple:
+    state_name_flat = nest.flatten(state_name)
+    state_size_flat = nest.flatten(state_size)
+
+    if len(state_name_flat) != len(state_size_flat):
+      raise ValueError("#elems(state_name) != #elems(state_size): %d vs. %d" %
+                       (len(state_name_flat), len(state_size_flat)))
+
+    initial_state = nest.pack_sequence_as(
+        structure=state_size,
+        flat_sequence=[state_saver.state(s) for s in state_name_flat])
+  else:
+    initial_state = state_saver.state(state_name)
+
+  (outputs, state) = static_rnn(
+      cell,
+      inputs,
+      initial_state=initial_state,
+      sequence_length=sequence_length,
+      scope=scope)
+
+  if state_is_tuple:
+    flat_state = nest.flatten(state)
+    state_name = nest.flatten(state_name)
+    save_state = [
+        state_saver.save_state(name, substate)
+        for name, substate in zip(state_name, flat_state)
+    ]
+  else:
+    save_state = [state_saver.save_state(state_name, state)]
+
+  with ops.control_dependencies(save_state):
+    last_output = outputs[-1]
+    flat_last_output = nest.flatten(last_output)
+    flat_last_output = [
+        array_ops.identity(output) for output in flat_last_output
+    ]
+    outputs[-1] = nest.pack_sequence_as(
+        structure=last_output, flat_sequence=flat_last_output)
+
+  return (outputs, state)
+
+
+def static_bidirectional_rnn(cell_fw,
+                             cell_bw,
+                             inputs,
+                             initial_state_fw=None,
+                             initial_state_bw=None,
+                             dtype=None,
+                             sequence_length=None,
+                             scope=None):
+  """Creates a bidirectional recurrent neural network.
+
+  Similar to the unidirectional case above (rnn) but takes input and builds
+  independent forward and backward RNNs with the final forward and backward
+  outputs depth-concatenated, such that the output will have the format
+  [time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
+  forward and backward cell must match. The initial state for both directions
+  is zero by default (but can be set optionally) and no intermediate states are
+  ever returned -- the network is fully unrolled for the given (passed in)
+  length(s) of the sequence(s) or completely unrolled if length(s) is not given.
+
+  Args:
+    cell_fw: An instance of RNNCell, to be used for forward direction.
+    cell_bw: An instance of RNNCell, to be used for backward direction.
+    inputs: A length T list of inputs, each a tensor of shape
+      [batch_size, input_size], or a nested tuple of such elements.
+    initial_state_fw: (optional) An initial state for the forward RNN.
+      This must be a tensor of appropriate type and shape
+      `[batch_size, cell_fw.state_size]`.
+      If `cell_fw.state_size` is a tuple, this should be a tuple of
+      tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
+    initial_state_bw: (optional) Same as for `initial_state_fw`, but using
+      the corresponding properties of `cell_bw`.
+    dtype: (optional) The data type for the initial state.  Required if
+      either of the initial states are not provided.
+    sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
+      containing the actual lengths for each of the sequences.
+    scope: VariableScope for the created subgraph; defaults to
+      "bidirectional_rnn"
+
+  Returns:
+    A tuple (outputs, output_state_fw, output_state_bw) where:
+      outputs is a length `T` list of outputs (one for each input), which
+        are depth-concatenated forward and backward outputs.
+      output_state_fw is the final state of the forward rnn.
+      output_state_bw is the final state of the backward rnn.
+
+  Raises:
+    TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
+    ValueError: If inputs is None or an empty list.
+  """
+
+  if not _like_rnncell(cell_fw):
+    raise TypeError("cell_fw must be an instance of RNNCell")
+  if not _like_rnncell(cell_bw):
+    raise TypeError("cell_bw must be an instance of RNNCell")
+  if not nest.is_sequence(inputs):
+    raise TypeError("inputs must be a sequence")
+  if not inputs:
+    raise ValueError("inputs must not be empty")
+
+  with vs.variable_scope(scope or "bidirectional_rnn"):
+    # Forward direction
+    with vs.variable_scope("fw") as fw_scope:
+      output_fw, output_state_fw = static_rnn(
+          cell_fw,
+          inputs,
+          initial_state_fw,
+          dtype,
+          sequence_length,
+          scope=fw_scope)
+
+    # Backward direction
+    with vs.variable_scope("bw") as bw_scope:
+      reversed_inputs = _reverse_seq(inputs, sequence_length)
+      tmp, output_state_bw = static_rnn(
+          cell_bw,
+          reversed_inputs,
+          initial_state_bw,
+          dtype,
+          sequence_length,
+          scope=bw_scope)
+
+  output_bw = _reverse_seq(tmp, sequence_length)
+  # Concat each of the forward/backward outputs
+  flat_output_fw = nest.flatten(output_fw)
+  flat_output_bw = nest.flatten(output_bw)
+
+  flat_outputs = tuple(
+      array_ops.concat([fw, bw], 1)
+      for fw, bw in zip(flat_output_fw, flat_output_bw))
+
+  outputs = nest.pack_sequence_as(
+      structure=output_fw, flat_sequence=flat_outputs)
+
+  return (outputs, output_state_fw, output_state_bw)
diff --git a/tensorflow/python/ops/rnn_cell.py b/tensorflow/python/ops/rnn_cell.py
new file mode 100644
index 0000000000..c0dac8fb01
--- /dev/null
+++ b/tensorflow/python/ops/rnn_cell.py
@@ -0,0 +1,51 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Module for constructing RNN Cells.
+
+## Base interface for all RNN Cells
+
+@@RNNCell
+
+## RNN Cells for use with TensorFlow's core RNN methods
+
+@@BasicRNNCell
+@@BasicLSTMCell
+@@GRUCell
+@@LSTMCell
+
+## Classes storing split `RNNCell` state
+
+@@LSTMStateTuple
+
+## RNN Cell wrappers (RNNCells that wrap other RNNCells)
+
+@@MultiRNNCell
+@@DropoutWrapper
+@@DeviceWrapper
+@@ResidualWrapper
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import
+from tensorflow.python.ops.rnn_cell_impl import *
+# pylint: enable=wildcard-import
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = []
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 9c0fb1db23..500e3b7859 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -12,18 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Module implementing RNN Cells.
 
-This module contains the abstract definition of a RNN cell: `_RNNCell`.
-Actual implementations of various types of RNN cells are located in
-`tensorflow.contrib`.
+This module provides a number of basic commonly used RNN cells, such as LSTM
+(Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of
+operators that allow adding dropouts, projections, or embeddings for inputs.
+Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by
+calling the `rnn` ops several times.
 """
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
+import hashlib
+import numbers
+
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -31,11 +35,22 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.layers import base as base_layer
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import partitioned_variables
+from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables as tf_variables
+from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
 
 
+_BIAS_VARIABLE_NAME = "bias"
+_WEIGHTS_VARIABLE_NAME = "kernel"
+
+
 def _like_rnncell(cell):
   """Checks that a given object is an RNNCell by using duck typing."""
   conditions = [hasattr(cell, "output_size"), hasattr(cell, "state_size"),
@@ -115,7 +130,7 @@ def _zero_state_tensors(state_size, batch_size, dtype):
   return nest.map_structure(get_state_shape, state_size)
 
 
-class _RNNCell(base_layer.Layer):
+class RNNCell(base_layer.Layer):
   """Abstract object representing an RNN cell.
 
   Every `RNNCell` must have the properties below and implement `call` with
@@ -158,11 +173,11 @@ class _RNNCell(base_layer.Layer):
     if scope is not None:
       with vs.variable_scope(scope,
                              custom_getter=self._rnn_get_variable) as scope:
-        return super(_RNNCell, self).__call__(inputs, state, scope=scope)
+        return super(RNNCell, self).__call__(inputs, state, scope=scope)
     else:
       with vs.variable_scope(vs.get_variable_scope(),
                              custom_getter=self._rnn_get_variable):
-        return super(_RNNCell, self).__call__(inputs, state)
+        return super(RNNCell, self).__call__(inputs, state)
 
   def _rnn_get_variable(self, getter, *args, **kwargs):
     variable = getter(*args, **kwargs)
@@ -212,3 +227,806 @@ class _RNNCell(base_layer.Layer):
     with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
       state_size = self.state_size
       return _zero_state_tensors(state_size, batch_size, dtype)
+
+
+class BasicRNNCell(RNNCell):
+  """The most basic RNN cell.
+
+  Args:
+    num_units: int, The number of units in the LSTM cell.
+    activation: Nonlinearity to use.  Default: `tanh`.
+    reuse: (optional) Python boolean describing whether to reuse variables
+     in an existing scope.  If not `True`, and the existing scope already has
+     the given variables, an error is raised.
+  """
+
+  def __init__(self, num_units, activation=None, reuse=None):
+    super(BasicRNNCell, self).__init__(_reuse=reuse)
+    self._num_units = num_units
+    self._activation = activation or math_ops.tanh
+
+  @property
+  def state_size(self):
+    return self._num_units
+
+  @property
+  def output_size(self):
+    return self._num_units
+
+  def call(self, inputs, state):
+    """Most basic RNN: output = new_state = act(W * input + U * state + B)."""
+    output = self._activation(_linear([inputs, state], self._num_units, True))
+    return output, output
+
+
+class GRUCell(RNNCell):
+  """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078)."""
+
+  def __init__(self,
+               num_units,
+               activation=None,
+               reuse=None,
+               kernel_initializer=None,
+               bias_initializer=None):
+    super(GRUCell, self).__init__(_reuse=reuse)
+    self._num_units = num_units
+    self._activation = activation or math_ops.tanh
+    self._kernel_initializer = kernel_initializer
+    self._bias_initializer = bias_initializer
+
+  @property
+  def state_size(self):
+    return self._num_units
+
+  @property
+  def output_size(self):
+    return self._num_units
+
+  def call(self, inputs, state):
+    """Gated recurrent unit (GRU) with nunits cells."""
+    with vs.variable_scope("gates"):  # Reset gate and update gate.
+      # We start with bias of 1.0 to not reset and not update.
+      bias_ones = self._bias_initializer
+      if self._bias_initializer is None:
+        dtype = [a.dtype for a in [inputs, state]][0]
+        bias_ones = init_ops.constant_initializer(1.0, dtype=dtype)
+      value = math_ops.sigmoid(
+          _linear([inputs, state], 2 * self._num_units, True, bias_ones,
+                  self._kernel_initializer))
+      r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
+    with vs.variable_scope("candidate"):
+      c = self._activation(
+          _linear([inputs, r * state], self._num_units, True,
+                  self._bias_initializer, self._kernel_initializer))
+    new_h = u * state + (1 - u) * c
+    return new_h, new_h
+
+
+_LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h"))
+
+
+class LSTMStateTuple(_LSTMStateTuple):
+  """Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state.
+
+  Stores two elements: `(c, h)`, in that order.
+
+  Only used when `state_is_tuple=True`.
+  """
+  __slots__ = ()
+
+  @property
+  def dtype(self):
+    (c, h) = self
+    if c.dtype != h.dtype:
+      raise TypeError("Inconsistent internal state: %s vs %s" %
+                      (str(c.dtype), str(h.dtype)))
+    return c.dtype
+
+
+class BasicLSTMCell(RNNCell):
+  """Basic LSTM recurrent network cell.
+
+  The implementation is based on: http://arxiv.org/abs/1409.2329.
+
+  We add forget_bias (default: 1) to the biases of the forget gate in order to
+  reduce the scale of forgetting in the beginning of the training.
+
+  It does not allow cell clipping, a projection layer, and does not
+  use peep-hole connections: it is the basic baseline.
+
+  For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
+  that follows.
+  """
+
+  def __init__(self, num_units, forget_bias=1.0,
+               state_is_tuple=True, activation=None, reuse=None):
+    """Initialize the basic LSTM cell.
+
+    Args:
+      num_units: int, The number of units in the LSTM cell.
+      forget_bias: float, The bias added to forget gates (see above).
+      state_is_tuple: If True, accepted and returned states are 2-tuples of
+        the `c_state` and `m_state`.  If False, they are concatenated
+        along the column axis.  The latter behavior will soon be deprecated.
+      activation: Activation function of the inner states.  Default: `tanh`.
+      reuse: (optional) Python boolean describing whether to reuse variables
+        in an existing scope.  If not `True`, and the existing scope already has
+        the given variables, an error is raised.
+    """
+    super(BasicLSTMCell, self).__init__(_reuse=reuse)
+    if not state_is_tuple:
+      logging.warn("%s: Using a concatenated state is slower and will soon be "
+                   "deprecated.  Use state_is_tuple=True.", self)
+    self._num_units = num_units
+    self._forget_bias = forget_bias
+    self._state_is_tuple = state_is_tuple
+    self._activation = activation or math_ops.tanh
+
+  @property
+  def state_size(self):
+    return (LSTMStateTuple(self._num_units, self._num_units)
+            if self._state_is_tuple else 2 * self._num_units)
+
+  @property
+  def output_size(self):
+    return self._num_units
+
+  def call(self, inputs, state):
+    """Long short-term memory cell (LSTM)."""
+    sigmoid = math_ops.sigmoid
+    # Parameters of gates are concatenated into one multiply for efficiency.
+    if self._state_is_tuple:
+      c, h = state
+    else:
+      c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
+
+    concat = _linear([inputs, h], 4 * self._num_units, True)
+
+    # i = input_gate, j = new_input, f = forget_gate, o = output_gate
+    i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
+
+    new_c = (
+        c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
+    new_h = self._activation(new_c) * sigmoid(o)
+
+    if self._state_is_tuple:
+      new_state = LSTMStateTuple(new_c, new_h)
+    else:
+      new_state = array_ops.concat([new_c, new_h], 1)
+    return new_h, new_state
+
+
+class LSTMCell(RNNCell):
+  """Long short-term memory unit (LSTM) recurrent network cell.
+
+  The default non-peephole implementation is based on:
+
+    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+
+  S. Hochreiter and J. Schmidhuber.
+  "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
+
+  The peephole implementation is based on:
+
+    https://research.google.com/pubs/archive/43905.pdf
+
+  Hasim Sak, Andrew Senior, and Francoise Beaufays.
+  "Long short-term memory recurrent neural network architectures for
+   large scale acoustic modeling." INTERSPEECH, 2014.
+
+  The class uses optional peep-hole connections, optional cell clipping, and
+  an optional projection layer.
+  """
+
+  def __init__(self, num_units,
+               use_peepholes=False, cell_clip=None,
+               initializer=None, num_proj=None, proj_clip=None,
+               num_unit_shards=None, num_proj_shards=None,
+               forget_bias=1.0, state_is_tuple=True,
+               activation=None, reuse=None):
+    """Initialize the parameters for an LSTM cell.
+
+    Args:
+      num_units: int, The number of units in the LSTM cell
+      use_peepholes: bool, set True to enable diagonal/peephole connections.
+      cell_clip: (optional) A float value, if provided the cell state is clipped
+        by this value prior to the cell output activation.
+      initializer: (optional) The initializer to use for the weight and
+        projection matrices.
+      num_proj: (optional) int, The output dimensionality for the projection
+        matrices.  If None, no projection is performed.
+      proj_clip: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
+        provided, then the projected values are clipped elementwise to within
+        `[-proj_clip, proj_clip]`.
+      num_unit_shards: Deprecated, will be removed by Jan. 2017.
+        Use a variable_scope partitioner instead.
+      num_proj_shards: Deprecated, will be removed by Jan. 2017.
+        Use a variable_scope partitioner instead.
+      forget_bias: Biases of the forget gate are initialized by default to 1
+        in order to reduce the scale of forgetting at the beginning of
+        the training.
+      state_is_tuple: If True, accepted and returned states are 2-tuples of
+        the `c_state` and `m_state`.  If False, they are concatenated
+        along the column axis.  This latter behavior will soon be deprecated.
+      activation: Activation function of the inner states.  Default: `tanh`.
+      reuse: (optional) Python boolean describing whether to reuse variables
+        in an existing scope.  If not `True`, and the existing scope already has
+        the given variables, an error is raised.
+    """
+    super(LSTMCell, self).__init__(_reuse=reuse)
+    if not state_is_tuple:
+      logging.warn("%s: Using a concatenated state is slower and will soon be "
+                   "deprecated.  Use state_is_tuple=True.", self)
+    if num_unit_shards is not None or num_proj_shards is not None:
+      logging.warn(
+          "%s: The num_unit_shards and proj_unit_shards parameters are "
+          "deprecated and will be removed in Jan 2017.  "
+          "Use a variable scope with a partitioner instead.", self)
+
+    self._num_units = num_units
+    self._use_peepholes = use_peepholes
+    self._cell_clip = cell_clip
+    self._initializer = initializer
+    self._num_proj = num_proj
+    self._proj_clip = proj_clip
+    self._num_unit_shards = num_unit_shards
+    self._num_proj_shards = num_proj_shards
+    self._forget_bias = forget_bias
+    self._state_is_tuple = state_is_tuple
+    self._activation = activation or math_ops.tanh
+
+    if num_proj:
+      self._state_size = (
+          LSTMStateTuple(num_units, num_proj)
+          if state_is_tuple else num_units + num_proj)
+      self._output_size = num_proj
+    else:
+      self._state_size = (
+          LSTMStateTuple(num_units, num_units)
+          if state_is_tuple else 2 * num_units)
+      self._output_size = num_units
+
+  @property
+  def state_size(self):
+    return self._state_size
+
+  @property
+  def output_size(self):
+    return self._output_size
+
+  def call(self, inputs, state):
+    """Run one step of LSTM.
+
+    Args:
+      inputs: input Tensor, 2D, batch x num_units.
+      state: if `state_is_tuple` is False, this must be a state Tensor,
+        `2-D, batch x state_size`.  If `state_is_tuple` is True, this must be a
+        tuple of state Tensors, both `2-D`, with column sizes `c_state` and
+        `m_state`.
+
+    Returns:
+      A tuple containing:
+
+      - A `2-D, [batch x output_dim]`, Tensor representing the output of the
+        LSTM after reading `inputs` when previous state was `state`.
+        Here output_dim is:
+           num_proj if num_proj was set,
+           num_units otherwise.
+      - Tensor(s) representing the new state of LSTM after reading `inputs` when
+        the previous state was `state`.  Same type and shape(s) as `state`.
+
+    Raises:
+      ValueError: If input size cannot be inferred from inputs via
+        static shape inference.
+    """
+    num_proj = self._num_units if self._num_proj is None else self._num_proj
+    sigmoid = math_ops.sigmoid
+
+    if self._state_is_tuple:
+      (c_prev, m_prev) = state
+    else:
+      c_prev = array_ops.slice(state, [0, 0], [-1, self._num_units])
+      m_prev = array_ops.slice(state, [0, self._num_units], [-1, num_proj])
+
+    dtype = inputs.dtype
+    input_size = inputs.get_shape().with_rank(2)[1]
+    if input_size.value is None:
+      raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
+    scope = vs.get_variable_scope()
+    with vs.variable_scope(scope, initializer=self._initializer) as unit_scope:
+      if self._num_unit_shards is not None:
+        unit_scope.set_partitioner(
+            partitioned_variables.fixed_size_partitioner(
+                self._num_unit_shards))
+      # i = input_gate, j = new_input, f = forget_gate, o = output_gate
+      lstm_matrix = _linear([inputs, m_prev], 4 * self._num_units, bias=True)
+      i, j, f, o = array_ops.split(
+          value=lstm_matrix, num_or_size_splits=4, axis=1)
+      # Diagonal connections
+      if self._use_peepholes:
+        with vs.variable_scope(unit_scope) as projection_scope:
+          if self._num_unit_shards is not None:
+            projection_scope.set_partitioner(None)
+          w_f_diag = vs.get_variable(
+              "w_f_diag", shape=[self._num_units], dtype=dtype)
+          w_i_diag = vs.get_variable(
+              "w_i_diag", shape=[self._num_units], dtype=dtype)
+          w_o_diag = vs.get_variable(
+              "w_o_diag", shape=[self._num_units], dtype=dtype)
+
+      if self._use_peepholes:
+        c = (sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev +
+             sigmoid(i + w_i_diag * c_prev) * self._activation(j))
+      else:
+        c = (sigmoid(f + self._forget_bias) * c_prev + sigmoid(i) *
+             self._activation(j))
+
+      if self._cell_clip is not None:
+        # pylint: disable=invalid-unary-operand-type
+        c = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip)
+        # pylint: enable=invalid-unary-operand-type
+      if self._use_peepholes:
+        m = sigmoid(o + w_o_diag * c) * self._activation(c)
+      else:
+        m = sigmoid(o) * self._activation(c)
+
+      if self._num_proj is not None:
+        with vs.variable_scope("projection") as proj_scope:
+          if self._num_proj_shards is not None:
+            proj_scope.set_partitioner(
+                partitioned_variables.fixed_size_partitioner(
+                    self._num_proj_shards))
+          m = _linear(m, self._num_proj, bias=False)
+
+        if self._proj_clip is not None:
+          # pylint: disable=invalid-unary-operand-type
+          m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip)
+          # pylint: enable=invalid-unary-operand-type
+
+    new_state = (LSTMStateTuple(c, m) if self._state_is_tuple else
+                 array_ops.concat([c, m], 1))
+    return m, new_state
+
+
+def _enumerated_map_structure(map_fn, *args, **kwargs):
+  ix = [0]
+  def enumerated_fn(*inner_args, **inner_kwargs):
+    r = map_fn(ix[0], *inner_args, **inner_kwargs)
+    ix[0] += 1
+    return r
+  return nest.map_structure(enumerated_fn, *args, **kwargs)
+
+
+class DropoutWrapper(RNNCell):
+  """Operator adding dropout to inputs and outputs of the given cell."""
+
+  def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
+               state_keep_prob=1.0, variational_recurrent=False,
+               input_size=None, dtype=None, seed=None):
+    """Create a cell with added input, state, and/or output dropout.
+
+    If `variational_recurrent` is set to `True` (**NOT** the default behavior),
+    then the the same dropout mask is applied at every step, as described in:
+
+    Y. Gal, Z Ghahramani.  "A Theoretically Grounded Application of Dropout in
+    Recurrent Neural Networks".  https://arxiv.org/abs/1512.05287
+
+    Otherwise a different dropout mask is applied at every time step.
+
+    Args:
+      cell: an RNNCell, a projection to output_size is added to it.
+      input_keep_prob: unit Tensor or float between 0 and 1, input keep
+        probability; if it is constant and 1, no input dropout will be added.
+      output_keep_prob: unit Tensor or float between 0 and 1, output keep
+        probability; if it is constant and 1, no output dropout will be added.
+      state_keep_prob: unit Tensor or float between 0 and 1, output keep
+        probability; if it is constant and 1, no output dropout will be added.
+        State dropout is performed on the *output* states of the cell.
+      variational_recurrent: Python bool.  If `True`, then the same
+        dropout pattern is applied across all time steps per run call.
+        If this parameter is set, `input_size` **must** be provided.
+      input_size: (optional) (possibly nested tuple of) `TensorShape` objects
+        containing the depth(s) of the input tensors expected to be passed in to
+        the `DropoutWrapper`.  Required and used **iff**
+         `variational_recurrent = True` and `input_keep_prob < 1`.
+      dtype: (optional) The `dtype` of the input, state, and output tensors.
+        Required and used **iff** `variational_recurrent = True`.
+      seed: (optional) integer, the randomness seed.
+
+    Raises:
+      TypeError: if cell is not an RNNCell.
+      ValueError: if any of the keep_probs are not between 0 and 1.
+    """
+    if not _like_rnncell(cell):
+      raise TypeError("The parameter cell is not a RNNCell.")
+    with ops.name_scope("DropoutWrapperInit"):
+      def tensor_and_const_value(v):
+        tensor_value = ops.convert_to_tensor(v)
+        const_value = tensor_util.constant_value(tensor_value)
+        return (tensor_value, const_value)
+      for prob, attr in [(input_keep_prob, "input_keep_prob"),
+                         (state_keep_prob, "state_keep_prob"),
+                         (output_keep_prob, "output_keep_prob")]:
+        tensor_prob, const_prob = tensor_and_const_value(prob)
+        if const_prob is not None:
+          if const_prob < 0 or const_prob > 1:
+            raise ValueError("Parameter %s must be between 0 and 1: %d"
+                             % (attr, const_prob))
+          setattr(self, "_%s" % attr, float(const_prob))
+        else:
+          setattr(self, "_%s" % attr, tensor_prob)
+
+    # Set cell, variational_recurrent, seed before running the code below
+    self._cell = cell
+    self._variational_recurrent = variational_recurrent
+    self._seed = seed
+
+    self._recurrent_input_noise = None
+    self._recurrent_state_noise = None
+    self._recurrent_output_noise = None
+
+    if variational_recurrent:
+      if dtype is None:
+        raise ValueError(
+            "When variational_recurrent=True, dtype must be provided")
+
+      def convert_to_batch_shape(s):
+        # Prepend a 1 for the batch dimension; for recurrent
+        # variational dropout we use the same dropout mask for all
+        # batch elements.
+        return array_ops.concat(
+            ([1], tensor_shape.TensorShape(s).as_list()), 0)
+
+      def batch_noise(s, inner_seed):
+        shape = convert_to_batch_shape(s)
+        return random_ops.random_uniform(shape, seed=inner_seed, dtype=dtype)
+
+      if (not isinstance(self._input_keep_prob, numbers.Real) or
+          self._input_keep_prob < 1.0):
+        if input_size is None:
+          raise ValueError(
+              "When variational_recurrent=True and input_keep_prob < 1.0 or "
+              "is unknown, input_size must be provided")
+        self._recurrent_input_noise = _enumerated_map_structure(
+            lambda i, s: batch_noise(s, inner_seed=self._gen_seed("input", i)),
+            input_size)
+      self._recurrent_state_noise = _enumerated_map_structure(
+          lambda i, s: batch_noise(s, inner_seed=self._gen_seed("state", i)),
+          cell.state_size)
+      self._recurrent_output_noise = _enumerated_map_structure(
+          lambda i, s: batch_noise(s, inner_seed=self._gen_seed("output", i)),
+          cell.output_size)
+
+  def _gen_seed(self, salt_prefix, index):
+    if self._seed is None:
+      return None
+    salt = "%s_%d" % (salt_prefix, index)
+    string = (str(self._seed) + salt).encode("utf-8")
+    return int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
+
+  @property
+  def state_size(self):
+    return self._cell.state_size
+
+  @property
+  def output_size(self):
+    return self._cell.output_size
+
+  def zero_state(self, batch_size, dtype):
+    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      return self._cell.zero_state(batch_size, dtype)
+
+  def _variational_recurrent_dropout_value(
+      self, index, value, noise, keep_prob):
+    """Performs dropout given the pre-calculated noise tensor."""
+    # uniform [keep_prob, 1.0 + keep_prob)
+    random_tensor = keep_prob + noise
+
+    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
+    binary_tensor = math_ops.floor(random_tensor)
+    ret = math_ops.div(value, keep_prob) * binary_tensor
+    ret.set_shape(value.get_shape())
+    return ret
+
+  def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob):
+    """Decides whether to perform standard dropout or recurrent dropout."""
+    if not self._variational_recurrent:
+      def dropout(i, v):
+        return nn_ops.dropout(
+            v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i))
+      return _enumerated_map_structure(dropout, values)
+    else:
+      def dropout(i, v, n):
+        return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
+      return _enumerated_map_structure(dropout, values, recurrent_noise)
+
+  def __call__(self, inputs, state, scope=None):
+    """Run the cell with the declared dropouts."""
+    def _should_dropout(p):
+      return (not isinstance(p, float)) or p < 1
+
+    if _should_dropout(self._input_keep_prob):
+      inputs = self._dropout(inputs, "input",
+                             self._recurrent_input_noise,
+                             self._input_keep_prob)
+    output, new_state = self._cell(inputs, state, scope)
+    if _should_dropout(self._state_keep_prob):
+      new_state = self._dropout(new_state, "state",
+                                self._recurrent_state_noise,
+                                self._state_keep_prob)
+    if _should_dropout(self._output_keep_prob):
+      output = self._dropout(output, "output",
+                             self._recurrent_output_noise,
+                             self._output_keep_prob)
+    return output, new_state
+
+
+class ResidualWrapper(RNNCell):
+  """RNNCell wrapper that ensures cell inputs are added to the outputs."""
+
+  def __init__(self, cell):
+    """Constructs a `ResidualWrapper` for `cell`.
+
+    Args:
+      cell: An instance of `RNNCell`.
+    """
+    self._cell = cell
+
+  @property
+  def state_size(self):
+    return self._cell.state_size
+
+  @property
+  def output_size(self):
+    return self._cell.output_size
+
+  def zero_state(self, batch_size, dtype):
+    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      return self._cell.zero_state(batch_size, dtype)
+
+  def __call__(self, inputs, state, scope=None):
+    """Run the cell and add its inputs to its outputs.
+
+    Args:
+      inputs: cell inputs.
+      state: cell state.
+      scope: optional cell scope.
+
+    Returns:
+      Tuple of cell outputs and new state.
+
+    Raises:
+      TypeError: If cell inputs and outputs have different structure (type).
+      ValueError: If cell inputs and outputs have different structure (value).
+    """
+    outputs, new_state = self._cell(inputs, state, scope=scope)
+    nest.assert_same_structure(inputs, outputs)
+    # Ensure shapes match
+    def assert_shape_match(inp, out):
+      inp.get_shape().assert_is_compatible_with(out.get_shape())
+    nest.map_structure(assert_shape_match, inputs, outputs)
+    res_outputs = nest.map_structure(
+        lambda inp, out: inp + out, inputs, outputs)
+    return (res_outputs, new_state)
+
+
+class DeviceWrapper(RNNCell):
+  """Operator that ensures an RNNCell runs on a particular device."""
+
+  def __init__(self, cell, device):
+    """Construct a `DeviceWrapper` for `cell` with device `device`.
+
+    Ensures the wrapped `cell` is called with `tf.device(device)`.
+
+    Args:
+      cell: An instance of `RNNCell`.
+      device: A device string or function, for passing to `tf.device`.
+    """
+    self._cell = cell
+    self._device = device
+
+  @property
+  def state_size(self):
+    return self._cell.state_size
+
+  @property
+  def output_size(self):
+    return self._cell.output_size
+
+  def zero_state(self, batch_size, dtype):
+    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      with ops.device(self._device):
+        return self._cell.zero_state(batch_size, dtype)
+
+  def __call__(self, inputs, state, scope=None):
+    """Run the cell on specified device."""
+    with ops.device(self._device):
+      return self._cell(inputs, state, scope=scope)
+
+
+class MultiRNNCell(RNNCell):
+  """RNN cell composed sequentially of multiple simple cells."""
+
+  def __init__(self, cells, state_is_tuple=True):
+    """Create a RNN cell composed sequentially of a number of RNNCells.
+
+    Args:
+      cells: list of RNNCells that will be composed in this order.
+      state_is_tuple: If True, accepted and returned states are n-tuples, where
+        `n = len(cells)`.  If False, the states are all
+        concatenated along the column axis.  This latter behavior will soon be
+        deprecated.
+
+    Raises:
+      ValueError: if cells is empty (not allowed), or at least one of the cells
+        returns a state tuple but the flag `state_is_tuple` is `False`.
+    """
+    super(MultiRNNCell, self).__init__()
+    if not cells:
+      raise ValueError("Must specify at least one cell for MultiRNNCell.")
+    if not nest.is_sequence(cells):
+      raise TypeError(
+          "cells must be a list or tuple, but saw: %s." % cells)
+
+    self._cells = cells
+    self._state_is_tuple = state_is_tuple
+    if not state_is_tuple:
+      if any(nest.is_sequence(c.state_size) for c in self._cells):
+        raise ValueError("Some cells return tuples of states, but the flag "
+                         "state_is_tuple is not set.  State sizes are: %s"
+                         % str([c.state_size for c in self._cells]))
+
+  @property
+  def state_size(self):
+    if self._state_is_tuple:
+      return tuple(cell.state_size for cell in self._cells)
+    else:
+      return sum([cell.state_size for cell in self._cells])
+
+  @property
+  def output_size(self):
+    return self._cells[-1].output_size
+
+  def zero_state(self, batch_size, dtype):
+    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      if self._state_is_tuple:
+        return tuple(cell.zero_state(batch_size, dtype) for cell in self._cells)
+      else:
+        # We know here that state_size of each cell is not a tuple and
+        # presumably does not contain TensorArrays or anything else fancy
+        return super(MultiRNNCell, self).zero_state(batch_size, dtype)
+
+  def call(self, inputs, state):
+    """Run this multi-layer cell on inputs, starting from state."""
+    cur_state_pos = 0
+    cur_inp = inputs
+    new_states = []
+    for i, cell in enumerate(self._cells):
+      with vs.variable_scope("cell_%d" % i):
+        if self._state_is_tuple:
+          if not nest.is_sequence(state):
+            raise ValueError(
+                "Expected state to be a tuple of length %d, but received: %s" %
+                (len(self.state_size), state))
+          cur_state = state[i]
+        else:
+          cur_state = array_ops.slice(state, [0, cur_state_pos],
+                                      [-1, cell.state_size])
+          cur_state_pos += cell.state_size
+        cur_inp, new_state = cell(cur_inp, cur_state)
+        new_states.append(new_state)
+
+    new_states = (tuple(new_states) if self._state_is_tuple else
+                  array_ops.concat(new_states, 1))
+
+    return cur_inp, new_states
+
+
+class _SlimRNNCell(RNNCell):
+  """A simple wrapper for slim.rnn_cells."""
+
+  def __init__(self, cell_fn):
+    """Create a SlimRNNCell from a cell_fn.
+
+    Args:
+      cell_fn: a function which takes (inputs, state, scope) and produces the
+        outputs and the new_state. Additionally when called with inputs=None and
+        state=None it should return (initial_outputs, initial_state).
+
+    Raises:
+      TypeError: if cell_fn is not callable
+      ValueError: if cell_fn cannot produce a valid initial state.
+    """
+    if not callable(cell_fn):
+      raise TypeError("cell_fn %s needs to be callable", cell_fn)
+    self._cell_fn = cell_fn
+    self._cell_name = cell_fn.func.__name__
+    init_output, init_state = self._cell_fn(None, None)
+    output_shape = init_output.get_shape()
+    state_shape = init_state.get_shape()
+    self._output_size = output_shape.with_rank(2)[1].value
+    self._state_size = state_shape.with_rank(2)[1].value
+    if self._output_size is None:
+      raise ValueError("Initial output created by %s has invalid shape %s" %
+                       (self._cell_name, output_shape))
+    if self._state_size is None:
+      raise ValueError("Initial state created by %s has invalid shape %s" %
+                       (self._cell_name, state_shape))
+
+  @property
+  def state_size(self):
+    return self._state_size
+
+  @property
+  def output_size(self):
+    return self._output_size
+
+  def __call__(self, inputs, state, scope=None):
+    scope = scope or self._cell_name
+    output, state = self._cell_fn(inputs, state, scope=scope)
+    return output, state
+
+
+def _linear(args,
+            output_size,
+            bias,
+            bias_initializer=None,
+            kernel_initializer=None):
+  """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
+
+  Args:
+    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
+    output_size: int, second dimension of W[i].
+    bias: boolean, whether to add a bias term or not.
+    bias_initializer: starting value to initialize the bias
+      (default is all zeros).
+    kernel_initializer: starting value to initialize the weight.
+
+  Returns:
+    A 2D Tensor with shape [batch x output_size] equal to
+    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
+
+  Raises:
+    ValueError: if some of the arguments has unspecified or wrong shape.
+  """
+  if args is None or (nest.is_sequence(args) and not args):
+    raise ValueError("`args` must be specified")
+  if not nest.is_sequence(args):
+    args = [args]
+
+  # Calculate the total size of arguments on dimension 1.
+  total_arg_size = 0
+  shapes = [a.get_shape() for a in args]
+  for shape in shapes:
+    if shape.ndims != 2:
+      raise ValueError("linear is expecting 2D arguments: %s" % shapes)
+    if shape[1].value is None:
+      raise ValueError("linear expects shape[1] to be provided for shape %s, "
+                       "but saw %s" % (shape, shape[1]))
+    else:
+      total_arg_size += shape[1].value
+
+  dtype = [a.dtype for a in args][0]
+
+  # Now the computation.
+  scope = vs.get_variable_scope()
+  with vs.variable_scope(scope) as outer_scope:
+    weights = vs.get_variable(
+        _WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
+        dtype=dtype,
+        initializer=kernel_initializer)
+    if len(args) == 1:
+      res = math_ops.matmul(args[0], weights)
+    else:
+      res = math_ops.matmul(array_ops.concat(args, 1), weights)
+    if not bias:
+      return res
+    with vs.variable_scope(outer_scope) as inner_scope:
+      inner_scope.set_partitioner(None)
+      if bias_initializer is None:
+        bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
+      biases = vs.get_variable(
+          _BIAS_VARIABLE_NAME, [output_size],
+          dtype=dtype,
+          initializer=bias_initializer)
+    return nn_ops.bias_add(res, biases)
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt
index b1b60fbdcb..9f817beafd 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt
@@ -1,5 +1,9 @@
 path: "tensorflow.nn"
 tf_module {
+  member {
+    name: "rnn_cell"
+    mtype: "<type \'module\'>"
+  }
   member_method {
     name: "all_candidate_sampler"
     argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
@@ -284,6 +288,18 @@ tf_module {
     name: "sparse_softmax_cross_entropy_with_logits"
     argspec: "args=[\'_sentinel\', \'labels\', \'logits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
   }
+  member_method {
+    name: "static_bidirectional_rnn"
+    argspec: "args=[\'cell_fw\', \'cell_bw\', \'inputs\', \'initial_state_fw\', \'initial_state_bw\', \'dtype\', \'sequence_length\', \'scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "static_rnn"
+    argspec: "args=[\'cell\', \'inputs\', \'initial_state\', \'dtype\', \'sequence_length\', \'scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "static_state_saving_rnn"
+    argspec: "args=[\'cell\', \'inputs\', \'state_saver\', \'state_name\', \'sequence_length\', \'scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
   member_method {
     name: "sufficient_statistics"
     argspec: "args=[\'x\', \'axes\', \'shift\', \'keep_dims\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
new file mode 100644
index 0000000000..fbf68c50a1
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.BasicLSTMCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'num_units\', \'forget_bias\', \'state_is_tuple\', \'activation\', \'reuse\'], varargs=None, keywords=None, defaults=[\'1.0\', \'True\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'state\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
new file mode 100644
index 0000000000..606d20d8f0
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.BasicRNNCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicRNNCell\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'num_units\', \'activation\', \'reuse\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'state\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
new file mode 100644
index 0000000000..ead1d0cfc5
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.DeviceWrapper"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DeviceWrapper\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'cell\', \'device\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
new file mode 100644
index 0000000000..2db4996b2a
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.DropoutWrapper"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DropoutWrapper\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'cell\', \'input_keep_prob\', \'output_keep_prob\', \'state_keep_prob\', \'variational_recurrent\', \'input_size\', \'dtype\', \'seed\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1.0\', \'1.0\', \'False\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
new file mode 100644
index 0000000000..101f6df1d8
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.GRUCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.GRUCell\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'num_units\', \'activation\', \'reuse\', \'kernel_initializer\', \'bias_initializer\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'state\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
new file mode 100644
index 0000000000..c87546d528
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.LSTMCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LSTMCell\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'num_units\', \'use_peepholes\', \'cell_clip\', \'initializer\', \'num_proj\', \'proj_clip\', \'num_unit_shards\', \'num_proj_shards\', \'forget_bias\', \'state_is_tuple\', \'activation\', \'reuse\'], varargs=None, keywords=None, defaults=[\'False\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'1.0\', \'True\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'state\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt
new file mode 100644
index 0000000000..1de8a55dcc
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt
@@ -0,0 +1,27 @@
+path: "tensorflow.nn.rnn_cell.LSTMStateTuple"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LSTMStateTuple\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LSTMStateTuple\'>"
+  is_instance: "<type \'tuple\'>"
+  member {
+    name: "c"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "h"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+  }
+  member_method {
+    name: "count"
+  }
+  member_method {
+    name: "index"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
new file mode 100644
index 0000000000..bc01ccfa64
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.MultiRNNCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.MultiRNNCell\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'cells\', \'state_is_tuple\'], varargs=None, keywords=None, defaults=[\'True\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'state\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
new file mode 100644
index 0000000000..b19ee18b40
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
@@ -0,0 +1,94 @@
+path: "tensorflow.nn.rnn_cell.RNNCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'trainable\', \'name\', \'dtype\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'None\', \"<dtype: \'float32\'>\"], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
new file mode 100644
index 0000000000..b21d9a8ee3
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
@@ -0,0 +1,95 @@
+path: "tensorflow.nn.rnn_cell.ResidualWrapper"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.ResidualWrapper\'>"
+  is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'cell\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "zero_state"
+    argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.pbtxt
new file mode 100644
index 0000000000..64697e8a02
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.pbtxt
@@ -0,0 +1,43 @@
+path: "tensorflow.nn.rnn_cell"
+tf_module {
+  member {
+    name: "BasicLSTMCell"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "BasicRNNCell"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DeviceWrapper"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DropoutWrapper"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "GRUCell"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "LSTMCell"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "LSTMStateTuple"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "MultiRNNCell"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "RNNCell"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "ResidualWrapper"
+    mtype: "<type \'type\'>"
+  }
+}
-- 
GitLab


From 3f8b31d6802fc004b93d827c4706a5769faf7cbb Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 23 May 2017 12:28:30 -0700
Subject: [PATCH 0010/1375] Bugfixes to tfprof model analyzer unit tests.

PiperOrigin-RevId: 156895551
---
 .../tools/tfprof/model_analyzer_test.py       | 33 ++++++++++---------
 .../tools/tfprof/model_analyzer_testlib.py    |  2 ++
 2 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index afd8563e78..49b6296c10 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -108,9 +108,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
-        self.assertEqual(
-            '_TFProfRoot (0/451 params, 0/10.44k flops, 0B/5.28KB)\n  model_analyzer_testlib.py:33:BuildSmallModel:image = array_ops... (0/0 params, 0/0 flops, 0B/864B)\n  model_analyzer_testlib.py:37:BuildSmallModel:initializer=init_... (0/1 params, 0/0 flops, 0B/0B)\n  model_analyzer_testlib.py:41:BuildSmallModel:initializer=init_... (0/162 params, 0/0 flops, 0B/1.30KB)\n  model_analyzer_testlib.py:42:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/5.83k flops, 0B/432B)\n  model_analyzer_testlib.py:46:BuildSmallModel:initializer=init_... (0/288 params, 0/0 flops, 0B/2.30KB)\n  model_analyzer_testlib.py:47:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/4.61k flops, 0B/384B)\n',
-            f.read())
+        self.assertEqual('_TFProfRoot (', f.read()[0:13])
         # pylint: enable=line-too-long
 
   def testComplexCodeView(self):
@@ -138,25 +136,28 @@ class PrintModelAnalysisTest(test.TestCase):
 
       # pylint: disable=line-too-long
       with gfile.Open(outfile, 'r') as f:
-        self.assertEqual(
-            '_TFProfRoot (0/2.84k params, 0/54.08k flops)\n  model_analyzer_testlib.py:56:BuildFullModel:seq.append(array_... (0/1.80k params, 0/41.76k flops)\n    model_analyzer_testlib.py:33:BuildSmallModel:image = array_ops... (0/0 params, 0/0 flops)\n    model_analyzer_testlib.py:37:BuildSmallModel:initializer=init_... (0/4 params, 0/0 flops)\n    model_analyzer_testlib.py:41:BuildSmallModel:initializer=init_... (0/648 params, 0/0 flops)\n    model_analyzer_testlib.py:42:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/23.33k flops)\n    model_analyzer_testlib.py:46:BuildSmallModel:initializer=init_... (0/1.15k params, 0/0 flops)\n    model_analyzer_testlib.py:47:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/18.43k flops)\n  model_analyzer_testlib.py:60:BuildFullModel:cell, array_ops.c... (0/1.04k params, 0/4.13k flops)\n  model_analyzer_testlib.py:62:BuildFullModel:target = array_op... (0/0 params, 0/0 flops)\n  model_analyzer_testlib.py:63:BuildFullModel:loss = nn_ops.l2_... (0/0 params, 0/0 flops)\n  model_analyzer_testlib.py:65:BuildFullModel:return sgd_op.min... (0/0 params, 0/8.19k flops)\n',
-            f.read())
+        self.assertEqual('_TFProfRoot (0', f.read()[:14])
 
       self.assertLess(0, tfprof_node.total_exec_micros)
       self.assertEqual(2844, tfprof_node.total_parameters)
       self.assertEqual(54080, tfprof_node.total_float_ops)
       self.assertEqual(5, len(tfprof_node.children))
       self.assertEqual('_TFProfRoot', tfprof_node.name)
-      self.assertEqual('model_analyzer_testlib.py:56:BuildFullModel:seq.append(array_...',
-                       tfprof_node.children[0].name)
-      self.assertEqual('model_analyzer_testlib.py:60:BuildFullModel:cell, array_ops.c...',
-                       tfprof_node.children[1].name)
-      self.assertEqual('model_analyzer_testlib.py:62:BuildFullModel:target = array_op...',
-                       tfprof_node.children[2].name)
-      self.assertEqual('model_analyzer_testlib.py:63:BuildFullModel:loss = nn_ops.l2_...',
-                       tfprof_node.children[3].name)
-      self.assertEqual('model_analyzer_testlib.py:65:BuildFullModel:return sgd_op.min...',
-                       tfprof_node.children[4].name)
+      self.assertEqual(
+          'model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_...',
+          tfprof_node.children[0].name)
+      self.assertEqual(
+          'model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c...',
+          tfprof_node.children[1].name)
+      self.assertEqual(
+          'model_analyzer_testlib.py:64:BuildFullModel:target = array_op...',
+          tfprof_node.children[2].name)
+      self.assertEqual(
+          'model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_...',
+          tfprof_node.children[3].name)
+      self.assertEqual(
+          'model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min...',
+          tfprof_node.children[4].name)
       # pylint: enable=line-too-long
 
   def testCodeViewLeafGraphNode(self):
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
index 1234b15199..fea08b2db8 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
@@ -21,9 +21,11 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import rnn
 from tensorflow.python.ops import rnn_cell
+from tensorflow.python.ops import tensor_array_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.training import gradient_descent
 
-- 
GitLab


From 624442474ef48e7dc4075f4d4a1fa41bcd1b5987 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 23 May 2017 12:30:00 -0700
Subject: [PATCH 0011/1375] Bugfix to error message reporting when portpicker
 is not available.

PiperOrigin-RevId: 156895715
---
 tensorflow/python/framework/test_util.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 485bb3b109..6b6179f6c5 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -29,9 +29,11 @@ import threading
 import numpy as np
 import six
 
+_portpicker_import_error = None
 try:
   import portpicker  # pylint: disable=g-import-not-at-top
-except ImportError as _portpicker_import_error:
+except ImportError as _error:
+  _portpicker_import_error = _error
   portpicker = None
 
 # pylint: disable=g-import-not-at-top
@@ -820,8 +822,8 @@ def create_local_cluster(num_workers, num_ps, protocol="grpc"):
   Raises:
     ImportError: if portpicker module was not found at load time
   """
-  if not portpicker:
-    raise _portpicker_import_error
+  if _portpicker_import_error:
+    raise _portpicker_import_error  # pylint: disable=raising-bad-type
   worker_ports = [portpicker.pick_unused_port() for _ in range(num_workers)]
   ps_ports = [portpicker.pick_unused_port() for _ in range(num_ps)]
   cluster_dict = {
-- 
GitLab


From 30b6d613a085dd8d753028e4f9a279cfd9a58ac0 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 24 May 2017 14:25:41 -0700
Subject: [PATCH 0012/1375] C API/Windows: Export API functions that were
 accidentally left out.

PiperOrigin-RevId: 157037270
---
 tensorflow/c/c_api.h | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h
index ec9b01b388..c55bf70b9f 100644
--- a/tensorflow/c/c_api.h
+++ b/tensorflow/c/c_api.h
@@ -961,8 +961,9 @@ typedef struct TF_WhileParams {
 // - Reference-type inputs
 // - Directly referencing external tensors from the cond/body graphs (this is
 //   possible in the Python API)
-TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs, int ninputs,
-                           TF_Status* status);
+TF_CAPI_EXPORT extern TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs,
+                                                 int ninputs,
+                                                 TF_Status* status);
 
 // Builds the while loop specified by `params` and returns the output tensors of
 // the while loop in `outputs`. `outputs` should be allocated to size
@@ -972,13 +973,14 @@ TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs, int ninputs,
 //
 // Either this or TF_AbortWhile() must be called after a successful
 // TF_NewWhile() call.
-void TF_FinishWhile(const TF_WhileParams* params, TF_Status* status,
-                    TF_Output* outputs);
+TF_CAPI_EXPORT extern void TF_FinishWhile(const TF_WhileParams* params,
+                                          TF_Status* status,
+                                          TF_Output* outputs);
 
 // Frees `params`s resources without building a while loop. `params` is no
 // longer valid after this returns. Either this or TF_FinishWhile() must be
 // called after a successful TF_NewWhile() call.
-void TF_AbortWhile(const TF_WhileParams* params);
+TF_CAPI_EXPORT extern void TF_AbortWhile(const TF_WhileParams* params);
 
 // Adds operations to compute the partial derivatives of sum of `y`s w.r.t `x`s,
 // i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
@@ -994,8 +996,9 @@ void TF_AbortWhile(const TF_WhileParams* params);
 // supports. See
 // https://www.tensorflow.org/code/tensorflow/cc/gradients/README.md
 // for instructions on how to add C++ more gradients.
-void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny, TF_Output* x, int nx,
-                     TF_Output* dx, TF_Status* status, TF_Output* dy);
+TF_CAPI_EXPORT void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny,
+                                    TF_Output* x, int nx, TF_Output* dx,
+                                    TF_Status* status, TF_Output* dy);
 
 // TODO(josh11b): Register OpDef, available to all operations added
 // to this graph.
@@ -1032,7 +1035,7 @@ TF_CAPI_EXPORT extern TF_Session* TF_NewSession(TF_Graph* graph,
 //
 // If successful, populates `graph` with the contents of the Graph and
 // `meta_graph_def` with the MetaGraphDef of the loaded model.
-TF_Session* TF_LoadSessionFromSavedModel(
+TF_CAPI_EXPORT extern TF_Session* TF_LoadSessionFromSavedModel(
     const TF_SessionOptions* session_options, const TF_Buffer* run_options,
     const char* export_dir, const char* const* tags, int tags_len,
     TF_Graph* graph, TF_Buffer* meta_graph_def, TF_Status* status);
-- 
GitLab


From bbd27aa2a4e0659e853e55c185e8919fb05ba72f Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 24 May 2017 08:19:15 -0700
Subject: [PATCH 0013/1375] Add metadata to generated doc pages.

PiperOrigin-RevId: 156990440
---
 tensorflow/tools/docs/pretty_docs.py | 78 ++++++++++++++++++++++++++--
 1 file changed, 74 insertions(+), 4 deletions(-)

diff --git a/tensorflow/tools/docs/pretty_docs.py b/tensorflow/tools/docs/pretty_docs.py
index 918d475e0d..a8e0ab2613 100644
--- a/tensorflow/tools/docs/pretty_docs.py
+++ b/tensorflow/tools/docs/pretty_docs.py
@@ -12,12 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Classes for converting parsed doc content into markdown pages."""
+"""A module for converting parsed doc content into markdown pages.
+
+The adjacent `parser` module creates `PageInfo` objects, containing all data
+necessary to document an element of the TensorFlow API.
+
+This module contains one public function, which handels the conversion of these
+`PageInfo` objects into a markdown string:
+
+    md_page = build_md_page(page_info)
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import itertools
+
 
 def build_md_page(page_info):
   """Given a PageInfo object, return markdown for the page.
@@ -46,7 +57,9 @@ def build_md_page(page_info):
 
 def _build_function_page(page_info):
   """Given a FunctionPageInfo object Return the page as an md string."""
-  parts = ['# %s\n\n' % page_info.full_name]
+  parts = [_Metadata(page_info.full_name).build_html()]
+
+  parts.append('# %s\n\n' % page_info.full_name)
 
   if page_info.aliases:
     parts.extend('### `%s`\n' % name
@@ -70,7 +83,17 @@ def _build_function_page(page_info):
 
 def _build_class_page(page_info):
   """Given a ClassPageInfo object Return the page as an md string."""
-  parts = ['# {page_info.full_name}\n\n'.format(page_info=page_info)]
+  meta_data = _Metadata(page_info.full_name)
+  for item in itertools.chain(
+      page_info.classes,
+      page_info.properties,
+      page_info.methods,
+      page_info.other_members):
+    meta_data.append(item)
+
+  parts = [meta_data.build_html()]
+
+  parts.append('# {page_info.full_name}\n\n'.format(page_info=page_info))
 
   if page_info.aliases:
     parts.extend('### `class %s`\n' % name for name in page_info.aliases)
@@ -150,8 +173,17 @@ def _build_class_page(page_info):
 
 def _build_module_page(page_info):
   """Given a ClassPageInfo object Return the page as an md string."""
+  meta_data = _Metadata(page_info.full_name)
+
+  # Objects with their own pages are not added to the matadata list for the
+  # module, as the only thing on the module page is a link to the object's page.
+  for item in page_info.other_members:
+    meta_data.append(item)
+
+  parts = [meta_data.build_html()]
 
-  parts = ['# Module: {full_name}\n\n'.format(full_name=page_info.full_name)]
+  parts.append(
+      '# Module: {full_name}\n\n'.format(full_name=page_info.full_name))
 
   if page_info.aliases:
     parts.extend('### Module `%s`\n' % name for name in page_info.aliases)
@@ -261,3 +293,41 @@ def _build_function_details(function_details):
     parts.append(''.join(sub))
 
   return '\n'.join(parts)
+
+
+class _Metadata(object):
+  """A class for building a page's Metadata block.
+
+  Attributes:
+    name: The name of the page being described by the Metadata block.
+  """
+
+  def __init__(self, name):
+    """Creata a Metadata builder.
+
+    Args:
+      name: The name of the page being described by the Metadata block.
+    """
+    self.name = name
+    self._content = []
+
+  def append(self, item):
+    """Add an item from the page to the Metadata block.
+
+    Args:
+      item: The parsed page section to add.
+    """
+    self._content.append(item.short_name)
+
+  def build_html(self):
+    """Return the Metadata block as an Html string."""
+    schema = 'http://developers.google.com/ReferenceObject'
+    parts = ['<div itemscope itemtype="%s">' % schema]
+
+    parts.append('<meta itemprop="name" content="%s" />' % self.name)
+    for item in self._content:
+      parts.append('<meta itemprop="property" content="%s"/>' % item)
+
+    parts.extend(['</div>', '', ''])
+
+    return '\n'.join(parts)
-- 
GitLab


From 16f24d92b2a78a73eec9c52bfcd6c6e01c9de103 Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Thu, 25 May 2017 16:43:54 -0700
Subject: [PATCH 0014/1375] Updating the release notes and the rc number.

---
 README.md                                     | 12 +++++-----
 RELEASE.md                                    |  4 ++++
 tensorflow/core/public/version.h              |  2 +-
 tensorflow/docs_src/install/install_c.md      |  2 +-
 tensorflow/docs_src/install/install_go.md     |  2 +-
 tensorflow/docs_src/install/install_java.md   | 18 +++++++--------
 tensorflow/docs_src/install/install_linux.md  | 22 +++++++++----------
 tensorflow/docs_src/install/install_mac.md    | 10 ++++-----
 .../docs_src/install/install_sources.md       |  4 ++--
 .../docs_src/install/install_windows.md       |  4 ++--
 tensorflow/tools/pip_package/setup.py         |  2 +-
 11 files changed, 43 insertions(+), 39 deletions(-)

diff --git a/README.md b/README.md
index 21bbe3531b..a88e72880b 100644
--- a/README.md
+++ b/README.md
@@ -34,12 +34,12 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=cpu,OS=windows/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=cpu,OS=windows/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=gpu,OS=windows/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=gpu,OS=windows/))
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=cpu,OS=windows/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=cpu,OS=windows/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=gpu,OS=windows/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=gpu,OS=windows/))
 * Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
diff --git a/RELEASE.md b/RELEASE.md
index 6edde017a6..9e9f4bcc22 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -23,6 +23,10 @@
 * TensorFlow C library now available for Windows.
 * We released a new open-source version of TensorBoard.
 * [`SavedModel CLI`](https://www.tensorflow.org/versions/master/programmers_guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel
+* Android releases of TensorFlow are now pushed to jcenter for easier
+  integration into apps. See
+  https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md
+  for more details.
 
 ## Breaking Changes to the API
 * `org.tensorflow.contrib.android.TensorFlowInferenceInterface` now throws exceptions where possible and has simplified method signatures.
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 89f4d6cb4e..818e93a238 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX "-rc0"
+#define TF_VERSION_SUFFIX "-rc1"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 96d4af818f..4630d6b278 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc1.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 236fce5720..6b70f634f6 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc1.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 695739f8c3..141a5e9d38 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0-rc0</version>
+  <version>1.2.0-rc1</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0-rc0</version>
+                 <version>1.2.0-rc1</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc1.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc1.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc1.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc1.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0-rc0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.0-rc1.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following comand line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 9ab6dd21d3..bf59ef8a16 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -271,7 +271,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc0-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -458,7 +458,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -626,14 +626,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -645,14 +645,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -664,14 +664,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -683,14 +683,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index ac1785f5a4..f845a3540a 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -109,7 +109,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc0-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -230,7 +230,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc0-py2-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc0-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc0-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index e9d852f1a8..865412fcd2 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -320,10 +320,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0rc0 on Linux:
+for TensorFlow 1.2.0rc1 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc1-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index be224848f1..95c6b8dd39 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -114,12 +114,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc1-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc1-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 3a6050faa1..a85a220270 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0-rc0'
+_VERSION = '1.2.0-rc1'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
-- 
GitLab


From 0a2fef0e27c3a9b988cb25fa9964597e5be8dc1f Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Fri, 26 May 2017 09:57:25 -0700
Subject: [PATCH 0015/1375] Announce upgrade to cuDNN 6.0.

---
 RELEASE.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/RELEASE.md b/RELEASE.md
index 8d43197ee3..5b35f91477 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -49,6 +49,13 @@
   processing of the rnn.  For RNN decoding, this functionality has been replaced
   with an alternative API in `tf.contrib.seq2seq`.
 
+## Deprecations
+
+* TensorFlow 1.2 may be the last time we build with cuDNN 5.1. Starting with
+  TensorFlow 1.3, we will try to build all our prebuilt binaries with cuDNN 6.0.
+  While we will try to keep our source code compatible with cuDNN 5.1, it will
+  be best effort.
+
 ## Breaking Changes to the API
 * `org.tensorflow.contrib.android.TensorFlowInferenceInterface` now throws exceptions where possible and has simplified method signatures.
 
-- 
GitLab


From d101472296f8881aee75d8c1f8a2de89cffcd2e5 Mon Sep 17 00:00:00 2001
From: Neal Wu <neal@nealwu.com>
Date: Wed, 31 May 2017 09:24:38 -0700
Subject: [PATCH 0016/1375] Fixed up the documentation for
 tf.contrib.layers.batch_norm and tf.layers.batch_normalization explaining how
 to include the update ops in the train_op. Also simplified the existing
 tf.layers.batch_normalization documentation to the least error-prone option
 (adding to the train_op). (#10308)

PiperOrigin-RevId: 156609483
---
 .../contrib/layers/python/layers/layers.py    | 10 +++++---
 tensorflow/python/layers/normalization.py     | 25 ++++++-------------
 2 files changed, 14 insertions(+), 21 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 7a429f75bb..8ccb2cc8e5 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -393,16 +393,18 @@ def batch_norm(inputs,
 
   Can be used as a normalizer function for conv2d and fully_connected.
 
-  Note: When is_training is True the moving_mean and moving_variance need to be
-  updated, by default the update_ops are placed in `tf.GraphKeys.UPDATE_OPS` so
-  they need to be added as a dependency to the `train_op`, example:
+  Note: when training, the moving_mean and moving_variance need to be updated.
+  By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
+  need to be added as a dependency to the `train_op`. For example:
 
+  ```python
     update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
     with tf.control_dependencies(update_ops):
       train_op = optimizer.minimize(loss)
+  ```
 
   One can set updates_collections=None to force the updates in place, but that
-  can have speed penalty, especially in distributed settings.
+  can have a speed penalty, especially in distributed settings.
 
   Args:
     inputs: A tensor with 2 or more dimensions, where the first dimension has
diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py
index 8733d65769..ea6f55281e 100644
--- a/tensorflow/python/layers/normalization.py
+++ b/tensorflow/python/layers/normalization.py
@@ -363,23 +363,14 @@ def batch_normalization(inputs,
 
   Sergey Ioffe, Christian Szegedy
 
-  Note: the operations which update the `moving_mean` and `moving_variance`
-  variables will not be added as dependencies of your training operation and so
-  must be run separately. For example:
-
-  ```
-  extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
-  sess.run([train_op, extra_update_ops], ...)
-  ```
-  Alternatively, add the operations as a dependency to your training operation
-  manually, and then just run your training operation as normal:
-
-  ```
-  extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
-  with tf.control_dependencies(extra_update_ops):
-    train_op = optimizer.minimize(loss)
-  ...
-  sess.run([train_op], ...)
+  Note: when training, the moving_mean and moving_variance need to be updated.
+  By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
+  need to be added as a dependency to the `train_op`. For example:
+
+  ```python
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+    with tf.control_dependencies(update_ops):
+      train_op = optimizer.minimize(loss)
   ```
 
   Arguments:
-- 
GitLab


From 852453b715ee5241e68b840da8ee2255a8bb0cff Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 May 2017 06:26:31 -0700
Subject: [PATCH 0017/1375] Fix attention score padding. Use -inf as default
 instead of 0 for softmax.

PiperOrigin-RevId: 157214300
---
 .../kernel_tests/attention_wrapper_test.py    | 55 ++++++++++---------
 .../kernel_tests/beam_search_decoder_test.py  |  2 +-
 .../seq2seq/python/ops/attention_wrapper.py   | 31 ++++++++++-
 3 files changed, 60 insertions(+), 28 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
index ea34333360..56c1a2ba02 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
@@ -84,7 +84,7 @@ class AttentionWrapperTest(test.TestCase):
                          expected_final_alignment_history=None,
                          attention_layer_size=6,
                          name=''):
-    encoder_sequence_length = [3, 2, 3, 1, 0]
+    encoder_sequence_length = [3, 2, 3, 1, 1]
     decoder_sequence_length = [2, 0, 1, 2, 3]
     batch_size = 5
     encoder_max_time = 8
@@ -190,16 +190,17 @@ class AttentionWrapperTest(test.TestCase):
 
     expected_final_output = BasicDecoderOutput(
         rnn_output=ResultSummary(
-            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.00083043973),
-        sample_id=ResultSummary(shape=(5, 3), dtype=dtype('int32'), mean=2.0))
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0052250605),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
     expected_final_state = AttentionWrapperState(
         cell_state=LSTMStateTuple(
             c=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0039763632),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040092287),
             h=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019849765)),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0020015112)),
         attention=ResultSummary(
-            shape=(5, 6), dtype=dtype('float32'), mean=-0.00081052497),
+            shape=(5, 6), dtype=dtype('float32'), mean=-0.0052052638),
         time=3,
         alignments=ResultSummary(
             shape=(5, 8), dtype=dtype('float32'), mean=0.125),
@@ -221,17 +222,17 @@ class AttentionWrapperTest(test.TestCase):
 
     expected_final_output = BasicDecoderOutput(
         rnn_output=ResultSummary(
-            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.00040482997),
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0039222687),
         sample_id=ResultSummary(
-            shape=(5, 3), dtype=dtype('int32'), mean=1.8666666666666667))
+            shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
     expected_final_state = AttentionWrapperState(
         cell_state=LSTMStateTuple(
             c=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0039785588),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040052128),
             h=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019861322)),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019996136)),
         attention=ResultSummary(
-            shape=(5, 6), dtype=dtype('float32'), mean=-0.00038488387),
+            shape=(5, 6), dtype=dtype('float32'), mean=-0.0039024551),
         time=3,
         alignments=ResultSummary(
             shape=(5, 8), dtype=dtype('float32'), mean=0.125),
@@ -248,16 +249,17 @@ class AttentionWrapperTest(test.TestCase):
 
     expected_final_output = BasicDecoderOutput(
         rnn_output=ResultSummary(
-            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.00084602338),
-        sample_id=ResultSummary(shape=(5, 3), dtype=dtype('int32'), mean=2.0))
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0052615386),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
     expected_final_state = AttentionWrapperState(
         cell_state=LSTMStateTuple(
             c=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0039764317),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.004009536),
             h=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019850098)),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0020016613)),
         attention=ResultSummary(
-            shape=(5, 6), dtype=dtype('float32'), mean=-0.00080144603),
+            shape=(5, 6), dtype=dtype('float32'), mean=-0.0051812846),
         time=3,
         alignments=ResultSummary(
             shape=(5, 8), dtype=dtype('float32'), mean=0.125),
@@ -276,16 +278,17 @@ class AttentionWrapperTest(test.TestCase):
 
     expected_final_output = BasicDecoderOutput(
         rnn_output=ResultSummary(
-            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.00084602338),
-        sample_id=ResultSummary(shape=(5, 3), dtype=dtype('int32'), mean=2.0))
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0052615386),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
     expected_final_state = AttentionWrapperState(
         cell_state=LSTMStateTuple(
             c=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0039764317),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.004009536),
             h=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019850098)),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0020016613)),
         attention=ResultSummary(
-            shape=(5, 6), dtype=dtype('float32'), mean=-0.00080144603),
+            shape=(5, 6), dtype=dtype('float32'), mean=-0.0051812846),
         time=3,
         alignments=ResultSummary(
             shape=(5, 8), dtype=dtype('float32'), mean=0.125),
@@ -303,17 +306,17 @@ class AttentionWrapperTest(test.TestCase):
 
     expected_final_output = BasicDecoderOutput(
         rnn_output=ResultSummary(
-            shape=(5, 3, 10), dtype=dtype('float32'), mean=0.019546926),
+            shape=(5, 3, 10), dtype=dtype('float32'), mean=0.11815784),
         sample_id=ResultSummary(
-            shape=(5, 3), dtype=dtype('int32'), mean=2.7999999999999998))
+            shape=(5, 3), dtype=dtype('int32'), mean=4.5999999999999996))
     expected_final_state = AttentionWrapperState(
         cell_state=LSTMStateTuple(
             c=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.0041728448),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0063607907),
             h=ResultSummary(
-                shape=(5, 9), dtype=dtype('float32'), mean=-0.002085865)),
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.00323448)),
         attention=ResultSummary(
-            shape=(5, 10), dtype=dtype('float32'), mean=0.019546915),
+            shape=(5, 10), dtype=dtype('float32'), mean=0.11815783),
         time=3,
         alignments=ResultSummary(
             shape=(5, 8), dtype=dtype('float32'), mean=0.125),
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
index 873a39154f..fe4b12867e 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
@@ -224,7 +224,7 @@ class TestBeamStep(test.TestCase):
 class BeamSearchDecoderTest(test.TestCase):
 
   def _testDynamicDecodeRNN(self, time_major, has_attention):
-    encoder_sequence_length = [3, 2, 3, 1, 0]
+    encoder_sequence_length = [3, 2, 3, 1, 1]
     decoder_sequence_length = [2, 0, 1, 2, 3]
     batch_size = 5
     decoder_max_time = 4
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index 64ffb6ca40..637175674b 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -112,6 +112,18 @@ def _prepare_memory(memory, memory_sequence_length, check_inner_dims_defined):
   return nest.map_structure(lambda m: _maybe_mask(m, seq_len_mask), memory)
 
 
+def _maybe_mask_score(score, memory_sequence_length, score_mask_value):
+  if memory_sequence_length is None:
+    return score
+  message = ("All values in memory_sequence_length must greater than zero.")
+  with ops.control_dependencies(
+      [check_ops.assert_positive(memory_sequence_length, message=message)]):
+    score_mask = array_ops.sequence_mask(
+        memory_sequence_length, maxlen=array_ops.shape(score)[1])
+    score_mask_values = score_mask_value * array_ops.ones_like(score)
+    return array_ops.where(score_mask, score, score_mask_values)
+
+
 class _BaseAttentionMechanism(AttentionMechanism):
   """A base AttentionMechanism class providing common functionality.
 
@@ -127,6 +139,7 @@ class _BaseAttentionMechanism(AttentionMechanism):
                memory_sequence_length=None,
                memory_layer=None,
                check_inner_dims_defined=True,
+               score_mask_value=float("-inf"),
                name=None):
     """Construct base AttentionMechanism class.
 
@@ -149,6 +162,9 @@ class _BaseAttentionMechanism(AttentionMechanism):
       check_inner_dims_defined: Python boolean.  If `True`, the `memory`
         argument's shape is checked to ensure all but the two outermost
         dimensions are fully defined.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
       name: Name to use when creating ops.
     """
     if (query_layer is not None
@@ -164,7 +180,10 @@ class _BaseAttentionMechanism(AttentionMechanism):
     if not callable(probability_fn):
       raise TypeError("probability_fn must be callable, saw type: %s" %
                       type(probability_fn).__name__)
-    self._probability_fn = probability_fn
+    self._probability_fn = lambda score, prev: (  # pylint:disable=g-long-lambda
+        probability_fn(
+            _maybe_mask_score(score, memory_sequence_length, score_mask_value),
+            prev))
     with ops.name_scope(
         name, "BaseAttentionMechanismInit", nest.flatten(memory)):
       self._values = _prepare_memory(
@@ -245,6 +264,7 @@ class LuongAttention(_BaseAttentionMechanism):
                memory_sequence_length=None,
                scale=False,
                probability_fn=None,
+               score_mask_value=float("-inf"),
                name="LuongAttention"):
     """Construct the AttentionMechanism mechanism.
 
@@ -260,6 +280,9 @@ class LuongAttention(_BaseAttentionMechanism):
         probabilities.  The default is @{tf.nn.softmax}. Other options include
         @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}.
         Its signature should be: `probabilities = probability_fn(score)`.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
       name: Name to use when creating ops.
     """
     # For LuongAttention, we only transform the memory layer; thus
@@ -274,6 +297,7 @@ class LuongAttention(_BaseAttentionMechanism):
         memory=memory,
         probability_fn=wrapped_probability_fn,
         memory_sequence_length=memory_sequence_length,
+        score_mask_value=score_mask_value,
         name=name)
     self._num_units = num_units
     self._scale = scale
@@ -362,6 +386,7 @@ class BahdanauAttention(_BaseAttentionMechanism):
                memory_sequence_length=None,
                normalize=False,
                probability_fn=None,
+               score_mask_value=float("-inf"),
                name="BahdanauAttention"):
     """Construct the Attention mechanism.
 
@@ -377,6 +402,9 @@ class BahdanauAttention(_BaseAttentionMechanism):
         probabilities.  The default is @{tf.nn.softmax}. Other options include
         @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}.
         Its signature should be: `probabilities = probability_fn(score)`.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
       name: Name to use when creating ops.
     """
     if probability_fn is None:
@@ -390,6 +418,7 @@ class BahdanauAttention(_BaseAttentionMechanism):
         memory=memory,
         probability_fn=wrapped_probability_fn,
         memory_sequence_length=memory_sequence_length,
+        score_mask_value=score_mask_value,
         name=name)
     self._num_units = num_units
     self._normalize = normalize
-- 
GitLab


From 2b2dc6e8a36d2c1076dc9bed79f1cc76143c2e96 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 May 2017 13:45:47 -0700
Subject: [PATCH 0018/1375] Force GPU device objects that refer to the same
 physical card using the same stream id to use the same cuda stream objects.
 This avoids confusing the per-device memory allocator in ways that cause
 memory corruption.

Fixes https://github.com/tensorflow/serving/issues/335.

PiperOrigin-RevId: 157258318
---
 .../core/common_runtime/gpu/gpu_device.cc     | 98 +++++++++++++------
 .../core/common_runtime/gpu/gpu_device.h      | 16 ++-
 .../python/kernel_tests/basic_gpu_test.py     | 57 ++++++++++-
 3 files changed, 131 insertions(+), 40 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index e2ad18f33b..d5dba27f45 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -24,6 +24,8 @@ limitations under the License.
 #include <stdlib.h>
 #include <string.h>
 #include <algorithm>
+#include <map>
+#include <tuple>
 #include <vector>
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
@@ -174,6 +176,63 @@ class EigenCudaStreamDevice : public ::Eigen::StreamInterface {
   TF_DISALLOW_COPY_AND_ASSIGN(EigenCudaStreamDevice);
 };
 
+// This factory helps to ensure that different GPU device objects that refer to
+// the same physical device and stream group id use the same stream group
+// object (and therefore the same CUDA streams). This is necessary since there
+// is a single memory allocator per device (see ProcessState::GetGPUAllocator)
+// and allocators must not be shared across streams.
+class BaseGPUDevice::StreamGroupFactory {
+ public:
+  // Returns the unique stream group for use with the stream defined by
+  // {gpu_id, stream_group_within_gpu}, creating it if it does not yet exist.
+  // This function is thread safe.
+  BaseGPUDevice::StreamGroup* GetOrCreate(int gpu_id,
+                                          int stream_group_within_gpu,
+                                          gpu::StreamExecutor* executor) {
+    mutex_lock guard(lock_);
+    StreamGroup* group = &streams_[key_type(gpu_id, stream_group_within_gpu)];
+    if (!group->compute) {
+      group->compute = new gpu::Stream(executor);
+      group->compute->Init();
+      VLOG(2) << "Created stream[" << stream_group_within_gpu
+              << "] = " << group->compute;
+
+      group->host_to_device = new gpu::Stream(executor);
+      group->host_to_device->Init();
+      VLOG(2) << "Created host_to_device_stream[" << stream_group_within_gpu
+              << "] = " << group->host_to_device;
+
+      group->device_to_host = new gpu::Stream(executor);
+      group->device_to_host->Init();
+      VLOG(2) << "Created device_to_host_stream[" << stream_group_within_gpu
+              << "] = " << group->device_to_host;
+
+      group->device_to_device = new gpu::Stream(executor);
+      group->device_to_device->Init();
+      VLOG(2) << "Created device_to_device_stream[" << stream_group_within_gpu
+              << "] = " << group->device_to_host;
+    }
+    return group;
+  }
+
+  // Returns a reference to the StreamGroupFactory singleton. Note that this is
+  // never destroyed, so the objects it owns are never deleted.
+  static StreamGroupFactory& Global() {
+    static StreamGroupFactory* instance = new StreamGroupFactory();
+    return *instance;
+  }
+
+ private:
+  mutex lock_;
+  using key_type = std::tuple<int, int>;
+  std::map<key_type, StreamGroup> streams_;
+
+  // StreamGroupFactory cannot be created directly; Call
+  // StreamGroupFactory::Global() to get the global instance.
+  StreamGroupFactory() = default;
+  TF_DISALLOW_COPY_AND_ASSIGN(StreamGroupFactory);
+};
+
 BaseGPUDevice::BaseGPUDevice(const SessionOptions& options, const string& name,
                              Bytes memory_limit, const DeviceLocality& locality,
                              int gpu_id, const string& physical_device_desc,
@@ -193,12 +252,6 @@ BaseGPUDevice::BaseGPUDevice(const SessionOptions& options, const string& name,
 BaseGPUDevice::~BaseGPUDevice() {
   delete gpu_device_info_;
   for (auto ctx : device_contexts_) ctx->Unref();
-  for (auto& stream_group : streams_) {
-    delete stream_group.compute;
-    delete stream_group.host_to_device;
-    delete stream_group.device_to_host;
-    delete stream_group.device_to_device;
-  }
 }
 
 Status BaseGPUDevice::Init(const SessionOptions& options) {
@@ -217,27 +270,8 @@ Status BaseGPUDevice::Init(const SessionOptions& options) {
 
   // Create the specified number of GPU streams
   for (int i = 0; i < max_streams_; i++) {
-    auto stream = new gpu::Stream(executor_);
-    stream->Init();
-    VLOG(2) << "Created stream[" << i << "] = " << stream;
-
-    auto host_to_device_stream = new gpu::Stream(executor_);
-    host_to_device_stream->Init();
-    VLOG(2) << "Created host_to_device_stream[" << i
-            << "] = " << host_to_device_stream;
-
-    auto device_to_host_stream = new gpu::Stream(executor_);
-    device_to_host_stream->Init();
-    VLOG(2) << "Created device_to_host_stream[" << i
-            << "] = " << device_to_host_stream;
-
-    auto device_to_device_stream = new gpu::Stream(executor_);
-    device_to_device_stream->Init();
-    VLOG(2) << "Created device_to_device_stream[" << i
-            << "] = " << device_to_device_stream;
-
-    streams_.push_back({stream, host_to_device_stream, device_to_host_stream,
-                        device_to_device_stream});
+    streams_.push_back(
+        StreamGroupFactory::Global().GetOrCreate(gpu_id_, i, executor_));
 
     size_t scratch_buffer_size = Eigen::kCudaScratchSize + sizeof(unsigned int);
     void* scratch_buffer = gpu_allocator_->AllocateRaw(
@@ -259,12 +293,12 @@ Status BaseGPUDevice::Init(const SessionOptions& options) {
           "Failed to memcopy into scratch buffer for device ", gpu_id_);
     }
 
-    device_contexts_.push_back(
-        new GPUDeviceContext(i, stream, host_to_device_stream,
-                             device_to_host_stream, device_to_device_stream));
+    device_contexts_.push_back(new GPUDeviceContext(
+        i, streams_.back()->compute, streams_.back()->host_to_device,
+        streams_.back()->device_to_host, streams_.back()->device_to_device));
   }
   gpu_device_info_ = new GpuDeviceInfo;
-  gpu_device_info_->stream = streams_[0].compute;
+  gpu_device_info_->stream = streams_[0]->compute;
   gpu_device_info_->default_context = device_contexts_[0];
   gpu_device_info_->event_mgr = em_.get();
   gpu_device_info_->gpu_id = gpu_id_;
@@ -511,7 +545,7 @@ void BaseGPUDevice::ReinitializeDevice(OpKernelContext* context,
       static_cast<ConcretePerOpGpuDevice*>(device);
   DCHECK(concrete_device);
   const cudaStream_t* cuda_stream = reinterpret_cast<const cudaStream_t*>(
-      streams_[stream_id].compute->implementation()->CudaStreamMemberHack());
+      streams_[stream_id]->compute->implementation()->CudaStreamMemberHack());
   concrete_device->Reinitialize(context, cuda_stream, gpu_id_, allocator,
                                 scratch_[stream_id]);
 }
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.h b/tensorflow/core/common_runtime/gpu/gpu_device.h
index 370b3cc4f6..08c58867ee 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.h
@@ -20,7 +20,11 @@ limitations under the License.
 #ifndef TENSORFLOW_COMMON_RUNTIME_GPU_GPU_DEVICE_H_
 #define TENSORFLOW_COMMON_RUNTIME_GPU_GPU_DEVICE_H_
 
+#include <memory>
+#include <string>
+#include <unordered_map>
 #include <vector>
+
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h"
@@ -96,12 +100,14 @@ class BaseGPUDevice : public LocalDevice {
 
  private:
   struct StreamGroup {
-    gpu::Stream* compute;
-    gpu::Stream* host_to_device;
-    gpu::Stream* device_to_host;
-    gpu::Stream* device_to_device;
+    gpu::Stream* compute = nullptr;
+    gpu::Stream* host_to_device = nullptr;
+    gpu::Stream* device_to_host = nullptr;
+    gpu::Stream* device_to_device = nullptr;
   };
-  gtl::InlinedVector<StreamGroup, 4> streams_;
+  class StreamGroupFactory;
+
+  gtl::InlinedVector<StreamGroup*, 4> streams_;
   gtl::InlinedVector<char*, 4> scratch_;
   std::vector<GPUDeviceContext*> device_contexts_;
   GpuDeviceInfo* gpu_device_info_ = nullptr;
diff --git a/tensorflow/python/kernel_tests/basic_gpu_test.py b/tensorflow/python/kernel_tests/basic_gpu_test.py
index 0438d95bc4..e6d0c06d14 100644
--- a/tensorflow/python/kernel_tests/basic_gpu_test.py
+++ b/tensorflow/python/kernel_tests/basic_gpu_test.py
@@ -18,15 +18,21 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
+import itertools
+import threading
 
 import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
 
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.framework import random_seed
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
 from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args
 from tensorflow.python.platform import test
 
@@ -219,5 +225,50 @@ class BroadcastSimpleTest(test.TestCase):
     self._compareGpu(x, y + 0.1, np.floor_divide, math_ops.floordiv)
 
 
-if __name__ == "__main__":
+class GpuMultiSessionMemoryTest(test_util.TensorFlowTestCase):
+  """Tests concurrent sessions executing on the same GPU."""
+
+  def _run_session(self, results):
+    n_iterations = 500
+    with self.test_session(use_gpu=True) as s:
+      data = variables.Variable(1.0)
+      with ops.device('/gpu:0'):
+        random_seed.set_random_seed(1)
+        matrix1 = variables.Variable(
+            random_ops.truncated_normal([1024, 1]), name='matrix1')
+        matrix2 = variables.Variable(
+            random_ops.truncated_normal([1, 1024]), name='matrix2')
+        x1 = math_ops.multiply(data, matrix1, name='x1')
+        x3 = math_ops.matmul(x1, math_ops.matmul(matrix2, matrix1))
+        x4 = math_ops.matmul(array_ops.transpose(x3), x3, name='x4')
+        s.run(variables.global_variables_initializer())
+
+        for _ in xrange(n_iterations):
+          value = s.run(x4)
+          results.append(value)
+          if value != results[0]:
+            break
+
+  def testConcurrentSessions(self):
+    if not test.is_gpu_available():
+      return
+
+    n_threads = 4
+    results = [[]] * n_threads
+    threads = [
+        threading.Thread(target=self._run_session, args=(results[i],))
+        for i in xrange(n_threads)
+    ]
+    for thread in threads:
+      thread.start()
+    for thread in threads:
+      thread.join()
+
+    flat_results = [x for x in itertools.chain(*results)]
+    self.assertNotEqual(0, len(flat_results))
+    for result in flat_results:
+      self.assertEqual(result, flat_results[0])
+
+
+if __name__ == '__main__':
   test.main()
-- 
GitLab


From 621cb8e91691e74f23eb3394981c4ad17389c8e9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 May 2017 15:30:26 -0700
Subject: [PATCH 0019/1375] Fixes compatibility of contrib feature_column with
 core make_parse_example_spec.

PiperOrigin-RevId: 157270783
---
 tensorflow/contrib/layers/BUILD               |  1 +
 .../layers/python/layers/feature_column.py    | 16 ++++-----
 .../python/layers/feature_column_test.py      | 36 ++++++++++++-------
 3 files changed, 33 insertions(+), 20 deletions(-)

diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD
index 03af377149..c7cae56049 100644
--- a/tensorflow/contrib/layers/BUILD
+++ b/tensorflow/contrib/layers/BUILD
@@ -237,6 +237,7 @@ py_test(
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/feature_column",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index e1a27335ab..68159fe9b9 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -476,7 +476,7 @@ class _SparseColumn(
     return self._do_transform(input_tensor)
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
   @property
@@ -802,7 +802,7 @@ class _WeightedSparseColumn(
         inputs.get(self.sparse_id_column), inputs.get(self.weight_column_name))
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
   @property
@@ -960,7 +960,7 @@ class _OneHotColumn(
     return self._to_dnn_input_layer(inputs.get(self.sparse_id_column))
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
 
@@ -1110,7 +1110,7 @@ class _EmbeddingColumn(
     return inputs.get(self.sparse_id_column)
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
 
@@ -1467,7 +1467,7 @@ class _ScatteredEmbeddingColumn(
     return inputs.get(self.column_name)
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
 
@@ -1817,7 +1817,7 @@ class _RealValuedColumn(
         self._normalized_input_tensor(inputs.get(self.name)))
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
 
@@ -2081,7 +2081,7 @@ class _BucketizedColumn(
         _LazyBuilderByColumnsToTensor(columns_to_tensors))
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
   @property
@@ -2311,7 +2311,7 @@ class _CrossedColumn(
         _LazyBuilderByColumnsToTensor(columns_to_tensors))
 
   @property
-  def _parse_example_config(self):
+  def _parse_example_spec(self):
     return self.config
 
   @property
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_test.py b/tensorflow/contrib/layers/python/layers/feature_column_test.py
index b6a8b6bdda..ce3bc23cf6 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_test.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_test.py
@@ -27,6 +27,7 @@ import numpy as np
 
 from tensorflow.contrib.layers.python.layers import feature_column as fc
 from tensorflow.contrib.layers.python.layers import feature_column_ops
+from tensorflow.python.feature_column import feature_column as fc_core
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
@@ -697,11 +698,6 @@ class FeatureColumnTest(test.TestCase):
                                                 "str_id_weights_column")
     real_valued_col1 = fc.real_valued_column("real_valued_column1")
     real_valued_col2 = fc.real_valued_column("real_valued_column2", 5)
-    real_valued_col3 = fc._real_valued_var_len_column(
-        "real_valued_column3", is_sparse=True)
-    real_valued_col4 = fc._real_valued_var_len_column(
-        "real_valued_column4", dtype=dtypes.int64, default_value=0,
-        is_sparse=False)
     bucketized_col1 = fc.bucketized_column(
         fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
     bucketized_col2 = fc.bucketized_column(
@@ -717,8 +713,8 @@ class FeatureColumnTest(test.TestCase):
     feature_columns = set([
         sparse_col, embedding_col, weighted_id_col, int32_sparse_id_col,
         int64_sparse_id_col, real_valued_col1, real_valued_col2,
-        real_valued_col3, real_valued_col4, bucketized_col1, bucketized_col2,
-        cross_col, one_hot_col, scattered_embedding_col
+        bucketized_col1, bucketized_col2, cross_col, one_hot_col,
+        scattered_embedding_col
     ])
     expected_config = {
         "sparse_column":
@@ -739,11 +735,6 @@ class FeatureColumnTest(test.TestCase):
         "real_valued_column2":
             parsing_ops.FixedLenFeature(
                 [5], dtype=dtypes.float32),
-        "real_valued_column3":
-            parsing_ops.VarLenFeature(dtype=dtypes.float32),
-        "real_valued_column4":
-            parsing_ops.FixedLenSequenceFeature(
-                [], dtype=dtypes.int64, allow_missing=True, default_value=0),
         "real_valued_column_for_bucketization1":
             parsing_ops.FixedLenFeature(
                 [1], dtype=dtypes.float32),
@@ -763,6 +754,10 @@ class FeatureColumnTest(test.TestCase):
     config = fc.create_feature_spec_for_parsing(feature_columns)
     self.assertDictEqual(expected_config, config)
 
+    # Tests that contrib feature columns work with core library:
+    config_core = fc_core.make_parse_example_spec(feature_columns)
+    self.assertDictEqual(expected_config, config_core)
+
     # Test that the same config is parsed out if we pass a dictionary.
     feature_columns_dict = {
         str(i): val
@@ -771,6 +766,23 @@ class FeatureColumnTest(test.TestCase):
     config = fc.create_feature_spec_for_parsing(feature_columns_dict)
     self.assertDictEqual(expected_config, config)
 
+  def testCreateFeatureSpec_ExperimentalColumns(self):
+    real_valued_col0 = fc._real_valued_var_len_column(
+        "real_valued_column0", is_sparse=True)
+    real_valued_col1 = fc._real_valued_var_len_column(
+        "real_valued_column1", dtype=dtypes.int64, default_value=0,
+        is_sparse=False)
+    feature_columns = set([real_valued_col0, real_valued_col1])
+    expected_config = {
+        "real_valued_column0": parsing_ops.VarLenFeature(dtype=dtypes.float32),
+        "real_valued_column1":
+            parsing_ops.FixedLenSequenceFeature(
+                [], dtype=dtypes.int64, allow_missing=True, default_value=0),
+    }
+
+    config = fc.create_feature_spec_for_parsing(feature_columns)
+    self.assertDictEqual(expected_config, config)
+
   def testCreateFeatureSpec_RealValuedColumnWithDefaultValue(self):
     real_valued_col1 = fc.real_valued_column(
         "real_valued_column1", default_value=2)
-- 
GitLab


From 4f586f145be2a8aa66540be15e0f8ad3f8ff970c Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Mon, 29 May 2017 15:14:46 -0700
Subject: [PATCH 0020/1375] [tf contrib seq2seq] Bugfixes to BeamSearchDecoder

Implementation by Cinjon Resnick.  He can't push this since he's traveling.
I just copied the fix and added some small syntax tweaks to make the unit
tests pass.  More comprehensive unit tests will come in the near future.

Fixes at least part of #9904.

BeamSearchDecoder:
1. Fix the bug where we don't pass the next cell state through.
2. Gather the cell state (and attention if that's a part of the model
   as an AttentionWrapper on the cell) according to the next_beam_ids.
PiperOrigin-RevId: 157415564
---
 .../kernel_tests/beam_search_decoder_test.py  |   2 +
 .../seq2seq/python/ops/beam_search_decoder.py | 119 ++++++++++++++----
 2 files changed, 96 insertions(+), 25 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
index fe4b12867e..360eeae67a 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
@@ -141,6 +141,7 @@ class TestBeamStep(test.TestCase):
     outputs, next_beam_state = beam_search_decoder._beam_search_step(
         time=2,
         logits=logits,
+        next_cell_state=dummy_cell_state,
         beam_state=beam_state,
         batch_size=ops.convert_to_tensor(self.batch_size),
         beam_width=self.beam_width,
@@ -195,6 +196,7 @@ class TestBeamStep(test.TestCase):
     outputs, next_beam_state = beam_search_decoder._beam_search_step(
         time=2,
         logits=logits,
+        next_cell_state=dummy_cell_state,
         beam_state=beam_state,
         batch_size=ops.convert_to_tensor(self.batch_size),
         beam_width=self.beam_width,
diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
index c9be517fad..483f866fd6 100644
--- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
@@ -110,6 +110,15 @@ def tile_batch(t, multiplier, name=None):
     return tiled
 
 
+def _check_maybe(t):
+  if isinstance(t, tensor_array_ops.TensorArray):
+    raise TypeError(
+        "TensorArray state is not supported by BeamSearchDecoder: %s" % t.name)
+  if t.shape.ndims is None:
+    raise ValueError(
+        "Expected tensor (%s) to have known rank, but ndims == None." % t)
+
+
 class BeamSearchDecoder(decoder.Decoder):
   """BeamSearch sampling decoder."""
 
@@ -351,13 +360,7 @@ class BeamSearchDecoder(decoder.Decoder):
       TypeError: If t is an instance of TensorArray.
       ValueError: If the rank of t is not statically known.
     """
-    if isinstance(t, tensor_array_ops.TensorArray):
-      raise TypeError(
-          "TensorArray state is not supported by BeamSearchDecoder: %s"
-          % t.name)
-    if t.shape.ndims is None:
-      raise ValueError(
-          "Expected tensor (%s) to have known rank, but ndims == None." % t)
+    _check_maybe(t)
     if t.shape.ndims >= 1:
       return self._split_batch_beams(t, s)
     else:
@@ -380,13 +383,7 @@ class BeamSearchDecoder(decoder.Decoder):
       TypeError: If t is an instance of TensorArray.
       ValueError:  If the rank of t is not statically known.
     """
-    if isinstance(t, tensor_array_ops.TensorArray):
-      raise TypeError(
-          "TensorArray state is not supported by BeamSearchDecoder: %s"
-          % t.name)
-    if t.shape.ndims is None:
-      raise ValueError(
-          "Expected tensor (%s) to have known rank, but ndims == None." % t)
+    _check_maybe(t)
     if t.shape.ndims >= 2:
       return self._merge_batch_beams(t, s)
     else:
@@ -417,7 +414,6 @@ class BeamSearchDecoder(decoder.Decoder):
           self._maybe_merge_batch_beams,
           cell_state, self._cell.state_size)
       cell_outputs, next_cell_state = self._cell(inputs, cell_state)
-
       cell_outputs = nest.map_structure(
           lambda out: self._split_batch_beams(out, out.shape[1:]), cell_outputs)
       next_cell_state = nest.map_structure(
@@ -430,11 +426,13 @@ class BeamSearchDecoder(decoder.Decoder):
       beam_search_output, beam_search_state = _beam_search_step(
           time=time,
           logits=cell_outputs,
+          next_cell_state=next_cell_state,
           beam_state=state,
           batch_size=batch_size,
           beam_width=beam_width,
           end_token=end_token,
           length_penalty_weight=length_penalty_weight)
+
       finished = beam_search_state.finished
       sample_ids = beam_search_output.predicted_ids
       next_inputs = control_flow_ops.cond(
@@ -444,8 +442,8 @@ class BeamSearchDecoder(decoder.Decoder):
     return (beam_search_output, beam_search_state, next_inputs, finished)
 
 
-def _beam_search_step(time, logits, beam_state, batch_size, beam_width,
-                      end_token, length_penalty_weight):
+def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
+                      beam_width, end_token, length_penalty_weight):
   """Performs a single step of Beam Search Decoding.
 
   Args:
@@ -454,6 +452,8 @@ def _beam_search_step(time, logits, beam_state, batch_size, beam_width,
       continuations.
     logits: Logits at the current time step. A tensor of shape
       `[batch_size, beam_width, vocab_size]`
+    next_cell_state: The next state from the cell, e.g. an instance of
+      AttentionWrapperState if the cell is attentional.
     beam_state: Current state of the beam search.
       An instance of `BeamSearchDecoderState`.
     batch_size: The batch size for this input.
@@ -522,8 +522,7 @@ def _beam_search_step(time, logits, beam_state, batch_size, beam_width,
       gather_from=total_probs,
       range_input=batch_size,
       range_size=beam_width * vocab_size,
-      final_shape=[static_batch_size, beam_width])
-
+      gather_shape=[-1])
   next_word_ids = math_ops.to_int32(word_indices % vocab_size)
   next_beam_ids = math_ops.to_int32(word_indices / vocab_size)
 
@@ -533,7 +532,7 @@ def _beam_search_step(time, logits, beam_state, batch_size, beam_width,
       gather_from=previously_finished,
       range_input=batch_size,
       range_size=beam_width,
-      final_shape=[static_batch_size, beam_width])
+      gather_shape=[-1])
   next_finished = math_ops.logical_or(previously_finished,
                                       math_ops.equal(next_word_ids, end_token))
 
@@ -549,11 +548,26 @@ def _beam_search_step(time, logits, beam_state, batch_size, beam_width,
       gather_from=beam_state.lengths,
       range_input=batch_size,
       range_size=beam_width,
-      final_shape=[static_batch_size, beam_width])
+      gather_shape=[-1])
   next_prediction_len += lengths_to_add
 
+  # Pick out the cell_states according to the next_beam_ids. We use a
+  # different gather_shape here because the cell_state tensors, i.e.
+  # the tensors that would be gathered from, all have dimension
+  # greater than two and we need to preserve those dimensions.
+  # pylint: disable=g-long-lambda
+  next_cell_state = nest.map_structure(
+      lambda gather_from: _maybe_tensor_gather_helper(
+          gather_indices=next_beam_ids,
+          gather_from=gather_from,
+          range_input=batch_size,
+          range_size=beam_width,
+          gather_shape=[batch_size * beam_width, -1]),
+      next_cell_state)
+  # pylint: enable=g-long-lambda
+
   next_state = BeamSearchDecoderState(
-      cell_state=beam_state.cell_state,
+      cell_state=next_cell_state,
       log_probs=next_beam_probs,
       lengths=next_prediction_len,
       finished=next_finished)
@@ -639,12 +653,67 @@ def _mask_probs(probs, eos_token, finished):
   return finished_examples + non_finished_examples
 
 
+def _maybe_tensor_gather_helper(gather_indices, gather_from, range_input,
+                                range_size, gather_shape):
+  """Maybe applies _tensor_gather_helper.
+
+  This applies _tensor_gather_helper when the gather_from dims is at least as
+  big as the length of gather_shape. This is used in conjunction with nest so
+  that we don't apply _tensor_gather_helper to inapplicable values like scalars.
+
+  Args:
+    gather_indices: The tensor indices that we use to gather.
+    gather_from: The tensor that we are gathering from.
+    range_input: The input range to use. Likely equal to batch_size.
+    range_size: The number of values in each range. Likely equal to beam_width.
+    gather_shape: What we should reshape gather_from to in order to preserve the
+      correct values. An example is when gather_from is the attention from an
+      AttentionWrapperState with shape [batch_size, beam_width, attention_size].
+      There, we want to preserve the attention_size elements, so gather_shape is
+      [batch_size * beam_width, -1]. Then, upon reshape, we still have the
+      attention_size as desired.
+
+  Returns:
+    output: Gathered tensor of shape tf.shape(gather_from)[:1+len(gather_shape)]
+      or the original tensor if its dimensions are too small.
+  """
+  _check_maybe(gather_from)
+  if gather_from.shape.ndims >= len(gather_shape):
+    return _tensor_gather_helper(gather_indices, gather_from, range_input,
+                                 range_size, gather_shape)
+  else:
+    return gather_from
+
+
 def _tensor_gather_helper(gather_indices, gather_from, range_input, range_size,
-                          final_shape):
+                          gather_shape):
+  """Helper for gathering the right indices from the tensor.
+
+  This works by reshaping gather_from to gather_shape (e.g. [-1]) and then
+  gathering from that according to the gather_indices, which are offset by
+  the right amounts in order to preserve the batch order.
+
+  Args:
+    gather_indices: The tensor indices that we use to gather.
+    gather_from: The tensor that we are gathering from.
+    range_input: The input range to use. Likely equal to batch_size.
+    range_size: The number of values in each range. Likely equal to beam_width.
+    gather_shape: What we should reshape gather_from to in order to preserve the
+      correct values. An example is when gather_from is the attention from an
+      AttentionWrapperState with shape [batch_size, beam_width, attention_size].
+      There, we want to preserve the attention_size elements, so gather_shape is
+      [batch_size * beam_width, -1]. Then, upon reshape, we still have the
+      attention_size as desired.
+
+  Returns:
+    output: Gathered tensor of shape tf.shape(gather_from)[:1+len(gather_shape)]
+  """
   range_ = array_ops.expand_dims(math_ops.range(range_input) * range_size, 1)
   gather_indices = array_ops.reshape(gather_indices + range_, [-1])
   output = array_ops.gather(
-      array_ops.reshape(gather_from, [-1]), gather_indices)
+      array_ops.reshape(gather_from, gather_shape), gather_indices)
+  final_shape = array_ops.shape(gather_from)[:1 + len(gather_shape)]
+  final_static_shape = gather_from.shape[:1 + len(gather_shape)]
   output = array_ops.reshape(output, final_shape)
-  output.set_shape(final_shape)
+  output.set_shape(final_static_shape)
   return output
-- 
GitLab


From b8e8fc2987bdf1f87c70d2d7f64d7b1b1c799c5f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 30 May 2017 11:23:21 -0700
Subject: [PATCH 0021/1375] Add SampleEmbeddingHelper to do sampling at
 inference time

PiperOrigin-RevId: 157487623
---
 tensorflow/contrib/seq2seq/__init__.py        |  1 +
 .../python/kernel_tests/basic_decoder_test.py | 72 +++++++++++++++++++
 .../contrib/seq2seq/python/ops/helper.py      | 40 +++++++++++
 3 files changed, 113 insertions(+)

diff --git a/tensorflow/contrib/seq2seq/__init__.py b/tensorflow/contrib/seq2seq/__init__.py
index dc159b93a3..d36d7e16de 100644
--- a/tensorflow/contrib/seq2seq/__init__.py
+++ b/tensorflow/contrib/seq2seq/__init__.py
@@ -47,6 +47,7 @@ _allowed_symbols = [
     "FinalBeamSearchDecoderOutput",
     "gather_tree",
     "GreedyEmbeddingHelper",
+    "SampleEmbeddingHelper",
     "ScheduledEmbeddingTrainingHelper",
     "ScheduledOutputTrainingHelper",
     "TrainingHelper",
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
index 600adea189..cb12bc9450 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
@@ -27,8 +27,10 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.layers import core as layers_core
+from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import test
 # pylint: enable=g-import-not-at-top
 
@@ -189,6 +191,76 @@ class BasicDecoderTest(test.TestCase):
       self.assertAllEqual(expected_step_next_inputs,
                           sess_results["step_next_inputs"])
 
+  def testStepWithSampleEmbeddingHelper(self):
+    batch_size = 5
+    vocabulary_size = 7
+    cell_depth = vocabulary_size  # cell's logits must match vocabulary size
+    input_depth = 10
+    np.random.seed(0)
+    start_tokens = np.random.randint(0, vocabulary_size, size=batch_size)
+    end_token = 1
+
+    with self.test_session(use_gpu=True) as sess:
+      with variable_scope.variable_scope(
+          "testStepWithSampleEmbeddingHelper",
+          initializer=init_ops.constant_initializer(0.01)):
+        embeddings = np.random.randn(vocabulary_size,
+                                     input_depth).astype(np.float32)
+        cell = rnn_cell.LSTMCell(vocabulary_size)
+        helper = helper_py.SampleEmbeddingHelper(embeddings, start_tokens,
+                                                 end_token, seed=0)
+        my_decoder = basic_decoder.BasicDecoder(
+            cell=cell,
+            helper=helper,
+            initial_state=cell.zero_state(
+                dtype=dtypes.float32, batch_size=batch_size))
+        output_size = my_decoder.output_size
+        output_dtype = my_decoder.output_dtype
+        self.assertEqual(
+            basic_decoder.BasicDecoderOutput(cell_depth,
+                                             tensor_shape.TensorShape([])),
+            output_size)
+        self.assertEqual(
+            basic_decoder.BasicDecoderOutput(dtypes.float32, dtypes.int32),
+            output_dtype)
+
+        (first_finished, first_inputs, first_state) = my_decoder.initialize()
+        (step_outputs, step_state, step_next_inputs,
+         step_finished) = my_decoder.step(
+             constant_op.constant(0), first_inputs, first_state)
+        batch_size_t = my_decoder.batch_size
+
+        self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
+        self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
+        self.assertTrue(
+            isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
+        self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape())
+        self.assertEqual((batch_size,), step_outputs[1].get_shape())
+        self.assertEqual((batch_size, cell_depth), first_state[0].get_shape())
+        self.assertEqual((batch_size, cell_depth), first_state[1].get_shape())
+        self.assertEqual((batch_size, cell_depth), step_state[0].get_shape())
+        self.assertEqual((batch_size, cell_depth), step_state[1].get_shape())
+
+        sess.run(variables.global_variables_initializer())
+        sess_results = sess.run({
+            "batch_size": batch_size_t,
+            "first_finished": first_finished,
+            "first_inputs": first_inputs,
+            "first_state": first_state,
+            "step_outputs": step_outputs,
+            "step_state": step_state,
+            "step_next_inputs": step_next_inputs,
+            "step_finished": step_finished
+        })
+
+        sample_ids = sess_results["step_outputs"].sample_id
+        expected_step_finished = (sample_ids == end_token)
+        expected_step_next_inputs = embeddings[sample_ids]
+        self.assertAllEqual(expected_step_finished,
+                            sess_results["step_finished"])
+        self.assertAllEqual(expected_step_next_inputs,
+                            sess_results["step_next_inputs"])
+
   def testStepWithScheduledEmbeddingTrainingHelper(self):
     sequence_length = [3, 4, 3, 1, 0]
     batch_size = 5
diff --git a/tensorflow/contrib/seq2seq/python/ops/helper.py b/tensorflow/contrib/seq2seq/python/ops/helper.py
index bdd7d7ca73..bee7547935 100644
--- a/tensorflow/contrib/seq2seq/python/ops/helper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/helper.py
@@ -41,6 +41,7 @@ __all__ = [
     "Helper",
     "TrainingHelper",
     "GreedyEmbeddingHelper",
+    "SampleEmbeddingHelper",
     "CustomHelper",
     "ScheduledEmbeddingTrainingHelper",
     "ScheduledOutputTrainingHelper",
@@ -512,3 +513,42 @@ class GreedyEmbeddingHelper(Helper):
         lambda: self._start_inputs,
         lambda: self._embedding_fn(sample_ids))
     return (finished, next_inputs, state)
+
+
+class SampleEmbeddingHelper(GreedyEmbeddingHelper):
+  """A helper for use during inference.
+
+  Uses sampling (from a distribution) instead of argmax and passes the
+  result through an embedding layer to get the next input.
+  """
+
+  def __init__(self, embedding, start_tokens, end_token, seed=None):
+    """Initializer.
+
+    Args:
+      embedding: A callable that takes a vector tensor of `ids` (argmax ids),
+        or the `params` argument for `embedding_lookup`. The returned tensor
+        will be passed to the decoder input.
+      start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
+      end_token: `int32` scalar, the token that marks end of decoding.
+      seed: The sampling seed.
+
+    Raises:
+      ValueError: if `start_tokens` is not a 1D tensor or `end_token` is not a
+        scalar.
+    """
+    super(SampleEmbeddingHelper, self).__init__(
+        embedding, start_tokens, end_token)
+    self._seed = seed
+
+  def sample(self, time, outputs, state, name=None):
+    """sample for SampleEmbeddingHelper."""
+    del time, state  # unused by sample_fn
+    # Outputs are logits, we sample instead of argmax (greedy).
+    if not isinstance(outputs, ops.Tensor):
+      raise TypeError("Expected outputs to be a single Tensor, got: %s" %
+                      type(outputs))
+    sample_id_sampler = categorical.Categorical(logits=outputs)
+    sample_ids = sample_id_sampler.sample(seed=self._seed)
+
+    return sample_ids
-- 
GitLab


From 93e5eaad37467be2516bd49e4ead080522ef6c3a Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Tue, 30 May 2017 15:20:53 -0700
Subject: [PATCH 0022/1375] Refactoring of layer name autogeneration, to remove
 a graph serialization warning.

PiperOrigin-RevId: 157520123
---
 .../contrib/keras/python/keras/backend.py     | 14 +++-------
 tensorflow/python/layers/base.py              | 28 ++++++++++---------
 2 files changed, 19 insertions(+), 23 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index 905ef13e14..ed2b251b31 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -33,6 +33,7 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes as dtypes_module
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.layers import base as tf_base_layers
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import clip_ops
 from tensorflow.python.ops import control_flow_ops
@@ -261,16 +262,9 @@ def get_uid(prefix=''):
     2
   ```
   """
-  layer_name_uids_collection = ops.get_collection('LAYER_NAME_UIDS')
-  if not layer_name_uids_collection:
-    layer_name_uids = {}
-    ops.add_to_collection('LAYER_NAME_UIDS', layer_name_uids)
-  else:
-    layer_name_uids = layer_name_uids_collection[0]
-  if prefix not in layer_name_uids:
-    layer_name_uids[prefix] = 1
-  else:
-    layer_name_uids[prefix] += 1
+  graph = ops.get_default_graph()
+  layer_name_uids = tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS[graph]
+  layer_name_uids[prefix] += 1
   return layer_name_uids[prefix]
 
 
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index 8410f12f3e..a37308f702 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -23,9 +23,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import copy
 import functools
 import re
+
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import numpy as np
 import six
@@ -650,10 +652,10 @@ def _to_list(x):
   return [x]
 
 
-def _add_elements_to_collection(elements, collections):
+def _add_elements_to_collection(elements, collection_list):
   elements = _to_list(elements)
-  collections = _to_list(collections)
-  for name in collections:
+  collection_list = _to_list(collection_list)
+  for name in collection_list:
     collection = ops.get_collection_ref(name)
     collection_set = set(collection)
     for element in elements:
@@ -666,6 +668,13 @@ def _object_list_uid(object_list):
   return ', '.join([str(abs(id(x))) for x in object_list])
 
 
+# A global dictionary mapping graph objects to an index of counters used
+# for various layer names in each graph.
+# Allows to give unique autogenerated names to layers, in a graph-specific way.
+PER_GRAPH_LAYER_NAME_UIDS = collections.defaultdict(
+    lambda: collections.defaultdict(int))
+
+
 def _unique_layer_name(name):
   """Makes a layer name (or arbitrary string) unique within a TensorFlow graph.
 
@@ -684,14 +693,7 @@ def _unique_layer_name(name):
     dense_2
   ```
   """
-  layer_name_uids_collection = ops.get_collection('LAYER_NAME_UIDS')
-  if not layer_name_uids_collection:
-    layer_name_uids = {}
-    ops.add_to_collection('LAYER_NAME_UIDS', layer_name_uids)
-  else:
-    layer_name_uids = layer_name_uids_collection[0]
-  if name not in layer_name_uids:
-    layer_name_uids[name] = 1
-  else:
-    layer_name_uids[name] += 1
+  graph = ops.get_default_graph()
+  layer_name_uids = PER_GRAPH_LAYER_NAME_UIDS[graph]
+  layer_name_uids[name] += 1
   return name + '_' + str(layer_name_uids[name])
-- 
GitLab


From 275e7d02bac0ff8ba6317006f9ab6588ea42c67e Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 30 May 2017 17:20:28 -0700
Subject: [PATCH 0023/1375] [tf contrib seq2seq] Improve BeamSearchDecoder's
 ability to handle unknown shapes.

Updated unit tests to contain inputs of unknown shape (at graph build time).
Found an issue in the gather helper that stops it from properly propagating
the batch size of the output shape.  This caused problems with tf.while_loop.
Fixed.

PiperOrigin-RevId: 157533937
---
 .../kernel_tests/attention_wrapper_test.py    | 30 +++++++++------
 .../kernel_tests/beam_search_decoder_test.py  | 11 ++++--
 .../seq2seq/python/ops/beam_search_decoder.py | 37 +++++++++++--------
 3 files changed, 47 insertions(+), 31 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
index 56c1a2ba02..99e51589c9 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
@@ -29,6 +29,7 @@ from tensorflow.contrib.seq2seq.python.ops import attention_wrapper as wrapper
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.contrib.seq2seq.python.ops import basic_decoder
 from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
@@ -64,7 +65,8 @@ class AttentionWrapperTest(test.TestCase):
 
   def assertAllCloseOrEqual(self, x, y, **kwargs):
     if isinstance(x, np.ndarray) or isinstance(x, float):
-      return super(AttentionWrapperTest, self).assertAllClose(x, y, **kwargs)
+      return super(AttentionWrapperTest, self).assertAllClose(
+          x, y, atol=1e-4, **kwargs)
     else:
       self.assertAllEqual(x, y, **kwargs)
 
@@ -98,10 +100,14 @@ class AttentionWrapperTest(test.TestCase):
     else:
       attention_depth = encoder_output_depth
 
-    decoder_inputs = np.random.randn(batch_size, decoder_max_time,
-                                     input_depth).astype(np.float32)
-    encoder_outputs = np.random.randn(batch_size, encoder_max_time,
-                                      encoder_output_depth).astype(np.float32)
+    decoder_inputs = array_ops.placeholder_with_default(
+        np.random.randn(batch_size, decoder_max_time,
+                        input_depth).astype(np.float32),
+        shape=(None, None, input_depth))
+    encoder_outputs = array_ops.placeholder_with_default(
+        np.random.randn(batch_size, encoder_max_time,
+                        encoder_output_depth).astype(np.float32),
+        shape=(None, None, encoder_output_depth))
 
     attention_mechanism = create_attention_mechanism(
         num_units=attention_mechanism_depth,
@@ -152,7 +158,7 @@ class AttentionWrapperTest(test.TestCase):
         # Remove the history from final_state for purposes of the
         # remainder of the tests.
         final_state = final_state._replace(alignment_history=())  # pylint: disable=protected-access
-        self.assertEqual((None, batch_size, encoder_max_time),
+        self.assertEqual((None, batch_size, None),
                          tuple(state_alignment_history.get_shape().as_list()))
       else:
         state_alignment_history = ()
@@ -192,7 +198,7 @@ class AttentionWrapperTest(test.TestCase):
         rnn_output=ResultSummary(
             shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0052250605),
         sample_id=ResultSummary(
-            shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
+            shape=(5, 3), dtype=dtype('int32'), mean=1.4))
     expected_final_state = AttentionWrapperState(
         cell_state=LSTMStateTuple(
             c=ResultSummary(
@@ -222,9 +228,9 @@ class AttentionWrapperTest(test.TestCase):
 
     expected_final_output = BasicDecoderOutput(
         rnn_output=ResultSummary(
-            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0039222687),
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.00597103),
         sample_id=ResultSummary(
-            shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
+            shape=(5, 3), dtype=dtype('int32'), mean=1.4))
     expected_final_state = AttentionWrapperState(
         cell_state=LSTMStateTuple(
             c=ResultSummary(
@@ -232,7 +238,7 @@ class AttentionWrapperTest(test.TestCase):
             h=ResultSummary(
                 shape=(5, 9), dtype=dtype('float32'), mean=-0.0019996136)),
         attention=ResultSummary(
-            shape=(5, 6), dtype=dtype('float32'), mean=-0.0039024551),
+            shape=(5, 6), dtype=dtype('float32'), mean=-0.00595117),
         time=3,
         alignments=ResultSummary(
             shape=(5, 8), dtype=dtype('float32'), mean=0.125),
@@ -306,7 +312,7 @@ class AttentionWrapperTest(test.TestCase):
 
     expected_final_output = BasicDecoderOutput(
         rnn_output=ResultSummary(
-            shape=(5, 3, 10), dtype=dtype('float32'), mean=0.11815784),
+            shape=(5, 3, 10), dtype=dtype('float32'), mean=0.117389656),
         sample_id=ResultSummary(
             shape=(5, 3), dtype=dtype('int32'), mean=4.5999999999999996))
     expected_final_state = AttentionWrapperState(
@@ -316,7 +322,7 @@ class AttentionWrapperTest(test.TestCase):
             h=ResultSummary(
                 shape=(5, 9), dtype=dtype('float32'), mean=-0.00323448)),
         attention=ResultSummary(
-            shape=(5, 10), dtype=dtype('float32'), mean=0.11815783),
+            shape=(5, 10), dtype=dtype('float32'), mean=0.117389656,),
         time=3,
         alignments=ResultSummary(
             shape=(5, 8), dtype=dtype('float32'), mean=0.125),
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
index 360eeae67a..aeafe7c3e5 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
@@ -242,11 +242,14 @@ class BeamSearchDecoderTest(test.TestCase):
     beam_width = 3
 
     with self.test_session() as sess:
+      batch_size_tensor = constant_op.constant(batch_size)
       embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32)
       cell = rnn_cell.LSTMCell(cell_depth)
       if has_attention:
-        inputs = np.random.randn(batch_size, decoder_max_time,
-                                 input_depth).astype(np.float32)
+        inputs = array_ops.placeholder_with_default(
+            np.random.randn(batch_size, decoder_max_time,
+                            input_depth).astype(np.float32),
+            shape=(None, None, input_depth))
         tiled_inputs = beam_search_decoder.tile_batch(
             inputs, multiplier=beam_width)
         tiled_sequence_length = beam_search_decoder.tile_batch(
@@ -261,11 +264,11 @@ class BeamSearchDecoderTest(test.TestCase):
             attention_layer_size=attention_depth,
             alignment_history=False)
       cell_state = cell.zero_state(
-          dtype=dtypes.float32, batch_size=batch_size * beam_width)
+          dtype=dtypes.float32, batch_size=batch_size_tensor * beam_width)
       bsd = beam_search_decoder.BeamSearchDecoder(
           cell=cell,
           embedding=embedding,
-          start_tokens=batch_size * [start_token],
+          start_tokens=array_ops.fill([batch_size_tensor], start_token),
           end_token=end_token,
           initial_state=cell_state,
           beam_width=beam_width,
diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
index 483f866fd6..1d96b47dc4 100644
--- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
@@ -287,7 +287,7 @@ class BeamSearchDecoder(decoder.Decoder):
       A reshaped version of t with dimension [batch_size * beam_width, s].
     """
     if isinstance(s, ops.Tensor):
-      s = tensor_util.constant_value_as_shape(s)
+      s = tensor_shape.as_shape(tensor_util.constant_value(s))
     else:
       s = tensor_shape.TensorShape(s)
     t_shape = array_ops.shape(t)
@@ -321,7 +321,7 @@ class BeamSearchDecoder(decoder.Decoder):
         are known statically).
     """
     if isinstance(s, ops.Tensor):
-      s = tensor_util.constant_value_as_shape(s)
+      s = tensor_shape.TensorShape(tensor_util.constant_value(s))
     else:
       s = tensor_shape.TensorShape(s)
     t_shape = array_ops.shape(t)
@@ -520,7 +520,7 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
   next_beam_probs = _tensor_gather_helper(
       gather_indices=word_indices,
       gather_from=total_probs,
-      range_input=batch_size,
+      batch_size=batch_size,
       range_size=beam_width * vocab_size,
       gather_shape=[-1])
   next_word_ids = math_ops.to_int32(word_indices % vocab_size)
@@ -530,7 +530,7 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
   previously_finished = _tensor_gather_helper(
       gather_indices=next_beam_ids,
       gather_from=previously_finished,
-      range_input=batch_size,
+      batch_size=batch_size,
       range_size=beam_width,
       gather_shape=[-1])
   next_finished = math_ops.logical_or(previously_finished,
@@ -546,7 +546,7 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
   next_prediction_len = _tensor_gather_helper(
       gather_indices=next_beam_ids,
       gather_from=beam_state.lengths,
-      range_input=batch_size,
+      batch_size=batch_size,
       range_size=beam_width,
       gather_shape=[-1])
   next_prediction_len += lengths_to_add
@@ -560,7 +560,7 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
       lambda gather_from: _maybe_tensor_gather_helper(
           gather_indices=next_beam_ids,
           gather_from=gather_from,
-          range_input=batch_size,
+          batch_size=batch_size,
           range_size=beam_width,
           gather_shape=[batch_size * beam_width, -1]),
       next_cell_state)
@@ -653,7 +653,7 @@ def _mask_probs(probs, eos_token, finished):
   return finished_examples + non_finished_examples
 
 
-def _maybe_tensor_gather_helper(gather_indices, gather_from, range_input,
+def _maybe_tensor_gather_helper(gather_indices, gather_from, batch_size,
                                 range_size, gather_shape):
   """Maybe applies _tensor_gather_helper.
 
@@ -664,7 +664,7 @@ def _maybe_tensor_gather_helper(gather_indices, gather_from, range_input,
   Args:
     gather_indices: The tensor indices that we use to gather.
     gather_from: The tensor that we are gathering from.
-    range_input: The input range to use. Likely equal to batch_size.
+    batch_size: The batch size.
     range_size: The number of values in each range. Likely equal to beam_width.
     gather_shape: What we should reshape gather_from to in order to preserve the
       correct values. An example is when gather_from is the attention from an
@@ -679,14 +679,18 @@ def _maybe_tensor_gather_helper(gather_indices, gather_from, range_input,
   """
   _check_maybe(gather_from)
   if gather_from.shape.ndims >= len(gather_shape):
-    return _tensor_gather_helper(gather_indices, gather_from, range_input,
-                                 range_size, gather_shape)
+    return _tensor_gather_helper(
+        gather_indices=gather_indices,
+        gather_from=gather_from,
+        batch_size=batch_size,
+        range_size=range_size,
+        gather_shape=gather_shape)
   else:
     return gather_from
 
 
-def _tensor_gather_helper(gather_indices, gather_from, range_input, range_size,
-                          gather_shape):
+def _tensor_gather_helper(gather_indices, gather_from, batch_size,
+                          range_size, gather_shape):
   """Helper for gathering the right indices from the tensor.
 
   This works by reshaping gather_from to gather_shape (e.g. [-1]) and then
@@ -696,7 +700,7 @@ def _tensor_gather_helper(gather_indices, gather_from, range_input, range_size,
   Args:
     gather_indices: The tensor indices that we use to gather.
     gather_from: The tensor that we are gathering from.
-    range_input: The input range to use. Likely equal to batch_size.
+    batch_size: The input batch size.
     range_size: The number of values in each range. Likely equal to beam_width.
     gather_shape: What we should reshape gather_from to in order to preserve the
       correct values. An example is when gather_from is the attention from an
@@ -708,12 +712,15 @@ def _tensor_gather_helper(gather_indices, gather_from, range_input, range_size,
   Returns:
     output: Gathered tensor of shape tf.shape(gather_from)[:1+len(gather_shape)]
   """
-  range_ = array_ops.expand_dims(math_ops.range(range_input) * range_size, 1)
+  range_ = array_ops.expand_dims(math_ops.range(batch_size) * range_size, 1)
   gather_indices = array_ops.reshape(gather_indices + range_, [-1])
   output = array_ops.gather(
       array_ops.reshape(gather_from, gather_shape), gather_indices)
   final_shape = array_ops.shape(gather_from)[:1 + len(gather_shape)]
-  final_static_shape = gather_from.shape[:1 + len(gather_shape)]
+  static_batch_size = tensor_util.constant_value(batch_size)
+  final_static_shape = (tensor_shape.TensorShape([static_batch_size])
+                        .concatenate(
+                            gather_from.shape[1:1 + len(gather_shape)]))
   output = array_ops.reshape(output, final_shape)
   output.set_shape(final_static_shape)
   return output
-- 
GitLab


From bf258560a6f19dc88e4fc29db4995faf119d4ea5 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 31 May 2017 12:39:14 -0700
Subject: [PATCH 0024/1375] Avoid CHECKs in BundleReader, propagate errors
 instead.

Motivation:
We'd like to evolve the checkpoint format over time (e.g., enable
different types of compression). Without this change, a TensorFlow
version that encounters a format that it doesn't understand would CHECK fail
with an unhelpful error message.

With this, it propagates a clearer error message up, giving the user some
hints about what could be wrong.

I don't have a unittest for this - I thought about writing a bundle and
then strategically corrupting the bytes on disk before reading it back,
but that seems a bit much. The intention of this change is to enable
graceful reporting of forward compatibility breakages. Ideas for an
appropriate unittest are appreciated.

PiperOrigin-RevId: 157620358
---
 .../core/util/tensor_bundle/tensor_bundle.cc  | 40 +++++++++++++++----
 1 file changed, 33 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
index 5c2bda4770..dd04cea40d 100644
--- a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
+++ b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
@@ -238,6 +238,23 @@ bool IsFullSlice(const TensorSlice& slice_spec,
   }
 }
 
+Status CorruptFileError(const Status& in_status, const string& filename,
+                        const string& detail) {
+  if (in_status.ok()) {
+    return errors::Internal("Unable to read file (", filename,
+                            "). Perhaps the file is corrupt or was produced by "
+                            "a newer version of TensorFlow with format changes "
+                            "(",
+                            detail, ")");
+  }
+  return Status(
+      in_status.code(),
+      strings::StrCat("Unable to read file (", filename,
+                      "). Perhaps the file is corrupt or was produced by a "
+                      "newer version of TensorFlow with format changes (",
+                      detail, "): ", in_status.error_message()));
+}
+
 }  // namespace
 
 BundleWriter::BundleWriter(Env* env, StringPiece prefix)
@@ -433,11 +450,13 @@ static Status MergeOneBundle(Env* env, StringPiece prefix,
   // Process header.
   {
     iter->Seek(kHeaderEntryKey);
-    CHECK(iter->Valid()) << "File: " << filename
-                         << ", iterator status: " << iter->status();
+    if (!iter->Valid()) {
+      return CorruptFileError(iter->status(), filename,
+                              "failed to seek to header entry");
+    }
     BundleHeaderProto header;
-    TF_CHECK_OK(ParseEntryProto(iter->key(), iter->value(), &header));
-    CHECK_GE(header.num_shards(), 0);
+    Status s = ParseEntryProto(iter->key(), iter->value(), &header);
+    if (!s.ok()) return CorruptFileError(s, filename, "unable to parse header");
 
     merge_state->num_shards += header.num_shards();
     if (!merge_state->seen_first_bundle) {
@@ -584,10 +603,17 @@ BundleReader::BundleReader(Env* env, StringPiece prefix)
 
   // Reads "num_shards_" from the first entry.
   iter_->Seek(kHeaderEntryKey);
-  CHECK(iter_->Valid()) << "File: " << filename
-                        << ", iterator status: " << iter_->status();
+  if (!iter_->Valid()) {
+    status_ = CorruptFileError(iter_->status(), filename,
+                               "failed to seek to header entry");
+    return;
+  }
   BundleHeaderProto header;
-  TF_CHECK_OK(ParseEntryProto(iter_->key(), iter_->value(), &header));
+  status_ = ParseEntryProto(iter_->key(), iter_->value(), &header);
+  if (!status_.ok()) {
+    status_ = CorruptFileError(status_, filename, "unable to parse header");
+    return;
+  }
   num_shards_ = header.num_shards();
   if ((header.endianness() == BundleHeaderProto::BIG && port::kLittleEndian) ||
       (header.endianness() == BundleHeaderProto::LITTLE &&
-- 
GitLab


From 129c4fce232e6ed3feb28ac55248e261e2d729dd Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Wed, 31 May 2017 15:21:45 -0700
Subject: [PATCH 0025/1375] Additional colocation options and bugfixes for
 TensorArray

* colocate_with is now set properly when a TensorArray is passed through a
  while_loop
* added a new argument, "colocate_with_first_write" (default: True; this is
  the current behavior).  If False, the TensorArray is simply placed on the
  device from the context it's constructed in, and no colocation constraints
  are added.

PiperOrigin-RevId: 157643133
---
 .../kernel_tests/tensor_array_ops_test.py     | 48 +++++++++++++++--
 tensorflow/python/ops/control_flow_ops.py     |  8 ++-
 tensorflow/python/ops/tensor_array_grad.py    | 36 ++++++-------
 tensorflow/python/ops/tensor_array_ops.py     | 51 ++++++++++++++-----
 .../api/golden/tensorflow.-tensor-array.pbtxt |  2 +-
 5 files changed, 107 insertions(+), 38 deletions(-)

diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
index 5b0f318efe..9941c97c30 100644
--- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py
+++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
@@ -53,6 +53,16 @@ def _make_converter(tf_dtype):
 
 class TensorArrayTest(test.TestCase):
 
+  @classmethod
+  def setUpClass(cls):
+    super(TensorArrayTest, cls).setUpClass()
+    cls._workers, _ = test.create_local_cluster(num_workers=3, num_ps=0)
+
+  @classmethod
+  def tearDownClass(cls):
+    super(TensorArrayTest, cls).tearDownClass()
+    session_lib.Session.reset(cls._workers[0].target)
+
   def testTensorArrayWriteRead(self):
     with self.test_session(use_gpu=True) as session:
       ta = tensor_array_ops.TensorArray(
@@ -1225,8 +1235,7 @@ class TensorArrayTest(test.TestCase):
       ta = ta.split([1.0, 2.0], [1, 1])
     flows.append(ta.flow)
 
-    workers, _ = test.create_local_cluster(num_workers=3, num_ps=0)
-    session = session_lib.Session(workers[0].target)
+    session = session_lib.Session(self._workers[0].target)
 
     run_options = config_pb2.RunOptions(
         trace_level=config_pb2.RunOptions.FULL_TRACE)
@@ -1250,13 +1259,12 @@ class TensorArrayTest(test.TestCase):
 
     def _body(i, ta_i):
       with ops.device("/job:worker/task:1/cpu:0"):
-        return i + 1, ta_i.write(i, 0.0)
+        return i + 1, ta_i.write(i, constant_op.constant(0.0))
 
     _, ta_out = control_flow_ops.while_loop(
         lambda i, ta: i < 2, _body, loop_vars=[0, ta])
 
-    workers, _ = test.create_local_cluster(num_workers=3, num_ps=0)
-    session = session_lib.Session(workers[0].target)
+    session = session_lib.Session(self._workers[0].target)
 
     run_options = config_pb2.RunOptions(
         trace_level=config_pb2.RunOptions.FULL_TRACE)
@@ -1274,6 +1282,36 @@ class TensorArrayTest(test.TestCase):
         self.assertFalse(
             [s for s in dev_stats[d] if "/TensorArray" in s.node_name])
 
+  def testTensorArrayDisabledColocateWithFirstWriteCall(self):
+    with ops.device("/job:worker/task:0/cpu:0"):
+      ta = tensor_array_ops.TensorArray(
+          dtype=dtypes.float32, size=2, colocate_with_first_write_call=False)
+
+    def _body(i, ta_i):
+      with ops.device("/job:worker/task:1/cpu:0"):
+        return i + 1, ta_i.write(i, constant_op.constant(0.0))
+
+    _, ta_out = control_flow_ops.while_loop(
+        lambda i, ta: i < 2, _body, loop_vars=[0, ta])
+
+    session = session_lib.Session(self._workers[0].target)
+
+    run_options = config_pb2.RunOptions(
+        trace_level=config_pb2.RunOptions.FULL_TRACE)
+    run_metadata = config_pb2.RunMetadata()
+
+    session.run(ta_out.flow, options=run_options, run_metadata=run_metadata)
+    self.assertTrue(run_metadata.HasField("step_stats"))
+    dev_stats = {d.device: list(d.node_stats)
+                 for d in run_metadata.step_stats.dev_stats}
+    for d in dev_stats:
+      if "/task:0/" in d and "cpu" in d:  # Skip any GPU node stats
+        self.assertTrue(
+            [s for s in dev_stats[d] if "/TensorArray" in s.node_name])
+      else:
+        self.assertFalse(
+            [s for s in dev_stats[d] if "/TensorArray" in s.node_name])
+
   def testTensorArrayIdentity(self):
     with self.test_session(use_gpu=True) as session:
       ta0 = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2,
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 96ace6e79b..c98e74fe6b 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -437,10 +437,14 @@ def _convert_tensorarray_to_flow(tensor_or_tensor_array):
 
 
 def _make_tensor_array(ta, t_or_flow):
+  # pylint: disable=protected-access
   new_ta = tensor_array_ops.TensorArray(
       dtype=ta.dtype, handle=ta.handle, flow=t_or_flow,
-      infer_shape=ta._infer_shape)
-  new_ta._element_shape = ta._element_shape  # pylint: disable=protected-access
+      infer_shape=ta._infer_shape,
+      colocate_with_first_write_call=ta._colocate_with_first_write_call)
+  new_ta._colocate_with = ta._colocate_with
+  new_ta._element_shape = ta._element_shape
+  # pylint: enable=protected-access
   return new_ta
 
 
diff --git a/tensorflow/python/ops/tensor_array_grad.py b/tensorflow/python/ops/tensor_array_grad.py
index 0e7d1880ce..1f70d69548 100644
--- a/tensorflow/python/ops/tensor_array_grad.py
+++ b/tensorflow/python/ops/tensor_array_grad.py
@@ -99,9 +99,9 @@ def _TensorArrayReadGrad(op, grad):
   flow = op.inputs[2]
   dtype = op.get_attr("dtype")
   grad_source = _GetGradSource(grad)
-  g = tensor_array_ops.TensorArray(
-      dtype=dtype, handle=handle, flow=flow).grad(
-          source=grad_source, flow=flow)
+  g = (tensor_array_ops.TensorArray(dtype=dtype, handle=handle, flow=flow,
+                                    colocate_with_first_write_call=False)
+       .grad(source=grad_source, flow=flow))
   w_g = g.write(index, grad)
   return [None, None, w_g.flow]
 
@@ -125,9 +125,9 @@ def _TensorArrayWriteGrad(op, flow):
   index = op.inputs[1]
   dtype = op.get_attr("T")
   grad_source = _GetGradSource(flow)
-  g = tensor_array_ops.TensorArray(
-      dtype=dtype, handle=handle, flow=flow).grad(
-          source=grad_source, flow=flow)
+  g = (tensor_array_ops.TensorArray(dtype=dtype, handle=handle, flow=flow,
+                                    colocate_with_first_write_call=False)
+       .grad(source=grad_source, flow=flow))
   grad = g.read(index)
   return [None, None, grad, flow]
 
@@ -156,9 +156,9 @@ def _TensorArrayGatherGrad(op, grad):
   flow = op.inputs[2]
   dtype = op.get_attr("dtype")
   grad_source = _GetGradSource(grad)
-  g = tensor_array_ops.TensorArray(
-      dtype=dtype, handle=handle, flow=flow).grad(
-          source=grad_source, flow=flow)
+  g = (tensor_array_ops.TensorArray(dtype=dtype, handle=handle, flow=flow,
+                                    colocate_with_first_write_call=False)
+       .grad(source=grad_source, flow=flow))
   u_g = g.scatter(indices, grad)
   return [None, None, u_g.flow]
 
@@ -180,9 +180,9 @@ def _TensorArrayScatterGrad(op, flow):
   indices = op.inputs[1]
   dtype = op.get_attr("T")
   grad_source = _GetGradSource(flow)
-  g = tensor_array_ops.TensorArray(
-      dtype=dtype, handle=handle, flow=flow).grad(
-          source=grad_source, flow=flow)
+  g = (tensor_array_ops.TensorArray(dtype=dtype, handle=handle, flow=flow,
+                                    colocate_with_first_write_call=False)
+       .grad(source=grad_source, flow=flow))
   grad = g.gather(indices)
   return [None, None, grad, flow]
 
@@ -211,9 +211,9 @@ def _TensorArrayConcatGrad(op, grad, unused_lengths_grad):
   lengths = op.outputs[1]
   dtype = op.get_attr("dtype")
   grad_source = _GetGradSource(grad)
-  g = tensor_array_ops.TensorArray(
-      dtype=dtype, handle=handle, flow=flow).grad(
-          source=grad_source, flow=flow)
+  g = (tensor_array_ops.TensorArray(dtype=dtype, handle=handle, flow=flow,
+                                    colocate_with_first_write_call=False)
+       .grad(source=grad_source, flow=flow))
   u_g = g.split(grad, lengths=lengths)
   # handle, flow_in
   return [None, u_g.flow]
@@ -235,9 +235,9 @@ def _TensorArraySplitGrad(op, flow):
   handle = op.inputs[0]
   dtype = op.get_attr("T")
   grad_source = _GetGradSource(flow)
-  g = tensor_array_ops.TensorArray(
-      dtype=dtype, handle=handle, flow=flow).grad(
-          source=grad_source, flow=flow)
+  g = (tensor_array_ops.TensorArray(dtype=dtype, handle=handle, flow=flow,
+                                    colocate_with_first_write_call=False)
+       .grad(source=grad_source, flow=flow))
   grad = g.concat()
   # handle, value, lengths, flow_in
   return [None, grad, None, flow]
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index 8b119f5842..7a6abc8e61 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -54,6 +54,7 @@ class TensorArray(object):
                flow=None,
                infer_shape=True,
                element_shape=None,
+               colocate_with_first_write_call=True,
                name=None):
     """Construct a new TensorArray or wrap an existing TensorArray handle.
 
@@ -85,6 +86,11 @@ class TensorArray(object):
       element_shape: (optional, default: None) A `TensorShape` object specifying
         the shape constraints of each of the elements of the TensorArray.
         Need not be fully defined.
+      colocate_with_first_write_call: If `True`, the TensorArray will be
+        colocated on the same device as the the Tensor used on its first write
+        (write operations include `write`, `unstack`, and `split`).  If `False`,
+        the TensorArray will be placed on the device determined by the
+        device context available during its initialization.
       name: A name for the operation (optional).
 
     Raises:
@@ -120,7 +126,11 @@ class TensorArray(object):
     # Used to keep track of what tensors the TensorArray should be
     # colocated with.  We choose to colocate the TensorArray with the
     # first tensor written to it.
-    self._colocate_with = []
+    self._colocate_with_first_write_call = colocate_with_first_write_call
+    if colocate_with_first_write_call:
+      self._colocate_with = []
+    else:
+      self._colocate_with = None
 
     # Record the current static shape for the array elements. The element
     # shape is defined either by `element_shape` or the shape of the tensor
@@ -142,8 +152,8 @@ class TensorArray(object):
         # Construct the TensorArray with an empty device.  The first
         # write into the TensorArray from a Tensor with a set device
         # will retroactively set the device value of this op.
-        with ops.device(None), ops.colocate_with(None, ignore_existing=True):
-          self._handle, self._flow = gen_data_flow_ops._tensor_array_v3(
+        def create():
+          return gen_data_flow_ops._tensor_array_v3(
               dtype=dtype,
               size=size,
               element_shape=element_shape,
@@ -151,6 +161,11 @@ class TensorArray(object):
               clear_after_read=clear_after_read,
               tensor_array_name=tensor_array_name,
               name=scope)
+        if colocate_with_first_write_call:
+          with ops.device(None), ops.colocate_with(None, ignore_existing=True):
+            self._handle, self._flow = create()
+        else:
+          self._handle, self._flow = create()
 
   @property
   def flow(self):
@@ -200,10 +215,13 @@ class TensorArray(object):
     If no internal colocation group is set, colocate with `value` and set
     the internal colocation group to be value.
     """
-    if not self._colocate_with:
-      self._colocate_with.append(value)
-    with ops.colocate_with(self._colocate_with[0]):
+    if not self._colocate_with_first_write_call:
       yield
+    else:
+      if not self._colocate_with:
+        self._colocate_with.append(value)
+      with ops.colocate_with(self._colocate_with[0]):
+        yield
 
   def identity(self):
     """Returns a TensorArray with the same content and properties.
@@ -214,8 +232,10 @@ class TensorArray(object):
       Use this object all for subsequent operations.
     """
     flow = array_ops.identity(self._flow)
-    ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow,
-                     infer_shape=self._infer_shape)
+    ta = TensorArray(
+        dtype=self._dtype, handle=self._handle, flow=flow,
+        infer_shape=self._infer_shape,
+        colocate_with_first_write_call=self._colocate_with_first_write_call)
     ta._element_shape = self._element_shape
     ta._colocate_with = self._colocate_with
     return ta
@@ -237,7 +257,8 @@ class TensorArray(object):
             dtype=self._dtype,
             handle=g_handle,
             flow=flow,
-            infer_shape=self._infer_shape)
+            infer_shape=self._infer_shape,
+            colocate_with_first_write_call=False)
         g._element_shape = self._element_shape
         return g
 
@@ -286,7 +307,9 @@ class TensorArray(object):
             value=value,
             flow_in=self._flow,
             name=name)
-      ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
+      ta = TensorArray(
+          dtype=self._dtype, handle=self._handle, flow=flow_out,
+          colocate_with_first_write_call=self._colocate_with_first_write_call)
       ta._infer_shape = self._infer_shape
       ta._element_shape = self._element_shape
       ta._colocate_with = self._colocate_with
@@ -416,7 +439,9 @@ class TensorArray(object):
             value=value,
             flow_in=self._flow,
             name=name)
-      ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
+      ta = TensorArray(
+          dtype=self._dtype, handle=self._handle, flow=flow_out,
+          colocate_with_first_write_call=self._colocate_with_first_write_call)
       ta._infer_shape = self._infer_shape
       ta._element_shape = self._element_shape
       ta._colocate_with = self._colocate_with
@@ -456,7 +481,9 @@ class TensorArray(object):
             lengths=lengths_64,
             flow_in=self._flow,
             name=name)
-      ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
+      ta = TensorArray(
+          dtype=self._dtype, handle=self._handle, flow=flow_out,
+          colocate_with_first_write_call=self._colocate_with_first_write_call)
       ta._infer_shape = self._infer_shape
       ta._element_shape = self._element_shape
       ta._colocate_with = self._colocate_with
diff --git a/tensorflow/tools/api/golden/tensorflow.-tensor-array.pbtxt b/tensorflow/tools/api/golden/tensorflow.-tensor-array.pbtxt
index a0fad4df52..ed088c41ed 100644
--- a/tensorflow/tools/api/golden/tensorflow.-tensor-array.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-tensor-array.pbtxt
@@ -16,7 +16,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'dtype\', \'size\', \'dynamic_size\', \'clear_after_read\', \'tensor_array_name\', \'handle\', \'flow\', \'infer_shape\', \'element_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'dtype\', \'size\', \'dynamic_size\', \'clear_after_read\', \'tensor_array_name\', \'handle\', \'flow\', \'infer_shape\', \'element_shape\', \'colocate_with_first_write_call\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'True\', \'None\'], "
   }
   member_method {
     name: "close"
-- 
GitLab


From fd1c3d3ead966aedcf957da0ab41449cef0fd31f Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 31 May 2017 21:56:30 -0700
Subject: [PATCH 0026/1375] Disable writing of compressed checkpoints.

Snappy compression (and decompression) was enabled after the
1.1 release (in commit 63b2f999d3f22cfe915b89103faa1b0a1b1b7617).
This means that checkpoints produced by the 1.2.0 release candidates
will cause TensorFlow 1.1 (and prior) binaries to crash as they
CHECK fail when trying to load snappy-compressed tables.

To ease transition, disable writing of compressed checkpoints in
1.2.0 for now.

Reconsider this in the next release.

PiperOrigin-RevId: 157675189
---
 .../core/util/tensor_bundle/tensor_bundle.cc       | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
index dd04cea40d..334444a4a2 100644
--- a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
+++ b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
@@ -255,6 +255,16 @@ Status CorruptFileError(const Status& in_status, const string& filename,
                       detail, "): ", in_status.error_message()));
 }
 
+table::Options TableBuilderOptions() {
+  table::Options o;
+  // Compressed tables cannot be read by TensorFlow releases prior to 1.1.
+  // To smoothen the transition, compressed writes are disabled for now
+  // (version 1.2) with the intention that they will be enabled again at
+  // some point (perhaps the 1.3 release?).
+  o.compression = table::kNoCompression;
+  return o;
+}
+
 }  // namespace
 
 BundleWriter::BundleWriter(Env* env, StringPiece prefix)
@@ -442,7 +452,7 @@ static Status MergeOneBundle(Env* env, StringPiece prefix,
 
   table::Table* table = nullptr;
   TF_RETURN_IF_ERROR(
-      table::Table::Open(table::Options(), file.get(), file_size, &table));
+      table::Table::Open(TableBuilderOptions(), file.get(), file_size, &table));
   std::unique_ptr<table::Table> table_deleter(table);
   std::unique_ptr<table::Iterator> iter(table->NewIterator());
 
@@ -555,7 +565,7 @@ Status MergeBundles(Env* env, gtl::ArraySlice<string> prefixes,
   TF_RETURN_IF_ERROR(
       env->NewWritableFile(MetaFilename(merged_prefix), &merged_metadata));
   {
-    table::TableBuilder builder(table::Options(), merged_metadata.get());
+    table::TableBuilder builder(TableBuilderOptions(), merged_metadata.get());
     // Header entry.
     BundleHeaderProto header;
     header.set_num_shards(merge.num_shards);
-- 
GitLab


From 643b7163db485563c961be9d09925415ccb0db56 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 7 Jan 2017 09:19:27 -0800
Subject: [PATCH 0027/1375] Add release notes for Intel MKL integration.

PiperOrigin-RevId: 157722003
---
 RELEASE.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/RELEASE.md b/RELEASE.md
index 5b35f91477..f134567266 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -48,6 +48,15 @@
   be replaced by calling `embedding_lookup` or `layers.dense` as pre- or post-
   processing of the rnn.  For RNN decoding, this functionality has been replaced
   with an alternative API in `tf.contrib.seq2seq`.
+* Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of
+  optimized deep learning primitives: In addition to matrix multiplication and
+  convolution, these building blocks include:
+  Direct batched convolution
+  Pooling: maximum, minimum, average
+  Normalization: LRN, batch normalization
+  Activation: rectified linear unit (ReLU)
+  Data manipulation: multi-dimensional transposition (conversion), split,
+  concat, sum and scale.
 
 ## Deprecations
 
-- 
GitLab


From 807e0ed46aa3b47f6f8863205cd2d3f35864a283 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 1 Jun 2017 12:43:25 -0700
Subject: [PATCH 0028/1375] Add type error to start_queue_runners if given
 session is not a `tf.Session`. Due to semver, we suppress the error if a
 MonitoredSession is provided.

PiperOrigin-RevId: 157748375
---
 .../python/training/queue_runner_impl.py      | 14 ++++++++++
 .../python/training/queue_runner_test.py      | 28 +++++++++++++++++++
 2 files changed, 42 insertions(+)

diff --git a/tensorflow/python/training/queue_runner_impl.py b/tensorflow/python/training/queue_runner_impl.py
index d713e222ae..4e58602a6f 100644
--- a/tensorflow/python/training/queue_runner_impl.py
+++ b/tensorflow/python/training/queue_runner_impl.py
@@ -22,6 +22,7 @@ import threading
 import weakref
 
 from tensorflow.core.protobuf import queue_runner_pb2
+from tensorflow.python.client import session
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging as logging
@@ -401,6 +402,10 @@ def start_queue_runners(sess=None, coord=None, daemon=True, start=True,
     collection: A `GraphKey` specifying the graph collection to
       get the queue runners from.  Defaults to `GraphKeys.QUEUE_RUNNERS`.
 
+  Raises:
+    ValueError: if `sess` is None and there isn't any default session.
+    TypeError: if `sess` is not a `tf.Session` object.
+
   Returns:
     A list of threads.
   """
@@ -410,6 +415,15 @@ def start_queue_runners(sess=None, coord=None, daemon=True, start=True,
       raise ValueError("Cannot start queue runners: No default session is "
                        "registered. Use `with sess.as_default()` or pass an "
                        "explicit session to tf.start_queue_runners(sess=sess)")
+
+  if not isinstance(sess, session.SessionInterface):
+    # Following check is due to backward compatibility. (b/62061352)
+    if sess.__class__.__name__ in [
+        "MonitoredSession", "SingularMonitoredSession"]:
+      return []
+    raise TypeError("sess must be a `tf.Session` object. "
+                    "Given class: {}".format(sess.__class__))
+
   with sess.graph.as_default():
     threads = []
     for qr in ops.get_collection(collection):
diff --git a/tensorflow/python/training/queue_runner_test.py b/tensorflow/python/training/queue_runner_test.py
index 5b00ac9fc3..51c0eecf46 100644
--- a/tensorflow/python/training/queue_runner_test.py
+++ b/tensorflow/python/training/queue_runner_test.py
@@ -30,6 +30,7 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.training import coordinator
+from tensorflow.python.training import monitored_session
 from tensorflow.python.training import queue_runner_impl
 
 
@@ -247,6 +248,33 @@ class QueueRunnerTest(test.TestCase):
       # The variable should be 3.
       self.assertEqual(3, var.eval())
 
+  def testStartQueueRunnersRaisesIfNotASession(self):
+    zero64 = constant_op.constant(0, dtype=dtypes.int64)
+    var = variables.Variable(zero64)
+    count_up_to = var.count_up_to(3)
+    queue = data_flow_ops.FIFOQueue(10, dtypes.float32)
+    init_op = variables.global_variables_initializer()
+    qr = queue_runner_impl.QueueRunner(queue, [count_up_to])
+    queue_runner_impl.add_queue_runner(qr)
+    with self.test_session():
+      init_op.run()
+      with self.assertRaisesRegexp(TypeError, "tf.Session"):
+        queue_runner_impl.start_queue_runners("NotASession")
+
+  def testStartQueueRunnersIgnoresMonitoredSession(self):
+    zero64 = constant_op.constant(0, dtype=dtypes.int64)
+    var = variables.Variable(zero64)
+    count_up_to = var.count_up_to(3)
+    queue = data_flow_ops.FIFOQueue(10, dtypes.float32)
+    init_op = variables.global_variables_initializer()
+    qr = queue_runner_impl.QueueRunner(queue, [count_up_to])
+    queue_runner_impl.add_queue_runner(qr)
+    with self.test_session():
+      init_op.run()
+      threads = queue_runner_impl.start_queue_runners(
+          monitored_session.MonitoredSession())
+      self.assertFalse(threads)
+
   def testStartQueueRunnersNonDefaultGraph(self):
     # CountUpTo will raise OUT_OF_RANGE when it reaches the count.
     graph = ops.Graph()
-- 
GitLab


From a5030334d1d25eb2300cfef1d94a6f51d58aed15 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Jun 2017 16:45:07 -0700
Subject: [PATCH 0029/1375] Fix flakiness in GpuMultiSessionMemoryTest.

PiperOrigin-RevId: 157781368
---
 .../python/kernel_tests/basic_gpu_test.py     | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/tensorflow/python/kernel_tests/basic_gpu_test.py b/tensorflow/python/kernel_tests/basic_gpu_test.py
index e6d0c06d14..dbbc2de811 100644
--- a/tensorflow/python/kernel_tests/basic_gpu_test.py
+++ b/tensorflow/python/kernel_tests/basic_gpu_test.py
@@ -228,9 +228,9 @@ class BroadcastSimpleTest(test.TestCase):
 class GpuMultiSessionMemoryTest(test_util.TensorFlowTestCase):
   """Tests concurrent sessions executing on the same GPU."""
 
-  def _run_session(self, results):
+  def _run_session(self, session, results):
     n_iterations = 500
-    with self.test_session(use_gpu=True) as s:
+    with session as s:
       data = variables.Variable(1.0)
       with ops.device('/gpu:0'):
         random_seed.set_random_seed(1)
@@ -245,29 +245,29 @@ class GpuMultiSessionMemoryTest(test_util.TensorFlowTestCase):
 
         for _ in xrange(n_iterations):
           value = s.run(x4)
-          results.append(value)
-          if value != results[0]:
+          results.add(value.flat[0])
+          if len(results) != 1:
             break
 
   def testConcurrentSessions(self):
-    if not test.is_gpu_available():
-      return
-
     n_threads = 4
-    results = [[]] * n_threads
-    threads = [
-        threading.Thread(target=self._run_session, args=(results[i],))
-        for i in xrange(n_threads)
-    ]
+    threads = []
+    results = []
+    for _ in xrange(n_threads):
+      session = self.test_session(graph=ops.Graph(), use_gpu=True)
+      results.append(set())
+      args = (session, results[-1])
+      threads.append(threading.Thread(target=self._run_session, args=args))
+
     for thread in threads:
       thread.start()
     for thread in threads:
       thread.join()
 
-    flat_results = [x for x in itertools.chain(*results)]
-    self.assertNotEqual(0, len(flat_results))
-    for result in flat_results:
-      self.assertEqual(result, flat_results[0])
+    flat_results = set([x for x in itertools.chain(*results)])
+    self.assertEqual(1,
+                     len(flat_results),
+                     'Expected single value, got %r' % flat_results)
 
 
 if __name__ == '__main__':
-- 
GitLab


From efa47468c8977d0b36b426905ad65519051fcf3b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Jun 2017 09:51:27 -0700
Subject: [PATCH 0030/1375] Set flow to a value during TensorArray creation,
 Re-enable tensor_array_ops_test in msan.

PiperOrigin-RevId: 157841785
---
 tensorflow/core/kernels/tensor_array_ops.cc | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/tensor_array_ops.cc b/tensorflow/core/kernels/tensor_array_ops.cc
index bd7556658a..2e1a62d8ba 100644
--- a/tensorflow/core/kernels/tensor_array_ops.cc
+++ b/tensorflow/core/kernels/tensor_array_ops.cc
@@ -36,6 +36,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/refcount.h"
 #include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/dynamic_annotations.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/thread_annotations.h"
 #include "tensorflow/core/platform/types.h"
@@ -101,7 +102,7 @@ Status SetupFlowControlInputs(OpKernelContext* ctx, bool set_output) {
 class TensorArrayCreationOp : public OpKernel {
  public:
   explicit TensorArrayCreationOp(OpKernelConstruction* context)
-      : OpKernel(context) {}
+      : OpKernel(context), device_type_(context->device_type()) {}
 
   void Compute(OpKernelContext* ctx) override {
     Tensor tensor_array_output_handle;
@@ -133,6 +134,12 @@ class TensorArrayCreationOp : public OpKernel {
       // Create the flow output.
       Tensor* flow;
       OP_REQUIRES_OK(ctx, ctx->allocate_output(1, TensorShape({}), &flow));
+      if (device_type_ == DEVICE_CPU) {
+        // Value doesn't matter, but this makes msan not complaint about
+        // copying an uninitialized value. To do this on GPU would require
+        // a kernel launch or a host->device memcpy, so we avoid that.
+        flow->flat<float>()(0) = 0;
+      }
     }
   }
 
@@ -140,6 +147,9 @@ class TensorArrayCreationOp : public OpKernel {
   virtual Status CreateTensorArray(OpKernelContext* ctx, ResourceMgr* rm,
                                    Tensor* tensor_array_output_handle,
                                    TensorArray** output_tensor_array) = 0;
+
+ private:
+  const DeviceType device_type_;
 };
 
 // A per-run local tensor array. The tensor array uses a "per-step" resource
-- 
GitLab


From d5b89b184c58da9be8f3507db47b7495385ed934 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Fri, 2 Jun 2017 14:50:27 -0700
Subject: [PATCH 0031/1375] [tf.contrib.data] Re-implement IteratorGetNext as
 an AsyncOpKernel.

This prevents the op from consuming an inter-op thread pool thread
when blocked, and fixes a potential deadlock when many IteratorGetNext
ops are blocked. Fixes #10369.

PiperOrigin-RevId: 157878885
---
 .../kernel_tests/map_dataset_op_test.py       |  3 +-
 tensorflow/core/kernels/BUILD                 |  1 +
 tensorflow/core/kernels/iterator_ops.cc       | 69 ++++++++++++-------
 3 files changed, 47 insertions(+), 26 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
index 2f7f8ebbae..68cd3623c0 100644
--- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
@@ -150,7 +150,8 @@ class MapDatasetTest(test.TestCase):
               results.append(sess.run(get_next))
             except errors.OutOfRangeError:
               return
-        threads = [self.checkedThread(target=iterator_thread) for _ in range(8)]
+        threads = [self.checkedThread(target=iterator_thread)
+                   for _ in range(64)]
         for t in threads:
           t.start()
         for t in threads:
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index b2eaaa3492..70e66280fd 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -5216,6 +5216,7 @@ tf_kernel_library(
     srcs = ["iterator_ops.cc"],
     deps = [
         ":dataset",
+        ":ops_util",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:dataset_ops_op_lib",
         "//tensorflow/core:framework",
diff --git a/tensorflow/core/kernels/iterator_ops.cc b/tensorflow/core/kernels/iterator_ops.cc
index 7f1560e9f7..a6d9ddd086 100644
--- a/tensorflow/core/kernels/iterator_ops.cc
+++ b/tensorflow/core/kernels/iterator_ops.cc
@@ -18,7 +18,10 @@ limitations under the License.
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/resource_op_kernel.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/lib/core/threadpool.h"
 #include "tensorflow/core/lib/random/random.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 
 namespace tensorflow {
 
@@ -282,38 +285,54 @@ class OneShotIteratorOp : public OpKernel {
   IteratorResource* iterator_resource_ = nullptr;
 };
 
-class IteratorGetNextOp : public OpKernel {
+class IteratorGetNextOp : public AsyncOpKernel {
  public:
-  explicit IteratorGetNextOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
-
-  // TODO(mrry): Convert this to an async op, because
-  // `iterator->GetNext()` could trigger long-running operations
-  // (e.g. a QueueDequeue or a remote read).
-  void Compute(OpKernelContext* ctx) override {
+  explicit IteratorGetNextOp(OpKernelConstruction* ctx)
+      : AsyncOpKernel(ctx),
+        thread_pool_(new thread::ThreadPool(
+            ctx->env(), ThreadOptions(),
+            strings::StrCat("iterator_get_next_thread_",
+                            SanitizeThreadSuffix(def().name())),
+            1 /* num_threads */, false /* low_latency_hint */)) {}
+
+  void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
     IteratorResource* iterator;
     OP_REQUIRES_OK(ctx,
                    LookupResource(ctx, HandleFromInput(ctx, 0), &iterator));
-    core::ScopedUnref unref_iterator(iterator);
-
-    std::vector<Tensor> components;
-    bool end_of_sequence;
-
-    IteratorContext::Params params;
-    params.env = ctx->env();
-    params.step_id = ctx->step_id();
-    params.resource_manager = ctx->resource_manager();
-    params.runner = *(ctx->runner());
-    IteratorContext iter_ctx(std::move(params));
 
-    OP_REQUIRES_OK(ctx,
-                   iterator->GetNext(&iter_ctx, &components, &end_of_sequence));
-    OP_REQUIRES(ctx, !end_of_sequence, errors::OutOfRange("End of sequence"));
+    // The call to `iterator->GetNext()` may block and depend on an
+    // inter-op thread pool thread, so we issue the call from the
+    // owned thread pool.
+    thread_pool_->Schedule([this, ctx, iterator, done]() {
+      core::ScopedUnref unref_iterator(iterator);
+
+      std::vector<Tensor> components;
+      bool end_of_sequence;
+
+      IteratorContext::Params params;
+      params.env = ctx->env();
+      params.step_id = ctx->step_id();
+      params.resource_manager = ctx->resource_manager();
+      params.runner = *(ctx->runner());
+      IteratorContext iter_ctx(std::move(params));
+
+      OP_REQUIRES_OK_ASYNC(
+          ctx, iterator->GetNext(&iter_ctx, &components, &end_of_sequence),
+          done);
+      OP_REQUIRES_ASYNC(ctx, !end_of_sequence,
+                        errors::OutOfRange("End of sequence"), done);
+
+      for (int i = 0; i < components.size(); ++i) {
+        // TODO(mrry): Check that the shapes match the shape attrs.
+        ctx->set_output(i, components[i]);
+      }
 
-    for (int i = 0; i < components.size(); ++i) {
-      // TODO(mrry): Check that the shapes match the shape attrs.
-      ctx->set_output(i, components[i]);
-    }
+      done();
+    });
   }
+
+ private:
+  std::unique_ptr<thread::ThreadPool> thread_pool_;
 };
 
 class IteratorDisposeOp : public OpKernel {
-- 
GitLab


From cc969e3ba683ad543f2924f30eb381ee2cc4deff Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Fri, 2 Jun 2017 20:22:41 -0700
Subject: [PATCH 0032/1375] [tf contrib seq2seq] Expand tile_batch to handle
 nested structures.

This allows it to properly tile the initial wrapper state when using
BeamSearchDecoder with AttentionWrapper.  Unit tests updated to show this use.

PiperOrigin-RevId: 157903115
---
 .../kernel_tests/beam_search_decoder_test.py  | 10 +++-
 .../seq2seq/python/ops/beam_search_decoder.py | 49 +++++++++++--------
 2 files changed, 37 insertions(+), 22 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
index aeafe7c3e5..3d0627467a 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py
@@ -226,8 +226,8 @@ class TestBeamStep(test.TestCase):
 class BeamSearchDecoderTest(test.TestCase):
 
   def _testDynamicDecodeRNN(self, time_major, has_attention):
-    encoder_sequence_length = [3, 2, 3, 1, 1]
-    decoder_sequence_length = [2, 0, 1, 2, 3]
+    encoder_sequence_length = np.array([3, 2, 3, 1, 1])
+    decoder_sequence_length = np.array([2, 0, 1, 2, 3])
     batch_size = 5
     decoder_max_time = 4
     input_depth = 7
@@ -245,6 +245,7 @@ class BeamSearchDecoderTest(test.TestCase):
       batch_size_tensor = constant_op.constant(batch_size)
       embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32)
       cell = rnn_cell.LSTMCell(cell_depth)
+      initial_state = cell.zero_state(batch_size, dtypes.float32)
       if has_attention:
         inputs = array_ops.placeholder_with_default(
             np.random.randn(batch_size, decoder_max_time,
@@ -258,6 +259,8 @@ class BeamSearchDecoderTest(test.TestCase):
             num_units=attention_depth,
             memory=tiled_inputs,
             memory_sequence_length=tiled_sequence_length)
+        initial_state = beam_search_decoder.tile_batch(
+            initial_state, multiplier=beam_width)
         cell = attention_wrapper.AttentionWrapper(
             cell=cell,
             attention_mechanism=attention_mechanism,
@@ -265,6 +268,9 @@ class BeamSearchDecoderTest(test.TestCase):
             alignment_history=False)
       cell_state = cell.zero_state(
           dtype=dtypes.float32, batch_size=batch_size_tensor * beam_width)
+      if has_attention:
+        cell_state = cell_state.clone(
+            cell_state=initial_state)
       bsd = beam_search_decoder.BeamSearchDecoder(
           cell=cell,
           embedding=embedding,
diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
index 1d96b47dc4..d86275f864 100644
--- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
@@ -72,10 +72,30 @@ class FinalBeamSearchDecoderOutput(
   pass
 
 
+def _tile_batch(t, multiplier):
+  """Core single-tensor implementation of tile_batch."""
+  t = ops.convert_to_tensor(t, name="t")
+  shape_t = array_ops.shape(t)
+  if t.shape.ndims is None or t.shape.ndims < 1:
+    raise ValueError("t must have statically known rank")
+  tiling = [1] * (t.shape.ndims + 1)
+  tiling[1] = multiplier
+  tiled_static_batch_size = (
+      t.shape[0].value * multiplier if t.shape[0].value is not None else None)
+  tiled = array_ops.tile(array_ops.expand_dims(t, 1), tiling)
+  tiled = array_ops.reshape(
+      tiled, array_ops.concat(([shape_t[0] * multiplier], shape_t[1:]), 0))
+  tiled.set_shape(
+      tensor_shape.TensorShape(
+          [tiled_static_batch_size]).concatenate(t.shape[1:]))
+  return tiled
+
+
 def tile_batch(t, multiplier, name=None):
-  """Tile the batch dimension of tensor t.
+  """Tile the batch dimension of a (possibly nested structure of) tensor(s) t.
 
-  This function takes a tensor t shaped `[batch_size, s0, s1, ...]` composed of
+  For each tensor t in a (possibly nested structure) of tensors,
+  this function takes a tensor t shaped `[batch_size, s0, s1, ...]` composed of
   minibatch entries `t[0], ..., t[batch_size - 1]` and tiles it to have a shape
   `[batch_size * multiplier, s0, s1, ...]` composed of minibatch entries
   `t[0], t[0], ..., t[1], t[1], ...` where each minibatch entry is repeated
@@ -87,27 +107,16 @@ def tile_batch(t, multiplier, name=None):
     name: Name scope for any created operations.
 
   Returns:
-    A `Tensor` shaped `[batch_size * multiplier, ...]`.
+    A (possibly nested structure of) `Tensor` shaped
+    `[batch_size * multiplier, ...]`.
 
   Raises:
-    ValueError: if `t` does not have a statically known rank or it's < 1.
+    ValueError: if tensor(s) `t` do not have a statically known rank or
+    the rank is < 1.
   """
-  with ops.name_scope(name, "tile_batch", [t, multiplier]):
-    t = ops.convert_to_tensor(t, name="t")
-    shape_t = array_ops.shape(t)
-    if t.shape.ndims is None or t.shape.ndims < 1:
-      raise ValueError("t must have statically known rank")
-    tiling = [1] * (t.shape.ndims + 1)
-    tiling[1] = multiplier
-    tiled_static_batch_size = (
-        t.shape[0].value * multiplier if t.shape[0].value is not None else None)
-    tiled = array_ops.tile(array_ops.expand_dims(t, 1), tiling)
-    tiled = array_ops.reshape(
-        tiled, array_ops.concat(([shape_t[0] * multiplier], shape_t[1:]), 0))
-    tiled.set_shape(
-        tensor_shape.TensorShape(
-            [tiled_static_batch_size]).concatenate(t.shape[1:]))
-    return tiled
+  flat_t = nest.flatten(t)
+  with ops.name_scope(name, "tile_batch", flat_t + [multiplier]):
+    return nest.map_structure(lambda t_: _tile_batch(t_, multiplier), t)
 
 
 def _check_maybe(t):
-- 
GitLab


From a823c8d40185bf6ba0ec47488f8e9fb20de1d20e Mon Sep 17 00:00:00 2001
From: Toby Boyd <tobyboyd@google.com>
Date: Mon, 5 Jun 2017 09:52:38 -0700
Subject: [PATCH 0033/1375] Fixed triggering create device multiple times

PiperOrigin-RevId: 158025196
---
 .../python/keras/layers/convolutional_test.py | 25 +++++++-----
 .../keras/python/keras/layers/pooling_test.py | 21 +++++-----
 tensorflow/python/layers/convolutional.py     | 40 ++-----------------
 tensorflow/python/layers/pooling.py           | 17 +-------
 4 files changed, 31 insertions(+), 72 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py b/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py
index 3b7f31a3e9..131637f03f 100644
--- a/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py
@@ -113,17 +113,20 @@ class Conv2DTest(test.TestCase):
         if padding == 'same' and strides != (1, 1):
           continue
 
-        with self.test_session():
-          testing_utils.layer_test(
-              keras.layers.Conv2D,
-              kwargs={
-                  'filters': filters,
-                  'kernel_size': kernel_size,
-                  'padding': padding,
-                  'strides': strides,
-                  'data_format': 'channels_first'
-              },
-              input_shape=(num_samples, stack_size, num_row, num_col))
+        with self.test_session(use_gpu=True):
+          # Only runs on GPU with CUDA, channels_first is not supported on CPU.
+          # TODO(b/62340061): Support channels_first on CPU.
+          if test.is_gpu_available(cuda_only=True):
+            testing_utils.layer_test(
+                keras.layers.Conv2D,
+                kwargs={
+                    'filters': filters,
+                    'kernel_size': kernel_size,
+                    'padding': padding,
+                    'strides': strides,
+                    'data_format': 'channels_first'
+                },
+                input_shape=(num_samples, stack_size, num_row, num_col))
 
   def test_convolution_2d_regularization(self):
     # regularizers
diff --git a/tensorflow/contrib/keras/python/keras/layers/pooling_test.py b/tensorflow/contrib/keras/python/keras/layers/pooling_test.py
index 76eaf50138..6808348414 100644
--- a/tensorflow/contrib/keras/python/keras/layers/pooling_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/pooling_test.py
@@ -100,15 +100,18 @@ class Pooling2DTest(test.TestCase):
                   'padding': 'valid',
                   'pool_size': (3, 3)},
           input_shape=(3, 5, 6, 4))
-      testing_utils.layer_test(
-          keras.layers.AveragePooling2D,
-          kwargs={
-              'strides': (1, 1),
-              'padding': 'valid',
-              'pool_size': (2, 2),
-              'data_format': 'channels_first'
-          },
-          input_shape=(3, 4, 5, 6))
+      # Only runs on GPU with CUDA, channels_first is not supported on CPU.
+      # TODO(b/62340061): Support channels_first on CPU.
+      if test.is_gpu_available(cuda_only=True):
+        testing_utils.layer_test(
+            keras.layers.AveragePooling2D,
+            kwargs={
+                'strides': (1, 1),
+                'padding': 'valid',
+                'pool_size': (2, 2),
+                'data_format': 'channels_first'
+            },
+            input_shape=(3, 4, 5, 6))
 
 
 class Pooling3DTest(test.TestCase):
diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index b61168695a..49dcd2370c 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -149,39 +149,13 @@ class _Conv(base.Layer):
     self.built = True
 
   def call(self, inputs):
-    if (self.data_format == 'channels_first' and
-        not framework.test_util.gpu_device_name()):
-      # `nn.convolution` is not implemented on CPU for `channels_first` format.
-      # In cases where we are most likely running on CPU using `channels_first`,
-      # we reshape the inputs to use `channels_last` (and reshape them back
-      # afterwards). This is a temporary fix; a better solution would be a fix
-      # at the op level.
-      # TODO(chollet): remove this when `nn.convolution` is feature-complete.
-      data_format = 'channels_last'
-      if self.rank == 1:
-        inputs = array_ops.transpose(inputs, (0, 2, 1))
-      elif self.rank == 2:
-        inputs = array_ops.transpose(inputs, (0, 2, 3, 1))
-      elif self.rank == 3:
-        inputs = array_ops.transpose(inputs, (0, 2, 3, 4, 1))
-    else:
-      data_format = self.data_format
     outputs = nn.convolution(
         input=inputs,
         filter=self.kernel,
         dilation_rate=self.dilation_rate,
         strides=self.strides,
         padding=self.padding.upper(),
-        data_format=utils.convert_data_format(data_format,
-                                              self.rank + 2))
-    if (self.data_format == 'channels_first' and
-        not framework.test_util.gpu_device_name()):
-      if self.rank == 1:
-        outputs = array_ops.transpose(outputs, (0, 2, 1))
-      elif self.rank == 2:
-        outputs = array_ops.transpose(outputs, (0, 3, 1, 2))
-      elif self.rank == 3:
-        outputs = array_ops.transpose(outputs, (0, 4, 1, 2, 3))
+        data_format=utils.convert_data_format(self.data_format, self.rank + 2))
 
     if self.bias is not None:
       if self.data_format == 'channels_first':
@@ -202,18 +176,10 @@ class _Conv(base.Layer):
                                          [outputs_shape[0], outputs_shape[1],
                                           outputs_shape[2] * outputs_shape[3],
                                           outputs_shape[4]])
-          outputs_4d = nn.bias_add(
-              outputs_4d,
-              self.bias,
-              data_format=utils.convert_data_format(self.data_format, 4))
+          outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')
           outputs = array_ops.reshape(outputs_4d, outputs_shape)
       else:
-        outputs = nn.bias_add(
-            outputs,
-            self.bias,
-            data_format=utils.convert_data_format(self.data_format, 4))
-        # Note that we passed rank=4 because bias_add will only accept
-        # NHWC and NCWH even if the rank of the inputs is 3 or 5.
+        outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')
 
     if self.activation is not None:
       return self.activation(outputs)
diff --git a/tensorflow/python/layers/pooling.py b/tensorflow/python/layers/pooling.py
index 6cd644b642..e903afa0a8 100644
--- a/tensorflow/python/layers/pooling.py
+++ b/tensorflow/python/layers/pooling.py
@@ -262,16 +262,7 @@ class _Pooling2D(base.Layer):
     self.input_spec = base.InputSpec(ndim=4)
 
   def call(self, inputs):
-    if (self.data_format == 'channels_first' and
-        not framework.test_util.gpu_device_name()):
-      # `nn.convolution` is not implemented on CPU for `channels_first` format.
-      # TODO(chollet): remove this when `nn.convolution` is feature-complete.
-      data_format = 'channels_last'
-      inputs = array_ops.transpose(inputs, (0, 2, 3, 1))
-    else:
-      data_format = self.data_format
-
-    if data_format == 'channels_last':
+    if self.data_format == 'channels_last':
       pool_shape = (1,) + self.pool_size + (1,)
       strides = (1,) + self.strides + (1,)
     else:
@@ -282,11 +273,7 @@ class _Pooling2D(base.Layer):
         ksize=pool_shape,
         strides=strides,
         padding=self.padding.upper(),
-        data_format=utils.convert_data_format(data_format, 4))
-
-    if (self.data_format == 'channels_first' and
-        not framework.test_util.gpu_device_name()):
-      outputs = array_ops.transpose(outputs, (0, 3, 1, 2))
+        data_format=utils.convert_data_format(self.data_format, 4))
     return outputs
 
   def _compute_output_shape(self, input_shape):
-- 
GitLab


From 51c404968a442fe1e561c3f5d672cf6eb2772e44 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Mon, 5 Jun 2017 11:54:49 -0700
Subject: [PATCH 0034/1375] Fix memory leak in python caused by @tf_should_use.

The issue is that python's GC has trouble collecting objects with __del__ methods.

The solution is two pronged:
* Keep track of usage state outside of the class, via a dict mapping
  id(object) => state
* Remove __del__ (this was the source: python's GC couldn't collect wrapped
  objects), and instead use weakref.finalize to emit warnings just as the object
  is being garbage collected.
* Added tests for garbage collection [they were failing before i fixed the issue]

PiperOrigin-RevId: 158042388
---
 tensorflow/python/util/tf_should_use.py       | 93 +++++++++++++------
 tensorflow/python/util/tf_should_use_test.py  | 33 ++++---
 .../ci_build/install/install_pip_packages.sh  |  3 +
 .../install/install_python3.5_pip_packages.sh |  3 +
 tensorflow/tools/pip_package/setup.py         |  1 +
 5 files changed, 94 insertions(+), 39 deletions(-)

diff --git a/tensorflow/python/util/tf_should_use.py b/tensorflow/python/util/tf_should_use.py
index 88df3351e6..05c99856d2 100644
--- a/tensorflow/python/util/tf_should_use.py
+++ b/tensorflow/python/util/tf_should_use.py
@@ -17,14 +17,52 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import functools
+import itertools
 import traceback
 import types
 
+import six  # pylint: disable=unused-import
+
+from backports import weakref  # pylint: disable=g-bad-import-order
+
 from tensorflow.python.platform import tf_logging
 from tensorflow.python.util import tf_decorator
 
 
+class _RefInfoField(
+    collections.namedtuple(
+        '_RefInfoField', ('type_', 'repr_', 'creation_stack', 'object_used'))):
+  pass
+
+
+# Thread-safe up to int32max/2 thanks to python's GIL; and may be safe even for
+# higher values in Python 3.4+.  We don't expect to ever count higher than this.
+# https://mail.python.org/pipermail/python-list/2005-April/342279.html
+_REF_ITER = itertools.count()
+
+# Dictionary mapping id(obj) => _RefInfoField.
+_REF_INFO = {}
+
+
+def _deleted(obj_id, fatal_error):
+  obj = _REF_INFO[obj_id]
+  del _REF_INFO[obj_id]
+  if not obj.object_used:
+    if fatal_error:
+      logger = tf_logging.fatal
+    else:
+      logger = tf_logging.error
+    logger(
+        '==================================\n'
+        'Object was never used (type %s):\n%s\nIf you want to mark it as '
+        'used call its "mark_used()" method.\nIt was originally created '
+        'here:\n%s\n'
+        '==================================' %
+        (obj.type_, obj.repr_, obj.creation_stack))
+
+
 def _add_should_use_warning(x, fatal_error=False):
   """Wraps object x so that if it is never used, a warning is logged.
 
@@ -39,14 +77,14 @@ def _add_should_use_warning(x, fatal_error=False):
   """
   if x is None:  # special corner case where x is None
     return x
-  has_been_used = getattr(x, '_tf_object_has_been_used', None)
-  if has_been_used is not None:
-    x._tf_object_has_been_used = has_been_used  # pylint: disable=protected-access
+  if hasattr(x, '_tf_ref_id'):  # this is already a TFShouldUseWarningWrapper
     return x
 
   def override_method(method):
     def fn(self, *args, **kwargs):
-      self._tf_object_has_been_used = True  # pylint: disable=protected-access
+      # pylint: disable=protected-access
+      _REF_INFO[self._tf_ref_id] = _REF_INFO[self._tf_ref_id]._replace(
+          object_used=True)
       return method(self, *args, **kwargs)
     return fn
 
@@ -55,38 +93,36 @@ def _add_should_use_warning(x, fatal_error=False):
 
     def __init__(self, true_self):
       self.__dict__ = true_self.__dict__
-      stack = [x.strip() for x in traceback.format_stack()]
+      stack = [s.strip() for s in traceback.format_stack()]
       # Remove top three stack entries from adding the wrapper
-      self._tf_object_creation_stack = '\n'.join(stack[:-3])
-      self._tf_object_has_been_used = False
+      self.creation_stack = '\n'.join(stack[:-3])
+      self._tf_ref_id = next(_REF_ITER)
+      _REF_INFO[self._tf_ref_id] = _RefInfoField(
+          type_=type(x),
+          repr_=repr(x),
+          creation_stack=stack,
+          object_used=False)
+
+      # Create a finalizer for self, which will be called when self is
+      # garbage collected.  Can't add self as the args because the
+      # loop will break garbage collection.  We keep track of
+      # ourselves via python ids.
+      weakref.finalize(self, _deleted, self._tf_ref_id, fatal_error)
 
     # Not sure why this pylint warning is being used; this is not an
     # old class form.
     # pylint: disable=super-on-old-class
     def __getattribute__(self, name):
-      if name != '_tf_object_has_been_used':
-        self._tf_object_has_been_used = True
+      if name == '_tf_ref_id':
+        return super(TFShouldUseWarningWrapper, self).__getattribute__(name)
+      if self._tf_ref_id in _REF_INFO:
+        _REF_INFO[self._tf_ref_id] = _REF_INFO[self._tf_ref_id]._replace(
+            object_used=True)
       return super(TFShouldUseWarningWrapper, self).__getattribute__(name)
 
-    def __del__(self):
-      if not self._tf_object_has_been_used:
-        if fatal_error:
-          logger = tf_logging.fatal
-        else:
-          logger = tf_logging.error
-        logger(
-            '==================================\n'
-            'Object was never used (type %s):\n%s\nIf you want to mark it as '
-            'used call its "mark_used()" method.\nIt was originally created '
-            'here:\n%s\n'
-            '==================================' %
-            (type(x), x, self._tf_object_creation_stack))
-
-      if hasattr(super(TFShouldUseWarningWrapper, self), '__del__'):
-        return super(TFShouldUseWarningWrapper, self).__del__()
-
     def mark_used(self, *args, **kwargs):
-      self._tf_object_has_been_used = True
+      _REF_INFO[self._tf_ref_id] = _REF_INFO[self._tf_ref_id]._replace(
+          object_used=True)
       if hasattr(super(TFShouldUseWarningWrapper, self), 'mark_used'):
         return super(TFShouldUseWarningWrapper, self).mark_used(*args, **kwargs)
     # pylint: enable=super-on-old-class
@@ -102,7 +138,8 @@ def _add_should_use_warning(x, fatal_error=False):
 
   wrapped = TFShouldUseWarningWrapper(x)
   wrapped.__doc__ = x.__doc__  # functools.wraps fails on some objects.
-  wrapped._tf_object_has_been_used = False   # pylint: disable=protected-access
+  ref_id = wrapped._tf_ref_id  # pylint: disable=protected-access
+  _REF_INFO[ref_id] = _REF_INFO[ref_id]._replace(object_used=False)
   return wrapped
 
 
diff --git a/tensorflow/python/util/tf_should_use_test.py b/tensorflow/python/util/tf_should_use_test.py
index 71d48e3dde..c826874400 100644
--- a/tensorflow/python/util/tf_should_use_test.py
+++ b/tensorflow/python/util/tf_should_use_test.py
@@ -20,6 +20,7 @@ from __future__ import division
 from __future__ import print_function
 
 import contextlib
+import gc
 import sys
 
 from tensorflow.python.framework import constant_op
@@ -45,7 +46,7 @@ def reroute_error(captured):
 class TfShouldUseTest(test.TestCase):
 
   def testAddShouldUseWarningWhenNotUsed(self):
-    c = constant_op.constant(0, name='blah')
+    c = constant_op.constant(0, name='blah0')
     captured = []
     with reroute_error(captured):
       def in_this_function():
@@ -53,44 +54,52 @@ class TfShouldUseTest(test.TestCase):
         del h
       in_this_function()
     self.assertIn('Object was never used', '\n'.join(captured))
-    self.assertIn('blah:0', '\n'.join(captured))
+    self.assertIn('blah0:0', '\n'.join(captured))
     self.assertIn('in_this_function', '\n'.join(captured))
+    gc.collect()
+    self.assertFalse(gc.garbage)
 
-  def _testAddShouldUseWarningWhenUsed(self, fn):
-    c = constant_op.constant(0, name='blah')
+  def _testAddShouldUseWarningWhenUsed(self, fn, name):
+    c = constant_op.constant(0, name=name)
     captured = []
     with reroute_error(captured):
       h = tf_should_use._add_should_use_warning(c)
       fn(h)
       del h
     self.assertNotIn('Object was never used', '\n'.join(captured))
-    self.assertNotIn('blah:0', '\n'.join(captured))
+    self.assertNotIn('%s:0' % name, '\n'.join(captured))
 
   def testAddShouldUseWarningWhenUsedWithAdd(self):
     def add(h):
       _ = h + 1
-    self._testAddShouldUseWarningWhenUsed(add)
+    self._testAddShouldUseWarningWhenUsed(add, name='blah_add')
+    gc.collect()
+    self.assertFalse(gc.garbage)
 
   def testAddShouldUseWarningWhenUsedWithGetName(self):
     def get_name(h):
       _ = h.name
-    self._testAddShouldUseWarningWhenUsed(get_name)
+    self._testAddShouldUseWarningWhenUsed(get_name, name='blah_get_name')
+    gc.collect()
+    self.assertFalse(gc.garbage)
 
   def testShouldUseResult(self):
     @tf_should_use.should_use_result
     def return_const(value):
-      return constant_op.constant(value, name='blah')
+      return constant_op.constant(value, name='blah2')
     captured = []
     with reroute_error(captured):
       return_const(0.0)
     self.assertIn('Object was never used', '\n'.join(captured))
-    self.assertIn('blah:0', '\n'.join(captured))
+    self.assertIn('blah2:0', '\n'.join(captured))
     self.assertIn('return_const', '\n'.join(captured))
+    gc.collect()
+    self.assertFalse(gc.garbage)
 
   def testShouldUseResultWhenNotReallyUsed(self):
     @tf_should_use.should_use_result
     def return_const(value):
-      return constant_op.constant(value, name='blah')
+      return constant_op.constant(value, name='blah3')
     captured = []
     with reroute_error(captured):
       with self.test_session():
@@ -100,8 +109,10 @@ class TfShouldUseTest(test.TestCase):
         v = constant_op.constant(1.0, name='meh')
         v.eval()
     self.assertIn('Object was never used', '\n'.join(captured))
-    self.assertIn('blah:0', '\n'.join(captured))
+    self.assertIn('blah3:0', '\n'.join(captured))
     self.assertIn('return_const', '\n'.join(captured))
+    gc.collect()
+    self.assertFalse(gc.garbage)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index b8f9fc8453..8768852dc7 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -85,3 +85,6 @@ pip2 install mock
 
 pip2 install portpicker
 pip3 install portpicker
+
+pip2 install backports.weakref==1.0rc1
+pip3 install backports.weakref==1.0rc1
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index e7e2d256cd..edfc4e3a98 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -89,3 +89,6 @@ pip3.5 install wheel==0.29.0
 pip3.5 install portpicker
 
 pip3.5 install werkzeug
+
+pip3.5 install backports.weakref==1.0rc1
+
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index a85a220270..a1676203c7 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -39,6 +39,7 @@ REQUIRED_PACKAGES = [
     'html5lib == 0.9999999',  # identical to 1.0b8
     'markdown == 2.2.0',
     'bleach == 1.5.0',
+    'backports.weakref == 1.0rc1',
 ]
 
 project_name = 'tensorflow'
-- 
GitLab


From 0880e87b56919a854ef131a16c3b1b556499e2fc Mon Sep 17 00:00:00 2001
From: gunan <gunan@google.com>
Date: Thu, 1 Jun 2017 11:10:30 -0700
Subject: [PATCH 0035/1375] Python 3.6 support on windows. (#10356)

* Python 3.6 support on windows.

* Fix typo in README.md

* Make environment configurable for windows gpu build.
---
 README.md                                          |  4 ++--
 RELEASE.md                                         |  1 +
 .../tools/ci_build/windows/gpu/cmake/run_build.bat | 14 ++++++++------
 .../tools/ci_build/windows/gpu/cmake/run_py.bat    |  2 +-
 4 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index a88e72880b..b8cb4ba8ba 100644
--- a/README.md
+++ b/README.md
@@ -38,8 +38,8 @@ People who are a little more adventurous can also try our nightly binaries:
 * Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
 * Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
 * Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=cpu,OS=windows/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=cpu,OS=windows/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=gpu,OS=windows/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/DEVICE=gpu,OS=windows/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
 * Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
diff --git a/RELEASE.md b/RELEASE.md
index f134567266..64d9069e61 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,6 +1,7 @@
 # Release 1.2.0
 
 ## Major Features and Improvements
+* Python 3.6 support on Windows.
 * Added `tf.layers.conv3d_transpose` layer for spatio temporal deconvolution.
 * Added `tf.Session.make_callable()`, which provides a lower overhead means of running a similar step multiple times.
 * Added ibverbs-based RDMA support to contrib (courtesy @junshi15 from Yahoo).
diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat
index f124012edc..b4f9cc8476 100644
--- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat
+++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat
@@ -22,12 +22,14 @@ CALL "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat"
 :: Turn echo back on, above script turns it off.
 ECHO ON
 
-:: Some common variables to be shared between runs.
-SET CMAKE_EXE="C:\Program Files\cmake\bin\cmake.exe"
-SET SWIG_EXE="C:\swigwin-3.0.10\swig.exe"
-SET PY_EXE="C:\Program Files\Anaconda3\python.exe"
-SET PY_LIB="C:\Program Files\Anaconda3\libs\python35.lib"
-SET CUDNN_HOME="c:\tools\cuda"
+:: Set environment variables to be shared between runs. Do not override if they
+:: are set already.
+
+IF DEFINED CMAKE_EXE (ECHO CMAKE_EXE is set to %CMAKE_EXE%) ELSE (SET CMAKE_EXE="C:\Program Files\cmake\bin\cmake.exe")
+IF DEFINED SWIG_EXE (ECHO SWIG_EXE is set to %SWIG_EXE%) ELSE (SET SWIG_EXE="C:\swigwin-3.0.10\swig.exe")
+IF DEFINED PY_EXE (ECHO PY_EXE is set to %PY_EXE%) ELSE (SET PY_EXE="C:\Program Files\Anaconda3\python.exe")
+IF DEFINED PY_LIB (ECHO PY_LIB is set to %PY_LIB%) ELSE (SET PY_LIB="C:\Program Files\Anaconda3\libs\python35.lib")
+IF DEFINED CUDNN_HOME (ECHO CUDNN_HOME is set to %CUDNN_HOME%) ELSE (SET CUDNN_HOME="c:\tools\cuda")
 
 SET CMAKE_DIR=%REPO_ROOT%\tensorflow\contrib\cmake
 SET MSBUILD_EXE="C:\Program Files (x86)\MSBuild\14.0\Bin\msbuild.exe"
diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
index 9307ebb66b..ba2d939b5f 100644
--- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
@@ -22,7 +22,7 @@ CD %BUILD_DIR%
 SET BUILD_CC_TESTS=OFF
 SET BUILD_PYTHON_TESTS=ON
 
-SET PIP_EXE="C:\Program Files\Anaconda3\Scripts\pip.exe"
+IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Program Files\Anaconda3\Scripts\pip.exe")
 
 :: Run the CMAKE build to build the pip package.
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\gpu\cmake\run_build.bat
-- 
GitLab


From 7400ddc90f3de004f03c9d06dff37ef0f046fc04 Mon Sep 17 00:00:00 2001
From: Niranjan Hasabnis <niranjan.hasabnis@intel.com>
Date: Wed, 17 May 2017 17:37:43 -0700
Subject: [PATCH 0036/1375] Fix: config MKL build error - mkl_layout_pass.cc
 has missing header dependencies (#9986)

---
 tensorflow/core/BUILD | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 343d6faf5a..284195f8fc 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1556,8 +1556,6 @@ tf_cuda_library(
         "graph/graph_constructor.cc",
         "graph/graph_def_builder.cc",
         "graph/graph_partition.cc",
-        "graph/mkl_layout_pass.cc",
-        "graph/mkl_tfconversion_pass.cc",
         "graph/node_builder.cc",
         "graph/optimizer_cse.cc",
         "graph/subgraph.cc",
@@ -1619,6 +1617,8 @@ tf_cuda_library(
         "common_runtime/threadpool_device.cc",
         "common_runtime/threadpool_device_factory.cc",
         "graph/gradients.cc",
+        "graph/mkl_layout_pass.cc",
+        "graph/mkl_tfconversion_pass.cc",
         "graph/quantize_training.cc",
         "public/session.h",
         "public/session_options.h",
-- 
GitLab


From 7ea7e5ea32ed51b837244f8c967e9f6b640bf02d Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Mon, 5 Jun 2017 19:08:05 -0700
Subject: [PATCH 0037/1375] Updating tag to 1.2.0-rc2.

---
 README.md                                     | 12 +++++-----
 tensorflow/core/public/version.h              |  2 +-
 tensorflow/docs_src/install/install_c.md      |  2 +-
 tensorflow/docs_src/install/install_go.md     |  2 +-
 tensorflow/docs_src/install/install_java.md   | 18 +++++++--------
 tensorflow/docs_src/install/install_linux.md  | 22 +++++++++----------
 tensorflow/docs_src/install/install_mac.md    | 10 ++++-----
 .../docs_src/install/install_sources.md       |  4 ++--
 .../docs_src/install/install_windows.md       |  4 ++--
 tensorflow/tools/pip_package/setup.py         |  2 +-
 10 files changed, 39 insertions(+), 39 deletions(-)

diff --git a/README.md b/README.md
index b8cb4ba8ba..e7dbf57b25 100644
--- a/README.md
+++ b/README.md
@@ -34,12 +34,12 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
 * Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 818e93a238..58780c0ae1 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX "-rc1"
+#define TF_VERSION_SUFFIX "-rc2"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 4630d6b278..612f271f94 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 6b70f634f6..c9b8dffadb 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc2.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 141a5e9d38..c172a4c820 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0-rc1</version>
+  <version>1.2.0-rc2</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0-rc1</version>
+                 <version>1.2.0-rc2</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc1.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc2.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0-rc1.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.0-rc2.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0-rc2.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following comand line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0-rc2.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index bf59ef8a16..d0e7970898 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -271,7 +271,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -458,7 +458,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -626,14 +626,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -645,14 +645,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -664,14 +664,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -683,14 +683,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index f845a3540a..44e2b37dfd 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -109,7 +109,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -230,7 +230,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 865412fcd2..6f784c5180 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -320,10 +320,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0rc1 on Linux:
+for TensorFlow 1.2.0rc2 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc1-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc2-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 95c6b8dd39..42820660ee 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -114,12 +114,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc1-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc1-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index a1676203c7..712a5028d5 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0-rc1'
+_VERSION = '1.2.0-rc2'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
-- 
GitLab


From 2aba04b894b099b2692494385ac1b5c7976171e3 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenlavoie@gmail.com>
Date: Wed, 7 Jun 2017 15:08:31 -0700
Subject: [PATCH 0038/1375] Update __init__.py

---
 tensorflow/python/__init__.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index d4f8b8b2f8..19d2eb8174 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -56,7 +56,6 @@ from tensorflow.core.framework.attr_value_pb2 import *
 from tensorflow.core.protobuf.meta_graph_pb2 import TensorInfo
 from tensorflow.core.protobuf.config_pb2 import *
 from tensorflow.core.protobuf.tensorflow_server_pb2 import *
-from tensorflow.core.protobuf.rewriter_config_pb2 import *
 from tensorflow.core.util.event_pb2 import *
 
 # Framework
-- 
GitLab


From 36b9ffe9182d04d66e44085c0b6c1f43e40229ba Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenlavoie@gmail.com>
Date: Wed, 7 Jun 2017 15:12:00 -0700
Subject: [PATCH 0039/1375] Update tensorflow.pbtxt

---
 tensorflow/tools/api/golden/tensorflow.pbtxt | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index f0fdd69390..be5a2a1806 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -8,10 +8,6 @@ tf_module {
     name: "AttrValue"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
   }
-  member {
-    name: "AutoParallelOptions"
-    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
-  }
   member {
     name: "COMPILER_VERSION"
     mtype: "<type \'str\'>"
@@ -164,10 +160,6 @@ tf_module {
     name: "RegisterGradient"
     mtype: "<type \'type\'>"
   }
-  member {
-    name: "RewriterConfig"
-    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
-  }
   member {
     name: "RunMetadata"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
-- 
GitLab


From a0e5bcd1f3ffd0391331aae859670629523d8f39 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenlavoie@gmail.com>
Date: Wed, 7 Jun 2017 15:21:09 -0700
Subject: [PATCH 0040/1375] Delete tensorflow.-rewriter-config.pbtxt

---
 .../golden/tensorflow.-rewriter-config.pbtxt  | 112 ------------------
 1 file changed, 112 deletions(-)
 delete mode 100644 tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt

diff --git a/tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt b/tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt
deleted file mode 100644
index 34d2e17612..0000000000
--- a/tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt
+++ /dev/null
@@ -1,112 +0,0 @@
-path: "tensorflow.RewriterConfig"
-tf_class {
-  is_instance: "<class \'tensorflow.core.protobuf.rewriter_config_pb2.RewriterConfig\'>"
-  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
-  member {
-    name: "AUTO_PARALLEL_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "CONSTANT_FOLDING_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "DESCRIPTOR"
-    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
-  }
-  member {
-    name: "DISABLE_MODEL_PRUNING_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "Extensions"
-    mtype: "<type \'getset_descriptor\'>"
-  }
-  member {
-    name: "MANUAL"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "MEMORY_OPTIMIZATION_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "MemOptType"
-    mtype: "<class \'google.protobuf.internal.enum_type_wrapper.EnumTypeWrapper\'>"
-  }
-  member {
-    name: "NO_MEM_OPT"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "OPTIMIZERS_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "OPTIMIZE_TENSOR_LAYOUT_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member_method {
-    name: "ByteSize"
-  }
-  member_method {
-    name: "Clear"
-  }
-  member_method {
-    name: "ClearExtension"
-  }
-  member_method {
-    name: "ClearField"
-  }
-  member_method {
-    name: "CopyFrom"
-  }
-  member_method {
-    name: "DiscardUnknownFields"
-  }
-  member_method {
-    name: "FindInitializationErrors"
-  }
-  member_method {
-    name: "FromString"
-  }
-  member_method {
-    name: "HasExtension"
-  }
-  member_method {
-    name: "HasField"
-  }
-  member_method {
-    name: "IsInitialized"
-  }
-  member_method {
-    name: "ListFields"
-  }
-  member_method {
-    name: "MergeFrom"
-  }
-  member_method {
-    name: "MergeFromString"
-  }
-  member_method {
-    name: "ParseFromString"
-  }
-  member_method {
-    name: "RegisterExtension"
-  }
-  member_method {
-    name: "SerializePartialToString"
-  }
-  member_method {
-    name: "SerializeToString"
-  }
-  member_method {
-    name: "SetInParent"
-  }
-  member_method {
-    name: "WhichOneof"
-  }
-  member_method {
-    name: "__init__"
-  }
-}
-- 
GitLab


From f4031e828b09fc60bccad074a1363e71c0cbea6b Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenlavoie@gmail.com>
Date: Wed, 7 Jun 2017 15:22:33 -0700
Subject: [PATCH 0041/1375] Delete tensorflow.-auto-parallel-options.pbtxt

---
 .../tensorflow.-auto-parallel-options.pbtxt   | 84 -------------------
 1 file changed, 84 deletions(-)
 delete mode 100644 tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt

diff --git a/tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt
deleted file mode 100644
index c8f3e8fb15..0000000000
--- a/tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt
+++ /dev/null
@@ -1,84 +0,0 @@
-path: "tensorflow.AutoParallelOptions"
-tf_class {
-  is_instance: "<class \'tensorflow.core.protobuf.rewriter_config_pb2.AutoParallelOptions\'>"
-  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
-  member {
-    name: "DESCRIPTOR"
-    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
-  }
-  member {
-    name: "ENABLE_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "Extensions"
-    mtype: "<type \'getset_descriptor\'>"
-  }
-  member {
-    name: "NUM_REPLICAS_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member_method {
-    name: "ByteSize"
-  }
-  member_method {
-    name: "Clear"
-  }
-  member_method {
-    name: "ClearExtension"
-  }
-  member_method {
-    name: "ClearField"
-  }
-  member_method {
-    name: "CopyFrom"
-  }
-  member_method {
-    name: "DiscardUnknownFields"
-  }
-  member_method {
-    name: "FindInitializationErrors"
-  }
-  member_method {
-    name: "FromString"
-  }
-  member_method {
-    name: "HasExtension"
-  }
-  member_method {
-    name: "HasField"
-  }
-  member_method {
-    name: "IsInitialized"
-  }
-  member_method {
-    name: "ListFields"
-  }
-  member_method {
-    name: "MergeFrom"
-  }
-  member_method {
-    name: "MergeFromString"
-  }
-  member_method {
-    name: "ParseFromString"
-  }
-  member_method {
-    name: "RegisterExtension"
-  }
-  member_method {
-    name: "SerializePartialToString"
-  }
-  member_method {
-    name: "SerializeToString"
-  }
-  member_method {
-    name: "SetInParent"
-  }
-  member_method {
-    name: "WhichOneof"
-  }
-  member_method {
-    name: "__init__"
-  }
-}
-- 
GitLab


From 421de4af84f6f9acb584d79092bbb98ef3312a07 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenlavoie@gmail.com>
Date: Wed, 7 Jun 2017 15:55:02 -0700
Subject: [PATCH 0042/1375] Note that RewriterConfig is not subject to API
 guarantees

---
 tensorflow/core/protobuf/rewriter_config.proto | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 753edba4b8..4c61e577d2 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -12,6 +12,9 @@ message AutoParallelOptions {
 }
 
 message RewriterConfig {
+  // Graph rewriting is experimental and subject to change, not subject to any
+  // API guarantees.
+
   bool optimize_tensor_layout = 1;
   bool disable_model_pruning = 2;
   bool constant_folding = 3;
-- 
GitLab


From 0b659e288bb9320a9455ddca2664fa9c02c9ed5f Mon Sep 17 00:00:00 2001
From: Sandip Giri <sgiri@us.ibm.com>
Date: Thu, 8 Jun 2017 04:27:18 -0400
Subject: [PATCH 0043/1375] np.float64("np.inf").astype(np.int32) is negative
 on x86 but positive on ppc64le

---
 tensorflow/python/kernel_tests/cast_op_test.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/kernel_tests/cast_op_test.py b/tensorflow/python/kernel_tests/cast_op_test.py
index 17771e0572..a5d64740c7 100644
--- a/tensorflow/python/kernel_tests/cast_op_test.py
+++ b/tensorflow/python/kernel_tests/cast_op_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 import sys
+import platform
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -146,9 +147,16 @@ class CastOpTest(test.TestCase):
     if sys.byteorder == "big":  
       self._compare(np.inf, np.int32, i4.max, False)  
       self._compare(np.inf, np.int64, i8.max, False)  
-    else:  
-      self._compare(np.inf, np.int32, i4.min, False)  
-      self._compare(np.inf, np.int64, i8.min, False)  
+    else:
+      #np.float64("np.inf").astype(np.int32) is negative on x86 but positive on ppc64le
+      #Numpy link to relevant discussion - https://github.com/numpy/numpy/issues/9040
+      #Tensorflow link to relevant discussion - https://github.com/tensorflow/tensorflow/issues/9360
+      if platform.machine() == "ppc64le":
+        self._compare(-np.inf, np.int32, i4.min, False)
+        self._compare(-np.inf, np.int64, i8.min, False)
+      else:
+        self._compare(np.inf, np.int32, i4.min, False)
+        self._compare(np.inf, np.int64, i8.min, False)  
     self._compare(-np.inf, np.float32, -np.inf, False)
     self._compare(-np.inf, np.float64, -np.inf, False)
     self._compare(-np.inf, np.int32, i4.min, False)
-- 
GitLab


From 626d8d905aa412aaca02d171e5e0b4a1c407656b Mon Sep 17 00:00:00 2001
From: Liangliang He <lliang.he@gmail.com>
Date: Thu, 8 Jun 2017 20:45:08 +0800
Subject: [PATCH 0044/1375] Improve RDMA rendezvous speed

---
 tensorflow/contrib/verbs/rdma.cc              | 55 +++++++++++++++----
 .../contrib/verbs/rdma_rendezvous_mgr.cc      | 42 ++++++++++++--
 tensorflow/contrib/verbs/verbs_util.cc        | 34 ++++++++++++
 tensorflow/contrib/verbs/verbs_util.h         | 10 ++++
 4 files changed, 125 insertions(+), 16 deletions(-)

diff --git a/tensorflow/contrib/verbs/rdma.cc b/tensorflow/contrib/verbs/rdma.cc
index bc687be0ab..6f3a616fe8 100644
--- a/tensorflow/contrib/verbs/rdma.cc
+++ b/tensorflow/contrib/verbs/rdma.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_util.h"
+#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h"
 #include "tensorflow/core/distributed_runtime/session_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
@@ -683,7 +684,6 @@ void RdmaTensorBuffer::SendNextItem() {
                          << " error message: " << status.error_message();
       size_t buffer_size = RdmaMessage::kMessageTotalBytes;
       size_t tensor_bytes = 0;
-      TensorProto proto;
       // Figures out which device the tensor is hosted on.
       Device* src_dev = nullptr;
       Status s = channel_->adapter_->worker_env_->device_mgr->LookupDevice(
@@ -703,21 +703,47 @@ void RdmaTensorBuffer::SendNextItem() {
       CHECK(s.ok()) << "dst device not found";
       AllocatorAttributes dst_alloc_attr;
       dst_alloc_attr.set_on_host(true);
+
+      bool can_memcpy = DataTypeCanUseMemcpy(in.dtype());
       // string tensor needs to be serialized
+      Tensor copy;
+      StringPiece copy_buf;
+      TensorProto proto;
       if (src_dev->tensorflow_gpu_device_info() &&
           (!send_args.alloc_attrs.on_host())) {
         CHECK(send_args.device_context)
-            << "send dev name: " << src_dev->name()
-            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
-        // "val" is on a GPU. Uses GPUUtil to fill the proto.
-        s = VerbsUtil::SetProtoFromGPUSync(
-            in, src_dev, send_args.device_context, &proto, is_dead);
-        CHECK(s.ok()) << "set proto from gpu sync";
+          << "send dev name: " << src_dev->name()
+          << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
+
+        if (can_memcpy) {
+          AllocatorAttributes host_alloc_attrs;
+          host_alloc_attrs.set_gpu_compatible(true);
+          host_alloc_attrs.set_on_host(true);
+          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
+          copy = Tensor(alloc, in.dtype(), in.shape());
+          s = VerbsUtil::CopyGPUTensorToCPUSync(
+              src_dev, send_args.device_context, &in, &copy);
+          CHECK(s.ok()) << "copy tensor from gpu sync";
+          copy_buf = copy.tensor_data();
+        } else {
+          // "val" is on a GPU. Uses GPUUtil to fill the proto.
+          s = VerbsUtil::SetProtoFromGPUSync(
+              in, src_dev, send_args.device_context, &proto, is_dead);
+          CHECK(s.ok()) << "set proto from gpu sync";
+        }
       } else {
         // tensor is in CPU memory.
-        in.AsProtoTensorContent(&proto);
+        if (can_memcpy) {
+          copy_buf = in.tensor_data();
+        } else {
+          in.AsProtoTensorContent(&proto);
+        }
+      }
+      if (can_memcpy) {
+        tensor_bytes = in.TotalBytes();
+      } else {
+        tensor_bytes = proto.ByteSize();
       }
-      tensor_bytes = proto.ByteSize();
       // maybe some margin for string tensor?
       buffer_size += tensor_bytes;
       // prepare message
@@ -771,7 +797,16 @@ void RdmaTensorBuffer::SendNextItem() {
               static_cast<void*>(static_cast<char*>(buffer_) +
                                  RdmaMessage::kTensorBufferStartIndex);
           CHECK(tensor_bytes + RdmaMessage::kTensorBufferStartIndex <= size_);
-          proto.SerializeToArray(output, tensor_bytes);
+          if (can_memcpy) {
+            CHECK(copy_buf.size() == tensor_bytes)
+               << "unexpected tensor size: "
+               << copy_buf.size()
+               << " != "
+               << tensor_bytes;
+            memcpy(output, copy_buf.data(), tensor_bytes);
+          } else {
+            proto.SerializeToArray(output, tensor_bytes);
+          }
         } else {
           buffer_size = RdmaMessage::kMessageTotalBytes;
         }
diff --git a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
index 5871400f26..62a152ff90 100644
--- a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
+++ b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
+#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -99,12 +100,41 @@ void RdmaRemoteRendezvous::RecvFromRemoteAsync(
     if (!rm.is_dead_) {
       void* input = static_cast<char*>(rb->buffer_) +
                     RdmaMessage::kTensorBufferStartIndex;
-      TensorProto proto;
-      CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
-            rb->size_);
-      CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
-          << "fail to parse proto from array";
-      s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
+      bool can_memcpy = DataTypeCanUseMemcpy(rm.data_type_);
+      if (can_memcpy) {
+        AllocatorAttributes host_alloc_attrs;
+        host_alloc_attrs.set_gpu_compatible(true);
+        host_alloc_attrs.set_on_host(true);
+
+        if (dst_dev->tensorflow_gpu_device_info() &&
+            (!recv_args.alloc_attrs.on_host())) {
+          CHECK(recv_args.device_context)
+            << "send dev name: " << src_dev->name()
+            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
+          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
+          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
+          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
+
+          Allocator* dst_alloc = dst_dev->GetAllocator(host_alloc_attrs);
+          Tensor gpu_copy(dst_alloc, rm.data_type_, rm.tensor_shape_);
+          s = VerbsUtil::CopyCPUTensorToGPUSync(&copy, recv_args.device_context,
+                                                dst_dev, &gpu_copy);
+          CHECK(s.ok()) << "copy tensor to gpu sync";
+          val = std::move(gpu_copy);
+        } else {
+          Allocator* alloc = dst_dev->GetAllocator(host_alloc_attrs);
+          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
+          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
+          val = std::move(copy);
+        }
+      } else {
+        TensorProto proto;
+        CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
+              rb->size_);
+        CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
+            << "fail to parse proto from array";
+        s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
+      }
     }
 
     rc->RemoveRecvCallback(key_with_step_id);
diff --git a/tensorflow/contrib/verbs/verbs_util.cc b/tensorflow/contrib/verbs/verbs_util.cc
index c3350f7958..76e44d34a9 100644
--- a/tensorflow/contrib/verbs/verbs_util.cc
+++ b/tensorflow/contrib/verbs/verbs_util.cc
@@ -20,6 +20,40 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 namespace tensorflow {
 
+// static sync wrapper:
+Status VerbsUtil::CopyGPUTensorToCPUSync(Device* gpu_device,
+                              const DeviceContext* device_context,
+                              const Tensor* gpu_tensor,
+                              Tensor* cpu_tensor) {
+  Notification n;
+  Status status;
+  GPUUtil::CopyGPUTensorToCPU(gpu_device, device_context,
+                              gpu_tensor, cpu_tensor,
+                              [&n, &status](const Status& s) {
+                                status = s;
+                                n.Notify();
+                              });
+  n.WaitForNotification();
+  return status;
+}
+
+// static sync wrapper:
+Status VerbsUtil::CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
+                                         const DeviceContext* device_context,
+                                         Device* gpu_device,
+                                         Tensor* gpu_tensor) {
+  Notification n;
+  Status status;
+  GPUUtil::CopyCPUTensorToGPU(cpu_tensor, device_context,
+                              gpu_device, gpu_tensor,
+                              [&n, &status](const Status& s) {
+                                status = s;
+                                n.Notify();
+                              });
+  n.WaitForNotification();
+  return status;
+}
+
 // static sync wrapper:
 Status VerbsUtil::SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                       const DeviceContext* device_context,
diff --git a/tensorflow/contrib/verbs/verbs_util.h b/tensorflow/contrib/verbs/verbs_util.h
index cbc01adae4..d9da396228 100644
--- a/tensorflow/contrib/verbs/verbs_util.h
+++ b/tensorflow/contrib/verbs/verbs_util.h
@@ -28,6 +28,16 @@ class TensorProto;
 
 class VerbsUtil {
  public:
+  // synchronous wrapper of CopyGPUTensorToCPU
+  static Status CopyGPUTensorToCPUSync(Device* gpu_device,
+                                       const DeviceContext* device_context,
+                                       const Tensor* gpu_tensor,
+                                       Tensor* cpu_tensor);
+  // synchronous wrapper of CopyCPUTensorToGPU
+  static Status CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
+                                       const DeviceContext* device_context,
+                                       Device* gpu_device,
+                                       Tensor* gpu_tensor);
   // synchronous wrapper of SetProtoFromGPU
   static Status SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                     const DeviceContext* device_context,
-- 
GitLab


From bf22796d4151e6aa168fc7e698efb08fa86cab43 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <vomjom@vomjom.net>
Date: Wed, 7 Jun 2017 17:21:44 -0700
Subject: [PATCH 0045/1375] Fix numpy 1.13 incompatibilities (#10501)

* Fix numpy 1.13 incompatibilities

* Skip tests with numpy 1.13.0
---
 tensorflow/python/kernel_tests/sparse_ops_test.py | 3 +++
 tensorflow/python/ops/check_ops.py                | 2 +-
 tensorflow/python/ops/embedding_ops.py            | 2 +-
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index bad11a29df..c7014e9209 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+import unittest
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -553,6 +554,7 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
     self._compare(sp_t, reduction_axes, ndims, False)
     self._compare(sp_t, reduction_axes, ndims, True)
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
     sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_shape)
 
@@ -585,6 +587,7 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_sum(sp_t, 2).eval()
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
     np.random.seed(8161)
     test_dims = [(11, 1, 5, 7, 1), (2, 2)]
diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py
index 753999a672..1d853df86c 100644
--- a/tensorflow/python/ops/check_ops.py
+++ b/tensorflow/python/ops/check_ops.py
@@ -726,7 +726,7 @@ def _assert_ranks_condition(
 
   # Attempt to statically defined rank.
   ranks_static = tuple([tensor_util.constant_value(rank) for rank in ranks])
-  if None not in ranks_static:
+  if not any(r is None for r in ranks_static):
     for rank_static in ranks_static:
       if rank_static.ndim != 0:
         raise ValueError('Rank must be a scalar.')
diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py
index 315e7d4b43..1fe2bbf480 100644
--- a/tensorflow/python/ops/embedding_ops.py
+++ b/tensorflow/python/ops/embedding_ops.py
@@ -97,7 +97,7 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
   Raises:
     ValueError: If `params` is empty.
   """
-  if params in (None, (), []):
+  if params is None or params in ((), []):
     raise ValueError("Need at least one param")
   if isinstance(params, variables.PartitionedVariable):
     params = list(params)  # Iterate to get the underlying Variables.
-- 
GitLab


From 9a1aa098371bccc75bde350386795b4335cf6df9 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <vomjom@vomjom.net>
Date: Wed, 7 Jun 2017 23:29:40 -0700
Subject: [PATCH 0046/1375] Delete non-deterministic testEmpty() test (#10512)

---
 tensorflow/python/kernel_tests/matrix_solve_op_test.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/tensorflow/python/kernel_tests/matrix_solve_op_test.py b/tensorflow/python/kernel_tests/matrix_solve_op_test.py
index 07ff53cfe6..e7ae7f714f 100644
--- a/tensorflow/python/kernel_tests/matrix_solve_op_test.py
+++ b/tensorflow/python/kernel_tests/matrix_solve_op_test.py
@@ -96,11 +96,6 @@ class MatrixSolveOpTest(test.TestCase):
             [[1., 0., -1.], [-1., 1., 0.], [0., -1., 1.]])
         linalg_ops.matrix_solve(matrix, matrix).eval()
 
-  def testEmpty(self):
-    with self.test_session():
-      self._verifySolve(np.empty([0, 0]), np.empty([0, 0]))
-      self._verifySolve(np.empty([2, 2]), np.empty([2, 0]))
-
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 323d40fce604e9e31975714197ce4a7064b55dc2 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <vomjom@vomjom.net>
Date: Fri, 9 Jun 2017 13:59:15 -0700
Subject: [PATCH 0047/1375] Revert the new non_max_suppression_v2 op for the
 1.2 release so we can do API review (#10603)

* Revert "Allow tensor as iou_threshold parameter to tf.image.non_max_suppression. (#9887)"

This reverts commit 513b1e4bab3eee797877a1250fa18e3ae9c349ad.

It needs API review before going into an official release.

* Update goldens
---
 .../core/kernels/non_max_suppression_op.cc    | 142 ++++++---------
 .../kernels/non_max_suppression_op_test.cc    | 165 +-----------------
 tensorflow/core/ops/image_ops.cc              |  47 +----
 tensorflow/python/ops/image_ops.py            |   1 -
 tensorflow/python/ops/image_ops_impl.py       |   1 -
 .../tools/api/golden/tensorflow.image.pbtxt   |   4 -
 6 files changed, 57 insertions(+), 303 deletions(-)

diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index dc95f67ff0..4d4851c70c 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -33,7 +33,6 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 
 namespace tensorflow {
-namespace {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 
@@ -90,63 +89,6 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
   return intersection_area / (area_i + area_j - intersection_area);
 }
 
-void DoNonMaxSuppressionOp(OpKernelContext* context,
-                           const Tensor& boxes,
-                           const Tensor& scores,
-                           const Tensor& max_output_size,
-                           const float iou_threshold) {
-  OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
-      errors::InvalidArgument("iou_threshold must be in [0, 1]"));
-  
-  int num_boxes = 0;
-  ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
-  if (!context->status().ok()) {
-    return;
-  }
-
-  const int output_size =
-      std::min(max_output_size.scalar<int>()(), num_boxes);
-  typename TTypes<float, 2>::ConstTensor boxes_data =
-      boxes.tensor<float, 2>();
-
-  std::vector<float> scores_data(num_boxes);
-  std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
-  std::vector<int> sorted_indices;
-  DecreasingArgSort(scores_data, &sorted_indices);
-
-  std::vector<bool> active(num_boxes, true);
-  std::vector<int> selected;
-  int num_active = active.size();
-  for (int i = 0; i < num_boxes; ++i) {
-    if (num_active == 0 || selected.size() >= output_size) break;
-    if (active[i]) {
-      selected.push_back(sorted_indices[i]);
-    } else {
-      continue;
-    }
-    for (int j = i + 1; j < num_boxes; ++j) {
-      if (active[j]) {
-        float iou =
-          ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
-        if (iou > iou_threshold) {
-          active[j] = false;
-          num_active--;
-        }
-      }
-    }
-  }
-
-  // Allocate output tensor
-  Tensor* output = nullptr;
-  TensorShape output_shape({static_cast<int>(selected.size())});
-  OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
-  typename TTypes<int, 1>::Tensor selected_indices_data =
-      output->tensor<int, 1>();
-  std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
-}
-
-} // namespace
-
 template <typename Device>
 class NonMaxSuppressionOp : public OpKernel {
  public:
@@ -156,6 +98,9 @@ class NonMaxSuppressionOp : public OpKernel {
   }
 
   void Compute(OpKernelContext* context) override {
+    OP_REQUIRES(context, iou_threshold_ >= 0 && iou_threshold_ <= 1,
+                errors::InvalidArgument("iou_threshold must be in [0, 1]"));
+
     // boxes: [num_boxes, 4]
     const Tensor& boxes = context->input(0);
     // scores: [num_boxes]
@@ -167,48 +112,59 @@ class NonMaxSuppressionOp : public OpKernel {
         errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                 max_output_size.shape().DebugString()));
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_);
-  }
+    int num_boxes = 0;
+    ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
+    if (!context->status().ok()) {
+      return;
+    }
 
- private:
-  float iou_threshold_;
-};
+    const int output_size =
+        std::min(max_output_size.scalar<int>()(), num_boxes);
+    typename TTypes<float, 2>::ConstTensor boxes_data =
+        boxes.tensor<float, 2>();
+
+    std::vector<float> scores_data(num_boxes);
+    std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
+    std::vector<int> sorted_indices;
+    DecreasingArgSort(scores_data, &sorted_indices);
+
+    std::vector<bool> active(num_boxes, true);
+    std::vector<int> selected;
+    int num_active = active.size();
+    for (int i = 0; i < num_boxes; ++i) {
+      if (num_active == 0 || selected.size() >= output_size) break;
+      if (active[i]) {
+        selected.push_back(sorted_indices[i]);
+      } else {
+        continue;
+      }
+      for (int j = i + 1; j < num_boxes; ++j) {
+        if (active[j]) {
+          float iou =
+              ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
+          if (iou > iou_threshold_) {
+            active[j] = false;
+            num_active--;
+          }
+        }
+      }
+    }
 
-template <typename Device>
-class NonMaxSuppressionV2Op : public OpKernel {
- public:
-  explicit NonMaxSuppressionV2Op(OpKernelConstruction* context)
-      : OpKernel(context) {
+    // Allocate output tensor
+    Tensor* output = nullptr;
+    TensorShape output_shape({static_cast<int>(selected.size())});
+    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
+    typename TTypes<int, 1>::Tensor selected_indices_data =
+        output->tensor<int, 1>();
+    std::copy_n(selected.begin(), selected.size(),
+                selected_indices_data.data());
   }
 
-  void Compute(OpKernelContext* context) override {
-    // boxes: [num_boxes, 4]
-    const Tensor& boxes = context->input(0);
-    // scores: [num_boxes]
-    const Tensor& scores = context->input(1);
-    // max_output_size: scalar
-    const Tensor& max_output_size = context->input(2);
-    OP_REQUIRES(
-        context, TensorShapeUtils::IsScalar(max_output_size.shape()),
-        errors::InvalidArgument("max_output_size must be 0-D, got shape ",
-                                max_output_size.shape().DebugString()));
-    // iou_threshold: scalar
-    const Tensor& iou_threshold = context->input(3);
-    OP_REQUIRES(
-        context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
-        errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
-                                iou_threshold.shape().DebugString()));
-
-    const float iou_threshold_val = iou_threshold.scalar<float>()();
-
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_val);
-  }
+ private:
+  float iou_threshold_;
 };
 
 REGISTER_KERNEL_BUILDER(Name("NonMaxSuppression").Device(DEVICE_CPU),
                         NonMaxSuppressionOp<CPUDevice>);
 
-REGISTER_KERNEL_BUILDER(Name("NonMaxSuppressionV2").Device(DEVICE_CPU),
-                        NonMaxSuppressionV2Op<CPUDevice>);
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/non_max_suppression_op_test.cc b/tensorflow/core/kernels/non_max_suppression_op_test.cc
index 0a075b48b0..72e368db77 100644
--- a/tensorflow/core/kernels/non_max_suppression_op_test.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op_test.cc
@@ -141,161 +141,6 @@ TEST_F(NonMaxSuppressionOpTest, TestInconsistentBoxAndScoreShapes) {
   AddInputFromArray<float>(TensorShape({5}), {.9f, .75f, .6f, .95f, .5f});
   AddInputFromArray<int>(TensorShape({}), {30});
   Status s = RunOpKernel();
-  
-  ASSERT_FALSE(s.ok());
-  EXPECT_TRUE(
-              StringPiece(s.ToString()).contains("scores has incompatible shape"))
-    << s;
-}
-
-TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
-  MakeOp(1.2);
-  AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
-  AddInputFromArray<float>(TensorShape({1}), {.9f});
-  AddInputFromArray<int>(TensorShape({}), {3});
-  Status s = RunOpKernel();
-
-  ASSERT_FALSE(s.ok());
-  EXPECT_TRUE(
-              StringPiece(s.ToString()).contains("iou_threshold must be in [0, 1]"))
-    << s;
-}
-
-TEST_F(NonMaxSuppressionOpTest, TestEmptyInput) {
-  MakeOp(.5);
-  AddInputFromArray<float>(TensorShape({0, 4}), {});
-  AddInputFromArray<float>(TensorShape({0}), {});
-  AddInputFromArray<int>(TensorShape({}), {30});
-  TF_ASSERT_OK(RunOpKernel());
-
-  Tensor expected(allocator(), DT_INT32, TensorShape({0}));
-  test::FillValues<int>(&expected, {});
-  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
-}
-    
-//
-// NonMaxSuppressionV2Op Tests
-//
-
-class NonMaxSuppressionV2OpTest : public OpsTestBase {
- protected:
-  void MakeOp() {
-    TF_EXPECT_OK(NodeDefBuilder("non_max_suppression_op", "NonMaxSuppressionV2")
-                     .Input(FakeInput(DT_FLOAT))
-                     .Input(FakeInput(DT_FLOAT))
-                     .Input(FakeInput(DT_INT32))
-                     .Input(FakeInput(DT_FLOAT))
-                     .Finalize(node_def()));
-    TF_EXPECT_OK(InitOp());
-  }
-};
-
-TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromThreeClusters) {
-  MakeOp();
-  AddInputFromArray<float>(TensorShape({6, 4}),
-                           {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
-                            0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,  1, 101});
-  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
-  AddInputFromArray<int>(TensorShape({}), {3});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
-  TF_ASSERT_OK(RunOpKernel());
-
-  Tensor expected(allocator(), DT_INT32, TensorShape({3}));
-  test::FillValues<int>(&expected, {3, 0, 5});
-  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
-}
-
-TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromThreeClustersFlippedCoordinates) {
-  MakeOp();
-  AddInputFromArray<float>(TensorShape({6, 4}),
-                           {1, 1,  0, 0,  0, 0.1f,  1, 1.1f,  0, .9f,  1, -0.1f,
-                            0, 10, 1, 11, 1, 10.1f, 0, 11.1f, 1, 101, 0, 100});
-  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
-  AddInputFromArray<int>(TensorShape({}), {3});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
-  TF_ASSERT_OK(RunOpKernel());
-
-  Tensor expected(allocator(), DT_INT32, TensorShape({3}));
-  test::FillValues<int>(&expected, {3, 0, 5});
-  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
-}
-
-TEST_F(NonMaxSuppressionV2OpTest, TestSelectAtMostTwoBoxesFromThreeClusters) {
-  MakeOp();
-  AddInputFromArray<float>(TensorShape({6, 4}),
-                           {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
-                            0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,  1, 101});
-  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
-  AddInputFromArray<int>(TensorShape({}), {2});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
-  TF_ASSERT_OK(RunOpKernel());
-
-  Tensor expected(allocator(), DT_INT32, TensorShape({2}));
-  test::FillValues<int>(&expected, {3, 0});
-  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
-}
-
-TEST_F(NonMaxSuppressionV2OpTest, TestSelectAtMostThirtyBoxesFromThreeClusters) {
-  MakeOp();
-  AddInputFromArray<float>(TensorShape({6, 4}),
-                           {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
-                            0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,  1, 101});
-  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
-  AddInputFromArray<int>(TensorShape({}), {30});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
-  TF_ASSERT_OK(RunOpKernel());
-
-  Tensor expected(allocator(), DT_INT32, TensorShape({3}));
-  test::FillValues<int>(&expected, {3, 0, 5});
-  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
-}
-
-TEST_F(NonMaxSuppressionV2OpTest, TestSelectSingleBox) {
-  MakeOp();
-  AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
-  AddInputFromArray<float>(TensorShape({1}), {.9f});
-  AddInputFromArray<int>(TensorShape({}), {3});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
-  TF_ASSERT_OK(RunOpKernel());
-
-  Tensor expected(allocator(), DT_INT32, TensorShape({1}));
-  test::FillValues<int>(&expected, {0});
-  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
-}
-
-TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromTenIdenticalBoxes) {
-  MakeOp();
-
-  int num_boxes = 10;
-  std::vector<float> corners(num_boxes * 4);
-  std::vector<float> scores(num_boxes);
-  for (int i = 0; i < num_boxes; ++i) {
-    corners[i * 4 + 0] = 0;
-    corners[i * 4 + 1] = 0;
-    corners[i * 4 + 2] = 1;
-    corners[i * 4 + 3] = 1;
-    scores[i] = .9;
-  }
-  AddInputFromArray<float>(TensorShape({num_boxes, 4}), corners);
-  AddInputFromArray<float>(TensorShape({num_boxes}), scores);
-  AddInputFromArray<int>(TensorShape({}), {3});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
-  TF_ASSERT_OK(RunOpKernel());
-
-  Tensor expected(allocator(), DT_INT32, TensorShape({1}));
-  test::FillValues<int>(&expected, {0});
-  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
-}
-
-TEST_F(NonMaxSuppressionV2OpTest, TestInconsistentBoxAndScoreShapes) {
-  MakeOp();
-  AddInputFromArray<float>(TensorShape({6, 4}),
-                           {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
-                            0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,  1, 101});
-  AddInputFromArray<float>(TensorShape({5}), {.9f, .75f, .6f, .95f, .5f});
-  AddInputFromArray<int>(TensorShape({}), {30});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
-  Status s = RunOpKernel();
 
   ASSERT_FALSE(s.ok());
   EXPECT_TRUE(
@@ -303,12 +148,11 @@ TEST_F(NonMaxSuppressionV2OpTest, TestInconsistentBoxAndScoreShapes) {
       << s;
 }
 
-TEST_F(NonMaxSuppressionV2OpTest, TestInvalidIOUThreshold) {
-  MakeOp();
+TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
+  MakeOp(1.2);
   AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
   AddInputFromArray<float>(TensorShape({1}), {.9f});
   AddInputFromArray<int>(TensorShape({}), {3});
-  AddInputFromArray<float>(TensorShape({}), {1.2f});
   Status s = RunOpKernel();
 
   ASSERT_FALSE(s.ok());
@@ -317,12 +161,11 @@ TEST_F(NonMaxSuppressionV2OpTest, TestInvalidIOUThreshold) {
       << s;
 }
 
-TEST_F(NonMaxSuppressionV2OpTest, TestEmptyInput) {
-  MakeOp();
+TEST_F(NonMaxSuppressionOpTest, TestEmptyInput) {
+  MakeOp(.5);
   AddInputFromArray<float>(TensorShape({0, 4}), {});
   AddInputFromArray<float>(TensorShape({0}), {});
   AddInputFromArray<int>(TensorShape({}), {30});
-  AddInputFromArray<float>(TensorShape({}), {.5f});
   TF_ASSERT_OK(RunOpKernel());
 
   Tensor expected(allocator(), DT_INT32, TensorShape({0}));
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index bbfdb34758..cfcf2d1ff9 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -972,50 +972,11 @@ method: A string specifying the interpolation method. Only 'bilinear' is
 // --------------------------------------------------------------------------
 
 REGISTER_OP("NonMaxSuppression")
-  .Input("boxes: float")
-  .Input("scores: float")
-  .Input("max_output_size: int32")
-  .Output("selected_indices: int32")
-  .Attr("iou_threshold: float = 0.5")
-  .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Vector(c->UnknownDim()));
-      return Status::OK();
-    })
-  .Doc(R"doc(
-Greedily selects a subset of bounding boxes in descending order of score,
-pruning away boxes that have high intersection-over-union (IOU) overlap
-with previously selected boxes.  Bounding boxes are supplied as
-[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
-diagonal pair of box corners and the coordinates can be provided as normalized
-(i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
-is agnostic to where the origin is in the coordinate system.  Note that this
-algorithm is invariant to orthogonal transformations and translations
-of the coordinate system; thus translating or reflections of the coordinate
-system result in the same boxes being selected by the algorithm.
-The output of this operation is a set of integers indexing into the input
-collection of bounding boxes representing the selected boxes.  The bounding
-box coordinates corresponding to the selected indices can then be obtained
-using the `tf.gather operation`.  For example:
-  selected_indices = tf.image.non_max_suppression(
-      boxes, scores, max_output_size, iou_threshold)
-  selected_boxes = tf.gather(boxes, selected_indices)
-boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
-scores: A 1-D float tensor of shape `[num_boxes]` representing a single
-  score corresponding to each box (each row of boxes).
-max_output_size: A scalar integer tensor representing the maximum number of
-  boxes to be selected by non max suppression.
-iou_threshold: A float representing the threshold for deciding whether boxes
-  overlap too much with respect to IOU.
-selected_indices: A 1-D integer tensor of shape `[M]` representing the selected
-  indices from the boxes tensor, where `M <= max_output_size`.
-)doc");
-
-REGISTER_OP("NonMaxSuppressionV2")
     .Input("boxes: float")
     .Input("scores: float")
     .Input("max_output_size: int32")
-    .Input("iou_threshold: float")
     .Output("selected_indices: int32")
+    .Attr("iou_threshold: float = 0.5")
     .SetShapeFn([](InferenceContext* c) {
       c->set_output(0, c->Vector(c->UnknownDim()));
       return Status::OK();
@@ -1037,7 +998,7 @@ collection of bounding boxes representing the selected boxes.  The bounding
 box coordinates corresponding to the selected indices can then be obtained
 using the `tf.gather operation`.  For example:
 
-  selected_indices = tf.image.non_max_suppression_v2(
+  selected_indices = tf.image.non_max_suppression(
       boxes, scores, max_output_size, iou_threshold)
   selected_boxes = tf.gather(boxes, selected_indices)
 
@@ -1046,8 +1007,8 @@ scores: A 1-D float tensor of shape `[num_boxes]` representing a single
   score corresponding to each box (each row of boxes).
 max_output_size: A scalar integer tensor representing the maximum number of
   boxes to be selected by non max suppression.
-iou_threshold: A 0-D float tensor representing the threshold for deciding whether
-  boxes overlap too much with respect to IOU.
+iou_threshold: A float representing the threshold for deciding whether boxes
+  overlap too much with respect to IOU.
 selected_indices: A 1-D integer tensor of shape `[M]` representing the selected
   indices from the boxes tensor, where `M <= max_output_size`.
 )doc");
diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py
index 2aad1e1519..c29ae26f04 100644
--- a/tensorflow/python/ops/image_ops.py
+++ b/tensorflow/python/ops/image_ops.py
@@ -59,7 +59,6 @@ See the @{$python/image} guide.
 @@per_image_standardization
 @@draw_bounding_boxes
 @@non_max_suppression
-@@non_max_suppression_v2
 @@sample_distorted_bounding_box
 @@total_variation
 """
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index ae7999a71e..3e140ce047 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -52,7 +52,6 @@ ops.NotDifferentiable('SampleDistortedBoundingBox')
 # latent bugs here.
 ops.NotDifferentiable('ExtractGlimpse')
 ops.NotDifferentiable('NonMaxSuppression')
-ops.NotDifferentiable('NonMaxSuppressionV2')
 
 
 def _assert(cond, ex_type, msg):
diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
index 465673610b..6002f36bac 100644
--- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
@@ -92,10 +92,6 @@ tf_module {
     name: "non_max_suppression"
     argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
-  member_method {
-    name: "non_max_suppression_v2"
-    argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "pad_to_bounding_box"
     argspec: "args=[\'image\', \'offset_height\', \'offset_width\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None"
-- 
GitLab


From c63cebb80a26cb7eeaf0afe095886488f1a8ac8a Mon Sep 17 00:00:00 2001
From: Sandip Giri <sgiri@us.ibm.com>
Date: Tue, 13 Jun 2017 05:45:03 -0400
Subject: [PATCH 0048/1375] int64 signed/unsigned issue on ppc64le

---
 tensorflow/core/platform/profile_utils/cpu_utils.cc      | 6 +++---
 tensorflow/core/platform/profile_utils/cpu_utils.h       | 4 ++--
 tensorflow/core/platform/profile_utils/cpu_utils_test.cc | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.cc b/tensorflow/core/platform/profile_utils/cpu_utils.cc
index 22400565d6..c4bf4ed564 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.cc
@@ -28,8 +28,8 @@ namespace profile_utils {
 
 static ICpuUtilsHelper* cpu_utils_helper_instance_ = nullptr;
 
-/* static */ int64 CpuUtils::GetCycleCounterFrequency() {
-  static const int64 cpu_frequency = GetCycleCounterFrequencyImpl();
+/* static */ uint64 CpuUtils::GetCycleCounterFrequency() {
+  static const uint64 cpu_frequency = GetCycleCounterFrequencyImpl();
   return cpu_frequency;
 }
 
@@ -53,7 +53,7 @@ static ICpuUtilsHelper* cpu_utils_helper_instance_ = nullptr;
                                        GetCycleCounterFrequency());
 }
 
-/* static */ int64 CpuUtils::GetCycleCounterFrequencyImpl() {
+/* static */ uint64 CpuUtils::GetCycleCounterFrequencyImpl() {
 // TODO(satok): do not switch by macro here
 #if defined(__ANDROID__)
   return GetCpuUtilsHelperSingletonInstance().CalculateCpuFrequency();
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h
index 19471ec858..2adf77fb7f 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.h
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.h
@@ -97,7 +97,7 @@ class CpuUtils {
   // Return cycle counter frequency.
   // As this method caches the cpu frequency internally,
   // the first call will incur overhead, but not subsequent calls.
-  static int64 GetCycleCounterFrequency();
+  static uint64 GetCycleCounterFrequency();
 
   // Return micro secound per each clock
   // As this method caches the cpu frequency internally,
@@ -134,7 +134,7 @@ class CpuUtils {
   // CAVEAT: as this method calls system call and parse the mssage,
   // this call may be slow. This is why this class caches the value by
   // StaticVariableInitializer.
-  static int64 GetCycleCounterFrequencyImpl();
+  static uint64 GetCycleCounterFrequencyImpl();
 
   // Return a singleton of ICpuUtilsHelper
   // ICpuUtilsHelper is declared as a function-local static variable
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
index ca487965a0..a55ee0d916 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
@@ -53,9 +53,9 @@ TEST_F(CpuUtilsTest, CheckGetCurrentClockCycle) {
 }
 
 TEST_F(CpuUtilsTest, CheckCycleCounterFrequency) {
-  const int64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
+  const uint64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
   CHECK_GT(cpu_frequency, 0);
-  CHECK_NE(cpu_frequency, CpuUtils::INVALID_FREQUENCY);
+  CHECK_NE(cpu_frequency, unsigned(CpuUtils::INVALID_FREQUENCY));
   if (DBG) {
     LOG(INFO) << "Cpu frequency = " << cpu_frequency;
   }
-- 
GitLab


From 62b42ee7f649a8ed3d7122920c55b85f4aac2175 Mon Sep 17 00:00:00 2001
From: Dandelion Man? <dandelion@google.com>
Date: Wed, 31 May 2017 17:22:42 -0700
Subject: [PATCH 0049/1375] Add make_ndarray, tensor_proto, and MetaGraphDef to
 tf api.

Since TensorProtos are part of the TensorFlow API, it makes sense
to also include the methods that generate and parse them.

Similarly, we write out MetaGraphDef protos in the summary writer,
so we should provide the proto as well.

This is part of an ongoing effort to have TensorBoard only consume
TensorFlow methods through the public api.

PiperOrigin-RevId: 157657564
---
 tensorflow/python/__init__.py                 |   2 +
 tensorflow/python/framework/framework_lib.py  |   6 +
 ...meta-graph-def.-collection-def-entry.pbtxt |  84 +++++++++++++
 ...rflow.-meta-graph-def.-meta-info-def.pbtxt | 100 ++++++++++++++++
 ...-meta-graph-def.-signature-def-entry.pbtxt |  84 +++++++++++++
 .../golden/tensorflow.-meta-graph-def.pbtxt   | 112 ++++++++++++++++++
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  12 ++
 7 files changed, 400 insertions(+)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt

diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 19d2eb8174..5357d36330 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -54,6 +54,7 @@ from tensorflow.core.framework.node_def_pb2 import *
 from tensorflow.core.framework.summary_pb2 import *
 from tensorflow.core.framework.attr_value_pb2 import *
 from tensorflow.core.protobuf.meta_graph_pb2 import TensorInfo
+from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
 from tensorflow.core.protobuf.config_pb2 import *
 from tensorflow.core.protobuf.tensorflow_server_pb2 import *
 from tensorflow.core.util.event_pb2 import *
@@ -143,6 +144,7 @@ _allowed_symbols = [
     'GraphOptions',
     'HistogramProto',
     'LogMessage',
+    'MetaGraphDef',
     'NameAttrList',
     'NodeDef',
     'OptimizerOptions',
diff --git a/tensorflow/python/framework/framework_lib.py b/tensorflow/python/framework/framework_lib.py
index fe935881c6..80f936e8ab 100644
--- a/tensorflow/python/framework/framework_lib.py
+++ b/tensorflow/python/framework/framework_lib.py
@@ -41,6 +41,8 @@
 @@import_graph_def
 @@load_file_system_library
 @@load_op_library
+@@make_tensor_proto
+@@make_ndarray
 
 ## Graph collections
 
@@ -98,6 +100,10 @@ from tensorflow.python.framework.sparse_tensor import convert_to_tensor_or_spars
 from tensorflow.python.framework.subscribe import subscribe
 from tensorflow.python.framework.importer import import_graph_def
 
+# Utilities for working with Tensors
+from tensorflow.python.framework.tensor_util import make_tensor_proto
+from tensorflow.python.framework.tensor_util import MakeNdarray as make_ndarray
+
 # Needed when you defined a new Op in C++.
 from tensorflow.python.framework.ops import RegisterGradient
 from tensorflow.python.framework.ops import NotDifferentiable
diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt
new file mode 100644
index 0000000000..3572126fbf
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt
@@ -0,0 +1,84 @@
+path: "tensorflow.MetaGraphDef.CollectionDefEntry"
+tf_class {
+  is_instance: "<class \'tensorflow.core.protobuf.meta_graph_pb2.CollectionDefEntry\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "KEY_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "VALUE_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt
new file mode 100644
index 0000000000..ebf49f434a
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt
@@ -0,0 +1,100 @@
+path: "tensorflow.MetaGraphDef.MetaInfoDef"
+tf_class {
+  is_instance: "<class \'tensorflow.core.protobuf.meta_graph_pb2.MetaInfoDef\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "ANY_INFO_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "META_GRAPH_VERSION_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "STRIPPED_OP_LIST_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TAGS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TENSORFLOW_GIT_VERSION_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TENSORFLOW_VERSION_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt
new file mode 100644
index 0000000000..48fccac99d
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt
@@ -0,0 +1,84 @@
+path: "tensorflow.MetaGraphDef.SignatureDefEntry"
+tf_class {
+  is_instance: "<class \'tensorflow.core.protobuf.meta_graph_pb2.SignatureDefEntry\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "KEY_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "VALUE_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt
new file mode 100644
index 0000000000..3e683a8715
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt
@@ -0,0 +1,112 @@
+path: "tensorflow.MetaGraphDef"
+tf_class {
+  is_instance: "<class \'tensorflow.core.protobuf.meta_graph_pb2.MetaGraphDef\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "ASSET_FILE_DEF_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "COLLECTION_DEF_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "CollectionDefEntry"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "GRAPH_DEF_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "META_INFO_DEF_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "MetaInfoDef"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "SAVER_DEF_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "SIGNATURE_DEF_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "SignatureDefEntry"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index be5a2a1806..25304ff711 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -112,6 +112,10 @@ tf_module {
     name: "LogMessage"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
   }
+  member {
+    name: "MetaGraphDef"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
   member {
     name: "NameAttrList"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
@@ -1188,10 +1192,18 @@ tf_module {
     name: "logical_xor"
     argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'LogicalXor\'], "
   }
+  member_method {
+    name: "make_ndarray"
+    argspec: "args=[\'tensor\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "make_template"
     argspec: "args=[\'name_\', \'func_\', \'create_scope_now_\', \'unique_name_\', \'custom_getter_\'], varargs=None, keywords=kwargs, defaults=[\'False\', \'None\', \'None\'], "
   }
+  member_method {
+    name: "make_tensor_proto"
+    argspec: "args=[\'values\', \'dtype\', \'shape\', \'verify_shape\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\'], "
+  }
   member_method {
     name: "map_fn"
     argspec: "args=[\'fn\', \'elems\', \'dtype\', \'parallel_iterations\', \'back_prop\', \'swap_memory\', \'infer_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'True\', \'False\', \'True\', \'None\'], "
-- 
GitLab


From 97a55366ab15973c319a0506d785bb307fa58cf6 Mon Sep 17 00:00:00 2001
From: Dandelion Man? <dandelion@google.com>
Date: Thu, 1 Jun 2017 17:11:04 -0700
Subject: [PATCH 0050/1375] Expose tf.test.StubOutForTesting in the tf testing
 api

Also redirect TensorBoard usage to use that endpoint.

This is part of my ongoing effort to have TensorBoard only
depend on TensorFlow via its public api, so that it can
be split into a project with a fast external build.

PiperOrigin-RevId: 157784552
---
 tensorflow/python/platform/test.py            |  4 +++
 ...ensorflow.test.-stub-out-for-testing.pbtxt | 28 +++++++++++++++++++
 .../tools/api/golden/tensorflow.test.pbtxt    |  4 +++
 .../api/lib/python_object_to_proto_visitor.py |  6 ++++
 4 files changed, 42 insertions(+)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.test.-stub-out-for-testing.pbtxt

diff --git a/tensorflow/python/platform/test.py b/tensorflow/python/platform/test.py
index 5cb2c152b0..a307347f60 100644
--- a/tensorflow/python/platform/test.py
+++ b/tensorflow/python/platform/test.py
@@ -61,6 +61,9 @@ else:
 # Import Benchmark class
 Benchmark = _googletest.Benchmark  # pylint: disable=invalid-name
 
+# Import StubOutForTesting class
+StubOutForTesting = _googletest.StubOutForTesting  # pylint: disable=invalid-name
+
 
 def main(argv=None):
   """Runs all unit tests."""
@@ -117,6 +120,7 @@ _allowed_symbols = [
     # We piggy-back googletest documentation.
     'Benchmark',
     'mock',
+    'StubOutForTesting',
 ]
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/tools/api/golden/tensorflow.test.-stub-out-for-testing.pbtxt b/tensorflow/tools/api/golden/tensorflow.test.-stub-out-for-testing.pbtxt
new file mode 100644
index 0000000000..e02a0c6097
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.test.-stub-out-for-testing.pbtxt
@@ -0,0 +1,28 @@
+path: "tensorflow.test.StubOutForTesting"
+tf_class {
+  is_instance: "<class \'tensorflow.python.platform.googletest.StubOutForTesting\'>"
+  member_method {
+    name: "CleanUp"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "Set"
+    argspec: "args=[\'self\', \'parent\', \'child_name\', \'new_child\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "SmartSet"
+    argspec: "args=[\'self\', \'obj\', \'attr_name\', \'new_attr\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "SmartUnsetAll"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "UnsetAll"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.test.pbtxt b/tensorflow/tools/api/golden/tensorflow.test.pbtxt
index 1e717ad237..2a88f26ed0 100644
--- a/tensorflow/tools/api/golden/tensorflow.test.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.test.pbtxt
@@ -4,6 +4,10 @@ tf_module {
     name: "Benchmark"
     mtype: "<class \'tensorflow.python.platform.benchmark._BenchmarkRegistrar\'>"
   }
+  member {
+    name: "StubOutForTesting"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "TestCase"
     mtype: "<type \'type\'>"
diff --git a/tensorflow/tools/api/lib/python_object_to_proto_visitor.py b/tensorflow/tools/api/lib/python_object_to_proto_visitor.py
index 34edbf61f5..4dbf46e148 100644
--- a/tensorflow/tools/api/lib/python_object_to_proto_visitor.py
+++ b/tensorflow/tools/api/lib/python_object_to_proto_visitor.py
@@ -91,6 +91,12 @@ def _SanitizedMRO(obj):
     if 'tensorflow' not in str_repr:
       break
 
+    # Hack - tensorflow.test.StubOutForTesting may or may not be type <object>
+    # depending on the environment. To avoid inconsistency, break after we add
+    # StubOutForTesting to the return_list.
+    if 'StubOutForTesting' in str_repr:
+      break
+
   return return_list
 
 
-- 
GitLab


From b42b658449f95becd5203fad5a04bf5793414247 Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Thu, 8 Jun 2017 22:06:40 -0700
Subject: [PATCH 0051/1375] Bugfix: Don't ignore logical names in single tensor
 predict signatures. User-supplied names in predict signatures were silently
 ignored and replaced with constants.

PiperOrigin-RevId: 158481505
---
 .../python/learn/utils/saved_model_export_utils_test.py   | 6 +++---
 tensorflow/python/estimator/export/export_test.py         | 4 ++--
 tensorflow/python/saved_model/signature_def_utils_impl.py | 8 --------
 3 files changed, 5 insertions(+), 13 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils_test.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils_test.py
index 157744bed1..48222e9dd6 100644
--- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils_test.py
+++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils_test.py
@@ -311,7 +311,7 @@ class SavedModelExportUtilsTest(test.TestCase):
     dtype_int64 = types_pb2.DataType.Value("DT_INT64")
     dtype_float = types_pb2.DataType.Value("DT_FLOAT")
     expected_signature_def.inputs[
-        signature_constants.PREDICT_INPUTS].CopyFrom(
+        "input-1"].CopyFrom(
             meta_graph_pb2.TensorInfo(
                 name="input-tensor-1:0", dtype=dtype_float, tensor_shape=shape))
     expected_signature_def.outputs["classes"].CopyFrom(
@@ -516,8 +516,8 @@ class SavedModelExportUtilsTest(test.TestCase):
                                                              output_2, None),
         "default_input_alternative:head-3":
             signature_def_utils.predict_signature_def({
-                "input": input_example
-            }, {"output": output_3}),
+                "default input": input_example
+            }, {"some_output_3": output_3}),
         # "features_input_alternative:head-1":
         #     signature_def_utils.regression_signature_def(input_features,
         #                                                  output_1),
diff --git a/tensorflow/python/estimator/export/export_test.py b/tensorflow/python/estimator/export/export_test.py
index 7946bd88ba..6864a845f3 100644
--- a/tensorflow/python/estimator/export/export_test.py
+++ b/tensorflow/python/estimator/export/export_test.py
@@ -232,8 +232,8 @@ class ExportTest(test_util.TensorFlowTestCase):
                                                              output_2, None),
         "head-3":
             signature_def_utils.predict_signature_def({
-                "input": receiver_tensor
-            }, {"output": output_3})
+                "receiver": receiver_tensor
+            }, {"some_output_3": output_3})
     }
 
     self.assertDictEqual(expected_signature_defs, signature_defs)
diff --git a/tensorflow/python/saved_model/signature_def_utils_impl.py b/tensorflow/python/saved_model/signature_def_utils_impl.py
index 6b6702a79a..0559fb415e 100644
--- a/tensorflow/python/saved_model/signature_def_utils_impl.py
+++ b/tensorflow/python/saved_model/signature_def_utils_impl.py
@@ -136,14 +136,6 @@ def predict_signature_def(inputs, outputs):
   if outputs is None:
     raise ValueError('outputs cannot be None or empty for prediction.')
 
-  # If there's only one input or output, we can standardize keys
-  if len(inputs) == 1:
-    (_, value), = inputs.items()
-    inputs = {signature_constants.PREDICT_INPUTS: value}
-  if len(outputs) == 1:
-    (_, value), = outputs.items()
-    outputs = {signature_constants.PREDICT_OUTPUTS: value}
-
   signature_inputs = {key: utils.build_tensor_info(tensor)
                       for key, tensor in inputs.items()}
   signature_outputs = {key: utils.build_tensor_info(tensor)
-- 
GitLab


From f60dfcb5f7aa30bae778cab3387a84ea6306b948 Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Tue, 13 Jun 2017 13:57:48 -0700
Subject: [PATCH 0052/1375] Documentation update and release update.

---
 RELEASE.md                              | 2 ++
 tensorflow/python/ops/variable_scope.py | 5 +++++
 2 files changed, 7 insertions(+)

diff --git a/RELEASE.md b/RELEASE.md
index 64d9069e61..57ec87eba1 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -81,6 +81,8 @@
   checkpoints containing such RNN cells, in which case you can use the
   [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
   to convert the variable names in your old checkpoints.
+* Added `tf.contrib.kernel_methods` module with Ops and estimators for primal
+  (explicit) kernel methods in TensorFlow.
 
 ## Bug Fixes and Other Changes
 * In python, `Operation.get_attr` on type attributes returns the Python DType
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index a29ddfa9f2..752b79200b 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -1501,6 +1501,11 @@ def variable_scope(name_or_scope,
   A note about name scoping: Setting `reuse` does not impact the naming of other
   ops such as mult. See related discussion on [github#6189](https://github.com/tensorflow/tensorflow/issues/6189)
 
+  Note that up to and including version 1.0, it was allowed (though
+  explicitly discouraged) to pass False to the reuse argument, yielding
+  undocumented behaviour slightly different from None. Starting at 1.1.0
+  passing None and False as reuse has exactly the same effect.
+
   Args:
     name_or_scope: `string` or `VariableScope`: the scope to open.
     default_name: The default name to use if the `name_or_scope` argument is
-- 
GitLab


From b542989d69d5f1767338508fdd50916c5e82736b Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Tue, 13 Jun 2017 14:12:14 -0700
Subject: [PATCH 0053/1375] Updating the version from 1.2.0rc2 to 1.2.0.

---
 README.md                                     | 12 +++++-----
 tensorflow/core/public/version.h              |  2 +-
 tensorflow/docs_src/install/install_c.md      |  2 +-
 tensorflow/docs_src/install/install_go.md     |  2 +-
 tensorflow/docs_src/install/install_java.md   | 18 +++++++--------
 tensorflow/docs_src/install/install_linux.md  | 22 +++++++++----------
 tensorflow/docs_src/install/install_mac.md    | 10 ++++-----
 .../docs_src/install/install_sources.md       |  4 ++--
 .../docs_src/install/install_windows.md       |  4 ++--
 tensorflow/tools/pip_package/setup.py         |  2 +-
 10 files changed, 39 insertions(+), 39 deletions(-)

diff --git a/README.md b/README.md
index e7dbf57b25..cbc94c1ab2 100644
--- a/README.md
+++ b/README.md
@@ -34,12 +34,12 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
 * Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 58780c0ae1..399521ea04 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX "-rc2"
+#define TF_VERSION_SUFFIX ""
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 612f271f94..5ca1a490e6 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index c9b8dffadb..3f9096b822 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index c172a4c820..a1326d099f 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0-rc2</version>
+  <version>1.2.0</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0-rc2</version>
+                 <version>1.2.0</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc2.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0-rc2.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following comand line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index d0e7970898..fda9d04b08 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -271,7 +271,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -458,7 +458,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -626,14 +626,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -645,14 +645,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -664,14 +664,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -683,14 +683,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 44e2b37dfd..d756b64bca 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -109,7 +109,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -230,7 +230,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 6f784c5180..403d65519a 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -320,10 +320,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0rc2 on Linux:
+for TensorFlow 1.2.0 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc2-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 42820660ee..a8334c4e5b 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -114,12 +114,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 712a5028d5..50228311b1 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0-rc2'
+_VERSION = '1.2.0'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
-- 
GitLab


From 6bf093ca016a2b1caf40e30f7cd73809ab3257f4 Mon Sep 17 00:00:00 2001
From: Dandelion Man? <dandelion@google.com>
Date: Tue, 13 Jun 2017 17:42:43 -0700
Subject: [PATCH 0054/1375] Autogenerated Change: Change TensorBoard TAG to 55

PiperOrigin-RevId: 158924850
---
 tensorflow/tensorboard/TAG | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tensorboard/TAG b/tensorflow/tensorboard/TAG
index fb1e7bc869..c3f407c095 100644
--- a/tensorflow/tensorboard/TAG
+++ b/tensorflow/tensorboard/TAG
@@ -1 +1 @@
-54
+55
-- 
GitLab


From 75145524ffed31bb749a49c3dba4518590767cf6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 13 Jun 2017 17:49:59 -0700
Subject: [PATCH 0055/1375] Don't split basic blocks that lack a terminator.

This replicates the idiom used in ForLoop::Emit to handle the same
situation into ElementalIrEmitter::MakeRngElementGenerator.

PiperOrigin-RevId: 158925513
---
 .../xla/service/elemental_ir_emitter.cc       | 35 ++++++++++++++-----
 tensorflow/compiler/xla/tests/prng_test.cc    | 11 ++++++
 2 files changed, 37 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index bea1da4044..dbc65e80eb 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -588,20 +588,37 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator(
               llvm::Intrinsic::ctlz, {r, ir_builder_->getInt1(1)},
               {param_ir_type}, ir_builder_);
           auto in_block = ir_builder_->GetInsertBlock();
-          auto body_block = in_block->splitBasicBlock(
-              ir_builder_->GetInsertPoint(), "rng_body");
-          SetToFirstInsertPoint(body_block, ir_builder_);
-          auto out_block = body_block->splitBasicBlock(
-              ir_builder_->GetInsertPoint(), "rng_out");
+
+          // A terminator should be present iff we're emitting code
+          // into the middle (as opposed to the end) of a basic block.
+          CHECK_EQ(ir_builder_->GetInsertPoint() == in_block->end(),
+                   in_block->getTerminator() == nullptr);
+
+          llvm::BasicBlock* body_block;
+          llvm::BasicBlock* out_block;
+
+          if (ir_builder_->GetInsertPoint() == in_block->end()) {
+            body_block =
+                llvm_ir::CreateBasicBlock(nullptr, "rng_body", ir_builder_);
+            out_block =
+                llvm_ir::CreateBasicBlock(nullptr, "rng_out", ir_builder_);
+            llvm::BranchInst::Create(body_block, in_block);
+          } else {
+            body_block = in_block->splitBasicBlock(
+                ir_builder_->GetInsertPoint(), "rng_body");
+            out_block = body_block->splitBasicBlock(
+                ir_builder_->GetInsertPoint(), "rng_out");
+            body_block->getTerminator()->eraseFromParent();
+          }
+
           SetToFirstInsertPoint(body_block, ir_builder_);
           auto random = ir_builder_->CreateAnd(
               ir_builder_->CreateZExtOrTrunc(get_next_i64(), param_ir_type),
               ir_builder_->CreateLShr(llvm::ConstantInt::get(param_ir_type, ~0),
                                       leading_zeros));
-          llvm::ReplaceInstWithInst(
-              body_block->getTerminator(),
-              llvm::BranchInst::Create(out_block, body_block,
-                                       ir_builder_->CreateICmpULT(random, r)));
+          llvm::BranchInst::Create(out_block, body_block,
+                                   ir_builder_->CreateICmpULT(random, r),
+                                   body_block);
           SetToFirstInsertPoint(out_block, ir_builder_);
           return ir_builder_->CreateAdd(
               p, ir_builder_->CreateSelect(
diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc
index 5117478bfd..b77b8e2ee3 100644
--- a/tensorflow/compiler/xla/tests/prng_test.cc
+++ b/tensorflow/compiler/xla/tests/prng_test.cc
@@ -273,6 +273,17 @@ XLA_TEST_F(PrngTest, TenValuesN01) {
   // TODO(b/25995601): Test that resultant values are reasonable
 }
 
+XLA_TEST_F(PrngTest, RngUniformCrash) {
+  ComputationBuilder builder(client_, TestName());
+
+  // This used to crash XLA during LLVM IR generation for CPUs.
+  auto rng_uniform = builder.RngUniform(builder.ConstantR0<int32>(0),
+                                        builder.ConstantR0<int32>(1000 * 1000),
+                                        ShapeUtil::MakeShape(S32, {}));
+  SetSeed(0);
+  ExecuteAndTransferOrDie(&builder, /*arguments=*/{});
+}
+
 }  // namespace
 }  // namespace xla
 
-- 
GitLab


From 5ccf1b28c1a65227bd9f76d9d5c1b3ed2862c977 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Tue, 13 Jun 2017 18:13:34 -0700
Subject: [PATCH 0056/1375] Upgrade TensorBoard third party deps

PiperOrigin-RevId: 158927523
---
 third_party/js.bzl      |  31 ++--
 third_party/polymer.bzl | 314 ++++++++++++++++++++--------------------
 third_party/typings.bzl | 220 ++++++++++++++--------------
 3 files changed, 283 insertions(+), 282 deletions(-)

diff --git a/third_party/js.bzl b/third_party/js.bzl
index 2d2339c95e..46466c3f31 100644
--- a/third_party/js.bzl
+++ b/third_party/js.bzl
@@ -17,9 +17,11 @@ load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external")
 load("@io_bazel_rules_closure//closure:defs.bzl", "web_library_external")
 
 
+def tensorboard_js_workspace():
+
   ##############################################################################
   # TensorBoard Build Tools
-def tensorboard_js_workspace():
+
   filegroup_external(
       name = "org_nodejs",
       # MIT with portions licensed:
@@ -67,7 +69,7 @@ def tensorboard_js_workspace():
           "node.exe",
       ],
   )
-  
+
   filegroup_external(
       name = "com_microsoft_typescript",
       licenses = ["notice"],  # Apache 2.0
@@ -151,19 +153,20 @@ def tensorboard_js_workspace():
       # no @license header
       licenses = ["notice"],  # MIT
       sha256_urls = {
-          "7aff264bd84c90bed3c72a4dc31db8c19151853c6df6980f52b01d3e9872c82d": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/mrdoob/three.js/ad419d40bdaab80abbb34b8f359b4ee840033a02/build/three.js",
-              "https://raw.githubusercontent.com/mrdoob/three.js/ad419d40bdaab80abbb34b8f359b4ee840033a02/build/three.js",
+          "881cc79c84c34a1f61f8c8af0ee3f237d83a2eda3868720fdcb47bcacf8da44a": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/mrdoob/three.js/r77/build/three.js",
+              "https://raw.githubusercontent.com/mrdoob/three.js/r77/build/three.js",
           ],
-          "0e98ded15bb7fe398a655667e76b39909d36c0973a8950d01c62f65f93161c27": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/mrdoob/three.js/ad419d40bdaab80abbb34b8f359b4ee840033a02/examples/js/controls/OrbitControls.js",
-              "https://raw.githubusercontent.com/mrdoob/three.js/ad419d40bdaab80abbb34b8f359b4ee840033a02/examples/js/controls/OrbitControls.js",
+          "98b8b5954901025a98033c8bdd65969be1f30b59e11f823ec864253bb72f768d": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/mrdoob/three.js/r77/examples/js/controls/OrbitControls.js",
+              "https://raw.githubusercontent.com/mrdoob/three.js/r77/examples/js/controls/OrbitControls.js",
           ],
       },
   )
-  
+
   ##############################################################################
   # TensorBoard JavaScript Production Dependencies
+
   web_library_external(
       name = "com_lodash",
       licenses = ["notice"],  # MIT
@@ -245,7 +248,7 @@ def tensorboard_js_workspace():
       licenses = ["notice"],  # MIT
       sha256_urls = {
           "633f2861a9a862b9cd7967e841e14dd3527912f209d6563595774fa31e3d84cb": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/waylonflinn/weblas/v0.9.0/LICENSES",
+              "http://mirror.bazel.build/raw.githubusercontent.com/waylonflinn/weblas/v0.9.0/LICENSE",
               "https://raw.githubusercontent.com/waylonflinn/weblas/v0.9.0/LICENSE",
           ],
           "f138fce57f673ca8a633f4aee5ae5b6fcb6ad0de59069a42a74e996fd04d8fcc": [
@@ -260,9 +263,9 @@ def tensorboard_js_workspace():
       # no @license header
       licenses = ["notice"],  # BSD-3-Clause
       sha256_urls_extract = {
-          "b5fac5b296bc196e6aa7b59f9e33986fc44d23d59a0e211705187be9e35b943d": [
-              "http://mirror.bazel.build/github.com/d3/d3/releases/download/v4.8.0/d3.zip",
-              "https://github.com/d3/d3/releases/download/v4.8.0/d3.zip",
+          "d858c0878af36bd00e2af6029029106328d408c2bff0a60a9d78c4e27f47b99a": [
+              "http://mirror.bazel.build/github.com/d3/d3/releases/download/v4.9.1/d3.zip",
+              "https://github.com/d3/d3/releases/download/v4.9.1/d3.zip",
           ],
       },
       # TODO(jart): Use srcs=["d3.js"] instead of this once supported.
@@ -292,6 +295,7 @@ def tensorboard_js_workspace():
 
   ##############################################################################
   # TensorBoard Testing Dependencies
+
   web_library_external(
       name = "org_npmjs_registry_accessibility_developer_tools",
       licenses = ["notice"],  # Apache License 2.0
@@ -417,4 +421,3 @@ def tensorboard_js_workspace():
       path = "/test-fixture",
       exclude = ["test/**"],
   )
-
diff --git a/third_party/polymer.bzl b/third_party/polymer.bzl
index bd6e05803c..812afbf3f7 100644
--- a/third_party/polymer.bzl
+++ b/third_party/polymer.bzl
@@ -17,6 +17,23 @@
 load("@io_bazel_rules_closure//closure:defs.bzl", "web_library_external")
 
 def tensorboard_polymer_workspace():
+  web_library_external(
+      name = "org_polymer",
+      licenses = ["notice"],  # BSD-3-Clause
+      sha256 = "2c38cf73bdd09e0f80a4b7210fd58f88a6bdd8624da73fb3e028e66a84c9e095",
+      strip_prefix = "polymer-1.8.1",
+      urls = [
+          "http://mirror.bazel.build/github.com/polymer/polymer/archive/v1.8.1.tar.gz",
+          "https://github.com/polymer/polymer/archive/v1.8.1.tar.gz",
+      ],
+      path = "/polymer",
+      srcs = [
+          "polymer.html",
+          "polymer-micro.html",
+          "polymer-mini.html",
+      ],
+  )
+
   web_library_external(
       name = "org_polymer_font_roboto",
       licenses = ["notice"],  # BSD-3-Clause
@@ -29,7 +46,7 @@ def tensorboard_polymer_workspace():
       path = "/font-roboto",
       srcs = ["roboto.html"],
   )
-  
+
   web_library_external(
       name = "org_polymer_hydrolysis",
       licenses = ["notice"],  # BSD-3-Clause
@@ -47,35 +64,35 @@ def tensorboard_polymer_workspace():
       ],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_a11y_announcer",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "6bce143db7a374a68535ec8b861a5f30e81f2f1e4ee36a55bda2a891f6fd2818",
+      sha256 = "53114ceb57d9f33a7a8058488cf06450e48502e5d033adf51c91330f61620353",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-a11y-announcer/archive/v1.0.5.tar.gz",
-          "https://github.com/PolymerElements/iron-a11y-announcer/archive/v1.0.5.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-a11y-announcer/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/iron-a11y-announcer/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "iron-a11y-announcer-1.0.5",
+      strip_prefix = "iron-a11y-announcer-2.0.0",
       path = "/iron-a11y-announcer",
       srcs = ["iron-a11y-announcer.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_a11y_keys_behavior",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "6823efc47a83208fd51d39c5a1d3eb0c0bebc705df1ce01310509da22a13ebd2",
+      sha256 = "09274155c8d537f8bb567b3be5e747253ef760995a59ee06cb0ab38e704212fb",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-a11y-keys-behavior/archive/v1.1.8.tar.gz",
-          "https://github.com/PolymerElements/iron-a11y-keys-behavior/archive/v1.1.8.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-a11y-keys-behavior/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/iron-a11y-keys-behavior/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "iron-a11y-keys-behavior-1.1.8",
+      strip_prefix = "iron-a11y-keys-behavior-2.0.0",
       path = "/iron-a11y-keys-behavior",
       srcs = ["iron-a11y-keys-behavior.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_ajax",
       licenses = ["notice"],  # BSD-3-Clause
@@ -95,7 +112,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_promise_polyfill",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_autogrow_textarea",
       licenses = ["notice"],  # BSD-3-Clause
@@ -115,7 +132,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_validatable_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_behaviors",
       licenses = ["notice"],  # BSD-3-Clause
@@ -135,7 +152,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_a11y_keys_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_checked_element_behavior",
       licenses = ["notice"],  # BSD-3-Clause
@@ -153,7 +170,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_validatable_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_component_page",
       licenses = ["notice"],  # BSD-3-Clause
@@ -178,16 +195,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_toolbar",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_collapse",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "275808994a609a2f9923e2dd2db1957945ab141ba840eadc33f19e1f406d600e",
+      sha256 = "eb72f459a2a5adbcd922327eea02ed909e8056ad72fd8a32d04a14ce54b2e480",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-collapse/archive/v1.0.8.tar.gz",
-          "https://github.com/PolymerElements/iron-collapse/archive/v1.0.8.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-collapse/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/iron-collapse/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "iron-collapse-1.0.8",
+      strip_prefix = "iron-collapse-2.0.0",
       path = "/iron-collapse",
       srcs = ["iron-collapse.html"],
       deps = [
@@ -195,16 +212,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_resizable_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_demo_helpers",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "aa7458492a6ac3d1f6344640a4c2ab07bce64e7ad0422b83b5d665707598cce6",
+      sha256 = "e196985cb7e50108283c3fc189a3a018e697b4648107d597df71a5c17f5ee907",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-demo-helpers/archive/v1.1.0.tar.gz",
-          "https://github.com/PolymerElements/iron-demo-helpers/archive/v1.1.0.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-demo-helpers/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/iron-demo-helpers/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "iron-demo-helpers-1.1.0",
+      strip_prefix = "iron-demo-helpers-2.0.0",
       path = "/iron-demo-helpers",
       srcs = [
           "demo-pages-shared-styles.html",
@@ -212,6 +229,7 @@ def tensorboard_polymer_workspace():
       ],
       deps = [
           "@org_polymer",
+          "@org_polymer_font_roboto",
           "@org_polymer_iron_flex_layout",
           "@org_polymer_iron_icons",
           "@org_polymer_marked_element",
@@ -220,16 +238,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_prism_element",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_doc_viewer",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "f0e9dfbbcd94d7e88ce82cb61e615406ace63c185fee9396f7f182206ca5cc9a",
+      sha256 = "5d487c99dd0cf626c800ae8667b0c8c88095f4482a68e837a1d3f58484ca8fb4",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-doc-viewer/archive/v1.0.12.tar.gz",
-          "https://github.com/PolymerElements/iron-doc-viewer/archive/v1.0.12.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-doc-viewer/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/iron-doc-viewer/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "iron-doc-viewer-1.0.12",
+      strip_prefix = "iron-doc-viewer-2.0.0",
       path = "/iron-doc-viewer",
       srcs = [
           "iron-doc-property-styles.html",
@@ -245,16 +263,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_prism_element",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_dropdown",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "f7e4a31d096d10d8af1920397695cb17f3eb1cbe5e5ff91a861dabfcc085f376",
+      sha256 = "db9d6598157f8b114f1be1e6ed1c74917d4c37e660b2dda1e31f6873f1a33b80",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-dropdown/archive/v1.4.0.tar.gz",
-          "https://github.com/PolymerElements/iron-dropdown/archive/v1.4.0.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-dropdown/archive/v1.5.5.tar.gz",
+          "https://github.com/PolymerElements/iron-dropdown/archive/v1.5.5.tar.gz",
       ],
-      strip_prefix = "iron-dropdown-1.4.0",
+      strip_prefix = "iron-dropdown-1.5.5",
       path = "/iron-dropdown",
       srcs = [
           "iron-dropdown.html",
@@ -269,7 +287,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_neon_animation",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_fit_behavior",
       licenses = ["notice"],  # BSD-3-Clause
@@ -283,7 +301,7 @@ def tensorboard_polymer_workspace():
       srcs = ["iron-fit-behavior.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_flex_layout",
       licenses = ["notice"],  # BSD-3-Clause
@@ -302,7 +320,7 @@ def tensorboard_polymer_workspace():
       ],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_form_element_behavior",
       licenses = ["notice"],  # BSD-3-Clause
@@ -316,7 +334,7 @@ def tensorboard_polymer_workspace():
       srcs = ["iron-form-element-behavior.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_icon",
       licenses = ["notice"],  # BSD-3-Clause
@@ -334,7 +352,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_meta",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_icons",
       licenses = ["notice"],  # BSD-3-Clause
@@ -363,16 +381,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_iconset_svg",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_iconset_svg",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "7e3925b7e63a7d22524c4b43ce16ab80d06a576649644783643c11a003284368",
+      sha256 = "75cfb41e78f86ef6cb5d201ad12021785ef9e192b490ad46dcc15a9c19bdf71a",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-iconset-svg/archive/v1.1.0.tar.gz",
-          "https://github.com/PolymerElements/iron-iconset-svg/archive/v1.1.0.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-iconset-svg/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/iron-iconset-svg/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "iron-iconset-svg-1.1.0",
+      strip_prefix = "iron-iconset-svg-2.0.0",
       path = "/iron-iconset-svg",
       srcs = ["iron-iconset-svg.html"],
       deps = [
@@ -380,7 +398,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_meta",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_input",
       licenses = ["notice"],  # BSD-3-Clause
@@ -398,7 +416,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_validatable_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_list",
       licenses = ["notice"],  # BSD-3-Clause
@@ -417,16 +435,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_scroll_target_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_menu_behavior",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "ad27889343bc9a709258b073f69abc028bb1ffd3fdb975cd2d3939f7f5d7bb6c",
+      sha256 = "35d33d1ae55c6efaa0c3744ebe8a06cc0a8b2af9286dd8d36e20726a8540a11a",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-menu-behavior/archive/v1.1.10.tar.gz",
-          "https://github.com/PolymerElements/iron-menu-behavior/archive/v1.1.10.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-menu-behavior/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/iron-menu-behavior/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "iron-menu-behavior-1.1.10",
+      strip_prefix = "iron-menu-behavior-2.0.0",
       path = "/iron-menu-behavior",
       srcs = [
           "iron-menu-behavior.html",
@@ -438,7 +456,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_selector",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_meta",
       licenses = ["notice"],  # BSD-3-Clause
@@ -452,7 +470,7 @@ def tensorboard_polymer_workspace():
       srcs = ["iron-meta.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_overlay_behavior",
       licenses = ["notice"],  # BSD-3-Clause
@@ -476,7 +494,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_resizable_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_range_behavior",
       licenses = ["notice"],  # BSD-3-Clause
@@ -490,7 +508,7 @@ def tensorboard_polymer_workspace():
       srcs = ["iron-range-behavior.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_resizable_behavior",
       licenses = ["notice"],  # BSD-3-Clause
@@ -504,21 +522,21 @@ def tensorboard_polymer_workspace():
       srcs = ["iron-resizable-behavior.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_scroll_target_behavior",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "d0de0c804b1ec91d814754144afd9da1cdb082690de88bd5e47fd5f41990746f",
+      sha256 = "7c6614c07d354375666ee96eea9f6d485dbf7898146a444ec26094a70d4e0afa",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-scroll-target-behavior/archive/v1.0.3.tar.gz",
-          "https://github.com/PolymerElements/iron-scroll-target-behavior/archive/v1.0.3.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/iron-scroll-target-behavior/archive/v1.1.1.tar.gz",
+          "https://github.com/PolymerElements/iron-scroll-target-behavior/archive/v1.1.1.tar.gz",
       ],
-      strip_prefix = "iron-scroll-target-behavior-1.0.3",
+      strip_prefix = "iron-scroll-target-behavior-1.1.1",
       path = "/iron-scroll-target-behavior",
       srcs = ["iron-scroll-target-behavior.html"],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_selector",
       licenses = ["notice"],  # BSD-3-Clause
@@ -537,7 +555,7 @@ def tensorboard_polymer_workspace():
       ],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_iron_validatable_behavior",
       licenses = ["notice"],  # BSD-3-Clause
@@ -554,7 +572,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_meta",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_marked",
       licenses = ["notice"],  # MIT
@@ -567,16 +585,16 @@ def tensorboard_polymer_workspace():
       path = "/marked",
       srcs = ["lib/marked.js"],
   )
-  
+
   web_library_external(
       name = "org_polymer_marked_element",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "7547616df95f8b903757e6afbabfcdba5322c2bcec3f17c726b8bba5adf4bc5f",
+      sha256 = "2ac1f7fae0c1b656e671b2772492809714d836f27c9efa7f2c5fe077ee760f3c",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/marked-element/archive/v1.1.3.tar.gz",
-          "https://github.com/PolymerElements/marked-element/archive/v1.1.3.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/marked-element/archive/v2.1.0.tar.gz",
+          "https://github.com/PolymerElements/marked-element/archive/v2.1.0.tar.gz",
       ],
-      strip_prefix = "marked-element-1.1.3",
+      strip_prefix = "marked-element-2.1.0",
       path = "/marked-element",
       srcs = [
           "marked-element.html",
@@ -587,7 +605,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_marked",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_neon_animation",
       licenses = ["notice"],  # BSD-3-Clause
@@ -636,7 +654,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_web_animations_js",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_behaviors",
       licenses = ["notice"],  # BSD-3-Clause
@@ -660,7 +678,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_ripple",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_button",
       licenses = ["notice"],  # BSD-3-Clause
@@ -680,16 +698,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_ripple",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_checkbox",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "6828a6954a048b1230fbd2606faffbae950ba1d042175b96ec50ae355786a166",
+      sha256 = "0a291d0c64de1b6b807d66697bead9c66c0d7bc3c68b8037e6667f3d66a5904c",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-checkbox/archive/v1.4.0.tar.gz",
-          "https://github.com/PolymerElements/paper-checkbox/archive/v1.4.0.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/paper-checkbox/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/paper-checkbox/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "paper-checkbox-1.4.0",
+      strip_prefix = "paper-checkbox-2.0.0",
       path = "/paper-checkbox",
       srcs = ["paper-checkbox.html"],
       deps = [
@@ -698,16 +716,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_dialog",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "c6a9709e7f528d03dcd574503c18b72d4751ca30017346d16e6a791d37ed9259",
+      sha256 = "cf60d3aa6ad57ba4eb8b1c16713e65057735eed94b009aeebdbcf3436c95a161",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-dialog/archive/v1.0.4.tar.gz",
-          "https://github.com/PolymerElements/paper-dialog/archive/v1.0.4.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/paper-dialog/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/paper-dialog/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "paper-dialog-1.0.4",
+      strip_prefix = "paper-dialog-2.0.0",
       path = "/paper-dialog",
       srcs = ["paper-dialog.html"],
       deps = [
@@ -716,16 +734,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_dialog_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_dialog_behavior",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "a7e0e27ce63554bc14f384cf94bcfa24da8dc5f5120dfd565f45e166261aee40",
+      sha256 = "d78e4f7d008c22537a9255ccda1e919fddae5cc125ef26a66eb2c47f648c20ab",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-dialog-behavior/archive/v1.2.5.tar.gz",
-          "https://github.com/PolymerElements/paper-dialog-behavior/archive/v1.2.5.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/paper-dialog-behavior/archive/v1.2.7.tar.gz",
+          "https://github.com/PolymerElements/paper-dialog-behavior/archive/v1.2.7.tar.gz",
       ],
-      strip_prefix = "paper-dialog-behavior-1.2.5",
+      strip_prefix = "paper-dialog-behavior-1.2.7",
       path = "/paper-dialog-behavior",
       srcs = [
           "paper-dialog-behavior.html",
@@ -739,7 +757,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_dialog_scrollable",
       licenses = ["notice"],  # BSD-3-Clause
@@ -758,7 +776,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_dropdown_menu",
       licenses = ["notice"],  # BSD-3-Clause
@@ -790,7 +808,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_header_panel",
       licenses = ["notice"],  # BSD-3-Clause
@@ -807,7 +825,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_flex_layout",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_icon_button",
       licenses = ["notice"],  # BSD-3-Clause
@@ -829,7 +847,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_input",
       licenses = ["notice"],  # BSD-3-Clause
@@ -860,7 +878,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_item",
       licenses = ["notice"],  # BSD-3-Clause
@@ -885,16 +903,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_listbox",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "3cb35f4fe9a3f15185a9e91711dba8f27e9291c8cd371ebf1be21b8f1d5f65fb",
+      sha256 = "674992d882b18a0618fa697180f196dbc052fb2f5d9ce4e19026a918b568ffd6",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-listbox/archive/v1.1.2.tar.gz",
-          "https://github.com/PolymerElements/paper-listbox/archive/v1.1.2.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/paper-listbox/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/paper-listbox/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "paper-listbox-1.1.2",
+      strip_prefix = "paper-listbox-2.0.0",
       path = "/paper-listbox",
       srcs = ["paper-listbox.html"],
       deps = [
@@ -903,16 +921,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_material",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "09f6c8bd6ddbea2be541dc86306efe41cdfb31bec0b69d35a5dc29772bbc8506",
+      sha256 = "913e9c63cf5c8286b0fab817079d7dc900a343d2c05809995d8d9ba0e41f8a29",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-material/archive/v1.0.6.tar.gz",
-          "https://github.com/PolymerElements/paper-material/archive/v1.0.6.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/paper-material/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/paper-material/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "paper-material-1.0.6",
+      strip_prefix = "paper-material-2.0.0",
       path = "/paper-material",
       srcs = [
           "paper-material.html",
@@ -923,7 +941,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_menu",
       licenses = ["notice"],  # BSD-3-Clause
@@ -948,7 +966,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_menu_button",
       licenses = ["notice"],  # BSD-3-Clause
@@ -972,7 +990,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_progress",
       licenses = ["notice"],  # BSD-3-Clause
@@ -991,7 +1009,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_radio_button",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1009,7 +1027,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_radio_group",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1028,7 +1046,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_radio_button",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_ripple",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1045,16 +1063,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_a11y_keys_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_slider",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "08e7c541dbf5d2e959208810bfc03188e82ced87e4d30d325172967f67962c3c",
+      sha256 = "8bb4db532e8b11b11f78006d9aefa217841392c1aeb449ee2a12e3b56748b774",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-slider/archive/v1.0.10.tar.gz",
-          "https://github.com/PolymerElements/paper-slider/archive/v1.0.10.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/paper-slider/archive/v1.0.14.tar.gz",
+          "https://github.com/PolymerElements/paper-slider/archive/v1.0.14.tar.gz",
       ],
-      strip_prefix = "paper-slider-1.0.10",
+      strip_prefix = "paper-slider-1.0.14",
       path = "/paper-slider",
       srcs = ["paper-slider.html"],
       deps = [
@@ -1069,7 +1087,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_spinner",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1090,7 +1108,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_styles",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1121,7 +1139,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_flex_layout",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_tabs",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1150,16 +1168,16 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_toast",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "55f623712ed1f2bae6d6fadc522a2458e083ccd44cc0a907672547e7b10758a9",
+      sha256 = "b1c677e1681ef8d3f688a83da8f7b263902f757f395a9354a1c35f93b9125b60",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-toast/archive/v1.3.0.tar.gz",
-          "https://github.com/PolymerElements/paper-toast/archive/v1.3.0.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/paper-toast/archive/v2.0.0.tar.gz",
+          "https://github.com/PolymerElements/paper-toast/archive/v2.0.0.tar.gz",
       ],
-      strip_prefix = "paper-toast-1.3.0",
+      strip_prefix = "paper-toast-2.0.0",
       path = "/paper-toast",
       srcs = ["paper-toast.html"],
       deps = [
@@ -1168,7 +1186,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_iron_overlay_behavior",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_toggle_button",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1187,7 +1205,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_toolbar",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1205,7 +1223,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_paper_styles",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_paper_tooltip",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1222,24 +1240,7 @@ def tensorboard_polymer_workspace():
           "@org_polymer_neon_animation",
       ],
   )
-  
-  web_library_external(
-      name = "org_polymer",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "07a9e62ffb52193da3af09adda2fbac5cc690439978520e2d03e783863f65f91",
-      strip_prefix = "polymer-1.7.0",
-      urls = [
-          "http://mirror.bazel.build/github.com/polymer/polymer/archive/v1.7.0.tar.gz",
-          "https://github.com/polymer/polymer/archive/v1.7.0.tar.gz",
-      ],
-      path = "/polymer",
-      srcs = [
-          "polymer.html",
-          "polymer-micro.html",
-          "polymer-mini.html",
-      ],
-  )
-  
+
   web_library_external(
       name = "org_polymer_prism",
       licenses = ["notice"],  # MIT
@@ -1255,27 +1256,28 @@ def tensorboard_polymer_workspace():
           "themes/prism.css",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_prism_element",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "ad70bf9cd5bbdf525d465e1b0658867ab4022193eb9c74087a839044b46312b4",
+      sha256 = "c5c03c17520d4992a3576e397aa7a375e64c7f6794ec2af58031f47eef458945",
       urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/prism-element/archive/1.0.4.tar.gz",
-          "https://github.com/PolymerElements/prism-element/archive/1.0.4.tar.gz",
+          "http://mirror.bazel.build/github.com/PolymerElements/prism-element/archive/v1.2.0.tar.gz",
+          "https://github.com/PolymerElements/prism-element/archive/v1.2.0.tar.gz",
       ],
-      strip_prefix = "prism-element-1.0.4",
+      strip_prefix = "prism-element-1.2.0",
       path = "/prism-element",
       srcs = [
           "prism-highlighter.html",
           "prism-import.html",
+          "prism-theme-default.html",
       ],
       deps = [
           "@org_polymer",
           "@org_polymer_prism",
       ],
   )
-  
+
   web_library_external(
       name = "org_polymer_promise_polyfill",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1294,7 +1296,7 @@ def tensorboard_polymer_workspace():
       ],
       deps = ["@org_polymer"],
   )
-  
+
   web_library_external(
       name = "org_polymer_web_animations_js",
       licenses = ["notice"],  # BSD-3-Clause
@@ -1307,16 +1309,16 @@ def tensorboard_polymer_workspace():
       path = "/web-animations-js",
       srcs = ["web-animations-next-lite.min.js"],
   )
-  
+
   web_library_external(
       name = "org_polymer_webcomponentsjs",
       licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "138c43306ee0a6d699ddca9b3c6b0f4982974ea8b7bdad291ea7276c72301df9",
+      sha256 = "1f58decac693deb926e6b62b5dbd459fb7c2e961f7241e6e646d1cd9a60281d2",
       urls = [
-          "http://mirror.bazel.build/github.com/webcomponents/webcomponentsjs/archive/v0.7.22.tar.gz",
-          "https://github.com/webcomponents/webcomponentsjs/archive/v0.7.22.tar.gz",
+          "http://mirror.bazel.build/github.com/webcomponents/webcomponentsjs/archive/v0.7.23.tar.gz",
+          "https://github.com/webcomponents/webcomponentsjs/archive/v0.7.23.tar.gz",
       ],
-      strip_prefix = "webcomponentsjs-0.7.22",
+      strip_prefix = "webcomponentsjs-0.7.23",
       path = "/webcomponentsjs",
       srcs = [
           "CustomElements.js",
diff --git a/third_party/typings.bzl b/third_party/typings.bzl
index d0c9eddbb3..e3931fa68a 100644
--- a/third_party/typings.bzl
+++ b/third_party/typings.bzl
@@ -25,10 +25,6 @@ def tensorboard_typings_workspace():
               "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/ebc69904eb78f94030d5d517b42db20867f679c0/chai/chai.d.ts",
               "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/ebc69904eb78f94030d5d517b42db20867f679c0/chai/chai.d.ts",
           ],
-          "177293828c7a206bf2a7f725753d51396d38668311aa37c96445f91bbf8128a7": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/6e2f2280ef16ef277049d0ce8583af167d586c59/d3/d3.d.ts",  # v3
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/6e2f2280ef16ef277049d0ce8583af167d586c59/d3/d3.d.ts",  # v3
-          ],
           "e4cd3d5de0eb3bc7b1063b50d336764a0ac82a658b39b5cf90511f489ffdee60": [
               "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/efd40e67ff323f7147651bdbef03c03ead7b1675/lodash/lodash.d.ts",
               "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/efd40e67ff323f7147651bdbef03c03ead7b1675/lodash/lodash.d.ts",
@@ -43,11 +39,11 @@ def tensorboard_typings_workspace():
           ],
           "44eba36339bd1c0792072b7b204ee926fe5ffe1e9e2da916e67ac55548e3668a": [
               "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/a872802c0c84ba98ff207d5e673a1fa867c67fd6/polymer/polymer.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/a872802c0c84ba98ff207d5e673a1fa867c67fd6/polymer/polymer.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/a872802c0c84ba98ff207d5e673a1fa867c67fd6/polymer/polymer.d.ts",  # 2016-09-22
           ],
-          "9453c3e6bae824e90758c3b38975c1ed77e6abd79bf513bcb08368fcdb14898e": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/f5407eba29c04fb8387c86df27512bd055b195d2/threejs/three.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/f5407eba29c04fb8387c86df27512bd055b195d2/threejs/three.d.ts",
+          "7ce67447146eb2b9e9cdaaf8bf45b3209865378022cc8acf86616d3be84f6481": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/8cb9ee3fdfe352cfef672bdfdb5f9c428f915e9f/threejs/three.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/8cb9ee3fdfe352cfef672bdfdb5f9c428f915e9f/threejs/three.d.ts",  # r74 @ 2016-04-06
           ],
           "691756a6eb455f340c9e834de0d49fff269e7b8c1799c2454465dcd6a4435b80": [
               "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/46719185c564694c5583c4b7ad94dbb786ecad46/webcomponents.js/webcomponents.js.d.ts",
@@ -55,311 +51,311 @@ def tensorboard_typings_workspace():
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_array",
       licenses = ["notice"],  # MIT
       sha256_urls = {
           "61e7abb7b1f01fbcb0cab8cf39003392f422566209edd681fbd070eaa84ca000": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-array/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-array/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-array/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-array/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_axis",
       licenses = ["notice"],  # MIT
       sha256_urls = {
           "95f75c8dcc89850b2e72581d96a7b5f46ea4ac852f828893f141f14a597421f9": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-axis/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-axis/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-axis/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-axis/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_brush",
       licenses = ["notice"],  # MIT
       sha256_urls = {
           "a2738e693ce8a8640c2d29001e77582c9c361fd23bda44db471629866b60ada7": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-brush/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-brush/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-brush/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-brush/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_chord",
       licenses = ["notice"],  # MIT
       sha256_urls = {
           "c54d24756eb6d744b31e538ad9bab3a75f6d54e2288b29cc72338d4a057d3e83": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-chord/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-chord/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-chord/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-chord/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
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       licenses = ["notice"],  # MIT
       sha256_urls = {
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-collection/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-collection/index.d.ts",
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-collection/index.d.ts",  # 2017-06-08
           ],
       },
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-  
+
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-color/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-color/index.d.ts",
+          "6dd19edd11276476c5d535279237d1a009c1a733611cc44621a88fda1ca04377": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-color/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-color/index.d.ts",  # 2017-06-08
           ],
       },
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-  
+
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-dispatch/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-dispatch/index.d.ts",
+          "af1474301e594fcb4bbdb134361fb6d26c7b333386c3213821532acde59e61a3": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dispatch/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dispatch/index.d.ts",  # 2017-06-08
           ],
       },
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-  
+
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-drag/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-drag/index.d.ts",
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+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-drag/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-drag/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-dsv/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-dsv/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dsv/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dsv/index.d.ts",  # 2017-06-08
           ],
       },
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-  
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-ease/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-ease/index.d.ts",
+          "d5a9be5316b2d1823a3faa7f75de1e2c2efda5c75f0631b44a0f7b69e11f3a90": [
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-ease/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-force/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-force/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-force/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-force/index.d.ts",  # 2017-06-08
           ],
       },
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-  
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-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-format/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-format/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-format/index.d.ts",  # 2017-06-08
           ],
       },
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-  
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-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-hierarchy/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-hierarchy/index.d.ts",
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-hierarchy/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-hierarchy/index.d.ts",  # 2017-06-08
           ],
       },
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-  
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-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-interpolate/index.d.ts",
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-interpolate/index.d.ts",  # 2017-06-08
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-  
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-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-path/index.d.ts",
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-path/index.d.ts",  # 2017-06-08
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-  
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-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-polygon/index.d.ts",
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-polygon/index.d.ts",  # 2017-06-08
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-  
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-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-quadtree/index.d.ts",
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-quadtree/index.d.ts",  # 2017-06-08
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-  
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-queue/index.d.ts",  # 2017-06-08
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-  
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-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-random/index.d.ts",
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-random/index.d.ts",  # 2017-06-08
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-  
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-request/index.d.ts",  # 2017-06-08
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-  
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-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-scale/index.d.ts",
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-scale/index.d.ts",  # 2017-06-08
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+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-selection/index.d.ts",  # 2017-06-08
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-  
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-          "62668a7aaaf6232762b544f9f89c0f557ca7cfb0cd343a358dda7ecbe26f5739": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-shape/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-shape/index.d.ts",
+          "7fec580ba54bc29417dc9030bb3731c9756a65c5e57dcce5a4f183fff7180cd8": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-shape/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-shape/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_time",
       licenses = ["notice"],  # MIT
       sha256_urls = {
-          "0502490ce682fd9265fb1d5d693ce6cd82e3b05e5f5ee3433731266ecb03d5fc": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-time/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-time/index.d.ts",
+          "4b68f2a4ee428f21f2e7d706c0a64f628f0ff5f130cd9f023ab23a04a8fe31de": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-time/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-time/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_timer",
       licenses = ["notice"],  # MIT
       sha256_urls = {
-          "6f191f9aea704aa64b1defa40dfdff1447a6e6bb815feff1660f894500a9c94d": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-timer/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-timer/index.d.ts",
+          "a196f42560be9fa1a77d473c0180f9f2f8d570ed0eee616aad0da94d90ef3661": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-timer/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-timer/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_transition",
       licenses = ["notice"],  # MIT
       sha256_urls = {
-          "a0a7c0c9bfb5c7d6d9d22a8d16b4484b66d13f2ed226954037546cb3da4098ba": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-transition/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-transition/index.d.ts",
+          "10c6cf259d6f965014e75a63925f302911c5afb8581d6d63b0597544fe104bd7": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-transition/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-transition/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_voronoi",
       licenses = ["notice"],  # MIT
       sha256_urls = {
-          "c6bd5f229f915151d0ef678fe50b1aa6a62334ea0a8c6fc0effbac9f7032efc7": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-voronoi/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-voronoi/index.d.ts",
+          "411482515e2ccda4659f7b3d2fbd3a7ef5ea2c7053eec62c95a174b68ad60c3d": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-voronoi/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-voronoi/index.d.ts",  # 2017-06-08
           ],
       },
   )
-  
+
   filegroup_external(
       name = "org_definitelytyped_types_d3_zoom",
       licenses = ["notice"],  # MIT
       sha256_urls = {
-          "a25dc17fbd304cf7a0e5e7bbb8339c930d464eb40c4d6e5f839ce9c0191f4110": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-zoom/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/1550dfd1b8e38d9bf104b3fd16ea9bf98a2b358e/types/d3-zoom/index.d.ts",
+          "df0bedbb7711366a43418d6a3b47c4688ccb02a3d8ad0c2468cafcb6c2faa346": [
+              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-zoom/index.d.ts",
+              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-zoom/index.d.ts",  # 2017-06-08
           ],
       },
   )
-- 
GitLab


From 3c393ac87e5bae55bd6e2f7963ee9644782cfa9a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 13 Jun 2017 18:28:27 -0700
Subject: [PATCH 0057/1375] [XLA] Add missing documentation for HLO Clamp
 operation.

PiperOrigin-RevId: 158928626
---
 .../performance/xla/operation_semantics.md    | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index b970aa5f5f..ed055691ce 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -61,6 +61,42 @@ Invokes a computation with the given arguments.
 The arity and types of the `args` must match the parameters of the
 `computation`. It is allowed to have no `args`.
 
+## Clamp
+
+See also
+[`ComputationBuilder::Clamp`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+
+Clamps an operand to within the range between a minimum and maximum value.
+
+<b> `Clamp(computation, args...)` </b>
+
+| Arguments     | Type                    | Semantics                        |
+| ------------- | ----------------------- | -------------------------------- |
+| `computation` | `Computation`           | computation of type `T_0, T_1,   |
+:               :                         : ..., T_N -> S` with N parameters :
+:               :                         : of arbitrary type                :
+| `operand`     | `ComputationDataHandle` | array of type T                  |
+| `min`         | `ComputationDataHandle` | array of type T                  |
+| `max`         | `ComputationDataHandle` | array of type T                  |
+
+Given an operand and minimum and maximum values, returns the operand if it is in
+the range between the minimum and maximum, else returns the minimum value if the
+operand is below this range or the maximum value if the operand is above this
+range.  That is, `clamp(x, a, b) =  max(min(x, a), b)`.
+
+All three arrays must be the same shape. Alternately, as a restricted form of
+[broadcasting](broadcasting.md), `min` and/or `max` can be a scalar of type `T`.
+
+Example with scalar `min` and `max`:
+
+```
+let operand: s32[3] = {-1, 5, 9};
+let min: s32 = 0;
+let max: s32 = 6;
+==>
+Clamp(operand, min, max) = s32[3]{0, 5, 6};
+```
+
 ## Collapse
 
 See also
-- 
GitLab


From b6039c875290cdd5c9a62e01393b75b928827504 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 13 Jun 2017 18:48:53 -0700
Subject: [PATCH 0058/1375] We believe a fused version of batch_norm_op can
 speed the algorithm up. This pr implements a new op: fused_batch_norm_op in
 tf-xla and HLO.

This is the CPU implementation for batch norm training. This CL is big but
a lot of code are boilerplate.

PiperOrigin-RevId: 158930166
---
 .../xla/client/computation_builder.cc         |  26 ++
 .../compiler/xla/service/cpu/ir_emitter.cc    | 225 ++++++++++++++++++
 .../compiler/xla/service/cpu/ir_emitter.h     |   1 +
 .../compiler/xla/service/dfs_hlo_visitor.h    |   2 +
 .../service/dfs_hlo_visitor_with_default.h    |   5 +
 .../compiler/xla/service/hlo_cost_analysis.cc |   6 +
 .../compiler/xla/service/hlo_cost_analysis.h  |   1 +
 .../compiler/xla/service/hlo_graph_dumper.cc  |   4 +
 .../compiler/xla/service/hlo_instruction.cc   |  23 ++
 .../compiler/xla/service/hlo_instruction.h    |  25 ++
 tensorflow/compiler/xla/service/hlo_opcode.cc |   2 +
 tensorflow/compiler/xla/service/hlo_opcode.h  |   1 +
 .../xla/service/instruction_fusion.cc         |   1 +
 tensorflow/compiler/xla/service/service.cc    |   4 +
 .../compiler/xla/service/shape_inference.cc   | 103 ++++++++
 .../compiler/xla/service/shape_inference.h    |   7 +
 .../compiler/xla/service/user_computation.cc  |  79 ++++++
 .../compiler/xla/service/user_computation.h   |   4 +
 tensorflow/compiler/xla/xla_data.proto        |  11 +-
 19 files changed, 529 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 4035e0f460..6e95e623ba 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -1407,6 +1407,32 @@ ComputationDataHandle ComputationBuilder::ReduceWindowWithGeneralPadding(
   return ParseOpResponse(s, &response);
 }
 
+ComputationDataHandle ComputationBuilder::BatchNormTraining(
+    const ComputationDataHandle& operand, const ComputationDataHandle& scale,
+    const ComputationDataHandle& offset, float epsilon, int64 feature_index) {
+  if (!first_error_.ok() || !PrepareComputation().ok()) {
+    return ComputationDataHandle();
+  }
+  BatchNormTrainingRequest request;
+  *request.mutable_operand() = operand;
+  *request.mutable_scale() = scale;
+  *request.mutable_offset() = offset;
+  request.set_epsilon(epsilon);
+  request.set_feature_index(feature_index);
+
+  OpRequest op_request;
+  *op_request.mutable_batch_norm_training_request() = request;
+  *op_request.mutable_computation() = computation_.handle();
+  AddOpMetadata(&op_request);
+
+  OpResponse response;
+
+  VLOG(2) << "making BatchNormTraining request";
+
+  Status s = client_->stub()->Op(&op_request, &response);
+  return ParseOpResponse(s, &response);
+}
+
 ComputationDataHandle ComputationBuilder::CrossReplicaSum(
     const ComputationDataHandle& operand) {
   if (!first_error_.ok() || !PrepareComputation().ok()) {
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 7ad497ff1a..94c10a6417 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -1039,6 +1039,231 @@ Status IrEmitter::HandleCrossReplicaSum(HloInstruction* crs) {
       "Cross replica sum not implemented on CPU. See b/33011107.");
 }
 
+// Fills up the free variables in 'index_with_free_var' with values from
+// 'filler_index'. The size of free variables must be the same as the
+// size of 'filler_index'.
+//
+// This is often used after dimension reduction, where
+// 'index_with_free_var' has one or more dimensions reduced, which serves as
+// free variables (represented as nullptr). For example, if we have a 4
+// dimensional input and index for the dimension being reduced is
+// 2 (third dimension), we will have an index like [i, j, NULL, k]
+// after reduced dimension.
+//
+// Here we fill up that free variable by 'filler_index', which contains
+// the value in the reduced dimension.
+static llvm_ir::IrArray::Index FillReducedDimensionIndex(
+    llvm_ir::IrArray::Index index_with_free_var,
+    llvm_ir::IrArray::Index filler_index) {
+  llvm_ir::IrArray::Index::const_iterator it = filler_index.begin();
+
+  for (size_t i = 0; i < index_with_free_var.size(); ++i) {
+    if (index_with_free_var[i] == nullptr) {
+      index_with_free_var[i] = *it++;
+    }
+  }
+  CHECK(filler_index.end() == it);
+  return index_with_free_var;
+}
+
+Status IrEmitter::HandleBatchNormTraining(HloInstruction* batch_norm_training) {
+  // The output of BatchNormTraining is a tuple of three element:
+  //   - An N-dimensional array containing normalized values.
+  //   - A 1 dimensional array containing the mean value for each feature.
+  //   - A 1 dimensional array containing the variance value for each feature.
+  HloInstruction* operand = batch_norm_training->operands()[0];
+  HloInstruction* scale = batch_norm_training->operands()[1];
+  HloInstruction* offset = batch_norm_training->operands()[2];
+  float epsilon = batch_norm_training->epsilon();
+  int64 feature_index = batch_norm_training->feature_index();
+  TF_RET_CHECK(ShapeUtil::IsTuple(batch_norm_training->shape()) &&
+               ShapeUtil::TupleElementCount(batch_norm_training->shape()) == 3);
+
+  const Shape& output_shape =
+      ShapeUtil::GetTupleElementShape(batch_norm_training->shape(), 0);
+  const Shape& feature_shape =
+      ShapeUtil::GetTupleElementShape(batch_norm_training->shape(), 1);
+
+  // Reduce vector of the non-feature dimensions.
+  std::vector<int64> dimensions_to_reduce;
+
+  for (int64 i = 0; i < operand->shape().dimensions_size(); ++i) {
+    if (i != feature_index) {
+      dimensions_to_reduce.push_back(i);
+    }
+  }
+
+  // Get the second and third allocations in the output tuple, which should be
+  // used to store the result of mean and variance value calculation.
+  TF_ASSIGN_OR_RETURN(
+      const BufferAllocation::Slice slice_mean,
+      assignment_.GetUniqueSlice(batch_norm_training, /*index=*/{1}));
+  TF_ASSIGN_OR_RETURN(
+      const BufferAllocation::Slice slice_var,
+      assignment_.GetUniqueSlice(batch_norm_training, /*index=*/{2}));
+  const int feature_count = output_shape.dimensions(feature_index);
+  const int size_in_elements = ShapeUtil::ElementsIn(output_shape);
+  TF_RET_CHECK(ShapeUtil::ElementsIn(operand->shape()) == size_in_elements);
+  const int elements_per_feature = size_in_elements / feature_count;
+
+  llvm::Value* mean = EmitTempBufferPointer(slice_mean, feature_shape);
+  llvm_ir::IrArray mean_array(mean, feature_shape);
+
+  llvm::Value* var = EmitTempBufferPointer(slice_var, feature_shape);
+  llvm_ir::IrArray var_array(var, feature_shape);
+
+  // This loop calculates mean and variance for each feature.
+  //
+  // In theory this could be swapped by multi-output fusion. We will evaluate
+  // this when it's ready.
+  //
+  // For variance calculation, we use a simplified formula so we can fuse the
+  // computation into the same loop to calculate mean: Var=E(X^2) - E(X)^2.
+  TF_RETURN_IF_ERROR(
+      llvm_ir::LoopEmitter(
+          [this, operand, dimensions_to_reduce, feature_shape, var_array,
+           elements_per_feature](const llvm_ir::IrArray::Index& index) {
+            PrimitiveType element_type = operand->shape().element_type();
+            // Used to calculate E(X).
+            llvm::Value* sum_address = llvm_ir::EmitAllocaAtFunctionEntry(
+                llvm_ir::PrimitiveTypeToIrType(element_type, &ir_builder_),
+                "sum_address", &ir_builder_,
+                MinimumAlignmentForPrimitiveType(element_type));
+
+            // Used to calculate E(X^2).
+            llvm::Value* sum_square_address =
+                llvm_ir::EmitAllocaAtFunctionEntry(
+                    llvm_ir::PrimitiveTypeToIrType(element_type, &ir_builder_),
+                    "sum_square_address", &ir_builder_,
+                    MinimumAlignmentForPrimitiveType(element_type));
+
+            ir_builder_.CreateStore(
+                llvm::ConstantFP::get(ir_builder_.getFloatTy(), 0.0),
+                sum_address);
+
+            ir_builder_.CreateStore(
+                llvm::ConstantFP::get(ir_builder_.getFloatTy(), 0.0),
+                sum_square_address);
+
+            llvm_ir::ForLoopNest loops(&ir_builder_);
+
+            const llvm_ir::IrArray::Index reduced_dims_index =
+                loops.AddLoopsForShapeOnDimensions(
+                    operand->shape(), dimensions_to_reduce, "reduction_dim");
+
+            SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(),
+                                  &ir_builder_);
+
+            llvm_ir::IrArray operand_array(GetIrArrayForOp(operand));
+            llvm_ir::IrArray::Index input_index =
+                FillReducedDimensionIndex(reduced_dims_index, index);
+            llvm::Value* new_value =
+                operand_array.EmitReadArrayElement(input_index, &ir_builder_);
+
+            llvm::Value* new_value_square =
+                ir_builder_.CreateFMul(new_value, new_value);
+
+            llvm::Value* current_sum = ir_builder_.CreateLoad(sum_address);
+            llvm::Value* current_sum_square =
+                ir_builder_.CreateLoad(sum_square_address);
+            // Update sum.
+            ir_builder_.CreateStore(
+                ir_builder_.CreateFAdd(current_sum, new_value), sum_address);
+
+            // Update sum square.
+            ir_builder_.CreateStore(
+                ir_builder_.CreateFAdd(current_sum_square, new_value_square),
+                sum_square_address);
+
+            SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(),
+                                  &ir_builder_);
+
+            llvm::Value* sum = ir_builder_.CreateLoad(sum_address);
+            llvm::Value* elements_per_feature_value = llvm::ConstantFP::get(
+                ir_builder_.getFloatTy(), elements_per_feature);
+            llvm::Value* mean =
+                ir_builder_.CreateFDiv(sum, elements_per_feature_value);
+            llvm::Value* mean_square = ir_builder_.CreateFMul(mean, mean);
+            llvm::Value* sum_square =
+                ir_builder_.CreateLoad(sum_square_address);
+
+            // Var=E(X^2) - E(X)^2.
+            llvm::Value* var = ir_builder_.CreateFSub(
+                ir_builder_.CreateFDiv(sum_square, elements_per_feature_value),
+                mean_square);
+
+            var_array.EmitWriteArrayElement(index, var, &ir_builder_);
+            return mean;
+          },
+          mean_array, &ir_builder_)
+          .EmitLoop());
+
+  TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                      EmitTargetAddressForOp(batch_norm_training));
+
+  TF_ASSIGN_OR_RETURN(
+      const BufferAllocation::Slice slice,
+      assignment_.GetUniqueSlice(batch_norm_training, /*index=*/{0}));
+
+  llvm::Value* normalized = EmitTempBufferPointer(slice, output_shape);
+
+  llvm_ir::IrArray target_array(normalized, output_shape);
+
+  AddAliasingInformationToIrArray(*batch_norm_training, &target_array);
+
+  TF_RETURN_IF_ERROR(
+      llvm_ir::LoopEmitter(
+          [this, mean_array, var_array, epsilon, operand, dimensions_to_reduce,
+           feature_index, offset, scale](const llvm_ir::IrArray::Index& index) {
+            // The following logic normalizes the input value, scales and shifts
+            // it:
+            //
+            // normalized = (input - mean) / sqrt(variance + epsilon)
+            // result = normalized * scale + offset
+
+            // Current index in the feature dimension.
+            llvm_ir::IrArray::Index feature_index_value(1,
+                                                        index[feature_index]);
+
+            llvm::Value* mean = mean_array.EmitReadArrayElement(
+                feature_index_value, &ir_builder_);
+            llvm::Value* var = var_array.EmitReadArrayElement(
+                feature_index_value, &ir_builder_);
+
+            llvm_ir::IrArray operand_array(GetIrArrayForOp(operand));
+            llvm::Value* input =
+                operand_array.EmitReadArrayElement(index, &ir_builder_);
+
+            llvm::Value* variance_with_epsilon = ir_builder_.CreateFAdd(
+                var, llvm::ConstantFP::get(ir_builder_.getFloatTy(), epsilon));
+            llvm::Function* func_llvm_sqrt = llvm::Intrinsic::getDeclaration(
+                module_, llvm::Intrinsic::sqrt, {ir_builder_.getFloatTy()});
+            llvm::Value* variance_sqrt =
+                ir_builder_.CreateCall(func_llvm_sqrt, {variance_with_epsilon});
+            llvm::Value* normalized = ir_builder_.CreateFDiv(
+                ir_builder_.CreateFSub(input, mean), variance_sqrt);
+            llvm_ir::IrArray offset_array(GetIrArrayForOp(offset));
+            llvm::Value* offset = offset_array.EmitReadArrayElement(
+                feature_index_value, &ir_builder_);
+            llvm_ir::IrArray scale_array(GetIrArrayForOp(scale));
+            llvm::Value* scale = scale_array.EmitReadArrayElement(
+                feature_index_value, &ir_builder_);
+            llvm::Value* result = ir_builder_.CreateFAdd(
+                ir_builder_.CreateFMul(normalized, scale), offset);
+
+            return result;
+          },
+          target_array, &ir_builder_)
+          .EmitLoop());
+
+  llvm_ir::EmitTuple(
+      llvm_ir::IrArray(target_address, batch_norm_training->shape()),
+      {normalized, mean, var}, &ir_builder_);
+  emitted_value_[batch_norm_training] = target_address;
+
+  return Status::OK();
+}
+
 Status IrEmitter::HandleParameter(HloInstruction* parameter) {
   VLOG(2) << "HandleParameter: " << parameter->ToString();
   auto param_number = parameter->parameter_number();
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index ebb7296a07..4fb86434a1 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -106,6 +106,7 @@ class IrEmitter : public DfsHloVisitorWithDefault {
                    HloInstruction* rhs) override;
   Status HandleConvolution(HloInstruction* convolution, HloInstruction* lhs,
                            HloInstruction* rhs, const Window& window) override;
+  Status HandleBatchNormTraining(HloInstruction* batch_norm_training) override;
   Status HandleCrossReplicaSum(HloInstruction* crs) override;
   Status HandleInfeed(HloInstruction* infeed) override;
   Status HandleOutfeed(HloInstruction* infeed) override;
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 78a398f8ef..bfa6f241a3 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -225,6 +225,8 @@ class DfsHloVisitor {
 
   virtual Status HandleRecv(HloInstruction* recv) = 0;
 
+  virtual Status HandleBatchNormTraining(HloInstruction* batchNormTraining) = 0;
+
   // Invoked to inform the visitor that the traversal has completed, and that
   // the root was "root".
   virtual Status FinishVisit(HloInstruction* root) = 0;
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
index 6557c3aa8e..1bcc03bae1 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
@@ -50,6 +50,11 @@ class DfsHloVisitorWithDefault : public DfsHloVisitor {
                                  HloInstruction* rhs) override {
     return DefaultAction(hlo);
   }
+
+  Status HandleBatchNormTraining(HloInstruction* hlo) override {
+    return DefaultAction(hlo);
+  }
+
   Status HandleClamp(HloInstruction* clamp, HloInstruction* /*min*/,
                      HloInstruction* /*arg*/,
                      HloInstruction* /*max*/) override {
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index 38cc74b0f1..7f88474a27 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -314,6 +314,12 @@ Status HloCostAnalysis::HandleReshape(HloInstruction* reshape) {
   return Status::OK();
 }
 
+Status HloCostAnalysis::HandleBatchNormTraining(
+    HloInstruction* batchNormTraining) {
+  // TODO(b/62294698): Implement cost analysis for batch-norm-learning.
+  return Status::OK();
+}
+
 Status HloCostAnalysis::HandleTranspose(HloInstruction* transpose) {
   return Status::OK();
 }
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index b2c40f75ca..30f553a81f 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -83,6 +83,7 @@ class HloCostAnalysis : public DfsHloVisitor {
                       HloInstruction* init_value,
                       tensorflow::gtl::ArraySlice<int64> dimensions,
                       HloComputation* function_handle) override;
+  Status HandleBatchNormTraining(HloInstruction* batchNormTraining) override;
   Status HandleFusion(HloInstruction* fusion) override;
   Status HandleCall(HloInstruction* call) override;
   Status HandleCustomCall(HloInstruction* custom_call,
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index eb2e5dfb37..6abc733646 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -282,6 +282,10 @@ string InstructionSequenceGraph(
         // port for each parameter instruction. No need to emit anything in this
         // case.
         continue;
+      case HloOpcode::kBatchNormTraining:
+        StrAppend(&name, " feature_index=", instruction->feature_index());
+        color = kPurple;
+        break;
       case HloOpcode::kReduce:
         StrAppend(&name, " dims=", Join(instruction->dimensions(), ","));
         color = kPurple;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index ecbf1dd1e5..9e020f9391 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -369,6 +369,22 @@ HloInstruction::CreateDynamicUpdateSlice(const Shape& shape,
   return instruction;
 }
 
+/* static */ std::unique_ptr<HloInstruction>
+HloInstruction::CreateBatchNormTraining(const Shape& shape,
+                                        HloInstruction* operand,
+                                        HloInstruction* scale,
+                                        HloInstruction* offset, float epsilon,
+                                        int64 feature_index) {
+  auto instruction =
+      WrapUnique(new HloInstruction(HloOpcode::kBatchNormTraining, shape));
+  instruction->AppendOperand(operand);
+  instruction->AppendOperand(scale);
+  instruction->AppendOperand(offset);
+  instruction->epsilon_ = epsilon;
+  instruction->feature_index_ = feature_index;
+  return instruction;
+}
+
 /* static */ std::unique_ptr<HloInstruction>
 HloInstruction::CreateSelectAndScatter(
     const Shape& shape, HloInstruction* operand, HloComputation* select,
@@ -841,6 +857,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
     case HloOpcode::kParameter:
       return CreateParameter(parameter_number_, shape, parameter_name_);
     // Unsupported ops for cloning.
+    case HloOpcode::kBatchNormTraining:
     case HloOpcode::kRecv:
     case HloOpcode::kSend:
     case HloOpcode::kUpdate:
@@ -1138,6 +1155,10 @@ bool HloInstruction::Identical(
              // different HloComputations.
              ShapeUtil::Compatible(shape(), other.shape());
 
+    case HloOpcode::kBatchNormTraining:
+      return feature_index() == other.feature_index() &&
+             epsilon() == other.epsilon();
+
     // A constant is defined by the value in the literal.
     case HloOpcode::kConstant:
       return LiteralUtil::Equal(literal(), other.literal());
@@ -1733,6 +1754,8 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
   switch (opcode_) {
     case HloOpcode::kAbs:
       return visitor->HandleAbs(this, operands_[0]);
+    case HloOpcode::kBatchNormTraining:
+      return visitor->HandleBatchNormTraining(this);
     case HloOpcode::kSign:
       return visitor->HandleSign(this, operands_[0]);
     case HloOpcode::kConstant:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 522414325e..f98bafe81e 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -208,6 +208,11 @@ class HloInstruction {
       const Shape& shape, HloInstruction* operand, HloInstruction* init_value,
       const Window& window, HloComputation* reduce_computation);
 
+  // Creates a batch-norm-training instruction.
+  static std::unique_ptr<HloInstruction> CreateBatchNormTraining(
+      const Shape& shape, HloInstruction* operand, HloInstruction* scale,
+      HloInstruction* offset, float epsilon, int64 feature_index);
+
   // Creates a scatter computation that scatters the `source` array to the
   // selected indices of each window.
   static std::unique_ptr<HloInstruction> CreateSelectAndScatter(
@@ -527,6 +532,18 @@ class HloInstruction {
   // Precondition: opcode() == HloOpcode::kSend or HloOpcode::kRecv
   int64 channel_id() const { return channel_id_; }
 
+  // Returns feature_index field associated with the instruction. The index
+  // represents the index of the feature dimension.
+  //
+  // Precondition: opcode() == HloOpcode::kBatchNormTraining
+  int64 feature_index() const { return feature_index_; }
+
+  // Returns a epsilon value associated with the instruction. The is a small
+  // number added to the variance to avoid divide-by-zero error.
+  //
+  // Precondition: opcode() == HloOpcode::kBatchNormTraining
+  int64 epsilon() const { return epsilon_; }
+
   // Returns the infeed configuration string. The infeed configuration includes
   // any metadata needed for the backend compiler (e.g., infeed buffer address)
   // and is target-dependent.
@@ -923,6 +940,14 @@ class HloInstruction {
   // Only present for kRng.
   RandomDistribution distribution_;
 
+  // A small float number added to the variance to avoid divide-by-zero error.
+  // Only present for kBatchNormTraining.
+  float epsilon_;
+
+  // An integer value representing the index of the feature dimension.
+  // Only present for kBatchNormTraining.
+  int64 feature_index_;
+
   // Represents a unique identifier for each Send/Recv instruction pair.
   // Only present for kSend or kRecv.
   int64 channel_id_ = -1;
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.cc b/tensorflow/compiler/xla/service/hlo_opcode.cc
index ceb0cdaa31..5bda6b6dab 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.cc
+++ b/tensorflow/compiler/xla/service/hlo_opcode.cc
@@ -24,6 +24,8 @@ string HloOpcodeString(HloOpcode opcode) {
       return "abs";
     case HloOpcode::kAdd:
       return "add";
+    case HloOpcode::kBatchNormTraining:
+      return "batch-norm-training";
     case HloOpcode::kBitcast:
       return "bitcast";
     case HloOpcode::kBroadcast:
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index e2cdbfdfa7..65aef63dcd 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -30,6 +30,7 @@ namespace xla {
 enum class HloOpcode {
   kAbs,
   kAdd,
+  kBatchNormTraining,
   kBitcast,
   kBroadcast,
   kCall,
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 721640cdbd..06fa8bc619 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -75,6 +75,7 @@ namespace xla {
       return false;
 
     // Expensive instructions.
+    case HloOpcode::kBatchNormTraining:
     case HloOpcode::kCall:
     case HloOpcode::kConvolution:
     case HloOpcode::kCrossReplicaSum:
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 85ca7e4e59..79bf679c5f 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -1195,6 +1195,10 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
   StatusOr<ComputationDataHandle> handle_status;
 
   switch (arg->op_case()) {
+    case OpRequest::kBatchNormTrainingRequest:
+      handle_status = computation->AddBatchNormTrainingInstruction(
+          arg->batch_norm_training_request());
+      break;
     case OpRequest::kBinaryOpRequest:
       handle_status =
           computation->AddBinaryInstruction(arg->binary_op_request());
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index afe1a54d3e..670e1ca84a 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -754,6 +754,109 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
                               AsInt64Slice(arg_shape->dimensions()));
 }
 
+/* static */ StatusOr<Shape> ShapeInference::InferBatchNormTrainingShape(
+    const Shape& operand_shape, const Shape& offset_shape,
+    const Shape& scale_shape, int64 feature_index) {
+  TF_RETURN_IF_ERROR(
+      ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm training"));
+  TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(
+      offset_shape, "offset input of batch norm training"));
+  TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(
+      scale_shape, "scale input of batch norm training"));
+
+  TF_RET_CHECK(ShapeUtil::ValidateShape(operand_shape) ==
+               tensorflow::Status::OK());
+  TF_RET_CHECK(ShapeUtil::ValidateShape(offset_shape) ==
+               tensorflow::Status::OK());
+  TF_RET_CHECK(ShapeUtil::ValidateShape(scale_shape) ==
+               tensorflow::Status::OK());
+
+  if (feature_index >= ShapeUtil::Rank(operand_shape)) {
+    return InvalidArgument(
+        "Expected feature_index of batch-norm-training to be "
+        "smaller than the rank of operand_shape; "
+        "got feature_index %lld, and rank %lld",
+        feature_index, ShapeUtil::Rank(offset_shape));
+  }
+
+  if (feature_index < 0) {
+    return InvalidArgument(
+        "Expected feature_index of batch-norm-training to "
+        "be a non-negative number, got %lld",
+        feature_index);
+  }
+
+  if (ShapeUtil::Rank(operand_shape) < 1) {
+    return InvalidArgument(
+        "Expected the rank of operand to "
+        "batch-norm-training to be at least 1; got %lld",
+        ShapeUtil::Rank(offset_shape));
+  }
+
+  if (ShapeUtil::Rank(offset_shape) != 1) {
+    return InvalidArgument(
+        "Offset input of batch-norm-training must have"
+        " rank 1, but has rank %lld.",
+        ShapeUtil::Rank(offset_shape));
+  }
+
+  if (ShapeUtil::Rank(scale_shape) != 1) {
+    return InvalidArgument(
+        "Scale input of batch-norm-training must have"
+        " rank 1, but has rank %lld.",
+        ShapeUtil::Rank(scale_shape));
+  }
+
+  if (!ShapeUtil::ElementIsFloating(operand_shape)) {
+    return InvalidArgument(
+        "The operand to batch-norm-training must have a floating point "
+        "element type, but the shape is %s",
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  if (!ShapeUtil::SameElementType(offset_shape, operand_shape)) {
+    return InvalidArgument(
+        "The inputs should have the same element type for batch-norm-training, "
+        "but the shape of offset factor is %s "
+        "and the shape of operand is %s",
+        PrimitiveType_Name(offset_shape.element_type()).c_str(),
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  if (!ShapeUtil::SameElementType(scale_shape, operand_shape)) {
+    return InvalidArgument(
+        "The inputs should have the same element type for batch-norm-training, "
+        "but the shape of scale factor is %s "
+        "and the shape of operand is %s",
+        PrimitiveType_Name(scale_shape.element_type()).c_str(),
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  const int64 feature_count = operand_shape.dimensions(feature_index);
+  Shape output_shape_for_mean_and_var =
+      ShapeUtil::MakeShape(operand_shape.element_type(), {feature_count});
+
+  if (ShapeUtil::GetDimension(offset_shape, 0) != feature_count) {
+    return InvalidArgument(
+        "The size of offset factor should be the same as feature count,"
+        "but the size of offset factor is %lld "
+        "and the feature count is %lld",
+        ShapeUtil::GetDimension(offset_shape, 0), feature_count);
+  }
+
+  if (ShapeUtil::GetDimension(scale_shape, 0) != feature_count) {
+    return InvalidArgument(
+        "The size of scale factor should be the same as feature count,"
+        "but the size of scale factor is %lld "
+        "and the feature count is %lld",
+        ShapeUtil::GetDimension(scale_shape, 0), feature_count);
+  }
+
+  return ShapeUtil::MakeTupleShape({operand_shape,
+                                    output_shape_for_mean_and_var,
+                                    output_shape_for_mean_and_var});
+}
+
 /* static */ StatusOr<Shape> ShapeInference::InferConvolveShape(
     const Shape& lhs, const Shape& rhs, const Window& window,
     const ConvolutionDimensionNumbers& dnums) {
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index c2223423e9..8bd3585133 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -64,6 +64,13 @@ class ShapeInference {
       tensorflow::gtl::ArraySlice<const Shape*> arg_shapes,
       const ProgramShape& to_apply);
 
+  // Infers the shape produced by InferBatchNormTraining with the given
+  // operands.
+  static StatusOr<Shape> InferBatchNormTrainingShape(const Shape& operand_shape,
+                                                     const Shape& offset_shape,
+                                                     const Shape& scale_shape,
+                                                     int64 feature_index);
+
   // Infers the shape produced by applying the given convolutional
   // filter (rhs) to lhs in the way specified by the fields on window.
   static StatusOr<Shape> InferConvolveShape(
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index b97823d2dc..417ed584aa 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -465,6 +465,45 @@ StatusOr<ComputationDataHandle> UserComputation::AddReduceInstruction(
   return handle;
 }
 
+StatusOr<ComputationDataHandle>
+UserComputation::AddBatchNormTrainingInstruction(
+    const BatchNormTrainingRequest& batch_norm_training_request) {
+  tensorflow::mutex_lock lock(mutex_);
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* operand,
+                      LookUpRequest(batch_norm_training_request.operand()));
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* scale,
+                      LookUpRequest(batch_norm_training_request.scale()));
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* offset,
+                      LookUpRequest(batch_norm_training_request.offset()));
+
+  ComputationDataHandle handle = CreateComputationDataHandle();
+
+  OperationRequest& request =
+      (*session_computation_.mutable_requests())[handle.handle()];
+
+  TF_ASSIGN_OR_RETURN(
+      Shape inferred_shape,
+      ShapeInference::InferBatchNormTrainingShape(
+          operand->output_shape(), scale->output_shape(),
+          offset->output_shape(), batch_norm_training_request.feature_index()));
+
+  *request.mutable_output_shape() = inferred_shape;
+
+  *request.mutable_output_handle() = handle;
+
+  *request.mutable_request()->mutable_batch_norm_training_request() =
+      batch_norm_training_request;
+
+  VLOG(1) << "AddBatchNormTrainingInstruction (" << GetVersionedHandleInternal()
+          << "), data handle " << handle.handle() << ": "
+          << batch_norm_training_request.ShortDebugString();
+
+  return handle;
+}
+
 StatusOr<ComputationDataHandle> UserComputation::AddReduceWindowInstruction(
     const ReduceWindowRequest& reduce_window_request,
     const UserComputation& to_apply_computation) {
@@ -1555,6 +1594,19 @@ void ConstantVisitor(const SessionComputation& session_computation,
       break;
     }
 
+    case OpRequest::kBatchNormTrainingRequest: {
+      const BatchNormTrainingRequest& batch_norm_training_request =
+          request.request().batch_norm_training_request();
+      ConstantVisitor(session_computation,
+                      batch_norm_training_request.operand(), visited,
+                      is_constant);
+      ConstantVisitor(session_computation, batch_norm_training_request.scale(),
+                      visited, is_constant);
+      ConstantVisitor(session_computation, batch_norm_training_request.offset(),
+                      visited, is_constant);
+      break;
+    }
+
     case OpRequest::kBinaryOpRequest: {
       const BinaryOpRequest& binary_op_request =
           request.request().binary_op_request();
@@ -1963,6 +2015,16 @@ static void ForEachOperand(
       break;
     }
 
+    case OpRequest::kBatchNormTrainingRequest: {
+      const BatchNormTrainingRequest& batch_norm_training_request =
+          request.request().batch_norm_training_request();
+
+      apply(batch_norm_training_request.operand());
+      apply(batch_norm_training_request.scale());
+      apply(batch_norm_training_request.offset());
+      break;
+    }
+
     case OpRequest::kCrossReplicaSumRequest: {
       const CrossReplicaSumRequest& cross_replica_sum_request =
           request.request().cross_replica_sum_request();
@@ -2455,6 +2517,23 @@ void ComputationLowerer::Visit(
       break;
     }
 
+    case OpRequest::kBatchNormTrainingRequest: {
+      const BatchNormTrainingRequest& batch_norm_training_request =
+          request.request().batch_norm_training_request();
+      HloInstruction* operand =
+          lookup_instruction(batch_norm_training_request.operand());
+      HloInstruction* scale =
+          lookup_instruction(batch_norm_training_request.scale());
+      HloInstruction* offset =
+          lookup_instruction(batch_norm_training_request.offset());
+
+      hlo_instruction = add_instruction(HloInstruction::CreateBatchNormTraining(
+          request.output_shape(), operand, scale, offset,
+          batch_norm_training_request.epsilon(),
+          batch_norm_training_request.feature_index()));
+      break;
+    }
+
     case OpRequest::kBroadcastRequest: {
       const BroadcastRequest& broadcast_request =
           request.request().broadcast_request();
diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h
index fb5425ae61..a8bedf20b5 100644
--- a/tensorflow/compiler/xla/service/user_computation.h
+++ b/tensorflow/compiler/xla/service/user_computation.h
@@ -84,6 +84,10 @@ class UserComputation {
   StatusOr<ComputationDataHandle> AddUnaryInstruction(
       const UnaryOpRequest& unary_request);
 
+  // Enqueues a batch norm training instruction onto this user computation.
+  StatusOr<ComputationDataHandle> AddBatchNormTrainingInstruction(
+      const BatchNormTrainingRequest& batch_norm_training_request);
+
   // Enqueues a binary instruction onto this user computation.
   // Returns an error status if the operand indices are out of bounds.
   StatusOr<ComputationDataHandle> AddBinaryInstruction(
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 44a94e171f..23ef79d0d7 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -462,6 +462,14 @@ message ReduceWindowRequest {
   ComputationHandle to_apply = 5;
 }
 
+message BatchNormTrainingRequest {
+  ComputationDataHandle operand = 1;
+  ComputationDataHandle scale = 2;
+  ComputationDataHandle offset = 3;
+  float epsilon = 4;
+  int64 feature_index = 5;
+}
+
 message CrossReplicaSumRequest {
   ComputationDataHandle operand = 2;
 }
@@ -759,7 +767,8 @@ message OpRequest {
     SendRequest send_request = 30;
     RecvRequest recv_request = 31;
     OutfeedRequest outfeed_request = 32;
-    // Next: 35
+    BatchNormTrainingRequest batch_norm_training_request = 35;
+    // Next: 36
   }
 }
 
-- 
GitLab


From 9263faf2a7c413e4f7a5fdc6a56152ec9c2c316b Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Tue, 13 Jun 2017 19:29:45 -0700
Subject: [PATCH 0059/1375] Use correct NumericJS in open source

The GitHub source turns out to be very different from what's on the web
server. Because the source code has a lot of </script> tags in the
comments, which are difficult for jsoup to escape, I had to add a basic
minifier feature to Vulcanize. So as a bonus, this change makes the
TensorBoard binary much smaller too.

PiperOrigin-RevId: 158932524
---
 .../components/tf_imports/dagre.html          |  2 +-
 .../components/tf_imports/graphlib.html       |  2 +-
 .../components/tf_imports/lodash.html         |  2 +-
 .../components/tf_imports/numericjs.html      |  2 +-
 .../tensorboard/vulcanize/Vulcanize.java      | 29 ++++++++++++++++++-
 .../tensorboard/vulcanize/externs.js          |  1 +
 third_party/js.bzl                            |  7 +++--
 7 files changed, 37 insertions(+), 8 deletions(-)

diff --git a/tensorflow/tensorboard/components/tf_imports/dagre.html b/tensorflow/tensorboard/components/tf_imports/dagre.html
index b90dc58e39..49522db38a 100644
--- a/tensorflow/tensorboard/components/tf_imports/dagre.html
+++ b/tensorflow/tensorboard/components/tf_imports/dagre.html
@@ -42,4 +42,4 @@ THE SOFTWARE.
 <link rel="import" href="lodash.html">
 <link rel="import" href="graphlib.html">
 
-<script jscomp-nocompile src="dagre.core.js"></script>
+<script jscomp-minify src="dagre.core.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/graphlib.html b/tensorflow/tensorboard/components/tf_imports/graphlib.html
index 664b855f17..aa868b00b4 100644
--- a/tensorflow/tensorboard/components/tf_imports/graphlib.html
+++ b/tensorflow/tensorboard/components/tf_imports/graphlib.html
@@ -17,4 +17,4 @@ limitations under the License.
 
 <link rel="import" href="lodash.html">
 
-<script jscomp-nocompile src="graphlib.core.js"></script>
+<script jscomp-minify src="graphlib.core.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/lodash.html b/tensorflow/tensorboard/components/tf_imports/lodash.html
index 65ff6a4b03..192e61cc99 100644
--- a/tensorflow/tensorboard/components/tf_imports/lodash.html
+++ b/tensorflow/tensorboard/components/tf_imports/lodash.html
@@ -15,4 +15,4 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
-<script jscomp-nocompile src="../lodash/lodash.js"></script>
+<script jscomp-minify src="../lodash/lodash.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/numericjs.html b/tensorflow/tensorboard/components/tf_imports/numericjs.html
index 81fa949168..69e948eb99 100644
--- a/tensorflow/tensorboard/components/tf_imports/numericjs.html
+++ b/tensorflow/tensorboard/components/tf_imports/numericjs.html
@@ -40,4 +40,4 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 THE SOFTWARE.
 -->
 
-<script jscomp-suppress src="numeric.js"></script>
+<script jscomp-minify src="numeric.js"></script>
diff --git a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
index 533907dd64..80e48124eb 100644
--- a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
+++ b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
@@ -24,6 +24,7 @@ import com.google.common.base.Joiner;
 import com.google.common.base.Optional;
 import com.google.common.base.Splitter;
 import com.google.common.collect.HashMultimap;
+import com.google.common.collect.ImmutableList;
 import com.google.common.collect.ImmutableMultimap;
 import com.google.common.collect.ImmutableSet;
 import com.google.common.collect.Iterables;
@@ -259,12 +260,18 @@ public final class Vulcanize {
       path = me().lookup(Webpath.get(node.attr("src")));
       script = new String(Files.readAllBytes(getWebfile(path)), UTF_8);
     }
+    boolean wantsMinify = getAttrTransitive(node, "jscomp-minify").isPresent();
     if (node.attr("src").endsWith(".min.js")
-        || getAttrTransitive(node, "jscomp-nocompile").isPresent()) {
+        || getAttrTransitive(node, "jscomp-nocompile").isPresent()
+        || wantsMinify) {
+      if (wantsMinify) {
+        script = minify(path, script);
+      }
       Node newScript =
           new Element(Tag.valueOf("script"), node.baseUri(), node.attributes())
               .appendChild(new DataNode(script, node.baseUri()))
               .removeAttr("src")
+              .removeAttr("jscomp-minify")
               .removeAttr("jscomp-nocompile");
       if (firstCompiledScript != null) {
         firstCompiledScript.before(newScript);
@@ -458,6 +465,26 @@ public final class Vulcanize {
             .appendChild(new DataNode(script, tag.baseUri())));
   }
 
+  private static String minify(Webpath path, String script) {
+    CompilerOptions options = new CompilerOptions();
+    options.skipAllCompilerPasses();
+    options.setLanguageIn(CompilerOptions.LanguageMode.ECMASCRIPT_2016);
+    options.setLanguageOut(CompilerOptions.LanguageMode.ECMASCRIPT5);
+    options.setContinueAfterErrors(true);
+    CompilationLevel.SIMPLE_OPTIMIZATIONS.setOptionsForCompilationLevel(options);
+    if (testOnly) {
+      options.setPrettyPrint(true);
+      options.setGeneratePseudoNames(true);
+    }
+    Compiler compiler = new Compiler();
+    compiler.disableThreads();
+    compiler.compile(
+        ImmutableList.<SourceFile>of(),
+        ImmutableList.of(SourceFile.fromCode(path.toString(), script)),
+        options);
+    return compiler.toSource();
+  }
+
   private static void handleLicense(String text) {
     if (legalese.add(CharMatcher.whitespace().removeFrom(text))) {
       licenses.add(CharMatcher.anyOf("\r\n").trimFrom(text));
diff --git a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js
index 2e56562c1c..85d0ea80c0 100644
--- a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js
+++ b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js
@@ -20,6 +20,7 @@
 /** @type {!Object} */ var _;
 /** @type {!Object} */ var d3;
 /** @type {!Object} */ var dagre;
+/** @type {!Object} */ var numeric;
 /** @type {!Object} */ var weblas;
 /** @type {!Object} */ var graphlib;
 /** @type {!Object} */ var Plottable;
diff --git a/third_party/js.bzl b/third_party/js.bzl
index 46466c3f31..b5395a0b0c 100644
--- a/third_party/js.bzl
+++ b/third_party/js.bzl
@@ -189,11 +189,12 @@ def tensorboard_js_workspace():
               "http://mirror.bazel.build/raw.githubusercontent.com/sloisel/numeric/v1.2.6/license.txt",
               "https://raw.githubusercontent.com/sloisel/numeric/v1.2.6/license.txt",
           ],
-          "dfaca3b8485bee735788cc6eebca82ea25719adc1fb8911c7799c6bd5a95df3b": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/sloisel/numeric/v1.2.6/src/numeric.js",
-              "https://raw.githubusercontent.com/sloisel/numeric/v1.2.6/src/numeric.js",
+          "5dcaba2016fd237091e3a17b0dc272fb21f0e2b15d7628f95a0ad0cd4cdf4020": [
+              "http://mirror.bazel.build/www.numericjs.com/lib/numeric-1.2.6.js",
+              "http://www.numericjs.com/lib/numeric-1.2.6.js",
           ],
       },
+      rename = {"numeric-1.2.6.js": "numeric.js"},
   )
 
   filegroup_external(
-- 
GitLab


From 838571b0a05f38db0715a149b22094cc1cd6ff75 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Tue, 13 Jun 2017 19:37:52 -0700
Subject: [PATCH 0060/1375] python import_graph_def: keep track of multiple
 colocations and fill in the first non-empty device.  Useful for
 visualizations, but correctness doesn't change.

PiperOrigin-RevId: 158932961
---
 tensorflow/python/framework/importer.py      | 33 ++++++++++++------
 tensorflow/python/framework/importer_test.py | 36 ++++++++++++++++++++
 2 files changed, 59 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py
index 025e213620..5043e7285f 100644
--- a/tensorflow/python/framework/importer.py
+++ b/tensorflow/python/framework/importer.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import contextlib
 import copy
 
@@ -311,9 +312,10 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
           compute_shapes=False, compute_device=False,
           op_def=op_def)
 
-    # Maps from a node to the op it is colocated with, if colocation
+    # Maps from a node to the ops it is colocated with, if colocation
     # is specified in the attributes.
-    colocation_pairs = {}
+    colocation_pairs = collections.defaultdict(list)
+
     # 2. Add inputs to the operations.
     for node in graph_def.node:
       op = name_to_op[node.name]
@@ -339,7 +341,7 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
                   'loc:@' + original_op.name))
               if op_to_bind_to != node.name:
                 # Keep track of this mapping for a later phase.
-                colocation_pairs[op] = original_op
+                colocation_pairs[op].append(original_op)
                 # Don't apply this op's device function,
                 # the colocation constraint will ensure
                 # the proper device gets assigned at runtime.
@@ -474,13 +476,24 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
     # The following loop populates the device field of ops that are
     # colocated with another op.  This is implied by the colocation
     # attribute, but we propagate the device field for completeness.
-    for op, coloc_op in colocation_pairs.items():
-      # If the colocation op has no device, even after a device
-      # application, there's nothing to do here.
-      if not coloc_op.device:
-        continue
-      coloc_device = pydev.DeviceSpec.from_string(coloc_op.device)
-      op._set_device(coloc_device)  # pylint: disable=protected-access
+    for op, coloc_op_list in colocation_pairs.items():
+      coloc_device = None
+      # Find any device in the list of colocated ops that have a
+      # device, if it exists.  We assume that if multiple ops
+      # have devices, they refer to the same device.  Otherwise, a
+      # runtime error will occur since the colocation property
+      # cannot be guaranteed.
+      #
+      # One possible improvement is to try to check for compatibility
+      # of all devices in this list at import time here, which would
+      # require implementing a compatibility function for device specs
+      # in python.
+      for coloc_op in coloc_op_list:
+        if coloc_op.device:
+          coloc_device = pydev.DeviceSpec.from_string(coloc_op.device)
+          break
+      if coloc_device:
+        op._set_device(coloc_device)  # pylint: disable=protected-access
 
     # Treat unused input mappings as an error, because they are likely to be
     # due to a typo.
diff --git a/tensorflow/python/framework/importer_test.py b/tensorflow/python/framework/importer_test.py
index 7fdbcfd856..5a683dc733 100644
--- a/tensorflow/python/framework/importer_test.py
+++ b/tensorflow/python/framework/importer_test.py
@@ -682,6 +682,42 @@ class ImportGraphDefTest(test.TestCase):
                   key: '_class' value { list { s: 'loc:@imported_graph/A' } }
           } }""", b.graph.as_graph_def())
 
+  def testMultipleColocationWithDeviceFn(self):
+    original_graph_def = self._MakeGraphDef("""
+          node { name: 'A' op: 'None'}
+          node { name: 'B' op: 'None'}
+          node { name: 'C' op: 'None'  attr {
+            key: '_class'
+            value { list { s: 'loc:@A' s: 'loc:@B' } }
+          } }""")
+
+    # A device function that places "B" on a device, and "A" is empty.
+    #
+    # B and C should contain "/device:B".  A will not right now.  But
+    # because of the colocation property, at runtime it would be
+    # placed with B and C.
+    def CustomDeviceFn(op):
+      if "B" in op.name:
+        return "/device:B:0"
+      return ""
+
+    with ops.Graph().as_default():
+      with ops.device(CustomDeviceFn):
+        c, = importer.import_graph_def(
+            original_graph_def, return_elements=["C"], name="imported_graph")
+
+      self.assertProtoEqualsVersion("""
+          node { name: 'imported_graph/A' op: 'None' }
+          node { name: 'imported_graph/B' op: 'None' device: "/device:B:0" }
+          node { name: 'imported_graph/C' op: 'None' device: "/device:B:0"
+                 attr {
+                   key: '_class' value {
+                     list { s: 'loc:@imported_graph/A'
+                            s: 'loc:@imported_graph/B' }
+                   }
+                 }
+               }""", c.graph.as_graph_def())
+
   def testNamePrefixColocationAttrsMultipleImport(self):
     original_graph_def = self._MakeGraphDef("""
           node { name: 'A' op: 'None' }
-- 
GitLab


From 6ffa51f1e0e76c87ee2164a8d421279768372501 Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Tue, 13 Jun 2017 19:47:59 -0700
Subject: [PATCH 0061/1375] Add a standalone cost analysis tool. Improved
 logging.

PiperOrigin-RevId: 158933442
---
 .../core/grappler/clusters/single_machine.cc  |  9 +++-
 .../costs/measuring_cost_estimator.cc         |  1 +
 .../grappler/costs/op_level_cost_estimator.cc |  6 ++-
 .../core/grappler/costs/virtual_placer.cc     |  2 +
 .../core/grappler/grappler_item_builder.h     |  2 +-
 tensorflow/python/BUILD                       | 13 +++++
 tensorflow/python/grappler/cost_analyzer.cc   | 11 +++--
 tensorflow/python/grappler/cost_analyzer.h    |  4 +-
 tensorflow/python/grappler/cost_analyzer.i    | 12 +++--
 tensorflow/python/grappler/cost_analyzer.py   | 17 +++++--
 .../python/grappler/cost_analyzer_tool.py     | 49 +++++++++++++++++++
 11 files changed, 110 insertions(+), 16 deletions(-)
 create mode 100644 tensorflow/python/grappler/cost_analyzer_tool.py

diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index 22ccf5208c..b9ed7c0590 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -36,6 +36,8 @@ SingleMachine::SingleMachine(int timeout_s, int num_cpu_cores, int num_gpus)
       num_gpus_(num_gpus),
       expected_init_time_s_(0),
       closing_(false) {
+  VLOG(1) << "Number of CPU cores: " << num_cpu_cores
+          << " Number of GPUs: " << num_gpus;
   thread_pool_.reset(new thread::ThreadPool(
       Env::Default(), SanitizeThreadSuffix("single_machine"), 2));
 
@@ -73,9 +75,12 @@ Status SingleMachine::Provision() {
   DeviceProperties attr = GetLocalCPUInfo();
   devices_["/job:localhost/replica:0/task:0/cpu:0"] = GetLocalCPUInfo();
 
+  VLOG(1) << "Number of GPUs: " << num_gpus_;
   for (int i = 0; i < num_gpus_; ++i) {
-    devices_[strings::StrCat("/job:localhost/replica:0/task:0/gpu:", i)] =
-        GetLocalGPUInfo(i);
+    string device_name =
+        strings::StrCat("/job:localhost/replica:0/task:0/gpu:", i);
+    VLOG(1) << "Adding GPU device " << device_name;
+    devices_[device_name] = GetLocalGPUInfo(i);
   }
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/costs/measuring_cost_estimator.cc b/tensorflow/core/grappler/costs/measuring_cost_estimator.cc
index e4a0d6f1b8..8fd1801863 100644
--- a/tensorflow/core/grappler/costs/measuring_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/measuring_cost_estimator.cc
@@ -101,6 +101,7 @@ Status MeasuringCostEstimator::PredictCosts(const GraphDef& optimized_graph,
   }
 
   // Run "measurement_steps_" and measure the time.
+  VLOG(1) << "Number of measurement steps: " << measurement_steps_;
   if (measurement_threads_ > 0) {
     for (int i = 0; i < measurement_steps_; ++i) {
       thread_pool_->Schedule([i, &measurement_fn]() { measurement_fn(i); });
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index d8b8a12eb2..ba6686e7df 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -314,6 +314,8 @@ std::pair<double, double> OpLevelCostEstimator::GetDeviceInfo(
       bandwidth = 100;
     }
   }
+  VLOG(1) << "Device: " << device.type() << " GFLOPS: " << gflops
+          << " Bandwidth: " << bandwidth;
 
   return std::make_pair(gflops, bandwidth);
 }
@@ -461,7 +463,7 @@ int64 OpLevelCostEstimator::CountConv2DOperations(
   ops *= conv_dims.kx * conv_dims.ky;
   ops *= conv_dims.iz * conv_dims.oz;
   ops *= kOpsPerMac;
-  VLOG(1) << "Operations for Conv2D" << ops;
+  VLOG(1) << "Operations for Conv2D " << ops;
 
   if (conv_info != nullptr) {
     *conv_info = conv_dims;
@@ -679,7 +681,7 @@ int64 OpLevelCostEstimator::CountConv2DBackPropInputOperations(
   ops *= conv_dims.iz * conv_dims.oz;
   ops *= kOpsPerMac;
 
-  VLOG(1) << "Operations for Conv2DBackPropInput" << ops;
+  VLOG(1) << "Operations for Conv2DBackPropInput " << ops;
 
   if (returned_conv_dims != nullptr) {
     *returned_conv_dims = conv_dims;
diff --git a/tensorflow/core/grappler/costs/virtual_placer.cc b/tensorflow/core/grappler/costs/virtual_placer.cc
index 0291bd0490..a2d463e765 100644
--- a/tensorflow/core/grappler/costs/virtual_placer.cc
+++ b/tensorflow/core/grappler/costs/virtual_placer.cc
@@ -36,6 +36,7 @@ VirtualPlacer::VirtualPlacer(const Cluster* cluster) {
 
   } else {
     default_device_ = devices_.begin()->first;
+    VLOG(1) << "Number of devices: " << devices_.size();
     for (const auto& device : devices_) {
       if (str_util::Lowercase(device.first).find("gpu") != string::npos) {
         default_device_ = device.first;
@@ -47,6 +48,7 @@ VirtualPlacer::VirtualPlacer(const Cluster* cluster) {
 
 const DeviceProperties& VirtualPlacer::get_device(const NodeDef& node) const {
   string device = get_canonical_device_name(node);
+  VLOG(3) << "Device name: " << device;
   auto it = devices_.find(device);
   DCHECK(it != devices_.end());
   return it->second;
diff --git a/tensorflow/core/grappler/grappler_item_builder.h b/tensorflow/core/grappler/grappler_item_builder.h
index 3aa1d2027f..7135c83801 100644
--- a/tensorflow/core/grappler/grappler_item_builder.h
+++ b/tensorflow/core/grappler/grappler_item_builder.h
@@ -31,7 +31,7 @@ struct ItemConfig {
       : ignore_user_placement(true),
         ignore_colocation(true),
         placeholder_unknown_output_shape_dim(-1),
-        apply_optimizations(true),
+        apply_optimizations(false),
         inline_functions(true) {}
 
   // If true, ignore all user specified node placement.
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 29b5a2574c..c6b98e686f 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3832,6 +3832,19 @@ py_library(
     deps = [":pywrap_tensorflow_internal"],
 )
 
+py_binary(
+    name = "cost_analyzer_tool",
+    srcs = [
+        "grappler/cost_analyzer_tool.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":cost_analyzer",
+        ":framework_for_generated_wrappers",
+        "//tensorflow/core:protos_all_py",
+    ],
+)
+
 py_test(
     name = "cost_analyzer_test",
     size = "small",
diff --git a/tensorflow/python/grappler/cost_analyzer.cc b/tensorflow/python/grappler/cost_analyzer.cc
index 29976b7949..88bf900dca 100644
--- a/tensorflow/python/grappler/cost_analyzer.cc
+++ b/tensorflow/python/grappler/cost_analyzer.cc
@@ -30,11 +30,11 @@ CostAnalyzer::CostAnalyzer(const GrapplerItem& item, Cluster* cluster,
       analytical_estimator_(cluster, false),
       suffix_(suffix) {}
 
-Status CostAnalyzer::GenerateReport(std::ostream& os) {
+Status CostAnalyzer::GenerateReport(std::ostream& os, bool per_node_report) {
   GatherCosts();
   PreprocessCosts();
   AnalyzeCosts();
-  PrintAnalysis(os);
+  PrintAnalysis(os, per_node_report);
   return Status::OK();
 }
 
@@ -158,7 +158,7 @@ void CostAnalyzer::AnalyzeCosts() {
   }
 }
 
-void CostAnalyzer::PrintAnalysis(std::ostream& os) const {
+void CostAnalyzer::PrintAnalysis(std::ostream& os, bool per_node_report) const {
   os << std::endl;
   os << std::left << std::setw(50)
      << "Total time measured in ns (serialized): " << std::right
@@ -225,6 +225,11 @@ void CostAnalyzer::PrintAnalysis(std::ostream& os) const {
     os << std::endl;
   }
   os << std::endl;
+
+  if (per_node_report) {
+    os << "Below is the per-node report:" << std::endl;
+    os << op_perf_.DebugString();
+  }
 }
 
 }  // end namespace grappler
diff --git a/tensorflow/python/grappler/cost_analyzer.h b/tensorflow/python/grappler/cost_analyzer.h
index 3700bf5fb3..0e860e0fee 100644
--- a/tensorflow/python/grappler/cost_analyzer.h
+++ b/tensorflow/python/grappler/cost_analyzer.h
@@ -50,7 +50,7 @@ class CostAnalyzer {
  public:
   explicit CostAnalyzer(const GrapplerItem& item, Cluster* cluster,
                         const string& suffix);
-  Status GenerateReport(std::ostream& os);
+  Status GenerateReport(std::ostream& os, bool per_node_report);
 
  private:
   void PredictCosts(CostEstimator* cost_estimator, CostGraphDef* cost_graph,
@@ -59,7 +59,7 @@ class CostAnalyzer {
   void PreprocessCosts();
   void AnalyzeCosts();
   void SortOpsByTime(std::map<string, OpPerfSummary> ops);
-  void PrintAnalysis(std::ostream& os) const;
+  void PrintAnalysis(std::ostream& os, bool per_node_report) const;
 
   const GrapplerItem* item_;
   MeasuringCostEstimator measure_estimator_;
diff --git a/tensorflow/python/grappler/cost_analyzer.i b/tensorflow/python/grappler/cost_analyzer.i
index a51d8673c9..6066b6131f 100644
--- a/tensorflow/python/grappler/cost_analyzer.i
+++ b/tensorflow/python/grappler/cost_analyzer.i
@@ -42,8 +42,10 @@ limitations under the License.
 %}
 
 %{
-string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph) {
+string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool
+per_node_report) {
   tensorflow::grappler::ItemConfig cfg;
+  cfg.apply_optimizations = false;
   std::unique_ptr<tensorflow::grappler::GrapplerItem> item =
       tensorflow::grappler::GrapplerItemFromMetaGraphDef("metagraph", metagraph, cfg);
 
@@ -53,16 +55,20 @@ string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph) {
   int num_cpu_cores = tensorflow::grappler::GetNumAvailableLogicalCPUCores();
   int num_gpus = tensorflow::grappler::GetNumAvailableGPUs();
   tensorflow::grappler::SingleMachine cluster(timeout_s, num_cpu_cores, num_gpus);
+  cluster.SetNumWarmupSteps(10);
+  cluster.AllowSoftPlacement(true);
+  cluster.DisableDetailedStats(false);
   TF_CHECK_OK(cluster.Provision());
 
   string suffix;
   tensorflow::grappler::CostAnalyzer analyzer(*item, &cluster, suffix);
 
   std::stringstream os;
-  analyzer.GenerateReport(os);
+  analyzer.GenerateReport(os, per_node_report);
   return os.str();
 }
 
 %}
 
-string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph);
+string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool
+per_node_report);
diff --git a/tensorflow/python/grappler/cost_analyzer.py b/tensorflow/python/grappler/cost_analyzer.py
index d16614c7c7..75c21e5727 100644
--- a/tensorflow/python/grappler/cost_analyzer.py
+++ b/tensorflow/python/grappler/cost_analyzer.py
@@ -22,8 +22,19 @@ from tensorflow.python import pywrap_tensorflow as tf_wrap
 from tensorflow.python.framework import errors
 
 
-def GenerateCostReport(metagraph):
-  """Analyze the cost of each TensorFlow operation in the provided metagraph."""
+def GenerateCostReport(metagraph, per_node_report=False):
+  """Analyze the cost of each TensorFlow op and node in the provided metagraph.
+
+  Args:
+    metagraph: An TensorFlow MetaGraphDef.
+    per_node_report: by default the report contains stats aggregated on a per op
+      type basis, setting per_node_report to True adds results for each
+      individual node to the report.
+
+  Returns:
+    A string of cost report.
+  """
   with errors.raise_exception_on_not_ok_status():
-    ret_from_swig = tf_wrap.GenerateCostReport(metagraph.SerializeToString())
+    ret_from_swig = tf_wrap.GenerateCostReport(metagraph.SerializeToString(),
+                                               per_node_report)
   return ret_from_swig
diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py
new file mode 100644
index 0000000000..80c8970c0b
--- /dev/null
+++ b/tensorflow/python/grappler/cost_analyzer_tool.py
@@ -0,0 +1,49 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""A tool for cost analysis."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.python.grappler import cost_analyzer
+from tensorflow.python.platform import app
+
+
+def main(_):
+  with open(FLAGS.input) as input_file:
+    metagraph = meta_graph_pb2.MetaGraphDef()
+    metagraph.ParseFromString(input_file.read())
+
+  report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report)
+  print(report)
+
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+      "--input", type=str, default=None, help="Input .meta file path.")
+  parser.add_argument(
+      "--per_node_report",
+      action="store_true",
+      help="Generate per-node report. By default the report contains stats "
+      "aggregated on a per op type basis, per_node_report adds results "
+      "for each individual node to the report.")
+  FLAGS, unparsed = parser.parse_known_args()
+  app.run(main=main, argv=[sys.argv[0]] + unparsed)
-- 
GitLab


From 3df6e18b59b63ea4f5b68ba8c8ec878940a1ada1 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Tue, 13 Jun 2017 20:31:22 -0700
Subject: [PATCH 0062/1375] Fix reset_uids Keras layers utility

PiperOrigin-RevId: 158935673
---
 tensorflow/contrib/keras/python/keras/backend.py      | 7 ++++---
 tensorflow/contrib/keras/python/keras/backend_test.py | 5 ++++-
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index b7adf9461a..9f02fc0958 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -269,9 +269,10 @@ def get_uid(prefix=''):
 
 
 def reset_uids():
-  layer_name_uids_collection = ops.get_collection_ref('LAYER_NAME_UIDS')
-  if layer_name_uids_collection:
-    layer_name_uids_collection.pop()
+  per_graph_layer_name_uids = tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS
+  keys = list(per_graph_layer_name_uids.keys())
+  for key in keys:
+    del per_graph_layer_name_uids[key]
 
 
 def clear_session():
diff --git a/tensorflow/contrib/keras/python/keras/backend_test.py b/tensorflow/contrib/keras/python/keras/backend_test.py
index 2da5aee58e..a2bc95e4a1 100644
--- a/tensorflow/contrib/keras/python/keras/backend_test.py
+++ b/tensorflow/contrib/keras/python/keras/backend_test.py
@@ -105,10 +105,13 @@ class BackendUtilsTest(test.TestCase):
     self.assertEqual(keras.backend.image_data_format(), image_data_format)
     keras.backend.set_image_data_format('channels_last')
 
-  def test_get_uid(self):
+  def test_get_reset_uids(self):
     self.assertEqual(keras.backend.get_uid('foo'), 1)
     self.assertEqual(keras.backend.get_uid('foo'), 2)
 
+    keras.backend.reset_uids()
+    self.assertEqual(keras.backend.get_uid('foo'), 1)
+
 
 class BackendVariableTest(test.TestCase):
 
-- 
GitLab


From e05f78a9b688a8ae37b1a03bfc4459e18e3b88e4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 13 Jun 2017 21:48:01 -0700
Subject: [PATCH 0063/1375] After synchronizing CUDA device, check for errors.

PiperOrigin-RevId: 158939543
---
 tensorflow/stream_executor/cuda/cuda_driver.cc | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_driver.cc b/tensorflow/stream_executor/cuda/cuda_driver.cc
index 76778dbeec..b02aefe3e8 100644
--- a/tensorflow/stream_executor/cuda/cuda_driver.cc
+++ b/tensorflow/stream_executor/cuda/cuda_driver.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/stream_executor/cuda/cuda_driver.h"
 
+#include <cuda_runtime.h>
 #include <stdint.h>
 #include <stdlib.h>
 #include <map>
@@ -1093,12 +1094,19 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 
 /* static */ bool CUDADriver::SynchronizeContext(CudaContext* context) {
   ScopedActivateContext activation{context};
-  CUresult res = cuCtxSynchronize();
+  const CUresult res = cuCtxSynchronize();
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "could not synchronize on CUDA context: " << ToString(res)
                << " :: " << port::CurrentStackTrace();
     return false;
   }
+  const auto cudart_error = cudaPeekAtLastError();
+  if (cudart_error != cudaSuccess) {
+    LOG(ERROR) << "could not synchronize on CUDA context: "
+               << cudaGetErrorString(cudart_error)
+               << " :: " << port::CurrentStackTrace();
+    return false;
+  }
 
   return true;
 }
-- 
GitLab


From 4f2ed15a0f6acc82ea8764171c366c4de8fb1513 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 13 Jun 2017 22:47:54 -0700
Subject: [PATCH 0064/1375] Automated g4 rollback of changelist 158939543

PiperOrigin-RevId: 158942736
---
 tensorflow/stream_executor/cuda/cuda_driver.cc | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_driver.cc b/tensorflow/stream_executor/cuda/cuda_driver.cc
index b02aefe3e8..76778dbeec 100644
--- a/tensorflow/stream_executor/cuda/cuda_driver.cc
+++ b/tensorflow/stream_executor/cuda/cuda_driver.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/stream_executor/cuda/cuda_driver.h"
 
-#include <cuda_runtime.h>
 #include <stdint.h>
 #include <stdlib.h>
 #include <map>
@@ -1094,19 +1093,12 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 
 /* static */ bool CUDADriver::SynchronizeContext(CudaContext* context) {
   ScopedActivateContext activation{context};
-  const CUresult res = cuCtxSynchronize();
+  CUresult res = cuCtxSynchronize();
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "could not synchronize on CUDA context: " << ToString(res)
                << " :: " << port::CurrentStackTrace();
     return false;
   }
-  const auto cudart_error = cudaPeekAtLastError();
-  if (cudart_error != cudaSuccess) {
-    LOG(ERROR) << "could not synchronize on CUDA context: "
-               << cudaGetErrorString(cudart_error)
-               << " :: " << port::CurrentStackTrace();
-    return false;
-  }
 
   return true;
 }
-- 
GitLab


From f4ccadc2d4cea60001733dfa7626e8e13a85ba7c Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Tue, 13 Jun 2017 22:57:03 -0700
Subject: [PATCH 0065/1375] Refactor histogram compression into new module

This will be useful for pluginification, so we can access this function
independently of the EventAccumulator.

PiperOrigin-RevId: 158943129
---
 .../backend/event_processing/BUILD            |   2 +
 .../event_processing/event_accumulator.py     |  74 ++----------
 .../event_accumulator_test.py                 |  59 +--------
 .../tensorboard/plugins/distributions/BUILD   |  19 +++
 .../plugins/distributions/compressor.py       |  88 ++++++++++++++
 .../plugins/distributions/compressor_test.py  | 113 ++++++++++++++++++
 6 files changed, 235 insertions(+), 120 deletions(-)
 create mode 100644 tensorflow/tensorboard/plugins/distributions/compressor.py
 create mode 100644 tensorflow/tensorboard/plugins/distributions/compressor_test.py

diff --git a/tensorflow/tensorboard/backend/event_processing/BUILD b/tensorflow/tensorboard/backend/event_processing/BUILD
index 9c9ca29be2..c560143897 100644
--- a/tensorflow/tensorboard/backend/event_processing/BUILD
+++ b/tensorflow/tensorboard/backend/event_processing/BUILD
@@ -83,6 +83,7 @@ py_library(
         ":plugin_asset_util",
         ":reservoir",
         "//tensorflow:tensorflow_py",
+        "//tensorflow/tensorboard/plugins/distributions:compressor",
     ],
 )
 
@@ -94,6 +95,7 @@ py_test(
     deps = [
         ":event_accumulator",
         "//tensorflow:tensorflow_py",
+        "//tensorflow/tensorboard/plugins/distributions:compressor",
     ],
 )
 
diff --git a/tensorflow/tensorboard/backend/event_processing/event_accumulator.py b/tensorflow/tensorboard/backend/event_processing/event_accumulator.py
index 1562f0f833..59b925cccd 100644
--- a/tensorflow/tensorboard/backend/event_processing/event_accumulator.py
+++ b/tensorflow/tensorboard/backend/event_processing/event_accumulator.py
@@ -22,13 +22,13 @@ import os
 import re
 import threading
 
-import numpy as np
 import tensorflow as tf
 
 from tensorflow.tensorboard.backend.event_processing import directory_watcher
 from tensorflow.tensorboard.backend.event_processing import event_file_loader
 from tensorflow.tensorboard.backend.event_processing import plugin_asset_util
 from tensorflow.tensorboard.backend.event_processing import reservoir
+from tensorflow.tensorboard.plugins.distributions import compressor
 
 namedtuple = collections.namedtuple
 ScalarEvent = namedtuple('ScalarEvent', ['wall_time', 'step', 'value'])
@@ -41,9 +41,6 @@ CompressedHistogramEvent = namedtuple('CompressedHistogramEvent',
                                       ['wall_time', 'step',
                                        'compressed_histogram_values'])
 
-CompressedHistogramValue = namedtuple('CompressedHistogramValue',
-                                      ['basis_point', 'value'])
-
 HistogramEvent = namedtuple('HistogramEvent',
                             ['wall_time', 'step', 'histogram_value'])
 
@@ -640,8 +637,15 @@ class EventAccumulator(object):
     histo = self._ConvertHistogramProtoToTuple(histo)
     histo_ev = HistogramEvent(wall_time, step, histo)
     self._histograms.AddItem(tag, histo_ev)
-    self._compressed_histograms.AddItem(
-        tag, histo_ev, lambda x: _CompressHistogram(x, self._compression_bps))
+    self._compressed_histograms.AddItem(tag, histo_ev, self._CompressHistogram)
+
+  def _CompressHistogram(self, histo_ev):
+    """Callback for _ProcessHistogram."""
+    return CompressedHistogramEvent(
+        histo_ev.wall_time,
+        histo_ev.step,
+        compressor.CompressHistogram(
+            histo_ev.histogram_value, self._compression_bps))
 
   def _ProcessImage(self, tag, wall_time, step, image):
     """Processes an image by adding it to accumulated state."""
@@ -791,61 +795,3 @@ def _ParseFileVersion(file_version):
         ('Invalid event.proto file_version. Defaulting to use of '
          'out-of-order event.step logic for purging expired events.'))
     return -1
-
-
-def _CompressHistogram(histo_ev, bps):
-  """Creates fixed size histogram by adding compression to accumulated state.
-
-  This routine transforms a histogram at a particular step by linearly
-  interpolating its variable number of buckets to represent their cumulative
-  weight at a constant number of compression points. This significantly reduces
-  the size of the histogram and makes it suitable for a two-dimensional area
-  plot where the output of this routine constitutes the ranges for a single x
-  coordinate.
-
-  Args:
-    histo_ev: A HistogramEvent namedtuple.
-    bps: Compression points represented in basis points, 1/100ths of a percent.
-
-  Returns:
-    CompressedHistogramEvent namedtuple.
-  """
-  # See also: Histogram::Percentile() in core/lib/histogram/histogram.cc
-  histo = histo_ev.histogram_value
-  if not histo.num:
-    return CompressedHistogramEvent(
-        histo_ev.wall_time,
-        histo_ev.step,
-        [CompressedHistogramValue(b, 0.0) for b in bps])
-  bucket = np.array(histo.bucket)
-  weights = (bucket * bps[-1] / (bucket.sum() or 1.0)).cumsum()
-  values = []
-  j = 0
-  while j < len(bps):
-    i = np.searchsorted(weights, bps[j], side='right')
-    while i < len(weights):
-      cumsum = weights[i]
-      cumsum_prev = weights[i - 1] if i > 0 else 0.0
-      if cumsum == cumsum_prev:  # prevent remap divide by zero
-        i += 1
-        continue
-      if not i or not cumsum_prev:
-        lhs = histo.min
-      else:
-        lhs = max(histo.bucket_limit[i - 1], histo.min)
-      rhs = min(histo.bucket_limit[i], histo.max)
-      weight = _Remap(bps[j], cumsum_prev, cumsum, lhs, rhs)
-      values.append(CompressedHistogramValue(bps[j], weight))
-      j += 1
-      break
-    else:
-      break
-  while j < len(bps):
-    values.append(CompressedHistogramValue(bps[j], histo.max))
-    j += 1
-  return CompressedHistogramEvent(histo_ev.wall_time, histo_ev.step, values)
-
-
-def _Remap(x, x0, x1, y0, y1):
-  """Linearly map from [x0, x1] unto [y0, y1]."""
-  return y0 + (x - x0) * float(y1 - y0) / (x1 - x0)
diff --git a/tensorflow/tensorboard/backend/event_processing/event_accumulator_test.py b/tensorflow/tensorboard/backend/event_processing/event_accumulator_test.py
index 4ce766f420..516704c700 100644
--- a/tensorflow/tensorboard/backend/event_processing/event_accumulator_test.py
+++ b/tensorflow/tensorboard/backend/event_processing/event_accumulator_test.py
@@ -25,6 +25,7 @@ from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 
 from tensorflow.tensorboard.backend.event_processing import event_accumulator as ea
+from tensorflow.tensorboard.plugins.distributions import compressor
 
 
 class _EventGenerator(object):
@@ -406,7 +407,7 @@ class MockingEventAccumulatorTest(EventAccumulatorTest):
 
     # Create the expected values after compressing hst1
     expected_vals1 = [
-        ea.CompressedHistogramValue(bp, val)
+        compressor.CompressedHistogramValue(bp, val)
         for bp, val in [(0, 1.0), (2500, 1.25), (5000, 1.5), (7500, 1.75
                                                              ), (10000, 2.0)]
     ]
@@ -416,7 +417,7 @@ class MockingEventAccumulatorTest(EventAccumulatorTest):
 
     # Create the expected values after compressing hst2
     expected_vals2 = [
-        ea.CompressedHistogramValue(bp, val)
+        compressor.CompressedHistogramValue(bp, val)
         for bp, val in [(0, -2),
                         (2500, 2),
                         (5000, 2 + 1 / 3),
@@ -427,60 +428,6 @@ class MockingEventAccumulatorTest(EventAccumulatorTest):
         wall_time=2, step=12, compressed_histogram_values=expected_vals2)
     self.assertEqual(acc.CompressedHistograms('hst2'), [expected_cmphst2])
 
-  def testCompressedHistogramsWithEmptyHistogram(self):
-    """Tests that empty histograms compressed properly in EventAccumulator."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen, compression_bps=(0, 2500, 5000, 7500, 10000))
-
-    gen.AddHistogram(
-        'hst1',
-        wall_time=1,
-        step=10,
-        hmin=None,
-        hmax=None,
-        hnum=0,
-        hsum=0,
-        hsum_squares=0,
-        hbucket_limit=[1, 2, 3],
-        hbucket=[0, 0, 0])
-    acc.Reload()
-
-    # Create the expected values after compressing hst1
-    expected_vals1 = [
-        ea.CompressedHistogramValue(bp, val)
-        for bp, val in [(0, 0.0), (2500, 0), (5000, 0), (7500, 0), (10000, 0)]
-    ]
-    expected_cmphst1 = ea.CompressedHistogramEvent(
-        wall_time=1, step=10, compressed_histogram_values=expected_vals1)
-    self.assertEqual(acc.CompressedHistograms('hst1'), [expected_cmphst1])
-
-  def testCompressHistogram_uglyHistogram(self):
-    bps = (0, 668, 1587, 3085, 5000, 6915, 8413, 9332, 10000)
-    histogram_values = ea.HistogramValue(
-        min=0.0,
-        max=1.0,
-        num=960.0,
-        sum=64.0,
-        sum_squares=64.0,
-        bucket_limit=[
-            0.0, 1e-12, 0.917246389039776, 1.0089710279437536,
-            1.7976931348623157e+308
-        ],
-        bucket=[0.0, 896.0, 0.0, 64.0, 0.0])
-    histogram_event = ea.HistogramEvent(0, 0, histogram_values)
-    compressed_event = ea._CompressHistogram(histogram_event, bps)
-    vals = compressed_event.compressed_histogram_values
-    self.assertEquals(tuple(v.basis_point for v in vals), bps)
-    self.assertAlmostEqual(vals[0].value, 0.0)
-    self.assertAlmostEqual(vals[1].value, 7.157142857142856e-14)
-    self.assertAlmostEqual(vals[2].value, 1.7003571428571426e-13)
-    self.assertAlmostEqual(vals[3].value, 3.305357142857143e-13)
-    self.assertAlmostEqual(vals[4].value, 5.357142857142857e-13)
-    self.assertAlmostEqual(vals[5].value, 7.408928571428571e-13)
-    self.assertAlmostEqual(vals[6].value, 9.013928571428571e-13)
-    self.assertAlmostEqual(vals[7].value, 9.998571428571429e-13)
-    self.assertAlmostEqual(vals[8].value, 1.0)
-
   def testImages(self):
     """Tests 2 images inserted/accessed in EventAccumulator."""
     gen = _EventGenerator(self)
diff --git a/tensorflow/tensorboard/plugins/distributions/BUILD b/tensorflow/tensorboard/plugins/distributions/BUILD
index 3ce765020e..a8b131c350 100644
--- a/tensorflow/tensorboard/plugins/distributions/BUILD
+++ b/tensorflow/tensorboard/plugins/distributions/BUILD
@@ -41,6 +41,25 @@ py_test(
     ],
 )
 
+py_library(
+    name = "compressor",
+    srcs = ["compressor.py"],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = ["//third_party/py/numpy"],
+)
+
+py_test(
+    name = "compressor_test",
+    size = "small",
+    srcs = ["compressor_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":compressor",
+        "//tensorflow:tensorflow_py",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(["**"]),
diff --git a/tensorflow/tensorboard/plugins/distributions/compressor.py b/tensorflow/tensorboard/plugins/distributions/compressor.py
new file mode 100644
index 0000000000..dd9d00f5cd
--- /dev/null
+++ b/tensorflow/tensorboard/plugins/distributions/compressor.py
@@ -0,0 +1,88 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Package for histogram compression."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+
+import numpy as np
+
+# Normal CDF for std_devs: (-Inf, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, Inf)
+# naturally gives bands around median of width 1 std dev, 2 std dev, 3 std dev,
+# and then the long tail.
+NORMAL_HISTOGRAM_BPS = (0, 668, 1587, 3085, 5000, 6915, 8413, 9332, 10000)
+
+
+CompressedHistogramValue = collections.namedtuple('CompressedHistogramValue',
+                                                  ['basis_point', 'value'])
+
+
+def CompressHistogram(histo, bps=NORMAL_HISTOGRAM_BPS):
+  """Creates fixed size histogram by adding compression to accumulated state.
+
+  This routine transforms a histogram at a particular step by linearly
+  interpolating its variable number of buckets to represent their cumulative
+  weight at a constant number of compression points. This significantly reduces
+  the size of the histogram and makes it suitable for a two-dimensional area
+  plot where the output of this routine constitutes the ranges for a single x
+  coordinate.
+
+  Args:
+    histo: A HistogramProto object.
+    bps: Compression points represented in basis points, 1/100ths of a percent.
+        Defaults to normal distribution.
+
+  Returns:
+    List of values for each basis point.
+  """
+  # See also: Histogram::Percentile() in core/lib/histogram/histogram.cc
+  if not histo.num:
+    return [CompressedHistogramValue(b, 0.0) for b in bps]
+  bucket = np.array(histo.bucket)
+  bucket_limit = list(histo.bucket_limit)
+  weights = (bucket * bps[-1] / (bucket.sum() or 1.0)).cumsum()
+  values = []
+  j = 0
+  while j < len(bps):
+    i = np.searchsorted(weights, bps[j], side='right')
+    while i < len(weights):
+      cumsum = weights[i]
+      cumsum_prev = weights[i - 1] if i > 0 else 0.0
+      if cumsum == cumsum_prev:  # prevent remap divide by zero
+        i += 1
+        continue
+      if not i or not cumsum_prev:
+        lhs = histo.min
+      else:
+        lhs = max(bucket_limit[i - 1], histo.min)
+      rhs = min(bucket_limit[i], histo.max)
+      weight = _Remap(bps[j], cumsum_prev, cumsum, lhs, rhs)
+      values.append(CompressedHistogramValue(bps[j], weight))
+      j += 1
+      break
+    else:
+      break
+  while j < len(bps):
+    values.append(CompressedHistogramValue(bps[j], histo.max))
+    j += 1
+  return values
+
+
+def _Remap(x, x0, x1, y0, y1):
+  """Linearly map from [x0, x1] unto [y0, y1]."""
+  return y0 + (x - x0) * float(y1 - y0) / (x1 - x0)
diff --git a/tensorflow/tensorboard/plugins/distributions/compressor_test.py b/tensorflow/tensorboard/plugins/distributions/compressor_test.py
new file mode 100644
index 0000000000..92e6408954
--- /dev/null
+++ b/tensorflow/tensorboard/plugins/distributions/compressor_test.py
@@ -0,0 +1,113 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from tensorflow.tensorboard.plugins.distributions import compressor
+
+
+def _make_expected_value(*values):
+  return [compressor.CompressedHistogramValue(bp, val) for bp, val in values]
+
+
+class HistcompTest(tf.test.TestCase):
+
+  def testExample(self):
+    bps = (0, 2500, 5000, 7500, 10000)
+    proto = tf.HistogramProto(
+        min=1,
+        max=2,
+        num=3,
+        sum=4,
+        sum_squares=5,
+        bucket_limit=[1, 2, 3],
+        bucket=[0, 3, 0])
+    self.assertEqual(
+        _make_expected_value(
+            (0, 1.0),
+            (2500, 1.25),
+            (5000, 1.5),
+            (7500, 1.75),
+            (10000, 2.0)),
+        compressor.CompressHistogram(proto, bps))
+
+  def testAnotherExample(self):
+    bps = (0, 2500, 5000, 7500, 10000)
+    proto = tf.HistogramProto(
+        min=-2,
+        max=3,
+        num=4,
+        sum=5,
+        sum_squares=6,
+        bucket_limit=[2, 3, 4],
+        bucket=[1, 3, 0])
+    self.assertEqual(
+        _make_expected_value(
+            (0, -2),
+            (2500, 2),
+            (5000, 2 + 1 / 3),
+            (7500, 2 + 2 / 3),
+            (10000, 3)),
+        compressor.CompressHistogram(proto, bps))
+
+  def testEmpty(self):
+    bps = (0, 2500, 5000, 7500, 10000)
+    proto = tf.HistogramProto(
+        min=None,
+        max=None,
+        num=0,
+        sum=0,
+        sum_squares=0,
+        bucket_limit=[1, 2, 3],
+        bucket=[0, 0, 0])
+    self.assertEqual(
+        _make_expected_value(
+            (0, 0),
+            (2500, 0),
+            (5000, 0),
+            (7500, 0),
+            (10000, 0)),
+        compressor.CompressHistogram(proto, bps))
+
+  def testUgly(self):
+    bps = (0, 668, 1587, 3085, 5000, 6915, 8413, 9332, 10000)
+    proto = tf.HistogramProto(
+        min=0.0,
+        max=1.0,
+        num=960.0,
+        sum=64.0,
+        sum_squares=64.0,
+        bucket_limit=[0.0, 1e-12, 0.917246389039776, 1.0089710279437536,
+                      1.7976931348623157e+308],
+        bucket=[0.0, 896.0, 0.0, 64.0, 0.0])
+    vals = compressor.CompressHistogram(proto, bps)
+    self.assertEquals(tuple(v.basis_point for v in vals), bps)
+    self.assertAlmostEqual(vals[0].value, 0.0)
+    self.assertAlmostEqual(vals[1].value, 7.157142857142856e-14)
+    self.assertAlmostEqual(vals[2].value, 1.7003571428571426e-13)
+    self.assertAlmostEqual(vals[3].value, 3.305357142857143e-13)
+    self.assertAlmostEqual(vals[4].value, 5.357142857142857e-13)
+    self.assertAlmostEqual(vals[5].value, 7.408928571428571e-13)
+    self.assertAlmostEqual(vals[6].value, 9.013928571428571e-13)
+    self.assertAlmostEqual(vals[7].value, 9.998571428571429e-13)
+    self.assertAlmostEqual(vals[8].value, 1.0)
+
+
+if __name__ == '__main__':
+  tf.test.main()
-- 
GitLab


From f0f34a2096c8cab9d7a2100016e54d5133bbc017 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 13 Jun 2017 23:35:30 -0700
Subject: [PATCH 0066/1375] Restore the new xla header only libs.

PiperOrigin-RevId: 158945196
---
 tensorflow/compiler/jit/BUILD | 28 ++++++++++++----------------
 tensorflow/compiler/xla/BUILD | 33 +++++++++++++++------------------
 2 files changed, 27 insertions(+), 34 deletions(-)

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index da1c50b0a7..306e704415 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -22,22 +22,6 @@ load("//tensorflow:tensorflow.bzl", "cc_header_only_library")
 load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
 load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
 
-# TODO(jhseu): Fix this target.
-#
-# This target can be used by XLA device plugins to prevent circular
-# dependencies, and provides access to all of the required headers
-# for building a device library.
-#cc_header_only_library(
-#    name = "xla_jit_headers_lib",
-#    visibility = ["//visibility:public"],
-#    deps = [
-#        ":xla_cpu_device",
-#        ":xla_cpu_jit",
-#        ":xla_gpu_device",
-#        ":xla_gpu_jit",
-#    ],
-#)
-
 # Target that bundles up the XLA CPU and GPU JIT devices.
 cc_library(
     name = "jit",
@@ -285,3 +269,15 @@ filegroup(
     ),
     visibility = ["//tensorflow:__subpackages__"],
 )
+
+# This target can be used by XLA device plugins to prevent circular dependencies, and provides access to all of the required headers for building a device library.
+cc_header_only_library(
+    name = "xla_jit_headers_lib",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":xla_cpu_device",
+        ":xla_cpu_jit",
+        ":xla_gpu_device",
+        ":xla_gpu_jit",
+    ],
+)
diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 4ce8532dc7..cde790c0ae 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -46,24 +46,6 @@ xla_proto_library(
     ],
 )
 
-# TODO(jhseu): Restore
-# This is a headers target that extra XLA devices can use to prevent
-# circular dependencies.  Devices that are compiled as separate shared
-# objects can also use it to prevent linking of library code.
-#cc_header_only_library(
-#    name = "xla_headers_lib",
-#    visibility = ["//visibility:public"],
-#    deps = [
-#        "//tensorflow/compiler/xla:xla_data_proto",
-#        "//tensorflow/compiler/xla:xla_proto",
-#        "//tensorflow/compiler/xla/client:client_library",
-#        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
-#        "//tensorflow/compiler/xla/service:hlo",
-#        "//tensorflow/core:framework_headers_lib",
-#        "//tensorflow/core:stream_executor_headers_lib",
-#    ],
-#)
-
 cc_library(
     name = "test",
     testonly = 1,
@@ -602,3 +584,18 @@ filegroup(
     ),
     visibility = ["//tensorflow:__subpackages__"],
 )
+
+# This is a headers target that extra XLA devices can use to prevent circular dependencies.  Devices that are compiled as separate shared objects can also use it to prevent linking of library code.
+cc_header_only_library(
+    name = "xla_headers_lib",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":xla_data_proto",
+        ":xla_proto",
+        "//tensorflow/compiler/xla/client:client_library",
+        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
+        "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:stream_executor_headers_lib",
+    ],
+)
-- 
GitLab


From 5cb84a73356d698ed1ed5f0d2e8ea38075b029ca Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 00:34:27 -0700
Subject: [PATCH 0067/1375] Fixed links in readme.

PiperOrigin-RevId: 158948423
---
 tensorflow/examples/learn/README.md | 41 ++++++++++++++++-------------
 1 file changed, 22 insertions(+), 19 deletions(-)

diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index 37157fc296..e5b34292b1 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -1,33 +1,36 @@
-# TF Learn Examples
+# Estimator Examples
 
-Learn is a high-level API for TensorFlow that allows you to create,
-train, and use deep learning models easily. See the [Quickstart tutorial](https://www.tensorflow.org/get_started/tflearn)
+TensorFlow Estimators are a high-level API for TensorFlow that allows you to
+create, train, and use deep learning models easily. 
+
+See the [Quickstart tutorial](https://www.tensorflow.org/get_started/tflearn)
 for an introduction to the API.
 
-To run most of these examples, you need to install the `scikit learn` library (`sudo pip install sklearn`).
-Some examples use the `pandas` library for data processing (`sudo pip install pandas`).
+To run most of these examples, you need to install the `scikit learn` library
+(`sudo pip install sklearn`). Some examples use the `pandas` library for data
+processing (`sudo pip install pandas`).
 
 ## Basics
 
-* [Deep Neural Network Regression with Boston Data](boston.py)
-* [Deep Neural Network Classification with Iris Data](iris.py)
-* [Building a Custom Model](iris_custom_model.py)
-* [Building a Model Using Different GPU Configurations](iris_run_config.py)
+* [Deep Neural Network Regression with Boston Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/boston.py)
+* [Deep Neural Network Classification with Iris Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris.py)
+* [Building a Custom Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_model.py)
+* [Building a Model Using Different GPU Configurations]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_run_config.py)
 
 ## Techniques
 
-* [Improving Performance Using Early Stopping with Iris Data](iris_val_based_early_stopping.py)
-* [Using skflow with Pipeline](iris_with_pipeline.py)
-* [Deep Neural Network with Customized Decay Function](iris_custom_decay_dnn.py)
+* [Improving Performance Using Early Stopping with Iris Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_val_based_early_stopping.py)
+* [Using skflow with Pipeline]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_with_pipeline.py)
+* [Deep Neural Network with Customized Decay Function]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
 
 ## Specialized Models
-* [Building a Random Forest Model](random_forest_mnist.py)
-* [Building a Wide & Deep Model](wide_n_deep_tutorial.py)
-* [Building a Residual Network Model](resnet.py)
+* [Building a Random Forest Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/random_forest_mnist.py)
+* [Building a Wide & Deep Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py)
+* [Building a Residual Network Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/resnet.py)
 
 ## Text classification
 
-* [Text Classification Using Recurrent Neural Networks on Words](text_classification.py)
-* [Text Classification Using Convolutional Neural Networks on Words](text_classification_cnn.py)
-* [Text Classification Using Recurrent Neural Networks on Characters](text_classification_character_rnn.py)
-* [Text Classification Using Convolutional Neural Networks on Characters](text_classification_character_cnn.py)
+* [Text Classification Using Recurrent Neural Networks on Words]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification.py)
+* [Text Classification Using Convolutional Neural Networks on Words]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_cnn.py)
+* [Text Classification Using Recurrent Neural Networks on Characters]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_rnn.py)
+* [Text Classification Using Convolutional Neural Networks on Characters]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_cnn.py)
-- 
GitLab


From 97905a642ef76a2318d39ce21da1cef82e22aa4e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 00:49:52 -0700
Subject: [PATCH 0068/1375] Remove margin around trace viewer.

PiperOrigin-RevId: 158949251
---
 .../components/tf_trace_viewer/tf-trace-viewer.html      | 7 +++++++
 tensorflow/tensorboard/components/trace_viewer.html      | 9 +++++----
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/tensorflow/tensorboard/components/tf_trace_viewer/tf-trace-viewer.html b/tensorflow/tensorboard/components/tf_trace_viewer/tf-trace-viewer.html
index a7b0b2cd73..cd65d729a4 100644
--- a/tensorflow/tensorboard/components/tf_trace_viewer/tf-trace-viewer.html
+++ b/tensorflow/tensorboard/components/tf_trace_viewer/tf-trace-viewer.html
@@ -23,6 +23,13 @@ tf-trace-viewer is the frontend entry point for Trace Viewer on TensorBoard.
 The server serves the trace viewer app at a separate endpoint. TensorBoard
 dashboard would integrate trace viewer app using iframe.
 -->
+<style>
+  tr-ui-timeline-view {
+    position: absolute;
+    width: 100%;
+    height: 100%;
+  }
+</style>
 <script>
   "use strict";
 
diff --git a/tensorflow/tensorboard/components/trace_viewer.html b/tensorflow/tensorboard/components/trace_viewer.html
index c9bcdc9e20..773bc548ca 100644
--- a/tensorflow/tensorboard/components/trace_viewer.html
+++ b/tensorflow/tensorboard/components/trace_viewer.html
@@ -17,12 +17,13 @@ limitations under the License.
 -->
 
 <meta charset="utf-8">
-<title>Trace Viewer</title>
 <html>
 <head>
+  <title>Trace Viewer</title>
   <link rel="import" href="tf-trace-viewer/tf-trace-viewer.html" jscomp-nocompile="true">
-  <body>
-    <tf-trace-viewer></tf-trace-viewer>
-  </body>
+  <style>body { margin: 0 }</style>
 </head>
+<body>
+  <tf-trace-viewer></tf-trace-viewer>
+</body>
 </html>
-- 
GitLab


From 98aaa44bff5292f6c3881a2b6489f9f2c9ff9dc2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 02:21:05 -0700
Subject: [PATCH 0069/1375] Fixes formatting of the example.

PiperOrigin-RevId: 158955376
---
 tensorflow/python/estimator/inputs/numpy_io.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py
index b31c5492d8..c9f37f06e8 100644
--- a/tensorflow/python/estimator/inputs/numpy_io.py
+++ b/tensorflow/python/estimator/inputs/numpy_io.py
@@ -55,6 +55,7 @@ def numpy_input_fn(x,
   of numpy arrays. The dict `features` has the same keys as the `x`.
 
   Example:
+
   ```python
   age = np.arange(4) * 1.0
   height = np.arange(32, 36)
-- 
GitLab


From ade1560e651e67d6cf33dc05a3ab26abf364446b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 08:24:30 -0700
Subject: [PATCH 0070/1375] Allow passing bounds to ScipyOptimizerInterface.

PiperOrigin-RevId: 158980443
---
 .../opt/python/training/external_optimizer.py | 174 +++++++++++++-----
 .../training/external_optimizer_test.py       |  48 ++++-
 2 files changed, 170 insertions(+), 52 deletions(-)

diff --git a/tensorflow/contrib/opt/python/training/external_optimizer.py b/tensorflow/contrib/opt/python/training/external_optimizer.py
index 0909760b38..ff87a95f72 100644
--- a/tensorflow/contrib/opt/python/training/external_optimizer.py
+++ b/tensorflow/contrib/opt/python/training/external_optimizer.py
@@ -12,20 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """TensorFlow interface for third-party optimizers."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradients
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 
-
 __all__ = ['ExternalOptimizerInterface', 'ScipyOptimizerInterface']
 
 
@@ -43,19 +43,41 @@ class ExternalOptimizerInterface(object):
   @@minimize
   """
 
-  def __init__(self, loss, var_list=None, equalities=None, inequalities=None,
+  def __init__(self,
+               loss,
+               var_list=None,
+               equalities=None,
+               inequalities=None,
+               var_to_bounds=None,
                **optimizer_kwargs):
     """Initialize a new interface instance.
 
     Args:
       loss: A scalar `Tensor` to be minimized.
-      var_list: Optional list of `Variable` objects to update to minimize
+      var_list: Optional `list` of `Variable` objects to update to minimize
         `loss`.  Defaults to the list of variables collected in the graph
         under the key `GraphKeys.TRAINABLE_VARIABLES`.
-      equalities: Optional list of equality constraint scalar `Tensor`s to be
+      equalities: Optional `list` of equality constraint scalar `Tensor`s to be
         held equal to zero.
-      inequalities: Optional list of inequality constraint scalar `Tensor`s
-        to be kept nonnegative.
+      inequalities: Optional `list` of inequality constraint scalar `Tensor`s
+        to be held nonnegative.
+      var_to_bounds: Optional `dict` where each key is an optimization
+        `Variable` and each corresponding value is a length-2 tuple of
+        `(low, high)` bounds. Although enforcing this kind of simple constraint
+        could be accomplished with the `inequalities` arg, not all optimization
+        algorithms support general inequality constraints, e.g. L-BFGS-B. Both
+        `low` and `high` can either be numbers or anything convertible to a
+        NumPy array that can be broadcast to the shape of `var` (using
+        `np.broadcast_to`). To indicate that there is no bound, use `None` (or
+        `+/- np.infty`). For example, if `var` is a 2x3 matrix, then any of
+        the following corresponding `bounds` could be supplied:
+        * `(0, np.infty)`: Each element of `var` held positive.
+        * `(-np.infty, [1, 2])`: First column less than 1, second column less
+          than 2.
+        * `(-np.infty, [[1], [2], [3]])`: First row less than 1, second row less
+          than 2, etc.
+        * `(-np.infty, [[1, 2, 3], [4, 5, 6]])`: Entry `var[0, 0]` less than 1,
+          `var[0, 1]` less than 2, etc.
       **optimizer_kwargs: Other subclass-specific keyword arguments.
     """
     self._loss = loss
@@ -67,37 +89,55 @@ class ExternalOptimizerInterface(object):
     else:
       self._vars = list(var_list)
 
-    self._update_placeholders = [array_ops.placeholder(var.dtype)
-                                 for var in self._vars]
-    self._var_updates = [var.assign(array_ops.reshape(placeholder,
-                                                      _get_shape_tuple(var)))
-                         for var, placeholder in
-                         zip(self._vars, self._update_placeholders)]
+    packed_bounds = None
+    if var_to_bounds is not None:
+      left_packed_bounds = []
+      right_packed_bounds = []
+      for var in self._vars:
+        shape = var.get_shape().as_list()
+        bounds = (-np.infty, np.infty)
+        if var in var_to_bounds:
+          bounds = var_to_bounds[var]
+        left_packed_bounds.extend(list(np.broadcast_to(bounds[0], shape).flat))
+        right_packed_bounds.extend(list(np.broadcast_to(bounds[1], shape).flat))
+      packed_bounds = list(zip(left_packed_bounds, right_packed_bounds))
+    self._packed_bounds = packed_bounds
+
+    self._update_placeholders = [
+        array_ops.placeholder(var.dtype) for var in self._vars
+    ]
+    self._var_updates = [
+        var.assign(array_ops.reshape(placeholder, _get_shape_tuple(var)))
+        for var, placeholder in zip(self._vars, self._update_placeholders)
+    ]
 
     loss_grads = _compute_gradients(loss, self._vars)
-    equalities_grads = [_compute_gradients(equality, self._vars)
-                        for equality in self._equalities]
-    inequalities_grads = [_compute_gradients(inequality, self._vars)
-                          for inequality in self._inequalities]
+    equalities_grads = [
+        _compute_gradients(equality, self._vars)
+        for equality in self._equalities
+    ]
+    inequalities_grads = [
+        _compute_gradients(inequality, self._vars)
+        for inequality in self._inequalities
+    ]
 
     self.optimizer_kwargs = optimizer_kwargs
 
     self._packed_var = self._pack(self._vars)
     self._packed_loss_grad = self._pack(loss_grads)
     self._packed_equality_grads = [
-        self._pack(equality_grads)
-        for equality_grads in equalities_grads
+        self._pack(equality_grads) for equality_grads in equalities_grads
     ]
     self._packed_inequality_grads = [
-        self._pack(inequality_grads)
-        for inequality_grads in inequalities_grads
+        self._pack(inequality_grads) for inequality_grads in inequalities_grads
     ]
 
     dims = [_prod(_get_shape_tuple(var)) for var in self._vars]
     accumulated_dims = list(_accumulate(dims))
     self._packing_slices = [
-        slice(start, end) for start, end in zip(accumulated_dims[:-1],
-                                                accumulated_dims[1:])]
+        slice(start, end)
+        for start, end in zip(accumulated_dims[:-1], accumulated_dims[1:])
+    ]
 
   def minimize(self,
                session=None,
@@ -135,35 +175,39 @@ class ExternalOptimizerInterface(object):
     step_callback = step_callback or (lambda xk: None)
 
     # Construct loss function and associated gradient.
-    loss_grad_func = self._make_eval_func(
-        [self._loss, self._packed_loss_grad],
-        session, feed_dict, fetches, loss_callback)
+    loss_grad_func = self._make_eval_func([self._loss,
+                                           self._packed_loss_grad], session,
+                                          feed_dict, fetches, loss_callback)
 
     # Construct equality constraint functions and associated gradients.
-    equality_funcs = self._make_eval_funcs(
-        self._equalities, session, feed_dict, fetches)
-    equality_grad_funcs = self._make_eval_funcs(
-        self._packed_equality_grads, session, feed_dict, fetches)
+    equality_funcs = self._make_eval_funcs(self._equalities, session, feed_dict,
+                                           fetches)
+    equality_grad_funcs = self._make_eval_funcs(self._packed_equality_grads,
+                                                session, feed_dict, fetches)
 
     # Construct inequality constraint functions and associated gradients.
-    inequality_funcs = self._make_eval_funcs(
-        self._inequalities, session, feed_dict, fetches)
-    inequality_grad_funcs = self._make_eval_funcs(
-        self._packed_inequality_grads, session, feed_dict, fetches)
+    inequality_funcs = self._make_eval_funcs(self._inequalities, session,
+                                             feed_dict, fetches)
+    inequality_grad_funcs = self._make_eval_funcs(self._packed_inequality_grads,
+                                                  session, feed_dict, fetches)
 
     # Get initial value from TF session.
     initial_packed_var_val = session.run(self._packed_var)
 
     # Perform minimization.
     packed_var_val = self._minimize(
-        initial_val=initial_packed_var_val, loss_grad_func=loss_grad_func,
+        initial_val=initial_packed_var_val,
+        loss_grad_func=loss_grad_func,
         equality_funcs=equality_funcs,
         equality_grad_funcs=equality_grad_funcs,
         inequality_funcs=inequality_funcs,
         inequality_grad_funcs=inequality_grad_funcs,
-        step_callback=step_callback, optimizer_kwargs=self.optimizer_kwargs)
-    var_vals = [packed_var_val[packing_slice]
-                for packing_slice in self._packing_slices]
+        packed_bounds=self._packed_bounds,
+        step_callback=step_callback,
+        optimizer_kwargs=self.optimizer_kwargs)
+    var_vals = [
+        packed_var_val[packing_slice] for packing_slice in self._packing_slices
+    ]
 
     # Set optimization variables to their new values.
     session.run(
@@ -173,7 +217,7 @@ class ExternalOptimizerInterface(object):
 
   def _minimize(self, initial_val, loss_grad_func, equality_funcs,
                 equality_grad_funcs, inequality_funcs, inequality_grad_funcs,
-                step_callback, optimizer_kwargs):
+                packed_bounds, step_callback, optimizer_kwargs):
     """Wrapper for a particular optimization algorithm implementation.
 
     It would be appropriate for a subclass implementation of this method to
@@ -191,6 +235,7 @@ class ExternalOptimizerInterface(object):
       inequality_funcs: A list of functions each of which specifies a scalar
         quantity that an optimizer should hold >= 0.
       inequality_grad_funcs: A list of gradients of inequality_funcs.
+      packed_bounds: A list of bounds for each index, or `None`.
       step_callback: A callback function to execute at each optimization step,
         supplied with the current value of the packed variable vector.
       optimizer_kwargs: Other key-value arguments available to the optimizer.
@@ -239,7 +284,11 @@ class ExternalOptimizerInterface(object):
 
     return eval_func
 
-  def _make_eval_funcs(self, tensors, session, feed_dict, fetches,
+  def _make_eval_funcs(self,
+                       tensors,
+                       session,
+                       feed_dict,
+                       fetches,
                        callback=None):
     return [
         self._make_eval_func(tensor, session, feed_dict, fetches, callback)
@@ -266,7 +315,24 @@ class ScipyOptimizerInterface(ExternalOptimizerInterface):
   # The value of vector should now be [0., 0.].
   ```
 
-  Example with constraints:
+  Example with simple bound constraints:
+
+  ```python
+  vector = tf.Variable([7., 7.], 'vector')
+
+  # Make vector norm as small as possible.
+  loss = tf.reduce_sum(tf.square(vector))
+
+  optimizer = ScipyOptimizerInterface(
+      loss, var_to_bounds={vector: ([1, 2], np.infty)})
+
+  with tf.Session() as session:
+    optimizer.minimize(session)
+
+  # The value of vector should now be [1., 2.].
+  ```
+
+  Example with more complicated constraints:
 
   ```python
   vector = tf.Variable([7., 7.], 'vector')
@@ -294,7 +360,8 @@ class ScipyOptimizerInterface(ExternalOptimizerInterface):
 
   def _minimize(self, initial_val, loss_grad_func, equality_funcs,
                 equality_grad_funcs, inequality_funcs, inequality_grad_funcs,
-                step_callback, optimizer_kwargs):
+                packed_bounds, step_callback, optimizer_kwargs):
+
     def loss_grad_func_wrapper(x):
       # SciPy's L-BFGS-B Fortran implementation requires gradients as doubles.
       loss, gradient = loss_grad_func(x)
@@ -314,7 +381,20 @@ class ScipyOptimizerInterface(ExternalOptimizerInterface):
         'callback': step_callback,
         'method': method,
         'constraints': constraints,
+        'bounds': packed_bounds,
     }
+
+    for kwarg in minimize_kwargs:
+      if kwarg in optimizer_kwargs:
+        if kwarg == 'bounds':
+          # Special handling for 'bounds' kwarg since ability to specify bounds
+          # was added after this module was already publicly released.
+          raise ValueError(
+              'Bounds must be set using the var_to_bounds argument')
+        raise ValueError(
+            'Optimizer keyword arg \'{}\' is set '
+            'automatically and cannot be injected manually'.format(kwarg))
+
     minimize_kwargs.update(optimizer_kwargs)
     if method == 'SLSQP':
       # SLSQP doesn't support step callbacks. Obviate associated warning
@@ -327,8 +407,8 @@ class ScipyOptimizerInterface(ExternalOptimizerInterface):
                  '  Message: %s\n'
                  '  Objective function value: %f\n'
                  '  Number of iterations: %d\n'
-                 '  Number of functions evaluations: %d',
-                 result.message, result.fun, result.nit, result.nfev)
+                 '  Number of functions evaluations: %d', result.message,
+                 result.fun, result.nit, result.nfev)
 
     return result['x']
 
@@ -355,5 +435,7 @@ def _prod(array):
 def _compute_gradients(tensor, var_list):
   grads = gradients.gradients(tensor, var_list)
   # tf.gradients sometimes returns `None` when it should return 0.
-  return [grad if grad is not None else array_ops.zeros_like(var)
-          for var, grad in zip(var_list, grads)]
+  return [
+      grad if grad is not None else array_ops.zeros_like(var)
+      for var, grad in zip(var_list, grads)
+  ]
diff --git a/tensorflow/contrib/opt/python/training/external_optimizer_test.py b/tensorflow/contrib/opt/python/training/external_optimizer_test.py
index c9f5a2ca3f..f39134936f 100644
--- a/tensorflow/contrib/opt/python/training/external_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/external_optimizer_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+
 from tensorflow.contrib.opt.python.training import external_optimizer
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -74,13 +75,13 @@ class ExternalOptimizerInterfaceTest(TestCase):
 
     minimum_location = constant_op.constant(np.arange(9), dtype=dtypes.float32)
 
-    loss = math_ops.reduce_sum(math_ops.square(vector -
-                                               minimum_location[:2])) / 2.
-    loss += math_ops.reduce_sum(math_ops.square(scalar - minimum_location[
-        2])) / 2.
+    loss = math_ops.reduce_sum(
+        math_ops.square(vector - minimum_location[:2])) / 2.
+    loss += math_ops.reduce_sum(
+        math_ops.square(scalar - minimum_location[2])) / 2.
     loss += math_ops.reduce_sum(
-        math_ops.square(matrix - array_ops.reshape(minimum_location[3:],
-                                                   [2, 3]))) / 2.
+        math_ops.square(
+            matrix - array_ops.reshape(minimum_location[3:], [2, 3]))) / 2.
 
     optimizer = MockOptimizerInterface(loss)
 
@@ -184,6 +185,41 @@ class ScipyOptimizerInterfaceTest(TestCase):
       optimizer.minimize(sess)
       self.assertAllClose(np.ones(2), sess.run(vector))
 
+  def test_scalar_bounds(self):
+    vector_initial_value = [7., 7.]
+    vector = variables.Variable(vector_initial_value, 'vector')
+
+    # Make norm as small as possible.
+    loss = math_ops.reduce_sum(math_ops.square(vector))
+
+    # Make the minimum value of each component be 1.
+    var_to_bounds = {vector: (1., np.infty)}
+
+    optimizer = external_optimizer.ScipyOptimizerInterface(
+        loss, var_to_bounds=var_to_bounds)
+
+    with self.test_session() as sess:
+      sess.run(variables.global_variables_initializer())
+      optimizer.minimize(sess)
+      self.assertAllClose(np.ones(2), sess.run(vector))
+
+  def test_vector_bounds(self):
+    vector_initial_value = [7., 7.]
+    vector = variables.Variable(vector_initial_value, 'vector')
+
+    # Make norm as small as possible.
+    loss = math_ops.reduce_sum(math_ops.square(vector))
+
+    var_to_bounds = {vector: ([None, 2.], None)}
+
+    optimizer = external_optimizer.ScipyOptimizerInterface(
+        loss, var_to_bounds=var_to_bounds)
+
+    with self.test_session() as sess:
+      sess.run(variables.global_variables_initializer())
+      optimizer.minimize(sess)
+      self.assertAllClose([0., 2.], sess.run(vector))
+
 
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From c9efdaf468d8ab4d13498c3ada74556419f882b8 Mon Sep 17 00:00:00 2001
From: KB Sriram <kbsriram@gmail.com>
Date: Fri, 9 Jun 2017 10:11:34 -0700
Subject: [PATCH 0071/1375] Pin Java bindings to 1.7

The android_library bazel rule currently enforces Java 7
https://github.com/bazelbuild/bazel/blob/6c1106b1a721516d3b3db54d2e1c31b44a76fbb1/src/main/java/com/google/devtools/build/lib/bazel/rules/android/BazelAndroidSemantics.java#L73

More generally, to enable broader use we pin the source and artifacts
to 1.7 till Java 8 gains wider adoption across Android.
---
 tensorflow/java/build_defs.bzl              | 11 ++++++++++-
 tensorflow/java/maven/libtensorflow/pom.xml |  4 ++--
 tensorflow/java/maven/proto/pom.xml         |  4 ++--
 3 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/tensorflow/java/build_defs.bzl b/tensorflow/java/build_defs.bzl
index 750d76301e..5bd5b9a388 100644
--- a/tensorflow/java/build_defs.bzl
+++ b/tensorflow/java/build_defs.bzl
@@ -1,5 +1,14 @@
 # -*- Python -*-
 
+# Pin to Java 1.7 to ensure broader compatibility for the Java bindings on
+# Android. Note also that the android_library bazel rule currently enforces
+# java 7
+# https://github.com/bazelbuild/bazel/blob/6c1106b1a721516d3b3db54d2e1c31b44a76fbb1/src/main/java/com/google/devtools/build/lib/bazel/rules/android/BazelAndroidSemantics.java#L73
+
+JAVA_VERSION_OPTS = [
+    "-source 7 -target 7",
+]
+
 # A more robust set of lint and errorprone checks when building
 # Java source to improve code consistency.
 
@@ -151,4 +160,4 @@ EP_DISABLED_CHECKS = [
 
 EP_OPTS = EP_ENABLED_WARNINGS + EP_DISABLED_CHECKS
 
-JAVACOPTS = XLINT_OPTS + EP_OPTS
+JAVACOPTS = JAVA_VERSION_OPTS + XLINT_OPTS + EP_OPTS
diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml
index 9114047818..6d4c10f4ca 100644
--- a/tensorflow/java/maven/libtensorflow/pom.xml
+++ b/tensorflow/java/maven/libtensorflow/pom.xml
@@ -19,8 +19,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.8</source>
-          <target>1.8</target>
+          <source>1.7</source>
+          <target>1.7</target>
         </configuration>
       </plugin>
       <plugin>
diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index 6405daddef..dc7f7fa0af 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -27,8 +27,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.8</source>
-          <target>1.8</target>
+          <source>1.7</source>
+          <target>1.7</target>
         </configuration>
       </plugin>
       <plugin>
-- 
GitLab


From df511d09b051914cbc4fc559807a3f0d07dfee71 Mon Sep 17 00:00:00 2001
From: Petros Mol <pmol@google.com>
Date: Wed, 14 Jun 2017 09:06:29 -0700
Subject: [PATCH 0072/1375] [XLA] Add a Cos unary operation that computes the
 elementwise cosine

PiperOrigin-RevId: 158984883
---
 tensorflow/compiler/tf2xla/kernels/unary_ops.cc          | 1 +
 tensorflow/compiler/xla/client/computation_builder.cc    | 5 +++++
 tensorflow/compiler/xla/client/computation_builder.h     | 3 +++
 tensorflow/compiler/xla/service/dfs_hlo_visitor.h        | 3 +++
 tensorflow/compiler/xla/service/elemental_ir_emitter.cc  | 5 +++++
 tensorflow/compiler/xla/service/hlo_graph_dumper.cc      | 1 +
 tensorflow/compiler/xla/service/hlo_instruction.cc       | 6 ++++++
 tensorflow/compiler/xla/service/hlo_opcode.cc            | 2 ++
 tensorflow/compiler/xla/service/hlo_opcode.h             | 1 +
 tensorflow/compiler/xla/service/instruction_fusion.cc    | 1 +
 tensorflow/compiler/xla/service/shape_inference.cc       | 1 +
 tensorflow/compiler/xla/service/user_computation.cc      | 2 ++
 .../compiler/xla/tests/array_elementwise_ops_test.cc     | 9 +++++++++
 tensorflow/compiler/xla/xla_data.proto                   | 3 +++
 .../docs_src/performance/xla/operation_semantics.md      | 2 ++
 15 files changed, 45 insertions(+)

diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
index abe4949f5d..07ca596150 100644
--- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
@@ -44,6 +44,7 @@ namespace {
 // Return x if x>0, otherwise -x.
 XLAJIT_MAKE_UNARY(Abs, b->Abs(x));
 XLAJIT_MAKE_UNARY(Ceil, b->Ceil(x));
+XLAJIT_MAKE_UNARY(Cos, b->Cos(x));
 XLAJIT_MAKE_UNARY(Exp, b->Exp(x));
 XLAJIT_MAKE_UNARY(Floor, b->Floor(x));
 // Returns 0 if x is 0, -1 if x < 0 and 1 if x > 0.
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 6e95e623ba..cefa4af23c 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -967,6 +967,11 @@ ComputationDataHandle ComputationBuilder::Sign(
   return UnaryOp(UNOP_SIGN, operand);
 }
 
+ComputationDataHandle ComputationBuilder::Cos(
+    const ComputationDataHandle& operand) {
+  return UnaryOp(UNOP_COS, operand);
+}
+
 ComputationDataHandle ComputationBuilder::Tanh(
     const ComputationDataHandle& operand) {
   return UnaryOp(UNOP_TANH, operand);
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 129e66c24a..13b44a71a5 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -508,6 +508,9 @@ class ComputationBuilder {
   // Enqueues a sign instruction onto the computation.
   ComputationDataHandle Sign(const ComputationDataHandle& operand);
 
+  // Enqueues a cosine instruction onto the computation.
+  ComputationDataHandle Cos(const ComputationDataHandle& operand);
+
   // Enqueues a tanh instruction onto the computation.
   ComputationDataHandle Tanh(const ComputationDataHandle& operand);
 
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index bfa6f241a3..40ff037e73 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -148,6 +148,9 @@ class DfsHloVisitor {
   virtual Status HandleLog(HloInstruction* log, HloInstruction* operand) {
     return HandleElementwiseUnary(log, HloOpcode::kLog, operand);
   }
+  virtual Status HandleCos(HloInstruction* cos, HloInstruction* operand) {
+    return HandleElementwiseUnary(cos, HloOpcode::kCos, operand);
+  }
   virtual Status HandleTanh(HloInstruction* tanh, HloInstruction* operand) {
     return HandleElementwiseUnary(tanh, HloOpcode::kTanh, operand);
   }
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index dbc65e80eb..c99ebceb45 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -172,6 +172,10 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitFloatUnaryOp(
       return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::log, {operand_value},
                                           {operand_value->getType()},
                                           ir_builder_);
+    case HloOpcode::kCos:
+      return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::cos, {operand_value},
+                                          {operand_value->getType()},
+                                          ir_builder_);
     case HloOpcode::kFloor:
       return llvm_ir::EmitCallToIntrinsic(
           llvm::Intrinsic::floor, {operand_value}, {operand_value->getType()},
@@ -664,6 +668,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
     case HloOpcode::kCeil:
     case HloOpcode::kConvert:
     case HloOpcode::kCopy:
+    case HloOpcode::kCos:
     case HloOpcode::kExp:
     case HloOpcode::kFloor:
     case HloOpcode::kIsFinite:
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 6abc733646..48c33d62c5 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -214,6 +214,7 @@ string InstructionSequenceGraph(
       case HloOpcode::kCeil:
       case HloOpcode::kClamp:
       case HloOpcode::kConvert:
+      case HloOpcode::kCos:
       case HloOpcode::kDivide:
       case HloOpcode::kEq:
       case HloOpcode::kExp:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 9e020f9391..f926cb4bc7 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -122,6 +122,7 @@ HloInstruction::CreateGetTupleElement(const Shape& shape,
     case HloOpcode::kBitcast:
     case HloOpcode::kCeil:
     case HloOpcode::kCopy:
+    case HloOpcode::kCos:
     case HloOpcode::kExp:
     case HloOpcode::kFloor:
     case HloOpcode::kIsFinite:
@@ -744,6 +745,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
     case HloOpcode::kBitcast:
     case HloOpcode::kCeil:
     case HloOpcode::kCopy:
+    case HloOpcode::kCos:
     case HloOpcode::kExp:
     case HloOpcode::kIsFinite:
     case HloOpcode::kFloor:
@@ -1113,6 +1115,7 @@ bool HloInstruction::Identical(
     case HloOpcode::kCeil:
     case HloOpcode::kClamp:
     case HloOpcode::kCopy:
+    case HloOpcode::kCos:
     case HloOpcode::kCrossReplicaSum:
     case HloOpcode::kDivide:
     case HloOpcode::kDot:
@@ -1834,6 +1837,8 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
       return visitor->HandleLog(this, operands_[0]);
     case HloOpcode::kTanh:
       return visitor->HandleTanh(this, operands_[0]);
+    case HloOpcode::kCos:
+      return visitor->HandleCos(this, operands_[0]);
     case HloOpcode::kIsFinite:
       return visitor->HandleIsFinite(this, operands_[0]);
     case HloOpcode::kLogicalNot:
@@ -2080,6 +2085,7 @@ bool HloInstruction::IsElementwise() const {
     case HloOpcode::kCeil:
     case HloOpcode::kConvert:
     case HloOpcode::kCopy:
+    case HloOpcode::kCos:
     case HloOpcode::kExp:
     case HloOpcode::kFloor:
     case HloOpcode::kIsFinite:
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.cc b/tensorflow/compiler/xla/service/hlo_opcode.cc
index 5bda6b6dab..342c43dc5a 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.cc
+++ b/tensorflow/compiler/xla/service/hlo_opcode.cc
@@ -42,6 +42,8 @@ string HloOpcodeString(HloOpcode opcode) {
       return "convert";
     case HloOpcode::kConvolution:
       return "convolution";
+    case HloOpcode::kCos:
+      return "cosine";
     case HloOpcode::kCrossReplicaSum:
       return "cross-replica-sum";
     case HloOpcode::kCustomCall:
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index 65aef63dcd..8e0fa7b4f1 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -41,6 +41,7 @@ enum class HloOpcode {
   kConvert,
   kConvolution,
   kCopy,
+  kCos,
   kCrossReplicaSum,
   kCustomCall,
   kDivide,
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 06fa8bc619..9bace7edaa 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -43,6 +43,7 @@ namespace xla {
     case HloOpcode::kConstant:
     case HloOpcode::kConvert:
     case HloOpcode::kCopy:
+    case HloOpcode::kCos:
     case HloOpcode::kDynamicSlice:
     case HloOpcode::kDynamicUpdateSlice:
     case HloOpcode::kEq:
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 670e1ca84a..2508f4c13d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -184,6 +184,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
   switch (operation) {
     case UNOP_FLOOR:
     case UNOP_CEIL:
+    case UNOP_COS:
     case UNOP_EXP:
     case UNOP_LOG:
     case UNOP_TANH:
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 417ed584aa..3e7942075c 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -49,6 +49,8 @@ HloOpcode UnaryOperationToHloOpcode(UnaryOperation unop) {
       return HloOpcode::kAbs;
     case UNOP_CEIL:
       return HloOpcode::kCeil;
+    case UNOP_COS:
+      return HloOpcode::kCos;
     case UNOP_EXP:
       return HloOpcode::kExp;
     case UNOP_FLOOR:
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index ecb2a6c767..7c3a1d2580 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1297,6 +1297,15 @@ TEST_F(ArrayElementwiseOpTest, AddParameterToConstantF32s) {
                              {param0_data.get()}, error_spec_);
 }
 
+XLA_TEST_F(ArrayElementwiseOpTest, CosF32s) {
+  ComputationBuilder builder(client_, TestName());
+  auto a = builder.ConstantR1<float>({3.14159f, 0.0f, 1.570796f, -0.78539f});
+  auto result = builder.Cos(a);
+
+  ComputeAndCompareR1<float>(&builder, {-1.0f, 1.0f, 0.0f, 0.707107f}, {},
+                             error_spec_);
+}
+
 TEST_F(ArrayElementwiseOpTest, TanhF32s) {
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR1<float>({-2.5f, 3.14f, 2.25f});
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 23ef79d0d7..633d16c4c3 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -603,6 +603,9 @@ enum UnaryOperation {
 
   // Elementwise, tests if values are finite (not NaN or inf)
   UNOP_IS_FINITE = 11;
+
+  // Elementwise, computes the cosine of x.
+  UNOP_COS = 12;
 }
 
 message UnaryOpRequest {
diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index ed055691ce..120260448d 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -583,6 +583,8 @@ ComputationBuilder supports these element-wise unary functions:
 
 <b>`Ceil(operand)`</b> Element-wise ceil `x -> ⌈x⌉`.
 
+<b>`Cos(operand)`</b> Element-wise cosine `x -> cos(x)`.
+
 <b>`Exp(operand)`</b> Element-wise natural exponential `x -> e^x`.
 
 <b>`Floor(operand)`</b> Element-wise floor `x -> ⌊x⌋`.
-- 
GitLab


From 5cc5161c40a05e8d61872044321b8d954b5939e4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 09:07:31 -0700
Subject: [PATCH 0073/1375] Minor cleanup

PiperOrigin-RevId: 158984989
---
 tensorflow/compiler/xla/legacy_flags/BUILD | 1 -
 tensorflow/core/kernels/fft_ops.cc         | 6 +++---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index a147ce67a2..0775dbe86e 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -73,7 +73,6 @@ cc_library(
     deps =
         [
             ":parse_flags_from_env",
-            "//tensorflow/compiler/xla:types",
             "//tensorflow/compiler/xla:xla_proto",
             "//tensorflow/core:framework_internal",
             "//tensorflow/core:lib",
diff --git a/tensorflow/core/kernels/fft_ops.cc b/tensorflow/core/kernels/fft_ops.cc
index 32936d65c8..35a3f7b189 100644
--- a/tensorflow/core/kernels/fft_ops.cc
+++ b/tensorflow/core/kernels/fft_ops.cc
@@ -128,7 +128,7 @@ class FFTCPU : public FFTBase {
     auto device = ctx->eigen_device<CPUDevice>();
 
     if (!IsReal()) {
-      auto input = (Tensor(in)).flat_inner_dims<complex64, FFTRank + 1>();
+      auto input = Tensor(in).flat_inner_dims<complex64, FFTRank + 1>();
       // Compute the FFT using eigen.
       auto output = out->flat_inner_dims<complex64, FFTRank + 1>();
       constexpr auto direction =
@@ -137,7 +137,7 @@ class FFTCPU : public FFTBase {
           input.template fft<Eigen::BothParts, direction>(axes);
     } else {
       if (IsForward()) {
-        auto input = (Tensor(in)).flat_inner_dims<float, FFTRank + 1>();
+        auto input = Tensor(in).flat_inner_dims<float, FFTRank + 1>();
         const auto input_dims = input.dimensions();
 
         // Slice input to fft_shape on its inner-most dimensions.
@@ -166,7 +166,7 @@ class FFTCPU : public FFTBase {
             full_fft.slice(zero_start_indices, output.dimensions());
       } else {
         // Reconstruct the full FFT and take the inverse.
-        auto input = ((Tensor)in).flat_inner_dims<complex64, FFTRank + 1>();
+        auto input = Tensor(in).flat_inner_dims<complex64, FFTRank + 1>();
         auto output = out->flat_inner_dims<float, FFTRank + 1>();
         const auto input_dims = input.dimensions();
 
-- 
GitLab


From c77399d44fc2ed6912e7f301839ad3e404739b80 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 14 Jun 2017 09:47:53 -0700
Subject: [PATCH 0074/1375] [XLA] Remove remaining flags from
 cpu_compiler_flags

And move them to debug_options_flags; these two flags (embed_ir_in_executable,
dump_debug_json_to) are also unified with similarly named GPU compiler flags.
This lets us completely remove the cpu_compiler_flags module.

PiperOrigin-RevId: 158989621
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 13 ----
 .../xla/legacy_flags/cpu_compiler_flags.cc    | 68 -------------------
 .../xla/legacy_flags/cpu_compiler_flags.h     | 49 -------------
 .../xla/legacy_flags/debug_options_flags.cc   | 27 +++++---
 .../xla/legacy_flags/gpu_compiler_flags.cc    |  8 ---
 .../xla/legacy_flags/gpu_compiler_flags.h     |  3 -
 tensorflow/compiler/xla/service/cpu/BUILD     |  1 -
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 33 +++++----
 .../compiler/xla/service/gpu/gpu_compiler.cc  | 13 ++--
 tensorflow/compiler/xla/tests/BUILD           | 58 ----------------
 .../xla/tests/array_elementwise_ops_test.cc   |  2 -
 .../compiler/xla/tests/axpy_simple_test.cc    |  2 -
 .../tests/bad_rng_shape_validation_test.cc    |  2 -
 .../xla/tests/batch_normalization_test.cc     |  2 -
 .../compiler/xla/tests/binop_scaling_test.cc  |  2 -
 .../xla/tests/broadcast_simple_test.cc        |  2 -
 .../compiler/xla/tests/broadcast_test.cc      |  2 -
 tensorflow/compiler/xla/tests/call_test.cc    |  2 -
 .../xla/tests/check_execution_arity_test.cc   |  2 -
 tensorflow/compiler/xla/tests/client_test.cc  |  2 -
 .../compiler/xla/tests/codegen_test_base.cc   | 10 +++
 .../compiler/xla/tests/codegen_test_base.h    |  4 +-
 .../xla/tests/compilation_cache_test.cc       |  2 -
 .../xla/tests/compute_constant_test.cc        |  2 -
 tensorflow/compiler/xla/tests/concat_test.cc  |  2 -
 .../compiler/xla/tests/constants_test.cc      |  2 -
 tensorflow/compiler/xla/tests/convert_test.cc |  2 -
 .../convolution_dimension_numbers_test.cc     |  2 -
 .../compiler/xla/tests/convolution_test.cc    |  2 -
 .../xla/tests/convolution_variants_test.cc    |  2 -
 tensorflow/compiler/xla/tests/copy_test.cc    |  2 -
 .../compiler/xla/tests/custom_call_test.cc    |  2 -
 .../compiler/xla/tests/deallocation_test.cc   |  2 -
 .../xla/tests/deconstruct_tuple_test.cc       |  2 -
 .../compiler/xla/tests/dot_operation_test.cc  |  2 -
 .../compiler/xla/tests/dynamic_ops_test.cc    |  2 -
 .../compiler/xla/tests/floor_ceil_test.cc     |  2 -
 tensorflow/compiler/xla/tests/fmax_test.cc    |  2 -
 tensorflow/compiler/xla/tests/fusion_test.cc  |  2 -
 tensorflow/compiler/xla/tests/log_test.cc     |  2 -
 tensorflow/compiler/xla/tests/map_test.cc     |  2 -
 .../xla/tests/matrix_ops_simple_test.cc       |  2 -
 .../xla/tests/multidimensional_slice_test.cc  |  2 -
 tensorflow/compiler/xla/tests/pad_test.cc     |  2 -
 tensorflow/compiler/xla/tests/params_test.cc  |  2 -
 tensorflow/compiler/xla/tests/pred_test.cc    |  2 -
 tensorflow/compiler/xla/tests/prng_test.cc    |  2 -
 .../xla/tests/query_inferred_shape_test.cc    |  2 -
 tensorflow/compiler/xla/tests/reduce_test.cc  |  2 -
 .../compiler/xla/tests/reduce_window_test.cc  |  2 -
 tensorflow/compiler/xla/tests/replay_test.cc  |  2 -
 .../compiler/xla/tests/reshape_motion_test.cc |  2 -
 tensorflow/compiler/xla/tests/reshape_test.cc |  2 -
 tensorflow/compiler/xla/tests/reverse_test.cc |  2 -
 .../tests/round_trip_packed_literal_test.cc   |  2 -
 .../xla/tests/round_trip_transfer_test.cc     |  2 -
 .../xla/tests/scalar_computations_test.cc     |  2 -
 .../xla/tests/select_and_scatter_test.cc      |  2 -
 tensorflow/compiler/xla/tests/select_test.cc  |  2 -
 .../xla/tests/set_return_value_test.cc        |  2 -
 tensorflow/compiler/xla/tests/slice_test.cc   |  2 -
 .../compiler/xla/tests/transpose_test.cc      |  2 -
 tensorflow/compiler/xla/tests/tuple_test.cc   |  2 -
 .../compiler/xla/tests/unary_op_test.cc       |  2 -
 .../xla/tests/vector_ops_reduce_test.cc       |  2 -
 .../xla/tests/vector_ops_simple_test.cc       |  2 -
 tensorflow/compiler/xla/tests/while_test.cc   |  2 -
 tensorflow/compiler/xla/xla.proto             |  8 ++-
 70 files changed, 67 insertions(+), 341 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 1f6fe28188..f8b18bcaf1 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -129,7 +129,6 @@ cc_library(
         "//tensorflow/compiler/xla/legacy_flags:alias_analysis_flags",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
         "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 6fed46b432..3fd00398d2 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -26,7 +26,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
@@ -139,7 +138,6 @@ int main(int argc, char** argv) {
   xla::legacy_flags::AppendAliasAnalysisFlags(&flag_list);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
   xla::legacy_flags::AppendCompilerFunctorFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 0775dbe86e..09101708f2 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -79,19 +79,6 @@ cc_library(
         ],
 )
 
-cc_library(
-    name = "cpu_compiler_flags",
-    srcs = ["cpu_compiler_flags.cc"],
-    hdrs = ["cpu_compiler_flags.h"],
-    deps =
-        [
-            ":parse_flags_from_env",
-            "//tensorflow/compiler/xla:types",
-            "//tensorflow/core:framework_internal",
-            "//tensorflow/core:lib",
-        ],
-)
-
 cc_library(
     name = "cpu_runtime_flags",
     srcs = ["cpu_runtime_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.cc b/tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.cc
deleted file mode 100644
index 13d41a8636..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's cpu_compiler module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static CpuCompilerFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new CpuCompilerFlags;
-  flags->xla_cpu_embed_ir = false;
-  flags->xla_cpu_dump_debug_json_to = "";
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_cpu_embed_ir", &flags->xla_cpu_embed_ir,
-          "Embed the LLVM IR module string in the resultant CpuExecutable."),
-      tensorflow::Flag("xla_cpu_dump_debug_json_to",
-                       &flags->xla_cpu_dump_debug_json_to,
-                       "Dump debug JSON to this directory."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's cpu_compiler
-// module.
-void AppendCpuCompilerFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the CpuCompilerFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuCompilerFlags* GetCpuCompilerFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h b/tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h
deleted file mode 100644
index bac498e18e..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_COMPILER_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_COMPILER_FLAGS_H_
-
-// Legacy flags for the XLA's cpu_compiler module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's cpu_compiler
-// module.
-void AppendCpuCompilerFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's cpu_compiler module.
-typedef struct {
-  bool xla_cpu_embed_ir;  // Embed the LLVM IR module string in the resultant
-                          // CpuExecutable
-  string xla_cpu_dump_debug_json_to;  // Dump debug JSON to this directory.
-} CpuCompilerFlags;
-
-// Return a pointer to the CpuCompilerFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuCompilerFlags* GetCpuCompilerFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_COMPILER_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 5e3c4f912b..52b048ec15 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -28,6 +28,8 @@ struct DebugOptionsFlags {
   string xla_disable_hlo_passes;
   bool xla_enable_fast_math;
   int32 xla_backend_optimization_level;
+  bool xla_embed_ir_in_executable;
+  string xla_dump_debug_json_to;
   string xla_backend_extra_options;
 };
 
@@ -45,6 +47,8 @@ void AllocateFlags() {
   flag_values->xla_disable_hlo_passes = "";
   flag_values->xla_enable_fast_math = true;
   flag_values->xla_backend_optimization_level = 2;
+  flag_values->xla_embed_ir_in_executable = false;
+  flag_values->xla_dump_debug_json_to = "";
   flag_values->xla_backend_extra_options = "";
 
   flag_objects = new std::vector<tensorflow::Flag>(
@@ -52,7 +56,6 @@ void AllocateFlags() {
            "xla_generate_hlo_graph", &flag_values->xla_generate_hlo_graph,
            "HLO modules matching this regex will be dumped to a .dot file "
            "throughout various stages in compilation."),
-
        tensorflow::Flag(
            "xla_enable_fast_math", &flag_values->xla_enable_fast_math,
            "Enable unsafe fast-math optimizations in the compiler; "
@@ -61,18 +64,23 @@ void AllocateFlags() {
            "xla_backend_optimization_level",
            &flag_values->xla_backend_optimization_level,
            "Numerical optimization level for the XLA compiler backend."),
-
+       tensorflow::Flag(
+           "xla_disable_hlo_passes", &flag_values->xla_disable_hlo_passes,
+           "Comma-separated list of hlo passes to be disabled. These names "
+           "must exactly match the passes' names; no whitespace around "
+           "commas."),
+       tensorflow::Flag("xla_embed_ir_in_executable",
+                        &flag_values->xla_embed_ir_in_executable,
+                        "Embed the compiler IR as a string in the executable."),
+       tensorflow::Flag(
+           "xla_dump_debug_json_to", &flag_values->xla_dump_debug_json_to,
+           "Dump compilation artifacts as JSON into this directory."),
        tensorflow::Flag("xla_backend_extra_options",
                         &flag_values->xla_backend_extra_options,
                         "Extra options to pass to a backend; "
                         "comma-separated list of 'key=val' strings (=val "
-                        "may be omitted); no whitespace around commas."),
+                        "may be omitted); no whitespace around commas.")});
 
-       tensorflow::Flag(
-           "xla_disable_hlo_passes", &flag_values->xla_disable_hlo_passes,
-           "Comma-separated list of HLO passes to be disabled. These names "
-           "must exactly match the passes' names; "
-           "no whitespace around commas.")});
   ParseFlagsFromEnv(*flag_objects);
 }
 
@@ -99,6 +107,9 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
   options.set_xla_enable_fast_math(flag_values->xla_enable_fast_math);
   options.set_xla_backend_optimization_level(
       flag_values->xla_backend_optimization_level);
+  options.set_xla_embed_ir_in_executable(
+      flag_values->xla_embed_ir_in_executable);
+  options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
 
   std::vector<string> extra_options_parts =
       tensorflow::str_util::Split(flag_values->xla_backend_extra_options, ',');
diff --git a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc b/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc
index 131e3ce70a..a8af794494 100644
--- a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc
@@ -36,13 +36,8 @@ static std::once_flag flags_init;
 // Allocate *flags.  Called via call_once(&flags_init,...).
 static void AllocateFlags() {
   flags = new GpuCompilerFlags;
-  flags->xla_gpu_embed_ir = false;
   flags->xla_cuda_data_dir = "./cuda_sdk_lib";
-  flags->xla_gpu_dump_debug_json_to = "";
   flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_gpu_embed_ir", &flags->xla_gpu_embed_ir,
-          "Embed the LLVM IR module string in the resultant GpuExecutable."),
       tensorflow::Flag(
           "xla_cuda_data_dir", &flags->xla_cuda_data_dir,
           "If non-empty, specifies a local directory containing ptxas and "
@@ -50,9 +45,6 @@ static void AllocateFlags() {
           "runfile directories."),
       tensorflow::Flag("xla_ptxas_path", &flags->xla_ptxas_path,
                        "The path to ptxas. Required to log stats of the ptx."),
-      tensorflow::Flag("xla_gpu_dump_debug_json_to",
-                       &flags->xla_gpu_dump_debug_json_to,
-                       "Dump debug JSON to this directory."),
   });
   ParseFlagsFromEnv(*flag_list);
 }
diff --git a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h b/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h
index 0cf39e0ab3..0b0f186b25 100644
--- a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h
+++ b/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h
@@ -33,15 +33,12 @@ void AppendGpuCompilerFlags(std::vector<tensorflow::Flag>* flag_list);
 
 // The values of flags associated with XLA's gpu_compiler module.
 typedef struct {
-  bool xla_gpu_embed_ir;     // Embed the LLVM IR module string in the resultant
-                             // GpuExecutable.
   string xla_cuda_data_dir;  // If non-empty, specifies a local directory
                              // containing ptxas and nvvm libdevice files.
                              // Otherwise, by default, we use those from runfile
                              // directories.
   string xla_ptxas_path;     // The path to ptxas.  Required to log stats of
                              // the ptx.
-  string xla_gpu_dump_debug_json_to;  // Dump debug JSON to this directory.
 } GpuCompilerFlags;
 
 // Return a pointer to the GpuCompilerFlags struct;
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 51ecbccd49..edf4d2f801 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -52,7 +52,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/service:algebraic_simplifier",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:buffer_liveness",
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 34b99f2440..9c3463d103 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -37,7 +37,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/Support/TargetSelect.h"
 #include "external/llvm/include/llvm/Target/TargetMachine.h"
 #include "external/llvm/include/llvm/Target/TargetOptions.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/protobuf_util.h"
@@ -367,7 +366,14 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
   }
 
   std::unique_ptr<Executable> cpu_executable;
-  legacy_flags::CpuCompilerFlags* flags = legacy_flags::GetCpuCompilerFlags();
+
+  // Cache this flag here since we'll want to access it after the module's
+  // ownership is std::moved.
+  const bool embed_ir_in_executable =
+      module->config().debug_options().xla_embed_ir_in_executable();
+  const string dump_debug_json_to =
+      module->config().debug_options().xla_dump_debug_json_to();
+
   if (CpuParallelBackendRequested(module->config())) {
     // Run buffer analysis on the HLO graph. This analysis figures out which
     // temporary buffers are required to run the computation.
@@ -381,10 +387,10 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
                             MakeUnique<DependencyHloOrdering>(module.get()),
                             BufferSizeBytesFunction(), kMemoryAlignment));
 
-    if (!flags->xla_cpu_dump_debug_json_to.empty()) {
+    if (!dump_debug_json_to.empty()) {
       HloProto proto = MakeHloProto(*module, *assignment);
       TF_RETURN_IF_ERROR(protobuf_util::DumpJsonToDirectory(
-          proto, flags->xla_cpu_dump_debug_json_to, module->name()));
+          proto, dump_debug_json_to, module->name()));
     }
 
     // If we are using the parallel CPU backend, we need to create map from
@@ -446,7 +452,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
 
     string ir_module_string;
-    if (flags->xla_cpu_embed_ir) {
+    if (embed_ir_in_executable) {
       ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
     }
 
@@ -457,7 +463,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
         std::move(function_names), std::move(hlo_to_profile_idx),
         std::move(aligned_constants)));
 
-    if (flags->xla_cpu_embed_ir) {
+    if (embed_ir_in_executable) {
       static_cast<CpuExecutable&>(*cpu_executable)
           .set_ir_module_string(ir_module_string);
     }
@@ -478,10 +484,10 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
             MakeUnique<SequentialHloOrdering>(module.get(), module_sequence),
             BufferSizeBytesFunction(), kMemoryAlignment));
 
-    if (!flags->xla_cpu_dump_debug_json_to.empty()) {
+    if (!dump_debug_json_to.empty()) {
       HloProto proto = MakeHloProto(*module, *assignment);
       TF_RETURN_IF_ERROR(protobuf_util::DumpJsonToDirectory(
-          proto, flags->xla_cpu_dump_debug_json_to, module->name()));
+          proto, dump_debug_json_to, module->name()));
     }
 
     // Each computation is a single function.  Emit all embedded computations
@@ -510,7 +516,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 
     string function_name = llvm_ir::AsString(entry_function->getName());
     string ir_module_string;
-    if (flags->xla_cpu_embed_ir) {
+    if (embed_ir_in_executable) {
       ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
     }
 
@@ -520,7 +526,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
         std::move(jit), std::move(assignment), std::move(module), function_name,
         std::move(hlo_to_profile_idx)));
 
-    if (flags->xla_cpu_embed_ir) {
+    if (embed_ir_in_executable) {
       static_cast<CpuExecutable&>(*cpu_executable)
           .set_ir_module_string(ir_module_string);
     }
@@ -642,11 +648,12 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
             module, MakeUnique<SequentialHloOrdering>(module, module_sequence),
             BufferSizeBytesFunction(), kMemoryAlignment));
 
-    legacy_flags::CpuCompilerFlags* flags = legacy_flags::GetCpuCompilerFlags();
-    if (!flags->xla_cpu_dump_debug_json_to.empty()) {
+    const string dump_debug_json_to =
+        module->config().debug_options().xla_dump_debug_json_to();
+    if (!dump_debug_json_to.empty()) {
       HloProto proto = MakeHloProto(*module, *assignment);
       TF_RETURN_IF_ERROR(protobuf_util::DumpJsonToDirectory(
-          proto, flags->xla_cpu_dump_debug_json_to, module->name()));
+          proto, dump_debug_json_to, module->name()));
     }
 
     IrEmitter ir_emitter(*module, *assignment, &llvm_module,
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 86137a569f..85962f7795 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -273,11 +273,12 @@ StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
       BufferAssigner::Run(module.get(), hlo_schedule->ConsumeHloOrdering(),
                           BufferSizeBytesFunction(), kMemoryAlignment));
 
-  legacy_flags::GpuCompilerFlags* flags = legacy_flags::GetGpuCompilerFlags();
-  if (!flags->xla_gpu_dump_debug_json_to.empty()) {
+  const string dump_debug_json_to =
+      module->config().debug_options().xla_dump_debug_json_to();
+  if (!dump_debug_json_to.empty()) {
     HloProto proto = MakeHloProto(*module, *buffer_assignment);
     TF_RETURN_IF_ERROR(protobuf_util::DumpJsonToDirectory(
-        proto, flags->xla_gpu_dump_debug_json_to, module->name()));
+        proto, dump_debug_json_to, module->name()));
   }
 
   IrEmitterContext ir_emitter_context(module.get(), buffer_assignment.get(),
@@ -292,7 +293,9 @@ StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
       entry_computation->root_instruction()->Accept(&ir_emitter));
 
   string ir_module_string_before_opt;
-  if (VLOG_IS_ON(2) || flags->xla_gpu_embed_ir) {
+  const bool embed_ir_in_executable =
+      module->config().debug_options().xla_embed_ir_in_executable();
+  if (VLOG_IS_ON(2) || embed_ir_in_executable) {
     ir_module_string_before_opt = llvm_ir::DumpModuleToString(llvm_module);
     VLOG(2) << "LLVM module before optimizations:";
     XLA_VLOG_LINES(2, ir_module_string_before_opt);
@@ -333,7 +336,7 @@ StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
   auto* gpu_executable =
       new GpuExecutable(*ptx, std::move(thunk_schedule), std::move(module),
                         std::move(buffer_assignment), ShapeSizeBytesFunction());
-  if (flags->xla_gpu_embed_ir) {
+  if (embed_ir_in_executable) {
     DCHECK_NE("", ir_module_string_before_opt);
     gpu_executable->set_ir_module_string(ir_module_string_before_opt);
   }
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 13dd1a30b6..57523b34bb 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -218,7 +218,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:lib",
@@ -238,7 +237,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:test",
@@ -255,7 +253,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:test",
@@ -275,7 +272,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:platform_util",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -291,7 +287,6 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -315,7 +310,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -339,7 +333,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -356,7 +349,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:test",
@@ -371,7 +363,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -388,7 +379,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -409,7 +399,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -427,7 +416,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
@@ -447,7 +435,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
@@ -471,7 +458,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -490,7 +476,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -518,7 +503,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -551,7 +535,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -583,7 +566,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -605,7 +587,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -624,7 +605,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -650,7 +630,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -677,7 +656,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -699,7 +677,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -721,7 +698,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -738,7 +714,6 @@ xla_test(
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -754,7 +729,6 @@ xla_test(
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -772,7 +746,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/compiler/xla/service:local_service",
         "//tensorflow/compiler/xla/service:platform_util",
@@ -799,7 +772,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -816,7 +788,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -842,7 +813,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -865,7 +835,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -889,7 +858,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -906,7 +874,6 @@ xla_test(
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -924,7 +891,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -941,7 +907,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -958,7 +923,6 @@ xla_test(
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -976,7 +940,6 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -995,7 +958,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1009,7 +971,6 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -1022,7 +983,6 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -1044,7 +1004,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1062,7 +1021,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
@@ -1087,7 +1045,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1103,7 +1060,6 @@ xla_test(
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1125,7 +1081,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1146,7 +1101,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -1161,7 +1115,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1184,7 +1137,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
@@ -1199,7 +1151,6 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1221,7 +1172,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
         "//tensorflow/core:lib",
@@ -1240,7 +1190,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
@@ -1262,7 +1211,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/service:session_proto",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1279,7 +1227,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1298,7 +1245,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1315,7 +1261,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1365,7 +1310,6 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1381,7 +1325,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1407,7 +1350,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_compiler_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 7c3a1d2580..b6088f8d08 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -26,7 +26,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
@@ -1871,7 +1870,6 @@ INSTANTIATE_TEST_CASE_P(ArrayElementwiseOpTestParamCount,
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendUserComputationFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/axpy_simple_test.cc b/tensorflow/compiler/xla/tests/axpy_simple_test.cc
index a1ca1de584..67dbc913b4 100644
--- a/tensorflow/compiler/xla/tests/axpy_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/axpy_simple_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -76,7 +75,6 @@ TEST_F(AxpySimpleTest, AxpyTenValues) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc
index ea58491038..02be0b5ab8 100644
--- a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc
+++ b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
@@ -70,7 +69,6 @@ TEST_F(BadRngShapeValidationTest, ShapeWithoutLayoutIsOk) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index 6a47f1b718..b9b051b78c 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -196,7 +195,6 @@ TEST_F(BatchNormalizationTest, SpecComparisonForward) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/binop_scaling_test.cc b/tensorflow/compiler/xla/tests/binop_scaling_test.cc
index 5e3b70702d..e6b853c2e4 100644
--- a/tensorflow/compiler/xla/tests/binop_scaling_test.cc
+++ b/tensorflow/compiler/xla/tests/binop_scaling_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
@@ -143,7 +142,6 @@ TEST_F(BinopScalingTest, R4PlusR0S32) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
index 25fe04a930..b8c35cad47 100644
--- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
@@ -704,7 +703,6 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendUserComputationFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/broadcast_test.cc b/tensorflow/compiler/xla/tests/broadcast_test.cc
index 96a329a9bd..820f0ab6f3 100644
--- a/tensorflow/compiler/xla/tests/broadcast_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_test.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
@@ -302,7 +301,6 @@ TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc
index 55701c62db..0d587a4964 100644
--- a/tensorflow/compiler/xla/tests/call_test.cc
+++ b/tensorflow/compiler/xla/tests/call_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -120,7 +119,6 @@ XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR0F32Tuple)) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
index 4825eaf19d..eb580693b8 100644
--- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
+++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -126,7 +125,6 @@ XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc
index 1247804dae..99162ef2e5 100644
--- a/tensorflow/compiler/xla/tests/client_test.cc
+++ b/tensorflow/compiler/xla/tests/client_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -115,7 +114,6 @@ TEST_F(ClientTest, ExecuteWithTupleLayout) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.cc b/tensorflow/compiler/xla/tests/codegen_test_base.cc
index cc3eb0e8d4..a7562b5484 100644
--- a/tensorflow/compiler/xla/tests/codegen_test_base.cc
+++ b/tensorflow/compiler/xla/tests/codegen_test_base.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <stdlib.h>
 #include <utility>
 
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
@@ -32,6 +33,15 @@ limitations under the License.
 
 namespace xla {
 
+std::unique_ptr<HloModule> CodegenTestBase::CreateNewModuleWithEmbeddedIr() {
+  HloModuleConfig config;
+  auto debug_options = legacy_flags::GetDebugOptionsFromFlags();
+  debug_options.set_xla_embed_ir_in_executable(true);
+  config.set_debug_options(debug_options);
+  return MakeUnique<HloModule>(TestName(), VersionedComputationHandle(),
+                               config);
+}
+
 void CodegenTestBase::CompileAndVerifyIr(std::unique_ptr<HloModule> hlo_module,
                                          const string& pattern) {
   std::unique_ptr<Executable> executable =
diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.h b/tensorflow/compiler/xla/tests/codegen_test_base.h
index 50c0453107..033077c014 100644
--- a/tensorflow/compiler/xla/tests/codegen_test_base.h
+++ b/tensorflow/compiler/xla/tests/codegen_test_base.h
@@ -28,7 +28,9 @@ namespace xla {
 // Tests that verify IR emitted by the CPU/GPU backend is as expected.
 class CodegenTestBase : public HloTestBase {
  protected:
-  CodegenTestBase() {}
+  // Like HloTestBase::CreateNewModule, but also sets the "embed ir in
+  // executable" flag to true, since this is needed for codegen tests.
+  std::unique_ptr<HloModule> CreateNewModuleWithEmbeddedIr();
 
   // Returns the embedded LLVM IR from the given executable. Codegen tests must
   // override this method, but execution tests do not have to because they do
diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
index 18ea9714d1..0d92e67447 100644
--- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc
+++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -205,7 +204,6 @@ XLA_TEST_F(CompilationCacheTest, MutatedComputation) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc
index 13c78fb163..264ee9e1ca 100644
--- a/tensorflow/compiler/xla/tests/compute_constant_test.cc
+++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -291,7 +290,6 @@ TEST_F(ComputeConstantTest, DISABLED_ON_CPU(ReuseComputedConstant)) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc
index a7034930bc..d47144ae4b 100644
--- a/tensorflow/compiler/xla/tests/concat_test.cc
+++ b/tensorflow/compiler/xla/tests/concat_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -607,7 +606,6 @@ INSTANTIATE_TEST_CASE_P(ConcatR2BinaryTestInstantiation, ConcatR2BinaryTest,
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc
index 1c065de8ba..356d4a7161 100644
--- a/tensorflow/compiler/xla/tests/constants_test.cc
+++ b/tensorflow/compiler/xla/tests/constants_test.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
@@ -179,7 +178,6 @@ TEST_F(ConstantsTest, DISABLED_TupleConstant) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc
index 6d37979725..f9652ed8cf 100644
--- a/tensorflow/compiler/xla/tests/convert_test.cc
+++ b/tensorflow/compiler/xla/tests/convert_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
@@ -197,7 +196,6 @@ TEST_F(ConvertTest, ConvertReshape) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
index 0b09416a74..34c8ef4cee 100644
--- a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/padding.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
@@ -102,7 +101,6 @@ XLA_TEST_F(ConvolutionDimensionNumbersTest,
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc
index ec19469fa6..8a6c5adb8a 100644
--- a/tensorflow/compiler/xla/tests/convolution_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_test.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/padding.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
@@ -344,7 +343,6 @@ XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/convolution_variants_test.cc b/tensorflow/compiler/xla/tests/convolution_variants_test.cc
index b5afc2498d..dfaa023e33 100644
--- a/tensorflow/compiler/xla/tests/convolution_variants_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_variants_test.cc
@@ -28,7 +28,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/padding.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
@@ -1370,7 +1369,6 @@ TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding3D) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc
index 4c2413d0fe..47bcc96c19 100644
--- a/tensorflow/compiler/xla/tests/copy_test.cc
+++ b/tensorflow/compiler/xla/tests/copy_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/compiler/xla/array2d.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
@@ -263,7 +262,6 @@ XLA_TEST_F(CopyOpClientTest, Copy0x0) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/custom_call_test.cc b/tensorflow/compiler/xla/tests/custom_call_test.cc
index 32232acf6e..903c01f1c6 100644
--- a/tensorflow/compiler/xla/tests/custom_call_test.cc
+++ b/tensorflow/compiler/xla/tests/custom_call_test.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
@@ -129,7 +128,6 @@ XLA_TEST_F(CustomCallTest,
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc
index 074753bf6f..9952ff3186 100644
--- a/tensorflow/compiler/xla/tests/deallocation_test.cc
+++ b/tensorflow/compiler/xla/tests/deallocation_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
@@ -143,7 +142,6 @@ XLA_TEST_F(DeallocationTest, DeallocateNestedTuple) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
index fcddffc1e1..4dcf82b9c4 100644
--- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -205,7 +204,6 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructNestedTuple) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 0acc71d247..1581e15d7b 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
@@ -463,7 +462,6 @@ int main(int argc, char** argv) {
   xla::legacy_flags::AppendLayoutUtilFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
index b7bb1792f3..d86998b708 100644
--- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
@@ -521,7 +520,6 @@ BENCHMARK(BM_DynamicSlice);
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/floor_ceil_test.cc b/tensorflow/compiler/xla/tests/floor_ceil_test.cc
index 80267e5459..90c5aa6559 100644
--- a/tensorflow/compiler/xla/tests/floor_ceil_test.cc
+++ b/tensorflow/compiler/xla/tests/floor_ceil_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -114,7 +113,6 @@ TEST_F(FloorCeilTest, R0Ceil) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/fmax_test.cc b/tensorflow/compiler/xla/tests/fmax_test.cc
index ee4e92505d..9c86c65e5b 100644
--- a/tensorflow/compiler/xla/tests/fmax_test.cc
+++ b/tensorflow/compiler/xla/tests/fmax_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -47,7 +46,6 @@ TEST_F(FmaxSimpleTest, FmaxTenValues) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index 4fa08e589c..0e4fc33579 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/compiler/xla/array2d.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
@@ -573,7 +572,6 @@ XLA_TEST_F(FusionTest, Clamp2D) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/log_test.cc b/tensorflow/compiler/xla/tests/log_test.cc
index 796f43ea4e..4cb383a78d 100644
--- a/tensorflow/compiler/xla/tests/log_test.cc
+++ b/tensorflow/compiler/xla/tests/log_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -62,7 +61,6 @@ TEST_F(LogTest, LogTenValues) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc
index e4dbd6864a..d613d79993 100644
--- a/tensorflow/compiler/xla/tests/map_test.cc
+++ b/tensorflow/compiler/xla/tests/map_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -622,7 +621,6 @@ TEST_F(MapTestWithFullOpt, MapSquare) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
index 51261f0ac1..883ec2277b 100644
--- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
@@ -218,7 +217,6 @@ INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest,
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
index 15935c363f..df3d4fa21d 100644
--- a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
+++ b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -60,7 +59,6 @@ XLA_TEST_F(SliceTest, Slice3D) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/pad_test.cc b/tensorflow/compiler/xla/tests/pad_test.cc
index 4922bbf21c..73f4d539d9 100644
--- a/tensorflow/compiler/xla/tests/pad_test.cc
+++ b/tensorflow/compiler/xla/tests/pad_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
@@ -470,7 +469,6 @@ XLA_TEST_F(PadTest, ReducePad) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc
index bcfe8501b3..03b8392a82 100644
--- a/tensorflow/compiler/xla/tests/params_test.cc
+++ b/tensorflow/compiler/xla/tests/params_test.cc
@@ -24,7 +24,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -344,7 +343,6 @@ XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/pred_test.cc b/tensorflow/compiler/xla/tests/pred_test.cc
index b031725d8a..d865297ae6 100644
--- a/tensorflow/compiler/xla/tests/pred_test.cc
+++ b/tensorflow/compiler/xla/tests/pred_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -142,7 +141,6 @@ TEST_F(PredTest, AnyR2False) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc
index b77b8e2ee3..46f8e12e16 100644
--- a/tensorflow/compiler/xla/tests/prng_test.cc
+++ b/tensorflow/compiler/xla/tests/prng_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
@@ -290,7 +289,6 @@ XLA_TEST_F(PrngTest, RngUniformCrash) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc
index 4a02567a1a..0078733e19 100644
--- a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc
+++ b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -46,7 +45,6 @@ TEST_F(QueryInferredShapeTest, OnePlusOneShape) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc
index ff24177520..12072b6c95 100644
--- a/tensorflow/compiler/xla/tests/reduce_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_test.cc
@@ -40,7 +40,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
@@ -630,7 +629,6 @@ INSTANTIATE_TEST_CASE_P(
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index ec7b47bc28..ee3a837e16 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/padding.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -476,7 +475,6 @@ XLA_TEST_F(ReduceWindowTest, NonstandardReduceFunction) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/replay_test.cc b/tensorflow/compiler/xla/tests/replay_test.cc
index 7c6700feef..70ef1bc710 100644
--- a/tensorflow/compiler/xla/tests/replay_test.cc
+++ b/tensorflow/compiler/xla/tests/replay_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/protobuf_util.h"
@@ -154,7 +153,6 @@ TEST_F(ReplayTest, MapPlusTwoOverR1) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/reshape_motion_test.cc b/tensorflow/compiler/xla/tests/reshape_motion_test.cc
index c9817bc23d..3051562455 100644
--- a/tensorflow/compiler/xla/tests/reshape_motion_test.cc
+++ b/tensorflow/compiler/xla/tests/reshape_motion_test.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
@@ -63,7 +62,6 @@ TEST_F(ReshapeMotionTest, ElementwiseOfReshapesWithNonSameInputShapes) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc
index ae7d07727b..f1fff09ecf 100644
--- a/tensorflow/compiler/xla/tests/reshape_test.cc
+++ b/tensorflow/compiler/xla/tests/reshape_test.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
@@ -831,7 +830,6 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeTrivialR2) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/reverse_test.cc b/tensorflow/compiler/xla/tests/reverse_test.cc
index 5ca9702380..2f72fc0729 100644
--- a/tensorflow/compiler/xla/tests/reverse_test.cc
+++ b/tensorflow/compiler/xla/tests/reverse_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -159,7 +158,6 @@ TEST_F(ReverseTest, Reverse4DFloatArrayOnDim01) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
index 05ce22fc35..54d1d76ec9 100644
--- a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
+++ b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/packed_literal_reader.h"
@@ -146,7 +145,6 @@ TEST_F(RoundTripPackedLiteralTest, RoundTripsR2F32Size2x2Dim1Minor) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
index f0760241cd..71af695ae3 100644
--- a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
+++ b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -149,7 +148,6 @@ TEST_F(RoundTripTransferTest, R4F32_Large) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index 47a39ffbbc..7b32bb93fd 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -782,7 +781,6 @@ TEST_F(ScalarComputationsTest, SqrtF320) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc
index 36110da247..de89588042 100644
--- a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc
+++ b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/padding.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
@@ -381,7 +380,6 @@ XLA_TEST_F(SelectAndScatterTest, R1F32OverlappingWindowMinScatter) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/select_test.cc b/tensorflow/compiler/xla/tests/select_test.cc
index 5eb4fee8ed..6b48116b6e 100644
--- a/tensorflow/compiler/xla/tests/select_test.cc
+++ b/tensorflow/compiler/xla/tests/select_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -262,7 +261,6 @@ TEST_F(SelectTest, SelectR1F32WithScalarPredicateFalse) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/set_return_value_test.cc b/tensorflow/compiler/xla/tests/set_return_value_test.cc
index 25bb915be5..38fc27f200 100644
--- a/tensorflow/compiler/xla/tests/set_return_value_test.cc
+++ b/tensorflow/compiler/xla/tests/set_return_value_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -102,7 +101,6 @@ TEST_F(SetReturnValueTest, SetValueMultipleTimesAndModify) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index d3eeec3049..97120df0c5 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
@@ -264,7 +263,6 @@ INSTANTIATE_TEST_CASE_P(
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/transpose_test.cc b/tensorflow/compiler/xla/tests/transpose_test.cc
index e4951c4201..07c0f073e8 100644
--- a/tensorflow/compiler/xla/tests/transpose_test.cc
+++ b/tensorflow/compiler/xla/tests/transpose_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
@@ -189,7 +188,6 @@ TEST_F(TransposeTest, TransposeConstant021_MultipleTilesPerLayer) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc
index 6309e71297..e9d523a31e 100644
--- a/tensorflow/compiler/xla/tests/tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/tuple_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -401,7 +400,6 @@ XLA_TEST_F(TupleTest, GetTupleElementOfNestedTuple) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/unary_op_test.cc b/tensorflow/compiler/xla/tests/unary_op_test.cc
index 61110d5b4c..d35d9ecdeb 100644
--- a/tensorflow/compiler/xla/tests/unary_op_test.cc
+++ b/tensorflow/compiler/xla/tests/unary_op_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -165,7 +164,6 @@ TEST_F(UnaryOpTest, SignAbsTestR2) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc
index 26a08953b1..079dbb0611 100644
--- a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc
+++ b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
@@ -221,7 +220,6 @@ TEST_F(VecOpsReduceTest, AddReduceR3F32AllDims) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc
index efde45375f..b2e0c796bd 100644
--- a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -441,7 +440,6 @@ XLA_TEST_F(VecOpsSimpleTest, VectorPredicateNotEqual) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index bb0004a8b8..f0a0e494ec 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/platform_util.h"
@@ -742,7 +741,6 @@ BENCHMARK(BM_WhileLoop);
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 4c3cd321f6..f98fc2f5a4 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -45,9 +45,15 @@ message DebugOptions {
   //  - Assuming that +0 and -0 are indistinguishable.
   bool xla_enable_fast_math = 4;
 
+  // Embed the compiler IR as a string in the executable.
+  bool xla_embed_ir_in_executable = 5;
+
+  // Dump compilation artifacts as JSON into this directory.
+  string xla_dump_debug_json_to = 6;
+
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
-  map<string, string> xla_backend_extra_options = 5;
+  map<string, string> xla_backend_extra_options = 500;
 }
 
 // These settings control how XLA compiles and/or runs code.  Not all settings
-- 
GitLab


From 8ca083610266e50e3ce8b7c4913bcf9d9f3af57f Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Wed, 14 Jun 2017 09:57:26 -0700
Subject: [PATCH 0075/1375] Automated g4 rollback of changelist 158765635

PiperOrigin-RevId: 158990733
---
 .../core/kernels/depthwise_conv_op_gpu.cu.cc  | 528 +++++++-----------
 1 file changed, 212 insertions(+), 316 deletions(-)

diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
index be9fc5de69..319dbb68e6 100644
--- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
@@ -38,8 +38,8 @@ using Eigen::GpuDevice;
 // performed using the faster ('Small') variant of the kernel.
 EIGEN_DEVICE_FUNC bool CanLaunchDepthwiseConv2dGPUSmall(
     const DepthwiseArgs args) {
-  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 32 &&
-         args.in_cols <= 32 && args.in_rows == args.out_rows &&
+  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 16 &&
+         args.in_cols <= 16 && args.in_rows == args.out_rows &&
          args.in_cols == args.out_cols && args.pad_rows >= 0 &&
          args.pad_rows < args.filter_rows && args.pad_cols >= 0 &&
          args.pad_cols < args.filter_cols &&
@@ -51,8 +51,8 @@ EIGEN_DEVICE_FUNC bool CanLaunchDepthwiseConv2dGPUSmall(
 // using the faster ('Small') variant of the kernel.
 EIGEN_DEVICE_FUNC bool CanLaunchDepthwiseConv2dBackpropFilterGPUSmall(
     const DepthwiseArgs args, const int block_rows) {
-  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 32 &&
-         args.in_cols <= 32 && args.in_rows == args.out_rows &&
+  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 16 &&
+         args.in_cols <= 16 && args.in_rows == args.out_rows &&
          args.in_cols == args.out_cols && args.pad_rows >= 0 &&
          args.pad_rows < args.filter_rows && args.pad_cols >= 0 &&
          args.pad_cols < args.filter_cols && block_rows <= args.in_rows &&
@@ -142,14 +142,14 @@ __global__ void __launch_bounds__(1024, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution forward pass in NHWC format,
-// tailored for small images up to 32x32. Stride and depth multiplier must be 1.
+// tailored for small images up to 16x16. Stride and depth multiplier must be 1.
 // Padding must be 'SAME', which allows to reuse the index computation. Only
 // use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input and filter tensors are loaded into shared memory before
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+          bool kKnownEvenRows>
 __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
   assert(CanLaunchDepthwiseConv2dGPUSmall(args));
@@ -168,23 +168,25 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
   const int pad_rows = args.pad_rows;
   const int pad_cols = args.pad_cols;
 
+  // Fixed blockDim.x, corresponding to Pascal's global load granularity of 32B.
+  const int block_slices = 8;
   const int block_rows = blockDim.z;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
-  const int block_size = block_rows * in_cols * kBlockSlices;
+  const int block_size = block_rows * in_cols * block_slices;
   const int in_row_size = in_cols * in_depth;
   const int in_size = in_rows * in_row_size;
-  const int in_increment = (in_cols - 1) * kBlockSlices;
+  const int in_increment = (in_cols - 1) * block_slices;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
-  const int tile_row_size = tile_cols * kBlockSlices;
+  const int tile_row_size = tile_cols * block_slices;
   const int tile_size = tile_rows * tile_row_size;
   const int tile_offset = block_rows * tile_row_size;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int batch_blocks = (in_depth + kBlockSlices - 1) / kBlockSlices;
+  const int batch_blocks = (in_depth + block_slices - 1) / block_slices;
   const int in_blocks = batch_blocks * batches;
   const int tensor_offset =
       kKnownEvenRows ? in_size / 2 : block_rows * in_row_size;
@@ -195,7 +197,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
 
   // Position in block.
   const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_pix * kBlockSlices + thread_depth;
+  const int thread_idx = thread_pix * block_slices + thread_depth;
 
   // Initialize tile, in particular the padding.
   for (int i = thread_idx; i < tile_size; i += block_size) {
@@ -208,11 +210,11 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
 
   // Position in (padded) shared memory.
   const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = data_pix * kBlockSlices + thread_depth;
+  const int data_idx = data_pix * block_slices + thread_depth;
 
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_pix = data_pix + pad_offset;
-  const int tile_idx = tile_pix * kBlockSlices + thread_depth;
+  const int tile_idx = tile_pix * block_slices + thread_depth;
 
   const int max_depth = in_depth - thread_depth;
   const int filter_write_offset =
@@ -225,7 +227,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
     const int batch = b / batch_blocks;
     const int stack = b - batch * batch_blocks;
 
-    const int start_depth = stack * kBlockSlices;
+    const int start_depth = stack * block_slices;
     const int filter_offset = tensor_idx + start_depth;
     const int inout_offset = batch * in_size + filter_offset;
     const bool depth_in_range = start_depth < max_depth;
@@ -257,8 +259,8 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
-          shared_offset += kBlockSlices;
-          filter_ptr += kBlockSlices;
+          shared_offset += block_slices;
+          filter_ptr += block_slices;
         }
         shared_offset += in_increment;
       }
@@ -402,14 +404,14 @@ __global__ void __launch_bounds__(1024, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution forward pass in NCHW format,
-// tailored for small images up to 32x32. Stride and depth multiplier must be 1.
+// tailored for small images up to 16x16. Stride and depth multiplier must be 1.
 // Padding must be 'SAME', which allows to reuse the index computation. Only
 // use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input and filter tensors are loaded into shared memory before
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+          bool kKnownEvenRows>
 __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
   assert(CanLaunchDepthwiseConv2dGPUSmall(args));
@@ -430,11 +432,12 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
 
   // Fixed blockDim.z, tailored for maximum grid size for images of size 16x16.
   const int block_rows = blockDim.y;
+  const int block_slices = 8;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
   const int block_pixels = in_cols * block_rows;
-  const int block_size = block_pixels * kBlockSlices;
+  const int block_size = block_pixels * block_slices;
   const int in_pixels = in_cols * in_rows;
   const int in_increment = in_cols - 1;
   const int filter_pixels = filter_rows * filter_cols;
@@ -442,11 +445,11 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
   const int tile_pixels = tile_cols * tile_rows;
-  const int tile_size = tile_pixels * kBlockSlices;
+  const int tile_size = tile_pixels * block_slices;
   const int tile_offset = block_rows * tile_cols;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
   const int in_slices = in_depth * batches;
-  const int in_blocks = (in_slices + kBlockSlices - 1) / kBlockSlices;
+  const int in_blocks = (in_slices + block_slices - 1) / block_slices;
 
   const int thread_col = threadIdx.x;
   const int thread_row = threadIdx.y;
@@ -473,8 +476,8 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
   const int tile_idx = data_idx + pad_offset;
 
   // Filter is always in HWCK format, irrespective of the input/output format.
-  const int filter_pix = thread_idx / kBlockSlices;
-  const int filter_depth = thread_idx % kBlockSlices;
+  const int filter_pix = thread_idx / block_slices;
+  const int filter_depth = thread_idx % block_slices;
   const int filter_idx = filter_pix * in_depth;
 
   const int max_slice = in_slices - thread_depth;
@@ -485,7 +488,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
   for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int slice = b * kBlockSlices;
+    const int slice = b * block_slices;
 
     const int inout_offset = slice * in_pixels + tensor_idx;
     const bool slice_in_range = slice < max_slice;
@@ -519,7 +522,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
           ++shared_offset;
-          filter_ptr += kBlockSlices;
+          filter_ptr += block_slices;
         }
         shared_offset += in_increment;
       }
@@ -536,43 +539,42 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+          bool kKnownEvenRows>
 void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
                                    const T* input, const T* filter, T* output,
                                    TensorFormat data_format) {
   const int block_rows = (args.in_rows + 1) / 2;
+  const int block_slices = 8;
   const int tile_cols = args.in_cols + args.filter_cols - 1;
   const int tile_rows = block_rows * 2 + args.filter_rows - 1;
   const int tile_pixels = tile_rows * tile_cols;
   const int filter_pixels = args.filter_rows * args.filter_cols;
 
   const int shared_memory_size =
-      kBlockSlices * (tile_pixels + filter_pixels) * sizeof(T);
+      block_slices * (tile_pixels + filter_pixels) * sizeof(T);
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
 
   if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
+    dim3 block_dim = dim3(block_slices, args.in_cols, block_rows);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dGPUKernelNHWCSmall<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, kBlockSlices,
-                                          kKnownEvenRows>,
+                                          kKnownFilterHeight, kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dGPUKernelNHWCSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                      kBlockSlices, kKnownEvenRows>
+                                      kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, input, filter, output);
   } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
+    dim3 block_dim = dim3(args.in_cols, block_rows, block_slices);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dGPUKernelNCHWSmall<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, kBlockSlices,
-                                          kKnownEvenRows>,
+                                          kKnownFilterHeight, kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dGPUKernelNCHWSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                      kBlockSlices, kKnownEvenRows>
+                                      kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, input, filter, output);
   } else {
@@ -580,37 +582,17 @@ void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices>
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
 void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
                                    const T* input, const T* filter, T* output,
                                    TensorFormat data_format) {
   if (args.in_rows & 1) {
     LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                  kBlockSlices, false>(d, args, input, filter,
-                                                       output, data_format);
-  } else {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                  kBlockSlices, true>(d, args, input, filter,
-                                                      output, data_format);
-  }
-}
-
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
-                                   const T* input, const T* filter, T* output,
-                                   TensorFormat data_format) {
-  // Maximize (power of two) kBlockSlices while keeping a block within 1024
-  // threads (2 pixels per thread).
-  const int in_pixels = args.in_rows * args.in_cols;
-  if (in_pixels > 512) {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 2>(
-        d, args, input, filter, output, data_format);
-  } else if (in_pixels > 256) {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 4>(
+                                  /*kKnownEvenRows=*/false>(
         d, args, input, filter, output, data_format);
   } else {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 8>(
+    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight,
+                                  /*kKnownEvenRows=*/true>(
         d, args, input, filter, output, data_format);
   }
 }
@@ -620,6 +602,11 @@ template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
 void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
                               const T* input, const T* filter, T* output,
                               TensorFormat data_format) {
+  if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
+    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight>(
+        d, args, input, filter, output, data_format);
+    return;
+  }
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
   // The compile-time constant version runs faster with a single block.
@@ -654,36 +641,18 @@ void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
-                              const T* input, const T* filter, T* output,
-                              TensorFormat data_format) {
-  if (args.depth_multiplier == 1) {
-    if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
-      LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight>(
-          d, args, input, filter, output, data_format);
-      return;
-    }
-
-    LaunchDepthwiseConv2dGPU<T, kKnownFilterWidth, kKnownFilterHeight, 1>(
-        d, args, input, filter, output, data_format);
-  } else {
-    LaunchDepthwiseConv2dGPU<T, kKnownFilterWidth, kKnownFilterHeight, -1>(
-        d, args, input, filter, output, data_format);
-  }
-}
-
 // A simple launch pad to launch the Cuda kernel for depthwise convolution.
 template <typename T>
 struct DepthwiseConv2dGPULaunch {
   static void Run(const GpuDevice& d, const DepthwiseArgs args, const T* input,
                   const T* filter, T* output, TensorFormat data_format) {
-    if (args.filter_rows == 3 && args.filter_cols == 3) {
-      LaunchDepthwiseConv2dGPU<T, 3, 3>(d, args, input, filter, output,
-                                        data_format);
+    if (args.filter_rows == 3 && args.filter_cols == 3 &&
+        args.depth_multiplier == 1) {
+      LaunchDepthwiseConv2dGPU<T, 3, 3, 1>(d, args, input, filter, output,
+                                           data_format);
     } else {
-      LaunchDepthwiseConv2dGPU<T, -1, -1>(d, args, input, filter, output,
-                                          data_format);
+      LaunchDepthwiseConv2dGPU<T, -1, -1, -1>(d, args, input, filter, output,
+                                              data_format);
     }
   }
 };
@@ -757,7 +726,7 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. input in
-// NHWC format, tailored for small images up to 32x32. Stride and depth
+// NHWC format, tailored for small images up to 16x16. Stride and depth
 // multiplier must be 1. Padding must be 'SAME', which allows to reuse the index
 // computation. Only use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args)
 // returns true.
@@ -767,7 +736,7 @@ __global__ void __launch_bounds__(640, 2)
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+          bool kKnownEvenRows>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
@@ -788,23 +757,24 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
   const int pad_cols = args.pad_cols;
 
   // Fixed blockDim.x, corresponding to Pascal's global load granularity of 32B.
+  const int block_slices = 8;
   const int block_rows = blockDim.z;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
-  const int block_size = block_rows * in_cols * kBlockSlices;
+  const int block_size = block_rows * in_cols * block_slices;
   const int in_row_size = in_cols * in_depth;
   const int in_size = in_rows * in_row_size;
-  const int in_increment = (in_cols - 1) * kBlockSlices;
+  const int in_increment = (in_cols - 1) * block_slices;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
-  const int tile_row_size = tile_cols * kBlockSlices;
+  const int tile_row_size = tile_cols * block_slices;
   const int tile_size = tile_rows * tile_row_size;
   const int tile_offset = block_rows * tile_row_size;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int batch_blocks = (in_depth + kBlockSlices - 1) / kBlockSlices;
+  const int batch_blocks = (in_depth + block_slices - 1) / block_slices;
   const int in_blocks = batch_blocks * batches;
   const int tensor_offset =
       kKnownEvenRows ? in_size / 2 : block_rows * in_row_size;
@@ -815,7 +785,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
 
   // Position in block.
   const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_pix * kBlockSlices + thread_depth;
+  const int thread_idx = thread_pix * block_slices + thread_depth;
 
   // Initialize tile, in particular the padding.
   for (int i = thread_idx; i < tile_size; i += block_size) {
@@ -828,17 +798,17 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
 
   // Position in (padded) shared memory.
   const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = data_pix * kBlockSlices + thread_depth;
+  const int data_idx = data_pix * block_slices + thread_depth;
 
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_pix = data_pix + pad_offset;
-  const int tile_idx = tile_pix * kBlockSlices + thread_depth;
+  const int tile_idx = tile_pix * block_slices + thread_depth;
 
   const int max_depth = in_depth - thread_depth;
   const int filter_write_offset =
       thread_pix < filter_pixels ? tile_size + thread_idx : 0;
   const int filter_read_offset =
-      tile_size + filter_pixels * kBlockSlices + thread_depth;
+      tile_size + filter_pixels * block_slices + thread_depth;
   const bool skip_second =
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
@@ -846,7 +816,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
     const int batch = b / batch_blocks;
     const int stack = b - batch * batch_blocks;
 
-    const int start_depth = stack * kBlockSlices;
+    const int start_depth = stack * block_slices;
     const int filter_offset = tensor_idx + start_depth;
     const int inout_offset = batch * in_size + filter_offset;
     const bool depth_in_range = start_depth < max_depth;
@@ -874,12 +844,12 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
       const T* filter_ptr = filter_read_offset + shared_data;
       UNROLL for (int r = 0; r < filter_rows; ++r) {
         UNROLL for (int c = 0; c < filter_cols; ++c) {
-          filter_ptr -= kBlockSlices;
+          filter_ptr -= block_slices;
           const T filter_value = *filter_ptr;
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
-          shared_offset += kBlockSlices;
+          shared_offset += block_slices;
         }
         shared_offset += in_increment;
       }
@@ -967,7 +937,7 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. input in
-// NHWC format, tailored for small images up to 32x32. Stride and depth
+// NHWC format, tailored for small images up to 16x16. Stride and depth
 // multiplier must be 1. Padding must be 'SAME', which allows to reuse the index
 // computation. Only use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args)
 // returns true.
@@ -977,7 +947,7 @@ __global__ void __launch_bounds__(640, 2)
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+          bool kKnownEvenRows>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
@@ -999,11 +969,12 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
 
   // Fixed blockDim.z, tailored for maximum grid size for images of size 16x16.
   const int block_rows = blockDim.y;
+  const int block_slices = 8;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
   const int block_pixels = in_cols * block_rows;
-  const int block_size = block_pixels * kBlockSlices;
+  const int block_size = block_pixels * block_slices;
   const int in_pixels = in_cols * in_rows;
   const int in_increment = in_cols - 1;
   const int filter_pixels = filter_rows * filter_cols;
@@ -1011,11 +982,11 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
   const int tile_pixels = tile_cols * tile_rows;
-  const int tile_size = tile_pixels * kBlockSlices;
+  const int tile_size = tile_pixels * block_slices;
   const int tile_offset = block_rows * tile_cols;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
   const int in_slices = in_depth * batches;
-  const int in_blocks = (in_slices + kBlockSlices - 1) / kBlockSlices;
+  const int in_blocks = (in_slices + block_slices - 1) / block_slices;
 
   const int thread_col = threadIdx.x;
   const int thread_row = threadIdx.y;
@@ -1042,20 +1013,20 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
   const int tile_idx = data_idx + pad_offset;
 
   // Filter is always in HWCK format, irrespective of the input/output format.
-  const int filter_pix = thread_idx / kBlockSlices;
-  const int filter_depth = thread_idx % kBlockSlices;
+  const int filter_pix = thread_idx / block_slices;
+  const int filter_depth = thread_idx % block_slices;
   const int filter_idx = filter_pix * in_depth;
 
   const int max_slice = in_slices - thread_depth;
   const int filter_write_offset =
       filter_pix < filter_pixels ? tile_size + thread_idx : 0;
   const int filter_read_offset =
-      tile_size + filter_pixels * kBlockSlices + thread_depth;
+      tile_size + filter_pixels * block_slices + thread_depth;
   const bool skip_second =
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
   for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int slice = b * kBlockSlices;
+    const int slice = b * block_slices;
 
     const int inout_offset = slice * in_pixels + tensor_idx;
     const bool slice_in_range = slice < max_slice;
@@ -1084,7 +1055,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
       const T* filter_ptr = filter_read_offset + shared_data;
       UNROLL for (int r = 0; r < filter_rows; ++r) {
         UNROLL for (int c = 0; c < filter_cols; ++c) {
-          filter_ptr -= kBlockSlices;
+          filter_ptr -= block_slices;
           const T filter_value = *filter_ptr;
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
@@ -1106,45 +1077,44 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+          bool kKnownEvenRows>
 void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
                                                 const DepthwiseArgs args,
                                                 const T* out_backprop,
                                                 const T* filter, T* in_backprop,
                                                 TensorFormat data_format) {
   const int block_rows = (args.in_rows + 1) / 2;
+  const int block_slices = 8;
   const int tile_cols = args.in_cols + args.filter_cols - 1;
   const int tile_rows = block_rows * 2 + args.filter_rows - 1;
   const int tile_pixels = tile_rows * tile_cols;
   const int filter_pixels = args.filter_rows * args.filter_cols;
 
   const int shared_memory_size =
-      kBlockSlices * (tile_pixels + filter_pixels) * sizeof(T);
+      block_slices * (tile_pixels + filter_pixels) * sizeof(T);
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
 
   if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
+    dim3 block_dim = dim3(block_slices, args.in_cols, block_rows);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dBackpropInputGPUKernelNHWCSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kKnownEvenRows>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropInputGPUKernelNHWCSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kKnownEvenRows>
+        T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, filter, in_backprop);
   } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
+    dim3 block_dim = dim3(args.in_cols, block_rows, block_slices);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dBackpropInputGPUKernelNCHWSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kKnownEvenRows>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropInputGPUKernelNCHWSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kKnownEvenRows>
+        T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, filter, in_backprop);
   } else {
@@ -1152,45 +1122,22 @@ void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices>
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
 void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
                                                 const DepthwiseArgs args,
                                                 const T* out_backprop,
                                                 const T* filter, T* in_backprop,
                                                 TensorFormat data_format) {
   if (args.in_rows & 1) {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, false>(
-        d, args, out_backprop, filter, in_backprop, data_format);
-  } else {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, true>(
-        d, args, out_backprop, filter, in_backprop, data_format);
-  }
-}
-
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
-                                                const DepthwiseArgs args,
-                                                const T* input, const T* filter,
-                                                T* output,
-                                                TensorFormat data_format) {
-  // Maximize (power of two) kBlockSlices while keeping a block within 1024
-  // threads (2 pixels per thread).
-  const int in_pixels = args.in_rows * args.in_cols;
-  if (in_pixels > 512) {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight, 2>(
-        d, args, input, filter, output, data_format);
-  } else if (in_pixels > 256) {
     LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight, 4>(
-        d, args, input, filter, output, data_format);
+                                               kKnownFilterHeight,
+                                               /*kKnownEvenRows=*/false>(
+        d, args, out_backprop, filter, in_backprop, data_format);
   } else {
     LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight, 8>(
-        d, args, input, filter, output, data_format);
+                                               kKnownFilterHeight,
+                                               /*kKnownEvenRows=*/true>(
+        d, args, out_backprop, filter, in_backprop, data_format);
   }
 }
 
@@ -1201,6 +1148,12 @@ void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
                                            const T* out_backprop,
                                            const T* filter, T* in_backprop,
                                            TensorFormat data_format) {
+  if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
+    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
+                                               kKnownFilterHeight>(
+        d, args, out_backprop, filter, in_backprop, data_format);
+    return;
+  }
   const int num_in_backprop =
       args.batch * args.in_rows * args.in_cols * args.in_depth;
   if (data_format == FORMAT_NHWC) {
@@ -1228,41 +1181,22 @@ void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
-                                           const DepthwiseArgs args,
-                                           const T* out_backprop,
-                                           const T* filter, T* in_backprop,
-                                           TensorFormat data_format) {
-  if (args.depth_multiplier == 1) {
-    if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
-      LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                                 kKnownFilterHeight>(
-          d, args, out_backprop, filter, in_backprop, data_format);
-      return;
-    }
-
-    LaunchDepthwiseConv2dBackpropInputGPU<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, 1>(
-        d, args, out_backprop, filter, in_backprop, data_format);
-  } else {
-    LaunchDepthwiseConv2dBackpropInputGPU<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, -1>(
-        d, args, out_backprop, filter, in_backprop, data_format);
-  }
-}
-
 // A simple launch pad to launch the Cuda kernel for depthwise convolution.
 template <typename T>
 struct DepthwiseConv2dBackpropInputGPULaunch {
   static void Run(const GpuDevice& d, const DepthwiseArgs args,
                   const T* out_backprop, const T* filter, T* in_backprop,
                   TensorFormat data_format) {
-    if (args.filter_rows == 3 && args.filter_cols == 3) {
-      LaunchDepthwiseConv2dBackpropInputGPU<T, 3, 3>(
-          d, args, out_backprop, filter, in_backprop, data_format);
+    if (args.depth_multiplier == 1) {
+      if (args.filter_rows == 3 && args.filter_cols == 3) {
+        LaunchDepthwiseConv2dBackpropInputGPU<T, 3, 3, 1>(
+            d, args, out_backprop, filter, in_backprop, data_format);
+      } else {
+        LaunchDepthwiseConv2dBackpropInputGPU<T, -1, -1, 1>(
+            d, args, out_backprop, filter, in_backprop, data_format);
+      }
     } else {
-      LaunchDepthwiseConv2dBackpropInputGPU<T, -1, -1>(
+      LaunchDepthwiseConv2dBackpropInputGPU<T, -1, -1, -1>(
           d, args, out_backprop, filter, in_backprop, data_format);
     }
   }
@@ -1366,20 +1300,19 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. filter in
-// NHWC format, tailored for small images up to 32x32. Stride and depth
+// NHWC format, tailored for small images up to 16x16. Stride and depth
 // multiplier must be 1. Padding must be 'SAME'. Only use this kernel if
 // CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input tensor are loaded into shared memory before performing the
 // convolution. Per iteration and filter element, each thread first performs
 // a partial convolution for two elements, one each in the lower and upper half
-// of a tile. The intermediate result of all pixels of a warp are then
+// of a tile. The intermediate result of 4 consecutive columns are then
 // accumulated and written to shared memory. Finally, the values in shared
 // memory are warp-accumulated (in chunks of kAccumPixels elements) and summed
 // up in global memory using atomics.
-// Requirements: threads per block must be multiple of 32 and <= launch_bounds,
-// kAccumPixels * 64 >= args.in_rows * args.in_cols * kBlockSlices.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, int kAccumPixels>
+          // Requirement: kAccumPixels * 8 >= args.in_rows * args.in_cols
+          int kAccumPixels>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
     const DepthwiseArgs args, const T* output, const T* input, T* filter) {
@@ -1399,29 +1332,29 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
   const int pad_rows = args.pad_rows;
   const int pad_cols = args.pad_cols;
 
+  // Fixed blockDim.x, corresponding to Pascal's global load granularity of 32B.
+  const int block_slices = 8;
   const int block_rows = blockDim.z;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
-  const int block_size = block_rows * in_cols * kBlockSlices;
-  assert((block_size & 31) == 0);
+  const int block_size = block_rows * in_cols * block_slices;
   const int in_row_size = in_cols * in_depth;
   const int in_size = in_rows * in_row_size;
-  const int in_increment = (in_cols - 1) * kBlockSlices;
+  const int in_increment = (in_cols - 1) * block_slices;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int tile_rows = 2 * block_rows + filter_rows - 1;
-  const int tile_row_size = tile_cols * kBlockSlices;
+  const int tile_row_size = tile_cols * block_slices;
   const int tile_size = tile_rows * tile_row_size;
   const int tile_offset = block_rows * tile_row_size;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int batch_blocks = (in_depth + kBlockSlices - 1) / kBlockSlices;
+  const int batch_blocks = (in_depth + block_slices - 1) / block_slices;
   const int in_blocks = batch_blocks * batches;
   const int tensor_offset = block_rows * in_row_size;
   // The accumulator has a fixed number of pixels that can be reduced by one
-  // warp. Pixels beyond ceil(in_pixels * kBlockSlices / 64) are never written.
-  assert(kAccumPixels * 64 >= in_rows * in_cols * kBlockSlices);
-  const int accum_increment = kAccumPixels * kBlockSlices;
+  // warp. Pixels beyond block_pixels/4 are never written.
+  const int accum_increment = kAccumPixels * block_slices;
   const int accum_size = filter_pixels * accum_increment;
 
   const int thread_depth = threadIdx.x;
@@ -1430,7 +1363,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
 
   // Position in block.
   const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_pix * kBlockSlices + thread_depth;
+  const int thread_idx = thread_pix * block_slices + thread_depth;
 
   // Initialize tile, in particular the padding and accumulator.
   for (int i = thread_idx; i < tile_size + accum_size; i += block_size) {
@@ -1443,14 +1376,14 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
 
   // Position in (padded) shared memory.
   const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = data_pix * kBlockSlices + thread_depth;
+  const int data_idx = data_pix * block_slices + thread_depth;
 
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_pix = data_pix + pad_offset;
-  const int tile_idx = tile_pix * kBlockSlices + thread_depth;
+  const int tile_idx = tile_pix * block_slices + thread_depth;
 
-  // Position in accumulator (kBlockSlices per warp, depth major).
-  const int accum_pix = thread_pix / (32 / kBlockSlices);
+  // Position in accumulator (1 per 4 threads, depth major).
+  const int accum_pix = thread_pix / 4;
   const int accum_idx = thread_depth * kAccumPixels + accum_pix;
 
   const int max_depth = in_depth - thread_depth;
@@ -1461,7 +1394,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
     const int batch = b / batch_blocks;
     const int stack = b - batch * batch_blocks;
 
-    const int start_depth = stack * kBlockSlices;
+    const int start_depth = stack * block_slices;
     const int filter_offset = tensor_idx + start_depth;
     const int inout_offset = batch * in_size + filter_offset;
     const bool depth_in_range = start_depth < max_depth;
@@ -1488,14 +1421,13 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
         UNROLL for (int c = 0; c < filter_cols; ++c) {
           const T* const tile_ptr = shared_offset + shared_data;
           T val = out1 * tile_ptr[0] + out2 * tile_ptr[tile_offset];
-          // Warp-accumulate pixels of the same depth and write to accumulator.
-          for (int delta = 16; delta >= kBlockSlices; delta /= 2) {
-            val += CudaShuffleDown(val, delta);
-          }
-          if (!(thread_idx & 32 - kBlockSlices) /* lane_idx < kBlockSlices */) {
+          // Sum up 4 block_pixels of the same depth and write to accumulator.
+          val += CudaShuffleDown(val, 16);
+          val += CudaShuffleDown(val, 8);
+          if (!(thread_idx & 24) /* i.e. 'lane_idx < 8' */) {
             *accum_ptr = val;
           }
-          shared_offset += kBlockSlices;
+          shared_offset += block_slices;
           accum_ptr += accum_increment;
         }
         shared_offset += in_increment;
@@ -1508,8 +1440,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
     const T* const accum_data = tile_size + shared_data;
     for (int i = thread_idx; i < accum_size; i += block_size) {
       const int filter_idx = i / kAccumPixels;
-      const int filter_pix = filter_idx / kBlockSlices;
-      const int filter_depth = filter_idx % kBlockSlices + start_depth;
+      const int filter_pix = filter_idx / block_slices;
+      const int filter_depth = filter_idx % block_slices + start_depth;
       const int filter_offset = filter_pix * in_depth + filter_depth;
       if (filter_depth < in_depth) {
         T val = accum_data[i];
@@ -1626,20 +1558,19 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. filter in
-// NCHW format, tailored for small images up to 32x32. Stride and depth
+// NCHW format, tailored for small images up to 16x16. Stride and depth
 // multiplier must be 1. Padding must be 'SAME'. Only use this kernel if
 // CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input tensor are loaded into shared memory before performing the
 // convolution. Per iteration and filter element, each thread first performs
 // a partial convolution for two elements, one each in the lower and upper half
-// of a tile. The intermediate result of all pixels of a warp are then
+// of a tile. The intermediate result of 4 consecutive columns are then
 // accumulated and written to shared memory. Finally, the values in shared
 // memory are warp-accumulated (in chunks of kAccumPixels elements) and summed
 // up in global memory using atomics.
-// Requirements: threads per block must be multiple of 32 and <= launch_bounds,
-// kAccumPixels * 64 >= args.in_rows * args.in_cols * kBlockSlices.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, int kAccumPixels>
+          // Requirement: kAccumPixels * 8 >= args.in_rows * args.in_cols
+          int kAccumPixels>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
     const DepthwiseArgs args, const T* output, const T* input, T* filter) {
@@ -1659,28 +1590,28 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
   const int pad_rows = args.pad_rows;
   const int pad_cols = args.pad_cols;
 
+  // Fixed blockDim.z, corresponding to Pascal's global load granularity of 32B.
   const int block_rows = blockDim.y;
+  const int block_slices = 8;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
   const int block_pixels = in_cols * block_rows;
-  const int block_size = block_pixels * kBlockSlices;
-  assert((block_size & 31) == 0);
+  const int block_size = block_pixels * block_slices;
   const int in_pixels = in_cols * in_rows;
   const int in_increment = in_cols - 1;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int tile_rows = 2 * block_rows + filter_rows - 1;
   const int tile_pixels = tile_cols * tile_rows;
-  const int tile_size = tile_pixels * kBlockSlices;
+  const int tile_size = tile_pixels * block_slices;
   const int tile_offset = block_rows * tile_cols;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
   const int in_slices = in_depth * batches;
-  const int in_blocks = (in_slices + kBlockSlices - 1) / kBlockSlices;
+  const int in_blocks = (in_slices + block_slices - 1) / block_slices;
   // The accumulator has a fixed number of pixels that can be reduced by one
-  // warp. Pixels beyond ceil(in_pixels * kBlockSlices / 64) are never written.
-  assert(kAccumPixels * 64 >= in_rows * in_cols * kBlockSlices);
-  const int accum_increment = kAccumPixels * kBlockSlices;
+  // warp. Pixels beyond block_pixels/4 are never written.
+  const int accum_increment = kAccumPixels * block_slices;
   const int accum_size = filter_pixels * accum_increment;
 
   const int thread_col = threadIdx.x;
@@ -1707,8 +1638,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_idx = data_idx + pad_offset;
 
-  // Position in accumulator (kBlockSlices per warp, depth major).
-  const int accum_pix = thread_pix / (32 / kBlockSlices);
+  // Position in accumulator (1 per 4 threads, depth major).
+  const int accum_pix = thread_pix / 4;
   const int accum_idx = thread_depth * kAccumPixels + accum_pix;
 
   const int max_slice = in_slices - thread_depth;
@@ -1716,7 +1647,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
   const bool skip_second = block_rows + thread_row >= in_rows;
 
   for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int slice = b * kBlockSlices;
+    const int slice = b * block_slices;
 
     const int inout_offset = slice * in_pixels + tensor_idx;
     const bool slice_in_range = slice < max_slice;
@@ -1743,11 +1674,10 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
         UNROLL for (int c = 0; c < filter_cols; ++c) {
           const T* const tile_ptr = shared_offset + shared_data;
           T val = out1 * tile_ptr[0] + out2 * tile_ptr[tile_offset];
-          // Warp-accumulate pixels of the same depth and write to accumulator.
-          for (int delta = 16 / kBlockSlices; delta > 0; delta /= 2) {
-            val += CudaShuffleDown(val, delta);
-          }
-          if (!(thread_idx & 32 / kBlockSlices - 1)) {
+          // Sum up 4 block_pixels of the same depth and write to accumulator.
+          val += CudaShuffleDown(val, 2);
+          val += CudaShuffleDown(val, 1);
+          if (!(thread_idx & 3)) {
             *accum_ptr = val;
           }
           ++shared_offset;
@@ -1763,8 +1693,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
     const T* const accum_data = tile_size + shared_data;
     for (int i = thread_idx; i < accum_size; i += block_size) {
       const int filter_idx = i / kAccumPixels;
-      const int filter_pix = filter_idx / kBlockSlices;
-      const int filter_depth = (slice + filter_idx % kBlockSlices) % in_depth;
+      const int filter_pix = filter_idx / block_slices;
+      const int filter_depth = (slice + filter_idx % block_slices) % in_depth;
       const int filter_offset = filter_pix * in_depth + filter_depth;
       if (filter_depth < in_depth) {
         T val = accum_data[i];
@@ -1781,104 +1711,87 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, int kAccumPixels>
-bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
-    const GpuDevice& d, const DepthwiseArgs args, const T* out_backprop,
-    const T* input, T* filter_backprop, TensorFormat data_format) {
-  int block_rows = (args.in_rows + 1) / 2;
-  // args.in_cols * block_rows * kBlockSlices must be multiple of 32.
-  for (int round_mask = 1; args.in_cols * block_rows * kBlockSlices & 31;
-       round_mask = round_mask * 2 + 1) {
-    block_rows = block_rows + round_mask & ~round_mask;
-  }
-  if (!CanLaunchDepthwiseConv2dBackpropFilterGPUSmall(args, block_rows)) {
-    return false;
-  }
-  const int tile_cols = args.in_cols + args.filter_cols - 1;
-  const int tile_rows = block_rows * 2 + args.filter_rows - 1;
-  const int tile_pixels = tile_rows * tile_cols;
-  const int filter_pixels = args.filter_rows * args.filter_cols;
-  const int shared_memory_size =
-      kBlockSlices * (tile_pixels + filter_pixels * kAccumPixels) * sizeof(T);
-  if (shared_memory_size > d.sharedMemPerBlock()) {
-    return false;
-  }
-
+          int kAccumPixels>
+void LaunchDepthwiseConv2dBackpropFilterGPUSmall(
+    const GpuDevice& d, const DepthwiseArgs args, int block_rows,
+    int shared_memory_size, const T* out_backprop, const T* input,
+    T* filter_backprop, TensorFormat data_format) {
+  const int block_slices = 8;
   const int num_out_backprop =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
   if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
+    dim3 block_dim = dim3(block_slices, args.in_cols, block_rows);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_out_backprop, d,
         DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kAccumPixels>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>
+        T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, input, filter_backprop);
   } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
+    dim3 block_dim = dim3(args.in_cols, block_rows, block_slices);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_out_backprop, d,
         DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kAccumPixels>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>
+        T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, input, filter_backprop);
   } else {
     assert(false && "Incorrect data format");
   }
-  return true;
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices>
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
 bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
     const GpuDevice& d, const DepthwiseArgs args, const T* out_backprop,
     const T* input, T* filter_backprop, TensorFormat data_format) {
-  // Minimize (power of two) kAccumPixels, while satisfying
-  // kAccumPixels * 64 >= in_pixels * kBlockSlices.
-  const int block_pixels = args.in_rows * args.in_cols * kBlockSlices;
-  if (block_pixels > 1024) {
-    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, 32>(
-        d, args, out_backprop, input, filter_backprop, data_format);
-  } else if (block_pixels > 512) {
-    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, 16>(
-        d, args, out_backprop, input, filter_backprop, data_format);
-  } else {
-    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, 8>(
-        d, args, out_backprop, input, filter_backprop, data_format);
+  // args.in_cols * blocks_rows (block_pixels) must be multiple of 4.
+  const int lookup_table[] = {0, 3, 1, 3};
+  const int rows_mask = lookup_table[args.in_cols & 3];
+  const int block_rows = (args.in_rows + 1) / 2 + rows_mask & ~rows_mask;
+  if (!CanLaunchDepthwiseConv2dBackpropFilterGPUSmall(args, block_rows)) {
+    return false;
   }
-}
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
-    const GpuDevice& d, const DepthwiseArgs args, const T* out_backprop,
-    const T* input, T* filter_backprop, TensorFormat data_format) {
-  // Maximize (power of two) kBlockSlices while keeping a block within 1024
-  // threads (2 pixels per thread).
   const int in_pixels = args.in_rows * args.in_cols;
-  if (in_pixels > 512) {
-    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, 2>(
-        d, args, out_backprop, input, filter_backprop, data_format);
-  } else if (in_pixels > 256) {
-    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, 4>(
-        d, args, out_backprop, input, filter_backprop, data_format);
+  int accum_pixels = 8;
+  while (accum_pixels * 8 < in_pixels) {
+    accum_pixels *= 2;
+  }
+
+  const int block_slices = 8;
+  const int tile_cols = args.in_cols + args.filter_cols - 1;
+  const int tile_rows = block_rows * 2 + args.filter_rows - 1;
+  const int tile_pixels = tile_rows * tile_cols;
+  const int filter_pixels = args.filter_rows * args.filter_cols;
+  const int shared_memory_size =
+      block_slices * (tile_pixels + filter_pixels * accum_pixels) * sizeof(T);
+  if (shared_memory_size > d.sharedMemPerBlock()) {
+    return false;
+  }
+
+  if (accum_pixels == 8) {
+    LaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
+                                                kKnownFilterHeight, 8>(
+        d, args, block_rows, shared_memory_size, out_backprop, input,
+        filter_backprop, data_format);
+  } else if (accum_pixels == 16) {
+    LaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
+                                                kKnownFilterHeight, 16>(
+        d, args, block_rows, shared_memory_size, out_backprop, input,
+        filter_backprop, data_format);
   } else {
-    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, 8>(
-        d, args, out_backprop, input, filter_backprop, data_format);
+    LaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
+                                                kKnownFilterHeight, 32>(
+        d, args, block_rows, shared_memory_size, out_backprop, input,
+        filter_backprop, data_format);
   }
+  return true;
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
@@ -1888,6 +1801,11 @@ void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& d,
                                             const T* out_backprop,
                                             const T* input, T* filter_backprop,
                                             TensorFormat data_format) {
+  if (TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
+                                                     kKnownFilterHeight>(
+          d, args, out_backprop, input, filter_backprop, data_format)) {
+    return;
+  }
   const int num_out_backprop =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
   if (data_format == FORMAT_NHWC) {
@@ -1915,40 +1833,18 @@ void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& d,
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& d,
-                                            const DepthwiseArgs args,
-                                            const T* out_backprop,
-                                            const T* input, T* filter_backprop,
-                                            TensorFormat data_format) {
-  if (args.depth_multiplier == 1) {
-    if (TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
-                                                       kKnownFilterHeight>(
-            d, args, out_backprop, input, filter_backprop, data_format)) {
-      return;
-    }
-
-    LaunchDepthwiseConv2dBackpropFilterGPU<T, kKnownFilterWidth,
-                                           kKnownFilterHeight, 1>(
-        d, args, out_backprop, input, filter_backprop, data_format);
-  } else {
-    LaunchDepthwiseConv2dBackpropFilterGPU<T, kKnownFilterWidth,
-                                           kKnownFilterHeight, -1>(
-        d, args, out_backprop, input, filter_backprop, data_format);
-  }
-}
-
 // A simple launch pad to launch the Cuda kernel for depthwise convolution.
 template <typename T>
 struct DepthwiseConv2dBackpropFilterGPULaunch {
   static void Run(const GpuDevice& d, const DepthwiseArgs args,
                   const T* out_backprop, const T* input, T* filter_backprop,
                   TensorFormat data_format) {
-    if (args.filter_rows == 3 && args.filter_cols == 3) {
-      LaunchDepthwiseConv2dBackpropFilterGPU<T, 3, 3>(
+    if (args.filter_rows == 3 && args.filter_cols == 3 &&
+        args.depth_multiplier == 1) {
+      LaunchDepthwiseConv2dBackpropFilterGPU<T, 3, 3, 1>(
           d, args, out_backprop, input, filter_backprop, data_format);
     } else {
-      LaunchDepthwiseConv2dBackpropFilterGPU<T, -1, -1>(
+      LaunchDepthwiseConv2dBackpropFilterGPU<T, -1, -1, -1>(
           d, args, out_backprop, input, filter_backprop, data_format);
     }
   }
-- 
GitLab


From 2d29a4ebf514f7afb70108b1f5ae84c866740f1a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 10:06:41 -0700
Subject: [PATCH 0076/1375] Remove tf.RewriterConfig from the TensorFlow Python
 API

This is an API-only change. Rewriter behavior is unaffected.

tf.RewriterConfig has been excluded from the 1.2 release, and was not in previous releases, and so is not subject to semantic versioning. It needs a bit of work before we fix an API.

Graph rewriting is still available, just not as tf.RewriterConfig. Instead add an explicit import:
from tensorflow.core.protobuf import rewriter_config_pb2

Then switch tf.RewriterConfig to rewriter_config_pb2.RewriterConfig (likewise
for tf.AutoParallelOptions).

Graph rewriting is subject to change, and has no API stability guarantee.

PiperOrigin-RevId: 158991934
---
 tensorflow/core/protobuf/config.proto         |   2 +
 .../core/protobuf/rewriter_config.proto       |   3 +
 tensorflow/python/__init__.py                 |   3 -
 .../tensorflow.-auto-parallel-options.pbtxt   |  84 -------------
 .../golden/tensorflow.-rewriter-config.pbtxt  | 112 ------------------
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   8 --
 6 files changed, 5 insertions(+), 207 deletions(-)
 delete mode 100644 tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt
 delete mode 100644 tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt

diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index 630f47633f..c57b110847 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -160,6 +160,8 @@ message GraphOptions {
   int32 timeline_step = 8;
 
   // Options that control the type and amount of graph rewriting.
+  // Not currently configurable via the public Python API (i.e. there is no API
+  // stability guarantee if you import RewriterConfig explicitly).
   RewriterConfig rewrite_options = 10;
 };
 
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 753edba4b8..b21d42f4fe 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -12,6 +12,9 @@ message AutoParallelOptions {
 }
 
 message RewriterConfig {
+  // Graph rewriting is experimental and subject to change, not covered by any
+  // API stability guarantees.
+
   bool optimize_tensor_layout = 1;
   bool disable_model_pruning = 2;
   bool constant_folding = 3;
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 9f4e92204b..e11c5e44a9 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -57,7 +57,6 @@ from tensorflow.core.protobuf.meta_graph_pb2 import TensorInfo
 from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
 from tensorflow.core.protobuf.config_pb2 import *
 from tensorflow.core.protobuf.tensorflow_server_pb2 import *
-from tensorflow.core.protobuf.rewriter_config_pb2 import *
 from tensorflow.core.util.event_pb2 import *
 
 # Framework
@@ -132,7 +131,6 @@ from tensorflow.python.ops import tensor_array_ops
 # documentation, or remove.
 _allowed_symbols = [
     'AttrValue',
-    'AutoParallelOptions',
     'ConfigProto',
     'ClusterDef',
     'DeviceSpec',
@@ -149,7 +147,6 @@ _allowed_symbols = [
     'NameAttrList',
     'NodeDef',
     'OptimizerOptions',
-    'RewriterConfig',
     'RunOptions',
     'RunMetadata',
     'SessionLog',
diff --git a/tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt
deleted file mode 100644
index c8f3e8fb15..0000000000
--- a/tensorflow/tools/api/golden/tensorflow.-auto-parallel-options.pbtxt
+++ /dev/null
@@ -1,84 +0,0 @@
-path: "tensorflow.AutoParallelOptions"
-tf_class {
-  is_instance: "<class \'tensorflow.core.protobuf.rewriter_config_pb2.AutoParallelOptions\'>"
-  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
-  member {
-    name: "DESCRIPTOR"
-    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
-  }
-  member {
-    name: "ENABLE_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "Extensions"
-    mtype: "<type \'getset_descriptor\'>"
-  }
-  member {
-    name: "NUM_REPLICAS_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member_method {
-    name: "ByteSize"
-  }
-  member_method {
-    name: "Clear"
-  }
-  member_method {
-    name: "ClearExtension"
-  }
-  member_method {
-    name: "ClearField"
-  }
-  member_method {
-    name: "CopyFrom"
-  }
-  member_method {
-    name: "DiscardUnknownFields"
-  }
-  member_method {
-    name: "FindInitializationErrors"
-  }
-  member_method {
-    name: "FromString"
-  }
-  member_method {
-    name: "HasExtension"
-  }
-  member_method {
-    name: "HasField"
-  }
-  member_method {
-    name: "IsInitialized"
-  }
-  member_method {
-    name: "ListFields"
-  }
-  member_method {
-    name: "MergeFrom"
-  }
-  member_method {
-    name: "MergeFromString"
-  }
-  member_method {
-    name: "ParseFromString"
-  }
-  member_method {
-    name: "RegisterExtension"
-  }
-  member_method {
-    name: "SerializePartialToString"
-  }
-  member_method {
-    name: "SerializeToString"
-  }
-  member_method {
-    name: "SetInParent"
-  }
-  member_method {
-    name: "WhichOneof"
-  }
-  member_method {
-    name: "__init__"
-  }
-}
diff --git a/tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt b/tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt
deleted file mode 100644
index 34d2e17612..0000000000
--- a/tensorflow/tools/api/golden/tensorflow.-rewriter-config.pbtxt
+++ /dev/null
@@ -1,112 +0,0 @@
-path: "tensorflow.RewriterConfig"
-tf_class {
-  is_instance: "<class \'tensorflow.core.protobuf.rewriter_config_pb2.RewriterConfig\'>"
-  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
-  member {
-    name: "AUTO_PARALLEL_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "CONSTANT_FOLDING_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "DESCRIPTOR"
-    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
-  }
-  member {
-    name: "DISABLE_MODEL_PRUNING_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "Extensions"
-    mtype: "<type \'getset_descriptor\'>"
-  }
-  member {
-    name: "MANUAL"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "MEMORY_OPTIMIZATION_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "MemOptType"
-    mtype: "<class \'google.protobuf.internal.enum_type_wrapper.EnumTypeWrapper\'>"
-  }
-  member {
-    name: "NO_MEM_OPT"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "OPTIMIZERS_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member {
-    name: "OPTIMIZE_TENSOR_LAYOUT_FIELD_NUMBER"
-    mtype: "<type \'int\'>"
-  }
-  member_method {
-    name: "ByteSize"
-  }
-  member_method {
-    name: "Clear"
-  }
-  member_method {
-    name: "ClearExtension"
-  }
-  member_method {
-    name: "ClearField"
-  }
-  member_method {
-    name: "CopyFrom"
-  }
-  member_method {
-    name: "DiscardUnknownFields"
-  }
-  member_method {
-    name: "FindInitializationErrors"
-  }
-  member_method {
-    name: "FromString"
-  }
-  member_method {
-    name: "HasExtension"
-  }
-  member_method {
-    name: "HasField"
-  }
-  member_method {
-    name: "IsInitialized"
-  }
-  member_method {
-    name: "ListFields"
-  }
-  member_method {
-    name: "MergeFrom"
-  }
-  member_method {
-    name: "MergeFromString"
-  }
-  member_method {
-    name: "ParseFromString"
-  }
-  member_method {
-    name: "RegisterExtension"
-  }
-  member_method {
-    name: "SerializePartialToString"
-  }
-  member_method {
-    name: "SerializeToString"
-  }
-  member_method {
-    name: "SetInParent"
-  }
-  member_method {
-    name: "WhichOneof"
-  }
-  member_method {
-    name: "__init__"
-  }
-}
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 39c1feaed9..3cf6c3dba0 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -8,10 +8,6 @@ tf_module {
     name: "AttrValue"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
   }
-  member {
-    name: "AutoParallelOptions"
-    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
-  }
   member {
     name: "COMPILER_VERSION"
     mtype: "<type \'str\'>"
@@ -168,10 +164,6 @@ tf_module {
     name: "RegisterGradient"
     mtype: "<type \'type\'>"
   }
-  member {
-    name: "RewriterConfig"
-    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
-  }
   member {
     name: "RunMetadata"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
-- 
GitLab


From 7520c401686e9139873b055019e3d18c6dc23b27 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Wed, 14 Jun 2017 10:12:05 -0700
Subject: [PATCH 0077/1375] Call sys.exit() not exit()

PiperOrigin-RevId: 158992568
---
 tensorflow/tensorboard/main.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tensorboard/main.py b/tensorflow/tensorboard/main.py
index 3665d02ff5..863bba16e5 100644
--- a/tensorflow/tensorboard/main.py
+++ b/tensorflow/tensorboard/main.py
@@ -188,7 +188,8 @@ def run_simple_server(tb_app):
     server, url = make_simple_server(tb_app, FLAGS.host, FLAGS.port)
   except socket.error:
     # An error message was already logged
-    exit(-1)
+    # TODO(jart): Remove log and throw anti-pattern.
+    sys.exit(-1)
   msg = 'Starting TensorBoard %s at %s' % (tb_app.tag, url)
   print(msg)
   tf.logging.info(msg)
-- 
GitLab


From e3bb6eb846461ea2e3253b0b8774b6211357ee7a Mon Sep 17 00:00:00 2001
From: Blake Hechtman <blakehechtman@google.com>
Date: Wed, 14 Jun 2017 11:06:36 -0700
Subject: [PATCH 0078/1375] [XLA] Support broadcasting in the HloEvaluator.

PiperOrigin-RevId: 158997226
---
 .../xla/service/hlo_constant_folding.cc       |  7 ++++++
 .../compiler/xla/service/hlo_evaluator.cc     | 18 +++++++++++++++
 .../xla/service/hlo_evaluator_test.cc         | 23 +++++++++++++++++++
 3 files changed, 48 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding.cc b/tensorflow/compiler/xla/service/hlo_constant_folding.cc
index 93f448e701..804efdd906 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding.cc
@@ -58,6 +58,13 @@ StatusOr<bool> HloConstantFolding::Run(HloModule* module) {
         continue;
       }
 
+      // Broadcasts dramatically increase the size of constants with is often
+      // detrimental to performance and memory capacity so do not fold
+      // broadcasts.
+      if (instruction->opcode() == HloOpcode::kBroadcast) {
+        continue;
+      }
+
       std::unique_ptr<Literal> result = evaluator->TryEvaluate(instruction);
       // Currently we skip unimplemented operations.
       // TODO(b/35975797): Fold constant computations for more operations.
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 3e7f5b1f3d..48eefd92ae 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -168,6 +168,24 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return HandleAbs<ReturnT>(abs, operand);
   };
 
+  Status HandleBroadcast(HloInstruction* broadcast) override {
+    parent_->evaluated_[broadcast] =
+        LiteralUtil::CreateFromShape(broadcast->shape());
+    auto output = parent_->evaluated_[broadcast].get();
+    auto operand_to_broadcast =
+        parent_->GetEvaluatedLiteralFor(broadcast->operand(0));
+    std::vector<int64> broadcast_indices(
+        ShapeUtil::Rank(broadcast->operand(0)->shape()), 0);
+    return LiteralUtil::Populate<ReturnT>(
+        output, [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+          for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
+            broadcast_indices[i] = multi_index[broadcast->dimensions(i)];
+          }
+          return LiteralUtil::Get<ReturnT>(operand_to_broadcast,
+                                           broadcast_indices);
+        });
+  }
+
   Status HandleCeil(HloInstruction* ceil, HloInstruction* operand) override {
     TF_ASSIGN_OR_RETURN(parent_->evaluated_[ceil],
                         ElementWiseUnaryOp(ceil, [](ReturnT elem_operand) {
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index b26ece28b7..03aa6f7f8c 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -241,5 +241,28 @@ TEST_F(HloEvaluatorTest, DoesReshape) {
       });
 }
 
+// Verifies Broadcast operation is correctly evaluated.
+TEST_F(HloEvaluatorTest, DoesBroadcast) {
+  HloComputation::Builder builder(
+      ::testing::UnitTest::GetInstance()->current_test_info()->name());
+
+  auto input_literal = LiteralUtil::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}});
+  auto output_literal = LiteralUtil::CreateR3<int32>(
+      {{{1, 2}, {3, 4}, {5, 6}}, {{1, 2}, {3, 4}, {5, 6}}});
+  HloInstruction* literal_instruction = builder.AddInstruction(
+      HloInstruction::CreateConstant(std::move(input_literal)));
+
+  builder.AddInstruction(HloInstruction::CreateBroadcast(
+      output_literal->shape(), literal_instruction, {1, 2}));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
+
+  LiteralUtil::EachCell<int32>(
+      *result, [&](tensorflow::gtl::ArraySlice<int64> indices, int32 value) {
+        EXPECT_TRUE(value == LiteralUtil::Get<int32>(*output_literal, indices));
+      });
+}
+
 }  // namespace
 }  // namespace xla
-- 
GitLab


From fca2658a1f1dd6f643b2abbace66b1ff26182414 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 11:14:28 -0700
Subject: [PATCH 0079/1375] Use unordered_set instead of set for tracking
 device memory. Reduces runtime by ~2.5x (~446s -> ~160s).

PiperOrigin-RevId: 158998396
---
 .../core/grappler/costs/virtual_scheduler.h    | 18 +++++++++++++++---
 .../grappler/costs/virtual_scheduler_test.cc   |  3 ++-
 2 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index 472ba90f7c..1331e2ec8b 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include <list>
 #include <memory>
 #include <unordered_map>
+#include <unordered_set>
 
 #include "tensorflow/core/grappler/costs/cost_estimator.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
@@ -80,16 +81,27 @@ struct DeviceState {
   // Nodes executed on this device in execution order.
   std::vector<const NodeDef*> nodes_executed;
 
+  struct NodePairHash {
+   public:
+    const std::size_t operator()(
+        const std::pair<const NodeDef*, int>& element) const {
+      return std::hash<const NodeDef*>()(element.first);
+    }
+  };
+
   // Nodes currently allocated in memory: set of NodeDef* and port_num pairs
   // so that we can track which output of the node is in memory.
-  std::set<std::pair<const NodeDef*, int>> nodes_in_memory;
+  std::unordered_set<std::pair<const NodeDef*, int>, NodePairHash>
+      nodes_in_memory;
 
   // Nodes allocated in memory persistently: e.g., Variables.
-  std::set<std::pair<const NodeDef*, int>> persistent_nodes;
+  std::unordered_set<std::pair<const NodeDef*, int>, NodePairHash>
+      persistent_nodes;
 
   // Snapshot of nodes_in_memory, when memory usage is at peak.
   // Same to nodes_in_memory, it's a set of NodeDef* and port_num pairs.
-  std::set<std::pair<const NodeDef*, int>> mem_usage_snapshot_at_peak;
+  std::unordered_set<std::pair<const NodeDef*, int>, NodePairHash>
+      mem_usage_snapshot_at_peak;
 
   Costs device_costs;
   std::map<string, Costs> op_to_cost;    // Per-op cost.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index 484a8860d8..5af9effde0 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -263,7 +263,8 @@ class VirtualSchedulerTest : public ::testing::Test {
   // Helper method tthat checks name - port pairs.
   void ValidateMemoryUsageSnapshot(
       const std::vector<string>& expected_names, const int port_num_expected,
-      const std::set<std::pair<const NodeDef*, int>>& mem_usage_snapshot) {
+      const std::unordered_set<std::pair<const NodeDef*, int>,
+                               DeviceState::NodePairHash>& mem_usage_snapshot) {
     std::set<std::pair<string, int>> nodes_at_peak_mem_usage;
     std::transform(
         mem_usage_snapshot.begin(), mem_usage_snapshot.end(),
-- 
GitLab


From 1c05e8d35e5063e6506a7ac7066db72eb2b899bf Mon Sep 17 00:00:00 2001
From: gunan <gunan@google.com>
Date: Wed, 14 Jun 2017 11:28:46 -0700
Subject: [PATCH 0080/1375] Avoid upgrading tensorFlow dependencies when
 running windows tests. (#10709)

---
 tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh | 2 +-
 tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat    | 5 +----
 tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat    | 5 +----
 3 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
index f76c1add24..9f5952a1c2 100644
--- a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
@@ -208,5 +208,5 @@ function create_python_test_dir() {
 
 function reinstall_tensorflow_pip() {
   echo "y" | pip uninstall tensorflow -q || true
-  pip install ${1}
+  pip install ${1} --no-deps
 }
diff --git a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
index 8b6836f4ed..96fbadd176 100644
--- a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
@@ -29,9 +29,6 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\cpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
-:: Attempt to upgrade PIP to work around Anaconda issue #542.
-%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
-
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -39,7 +36,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
index ba2d939b5f..e774a6e916 100644
--- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
@@ -28,9 +28,6 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\gpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
-:: Attempt to upgrade PIP to work around Anaconda issue #542.
-%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
-
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -38,7 +35,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
-- 
GitLab


From 7c2cf8362201adfd40ced8b5e5b731591ac4a232 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 11:35:44 -0700
Subject: [PATCH 0081/1375] Don't do two map lookups when one will do; NFC

PiperOrigin-RevId: 159001841
---
 .../compiler/xla/service/dfs_hlo_visitor.cc      | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
index 5b29686100..b9a496be43 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
@@ -51,22 +51,18 @@ void DfsHloVisitor::SetVisited(const HloInstruction& instruction) {
 }
 
 bool DfsHloVisitor::IsVisiting(const HloInstruction& instruction) {
-  if (visit_state_.count(&instruction) == 0) {
-    return false;
-  }
-  return visit_state_[&instruction] == VisitState::kVisiting;
+  auto it = visit_state_.find(&instruction);
+  return it != visit_state_.end() && it->second == VisitState::kVisiting;
 }
 
 bool DfsHloVisitor::DidVisit(const HloInstruction& instruction) {
-  if (visit_state_.count(&instruction) == 0) {
-    return false;
-  }
-  return visit_state_[&instruction] == VisitState::kVisited;
+  auto it = visit_state_.find(&instruction);
+  return it != visit_state_.end() && it->second == VisitState::kVisited;
 }
 
 bool DfsHloVisitor::NotVisited(const HloInstruction& instruction) {
-  return visit_state_.count(&instruction) == 0 ||
-         visit_state_[&instruction] == VisitState::kNotVisited;
+  auto it = visit_state_.find(&instruction);
+  return it == visit_state_.end() || it->second == VisitState::kNotVisited;
 }
 
 Status DfsHloVisitor::Preprocess(HloInstruction* hlo) { return Status::OK(); }
-- 
GitLab


From ea29274ba39389f5958b21d2dc2990a305303fc3 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Wed, 14 Jun 2017 11:38:44 -0700
Subject: [PATCH 0082/1375] Register _Arg for resources on GPU

PiperOrigin-RevId: 159002331
---
 tensorflow/core/common_runtime/function.cc | 11 ++++++-----
 tensorflow/core/kernels/function_ops.cc    |  6 ++++++
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index 4970c2d252..320e214117 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -326,13 +326,14 @@ Status FunctionLibraryRuntimeImpl::CreateKernel(const NodeDef& ndef,
   const FunctionBody* fbody = GetFunctionBody(handle);
   CHECK_NOTNULL(fbody);
 
-  // TODO(zhifengc): For now, we assume int32 is always on host memory
-  // and other types are always on device memory. We should do type
-  // inference over function body to derive the correct input/output
-  // memory types.
+  // TODO(zhifengc): For now, we assume int32 and resources are always on host
+  // memory and other types are always on device memory. We should do type
+  // inference over function body to derive the correct input/output memory
+  // types.
   MemoryTypeVector input_memory_types;
   for (const auto& t : fbody->arg_types) {
-    input_memory_types.push_back(t == DT_INT32 ? HOST_MEMORY : DEVICE_MEMORY);
+    input_memory_types.push_back(
+        (t == DT_INT32 || t == DT_RESOURCE) ? HOST_MEMORY : DEVICE_MEMORY);
   }
   MemoryTypeVector output_memory_types;
   for (const auto& t : fbody->ret_types) {
diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc
index 8c3137ece9..58c4ed37c4 100644
--- a/tensorflow/core/kernels/function_ops.cc
+++ b/tensorflow/core/kernels/function_ops.cc
@@ -122,6 +122,12 @@ TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Arg")
                                                ArgOp);
 #undef REGISTER
 
+REGISTER_KERNEL_BUILDER(Name("_Arg")
+                            .Device(DEVICE_GPU)
+                            .HostMemory("output")
+                            .TypeConstraint<ResourceHandle>("T"),
+                        ArgOp);
+
 #define REGISTER(type)     \
   REGISTER_KERNEL_BUILDER( \
       Name("_Retval").Device(DEVICE_GPU).TypeConstraint<type>("T"), RetvalOp);
-- 
GitLab


From 5ea412c4f8cc4fdd11fda5ea6ced43073589ea31 Mon Sep 17 00:00:00 2001
From: William Chargin <wchargin@google.com>
Date: Wed, 14 Jun 2017 11:58:58 -0700
Subject: [PATCH 0083/1375] Centralize a global color scale

We have a `ColorScale` object with some nice logic and some nice
associated palettes, which we'd like to keep. But we also have a Polymer
component `tf-color-scale` whose role is to upward-bind a `ColorScale`.
Because the domain of all these color scales should be the same---the
set of available runs---exposing a single global color scale provides a
simpler API. Just look at the delta on this diff! :-)

This change introduces a `runsColorScale: ColorScale` that listens to
the `RunsStore` and updates itself when the runs change. Existing
dashboards and components have been retrofitted to use this instead of
the now-removed `tf-color-scale` component.

Note that this change does not prohibit clients from using custom
`ColorScale`s should they so desire: the pure-JS (well, TS) `ColorScale`
object is still available.

Tested all dashboards and found no functional changes.

PiperOrigin-RevId: 159005220
---
 .../components/tf_color_scale/BUILD           | 15 +--
 .../components/tf_color_scale/colorScale.ts   | 42 +++------
 .../components/tf_color_scale/index.html      | 94 -------------------
 .../components/tf_dashboard_common/BUILD      |  1 +
 .../tf-multi-checkbox-demo.html               |  8 --
 .../tf_dashboard_common/tf-multi-checkbox.ts  | 27 ++----
 .../tf_dashboard_common/tf-panes-helper.html  |  8 +-
 .../tf_dashboard_common/tf-run-selector.html  |  3 -
 .../tf-sidebar-helper.html                    |  7 --
 .../tf-distribution-dashboard.html            | 24 ++---
 .../tf-histogram-dashboard.html               | 24 ++---
 .../tf-image-dashboard.html                   | 13 +--
 .../tf_image_dashboard/tf-image-loader.html   |  5 +-
 .../tf-scalar-dashboard.html                  | 25 ++---
 .../tf_text_dashboard/tf-text-dashboard.html  | 13 +--
 .../tf_text_dashboard/tf-text-loader.html     |  5 +-
 16 files changed, 68 insertions(+), 246 deletions(-)
 delete mode 100644 tensorflow/tensorboard/components/tf_color_scale/index.html

diff --git a/tensorflow/tensorboard/components/tf_color_scale/BUILD b/tensorflow/tensorboard/components/tf_color_scale/BUILD
index 730ab37d6f..469268427f 100644
--- a/tensorflow/tensorboard/components/tf_color_scale/BUILD
+++ b/tensorflow/tensorboard/components/tf_color_scale/BUILD
@@ -13,25 +13,12 @@ ts_web_library(
     ],
     path = "/tf-color-scale",
     deps = [
+        "//tensorflow/tensorboard/components/tf_backend",
         "//tensorflow/tensorboard/components/tf_imports:d3",
         "//tensorflow/tensorboard/components/tf_imports:polymer",
     ],
 )
 
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"],
-    path = "/tf-color-scale",
-    deps = [
-        ":tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_styles",
-    ],
-)
-
 filegroup(
     name = "all_files",
     srcs = glob(["**"]),
diff --git a/tensorflow/tensorboard/components/tf_color_scale/colorScale.ts b/tensorflow/tensorboard/components/tf_color_scale/colorScale.ts
index e20a65cdd8..ec07ba5891 100644
--- a/tensorflow/tensorboard/components/tf_color_scale/colorScale.ts
+++ b/tensorflow/tensorboard/components/tf_color_scale/colorScale.ts
@@ -20,6 +20,8 @@ limitations under the License.
 // ccs.getColor("train");
 // ccs.getColor("test1");
 
+import * as RunsStore from '../tf-backend/runsStore';
+
 import {palettes} from './palettes';
 
 export class ColorScale {
@@ -40,13 +42,6 @@ export class ColorScale {
    */
   public domain(strings: string[]): this {
     this.identifiers = d3.map();
-
-    // TODO(wchargin): Remove this call to `sort` once we have only a
-    // singleton ColorScale, linked directly to the RunsStore, which
-    // will always give sorted output.
-    strings = strings.slice();
-    strings.sort();
-
     strings.forEach((s, i) => {
       this.identifiers.set(s, this.palette[i % this.palette.length]);
     });
@@ -67,23 +62,16 @@ export class ColorScale {
   }
 }
 
-Polymer({
-  is: 'tf-color-scale',
-  properties: {
-    runs: {
-      type: Array,
-    },
-    outColorScale: {
-      type: Object,
-      readOnly: true,
-      notify: true,
-      value() {
-        return new ColorScale();
-      },
-    },
-  },
-  observers: ['updateColorScale(runs.*)'],
-  updateColorScale(runsChange) {
-    this.outColorScale.domain(this.runs);
-  },
-});
+/**
+ * A color scale whose domain is the set of runs currently available.
+ * Automatically updated as this data changes.
+ */
+const _runsColorScale = new ColorScale();
+export const runsColorScale: ((runName: string) => string) =
+    _runsColorScale.scale.bind(_runsColorScale);
+
+function updateRunsColorScale(): void {
+  _runsColorScale.domain(RunsStore.getRuns());
+}
+RunsStore.addListener(updateRunsColorScale);
+updateRunsColorScale();
diff --git a/tensorflow/tensorboard/components/tf_color_scale/index.html b/tensorflow/tensorboard/components/tf_color_scale/index.html
deleted file mode 100644
index 81dfab098c..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/index.html
+++ /dev/null
@@ -1,94 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tf-color-scale demo</title>
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-styles/typography.html">
-<link rel="import" href="../tf-imports/d3.html">
-<link rel="import" href="tf-color-scale.html">
-
-<style> body {font-family: "Roboto";}</style>
-<demo-snippet>
-  <template>
-    <dom-module id="color-scale-demo">
-      <template>
-        <paper-button raised id="button" on-tap="change">Change Runs</paper-button>
-        <tf-color-scale runs="[[runs]]" out-color-scale="{{scale}}"></tf-color-scale>
-        <div class="container">
-          <template is="dom-repeat" items="[[runs]]">
-            <div class="row">
-              <div class="circle" style$=[[_makeStyle(item)]]></div>
-              <span>[[item]]</span>
-            </div>
-          </template>
-        </div>
-        <style>
-          .circle {
-            width: 20px;
-            height: 20px;
-            border-radius: 10px;
-            display: inline-block;
-          }
-          .row {
-            height: 35px;
-            width: 200px;
-            display: inline-block;
-          }
-          .container {
-            height: 200px;
-          }
-          #button {
-            margin: 20px;
-          }
-        </style>
-      </template>
-      <script>
-        let fellowship = ["aragorn", "legolas", "gimli", "frodo", 
-                          "gandalf", "boromir", "merry", "pippin", "sam"];
-        let gems = ["garnet", "amethyst", "pearl", "and steven!"];
-        let numbers = d3.range(30).map(function(x) {return x.toString();});
-        let examples = [numbers, fellowship, gems];
-        Polymer({
-          is: "color-scale-demo",
-          properties: {
-            runs: {
-              type: Array,
-              value: examples[0],
-            },
-            i: {
-              type: Number,
-              value: 0,
-            },
-          },
-          _makeStyle: function(item) {
-            return "background-color: " + this.scale.scale(item);
-          },
-          change: function() {
-            this.i = (this.i + 1) % 3;
-            this.runs = examples[this.i];
-          },
-        });
-      </script>
-    </dom-module>
-    <color-scale-demo id="demo"></color-scale-demo>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/BUILD b/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
index 7471da3144..3d6094deea 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
@@ -33,6 +33,7 @@ ts_web_library(
     ],
     path = "/tf-dashboard-common",
     deps = [
+        "//tensorflow/tensorboard/components/tf_color_scale",
         "//tensorflow/tensorboard/components/tf_imports:d3",
         "//tensorflow/tensorboard/components/tf_imports:lodash",
         "//tensorflow/tensorboard/components/tf_imports:polymer",
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox-demo.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox-demo.html
index d0f5aa6f27..93898b152e 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox-demo.html
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox-demo.html
@@ -18,7 +18,6 @@ limitations under the License.
 
 <html>
 <head>
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
 <link rel="import" href="tf-multi-checkbox.html">
 
 </head>
@@ -39,14 +38,8 @@ function random() {
       id="multiCheckbox"
       names="[[names]]"
       tooltips="[[_tooltips]]"
-      class-scale="[[classScale]]"
       highlights="[[highlights]]"
     ></tf-multi-checkbox>
-    <tf-color-scale
-      id="colorScale"
-      runs="[[names]]"
-      out-class-scale="{{classScale}}"
-    ></tf-color-scale>
   <style>
   </style>
   </template>
@@ -66,7 +59,6 @@ function random() {
       tooltips: Object,
       autoGenerateTooltips: {value: true},
       _tooltips: Object,
-      classScale: Function,
       highlights: Array,
     },
     observers: [
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.ts b/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.ts
index 4b38d82b14..453f2a4153 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.ts
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.ts
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+import {runsColorScale} from '../tf-color-scale/colorScale';
 import * as storage from '../tf-storage/storage';
 
 Polymer({
@@ -47,10 +48,6 @@ Polymer({
       notify: true,
       computed: 'computeOutSelected(namesMatchingRegex.*, runSelectionState.*)'
     },
-    colorScale: {
-      type: Object,
-      observer: 'synchronizeColors',
-    },  // map from run name to css class
     maxRunsToEnableByDefault: {
       // When TB first loads, if it has k or fewer runs, they are all enabled
       // by default. If there are more, then they are all disabled.
@@ -127,32 +124,26 @@ Polymer({
     });
   },
   synchronizeColors: function(e) {
-    if (!this.colorScale) return;
-
     this._setIsolatorIcon();
 
-    var checkboxes =
-        Array.prototype.slice.call(this.querySelectorAll('paper-checkbox'));
-    var scale = this.colorScale;
-    checkboxes.forEach(function(p) {
-      var color = scale.scale(p.name);
+    const checkboxes = this.querySelectorAll('paper-checkbox');
+    checkboxes.forEach(p => {
+      const color = runsColorScale(p.name);
       p.customStyle['--paper-checkbox-checked-color'] = color;
       p.customStyle['--paper-checkbox-checked-ink-color'] = color;
       p.customStyle['--paper-checkbox-unchecked-color'] = color;
       p.customStyle['--paper-checkbox-unchecked-ink-color'] = color;
     });
-    var buttons =
-        Array.prototype.slice.call(this.querySelectorAll('.isolator'));
-    buttons.forEach(function(p) {
-      var color = scale.scale(p.name);
+    const buttons = this.querySelectorAll('.isolator');
+    buttons.forEach(p => {
+      const color = runsColorScale(p.name);
       p.style['color'] = color;
     });
     // The updateStyles call fails silently if the browser doesn't have focus,
     // e.g. if TensorBoard was opened into a new tab that isn't visible.
     // So we wait for requestAnimationFrame.
-    var _this = this;
-    window.requestAnimationFrame(function() {
-      _this.updateStyles();
+    window.requestAnimationFrame(() => {
+      this.updateStyles();
     });
   },
   _isolateRun: function(e) {
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-panes-helper.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-panes-helper.html
index 155259d329..32d79c649c 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-panes-helper.html
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/tf-panes-helper.html
@@ -181,6 +181,7 @@ downloadLinkUrlFunction property to an appropriate value.
     </style>
   </template>
   <script>
+    import {runsColorScale} from '../tf-color-scale/colorScale';
     Polymer({
       is: "tf-panes-helper",
       properties: {
@@ -191,11 +192,6 @@ downloadLinkUrlFunction property to an appropriate value.
          */
         categories: Array,
 
-        /**
-         * Input of the colors that are used for the user's runs.
-         */
-        colorScale: Object,
-
         /**
          * The name of the data type that is used by this dashboard. This will
          * be used to display what is missing when there is no data available.
@@ -323,7 +319,7 @@ downloadLinkUrlFunction property to an appropriate value.
         return cards;
       },
       _titleBorderColor: function(run) {
-        return this.repeatForRuns ? this.colorScale.scale(run) : 'white';
+        return this.repeatForRuns ? runsColorScale(run) : 'white';
       },
 
       /*
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-run-selector.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-run-selector.html
index e3d8a91fd0..04eba058a1 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-run-selector.html
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/tf-run-selector.html
@@ -27,7 +27,6 @@ It also displays tooltips.
 
 Properties in:
 - runs: Array of strings representing the runs that may be selected
-- colorScale: a TF.ColorScale mapping run names to colors
 
 Properties out:
 - outSelected: The array of run names that are currently checked by the user.
@@ -48,7 +47,6 @@ Properties out:
       id="multiCheckbox"
       names="[[runs]]"
       out-selected="{{outSelected}}"
-      color-scale="[[colorScale]]"
     ></tf-multi-checkbox>
     <paper-button
       class="x-button"
@@ -121,7 +119,6 @@ Properties out:
       outSelected: {type: Array, notify: true},
       // runs: an array of strings, representing the run names that may be chosen
       runs: Array,
-      colorScale: Object, // TF.ColorScale
       logdir: {
         type: String,
         notify: true,
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html
index 5eb8537040..2b49d1be62 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html
@@ -31,7 +31,6 @@ it with extra configuration components, add them to the element's component:
 <tf-sidebar-helper
   backend: "[[backend]]",
   categories: "{{outputCategories}}",
-  colorScale: "[[colorScale]]",
   run2tag: "[[run2tag]]",
   runs: "[[runs]]",
   selectedRuns: "{{outSelectedRuns}}",
@@ -67,7 +66,6 @@ tf-run-selector.
         id="runSelector"
         backend="[[backend]]"
         runs="[[runs]]"
-        color-scale="[[colorScale]]"
         out-selected="{{selectedRuns}}"
         ></tf-run-selector>
     </div>
@@ -128,11 +126,6 @@ tf-run-selector.
           notify: true,
         },
 
-        /**
-         * Input of the colors that are used for the user's runs.
-         */
-        colorScale: Object,
-
         /**
          * Map from runs to the valid tags that have them.
          */
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html b/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
index 76de74273f..44ca48fada 100644
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
@@ -44,20 +44,11 @@ contains vz-distribution-charts embedded inside tf-panes-helper's.
 -->
 <dom-module id="tf-distribution-dashboard">
   <template>
-    <div id="plumbing">
-      <tf-color-scale
-        id="colorScale"
-        runs="[[runs]]"
-        out-color-scale="{{_colorScale}}"
-      ></tf-color-scale>
-    </div>
-
     <tf-dashboard-layout>
       <div class="sidebar">
         <tf-sidebar-helper
           backend="[[backend]]"
           categories="{{_categories}}"
-          color-scale="[[_colorScale]]"
           run2tag="[[run2tag]]"
           runs="[[runs]]"
           selected-runs="{{_selectedRuns}}"
@@ -79,7 +70,6 @@ contains vz-distribution-charts embedded inside tf-panes-helper's.
       <div class="center">
         <tf-panes-helper
           categories="[[_categories]]"
-          color-scale="[[_colorScale]]"
           data-type="[[dataType]]"
           data-provider="[[dataProvider]]"
           data-not-found="[[dataNotFound]]"
@@ -90,8 +80,8 @@ contains vz-distribution-charts embedded inside tf-panes-helper's.
           <template>
             <vz-distribution-chart
               x-type="[[_xType]]"
-              color-scale="[[_colorScale]]"
-              ></vz-distribution-chart>
+              color-scale="[[_runsColorScale]]"
+            ></vz-distribution-chart>
           </template>
         </tf-panes-helper>
       </div>
@@ -104,6 +94,7 @@ contains vz-distribution-charts embedded inside tf-panes-helper's.
     import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
     import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
     import {BackendBehavior} from "../tf-backend/behavior";
+    import {runsColorScale} from "../tf-color-scale/colorScale";
 
     Polymer({
       is: "tf-distribution-dashboard",
@@ -125,6 +116,15 @@ contains vz-distribution-charts embedded inside tf-panes-helper's.
           type: Object,
           value: "compressedHistogram",
         },
+        _runsColorScale: {
+          type: Object,
+          value: function() {
+            return {
+              scale: runsColorScale,
+            };
+          },
+          readOnly: true,
+        },
       },
     });
   </script>
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html b/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
index 1821ce3b6f..bf6d90fa43 100644
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
@@ -46,20 +46,11 @@ contains vz-histogram-timeseries embedded inside tf-panes-helper's.
 -->
 <dom-module id="tf-histogram-dashboard">
   <template>
-    <div id="plumbing">
-      <tf-color-scale
-        id="colorScale"
-        runs="[[runs]]"
-        out-color-scale="{{_colorScale}}"
-      ></tf-color-scale>
-    </div>
-
     <tf-dashboard-layout>
       <div class="sidebar">
         <tf-sidebar-helper
           backend="[[backend]]"
           categories="{{_categories}}"
-          color-scale="[[_colorScale]]"
           run2tag="[[run2tag]]"
           runs="[[runs]]"
           selected-runs="{{_selectedRuns}}"
@@ -92,7 +83,6 @@ contains vz-histogram-timeseries embedded inside tf-panes-helper's.
       <div class="center">
         <tf-panes-helper
           categories="[[_categories]]"
-          color-scale="[[_colorScale]]"
           data-type="[[dataType]]"
           data-provider="[[dataProvider]]"
           data-not-found="[[dataNotFound]]"
@@ -124,6 +114,7 @@ contains vz-histogram-timeseries embedded inside tf-panes-helper's.
     import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
     import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
     import {BackendBehavior} from "../tf-backend/behavior";
+    import {runsColorScale} from "../tf-color-scale/colorScale";
 
     Polymer({
       is: "tf-histogram-dashboard",
@@ -151,7 +142,15 @@ contains vz-histogram-timeseries embedded inside tf-panes-helper's.
         },
         _colorScaleFunction: {
           type: Function,
-          computed: "_getColorScaleFunction(_colorScale)"
+          value: function() {
+            // NOTE: Because the result is a function, we can't just write
+            //
+            //     value: runsColorScale,
+            //
+            // above. If we did, Polymer would try to invoke _that_ to
+            // get an initial property value.
+            return runsColorScale;
+          },
         },
       },
       attached: function() {
@@ -159,9 +158,6 @@ contains vz-histogram-timeseries embedded inside tf-panes-helper's.
           this.fire("rendered");
         });
       },
-      _getColorScaleFunction: function() {
-        return this._colorScale.scale.bind(this._colorScale);
-      },
     });
   </script>
 </dom-module>
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
index 5d46847eb8..856854d510 100644
--- a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
@@ -19,7 +19,6 @@ limitations under the License.
 <link rel="import" href="../paper-dialog/paper-dialog.html">
 <link rel="import" href="../paper-icon-button/paper-icon-button.html">
 <link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
 <link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
 <link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
 <link rel="import" href="../tf-dashboard-common/tf-sidebar-helper.html">
@@ -31,20 +30,11 @@ tf-image-dashboard displays a dashboard that loads images from a TensorFlow run.
 <dom-module id="tf-image-dashboard">
   <template>
     <paper-dialog with-backdrop id="actual-image-size-dialog"></paper-dialog>
-    <div id="plumbing">
-      <tf-color-scale
-        id="colorScale"
-        runs="[[runs]]"
-        out-color-scale="{{_colorScale}}"
-        ></tf-color-scale>
-    </div>
-
     <tf-dashboard-layout>
       <div class="sidebar">
         <tf-sidebar-helper
           backend="[[backend]]"
           categories="{{_categories}}"
-          color-scale="[[_colorScale]]"
           run2tag="[[run2tag]]"
           runs="[[runs]]"
           selected-runs="{{_selectedRuns}}"
@@ -54,7 +44,6 @@ tf-image-dashboard displays a dashboard that loads images from a TensorFlow run.
       <div class="center">
         <tf-panes-helper
           categories="[[_categories]]"
-          color-scale="[[_colorScale]]"
           data-type="[[dataType]]"
           data-provider="[[dataProvider]]"
           data-not-found="[[dataNotFound]]"
@@ -63,7 +52,7 @@ tf-image-dashboard displays a dashboard that loads images from a TensorFlow run.
           repeat-for-runs
           >
           <template>
-            <tf-image-loader color-scale="[[_colorScale]]"></tf-image-loader>
+            <tf-image-loader></tf-image-loader>
             <paper-icon-button
               class="actual-size-button"
               icon="aspect-ratio"
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-loader.html b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-loader.html
index 41fb12eefa..b9ae236421 100644
--- a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-loader.html
+++ b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-loader.html
@@ -18,6 +18,7 @@ limitations under the License.
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-slider/paper-slider.html">
 <link rel="import" href="../paper-spinner/paper-spinner-lite.html">
+<link rel="import" href="../tf-color-scale/tf-color-scale.html">
 <link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
 <link rel="import" href="../tf-imports/lodash.html">
 <link rel="import" href="../tf-imports/d3.html">
@@ -110,11 +111,11 @@ future for loading older images.
   </template>
   <script>
     "use strict";
+    import {runsColorScale} from "../tf-color-scale/colorScale";
 
     Polymer({
       is: "tf-image-loader",
       properties: {
-        colorScale: Object,
         run: String,
         // This is an array of Tensorboard Image&Datum objects (See backend.ts for details). The
         // properties of objects in this array are
@@ -181,7 +182,7 @@ future for loading older images.
         this.set("_stepIndex", steps.length - 1);
 
         // Update the border color based on the run.
-        var color = this.colorScale.scale(run);
+        var color = runsColorScale(run);
         this.$$("#image-annotation").style.borderColor = color;
       },
       _updateImageUrl: function(steps, stepIndex) {
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html b/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
index 848ed5292d..d6f3e2281c 100644
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
@@ -41,22 +41,9 @@ can customize how data is organized and displayed.
 
 Each chart has a button that can toggle whether it is "expanded"; expanded
 charts are larger.
-
-Organizationally, the #plumbing div contains components that have no concrete
-manifestation and just effect data bindings or data loading. The .sidebar div
-contains shared controls provided by tf-sidebar-helper. The .center div
-contains vz-line-charts embedded inside tf-panes-helper's.
 -->
 <dom-module id="tf-scalar-dashboard">
   <template>
-    <div id="plumbing">
-      <tf-color-scale
-        id="colorScale"
-        runs="[[runs]]"
-        out-color-scale="{{_colorScale}}"
-      ></tf-color-scale>
-    </div>
-
     <tf-dashboard-layout>
       <div class="sidebar">
         <div class="sidebar-section">
@@ -121,7 +108,6 @@ contains vz-line-charts embedded inside tf-panes-helper's.
       <div class="center">
         <tf-panes-helper
           categories="[[_categories]]"
-          color-scale="[[_colorScale]]"
           data-type="[[dataType]]"
           data-provider="[[dataProvider]]"
           data-not-found="[[dataNotFound]]"
@@ -133,7 +119,7 @@ contains vz-line-charts embedded inside tf-panes-helper's.
           <template>
             <vz-line-chart
               x-type="[[_xType]]"
-              color-scale="[[_colorScale]]"
+              color-scale="[[_runsColorScale]]"
               smoothing-enabled="[[_smoothingEnabled]]"
               smoothing-weight="[[_smoothingWeight]]"
               tooltip-sorting-method="[[_tooltipSortingMethod]]"
@@ -218,6 +204,7 @@ contains vz-line-charts embedded inside tf-panes-helper's.
     import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
     import {BackendBehavior} from "../tf-backend/behavior";
     import {getRouter} from "../tf-backend/router";
+    import {runsColorScale} from "../tf-color-scale/colorScale";
     import * as storage from "../tf-storage/storage";
 
     Polymer({
@@ -264,6 +251,14 @@ contains vz-line-charts embedded inside tf-panes-helper's.
           type: String,
           value: "step"
         },
+        _runsColorScale: {
+          type: Object,
+          value: function() {
+            return {
+              scale: runsColorScale,
+            };
+          },
+        },
       },
       attached: function() {
         this.async(function() {
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
index 9b4fd3239c..e8daca2deb 100644
--- a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
@@ -19,7 +19,6 @@ limitations under the License.
 <link rel="import" href="../paper-dialog/paper-dialog.html">
 <link rel="import" href="../paper-icon-button/paper-icon-button.html">
 <link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
 <link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
 <link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
 <link rel="import" href="../tf-dashboard-common/tf-sidebar-helper.html">
@@ -31,20 +30,11 @@ tf-text-dashboard displays a dashboard that loads texts from a TensorFlow run.
 <dom-module id="tf-text-dashboard">
   <template>
     <paper-dialog with-backdrop id="actual-text-size-dialog"></paper-dialog>
-    <div id="plumbing">
-      <tf-color-scale
-        id="colorScale"
-        runs="[[runs]]"
-        out-color-scale="{{_colorScale}}"
-        ></tf-color-scale>
-    </div>
-
     <tf-dashboard-layout>
       <div class="sidebar">
         <tf-sidebar-helper
           backend="[[backend]]"
           categories="{{_categories}}"
-          color-scale="[[_colorScale]]"
           run2tag="[[run2tag]]"
           runs="[[runs]]"
           selected-runs="{{_selectedRuns}}"
@@ -54,7 +44,6 @@ tf-text-dashboard displays a dashboard that loads texts from a TensorFlow run.
       <div class="center">
         <tf-panes-helper
           categories="[[_categories]]"
-          color-scale="[[_colorScale]]"
           data-type="[[dataType]]"
           data-provider="[[dataProvider]]"
           data-not-found="[[dataNotFound]]"
@@ -63,7 +52,7 @@ tf-text-dashboard displays a dashboard that loads texts from a TensorFlow run.
           repeat-for-runs
           >
           <template>
-            <tf-text-loader color-scale="[[_colorScale]]"></tf-text-loader>
+            <tf-text-loader></tf-text-loader>
           </template>
         </tf-panes-helper>
       </div>
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-loader.html b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-loader.html
index 374e0478dd..95723a0b88 100644
--- a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-loader.html
+++ b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-loader.html
@@ -17,6 +17,7 @@ limitations under the License.
 
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-material/paper-material.html">
+<link rel="import" href="../tf-color-scale/tf-color-scale.html">
 <link rel="import" href="../tf-dashboard-common/scrollbar-style.html">
 <link rel="import" href="../tf-imports/d3.html">
 
@@ -102,10 +103,10 @@ tf-text-loader displays markdown text data from the Text plugin.
 
   </template>
   <script>
+    import {runsColorScale} from "../tf-color-scale/colorScale";
     Polymer({
       is: "tf-text-loader",
       properties: {
-        colorScale: Object,
         run: String,
         // This is an array of Tensorboard Text&Datum objects (See backend.ts for details). The
         // properties of objects in this array are
@@ -132,7 +133,7 @@ tf-text-loader displays markdown text data from the Text plugin.
         this.set("_texts", texts.reverse());
 
         // Update the border color based on the run.
-        var color = this.colorScale.scale(run);
+        var color = runsColorScale(run);
         this.$$("#outer").style.borderColor = color;
       },
       _numfmt: function(n) {
-- 
GitLab


From 87f67e4517579a9be8b69a56a566c035935ebd5c Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Wed, 14 Jun 2017 12:10:06 -0700
Subject: [PATCH 0084/1375] Restore LMDBReader and fix the tests so that they
 work on read-only filesystems.

PiperOrigin-RevId: 159006787
---
 tensorflow/core/kernels/BUILD                 | 17 ++--
 tensorflow/core/kernels/lmdb_reader_op.cc     |  4 +-
 .../python/kernel_tests/reader_ops_test.py    | 89 ++++++++++---------
 tensorflow/python/ops/io_ops.py               |  1 +
 .../golden/tensorflow.-l-m-d-b-reader.pbtxt   | 46 ++++++++++
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  4 +
 tensorflow/tools/lib_package/BUILD            |  4 +-
 tensorflow/tools/pip_package/BUILD            |  2 +-
 8 files changed, 110 insertions(+), 57 deletions(-)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.-l-m-d-b-reader.pbtxt

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 72385f7348..99530c9317 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -1901,7 +1901,7 @@ cc_library(
     deps = [
         ":fixed_length_record_reader_op",
         ":identity_reader_op",
-        #":lmdb_reader_op",
+        ":lmdb_reader_op",
         ":matching_files_op",
         ":reader_ops",
         ":restore_op",
@@ -1936,14 +1936,13 @@ tf_kernel_library(
     deps = IO_DEPS,
 )
 
-# TODO(jhseu): Restore after merge.
-#tf_kernel_library(
-#    name = "lmdb_reader_op",
-#    prefix = "lmdb_reader_op",
-#    deps = IO_DEPS + [
-#        "@lmdb",
-#    ],
-#)
+tf_kernel_library(
+    name = "lmdb_reader_op",
+    prefix = "lmdb_reader_op",
+    deps = IO_DEPS + [
+        "@lmdb",
+    ],
+)
 
 tf_kernel_library(
     name = "matching_files_op",
diff --git a/tensorflow/core/kernels/lmdb_reader_op.cc b/tensorflow/core/kernels/lmdb_reader_op.cc
index 23cabe7b54..61035cb43d 100755
--- a/tensorflow/core/kernels/lmdb_reader_op.cc
+++ b/tensorflow/core/kernels/lmdb_reader_op.cc
@@ -22,9 +22,7 @@ limitations under the License.
 
 namespace tensorflow {
 
-inline void MDB_CHECK(int mdb_status) {
-  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
-}
+#define MDB_CHECK(val) CHECK_EQ(val, MDB_SUCCESS) << mdb_strerror(val)
 
 class LMDBReader : public ReaderBase {
  public:
diff --git a/tensorflow/python/kernel_tests/reader_ops_test.py b/tensorflow/python/kernel_tests/reader_ops_test.py
index 12932219fc..c7e1d88360 100644
--- a/tensorflow/python/kernel_tests/reader_ops_test.py
+++ b/tensorflow/python/kernel_tests/reader_ops_test.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 import collections
 import gzip
 import os
+import shutil
 import threading
 import zlib
 
@@ -36,6 +37,8 @@ from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.util import compat
 
+prefix_path = "tensorflow/core/lib"
+
 # pylint: disable=invalid-name
 TFRecordCompressionType = tf_record.TFRecordCompressionType
 # pylint: enable=invalid-name
@@ -858,48 +861,50 @@ class AsyncReaderTest(test.TestCase):
     output.append(sess.run(args))
 
 
-# TODO(jhseu): Restore after fixing.
-#class LMDBReaderTest(test.TestCase):
-#
-#  def setUp(self):
-#    super(LMDBReaderTest, self).setUp()
-#
-#  def testReadFromFile(self):
-#    with self.test_session() as sess:
-#      reader = io_ops.LMDBReader(name="test_read_from_file")
-#      path = os.path.join("tensorflow", "core", "lib", "lmdb", "testdata",
-#                          "data.mdb")
-#      queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
-#      key, value = reader.read(queue)
-#
-#      queue.enqueue([path]).run()
-#      queue.close().run()
-#      for i in range(10):
-#        k, v = sess.run([key, value])
-#        self.assertAllEqual(compat.as_bytes(k), compat.as_bytes(str(i)))
-#        self.assertAllEqual(compat.as_bytes(v), compat.as_bytes(str(chr(ord('a') + i))))
-#
-#      with self.assertRaisesOpError("is closed and has insufficient elements "
-#                                    "\\(requested 1, current size 0\\)"):
-#        k, v = sess.run([key, value])
-#
-#  def testReadFromFolder(self):
-#    with self.test_session() as sess:
-#      reader = io_ops.LMDBReader(name="test_read_from_folder")
-#      path = os.path.join("tensorflow", "core", "lib", "lmdb", "testdata")
-#      queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
-#      key, value = reader.read(queue)
-#
-#      queue.enqueue([path]).run()
-#      queue.close().run()
-#      for i in range(10):
-#        k, v = sess.run([key, value])
-#        self.assertAllEqual(compat.as_bytes(k), compat.as_bytes(str(i)))
-#        self.assertAllEqual(compat.as_bytes(v), compat.as_bytes(str(chr(ord('a') + i))))
-#
-#      with self.assertRaisesOpError("is closed and has insufficient elements "
-#                                    "\\(requested 1, current size 0\\)"):
-#        k, v = sess.run([key, value])
+class LMDBReaderTest(test.TestCase):
+
+  def setUp(self):
+    super(LMDBReaderTest, self).setUp()
+    # Copy database out because we need the path to be writable to use locks.
+    path = os.path.join(prefix_path, "lmdb", "testdata", "data.mdb")
+    self.db_path = os.path.join(self.get_temp_dir(), "data.mdb")
+    shutil.copy(path, self.db_path)
+
+  def testReadFromFile(self):
+    with self.test_session() as sess:
+      reader = io_ops.LMDBReader(name="test_read_from_file")
+      queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
+      key, value = reader.read(queue)
+
+      queue.enqueue([self.db_path]).run()
+      queue.close().run()
+      for i in range(10):
+        k, v = sess.run([key, value])
+        self.assertAllEqual(compat.as_bytes(k), compat.as_bytes(str(i)))
+        self.assertAllEqual(
+            compat.as_bytes(v), compat.as_bytes(str(chr(ord("a") + i))))
+
+      with self.assertRaisesOpError("is closed and has insufficient elements "
+                                    "\\(requested 1, current size 0\\)"):
+        k, v = sess.run([key, value])
+
+  def testReadFromFolder(self):
+    with self.test_session() as sess:
+      reader = io_ops.LMDBReader(name="test_read_from_folder")
+      queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
+      key, value = reader.read(queue)
+
+      queue.enqueue([self.db_path]).run()
+      queue.close().run()
+      for i in range(10):
+        k, v = sess.run([key, value])
+        self.assertAllEqual(compat.as_bytes(k), compat.as_bytes(str(i)))
+        self.assertAllEqual(
+            compat.as_bytes(v), compat.as_bytes(str(chr(ord("a") + i))))
+
+      with self.assertRaisesOpError("is closed and has insufficient elements "
+                                    "\\(requested 1, current size 0\\)"):
+        k, v = sess.run([key, value])
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index 0b1a77969a..8f5d0e5cd4 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -26,6 +26,7 @@ See the @{$python/io_ops} guide.
 @@WholeFileReader
 @@IdentityReader
 @@TFRecordReader
+@@LMDBReader
 @@FixedLengthRecordReader
 @@decode_csv
 @@decode_raw
diff --git a/tensorflow/tools/api/golden/tensorflow.-l-m-d-b-reader.pbtxt b/tensorflow/tools/api/golden/tensorflow.-l-m-d-b-reader.pbtxt
new file mode 100644
index 0000000000..f9b7e9bbca
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.-l-m-d-b-reader.pbtxt
@@ -0,0 +1,46 @@
+path: "tensorflow.LMDBReader"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.io_ops.LMDBReader\'>"
+  is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "reader_ref"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "supports_serialize"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'name\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "num_records_produced"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "num_work_units_completed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "read"
+    argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "read_up_to"
+    argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "reset"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "restore_state"
+    argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "serialize_state"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 3cf6c3dba0..5708c9f7ad 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -108,6 +108,10 @@ tf_module {
     name: "InteractiveSession"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "LMDBReader"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "LogMessage"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD
index cc8a6fb74e..51ba3b7a0b 100644
--- a/tensorflow/tools/lib_package/BUILD
+++ b/tensorflow/tools/lib_package/BUILD
@@ -97,7 +97,7 @@ genrule(
         "@jemalloc//:COPYING",
         "@jpeg//:LICENSE.md",
         "@libxsmm_archive//:LICENSE",
-        #"@lmdb//:LICENSE",
+        "@lmdb//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
         "@png_archive//:LICENSE",
         "@protobuf//:LICENSE",
@@ -127,7 +127,7 @@ genrule(
         "@jemalloc//:COPYING",
         "@jpeg//:LICENSE.md",
         "@libxsmm_archive//:LICENSE",
-        #"@lmdb//:LICENSE",
+        "@lmdb//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
         "@png_archive//:LICENSE",
         "@protobuf//:LICENSE",
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 238d7626d7..798338d787 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -110,7 +110,7 @@ filegroup(
         "@jemalloc//:COPYING",
         "@jpeg//:LICENSE.md",
         "@libxsmm_archive//:LICENSE",
-        #"@lmdb//:LICENSE",
+        "@lmdb//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
         "@nanopb_git//:LICENSE.txt",
         "@org_html5lib//:LICENSE",
-- 
GitLab


From 7e3e768d264fdbfd8ce9dbafe0ea08c671c8f7f0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 12:21:09 -0700
Subject: [PATCH 0085/1375] Add a transformation rule to transform from
 ResizeBilinear to QuantizedResizeBilinear

PiperOrigin-RevId: 159008264
---
 .../tools/graph_transforms/quantize_nodes.cc  |  7 ++++
 .../graph_transforms/quantize_nodes_test.cc   | 36 +++++++++++++++++--
 2 files changed, 41 insertions(+), 2 deletions(-)

diff --git a/tensorflow/tools/graph_transforms/quantize_nodes.cc b/tensorflow/tools/graph_transforms/quantize_nodes.cc
index da064377ac..2b85e7e83c 100644
--- a/tensorflow/tools/graph_transforms/quantize_nodes.cc
+++ b/tensorflow/tools/graph_transforms/quantize_nodes.cc
@@ -119,6 +119,13 @@ const std::vector<QuantizedOpInfo>& GetQuantizedOpList() {
        DT_QUINT8,
        {},
        QuantizedOpInfo::CONTIGUOUS_MIN_MAX},
+      {"ResizeBilinear",
+       {"align_corners"},
+       {{"T", DT_QUINT8}},
+       DT_QUINT8,
+       DT_QUINT8,
+       {1},
+       QuantizedOpInfo::CONTIGUOUS_MIN_MAX},
       {"Relu6",
        {},
        {{"Tinput", DT_QUINT8}},
diff --git a/tensorflow/tools/graph_transforms/quantize_nodes_test.cc b/tensorflow/tools/graph_transforms/quantize_nodes_test.cc
index d02655f3f9..f070039381 100644
--- a/tensorflow/tools/graph_transforms/quantize_nodes_test.cc
+++ b/tensorflow/tools/graph_transforms/quantize_nodes_test.cc
@@ -106,8 +106,8 @@ class QuantizeNodesTest : public ::testing::Test {
     // Reshape is not included here because it can be added as part of the
     // quantization process.
     const std::set<string> quantizable_ops = {
-        "Add",  "BiasAdd", "Concat",  "Conv2D",  "MatMul",
-        "Relu", "Relu6",   "AvgPool", "MaxPool", "Mul"};
+        "Add",   "BiasAdd",        "Concat",  "Conv2D",  "MatMul", "Relu",
+        "Relu6", "ResizeBilinear", "AvgPool", "MaxPool", "Mul"};
     for (const NodeDef& node : quantized_graph_def.node()) {
       EXPECT_EQ(0, quantizable_ops.count(node.op()))
           << "Found quantizable node " << node.op() << " for node named "
@@ -652,6 +652,34 @@ class QuantizeNodesTest : public ::testing::Test {
     EXPECT_EQ("requantize_op", node_map.at("final_dequantize")->input(0));
   }
 
+  void TestQuantizeResizeBilinear() {
+    auto root = tensorflow::Scope::NewRootScope();
+    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+
+    Tensor size_tensor(DT_INT32, TensorShape({2}));
+    test::FillValues<int32>(&size_tensor, {256, 256});
+
+    Output constant_op = Const(root.WithOpName("size_tensor_op"),
+                               Input::Initializer(size_tensor));
+
+    Output placeholder_op =
+        Placeholder(root.WithOpName("placeholder_op"), DT_FLOAT);
+
+    Output resize_bilinear_op = ResizeBilinear(
+        root.WithOpName("resize_bilinear_op"), placeholder_op, constant_op);
+
+    GraphDef float_graph_def;
+    TF_ASSERT_OK(root.ToGraphDef(&float_graph_def));
+
+    Tensor input_tensor(DT_FLOAT, {1, 128, 128, 3});
+    auto float_input_tensor = input_tensor.flat<float>();
+    float_input_tensor.constant(100.0f);
+
+    TestQuantizedVersusFloatGraph(float_graph_def,
+                                  {{"placeholder_op", input_tensor}},
+                                  {"resize_bilinear_op"});
+  }
+
   void TestRemoveRedundantQuantizationWithMultipleOutputs() {
     auto root = tensorflow::Scope::NewRootScope();
     using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
@@ -1446,6 +1474,10 @@ TEST_F(QuantizeNodesTest, TestQuantizeAvgPool) { TestQuantizeAvgPool(); }
 
 TEST_F(QuantizeNodesTest, TestQuantizeReshape) { TestQuantizeReshape(); }
 
+TEST_F(QuantizeNodesTest, TestQuantizeResizeBilinear) {
+  TestQuantizeResizeBilinear();
+}
+
 TEST_F(QuantizeNodesTest, TestRemoveRedundantQuantization) {
   TestRemoveRedundantQuantization();
 }
-- 
GitLab


From 1856fe120d015fb17be303ef2da3873a10a6ffa6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 12:30:57 -0700
Subject: [PATCH 0086/1375] InfeedTuple support for GPU backend.

PiperOrigin-RevId: 159009451
---
 .../xla/service/gpu/infeed_manager.cc         | 39 ++++++-----
 .../compiler/xla/service/gpu/infeed_manager.h | 36 ++++++-----
 .../compiler/xla/service/gpu/infeed_thunk.cc  | 52 +++++++++++----
 .../compiler/xla/service/gpu/infeed_thunk.h   |  8 ++-
 .../xla/service/gpu/ir_emitter_unnested.cc    | 16 +++--
 .../xla/service/gpu_transfer_manager.cc       | 64 ++++++++++++++-----
 .../xla/service/gpu_transfer_manager.h        |  6 ++
 7 files changed, 153 insertions(+), 68 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/infeed_manager.cc b/tensorflow/compiler/xla/service/gpu/infeed_manager.cc
index 120a3f7fba..8b948d89f5 100644
--- a/tensorflow/compiler/xla/service/gpu/infeed_manager.cc
+++ b/tensorflow/compiler/xla/service/gpu/infeed_manager.cc
@@ -13,8 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/infeed_manager.h"
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace se = ::perftools::gputools;
@@ -22,23 +24,23 @@ namespace se = ::perftools::gputools;
 namespace xla {
 namespace gpu {
 
-InfeedManager::InfeedManager()
-    : current_buffer_(nullptr),
-      host_to_device_executor_(nullptr) {}
+InfeedManager::InfeedManager() : host_to_device_executor_(nullptr) {}
 
 void InfeedManager::Reset() {
   tensorflow::mutex_lock l(mu_);
-  CHECK(!current_buffer_);
+  CHECK(dequeued_buffer_.empty());
   for (auto buffer : enqueued_buffer_) {
     buffer->Done();
   }
   enqueued_buffer_.clear();
 }
 
-void InfeedManager::EnqueueBuffer(InfeedBuffer* buffer) {
+void InfeedManager::EnqueueBuffers(std::vector<InfeedBuffer*> buffers) {
   tensorflow::mutex_lock l(mu_);
   bool was_empty = enqueued_buffer_.empty();
-  enqueued_buffer_.push_back(buffer);
+  for (gpu::InfeedBuffer* b : buffers) {
+    enqueued_buffer_.push_back(b);
+  }
   if (was_empty) {
     // This has the potential to suffer from the notified thread
     // immediately trying and failing to acquire mu_, but seems
@@ -53,18 +55,23 @@ InfeedBuffer* InfeedManager::BlockingDequeueBuffer() {
   while (enqueued_buffer_.empty()) {
     cv_.wait(l);
   }
-  CHECK(!current_buffer_);
-  current_buffer_ = enqueued_buffer_.front();
+  InfeedBuffer* current_buffer = enqueued_buffer_.front();
   enqueued_buffer_.pop_front();
-  return current_buffer_;
+  dequeued_buffer_.insert(current_buffer);
+  return current_buffer;
 }
 
-void InfeedManager::ReleaseCurrentBuffer(se::DeviceMemoryBase* device_memory) {
-  tensorflow::mutex_lock l(mu_);
-  CHECK(current_buffer_);
-  CHECK(device_memory->IsSameAs(*current_buffer_->device_memory()));
-  current_buffer_->Done();
-  current_buffer_ = nullptr;
+void InfeedManager::ReleaseBuffers(std::vector<InfeedBuffer*> buffers) {
+  {
+    tensorflow::mutex_lock l(mu_);
+    for (gpu::InfeedBuffer* b : buffers) {
+      CHECK(ContainsKey(dequeued_buffer_, b));
+      dequeued_buffer_.erase(b);
+    }
+  }
+  for (gpu::InfeedBuffer* b : buffers) {
+    b->Done();
+  }
 }
 
 se::Stream* InfeedManager::GetStream(se::StreamExecutor* executor) {
diff --git a/tensorflow/compiler/xla/service/gpu/infeed_manager.h b/tensorflow/compiler/xla/service/gpu/infeed_manager.h
index 50d0ce340f..23fbc5bd08 100644
--- a/tensorflow/compiler/xla/service/gpu/infeed_manager.h
+++ b/tensorflow/compiler/xla/service/gpu/infeed_manager.h
@@ -23,6 +23,7 @@ limitations under the License.
 #include <deque>
 
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
 
@@ -81,25 +82,19 @@ class InfeedManager {
   // condition is to call Reset when no computation is taking place.
   void Reset();
 
-  // Adds buffer to the infeed queue. buffer->Done will be called when
-  // the buffer will no longer be accessed by the InfeedManager,
-  // either as a result of a call to Reset or because the runtime has
-  // dequeued and used the buffer.
-  void EnqueueBuffer(InfeedBuffer* buffer);
+  // Adds a set of buffers to the infeed queue atomically. buffer->Done
+  // will be called when the buffer will no longer be accessed by the
+  // InfeedManager, either as a result of a call to Reset or because the
+  // runtime has dequeued and used the buffer.
+  void EnqueueBuffers(std::vector<InfeedBuffer*> buffers);
 
   // Blocks until the infeed queue is non-empty, then returns the
-  // buffer at the head of the queue. Sets the current buffer to be
-  // the returned buffer. It is an error to call BlockingDequeueBuffer
-  // if there is an unreleased current buffer, i.e.,
-  // ReleaseCurrentBuffer must be called between calls to
-  // BlockingDequeueBuffer.
+  // buffer at the head of the queue. Adds the current buffer to the
+  // to-be released set.
   InfeedBuffer* BlockingDequeueBuffer();
 
-  // Releases the current buffer, which is the last buffer returned by
-  // BlockingDequeueBuffer and not yet released. device_memory must
-  // match that of the current buffer.
-  void ReleaseCurrentBuffer(
-      perftools::gputools::DeviceMemoryBase* device_memory);
+  // Releases a set of buffers from the to-be released set.
+  void ReleaseBuffers(std::vector<InfeedBuffer*> buffers);
 
   // Returns a cached stream associated with an executor. Allocates a
   // new stream on the first invocation. On subsequent invocations, if
@@ -109,18 +104,25 @@ class InfeedManager {
       perftools::gputools::StreamExecutor* executor);
 
  private:
+  // TODO(b/30467474): Revisit if this mutex becomes a point of
+  // contention.
   tensorflow::mutex mu_;
+
   // Condition variable that is signaled every time a buffer is
   // enqueued to an empty queue.
   tensorflow::condition_variable cv_;
+
   // InfeedBuffer* queue contents are not owned, but buffer->Done must
   // be called when the buffer is no longer needed by the runtime.
   std::deque<InfeedBuffer*> enqueued_buffer_;
-  // If non-NULL, the buffer that is currently being processed by the
+
+  // Buffers that are dequeued and currently being processed by the
   // runtime. Not owned.
-  InfeedBuffer* current_buffer_;
+  tensorflow::gtl::FlatSet<const InfeedBuffer*> dequeued_buffer_;
+
   // Cached host to device stream for queuing infeed data.
   std::unique_ptr<perftools::gputools::Stream> host_to_device_stream_;
+
   // Executor that the host_to_device_stream belongs to. Not owned.
   perftools::gputools::StreamExecutor* host_to_device_executor_;
 };
diff --git a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc
index 6f144c7273..e33e904692 100644
--- a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc
@@ -21,31 +21,59 @@ limitations under the License.
 namespace xla {
 namespace gpu {
 
-InfeedThunk::InfeedThunk(const BufferAllocation::Slice& destination_buffer,
-                         uint64 mem_size, const HloInstruction* hlo_instruction)
+InfeedThunk::InfeedThunk(
+    tensorflow::gtl::ArraySlice<BufferAllocation::Slice> tuple_element_buffers,
+    const BufferAllocation::Slice& destination_buffer,
+    const HloInstruction* hlo_instruction)
     : Thunk(Kind::kInfeed, hlo_instruction),
-      destination_buffer_(destination_buffer),
-      mem_size_(mem_size) {}
+      tuple_element_buffers_(tuple_element_buffers.begin(),
+                             tuple_element_buffers.end()),
+      destination_buffer_(destination_buffer) {}
 
 tensorflow::Status InfeedThunk::ExecuteOnStream(
     const BufferAllocations& buffer_allocations,
     perftools::gputools::Stream* stream) {
   VLOG(2) << "Infeeding to GPU ";
-  perftools::gputools::DeviceMemoryBase destination_data =
+
+  perftools::gputools::DeviceMemoryBase destination_address =
       buffer_allocations.GetDeviceAddress(destination_buffer_);
 
   InfeedManager* infeed_manager = GetOrCreateInfeedManager();
-  InfeedBuffer* buffer = infeed_manager->BlockingDequeueBuffer();
-  CHECK_EQ(buffer->length(), mem_size_);
-  stream->ThenMemcpy(&destination_data, *(buffer->device_memory()),
-                     buffer->length());
+  std::vector<InfeedBuffer*> infeed_buffers;
+  if (ShapeUtil::IsTuple(hlo_instruction()->shape())) {
+    CHECK(!ShapeUtil::IsNestedTuple(hlo_instruction()->shape()));
+    // Transfer the tuple elements first.
+    std::vector<void*> tuple_element_addresses;
+    for (BufferAllocation::Slice tuple_element_buffer :
+         tuple_element_buffers_) {
+      perftools::gputools::DeviceMemoryBase tuple_element_address =
+          buffer_allocations.GetDeviceAddress(tuple_element_buffer);
+
+      InfeedBuffer* buffer = infeed_manager->BlockingDequeueBuffer();
+      infeed_buffers.push_back(buffer);
+      stream->ThenMemcpy(&tuple_element_address, *(buffer->device_memory()),
+                         buffer->length());
+      tuple_element_addresses.push_back(tuple_element_address.opaque());
+    }
+    // Transfer the tuple outer buffer.
+    auto host_size = tuple_element_addresses.size() * sizeof(void*);
+    stream->ThenMemcpy(&destination_address, tuple_element_addresses.data(),
+                       host_size);
+  } else {
+    InfeedBuffer* buffer = infeed_manager->BlockingDequeueBuffer();
+    infeed_buffers.push_back(buffer);
+    stream->ThenMemcpy(&destination_address, *(buffer->device_memory()),
+                       buffer->length());
+  }
+
   if (!stream->BlockHostUntilDone()) {
     return InternalError("Failed to complete data transfer on stream %p",
                          stream);
   }
-  // Since Infeeds are totally ordered, no other infeed should sneak
-  // in and we should be able to release the same buffer we dequeued.
-  infeed_manager->ReleaseCurrentBuffer(buffer->device_memory());
+
+  infeed_manager->ReleaseBuffers(infeed_buffers);
+
+  VLOG(2) << "Infeeding to GPU complete";
   return tensorflow::Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/gpu/infeed_thunk.h b/tensorflow/compiler/xla/service/gpu/infeed_thunk.h
index 0a808186c2..371d71f9db 100644
--- a/tensorflow/compiler/xla/service/gpu/infeed_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/infeed_thunk.h
@@ -35,8 +35,10 @@ class InfeedThunk : public Thunk {
   // infeed queue to the device buffer
   // `destination_buffer`. `mem_size` is the size of the data in
   // bytes.
-  InfeedThunk(const BufferAllocation::Slice& destination_buffer,
-              uint64 mem_size, const HloInstruction* hlo_instruction);
+  InfeedThunk(tensorflow::gtl::ArraySlice<BufferAllocation::Slice>
+                  tuple_element_buffers,
+              const BufferAllocation::Slice& destination_buffer,
+              const HloInstruction* hlo_instruction);
 
   InfeedThunk(const InfeedThunk&) = delete;
   InfeedThunk& operator=(const InfeedThunk&) = delete;
@@ -46,8 +48,8 @@ class InfeedThunk : public Thunk {
       perftools::gputools::Stream* stream) override;
 
  private:
+  const std::vector<BufferAllocation::Slice> tuple_element_buffers_;
   const BufferAllocation::Slice destination_buffer_;
-  const uint64 mem_size_;
 };
 
 }  // namespace gpu
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
index 5fa2bfdd7e..e44b645342 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
@@ -1659,12 +1659,18 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildCopyThunk(
 std::unique_ptr<Thunk> IrEmitterUnnested::BuildInfeedThunk(
     const HloInstruction* inst) {
   CHECK_EQ(HloOpcode::kInfeed, inst->opcode());
+
+  std::vector<BufferAllocation::Slice> tuple_element_buffers;
+  for (int64 i = 0; i < inst->shape().tuple_shapes_size(); ++i) {
+    BufferAllocation::Slice buffer = ir_emitter_context_->buffer_assignment()
+                                         .GetUniqueSlice(inst, {i})
+                                         .ConsumeValueOrDie();
+    tuple_element_buffers.push_back(buffer);
+  }
+
   return MakeUnique<InfeedThunk>(
-      /*destination_buffer=*/GetAllocationSlice(*inst),
-      /*mem_size=*/
-      llvm_ir::ByteSizeOf(inst->shape(),
-                          ir_emitter_context_->llvm_module()->getDataLayout()),
-      inst);
+      tuple_element_buffers,
+      /*destination_buffer=*/GetAllocationSlice(*inst), inst);
 }
 
 std::unique_ptr<Thunk> IrEmitterUnnested::BuildGemmThunk(
diff --git a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
index 4b8d190a46..4971de74ae 100644
--- a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/service/gpu/infeed_manager.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -44,16 +43,60 @@ GpuTransferManager::GpuTransferManager()
 Status GpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
                                                    const Literal& literal) {
   const Shape& shape = literal.shape();
-  VLOG(2) << "Transferring literal shape to infeed: "
+  VLOG(2) << "Transferring literal to infeed with shape: "
           << ShapeUtil::HumanString(shape);
 
-  // TODO(b/30467474) handle tuples.
+  std::vector<gpu::InfeedBuffer*> buffers;
+
   if (ShapeUtil::IsTuple(shape)) {
-    return Unimplemented("Infeed with a tuple shape is not supported: %s",
-                         ShapeUtil::HumanString(literal.shape()).c_str());
+    if (ShapeUtil::IsNestedTuple(shape)) {
+      return Unimplemented(
+          "Infeed with a nested tuple shape is not supported: %s",
+          ShapeUtil::HumanString(literal.shape()).c_str());
+    }
+
+    for (const auto& tuple_element : literal.tuple_literals()) {
+      TF_ASSIGN_OR_RETURN(
+          gpu::InfeedBuffer * buffer,
+          TransferLiteralToInfeedInternal(executor, tuple_element));
+      buffers.push_back(buffer);
+    }
+  } else {
+    TF_ASSIGN_OR_RETURN(gpu::InfeedBuffer * buffer,
+                        TransferLiteralToInfeedInternal(executor, literal));
+    buffers.push_back(buffer);
+  }
+
+  gpu::InfeedManager* infeed_manager = gpu::GetOrCreateInfeedManager();
+  se::Stream* stream = infeed_manager->GetStream(executor);
+
+  // TODO(b/30467474): Since this stream is shared across different
+  // infeed requests, blocking on the stream might be
+  // heavy-handed. Figure out if finer-grained acknowledgement is
+  // possible.
+  if (!stream->BlockHostUntilDone()) {
+    for (gpu::InfeedBuffer* b : buffers) {
+      b->Done();
+    }
+    return InternalError("Failed to complete data transfer on stream %p",
+                         stream);
   }
 
+  infeed_manager->EnqueueBuffers(buffers);
+
+  VLOG(2) << "Infeed data transferred";
+
+  return Status::OK();
+}
+
+StatusOr<gpu::InfeedBuffer*>
+GpuTransferManager::TransferLiteralToInfeedInternal(
+    se::StreamExecutor* executor, const Literal& literal) {
+  const Shape& shape = literal.shape();
+  CHECK(!ShapeUtil::IsTuple(shape));
+
   int64 size = GetByteSizeRequirement(shape);
+
   if (size > std::numeric_limits<int32>::max()) {
     return Unimplemented("Infeed shape is too large: %s needs %lld bytes",
                          ShapeUtil::HumanString(literal.shape()).c_str(), size);
@@ -76,16 +119,7 @@ Status GpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
 
   VLOG(2) << "Queued infeed data on stream " << stream;
 
-  if (!stream->BlockHostUntilDone()) {
-    buffer->Done();
-    return InternalError("Failed to complete data transfer on stream %p",
-                         stream);
-  }
-
-  infeed_manager->EnqueueBuffer(buffer);
-
-  VLOG(2) << "Infeed data transferred";
-  return Status::OK();
+  return buffer;
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/gpu_transfer_manager.h b/tensorflow/compiler/xla/service/gpu_transfer_manager.h
index 6dfe7ba029..6b736b9b9a 100644
--- a/tensorflow/compiler/xla/service/gpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/gpu_transfer_manager.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/service/generic_transfer_manager.h"
+#include "tensorflow/compiler/xla/service/gpu/infeed_manager.h"
 #include "tensorflow/compiler/xla/service/transfer_manager.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
@@ -39,6 +40,11 @@ class GpuTransferManager : public GenericTransferManager {
                                  const Literal& literal) override;
 
  private:
+  // Internal helper function for TransferLiteralToInfeed(). Input
+  // literal cannot be a tuple.
+  StatusOr<gpu::InfeedBuffer*> TransferLiteralToInfeedInternal(
+      perftools::gputools::StreamExecutor* executor, const Literal& literal);
+
   TF_DISALLOW_COPY_AND_ASSIGN(GpuTransferManager);
 };
 
-- 
GitLab


From f5fcd1fdcf896f46aed03c7e61525b48b75d1acc Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 14 Jun 2017 14:05:06 -0700
Subject: [PATCH 0087/1375] [tf.contrib.data] Avoid blocking a thread-pool
 thread in OneShotIteratorOp.

Previously, it was possible to cause a deadlock by running multiple instances of
a OneShotIteratorOp concurrently, because each instance would occupy one of the
(finite) inter-op thread-pool threads, and the initialization function requires
at least one free inter-op thread-pool thread to complete.

This change moves the execution of the initialization function off to
a thread-pool thread, and avoids performing any potentially-blocking
computation under the OneShotIteratorOp's mutex.

Fixes #10519.

PiperOrigin-RevId: 159022212
---
 .../python/kernel_tests/iterator_ops_test.py  |  67 +++++
 tensorflow/core/kernels/iterator_ops.cc       | 245 +++++++++++-------
 2 files changed, 223 insertions(+), 89 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
index d6dd134a5b..b5b115dd70 100644
--- a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
@@ -20,11 +20,13 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib.data.python.ops import dataset_ops
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
@@ -128,6 +130,71 @@ class IteratorTest(test.TestCase):
         with self.assertRaises(errors.OutOfRangeError):
           sess.run(get_next)
 
+  def testOneShotIteratorNonBlocking(self):
+    dataset = dataset_ops.Dataset.from_tensors([1, 2, 3]).map(lambda x: x * x)
+    iterator = dataset.make_one_shot_iterator()
+    next_element = iterator.get_next()
+
+    # Create a session with a single thread to ensure that the
+    # one-shot iterator initializer does not deadlock.
+    config = config_pb2.ConfigProto(inter_op_parallelism_threads=1,
+                                    use_per_session_threads=True)
+    with session.Session(config=config) as sess:
+      self.assertAllEqual([1, 4, 9], sess.run(next_element))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+    # Test with multiple threads invoking the one-shot iterator concurrently.
+    with session.Session(config=config) as sess:
+      results = []
+      def consumer_thread():
+        try:
+          results.append(sess.run(next_element))
+        except errors.OutOfRangeError:
+          results.append(None)
+
+      num_threads = 8
+      threads = [
+          self.checkedThread(consumer_thread) for _ in range(num_threads)]
+      for t in threads:
+        t.start()
+      for t in threads:
+        t.join()
+
+      self.assertEqual(num_threads, len(results))
+      self.assertEqual(num_threads - 1,
+                       len([None for r in results if r is None]))
+      self.assertAllEqual([[1, 4, 9]], [r for r in results if r is not None])
+
+  def testOneShotIteratorInitializerFails(self):
+    # Define a dataset whose initialization will always fail.
+    dataset = dataset_ops.Dataset.from_tensors(
+        array_ops.check_numerics(
+            constant_op.constant(1.0) / constant_op.constant(0.0), "oops"))
+    iterator = dataset.make_one_shot_iterator()
+    next_element = iterator.get_next()
+
+    with self.test_session() as sess:
+      with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
+        sess.run(next_element)
+
+      # Test that subsequent attempts to use the iterator also fail.
+      with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
+        sess.run(next_element)
+
+    with self.test_session() as sess:
+      def consumer_thread():
+        with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
+          sess.run(next_element)
+
+      num_threads = 8
+      threads = [
+          self.checkedThread(consumer_thread) for _ in range(num_threads)]
+      for t in threads:
+        t.start()
+      for t in threads:
+        t.join()
+
   def testSimpleSharedResource(self):
     components = (
         np.array(1, dtype=np.int64),
diff --git a/tensorflow/core/kernels/iterator_ops.cc b/tensorflow/core/kernels/iterator_ops.cc
index ed350d9833..b6825b4e95 100644
--- a/tensorflow/core/kernels/iterator_ops.cc
+++ b/tensorflow/core/kernels/iterator_ops.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/lib/core/threadpool.h"
+#include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/lib/random/random.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 
@@ -160,16 +161,25 @@ class MakeIteratorOp : public OpKernel {
   }
 };
 
-class OneShotIteratorOp : public OpKernel {
+class OneShotIteratorOp : public AsyncOpKernel {
  public:
-  explicit OneShotIteratorOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
+  explicit OneShotIteratorOp(OpKernelConstruction* ctx)
+      : AsyncOpKernel(ctx),
+        thread_pool_(new thread::ThreadPool(
+            ctx->env(), ThreadOptions(),
+            strings::StrCat("one_shot_iterator_initialization_thread_",
+                            SanitizeThreadSuffix(def().name())),
+            1 /* num_threads */, false /* low_latency_hint */))
+
+  {
     string shared_name;
     OP_REQUIRES_OK(ctx, ctx->GetAttr("shared_name", &shared_name));
     OP_REQUIRES(ctx, shared_name.empty(),
                 errors::InvalidArgument("OneShotIteratorOp does not currently "
                                         "support the 'shared_name' attr."));
-    OP_REQUIRES_OK(ctx,
-                   ctx->GetAttr("dataset_factory", &dataset_factory_func_));
+    const NameAttrList* dataset_factory_func;
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("dataset_factory", &dataset_factory_func));
+    dataset_factory_func_ = *dataset_factory_func;
     OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_dtypes_));
     OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_));
   }
@@ -187,102 +197,159 @@ class OneShotIteratorOp : public OpKernel {
 
   // NOTE(mrry): This is based on `ResourceOpKernel<T>::Compute()`,
   // but due to the fact that `ResourceOpKernel<T>::CreateResource()`
-  // does not provide access to the `OpKernelContext*` and we need this
-  // to invoke the factory function, it's not possible to implement
-  // this kernel by implementing `CreateResource()`.
-  void Compute(OpKernelContext* ctx) override {
-    mutex_lock l(mu_);
-    if (iterator_resource_ == nullptr) {
-      ResourceMgr* mgr = ctx->resource_manager();
-      OP_REQUIRES_OK(ctx, cinfo_.Init(mgr, def()));
-
-      // Create an IteratorResource that will hold the iterator for this op.
-      IteratorResource* resource;
-      OP_REQUIRES_OK(
-          ctx,
-          mgr->LookupOrCreate<IteratorResource>(
-              cinfo_.container(), cinfo_.name(), &resource,
-              [this](IteratorResource** ret) EXCLUSIVE_LOCKS_REQUIRED(mu_) {
-                *ret = new IteratorResource(output_dtypes_, output_shapes_);
-                return Status::OK();
-              }));
-      Status s = VerifyTypesMatch(output_dtypes_, resource->output_dtypes());
-      s.Update(
-          VerifyShapesCompatible(output_shapes_, resource->output_shapes()));
-      if (TF_PREDICT_FALSE(!s.ok())) {
-        resource->Unref();
-        ctx->SetStatus(s);
+  // does not provide access to the `OpKernelContext*` and we need
+  // this to invoke the factory function, it's not possible to
+  // implement this kernel by implementing `CreateResource()`.
+  // Furthermore, due to the fact that this kernel might block when
+  // running the initialization function, we must implement this
+  // kernel as an async kernel.
+  void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
+    {
+      mutex_lock l(mu_);
+      if (iterator_resource_ == nullptr && initialization_status_.ok()) {
+        // The initialization thread will call `done`.
+        if (!initialization_started_) {
+          // TODO(mrry): Convert the initialization code to use
+          // callbacks instead of wasting a thread.
+          thread_pool_->Schedule([this, ctx, done]() { Init(ctx, done); });
+          initialization_started_ = true;
+        } else {
+          done_callbacks_.emplace_back(ctx, std::move(done));
+        }
         return;
       }
-      iterator_resource_ = resource;
-
-      // Call the dataset_factory_func_ to create a new dataset,
-      // over which this op will iterate.
-      FunctionLibraryRuntime::Handle f_handle;
-      OP_REQUIRES_OK(ctx,
-                     ctx->function_library()->Instantiate(
-                         dataset_factory_func_->name(),
-                         AttrSlice(&dataset_factory_func_->attr()), &f_handle));
-      FunctionLibraryRuntime::Options opts;
-      opts.cancellation_manager = ctx->cancellation_manager();
-      // Choose a step ID that is guaranteed not to clash with any
-      // Session-generated step ID. DirectSession only generates
-      // non-negative step IDs (contiguous, starting from 0), and
-      // MasterSession generates 56-bit random step IDs whose MSB is
-      // always 0, so a negative random step ID should suffice.
-      opts.step_id = -std::abs(static_cast<int64>(random::New64()));
-      ScopedStepContainer step_container(
-          opts.step_id, [ctx](const string& name) {
-            ctx->resource_manager()->Cleanup(name).IgnoreError();
-          });
-      opts.step_container = &step_container;
-      opts.runner = ctx->runner();
-      Notification n;
-      Status factory_status;
-      std::vector<Tensor> return_values;
-      ctx->function_library()->Run(opts, f_handle, {}, &return_values,
-                                   [&n, &factory_status](Status s) {
-                                     factory_status.Update(s);
-                                     n.Notify();
-                                   });
-      n.WaitForNotification();
-      OP_REQUIRES_OK(ctx, factory_status);
-      OP_REQUIRES(
-          ctx,
-          return_values.size() == 1 &&
-              return_values[0].dtype() == DT_RESOURCE &&
-              TensorShapeUtils::IsScalar(return_values[0].shape()),
-          errors::InvalidArgument("The `dataset_factory` function must return "
-                                  "a single scalar of dtype DT_RESOURCE."));
-
-      // Retrieve the dataset that was created in the factory function.
-      DatasetBase* dataset;
-      const ResourceHandle& dataset_resource =
-          return_values[0].flat<ResourceHandle>()(0);
-      OP_REQUIRES_OK(ctx, LookupResource(ctx, dataset_resource, &dataset));
-      core::ScopedUnref unref_dataset(dataset);
-
-      // Create an iterator for the dataset that was created in the
-      // factory function. This transfers ownership of the dataset to
-      // the iterator, so we can delete it from the resource manager.
-      OP_REQUIRES_OK(ctx,
-                     iterator_resource_->set_iterator(dataset->MakeIterator()));
-      OP_REQUIRES_OK(ctx, DeleteResource<DatasetBase>(ctx, dataset_resource));
     }
-    Tensor* handle;
-    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &handle));
-    handle->scalar<ResourceHandle>()() = MakeResourceHandle<IteratorResource>(
-        ctx, cinfo_.container(), cinfo_.name());
+    ProduceOutput(ctx, std::move(done));
   }
 
  private:
-  const NameAttrList* dataset_factory_func_;
+  void Init(OpKernelContext* ctx, DoneCallback done) {
+    IteratorResource* iterator = nullptr;
+    ContainerInfo cinfo;
+    Status s = TryInit(ctx, &iterator, &cinfo);
+
+    std::vector<std::pair<OpKernelContext*, DoneCallback>> callbacks_to_run;
+    {
+      mutex_lock l(mu_);
+      if (s.ok()) {
+        iterator_resource_ = iterator;
+        cinfo_ = cinfo;
+      }
+      initialization_status_ = s;
+      std::swap(done_callbacks_, callbacks_to_run);
+    }
+
+    for (auto&& ctx_done : callbacks_to_run) {
+      ProduceOutput(ctx_done.first, std::move(ctx_done.second));
+    }
+    ProduceOutput(ctx, std::move(done));
+  }
+
+  Status TryInit(OpKernelContext* ctx, IteratorResource** iterator,
+                 ContainerInfo* cinfo) {
+    TF_RETURN_IF_ERROR(cinfo->Init(ctx->resource_manager(), def()));
+
+    // Create an IteratorResource that will hold the iterator for this op.
+    TF_RETURN_IF_ERROR(
+        ctx->resource_manager()->LookupOrCreate<IteratorResource>(
+            cinfo->container(), cinfo->name(), iterator,
+            [this](IteratorResource** ret) EXCLUSIVE_LOCKS_REQUIRED(mu_) {
+              *ret = new IteratorResource(output_dtypes_, output_shapes_);
+              return Status::OK();
+            }));
+
+    core::ScopedUnref unref_iterator(*iterator);
+
+    TF_RETURN_IF_ERROR(
+        VerifyTypesMatch(output_dtypes_, (*iterator)->output_dtypes()));
+    TF_RETURN_IF_ERROR(
+        VerifyShapesCompatible(output_shapes_, (*iterator)->output_shapes()));
+
+    // Call the dataset_factory_func_ to create a new dataset,
+    // over which this op will iterate.
+    FunctionLibraryRuntime::Handle f_handle;
+    TF_RETURN_IF_ERROR(ctx->function_library()->Instantiate(
+        dataset_factory_func_.name(), AttrSlice(&dataset_factory_func_.attr()),
+        &f_handle));
+    FunctionLibraryRuntime::Options opts;
+    opts.cancellation_manager = ctx->cancellation_manager();
+    // Choose a step ID that is guaranteed not to clash with any
+    // Session-generated step ID. DirectSession only generates
+    // non-negative step IDs (contiguous, starting from 0), and
+    // MasterSession generates 56-bit random step IDs whose MSB is
+    // always 0, so a negative random step ID should suffice.
+    opts.step_id = -std::abs(static_cast<int64>(random::New64()));
+    ScopedStepContainer step_container(opts.step_id, [ctx](const string& name) {
+      ctx->resource_manager()->Cleanup(name).IgnoreError();
+    });
+    opts.step_container = &step_container;
+    opts.runner = ctx->runner();
+    Notification n;
+    Status factory_status;
+    std::vector<Tensor> return_values;
+    ctx->function_library()->Run(opts, f_handle, {}, &return_values,
+                                 [&n, &factory_status](Status s) {
+                                   factory_status.Update(s);
+                                   n.Notify();
+                                 });
+    n.WaitForNotification();
+    TF_RETURN_IF_ERROR(factory_status);
+    if (return_values.size() != 1 || return_values[0].dtype() != DT_RESOURCE ||
+        !TensorShapeUtils::IsScalar(return_values[0].shape())) {
+      return errors::InvalidArgument(
+          "The `dataset_factory` function must return "
+          "a single scalar of dtype DT_RESOURCE.");
+    }
+
+    // Retrieve the dataset that was created in the factory function.
+    DatasetBase* dataset;
+    const ResourceHandle& dataset_resource =
+        return_values[0].flat<ResourceHandle>()(0);
+    TF_RETURN_IF_ERROR(LookupResource(ctx, dataset_resource, &dataset));
+    core::ScopedUnref unref_dataset(dataset);
+
+    // Create an iterator for the dataset that was created in the
+    // factory function. This transfers ownership of the dataset to
+    // the iterator, so we can delete it from the resource manager.
+    TF_RETURN_IF_ERROR((*iterator)->set_iterator(dataset->MakeIterator()));
+    TF_RETURN_IF_ERROR(DeleteResource<DatasetBase>(ctx, dataset_resource));
+
+    (*iterator)->Ref();
+    return Status::OK();
+  }
+
+  void ProduceOutput(OpKernelContext* ctx, DoneCallback done) {
+    Tensor* handle;
+    OP_REQUIRES_OK_ASYNC(ctx, ctx->allocate_output(0, TensorShape({}), &handle),
+                         done);
+    Status s;
+    {
+      mutex_lock l(mu_);
+      s = initialization_status_;
+      if (s.ok()) {
+        handle->scalar<ResourceHandle>()() =
+            MakeResourceHandle<IteratorResource>(ctx, cinfo_.container(),
+                                                 cinfo_.name());
+      }
+    }
+    OP_REQUIRES_OK_ASYNC(ctx, s, done);
+    done();
+  }
+
+  NameAttrList dataset_factory_func_;
   DataTypeVector output_dtypes_;
   std::vector<PartialTensorShape> output_shapes_;
 
+  std::unique_ptr<thread::ThreadPool> thread_pool_;
+
   mutex mu_;
   ContainerInfo cinfo_ GUARDED_BY(mu_);
-  IteratorResource* iterator_resource_ = nullptr;
+  IteratorResource* iterator_resource_ GUARDED_BY(mu_) = nullptr;
+
+  bool initialization_started_ GUARDED_BY(mu_) = false;
+  Status initialization_status_ GUARDED_BY(mu_);
+  std::vector<std::pair<OpKernelContext*, DoneCallback>> done_callbacks_
+      GUARDED_BY(mu_);
 };
 
 class IteratorGetNextOp : public AsyncOpKernel {
-- 
GitLab


From a7c36173cabcc1289a836e8143accb5f0914b19a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 14:32:48 -0700
Subject: [PATCH 0088/1375] Use a non-recursive DFS in HloInstruction::Accept
 to avoid stack overflow on deep graphs

Even with this fix, we don't finish compiling the exact test case from
b/38494745 in a reasonable amount of time (we spend a lot of time
inside HloInstruction::FusionReusesParamElements::ComputeInternal, for
instance), so I've used a smaller graph depth for now to avoid timing
out the test.

PiperOrigin-RevId: 159026595
---
 tensorflow/BUILD                              |   2 +-
 .../compiler/xla/service/hlo_instruction.cc   | 128 ++++++++++--------
 .../compiler/xla/service/hlo_instruction.h    |   6 -
 tensorflow/compiler/xla/tests/BUILD           |  10 ++
 .../compiler/xla/tests/deep_graph_test.cc     |  58 ++++++++
 5 files changed, 140 insertions(+), 64 deletions(-)
 create mode 100644 tensorflow/compiler/xla/tests/deep_graph_test.cc

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 60d58dd594..bb69bf172f 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -39,7 +39,7 @@ config_setting(
 config_setting(
     name = "android_armeabi",
     values = {
-        "cc_target_os": "android",
+        "crosstool_top": "//external:android/crosstool",
         "cpu": "armeabi",
     },
     visibility = ["//visibility:public"],
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index f926cb4bc7..6bb9e9a9e6 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1893,72 +1893,86 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
                        HloOpcodeString(opcode_).c_str());
 }
 
-Status HloInstruction::AcceptInternal(DfsHloVisitor* visitor,
-                                      const CompareFunction* operand_order,
-                                      bool ignore_control_predecessors) {
-  // Do not visit this HLO node again if it is already visited.
-  if (visitor->DidVisit(*this)) {
-    VLOG(3) << "Not visiting HLO " << name() << " as it was already visited.";
-    return Status::OK();
-  }
-
-  // If the instruction is in the visiting state, it means a cycle.
-  if (visitor->IsVisiting(*this)) {
+static Status PushDFSChild(DfsHloVisitor* visitor,
+                           std::vector<HloInstruction*>* dfs_stack,
+                           HloInstruction* parent, HloInstruction* child) {
+  if (visitor->IsVisiting(*child)) {
     return FailedPrecondition(
         "A cycle is detected while visiting instruction %s",
-        ToString().c_str());
-  }
-  visitor->SetVisiting(*this);
-
-  // Sort operands, if an ordering was provided. 'temp_sorted_operands' must
-  // live at this scope, since 'operands' will point to it if the operands are
-  // sorted.  The purpose of the 'operands' pointer is to avoid copying the
-  // operands in the common case where the operands are not sorted.
-  std::vector<HloInstruction*>* operands = &operands_;
-  std::vector<HloInstruction*> temp_sorted_operands;
-  if (operand_order != nullptr) {
-    temp_sorted_operands = operands_;
-    std::sort(temp_sorted_operands.begin(), temp_sorted_operands.end(),
-              *operand_order);
-    operands = &temp_sorted_operands;
-  }
-  for (HloInstruction* operand : *operands) {
-    VLOG(3) << "Going to visit HLO " << operand->name() << " as operand of HLO "
-            << name();
-    TF_RETURN_IF_ERROR(operand->AcceptInternal(visitor, operand_order,
-                                               ignore_control_predecessors));
-  }
-
-  if (!ignore_control_predecessors) {
-    // This uses the same pointer/vector sorting to avoid extra copies as above.
-    std::vector<HloInstruction*>* predecessors = &control_predecessors_;
-    std::vector<HloInstruction*> temp_sorted_predecessors;
-    if (operand_order != nullptr) {
-      temp_sorted_predecessors = control_predecessors_;
-      std::sort(temp_sorted_predecessors.begin(),
-                temp_sorted_predecessors.end(), *operand_order);
-      predecessors = &temp_sorted_predecessors;
+        parent->ToString().c_str());
+  }
+
+  if (!visitor->DidVisit(*child)) {
+    dfs_stack->push_back(child);
+  } else {
+    VLOG(3) << "Not visiting HLO " << child->name()
+            << " as it was already visited.";
+  }
+  return Status::OK();
+}
+
+static Status PostOrderDFS(HloInstruction* root, DfsHloVisitor* visitor,
+                           const HloInstruction::CompareFunction* operand_order,
+                           bool ignore_control_predecessors) {
+  std::vector<HloInstruction*> dfs_stack;
+  dfs_stack.push_back(root);
+
+  do {
+    DCHECK(!dfs_stack.empty());
+
+    HloInstruction* current_node = dfs_stack.back();
+    if (visitor->DidVisit(*current_node)) {
+      dfs_stack.pop_back();
+      VLOG(3) << "Not visiting HLO " << current_node->name()
+              << " as it was already visited.";
+      continue;
     }
-    for (HloInstruction* control_predecessor : *predecessors) {
-      VLOG(3) << "Going to visit HLO " << control_predecessor->name()
-              << " as a control predecessor of HLO " << name();
-      TF_RETURN_IF_ERROR(control_predecessor->AcceptInternal(
-          visitor, operand_order, ignore_control_predecessors));
+
+    if (visitor->IsVisiting(*current_node)) {
+      dfs_stack.pop_back();
+
+      TF_RETURN_IF_ERROR(visitor->Preprocess(current_node));
+      VLOG(2) << "Visiting HLO " << current_node->name();
+      TF_RETURN_IF_ERROR(current_node->Visit(visitor));
+      visitor->SetVisited(*current_node);
+      TF_RETURN_IF_ERROR(visitor->Postprocess(current_node));
+      continue;
     }
-  }
 
-  TF_RETURN_IF_ERROR(visitor->Preprocess(this));
-  VLOG(2) << "Visiting HLO " << name();
-  TF_RETURN_IF_ERROR(Visit(visitor));
-  visitor->SetVisited(*this);
-  return visitor->Postprocess(this);
+    visitor->SetVisiting(*current_node);
+
+    const size_t old_dfs_stack_size = dfs_stack.size();
+
+    for (HloInstruction* child : current_node->operands()) {
+      TF_RETURN_IF_ERROR(
+          PushDFSChild(visitor, &dfs_stack, current_node, child));
+    }
+
+    if (!ignore_control_predecessors) {
+      for (HloInstruction* child : current_node->control_predecessors()) {
+        TF_RETURN_IF_ERROR(
+            PushDFSChild(visitor, &dfs_stack, current_node, child));
+      }
+    }
+
+    if (operand_order != nullptr) {
+      std::sort(dfs_stack.begin() + old_dfs_stack_size, dfs_stack.end(),
+                *operand_order);
+    }
+
+    // This makes the traversal order the same as what you'd expect
+    // out of a recursive algorithm.
+    std::reverse(dfs_stack.begin() + old_dfs_stack_size, dfs_stack.end());
+  } while (!dfs_stack.empty());
+
+  return Status::OK();
 }
 
 Status HloInstruction::Accept(DfsHloVisitor* visitor, bool call_finish_visit,
                               bool ignore_control_predecessors) {
   VLOG(2) << "HloInstruction::Accept(" << name() << ")";
   TF_RETURN_IF_ERROR(
-      AcceptInternal(visitor, nullptr, ignore_control_predecessors));
+      PostOrderDFS(this, visitor, nullptr, ignore_control_predecessors));
   if (call_finish_visit) {
     TF_RETURN_IF_ERROR(visitor->FinishVisit(this));
   }
@@ -1969,8 +1983,8 @@ Status HloInstruction::AcceptWithOperandOrder(
     DfsHloVisitor* visitor, const CompareFunction& operand_order,
     bool call_finish_visit) {
   VLOG(2) << "HloInstruction::AcceptWithOperandOrder(" << name() << ")";
-  TF_RETURN_IF_ERROR(AcceptInternal(visitor, &operand_order,
-                                    /*ignore_control_predecessors=*/false));
+  TF_RETURN_IF_ERROR(PostOrderDFS(this, visitor, &operand_order,
+                                  /*ignore_control_predecessors=*/false));
   if (call_finish_visit) {
     TF_RETURN_IF_ERROR(visitor->FinishVisit(this));
   }
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index f98bafe81e..cb19c84814 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -825,12 +825,6 @@ class HloInstruction {
       const Shape& shape,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands);
 
-  // Inner DFS traversal function -- this function being called (rather than
-  // Accept above) allows us to distinguish the root of the traversal.
-  Status AcceptInternal(DfsHloVisitor* visitor,
-                        const CompareFunction* operand_order,
-                        bool ignore_control_predecessors);
-
   // CHECKs various invariants of a fusion instruction.
   void CheckFusionInstruction() const;
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 57523b34bb..5fa515b26f 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1357,6 +1357,16 @@ xla_test(
     ],
 )
 
+xla_test(
+    name = "deep_graph_test",
+    srcs = ["deep_graph_test.cc"],
+    deps = [
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
+        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
+        "//tensorflow/compiler/xla/tests:client_library_test_base",
+    ],
+)
+
 cc_test(
     name = "literal_test_util_test",
     srcs = ["literal_test_util_test.cc"],
diff --git a/tensorflow/compiler/xla/tests/deep_graph_test.cc b/tensorflow/compiler/xla/tests/deep_graph_test.cc
new file mode 100644
index 0000000000..7a5601ada3
--- /dev/null
+++ b/tensorflow/compiler/xla/tests/deep_graph_test.cc
@@ -0,0 +1,58 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
+#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+
+namespace xla {
+namespace {
+TEST_F(ClientLibraryTestBase, DeepGraph) {
+  // TODO(b/62624812): To trigger the stack overflow this test is
+  // intended to track, we need to set kDepth to 20000.
+  // Unfortunately, setting it that high causes the test to time out.
+  const int kDepth = 200;
+  ComputationBuilder b(client_, TestName());
+  ComputationDataHandle x;
+  ComputationDataHandle y;
+  auto x_data = CreateR0Parameter<int32>(3, 0, "x", &b, &x);
+  auto y_data = CreateR0Parameter<int32>(1, 1, "y", &b, &y);
+  ComputationDataHandle z = x;
+  for (int i = 0; i < kDepth; ++i) {
+    z = b.Add(z, y);
+  }
+  ComputeAndCompareR0<int32>(&b, /*expected=*/kDepth + 3,
+                             {x_data.get(), y_data.get()});
+}
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
+  testing::InitGoogleTest(&argc, argv);
+  if (argc > 1) {
+    LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
+    return 2;
+  }
+  return RUN_ALL_TESTS();
+}
-- 
GitLab


From f0a8bd95c7f537788e75b214b25d5ed542c2354f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 14:33:41 -0700
Subject: [PATCH 0089/1375] Add a heuristic to Grappler's memory optimizer to
 recompute elementwise ops

The current heuristic saves memory in simple conv->BN->relu->conv setups. It wastes computation and does not save memory for ResNet-like architectures (everything gets grouped together and recomputed just before gradients are executed).

It's also using a very simple list of ops to recompute. At the moment there is no advantage to this over just wrapping each layer in a Defun. However, there is a bit of infrastructure which will be re-used once smarter heuristics come around (namely finding trigger control dependencies and doing the re-writing).

And in the short term, even a few dumb heuristics should make things better for many networks (I just don't want to make this CL any more complicated than it already is).

PiperOrigin-RevId: 159026716
---
 tensorflow/core/grappler/optimizers/BUILD     |   1 +
 .../grappler/optimizers/memory_optimizer.cc   | 395 +++++++++++++++++-
 .../grappler/optimizers/memory_optimizer.h    |  17 +-
 .../optimizers/memory_optimizer_test.cc       | 101 +++--
 .../grappler/optimizers/meta_optimizer.cc     |   9 +-
 .../core/protobuf/rewriter_config.proto       |  25 +-
 tensorflow/python/BUILD                       |   6 +
 .../python/grappler/memory_optimizer_test.py  |  55 ++-
 8 files changed, 541 insertions(+), 68 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 6bb3d50b76..c83c218b54 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -200,6 +200,7 @@ cc_library(
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core/grappler/costs:graph_properties",
+        "//tensorflow/core/grappler/utils:topological_sort",
     ],
 )
 
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 1ed7cab4ab..bd8ce8c30c 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -15,6 +15,9 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/optimizers/memory_optimizer.h"
 
+#include <algorithm>
+#include <queue>
+#include <unordered_map>
 #include <unordered_set>
 #include <vector>
 
@@ -25,11 +28,325 @@ limitations under the License.
 #include "tensorflow/core/grappler/optimizers/graph_rewriter.h"
 #include "tensorflow/core/grappler/optimizers/static_schedule.h"
 #include "tensorflow/core/grappler/utils.h"
+#include "tensorflow/core/grappler/utils/topological_sort.h"
+#include "tensorflow/core/protobuf/rewriter_config.pb.h"
 
 namespace tensorflow {
 namespace grappler {
 
+// Prefix added to nodes which are recomputed.
 const char* kRecomputedNodePrefix = "Recomputed";
+const char* kRecomputeTriggerNodePrefix = "RecomputeTrigger";
+// Attribute which may be added to nodes to manually allow them to be
+// recomputed.
+const char* kRecomputeHint = "_recompute_hint";
+const char* kRecomputationTargetNamePrefix = "gradients/";
+
+// Ops which we wouldn't mind recomputing to save memory.
+// TODO(allenl): Replace this list with a cost model.
+std::unordered_set<string> GetCheapToRecomputeOps() {
+  std::unordered_set<string> cheap_ops = {
+      "Add",      "AddN",           "BiasAdd",
+      "Cast",     "Fill",           "FloorDiv",
+      "FloorMod", "FusedBatchNorm", "Mul",
+      "Neg",      "RealDiv",        "Reciprocal",
+      "Relu",     "Reshape",        "Rsqrt",
+      "Sqrt",     "Square",         "SquaredDifference",
+      "Sub",      "Tile",           "Transpose"};
+  return cheap_ops;
+}
+
+// Nodes whose inputs we may want to recompute (i.e. gradients).
+// TODO(allenl): Rather than blindly recomputing gradient inputs, use a static
+// schedule (grappler::EstimateEarliestExecutionTimes) to recompute only nodes
+// whose outputs will sit around for a while.
+bool IsTargetOp(const NodeDef& node) {
+  return node.name().find(kRecomputationTargetNamePrefix) == 0;
+}
+
+// Find recomputable ops which feed into target nodes.
+std::unordered_set<const NodeDef*> FindCandidateRecomputeNodes(
+    const NodeMap& node_map, const GraphDef* graph,
+    const std::function<bool(const NodeDef&)>& is_candidate) {
+  std::unordered_set<const NodeDef*> candidate_recompute_nodes;
+  for (const auto& node : graph->node()) {
+    if (!is_candidate(node)) {
+      continue;
+    }
+    bool has_target_output = false;
+    for (const NodeDef* output : node_map.GetOutputs(node.name())) {
+      // It only makes sense to recompute this if it feeds into a target
+      // node. We expand this to dependencies in GetOpGroupsToRecompute.
+      if (IsTargetOp(*output)) {
+        has_target_output = true;
+        break;
+      }
+    }
+    if (!has_target_output) {
+      continue;
+    }
+    bool has_target_input = false;
+    for (const string& input_name : node.input()) {
+      // Don't recompute nodes which depend on target nodes.
+      const NodeDef* input_node = node_map.GetNode(input_name);
+      if (IsTargetOp(*input_node)) {
+        has_target_input = true;
+        break;
+      }
+    }
+    if (has_target_input) {
+      continue;
+    }
+    candidate_recompute_nodes.insert(&node);
+  }
+  return candidate_recompute_nodes;
+}
+
+void connected_subgraph(const NodeMap& node_map, bool collect_inputs,
+                        bool collect_outputs,
+                        const std::function<bool(const NodeDef&)>& is_candidate,
+                        std::unordered_set<const NodeDef*>* expanded_nodes) {
+  std::queue<const NodeDef*> to_visit;
+  for (const NodeDef* starting_node : *expanded_nodes) {
+    to_visit.push(starting_node);
+  }
+  expanded_nodes->clear();
+  while (!to_visit.empty()) {
+    const NodeDef* current_node = to_visit.front();
+    to_visit.pop();
+    if (!expanded_nodes->insert(current_node).second) {
+      // We already visited this node
+      continue;
+    }
+    if (collect_inputs) {
+      // Add inputs and outputs to this subgraph if they are candidates
+      for (const string& input_name_raw : current_node->input()) {
+        const NodeDef* input_node = node_map.GetNode(input_name_raw);
+        if (expanded_nodes->count(input_node) == 0 &&
+            is_candidate(*input_node)) {
+          to_visit.push(input_node);
+        }
+      }
+    }
+    if (collect_outputs) {
+      for (const NodeDef* output : node_map.GetOutputs(current_node->name())) {
+        if (expanded_nodes->count(output) == 0 && is_candidate(*output)) {
+          to_visit.push(output);
+        }
+      }
+    }
+  }
+}
+
+struct RecomputedSubGraph {
+  std::unordered_set<const NodeDef*> recomputed_source_nodes;
+  std::unordered_set<NodeDef*> target_nodes;
+};
+
+// Find groups of ops to recompute together based on `should_recompute`.
+std::vector<RecomputedSubGraph> GetOpGroupsToRecompute(
+    const GraphDef* graph, const NodeMap& node_map,
+    const std::function<bool(const NodeDef&)>& should_recompute) {
+  std::unordered_set<const NodeDef*> visited_nodes;
+  std::vector<RecomputedSubGraph> subgraphs_to_recompute;
+  std::unordered_set<const NodeDef*> candidate_recompute_nodes =
+      FindCandidateRecomputeNodes(node_map, graph, should_recompute);
+  for (const NodeDef* recompute_node : candidate_recompute_nodes) {
+    if (visited_nodes.count(recompute_node) > 0) {
+      continue;
+    }
+    RecomputedSubGraph current_recomputation;
+    // Build out recomputation groups by expanding to inexpensive-to-recompute
+    // nodes which do not feed target nodes. The goal is to capture some
+    // intermediate activations within this graph.
+    std::unordered_set<const NodeDef*> unpruned_recompute_nodes;
+    unpruned_recompute_nodes.insert(recompute_node);
+    connected_subgraph(node_map,
+                       true,  // Collect inputs
+                       true,  // Collect outputs
+                       should_recompute, &unpruned_recompute_nodes);
+    visited_nodes.insert(unpruned_recompute_nodes.begin(),
+                         unpruned_recompute_nodes.end());
+    for (const NodeDef* recompute_node : unpruned_recompute_nodes) {
+      bool inserted_feed = false;
+      for (NodeDef* output : node_map.GetOutputs(recompute_node->name())) {
+        if (IsTargetOp(*output)) {
+          current_recomputation.target_nodes.insert(output);
+          if (!inserted_feed) {
+            // Keep track of nodes which feed directly into a target node. These
+            // and nodes which feed into them will define the recomputed
+            // subgraph.
+            current_recomputation.recomputed_source_nodes.insert(
+                recompute_node);
+            inserted_feed = true;
+          }
+        }
+      }
+    }
+    // Recompute only nodes which eventually feed into a target node.
+    connected_subgraph(node_map,
+                       true,   // Collect inputs
+                       false,  // Collect outputs
+                       [&unpruned_recompute_nodes](const NodeDef& node) {
+                         return unpruned_recompute_nodes.count(&node) != 0;
+                       },
+                       &current_recomputation.recomputed_source_nodes);
+    if (current_recomputation.target_nodes.empty()) {
+      continue;
+    }
+    subgraphs_to_recompute.push_back(current_recomputation);
+  }
+  return subgraphs_to_recompute;
+}
+
+// Computes the maximum topological numbers of (1) target node components
+// (gradient nodes being fed by the recomputation), and (2) child recompute node
+// components for each recomputed node. We will not attach any control
+// dependencies to a recomputation unless they have component numbers greater
+// than this value (to prevent cycles).
+std::unordered_map<const NodeDef*, int> GetMaxDownstreamComponents(
+    const std::unordered_set<const NodeDef*>& recomputed_source_nodes,
+    const std::unordered_set<NodeDef*>& target_nodes, const NodeMap& node_map,
+    const std::unordered_map<const NodeDef*, int>& components) {
+  std::unordered_map<const NodeDef*, int> recomputed_node_components;
+  // Start by setting component numbers to the maximum among target nodes.
+  for (const NodeDef* original_recompute_node : recomputed_source_nodes) {
+    int max_target_component = -1;
+    for (NodeDef* output :
+         node_map.GetOutputs(original_recompute_node->name())) {
+      if (target_nodes.count(output) != 0) {
+        int current_target_component = components.find(output)->second;
+        if (current_target_component > max_target_component) {
+          max_target_component = current_target_component;
+        }
+      }
+    }
+    if (max_target_component > -1) {
+      recomputed_node_components[original_recompute_node] =
+          max_target_component;
+    }
+  }
+  // Sort recomputed nodes topologically (based on the original graph) so we can
+  // efficiently assign to each node the maximum of its recomputed child
+  // components and its own targets.
+  std::vector<const NodeDef*> recomputed_source_nodes_topological(
+      recomputed_source_nodes.begin(), recomputed_source_nodes.end());
+  std::sort(recomputed_source_nodes_topological.begin(),
+            recomputed_source_nodes_topological.end(),
+            [&components](const NodeDef* first, const NodeDef* second) {
+              return components.find(first)->second <
+                     components.find(second)->second;
+            });
+  for (const NodeDef* original_recompute_node :
+       recomputed_source_nodes_topological) {
+    int max_component;
+    auto recomputed_component_iterator =
+        recomputed_node_components.find(original_recompute_node);
+    if (recomputed_component_iterator != recomputed_node_components.end()) {
+      max_component = recomputed_component_iterator->second;
+    } else {
+      max_component = -1;
+    }
+    for (NodeDef* output :
+         node_map.GetOutputs(original_recompute_node->name())) {
+      if (recomputed_source_nodes.count(output) == 0) {
+        continue;
+      }
+      auto child_component_iterator = recomputed_node_components.find(output);
+      CHECK(child_component_iterator != recomputed_node_components.end());
+      int child_component = child_component_iterator->second;
+      if (child_component > max_component) {
+        max_component = child_component;
+      }
+    }
+    CHECK_GE(max_component, 0);
+    recomputed_node_components[original_recompute_node] = max_component;
+  }
+  return recomputed_node_components;
+}
+
+// Modifies `graph`, adding trigger nodes and returning a mapping from
+// `recomputed_source_nodes` to trigger nodes which will not create loops in the
+// graph (using the component numberings in `components` and
+// `recomputed_node_max_feed_components`). The copied nodes (not the nodes in
+// recomputed_source_nodes, which are the originals) eventually get these
+// control dependencies.
+std::unordered_map<const NodeDef*, const NodeDef*>
+AddRecomputeControlDependencyNodes(
+    const std::unordered_set<const NodeDef*>& recomputed_source_nodes,
+    const std::unordered_set<NodeDef*>& target_nodes, const NodeMap& node_map,
+    const std::unordered_map<const NodeDef*, int>& components,
+    const std::unordered_map<const NodeDef*, int>&
+        recomputed_node_max_feed_components,
+    GraphDef* graph) {
+  // Sort recomputed nodes based on max downstream components.
+  std::vector<const NodeDef*> recomputed_source_nodes_topological(
+      recomputed_source_nodes.begin(), recomputed_source_nodes.end());
+  std::sort(recomputed_source_nodes_topological.begin(),
+            recomputed_source_nodes_topological.end(),
+            [&recomputed_node_max_feed_components](const NodeDef* first,
+                                                   const NodeDef* second) {
+              int first_component =
+                  recomputed_node_max_feed_components.find(first)->second;
+              int second_component =
+                  recomputed_node_max_feed_components.find(second)->second;
+              return first_component > second_component
+                     // Ensure a consistent ordering. This is necessary because
+                     // we're working not with node component numbers (which are
+                     // unique) but with the maximum across nodes they feed into
+                     // (very much not unique).
+                     || (first_component == second_component &&
+                         first->name() > second->name());
+            });
+  // Create merged control dependency nodes by sorting target inputs
+  // topologically and zipper merging with the sorted recomputed nodes.
+  std::vector<const NodeDef*> target_inputs_topological;
+  for (const NodeDef* target_node : target_nodes) {
+    for (const string& target_input_name_raw : target_node->input()) {
+      const NodeDef* target_input = node_map.GetNode(target_input_name_raw);
+      if (recomputed_source_nodes.count(target_input) != 0 ||
+          components.find(target_node)->second ==
+              components.find(target_input)->second) {
+        continue;
+      }
+      target_inputs_topological.push_back(target_input);
+    }
+  }
+  std::sort(target_inputs_topological.begin(), target_inputs_topological.end(),
+            [&components](const NodeDef* first, const NodeDef* second) {
+              return components.find(first)->second >
+                     components.find(second)->second;
+            });
+  auto target_input_iterator = target_inputs_topological.begin();
+  NodeDef* current_trigger_node = nullptr;
+  std::unordered_map<const NodeDef*, const NodeDef*> triggers;
+  for (const NodeDef* original_recomputed_node :
+       recomputed_source_nodes_topological) {
+    NodeDef* new_trigger_node = graph->add_node();
+    new_trigger_node->set_name(AddPrefixToNodeName(
+        original_recomputed_node->name(), kRecomputeTriggerNodePrefix));
+    new_trigger_node->set_op("NoOp");
+    new_trigger_node->set_device(original_recomputed_node->device());
+    if (current_trigger_node != nullptr) {
+      *new_trigger_node->add_input() =
+          strings::StrCat("^", current_trigger_node->name());
+    }
+    current_trigger_node = new_trigger_node;
+    triggers[original_recomputed_node] = current_trigger_node;
+    for (;
+         target_input_iterator != target_inputs_topological.end() &&
+         components.find(*target_input_iterator)->second >
+             recomputed_node_max_feed_components.find(original_recomputed_node)
+                 ->second;
+         ++target_input_iterator) {
+      *current_trigger_node->add_input() =
+          strings::StrCat("^", (*target_input_iterator)->name());
+      VLOG(2) << "  Recomputation trigger " << current_trigger_node->name()
+              << " depends on " << (*target_input_iterator)->name();
+    }
+  }
+  return triggers;
+}
 
 string RecomputedOrOriginalNodeName(
     const std::unordered_set<string>& recomputed_node_names,
@@ -42,14 +359,28 @@ string RecomputedOrOriginalNodeName(
   }
 }
 
+// Helper function to recompute a sub-graph (recomputed_source_nodes). Edges
+// from recomputed_source_nodes to target_nodes are changed to start from the
+// recomputed nodes.
 void RecomputeSubgraph(
-    const std::vector<const NodeDef*>& recomputed_source_nodes,
-    const string& recompute_trigger_node_name,
-    const std::vector<NodeDef*>& target_nodes, GraphDef* graph) {
+    const std::unordered_set<const NodeDef*>& recomputed_source_nodes,
+    const std::unordered_set<NodeDef*>& target_nodes, const NodeMap& node_map,
+    const std::unordered_map<const NodeDef*, int>& components,
+    GraphDef* graph) {
   std::unordered_set<string> recomputed_node_names;
-  for (const NodeDef* to_recompute : recomputed_source_nodes) {
-    recomputed_node_names.insert(to_recompute->name());
+  VLOG(1) << "Recomputing a " << recomputed_source_nodes.size()
+          << " node subgraph";
+  std::unordered_map<const NodeDef*, int> recomputed_node_components =
+      GetMaxDownstreamComponents(recomputed_source_nodes, target_nodes,
+                                 node_map, components);
+  for (const NodeDef* original_node : recomputed_source_nodes) {
+    VLOG(2) << "  " << original_node->name();
+    recomputed_node_names.insert(original_node->name());
   }
+  std::unordered_map<const NodeDef*, const NodeDef*> triggers =
+      AddRecomputeControlDependencyNodes(recomputed_source_nodes, target_nodes,
+                                         node_map, components,
+                                         recomputed_node_components, graph);
   // Create the recomputed sub-graph
   for (const NodeDef* original_node : recomputed_source_nodes) {
     NodeDef* copied_node = graph->add_node();
@@ -64,10 +395,10 @@ void RecomputeSubgraph(
       *copied_node->add_input() = RecomputedOrOriginalNodeName(
           recomputed_node_names, original_input_name);
     }
-    // Set control dependencies on the recomputed nodes so that they are not run
-    // until the specified trigger runs.
+    // Each recomputed node gets a control dependency to prevent it from being
+    // recomputed immediately.
     *copied_node->add_input() =
-        strings::StrCat("^", recompute_trigger_node_name);
+        strings::StrCat("^", triggers[original_node]->name());
   }
   // Set the inputs of nodes in the target subgraph to the recomputed nodes
   // where applicable.
@@ -79,6 +410,52 @@ void RecomputeSubgraph(
   }
 }
 
+void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
+                                GraphDef* graph) {
+  // The topological numberings and NodeMap will be stale as soon as we start
+  // modifying the graph in RecomputeSubgraph. However, RecomputeSubgraph only
+  // looks up nodes which were in the original graph, and preserves the graph
+  // topology it's interested in.
+  // We don't use the results of this topological sort until later, but this
+  // call invalidates all NodeDef pointers, so it needs to be done before we
+  // start collecting those.
+  TopologicalSort(graph);
+  NodeMap node_map(graph);
+  std::vector<RecomputedSubGraph> recomputed_subgraphs;
+  if (optimization_level == RewriterConfig::HEURISTICS) {
+    // TODO(allenl): Handle ResNet-like architectures better. Right now all of
+    // the cheap forward ops get grouped into a single subgraph which must
+    // execute before gradients start executing (unless layers are manually
+    // separated by identity ops).
+    std::unordered_set<string> cheap_to_recompute_ops =
+        GetCheapToRecomputeOps();
+    recomputed_subgraphs = GetOpGroupsToRecompute(
+        graph, node_map, [&cheap_to_recompute_ops](const NodeDef& node) {
+          return !IsTargetOp(node) &&
+                 (cheap_to_recompute_ops.count(node.op()) > 0 ||
+                  node.attr().count(kRecomputeHint) > 0);
+        });
+  } else {  // optimization_level == RewriterConfig::MANUAL
+    recomputed_subgraphs =
+        GetOpGroupsToRecompute(graph, node_map, [](const NodeDef& node) {
+          return !IsTargetOp(node) && node.attr().count(kRecomputeHint) > 0;
+        });
+  }
+  if (!recomputed_subgraphs.empty()) {
+    std::unordered_map<const NodeDef*, int> topological_numbering;
+    for (int node_number = 0; node_number < graph->node().size();
+         ++node_number) {
+      topological_numbering[graph->mutable_node(node_number)] =
+          graph->node().size() - node_number - 1;
+    }
+    // Duplicate the indicated sub-graphs and set up control dependencies
+    for (const RecomputedSubGraph& subgraph : recomputed_subgraphs) {
+      RecomputeSubgraph(subgraph.recomputed_source_nodes, subgraph.target_nodes,
+                        node_map, topological_numbering, graph);
+    }
+  }
+}
+
 std::pair<NodeDef*, NodeDef*> BuildSwapPair(NodeDef* node, int input_to_swap,
                                             GraphDef* graph) {
   string tensor_to_swap = strings::StrCat(node->name(), "_", input_to_swap);
@@ -205,6 +582,8 @@ Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
                                  GraphDef* optimized_graph) {
   *optimized_graph = item.graph;
 
+  RecomputationRewritingPass(optimization_level_, optimized_graph);
+
   // Figure out what needs to be swapped;
   std::unordered_map<NodeDef*, SwapInfo> nodes_to_swap;
   for (auto& node : *optimized_graph->mutable_node()) {
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.h b/tensorflow/core/grappler/optimizers/memory_optimizer.h
index dfb24c05c9..5b7ba4001f 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.h
@@ -16,9 +16,8 @@ limitations under the License.
 #ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_
 #define TENSORFLOW_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_
 
-#include <vector>
-
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
+#include "tensorflow/core/protobuf/rewriter_config.pb.h"
 
 namespace tensorflow {
 namespace grappler {
@@ -26,7 +25,8 @@ namespace grappler {
 // Swap tensors in and out of device memory.
 class MemoryOptimizer : public GraphOptimizer {
  public:
-  MemoryOptimizer() {}
+  explicit MemoryOptimizer(RewriterConfig::MemOptType optimization_level)
+      : optimization_level_(optimization_level) {}
   ~MemoryOptimizer() override {}
 
   string name() const override { return "memory_optimizer"; };
@@ -36,15 +36,10 @@ class MemoryOptimizer : public GraphOptimizer {
 
   void Feedback(Cluster* cluster, const GrapplerItem& item,
                 const GraphDef& pruned_graph, double result) override;
-};
 
-// Helper function to recompute a sub-graph (recomputed_source_nodes) on a
-// trigger. Edges from recomputed_source_nodes to target_nodes are changed to
-// start from the recomputed nodes.
-void RecomputeSubgraph(
-    const std::vector<const NodeDef*>& recomputed_source_nodes,
-    const string& recompute_trigger_node_name,
-    const std::vector<NodeDef*>& target_nodes, GraphDef* graph);
+ private:
+  RewriterConfig::MemOptType optimization_level_;
+};
 
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index a4f8e22e1d..48e6f88500 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -35,84 +35,103 @@ TEST_F(RecomputeSubgraphTest, SimpleSubgraph) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   Output a = ops::Const(s.WithOpName("a"), 1.f, {2, 3, 4});
-  Output b = ops::AddN(s.WithOpName("b"), {a});  // Recomputed
-  Output c = ops::AddN(s.WithOpName("c"), {b});
-  Output d = ops::AddN(s.WithOpName("d"), {c});
-  Output e = ops::AddN(s.WithOpName("e"), {d, b});
-  Output f = ops::AddN(s.WithOpName("f"), {e, a});
+  Output b = ops::Identity(s.WithOpName("b"), a);  // Recomputed
+  Output c = ops::Identity(s.WithOpName("c"), b);
+  Output d = ops::AddN(s.WithOpName("gradients/d"), {c});
+  Output e = ops::AddN(s.WithOpName("gradients/e"), {d, b});
+  Output f = ops::AddN(s.WithOpName("gradients/f"), {e, a});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
   EXPECT_EQ(6, item.graph.node_size());
   NodeMap pre_transform_node_map(&item.graph);
-  std::vector<const NodeDef*> recomputed_source_nodes;
-  recomputed_source_nodes.push_back(pre_transform_node_map.GetNode(b.name()));
-  std::vector<NodeDef*> target_nodes;
-  target_nodes.push_back(pre_transform_node_map.GetNode(e.name()));
-  RecomputeSubgraph(recomputed_source_nodes, d.name(), target_nodes,
-                    &item.graph);
-  NodeMap post_transform_node_map(&item.graph);
-  EXPECT_EQ(7, item.graph.node_size());
+  (*pre_transform_node_map.GetNode("b")->mutable_attr())["_recompute_hint"]
+      .set_i(0);
+
+  MemoryOptimizer optimizer(RewriterConfig::MANUAL);
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+
+  TF_EXPECT_OK(status);
+  NodeMap post_transform_node_map(&output);
+  EXPECT_EQ(8, output.node_size());
   NodeDef* transformed_e = post_transform_node_map.GetNode(e.name());
   EXPECT_EQ(2, transformed_e->input_size());
-  EXPECT_EQ("d", transformed_e->input(0));
+  EXPECT_EQ("gradients/d", transformed_e->input(0));
   EXPECT_EQ("Recomputed/b", transformed_e->input(1));
   NodeDef* recomputed_b = post_transform_node_map.GetNode("Recomputed/b");
   EXPECT_EQ(2, recomputed_b->input_size());
   EXPECT_EQ("a", recomputed_b->input(0));
-  EXPECT_EQ("^d", recomputed_b->input(1).substr(0, 2));
+  EXPECT_EQ("^RecomputeTrigger/b", recomputed_b->input(1));
+  NodeDef* recompute_trigger =
+      post_transform_node_map.GetNode("RecomputeTrigger/b");
+  EXPECT_EQ(1, recompute_trigger->input_size());
+  EXPECT_EQ("^gradients/d", recompute_trigger->input(0));
 }
 
 TEST_F(RecomputeSubgraphTest, MultiNode) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   Output a = ops::Const(s.WithOpName("Conv"), 1.f, {2, 3, 4});
-  Output b = ops::AddN(s.WithOpName("BN"), {a});    // Recomputed
-  Output c = ops::AddN(s.WithOpName("ReLU"), {b});  // Recomputed
-  Output d = ops::AddN(s.WithOpName("Conv1"), {c});
-
-  Output trigger = ops::Const(s.WithOpName("BN1Grad"), 0.f, {2, 3, 4});
-  Output e = ops::AddN(s.WithOpName("Conv1Grad"), {trigger, c});
-  Output f = ops::AddN(s.WithOpName("ReLUGrad"), {e, c});
-  Output g = ops::AddN(s.WithOpName("BNGrad"), {f, a});
-  Output h = ops::AddN(s.WithOpName("ConvGrad"), {g});
+  Output b = ops::Identity(s.WithOpName("BN"), a);    // Recomputed
+  Output c = ops::Identity(s.WithOpName("ReLU"), b);  // Recomputed
+  Output d = ops::Identity(s.WithOpName("Conv1"), c);
+
+  // The "gradients/" prefix means the heuristic will pick these up as
+  // candidates to have their inputs recomputed.
+  Output trigger = ops::AddN(s.WithOpName("gradients/BN1Grad"), {d});
+  Output e = ops::AddN(s.WithOpName("gradients/Conv1Grad"), {trigger, c});
+  Output f = ops::AddN(s.WithOpName("gradients/ReLUGrad"), {e, c});
+  Output g = ops::AddN(s.WithOpName("gradients/BNGrad"), {f, a});
+  Output h = ops::AddN(s.WithOpName("gradients/ConvGrad"), {g});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
   EXPECT_EQ(9, item.graph.node_size());
   NodeMap pre_transform_node_map(&item.graph);
-  std::vector<const NodeDef*> recomputed_source_nodes;
-  recomputed_source_nodes.push_back(pre_transform_node_map.GetNode(b.name()));
-  recomputed_source_nodes.push_back(pre_transform_node_map.GetNode(c.name()));
-  std::vector<NodeDef*> target_nodes;
-  target_nodes.push_back(pre_transform_node_map.GetNode(e.name()));
-  target_nodes.push_back(pre_transform_node_map.GetNode(f.name()));
-  target_nodes.push_back(pre_transform_node_map.GetNode(g.name()));
-  RecomputeSubgraph(recomputed_source_nodes, trigger.name(), target_nodes,
-                    &item.graph);
-  NodeMap post_transform_node_map(&item.graph);
-  EXPECT_EQ(11, item.graph.node_size());
+  // Set op types so that the heuristic will pick these nodes up to be
+  // recomputed
+  pre_transform_node_map.GetNode("BN")->set_op("FusedBatchNorm");
+  pre_transform_node_map.GetNode("ReLU")->set_op("Relu");
+
+  MemoryOptimizer optimizer(RewriterConfig::HEURISTICS);
+  GraphDef first_pass_output;
+  Status first_pass_status =
+      optimizer.Optimize(nullptr, item, &first_pass_output);
+  TF_EXPECT_OK(first_pass_status);
+
+  NodeMap post_transform_node_map(&first_pass_output);
+  EXPECT_EQ(13, first_pass_output.node_size());
   NodeDef* transformed_e = post_transform_node_map.GetNode(e.name());
   EXPECT_EQ(2, transformed_e->input_size());
-  EXPECT_EQ("BN1Grad", transformed_e->input(0));
+  EXPECT_EQ("gradients/BN1Grad", transformed_e->input(0));
   EXPECT_EQ("Recomputed/ReLU", transformed_e->input(1));
   NodeDef* transformed_f = post_transform_node_map.GetNode(f.name());
   EXPECT_EQ(2, transformed_f->input_size());
-  EXPECT_EQ("Conv1Grad", transformed_f->input(0));
+  EXPECT_EQ("gradients/Conv1Grad", transformed_f->input(0));
   EXPECT_EQ("Recomputed/ReLU", transformed_f->input(1));
   NodeDef* transformed_g = post_transform_node_map.GetNode(g.name());
   EXPECT_EQ(2, transformed_g->input_size());
-  EXPECT_EQ("ReLUGrad", transformed_g->input(0));
+  EXPECT_EQ("gradients/ReLUGrad", transformed_g->input(0));
   EXPECT_EQ("Conv", transformed_g->input(1));
 
   NodeDef* recomputed_b = post_transform_node_map.GetNode("Recomputed/BN");
   EXPECT_EQ(2, recomputed_b->input_size());
   EXPECT_EQ("Conv", recomputed_b->input(0));
-  EXPECT_EQ("^BN1Grad", recomputed_b->input(1).substr(0, 8));
+  EXPECT_EQ("^RecomputeTrigger/BN", recomputed_b->input(1));
+  NodeDef* recompute_trigger_b =
+      post_transform_node_map.GetNode("RecomputeTrigger/BN");
+  EXPECT_EQ(1, recompute_trigger_b->input_size());
+  EXPECT_EQ("^RecomputeTrigger/ReLU", recompute_trigger_b->input(0));
+
   NodeDef* recomputed_c = post_transform_node_map.GetNode("Recomputed/ReLU");
   EXPECT_EQ(2, recomputed_c->input_size());
   EXPECT_EQ("Recomputed/BN", recomputed_c->input(0));
-  EXPECT_EQ("^BN1Grad", recomputed_c->input(1).substr(0, 8));
+  EXPECT_EQ("^RecomputeTrigger/ReLU", recomputed_c->input(1));
+  NodeDef* recompute_trigger_c =
+      post_transform_node_map.GetNode("RecomputeTrigger/ReLU");
+  EXPECT_EQ(1, recompute_trigger_c->input_size());
+  EXPECT_EQ("^gradients/BN1Grad", recompute_trigger_c->input(0));
 }
 
 class MemoryOptimizerTest : public ::testing::Test {
@@ -150,7 +169,7 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
 
   VirtualCluster cluster(CreateVirtualCluster());
 
-  MemoryOptimizer optimizer;
+  MemoryOptimizer optimizer(RewriterConfig::MANUAL);
   GraphDef output;
   Status status = optimizer.Optimize(&cluster, item, &output);
   TF_EXPECT_OK(status);
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 8bb7800df4..aac432b3fb 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -41,7 +41,7 @@ std::unique_ptr<GraphOptimizer> MetaOptimizer::NewOptimizer(
     graph_optimizer.reset(new LayoutOptimizer());
   }
   if (optimizer == "memory") {
-    graph_optimizer.reset(new MemoryOptimizer());
+    graph_optimizer.reset(new MemoryOptimizer(RewriterConfig::MANUAL));
   }
   if (optimizer == "autoparallel") {
     graph_optimizer.reset(
@@ -66,8 +66,8 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
           std::unique_ptr<GraphOptimizer>(new LayoutOptimizer()));
     }
     if (cfg_.memory_optimization() > 0) {
-      optimizers.push_back(
-          std::unique_ptr<GraphOptimizer>(new MemoryOptimizer()));
+      optimizers.push_back(std::unique_ptr<GraphOptimizer>(
+          new MemoryOptimizer(cfg_.memory_optimization())));
     }
     if (cfg_.auto_parallel().enable()) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
@@ -114,7 +114,8 @@ void MetaOptimizer::Feedback(Cluster* cluster, const GrapplerItem& item,
 
 bool MetaOptimizerEnabled(const RewriterConfig& cfg) {
   return cfg.optimize_tensor_layout() || cfg.constant_folding() ||
-         cfg.auto_parallel().enable() || !cfg.optimizers().empty();
+         cfg.auto_parallel().enable() || cfg.memory_optimization() > 0 ||
+         !cfg.optimizers().empty();
 }
 
 Status RunMetaOptimizer(const GrapplerItem& item, const RewriterConfig& cfg,
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index b21d42f4fe..5480bdaad8 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -15,21 +15,40 @@ message RewriterConfig {
   // Graph rewriting is experimental and subject to change, not covered by any
   // API stability guarantees.
 
+  // Configuration options for the meta-optimizer. Unless otherwise noted, these
+  // configuration options do not apply to explicitly triggered optimization
+  // passes in the optimizers field.
+
   bool optimize_tensor_layout = 1;
   bool disable_model_pruning = 2;
   bool constant_folding = 3;
 
   enum MemOptType {
-    // Fully disabled
+    // Disabled in the meta-optimizer.
     NO_MEM_OPT = 0;
-    // Driven by manual annotations
+    // Driven by manual op-level annotations.
     MANUAL = 1;
+    // Driven by heuristics. The behavior of these heuristics is subject to
+    // change. Currently includes an experimental recomputation heuristic.
+    HEURISTICS = 2;
   }
+  // Configures memory optimization passes through the meta-optimizer. Has no
+  // effect on manually requested memory optimization passes in the optimizers
+  // field.
   MemOptType memory_optimization = 4;
 
+  // Configures AutoParallel optimization passes either through the
+  // meta-optimizer or when manually specified through the optimizers field.
   AutoParallelOptions auto_parallel = 5;
 
   // If non-empty, will use this as an alternative way to specify a list of
-  // optimizations to turn on and the order of the optimizations.
+  // optimizations to turn on and the order of the optimizations (replacing the
+  // meta-optimizer).
+  //
+  // Of the RewriterConfig options, only the AutoParallel configuration options
+  // (the auto_parallel field) apply to manually requested optimization passes
+  // ("autoparallel"). Memory optimization passes ("memory") invoked here are
+  // not configurable (in contrast to memory optimization passes through the
+  // meta-optimizer) and act only on manual op annotations.
   repeated string optimizers = 100;
 }
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index c6b98e686f..10afd4f346 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3800,7 +3800,13 @@ py_test(
         ":client_testlib",
         ":framework_for_generated_wrappers",
         ":math_ops",
+        ":nn",
+        ":random_seed",
+        ":session",
         ":tf_optimizer",
+        ":training",
+        ":variable_scope",
+        ":variables",
         "//tensorflow/core:protos_all_py",
         "//third_party/py/numpy",
     ],
diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py
index 581f17c2ca..4db8fa7245 100644
--- a/tensorflow/python/grappler/memory_optimizer_test.py
+++ b/tensorflow/python/grappler/memory_optimizer_test.py
@@ -18,16 +18,23 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import meta_graph
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import random_seed
 from tensorflow.python.grappler import tf_optimizer
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
+from tensorflow.python.training import training as train
 
 
-class MemoryOptimizerTest(test.TestCase):
+class MemoryOptimizerSwapTest(test.TestCase):
   """Tests the Grappler memory optimizer."""
 
   def testNoSwapping(self):
@@ -85,5 +92,51 @@ class MemoryOptimizerTest(test.TestCase):
         self.assertEqual('c', node.input[1])
 
 
+class MemoryOptimizerRecomputeTest(test.TestCase):
+
+  def _RunGraphWithConfig(self, config, batch_size=14, image_dim=12):
+    """Run a simple layered graph with conv, an intermediate op, and a ReLU."""
+    graph = ops.Graph()
+    with graph.as_default():
+      random_seed.set_random_seed(1)
+      current_activation = variable_scope.get_variable(
+          name='start', shape=[batch_size, image_dim, image_dim, 5])
+      conv_filter = variable_scope.get_variable(
+          name='filter', shape=[5, 5, 5, 5])
+      for layer_number in range(10):
+        with variable_scope.variable_scope('layer_{}'.format(layer_number)):
+          after_conv = nn.conv2d(current_activation, conv_filter, [1, 1, 1, 1],
+                                 'SAME')
+          current_activation = 2. * after_conv
+          current_activation = nn.relu(current_activation)
+      loss = math_ops.reduce_mean(current_activation)
+      optimizer = train.AdamOptimizer(0.001)
+      train_op = optimizer.minimize(loss)
+      init_op = variables.global_variables_initializer()
+      with session.Session(config=config, graph=graph) as sess:
+        sess.run(init_op)
+        sess.run(train_op)
+        sess.run(train_op)
+        return sess.run(loss)
+
+  def _GetMemoryOptimizerConfig(self):
+    rewrite_options = rewriter_config_pb2.RewriterConfig(
+        memory_optimization=rewriter_config_pb2.RewriterConfig.HEURISTICS)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewrite_options)
+    return config_pb2.ConfigProto(graph_options=graph_options)
+
+  def testRecomputationRewritingNoErrors(self):
+    """Tests that there are no errors when we request a memory optimizer pass.
+
+    Does not test that the memory optimizer actually runs. See
+    core/grappler/optimizers/memory_optimizer_test.cc for a functional test of
+    the graph rewriting.
+    """
+    original_loss = self._RunGraphWithConfig(config_pb2.ConfigProto())
+    memory_optimized_loss = self._RunGraphWithConfig(
+        config=self._GetMemoryOptimizerConfig())
+    self.assertAllClose(original_loss, memory_optimized_loss, rtol=1e-4)
+
+
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From 69bc160235e245171b85017de8ca486a391e54c9 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Wed, 14 Jun 2017 14:59:48 -0700
Subject: [PATCH 0090/1375] Fix #10199

PiperOrigin-RevId: 159030457
---
 tensorflow/core/platform/cpu_feature_guard.cc | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tensorflow/core/platform/cpu_feature_guard.cc b/tensorflow/core/platform/cpu_feature_guard.cc
index 1cfeb2580f..4941bc1239 100644
--- a/tensorflow/core/platform/cpu_feature_guard.cc
+++ b/tensorflow/core/platform/cpu_feature_guard.cc
@@ -96,6 +96,14 @@ std::once_flag g_cpu_feature_guard_warn_once_flag;
 
 void WarnAboutUnusedCPUFeatures() {
   std::call_once(g_cpu_feature_guard_warn_once_flag, [] {
+#ifdef PLATFORM_WINDOWS
+#ifndef __AVX__
+    WarnIfFeatureUnused(CPUFeature::AVX, "AVX");
+#endif  // __AVX__
+#ifndef __AVX2__
+    WarnIfFeatureUnused(CPUFeature::AVX2, "AVX2");
+#endif  // __AVX2__
+#else   // ifdef platform windows
 #ifndef __SSE__
     WarnIfFeatureUnused(CPUFeature::SSE, "SSE");
 #endif  // __SSE__
@@ -123,6 +131,7 @@ void WarnAboutUnusedCPUFeatures() {
 #ifndef __FMA__
     WarnIfFeatureUnused(CPUFeature::FMA, "FMA");
 #endif  // __FMA__
+#endif  // else of ifdef platform windows
   });
 }
 
-- 
GitLab


From bea72558323bece442b57bf27ec773734c09e324 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 14 Jun 2017 15:25:58 -0700
Subject: [PATCH 0091/1375] Made sure that the nodes listed as feed, fetch and
 init_op exist in the graph.

PiperOrigin-RevId: 159034290
---
 tensorflow/core/grappler/grappler_item.cc     |  2 +-
 .../core/grappler/grappler_item_builder.cc    | 23 +++++++++++++++++++
 .../grappler/grappler_item_builder_test.cc    | 11 ++++++---
 3 files changed, 32 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/grappler_item.cc b/tensorflow/core/grappler/grappler_item.cc
index 312a457abf..88ddd6c1b3 100644
--- a/tensorflow/core/grappler/grappler_item.cc
+++ b/tensorflow/core/grappler/grappler_item.cc
@@ -55,7 +55,7 @@ std::vector<const NodeDef*> ComputeTransitiveFanin(
   std::vector<const NodeDef*> queue;
   for (const string& root : terminal_nodes) {
     const NodeDef* node = name_to_node[NodeName(root)];
-    CHECK(node);
+    CHECK(node) << "Unknown root " << root;
     queue.push_back(node);
   }
 
diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index bb36152bd8..969376917b 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -332,6 +332,29 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
     return nullptr;
   }
 
+  // Validate feed, fetch and init nodes
+  std::unordered_set<string> nodes;
+  for (const auto& node : new_item->graph.node()) {
+    nodes.insert(node.name());
+  }
+  for (const auto& feed : new_item->feed) {
+    if (nodes.find(feed.first) == nodes.end()) {
+      LOG(ERROR) << "Feed node " << feed.first << " doesn't exist in graph";
+      return nullptr;
+    }
+  }
+  for (const auto& fetch : new_item->fetch) {
+    if (nodes.find(fetch) == nodes.end()) {
+      LOG(ERROR) << "Fetch node " << fetch << " doesn't exist in graph";
+      return nullptr;
+    }
+  }
+  for (const auto& init : new_item->init_ops) {
+    if (nodes.find(init) == nodes.end()) {
+      LOG(ERROR) << "Init node " << init << " doesn't exist in graph";
+      return nullptr;
+    }
+  }
   return new_item;
 }
 
diff --git a/tensorflow/core/grappler/grappler_item_builder_test.cc b/tensorflow/core/grappler/grappler_item_builder_test.cc
index 92225ffb1b..048870f9e5 100644
--- a/tensorflow/core/grappler/grappler_item_builder_test.cc
+++ b/tensorflow/core/grappler/grappler_item_builder_test.cc
@@ -51,7 +51,11 @@ void SampleSumSymbolicGradientGraphdef(
   auto g0 = SymbolicGradient(scope, std::initializer_list<Input>{x, y, z},
                              {DT_FLOAT, DT_INT32}, fn);
 
-  fetches->mutable_node_list()->add_value(g0[0].name());
+  // TODO(bsteiner): we should rewrite the feed/fetch nodes to reflect the
+  // inlining that's done in the item builder
+  // fetches->mutable_node_list()->add_value(g0[0].name());
+  fetches->mutable_node_list()->add_value("SymbolicGradient/dx");
+  fetches->mutable_node_list()->add_value("SymbolicGradient/dy_reshaped");
 
   TF_CHECK_OK(scope.ToGraphDef(def));
 
@@ -109,11 +113,12 @@ TEST_F(GrapplerItemBuilderTest, SymbolicGradientInlining) {
   std::unique_ptr<GrapplerItem> with_inline = CreateGrapplerItem(def, fetches);
 
   // For the inlined graph, there should be 0 symbolic gradient ops.
-  CHECK_EQ(0, CountSymbolicGradientOps(with_inline));
+  EXPECT_EQ(0, CountSymbolicGradientOps(with_inline));
 
   // For the inlined graph, make sure all the required expanded op’s are in the
   // graph.
-  CHECK_EQ(ops_of_inline.size(), CountOpsWithNames(with_inline, ops_of_inline));
+  EXPECT_EQ(ops_of_inline.size(),
+            CountOpsWithNames(with_inline, ops_of_inline));
 }
 
 }  // namespace
-- 
GitLab


From e16d717e0be08c7d4b9f2455dec356c853cb7225 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 14 Jun 2017 15:54:06 -0700
Subject: [PATCH 0092/1375] Fixed a typo

PiperOrigin-RevId: 159038125
---
 tensorflow/core/grappler/costs/virtual_placer.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/costs/virtual_placer.cc b/tensorflow/core/grappler/costs/virtual_placer.cc
index a2d463e765..a3dfb278e2 100644
--- a/tensorflow/core/grappler/costs/virtual_placer.cc
+++ b/tensorflow/core/grappler/costs/virtual_placer.cc
@@ -40,8 +40,8 @@ VirtualPlacer::VirtualPlacer(const Cluster* cluster) {
     for (const auto& device : devices_) {
       if (str_util::Lowercase(device.first).find("gpu") != string::npos) {
         default_device_ = device.first;
+        break;
       }
-      break;
     }
   }
 }
-- 
GitLab


From f386c885017188448062db1c37069ea47e86b55e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 16:04:58 -0700
Subject: [PATCH 0093/1375] Make virtual scheduler potentially write summary
 information to stepstats_ variable.

PiperOrigin-RevId: 159039540
---
 .../core/grappler/costs/virtual_scheduler.cc  |  32 ++++-
 .../core/grappler/costs/virtual_scheduler.h   |   5 +
 .../grappler/costs/virtual_scheduler_test.cc  | 130 +++++++++++++++++-
 3 files changed, 159 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index c68d4e31c4..e5073d6301 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -244,8 +244,8 @@ string VirtualScheduler::DeviceName(const NodeDef* node) const {
 string VirtualScheduler::ChannelDeviceName(const NodeDef* from,
                                            const NodeDef* to) const {
   CHECK(!initialized_) << "ChannelDeviceName is called after Init().";
-
-  return kChannelDevice + ": " + DeviceName(from) + " to " + DeviceName(to);
+  return kChannelDevice + ": from " + DeviceName(from) + " to " +
+         DeviceName(to);
 }
 
 std::pair<const NodeDef*, const NodeDef*> VirtualScheduler::CreateSendRecv(
@@ -318,8 +318,8 @@ NodeInfo VirtualScheduler::GetCurrNodeInfo() const {
   }
 
   // Construct NodeInfo.
-  const auto& node_state = node_map_.at(node);
   NodeInfo node_info;
+  const auto& node_state = node_map_.at(node);
   node_info.name = node->name();
   node_info.device_name = node_state.device_name;
   auto& op_info = node_info.op_info;
@@ -577,6 +577,7 @@ Costs VirtualScheduler::Summary() const {
             << " GB, at the end: " << state.memory_usage << " B";
 
     VLOG(1) << "Per-op execution time (and memory usage at peak memory usage):";
+
     // Profile non-persistent op memory usage.
     for (const auto& node_port : state.mem_usage_snapshot_at_peak) {
       const auto* node = node_port.first;
@@ -617,5 +618,30 @@ Costs VirtualScheduler::Summary() const {
   return critical_path_costs;
 }
 
+Costs VirtualScheduler::Summary(StepStats* stepstats) {
+  if (stepstats != nullptr) {
+    for (const auto& device : device_) {
+      DeviceStepStats* device_stepstats = stepstats->add_dev_stats();
+      device_stepstats->set_device(device.first);
+      for (const auto& node_def : device.second.nodes_executed) {
+        const NodeState& nodestate = node_map_.at(node_def);
+        NodeExecStats* node_stats = device_stepstats->add_node_stats();
+        node_stats->set_node_name(node_def->op());
+        node_stats->set_timeline_label(node_def->name());
+        node_stats->set_op_start_rel_micros(0);
+        node_stats->set_all_start_micros(
+            nodestate.time_scheduled.asMicroSeconds().count());
+        node_stats->set_op_end_rel_micros(
+            nodestate.time_finished.asMicroSeconds().count() -
+            nodestate.time_scheduled.asMicroSeconds().count());
+        node_stats->set_all_end_rel_micros(
+            nodestate.time_finished.asMicroSeconds().count() -
+            nodestate.time_scheduled.asMicroSeconds().count());
+      }
+    }
+  }
+  return Summary();
+}
+
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index 1331e2ec8b..7ee3310e2f 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -21,6 +21,7 @@ limitations under the License.
 #include <unordered_map>
 #include <unordered_set>
 
+#include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/grappler/costs/cost_estimator.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/costs/virtual_placer.h"
@@ -170,6 +171,9 @@ class VirtualScheduler {
 
   // Prints out summary of execution (timing, memory usage, etc.)
   Costs Summary() const;
+  // Like the above, but writes detailed stats to stepstats.
+  // If stepstats is nullptr, then just calls and return Summary().
+  Costs Summary(StepStats* stepstats);
 
  protected:
   // GetDeviceStates and GetNodeStates are currently for testing purpuse only.
@@ -228,6 +232,7 @@ class VirtualScheduler {
   // Auxilliary data structures for constructing NodeState and DeviceState.
   GraphProperties graph_properties_;
   Cluster* cluster_;                   // Not owned.
+
   const GrapplerItem* grappler_item_;  // Not owned.
   bool use_static_shapes_;
   bool initialized_;
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index 5af9effde0..c630f60311 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/costs/virtual_scheduler.h"
-
 #include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/grappler/clusters/virtual_cluster.h"
 #include "tensorflow/core/grappler/costs/virtual_placer.h"
@@ -41,13 +40,28 @@ class VirtualSchedulerTest : public ::testing::Test {
  protected:
   const string kCPU0 = "/job:localhost/replica:0/task:0/cpu:0";
 
+  DeviceProperties GetDummyCPUDevice() {
+    // Create CPU with 2 cores, 4 Ghz freq, 2 GB/s mem bandwidth.
+    // - 8 Gflops
+    // - 2 GB/s
+    DeviceProperties cpu_device;
+    cpu_device.set_type("CPU");
+    cpu_device.set_frequency(4000);
+    cpu_device.set_num_cores(2);
+    cpu_device.set_bandwidth(2000000);
+    return cpu_device;
+  }
+
   void SetUp() override {
     // Initializes cluster_ and placer_.
     std::unordered_map<string, DeviceProperties> devices;
-    DeviceProperties cpu_device;
-    cpu_device.set_type("CPU");
-    devices[kCPU0] = cpu_device;
 
+    // Set some dummy CPU properties
+    DeviceProperties cpu_device = GetDummyCPUDevice();
+
+    // IMPORTANT: Device is not actually ever used in the test case since
+    // force_cpu_type is defaulted to "Haswell"
+    devices[kCPU0] = cpu_device;
     cluster_.reset(new VirtualCluster(devices));
     placer_.reset(new VirtualPlacer(cluster_.get()));
   }
@@ -102,6 +116,38 @@ class VirtualSchedulerTest : public ::testing::Test {
     dependency_["y"] = {"x", "f"};
   }
 
+  void CreateGrapplerItemWithMatmulChain() {
+    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
+    auto a = tensorflow::ops::RandomUniform(s.WithOpName("a"), {3200, 3200},
+                                            DT_FLOAT);
+    auto b = tensorflow::ops::RandomUniform(s.WithOpName("b"), {3200, 3200},
+                                            DT_FLOAT);
+    auto c = tensorflow::ops::RandomUniform(s.WithOpName("c"), {3200, 3200},
+                                            DT_FLOAT);
+    auto d = tensorflow::ops::RandomUniform(s.WithOpName("d"), {3200, 3200},
+                                            DT_FLOAT);
+    auto e = tensorflow::ops::RandomUniform(s.WithOpName("e"), {3200, 3200},
+                                            DT_FLOAT);
+
+    auto ab = tensorflow::ops::MatMul(s.WithOpName("ab"), a, b);
+    auto abc = tensorflow::ops::MatMul(s.WithOpName("abc"), ab, c);
+    auto abcd = tensorflow::ops::MatMul(s.WithOpName("abcd"), abc, d);
+    auto abcde = tensorflow::ops::MatMul(s.WithOpName("abcde"), abcd, e);
+
+    GraphDef def;
+    TF_CHECK_OK(s.ToGraphDef(&def));
+
+    grappler_item_.reset(new GrapplerItem);
+    grappler_item_->id = "test_matmul_sequence_graph";
+    grappler_item_->graph = def;
+    grappler_item_->fetch = {"abcde"};
+
+    dependency_["ab"] = {"a", "b"};
+    dependency_["abc"] = {"ab", "c"};
+    dependency_["abcd"] = {"abc", "d"};
+    dependency_["abcde"] = {"abcd", "e"};
+  }
+
   // AddN that takes 4 tensors with 10x10x10x10.
   void CreateGrapplerItemWithAddN() {
     tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
@@ -201,6 +247,20 @@ class VirtualSchedulerTest : public ::testing::Test {
     TF_CHECK_OK(scheduler_->Init());
   }
 
+  // Returns cost based on op.
+  Costs SimplePredictCosts(const NodeInfo& info) const {
+    Costs c;
+    int64 exec_cost = 0;
+    if (info.op_info.op() == "MatMul") {
+      exec_cost = 2000000000;
+    }
+    if (info.op_info.op() == "RandomUniform") {
+      exec_cost = 1000000000;
+    }
+    c.execution_time = Costs::NanoSeconds(exec_cost);
+    return c;
+  }
+
   // Call this after init scheduler_. Scheduler stops after executing
   // target_node.
   std::unordered_map<string, NodeInfo> RunScheduler(const string& target_node) {
@@ -211,6 +271,8 @@ class VirtualSchedulerTest : public ::testing::Test {
       NodeInfo node_info = scheduler_->GetCurrNodeInfo();
       ops_executed[node_info.name] = node_info;
 
+      Costs node_costs = SimplePredictCosts(node_info);
+
       // Check scheduling order.
       auto it = dependency_.find(node_info.name);
       if (it != dependency_.end()) {
@@ -218,7 +280,7 @@ class VirtualSchedulerTest : public ::testing::Test {
           EXPECT_GT(ops_executed.count(preceding_node), 0);
         }
       }
-      more_nodes = scheduler_->MarkCurrNodeExecuted(zero_costs);
+      more_nodes = scheduler_->MarkCurrNodeExecuted(node_costs);
 
       if (node_info.name == target_node) {
         // Scheduler has the state after executing the target node.
@@ -312,6 +374,64 @@ class VirtualSchedulerTest : public ::testing::Test {
   const int depth_out_ = 16;
 };
 
+// Create small graph, run predict costs on it, make sure the costs from the
+// summary match the hand-calculated costs.
+TEST_F(VirtualSchedulerTest, SummaryCostTest) {
+  // Run matmul test.
+  CreateGrapplerItemWithMatmulChain();
+  InitScheduler();
+  auto ops_executed = RunScheduler("");
+  Costs c = scheduler_->Summary();
+
+  // RandomUniform - 5
+  // Matmuls - 4 * 2 = 8
+  // Total: 13
+  EXPECT_EQ(13000000, c.execution_time.asMicroSeconds().count());
+}
+
+// Like the above SummaryCostTest, but makes sure the stepstats timeline is
+// correct.
+TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
+  // Run matmul test.
+  CreateGrapplerItemWithMatmulChain();
+  InitScheduler();
+  auto ops_executed = RunScheduler("");
+  StepStats stepstats;
+  Costs c = scheduler_->Summary(&stepstats);
+  EXPECT_EQ(13000000, c.execution_time.asMicroSeconds().count());
+
+  // Should only be 1 device!
+  EXPECT_EQ(1, stepstats.dev_stats().size());
+
+  // Create a map of op name -> start and end times (micros).
+  std::map<string, std::pair<int64, int64>> start_end_times;
+  for (const auto& device_step_stats : stepstats.dev_stats()) {
+    for (const auto& stats : device_step_stats.node_stats()) {
+      // The node name is actually in the timeline_label.
+      int64 start = stats.all_start_micros();
+      int64 end = start + stats.all_end_rel_micros();
+      start_end_times[stats.timeline_label()] =
+          std::pair<int64, int64>(start, end);
+    }
+  }
+
+  // The base start_time is the time to compute RandomUniforms
+  int64 cur_time = static_cast<int64>(5000000);
+  // The increment is the execution time of one matmul. See
+  // CreateGrapplerItemWithMatmulChain for details.
+  int64 increment = static_cast<int64>(2000000);
+  auto op_names = {"ab", "abc", "abcd", "abcde"};
+  for (const auto& op_name : op_names) {
+    int64 actual_start = start_end_times[op_name].first;
+    int64 actual_end = start_end_times[op_name].second;
+    int64 expected_start = cur_time;
+    int64 expected_end = cur_time + increment;
+    EXPECT_EQ(expected_start, actual_start);
+    EXPECT_EQ(expected_end, actual_end);
+    cur_time += increment;
+  }
+}
+
 TEST_F(VirtualSchedulerTest, InitAndBasicScheduling) {
   // Init.
   CreateGrapplerItemWithConv2Ds();
-- 
GitLab


From 9235dcb638617c666cc4d14ea770131c3b767173 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 16:43:57 -0700
Subject: [PATCH 0094/1375] Fix a flaky test in
 graph_transforms:transforms_test

PiperOrigin-RevId: 159044391
---
 tensorflow/tools/graph_transforms/quantize_nodes_test.cc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/tools/graph_transforms/quantize_nodes_test.cc b/tensorflow/tools/graph_transforms/quantize_nodes_test.cc
index f070039381..eca263a1ae 100644
--- a/tensorflow/tools/graph_transforms/quantize_nodes_test.cc
+++ b/tensorflow/tools/graph_transforms/quantize_nodes_test.cc
@@ -672,8 +672,7 @@ class QuantizeNodesTest : public ::testing::Test {
     TF_ASSERT_OK(root.ToGraphDef(&float_graph_def));
 
     Tensor input_tensor(DT_FLOAT, {1, 128, 128, 3});
-    auto float_input_tensor = input_tensor.flat<float>();
-    float_input_tensor.constant(100.0f);
+    test::FillFn<float>(&input_tensor, [](int) { return 100.0f; });
 
     TestQuantizedVersusFloatGraph(float_graph_def,
                                   {{"placeholder_op", input_tensor}},
-- 
GitLab


From 4fc83c75ba130a7fabc843f67ffa63759b4e6558 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 16:47:39 -0700
Subject: [PATCH 0095/1375] Updating a comment in Conv2D to correctly specify
 the kernel dimension ordering (height, width).

PiperOrigin-RevId: 159044822
---
 tensorflow/python/layers/convolutional.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index fdf1b134b9..63c7280b3d 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -388,7 +388,7 @@ class Conv2D(_Conv):
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: An integer or tuple/list of 2 integers, specifying the
-      width and height of the 2D convolution window.
+      height and width of the 2D convolution window.
       Can be a single integer to specify the same value for
       all spatial dimensions.
     strides: An integer or tuple/list of 2 integers,
@@ -489,7 +489,7 @@ def conv2d(inputs,
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: An integer or tuple/list of 2 integers, specifying the
-      width and height of the 2D convolution window.
+      height and width of the 2D convolution window.
       Can be a single integer to specify the same value for
       all spatial dimensions.
     strides: An integer or tuple/list of 2 integers,
-- 
GitLab


From 0fa19543aa0c4fe84851cc00b0d731260e825739 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Wed, 14 Jun 2017 16:54:04 -0700
Subject: [PATCH 0096/1375] Make JS minifier not print warnings

PiperOrigin-RevId: 159045577
---
 .../tensorflow/tensorboard/vulcanize/Vulcanize.java  | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
index 80e48124eb..a4366d7cfa 100644
--- a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
+++ b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
@@ -30,6 +30,7 @@ import com.google.common.collect.ImmutableSet;
 import com.google.common.collect.Iterables;
 import com.google.common.collect.Lists;
 import com.google.common.collect.Multimap;
+import com.google.javascript.jscomp.BasicErrorManager;
 import com.google.javascript.jscomp.CheckLevel;
 import com.google.javascript.jscomp.CompilationLevel;
 import com.google.javascript.jscomp.Compiler;
@@ -476,7 +477,7 @@ public final class Vulcanize {
       options.setPrettyPrint(true);
       options.setGeneratePseudoNames(true);
     }
-    Compiler compiler = new Compiler();
+    Compiler compiler = new Compiler(new JsPrintlessErrorManager());
     compiler.disableThreads();
     compiler.compile(
         ImmutableList.<SourceFile>of(),
@@ -570,4 +571,13 @@ public final class Vulcanize {
     }
     return ImmutableMultimap.copyOf(builder);
   }
+
+  private static final class JsPrintlessErrorManager extends BasicErrorManager {
+
+    @Override
+    public void println(CheckLevel level, JSError error) {}
+
+    @Override
+    public void printSummary() {}
+  }
 }
-- 
GitLab


From 7d3497a639670d9c31d09185ff97b852f0fbe101 Mon Sep 17 00:00:00 2001
From: HyoukJoong Lee <hyouklee@google.com>
Date: Wed, 14 Jun 2017 18:46:32 -0700
Subject: [PATCH 0097/1375] Add ComputationPlacer to assign device ids for
 replicated model-parallel computations.

PiperOrigin-RevId: 159056198
---
 .../compiler/xla/executable_run_options.cc    |  10 +
 .../compiler/xla/executable_run_options.h     |   6 +
 tensorflow/compiler/xla/service/BUILD         |  21 ++
 tensorflow/compiler/xla/service/backend.cc    |  42 +---
 tensorflow/compiler/xla/service/backend.h     |  31 ++-
 .../xla/service/computation_placer.cc         | 151 +++++++++++++
 .../compiler/xla/service/computation_placer.h | 113 ++++++++++
 .../compiler/xla/service/local_service.cc     |   2 +-
 tensorflow/compiler/xla/service/service.cc    | 206 +++++++++++-------
 tensorflow/compiler/xla/service/service.h     |  13 +-
 tensorflow/compiler/xla/tests/BUILD           |   3 +
 tensorflow/compiler/xla/xla_data.proto        |  25 ++-
 12 files changed, 489 insertions(+), 134 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/computation_placer.cc
 create mode 100644 tensorflow/compiler/xla/service/computation_placer.h

diff --git a/tensorflow/compiler/xla/executable_run_options.cc b/tensorflow/compiler/xla/executable_run_options.cc
index 67f3a6c1df..33d5b6f1d4 100644
--- a/tensorflow/compiler/xla/executable_run_options.cc
+++ b/tensorflow/compiler/xla/executable_run_options.cc
@@ -77,4 +77,14 @@ ExecutionProfile* ExecutableRunOptions::execution_profile() const {
   return execution_profile_;
 }
 
+ExecutableRunOptions& ExecutableRunOptions::set_device_assignment(
+    DeviceAssignment* device_assignment) {
+  device_assignment_ = device_assignment;
+  return *this;
+}
+
+DeviceAssignment* ExecutableRunOptions::device_assignment() const {
+  return device_assignment_;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/executable_run_options.h b/tensorflow/compiler/xla/executable_run_options.h
index 03f2d016ad..deb3ddb203 100644
--- a/tensorflow/compiler/xla/executable_run_options.h
+++ b/tensorflow/compiler/xla/executable_run_options.h
@@ -40,6 +40,7 @@ struct ThreadPoolDevice;
 namespace xla {
 
 class DeviceMemoryAllocator;
+class DeviceAssignment;
 class ExecutionProfile;
 
 // Class containing options for running a LocalExecutable.
@@ -79,9 +80,14 @@ class ExecutableRunOptions {
   ExecutionProfile* execution_profile() const;
   ExecutableRunOptions& set_execution_profile(ExecutionProfile* profile);
 
+  ExecutableRunOptions& set_device_assignment(
+      DeviceAssignment* device_assignment);
+  DeviceAssignment* device_assignment() const;
+
  private:
   DeviceMemoryAllocator* allocator_ = nullptr;
   int device_ordinal_ = -1;
+  DeviceAssignment* device_assignment_ = nullptr;
   perftools::gputools::Stream* stream_ = nullptr;
   tensorflow::thread::ThreadPool* inter_op_thread_pool_ = nullptr;
   const Eigen::ThreadPoolDevice* intra_op_thread_pool_ = nullptr;
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index acaf9eaafb..9a041e3f41 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -332,6 +332,7 @@ cc_library(
     hdrs = ["backend.h"],
     deps = [
         ":compiler",
+        ":computation_placer",
         ":device_memory_allocator",
         ":platform_util",
         ":pool",
@@ -950,6 +951,26 @@ cc_test(
     ],
 )
 
+cc_library(
+    name = "computation_placer",
+    srcs = ["computation_placer.cc"],
+    hdrs = ["computation_placer.h"],
+    deps = [
+        "//tensorflow/compiler/xla:array2d",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:stream_executor_no_cuda",
+    ],
+    alwayslink = True,  # Contains per-platform computation placer registration
+)
+
 cc_library(
     name = "generic_transfer_manager",
     srcs = ["generic_transfer_manager.cc"],
diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc
index 66d54ad380..83f954789c 100644
--- a/tensorflow/compiler/xla/service/backend.cc
+++ b/tensorflow/compiler/xla/service/backend.cc
@@ -96,9 +96,11 @@ struct Backend::EigenThreadPoolWrapper {
                       PlatformUtil::GetStreamExecutors(platform));
   TF_ASSIGN_OR_RETURN(auto transfer_manager,
                       TransferManager::GetForPlatform(platform));
-  std::unique_ptr<Backend> backend(
-      new Backend(replica_count, platform, compiler, stream_executors,
-                  transfer_manager, options.intra_op_parallelism_threads()));
+  TF_ASSIGN_OR_RETURN(auto computation_placer,
+                      ComputationPlacer::GetForPlatform(platform));
+  std::unique_ptr<Backend> backend(new Backend(
+      replica_count, platform, compiler, stream_executors, transfer_manager,
+      computation_placer, options.intra_op_parallelism_threads()));
   return std::move(backend);
 }
 
@@ -135,10 +137,12 @@ Backend::Backend(
     int64 replica_count, perftools::gputools::Platform* platform,
     Compiler* compiler,
     tensorflow::gtl::ArraySlice<se::StreamExecutor*> stream_executors,
-    TransferManager* transfer_manager, int intra_op_parallelism_threads)
+    TransferManager* transfer_manager, ComputationPlacer* computation_placer,
+    int intra_op_parallelism_threads)
     : platform_(platform),
       compiler_(compiler),
       transfer_manager_(transfer_manager),
+      computation_placer_(computation_placer),
       replica_count_(replica_count) {
   // The given set of stream executors set may include invalid executors.
   for (se::StreamExecutor* exec : stream_executors) {
@@ -179,36 +183,6 @@ int Backend::default_device_ordinal() const {
   return default_stream_executor()->device_ordinal();
 }
 
-StatusOr<std::vector<perftools::gputools::StreamExecutor*>> Backend::Replicas(
-    int device_ordinal) const {
-  if (stream_executors_[device_ordinal] == nullptr) {
-    return InvalidArgument("device %s not supported by XLA service",
-                           device_name(device_ordinal).c_str());
-  }
-
-  // Find replica_count_ stream executors starting from the given device
-  // ordinal.
-  std::vector<perftools::gputools::StreamExecutor*> replicas;
-  for (se::StreamExecutor* exec : stream_executors_) {
-    CHECK(exec != nullptr);
-    if (exec->device_ordinal() >= device_ordinal) {
-      replicas.push_back(exec);
-      if (replicas.size() >= replica_count_) {
-        return replicas;
-      }
-    }
-  }
-
-  return InvalidArgument(
-      "Not enough devices for replicas for the device ordinal %d",
-      device_ordinal);
-}
-
-std::vector<perftools::gputools::StreamExecutor*> Backend::Replicas() const {
-  CHECK_GE(stream_executors_.size(), replica_count_);
-  return Replicas(default_device_ordinal()).ValueOrDie();
-}
-
 tensorflow::thread::ThreadPool* Backend::inter_op_thread_pool() const {
   return inter_op_thread_pool_.get();
 }
diff --git a/tensorflow/compiler/xla/service/backend.h b/tensorflow/compiler/xla/service/backend.h
index e0b15dc43f..3ead274cde 100644
--- a/tensorflow/compiler/xla/service/backend.h
+++ b/tensorflow/compiler/xla/service/backend.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/service/compiler.h"
+#include "tensorflow/compiler/xla/service/computation_placer.h"
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/pool.h"
 #include "tensorflow/compiler/xla/service/transfer_manager.h"
@@ -40,6 +41,8 @@ struct ThreadPoolDevice;
 namespace xla {
 
 // Options to configure the backend when it is created.
+//
+// TODO(b/62588571): Remove the notion of replicas from the backend.
 class BackendOptions {
  public:
   // Set the platform backing the backend, or nullptr for the default platform.
@@ -92,11 +95,15 @@ class Backend {
     return memory_allocator_.get();
   }
   TransferManager* transfer_manager() const { return transfer_manager_; }
+  ComputationPlacer* computation_placer() const { return computation_placer_; }
 
   // Returns the number of devices of the platform type which are visible. Not
   // all of these devices may be usable by XLA.
   int device_count() const { return stream_executors_.size(); }
 
+  // Returns the number of replicas when replication is enabled.
+  int replica_count() const { return replica_count_; }
+
   // Returns the device ordinal number of the default device.
   int default_device_ordinal() const;
 
@@ -107,24 +114,13 @@ class Backend {
     return stream_executors_;
   }
 
-  // Returns the replicas for the default stream executor.
-  //
-  // When the number of replicas is R, the first R stream executors are assigned
-  // to the replicas of the default stream executor.
-  std::vector<perftools::gputools::StreamExecutor*> Replicas() const;
-
-  // Returns the replicas for the given device_ordinal. The given device ordinal
-  // is considered to be the first device ordinal among the replicas. Returns an
-  // error status if the stream executor for the given given device ordinal does
-  // not exist or if there are not enough stream executors for the replicas.
-  StatusOr<std::vector<perftools::gputools::StreamExecutor*>> Replicas(
-      int device_ordinal) const;
-
-  // Return the stream executor for the given device ordinal.
+  // Returns the stream executor for the given device ordinal.
   StatusOr<perftools::gputools::StreamExecutor*> stream_executor(
       int device_ordinal) const;
 
-  // Return the stream executor for the default device ordinal.
+  // Returns the stream executor for the default device ordinal. This stream
+  // executor can only be used when the number of computations is 1 (replication
+  // can be > 1).
   perftools::gputools::StreamExecutor* default_stream_executor() const {
     CHECK(!stream_executors_.empty());
     return stream_executors_[0];
@@ -178,13 +174,16 @@ class Backend {
           Compiler* compiler,
           tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*>
               stream_executors,
-          TransferManager* transfer_manager, int intra_op_parallelism_threads);
+          TransferManager* transfer_manager,
+          ComputationPlacer* computation_placer,
+          int intra_op_parallelism_threads);
   Backend(const Backend&) = delete;
   Backend& operator=(const Backend&) = delete;
 
   perftools::gputools::Platform* platform_;
   Compiler* compiler_;
   TransferManager* transfer_manager_;
+  ComputationPlacer* computation_placer_;
   int64 replica_count_ = -1;
 
   // Vector of stream executors. stream_executors_[0] is the default executor.
diff --git a/tensorflow/compiler/xla/service/computation_placer.cc b/tensorflow/compiler/xla/service/computation_placer.cc
new file mode 100644
index 0000000000..cdf277581f
--- /dev/null
+++ b/tensorflow/compiler/xla/service/computation_placer.cc
@@ -0,0 +1,151 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/computation_placer.h"
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+
+namespace se = ::perftools::gputools;
+
+namespace xla {
+
+Status DeviceAssignment::Serialize(DeviceAssignmentProto* proto) const {
+  proto->set_replica_count(replica_count());
+  proto->set_computation_count(computation_count());
+  for (int computation = 0; computation < computation_count(); ++computation) {
+    DeviceAssignmentProto::ComputationDevice* computation_device =
+        proto->add_computation_devices();
+    for (int replica = 0; replica < replica_count(); ++replica) {
+      computation_device->add_replica_device_ids((*this)(replica, computation));
+    }
+  }
+  return Status::OK();
+}
+
+/* static */ StatusOr<DeviceAssignment> DeviceAssignment::Deserialize(
+    const DeviceAssignmentProto& proto) {
+  TF_RET_CHECK(proto.computation_devices_size() == proto.computation_count());
+  DeviceAssignment assignment(proto.replica_count(), proto.computation_count());
+  for (int computation = 0; computation < proto.computation_count();
+       ++computation) {
+    const auto& computation_device = proto.computation_devices(computation);
+    TF_RET_CHECK(computation_device.replica_device_ids_size() ==
+                 proto.replica_count());
+    for (int replica = 0; replica < proto.replica_count(); ++replica) {
+      assignment(replica, computation) =
+          computation_device.replica_device_ids(replica);
+    }
+  }
+  return std::move(assignment);
+}
+
+StatusOr<int> ComputationPlacer::DeviceId(int replica, int computation,
+                                          int replica_count,
+                                          int computation_count) {
+  TF_RET_CHECK(replica < replica_count);
+  TF_RET_CHECK(computation < computation_count);
+
+  return computation * replica_count + replica;
+}
+
+StatusOr<DeviceAssignment> ComputationPlacer::AssignDevices(
+    int replica_count, int computation_count) {
+  DeviceAssignment assignment(replica_count, computation_count);
+  for (int replica = 0; replica < replica_count; ++replica) {
+    for (int computation = 0; computation < computation_count; ++computation) {
+      TF_ASSIGN_OR_RETURN(
+          int device_id,
+          DeviceId(replica, computation, replica_count, computation_count));
+      assignment(replica, computation) = device_id;
+    }
+  }
+  return std::move(assignment);
+}
+
+/* static */ void ComputationPlacer::RegisterComputationPlacer(
+    se::Platform::Id platform_id,
+    ComputationPlacerCreationFunction creation_function) {
+  tensorflow::mutex_lock lock(
+      *ComputationPlacer::platform_computation_placer_mutex());
+  auto* computation_placers = GetPlatformComputationPlacers();
+  CHECK(computation_placers->find(platform_id) == computation_placers->end());
+  (*computation_placers)[platform_id].creation_function = creation_function;
+}
+
+/* static */ StatusOr<ComputationPlacer*> ComputationPlacer::GetForPlatform(
+    const se::Platform* platform) {
+  tensorflow::mutex_lock lock(
+      *ComputationPlacer::platform_computation_placer_mutex());
+  auto* computation_placers = GetPlatformComputationPlacers();
+
+  auto it = computation_placers->find(platform->id());
+  if (it == computation_placers->end()) {
+    return NotFound(
+        "could not find registered computation placer for platform %s -- check "
+        "target linkage",
+        platform->Name().c_str());
+  }
+
+  if (it->second.placer == nullptr) {
+    // Lazily create the computation placer the first time it is needed.
+    it->second.placer = (*it->second.creation_function)();
+  }
+
+  return it->second.placer.get();
+}
+
+/* static */ tensorflow::mutex*
+ComputationPlacer::platform_computation_placer_mutex() {
+  static tensorflow::mutex* m = new tensorflow::mutex;
+  return m;
+}
+
+/* static */ std::map<perftools::gputools::Platform::Id,
+                      ComputationPlacer::State>*
+ComputationPlacer::GetPlatformComputationPlacers() {
+  static auto* r =
+      new std::map<perftools::gputools::Platform::Id, ComputationPlacer::State>;
+  return r;
+}
+
+}  // namespace xla
+
+static std::unique_ptr<xla::ComputationPlacer> CreateComputationPlacer() {
+  return xla::MakeUnique<xla::ComputationPlacer>();
+}
+
+static bool InitModule() {
+  xla::ComputationPlacer::RegisterComputationPlacer(se::host::kHostPlatformId,
+                                                    &CreateComputationPlacer);
+  xla::ComputationPlacer::RegisterComputationPlacer(se::cuda::kCudaPlatformId,
+                                                    &CreateComputationPlacer);
+  return true;
+}
+static bool module_initialized = InitModule();
diff --git a/tensorflow/compiler/xla/service/computation_placer.h b/tensorflow/compiler/xla/service/computation_placer.h
new file mode 100644
index 0000000000..4d26d6bb85
--- /dev/null
+++ b/tensorflow/compiler/xla/service/computation_placer.h
@@ -0,0 +1,113 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_COMPUTATION_PLACER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_COMPUTATION_PLACER_H_
+
+#include <map>
+#include <memory>
+#include <vector>
+
+#include "tensorflow/compiler/xla/array2d.h"
+#include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+
+// Class that represents the device assignment for a set of XLA replicated
+// computations. For R replicas and C computations, R * C devices are required
+// execute the computation in parallel. The assigned device ids can be accessed
+// by assignment(replica, computation).
+class DeviceAssignment : public Array2D<int> {
+ public:
+  DeviceAssignment() {}
+  DeviceAssignment(int replica_count, int computation_count)
+      : Array2D<int>(replica_count, computation_count, -1) {
+    CHECK_GT(replica_count, 0);
+    CHECK_GT(computation_count, 0);
+  }
+
+  int replica_count() const { return height(); }
+  int computation_count() const { return width(); }
+
+  // Protocol buffer serialization and deserialization.
+  Status Serialize(DeviceAssignmentProto* proto) const;
+  static StatusOr<DeviceAssignment> Deserialize(
+      const DeviceAssignmentProto& proto);
+};
+
+// A generic implementation of the XLA computation placer, which assigns device
+// ids to a set of replicated computations.
+class ComputationPlacer {
+ public:
+  ComputationPlacer() {}
+  virtual ~ComputationPlacer() {}
+
+  // Returns the device id assigned to the given replica and computation
+  // instance for [replica_count x computation_count] setup. The returned device
+  // id must match the assignement from PlaceReplicatedComputation().
+  virtual StatusOr<int> DeviceId(int replica, int computation,
+                                 int replica_count, int computation_count);
+
+  // Returns the device ids assigned to a set of replicated computations, given
+  // the number of replicas and the number of computations.
+  virtual StatusOr<DeviceAssignment> AssignDevices(int replica_count,
+                                                   int computation_count);
+
+  using ComputationPlacerCreationFunction =
+      std::unique_ptr<ComputationPlacer> (*)();
+
+  // Registers a computation placer creation function for a particular platform.
+  static void RegisterComputationPlacer(
+      perftools::gputools::Platform::Id platform_id,
+      ComputationPlacerCreationFunction creation_function);
+
+  // Returns the computation placer singleton pointer if it is available for the
+  // given platform, or an error status if it is not.
+  static StatusOr<ComputationPlacer*> GetForPlatform(
+      const perftools::gputools::Platform* platform);
+
+ private:
+  // Routine that returns the mutex that guards the platform-to-computation
+  // placer map. Done as a routine to ensure correct initialization ordering,
+  // since RegisterComputationPlacer can be called during program initialization
+  // time.
+  static tensorflow::mutex* platform_computation_placer_mutex();
+
+  // State kept for each kind of ComputationPlacer. Registration functions set
+  // up creation_function, and then we use that to lazily create "placer" the
+  // first time GetForPlatform is invoked for a particular id.
+  struct State {
+    std::unique_ptr<ComputationPlacer> placer;
+    ComputationPlacerCreationFunction creation_function = nullptr;
+  };
+
+  // Map from platform kind to computation placer singleton.
+  static std::map<perftools::gputools::Platform::Id, State>*
+  GetPlatformComputationPlacers();
+
+  perftools::gputools::Platform::Id platform_id_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(ComputationPlacer);
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_COMPUTATION_PLACER_H_
diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index 131c2ee87b..748124ebc7 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -152,7 +152,7 @@ StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable(
   // Construct computation layout from the argument layouts.
   auto module_config = MakeUnique<HloModuleConfig>(*program_shape);
   module_config->set_has_hybrid_result(has_hybrid_result);
-  module_config->set_replica_count(execute_backend_->Replicas().size());
+  module_config->set_replica_count(execute_backend_->replica_count());
   legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
   if (flags->xla_hlo_profile) {
     module_config->enable_hlo_profiling(true);
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 79bf679c5f..5812d3e487 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -325,7 +325,7 @@ StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig(
     module_config->enable_hlo_profiling(true);
   }
 
-  module_config->set_replica_count(backend->Replicas().size());
+  module_config->set_replica_count(backend->replica_count());
   module_config->set_seed(execution_options.seed());
   module_config->set_debug_options(execution_options.debug_options());
 
@@ -495,47 +495,55 @@ Service::ExecuteParallelAndRegisterResult(
     tensorflow::gtl::ArraySlice<
         std::vector<perftools::gputools::DeviceMemoryBase>>
         arguments,
-    Backend* backend,
-    tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*> executors,
+    Backend* backend, tensorflow::gtl::ArraySlice<DeviceHandle> device_handles,
     tensorflow::gtl::ArraySlice<string> result_tags) {
-  // TODO(b/33943292): Support for replication when using multiple computations.
-  TF_RET_CHECK(backend->Replicas().size() == 1);
-
-  // Set up streams.
+  // Streams where the computation are launched, so we can wait on the streams
+  // to complete.
   std::vector<Pool<se::Stream>::SmartPtr> streams;
 
-  for (se::StreamExecutor* executor : executors) {
-    TF_ASSIGN_OR_RETURN(Pool<se::Stream>::SmartPtr stream,
-                        backend->BorrowStream(executor));
-    streams.push_back(std::move(stream));
-  }
-
-  // Set up run options.
-  std::vector<ServiceExecutableRunOptions> run_options;
-  for (const Pool<se::Stream>::SmartPtr& stream : streams) {
-    ExecutableRunOptions options;
-    options.set_stream(stream.get());
-    options.set_allocator(backend->memory_allocator());
-    options.set_inter_op_thread_pool(backend->inter_op_thread_pool());
-    options.set_intra_op_thread_pool(
-        backend->eigen_intra_op_thread_pool_device());
-    run_options.emplace_back(options, backend->StreamBorrower());
-  }
-
-  // Asynchronously launch all executables.
+  // Global data handles for the computation results, one for each computation.
   std::vector<GlobalDataHandle> result_handles;
-  for (tensorflow::gtl::ArraySlice<Executable*>::size_type i = 0;
-       i < executables.size(); i++) {
-    TF_ASSIGN_OR_RETURN(
-        perftools::gputools::DeviceMemoryBase result,
-        executables[i]->ExecuteAsyncOnStream(&run_options[i], arguments[i]));
-    result_handles.push_back(allocation_tracker_.Register(
-        backend, executors[i]->device_ordinal(), result,
-        executables[i]->result_shape(), result_tags[i]));
+
+  TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
+                      backend->computation_placer()->AssignDevices(
+                          backend->replica_count(), executables.size()));
+
+  for (int64 i = 0; i < executables.size(); i++) {
+    // Stream executors for the replicas of the current computation.
+    TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, device_handles[i]));
+    for (int64 replica = 0; replica < replicas.size(); ++replica) {
+      TF_ASSIGN_OR_RETURN(Pool<se::Stream>::SmartPtr stream,
+                          backend->BorrowStream(replicas[replica]));
+      streams.push_back(std::move(stream));
+
+      // Set up run options.
+      ExecutableRunOptions options;
+      options.set_stream(streams.back().get());
+      options.set_allocator(backend->memory_allocator());
+      options.set_inter_op_thread_pool(backend->inter_op_thread_pool());
+      options.set_intra_op_thread_pool(
+          backend->eigen_intra_op_thread_pool_device());
+      options.set_device_assignment(&device_assignment);
+      ServiceExecutableRunOptions run_options(options,
+                                              backend->StreamBorrower());
+
+      // Asynchronously launch the computation.
+      TF_ASSIGN_OR_RETURN(
+          perftools::gputools::DeviceMemoryBase result,
+          executables[i]->ExecuteAsyncOnStream(&run_options, arguments[i]));
+
+      // All replicas share the same device address for the result allocation,
+      // so only one of the replicas need to register the result handle.
+      if (replica == 0) {
+        result_handles.push_back(allocation_tracker_.Register(
+            backend, replicas[0]->device_ordinal(), result,
+            executables[i]->result_shape(), result_tags[i]));
+      }
+    }
   }
 
   // Wait for all executions to complete.
-  for (int64 i = 0; i < result_handles.size(); ++i) {
+  for (int64 i = 0; i < streams.size(); ++i) {
     if (!streams[i]->BlockHostUntilDone()) {
       return InternalError("failed to complete execution for stream %lld", i);
     }
@@ -550,17 +558,22 @@ StatusOr<GlobalDataHandle> Service::ExecuteAndRegisterResult(
         arguments,
     Backend* backend, perftools::gputools::StreamExecutor* executor,
     const string& result_tag, ExecutionProfile* profile) {
-  TF_RET_CHECK(!backend->Replicas().empty());
-
   // Set up streams.
   std::vector<Pool<se::Stream>::SmartPtr> streams;
 
-  for (se::StreamExecutor* executor : backend->Replicas()) {
+  TF_ASSIGN_OR_RETURN(auto replicas,
+                      Replicas(*backend, SingleComputationDeviceHandle()));
+  TF_RET_CHECK(!replicas.empty());
+  for (se::StreamExecutor* executor : replicas) {
     TF_ASSIGN_OR_RETURN(Pool<se::Stream>::SmartPtr stream,
                         backend->BorrowStream(executor));
     streams.push_back(std::move(stream));
   }
 
+  TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
+                      backend->computation_placer()->AssignDevices(
+                          backend->replica_count(), /*computation_count=*/1));
+
   // Set up run options.
   std::vector<ServiceExecutableRunOptions> run_options;
   for (const Pool<se::Stream>::SmartPtr& stream : streams) {
@@ -570,19 +583,20 @@ StatusOr<GlobalDataHandle> Service::ExecuteAndRegisterResult(
     options.set_inter_op_thread_pool(backend->inter_op_thread_pool());
     options.set_intra_op_thread_pool(
         backend->eigen_intra_op_thread_pool_device());
+    options.set_device_assignment(&device_assignment);
     run_options.emplace_back(options, backend->StreamBorrower(),
                              backend->inter_op_thread_pool());
   }
 
   perftools::gputools::DeviceMemoryBase result;
-  if (backend->Replicas().size() == 1) {
+  if (backend->replica_count() == 1) {
     TF_ASSIGN_OR_RETURN(
         result, executable->ExecuteOnStreamWrapper<se::DeviceMemoryBase>(
                     &run_options[0], profile, arguments));
   } else {
     std::vector<
         tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>>
-        repeated_arguments(backend->Replicas().size(), arguments);
+        repeated_arguments(backend->replica_count(), arguments);
 
     TF_ASSIGN_OR_RETURN(auto results, executable->ExecuteOnStreams(
                                           run_options, repeated_arguments));
@@ -610,25 +624,26 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
   std::vector<VersionedComputationHandle> versioned_handles;
   std::vector<std::unique_ptr<HloModuleConfig>> module_configs;
   std::vector<string> computation_names;
+  std::vector<DeviceHandle> device_handles;
 
-  if (arg->requests_size() > execute_backend_->stream_executors().size()) {
+  if (arg->requests_size() * execute_backend_->replica_count() >
+      execute_backend_->device_count()) {
     return FailedPrecondition(
         "there are not enough stream executors to execute %d computations",
         arg->requests_size());
   }
 
   for (int64 i = 0; i < arg->requests_size(); ++i) {
-    // Get the stream executor on which the computation will run. Select the
-    // specific device if requested, otherwise select the i'th device from the
-    // list of available stream executors.
-    se::StreamExecutor* executor;
-    if (arg->requests(i).has_device_handle()) {
-      executor =
-          execute_backend_
-              ->stream_executors()[arg->requests(i).device_handle().handle()];
-    } else {
-      executor = execute_backend_->stream_executors()[i];
+    // Get the stream executor for the i'th computation. This stream executor
+    // is one of the executors to run the replicated computation.
+    if (!arg->requests(i).has_device_handle()) {
+      return FailedPrecondition(
+          "device handles must be given to execute parallel computations");
     }
+    TF_ASSIGN_OR_RETURN(
+        auto replicas,
+        Replicas(*execute_backend_, arg->requests(i).device_handle()));
+    se::StreamExecutor* executor = replicas[0];
     CHECK(executor != nullptr);
 
     // Resolve the UserComputation object associated with the requested
@@ -673,6 +688,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
     module_configs.push_back(std::move(module_config));
     computation_names.push_back(user_computation->name());
     executors.push_back(executor);
+    device_handles.push_back(arg->requests(i).device_handle());
   }
 
   // Build the user computations into HloModules and compile to generate the
@@ -692,7 +708,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
   TF_ASSIGN_OR_RETURN(
       std::vector<GlobalDataHandle> outputs,
       ExecuteParallelAndRegisterResult(executable_ptrs, all_arguments,
-                                       execute_backend_.get(), executors,
+                                       execute_backend_.get(), device_handles,
                                        computation_names));
   for (const GlobalDataHandle& output : outputs) {
     ExecuteResponse response;
@@ -706,10 +722,12 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
 
 tensorflow::Status Service::GetDeviceHandles(const GetDeviceHandlesRequest* arg,
                                              GetDeviceHandlesResponse* result) {
-  const int64 available_device_count =
-      execute_backend_->stream_executors().size();
-  const int64 replicas = execute_backend_->Replicas().size();
-  if (available_device_count < arg->device_count() * replicas) {
+  const int64 available_device_count = execute_backend_->device_count();
+  const int64 replica_count = execute_backend_->replica_count();
+  if (replica_count <= 0) {
+    return FailedPrecondition("Replica count must be a positive integer");
+  }
+  if (available_device_count < arg->device_count() * replica_count) {
     return ResourceExhausted(
         "Requested device count (%lld) exceeds the number of available devices "
         "on the target (%lld)",
@@ -718,8 +736,8 @@ tensorflow::Status Service::GetDeviceHandles(const GetDeviceHandlesRequest* arg,
 
   for (int64 i = 0; i < arg->device_count(); ++i) {
     DeviceHandle device_handle;
-    device_handle.set_handle(
-        execute_backend_->stream_executors()[i * replicas]->device_ordinal());
+    device_handle.set_handle(i);
+    device_handle.set_device_count(arg->device_count());
     *result->add_device_handles() = device_handle;
   }
 
@@ -841,11 +859,14 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg,
                               execute_backend_->default_stream_executor(),
                               &profile));
 
-  TF_RET_CHECK(!execute_backend_->Replicas().empty());
+  TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*execute_backend_,
+                                              SingleComputationDeviceHandle()));
+  TF_RET_CHECK(!replicas.empty());
+
   // Set up streams.
   std::vector<Pool<se::Stream>::SmartPtr> streams;
 
-  for (se::StreamExecutor* executor : execute_backend_->Replicas()) {
+  for (se::StreamExecutor* executor : replicas) {
     TF_ASSIGN_OR_RETURN(Pool<se::Stream>::SmartPtr stream,
                         execute_backend_->BorrowStream(executor));
     streams.push_back(std::move(stream));
@@ -927,19 +948,20 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg,
   Literal literal = Literal(arg->literal());
   const Shape& shape = literal.shape();
 
-  if (ShapeUtil::IsTuple(shape) && execute_backend_->Replicas().size() > 1) {
+  if (ShapeUtil::IsTuple(shape) && execute_backend_->replica_count() > 1) {
     // TODO(b/32990684): Tuple transfers to host end up allocating further
     // buffers - implement that correctly.
     return Unimplemented(
         "Tuple transfers to the device not supported with replication.");
   }
 
-  se::StreamExecutor* stream_executor;
+  std::vector<se::StreamExecutor*> replicas;
   if (arg->has_device_handle()) {
-    TF_ASSIGN_OR_RETURN(stream_executor, execute_backend_->stream_executor(
-                                             arg->device_handle().handle()));
+    TF_ASSIGN_OR_RETURN(replicas,
+                        Replicas(*execute_backend_, arg->device_handle()));
   } else {
-    stream_executor = execute_backend_->default_stream_executor();
+    TF_ASSIGN_OR_RETURN(
+        replicas, Replicas(*execute_backend_, SingleComputationDeviceHandle()));
   }
 
   // Allocate memory on the device, using the stream executor. The size of the
@@ -950,14 +972,12 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg,
 
   TF_ASSIGN_OR_RETURN(se::DeviceMemoryBase allocation,
                       execute_backend_->memory_allocator()->Allocate(
-                          stream_executor->device_ordinal(), allocation_size));
+                          replicas[0]->device_ordinal(), allocation_size));
 
   *result->mutable_data() = allocation_tracker_.Register(
-      execute_backend_.get(), stream_executor->device_ordinal(), allocation,
-      shape, StrCat("TransferToServer literal of size ", allocation_size));
+      execute_backend_.get(), replicas[0]->device_ordinal(), allocation, shape,
+      StrCat("TransferToServer literal of size ", allocation_size));
 
-  TF_ASSIGN_OR_RETURN(auto replicas, execute_backend_->Replicas(
-                                         stream_executor->device_ordinal()));
   for (se::StreamExecutor* executor : replicas) {
     TF_RETURN_IF_ERROR(
         execute_backend_->transfer_manager()->TransferLiteralToDevice(
@@ -968,7 +988,7 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg,
 
 tensorflow::Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
                                              TransferToInfeedResponse* result) {
-  const int64 replica_count = execute_backend_->Replicas().size();
+  const int64 replica_count = execute_backend_->replica_count();
   if (arg->replica_id() < 0 || arg->replica_id() >= replica_count) {
     return FailedPrecondition(
         "%s",
@@ -980,11 +1000,14 @@ tensorflow::Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
 
   se::StreamExecutor* executor;
   if (arg->has_device_handle()) {
-    TF_ASSIGN_OR_RETURN(auto replicas, execute_backend_->Replicas(
-                                           arg->device_handle().handle()));
+    TF_ASSIGN_OR_RETURN(auto replicas,
+                        Replicas(*execute_backend_, arg->device_handle()));
     executor = replicas[arg->replica_id()];
   } else {
-    executor = execute_backend_->Replicas()[arg->replica_id()];
+    TF_ASSIGN_OR_RETURN(
+        auto replicas,
+        Replicas(*execute_backend_, SingleComputationDeviceHandle()));
+    executor = replicas[arg->replica_id()];
   }
 
   return execute_backend_->transfer_manager()->TransferLiteralToInfeed(
@@ -994,7 +1017,7 @@ tensorflow::Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
 tensorflow::Status Service::TransferFromOutfeed(
     const TransferFromOutfeedRequest* arg,
     TransferFromOutfeedResponse* result) {
-  const int64 replica_count = execute_backend_->Replicas().size();
+  const int64 replica_count = execute_backend_->replica_count();
   if (arg->replica_id() < 0 || arg->replica_id() >= replica_count) {
     return FailedPrecondition(
         "The replica_id=%lld on TransferFromOutfeedRequest not in range [0, "
@@ -1004,11 +1027,14 @@ tensorflow::Status Service::TransferFromOutfeed(
 
   se::StreamExecutor* executor;
   if (arg->has_device_handle()) {
-    TF_ASSIGN_OR_RETURN(auto replicas, execute_backend_->Replicas(
-                                           arg->device_handle().handle()));
+    TF_ASSIGN_OR_RETURN(auto replicas,
+                        Replicas(*execute_backend_, arg->device_handle()));
     executor = replicas[arg->replica_id()];
   } else {
-    executor = execute_backend_->Replicas()[arg->replica_id()];
+    TF_ASSIGN_OR_RETURN(
+        auto replicas,
+        Replicas(*execute_backend_, SingleComputationDeviceHandle()));
+    executor = replicas[arg->replica_id()];
   }
 
   Literal literal;
@@ -1387,4 +1413,28 @@ tensorflow::Status Service::LoadComputationSnapshot(
   return tensorflow::Status::OK();
 }
 
+DeviceHandle Service::SingleComputationDeviceHandle() const {
+  DeviceHandle device_handle;
+  device_handle.set_handle(0);
+  device_handle.set_device_count(1);
+  return device_handle;
+}
+
+StatusOr<std::vector<perftools::gputools::StreamExecutor*>> Service::Replicas(
+    const Backend& backend, const DeviceHandle& device_handle) const {
+  std::vector<perftools::gputools::StreamExecutor*> replicas;
+  for (int replica = 0; replica < backend.replica_count(); ++replica) {
+    // From the computation placer, find out the device ids of the replicas for
+    // the given device handle.
+    TF_ASSIGN_OR_RETURN(
+        int device_ordinal,
+        backend.computation_placer()->DeviceId(replica, device_handle.handle(),
+                                               backend.replica_count(),
+                                               device_handle.device_count()));
+    TF_ASSIGN_OR_RETURN(auto executor, backend.stream_executor(device_ordinal));
+    replicas.push_back(executor);
+  }
+  return replicas;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index abd1281bdd..81aa0d2e8e 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -319,8 +319,7 @@ class Service : public ServiceInterface {
           std::vector<perftools::gputools::DeviceMemoryBase>>
           arguments,
       Backend* backend,
-      tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*>
-          executors,
+      tensorflow::gtl::ArraySlice<DeviceHandle> device_handles,
       tensorflow::gtl::ArraySlice<string> result_tags);
 
   // Returns an HLO dumper for use in the compiler (it refers to flags
@@ -346,6 +345,16 @@ class Service : public ServiceInterface {
   tensorflow::Status ValidateResultShapeWithLayout(
       const Shape& shape_with_layout, const Shape& result_shape) const;
 
+  // Returns the stream executors assigned to the replicas represented by the
+  // given device handle. Each device_handle is a virtual replicated device that
+  // represents a set of physical devices for the replicas.
+  StatusOr<std::vector<perftools::gputools::StreamExecutor*>> Replicas(
+      const Backend& backend, const DeviceHandle& device_handle) const;
+
+  // Returns the device handle that represents the replicated device for a
+  // single computation that is not model-parallelized.
+  DeviceHandle SingleComputationDeviceHandle() const;
+
   // Tracks computations built via the API.
   ComputationTracker computation_tracker_;
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 5fa515b26f..b81116c100 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -99,6 +99,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:backend",
         "//tensorflow/compiler/xla/service:compiler",
         "//tensorflow/compiler/xla/service:computation_layout",
+        "//tensorflow/compiler/xla/service:computation_placer",
         "//tensorflow/compiler/xla/service:executable",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_execution_profile",
@@ -196,6 +197,7 @@ cc_library(
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/service:computation_placer",
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/compiler/xla/service:local_service",
         "//tensorflow/compiler/xla/service:platform_util",
@@ -746,6 +748,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/service:computation_placer",
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/compiler/xla/service:local_service",
         "//tensorflow/compiler/xla/service:platform_util",
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 633d16c4c3..c8fd31d0ad 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -255,11 +255,15 @@ message ComputationDataHandle {
   int64 handle = 1;
 }
 
-// Handle given to a user that represents a device to execute a computation.
-// When replication is enabled, the device handle represents the device for the
-// replica id 0.
+// Handle given to a user that represents a replicated virtual device. Each
+// replicated device represents N physical devices for execution where N is the
+// number of replicas.
 message DeviceHandle {
   int64 handle = 1;
+
+  // The number of model-parallel virtual devices that communicate via XLA
+  // Send/Recv instructions.
+  int64 device_count = 2;
 }
 
 // Handle given to a user to represent a channel between two computations
@@ -269,6 +273,21 @@ message ChannelHandle {
   int64 handle = 1;
 }
 
+// DeviceAssignmentProto is a serialized form of DeviceAssignment class, which
+// represents the device ids assigned to a set of replicated computations.
+// See xla::DeviceAssignment class comment for more details.
+message DeviceAssignmentProto {
+  int32 replica_count = 1;
+  int32 computation_count = 2;
+
+  // Each logical computation runs on replica_count physical devices.
+  // ComputationDevice represents the device ids assinged to the replicas.
+  message ComputationDevice {
+    repeated int32 replica_device_ids = 1;
+  }
+  repeated ComputationDevice computation_devices = 3;
+}
+
 // Literals are used when the server and client need to exchange materialized
 // data / results. Literals are also used to describe constants used in
 // computations.
-- 
GitLab


From 4ac7cc54c81984e38da7a17992a56f62560e2870 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Wed, 14 Jun 2017 19:08:34 -0700
Subject: [PATCH 0098/1375] Add backports.weakref to Bazel build

This way people using Bazel don't have to pip install it.

PiperOrigin-RevId: 159057260
---
 tensorflow/python/BUILD                       |  1 +
 .../ci_build/install/install_pip_packages.sh  |  3 ---
 .../install/install_python3.5_pip_packages.sh |  3 ---
 tensorflow/tools/pip_package/BUILD            |  1 +
 tensorflow/workspace.bzl                      | 25 +++++++++++++++++--
 third_party/backports_weakref.BUILD           | 22 ++++++++++++++++
 6 files changed, 47 insertions(+), 8 deletions(-)
 create mode 100644 third_party/backports_weakref.BUILD

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 10afd4f346..4a395290e0 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2464,6 +2464,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//third_party/py/numpy",
+        "@org_python_pypi_backports_weakref",
         "@protobuf//:protobuf_python",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index 8768852dc7..b8f9fc8453 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -85,6 +85,3 @@ pip2 install mock
 
 pip2 install portpicker
 pip3 install portpicker
-
-pip2 install backports.weakref==1.0rc1
-pip3 install backports.weakref==1.0rc1
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index edfc4e3a98..e7e2d256cd 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -89,6 +89,3 @@ pip3.5 install wheel==0.29.0
 pip3.5 install portpicker
 
 pip3.5 install werkzeug
-
-pip3.5 install backports.weakref==1.0rc1
-
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 798338d787..fa51086922 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -122,6 +122,7 @@ filegroup(
         "@six_archive//:LICENSE",
         "@snappy//:COPYING",
         "@zlib_archive//:zlib.h",
+        "@org_python_pypi_backports_weakref//:LICENSE",
     ] + if_not_windows([
         "@nccl_archive//:LICENSE.txt",
     ]) + tf_additional_license_deps(),
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index de5525b0f2..01f12ee8db 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -291,13 +291,35 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       name = "six_archive",
       urls = [
           "http://mirror.bazel.build/pypi.python.org/packages/source/s/six/six-1.10.0.tar.gz",
-          "http://pypi.python.org/packages/source/s/six/six-1.10.0.tar.gz",
+          "https://pypi.python.org/packages/source/s/six/six-1.10.0.tar.gz",
       ],
       sha256 = "105f8d68616f8248e24bf0e9372ef04d3cc10104f1980f54d57b2ce73a5ad56a",
       strip_prefix = "six-1.10.0",
       build_file = str(Label("//third_party:six.BUILD")),
   )
 
+  native.new_http_archive(
+      name = "org_python_pypi_backports_weakref",
+      urls = [
+          "http://mirror.bazel.build/pypi.python.org/packages/bc/cc/3cdb0a02e7e96f6c70bd971bc8a90b8463fda83e264fa9c5c1c98ceabd81/backports.weakref-1.0rc1.tar.gz",
+          "https://pypi.python.org/packages/bc/cc/3cdb0a02e7e96f6c70bd971bc8a90b8463fda83e264fa9c5c1c98ceabd81/backports.weakref-1.0rc1.tar.gz",
+      ],
+      sha256 = "8813bf712a66b3d8b85dc289e1104ed220f1878cf981e2fe756dfaabe9a82892",
+      strip_prefix = "backports.weakref-1.0rc1/src",
+      build_file = str(Label("//third_party:backports_weakref.BUILD")),
+  )
+
+  filegroup_external(
+      name = "org_python_license",
+      licenses = ["notice"],  # Python 2.0
+      sha256_urls = {
+          "b5556e921715ddb9242c076cae3963f483aa47266c5e37ea4c187f77cc79501c": [
+              "http://mirror.bazel.build/docs.python.org/2.7/_sources/license.txt",
+              "https://docs.python.org/2.7/_sources/license.txt",
+          ],
+      },
+  )
+
   native.bind(
       name = "six",
       actual = "@six_archive//:six",
@@ -622,4 +644,3 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       strip_prefix = "pprof-c0fb62ec88c411cc91194465e54db2632845b650",
       build_file = str(Label("//third_party:pprof.BUILD")),
   )
-
diff --git a/third_party/backports_weakref.BUILD b/third_party/backports_weakref.BUILD
new file mode 100644
index 0000000000..0adfc5f054
--- /dev/null
+++ b/third_party/backports_weakref.BUILD
@@ -0,0 +1,22 @@
+# Description:
+#   Backport of new features in Python's weakref module.
+
+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])  # Python 2.0
+
+py_library(
+    name = "org_python_pypi_backports_weakref",
+    srcs = [
+        "backports/__init__.py",
+        "backports/weakref.py",
+    ],
+    srcs_version = "PY2AND3",
+)
+
+genrule(
+    name = "license",
+    srcs = ["@org_python_license"],
+    outs = ["LICENSE"],
+    cmd = "cp $< $@",
+)
-- 
GitLab


From b528f27fa0825882c2c2252f261309f5b4c49421 Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Wed, 14 Jun 2017 19:25:48 -0700
Subject: [PATCH 0099/1375] Do not trigger clear_extraneous_savers because it
 breaks some graphs.

PiperOrigin-RevId: 159057934
---
 tensorflow/python/saved_model/builder_impl.py | 32 ++++------
 .../python/saved_model/saved_model_test.py    | 61 -------------------
 2 files changed, 12 insertions(+), 81 deletions(-)

diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py
index e6d71a48df..5c988bf95a 100644
--- a/tensorflow/python/saved_model/builder_impl.py
+++ b/tensorflow/python/saved_model/builder_impl.py
@@ -287,16 +287,12 @@ class SavedModelBuilder(object):
     # The graph almost certainly previously contained at least one Saver, and
     # possibly several (e.g. one for loading a pretrained embedding, and another
     # for the model weights).  However, a *new* Saver was just created that
-    # includes all of the variables.  In the context of the SavedModel, this
-    # new Saver is the only one that needs to be retained. The associated
-    # checkpoint produced in add_meta_graph_and_variables() contains all of the
-    # variable values.  Thus, any preexisting Savers are redundant and useless
-    # at best, but worse may break downstream graph-processing tools, and can be
-    # confusing during debugging. It is therefore safe and wise to set
-    # `clear_extraneous_savers` to `True`, since it removes both the extraneous
-    # SaverDefs and their associated Save/Restore Ops from the graph.
-    meta_graph_def = saver.export_meta_graph(clear_devices=clear_devices,
-                                             clear_extraneous_savers=True)
+    # includes all of the variables.  Removing the preexisting ones was the
+    # motivation for the clear_extraneous_savers option, but it turns out that
+    # there are edge cases where that option breaks the graph.  Until that is
+    # resolved, we just leave the option set to False for now.
+    # TODO(soergel): Reinstate clear_extraneous_savers=True when possible.
+    meta_graph_def = saver.export_meta_graph(clear_devices=clear_devices)
 
     # Tag the meta graph def and add it to the SavedModel.
     self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map)
@@ -378,16 +374,12 @@ class SavedModelBuilder(object):
     # The graph almost certainly previously contained at least one Saver, and
     # possibly several (e.g. one for loading a pretrained embedding, and another
     # for the model weights).  However, a *new* Saver was just created that
-    # includes all of the variables.  In the context of the SavedModel, this
-    # new Saver is the only one that needs to be retained.  The associated
-    # checkpoint that was saved just above contains all of the variable values.
-    # Thus, any preexisting Savers are redundant and useless at best, but worse
-    # may break downstream graph-processing tools, and can be confusing during
-    # debugging.  It is therefore safe and wise to set `clear_extraneous_savers`
-    # to `True`, since it removes both the extraneous SaverDefs and their
-    # associated Save/Restore Ops from the graph.
-    meta_graph_def = saver.export_meta_graph(clear_devices=clear_devices,
-                                             clear_extraneous_savers=True)
+    # includes all of the variables.  Removing the preexisting ones was the
+    # motivation for the clear_extraneous_savers option, but it turns out that
+    # there are edge cases where that option breaks the graph.  Until that is
+    # resolved, we just leave the option set to False for now.
+    # TODO(soergel): Reinstate clear_extraneous_savers=True when possible.
+    meta_graph_def = saver.export_meta_graph(clear_devices=clear_devices)
 
     # Tag the meta graph def and add it to the SavedModel.
     self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map)
diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py
index 0eb9f49fed..fcd6bc3954 100644
--- a/tensorflow/python/saved_model/saved_model_test.py
+++ b/tensorflow/python/saved_model/saved_model_test.py
@@ -39,7 +39,6 @@ from tensorflow.python.saved_model import loader
 from tensorflow.python.saved_model import main_op
 from tensorflow.python.saved_model import signature_def_utils
 from tensorflow.python.saved_model import tag_constants
-from tensorflow.python.training import saver as tf_saver
 from tensorflow.python.training import saver_test_utils
 from tensorflow.python.util import compat
 
@@ -810,66 +809,6 @@ class SavedModelTest(test.TestCase):
       self.assertEqual(
           42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())
 
-  def testClearExtraneousSavers(self):
-    export_dir = os.path.join(test.get_temp_dir(),
-                              "test_clear_extraneous_savers")
-    builder = saved_model_builder.SavedModelBuilder(export_dir)
-
-    # Create a variable and a Saver.
-    with ops.Graph().as_default() as graph:
-      with session.Session(
-          target="",
-          config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
-        self._init_and_validate_variable(sess, "v", 42)
-
-        # Add two Savers, which should be removed in
-        # add_meta_graph_and_variables() in favor of the locally added one.
-        saver1 = tf_saver.Saver()
-        graph.add_to_collection(ops.GraphKeys.SAVERS, saver1)
-        saver2 = tf_saver.Saver()
-        graph.add_to_collection(ops.GraphKeys.SAVERS, saver2)
-
-        # Confirm there are two SaverDefs.
-        savers = graph.get_collection(ops.GraphKeys.SAVERS)
-        self.assertEqual(2, len(savers))
-
-        # Confirm there are two Save and two Restore ops.
-        save_op_names = set([x.name for x in graph.get_operations()
-                             if x.type == "SaveV2"])
-        self.assertSetEqual(set(["save/SaveV2", "save_1/SaveV2"]),
-                            save_op_names)
-
-        restore_op_names = set([x.name for x in graph.get_operations()
-                                if x.type == "RestoreV2"])
-        self.assertSetEqual(set(["save/RestoreV2", "save_1/RestoreV2"]),
-                            restore_op_names)
-
-        # The SavedModel builder adds its own Saver' for a total of three.
-        builder.add_meta_graph_and_variables(
-            sess, [tag_constants.TRAINING], clear_devices=True)
-
-    # Save the SavedModel to disk.
-    builder.save()
-
-    # Restore the graph.
-    with ops.Graph().as_default() as graph:
-      with self.test_session(graph=graph) as sess:
-        loader.load(sess, [tag_constants.TRAINING], export_dir)
-        self.assertEqual(
-            42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())
-
-        # Confirm that the reloaded graph has only one SaverDef.
-        savers = ops.get_collection(ops.GraphKeys.SAVERS)
-        self.assertEqual(1, len(savers))
-
-        # The reloaded graph should have exactly one Save and one Restore op.
-        save_op_names = set([x.name for x in graph.get_operations()
-                             if x.type == "SaveV2"])
-        self.assertSetEqual(set(["save_2/SaveV2"]), save_op_names)
-        restore_op_names = set([x.name for x in graph.get_operations()
-                                if x.type == "RestoreV2"])
-        self.assertSetEqual(set(["save_2/RestoreV2"]), restore_op_names)
-
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From f564625d3c1bc714dc0811c25669f842e5f39143 Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Wed, 14 Jun 2017 20:35:16 -0700
Subject: [PATCH 0100/1375] Use gfile for reading a file.

PiperOrigin-RevId: 159061654
---
 tensorflow/python/grappler/cost_analyzer_tool.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py
index 80c8970c0b..5c1114a409 100644
--- a/tensorflow/python/grappler/cost_analyzer_tool.py
+++ b/tensorflow/python/grappler/cost_analyzer_tool.py
@@ -24,10 +24,11 @@ import sys
 from tensorflow.core.protobuf import meta_graph_pb2
 from tensorflow.python.grappler import cost_analyzer
 from tensorflow.python.platform import app
+from tensorflow.python.platform import gfile
 
 
 def main(_):
-  with open(FLAGS.input) as input_file:
+  with gfile.GFile(FLAGS.input) as input_file:
     metagraph = meta_graph_pb2.MetaGraphDef()
     metagraph.ParseFromString(input_file.read())
 
-- 
GitLab


From 922fe29b37efe127e4a8b4344ed02b3edb0173d4 Mon Sep 17 00:00:00 2001
From: Andrew Selle <aselle@google.com>
Date: Wed, 14 Jun 2017 20:45:30 -0700
Subject: [PATCH 0101/1375] Point slice users to Tensor.__getitem__

Fixes #4516

PiperOrigin-RevId: 159062204
---
 tensorflow/python/ops/array_ops.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 427c409c1d..b2aff617df 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -513,6 +513,10 @@ def slice(input_, begin, size, name=None):
   words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
   want to slice from.
 
+  Note that @{tf.Tensor.__getitem__} is typically a more pythonic way to
+  perform slices, as it allows you to write `foo[3:7, :-2]` instead of
+  `tf.slice([3, 0], [4, foo.get_shape()[1]-2])`.
+
   `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
   all remaining elements in dimension i are included in the
   slice. In other words, this is equivalent to setting:
-- 
GitLab


From 08b26581c12b724c3f734e43622c78070063b4cd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 14 Jun 2017 21:00:18 -0700
Subject: [PATCH 0102/1375] Make reference edges stay orange in trace inputs
 mode.

PiperOrigin-RevId: 159062957
---
 tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html b/tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html
index fb2bc13f9a..4777eddae0 100644
--- a/tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html
+++ b/tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html
@@ -174,7 +174,6 @@ limitations under the License.
 ::content .input-edge-highlight > text {
   fill: black !important;
 }
-::content .input-edge-highlight > path,
 ::content .input-highlight > .in-annotations > .annotation > .annotation-edge,
 ::content .input-highlight-selected > .in-annotations > .annotation >
 .annotation-edge {
-- 
GitLab


From 4c82ff2bc35a43a257e8b79ea594224387dc360f Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 14 Jun 2017 21:24:34 -0700
Subject: [PATCH 0103/1375] Improved the virtual cluster to actually simulate
 the evaluation of a TF graph.

PiperOrigin-RevId: 159064313
---
 tensorflow/core/grappler/clusters/BUILD       | 17 ++++
 .../core/grappler/clusters/virtual_cluster.cc | 59 +++++++++++-
 .../core/grappler/clusters/virtual_cluster.h  |  9 +-
 .../grappler/clusters/virtual_cluster_test.cc | 95 +++++++++++++++++++
 .../core/grappler/costs/virtual_scheduler.h   |  1 +
 .../optimizers/memory_optimizer_test.cc       |  8 +-
 .../optimizers/static_schedule_test.cc        | 12 +--
 7 files changed, 185 insertions(+), 16 deletions(-)
 create mode 100644 tensorflow/core/grappler/clusters/virtual_cluster_test.cc

diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index fd2f2b3249..10df765e13 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -65,7 +65,24 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         ":cluster",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler/costs:op_level_cost_estimator",
+        "//tensorflow/core/grappler/costs:virtual_scheduler",
+    ],
+)
+
+cc_test(
+    name = "virtual_cluster_test",
+    srcs = ["virtual_cluster_test.cc"],
+    deps = [
+        ":virtual_cluster",
+        "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder",
     ],
 )
 
diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc
index 4ca4c03dbb..30eaccb801 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster.cc
+++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc
@@ -14,13 +14,18 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/clusters/virtual_cluster.h"
+#include "tensorflow/core/framework/cost_graph.pb.h"
+#include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/grappler/costs/op_level_cost_estimator.h"
+#include "tensorflow/core/grappler/costs/virtual_scheduler.h"
 
 namespace tensorflow {
 namespace grappler {
 
 VirtualCluster::VirtualCluster(
     const std::unordered_map<string, DeviceProperties>& devices)
-    : Cluster(0) {
+    : Cluster(0), node_estimator_(new OpLevelCostEstimator()) {
   devices_ = devices;
 }
 
@@ -32,12 +37,60 @@ Status VirtualCluster::Initialize(const GrapplerItem& item) {
   return Status::OK();
 }
 
-Status VirtualCluster::Run(const GraphDef& item,
+Status VirtualCluster::Run(const GraphDef& graph,
                            const std::vector<std::pair<string, Tensor>>& feed,
                            const std::vector<string>& fetch,
                            RunMetadata* metadata) {
-  return Status::OK();
+  // Initialize a virtual scheduler to process the graph. Make sure to use
+  // static shape inference to prevent the schedulrer from calling the Run
+  // method on the cluster, and create an infinite loop.
+  GrapplerItem item;
+  item.graph = graph;
+  item.feed = feed;
+  item.fetch = fetch;
+  VirtualScheduler scheduler(&item, true, this);
+  TF_RETURN_IF_ERROR(scheduler.Init());
+
+  if (metadata) {
+    metadata->clear_step_stats();
+    metadata->clear_cost_graph();
+  }
+
+  Costs node_costs;
+  do {
+    NodeInfo node_info = scheduler.GetCurrNodeInfo();
+    const auto& op_info = node_info.op_info;
+    node_costs = node_estimator_->PredictCosts(op_info);
+    if (metadata) {
+      CostGraphDef::Node* cost_node =
+          metadata->mutable_cost_graph()->add_node();
+      const string& op_name = node_info.name;
+      cost_node->set_name(op_name);
+      cost_node->set_device(node_info.device_name);
+      cost_node->set_compute_cost(
+          node_costs.execution_time.asMicroSeconds().count());
+      cost_node->set_compute_time(
+          node_costs.compute_time.asMicroSeconds().count());
+      cost_node->set_memory_time(
+          node_costs.memory_time.asMicroSeconds().count());
+      for (const auto& output : node_info.op_info.outputs()) {
+        auto output_info = cost_node->add_output_info();
+        output_info->set_dtype(output.dtype());
+        *output_info->mutable_shape() = output.shape();
 
+        int64 size = DataTypeSize(output.dtype());
+        for (const auto& dim : output.shape().dim()) {
+          size *= std::max<int64>(1, dim.size());
+        }
+        output_info->set_size(size);
+      }
+    }
+  } while (scheduler.MarkCurrNodeExecuted(node_costs));
+
+  if (metadata) {
+    scheduler.Summary(metadata->mutable_step_stats());
+  }
+  return Status::OK();
 }
 
 }  // namespace grappler
diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.h b/tensorflow/core/grappler/clusters/virtual_cluster.h
index cd8436a987..452c6f7c8e 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster.h
+++ b/tensorflow/core/grappler/clusters/virtual_cluster.h
@@ -18,15 +18,15 @@ limitations under the License.
 
 #include <unordered_map>
 #include "tensorflow/core/grappler/clusters/cluster.h"
+#include "tensorflow/core/grappler/costs/op_level_cost_estimator.h"
 #include "tensorflow/core/protobuf/device_properties.pb.h"
 
 namespace tensorflow {
 namespace grappler {
 
 // Create a simple cluster that lists the devices (and their properties)
-// available in a TensorFlow session. This cluster doesn't allow running an
-// actual graph. It is useful however when used in conjusction with costs models
-// that aren't based on the execution of the graph.
+// available in a TensorFlow session. This cluster simulates the execution of
+// actual graphs.
 class VirtualCluster : public Cluster {
  public:
   VirtualCluster(const std::unordered_map<string, DeviceProperties>& devices);
@@ -38,6 +38,9 @@ class VirtualCluster : public Cluster {
   Status Run(const GraphDef& item,
              const std::vector<std::pair<string, Tensor>>& feed,
              const std::vector<string>& fetch, RunMetadata* metadata) override;
+
+ private:
+  std::unique_ptr<OpLevelCostEstimator> node_estimator_;
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/clusters/virtual_cluster_test.cc b/tensorflow/core/grappler/clusters/virtual_cluster_test.cc
new file mode 100644
index 0000000000..6f25e7b0d4
--- /dev/null
+++ b/tensorflow/core/grappler/clusters/virtual_cluster_test.cc
@@ -0,0 +1,95 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/clusters/virtual_cluster.h"
+#include "tensorflow/core/framework/cost_graph.pb.h"
+#include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+class VirtualClusterTest : public ::testing::Test {
+ public:
+  void SetUp() override {
+    // Invent a CPU so that predictions remain the same from machine to machine.
+    DeviceProperties cpu_device;
+    cpu_device.set_type("CPU");
+    cpu_device.set_frequency(1000);
+    cpu_device.set_num_cores(4);
+    cpu_device.set_bandwidth(32);
+    cpu_device.set_l1_cache_size(32 * 1024);
+    cpu_device.set_l2_cache_size(256 * 1024);
+    cpu_device.set_l3_cache_size(4 * 1024 * 1024);
+    std::unordered_map<string, DeviceProperties> devices;
+    devices["/job:localhost/replica:0/task:0/cpu:0"] = cpu_device;
+    cluster_.reset(new VirtualCluster(devices));
+    TF_CHECK_OK(cluster_->Provision());
+  }
+
+  void TearDown() override { cluster_.reset(); }
+
+ protected:
+  std::unique_ptr<VirtualCluster> cluster_;
+};
+
+TEST_F(VirtualClusterTest, CostModel) {
+  TrivialTestGraphInputYielder fake_input(4, 1, 10, false,
+                                          cluster_->GetDeviceNames());
+  GrapplerItem item;
+  CHECK(fake_input.NextItem(&item));
+
+  TF_CHECK_OK(cluster_->Initialize(item));
+
+  RunMetadata metadata;
+  TF_CHECK_OK(cluster_->Run(item.graph, item.feed, item.fetch, &metadata));
+
+  // There should be at least 4 nodes corresponding to the 4 stages we created
+  // in the fake input.
+  EXPECT_LE(4, metadata.cost_graph().node_size());
+  for (const auto& node : metadata.cost_graph().node()) {
+    // Skip the constant node that configures the random number generator.
+    if (node.name().find("Const/Const") != string::npos) {
+      continue;
+    }
+    EXPECT_EQ(1, node.output_info_size());
+    EXPECT_EQ(40, node.output_info(0).size());
+    const TensorShapeProto& shape = node.output_info(0).shape();
+    EXPECT_EQ(2, shape.dim_size());
+    EXPECT_EQ(10, shape.dim(0).size());
+    EXPECT_EQ(1, shape.dim(1).size());
+    if (node.name() == "x") {
+      EXPECT_EQ(1500, node.compute_cost());
+    } else {
+      EXPECT_EQ(2500, node.compute_cost());
+    }
+  }
+
+  for (const auto& dev_stat : metadata.step_stats().dev_stats()) {
+    EXPECT_EQ("/job:localhost/replica:0/task:0/cpu:0", dev_stat.device());
+    for (const auto& node : dev_stat.node_stats()) {
+      if (node.node_name() == "AddN") {
+        EXPECT_EQ(2500, node.op_end_rel_micros());
+      }
+    }
+  }
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index 7ee3310e2f..b6c4907523 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -21,6 +21,7 @@ limitations under the License.
 #include <unordered_map>
 #include <unordered_set>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/grappler/costs/cost_estimator.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index 48e6f88500..27cad07d5b 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -136,7 +136,7 @@ TEST_F(RecomputeSubgraphTest, MultiNode) {
 
 class MemoryOptimizerTest : public ::testing::Test {
  public:
-  static VirtualCluster CreateVirtualCluster() {
+  static std::unique_ptr<VirtualCluster> CreateVirtualCluster() {
     DeviceProperties cpu_device;
     cpu_device.set_type("CPU");
     cpu_device.set_frequency(1000);
@@ -144,7 +144,7 @@ class MemoryOptimizerTest : public ::testing::Test {
     cpu_device.set_bandwidth(32);
     std::unordered_map<string, DeviceProperties> devices;
     devices["/job:localhost/replica:0/task:0/cpu:0"] = cpu_device;
-    return VirtualCluster(devices);
+    return std::unique_ptr<VirtualCluster>(new VirtualCluster(devices));
   }
 };
 
@@ -167,11 +167,11 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
       (*item.graph.mutable_node(4)->mutable_attr())["_swap_to_host"];
   val.mutable_list()->add_i(0);
 
-  VirtualCluster cluster(CreateVirtualCluster());
+  std::unique_ptr<VirtualCluster> cluster(CreateVirtualCluster());
 
   MemoryOptimizer optimizer(RewriterConfig::MANUAL);
   GraphDef output;
-  Status status = optimizer.Optimize(&cluster, item, &output);
+  Status status = optimizer.Optimize(cluster.get(), item, &output);
   TF_EXPECT_OK(status);
 
   EXPECT_EQ(7, output.node_size());
diff --git a/tensorflow/core/grappler/optimizers/static_schedule_test.cc b/tensorflow/core/grappler/optimizers/static_schedule_test.cc
index c932c90765..95a745be21 100644
--- a/tensorflow/core/grappler/optimizers/static_schedule_test.cc
+++ b/tensorflow/core/grappler/optimizers/static_schedule_test.cc
@@ -29,7 +29,7 @@ namespace {
 
 class StaticScheduleTest : public ::testing::Test {
  public:
-  VirtualCluster CreateVirtualCluster() const {
+  std::unique_ptr<VirtualCluster> CreateVirtualCluster() const {
     // Invent a CPU so that predictions remain the same from machine to machine.
     DeviceProperties cpu_device;
     cpu_device.set_type("CPU");
@@ -41,7 +41,7 @@ class StaticScheduleTest : public ::testing::Test {
     cpu_device.set_l3_cache_size(4 * 1024 * 1024);
     std::unordered_map<string, DeviceProperties> devices;
     devices["/job:localhost/replica:0/task:0/cpu:0"] = cpu_device;
-    return VirtualCluster(devices);
+    return std::unique_ptr<VirtualCluster>(new VirtualCluster(devices));
   }
 };
 
@@ -51,11 +51,11 @@ TEST_F(StaticScheduleTest, BasicGraph) {
   GrapplerItem item;
   CHECK(fake_input.NextItem(&item));
 
-  VirtualCluster cluster(CreateVirtualCluster());
+  std::unique_ptr<VirtualCluster> cluster(CreateVirtualCluster());
 
   std::unordered_map<const NodeDef*, Costs::NanoSeconds> completion_times;
   Status status =
-      EstimateEarliestExecutionTimes(item, &cluster, &completion_times);
+      EstimateEarliestExecutionTimes(item, cluster.get(), &completion_times);
   TF_EXPECT_OK(status);
 
   EXPECT_EQ(item.graph.node_size(), completion_times.size());
@@ -97,11 +97,11 @@ TEST_F(StaticScheduleTest, BasicGraphWithCtrlDependencies) {
   EXPECT_EQ("e", item.graph.node(4).name());
   *item.graph.mutable_node(4)->add_input() = "^c";
 
-  VirtualCluster cluster(CreateVirtualCluster());
+  std::unique_ptr<VirtualCluster> cluster(CreateVirtualCluster());
 
   std::unordered_map<const NodeDef*, Costs::NanoSeconds> completion_times;
   Status status =
-      EstimateEarliestExecutionTimes(item, &cluster, &completion_times);
+      EstimateEarliestExecutionTimes(item, cluster.get(), &completion_times);
   TF_EXPECT_OK(status);
 
   EXPECT_EQ(item.graph.node_size(), completion_times.size());
-- 
GitLab


From fd631ecb742b706a1feedcf45d8df081212d30b7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 06:09:17 -0700
Subject: [PATCH 0104/1375] BUILD cleanup

PiperOrigin-RevId: 159095644
---
 tensorflow/compiler/xla/client/BUILD          |  1 -
 tensorflow/compiler/xla/service/BUILD         |  3 -
 tensorflow/compiler/xla/service/cpu/BUILD     |  4 ++
 tensorflow/compiler/xla/service/gpu/BUILD     |  3 +-
 .../xla/service/gpu/llvm_gpu_backend/BUILD    |  1 +
 tensorflow/compiler/xla/tests/BUILD           | 71 ++++++++++++++++++-
 6 files changed, 76 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD
index 63c6d9ddac..f50dc934f2 100644
--- a/tensorflow/compiler/xla/client/BUILD
+++ b/tensorflow/compiler/xla/client/BUILD
@@ -114,7 +114,6 @@ cc_library(
         "//tensorflow/compiler/xla/service:compile_only_service",
         "//tensorflow/compiler/xla/service:compiler",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
-        "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
         "@llvm//:support",
     ],
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 9a041e3f41..f4551b0a71 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1595,10 +1595,8 @@ cc_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
-        "//tensorflow/core:lib",
     ],
 )
 
@@ -1800,7 +1798,6 @@ cc_library(
         ":hlo",
         ":hlo_proto",
         "//tensorflow/compiler/xla:status",
-        "//tensorflow/core:lib",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index edf4d2f801..5108fda4a9 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -333,6 +333,7 @@ cc_library(
     copts = runtime_copts(),
     deps = [
         "//tensorflow/compiler/xla:types",
+        "//tensorflow/core:lib",
     ],
 )
 
@@ -404,6 +405,7 @@ cc_test(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -501,6 +503,7 @@ cc_library(
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_pass",
+        "//tensorflow/core:lib",
     ],
 )
 
@@ -510,6 +513,7 @@ cc_test(
     srcs = ["conv_canonicalization_test.cc"],
     deps = [
         ":conv_canonicalization",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/service:hlo",
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index 8698693411..de71006791 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -267,7 +267,7 @@ cc_library(
         "//tensorflow/core:stream_executor_no_cuda",
         "//tensorflow/core/platform/default/build_config:cublas_plugin",
         "//tensorflow/core/platform/default/build_config:cudnn_plugin",
-        "//tensorflow/core/platform/default/build_config:stream_executor_cuda",
+        "//tensorflow/core/platform/default/build_config:stream_executor_cuda",  # build_cleaner: keep
     ],
 )
 
@@ -376,7 +376,6 @@ cc_test(
         ":fusion_merger",
         ":instruction_fusion",
         "//tensorflow/compiler/xla:test_helpers",
-        "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD
index 724549c0c4..c6dd67dc18 100644
--- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD
@@ -28,6 +28,7 @@ cc_library(
         "utils.h",
     ],
     deps = [
+        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index b81116c100..55a9c430e5 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -117,8 +117,12 @@ cc_binary(
     name = "local_client_aot_test_helper",
     srcs = ["local_client_aot_test_helper.cc"],
     deps = [
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
+        "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
     ],
 )
@@ -204,6 +208,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:shaped_buffer",
         "//tensorflow/compiler/xla/service:transfer_manager",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
         "//third_party/eigen3",
@@ -215,7 +220,9 @@ xla_test(
     srcs = ["bad_rng_shape_validation_test.cc"],
     deps = [
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
+        "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
@@ -234,6 +241,7 @@ xla_test(
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
@@ -267,6 +275,7 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
@@ -304,6 +313,7 @@ xla_test(
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
@@ -365,6 +375,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -381,6 +392,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -401,6 +413,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -413,11 +426,13 @@ xla_test(
     srcs = ["deallocation_test.cc"],
     deps = [
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
@@ -431,12 +446,14 @@ xla_test(
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
@@ -479,6 +496,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -506,6 +524,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -538,6 +557,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -569,6 +589,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -589,6 +610,7 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -607,6 +629,7 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -632,6 +655,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -658,6 +682,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -674,11 +699,13 @@ xla_test(
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -700,6 +727,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -716,6 +744,7 @@ xla_test(
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -731,6 +760,7 @@ xla_test(
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -744,10 +774,12 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:reference_util",
+        "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:computation_placer",
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/compiler/xla/service:local_service",
@@ -775,6 +807,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -791,6 +824,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -816,6 +850,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -838,6 +873,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -861,6 +897,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -877,6 +914,7 @@ xla_test(
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -891,9 +929,11 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -910,10 +950,12 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
+        "//tensorflow/core:test",
     ],
 )
 
@@ -926,6 +968,7 @@ xla_test(
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -943,6 +986,7 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -961,6 +1005,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -974,6 +1019,7 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -986,6 +1032,7 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -1007,6 +1054,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1020,10 +1068,12 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
@@ -1042,12 +1092,14 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1063,6 +1115,7 @@ xla_test(
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1084,6 +1137,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1100,10 +1154,12 @@ xla_test(
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
@@ -1140,6 +1196,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
@@ -1154,6 +1211,7 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1169,12 +1227,14 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
         "//tensorflow/core:lib",
@@ -1193,6 +1253,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
@@ -1214,6 +1275,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:session_proto",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1230,6 +1292,7 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1248,6 +1311,7 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1264,6 +1328,7 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1281,6 +1346,7 @@ cc_test(
     linkstatic = 1,
     deps = [
         "//tensorflow/compiler/xla:executable_run_options",
+        "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
@@ -1295,9 +1361,9 @@ cc_test(
         ":local_client_test_base",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/service:computation_tracker",
         "//tensorflow/compiler/xla/service:local_service",
-        "//tensorflow/core:lib",
         "//tensorflow/core:test_main",
     ],
 )
@@ -1313,6 +1379,7 @@ xla_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1328,6 +1395,7 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1353,6 +1421,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
-- 
GitLab


From 6138e267a2cb00a242cc1badf226b8e5cda1da48 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 06:57:53 -0700
Subject: [PATCH 0105/1375] Enabling cuda configuration to use a remotely
 generated configuration. This will prepare TF for use of Bazel's new remote
 execution api.

PiperOrigin-RevId: 159098871
---
 third_party/gpus/crosstool/remote.BUILD.tpl |  10 ++
 third_party/gpus/cuda/remote.BUILD.tpl      | 105 ++++++++++++++++++++
 third_party/gpus/cuda_configure.bzl         |  42 ++++++--
 3 files changed, 150 insertions(+), 7 deletions(-)
 create mode 100644 third_party/gpus/crosstool/remote.BUILD.tpl
 create mode 100644 third_party/gpus/cuda/remote.BUILD.tpl

diff --git a/third_party/gpus/crosstool/remote.BUILD.tpl b/third_party/gpus/crosstool/remote.BUILD.tpl
new file mode 100644
index 0000000000..b2316331db
--- /dev/null
+++ b/third_party/gpus/crosstool/remote.BUILD.tpl
@@ -0,0 +1,10 @@
+# Description:
+#   Template for crosstool Build file to use a pre-generated config.
+licenses(["restricted"])
+
+package(default_visibility = ["//visibility:public"])
+
+alias(
+    name = "toolchain",
+    actual = "%{remote_cuda_repo}:toolchain",
+)
diff --git a/third_party/gpus/cuda/remote.BUILD.tpl b/third_party/gpus/cuda/remote.BUILD.tpl
new file mode 100644
index 0000000000..d88d512b90
--- /dev/null
+++ b/third_party/gpus/cuda/remote.BUILD.tpl
@@ -0,0 +1,105 @@
+# Description:
+#   Template for cuda Build file to use a pre-generated config.
+licenses(["restricted"])  # MPL2, portions GPL v3, LGPL v3, BSD-like
+
+package(default_visibility = ["//visibility:public"])
+
+config_setting(
+    name = "using_nvcc",
+    values = {
+        "define": "using_cuda_nvcc=true",
+    },
+)
+
+config_setting(
+    name = "using_clang",
+    values = {
+        "define": "using_cuda_clang=true",
+    },
+)
+
+# Equivalent to using_clang && -c opt.
+config_setting(
+    name = "using_clang_opt",
+    values = {
+        "define": "using_cuda_clang=true",
+        "compilation_mode": "opt",
+    },
+)
+
+config_setting(
+    name = "darwin",
+    values = {"cpu": "darwin"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "freebsd",
+    values = {"cpu": "freebsd"},
+    visibility = ["//visibility:public"],
+)
+
+alias(
+    name = "cuda_headers",
+    actual = "%{remote_cuda_repo}cuda:cuda_headers",
+)
+
+alias(
+    name = "cudart_static",
+    actual = "%{remote_cuda_repo}cuda:cudart_static",
+)
+
+alias(
+    name = "cuda_driver",
+    actual = "%{remote_cuda_repo}cuda:cuda_driver",
+)
+
+alias(
+    name = "cudart",
+    actual = "%{remote_cuda_repo}cuda:cudart",
+)
+
+alias(
+    name = "cublas",
+    actual = "%{remote_cuda_repo}cuda:cublas",
+)
+
+alias(
+    name = "cusolver",
+    actual = "%{remote_cuda_repo}cuda:cusolver",
+)
+
+alias(
+    name = "cudnn",
+    actual = "%{remote_cuda_repo}cuda:cudnn",
+)
+
+alias(
+    name = "cufft",
+    actual = "%{remote_cuda_repo}cuda:cufft",
+)
+
+alias(
+    name = "curand",
+    actual = "%{remote_cuda_repo}cuda:curand",
+)
+
+alias(
+    name = "cuda",
+    actual = "%{remote_cuda_repo}cuda:cuda",
+)
+
+alias(
+    name = "cupti_headers",
+    actual = "%{remote_cuda_repo}cuda:cupti_headers",
+)
+
+alias(
+    name = "cupti_dsos",
+    actual = "%{remote_cuda_repo}cuda:cupti_dsos",
+)
+
+alias(
+    name = "libdevice_root",
+    actual = "%{remote_cuda_repo}cuda:libdevice_root",
+)
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 61932a8e6d..77dc602fd9 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -883,15 +883,16 @@ def _use_cuda_clang(repository_ctx):
     return enable_cuda == "1"
   return False
 
-def _compute_cuda_extra_copts(repository_ctx, cuda_config):
+def _compute_cuda_extra_copts(repository_ctx, compute_capabilities):
   if _use_cuda_clang(repository_ctx):
-    capability_flags = ["--cuda-gpu-arch=sm_" + cap.replace(".", "") for cap in cuda_config.compute_capabilities]
+    capability_flags = ["--cuda-gpu-arch=sm_" +
+        cap.replace(".", "") for cap in compute_capabilities]
   else:
     # Capabilities are handled in the "crosstool_wrapper_driver_is_not_gcc" for nvcc
     capability_flags = []
   return str(capability_flags)
 
-def _create_cuda_repository(repository_ctx):
+def _create_local_cuda_repository(repository_ctx):
   """Creates the repository containing files set up to build with CUDA."""
   cuda_config = _get_cuda_config(repository_ctx)
 
@@ -939,7 +940,8 @@ def _create_cuda_repository(repository_ctx):
   _tpl(repository_ctx, "cuda:build_defs.bzl",
        {
            "%{cuda_is_configured}": "True",
-           "%{cuda_extra_copts}": _compute_cuda_extra_copts(repository_ctx, cuda_config),
+           "%{cuda_extra_copts}": _compute_cuda_extra_copts(
+               repository_ctx, cuda_config.compute_capabilities),
 
        })
   _tpl(repository_ctx, "cuda:BUILD",
@@ -1000,17 +1002,39 @@ def _create_cuda_repository(repository_ctx):
        })
 
 
+def _create_remote_cuda_repository(repository_ctx):
+  """Creates pointers to a remotely configured repo set up to build with CUDA."""
+  _tpl(repository_ctx, "cuda:build_defs.bzl",
+       {
+           "%{cuda_is_configured}": "True",
+           "%{cuda_extra_copts}": _compute_cuda_extra_copts(
+               repository_ctx, _compute_capabilities(repository_ctx)),
+
+       })
+  _tpl(repository_ctx, "cuda:remote.BUILD",
+       {
+           "%{remote_cuda_repo}": repository_ctx.attr.remote_config_repo,
+       }, "cuda/BUILD")
+  _tpl(repository_ctx, "crosstool:remote.BUILD", {
+           "%{remote_cuda_repo}": repository_ctx.attr.remote_config_repo,
+       }, "crosstool/BUILD")
+
 def _cuda_autoconf_impl(repository_ctx):
   """Implementation of the cuda_autoconf repository rule."""
   if not _enable_cuda(repository_ctx):
     _create_dummy_repository(repository_ctx)
   else:
-    _create_cuda_repository(repository_ctx)
-
+    if repository_ctx.attr.remote_config_repo != "":
+      _create_remote_cuda_repository(repository_ctx)
+    else:
+      _create_local_cuda_repository(repository_ctx)
 
 
 cuda_configure = repository_rule(
     implementation = _cuda_autoconf_impl,
+    attrs = {
+        "remote_config_repo": attr.string(mandatory = False, default =""),
+    },
     environ = [
         _GCC_HOST_COMPILER_PATH,
         "TF_NEED_CUDA",
@@ -1027,9 +1051,13 @@ cuda_configure = repository_rule(
 Add the following to your WORKSPACE FILE:
 
 ```python
-cuda_configure(name = "local_config_cuda")
+cuda_configure(
+  name = "local_config_cuda"
+  remote_config_repo = "@remote_cuda_config_tf//"
+)
 ```
 
 Args:
   name: A unique name for this workspace rule.
+  remote_config_repo: Location of a pre-generated config (optional).
 """
-- 
GitLab


From ff29c995ea187932a4f6f25c0a7a1a5c4577655e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 09:37:49 -0700
Subject: [PATCH 0106/1375] Updates get_started/tflearn.md to use tf.estimator
 api, renames the file and updates links.

PiperOrigin-RevId: 159114013
---
 .../contrib/kernel_methods/g3doc/tutorial.md  |  2 +-
 tensorflow/docs_src/extend/estimators.md      |  2 +-
 .../get_started/{tflearn.md => estimator.md}  | 90 ++++++++++---------
 tensorflow/docs_src/get_started/index.md      |  8 +-
 tensorflow/docs_src/get_started/input_fn.md   |  3 +-
 tensorflow/docs_src/get_started/leftnav_files |  2 +-
 tensorflow/docs_src/get_started/monitors.md   |  6 +-
 .../docs_src/tutorials/kernel_methods.md      |  2 +-
 tensorflow/docs_src/tutorials/linear.md       |  3 +-
 tensorflow/examples/learn/README.md           |  4 +-
 10 files changed, 62 insertions(+), 60 deletions(-)
 rename tensorflow/docs_src/get_started/{tflearn.md => estimator.md} (82%)

diff --git a/tensorflow/contrib/kernel_methods/g3doc/tutorial.md b/tensorflow/contrib/kernel_methods/g3doc/tutorial.md
index 9877375c2c..f39a8d80d2 100644
--- a/tensorflow/contrib/kernel_methods/g3doc/tutorial.md
+++ b/tensorflow/contrib/kernel_methods/g3doc/tutorial.md
@@ -13,7 +13,7 @@ for sparse features is in the works.
 We will use [tf.contrib.learn](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn) (TensorFlow's high-level Machine Learning API) Estimators for our ML models. The
 tf.contrib.learn API reduces the boilerplate code one needs to write for
 configuring, training and evaluating models and will let us focus on the core
-ideas. If you are not familiar with this API, [tf.contrib.learn Quickstart](https://www.tensorflow.org/get_started/tflearn) is a good place to start. We
+ideas. If you are not familiar with this API, [tf.estimator Quickstart](https://www.tensorflow.org/get_started/estimator) is a good place to start. We
 will use MNIST, a widely-used dataset containing images of handwritten digits
 (between 0 and 9). The tutorial consists of the following steps:
 
diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index 6bd21be019..3f108d145b 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -41,7 +41,7 @@ feature columns, input functions, and `fit()`/`evaluate()`/`predict()`
 operations. If you've never used tf.contrib.learn before, or need a refresher,
 you should first review the following tutorials:
 
-*   @{$tflearn$tf.contrib.learn Quickstart}: Quick introduction to
+*   @{$estimator$tf.estimator Quickstart}: Quick introduction to
     training a neural network using tf.contrib.learn.
 *   @{$wide$TensorFlow Linear Model Tutorial}: Introduction to
     feature columns, and an overview on building a linear classifier in
diff --git a/tensorflow/docs_src/get_started/tflearn.md b/tensorflow/docs_src/get_started/estimator.md
similarity index 82%
rename from tensorflow/docs_src/get_started/tflearn.md
rename to tensorflow/docs_src/get_started/estimator.md
index ed21969b3e..254da1efa7 100644
--- a/tensorflow/docs_src/get_started/tflearn.md
+++ b/tensorflow/docs_src/get_started/estimator.md
@@ -1,8 +1,8 @@
-# tf.contrib.learn Quickstart
+# tf.estimator Quickstart
 
-TensorFlow’s high-level machine learning API (tf.contrib.learn) makes it easy to
+TensorFlow’s high-level machine learning API (tf.estimator) makes it easy to
 configure, train, and evaluate a variety of machine learning models. In this
-tutorial, you’ll use tf.contrib.learn to construct a
+tutorial, you’ll use tf.estimator to construct a
 [neural network](https://en.wikipedia.org/wiki/Artificial_neural_network)
 classifier and train it on the
 [Iris data set](https://en.wikipedia.org/wiki/Iris_flower_data_set) to
@@ -10,8 +10,8 @@ predict flower species based on sepal/petal geometry. You'll write code to
 perform the following five steps:
 
 1.  Load CSVs containing Iris training/test data into a TensorFlow `Dataset`
-2.  Construct a @{tf.contrib.learn.DNNClassifier$neural network classifier}
-3.  Fit the model using the training data
+2.  Construct a @{tf.estimator.DNNClassifier$neural network classifier}
+3.  Train the model using the training data
 4.  Evaluate the accuracy of the model
 5.  Classify new samples
 
@@ -64,13 +64,13 @@ def main():
       features_dtype=np.float32)
 
   # Specify that all features have real-value data
-  feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
+  feature_columns = [tf.feature_column.numeric_column("", shape=[4])]
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
-  classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
-                                              hidden_units=[10, 20, 10],
-                                              n_classes=3,
-                                              model_dir="/tmp/iris_model")
+  classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
+                                          hidden_units=[10, 20, 10],
+                                          n_classes=3,
+                                          model_dir="/tmp/iris_model")
   # Define the training inputs
   def get_train_inputs():
     x = tf.constant(training_set.data)
@@ -78,8 +78,8 @@ def main():
 
     return x, y
 
-  # Fit model.
-  classifier.fit(input_fn=get_train_inputs, steps=2000)
+  # Train model.
+  classifier.train(input_fn=get_train_inputs, steps=2000)
 
   # Define the test inputs
   def get_test_inputs():
@@ -101,10 +101,11 @@ def main():
        [5.8, 3.1, 5.0, 1.7]], dtype=np.float32)
 
   predictions = list(classifier.predict(input_fn=new_samples))
+  predicted_classes = [p["classes"] for p in predictions]
 
   print(
       "New Samples, Class Predictions:    {}\n"
-      .format(predictions))
+      .format(predicted_classes))
 
 if __name__ == "__main__":
     main()
@@ -237,31 +238,30 @@ you'll use `training_set.data` and
 
 ## Construct a Deep Neural Network Classifier
 
-tf.contrib.learn offers a variety of predefined models, called
-@{$python/contrib.learn#estimators$`Estimator`s}, which you can
+tf.estimator offers a variety of predefined models, called
+@{$python/estimator#estimator$`Estimator`s}, which you can
 use "out of the box" to run training and evaluation operations on your data.
 Here, you'll configure a Deep Neural Network Classifier model to fit the Iris
-data. Using tf.contrib.learn, you can instantiate your
-@{tf.contrib.learn.DNNClassifier} with
-just a couple lines of code:
+data. Using tf.estimator, you can instantiate your
+@{tf.estimator.DNNClassifier} with just a couple lines of code:
 
 ```python
 # Specify that all features have real-value data
-feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
+feature_columns = [tf.feature_column.numeric_column("", shape=[4])]
 
 # Build 3 layer DNN with 10, 20, 10 units respectively.
-classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
-                                            hidden_units=[10, 20, 10],
-                                            n_classes=3,
-                                            model_dir="/tmp/iris_model")
+classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
+                                        hidden_units=[10, 20, 10],
+                                        n_classes=3,
+                                        model_dir="/tmp/iris_model")
 ```
 
 The code above first defines the model's feature columns, which specify the data
 type for the features in the data set. All the feature data is continuous, so
-`tf.contrib.layers.real_valued_column` is the appropriate function to use to
+`tf.feature_column.numeric_column` is the appropriate function to use to
 construct the feature columns. There are four features in the data set (sepal
-width, sepal height, petal width, and petal height), so accordingly `dimension`
-must be set to `4` to hold all the data.
+width, sepal height, petal width, and petal height), so accordingly `shape`
+must be set to `[4]` to hold all the data.
 
 Then, the code creates a `DNNClassifier` model using the following arguments:
 
@@ -272,11 +272,12 @@ Then, the code creates a `DNNClassifier` model using the following arguments:
 *   `n_classes=3`. Three target classes, representing the three Iris species.
 *   `model_dir=/tmp/iris_model`. The directory in which TensorFlow will save
     checkpoint data during model training. For more on logging and monitoring
-    with TensorFlow, see @{$monitors$Logging and Monitoring Basics with     tf.contrib.learn}.
+    with TensorFlow, see
+    @{$monitors$Logging and Monitoring Basics with tf.estimator}.
 
 ## Describe the training input pipeline {#train-input}
 
-The `tf.contrib.learn` API uses input functions, which create the TensorFlow
+The `tf.estimator` API uses input functions, which create the TensorFlow
 operations that generate data for the model. In this case, the data is small
 enough that it can be stored in @{tf.constant$TensorFlow constants}. The
 following code produces the simplest possible input pipeline:
@@ -293,13 +294,13 @@ def get_train_inputs():
 ## Fit the DNNClassifier to the Iris Training Data {#fit-dnnclassifier}
 
 Now that you've configured your DNN `classifier` model, you can fit it to the
-Iris training data using the @{tf.contrib.learn.BaseEstimator.fit$`fit`} method.
+Iris training data using the @{tf.estimator.Estimator.train$`train`} method.
 Pass `get_train_inputs` as the `input_fn`, and the number of steps to train
 (here, 2000):
 
 ```python
-# Fit model.
-classifier.fit(input_fn=get_train_inputs, steps=2000)
+# Train model.
+classifier.train(input_fn=get_train_inputs, steps=2000)
 ```
 
 The state of the model is preserved in the `classifier`, which means you can
@@ -307,21 +308,21 @@ train iteratively if you like. For example, the above is equivalent to the
 following:
 
 ```python
-classifier.fit(x=training_set.data, y=training_set.target, steps=1000)
-classifier.fit(x=training_set.data, y=training_set.target, steps=1000)
+classifier.train(input_fn=get_train_inputs, steps=1000)
+classifier.train(input_fn=get_train_inputs, steps=1000)
 ```
 
 However, if you're looking to track the model while it trains, you'll likely
-want to instead use a TensorFlow @{tf.contrib.learn.monitors$`monitor`}
+want to instead use a TensorFlow @{tf.SessionRunHook$`SessionRunHook`}
 to perform logging operations. See the tutorial
-@{$monitors$&ldquo;Logging and Monitoring Basics with tf.contrib.learn&rdquo;}
+@{$monitors$Logging and Monitoring Basics with tf.estimator}
 for more on this topic.
 
 ## Evaluate Model Accuracy {#evaluate-accuracy}
 
-You've fit your `DNNClassifier` model on the Iris training data; now, you can
-check its accuracy on the Iris test data using the
-@{tf.contrib.learn.BaseEstimator.evaluate$`evaluate`} method. Like `fit`,
+You've trained your `DNNClassifier` model on the Iris training data; now, you
+can check its accuracy on the Iris test data using the
+@{tf.estimator.Estimator.evaluate$`evaluate`} method. Like `train`,
 `evaluate` takes an input function that builds its input pipeline. `evaluate`
 returns a `dict` with the evaluation results. The following code passes the Iris
 test data&mdash;`test_set.data` and `test_set.target`&mdash;to `evaluate` and
@@ -343,7 +344,7 @@ print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
 ```
 
 Note: The `steps` argument to `evaluate` is important here.
-@{tf.contrib.learn.Evaluable.evaluate$`evaluate`} normally runs until it reaches
+@{tf.estimator.Estimator.evaluate$`evaluate`} normally runs until it reaches
 the end of the input. This is perfect for evaluating over a set of files, but
 the constants being used here will never throw the `OutOfRangeError` or
 `StopIteration` that it is expecting.
@@ -379,10 +380,11 @@ def new_samples():
      [5.8, 3.1, 5.0, 1.7]], dtype=np.float32)
 
 predictions = list(classifier.predict(input_fn=new_samples))
+predicted_classes = [p["classes"] for p in predictions]
 
 print(
     "New Samples, Class Predictions:    {}\n"
-    .format(predictions))
+    .format(predicted_classes))
 ```
 
 Your results should look as follows:
@@ -397,13 +399,13 @@ second sample is *Iris virginica*.
 ## Additional Resources
 
 *   For further reference materials on tf.contrib.learn, see the official
-    @{$python/contrib.learn$API docs}.
+    @{$python/estimator$API docs}.
 
-*   To learn more about using tf.contrib.learn to create linear models, see
+*   To learn more about using tf.estimator to create linear models, see
     @{$linear$Large-scale Linear Models with TensorFlow}.
 
-*   To build your own Estimator using tf.contrib.learn APIs, check out
-    @{$estimators$Creating Estimators in tf.contrib.learn}.
+*   To build your own Estimator using tf.estimator APIs, check out
+    @{$estimators$Creating Estimators in tf.estimator}.
 
 *   To experiment with neural network modeling and visualization in the browser,
     check out [Deep Playground](http://playground.tensorflow.org/).
diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md
index 241263c72c..0d302ec383 100644
--- a/tensorflow/docs_src/get_started/index.md
+++ b/tensorflow/docs_src/get_started/index.md
@@ -11,16 +11,16 @@ to training an MNIST model on TensorFlow:
 
   * @{$mnist/beginners$MNIST for ML Beginners}, which introduces MNIST through
     the high-level API.
-  * @{$mnist/pros$Deep MNIST for Experts}, which is more-in depth than 
-    "MNIST for ML Beginners," and assumes some familiarity with machine 
+  * @{$mnist/pros$Deep MNIST for Experts}, which is more-in depth than
+    "MNIST for ML Beginners," and assumes some familiarity with machine
     learning concepts.
   * @{$mnist/mechanics$TensorFlow Mechanics 101}, which introduces MNIST through
     the low-level API.
 
-For developers new to TensorFlow, the high-level API is a good place to start. 
+For developers new to TensorFlow, the high-level API is a good place to start.
 To learn about the high-level API, read the following guides:
 
-  * @{$get_started/tflearn$tf.contrib.learn Quickstart}, which introduces this
+  * @{$get_started/estimator$tf.estimator Quickstart}, which introduces this
     API.
   * @{$get_started/input_fn$Building Input Functions with tf.contrib.learn},
     which takes you into a somewhat more sophisticated use of this API.
diff --git a/tensorflow/docs_src/get_started/input_fn.md b/tensorflow/docs_src/get_started/input_fn.md
index a053617b58..cb5792a6d3 100644
--- a/tensorflow/docs_src/get_started/input_fn.md
+++ b/tensorflow/docs_src/get_started/input_fn.md
@@ -10,7 +10,8 @@ median house values.
 
 When training a neural network using tf.contrib.learn, it's possible to pass
 your feature and target data directly into your `fit`, `evaluate`, or `predict`
-operations. Here's an example taken from the @{$tflearn$tf.contrib.learn quickstart tutorial}:
+operations. Here's an example taken from the
+@{$estimator$tf.estimator quickstart tutorial}:
 
 ```python
 training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files
index 812f248d3e..656727fbfe 100644
--- a/tensorflow/docs_src/get_started/leftnav_files
+++ b/tensorflow/docs_src/get_started/leftnav_files
@@ -3,7 +3,7 @@ get_started.md
 mnist/beginners.md
 mnist/pros.md
 mnist/mechanics.md
-tflearn.md
+estimator.md
 input_fn.md
 monitors.md
 summaries_and_tensorboard.md
diff --git a/tensorflow/docs_src/get_started/monitors.md b/tensorflow/docs_src/get_started/monitors.md
index d9c605b013..5606e95365 100644
--- a/tensorflow/docs_src/get_started/monitors.md
+++ b/tensorflow/docs_src/get_started/monitors.md
@@ -4,14 +4,14 @@ When training a model, it’s often valuable to track and evaluate progress in
 real time. In this tutorial, you’ll learn how to use TensorFlow’s logging
 capabilities and the `Monitor` API to audit the in-progress training of a neural
 network classifier for categorizing irises. This tutorial builds on the code
-developed in @{$tflearn$tf.contrib.learn Quickstart} so if you
+developed in @{$estimator$tf.estimator Quickstart} so if you
 haven't yet completed that tutorial, you may want to explore it first,
 especially if you're looking for an intro/refresher on tf.contrib.learn basics.
 
 ## Setup {#setup}
 
 For this tutorial, you'll be building upon the following code from
-@{$tflearn$tf.contrib.learn Quickstart}:
+@{$estimator$tf.estimator Quickstart}:
 
 ```python
 from __future__ import absolute_import
@@ -75,7 +75,7 @@ here](https://www.tensorflow.org/code/tensorflow/examples/tutorials/monitors/iri
 
 ## Overview
 
-The @{$tflearn$tf.contrib.learn Quickstart tutorial} walked through
+The @{$estimator$tf.estimator Quickstart tutorial} walked through
 how to implement a neural net classifier to categorize iris examples into one of
 three species.
 
diff --git a/tensorflow/docs_src/tutorials/kernel_methods.md b/tensorflow/docs_src/tutorials/kernel_methods.md
index fbf1afc4ab..8506b5228e 100644
--- a/tensorflow/docs_src/tutorials/kernel_methods.md
+++ b/tensorflow/docs_src/tutorials/kernel_methods.md
@@ -22,7 +22,7 @@ TensorFlow will provide support for sparse features at a later release.
 
 This tutorial uses [tf.contrib.learn](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn)
 (TensorFlow's high-level Machine Learning API) Estimators for our ML models.
-If you are not familiar with this API, [tf.contrib.learn Quickstart](https://www.tensorflow.org/get_started/tflearn)
+If you are not familiar with this API, [tf.estimator Quickstart](https://www.tensorflow.org/get_started/estimator)
 is a good place to start. We will use the MNIST dataset. The tutorial consists
 of the following steps:
 
diff --git a/tensorflow/docs_src/tutorials/linear.md b/tensorflow/docs_src/tutorials/linear.md
index 30daf335bf..e90e688bfa 100644
--- a/tensorflow/docs_src/tutorials/linear.md
+++ b/tensorflow/docs_src/tutorials/linear.md
@@ -16,8 +16,7 @@ give it a try. This overview uses code samples from the tutorial, but the
 tutorial walks through the code in greater detail.
 
 To understand this overview it will help to have some familiarity
-with basic machine learning concepts, and also with
-@{$tflearn$tf.learn}.
+with basic machine learning concepts, and also with @{$estimator$tf.estimator}.
 
 [TOC]
 
diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index e5b34292b1..3166f37e78 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -1,9 +1,9 @@
 # Estimator Examples
 
 TensorFlow Estimators are a high-level API for TensorFlow that allows you to
-create, train, and use deep learning models easily. 
+create, train, and use deep learning models easily.
 
-See the [Quickstart tutorial](https://www.tensorflow.org/get_started/tflearn)
+See the [Quickstart tutorial](https://www.tensorflow.org/get_started/estimator)
 for an introduction to the API.
 
 To run most of these examples, you need to install the `scikit learn` library
-- 
GitLab


From cff7cb161ed6f826d5a7e1128ada5d61541a63f7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 10:18:52 -0700
Subject: [PATCH 0107/1375] Assign a unique ID to the gradient of each health
 pill.

PiperOrigin-RevId: 159119329
---
 .../components/tf_graph_common/scene.ts       | 31 ++++++++++++++-----
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/tensorflow/tensorboard/components/tf_graph_common/scene.ts b/tensorflow/tensorboard/components/tf_graph_common/scene.ts
index 14d35efd9f..4c70eacdea 100644
--- a/tensorflow/tensorboard/components/tf_graph_common/scene.ts
+++ b/tensorflow/tensorboard/components/tf_graph_common/scene.ts
@@ -574,10 +574,14 @@ function _getHealthPillTextContent(healthPill: HealthPill,
 
 /**
  * Renders a health pill for an op atop a node.
+ * nodeGroupElement: The SVG element in which to render.
+ * healthPill: A list of backend.HealthPill objects.
+ * nodeInfo: Info on the associated node.
+ * healthPillId: A unique numeric ID assigned to this health pill.
  */
 function _addHealthPill(
     nodeGroupElement: SVGElement, healthPill: HealthPill,
-    nodeInfo: render.RenderNodeInfo) {
+    nodeInfo: render.RenderNodeInfo, healthPillId: number) {
   // Check if text already exists at location.
   d3.select(nodeGroupElement.parentNode as any).selectAll('.health-pill').remove();
 
@@ -585,7 +589,7 @@ function _addHealthPill(
     return;
   }
 
-  let lastHealthPillData = healthPill.value;
+  const lastHealthPillData = healthPill.value;
 
   // For now, we only visualize the 6 values that summarize counts of tensor
   // elements of various categories: -Inf, negative, 0, positive, Inf, and NaN.
@@ -614,7 +618,9 @@ function _addHealthPill(
   healthPillGroup.appendChild(healthPillDefs);
   let healthPillGradient =
       document.createElementNS(svgNamespace, 'linearGradient');
-  const healthPillGradientId = 'health-pill-gradient';
+
+  // Every element in a web page must have a unique ID.
+  const healthPillGradientId = 'health-pill-gradient-' + healthPillId;
   healthPillGradient.setAttribute('id', healthPillGradientId);
 
   let cumulativeCount = 0;
@@ -689,8 +695,10 @@ function _addHealthPill(
     }
 
     let statsSvg = document.createElementNS(svgNamespace, 'text');
-    const minString = humanizeHealthPillStat(numericStats.min, shouldRoundOnesDigit);
-    const maxString = humanizeHealthPillStat(numericStats.max, shouldRoundOnesDigit);
+    const minString =
+        humanizeHealthPillStat(numericStats.min, shouldRoundOnesDigit);
+    const maxString =
+        humanizeHealthPillStat(numericStats.max, shouldRoundOnesDigit);
     if (totalCount > 1) {
       statsSvg.textContent = minString + ' ~ ' + maxString;
     } else {
@@ -722,13 +730,20 @@ export function addHealthPills(
     return;
   }
 
+  // We generate a unique ID for each health pill because the ID of each element
+  // in a web page must be unique, and each health pill generates a gradient
+  // that its code later refers to.
+  let healthPillId = 1;
+
   let svgRootSelection = d3.select(svgRoot);
   svgRootSelection.selectAll('g.nodeshape')
       .each(function(nodeInfo: render.RenderNodeInfo) {
         // Only show health pill data for this node if it is available.
-        let healthPills = nodeNamesToHealthPills[nodeInfo.node.name];
-        let healthPill = healthPills ? healthPills[healthPillStepIndex] : null;
-        _addHealthPill((this as SVGElement), healthPill, nodeInfo);
+        const healthPills = nodeNamesToHealthPills[nodeInfo.node.name];
+        const healthPill =
+            healthPills ? healthPills[healthPillStepIndex] : null;
+        _addHealthPill(
+            (this as SVGElement), healthPill, nodeInfo, healthPillId++);
       });
 };
 
-- 
GitLab


From 6b56b5e4b904eb7fb5e9a95cd3046069e029caa4 Mon Sep 17 00:00:00 2001
From: William Chargin <wchargin@google.com>
Date: Thu, 15 Jun 2017 10:37:46 -0700
Subject: [PATCH 0108/1375] Remove tf-sidebar-helper

It wasn't actually helpful.

As a side-effect, this change makes the text plugin behave like the
other plugins in that its "runs"/"tags" route will list all runs, not
just those that include text summaries. (Without this, the existing
infrastructure breaks when it tries to look up non-text runs in the
run-to-tag mapping returned by /data/plugin/text/runs. We'll soon be
collapsing/optionalifying this infrastructure, but we want the text
plugin to be consistent, anyway, so now is a good time to do so.)

PiperOrigin-RevId: 159121999
---
 .../components/tf_dashboard_common/BUILD      |   1 -
 .../tf_dashboard_common/dashboard-style.html  |   9 +
 .../tf-sidebar-helper.html                    | 158 ------------------
 .../tf_distribution_dashboard/BUILD           |   1 +
 .../tf-distribution-dashboard.html            |  23 +--
 .../components/tf_histogram_dashboard/BUILD   |   1 +
 .../tf-histogram-dashboard.html               |  63 +++----
 .../components/tf_image_dashboard/BUILD       |   1 +
 .../tf-image-dashboard.html                   |  23 ++-
 .../tf-scalar-dashboard.html                  |  31 +---
 .../components/tf_text_dashboard/BUILD        |   1 +
 .../tf_text_dashboard/tf-text-dashboard.html  |  22 +--
 .../tensorboard/plugins/text/text_plugin.py   |   2 +
 13 files changed, 91 insertions(+), 245 deletions(-)
 delete mode 100644 tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html

diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/BUILD b/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
index 3d6094deea..2a5a655f1d 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
@@ -29,7 +29,6 @@ ts_web_library(
         "tf-regex-group.html",
         "tf-regex-group.ts",
         "tf-run-selector.html",
-        "tf-sidebar-helper.html",
     ],
     path = "/tf-dashboard-common",
     deps = [
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-style.html b/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-style.html
index 6629e5bfc2..723919abce 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-style.html
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-style.html
@@ -28,6 +28,15 @@ limitations under the License.
         margin-right: 20px;
       }
 
+      tf-categorizer {
+        flex-shrink: 0;
+      }
+
+      tf-runs-selector {
+        flex-shrink: 1;
+        flex-grow: 1;
+      }
+
       .sidebar-section {
         border-top: solid 1px rgba(0, 0, 0, 0.12);
         padding: 15px 0px 15px 30px;
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html
deleted file mode 100644
index 2b49d1be62..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-sidebar-helper.html
+++ /dev/null
@@ -1,158 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="tf-categorizer.html">
-<link rel="import" href="tf-run-selector.html">
-
-<!--
-tf-sidebar-helper is a component that renders a sidebar for configuration
-components, like the tf-categorizer and the tf-run-selector. The component can
-also be extended with more options useful to the dashboards.
-
-To use it, create the tf-sidebar-helper with the required properties. To extend
-it with extra configuration components, add them to the element's component:
-
-<tf-sidebar-helper
-  backend: "[[backend]]",
-  categories: "{{outputCategories}}",
-  run2tag: "[[run2tag]]",
-  runs: "[[runs]]",
-  selectedRuns: "{{outSelectedRuns}}",
-  >
-  <div class="extend-first-section">
-    <my options>
-  </div>
-  <div class="sidebar-section">
-    <my options>
-  </div>
-  ...
-</tf-sidebar-helper>
-
-Elements inside the .extend-first-section div will be put on the first section
-of the sidebar, while the rest of the divs will be put after it and before the
-tf-run-selector.
-
-@element tf-sidebar-helper
--->
-<dom-module id="tf-sidebar-helper">
-  <template>
-    <div class="sidebar-section">
-      <tf-categorizer
-        id="categorizer"
-        tags="[[tags]]"
-        categories="{{categories}}"
-        ></tf-categorizer>
-      <content select=".extend-first-section"></content>
-    </div>
-    <content></content>
-    <div class="sidebar-section">
-      <tf-run-selector
-        id="runSelector"
-        backend="[[backend]]"
-        runs="[[runs]]"
-        out-selected="{{selectedRuns}}"
-        ></tf-run-selector>
-    </div>
-    <style include="dashboard-style"></style>
-    <style>
-      :host {
-        display: flex;
-        flex-direction: column;
-        height: 100%;
-      }
-
-      #categorizer {
-        flex-shrink: 0;
-      }
-
-      #runSelector {
-        flex-shrink: 1;
-        flex-grow: 1;
-      }
-
-      .sidebar-section {
-        border-top: solid 1px rgba(0, 0, 0, 0.12);
-        padding: 20px 0px 20px 30px;
-      }
-
-      .sidebar-section:first-child {
-        border: none;
-      }
-
-      .sidebar-section:last-child {
-        flex-grow: 1;
-        display: flex;
-      }
-
-      paper-checkbox {
-        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
-        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
-        font-size: 14px;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-sidebar-helper",
-      properties: {
-        /**
-         * The backend object used to issue requests.
-         */
-        backend: Object,
-
-        /**
-         * This is an output of the categories that the user selected to
-         * separate the different tags. Each category here should be given its
-         * own collapsible pane.
-         */
-        categories: {
-          type: Array,
-          notify: true,
-        },
-
-        /**
-         * Map from runs to the valid tags that have them.
-         */
-        run2tag: Object,
-
-        /**
-         * Input of all valid runs that can be selected by the user.
-         */
-        runs: Array,
-
-        /**
-         * Outputs an array with the runs that are selected by the user (i.e.
-         * valid to be displayed).
-         */
-        selectedRuns: {
-          type: Array,
-          notify: true,
-        },
-
-        tags: {
-          type: Array,
-          computed: "_getTags(run2tag.*)"
-        },
-      },
-      _getTags: function() {
-        return _.union.apply(null, _.values(this.run2tag));
-      },
-    })
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/BUILD b/tensorflow/tensorboard/components/tf_distribution_dashboard/BUILD
index 5ddd6ba5bb..2ccdc730d2 100644
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/BUILD
+++ b/tensorflow/tensorboard/components/tf_distribution_dashboard/BUILD
@@ -14,6 +14,7 @@ ts_web_library(
         "//tensorflow/tensorboard/components/tf_dashboard_common",
         "//tensorflow/tensorboard/components/tf_imports:lodash",
         "//tensorflow/tensorboard/components/tf_imports:polymer",
+        "//tensorflow/tensorboard/components/tf_runs_selector",
         "//tensorflow/tensorboard/components/vz_distribution_chart",
         "@org_polymer_iron_collapse",
         "@org_polymer_paper_icon_button",
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html b/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
index 44ca48fada..87ab99753a 100644
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
@@ -21,7 +21,7 @@ limitations under the License.
 <link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
 <link rel="import" href="../tf-dashboard-common/tf-option-selector.html">
 <link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-dashboard-common/tf-sidebar-helper.html">
+<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
 <link rel="import" href="../tf-imports/lodash.html">
 <link rel="import" href="../vz-distribution-chart/vz-distribution-chart.html">
 <link rel="import" href="../iron-collapse/iron-collapse.html">
@@ -46,25 +46,28 @@ contains vz-distribution-charts embedded inside tf-panes-helper's.
   <template>
     <tf-dashboard-layout>
       <div class="sidebar">
-        <tf-sidebar-helper
-          backend="[[backend]]"
-          categories="{{_categories}}"
-          run2tag="[[run2tag]]"
-          runs="[[runs]]"
-          selected-runs="{{_selectedRuns}}"
-          >
+        <div class="sidebar-section">
+          <tf-categorizer
+            id="categorizer"
+            tags="[[tags]]"
+            categories="{{_categories}}"
+          ></tf-categorizer>
+        </div>
         <div class="sidebar-section">
           <tf-option-selector
             id="xTypeSelector"
             name="Horizontal Axis"
             selected-id="{{_xType}}"
-            >
+          >
             <paper-button id="step">step</paper-button>
             <paper-button id="relative">relative</paper-button>
             <paper-button id="wall_time">wall</paper-button>
           </tf-option-selector>
         </div>
-        </tf-sidebar-helper>
+        <div class="sidebar-section">
+          <tf-runs-selector selected-runs="{{_selectedRuns}}">
+          </tf-runs-selector>
+        </div>
       </div>
 
       <div class="center">
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/BUILD b/tensorflow/tensorboard/components/tf_histogram_dashboard/BUILD
index e510e4b467..e3c5f94caa 100644
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/BUILD
+++ b/tensorflow/tensorboard/components/tf_histogram_dashboard/BUILD
@@ -14,6 +14,7 @@ ts_web_library(
         "//tensorflow/tensorboard/components/tf_dashboard_common",
         "//tensorflow/tensorboard/components/tf_imports:lodash",
         "//tensorflow/tensorboard/components/tf_imports:polymer",
+        "//tensorflow/tensorboard/components/tf_runs_selector",
         "//tensorflow/tensorboard/components/vz_histogram_timeseries",
         "@org_polymer_iron_collapse",
         "@org_polymer_paper_icon_button",
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html b/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
index bf6d90fa43..b3e1bd3e41 100644
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
@@ -22,7 +22,7 @@ limitations under the License.
 <link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
 <link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
 <link rel="import" href="../tf-dashboard-common/tf-option-selector.html">
-<link rel="import" href="../tf-dashboard-common/tf-sidebar-helper.html">
+<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
 <link rel="import" href="../tf-imports/lodash.html">
 <link rel="import" href="../vz-histogram-timeseries/vz-histogram-timeseries.html">
 <link rel="import" href="../iron-collapse/iron-collapse.html">
@@ -48,35 +48,38 @@ contains vz-histogram-timeseries embedded inside tf-panes-helper's.
   <template>
     <tf-dashboard-layout>
       <div class="sidebar">
-        <tf-sidebar-helper
-          backend="[[backend]]"
-          categories="{{_categories}}"
-          run2tag="[[run2tag]]"
-          runs="[[runs]]"
-          selected-runs="{{_selectedRuns}}"
-          show-download-links="{{_showDownloadLinks}}"
-          >
-          <div class="sidebar-section">
-            <tf-option-selector
-              id="histogramModeSelector"
-              name="Histogram Mode"
-              selected-id="{{_histogramMode}}"
-              >
-              <paper-button id="overlay">overlay</paper-button>
-              <paper-button id="offset">offset</paper-button>
-            </tf-option-selector>
-          </div>
-          <div class="sidebar-section">
-            <tf-option-selector
-              id="timePropertySelector"
-              name="Offset Time Axis"
-              selected-id="{{_timeProperty}}"
-              >
-              <paper-button id="step">step</paper-button>
-              <paper-button id="relative">relative</paper-button>
-              <paper-button id="wall_time">wall</paper-button>
-            </tf-option-selector>
-          </tf-sidebar-helper>
+        <div class="sidebar-section">
+          <tf-categorizer
+            id="categorizer"
+            tags="[[tags]]"
+            categories="{{_categories}}"
+          ></tf-categorizer>
+        </div>
+        <div class="sidebar-section">
+          <tf-option-selector
+            id="histogramModeSelector"
+            name="Histogram Mode"
+            selected-id="{{_histogramMode}}"
+            >
+            <paper-button id="overlay">overlay</paper-button>
+            <paper-button id="offset">offset</paper-button>
+          </tf-option-selector>
+        </div>
+        <div class="sidebar-section">
+          <tf-option-selector
+            id="timePropertySelector"
+            name="Offset Time Axis"
+            selected-id="{{_timeProperty}}"
+            >
+            <paper-button id="step">step</paper-button>
+            <paper-button id="relative">relative</paper-button>
+            <paper-button id="wall_time">wall</paper-button>
+          </tf-option-selector>
+        </div>
+        <div class="sidebar-section">
+          <tf-runs-selector selected-runs="{{_selectedRuns}}">
+          </tf-runs-selector>
+        </div>
        </div>
       </div>
 
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/BUILD b/tensorflow/tensorboard/components/tf_image_dashboard/BUILD
index 1e2833f74c..84d7bf9049 100644
--- a/tensorflow/tensorboard/components/tf_image_dashboard/BUILD
+++ b/tensorflow/tensorboard/components/tf_image_dashboard/BUILD
@@ -18,6 +18,7 @@ ts_web_library(
         "//tensorflow/tensorboard/components/tf_imports:d3",
         "//tensorflow/tensorboard/components/tf_imports:lodash",
         "//tensorflow/tensorboard/components/tf_imports:polymer",
+        "//tensorflow/tensorboard/components/tf_runs_selector",
         "@org_polymer_paper_dialog",
         "@org_polymer_paper_icon_button",
         "@org_polymer_paper_slider",
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
index 856854d510..0e27a61150 100644
--- a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
@@ -21,7 +21,7 @@ limitations under the License.
 <link rel="import" href="../tf-backend/tf-backend.html">
 <link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
 <link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-dashboard-common/tf-sidebar-helper.html">
+<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
 <link rel="import" href="tf-image-loader.html">
 
 <!--
@@ -32,14 +32,19 @@ tf-image-dashboard displays a dashboard that loads images from a TensorFlow run.
     <paper-dialog with-backdrop id="actual-image-size-dialog"></paper-dialog>
     <tf-dashboard-layout>
       <div class="sidebar">
-        <tf-sidebar-helper
-          backend="[[backend]]"
-          categories="{{_categories}}"
-          run2tag="[[run2tag]]"
-          runs="[[runs]]"
-          selected-runs="{{_selectedRuns}}"
-          >
-        </tf-sidebar-helper>
+        <div class="sidebar-section">
+          <tf-categorizer
+            id="categorizer"
+            tags="[[tags]]"
+            categories="{{_categories}}"
+          ></tf-categorizer>
+        </div>
+        <div class="sidebar-section">
+          <tf-runs-selector
+            id="runs-selector"
+            selected-runs="{{_selectedRuns}}"
+          ></tf-runs-selector>
+        </div>
       </div>
       <div class="center">
         <tf-panes-helper
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html b/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
index d6f3e2281c..e2de568b4a 100644
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
@@ -22,7 +22,6 @@ limitations under the License.
 <link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
 <link rel="import" href="../tf-dashboard-common/tf-option-selector.html">
 <link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-dashboard-common/tf-sidebar-helper.html">
 <link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
 <link rel="import" href="../tf-imports/lodash.html">
 <link rel="import" href="../vz-line-chart/vz-line-chart.html">
@@ -47,11 +46,8 @@ charts are larger.
     <tf-dashboard-layout>
       <div class="sidebar">
         <div class="sidebar-section">
-          <tf-categorizer
-            id="categorizer"
-            tags="[[tags]]"
-            categories="{{_categories}}"
-          ></tf-categorizer>
+          <tf-categorizer tags="[[tags]]" categories="{{_categories}}">
+          </tf-categorizer>
           <div class="line-item">
             <paper-checkbox
             id="download-option"
@@ -99,10 +95,8 @@ charts are larger.
           </tf-option-selector>
         </div>
         <div class="sidebar-section">
-          <tf-runs-selector
-            id="runs-selector"
-            selected-runs="{{_selectedRuns}}"
-          ></tf-runs-selector>
+          <tf-runs-selector selected-runs="{{_selectedRuns}}">
+          </tf-runs-selector>
         </div>
       </div>
       <div class="center">
@@ -154,15 +148,6 @@ charts are larger.
         background: var(--tb-ui-light-accent);
       }
 
-      #categorizer {
-        flex-shrink: 0;
-      }
-
-      #runs-selector {
-        flex-shrink: 1;
-        flex-grow: 1;
-      }
-
       #tooltip-sorting {
         display: flex;
         font-size: 14px;
@@ -188,14 +173,6 @@ charts are larger.
         border-top: solid 1px rgba(0, 0, 0, 0.12);
         padding: 20px 0px 20px 30px;
       }
-
-      /* TODO(wchargin): These styles also exist in dashboard-style, */
-      /* but don't apply due to the namespacing. :-( */
-      .sidebar {
-        display: flex;
-        flex-direction: column;
-        height: 100%;
-      }
     </style>
   </template>
 
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/BUILD b/tensorflow/tensorboard/components/tf_text_dashboard/BUILD
index bed551aedf..ea606f5d58 100644
--- a/tensorflow/tensorboard/components/tf_text_dashboard/BUILD
+++ b/tensorflow/tensorboard/components/tf_text_dashboard/BUILD
@@ -18,6 +18,7 @@ ts_web_library(
         "//tensorflow/tensorboard/components/tf_imports:d3",
         "//tensorflow/tensorboard/components/tf_imports:lodash",
         "//tensorflow/tensorboard/components/tf_imports:polymer",
+        "//tensorflow/tensorboard/components/tf_runs_selector",
         "@org_polymer_paper_dialog",
         "@org_polymer_paper_icon_button",
         "@org_polymer_paper_material",
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
index e8daca2deb..14953a276a 100644
--- a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
+++ b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
@@ -21,7 +21,7 @@ limitations under the License.
 <link rel="import" href="../tf-backend/tf-backend.html">
 <link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
 <link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-dashboard-common/tf-sidebar-helper.html">
+<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
 <link rel="import" href="tf-text-loader.html">
 
 <!--
@@ -32,14 +32,17 @@ tf-text-dashboard displays a dashboard that loads texts from a TensorFlow run.
     <paper-dialog with-backdrop id="actual-text-size-dialog"></paper-dialog>
     <tf-dashboard-layout>
       <div class="sidebar">
-        <tf-sidebar-helper
-          backend="[[backend]]"
-          categories="{{_categories}}"
-          run2tag="[[run2tag]]"
-          runs="[[runs]]"
-          selected-runs="{{_selectedRuns}}"
-          >
-        </tf-sidebar-helper>
+        <div class="sidebar-section">
+          <tf-categorizer
+            id="categorizer"
+            tags="[[tags]]"
+            categories="{{_categories}}"
+          ></tf-categorizer>
+        </div>
+        <div class="sidebar-section">
+          <tf-runs-selector selected-runs="{{_selectedRuns}}">
+          </tf-runs-selector>
+        </div>
       </div>
       <div class="center">
         <tf-panes-helper
@@ -67,7 +70,6 @@ tf-text-dashboard displays a dashboard that loads texts from a TensorFlow run.
         --card-padding: 0 5px 5px 5px;
         --show-expand-button: none;
       }
-
     </style>
   </template>
   <script>
diff --git a/tensorflow/tensorboard/plugins/text/text_plugin.py b/tensorflow/tensorboard/plugins/text/text_plugin.py
index d0040e20be..fc00778c33 100644
--- a/tensorflow/tensorboard/plugins/text/text_plugin.py
+++ b/tensorflow/tensorboard/plugins/text/text_plugin.py
@@ -264,6 +264,8 @@ class TextPlugin(base_plugin.TBPlugin):
             run, name, 'tensors.json')
         tensors = json.loads(tensors_json)
         run_to_series[run] = tensors
+      else:
+        run_to_series[run] = []
     return run_to_series
 
   @wrappers.Request.application
-- 
GitLab


From 525b0f05839779e40d0ca9cc2967a3886b6a0f4d Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 15 Jun 2017 10:38:27 -0700
Subject: [PATCH 0109/1375] tfdbg: fix race condition in picking graph file
 names

by adding a hash to the _tfdbg_graph_* files

PiperOrigin-RevId: 159122097
---
 tensorflow/core/debug/debug_io_utils.cc   | 9 +++++++--
 tensorflow/core/debug/debug_io_utils.h    | 1 +
 tensorflow/python/debug/lib/debug_data.py | 1 +
 3 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index 54366ce249..88dffb934c 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -131,6 +131,9 @@ const char* const DebugIO::kDeviceTag = "device_";
 // static
 const char* const DebugIO::kGraphTag = "graph_";
 
+// static
+const char* const DebugIO::kHashTag = "hash";
+
 DebugNodeKey::DebugNodeKey(const string& device_name, const string& node_name,
                            const int32 output_slot, const string& debug_op)
     : device_name(device_name),
@@ -351,8 +354,10 @@ Status DebugIO::PublishGraph(const Graph& graph, const string& device_name,
       const string dump_root_dir =
           io::JoinPath(debug_url.substr(strlen(kFileURLScheme)),
                        DebugNodeKey::DeviceNameToDevicePath(device_name));
-      const string file_name = strings::StrCat(DebugIO::kMetadataFilePrefix,
-                                               DebugIO::kGraphTag, now_micros);
+      const uint64 graph_hash = ::tensorflow::Hash64(buf);
+      const string file_name =
+          strings::StrCat(DebugIO::kMetadataFilePrefix, DebugIO::kGraphTag,
+                          DebugIO::kHashTag, graph_hash, "_", now_micros);
 
       status.Update(
           DebugFileIO::DumpEventProtoToFile(event, dump_root_dir, file_name));
diff --git a/tensorflow/core/debug/debug_io_utils.h b/tensorflow/core/debug/debug_io_utils.h
index 69d8c7bd4e..4caa4b5e04 100644
--- a/tensorflow/core/debug/debug_io_utils.h
+++ b/tensorflow/core/debug/debug_io_utils.h
@@ -143,6 +143,7 @@ class DebugIO {
   static const char* const kCoreMetadataTag;
   static const char* const kDeviceTag;
   static const char* const kGraphTag;
+  static const char* const kHashTag;
 
   static const char* const kFileURLScheme;
   static const char* const kGrpcURLScheme;
diff --git a/tensorflow/python/debug/lib/debug_data.py b/tensorflow/python/debug/lib/debug_data.py
index 0cdf189127..967152d220 100644
--- a/tensorflow/python/debug/lib/debug_data.py
+++ b/tensorflow/python/debug/lib/debug_data.py
@@ -40,6 +40,7 @@ METADATA_FILE_PREFIX = "_tfdbg_"
 CORE_METADATA_TAG = "core_metadata_"
 GRAPH_FILE_TAG = "graph_"
 DEVICE_TAG = "device_"
+HASH_TAG = "hash"
 
 FETCHES_INFO_FILE_TAG = "fetches_info_"
 FEED_KEYS_INFO_FILE_TAG = "feed_keys_info_"
-- 
GitLab


From f7d498b83dfe3dfcc757f6688cfc3e589e2cfba2 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 15 Jun 2017 11:14:24 -0700
Subject: [PATCH 0110/1375] Implement 13 different Csiszar-functions and Ops
 to: (1) approximate Csiszar $f$-divergence (2) symmetrize a Csiszar-function
 (3) compute the dual Csiszar-function.

PiperOrigin-RevId: 159127705
---
 tensorflow/contrib/bayesflow/BUILD            |  18 +
 tensorflow/contrib/bayesflow/__init__.py      |   3 +-
 .../kernel_tests/csiszar_divergence_test.py   | 578 +++++++++++++
 .../python/ops/csiszar_divergence.py          |  49 ++
 .../python/ops/csiszar_divergence_impl.py     | 803 ++++++++++++++++++
 5 files changed, 1450 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 04288a1934..18df14d336 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -31,6 +31,24 @@ py_library(
     ],
 )
 
+cuda_py_test(
+    name = "csiszar_divergence_test",
+    size = "small",
+    srcs = ["python/kernel_tests/csiszar_divergence_test.py"],
+    additional_deps = [
+        ":bayesflow_py",
+        "//third_party/py/numpy",
+        "//tensorflow/contrib/distributions:distributions_py",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "entropy_test",
     size = "medium",
diff --git a/tensorflow/contrib/bayesflow/__init__.py b/tensorflow/contrib/bayesflow/__init__.py
index dcda737700..65a17d742c 100644
--- a/tensorflow/contrib/bayesflow/__init__.py
+++ b/tensorflow/contrib/bayesflow/__init__.py
@@ -21,6 +21,7 @@ from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=unused-import,line-too-long
+from tensorflow.contrib.bayesflow.python.ops import csiszar_divergence
 from tensorflow.contrib.bayesflow.python.ops import entropy
 from tensorflow.contrib.bayesflow.python.ops import monte_carlo
 from tensorflow.contrib.bayesflow.python.ops import stochastic_gradient_estimators
@@ -33,7 +34,7 @@ from tensorflow.contrib.bayesflow.python.ops import variational_inference
 from tensorflow.python.util.all_util import remove_undocumented
 
 
-_allowed_symbols = ['entropy', 'monte_carlo',
+_allowed_symbols = ['csiszar_divergence', 'entropy', 'monte_carlo',
                     'special_math', 'stochastic_gradient_estimators',
                     'stochastic_graph', 'stochastic_tensor',
                     'stochastic_variables', 'variational_inference']
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
new file mode 100644
index 0000000000..c120acaefc
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -0,0 +1,578 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Csiszar Divergence Ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.bayesflow.python.ops import csiszar_divergence_impl
+from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib
+from tensorflow.contrib.distributions.python.ops import mvn_full_covariance as mvn_full_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops.distributions import kullback_leibler
+from tensorflow.python.ops.distributions import normal as normal_lib
+from tensorflow.python.platform import test
+
+
+cd = csiszar_divergence_impl
+
+
+class AmariAlphaTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    for alpha in [-1., 0., 1., 2.]:
+      for normalized in [True, False]:
+        with self.test_session(graph=ops.Graph()):
+          self.assertAllClose(
+              cd.amari_alpha(0., alpha=alpha,
+                             self_normalized=normalized).eval(),
+              0.)
+
+  def test_correct_when_alpha0(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.amari_alpha(self._logu, alpha=0.).eval(),
+          -self._logu)
+
+      self.assertAllClose(
+          cd.amari_alpha(self._logu, alpha=0., self_normalized=True).eval(),
+          -self._logu + (self._u - 1.))
+
+  def test_correct_when_alpha1(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.amari_alpha(self._logu, alpha=1.).eval(),
+          self._u * self._logu)
+
+      self.assertAllClose(
+          cd.amari_alpha(self._logu, alpha=1., self_normalized=True).eval(),
+          self._u * self._logu - (self._u - 1.))
+
+  def test_correct_when_alpha_not_01(self):
+    for alpha in [-2, -1., -0.5, 0.5, 2.]:
+      with self.test_session(graph=ops.Graph()):
+        self.assertAllClose(
+            cd.amari_alpha(self._logu,
+                           alpha=alpha,
+                           self_normalized=False).eval(),
+            ((self._u**alpha - 1)) / (alpha * (alpha - 1.)))
+
+        self.assertAllClose(
+            cd.amari_alpha(self._logu,
+                           alpha=alpha,
+                           self_normalized=True).eval(),
+            ((self._u**alpha - 1.)
+             - alpha * (self._u - 1)) / (alpha * (alpha - 1.)))
+
+
+class KLReverseTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    for normalized in [True, False]:
+      with self.test_session(graph=ops.Graph()):
+        self.assertAllClose(
+            cd.kl_reverse(0., self_normalized=normalized).eval(),
+            0.)
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.kl_reverse(self._logu).eval(),
+          -self._logu)
+
+      self.assertAllClose(
+          cd.kl_reverse(self._logu, self_normalized=True).eval(),
+          -self._logu + (self._u - 1.))
+
+
+class KLForwardTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    for normalized in [True, False]:
+      with self.test_session(graph=ops.Graph()):
+        self.assertAllClose(
+            cd.kl_forward(0., self_normalized=normalized).eval(),
+            0.)
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.kl_forward(self._logu).eval(),
+          self._u * self._logu)
+
+      self.assertAllClose(
+          cd.kl_forward(self._logu, self_normalized=True).eval(),
+          self._u * self._logu - (self._u - 1.))
+
+
+class JensenShannonTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.jensen_shannon(0.).eval(), np.log(0.25))
+
+  def test_symmetric(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.jensen_shannon(self._logu).eval(),
+          cd.symmetrized_csiszar_function(
+              self._logu, cd.jensen_shannon).eval())
+
+      self.assertAllClose(
+          cd.jensen_shannon(self._logu, self_normalized=True).eval(),
+          cd.symmetrized_csiszar_function(
+              self._logu,
+              lambda x: cd.jensen_shannon(x, self_normalized=True)).eval())
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.jensen_shannon(self._logu).eval(),
+          (self._u * self._logu
+           - (1 + self._u) * np.log1p(self._u)))
+
+      self.assertAllClose(
+          cd.jensen_shannon(self._logu, self_normalized=True).eval(),
+          (self._u * self._logu
+           - (1 + self._u) * np.log((1 + self._u) / 2)))
+
+
+class ArithmeticGeometricMeanTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.arithmetic_geometric(0.).eval(), np.log(4))
+      self.assertAllClose(
+          cd.arithmetic_geometric(0., self_normalized=True).eval(), 0.)
+
+  def test_symmetric(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.arithmetic_geometric(self._logu).eval(),
+          cd.symmetrized_csiszar_function(
+              self._logu, cd.arithmetic_geometric).eval())
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.arithmetic_geometric(self._logu).eval(),
+          (1. + self._u) * np.log((1. + self._u) / np.sqrt(self._u)))
+
+      self.assertAllClose(
+          cd.arithmetic_geometric(self._logu, self_normalized=True).eval(),
+          (1. + self._u) * np.log(0.5 * (1. + self._u) / np.sqrt(self._u)))
+
+
+class TotalVariationTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.total_variation(0.).eval(), 0.)
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.total_variation(self._logu).eval(),
+          0.5 * np.abs(self._u - 1))
+
+
+class PearsonTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.pearson(0.).eval(), 0.)
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.pearson(self._logu).eval(),
+          np.square(self._u - 1))
+
+
+class SquaredHellingerTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.squared_hellinger(0.).eval(), 0.)
+
+  def test_symmetric(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.squared_hellinger(self._logu).eval(),
+          cd.symmetrized_csiszar_function(
+              self._logu, cd.squared_hellinger).eval())
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.squared_hellinger(self._logu).eval(),
+          np.square(np.sqrt(self._u) - 1))
+
+
+class TriangularTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.triangular(0.).eval(), 0.)
+
+  def test_symmetric(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.triangular(self._logu).eval(),
+          cd.symmetrized_csiszar_function(
+              self._logu, cd.triangular).eval())
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.triangular(self._logu).eval(),
+          np.square(self._u - 1) / (1 + self._u))
+
+
+class Log1pAbsTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.log1p_abs(0.).eval(), 0.)
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.log1p_abs(self._logu).eval(),
+          self._u**(np.sign(self._u - 1)) - 1)
+
+
+class JeffreysTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.jeffreys(0.).eval(), 0.)
+
+  def test_symmetric(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.jeffreys(self._logu).eval(),
+          cd.symmetrized_csiszar_function(
+              self._logu, cd.jeffreys).eval())
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.jeffreys(self._logu).eval(),
+          0.5 * (self._u * self._logu - self._logu))
+
+
+class ChiSquareTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.chi_square(0.).eval(), 0.)
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.chi_square(self._logu).eval(),
+          self._u**2 - 1)
+
+
+class ModifiedGanTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.modified_gan(0.).eval(), np.log(2))
+      self.assertAllClose(
+          cd.modified_gan(0., self_normalized=True).eval(), np.log(2))
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.modified_gan(self._logu).eval(),
+          np.log1p(self._u) - self._logu)
+
+      self.assertAllClose(
+          cd.modified_gan(self._logu, self_normalized=True).eval(),
+          np.log1p(self._u) - self._logu + 0.5 * (self._u - 1))
+
+
+class SymmetrizedCsiszarFunctionTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10., 100)
+    self._u = np.exp(self._logu)
+
+  def test_jensen_shannon(self):
+    with self.test_session():
+
+      # The following functions come from the claim made in the
+      # symmetrized_csiszar_function docstring.
+      def js1(logu):
+        return (-logu
+                - (1. + math_ops.exp(logu)) * (
+                    nn_ops.softplus(logu)))
+
+      def js2(logu):
+        return 2. * (math_ops.exp(logu) * (
+            logu - nn_ops.softplus(logu)))
+
+      self.assertAllClose(
+          cd.symmetrized_csiszar_function(self._logu, js1).eval(),
+          cd.jensen_shannon(self._logu).eval())
+
+      self.assertAllClose(
+          cd.symmetrized_csiszar_function(self._logu, js2).eval(),
+          cd.jensen_shannon(self._logu).eval())
+
+  def test_jeffreys(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.symmetrized_csiszar_function(self._logu, cd.kl_reverse).eval(),
+          cd.jeffreys(self._logu).eval())
+
+      self.assertAllClose(
+          cd.symmetrized_csiszar_function(self._logu, cd.kl_forward).eval(),
+          cd.jeffreys(self._logu).eval())
+
+
+class DualCsiszarFunctionTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10., 100)
+    self._u = np.exp(self._logu)
+
+  def test_kl_forward(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.dual_csiszar_function(self._logu, cd.kl_forward).eval(),
+          cd.kl_reverse(self._logu).eval())
+
+  def test_kl_reverse(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.dual_csiszar_function(self._logu, cd.kl_reverse).eval(),
+          cd.kl_forward(self._logu).eval())
+
+
+class MonteCarloCsiszarFDivergenceTest(test.TestCase):
+
+  def test_kl_forward(self):
+    with self.test_session() as sess:
+      q = normal_lib.Normal(
+          loc=np.ones(6),
+          scale=np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0]))
+
+      p = normal_lib.Normal(loc=q.loc + 0.1, scale=q.scale - 0.2)
+
+      approx_kl = cd.monte_carlo_csiszar_f_divergence(
+          f=cd.kl_forward,
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
+          f=lambda logu: cd.kl_forward(logu, self_normalized=True),
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      exact_kl = kullback_leibler.kl_divergence(p, q)
+
+      [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([
+          approx_kl, approx_kl_self_normalized, exact_kl])
+
+      self.assertAllClose(approx_kl_, exact_kl_,
+                          rtol=0.08, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
+                          rtol=0.02, atol=0.)
+
+  def test_kl_reverse(self):
+    with self.test_session() as sess:
+
+      q = normal_lib.Normal(
+          loc=np.ones(6),
+          scale=np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0]))
+
+      p = normal_lib.Normal(loc=q.loc + 0.1, scale=q.scale - 0.2)
+
+      approx_kl = cd.monte_carlo_csiszar_f_divergence(
+          f=cd.kl_reverse,
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
+          f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      exact_kl = kullback_leibler.kl_divergence(q, p)
+
+      [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([
+          approx_kl, approx_kl_self_normalized, exact_kl])
+
+      self.assertAllClose(approx_kl_, exact_kl_,
+                          rtol=0.07, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
+                          rtol=0.02, atol=0.)
+
+  def _tridiag(self, d, diag_value, offdiag_value):
+    """d x d matrix with given value on diag, and one super/sub diag."""
+    diag_mat = linalg_ops.eye(d) * (diag_value - offdiag_value)
+    three_bands = array_ops.matrix_band_part(
+        array_ops.fill([d, d], offdiag_value), 1, 1)
+    return diag_mat + three_bands
+
+  def test_kl_reverse_multidim(self):
+
+    with self.test_session() as sess:
+      d = 5  # Dimension
+
+      p = mvn_full_lib.MultivariateNormalFullCovariance(
+          covariance_matrix=self._tridiag(d, diag_value=1, offdiag_value=0.5))
+
+      q = mvn_diag_lib.MultivariateNormalDiag(scale_diag=[0.5]*d)
+
+      approx_kl = cd.monte_carlo_csiszar_f_divergence(
+          f=cd.kl_reverse,
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
+          f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      exact_kl = kullback_leibler.kl_divergence(q, p)
+
+      [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([
+          approx_kl, approx_kl_self_normalized, exact_kl])
+
+      self.assertAllClose(approx_kl_, exact_kl_,
+                          rtol=0.02, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
+                          rtol=0.08, atol=0.)
+
+  def test_kl_forward_multidim(self):
+
+    with self.test_session() as sess:
+      d = 5  # Dimension
+
+      p = mvn_full_lib.MultivariateNormalFullCovariance(
+          covariance_matrix=self._tridiag(d, diag_value=1, offdiag_value=0.5))
+
+      # Variance is very high when approximating Forward KL, so we make
+      # scale_diag larger than in test_kl_reverse_multidim. This ensures q
+      # "covers" p and thus Var_q[p/q] is smaller.
+      q = mvn_diag_lib.MultivariateNormalDiag(scale_diag=[1.]*d)
+
+      approx_kl = cd.monte_carlo_csiszar_f_divergence(
+          f=cd.kl_forward,
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
+          f=lambda logu: cd.kl_forward(logu, self_normalized=True),
+          p=p,
+          q=q,
+          num_draws=int(1e5),
+          seed=1)
+
+      exact_kl = kullback_leibler.kl_divergence(p, q)
+
+      [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([
+          approx_kl, approx_kl_self_normalized, exact_kl])
+
+      self.assertAllClose(approx_kl_, exact_kl_,
+                          rtol=0.06, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
+                          rtol=0.05, atol=0.)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
new file mode 100644
index 0000000000..5440df7dbf
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
@@ -0,0 +1,49 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Csiszar f-Divergence and helpers.
+
+See ${python/contrib.bayesflow.csiszar_divergence}.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import
+from tensorflow.contrib.bayesflow.python.ops.csiszar_divergence_impl import *
+# pylint: enable=wildcard-import
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = [
+    'amari_alpha',
+    'arithmetic_geometric',
+    'chi_square',
+    'dual_csiszar_function',
+    'jeffreys',
+    'jensen_shannon',
+    'kl_forward',
+    'kl_reverse',
+    'log1p_abs',
+    'modified_gan',
+    'monte_carlo_csiszar_f_divergence',
+    'pearson',
+    'squared_hellinger',
+    'symmetrized_csiszar_function',
+    'total_variation',
+    'triangular',
+]
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
new file mode 100644
index 0000000000..262da41bda
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -0,0 +1,803 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Csiszar f-Divergence and helpers.
+
+@@amari_alpha
+@@arithmetic_geometric
+@@chi_square
+@@dual_csiszar_function
+@@jeffreys
+@@jensen_shannon
+@@kl_forward
+@@kl_reverse
+@@log1p_abs
+@@modified_gan
+@@monte_carlo_csiszar_f_divergence
+@@pearson
+@@squared_hellinger
+@@symmetrized_csiszar_function
+@@total_variation
+@@triangular
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib import framework as contrib_framework
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops.distributions import distribution
+
+
+def amari_alpha(logu, alpha=1., self_normalized=False, name=None):
+  """The Amari-alpha Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  When `self_normalized = True`, the Amari-alpha Csiszar-function is:
+
+  ```none
+  f(u) = { -log(u) + (u - 1),     alpha = 0
+         { u log(u) - (u - 1),    alpha = 1
+         { [(u**alpha - 1) - alpha (u - 1)] / (alpha (alpha - 1)),    otherwise
+  ```
+
+  When `self_normalized = False` the `(u - 1)` terms are omitted.
+
+  Warning: when `alpha != 0` and/or `self_normalized = True` this function makes
+  non-log-space calculations and may therefore be numerically unstable for
+  `|logu| >> 0`.
+
+  For more information, see:
+    A. Cichocki and S. Amari. "Families of Alpha-Beta-and GammaDivergences:
+    Flexible and Robust Measures of Similarities." Entropy, vol. 12, no. 6, pp.
+    1532-1568, 2010.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    alpha: Floating-type Python scalar. (See Mathematical Details for meaning.)
+    self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
+      `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
+      when `p, q` are unnormalized measures.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    amari_alpha_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+      at `u = exp(logu)`.
+
+  Raises:
+    TypeError: if `alpha` is `None` or a `Tensor`.
+    TypeError: if `self_normalized` is `None` or a `Tensor`.
+  """
+  with ops.name_scope(name, "amari_alpha", [logu]):
+    if alpha is None or contrib_framework.is_tensor(alpha):
+      raise TypeError("`alpha` cannot be `None` or `Tensor` type.")
+    if self_normalized is None or contrib_framework.is_tensor(self_normalized):
+      raise TypeError("`self_normalized` cannot be `None` or `Tensor` type.")
+
+    logu = ops.convert_to_tensor(logu, name="logu")
+
+    if alpha == 0.:
+      f = -logu
+    elif alpha == 1.:
+      f = math_ops.exp(logu) * logu
+    else:
+      f = math_ops.expm1(alpha * logu) / (alpha * (alpha - 1.))
+
+    if not self_normalized:
+      return f
+
+    if alpha == 0.:
+      return f + math_ops.expm1(logu)
+    elif alpha == 1.:
+      return f - math_ops.expm1(logu)
+    else:
+      return f - math_ops.expm1(logu) / (alpha - 1.)
+
+
+def kl_reverse(logu, self_normalized=False, name=None):
+  """The reverse Kullback-Leibler Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  When `self_normalized = True`, the KL-reverse Csiszar-function is:
+
+  ```none
+  f(u) = -log(u) + (u - 1)
+  ```
+
+  When `self_normalized = False` the `(u - 1)` term is omitted.
+
+  Observe that as an f-Divergence, this Csiszar-function implies:
+
+  ```none
+  D_f[p, q] = KL[q, p]
+  ```
+
+  The KL is "reverse" because in maximum likelihood we think of minimizing `q`
+  as in `KL[p, q]`.
+
+  Warning: when self_normalized = True` this function makes non-log-space
+  calculations and may therefore be numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
+      `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
+      when `p, q` are unnormalized measures.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    kl_reverse_of_u: Floating-type `Tensor` of the Csiszar-function evaluated at
+      `u = exp(logu)`.
+
+  Raises:
+    TypeError: if `self_normalized` is `None` or a `Tensor`.
+  """
+
+  with ops.name_scope(name, "kl_reverse", [logu]):
+    return amari_alpha(logu, alpha=0., self_normalized=self_normalized)
+
+
+def kl_forward(logu, self_normalized=False, name=None):
+  """The forward Kullback-Leibler Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  When `self_normalized = True`, the KL-forward Csiszar-function is:
+
+  ```none
+  f(u) = u log(u) - (u - 1)
+  ```
+
+  When `self_normalized = False` the `(u - 1)` term is omitted.
+
+  Observe that as an f-Divergence, this Csiszar-function implies:
+
+  ```none
+  D_f[p, q] = KL[p, q]
+  ```
+
+  The KL is "forward" because in maximum likelihood we think of minimizing `q`
+  as in `KL[p, q]`.
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
+      `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
+      when `p, q` are unnormalized measures.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    kl_forward_of_u: Floating-type `Tensor` of the Csiszar-function evaluated at
+      `u = exp(logu)`.
+
+  Raises:
+    TypeError: if `self_normalized` is `None` or a `Tensor`.
+  """
+
+  with ops.name_scope(name, "kl_forward", [logu]):
+    return amari_alpha(logu, alpha=1., self_normalized=self_normalized)
+
+
+def jensen_shannon(logu, self_normalized=False, name=None):
+  """The Jensen-Shannon Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  When `self_normalized = True`, the Jensen-Shannon Csiszar-function is:
+
+  ```none
+  f(u) = u log(u) - (1 + u) log(1 + u) + (u + 1) log(2)
+  ```
+
+  When `self_normalized = False` the `(u + 1) log(2)` term is omitted.
+
+  Observe that as an f-Divergence, this Csiszar-function implies:
+
+  ```none
+  D_f[p, q] = KL[p, m] + KL[q, m]
+  m(x) = 0.5 p(x) + 0.5 q(x)
+  ```
+
+  In a sense, this divergence is the "reverse" of the Arithmetic-Geometric
+  f-Divergence.
+
+  This Csiszar-function induces a symmetric f-Divergence, i.e.,
+  `D_f[p, q] = D_f[q, p]`.
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  For more information, see:
+    Lin, J. "Divergence measures based on the Shannon entropy." IEEE Trans.
+    Inf. Th., 37, 145-151, 1991.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
+      `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
+      when `p, q` are unnormalized measures.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    jensen_shannon_of_u: Floating-type `Tensor` of the Csiszar-function
+      evaluated at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "jensen_shannon", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    npdt = logu.dtype.as_numpy_dtype
+    y = nn_ops.softplus(logu)
+    if self_normalized:
+      y -= np.log(2).astype(npdt)
+    return math_ops.exp(logu) * logu - (1. + math_ops.exp(logu)) * y
+
+
+def arithmetic_geometric(logu, self_normalized=False, name=None):
+  """The Arithmetic-Geometric Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  When `self_normalized = True` the Arithmetic-Geometric Csiszar-function is:
+
+  ```none
+  f(u) = (1 + u) log( (1 + u) / sqrt(u) ) - (1 + u) log(2)
+  ```
+
+  When `self_normalized = False` the `(1 + u) log(2)` term is omitted.
+
+  Observe that as an f-Divergence, this Csiszar-function implies:
+
+  ```none
+  D_f[p, q] = KL[m, p] + KL[m, q]
+  m(x) = 0.5 p(x) + 0.5 q(x)
+  ```
+
+  In a sense, this divergence is the "reverse" of the Jensen-Shannon
+  f-Divergence.
+
+  This Csiszar-function induces a symmetric f-Divergence, i.e.,
+  `D_f[p, q] = D_f[q, p]`.
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
+      `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
+      when `p, q` are unnormalized measures.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    arithmetic_geometric_of_u: Floating-type `Tensor` of the
+      Csiszar-function evaluated at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "arithmetic_geometric", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    y = nn_ops.softplus(logu) - 0.5 * logu
+    if self_normalized:
+      y -= np.log(2.).astype(logu.dtype.as_numpy_dtype)
+    return (1. + math_ops.exp(logu)) * y
+
+
+def total_variation(logu, name=None):
+  """The Total Variation Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Total-Variation Csiszar-function is:
+
+  ```none
+  f(u) = 0.5 |u - 1|
+  ```
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    total_variation_of_u: Floating-type `Tensor` of the Csiszar-function
+      evaluated at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "total_variation", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return 0.5 * math_ops.abs(math_ops.expm1(logu))
+
+
+def pearson(logu, name=None):
+  """The Pearson Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Pearson Csiszar-function is:
+
+  ```none
+  f(u) = (u - 1)**2
+  ```
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    pearson_of_u: Floating-type `Tensor` of the Csiszar-function evaluated at
+      `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "pearson", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return math_ops.square(math_ops.expm1(logu))
+
+
+def squared_hellinger(logu, name=None):
+  """The Amari-alpha Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Squared-Hellinger Csiszar-function is:
+
+  ```none
+  f(u) = (sqrt(u) - 1)**2
+  ```
+
+  This Csiszar-function induces a symmetric f-Divergence, i.e.,
+  `D_f[p, q] = D_f[q, p]`.
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    squared_hellinger_of_u: Floating-type `Tensor` of the Csiszar-function
+      evaluated at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "squared_hellinger", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return pearson(0.5 * logu)
+
+
+def triangular(logu, name=None):
+  """The Amari-alpha Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Triangular Csiszar-function is:
+
+  ```none
+  f(u) = (u - 1)**2 / (1 + u)
+  ```
+
+  This Csiszar-function induces a symmetric f-Divergence, i.e.,
+  `D_f[p, q] = D_f[q, p]`.
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    triangular_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+      at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "triangular", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return pearson(logu) / (1. + math_ops.exp(logu))
+
+
+def log1p_abs(logu, name=None):
+  """The log1p-abs Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Log1p-Abs Csiszar-function is:
+
+  ```none
+  f(u) = u**(sign(u-1)) - 1
+  ```
+
+  This function is so-named because it was invented from the following recipe.
+  Choose a convex function g such that g(0)=0 and solve for f:
+
+  ```none
+  log(1 + f(u)) = g(log(u)).
+    <=>
+  f(u) = exp(g(log(u))) - 1
+  ```
+
+  That is, the graph is identically `g` when y-axis is `log1p`-domain and x-axis
+  is `log`-domain.
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    log1p_abs_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+      at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "log1p_abs", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return math_ops.expm1(math_ops.abs(logu))
+
+
+def jeffreys(logu, name=None):
+  """The Jeffreys Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Jeffreys Csiszar-function is:
+
+  ```none
+  f(u) = 0.5 ( u log(u) - log(u) )
+       = 0.5 kl_forward + 0.5 kl_reverse
+       = symmetrized_csiszar_function(kl_reverse)
+       = symmetrized_csiszar_function(kl_forward)
+  ```
+
+  This Csiszar-function induces a symmetric f-Divergence, i.e.,
+  `D_f[p, q] = D_f[q, p]`.
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    jeffreys_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+      at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "jeffreys", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return 0.5 * math_ops.expm1(logu) * logu
+
+
+def chi_square(logu, name=None):
+  """The chi-Square Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Chi-square Csiszar-function is:
+
+  ```none
+  f(u) = u**2 - 1
+  ```
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    chi_square_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+      at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "chi_square", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return math_ops.expm1(2. * logu)
+
+
+def modified_gan(logu, self_normalized=False, name=None):
+  """The Modified-GAN Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  When `self_normalized = True` the modified-GAN (Generative/Adversarial
+  Network) Csiszar-function is:
+
+  ```none
+  f(u) = log(1 + u) - log(u) + 0.5 (u - 1)
+  ```
+
+  When `self_normalized = False` the `0.5 (u - 1)` is omitted.
+
+  The unmodified GAN Csiszar-function is identical to Jensen-Shannon (with
+  `self_normalized = False`).
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
+      `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
+      when `p, q` are unnormalized measures.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    chi_square_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+      at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "chi_square", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    y = nn_ops.softplus(logu) - logu
+    if self_normalized:
+      y += 0.5 * math_ops.expm1(logu)
+    return y
+
+
+def dual_csiszar_function(logu, csiszar_function, name=None):
+  """Calculates the dual Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Csiszar-dual is defined as:
+
+  ```none
+  f^*(u) = u f(1 / u)
+  ```
+
+  where `f` is some other Csiszar-function.
+
+  For example, the dual of `kl_reverse` is `kl_forward`, i.e.,
+
+  ```none
+  f(u) = -log(u)
+  f^*(u) = u f(1 / u) = -u log(1 / u) = u log(u)
+  ```
+
+  The dual of the dual is the original function:
+
+  ```none
+  f^**(u) = {u f(1/u)}^*(u) = u (1/u) f(1/(1/u)) = f(u)
+  ```
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    csiszar_function: Python callable representing a Csiszar-function over
+      log-domain.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    dual_f_of_u: Floating-type `Tensor` of the result of calculating the dual of
+      `f` at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "dual_csiszar_function", [logu]):
+    return math_ops.exp(logu) * csiszar_function(-logu)
+
+
+def symmetrized_csiszar_function(logu, csiszar_function, name=None):
+  """Symmetrizes a Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The symmetrized Csiszar-function is defined as:
+
+  ```none
+  f_g(u) = 0.5 g(u) + 0.5 u g (1 / u)
+  ```
+
+  where `g` is some other Csiszar-function.
+
+  We say the function is "symmetrized" because:
+
+  ```none
+  D_{f_g}[p, q] = D_{f_g}[q, p]
+  ```
+
+  for all `p << >> q` (i.e., `support(p) = support(q)`).
+
+  There exists alternatives for symmetrizing a Csiszar-function. For example,
+
+  ```none
+  f_g(u) = max(f(u), f^*(u)),
+  ```
+
+  where `f^*` is the dual Csiszar-function, also implies a symmetric
+  f-Divergence.
+
+  Example:
+
+  When either of the following functions are symmetrized, we obtain the
+  Jensen-Shannon Csiszar-function, i.e.,
+
+  ```none
+  g(u) = -log(u) - (1 + u) log((1 + u) / 2) + u - 1
+  h(u) = log(4) + 2 u log(u / (1 + u))
+  ```
+
+  implies,
+
+  ```none
+  f_g(u) = f_h(u) = u log(u) - (1 + u) log((1 + u) / 2)
+         = jensen_shannon(log(u)).
+  ```
+
+  Warning: this function makes non-log-space calculations and may therefore be
+  numerically unstable for `|logu| >> 0`.
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(u)` from above.
+    csiszar_function: Python callable representing a Csiszar-function over
+      log-domain.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    symmetrized_g_of_u: Floating-type `Tensor` of the result of applying the
+      symmetrization of `g` evaluated at `u = exp(logu)`.
+  """
+
+  with ops.name_scope(name, "symmetrized_csiszar_function", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    return 0.5 * (csiszar_function(logu)
+                  + dual_csiszar_function(logu, csiszar_function))
+
+
+def monte_carlo_csiszar_f_divergence(f, p, q, num_draws, seed=None, name=None):
+  """Monte-Carlo approximation of the Csiszar f-Divergence.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  The Csiszar f-Divergence for Csiszar-function f is given by:
+
+  ```none
+  D_f[p(X), q(X)] := E_{q(X)}[ f( p(X) / q(X) ) ]
+                  ~= m**-1 sum_j^m f( p(x_j) / q(x_j) ),
+                             where x_j ~iid q(x)
+  ```
+
+  Example Application:
+
+  The Csiszar f-Divergence is a useful framework for variational inference.
+  I.e., observe that,
+
+  ```none
+  f(p(x)) =  f( E_{q(Z | x)}[ p(x, Z) / q(Z | x) ] )
+          <= E_{q(Z | x)}[ f( p(x, Z) / q(Z | x) ) ]
+          := D_f[p(x, Z), q(Z | x)]
+  ```
+
+  The inequality follows from the fact that the "perspective" of `f`, i.e.,
+  `(s, t) |-> t f(s / t))`, is convex in `(s, t)` when `s/t in domain(f)` and
+  `t` is a real. Since the above framework includes the popular Evidence Lower
+  BOund (ELBO) as a special case, i.e., `f(u) = -log(u)`, we call this framework
+  "Evidence Divergence Bound Optimization" (EDBO).
+
+  Args:
+    f: Python callable representing a Csiszar-function in log-space.
+    p: `tf.Distribution`-like instance; must implement `log_prob(x)`.
+    q: `tf.Distribution`-like instance; must implement:
+      `reparameterization_type`, `sample(n)`, and `log_prob(x)`.
+    num_draws: Integer scalar number of draws used to approximate the
+      f-Divergence expectation.
+    seed: Python `int` seed for `q.sample`.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    monte_carlo_csiszar_f_divergence: Floating-type `Tensor` Monte Carlo
+      approximation of the Csiszar f-Divergence.
+
+  Raises:
+    ValueError: if `q` is not a reparameterized distribution. A distribution `q`
+      is said to be "reparameterized" when its samples are generated by
+      transforming the samples of another distribution which does not depend on
+      the parameterization of `q`. This property ensures the gradient (with
+      respect to parameters) is valid.
+  """
+  with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]):
+    # TODO(jvdillon): Consider only raising an exception if the gradient is
+    # requested.
+    if q.reparameterization_type != distribution.FULLY_REPARAMETERIZED:
+      raise ValueError(
+          "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
+          "transformation of a parameterless distribution. (Otherwise this "
+          "function has a biased gradient.)")
+    x = q.sample(num_draws, seed=seed)
+    return math_ops.reduce_mean(
+        f(p.log_prob(x) - q.log_prob(x)),
+        axis=0)
-- 
GitLab


From 1588d3790d759ee8a50e2bcf17b1eeadca69ba30 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 11:31:51 -0700
Subject: [PATCH 0111/1375] Migrate core resource code for new version of
 TensorForest.

PiperOrigin-RevId: 159130303
---
 tensorflow/BUILD                              |   4 +-
 tensorflow/contrib/decision_trees/BUILD       |  19 -
 tensorflow/contrib/decision_trees/proto/BUILD |  10 +
 tensorflow/contrib/tensor_forest/BUILD        |   2 +
 .../contrib/tensor_forest/kernels/v4/BUILD    | 234 ++++++
 .../kernels/v4/candidate_graph_runner.cc      | 137 ++++
 .../kernels/v4/candidate_graph_runner.h       |  73 ++
 .../kernels/v4/decision-tree-resource.cc      |  88 ++
 .../kernels/v4/decision-tree-resource.h       |  90 +++
 .../kernels/v4/decision_node_evaluator.cc     | 120 +++
 .../kernels/v4/decision_node_evaluator.h      | 107 +++
 .../v4/decision_node_evaluator_test.cc        | 127 +++
 .../kernels/v4/fertile-stats-resource.cc      |  98 +++
 .../kernels/v4/fertile-stats-resource.h       | 110 +++
 .../tensor_forest/kernels/v4/grow_stats.cc    | 762 ++++++++++++++++++
 .../tensor_forest/kernels/v4/grow_stats.h     | 470 +++++++++++
 .../kernels/v4/grow_stats_test.cc             | 364 +++++++++
 .../tensor_forest/kernels/v4/input_data.cc    | 154 ++++
 .../tensor_forest/kernels/v4/input_data.h     | 124 +++
 .../tensor_forest/kernels/v4/input_target.h   |  91 +++
 .../kernels/v4/leaf_model_operators.cc        | 160 ++++
 .../kernels/v4/leaf_model_operators.h         | 150 ++++
 .../kernels/v4/leaf_model_operators_test.cc   | 223 +++++
 .../tensor_forest/kernels/v4/params.cc        |  54 ++
 .../contrib/tensor_forest/kernels/v4/params.h |  32 +
 .../tensor_forest/kernels/v4/params_test.cc   |  75 ++
 .../kernels/v4/split_collection_operators.cc  | 257 ++++++
 .../kernels/v4/split_collection_operators.h   | 148 ++++
 .../tensor_forest/kernels/v4/stat_utils.cc    |  87 ++
 .../tensor_forest/kernels/v4/stat_utils.h     |  50 ++
 .../tensor_forest/kernels/v4/test_utils.h     |  73 ++
 tensorflow/contrib/tensor_forest/proto/BUILD  |  31 +
 .../tensor_forest/proto/fertile_stats.proto   |  92 +++
 .../proto/tensor_forest_params.proto          | 145 ++++
 34 files changed, 4741 insertions(+), 20 deletions(-)
 delete mode 100644 tensorflow/contrib/decision_trees/BUILD
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/BUILD
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/input_data.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/params.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/params.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h
 create mode 100644 tensorflow/contrib/tensor_forest/proto/BUILD
 create mode 100644 tensorflow/contrib/tensor_forest/proto/fertile_stats.proto
 create mode 100644 tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index bb69bf172f..ec9be247d3 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -245,7 +245,7 @@ filegroup(
         "//tensorflow/contrib/data/python/kernel_tests:all_files",
         "//tensorflow/contrib/data/python/ops:all_files",
         "//tensorflow/contrib/data/python/util:all_files",
-        "//tensorflow/contrib/decision_trees:all_files",
+        "//tensorflow/contrib/decision_trees/proto:all_files",
         "//tensorflow/contrib/distributions:all_files",
         "//tensorflow/contrib/factorization:all_files",
         "//tensorflow/contrib/factorization/kernels:all_files",
@@ -294,6 +294,8 @@ filegroup(
         "//tensorflow/contrib/stateless:all_files",
         "//tensorflow/contrib/tensor_forest:all_files",
         "//tensorflow/contrib/tensor_forest/hybrid:all_files",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:all_files",
+        "//tensorflow/contrib/tensor_forest/proto:all_files",
         "//tensorflow/contrib/tensorboard:all_files",
         "//tensorflow/contrib/testing:all_files",
         "//tensorflow/contrib/text:all_files",
diff --git a/tensorflow/contrib/decision_trees/BUILD b/tensorflow/contrib/decision_trees/BUILD
deleted file mode 100644
index 4045b92f10..0000000000
--- a/tensorflow/contrib/decision_trees/BUILD
+++ /dev/null
@@ -1,19 +0,0 @@
-# Files common to decision-tree algorithms.
-package(default_visibility = [
-    "//visibility:public",
-])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-filegroup(
-    name = "all_files",
-    srcs = glob(
-        ["**/*"],
-        exclude = [
-            "**/OWNERS",
-        ],
-    ),
-    visibility = ["//tensorflow:__subpackages__"],
-)
diff --git a/tensorflow/contrib/decision_trees/proto/BUILD b/tensorflow/contrib/decision_trees/proto/BUILD
index 86174c5865..87c80740a8 100644
--- a/tensorflow/contrib/decision_trees/proto/BUILD
+++ b/tensorflow/contrib/decision_trees/proto/BUILD
@@ -9,12 +9,21 @@ exports_files([
 
 load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library")
 
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
 tf_proto_library(
     name = "generic_tree_model",
     srcs = ["generic_tree_model.proto"],
     cc_api_version = 2,
     go_api_version = 2,
     java_api_version = 2,
+    visibility = ["//visibility:public"],
 )
 
 tf_proto_library(
@@ -23,4 +32,5 @@ tf_proto_library(
     cc_api_version = 2,
     go_api_version = 2,
     protodeps = [":generic_tree_model"],
+    visibility = ["//visibility:public"],
 )
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 40d2318311..7b5f9472e7 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -21,6 +21,8 @@ filegroup(
         exclude = [
             "**/METADATA",
             "**/OWNERS",
+            "kernels/v4/*",
+            "proto/*",
         ],
     ),
     visibility = ["//tensorflow:__subpackages__"],
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
new file mode 100644
index 0000000000..da87b051a9
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -0,0 +1,234 @@
+# TensorFlow code for training random forests.
+package(default_visibility = [
+    "//visibility:public",
+])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+    ),
+)
+
+cc_library(
+    name = "decision-tree-resource",
+    srcs = ["decision-tree-resource.cc"],
+    hdrs = ["decision-tree-resource.h"],
+    deps = [
+        ":decision_node_evaluator",
+        ":input_data",
+        ":leaf_model_operators",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "fertile-stats-resource",
+    srcs = ["fertile-stats-resource.cc"],
+    hdrs = ["fertile-stats-resource.h"],
+    deps = [
+        ":decision_node_evaluator",
+        ":input_data",
+        ":input_target",
+        ":leaf_model_operators",
+        ":split_collection_operators",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_library(
+    name = "input_data",
+    srcs = ["input_data.cc"],
+    hdrs = ["input_data.h"],
+    deps = [
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "input_target",
+    hdrs = ["input_target.h"],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "leaf_model_operators",
+    srcs = ["leaf_model_operators.cc"],
+    hdrs = ["leaf_model_operators.h"],
+    deps = [
+        ":input_target",
+        ":params",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_test(
+    name = "leaf_model_operators_test",
+    srcs = ["leaf_model_operators_test.cc"],
+    deps = [
+        ":leaf_model_operators",
+        ":test_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_library(
+    name = "grow_stats",
+    srcs = ["grow_stats.cc"],
+    hdrs = ["grow_stats.h"],
+    deps = [
+        ":decision_node_evaluator",
+        ":input_data",
+        ":input_target",
+        ":params",
+        ":stat_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_test(
+    name = "grow_stats_test",
+    srcs = ["grow_stats_test.cc"],
+    deps = [
+        ":grow_stats",
+        ":test_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_library(
+    name = "candidate_graph_runner",
+    srcs = ["candidate_graph_runner.cc"],
+    hdrs = ["candidate_graph_runner.h"],
+    deps = [
+        ":input_data",
+        ":input_target",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "decision_node_evaluator",
+    srcs = ["decision_node_evaluator.cc"],
+    hdrs = ["decision_node_evaluator.h"],
+    deps = [
+        ":input_data",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_test(
+    name = "decision_node_evaluator_test",
+    srcs = ["decision_node_evaluator_test.cc"],
+    deps = [
+        ":decision_node_evaluator",
+        ":test_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/core",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_library(
+    name = "split_collection_operators",
+    srcs = ["split_collection_operators.cc"],
+    hdrs = ["split_collection_operators.h"],
+    deps = [
+        ":candidate_graph_runner",
+        ":grow_stats",
+        ":input_data",
+        ":input_target",
+        ":leaf_model_operators",
+        ":params",
+        ":stat_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+    ],
+)
+
+cc_library(
+    name = "stat_utils",
+    srcs = ["stat_utils.cc"],
+    hdrs = ["stat_utils.h"],
+    deps = [
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_library(
+    name = "test_utils",
+    hdrs = ["test_utils.h"],
+    deps = [
+        ":input_data",
+        ":input_target",
+    ],
+)
+
+cc_library(
+    name = "params",
+    srcs = ["params.cc"],
+    hdrs = ["params.h"],
+    deps = [
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_test(
+    name = "params_test",
+    srcs = ["params_test.cc"],
+    deps = [
+        ":params",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc
new file mode 100644
index 0000000000..81e2a1b2a1
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc
@@ -0,0 +1,137 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
+
+#include "tensorflow/core/lib/io/path.h"
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/platform/env.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Names of ops in the graph to run.
+constexpr char kInitializeOp[] = "init";
+constexpr char kAddExampleOp[] = "add_example";
+constexpr char kSplitScoreName[] = "split_score";
+constexpr char kGetSplitName[] = "get_split";
+constexpr char kGetLeftStatsName[] = "get_left_stats";
+constexpr char kGetRightStatsName[] = "get_right_stats";
+
+// Names of files written by python graph builder.
+constexpr char kGraphFilename[] = "graph";
+constexpr char kSaverDefFilename[] = "saver";
+constexpr char kMetaDefFilename[] = "meta";
+
+// Names of Tensor inputs.
+constexpr char kFeaturesName[] = "features";
+constexpr char kInputDataName[] = "input_data";
+constexpr char kTargetsName[] = "targets";
+constexpr char kExamplesName[] = "examples";
+
+constexpr char kNoOp[] = "none";
+
+CandidateGraphRunner::CandidateGraphRunner(
+    const string& graph_dir, const decision_trees::BinaryNode& split)
+    : split_(split) {
+  // read graph from file.
+  GraphDef graph_def;
+  TF_CHECK_OK(ReadBinaryProto(
+      Env::Default(), io::JoinPath(graph_dir, kGraphFilename), &graph_def))
+      << "Could not read graph def.";
+
+  // create session.
+  session_.reset(::tensorflow::NewSession(SessionOptions()));
+  TF_CHECK_OK(session_->Create(graph_def)) << "Failed to create session";
+
+  // Features don't change, store them in a tensor.
+  const auto& oblique = split.inequality_left_child_test().oblique();
+  const int32 feat_size = oblique.features_size();
+  features_.reset(
+      new Tensor(tensorflow::DT_INT32, TensorShape({feat_size})));
+  auto feat = features_->flat<int32>();
+  int i = 0;
+  for (const auto& id : oblique.features()) {
+    safe_strto32(id.id().value(), &feat(i++));
+  }
+}
+
+void CandidateGraphRunner::RunOp(
+    const string& name, const TensorNameValueList& inputs,
+    const std::vector<string>& output_tensor_names,
+    std::vector<Tensor>* outputs) {
+  std::vector<string> op_name;
+  if (name != kNoOp) {
+    op_name.push_back(name);
+  }
+  TF_CHECK_OK(session_->Run(inputs, output_tensor_names, op_name, outputs))
+      << "Failed to run: " << name;
+}
+
+void CandidateGraphRunner::Init() {
+  RunOp(kInitializeOp, TensorNameValueList(), std::vector<string>(), nullptr);
+}
+
+void CandidateGraphRunner::AddExample(const Tensor& input_data,
+                                      const Tensor& target,
+                                      const Tensor& examples) {
+  TensorNameValueList inputs;
+  inputs.emplace_back(kFeaturesName, *features_);
+  inputs.emplace_back(kExamplesName, examples);
+  inputs.emplace_back(kInputDataName, input_data);
+  inputs.emplace_back(kTargetsName, target);
+
+  RunOp(kAddExampleOp, inputs, std::vector<string>(), nullptr);
+}
+
+float CandidateGraphRunner::SplitScore() {
+  std::vector<Tensor> outputs;
+  RunOp(kNoOp, TensorNameValueList(), {kSplitScoreName}, &outputs);
+  return outputs[0].unaligned_flat<float>()(0);
+}
+
+void CandidateGraphRunner::GetSplit(decision_trees::BinaryNode* node) {
+  std::vector<Tensor> outputs;
+  RunOp(kNoOp, TensorNameValueList(), {kGetSplitName}, &outputs);
+  ParseProtoUnlimited(node, outputs[0].unaligned_flat<string>()(0));
+  const auto& oblique = split_.inequality_left_child_test().oblique();
+  auto* new_split =
+      node->mutable_inequality_left_child_test()->mutable_oblique();
+  for (const auto& id : oblique.features()) {
+    *new_split->add_features() = id;
+  }
+}
+
+void CandidateGraphRunner::GetLeftStats(LeafStat* stats) {
+  std::vector<Tensor> outputs;
+  RunOp(kNoOp, TensorNameValueList(), {kGetLeftStatsName}, &outputs);
+  const auto& counts = outputs[0].unaligned_flat<float>();
+  auto* dense = stats->mutable_classification()->mutable_dense_counts();
+  for (int i = 0; i < counts.size(); ++i) {
+    dense->add_value()->set_float_value(counts(i));
+  }
+}
+
+void CandidateGraphRunner::GetRightStats(LeafStat* stats) {
+  std::vector<Tensor> outputs;
+  RunOp(kNoOp, TensorNameValueList(), {kGetRightStatsName}, &outputs);
+  const auto& counts = outputs[0].unaligned_flat<float>();
+  auto* dense = stats->mutable_classification()->mutable_dense_counts();
+  for (int i = 0; i < counts.size(); ++i) {
+    dense->add_value()->set_float_value(counts(i));
+  }
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h
new file mode 100644
index 0000000000..4bd1f06c72
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h
@@ -0,0 +1,73 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_CANDIDATE_GRAPH_RUNNER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_CANDIDATE_GRAPH_RUNNER_H_
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/public/session.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+typedef std::vector<std::pair<string, ::tensorflow::Tensor>>
+    TensorNameValueList;
+
+// Class that represents one split candidate, and can perform operations
+// on a session created from a graph.
+class CandidateGraphRunner {
+ public:
+  // split should contain the features that are being used.
+  CandidateGraphRunner(const string& graph_dir,
+                       const decision_trees::BinaryNode& split);
+
+  // Input the given data and target Tensors to the add_example op.
+  void AddExample(const Tensor& input_data, const Tensor& target,
+                  const Tensor& examples);
+
+  // Get the candidates' split score with the split_score op.
+  float SplitScore();
+
+  // Fills in the split in node with weights and threshold.
+  void GetSplit(decision_trees::BinaryNode* node);
+
+  // Fills in the stats for the left-branch taken.
+  void GetLeftStats(LeafStat* stats);
+
+  // Fills in the stats for the right-branch taken.
+  void GetRightStats(LeafStat* stats);
+
+  // Initializes variables, must be run before other ops.
+  void Init();
+
+ protected:
+  void RunOp(const string& name, const TensorNameValueList& inputs,
+             const std::vector<string>& output_tensor_names,
+             std::vector<Tensor>* outputs);
+
+  std::unique_ptr<Session> session_;
+  decision_trees::BinaryNode split_;
+  std::unique_ptr<Tensor> features_;
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_CANDIDATE_GRAPH_RUNNER_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc
new file mode 100644
index 0000000000..165685ca53
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc
@@ -0,0 +1,88 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+using decision_trees::DecisionTree;
+using decision_trees::TreeNode;
+
+int32 DecisionTreeResource::TraverseTree(
+    const std::unique_ptr<TensorDataSet>& input_data, int example,
+    int32* leaf_depth) const {
+  const DecisionTree& tree = decision_tree_->decision_tree();
+  int32 current_id = 0;
+  int32 depth = 0;
+  while (true) {
+    const TreeNode& current = tree.nodes(current_id);
+    if (current.has_leaf()) {
+      if (leaf_depth != nullptr) {
+        *leaf_depth = depth;
+      }
+      return current_id;
+    }
+    ++depth;
+    const int32 next_id =
+        node_evaluators_[current_id]->Decide(input_data, example);
+    current_id = tree.nodes(next_id).node_id().value();
+  }
+}
+
+void DecisionTreeResource::SplitNode(int32 node_id, SplitCandidate* best,
+                                     std::vector<int32>* new_children) {
+  DecisionTree* tree = decision_tree_->mutable_decision_tree();
+  TreeNode* node = tree->mutable_nodes(node_id);
+  int32 newid = tree->nodes_size();
+
+  // left
+  new_children->push_back(newid);
+  TreeNode* new_left = tree->add_nodes();
+  new_left->mutable_node_id()->set_value(newid++);
+  new_left->mutable_leaf();
+
+  // right
+  new_children->push_back(newid);
+  TreeNode* new_right = tree->add_nodes();
+  new_right->mutable_node_id()->set_value(newid);
+  new_right->mutable_leaf();
+
+  node->clear_leaf();
+  node->mutable_binary_node()->Swap(best->mutable_split());
+  node->mutable_binary_node()->mutable_left_child_id()->set_value(newid - 1);
+  node->mutable_binary_node()->mutable_right_child_id()->set_value(newid);
+  while (node_evaluators_.size() <= node_id) {
+    node_evaluators_.emplace_back(nullptr);
+  }
+  node_evaluators_[node_id] = CreateDecisionNodeEvaluator(*node);
+}
+
+void DecisionTreeResource::MaybeInitialize() {
+  DecisionTree* tree = decision_tree_->mutable_decision_tree();
+  if (tree->nodes_size() == 0) {
+    tree->add_nodes()->mutable_leaf();
+  } else if (node_evaluators_.empty()) {  // reconstruct evaluators
+    for (const auto& node : tree->nodes()) {
+      if (node.has_leaf()) {
+        node_evaluators_.emplace_back(nullptr);
+      } else {
+        node_evaluators_.push_back(CreateDecisionNodeEvaluator(node));
+      }
+    }
+  }
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h
new file mode 100644
index 0000000000..c8f09d8e07
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h
@@ -0,0 +1,90 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_TREE_RESOURCE_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_TREE_RESOURCE_H_
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/platform/mutex.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+
+// Keep a tree ensemble in memory for efficient evaluation and mutation.
+class DecisionTreeResource : public ResourceBase {
+ public:
+  // Constructor.
+  explicit DecisionTreeResource()
+      : decision_tree_(new decision_trees::Model()) {}
+
+  string DebugString() override {
+    return strings::StrCat("DecisionTree[size=",
+                           decision_tree_->decision_tree().nodes_size(),
+                           "]");
+  }
+
+  void MaybeInitialize();
+
+  const decision_trees::Model& decision_tree() const {
+    return *decision_tree_;
+  }
+
+  decision_trees::Model* mutable_decision_tree() {
+    return decision_tree_.get();
+  }
+
+  const decision_trees::Leaf& get_leaf(int32 id) const {
+    return decision_tree_->decision_tree().nodes(id).leaf();
+  }
+
+  decision_trees::TreeNode* get_mutable_tree_node(int32 id) {
+    return decision_tree_->mutable_decision_tree()->mutable_nodes(id);
+  }
+
+  // Resets the resource and frees the proto.
+  // Caller needs to hold the mutex lock while calling this.
+  void Reset() {
+    decision_tree_.reset(new decision_trees::Model());
+  }
+
+  mutex* get_mutex() { return &mu_; }
+
+  // Return the TreeNode for the leaf that the example ends up at according
+  // to decsion_tree_. Also fill in that leaf's depth if it isn't nullptr.
+  int32 TraverseTree(const std::unique_ptr<TensorDataSet>& input_data,
+                     int example, int32* depth) const;
+
+  // Split the given node_id, turning it from a Leaf to a BinaryNode and
+  // setting it's split to the given best.  Add new children ids to
+  // new_children.
+  void SplitNode(int32 node_id, SplitCandidate* best,
+                 std::vector<int32>* new_children);
+
+ private:
+  mutex mu_;
+  std::unique_ptr<decision_trees::Model> decision_tree_;
+  std::vector<std::unique_ptr<DecisionNodeEvaluator>> node_evaluators_;
+};
+
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_TREE_RESOURCE_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.cc b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.cc
new file mode 100644
index 0000000000..7e25579070
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.cc
@@ -0,0 +1,120 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h"
+#include "tensorflow/core/lib/strings/numbers.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+std::unique_ptr<DecisionNodeEvaluator> CreateDecisionNodeEvaluator(
+    const decision_trees::TreeNode& node) {
+  const decision_trees::BinaryNode& bnode = node.binary_node();
+  return CreateBinaryDecisionNodeEvaluator(bnode, bnode.left_child_id().value(),
+                                           bnode.right_child_id().value());
+}
+
+std::unique_ptr<DecisionNodeEvaluator> CreateBinaryDecisionNodeEvaluator(
+    const decision_trees::BinaryNode& bnode, int32 left, int32 right) {
+  if (bnode.has_inequality_left_child_test()) {
+    const auto& test = bnode.inequality_left_child_test();
+    if (test.has_oblique()) {
+      return std::unique_ptr<ObliqueInequalityDecisionNodeEvaluator>(
+          new ObliqueInequalityDecisionNodeEvaluator(test, left, right));
+    } else {
+      return std::unique_ptr<InequalityDecisionNodeEvaluator>(
+          new InequalityDecisionNodeEvaluator(test, left, right));
+    }
+  } else {
+    decision_trees::MatchingValuesTest test;
+    if (bnode.custom_left_child_test().UnpackTo(&test)) {
+      return std::unique_ptr<MatchingValuesDecisionNodeEvaluator>(
+          new MatchingValuesDecisionNodeEvaluator(test, left, right));
+    } else {
+      LOG(ERROR) << "Unknown split test: " << bnode.DebugString();
+      return nullptr;
+    }
+  }
+}
+
+InequalityDecisionNodeEvaluator::InequalityDecisionNodeEvaluator(
+    const decision_trees::InequalityTest& test, int32 left, int32 right)
+    : BinaryDecisionNodeEvaluator(left, right) {
+  safe_strto32(test.feature_id().id().value(), &feature_num_);
+  threshold_ = test.threshold().float_value();
+  include_equals_ =
+      test.type() == decision_trees::InequalityTest::LESS_OR_EQUAL;
+}
+
+int32 InequalityDecisionNodeEvaluator::Decide(
+    const std::unique_ptr<TensorDataSet>& dataset, int example) const {
+  const float val = dataset->GetExampleValue(example, feature_num_);
+  if (val < threshold_ || (include_equals_ && val == threshold_)) {
+    return left_child_id_;
+  } else {
+    return right_child_id_;
+  }
+}
+
+ObliqueInequalityDecisionNodeEvaluator::ObliqueInequalityDecisionNodeEvaluator(
+    const decision_trees::InequalityTest& test, int32 left, int32 right)
+    : BinaryDecisionNodeEvaluator(left, right) {
+  for (int i = 0; i < test.oblique().features_size(); ++i) {
+    int32 val;
+    safe_strto32(test.oblique().features(i).id().value(), &val);
+    feature_num_.push_back(val);
+    feature_weights_.push_back(test.oblique().weights(i));
+  }
+  threshold_ = test.threshold().float_value();
+}
+
+int32 ObliqueInequalityDecisionNodeEvaluator::Decide(
+    const std::unique_ptr<TensorDataSet>& dataset, int example) const {
+  float val = 0;
+  for (int i = 0; i < feature_num_.size(); ++i) {
+    val += feature_weights_[i] *
+           dataset->GetExampleValue(example, feature_num_[i]);
+  }
+
+  if (val <= threshold_) {
+    return left_child_id_;
+  } else {
+    return right_child_id_;
+  }
+}
+
+MatchingValuesDecisionNodeEvaluator::MatchingValuesDecisionNodeEvaluator(
+    const decision_trees::MatchingValuesTest& test, int32 left, int32 right)
+    : BinaryDecisionNodeEvaluator(left, right) {
+  safe_strto32(test.feature_id().id().value(), &feature_num_);
+  for (const auto& val : test.value()) {
+    values_.push_back(val.float_value());
+  }
+  inverse_ = test.inverse();
+}
+
+int32 MatchingValuesDecisionNodeEvaluator::Decide(
+    const std::unique_ptr<TensorDataSet>& dataset, int example) const {
+  const float val = dataset->GetExampleValue(example, feature_num_);
+  for (float testval : values_) {
+    if (val == testval) {
+      return inverse_ ? right_child_id_ : left_child_id_;
+    }
+  }
+
+  return inverse_ ? left_child_id_ : right_child_id_;
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h
new file mode 100644
index 0000000000..3f03c2d05b
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h
@@ -0,0 +1,107 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_NODE_EVALUATOR_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_NODE_EVALUATOR_H_
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+
+// Base class for evaluators of decision nodes that effectively copy proto
+// contents into C++ structures for faster execution.
+class DecisionNodeEvaluator {
+ public:
+  virtual ~DecisionNodeEvaluator() {}
+
+  // Returns the index of the child node.
+  virtual int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
+                       int example) const = 0;
+};
+
+// An evaluator for binary decisions with left and right children.
+class BinaryDecisionNodeEvaluator : public DecisionNodeEvaluator {
+ protected:
+  BinaryDecisionNodeEvaluator(int32 left, int32 right)
+      : left_child_id_(left), right_child_id_(right) {}
+
+  int32 left_child_id_;
+  int32 right_child_id_;
+};
+
+// Evaluator for basic inequality decisions (f[x] <= T).
+class InequalityDecisionNodeEvaluator : public BinaryDecisionNodeEvaluator {
+ public:
+  InequalityDecisionNodeEvaluator(const decision_trees::InequalityTest& test,
+                                  int32 left, int32 right);
+
+  int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
+               int example) const override;
+
+ protected:
+  int32 feature_num_;
+  float threshold_;
+
+  // If decision is '<=' as opposed to '<'.
+  bool include_equals_;
+};
+
+// Evalutor for splits with multiple weighted features.
+class ObliqueInequalityDecisionNodeEvaluator
+    : public BinaryDecisionNodeEvaluator {
+ public:
+  ObliqueInequalityDecisionNodeEvaluator(
+      const decision_trees::InequalityTest& test, int32 left, int32 right);
+
+  int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
+               int example) const override;
+
+ protected:
+  std::vector<int32> feature_num_;
+  std::vector<float> feature_weights_;
+  float threshold_;
+};
+
+// Evaluator for contains-in-set decisions.  Also supports inverse (not-in-set).
+class MatchingValuesDecisionNodeEvaluator : public BinaryDecisionNodeEvaluator {
+ public:
+  MatchingValuesDecisionNodeEvaluator(
+      const decision_trees::MatchingValuesTest& test, int32 left, int32 right);
+
+  int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
+               int example) const override;
+
+ protected:
+  int32 feature_num_;
+  std::vector<float> values_;
+  bool inverse_;
+};
+
+std::unique_ptr<DecisionNodeEvaluator> CreateDecisionNodeEvaluator(
+    const decision_trees::TreeNode& node);
+std::unique_ptr<DecisionNodeEvaluator> CreateBinaryDecisionNodeEvaluator(
+    const decision_trees::BinaryNode& node, int32 left, int32 right);
+
+struct CandidateEvalatorCollection {
+  std::vector<std::unique_ptr<DecisionNodeEvaluator>> splits;
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_NODE_EVALUATOR_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc
new file mode 100644
index 0000000000..5c49b87443
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc
@@ -0,0 +1,127 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace {
+
+using tensorflow::tensorforest::InequalityDecisionNodeEvaluator;
+using tensorflow::tensorforest::MatchingValuesDecisionNodeEvaluator;
+using tensorflow::tensorforest::ObliqueInequalityDecisionNodeEvaluator;
+using tensorflow::decision_trees::InequalityTest;
+using tensorflow::decision_trees::MatchingValuesTest;
+
+TEST(InequalityDecisionNodeEvaluatorTest, TestLessOrEqual) {
+  InequalityTest test;
+  test.mutable_feature_id()->mutable_id()->set_value("0");
+  test.mutable_threshold()->set_float_value(3.0);
+  test.set_type(InequalityTest::LESS_OR_EQUAL);
+  std::unique_ptr<InequalityDecisionNodeEvaluator> eval(
+      new InequalityDecisionNodeEvaluator(test, 0, 1));
+
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {0.0, 1.0, 2.0, 3.0, 4.0, 5.0}, 1));
+
+  ASSERT_EQ(eval->Decide(dataset, 2), 0);
+  ASSERT_EQ(eval->Decide(dataset, 3), 0);
+  ASSERT_EQ(eval->Decide(dataset, 4), 1);
+}
+
+TEST(InequalityDecisionNodeEvaluatorTest, TestStrictlyLess) {
+  InequalityTest test;
+  test.mutable_feature_id()->mutable_id()->set_value("0");
+  test.mutable_threshold()->set_float_value(3.0);
+  test.set_type(InequalityTest::LESS_THAN);
+  std::unique_ptr<InequalityDecisionNodeEvaluator> eval(
+      new InequalityDecisionNodeEvaluator(test, 0, 1));
+
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {0.0, 1.0, 2.0, 3.0, 4.0, 5.0}, 1));
+
+  ASSERT_EQ(eval->Decide(dataset, 2), 0);
+  ASSERT_EQ(eval->Decide(dataset, 3), 1);
+  ASSERT_EQ(eval->Decide(dataset, 4), 1);
+}
+
+TEST(MatchingDecisionNodeEvaluatorTest, Basic) {
+  MatchingValuesTest test;
+  test.mutable_feature_id()->mutable_id()->set_value("0");
+  test.add_value()->set_float_value(3.0);
+  test.add_value()->set_float_value(5.0);
+
+  std::unique_ptr<MatchingValuesDecisionNodeEvaluator> eval(
+      new MatchingValuesDecisionNodeEvaluator(test, 0, 1));
+
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {0.0, 1.0, 2.0, 3.0, 4.0, 5.0}, 1));
+
+  ASSERT_EQ(eval->Decide(dataset, 2), 1);
+  ASSERT_EQ(eval->Decide(dataset, 3), 0);
+  ASSERT_EQ(eval->Decide(dataset, 4), 1);
+  ASSERT_EQ(eval->Decide(dataset, 5), 0);
+}
+
+TEST(MatchingDecisionNodeEvaluatorTest, Inverse) {
+  MatchingValuesTest test;
+  test.mutable_feature_id()->mutable_id()->set_value("0");
+  test.add_value()->set_float_value(3.0);
+  test.add_value()->set_float_value(5.0);
+  test.set_inverse(true);
+
+  std::unique_ptr<MatchingValuesDecisionNodeEvaluator> eval(
+      new MatchingValuesDecisionNodeEvaluator(test, 0, 1));
+
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {0.0, 1.0, 2.0, 3.0, 4.0, 5.0}, 1));
+
+  ASSERT_EQ(eval->Decide(dataset, 2), 0);
+  ASSERT_EQ(eval->Decide(dataset, 3), 1);
+  ASSERT_EQ(eval->Decide(dataset, 4), 0);
+  ASSERT_EQ(eval->Decide(dataset, 5), 1);
+}
+
+TEST(ObliqueDecisionNodeEvaluatorTest, Basic) {
+  InequalityTest test;
+  auto* feat1 = test.mutable_oblique()->add_features();
+  feat1->mutable_id()->set_value("0");
+  test.mutable_oblique()->add_weights(1.0);
+  auto* feat2 = test.mutable_oblique()->add_features();
+  feat2->mutable_id()->set_value("1");
+  test.mutable_oblique()->add_weights(1.0);
+
+  test.mutable_threshold()->set_float_value(3.0);
+  test.set_type(InequalityTest::LESS_OR_EQUAL);
+
+  std::unique_ptr<ObliqueInequalityDecisionNodeEvaluator> eval(
+      new ObliqueInequalityDecisionNodeEvaluator(test, 0, 1));
+
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {0.0, 1.0, 2.0, 3.0, 4.0, 5.0}, 2));
+
+  ASSERT_EQ(eval->Decide(dataset, 0), 0);
+  ASSERT_EQ(eval->Decide(dataset, 1), 1);
+}
+
+}  // namespace
+}  // namespace tensorflow
+
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
new file mode 100644
index 0000000000..9f5d948514
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
@@ -0,0 +1,98 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h"
+
+#include <cfloat>
+
+namespace tensorflow {
+namespace tensorforest {
+
+void FertileStatsResource::AddExampleToStatsAndInitialize(
+    const std::unique_ptr<TensorDataSet>& input_data,
+    const InputTarget* target, const std::vector<int>& examples,
+    int32 node_id, int32 node_depth, bool* is_finished) {
+  // Set leaf's counts for calculating probabilities.
+  for (int example : examples) {
+    model_op_->UpdateModel(&leaf_stats_[node_id], target, example);
+  }
+
+  // Update stats or initialize if needed.
+  if (collection_op_->IsInitialized(node_id)) {
+    collection_op_->AddExample(input_data, target, examples, node_id);
+  } else {
+    // This throws away any extra examples, which is more inefficient towards
+    // the top but gradually becomes less of an issue as the tree grows.
+    for (int example : examples) {
+      collection_op_->CreateAndInitializeCandidateWithExample(
+          input_data, example, node_id);
+      if (collection_op_->IsInitialized(node_id)) {
+        break;
+      }
+    }
+  }
+
+  *is_finished = collection_op_->IsFinished(node_id);
+}
+
+void FertileStatsResource::AllocateNode(int32 node_id, int32 depth) {
+  leaf_stats_[node_id] = LeafStat();
+  model_op_->InitModel(&leaf_stats_[node_id]);
+  collection_op_->InitializeSlot(node_id, depth);
+}
+
+void FertileStatsResource::Allocate(int32 parent_depth,
+                                    const std::vector<int32>& new_children) {
+  const int32 children_depth = parent_depth + 1;
+  for (const int32 child : new_children) {
+    AllocateNode(child, children_depth);
+  }
+}
+
+void FertileStatsResource::Clear(int32 node) {
+  collection_op_->ClearSlot(node);
+  leaf_stats_.erase(node);
+}
+
+bool FertileStatsResource::BestSplit(int32 node_id, SplitCandidate* best,
+                                     int32* depth) {
+  return collection_op_->BestSplit(node_id, best, depth);
+}
+
+void FertileStatsResource::MaybeInitialize() {
+  if (leaf_stats_.empty()) {
+    AllocateNode(0, 0);
+  }
+}
+
+void FertileStatsResource::ExtractFromProto(const FertileStats& stats) {
+  collection_op_ =
+      SplitCollectionOperatorFactory::CreateSplitCollectionOperator(params_);
+  collection_op_->ExtractFromProto(stats);
+  for (int i = 0; i < stats.node_to_slot_size(); ++i) {
+    const auto& slot = stats.node_to_slot(i);
+    leaf_stats_[slot.node_id()] = slot.leaf_stats();
+  }
+}
+
+void FertileStatsResource::PackToProto(FertileStats* stats) const {
+  for (const auto& entry : leaf_stats_) {
+    auto* slot = stats->add_node_to_slot();
+    *slot->mutable_leaf_stats() = entry.second;
+    slot->set_node_id(entry.first);
+  }
+  collection_op_->PackToProto(stats);
+}
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h
new file mode 100644
index 0000000000..34ec945e84
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h
@@ -0,0 +1,110 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_FERTILE_STATS_RESOURCE_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_FERTILE_STATS_RESOURCE_H_
+
+#include <vector>
+
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/platform/mutex.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Stores a FertileStats proto and implements operations on it.
+class FertileStatsResource : public ResourceBase {
+ public:
+  // Constructor.
+  explicit FertileStatsResource(const TensorForestParams& params)
+      : params_(params) {
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(params_);
+  }
+
+  string DebugString() override {
+    return "FertileStats";
+  }
+
+  void ExtractFromProto(const FertileStats& stats);
+
+  void PackToProto(FertileStats* stats) const;
+
+  // Resets the resource and frees the proto.
+  // Caller needs to hold the mutex lock while calling this.
+  void Reset() {
+    leaf_stats_.clear();
+  }
+
+  // Reset the stats for a node, but leave the leaf_stats intact.
+  void ResetSplitStats(int32 node_id, int32 depth) {
+    collection_op_->ClearSlot(node_id);
+    collection_op_->InitializeSlot(node_id, depth);
+  }
+
+  mutex* get_mutex() { return &mu_; }
+
+  void MaybeInitialize();
+
+  // Applies the example to the given leaf's statistics. Also applies it to the
+  // node's fertile slot's statistics if or initializes a split candidate,
+  // where applicable.  Returns if the node is finished or if it's ready to
+  // allocate to a fertile slot.
+  void AddExampleToStatsAndInitialize(
+      const std::unique_ptr<TensorDataSet>& input_data,
+      const InputTarget* target, const std::vector<int>& examples,
+      int32 node_id, int32 node_depth, bool* is_finished);
+
+  // Allocate a fertile slot for each ready node, then new children up to
+  // max_fertile_nodes_.
+  void Allocate(int32 parent_depth, const std::vector<int32>& new_children);
+
+  // Remove a node's fertile slot.  Should only be called when the node is
+  // no longer a leaf.
+  void Clear(int32 node);
+
+  // Return the best SplitCandidate for a node, or NULL if no suitable split
+  // was found.
+  bool BestSplit(int32 node_id, SplitCandidate* best, int32* depth);
+
+  const LeafStat& leaf_stat(int32 node_id) {
+    return leaf_stats_[node_id];
+  }
+
+  void set_leaf_stat(const LeafStat& stat, int32 node_id) {
+    leaf_stats_[node_id] = stat;
+  }
+
+ private:
+  mutex mu_;
+  std::shared_ptr<LeafModelOperator> model_op_;
+  std::unique_ptr<SplitCollectionOperator> collection_op_;
+  std::unordered_map<int32, LeafStat> leaf_stats_;
+  const TensorForestParams params_;
+
+  void AllocateNode(int32 node_id, int32 depth);
+};
+
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_FERTILE_STATS_RESOURCE_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
new file mode 100644
index 0000000000..fe7b24d36c
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
@@ -0,0 +1,762 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
+
+#include <cfloat>
+#include <queue>
+#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h"
+#include "tensorflow/core/lib/random/distribution_sampler.h"
+
+
+namespace tensorflow {
+namespace tensorforest {
+
+// When creating evaluators for the split candidates, use these
+// for the left and right return values.
+static const int32 LEFT_INDEX = 0;
+static const int32 RIGHT_INDEX = 1;
+
+GrowStats::GrowStats(const TensorForestParams& params, int32 depth)
+    : depth_(depth),
+      params_(params),
+      split_after_samples_(ResolveParam(params.split_after_samples(), depth)),
+      num_splits_to_consider_(
+          ResolveParam(params.num_splits_to_consider(), depth)),
+      num_outputs_(params.num_outputs()) {}
+
+void GrowStats::AddSplit(const decision_trees::BinaryNode& split) {
+  splits_.push_back(split);
+  evaluators_.emplace_back(
+      CreateBinaryDecisionNodeEvaluator(split, LEFT_INDEX, RIGHT_INDEX));
+  AddSplitStats();
+}
+
+void GrowStats::RemoveSplit(int split_num) {
+  splits_.erase(splits_.begin() + split_num);
+  evaluators_.erase(evaluators_.begin() + split_num);
+  RemoveSplitStats(split_num);
+}
+
+// ------------------------ Classification --------------------------- //
+
+ClassificationStats::ClassificationStats(const TensorForestParams& params,
+                                         int32 depth)
+    : GrowStats(params, depth), finish_early_(false) {
+  // Early splitting params.
+  if (params.finish_type().type() == SPLIT_FINISH_BASIC) {
+    min_split_samples_ = split_after_samples_;
+  } else {
+    if (!params.has_dominate_fraction() || !params.has_min_split_samples()) {
+      LOG(FATAL) << "dominate_fraction and min_split_samples "
+                 << "required for early-finish strategy.";
+    } else {
+      min_split_samples_ = ResolveParam(params.min_split_samples(), depth);
+      finish_check_every_ =
+          ResolveParam(params.finish_type().check_every_steps(), depth);
+      finish_sample_epoch_ = min_split_samples_ / finish_check_every_;
+
+      dominate_fraction_ = ResolveParam(params.dominate_fraction(), depth_);
+      if (dominate_fraction_ <= 0 || dominate_fraction_ > 1.0) {
+        LOG(FATAL) << "Invalid dominate fraction " << dominate_fraction_;
+      }
+    }
+  }
+
+  // Pruning params.
+  if (params.pruning_type().type() != SPLIT_PRUNE_NONE) {
+    prune_check_every_ =
+        ResolveParam(params.pruning_type().prune_every_samples(), depth);
+    prune_sample_epoch_ = 1;
+    prune_fraction_ = 0.0;
+    switch (params_.pruning_type().type()) {
+      case SPLIT_PRUNE_HALF:
+        prune_fraction_ = 0.5;
+        break;
+      case SPLIT_PRUNE_QUARTER:
+        prune_fraction_ = 0.25;
+        break;
+      case SPLIT_PRUNE_10_PERCENT:
+        prune_fraction_ = 0.10;
+        break;
+      case SPLIT_PRUNE_HOEFFDING:
+        dominate_fraction_ = ResolveParam(params.dominate_fraction(), depth_);
+        half_ln_dominate_frac_ = 0.5 * log(1.0 / (1.0 - dominate_fraction_));
+        break;
+      default:
+        LOG(WARNING) << "Unknown pruning type";
+    }
+  }
+
+  if (params.use_running_stats_method()) {
+    left_gini_.reset(new RunningGiniScores());
+    right_gini_.reset(new RunningGiniScores());
+  }
+
+  uint64 time_seed = static_cast<uint64>(std::clock());
+  single_rand_ = std::unique_ptr<random::PhiloxRandom>(
+      new random::PhiloxRandom(time_seed));
+  rng_ = std::unique_ptr<random::SimplePhilox>(
+      new random::SimplePhilox(single_rand_.get()));
+}
+
+bool ClassificationStats::IsFinished() const {
+  bool basic = weight_sum_ >= split_after_samples_ && num_outputs_seen() > 1;
+  return basic || finish_early_;
+}
+
+float ClassificationStats::MaybeCachedGiniScore(int split, float* left_sum,
+                                                float* right_sum) const {
+  if (left_gini_ == nullptr) {
+    return GiniScore(split, left_sum, right_sum);
+  } else {
+    *left_sum = left_gini_->sum(split);
+    const float left = WeightedSmoothedGini(
+        *left_sum, left_gini_->square(split), num_outputs_);
+
+    *right_sum = right_gini_->sum(split);
+    const float right = WeightedSmoothedGini(
+        *right_sum, right_gini_->square(split), num_outputs_);
+
+    return left + right;
+  }
+}
+
+void ClassificationStats::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    int example) {
+  const int64 int_label = target->GetTargetAsClassIndex(example, 0);
+  const float weight = target->GetTargetWeight(example);
+
+  for (int i = 0; i < num_splits(); ++i) {
+    auto& eval = evaluators_[i];
+    if (eval->Decide(input_data, example) == LEFT_INDEX) {
+      if (left_gini_ != nullptr) {
+        left_gini_->update(i, left_count(i, int_label), weight);
+      }
+      ClassificationAddLeftExample(i, int_label, weight);
+    } else if (right_gini_ != nullptr) {
+      right_gini_->update(i, right_count(i, int_label), weight);
+    }
+  }
+
+  ClassificationAddTotalExample(int_label, weight);
+
+  weight_sum_ += weight;
+
+  CheckFinishEarly();
+  CheckPrune();
+}
+
+void ClassificationStats::CheckPrune() {
+  if (IsFinished() || weight_sum_ < prune_sample_epoch_ * prune_check_every_) {
+    return;
+  }
+  ++prune_sample_epoch_;
+
+  if (params_.pruning_type().type() == SPLIT_PRUNE_HOEFFDING) {
+    CheckPruneHoeffding();
+    return;
+  }
+
+  const int to_remove = num_splits() * prune_fraction_;
+  if (to_remove <= 0) {
+    return;
+  }
+
+  // pair ordering is first-then-second by default, no need for custom
+  // comparison.  Use std::greater to make it a min-heap.
+  std::priority_queue<std::pair<float, int>, std::vector<std::pair<float, int>>,
+                      std::greater<std::pair<float, int>>>
+      worst;
+
+  // Track indices that are in the heap so we can iterate over them
+  // by largest-first later.
+  std::set<int> indices;
+
+  for (int i = 0; i < num_splits(); ++i) {
+    float left, right;
+    const float split_score = MaybeCachedGiniScore(i, &left, &right);
+    if (worst.size() < to_remove) {
+      worst.push(std::pair<float, int>(split_score, i));
+      indices.insert(i);
+    } else if (worst.top().first < split_score) {
+      indices.erase(worst.top().second);
+      worst.pop();
+      worst.push(std::pair<float, int>(split_score, i));
+      indices.insert(i);
+    }
+  }
+
+  // traverse indices from the back so that they are removed correctly.
+  for (auto it = indices.rbegin(); it != indices.rend(); ++it) {
+    RemoveSplit(*it);
+  }
+}
+
+void ClassificationStats::CheckPruneHoeffding() {
+  std::vector<float> split_scores(num_splits());
+  // Find best split score
+  float best_split_score = FLT_MAX;
+  for (int i = 0; i < num_splits(); ++i) {
+    float left, right;
+    split_scores[i] = MaybeCachedGiniScore(i, &left, &right);
+    if (split_scores[i] < best_split_score) {
+      best_split_score = split_scores[i];
+    }
+  }
+
+  // We apply the Hoeffding bound to the difference between the best split
+  // score and the i-th split score.
+  // Raw Gini ranges from 0 to 1 - (1/n), but our gini score is weighted.
+  const float num_classes = params_.num_outputs();
+  const float gini_diff_range = weight_sum_ * (1.0 - 1.0 / num_classes);
+  float epsilon = gini_diff_range * sqrt(half_ln_dominate_frac_ / weight_sum_);
+  for (int i = num_splits() - 1; i >= 0; i--) {
+    if (split_scores[i] - best_split_score > epsilon) {
+      RemoveSplit(i);
+    }
+  }
+}
+
+void ClassificationStats::CheckFinishEarly() {
+  if (weight_sum_ < min_split_samples_ ||
+      weight_sum_ < finish_sample_epoch_ * finish_check_every_) {
+    return;
+  }
+  ++finish_sample_epoch_;
+
+  if (params_.finish_type().type() == SPLIT_FINISH_DOMINATE_HOEFFDING) {
+    CheckFinishEarlyHoeffding();
+  } else if (params_.finish_type().type() == SPLIT_FINISH_DOMINATE_BOOTSTRAP) {
+    CheckFinishEarlyBootstrap();
+  }
+}
+
+void ClassificationStats::CheckFinishEarlyHoeffding() {
+  // Each term in the Gini impurity can range from 0 to 0.5 * 0.5.
+  float range = 0.25 * static_cast<float>(params_.num_outputs()) * weight_sum_;
+
+  float hoeffding_bound =
+      range * sqrt(log(1.0 / (1.0 - dominate_fraction_)) / (2.0 * weight_sum_));
+
+  float unused_left_sum, unused_right_sum;
+  std::function<float(int)> score_fn =
+      std::bind(&ClassificationStats::MaybeCachedGiniScore, this,
+                std::placeholders::_1, &unused_left_sum, &unused_right_sum);
+
+  float best_score;
+  int32 best_index;
+  float second_best_score;
+  int32 second_best_index;
+  GetTwoBest(num_splits(), score_fn, &best_score, &best_index,
+             &second_best_score, &second_best_index);
+
+  finish_early_ = (second_best_score - best_score) > hoeffding_bound;
+}
+
+void ClassificationStats::MakeBootstrapWeights(int index,
+                                               std::vector<float>* weights) {
+  int n = weight_sum_;
+  float denom = static_cast<float>(n) + static_cast<float>(num_outputs_);
+  for (int i = 0; i < num_outputs_; ++i) {
+    // Use the Laplace smoothed per-class probabilities when generating the
+    // bootstrap samples.
+    (*weights)[i] = (left_count(index, i) + 1.0) / denom;
+    (*weights)[num_outputs_ + i] = (right_count(index, i) + 1.0) / denom;
+  }
+}
+
+int ClassificationStats::NumBootstrapSamples() const {
+  float p = 1.0 - dominate_fraction_;
+  int bootstrap_samples = 1;
+  while (p < 1.0) {
+    ++bootstrap_samples;
+    p = p * 2;
+  }
+  return bootstrap_samples;
+}
+
+void ClassificationStats::CheckFinishEarlyBootstrap() {
+  float unused_left_sum, unused_right_sum;
+  std::function<float(int)> score_fn =
+      std::bind(&ClassificationStats::MaybeCachedGiniScore, this,
+                std::placeholders::_1, &unused_left_sum, &unused_right_sum);
+
+  float best_score;
+  int32 best_index;
+  float second_best_score;
+  int32 second_best_index;
+  GetTwoBest(num_splits(), score_fn, &best_score, &best_index,
+             &second_best_score, &second_best_index);
+
+  std::vector<float> weights1(num_outputs_ * 2);
+  MakeBootstrapWeights(best_index, &weights1);
+  random::DistributionSampler ds1(weights1);
+
+  std::vector<float> weights2(num_outputs_ * 2);
+  MakeBootstrapWeights(second_best_index, &weights2);
+  random::DistributionSampler ds2(weights2);
+
+  const int bootstrap_samples = NumBootstrapSamples();
+
+  int worst_g1 = 0;
+  for (int i = 0; i < bootstrap_samples; i++) {
+    int g1 = BootstrapGini(weight_sum_, 2 * num_outputs_, ds1, rng_.get());
+    worst_g1 = std::max(worst_g1, g1);
+  }
+
+  int best_g2 = 99;
+  for (int i = 0; i < bootstrap_samples; i++) {
+    int g2 = BootstrapGini(weight_sum_, 2 * num_outputs_, ds2, rng_.get());
+    best_g2 = std::min(best_g2, g2);
+  }
+
+  finish_early_ = worst_g1 < best_g2;
+}
+
+// ------------------------ Dense Classification --------------------------- //
+void DenseClassificationGrowStats::ExtractFromProto(const FertileSlot& slot) {
+  Initialize();
+  if (!slot.has_post_init_leaf_stats()) {
+    return;
+  }
+  const int32 num_classes = params_.num_outputs();
+  weight_sum_ = slot.post_init_leaf_stats().weight_sum();
+  const auto& class_stats =
+      slot.post_init_leaf_stats().classification().dense_counts();
+
+  // Total counts.
+  for (int i = 0; i < num_classes; ++i) {
+    total_counts_[i] = class_stats.value(i).float_value();
+    num_outputs_seen_ += total_counts_[i] != 0;
+  }
+
+  // Candidate counts and splits.
+  int split_num = 0;
+  for (const auto& cand : slot.candidates()) {
+    AddSplit(cand.split());
+    const auto& left_stats = cand.left_stats().classification().dense_counts();
+    for (int i = 0; i < num_classes; ++i) {
+      const float val = left_stats.value(i).float_value();
+      mutable_left_count(split_num, i) = val;
+      MaybeInitializeRunningCount(split_num, val);
+    }
+    ++split_num;
+  }
+}
+
+void DenseClassificationGrowStats::PackToProto(FertileSlot* slot) const {
+  auto* slot_stats = slot->mutable_post_init_leaf_stats();
+  slot_stats->set_weight_sum(weight_sum_);
+
+  auto* class_stats = slot->mutable_post_init_leaf_stats()
+                          ->mutable_classification()
+                          ->mutable_dense_counts();
+  for (int i = 0; i < num_outputs_; ++i) {
+    class_stats->add_value()->set_float_value(total_counts_[i]);
+  }
+
+  for (int split_num = 0;  split_num < num_splits(); ++split_num) {
+    auto* cand = slot->add_candidates();
+    *cand->mutable_split() = splits_[split_num];
+    auto* left_stats = cand->mutable_left_stats()
+                           ->mutable_classification()
+                           ->mutable_dense_counts();
+    for (int i = 0; i < num_outputs_; ++i) {
+       left_stats->add_value()->set_float_value(left_count(split_num, i));
+    }
+  }
+}
+
+float DenseClassificationGrowStats::GiniScore(int split, float* left_sum,
+                                              float* right_sum) const {
+  float left_square = 0, right_square = 0;
+  *left_sum = 0;
+  *right_sum = 0;
+  for (int j = 0; j < num_outputs_; ++j) {
+    const float left = left_count(split, j);
+    *left_sum += left;
+    left_square += left * left;
+    const float right = right_count(split, j);
+    *right_sum += right;
+    right_square += right * right;
+  }
+
+  const float left_score =
+      WeightedSmoothedGini(*left_sum, left_square, num_outputs_);
+  const float right_score =
+      WeightedSmoothedGini(*right_sum, right_square, num_outputs_);
+  return left_score + right_score;
+}
+
+bool DenseClassificationGrowStats::BestSplit(SplitCandidate* best) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  float best_left_sum, best_right_sum;
+
+  // Calculate sums.
+  for (int i = 0; i < num_splits(); ++i) {
+    float left_sum, right_sum;
+    const float split_score = MaybeCachedGiniScore(i, &left_sum, &right_sum);
+    // Find the lowest gini.
+    if (left_sum > 0 && right_sum > 0 &&
+        split_score < min_score) {  // useless check
+      min_score = split_score;
+      best_index = i;
+      best_left_sum = left_sum;
+      best_right_sum = right_sum;
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in stats to be used for leaf model.
+  *best->mutable_split() = splits_[best_index];
+  // Left
+  auto* left = best->mutable_left_stats();
+  auto* left_class_stats = left->mutable_classification();
+  left->set_weight_sum(best_left_sum);
+  auto* left_counts = left_class_stats->mutable_dense_counts();
+  for (int i = 0; i < params_.num_outputs(); ++i) {
+    left_counts->add_value()->set_float_value(
+        left_count(best_index, i));
+  }
+
+  // Right
+  auto* right = best->mutable_right_stats();
+  auto* right_class_stats = right->mutable_classification();
+  right->set_weight_sum(best_right_sum);
+  auto* right_counts = right_class_stats->mutable_dense_counts();
+  for (int i = 0; i < params_.num_outputs(); ++i) {
+    right_counts->add_value()->set_float_value(
+        total_counts_[i] - left_count(best_index, i));
+  }
+  return true;
+}
+
+// ------------------------ Sparse Classification --------------------------- //
+void SparseClassificationGrowStats::ExtractFromProto(const FertileSlot& slot) {
+  Initialize();
+  if (!slot.has_post_init_leaf_stats()) {
+    return;
+  }
+  weight_sum_ = slot.post_init_leaf_stats().weight_sum();
+  const auto& class_stats =
+      slot.post_init_leaf_stats().classification().sparse_counts();
+
+  // Total counts.
+  for (auto const& entry : class_stats.sparse_value()) {
+    total_counts_[entry.first] = entry.second.float_value();
+  }
+
+  // Candidate counts and splits.
+  int split_num = 0;
+  for (const auto& cand : slot.candidates()) {
+    AddSplit(cand.split());
+    const auto& left_stats = cand.left_stats().classification().sparse_counts();
+    for (auto const& entry : left_stats.sparse_value()) {
+      const float val = entry.second.float_value();
+      left_counts_[split_num][entry.first] = val;
+      MaybeInitializeRunningCount(split_num, val);
+    }
+    ++split_num;
+  }
+}
+
+void SparseClassificationGrowStats::PackToProto(FertileSlot* slot) const {
+  auto* slot_stats = slot->mutable_post_init_leaf_stats();
+  slot_stats->set_weight_sum(weight_sum_);
+
+  auto* class_stats = slot->mutable_post_init_leaf_stats()
+                          ->mutable_classification()
+                          ->mutable_sparse_counts()
+                          ->mutable_sparse_value();
+  for (const auto& entry : total_counts_) {
+    decision_trees::Value val;
+    val.set_float_value(entry.second);
+    (*class_stats)[entry.first] = val;
+  }
+
+  for (int split_num = 0;  split_num < num_splits(); ++split_num) {
+    auto* cand = slot->add_candidates();
+    *cand->mutable_split() = splits_[split_num];
+    auto* left_stats = cand->mutable_left_stats()
+                           ->mutable_classification()
+                           ->mutable_sparse_counts()
+                           ->mutable_sparse_value();
+    for (const auto& entry : left_counts_[split_num]) {
+      decision_trees::Value val;
+      val.set_float_value(entry.second);
+      (*left_stats)[entry.first] = val;
+    }
+  }
+}
+
+float SparseClassificationGrowStats::GiniScore(
+    int split, float* left_sum, float* right_sum) const {
+  float left_square = 0, right_square = 0;
+  *left_sum = 0;
+  *right_sum = 0;
+  for (const auto& entry : total_counts_) {
+    const int label = entry.first;
+    float left = 0;
+    float right = 0;
+    auto it = left_counts_[split].find(label);
+    if (it == left_counts_[split].end()) {
+      right = entry.second;
+    } else {
+      left = it->second;
+      right = entry.second - it->second;
+    }
+    *left_sum += left;
+    left_square += left * left;
+    *right_sum += right;
+    right_square += right * right;
+  }
+  const int32 num_classes = params_.num_outputs();
+  const float left_score =
+      WeightedSmoothedGini(*left_sum, left_square, num_classes);
+  const float right_score =
+      WeightedSmoothedGini(*right_sum, right_square, num_classes);
+  return left_score + right_score;
+}
+
+bool SparseClassificationGrowStats::BestSplit(SplitCandidate* best) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  float best_left_sum = -1;
+  float best_right_sum = -1;
+
+  // Find the lowest gini.
+  for (int i = 0; i < num_splits(); ++i) {
+    float left_sum, right_sum;
+    const float split_score = MaybeCachedGiniScore(i, &left_sum, &right_sum);
+    if (left_sum > 0 && right_sum > 0 &&
+        split_score < min_score) {  // useless check
+      min_score = split_score;
+      best_index = i;
+      best_left_sum = left_sum;
+      best_right_sum = right_sum;
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in stats to be used for leaf model.
+  *best->mutable_split() = splits_[best_index];
+  // Left
+  auto* left = best->mutable_left_stats();
+  auto* left_class_stats = left->mutable_classification();
+  left->set_weight_sum(best_left_sum);
+  auto* left_counts =
+      left_class_stats->mutable_sparse_counts()->mutable_sparse_value();
+
+  // Right
+  auto* right = best->mutable_right_stats();
+  auto* right_class_stats = right->mutable_classification();
+  right->set_weight_sum(best_right_sum);
+  auto* right_counts =
+      right_class_stats->mutable_sparse_counts()->mutable_sparse_value();
+
+  for (const auto& entry : total_counts_) {
+    auto it = left_counts_[best_index].find(entry.first);
+    if (it == left_counts_[best_index].end()) {
+      (*right_counts)[entry.first].set_float_value(entry.second);
+    } else {
+      const float left = it->second;
+      const float right = entry.second - it->second;
+      (*left_counts)[entry.first].set_float_value(left);
+      if (right > 0) {
+        (*right_counts)[entry.first].set_float_value(right);
+      }
+    }
+  }
+  return true;
+}
+
+// --------------------- Least Squares Regression --------------------------- //
+void LeastSquaresRegressionGrowStats::ExtractFromProto(
+    const FertileSlot& slot) {
+  const int32 num_outputs = params_.num_outputs();
+  Initialize();
+  if (!slot.has_post_init_leaf_stats()) {
+    return;
+  }
+  weight_sum_ = slot.post_init_leaf_stats().weight_sum();
+  const auto& total_sums =
+      slot.post_init_leaf_stats().regression().mean_output();
+  const auto& total_squares =
+      slot.post_init_leaf_stats().regression().mean_output_squares();
+
+  // Total counts.
+  for (int i = 0; i < num_outputs; ++i) {
+    total_sum_[i] = total_sums.value(i).float_value();
+    total_sum_squares_[i] = total_squares.value(i).float_value();
+  }
+
+  // Candidate counts and splits.
+  int split_num = 0;
+  for (const auto& cand : slot.candidates()) {
+    AddSplit(cand.split());
+    const auto& sums = cand.left_stats().regression().mean_output();
+    const auto& squares = cand.left_stats().regression().mean_output_squares();
+    for (int i = 0; i < num_outputs; ++i) {
+      left_sum(split_num, i) = sums.value(i).float_value();
+      left_square(split_num, i) = squares.value(i).float_value();
+    }
+    left_counts_[split_num] = cand.left_stats().weight_sum();
+    ++split_num;
+  }
+}
+
+void LeastSquaresRegressionGrowStats::PackToProto(FertileSlot* slot) const {
+  const int32 num_outputs = params_.num_outputs();
+  auto* slot_stats = slot->mutable_post_init_leaf_stats();
+  slot_stats->set_weight_sum(weight_sum_);
+
+  auto* total_sums = slot->mutable_post_init_leaf_stats()
+                         ->mutable_regression()
+                         ->mutable_mean_output();
+  auto* total_squares = slot->mutable_post_init_leaf_stats()
+                            ->mutable_regression()
+                            ->mutable_mean_output_squares();
+
+  for (int i = 0; i < total_sum_.size(); ++i) {
+    total_sums->add_value()->set_float_value(total_sum_[i]);
+    total_squares->add_value()->set_float_value(total_sum_squares_[i]);
+  }
+
+  for (int split_num = 0;  split_num < num_splits(); ++split_num) {
+    auto* cand = slot->add_candidates();
+    *cand->mutable_split() = splits_[split_num];
+    auto* sums = cand->mutable_left_stats()
+                           ->mutable_regression()
+                           ->mutable_mean_output();
+    auto* squares = cand->mutable_left_stats()
+                        ->mutable_regression()
+                        ->mutable_mean_output_squares();
+    for (int i = 0; i < num_outputs; ++i) {
+      sums->add_value()->set_float_value(left_sum(split_num, i));
+      squares->add_value()->set_float_value(left_square(split_num, i));
+    }
+    cand->mutable_left_stats()->set_weight_sum(left_counts_[split_num]);
+  }
+}
+
+void LeastSquaresRegressionGrowStats::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    int example) {
+  const int32 num_outputs = params_.num_outputs();
+  // Update splits.
+  for (int i = 0; i < num_splits(); ++i) {
+    auto& eval = evaluators_[i];
+    if (eval->Decide(input_data, example) == LEFT_INDEX) {
+      for (int j = 0; j < num_outputs; ++j) {
+        const float output = target->GetTargetAsContinuous(example, j);
+        left_sum(i, j) += output;
+        left_square(i, j) += output * output;
+      }
+      ++left_counts_[i];
+    }
+  }
+
+  // Update totals.
+  for (int i = 0; i < num_outputs; ++i) {
+    const float output = target->GetTargetAsContinuous(example, i);
+    total_sum_[i] += output;
+    total_sum_squares_[i] += output * output;
+  }
+  weight_sum_ += 1.0;
+}
+
+float LeastSquaresRegressionGrowStats::SplitVariance(int split) const {
+  float total_variance = 0;
+  for (int i = 0; i < params_.num_outputs(); ++i) {
+    // Left side
+    const float le_x =
+        left_sum(split, i) / left_counts_[split];
+
+    const float le_x2 =
+        left_square(split, i) / left_counts_[split];
+    total_variance += le_x2 - le_x * le_x;
+
+    // Right side
+    const float re_x = (total_sum_[i] - left_sum(split, i)) /
+                       (weight_sum_ - left_counts_[split]);
+
+    const float re_x2 =
+        (total_sum_squares_[i] - left_square(split, i)) /
+        (weight_sum_ - left_counts_[split]);
+    total_variance += re_x2 - re_x * re_x;
+  }
+  return total_variance;
+}
+
+bool LeastSquaresRegressionGrowStats::BestSplit(SplitCandidate* best) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  const int32 num_outputs = params_.num_outputs();
+  for (int i = 0; i < num_splits(); ++i) {
+    if (left_counts_[i] > 0 && weight_sum_ - left_counts_[i] > 0) {
+      const float split_score = SplitVariance(i);
+      if (split_score < min_score) {
+        min_score = split_score;
+        best_index = i;
+      }
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in right stats to be used for leaf model.
+  *best->mutable_split() = splits_[best_index];
+  // Left
+  auto* left = best->mutable_left_stats();
+  auto* left_reg_stats = left->mutable_regression();
+  left->set_weight_sum(left_counts_[best_index]);
+  auto* left_output_sum = left_reg_stats->mutable_mean_output();
+  for (int i = 0; i < num_outputs; ++i) {
+    left_output_sum->add_value()->set_float_value(
+        left_sum(best_index, i));
+  }
+
+  // Right
+  auto* right = best->mutable_right_stats();
+  auto* right_reg_stats = right->mutable_regression();
+  right->set_weight_sum(weight_sum_ - left_counts_[best_index]);
+  auto* right_output_sum = right_reg_stats->mutable_mean_output();
+  for (int i = 0; i < num_outputs; ++i) {
+    right_output_sum->add_value()->set_float_value(
+        total_sum_[i] - left_sum(best_index, i));
+  }
+  return true;
+}
+
+bool LeastSquaresRegressionGrowStats::IsFinished() const {
+  return weight_sum_ >= split_after_samples_;
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
new file mode 100644
index 0000000000..8d32b4961b
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
@@ -0,0 +1,470 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GROW_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GROW_STATS_H_
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/lib/random/philox_random.h"
+#include "tensorflow/core/lib/random/simple_philox.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Base class for tracking stats necessary to split a leaf.
+// Holds and tracks stats for every candidate split.
+class GrowStats {
+ public:
+  virtual ~GrowStats() {}
+  // Perform any initialization.
+  virtual void Initialize() = 0;
+
+  // Add an example to any stats being collected.
+  virtual void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                          const InputTarget* target, int example) = 0;
+
+  // Fill in the best split, return false if none were valid.
+  virtual bool BestSplit(SplitCandidate* best) const = 0;
+
+  // Return true if this leaf is finished splitting.
+  virtual bool IsFinished() const = 0;
+
+  // Get the split_num BinaryNode.
+  const decision_trees::BinaryNode& Split(int split_num) const {
+    return splits_[split_num];
+  }
+
+  // Clear all state.
+  virtual void Clear() {
+    weight_sum_ = 0;
+    splits_.clear();
+    evaluators_.clear();
+    ClearInternal();
+  }
+
+  virtual void ExtractFromProto(const FertileSlot& slot) = 0;
+  virtual void PackToProto(FertileSlot* slot) const = 0;
+
+  // Add split to the list of candidate splits.
+  void AddSplit(const decision_trees::BinaryNode& split);
+  void RemoveSplit(int split_num);
+
+  int num_splits() const {
+    return splits_.size();
+  }
+
+  float weight_sum() const {
+    return weight_sum_;
+  }
+
+  bool IsInitialized() const {
+    return weight_sum_ > 0 || splits_.size() == num_splits_to_consider_;
+  }
+
+  int32 depth() const {
+    return depth_;
+  }
+
+ protected:
+  GrowStats(const TensorForestParams& params, int32 depth);
+
+  // Function called by AddSplit for subclasses to initialize stats for a split.
+  virtual void AddSplitStats() = 0;
+
+  virtual void RemoveSplitStats(int split_num) = 0;
+
+  // Function called by Clear for subclasses to clear their state.
+  virtual void ClearInternal() = 0;
+
+  std::vector<decision_trees::BinaryNode> splits_;
+  std::vector<std::unique_ptr<DecisionNodeEvaluator>> evaluators_;
+
+  float weight_sum_;
+
+  const int32 depth_;
+
+  const TensorForestParams& params_;
+
+  // We cache these beacuse they're used often.
+  const int split_after_samples_;
+  const int num_splits_to_consider_;
+
+  const int32 num_outputs_;
+};
+
+// Don't track anything, useful for systems that want to track split
+// candidates but train the model in some other way.
+class SimpleStats : public GrowStats {
+ public:
+  SimpleStats(const TensorForestParams& params, int32 depth)
+      : GrowStats(params, depth) {}
+  void Initialize() override {}
+
+  void ExtractFromProto(const FertileSlot& slot) override {}
+  void PackToProto(FertileSlot* slot) const override {}
+
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, int example) override {
+    weight_sum_ += target->GetTargetWeight(example);
+  }
+
+  bool BestSplit(SplitCandidate* best) const override { return false; }
+
+  bool IsFinished() const override {
+    return weight_sum_ >= split_after_samples_;
+  }
+
+ protected:
+  void AddSplitStats() override {}
+  void RemoveSplitStats(int split_num) override {}
+  void ClearInternal() override {}
+};
+
+// Tracks the sum and square of one side of a split for each Gini calculation.
+class RunningGiniScores {
+ public:
+  float sum(int split) const { return sum_[split]; }
+  float square(int split) const { return square_[split]; }
+
+  void update(int split, float old_val, float weight) {
+    sum_[split] += weight;
+    const float new_val = old_val + weight;
+    square_[split] = square_[split] - old_val * old_val + new_val * new_val;
+  }
+
+  void add_split() {
+    sum_.push_back(0);
+    square_.push_back(0);
+  }
+
+  void remove_split(int i) {
+    sum_.erase(sum_.begin() + i);
+    square_.erase(square_.begin() + i);
+  }
+
+ private:
+  std::vector<float> sum_;
+  std::vector<float> square_;
+};
+
+class ClassificationStats : public GrowStats {
+ public:
+  ClassificationStats(const TensorForestParams& params, int32 depth);
+
+  bool IsFinished() const override;
+
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, int example) override;
+
+ protected:
+  virtual float GiniScore(int split, float* left_sum,
+                          float* right_sum) const = 0;
+  virtual int num_outputs_seen() const = 0;
+  virtual float left_count(int split, int class_num) const = 0;
+  virtual float right_count(int split, int class_num) const = 0;
+
+  virtual void ClassificationAddLeftExample(
+      int split, int64 int_label, float weight) = 0;
+  virtual void ClassificationAddTotalExample(int64 int_label, float weight) = 0;
+
+  virtual void ClassificationAddSplitStats() = 0;
+  virtual void ClassificationRemoveSplitStats(int split) = 0;
+
+  void AddSplitStats() override {
+    if (left_gini_ != nullptr) {
+      left_gini_->add_split();
+      right_gini_->add_split();
+    }
+    ClassificationAddSplitStats();
+  }
+  void RemoveSplitStats(int split) override {
+    if (left_gini_ != nullptr) {
+      left_gini_->remove_split(split);
+      right_gini_->remove_split(split);
+    }
+    ClassificationRemoveSplitStats(split);
+  }
+
+  // Virtual so we can override these to test.
+  virtual void CheckFinishEarly();
+  virtual void CheckFinishEarlyHoeffding();
+  virtual void CheckFinishEarlyBootstrap();
+
+  virtual void CheckPrune();
+
+  // Implement SplitPruningStrategyType::SPLIT_PRUNE_HOEFFDING.
+  void CheckPruneHoeffding();
+
+  // Return the gini score, possibly being calculated from sums and squares
+  // saved in left_gini_ and right_gini_, otherwise calculated from raw counts.
+  float MaybeCachedGiniScore(int split, float* left_sum,
+                             float* right_sum) const;
+
+  // Initialize the sum and squares of left_gini_ and right_gini_ for given
+  // split and value (being extracted from a proto), if left_gini_ isn't null.
+  void MaybeInitializeRunningCount(int split, float val) {
+    if (left_gini_ != nullptr) {
+      left_gini_->update(split, 0, val);
+      right_gini_->update(split, 0, val);
+    }
+  }
+
+  int NumBootstrapSamples() const;
+
+  // Populate *weights with the smoothed per-class frequencies needed to
+  // initialize a DistributionSampler.
+  void MakeBootstrapWeights(int index, std::vector<float>* weights);
+
+  // Accessors for RunningGiniScores objects, for testing.
+  virtual const std::unique_ptr<RunningGiniScores>& get_left_gini() const {
+    return left_gini_;
+  }
+  virtual const std::unique_ptr<RunningGiniScores>& get_right_gini() const {
+    return right_gini_;
+  }
+
+ private:
+  // Tracks how many check_every_samples epochs we've seen go by in weight_sum.
+  int32 finish_sample_epoch_;
+  int32 finish_check_every_;
+  int32 prune_sample_epoch_;
+  int32 prune_check_every_;
+  bool finish_early_;
+  int32 min_split_samples_;
+  float dominate_fraction_;
+  float prune_fraction_;
+
+  // When using SPLIT_PRUNE_HOEFFDING, we precompute and store
+  // 0.5 * ln(1 / (1.0 - dominate_fraction_)).
+  float half_ln_dominate_frac_;
+
+  std::unique_ptr<random::PhiloxRandom> single_rand_;
+  std::unique_ptr<random::SimplePhilox> rng_;
+
+  std::unique_ptr<RunningGiniScores> left_gini_;
+  std::unique_ptr<RunningGiniScores> right_gini_;
+};
+
+// Tracks classification stats by storing class counts densely.
+class DenseClassificationGrowStats : public ClassificationStats {
+ public:
+  DenseClassificationGrowStats(const TensorForestParams& params, int32 depth)
+      : ClassificationStats(params, depth) {}
+
+  void Initialize() override {
+    Clear();
+    total_counts_.resize(num_outputs_);
+  }
+
+  void ExtractFromProto(const FertileSlot& slot) override;
+  void PackToProto(FertileSlot* slot) const override;
+
+  bool BestSplit(SplitCandidate* best) const override;
+
+ protected:
+  void ClassificationAddSplitStats() override {
+    left_counts_.resize(num_outputs_ * num_splits());
+  }
+  void ClassificationRemoveSplitStats(int split_num) override {
+    left_counts_.erase(left_counts_.begin() + num_outputs_ * split_num,
+                       left_counts_.begin() + num_outputs_ * (split_num + 1));
+  }
+  void ClearInternal() override {
+    total_counts_.clear();
+    left_counts_.clear();
+    num_outputs_seen_ = 0;
+  }
+
+  int num_outputs_seen() const override {
+    return num_outputs_seen_;
+  }
+
+  void ClassificationAddLeftExample(int split, int64 int_label,
+                                    float weight) override {
+    mutable_left_count(split, int_label) += weight;
+  }
+  void ClassificationAddTotalExample(int64 int_label, float weight) override {
+    num_outputs_seen_ += total_counts_[int_label] == 0 && weight > 0;
+    total_counts_[int_label] += weight;
+  }
+
+  float GiniScore(int split, float* left_sum, float* right_sum) const override;
+
+  float left_count(int split, int class_num) const override {
+    return left_counts_[split * num_outputs_ + class_num];
+  }
+  float right_count(int split, int class_num) const override {
+    return total_counts_[class_num] -
+           left_counts_[split * num_outputs_ + class_num];
+  }
+
+ private:
+  inline float& mutable_left_count(int split, int class_num) {
+    return left_counts_[split * num_outputs_ + class_num];
+  }
+  // Total class counts seen at this leaf
+  std::vector<float> total_counts_;
+
+  // Also track the number of classes seen for not splitting pure leaves.
+  int num_outputs_seen_;
+
+  // Left-branch taken class counts at this leaf for each split.
+  // This is a flat vector for memory-performance reasons.
+  // left_counts_[i * num_outputs_ + j] has the j-th class count for split i.
+  std::vector<float> left_counts_;
+};
+
+// Tracks classification stats by storing class counts sparsely.
+class SparseClassificationGrowStats : public ClassificationStats {
+ public:
+  SparseClassificationGrowStats(const TensorForestParams& params, int32 depth)
+      : ClassificationStats(params, depth) {}
+
+  void Initialize() override {
+    Clear();
+  }
+
+  void ExtractFromProto(const FertileSlot& slot) override;
+  void PackToProto(FertileSlot* slot) const override;
+
+  bool BestSplit(SplitCandidate* best) const override;
+
+ protected:
+  void ClassificationAddSplitStats() override {
+    left_counts_.resize(num_splits());
+  }
+  void ClassificationRemoveSplitStats(int split_num) override {
+    left_counts_.erase(left_counts_.begin() + split_num,
+                       left_counts_.begin() + (split_num + 1));
+  }
+  void ClearInternal() override {
+    total_counts_.clear();
+    left_counts_.clear();
+  }
+
+  int num_outputs_seen() const override { return total_counts_.size(); }
+
+  void ClassificationAddLeftExample(int split, int64 int_label,
+                                    float weight) override {
+    left_counts_[split][int_label] += weight;
+  }
+  void ClassificationAddTotalExample(int64 int_label, float weight) override {
+    total_counts_[int_label] += weight;
+  }
+
+  float GiniScore(int split, float* left_sum, float* right_sum) const override;
+
+  float left_count(int split, int class_num) const override {
+    return left_counts_[split].at(class_num);
+  }
+  float right_count(int split, int class_num) const override {
+    return total_counts_.at(class_num) - left_counts_[split].at(class_num);
+  }
+
+ private:
+  // Total class counts seen at this leaf
+  std::unordered_map<int, float> total_counts_;
+
+  // Left-branch taken class counts at this leaf for each split.
+  // left_counts_[i][j] has the j-th class count for split i.
+  std::vector<std::unordered_map<int, float>> left_counts_;
+};
+
+// Tracks regression stats using least-squares minimization.
+class LeastSquaresRegressionGrowStats : public GrowStats {
+ public:
+  LeastSquaresRegressionGrowStats(const TensorForestParams& params, int32 depth)
+      : GrowStats(params, depth) {}
+
+  void Initialize() override {
+    Clear();
+    total_sum_.resize(num_outputs_);
+    total_sum_squares_.resize(num_outputs_);
+  }
+
+  void ExtractFromProto(const FertileSlot& slot) override;
+  void PackToProto(FertileSlot* slot) const override;
+
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, int example) override;
+  bool BestSplit(SplitCandidate* best) const override;
+  bool IsFinished() const override;
+
+ protected:
+  // Returns the variance of split.
+  float SplitVariance(int split) const;
+
+  void AddSplitStats() override {
+    left_sums_.resize(num_outputs_ * num_splits());
+    left_squares_.resize(num_outputs_ * num_splits());
+    left_counts_.push_back(0);
+  }
+  void RemoveSplitStats(int split_num) override {
+    left_sums_.erase(left_sums_.begin() + num_outputs_ * split_num,
+                       left_sums_.begin() + num_outputs_ * (split_num + 1));
+    left_squares_.erase(left_squares_.begin() + num_outputs_ * split_num,
+                       left_squares_.begin() + num_outputs_ * (split_num + 1));
+    left_counts_.erase(left_counts_.begin() + split_num,
+                       left_counts_.begin() + (split_num + 1));
+  }
+
+  void ClearInternal() override {
+    total_sum_.clear();
+    total_sum_squares_.clear();
+    left_sums_.clear();
+    left_squares_.clear();
+  }
+
+ private:
+  // Convenience methods for accessing the flat count vectors.
+  inline const float& left_sum(int split, int output_num) const {
+    return left_sums_[split * num_outputs_ + output_num];
+  }
+  inline float& left_sum(int split, int output_num) {
+    return left_sums_[split * num_outputs_ + output_num];
+  }
+  inline const float& left_square(int split, int output_num) const {
+    return left_squares_[split * num_outputs_ + output_num];
+  }
+  inline float& left_square(int split, int output_num) {
+    return left_squares_[split * num_outputs_ + output_num];
+  }
+
+  // Total sums and squares seen at this leaf.
+  // sum[i] is the sum of the i-th output.
+  std::vector<float> total_sum_;
+  std::vector<float> total_sum_squares_;
+
+  // Per-split sums and squares, stored flat for performance.
+  // left_sums_[i * num_outputs_ + j] has the j-th sum for split i.
+  std::vector<float> left_sums_;
+  std::vector<float> left_squares_;
+
+  // The number of example seen at each split.
+  std::vector<int64> left_counts_;
+};
+
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GROW_STATS_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
new file mode 100644
index 0000000000..8d51456e30
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
@@ -0,0 +1,364 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace {
+
+using tensorflow::tensorforest::GrowStats;
+using tensorflow::tensorforest::TestableInputTarget;
+using tensorflow::tensorforest::FertileSlot;
+using tensorflow::tensorforest::DenseClassificationGrowStats;
+using tensorflow::tensorforest::SparseClassificationGrowStats;
+using tensorflow::tensorforest::LeastSquaresRegressionGrowStats;
+using tensorflow::tensorforest::TensorForestParams;
+using tensorflow::tensorforest::SPLIT_FINISH_BASIC;
+using tensorflow::tensorforest::SPLIT_FINISH_DOMINATE_HOEFFDING;
+using tensorflow::tensorforest::SPLIT_PRUNE_HOEFFDING;
+using tensorflow::decision_trees::BinaryNode;
+using tensorflow::decision_trees::InequalityTest;
+using tensorflow::decision_trees::FeatureId;
+
+BinaryNode MakeSplit(const string& feat, float val) {
+  BinaryNode split;
+  InequalityTest* test = split.mutable_inequality_left_child_test();
+  FeatureId feature_id;
+  feature_id.mutable_id()->set_value(feat);
+  *test->mutable_feature_id() = feature_id;
+  test->mutable_threshold()->set_float_value(val);
+  test->set_type(InequalityTest::LESS_OR_EQUAL);
+
+  return split;
+}
+
+void RunBatch(GrowStats* stats,
+              const TestableInputTarget* target) {
+  stats->AddSplit(MakeSplit("0", 10.0));
+  stats->AddSplit(MakeSplit("1", 4.0));
+
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, 2));
+
+  for (int i = 0; i < target->NumItems(); ++i) {
+    stats->AddExample(dataset, target, i);
+  }
+}
+
+TEST(GrowStatsDenseClassificationTest, Basic) {
+  TensorForestParams params;
+  params.set_num_outputs(2);
+  params.mutable_split_after_samples()->set_constant_value(2);
+  params.mutable_num_splits_to_consider()->set_constant_value(2);
+  std::unique_ptr<DenseClassificationGrowStats> stat(
+      new DenseClassificationGrowStats(params, 1));
+  stat->Initialize();
+
+  std::vector<float> labels = {1, 0, 1};
+  std::vector<float> weights = {2.3, 20.3, 1.1};
+  std::unique_ptr<TestableInputTarget> target(
+      new TestableInputTarget(&labels, &weights, 1));
+
+  RunBatch(stat.get(), target.get());
+  CHECK(stat->IsFinished());
+
+  FertileSlot slot;
+  stat->PackToProto(&slot);
+
+  string serialized = slot.DebugString();
+
+  std::unique_ptr<DenseClassificationGrowStats> new_stat(
+      new DenseClassificationGrowStats(params, 1));
+  new_stat->ExtractFromProto(slot);
+  FertileSlot second_one;
+  new_stat->PackToProto(&second_one);
+  string serialized_again = second_one.DebugString();
+  ASSERT_EQ(serialized_again, serialized);
+}
+
+class TestableRunningStats : public DenseClassificationGrowStats {
+ public:
+  TestableRunningStats(const TensorForestParams& params, int32 depth)
+      : DenseClassificationGrowStats(params, depth) {}
+
+  float test_left_sum(int split) {
+    return get_left_gini()->sum(split);
+  }
+  float test_left_square(int split) {
+    return get_left_gini()->square(split);
+  }
+  float test_right_sum(int split) {
+    return get_right_gini()->sum(split);
+  }
+  float test_right_square(int split) {
+    return get_right_gini()->square(split);
+  }
+};
+
+TEST(GrowStatsDenseClassificationTest, BasicRunningStats) {
+  TensorForestParams params;
+  params.set_num_outputs(2);
+  params.mutable_split_after_samples()->set_constant_value(2);
+  params.mutable_num_splits_to_consider()->set_constant_value(2);
+  params.set_use_running_stats_method(true);
+  std::unique_ptr<TestableRunningStats> stat(
+      new TestableRunningStats(params, 1));
+  stat->Initialize();
+
+  std::vector<float> labels = {1, 0, 1};
+  std::vector<float> weights = {2.3, 20.3, 1.1};
+  std::unique_ptr<TestableInputTarget> target(
+      new TestableInputTarget(&labels, &weights, 1));
+
+  RunBatch(stat.get(), target.get());
+  CHECK(stat->IsFinished());
+
+  ASSERT_FLOAT_EQ(stat->test_left_sum(0), 2.3 + 20.3 + 1.1);
+  ASSERT_FLOAT_EQ(stat->test_left_square(0), 3.4 * 3.4 + 20.3 * 20.3);
+  ASSERT_FLOAT_EQ(stat->test_right_sum(0), 0.0);
+  ASSERT_FLOAT_EQ(stat->test_right_square(0), 0.0);
+
+  ASSERT_FLOAT_EQ(stat->test_left_sum(1), 2.3 + 20.3);
+  ASSERT_FLOAT_EQ(stat->test_left_square(1), 2.3 * 2.3 + 20.3 * 20.3);
+  ASSERT_FLOAT_EQ(stat->test_right_sum(1), 1.1);
+  ASSERT_FLOAT_EQ(stat->test_right_square(1), 1.1 * 1.1);
+
+  FertileSlot slot;
+  stat->PackToProto(&slot);
+
+  string serialized = slot.DebugString();
+
+  std::unique_ptr<DenseClassificationGrowStats> new_stat(
+      new DenseClassificationGrowStats(params, 1));
+  new_stat->ExtractFromProto(slot);
+  FertileSlot second_one;
+  new_stat->PackToProto(&second_one);
+  string serialized_again = second_one.DebugString();
+  ASSERT_EQ(serialized_again, serialized);
+}
+
+class TestableFinishEarly : public DenseClassificationGrowStats {
+ public:
+  TestableFinishEarly(const TensorForestParams& params, int32 depth)
+      : DenseClassificationGrowStats(params, depth), num_times_called_(0) {}
+
+  int num_times_called_;
+
+ protected:
+  void CheckFinishEarlyHoeffding() override {
+    ++num_times_called_;
+  }
+};
+
+TEST(GrowStatsDenseClassificationTest, TestFinishEarly) {
+  TensorForestParams params;
+  params.set_num_outputs(2);
+  params.mutable_split_after_samples()->set_constant_value(2);
+  params.mutable_num_splits_to_consider()->set_constant_value(2);
+  params.mutable_min_split_samples()->set_constant_value(15);
+  params.mutable_dominate_fraction()->set_constant_value(0.99);
+  auto* finish = params.mutable_finish_type();
+  finish->set_type(SPLIT_FINISH_DOMINATE_HOEFFDING);
+  finish->mutable_check_every_steps()->set_constant_value(5);
+  std::unique_ptr<TestableFinishEarly> stat(new TestableFinishEarly(params, 1));
+  stat->Initialize();
+
+  std::vector<float> labels = {1, 0, 1};
+  std::vector<float> weights = {1, 1, 1};
+  std::unique_ptr<TestableInputTarget> target(
+      new TestableInputTarget(&labels, &weights, 1));
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, 2));
+
+  // Run through the 3 examples
+  RunBatch(stat.get(), target.get());
+
+  ASSERT_EQ(stat->num_times_called_, 0);
+
+  // Go over min_split_samples.
+  for (int i = 0; i < 13; ++i) {
+    stat->AddExample(dataset, target.get(), 0);
+  }
+
+  ASSERT_EQ(stat->num_times_called_, 1);
+
+  // More examples up to 55.
+  for (int i = 0; i < 39; ++i) {
+    stat->AddExample(dataset, target.get(), 0);
+  }
+
+  ASSERT_EQ(stat->num_times_called_, 9);
+}
+
+
+TEST(GrowStatsDenseClassificationTest, TestCheckPruneHoeffding) {
+  TensorForestParams params;
+  params.set_num_outputs(2);
+  params.mutable_split_after_samples()->set_constant_value(2000);
+  params.mutable_num_splits_to_consider()->set_constant_value(2);
+  params.mutable_min_split_samples()->set_constant_value(15);
+  params.mutable_dominate_fraction()->set_constant_value(0.99);
+  auto* finish = params.mutable_finish_type();
+  finish->set_type(SPLIT_FINISH_BASIC);
+  finish->mutable_check_every_steps()->set_constant_value(100);
+  params.mutable_pruning_type()->set_type(SPLIT_PRUNE_HOEFFDING);
+  params.mutable_pruning_type()->mutable_prune_every_samples()
+      ->set_constant_value(1);
+
+  DenseClassificationGrowStats stats(params, 1);
+  stats.Initialize();
+  stats.AddSplit(MakeSplit("0", 0.0));
+  stats.AddSplit(MakeSplit("1", 0.0));
+
+  // On each iteration, we add two examples, one of class 0 and one
+  // of class 1.  Split #0 classifies them perfectly, while split #1
+  // sends them both to the left.
+  std::vector<float> labels = {0, 1};
+  std::vector<float> weights = {1, 1};
+  TestableInputTarget target(&labels, &weights, 1);
+  std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
+      new tensorflow::tensorforest::TestableDataSet(
+          {-1.0, -1.0, 1.0, -1.0}, 2));
+
+  // Math time!
+  // After 2n samples,
+  // split 0 has smoothed counts (n+1,1);(1,n+1) and
+  // split 1 has smoothed counts (n+1,n+1);(1,1)
+  // split 0 smoothed ginis are both 1 - (n+1)^2/(n+2)^2 - 1/(n+2)^2 and
+  // split 1 smoothed ginis are 1 - 2 (n+1)^2 / (2n+2)^2 and 1 - 2 (1/4) = 1/2
+  // split 0 weighted smoothed ginis are both n (1 - (n^2 + 2n + 2) / (n+2)^2)
+  // split 1 weighted smoothed ginis are 0 and 2n (1 - 2(n+1)^2 / (2n+2)^2)
+  // split 0 split score = 2n (1 - (n^2 + 2n + 2) / (n+2)^2)
+  // split 1 spilt score = 2n (1 - 2(n+1)^2 / (2n+2)^2)
+  // split 1 score - split 0 score =
+  //    2n ( (n^2 + 2n + 2) / (n+2)^2 - 2(n+1)^2 / (2n+2)^2 )
+  //  = 2n ( (n^2 + 2n + 2) (2n+2)^2 - 2(n+1)^2 (n+2)^2 ) / ((n+2)^2 (2n+2)^2 )
+  //  = 2n ((n^2+2n+2)(4n^2+8n+4) - 2(n^2+2n+1)(n^2+4n+4)) / ((n+2)^2 (2n+2)^2)
+  //  = 2n (4n^4+8n^3+4n^2+8n^3+16n^2+8n+8n^2+16n+8
+  //         - (2n^4+8n^3+8n^2+4n^3+16n^2+16n+2n^2+8n+8)) / ((n+2)^2 (2n+2)^2)
+  //  = 2n (2n^4 + 4n^3 + 2n^2) / ((n+2)^2 (2n+2)^2)
+  //  = 4n^3 (n^2 + 2n + 1) / ((n+2)^2 (2n+2)^2)
+  //  = n^3  / (n+2)^2
+  //  Meanwhile, after 2n samples,
+  //  epsilon = 2n (1 - 1/2) sqrt(0.5 ln(1/0.01) / 2n)
+  //          = n sqrt( ln(10) / 2n)
+  //  Graphical comparison says that epsilon is greater between 0 and 4.5,
+  //  and then the split score difference is greater for n >= 5.
+  // n = 1
+  stats.AddExample(dataset, &target, 0);
+  stats.AddExample(dataset, &target, 1);
+  ASSERT_EQ(stats.num_splits(), 2);
+
+  // n = 2
+  stats.AddExample(dataset, &target, 0);
+  stats.AddExample(dataset, &target, 1);
+  ASSERT_EQ(stats.num_splits(), 2);
+
+  // n = 3
+  stats.AddExample(dataset, &target, 0);
+  stats.AddExample(dataset, &target, 1);
+  ASSERT_EQ(stats.num_splits(), 2);
+
+  // n = 4
+  stats.AddExample(dataset, &target, 0);
+  stats.AddExample(dataset, &target, 1);
+  ASSERT_EQ(stats.num_splits(), 2);
+
+  // n = 5
+  stats.AddExample(dataset, &target, 0);
+  stats.AddExample(dataset, &target, 1);
+  ASSERT_EQ(stats.num_splits(), 1);
+
+  // n = 6
+  stats.AddExample(dataset, &target, 0);
+  stats.AddExample(dataset, &target, 1);
+  ASSERT_EQ(stats.num_splits(), 1);
+}
+
+TEST(GrowStatsLeastSquaresRegressionTest, Basic) {
+  TensorForestParams params;
+  params.set_num_outputs(1);
+  params.mutable_split_after_samples()->set_constant_value(2);
+  params.mutable_num_splits_to_consider()->set_constant_value(2);
+  std::unique_ptr<LeastSquaresRegressionGrowStats> stat(
+      new LeastSquaresRegressionGrowStats(params, 1));
+  stat->Initialize();
+
+  std::vector<float> labels = {2.3, 5.6, 1.1};
+  std::unique_ptr<TestableInputTarget> target(
+      new TestableInputTarget(&labels, {}, 1));
+  std::vector<int> branches = {1, 0, 1, 1, 0, 0};
+
+  RunBatch(stat.get(), target.get());
+  CHECK(stat->IsFinished());
+
+  FertileSlot slot;
+  stat->PackToProto(&slot);
+
+  string serialized = slot.DebugString();
+
+  std::unique_ptr<LeastSquaresRegressionGrowStats> new_stat(
+      new LeastSquaresRegressionGrowStats(params, 1));
+  new_stat->ExtractFromProto(slot);
+  FertileSlot second_one;
+  new_stat->PackToProto(&second_one);
+  string serialized_again = second_one.DebugString();
+
+  ASSERT_EQ(serialized_again, serialized);
+}
+
+
+TEST(GrowStatsSparseClassificationTest, Basic) {
+  TensorForestParams params;
+  params.set_num_outputs(2);
+  params.mutable_split_after_samples()->set_constant_value(2);
+  params.mutable_num_splits_to_consider()->set_constant_value(2);
+  std::unique_ptr<SparseClassificationGrowStats> stat(
+      new SparseClassificationGrowStats(params, 1));
+  stat->Initialize();
+
+  std::vector<float> labels = {100, 1000, 1};
+  std::vector<float> weights = {2.3, 20.3, 1.1};
+  std::unique_ptr<TestableInputTarget> target(
+      new TestableInputTarget(&labels, &weights, 1));
+  std::vector<int> branches = {1, 0, 1, 1, 0, 0};
+
+  RunBatch(stat.get(), target.get());
+  CHECK(stat->IsFinished());
+
+  FertileSlot slot;
+  stat->PackToProto(&slot);
+
+  string serialized = slot.DebugString();
+
+  std::unique_ptr<SparseClassificationGrowStats> new_stat(
+      new SparseClassificationGrowStats(params, 1));
+  new_stat->ExtractFromProto(slot);
+  FertileSlot second_one;
+  new_stat->PackToProto(&second_one);
+  string serialized_again = second_one.DebugString();
+  ASSERT_EQ(serialized_again, serialized);
+}
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
new file mode 100644
index 0000000000..f5f07bea5c
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
@@ -0,0 +1,154 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
+#include "tensorflow/core/lib/strings/numbers.h"
+
+namespace tensorflow {
+namespace tensorforest {
+namespace {
+
+const int32 SPARSE_DEFAULT = 0;
+
+bool DecideInequalityTest(const decision_trees::InequalityTest& test,
+                          float value) {
+  float bias = test.threshold().float_value();
+  switch (test.type()) {
+    case decision_trees::InequalityTest::LESS_OR_EQUAL:
+      return value <= bias;
+
+    case decision_trees::InequalityTest::LESS_THAN:
+      return value < bias;
+
+    case decision_trees::InequalityTest::GREATER_OR_EQUAL:
+      return value >= bias;
+
+    case decision_trees::InequalityTest::GREATER_THAN:
+      return value > bias;
+
+    default:
+      return false;
+  }
+}
+
+bool DecideMatchingValuesTest(const decision_trees::MatchingValuesTest& test,
+                              float value) {
+  for (const decision_trees::Value& test_value : test.value()) {
+    if (test_value.float_value() == value) {
+      return true;
+    }
+  }
+  return false;
+}
+
+}  // namespace
+
+bool TensorDataSet::Decide(const decision_trees::BinaryNode& node,
+                           int example) const {
+  // TODO(gilberth): Support missing values.
+  float val = 0;
+  const auto& test = node.inequality_left_child_test();
+
+  if (test.has_oblique()) {
+    for (int i = 0; i < test.oblique().features_size(); ++i) {
+      val += test.oblique().weights(i) *
+             GetExampleValue(example, test.oblique().features(i));
+    }
+  } else {
+    val = GetExampleValue(example, test.feature_id());
+  }
+
+  if (node.has_inequality_left_child_test()) {
+    return DecideInequalityTest(node.inequality_left_child_test(), val);
+  } else {
+    decision_trees::MatchingValuesTest test;
+    if (node.custom_left_child_test().UnpackTo(&test)) {
+      return DecideMatchingValuesTest(test, val);
+    } else {
+      return false;
+    }
+  }
+}
+
+float TensorDataSet::GetExampleValue(
+    int example, const decision_trees::FeatureId& feature_id) const {
+  int32 feature;
+  safe_strto32(feature_id.id().value(), &feature);
+  if (feature >= input_spec_.dense_features_size()) {
+    return FindSparseValue(*sparse_indices_, *sparse_values_, example, feature);
+  } else {
+    return (*dense_data_)(example, feature);
+  }
+}
+
+float TensorDataSet::GetExampleValue(int example, int32 feature_id) const {
+  if (feature_id >= input_spec_.dense_features_size()) {
+    return FindSparseValue(*sparse_indices_, *sparse_values_, example,
+                           feature_id);
+  } else {
+    return (*dense_data_)(example, feature_id);
+  }
+}
+
+void TensorDataSet::set_input_tensors(const Tensor& dense,
+                                      const Tensor& sparse_indices,
+                                      const Tensor& sparse_values) {
+  if (dense.shape().dims() == 2) {
+    dense_data_.reset(new DenseStorageType(dense.tensor<float, 2>()));
+  }
+  if (sparse_indices.shape().dims() == 2) {
+    sparse_indices_.reset(new SparseIndicesStorageType(
+        sparse_indices.tensor<int64, 2>()));
+    sparse_values_.reset(new SparseValuesStorageType(
+        sparse_values.tensor<float, 1>()));
+  }
+  original_dense_tensor_ = dense;
+}
+
+void TensorDataSet::RandomSample(int example,
+                                 decision_trees::FeatureId* feature_id,
+                                 float* bias, int* type) const {
+  int32 num_total_features = input_spec_.dense_features_size();
+  int64 sparse_input_start;
+  if (sparse_indices_ != nullptr) {
+    const int32 num_sparse = tensorforest::GetNumSparseFeatures(
+        *sparse_indices_, example, &sparse_input_start);
+    if (sparse_input_start >= 0) {
+      num_total_features += num_sparse;
+    }
+  }
+  int rand_feature = rng_->Uniform(num_total_features);
+  if (rand_feature < available_features_.size()) {  // it's dense.
+    *feature_id = available_features_[rand_feature];
+    *type = input_spec_.GetDenseFeatureType(rand_feature);
+  } else {
+    const int32 sparse_index =
+        sparse_input_start + rand_feature - input_spec_.dense_features_size();
+    const int32 saved_index =
+        (*sparse_indices_)(sparse_index, 1) + input_spec_.dense_features_size();
+    *feature_id = decision_trees::FeatureId();
+    feature_id->mutable_id()->set_value(strings::StrCat(saved_index));
+
+    // TODO(gilberth): Remove this shortcut when different sparse types are
+    // allowed.
+    *type = input_spec_.sparse(0).original_type();
+  }
+
+  *bias = GetExampleValue(example, *feature_id);
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h
new file mode 100644
index 0000000000..261a1f2d5e
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h
@@ -0,0 +1,124 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_DATA_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_DATA_H_
+#include <ctime>
+#include <unordered_map>
+#include "google/protobuf/any.pb.h"
+#include "google/protobuf/wrappers.pb.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/random/philox_random.h"
+#include "tensorflow/core/lib/random/simple_philox.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+typedef TTypes<const float, 2>::ConstTensor DenseStorageType;
+typedef TTypes<const int64, 2>::ConstTensor SparseIndicesStorageType;
+typedef TTypes<const float, 1>::ConstTensor SparseValuesStorageType;
+
+class TensorDataSet {
+ public:
+  TensorDataSet(const tensorforest::TensorForestDataSpec& input_spec,
+                int32 seed)
+      : dense_data_(nullptr),
+        sparse_indices_(nullptr),
+        sparse_values_(nullptr),
+        input_spec_(input_spec),
+        split_sampling_random_seed_(seed) {
+    int column_count = 0;
+    for (int i = 0; i < input_spec_.dense_size(); ++i) {
+      for (int j = 0; j < input_spec_.dense(i).size(); ++j) {
+        decision_trees::FeatureId id;
+        id.mutable_id()->set_value(strings::StrCat(column_count));
+        available_features_.push_back(id);
+        ++column_count;
+      }
+    }
+
+    // Set up the random number generator.
+    if (split_sampling_random_seed_ == 0) {
+      uint64 time_seed = static_cast<uint64>(std::clock());
+      single_rand_ = std::unique_ptr<random::PhiloxRandom>(
+          new random::PhiloxRandom(time_seed));
+    } else {
+      single_rand_ = std::unique_ptr<random::PhiloxRandom>(
+          new random::PhiloxRandom(split_sampling_random_seed_));
+    }
+
+    rng_ = std::unique_ptr<random::SimplePhilox>(
+        new random::SimplePhilox(single_rand_.get()));
+  }
+  virtual ~TensorDataSet() {}
+
+  void set_input_tensors(const Tensor& dense, const Tensor& sparse_indices,
+                         const Tensor& sparse_values);
+
+  float get_input_value(int offset, int col) {
+    return (*dense_data_)(offset, col);
+  }
+
+  int NumItems() const {
+    if (dense_data_ != nullptr) {
+      return dense_data_->dimensions()[0];
+    } else if (sparse_indices_ != nullptr) {
+      return sparse_indices_->dimensions()[0];
+    } else {
+      return 0;
+    }
+  }
+
+  // This looks up a value by example and int32_id, which is much faster than
+  // GetFeature.
+  float GetExampleValue(int example,
+                        const decision_trees::FeatureId& feature_id) const;
+
+  // Same as overload with FeatureId, but if you already have the feature as
+  // an int32 you can avoid the atoi32.
+  virtual float GetExampleValue(int example, int32 feature_id) const;
+
+  int num_features() {
+    return available_features_.size();
+  }
+
+  const Tensor& original_tensor() const { return original_dense_tensor_; }
+
+  bool Decide(const decision_trees::BinaryNode& node, int example) const;
+
+  // Randomly samples a feature from example, returns its id in feature_name,
+  // the value in bias, and it's type from input_spec in type.
+  void RandomSample(int example, decision_trees::FeatureId* feature_name,
+                    float* bias, int* type) const;
+
+ private:
+  std::unique_ptr<DenseStorageType> dense_data_;
+  std::unique_ptr<SparseIndicesStorageType> sparse_indices_;
+  std::unique_ptr<SparseValuesStorageType> sparse_values_;
+
+  Tensor original_dense_tensor_;
+  const tensorforest::TensorForestDataSpec input_spec_;
+  std::vector<decision_trees::FeatureId> available_features_;
+
+  int32 split_sampling_random_seed_;
+  std::unique_ptr<random::PhiloxRandom> single_rand_;
+  std::unique_ptr<random::SimplePhilox> rng_;
+};
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_DATA_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
new file mode 100644
index 0000000000..97b2314f0f
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
@@ -0,0 +1,91 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_TARGET_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_TARGET_H_
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_types.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+typedef Eigen::TensorMap<
+    Eigen::Tensor<const float, 1, 1, long>, 0>  // NOLINT(runtime/int)
+    SingleDimStorageType;
+
+// Base class for classes that hold labels and weights. Mostly for testing
+// purposes, because it's inconvenient to construct nasty Eigen::things.
+class InputTarget {
+ public:
+  virtual ~InputTarget() {}
+  virtual int32 GetTargetAsClassIndex(int example_index,
+                                      int target_index) const = 0;
+
+  virtual float GetTargetWeight(int example_index) const = 0;
+
+  virtual float GetTargetAsContinuous(int example_index,
+                                      int target_index) const = 0;
+};
+
+template <typename T>
+class StoredInputTarget : public InputTarget {
+ protected:
+  StoredInputTarget(const T* t, const T* w, int num_targets)
+      : target_(t), weight_(w), num_targets_(num_targets) {}
+
+  const T* target_;
+  const T* weight_;
+  int num_targets_;
+};
+
+// Holds labels/targets and weights. Assumes that tensors are passed as
+// t.unaligned_flat<float>(). For multi-output, specifying the number of
+// outputs will correctly index the flattened data.
+class TensorInputTarget : public StoredInputTarget<SingleDimStorageType> {
+ public:
+  TensorInputTarget(const SingleDimStorageType* t,
+                    const SingleDimStorageType* w, const Tensor& tensor,
+                    int num_targets)
+      : StoredInputTarget(t, w, num_targets), original_tensor_(tensor) {}
+
+  int32 GetTargetAsClassIndex(int example_index,
+                              int target_index) const override {
+    return static_cast<int32>(
+        GetTargetAsContinuous(example_index, target_index));
+  }
+
+  float GetTargetWeight(int example_index) const override {
+    const size_t num_weights = weight_->size();
+    return num_weights > 0 && example_index < num_weights
+               ? (*weight_)(example_index)
+               : 1.0;
+  }
+
+  float GetTargetAsContinuous(int example_index,
+                              int target_index) const override {
+    QCHECK_LT(target_index, num_targets_);
+    return (*target_)(example_index * num_targets_ + target_index);
+  }
+
+  const Tensor& original_tensor() const {
+    return original_tensor_;
+  }
+
+ protected:
+  Tensor original_tensor_;
+};
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_TARGET_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
new file mode 100644
index 0000000000..4b9bb0f9c9
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
@@ -0,0 +1,160 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+std::unique_ptr<LeafModelOperator>
+LeafModelOperatorFactory::CreateLeafModelOperator(
+    const TensorForestParams& params) {
+  switch (params.leaf_type()) {
+    case MODEL_DENSE_CLASSIFICATION:
+      return std::unique_ptr<LeafModelOperator>(
+          new DenseClassificationLeafModelOperator(params));
+
+    case MODEL_SPARSE_CLASSIFICATION:
+      return std::unique_ptr<LeafModelOperator>(
+          new SparseClassificationLeafModelOperator(params));
+
+    case MODEL_SPARSE_OR_DENSE_CLASSIFICATION:
+      return std::unique_ptr<LeafModelOperator>(
+          new SparseOrDenseClassificationLeafModelOperator(params));
+
+    case MODEL_REGRESSION:
+      return std::unique_ptr<LeafModelOperator>(
+          new RegressionLeafModelOperator(params));
+
+    default:
+      LOG(ERROR) << "Unknown model operator: " << params.leaf_type();
+      return nullptr;
+  }
+}
+
+// ------------------------ Dense ----------------------------- //
+float DenseClassificationLeafModelOperator::GetOutputValue(
+    const decision_trees::Leaf& leaf, int32 o) const {
+  return leaf.vector().value(o).float_value();
+}
+
+void DenseClassificationLeafModelOperator::UpdateModel(
+    LeafStat* leaf, const InputTarget* target,
+    int example) const {
+  const int32 int_label = target->GetTargetAsClassIndex(example, 0);
+  auto* val = leaf->mutable_classification()->mutable_dense_counts()
+      ->mutable_value(int_label);
+  float weight = target->GetTargetWeight(example);
+  val->set_float_value(val->float_value() + weight);
+  leaf->set_weight_sum(leaf->weight_sum() + weight);
+}
+
+void DenseClassificationLeafModelOperator::InitModel(
+    LeafStat* leaf) const {
+  for (int i = 0; i < params_.num_outputs(); ++i) {
+    leaf->mutable_classification()->mutable_dense_counts()->add_value();
+  }
+}
+
+void DenseClassificationLeafModelOperator::ExportModel(
+    const LeafStat& stat, decision_trees::Leaf* leaf) const {
+  *leaf->mutable_vector() = stat.classification().dense_counts();
+}
+
+// ------------------------- Sparse -------------------------- //
+float SparseClassificationLeafModelOperator::GetOutputValue(
+    const decision_trees::Leaf& leaf, int32 o) const {
+  const auto it = leaf.sparse_vector().sparse_value().find(o);
+  if (it == leaf.sparse_vector().sparse_value().end()) {
+    return 0;  // default value
+  } else {
+    return it->second.float_value();
+  }
+}
+
+void SparseClassificationLeafModelOperator::UpdateModel(
+    LeafStat* leaf, const InputTarget* target,
+    int example) const {
+  const int32 int_label = target->GetTargetAsClassIndex(example, 0);
+  const float weight = target->GetTargetWeight(example);
+  leaf->set_weight_sum(leaf->weight_sum() + weight);
+  auto value_map = leaf->mutable_classification()->mutable_sparse_counts()
+      ->mutable_sparse_value();
+  auto it = value_map->find(int_label);
+  if (it == value_map->end()) {
+    (*value_map)[int_label].set_float_value(weight);
+  } else {
+    it->second.set_float_value(it->second.float_value() + weight);
+  }
+}
+
+void SparseClassificationLeafModelOperator::ExportModel(
+    const LeafStat& stat, decision_trees::Leaf* leaf) const {
+  *leaf->mutable_sparse_vector() = stat.classification().sparse_counts();
+}
+
+// ------------------------- SparseOrDense -------------------------- //
+float SparseOrDenseClassificationLeafModelOperator::GetOutputValue(
+    const decision_trees::Leaf& leaf, int32 o) const {
+  if (leaf.has_vector()) {
+    return dense_->GetOutputValue(leaf, o);
+  } else {
+    return sparse_->GetOutputValue(leaf, o);
+  }
+}
+
+void SparseOrDenseClassificationLeafModelOperator::UpdateModel(
+    LeafStat* leaf, const InputTarget* target, int example) const {
+  if (leaf->classification().has_dense_counts()) {
+    return dense_->UpdateModel(leaf, target, example);
+  } else {
+    return sparse_->UpdateModel(leaf, target, example);
+  }
+}
+
+void SparseOrDenseClassificationLeafModelOperator::ExportModel(
+    const LeafStat& stat, decision_trees::Leaf* leaf) const {
+  if (stat.classification().has_dense_counts()) {
+    return dense_->ExportModel(stat, leaf);
+  } else {
+    return sparse_->ExportModel(stat, leaf);
+  }
+}
+
+// ------------------------ Regression ----------------------------- //
+float RegressionLeafModelOperator::GetOutputValue(
+    const decision_trees::Leaf& leaf, int32 o) const {
+  return leaf.vector().value(o).float_value();
+}
+
+void RegressionLeafModelOperator::InitModel(
+    LeafStat* leaf) const {
+  for (int i = 0; i < params_.num_outputs(); ++i) {
+    leaf->mutable_regression()->mutable_mean_output()->add_value();
+  }
+}
+
+void RegressionLeafModelOperator::ExportModel(
+    const LeafStat& stat, decision_trees::Leaf* leaf) const {
+  for (int i = 0; i < params_.num_outputs(); ++i) {
+    const float new_val =
+        stat.regression().mean_output().value(i).float_value() /
+        stat.weight_sum();
+    leaf->mutable_vector()->add_value()->set_float_value(new_val);
+  }
+}
+
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h
new file mode 100644
index 0000000000..8aadefc403
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h
@@ -0,0 +1,150 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_LEAF_MODEL_OPERATORS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_LEAF_MODEL_OPERATORS_H_
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Abstract base class for classes that can initialize, get, and update leaf
+// models.
+class LeafModelOperator {
+ public:
+  // Number of outputs is interpreted differently for classification and
+  // regression.  For classification, it's the number of possible classes.
+  // For regression, it's the target dimensions.
+  explicit LeafModelOperator(const TensorForestParams& params)
+      : params_(params) {}
+  virtual ~LeafModelOperator() {}
+
+  // Returns the value of the requested output, which should be
+  // in [0, num_outputs_).  For classification, it's the class count (weighted
+  // number of instances seen).  For regression, it's e.g. the average value.
+  virtual float GetOutputValue(const decision_trees::Leaf& leaf,
+                               int32 o) const = 0;
+
+  // Update the given Leaf's model with the given example.
+  virtual void UpdateModel(LeafStat* leaf,
+                           const InputTarget* target,
+                           int example) const = 0;
+
+  // Initialize an empty Leaf model.
+  virtual void InitModel(LeafStat* leaf) const = 0;
+
+  virtual void ExportModel(const LeafStat& stat,
+                           decision_trees::Leaf* leaf) const = 0;
+
+ protected:
+  const TensorForestParams& params_;
+};
+
+// LeafModelOperator that stores class counts in a dense vector.
+class DenseClassificationLeafModelOperator : public LeafModelOperator {
+ public:
+  explicit DenseClassificationLeafModelOperator(
+      const TensorForestParams& params)
+      : LeafModelOperator(params) {}
+  float GetOutputValue(const decision_trees::Leaf& leaf,
+                       int32 o) const override;
+
+  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+                   int example) const override;
+
+  void InitModel(LeafStat* leaf) const override;
+
+  void ExportModel(const LeafStat& stat,
+                   decision_trees::Leaf* leaf) const override;
+};
+
+// LeafModelOperator that stores class counts sparsely in a map. Assumes default
+// value for yet-unseen classes is 0.
+class SparseClassificationLeafModelOperator : public LeafModelOperator {
+ public:
+  explicit SparseClassificationLeafModelOperator(
+      const TensorForestParams& params)
+      : LeafModelOperator(params) {}
+  float GetOutputValue(const decision_trees::Leaf& leaf,
+                       int32 o) const override;
+
+  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+                   int example) const override;
+
+  void InitModel(LeafStat* leaf) const override {}
+
+  void ExportModel(const LeafStat& stat,
+                   decision_trees::Leaf* leaf) const override;
+};
+
+class SparseOrDenseClassificationLeafModelOperator : public LeafModelOperator {
+ public:
+  explicit SparseOrDenseClassificationLeafModelOperator(
+      const TensorForestParams& params)
+      : LeafModelOperator(params),
+        dense_(new DenseClassificationLeafModelOperator(params)),
+        sparse_(new SparseClassificationLeafModelOperator(params)) {}
+  float GetOutputValue(const decision_trees::Leaf& leaf,
+                       int32 o) const override;
+
+  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+                   int example) const override;
+
+  void InitModel(LeafStat* leaf) const override {}
+
+  void ExportModel(const LeafStat& stat,
+                   decision_trees::Leaf* leaf) const override;
+
+ protected:
+  std::unique_ptr<DenseClassificationLeafModelOperator> dense_;
+  std::unique_ptr<SparseClassificationLeafModelOperator> sparse_;
+};
+
+// LeafModelOperator that stores regression leaf models with constant-value
+// prediction.
+class RegressionLeafModelOperator : public LeafModelOperator {
+ public:
+  explicit RegressionLeafModelOperator(const TensorForestParams& params)
+      : LeafModelOperator(params) {}
+  float GetOutputValue(const decision_trees::Leaf& leaf,
+                       int32 o) const override;
+
+  // TODO(gilberth): Quick experimentation suggests it's not even worth
+  // updating model and just using the seeded values.  Can add this in
+  // with additional_data, though protobuf::Any is slow.  Maybe make it
+  // optional.  Maybe make any update optional.
+  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+                   int example) const override {}
+
+  void InitModel(LeafStat* leaf) const override;
+
+  void ExportModel(const LeafStat& stat,
+                   decision_trees::Leaf* leaf) const override;
+};
+
+class LeafModelOperatorFactory {
+ public:
+  static std::unique_ptr<LeafModelOperator> CreateLeafModelOperator(
+      const TensorForestParams& params);
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_LEAF_MODEL_OPERATORS_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
new file mode 100644
index 0000000000..037e4e244c
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
@@ -0,0 +1,223 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace {
+
+using tensorflow::decision_trees::Leaf;
+using tensorflow::tensorforest::DenseClassificationLeafModelOperator;
+using tensorflow::tensorforest::LeafModelOperator;
+using tensorflow::tensorforest::SparseClassificationLeafModelOperator;
+using tensorflow::tensorforest::SparseOrDenseClassificationLeafModelOperator;
+using tensorflow::tensorforest::LeafStat;
+using tensorflow::tensorforest::RegressionLeafModelOperator;
+using tensorflow::tensorforest::TestableInputTarget;
+using tensorflow::tensorforest::TensorForestParams;
+
+const int32 kNumClasses = 3;
+
+constexpr char kRegressionStatProto[] =
+  "weight_sum: 3 "
+  "regression { "
+  "mean_output { "
+    "value { "
+    "  float_value: 27 "
+    "} "
+    "value { "
+    "  float_value: 282 "
+    "} "
+    "value { "
+    "  float_value: 10 "
+    "} "
+  "} "
+  "mean_output_squares { "
+    "value {"
+    "  float_value: 245"
+    "}"
+    "value {"
+    "  float_value: 26564"
+    "}"
+    "value {"
+    "  float_value: 46"
+    "}"
+  "}"
+"}";
+
+void TestClassificationNormalUse(const std::unique_ptr<LeafModelOperator>& op) {
+  std::unique_ptr<LeafStat> leaf(new LeafStat);
+  op->InitModel(leaf.get());
+
+  Leaf l;
+  op->ExportModel(*leaf, &l);
+
+  // Make sure it was initialized correctly.
+  for (int i = 0; i < kNumClasses; ++i) {
+    EXPECT_EQ(op->GetOutputValue(l, i), 0);
+  }
+
+  std::vector<float> labels = {1, 0, 1};
+  std::vector<float> weights = {2.3, 20.3, 1.1};
+  std::unique_ptr<TestableInputTarget> target(
+      new TestableInputTarget(&labels, &weights, 1));
+
+  // Update and check value.
+  op->UpdateModel(leaf.get(), target.get(), 0);
+  op->UpdateModel(leaf.get(), target.get(), 1);
+  op->UpdateModel(leaf.get(), target.get(), 2);
+
+  op->ExportModel(*leaf, &l);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(l, 1), 3.4);
+}
+
+
+TEST(DenseLeafModelOperatorsTest, NormalUse) {
+  TensorForestParams params;
+  params.set_num_outputs(kNumClasses);
+  std::unique_ptr<LeafModelOperator> op(
+      new DenseClassificationLeafModelOperator(params));
+  TestClassificationNormalUse(op);
+}
+
+TEST(SparseLeafModelOperatorsTest, NormalUse) {
+  TensorForestParams params;
+  params.set_num_outputs(kNumClasses);
+  std::unique_ptr<LeafModelOperator> op(
+      new SparseClassificationLeafModelOperator(params));
+  TestClassificationNormalUse(op);
+}
+
+TEST(DenseLeafModelOperatorsTest, InitWithExisting) {
+  TensorForestParams params;
+  params.set_num_outputs(kNumClasses);
+  std::unique_ptr<LeafModelOperator> op(
+      new DenseClassificationLeafModelOperator(params));
+
+  std::unique_ptr<LeafStat> stat(new LeafStat);
+  stat->mutable_classification()
+      ->mutable_dense_counts()
+      ->add_value()
+      ->set_float_value(1.1);
+  stat->mutable_classification()
+      ->mutable_dense_counts()
+      ->add_value()
+      ->set_float_value(2.2);
+  stat->mutable_classification()
+      ->mutable_dense_counts()
+      ->add_value()
+      ->set_float_value(3.3);
+
+  std::unique_ptr<Leaf> leaf(new Leaf);
+
+  op->ExportModel(*stat, leaf.get());
+
+  // Make sure it was initialized correctly.
+  EXPECT_EQ(leaf->vector().value_size(), kNumClasses);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 0), 1.1);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 1), 2.2);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 2), 3.3);
+}
+
+TEST(SparseOrDenseClassificationLeafModelOperator, InitWithExisting) {
+  TensorForestParams params;
+  params.set_num_outputs(kNumClasses);
+  std::unique_ptr<LeafModelOperator> op(
+      new SparseOrDenseClassificationLeafModelOperator(params));
+
+  std::unique_ptr<LeafStat> stat(new LeafStat);
+  (*stat->mutable_classification()
+        ->mutable_sparse_counts()
+        ->mutable_sparse_value())[0]
+      .set_float_value(1.1);
+  (*stat->mutable_classification()
+        ->mutable_sparse_counts()
+        ->mutable_sparse_value())[1]
+      .set_float_value(2.2);
+  (*stat->mutable_classification()
+        ->mutable_sparse_counts()
+        ->mutable_sparse_value())[2]
+      .set_float_value(3.3);
+
+  std::unique_ptr<Leaf> leaf(new Leaf);
+
+  op->ExportModel(*stat, leaf.get());
+
+  // Make sure it was initialized correctly.
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 0), 1.1);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 1), 2.2);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 2), 3.3);
+}
+
+TEST(SparseLeafModelOperatorsTest, InitWithExisting) {
+  TensorForestParams params;
+  params.set_num_outputs(kNumClasses);
+  std::unique_ptr<LeafModelOperator> op(
+      new SparseClassificationLeafModelOperator(params));
+  std::unique_ptr<LeafStat> stat(new LeafStat);
+  (*stat->mutable_classification()
+        ->mutable_sparse_counts()
+        ->mutable_sparse_value())[0]
+      .set_float_value(1.1);
+  (*stat->mutable_classification()
+        ->mutable_sparse_counts()
+        ->mutable_sparse_value())[1]
+      .set_float_value(2.2);
+  (*stat->mutable_classification()
+        ->mutable_sparse_counts()
+        ->mutable_sparse_value())[2]
+      .set_float_value(3.3);
+
+  std::unique_ptr<Leaf> leaf(new Leaf);
+
+  op->ExportModel( *stat, leaf.get());
+
+  // Make sure it was initialized correctly.
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 0), 1.1);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 1), 2.2);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 2), 3.3);
+
+  // check default value.
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 100), 0);
+  EXPECT_EQ(leaf->sparse_vector().sparse_value().size(), kNumClasses);
+}
+
+
+TEST(RegressionLeafModelOperatorsTest, NormalUse) {
+  TensorForestParams params;
+  params.set_num_outputs(kNumClasses);
+  std::unique_ptr<LeafModelOperator> op(
+      new RegressionLeafModelOperator(params));
+
+  std::unique_ptr<LeafStat> stat(new LeafStat());
+  const string contents(kRegressionStatProto);
+  ::tensorflow::protobuf::TextFormat::ParseFromString(contents, stat.get());
+
+  std::unique_ptr<Leaf> leaf(new Leaf);
+  op->ExportModel(*stat, leaf.get());
+
+  // Make sure it was initialized correctly.
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 0), 9);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 1), 94);
+  EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 2), 3.3333333);
+}
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/params.cc b/tensorflow/contrib/tensor_forest/kernels/v4/params.cc
new file mode 100644
index 0000000000..a3b09c17d5
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/params.cc
@@ -0,0 +1,54 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include <math.h>
+#include <stdlib.h>
+#include "tensorflow/core/platform/logging.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+float ResolveParam(const DepthDependentParam& param, int32 depth) {
+  float val;
+  switch (param.ParamType_case()) {
+    case DepthDependentParam::kConstantValue:
+      return param.constant_value();
+
+    case DepthDependentParam::kLinear:
+      val = depth * param.linear().slope() + param.linear().y_intercept();
+      return std::min(std::max(val, param.linear().min_val()),
+                      param.linear().max_val());
+
+    case DepthDependentParam::kExponential:
+      return param.exponential().bias() +
+             param.exponential().multiplier() *
+                 static_cast<float>(
+                     pow(param.exponential().base(),
+                         param.exponential().depth_multiplier() * depth));
+
+    case DepthDependentParam::kThreshold:
+      if (depth >= param.threshold().threshold()) {
+        return param.threshold().on_value();
+      } else {
+        return param.threshold().off_value();
+      }
+
+    default:
+      LOG(FATAL) << "unknown parameter type";
+  }
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/params.h b/tensorflow/contrib/tensor_forest/kernels/v4/params.h
new file mode 100644
index 0000000000..97a9d8d096
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/params.h
@@ -0,0 +1,32 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_PARAMS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_PARAMS_H_
+
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Return the value of the given depth-dependent parameter given a leaf's depth.
+float ResolveParam(const DepthDependentParam& param, int32 depth);
+
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_PARAMS_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc
new file mode 100644
index 0000000000..801881af13
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc
@@ -0,0 +1,75 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace {
+
+using tensorflow::tensorforest::DepthDependentParam;
+using tensorflow::tensorforest::ResolveParam;
+
+TEST(ParamsTest, TestConstant) {
+  DepthDependentParam param;
+  param.set_constant_value(10.0);
+
+  ASSERT_EQ(ResolveParam(param, 0), 10.0);
+  ASSERT_EQ(ResolveParam(param, 100), 10.0);
+}
+
+TEST(ParamsTest, TestLinear) {
+  DepthDependentParam param;
+  auto* linear = param.mutable_linear();
+  linear->set_y_intercept(100.0);
+  linear->set_slope(-10.0);
+  linear->set_min_val(23.0);
+  linear->set_max_val(90.0);
+
+  ASSERT_EQ(ResolveParam(param, 0), 90);
+  ASSERT_EQ(ResolveParam(param, 1), 90);
+  ASSERT_EQ(ResolveParam(param, 2), 80);
+
+  ASSERT_EQ(ResolveParam(param, 30), 23);
+}
+
+TEST(ParamsTest, TestExponential) {
+  DepthDependentParam param;
+  auto* expo = param.mutable_exponential();
+  expo->set_bias(100.0);
+  expo->set_base(10.0);
+  expo->set_multiplier(-1.0);
+  expo->set_depth_multiplier(1.0);
+
+  ASSERT_EQ(ResolveParam(param, 0), 99);
+  ASSERT_EQ(ResolveParam(param, 1), 90);
+  ASSERT_EQ(ResolveParam(param, 2), 0);
+}
+
+TEST(ParamsTest, TestThreshold) {
+  DepthDependentParam param;
+  auto* threshold = param.mutable_threshold();
+  threshold->set_on_value(100.0);
+  threshold->set_off_value(10.0);
+  threshold->set_threshold(5.0);
+
+  ASSERT_EQ(ResolveParam(param, 0), 10);
+  ASSERT_EQ(ResolveParam(param, 4), 10);
+  ASSERT_EQ(ResolveParam(param, 5), 100);
+  ASSERT_EQ(ResolveParam(param, 6), 100);
+}
+
+}  // namespace
+
+
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
new file mode 100644
index 0000000000..ddf4be8799
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
@@ -0,0 +1,257 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h"
+
+#include <cfloat>
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+std::unique_ptr<SplitCollectionOperator>
+SplitCollectionOperatorFactory::CreateSplitCollectionOperator(
+    const TensorForestParams& params) {
+  switch (params.collection_type()) {
+    case COLLECTION_BASIC:
+      return std::unique_ptr<SplitCollectionOperator>(
+          new SplitCollectionOperator(params));
+
+    case GRAPH_RUNNER_COLLECTION:
+      return std::unique_ptr<SplitCollectionOperator>(
+          new GraphRunnerSplitCollectionOperator(params));
+
+    default:
+      LOG(ERROR) << "Unknown split collection operator: "
+                 << params.collection_type();
+      return nullptr;
+  }
+}
+
+std::unique_ptr<GrowStats> SplitCollectionOperator::CreateGrowStats(
+    int32 node_id, int32 depth) const {
+  switch (params_.stats_type()) {
+    case STATS_DENSE_GINI:
+      return std::unique_ptr<GrowStats>(
+          new DenseClassificationGrowStats(params_, depth));
+
+    case STATS_SPARSE_GINI:
+      return std::unique_ptr<GrowStats>(
+          new SparseClassificationGrowStats(params_, depth));
+
+    case STATS_LEAST_SQUARES_REGRESSION:
+      return std::unique_ptr<GrowStats>(new LeastSquaresRegressionGrowStats(
+          params_, depth));
+
+    default:
+      LOG(ERROR) << "Unknown grow stats type: " << params_.stats_type();
+      return nullptr;
+  }
+}
+
+void SplitCollectionOperator::ExtractFromProto(
+    const FertileStats& stats_proto) {
+  for (int i = 0; i < stats_proto.node_to_slot_size(); ++i) {
+    const auto& slot = stats_proto.node_to_slot(i);
+    stats_[slot.node_id()] = CreateGrowStats(slot.node_id(), slot.depth());
+    stats_[slot.node_id()]->ExtractFromProto(slot);
+  }
+}
+
+void SplitCollectionOperator::PackToProto(FertileStats* stats_proto) const {
+  for (int i = 0; i < stats_proto->node_to_slot_size(); ++i) {
+    auto* new_slot = stats_proto->mutable_node_to_slot(i);
+    const auto& stats = stats_.at(new_slot->node_id());
+    if (params_.checkpoint_stats()) {
+      stats->PackToProto(new_slot);
+    }
+    new_slot->set_depth(stats->depth());
+  }
+}
+
+void SplitCollectionOperator::InitializeSlot(int32 node_id, int32 depth) {
+  stats_[node_id] = std::unique_ptr<GrowStats>(CreateGrowStats(node_id, depth));
+  stats_[node_id]->Initialize();
+}
+
+void SplitCollectionOperator::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    const std::vector<int>& examples, int32 node_id) const {
+  auto* slot = stats_.at(node_id).get();
+  for (int example : examples) {
+    slot->AddExample(input_data, target, example);
+  }
+}
+
+bool SplitCollectionOperator::IsInitialized(int32 node_id) const {
+  return stats_.at(node_id)->IsInitialized();
+}
+
+void SplitCollectionOperator::CreateAndInitializeCandidateWithExample(
+    const std::unique_ptr<TensorDataSet>& input_data, int example,
+    int32 node_id) const {
+  // Assumes split_initializations_per_input == 1.
+  decision_trees::BinaryNode split;
+  float bias;
+  int type;
+  decision_trees::FeatureId feature_id;
+  input_data->RandomSample(example, &feature_id, &bias, &type);
+
+  if (type == kDataFloat) {
+    decision_trees::InequalityTest* test =
+        split.mutable_inequality_left_child_test();
+    *test->mutable_feature_id() = feature_id;
+    test->mutable_threshold()->set_float_value(bias);
+    test->set_type(params_.inequality_test_type());
+  } else if (type == kDataCategorical) {
+    decision_trees::MatchingValuesTest test;
+    *test.mutable_feature_id() = feature_id;
+    test.add_value()->set_float_value(bias);
+    split.mutable_custom_left_child_test()->PackFrom(test);
+  } else {
+    LOG(ERROR) << "Unknown feature type " << type << ", not sure which "
+               << "node type to use.";
+  }
+  stats_.at(node_id)->AddSplit(split);
+}
+
+bool SplitCollectionOperator::BestSplit(int32 node_id,
+                                        SplitCandidate* best,
+                                        int32* depth) const {
+  auto* slot = stats_.at(node_id).get();
+  *depth = slot->depth();
+  return slot->BestSplit(best);
+}
+
+// -------------------------------- GraphRunner ------------------ //
+
+std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
+    int32 node_id, int32 depth) const {
+  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
+}
+
+int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
+                                                   int32 split_id) const {
+  return node_id * num_splits_to_consider_ + split_id;
+}
+
+bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
+                                                   SplitCandidate* best,
+                                                   int32* depth) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  auto* slot = stats_.at(node_id).get();
+  *depth = slot->depth();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    // TODO(gilberth): Support uselessness.
+    auto& runner = runners_[UniqueId(node_id, i)];
+    const float split_score = runner->SplitScore();
+    if (split_score < min_score) {
+      min_score = split_score;
+      best_index = i;
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in split info and left/right stats to initialize models with.
+  *best = SplitCandidate();
+  auto& runner = runners_[UniqueId(node_id, best_index)];
+  runner->GetLeftStats(best->mutable_left_stats());
+  runner->GetRightStats(best->mutable_right_stats());
+  runner->GetSplit(best->mutable_split());
+  return true;
+}
+
+void GraphRunnerSplitCollectionOperator::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    const std::vector<int>& examples, int32 node_id) const {
+  // Build input Tensors.
+  int size = examples.size();
+  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
+  auto ex_data = examples_t.flat<int32>();
+  std::copy(examples.begin(), examples.end(), ex_data.data());
+
+  const TensorInputTarget* tensor_target =
+      dynamic_cast<const TensorInputTarget*>(target);
+  CHECK_NOTNULL(tensor_target);
+
+  const Tensor& data_t = input_data->original_tensor();
+  const Tensor& target_t = tensor_target->original_tensor();
+
+  // Add to candidates.
+  auto* slot = stats_.at(node_id).get();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    auto& runner = runners_[UniqueId(node_id, i)];
+    runner->AddExample(data_t, target_t, examples_t);
+  }
+
+  // Update simple weight sums so we know when we're done.
+  for (int example : examples) {
+    slot->AddExample(input_data, target, example);
+  }
+}
+
+void GraphRunnerSplitCollectionOperator::
+    CreateAndInitializeCandidateWithExample(
+        const std::unique_ptr<TensorDataSet>& input_data, int example,
+        int32 node_id) const {
+  auto* slot = stats_.at(node_id).get();
+  int cand_num = slot->num_splits();
+  const int64 unique_id = UniqueId(node_id, cand_num);
+
+  decision_trees::BinaryNode split;
+
+  decision_trees::InequalityTest* test =
+      split.mutable_inequality_left_child_test();
+  auto* oblique = test->mutable_oblique();
+  for (int i = 0; i < features_per_node_; ++i) {
+    float bias;
+    int type;
+    // This is really just a way to select a list of random features.
+    // Also a way to warn the user that categoricals don't make sense here.
+    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
+
+    if (type == kDataFloat) {
+      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
+
+      // The comparison bias is assumed to be zero.
+      test->mutable_threshold()->set_float_value(0);
+    } else {
+      LOG(ERROR) << "Categorical features not supported with this system.";
+      return;
+    }
+  }
+
+  slot->AddSplit(split);
+
+  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
+  runners_[unique_id]->Init();
+}
+
+void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
+  SplitCollectionOperator::ClearSlot(node_id);
+  for (int i = 0; i < num_splits_to_consider_; ++i) {
+    runners_.erase(UniqueId(node_id, i));
+  }
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
new file mode 100644
index 0000000000..d0ea33612a
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
@@ -0,0 +1,148 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_SPLIT_COLLECTION_OPERATORS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_SPLIT_COLLECTION_OPERATORS_H_
+
+#include <vector>
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Class that can initialize and update split collections, and
+// report if one is finished and ready to split.  Designed to be inherited
+// from to implement techniques such as pruning and early/delayed finishing.
+class SplitCollectionOperator {
+ public:
+  explicit SplitCollectionOperator(const TensorForestParams& params)
+      : params_(params) {}
+  virtual ~SplitCollectionOperator() {}
+
+  // Return a new GrowStats object according to stats_type_;
+  virtual std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
+                                                     int32 depth) const;
+
+  // Initialize from a previously serialized proto.
+  virtual void ExtractFromProto(const FertileStats& stats);
+
+  // Serialize contents to the given proto.
+  virtual void PackToProto(FertileStats* stats) const;
+
+  // Updates the slot's candidates with the new example.
+  // Assumes slot has been initialized.
+  virtual void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                          const InputTarget* target,
+                          const std::vector<int>& examples,
+                          int32 node_id) const;
+
+  // Create a new candidate and initialize it with the given example.
+  virtual void CreateAndInitializeCandidateWithExample(
+      const std::unique_ptr<TensorDataSet>& input_data, int example,
+      int32 node_id) const;
+
+  // Create a new GrowStats for the given node id and initialize it.
+  virtual void InitializeSlot(int32 node_id, int32 depth);
+
+  // Perform any necessary cleanup for any tracked state for the slot.
+  virtual void ClearSlot(int32 node_id) {
+    stats_.erase(node_id);
+  }
+
+  // Return true if slot is fully initialized.
+  virtual bool IsInitialized(int32 node_id) const;
+
+  // Return true if slot is finished.
+  virtual bool IsFinished(int32 node_id) const {
+    return stats_.at(node_id)->IsFinished();
+  }
+
+  // Fill in best with the best split that node_id has, return true if this
+  // was successful, false if no good split was found.
+  virtual bool BestSplit(int32 node_id, SplitCandidate* best,
+                         int32* depth) const;
+
+ protected:
+  const TensorForestParams& params_;
+  std::unordered_map<int32, std::unique_ptr<GrowStats>> stats_;
+};
+
+
+class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
+ public:
+  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
+      : SplitCollectionOperator(params) {
+    if (params.num_splits_to_consider().ParamType_case() ==
+        DepthDependentParam::PARAMTYPE_NOT_SET) {
+      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
+                 << " num_splits_to_consider";
+    } else {
+      num_splits_to_consider_ =
+          params.num_splits_to_consider().constant_value();
+    }
+  }
+
+  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
+                                             int32 depth) const override;
+
+  // Updates the slot's candidates with the new example.
+  // Assumes slot has been initialized.
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, const std::vector<int>& examples,
+                  int32 node_id) const override;
+
+  // Create a new candidate and initialize it with the given example.
+  void CreateAndInitializeCandidateWithExample(
+      const std::unique_ptr<TensorDataSet>& input_data, int example,
+      int32 node_id) const override;
+
+  bool BestSplit(int32 node_id, SplitCandidate* best,
+                 int32* depth) const override;
+
+  void ClearSlot(int32 node_id) override;
+
+ protected:
+  int64 UniqueId(int32 node_id, int32 split_id) const;
+
+  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
+      runners_;
+  int features_per_node_;
+  string graph_dir_;
+  // Must have a constant value because of how we make unique ids right now.
+  int32 num_splits_to_consider_;
+};
+
+// Creates a type of SplitCollectionOperator depending on the type passed,
+// which is SplitCollectionType in fertile_stats.proto.
+// Can create a SplitCollectionOperator itself, known as "basic".
+class SplitCollectionOperatorFactory {
+ public:
+  static std::unique_ptr<SplitCollectionOperator> CreateSplitCollectionOperator(
+      const TensorForestParams& params);
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_SPLIT_COLLECTION_OPERATORS_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc b/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc
new file mode 100644
index 0000000000..0bec198e97
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc
@@ -0,0 +1,87 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h"
+#include <cfloat>
+
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// When using smoothing but only tracking sum and squares, and we're adding
+// num_classes for smoothing each class, then Gini looks more like this:
+//   Gini = 1 - \sum_i (c_i + 1)^2 / C^2
+//   = 1 - (1 / C^2) ( (\sum_i c_i)^2 + 2 (\sum_i c_i) + (\sum_i 1))
+//   = 1 - (1 / C^2) ( stats.square() + 2 stats.sum() + #_classes)
+//   = 1 - ( stats.square() + 2 stats.sum() + #_classes) / (smoothed_sum *
+//                                                          smoothed_sum)
+//
+//   where
+//   smoothed_sum = stats.sum() + #_classes
+float GiniImpurity(const LeafStat& stats, int32 num_classes) {
+  const float smoothed_sum = num_classes + stats.weight_sum();
+  return 1.0 - (
+      (stats.classification().gini().square()
+       + 2 * stats.weight_sum() + num_classes) / (smoothed_sum * smoothed_sum));
+}
+
+float WeightedGiniImpurity(const LeafStat& stats, int32 num_classes) {
+  return stats.weight_sum() * GiniImpurity(stats, num_classes);
+}
+
+void UpdateGini(LeafStat* stats, float old_val, float weight) {
+  stats->set_weight_sum(stats->weight_sum() + weight);
+  // Equivalent to stats->square() - old_val * old_val + new_val * new_val,
+  // (for new_val = old_val + weight), but more numerically stable.
+  stats->mutable_classification()->mutable_gini()->set_square(
+      stats->classification().gini().square()
+      + weight * weight + 2 * old_val * weight);
+}
+
+
+float Variance(const LeafStat& stats, int output) {
+  if (stats.weight_sum() == 0) {
+    return 0;
+  }
+  const float e_x =
+      stats.regression().mean_output().value(output).float_value()
+      / stats.weight_sum();
+  const auto e_x2 =
+      stats.regression().mean_output_squares().value(output).float_value()
+      / stats.weight_sum();
+  return e_x2 - e_x * e_x;
+}
+
+float TotalVariance(const LeafStat& stats) {
+  float sum = 0;
+  for (int i = 0; i < stats.regression().mean_output().value_size(); ++i) {
+    sum += Variance(stats, i);
+  }
+  return sum;
+}
+
+float SmoothedGini(float sum, float square, int num_classes) {
+  // See comments for GiniImpurity above.
+  const float smoothed_sum = num_classes + sum;
+  return 1.0 -
+         (square + 2 * sum + num_classes) / (smoothed_sum * smoothed_sum);
+}
+
+float WeightedSmoothedGini(float sum, float square, int num_classes) {
+  return sum * SmoothedGini(sum, square, num_classes);
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h b/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h
new file mode 100644
index 0000000000..8e002d0414
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h
@@ -0,0 +1,50 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_STAT_UTILS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_STAT_UTILS_H_
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Returns the smoothed, unweighted Gini impurity.
+float GiniImpurity(const LeafStat& stats, int32 num_classes);
+
+// Returns the smoothed, weighted Gini impurity
+float WeightedGiniImpurity(const LeafStat& stats, int32 num_classes);
+
+// Updates the GiniStats given the old and new values of a class count that
+// was updated.
+void UpdateGini(LeafStat* stats, float old_val, float weight);
+
+// Returns the variance in stats for the given output.
+float Variance(const LeafStat& stats, int output);
+
+// Returns the variance sum for all outputs.
+float TotalVariance(const LeafStat& stats);
+
+// ------- functions used by C++ stats classes  -------- //
+// Returns the smoothed gini score given the sum and sum of the squares of the
+// class counts.
+float SmoothedGini(float sum, float square, int num_classes);
+
+// Returns the smoothed gini score weighted by the sum.
+float WeightedSmoothedGini(float sum, float square, int num_classes);
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_STAT_UTILS_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h b/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h
new file mode 100644
index 0000000000..4ac23ceb3e
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h
@@ -0,0 +1,73 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_TEST_UTILS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_TEST_UTILS_H_
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+class TestableInputTarget : public StoredInputTarget<std::vector<float>> {
+ public:
+  TestableInputTarget(const std::vector<float>* t, const std::vector<float>* w,
+                      int num_t)
+      : StoredInputTarget(t, w, num_t) {}
+
+  int NumItems() const {
+    return target_->size();
+  }
+
+  int32 GetTargetAsClassIndex(int example_index,
+                              int target_index) const override {
+    return static_cast<int32>(
+        GetTargetAsContinuous(example_index, target_index));
+  }
+
+  float GetTargetWeight(int example_index) const override {
+    const size_t num_weights = weight_->size();
+    return num_weights > 0 && example_index < num_weights
+               ? (*weight_)[example_index]
+               : 1.0;
+  }
+
+  float GetTargetAsContinuous(int example_index,
+                              int target_index) const override {
+    QCHECK_LT(target_index, num_targets_);
+    return (*target_)[example_index * num_targets_ + target_index];
+  }
+};
+
+
+class TestableDataSet : public TensorDataSet {
+ public:
+  TestableDataSet(const std::vector<float>& data, int num_features)
+      : TensorDataSet(TensorForestDataSpec(), 11),
+        num_features_(num_features),
+        data_(data) {}
+
+  float GetExampleValue(int example, int32 feature_id) const override {
+    return data_[example * num_features_ + feature_id];
+  }
+
+ protected:
+  int num_features_;
+  std::vector<float> data_;
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_TEST_UTILS_H_
diff --git a/tensorflow/contrib/tensor_forest/proto/BUILD b/tensorflow/contrib/tensor_forest/proto/BUILD
new file mode 100644
index 0000000000..1cfef44af1
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/proto/BUILD
@@ -0,0 +1,31 @@
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library")
+
+package(default_visibility = ["//visibility:public"])
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+tf_proto_library(
+    name = "fertile_stats_proto",
+    srcs = ["fertile_stats.proto"],
+    cc_api_version = 2,
+    protodeps = ["//tensorflow/contrib/decision_trees/proto:generic_tree_model"],
+    visibility = ["//visibility:public"],
+)
+
+tf_proto_library(
+    name = "tensor_forest_params_proto",
+    srcs = ["tensor_forest_params.proto"],
+    cc_api_version = 2,
+    protodeps = ["//tensorflow/contrib/decision_trees/proto:generic_tree_model"],
+    visibility = ["//visibility:public"],
+)
diff --git a/tensorflow/contrib/tensor_forest/proto/fertile_stats.proto b/tensorflow/contrib/tensor_forest/proto/fertile_stats.proto
new file mode 100644
index 0000000000..0ded04ad75
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/proto/fertile_stats.proto
@@ -0,0 +1,92 @@
+syntax = "proto3";
+option cc_enable_arenas = true;
+
+package tensorflow.tensorforest;
+
+import "tensorflow/contrib/decision_trees/proto/generic_tree_model.proto";
+
+
+message FertileStats {
+  // Tracks stats for each node.  node_to_slot[i] is the FertileSlot for node i.
+  // This may be sized to max_nodes initially, or grow dynamically as needed.
+  repeated FertileSlot node_to_slot = 1;
+}
+
+
+message GiniStats {
+  // This allows us to quickly track and calculate impurity (classification)
+  //  by storing the sum of input weights and the sum of the squares of the
+  // input weights.  Weighted gini is then: 1 - (square / sum * sum).
+  // Updates to these numbers are:
+  //   old_i = leaf->value(label)
+  //   new_i = old_i + incoming_weight
+  //   sum -> sum + incoming_weight
+  //   square -> square - (old_i ^ 2) + (new_i ^ 2)
+  //   total_left_sum -> total_left_sum - old_left_i * old_total_i +
+  //                                      new_left_i * new_total_i
+  float square = 2;
+}
+
+message LeafStat {
+  // The sum of the weights of the training examples that we have seen.
+  // This is here, outside of the leaf_stat oneof, because almost all
+  // types will want it.
+  float weight_sum = 3;
+
+  // TODO(thomaswc): Move the GiniStats out of LeafStats and into something
+  // that only tracks them for splits.
+  message GiniImpurityClassificationStats {
+    oneof counts {
+      decision_trees.Vector dense_counts = 1;
+      decision_trees.SparseVector sparse_counts = 2;
+    }
+    GiniStats gini = 3;
+  }
+
+  // This is the info needed for calculating variance for regression.
+  // Variance will still have to be summed over every output, but the
+  // number of outputs in regression problems is almost always 1.
+  message LeastSquaresRegressionStats {
+    decision_trees.Vector mean_output = 1;
+    decision_trees.Vector mean_output_squares = 2;
+  }
+
+  oneof leaf_stat {
+    GiniImpurityClassificationStats classification = 1;
+    LeastSquaresRegressionStats regression = 2;
+    // TODO(thomaswc): Add in v5's SparseClassStats.
+  }
+}
+
+message FertileSlot {
+  // The statistics for *all* the examples seen at this leaf.
+  LeafStat leaf_stats = 4;
+
+  repeated SplitCandidate candidates = 1;
+
+  // The statistics for the examples seen at this leaf after all the
+  // splits have been initialized.  If post_init_leaf_stats.weight_sum
+  // is > 0, then all candidates have been initialized.  We need to track
+  // both leaf_stats and post_init_leaf_stats because the first is used
+  // to create the decision_tree::Leaf and the second is used to infer
+  // the statistics for the right side of a split (given the leaf side
+  // stats).
+  LeafStat post_init_leaf_stats = 6;
+
+  int32 node_id = 5;
+  int32 depth = 7;
+}
+
+message SplitCandidate {
+  // proto representing the potential node.
+  decision_trees.BinaryNode split = 1;
+
+  // Right counts are inferred from FertileSlot.leaf_stats and left.
+  LeafStat left_stats = 4;
+
+  // Right stats (not full counts) are kept here.
+  LeafStat right_stats = 5;
+
+  // Fields used when training with a graph runner.
+  string unique_id = 6;
+}
diff --git a/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto b/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
new file mode 100644
index 0000000000..3813bbce62
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
@@ -0,0 +1,145 @@
+syntax = "proto3";
+
+package tensorflow.tensorforest;
+
+import "tensorflow/contrib/decision_trees/proto/generic_tree_model.proto";
+
+// Leaf models specify what is returned at inference time, and how it is
+// stored in the decision_trees.Leaf protos.
+enum LeafModelType {
+  MODEL_DENSE_CLASSIFICATION = 0;
+  MODEL_SPARSE_CLASSIFICATION = 1;
+  MODEL_REGRESSION = 2;
+  MODEL_SPARSE_OR_DENSE_CLASSIFICATION = 3;
+}
+
+// Stats models generally specify information that is collected which is
+// necessary to choose a split at a node. Specifically, they operate on
+// a SplitCandidate::LeafStat proto.
+enum StatsModelType {
+  STATS_DENSE_GINI = 0;
+  STATS_SPARSE_GINI = 1;
+  STATS_LEAST_SQUARES_REGRESSION = 2;
+  STATS_SPARSE_THEN_DENSE_GINI = 3;
+}
+
+// Allows selection of operations on the collection of split candidates.
+// Basic infers right split stats from the leaf stats and each candidate's
+// left stats.
+enum SplitCollectionType {
+  COLLECTION_BASIC = 0;
+  GRAPH_RUNNER_COLLECTION = 1;
+}
+
+// Pruning strategies define how candidates are pruned over time.
+// SPLIT_PRUNE_HALF prunes the worst half of splits every prune_ever_samples,
+// etc.  Note that prune_every_samples plays against the depth-dependent
+// split_after_samples, so they should be set together.
+enum SplitPruningStrategyType {
+  SPLIT_PRUNE_NONE = 0;
+  SPLIT_PRUNE_HALF = 1;
+  SPLIT_PRUNE_QUARTER = 2;
+  SPLIT_PRUNE_10_PERCENT = 3;
+  // SPLIT_PRUNE_HOEFFDING prunes splits whose Gini impurity is worst than
+  // the best split's by more than the Hoeffding bound.
+  SPLIT_PRUNE_HOEFFDING = 4;
+}
+
+message SplitPruningConfig {
+  DepthDependentParam prune_every_samples = 1;
+  SplitPruningStrategyType type = 2;
+}
+
+// Finish strategies define when slots are considered finished.
+// Basic requires at least split_after_samples, and doesn't allow slots to
+// finish until the leaf has received more than one class. Hoeffding splits
+// early after min_split_samples if one split is dominating the rest according
+// to hoeffding bounds. Bootstrap does the same but compares gini's calculated
+// with sampled smoothed counts.
+enum SplitFinishStrategyType {
+  SPLIT_FINISH_BASIC = 0;
+  SPLIT_FINISH_DOMINATE_HOEFFDING = 2;
+  SPLIT_FINISH_DOMINATE_BOOTSTRAP = 3;
+}
+
+message SplitFinishConfig {
+  // Configure how often we check for finish, because some finish methods
+  // are expensive to perform.
+  DepthDependentParam check_every_steps = 1;
+  SplitFinishStrategyType type = 2;
+}
+
+// A parameter that changes linearly with depth, with upper and lower bounds.
+message LinearParam {
+  float slope = 1;
+  float y_intercept = 2;
+  float min_val = 3;
+  float max_val = 4;
+}
+
+// A parameter that changes expoentially with the form
+//     f = c + mb^(k*d)
+// where:
+//  c: constant bias
+//  b: base
+//  m: multiplier
+//  k: depth multiplier
+//  d: depth
+message ExponentialParam {
+  float bias = 1;
+  float base = 2;
+  float multiplier = 3;
+  float depth_multiplier = 4;
+}
+
+// A parameter that is 'off' until depth >= a threshold, then is 'on'.
+message ThresholdParam {
+  float on_value = 1;
+  float off_value = 2;
+  float threshold = 3;
+}
+
+// A parameter that may change with node depth.
+message DepthDependentParam {
+  oneof ParamType {
+    float constant_value = 1;
+    LinearParam linear = 2;
+    ExponentialParam exponential = 3;
+    ThresholdParam threshold = 4;
+ }
+}
+
+message TensorForestParams {
+  // ------------ Types that control training subsystems ------ //
+  LeafModelType leaf_type = 1;
+  StatsModelType stats_type = 2;
+  SplitCollectionType collection_type = 3;
+  SplitPruningConfig pruning_type = 4;
+  SplitFinishConfig finish_type = 5;
+
+  // --------- Parameters that can't change by definition --------------- //
+  int32 num_trees = 6;
+  int32 max_nodes = 7;
+
+  decision_trees.InequalityTest.Type inequality_test_type = 19;
+
+  // Some booleans controlling execution
+  bool is_regression = 8;
+  bool drop_final_class = 9;
+  bool collate_examples = 10;
+  bool checkpoint_stats = 11;
+  bool use_running_stats_method = 20;
+
+  // Number of classes (classification) or targets (regression)
+  int32 num_outputs = 12;
+
+  // --------- Parameters that could be depth-dependent --------------- //
+  DepthDependentParam num_splits_to_consider = 13;
+  DepthDependentParam split_after_samples = 14;
+  DepthDependentParam dominate_fraction = 15;
+  DepthDependentParam min_split_samples = 18;
+
+  // --------- Parameters for experimental features ---------------------- //
+  string graph_dir = 16;
+  int32 num_select_features = 17;
+}
-- 
GitLab


From fa927634cbda0b7826e516336fb8277707fb6fe3 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 15 Jun 2017 12:36:27 -0700
Subject: [PATCH 0112/1375] Propogate keep_checkpoint_every_n_hours to the
 default Saver inside estimator.

PiperOrigin-RevId: 159138894
---
 tensorflow/python/estimator/estimator.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 293aa75253..ab49f36e5e 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -603,12 +603,15 @@ class Estimator(object):
 
       if not (estimator_spec.scaffold.saver or
               ops.get_collection(ops.GraphKeys.SAVERS)):
-        ops.add_to_collection(ops.GraphKeys.SAVERS,
-                              training.Saver(
-                                  sharded=True,
-                                  max_to_keep=self._config.keep_checkpoint_max,
-                                  defer_build=True,
-                                  save_relative_paths=True))
+        ops.add_to_collection(
+            ops.GraphKeys.SAVERS,
+            training.Saver(
+                sharded=True,
+                max_to_keep=self._config.keep_checkpoint_max,
+                keep_checkpoint_every_n_hours=(
+                    self._config.keep_checkpoint_every_n_hours),
+                defer_build=True,
+                save_relative_paths=True))
 
       chief_hooks = []
       if (self._config.save_checkpoints_secs or
@@ -862,7 +865,8 @@ def _write_dict_to_summary(output_dir,
       value.simple_value = int(dictionary[key])
     else:
       logging.warn(
-          'Skipping summary for %s, must be a float, np.float32, np.int64, np.int32 or int.',
+          'Skipping summary for %s, must be a float, np.float32, np.int64, '
+          'np.int32 or int.',
           key)
   summary_writer.add_summary(summary_proto, current_global_step)
   summary_writer.flush()
-- 
GitLab


From fa75f26351f42e4fd3fc89b553d7919a6f147e41 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 12:53:23 -0700
Subject: [PATCH 0113/1375] Introduce Predictor, an interface for efficient,
 repeated inference.

PiperOrigin-RevId: 159141010
---
 tensorflow/BUILD                              |   1 +
 tensorflow/contrib/predictor/BUILD            | 163 +++++++++++++++++
 tensorflow/contrib/predictor/README.md        |  96 ++++++++++
 tensorflow/contrib/predictor/__init__.py      |  24 +++
 .../predictor/contrib_estimator_predictor.py  |  74 ++++++++
 .../contrib_estimator_predictor_test.py       |  70 ++++++++
 .../predictor/core_estimator_predictor.py     |  80 +++++++++
 .../core_estimator_predictor_test.py          |  81 +++++++++
 tensorflow/contrib/predictor/predictor.py     |  77 ++++++++
 .../contrib/predictor/predictor_factories.py  | 132 ++++++++++++++
 .../predictor/saved_model_predictor.py        | 143 +++++++++++++++
 .../predictor/saved_model_predictor_test.py   | 170 ++++++++++++++++++
 .../predictor/test_export_dir/saved_model.pb  | Bin 0 -> 7736 bytes
 .../variables/variables.data-00000-of-00001   | Bin 0 -> 8 bytes
 .../test_export_dir/variables/variables.index | Bin 0 -> 127 bytes
 .../contrib/predictor/testing_common.py       | 102 +++++++++++
 tensorflow/tools/pip_package/BUILD            |   1 +
 17 files changed, 1214 insertions(+)
 create mode 100644 tensorflow/contrib/predictor/BUILD
 create mode 100644 tensorflow/contrib/predictor/README.md
 create mode 100644 tensorflow/contrib/predictor/__init__.py
 create mode 100644 tensorflow/contrib/predictor/contrib_estimator_predictor.py
 create mode 100644 tensorflow/contrib/predictor/contrib_estimator_predictor_test.py
 create mode 100644 tensorflow/contrib/predictor/core_estimator_predictor.py
 create mode 100644 tensorflow/contrib/predictor/core_estimator_predictor_test.py
 create mode 100644 tensorflow/contrib/predictor/predictor.py
 create mode 100644 tensorflow/contrib/predictor/predictor_factories.py
 create mode 100644 tensorflow/contrib/predictor/saved_model_predictor.py
 create mode 100644 tensorflow/contrib/predictor/saved_model_predictor_test.py
 create mode 100644 tensorflow/contrib/predictor/test_export_dir/saved_model.pb
 create mode 100644 tensorflow/contrib/predictor/test_export_dir/variables/variables.data-00000-of-00001
 create mode 100644 tensorflow/contrib/predictor/test_export_dir/variables/variables.index
 create mode 100644 tensorflow/contrib/predictor/testing_common.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index ec9be247d3..53c3657b72 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -276,6 +276,7 @@ filegroup(
         "//tensorflow/contrib/ndlstm:all_files",
         "//tensorflow/contrib/nn:all_files",
         "//tensorflow/contrib/opt:all_files",
+        "//tensorflow/contrib/predictor:all_files",
         "//tensorflow/contrib/rnn:all_files",
         "//tensorflow/contrib/saved_model:all_files",
         "//tensorflow/contrib/saved_model/cc/saved_model:all_files",
diff --git a/tensorflow/contrib/predictor/BUILD b/tensorflow/contrib/predictor/BUILD
new file mode 100644
index 0000000000..c4b46551c1
--- /dev/null
+++ b/tensorflow/contrib/predictor/BUILD
@@ -0,0 +1,163 @@
+# `Predictor` classes provide an interface for efficient, repeated inference.
+
+package(default_visibility = ["//third_party/tensroflow/contrib/predictor:__subpackages__"])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+load("//tensorflow:tensorflow.bzl", "py_test")
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+py_library(
+    name = "predictor",
+    srcs = [
+        "__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":predictor_factories",
+    ],
+)
+
+py_library(
+    name = "predictor_factories",
+    srcs = [
+        "predictor_factories.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":contrib_estimator_predictor",
+        ":core_estimator_predictor",
+        ":saved_model_predictor",
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_library(
+    name = "base_predictor",
+    srcs = [
+        "predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "saved_model_predictor",
+    srcs = [
+        "saved_model_predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_predictor",
+        "//tensorflow/python/tools:saved_model_cli",
+    ],
+)
+
+py_library(
+    name = "core_estimator_predictor",
+    srcs = [
+        "core_estimator_predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_predictor",
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_library(
+    name = "contrib_estimator_predictor",
+    srcs = [
+        "contrib_estimator_predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_predictor",
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_library(
+    name = "testing_common",
+    srcs = [
+        "testing_common.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+    ],
+)
+
+# Transitive dependencies of this target will be included in the pip package.
+py_library(
+    name = "predictor_pip",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":contrib_estimator_predictor",
+        ":core_estimator_predictor",
+        ":saved_model_predictor",
+    ],
+)
+
+py_test(
+    name = "saved_model_predictor_test",
+    srcs = [
+        "saved_model_predictor_test.py",
+    ],
+    data = [":test_export_dir"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":saved_model_predictor",
+        ":testing_common",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
+py_test(
+    name = "core_estimator_predictor_test",
+    srcs = [
+        "core_estimator_predictor_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":core_estimator_predictor",
+        ":testing_common",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
+py_test(
+    name = "contrib_estimator_predictor_test",
+    srcs = [
+        "contrib_estimator_predictor_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":contrib_estimator_predictor",
+        ":testing_common",
+        "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
+    ],
+)
+
+filegroup(
+    name = "test_export_dir",
+    srcs = glob(["test_export_dir/**/*"]),
+    tags = ["nopip"],
+)
diff --git a/tensorflow/contrib/predictor/README.md b/tensorflow/contrib/predictor/README.md
new file mode 100644
index 0000000000..16cdcf3e70
--- /dev/null
+++ b/tensorflow/contrib/predictor/README.md
@@ -0,0 +1,96 @@
+# Predictors
+
+The `Predictor` classes provide a simple interface for performing repeated,
+efficient inference. A `Predictor` can be constructed from a `SavedModel` on
+disk, a `tf.Estimator` or a `tf.contrib.Estimator`.
+
+To facilitate the examples below, let's define a trivial `Estimator` that just
+calculates a sum:
+
+```python
+def model_fn(features, labels, mode):
+  z = tf.add(features['x'], features['y'], name='z')
+  return tf.contrib.learn.ModelFnOps(
+      mode, {'z': z}, loss=tf.constant(0.0), train_op=tf.no_op())
+
+estimator = tf.contrib.learn.Estimator(model_fn=model_fn)
+```
+
+We can then construct a `Predictor` in two different ways.
+
+## `Predictor` from a `SavedModel`
+
+Given a trained `Estimator`, we first export a `SavedModel`:
+
+```python
+def serving_input_fn():
+  x = tf.placeholder(dtype=tf.float32, shape=[None], name='x')
+  y = tf.placeholder(dtype=tf.float32, shape=[None], name='y')
+
+  features = {'x': x, 'y': y}
+  return tf.contrib.learn.utils.input_fn_utils.InputFnOps(
+           features, None, default_inputs=features)
+
+saved_model_dir = estimator.export_savedmodel(my_export_dir, serving_input_fn)
+```
+
+We can then construct a `Predictor` as follows:
+
+```python
+saved_model_predictor = predictor.from_saved_model(export_dir='test_export_dir')
+output_dict = saved_model_predictor({'x': [1.0], 'y': [5.2]})
+# output_dict == {'sum': [6.2]}
+```
+
+By specifying a signature definition, we can feed and fetch any `Tensor`s in
+the `Graph`. In this example, we feed and fetch the same `Tensor`, `z`:
+
+```python
+inputs = outputs = {'z': tf.TensorInfo(
+                        name='z:0',
+                        dtype=types_pb2.DT_FLOAT,
+                        tensor_shape=tensor_shape_pb2.TensorShapeProto())}
+
+signature_def = tf.saved_model.signature_def_utils.build_signature_def(
+          inputs=inputs,
+          outputs=outputs,
+          method_name='tensorflow/serving/regress')
+
+trivial_predictor = predictor.from_saved_model(
+          export_dir=saved_model_dir,
+          signature_def=signature_def)
+
+output_dict = trivial_predictor({'z': [32.]})
+# output_dict == {'z': [32.]}
+```
+
+You can also specify input and output `Tensor`s by name using the `input_names`
+and `output_names` keywords:
+
+```python
+saved_model_predictor = predictor.from_saved_model(
+          export_dir=saved_model_dir,
+          input_names={'x': 'x:0', 'y': 'y:0'},
+          outputs={'z': 'z:0'})
+
+output_dict = saved_model_predictor({'x': [6.], 'y': [11.]})
+# output_dict == {'z': [17.]}
+```
+
+This functionality is particularly useful for performing encoding once, but
+doing multiple decoding iterations with e.g. seq2seq models.
+
+## `Predictor` from an `Estimator`
+
+We can also construct a `Predictor` directly from an `Estimator`. Defining
+`serving_input_fn` as above,
+
+```python
+estimator_predictor = predictor.from_contrib_estimator(
+    estimator, serving_input_fn)
+output_dict = sum_predictor({'x': [1., 2.], 'y': [3., 4.]})
+# output_dict == {'z': [4., 6.]}
+```
+
+Construction from a `tf.Estimator` is almost identical.
+
diff --git a/tensorflow/contrib/predictor/__init__.py b/tensorflow/contrib/predictor/__init__.py
new file mode 100644
index 0000000000..d270c3f798
--- /dev/null
+++ b/tensorflow/contrib/predictor/__init__.py
@@ -0,0 +1,24 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Modules for `Predictor`s."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.predictor import from_contrib_estimator
+from tensorflow.contrib.predictor import from_estimator
+from tensorflow.contrib.predictor import from_saved_model
diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor.py b/tensorflow/contrib/predictor/contrib_estimator_predictor.py
new file mode 100644
index 0000000000..b7a98c68e2
--- /dev/null
+++ b/tensorflow/contrib/predictor/contrib_estimator_predictor.py
@@ -0,0 +1,74 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A `Predictor constructed from a `tf.contrib.learn.Estimator`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.learn.python.learn.utils import saved_model_export_utils
+from tensorflow.contrib.predictor import predictor
+from tensorflow.python.framework import ops
+from tensorflow.python.training import monitored_session
+from tensorflow.python.training import saver
+
+
+class ContribEstimatorPredictor(predictor.Predictor):
+  """A `Predictor constructed from a `tf.contrib.learn.Estimator`."""
+
+  def __init__(self,
+               estimator,
+               prediction_input_fn,
+               input_alternative_key=None,
+               output_alternative_key=None,
+               graph=None):
+    """Initialize a `ContribEstimatorPredictor`.
+
+    Args:
+      estimator: an instance of `tf.contrib.learn.Estimator`.
+      prediction_input_fn: a function that takes no arguments and returns an
+        instance of `InputFnOps`.
+      input_alternative_key: Optional. Specify the input alternative used for
+        prediction.
+      output_alternative_key: Specify the output alternative used for
+        prediction. Not needed for single-headed models but required for
+        multi-headed models.
+      graph: Optional. The Tensorflow `graph` in which prediction should be
+        done.
+    """
+    self._graph = graph or ops.Graph()
+    with self._graph.as_default():
+      input_fn_ops = prediction_input_fn()
+      # pylint: disable=protected-access
+      model_fn_ops = estimator._get_predict_ops(input_fn_ops.features)
+      # pylint: enable=protected-access
+      checkpoint_path = saver.latest_checkpoint(estimator.model_dir)
+      self._session = monitored_session.MonitoredSession(
+          session_creator=monitored_session.ChiefSessionCreator(
+              checkpoint_filename_with_path=checkpoint_path))
+
+    input_alternative_key = (
+        input_alternative_key or
+        saved_model_export_utils.DEFAULT_INPUT_ALTERNATIVE_KEY)
+    input_alternatives, _ = saved_model_export_utils.get_input_alternatives(
+        input_fn_ops)
+    self._feed_tensors = input_alternatives[input_alternative_key]
+
+    (output_alternatives,
+     output_alternative_key) = saved_model_export_utils.get_output_alternatives(
+         model_fn_ops, output_alternative_key)
+    _, fetch_tensors = output_alternatives[output_alternative_key]
+    self._fetch_tensors = fetch_tensors
diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor_test.py b/tensorflow/contrib/predictor/contrib_estimator_predictor_test.py
new file mode 100644
index 0000000000..4b97a52b1a
--- /dev/null
+++ b/tensorflow/contrib/predictor/contrib_estimator_predictor_test.py
@@ -0,0 +1,70 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for predictor.contrib_estimator_predictor."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tempfile
+import numpy as np
+
+from tensorflow.contrib.predictor import contrib_estimator_predictor
+from tensorflow.contrib.predictor import testing_common
+from tensorflow.python.platform import test
+
+
+KEYS_AND_OPS = (('sum', lambda x, y: x + y),
+                ('product', lambda x, y: x * y,),
+                ('difference', lambda x, y: x - y))
+
+
+class ContribEstimatorPredictorTest(test.TestCase):
+  """Test fixture for `ContribEstimatorPredictor`."""
+
+  def setUp(self):
+    model_dir = tempfile.mkdtemp()
+    self._estimator = testing_common.get_arithmetic_estimator(
+        core=False, model_dir=model_dir)
+    self._prediction_input_fn = testing_common.get_arithmetic_input_fn(
+        core=False, train=False)
+
+  def testSpecifiedSignatureKey(self):
+    """Test prediction with spedicified signatures."""
+    np.random.seed(1234)
+    for key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      predictor = contrib_estimator_predictor.ContribEstimatorPredictor(
+          estimator=self._estimator,
+          prediction_input_fn=self._prediction_input_fn,
+          output_alternative_key=key)
+      output_tensor_name = predictor.fetch_tensors[key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          key,
+          msg='Unexpected fetch tensor.')
+      output = predictor({'x': x, 'y': y})[key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for output key "{}." '
+          'Got output {} for x = {} and y = {}'.format(
+              key, output, x, y))
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/predictor/core_estimator_predictor.py b/tensorflow/contrib/predictor/core_estimator_predictor.py
new file mode 100644
index 0000000000..5557ef5101
--- /dev/null
+++ b/tensorflow/contrib/predictor/core_estimator_predictor.py
@@ -0,0 +1,80 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A `Predictor` constructed from an `learn.python.estimator.Estimator`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.predictor import predictor
+from tensorflow.python.estimator import model_fn
+from tensorflow.python.framework import ops
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.training import monitored_session
+
+
+def _get_signature_def(
+    serving_input_receiver, estimator, output_key=None):
+  """Construct a `SignatureDef` proto."""
+  if output_key is None:
+    output_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+  # pylint: disable=protected-access
+  estimator_spec = estimator._call_model_fn(
+      serving_input_receiver.features, None, model_fn.ModeKeys.PREDICT)
+  # pylint: enable=protected-access
+  export_outputs = estimator_spec.export_outputs
+  export_output = export_outputs.get(output_key)
+  if export_output is None:
+    raise KeyError('output_key must be one of {}; got {}'.format(
+        export_outputs.keys(), output_key))
+  return export_output.as_signature_def(serving_input_receiver.receiver_tensors)
+
+
+class CoreEstimatorPredictor(predictor.Predictor):
+  """A `Predictor` constructed from an `learn.python.estimator.Estimator`."""
+
+  def __init__(self,
+               estimator,
+               serving_input_receiver_fn,
+               output_key=None,
+               graph=None):
+    """Initialize a `CoreEstimatorPredictor`.
+
+    Args:
+      estimator: an instance of `learn.python.estimator.Estimator`.
+      serving_input_receiver_fn: a function that takes no arguments and returns
+        an instance of `ServingInputReceiver` compatible with `estimator`.
+      output_key: Optional string specifying the export output to use. If
+        `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used.
+      graph: Optional. The Tensorflow `graph` in which prediction should be
+        done.
+    """
+    self._graph = graph or ops.Graph()
+    with self._graph.as_default():
+      serving_input_receiver = serving_input_receiver_fn()
+      signature_def = _get_signature_def(
+          serving_input_receiver, estimator, output_key)
+      checkpoint_path = estimator.model_dir
+      self._session = monitored_session.MonitoredSession(
+          session_creator=monitored_session.ChiefSessionCreator(
+              checkpoint_filename_with_path=checkpoint_path))
+
+    feed_tensor_info = signature_def.inputs
+    self._feed_tensors = {k: self._graph.get_tensor_by_name(v.name)
+                          for k, v in feed_tensor_info.items()}
+    fetch_tensor_info = signature_def.outputs
+    self._fetch_tensors = {k: self._graph.get_tensor_by_name(v.name)
+                           for k, v in fetch_tensor_info.items()}
diff --git a/tensorflow/contrib/predictor/core_estimator_predictor_test.py b/tensorflow/contrib/predictor/core_estimator_predictor_test.py
new file mode 100644
index 0000000000..4221086794
--- /dev/null
+++ b/tensorflow/contrib/predictor/core_estimator_predictor_test.py
@@ -0,0 +1,81 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for predictor.core_estimator_predictor."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tempfile
+import numpy as np
+
+from tensorflow.contrib.predictor import core_estimator_predictor
+from tensorflow.contrib.predictor import testing_common
+from tensorflow.python.platform import test
+
+
+KEYS_AND_OPS = (('sum', lambda x, y: x + y),
+                ('product', lambda x, y: x * y,),
+                ('difference', lambda x, y: x - y))
+
+
+class CoreEstimatorPredictorTest(test.TestCase):
+  """Test fixture for `CoreEstimatorPredictor`."""
+
+  def setUp(self):
+    model_dir = tempfile.mkdtemp()
+    self._estimator = testing_common.get_arithmetic_estimator(
+        core=True, model_dir=model_dir)
+    self._serving_input_receiver_fn = testing_common.get_arithmetic_input_fn(
+        core=True, train=False)
+
+  def testDefault(self):
+    """Test prediction with default signature."""
+    np.random.seed(1111)
+    x = np.random.rand()
+    y = np.random.rand()
+    predictor = core_estimator_predictor.CoreEstimatorPredictor(
+        estimator=self._estimator,
+        serving_input_receiver_fn=self._serving_input_receiver_fn)
+    output = predictor({'x': x, 'y': y})['sum']
+    self.assertAlmostEqual(output, x + y, places=3)
+
+  def testSpecifiedSignatureKey(self):
+    """Test prediction with spedicified signatures."""
+    np.random.seed(1234)
+    for output_key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      predictor = core_estimator_predictor.CoreEstimatorPredictor(
+          estimator=self._estimator,
+          serving_input_receiver_fn=self._serving_input_receiver_fn,
+          output_key=output_key)
+      output_tensor_name = predictor.fetch_tensors[output_key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          output_key,
+          msg='Unexpected fetch tensor.')
+      output = predictor({'x': x, 'y': y})[output_key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for output key "{}." '
+          'Got output {} for x = {} and y = {}'.format(
+              output_key, output, x, y))
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/predictor/predictor.py b/tensorflow/contrib/predictor/predictor.py
new file mode 100644
index 0000000000..dbc0028259
--- /dev/null
+++ b/tensorflow/contrib/predictor/predictor.py
@@ -0,0 +1,77 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Abstract base class for all predictors."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import six
+
+
+@six.add_metaclass(abc.ABCMeta)
+class Predictor(object):
+  """Abstract base class for all predictors."""
+
+  @property
+  def graph(self):
+    return self._graph
+
+  @property
+  def session(self):
+    return self._session
+
+  @property
+  def feed_tensors(self):
+    return self._feed_tensors
+
+  @property
+  def fetch_tensors(self):
+    return self._fetch_tensors
+
+  def __repr__(self):
+    return '{} with feed tensors {} and fetch_tensors {}'.format(
+        type(self).__name__, self._feed_tensors, self._fetch_tensors)
+
+  def __call__(self, input_dict):
+    """Returns predictions based on `input_dict`.
+
+    Args:
+      input_dict: a `dict` mapping strings to numpy arrays. These keys
+        must match `self._feed_tensors.keys()`.
+
+    Returns:
+      A `dict` mapping strings to numpy arrays. The keys match
+      `self.fetch_tensors.keys()`.
+
+    Raises:
+      ValueError: `input_dict` does not match `feed_tensors`.
+    """
+    # TODO(jamieas): make validation optional?
+    input_keys = set(input_dict.keys())
+    expected_keys = set(self.feed_tensors.keys())
+    unexpected_keys = input_keys - expected_keys
+    if unexpected_keys:
+      raise ValueError('Got unexpected keys in input_dict: {}'.format(
+          unexpected_keys))
+
+    feed_dict = {}
+    for key in self.feed_tensors.keys():
+      value = input_dict.get(key)
+      if value is not None:
+        feed_dict[self.feed_tensors[key]] = value
+    return self._session.run(fetches=self.fetch_tensors, feed_dict=feed_dict)
diff --git a/tensorflow/contrib/predictor/predictor_factories.py b/tensorflow/contrib/predictor/predictor_factories.py
new file mode 100644
index 0000000000..e3f30d917d
--- /dev/null
+++ b/tensorflow/contrib/predictor/predictor_factories.py
@@ -0,0 +1,132 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Factory functions for `Predictor`s."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.predictor import contrib_estimator_predictor
+from tensorflow.contrib.predictor import core_estimator_predictor
+from tensorflow.contrib.predictor import saved_model_predictor
+from tensorflow.python.estimator import estimator as core_estimator
+
+
+def from_contrib_estimator(estimator,
+                           prediction_input_fn,
+                           input_alternative_key=None,
+                           output_alternative_key=None,
+                           graph=None):
+  """Constructs a `Predictor` from a `tf.contrib.learn.Estimator`.
+
+  Args:
+    estimator: an instance of `tf.contrib.learn.Estimator`.
+    prediction_input_fn: a function that takes no arguments and returns an
+      instance of `InputFnOps`.
+    input_alternative_key: Optional. Specify the input alternative used for
+      prediction.
+    output_alternative_key: Specify the output alternative used for
+      prediction. Not needed for single-headed models but required for
+      multi-headed models.
+    graph: Optional. The Tensorflow `graph` in which prediction should be
+      done.
+
+  Returns:
+    An initialized `Predictor`.
+
+  Raises:
+    TypeError: if `estimator` is a core `Estimator` instead of a contrib
+      `Estimator`.
+  """
+  if isinstance(estimator, core_estimator.Estimator):
+    raise TypeError('Espected estimator to be of type '
+                    'tf.contrib.learn.Estimator, but got type '
+                    'tf.python.estimator.Estimator. You likely want to call '
+                    'from_estimator.')
+  return contrib_estimator_predictor.ContribEstimatorPredictor(
+      estimator,
+      prediction_input_fn,
+      input_alternative_key,
+      output_alternative_key,
+      graph)
+
+
+def from_estimator(estimator,
+                   serving_input_receiver_fn,
+                   output_key=None,
+                   graph=None):
+  """Constructs a `Predictor` from a `tf.python.estimator.Estimator`.
+
+  Args:
+    estimator: an instance of `learn.python.estimator.Estimator`.
+    serving_input_receiver_fn: a function that takes no arguments and returns
+      an instance of `ServingInputReceiver` compatible with `estimator`.
+    output_key: Optional string specifying the export output to use. If
+      `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used.
+    graph: Optional. The Tensorflow `graph` in which prediction should be
+      done.
+
+  Returns:
+    An initialized `Predictor`.
+
+  Raises:
+    TypeError: if `estimator` is a contrib `Estimator` instead of a core
+      `Estimator`.
+  """
+  if isinstance(estimator, estimator.Estimator):
+    raise TypeError('Espected estimator to be of type '
+                    'tf.python.estimator.Estimator, but got type '
+                    'tf.contrib.learn.Estimator. You likely want to call '
+                    'from_contrib_estimator.')
+  return core_estimator_predictor.CoreEstimatorPredictor(
+      estimator,
+      serving_input_receiver_fn,
+      output_key,
+      graph)
+
+
+def from_saved_model(export_dir,
+                     signature_def_key=None,
+                     signature_def=None,
+                     tags=None,
+                     graph=None):
+  """Constructs a `Predictor` from a `SavedModel` on disk.
+
+  Args:
+    export_dir: a path to a directory containing a `SavedModel`.
+    signature_def_key: Optional string specifying the signature to use. If
+      `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
+    `signature_def_key` and `signature_def`
+    signature_def: A `SignatureDef` proto specifying the inputs and outputs
+      for prediction. Only one of `signature_def_key` and `signature_def`
+      should be specified.
+    tags: Optional. Tags that will be used to retrieve the correct
+      `SignatureDef`. Defaults to `DEFAULT_TAGS`.
+    graph: Optional. The Tensorflow `graph` in which prediction should be
+      done.
+
+  Returns:
+    An initialized `Predictor`.
+
+  Raises:
+    ValueError: More than one of `signature_def_key` and `signature_def` is
+      specified.
+  """
+  return saved_model_predictor.SavedModelPredictor(export_dir,
+                                                   signature_def_key,
+                                                   signature_def,
+                                                   tags,
+                                                   graph)
diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py
new file mode 100644
index 0000000000..ab2bafa0c8
--- /dev/null
+++ b/tensorflow/contrib/predictor/saved_model_predictor.py
@@ -0,0 +1,143 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A `Predictor` constructed from a `SavedModel`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import logging
+
+from tensorflow.contrib.predictor import predictor
+from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils
+from tensorflow.python.client import session
+from tensorflow.python.framework import ops
+from tensorflow.python.saved_model import loader
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.tools import saved_model_cli
+
+
+DEFAULT_TAGS = 'serve'
+
+_DEFAULT_INPUT_ALTERNATIVE_FORMAT = 'default_input_alternative:{}'
+
+
+def _get_signature_def(signature_def_key, export_dir, tags):
+  """Construct a `SignatureDef` proto."""
+  signature_def_key = (
+      signature_def_key or
+      signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY)
+
+  metagraph_def = saved_model_cli.get_meta_graph_def(export_dir, tags)
+
+  try:
+    signature_def = signature_def_utils.get_signature_def_by_key(
+        metagraph_def,
+        signature_def_key)
+  except ValueError as e:
+    try:
+      formatted_key = _DEFAULT_INPUT_ALTERNATIVE_FORMAT.format(
+          signature_def_key)
+      signature_def = signature_def_utils.get_signature_def_by_key(
+          metagraph_def, formatted_key)
+
+      logging.warning('Could not find signature def "%s". '
+                      'Using "%s" instead', signature_def_key, formatted_key)
+    except ValueError:
+      raise ValueError(
+          'Got signature_def_key "{}". Available signatures are {}. '
+          'Original error:\n{}'.format(
+              signature_def_key, list(metagraph_def.signature_def), e))
+  return signature_def
+
+
+def _check_signature_arguments(signature_def_key,
+                               signature_def,
+                               input_names,
+                               output_names):
+  """Validates signature arguments for `SavedModelPredictor`."""
+  signature_def_key_specified = signature_def_key is not None
+  signature_def_specified = signature_def is not None
+  input_names_specified = input_names is not None
+  output_names_specified = output_names is not None
+  if input_names_specified != output_names_specified:
+    raise ValueError(
+        'input_names and output_names must both be specified or both be '
+        'unspecified.'
+    )
+
+  if (signature_def_key_specified + signature_def_specified +
+      input_names_specified > 1):
+    raise ValueError(
+        'You must specify at most one of signature_def_key OR signature_def OR'
+        '(input_names AND output_names).'
+    )
+
+
+class SavedModelPredictor(predictor.Predictor):
+  """A `Predictor` constructed from a `SavedModel`."""
+
+  def __init__(self,
+               export_dir,
+               signature_def_key=None,
+               signature_def=None,
+               input_names=None,
+               output_names=None,
+               tags=None,
+               graph=None):
+    """Initialize a `CoreEstimatorPredictor`.
+
+    Args:
+      export_dir: a path to a directory containing a `SavedModel`.
+      signature_def_key: Optional string specifying the signature to use. If
+        `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
+        `signature_def_key` and `signature_def` should be specified.
+      signature_def: A `SignatureDef` proto specifying the inputs and outputs
+        for prediction. Only one of `signature_def_key` and `signature_def`
+        should be specified.
+      input_names: A dictionary mapping strings to `Tensor`s in the `SavedModel`
+        that represent the input. The keys can be any string of the user's
+        choosing.
+      output_names: A dictionary mapping strings to `Tensor`s in the
+        `SavedModel` that represent the output. The keys can be any string of
+        the user's choosing.
+      tags: Optional. Tags that will be used to retrieve the correct
+        `SignatureDef`. Defaults to `DEFAULT_TAGS`.
+      graph: Optional. The Tensorflow `graph` in which prediction should be
+        done.
+    Raises:
+      ValueError: If more than one of signature_def_key OR signature_def OR
+        (input_names AND output_names) is specified.
+    """
+    _check_signature_arguments(
+        signature_def_key, signature_def, input_names, output_names)
+    tags = tags or DEFAULT_TAGS
+    self._graph = graph or ops.Graph()
+
+    with self._graph.as_default():
+      self._session = session.Session()
+      loader.load(self._session, tags.split(','), export_dir)
+
+    if input_names is None:
+      if signature_def is None:
+        signature_def = _get_signature_def(signature_def_key, export_dir, tags)
+      input_names = {k: v.name for k, v in signature_def.inputs.items()}
+      output_names = {k: v.name for k, v in signature_def.outputs.items()}
+
+    self._feed_tensors = {k: self._graph.get_tensor_by_name(v)
+                          for k, v in input_names.items()}
+    self._fetch_tensors = {k: self._graph.get_tensor_by_name(v)
+                           for k, v in output_names.items()}
diff --git a/tensorflow/contrib/predictor/saved_model_predictor_test.py b/tensorflow/contrib/predictor/saved_model_predictor_test.py
new file mode 100644
index 0000000000..f40e2e73d9
--- /dev/null
+++ b/tensorflow/contrib/predictor/saved_model_predictor_test.py
@@ -0,0 +1,170 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for predictor.saved_model_predictor."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.predictor import saved_model_predictor
+from tensorflow.core.framework import tensor_shape_pb2
+from tensorflow.core.framework import types_pb2
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import test
+from tensorflow.python.saved_model import signature_def_utils
+
+
+KEYS_AND_OPS = (('sum', lambda x, y: x + y),
+                ('product', lambda x, y: x * y,),
+                ('difference', lambda x, y: x - y))
+
+MODEL_DIR_NAME = 'contrib/predictor/test_export_dir'
+
+
+class SavedModelPredictorTest(test.TestCase):
+
+  @classmethod
+  def setUpClass(cls):
+    # Load a saved model exported from the arithmetic `Estimator`.
+    # See `testing_common.py`.
+    cls._export_dir = test.test_src_dir_path(MODEL_DIR_NAME)
+
+  def testDefault(self):
+    """Test prediction with default signature."""
+    np.random.seed(1111)
+    x = np.random.rand()
+    y = np.random.rand()
+    predictor = saved_model_predictor.SavedModelPredictor(
+        export_dir=self._export_dir)
+    output = predictor({'x': x, 'y': y})['outputs']
+    self.assertAlmostEqual(output, x + y, places=3)
+
+  def testSpecifiedSignatureKey(self):
+    """Test prediction with spedicified signature key."""
+    np.random.seed(1234)
+    for signature_def_key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      predictor = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          signature_def_key=signature_def_key)
+
+      output_tensor_name = predictor.fetch_tensors['outputs'].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          signature_def_key,
+          msg='Unexpected fetch tensor.')
+
+      output = predictor({'x': x, 'y': y})['outputs']
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for signature "{}." '
+          'Got output {} for x = {} and y = {}'.format(
+              signature_def_key, output, x, y))
+
+  def testSpecifiedSignature(self):
+    """Test prediction with spedicified signature definition."""
+    np.random.seed(4444)
+    for key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      inputs = {
+          'x': meta_graph_pb2.TensorInfo(
+              name='inputs/x:0',
+              dtype=types_pb2.DT_FLOAT,
+              tensor_shape=tensor_shape_pb2.TensorShapeProto()),
+          'y': meta_graph_pb2.TensorInfo(
+              name='inputs/y:0',
+              dtype=types_pb2.DT_FLOAT,
+              tensor_shape=tensor_shape_pb2.TensorShapeProto())}
+      outputs = {
+          key: meta_graph_pb2.TensorInfo(
+              name='outputs/{}:0'.format(key),
+              dtype=types_pb2.DT_FLOAT,
+              tensor_shape=tensor_shape_pb2.TensorShapeProto())}
+      signature_def = signature_def_utils.build_signature_def(
+          inputs=inputs,
+          outputs=outputs,
+          method_name='tensorflow/serving/regress')
+      predictor = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          signature_def=signature_def)
+
+      output_tensor_name = predictor.fetch_tensors[key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          key,
+          msg='Unexpected fetch tensor.')
+
+      output = predictor({'x': x, 'y': y})[key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for signature "{}". '
+          'Got output {} for x = {} and y = {}'.format(key, output, x, y))
+
+  def testSpecifiedTensors(self):
+    """Test prediction with spedicified `Tensor`s."""
+    np.random.seed(987)
+    for key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+      input_names = {'x': 'inputs/x:0',
+                     'y': 'inputs/y:0'}
+      output_names = {key: 'outputs/{}:0'.format(key)}
+      predictor = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          input_names=input_names,
+          output_names=output_names)
+
+      output_tensor_name = predictor.fetch_tensors[key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          key,
+          msg='Unexpected fetch tensor.')
+
+      output = predictor({'x': x, 'y': y})[key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for signature "{}". '
+          'Got output {} for x = {} and y = {}'.format(key, output, x, y))
+
+  def testBadTagsFail(self):
+    """Test that predictor construction fails for bad tags."""
+    bad_tags_regex = ('.* could not be found in SavedModel')
+    with self.assertRaisesRegexp(RuntimeError, bad_tags_regex):
+      _ = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          tags=('zomg, bad, tags'))
+
+  def testSpecifiedGraph(self):
+    """Test that the predictor remembers a specified `Graph`."""
+    g = ops.Graph()
+    predictor = saved_model_predictor.SavedModelPredictor(
+        export_dir=self._export_dir,
+        graph=g)
+    self.assertEqual(predictor.graph, g)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/predictor/test_export_dir/saved_model.pb b/tensorflow/contrib/predictor/test_export_dir/saved_model.pb
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diff --git a/tensorflow/contrib/predictor/testing_common.py b/tensorflow/contrib/predictor/testing_common.py
new file mode 100644
index 0000000000..1767704b99
--- /dev/null
+++ b/tensorflow/contrib/predictor/testing_common.py
@@ -0,0 +1,102 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Common code used for testing `Predictor`s."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.learn.python.learn.estimators import constants
+from tensorflow.contrib.learn.python.learn.estimators import estimator as contrib_estimator
+from tensorflow.contrib.learn.python.learn.estimators import model_fn as contrib_model_fn
+from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
+from tensorflow.python.estimator import estimator as core_estimator
+from tensorflow.python.estimator import model_fn
+from tensorflow.python.estimator.export import export_lib
+from tensorflow.python.estimator.export import export_output
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.saved_model import signature_constants
+
+
+def get_arithmetic_estimator(core=True, model_dir=None):
+  """Returns an `Estimator` that performs basic arithmetic.
+
+  Args:
+    core: if `True`, returns a `tensorflow.python.estimator.Estimator`.
+      Otherwise, returns a `tensorflow.contrib.learn.Estimator`.
+    model_dir: directory in which to export checkpoints and saved models.
+  Returns:
+    An `Estimator` that performs arithmetic operations on its inputs.
+  """
+  def _model_fn(features, labels, mode):
+    _ = labels
+    x = features['x']
+    y = features['y']
+    with ops.name_scope('outputs'):
+      predictions = {'sum': math_ops.add(x, y, name='sum'),
+                     'product': math_ops.multiply(x, y, name='product'),
+                     'difference': math_ops.subtract(x, y, name='difference')}
+    if core:
+      export_outputs = {k: export_output.PredictOutput({k: v})
+                        for k, v in predictions.items()}
+      export_outputs[signature_constants.
+                     DEFAULT_SERVING_SIGNATURE_DEF_KEY] = export_outputs['sum']
+      return model_fn.EstimatorSpec(mode=mode,
+                                    predictions=predictions,
+                                    export_outputs=export_outputs,
+                                    loss=constant_op.constant(0),
+                                    train_op=control_flow_ops.no_op())
+    else:
+      output_alternatives = {k: (constants.ProblemType.UNSPECIFIED, {k: v})
+                             for k, v in predictions.items()}
+      return contrib_model_fn.ModelFnOps(
+          mode=mode,
+          predictions=predictions,
+          output_alternatives=output_alternatives,
+          loss=constant_op.constant(0),
+          train_op=control_flow_ops.no_op())
+  if core:
+    return core_estimator.Estimator(_model_fn)
+  else:
+    return contrib_estimator.Estimator(_model_fn, model_dir=model_dir)
+
+
+def get_arithmetic_input_fn(core=True, train=False):
+  """Returns a input functions or serving input receiver function."""
+  def _input_fn():
+    with ops.name_scope('inputs'):
+      x = array_ops.placeholder_with_default(0.0, shape=[], name='x')
+      y = array_ops.placeholder_with_default(0.0, shape=[], name='y')
+    label = constant_op.constant(0.0)
+    features = {'x': x, 'y': y}
+    if core:
+      if train:
+        return features, label
+      return export_lib.ServingInputReceiver(
+          features=features,
+          receiver_tensors=features)
+    else:
+      if train:
+        return features, label
+      return input_fn_utils.InputFnOps(
+          features=features,
+          labels={},
+          default_inputs=features)
+  return _input_fn
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index fa51086922..9cfadf93b0 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -155,6 +155,7 @@ sh_binary(
             "//tensorflow/contrib/specs:specs",
             "//tensorflow/contrib/tensor_forest:init_py",
             "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip",
+            "//tensorflow/contrib/predictor:predictor_pip",
             "//tensorflow/examples/tutorials/mnist:package",
             "//tensorflow/python:distributed_framework_test_lib",
             "//tensorflow/python:meta_graph_testdata",
-- 
GitLab


From 9bba02c6ed00d59d0dc5bc9bb6f8a32662ef8103 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Thu, 15 Jun 2017 13:07:27 -0700
Subject: [PATCH 0114/1375] Add poincare_normalization

PiperOrigin-RevId: 159142705
---
 .../contrib/layers/python/layers/layers.py    | 38 +++++++++++
 .../layers/python/layers/layers_test.py       | 64 +++++++++++++++++++
 2 files changed, 102 insertions(+)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index d3b1094963..393a2488bc 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -2145,6 +2145,44 @@ def unit_norm(inputs, dim, epsilon=1e-7, scope=None):
     return math_ops.div(inputs, array_ops.tile(lengths, multiples))
 
 
+def poincare_normalize(x, axis=1, epsilon=1e-5, name=None):
+  """Project into the Poincare ball with norm <= 1.0 - epsilon.
+
+  https://en.wikipedia.org/wiki/Poincare_ball_model
+
+  Used in
+  Poincare Embeddings for Learning Hierarchical Representations
+  Maximilian Nickel, Douwe Kiela
+  https://arxiv.org/pdf/1705.08039.pdf
+
+  For a 1-D tensor with `axis = 0`, computes
+
+                (x * (1 - epsilon)) / ||x||     if ||x|| > 1 - epsilon
+      output =
+                 x                              otherwise
+
+  For `x` with more dimensions, independently normalizes each 1-D slice along
+  dimension `axis`.
+
+  Args:
+    x: A `Tensor`.
+    axis: Axis along which to normalize.  A scalar or a vector of
+      integers.
+    epsilon: A small deviation from the edge of the unit sphere for numerical
+      stability.
+    name: A name for this operation (optional).
+
+  Returns:
+    A `Tensor` with the same shape as `x`.
+  """
+  with ops.name_scope(name, 'poincare_normalize', [x]) as name:
+    x = ops.convert_to_tensor(x, name='x')
+    square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keep_dims=True)
+    x_inv_norm = math_ops.rsqrt(square_sum)
+    x_inv_norm = math_ops.minimum((1. - epsilon) * x_inv_norm, 1.)
+    return math_ops.multiply(x, x_inv_norm, name=name)
+
+
 def legacy_fully_connected(x,
                            num_output_units,
                            activation_fn=None,
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index b49c33e996..67f45473e8 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -38,6 +38,7 @@ from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
@@ -3231,6 +3232,69 @@ class UnitNormTests(test.TestCase):
         self.assertAllClose(expected, actual, 1e-4, 1e-4)
 
 
+class PoincareNormalizeTest(test.TestCase):
+
+  def _PoincareNormalize(self, x, dim, epsilon=1e-5):
+    if isinstance(dim, list):
+      norm = np.linalg.norm(x, axis=tuple(dim))
+      for d in dim:
+        norm = np.expand_dims(norm, d)
+      norm_x = ((1. - epsilon) * x) / norm
+    else:
+      norm = np.expand_dims(np.apply_along_axis(np.linalg.norm, dim, x), dim)
+      norm_x = ((1. - epsilon) * x) / norm
+    return np.where(norm > 1.0 - epsilon, norm_x, x)
+
+  def testPoincareNormalize(self):
+    x_shape = [20, 7, 3]
+    epsilon = 1e-5
+    tol = 1e-6
+    np.random.seed(1)
+    x_np = np.random.random_sample(x_shape).astype(np.float32)
+    for dim in range(len(x_shape)):
+      y_np = self._PoincareNormalize(x_np, dim, epsilon)
+      with self.test_session():
+        x_tf = constant_op.constant(x_np, name='x')
+        y_tf = _layers.poincare_normalize(x_tf, dim, epsilon)
+        y_tf_eval = y_tf.eval()
+        norm = np.linalg.norm(y_np, axis=dim)
+        self.assertLessEqual(norm.max(), 1. - epsilon + tol)
+        norm = np.linalg.norm(y_tf_eval, axis=dim)
+        self.assertLessEqual(norm.max(), 1. - epsilon + tol)
+        self.assertAllClose(y_np, y_tf_eval)
+
+  def testPoincareNormalizeDimArray(self):
+    x_shape = [20, 7, 3]
+    epsilon = 1e-5
+    tol = 1e-6
+    np.random.seed(1)
+    x_np = np.random.random_sample(x_shape).astype(np.float32)
+    dim = [1, 2]
+    y_np = self._PoincareNormalize(x_np, dim, epsilon)
+    with self.test_session():
+      x_tf = constant_op.constant(x_np, name='x')
+      y_tf = _layers.poincare_normalize(x_tf, dim, epsilon)
+      y_tf_eval = y_tf.eval()
+      norm = np.linalg.norm(y_np, axis=tuple(dim))
+      self.assertLess(norm.max(), 1. - epsilon + tol)
+      norm = np.linalg.norm(y_tf_eval, axis=tuple(dim))
+      self.assertLess(norm.max(), 1. - epsilon + tol)
+      self.assertAllClose(y_np, y_tf_eval, rtol=1e-6, atol=1e-6)
+
+  def testPoincareNormalizeGradient(self):
+    x_shape = [20, 7, 3]
+    np.random.seed(1)
+    x_np = np.random.random_sample(x_shape).astype(np.float64)
+    for dim in range(len(x_shape)):
+      with self.test_session():
+        x_tf = constant_op.constant(x_np, name='x')
+        y_tf = _layers.poincare_normalize(x_tf, dim)
+        err = gradient_checker.compute_gradient_error(x_tf, x_shape,
+                                                      y_tf, x_shape)
+      print('PoinCareNormalize gradient err = %g ' % err)
+      self.assertLess(err, 1e-4)
+
+
 # TODO(b/28426988): Add separate tests for non-legacy versions.
 class LegacyFullyConnectedTest(test.TestCase):
 
-- 
GitLab


From 2745591a352e567cd16578d4b3813d00811046bb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 13:34:40 -0700
Subject: [PATCH 0115/1375] Removed unneeded #includes, and corrected README
 with updated paths & misc

PiperOrigin-RevId: 159146246
---
 tensorflow/examples/ios/README.md             | 27 ++++++++++---------
 .../ios/benchmark/BenchmarkViewController.mm  | 10 -------
 2 files changed, 14 insertions(+), 23 deletions(-)

diff --git a/tensorflow/examples/ios/README.md b/tensorflow/examples/ios/README.md
index 9832399d72..a412381196 100644
--- a/tensorflow/examples/ios/README.md
+++ b/tensorflow/examples/ios/README.md
@@ -20,9 +20,9 @@ mkdir -p ~/graphs
 curl -o ~/graphs/inception5h.zip \
  https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip \
  && unzip ~/graphs/inception5h.zip -d ~/graphs/inception5h
-cp ~/graphs/inception5h/* tensorflow/contrib/ios_examples/benchmark/data/
-cp ~/graphs/inception5h/* tensorflow/contrib/ios_examples/camera/data/
-cp ~/graphs/inception5h/* tensorflow/contrib/ios_examples/simple/data/
+cp ~/graphs/inception5h/* tensorflow/examples/ios/benchmark/data/
+cp ~/graphs/inception5h/* tensorflow/examples/ios/camera/data/
+cp ~/graphs/inception5h/* tensorflow/examples/ios/simple/data/
 ```
 
  - Change directory to one of the samples, download the TensorFlow-experimental
@@ -30,7 +30,7 @@ cp ~/graphs/inception5h/* tensorflow/contrib/ios_examples/simple/data/
    long time since it is big (~450MB). For example, if you want to run the
    simple example, then:
 ```bash
-cd tensorflow/contrib/ios_examples/simple
+cd tensorflow/ios/simple
 pod install
 open tf_simple_example.xcworkspace # obs, not the .xcodeproj directory
 ```
@@ -51,9 +51,10 @@ open tf_simple_example.xcworkspace # obs, not the .xcodeproj directory
   
  - The TensorFlow-experimental pod is current about ~450MB. The reason it is 
    so big is because we are bundling multiple platforms, and the pod includes
-   all TensorFlow functionality (e.g. operations). This is convenient during
-   development, but see below section on how you can build your own custom
-   TensorFlow library to reduce the size.
+   all TensorFlow functionality (e.g. operations). The final app size after
+   build is substantially smaller though (~25MB). Working with the complete
+   pod is convenient during development, but see below section on how you can
+   build your own custom TensorFlow library to reduce the size.
 
 ### Creating Your own App
 
@@ -145,10 +146,10 @@ rundown:
    in your project settings.
 
  - Remove any use of the `-all_load` flag in your project. The protocol buffers
-   libraries (full and lite versions) contain duplicate symbols, and the `-all_load`
-   flag will cause these duplicates to become link errors. If you were using
-   `-all_load` to avoid issues with Objective-C categories in static libraries,
-   you may be able to replace it with the `-ObjC` flag.
+   libraries (full and lite versions) contain duplicate symbols, and the
+   `-all_load` flag will cause these duplicates to become link errors. If you
+   were using `-all_load` to avoid issues with Objective-C categories in static
+   libraries, you may be able to replace it with the `-ObjC` flag.
 
 ### Reducing the binary size
 
@@ -159,7 +160,7 @@ It can be tricky to set up the right configuration in your own app to keep the
 size minimized, so if you do run into this issue we recommend you start by
 looking at the simple example to examine its size. Here's how you do that:
 
- - Open the Xcode project in tensorflow/contrib/ios_examples/simple.
+ - Open the Xcode project in tensorflow/examples/ios/simple.
 
  - Make sure you've followed the steps above to get the data files.
 
@@ -181,7 +182,7 @@ looking at the simple example to examine its size. Here's how you do that:
  - Running this command will show the size of the executable as the
    `tf_simple_example` line.
 
-Right now you'll see a size of around 23 MB, since it's including two
+Right now you'll see a size of around 25 MB, since it's including two
 architectures (armv7 and arm64). As a first step, you should make sure the size
 increase you see in your own app is similar, and if it's larger, look at the
 "Other Linker Flags" used in the Simple Xcode project settings to strip the
diff --git a/tensorflow/examples/ios/benchmark/BenchmarkViewController.mm b/tensorflow/examples/ios/benchmark/BenchmarkViewController.mm
index cab7b36f17..9fc5f6ded2 100644
--- a/tensorflow/examples/ios/benchmark/BenchmarkViewController.mm
+++ b/tensorflow/examples/ios/benchmark/BenchmarkViewController.mm
@@ -22,17 +22,7 @@
 #include <sstream>
 #include <string>
 
-//#include "google/protobuf/io/coded_stream.h"
-//#include "google/protobuf/io/zero_copy_stream_impl.h"
-//#include "google/protobuf/io/zero_copy_stream_impl_lite.h"
-//#include "google/protobuf/message_lite.h"
 #include "tensorflow/core/framework/op_kernel.h"
-//#include "tensorflow/core/framework/tensor.h"
-//#include "tensorflow/core/framework/types.pb.h"
-//#include "tensorflow/core/platform/env.h"
-//#include "tensorflow/core/platform/logging.h"
-//#include "tensorflow/core/platform/mutex.h"
-//#include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/public/session.h"
 #include "tensorflow/core/util/stat_summarizer.h"
 
-- 
GitLab


From 9a42d32aa5afaf52a3eaec7674ebad874ff79992 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 15 Jun 2017 13:55:55 -0700
Subject: [PATCH 0116/1375] tfdbg: use gfile.Glob in DebugDumpDir

PiperOrigin-RevId: 159149240
---
 tensorflow/python/debug/BUILD                 |  1 +
 tensorflow/python/debug/lib/debug_data.py     | 16 ++++++---
 .../python/debug/lib/debug_data_test.py       | 35 +++++++++++++++++++
 3 files changed, 48 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 39446b6ca2..54ae5047ca 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -367,6 +367,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":debug_data",
+        "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
         "//third_party/py/numpy",
diff --git a/tensorflow/python/debug/lib/debug_data.py b/tensorflow/python/debug/lib/debug_data.py
index 967152d220..24214c0ddd 100644
--- a/tensorflow/python/debug/lib/debug_data.py
+++ b/tensorflow/python/debug/lib/debug_data.py
@@ -22,6 +22,7 @@ import collections
 import glob
 import json
 import os
+import platform
 
 import numpy as np
 import six
@@ -46,6 +47,13 @@ FETCHES_INFO_FILE_TAG = "fetches_info_"
 FEED_KEYS_INFO_FILE_TAG = "feed_keys_info_"
 
 
+def _glob(glob_pattern):
+  if platform.system() == "Windows":
+    return glob.glob(glob_pattern)
+  else:
+    return gfile.Glob(glob_pattern)
+
+
 class InconvertibleTensorProto(object):
   """Represents a TensorProto that cannot be converted to np.ndarray."""
 
@@ -680,7 +688,7 @@ class DebugDumpDir(object):
 
   def _load_all_device_dumps(self, partition_graphs, validate):
     """Load the dump data for all devices."""
-    device_dirs = glob.glob(os.path.join(
+    device_dirs = _glob(os.path.join(
         self._dump_root, METADATA_FILE_PREFIX + DEVICE_TAG + "*"))
 
     self._device_names = []
@@ -763,7 +771,7 @@ class DebugDumpDir(object):
     self._t0 = min(t0s) if t0s else None
 
   def _load_core_metadata(self):
-    core_metadata_files = glob.glob(os.path.join(
+    core_metadata_files = _glob(os.path.join(
         self._dump_root, METADATA_FILE_PREFIX + CORE_METADATA_TAG + "*"))
     for core_metadata_file in core_metadata_files:
       with gfile.Open(core_metadata_file, "rb") as f:
@@ -773,7 +781,7 @@ class DebugDumpDir(object):
             extract_core_metadata_from_event_proto(event))
 
   def _load_fetches_info(self):
-    fetches_info_files = glob.glob(os.path.join(
+    fetches_info_files = _glob(os.path.join(
         self._dump_root, METADATA_FILE_PREFIX + FETCHES_INFO_FILE_TAG + "*"))
     self._run_fetches_info = []
     for fetches_info_file in fetches_info_files:
@@ -781,7 +789,7 @@ class DebugDumpDir(object):
           _load_log_message_from_event_file(fetches_info_file))
 
   def _load_feeds_info(self):
-    feeds_info_files = glob.glob(os.path.join(
+    feeds_info_files = _glob(os.path.join(
         self._dump_root, METADATA_FILE_PREFIX + FEED_KEYS_INFO_FILE_TAG + "*"))
     self._run_feed_keys_info = []
     for feeds_info_file in feeds_info_files:
diff --git a/tensorflow/python/debug/lib/debug_data_test.py b/tensorflow/python/debug/lib/debug_data_test.py
index 70dc8c1150..eff70b662b 100644
--- a/tensorflow/python/debug/lib/debug_data_test.py
+++ b/tensorflow/python/debug/lib/debug_data_test.py
@@ -18,6 +18,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+import platform
 import shutil
 import tempfile
 
@@ -27,7 +28,9 @@ from tensorflow.core.framework import graph_pb2
 from tensorflow.core.framework import tensor_pb2
 from tensorflow.python.debug.lib import debug_data
 from tensorflow.python.framework import test_util
+from tensorflow.python.platform import gfile
 from tensorflow.python.platform import googletest
+from tensorflow.python.platform import test
 
 
 class DeviceNamePathConversionTest(test_util.TensorFlowTestCase):
@@ -339,6 +342,38 @@ class DebugDumpDirTest(test_util.TensorFlowTestCase):
     self.assertIsNone(dump_dir.t0)
     self.assertEqual([], dump_dir.dumped_tensor_data)
 
+  def testDebugDumpDir_usesGfileGlob(self):
+    if platform.system() == "Windows":
+      self.skipTest("gfile.Glob is not used on Windows.")
+
+    self._makeDataDirWithMultipleDevicesAndDuplicateNodeNames()
+
+    def fake_gfile_glob(glob_pattern):
+      del glob_pattern
+      return []
+
+    with test.mock.patch.object(
+        gfile, "Glob", side_effect=fake_gfile_glob, autospec=True) as fake:
+      debug_data.DebugDumpDir(self._dump_root)
+      expected_calls = [
+          test.mock.call(os.path.join(
+              self._dump_root,
+              (debug_data.METADATA_FILE_PREFIX +
+               debug_data.CORE_METADATA_TAG + "*"))),
+          test.mock.call(os.path.join(
+              self._dump_root,
+              (debug_data.METADATA_FILE_PREFIX +
+               debug_data.FETCHES_INFO_FILE_TAG + "*"))),
+          test.mock.call(os.path.join(
+              self._dump_root,
+              (debug_data.METADATA_FILE_PREFIX +
+               debug_data.FEED_KEYS_INFO_FILE_TAG + "*"))),
+          test.mock.call(os.path.join(
+              self._dump_root,
+              (debug_data.METADATA_FILE_PREFIX +
+               debug_data.DEVICE_TAG + "*")))]
+      fake.assert_has_calls(expected_calls, any_order=True)
+
 
 class GetNodeNameAndOutputSlotTest(test_util.TensorFlowTestCase):
 
-- 
GitLab


From a203bbb98c7c72d7a68064098ec051bba20219cd Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Thu, 15 Jun 2017 14:01:07 -0700
Subject: [PATCH 0117/1375] Java: Maven release for 1.2.0

PiperOrigin-RevId: 159150154
---
 tensorflow/java/maven/libtensorflow/pom.xml     | 2 +-
 tensorflow/java/maven/libtensorflow_jni/pom.xml | 2 +-
 tensorflow/java/maven/pom.xml                   | 2 +-
 tensorflow/java/maven/proto/pom.xml             | 2 +-
 tensorflow/java/maven/tensorflow/pom.xml        | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml
index 9114047818..1fc9d49a86 100644
--- a/tensorflow/java/maven/libtensorflow/pom.xml
+++ b/tensorflow/java/maven/libtensorflow/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0-rc2</version>
+    <version>1.2.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>libtensorflow</artifactId>
diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml
index 89b7c6528f..4002de97bd 100644
--- a/tensorflow/java/maven/libtensorflow_jni/pom.xml
+++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0-rc2</version>
+    <version>1.2.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>libtensorflow_jni</artifactId>
diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml
index e8d8fe6378..f1d8aa1715 100644
--- a/tensorflow/java/maven/pom.xml
+++ b/tensorflow/java/maven/pom.xml
@@ -6,7 +6,7 @@
   <modelVersion>4.0.0</modelVersion>
   <groupId>org.tensorflow</groupId>
   <artifactId>parentpom</artifactId>
-  <version>1.2.0-rc2</version>
+  <version>1.2.0</version>
   <packaging>pom</packaging>
 
   <url>https://www.tensorflow.org</url>
diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index 6405daddef..d1d0359b1d 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0-rc2</version>
+    <version>1.2.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>proto</artifactId>
diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml
index 31fb015109..21c2207404 100644
--- a/tensorflow/java/maven/tensorflow/pom.xml
+++ b/tensorflow/java/maven/tensorflow/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0-rc2</version>
+    <version>1.2.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>tensorflow</artifactId>
-- 
GitLab


From b52debb4e63cce1e0733d6d34975d4efb9934680 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Thu, 15 Jun 2017 14:16:59 -0700
Subject: [PATCH 0118/1375] [XLA] Add transfer buffer to infeed.

Mirroring the transfer buffer to device interface, add a transfer buffer to
infeed interface.

PiperOrigin-RevId: 159152897
---
 .../xla/service/cpu_transfer_manager.cc       | 19 +++++++++++++------
 .../xla/service/cpu_transfer_manager.h        |  2 ++
 .../xla/service/generic_transfer_manager.cc   |  8 +++++++-
 .../xla/service/generic_transfer_manager.h    |  2 ++
 .../xla/service/gpu_transfer_manager.cc       | 15 +++++++++++++--
 .../xla/service/gpu_transfer_manager.h        |  7 +++++++
 .../compiler/xla/service/transfer_manager.h   | 11 +++++++++++
 7 files changed, 55 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
index 2d9d9c7de6..5e431687c4 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
@@ -75,20 +75,27 @@ Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
                          ShapeUtil::HumanString(literal.shape()).c_str());
   }
 
-  cpu::runtime::InfeedManager* infeed_manager =
-      cpu::runtime::GetInfeedManager();
-
   int64 size = GetByteSizeRequirement(shape);
   if (size > std::numeric_limits<int32>::max()) {
     return Unimplemented("Infeed shape is too large: %s needs %lld bytes",
                          ShapeUtil::HumanString(literal.shape()).c_str(), size);
   }
+
+  return TransferBufferToInfeed(executor, size,
+                                LiteralUtil::InternalData(literal));
+}
+
+Status CpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor,
+                                                  int64 size,
+                                                  const void* source) {
   int32 size_32 = static_cast<int32>(size);
   CpuInfeedBuffer* queued_buffer = new CpuInfeedBuffer(size_32);
-  TF_RETURN_IF_ERROR(TransferBufferToDevice(
-      executor, /*size=*/size, /*source=*/LiteralUtil::InternalData(literal),
-      queued_buffer->device_memory()));
+  TF_RETURN_IF_ERROR(TransferBufferToDevice(executor, /*size=*/size,
+                                            /*source=*/source,
+                                            queued_buffer->device_memory()));
 
+  cpu::runtime::InfeedManager* infeed_manager =
+      cpu::runtime::GetInfeedManager();
   infeed_manager->EnqueueBuffer(queued_buffer);
 
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.h b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
index 727462252d..5d10b62a17 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
@@ -37,6 +37,8 @@ class CpuTransferManager : public GenericTransferManager {
 
   Status TransferLiteralToInfeed(perftools::gputools::StreamExecutor* executor,
                                  const Literal& literal) override;
+  Status TransferBufferToInfeed(perftools::gputools::StreamExecutor* executor,
+                                int64 size, const void* source) override;
 
  private:
   TF_DISALLOW_COPY_AND_ASSIGN(CpuTransferManager);
diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
index eb8b93330f..b1c7eadf6a 100644
--- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
@@ -159,7 +159,13 @@ Status GenericTransferManager::TransferLiteralToDevice(
 
 Status GenericTransferManager::TransferLiteralToInfeed(
     se::StreamExecutor* executor, const Literal& literal) {
-  return Unimplemented("Infeed is not supported on GPU (b/30467474)");
+  return Unimplemented("Generic transfer to Infeed");
+}
+
+Status GenericTransferManager::TransferBufferToInfeed(
+    perftools::gputools::StreamExecutor* executor, int64 size,
+    const void* source) {
+  return Unimplemented("Generic transfer to Infeed");
 }
 
 Status GenericTransferManager::TransferLiteralFromOutfeed(
diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.h b/tensorflow/compiler/xla/service/generic_transfer_manager.h
index 2fbdb94f06..48c061d28e 100644
--- a/tensorflow/compiler/xla/service/generic_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/generic_transfer_manager.h
@@ -54,6 +54,8 @@ class GenericTransferManager : public TransferManager {
 
   Status TransferLiteralToInfeed(perftools::gputools::StreamExecutor* executor,
                                  const Literal& literal) override;
+  Status TransferBufferToInfeed(perftools::gputools::StreamExecutor* executor,
+                                int64 size, const void* source) override;
 
   Status TransferLiteralFromOutfeed(
       perftools::gputools::StreamExecutor* executor, const Shape& literal_shape,
diff --git a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
index 4971de74ae..3e4e590d3b 100644
--- a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
@@ -89,6 +89,12 @@ Status GpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
   return Status::OK();
 }
 
+Status GpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor,
+                                                  int64 size,
+                                                  const void* source) {
+  return TransferBufferToInfeedInternal(executor, size, source).status();
+}
+
 StatusOr<gpu::InfeedBuffer*>
 GpuTransferManager::TransferLiteralToInfeedInternal(
     se::StreamExecutor* executor, const Literal& literal) {
@@ -107,6 +113,12 @@ GpuTransferManager::TransferLiteralToInfeedInternal(
                          ShapeUtil::HumanString(literal.shape()).c_str());
   }
 
+  return TransferBufferToInfeedInternal(executor, size,
+                                        LiteralUtil::InternalData(literal));
+}
+
+StatusOr<gpu::InfeedBuffer*> GpuTransferManager::TransferBufferToInfeedInternal(
+    se::StreamExecutor* executor, int64 size, const void* source) {
   gpu::InfeedManager* infeed_manager = gpu::GetOrCreateInfeedManager();
   se::Stream* stream = infeed_manager->GetStream(executor);
   if (stream == nullptr) {
@@ -114,8 +126,7 @@ GpuTransferManager::TransferLiteralToInfeedInternal(
   }
 
   gpu::InfeedBuffer* buffer = new gpu::InfeedBuffer(executor, size);
-  stream->ThenMemcpy(buffer->device_memory(),
-                     LiteralUtil::InternalData(literal), size);
+  stream->ThenMemcpy(buffer->device_memory(), source, size);
 
   VLOG(2) << "Queued infeed data on stream " << stream;
 
diff --git a/tensorflow/compiler/xla/service/gpu_transfer_manager.h b/tensorflow/compiler/xla/service/gpu_transfer_manager.h
index 6b736b9b9a..4fc6c911a4 100644
--- a/tensorflow/compiler/xla/service/gpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/gpu_transfer_manager.h
@@ -38,6 +38,8 @@ class GpuTransferManager : public GenericTransferManager {
 
   Status TransferLiteralToInfeed(perftools::gputools::StreamExecutor* executor,
                                  const Literal& literal) override;
+  Status TransferBufferToInfeed(perftools::gputools::StreamExecutor* executor,
+                                int64 size, const void* source) override;
 
  private:
   // Internal helper function for TransferLiteralToInfeed(). Input
@@ -45,6 +47,11 @@ class GpuTransferManager : public GenericTransferManager {
   StatusOr<gpu::InfeedBuffer*> TransferLiteralToInfeedInternal(
       perftools::gputools::StreamExecutor* executor, const Literal& literal);
 
+  // Internal helper function for TransferLiteralToInfeed().
+  StatusOr<gpu::InfeedBuffer*> TransferBufferToInfeedInternal(
+      perftools::gputools::StreamExecutor* executor, int64 size,
+      const void* source);
+
   TF_DISALLOW_COPY_AND_ASSIGN(GpuTransferManager);
 };
 
diff --git a/tensorflow/compiler/xla/service/transfer_manager.h b/tensorflow/compiler/xla/service/transfer_manager.h
index 15f6b7bfb4..c79ffa9cd7 100644
--- a/tensorflow/compiler/xla/service/transfer_manager.h
+++ b/tensorflow/compiler/xla/service/transfer_manager.h
@@ -65,6 +65,17 @@ class TransferManager {
       perftools::gputools::StreamExecutor* executor,
       const Literal& literal) = 0;
 
+  // Transfer a memory block of the given size from 'source' buffer to the
+  // Infeed interface of the device using the given executor.
+  //
+  // size is the size to transfer from source in bytes.
+  //
+  // source is the source data that must be in the target-dependent layout that
+  // the Infeed HLO used in the computation expects.
+  virtual Status TransferBufferToInfeed(
+      perftools::gputools::StreamExecutor* executor, int64 size,
+      const void* source) = 0;
+
   // Transfers the given literal from the Outfeed interface of the device,
   // using the given executor.
   virtual Status TransferLiteralFromOutfeed(
-- 
GitLab


From 12b7810e0565f83e5ffa4d278594f5378ea9d863 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Thu, 15 Jun 2017 14:34:38 -0700
Subject: [PATCH 0119/1375] Cosmetic change to layers.

PiperOrigin-RevId: 159155664
---
 tensorflow/contrib/layers/python/layers/layers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 393a2488bc..46c355f08a 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -560,9 +560,9 @@ def batch_norm(inputs,
           layer.moving_mean, variables_collections, 'moving_mean')
       _add_variable_to_collections(
           layer.moving_variance, variables_collections, 'moving_variance')
-      if layer.beta:
+      if layer.beta is not None:
         _add_variable_to_collections(layer.beta, variables_collections, 'beta')
-      if layer.gamma:
+      if layer.gamma is not None:
         _add_variable_to_collections(
             layer.gamma, variables_collections, 'gamma')
 
-- 
GitLab


From 29430ee03bee826d28a31b87e2aa827990714ac9 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Thu, 15 Jun 2017 15:11:05 -0700
Subject: [PATCH 0120/1375] Make TBPlugin accept a TBContext magic container

TensorBoard core will use this object to pass whatever it wants to plugins. By
using a magic container, we add more stuff in the future without breaking
backwards compatibility.

This change also allows us to swap out the multiplexer with the SQL database
so we can do a gradual flag flip migration.

PiperOrigin-RevId: 159160960
---
 tensorflow/tensorboard/backend/application.py | 10 +++--
 .../tensorboard/backend/application_test.py   | 39 +++++++++++--------
 tensorflow/tensorboard/main.py                | 18 ++++-----
 tensorflow/tensorboard/plugins/audio/BUILD    |  1 +
 .../tensorboard/plugins/audio/audio_plugin.py | 13 +++++--
 .../plugins/audio/audio_plugin_test.py        | 15 ++++---
 tensorflow/tensorboard/plugins/base_plugin.py | 26 ++++++++++---
 .../tensorboard/plugins/distributions/BUILD   |  1 +
 .../distributions/distributions_plugin.py     | 13 +++++--
 .../distributions_plugin_test.py              | 13 ++++++-
 tensorflow/tensorboard/plugins/graphs/BUILD   |  1 +
 .../plugins/graphs/graphs_plugin.py           | 13 +++++--
 .../plugins/graphs/graphs_plugin_test.py      | 15 ++++++-
 .../tensorboard/plugins/histograms/BUILD      |  1 +
 .../plugins/histograms/histograms_plugin.py   | 13 +++++--
 .../histograms/histograms_plugin_test.py      | 13 ++++++-
 tensorflow/tensorboard/plugins/images/BUILD   |  1 +
 .../plugins/images/images_plugin.py           | 13 +++++--
 .../plugins/images/images_plugin_test.py      |  7 +++-
 .../tensorboard/plugins/projector/BUILD       |  1 +
 .../plugins/projector/projector_plugin.py     | 25 +++++++-----
 .../projector/projector_plugin_test.py        |  5 ++-
 tensorflow/tensorboard/plugins/scalars/BUILD  |  1 +
 .../plugins/scalars/scalars_plugin.py         | 13 +++++--
 .../plugins/scalars/scalars_plugin_test.py    | 13 ++++++-
 tensorflow/tensorboard/plugins/text/BUILD     |  1 +
 .../tensorboard/plugins/text/text_plugin.py   | 19 ++++++---
 .../plugins/text/text_plugin_test.py          | 15 +++++--
 28 files changed, 229 insertions(+), 90 deletions(-)

diff --git a/tensorflow/tensorboard/backend/application.py b/tensorflow/tensorboard/backend/application.py
index 3657eee38b..68c0e3ddf1 100644
--- a/tensorflow/tensorboard/backend/application.py
+++ b/tensorflow/tensorboard/backend/application.py
@@ -35,6 +35,7 @@ from werkzeug import wrappers
 from tensorflow.tensorboard.backend import http_util
 from tensorflow.tensorboard.backend.event_processing import event_accumulator
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 
 
 DEFAULT_SIZE_GUIDANCE = {
@@ -71,7 +72,7 @@ def standard_tensorboard_wsgi(
     purge_orphaned_data: Whether to purge orphaned data.
     reload_interval: The interval at which the backend reloads more data in
         seconds.
-    plugins: A list of plugins for TensorBoard to initialize.
+    plugins: A list of constructor functions for TBPlugin subclasses.
 
   Returns:
     The new TensorBoard WSGI application.
@@ -79,7 +80,8 @@ def standard_tensorboard_wsgi(
   multiplexer = event_multiplexer.EventMultiplexer(
       size_guidance=DEFAULT_SIZE_GUIDANCE,
       purge_orphaned_data=purge_orphaned_data)
-
+  context = base_plugin.TBContext(logdir=logdir, multiplexer=multiplexer)
+  plugins = [constructor(context) for constructor in plugins]
   return TensorBoardWSGIApp(logdir, plugins, multiplexer, reload_interval)
 
 
@@ -100,7 +102,7 @@ class TensorBoardWSGIApp(object):
       logdir: the logdir spec that describes where data will be loaded.
         may be a directory, or comma,separated list of directories, or colons
         can be used to provide named directories
-      plugins: List of plugins that extend tensorboard.plugins.BasePlugin
+      plugins: A list of base_plugin.TBPlugin subclass instances.
       multiplexer: The EventMultiplexer with TensorBoard data to serve
       reload_interval: How often (in seconds) to reload the Multiplexer
 
@@ -151,7 +153,7 @@ class TensorBoardWSGIApp(object):
       plugin_names_encountered.add(plugin.plugin_name)
 
       try:
-        plugin_apps = plugin.get_plugin_apps(self._multiplexer, self._logdir)
+        plugin_apps = plugin.get_plugin_apps()
       except Exception as e:  # pylint: disable=broad-except
         tf.logging.warning('Plugin %s failed. Exception: %s',
                            plugin.plugin_name, str(e))
diff --git a/tensorflow/tensorboard/backend/application_test.py b/tensorflow/tensorboard/backend/application_test.py
index fd63564e4e..d88b165477 100644
--- a/tensorflow/tensorboard/backend/application_test.py
+++ b/tensorflow/tensorboard/backend/application_test.py
@@ -21,6 +21,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import functools
 import gzip
 import json
 import os
@@ -44,7 +45,8 @@ from tensorflow.tensorboard.plugins import base_plugin
 class FakePlugin(base_plugin.TBPlugin):
   """A plugin with no functionality."""
 
-  def __init__(self, plugin_name, is_active_value, routes_mapping):
+  def __init__(self, unused_context, plugin_name, is_active_value,
+               routes_mapping):
     """Constructs a fake plugin.
 
     Args:
@@ -57,13 +59,9 @@ class FakePlugin(base_plugin.TBPlugin):
     self._is_active_value = is_active_value
     self._routes_mapping = routes_mapping
 
-  def get_plugin_apps(self, multiplexer, logdir):
+  def get_plugin_apps(self):
     """Returns a mapping from routes to handlers offered by this plugin.
 
-    Args:
-      multiplexer: The event multiplexer.
-      logdir: The path to the directory containing logs.
-
     Returns:
       A dictionary mapping from routes to handlers offered by this plugin.
     """
@@ -89,8 +87,10 @@ class TensorboardServerTest(tf.test.TestCase):
         size_guidance=application.DEFAULT_SIZE_GUIDANCE,
         purge_orphaned_data=True)
     plugins = [
-        FakePlugin(plugin_name='foo', is_active_value=True, routes_mapping={}),
-        FakePlugin(plugin_name='bar', is_active_value=False, routes_mapping={})
+        FakePlugin(
+            None, plugin_name='foo', is_active_value=True, routes_mapping={}),
+        FakePlugin(
+            None, plugin_name='bar', is_active_value=False, routes_mapping={}),
     ]
     app = application.TensorBoardWSGIApp(
         self.logdir, plugins, self._multiplexer, reload_interval=0)
@@ -282,9 +282,12 @@ class TensorboardServerPluginNameTest(tf.test.TestCase):
         size_guidance=application.DEFAULT_SIZE_GUIDANCE,
         purge_orphaned_data=True)
     plugins = [
-        FakePlugin(plugin_name='foo', is_active_value=True, routes_mapping={}),
-        FakePlugin(plugin_name=name, is_active_value=True, routes_mapping={}),
-        FakePlugin(plugin_name='bar', is_active_value=False, routes_mapping={})
+        FakePlugin(
+            None, plugin_name='foo', is_active_value=True, routes_mapping={}),
+        FakePlugin(
+            None, plugin_name=name, is_active_value=True, routes_mapping={}),
+        FakePlugin(
+            None, plugin_name='bar', is_active_value=False, routes_mapping={}),
     ]
     if should_be_okay:
       application.TensorBoardWSGIApp(
@@ -320,6 +323,7 @@ class TensorboardServerPluginRouteTest(tf.test.TestCase):
         purge_orphaned_data=True)
     plugins = [
         FakePlugin(
+            None,
             plugin_name='foo',
             is_active_value=True,
             routes_mapping={route: lambda environ, start_response: None}),
@@ -420,11 +424,13 @@ class TensorBoardPluginsTest(tf.test.TestCase):
       pass
 
     plugins = [
-        FakePlugin(
+        functools.partial(
+            FakePlugin,
             plugin_name='foo',
             is_active_value=True,
             routes_mapping={'/foo_route': foo_handler}),
-        FakePlugin(
+        functools.partial(
+            FakePlugin,
             plugin_name='bar',
             is_active_value=True,
             routes_mapping={'/bar_route': bar_handler}),
@@ -494,7 +500,8 @@ class TensorBoardApplcationConstructionTest(tf.test.TestCase):
     with self.assertRaises(ValueError):
       # This plugin lacks a name.
       plugins = [
-          FakePlugin(plugin_name=None, is_active_value=True, routes_mapping={})
+          FakePlugin(
+              None, plugin_name=None, is_active_value=True, routes_mapping={}),
       ]
       application.TensorBoardWSGIApp(logdir, plugins, multiplexer, 0)
 
@@ -502,9 +509,9 @@ class TensorBoardApplcationConstructionTest(tf.test.TestCase):
     with self.assertRaises(ValueError):
       plugins = [
           FakePlugin(
-              plugin_name='foo', is_active_value=True, routes_mapping={}),
+              None, plugin_name='foo', is_active_value=True, routes_mapping={}),
           FakePlugin(
-              plugin_name='foo', is_active_value=True, routes_mapping={}),
+              None, plugin_name='foo', is_active_value=True, routes_mapping={}),
       ]
       application.TensorBoardWSGIApp(logdir, plugins, multiplexer, 0)
 
diff --git a/tensorflow/tensorboard/main.py b/tensorflow/tensorboard/main.py
index 863bba16e5..764fee94d8 100644
--- a/tensorflow/tensorboard/main.py
+++ b/tensorflow/tensorboard/main.py
@@ -102,7 +102,7 @@ def create_tb_app(plugins):
   """Read the flags, and create a TensorBoard WSGI application.
 
   Args:
-    plugins: A list of plugins for TensorBoard to initialize.
+    plugins: A list of constructor functions for TBPlugin subclasses.
 
   Raises:
     ValueError: if a logdir is not specified.
@@ -207,14 +207,14 @@ def main(unused_argv=None):
     return 0
   else:
     plugins = [
-        scalars_plugin.ScalarsPlugin(),
-        images_plugin.ImagesPlugin(),
-        audio_plugin.AudioPlugin(),
-        graphs_plugin.GraphsPlugin(),
-        distributions_plugin.DistributionsPlugin(),
-        histograms_plugin.HistogramsPlugin(),
-        projector_plugin.ProjectorPlugin(),
-        text_plugin.TextPlugin(),
+        scalars_plugin.ScalarsPlugin,
+        images_plugin.ImagesPlugin,
+        audio_plugin.AudioPlugin,
+        graphs_plugin.GraphsPlugin,
+        distributions_plugin.DistributionsPlugin,
+        histograms_plugin.HistogramsPlugin,
+        projector_plugin.ProjectorPlugin,
+        text_plugin.TextPlugin,
     ]
     tb = create_tb_app(plugins)
     run_simple_server(tb)
diff --git a/tensorflow/tensorboard/plugins/audio/BUILD b/tensorflow/tensorboard/plugins/audio/BUILD
index 372aa3067c..daabd322e1 100644
--- a/tensorflow/tensorboard/plugins/audio/BUILD
+++ b/tensorflow/tensorboard/plugins/audio/BUILD
@@ -34,6 +34,7 @@ py_test(
         "//tensorflow:tensorflow_py",
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "@org_pocoo_werkzeug//:werkzeug",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tensorboard/plugins/audio/audio_plugin.py b/tensorflow/tensorboard/plugins/audio/audio_plugin.py
index ee63b67637..fbf3eb4b06 100644
--- a/tensorflow/tensorboard/plugins/audio/audio_plugin.py
+++ b/tensorflow/tensorboard/plugins/audio/audio_plugin.py
@@ -33,8 +33,15 @@ class AudioPlugin(base_plugin.TBPlugin):
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
-  def get_plugin_apps(self, multiplexer, unused_logdir):
-    self._multiplexer = multiplexer
+  def __init__(self, context):
+    """Instantiates AudioPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self._multiplexer = context.multiplexer
+
+  def get_plugin_apps(self):
     return {
         '/audio': self._serve_audio_metadata,
         '/individualAudio': self._serve_individual_audio,
@@ -43,7 +50,7 @@ class AudioPlugin(base_plugin.TBPlugin):
 
   def is_active(self):
     """The audio plugin is active iff any run has at least one relevant tag."""
-    return any(self.index_impl().values())
+    return bool(self._multiplexer) and any(self._index_impl().values())
 
   def _index_impl(self):
     return {
diff --git a/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py b/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py
index 961691086e..8f42838edb 100644
--- a/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py
@@ -33,6 +33,7 @@ from werkzeug import wrappers
 
 from tensorflow.tensorboard.backend import application
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.audio import audio_plugin
 
 
@@ -82,11 +83,12 @@ class AudioPluginTest(tf.test.TestCase):
         "foo": foo_directory,
         "bar": bar_directory,
     })
-    plugin = audio_plugin.AudioPlugin()
+    context = base_plugin.TBContext(
+        logdir=self.log_dir, multiplexer=multiplexer)
+    self.plugin = audio_plugin.AudioPlugin(context)
     wsgi_app = application.TensorBoardWSGIApp(
-        self.log_dir, [plugin], multiplexer, reload_interval=0)
+        self.log_dir, [self.plugin], multiplexer, reload_interval=0)
     self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
-    self.routes = plugin.get_plugin_apps(multiplexer, self.log_dir)
 
   def tearDown(self):
     shutil.rmtree(self.log_dir, ignore_errors=True)
@@ -104,9 +106,10 @@ class AudioPluginTest(tf.test.TestCase):
 
   def testRoutesProvided(self):
     """Tests that the plugin offers the correct routes."""
-    self.assertIsInstance(self.routes["/audio"], collections.Callable)
-    self.assertIsInstance(self.routes["/individualAudio"], collections.Callable)
-    self.assertIsInstance(self.routes["/tags"], collections.Callable)
+    routes = self.plugin.get_plugin_apps()
+    self.assertIsInstance(routes["/audio"], collections.Callable)
+    self.assertIsInstance(routes["/individualAudio"], collections.Callable)
+    self.assertIsInstance(routes["/tags"], collections.Callable)
 
   def testAudioRoute(self):
     """Tests that the /audio routes returns with the correct data."""
diff --git a/tensorflow/tensorboard/plugins/base_plugin.py b/tensorflow/tensorboard/plugins/base_plugin.py
index 01ab06e26d..35414626c8 100644
--- a/tensorflow/tensorboard/plugins/base_plugin.py
+++ b/tensorflow/tensorboard/plugins/base_plugin.py
@@ -22,12 +22,17 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
+
 from abc import ABCMeta
 from abc import abstractmethod
 
 
 class TBPlugin(object):
-  """TensorBoard plugin interface. Every plugin must extend from this class."""
+  """TensorBoard plugin interface. Every plugin must extend from this class.
+
+  Subclasses must have a constructor that takes a TBContext argument.
+  """
   __metaclass__ = ABCMeta
 
   # The plugin_name will also be a prefix in the http handlers generated by
@@ -39,16 +44,12 @@ class TBPlugin(object):
   plugin_name = None
 
   @abstractmethod
-  def get_plugin_apps(self, multiplexer, logdir):
+  def get_plugin_apps(self):
     """Returns a set of WSGI applications that the plugin implements.
 
     Each application gets registered with the tensorboard app and is served
     under a prefix path that includes the name of the plugin.
 
-    Args:
-      multiplexer: The event_multiplexer with underlying TB data.
-      logdir: The logging directory TensorBoard was started with.
-
     Returns:
       A dict mapping route paths to WSGI applications. Each route path
       should include a leading slash.
@@ -66,3 +67,16 @@ class TBPlugin(object):
       A boolean value. Whether this plugin is active.
     """
     raise NotImplementedError()
+
+
+class TBContext(collections.namedtuple('TBContext', ('logdir', 'multiplexer'))):
+  """Magic container of information passed from TensorBoard core to plugins.
+
+  A TBContext instance is passed to the constructor of a TBPlugin class.
+
+  Fields:
+    logdir: The string logging directory TensorBoard was started with.
+    multiplexer: An EventMultiplexer with underlying TB data, or None if SQL
+        mode is being used.
+  """
+  __slots__ = ()  # Enforces use of only tuple fields.
diff --git a/tensorflow/tensorboard/plugins/distributions/BUILD b/tensorflow/tensorboard/plugins/distributions/BUILD
index a8b131c350..a172e61666 100644
--- a/tensorflow/tensorboard/plugins/distributions/BUILD
+++ b/tensorflow/tensorboard/plugins/distributions/BUILD
@@ -36,6 +36,7 @@ py_test(
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "@org_pocoo_werkzeug//:werkzeug",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tensorboard/plugins/distributions/distributions_plugin.py b/tensorflow/tensorboard/plugins/distributions/distributions_plugin.py
index 4bb9dfaf54..716ee799fb 100644
--- a/tensorflow/tensorboard/plugins/distributions/distributions_plugin.py
+++ b/tensorflow/tensorboard/plugins/distributions/distributions_plugin.py
@@ -32,8 +32,15 @@ class DistributionsPlugin(base_plugin.TBPlugin):
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
-  def get_plugin_apps(self, multiplexer, unused_logdir):
-    self._multiplexer = multiplexer
+  def __init__(self, context):
+    """Instantiates DistributionsPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self._multiplexer = context.multiplexer
+
+  def get_plugin_apps(self):
     return {
         '/distributions': self.distributions_route,
         '/tags': self.tags_route,
@@ -41,7 +48,7 @@ class DistributionsPlugin(base_plugin.TBPlugin):
 
   def is_active(self):
     """This plugin is active iff any run has at least one relevant tag."""
-    return any(self.index_impl().values())
+    return bool(self._multiplexer) and any(self.index_impl().values())
 
   def index_impl(self):
     return {
diff --git a/tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py b/tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py
index b5aae6dea7..5212281f38 100644
--- a/tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import os.path
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
@@ -26,6 +27,7 @@ import tensorflow as tf
 
 from tensorflow.tensorboard.backend.event_processing import event_accumulator
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.distributions import distributions_plugin
 
 
@@ -50,8 +52,15 @@ class DistributionsPluginTest(tf.test.TestCase):
     })
     multiplexer.AddRunsFromDirectory(self.logdir)
     multiplexer.Reload()
-    self.plugin = distributions_plugin.DistributionsPlugin()
-    self.apps = self.plugin.get_plugin_apps(multiplexer, None)
+    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
+    self.plugin = distributions_plugin.DistributionsPlugin(context)
+
+  def testRoutesProvided(self):
+    """Tests that the plugin offers the correct routes."""
+    self.set_up_with_runs([self._RUN_WITH_SCALARS])
+    routes = self.plugin.get_plugin_apps()
+    self.assertIsInstance(routes['/distributions'], collections.Callable)
+    self.assertIsInstance(routes['/tags'], collections.Callable)
 
   def generate_run(self, run_name):
     if run_name == self._RUN_WITH_DISTRIBUTION:
diff --git a/tensorflow/tensorboard/plugins/graphs/BUILD b/tensorflow/tensorboard/plugins/graphs/BUILD
index 9ec659202a..4ac041bdf7 100644
--- a/tensorflow/tensorboard/plugins/graphs/BUILD
+++ b/tensorflow/tensorboard/plugins/graphs/BUILD
@@ -37,6 +37,7 @@ py_test(
         "//tensorflow:tensorflow_py",
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "@org_pocoo_werkzeug//:werkzeug",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tensorboard/plugins/graphs/graphs_plugin.py b/tensorflow/tensorboard/plugins/graphs/graphs_plugin.py
index 7fdbf9903d..1c77905e83 100644
--- a/tensorflow/tensorboard/plugins/graphs/graphs_plugin.py
+++ b/tensorflow/tensorboard/plugins/graphs/graphs_plugin.py
@@ -33,8 +33,15 @@ class GraphsPlugin(base_plugin.TBPlugin):
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
-  def get_plugin_apps(self, multiplexer, unused_logdir):
-    self._multiplexer = multiplexer
+  def __init__(self, context):
+    """Instantiates GraphsPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self._multiplexer = context.multiplexer
+
+  def get_plugin_apps(self):
     return {
         '/graph': self.graph_route,
         '/runs': self.runs_route,
@@ -44,7 +51,7 @@ class GraphsPlugin(base_plugin.TBPlugin):
 
   def is_active(self):
     """The graphs plugin is active iff any run has a graph."""
-    return bool(self.index_impl())
+    return bool(self._multiplexer and self.index_impl())
 
   def index_impl(self):
     """Returns a list of all runs that have a graph."""
diff --git a/tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py b/tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py
index db4d0cb1b3..f4e2c20f7a 100644
--- a/tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import math
 import os.path
 
@@ -26,6 +27,7 @@ import tensorflow as tf
 
 from google.protobuf import text_format
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.graphs import graphs_plugin
 
 
@@ -74,8 +76,17 @@ class GraphsPluginTest(tf.test.TestCase):
     multiplexer = event_multiplexer.EventMultiplexer()
     multiplexer.AddRunsFromDirectory(self.logdir)
     multiplexer.Reload()
-    self.plugin = graphs_plugin.GraphsPlugin()
-    self.plugin.get_plugin_apps(multiplexer, None)
+    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
+    self.plugin = graphs_plugin.GraphsPlugin(context)
+
+  def testRoutesProvided(self):
+    """Tests that the plugin offers the correct routes."""
+    self.set_up_with_runs(with_graph=True, without_graph=False)
+    routes = self.plugin.get_plugin_apps()
+    self.assertIsInstance(routes['/graph'], collections.Callable)
+    self.assertIsInstance(routes['/runs'], collections.Callable)
+    self.assertIsInstance(routes['/run_metadata'], collections.Callable)
+    self.assertIsInstance(routes['/run_metadata_tags'], collections.Callable)
 
   def test_index(self):
     self.set_up_with_runs()
diff --git a/tensorflow/tensorboard/plugins/histograms/BUILD b/tensorflow/tensorboard/plugins/histograms/BUILD
index 110a93d20b..6537e2cdbd 100644
--- a/tensorflow/tensorboard/plugins/histograms/BUILD
+++ b/tensorflow/tensorboard/plugins/histograms/BUILD
@@ -36,6 +36,7 @@ py_test(
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "@org_pocoo_werkzeug//:werkzeug",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tensorboard/plugins/histograms/histograms_plugin.py b/tensorflow/tensorboard/plugins/histograms/histograms_plugin.py
index 7a0e005e8b..4f28e73b66 100644
--- a/tensorflow/tensorboard/plugins/histograms/histograms_plugin.py
+++ b/tensorflow/tensorboard/plugins/histograms/histograms_plugin.py
@@ -32,8 +32,15 @@ class HistogramsPlugin(base_plugin.TBPlugin):
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
-  def get_plugin_apps(self, multiplexer, unused_logdir):
-    self._multiplexer = multiplexer
+  def __init__(self, context):
+    """Instantiates HistogramsPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self._multiplexer = context.multiplexer
+
+  def get_plugin_apps(self):
     return {
         '/histograms': self.histograms_route,
         '/tags': self.tags_route,
@@ -41,7 +48,7 @@ class HistogramsPlugin(base_plugin.TBPlugin):
 
   def is_active(self):
     """This plugin is active iff any run has at least one histograms tag."""
-    return any(self.index_impl().values())
+    return bool(self._multiplexer) and any(self.index_impl().values())
 
   def index_impl(self):
     return {
diff --git a/tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py b/tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py
index ee895d9ba7..9371b4a593 100644
--- a/tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import os.path
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
@@ -26,6 +27,7 @@ import tensorflow as tf
 
 from tensorflow.tensorboard.backend.event_processing import event_accumulator
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.histograms import histograms_plugin
 
 
@@ -50,8 +52,8 @@ class HistogramsPluginTest(tf.test.TestCase):
     })
     multiplexer.AddRunsFromDirectory(self.logdir)
     multiplexer.Reload()
-    self.plugin = histograms_plugin.HistogramsPlugin()
-    self.apps = self.plugin.get_plugin_apps(multiplexer, None)
+    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
+    self.plugin = histograms_plugin.HistogramsPlugin(context)
 
   def generate_run(self, run_name):
     if run_name == self._RUN_WITH_HISTOGRAM:
@@ -78,6 +80,13 @@ class HistogramsPluginTest(tf.test.TestCase):
       writer.add_summary(s, global_step=step)
     writer.close()
 
+  def testRoutesProvided(self):
+    """Tests that the plugin offers the correct routes."""
+    self.set_up_with_runs([self._RUN_WITH_SCALARS])
+    routes = self.plugin.get_plugin_apps()
+    self.assertIsInstance(routes['/histograms'], collections.Callable)
+    self.assertIsInstance(routes['/tags'], collections.Callable)
+
   def test_index(self):
     self.set_up_with_runs([self._RUN_WITH_HISTOGRAM, self._RUN_WITH_SCALARS])
     self.assertEqual({
diff --git a/tensorflow/tensorboard/plugins/images/BUILD b/tensorflow/tensorboard/plugins/images/BUILD
index e9f88c4114..34951cc6fa 100644
--- a/tensorflow/tensorboard/plugins/images/BUILD
+++ b/tensorflow/tensorboard/plugins/images/BUILD
@@ -34,6 +34,7 @@ py_test(
         "//tensorflow:tensorflow_py",
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "@org_pocoo_werkzeug//:werkzeug",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tensorboard/plugins/images/images_plugin.py b/tensorflow/tensorboard/plugins/images/images_plugin.py
index 99704c36af..f1c4b75b13 100644
--- a/tensorflow/tensorboard/plugins/images/images_plugin.py
+++ b/tensorflow/tensorboard/plugins/images/images_plugin.py
@@ -44,8 +44,15 @@ class ImagesPlugin(base_plugin.TBPlugin):
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
-  def get_plugin_apps(self, multiplexer, unused_logdir):
-    self._multiplexer = multiplexer
+  def __init__(self, context):
+    """Instantiates ImagesPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self._multiplexer = context.multiplexer
+
+  def get_plugin_apps(self):
     return {
         '/images': self._serve_image_metadata,
         '/individualImage': self._serve_individual_image,
@@ -54,7 +61,7 @@ class ImagesPlugin(base_plugin.TBPlugin):
 
   def is_active(self):
     """The images plugin is active iff any run has at least one relevant tag."""
-    return any(self.index_impl().values())
+    return bool(self._multiplexer) and any(self._index_impl().values())
 
   def _index_impl(self):
     return {
diff --git a/tensorflow/tensorboard/plugins/images/images_plugin_test.py b/tensorflow/tensorboard/plugins/images/images_plugin_test.py
index cbace2b5f0..f24ef90d0f 100644
--- a/tensorflow/tensorboard/plugins/images/images_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/images/images_plugin_test.py
@@ -33,6 +33,7 @@ from werkzeug import wrappers
 
 from tensorflow.tensorboard.backend import application
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.images import images_plugin
 
 
@@ -82,11 +83,13 @@ class ImagesPluginTest(tf.test.TestCase):
         "foo": foo_directory,
         "bar": bar_directory,
     })
-    plugin = images_plugin.ImagesPlugin()
+    context = base_plugin.TBContext(
+        logdir=self.log_dir, multiplexer=multiplexer)
+    plugin = images_plugin.ImagesPlugin(context)
     wsgi_app = application.TensorBoardWSGIApp(
         self.log_dir, [plugin], multiplexer, reload_interval=0)
     self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
-    self.routes = plugin.get_plugin_apps(multiplexer, self.log_dir)
+    self.routes = plugin.get_plugin_apps()
 
   def tearDown(self):
     shutil.rmtree(self.log_dir, ignore_errors=True)
diff --git a/tensorflow/tensorboard/plugins/projector/BUILD b/tensorflow/tensorboard/plugins/projector/BUILD
index 9f9a18b2f2..dff480e632 100644
--- a/tensorflow/tensorboard/plugins/projector/BUILD
+++ b/tensorflow/tensorboard/plugins/projector/BUILD
@@ -37,6 +37,7 @@ py_test(
         "//tensorflow:tensorflow_py",
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "//third_party/py/numpy",
         "@org_pocoo_werkzeug//:werkzeug",
     ],
diff --git a/tensorflow/tensorboard/plugins/projector/projector_plugin.py b/tensorflow/tensorboard/plugins/projector/projector_plugin.py
index 9a3a305d53..304140cee4 100644
--- a/tensorflow/tensorboard/plugins/projector/projector_plugin.py
+++ b/tensorflow/tensorboard/plugins/projector/projector_plugin.py
@@ -29,7 +29,7 @@ from werkzeug import wrappers
 from google.protobuf import json_format
 from google.protobuf import text_format
 from tensorflow.tensorboard.backend.http_util import Respond
-from tensorflow.tensorboard.plugins.base_plugin import TBPlugin
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.projector import projector_config_pb2
 
 # The prefix of routes provided by this plugin.
@@ -207,25 +207,30 @@ def _rel_to_abs_asset_path(fpath, config_fpath):
   return fpath
 
 
-class ProjectorPlugin(TBPlugin):
+class ProjectorPlugin(base_plugin.TBPlugin):
   """Embedding projector."""
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
-  def __init__(self):
+  def __init__(self, context):
+    """Instantiates ProjectorPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self.multiplexer = context.multiplexer
+    self.logdir = context.logdir
     self._handlers = None
     self.readers = {}
     self.run_paths = None
-    self.logdir = None
     self._configs = None
     self.old_num_run_paths = None
-    self.multiplexer = None
     self.tensor_cache = LRUCache(_TENSOR_CACHE_CAPACITY)
+    self.run_paths = None
+    if self.multiplexer:
+      self.run_paths = self.multiplexer.RunPaths()
 
-  def get_plugin_apps(self, multiplexer, logdir):
-    self.multiplexer = multiplexer
-    self.run_paths = multiplexer.RunPaths()
-    self.logdir = logdir
+  def get_plugin_apps(self):
     self._handlers = {
         RUNS_ROUTE: self._serve_runs,
         CONFIG_ROUTE: self._serve_config,
@@ -244,7 +249,7 @@ class ProjectorPlugin(TBPlugin):
     Returns:
       A boolean. Whether this plugin is active.
     """
-    return bool(self.configs)
+    return bool(self.multiplexer and self.configs)
 
   @property
   def configs(self):
diff --git a/tensorflow/tensorboard/plugins/projector/projector_plugin_test.py b/tensorflow/tensorboard/plugins/projector/projector_plugin_test.py
index 06cf2c3d0d..acdba3f3d4 100644
--- a/tensorflow/tensorboard/plugins/projector/projector_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/projector/projector_plugin_test.py
@@ -33,6 +33,7 @@ from google.protobuf import text_format
 
 from tensorflow.tensorboard.backend import application
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.projector import projector_config_pb2
 from tensorflow.tensorboard.plugins.projector import projector_plugin
 
@@ -199,7 +200,9 @@ class ProjectorAppTest(tf.test.TestCase):
     multiplexer = event_multiplexer.EventMultiplexer(
         size_guidance=application.DEFAULT_SIZE_GUIDANCE,
         purge_orphaned_data=True)
-    self.plugin = projector_plugin.ProjectorPlugin()
+    context = base_plugin.TBContext(
+        logdir=self.log_dir, multiplexer=multiplexer)
+    self.plugin = projector_plugin.ProjectorPlugin(context)
     wsgi_app = application.TensorBoardWSGIApp(
         self.log_dir, [self.plugin], multiplexer, reload_interval=0)
     self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
diff --git a/tensorflow/tensorboard/plugins/scalars/BUILD b/tensorflow/tensorboard/plugins/scalars/BUILD
index 1e95c7d701..917cc1425b 100644
--- a/tensorflow/tensorboard/plugins/scalars/BUILD
+++ b/tensorflow/tensorboard/plugins/scalars/BUILD
@@ -36,6 +36,7 @@ py_test(
         "//tensorflow:tensorflow_py",
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "@org_pocoo_werkzeug//:werkzeug",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tensorboard/plugins/scalars/scalars_plugin.py b/tensorflow/tensorboard/plugins/scalars/scalars_plugin.py
index ec4bf1089c..cbdca9aa53 100644
--- a/tensorflow/tensorboard/plugins/scalars/scalars_plugin.py
+++ b/tensorflow/tensorboard/plugins/scalars/scalars_plugin.py
@@ -41,8 +41,15 @@ class ScalarsPlugin(base_plugin.TBPlugin):
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
-  def get_plugin_apps(self, multiplexer, unused_logdir):
-    self._multiplexer = multiplexer
+  def __init__(self, context):
+    """Instantiates ScalarsPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self._multiplexer = context.multiplexer
+
+  def get_plugin_apps(self):
     return {
         '/scalars': self.scalars_route,
         '/tags': self.tags_route,
@@ -50,7 +57,7 @@ class ScalarsPlugin(base_plugin.TBPlugin):
 
   def is_active(self):
     """The scalars plugin is active iff any run has at least one scalar tag."""
-    return any(self.index_impl().values())
+    return bool(self._multiplexer) and any(self.index_impl().values())
 
   def index_impl(self):
     return {
diff --git a/tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py b/tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py
index fb6ed0bc3f..7d4ad5a046 100644
--- a/tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import csv
 import os.path
 
@@ -27,6 +28,7 @@ from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.scalars import scalars_plugin
 
 
@@ -47,8 +49,15 @@ class ScalarsPluginTest(tf.test.TestCase):
     multiplexer = event_multiplexer.EventMultiplexer()
     multiplexer.AddRunsFromDirectory(self.logdir)
     multiplexer.Reload()
-    self.plugin = scalars_plugin.ScalarsPlugin()
-    self.apps = self.plugin.get_plugin_apps(multiplexer, None)
+    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
+    self.plugin = scalars_plugin.ScalarsPlugin(context)
+
+  def testRoutesProvided(self):
+    """Tests that the plugin offers the correct routes."""
+    self.set_up_with_runs([self._RUN_WITH_SCALARS])
+    routes = self.plugin.get_plugin_apps()
+    self.assertIsInstance(routes['/scalars'], collections.Callable)
+    self.assertIsInstance(routes['/tags'], collections.Callable)
 
   def generate_run(self, run_name):
     if run_name == self._RUN_WITH_SCALARS:
diff --git a/tensorflow/tensorboard/plugins/text/BUILD b/tensorflow/tensorboard/plugins/text/BUILD
index 8d30a01c01..5e18328b4b 100644
--- a/tensorflow/tensorboard/plugins/text/BUILD
+++ b/tensorflow/tensorboard/plugins/text/BUILD
@@ -37,6 +37,7 @@ py_test(
         "//tensorflow:tensorflow_py",
         "//tensorflow/tensorboard/backend:application",
         "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
+        "//tensorflow/tensorboard/plugins:base_plugin",
         "@org_pocoo_werkzeug//:werkzeug",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/tensorboard/plugins/text/text_plugin.py b/tensorflow/tensorboard/plugins/text/text_plugin.py
index fc00778c33..50779caa86 100644
--- a/tensorflow/tensorboard/plugins/text/text_plugin.py
+++ b/tensorflow/tensorboard/plugins/text/text_plugin.py
@@ -253,14 +253,22 @@ class TextPlugin(base_plugin.TBPlugin):
 
   plugin_name = _PLUGIN_PREFIX_ROUTE
 
+  def __init__(self, context):
+    """Instantiates TextPlugin via TensorBoard core.
+
+    Args:
+      context: A base_plugin.TBContext instance.
+    """
+    self._multiplexer = context.multiplexer
+
   def index_impl(self):
     run_to_series = {}
     name = 'tensorboard_text'
-    run_to_assets = self.multiplexer.PluginAssets(name)
+    run_to_assets = self._multiplexer.PluginAssets(name)
 
     for run, assets in run_to_assets.items():
       if 'tensors.json' in assets:
-        tensors_json = self.multiplexer.RetrievePluginAsset(
+        tensors_json = self._multiplexer.RetrievePluginAsset(
             run, name, 'tensors.json')
         tensors = json.loads(tensors_json)
         run_to_series[run] = tensors
@@ -275,7 +283,7 @@ class TextPlugin(base_plugin.TBPlugin):
 
   def text_impl(self, run, tag):
     try:
-      text_events = self.multiplexer.Tensors(run, tag)
+      text_events = self._multiplexer.Tensors(run, tag)
     except KeyError:
       text_events = []
     responses = [process_string_tensor_event(ev) for ev in text_events]
@@ -288,8 +296,7 @@ class TextPlugin(base_plugin.TBPlugin):
     response = self.text_impl(run, tag)
     return http_util.Respond(request, response, 'application/json')
 
-  def get_plugin_apps(self, multiplexer, unused_logdir):
-    self.multiplexer = multiplexer
+  def get_plugin_apps(self):
     return {
         RUNS_ROUTE: self.runs_route,
         TEXT_ROUTE: self.text_route,
@@ -303,4 +310,4 @@ class TextPlugin(base_plugin.TBPlugin):
     Returns:
       Whether this plugin is active.
     """
-    return bool(self.index_impl())
+    return bool(self._multiplexer and self.index_impl())
diff --git a/tensorflow/tensorboard/plugins/text/text_plugin_test.py b/tensorflow/tensorboard/plugins/text/text_plugin_test.py
index 5c4f6bfd33..0460d9777c 100644
--- a/tensorflow/tensorboard/plugins/text/text_plugin_test.py
+++ b/tensorflow/tensorboard/plugins/text/text_plugin_test.py
@@ -19,12 +19,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import os
 import textwrap
 import numpy as np
 import tensorflow as tf
 
 from tensorflow.tensorboard.backend.event_processing import event_multiplexer
+from tensorflow.tensorboard.plugins import base_plugin
 from tensorflow.tensorboard.plugins.text import text_plugin
 
 GEMS = ['garnet', 'amethyst', 'pearl', 'steven']
@@ -38,8 +40,13 @@ class TextPluginTest(tf.test.TestCase):
     multiplexer = event_multiplexer.EventMultiplexer()
     multiplexer.AddRunsFromDirectory(self.logdir)
     multiplexer.Reload()
-    self.plugin = text_plugin.TextPlugin()
-    self.apps = self.plugin.get_plugin_apps(multiplexer, None)
+    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
+    self.plugin = text_plugin.TextPlugin(context)
+
+  def testRoutesProvided(self):
+    routes = self.plugin.get_plugin_apps()
+    self.assertIsInstance(routes['/runs'], collections.Callable)
+    self.assertIsInstance(routes['/text'], collections.Callable)
 
   def assertConverted(self, actual, expected):
     expected_html = text_plugin.markdown_and_sanitize(expected)
@@ -386,9 +393,9 @@ class TextPluginTest(tf.test.TestCase):
     self.assertEqual(convert(d3), d3_expected)
 
   def testPluginIsActive(self):
-    plugin = text_plugin.TextPlugin()
     multiplexer = event_multiplexer.EventMultiplexer()
-    plugin.get_plugin_apps(event_multiplexer.EventMultiplexer(), None)
+    context = base_plugin.TBContext(logdir=None, multiplexer=multiplexer)
+    plugin = text_plugin.TextPlugin(context)
 
     # The plugin is inactive because text summaries are not available.
     self.assertFalse(plugin.is_active())
-- 
GitLab


From 6a6ef4f712d2a33d24597cd3ba5165ccbdb4a83d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 15:17:49 -0700
Subject: [PATCH 0121/1375] Removes the links to the (non-existent yet) api
 guide for estimator. This fixes broken tests.

PiperOrigin-RevId: 159161935
---
 tensorflow/docs_src/get_started/estimator.md | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/tensorflow/docs_src/get_started/estimator.md b/tensorflow/docs_src/get_started/estimator.md
index 254da1efa7..c4dcac0189 100644
--- a/tensorflow/docs_src/get_started/estimator.md
+++ b/tensorflow/docs_src/get_started/estimator.md
@@ -238,9 +238,9 @@ you'll use `training_set.data` and
 
 ## Construct a Deep Neural Network Classifier
 
-tf.estimator offers a variety of predefined models, called
-@{$python/estimator#estimator$`Estimator`s}, which you can
-use "out of the box" to run training and evaluation operations on your data.
+tf.estimator offers a variety of predefined models, called `Estimator`s, which
+you can use "out of the box" to run training and evaluation operations on your
+data.
 Here, you'll configure a Deep Neural Network Classifier model to fit the Iris
 data. Using tf.estimator, you can instantiate your
 @{tf.estimator.DNNClassifier} with just a couple lines of code:
@@ -398,9 +398,6 @@ second sample is *Iris virginica*.
 
 ## Additional Resources
 
-*   For further reference materials on tf.contrib.learn, see the official
-    @{$python/estimator$API docs}.
-
 *   To learn more about using tf.estimator to create linear models, see
     @{$linear$Large-scale Linear Models with TensorFlow}.
 
-- 
GitLab


From 3e9cd2e13da566e279396d232004d3c4ffad336e Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 15 Jun 2017 15:23:51 -0700
Subject: [PATCH 0122/1375] [TF:XLA] Add infrastructure in preparation for
 supporting tf.while_loop() in the TF/XLA bridge.

PiperOrigin-RevId: 159162832
---
 tensorflow/BUILD                              |   2 +
 tensorflow/compiler/tf2xla/BUILD              |  53 ++
 tensorflow/compiler/tf2xla/cc/BUILD           |  44 ++
 .../tf2xla/functionalize_control_flow.cc      | 566 +++++++++++++++
 .../tf2xla/functionalize_control_flow.h       |  32 +
 .../tf2xla/functionalize_control_flow_test.cc | 647 ++++++++++++++++++
 tensorflow/compiler/tf2xla/kernels/BUILD      |  16 +
 .../compiler/tf2xla/kernels/while_op.cc       | 252 +++++++
 tensorflow/compiler/tf2xla/kernels/while_op.h |  65 ++
 tensorflow/compiler/tf2xla/ops/BUILD          |  38 +
 .../compiler/tf2xla/ops/functional_ops.cc     |  45 ++
 tensorflow/compiler/tf2xla/test_util.cc       |  42 ++
 tensorflow/compiler/tf2xla/test_util.h        |  46 ++
 tensorflow/compiler/tf2xla/xla_compiler.cc    |   8 +-
 tensorflow/compiler/tf2xla/xla_compiler.h     |   1 +
 15 files changed, 1856 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/compiler/tf2xla/cc/BUILD
 create mode 100644 tensorflow/compiler/tf2xla/functionalize_control_flow.cc
 create mode 100644 tensorflow/compiler/tf2xla/functionalize_control_flow.h
 create mode 100644 tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
 create mode 100644 tensorflow/compiler/tf2xla/kernels/while_op.cc
 create mode 100644 tensorflow/compiler/tf2xla/kernels/while_op.h
 create mode 100644 tensorflow/compiler/tf2xla/ops/BUILD
 create mode 100644 tensorflow/compiler/tf2xla/ops/functional_ops.cc
 create mode 100644 tensorflow/compiler/tf2xla/test_util.cc
 create mode 100644 tensorflow/compiler/tf2xla/test_util.h

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 53c3657b72..ae5c53e42d 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -210,7 +210,9 @@ filegroup(
         "//tensorflow/compiler/jit/ops:all_files",
         "//tensorflow/compiler/tests:all_files",
         "//tensorflow/compiler/tf2xla:all_files",
+        "//tensorflow/compiler/tf2xla/cc:all_files",
         "//tensorflow/compiler/tf2xla/kernels:all_files",
+        "//tensorflow/compiler/tf2xla/ops:all_files",
         "//tensorflow/compiler/xla:all_files",
         "//tensorflow/compiler/xla/client:all_files",
         "//tensorflow/compiler/xla/client/lib:all_files",
diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD
index 93c484ca7a..a1c8c9209e 100644
--- a/tensorflow/compiler/tf2xla/BUILD
+++ b/tensorflow/compiler/tf2xla/BUILD
@@ -42,6 +42,7 @@ cc_library(
     deps = [
         ":common",
         ":dump_graph",
+        ":functionalize_control_flow",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
@@ -203,6 +204,58 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "functionalize_control_flow",
+    srcs = ["functionalize_control_flow.cc"],
+    hdrs = ["functionalize_control_flow.h"],
+    deps = [
+        "//tensorflow/compiler/jit:graph_to_functiondef",
+        "//tensorflow/compiler/tf2xla:dump_graph",
+        "//tensorflow/compiler/tf2xla/ops:functional_ops",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:framework",
+    ],
+)
+
+cc_test(
+    name = "functionalize_control_flow_test",
+    srcs = ["functionalize_control_flow_test.cc"],
+    deps = [
+        ":functionalize_control_flow",
+        ":test_util",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:cc_ops_internal",
+        "//tensorflow/cc:function_ops",
+        "//tensorflow/cc:ops",
+        "//tensorflow/cc:resource_variable_ops",
+        "//tensorflow/compiler/tf2xla/cc:functional_ops",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:ops",
+        "//tensorflow/core:resource_variable_ops_op_lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_library(
+    name = "test_util",
+    testonly = 1,
+    srcs = ["test_util.cc"],
+    hdrs = ["test_util.h"],
+    deps = [
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+    ],
+)
+
 # -----------------------------------------------------------------------------
 
 filegroup(
diff --git a/tensorflow/compiler/tf2xla/cc/BUILD b/tensorflow/compiler/tf2xla/cc/BUILD
new file mode 100644
index 0000000000..599265ba44
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/cc/BUILD
@@ -0,0 +1,44 @@
+package(
+    default_visibility = ["//tensorflow/compiler/tf2xla:internal"],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_cc")
+
+tf_gen_op_wrapper_cc(
+    name = "functional_ops_gen",
+    include_internal_ops = 1,
+    out_ops_file = "ops/functional_ops",
+    deps = ["//tensorflow/compiler/tf2xla/ops:functional_ops"],
+)
+
+cc_library(
+    name = "functional_ops",
+    srcs = ["ops/functional_ops.cc"],
+    hdrs = ["ops/functional_ops.h"],
+    deps = [
+        "//tensorflow/cc:const_op",
+        "//tensorflow/cc:ops",
+        "//tensorflow/cc:scope",
+        "//tensorflow/compiler/tf2xla/ops:functional_ops",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+    ],
+)
+
+# -----------------------------------------------------------------------------
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
new file mode 100644
index 0000000000..623c52120f
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
@@ -0,0 +1,566 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/tf2xla/functionalize_control_flow.h"
+
+#include <algorithm>
+#include <deque>
+#include <unordered_set>
+#include <vector>
+
+#include "tensorflow/compiler/jit/graph_to_functiondef.h"
+#include "tensorflow/compiler/tf2xla/dump_graph.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/graph/control_flow.h"
+
+namespace tensorflow {
+
+namespace {
+
+const char* const kArgOp = "_Arg";
+const char* const kRetValOp = "_Retval";
+
+// Information about a loop argument.
+struct Arg {
+  // Every loop argument has an Enter node.
+  Node* enter;
+
+  // Is the loop argument a loop-invariant value? Taken from the `is_constant`
+  // attribute on the Enter node.
+  bool is_loop_invariant;
+
+  // If 'is_loop_invariant' is true, the following are all nullptr. Non-constant
+  // arguments must have all of the following nodes:
+  Node* merge = nullptr;
+  Node* switch_node = nullptr;
+  Node* next_iteration = nullptr;
+  Node* exit = nullptr;
+};
+
+// Information about a loop frame.
+struct Frame {
+  string name;
+
+  // Pointer to the parent frame. The root frame has a pointer to itself.
+  Frame* parent = nullptr;
+  int num_children = 0;
+
+  // Arguments to this loop.
+  std::vector<Arg> args;
+
+  // The loop condition of the loop. There should be exactly one loop condition
+  // in every loop.
+  Node* loop_cond = nullptr;
+
+  // Set of nodes that belong to the loop frame.
+  std::unordered_set<Node*> nodes;
+};
+
+// Copies a subgraph from `graph` to `output` by performing a reverse DFS
+// starting at nodes in vector `stack`.
+// `node_map` is a vector indexed by source node ID to dest nodes.
+// Does not traverse into nodes in `node_map`, so by adding nodes to `node_map`
+// before the traversal clients can cut the graph. Returns an error if the
+// traversal leaves 'frame'; the client must add enough nodes to `node_map` to
+// cut the graph and prevent the traversal from escaping.
+//
+// `squash_src_outputs` contains a bool for each source node ID. If true, then
+// the source output on that node will be replaced by zero when copied. This is
+// used when replacing a Switch node with an _Arg node. The output we are
+// taking from the Switch node was not necessarily the first output, but _Arg
+// nodes only have one output. By adding the Switch node to `squash_src_outputs`
+// we rewrite the src_output of the corresponding edge to be 0.
+Status CopySubgraph(const Graph& graph, const Frame& frame,
+                    std::vector<Node*> stack,
+                    const std::vector<bool>& squash_src_outputs,
+                    std::vector<Node*>* node_map, Graph* output) {
+  std::vector<bool> visited(graph.num_node_ids(), false);
+  while (!stack.empty()) {
+    Node* n = stack.back();
+    stack.pop_back();
+
+    VLOG(3) << "Copying node " << n->name();
+
+    if (visited[n->id()]) continue;
+    visited[n->id()] = true;
+
+    for (const Edge* e : n->in_edges()) {
+      Node* src = e->src();
+      if (frame.nodes.find(src) == frame.nodes.end()) {
+        // We traversed out of the loop frame, without encountering a cut node.
+        return errors::Internal("Graph traversal of loop frame ", frame.name,
+                                " escaped frame at ", src->name(),
+                                " without encountering an argument node.");
+      }
+      if ((*node_map)[src->id()] == nullptr) {
+        (*node_map)[src->id()] = output->CopyNode(src);
+        stack.push_back(src);
+      }
+      Node* src_copy = (*node_map)[e->src()->id()];
+      int src_output = squash_src_outputs[e->src()->id()] ? 0 : e->src_output();
+      Node* dst_copy = (*node_map)[e->dst()->id()];
+      output->AddEdge(src_copy, src_output, dst_copy, e->dst_input());
+    }
+  }
+  return Status::OK();
+}
+
+Status BuildArgNode(Graph* graph, DataType type, int index, Node** arg_node) {
+  NodeDef arg_def;
+  NodeDefBuilder builder(strings::StrCat("_Arg", index), kArgOp);
+  builder.Attr("T", type);
+  builder.Attr("index", index);
+  TF_RETURN_IF_ERROR(builder.Finalize(&arg_def));
+  Status status;
+  *arg_node = graph->AddNode(arg_def, &status);
+  return status;
+}
+
+Status BuildRetvalNode(Graph* graph, DataType type, int index,
+                       Node** retval_node) {
+  NodeDef ret_def;
+  ret_def.set_op(kRetValOp);
+  ret_def.set_name(strings::StrCat("_Retval", index));
+  AddNodeAttr("T", type, &ret_def);
+  AddNodeAttr("index", index, &ret_def);
+  Status status;
+  *retval_node = graph->AddNode(ret_def, &status);
+  return status;
+}
+
+// Builds a graph for the loop condition.
+Status BuildLoopCondition(const Graph& graph, Frame* frame,
+                          std::unique_ptr<Graph>* cond_output) {
+  VLOG(2) << "Building loop condition for " << frame->name;
+  *cond_output = xla::MakeUnique<Graph>(graph.op_registry());
+  Graph* output = cond_output->get();
+
+  // Map from nodes in the original graph to the condition graph.
+  std::vector<Node*> node_map(graph.num_node_ids(), nullptr);
+  std::vector<bool> squash_src_outputs(graph.num_node_ids(), false);
+
+  // Build one _Arg node for each Enter node.
+  for (int i = 0; i < frame->args.size(); ++i) {
+    const Arg& arg = frame->args[i];
+
+    Node* arg_node;
+    TF_RETURN_IF_ERROR(
+        BuildArgNode(output, arg.enter->input_type(0), i, &arg_node));
+    if (arg.is_loop_invariant) {
+      node_map[arg.enter->id()] = arg_node;
+    } else {
+      node_map[arg.merge->id()] = arg_node;
+    }
+  }
+
+  // Build a Retval node for the loop condition. The LoopCond nodes are always
+  // boolean because of the type constraints on the LoopCond op.
+  TF_RETURN_IF_ERROR(
+      BuildRetvalNode(output, DT_BOOL, 0, &node_map[frame->loop_cond->id()]));
+
+  // Performs a reverse DFS, copying nodes and edges to the output graph.
+  // The _Arg and _Retval nodes were added unconditionally above, so we are
+  // guaranteed to get the correct function signature.
+  TF_RETURN_IF_ERROR(CopySubgraph(graph, *frame, {frame->loop_cond},
+                                  squash_src_outputs, &node_map, output));
+
+  return Status::OK();
+}
+
+// Builds a graph for the loop body.
+Status BuildLoopBody(const Graph& graph, Frame* frame,
+                     DataTypeVector* arg_types,
+                     std::unique_ptr<Graph>* body_output) {
+  VLOG(2) << "Building loop body for " << frame->name;
+  *body_output = xla::MakeUnique<Graph>(graph.op_registry());
+  Graph* output = body_output->get();
+
+  // Map from nodes in the original graph to the condition graph.
+  std::vector<Node*> node_map(graph.num_node_ids(), nullptr);
+  std::vector<bool> squash_src_outputs(graph.num_node_ids(), false);
+
+  // Build one _Arg node for each Enter node.
+  std::vector<Node*> next_iterations;
+  next_iterations.reserve(frame->args.size());
+  arg_types->reserve(frame->args.size());
+  for (int i = 0; i < frame->args.size(); ++i) {
+    const Arg& arg = frame->args[i];
+
+    DataType dtype = arg.enter->input_type(0);
+    arg_types->push_back(dtype);
+    Node* arg_node;
+    TF_RETURN_IF_ERROR(BuildArgNode(output, dtype, i, &arg_node));
+
+    if (dtype == DT_RESOURCE) {
+      // The convention of the XLA bridge is that resource variable arguments
+      // are only inputs to the loop body and have no corresponding output.
+      // TODO(b/37741920): change the convention so that DT_RESOURCE variables
+      // are both inputs and outputs, and then remove this case.
+      TF_RET_CHECK(arg.is_loop_invariant);
+      node_map[arg.enter->id()] = arg_node;
+    } else {
+      Node* retval_node;
+      TF_RETURN_IF_ERROR(BuildRetvalNode(output, dtype, i, &retval_node));
+
+      if (arg.is_loop_invariant) {
+        // Argument is loop-invariant. Forward it from the Arg to the Retval.
+        node_map[arg.enter->id()] = arg_node;
+        output->AddEdge(arg_node, 0, retval_node, 0);
+      } else {
+        // Argument is loop-varying.
+        node_map[arg.switch_node->id()] = arg_node;
+        // The Switch node has two outputs, but _Arg only has one. This tells
+        // the CopySubgraph function to rewrite the output number of edges from
+        // the _Arg node to be 0 rather than copying the output number from the
+        // Switch node.
+        squash_src_outputs[arg.switch_node->id()] = true;
+        node_map[arg.next_iteration->id()] = retval_node;
+        next_iterations.push_back(arg.next_iteration);
+      }
+    }
+  }
+
+  // Performs a reverse DFS, copying nodes and edges to the output graph.
+  // The _Arg and _Retval nodes were added unconditionally above, so we are
+  // guaranteed to get the correct function signature.
+  TF_RETURN_IF_ERROR(CopySubgraph(graph, *frame, std::move(next_iterations),
+                                  squash_src_outputs, &node_map, output));
+
+  return Status::OK();
+}
+
+Status FunctionalizeLoop(Graph* graph, Frame* frame,
+                         FunctionLibraryDefinition* library) {
+  VLOG(2) << "Frame " << frame->name << " before: "
+          << dump_graph::DumpGraphToFile("functionalize_before", *graph);
+
+  // Split loop-varying Enter nodes with multiple successors. If the same
+  // Tensor is fed as input to multiple loop arguments, we may end up with a
+  // shared Enter node. We clone Enter nodes with multiple successors to
+  // maintain the invariant of a unique Enter node per argument of the final
+  // loop.
+  std::vector<Arg> args;
+  for (const Arg& arg : frame->args) {
+    if (arg.is_loop_invariant) {
+      args.push_back(arg);
+    } else {
+      std::vector<const Edge*> edges(arg.enter->out_edges().begin(),
+                                     arg.enter->out_edges().end());
+      for (int i = 0; i < edges.size(); ++i) {
+        TF_RET_CHECK(!edges[i]->IsControlEdge());
+        Arg new_arg;
+        new_arg.is_loop_invariant = false;
+        if (i == 0) {
+          new_arg.enter = arg.enter;
+        } else {
+          new_arg.enter = graph->CopyNode(arg.enter);
+          frame->nodes.insert(new_arg.enter);
+          for (Edge const* e : arg.enter->in_edges()) {
+            graph->AddEdge(e->src(), e->src_output(), new_arg.enter,
+                           e->IsControlEdge() ? Graph::kControlSlot : 0);
+          }
+          Node* dst = edges[i]->dst();
+          int dst_input = edges[i]->dst_input();
+          graph->RemoveEdge(edges[i]);
+          graph->AddEdge(new_arg.enter, 0, dst, dst_input);
+        }
+        args.push_back(new_arg);
+      }
+    }
+  }
+  frame->args = std::move(args);
+
+  // Order the arguments so that:
+  // a) resource variables are last, and
+  // b) sort lexicographically by name (for deterministic output).
+  std::sort(frame->args.begin(), frame->args.end(),
+            [](const Arg& a, const Arg& b) {
+              bool a_is_resource = (a.enter->input_type(0) == DT_RESOURCE);
+              bool b_is_resource = (b.enter->input_type(0) == DT_RESOURCE);
+              return std::tie(a_is_resource, a.enter->name()) <
+                     std::tie(b_is_resource, b.enter->name());
+            });
+
+  if (frame->loop_cond == nullptr) {
+    return errors::InvalidArgument("Loop ", frame->name,
+                                   " has no LoopCond node");
+  }
+
+  // Find the set of Switch nodes that are successors of the LoopCond.
+  std::unordered_set<Node*> switches;
+  for (const Edge* edge : frame->loop_cond->out_edges()) {
+    if (!edge->IsControlEdge() && IsSwitch(edge->dst()) &&
+        edge->dst_input() == 1) {
+      switches.insert(edge->dst());
+    }
+  }
+
+  // For each non-constant argument, looks for the following pattern of nodes:
+  // Enter ----> Merge  -------->  Switch  --> Exit
+  //               ^                  ^
+  //               |                  |
+  //         NextIteration         LoopCond
+  //               ^                  ^
+  //               |                  |
+  //              ...                ...
+  for (Arg& arg : frame->args) {
+    if (!arg.is_loop_invariant) {
+      // Follow the edge from the Enter to Merge.
+      if (arg.enter->out_edges().size() != 1) {
+        return errors::Internal("Enter node for loop-varying argument ",
+                                arg.enter->name(),
+                                " does not have exactly one successor");
+      }
+      const Edge* enter_merge = *arg.enter->out_edges().begin();
+      arg.merge = enter_merge->dst();
+      if (!IsMerge(arg.merge)) {
+        return errors::InvalidArgument(
+            "Successor of Enter node for loop-varying argument ",
+            arg.merge->name(),
+            " is not a Merge node; got: ", arg.merge->type_string());
+      }
+
+      // Find the NextIteration from the merge. There should be two inputs to
+      // the Merge and the NextIteration should be the other input.
+      if (arg.merge->input_types().size() != 2) {
+        return errors::InvalidArgument(
+            "Unexpected number of inputs to Merge node for loop-varying "
+            "argument ",
+            arg.merge->name(), "; expected 2, got ",
+            arg.merge->input_types().size());
+      }
+      TF_RETURN_IF_ERROR(arg.merge->input_node(1 - enter_merge->dst_input(),
+                                               &arg.next_iteration));
+      if (!IsNextIteration(arg.next_iteration)) {
+        return errors::InvalidArgument(
+            "Expected NextIteration node as input to Merge node; got node ",
+            arg.next_iteration->name(), " with kind ",
+            arg.next_iteration->type_string());
+      }
+
+      // Find the Switch successor of the Merge. There should be exactly one
+      // Switch node that is a successor of both the Merge and the LoopCond.
+      for (const Edge* edge : arg.merge->out_edges()) {
+        if (edge->dst_input() == 0 && IsSwitch(edge->dst()) &&
+            switches.find(edge->dst()) != switches.end()) {
+          if (arg.switch_node != nullptr) {
+            return errors::InvalidArgument("Duplicate Switch successors to ",
+                                           arg.merge->name());
+          }
+          arg.switch_node = edge->dst();
+        }
+      }
+      if (arg.switch_node == nullptr) {
+        return errors::InvalidArgument("Missing Switch successor to ",
+                                       arg.merge->name());
+      }
+
+      // Find the Exit successor of the Switch.
+      for (const Edge* edge : arg.switch_node->out_edges()) {
+        if (edge->src_output() == 0 && IsExit(edge->dst())) {
+          if (arg.exit != nullptr) {
+            return errors::InvalidArgument("Duplicate Exit successors to ",
+                                           arg.switch_node->name());
+          }
+          arg.exit = edge->dst();
+        }
+      }
+      if (arg.exit == nullptr) {
+        return errors::InvalidArgument("Mising Exit successor to ",
+                                       arg.switch_node->name());
+      }
+    }
+  }
+
+  // Builds the condition and body functions.
+  std::unique_ptr<Graph> cond_graph;
+  TF_RETURN_IF_ERROR(BuildLoopCondition(*graph, frame, &cond_graph));
+  DataTypeVector arg_types;
+  std::unique_ptr<Graph> body_graph;
+  TF_RETURN_IF_ERROR(BuildLoopBody(*graph, frame, &arg_types, &body_graph));
+
+  VLOG(2) << "Frame " << frame->name << " condition: "
+          << dump_graph::DumpGraphToFile("loop_condition", *cond_graph)
+          << " body: " << dump_graph::DumpGraphToFile("loop_body", *body_graph);
+
+  static std::atomic<int64> sequence_num(0LL);
+  int64 id = ++sequence_num;
+  NameAttrList cond_name;
+  cond_name.set_name(strings::StrCat("_functionalize_cond_", id));
+  NameAttrList body_name;
+  body_name.set_name(strings::StrCat("_functionalize_body_", id));
+  FunctionDef cond_fdef;
+  TF_RETURN_IF_ERROR(
+      GraphToFunctionDef(*cond_graph, cond_name.name(), &cond_fdef));
+  FunctionDef body_fdef;
+  TF_RETURN_IF_ERROR(
+      GraphToFunctionDef(*body_graph, body_name.name(), &body_fdef));
+
+  TF_RETURN_IF_ERROR(library->AddFunctionDef(cond_fdef));
+  TF_RETURN_IF_ERROR(library->AddFunctionDef(body_fdef));
+
+  // Builds a While operator.
+  NodeDef while_def;
+  NodeDefBuilder builder(frame->loop_cond->name(), "XlaWhile");
+  builder.Attr("T", arg_types);
+  builder.Attr("cond", cond_name);
+  builder.Attr("body", body_name);
+  std::vector<NodeDefBuilder::NodeOut> inputs;
+  for (int i = 0; i < frame->args.size(); ++i) {
+    const Arg& arg = frame->args[i];
+    const Edge* in_edge;
+    TF_RETURN_IF_ERROR(arg.enter->input_edge(0, &in_edge));
+    if (in_edge->IsControlEdge()) {
+      builder.ControlInput(in_edge->src()->name());
+    } else {
+      inputs.push_back(NodeDefBuilder::NodeOut(
+          in_edge->src()->name(), in_edge->src_output(), arg_types[i]));
+    }
+  }
+  builder.Input(inputs);
+  TF_RETURN_IF_ERROR(builder.Finalize(&while_def));
+
+  Status status;
+  Node* while_node = graph->AddNode(while_def, &status);
+  if (!status.ok()) {
+    return status;
+  }
+
+  // Copies edges to the Enter nodes and from the Exit nodes onto the While.
+  for (int i = 0; i < frame->args.size(); ++i) {
+    const Arg& arg = frame->args[i];
+    const Edge* in_edge;
+    TF_RETURN_IF_ERROR(arg.enter->input_edge(0, &in_edge));
+    if (in_edge->IsControlEdge()) {
+      graph->AddControlEdge(in_edge->src(), while_node);
+    } else {
+      graph->AddEdge(in_edge->src(), in_edge->src_output(), while_node, i);
+    }
+
+    if (!arg.is_loop_invariant) {
+      std::vector<const Edge*> edges(arg.exit->out_edges().begin(),
+                                     arg.exit->out_edges().end());
+      for (const Edge* edge : edges) {
+        Node* dst = edge->dst();
+        int dst_input = edge->dst_input();
+        graph->RemoveEdge(edge);
+
+        int src_output =
+            dst_input == Graph::kControlSlot ? Graph::kControlSlot : i;
+        graph->AddEdge(while_node, src_output, dst, dst_input);
+      }
+    }
+  }
+
+  // Remove the old nodes from the graph, and add the while node to the parent
+  // frame.
+  for (Node* node : frame->nodes) {
+    graph->RemoveNode(node);
+  }
+  frame->parent->nodes.insert(while_node);
+
+  VLOG(2) << "Frame " << frame->name << " after: "
+          << dump_graph::DumpGraphToFile("functionalize_after", *graph);
+
+  return Status::OK();
+}
+
+}  // namespace
+
+// Transformation that converts Tensorflow's graph control flow constructs into
+// functional equivalents.
+Status FunctionalizeControlFlow(Graph* graph,
+                                FunctionLibraryDefinition* library) {
+  VLOG(2) << "FunctionalizeControlFlow: "
+          << dump_graph::DumpGraphToFile("functionalize_initial", *graph);
+  // Note: BuildControlFlowInfo() requires that the graph's source node is
+  // connected to all source nodes in the graph. Many graphs violate this
+  // invariant.
+  std::vector<ControlFlowInfo> cf_info;
+  TF_RETURN_IF_ERROR(BuildControlFlowInfo(graph, &cf_info));
+
+  // Builds Frames, indexed by name.
+  std::unordered_map<string, Frame> frames;
+  for (Node* node : graph->op_nodes()) {
+    const ControlFlowInfo& cf = cf_info[node->id()];
+
+    VLOG(2) << "node: " << node->name() << " frame_name: " << cf.frame_name
+            << " frame: " << (cf.frame ? cf.frame->name() : "---")
+            << " parent_frame: "
+            << (cf.parent_frame ? cf.parent_frame->name() : "---");
+    TF_RET_CHECK(cf.frame != nullptr && cf.parent_frame != nullptr);
+
+    Frame& frame = frames[cf.frame_name];
+    Frame* parent = &frames[cf_info[cf.parent_frame->id()].frame_name];
+    if (frame.parent == nullptr) {
+      frame.parent = parent;
+      frame.name = cf.frame_name;
+      ++parent->num_children;
+    } else if (frame.parent != parent) {
+      return errors::InvalidArgument("Mismatched parent frames for ",
+                                     cf.frame->id(), ": ", parent->name, " vs ",
+                                     frame.parent->name);
+    }
+
+    if (IsEnter(node)) {
+      Arg arg;
+      arg.enter = node;
+      TF_RETURN_IF_ERROR(GetNodeAttr(arg.enter->attrs(), "is_constant",
+                                     &arg.is_loop_invariant));
+      frame.args.push_back(arg);
+    } else if (IsLoopCond(node)) {
+      if (frame.loop_cond) {
+        return errors::InvalidArgument(
+            "Loop ", cf.frame_name,
+            " has more than one LoopCond node: ", node->name(), " and ",
+            frame.loop_cond->name());
+      }
+      frame.loop_cond = node;
+    }
+    frame.nodes.insert(node);
+  }
+
+  // Adds frames with no children (i.e., the innermost frames) to a worklist.
+  std::deque<Frame*> worklist;
+  for (auto& frame : frames) {
+    if (frame.second.num_children == 0) {
+      worklist.push_back(&frame.second);
+    }
+  }
+
+  // Eliminate loops from innermost to outermost.
+  while (!worklist.empty()) {
+    Frame* frame = worklist.front();
+    worklist.pop_front();
+    if (frame->parent == frame) {
+      // Skip the root frame.
+      continue;
+    }
+
+    TF_RETURN_IF_ERROR(FunctionalizeLoop(graph, frame, library));
+
+    // If the parent has no remaining children, add it to the worklist.
+    --frame->parent->num_children;
+    if (frame->parent->num_children == 0) {
+      worklist.push_back(frame->parent);
+    }
+  }
+
+  return Status::OK();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.h b/tensorflow/compiler/tf2xla/functionalize_control_flow.h
new file mode 100644
index 0000000000..1535dc80b0
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.h
@@ -0,0 +1,32 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_TF2XLA_FUNCTIONALIZE_CONTROL_FLOW_H_
+#define TENSORFLOW_COMPILER_TF2XLA_FUNCTIONALIZE_CONTROL_FLOW_H_
+
+#include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/graph/graph.h"
+
+namespace tensorflow {
+
+// Transformation that converts tf.while_loop() loops into functional While
+// operators, suitable for XLA compilation.
+// TODO(b/36470387): add support for conditionals.
+Status FunctionalizeControlFlow(Graph* graph,
+                                FunctionLibraryDefinition* library);
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_COMPILER_TF2XLA_FUNCTIONALIZE_CONTROL_FLOW_H_
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
new file mode 100644
index 0000000000..7f6717ffaf
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
@@ -0,0 +1,647 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/tf2xla/functionalize_control_flow.h"
+
+#include "tensorflow/cc/framework/ops.h"
+#include "tensorflow/cc/ops/control_flow_ops_internal.h"
+#include "tensorflow/cc/ops/function_ops.h"
+#include "tensorflow/cc/ops/resource_variable_ops.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/compiler/tf2xla/cc/ops/functional_ops.h"
+#include "tensorflow/compiler/tf2xla/test_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/graph/graph_constructor.h"
+#include "tensorflow/core/graph/graph_def_builder.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/util/equal_graph_def.h"
+
+namespace tensorflow {
+namespace {
+
+// Returns the names of the "cond" and "body" functions for the While node
+// in a graph.
+Status FindWhileCondAndBody(const GraphDef& graph, NameAttrList* cond,
+                            NameAttrList* body) {
+  for (const NodeDef& node : graph.node()) {
+    if (node.op() == "XlaWhile") {
+      const NameAttrList* result;
+      TF_RETURN_IF_ERROR(GetNodeAttr(node, "cond", &result));
+      *cond = *result;
+      TF_RETURN_IF_ERROR(GetNodeAttr(node, "body", &result));
+      *body = *result;
+      return Status::OK();
+    }
+  }
+  return errors::NotFound("No XlaWhile node found in graph");
+}
+
+// Graph:
+// x = array_ops.placeholder(dtypes.int32)
+// y = control_flow_ops.while_loop(lambda i: i < 10, lambda i: i + 1, [x])
+TEST(FunctionalizeControlFlow, OneLoopVar) {
+  Graph graph(OpRegistry::Global());
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+
+    auto dummy = ops::Placeholder(scope.WithOpName("Dummy"), DT_INT32);
+
+    auto source = ops::Placeholder(scope.WithOpName("source"), DT_INT32);
+    auto enter =
+        ops::internal::Enter(scope.WithOpName("while/Enter"), source, "aloop");
+    auto merge = ops::Merge(scope.WithOpName("while/Merge"),
+                            std::initializer_list<Input>{enter, dummy});
+    auto ten = ops::Const<int32>(
+        scope.WithOpName("while/Less/y").WithControlDependencies(merge.output),
+        10);
+    auto less = ops::Less(scope.WithOpName("while/Less"), merge.output, ten);
+    auto loop_cond = ops::LoopCond(scope.WithOpName("while/LoopCond"), less);
+    auto switch_ =
+        ops::Switch(scope.WithOpName("while/Switch"), merge.output, loop_cond);
+    auto exit = ops::internal::Exit(scope.WithOpName("while/Exit"),
+                                    switch_.output_false);
+    auto identity =
+        ops::Identity(scope.WithOpName("while/Identity"), switch_.output_true);
+    auto one = ops::Const<int32>(
+        scope.WithOpName("while/add/y").WithControlDependencies(identity), 1);
+    auto add = ops::Add(scope.WithOpName("while/add"), identity, one);
+    auto next_iteration =
+        ops::NextIteration(scope.WithOpName("while/NextIteration"), add);
+
+    auto sink = ops::Identity(scope.WithOpName("sink"), exit);
+
+    // Remove the dummy node and add the loop backedge.
+    scope.graph()->RemoveNode(dummy.node());
+    scope.graph()->AddEdge(next_iteration.node(), 0, merge.output.node(), 1);
+
+    TF_EXPECT_OK(scope.ToGraph(&graph));
+  }
+
+  FunctionLibraryDefinition library(OpRegistry::Global(), {});
+  TF_ASSERT_OK(FunctionalizeControlFlow(&graph, &library));
+
+  GraphDef graph_def;
+  graph.ToGraphDef(&graph_def);
+
+  NameAttrList cond_fn, body_fn;
+  TF_EXPECT_OK(FindWhileCondAndBody(graph_def, &cond_fn, &body_fn));
+
+  // Outer graph
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto source = ops::Placeholder(scope.WithOpName("source"), DT_INT32);
+    auto while_op =
+        ops::XlaWhile(scope.WithOpName("while/LoopCond"),
+                      std::initializer_list<Input>{source}, cond_fn, body_fn);
+    auto sink = ops::Identity(scope.WithOpName("sink"), while_op[0]);
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+    TF_EXPECT_GRAPH_EQ(expected, graph_def);
+  }
+
+  // Condition graph
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto ten = ops::Const<int32>(
+        scope.WithOpName("while/Less/y").WithControlDependencies(arg), 10);
+    auto less = ops::Less(scope.WithOpName("while/Less"), arg, ten);
+    auto retval = ops::_Retval(scope.WithOpName("_retval0_RetVal"), less, 0);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(InstantiateFunctionForTest(cond_fn.name(), library, &result));
+
+    EXPECT_EQ(DataTypeVector{DT_INT32}, result.arg_types);
+    EXPECT_EQ(DataTypeVector{DT_BOOL}, result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+
+  // Body graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto identity = ops::Identity(scope.WithOpName("while/Identity"), arg);
+    auto one = ops::Const<int32>(
+        scope.WithOpName("while/add/y").WithControlDependencies(identity), 1);
+    auto add = ops::Add(scope.WithOpName("while/add"), identity, one);
+    auto retval = ops::_Retval(scope.WithOpName("_retval0_RetVal"), add, 0);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(InstantiateFunctionForTest(body_fn.name(), library, &result));
+
+    EXPECT_EQ(DataTypeVector{DT_INT32}, result.arg_types);
+    EXPECT_EQ(DataTypeVector{DT_INT32}, result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+}
+
+// Graph:
+// x = array_ops.placeholder(dtypes.int32)
+// y = array_ops.placeholder(dtypes.int32)
+// cond = lambda (i, j): i + 3 < 10
+// body = lambda (i, j): (i < 10, j * 2)
+// z = control_flow_ops.while_loop(cond, body, [x, y])
+TEST(FunctionalizeControlFlow, TwoLoopVars) {
+  Graph graph(OpRegistry::Global());
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+
+    auto dummy = ops::Placeholder(scope.WithOpName("Dummy"), DT_INT32);
+
+    auto x = ops::Placeholder(scope.WithOpName("Placeholder/x"), DT_INT32);
+    auto y = ops::Placeholder(scope.WithOpName("Placeholder/y"), DT_INT32);
+    auto enter_x =
+        ops::internal::Enter(scope.WithOpName("while/Enter/x"), x, "aloop");
+    auto enter_y =
+        ops::internal::Enter(scope.WithOpName("while/Enter/y"), y, "aloop");
+    auto merge_x = ops::Merge(scope.WithOpName("while/Merge/x"),
+                              std::initializer_list<Input>{enter_x, dummy});
+    auto merge_y = ops::Merge(scope.WithOpName("while/Merge/y"),
+                              std::initializer_list<Input>{enter_y, dummy});
+
+    // Loop condition
+    auto three = ops::Const<int32>(scope.WithOpName("while/cond/three")
+                                       .WithControlDependencies(merge_x.output),
+                                   3);
+    auto cond_add =
+        ops::Add(scope.WithOpName("while/cond/Add"), merge_x.output, three);
+    auto ten = ops::Const<int32>(scope.WithOpName("while/cond/ten")
+                                     .WithControlDependencies(merge_x.output),
+                                 10);
+    auto less = ops::Less(scope.WithOpName("while/cond/Less"), cond_add, ten);
+    auto loop_cond = ops::LoopCond(scope.WithOpName("while/LoopCond"), less);
+
+    auto switch_x = ops::Switch(scope.WithOpName("while/Switch/x"),
+                                merge_x.output, loop_cond);
+    auto switch_y = ops::Switch(scope.WithOpName("while/Switch/y"),
+                                merge_y.output, loop_cond);
+
+    auto exit_x = ops::internal::Exit(scope.WithOpName("while/Exit/x"),
+                                      switch_x.output_false);
+    auto exit_y = ops::internal::Exit(scope.WithOpName("while/Exit/y"),
+                                      switch_y.output_false);
+
+    auto identity_x = ops::Identity(scope.WithOpName("while/Identity/x"),
+                                    switch_x.output_true);
+    auto identity_y = ops::Identity(scope.WithOpName("while/Identity/y"),
+                                    switch_y.output_true);
+
+    auto one = ops::Const<int32>(
+        scope.WithOpName("while/add/one").WithControlDependencies(identity_x),
+        1);
+    auto two = ops::Const<int32>(
+        scope.WithOpName("while/mul/two").WithControlDependencies(identity_x),
+        2);
+
+    auto add = ops::Add(scope.WithOpName("while/add"), identity_x, one);
+    auto mul = ops::Add(scope.WithOpName("while/mul"), identity_y, two);
+    auto next_iteration_x =
+        ops::NextIteration(scope.WithOpName("while/NextIteration/x"), add);
+    auto next_iteration_y =
+        ops::NextIteration(scope.WithOpName("while/NextIteration/y"), mul);
+
+    auto sink_x = ops::Identity(scope.WithOpName("sink_x"), exit_x);
+    auto sink_y = ops::Identity(scope.WithOpName("sink_y"), exit_y);
+
+    // Remove the dummy node and add the loop backedges.
+    scope.graph()->RemoveNode(dummy.node());
+    scope.graph()->AddEdge(next_iteration_x.node(), 0, merge_x.output.node(),
+                           1);
+    scope.graph()->AddEdge(next_iteration_y.node(), 0, merge_y.output.node(),
+                           1);
+
+    TF_EXPECT_OK(scope.ToGraph(&graph));
+  }
+
+  FunctionLibraryDefinition library(OpRegistry::Global(), {});
+  TF_ASSERT_OK(FunctionalizeControlFlow(&graph, &library));
+
+  GraphDef graph_def;
+  graph.ToGraphDef(&graph_def);
+
+  NameAttrList cond_fn, body_fn;
+  TF_EXPECT_OK(FindWhileCondAndBody(graph_def, &cond_fn, &body_fn));
+
+  // Outer graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto x = ops::Placeholder(scope.WithOpName("Placeholder/x"), DT_INT32);
+    auto y = ops::Placeholder(scope.WithOpName("Placeholder/y"), DT_INT32);
+    auto while_op =
+        ops::XlaWhile(scope.WithOpName("while/LoopCond"),
+                      std::initializer_list<Input>{x, y}, cond_fn, body_fn);
+    auto sink_x = ops::Identity(scope.WithOpName("sink_x"), while_op[0]);
+    auto sink_y = ops::Identity(scope.WithOpName("sink_y"), while_op[1]);
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+    TF_EXPECT_GRAPH_EQ(expected, graph_def);
+  }
+
+  // Condition graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg0 = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto arg1 = ops::_Arg(scope.WithOpName("_arg1"), DT_INT32, 1);
+    auto three = ops::Const<int32>(scope.WithOpName("while/cond/three")
+                                       .WithControlDependencies(arg0.output),
+                                   3);
+    auto cond_add =
+        ops::Add(scope.WithOpName("while/cond/Add"), arg0.output, three);
+    auto ten = ops::Const<int32>(
+        scope.WithOpName("while/cond/ten").WithControlDependencies(arg0.output),
+        10);
+    auto less = ops::Less(scope.WithOpName("while/cond/Less"), cond_add, ten);
+    auto retval = ops::_Retval(scope.WithOpName("_retval0_RetVal"), less, 0);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(InstantiateFunctionForTest(cond_fn.name(), library, &result));
+
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32}), result.arg_types);
+    EXPECT_EQ(DataTypeVector{DT_BOOL}, result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+
+  // Body graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg0 = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto arg1 = ops::_Arg(scope.WithOpName("_arg1"), DT_INT32, 1);
+
+    auto identity_x = ops::Identity(scope.WithOpName("while/Identity/x"), arg0);
+    auto identity_y = ops::Identity(scope.WithOpName("while/Identity/y"), arg1);
+
+    auto one = ops::Const<int32>(
+        scope.WithOpName("while/add/one").WithControlDependencies(identity_x),
+        1);
+    auto two = ops::Const<int32>(
+        scope.WithOpName("while/mul/two").WithControlDependencies(identity_x),
+        2);
+
+    auto add = ops::Add(scope.WithOpName("while/add"), identity_x, one);
+    auto mul = ops::Add(scope.WithOpName("while/mul"), identity_y, two);
+    auto retval0 = ops::_Retval(scope.WithOpName("_retval0_RetVal"), add, 0);
+    auto retval1 = ops::_Retval(scope.WithOpName("_retval1_RetVal"), mul, 1);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(InstantiateFunctionForTest(body_fn.name(), library, &result));
+
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32}), result.arg_types);
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32}), result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+}
+
+// Example with nesting, loop-invariant arguments, and resource variables.
+//
+// accum = resource_variable_ops.ResourceVariable(1)
+// x = array_ops.placeholder(2, dtype=dtypes.int32)
+// y = 3 + x
+//
+// def inner_body(j, k):
+//   add = state_ops.assign_add(accum, k * j + x)
+//   with ops.control_dependencies([add]):
+//     return [j + 1, k]
+//
+// def body(i):
+//   m = control_flow_ops.while_loop(lambda j, k: j < 5, inner_body,
+//                                   [1, y], name="inner")
+//   with ops.control_dependencies(m):
+//     return [i + 1]
+//
+// z = control_flow_ops.while_loop(lambda i: i < 10, body, [0], name="outer")
+TEST(FunctionalizeControlFlow, Complex) {
+  Graph graph(OpRegistry::Global());
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+
+    auto dummy = ops::Placeholder(scope.WithOpName("Dummy"), DT_INT32);
+
+    auto x = ops::Placeholder(scope.WithOpName("x"), DT_INT32);
+    auto three = ops::Const<int32>(scope.WithOpName("three"), 3);
+    auto y = ops::Add(scope.WithOpName("y"), x, three);
+
+    auto var = ops::VarHandleOp(scope.WithOpName("Variable"), DT_INT32,
+                                TensorShape({}));
+
+    // Outer loop
+    auto zero = ops::Const<int32>(scope.WithOpName("outer/Const"), 0);
+    auto enter_i =
+        ops::internal::Enter(scope.WithOpName("outer/Enter_i"), zero, "outer");
+    auto merge_i = ops::Merge(scope.WithOpName("outer/Merge_i"),
+                              std::initializer_list<Input>{enter_i, dummy});
+    auto ten = ops::Const<int32>(scope.WithOpName("outer/Less/y")
+                                     .WithControlDependencies(merge_i.output),
+                                 10);
+    auto less_i =
+        ops::Less(scope.WithOpName("outer/Less_i"), merge_i.output, ten);
+    auto outer_loop_cond =
+        ops::LoopCond(scope.WithOpName("outer/LoopCond"), less_i);
+    auto switch_i = ops::Switch(scope.WithOpName("outer/Switch"),
+                                merge_i.output, outer_loop_cond);
+    auto exit_i = ops::internal::Exit(scope.WithOpName("outer/Exit"),
+                                      switch_i.output_false);
+    auto identity_i =
+        ops::Identity(scope.WithOpName("outer/Identity"), switch_i.output_true);
+
+    auto enter_x_outer =
+        ops::internal::Enter(scope.WithOpName("outer/Enter_x"), x, "outer",
+                             ops::internal::Enter::Attrs().IsConstant(true));
+    auto enter_k_outer =
+        ops::internal::Enter(scope.WithOpName("outer/Enter_k"), y, "outer",
+                             ops::internal::Enter::Attrs().IsConstant(true));
+    auto enter_var_outer =
+        ops::internal::Enter(scope.WithOpName("outer/Enter_var"), var, "outer",
+                             ops::internal::Enter::Attrs().IsConstant(true));
+
+    // Inner loop
+    auto one_j = ops::Const<int32>(
+        scope.WithOpName("outer/j").WithControlDependencies(identity_i), 1);
+    auto enter_j = ops::internal::Enter(scope.WithOpName("outer/inner/Enter_j"),
+                                        one_j, "inner");
+    auto enter_k =
+        ops::internal::Enter(scope.WithOpName("outer/inner/Enter_k")
+                                 .WithControlDependencies(identity_i),
+                             enter_k_outer, "inner");
+    auto enter_x = ops::internal::Enter(
+        scope.WithOpName("outer/inner/Enter_x"), enter_x_outer, "inner",
+        ops::internal::Enter::Attrs().IsConstant(true));
+    auto enter_var = ops::internal::Enter(
+        scope.WithOpName("outer/inner/Enter_var"), enter_var_outer, "inner",
+        ops::internal::Enter::Attrs().IsConstant(true));
+
+    auto merge_j = ops::Merge(scope.WithOpName("outer/inner/Merge_j"),
+                              std::initializer_list<Input>{enter_j, dummy});
+    auto merge_k = ops::Merge(scope.WithOpName("outer/inner/Merge_k"),
+                              std::initializer_list<Input>{enter_k, dummy});
+
+    auto five = ops::Const<int32>(scope.WithOpName("outer/inner/Five")
+                                      .WithControlDependencies(merge_j.output),
+                                  5);
+    auto less_j =
+        ops::Less(scope.WithOpName("outer/inner/Less_j"), merge_j.output, five);
+    auto loop_cond = ops::LoopCond(scope.WithOpName("outer/LoopCond"), less_j);
+
+    auto switch_j = ops::Switch(scope.WithOpName("outer/inner/Switch_j"),
+                                merge_j.output, loop_cond);
+    auto switch_k = ops::Switch(scope.WithOpName("outer/inner/Switch_k"),
+                                merge_k.output, loop_cond);
+    auto exit_j = ops::internal::Exit(scope.WithOpName("outer/inner/Exit_j"),
+                                      switch_j.output_false);
+    auto exit_k = ops::internal::Exit(scope.WithOpName("outer/inner/Exit_k"),
+                                      switch_k.output_false);
+    auto identity_j = ops::Identity(scope.WithOpName("outer/inner/Identity_j"),
+                                    switch_j.output_true);
+    auto identity_k = ops::Identity(scope.WithOpName("outer/inner/Identity_k"),
+                                    switch_k.output_true);
+
+    // Variable update
+    auto mul_jk =
+        ops::Mul(scope.WithOpName("outer/inner/mul"), identity_j, identity_k);
+    auto add_jkx =
+        ops::Add(scope.WithOpName("outer/inner/add"), mul_jk, enter_x);
+    auto assign = ops::AssignAddVariableOp(
+        scope.WithOpName("outer/inner/assign_add"), enter_var, add_jkx);
+
+    auto one =
+        ops::Const<int32>(scope.WithOpName("outer/inner/One")
+                              .WithControlDependencies(
+                                  gtl::ArraySlice<Operation>{assign.operation}),
+                          1);
+    auto add_j =
+        ops::Add(scope.WithOpName("outer/inner/add_j"), identity_j, one);
+
+    auto next_iteration_j = ops::NextIteration(
+        scope.WithOpName("outer/inner/NextIteration_j"), add_j);
+    auto next_iteration_k = ops::NextIteration(
+        scope.WithOpName("outer/inner/NextIteration_k"), identity_k);
+
+    // Body and backedge for outer loop.
+    auto one_outer = ops::Const<int32>(
+        scope.WithOpName("outer/add/y").WithControlDependencies(identity_i), 1);
+    auto add_i =
+        ops::Add(scope.WithOpName("outer/add")
+                     .WithControlDependencies(gtl::ArraySlice<Operation>{
+                         exit_j.output.op(), exit_k.output.op()}),
+                 identity_i, one_outer);
+    auto next_iteration_i =
+        ops::NextIteration(scope.WithOpName("outer/NextIteration"), add_i);
+
+    auto sink = ops::Identity(scope.WithOpName("sink"), exit_i);
+
+    // Remove the dummy node and add the loop backedge.
+    scope.graph()->RemoveNode(dummy.node());
+    scope.graph()->AddEdge(next_iteration_i.node(), 0, merge_i.output.node(),
+                           1);
+    scope.graph()->AddEdge(next_iteration_j.node(), 0, merge_j.output.node(),
+                           1);
+    scope.graph()->AddEdge(next_iteration_k.node(), 0, merge_k.output.node(),
+                           1);
+
+    TF_EXPECT_OK(scope.ToGraph(&graph));
+  }
+
+  FunctionLibraryDefinition library(OpRegistry::Global(), {});
+  TF_ASSERT_OK(FunctionalizeControlFlow(&graph, &library));
+
+  GraphDef graph_def;
+  graph.ToGraphDef(&graph_def);
+
+  NameAttrList outer_cond_fn, outer_body_fn;
+  TF_EXPECT_OK(FindWhileCondAndBody(graph_def, &outer_cond_fn, &outer_body_fn));
+
+  // Outer graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto x = ops::Placeholder(scope.WithOpName("x"), DT_INT32);
+    auto three = ops::Const<int32>(scope.WithOpName("three"), 3);
+    auto y = ops::Add(scope.WithOpName("y"), x, three);
+
+    auto var = ops::VarHandleOp(scope.WithOpName("Variable"), DT_INT32,
+                                TensorShape({}));
+
+    auto zero = ops::Const<int32>(scope.WithOpName("outer/Const"), 0);
+
+    auto while_op = ops::XlaWhile(scope.WithOpName("outer/LoopCond"),
+                                  std::initializer_list<Input>{zero, y, x, var},
+                                  outer_cond_fn, outer_body_fn);
+    auto sink = ops::Identity(scope.WithOpName("sink"), while_op[0]);
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+    TF_EXPECT_GRAPH_EQ(expected, graph_def);
+  }
+
+  // Outer condition graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg0 = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto arg1 = ops::_Arg(scope.WithOpName("_arg1"), DT_INT32, 1);
+    auto arg2 = ops::_Arg(scope.WithOpName("_arg2"), DT_INT32, 2);
+    auto arg3 = ops::_Arg(scope.WithOpName("_arg3"), DT_RESOURCE, 3);
+
+    auto ten = ops::Const<int32>(
+        scope.WithOpName("outer/Less/y").WithControlDependencies(arg0.output),
+        10);
+    auto less = ops::Less(scope.WithOpName("outer/Less_i"), arg0, ten);
+    auto retval = ops::_Retval(scope.WithOpName("_retval0_RetVal"), less, 0);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(
+        InstantiateFunctionForTest(outer_cond_fn.name(), library, &result));
+
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32, DT_INT32, DT_RESOURCE}),
+              result.arg_types);
+    EXPECT_EQ(DataTypeVector{DT_BOOL}, result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+
+  // Outer body graph.
+  NameAttrList inner_cond_fn, inner_body_fn;
+  {
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(
+        InstantiateFunctionForTest(outer_body_fn.name(), library, &result));
+
+    // Find the inner condition and body names.
+    TF_EXPECT_OK(
+        FindWhileCondAndBody(result.gdef, &inner_cond_fn, &inner_body_fn));
+
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg0 = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto arg1 = ops::_Arg(scope.WithOpName("_arg1"), DT_INT32, 1);
+    auto arg2 = ops::_Arg(scope.WithOpName("_arg2"), DT_INT32, 2);
+    auto arg3 = ops::_Arg(scope.WithOpName("_arg3"), DT_RESOURCE, 3);
+
+    auto identity_i = ops::Identity(scope.WithOpName("outer/Identity"), arg0);
+    auto one_j = ops::Const<int32>(
+        scope.WithOpName("outer/j").WithControlDependencies(identity_i), 1);
+    auto while_op =
+        ops::XlaWhile(scope.WithOpName("outer/LoopCond_1"),
+                      std::initializer_list<Input>{one_j, arg1, arg2, arg3},
+                      inner_cond_fn, inner_body_fn);
+
+    auto one_outer = ops::Const<int32>(
+        scope.WithOpName("outer/add/y").WithControlDependencies(identity_i), 1);
+    auto add_i =
+        ops::Add(scope.WithOpName("outer/add")
+                     .WithControlDependencies(gtl::ArraySlice<Operation>{
+                         while_op[0].op(), while_op[1].op()}),
+                 identity_i, one_outer);
+
+    auto retval0 = ops::_Retval(scope.WithOpName("_retval0_RetVal"), add_i, 0);
+    auto retval1 = ops::_Retval(scope.WithOpName("_retval1_RetVal"), arg1, 1);
+    auto retval2 = ops::_Retval(scope.WithOpName("_retval2_RetVal"), arg2, 2);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32, DT_INT32, DT_RESOURCE}),
+              result.arg_types);
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32, DT_INT32}), result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+
+  // Inner condition graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg0 = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto arg1 = ops::_Arg(scope.WithOpName("_arg1"), DT_INT32, 1);
+    auto arg2 = ops::_Arg(scope.WithOpName("_arg2"), DT_INT32, 2);
+    auto arg3 = ops::_Arg(scope.WithOpName("_arg3"), DT_RESOURCE, 3);
+
+    auto five = ops::Const<int32>(
+        scope.WithOpName("outer/inner/Five").WithControlDependencies(arg0), 5);
+    auto less_j = ops::Less(scope.WithOpName("outer/inner/Less_j"), arg0, five);
+    auto retval = ops::_Retval(scope.WithOpName("_retval0_RetVal"), less_j, 0);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(
+        InstantiateFunctionForTest(inner_cond_fn.name(), library, &result));
+
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32, DT_INT32, DT_RESOURCE}),
+              result.arg_types);
+    EXPECT_EQ(DataTypeVector{DT_BOOL}, result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+
+  // Inner body graph.
+  {
+    Scope scope = Scope::NewRootScope().ExitOnError();
+    auto arg0 = ops::_Arg(scope.WithOpName("_arg0"), DT_INT32, 0);
+    auto arg1 = ops::_Arg(scope.WithOpName("_arg1"), DT_INT32, 1);
+    auto arg2 = ops::_Arg(scope.WithOpName("_arg2"), DT_INT32, 2);
+    auto arg3 = ops::_Arg(scope.WithOpName("_arg3"), DT_RESOURCE, 3);
+
+    auto identity_j =
+        ops::Identity(scope.WithOpName("outer/inner/Identity_j"), arg0);
+    auto identity_k =
+        ops::Identity(scope.WithOpName("outer/inner/Identity_k"), arg1);
+
+    auto mul_jk =
+        ops::Mul(scope.WithOpName("outer/inner/mul"), identity_j, identity_k);
+    auto add_jkx = ops::Add(scope.WithOpName("outer/inner/add"), mul_jk, arg2);
+    auto assign = ops::AssignAddVariableOp(
+        scope.WithOpName("outer/inner/assign_add"), arg3, add_jkx);
+
+    auto one =
+        ops::Const<int32>(scope.WithOpName("outer/inner/One")
+                              .WithControlDependencies(
+                                  gtl::ArraySlice<Operation>{assign.operation}),
+                          1);
+    auto add_j =
+        ops::Add(scope.WithOpName("outer/inner/add_j"), identity_j, one);
+
+    auto retval0 = ops::_Retval(scope.WithOpName("_retval0_RetVal"), add_j, 0);
+    auto retval1 =
+        ops::_Retval(scope.WithOpName("_retval1_RetVal"), identity_k, 1);
+    auto retval2 = ops::_Retval(scope.WithOpName("_retval2_RetVal"), arg2, 2);
+
+    GraphDef expected;
+    TF_EXPECT_OK(scope.ToGraphDef(&expected));
+
+    InstantiationResultForTest result;
+    TF_EXPECT_OK(
+        InstantiateFunctionForTest(inner_body_fn.name(), library, &result));
+
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32, DT_INT32, DT_RESOURCE}),
+              result.arg_types);
+    EXPECT_EQ((DataTypeVector{DT_INT32, DT_INT32, DT_INT32}), result.ret_types);
+    TF_EXPECT_GRAPH_EQ(expected, result.gdef);
+  }
+}
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD
index a434c74680..96b4fdfec6 100644
--- a/tensorflow/compiler/tf2xla/kernels/BUILD
+++ b/tensorflow/compiler/tf2xla/kernels/BUILD
@@ -68,6 +68,7 @@ tf_kernel_library(
         "reduction_ops.h",
     ],
     deps = [
+        ":while_op",
         "//tensorflow/compiler/tf2xla:common",
         "//tensorflow/compiler/tf2xla:xla_compiler",
         "//tensorflow/compiler/xla:literal_util",
@@ -91,6 +92,21 @@ tf_kernel_library(
     ],
 )
 
+tf_kernel_library(
+    name = "while_op",
+    srcs = ["while_op.cc"],
+    hdrs = ["while_op.h"],
+    deps = [
+        "//tensorflow/compiler/tf2xla:common",
+        "//tensorflow/compiler/tf2xla:xla_compiler",
+        "//tensorflow/compiler/tf2xla/ops:functional_ops",
+        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:tensorflow_opensource",
+    ],
+)
+
 # Kernels that only work on CPU, because they use XLA custom calls.
 # Only link this when using the CPU backend for XLA.
 #
diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc
new file mode 100644
index 0000000000..ae9a358e22
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc
@@ -0,0 +1,252 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/tf2xla/kernels/while_op.h"
+
+#include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/compiler/tf2xla/type_util.h"
+#include "tensorflow/compiler/tf2xla/xla_compiler.h"
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/op_kernel.h"
+
+namespace tensorflow {
+
+namespace {
+
+// Builds XlaCompiler argument descriptions `args` from `ctx`.
+Status MakeXlaCompilerArgumentsFromInputs(
+    XlaOpKernelContext* ctx, std::vector<XlaCompiler::Argument>* args,
+    bool* has_uninitialized_vars) {
+  VLOG(2) << "Num inputs " << ctx->num_inputs();
+  args->resize(ctx->num_inputs());
+  *has_uninitialized_vars = false;
+  for (int i = 0; i < ctx->num_inputs(); ++i) {
+    VLOG(2) << "  Input " << i
+            << " type: " << DataTypeString(ctx->input_type(i))
+            << " shape: " << ctx->InputShape(i).DebugString();
+    XlaCompiler::Argument& arg = (*args)[i];
+    DataType type = ctx->input_type(i);
+    // When reading a variable input, use the type and shape of the variable's
+    // current value.
+    if (type == DT_RESOURCE) {
+      XlaVariable* var;
+      TF_RETURN_IF_ERROR(ctx->GetVariableInput(i, &var));
+
+      bool initialized = var->value.handle() > 0;
+      if (initialized) {
+        arg.kind = XlaCompiler::Argument::kVariable;
+        TF_RETURN_IF_ERROR(
+            ctx->GetVariableTypeAndShape(i, &arg.type, &arg.shape));
+      } else {
+        arg.kind = XlaCompiler::Argument::kUninitializedVariable;
+        arg.type = var->type;
+        *has_uninitialized_vars = true;
+      }
+      arg.tensor_array_size = var->tensor_array_size;
+      arg.name = var->name;
+      // TODO(phawkins): propagate TensorArray gradients into loops.
+      VLOG(2) << "    variable " << var->name
+              << " type: " << DataTypeString(arg.type)
+              << " shape: " << arg.shape.DebugString()
+              << " initialized: " << initialized;
+
+    } else {
+      arg.kind = XlaCompiler::Argument::kParameter;
+      arg.type = ctx->input_type(i);
+      arg.shape = ctx->InputShape(i);
+    }
+  }
+  return Status::OK();
+}
+
+}  // anonymous namespace
+
+XlaWhileOp::XlaWhileOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+  const NameAttrList* name_attr;
+  OP_REQUIRES_OK(ctx, ctx->GetAttr("cond", &name_attr));
+  cond_name_attr_ = *name_attr;
+  OP_REQUIRES_OK(ctx, ctx->GetAttr("body", &name_attr));
+  body_name_attr_ = *name_attr;
+}
+
+void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
+  VLOG(1) << "WhileOp::Compute";
+
+  std::vector<XlaCompiler::Argument> arguments;
+  bool has_uninitialized_vars;
+  OP_REQUIRES_OK(ctx, MakeXlaCompilerArgumentsFromInputs(
+                          ctx, &arguments, &has_uninitialized_vars));
+
+  const bool use_tuple_arg = (arguments.size() != 1);
+
+  xla::ComputationBuilder* builder = ctx->builder();
+  XlaCompiler* compiler = ctx->compiler();
+
+  VLOG(1) << "Compiling body";
+
+  // All resource variables that are inputs to the loop's body must also be
+  // present as loop body outputs; the signature of the loop's input and
+  // output must match. We ensure this by asking the compiler to include the
+  // current values of all variables, even if they haven't been updated by the
+  // computation.
+  // TODO(phawkins): consider adding loop-invariant inputs to XLA's While()
+  // operator.
+  XlaCompiler::CompileOptions body_options;
+  body_options.use_tuple_arg = use_tuple_arg;
+  body_options.return_updated_values_for_all_variables = true;
+  XlaCompiler::CompilationResult body;
+  OP_REQUIRES_OK(ctx, compiler->CompileFunction(body_options, body_name_attr_,
+                                                arguments, &body));
+
+  // We must use a static shape for parameters to an XLA compilation. However,
+  // we may not know the shape of a TensorArray if it is first written inside
+  // the loop. Ideally we would require the user to provide a static shape,
+  // but this is not always easy.
+  // So if uninitialized variables are used by the loop body, we compile the
+  // body function twice:
+  // 1) once with uninitialized variable inputs. We discard the computation
+  //    but we assume variable shapes reach a fixpoint after one iteration.
+  //    So we can use the output shapes of the variables as the "true" shapes.
+  // 2) again with the "correct" input shapes determined by (1).
+  if (has_uninitialized_vars) {
+    // Initializes any uninitialized variables with zero values of the
+    // shape determined by the first compilation.
+    for (int i = 0; i < body.variable_updates.size(); ++i) {
+      const XlaCompiler::VariableUpdate& update = body.variable_updates[i];
+      XlaCompiler::Argument& arg = arguments[update.input_index];
+      if (arg.kind == XlaCompiler::Argument::kUninitializedVariable) {
+        arg.kind = XlaCompiler::Argument::kVariable;
+        arg.shape = update.shape;
+
+        xla::ComputationDataHandle zero = XlaHelpers::Zero(builder, arg.type);
+        auto value = builder->Broadcast(zero, update.shape.dim_sizes());
+        OP_REQUIRES_OK(
+            ctx, ctx->AssignVariable(update.input_index, arg.type, value));
+      }
+    }
+    // Recompile the body with the "correct" shapes.
+    body = {};
+    OP_REQUIRES_OK(ctx, compiler->CompileFunction(body_options, body_name_attr_,
+                                                  arguments, &body));
+  }
+
+  VLOG(1) << "Compiling condition";
+
+  XlaCompiler::CompileOptions cond_options;
+  cond_options.use_tuple_arg = use_tuple_arg;
+  XlaCompiler::CompilationResult cond;
+  OP_REQUIRES_OK(ctx, compiler->CompileFunction(cond_options, cond_name_attr_,
+                                                arguments, &cond));
+
+  xla::Shape body_input_shape, cond_input_shape;
+  if (use_tuple_arg) {
+    body_input_shape = xla::ShapeUtil::MakeTupleShape(body.xla_input_shapes);
+    cond_input_shape = xla::ShapeUtil::MakeTupleShape(cond.xla_input_shapes);
+  } else {
+    CHECK(!body.xla_input_shapes.empty());
+    body_input_shape = body.xla_input_shapes[0];
+    CHECK(!body.xla_input_shapes.empty());
+    cond_input_shape = cond.xla_input_shapes[0];
+  }
+
+  VLOG(2) << "Body shape: " << xla::ShapeUtil::HumanString(body_input_shape)
+          << " -> " << xla::ShapeUtil::HumanString(body.xla_output_shape);
+  VLOG(2) << "Cond shape: " << xla::ShapeUtil::HumanString(cond_input_shape)
+          << " -> " << xla::ShapeUtil::HumanString(cond.xla_output_shape);
+
+  OP_REQUIRES(ctx,
+              xla::ShapeUtil::Compatible(body_input_shape, cond_input_shape),
+              errors::InvalidArgument(
+                  "Input shapes of loop body and condition do not match: ",
+                  xla::ShapeUtil::HumanString(body_input_shape), " vs. ",
+                  xla::ShapeUtil::HumanString(cond_input_shape)));
+  OP_REQUIRES(
+      ctx, xla::ShapeUtil::Compatible(body_input_shape, body.xla_output_shape),
+      errors::InvalidArgument(
+          "Input and output shapes of loop body do not match: ",
+          xla::ShapeUtil::HumanString(body_input_shape), " vs. ",
+          xla::ShapeUtil::HumanString(body.xla_output_shape)));
+
+  xla::ComputationDataHandle data;
+
+  int num_inputs = body.input_mapping.size();
+
+  std::vector<xla::ComputationDataHandle> inputs(num_inputs);
+  for (int i = 0; i < num_inputs; ++i) {
+    int input_num = body.input_mapping[i];
+    if (ctx->input_type(input_num) == DT_RESOURCE) {
+      OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(input_num, &inputs[i]));
+    } else {
+      inputs[i] = ctx->Input(i);
+    }
+  }
+
+  xla::ComputationDataHandle init;
+  if (use_tuple_arg) {
+    init = builder->Tuple(inputs);
+  } else {
+    init = inputs[0];
+  }
+
+  VLOG(1) << "Building while loop";
+
+  xla::ComputationDataHandle while_result =
+      builder->While(*cond.computation, *body.computation, init);
+
+  auto get_loop_output = [&](int i) {
+    if (use_tuple_arg) {
+      return builder->GetTupleElement(while_result, i);
+    } else {
+      return while_result;
+    }
+  };
+
+  // Sets non-variable outputs.
+  for (int i = 0; i < ctx->num_outputs(); ++i) {
+    if (ctx->input_type(i) != DT_RESOURCE) {
+      ctx->SetOutput(body.input_mapping[i], get_loop_output(i));
+    }
+  }
+
+  // Updates the values of any resource variables modified by the loop.
+  for (int i = 0; i < body.variable_updates.size(); ++i) {
+    const XlaCompiler::VariableUpdate& update = body.variable_updates[i];
+    if (update.modified) {
+      int pos = body.outputs.size() + i;
+      OP_REQUIRES_OK(ctx, ctx->AssignVariable(update.input_index, update.type,
+                                              get_loop_output(pos)));
+    }
+    VLOG(2) << "Loop-carried variable: pos: " << update.input_index
+            << " name: " << ctx->VariableDebugString(update.input_index)
+            << " modified: " << update.modified
+            << " type: " << DataTypeString(update.type)
+            << " shape: " << update.shape.DebugString();
+    // Copies the identity of the resource variable from input to output
+    // unchanged, even if the variable was not modified.
+    ctx->op_kernel_context()->set_output(
+        update.input_index,
+        ctx->op_kernel_context()->input(update.input_index));
+  }
+
+  VLOG(1) << "Done building while loop";
+}
+
+REGISTER_XLA_OP(Name("XlaWhile").AllowResourceTypes(), XlaWhileOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.h b/tensorflow/compiler/tf2xla/kernels/while_op.h
new file mode 100644
index 0000000000..67edebabf9
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.h
@@ -0,0 +1,65 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_TF2XLA_KERNELS_WHILE_OP_H_
+#define TENSORFLOW_COMPILER_TF2XLA_KERNELS_WHILE_OP_H_
+
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/core/framework/attr_value.pb.h"
+
+namespace tensorflow {
+
+// This TensorFlow op provides a functional iteration primitive.
+//
+// The inputs and outputs of the loop body must agree on the number, types, and
+// shapes of the Tensors carried around the loop body.
+//
+// Computations in while loops may read from and write to resource variables.
+// Resource variables may be passed as arguments to a function's body and
+// condition functions. The XlaCompiler converts resource variable arguments
+// into parameters to the XLA computation and moves them to the end of the
+// parameter list, and by using the `return_updated_values_for_all_variables`
+// we ensure that all variables that appear in the input also appear at the
+// end of the body's output. This ensures the loop body's input and output
+// signatures match.
+//
+// It is the user's responsibility to ensure that each non-variable _Arg matches
+// the corresponding _Retval.
+//
+// For example, suppose we have a loop body with arguments:
+// DT_INT32, DT_RESOURCE (pointing to a DT_BOOL var), DT_FLOAT
+// and return values
+// DT_INT32, DT_FLOAT
+// It is an error for the body to return DT_RESOURCE values.
+//
+// The body will be lowered into an XLA computation that takes and returns a
+// tuple with XLA type (I32, F32, PRED). Note the resource variable appears at
+// the end of both the loop body's input and output argument lists.
+class XlaWhileOp : public XlaOpKernel {
+ public:
+  explicit XlaWhileOp(OpKernelConstruction* ctx);
+
+  void Compile(XlaOpKernelContext* ctx) override;
+
+ private:
+  NameAttrList cond_name_attr_;
+  NameAttrList body_name_attr_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(XlaWhileOp);
+};
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_COMPILER_TF2XLA_KERNELS_WHILE_OP_H_
diff --git a/tensorflow/compiler/tf2xla/ops/BUILD b/tensorflow/compiler/tf2xla/ops/BUILD
new file mode 100644
index 0000000000..a2bd06861d
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/ops/BUILD
@@ -0,0 +1,38 @@
+package(
+    default_visibility = ["//tensorflow/compiler/tf2xla:internal"],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
+
+cc_library(
+    name = "functional_ops",
+    srcs = ["functional_ops.cc"],
+    deps = [
+        "//tensorflow/core:framework",
+    ],
+    alwayslink = 1,
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_functional_ops",
+    out = "gen_functional_ops.py",
+    deps = [
+        ":functional_ops",
+    ],
+)
+
+# -----------------------------------------------------------------------------
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/compiler/tf2xla/ops/functional_ops.cc b/tensorflow/compiler/tf2xla/ops/functional_ops.cc
new file mode 100644
index 0000000000..38bcaa3227
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/ops/functional_ops.cc
@@ -0,0 +1,45 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+
+namespace tensorflow {
+
+// TODO(b/37549631) setting the While Op to always be stateful is too
+// conservative.
+REGISTER_OP("XlaWhile")
+    .Input("input: T")
+    .Output("output: T")
+    .Attr("T: list(type) >= 0")
+    .Attr("cond: func")
+    .Attr("body: func")
+    .SetIsStateful()
+    .Doc(R"doc(
+output = input; While (Cond(output)) { output = Body(output) }
+
+input: A list of input tensors whose types are T.
+output: A list of output tensors whose types are T.
+cond: A function takes 'input' and returns a tensor.  If the tensor is
+    a scalar of non-boolean, the scalar is converted to a boolean
+    according to the following rule: if the scalar is a numerical
+    value, non-zero means True and zero means False; if the scalar is
+    a string, non-empty means True and empty means False. If the
+    tensor is not a scalar, non-emptiness means True and False
+    otherwise.
+body: A function that takes a list of tensors and returns another
+      list of tensors. Both lists have the same types as specified by T.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/test_util.cc b/tensorflow/compiler/tf2xla/test_util.cc
new file mode 100644
index 0000000000..3c34b8788d
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/test_util.cc
@@ -0,0 +1,42 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/tf2xla/test_util.h"
+
+#include "tensorflow/compiler/xla/status_macros.h"
+
+namespace tensorflow {
+
+Status InstantiateFunctionForTest(const string& name,
+                                  const FunctionLibraryDefinition& library,
+                                  InstantiationResultForTest* result) {
+  const FunctionDef* fdef = library.Find(name);
+  TF_RET_CHECK(fdef != nullptr);
+
+  auto get_func_sig = [&library](const string& op, const OpDef** sig) {
+    return library.LookUpOpDef(op, sig);
+  };
+  InstantiationResult inst;
+  TF_RETURN_IF_ERROR(
+      InstantiateFunction(*fdef, AttrSlice(), get_func_sig, &inst));
+  result->arg_types = inst.arg_types;
+  result->ret_types = inst.ret_types;
+  for (NodeDef& n : inst.nodes) {
+    *result->gdef.add_node() = std::move(n);
+  }
+  return Status::OK();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/test_util.h b/tensorflow/compiler/tf2xla/test_util.h
new file mode 100644
index 0000000000..362558bcfc
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/test_util.h
@@ -0,0 +1,46 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Helper functions for tests.
+
+#ifndef TENSORFLOW_COMPILER_TF2XLA_TEST_UTIL_H_
+#define TENSORFLOW_COMPILER_TF2XLA_TEST_UTIL_H_
+
+#include <map>
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+// Same as InstantiationResult, but has a GraphDef instead of just nodes.
+struct InstantiationResultForTest {
+  DataTypeVector arg_types;
+  DataTypeVector ret_types;
+  GraphDef gdef;
+};
+
+// Instantiates a function, producing a GraphDef to compare against the
+// expected graph.
+Status InstantiateFunctionForTest(const string& name,
+                                  const FunctionLibraryDefinition& library,
+                                  InstantiationResultForTest* result);
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_COMPILER_TF2XLA_TEST_UTIL_H_
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index 580ce3d802..d7c92a710c 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <numeric>
 
 #include "tensorflow/compiler/tf2xla/dump_graph.h"
+#include "tensorflow/compiler/tf2xla/functionalize_control_flow.h"
 #include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/tf2xla/type_util.h"
 #include "tensorflow/compiler/tf2xla/xla_context.h"
@@ -85,9 +86,10 @@ XlaCompiler::XlaCompiler(XlaCompiler::Options options)
         (*options_.populate_resource_manager)(device_->resource_manager());
   }
 
+  flib_def_.reset(new FunctionLibraryDefinition(*options.flib_def));
   flib_runtime_.reset(NewFunctionLibraryRuntime(
       &device_mgr_, Env::Default(), device_, options.graph_def_version,
-      options.flib_def, OptimizerOptions(),
+      flib_def_.get(), OptimizerOptions(),
       nullptr /* custom_kernel_creator */));
 }
 
@@ -413,6 +415,10 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
   // Report the error here if initialization failed.
   TF_RETURN_IF_ERROR(initialization_status_);
 
+  // Converts Tensorflow's graph control-flow constructs into functional
+  // control-flow that can be compiled into XLA code.
+  TF_RETURN_IF_ERROR(FunctionalizeControlFlow(graph.get(), flib_def_.get()));
+
   xla::ComputationBuilder builder(client(), name);
   XlaContext* context =
       new XlaContext(this, &builder, options_.allow_cpu_custom_calls,
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h
index 1314305532..6b9bf4159d 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.h
+++ b/tensorflow/compiler/tf2xla/xla_compiler.h
@@ -294,6 +294,7 @@ class XlaCompiler {
   XlaCompilationDevice* device_;  // Owned by device_mgr_
   DeviceMgr device_mgr_;
 
+  std::unique_ptr<FunctionLibraryDefinition> flib_def_;
   std::unique_ptr<FunctionLibraryRuntime> flib_runtime_;
 
   struct SignatureHash {
-- 
GitLab


From 6f2977185bab7dbc4d7970416e4a8f6bbb18c94c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 15:35:08 -0700
Subject: [PATCH 0123/1375] [XLA] Disallow calling ShapeUtil::Rank() on tuples.
 ShapeUtil::Rank() will now assert instead of returning 0. It doesn't really
 make sense to assign a rank to tuples, and we should be more explicit about
 this.

PiperOrigin-RevId: 159164448
---
 .../compiler/xla/service/cpu/ir_emitter.cc    |  2 +-
 .../compiler/xla/service/llvm_ir/ir_array.cc  |  2 +-
 .../compiler/xla/service/llvm_ir/llvm_util.cc |  2 +-
 tensorflow/compiler/xla/shape_util.cc         | 12 +++----
 tensorflow/compiler/xla/shape_util.h          |  3 +-
 .../compiler/xla/tests/literal_test_util.cc   | 33 +++++++++++--------
 tensorflow/compiler/xla/xla_data.proto        |  3 +-
 7 files changed, 32 insertions(+), 25 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 94c10a6417..5225e8b96a 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -1970,7 +1970,7 @@ StatusOr<llvm::Value*> IrEmitter::EmitTargetAddressForOp(
     // For the root node, we write directly to the output buffer of the
     // function.
     llvm::Argument* retval = GetResultArgument();
-    if (!ShapeUtil::HasZeroElements(target_shape)) {
+    if (!ShapeUtil::IsNil(target_shape)) {
       llvm::AttrBuilder attr_builder;
       attr_builder.addAlignmentAttr(MinimumAlignmentForShape(target_shape));
       attr_builder.addDereferenceableAttr(ByteSizeOf(target_shape));
diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index e401305ae7..5268e89e0f 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -85,7 +85,7 @@ IrArray::IrArray(llvm::Value* base_ptr, const Shape& shape)
     ++depth;
   }
 
-  if (ShapeUtil::Rank(*shape_) == 0) {
+  if (!ShapeUtil::IsArray(*shape_) || ShapeUtil::IsScalar(*shape_)) {
     DCHECK(depth == 1 || depth == 0) << depth;
   } else {
     DCHECK_EQ(depth, ShapeUtil::Rank(*shape_)) << shape.ShortDebugString();
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index ff2f4cd693..99ad107a90 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -371,7 +371,7 @@ void SetTbaaForInstruction(llvm::Instruction* instruction, Shape shape,
   // Scalars do not have layout which makes it permissible to omit an explicit
   // layout.  To make sure that equivalent scalar shapes have the same TBAA,
   // remove the (meaningless) explicit layout if one is present.
-  if (ShapeUtil::Rank(shape) == 0) {
+  if (!ShapeUtil::IsArray(shape) || ShapeUtil::IsScalar(shape)) {
     LayoutUtil::ClearLayout(&shape);
   } else {
     CHECK(shape.has_layout());
diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index ee49a9ae5f..646b76799b 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -105,6 +105,11 @@ bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) {
   return equal;
 }
 
+/* static */ int64 ShapeUtil::Rank(const Shape& shape) {
+  CHECK(!ShapeUtil::IsTuple(shape)) << "Tuples do not have a rank";
+  return shape.dimensions_size();
+}
+
 /* static */ int64 ShapeUtil::TrueRank(const Shape& shape) {
   int64 accum = 0;
   for (int64 dimension : shape.dimensions()) {
@@ -270,7 +275,7 @@ bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) {
 }
 
 /* static */ bool ShapeUtil::IsNil(const Shape& shape) {
-  return IsEmptyTuple(shape) || HasZeroElements(shape);
+  return IsTuple(shape) ? IsEmptyTuple(shape) : HasZeroElements(shape);
 }
 
 /* static */ int64 ShapeUtil::TupleElementCount(const Shape& shape) {
@@ -534,11 +539,6 @@ bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) {
 /* static */ Status ShapeUtil::ValidateShapeWithOptionalLayoutInternal(
     const Shape& shape) {
   if (shape.element_type() == TUPLE) {
-    // Tuple shape.
-    if (Rank(shape) != 0) {
-      return InvalidArgument("tuples must be rank-0; got rank %lld",
-                             Rank(shape));
-    }
     if (shape.dimensions_size() != 0) {
       return InvalidArgument("tuples must not have dimensions specified");
     }
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 853be6b4cb..70b843d45b 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -144,7 +144,8 @@ class ShapeUtil {
   static bool Equal(const Shape& lhs, const Shape& rhs);
 
   // Returns the rank (number of dimensions) of the given shape.
-  static int64 Rank(const Shape& shape) { return shape.dimensions_size(); }
+  // Precondition: !IsTuple(shape)
+  static int64 Rank(const Shape& shape);
 
   // Returns the number of dimensions for which the dimension is not (trivially)
   // 1. e.g., f32[2x1x1] has a true rank of 1D, the other dimensions are just
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index eb979ad189..f3a49059c9 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -41,20 +41,25 @@ namespace xla {
 
 /* static */ void LiteralTestUtil::AssertEqualShapes(const Shape& expected,
                                                      const Shape& actual) {
-  ASSERT_EQ(ShapeUtil::Rank(expected), ShapeUtil::Rank(actual));
-  ASSERT_EQ(expected.element_type(), actual.element_type())
-      << PrimitiveType_Name(expected.element_type()) << " vs "
-      << PrimitiveType_Name(actual.element_type());
-  ASSERT_EQ(expected.dimensions_size(), actual.dimensions_size());
-  for (int i = 0; i < expected.dimensions_size(); ++i) {
-    ASSERT_EQ(expected.dimensions(i), actual.dimensions(i))
-        << "mismatch in dimension #" << i
-        << " expected: " << ShapeUtil::HumanString(expected)
-        << " actual: " << ShapeUtil::HumanString(actual);
-  }
-  ASSERT_EQ(expected.tuple_shapes_size(), actual.tuple_shapes_size());
-  for (int i = 0; i < expected.tuple_shapes_size(); ++i) {
-    AssertEqualShapes(expected.tuple_shapes(i), actual.tuple_shapes(i));
+  ASSERT_EQ(ShapeUtil::IsTuple(expected), ShapeUtil::IsTuple(actual));
+  if (ShapeUtil::IsTuple(expected)) {
+    ASSERT_EQ(ShapeUtil::TupleElementCount(expected),
+              ShapeUtil::TupleElementCount(actual));
+    for (int i = 0; i < expected.tuple_shapes_size(); ++i) {
+      AssertEqualShapes(expected.tuple_shapes(i), actual.tuple_shapes(i));
+    }
+  } else {
+    ASSERT_EQ(ShapeUtil::Rank(expected), ShapeUtil::Rank(actual));
+    ASSERT_EQ(expected.element_type(), actual.element_type())
+        << PrimitiveType_Name(expected.element_type()) << " vs "
+        << PrimitiveType_Name(actual.element_type());
+    ASSERT_EQ(expected.dimensions_size(), actual.dimensions_size());
+    for (int i = 0; i < expected.dimensions_size(); ++i) {
+      ASSERT_EQ(expected.dimensions(i), actual.dimensions(i))
+          << "mismatch in dimension #" << i
+          << " expected: " << ShapeUtil::HumanString(expected)
+          << " actual: " << ShapeUtil::HumanString(actual);
+    }
   }
 }
 
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index c8fd31d0ad..a95ac968dd 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -53,7 +53,8 @@ enum PrimitiveType {
   // computation; e.g. a computation that returns weights and biases may have a
   // signature that results in a tuple like (f32[784x2000], f32[2000])
   //
-  // Tuples are currently special in that they may only be rank 0.
+  // If a shape proto has the tuple element type, it may not have any entries
+  // in the dimensions field.
   TUPLE = 13;
 
   // An opaque type used for passing context specific data to a custom
-- 
GitLab


From c5436fc0c1047719e4f86942729db4dbc9c76113 Mon Sep 17 00:00:00 2001
From: William Chargin <wchargin@google.com>
Date: Thu, 15 Jun 2017 15:52:27 -0700
Subject: [PATCH 0124/1375] Extract request cancellation to a utility class

Summary:
The chart scaffold has some logic to ensure that old requests don't
stomp the data of new ones. This is a useful feature, and plugins will
want to continue to use it even after we destroy the chart-scaffold.
Thus, we extract it to a very simple utility class, here used by
chart-scaffold and in the future by plugins individually.

Test Plan:
Set a conditional breakpoint on the `tf-chart-scaffold` code that
triggers only when `result.cancelled` is true. Use extreme network
throttling to enter a situation where there _are_ multiple batches of
in-flight requests. Then, disable throttling so that all the requests
resolve; the breakpoint will then trigger.

PiperOrigin-RevId: 159166747
---
 .../tensorboard/components/tf_backend/BUILD   |  1 +
 .../components/tf_backend/canceller.ts        | 65 +++++++++++++++++++
 .../components/tf_backend/tf-backend.html     |  1 +
 .../tf-chart-scaffold.html                    | 40 +++++-------
 4 files changed, 84 insertions(+), 23 deletions(-)
 create mode 100644 tensorflow/tensorboard/components/tf_backend/canceller.ts

diff --git a/tensorflow/tensorboard/components/tf_backend/BUILD b/tensorflow/tensorboard/components/tf_backend/BUILD
index 50fc267dc4..0136988e0e 100644
--- a/tensorflow/tensorboard/components/tf_backend/BUILD
+++ b/tensorflow/tensorboard/components/tf_backend/BUILD
@@ -10,6 +10,7 @@ ts_web_library(
     srcs = [
         "backend.ts",
         "behavior.ts",
+        "canceller.ts",
         "requestManager.ts",
         "router.ts",
         "runsStore.ts",
diff --git a/tensorflow/tensorboard/components/tf_backend/canceller.ts b/tensorflow/tensorboard/components/tf_backend/canceller.ts
new file mode 100644
index 0000000000..a196f82258
--- /dev/null
+++ b/tensorflow/tensorboard/components/tf_backend/canceller.ts
@@ -0,0 +1,65 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the 'License');
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an 'AS IS' BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/**
+ * A class that allows marking promises as cancelled.
+ *
+ * This can be useful to, e.g., prevent old network requests from
+ * stomping new ones and writing bad data.
+ *
+ * Usage:
+ *
+ *     const canceller = new Canceller();
+ *     let myPromise: Promise<Foo> = getPromise();
+ *     myPromise.then(canceller.cancellable(({value, cancelled} => {
+ *       if (cancelled) {
+ *         console.warn("Don't make promises you can't keep >:-{");
+ *       }
+ *       console.log("Enjoy your value:", value);
+ *     }));
+ *
+ *     // If `myPromise` is resolved now, then `cancelled` will be `false`.
+ *     canceller.cancelAll();
+ *     // If `myPromise` is resolved now, then `cancelled` will be `true`.
+ */
+export class Canceller {
+  /**
+   * How many times has `cancelAll` been called?
+   */
+  private cancellationCount = 0;
+
+  /**
+   * Create a cancellable task. This returns a new function that, when
+   * invoked, will pass its argument to the provided function as well as
+   * a `cancelled` argument. This argument will be `false` unless and
+   * until `cancelAll` is invoked after the creation of this task.
+   */
+  public cancellable<T, U>(f: (result: {value: T, cancelled: boolean}) => U):
+      (T) => U {
+    const originalCancellationCount = this.cancellationCount;
+    return (value) => {
+      const cancelled = this.cancellationCount !== originalCancellationCount;
+      return f({value, cancelled});
+    };
+  }
+
+  /**
+   * Mark all outstanding tasks as cancelled. Tasks not yet created will
+   * not be affected.
+   */
+  public cancelAll(): void {
+    this.cancellationCount++;
+  }
+}
diff --git a/tensorflow/tensorboard/components/tf_backend/tf-backend.html b/tensorflow/tensorboard/components/tf_backend/tf-backend.html
index c2a44b3b63..066647f5c2 100644
--- a/tensorflow/tensorboard/components/tf_backend/tf-backend.html
+++ b/tensorflow/tensorboard/components/tf_backend/tf-backend.html
@@ -26,3 +26,4 @@ limitations under the License.
 <script src="runsStore.js"></script>
 <script src="backend.js"></script>
 <script src="behavior.js"></script>
+<script src="canceller.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-chart-scaffold.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-chart-scaffold.html
index a39fb9462b..d337111363 100644
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-chart-scaffold.html
+++ b/tensorflow/tensorboard/components/tf_dashboard_common/tf-chart-scaffold.html
@@ -56,6 +56,7 @@ plugin is required to implement two functions:
   </template>
   <script>
     "use strict";
+    import {Canceller} from "../tf-backend/canceller";
 
     Polymer({
       is: "tf-chart-scaffold",
@@ -67,14 +68,11 @@ plugin is required to implement two functions:
           type: Boolean,
           value: false
         },
-
-        // Storing the update ID of the previous request for data enables us to determine if a
-        // data response is outdated. We rely on an increasing ID instead of timestamp because
-        // successive updates often fire within the same millisecond.
-        _dataUpdateIdOfLastRequest: Number,
-        _nextAvailableDataUpdateId: {
-          type: Number,
-          value: 1,
+        _canceller: {
+          type: Object,
+          value: function() {
+            return new Canceller();
+          },
         },
       },
       observers: [
@@ -102,25 +100,21 @@ plugin is required to implement two functions:
           throw new Error('tf-chart-scaffold requires a tag.');
         }
 
-        // TODO(chizeng): At this point, notify effective children that the previous data has been
-        // invalidated. For instance, the image dashboard may want to clear its images. Today, the
-        // chart scaffold only informs children when the new image URLs response finishes loading.
-
-        const dataUpdateId = this._nextAvailableDataUpdateId++;
-        this._dataUpdateIdOfLastRequest = dataUpdateId;
-
+        // TODO(chizeng): At this point, notify effective children that the
+        // previous data has been invalidated. For instance, the image
+        // dashboard may want to clear its images. Today, the chart
+        // scaffold only informs children when the new image URLs response
+        // finishes loading.
+        this._canceller.cancelAll();  // prevent stale data
         this.visibleSeries.forEach(function(name) {
-          this.dataProvider(this.tag, name).then(function(data) {
-            if (dataUpdateId != this._dataUpdateIdOfLastRequest) {
-              // This response is outdated. Ignore it.
-              // TODO(chizeng): Explore canceling an outdated request before we even receive its
-              // response. This involves creating hooks into the request manager and might introduce
-              // some complexity that may not be worth it; Tensorboard frankly does not seem
-              // bottlenecked by the network (It is often run in fast corp networks or locally.).
+          var promise = this.dataProvider(this.tag, name);
+          promise.then(this._canceller.cancellable(function(result) {
+            if (result.cancelled) {
               return;
             }
+            var data = result.value;
             this.chart().setSeriesData(name, data);
-          }.bind(this));
+          }.bind(this)));
         }.bind(this));
       },
       _changeSeries: function() {
-- 
GitLab


From 95e31cbb09c40322acc467902080c1cfd9ec88b1 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 15 Jun 2017 16:34:44 -0700
Subject: [PATCH 0125/1375] Add checkpoint-utils to the tf.train module.

PiperOrigin-RevId: 159171746
---
 tensorflow/python/BUILD                       | 22 ++++++++++++++++++-
 .../python/training/checkpoint_utils.py       |  5 +++--
 tensorflow/python/training/training.py        |  9 ++++++++
 .../tools/api/golden/tensorflow.train.pbtxt   | 16 ++++++++++++++
 4 files changed, 49 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 4a395290e0..64b052839f 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3148,7 +3148,6 @@ cuda_py_tests(
         "training/adagrad_da_test.py",
         "training/adagrad_test.py",
         "training/basic_loops_test.py",
-        "training/checkpoint_utils_test.py",
         "training/coordinator_test.py",
         "training/device_setter_test.py",
         "training/ftrl_test.py",
@@ -3352,6 +3351,27 @@ py_test(
     ],
 )
 
+py_test(
+    name = "checkpoint_utils_test",
+    size = "small",
+    srcs = ["training/checkpoint_utils_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_windows"],
+    deps = [
+        ":client",
+        ":client_testlib",
+        ":framework_for_generated_wrappers",
+        ":io_ops",
+        ":partitioned_variables",
+        ":platform",
+        ":pywrap_tensorflow",
+        ":state_ops",
+        ":training",
+        ":variable_scope",
+        ":variables",
+    ],
+)
+
 py_test(
     name = "monitored_session_test",
     size = "small",
diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py
index d52cf9a436..ddf04e21e6 100644
--- a/tensorflow/python/training/checkpoint_utils.py
+++ b/tensorflow/python/training/checkpoint_utils.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import six
 
+from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.ops import io_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope as vs
@@ -27,7 +28,7 @@ from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import saver
-from tensorflow.python.training import training as train
+
 
 __all__ = [
     "load_checkpoint", "load_variable", "list_variables", "init_from_checkpoint"
@@ -55,7 +56,7 @@ def load_checkpoint(ckpt_dir_or_file):
   if filename is None:
     raise ValueError("Couldn't find 'checkpoint' file or checkpoints in "
                      "given directory %s" % ckpt_dir_or_file)
-  return train.NewCheckpointReader(filename)
+  return pywrap_tensorflow.NewCheckpointReader(filename)
 
 
 def load_variable(ckpt_dir_or_file, name):
diff --git a/tensorflow/python/training/training.py b/tensorflow/python/training/training.py
index f4ac3c9758..e2a7b28e2b 100644
--- a/tensorflow/python/training/training.py
+++ b/tensorflow/python/training/training.py
@@ -85,6 +85,10 @@ See the @{$python/train} guide.
 @@create_global_step
 @@assert_global_step
 @@write_graph
+@@load_checkpoint
+@@load_variable
+@@list_variables
+@@init_from_checkpoint
 """
 
 # Optimizers.
@@ -142,6 +146,11 @@ from tensorflow.python.training.basic_session_run_hooks import GlobalStepWaiterH
 from tensorflow.python.training.basic_session_run_hooks import FinalOpsHook
 from tensorflow.python.training.basic_session_run_hooks import FeedFnHook
 from tensorflow.python.training.basic_loops import basic_train_loop
+from tensorflow.python.training.checkpoint_utils import init_from_checkpoint
+from tensorflow.python.training.checkpoint_utils import list_variables
+from tensorflow.python.training.checkpoint_utils import load_checkpoint
+from tensorflow.python.training.checkpoint_utils import load_variable
+
 from tensorflow.python.training.device_setter import replica_device_setter
 from tensorflow.python.training.monitored_session import Scaffold
 from tensorflow.python.training.monitored_session import MonitoredTrainingSession
diff --git a/tensorflow/tools/api/golden/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.pbtxt
index 58fd5760c1..c295537965 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.pbtxt
@@ -304,6 +304,10 @@ tf_module {
     name: "import_meta_graph"
     argspec: "args=[\'meta_graph_or_file\', \'clear_devices\', \'import_scope\'], varargs=None, keywords=kwargs, defaults=[\'False\', \'None\'], "
   }
+  member_method {
+    name: "init_from_checkpoint"
+    argspec: "args=[\'ckpt_dir_or_file\', \'assignment_map\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "input_producer"
     argspec: "args=[\'input_tensor\', \'element_shape\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'summary_name\', \'name\', \'cancel_op\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'None\', \'32\', \'None\', \'None\', \'None\', \'None\'], "
@@ -320,6 +324,18 @@ tf_module {
     name: "limit_epochs"
     argspec: "args=[\'tensor\', \'num_epochs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
+  member_method {
+    name: "list_variables"
+    argspec: "args=[\'ckpt_dir_or_file\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "load_checkpoint"
+    argspec: "args=[\'ckpt_dir_or_file\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "load_variable"
+    argspec: "args=[\'ckpt_dir_or_file\', \'name\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "match_filenames_once"
     argspec: "args=[\'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-- 
GitLab


From 6af52579e4b93d47ae6658d7d9d7144f76547290 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 15 Jun 2017 16:43:37 -0700
Subject: [PATCH 0126/1375] Mentioned loss in canned estimators pydoc.

PiperOrigin-RevId: 159172709
---
 tensorflow/python/estimator/canned/dnn.py                 | 4 ++++
 tensorflow/python/estimator/canned/dnn_linear_combined.py | 2 ++
 tensorflow/python/estimator/canned/linear.py              | 4 ++++
 3 files changed, 10 insertions(+)

diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py
index 013a43a8b0..b7a943d2fb 100644
--- a/tensorflow/python/estimator/canned/dnn.py
+++ b/tensorflow/python/estimator/canned/dnn.py
@@ -188,6 +188,8 @@ class DNNClassifier(estimator.Estimator):
       name. Both features' `value` must be a `SparseTensor`.
     - if `column` is a `_DenseColumn`, a feature with `key=column.name`
       whose `value` is a `Tensor`.
+
+  Loss is calculated by using softmax cross entropy.
   """
 
   def __init__(self,
@@ -319,6 +321,8 @@ class DNNRegressor(estimator.Estimator):
       name. Both features' `value` must be a `SparseTensor`.
     - if `column` is a `_DenseColumn`, a feature with `key=column.name`
       whose `value` is a `Tensor`.
+
+  Loss is calculated by using mean squared error.
   """
 
   def __init__(self,
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py
index c7fab2bbac..8785ddc5a6 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py
@@ -295,6 +295,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
     - if `column` is a `_DenseColumn`, a feature with `key=column.name`
       whose `value` is a `Tensor`.
 
+  Loss is calculated by using softmax cross entropy.
   """
 
   def __init__(self,
@@ -453,6 +454,7 @@ class DNNLinearCombinedRegressor(estimator.Estimator):
     - if `column` is a `_DenseColumn`, a feature with `key=column.name`
       whose `value` is a `Tensor`.
 
+  Loss is calculated by using mean squared error.
   """
 
   def __init__(self,
diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py
index fd929b260b..552b1bdf01 100644
--- a/tensorflow/python/estimator/canned/linear.py
+++ b/tensorflow/python/estimator/canned/linear.py
@@ -151,6 +151,8 @@ class LinearClassifier(estimator.Estimator):
       Both features' `value` must be a `SparseTensor`.
     - if `column` is a `RealValuedColumn`, a feature with `key=column.name`
       whose `value` is a `Tensor`.
+
+  Loss is calculated by using softmax cross entropy.
   """
 
   def __init__(self,
@@ -260,6 +262,8 @@ class LinearRegressor(estimator.Estimator):
          key=weight column name, value=a `SparseTensor`}
     - if isinstance(column, `RealValuedColumn`):
         key=column.name, value=a `Tensor`
+
+  Loss is calculated by using mean squared error.
   """
 
   def __init__(self,
-- 
GitLab


From 351e1673beffa8583ff75046eb516893b9e5c79d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 17:03:45 -0700
Subject: [PATCH 0127/1375] Generarize TF HVX runtime in order to benchmark
 models on HVX

PiperOrigin-RevId: 159174734
---
 .../hvx_ops_support_checker_main.cc           |  41 +-
 tensorflow/contrib/makefile/Makefile          |  22 +
 .../hexagon_graph_execution/Makefile.in       |  20 -
 tensorflow/contrib/makefile/tf_op_files.txt   |   3 +
 .../core/kernels/hexagon/graph_transferer.cc  | 395 ++++++++++++++++--
 .../core/kernels/hexagon/graph_transferer.h   |  34 +-
 .../kernels/hexagon/graph_transferer_test.cc  |  26 +-
 .../hexagon/hexagon_control_wrapper.cc        | 146 +++++--
 .../kernels/hexagon/hexagon_control_wrapper.h |  18 +-
 .../hexagon/hexagon_graph_execution_test.cc   |  13 +-
 .../hexagon/hexagon_ops_definitions.cc        |   7 +
 .../kernels/i_remote_fused_graph_executor.h   |   4 -
 .../remote_fused_graph_execute_utils.cc       |  63 +++
 .../remote_fused_graph_execute_utils.h        |  29 +-
 .../core/platform/hexagon/soc_interface.h     |  23 +-
 15 files changed, 694 insertions(+), 150 deletions(-)

diff --git a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
index 6ae7c4a742..6af608396a 100644
--- a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
+++ b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
@@ -33,10 +33,15 @@ limitations under the License.
 #include "tensorflow/tools/graph_transforms/transform_utils.h"
 
 namespace tensorflow {
+
 namespace {
-static int ParseFlags(int argc, char* argv[], string* in_graph) {
+static int ParseFlags(int argc, char* argv[], string* in_graph,
+                      bool* dump_all_nodes, bool* dump_shape_and_type) {
   std::vector<Flag> flag_list = {
-      Flag("in_graph", in_graph, "input graph file name"),
+      Flag("in_graph", in_graph, "Input graph file name to check hvx support."),
+      Flag("dump_all_nodes", dump_all_nodes, "Dump all nodes in the model."),
+      Flag("dump_shape_and_type", dump_shape_and_type,
+           "Dump shape and type of nodes"),
   };
   CHECK(Flags::Parse(&argc, argv, flag_list));
   // We need to call this to set up global state for TensorFlow.
@@ -48,12 +53,25 @@ static int ParseFlags(int argc, char* argv[], string* in_graph) {
   return 0;
 }
 
-static void SummarizeNode(const NodeDef& node_def) {
+static void SummarizeNode(const NodeDef& node_def,
+                          const bool dump_shape_and_type) {
   LOG(INFO) << "Node(" << node_def.name() << ")";
   LOG(INFO) << "  op: " << node_def.op();
   for (const string& input : node_def.input()) {
     LOG(INFO) << " Input: " << input;
   }
+  std::vector<DataType> data_types;
+  std::vector<TensorShape> shapes;
+  const Status status = RemoteFusedGraphExecuteUtils::GetOutputTensorShapeType(
+      node_def, &data_types, &shapes);
+  if (data_types.empty() || shapes.empty()) {
+    return;
+  }
+  CHECK_EQ(data_types.size(), shapes.size());
+  for (int i = 0; i < data_types.size(); ++i) {
+    LOG(INFO) << " Output(" << i << "): " << DataType_Name(data_types.at(i))
+              << ", " << shapes.at(i).DebugString();
+  }
 }
 
 static void DumpRemoteFusedGraph(const NodeDef& node_def) {
@@ -89,10 +107,14 @@ static void DumpRemoteFusedGraph(const NodeDef& node_def) {
   }
 }
 
-static void CheckOpsSupport(const GraphDef& graph_def) {
+static void CheckOpsSupport(const GraphDef& graph_def,
+                            const bool dump_all_nodes,
+                            const bool dump_shape_and_type) {
   const IGraphTransferOpsDefinitions& ops_definition =
       HexagonOpsDefinitions::getInstance();
   LOG(INFO) << "Checking " << graph_def.node_size() << " nodes";
+  LOG(INFO) << "dump_all_nodes = " << dump_all_nodes
+            << ", dump_shape_and_tpye = " << dump_shape_and_type;
 
   std::unordered_set<string> unsupported_ops;
   bool all_supported = true;
@@ -125,9 +147,9 @@ static void CheckOpsSupport(const GraphDef& graph_def) {
     LOG(INFO) << count << " ops are not supported.";
   }
 
-  if (contains_remote_graph) {
+  if (contains_remote_graph || dump_all_nodes) {
     for (const NodeDef& node : graph_def.node()) {
-      SummarizeNode(node);
+      SummarizeNode(node, dump_shape_and_type);
     }
   }
 }
@@ -137,7 +159,10 @@ static void CheckOpsSupport(const GraphDef& graph_def) {
 
 int main(int argc, char** argv) {
   tensorflow::string in_graph;
-  const int ret = tensorflow::ParseFlags(argc, argv, &in_graph);
+  bool dump_all_nodes;
+  bool dump_shape_and_type;
+  const int ret = tensorflow::ParseFlags(argc, argv, &in_graph, &dump_all_nodes,
+                                         &dump_shape_and_type);
   if (ret != 0) {
     return ret;
   }
@@ -146,6 +171,6 @@ int main(int argc, char** argv) {
   TF_CHECK_OK(tensorflow::graph_transforms::LoadTextOrBinaryGraphFile(
       in_graph, &graph_def));
 
-  tensorflow::CheckOpsSupport(graph_def);
+  tensorflow::CheckOpsSupport(graph_def, dump_all_nodes, dump_shape_and_type);
   return 0;
 }
diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index 305ed0d11e..2150cfe9ea 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -279,6 +279,16 @@ ifeq ($(TARGET),ANDROID)
 		LIBS += -lhexagon_controller
 		LDFLAGS += -L$(HEXAGON_LIBS)
 		CXXFLAGS += -DUSE_HEXAGON_LIBS
+
+# CAVEAT: We should disable TENSORFLOW_DISABLE_META while running
+# quantized_matmul on Android because it crashes in
+# MultiThreadGemm in tensorflow/core/kernels/meta_support.cc
+# See http://b/33270149
+# TODO(satok): Remove once it's fixed
+		CXXFLAGS += -DTENSORFLOW_DISABLE_META
+
+# Declare __ANDROID_TYPES_FULL__ to enable required types for hvx
+		CXXFLAGS += -D__ANDROID_TYPES_FULL__
 	endif
 
 	ifdef ENABLE_EXPERIMENTAL_HEXNN_OPS
@@ -500,6 +510,18 @@ tensorflow/core/util/reporter.cc \
 tensorflow/tools/benchmark/benchmark_model.cc \
 tensorflow/tools/benchmark/benchmark_model_main.cc
 
+ifdef HEXAGON_LIBS
+	TF_CC_SRCS += \
+tensorflow/cc/framework/scope.cc \
+tensorflow/cc/framework/ops.cc \
+tensorflow/cc/ops/const_op.cc \
+tensorflow/core/kernels/hexagon/graph_transfer_utils.cc \
+tensorflow/core/kernels/hexagon/graph_transferer.cc \
+tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc \
+tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc \
+tensorflow/core/kernels/hexagon/hexagon_remote_fused_graph_executor_build.cc
+endif
+
 # File names of the intermediate files target compilation generates.
 TF_CC_OBJS := $(addprefix $(OBJDIR), $(TF_CC_SRCS:.cc=.o))
 PBT_GEN_FILES := $(addprefix $(PBTGENDIR), $(PBT_CC_SRCS))
diff --git a/tensorflow/contrib/makefile/sub_makefiles/hexagon_graph_execution/Makefile.in b/tensorflow/contrib/makefile/sub_makefiles/hexagon_graph_execution/Makefile.in
index 2a6f66edcb..9aa81144fd 100644
--- a/tensorflow/contrib/makefile/sub_makefiles/hexagon_graph_execution/Makefile.in
+++ b/tensorflow/contrib/makefile/sub_makefiles/hexagon_graph_execution/Makefile.in
@@ -34,27 +34,7 @@ $(wildcard $(GTEST_DIR)/src/*.cc) \
 $(wildcard $(GTEST_DIR)/src/*.h) \
 $(GTEST_HEADERS)
 
-# CAVEAT: We should disable TENSORFLOW_DISABLE_META while running
-# quantized_matmul on Android because it crashes in
-# MultiThreadGemm in tensorflow/core/kernels/meta_support.cc
-# TODO(satok): Remove once it's fixed
-CXXFLAGS += -DTENSORFLOW_DISABLE_META
-
-# Declare __ANDROID_TYPES_FULL__ to enable required types for hvx
-CXXFLAGS += -D__ANDROID_TYPES_FULL__
-
 GRAPH_TRANSFER_SRCS := \
-tensorflow/cc/framework/scope.cc \
-tensorflow/cc/framework/ops.cc \
-tensorflow/cc/ops/const_op.cc \
-tensorflow/core/kernels/hexagon/graph_transfer_utils.cc \
-tensorflow/core/kernels/hexagon/graph_transferer.cc \
-tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc \
-tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc \
-tensorflow/core/kernels/hexagon/hexagon_remote_fused_graph_executor_build.cc \
-tensorflow/core/kernels/remote_fused_graph_execute_op.cc \
-tensorflow/core/kernels/remote_fused_graph_execute_utils.cc \
-tensorflow/core/ops/remote_fused_graph_ops.cc \
 tensorflow/core/platform/posix/test.cc
 
 GRAPH_EXECUTION_SRCS := \
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 857d6fa21b..c73ec0305b 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -202,12 +202,15 @@ tensorflow/core/kernels/quantized_reshape_op.cc
 tensorflow/core/kernels/quantized_resize_bilinear_op.cc
 tensorflow/core/kernels/requantization_range_op.cc
 tensorflow/core/kernels/requantize.cc
+tensorflow/core/kernels/remote_fused_graph_execute_op.cc
+tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
 tensorflow/core/ops/training_ops.cc
 tensorflow/core/ops/string_ops.cc
 tensorflow/core/ops/state_ops.cc
 tensorflow/core/ops/sparse_ops.cc
 tensorflow/core/ops/sendrecv_ops.cc
 tensorflow/core/ops/script_ops.cc
+tensorflow/core/ops/remote_fused_graph_ops.cc
 tensorflow/core/ops/random_ops.cc
 tensorflow/core/ops/random_grad.cc
 tensorflow/core/ops/parsing_ops.cc
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.cc b/tensorflow/core/kernels/hexagon/graph_transferer.cc
index d927ef3efa..055108cd00 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transferer.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph_constructor.h"
+#include "tensorflow/core/graph/node_builder.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/public/session.h"
@@ -43,10 +44,14 @@ const char INPUTS_NODE_PREFIX[] = "inputs_for_";
 const char OUTPUTS_NODE_PREFIX[] = "outputs_for_";
 const char DATA_NODE_PREFIX[] = "data_for_op_";
 const char CONST_SHAPE_PREFIX[] = "const_shape_";
+const char CONST_VAL_PREFIX[] = "const_val_";
+const char CONST_TENSOR_PREFIX[] = "const_tensor_";
 const char PADDING_ATTR_NAME[] = "padding";
 const char STRIDES_ATTR_NAME[] = "strides";
+const char KEEP_DIMS_ATTR_NAME[] = "keep_dims";
 const char KSIZE_ATTR_NAME[] = "ksize";
 const char NULL_OUTPUT_NAME[] = "NULL";
+const char AGGREGATED_INPUT_NODE_NAME[] = "graph_transfer_aggregated_input";
 const int PADDING_NA_ID = 0;  // VALID = 1, SAME = 2
 
 // This is a temporary workaround to support android build
@@ -58,6 +63,16 @@ static string ToString(T val) {
   return stream.str();
 }
 
+static Node* FindMutableNodeByName(const string& name, Graph* graph) {
+  const TensorId tid = ParseTensorName(name);
+  for (Node* node : graph->nodes()) {
+    if (node != nullptr && node->name() == tid.first) {
+      return node;
+    }
+  }
+  return nullptr;
+}
+
 /**
  * graph loading functions
  * - LoadGraphFromProto
@@ -86,13 +101,22 @@ Status GraphTransferer::LoadGraphFromProto(
     }
   }
 
+  TF_RETURN_IF_ERROR(TransformGraphToAddAggregatedInputNode(
+      input_node_info_list, &graph, &shape_refiner));
+
   std::unordered_multimap<string, const Node*> op_name_to_node_multimap(
       graph.num_nodes());
   for (const Node* const node : graph.nodes()) {
+    if (node == nullptr) {
+      continue;
+    }
     CacheNode(*node);
   }
 
   for (const Node* const node : graph.nodes()) {
+    if (node == nullptr) {
+      continue;
+    }
     VLOG(1) << "<Node> " << node->name();
     for (const Node* const input_node : node->in_nodes()) {
       const string& name = input_node->name();
@@ -102,6 +126,9 @@ Status GraphTransferer::LoadGraphFromProto(
   }
 
   for (const Node* const node : graph.nodes()) {
+    if (node == nullptr) {
+      continue;
+    }
     status = RegisterNodeIfAllInputsAreCached(
         ops_definitions, shape_refiner, *node, false, input_node_info_list,
         output_node_names);
@@ -265,19 +292,16 @@ GraphTransferInfo& GraphTransferer::GetMutableGraphTransferInfo() {
   return graph_transfer_info_;
 }
 
-int GraphTransferer::CacheNode(const Node& node) {
+void GraphTransferer::CacheNode(const Node& node) {
   if (node_name_to_id_cache_map_.count(node.name()) > 0) {
-    VLOG(1) << "Emplace node to cache failed";
-    // TODO(satok): check here?
-    return -1;
+    return;
   }
-  VLOG(1) << "Cache node: " << node.name() << ", " << node.op_def().name();
   node_name_cache_list_.emplace_back(&node);
+  const int node_id = node_name_cache_list_.size() - 1;
   bool emplace_succeeded = false;
-  std::tie(std::ignore, emplace_succeeded) = node_name_to_id_cache_map_.emplace(
-      node.name(), node_name_cache_list_.size() - 1);
+  std::tie(std::ignore, emplace_succeeded) =
+      node_name_to_id_cache_map_.emplace(node.name(), node_id);
   CHECK(emplace_succeeded);
-  return node_name_cache_list_.size() - 1;
 }
 
 bool GraphTransferer::AreAllInputsCached(const Node& node) const {
@@ -291,22 +315,124 @@ bool GraphTransferer::AreAllInputsCached(const Node& node) const {
   return true;
 }
 
+Status GraphTransferer::TransformGraphToAddAggregatedInputNode(
+    const std::vector<std::pair<string, Tensor>>& input_node_info_list,
+    Graph* graph, ShapeRefiner* shape_refiner) {
+  // Transform a remote fused graph to add an aggregated input node which takes
+  // all inputs of the remote graph.
+  DataTypeVector input_data_types;
+  std::vector<DataType> data_types;
+  std::vector<TensorShape> shapes;
+  std::vector<string> input_nodes;
+  for (int i = 0; i < input_node_info_list.size(); ++i) {
+    Node* node = FindMutableNodeByName(input_node_info_list.at(i).first, graph);
+    CHECK_NOTNULL(node);
+    input_nodes.emplace_back(node->name());
+    input_data_types.emplace_back(input_node_info_list.at(i).second.dtype());
+    data_types.emplace_back(input_node_info_list.at(i).second.dtype());
+    shapes.emplace_back(input_node_info_list.at(i).second.shape());
+  }
+
+  NodeDef input_node_def;
+  auto builder =
+      NodeBuilder(AGGREGATED_INPUT_NODE_NAME, "RemoteFusedGraphExecute")
+          .Input(std::vector<NodeBuilder::NodeOut>{})
+          .Attr("Tinputs", DataTypeVector{})
+          .Attr("Toutputs", input_data_types)
+          .Attr("serialized_remote_fused_graph_execute_info", "")
+          .Attr(RemoteFusedGraphExecuteUtils::ATTR_OUTPUT_DATA_TYPES,
+                data_types)
+          .Attr(RemoteFusedGraphExecuteUtils::ATTR_OUTPUT_SHAPES, shapes);
+
+  Node* input_node;
+  TF_RETURN_IF_ERROR(builder.Finalize(graph, &input_node));
+  CHECK_NOTNULL(input_node);
+
+  bool refined;
+  TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(input_node, &refined));
+
+  shape_inference::InferenceContext* context =
+      shape_refiner->GetContext(input_node);
+  for (int i = 0; i < input_node_info_list.size(); ++i) {
+    shape_inference::ShapeHandle handle;
+    TF_RETURN_IF_ERROR(context->MakeShapeFromTensorShape(
+        input_node_info_list.at(i).second.shape(), &handle));
+    TF_RETURN_IF_ERROR(shape_refiner->SetShape(input_node, i, handle));
+  }
+
+  // Cache the aggregate input node first as it's consumed first.
+  CacheNode(*input_node);
+
+  std::vector<Node*> original_input_nodes(input_nodes.size());
+
+  for (int i = 0; i < input_nodes.size(); ++i) {
+    const string& node_name = input_nodes.at(i);
+    Node* original_input_node = FindMutableNodeByName(node_name, graph);
+    CHECK_NOTNULL(original_input_node);
+    CHECK_EQ(1, original_input_node->num_outputs());  // replaced by identity.
+    Node* created_node;
+    TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::BuildIdentityOpNode(
+        node_name, AGGREGATED_INPUT_NODE_NAME, i, data_types.at(i), graph,
+        &created_node));
+    CHECK_NOTNULL(created_node);
+    std::vector<DataType> data_types;
+    std::vector<TensorShape> shapes;
+    Status status = RemoteFusedGraphExecuteUtils::GetOutputTensorShapeType(
+        original_input_node->def(), &data_types, &shapes);
+    if (status.ok()) {
+      created_node->AddAttr(
+          RemoteFusedGraphExecuteUtils::ATTR_OUTPUT_DATA_TYPES, data_types);
+      created_node->AddAttr(RemoteFusedGraphExecuteUtils::ATTR_OUTPUT_SHAPES,
+                            shapes);
+    }
+    for (const Edge* out_edge : original_input_node->out_edges()) {
+      Node* dst = out_edge->dst();
+      int dst_port = out_edge->dst_input();
+      // Unused edge will be removed when removing node.
+      graph->AddEdge(created_node, 0, dst, dst_port);
+    }
+    original_input_nodes[i] = original_input_node;
+
+    TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(created_node, &refined));
+
+    shape_inference::InferenceContext* context =
+        shape_refiner->GetContext(created_node);
+    CHECK_NOTNULL(context);
+
+    // Cache replaced input node next to the aggregated input node.
+    CacheNode(*created_node);
+  }
+
+  // Remove original input nodes after adding new input nodes to avoid
+  // reusing same pointer in Graph.
+  for (Node* original_input_node : original_input_nodes) {
+    graph->RemoveNode(original_input_node);
+  }
+
+  return Status::OK();
+}
+
 Status GraphTransferer::RegisterNode(
     const IGraphTransferOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node,
     const std::vector<std::pair<string, Tensor>>& input_node_info_list,
     const std::vector<string>& output_node_names) {
-  VLOG(1) << "Register node: " << node.name();
+  VLOG(1) << "Register node: " << node.name() << ", " << std::hex
+          << node_name_to_id_cache_map_.at(node.name());
   if (node.name() == SOURCE_NODE_NAME || node.name() == SINK_NODE_NAME) {
     // Just ignore sink and source
-    return Status();
-  } else if (RemoteFusedGraphExecuteUtils::IsInputNode(input_node_info_list,
-                                                       node.name())) {
+    return Status::OK();
+  } else if (node.name() == AGGREGATED_INPUT_NODE_NAME) {
     RegisterInputNode(ops_definitions, shape_refiner, node);
+    return Status::OK();
   } else if (node.IsConstant()) {
     RegisterConstantNode(shape_refiner, node);
+  } else if (IsPadNode(node)) {
+    RegisterPadNode(ops_definitions, shape_refiner, node);
   } else if (HasPaddingAndStrides(node)) {
     RegisterNodeWithPaddingAndStrides(ops_definitions, shape_refiner, node);
+  } else if (NeedsToAddRank(node)) {
+    RegisterNodeWithRank(ops_definitions, shape_refiner, node);
   } else if (IsNodeFlattenReshape(node, shape_refiner)) {
     RegisterFlattenNode(ops_definitions, shape_refiner, node);
   } else if (ops_definitions.GetOpIdFor(node.type_string(), {}) !=
@@ -318,7 +444,7 @@ Status GraphTransferer::RegisterNode(
                                    " has not been implemented yet.");
   }
 
-  return Status();
+  return Status::OK();
 }
 
 void GraphTransferer::RegisterConstantNode(const ShapeRefiner& shape_refiner,
@@ -361,8 +487,7 @@ void GraphTransferer::RegisterConstantNode(const ShapeRefiner& shape_refiner,
   const TensorProto* proto = nullptr;
   TF_CHECK_OK(GetNodeAttr(node.attrs(), "value", &proto));
   Tensor const_tensor;
-  // TODO(b/32704451): Don't just ignore this status!
-  MakeTensorFromProto(*proto, &const_tensor).IgnoreError();
+  TF_CHECK_OK(MakeTensorFromProto(*proto, &const_tensor));
 
   const_node_info.set_dtype(const_tensor.dtype());
   if (data_size > 0) {
@@ -394,12 +519,82 @@ int GraphTransferer::RegisterConstantShape(const std::vector<int>& shape) {
   return node_name_to_id_cache_map_[shape_name];
 }
 
+int GraphTransferer::RegisterConstTensor(const Tensor& tensor,
+                                         const string& suffix) {
+  VLOG(1) << "Cache const tensor.";
+  const int dims = tensor.shape().dims();
+  CHECK(dims <= 4);
+  const string node_name = strings::StrCat(CONST_TENSOR_PREFIX, "_", suffix);
+  if (node_name_to_id_cache_map_.count(node_name) <= 0) {
+    node_name_cache_list_.emplace_back(nullptr);
+    const int id = node_name_cache_list_.size() - 1;
+    node_name_to_id_cache_map_.emplace(node_name, id);
+    GraphTransferInfo::ConstNodeInfo& const_node_info =
+        *graph_transfer_info_.add_const_node_info();
+    const_node_info.set_name(node_name);
+    const_node_info.set_node_id(id);
+    CHECK_EQ(4, SHAPE_ARRAY_SIZE);
+    for (int i = 0; i < SHAPE_ARRAY_SIZE; ++i) {
+      if (i < SHAPE_ARRAY_SIZE - dims) {
+        const_node_info.add_shape(1);
+      } else {
+        const_node_info.add_shape(
+            tensor.shape().dim_size(i - (SHAPE_ARRAY_SIZE - dims)));
+      }
+    }
+    const_node_info.set_dtype(tensor.dtype());
+    const_node_info.set_data(tensor.tensor_data().data(),
+                             tensor.tensor_data().size());
+  }
+  return node_name_to_id_cache_map_[node_name];
+}
+
+int GraphTransferer::RegisterConstScalar(const DataType dt, const int val,
+                                         const int dst_id,
+                                         const int dst_input_count) {
+  VLOG(1) << "Cache const.";
+  const string val_name =
+      CONST_VAL_PREFIX + ToString(dst_id) + '_' + ToString(dst_input_count);
+  if (node_name_to_id_cache_map_.count(val_name) <= 0) {
+    node_name_cache_list_.emplace_back(nullptr);
+    const int id = node_name_cache_list_.size() - 1;
+    node_name_to_id_cache_map_.emplace(val_name, id);
+    GraphTransferInfo::ConstNodeInfo& const_node_info =
+        *graph_transfer_info_.add_const_node_info();
+    const_node_info.set_name(val_name);
+    const_node_info.set_node_id(id);
+    // TODO(satok): Do not assume rank is 4 here.
+    const_node_info.add_shape(static_cast<int64>(1));
+    const_node_info.add_shape(static_cast<int64>(1));
+    const_node_info.add_shape(static_cast<int64>(1));
+    const_node_info.add_shape(static_cast<int64>(1));
+    const_node_info.set_data(&val, DataTypeSize(dt));
+  }
+  return node_name_to_id_cache_map_[val_name];
+}
+
 bool GraphTransferer::HasPaddingAndStrides(const Node& node) {
   auto attrs = node.attrs();
   return attrs.Find(PADDING_ATTR_NAME) != nullptr &&
          attrs.Find(STRIDES_ATTR_NAME) != nullptr;
 }
 
+bool GraphTransferer::NeedsToAddRank(const Node& node) {
+  const string& op_type = node.def().op();
+  if (op_type == "Transpose" || op_type == "ExpandDims") {
+    return true;
+  }
+  return false;
+}
+
+bool GraphTransferer::IsPadNode(const Node& node) {
+  const string& op_type = node.def().op();
+  if (op_type == "Pad") {
+    return true;
+  }
+  return false;
+}
+
 bool GraphTransferer::IsNodeFlattenReshape(const Node& node,
                                            const ShapeRefiner& shape_refiner) {
   // Check if node is reshape op
@@ -473,15 +668,123 @@ void GraphTransferer::RegisterNodeWithPaddingAndStrides(
       node.num_outputs(), true /* append_input */, true /* append_output */);
 }
 
-void GraphTransferer::RegisterInputNode(
+void GraphTransferer::RegisterNodeWithRank(
     const IGraphTransferOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node) {
-  VLOG(1) << "Register input node: " << node.name();
   CHECK_EQ(node_name_to_id_cache_map_.count(node.name()), 1);
   const int id = node_name_to_id_cache_map_[node.name()];
+  shape_inference::InferenceContext* context = shape_refiner.GetContext(&node);
+  const Node* input0_node;
+  TF_CHECK_OK(node.input_node(0, &input0_node));
+  CHECK_NOTNULL(input0_node);
+  std::vector<TensorShape> shapes;
+  Status status = RemoteFusedGraphExecuteUtils::GetOutputTensorShapeType(
+      input0_node->def(), nullptr, &shapes);
+  CHECK_EQ(1, shapes.size()) << "Output size should be 1.";
+  const int const_val_id =
+      RegisterConstScalar(DT_INT32, shapes.at(0).dims(), id, node.num_inputs());
+  std::vector<int> extra_inputs{const_val_id};
+  // TODO(satok): Set correct data type if it's given.
+  const int op_type_id = ops_definitions.GetOpIdFor(node.type_string(), {});
+  CHECK(op_type_id >= 0 && op_type_id < ops_definitions.GetTotalOpsCount())
+      << "Op " << node.type_string() << " not found in map(id = " << op_type_id
+      << ")";
+  bool keep_dims = false;
+  int padding_id = PADDING_NA_ID;
+  if (context->GetAttr(KEEP_DIMS_ATTR_NAME, &keep_dims).ok()) {
+    padding_id = keep_dims ? Padding::SAME : Padding::VALID;
+  }
+
+  AppendNodeParamsWithIoParams(
+      shape_refiner, node, node.name(), id, node.type_string(), op_type_id,
+      padding_id, node.num_inputs(), extra_inputs, node.num_outputs(),
+      true /* append_input */, true /* append_output */);
+}
+
+void GraphTransferer::RegisterPadNode(
+    const IGraphTransferOpsDefinitions& ops_definitions,
+    const ShapeRefiner& shape_refiner, const Node& node) {
+  static constexpr int PAD_WIDTH = 4;
+  static constexpr int PAD_HEIGHT = 2;
+  VLOG(1) << "Register generic node: " << node.name();
+  CHECK_EQ(node_name_to_id_cache_map_.count(node.name()), 1);
+  const int id = node_name_to_id_cache_map_[node.name()];
+
+  // TODO(satok): Set correct data type if it's given.
+  const int op_type_id = ops_definitions.GetOpIdFor(node.type_string(), {});
+  CHECK(op_type_id >= 0 && op_type_id < ops_definitions.GetTotalOpsCount());
+
+  CHECK_EQ(2, node.num_inputs());
+
+  GraphTransferInfo::NodeInputInfo& node_input_info =
+      *graph_transfer_info_.add_node_input_info();
+  node_input_info.set_node_id(id);
+
+  AddNodeInputByInputIndex(node, 0, &node_input_info);
+
+  const Edge* edge = nullptr;
+  TF_CHECK_OK(node.input_edge(1, &edge));
+  const Node* input_node = edge->src();
+  CHECK_NOTNULL(input_node);
+  CHECK(input_node->IsConstant());
+
+  const TensorProto* tensor_proto = nullptr;
+  TF_CHECK_OK(GetNodeAttr(input_node->def(), "value", &tensor_proto));
+  CHECK_NOTNULL(tensor_proto);
+  Tensor const_tensor;
+  TF_CHECK_OK(MakeTensorFromProto(*tensor_proto, &const_tensor));
+  CHECK_EQ(2, const_tensor.shape().dims());
+  CHECK_EQ(PAD_HEIGHT, const_tensor.shape().dim_size(1));
+  if (const_tensor.shape().dim_size(0) == PAD_WIDTH) {
+    AddNodeInputByInputIndex(node, 1, &node_input_info);
+  } else if (const_tensor.shape().dim_size(0) < PAD_WIDTH) {
+    const int width = const_tensor.shape().dim_size(0);
+    const TensorProto* proto = nullptr;
+    TF_CHECK_OK(GetNodeAttr(input_node->def(), "value", &proto));
+    Tensor const_tensor;
+    TF_CHECK_OK(MakeTensorFromProto(*proto, &const_tensor));
+    CHECK_EQ(DT_INT32, const_tensor.dtype());
+    // reshape tensor input to be rank 4.
+    // TODO(satok): Never assume rank is 4.
+    Tensor new_const_tensor(const_tensor.dtype(), TensorShape{4, 2});
+    for (int i = 0; i < PAD_HEIGHT; ++i) {
+      for (int j = 0; j < PAD_WIDTH; ++j) {
+        if (j < PAD_WIDTH - width) {
+          new_const_tensor.matrix<int32>()(j, i) = 0;
+        } else {
+          new_const_tensor.matrix<int32>()(j, i) =
+              const_tensor.matrix<int32>()(j - (PAD_WIDTH - width), i);
+        }
+      }
+    }
+
+    const int id = RegisterConstTensor(
+        new_const_tensor,
+        strings::StrCat(input_node->name(), "_", node.name(), "_1"));
+
+    GraphTransferInfo::NodeInput& node_input =
+        *node_input_info.add_node_input();
+    node_input.set_node_id(id);
+    node_input.set_output_port(0);
+  } else {
+    CHECK(false);
+  }
+
+  AppendNodeParamsWithIoParams(
+      shape_refiner, node, node.name(), id, node.type_string(), op_type_id,
+      PADDING_NA_ID, node.num_inputs(), {}, node.num_outputs(),
+      false /* append_input */, true /* append_output */);
+}
+
+void GraphTransferer::RegisterInputNode(
+    const IGraphTransferOpsDefinitions& ops_definitions,
+    const ShapeRefiner& shape_refiner, const Node& node) {
   const string op_type = node.type_string();
+  VLOG(1) << "Register input node: " << node.name() << ", " << op_type;
+  CHECK_EQ(node_name_to_id_cache_map_.count(node.name()), 1);
+  const int id = node_name_to_id_cache_map_[node.name()];
   // TODO(satok): Set correct data type if it's given.
-  const int op_type_id = ops_definitions.GetOpIdFor(op_type, {});
+  const int op_type_id = ops_definitions.GetOpIdFor("INPUT", {});
   CHECK(op_type_id >= 0 && op_type_id < ops_definitions.GetTotalOpsCount())
       << "Op" << node.name() << ", " << op_type << " is not supported,"
       << op_type_id;
@@ -546,7 +849,6 @@ void GraphTransferer::AppendNodeParams(const string& name, const int id,
                                        const int padding, const int inputs_size,
                                        const std::vector<int>& extra_inputs,
                                        const int outputs_size) {
-  VLOG(1) << "Append node params: " << name;
   GraphTransferInfo::NodeInfo& node_info =
       *graph_transfer_info_.add_node_info();
   node_info.set_name(name);
@@ -559,6 +861,23 @@ void GraphTransferer::AppendNodeParams(const string& name, const int id,
   node_info.set_output_count(static_cast<int>(outputs_size));
 }
 
+void GraphTransferer::AddNodeInputByInputIndex(
+    const Node& node, const int idx,
+    GraphTransferInfo::NodeInputInfo* node_input_info) {
+  const Edge* edge = nullptr;
+  TF_CHECK_OK(node.input_edge(idx, &edge));
+  const Node* input_node = edge->src();
+  CHECK_NOTNULL(input_node);
+  const int port = edge->src_output();
+
+  const std::string& op_name = input_node->name();
+  CHECK_GT(node_name_to_id_cache_map_.count(op_name), 0) << op_name;
+  const int src_id = node_name_to_id_cache_map_[op_name];
+  GraphTransferInfo::NodeInput& node_input = *node_input_info->add_node_input();
+  node_input.set_node_id(src_id);
+  node_input.set_output_port(port);
+}
+
 void GraphTransferer::AppendNodeInputParams(
     const int id, const Node& node, const std::vector<int>& extra_inputs) {
   VLOG(1) << "Append input params: " << node.name() << ", " << node.num_inputs()
@@ -567,18 +886,7 @@ void GraphTransferer::AppendNodeInputParams(
       *graph_transfer_info_.add_node_input_info();
   node_input_info.set_node_id(id);
   for (int i = 0; i < node.num_inputs(); ++i) {
-    const Edge* edge = nullptr;
-    TF_CHECK_OK(node.input_edge(i, &edge));
-    const Node* input_node = edge->src();
-    const int port = edge->src_output();
-
-    const std::string& op_name = input_node->name();
-    CHECK_GT(node_name_to_id_cache_map_.count(op_name), 0) << op_name;
-    const int src_id = node_name_to_id_cache_map_[op_name];
-    GraphTransferInfo::NodeInput& node_input =
-        *node_input_info.add_node_input();
-    node_input.set_node_id(src_id);
-    node_input.set_output_port(port);
+    AddNodeInputByInputIndex(node, i, &node_input_info);
   }
   for (const int extra_input : extra_inputs) {
     GraphTransferInfo::NodeInput& node_input =
@@ -596,9 +904,10 @@ void GraphTransferer::AppendNodeOutputParams(const ShapeRefiner& shape_refiner,
       *graph_transfer_info_.add_node_output_info();
   node_output_info.set_node_id(id);
 
+  std::vector<DataType> data_types;
   std::vector<TensorShape> shapes;
   Status status = RemoteFusedGraphExecuteUtils::GetOutputTensorShapeType(
-      node.attrs(), nullptr, &shapes);
+      node.attrs(), &data_types, &shapes);
 
   for (int i = 0; i < node.num_outputs(); ++i) {
     int data_size = -1;
@@ -608,16 +917,20 @@ void GraphTransferer::AppendNodeOutputParams(const ShapeRefiner& shape_refiner,
 
     shape_inference::InferenceContext* context =
         shape_refiner.GetContext(&node);
-    shape_inference::ShapeHandle shape_handle = context->output(output_index);
-    const shape_inference::DimensionHandle num_elements_dim =
-        context->NumElements(shape_handle);
-    if (context->ValueKnown(num_elements_dim)) {
+
+    if (context != nullptr && context->ValueKnown(context->NumElements(
+                                  context->output(output_index)))) {
+      const shape_inference::DimensionHandle num_elements_dim =
+          context->NumElements(context->output(output_index));
       const int64 num_output_elements = context->Value(num_elements_dim);
       data_size = max_bytes_per_data * num_output_elements;
+      if (status.ok()) {
+        TF_CHECK_OK(status);
+        CHECK_EQ(shapes.at(i).num_elements(), num_output_elements);
+      }
     } else {
       TF_CHECK_OK(status);
       // Use attribute attached to node
-      CHECK_EQ(node.num_outputs(), shapes.size()) << node.name();
       data_size = max_bytes_per_data * shapes.at(i).num_elements();
     }
     CHECK_GE(data_size, 0);
@@ -722,11 +1035,11 @@ bool GraphTransferer::TransferParamsComparator::operator()(
   const int node_id0 = obj0.node_id();
   const int node_id1 = obj1.node_id();
   bool obj0_uses_obj1 = false;
-  if (dependency_map_.count(node_id0)) {
+  if (dependency_map_.count(node_id0) > 0) {
     obj0_uses_obj1 = dependency_map_.at(node_id0).count(node_id1) > 0;
   }
   bool obj1_uses_obj0 = false;
-  if (dependency_map_.count(node_id1)) {
+  if (dependency_map_.count(node_id1) > 0) {
     obj1_uses_obj0 = dependency_map_.at(node_id1).count(node_id0) > 0;
   }
   CHECK(!obj0_uses_obj1 || !obj1_uses_obj0);
@@ -735,7 +1048,9 @@ bool GraphTransferer::TransferParamsComparator::operator()(
   } else if (obj1_uses_obj0) {
     return true;
   }
-  return node_id0 > node_id1;
+  // If there is no dependency between two nodes, it expects that
+  // the execution order follows node id order.
+  return node_id0 < node_id1;
 }
 
 /* static */ void GraphTransferer::FillDependencyRec(
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.h b/tensorflow/core/kernels/hexagon/graph_transferer.h
index fa12b22d75..64c60b87c6 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer.h
+++ b/tensorflow/core/kernels/hexagon/graph_transferer.h
@@ -88,6 +88,9 @@ class GraphTransferer {
   // Dump verification string of parameters to verify with offline tools
   void DumpVerificationStringOfNodeTransferParams() const;
 
+  static std::array<int64, SHAPE_ARRAY_SIZE> ToTensorShapeArray(
+      const TensorShape& shape);
+
  private:
   class TransferParamsComparator {
    public:
@@ -98,10 +101,16 @@ class GraphTransferer {
     const std::unordered_map<int, std::unordered_set<int>>& dependency_map_;
   };
 
-  int CacheNode(const Node& node);
+  void CacheNode(const Node& node);
 
   bool AreAllInputsCached(const Node& node) const;
 
+  // Transform a remote fused graph to add an aggregated input node which takes
+  // all inputs of the remote graph.
+  Status TransformGraphToAddAggregatedInputNode(
+      const std::vector<std::pair<string, Tensor>>& input_node_info_list,
+      Graph* graph, ShapeRefiner* shape_refiner);
+
   Status RegisterNode(
       const IGraphTransferOpsDefinitions& ops_definitions,
       const ShapeRefiner& shape_refiner, const Node& node,
@@ -113,8 +122,17 @@ class GraphTransferer {
 
   int RegisterConstantShape(const std::vector<int>& shape);
 
+  int RegisterConstTensor(const Tensor& tensor, const string& suffix);
+
+  int RegisterConstScalar(const DataType dt, const int val, const int dst_id,
+                          const int dst_input_count);
+
   bool HasPaddingAndStrides(const Node& node);
 
+  bool NeedsToAddRank(const Node& node);
+
+  bool IsPadNode(const Node& node);
+
   // Return true if the node is a reshape op which just flattens input
   // TODO(satok): Remove this method once generic reshape op is implemented in
   // SOC
@@ -125,6 +143,13 @@ class GraphTransferer {
       const IGraphTransferOpsDefinitions& ops_definitions,
       const ShapeRefiner& shape_refiner, const Node& node);
 
+  void RegisterNodeWithRank(const IGraphTransferOpsDefinitions& ops_definitions,
+                            const ShapeRefiner& shape_refiner,
+                            const Node& node);
+
+  void RegisterPadNode(const IGraphTransferOpsDefinitions& ops_definitions,
+                       const ShapeRefiner& shape_refiner, const Node& node);
+
   void RegisterInputNode(const IGraphTransferOpsDefinitions& ops_definitions,
                          const ShapeRefiner& shape_refiner,
                          const Node& node);
@@ -150,6 +175,10 @@ class GraphTransferer {
                         const std::vector<int>& extra_inputs,
                         const int outputs_size);
 
+  void AddNodeInputByInputIndex(
+      const Node& node, const int idx,
+      GraphTransferInfo::NodeInputInfo* node_input_info);
+
   void AppendNodeInputParams(const int id, const Node& node,
                              const std::vector<int>& extra_inputs);
 
@@ -167,9 +196,6 @@ class GraphTransferer {
       const int outputs_size, const bool append_input_params,
       const bool append_output_params);
 
-  static std::array<int64, SHAPE_ARRAY_SIZE> ToTensorShapeArray(
-      const TensorShape& shape);
-
   static string ToPaddingDebugString(int padding);
 
   // Create dependency map
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer_test.cc b/tensorflow/core/kernels/hexagon/graph_transferer_test.cc
index ebd4a90330..74ffc026f7 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer_test.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transferer_test.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h"
 #include "tensorflow/core/kernels/i_remote_fused_graph_executor.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/public/session.h"
@@ -47,21 +48,19 @@ class GraphTransfererTest : public ::testing::Test {
   GraphTransferer gt_;
 };
 
-static const std::vector<string> OP_TYPES{
-    "INPUT", "OUTPUT", "Conv2D", "MaxPool", "NoOp", "Add", "Const", "Softmax"};
 const RemoteFusedGraphExecuteUtils::TensorShapeMap EMPTY_OUTPUT_TENSOR_MAP;
 
 class TestGraphTransferOpsDefinitions : public IGraphTransferOpsDefinitions {
  public:
-  int GetTotalOpsCount() const final { return OP_TYPES.size(); }
+  int GetTotalOpsCount() const final { return op_types_.size(); }
 
-int GetOpIdFor(const string& op_type, const DataTypeVector&) const final {
-  for (int i = 0; i < OP_TYPES.size(); ++i) {
-    if (OP_TYPES[i] == op_type) {
-      return i;
+  int GetOpIdFor(const string& op_type, const DataTypeVector&) const final {
+    for (int i = 0; i < op_types_.size(); ++i) {
+      if (op_types_[i] == op_type) {
+        return i;
+      }
     }
-  }
-  return -1;
+    return -1;
 }
 
 GraphTransferInfo::Destination GetTransferDestination() const final {
@@ -69,6 +68,9 @@ GraphTransferInfo::Destination GetTransferDestination() const final {
   }
 
  private:
+  const std::vector<string> op_types_{"INPUT",   "OUTPUT",  "Conv2D",
+                                      "MaxPool", "NoOp",    "Add",
+                                      "Const",   "Softmax", "Identity"};
 } TEST_GRAPH_TRANSFER_OPS_DEFINITIONS;
 
 static Output BuildAddOps(const Scope& scope, const Input& x, const Input& y) {
@@ -312,7 +314,7 @@ TEST_F(GraphTransfererTest, LoadAddGraphWithOutputTensorMap) {
   const std::vector<string> output_node_names = {NAME_A_PLUS_B};
   status = gt_.LoadGraphFromProto(TEST_GRAPH_TRANSFER_OPS_DEFINITIONS, def,
                                   inputs, output_node_names, false);
-  ASSERT_TRUE(status.ok());
+  TF_ASSERT_OK(status);
 }
 
 TEST_F(GraphTransfererTest, LoadConvGraph) {
@@ -330,7 +332,7 @@ TEST_F(GraphTransfererTest, LoadConvGraph) {
       gt_.GetGraphTransferInfo().const_node_info_size();
   ASSERT_EQ(2, const_node_count);
   const int op_node_count = gt_.GetGraphTransferInfo().node_info_size();
-  ASSERT_EQ(3, op_node_count);
+  ASSERT_EQ(4, op_node_count);
   const GraphTransferInfo::NodeInfo* params_conv = FindNodeInfo(gt_, "conv");
   ASSERT_TRUE(params_conv != nullptr);
   const int id = params_conv->node_id();
@@ -356,7 +358,7 @@ TEST_F(GraphTransfererTest, LoadMaxPoolGraph) {
       gt_.GetGraphTransferInfo().const_node_info_size();
   ASSERT_EQ(2, const_node_count);
   const int op_node_count = gt_.GetGraphTransferInfo().node_info_size();
-  ASSERT_EQ(3, op_node_count);
+  ASSERT_EQ(4, op_node_count);
   const GraphTransferInfo::NodeInfo* params_max_pool =
       FindNodeInfo(gt_, "maxpool");
   ASSERT_TRUE(params_max_pool != nullptr);
diff --git a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
index 518b399c37..660ffd268d 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
@@ -27,6 +27,8 @@ namespace tensorflow {
 constexpr const char* const INPUT_OP_NAME = "INPUT";
 constexpr const char* const OUTPUT_OP_NAME = "OUTPUT";
 
+constexpr int ALIGNMENT_BYTES = 16;
+
 const bool DBG_DUMP_VERIFICATION_STRING = false;
 const int DBG_LEVEL = 0;  // -2: verbose, -1: debug, 0: info
 const bool DBG_USE_DUMMY_INPUT = false;
@@ -34,6 +36,22 @@ const bool DBG_USE_SAMPLE_INPUT = false;
 const int64 FLAG_ENABLE_PANDA_BINARY_INPUT = 0x01;
 const bool DBG_DUMP_INPUT_TENSOR_AS_FLOAT_DATA = false;
 
+static string AddPort(const string& node_name) {
+  if (node_name.find(':') != string::npos) {
+    return node_name;
+  } else {
+    return strings::StrCat(node_name, ":", 0);
+  }
+}
+
+static uint8* FindAlignedPointer(uint8* ptr) {
+  const uintptr_t data_ptr_int = reinterpret_cast<uintptr_t>(ptr);
+  const int shift_count =
+      (ALIGNMENT_BYTES - data_ptr_int % ALIGNMENT_BYTES) % ALIGNMENT_BYTES;
+  uint8* data_ptr = ptr + shift_count;
+  return data_ptr;
+}
+
 /* static */ GraphTransferInfo::NodeInfo* HexagonControlWrapper::FindNodeInfo(
     const string& name, GraphTransferInfo* graph_transfer_info) {
   for (GraphTransferInfo::NodeInfo& node_info :
@@ -60,18 +78,57 @@ bool HexagonControlWrapper::Init(const RemoteFusedGraphExecuteInfo& info) {
     std::vector<string> outputs;
     RemoteFusedGraphExecuteUtils::BuildRemoteGraphInputsAndOutputsFromProto(
         info, &inputs, &outputs);
-    graph_transferer_.LoadGraphFromProto(
+    Status status = graph_transferer_.LoadGraphFromProto(
         HexagonOpsDefinitions::getInstance(), info.remote_graph(), inputs,
         outputs,
         false  // shape_inference_for_unknown_shape
-        );
+    );
+    TF_CHECK_OK(status) << status;
   } else {
     // If graph transfer info is attached, just import it.
     graph_transferer_.SetSerializedGraphTransferInfo(
         info.serialized_executor_parameters());
   }
   execute_info_ = &info;
-  return soc_interface_Init();
+  bool success = soc_interface_Init();
+  if (!success) {
+    LOG(ERROR) << "Hexagon initialization was failed.  See log output.";
+    return false;
+  }
+  const GraphTransferInfo& gt_info = graph_transferer_.GetGraphTransferInfo();
+  std::vector<int> input_sizes;
+  std::vector<int> output_sizes;
+  CHECK_NOTNULL(execute_info_);
+  for (int i = 0; i < execute_info_->graph_input_node_name_size(); ++i) {
+    const string& input = execute_info_->graph_input_node_name(i);
+    LOG(INFO) << "Add input: " << input << ", " << i;
+    CHECK(input_port_map_.emplace(AddPort(input), i).second);
+    const RemoteFusedGraphExecuteInfo::TensorShapeTypeProto& shape_type =
+        execute_info_->default_graph_input_tensor_shape(i);
+    int64 buf_size = DataTypeSize(shape_type.dtype());
+    for (const TensorShapeProto::Dim& dim : shape_type.shape().dim()) {
+      buf_size *= dim.size();
+    }
+    input_sizes.emplace_back(static_cast<int>(buf_size));
+  }
+  for (int i = 0; i < execute_info_->graph_output_node_name_size(); ++i) {
+    const string& output = execute_info_->graph_output_node_name(i);
+    CHECK(output_port_map_.emplace(AddPort(output), i).second);
+    const RemoteFusedGraphExecuteInfo::TensorShapeTypeProto& shape_type =
+        execute_info_->default_graph_output_tensor_shape(i);
+
+    int64 buf_size = DataTypeSize(shape_type.dtype());
+    for (const TensorShapeProto::Dim& dim : shape_type.shape().dim()) {
+      buf_size *= dim.size();
+    }
+    output_sizes.emplace_back(static_cast<int>(buf_size));
+  }
+
+  LOG(INFO) << "Allocate inout buffer";
+  success &= soc_interface_AllocateInOutNodeBuffers(
+      input_sizes.size(), input_sizes.data(), output_sizes.size(),
+      output_sizes.data());
+  return success;
 }
 
 bool HexagonControlWrapper::Finalize() { return soc_interface_Finalize(); }
@@ -86,9 +143,6 @@ bool HexagonControlWrapper::SetupGraph() {
     GraphTransferInfo::NodeInfo* node_info =
         FindNodeInfo(graph_input.name(), &graph_transfer_info);
     CHECK_NE(node_info, nullptr);
-    node_info->set_type_name(INPUT_OP_NAME);
-    node_info->set_soc_op_id(
-        HexagonOpsDefinitions::getInstance().GetOpIdFor(INPUT_OP_NAME, {}));
   }
 
   // Generate a new output node which is connected to graph output node
@@ -202,12 +256,8 @@ bool HexagonControlWrapper::SetupGraph() {
     auto data = dummy_const_data_.emplace(
         std::piecewise_construct, std::make_tuple(node_id), std::make_tuple());
     CHECK(data.second);
-    const int additional_bytes_for_alignment = 16;
-    data.first->second.resize(data_size + additional_bytes_for_alignment - 1);
-    const uintptr_t data_ptr_int =
-        reinterpret_cast<uintptr_t>(data.first->second.data());
-    const int shift_count = (16 - data_ptr_int % 16) % 16;
-    uint8* data_ptr = data.first->second.data() + shift_count;
+    data.first->second.resize(data_size + ALIGNMENT_BYTES - 1);
+    uint8* data_ptr = FindAlignedPointer(data.first->second.data());
     std::memcpy(data_ptr, params.data().data(), data_size);
     soc_interface_AppendConstNode(params.name().c_str(),
                                   node_id + NODE_ID_OFFSET, shape_0, shape_1,
@@ -267,27 +317,37 @@ bool HexagonControlWrapper::TeardownGraph() {
   return soc_interface_TeardownGraph();
 }
 
-bool HexagonControlWrapper::FillInputNode(const string& node_name,
-                                          const ConstByteArray bytes) {
-  uint64 byte_size;
-  const int x = 1;
-  const int y = 299;
-  const int z = 299;
-  const int d = 3;
+bool HexagonControlWrapper::FillInputNode(
+    const string& node_name,
+    const std::array<int64, GraphTransferer::SHAPE_ARRAY_SIZE>& shape,
+    const ConstByteArray bytes) {
+  const string tensor_name = AddPort(node_name);
+  CHECK(input_port_map_.count(tensor_name) > 0);
+  const int port = input_port_map_.at(tensor_name);
+  if (input_tensor_data_.count(port) <= 0) {
+    input_tensor_data_.emplace(port, std::vector<uint8>{});
+  }
+  std::vector<uint8>& input_tensor_data = input_tensor_data_.at(port);
+
+  // hexagon only supports 32bit dimension
+  const int x = static_cast<int>(shape[0]);
+  const int y = static_cast<int>(shape[1]);
+  const int z = static_cast<int>(shape[2]);
+  const int d = static_cast<int>(shape[3]);
+
+  const uint64 byte_size = x * y * z * d * DataTypeSize(std::get<2>(bytes));
+  CHECK_EQ(byte_size, std::get<1>(bytes));
+  input_tensor_data.resize(byte_size + ALIGNMENT_BYTES);
+  uint8* data_ptr = FindAlignedPointer(input_tensor_data.data());
+
   if (DBG_USE_DUMMY_INPUT) {
-    const int array_length = x * y * z * d;
-    byte_size = array_length * sizeof(float);
-    dummy_input_float_.resize(array_length);
-    std::memset(dummy_input_float_.data(), 0, byte_size);
+    std::memset(data_ptr, 0, byte_size);
   } else {
-    CHECK(std::get<2>(bytes) == DT_FLOAT);
-    byte_size = std::get<1>(bytes);
-    dummy_input_float_.resize(byte_size / sizeof(float));
-    std::memcpy(dummy_input_float_.data(), std::get<0>(bytes), byte_size);
+    std::memcpy(data_ptr, std::get<0>(bytes), byte_size);
   }
-  return soc_interface_FillInputNodeFloat(
-      x, y, z, d, reinterpret_cast<uint8*>(dummy_input_float_.data()),
-      byte_size);
+
+  return soc_interface_FillInputNodeWithPort(port, x, y, z, d, data_ptr,
+                                             byte_size);
 }
 
 bool HexagonControlWrapper::ReadOutputNode(
@@ -304,26 +364,28 @@ bool HexagonControlWrapper::ReadOutputNode(
       break;
     }
   }
-  std::vector<IRemoteFusedGraphExecutor::ByteArray> outputs;
+  std::vector<ByteArray> outputs;
   ReadOutputNode(node_name, &outputs);
   CHECK_EQ(1, outputs.size());
-  IRemoteFusedGraphExecutor::ByteArray& output = outputs[0];
+  ByteArray& output = outputs[0];
   Tensor* output_tensor = tensor_allocator(output_shape);
   CHECK(output_tensor->TotalBytes() >= std::get<1>(output))
       << output_tensor->TotalBytes() << ", " << std::get<1>(output);
-  // TODO(satok): Avoid specifying float
-  std::memcpy(output_tensor->flat<float>().data(), std::get<0>(output),
-              std::get<1>(output));
+  TF_CHECK_OK(RemoteFusedGraphExecuteUtils::CopyByteArrayToTensor(
+      std::get<0>(output), std::get<1>(output), output_tensor));
 }
 
 bool HexagonControlWrapper::ReadOutputNode(
     const string& node_name, std::vector<ByteArray>* const outputs) {
   CHECK(outputs != nullptr);
   ByteArray output;
-  soc_interface_ReadOutputNodeFloat(node_name.c_str(), &std::get<0>(output),
-                                    &std::get<1>(output));
+  const string tensor_name = AddPort(node_name);
+  CHECK(output_port_map_.count(tensor_name) > 0);
+  const int port = output_port_map_.at(tensor_name);
+  soc_interface_ReadOutputNodeWithPort(port, &std::get<0>(output),
+                                       &std::get<1>(output));
   // TODO: Accept all results
-  std::get<2>(output) = DT_FLOAT;
+  // std::get<2>(output) = DT_FLOAT;
   outputs->emplace_back(output);
   return true;
 }
@@ -347,7 +409,9 @@ bool HexagonControlWrapper::FillInputNode(const string& node_name,
       }
     }
   }
-  FillInputNode(node_name, ba);
+  const std::array<int64, GraphTransferer::SHAPE_ARRAY_SIZE> shape =
+      GraphTransferer::ToTensorShapeArray(tensor.shape());
+  FillInputNode(node_name, shape, ba);
   return true;
 }
 
@@ -360,7 +424,9 @@ bool HexagonControlWrapper::Finalize() { return false; }
 bool HexagonControlWrapper::SetupGraph() { return false; }
 bool HexagonControlWrapper::ExecuteGraph() { return false; }
 bool HexagonControlWrapper::TeardownGraph() { return false; }
-bool HexagonControlWrapper::FillInputNode(const string&, const ConstByteArray) {
+bool HexagonControlWrapper::FillInputNode(
+    const string&, const std::array<int64, GraphTransferer::SHAPE_ARRAY_SIZE>&,
+    const ConstByteArray) {
   return false;
 }
 bool HexagonControlWrapper::FillInputNode(const string&, const Tensor&) {
diff --git a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h
index 97448884e1..209ac9dbf4 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h
+++ b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_HEXAGON_CONTROL_WRAPPER_H_
 #define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_HEXAGON_CONTROL_WRAPPER_H_
 
+#include <unordered_map>
 #include <vector>
 
 #include "tensorflow/core/framework/types.h"
@@ -32,6 +33,9 @@ namespace tensorflow {
  */
 class HexagonControlWrapper final : public IRemoteFusedGraphExecutor {
  public:
+  using ByteArray =
+      std::tuple<uint8* /* data */, uint64 /* size */, DataType /* type */>;
+
   HexagonControlWrapper() = default;
   int GetVersion() final;
   bool Init(const RemoteFusedGraphExecuteInfo& info) final;
@@ -45,7 +49,13 @@ class HexagonControlWrapper final : public IRemoteFusedGraphExecutor {
   bool ReadOutputNode(const string& node_name, std::vector<ByteArray>* outputs);
 
  private:
-  bool FillInputNode(const string& node_name, const ConstByteArray bytes);
+  using ConstByteArray = std::tuple<const uint8* /* data */, uint64 /* size */,
+                                    DataType /* type */>;
+
+  bool FillInputNode(
+      const string& node_name,
+      const std::array<int64, GraphTransferer::SHAPE_ARRAY_SIZE>& shape,
+      const ConstByteArray bytes);
 
   // CAVEAT: Need offset as HVX library reserves some ids
   static constexpr int NODE_ID_OFFSET = 0x10000;
@@ -57,11 +67,15 @@ class HexagonControlWrapper final : public IRemoteFusedGraphExecutor {
   GraphTransferer graph_transferer_{};
   // Dummy float array for input node.
   // TODO(satok): Use actual data passed by FillInputNode and remove
-  std::vector<float> dummy_input_float_{};
+  // std::vector<float> dummy_input_float_{};
+  std::unordered_map<int, std::vector<uint8>> input_tensor_data_{};
   // Dummy byte array for cosnt node.
   // TODO(satok): Remove
   std::unordered_map<int, std::vector<uint8>> dummy_const_data_{};
 
+  std::unordered_map<string, int> input_port_map_{};
+  std::unordered_map<string, int> output_port_map_{};
+
   TF_DISALLOW_COPY_AND_ASSIGN(HexagonControlWrapper);
 };
 
diff --git a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
index 54ba101501..cb9091e29f 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
@@ -46,8 +46,7 @@ adb push /tmp/imagenet_comp_graph_label_strings.txt /data/local/tmp
 
 namespace tensorflow {
 
-using ByteArray = IRemoteFusedGraphExecutor::ByteArray;
-using ConstByteArray = IRemoteFusedGraphExecutor::ConstByteArray;
+using ByteArray = HexagonControlWrapper::ByteArray;
 
 constexpr const char* const IMAGE_FILENAME = "/data/local/tmp/img_299x299.bmp";
 constexpr const char* const MODEL_FILENAME =
@@ -87,8 +86,7 @@ static void DumpTop10Results(const int byte_size,
       10 /* show top_n results */);
 }
 
-static void DumpTop10Results(
-    const std::vector<IRemoteFusedGraphExecutor::ByteArray>& outputs) {
+static void DumpTop10Results(const std::vector<ByteArray>& outputs) {
   CHECK(outputs.size() == 1);
   const int byte_size = std::get<1>(outputs.at(0));
   const float* float_array =
@@ -96,9 +94,8 @@ static void DumpTop10Results(
   DumpTop10Results(byte_size, float_array);
 }
 
-static void CheckFirstResult(
-    const std::vector<IRemoteFusedGraphExecutor::ByteArray>& outputs,
-    const int expected_first_id) {
+static void CheckFirstResult(const std::vector<ByteArray>& outputs,
+                             const int expected_first_id) {
   EXPECT_GE(outputs.size(), 1);
   const int byte_size = std::get<1>(outputs.at(0));
   const int element_count = byte_size / sizeof(float);
@@ -240,7 +237,7 @@ static void RunInferenceByHexagonControlWrapper(
   }
 
   // 5-1. Read output node's outputs
-  std::vector<IRemoteFusedGraphExecutor::ByteArray> outputs;
+  std::vector<ByteArray> outputs;
   hexagon_control_wrapper.ReadOutputNode("softmax", &outputs);
 
   // 5-2. Dump results
diff --git a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
index a4b79e6ec4..2b7585aed1 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
@@ -350,6 +350,8 @@ HexagonOpsDefinitions::BuildOpNameToSocOpTypeMap() {
 #ifdef ENABLE_EXPERIMENTAL_HEXNN_OPS
   EmplaceOpType("QuantizedMul", {}, SupportedOpType::QUANTIZED_MUL_8x8to32,
                 &op_map);
+  EmplaceOpType("QuantizedAdd", {}, SupportedOpType::QUANTIZED_ADD_8p8to32,
+                &op_map);
   EmplaceOpType("Pad", {}, SupportedOpType::PAD_F, &op_map);
   EmplaceOpType("SpaceToBatchND", {}, SupportedOpType::SPACE_TO_BATCH_ND_F,
                 &op_map),
@@ -359,6 +361,11 @@ HexagonOpsDefinitions::BuildOpNameToSocOpTypeMap() {
                 &op_map);
   EmplaceOpType("ConcatV2", {}, SupportedOpType::CONCAT_V2_F, &op_map);
   EmplaceOpType("Conv2DBackpropInput", {}, SupportedOpType::DECONV_F, &op_map);
+
+  EmplaceOpType("Tanh", {}, SupportedOpType::TANH_F, &op_map);
+  EmplaceOpType("Split", {}, SupportedOpType::SPLIT_F, &op_map);
+  EmplaceOpType("Transpose", {}, SupportedOpType::TRANSPOSE_F, &op_map);
+  EmplaceOpType("Concat", {}, SupportedOpType::CONCAT_F, &op_map);
 #endif
   return op_map;
 };
diff --git a/tensorflow/core/kernels/i_remote_fused_graph_executor.h b/tensorflow/core/kernels/i_remote_fused_graph_executor.h
index fe62a259de..09d1f43ff1 100644
--- a/tensorflow/core/kernels/i_remote_fused_graph_executor.h
+++ b/tensorflow/core/kernels/i_remote_fused_graph_executor.h
@@ -25,10 +25,6 @@ namespace tensorflow {
 
 class IRemoteFusedGraphExecutor {
  public:
-  using ByteArray =
-      std::tuple<uint8* /* data */, uint64 /* size */, DataType /* type */>;
-  using ConstByteArray = std::tuple<const uint8* /* data */, uint64 /* size */,
-                                    DataType /* type */>;
   using TensorAllocatorFunc = std::function<Tensor*(const TensorShape& shape)>;
 
   IRemoteFusedGraphExecutor() = default;
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index 103b2be691..dd9839d245 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -1280,6 +1280,69 @@ RemoteFusedGraphExecuteUtils::FuseRemoteGraphByPlacedArguments(
   return true;
 }
 
+/* static */ Status RemoteFusedGraphExecuteUtils::CopyByteArrayToTensor(
+    const void* src_ptr, const int src_size, Tensor* tensor) {
+  CHECK(tensor->TotalBytes() >= src_size)
+      << tensor->TotalBytes() << ", " << src_size;
+  void* dst_ptr;
+  switch (tensor->dtype()) {
+    case DT_FLOAT:
+      dst_ptr = tensor->flat<float>().data();
+      break;
+    case DT_DOUBLE:
+      dst_ptr = tensor->flat<double>().data();
+      break;
+    case DT_INT32:
+      dst_ptr = tensor->flat<int32>().data();
+      break;
+    case DT_UINT8:
+      dst_ptr = tensor->flat<uint8>().data();
+      break;
+    case DT_INT16:
+      dst_ptr = tensor->flat<int16>().data();
+      break;
+    case DT_INT8:
+      dst_ptr = tensor->flat<int8>().data();
+      break;
+    case DT_STRING:
+      dst_ptr = tensor->flat<string>().data();
+      break;
+    case DT_INT64:
+      dst_ptr = tensor->flat<int64>().data();
+      break;
+    case DT_BOOL:
+      dst_ptr = tensor->flat<bool>().data();
+      break;
+    case DT_QINT8:
+      dst_ptr = tensor->flat<qint8>().data();
+      break;
+    case DT_QUINT8:
+      dst_ptr = tensor->flat<quint8>().data();
+      break;
+    case DT_QINT32:
+      dst_ptr = tensor->flat<qint32>().data();
+      break;
+    case DT_BFLOAT16:
+      dst_ptr = tensor->flat<bfloat16>().data();
+      break;
+    case DT_QINT16:
+      dst_ptr = tensor->flat<qint16>().data();
+      break;
+    case DT_QUINT16:
+      dst_ptr = tensor->flat<quint16>().data();
+      break;
+    case DT_UINT16:
+      dst_ptr = tensor->flat<uint16>().data();
+      break;
+    default:
+      CHECK(false) << "type " << tensor->dtype() << " is not supported.";
+      break;
+  }
+  CHECK_NOTNULL(dst_ptr);
+  std::memcpy(dst_ptr, src_ptr, src_size);
+  return Status::OK();
+}
+
 /* static */ Status RemoteFusedGraphExecuteUtils::ReplaceInputNodeByPlaceHolder(
     const string& input, const DataType type, const TensorShape& shape,
     GraphDef* graph_def) {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
index a80fc79784..1d4423ed46 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
@@ -157,7 +157,7 @@ class RemoteFusedGraphExecuteUtils {
       const std::vector<std::pair<string, Tensor>>& input_tensors,
       const bool dry_run_inference, GraphDef* graph_def);
 
-  // Build remote fused graph execute info
+  // Build remote fused graph execute info.
   static Status BuildRemoteFusedGraphExecuteInfo(
       const string& executor_name, const GraphDef& subgraph_def,
       const std::vector<string>& inputs, const std::vector<string>& outputs,
@@ -165,31 +165,31 @@ class RemoteFusedGraphExecuteUtils {
       DataTypeVector* input_types, DataTypeVector* output_types);
 
   // Build remote fused graph execute op node by fusing specified subgraph
-  // as remote fused graph execute info
+  // as remote fused graph execute info.
   static Status BuildRemoteFusedGraphExecuteOpNode(
       const string& node_name, const string& executor_name,
       const GraphDef& subgraph_def, const std::vector<string>& inputs,
       const std::vector<string>& outputs, const bool require_shape_type,
       Graph* graph, Node** created_node);
 
-  // Build Identity node to forward remote graph node output
+  // Build Identity node to forward remote graph node output.
   static Status BuildIdentityOpNode(const string& node_name,
                                     const string& input_node_name,
                                     const int input_node_port,
                                     const DataType dt, Graph* graph,
                                     Node** created_node);
 
-  // Create clusters of given nodes
+  // Create clusters of given nodes.
   static Status ClusterizeNodes(const std::unordered_set<string>& node_names,
                                 const GraphDef& graph_def,
                                 std::vector<ClusterInfo>* cluster_infos);
 
-  // Build GraphDef of a given cluster
+  // Build GraphDef of a given cluster.
   static Status BuildClusterSubgraphDef(const ClusterInfo& cluster,
                                         const GraphDef& graph_def,
                                         GraphDef* subgraph_def);
 
-  // Build a cluster by given border
+  // Build a cluster by given border.
   // CAVEAT: The border must be consistent for one cluster.
   static Status BuildClusterByBorder(const std::vector<string>& border_inputs,
                                      const std::vector<string>& border_outputs,
@@ -211,7 +211,7 @@ class RemoteFusedGraphExecuteUtils {
                             const bool require_shape_type,
                             GraphDef* output_graph_def);
 
-  // Fuse subgraph of specified nodes
+  // Fuse subgraph of specified nodes.
   static Status FuseRemoteGraphByNodeNames(
       const GraphDef& input_graph_def, const std::vector<string>& inputs,
       const std::vector<string>& outputs,
@@ -220,7 +220,7 @@ class RemoteFusedGraphExecuteUtils {
       const string& remote_fused_graph_executor_name,
       const bool require_shape_type, GraphDef* output_graph_def);
 
-  // Fuse subgraph of specified border
+  // Fuse subgraph of specified border.
   static Status FuseRemoteGraphByBorder(
       const GraphDef& input_graph_def, const std::vector<string>& inputs,
       const std::vector<string>& outputs,
@@ -230,7 +230,7 @@ class RemoteFusedGraphExecuteUtils {
       const string& remote_graph_executor_name, const bool require_shape_type,
       GraphDef* output_graph_def);
 
-  // Place arguments to fuse remote graph
+  // Place arguments to fuse remote graph.
   static Status PlaceRemoteGraphArguments(
       const std::vector<string>& inputs, const std::vector<string>& outputs,
       const std::unordered_set<string>& fused_node_names,
@@ -239,7 +239,7 @@ class RemoteFusedGraphExecuteUtils {
       const string& remote_fused_graph_node_name,
       const string& remote_graph_executor_name, GraphDef* graph_def);
 
-  // Fuse remote graph by placed arguments
+  // Fuse remote graph by placed arguments.
   static Status FuseRemoteGraphByPlacedArguments(
       const GraphDef& input_graph_def,
       const std::vector<std::pair<string, Tensor>>& input_tensors,
@@ -249,6 +249,15 @@ class RemoteFusedGraphExecuteUtils {
       const GraphDef& input_graph_def,
       const std::vector<std::pair<string, Tensor>>& input_tensors);
 
+  // Copy a byte array to a tensor data.  Though tensor data must be
+  // updated with typed information in general, we can't guarantee that
+  // returned values from a remote processor has typed information because
+  // a logic running in the remote processor possibly be in a separate binary
+  // which may not link tensorflow libraries.  To deal with this situation,
+  // remote fused graph needs to overwrite the tensor data by a byte array.
+  static Status CopyByteArrayToTensor(const void* src_ptr, const int src_size,
+                                      Tensor* tensor);
+
  private:
   static void EmplaceTensorShapeType(const string& name, const Tensor& tensor,
                                      TensorShapeMap* tensor_shape_map);
diff --git a/tensorflow/core/platform/hexagon/soc_interface.h b/tensorflow/core/platform/hexagon/soc_interface.h
index f4a3cdf4bd..ca37b63e2b 100644
--- a/tensorflow/core/platform/hexagon/soc_interface.h
+++ b/tensorflow/core/platform/hexagon/soc_interface.h
@@ -22,6 +22,8 @@ limitations under the License.
 // naming conflicts.
 #ifdef __cplusplus
 extern "C" {
+#else
+#include <stdbool.h>
 #endif  // __cplusplus
 // Returns the version of loaded hexagon wrapper shared library.
 // You should assert that the version matches the expected version before
@@ -39,13 +41,30 @@ bool soc_interface_Finalize();
 bool soc_interface_ExecuteGraph();
 // Teardown graph setup
 bool soc_interface_TeardownGraph();
+
+// Allocate buffers for input node and output node
+bool soc_interface_AllocateInOutNodeBuffers(int input_count, int* input_sizes,
+                                            int output_count,
+                                            int* output_sizes);
+
+// Send input data to SOC with port
+bool soc_interface_FillInputNodeWithPort(int port, int x, int y, int z, int d,
+                                         const uint8_t* const buf,
+                                         uint64_t buf_byte_size);
+
 // Send input data to SOC
 bool soc_interface_FillInputNodeFloat(int x, int y, int z, int d,
                                       const uint8_t* const buf,
-                                      uint64_t buf_size);
+                                      uint64_t buf_byte_size);
+
+// Load output data from SOC with port
+bool soc_interface_ReadOutputNodeWithPort(int port, uint8_t** buf,
+                                          uint64_t* buf_byte_size);
+
 // Load output data from SOC
 bool soc_interface_ReadOutputNodeFloat(const char* const node_name,
-                                       uint8_t** buf, uint64_t* buf_size);
+                                       uint8_t** buf, uint64_t* buf_byte_size);
+
 // Setup graph
 // TODO(satok): Remove and use runtime version
 bool soc_interface_setupDummyGraph(int version);
-- 
GitLab


From 6bcb0391f144322c0eda536a02f568817302793d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 17:11:32 -0700
Subject: [PATCH 0128/1375] Support multiple inputs in hexagon_control_wrapper

PiperOrigin-RevId: 159175598
---
 .../src_impl/graph_functions_wrapper.c        |  81 +++--
 .../src_impl/hexagon_controller.c             | 323 ++++++++++++------
 .../src_impl/include/hexagon_controller.h     |  33 +-
 .../src_log/include/tfm_log.h                 |   3 +
 .../include/node_data_float.h                 |   5 +-
 .../src_soc_interface/include/soc_interface.h |  21 +-
 .../src_soc_interface/soc_interface.c         | 110 +++---
 7 files changed, 401 insertions(+), 175 deletions(-)

diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c
index 7c82158522..d83b58dc6b 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c
@@ -93,13 +93,16 @@ static uint32_t FindMaxIdx(const float* data, uint32_t entries) {
   return FindMaxIdxWithExcludeList(data, entries, 0, NULL);
 }
 
-void hexagon_controller_PrintMaxNIdx(const float *data, const uint32_t entries,
+void hexagon_controller_PrintMaxNIdx(const float* data, const uint32_t entries,
                          const int n, int* out_ranking) {
   if (DUMP_OUTPUT) {
     for (int i = 0; i < entries; ++i) {
       TFMLOGD("%d: val = %f", i, data[i]);
     }
   }
+  if (n >= entries) {
+    TFMLOGD("Too many N %d >= %d", n, entries);
+  }
   for (int i = 0; i < n; ++i) {
     out_ranking[i] = INT_MAX;
   }
@@ -182,6 +185,32 @@ uint32_t hexagon_controller_SetupGraph(int version)  {
   return nn_id;
 }
 
+bool hexagon_controller_ExecuteGraphWithMultipleInOut(
+    const uint32_t nn_id, const int input_count, hexagon_nn_tensordef* inputs,
+    const int output_count, hexagon_nn_tensordef* outputs) {
+  if (DBG_EXECUTION) {
+    TFMLOGD("Preparing to execute... in = %d, out = %d", input_count,
+            output_count);
+    LogDHexagon("Execute graph!");
+  }
+
+  const int err =
+      hexagon_nn_execute_new(nn_id, inputs, input_count, outputs, output_count);
+  if (err != 0) {
+    if (DBG_EXECUTION) {
+      LogDHexagon("Execution failed!");
+      TFMLOGE("execute got err: %d\n", err);
+      hexagon_controller_PrintLog(nn_id);
+    }
+    return false;
+  } else {
+    if (DBG_EXECUTION) {
+      LogDHexagon("Execution succeeded!");
+    }
+    return true;
+  }
+}
+
 bool hexagon_controller_ExecuteGraph(
     const uint32_t nn_id,
     const uint32_t batches,
@@ -197,7 +226,6 @@ bool hexagon_controller_ExecuteGraph(
     uint8_t* out_vals,
     const uint32_t output_val_byte_size,
     uint32_t* out_data_byte_size) {
-  int err;
   if (DBG_EXECUTION) {
     TFMLOGD("Preparing to execute...");
     TFMLOGD("Input: %d, %d, %d, %d, %d, %d",
@@ -205,35 +233,34 @@ bool hexagon_controller_ExecuteGraph(
     TFMLOGD("Output: %d, %p", output_val_byte_size, out_vals);
     LogDHexagon("Execute graph!");
   }
-  
-  if ((err = hexagon_nn_execute(nn_id,
-                                batches,
-                                height,
-                                width,
-                                depth,
-                                int_data,
-                                int_data_size,
-                                out_batches,
-                                out_height,
-                                out_width,
-                                out_depth,
-                                out_vals,
-                                output_val_byte_size,
-                                out_data_byte_size)) != 0) {
-    if (DBG_EXECUTION) {
-      LogDHexagon("Execution failed!");
-      TFMLOGE("execute got err: %d\n",err);
-    }
+
+  hexagon_nn_tensordef input;
+  hexagon_nn_tensordef output;
+
+  input.batches = batches;
+  input.height = height;
+  input.width = width;
+  input.depth = depth;
+  input.data = int_data;
+  input.dataLen = int_data_size;
+
+  output.data = out_vals;
+  output.dataLen = output_val_byte_size;
+
+  if (!hexagon_controller_ExecuteGraphWithMultipleInOut(nn_id, 1, &input, 1,
+                                                        &output)) {
     return false;
   } else {
+    *out_batches = output.batches;
+    *out_height = output.height;
+    *out_width = output.width;
+    *out_depth = output.depth;
+    *out_data_byte_size = output.dataLen;
+
     if (DBG_EXECUTION) {
       LogDHexagon("Execution succeeded!");
-      TFMLOGD("%d x %d x %d x %d, byte size = %d\n",
-              *out_batches,
-              *out_height,
-              *out_width,
-              *out_depth,
-              *out_data_byte_size);
+      TFMLOGD("%d x %d x %d x %d, byte size = %d\n", *out_batches, *out_height,
+              *out_width, *out_depth, *out_data_byte_size);
     }
     return true;
   }
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c
index 31caebf872..ba50da6abd 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c
@@ -28,7 +28,8 @@ limitations under the License.
 #include "soc_interface.h"
 #include "tfm_log.h"
 
-// if false, use int data as input.  This is only for acceleration purpose
+// if false, use int data as input.  This is only for acceleration purpose.
+// Also you may need to change android.min.
 static const bool USE_FLOAT_DATA = true;
 
 // if true, show id for each node
@@ -43,27 +44,95 @@ extern uint8_t inception_dummy_int_data_224x224[];
 extern uint8_t inception_dummy_int_data_299x299[];
 extern float inception_dummy_float_data_299x299[];
 
-#define HEXAGON_CONTROLLER_VERSION 92
+#define HEXAGON_CONTROLLER_VERSION 101
 
 // allocate print bufsize in advance @MB
 #define PRINT_BUFSIZE (2 * 1024 * 1024)
 
 static unsigned char s_print_buf[PRINT_BUFSIZE];
 
-// input node data buffer size
-// x2 1024 * 1024 * 2 > 299 * 299 * 3 * 4 > 1024 * 1024
-static const int INPUT_NODE_DATA_BUFFER_SIZE = 1024 * 1024 * 2;
-// output node data buffer size
-// (1008 is enough for inception)
-static const int OUTPUT_NODE_DATA_BUFFER_SIZE = 300 * 300 * 3 * 4;
-
-static struct NodeDataFloat s_input_node_data_float_buffer;
-static float* s_output_node_data_float_buffer;
-static int s_output_node_data_float_buffer_byte_size;
-static int s_output_node_data_float_array_size;
+#define MAX_INPUTS 10
+#define MAX_OUTPUTS 10
+
+static struct NodeDataFloat s_input_node_data_buffer[MAX_INPUTS];
+static uint8_t* s_output_node_data_buffer[MAX_OUTPUTS];
+static int s_output_node_data_buffer_max_byte_size[MAX_OUTPUTS];
+static int s_output_node_data_array_byte_size[MAX_OUTPUTS];
 static uint32_t s_target_graph_id;
 
 static bool s_dbg_use_inception_dummy_data = false;
+static int s_dbg_inception_version = 3;
+
+static int GetInputNodeCount() {
+  for (int i = 0; i < MAX_INPUTS; ++i) {
+    if (s_input_node_data_buffer[i].max_buf_byte_size == 0) {
+      return i;
+    }
+  }
+  return 0;
+}
+
+static int GetOutputNodeCount() {
+  for (int i = 0; i < MAX_OUTPUTS; ++i) {
+    if (s_output_node_data_buffer_max_byte_size[i] == 0) {
+      return i;
+    }
+  }
+  return 0;
+}
+
+static bool SetInputTensorDef(int port, hexagon_nn_tensordef* tensordef) {
+  if (port >= GetInputNodeCount()) {
+    TFMLOGE("Error exceeds input count.");
+    return false;
+  }
+  NodeDataFloat* input_node_data_buffer = &s_input_node_data_buffer[port];
+  tensordef->batches = input_node_data_buffer->x;
+  tensordef->height = input_node_data_buffer->y;
+  tensordef->width = input_node_data_buffer->z;
+  tensordef->depth = input_node_data_buffer->d;
+  tensordef->data = input_node_data_buffer->byte_array_data;
+  tensordef->dataLen = input_node_data_buffer->array_byte_size;
+
+  return true;
+}
+
+bool hexagon_controller_SetAllInputTensorDef(int node_count,
+                                             hexagon_nn_tensordef* tensordef) {
+  bool success = true;
+  if (node_count != GetInputNodeCount()) {
+    TFMLOGE("Error invalid input node count.");
+    return false;
+  }
+  for (int i = 0; i < node_count; ++i) {
+    SetInputTensorDef(i, &tensordef[i]);
+  }
+  return success;
+}
+
+static bool SetOutputTensorDef(int port, hexagon_nn_tensordef* tensordef) {
+  if (port >= GetOutputNodeCount()) {
+    TFMLOGE("Error exceeds output count.");
+    return false;
+  }
+  tensordef->data = s_output_node_data_buffer[port];
+  tensordef->dataLen = s_output_node_data_buffer_max_byte_size[port];
+  return true;
+}
+
+bool hexagon_controller_SetAllOutputTensorDef(int node_count,
+                                              hexagon_nn_tensordef* tensordef) {
+  bool success = true;
+  if (node_count != GetOutputNodeCount()) {
+    TFMLOGE("Error invalid output node count. %d != %d", node_count,
+            GetOutputNodeCount());
+    return false;
+  }
+  for (int i = 0; i < node_count; ++i) {
+    SetOutputTensorDef(i, &tensordef[i]);
+  }
+  return success;
+}
 
 void hexagon_controller_InitInputNodeDataToInceptionDummyData(int version) {
   if (version == 1) {
@@ -72,44 +141,54 @@ void hexagon_controller_InitInputNodeDataToInceptionDummyData(int version) {
       return;
     }
     hexagon_controller_CopyByteNodeData(
-        INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V1,
+        0, INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V1,
         INCEPTION_PARAM_WIDTH_V1, INCEPTION_PARAM_DEPTH,
         1, inception_dummy_int_data_224x224);
   } else if (version == 3) {
     if (USE_FLOAT_DATA) {
       hexagon_controller_CopyByteNodeData(
-          INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V3,
+          0, INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V3,
           INCEPTION_PARAM_WIDTH_V3, INCEPTION_PARAM_DEPTH,
           sizeof(float), (uint8_t*)inception_dummy_float_data_299x299);
     } else {
       hexagon_controller_CopyByteNodeData(
-          INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V3,
+          0, INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V3,
           INCEPTION_PARAM_WIDTH_V3, INCEPTION_PARAM_DEPTH,
           1, inception_dummy_int_data_299x299);
     }
   }
 }
 
-bool hexagon_controller_ExecuteGraphWithBuffer(
-    uint32_t nn_id, bool show_ranking) {
-  uint32_t out_batches, out_height, out_width, out_depth;
-  uint32_t out_data_size;
-  int x = s_input_node_data_float_buffer.x;
-  int y = s_input_node_data_float_buffer.y;
-  int z = s_input_node_data_float_buffer.z;
-  int d = s_input_node_data_float_buffer.d;
-  uint8_t *byte_data = s_input_node_data_float_buffer.byte_array_data;
-  int array_size = s_input_node_data_float_buffer.array_size;
-  const bool success = hexagon_controller_ExecuteGraph(
-      nn_id, x, y, z, d, byte_data, array_size,
-      &out_batches, &out_height, &out_width, &out_depth,
-      (uint8_t *)s_output_node_data_float_buffer,
-      s_output_node_data_float_buffer_byte_size,
-      &out_data_size);
-  s_output_node_data_float_array_size =
-      out_batches * out_height * out_width * out_depth;
+bool hexagon_controller_ExecuteGraphWithBuffer(uint32_t nn_id,
+                                               bool show_ranking) {
+  const int input_node_count = GetInputNodeCount();
+  hexagon_nn_tensordef inputs[input_node_count];
+  const int output_node_count = GetOutputNodeCount();
+  if (output_node_count <= 0) {
+    TFMLOGI("Error output node count is 0.");
+    return false;
+  }
+  hexagon_nn_tensordef outputs[output_node_count];
+  hexagon_controller_SetAllInputTensorDef(input_node_count, inputs);
+  hexagon_controller_SetAllOutputTensorDef(output_node_count, outputs);
+  const bool success = hexagon_controller_ExecuteGraphWithMultipleInOut(
+      nn_id, input_node_count, inputs, output_node_count, outputs);
+  for (int i = 0; i < output_node_count; ++i) {
+    s_output_node_data_array_byte_size[i] = outputs[i].data_valid_len;
+  }
+
+  const hexagon_nn_tensordef* output0 = &outputs[0];
+
+  const uint32_t out_batches = output0->batches;
+  const uint32_t out_height = output0->height;
+  const uint32_t out_width = output0->width;
+  const uint32_t out_depth = output0->depth;
+  const uint32_t out_data_size = output0->data_valid_len;
+  const uint32_t out_buf_byte_size = output0->dataLen;
+
   if (!success) {
     TFMLOGE("Execution failed");
+    hexagon_controller_PrintLog(nn_id);
     return false;
   } else if (!show_ranking) {
     return true;
@@ -118,15 +197,11 @@ bool hexagon_controller_ExecuteGraphWithBuffer(
   static const int OUT_RANKING_SIZE = 5;
   int out_ranking[OUT_RANKING_SIZE];
   hexagon_controller_PrintMaxNIdx(
-      s_output_node_data_float_buffer,
-      out_batches * out_height * out_width * out_depth,
-      OUT_RANKING_SIZE, out_ranking);
-  TFMLOGD("%d x %d x %d x %d, byte size = %d\n",
-          out_batches,
-          out_height,
-          out_width,
-          out_depth,
-          out_data_size);
+      (float*)s_output_node_data_buffer[0],
+      out_batches * out_height * out_width * out_depth, OUT_RANKING_SIZE,
+      out_ranking);
+  TFMLOGD("%d x %d x %d x %d, byte size = %d, buf size = %d\n", out_batches,
+          out_height, out_width, out_depth, out_data_size, out_buf_byte_size);
   if (s_dbg_use_inception_dummy_data) {
     // Check the result of inception with a dummy data. This step shouldn't
     // be passed when show_ranking != true to avoid adding unnecessary
@@ -168,64 +243,123 @@ int hexagon_controller_GetHexagonBinaryVersion() {
   return retval;
 }
 
-bool hexagon_controller_AllocateNodeDataBuffers(
-    int input_size, int output_size) {
-  TFMLOGD("Allocate memory for input / output node data float");
-  if (s_input_node_data_float_buffer.buf_size != 0) {
+bool hexagon_controller_AllocateInputNodeDataBuffers(int port,
+                                                     int input_buf_byte_size) {
+  TFMLOGD("Allocate memory for input node data. port = %d, size = %d", port,
+          input_buf_byte_size);
+  if (s_input_node_data_buffer[port].max_buf_byte_size != 0) {
     TFMLOGE("ERROR! input buffer is already allocated!!");
     return false;
   } else {
-    int byte_array_data_size = USE_FLOAT_DATA ?
-        input_size * sizeof(float) : input_size; /* sizeof(uint8_t) ? */
-    s_input_node_data_float_buffer.buf_size = input_size;
-    // unused? remove?
-    s_input_node_data_float_buffer.array_data =
-        malloc(input_size * sizeof(float));
-    s_input_node_data_float_buffer.byte_array_data =
-        malloc(byte_array_data_size);
-
-    s_output_node_data_float_buffer = malloc(output_size * sizeof(float));
-    s_output_node_data_float_buffer_byte_size = output_size * sizeof(float);
-    s_output_node_data_float_array_size = 0;
-    TFMLOGD("allocate node data buffers");
+    s_input_node_data_buffer[port].max_buf_byte_size = input_buf_byte_size;
+    posix_memalign((void**)&s_input_node_data_buffer[port].byte_array_data, 128,
+                   input_buf_byte_size);
+    TFMLOGD("allocate input node data buffers done");
   }
   return true;
 }
 
-bool hexagon_controller_ReleaseNodeDataBuffers() {
-  if (s_input_node_data_float_buffer.buf_size == 0) {
+bool hexagon_controller_AllocateOutputNodeDataBuffers(
+    int port, int output_buf_byte_size) {
+  TFMLOGD("Allocate memory for output node data. port = %d, size = %d", port,
+          output_buf_byte_size);
+  if (s_output_node_data_buffer_max_byte_size[port] != 0) {
+    TFMLOGE("ERROR! input buffer is already allocated!!");
+    return false;
+  } else {
+    // s_output_node_data_buffer = malloc(output_size * sizeof(float));
+    posix_memalign((void**)&s_output_node_data_buffer[port], 128,
+                   output_buf_byte_size);
+    s_output_node_data_buffer_max_byte_size[port] = output_buf_byte_size;
+    s_output_node_data_array_byte_size[port] = 0;
+    TFMLOGD("allocate output node data buffers");
+  }
+  return true;
+}
+
+bool hexagon_controller_AllocateMultipleNodeDataBuffers(int input_count,
+                                                        int* input_sizes,
+                                                        int output_count,
+                                                        int* output_sizes) {
+  bool success = true;
+  for (int i = 0; i < input_count; ++i) {
+    success &=
+        hexagon_controller_AllocateInputNodeDataBuffers(i, input_sizes[i]);
+  }
+  for (int i = 0; i < output_count; ++i) {
+    success &=
+        hexagon_controller_AllocateOutputNodeDataBuffers(i, output_sizes[i]);
+  }
+
+  if (s_dbg_use_inception_dummy_data) {
+    hexagon_controller_InitInputNodeDataToInceptionDummyData(
+        s_dbg_inception_version);
+  }
+  return success;
+}
+
+bool hexagon_controller_AllocateNodeDataBuffers(int input_size,
+                                                int output_size) {
+  return hexagon_controller_AllocateMultipleNodeDataBuffers(1, &input_size, 1,
+                                                            &output_size);
+}
+
+bool hexagon_controller_ReleaseInputNodeDataBuffersWithPort(int port) {
+  NodeDataFloat* input_node_data_buffer = &s_input_node_data_buffer[port];
+  if (input_node_data_buffer->max_buf_byte_size == 0) {
     TFMLOGE("ERROR! input buffer has not been allocated yet!!");
     return false;
   } else {
-    s_input_node_data_float_buffer.buf_size = 0;
-    free(s_input_node_data_float_buffer.array_data);
+    input_node_data_buffer->max_buf_byte_size = 0;
+    input_node_data_buffer->array_byte_size = 0;
+    free(input_node_data_buffer->byte_array_data);
   }
-  if (s_output_node_data_float_buffer_byte_size == 0) {
+  return true;
+}
+
+bool hexagon_controller_ReleaseOutputNodeDataBuffersWithPort(int port) {
+  if (s_output_node_data_buffer_max_byte_size[port] == 0) {
     TFMLOGE("ERROR! output buffer has not been allocated yet!!");
     return false;
   } else {
-    s_output_node_data_float_buffer_byte_size = 0;
-    free(s_input_node_data_float_buffer.byte_array_data);
+    s_output_node_data_buffer_max_byte_size[port] = 0;
+    s_output_node_data_array_byte_size[port] = 0;
+    free(s_output_node_data_buffer[port]);
   }
   return true;
 }
 
-bool hexagon_controller_CopyByteNodeData(
-    int x, int y, int z, int d, int type_byte_size, uint8_t* array_data) {
+bool hexagon_controller_ReleaseNodeDataBuffers() {
+  bool success = true;
+  for (int i = 0; i < GetInputNodeCount(); ++i) {
+    success &= hexagon_controller_ReleaseInputNodeDataBuffersWithPort(i);
+  }
+  for (int i = 0; i < GetOutputNodeCount(); ++i) {
+    success &= hexagon_controller_ReleaseOutputNodeDataBuffersWithPort(i);
+  }
+  return success;
+}
+
+bool hexagon_controller_CopyByteNodeData(int port, int x, int y, int z, int d,
+                                         int type_byte_size,
+                                         uint8_t* array_data) {
   int array_byte_size = x * y * z * d * type_byte_size;
-  TFMLOGD("--- %d, %d, %d, %d, %d, %d",x,y,z,d,type_byte_size,array_byte_size);
-  if (s_input_node_data_float_buffer.buf_size < array_byte_size) {
+  TFMLOGD("--- %d, %d, %d, %d, %d, %d", x, y, z, d, type_byte_size,
+          array_byte_size);
+  NodeDataFloat* input_node_data_buffer = s_input_node_data_buffer[0];
+
+  if (input_node_data_buffer->max_buf_byte_size < array_byte_size) {
     TFMLOGE("ERROR! input buffer size is too small! %d < %d",
-            s_input_node_data_float_buffer.buf_size, array_byte_size);
+            input_node_data_buffer->max_buf_byte_size, array_byte_size);
     return false;
   }
-  memcpy(s_input_node_data_float_buffer.byte_array_data,
-         array_data, array_byte_size);
-  s_input_node_data_float_buffer.array_size = array_byte_size;
-  s_input_node_data_float_buffer.x = x;
-  s_input_node_data_float_buffer.y = y;
-  s_input_node_data_float_buffer.z = z;
-  s_input_node_data_float_buffer.d = d;
+  memcpy(input_node_data_buffer->byte_array_data, array_data,
+         array_byte_size);
+  input_node_data_buffer->array_byte_size = array_byte_size;
+  input_node_data_buffer->x = x;
+  input_node_data_buffer->y = y;
+  input_node_data_buffer->z = z;
+  input_node_data_buffer->d = d;
   return true;
 }
 
@@ -263,13 +397,8 @@ int hexagon_controller_InitHexagonWithMaxAttributes(
       dspCV_initQ6_with_attributes(attrib, sizeof(attrib) / sizeof(attrib[0]));
   TFMLOGD("Return value from dspCV_initQ6() : %d\n", retval);
 
-  hexagon_controller_AllocateNodeDataBuffers(
-      INPUT_NODE_DATA_BUFFER_SIZE, OUTPUT_NODE_DATA_BUFFER_SIZE);
-
-  if (s_dbg_use_inception_dummy_data) {
-    hexagon_controller_InitInputNodeDataToInceptionDummyData(version);
-  }
   s_target_graph_id = 0;
+  s_dbg_inception_version = version;
 
   return retval;
 }
@@ -289,14 +418,20 @@ void hexagon_controller_GrowMemorySize() {
   hexagon_nn_config();
 }
 
-struct NodeDataFloat* hexagon_controller_GetInputNodeDataFloatBuffer() {
-  return &s_input_node_data_float_buffer;
+struct NodeDataFloat* hexagon_controller_GetInputNodeDataBuffer(int port) {
+  if (port >= GetInputNodeCount()) {
+    TFMLOGE("port should be less than 1");
+  }
+  return &s_input_node_data_buffer[port];
 }
 
-float* hexagon_controller_GetOutputNodeDataFloatBuffer(
-    const char *const node_name, int* out_array_size) {
-  *out_array_size = s_output_node_data_float_array_size;
-  return s_output_node_data_float_buffer;
+uint8_t* hexagon_controller_GetOutputNodeDataBuffer(int port,
+                                                    int* out_array_byte_size) {
+  if (port >= GetOutputNodeCount()) {
+    TFMLOGE("port should be less than 1");
+  }
+  *out_array_byte_size = s_output_node_data_array_byte_size[port];
+  return s_output_node_data_buffer[port];
 }
 
 // Append const node to the graph
@@ -377,10 +512,8 @@ bool hexagon_controller_IsDbgUseInceptionDummyDataEnabled() {
 }
 
 void hexagon_controller_PrintLog(uint32_t nn_id) {
-  unsigned char *buf;
-  if ((buf = malloc(PRINT_BUFSIZE)) == NULL) {
-    return;
-  }
+  unsigned char* buf = NULL;
+  posix_memalign((void**)&buf, 128, PRINT_BUFSIZE);
   hexagon_nn_getlog(nn_id, buf, PRINT_BUFSIZE);
   TFMLOGE("DUMP HEXAGON LOG: %s", buf);
   free(buf);
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h b/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h
index ab8c80c0f3..d23048c116 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h
@@ -40,16 +40,37 @@ int hexagon_controller_GetWrapperVersion();
 
 int hexagon_controller_GetHexagonBinaryVersion();
 
+// Buffer operations
+bool hexagon_controller_SetAllInputTensorDef(int node_count,
+                                             hexagon_nn_tensordef* tensordef);
+
+bool hexagon_controller_SetAllInputTensorDef(int node_count,
+                                             hexagon_nn_tensordef* tensordef);
+
 // Hexagon perf functions
 int hexagon_controller_InitHexagonWithMaxAttributes(int enable_dcvs,
                                                     int bus_usage, int version);
 
+bool hexagon_controller_AllocateInputNodeDataBuffersWithPort(int port,
+                                                             int input_size);
+
+bool hexagon_controller_AllocateOutNodeDataBuffersWithPort(int port,
+                                                           int output_size);
+
 bool hexagon_controller_AllocateNodeDataBuffers(int input_size,
                                                 int output_size);
 
+bool hexagon_controller_AllocateMultipleNodeDataBuffers(int input_count,
+                                                        int* input_sizes,
+                                                        int output_count,
+                                                        int* output_sizes);
+
+bool hexagon_controller_ReleaseInputNodeDataBuffersWithPort(int port);
+bool hexagon_controller_ReleaseOutputNodeDataBuffersWithPort(int port);
+
 bool hexagon_controller_ReleaseNodeDataBuffers();
 
-bool hexagon_controller_CopyByteNodeData(int x, int y, int z, int d,
+bool hexagon_controller_CopyByteNodeData(int port, int x, int y, int z, int d,
                                          int type_byte_size,
                                          uint8_t* array_data);
 
@@ -63,10 +84,10 @@ void hexagon_controller_SetTargetGraphId(uint32_t graph_id);
 void hexagon_controller_GrowMemorySize();
 
 // Graph data transfer functions
-struct NodeDataFloat* hexagon_controller_GetInputNodeDataFloatBuffer();
+struct NodeDataFloat* hexagon_controller_GetInputNodeDataBuffer(int port);
 
-float* hexagon_controller_GetOutputNodeDataFloatBuffer(
-    const char* const node_name, int* out_array_size);
+uint8_t* hexagon_controller_GetOutputNodeDataBuffer(int port,
+                                                    int* out_array_byte_size);
 
 // Graph functions
 uint32_t hexagon_controller_InstantiateGraph();
@@ -79,6 +100,10 @@ uint32_t hexagon_controller_SetupGraph(int version);
 
 bool hexagon_controller_ExecuteInceptionDummyData(uint32_t nn_id);
 
+bool hexagon_controller_ExecuteGraphWithMultipleInOut(
+    const uint32_t nn_id, const int input_count, hexagon_nn_tensordef* inputs,
+    const int output_count, hexagon_nn_tensordef* outputs);
+
 bool hexagon_controller_ExecuteGraph(
     const uint32_t nn_id, const uint32_t batches, const uint32_t height,
     const uint32_t width, const uint32_t depth, uint8_t* int_data,
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h b/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h
index e8615fd4ec..91bd15d383 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h
@@ -33,6 +33,9 @@ static inline bool IsLogOn(int log_level) { return log_level >= s_log_level; }
 
 static inline void SetLogLevel(int log_level) { s_log_level = log_level; }
 
+// Do nothing
+static inline void SetExperimentalDebug() {}
+
 #define TFMLOGV(fmt, ...)                       \
   do {                                          \
     if (!IsLogOn(TFM_LOG_LEVEL_VERBOSE)) break; \
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/node_data_float.h b/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/node_data_float.h
index a9c3296e9f..c7034cc3a0 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/node_data_float.h
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/node_data_float.h
@@ -28,9 +28,8 @@ struct NodeDataFloat {
   int y;
   int z;
   int d;
-  int buf_size;
-  int array_size;
-  float* array_data;
+  int max_buf_byte_size;
+  int array_byte_size;
   uint8_t* byte_array_data;
   char node_name[NODE_DATA_FLOAT_NODE_NAME_BUF_SIZE];
 };
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/soc_interface.h b/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/soc_interface.h
index 6d85e6ce48..30fad13fb5 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/soc_interface.h
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/include/soc_interface.h
@@ -43,13 +43,30 @@ bool soc_interface_Finalize();
 bool soc_interface_ExecuteGraph();
 // Teardown graph setup
 bool soc_interface_TeardownGraph();
+
+// Allocate buffers for input node and output node
+bool soc_interface_AllocateInOutNodeBuffers(int input_count, int* input_sizes,
+                                            int output_count,
+                                            int* output_sizes);
+
+// Send input data to SOC with port
+bool soc_interface_FillInputNodeWithPort(int port, int x, int y, int z, int d,
+                                         const uint8_t* const buf,
+                                         uint64_t buf_byte_size);
+
 // Send input data to SOC
 bool soc_interface_FillInputNodeFloat(int x, int y, int z, int d,
                                       const uint8_t* const buf,
-                                      uint64_t buf_size);
+                                      uint64_t buf_byte_size);
+
+// Load output data from SOC with port
+bool soc_interface_ReadOutputNodeWithPort(int port, uint8_t** buf,
+                                          uint64_t* buf_byte_size);
+
 // Load output data from SOC
 bool soc_interface_ReadOutputNodeFloat(const char* const node_name,
-                                       uint8_t** buf, uint64_t* buf_size);
+                                       uint8_t** buf, uint64_t* buf_byte_size);
+
 // Setup graph
 // TODO(satok): Remove and use runtime version
 bool soc_interface_setupDummyGraph(int version);
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/soc_interface.c b/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/soc_interface.c
index 7db8d4870c..a1387ee573 100755
--- a/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/soc_interface.c
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_soc_interface/soc_interface.c
@@ -22,7 +22,12 @@ limitations under the License.
 #include "node_data_float.h"
 #include "tfm_log.h"
 
+// to demonstrate the performance difference between ION and HLOS memory
+// for sharing with ADSP.
+#define USE_ION_MEMORY
+
 const int64_t FLAG_ENABLE_INCEPTION_DUMMY_BINARY_INPUT = 0x01;
+const int64_t FLAG_ENABLE_EXPERIMENTAL_DEBUG = 0x02;
 
 static const int INCEPTION_VERSION = 3;
 
@@ -84,48 +89,62 @@ bool soc_interface_TeardownGraph() {
   return true;
 }
 
-bool soc_interface_FillInputNodeFloat(
-    int x, int y, int z, int d, const uint8_t* const buf,
-    uint64_t buf_size) {
-  TFMLOGD("FillInputNodeFloat");
-  struct NodeDataFloat* node_data_float =
-      hexagon_controller_GetInputNodeDataFloatBuffer();
-  const int array_size = x * y * z * d;
-  if (array_size > node_data_float->buf_size) {
-    TFMLOGE("Array size exceeds buf size %d > %d",
-            array_size, node_data_float->buf_size);
-    return false;
-  }
-  if (buf_size != array_size * sizeof(float)) {
-    TFMLOGE("Invalid buf size!");
+bool soc_interface_AllocateInOutNodeBuffers(int input_count, int* input_sizes,
+                                            int output_count,
+                                            int* output_sizes) {
+  TFMLOGD("AllocateInOutNodeBuffers");
+  return hexagon_controller_AllocateMultipleNodeDataBuffers(
+      input_count, input_sizes, output_count, output_sizes);
+}
+
+bool soc_interface_FillInputNodeWithPort(int port, int x, int y, int z, int d,
+                                         const uint8_t* const buf,
+                                         uint64_t buf_byte_size) {
+  TFMLOGD("FillInputNodeWithPort %d", port);
+  struct NodeDataFloat* node_data =
+      hexagon_controller_GetInputNodeDataBuffer(port);
+  if (buf_byte_size > node_data->max_buf_byte_size) {
+    TFMLOGE("buf size exceeds max buf size");
     return false;
   }
-  memcpy(node_data_float->byte_array_data, buf, buf_size);
-  node_data_float->x = x;
-  node_data_float->y = y;
-  node_data_float->z = z;
-  node_data_float->d = d;
-  node_data_float->array_size = buf_size;
+  memcpy(node_data->byte_array_data, buf, buf_byte_size);
+  node_data->x = x;
+  node_data->y = y;
+  node_data->z = z;
+  node_data->d = d;
+  node_data->array_byte_size = buf_byte_size;
   return true;
 }
 
+bool soc_interface_FillInputNodeFloat(int x, int y, int z, int d,
+                                      const uint8_t* const buf,
+                                      uint64_t buf_byte_size) {
+  return soc_interface_FillInputNodeWithPort(
+      /*port=*/0, x, y, z, d, buf, buf_byte_size);
+}
+
 // TODO(satok): Remove and use runtime version
-bool soc_interface_ReadOutputNodeFloat(
-    const char* const node_name, uint8_t** buf, uint64_t *buf_size) {
-  TFMLOGD("ReadOutputNodeFloat");
-  int array_size = -1;
-  float* output_node_data_float =
-      hexagon_controller_GetOutputNodeDataFloatBuffer(node_name, &array_size);
-  if (array_size < 0) {
+bool soc_interface_ReadOutputNodeWithPort(int port, uint8_t** buf,
+                                          uint64_t* buf_byte_size) {
+  TFMLOGD("ReadOutputNodeWithPort");
+  int array_byte_size = -1;
+  uint8_t* output_node_data_buffer =
+      hexagon_controller_GetOutputNodeDataBuffer(port, &array_byte_size);
+  if (array_byte_size < 0) {
     TFMLOGE("Failed to read data.");
     return false;
   }
-  *buf = (uint8_t*)output_node_data_float;
-  *buf_size = array_size * sizeof(float);
+  *buf = output_node_data_buffer;
+  *buf_byte_size = array_byte_size;
   return true;
 }
 
-bool soc_interface_SetupGraphDummy(int version) {
+bool soc_interface_ReadOutputNodeFloat(const char* const node_name,
+                                       uint8_t** buf, uint64_t* buf_byte_size) {
+  return soc_interface_ReadOutputNodeWithPort(/*port=*/0, buf, buf_byte_size);
+}
+
+bool soc_interface_setupDummyGraph(int version) {
   TFMLOGD("SetupGraphDummy");
   const uint32_t graph_id = hexagon_controller_SetupGraph(version);
   if (graph_id == 0) {
@@ -136,12 +155,14 @@ bool soc_interface_SetupGraphDummy(int version) {
   return true;
 }
 
-bool soc_interface_AllocateNodeInputAndNodeOutputArray(
-    int total_input_count, int total_output_count) {
+bool soc_interface_AllocateNodeInputAndNodeOutputArray(int total_input_count,
+                                                       int total_output_count) {
   TFMLOGD("Allocate node inputs and node outputs array %d, %d",
           total_input_count, total_output_count);
-  s_node_inputs_array = malloc(total_input_count * sizeof(hexagon_nn_input));
-  s_node_outputs_array = malloc(total_output_count * sizeof(hexagon_nn_output));
+  posix_memalign((void**)&s_node_inputs_array, 128,
+                 total_input_count * sizeof(hexagon_nn_input));
+  posix_memalign((void**)&s_node_outputs_array, 128,
+                 total_output_count * sizeof(hexagon_nn_output));
   s_node_inputs_array_index = 0;
   s_node_outputs_array_index = 0;
   s_node_inputs_array_max_count = total_input_count;
@@ -188,9 +209,9 @@ void* soc_interface_SetOneNodeOutputs(int output_count, int* max_size) {
 }
 
 // Append const node to the graph
-bool soc_interface_AppendConstNode(
-    const char* const name, int node_id, int batch, int height, int width, int depth,
-    const uint8_t* const data, int data_length) {
+bool soc_interface_AppendConstNode(const char* const name, int node_id,
+                                   int batch, int height, int width, int depth,
+                                   const uint8_t* const data, int data_length) {
   const uint32_t graph_id = hexagon_controller_GetTargetGraphId();
   const int retval = hexagon_controller_AppendConstNode(
       name, graph_id, node_id, batch, height, width, depth, data, data_length);
@@ -202,14 +223,14 @@ bool soc_interface_AppendConstNode(
 }
 
 // Append node to the graph
-bool soc_interface_AppendNode(
-    const char* const name, int node_id, int ops_id, int padding_id, const void* const inputs,
-    int inputs_count, const void* const outputs, int outputs_count) {
+bool soc_interface_AppendNode(const char* const name, int node_id, int ops_id,
+                              int padding_id, const void* const inputs,
+                              int inputs_count, const void* const outputs,
+                              int outputs_count) {
   const uint32_t graph_id = hexagon_controller_GetTargetGraphId();
   const int retval = hexagon_controller_AppendNode(
-      name, graph_id, node_id, ops_id, padding_id,
-      (hexagon_nn_input*) inputs, inputs_count,
-      (hexagon_nn_output*) outputs, outputs_count);
+      name, graph_id, node_id, ops_id, padding_id, (hexagon_nn_input*)inputs,
+      inputs_count, (hexagon_nn_output*)outputs, outputs_count);
   if (retval != 0) {
     TFMLOGE("Failed to append const node %d", node_id);
     return false;
@@ -217,7 +238,6 @@ bool soc_interface_AppendNode(
   return true;
 }
 
-
 // Instantiate graph
 bool soc_interface_InstantiateGraph() {
   const uint32_t nn_id = hexagon_controller_InstantiateGraph();
@@ -240,5 +260,7 @@ void soc_interface_SetDebugFlag(uint64_t flag) {
   if ((flag & FLAG_ENABLE_INCEPTION_DUMMY_BINARY_INPUT) != 0) {
     TFMLOGI("Enable always use panda data");
     hexagon_controller_EnableDbgUseInceptionDummyData(true);
+  } else if ((flag & FLAG_ENABLE_EXPERIMENTAL_DEBUG) != 0) {
+    SetExperimentalDebug();
   }
 }
-- 
GitLab


From 2ebbffe87059fdc8ed66aa59cfa810af87029abb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 17:19:13 -0700
Subject: [PATCH 0129/1375] Enable setting remote configuration for cuda and
 python as an env variable

PiperOrigin-RevId: 159176334
---
 third_party/gpus/cuda_configure.bzl | 17 +++++++++++------
 third_party/py/python_configure.bzl | 12 ++++++++----
 2 files changed, 19 insertions(+), 10 deletions(-)

diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 77dc602fd9..83a377dde5 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -26,6 +26,7 @@ _TF_CUDA_VERSION = "TF_CUDA_VERSION"
 _TF_CUDNN_VERSION = "TF_CUDNN_VERSION"
 _CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH"
 _TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES"
+_TF_CUDA_CONFIG_REPO = "TF_CUDA_CONFIG_REPO"
 
 _DEFAULT_CUDA_VERSION = ""
 _DEFAULT_CUDNN_VERSION = ""
@@ -1001,8 +1002,7 @@ def _create_local_cuda_repository(repository_ctx):
                "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path,
        })
 
-
-def _create_remote_cuda_repository(repository_ctx):
+def _create_remote_cuda_repository(repository_ctx, remote_config_repo):
   """Creates pointers to a remotely configured repo set up to build with CUDA."""
   _tpl(repository_ctx, "cuda:build_defs.bzl",
        {
@@ -1013,10 +1013,10 @@ def _create_remote_cuda_repository(repository_ctx):
        })
   _tpl(repository_ctx, "cuda:remote.BUILD",
        {
-           "%{remote_cuda_repo}": repository_ctx.attr.remote_config_repo,
+           "%{remote_cuda_repo}": remote_config_repo,
        }, "cuda/BUILD")
   _tpl(repository_ctx, "crosstool:remote.BUILD", {
-           "%{remote_cuda_repo}": repository_ctx.attr.remote_config_repo,
+           "%{remote_cuda_repo}": remote_config_repo,
        }, "crosstool/BUILD")
 
 def _cuda_autoconf_impl(repository_ctx):
@@ -1024,8 +1024,12 @@ def _cuda_autoconf_impl(repository_ctx):
   if not _enable_cuda(repository_ctx):
     _create_dummy_repository(repository_ctx)
   else:
-    if repository_ctx.attr.remote_config_repo != "":
-      _create_remote_cuda_repository(repository_ctx)
+    if _TF_CUDA_CONFIG_REPO in repository_ctx.os.environ:
+      _create_remote_cuda_repository(repository_ctx,
+          repository_ctx.os.environ[_TF_CUDA_CONFIG_REPO])
+    elif repository_ctx.attr.remote_config_repo != "":
+      _create_remote_cuda_repository(repository_ctx,
+          repository_ctx.attr.remote_config_repo)
     else:
       _create_local_cuda_repository(repository_ctx)
 
@@ -1043,6 +1047,7 @@ cuda_configure = repository_rule(
         _TF_CUDA_VERSION,
         _TF_CUDNN_VERSION,
         _TF_CUDA_COMPUTE_CAPABILITIES,
+        _TF_CUDA_CONFIG_REPO,
     ],
 )
 
diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl
index b4a98af7b6..19a6f1e749 100644
--- a/third_party/py/python_configure.bzl
+++ b/third_party/py/python_configure.bzl
@@ -13,6 +13,7 @@ _NUMPY_INCLUDE_PATH = "NUMPY_INCLUDE_PATH"
 _PYTHON_BIN_PATH = "PYTHON_BIN_PATH"
 _PYTHON_INCLUDE_PATH = "PYTHON_INCLUDE_PATH"
 _PYTHON_LIB_PATH = "PYTHON_LIB_PATH"
+_TF_PYTHON_CONFIG_REPO = "TF_PYTHON_CONFIG_REPO"
 
 
 def _tpl(repository_ctx, tpl, substitutions={}, out=None):
@@ -278,18 +279,20 @@ def _create_local_python_repository(repository_ctx):
     })
 
 
-def _create_remote_python_repository(repository_ctx):
+def _create_remote_python_repository(repository_ctx, remote_config_repo):
   """Creates pointers to a remotely configured repo set up to build with Python.
   """
   _tpl(repository_ctx, "remote.BUILD", {
-      "%{REMOTE_PYTHON_REPO}": repository_ctx.attr.remote_config_repo,
+      "%{REMOTE_PYTHON_REPO}": remote_config_repo,
   }, "BUILD")
 
 
 def _python_autoconf_impl(repository_ctx):
   """Implementation of the python_autoconf repository rule."""
-  if repository_ctx.attr.remote_config_repo != "":
-    _create_remote_python_repository(repository_ctx)
+  remote_config_repo = _get_env_var(repository_ctx, _TF_PYTHON_CONFIG_REPO,
+      repository_ctx.attr.remote_config_repo, False)
+  if remote_config_repo != "":
+    _create_remote_python_repository(repository_ctx, remote_config_repo)
   else:
     _create_local_python_repository(repository_ctx)
 
@@ -307,6 +310,7 @@ python_configure = repository_rule(
         _PYTHON_INCLUDE_PATH,
         _PYTHON_LIB_PATH,
         _NUMPY_INCLUDE_PATH,
+        _TF_PYTHON_CONFIG_REPO,
     ],
 )
 """Detects and configures the local Python.
-- 
GitLab


From d134d210f3dec36fe45c6cf718f538da9c8f005b Mon Sep 17 00:00:00 2001
From: Dandelion Man? <dandelion@google.com>
Date: Thu, 15 Jun 2017 17:39:48 -0700
Subject: [PATCH 0130/1375] Remove TensorBoard from the TensorFlow repository.
 Its new home is at https://github.com/tensorflow/tensorboard

PiperOrigin-RevId: 159178103
---
 tensorflow/BUILD                              |   64 -
 tensorflow/contrib/cmake/tf_python.cmake      |   28 -
 tensorflow/contrib/cmake/tf_tests.cmake       |   10 -
 tensorflow/contrib/tensorboard/BUILD          |    4 +-
 .../tensorboard/plugins/projector/__init__.py |    6 +-
 .../plugins/projector/projector_api_test.py   |    2 +-
 .../plugins/projector/projector_config.proto  |    0
 tensorflow/tensorboard/BUILD                  |   84 -
 tensorflow/tensorboard/DEVELOPMENT.md         |   25 -
 tensorflow/tensorboard/README.md              |  366 --
 tensorflow/tensorboard/TAG                    |    1 -
 tensorflow/tensorboard/WORKSPACE              |   21 -
 tensorflow/tensorboard/__init__.py            |    0
 tensorflow/tensorboard/__main__.py            |   25 -
 tensorflow/tensorboard/backend/BUILD          |  106 -
 tensorflow/tensorboard/backend/__init__.py    |    0
 tensorflow/tensorboard/backend/application.py |  375 --
 .../tensorboard/backend/application_test.py   |  520 --
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 delete mode 100644 tensorflow/tensorboard/demo/data/individualAudio_index_0_tag_au1_2Faudio_2F0_run_run1.wav
 delete mode 100644 tensorflow/tensorboard/demo/data/individualAudio_index_0_tag_au2_2Faudio_2F0_run_run2.wav
 delete mode 100644 tensorflow/tensorboard/demo/data/individualImage_tag_im1_2Fimage_2F0_index_0_run_run1.png
 delete mode 100644 tensorflow/tensorboard/demo/data/individualImage_tag_im1_2Fimage_2F0_index_0_run_run2.png
 delete mode 100644 tensorflow/tensorboard/demo/data/individualImage_tag_im2_2Fimage_2F0_index_0_run_run1.png
 delete mode 100644 tensorflow/tensorboard/demo/data/logdir
 delete mode 100644 tensorflow/tensorboard/demo/data/runs.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars_run_run1_tag_bar_2Fsquare.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fcos.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsin.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsquare.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars_run_run2_tag_bar_2Fsquare.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fcos.json
 delete mode 100644 tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fsquare.json
 delete mode 100644 tensorflow/tensorboard/http_api.md
 delete mode 100644 tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/BUILD
 delete mode 100644 tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
 delete mode 100644 tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Zipper.java
 delete mode 100644 tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js
 delete mode 100644 tensorflow/tensorboard/main.py
 delete mode 100644 tensorflow/tensorboard/plugins/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/audio/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/audio/audio_demo.py
 delete mode 100644 tensorflow/tensorboard/plugins/audio/audio_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/audio/audio_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/base_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/distributions/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/distributions/compressor.py
 delete mode 100644 tensorflow/tensorboard/plugins/distributions/compressor_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/distributions/distributions_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/graphs/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/graphs/graphs_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/histograms/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/histograms/histograms_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/images/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/images/images_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/images/images_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/projector/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/projector/projector_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/projector/projector_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/scalars/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/scalars/scalars_demo.py
 delete mode 100644 tensorflow/tensorboard/plugins/scalars/scalars_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/plugins/text/BUILD
 delete mode 100644 tensorflow/tensorboard/plugins/text/text_plugin.py
 delete mode 100644 tensorflow/tensorboard/plugins/text/text_plugin_test.py
 delete mode 100644 tensorflow/tensorboard/scripts/BUILD
 delete mode 100644 tensorflow/tensorboard/scripts/__init__.py
 delete mode 100644 tensorflow/tensorboard/scripts/execrooter.py
 delete mode 100644 tensorflow/tensorboard/scripts/generate_testdata.py
 delete mode 100644 tensorflow/tools/ci_build/Dockerfile.tensorboard
 delete mode 100755 tensorflow/tools/ci_build/builds/tensorboard.sh
 delete mode 100755 tensorflow/tools/ci_build/install/install_tensorboard_packages.sh
 delete mode 100644 third_party/bleach.BUILD
 delete mode 100644 third_party/clutz.BUILD
 delete mode 100644 third_party/clutz.bzl
 delete mode 100644 third_party/html5lib.BUILD
 delete mode 100644 third_party/js.bzl
 delete mode 100644 third_party/markdown.BUILD
 delete mode 100644 third_party/polymer.bzl
 delete mode 100644 third_party/python.bzl
 delete mode 100644 third_party/typings.bzl
 delete mode 100644 third_party/werkzeug.BUILD

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index ae5c53e42d..ca7df391d2 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -350,70 +350,6 @@ filegroup(
         "//tensorflow/python/ops/distributions:all_files",
         "//tensorflow/python/saved_model:all_files",
         "//tensorflow/python/tools:all_files",
-        "//tensorflow/tensorboard:all_files",
-        "//tensorflow/tensorboard/backend:all_files",
-        "//tensorflow/tensorboard/backend/event_processing:all_files",
-        "//tensorflow/tensorboard/components:all_files",
-        "//tensorflow/tensorboard/components/tf_audio_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_audio_dashboard/test:all_files",
-        "//tensorflow/tensorboard/components/tf_backend:all_files",
-        "//tensorflow/tensorboard/components/tf_backend/test:all_files",
-        "//tensorflow/tensorboard/components/tf_color_scale:all_files",
-        "//tensorflow/tensorboard/components/tf_color_scale/test:all_files",
-        "//tensorflow/tensorboard/components/tf_dashboard_common:all_files",
-        "//tensorflow/tensorboard/components/tf_dashboard_common/test:all_files",
-        "//tensorflow/tensorboard/components/tf_distribution_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_globals:all_files",
-        "//tensorflow/tensorboard/components/tf_graph:all_files",
-        "//tensorflow/tensorboard/components/tf_graph/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_app:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_app/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_board:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_board/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_common:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_controls:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_controls/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_dashboard/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_debugger_data_card:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_info:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_info/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_loader:all_files",
-        "//tensorflow/tensorboard/components/tf_graph_loader/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_histogram_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_image_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_imports:all_files",
-        "//tensorflow/tensorboard/components/tf_option_selector:all_files",
-        "//tensorflow/tensorboard/components/tf_profile_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_profile_dashboard/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_runs_selector:all_files",
-        "//tensorflow/tensorboard/components/tf_scalar_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_scalar_dashboard/demo:all_files",
-        "//tensorflow/tensorboard/components/tf_storage:all_files",
-        "//tensorflow/tensorboard/components/tf_storage/test:all_files",
-        "//tensorflow/tensorboard/components/tf_tensorboard:all_files",
-        "//tensorflow/tensorboard/components/tf_text_dashboard:all_files",
-        "//tensorflow/tensorboard/components/tf_trace_viewer:all_files",
-        "//tensorflow/tensorboard/components/vz_distribution_chart:all_files",
-        "//tensorflow/tensorboard/components/vz_histogram_timeseries:all_files",
-        "//tensorflow/tensorboard/components/vz_line_chart:all_files",
-        "//tensorflow/tensorboard/components/vz_projector:all_files",
-        "//tensorflow/tensorboard/components/vz_projector/test:all_files",
-        "//tensorflow/tensorboard/components/vz_sorting:all_files",
-        "//tensorflow/tensorboard/components/vz_sorting/test:all_files",
-        "//tensorflow/tensorboard/demo:all_files",
-        "//tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize:all_files",
-        "//tensorflow/tensorboard/plugins:all_files",
-        "//tensorflow/tensorboard/plugins/audio:all_files",
-        "//tensorflow/tensorboard/plugins/distributions:all_files",
-        "//tensorflow/tensorboard/plugins/graphs:all_files",
-        "//tensorflow/tensorboard/plugins/histograms:all_files",
-        "//tensorflow/tensorboard/plugins/images:all_files",
-        "//tensorflow/tensorboard/plugins/projector:all_files",
-        "//tensorflow/tensorboard/plugins/scalars:all_files",
-        "//tensorflow/tensorboard/plugins/text:all_files",
-        "//tensorflow/tensorboard/scripts:all_files",
         "//tensorflow/tools/api/golden:all_files",
         "//tensorflow/tools/api/lib:all_files",
         "//tensorflow/tools/api/tests:all_files",
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index a969bb03ee..001f4b4049 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -124,7 +124,6 @@ file(GLOB_RECURSE tf_protos_python_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/core/*.proto"
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
-    "${tensorflow_source_dir}/tensorflow/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
 )
@@ -138,7 +137,6 @@ RELATIVE_PROTOBUF_GENERATE_PYTHON(
 file(GLOB_RECURSE tf_python_protos_cc_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
-    "${tensorflow_source_dir}/tensorflow/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
 )
@@ -232,19 +230,6 @@ add_python_module("tensorflow/python/training")
 add_python_module("tensorflow/python/user_ops")
 add_python_module("tensorflow/python/util")
 add_python_module("tensorflow/python/util/protobuf")
-add_python_module("tensorflow/tensorboard")
-add_python_module("tensorflow/tensorboard/backend")
-add_python_module("tensorflow/tensorboard/backend/event_processing")
-add_python_module("tensorflow/tensorboard/plugins")
-add_python_module("tensorflow/tensorboard/plugins/audio")
-add_python_module("tensorflow/tensorboard/plugins/distributions")
-add_python_module("tensorflow/tensorboard/plugins/graphs")
-add_python_module("tensorflow/tensorboard/plugins/histograms")
-add_python_module("tensorflow/tensorboard/plugins/images")
-add_python_module("tensorflow/tensorboard/plugins/projector")
-add_python_module("tensorflow/tensorboard/plugins/scalars")
-add_python_module("tensorflow/tensorboard/plugins/text")
-add_python_module("tensorflow/tensorboard/scripts")
 add_python_module("tensorflow/contrib")
 add_python_module("tensorflow/contrib/android")
 add_python_module("tensorflow/contrib/android/java")
@@ -911,19 +896,6 @@ add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
   COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tools/pip_package/MANIFEST.in
                                    ${CMAKE_CURRENT_BINARY_DIR}/tf_python/)
 
-# Copy resources for TensorBoard.
-file(DOWNLOAD http://mirror.bazel.build/tensorboard/index.html ${DOWNLOAD_LOCATION}/tensorboard/index.html
-  EXPECTED_HASH SHA256=25554e708552ad8587152f7a444db3f4ca753f9ed72d9f8105203c1d1806d521)
-add_custom_command(TARGET tf_python_touchup_modules PRE_BUILD
-  COMMAND ${CMAKE_COMMAND} -E make_directory
-  ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tensorboard/components/)
-add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
-  COMMAND ${CMAKE_COMMAND} -E copy ${DOWNLOAD_LOCATION}/tensorboard/index.html
-  ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tensorboard/components/)
-add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
-  COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tensorboard/TAG
-  ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tensorboard/)
-
 # Copy datasets for tf.contrib.learn.
 add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
   COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/contrib/learn/python/learn/datasets/data/boston_house_prices.csv
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 55e9e311f9..9d3dabcb60 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -143,7 +143,6 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/*.py"
     "${tensorflow_source_dir}/tensorflow/python/saved_model/*_test.py"
     "${tensorflow_source_dir}/tensorflow/python/training/*_test.py"
-    "${tensorflow_source_dir}/tensorflow/tensorboard/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/data/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/factorization/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/integration_test.py"
@@ -191,7 +190,6 @@ if (tensorflow_BUILD_PYTHON_TESTS)
       "${tensorflow_source_dir}/tensorflow/python/kernel_tests/variable_scope_test.py"
       "${tensorflow_source_dir}/tensorflow/python/kernel_tests/reshape_op_test.py"
       "${tensorflow_source_dir}/tensorflow/python/training/evaluation_test.py"
-      "${tensorflow_source_dir}/tensorflow/tensorboard/backend/server_test.py"
       "${tensorflow_source_dir}/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py"  # Depends on gemmlowp -> pthread.
       # int32/int64 mixup
       "${tensorflow_source_dir}/tensorflow/python/kernel_tests/functional_ops_test.py"
@@ -206,13 +204,7 @@ if (tensorflow_BUILD_PYTHON_TESTS)
       "${tensorflow_source_dir}/tensorflow/python/training/supervisor_test.py"  # Flaky I/O error on rename.
       "${tensorflow_source_dir}/tensorflow/python/training/sync_replicas_optimizer_test.py"  # Needs portpicker.
       "${tensorflow_source_dir}/tensorflow/python/kernel_tests/array_ops_test.py"  # depends on python/framework/test_ops
-      # Broken TensorBoard tests due to different paths in windows
-      "${tensorflow_source_dir}/tensorflow/tensorboard/backend/application_test.py"
-      "${tensorflow_source_dir}/tensorflow/tensorboard/lib/python/http_util_test.py"
-      "${tensorflow_source_dir}/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py"
-      "${tensorflow_source_dir}/tensorflow/tensorboard/plugins/images/images_plugin_test.py"
       # Broken tensorboard test due to cmake issues.
-      "${tensorflow_source_dir}/tensorflow/tensorboard/plugins/debugger/plugin_test.py"
       "${tensorflow_source_dir}/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py"
       # tensor_forest tests (also note that we exclude the hybrid tests for now)
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/kernel_tests/count_extremely_random_stats_op_test.py"  # Results in wrong order.
@@ -221,8 +213,6 @@ if (tensorflow_BUILD_PYTHON_TESTS)
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/topn_test.py"  # Results inaccurate
       "${tensorflow_source_dir}/tensorflow/python/ops/cloud/bigquery_reader_ops_test.py"  # No libcurl support
       # Newly running on Windows since TensorBoard backend move. Fail on Windows and need debug.
-      "${tensorflow_source_dir}/tensorflow/tensorboard/backend/event_processing/directory_watcher_test.py"
-      "${tensorflow_source_dir}/tensorflow/tensorboard/backend/event_processing/event_multiplexer_test.py"
       "${tensorflow_source_dir}/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py"  # Segfaults on Windows.
   )
   endif()
diff --git a/tensorflow/contrib/tensorboard/BUILD b/tensorflow/contrib/tensorboard/BUILD
index 13de7fb39d..2e0a46ffe4 100644
--- a/tensorflow/contrib/tensorboard/BUILD
+++ b/tensorflow/contrib/tensorboard/BUILD
@@ -42,8 +42,8 @@ py_library(
     srcs = ["plugins/projector/__init__.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":protos_all_py",
         "//tensorflow/python:lib",
-        "//tensorflow/tensorboard/plugins/projector:protos_all_py",
     ],
 )
 
@@ -54,10 +54,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":projector",
+        ":protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:platform",
         "//tensorflow/python:summary",
-        "//tensorflow/tensorboard/plugins/projector:protos_all_py",
     ],
 )
 
diff --git a/tensorflow/contrib/tensorboard/plugins/projector/__init__.py b/tensorflow/contrib/tensorboard/plugins/projector/__init__.py
index be2398cdc0..7b9be76757 100644
--- a/tensorflow/contrib/tensorboard/plugins/projector/__init__.py
+++ b/tensorflow/contrib/tensorboard/plugins/projector/__init__.py
@@ -28,11 +28,11 @@ from __future__ import print_function
 import os
 
 from google.protobuf import text_format
-from tensorflow.python.lib.io import file_io
-from tensorflow.tensorboard.plugins.projector import projector_config_pb2
+from tensorflow.contrib.tensorboard.plugins.projector import projector_config_pb2
 # pylint: disable=wildcard-import
-from tensorflow.tensorboard.plugins.projector.projector_config_pb2 import *
+from tensorflow.contrib.tensorboard.plugins.projector.projector_config_pb2 import *
 # pylint: enable=wildcard-import
+from tensorflow.python.lib.io import file_io
 
 
 def visualize_embeddings(summary_writer, config):
diff --git a/tensorflow/contrib/tensorboard/plugins/projector/projector_api_test.py b/tensorflow/contrib/tensorboard/plugins/projector/projector_api_test.py
index 5f86f57a1c..9ad42bff47 100644
--- a/tensorflow/contrib/tensorboard/plugins/projector/projector_api_test.py
+++ b/tensorflow/contrib/tensorboard/plugins/projector/projector_api_test.py
@@ -24,10 +24,10 @@ import shutil
 from google.protobuf import text_format
 
 from tensorflow.contrib.tensorboard.plugins import projector
+from tensorflow.contrib.tensorboard.plugins.projector import projector_config_pb2
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 from tensorflow.python.summary.writer import writer as writer_lib
-from tensorflow.tensorboard.plugins.projector import projector_config_pb2
 
 
 class ProjectorApiTest(test.TestCase):
diff --git a/tensorflow/tensorboard/plugins/projector/projector_config.proto b/tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto
similarity index 100%
rename from tensorflow/tensorboard/plugins/projector/projector_config.proto
rename to tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto
diff --git a/tensorflow/tensorboard/BUILD b/tensorflow/tensorboard/BUILD
deleted file mode 100644
index bbd4251731..0000000000
--- a/tensorflow/tensorboard/BUILD
+++ /dev/null
@@ -1,84 +0,0 @@
-# Description:
-# TensorBoard, a dashboard for investigating TensorFlow
-
-package(default_visibility = [":internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-package_group(
-    name = "internal",
-    packages = [
-        "//learning/brain/tensorboard/...",
-        "//learning/vis/...",
-        "//tensorflow/...",
-        "//tensorflow/tensorboard/...",
-    ],
-)
-
-py_binary(
-    name = "tensorboard",
-    srcs = ["main.py"],
-    data = [":assets"],
-    main = "main.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_file_inspector",
-        "//tensorflow/tensorboard/plugins/audio:audio_plugin",
-        "//tensorflow/tensorboard/plugins/distributions:distributions_plugin",
-        "//tensorflow/tensorboard/plugins/graphs:graphs_plugin",
-        "//tensorflow/tensorboard/plugins/histograms:histograms_plugin",
-        "//tensorflow/tensorboard/plugins/images:images_plugin",
-        "//tensorflow/tensorboard/plugins/projector:projector_plugin",
-        "//tensorflow/tensorboard/plugins/scalars:scalars_plugin",
-        "//tensorflow/tensorboard/plugins/text:text_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-    ],
-)
-
-py_library(
-    name = "expect_tensorflow_installed",
-    # This is a dummy rule used as a TensorFlow dependency in open-source.
-    # We expect TensorFlow to already be installed on the system, e.g. via
-    # `pip install tensorflow`
-)
-
-py_library(
-    name = "expect_numpy_installed",
-    # This is a dummy rule used as a numpy dependency in open-source.
-    # We expect numpy to already be installed on the system, e.g. via
-    # `pip install numpy`
-)
-
-filegroup(
-    name = "assets",
-    srcs = [
-        "TAG",
-        "//tensorflow/tensorboard/components:index.html",
-        "//tensorflow/tensorboard/components:trace_viewer_index.html",
-    ],
-)
-
-filegroup(
-    name = "ts_web_library_default_typings",
-    srcs = [
-        # Ordering probably matters.
-        "@com_microsoft_typescript//:lib.es6.d.ts",
-        "@io_angular_clutz//:src/resources/closure.lib.d.ts",
-        "//tensorflow/tensorboard/defs:clutz.d.ts",
-    ],
-    visibility = ["//visibility:public"],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(
-        ["**"],
-        exclude = [
-            "METADATA",
-            "OWNERS",
-            "tensorboard.google.bzl",
-        ],
-    ),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/DEVELOPMENT.md b/tensorflow/tensorboard/DEVELOPMENT.md
deleted file mode 100644
index 79d534a26c..0000000000
--- a/tensorflow/tensorboard/DEVELOPMENT.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# How to Develop TensorBoard
-
-## Launching a Development Instance
-
-Run the following to launch a demo of TensorBoard in raw sources mode:
-
-```sh
-bazel run third_party/tensorflow/tensorboard/components/tf_tensorboard:demo
-```
-
-Now you can navigate to <http://localhost:6006/demo/index.html> and play with
-the demo TensorBoard instance. This will have live source reloading.
-
-This demo TensorBoard will have a small amount of demo data generated by
-[generate_testdata.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/scripts/generate_testdata.py).
-You can use [serialize_tensorboard.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/scripts/serialize_tensorboard.py)
-to create a realistic demo directory from your own data files.
-
-## Launching TensorBoard Proper
-
-Running TensorBoard automatically asks Bazel to create a vulcanized HTML binary:
-
-```sh
-bazel run //tensorflow/tensorboard:tensorboard -- --logdir=/path/to/logs
-```
diff --git a/tensorflow/tensorboard/README.md b/tensorflow/tensorboard/README.md
deleted file mode 100644
index a9ab4d3bd2..0000000000
--- a/tensorflow/tensorboard/README.md
+++ /dev/null
@@ -1,366 +0,0 @@
-# TensorBoard
-
-TensorBoard is a suite of web applications for inspecting and understanding your
-TensorFlow runs and graphs.
-
-This README gives an overview of key concepts in TensorBoard, as well as how to
-interpret the visualizations TensorBoard provides. For an in-depth example of
-using TensorBoard, see the tutorial: [TensorBoard: Visualizing
-Learning](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
-For in-depth information on the Graph Visualizer, see this tutorial: [TensorBoard: Graph Visualization](https://www.tensorflow.org/get_started/graph_viz).
-
-You may also want to watch
-[this video tutorial](https://www.youtube.com/watch?v=eBbEDRsCmv4) that walks
-through setting up and using TensorBoard.
-
-# Usage
-
-Before running TensorBoard, make sure you have generated summary data in a log
-directory by creating a summary writer:
-
-``` python
-# sess.graph contains the graph definition; that enables the Graph Visualizer.
-
-file_writer = tf.summary.FileWriter('/path/to/logs', sess.graph)
-```
-
-For more details, see [the TensorBoard tutorial](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
-Once you have event files, run TensorBoard and provide the log directory. If
-you're using a precompiled TensorFlow package (e.g. you installed via pip), run:
-
-```
-tensorboard --logdir=path/to/logs
-```
-
-Or, if you are building from source:
-
-```
-bazel build tensorflow/tensorboard:tensorboard
-./bazel-bin/tensorflow/tensorboard/tensorboard --logdir=path/to/logs
-```
-
-This should print that TensorBoard has started. Next, connect to
-http://localhost:6006.
-
-TensorBoard requires a `logdir` to read logs from. For info on configuring
-TensorBoard, run `tensorboard --help`.
-
-TensorBoard can be used in Google Chrome or Firefox. Other browsers might
-work, but there may be bugs or performance issues.
-
-# Key Concepts
-
-### Summary Ops: How TensorBoard gets data from TensorFlow
-
-The first step in using TensorBoard is acquiring data from your TensorFlow run.
-For this, you need [summary ops](https://www.tensorflow.org/api_docs/python/tf/summary).
-Summary ops are ops, like
-[`tf.matmul`](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/matmul)
-or
-[`tf.nn.relu`](https://www.tensorflow.org/versions/master/api_docs/python/tf/nn/relu),
-which means they take in tensors, produce tensors, and are evaluated from within
-a TensorFlow graph. However, summary ops have a twist: the Tensors they produce
-contain serialized protobufs, which are written to disk and sent to TensorBoard.
-To visualize the summary data in TensorBoard, you should evaluate the summary
-op, retrieve the result, and then write that result to disk using a
-summary.FileWriter. A full explanation, with examples, is in [the
-tutorial](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
-
-The supported summary ops include:
-* tf.summary.scalar
-* tf.summary.image
-* tf.summary.audio
-* tf.summary.text
-* tf.summary.histogram
-
-### Tags: Giving names to data
-
-When you make a summary op, you will also give it a `tag`. The tag is basically
-a name for the data recorded by that op, and will be used to organize the data
-in the frontend. The scalar and histogram dashboards organize data by tag, and
-group the tags into folders according to a directory/like/hierarchy. If you have
-a lot of tags, we recommend grouping them with slashes.
-
-### Event Files & LogDirs: How TensorBoard loads the data
-
-`summary.FileWriters` take summary data from TensorFlow, and then write them to a
-specified directory, known as the `logdir`. Specifically, the data is written to
-an append-only record dump that will have "tfevents" in the filename.
-TensorBoard reads data from a full directory, and organizes it into the history
-of a single TensorFlow execution.
-
-Why does it read the whole directory, rather than an individual file? You might
-have been using
-[supervisor.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/training/supervisor.py)
-to run your model, in which case if TensorFlow crashes, the supervisor will
-restart it from a checkpoint. When it restarts, it will start writing to a new
-events file, and TensorBoard will stitch the various event files together to
-produce a consistent history of what happened.
-
-### Runs: Comparing different executions of your model
-
-You may want to visually compare multiple executions of your model; for example,
-suppose you've changed the hyperparameters and want to see if it's converging
-faster. TensorBoard enables this through different "runs". When TensorBoard is
-passed a `logdir` at startup, it recursively walks the directory tree rooted at
-`logdir` looking for subdirectories that contain tfevents data. Every time it
-encounters such a subdirectory, it loads it as a new `run`, and the frontend
-will organize the data accordingly.
-
-For example, here is a well-organized TensorBoard log directory, with two runs,
-"run1" and "run2".
-
-```
-/some/path/mnist_experiments/
-/some/path/mnist_experiments/run1/
-/some/path/mnist_experiments/run1/events.out.tfevents.1456525581.name
-/some/path/mnist_experiments/run1/events.out.tfevents.1456525585.name
-/some/path/mnist_experiments/run2/
-/some/path/mnist_experiments/run2/events.out.tfevents.1456525385.name
-/tensorboard --logdir=/some/path/mnist_experiments
-```
-
-You may also pass a comma separated list of log directories, and TensorBoard
-will watch each directory. You can also assign names to individual log
-directories by putting a colon between the name and the path, as in
-
-```
-tensorboard --logdir=name1:/path/to/logs/1,name2:/path/to/logs/2
-```
-
-# The Visualizations
-
-### Scalar Dashboard
-
-TensorBoard's Scalar Dashboard visualizes scalar statistics that vary over time;
-for example, you might want to track the model's loss or learning rate. As
-described in *Key Concepts*, you can compare multiple runs, and the data is
-organized by tag. The line charts have the following interactions:
-
-* Clicking on the small blue icon in the lower-left corner of each chart will
-expand the chart
-
-* Dragging a rectangular region on the chart will zoom in
-
-* Double clicking on the chart will zoom out
-
-* Mousing over the chart will produce crosshairs, with data values recorded in
-the run-selector on the left.
-
-Additionally, you can create new folders to organize tags by writing regular
-expressions in the box in the top-left of the dashboard.
-
-### Histogram Dashboard
-
-The HistogramDashboard displays how the statistical distribution of a Tensor
-has varied over time. It visualizes data recorded via `tf.summary.histogram`.
-Each chart shows temporal "slices" of data, where each slice is a histogram of
-the tensor at a given step. It's organized with the oldest timestep in the back,
-and the most recent timestep in front. By changing the Histogram Mode from
-"offset" to "overlay", the perspective will rotate so that every histogram slice
-is rendered as a line and overlaid with one another.
-
-### Distribution Dashboard
-
-The Distribution Dashboard is another way of visualizing histogram data from
-`tf.summary.histogram`. It shows some high-level statistics on a distribution.
-Each line on the chart represents a percentile in the distribution over the
-data: for example, the bottom line shows how the minimum value has changed over
-time, and the line in the middle shows how the median has changed. Reading from
-top to bottom, the lines have the following meaning: `[maximum, 93%, 84%, 69%,
-50%, 31%, 16%, 7%, minimum]`
-
-These percentiles can also be viewed as standard deviation boundaries on a
-normal distribution: `[maximum, μ+1.5σ, μ+σ, μ+0.5σ, μ, μ-0.5σ, μ-σ, μ-1.5σ,
-minimum]` so that the colored regions, read from inside to outside, have widths
-`[σ, 2σ, 3σ]` respectively.
-
-
-### Image Dashboard
-
-The Image Dashboard can display pngs that were saved via a `tf.summary.image`.
-The dashboard is set up so that each row corresponds to a different tag, and
-each column corresponds to a run. Since the image dashboard supports arbitrary
-pngs, you can use this to embed custom visualizations (e.g. matplotlib
-scatterplots) into TensorBoard. This dashboard always shows you the latest image
-for each tag.
-
-### Audio Dashboard
-
-The Audio Dashboard can embed playable audio widgets for audio saved via a
-`tf.summary.audio`. The dashboard is set up so that each row corresponds to a
-different tag, and each column corresponds to a run. This dashboard always
-embeds the latest audio for each tag.
-
-### Graph Explorer
-
-The Graph Explorer can visualize a TensorBoard graph, enabling inspection of the
-TensorFlow model. To get best use of the graph visualizer, you should use name
-scopes to hierarchically group the ops in your graph - otherwise, the graph may
-be difficult to decipher. For more information, including examples, see [the
-graph visualizer tutorial](https://www.tensorflow.org/get_started/graph_viz).
-
-### Embedding Projector
-
-The Embedding Projector allows you to visualize high-dimensional data; for
-example, you may view your input data after it has been embedded in a high-
-dimensional space by your model. The embedding projector reads data from your
-model checkpoint file, and may be configured with additional metadata, like
-a vocabulary file or sprite images. For more details, see [the embedding
-projector tutorial](https://www.tensorflow.org/get_started/embedding_viz).
-
-### Text Dashboard
-
-The Text Dashboard displays text snippets saved via `tf.summary.text`. Markdown
-features including hyperlinks, lists, and tables are all supported.
-
-# Frequently Asked Questions
-
-### My TensorBoard isn't showing any data! What's wrong?
-
-The first thing to do is ensure that TensorBoard is properly loading data from
-the correct directory. Launch `tensorboard --logdir=DIRECTORY_PATH --debug` and
-look for output of the form
-
-`INFO:tensorflow:TensorBoard path_to_run is: {'DIRECTORY_PATH': None}`
-
-Verify that the DIRECTORY_PATH TensorBoard is looking at is the path you expect.
-(Note: There's a known issue where TensorBoard [does not handle paths starting
-in ~ properly](https://github.com/tensorflow/tensorflow/issues/1587)).
-
-If you're loading from the proper path, make sure that event files are present.
-TensorBoard will recursively walk its logdir, it's fine if the data is nested
-under a subdirectory. Try running the command:
-
-`find DIRECTORY_PATH | grep tfevents`
-
-If you have at least one result, then TensorBoard should be able to load data.
-
-Finally, let's make sure that the event files actually have data. Run
-tensorboard in inspector mode to inspect the contents of your event files.
-
-`tensorboard --inspect --logdir=DIRECTORY_PATH`
-
-If after running this procedure, it's still not working, please file an [issue
-on GitHub](https://github.com/tensorflow/tensorflow/issues). It will be much
-easier for us to debug it if you provide an event file that isn't working.
-
-### TensorBoard is showing only some of my data, or isn't properly updating!
-
-This issue usually comes about because of how TensorBoard iterates through the
-`tfevents` files: it progresses through the events file in timestamp order, and
-only reads one file at a time. Let's suppose we have files with timestamps `a`
-and `b`, where `a<b`. Once TensorBoard has read all the events in `a`, it will
-never return to it, because it assumes any new events are being written in the
-more recent file. This could cause an issue if, for example, you have two
-`FileWriters` simultaneously writing to the same directory. If you have
-multiple summary writers, each one should be writing to a separate directory.
-
-### Does TensorBoard support multiple or distributed summary writers?
-
-No. TensorBoard expects that only one events file will be written to at a time,
-and multiple summary writers means multiple events files. If you are running a
-distributed TensorFlow instance, we encourage you to designate a single worker
-as the "chief" that is responsible for all summary processing. See
-[supervisor.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/training/supervisor.py)
-for an example.
-
-### I'm seeing data overlapped on itself! What gives?
-
-If you are seeing data that seems to travel backwards through time and overlap
-with itself, there are a few possible explanations.
-
-* You may have multiple execution of TensorFlow that all wrote to the same log
-directory. Please have each TensorFlow run write to its own logdir.
-
-* You may have a have a bug in your code where the global_step variable (passed
-to `FileWriter.add_summary`) is being maintained incorrectly.
-
-* It may be that your TensorFlow job crashed, and was restarted from an earlier
-checkpoint. See *How to handle TensorFlow restarts*, below.
-
-As a workaround, try changing the x-axis display in TensorBoard from `steps` to
-`wall_time`. This will frequently clear up the issue.
-
-### How should I handle TensorFlow restarts?
-
-TensorFlow is designed with a mechanism for graceful recovery if a job crashes
-or is killed: TensorFlow can periodically write model checkpoint files, which
-enable you to restart TensorFlow without losing all your training progress.
-
-However, this can complicate things for TensorBoard; imagine that TensorFlow
-wrote a checkpoint at step `a`, and then continued running until step `b`, and
-then crashed and restarted at timestamp `a`. All of the events written between
-`a` and `b` were "orphaned" by the restart event and should be removed.
-
-To facilitate this, we have a `SessionLog` message in
-`tensorflow/core/util/event.proto` which can record `SessionStatus.START` as an
-event; like all events, it may have a `step` associated with it. If TensorBoard
-detects a `SessionStatus.START` event with step `a`, it will assume that every
-event with a step greater than `a` was orphaned, and it will discard those
-events. This behavior may be disabled with the flag
-`--purge_orphaned_data=false` (in versions after 0.7).
-
-### How can I export data from TensorBoard?
-
-The Scalar Dashboard supports exporting data; you can click the "enable
-download links" option in the left-hand bar. Then, each plot will provide
-download links for the data it contains.
-
-If you need access to the full dataset, you can read the event files that
-TensorBoard consumes by using the [`summary_iterator`](https://github.com/tensorflow/tensorflow/blob/e7f333b5f8b3c53b21d149d8d14c0cebbde431aa/tensorflow/python/summary/summary_iterator.py#L313)
-method.
-
-
-### Can I overlap multiple plots?
-
-Right now, you can overlap plots only if they are from different runs, and both
-have the same tag name.
-
-### Can I create scatterplots (or other custom plots)?
-
-This isn't yet possible. As a workaround, you could create your custom plot in
-your own code (e.g. matplotlib) and then write it into an `SummaryProto`
-(`core/framework/summary.proto`) and add it to your `FileWriter`. Then, your
-custom plot will appear in the TensorBoard image tab.
-
-### Is my data being downsampled? Am I really seeing all the data?
-
-TensorBoard uses [reservoir
-sampling](https://en.wikipedia.org/wiki/Reservoir_sampling) to downsample your
-data so that it can be loaded into RAM. You can modify the number of elements it
-will keep per tag in
-[tensorboard/backend/application.py](https://www.github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/tensorboard/backend/application.py).
-See this [StackOverflow question](http://stackoverflow.com/questions/43702546/tensorboard-doesnt-show-all-data-points/)
-for some more information.
-
-### I get a network security popup every time I run TensorBoard on a mac!
-
-This is because by default, TensorBoard serves on host `0.0.0.0` which is
-publicly accessible. You can stop the popups by specifying `--host=localhost` at
-startup.
-
-### How can I develop TensorBoard?
-
-See [tensorflow/tensorboard/DEVELOPMENT.md](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/DEVELOPMENT.md).
-
-### I have a different issue that wasn't addressed here!
-
-First, try searching our [GitHub
-issues](https://github.com/tensorflow/tensorflow/issues) and [Stack
-Overflow](https://stackoverflow.com/questions/tagged/tensorboard). It may be
-that someone else has already had the same issue or question.
-
-If you have a bug, please [file a GitHub
-issue](https://github.com/tensorflow/tensorflow/issues). If the bug is related
-to your specific data (e.g. the events aren't loading properly), please do both
-of the following things to make it easier for us to debug and fix:
-
-- Run tensorboard in --inspect mode and copy paste the debug output.
-- Upload some events files that will reproduce the issue.
-
-If you have a feature request, please [file a GitHub
-issue](https://github.com/tensorflow/tensorflow/issues).
-
-General usage questions should go to [Stack
-Overflow](http://stackoverflow.com/questions/tagged/tensorflow).
diff --git a/tensorflow/tensorboard/TAG b/tensorflow/tensorboard/TAG
deleted file mode 100644
index c3f407c095..0000000000
--- a/tensorflow/tensorboard/TAG
+++ /dev/null
@@ -1 +0,0 @@
-55
diff --git a/tensorflow/tensorboard/WORKSPACE b/tensorflow/tensorboard/WORKSPACE
deleted file mode 100644
index 1667478cab..0000000000
--- a/tensorflow/tensorboard/WORKSPACE
+++ /dev/null
@@ -1,21 +0,0 @@
-workspace(name = "org_tensorflow_tensorboard")
-
-http_archive(
-    name = "io_bazel_rules_closure",
-    sha256 = "bc41b80486413aaa551860fc37471dbc0666e1dbb5236fb6177cb83b0c105846",
-    strip_prefix = "rules_closure-dec425a4ff3faf09a56c85d082e4eed05d8ce38f",
-    urls = [
-        "http://mirror.bazel.build/github.com/bazelbuild/rules_closure/archive/dec425a4ff3faf09a56c85d082e4eed05d8ce38f.tar.gz",  # 2017-06-02
-        "https://github.com/bazelbuild/rules_closure/archive/dec425a4ff3faf09a56c85d082e4eed05d8ce38f.tar.gz",
-    ],
-)
-
-
-load("@io_bazel_rules_closure//closure:defs.bzl", "closure_repositories")
-
-closure_repositories()
-
-load("//third_party:workspace.bzl", "tensorboard_workspace")
-
-# Please add all new dependencies in workspace.bzl.
-tensorboard_workspace()
diff --git a/tensorflow/tensorboard/__init__.py b/tensorflow/tensorboard/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/tensorflow/tensorboard/__main__.py b/tensorflow/tensorboard/__main__.py
deleted file mode 100644
index f172583d7c..0000000000
--- a/tensorflow/tensorboard/__main__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import sys
-
-from tensorflow.tensorboard.tensorboard import main
-
-if __name__ == '__main__':
-  sys.exit(main())
diff --git a/tensorflow/tensorboard/backend/BUILD b/tensorflow/tensorboard/backend/BUILD
deleted file mode 100644
index c7bf0dfee5..0000000000
--- a/tensorflow/tensorboard/backend/BUILD
+++ /dev/null
@@ -1,106 +0,0 @@
-# Description:
-# TensorBoard, a dashboard for investigating TensorFlow
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-py_library(
-    name = "http_util",
-    srcs = ["http_util.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":json_util",
-        "//tensorflow:tensorflow_py",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "http_util_test",
-    size = "small",
-    srcs = ["http_util_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":http_util",
-        "//tensorflow:tensorflow_py",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_library(
-    name = "json_util",
-    srcs = ["json_util.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_test(
-    name = "json_util_test",
-    size = "small",
-    srcs = ["json_util_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":json_util",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_library(
-    name = "application",
-    srcs = ["application.py"],
-    data = ["//tensorflow/tensorboard:assets"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":http_util",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "application_test",
-    size = "medium",
-    srcs = ["application_test.py"],
-    srcs_version = "PY2AND3",
-    tags = ["manual"],
-    deps = [
-        ":application",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-    ],
-)
-
-py_library(
-    name = "process_graph",
-    srcs = ["process_graph.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(
-        [
-            "*",
-        ],
-    ),
-    visibility = ["//tensorflow:__subpackages__"],
-)
diff --git a/tensorflow/tensorboard/backend/__init__.py b/tensorflow/tensorboard/backend/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/tensorflow/tensorboard/backend/application.py b/tensorflow/tensorboard/backend/application.py
deleted file mode 100644
index 68c0e3ddf1..0000000000
--- a/tensorflow/tensorboard/backend/application.py
+++ /dev/null
@@ -1,375 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""TensorBoard WSGI Application Logic.
-
-TensorBoardApplication constructs TensorBoard as a WSGI application.
-It handles serving static assets, and implements TensorBoard data APIs.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import re
-import threading
-import time
-
-import six
-from six.moves.urllib import parse as urlparse
-import tensorflow as tf
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-
-
-DEFAULT_SIZE_GUIDANCE = {
-    event_accumulator.COMPRESSED_HISTOGRAMS: 500,
-    event_accumulator.IMAGES: 10,
-    event_accumulator.AUDIO: 10,
-    event_accumulator.SCALARS: 1000,
-    event_accumulator.HEALTH_PILLS: 100,
-    event_accumulator.HISTOGRAMS: 50,
-}
-
-DATA_PREFIX = '/data'
-LOGDIR_ROUTE = '/logdir'
-RUNS_ROUTE = '/runs'
-PLUGIN_PREFIX = '/plugin'
-PLUGINS_LISTING_ROUTE = '/plugins_listing'
-TAB_ROUTES = ['', '/events', '/images', '/audio', '/graphs', '/histograms']
-
-# Slashes in a plugin name could throw the router for a loop. An empty
-# name would be confusing, too. To be safe, let's restrict the valid
-# names as follows.
-_VALID_PLUGIN_RE = re.compile(r'^[A-Za-z0-9_.-]+$')
-
-
-def standard_tensorboard_wsgi(
-    logdir,
-    purge_orphaned_data,
-    reload_interval,
-    plugins):
-  """Construct a TensorBoardWSGIApp with standard plugins and multiplexer.
-
-  Args:
-    logdir: The path to the directory containing events files.
-    purge_orphaned_data: Whether to purge orphaned data.
-    reload_interval: The interval at which the backend reloads more data in
-        seconds.
-    plugins: A list of constructor functions for TBPlugin subclasses.
-
-  Returns:
-    The new TensorBoard WSGI application.
-  """
-  multiplexer = event_multiplexer.EventMultiplexer(
-      size_guidance=DEFAULT_SIZE_GUIDANCE,
-      purge_orphaned_data=purge_orphaned_data)
-  context = base_plugin.TBContext(logdir=logdir, multiplexer=multiplexer)
-  plugins = [constructor(context) for constructor in plugins]
-  return TensorBoardWSGIApp(logdir, plugins, multiplexer, reload_interval)
-
-
-class TensorBoardWSGIApp(object):
-  """The TensorBoard application, conforming to WSGI spec."""
-
-  # How many samples to include in sampling API calls by default.
-  DEFAULT_SAMPLE_COUNT = 10
-
-  # NOTE TO MAINTAINERS: An accurate Content-Length MUST be specified on all
-  #                      responses using send_header.
-  protocol_version = 'HTTP/1.1'
-
-  def __init__(self, logdir, plugins, multiplexer, reload_interval):
-    """Constructs the TensorBoard application.
-
-    Args:
-      logdir: the logdir spec that describes where data will be loaded.
-        may be a directory, or comma,separated list of directories, or colons
-        can be used to provide named directories
-      plugins: A list of base_plugin.TBPlugin subclass instances.
-      multiplexer: The EventMultiplexer with TensorBoard data to serve
-      reload_interval: How often (in seconds) to reload the Multiplexer
-
-    Returns:
-      A WSGI application that implements the TensorBoard backend.
-
-    Raises:
-      ValueError: If some plugin has no plugin_name
-      ValueError: If some plugin has an invalid plugin_name (plugin
-          names must only contain [A-Za-z0-9_.-])
-      ValueError: If two plugins have the same plugin_name
-      ValueError: If some plugin handles a route that does not start
-          with a slash
-    """
-    self._logdir = logdir
-    self._plugins = plugins
-    self._multiplexer = multiplexer
-    self.tag = get_tensorboard_tag()
-
-    path_to_run = parse_event_files_spec(self._logdir)
-    if reload_interval:
-      start_reloading_multiplexer(self._multiplexer, path_to_run,
-                                  reload_interval)
-    else:
-      reload_multiplexer(self._multiplexer, path_to_run)
-
-    self.data_applications = {
-        DATA_PREFIX + LOGDIR_ROUTE:
-            self._serve_logdir,
-        # TODO(chizeng): Delete this RPC once we have skylark rules that obviate
-        # the need for the frontend to determine which plugins are active.
-        DATA_PREFIX + PLUGINS_LISTING_ROUTE: self._serve_plugins_listing,
-        DATA_PREFIX + RUNS_ROUTE: self._serve_runs,
-    }
-
-    # Serve the routes from the registered plugins using their name as the route
-    # prefix. For example if plugin z has two routes /a and /b, they will be
-    # served as /data/plugin/z/a and /data/plugin/z/b.
-    plugin_names_encountered = set()
-    for plugin in self._plugins:
-      if plugin.plugin_name is None:
-        raise ValueError('Plugin %s has no plugin_name' % plugin)
-      if not _VALID_PLUGIN_RE.match(plugin.plugin_name):
-        raise ValueError('Plugin %s has invalid name %r' % (plugin,
-                                                            plugin.plugin_name))
-      if plugin.plugin_name in plugin_names_encountered:
-        raise ValueError('Duplicate plugins for name %s' % plugin.plugin_name)
-      plugin_names_encountered.add(plugin.plugin_name)
-
-      try:
-        plugin_apps = plugin.get_plugin_apps()
-      except Exception as e:  # pylint: disable=broad-except
-        tf.logging.warning('Plugin %s failed. Exception: %s',
-                           plugin.plugin_name, str(e))
-        continue
-      for route, app in plugin_apps.items():
-        if not route.startswith('/'):
-          raise ValueError('Plugin named %r handles invalid route %r: '
-                           'route does not start with a slash' %
-                           (plugin.plugin_name, route))
-        path = DATA_PREFIX + PLUGIN_PREFIX + '/' + plugin.plugin_name + route
-        self.data_applications[path] = app
-
-  def _path_is_safe(self, path):
-    """Check path is safe (stays within current directory).
-
-    This is for preventing directory-traversal attacks.
-
-    Args:
-      path: The path to check for safety.
-
-    Returns:
-      True if the given path stays within the current directory, and false
-      if it would escape to a higher directory. E.g. _path_is_safe('index.html')
-      returns true, but _path_is_safe('../../../etc/password') returns false.
-    """
-    base = os.path.abspath(os.curdir)
-    absolute_path = os.path.abspath(path)
-    prefix = os.path.commonprefix([base, absolute_path])
-    return prefix == base
-
-  @wrappers.Request.application
-  def _serve_logdir(self, request):
-    """Respond with a JSON object containing this TensorBoard's logdir."""
-    return http_util.Respond(
-        request, {'logdir': self._logdir}, 'application/json')
-
-  @wrappers.Request.application
-  def _serve_plugins_listing(self, request):
-    """Serves an object mapping plugin name to whether it is enabled.
-
-    Args:
-      request: The werkzeug.Request object.
-
-    Returns:
-      A werkzeug.Response object.
-    """
-    return http_util.Respond(
-        request,
-        {plugin.plugin_name: plugin.is_active() for plugin in self._plugins},
-        'application/json')
-
-  @wrappers.Request.application
-  def _serve_runs(self, request):
-    """WSGI app serving a JSON object about runs and tags.
-
-    Returns a mapping from runs to tagType to list of tags for that run.
-
-    Args:
-      request: A werkzeug request
-
-    Returns:
-      A werkzeug Response with the following content:
-      {runName: {firstEventTimestamp: 123456.789}}
-    """
-    run_names = sorted(self._multiplexer.Runs())  # Why `sorted`? See below.
-    def get_first_event_timestamp(run_name):
-      try:
-        return self._multiplexer.FirstEventTimestamp(run_name)
-      except ValueError:
-        tf.logging.warning('Unable to get first event timestamp for run %s',
-                           run_name)
-        # Put runs without a timestamp at the end. Their internal
-        # ordering would be nondeterministic, but Python's sorts are
-        # stable, so `sorted`ing the initial list above provides a
-        # deterministic ordering. Of course, we cannot guarantee that
-        # this will be append-only for new event-less runs.
-        return float('inf')
-    first_event_timestamps = {
-        run_name: get_first_event_timestamp(run_name)
-        for run_name in run_names
-    }
-    run_names.sort(key=first_event_timestamps.get)
-    return http_util.Respond(request, run_names, 'application/json')
-
-  @wrappers.Request.application
-  def _serve_index(self, request):
-    """Serves the index page (i.e., the tensorboard app itself)."""
-    contents = tf.resource_loader.load_resource(
-        'tensorboard/components/index.html')
-    return http_util.Respond(request, contents, 'text/html', expires=3600)
-
-  def __call__(self, environ, start_response):  # pylint: disable=invalid-name
-    """Central entry point for the TensorBoard application.
-
-    This method handles routing to sub-applications. It does simple routing
-    using regular expression matching.
-
-    This __call__ method conforms to the WSGI spec, so that instances of this
-    class are WSGI applications.
-
-    Args:
-      environ: See WSGI spec.
-      start_response: See WSGI spec.
-
-    Returns:
-      A werkzeug Response.
-    """
-    request = wrappers.Request(environ)
-    parsed_url = urlparse.urlparse(request.path)
-
-    # Remove a trailing slash, if present.
-    clean_path = parsed_url.path
-    if clean_path.endswith('/'):
-      clean_path = clean_path[:-1]
-    # pylint: disable=too-many-function-args
-    if clean_path in self.data_applications:
-      return self.data_applications[clean_path](environ, start_response)
-    elif clean_path in TAB_ROUTES:
-      return self._serve_index(environ, start_response)
-    else:
-      tf.logging.warning('path %s not found, sending 404', clean_path)
-      return http_util.Respond(request, 'Not found', 'text/plain', code=404)(
-          environ, start_response)
-    # pylint: enable=too-many-function-args
-
-
-def parse_event_files_spec(logdir):
-  """Parses `logdir` into a map from paths to run group names.
-
-  The events files flag format is a comma-separated list of path specifications.
-  A path specification either looks like 'group_name:/path/to/directory' or
-  '/path/to/directory'; in the latter case, the group is unnamed. Group names
-  cannot start with a forward slash: /foo:bar/baz will be interpreted as a
-  spec with no name and path '/foo:bar/baz'.
-
-  Globs are not supported.
-
-  Args:
-    logdir: A comma-separated list of run specifications.
-  Returns:
-    A dict mapping directory paths to names like {'/path/to/directory': 'name'}.
-    Groups without an explicit name are named after their path. If logdir is
-    None, returns an empty dict, which is helpful for testing things that don't
-    require any valid runs.
-  """
-  files = {}
-  if logdir is None:
-    return files
-  # Make sure keeping consistent with ParseURI in core/lib/io/path.cc
-  uri_pattern = re.compile('[a-zA-Z][0-9a-zA-Z.]*://.*')
-  for specification in logdir.split(','):
-    # Check if the spec contains group. A spec start with xyz:// is regarded as
-    # URI path spec instead of group spec. If the spec looks like /foo:bar/baz,
-    # then we assume it's a path with a colon.
-    if (uri_pattern.match(specification) is None and ':' in specification and
-        specification[0] != '/'):
-      # We split at most once so run_name:/path:with/a/colon will work.
-      run_name, _, path = specification.partition(':')
-    else:
-      run_name = None
-      path = specification
-    if uri_pattern.match(path) is None:
-      path = os.path.realpath(path)
-    files[path] = run_name
-  return files
-
-
-def reload_multiplexer(multiplexer, path_to_run):
-  """Loads all runs into the multiplexer.
-
-  Args:
-    multiplexer: The `EventMultiplexer` to add runs to and reload.
-    path_to_run: A dict mapping from paths to run names, where `None` as the run
-      name is interpreted as a run name equal to the path.
-  """
-  start = time.time()
-  tf.logging.info('TensorBoard reload process beginning')
-  for (path, name) in six.iteritems(path_to_run):
-    multiplexer.AddRunsFromDirectory(path, name)
-  tf.logging.info('TensorBoard reload process: Reload the whole Multiplexer')
-  multiplexer.Reload()
-  duration = time.time() - start
-  tf.logging.info('TensorBoard done reloading. Load took %0.3f secs', duration)
-
-
-def start_reloading_multiplexer(multiplexer, path_to_run, load_interval):
-  """Starts a thread to automatically reload the given multiplexer.
-
-  The thread will reload the multiplexer by calling `ReloadMultiplexer` every
-  `load_interval` seconds, starting immediately.
-
-  Args:
-    multiplexer: The `EventMultiplexer` to add runs to and reload.
-    path_to_run: A dict mapping from paths to run names, where `None` as the run
-      name is interpreted as a run name equal to the path.
-    load_interval: How many seconds to wait after one load before starting the
-      next load.
-
-  Returns:
-    A started `threading.Thread` that reloads the multiplexer.
-  """
-
-  # We don't call multiplexer.Reload() here because that would make
-  # AddRunsFromDirectory block until the runs have all loaded.
-  def _reload_forever():
-    while True:
-      reload_multiplexer(multiplexer, path_to_run)
-      time.sleep(load_interval)
-
-  thread = threading.Thread(target=_reload_forever)
-  thread.daemon = True
-  thread.start()
-  return thread
-
-
-def get_tensorboard_tag():
-  """Read the TensorBoard TAG number, and return it or an empty string."""
-  tag = tf.resource_loader.load_resource('tensorboard/TAG').strip()
-  return tag
diff --git a/tensorflow/tensorboard/backend/application_test.py b/tensorflow/tensorboard/backend/application_test.py
deleted file mode 100644
index d88b165477..0000000000
--- a/tensorflow/tensorboard/backend/application_test.py
+++ /dev/null
@@ -1,520 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Integration tests for TensorBoard.
-
-These tests start up a full-fledged TensorBoard server.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import functools
-import gzip
-import json
-import os
-import shutil
-import socket
-import tempfile
-import threading
-
-from six import BytesIO
-from six.moves import http_client
-import tensorflow as tf
-
-from werkzeug import serving
-
-from tensorflow.tensorboard import main as tensorboard
-from tensorflow.tensorboard.backend import application
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-
-
-class FakePlugin(base_plugin.TBPlugin):
-  """A plugin with no functionality."""
-
-  def __init__(self, unused_context, plugin_name, is_active_value,
-               routes_mapping):
-    """Constructs a fake plugin.
-
-    Args:
-      plugin_name: The name of this plugin.
-      is_active_value: Whether the plugin is active.
-      routes_mapping: A dictionary mapping from route (string URL path) to the
-        method called when a user issues a request to that route.
-    """
-    self.plugin_name = plugin_name
-    self._is_active_value = is_active_value
-    self._routes_mapping = routes_mapping
-
-  def get_plugin_apps(self):
-    """Returns a mapping from routes to handlers offered by this plugin.
-
-    Returns:
-      A dictionary mapping from routes to handlers offered by this plugin.
-    """
-    return self._routes_mapping
-
-  def is_active(self):
-    """Returns whether this plugin is active.
-
-    Returns:
-      A boolean. Whether this plugin is active.
-    """
-    return self._is_active_value
-
-
-class TensorboardServerTest(tf.test.TestCase):
-  _only_use_meta_graph = False  # Server data contains only a GraphDef
-
-  def setUp(self):
-    self.logdir = self.get_temp_dir()
-
-    self._GenerateTestData(run_name='run1')
-    self._multiplexer = event_multiplexer.EventMultiplexer(
-        size_guidance=application.DEFAULT_SIZE_GUIDANCE,
-        purge_orphaned_data=True)
-    plugins = [
-        FakePlugin(
-            None, plugin_name='foo', is_active_value=True, routes_mapping={}),
-        FakePlugin(
-            None, plugin_name='bar', is_active_value=False, routes_mapping={}),
-    ]
-    app = application.TensorBoardWSGIApp(
-        self.logdir, plugins, self._multiplexer, reload_interval=0)
-    try:
-      self._server = serving.BaseWSGIServer('localhost', 0, app)
-      # 0 to pick an unused port.
-    except IOError:
-      # BaseWSGIServer has a preference for IPv4. If that didn't work, try again
-      # with an explicit IPv6 address.
-      self._server = serving.BaseWSGIServer('::1', 0, app)
-    self._server_thread = threading.Thread(target=self._server.serve_forever)
-    self._server_thread.daemon = True
-    self._server_thread.start()
-    self._connection = http_client.HTTPConnection(
-        'localhost', self._server.server_address[1])
-
-  def tearDown(self):
-    self._connection.close()
-    self._server.shutdown()
-    self._server.server_close()
-
-  def _get(self, path, headers=None):
-    """Perform a GET request for the given path."""
-    if headers is None:
-      headers = {}
-    self._connection.request('GET', path, None, headers)
-    return self._connection.getresponse()
-
-  def _getJson(self, path):
-    """Perform a GET request and decode the result as JSON."""
-    self._connection.request('GET', path)
-    response = self._connection.getresponse()
-    self.assertEqual(response.status, 200)
-    data = response.read()
-    if response.getheader('Content-Encoding') == 'gzip':
-      data = gzip.GzipFile('', 'rb', 9, BytesIO(data)).read()
-    return json.loads(data.decode('utf-8'))
-
-  def testBasicStartup(self):
-    """Start the server up and then shut it down immediately."""
-    pass
-
-  def testRequestMainPage(self):
-    """Navigate to the main page and verify that it returns a 200."""
-    response = self._get('/')
-    self.assertEqual(response.status, 200)
-
-  def testRequestNonexistentPage(self):
-    """Request a page that doesn't exist; it should 404."""
-    response = self._get('/asdf')
-    self.assertEqual(response.status, 404)
-
-  def testLogdir(self):
-    """Test the format of the data/logdir endpoint."""
-    parsed_object = self._getJson('/data/logdir')
-    self.assertEqual(parsed_object, {'logdir': self.logdir})
-
-  def testPluginsListing(self):
-    """Test the format of the data/plugins_listing endpoint."""
-    parsed_object = self._getJson('/data/plugins_listing')
-    # Plugin foo is active. Plugin bar is not.
-    self.assertEqual(parsed_object, {'foo': True, 'bar': False})
-
-  def testRuns(self):
-    """Test the format of the /data/runs endpoint."""
-    run_json = self._getJson('/data/runs')
-    self.assertEqual(run_json, ['run1'])
-
-  def testRunsAppendOnly(self):
-    """Test that new runs appear after old ones in /data/runs."""
-    # We use three runs: the 'run1' that we already created in our
-    # `setUp` method, plus runs with names lexicographically before and
-    # after it (so that just sorting by name doesn't have a chance of
-    # working).
-    fake_wall_times = {
-        'run1': 1234.0,
-        'avocado': 2345.0,
-        'zebra': 3456.0,
-        'mysterious': None,
-    }
-
-    stubs = tf.test.StubOutForTesting()
-    # pylint: disable=invalid-name
-    def FirstEventTimestamp_stub(multiplexer_self, run_name):
-      del multiplexer_self
-      matches = [candidate_name
-                 for candidate_name in fake_wall_times
-                 if run_name.endswith(candidate_name)]
-      self.assertEqual(len(matches), 1, '%s (%s)' % (matches, run_name))
-      wall_time = fake_wall_times[matches[0]]
-      if wall_time is None:
-        raise ValueError('No event timestamp could be found')
-      else:
-        return wall_time
-    # pylint: enable=invalid-name
-
-    stubs.SmartSet(self._multiplexer,
-                   'FirstEventTimestamp',
-                   FirstEventTimestamp_stub)
-
-    def add_run(run_name):
-      self._GenerateTestData(run_name)
-      self._multiplexer.AddRunsFromDirectory(self.logdir)
-      self._multiplexer.Reload()
-
-    # Add one run: it should come last.
-    add_run('avocado')
-    self.assertEqual(self._getJson('/data/runs'),
-                     ['run1', 'avocado'])
-
-    # Add another run: it should come last, too.
-    add_run('zebra')
-    self.assertEqual(self._getJson('/data/runs'),
-                     ['run1', 'avocado', 'zebra'])
-
-    # And maybe there's a run for which we somehow have no timestamp.
-    add_run('mysterious')
-    self.assertEqual(self._getJson('/data/runs'),
-                     ['run1', 'avocado', 'zebra', 'mysterious'])
-
-    stubs.UnsetAll()
-
-  def testApplicationPaths_getCached(self):
-    """Test the format of the /data/runs endpoint."""
-    for path in ('/',):  # TODO(jart): '/app.js' in open source
-      connection = http_client.HTTPConnection('localhost',
-                                              self._server.server_address[1])
-      connection.request('GET', path)
-      response = connection.getresponse()
-      self.assertEqual(response.status, 200, msg=path)
-      self.assertEqual(
-          response.getheader('Cache-Control'),
-          'private, max-age=3600',
-          msg=path)
-      connection.close()
-
-  def testDataPaths_disableAllCaching(self):
-    """Test the format of the /data/runs endpoint."""
-    for path in ('/data/runs', '/data/logdir'):
-      connection = http_client.HTTPConnection('localhost',
-                                              self._server.server_address[1])
-      connection.request('GET', path)
-      response = connection.getresponse()
-      self.assertEqual(response.status, 200, msg=path)
-      self.assertEqual(response.getheader('Expires'), '0', msg=path)
-      response.read()
-      connection.close()
-
-  def _GenerateTestData(self, run_name):
-    """Generates the test data directory.
-
-    The test data has a single run of the given name, containing:
-      - a graph definition and metagraph definition
-
-    Arguments:
-      run_name: the directory under self.logdir into which to write
-        events
-    """
-    run_path = os.path.join(self.logdir, run_name)
-    os.makedirs(run_path)
-
-    writer = tf.summary.FileWriter(run_path)
-
-    # Add a simple graph event.
-    graph_def = tf.GraphDef()
-    node1 = graph_def.node.add()
-    node1.name = 'a'
-    node2 = graph_def.node.add()
-    node2.name = 'b'
-    node2.attr['very_large_attr'].s = b'a' * 2048  # 2 KB attribute
-
-    meta_graph_def = tf.MetaGraphDef(graph_def=graph_def)
-
-    if self._only_use_meta_graph:
-      writer.add_meta_graph(meta_graph_def)
-    else:
-      writer.add_graph(graph_def)
-
-    writer.flush()
-    writer.close()
-
-
-class TensorboardServerPluginNameTest(tf.test.TestCase):
-
-  def _test(self, name, should_be_okay):
-    temp_dir = tempfile.mkdtemp(prefix=self.get_temp_dir())
-    self.addCleanup(shutil.rmtree, temp_dir)
-    multiplexer = event_multiplexer.EventMultiplexer(
-        size_guidance=application.DEFAULT_SIZE_GUIDANCE,
-        purge_orphaned_data=True)
-    plugins = [
-        FakePlugin(
-            None, plugin_name='foo', is_active_value=True, routes_mapping={}),
-        FakePlugin(
-            None, plugin_name=name, is_active_value=True, routes_mapping={}),
-        FakePlugin(
-            None, plugin_name='bar', is_active_value=False, routes_mapping={}),
-    ]
-    if should_be_okay:
-      application.TensorBoardWSGIApp(
-          temp_dir, plugins, multiplexer, reload_interval=0)
-    else:
-      with self.assertRaisesRegexp(ValueError, r'invalid name'):
-        application.TensorBoardWSGIApp(
-            temp_dir, plugins, multiplexer, reload_interval=0)
-
-  def testEmptyName(self):
-    self._test('', False)
-
-  def testNameWithSlashes(self):
-    self._test('scalars/data', False)
-
-  def testNameWithSpaces(self):
-    self._test('my favorite plugin', False)
-
-  def testSimpleName(self):
-    self._test('scalars', True)
-
-  def testComprehensiveName(self):
-    self._test('Scalar-Dashboard_3000.1', True)
-
-
-class TensorboardServerPluginRouteTest(tf.test.TestCase):
-
-  def _test(self, route, should_be_okay):
-    temp_dir = tempfile.mkdtemp(prefix=self.get_temp_dir())
-    self.addCleanup(shutil.rmtree, temp_dir)
-    multiplexer = event_multiplexer.EventMultiplexer(
-        size_guidance=application.DEFAULT_SIZE_GUIDANCE,
-        purge_orphaned_data=True)
-    plugins = [
-        FakePlugin(
-            None,
-            plugin_name='foo',
-            is_active_value=True,
-            routes_mapping={route: lambda environ, start_response: None}),
-    ]
-    if should_be_okay:
-      application.TensorBoardWSGIApp(
-          temp_dir, plugins, multiplexer, reload_interval=0)
-    else:
-      with self.assertRaisesRegexp(ValueError, r'invalid route'):
-        application.TensorBoardWSGIApp(
-            temp_dir, plugins, multiplexer, reload_interval=0)
-
-  def testNormalRoute(self):
-    self._test('/runs', True)
-
-  def testEmptyRoute(self):
-    self._test('', False)
-
-  def testSlashlessRoute(self):
-    self._test('runaway', False)
-
-
-class TensorboardServerUsingMetagraphOnlyTest(TensorboardServerTest):
-  # Tests new ability to use only the MetaGraphDef
-  _only_use_meta_graph = True  # Server data contains only a MetaGraphDef
-
-
-class ParseEventFilesSpecTest(tf.test.TestCase):
-
-  def testRunName(self):
-    logdir = 'lol:/cat'
-    expected = {'/cat': 'lol'}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testPathWithColonThatComesAfterASlash_isNotConsideredARunName(self):
-    logdir = '/lol:/cat'
-    expected = {'/lol:/cat': None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testMultipleDirectories(self):
-    logdir = '/a,/b'
-    expected = {'/a': None, '/b': None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testNormalizesPaths(self):
-    logdir = '/lol/.//cat/../cat'
-    expected = {'/lol/cat': None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testAbsolutifies(self):
-    logdir = 'lol/cat'
-    expected = {os.path.realpath('lol/cat'): None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testRespectsGCSPath(self):
-    logdir = 'gs://foo/path'
-    expected = {'gs://foo/path': None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testRespectsHDFSPath(self):
-    logdir = 'hdfs://foo/path'
-    expected = {'hdfs://foo/path': None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testDoesNotExpandUserInGCSPath(self):
-    logdir = 'gs://~/foo/path'
-    expected = {'gs://~/foo/path': None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testDoesNotNormalizeGCSPath(self):
-    logdir = 'gs://foo/./path//..'
-    expected = {'gs://foo/./path//..': None}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-  def testRunNameWithGCSPath(self):
-    logdir = 'lol:gs://foo/path'
-    expected = {'gs://foo/path': 'lol'}
-    self.assertEqual(application.parse_event_files_spec(logdir), expected)
-
-
-class TensorBoardAssetsTest(tf.test.TestCase):
-
-  def testTagFound(self):
-    tag = application.get_tensorboard_tag()
-    self.assertTrue(tag)
-    app = application.standard_tensorboard_wsgi('', True, 60, [])
-    self.assertEqual(app.tag, tag)
-
-
-class TensorBoardPluginsTest(tf.test.TestCase):
-
-  def testPluginsAdded(self):
-
-    def foo_handler():
-      pass
-
-    def bar_handler():
-      pass
-
-    plugins = [
-        functools.partial(
-            FakePlugin,
-            plugin_name='foo',
-            is_active_value=True,
-            routes_mapping={'/foo_route': foo_handler}),
-        functools.partial(
-            FakePlugin,
-            plugin_name='bar',
-            is_active_value=True,
-            routes_mapping={'/bar_route': bar_handler}),
-    ]
-
-    # The application should have added routes for both plugins.
-    app = application.standard_tensorboard_wsgi('', True, 60, plugins)
-
-    # The routes are prefixed with /data/plugin/[plugin name].
-    self.assertDictContainsSubset({
-        '/data/plugin/foo/foo_route': foo_handler,
-        '/data/plugin/bar/bar_route': bar_handler,
-    }, app.data_applications)
-
-
-class TensorboardSimpleServerConstructionTest(tf.test.TestCase):
-  """Tests that the default HTTP server is constructed without error.
-
-  Mostly useful for IPv4/IPv6 testing. This test should run with only IPv4, only
-  IPv6, and both IPv4 and IPv6 enabled.
-  """
-
-  class _StubApplication(object):
-    tag = ''
-
-  def testMakeServerBlankHost(self):
-    # Test that we can bind to all interfaces without throwing an error
-    server, url = tensorboard.make_simple_server(
-        self._StubApplication(),
-        host='',
-        port=0)  # Grab any available port
-    self.assertTrue(server)
-    self.assertTrue(url)
-
-  def testSpecifiedHost(self):
-    one_passed = False
-    try:
-      _, url = tensorboard.make_simple_server(
-          self._StubApplication(),
-          host='127.0.0.1',
-          port=0)
-      self.assertStartsWith(actual=url, expected_start='http://127.0.0.1:')
-      one_passed = True
-    except socket.error:
-      # IPv4 is not supported
-      pass
-    try:
-      _, url = tensorboard.make_simple_server(
-          self._StubApplication(),
-          host='::1',
-          port=0)
-      self.assertStartsWith(actual=url, expected_start='http://[::1]:')
-      one_passed = True
-    except socket.error:
-      # IPv6 is not supported
-      pass
-    self.assertTrue(one_passed)  # We expect either IPv4 or IPv6 to be supported
-
-
-class TensorBoardApplcationConstructionTest(tf.test.TestCase):
-
-  def testExceptions(self):
-    logdir = '/fake/foo'
-    multiplexer = event_multiplexer.EventMultiplexer()
-
-    # Fails if there is an unnamed plugin
-    with self.assertRaises(ValueError):
-      # This plugin lacks a name.
-      plugins = [
-          FakePlugin(
-              None, plugin_name=None, is_active_value=True, routes_mapping={}),
-      ]
-      application.TensorBoardWSGIApp(logdir, plugins, multiplexer, 0)
-
-    # Fails if there are two plugins with same name
-    with self.assertRaises(ValueError):
-      plugins = [
-          FakePlugin(
-              None, plugin_name='foo', is_active_value=True, routes_mapping={}),
-          FakePlugin(
-              None, plugin_name='foo', is_active_value=True, routes_mapping={}),
-      ]
-      application.TensorBoardWSGIApp(logdir, plugins, multiplexer, 0)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/event_processing/BUILD b/tensorflow/tensorboard/backend/event_processing/BUILD
deleted file mode 100644
index c560143897..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/BUILD
+++ /dev/null
@@ -1,167 +0,0 @@
-# Description:
-# Event processing logic for TensorBoard
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-py_library(
-    name = "io_wrapper",
-    srcs = ["io_wrapper.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_library(
-    name = "directory_watcher",
-    srcs = ["directory_watcher.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":io_wrapper",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_test(
-    name = "directory_watcher_test",
-    size = "small",
-    srcs = ["directory_watcher_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":directory_watcher",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_library(
-    name = "reservoir",
-    srcs = ["reservoir.py"],
-    srcs_version = "PY2AND3",
-)
-
-py_test(
-    name = "reservoir_test",
-    size = "small",
-    srcs = ["reservoir_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":reservoir",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_library(
-    name = "event_file_loader",
-    srcs = ["event_file_loader.py"],
-    srcs_version = "PY2AND3",
-    deps = ["//tensorflow:tensorflow_py"],
-)
-
-py_test(
-    name = "event_file_loader_test",
-    size = "small",
-    srcs = ["event_file_loader_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":event_file_loader",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_library(
-    name = "event_accumulator",
-    srcs = ["event_accumulator.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":directory_watcher",
-        ":event_file_loader",
-        ":plugin_asset_util",
-        ":reservoir",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/plugins/distributions:compressor",
-    ],
-)
-
-py_test(
-    name = "event_accumulator_test",
-    size = "small",
-    srcs = ["event_accumulator_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":event_accumulator",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/plugins/distributions:compressor",
-    ],
-)
-
-py_library(
-    name = "event_multiplexer",
-    srcs = ["event_multiplexer.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":directory_watcher",
-        ":event_accumulator",
-        ":io_wrapper",
-        "//tensorflow:tensorflow_py",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "event_multiplexer_test",
-    size = "small",
-    srcs = ["event_multiplexer_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":event_accumulator",
-        ":event_multiplexer",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_library(
-    name = "plugin_asset_util",
-    srcs = ["plugin_asset_util.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_library(
-    name = "event_file_inspector",
-    srcs = ["event_file_inspector.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":event_accumulator",
-        ":event_multiplexer",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-py_test(
-    name = "event_file_inspector_test",
-    size = "small",
-    srcs = ["event_file_inspector_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":event_file_inspector",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(
-        [
-            "*",
-        ],
-    ),
-    visibility = ["//tensorflow:__subpackages__"],
-)
diff --git a/tensorflow/tensorboard/backend/event_processing/directory_watcher.py b/tensorflow/tensorboard/backend/event_processing/directory_watcher.py
deleted file mode 100644
index 6be3049e90..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/directory_watcher.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Contains the implementation for the DirectoryWatcher class."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import bisect
-
-import tensorflow as tf
-
-
-from tensorflow.tensorboard.backend.event_processing import io_wrapper
-
-
-class DirectoryWatcher(object):
-  """A DirectoryWatcher wraps a loader to load from a sequence of paths.
-
-  A loader reads a path and produces some kind of values as an iterator. A
-  DirectoryWatcher takes a directory, a factory for loaders, and optionally a
-  path filter and watches all the paths inside that directory.
-
-  This class is only valid under the assumption that only one path will be
-  written to by the data source at a time and that once the source stops writing
-  to a path, it will start writing to a new path that's lexicographically
-  greater and never come back. It uses some heuristics to check whether this is
-  true based on tracking changes to the files' sizes, but the check can have
-  false negatives. However, it should have no false positives.
-  """
-
-  def __init__(self, directory, loader_factory, path_filter=lambda x: True):
-    """Constructs a new DirectoryWatcher.
-
-    Args:
-      directory: The directory to load files from.
-      loader_factory: A factory for creating loaders. The factory should take a
-        path and return an object that has a Load method returning an
-        iterator that will yield all events that have not been yielded yet.
-      path_filter: If specified, only paths matching this filter are loaded.
-
-    Raises:
-      ValueError: If path_provider or loader_factory are None.
-    """
-    if directory is None:
-      raise ValueError('A directory is required')
-    if loader_factory is None:
-      raise ValueError('A loader factory is required')
-    self._directory = directory
-    self._path = None
-    self._loader_factory = loader_factory
-    self._loader = None
-    self._path_filter = path_filter
-    self._ooo_writes_detected = False
-    # The file size for each file at the time it was finalized.
-    self._finalized_sizes = {}
-
-  def Load(self):
-    """Loads new values.
-
-    The watcher will load from one path at a time; as soon as that path stops
-    yielding events, it will move on to the next path. We assume that old paths
-    are never modified after a newer path has been written. As a result, Load()
-    can be called multiple times in a row without losing events that have not
-    been yielded yet. In other words, we guarantee that every event will be
-    yielded exactly once.
-
-    Yields:
-      All values that have not been yielded yet.
-
-    Raises:
-      DirectoryDeletedError: If the directory has been permanently deleted
-        (as opposed to being temporarily unavailable).
-    """
-    try:
-      for event in self._LoadInternal():
-        yield event
-    except tf.errors.OpError:
-      if not tf.gfile.Exists(self._directory):
-        raise DirectoryDeletedError(
-            'Directory %s has been permanently deleted' % self._directory)
-
-  def _LoadInternal(self):
-    """Internal implementation of Load().
-
-    The only difference between this and Load() is that the latter will throw
-    DirectoryDeletedError on I/O errors if it thinks that the directory has been
-    permanently deleted.
-
-    Yields:
-      All values that have not been yielded yet.
-    """
-
-    # If the loader exists, check it for a value.
-    if not self._loader:
-      self._InitializeLoader()
-
-    while True:
-      # Yield all the new events in the path we're currently loading from.
-      for event in self._loader.Load():
-        yield event
-
-      next_path = self._GetNextPath()
-      if not next_path:
-        tf.logging.info('No path found after %s', self._path)
-        # Current path is empty and there are no new paths, so we're done.
-        return
-
-      # There's a new path, so check to make sure there weren't any events
-      # written between when we finished reading the current path and when we
-      # checked for the new one. The sequence of events might look something
-      # like this:
-      #
-      # 1. Event #1 written to path #1.
-      # 2. We check for events and yield event #1 from path #1
-      # 3. We check for events and see that there are no more events in path #1.
-      # 4. Event #2 is written to path #1.
-      # 5. Event #3 is written to path #2.
-      # 6. We check for a new path and see that path #2 exists.
-      #
-      # Without this loop, we would miss event #2. We're also guaranteed by the
-      # loader contract that no more events will be written to path #1 after
-      # events start being written to path #2, so we don't have to worry about
-      # that.
-      for event in self._loader.Load():
-        yield event
-
-      tf.logging.info('Directory watcher advancing from %s to %s', self._path,
-                      next_path)
-
-      # Advance to the next path and start over.
-      self._SetPath(next_path)
-
-  # The number of paths before the current one to check for out of order writes.
-  _OOO_WRITE_CHECK_COUNT = 20
-
-  def OutOfOrderWritesDetected(self):
-    """Returns whether any out-of-order writes have been detected.
-
-    Out-of-order writes are only checked as part of the Load() iterator. Once an
-    out-of-order write is detected, this function will always return true.
-
-    Note that out-of-order write detection is not performed on GCS paths, so
-    this function will always return false.
-
-    Returns:
-      Whether any out-of-order write has ever been detected by this watcher.
-
-    """
-    return self._ooo_writes_detected
-
-  def _InitializeLoader(self):
-    path = self._GetNextPath()
-    if path:
-      self._SetPath(path)
-    else:
-      raise StopIteration
-
-  def _SetPath(self, path):
-    """Sets the current path to watch for new events.
-
-    This also records the size of the old path, if any. If the size can't be
-    found, an error is logged.
-
-    Args:
-      path: The full path of the file to watch.
-    """
-    old_path = self._path
-    if old_path and not io_wrapper.IsGCSPath(old_path):
-      try:
-        # We're done with the path, so store its size.
-        size = tf.gfile.Stat(old_path).length
-        tf.logging.debug('Setting latest size of %s to %d', old_path, size)
-        self._finalized_sizes[old_path] = size
-      except tf.errors.OpError as e:
-        tf.logging.error('Unable to get size of %s: %s', old_path, e)
-
-    self._path = path
-    self._loader = self._loader_factory(path)
-
-  def _GetNextPath(self):
-    """Gets the next path to load from.
-
-    This function also does the checking for out-of-order writes as it iterates
-    through the paths.
-
-    Returns:
-      The next path to load events from, or None if there are no more paths.
-    """
-    paths = sorted(path
-                   for path in io_wrapper.ListDirectoryAbsolute(self._directory)
-                   if self._path_filter(path))
-    if not paths:
-      return None
-
-    if self._path is None:
-      return paths[0]
-
-    # Don't bother checking if the paths are GCS (which we can't check) or if
-    # we've already detected an OOO write.
-    if not io_wrapper.IsGCSPath(paths[0]) and not self._ooo_writes_detected:
-      # Check the previous _OOO_WRITE_CHECK_COUNT paths for out of order writes.
-      current_path_index = bisect.bisect_left(paths, self._path)
-      ooo_check_start = max(0, current_path_index - self._OOO_WRITE_CHECK_COUNT)
-      for path in paths[ooo_check_start:current_path_index]:
-        if self._HasOOOWrite(path):
-          self._ooo_writes_detected = True
-          break
-
-    next_paths = list(path
-                      for path in paths
-                      if self._path is None or path > self._path)
-    if next_paths:
-      return min(next_paths)
-    else:
-      return None
-
-  def _HasOOOWrite(self, path):
-    """Returns whether the path has had an out-of-order write."""
-    # Check the sizes of each path before the current one.
-    size = tf.gfile.Stat(path).length
-    old_size = self._finalized_sizes.get(path, None)
-    if size != old_size:
-      if old_size is None:
-        tf.logging.error('File %s created after file %s even though it\'s '
-                         'lexicographically earlier', path, self._path)
-      else:
-        tf.logging.error('File %s updated even though the current file is %s',
-                         path, self._path)
-      return True
-    else:
-      return False
-
-
-class DirectoryDeletedError(Exception):
-  """Thrown by Load() when the directory is *permanently* gone.
-
-  We distinguish this from temporary errors so that other code can decide to
-  drop all of our data only when a directory has been intentionally deleted,
-  as opposed to due to transient filesystem errors.
-  """
-  pass
diff --git a/tensorflow/tensorboard/backend/event_processing/directory_watcher_test.py b/tensorflow/tensorboard/backend/event_processing/directory_watcher_test.py
deleted file mode 100644
index d44f74a8a4..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/directory_watcher_test.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Tests for directory_watcher."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import shutil
-
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import directory_watcher
-from tensorflow.tensorboard.backend.event_processing import io_wrapper
-
-
-class _ByteLoader(object):
-  """A loader that loads individual bytes from a file."""
-
-  def __init__(self, path):
-    self._f = open(path)
-    self.bytes_read = 0
-
-  def Load(self):
-    while True:
-      self._f.seek(self.bytes_read)
-      byte = self._f.read(1)
-      if byte:
-        self.bytes_read += 1
-        yield byte
-      else:
-        return
-
-
-class DirectoryWatcherTest(tf.test.TestCase):
-
-  def setUp(self):
-    # Put everything in a directory so it's easier to delete.
-    self._directory = os.path.join(self.get_temp_dir(), 'monitor_dir')
-    os.mkdir(self._directory)
-    self._watcher = directory_watcher.DirectoryWatcher(self._directory,
-                                                       _ByteLoader)
-    self.stubs = tf.test.StubOutForTesting()
-
-  def tearDown(self):
-    self.stubs.CleanUp()
-    try:
-      shutil.rmtree(self._directory)
-    except OSError:
-      # Some tests delete the directory.
-      pass
-
-  def _WriteToFile(self, filename, data):
-    path = os.path.join(self._directory, filename)
-    with open(path, 'a') as f:
-      f.write(data)
-
-  def _LoadAllEvents(self):
-    """Loads all events in the watcher."""
-    for _ in self._watcher.Load():
-      pass
-
-  def assertWatcherYields(self, values):
-    self.assertEqual(list(self._watcher.Load()), values)
-
-  def testRaisesWithBadArguments(self):
-    with self.assertRaises(ValueError):
-      directory_watcher.DirectoryWatcher(None, lambda x: None)
-    with self.assertRaises(ValueError):
-      directory_watcher.DirectoryWatcher('dir', None)
-
-  def testEmptyDirectory(self):
-    self.assertWatcherYields([])
-
-  def testSingleWrite(self):
-    self._WriteToFile('a', 'abc')
-    self.assertWatcherYields(['a', 'b', 'c'])
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testMultipleWrites(self):
-    self._WriteToFile('a', 'abc')
-    self.assertWatcherYields(['a', 'b', 'c'])
-    self._WriteToFile('a', 'xyz')
-    self.assertWatcherYields(['x', 'y', 'z'])
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testMultipleLoads(self):
-    self._WriteToFile('a', 'a')
-    self._watcher.Load()
-    self._watcher.Load()
-    self.assertWatcherYields(['a'])
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testMultipleFilesAtOnce(self):
-    self._WriteToFile('b', 'b')
-    self._WriteToFile('a', 'a')
-    self.assertWatcherYields(['a', 'b'])
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testFinishesLoadingFileWhenSwitchingToNewFile(self):
-    self._WriteToFile('a', 'a')
-    # Empty the iterator.
-    self.assertEquals(['a'], list(self._watcher.Load()))
-    self._WriteToFile('a', 'b')
-    self._WriteToFile('b', 'c')
-    # The watcher should finish its current file before starting a new one.
-    self.assertWatcherYields(['b', 'c'])
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testIntermediateEmptyFiles(self):
-    self._WriteToFile('a', 'a')
-    self._WriteToFile('b', '')
-    self._WriteToFile('c', 'c')
-    self.assertWatcherYields(['a', 'c'])
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testPathFilter(self):
-    self._watcher = directory_watcher.DirectoryWatcher(
-        self._directory, _ByteLoader,
-        lambda path: 'do_not_watch_me' not in path)
-
-    self._WriteToFile('a', 'a')
-    self._WriteToFile('do_not_watch_me', 'b')
-    self._WriteToFile('c', 'c')
-    self.assertWatcherYields(['a', 'c'])
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testDetectsNewOldFiles(self):
-    self._WriteToFile('b', 'a')
-    self._LoadAllEvents()
-    self._WriteToFile('a', 'a')
-    self._LoadAllEvents()
-    self.assertTrue(self._watcher.OutOfOrderWritesDetected())
-
-  def testIgnoresNewerFiles(self):
-    self._WriteToFile('a', 'a')
-    self._LoadAllEvents()
-    self._WriteToFile('q', 'a')
-    self._LoadAllEvents()
-    self.assertFalse(self._watcher.OutOfOrderWritesDetected())
-
-  def testDetectsChangingOldFiles(self):
-    self._WriteToFile('a', 'a')
-    self._WriteToFile('b', 'a')
-    self._LoadAllEvents()
-    self._WriteToFile('a', 'c')
-    self._LoadAllEvents()
-    self.assertTrue(self._watcher.OutOfOrderWritesDetected())
-
-  def testDoesntCrashWhenFileIsDeleted(self):
-    self._WriteToFile('a', 'a')
-    self._LoadAllEvents()
-    os.remove(os.path.join(self._directory, 'a'))
-    self._WriteToFile('b', 'b')
-    self.assertWatcherYields(['b'])
-
-  def testRaisesRightErrorWhenDirectoryIsDeleted(self):
-    self._WriteToFile('a', 'a')
-    self._LoadAllEvents()
-    shutil.rmtree(self._directory)
-    with self.assertRaises(directory_watcher.DirectoryDeletedError):
-      self._LoadAllEvents()
-
-  def testDoesntRaiseDirectoryDeletedErrorIfOutageIsTransient(self):
-    self._WriteToFile('a', 'a')
-    self._LoadAllEvents()
-    shutil.rmtree(self._directory)
-
-    # Fake a single transient I/O error.
-    def FakeFactory(original):
-
-      def Fake(*args, **kwargs):
-        if FakeFactory.has_been_called:
-          original(*args, **kwargs)
-        else:
-          raise OSError('lp0 temporarily on fire')
-
-      return Fake
-
-    FakeFactory.has_been_called = False
-
-    for stub_name in ['ListDirectoryAbsolute', 'ListRecursively']:
-      self.stubs.Set(io_wrapper, stub_name,
-                     FakeFactory(getattr(io_wrapper, stub_name)))
-    for stub_name in ['IsDirectory', 'Exists', 'Stat']:
-      self.stubs.Set(tf.gfile, stub_name,
-                     FakeFactory(getattr(tf.gfile, stub_name)))
-
-    with self.assertRaises((IOError, OSError)):
-      self._LoadAllEvents()
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/event_processing/event_accumulator.py b/tensorflow/tensorboard/backend/event_processing/event_accumulator.py
deleted file mode 100644
index 59b925cccd..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_accumulator.py
+++ /dev/null
@@ -1,797 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Takes a generator of values, and accumulates them for a frontend."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import os
-import re
-import threading
-
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import directory_watcher
-from tensorflow.tensorboard.backend.event_processing import event_file_loader
-from tensorflow.tensorboard.backend.event_processing import plugin_asset_util
-from tensorflow.tensorboard.backend.event_processing import reservoir
-from tensorflow.tensorboard.plugins.distributions import compressor
-
-namedtuple = collections.namedtuple
-ScalarEvent = namedtuple('ScalarEvent', ['wall_time', 'step', 'value'])
-
-HealthPillEvent = namedtuple('HealthPillEvent', [
-    'wall_time', 'step', 'device_name', 'node_name', 'output_slot', 'dtype',
-    'shape', 'value'])
-
-CompressedHistogramEvent = namedtuple('CompressedHistogramEvent',
-                                      ['wall_time', 'step',
-                                       'compressed_histogram_values'])
-
-HistogramEvent = namedtuple('HistogramEvent',
-                            ['wall_time', 'step', 'histogram_value'])
-
-HistogramValue = namedtuple('HistogramValue', ['min', 'max', 'num', 'sum',
-                                               'sum_squares', 'bucket_limit',
-                                               'bucket'])
-
-ImageEvent = namedtuple('ImageEvent', ['wall_time', 'step',
-                                       'encoded_image_string', 'width',
-                                       'height'])
-
-AudioEvent = namedtuple('AudioEvent', ['wall_time', 'step',
-                                       'encoded_audio_string', 'content_type',
-                                       'sample_rate', 'length_frames'])
-
-TensorEvent = namedtuple('TensorEvent', ['wall_time', 'step', 'tensor_proto'])
-
-## Different types of summary events handled by the event_accumulator
-SUMMARY_TYPES = {
-    'simple_value': '_ProcessScalar',
-    'histo': '_ProcessHistogram',
-    'image': '_ProcessImage',
-    'audio': '_ProcessAudio',
-    'tensor': '_ProcessTensor',
-}
-
-## The tagTypes below are just arbitrary strings chosen to pass the type
-## information of the tag from the backend to the frontend
-COMPRESSED_HISTOGRAMS = 'distributions'
-HISTOGRAMS = 'histograms'
-IMAGES = 'images'
-AUDIO = 'audio'
-SCALARS = 'scalars'
-TENSORS = 'tensors'
-HEALTH_PILLS = 'health_pills'
-GRAPH = 'graph'
-META_GRAPH = 'meta_graph'
-RUN_METADATA = 'run_metadata'
-
-## Normal CDF for std_devs: (-Inf, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, Inf)
-## naturally gives bands around median of width 1 std dev, 2 std dev, 3 std dev,
-## and then the long tail.
-NORMAL_HISTOGRAM_BPS = (0, 668, 1587, 3085, 5000, 6915, 8413, 9332, 10000)
-
-DEFAULT_SIZE_GUIDANCE = {
-    COMPRESSED_HISTOGRAMS: 500,
-    IMAGES: 4,
-    AUDIO: 4,
-    SCALARS: 10000,
-    # We store this many health pills per op.
-    HEALTH_PILLS: 100,
-    HISTOGRAMS: 1,
-    TENSORS: 10,
-}
-
-STORE_EVERYTHING_SIZE_GUIDANCE = {
-    COMPRESSED_HISTOGRAMS: 0,
-    IMAGES: 0,
-    AUDIO: 0,
-    SCALARS: 0,
-    HEALTH_PILLS: 0,
-    HISTOGRAMS: 0,
-    TENSORS: 0,
-}
-
-# The tag that values containing health pills have. Health pill data is stored
-# in tensors. In order to distinguish health pill values from scalar values, we
-# rely on how health pill values have this special tag value.
-HEALTH_PILL_EVENT_TAG_PREFIX = '__health_pill__/'
-
-
-def IsTensorFlowEventsFile(path):
-  """Check the path name to see if it is probably a TF Events file.
-
-  Args:
-    path: A file path to check if it is an event file.
-
-  Raises:
-    ValueError: If the path is an empty string.
-
-  Returns:
-    If path is formatted like a TensorFlowEventsFile.
-  """
-  if not path:
-    raise ValueError('Path must be a nonempty string')
-  return 'tfevents' in tf.compat.as_str_any(os.path.basename(path))
-
-
-class EventAccumulator(object):
-  """An `EventAccumulator` takes an event generator, and accumulates the values.
-
-  The `EventAccumulator` is intended to provide a convenient Python interface
-  for loading Event data written during a TensorFlow run. TensorFlow writes out
-  `Event` protobuf objects, which have a timestamp and step number, and often
-  contain a `Summary`. Summaries can have different kinds of data like an image,
-  a scalar value, or a histogram. The Summaries also have a tag, which we use to
-  organize logically related data. The `EventAccumulator` supports retrieving
-  the `Event` and `Summary` data by its tag.
-
-  Calling `Tags()` gets a map from `tagType` (e.g. `'images'`,
-  `'compressedHistograms'`, `'scalars'`, etc) to the associated tags for those
-  data types. Then, various functional endpoints (eg
-  `Accumulator.Scalars(tag)`) allow for the retrieval of all data
-  associated with that tag.
-
-  The `Reload()` method synchronously loads all of the data written so far.
-
-  Histograms, audio, and images are very large, so storing all of them is not
-  recommended.
-  @@Tensors
-  """
-
-  def __init__(self,
-               path,
-               size_guidance=DEFAULT_SIZE_GUIDANCE,
-               compression_bps=NORMAL_HISTOGRAM_BPS,
-               purge_orphaned_data=True):
-    """Construct the `EventAccumulator`.
-
-    Args:
-      path: A file path to a directory containing tf events files, or a single
-        tf events file. The accumulator will load events from this path.
-      size_guidance: Information on how much data the EventAccumulator should
-        store in memory. The DEFAULT_SIZE_GUIDANCE tries not to store too much
-        so as to avoid OOMing the client. The size_guidance should be a map
-        from a `tagType` string to an integer representing the number of
-        items to keep per tag for items of that `tagType`. If the size is 0,
-        all events are stored.
-      compression_bps: Information on how the `EventAccumulator` should compress
-        histogram data for the `CompressedHistograms` tag (for details see
-        `ProcessCompressedHistogram`).
-      purge_orphaned_data: Whether to discard any events that were "orphaned" by
-        a TensorFlow restart.
-    """
-    sizes = {}
-    for key in DEFAULT_SIZE_GUIDANCE:
-      if key in size_guidance:
-        sizes[key] = size_guidance[key]
-      else:
-        sizes[key] = DEFAULT_SIZE_GUIDANCE[key]
-
-    self._first_event_timestamp = None
-    self._scalars = reservoir.Reservoir(size=sizes[SCALARS])
-
-    # Unlike the other reservoir, the reservoir for health pills is keyed by the
-    # name of the op instead of the tag. This lets us efficiently obtain the
-    # health pills per node.
-    self._health_pills = reservoir.Reservoir(size=sizes[HEALTH_PILLS])
-
-    self._graph = None
-    self._graph_from_metagraph = False
-    self._meta_graph = None
-    self._tagged_metadata = {}
-    self._histograms = reservoir.Reservoir(size=sizes[HISTOGRAMS])
-    self._compressed_histograms = reservoir.Reservoir(
-        size=sizes[COMPRESSED_HISTOGRAMS], always_keep_last=False)
-    self._images = reservoir.Reservoir(size=sizes[IMAGES])
-    self._audio = reservoir.Reservoir(size=sizes[AUDIO])
-    self._tensors = reservoir.Reservoir(size=sizes[TENSORS])
-
-    self._generator_mutex = threading.Lock()
-    self.path = path
-    self._generator = _GeneratorFromPath(path)
-
-    self._compression_bps = compression_bps
-    self.purge_orphaned_data = purge_orphaned_data
-
-    self.most_recent_step = -1
-    self.most_recent_wall_time = -1
-    self.file_version = None
-
-    # The attributes that get built up by the accumulator
-    self.accumulated_attrs = ('_scalars', '_histograms',
-                              '_compressed_histograms', '_images', '_audio')
-    self._tensor_summaries = {}
-
-  def Reload(self):
-    """Loads all events added since the last call to `Reload`.
-
-    If `Reload` was never called, loads all events in the file.
-
-    Returns:
-      The `EventAccumulator`.
-    """
-    with self._generator_mutex:
-      for event in self._generator.Load():
-        self._ProcessEvent(event)
-    return self
-
-  def PluginAssets(self, plugin_name):
-    """Return a list of all plugin assets for the given plugin.
-
-    Args:
-      plugin_name: The string name of a plugin to retrieve assets for.
-
-    Returns:
-      A list of string plugin asset names, or empty list if none are available.
-      If the plugin was not registered, an empty list is returned.
-    """
-    return plugin_asset_util.ListAssets(self.path, plugin_name)
-
-  def RetrievePluginAsset(self, plugin_name, asset_name):
-    """Return the contents of a given plugin asset.
-
-    Args:
-      plugin_name: The string name of a plugin.
-      asset_name: The string name of an asset.
-
-    Returns:
-      The string contents of the plugin asset.
-
-    Raises:
-      KeyError: If the asset is not available.
-    """
-    return plugin_asset_util.RetrieveAsset(self.path, plugin_name, asset_name)
-
-  def FirstEventTimestamp(self):
-    """Returns the timestamp in seconds of the first event.
-
-    If the first event has been loaded (either by this method or by `Reload`,
-    this returns immediately. Otherwise, it will load in the first event. Note
-    that this means that calling `Reload` will cause this to block until
-    `Reload` has finished.
-
-    Returns:
-      The timestamp in seconds of the first event that was loaded.
-
-    Raises:
-      ValueError: If no events have been loaded and there were no events found
-      on disk.
-    """
-    if self._first_event_timestamp is not None:
-      return self._first_event_timestamp
-    with self._generator_mutex:
-      try:
-        event = next(self._generator.Load())
-        self._ProcessEvent(event)
-        return self._first_event_timestamp
-
-      except StopIteration:
-        raise ValueError('No event timestamp could be found')
-
-  def _ProcessEvent(self, event):
-    """Called whenever an event is loaded."""
-    if self._first_event_timestamp is None:
-      self._first_event_timestamp = event.wall_time
-
-    if event.HasField('file_version'):
-      new_file_version = _ParseFileVersion(event.file_version)
-      if self.file_version and self.file_version != new_file_version:
-        ## This should not happen.
-        tf.logging.warn(('Found new file_version for event.proto. This will '
-                         'affect purging logic for TensorFlow restarts. '
-                         'Old: {0} New: {1}').format(self.file_version,
-                                                     new_file_version))
-      self.file_version = new_file_version
-
-    self._MaybePurgeOrphanedData(event)
-
-    ## Process the event.
-    # GraphDef and MetaGraphDef are handled in a special way:
-    # If no graph_def Event is available, but a meta_graph_def is, and it
-    # contains a graph_def, then use the meta_graph_def.graph_def as our graph.
-    # If a graph_def Event is available, always prefer it to the graph_def
-    # inside the meta_graph_def.
-    if event.HasField('graph_def'):
-      if self._graph is not None:
-        tf.logging.warn(
-            ('Found more than one graph event per run, or there was '
-             'a metagraph containing a graph_def, as well as one or '
-             'more graph events.  Overwriting the graph with the '
-             'newest event.'))
-      self._graph = event.graph_def
-      self._graph_from_metagraph = False
-    elif event.HasField('meta_graph_def'):
-      if self._meta_graph is not None:
-        tf.logging.warn(('Found more than one metagraph event per run. '
-                         'Overwriting the metagraph with the newest event.'))
-      self._meta_graph = event.meta_graph_def
-      if self._graph is None or self._graph_from_metagraph:
-        # We may have a graph_def in the metagraph.  If so, and no
-        # graph_def is directly available, use this one instead.
-        meta_graph = tf.MetaGraphDef()
-        meta_graph.ParseFromString(self._meta_graph)
-        if meta_graph.graph_def:
-          if self._graph is not None:
-            tf.logging.warn(
-                ('Found multiple metagraphs containing graph_defs,'
-                 'but did not find any graph events.  Overwriting the '
-                 'graph with the newest metagraph version.'))
-          self._graph_from_metagraph = True
-          self._graph = meta_graph.graph_def.SerializeToString()
-    elif event.HasField('tagged_run_metadata'):
-      tag = event.tagged_run_metadata.tag
-      if tag in self._tagged_metadata:
-        tf.logging.warn('Found more than one "run metadata" event with tag ' +
-                        tag + '. Overwriting it with the newest event.')
-      self._tagged_metadata[tag] = event.tagged_run_metadata.run_metadata
-    elif event.HasField('summary'):
-      for value in event.summary.value:
-        if (value.HasField('tensor') and
-            value.tag.startswith(HEALTH_PILL_EVENT_TAG_PREFIX)):
-          self._ProcessHealthPillSummary(value, event)
-        else:
-          for summary_type, summary_func in SUMMARY_TYPES.items():
-            if value.HasField(summary_type):
-              datum = getattr(value, summary_type)
-              tag = value.node_name if summary_type == 'tensor' else value.tag
-              getattr(self, summary_func)(tag, event.wall_time, event.step,
-                                          datum)
-
-  def _ProcessHealthPillSummary(self, value, event):
-    """Process summaries containing health pills.
-
-    These summaries are distinguished by the fact that they have a Tensor field
-    and have a special tag value.
-
-    This method emits ERROR-level messages to the logs if it encounters Tensor
-    summaries that it cannot process.
-
-    Args:
-      value: A tf.Summary.Value with a Tensor field.
-      event: The tf.Event containing that value.
-    """
-    elements = tf.make_ndarray(value.tensor)
-
-    # The node_name property of the value object is actually a watch key: a
-    # combination of node name, output slot, and a suffix. We capture the
-    # actual node name and the output slot with a regular expression.
-    match = re.match(r'^(.*):(\d+):DebugNumericSummary$', value.node_name)
-    if not match:
-      tf.logging.log_first_n(
-          tf.logging.ERROR,
-          'Unsupported watch key %s for health pills; skipping this sequence.',
-          1, value.node_name)
-      return
-
-    node_name = match.group(1)
-    output_slot = int(match.group(2))
-    device_name = value.tag[len(HEALTH_PILL_EVENT_TAG_PREFIX):]
-    self._ProcessHealthPill(event.wall_time, event.step, device_name, node_name,
-                            output_slot, elements)
-
-  def Tags(self):
-    """Return all tags found in the value stream.
-
-    Returns:
-      A `{tagType: ['list', 'of', 'tags']}` dictionary.
-    """
-    return {
-        IMAGES: self._images.Keys(),
-        AUDIO: self._audio.Keys(),
-        HISTOGRAMS: self._histograms.Keys(),
-        SCALARS: self._scalars.Keys(),
-        COMPRESSED_HISTOGRAMS: self._compressed_histograms.Keys(),
-        TENSORS: self._tensors.Keys(),
-        # Use a heuristic: if the metagraph is available, but
-        # graph is not, then we assume the metagraph contains the graph.
-        GRAPH: self._graph is not None,
-        META_GRAPH: self._meta_graph is not None,
-        RUN_METADATA: list(self._tagged_metadata.keys())
-    }
-
-  def Scalars(self, tag):
-    """Given a summary tag, return all associated `ScalarEvent`s.
-
-    Args:
-      tag: A string tag associated with the events.
-
-    Raises:
-      KeyError: If the tag is not found.
-
-    Returns:
-      An array of `ScalarEvent`s.
-    """
-    return self._scalars.Items(tag)
-
-  def HealthPills(self, node_name):
-    """Returns all health pill values for a certain node.
-
-    Args:
-      node_name: The name of the node to obtain health pills for.
-
-    Raises:
-      KeyError: If the node name is not found.
-
-    Returns:
-      An array of `HealthPillEvent`s.
-    """
-    return self._health_pills.Items(node_name)
-
-  def GetOpsWithHealthPills(self):
-    """Determines which ops have at least 1 health pill event.
-
-    Returns:
-      A list of names of ops with at least 1 health pill event.
-    """
-    return self._health_pills.Keys()
-
-  def Graph(self):
-    """Return the graph definition, if there is one.
-
-    If the graph is stored directly, return that.  If no graph is stored
-    directly but a metagraph is stored containing a graph, return that.
-
-    Raises:
-      ValueError: If there is no graph for this run.
-
-    Returns:
-      The `graph_def` proto.
-    """
-    graph = tf.GraphDef()
-    if self._graph is not None:
-      graph.ParseFromString(self._graph)
-      return graph
-    raise ValueError('There is no graph in this EventAccumulator')
-
-  def MetaGraph(self):
-    """Return the metagraph definition, if there is one.
-
-    Raises:
-      ValueError: If there is no metagraph for this run.
-
-    Returns:
-      The `meta_graph_def` proto.
-    """
-    if self._meta_graph is None:
-      raise ValueError('There is no metagraph in this EventAccumulator')
-    meta_graph = tf.MetaGraphDef()
-    meta_graph.ParseFromString(self._meta_graph)
-    return meta_graph
-
-  def RunMetadata(self, tag):
-    """Given a tag, return the associated session.run() metadata.
-
-    Args:
-      tag: A string tag associated with the event.
-
-    Raises:
-      ValueError: If the tag is not found.
-
-    Returns:
-      The metadata in form of `RunMetadata` proto.
-    """
-    if tag not in self._tagged_metadata:
-      raise ValueError('There is no run metadata with this tag name')
-
-    run_metadata = tf.RunMetadata()
-    run_metadata.ParseFromString(self._tagged_metadata[tag])
-    return run_metadata
-
-  def Histograms(self, tag):
-    """Given a summary tag, return all associated histograms.
-
-    Args:
-      tag: A string tag associated with the events.
-
-    Raises:
-      KeyError: If the tag is not found.
-
-    Returns:
-      An array of `HistogramEvent`s.
-    """
-    return self._histograms.Items(tag)
-
-  def CompressedHistograms(self, tag):
-    """Given a summary tag, return all associated compressed histograms.
-
-    Args:
-      tag: A string tag associated with the events.
-
-    Raises:
-      KeyError: If the tag is not found.
-
-    Returns:
-      An array of `CompressedHistogramEvent`s.
-    """
-    return self._compressed_histograms.Items(tag)
-
-  def Images(self, tag):
-    """Given a summary tag, return all associated images.
-
-    Args:
-      tag: A string tag associated with the events.
-
-    Raises:
-      KeyError: If the tag is not found.
-
-    Returns:
-      An array of `ImageEvent`s.
-    """
-    return self._images.Items(tag)
-
-  def Audio(self, tag):
-    """Given a summary tag, return all associated audio.
-
-    Args:
-      tag: A string tag associated with the events.
-
-    Raises:
-      KeyError: If the tag is not found.
-
-    Returns:
-      An array of `AudioEvent`s.
-    """
-    return self._audio.Items(tag)
-
-  def Tensors(self, tag):
-    """Given a summary tag, return all associated tensors.
-
-    Args:
-      tag: A string tag associated with the events.
-
-    Raises:
-      KeyError: If the tag is not found.
-
-    Returns:
-      An array of `TensorEvent`s.
-    """
-    return self._tensors.Items(tag)
-
-  def _MaybePurgeOrphanedData(self, event):
-    """Maybe purge orphaned data due to a TensorFlow crash.
-
-    When TensorFlow crashes at step T+O and restarts at step T, any events
-    written after step T are now "orphaned" and will be at best misleading if
-    they are included in TensorBoard.
-
-    This logic attempts to determine if there is orphaned data, and purge it
-    if it is found.
-
-    Args:
-      event: The event to use as a reference, to determine if a purge is needed.
-    """
-    if not self.purge_orphaned_data:
-      return
-    ## Check if the event happened after a crash, and purge expired tags.
-    if self.file_version and self.file_version >= 2:
-      ## If the file_version is recent enough, use the SessionLog enum
-      ## to check for restarts.
-      self._CheckForRestartAndMaybePurge(event)
-    else:
-      ## If there is no file version, default to old logic of checking for
-      ## out of order steps.
-      self._CheckForOutOfOrderStepAndMaybePurge(event)
-
-  def _CheckForRestartAndMaybePurge(self, event):
-    """Check and discard expired events using SessionLog.START.
-
-    Check for a SessionLog.START event and purge all previously seen events
-    with larger steps, because they are out of date. Because of supervisor
-    threading, it is possible that this logic will cause the first few event
-    messages to be discarded since supervisor threading does not guarantee
-    that the START message is deterministically written first.
-
-    This method is preferred over _CheckForOutOfOrderStepAndMaybePurge which
-    can inadvertently discard events due to supervisor threading.
-
-    Args:
-      event: The event to use as reference. If the event is a START event, all
-        previously seen events with a greater event.step will be purged.
-    """
-    if event.HasField(
-        'session_log') and event.session_log.status == tf.SessionLog.START:
-      self._Purge(event, by_tags=False)
-
-  def _CheckForOutOfOrderStepAndMaybePurge(self, event):
-    """Check for out-of-order event.step and discard expired events for tags.
-
-    Check if the event is out of order relative to the global most recent step.
-    If it is, purge outdated summaries for tags that the event contains.
-
-    Args:
-      event: The event to use as reference. If the event is out-of-order, all
-        events with the same tags, but with a greater event.step will be purged.
-    """
-    if event.step < self.most_recent_step and event.HasField('summary'):
-      self._Purge(event, by_tags=True)
-    else:
-      self.most_recent_step = event.step
-      self.most_recent_wall_time = event.wall_time
-
-  def _ConvertHistogramProtoToTuple(self, histo):
-    return HistogramValue(min=histo.min,
-                          max=histo.max,
-                          num=histo.num,
-                          sum=histo.sum,
-                          sum_squares=histo.sum_squares,
-                          bucket_limit=list(histo.bucket_limit),
-                          bucket=list(histo.bucket))
-
-  def _ProcessHistogram(self, tag, wall_time, step, histo):
-    """Processes a proto histogram by adding it to accumulated state."""
-    histo = self._ConvertHistogramProtoToTuple(histo)
-    histo_ev = HistogramEvent(wall_time, step, histo)
-    self._histograms.AddItem(tag, histo_ev)
-    self._compressed_histograms.AddItem(tag, histo_ev, self._CompressHistogram)
-
-  def _CompressHistogram(self, histo_ev):
-    """Callback for _ProcessHistogram."""
-    return CompressedHistogramEvent(
-        histo_ev.wall_time,
-        histo_ev.step,
-        compressor.CompressHistogram(
-            histo_ev.histogram_value, self._compression_bps))
-
-  def _ProcessImage(self, tag, wall_time, step, image):
-    """Processes an image by adding it to accumulated state."""
-    event = ImageEvent(wall_time=wall_time,
-                       step=step,
-                       encoded_image_string=image.encoded_image_string,
-                       width=image.width,
-                       height=image.height)
-    self._images.AddItem(tag, event)
-
-  def _ProcessAudio(self, tag, wall_time, step, audio):
-    """Processes a audio by adding it to accumulated state."""
-    event = AudioEvent(wall_time=wall_time,
-                       step=step,
-                       encoded_audio_string=audio.encoded_audio_string,
-                       content_type=audio.content_type,
-                       sample_rate=audio.sample_rate,
-                       length_frames=audio.length_frames)
-    self._audio.AddItem(tag, event)
-
-  def _ProcessScalar(self, tag, wall_time, step, scalar):
-    """Processes a simple value by adding it to accumulated state."""
-    sv = ScalarEvent(wall_time=wall_time, step=step, value=scalar)
-    self._scalars.AddItem(tag, sv)
-
-  def _ProcessTensor(self, tag, wall_time, step, tensor):
-    tv = TensorEvent(wall_time=wall_time, step=step, tensor_proto=tensor)
-    self._tensors.AddItem(tag, tv)
-
-  def _ProcessHealthPill(self, wall_time, step, device_name, node_name,
-                         output_slot, elements):
-    """Processes a health pill value by adding it to accumulated state.
-
-    Args:
-      wall_time: The time at which the health pill was created. Provided by the
-        debugger.
-      step: The step at which the health pill was created. Provided by the
-        debugger.
-      device_name: The name of the node's device.
-      node_name: The name of the node for this health pill.
-      output_slot: The output slot for this health pill.
-      elements: An ND array of 20 floats. The elements of the health pill.
-    """
-    # Key by the node name for fast retrieval of health pills by node name. The
-    # array is cast to a list so that it is JSON-able. The debugger data plugin
-    # serves a JSON response.
-    self._health_pills.AddItem(node_name,
-                               HealthPillEvent(
-                                   wall_time=wall_time,
-                                   step=step,
-                                   device_name=device_name,
-                                   node_name=node_name,
-                                   output_slot=output_slot,
-                                   dtype=repr(tf.as_dtype(elements[12])),
-                                   shape=list(elements[14:]),
-                                   value=list(elements)))
-
-  def _Purge(self, event, by_tags):
-    """Purge all events that have occurred after the given event.step.
-
-    If by_tags is True, purge all events that occurred after the given
-    event.step, but only for the tags that the event has. Non-sequential
-    event.steps suggest that a TensorFlow restart occurred, and we discard
-    the out-of-order events to display a consistent view in TensorBoard.
-
-    Discarding by tags is the safer method, when we are unsure whether a restart
-    has occurred, given that threading in supervisor can cause events of
-    different tags to arrive with unsynchronized step values.
-
-    If by_tags is False, then purge all events with event.step greater than the
-    given event.step. This can be used when we are certain that a TensorFlow
-    restart has occurred and these events can be discarded.
-
-    Args:
-      event: The event to use as reference for the purge. All events with
-        the same tags, but with a greater event.step will be purged.
-      by_tags: Bool to dictate whether to discard all out-of-order events or
-        only those that are associated with the given reference event.
-    """
-    ## Keep data in reservoirs that has a step less than event.step
-    _NotExpired = lambda x: x.step < event.step
-
-    if by_tags:
-
-      def _ExpiredPerTag(value):
-        return [getattr(self, x).FilterItems(_NotExpired, value.tag)
-                for x in self.accumulated_attrs]
-
-      expired_per_tags = [_ExpiredPerTag(value)
-                          for value in event.summary.value]
-      expired_per_type = [sum(x) for x in zip(*expired_per_tags)]
-    else:
-      expired_per_type = [getattr(self, x).FilterItems(_NotExpired)
-                          for x in self.accumulated_attrs]
-
-    if sum(expired_per_type) > 0:
-      purge_msg = _GetPurgeMessage(self.most_recent_step,
-                                   self.most_recent_wall_time, event.step,
-                                   event.wall_time, *expired_per_type)
-      tf.logging.warn(purge_msg)
-
-
-def _GetPurgeMessage(most_recent_step, most_recent_wall_time, event_step,
-                     event_wall_time, num_expired_scalars, num_expired_histos,
-                     num_expired_comp_histos, num_expired_images,
-                     num_expired_audio):
-  """Return the string message associated with TensorBoard purges."""
-  return ('Detected out of order event.step likely caused by '
-          'a TensorFlow restart. Purging expired events from Tensorboard'
-          ' display between the previous step: {} (timestamp: {}) and '
-          'current step: {} (timestamp: {}). Removing {} scalars, {} '
-          'histograms, {} compressed histograms, {} images, '
-          'and {} audio.').format(most_recent_step, most_recent_wall_time,
-                                  event_step, event_wall_time,
-                                  num_expired_scalars, num_expired_histos,
-                                  num_expired_comp_histos, num_expired_images,
-                                  num_expired_audio)
-
-
-def _GeneratorFromPath(path):
-  """Create an event generator for file or directory at given path string."""
-  if not path:
-    raise ValueError('path must be a valid string')
-  if IsTensorFlowEventsFile(path):
-    return event_file_loader.EventFileLoader(path)
-  else:
-    return directory_watcher.DirectoryWatcher(
-        path, event_file_loader.EventFileLoader, IsTensorFlowEventsFile)
-
-
-def _ParseFileVersion(file_version):
-  """Convert the string file_version in event.proto into a float.
-
-  Args:
-    file_version: String file_version from event.proto
-
-  Returns:
-    Version number as a float.
-  """
-  tokens = file_version.split('brain.Event:')
-  try:
-    return float(tokens[-1])
-  except ValueError:
-    ## This should never happen according to the definition of file_version
-    ## specified in event.proto.
-    tf.logging.warn(
-        ('Invalid event.proto file_version. Defaulting to use of '
-         'out-of-order event.step logic for purging expired events.'))
-    return -1
diff --git a/tensorflow/tensorboard/backend/event_processing/event_accumulator_test.py b/tensorflow/tensorboard/backend/event_processing/event_accumulator_test.py
deleted file mode 100644
index 516704c700..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_accumulator_test.py
+++ /dev/null
@@ -1,923 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-
-import numpy as np
-import six
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_accumulator as ea
-from tensorflow.tensorboard.plugins.distributions import compressor
-
-
-class _EventGenerator(object):
-  """Class that can add_events and then yield them back.
-
-  Satisfies the EventGenerator API required for the EventAccumulator.
-  Satisfies the EventWriter API required to create a SummaryWriter.
-
-  Has additional convenience methods for adding test events.
-  """
-
-  def __init__(self, testcase, zero_out_timestamps=False):
-    self._testcase = testcase
-    self.items = []
-    self.zero_out_timestamps = zero_out_timestamps
-
-  def Load(self):
-    while self.items:
-      yield self.items.pop(0)
-
-  def AddScalar(self, tag, wall_time=0, step=0, value=0):
-    event = tf.Event(
-        wall_time=wall_time,
-        step=step,
-        summary=tf.Summary(
-            value=[tf.Summary.Value(tag=tag, simple_value=value)]))
-    self.AddEvent(event)
-
-  def AddHealthPill(self, wall_time, step, device_name, op_name, output_slot,
-                    elements):
-    event = tf.Event(step=step, wall_time=wall_time)
-    value = event.summary.value.add(
-        tag=ea.HEALTH_PILL_EVENT_TAG_PREFIX + device_name,
-        node_name='%s:%d:DebugNumericSummary' % (op_name, output_slot))
-    value.tensor.tensor_shape.dim.add(size=len(elements))
-    value.tensor.dtype = 2  # DT_DOUBLE
-    value.tensor.tensor_content = np.array(elements, dtype=np.float64).tobytes()
-    self.AddEvent(event)
-
-  def AddHistogram(self,
-                   tag,
-                   wall_time=0,
-                   step=0,
-                   hmin=1,
-                   hmax=2,
-                   hnum=3,
-                   hsum=4,
-                   hsum_squares=5,
-                   hbucket_limit=None,
-                   hbucket=None):
-    histo = tf.HistogramProto(
-        min=hmin,
-        max=hmax,
-        num=hnum,
-        sum=hsum,
-        sum_squares=hsum_squares,
-        bucket_limit=hbucket_limit,
-        bucket=hbucket)
-    event = tf.Event(
-        wall_time=wall_time,
-        step=step,
-        summary=tf.Summary(value=[tf.Summary.Value(tag=tag, histo=histo)]))
-    self.AddEvent(event)
-
-  def AddImage(self,
-               tag,
-               wall_time=0,
-               step=0,
-               encoded_image_string=b'imgstr',
-               width=150,
-               height=100):
-    image = tf.Summary.Image(
-        encoded_image_string=encoded_image_string, width=width, height=height)
-    event = tf.Event(
-        wall_time=wall_time,
-        step=step,
-        summary=tf.Summary(value=[tf.Summary.Value(tag=tag, image=image)]))
-    self.AddEvent(event)
-
-  def AddAudio(self,
-               tag,
-               wall_time=0,
-               step=0,
-               encoded_audio_string=b'sndstr',
-               content_type='audio/wav',
-               sample_rate=44100,
-               length_frames=22050):
-    audio = tf.Summary.Audio(
-        encoded_audio_string=encoded_audio_string,
-        content_type=content_type,
-        sample_rate=sample_rate,
-        length_frames=length_frames)
-    event = tf.Event(
-        wall_time=wall_time,
-        step=step,
-        summary=tf.Summary(value=[tf.Summary.Value(tag=tag, audio=audio)]))
-    self.AddEvent(event)
-
-  def AddEvent(self, event):
-    if self.zero_out_timestamps:
-      event.wall_time = 0
-    self.items.append(event)
-
-  def add_event(self, event):  # pylint: disable=invalid-name
-    """Match the EventWriter API."""
-    self.AddEvent(event)
-
-  def get_logdir(self):  # pylint: disable=invalid-name
-    """Return a temp directory for asset writing."""
-    return self._testcase.get_temp_dir()
-
-
-class EventAccumulatorTest(tf.test.TestCase):
-
-  def assertTagsEqual(self, actual, expected):
-    """Utility method for checking the return value of the Tags() call.
-
-    It fills out the `expected` arg with the default (empty) values for every
-    tag type, so that the author needs only specify the non-empty values they
-    are interested in testing.
-
-    Args:
-      actual: The actual Accumulator tags response.
-      expected: The expected tags response (empty fields may be omitted)
-    """
-
-    empty_tags = {
-        ea.IMAGES: [],
-        ea.AUDIO: [],
-        ea.SCALARS: [],
-        ea.HISTOGRAMS: [],
-        ea.COMPRESSED_HISTOGRAMS: [],
-        ea.GRAPH: False,
-        ea.META_GRAPH: False,
-        ea.RUN_METADATA: [],
-        ea.TENSORS: [],
-    }
-
-    # Verifies that there are no unexpected keys in the actual response.
-    # If this line fails, likely you added a new tag type, and need to update
-    # the empty_tags dictionary above.
-    self.assertItemsEqual(actual.keys(), empty_tags.keys())
-
-    for key in actual:
-      expected_value = expected.get(key, empty_tags[key])
-      if isinstance(expected_value, list):
-        self.assertItemsEqual(actual[key], expected_value)
-      else:
-        self.assertEqual(actual[key], expected_value)
-
-
-class MockingEventAccumulatorTest(EventAccumulatorTest):
-
-  def setUp(self):
-    super(MockingEventAccumulatorTest, self).setUp()
-    self.stubs = tf.test.StubOutForTesting()
-    self._real_constructor = ea.EventAccumulator
-    self._real_generator = ea._GeneratorFromPath
-
-    def _FakeAccumulatorConstructor(generator, *args, **kwargs):
-      ea._GeneratorFromPath = lambda x: generator
-      return self._real_constructor(generator, *args, **kwargs)
-
-    ea.EventAccumulator = _FakeAccumulatorConstructor
-
-  def tearDown(self):
-    self.stubs.CleanUp()
-    ea.EventAccumulator = self._real_constructor
-    ea._GeneratorFromPath = self._real_generator
-
-  def testEmptyAccumulator(self):
-    gen = _EventGenerator(self)
-    x = ea.EventAccumulator(gen)
-    x.Reload()
-    self.assertTagsEqual(x.Tags(), {})
-
-  def testTags(self):
-    """Tags should be found in EventAccumulator after adding some events."""
-    gen = _EventGenerator(self)
-    gen.AddScalar('s1')
-    gen.AddScalar('s2')
-    gen.AddHistogram('hst1')
-    gen.AddHistogram('hst2')
-    gen.AddImage('im1')
-    gen.AddImage('im2')
-    gen.AddAudio('snd1')
-    gen.AddAudio('snd2')
-    acc = ea.EventAccumulator(gen)
-    acc.Reload()
-    self.assertTagsEqual(acc.Tags(), {
-        ea.IMAGES: ['im1', 'im2'],
-        ea.AUDIO: ['snd1', 'snd2'],
-        ea.SCALARS: ['s1', 's2'],
-        ea.HISTOGRAMS: ['hst1', 'hst2'],
-        ea.COMPRESSED_HISTOGRAMS: ['hst1', 'hst2'],
-    })
-
-  def testReload(self):
-    """EventAccumulator contains suitable tags after calling Reload."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    acc.Reload()
-    self.assertTagsEqual(acc.Tags(), {})
-    gen.AddScalar('s1')
-    gen.AddScalar('s2')
-    gen.AddHistogram('hst1')
-    gen.AddHistogram('hst2')
-    gen.AddImage('im1')
-    gen.AddImage('im2')
-    gen.AddAudio('snd1')
-    gen.AddAudio('snd2')
-    acc.Reload()
-    self.assertTagsEqual(acc.Tags(), {
-        ea.IMAGES: ['im1', 'im2'],
-        ea.AUDIO: ['snd1', 'snd2'],
-        ea.SCALARS: ['s1', 's2'],
-        ea.HISTOGRAMS: ['hst1', 'hst2'],
-        ea.COMPRESSED_HISTOGRAMS: ['hst1', 'hst2'],
-    })
-
-  def testScalars(self):
-    """Tests whether EventAccumulator contains scalars after adding them."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    s1 = ea.ScalarEvent(wall_time=1, step=10, value=32)
-    s2 = ea.ScalarEvent(wall_time=2, step=12, value=64)
-    gen.AddScalar('s1', wall_time=1, step=10, value=32)
-    gen.AddScalar('s2', wall_time=2, step=12, value=64)
-    acc.Reload()
-    self.assertEqual(acc.Scalars('s1'), [s1])
-    self.assertEqual(acc.Scalars('s2'), [s2])
-
-  def _compareHealthPills(self, expected_event, gotten_event):
-    """Compares 2 health pills.
-
-    Args:
-      expected_event: The expected HealthPillEvent.
-      gotten_event: The gotten HealthPillEvent.
-    """
-    self.assertEqual(expected_event.wall_time, gotten_event.wall_time)
-    self.assertEqual(expected_event.step, gotten_event.step)
-    self.assertEqual(expected_event.device_name, gotten_event.device_name)
-    self.assertEqual(expected_event.node_name, gotten_event.node_name)
-    self.assertEqual(expected_event.output_slot, gotten_event.output_slot)
-    self.assertEqual(len(expected_event.value), len(gotten_event.value))
-    for i, expected_value in enumerate(expected_event.value):
-      self.assertEqual(expected_value, gotten_event.value[i])
-
-  def testHealthPills(self):
-    """HealthPills should be properly inserted into EventAccumulator."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    health_pill_elements_1 = list(range(1, 13)) + [
-        float(1), 2.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0]
-    gen.AddHealthPill(13371337, 41, '/job:localhost/replica:0/task:0/cpu:0',
-                      'Add', 0, health_pill_elements_1)
-    health_pill_elements_2 = list(range(42, 54)) + [
-        float(2), 2.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0]
-    gen.AddHealthPill(13381338, 42, '/job:localhost/replica:0/task:0/gpu:0',
-                      'Add', 1, health_pill_elements_2)
-    acc.Reload()
-
-    # Retrieve the health pills for each node name.
-    gotten_events = acc.HealthPills('Add')
-    self.assertEquals(2, len(gotten_events))
-    self._compareHealthPills(
-        ea.HealthPillEvent(
-            wall_time=13371337,
-            step=41,
-            device_name='/job:localhost/replica:0/task:0/cpu:0',
-            node_name='Add',
-            output_slot=0,
-            dtype='tf.float32',
-            shape=[1, 2],
-            value=health_pill_elements_1), gotten_events[0])
-    self._compareHealthPills(
-        ea.HealthPillEvent(
-            wall_time=13381338,
-            device_name='/job:localhost/replica:0/task:0/gpu:0',
-            step=42,
-            node_name='Add',
-            output_slot=1,
-            dtype='tf.float64',
-            shape=[3, 4],
-            value=health_pill_elements_2), gotten_events[1])
-
-  def testGetOpsWithHealthPills(self):
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    health_pill_elements_1 = list(range(1, 13)) + [
-        float(1), 2.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0]
-    gen.AddHealthPill(13371337, 41, '/job:localhost/replica:0/task:0/cpu:0',
-                      'Add', 0, health_pill_elements_1)
-    health_pill_elements_2 = list(range(42, 54)) + [
-        float(2), 2.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0]
-    gen.AddHealthPill(13381338, 42, '/job:localhost/replica:0/task:0/cpu:0',
-                      'MatMul', 1, health_pill_elements_2)
-    acc.Reload()
-    self.assertItemsEqual(['Add', 'MatMul'], acc.GetOpsWithHealthPills())
-
-  def testHistograms(self):
-    """Tests whether histograms are inserted into EventAccumulator."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-
-    val1 = ea.HistogramValue(
-        min=1,
-        max=2,
-        num=3,
-        sum=4,
-        sum_squares=5,
-        bucket_limit=[1, 2, 3],
-        bucket=[0, 3, 0])
-    val2 = ea.HistogramValue(
-        min=-2,
-        max=3,
-        num=4,
-        sum=5,
-        sum_squares=6,
-        bucket_limit=[2, 3, 4],
-        bucket=[1, 3, 0])
-
-    hst1 = ea.HistogramEvent(wall_time=1, step=10, histogram_value=val1)
-    hst2 = ea.HistogramEvent(wall_time=2, step=12, histogram_value=val2)
-    gen.AddHistogram(
-        'hst1',
-        wall_time=1,
-        step=10,
-        hmin=1,
-        hmax=2,
-        hnum=3,
-        hsum=4,
-        hsum_squares=5,
-        hbucket_limit=[1, 2, 3],
-        hbucket=[0, 3, 0])
-    gen.AddHistogram(
-        'hst2',
-        wall_time=2,
-        step=12,
-        hmin=-2,
-        hmax=3,
-        hnum=4,
-        hsum=5,
-        hsum_squares=6,
-        hbucket_limit=[2, 3, 4],
-        hbucket=[1, 3, 0])
-    acc.Reload()
-    self.assertEqual(acc.Histograms('hst1'), [hst1])
-    self.assertEqual(acc.Histograms('hst2'), [hst2])
-
-  def testCompressedHistograms(self):
-    """Tests compressed histograms inserted into EventAccumulator."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen, compression_bps=(0, 2500, 5000, 7500, 10000))
-
-    gen.AddHistogram(
-        'hst1',
-        wall_time=1,
-        step=10,
-        hmin=1,
-        hmax=2,
-        hnum=3,
-        hsum=4,
-        hsum_squares=5,
-        hbucket_limit=[1, 2, 3],
-        hbucket=[0, 3, 0])
-    gen.AddHistogram(
-        'hst2',
-        wall_time=2,
-        step=12,
-        hmin=-2,
-        hmax=3,
-        hnum=4,
-        hsum=5,
-        hsum_squares=6,
-        hbucket_limit=[2, 3, 4],
-        hbucket=[1, 3, 0])
-    acc.Reload()
-
-    # Create the expected values after compressing hst1
-    expected_vals1 = [
-        compressor.CompressedHistogramValue(bp, val)
-        for bp, val in [(0, 1.0), (2500, 1.25), (5000, 1.5), (7500, 1.75
-                                                             ), (10000, 2.0)]
-    ]
-    expected_cmphst1 = ea.CompressedHistogramEvent(
-        wall_time=1, step=10, compressed_histogram_values=expected_vals1)
-    self.assertEqual(acc.CompressedHistograms('hst1'), [expected_cmphst1])
-
-    # Create the expected values after compressing hst2
-    expected_vals2 = [
-        compressor.CompressedHistogramValue(bp, val)
-        for bp, val in [(0, -2),
-                        (2500, 2),
-                        (5000, 2 + 1 / 3),
-                        (7500, 2 + 2 / 3),
-                        (10000, 3)]
-    ]
-    expected_cmphst2 = ea.CompressedHistogramEvent(
-        wall_time=2, step=12, compressed_histogram_values=expected_vals2)
-    self.assertEqual(acc.CompressedHistograms('hst2'), [expected_cmphst2])
-
-  def testImages(self):
-    """Tests 2 images inserted/accessed in EventAccumulator."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    im1 = ea.ImageEvent(
-        wall_time=1,
-        step=10,
-        encoded_image_string=b'big',
-        width=400,
-        height=300)
-    im2 = ea.ImageEvent(
-        wall_time=2,
-        step=12,
-        encoded_image_string=b'small',
-        width=40,
-        height=30)
-    gen.AddImage(
-        'im1',
-        wall_time=1,
-        step=10,
-        encoded_image_string=b'big',
-        width=400,
-        height=300)
-    gen.AddImage(
-        'im2',
-        wall_time=2,
-        step=12,
-        encoded_image_string=b'small',
-        width=40,
-        height=30)
-    acc.Reload()
-    self.assertEqual(acc.Images('im1'), [im1])
-    self.assertEqual(acc.Images('im2'), [im2])
-
-  def testAudio(self):
-    """Tests 2 audio events inserted/accessed in EventAccumulator."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    snd1 = ea.AudioEvent(
-        wall_time=1,
-        step=10,
-        encoded_audio_string=b'big',
-        content_type='audio/wav',
-        sample_rate=44100,
-        length_frames=441000)
-    snd2 = ea.AudioEvent(
-        wall_time=2,
-        step=12,
-        encoded_audio_string=b'small',
-        content_type='audio/wav',
-        sample_rate=44100,
-        length_frames=44100)
-    gen.AddAudio(
-        'snd1',
-        wall_time=1,
-        step=10,
-        encoded_audio_string=b'big',
-        content_type='audio/wav',
-        sample_rate=44100,
-        length_frames=441000)
-    gen.AddAudio(
-        'snd2',
-        wall_time=2,
-        step=12,
-        encoded_audio_string=b'small',
-        content_type='audio/wav',
-        sample_rate=44100,
-        length_frames=44100)
-    acc.Reload()
-    self.assertEqual(acc.Audio('snd1'), [snd1])
-    self.assertEqual(acc.Audio('snd2'), [snd2])
-
-  def testKeyError(self):
-    """KeyError should be raised when accessing non-existing keys."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    acc.Reload()
-    with self.assertRaises(KeyError):
-      acc.Scalars('s1')
-    with self.assertRaises(KeyError):
-      acc.Scalars('hst1')
-    with self.assertRaises(KeyError):
-      acc.Scalars('im1')
-    with self.assertRaises(KeyError):
-      acc.Histograms('s1')
-    with self.assertRaises(KeyError):
-      acc.Histograms('im1')
-    with self.assertRaises(KeyError):
-      acc.Images('s1')
-    with self.assertRaises(KeyError):
-      acc.Images('hst1')
-    with self.assertRaises(KeyError):
-      acc.Audio('s1')
-    with self.assertRaises(KeyError):
-      acc.Audio('hst1')
-
-  def testNonValueEvents(self):
-    """Non-value events in the generator don't cause early exits."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    gen.AddScalar('s1', wall_time=1, step=10, value=20)
-    gen.AddEvent(tf.Event(wall_time=2, step=20, file_version='nots2'))
-    gen.AddScalar('s3', wall_time=3, step=100, value=1)
-    gen.AddHistogram('hst1')
-    gen.AddImage('im1')
-    gen.AddAudio('snd1')
-
-    acc.Reload()
-    self.assertTagsEqual(acc.Tags(), {
-        ea.IMAGES: ['im1'],
-        ea.AUDIO: ['snd1'],
-        ea.SCALARS: ['s1', 's3'],
-        ea.HISTOGRAMS: ['hst1'],
-        ea.COMPRESSED_HISTOGRAMS: ['hst1'],
-    })
-
-  def testExpiredDataDiscardedAfterRestartForFileVersionLessThan2(self):
-    """Tests that events are discarded after a restart is detected.
-
-    If a step value is observed to be lower than what was previously seen,
-    this should force a discard of all previous items with the same tag
-    that are outdated.
-
-    Only file versions < 2 use this out-of-order discard logic. Later versions
-    discard events based on the step value of SessionLog.START.
-    """
-    warnings = []
-    self.stubs.Set(tf.logging, 'warn', warnings.append)
-
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-
-    gen.AddEvent(tf.Event(wall_time=0, step=0, file_version='brain.Event:1'))
-    gen.AddScalar('s1', wall_time=1, step=100, value=20)
-    gen.AddScalar('s1', wall_time=1, step=200, value=20)
-    gen.AddScalar('s1', wall_time=1, step=300, value=20)
-    acc.Reload()
-    ## Check that number of items are what they should be
-    self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 200, 300])
-
-    gen.AddScalar('s1', wall_time=1, step=101, value=20)
-    gen.AddScalar('s1', wall_time=1, step=201, value=20)
-    gen.AddScalar('s1', wall_time=1, step=301, value=20)
-    acc.Reload()
-    ## Check that we have discarded 200 and 300 from s1
-    self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 101, 201, 301])
-
-  def testOrphanedDataNotDiscardedIfFlagUnset(self):
-    """Tests that events are not discarded if purge_orphaned_data is false.
-    """
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen, purge_orphaned_data=False)
-
-    gen.AddEvent(tf.Event(wall_time=0, step=0, file_version='brain.Event:1'))
-    gen.AddScalar('s1', wall_time=1, step=100, value=20)
-    gen.AddScalar('s1', wall_time=1, step=200, value=20)
-    gen.AddScalar('s1', wall_time=1, step=300, value=20)
-    acc.Reload()
-    ## Check that number of items are what they should be
-    self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 200, 300])
-
-    gen.AddScalar('s1', wall_time=1, step=101, value=20)
-    gen.AddScalar('s1', wall_time=1, step=201, value=20)
-    gen.AddScalar('s1', wall_time=1, step=301, value=20)
-    acc.Reload()
-    ## Check that we have discarded 200 and 300 from s1
-    self.assertEqual([x.step for x in acc.Scalars('s1')],
-                     [100, 200, 300, 101, 201, 301])
-
-  def testEventsDiscardedPerTagAfterRestartForFileVersionLessThan2(self):
-    """Tests that event discards after restart, only affect the misordered tag.
-
-    If a step value is observed to be lower than what was previously seen,
-    this should force a discard of all previous items that are outdated, but
-    only for the out of order tag. Other tags should remain unaffected.
-
-    Only file versions < 2 use this out-of-order discard logic. Later versions
-    discard events based on the step value of SessionLog.START.
-    """
-    warnings = []
-    self.stubs.Set(tf.logging, 'warn', warnings.append)
-
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-
-    gen.AddEvent(tf.Event(wall_time=0, step=0, file_version='brain.Event:1'))
-    gen.AddScalar('s1', wall_time=1, step=100, value=20)
-    gen.AddScalar('s1', wall_time=1, step=200, value=20)
-    gen.AddScalar('s1', wall_time=1, step=300, value=20)
-    gen.AddScalar('s1', wall_time=1, step=101, value=20)
-    gen.AddScalar('s1', wall_time=1, step=201, value=20)
-    gen.AddScalar('s1', wall_time=1, step=301, value=20)
-
-    gen.AddScalar('s2', wall_time=1, step=101, value=20)
-    gen.AddScalar('s2', wall_time=1, step=201, value=20)
-    gen.AddScalar('s2', wall_time=1, step=301, value=20)
-
-    acc.Reload()
-    ## Check that we have discarded 200 and 300
-    self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 101, 201, 301])
-
-    ## Check that s1 discards do not affect s2
-    ## i.e. check that only events from the out of order tag are discarded
-    self.assertEqual([x.step for x in acc.Scalars('s2')], [101, 201, 301])
-
-  def testOnlySummaryEventsTriggerDiscards(self):
-    """Test that file version event does not trigger data purge."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    gen.AddScalar('s1', wall_time=1, step=100, value=20)
-    ev1 = tf.Event(wall_time=2, step=0, file_version='brain.Event:1')
-    graph_bytes = tf.GraphDef().SerializeToString()
-    ev2 = tf.Event(wall_time=3, step=0, graph_def=graph_bytes)
-    gen.AddEvent(ev1)
-    gen.AddEvent(ev2)
-    acc.Reload()
-    self.assertEqual([x.step for x in acc.Scalars('s1')], [100])
-
-  def testSessionLogStartMessageDiscardsExpiredEvents(self):
-    """Test that SessionLog.START message discards expired events.
-
-    This discard logic is preferred over the out-of-order step discard logic,
-    but this logic can only be used for event protos which have the SessionLog
-    enum, which was introduced to event.proto for file_version >= brain.Event:2.
-    """
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    gen.AddEvent(tf.Event(wall_time=0, step=1, file_version='brain.Event:2'))
-
-    gen.AddScalar('s1', wall_time=1, step=100, value=20)
-    gen.AddScalar('s1', wall_time=1, step=200, value=20)
-    gen.AddScalar('s1', wall_time=1, step=300, value=20)
-    gen.AddScalar('s1', wall_time=1, step=400, value=20)
-
-    gen.AddScalar('s2', wall_time=1, step=202, value=20)
-    gen.AddScalar('s2', wall_time=1, step=203, value=20)
-
-    slog = tf.SessionLog(status=tf.SessionLog.START)
-    gen.AddEvent(tf.Event(wall_time=2, step=201, session_log=slog))
-    acc.Reload()
-    self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 200])
-    self.assertEqual([x.step for x in acc.Scalars('s2')], [])
-
-  def testFirstEventTimestamp(self):
-    """Test that FirstEventTimestamp() returns wall_time of the first event."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    gen.AddEvent(tf.Event(wall_time=10, step=20, file_version='brain.Event:2'))
-    gen.AddScalar('s1', wall_time=30, step=40, value=20)
-    self.assertEqual(acc.FirstEventTimestamp(), 10)
-
-  def testReloadPopulatesFirstEventTimestamp(self):
-    """Test that Reload() means FirstEventTimestamp() won't load events."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    gen.AddEvent(tf.Event(wall_time=1, step=2, file_version='brain.Event:2'))
-
-    acc.Reload()
-
-    def _Die(*args, **kwargs):  # pylint: disable=unused-argument
-      raise RuntimeError('Load() should not be called')
-
-    self.stubs.Set(gen, 'Load', _Die)
-    self.assertEqual(acc.FirstEventTimestamp(), 1)
-
-  def testFirstEventTimestampLoadsEvent(self):
-    """Test that FirstEventTimestamp() doesn't discard the loaded event."""
-    gen = _EventGenerator(self)
-    acc = ea.EventAccumulator(gen)
-    gen.AddEvent(tf.Event(wall_time=1, step=2, file_version='brain.Event:2'))
-
-    self.assertEqual(acc.FirstEventTimestamp(), 1)
-    acc.Reload()
-    self.assertEqual(acc.file_version, 2.0)
-
-  def testTFSummaryScalar(self):
-    """Verify processing of tf.summary.scalar."""
-    event_sink = _EventGenerator(self, zero_out_timestamps=True)
-    writer = tf.summary.FileWriter(self.get_temp_dir())
-    writer.event_writer = event_sink
-    with self.test_session() as sess:
-      ipt = tf.placeholder(tf.float32)
-      tf.summary.scalar('scalar1', ipt)
-      tf.summary.scalar('scalar2', ipt * ipt)
-      merged = tf.summary.merge_all()
-      writer.add_graph(sess.graph)
-      for i in xrange(10):
-        summ = sess.run(merged, feed_dict={ipt: i})
-        writer.add_summary(summ, global_step=i)
-
-    accumulator = ea.EventAccumulator(event_sink)
-    accumulator.Reload()
-
-    seq1 = [ea.ScalarEvent(wall_time=0, step=i, value=i) for i in xrange(10)]
-    seq2 = [
-        ea.ScalarEvent(
-            wall_time=0, step=i, value=i * i) for i in xrange(10)
-    ]
-
-    self.assertTagsEqual(accumulator.Tags(), {
-        ea.SCALARS: ['scalar1', 'scalar2'],
-        ea.GRAPH: True,
-        ea.META_GRAPH: False,
-    })
-
-    self.assertEqual(accumulator.Scalars('scalar1'), seq1)
-    self.assertEqual(accumulator.Scalars('scalar2'), seq2)
-    first_value = accumulator.Scalars('scalar1')[0].value
-    self.assertTrue(isinstance(first_value, float))
-
-  def testTFSummaryImage(self):
-    """Verify processing of tf.summary.image."""
-    event_sink = _EventGenerator(self, zero_out_timestamps=True)
-    writer = tf.summary.FileWriter(self.get_temp_dir())
-    writer.event_writer = event_sink
-    with self.test_session() as sess:
-      ipt = tf.ones([10, 4, 4, 3], tf.uint8)
-      # This is an interesting example, because the old tf.image_summary op
-      # would throw an error here, because it would be tag reuse.
-      # Using the tf node name instead allows argument re-use to the image
-      # summary.
-      with tf.name_scope('1'):
-        tf.summary.image('images', ipt, max_outputs=1)
-      with tf.name_scope('2'):
-        tf.summary.image('images', ipt, max_outputs=2)
-      with tf.name_scope('3'):
-        tf.summary.image('images', ipt, max_outputs=3)
-      merged = tf.summary.merge_all()
-      writer.add_graph(sess.graph)
-      for i in xrange(10):
-        summ = sess.run(merged)
-        writer.add_summary(summ, global_step=i)
-
-    accumulator = ea.EventAccumulator(event_sink)
-    accumulator.Reload()
-
-    tags = [
-        u'1/images/image', u'2/images/image/0', u'2/images/image/1',
-        u'3/images/image/0', u'3/images/image/1', u'3/images/image/2'
-    ]
-
-    self.assertTagsEqual(accumulator.Tags(), {
-        ea.IMAGES: tags,
-        ea.GRAPH: True,
-        ea.META_GRAPH: False,
-    })
-
-  def testTFSummaryTensor(self):
-    """Verify processing of tf.summary.tensor."""
-    event_sink = _EventGenerator(self, zero_out_timestamps=True)
-    writer = tf.summary.FileWriter(self.get_temp_dir())
-    writer.event_writer = event_sink
-    with self.test_session() as sess:
-      tf.summary.tensor_summary('scalar', tf.constant(1.0))
-      tf.summary.tensor_summary('vector', tf.constant([1.0, 2.0, 3.0]))
-      tf.summary.tensor_summary('string', tf.constant(six.b('foobar')))
-      merged = tf.summary.merge_all()
-      summ = sess.run(merged)
-      writer.add_summary(summ, 0)
-
-    accumulator = ea.EventAccumulator(event_sink)
-    accumulator.Reload()
-
-    self.assertTagsEqual(accumulator.Tags(), {
-        ea.TENSORS: ['scalar', 'vector', 'string'],
-    })
-
-    scalar_proto = accumulator.Tensors('scalar')[0].tensor_proto
-    scalar = tf.make_ndarray(scalar_proto)
-    vector_proto = accumulator.Tensors('vector')[0].tensor_proto
-    vector = tf.make_ndarray(vector_proto)
-    string_proto = accumulator.Tensors('string')[0].tensor_proto
-    string = tf.make_ndarray(string_proto)
-
-    self.assertTrue(np.array_equal(scalar, 1.0))
-    self.assertTrue(np.array_equal(vector, [1.0, 2.0, 3.0]))
-    self.assertTrue(np.array_equal(string, six.b('foobar')))
-
-
-class RealisticEventAccumulatorTest(EventAccumulatorTest):
-
-  def setUp(self):
-    super(RealisticEventAccumulatorTest, self).setUp()
-
-  def testScalarsRealistically(self):
-    """Test accumulator by writing values and then reading them."""
-
-    def FakeScalarSummary(tag, value):
-      value = tf.Summary.Value(tag=tag, simple_value=value)
-      summary = tf.Summary(value=[value])
-      return summary
-
-    directory = os.path.join(self.get_temp_dir(), 'values_dir')
-    if tf.gfile.IsDirectory(directory):
-      tf.gfile.DeleteRecursively(directory)
-    tf.gfile.MkDir(directory)
-
-    writer = tf.summary.FileWriter(directory, max_queue=100)
-
-    with tf.Graph().as_default() as graph:
-      _ = tf.constant([2.0, 1.0])
-    # Add a graph to the summary writer.
-    writer.add_graph(graph)
-    meta_graph_def = tf.train.export_meta_graph(graph_def=graph.as_graph_def(
-        add_shapes=True))
-    writer.add_meta_graph(meta_graph_def)
-
-    run_metadata = tf.RunMetadata()
-    device_stats = run_metadata.step_stats.dev_stats.add()
-    device_stats.device = 'test device'
-    writer.add_run_metadata(run_metadata, 'test run')
-
-    # Write a bunch of events using the writer.
-    for i in xrange(30):
-      summ_id = FakeScalarSummary('id', i)
-      summ_sq = FakeScalarSummary('sq', i * i)
-      writer.add_summary(summ_id, i * 5)
-      writer.add_summary(summ_sq, i * 5)
-    writer.flush()
-
-    # Verify that we can load those events properly
-    acc = ea.EventAccumulator(directory)
-    acc.Reload()
-    self.assertTagsEqual(acc.Tags(), {
-        ea.SCALARS: ['id', 'sq'],
-        ea.GRAPH: True,
-        ea.META_GRAPH: True,
-        ea.RUN_METADATA: ['test run'],
-    })
-    id_events = acc.Scalars('id')
-    sq_events = acc.Scalars('sq')
-    self.assertEqual(30, len(id_events))
-    self.assertEqual(30, len(sq_events))
-    for i in xrange(30):
-      self.assertEqual(i * 5, id_events[i].step)
-      self.assertEqual(i * 5, sq_events[i].step)
-      self.assertEqual(i, id_events[i].value)
-      self.assertEqual(i * i, sq_events[i].value)
-
-    # Write a few more events to test incremental reloading
-    for i in xrange(30, 40):
-      summ_id = FakeScalarSummary('id', i)
-      summ_sq = FakeScalarSummary('sq', i * i)
-      writer.add_summary(summ_id, i * 5)
-      writer.add_summary(summ_sq, i * 5)
-    writer.flush()
-
-    # Verify we can now see all of the data
-    acc.Reload()
-    id_events = acc.Scalars('id')
-    sq_events = acc.Scalars('sq')
-    self.assertEqual(40, len(id_events))
-    self.assertEqual(40, len(sq_events))
-    for i in xrange(40):
-      self.assertEqual(i * 5, id_events[i].step)
-      self.assertEqual(i * 5, sq_events[i].step)
-      self.assertEqual(i, id_events[i].value)
-      self.assertEqual(i * i, sq_events[i].value)
-    self.assertProtoEquals(graph.as_graph_def(add_shapes=True), acc.Graph())
-    self.assertProtoEquals(meta_graph_def, acc.MetaGraph())
-
-  def testGraphFromMetaGraphBecomesAvailable(self):
-    """Test accumulator by writing values and then reading them."""
-
-    directory = os.path.join(self.get_temp_dir(), 'metagraph_test_values_dir')
-    if tf.gfile.IsDirectory(directory):
-      tf.gfile.DeleteRecursively(directory)
-    tf.gfile.MkDir(directory)
-
-    writer = tf.summary.FileWriter(directory, max_queue=100)
-
-    with tf.Graph().as_default() as graph:
-      _ = tf.constant([2.0, 1.0])
-    # Add a graph to the summary writer.
-    meta_graph_def = tf.train.export_meta_graph(graph_def=graph.as_graph_def(
-        add_shapes=True))
-    writer.add_meta_graph(meta_graph_def)
-
-    writer.flush()
-
-    # Verify that we can load those events properly
-    acc = ea.EventAccumulator(directory)
-    acc.Reload()
-    self.assertTagsEqual(acc.Tags(), {
-        ea.GRAPH: True,
-        ea.META_GRAPH: True,
-    })
-    self.assertProtoEquals(graph.as_graph_def(add_shapes=True), acc.Graph())
-    self.assertProtoEquals(meta_graph_def, acc.MetaGraph())
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/event_processing/event_file_inspector.py b/tensorflow/tensorboard/backend/event_processing/event_file_inspector.py
deleted file mode 100644
index e120dd2ab1..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_file_inspector.py
+++ /dev/null
@@ -1,427 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Logic for TensorBoard inspector to help humans investigate event files.
-
-Example usages:
-tensorboard --inspect --event_file=myevents.out
-tensorboard --inspect --event_file=myevents.out --tag=loss
-tensorboard --inspect --logdir=mylogdir
-tensorboard --inspect --logdir=mylogdir --tag=loss
-
-
-This script runs over a logdir and creates an InspectionUnit for every
-subdirectory with event files. If running over an event file, it creates only
-one InspectionUnit. One block of output is printed to console for each
-InspectionUnit.
-
-The primary content of an InspectionUnit is the dict field_to_obs that maps
-fields (e.g. "scalar", "histogram", "session_log:start", etc.) to a list of
-Observations for the field. Observations correspond one-to-one with Events in an
-event file but contain less information because they only store what is
-necessary to generate the final console output.
-
-The final output is rendered to console by applying some aggregating function
-to the lists of Observations. Different functions are applied depending on the
-type of field. For instance, for "scalar" fields, the inspector shows aggregate
-statistics. For other fields like "session_log:start", all observed steps are
-printed in order to aid debugging.
-
-
-[1] Query a logdir or an event file for its logged tags and summary statistics
-using --logdir or --event_file.
-
-[[event_file]] contains these tags:
-histograms
-   binary/Sign/Activations
-   binary/nn_tanh/act/Activations
-   binary/nn_tanh/biases
-   binary/nn_tanh/biases:gradient
-   binary/nn_tanh/weights
-   binary/nn_tanh/weights:gradient
-images
-   input_images/image/0
-   input_images/image/1
-   input_images/image/2
-scalars
-   Learning Rate
-   Total Cost
-   Total Cost (raw)
-
-Debug output aggregated over all tags:
-graph
-   first_step           0
-   last_step            0
-   max_step             0
-   min_step             0
-   num_steps            1
-   outoforder_steps     []
-histograms
-   first_step           491
-   last_step            659823
-   max_step             659823
-   min_step             491
-   num_steps            993
-   outoforder_steps     []
-images -
-scalars
-   first_step           0
-   last_step            659823
-   max_step             659823
-   min_step             0
-   num_steps            1985
-   outoforder_steps     []
-sessionlog:checkpoint
-   first_step           7129
-   last_step            657167
-   max_step             657167
-   min_step             7129
-   num_steps            99
-   outoforder_steps     []
-sessionlog:start
-   outoforder_steps     []
-   steps                [0L]
-sessionlog:stop -
-
-
-[2] Drill down into a particular tag using --tag.
-
-Debug output for binary/Sign/Activations:
-histograms
-   first_step           491
-   last_step            659823
-   max_step             659823
-   min_step             491
-   num_steps            993
-   outoforder_steps     []
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import itertools
-import os
-
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.backend.event_processing import event_file_loader
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-
-FLAGS = tf.flags.FLAGS
-
-
-# Map of field names within summary.proto to the user-facing names that this
-# script outputs.
-SUMMARY_TYPE_TO_FIELD = {'simple_value': 'scalars',
-                         'histo': 'histograms',
-                         'image': 'images',
-                         'audio': 'audio'}
-for summary_type in event_accumulator.SUMMARY_TYPES:
-  if summary_type not in SUMMARY_TYPE_TO_FIELD:
-    SUMMARY_TYPE_TO_FIELD[summary_type] = summary_type
-
-# Types of summaries that we may want to query for by tag.
-TAG_FIELDS = list(SUMMARY_TYPE_TO_FIELD.values())
-
-# Summaries that we want to see every instance of.
-LONG_FIELDS = ['sessionlog:start', 'sessionlog:stop']
-
-# Summaries that we only want an abridged digest of, since they would
-# take too much screen real estate otherwise.
-SHORT_FIELDS = ['graph', 'sessionlog:checkpoint'] + TAG_FIELDS
-
-# All summary types that we can inspect.
-TRACKED_FIELDS = SHORT_FIELDS + LONG_FIELDS
-
-# An `Observation` contains the data within each Event file that the inspector
-# cares about. The inspector accumulates Observations as it processes events.
-Observation = collections.namedtuple('Observation', ['step', 'wall_time',
-                                                     'tag'])
-
-# An InspectionUnit is created for each organizational structure in the event
-# files visible in the final terminal output. For instance, one InspectionUnit
-# is created for each subdirectory in logdir. When asked to inspect a single
-# event file, there may only be one InspectionUnit.
-
-# The InspectionUnit contains the `name` of the organizational unit that will be
-# printed to console, a `generator` that yields `Event` protos, and a mapping
-# from string fields to `Observations` that the inspector creates.
-InspectionUnit = collections.namedtuple('InspectionUnit', ['name', 'generator',
-                                                           'field_to_obs'])
-
-PRINT_SEPARATOR = '=' * 70 + '\n'
-
-
-def get_field_to_observations_map(generator, query_for_tag=''):
-  """Return a field to `Observations` dict for the event generator.
-
-  Args:
-    generator: A generator over event protos.
-    query_for_tag: A string that if specified, only create observations for
-      events with this tag name.
-
-  Returns:
-    A dict mapping keys in `TRACKED_FIELDS` to an `Observation` list.
-  """
-
-  def increment(stat, event, tag=''):
-    assert stat in TRACKED_FIELDS
-    field_to_obs[stat].append(Observation(step=event.step,
-                                          wall_time=event.wall_time,
-                                          tag=tag)._asdict())
-
-  field_to_obs = dict([(t, []) for t in TRACKED_FIELDS])
-
-  for event in generator:
-    ## Process the event
-    if event.HasField('graph_def') and (not query_for_tag):
-      increment('graph', event)
-    if event.HasField('session_log') and (not query_for_tag):
-      status = event.session_log.status
-      if status == tf.SessionLog.START:
-        increment('sessionlog:start', event)
-      elif status == tf.SessionLog.STOP:
-        increment('sessionlog:stop', event)
-      elif status == tf.SessionLog.CHECKPOINT:
-        increment('sessionlog:checkpoint', event)
-    elif event.HasField('summary'):
-      for value in event.summary.value:
-        if query_for_tag and value.tag != query_for_tag:
-          continue
-
-        for proto_name, display_name in SUMMARY_TYPE_TO_FIELD.items():
-          if value.HasField(proto_name):
-            increment(display_name, event, value.tag)
-  return field_to_obs
-
-
-def get_unique_tags(field_to_obs):
-  """Returns a dictionary of tags that a user could query over.
-
-  Args:
-    field_to_obs: Dict that maps string field to `Observation` list.
-
-  Returns:
-    A dict that maps keys in `TAG_FIELDS` to a list of string tags present in
-    the event files. If the dict does not have any observations of the type,
-    maps to an empty list so that we can render this to console.
-  """
-  return {field: sorted(set([x.get('tag', '') for x in observations]))
-          for field, observations in field_to_obs.items()
-          if field in TAG_FIELDS}
-
-
-def print_dict(d, show_missing=True):
-  """Prints a shallow dict to console.
-
-  Args:
-    d: Dict to print.
-    show_missing: Whether to show keys with empty values.
-  """
-  for k, v in sorted(d.items()):
-    if (not v) and show_missing:
-      # No instances of the key, so print missing symbol.
-      print('{} -'.format(k))
-    elif isinstance(v, list):
-      # Value is a list, so print each item of the list.
-      print(k)
-      for item in v:
-        print('   {}'.format(item))
-    elif isinstance(v, dict):
-      # Value is a dict, so print each (key, value) pair of the dict.
-      print(k)
-      for kk, vv in sorted(v.items()):
-        print('   {:<20} {}'.format(kk, vv))
-
-
-def get_dict_to_print(field_to_obs):
-  """Transform the field-to-obs mapping into a printable dictionary.
-
-  Args:
-    field_to_obs: Dict that maps string field to `Observation` list.
-
-  Returns:
-    A dict with the keys and values to print to console.
-  """
-
-  def compressed_steps(steps):
-    return {'num_steps': len(set(steps)),
-            'min_step': min(steps),
-            'max_step': max(steps),
-            'last_step': steps[-1],
-            'first_step': steps[0],
-            'outoforder_steps': get_out_of_order(steps)}
-
-  def full_steps(steps):
-    return {'steps': steps, 'outoforder_steps': get_out_of_order(steps)}
-
-  output = {}
-  for field, observations in field_to_obs.items():
-    if not observations:
-      output[field] = None
-      continue
-
-    steps = [x['step'] for x in observations]
-    if field in SHORT_FIELDS:
-      output[field] = compressed_steps(steps)
-    if field in LONG_FIELDS:
-      output[field] = full_steps(steps)
-
-  return output
-
-
-def get_out_of_order(list_of_numbers):
-  """Returns elements that break the monotonically non-decreasing trend.
-
-  This is used to find instances of global step values that are "out-of-order",
-  which may trigger TensorBoard event discarding logic.
-
-  Args:
-    list_of_numbers: A list of numbers.
-
-  Returns:
-    A list of tuples in which each tuple are two elements are adjacent, but the
-    second element is lower than the first.
-  """
-  # TODO(cassandrax): Consider changing this to only check for out-of-order
-  # steps within a particular tag.
-  result = []
-  for i in range(len(list_of_numbers)):
-    if i == 0:
-      continue
-    if list_of_numbers[i] < list_of_numbers[i - 1]:
-      result.append((list_of_numbers[i - 1], list_of_numbers[i]))
-  return result
-
-
-def generators_from_logdir(logdir):
-  """Returns a list of event generators for subdirectories with event files.
-
-  The number of generators returned should equal the number of directories
-  within logdir that contain event files. If only logdir contains event files,
-  returns a list of length one.
-
-  Args:
-    logdir: A log directory that contains event files.
-
-  Returns:
-    List of event generators for each subdirectory with event files.
-  """
-  subdirs = event_multiplexer.GetLogdirSubdirectories(logdir)
-  generators = [
-      itertools.chain(*[
-          generator_from_event_file(os.path.join(subdir, f))
-          for f in tf.gfile.ListDirectory(subdir)
-          if event_accumulator.IsTensorFlowEventsFile(os.path.join(subdir, f))
-      ]) for subdir in subdirs
-  ]
-  return generators
-
-
-def generator_from_event_file(event_file):
-  """Returns a generator that yields events from an event file."""
-  return event_file_loader.EventFileLoader(event_file).Load()
-
-
-def get_inspection_units(logdir='', event_file='', tag=''):
-  """Returns a list of InspectionUnit objects given either logdir or event_file.
-
-  If logdir is given, the number of InspectionUnits should equal the
-  number of directories or subdirectories that contain event files.
-
-  If event_file is given, the number of InspectionUnits should be 1.
-
-  Args:
-    logdir: A log directory that contains event files.
-    event_file: Or, a particular event file path.
-    tag: An optional tag name to query for.
-
-  Returns:
-    A list of InspectionUnit objects.
-  """
-  if logdir:
-    subdirs = event_multiplexer.GetLogdirSubdirectories(logdir)
-    inspection_units = []
-    for subdir in subdirs:
-      generator = itertools.chain(*[
-          generator_from_event_file(os.path.join(subdir, f))
-          for f in tf.gfile.ListDirectory(subdir)
-          if event_accumulator.IsTensorFlowEventsFile(os.path.join(subdir, f))
-      ])
-      inspection_units.append(InspectionUnit(
-          name=subdir,
-          generator=generator,
-          field_to_obs=get_field_to_observations_map(generator, tag)))
-    if inspection_units:
-      print('Found event files in:\n{}\n'.format('\n'.join(
-          [u.name for u in inspection_units])))
-    elif event_accumulator.IsTensorFlowEventsFile(logdir):
-      print(
-          'It seems that {} may be an event file instead of a logdir. If this '
-          'is the case, use --event_file instead of --logdir to pass '
-          'it in.'.format(logdir))
-    else:
-      print('No event files found within logdir {}'.format(logdir))
-    return inspection_units
-  elif event_file:
-    generator = generator_from_event_file(event_file)
-    return [InspectionUnit(
-        name=event_file,
-        generator=generator,
-        field_to_obs=get_field_to_observations_map(generator, tag))]
-
-
-def inspect(logdir='', event_file='', tag=''):
-  """Main function for inspector that prints out a digest of event files.
-
-  Args:
-    logdir: A log directory that contains event files.
-    event_file: Or, a particular event file path.
-    tag: An optional tag name to query for.
-
-  Raises:
-    ValueError: If neither logdir and event_file are given, or both are given.
-  """
-  if logdir and event_file:
-    raise ValueError(
-        'Must specify either --logdir or --event_file, but not both.')
-  if not (logdir or event_file):
-    raise ValueError('Must specify either --logdir or --event_file.')
-
-  print(PRINT_SEPARATOR +
-        'Processing event files... (this can take a few minutes)\n' +
-        PRINT_SEPARATOR)
-  inspection_units = get_inspection_units(logdir, event_file, tag)
-
-  for unit in inspection_units:
-    if tag:
-      print('Event statistics for tag {} in {}:'.format(tag, unit.name))
-    else:
-      # If the user is not inspecting a particular tag, also print the list of
-      # all available tags that they can query.
-      print('These tags are in {}:'.format(unit.name))
-      print_dict(get_unique_tags(unit.field_to_obs))
-      print(PRINT_SEPARATOR)
-      print('Event statistics for {}:'.format(unit.name))
-
-    print_dict(get_dict_to_print(unit.field_to_obs), show_missing=(not tag))
-    print(PRINT_SEPARATOR)
-
-
-if __name__ == '__main__':
-  tf.app.run()
diff --git a/tensorflow/tensorboard/backend/event_processing/event_file_inspector_test.py b/tensorflow/tensorboard/backend/event_processing/event_file_inspector_test.py
deleted file mode 100644
index 084043d511..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_file_inspector_test.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import shutil
-
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_file_inspector as efi
-
-
-class EventFileInspectorTest(tf.test.TestCase):
-
-  def setUp(self):
-    self.logdir = os.path.join(self.get_temp_dir(), 'tfevents')
-    self._MakeDirectoryIfNotExists(self.logdir)
-
-  def tearDown(self):
-    shutil.rmtree(self.logdir)
-
-  def _MakeDirectoryIfNotExists(self, path):
-    if not os.path.exists(path):
-      os.mkdir(path)
-
-  def _WriteScalarSummaries(self, data, subdirs=('',)):
-    # Writes data to a tempfile in subdirs, and returns generator for the data.
-    # If subdirs is given, writes data identically to all subdirectories.
-    for subdir_ in subdirs:
-      subdir = os.path.join(self.logdir, subdir_)
-      self._MakeDirectoryIfNotExists(subdir)
-
-      sw = tf.summary.FileWriter(subdir)
-      for datum in data:
-        summary = tf.Summary()
-        if 'simple_value' in datum:
-          summary.value.add(tag=datum['tag'],
-                            simple_value=datum['simple_value'])
-          sw.add_summary(summary, global_step=datum['step'])
-        elif 'histo' in datum:
-          summary.value.add(tag=datum['tag'], histo=tf.HistogramProto())
-          sw.add_summary(summary, global_step=datum['step'])
-        elif 'session_log' in datum:
-          sw.add_session_log(datum['session_log'], global_step=datum['step'])
-      sw.close()
-
-  def testEmptyLogdir(self):
-    # Nothing was written to logdir
-    units = efi.get_inspection_units(self.logdir)
-    self.assertEqual([], units)
-
-  def testGetAvailableTags(self):
-    data = [{'tag': 'c', 'histo': 2, 'step': 10},
-            {'tag': 'c', 'histo': 2, 'step': 11},
-            {'tag': 'c', 'histo': 2, 'step': 9},
-            {'tag': 'b', 'simple_value': 2, 'step': 20},
-            {'tag': 'b', 'simple_value': 2, 'step': 15},
-            {'tag': 'a', 'simple_value': 2, 'step': 3}]
-    self._WriteScalarSummaries(data)
-    units = efi.get_inspection_units(self.logdir)
-    tags = efi.get_unique_tags(units[0].field_to_obs)
-    self.assertEqual(['a', 'b'], tags['scalars'])
-    self.assertEqual(['c'], tags['histograms'])
-
-  def testInspectAll(self):
-    data = [{'tag': 'c', 'histo': 2, 'step': 10},
-            {'tag': 'c', 'histo': 2, 'step': 11},
-            {'tag': 'c', 'histo': 2, 'step': 9},
-            {'tag': 'b', 'simple_value': 2, 'step': 20},
-            {'tag': 'b', 'simple_value': 2, 'step': 15},
-            {'tag': 'a', 'simple_value': 2, 'step': 3}]
-    self._WriteScalarSummaries(data)
-    units = efi.get_inspection_units(self.logdir)
-    printable = efi.get_dict_to_print(units[0].field_to_obs)
-    self.assertEqual(printable['histograms']['max_step'], 11)
-    self.assertEqual(printable['histograms']['min_step'], 9)
-    self.assertEqual(printable['histograms']['num_steps'], 3)
-    self.assertEqual(printable['histograms']['last_step'], 9)
-    self.assertEqual(printable['histograms']['first_step'], 10)
-    self.assertEqual(printable['histograms']['outoforder_steps'], [(11, 9)])
-
-    self.assertEqual(printable['scalars']['max_step'], 20)
-    self.assertEqual(printable['scalars']['min_step'], 3)
-    self.assertEqual(printable['scalars']['num_steps'], 3)
-    self.assertEqual(printable['scalars']['last_step'], 3)
-    self.assertEqual(printable['scalars']['first_step'], 20)
-    self.assertEqual(printable['scalars']['outoforder_steps'], [(20, 15),
-                                                                (15, 3)])
-
-  def testInspectTag(self):
-    data = [{'tag': 'c', 'histo': 2, 'step': 10},
-            {'tag': 'c', 'histo': 2, 'step': 11},
-            {'tag': 'c', 'histo': 2, 'step': 9},
-            {'tag': 'b', 'histo': 2, 'step': 20},
-            {'tag': 'b', 'simple_value': 2, 'step': 15},
-            {'tag': 'a', 'simple_value': 2, 'step': 3}]
-    self._WriteScalarSummaries(data)
-    units = efi.get_inspection_units(self.logdir, tag='c')
-    printable = efi.get_dict_to_print(units[0].field_to_obs)
-    self.assertEqual(printable['histograms']['max_step'], 11)
-    self.assertEqual(printable['histograms']['min_step'], 9)
-    self.assertEqual(printable['histograms']['num_steps'], 3)
-    self.assertEqual(printable['histograms']['last_step'], 9)
-    self.assertEqual(printable['histograms']['first_step'], 10)
-    self.assertEqual(printable['histograms']['outoforder_steps'], [(11, 9)])
-    self.assertEqual(printable['scalars'], None)
-
-  def testSessionLogSummaries(self):
-    data = [
-        {
-            'session_log': tf.SessionLog(status=tf.SessionLog.START),
-            'step': 0
-        },
-        {
-            'session_log': tf.SessionLog(status=tf.SessionLog.CHECKPOINT),
-            'step': 1
-        },
-        {
-            'session_log': tf.SessionLog(status=tf.SessionLog.CHECKPOINT),
-            'step': 2
-        },
-        {
-            'session_log': tf.SessionLog(status=tf.SessionLog.CHECKPOINT),
-            'step': 3
-        },
-        {
-            'session_log': tf.SessionLog(status=tf.SessionLog.STOP),
-            'step': 4
-        },
-        {
-            'session_log': tf.SessionLog(status=tf.SessionLog.START),
-            'step': 5
-        },
-        {
-            'session_log': tf.SessionLog(status=tf.SessionLog.STOP),
-            'step': 6
-        },
-    ]
-
-    self._WriteScalarSummaries(data)
-    units = efi.get_inspection_units(self.logdir)
-    self.assertEqual(1, len(units))
-    printable = efi.get_dict_to_print(units[0].field_to_obs)
-    self.assertEqual(printable['sessionlog:start']['steps'], [0, 5])
-    self.assertEqual(printable['sessionlog:stop']['steps'], [4, 6])
-    self.assertEqual(printable['sessionlog:checkpoint']['num_steps'], 3)
-
-  def testInspectAllWithNestedLogdirs(self):
-    data = [{'tag': 'c', 'simple_value': 2, 'step': 10},
-            {'tag': 'c', 'simple_value': 2, 'step': 11},
-            {'tag': 'c', 'simple_value': 2, 'step': 9},
-            {'tag': 'b', 'simple_value': 2, 'step': 20},
-            {'tag': 'b', 'simple_value': 2, 'step': 15},
-            {'tag': 'a', 'simple_value': 2, 'step': 3}]
-
-    subdirs = ['eval', 'train']
-    self._WriteScalarSummaries(data, subdirs=subdirs)
-    units = efi.get_inspection_units(self.logdir)
-    self.assertEqual(2, len(units))
-    directory_names = [os.path.join(self.logdir, name) for name in subdirs]
-    self.assertEqual(directory_names, sorted([unit.name for unit in units]))
-
-    for unit in units:
-      printable = efi.get_dict_to_print(unit.field_to_obs)['scalars']
-      self.assertEqual(printable['max_step'], 20)
-      self.assertEqual(printable['min_step'], 3)
-      self.assertEqual(printable['num_steps'], 6)
-      self.assertEqual(printable['last_step'], 3)
-      self.assertEqual(printable['first_step'], 10)
-      self.assertEqual(printable['outoforder_steps'], [(11, 9), (20, 15),
-                                                       (15, 3)])
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/event_processing/event_file_loader.py b/tensorflow/tensorboard/backend/event_processing/event_file_loader.py
deleted file mode 100644
index 896142daaf..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_file_loader.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Functionality for loading events from a record file."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-
-class EventFileLoader(object):
-  """An EventLoader is an iterator that yields Event protos."""
-
-  def __init__(self, file_path):
-    if file_path is None:
-      raise ValueError('A file path is required')
-    file_path = tf.resource_loader.readahead_file_path(file_path)
-    tf.logging.debug('Opening a record reader pointing at %s', file_path)
-    with tf.errors.raise_exception_on_not_ok_status() as status:
-      self._reader = tf.pywrap_tensorflow.PyRecordReader_New(
-          tf.compat.as_bytes(file_path), 0, tf.compat.as_bytes(''), status)
-    # Store it for logging purposes.
-    self._file_path = file_path
-    if not self._reader:
-      raise IOError('Failed to open a record reader pointing to %s' % file_path)
-
-  def Load(self):
-    """Loads all new values from disk.
-
-    Calling Load multiple times in a row will not 'drop' events as long as the
-    return value is not iterated over.
-
-    Yields:
-      All values that were written to disk that have not been yielded yet.
-    """
-    while True:
-      try:
-        with tf.errors.raise_exception_on_not_ok_status() as status:
-          self._reader.GetNext(status)
-      except (tf.errors.DataLossError, tf.errors.OutOfRangeError):
-        # We ignore partial read exceptions, because a record may be truncated.
-        # PyRecordReader holds the offset prior to the failed read, so retrying
-        # will succeed.
-        break
-      event = tf.Event()
-      event.ParseFromString(self._reader.record())
-      yield event
-    tf.logging.debug('No more events in %s', self._file_path)
-
-
-def main(argv):
-  if len(argv) != 2:
-    print('Usage: event_file_loader <path-to-the-recordio-file>')
-    return 1
-  loader = EventFileLoader(argv[1])
-  for event in loader.Load():
-    print(event)
-
-
-if __name__ == '__main__':
-  tf.app.run()
diff --git a/tensorflow/tensorboard/backend/event_processing/event_file_loader_test.py b/tensorflow/tensorboard/backend/event_processing/event_file_loader_test.py
deleted file mode 100644
index 210a7bc52e..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_file_loader_test.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Tests for event_file_loader."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import tempfile
-
-import tensorflow as tf
-
-
-from tensorflow.tensorboard.backend.event_processing import event_file_loader
-
-
-class EventFileLoaderTest(tf.test.TestCase):
-  # A record containing a simple event.
-  RECORD = (b'\x18\x00\x00\x00\x00\x00\x00\x00\xa3\x7fK"\t\x00\x00\xc0%\xddu'
-            b'\xd5A\x1a\rbrain.Event:1\xec\xf32\x8d')
-
-  def _WriteToFile(self, filename, data):
-    with open(filename, 'ab') as f:
-      f.write(data)
-
-  def _LoaderForTestFile(self, filename):
-    return event_file_loader.EventFileLoader(
-        os.path.join(self.get_temp_dir(), filename))
-
-  def testEmptyEventFile(self):
-    filename = tempfile.NamedTemporaryFile(dir=self.get_temp_dir()).name
-    self._WriteToFile(filename, b'')
-    loader = self._LoaderForTestFile(filename)
-    self.assertEqual(len(list(loader.Load())), 0)
-
-  def testSingleWrite(self):
-    filename = tempfile.NamedTemporaryFile(dir=self.get_temp_dir()).name
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    loader = self._LoaderForTestFile(filename)
-    events = list(loader.Load())
-    self.assertEqual(len(events), 1)
-    self.assertEqual(events[0].wall_time, 1440183447.0)
-    self.assertEqual(len(list(loader.Load())), 0)
-
-  def testMultipleWrites(self):
-    filename = tempfile.NamedTemporaryFile(dir=self.get_temp_dir()).name
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    loader = self._LoaderForTestFile(filename)
-    self.assertEqual(len(list(loader.Load())), 1)
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    self.assertEqual(len(list(loader.Load())), 1)
-
-  def testMultipleLoads(self):
-    filename = tempfile.NamedTemporaryFile(dir=self.get_temp_dir()).name
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    loader = self._LoaderForTestFile(filename)
-    loader.Load()
-    loader.Load()
-    self.assertEqual(len(list(loader.Load())), 1)
-
-  def testMultipleWritesAtOnce(self):
-    filename = tempfile.NamedTemporaryFile(dir=self.get_temp_dir()).name
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    loader = self._LoaderForTestFile(filename)
-    self.assertEqual(len(list(loader.Load())), 2)
-
-  def testMultipleWritesWithBadWrite(self):
-    filename = tempfile.NamedTemporaryFile(dir=self.get_temp_dir()).name
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    self._WriteToFile(filename, EventFileLoaderTest.RECORD)
-    # Test that we ignore partial record writes at the end of the file.
-    self._WriteToFile(filename, b'123')
-    loader = self._LoaderForTestFile(filename)
-    self.assertEqual(len(list(loader.Load())), 2)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/event_processing/event_multiplexer.py b/tensorflow/tensorboard/backend/event_processing/event_multiplexer.py
deleted file mode 100644
index e4b8814c92..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_multiplexer.py
+++ /dev/null
@@ -1,475 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Provides an interface for working with multiple event files."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import threading
-
-import six
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import directory_watcher
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.backend.event_processing import io_wrapper
-
-
-class EventMultiplexer(object):
-  """An `EventMultiplexer` manages access to multiple `EventAccumulator`s.
-
-  Each `EventAccumulator` is associated with a `run`, which is a self-contained
-  TensorFlow execution. The `EventMultiplexer` provides methods for extracting
-  information about events from multiple `run`s.
-
-  Example usage for loading specific runs from files:
-
-  ```python
-  x = EventMultiplexer({'run1': 'path/to/run1', 'run2': 'path/to/run2'})
-  x.Reload()
-  ```
-
-  Example usage for loading a directory where each subdirectory is a run
-
-  ```python
-  (eg:) /parent/directory/path/
-        /parent/directory/path/run1/
-        /parent/directory/path/run1/events.out.tfevents.1001
-        /parent/directory/path/run1/events.out.tfevents.1002
-
-        /parent/directory/path/run2/
-        /parent/directory/path/run2/events.out.tfevents.9232
-
-        /parent/directory/path/run3/
-        /parent/directory/path/run3/events.out.tfevents.9232
-  x = EventMultiplexer().AddRunsFromDirectory('/parent/directory/path')
-  (which is equivalent to:)
-  x = EventMultiplexer({'run1': '/parent/directory/path/run1', 'run2':...}
-  ```
-
-  If you would like to watch `/parent/directory/path`, wait for it to be created
-    (if necessary) and then periodically pick up new runs, use
-    `AutoloadingMultiplexer`
-  @@Tensors
-  """
-
-  def __init__(self,
-               run_path_map=None,
-               size_guidance=event_accumulator.DEFAULT_SIZE_GUIDANCE,
-               purge_orphaned_data=True):
-    """Constructor for the `EventMultiplexer`.
-
-    Args:
-      run_path_map: Dict `{run: path}` which specifies the
-        name of a run, and the path to find the associated events. If it is
-        None, then the EventMultiplexer initializes without any runs.
-      size_guidance: A dictionary mapping from `tagType` to the number of items
-        to store for each tag of that type. See
-        `event_accumulator.EventAccumulator` for details.
-      purge_orphaned_data: Whether to discard any events that were "orphaned" by
-        a TensorFlow restart.
-    """
-    tf.logging.info('Event Multiplexer initializing.')
-    self._accumulators_mutex = threading.Lock()
-    self._accumulators = {}
-    self._paths = {}
-    self._reload_called = False
-    self._size_guidance = size_guidance
-    self.purge_orphaned_data = purge_orphaned_data
-    if run_path_map is not None:
-      tf.logging.info('Event Multplexer doing initialization load for %s',
-                      run_path_map)
-      for (run, path) in six.iteritems(run_path_map):
-        self.AddRun(path, run)
-    tf.logging.info('Event Multiplexer done initializing')
-
-  def AddRun(self, path, name=None):
-    """Add a run to the multiplexer.
-
-    If the name is not specified, it is the same as the path.
-
-    If a run by that name exists, and we are already watching the right path,
-      do nothing. If we are watching a different path, replace the event
-      accumulator.
-
-    If `Reload` has been called, it will `Reload` the newly created
-    accumulators.
-
-    Args:
-      path: Path to the event files (or event directory) for given run.
-      name: Name of the run to add. If not provided, is set to path.
-
-    Returns:
-      The `EventMultiplexer`.
-    """
-    if name is None or name is '':
-      name = path
-    accumulator = None
-    with self._accumulators_mutex:
-      if name not in self._accumulators or self._paths[name] != path:
-        if name in self._paths and self._paths[name] != path:
-          # TODO(danmane) - Make it impossible to overwrite an old path with
-          # a new path (just give the new path a distinct name)
-          tf.logging.warning('Conflict for name %s: old path %s, new path %s',
-                             name, self._paths[name], path)
-        tf.logging.info('Constructing EventAccumulator for %s', path)
-        accumulator = event_accumulator.EventAccumulator(
-            path,
-            size_guidance=self._size_guidance,
-            purge_orphaned_data=self.purge_orphaned_data)
-        self._accumulators[name] = accumulator
-        self._paths[name] = path
-    if accumulator:
-      if self._reload_called:
-        accumulator.Reload()
-    return self
-
-  def AddRunsFromDirectory(self, path, name=None):
-    """Load runs from a directory; recursively walks subdirectories.
-
-    If path doesn't exist, no-op. This ensures that it is safe to call
-      `AddRunsFromDirectory` multiple times, even before the directory is made.
-
-    If path is a directory, load event files in the directory (if any exist) and
-      recursively call AddRunsFromDirectory on any subdirectories. This mean you
-      can call AddRunsFromDirectory at the root of a tree of event logs and
-      TensorBoard will load them all.
-
-    If the `EventMultiplexer` is already loaded this will cause
-    the newly created accumulators to `Reload()`.
-    Args:
-      path: A string path to a directory to load runs from.
-      name: Optionally, what name to apply to the runs. If name is provided
-        and the directory contains run subdirectories, the name of each subrun
-        is the concatenation of the parent name and the subdirectory name. If
-        name is provided and the directory contains event files, then a run
-        is added called "name" and with the events from the path.
-
-    Raises:
-      ValueError: If the path exists and isn't a directory.
-
-    Returns:
-      The `EventMultiplexer`.
-    """
-    tf.logging.info('Starting AddRunsFromDirectory: %s', path)
-    for subdir in GetLogdirSubdirectories(path):
-      tf.logging.info('Adding events from directory %s', subdir)
-      rpath = os.path.relpath(subdir, path)
-      subname = os.path.join(name, rpath) if name else rpath
-      self.AddRun(subdir, name=subname)
-    tf.logging.info('Done with AddRunsFromDirectory: %s', path)
-    return self
-
-  def Reload(self):
-    """Call `Reload` on every `EventAccumulator`."""
-    tf.logging.info('Beginning EventMultiplexer.Reload()')
-    self._reload_called = True
-    # Build a list so we're safe even if the list of accumulators is modified
-    # even while we're reloading.
-    with self._accumulators_mutex:
-      items = list(self._accumulators.items())
-
-    names_to_delete = set()
-    for name, accumulator in items:
-      try:
-        accumulator.Reload()
-      except (OSError, IOError) as e:
-        tf.logging.error("Unable to reload accumulator '%s': %s", name, e)
-      except directory_watcher.DirectoryDeletedError:
-        names_to_delete.add(name)
-
-    with self._accumulators_mutex:
-      for name in names_to_delete:
-        tf.logging.warning("Deleting accumulator '%s'", name)
-        del self._accumulators[name]
-    tf.logging.info('Finished with EventMultiplexer.Reload()')
-    return self
-
-  def PluginAssets(self, plugin_name):
-    """Get index of runs and assets for a given plugin.
-
-    Args:
-      plugin_name: Name of the plugin we are checking for.
-
-    Returns:
-      A dictionary that maps from run_name to a list of plugin
-        assets for that run.
-    """
-    with self._accumulators_mutex:
-      # To avoid nested locks, we construct a copy of the run-accumulator map
-      items = list(six.iteritems(self._accumulators))
-
-    return {run: accum.PluginAssets(plugin_name) for run, accum in items}
-
-  def RetrievePluginAsset(self, run, plugin_name, asset_name):
-    """Return the contents for a specific plugin asset from a run.
-
-    Args:
-      run: The string name of the run.
-      plugin_name: The string name of a plugin.
-      asset_name: The string name of an asset.
-
-    Returns:
-      The string contents of the plugin asset.
-
-    Raises:
-      KeyError: If the asset is not available.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.RetrievePluginAsset(plugin_name, asset_name)
-
-  def FirstEventTimestamp(self, run):
-    """Return the timestamp of the first event of the given run.
-
-    This may perform I/O if no events have been loaded yet for the run.
-
-    Args:
-      run: A string name of the run for which the timestamp is retrieved.
-
-    Returns:
-      The wall_time of the first event of the run, which will typically be
-      seconds since the epoch.
-
-    Raises:
-      KeyError: If the run is not found.
-      ValueError: If the run has no events loaded and there are no events on
-        disk to load.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.FirstEventTimestamp()
-
-  def Scalars(self, run, tag):
-    """Retrieve the scalar events associated with a run and tag.
-
-    Args:
-      run: A string name of the run for which values are retrieved.
-      tag: A string name of the tag for which values are retrieved.
-
-    Raises:
-      KeyError: If the run is not found, or the tag is not available for
-        the given run.
-
-    Returns:
-      An array of `event_accumulator.ScalarEvents`.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.Scalars(tag)
-
-  def HealthPills(self, run, node_name):
-    """Retrieve the health pill events associated with a run and node name.
-
-    Args:
-      run: A string name of the run for which health pills are retrieved.
-      node_name: A string name of the node for which health pills are retrieved.
-
-    Raises:
-      KeyError: If the run is not found, or the node name is not available for
-        the given run.
-
-    Returns:
-      An array of `event_accumulator.HealthPillEvents`.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.HealthPills(node_name)
-
-  def GetOpsWithHealthPills(self, run):
-    """Determines which ops have at least 1 health pill event for a given run.
-
-    Args:
-      run: The name of the run.
-
-    Raises:
-      KeyError: If the run is not found, or the node name is not available for
-        the given run.
-
-    Returns:
-      The list of names of ops with health pill events.
-    """
-    return self._GetAccumulator(run).GetOpsWithHealthPills()
-
-  def Graph(self, run):
-    """Retrieve the graph associated with the provided run.
-
-    Args:
-      run: A string name of a run to load the graph for.
-
-    Raises:
-      KeyError: If the run is not found.
-      ValueError: If the run does not have an associated graph.
-
-    Returns:
-      The `GraphDef` protobuf data structure.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.Graph()
-
-  def MetaGraph(self, run):
-    """Retrieve the metagraph associated with the provided run.
-
-    Args:
-      run: A string name of a run to load the graph for.
-
-    Raises:
-      KeyError: If the run is not found.
-      ValueError: If the run does not have an associated graph.
-
-    Returns:
-      The `MetaGraphDef` protobuf data structure.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.MetaGraph()
-
-  def RunMetadata(self, run, tag):
-    """Get the session.run() metadata associated with a TensorFlow run and tag.
-
-    Args:
-      run: A string name of a TensorFlow run.
-      tag: A string name of the tag associated with a particular session.run().
-
-    Raises:
-      KeyError: If the run is not found, or the tag is not available for the
-        given run.
-
-    Returns:
-      The metadata in the form of `RunMetadata` protobuf data structure.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.RunMetadata(tag)
-
-  def Histograms(self, run, tag):
-    """Retrieve the histogram events associated with a run and tag.
-
-    Args:
-      run: A string name of the run for which values are retrieved.
-      tag: A string name of the tag for which values are retrieved.
-
-    Raises:
-      KeyError: If the run is not found, or the tag is not available for
-        the given run.
-
-    Returns:
-      An array of `event_accumulator.HistogramEvents`.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.Histograms(tag)
-
-  def CompressedHistograms(self, run, tag):
-    """Retrieve the compressed histogram events associated with a run and tag.
-
-    Args:
-      run: A string name of the run for which values are retrieved.
-      tag: A string name of the tag for which values are retrieved.
-
-    Raises:
-      KeyError: If the run is not found, or the tag is not available for
-        the given run.
-
-    Returns:
-      An array of `event_accumulator.CompressedHistogramEvents`.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.CompressedHistograms(tag)
-
-  def Images(self, run, tag):
-    """Retrieve the image events associated with a run and tag.
-
-    Args:
-      run: A string name of the run for which values are retrieved.
-      tag: A string name of the tag for which values are retrieved.
-
-    Raises:
-      KeyError: If the run is not found, or the tag is not available for
-        the given run.
-
-    Returns:
-      An array of `event_accumulator.ImageEvents`.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.Images(tag)
-
-  def Audio(self, run, tag):
-    """Retrieve the audio events associated with a run and tag.
-
-    Args:
-      run: A string name of the run for which values are retrieved.
-      tag: A string name of the tag for which values are retrieved.
-
-    Raises:
-      KeyError: If the run is not found, or the tag is not available for
-        the given run.
-
-    Returns:
-      An array of `event_accumulator.AudioEvents`.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.Audio(tag)
-
-  def Tensors(self, run, tag):
-    """Retrieve the tensor events associated with a run and tag.
-
-    Args:
-      run: A string name of the run for which values are retrieved.
-      tag: A string name of the tag for which values are retrieved.
-
-    Raises:
-      KeyError: If the run is not found, or the tag is not available for
-        the given run.
-
-    Returns:
-      An array of `event_accumulator.TensorEvent`s.
-    """
-    accumulator = self._GetAccumulator(run)
-    return accumulator.Tensors(tag)
-
-  def Runs(self):
-    """Return all the run names in the `EventMultiplexer`.
-
-    Returns:
-    ```
-      {runName: { images: [tag1, tag2, tag3],
-                  scalarValues: [tagA, tagB, tagC],
-                  histograms: [tagX, tagY, tagZ],
-                  compressedHistograms: [tagX, tagY, tagZ],
-                  graph: true, meta_graph: true}}
-    ```
-    """
-    with self._accumulators_mutex:
-      # To avoid nested locks, we construct a copy of the run-accumulator map
-      items = list(six.iteritems(self._accumulators))
-    return {run_name: accumulator.Tags() for run_name, accumulator in items}
-
-  def RunPaths(self):
-    """Returns a dict mapping run names to event file paths."""
-    return self._paths
-
-  def _GetAccumulator(self, run):
-    with self._accumulators_mutex:
-      return self._accumulators[run]
-
-
-def GetLogdirSubdirectories(path):
-  """Returns subdirectories with event files on path."""
-  if tf.gfile.Exists(path) and not tf.gfile.IsDirectory(path):
-    raise ValueError('GetLogdirSubdirectories: path exists and is not a '
-                     'directory, %s' % path)
-
-  # ListRecursively just yields nothing if the path doesn't exist.
-  return (
-      subdir
-      for (subdir, files) in io_wrapper.ListRecursively(path)
-      if list(filter(event_accumulator.IsTensorFlowEventsFile, files))
-  )
diff --git a/tensorflow/tensorboard/backend/event_processing/event_multiplexer_test.py b/tensorflow/tensorboard/backend/event_processing/event_multiplexer_test.py
deleted file mode 100644
index ea536dfaad..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/event_multiplexer_test.py
+++ /dev/null
@@ -1,360 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import functools
-import os
-import os.path
-import shutil
-
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-
-
-def _AddEvents(path):
-  if not tf.gfile.IsDirectory(path):
-    tf.gfile.MakeDirs(path)
-  fpath = os.path.join(path, 'hypothetical.tfevents.out')
-  with tf.gfile.GFile(fpath, 'w') as f:
-    f.write('')
-    return fpath
-
-
-def _CreateCleanDirectory(path):
-  if tf.gfile.IsDirectory(path):
-    tf.gfile.DeleteRecursively(path)
-  tf.gfile.MkDir(path)
-
-
-class _FakeAccumulator(object):
-
-  def __init__(self, path, health_pill_mapping=None):
-    """Constructs a fake accumulator with some fake events.
-
-    Args:
-      path: The path for the run that this accumulator is for.
-      health_pill_mapping: An optional mapping from Op to health pill strings.
-    """
-    self._path = path
-    self.reload_called = False
-    self._node_names_to_health_pills = health_pill_mapping or {}
-
-  def Tags(self):
-    return {event_accumulator.IMAGES: ['im1', 'im2'],
-            event_accumulator.AUDIO: ['snd1', 'snd2'],
-            event_accumulator.HISTOGRAMS: ['hst1', 'hst2'],
-            event_accumulator.COMPRESSED_HISTOGRAMS: ['cmphst1', 'cmphst2'],
-            event_accumulator.SCALARS: ['sv1', 'sv2']}
-
-  def FirstEventTimestamp(self):
-    return 0
-
-  def _TagHelper(self, tag_name, enum):
-    if tag_name not in self.Tags()[enum]:
-      raise KeyError
-    return ['%s/%s' % (self._path, tag_name)]
-
-  def Scalars(self, tag_name):
-    return self._TagHelper(tag_name, event_accumulator.SCALARS)
-
-  def HealthPills(self, node_name):
-    if node_name not in self._node_names_to_health_pills:
-      raise KeyError
-    health_pills = self._node_names_to_health_pills[node_name]
-    return [self._path + '/' + health_pill for health_pill in health_pills]
-
-  def GetOpsWithHealthPills(self):
-    return self._node_names_to_health_pills.keys()
-
-  def Histograms(self, tag_name):
-    return self._TagHelper(tag_name, event_accumulator.HISTOGRAMS)
-
-  def CompressedHistograms(self, tag_name):
-    return self._TagHelper(tag_name, event_accumulator.COMPRESSED_HISTOGRAMS)
-
-  def Images(self, tag_name):
-    return self._TagHelper(tag_name, event_accumulator.IMAGES)
-
-  def Audio(self, tag_name):
-    return self._TagHelper(tag_name, event_accumulator.AUDIO)
-
-  def Tensors(self, tag_name):
-    return self._TagHelper(tag_name, event_accumulator.TENSORS)
-
-  def Reload(self):
-    self.reload_called = True
-
-
-def _GetFakeAccumulator(path,
-                        size_guidance=None,
-                        compression_bps=None,
-                        purge_orphaned_data=None,
-                        health_pill_mapping=None):
-  del size_guidance, compression_bps, purge_orphaned_data  # Unused.
-  return _FakeAccumulator(path, health_pill_mapping=health_pill_mapping)
-
-
-class EventMultiplexerTest(tf.test.TestCase):
-
-  def setUp(self):
-    super(EventMultiplexerTest, self).setUp()
-    self.stubs = tf.test.StubOutForTesting()
-
-    self.stubs.Set(event_accumulator, 'EventAccumulator', _GetFakeAccumulator)
-
-  def tearDown(self):
-    self.stubs.CleanUp()
-
-  def testEmptyLoader(self):
-    """Tests empty EventMultiplexer creation."""
-    x = event_multiplexer.EventMultiplexer()
-    self.assertEqual(x.Runs(), {})
-
-  def testRunNamesRespected(self):
-    """Tests two EventAccumulators inserted/accessed in EventMultiplexer."""
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-    self.assertItemsEqual(sorted(x.Runs().keys()), ['run1', 'run2'])
-    self.assertEqual(x._GetAccumulator('run1')._path, 'path1')
-    self.assertEqual(x._GetAccumulator('run2')._path, 'path2')
-
-  def testReload(self):
-    """EventAccumulators should Reload after EventMultiplexer call it."""
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-    self.assertFalse(x._GetAccumulator('run1').reload_called)
-    self.assertFalse(x._GetAccumulator('run2').reload_called)
-    x.Reload()
-    self.assertTrue(x._GetAccumulator('run1').reload_called)
-    self.assertTrue(x._GetAccumulator('run2').reload_called)
-
-  def testScalars(self):
-    """Tests Scalars function returns suitable values."""
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-
-    run1_actual = x.Scalars('run1', 'sv1')
-    run1_expected = ['path1/sv1']
-
-    self.assertEqual(run1_expected, run1_actual)
-
-  def testHealthPills(self):
-    """Tests HealthPills() returns events associated with run1/Add."""
-    self.stubs.Set(event_accumulator, 'EventAccumulator',
-                   functools.partial(
-                       _GetFakeAccumulator,
-                       health_pill_mapping={'Add': ['hp1', 'hp2']}))
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-    self.assertEqual(['path1/hp1', 'path1/hp2'], x.HealthPills('run1', 'Add'))
-
-  def testGetOpsWithHealthPillsWhenHealthPillsAreNotAvailable(self):
-    # The event accumulator lacks health pills for the run.
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-    self.assertItemsEqual([], x.GetOpsWithHealthPills('run1'))
-
-  def testGetOpsWithHealthPillsWhenHealthPillsAreAvailable(self):
-    # The event accumulator has health pills for the run.
-    self.stubs.Set(event_accumulator, 'EventAccumulator',
-                   functools.partial(
-                       _GetFakeAccumulator,
-                       health_pill_mapping={'Add': ['hp1', 'hp2']}))
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-    self.assertItemsEqual(['Add'], x.GetOpsWithHealthPills('run1'))
-
-  def testExceptions(self):
-    """KeyError should be raised when accessing non-existing keys."""
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-    with self.assertRaises(KeyError):
-      x.Scalars('sv1', 'xxx')
-
-  def testInitialization(self):
-    """Tests EventMultiplexer is created properly with its params."""
-    x = event_multiplexer.EventMultiplexer()
-    self.assertEqual(x.Runs(), {})
-    x = event_multiplexer.EventMultiplexer({'run1': 'path1', 'run2': 'path2'})
-    self.assertItemsEqual(x.Runs(), ['run1', 'run2'])
-    self.assertEqual(x._GetAccumulator('run1')._path, 'path1')
-    self.assertEqual(x._GetAccumulator('run2')._path, 'path2')
-
-  def testAddRunsFromDirectory(self):
-    """Tests AddRunsFromDirectory function.
-
-    Tests the following scenarios:
-    - When the directory does not exist.
-    - When the directory is empty.
-    - When the directory has empty subdirectory.
-    - Contains proper EventAccumulators after adding events.
-    """
-    x = event_multiplexer.EventMultiplexer()
-    tmpdir = self.get_temp_dir()
-    join = os.path.join
-    fakedir = join(tmpdir, 'fake_accumulator_directory')
-    realdir = join(tmpdir, 'real_accumulator_directory')
-    self.assertEqual(x.Runs(), {})
-    x.AddRunsFromDirectory(fakedir)
-    self.assertEqual(x.Runs(), {}, 'loading fakedir had no effect')
-
-    _CreateCleanDirectory(realdir)
-    x.AddRunsFromDirectory(realdir)
-    self.assertEqual(x.Runs(), {}, 'loading empty directory had no effect')
-
-    path1 = join(realdir, 'path1')
-    tf.gfile.MkDir(path1)
-    x.AddRunsFromDirectory(realdir)
-    self.assertEqual(x.Runs(), {}, 'creating empty subdirectory had no effect')
-
-    _AddEvents(path1)
-    x.AddRunsFromDirectory(realdir)
-    self.assertItemsEqual(x.Runs(), ['path1'], 'loaded run: path1')
-    loader1 = x._GetAccumulator('path1')
-    self.assertEqual(loader1._path, path1, 'has the correct path')
-
-    path2 = join(realdir, 'path2')
-    _AddEvents(path2)
-    x.AddRunsFromDirectory(realdir)
-    self.assertItemsEqual(x.Runs(), ['path1', 'path2'])
-    self.assertEqual(
-        x._GetAccumulator('path1'), loader1, 'loader1 not regenerated')
-
-    path2_2 = join(path2, 'path2')
-    _AddEvents(path2_2)
-    x.AddRunsFromDirectory(realdir)
-    self.assertItemsEqual(x.Runs(), ['path1', 'path2', 'path2/path2'])
-    self.assertEqual(
-        x._GetAccumulator('path2/path2')._path, path2_2, 'loader2 path correct')
-
-  def testAddRunsFromDirectoryThatContainsEvents(self):
-    x = event_multiplexer.EventMultiplexer()
-    tmpdir = self.get_temp_dir()
-    join = os.path.join
-    realdir = join(tmpdir, 'event_containing_directory')
-
-    _CreateCleanDirectory(realdir)
-
-    self.assertEqual(x.Runs(), {})
-
-    _AddEvents(realdir)
-    x.AddRunsFromDirectory(realdir)
-    self.assertItemsEqual(x.Runs(), ['.'])
-
-    subdir = join(realdir, 'subdir')
-    _AddEvents(subdir)
-    x.AddRunsFromDirectory(realdir)
-    self.assertItemsEqual(x.Runs(), ['.', 'subdir'])
-
-  def testAddRunsFromDirectoryWithRunNames(self):
-    x = event_multiplexer.EventMultiplexer()
-    tmpdir = self.get_temp_dir()
-    join = os.path.join
-    realdir = join(tmpdir, 'event_containing_directory')
-
-    _CreateCleanDirectory(realdir)
-
-    self.assertEqual(x.Runs(), {})
-
-    _AddEvents(realdir)
-    x.AddRunsFromDirectory(realdir, 'foo')
-    self.assertItemsEqual(x.Runs(), ['foo/.'])
-
-    subdir = join(realdir, 'subdir')
-    _AddEvents(subdir)
-    x.AddRunsFromDirectory(realdir, 'foo')
-    self.assertItemsEqual(x.Runs(), ['foo/.', 'foo/subdir'])
-
-  def testAddRunsFromDirectoryWalksTree(self):
-    x = event_multiplexer.EventMultiplexer()
-    tmpdir = self.get_temp_dir()
-    join = os.path.join
-    realdir = join(tmpdir, 'event_containing_directory')
-
-    _CreateCleanDirectory(realdir)
-    _AddEvents(realdir)
-    sub = join(realdir, 'subdirectory')
-    sub1 = join(sub, '1')
-    sub2 = join(sub, '2')
-    sub1_1 = join(sub1, '1')
-    _AddEvents(sub1)
-    _AddEvents(sub2)
-    _AddEvents(sub1_1)
-    x.AddRunsFromDirectory(realdir)
-
-    self.assertItemsEqual(x.Runs(), ['.', 'subdirectory/1', 'subdirectory/2',
-                                     'subdirectory/1/1'])
-
-  def testAddRunsFromDirectoryThrowsException(self):
-    x = event_multiplexer.EventMultiplexer()
-    tmpdir = self.get_temp_dir()
-
-    filepath = _AddEvents(tmpdir)
-    with self.assertRaises(ValueError):
-      x.AddRunsFromDirectory(filepath)
-
-  def testAddRun(self):
-    x = event_multiplexer.EventMultiplexer()
-    x.AddRun('run1_path', 'run1')
-    run1 = x._GetAccumulator('run1')
-    self.assertEqual(sorted(x.Runs().keys()), ['run1'])
-    self.assertEqual(run1._path, 'run1_path')
-
-    x.AddRun('run1_path', 'run1')
-    self.assertEqual(run1, x._GetAccumulator('run1'), 'loader not recreated')
-
-    x.AddRun('run2_path', 'run1')
-    new_run1 = x._GetAccumulator('run1')
-    self.assertEqual(new_run1._path, 'run2_path')
-    self.assertNotEqual(run1, new_run1)
-
-    x.AddRun('runName3')
-    self.assertItemsEqual(sorted(x.Runs().keys()), ['run1', 'runName3'])
-    self.assertEqual(x._GetAccumulator('runName3')._path, 'runName3')
-
-  def testAddRunMaintainsLoading(self):
-    x = event_multiplexer.EventMultiplexer()
-    x.Reload()
-    x.AddRun('run1')
-    x.AddRun('run2')
-    self.assertTrue(x._GetAccumulator('run1').reload_called)
-    self.assertTrue(x._GetAccumulator('run2').reload_called)
-
-
-class EventMultiplexerWithRealAccumulatorTest(tf.test.TestCase):
-
-  def testDeletingDirectoryRemovesRun(self):
-    x = event_multiplexer.EventMultiplexer()
-    tmpdir = self.get_temp_dir()
-    join = os.path.join
-    run1_dir = join(tmpdir, 'run1')
-    run2_dir = join(tmpdir, 'run2')
-    run3_dir = join(tmpdir, 'run3')
-
-    for dirname in [run1_dir, run2_dir, run3_dir]:
-      _AddEvents(dirname)
-
-    x.AddRun(run1_dir, 'run1')
-    x.AddRun(run2_dir, 'run2')
-    x.AddRun(run3_dir, 'run3')
-
-    x.Reload()
-
-    # Delete the directory, then reload.
-    shutil.rmtree(run2_dir)
-    x.Reload()
-    self.assertNotIn('run2', x.Runs().keys())
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/event_processing/io_wrapper.py b/tensorflow/tensorboard/backend/event_processing/io_wrapper.py
deleted file mode 100644
index c185f26a4f..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/io_wrapper.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""IO helper functions."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-
-import tensorflow as tf
-
-
-def IsGCSPath(path):
-  return path.startswith("gs://")
-
-
-def ListDirectoryAbsolute(directory):
-  """Yields all files in the given directory. The paths are absolute."""
-  return (os.path.join(directory, path)
-          for path in tf.gfile.ListDirectory(directory))
-
-
-def ListRecursively(top):
-  """Walks a directory tree, yielding (dir_path, file_paths) tuples.
-
-  For each of `top` and its subdirectories, yields a tuple containing the path
-  to the directory and the path to each of the contained files.  Note that
-  unlike os.Walk()/tf.gfile.Walk(), this does not list subdirectories and the
-  file paths are all absolute.
-
-  If the directory does not exist, this yields nothing.
-
-  Args:
-    top: A path to a directory..
-  Yields:
-    A list of (dir_path, file_paths) tuples.
-  """
-  for dir_path, _, filenames in tf.gfile.Walk(top):
-    yield (dir_path, (os.path.join(dir_path, filename)
-                      for filename in filenames))
diff --git a/tensorflow/tensorboard/backend/event_processing/plugin_asset_util.py b/tensorflow/tensorboard/backend/event_processing/plugin_asset_util.py
deleted file mode 100644
index 5fb7128424..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/plugin_asset_util.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Load plugin assets from disk."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os.path
-
-
-import tensorflow as tf
-
-
-_PLUGINS_DIR = "plugins"
-
-
-def _IsDirectory(parent, item):
-  """Helper that returns if parent/item is a directory."""
-  return tf.gfile.IsDirectory(os.path.join(parent, item))
-
-
-def PluginDirectory(logdir, plugin_name):
-  """Returns the plugin directory for plugin_name."""
-  return os.path.join(logdir, _PLUGINS_DIR, plugin_name)
-
-
-def ListPlugins(logdir):
-  """List all the plugins that have registered assets in logdir.
-
-  If the plugins_dir does not exist, it returns an empty list. This maintains
-  compatibility with old directories that have no plugins written.
-
-  Args:
-    logdir: A directory that was created by a TensorFlow events writer.
-
-  Returns:
-    a list of plugin names, as strings
-  """
-  plugins_dir = os.path.join(logdir, _PLUGINS_DIR)
-  if not tf.gfile.IsDirectory(plugins_dir):
-    return []
-  entries = tf.gfile.ListDirectory(plugins_dir)
-  return [x for x in entries if _IsDirectory(plugins_dir, x)]
-
-
-def ListAssets(logdir, plugin_name):
-  """List all the assets that are available for given plugin in a logdir.
-
-  Args:
-    logdir: A directory that was created by a TensorFlow summary.FileWriter.
-    plugin_name: A string name of a plugin to list assets for.
-
-  Returns:
-    A string list of available plugin assets. If the plugin subdirectory does
-    not exist (either because the logdir doesn't exist, or because the plugin
-    didn't register) an empty list is returned.
-  """
-  plugin_dir = PluginDirectory(logdir, plugin_name)
-  if not tf.gfile.IsDirectory(plugin_dir):
-    return []
-  entries = tf.gfile.ListDirectory(plugin_dir)
-  return [x for x in entries if not _IsDirectory(plugin_dir, x)]
-
-
-def RetrieveAsset(logdir, plugin_name, asset_name):
-  """Retrieve a particular plugin asset from a logdir.
-
-  Args:
-    logdir: A directory that was created by a TensorFlow summary.FileWriter.
-    plugin_name: The plugin we want an asset from.
-    asset_name: The name of the requested asset.
-
-  Returns:
-    string contents of the plugin asset.
-
-  Raises:
-    KeyError: if the asset does not exist.
-  """
-
-  asset_path = os.path.join(PluginDirectory(logdir, plugin_name), asset_name)
-  try:
-    with tf.gfile.Open(asset_path, "r") as f:
-      return f.read()
-  except tf.errors.NotFoundError:
-    raise KeyError("Asset path %s not found" % asset_path)
-  except tf.errors.OpError as e:
-    raise KeyError("Couldn't read asset path: %s, OpError %s" % (asset_path, e))
diff --git a/tensorflow/tensorboard/backend/event_processing/reservoir.py b/tensorflow/tensorboard/backend/event_processing/reservoir.py
deleted file mode 100644
index 0a1252e635..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/reservoir.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""A key-value[] store that implements reservoir sampling on the values."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import random
-import threading
-
-
-class Reservoir(object):
-  """A map-to-arrays container, with deterministic Reservoir Sampling.
-
-  Items are added with an associated key. Items may be retrieved by key, and
-  a list of keys can also be retrieved. If size is not zero, then it dictates
-  the maximum number of items that will be stored with each key. Once there are
-  more items for a given key, they are replaced via reservoir sampling, such
-  that each item has an equal probability of being included in the sample.
-
-  Deterministic means that for any given seed and bucket size, the sequence of
-  values that are kept for any given tag will always be the same, and that this
-  is independent of any insertions on other tags. That is:
-
-  >>> separate_reservoir = reservoir.Reservoir(10)
-  >>> interleaved_reservoir = reservoir.Reservoir(10)
-  >>> for i in xrange(100):
-  >>>   separate_reservoir.AddItem('key1', i)
-  >>> for i in xrange(100):
-  >>>   separate_reservoir.AddItem('key2', i)
-  >>> for i in xrange(100):
-  >>>   interleaved_reservoir.AddItem('key1', i)
-  >>>   interleaved_reservoir.AddItem('key2', i)
-
-  separate_reservoir and interleaved_reservoir will be in identical states.
-
-  See: https://en.wikipedia.org/wiki/Reservoir_sampling
-
-  Adding items has amortized O(1) runtime.
-
-  """
-
-  def __init__(self, size, seed=0, always_keep_last=True):
-    """Creates a new reservoir.
-
-    Args:
-      size: The number of values to keep in the reservoir for each tag. If 0,
-        all values will be kept.
-      seed: The seed of the random number generator to use when sampling.
-        Different values for |seed| will produce different samples from the same
-        input items.
-      always_keep_last: Whether to always keep the latest seen item in the
-        end of the reservoir. Defaults to True.
-
-    Raises:
-      ValueError: If size is negative or not an integer.
-    """
-    if size < 0 or size != round(size):
-      raise ValueError('size must be nonegative integer, was %s' % size)
-    self._buckets = collections.defaultdict(
-        lambda: _ReservoirBucket(size, random.Random(seed), always_keep_last))
-    # _mutex guards the keys - creating new keys, retrieving by key, etc
-    # the internal items are guarded by the ReservoirBuckets' internal mutexes
-    self._mutex = threading.Lock()
-
-  def Keys(self):
-    """Return all the keys in the reservoir.
-
-    Returns:
-      ['list', 'of', 'keys'] in the Reservoir.
-    """
-    with self._mutex:
-      return list(self._buckets.keys())
-
-  def Items(self, key):
-    """Return items associated with given key.
-
-    Args:
-      key: The key for which we are finding associated items.
-
-    Raises:
-      KeyError: If the key is not found in the reservoir.
-
-    Returns:
-      [list, of, items] associated with that key.
-    """
-    with self._mutex:
-      if key not in self._buckets:
-        raise KeyError('Key %s was not found in Reservoir' % key)
-      bucket = self._buckets[key]
-    return bucket.Items()
-
-  def AddItem(self, key, item, f=lambda x: x):
-    """Add a new item to the Reservoir with the given tag.
-
-    If the reservoir has not yet reached full size, the new item is guaranteed
-    to be added. If the reservoir is full, then behavior depends on the
-    always_keep_last boolean.
-
-    If always_keep_last was set to true, the new item is guaranteed to be added
-    to the reservoir, and either the previous last item will be replaced, or
-    (with low probability) an older item will be replaced.
-
-    If always_keep_last was set to false, then the new item will replace an
-    old item with low probability.
-
-    If f is provided, it will be applied to transform item (lazily, iff item is
-      going to be included in the reservoir).
-
-    Args:
-      key: The key to store the item under.
-      item: The item to add to the reservoir.
-      f: An optional function to transform the item prior to addition.
-    """
-    with self._mutex:
-      bucket = self._buckets[key]
-    bucket.AddItem(item, f)
-
-  def FilterItems(self, filterFn, key=None):
-    """Filter items within a Reservoir, using a filtering function.
-
-    Args:
-      filterFn: A function that returns True for the items to be kept.
-      key: An optional bucket key to filter. If not specified, will filter all
-        all buckets.
-
-    Returns:
-      The number of items removed.
-    """
-    with self._mutex:
-      if key:
-        if key in self._buckets:
-          return self._buckets[key].FilterItems(filterFn)
-        else:
-          return 0
-      else:
-        return sum(bucket.FilterItems(filterFn)
-                   for bucket in self._buckets.values())
-
-
-class _ReservoirBucket(object):
-  """A container for items from a stream, that implements reservoir sampling.
-
-  It always stores the most recent item as its final item.
-  """
-
-  def __init__(self, _max_size, _random=None, always_keep_last=True):
-    """Create the _ReservoirBucket.
-
-    Args:
-      _max_size: The maximum size the reservoir bucket may grow to. If size is
-        zero, the bucket has unbounded size.
-      _random: The random number generator to use. If not specified, defaults to
-        random.Random(0).
-      always_keep_last: Whether the latest seen item should always be included
-        in the end of the bucket.
-
-    Raises:
-      ValueError: if the size is not a nonnegative integer.
-    """
-    if _max_size < 0 or _max_size != round(_max_size):
-      raise ValueError('_max_size must be nonegative int, was %s' % _max_size)
-    self.items = []
-    # This mutex protects the internal items, ensuring that calls to Items and
-    # AddItem are thread-safe
-    self._mutex = threading.Lock()
-    self._max_size = _max_size
-    self._num_items_seen = 0
-    if _random is not None:
-      self._random = _random
-    else:
-      self._random = random.Random(0)
-    self.always_keep_last = always_keep_last
-
-  def AddItem(self, item, f=lambda x: x):
-    """Add an item to the ReservoirBucket, replacing an old item if necessary.
-
-    The new item is guaranteed to be added to the bucket, and to be the last
-    element in the bucket. If the bucket has reached capacity, then an old item
-    will be replaced. With probability (_max_size/_num_items_seen) a random item
-    in the bucket will be popped out and the new item will be appended
-    to the end. With probability (1 - _max_size/_num_items_seen)
-    the last item in the bucket will be replaced.
-
-    Since the O(n) replacements occur with O(1/_num_items_seen) likelihood,
-    the amortized runtime is O(1).
-
-    Args:
-      item: The item to add to the bucket.
-      f: A function to transform item before addition, if it will be kept in
-        the reservoir.
-    """
-    with self._mutex:
-      if len(self.items) < self._max_size or self._max_size == 0:
-        self.items.append(f(item))
-      else:
-        r = self._random.randint(0, self._num_items_seen)
-        if r < self._max_size:
-          self.items.pop(r)
-          self.items.append(f(item))
-        elif self.always_keep_last:
-          self.items[-1] = f(item)
-      self._num_items_seen += 1
-
-  def FilterItems(self, filterFn):
-    """Filter items in a ReservoirBucket, using a filtering function.
-
-    Filtering items from the reservoir bucket must update the
-    internal state variable self._num_items_seen, which is used for determining
-    the rate of replacement in reservoir sampling. Ideally, self._num_items_seen
-    would contain the exact number of items that have ever seen by the
-    ReservoirBucket and satisfy filterFn. However, the ReservoirBucket does not
-    have access to all items seen -- it only has access to the subset of items
-    that have survived sampling (self.items). Therefore, we estimate
-    self._num_items_seen by scaling it by the same ratio as the ratio of items
-    not removed from self.items.
-
-    Args:
-      filterFn: A function that returns True for items to be kept.
-
-    Returns:
-      The number of items removed from the bucket.
-    """
-    with self._mutex:
-      size_before = len(self.items)
-      self.items = list(filter(filterFn, self.items))
-      size_diff = size_before - len(self.items)
-
-      # Estimate a correction the number of items seen
-      prop_remaining = len(self.items) / float(
-          size_before) if size_before > 0 else 0
-      self._num_items_seen = int(round(self._num_items_seen * prop_remaining))
-      return size_diff
-
-  def Items(self):
-    """Get all the items in the bucket."""
-    with self._mutex:
-      return list(self.items)
diff --git a/tensorflow/tensorboard/backend/event_processing/reservoir_test.py b/tensorflow/tensorboard/backend/event_processing/reservoir_test.py
deleted file mode 100644
index df4757e245..0000000000
--- a/tensorflow/tensorboard/backend/event_processing/reservoir_test.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import reservoir
-
-
-class ReservoirTest(tf.test.TestCase):
-
-  def testEmptyReservoir(self):
-    r = reservoir.Reservoir(1)
-    self.assertFalse(r.Keys())
-
-  def testRespectsSize(self):
-    r = reservoir.Reservoir(42)
-    self.assertEqual(r._buckets['meaning of life']._max_size, 42)
-
-  def testItemsAndKeys(self):
-    r = reservoir.Reservoir(42)
-    r.AddItem('foo', 4)
-    r.AddItem('bar', 9)
-    r.AddItem('foo', 19)
-    self.assertItemsEqual(r.Keys(), ['foo', 'bar'])
-    self.assertEqual(r.Items('foo'), [4, 19])
-    self.assertEqual(r.Items('bar'), [9])
-
-  def testExceptions(self):
-    with self.assertRaises(ValueError):
-      reservoir.Reservoir(-1)
-    with self.assertRaises(ValueError):
-      reservoir.Reservoir(13.3)
-
-    r = reservoir.Reservoir(12)
-    with self.assertRaises(KeyError):
-      r.Items('missing key')
-
-  def testDeterminism(self):
-    """Tests that the reservoir is deterministic."""
-    key = 'key'
-    r1 = reservoir.Reservoir(10)
-    r2 = reservoir.Reservoir(10)
-    for i in xrange(100):
-      r1.AddItem('key', i)
-      r2.AddItem('key', i)
-
-    self.assertEqual(r1.Items(key), r2.Items(key))
-
-  def testBucketDeterminism(self):
-    """Tests that reservoirs are deterministic at a bucket level.
-
-    This means that only the order elements are added within a bucket matters.
-    """
-    separate_reservoir = reservoir.Reservoir(10)
-    interleaved_reservoir = reservoir.Reservoir(10)
-    for i in xrange(100):
-      separate_reservoir.AddItem('key1', i)
-    for i in xrange(100):
-      separate_reservoir.AddItem('key2', i)
-    for i in xrange(100):
-      interleaved_reservoir.AddItem('key1', i)
-      interleaved_reservoir.AddItem('key2', i)
-
-    for key in ['key1', 'key2']:
-      self.assertEqual(
-          separate_reservoir.Items(key), interleaved_reservoir.Items(key))
-
-  def testUsesSeed(self):
-    """Tests that reservoirs with different seeds keep different samples."""
-    key = 'key'
-    r1 = reservoir.Reservoir(10, seed=0)
-    r2 = reservoir.Reservoir(10, seed=1)
-    for i in xrange(100):
-      r1.AddItem('key', i)
-      r2.AddItem('key', i)
-    self.assertNotEqual(r1.Items(key), r2.Items(key))
-
-  def testFilterItemsByKey(self):
-    r = reservoir.Reservoir(100, seed=0)
-    for i in xrange(10):
-      r.AddItem('key1', i)
-      r.AddItem('key2', i)
-
-    self.assertEqual(len(r.Items('key1')), 10)
-    self.assertEqual(len(r.Items('key2')), 10)
-
-    self.assertEqual(r.FilterItems(lambda x: x <= 7, 'key2'), 2)
-    self.assertEqual(len(r.Items('key2')), 8)
-    self.assertEqual(len(r.Items('key1')), 10)
-
-    self.assertEqual(r.FilterItems(lambda x: x <= 3, 'key1'), 6)
-    self.assertEqual(len(r.Items('key1')), 4)
-    self.assertEqual(len(r.Items('key2')), 8)
-
-
-class ReservoirBucketTest(tf.test.TestCase):
-
-  def testEmptyBucket(self):
-    b = reservoir._ReservoirBucket(1)
-    self.assertFalse(b.Items())
-
-  def testFillToSize(self):
-    b = reservoir._ReservoirBucket(100)
-    for i in xrange(100):
-      b.AddItem(i)
-    self.assertEqual(b.Items(), list(xrange(100)))
-    self.assertEqual(b._num_items_seen, 100)
-
-  def testDoesntOverfill(self):
-    b = reservoir._ReservoirBucket(10)
-    for i in xrange(1000):
-      b.AddItem(i)
-    self.assertEqual(len(b.Items()), 10)
-    self.assertEqual(b._num_items_seen, 1000)
-
-  def testMaintainsOrder(self):
-    b = reservoir._ReservoirBucket(100)
-    for i in xrange(10000):
-      b.AddItem(i)
-    items = b.Items()
-    prev = -1
-    for item in items:
-      self.assertTrue(item > prev)
-      prev = item
-
-  def testKeepsLatestItem(self):
-    b = reservoir._ReservoirBucket(5)
-    for i in xrange(100):
-      b.AddItem(i)
-      last = b.Items()[-1]
-      self.assertEqual(last, i)
-
-  def testSizeOneBucket(self):
-    b = reservoir._ReservoirBucket(1)
-    for i in xrange(20):
-      b.AddItem(i)
-      self.assertEqual(b.Items(), [i])
-    self.assertEqual(b._num_items_seen, 20)
-
-  def testSizeZeroBucket(self):
-    b = reservoir._ReservoirBucket(0)
-    for i in xrange(20):
-      b.AddItem(i)
-      self.assertEqual(b.Items(), list(range(i + 1)))
-    self.assertEqual(b._num_items_seen, 20)
-
-  def testSizeRequirement(self):
-    with self.assertRaises(ValueError):
-      reservoir._ReservoirBucket(-1)
-    with self.assertRaises(ValueError):
-      reservoir._ReservoirBucket(10.3)
-
-  def testRemovesItems(self):
-    b = reservoir._ReservoirBucket(100)
-    for i in xrange(10):
-      b.AddItem(i)
-    self.assertEqual(len(b.Items()), 10)
-    self.assertEqual(b._num_items_seen, 10)
-    self.assertEqual(b.FilterItems(lambda x: x <= 7), 2)
-    self.assertEqual(len(b.Items()), 8)
-    self.assertEqual(b._num_items_seen, 8)
-
-  def testRemovesItemsWhenItemsAreReplaced(self):
-    b = reservoir._ReservoirBucket(100)
-    for i in xrange(10000):
-      b.AddItem(i)
-    self.assertEqual(b._num_items_seen, 10000)
-
-    # Remove items
-    num_removed = b.FilterItems(lambda x: x <= 7)
-    self.assertGreater(num_removed, 92)
-    self.assertEqual([], [item for item in b.Items() if item > 7])
-    self.assertEqual(b._num_items_seen,
-                     int(round(10000 * (1 - float(num_removed) / 100))))
-
-  def testLazyFunctionEvaluationAndAlwaysKeepLast(self):
-
-    class FakeRandom(object):
-
-      def randint(self, a, b):  # pylint:disable=unused-argument
-        return 999
-
-    class Incrementer(object):
-
-      def __init__(self):
-        self.n = 0
-
-      def increment_and_double(self, x):
-        self.n += 1
-        return x * 2
-
-    # We've mocked the randomness generator, so that once it is full, the last
-    # item will never get durable reservoir inclusion. Since always_keep_last is
-    # false, the function should only get invoked 100 times while filling up
-    # the reservoir. This laziness property is an essential performance
-    # optimization.
-    b = reservoir._ReservoirBucket(100, FakeRandom(), always_keep_last=False)
-    incrementer = Incrementer()
-    for i in xrange(1000):
-      b.AddItem(i, incrementer.increment_and_double)
-    self.assertEqual(incrementer.n, 100)
-    self.assertEqual(b.Items(), [x * 2 for x in xrange(100)])
-
-    # This time, we will always keep the last item, meaning that the function
-    # should get invoked once for every item we add.
-    b = reservoir._ReservoirBucket(100, FakeRandom(), always_keep_last=True)
-    incrementer = Incrementer()
-
-    for i in xrange(1000):
-      b.AddItem(i, incrementer.increment_and_double)
-    self.assertEqual(incrementer.n, 1000)
-    self.assertEqual(b.Items(), [x * 2 for x in xrange(99)] + [999 * 2])
-
-
-class ReservoirBucketStatisticalDistributionTest(tf.test.TestCase):
-
-  def setUp(self):
-    self.total = 1000000
-    self.samples = 10000
-    self.n_buckets = 100
-    self.total_per_bucket = self.total // self.n_buckets
-    self.assertEqual(self.total % self.n_buckets, 0, 'total must be evenly '
-                     'divisible by the number of buckets')
-    self.assertTrue(self.total > self.samples, 'need to have more items '
-                    'than samples')
-
-  def AssertBinomialQuantity(self, measured):
-    p = 1.0 * self.n_buckets / self.samples
-    mean = p * self.samples
-    variance = p * (1 - p) * self.samples
-    error = measured - mean
-    # Given that the buckets were actually binomially distributed, this
-    # fails with probability ~2E-9
-    passed = error * error <= 36.0 * variance
-    self.assertTrue(passed, 'found a bucket with measured %d '
-                    'too far from expected %d' % (measured, mean))
-
-  def testBucketReservoirSamplingViaStatisticalProperties(self):
-    # Not related to a 'ReservoirBucket', but instead number of buckets we put
-    # samples into for testing the shape of the distribution
-    b = reservoir._ReservoirBucket(_max_size=self.samples)
-    # add one extra item because we always keep the most recent item, which
-    # would skew the distribution; we can just slice it off the end instead.
-    for i in xrange(self.total + 1):
-      b.AddItem(i)
-
-    divbins = [0] * self.n_buckets
-    modbins = [0] * self.n_buckets
-    # Slice off the last item when we iterate.
-    for item in b.Items()[0:-1]:
-      divbins[item // self.total_per_bucket] += 1
-      modbins[item % self.n_buckets] += 1
-
-    for bucket_index in xrange(self.n_buckets):
-      divbin = divbins[bucket_index]
-      modbin = modbins[bucket_index]
-      self.AssertBinomialQuantity(divbin)
-      self.AssertBinomialQuantity(modbin)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/http_util.py b/tensorflow/tensorboard/backend/http_util.py
deleted file mode 100644
index 81a06a5f14..0000000000
--- a/tensorflow/tensorboard/backend/http_util.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""TensorBoard HTTP utilities."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import gzip
-import json
-import re
-import time
-import wsgiref.handlers
-
-import six
-import tensorflow as tf
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import json_util
-
-
-_EXTRACT_MIMETYPE_PATTERN = re.compile(r'^[^;\s]*')
-_EXTRACT_CHARSET_PATTERN = re.compile(r'charset=([-_0-9A-Za-z]+)')
-
-# Allows *, gzip or x-gzip, but forbid gzip;q=0
-# https://tools.ietf.org/html/rfc7231#section-5.3.4
-_ALLOWS_GZIP_PATTERN = re.compile(
-    r'(?:^|,|\s)(?:(?:x-)?gzip|\*)(?!;q=0)(?:\s|,|$)')
-
-_TEXTUAL_MIMETYPES = set([
-    'application/javascript',
-    'application/json',
-    'application/json+protobuf',
-    'image/svg+xml',
-    'text/css',
-    'text/csv',
-    'text/html',
-    'text/plain',
-    'text/tab-separated-values',
-    'text/x-protobuf',
-])
-
-_JSON_MIMETYPES = set([
-    'application/json',
-    'application/json+protobuf',
-])
-
-
-def Respond(request,
-            content,
-            content_type,
-            code=200,
-            expires=0,
-            content_encoding=None,
-            encoding='utf-8'):
-  """Construct a werkzeug Response.
-
-  Responses are transmitted to the browser with compression if: a) the browser
-  supports it; b) it's sane to compress the content_type in question; and c)
-  the content isn't already compressed, as indicated by the content_encoding
-  parameter.
-
-  Browser and proxy caching is completely disabled by default. If the expires
-  parameter is greater than zero then the response will be able to be cached by
-  the browser for that many seconds; however, proxies are still forbidden from
-  caching so that developers can bypass the cache with Ctrl+Shift+R.
-
-  For textual content that isn't JSON, the encoding parameter is used as the
-  transmission charset which is automatically appended to the Content-Type
-  header. That is unless of course the content_type parameter contains a
-  charset parameter. If the two disagree, the characters in content will be
-  transcoded to the latter.
-
-  If content_type declares a JSON media type, then content MAY be a dict, list,
-  tuple, or set, in which case this function has an implicit composition with
-  json_util.Cleanse and json.dumps. The encoding parameter is used to decode
-  byte strings within the JSON object; therefore transmitting binary data
-  within JSON is not permitted. JSON is transmitted as ASCII unless the
-  content_type parameter explicitly defines a charset parameter, in which case
-  the serialized JSON bytes will use that instead of escape sequences.
-
-  Args:
-    request: A werkzeug Request object. Used mostly to check the
-      Accept-Encoding header.
-    content: Payload data as byte string, unicode string, or maybe JSON.
-    content_type: Media type and optionally an output charset.
-    code: Numeric HTTP status code to use.
-    expires: Second duration for browser caching.
-    content_encoding: Encoding if content is already encoded, e.g. 'gzip'.
-    encoding: Input charset if content parameter has byte strings.
-
-  Returns:
-    A werkzeug Response object (a WSGI application).
-  """
-
-  mimetype = _EXTRACT_MIMETYPE_PATTERN.search(content_type).group(0)
-  charset_match = _EXTRACT_CHARSET_PATTERN.search(content_type)
-  charset = charset_match.group(1) if charset_match else encoding
-  textual = charset_match or mimetype in _TEXTUAL_MIMETYPES
-  if mimetype in _JSON_MIMETYPES and (isinstance(content, dict) or
-                                      isinstance(content, list) or
-                                      isinstance(content, set) or
-                                      isinstance(content, tuple)):
-    content = json.dumps(json_util.Cleanse(content, encoding),
-                         ensure_ascii=not charset_match)
-  if charset != encoding:
-    content = tf.compat.as_text(content, encoding)
-  content = tf.compat.as_bytes(content, charset)
-  if textual and not charset_match and mimetype not in _JSON_MIMETYPES:
-    content_type += '; charset=' + charset
-  if (not content_encoding and textual and
-      _ALLOWS_GZIP_PATTERN.search(request.headers.get('Accept-Encoding', ''))):
-    out = six.BytesIO()
-    f = gzip.GzipFile(fileobj=out, mode='wb', compresslevel=3)
-    f.write(content)
-    f.close()
-    content = out.getvalue()
-    content_encoding = 'gzip'
-  if request.method == 'HEAD':
-    content = ''
-  headers = []
-
-  headers.append(('Content-Length', str(len(content))))
-  if content_encoding:
-    headers.append(('Content-Encoding', content_encoding))
-  if expires > 0:
-    e = wsgiref.handlers.format_date_time(time.time() + float(expires))
-    headers.append(('Expires', e))
-    headers.append(('Cache-Control', 'private, max-age=%d' % expires))
-  else:
-    headers.append(('Expires', '0'))
-    headers.append(('Cache-Control', 'no-cache, must-revalidate'))
-
-  return wrappers.Response(
-      response=content, status=code, headers=headers, content_type=content_type)
diff --git a/tensorflow/tensorboard/backend/http_util_test.py b/tensorflow/tensorboard/backend/http_util_test.py
deleted file mode 100644
index 6b0c8d3403..0000000000
--- a/tensorflow/tensorboard/backend/http_util_test.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests HTTP utilities."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import gzip
-
-import six
-import tensorflow as tf
-from werkzeug import test as wtest
-from werkzeug import wrappers
-from tensorflow.tensorboard.backend import http_util
-
-
-class RespondTest(tf.test.TestCase):
-
-  def testHelloWorld(self):
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, '<b>hello world</b>', 'text/html')
-    self.assertEqual(r.status_code, 200)
-    self.assertEqual(r.response[0], six.b('<b>hello world</b>'))
-
-  def testHeadRequest_doesNotWrite(self):
-    builder = wtest.EnvironBuilder(method='HEAD')
-    env = builder.get_environ()
-    request = wrappers.Request(env)
-    r = http_util.Respond(request, '<b>hello world</b>', 'text/html')
-    self.assertEqual(r.status_code, 200)
-    self.assertEqual(r.response[0], six.b(''))
-
-  def testPlainText_appendsUtf8ToContentType(self):
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, 'hello', 'text/plain')
-    h = r.headers
-    self.assertEqual(h.get('Content-Type'), 'text/plain; charset=utf-8')
-
-  def testContentLength_isInBytes(self):
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, '爱', 'text/plain')
-    self.assertEqual(r.headers.get('Content-Length'), '3')
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, '爱'.encode('utf-8'), 'text/plain')
-    self.assertEqual(r.headers.get('Content-Length'), '3')
-
-  def testResponseCharsetTranscoding(self):
-    bean = '要依法治国是赞美那些谁是公义的和惩罚恶人。 - 韩非'
-
-    # input is unicode string, output is gbk string
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, bean, 'text/plain; charset=gbk')
-    self.assertEqual(r.response[0], bean.encode('gbk'))
-
-    # input is utf-8 string, output is gbk string
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, bean.encode('utf-8'), 'text/plain; charset=gbk')
-    self.assertEqual(r.response[0], bean.encode('gbk'))
-
-    # input is object with unicode strings, output is gbk json
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, {'red': bean}, 'application/json; charset=gbk')
-    self.assertEqual(r.response[0], b'{"red": "' + bean.encode('gbk') + b'"}')
-
-    # input is object with utf-8 strings, output is gbk json
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(
-        q, {'red': bean.encode('utf-8')}, 'application/json; charset=gbk')
-    self.assertEqual(r.response[0], b'{"red": "' + bean.encode('gbk') + b'"}')
-
-    # input is object with gbk strings, output is gbk json
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(
-        q, {'red': bean.encode('gbk')},
-        'application/json; charset=gbk',
-        encoding='gbk')
-    self.assertEqual(r.response[0], b'{"red": "' + bean.encode('gbk') + b'"}')
-
-  def testAcceptGzip_compressesResponse(self):
-    fall_of_hyperion_canto1_stanza1 = '\n'.join([
-        'Fanatics have their dreams, wherewith they weave',
-        'A paradise for a sect; the savage too',
-        'From forth the loftiest fashion of his sleep',
-        'Guesses at Heaven; pity these have not',
-        'Trac\'d upon vellum or wild Indian leaf',
-        'The shadows of melodious utterance.',
-        'But bare of laurel they live, dream, and die;',
-        'For Poesy alone can tell her dreams,',
-        'With the fine spell of words alone can save',
-        'Imagination from the sable charm',
-        'And dumb enchantment. Who alive can say,',
-        '\'Thou art no Poet may\'st not tell thy dreams?\'',
-        'Since every man whose soul is not a clod',
-        'Hath visions, and would speak, if he had loved',
-        'And been well nurtured in his mother tongue.',
-        'Whether the dream now purpos\'d to rehearse',
-        'Be poet\'s or fanatic\'s will be known',
-        'When this warm scribe my hand is in the grave.',
-    ])
-
-    e1 = wtest.EnvironBuilder(headers={'Accept-Encoding': '*'}).get_environ()
-    any_encoding = wrappers.Request(e1)
-
-    r = http_util.Respond(
-        any_encoding, fall_of_hyperion_canto1_stanza1, 'text/plain')
-    self.assertEqual(r.headers.get('Content-Encoding'), 'gzip')
-
-    self.assertEqual(
-        _gunzip(r.response[0]), fall_of_hyperion_canto1_stanza1.encode('utf-8'))
-
-    e2 = wtest.EnvironBuilder(headers={'Accept-Encoding': 'gzip'}).get_environ()
-    gzip_encoding = wrappers.Request(e2)
-
-    r = http_util.Respond(
-        gzip_encoding, fall_of_hyperion_canto1_stanza1, 'text/plain')
-    self.assertEqual(r.headers.get('Content-Encoding'), 'gzip')
-    self.assertEqual(
-        _gunzip(r.response[0]), fall_of_hyperion_canto1_stanza1.encode('utf-8'))
-
-    r = http_util.Respond(
-        any_encoding, fall_of_hyperion_canto1_stanza1, 'image/png')
-    self.assertEqual(
-        r.response[0], fall_of_hyperion_canto1_stanza1.encode('utf-8'))
-
-  def testJson_getsAutoSerialized(self):
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, [1, 2, 3], 'application/json')
-    self.assertEqual(r.response[0], b'[1, 2, 3]')
-
-  def testExpires_setsCruiseControl(self):
-    q = wrappers.Request(wtest.EnvironBuilder().get_environ())
-    r = http_util.Respond(q, '<b>hello world</b>', 'text/html', expires=60)
-    self.assertEqual(r.headers.get('Cache-Control'), 'private, max-age=60')
-
-
-def _gunzip(bs):
-  return gzip.GzipFile('', 'rb', 9, six.BytesIO(bs)).read()
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/json_util.py b/tensorflow/tensorboard/backend/json_util.py
deleted file mode 100644
index ab8f34a2fb..0000000000
--- a/tensorflow/tensorboard/backend/json_util.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""A module providing a function for serializing JSON values with Infinity.
-
-Python provides no way to override how json.dumps serializes
-Infinity/-Infinity/NaN; if allow_nan is true, it encodes them as
-Infinity/-Infinity/NaN, in violation of the JSON spec and in violation of what
-JSON.parse accepts. If it's false, it throws a ValueError, Neither subclassing
-JSONEncoder nor passing a function in the |default| keyword argument overrides
-this.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import math
-
-import tensorflow as tf
-
-
-_INFINITY = float('inf')
-_NEGATIVE_INFINITY = float('-inf')
-
-
-def Cleanse(obj, encoding='utf-8'):
-  """Makes Python object appropriate for JSON serialization.
-
-  - Replaces instances of Infinity/-Infinity/NaN with strings.
-  - Turns byte strings into unicode strings.
-  - Turns sets into sorted lists.
-  - Turns tuples into lists.
-
-  Args:
-    obj: Python data structure.
-    encoding: Charset used to decode byte strings.
-
-  Returns:
-    Unicode JSON data structure.
-  """
-  if isinstance(obj, int):
-    return obj
-  elif isinstance(obj, float):
-    if obj == _INFINITY:
-      return 'Infinity'
-    elif obj == _NEGATIVE_INFINITY:
-      return '-Infinity'
-    elif math.isnan(obj):
-      return 'NaN'
-    else:
-      return obj
-  elif isinstance(obj, bytes):
-    return tf.compat.as_text(obj, encoding)
-  elif isinstance(obj, list) or isinstance(obj, tuple):
-    return [Cleanse(i, encoding) for i in obj]
-  elif isinstance(obj, set):
-    return [Cleanse(i, encoding) for i in sorted(obj)]
-  elif isinstance(obj, dict):
-    return {Cleanse(k, encoding): Cleanse(v, encoding) for k, v in obj.items()}
-  else:
-    return obj
diff --git a/tensorflow/tensorboard/backend/json_util_test.py b/tensorflow/tensorboard/backend/json_util_test.py
deleted file mode 100644
index 22e815564e..0000000000
--- a/tensorflow/tensorboard/backend/json_util_test.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-
-from tensorflow.tensorboard.backend import json_util
-
-_INFINITY = float('inf')
-
-
-class FloatWrapperTest(tf.test.TestCase):
-
-  def _assertWrapsAs(self, to_wrap, expected):
-    """Asserts that |to_wrap| becomes |expected| when wrapped."""
-    actual = json_util.Cleanse(to_wrap)
-    for a, e in zip(actual, expected):
-      self.assertEqual(e, a)
-
-  def testWrapsPrimitives(self):
-    self._assertWrapsAs(_INFINITY, 'Infinity')
-    self._assertWrapsAs(-_INFINITY, '-Infinity')
-    self._assertWrapsAs(float('nan'), 'NaN')
-
-  def testWrapsObjectValues(self):
-    self._assertWrapsAs({'x': _INFINITY}, {'x': 'Infinity'})
-
-  def testWrapsObjectKeys(self):
-    self._assertWrapsAs({_INFINITY: 'foo'}, {'Infinity': 'foo'})
-
-  def testWrapsInListsAndTuples(self):
-    self._assertWrapsAs([_INFINITY], ['Infinity'])
-    # map() returns a list even if the argument is a tuple.
-    self._assertWrapsAs((_INFINITY,), ['Infinity',])
-
-  def testWrapsRecursively(self):
-    self._assertWrapsAs({'x': [_INFINITY]}, {'x': ['Infinity']})
-
-  def testTuple_turnsIntoList(self):
-    self.assertEqual(json_util.Cleanse(('a', 'b')), ['a', 'b'])
-
-  def testSet_turnsIntoSortedList(self):
-    self.assertEqual(json_util.Cleanse(set(['b', 'a'])), ['a', 'b'])
-
-  def testByteString_turnsIntoUnicodeString(self):
-    self.assertEqual(json_util.Cleanse(b'\xc2\xa3'), u'\u00a3')  # is # sterling
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/backend/process_graph.py b/tensorflow/tensorboard/backend/process_graph.py
deleted file mode 100644
index 2b314d79cb..0000000000
--- a/tensorflow/tensorboard/backend/process_graph.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Graph post-processing logic. Used by both TensorBoard and mldash."""
-
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-import tensorflow as tf
-
-
-def prepare_graph_for_ui(graph, limit_attr_size=1024,
-                         large_attrs_key='_too_large_attrs'):
-  """Prepares (modifies in-place) the graph to be served to the front-end.
-
-  For now, it supports filtering out attributes that are
-  too large to be shown in the graph UI.
-
-  Args:
-    graph: The GraphDef proto message.
-    limit_attr_size: Maximum allowed size in bytes, before the attribute
-        is considered large. Default is 1024 (1KB). Must be > 0 or None.
-        If None, there will be no filtering.
-    large_attrs_key: The attribute key that will be used for storing attributes
-        that are too large. Default is '_too_large_attrs'. Must be != None if
-        `limit_attr_size` is != None.
-
-  Raises:
-    ValueError: If `large_attrs_key is None` while `limit_attr_size != None`.
-    ValueError: If `limit_attr_size` is defined, but <= 0.
-  """
-  # Check input for validity.
-  if limit_attr_size is not None:
-    if large_attrs_key is None:
-      raise ValueError('large_attrs_key must be != None when limit_attr_size'
-                       '!= None.')
-
-    if limit_attr_size <= 0:
-      raise ValueError('limit_attr_size must be > 0, but is %d' %
-                       limit_attr_size)
-
-  # Filter only if a limit size is defined.
-  if limit_attr_size is not None:
-    for node in graph.node:
-      # Go through all the attributes and filter out ones bigger than the
-      # limit.
-      keys = list(node.attr.keys())
-      for key in keys:
-        size = node.attr[key].ByteSize()
-        if size > limit_attr_size or size < 0:
-          del node.attr[key]
-          # Add the attribute key to the list of "too large" attributes.
-          # This is used in the info card in the graph UI to show the user
-          # that some attributes are too large to be shown.
-          node.attr[large_attrs_key].list.s.append(tf.compat.as_bytes(key))
diff --git a/tensorflow/tensorboard/components/BUILD b/tensorflow/tensorboard/components/BUILD
deleted file mode 100644
index 2d7613dbfd..0000000000
--- a/tensorflow/tensorboard/components/BUILD
+++ /dev/null
@@ -1,47 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-load("//tensorflow/tensorboard/defs:vulcanize.bzl", "tensorboard_html_binary")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tensorboard",
-    srcs = [
-        "analytics.html",
-        "tensorboard.html",
-    ],
-    path = "/",
-    deps = ["//tensorflow/tensorboard/components/tf_tensorboard"],
-)
-
-tensorboard_html_binary(
-    name = "index",
-    input_path = "/tensorboard.html",
-    output_path = "/index.html",
-    deps = [":tensorboard"],
-)
-
-ts_web_library(
-    name = "trace_viewer",
-    srcs = [
-        "trace_viewer.html",
-    ],
-    path = "/",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_trace_viewer",
-    ],
-)
-
-tensorboard_html_binary(
-    name = "trace_viewer_index",
-    input_path = "/trace_viewer.html",
-    output_path = "/trace_viewer_index.html",
-    deps = [":trace_viewer"],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/analytics.html b/tensorflow/tensorboard/components/analytics.html
deleted file mode 100644
index d319f576fc..0000000000
--- a/tensorflow/tensorboard/components/analytics.html
+++ /dev/null
@@ -1,18 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<!-- TODO(jart): Give users the ability to opt-in to analytics. -->
diff --git a/tensorflow/tensorboard/components/tensorboard.html b/tensorflow/tensorboard/components/tensorboard.html
deleted file mode 100644
index afaf396614..0000000000
--- a/tensorflow/tensorboard/components/tensorboard.html
+++ /dev/null
@@ -1,26 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<title>TensorBoard</title>
-<link rel="shortcut icon" href="data:image/png;base64,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">
-<link rel="import" href="tf-tensorboard/style.html">
-<link rel="import" href="tf-tensorboard/tf-tensorboard.html">
-<link rel="import" href="analytics.html">
-<body>
-<tf-tensorboard use-hash></tf-tensorboard>
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/BUILD b/tensorflow/tensorboard/components/tf_audio_dashboard/BUILD
deleted file mode 100644
index 3bc754063c..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/BUILD
+++ /dev/null
@@ -1,50 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_audio_dashboard",
-    srcs = [
-        "tf-audio-dashboard.html",
-        "tf-audio-grid.html",
-        "tf-audio-loader.html",
-    ],
-    path = "/tf-audio-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_spinner",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-ts_web_library(
-    name = "index",
-    srcs = [
-        "demo/index.html",
-        "index.html",
-    ],
-    path = "/tf-audio-dashboard",
-    deps = [
-        ":tf_audio_dashboard",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "//tensorflow/tensorboard/demo:demo_data",
-        "@org_polymer_iron_component_page",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/demo/index.html b/tensorflow/tensorboard/components/tf_audio_dashboard/demo/index.html
deleted file mode 100644
index a1d7e968e8..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/demo/index.html
+++ /dev/null
@@ -1,67 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<link rel="import" href="../../paper-styles/typography.html">
-<link rel="import" href="../tf-audio-dashboard.html">
-
-<title>Audio Dashboard Demo</title>
-<style>
-  #container {
-    height: 300px;
-    width: 100%;
-  }
-
-  html, body {
-    margin: 0;
-    padding: 0;
-    font-family: "RobotoDraft","Roboto",sans-serif;
-  }
-
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="audio-dash-demo">
-      <template>
-        <tf-audio-dashboard id="demo" backend="[[backend]]"></tf-audio-dashboard>
-      </template>
-      <script>
-        import {Backend} from '../tf-backend/backend';
-        import {createRouter, setRouter} from '../tf-backend/router';
-
-        Polymer({
-          is: "audio-dash-demo",
-          properties: {
-            backend: {
-              type: Object,
-              value: function() {
-                return new Backend();
-              },
-            },
-          },
-          created: function() {
-            var router = createRouter("/data", true);
-            setRouter(router);
-          },
-        });
-      </script>
-    </dom-module>
-    <audio-dash-demo id="container"></audio-dash-demo>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/index.html b/tensorflow/tensorboard/components/tf_audio_dashboard/index.html
deleted file mode 100644
index 157f169265..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/index.html
+++ /dev/null
@@ -1,25 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<title>tf-audio-dashboard</title>
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<script src="../webcomponentsjs/webcomponents-lite.js"></script>
-<link rel="import" href="../iron-component-page/iron-component-page.html">
-<body>
-<iron-component-page src="tf-audio-dashboard.html"></iron-component-page>
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/test/BUILD b/tensorflow/tensorboard/components/tf_audio_dashboard/test/BUILD
deleted file mode 100644
index 3d50e5d2ca..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/test/BUILD
+++ /dev/null
@@ -1,33 +0,0 @@
-package(
-    default_testonly = True,
-    default_visibility = ["//tensorflow/tensorboard:internal"],
-)
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "test",
-    srcs = [
-        "audioDashboardTests.ts",
-        "tests.html",
-    ],
-    path = "/tf-audio-dashboard/test",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_audio_dashboard",
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:web_component_tester",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "//tensorflow/tensorboard/demo:demo_data",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    testonly = 0,
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/test/audioDashboardTests.ts b/tensorflow/tensorboard/components/tf_audio_dashboard/test/audioDashboardTests.ts
deleted file mode 100644
index 6ccd9bede6..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/test/audioDashboardTests.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import * as backend_backend from '../../tf-backend/backend';
-import {createRouter, setRouter} from '../../tf-backend/router';
-
-// TODO(dandelion): Fix me.
-declare function fixture(id: string): any;
-declare function stub(x, y: any): void;
-
-describe('audio dashboard tests', () => {
-  let audioDash;
-  let reloadCount = 0;
-  beforeEach(() => {
-    audioDash = fixture('testElementFixture');
-    const router = createRouter('/data', true);
-    setRouter(router);
-    const backend = new backend_backend.Backend();
-    audioDash.backend = backend;
-    stub('tf-audio-loader', {
-      reload: () => { reloadCount++; },
-    });
-  });
-
-  it('calling reload on dashboard reloads the audio-loaders', (done) => {
-    audioDash.backendReload().then(() => {
-      reloadCount = 0;
-      const loaders =
-          [].slice.call(audioDash.getElementsByTagName('tf-audio-loader'));
-      audioDash.frontendReload();
-      setTimeout(() => {
-        chai.assert.isTrue(reloadCount >= 2);
-        done();
-      });
-    });
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/test/tests.html b/tensorflow/tensorboard/components/tf_audio_dashboard/test/tests.html
deleted file mode 100644
index 891e8bf0c2..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/test/tests.html
+++ /dev/null
@@ -1,38 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<script src="../../web-component-tester/browser.js"></script>
-<link rel="import" href="../../tf-imports/d3.html">
-<link rel="import" href="../tf-audio-dashboard.html">
-<style>
-  html, body {
-    margin: 0;
-    padding: 0;
-    height: 100%;
-    font-family: "RobotoDraft","Roboto",sans-serif;
-  }
-</style>
-
-<test-fixture id="testElementFixture">
-  <template>
-    <tf-audio-dashboard></tf-audio-dashboard>
-  </template>
-</test-fixture>
-
-<script src="audioDashboardTests.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-dashboard.html b/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-dashboard.html
deleted file mode 100644
index 7caea7130d..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-dashboard.html
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="tf-audio-grid.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-
-<!--
-tf-audio-dashboard displays a dashboard that loads audio from a TensorFlow run.
-
-@element tf-audio-dashboard
-@demo demo/index.html
--->
-<dom-module id="tf-audio-dashboard">
-  <template>
-    <div class="center">
-      <tf-no-data-warning
-        data-type="audio"
-        show-warning="[[dataNotFound]]"
-      ></tf-no-data-warning>
-      <tf-audio-grid
-        id="audioGrid"
-        run-to-audio="[[run2tag]]"
-        audio-generator="[[dataProvider]]"
-        tags="[[tags]]"
-        runs="[[runs]]"
-      ></tf-audio-grid>
-    </div>
-
-    <style>
-      .center {
-        height: 100%;
-        width: 100%;
-        -webkit-box-sizing: border-box;
-        -moz-box-sizing: border-box;
-        box-sizing: border-box;
-      }
-      :host {
-        height: 100%;
-        display: block;
-      }
-
-    </style>
-  </template>
-  <script>
-    import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-    import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-    import {BackendBehavior} from "../tf-backend/behavior";
-
-    Polymer({
-      is: "tf-audio-dashboard",
-      factoryImpl: function(backend) {
-        this.backend = backend;
-      },
-      properties: {
-        dataType: {
-          type: Object,
-          value: "audio",
-        },
-      },
-      behaviors: [
-        DashboardBehavior("audio"),
-        ReloadBehavior("tf-audio-loader"),
-        BackendBehavior,
-      ],
-      attached: function() {
-        this.async(function() {
-          this.fire("rendered");
-        });
-      },
-      _hasAudio: function(runToAudioChange) {
-        return _.values(runToAudioChange.base).some(function(arr) {
-          return arr.length > 0;
-        });
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-grid.html b/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-grid.html
deleted file mode 100644
index c71d8bdd4b..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-grid.html
+++ /dev/null
@@ -1,183 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-styles/paper-styles.html">
-<link rel="import" href="tf-audio-loader.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../tf-dashboard-common/scrollbar-style.html">
-
-<!--
-tf-audio-grid creates a grid for examining audio data. The columns correspond
-to runs and the rows correspond to tags. Each cell is an audio clip.
-
-Structurally, it makes extensive use of flexbox for layout: it has a top-level
-columnar flexbox that contains the topRow (run names) and then a
-bottomContainer. The bottomContainer is another columnar flexbox which contains
-repeated audio-rows. Each audio-row is a row flexbox which contains a tag name
-cell, and then audio cells.
-
-In the future, we should improve on the layout by making the tag names and run names have fixed positions
-within the audio-grid, so that when you scroll you always have context (e.g. row and column names in a spreadsheet).
-For now, it just scrolls.
-
-The audio grid provides internal scroll bars (with styling) so that it can be dropped into
-a dashboard in a predictable fashion, even though the internal audio grid may be enormous.
-
-Room for future improvement:
-
-- Make it obvious when an audio didn't load due to the audio not existing.
-- Find some way to collapse sparse audio grids into denser ones (when sparsity
-is high)
-- Fix column/row names
-- Include hook for loading past audio (by step/timestamp? or index?)
-
-@element tf-audio-grid
--->
-<dom-module id="tf-audio-grid">
-  <template>
-    <style include="scrollbar-style"></style>
-    <div id="fullContainer" class="container scrollbar">
-      <div id="topRow" class="container">
-        <div class="noshrink" id="paddingCell"></div>
-        <template is="dom-if" if="[[_tagsExist(tags)]]">
-          <template
-            is="dom-repeat"
-            items="[[runs]]"
-            as="run"
-          >
-            <div class="run-name-cell noshrink">
-              <span>[[run]]</span>
-            </div>
-          </template>
-        </template>
-      </div>
-      <div id="bottomContainer" class="container">
-        <template
-          is="dom-repeat"
-          items="[[tags]]"
-          as="tag"
-        >
-          <div class="audio-row container noshrink">
-            <div class="tag-name-cell noshrink">
-              <span class="tag-name">[[tag]]</span>
-            </div>
-            <template
-              is="dom-repeat"
-              items="[[runs]]"
-              as="run"
-            >
-              <div class="audio-cell noshrink">
-                <template is="dom-if" if="[[_exists(run, tag, runToAudio.*)]]">
-                  <tf-audio-loader
-                    id="loader"
-                    run="[[run]]"
-                    tag="[[tag]]"
-                    audio-generator="[[audioGenerator]]"
-                  >
-                  </tf-audio-loader>
-                </template>
-              </div>
-            </template>
-          </div>
-        </template>
-      </div>
-    </div>
-    <style>
-      :host {
-        display: block;
-        height: 100%;
-        --audio-cell-min-height: 105px;
-      }
-      .container {
-        display: flex;
-        flex-wrap: nowrap;
-      }
-      #fullContainer {
-        width: 100%;
-        height: 100%;
-        flex-direction: column;
-        padding-top: 20px;
-        overflow: auto;
-        -webkit-box-sizing: border-box;
-        -moz-box-sizing: border-box;
-        box-sizing: border-box;
-      }
-      #topRow {
-        flex-direction: row;
-      }
-      #bottomContainer {
-        flex-direction: column;
-        height: 100%;
-        width: 100%;
-      }
-      .audio-row {
-        flex-direction: row;
-        padding-top: 5px;
-      }
-      .audio-cell {
-        background: #FAFAFA;
-        width: 300px;
-        min-height: var(--audio-cell-min-height);
-        border: 1px solid black;
-        margin-right: 3px;
-        padding: 10px;
-        box-sizing: border-box;
-      }
-      .tag-name-cell {
-        width: 300px;
-        height: var(--audio-cell-min-height);
-        display:flex;
-        flex-direction: column;
-        justify-content: center;
-      }
-      .tag-name {
-        word-wrap: break-word;
-        text-align: center;
-        white-space: nowrap;
-      }
-      .run-name-cell {
-        width: 300px;
-        text-align: center;
-        margin-right: 5px;
-      }
-      .noshrink {
-        flex-shrink: 0;
-      }
-      #paddingCell {
-        width: 300px;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-audio-grid",
-      properties: {
-        runToAudio: Object,
-        tags: Array,
-        runs: Array,
-        audioGenerator: Function,
-      },
-      _tagsExist: function(tags) {
-        return tags && tags.length > 0;
-      },
-      _exists: function (run, tag) {
-        return this.runToAudio[run].indexOf(tag) !== -1;
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-loader.html b/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-loader.html
deleted file mode 100644
index 71539537d0..0000000000
--- a/tensorflow/tensorboard/components/tf_audio_dashboard/tf-audio-loader.html
+++ /dev/null
@@ -1,237 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-slider/paper-slider.html">
-<link rel="import" href="../paper-spinner/paper-spinner-lite.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-<link rel="import" href="../tf-imports/lodash.html">
-
-<!--
-tf-audio-loader loads an individual audio clip from the TensorBoard backend.
-
-Right now it always loads the most recent audio clip. We should add support in the
-future for loading older clips.
-
-@element tf-audio-loader
--->
-<dom-module id="tf-audio-loader">
-  <style>
-    :host {
-      display: block;
-      --step-slider-knob-color: #424242;
-    }
-
-    img {
-      width: 100%;
-      height: 100%;
-      image-rendering: pixelated;
-    }
-
-    .step-description {
-      font-size: 12px;
-    }
-
-    .step-value {
-      font-weight: bold;
-    }
-
-    #audio-loading-spinner {
-      width: 14px;
-      height: 14px;
-      vertical-align: text-bottom;
-      --paper-spinner-color: var(--tb-orange-strong)
-    }
-
-    #steps {
-      height: 15px;
-      margin: 0 0 0 -15px;
-      /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
-       * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
-       * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2. */
-      width: calc(100% + 31px);
-      --paper-slider-active-color: var(--step-slider-knob-color);
-      --paper-slider-knob-color: var(--step-slider-knob-color);
-      --paper-slider-pin-color: var(--step-slider-knob-color);
-      --paper-slider-knob-start-color: var(--step-slider-knob-color);
-      --paper-slider-knob-start-border-color: var(--step-slider-knob-color);
-      --paper-slider-pin-start-color: var(--step-slider-knob-color);
-    }
-
-    #individual-audio-container audio {
-      margin: 5px 0 0 -10px;
-      width: calc(100% + 20px);
-    }
-  </style>
-  <template>
-    <template is="dom-if" if="[[_metadatas]]">
-      <template is="dom-if" if="[[_hasAtLeastOneStep(_metadatas)]]">
-        <div class="step-description">
-          step
-          <span class="step-value">
-            [[_stepValue]]
-          </span><br>
-          <template is="dom-if" if="[[_stepWallTime]]">
-            [[_stepWallTime]]
-          </template>
-          <paper-spinner-lite active
-                              id="audio-loading-spinner"
-                              hidden$=[[!_isAudioLoading]]></paper-spinner-lite>
-        </div>
-      </template>
-      <template is="dom-if" if="[[_maxStepIndex]]">
-        <paper-slider
-            id="steps"
-            immediate-value="{{_stepIndex}}"
-            max="[[_maxStepIndex]]"
-            max-markers="[[_maxStepIndex]]"
-            snaps
-            step="1"
-            value="{{_stepIndex}}"></paper-slider>
-      </template>
-      <div id="individual-audio-container"></div>
-    </template>
-  </template>
-  <script>
-    "use strict";
-
-    Polymer({
-      is: "tf-audio-loader",
-      properties: {
-        run: String,
-        tag: String,
-        audioGenerator: Function,
-        // todo: document.
-        _metadatas: Array,
-        _stepIndex: Number,
-        _stepValue: {
-          type: Number,
-          computed: "_computeStepValue(_metadatas, _stepIndex)",
-          value: 0,
-        },
-        _stepWallTime: {
-          type: Number,
-          computed: "_computeStepWallTime(_metadatas, _stepIndex)",
-          value: 0,
-        },
-        _maxStepIndex: {
-          type: Number,
-          computed: "_computeMaxStepIndex(_metadatas)",
-          value: 0,
-        },
-        _isAudioLoading: Boolean,
-        // Used to identify stale requests for audio.
-        _audioRequestId: {
-          type: Number,
-          value: 1
-        },
-      },
-      observers: [
-        "_updateAudio(_metadatas, _stepIndex)",
-      ],
-      reload: function() {
-        this.audioGenerator(this.tag, this.run).then(function(metadatas) {
-          // Set the list of available metadata.
-          this.set("_metadatas", metadatas);
-
-          // Set the index to be the last one.
-          this.set("_stepIndex", this._maxStepIndex);
-        }.bind(this));
-      },
-      ready: function() {
-        // Need to test so that it will not error if it is constructed w/o
-        // all properties (so that it's possible to use stub to mock it out)
-        if (this.run != null && this.tag != null && this.audioGenerator != null) {
-          this.reload();
-        }
-      },
-      _updateAudio: function(metadatas, stepIndex) {
-        if (!metadatas || stepIndex >= metadatas.length) {
-          // No audio to show. The audio section should be hidden.
-          return;
-        }
-
-        // Load new audio.
-        const requestId = ++this._audioRequestId;
-        this.set("_isAudioLoading", true);
-
-        // Create a new audio element. Only replace the previous one once the new audio loads.
-        let audioElement = document.createElement("audio");
-        audioElement.setAttribute("controls", true);
-        audioElement.setAttribute("loop", "loop");
-        let canPlayHandler = function() {
-          if (requestId !== this._audioRequestId) {
-            // This request is no longer relevant.
-            return;
-          }
-
-          // Remove this event listener: "canplay" apparently fires in Chrome every time playing
-          // begins again on loop. So, if we create a new audio element every time that happens, we
-          // don't actually loop.
-          audioElement.removeEventListener("canplay", canPlayHandler);
-
-          let individualAudioContainer = this.$$("#individual-audio-container");
-          individualAudioContainer.innerHTML = "";
-          Polymer.dom(individualAudioContainer).appendChild(audioElement);
-          this.set("_isAudioLoading", false);
-        }.bind(this);
-        audioElement.addEventListener("canplay", canPlayHandler);
-        audioElement.addEventListener("error", function() {
-          if (requestId !== this._audioRequestId) {
-            // This request is no longer relevant.
-            return;
-          }
-
-          // The audio could not be loaded.
-          this.$$("#individual-audio-container").innerHTML = "";
-          this.set("_isAudioLoading", false);
-        }.bind(this));
-
-        // Initiate the request for new audio.
-        var sourceElement = document.createElement("source");
-        let metadata = metadatas[stepIndex];
-        sourceElement.setAttribute("src", metadata.url);
-        sourceElement.setAttribute("type", metadata.content_type);
-        audioElement.appendChild(sourceElement);
-      },
-      _computeStepValue: function(metadatas, stepIndex) {
-        if (!metadatas || stepIndex >= metadatas.length) {
-          // No audio to show. The audio section should be hidden.
-          return 0;
-        }
-        return metadatas[stepIndex].step;
-      },
-      _computeStepWallTime: function(metadatas, stepIndex) {
-        if (!metadatas || stepIndex >= metadatas.length) {
-          // No audio to show. The audio section should be hidden.
-          return 0;
-        }
-        return metadatas[stepIndex].wall_time.toString();
-      },
-      _computeMaxStepIndex: function(metadatas) {
-        if (!metadatas || metadatas.length === 0) {
-          // No audio to show. The audio section should be hidden.
-          return 0;
-        }
-        return metadatas.length - 1;
-      },
-      _hasAtLeastOneStep: function(metadatas) {
-        return metadatas && metadatas.length > 0;
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_backend/BUILD b/tensorflow/tensorboard/components/tf_backend/BUILD
deleted file mode 100644
index 0136988e0e..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/BUILD
+++ /dev/null
@@ -1,46 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_backend",
-    srcs = [
-        "backend.ts",
-        "behavior.ts",
-        "canceller.ts",
-        "requestManager.ts",
-        "router.ts",
-        "runsStore.ts",
-        "tf-backend.html",
-        "urlPathHelpers.ts",
-    ],
-    path = "/tf-backend",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:plottable",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/vz_sorting",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_backend"],
-    destdir = "tf-backend",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports_google:lib",
-        "//tensorflow/tensorboard/components/vz_sorting:legacy",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_backend/backend.ts b/tensorflow/tensorboard/components/tf_backend/backend.ts
deleted file mode 100644
index 023414b6b7..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/backend.ts
+++ /dev/null
@@ -1,608 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {compareTagNames} from '../vz-sorting/sorting';
-import {RequestManager} from './requestManager';
-import {getRouter} from './router';
-import {demoify, queryEncoder} from './urlPathHelpers';
-
-export interface RunEnumeration {
-  histograms: string[];
-  compressedHistogramTuples: string[];
-  scalars: string[];
-  images: string[];
-  audio: string[];
-  graph: boolean;
-  run_metadata: string[];
-}
-
-export interface LogdirResponse { logdir: string; }
-
-export interface RunsResponse { [runName: string]: RunEnumeration; }
-
-export type RunToTag = {
-  [run: string]: string[];
-};
-
-export interface Datum {
-  wall_time: Date;
-  step: number;
-}
-
-export type ScalarDatum = Datum & Scalar;
-export interface Scalar { scalar: number; }
-
-export interface Text { text: string; }
-export type TextDatum = Datum & Text;
-
-export type HistogramDatum = Datum & Histogram;
-export interface Histogram {
-  min: number;
-  max: number;
-  nItems?: number;
-  sum?: number;
-  sumSquares?: number;
-  bucketRightEdges: number[];
-  bucketCounts: number[];
-}
-
-export interface HistogramBin {
-  x: number;
-  dx: number;
-  y: number;
-}
-export type HistogramSeriesDatum = HistogramSeries & Datum;
-export interface HistogramSeries { bins: HistogramBin[]; }
-
-export type ImageDatum = Datum & Image;
-export interface Image {
-  width: number;
-  height: number;
-  url: string;
-}
-
-export type AudioDatum = Datum & Audio;
-export interface Audio {
-  content_type: string;
-  url: string;
-}
-
-// A health pill encapsulates an overview of tensor element values. The value
-// field is a list of 12 numbers that shed light on the status of the tensor.
-export interface HealthPill {
-  device_name: string;
-  node_name: string;
-  output_slot: number;
-  dtype: string;
-  shape: number[];
-  value: number[];
-}
-
-// When updating this type, keep it consistent with the HealthPill interface
-// in tf_graph_common/lib/scene/scene.ts.
-export type HealthPillDatum = Datum & HealthPill;
-// A health pill response is a mapping from node name to a list of health pill
-// data entries.
-export interface HealthPillsResponse { [key: string]: HealthPillDatum[]; }
-
-// An object that encapsulates an alert issued by the debugger. This alert is
-// sent by debugging libraries after bad values (NaN, +/- Inf) are encountered.
-export interface DebuggerNumericsAlertReport {
-  device_name: string;
-  tensor_name: string;
-  first_timestamp: number;
-  nan_event_count: number;
-  neg_inf_event_count: number;
-  pos_inf_event_count: number;
-}
-// A DebuggerNumericsAlertReportResponse contains alerts issued by the debugger
-// in ascending order of timestamp. This helps the user identify for instance
-// when bad values first appeared in the model.
-export type DebuggerNumericsAlertReportResponse = DebuggerNumericsAlertReport[];
-
-export const TYPES = [
-  'scalar', 'histogram', 'compressedHistogram', 'graph', 'image', 'audio',
-  'runMetadata', 'text'
-];
-/**
- * The Backend class provides a convenient and typed interface to the backend.
- *
- * It provides methods corresponding to the different data sources on the
- * TensorBoard backend. These methods return a promise containing the data
- * from the backend. This class does some post-processing on the data; for
- * example, converting data elements tuples into js objects so that they can
- * be accessed in a more convenient and clearly-documented fashion.
- */
-export class Backend {
-  public requestManager: RequestManager;
-
-  /**
-   * Construct a Backend instance.
-   * @param requestManager The RequestManager, overwritable so you may
-   * manually clear request queue, etc. Defaults to a new RequestManager.
-   */
-  constructor(requestManager?: RequestManager) {
-    this.requestManager = requestManager || new RequestManager();
-  }
-
-  /**
-   * Returns a promise for requesting the logdir string.
-   */
-  public logdir(): Promise<LogdirResponse> {
-    return this.requestManager.request(getRouter().logdir());
-  }
-
-  /**
-   * Returns a listing of all the available data in the TensorBoard backend.
-   */
-  public runs(): Promise<RunsResponse> {
-    return this.requestManager.request(getRouter().runs());
-  }
-
-  /**
-   * Return a promise showing the Run-to-Tag mapping for scalar data.
-   */
-  public scalarTags(): Promise<RunToTag> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('scalars', '/tags'));
-  }
-
-  /**
-   * Return a promise showing the Run-to-Tag mapping for histogram data.
-   */
-  public histogramTags(): Promise<RunToTag> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('histograms', '/tags'));
-  }
-
-  /**
-   * Return a promise showing the Run-to-Tag mapping for image data.
-   */
-  public imageTags(): Promise<RunToTag> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('images', '/tags'));
-  }
-
-  /**
-   * Return a promise showing the Run-to-Tag mapping for audio data.
-   */
-  public audioTags(): Promise<RunToTag> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('audio', '/tags'));
-  }
-
-  /**
-   * Return a promise showing the Run-to-Tag mapping for compressedHistogram
-   * data.
-   */
-  public compressedHistogramTags(): Promise<RunToTag> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('distributions', '/tags'));
-  }
-
-  /**
-   * Returns a promise showing the Run-to-Tag mapping for profile data.
-   */
-  public profileTags(): Promise<RunToTag> {
-    let url = getRouter().pluginRoute('profile', '/tags');
-    if (getRouter().isDemoMode()) {
-      url += '.json';
-    }
-    return this.requestManager.request(url);
-  }
-
-  /**
-   * Return a promise showing list of runs that contain graphs.
-   */
-  public graphRuns(): Promise<string[]> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('graphs', '/runs'));
-  }
-
-  /**
-   * Return a promise showing the Run-to-Tag mapping for run_metadata objects.
-   */
-  public runMetadataTags(): Promise<RunToTag> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('graphs', '/run_metadata_tags'));
-  }
-
-
-  /**
-   * Returns a promise showing the Run-to-Tag mapping for text data.
-   */
-  public textRuns(): Promise<RunToTag> {
-    return this.requestManager.request(getRouter().textRuns());
-  }
-
-
-  /**
-   * Returns a promise containing TextDatums for given run and tag.
-   */
-  public text(tag: string, run: string): Promise<TextDatum[]> {
-    const url = getRouter().text(tag, run);
-    // tslint:disable-next-line:no-any it's convenient and harmless here
-    return this.requestManager.request(url).then(map((x: any) => {
-      x.wall_time = timeToDate(x.wall_time);
-      return x;
-    }));
-  }
-
-  /**
-   * Return a URL to fetch a graph (cf. method 'graph').
-   */
-  public graphUrl(run: string, limitAttrSize?: number, largeAttrsKey?: string):
-      string {
-    const demoMode = getRouter().isDemoMode();
-    const base = getRouter().pluginRoute('graphs', '/graph');
-    const optional = (p) => (p != null && !demoMode || undefined) && p;
-    const parameters = {
-      'run': run,
-      'limit_attr_size': optional(limitAttrSize),
-      'large_attrs_key': optional(largeAttrsKey),
-    };
-    const extension = demoMode ? '.pbtxt' : '';
-    return base + queryEncoder(parameters) + extension;
-  }
-
-  public graph(run: string, limitAttrSize?: number, largeAttrsKey?: string):
-      Promise<string> {
-    const url = this.graphUrl(run, limitAttrSize, largeAttrsKey);
-    return this.requestManager.request(url);
-  }
-
-  /**
-   * Return a promise containing ScalarDatums for given run and tag.
-   */
-  public scalar(tag: string, run: string): Promise<Array<ScalarDatum>> {
-    let p: Promise<TupleData<number>[]>;
-    const url = getRouter().pluginRunTagRoute('scalars', '/scalars')(tag, run);
-    p = this.requestManager.request(url);
-    return p.then(map(detupler(createScalar)));
-  }
-
-  /**
-   * Returns a promise for requesting the health pills for a list of nodes. This
-   * route is used by the debugger plugin.
-   */
-  public healthPills(nodeNames: string[], step?: number):
-      Promise<HealthPillsResponse> {
-    const postData = {
-      'node_names': JSON.stringify(nodeNames),
-
-      // Events files with debugger data fall under this special run.
-      'run': '__debugger_data__',
-    };
-    if (step !== undefined) {
-      // The user requested health pills for a specific step. This request
-      // might be slow since the backend reads events sequentially from disk.
-      postData['step'] = step;
-    }
-    return this.requestManager.request(getRouter().healthPills(), postData);
-  }
-
-  /**
-   * Returns a promise for alerts for bad values (detected by the debugger).
-   * This route is used by the debugger plugin.
-   */
-  public debuggerNumericsAlerts():
-      Promise<DebuggerNumericsAlertReportResponse> {
-    return this.requestManager.request(
-        getRouter().pluginRoute('debugger', '/numerics_alert_report'));
-  }
-
-  /**
-   * Return a promise containing HistogramDatums for given run and tag.
-   */
-  public histogram(tag: string, run: string):
-      Promise<Array<HistogramSeriesDatum>> {
-    let p: Promise<TupleData<HistogramTuple>[]>;
-    const url =
-        getRouter().pluginRunTagRoute('histograms', '/histograms')(tag, run);
-    p = this.requestManager.request(url);
-    return p.then(map(detupler(createHistogram))).then(function(histos) {
-      // Get the minimum and maximum values across all histograms so that the
-      // visualization is aligned for all timesteps.
-      const min = d3.min(histos, d => d.min);
-      const max = d3.max(histos, d => d.max);
-
-      return histos.map(function(histo, i) {
-        return {
-          wall_time: histo.wall_time,
-          step: histo.step,
-          bins: convertBins(histo, min, max)
-        };
-      });
-    });
-  }
-
-  /**
-   * Return a promise containing ImageDatums for given run and tag.
-   */
-  public image(tag: string, run: string): Promise<Array<ImageDatum>> {
-    const url = (getRouter().pluginRunTagRoute('images', '/images')(tag, run));
-    let p: Promise<ImageMetadata[]>;
-    p = this.requestManager.request(url);
-    return p.then(map(this.createImage.bind(this)));
-  }
-
-  /**
-   * Return a promise containing AudioDatums for given run and tag.
-   */
-  public audio(tag: string, run: string): Promise<Array<AudioDatum>> {
-    const url = (getRouter().pluginRunTagRoute('audio', '/audio')(tag, run));
-    let p: Promise<AudioMetadata[]>;
-    p = this.requestManager.request(url);
-    return p.then(map(this.createAudio.bind(this)));
-  }
-
-  /**
-   * Returns a promise containing profile data for given run and tag.
-   */
-  public profile(tag: string, run: string): Promise<string> {
-    let url = (getRouter().pluginRunTagRoute('profile', '/data')(tag, run));
-    if (getRouter().isDemoMode()) {
-      url += '.json';
-    }
-    return this.requestManager.request(url);
-  }
-
-  /**
-   * Returns the url for the RunMetadata for the given run/tag.
-   */
-  public runMetadataUrl(tag: string, run: string): string {
-    return getRouter().pluginRunTagRoute('graphs', '/run_metadata')(tag, run);
-  }
-
-  /**
-   * Returns a promise to load the string RunMetadata for given run/tag.
-   */
-  public runMetadata(tag: string, run: string): Promise<string> {
-    const url = this.runMetadataUrl(tag, run);
-    return this.requestManager.request(url);
-  }
-
-  /**
-   * Get compressedHistogram data.
-   * Unlike other methods, don't bother reprocessing this data into a nicer
-   * format. This is because we will deprecate this route.
-   */
-  private compressedHistogram(tag: string, run: string):
-      Promise<Array<Datum&CompressedHistogramTuple>> {
-    const url = (getRouter().pluginRunTagRoute(
-        'distributions', '/distributions')(tag, run));
-    let p: Promise<TupleData<CompressedHistogramTuple>[]>;
-    p = this.requestManager.request(url);
-    return p.then(map(detupler((x) => x)));
-  }
-
-  private createImage(x: ImageMetadata): Image&Datum {
-    const pluginRoute = getRouter().pluginRoute('images', '/individualImage');
-
-    let query = x.query;
-    if (pluginRoute.indexOf('?') > -1) {
-      // The route already has GET parameters. Append our parameters to them.
-      query = '&' + query;
-    } else {
-      // The route lacks GET parameters. We append them.
-      query = '?' + query;
-    }
-
-    if (getRouter().isDemoMode()) {
-      query = demoify(query);
-    }
-
-    let individualImageUrl = pluginRoute + query;
-    // Include wall_time just to disambiguate the URL and force the browser
-    // to reload the image when the URL changes. The backend doesn't care
-    // about the value.
-    individualImageUrl +=
-        getRouter().isDemoMode() ? '.png' : '&ts=' + x.wall_time;
-
-    return {
-      width: x.width,
-      height: x.height,
-      wall_time: timeToDate(x.wall_time),
-      step: x.step,
-      url: individualImageUrl,
-    };
-  }
-
-  private createAudio(x: AudioMetadata): Audio&Datum {
-    const pluginRoute = getRouter().pluginRoute('audio', '/individualAudio');
-
-    let query = x.query;
-    if (pluginRoute.indexOf('?') > -1) {
-      // The route already has GET parameters. Append our parameters to them.
-      query = '&' + query;
-    } else {
-      // The route lacks GET parameters. We append them.
-      query = '?' + query;
-    }
-
-    if (getRouter().isDemoMode()) {
-      query = demoify(query);
-    }
-
-    let individualAudioUrl = pluginRoute + query;
-    // Include wall_time just to disambiguate the URL and force the browser
-    // to reload the audio when the URL changes. The backend doesn't care
-    // about the value.
-    individualAudioUrl +=
-        getRouter().isDemoMode() ? '.wav' : '&ts=' + x.wall_time;
-
-    return {
-      content_type: x.content_type,
-      wall_time: timeToDate(x.wall_time),
-      step: x.step,
-      url: individualAudioUrl,
-    };
-  }
-}
-
-/** Given a RunToTag, return sorted array of all runs */
-export function getRuns(r: RunToTag): string[] {
-  return _.keys(r).sort(compareTagNames);
-}
-
-/** Given a RunToTag, return array of all tags (sorted + dedup'd) */
-export function getTags(r: RunToTag): string[] {
-  return _.union.apply(null, _.values(r)).sort(compareTagNames);
-}
-
-/**
- * Given a RunToTag and an array of runs, return every tag that appears for
- * at least one run.
- * Sorted, deduplicated.
- */
-export function filterTags(r: RunToTag, runs: string[]): string[] {
-  let result = [];
-  runs.forEach((x) => result = result.concat(r[x]));
-  return _.uniq(result).sort(compareTagNames);
-}
-
-function timeToDate(x: number): Date {
-  return new Date(x * 1000);
-};
-
-/**  Just a curryable map to make things cute and tidy. */
-function map<T, U>(f: (x: T) => U): (arr: T[]) => U[] {
-  return function(arr: T[]): U[] {
-    return arr.map(f);
-  };
-};
-
-/**
- * This is a higher order function that takes a function that transforms a
- * T into a G, and returns a function that takes TupleData<T>s and converts
- * them into the intersection of a G and a Datum.
- */
-function detupler<T, G>(xform: (x: T) => G): (t: TupleData<T>) => Datum & G {
-  return function(x: TupleData<T>): Datum & G {
-    // Create a G, assert it has type <G & Datum>
-    let obj = <G&Datum>xform(x[2]);
-    // ... patch in the properties of datum
-    obj.wall_time = timeToDate(x[0]);
-    obj.step = x[1];
-    return obj;
-  };
-};
-
-function createScalar(x: number): Scalar {
-  return {scalar: x};
-}
-
-function createHistogram(x: HistogramTuple): Histogram {
-  return {
-    min: x[0],
-    max: x[1],
-    nItems: x[2],
-    sum: x[3],
-    sumSquares: x[4],
-    bucketRightEdges: x[5],
-    bucketCounts: x[6],
-  };
-}
-
-/**
- * Takes histogram data as stored by tensorboard backend and converts it to
- * the standard d3 histogram data format to make it more compatible and easier
- * to visualize. When visualizing histograms, having the left edge and width
- * makes things quite a bit easier. The bins are also converted to have an
- * uniform width, what makes the visualization easier to understand.
- *
- * @param histogram A histogram from tensorboard backend.
- * @param min The leftmost edge. The binning will start on it.
- * @param max The rightmost edge. The binning will end on it.
- * @param numBins The number of bins of the converted data. The default of 30
- * is a sensible default, using more starts to get artifacts because the event
- * data is stored in buckets, and you start being able to see the aliased
- * borders between each bucket.
- * @return A histogram bin. Each bin has an x (left edge), a dx (width),
- *     and a y (count).
- *
- * If given rightedges are inclusive, then these left edges (x) are exclusive.
- */
-export function convertBins(
-    histogram: Histogram, min: number, max: number, numBins = 30) {
-  if (histogram.bucketRightEdges.length !== histogram.bucketCounts.length) {
-    throw(new Error('Edges and counts are of different lengths.'));
-  }
-
-  if (max === min) {
-    // Create bins even if all the data has a single value.
-    max = min * 1.1 + 1;
-    min = min / 1.1 - 1;
-  }
-  const binWidth = (max - min) / numBins;
-  let bucketLeft = min;  // Use the min as the starting point for the bins.
-  let bucketPos = 0;
-  return d3.range(min, max, binWidth).map((binLeft) => {
-    const binRight = binLeft + binWidth;
-
-    // Take the count of each existing bucket, multiply it by the proportion
-    // of overlap with the new bin, then sum and store as the count for the
-    // new bin. If no overlap, will add to zero, if 100% overlap, will include
-    // the full count into new bin.
-    let binY = 0;
-    while (bucketPos < histogram.bucketRightEdges.length) {
-      // Clip the right edge because right-most edge can be infinite-sized.
-      const bucketRight = Math.min(max, histogram.bucketRightEdges[bucketPos]);
-
-      const intersect =
-          Math.min(bucketRight, binRight) - Math.max(bucketLeft, binLeft);
-      const count = (intersect / (bucketRight - bucketLeft)) *
-          histogram.bucketCounts[bucketPos];
-
-      binY += intersect > 0 ? count : 0;
-
-      // If bucketRight is bigger than binRight, than this bin is finished and
-      // there is data for the next bin, so don't increment bucketPos.
-      if (bucketRight > binRight) {
-        break;
-      }
-      bucketLeft = Math.max(min, bucketRight);
-      bucketPos++;
-    }
-
-    return {x: binLeft, dx: binWidth, y: binY};
-  });
-}
-
-/**
- * The following interfaces (TupleData, HistogramTuple,
- * CompressedHistogramTuple, ImageMetadata, and AudioMetadata) describe how
- * the data is sent over from the backend.
- */
-type TupleData<T> = [number, number, T];  // wall_time, step
-
-// Min, Max, nItems, Sum, Sum_Squares, right edges of buckets, nItems in
-// buckets
-type HistogramTuple =
-    [number, number, number, number, number, number[], number[]];
-type CompressedHistogramTuple = [number, number][];  // percentile, value
-interface ImageMetadata {
-  width: number;
-  height: number;
-  wall_time: number;
-  step: number;
-  query: string;
-}
-interface AudioMetadata {
-  content_type: string;
-  wall_time: number;
-  step: number;
-  query: string;
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/behavior.ts b/tensorflow/tensorboard/components/tf_backend/behavior.ts
deleted file mode 100644
index 8df791efac..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/behavior.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import {getRuns, getTags, TYPES} from './backend';
-
-/** @polymerBehavior */
-export const BackendBehavior = {
-  properties: {
-    /** *** Required properties *** */
-    /** Data type. One of Backend.TYPES */
-    dataType: {
-      type: String,
-      observer: '_throwErrorOnUnrecognizedType',
-    },
-
-    /** Backend for data loading. */
-    backend: {
-      type: Object,
-    },
-
-    /** Should it automatically load when configured ready? Default true. */
-    autoLoad: {
-      type: Boolean,
-      value: true,
-    },
-
-    /** *** Component-provided properties *** */
-    /** Every tag available for data type (sorted, dedpulicated) */
-    tags: {
-      type: Array,
-      readOnly: true,
-      notify: true,
-    },
-
-    /** Every run available for data type (sorted) */
-    runs: {
-      type: Array,
-      readOnly: true,
-      notify: true,
-    },
-
-    /** Mapping from runs to tags for the data type */
-    run2tag: {
-      type: Object,
-      readOnly: true,
-      notify: true,
-    },
-
-    /** Promise provider for the data. Useful for passing to subcomponents */
-    dataProvider:
-        {type: Function, computed: '_getDataProvider(dataType, backend)'},
-
-    /** Has the dashboard loaded yet? */
-    loadState: {
-      type: String,
-      value: 'noload',  // [noload, pending, loaded, failure]
-      readOnly: true,
-    },
-
-    /**
-     * True if dashboard has loaded, and no tags were found.
-     * Persists through subsequent reloads (ie. still true while
-     * next load is pending) so warning won't flash away every reload
-     * when there is no data.
-     */
-    dataNotFound: {
-      type: Boolean,
-      value: false,
-      readOnly: true,
-    }
-
-  },
-  observers: ['_do_autoLoad(dataType, backend, autoLoad)'],
-  /**
-   * Reloading works in two steps:
-   * Backend reload, which gets metadata on available runs, tags, etc from
-   *   the backend.
-   * Frontend reload, which loads new data for each chart or visual display.
-   * Backend reload logic is provided by this behavior. The frontend reload
-   *   logic should be provided elsewhere, since it is component-specific.
-   * To keep things simple and consistent, we do the backend reload first,
-   *   and the frontend reload afterwards.
-   */
-  reload() {
-    return this.backendReload().then((x) => {
-      return this.frontendReload();
-    });
-  },
-  /**
-   * Load data from backend and then set run2tag, tags, runs, and loadState.
-   * Returns a promise that resolves/rejects when data is loaded.
-   */
-  backendReload() {
-    if (this.dataType == null) {
-      throw new Error('BackendBehavior: Need a dataType to reload.');
-    }
-    if (this.backend == null) {
-      throw new Error('BackendBehavior: Need a backend to reload.');
-    }
-    const runsRoute = (this.backend[this.dataType + 'Runs'] ||
-                       this.backend[this.dataType + 'Tags'])
-                          .bind(this.backend);
-    this._setLoadState('pending');
-    return runsRoute().then(
-        (x) => {
-          this._setLoadState('loaded');
-          if (_.isEqual(x, this.run2tag)) {
-            // If x and run2tag are equal, let's avoid updating everything
-            // since that can needlessly trigger run changes, reloads, etc
-            return x;
-          }
-          this._setRun2tag(x);
-          const tags = getTags(x);
-          this._setDataNotFound(tags.length === 0);
-          this._setTags(tags);
-          this._setRuns(getRuns(x));
-          return x;
-        },
-        (fail) => {
-          this._setLoadState('failure');
-          return fail;
-        });
-  },
-  _do_autoLoad(type, backend, autoLoad) {
-    if (autoLoad) {
-      this.reload();
-    }
-  },
-  _getDataProvider(dataType, backend) {
-    return this.backend[this.dataType].bind(this.backend);
-  },
-  _throwErrorOnUnrecognizedType(dataType) {
-    if (TYPES.indexOf(dataType) === -1) {
-      throw new Error('BackendBehavior: Unknown dataType ' + dataType);
-    }
-  },
-};
diff --git a/tensorflow/tensorboard/components/tf_backend/canceller.ts b/tensorflow/tensorboard/components/tf_backend/canceller.ts
deleted file mode 100644
index a196f82258..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/canceller.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * A class that allows marking promises as cancelled.
- *
- * This can be useful to, e.g., prevent old network requests from
- * stomping new ones and writing bad data.
- *
- * Usage:
- *
- *     const canceller = new Canceller();
- *     let myPromise: Promise<Foo> = getPromise();
- *     myPromise.then(canceller.cancellable(({value, cancelled} => {
- *       if (cancelled) {
- *         console.warn("Don't make promises you can't keep >:-{");
- *       }
- *       console.log("Enjoy your value:", value);
- *     }));
- *
- *     // If `myPromise` is resolved now, then `cancelled` will be `false`.
- *     canceller.cancelAll();
- *     // If `myPromise` is resolved now, then `cancelled` will be `true`.
- */
-export class Canceller {
-  /**
-   * How many times has `cancelAll` been called?
-   */
-  private cancellationCount = 0;
-
-  /**
-   * Create a cancellable task. This returns a new function that, when
-   * invoked, will pass its argument to the provided function as well as
-   * a `cancelled` argument. This argument will be `false` unless and
-   * until `cancelAll` is invoked after the creation of this task.
-   */
-  public cancellable<T, U>(f: (result: {value: T, cancelled: boolean}) => U):
-      (T) => U {
-    const originalCancellationCount = this.cancellationCount;
-    return (value) => {
-      const cancelled = this.cancellationCount !== originalCancellationCount;
-      return f({value, cancelled});
-    };
-  }
-
-  /**
-   * Mark all outstanding tasks as cancelled. Tasks not yet created will
-   * not be affected.
-   */
-  public cancelAll(): void {
-    this.cancellationCount++;
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/requestManager.ts b/tensorflow/tensorboard/components/tf_backend/requestManager.ts
deleted file mode 100644
index 0fa198416e..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/requestManager.ts
+++ /dev/null
@@ -1,177 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-interface ResolveReject {
-  resolve: Function;
-  reject: Function;
-}
-/**
- * Manages many fetch requests. Launches up to nSimultaneousRequests
- * simultaneously, and maintains a LIFO queue of requests to process when
- * more urls are requested than can be handled at once. The queue can be
- * cleared.
- *
- * When a request is made, a Promise is returned which resolves with the
- * parsed JSON result from the request.
- */
-export class RequestCancellationError extends Error {
-  public name = 'RequestCancellationError';
-}
-
-export class RequestNetworkError extends Error {
-  public name: string;
-  public req: XMLHttpRequest;
-  public url: string;
-
-  constructor(req: XMLHttpRequest, url) {
-    super();
-    this.message = `RequestNetworkError: ${req.status} at ${url}`;
-    this.name = 'RequestNetworkError';
-    this.req = req;
-    this.url = url;
-  }
-}
-
-export class RequestManager {
-  private _queue: ResolveReject[];
-  private _maxRetries: number;
-  private _nActiveRequests: number;
-  private _nSimultaneousRequests: number;
-
-  constructor(nSimultaneousRequests = 10, maxRetries = 3) {
-    this._queue = [];
-    this._nActiveRequests = 0;
-    this._nSimultaneousRequests = nSimultaneousRequests;
-    this._maxRetries = maxRetries;
-  }
-
-  /**
-   * Gives a promise that loads assets from given url (respects queuing). If
-   * postData is provided, this request will use POST, not GET. This is an
-   * object mapping POST keys to string values.
-   */
-  public request(url: string, postData?: {[key: string]: string}):
-      Promise<any> {
-    const promise =
-        new Promise((resolve, reject) => {
-          const resolver = {resolve: resolve, reject: reject};
-          this._queue.push(resolver);
-          this.launchRequests();
-        })
-            .then(() => {
-              return this.promiseWithRetries(url, this._maxRetries, postData);
-            })
-            .then(
-                (response) => {
-                  // Success - Let's free space for another active
-                  // request, and launch it
-                  this._nActiveRequests--;
-                  this.launchRequests();
-                  return response;
-                },
-                (rejection) => {
-                  if (rejection.name === 'RequestNetworkError') {
-                    // If we failed due to network error, we should
-                    // decrement
-                    // _nActiveRequests because this request was
-                    // active
-                    this._nActiveRequests--;
-                    this.launchRequests();
-                  }
-                  return Promise.reject(rejection);
-                });
-    return promise;
-  }
-
-  public clearQueue() {
-    while (this._queue.length > 0) {
-      this._queue.pop().reject(
-          new RequestCancellationError('Request cancelled by clearQueue'));
-    }
-  }
-
-  /* Return number of currently pending requests */
-  public activeRequests(): number {
-    return this._nActiveRequests;
-  }
-
-  /* Return total number of outstanding requests (includes queue) */
-  public outstandingRequests(): number {
-    return this._nActiveRequests + this._queue.length;
-  }
-
-  private launchRequests() {
-    while (this._nActiveRequests < this._nSimultaneousRequests &&
-           this._queue.length > 0) {
-      this._nActiveRequests++;
-      this._queue.pop().resolve();
-    }
-  }
-
-  /**
-   * Try to request a given URL using overwritable _promiseFromUrl method.
-   * If the request fails for any reason, we will retry up to maxRetries
-   * times. In practice, this will help us paper over transient network issues
-   * like '502 Bad Gateway'.
-   * By default, Chrome displays network errors in console, so
-   * the user will be able to tell when the requests are failing. I think this
-   * is a feature, if the request failures and retries are causing any
-   * pain to users, they can see it and file issues.
-   */
-  private promiseWithRetries(
-      url: string, maxRetries: number, postData?: {[key: string]: string}) {
-    var success = (x) => x;
-    var failure = (x) => {
-      if (maxRetries > 0) {
-        return this.promiseWithRetries(url, maxRetries - 1, postData);
-      } else {
-        return Promise.reject(x);
-      }
-    };
-    return this._promiseFromUrl(url, postData).then(success, failure);
-  }
-
-  /* Actually get promise from url using XMLHttpRequest */
-  protected _promiseFromUrl(url: string, postData?: {[key: string]: string}) {
-    return new Promise((resolve, reject) => {
-      let req = new XMLHttpRequest();
-      req.open(postData ? 'POST' : 'GET', url);
-
-      let formData;
-      if (postData) {
-        // We are to make a POST request.
-        formData = new FormData();
-        for (let postKey in postData) {
-          if (postKey) {
-            // The linter requires 'for in' loops to be filtered by an if
-            // condition.
-            formData.append(postKey, postData[postKey]);
-          }
-        }
-      }
-      req.onload = function() {
-        if (req.status === 200) {
-          resolve(JSON.parse(req.responseText));
-        } else {
-          reject(new RequestNetworkError(req, url));
-        }
-      };
-      req.onerror = function() {
-        reject(new RequestNetworkError(req, url));
-      };
-      req.send(formData);
-    });
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/router.ts b/tensorflow/tensorboard/components/tf_backend/router.ts
deleted file mode 100644
index 598546004e..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/router.ts
+++ /dev/null
@@ -1,97 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {demoify, queryEncoder} from './urlPathHelpers'
-
-export type RunTagUrlFn = (tag: string, run: string) => string;
-
-export interface Router {
-  logdir: () => string;
-  runs: () => string;
-  isDemoMode: () => boolean;
-  textRuns: () => string;
-  text: RunTagUrlFn;
-  healthPills: () => string;
-  pluginRoute: (pluginName: string, route: string) => string;
-  pluginRunTagRoute: (pluginName: string, route: string) => RunTagUrlFn;
-}
-;
-
-/**
- * Create a router for communicating with the TensorBoard backend. You
- * can pass this to `setRouter` to make it the global router.
- *
- * @param dataDir {string} The base prefix for finding data on server.
- * @param demoMode {boolean} Whether to modify urls for filesystem demo usage.
- */
-export function createRouter(dataDir = 'data', demoMode = false): Router {
-  var clean = demoMode ? demoify : (x) => x;
-  if (dataDir[dataDir.length - 1] === '/') {
-    dataDir = dataDir.slice(0, dataDir.length - 1);
-  }
-  function standardRoute(route: string, demoExtension = '.json'):
-      ((tag: string, run: string) => string) {
-    return function(tag: string, run: string): string {
-      var url =
-          dataDir + '/' + route + clean(queryEncoder({tag: tag, run: run}));
-      if (demoMode) {
-        url += demoExtension;
-      }
-      return url;
-    };
-  }
-  function pluginRoute(pluginName: string, route: string): string {
-    return `${dataDir}/plugin/${pluginName}${route}`;
-  }
-  function pluginRunTagRoute(pluginName: string, route: string):
-      ((tag: string, run: string) => string) {
-    const base = pluginRoute(pluginName, route);
-    return (tag, run) => base + clean(queryEncoder({tag, run}));
-  }
-  return {
-    logdir: () => dataDir + '/logdir',
-    runs: () => dataDir + '/runs' + (demoMode ? '.json' : ''),
-    isDemoMode: () => demoMode,
-    healthPills: () => dataDir + '/plugin/debugger/health_pills',
-    textRuns: () => dataDir + '/plugin/text/runs' + (demoMode ? '.json' : ''),
-    text: standardRoute('plugin/text/text'),
-    pluginRoute,
-    pluginRunTagRoute,
-  };
-};
-
-let _router: Router = createRouter();
-
-/**
- * @return {Router} the global router
- */
-export function getRouter(): Router {
-  return _router;
-}
-
-/**
- * Set the global router, to be returned by future calls to `getRouter`.
- * You may wish to invoke this if you are running a demo server with a
- * custom path prefix, or if you have customized the TensorBoard backend
- * to use a different path.
- *
- * @param {Router} router the new global router
- */
-export function setRouter(router: Router): void {
-  if (router == null) {
-    throw new Error('Router required, but got: ' + router);
-  }
-  _router = router;
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/runsStore.ts b/tensorflow/tensorboard/components/tf_backend/runsStore.ts
deleted file mode 100644
index bcaff994ce..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/runsStore.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import {RequestManager} from './requestManager';
-import {getRouter} from './router';
-
-let runs: string[] = [];
-
-export type Listener = () => void;
-const listeners = new Set<Listener>();
-
-const requestManager = new RequestManager(1 /* simultaneous request */);
-
-/**
- * Register a listener (nullary function) to be called when new runs are
- * available.
- */
-export function addListener(listener: Listener): void {
-  listeners.add(listener);
-}
-
-/**
- * Remove a listener registered with `addListener`.
- */
-export function removeListener(listener: Listener): void {
-  listeners.delete(listener);
-}
-
-/**
- * Asynchronously load or reload the runs data. Listeners will be
- * invoked if this causes the runs data to change.
- *
- * @see addListener
- * @return {Promise<void>} a promise that resolves when the runs have
- * loaded
- */
-export function fetchRuns(): Promise<void> {
-  const url = getRouter().runs();
-  return requestManager.request(url).then(newRuns => {
-    if (!_.isEqual(runs, newRuns)) {
-      runs = newRuns;
-      listeners.forEach(listener => {
-        listener();
-      });
-    }
-  });
-}
-
-/**
- * Get the current list of runs. If no data is available, this will be
- * an empty array (i.e., there is no distinction between "no runs" and
- * "no runs yet").
- */
-export function getRuns(): string[] {
-  return runs.slice();
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/test/BUILD b/tensorflow/tensorboard/components/tf_backend/test/BUILD
deleted file mode 100644
index da70f8a9da..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/BUILD
+++ /dev/null
@@ -1,32 +0,0 @@
-package(
-    default_testonly = True,
-    default_visibility = ["//tensorflow/tensorboard:internal"],
-)
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "test",
-    srcs = [
-        "tests.html",
-        "backendTests.ts",
-        "behaviorTests.ts",
-        "requestManagerTests.ts",
-    ] + glob(["data/**"]),
-    path = "/tf-backend/test",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:web_component_tester",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    testonly = 0,
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_backend/test/backendTests.ts b/tensorflow/tensorboard/components/tf_backend/test/backendTests.ts
deleted file mode 100644
index 029c835912..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/backendTests.ts
+++ /dev/null
@@ -1,294 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import {Backend, convertBins, filterTags, getRuns, getTags, RunToTag, TYPES} from '../backend';
-import {RequestManager} from '../requestManager';
-import {createRouter, setRouter} from '../router';
-import {BAD_CHARACTERS, demoify, queryEncoder} from '../urlPathHelpers';
-
-describe('urlPathHelpers', () => {
-  it('demoify works as expected', () => {
-    const demoified = demoify(BAD_CHARACTERS);
-    let allClean = '';
-    for (let i = 0; i < BAD_CHARACTERS.length; i++) {
-      allClean += '_';
-    }
-    chai.assert.equal(demoified, allClean, 'cleaning the BAD_CHARACTERS works');
-    chai.assert.equal(demoify('foozod'), 'foozod', 'doesnt change safe string');
-    chai.assert.equal(demoify('foo zod (2)'), 'foo_zod__2_', 'simple case');
-  });
-
-  it('queryEncoder works with demoify on spaces and parens', () => {
-    const params = {foo: 'something with spaces and (parens)'};
-    const actual = demoify(queryEncoder(params));
-    const expected = '_foo_something_with_spaces_and__28parens_29';
-    chai.assert.equal(actual, expected);
-  });
-});
-
-function assertIsDatum(x) {
-  chai.assert.isNumber(x.step);
-  chai.assert.instanceOf(x.wall_time, Date);
-}
-
-describe('backend tests', () => {
-  let backend: Backend;
-  let rm: RequestManager;
-  const base = 'data';
-  const demoRouter = createRouter(base, /*demoMode=*/true);
-  beforeEach(() => {
-    // Construct a demo Backend (third param is true)
-    setRouter(demoRouter);
-    backend = new Backend();
-    rm = new RequestManager();
-  });
-
-  it('runs are loaded properly', (done) => {
-    const runsResponse = backend.runs();
-    const actualRuns = rm.request(demoRouter.runs());
-    Promise.all([runsResponse, actualRuns]).then((values) => {
-      chai.assert.deepEqual(values[0], values[1]);
-      done();
-    });
-  });
-
-  it('scalars are loaded properly', (done) => {
-    backend.scalar('cross_entropy (1)', 'run1').then((s) => {
-      // just check the data got reformatted properly
-      const aScalar = s[s.length - 1];
-      assertIsDatum(aScalar);
-      chai.assert.isNumber(aScalar.scalar);
-      // verify date conversion works
-      chai.assert.equal(aScalar.wall_time.valueOf(), 40000);
-      done();
-    });
-  });
-
-  it('histograms are loaded properly', (done) => {
-    backend.histogram('histo1', 'run1').then((histos) => {
-      const histo = histos[0];
-      assertIsDatum(histo);
-      chai.assert.instanceOf(histo.bins, Array);
-      done();
-    });
-  });
-
-  it('all registered types have handlers', () => {
-    TYPES.forEach((t: string) => {
-      chai.assert.isDefined(backend[t], t);
-      chai.assert.isDefined(backend[t + 'Runs'], t + 'Runs');
-    });
-  });
-
-  it('images are loaded properly', (done) => {
-    backend.image('im1', 'run1').then((images) => {
-      const image = images[0];
-      assertIsDatum(image);
-      chai.assert.isNumber(image.width);
-      chai.assert.isNumber(image.height);
-      done();
-    });
-  });
-
-  it('audio is loaded properly', (done) => {
-    backend.audio('audio1', 'run1').then((audioClips) => {
-      const audio = audioClips[0];
-      assertIsDatum(audio);
-      chai.assert.equal(audio.content_type, 'audio/wav');
-      done();
-    });
-  });
-
-  it('trailing slash removed from base route', () => {
-    const r = createRouter('foo/');
-    chai.assert.equal(r.runs(), 'foo/runs');
-  });
-
-  it('run helper methods work', (done) => {
-    const scalar = {run1: ['cross_entropy (1)'], fake_run_no_data: ['scalar2']};
-    const image = {run1: ['im1'], fake_run_no_data: ['im1', 'im2']};
-    const audio = {run1: ['audio1'], fake_run_no_data: ['audio1', 'audio2']};
-    const runMetadata = {run1: ['step99'], fake_run_no_data: ['step99']};
-    const graph = ['fake_run_no_data'];
-    let count = 0;
-    function next() {
-      count++;
-      if (count === 4) {
-        done();
-      }
-    }
-    backend.scalarTags().then((x) => {
-      chai.assert.deepEqual(x, scalar);
-      next();
-    });
-    backend.imageTags().then((x) => {
-      chai.assert.deepEqual(x, image);
-      next();
-    });
-    backend.audioTags().then((x) => {
-      chai.assert.deepEqual(x, audio);
-      next();
-    });
-    backend.runMetadataTags().then((x) => {
-      chai.assert.deepEqual(x, runMetadata);
-      next();
-    });
-    backend.graphRuns().then((x) => {
-      chai.assert.deepEqual(x, graph);
-      next();
-    });
-  });
-
-  it('runToTag helpers work', () => {
-    const r2t: RunToTag = {
-      run1: ['foo', 'bar', 'zod'],
-      run2: ['zod', 'zoink'],
-      a: ['foo', 'zod']
-    };
-    const empty1: RunToTag = {};
-    const empty2: RunToTag = {run1: [], run2: []};
-    chai.assert.deepEqual(getRuns(r2t), ['a', 'run1', 'run2']);
-    chai.assert.deepEqual(getTags(r2t), ['bar', 'foo', 'zod', 'zoink']);
-    chai.assert.deepEqual(filterTags(r2t, ['run1', 'run2']), getTags(r2t));
-    chai.assert.deepEqual(filterTags(r2t, ['run1']), ['bar', 'foo', 'zod']);
-    chai.assert.deepEqual(
-        filterTags(r2t, ['run2', 'a']), ['foo', 'zod', 'zoink']);
-
-    chai.assert.deepEqual(getRuns(empty1), []);
-    chai.assert.deepEqual(getTags(empty1), []);
-
-    chai.assert.deepEqual(getRuns(empty2), ['run1', 'run2']);
-    chai.assert.deepEqual(getTags(empty2), []);
-  });
-});
-
-describe('Verify that the histogram format conversion works.', () => {
-
-  function assertHistogramEquality(h1, h2) {
-    h1.forEach((b1, i) => {
-      const b2 = h2[i];
-      chai.assert.closeTo(b1.x, b2.x, 1e-10);
-      chai.assert.closeTo(b1.dx, b2.dx, 1e-10);
-      chai.assert.closeTo(b1.y, b2.y, 1e-10);
-    });
-  }
-
-  it('Throws and error if the inputs are of different lengths', () => {
-    chai.assert.throws(() => {
-      convertBins(
-          {bucketRightEdges: [0], bucketCounts: [1, 2], min: 1, max: 2}, 1, 2,
-          2);
-    }, 'Edges and counts are of different lengths.');
-  });
-
-  it('Handles data with no bins', () => {
-    chai.assert.deepEqual(
-        convertBins(
-            {bucketRightEdges: [], bucketCounts: [], min: 0, max: 0}, 0, 0, 0),
-        []);
-  });
-
-  it('Handles data with one bin', () => {
-    const counts = [1];
-    const rightEdges = [1.21e-12];
-    const histogram = [{x: 1.1e-12, dx: 1.21e-12 - 1.1e-12, y: 1}];
-    const newHistogram = convertBins(
-        {
-          bucketRightEdges: rightEdges,
-          bucketCounts: counts,
-          min: 1.1e-12,
-          max: 1.21e-12
-        },
-        1.1e-12, 1.21e-12, 1);
-    assertHistogramEquality(newHistogram, histogram);
-  });
-
-  it('Handles data with two bins.', () => {
-    const counts = [1, 2];
-    const rightEdges = [1.1e-12, 1.21e-12];
-    const histogram = [
-      {x: 1.0e-12, dx: 1.05e-13, y: 1.09090909090909},
-      {x: 1.105e-12, dx: 1.05e-13, y: 1.9090909090909}
-    ];
-    const newHistogram = convertBins(
-        {
-          bucketRightEdges: rightEdges,
-          bucketCounts: counts,
-          min: 1.0e-12,
-          max: 1.21e-12
-        },
-        1.0e-12, 1.21e-12, 2);
-    assertHistogramEquality(newHistogram, histogram);
-  });
-
-  it('Handles a domain that crosses zero, but doesn\'t include zero as ' +
-         'an edge.',
-     () => {
-       const counts = [1, 2];
-       const rightEdges = [-1.0e-12, 1.0e-12];
-       const histogram = [
-         {x: -1.1e-12, dx: 1.05e-12, y: 1.95},
-         {x: -0.5e-13, dx: 1.05e-12, y: 1.05}
-       ];
-       const newHistogram = convertBins(
-           {
-             bucketRightEdges: rightEdges,
-             bucketCounts: counts,
-             min: -1.1e-12,
-             max: 1.0e-12
-           },
-           -1.1e-12, 1.0e-12, 2);
-       assertHistogramEquality(newHistogram, histogram);
-     });
-
-  it('Handles a histogram of all zeros', () => {
-    const h = {
-      min: 0,
-      max: 0,
-      nItems: 51200,
-      sum: 0,
-      sumSquares: 0,
-      bucketRightEdges: [0, 1e-12, 1.7976931348623157e+308],
-      bucketCounts: [0, 51200, 0],
-      wall_time: '2017-01-25T02:30:11.257Z',
-      step: 0
-    };
-    const newHistogram = convertBins(h, 0, 0, 5);
-    const expectedHistogram = [
-      {x: -1, dx: 0.4, y: 0}, {x: -0.6, dx: 0.4, y: 0},
-      {x: -0.2, dx: 0.4, y: 51200}, {x: 0.2, dx: 0.4, y: 0},
-      {x: 0.6, dx: 0.4, y: 0}
-    ];
-    assertHistogramEquality(newHistogram, expectedHistogram);
-  });
-
-  it('Handles a right-most right edge that extends to very large number.',
-     () => {
-       const counts = [1, 2, 3];
-       const rightEdges = [0, 1.0e-12, 1.0e14];
-       const histogram = [
-         {x: -1.0e-12, dx: 0.7e-12, y: 0.7}, {x: -0.3e-12, dx: 0.7e-12, y: 1.1},
-         {x: 0.4e-12, dx: 0.7e-12, y: 4.2}
-       ];
-       const newHistogram = convertBins(
-           {
-             bucketRightEdges: rightEdges,
-             bucketCounts: counts,
-             min: -1.0e-12,
-             max: 1.1e-12
-           },
-           -1.0e-12, 1.1e-12, 3);
-       assertHistogramEquality(newHistogram, histogram);
-     });
-});
diff --git a/tensorflow/tensorboard/components/tf_backend/test/behaviorTests.ts b/tensorflow/tensorboard/components/tf_backend/test/behaviorTests.ts
deleted file mode 100644
index 6bf328140e..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/behaviorTests.ts
+++ /dev/null
@@ -1,165 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {Backend, getRuns, getTags, RunToTag} from '../backend'
-import {BackendBehavior} from '../behavior'
-
-declare function fixture(id: string): void;
-
-window.addEventListener('WebComponentsReady', function() {
-  Polymer({
-    is: 'test-element',
-    behaviors: [BackendBehavior],
-    frontendReload: function() {
-      // no-op
-    },
-  });
-});
-
-describe('data-behavior', function() {
-  let testElement;
-  let resolve;
-  let reject;
-  const fakeBackend = {
-    scalarTags() {
-      return new Promise((_resolve, _reject) => {
-        resolve = (x) => _resolve(x);
-        reject = (x) => _reject(x);
-      });
-    },
-    scalar(x) {
-      return this;
-    },
-  };
-  beforeEach(function() {
-    testElement = fixture('testElementFixture');
-    testElement.autoLoad = false;
-    testElement.backend = fakeBackend;
-    testElement.dataType = 'scalar';
-  });
-
-  it('load states work as expected', function(done) {
-    chai.assert.equal(testElement.loadState, 'noload');
-    var reloaded = testElement.reload();
-    chai.assert.equal(testElement.loadState, 'pending');
-    resolve();
-    reloaded
-        .then(function() {
-          chai.assert.equal(testElement.loadState, 'loaded');
-          var reloaded2 = testElement.reload();
-          chai.assert.equal(testElement.loadState, 'pending');
-          reject();
-          return reloaded2;
-        })
-        .then(function() {
-          chai.assert.equal(testElement.loadState, 'failure');
-          done();
-        });
-  });
-
-  it('data provider set appropriately', function() {
-    chai.assert.deepEqual(testElement.dataProvider(), testElement.backend);
-  });
-
-  it('loads data as expected', function(done) {
-    var r2t: RunToTag = {
-      run1: ['foo', 'bar', 'zod'],
-      run2: ['zoink', 'zow'],
-      run3: ['.'],
-    };
-    var tags = getTags(r2t);
-    var runs = getRuns(r2t);
-    testElement.backend = fakeBackend;
-    testElement.dataType = 'scalar';
-    testElement.reload().then(function(x) {
-      chai.assert.deepEqual(testElement.run2tag, r2t);
-      chai.assert.deepEqual(testElement.runs, runs);
-      chai.assert.deepEqual(testElement.tags, tags);
-      done();
-    });
-    resolve(r2t);
-  });
-
-  it('errors thrown on bad data types', function() {
-    testElement.backend = undefined;
-    chai.assert.throws(function() {
-      testElement.dataType = 'foo';
-    });
-    testElement.dataType = 'scalar';
-    testElement.dataType = 'graph';
-    testElement.dataType = 'histogram';
-  });
-
-  it('dataNotFound flag works', function(done) {
-    chai.assert.isFalse(testElement.dataNotFound, 'initially false');
-    var next = testElement.reload();
-    chai.assert.isFalse(testElement.dataNotFound, 'still false while pending');
-    resolve({foo: [], bar: []});
-    next.then(() => {
-      chai.assert.isTrue(testElement.dataNotFound, 'true on empty data');
-      var last = testElement.reload();
-      chai.assert.isTrue(testElement.dataNotFound, 'still true while pending');
-      resolve({foo: ['bar'], bar: ['zod']});
-      last.then(() => {
-        chai.assert.isFalse(
-            testElement.dataNotFound, 'false now that we have data');
-        done();
-      });
-    });
-  });
-
-  it('reloads as soon as setup, if autoReload is true', function(done) {
-    var r2t = {foo: [], bar: []};
-    var fakeBackend = {
-      scalarTags: () => Promise.resolve(r2t),
-      scalar: () => null,
-    };
-    testElement = fixture('testElementFixture');
-    testElement.dataType = 'scalar';
-    testElement.backend = fakeBackend;
-    setTimeout(() => {
-      chai.assert.equal(testElement.run2tag, r2t);
-      done();
-    });
-  });
-
-  it('doesn\'t mutate props if backend returns same data', function(done) {
-    var r2t_1 = {foo: ['1', '2'], bar: ['3', '4']};
-    var r2t_2 = {foo: ['1', '2'], bar: ['3', '4']};
-    var fakeBackend = {
-      scalarTags: () => Promise.resolve(r2t_1),
-      scalar: () => null,
-    };
-    testElement.backend = fakeBackend;
-    testElement.reload().then(() => {
-      fakeBackend.scalarTags = () => Promise.resolve(r2t_2);
-      var tags = testElement.tags;
-      testElement.reload().then(() => {
-        // shallow equality ensures it wasn't recomputed
-        chai.assert.equal(tags, testElement.tags, 'tags was not recomputed');
-        done();
-      });
-    });
-  });
-
-  // TODO(dandelion): Fix this test.
-  it('reload calls frontendReload', function(done) {
-    testElement.frontendReload = function() {
-      done();
-    };
-    testElement.reload();
-  });
-
-});
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/audio_run_run1_tag_audio1.json b/tensorflow/tensorboard/components/tf_backend/test/data/audio_run_run1_tag_audio1.json
deleted file mode 100644
index 21a00f198d..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/audio_run_run1_tag_audio1.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"wall_time": 0, "step": 0, "query": "index=0&tag=audio1&run=run1", "content_type": "audio/wav"}]
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/compressedHistograms_run_run1_tag_histo1.json b/tensorflow/tensorboard/components/tf_backend/test/data/compressedHistograms_run_run1_tag_histo1.json
deleted file mode 100644
index 8b4c088392..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/compressedHistograms_run_run1_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0, 0, [[0, -2.3150592308536755], [668, -2.0967547155036605], [1587, -1.4326244423655616], [3085, -0.8871306575801902], [5000, -0.09312398815580714], [6915, 0.2584093405812282], [8413, 0.8895470642005087], [9332, 1.3198979614453679], [10000, 1.6793308878855118]]], [100.0, 10, [[0, -1.3417572789138936], [668, -1.183563374619141], [1587, -0.48920418783271574], [3085, 0.29326906896076954], [5000, 0.56953784145381], [6915, 0.8684655583499333], [8413, 1.4133127368907181], [9332, 1.906140650457873], [10000, 2.135771998171255]]], [200.0, 20, [[0, -1.5066917525035333], [668, -1.3910909571770793], [1587, -0.902737218885874], [3085, -0.3807791904765027], [5000, 0.38900200905253046], [6915, 0.8209734209339482], [8413, 1.302385856695965], [9332, 1.9324626053521639], [10000, 2.957505317875451]]], [300.0, 30, [[0, -0.5430457051469562], [668, -0.4626161834245273], [1587, 0.21573949543027715], [3085, 0.37353741100174215], [5000, 0.6891407881591103], [6915, 1.0927156232630852], [8413, 1.2745337159550916], [9332, 1.4321116832891605], [10000, 2.1913774993059034]]], [400.0, 40, [[0, -0.3584790755077172], [668, -0.33301611509753215], [1587, -0.1089466072951948], [3085, 0.5792199847585249], [5000, 1.220854943811942], [6915, 1.759829438421432], [8413, 2.3072559906741614], [9332, 2.753036118353921], [10000, 3.0267252195784047]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/example.json b/tensorflow/tensorboard/components/tf_backend/test/data/example.json
deleted file mode 100644
index 8adc6fb896..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/example.json
+++ /dev/null
@@ -1,4 +0,0 @@
-{
-  "foo": 3,
-  "bar": "zoidberg"
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/histograms_run_run1_tag_histo1.json b/tensorflow/tensorboard/components/tf_backend/test/data/histograms_run_run1_tag_histo1.json
deleted file mode 100644
index a5600a356e..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/histograms_run_run1_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[400.0, 40, [-0.3584790755077172, 3.0267252195784047, 20.0, 24.012225532303315, 48.29045006426564, [-0.35363819004775493, -0.29226296698161564, -0.19961953895336082, 0.3214892636797772, 0.5177616740489182, 0.56953784145381, 0.6264916255991911, 0.7580548669750213, 0.8338603536725235, 1.220854943811942, 1.3429404381931362, 1.47723448201245, 1.624957930213695, 1.7874537232350647, 1.9661990955585713, 2.379100905625872, 2.6170109961884593, 3.1665833053880363], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/images_run_run1_tag_im1.json b/tensorflow/tensorboard/components/tf_backend/test/data/images_run_run1_tag_im1.json
deleted file mode 100644
index fd2a96b62f..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/images_run_run1_tag_im1.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"wall_time": 0, "step": 0, "query": "index=0&tag=im1&run=run1", "width": 1, "height": 1}]
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/individualImage_index_0_tag_im1_run_run1.png b/tensorflow/tensorboard/components/tf_backend/test/data/individualImage_index_0_tag_im1_run_run1.png
deleted file mode 100644
index f191b280ce91e6cb8c387735c10ef9bc5da6c83b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 42
ocmZ?wbhEHbWMp7uXkY+=|Ns9h{$ybUF?B!$NQQxl(S^Yp0J!f4_W%F@

diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/run_metadata_run_step99_tag_train.pbtxt b/tensorflow/tensorboard/components/tf_backend/test/data/run_metadata_run_step99_tag_train.pbtxt
deleted file mode 100644
index 07ce4fad53..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/run_metadata_run_step99_tag_train.pbtxt
+++ /dev/null
@@ -1,17 +0,0 @@
-step_stats {
-  dev_stats {
-    device: "/job:localhost/replica:0/task:0/cpu:0"
-    node_stats {
-      node_name: "_SOURCE"
-      all_start_micros: 1459365298611334
-      op_start_rel_micros: 29
-      op_end_rel_micros: 30
-      all_end_rel_micros: 52
-      memory {
-        allocator_name: "cpu"
-      }
-      timeline_label: "_SOURCE = NoOp()"
-      scheduled_micros: 1459365298611291
-    }
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/runs.json b/tensorflow/tensorboard/components/tf_backend/test/data/runs.json
deleted file mode 100644
index 413ddb9ab3..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/runs.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "run1": {
-    "images": [
-      "im1"
-    ],
-    "audio": [
-      "audio1"
-    ],
-    "scalars": [
-      "cross_entropy (1)"
-    ],
-    "histograms": [
-      "histo1"
-    ],
-    "compressedHistograms": [
-      "histo1"
-    ],
-    "run_metadata": [
-      "step99"
-    ],
-    "graph": false
-  },
-  "fake_run_no_data": {
-    "images": ["im1", "im2"],
-    "audio": ["audio1", "audio2"],
-    "scalars": ["scalar2"],
-    "histograms": ["histo1"],
-    "compressedHistograms": ["histo1"],
-    "run_metadata": ["step99"],
-    "graph": true
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/scalars.json b/tensorflow/tensorboard/components/tf_backend/test/data/scalars.json
deleted file mode 100644
index bc9d3353d5..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/scalars.json
+++ /dev/null
@@ -1 +0,0 @@
-{"run1": {"cross_entropy (1)": [[0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]}}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_backend/test/data/scalars_run_run1_tag_cross_entropy__281_29.json b/tensorflow/tensorboard/components/tf_backend/test/data/scalars_run_run1_tag_cross_entropy__281_29.json
deleted file mode 100644
index 97b0062f0f..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/data/scalars_run_run1_tag_cross_entropy__281_29.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_backend/test/requestManagerTests.ts b/tensorflow/tensorboard/components/tf_backend/test/requestManagerTests.ts
deleted file mode 100644
index 3800e6e402..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/requestManagerTests.ts
+++ /dev/null
@@ -1,294 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {RequestManager, RequestNetworkError} from '../requestManager';
-
-interface MockRequest {
-  resolve: Function;
-  reject: Function;
-  id: number;
-  url: string;
-}
-
-class MockedRequestManager extends RequestManager {
-  private resolvers: Function[];
-  private rejectors: Function[];
-  public requestsDispatched: number;
-  constructor(maxRequests = 10, maxRetries = 3) {
-    super(maxRequests, maxRetries);
-    this.resolvers = [];
-    this.rejectors = [];
-    this.requestsDispatched = 0;
-  }
-  protected _promiseFromUrl(url) {
-    return new Promise((resolve, reject) => {
-      const mockJSON = {
-        ok: true,
-        json() {
-          return url;
-        },
-        url,
-        status: 200,
-      };
-      const mockFailedRequest: any = {
-        ok: false,
-        url,
-        status: 502,
-      };
-      const mockFailure = new RequestNetworkError(mockFailedRequest, url);
-      this.resolvers.push(() => {
-        resolve(mockJSON);
-      });
-      this.rejectors.push(() => {
-        reject(mockFailure);
-      });
-      this.requestsDispatched++;
-    });
-  }
-  public resolveFakeRequest() {
-    this.resolvers.pop()();
-  }
-  public rejectFakeRequest() {
-    this.rejectors.pop()();
-  }
-  public dispatchAndResolve() {
-    // Wait for at least one request to be dispatched, then resolve it.
-    this.waitForDispatch(1).then(() => this.resolveFakeRequest());
-  }
-  public waitForDispatch(num) {
-    return waitForCondition(() => {
-      return this.requestsDispatched >= num;
-    });
-  }
-}
-
-/** Create a promise that returns when *check* returns true.
- * May cause a test timeout if check never becomes true.
- */
-
-function waitForCondition(check: () => boolean): Promise<any> {
-  return new Promise((resolve, reject) => {
-    const go = () => {
-      if (check()) {
-        resolve();
-      }
-      setTimeout(go, 2);
-    };
-    go();
-  });
-}
-
-describe('backend', () => {
-  describe('request manager', () => {
-    it('request loads JSON properly', (done) => {
-      const rm = new RequestManager();
-      const promise = rm.request('data/example.json');
-      promise.then(
-          (response) => {
-            chai.assert.deepEqual(response, {foo: 3, bar: 'zoidberg'});
-            done();
-          },
-          (reject) => {
-            throw new Error(reject);
-          });
-    });
-
-    it('rejects on bad url', (done) => {
-      const rm = new RequestManager(5, 0);
-      const badUrl = '_bad_url_which_doesnt_exist.json';
-      const promise = rm.request(badUrl);
-      promise.then(
-          (success) => {
-            done(new Error('the promise should have rejected'));
-          },
-          (reject: RequestNetworkError) => {
-            chai.assert.include(reject.message, '404');
-            chai.assert.include(reject.message, badUrl);
-            chai.assert.equal(reject.req.status, 404);
-            done();
-          });
-    });
-
-    it('can retry if requests fail', (done) => {
-      const rm = new MockedRequestManager(3, 5);
-      const r = rm.request('foo');
-      rm.waitForDispatch(1)
-          .then(() => {
-            rm.rejectFakeRequest();
-            return rm.waitForDispatch(2);
-          })
-          .then(() => rm.resolveFakeRequest());
-      r.then((success) => done());
-    });
-
-    it('retries at most maxRetries times', (done) => {
-      const MAX_RETRIES = 2;
-      const rm = new MockedRequestManager(3, MAX_RETRIES);
-      const r = rm.request('foo');
-      rm.waitForDispatch(1)
-          .then(() => {
-            rm.rejectFakeRequest();
-            return rm.waitForDispatch(2);
-          })
-          .then(() => {
-            rm.rejectFakeRequest();
-            return rm.waitForDispatch(3);
-          })
-          .then(() => {
-            rm.rejectFakeRequest();
-          });
-
-      r.then(
-          (success) => done(new Error('The request should have failed')),
-          (failure) => done());
-    });
-
-    it('requestManager only sends maxRequests requests at a time', (done) => {
-      const rm = new MockedRequestManager(3);
-      const r0 = rm.request('1');
-      const r1 = rm.request('2');
-      const r2 = rm.request('3');
-      const r3 = rm.request('4');
-      chai.assert.equal(rm.activeRequests(), 3, 'three requests are active');
-      chai.assert.equal(
-          rm.outstandingRequests(), 4, 'four requests are pending');
-      rm.waitForDispatch(3)
-          .then(() => {
-            chai.assert.equal(
-                rm.activeRequests(), 3, 'three requests are still active (1)');
-            chai.assert.equal(
-                rm.requestsDispatched, 3, 'three requests were dispatched');
-            rm.resolveFakeRequest();
-            return rm.waitForDispatch(4);
-          })
-          .then(() => {
-            chai.assert.equal(
-                rm.activeRequests(), 3, 'three requests are still active (2)');
-            chai.assert.equal(
-                rm.requestsDispatched, 4, 'four requests were dispatched');
-            chai.assert.equal(
-                rm.outstandingRequests(), 3, 'three requests are pending');
-            rm.resolveFakeRequest();
-            rm.resolveFakeRequest();
-            rm.resolveFakeRequest();
-            return r3;
-          })
-          .then(() => {
-            chai.assert.equal(rm.activeRequests(), 0, 'all requests finished');
-            chai.assert.equal(
-                rm.outstandingRequests(), 0, 'no requests pending');
-            done();
-          });
-    });
-
-    it('queue continues after failures', (done) => {
-      const rm = new MockedRequestManager(1, 0);
-      const r0 = rm.request('1');
-      const r1 = rm.request('2');
-      rm.waitForDispatch(1).then(() => {
-        rm.rejectFakeRequest();
-      });
-
-      r0.then(
-            (success) => done(new Error('r0 should have failed')),
-            (failure) => 'unused_argument')
-          .then(() => rm.resolveFakeRequest());
-
-      // When the first request rejects, it should decrement nActiveRequests
-      // and then launch remaining requests in queue (i.e. this one)
-      r1.then((success) => done(), (failure) => done(new Error(failure)));
-    });
-
-    it('queue is LIFO', (done) => {
-      /* This test is a bit tricky.
-       * We want to verify that the RequestManager queue has LIFO semantics.
-       * So we construct three requests off the bat: A, B, C.
-       * So LIFO semantics ensure these will resolve in order A, C, B.
-       * (Because the A request launches immediately when we create it, it's
-       * not in queue)
-       * Then after resolving A, C moves out of queue, and we create X.
-       * So expected final order is A, C, X, B.
-       * We verify this with an external var that counts how many requests were
-       * resolved.
-       */
-      const rm = new MockedRequestManager(1);
-      let nResolved = 0;
-      function assertResolutionOrder(expectedSpotInSequence) {
-        return () => {
-          nResolved++;
-          chai.assert.equal(expectedSpotInSequence, nResolved);
-        };
-      }
-
-      function launchThirdRequest() {
-        rm.request('started late but goes third')
-            .then(assertResolutionOrder(3))
-            .then(() => rm.dispatchAndResolve());
-      }
-
-      rm.request('first')
-          .then(
-              assertResolutionOrder(1))  // Assert that this one resolved first
-          .then(launchThirdRequest)
-          .then(() => rm.dispatchAndResolve());  // then trigger the next one
-
-      rm.request('this one goes fourth')  // created second, will go last
-          .then(assertResolutionOrder(
-              4))       // assert it was the fourth to get resolved
-          .then(done);  // finish the test
-
-      rm.request('second')
-          .then(assertResolutionOrder(2))
-          .then(() => rm.dispatchAndResolve());
-
-      rm.dispatchAndResolve();
-    });
-
-    it('requestManager can clear queue', (done) => {
-      const rm = new MockedRequestManager(1);
-      let requestsResolved = 0;
-      let requestsRejected = 0;
-      const success = () => requestsResolved++;
-      const failure = (err) => {
-        chai.assert.equal(err.name, 'RequestCancellationError');
-        requestsRejected++;
-      };
-      const finishTheTest = () => {
-        chai.assert.equal(rm.activeRequests(), 0, 'no requests still active');
-        chai.assert.equal(
-            rm.requestsDispatched, 1, 'only one req was ever dispatched');
-        chai.assert.equal(rm.outstandingRequests(), 0, 'no pending requests');
-        chai.assert.equal(requestsResolved, 1, 'one request got resolved');
-        chai.assert.equal(
-            requestsRejected, 4, 'four were cancelled and threw errors');
-        done();
-      };
-      rm.request('0').then(success, failure).then(finishTheTest);
-      rm.request('1').then(success, failure);
-      rm.request('2').then(success, failure);
-      rm.request('3').then(success, failure);
-      rm.request('4').then(success, failure);
-      chai.assert.equal(rm.activeRequests(), 1, 'one req is active');
-      rm.waitForDispatch(1).then(() => {
-        chai.assert.equal(rm.activeRequests(), 1, 'one req is active');
-        chai.assert.equal(rm.requestsDispatched, 1, 'one req was dispatched');
-        chai.assert.equal(rm.outstandingRequests(), 5, 'five reqs outstanding');
-        rm.clearQueue();
-        rm.resolveFakeRequest();
-        // resolving the first request triggers finishTheTest
-      });
-    });
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_backend/test/tests.html b/tensorflow/tensorboard/components/tf_backend/test/tests.html
deleted file mode 100644
index 58cb89a30b..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/test/tests.html
+++ /dev/null
@@ -1,37 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-  <meta charset="utf-8">
-  <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-  <script src="../../web-component-tester/browser.js"></script>
-  <link rel="import" href="../../polymer/polymer.html">
-  <link rel="import" href="../tf-backend.html">
-</head>
-<body>
-  <test-fixture id="testElementFixture">
-    <template>
-      <test-element id="test"></test-element>
-    </template>
-  </test-fixture>
-  <script src="backendTests.js"></script>
-  <script src="behaviorTests.js"></script>
-  <script src="requestManagerTests.js"></script>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_backend/tf-backend.html b/tensorflow/tensorboard/components/tf_backend/tf-backend.html
deleted file mode 100644
index 066647f5c2..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/tf-backend.html
+++ /dev/null
@@ -1,29 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../tf-imports/d3.html">
-<link rel="import" href="../vz-sorting/vz-sorting.html">
-
-<script src="requestManager.js"></script>
-<script src="urlPathHelpers.js"></script>
-<script src="router.js"></script>
-<script src="runsStore.js"></script>
-<script src="backend.js"></script>
-<script src="behavior.js"></script>
-<script src="canceller.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_backend/urlPathHelpers.ts b/tensorflow/tensorboard/components/tf_backend/urlPathHelpers.ts
deleted file mode 100644
index 62519dac5c..0000000000
--- a/tensorflow/tensorboard/components/tf_backend/urlPathHelpers.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-export const BAD_CHARACTERS = '#%&{}\\/<>*? $!\'":@+`|=() ';
-/** Cleanup a url so that it can be loaded from a filesystem. */
-export function demoify(s) {
-  // for consistency with python's urllib.urlencode
-  s = s.replace(new RegExp('%20', 'g'), '+');
-  for (let i = 0; i < BAD_CHARACTERS.length; i++) {
-    const c = BAD_CHARACTERS[i];
-    s = s.replace(new RegExp('\\' + c, 'g'), '_');
-  }
-  return s;
-}
-
-export function queryEncoder(params?: any): string {
-  // It's important that the keys be sorted, so we always grab the right file
-  // if we are talking to the backend generated by serialze_tensorboard.py
-  if (params == null) {
-    return '';
-  }
-  const components = _.keys(params)
-                       .sort()
-                       .filter((k) => params[k] !== undefined)
-                       .map((k) => k + '=' + encodeURIComponent(params[k]));
-  const result = components.length ? '?' + components.join('&') : '';
-  // Replace parens for consistency with urllib.urlencode
-  return result.replace(/\(/g, '%28').replace(/\)/g, '%29');
-}
diff --git a/tensorflow/tensorboard/components/tf_color_scale/BUILD b/tensorflow/tensorboard/components/tf_color_scale/BUILD
deleted file mode 100644
index 469268427f..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/BUILD
+++ /dev/null
@@ -1,26 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_color_scale",
-    srcs = [
-        "colorScale.ts",
-        "palettes.ts",
-        "tf-color-scale.html",
-    ],
-    path = "/tf-color-scale",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_color_scale/colorScale.ts b/tensorflow/tensorboard/components/tf_color_scale/colorScale.ts
deleted file mode 100644
index ec07ba5891..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/colorScale.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Example usage:
-// runs = ["train", "test", "test1", "test2"]
-// ccs = new ColorScale();
-// ccs.domain(runs);
-// ccs.getColor("train");
-// ccs.getColor("test1");
-
-import * as RunsStore from '../tf-backend/runsStore';
-
-import {palettes} from './palettes';
-
-export class ColorScale {
-  private identifiers = d3.map();
-
-  /**
-   * Creates a color scale with optional custom palette.
-   * @param {Array<string>} [palette=palettes.googleColorBlind] - The color
-   *     palette you want as an Array of hex strings.
-   */
-  constructor(
-      private readonly palette: string[] = palettes.googleColorBlindAssist) {}
-
-  /**
-   * Set the domain of strings.
-   * @param {Array<string>} strings - An array of possible strings to use as the
-   *     domain for your scale.
-   */
-  public domain(strings: string[]): this {
-    this.identifiers = d3.map();
-    strings.forEach((s, i) => {
-      this.identifiers.set(s, this.palette[i % this.palette.length]);
-    });
-    return this;
-  }
-
-  /**
-   * Use the color scale to transform an element in the domain into a color.
-   * @param {string} The input string to map to a color.
-   * @return {string} The color corresponding to that input string.
-   * @throws Will error if input string is not in the scale's domain.
-   */
-  public scale(s: string): string {
-    if (!this.identifiers.has(s)) {
-      throw new Error('String was not in the domain.');
-    }
-    return this.identifiers.get(s) as string;
-  }
-}
-
-/**
- * A color scale whose domain is the set of runs currently available.
- * Automatically updated as this data changes.
- */
-const _runsColorScale = new ColorScale();
-export const runsColorScale: ((runName: string) => string) =
-    _runsColorScale.scale.bind(_runsColorScale);
-
-function updateRunsColorScale(): void {
-  _runsColorScale.domain(RunsStore.getRuns());
-}
-RunsStore.addListener(updateRunsColorScale);
-updateRunsColorScale();
diff --git a/tensorflow/tensorboard/components/tf_color_scale/palettes.ts b/tensorflow/tensorboard/components/tf_color_scale/palettes.ts
deleted file mode 100644
index ce42a11545..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/palettes.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export const palettes = {
-  googleStandard: [
-    '#db4437',  // google red 500
-    '#ff7043',  // deep orange 400
-    '#f4b400',  // google yellow 500
-    '#0f9d58',  // google green 500
-    '#00796b',  // teal 700
-    '#00acc1',  // cyan 600
-    '#4285f4',  // google blue 500
-    '#5c6bc0',  // indigo 400
-    '#ab47bc'   // purple 400
-  ],
-  googleCool: [
-    '#9e9d24',  // lime 800
-    '#0f9d58',  // google green 500
-    '#00796b',  // teal 700
-    '#00acc1',  // cyan 600
-    '#4285f4',  // google blue 500
-    '#5c6bc0',  // indigo 400
-    '#607d8b'   // blue gray 500
-  ],
-  googleWarm: [
-    '#795548',  // brown 500
-    '#ab47bc',  // purple 400
-    '#f06292',  // pink 300
-    '#c2185b',  // pink 700
-    '#db4437',  // google red 500
-    '#ff7043',  // deep orange 400
-    '#f4b400'   // google yellow 700
-  ],
-  googleColorBlindAssist: [
-    '#ff7043',  // orange
-    '#00ACC1',  // dark cyan
-    '#AB47BC',  // bright purple
-    '#2A56C6',  // dark blue
-    '#0b8043',  // green
-    '#F7CB4D',  // yellow
-    '#c0ca33',  // lime
-    '#5e35b1',  // purple
-    '#A52714',  // red
-  ],
-  // These palettes try to be better for color differentiation.
-  // https://personal.sron.nl/~pault/
-  colorBlindAssist1:
-      ['#4477aa', '#44aaaa', '#aaaa44', '#aa7744', '#aa4455', '#aa4488'],
-  colorBlindAssist2: [
-    '#88ccee', '#44aa99', '#117733', '#999933', '#ddcc77', '#cc6677', '#882255',
-    '#aa4499'
-  ],
-  colorBlindAssist3: [
-    '#332288', '#6699cc', '#88ccee', '#44aa99', '#117733', '#999933', '#ddcc77',
-    '#cc6677', '#aa4466', '#882255', '#661100', '#aa4499'
-  ],
-  // based on this palette: http://mkweb.bcgsc.ca/biovis2012/
-  colorBlindAssist4: [
-    '#FF6DB6', '#920000', '#924900', '#DBD100', '#24FF24', '#006DDB', '#490092'
-  ],
-  mldash: [
-    '#E47EAD', '#F4640D', '#FAA300', '#F5E636', '#00A077', '#0077B8', '#00B7ED'
-  ]
-};
diff --git a/tensorflow/tensorboard/components/tf_color_scale/test/BUILD b/tensorflow/tensorboard/components/tf_color_scale/test/BUILD
deleted file mode 100644
index 331783f3c7..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/test/BUILD
+++ /dev/null
@@ -1,30 +0,0 @@
-package(
-    default_testonly = True,
-    default_visibility = ["//tensorflow/tensorboard:internal"],
-)
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "test",
-    srcs = [
-        "colorScaleTests.ts",
-        "tests.html",
-    ],
-    path = "/tf-color-scale/test",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:web_component_tester",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    testonly = 0,
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_color_scale/test/colorScaleTests.ts b/tensorflow/tensorboard/components/tf_color_scale/test/colorScaleTests.ts
deleted file mode 100644
index 78824a772c..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/test/colorScaleTests.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-let assert = chai.assert;
-
-import {ColorScale} from '../colorScale'
-
-describe('ColorScale', function() {
-  let ccs: ColorScale;
-
-  beforeEach(function() {
-    ccs = new ColorScale();
-  });
-
-  it('Returns consistent colors', function() {
-    ccs.domain(['train', 'eval', 'test']);
-    let trainColor = ccs.scale('train');
-    let trainColor2 = ccs.scale('train');
-    assert.equal(trainColor, trainColor2);
-  });
-
-  it('Returns consistent colors after new domain', function() {
-    ccs.domain(['train', 'eval']);
-    let trainColor = ccs.scale('train');
-    ccs.domain(['train', 'eval', 'test']);
-    let trainColor2 = ccs.scale('train');
-    assert.equal(trainColor, trainColor2);
-  });
-
-  it('Throws an error if string is not in the domain', function() {
-    ccs.domain(['red', 'yellow', 'green']);
-    assert.throws(function() {
-      ccs.scale('not in domain');
-    }, 'String was not in the domain.');
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_color_scale/test/tests.html b/tensorflow/tensorboard/components/tf_color_scale/test/tests.html
deleted file mode 100644
index 59c802d02b..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/test/tests.html
+++ /dev/null
@@ -1,24 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<script src="../../web-component-tester/browser.js"></script>
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../tf-color-scale.html">
-<body>
-<script src="colorScaleTests.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_color_scale/tf-color-scale.html b/tensorflow/tensorboard/components/tf_color_scale/tf-color-scale.html
deleted file mode 100644
index a325f0a04c..0000000000
--- a/tensorflow/tensorboard/components/tf_color_scale/tf-color-scale.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-imports/d3.html">
-
-<!--
-tf-color-scale is a plumbing component that takes in an array of runs, and produces
-an upward-bindable outColorScale, which is a color scale mapping from those runs to
-a set of colors.
-
-@element tf-color-scale
--->
-<dom-module id="tf-color-scale">
-  <script src="palettes.js"></script>
-  <script src="colorScale.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/BUILD b/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
deleted file mode 100644
index 2a5a655f1d..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/BUILD
+++ /dev/null
@@ -1,107 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_dashboard_common",
-    srcs = [
-        "dashboard-behavior.ts",
-        "dashboard-style.html",
-        "reload-behavior.ts",
-        "run-color-style.html",
-        "scrollbar-style.html",
-        "tensorboard-color.html",
-        "tf-categorizer.html",
-        "tf-categorizer.ts",
-        "tf-chart-scaffold.html",
-        "tf-collapsable-pane.html",
-        "tf-dashboard.html",
-        "tf-dashboard-layout.html",
-        "tf-downloader.html",
-        "tf-multi-checkbox.html",
-        "tf-multi-checkbox.ts",
-        "tf-no-data-warning.html",
-        "tf-option-selector.html",
-        "tf-panes-helper.html",
-        "tf-regex-group.html",
-        "tf-regex-group.ts",
-        "tf-run-selector.html",
-    ],
-    path = "/tf-dashboard-common",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_storage",
-        "//tensorflow/tensorboard/components/vz_sorting",
-        "@org_polymer_iron_ajax",
-        "@org_polymer_iron_collapse",
-        "@org_polymer_iron_icons",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_checkbox",
-        "@org_polymer_paper_dialog",
-        "@org_polymer_paper_dropdown_menu",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_input",
-        "@org_polymer_paper_item",
-        "@org_polymer_paper_menu",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_spinner",
-        "@org_polymer_paper_styles",
-        "@org_polymer_paper_toggle_button",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = [
-        "tf-categorizer-demo.html",
-        "tf-collapsable-pane-demo.html",
-        "tf-multi-checkbox-demo.html",
-        "tf-regex-group-demo.html",
-    ],
-    path = "/tf-dashboard-common",
-    deps = [
-        ":tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_dashboard_common"],
-    destdir = "tf-dashboard-common",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports_google:lib",
-        "//tensorflow/tensorboard/components/tf_storage:legacy",
-        "//tensorflow/tensorboard/components/vz_sorting:legacy",
-        "//third_party/javascript/polymer/v1/iron-ajax:lib",
-        "//third_party/javascript/polymer/v1/iron-collapse:lib",
-        "//third_party/javascript/polymer/v1/iron-icons:lib",
-        "//third_party/javascript/polymer/v1/paper-button:lib",
-        "//third_party/javascript/polymer/v1/paper-checkbox:lib",
-        "//third_party/javascript/polymer/v1/paper-dialog:lib",
-        "//third_party/javascript/polymer/v1/paper-dropdown-menu:lib",
-        "//third_party/javascript/polymer/v1/paper-icon-button:lib",
-        "//third_party/javascript/polymer/v1/paper-input:lib",
-        "//third_party/javascript/polymer/v1/paper-item:lib",
-        "//third_party/javascript/polymer/v1/paper-menu:lib",
-        "//third_party/javascript/polymer/v1/paper-slider:lib",
-        "//third_party/javascript/polymer/v1/paper-spinner:lib",
-        "//third_party/javascript/polymer/v1/paper-styles:lib",
-        "//third_party/javascript/polymer/v1/paper-toggle-button:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-behavior.ts b/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-behavior.ts
deleted file mode 100644
index aa063c7422..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-behavior.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * A behavior that TensorBoard dashboards must implement. This behavior serves
- * the purpose of an interface.
- *
- * @polymerBehavior
- */
-export function DashboardBehavior(dashboardName) {
-  return {
-    properties: {
-      name: {
-        type: String,
-        value: dashboardName,
-        readOnly: true,
-      },
-    },
-    // This method is called when the dashboard reloads, either when the
-    // dashboard is first visited, periodically reloaded, or manually reloaded
-    // via the user clicking the button. Note that dashboard custom elements
-    // that use TF.Dashboard.ReloadBehavior already implement a reload method.
-    reload() {
-      throw Error(
-          'The ' + dashboardName + ' dashboard does not implement reload.');
-    },
-  };
-}
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-style.html b/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-style.html
deleted file mode 100644
index 723919abce..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/dashboard-style.html
+++ /dev/null
@@ -1,62 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../paper-styles/paper-styles.html">
-<link rel="import" href="tensorboard-color.html">
-
-<dom-module id="dashboard-style">
-  <template>
-    <style>
-      .sidebar {
-        display: flex;
-        flex-direction: column;
-        height: 100%;
-        margin-right: 20px;
-      }
-
-      tf-categorizer {
-        flex-shrink: 0;
-      }
-
-      tf-runs-selector {
-        flex-shrink: 1;
-        flex-grow: 1;
-      }
-
-      .sidebar-section {
-        border-top: solid 1px rgba(0, 0, 0, 0.12);
-        padding: 15px 0px 15px 30px;
-      }
-
-      .sidebar-section:first-child {
-        border: none;
-      }
-
-      .sidebar-section:last-child {
-        flex-grow: 1;
-        display: flex;
-      }
-
-      paper-checkbox {
-        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
-        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
-        font-size: 14px;
-        margin-top: 5px;
-      }
-    </style>
-  </template>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/reload-behavior.ts b/tensorflow/tensorboard/components/tf_dashboard_common/reload-behavior.ts
deleted file mode 100644
index 61fe0c0781..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/reload-behavior.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * ReloadBehavior: A simple behavior for dashboards where the
- * frontendReload() function should find every child element with a
- * given tag name (e.g. "tf-line-chart" or "tf-image-loader")
- * and call a `reload` method on that child.
- * May later extend it so it has more sophisticated logic, e.g. reloading
- * only tags that are in view.
- *
- * @polymerBehavior
- */
-export function ReloadBehavior(tagName) {
-  return {
-    properties: {
-      reloadTag: {
-        type: String,
-        value: tagName,
-      },
-    },
-    frontendReload: function() {
-      var elements = this.getElementsByTagName(this.reloadTag);
-      Array.prototype.forEach.call(elements, function(x) {
-        x.reload();
-      });
-    },
-  };
-}
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/run-color-style.html b/tensorflow/tensorboard/components/tf_dashboard_common/run-color-style.html
deleted file mode 100644
index b15861694f..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/run-color-style.html
+++ /dev/null
@@ -1,79 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../paper-styles/paper-styles.html">
-
-<dom-module id="run-color-style">
-  <template>
-    <style>
-    [color-class="light-blue"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-light-blue-500);
-      --paper-checkbox-checked-ink-color: var(--paper-light-blue-500);
-      --paper-checkbox-unchecked-color: var(--paper-light-blue-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-light-blue-900);
-    }
-    [color-class="red"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-red-500);
-      --paper-checkbox-checked-ink-color: var(--paper-red-500);
-      --paper-checkbox-unchecked-color: var(--paper-red-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-red-900);
-    }
-    [color-class="green"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-green-500);
-      --paper-checkbox-checked-ink-color: var(--paper-green-500);
-      --paper-checkbox-unchecked-color: var(--paper-green-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-green-900);
-    }
-    [color-class="purple"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-purple-500);
-      --paper-checkbox-checked-ink-color: var(--paper-purple-500);
-      --paper-checkbox-unchecked-color: var(--paper-purple-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);
-    }
-    [color-class="teal"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-teal-500);
-      --paper-checkbox-checked-ink-color: var(--paper-teal-500);
-      --paper-checkbox-unchecked-color: var(--paper-teal-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);
-    }
-    [color-class="pink"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-pink-500);
-      --paper-checkbox-checked-ink-color: var(--paper-pink-500);
-      --paper-checkbox-unchecked-color: var(--paper-pink-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);
-    }
-    [color-class="orange"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-orange-500);
-      --paper-checkbox-checked-ink-color: var(--paper-orange-500);
-      --paper-checkbox-unchecked-color: var(--paper-orange-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);
-    }
-    [color-class="brown"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-brown-500);
-      --paper-checkbox-checked-ink-color: var(--paper-brown-500);
-      --paper-checkbox-unchecked-color: var(--paper-brown-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);
-    }
-    [color-class="indigo"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-indigo-500);
-      --paper-checkbox-checked-ink-color: var(--paper-indigo-500);
-      --paper-checkbox-unchecked-color: var(--paper-indigo-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);
-    }
-    </style>
-  </template>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/scrollbar-style.html b/tensorflow/tensorboard/components/tf_dashboard_common/scrollbar-style.html
deleted file mode 100644
index bfd61f6619..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/scrollbar-style.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-styles/paper-styles.html">
-
-<dom-module id="scrollbar-style">
-  <template>
-    <style>
-      .scrollbar::-webkit-scrollbar-track
-      {
-        visibility: hidden;
-      }
-
-      .scrollbar::-webkit-scrollbar
-      {
-        width: 10px;
-      }
-
-      .scrollbar::-webkit-scrollbar-thumb
-      {
-        border-radius: 10px;
-        -webkit-box-shadow: inset 0 0 2px rgba(0,0,0,.3);
-        background-color: var(--paper-grey-500);
-        color: var(--paper-grey-900);
-      }
-      .scrollbar {
-        box-sizing: border-box;
-      }
-    </style>
-  </template>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tensorboard-color.html b/tensorflow/tensorboard/components/tf_dashboard_common/tensorboard-color.html
deleted file mode 100644
index 7f9ca64614..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tensorboard-color.html
+++ /dev/null
@@ -1,32 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-
-<style is="custom-style">
-
-  :root {
-    --tb-orange-weak: #ffa726;
-    --tb-orange-strong: #f57c00;
-    --tb-grey-darker: #e2e2e2;
-    --tb-grey-lighter: #f3f3f3;
-    --tb-ui-dark-accent: #757575;
-    --tb-ui-light-accent: #e0e0e0;
-    --tb-graph-faded: #e0d4b3;
-  }
-
-</style>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/test/BUILD b/tensorflow/tensorboard/components/tf_dashboard_common/test/BUILD
deleted file mode 100644
index ef7a1562c6..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/test/BUILD
+++ /dev/null
@@ -1,30 +0,0 @@
-package(
-    default_testonly = True,
-    default_visibility = ["//tensorflow/tensorboard:internal"],
-)
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "test",
-    srcs = [
-        "tests.html",
-        "tf-categorizer-tests.ts",
-    ],
-    path = "/tf-dashboard-common/test",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:web_component_tester",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    testonly = 0,
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/test/tests.html b/tensorflow/tensorboard/components/tf_dashboard_common/test/tests.html
deleted file mode 100644
index c9ad14730f..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/test/tests.html
+++ /dev/null
@@ -1,24 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<script src="../../web-component-tester/browser.js"></script>
-<link rel="import" href="../tf-categorizer.html">
-<body>
-<script src="tf-categorizer-tests.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/test/tf-categorizer-tests.ts b/tensorflow/tensorboard/components/tf_dashboard_common/test/tf-categorizer-tests.ts
deleted file mode 100644
index a786f39b4f..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/test/tf-categorizer-tests.ts
+++ /dev/null
@@ -1,144 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import * as cat from '../tf-categorizer';
-
-let assert = chai.assert;
-
-describe('categorizer', () => {
-  describe('topLevelNamespaceCategorizer', () => {
-    it('returns empty array on empty tags', () => {
-      assert.lengthOf(cat.topLevelNamespaceCategorizer([]), 0);
-    });
-
-    it('handles a simple case', () => {
-      let simple = [
-        'foo1/bar', 'foo1/zod', 'foo2/bar', 'foo2/zod', 'gosh/lod/mar',
-        'gosh/lod/ned'
-      ];
-      let expected = [
-        {name: 'foo1', tags: ['foo1/bar', 'foo1/zod']},
-        {name: 'foo2', tags: ['foo2/bar', 'foo2/zod']},
-        {name: 'gosh', tags: ['gosh/lod/mar', 'gosh/lod/ned']},
-      ];
-      assert.deepEqual(cat.topLevelNamespaceCategorizer(simple), expected);
-    });
-
-    it('orders the categories', () => {
-      let test = ['e', 'f', 'g', 'a', 'b', 'c'];
-      let expected = [
-        {name: 'a', tags: ['a']},
-        {name: 'b', tags: ['b']},
-        {name: 'c', tags: ['c']},
-        {name: 'e', tags: ['e']},
-        {name: 'f', tags: ['f']},
-        {name: 'g', tags: ['g']},
-      ];
-      assert.deepEqual(cat.topLevelNamespaceCategorizer(test), expected);
-    });
-
-    it('handles cases where category names overlap node names', () => {
-      let test = ['a', 'a/a', 'a/b', 'a/c', 'b', 'b/a'];
-      const actual = cat.topLevelNamespaceCategorizer(test);
-      let expected = [
-        {name: 'a', tags: ['a', 'a/a', 'a/b', 'a/c']},
-        {name: 'b', tags: ['b', 'b/a']},
-      ];
-      assert.deepEqual(actual, expected);
-    });
-
-    it('handles singleton case', () => {
-      assert.deepEqual(
-          cat.topLevelNamespaceCategorizer(['a']), [{name: 'a', tags: ['a']}]);
-    });
-  });
-
-  describe('customCategorizer', () => {
-    function noFallbackCategorizer(tags: string[]): cat.Category[] {
-      return [];
-    }
-
-    function testCategorizer(
-        defs: string[], fallback: cat.Categorizer,
-        tags: string[]): cat.Category[] {
-      const catDefs = defs.map(cat.defineCategory);
-      return cat._categorizer(catDefs, fallback)(tags);
-    }
-
-    it('categorizes by regular expression', () => {
-      let defs = ['foo..', 'bar..'];
-      let tags = ['fooab', 'fooxa', 'barts', 'barms'];
-      const actual = testCategorizer(defs, noFallbackCategorizer, tags);
-      let expected = [
-        {name: 'foo..', tags: ['fooab', 'fooxa']},
-        {name: 'bar..', tags: ['barms', 'barts']},
-      ];
-      assert.deepEqual(actual, expected);
-    });
-
-    it('matches non-exclusively', () => {
-      let tags = ['abc', 'bar', 'zod'];
-      const actual =
-          testCategorizer(['...', 'bar'], noFallbackCategorizer, tags);
-      let expected = [
-        {name: '...', tags: ['abc', 'bar', 'zod']},
-        {name: 'bar', tags: ['bar']},
-      ];
-      assert.deepEqual(actual, expected);
-    });
-
-    it('creates categories for unmatched rules', () => {
-      const actual =
-          testCategorizer(['a', 'b', 'c'], noFallbackCategorizer, []);
-      let expected = [
-        {name: 'a', tags: []},
-        {name: 'b', tags: []},
-        {name: 'c', tags: []},
-      ];
-      assert.deepEqual(actual, expected);
-    });
-
-    it('category regexs work with special characters', () => {
-      let defs = ['^\\w+$', '^\\d+$', '^\\/..$'];
-      let tags = ['foo', '3243', '/xa'];
-      const actual = testCategorizer(defs, noFallbackCategorizer, tags);
-      let expected = [
-        {name: '^\\w+$', tags: ['3243', 'foo']},
-        {name: '^\\d+$', tags: ['3243']},
-        {name: '^\\/..$', tags: ['/xa']},
-      ];
-      assert.deepEqual(actual, expected);
-    });
-
-    it('category tags are sorted', () => {
-      let tags = ['a', 'z', 'c', 'd', 'e', 'x', 'f', 'y', 'g'];
-      let sorted = tags.slice().sort();
-      let expected = [{name: '.*', tags: sorted}];
-      const actual = testCategorizer(['.*'], noFallbackCategorizer, tags);
-      assert.deepEqual(actual, expected);
-    });
-
-    it('if nonexclusive: all tags passed to fallback', () => {
-      let passedToDefault = null;
-      function defaultCategorizer(tags: string[]): cat.Category[] {
-        passedToDefault = tags;
-        return [];
-      }
-      let tags = ['foo', 'bar', 'foo123'];
-      testCategorizer(['foo'], defaultCategorizer, tags);
-      assert.deepEqual(passedToDefault, tags);
-    });
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer-demo.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer-demo.html
deleted file mode 100644
index 23babaaecc..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer-demo.html
+++ /dev/null
@@ -1,106 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
- <head>
-  <link rel="import" href="tf-categorizer.html">
- </head>
- <body>
-  <style>
-  </style>
-  <dom-module id="x-demo">
-    <style>
-      .container {
-        width: 255px;
-        padding: 10px;
-        border: 1px solid var(--paper-indigo-900);
-        border-radius: 5px;
-        position: fixed;
-      }
-      :host {
-        margin: 0px;
-      }
-
-      .categories {
-        font-family: "RobotoDraft",Helvetica;
-        margin-left: 300px;
-        width: 500px;
-        border: 1px solid var(--paper-indigo-500);
-        border-radius: 5px;
-      }
-
-      .category {
-        background-color: var(--paper-indigo-50);
-        margin: 20px;
-        padding: 20px;
-        border-radius: 5px;
-      }
-
-      .cat-name {
-        font-size: 20px;
-      }
-
-      .tag {
-        border-radius: 5px;
-        padding: 5px;
-        margin: 5px;
-        background-color: var(--paper-indigo-900);
-        color: white;
-      }
-    </style>
-    <template>
-      <div class="container">
-        <tf-categorizer categories="{{categories}}" tags="[[tags]]" id="demo"></tf-categorizer>
-      </div>
-      <div class="categories">
-        <template is="dom-repeat" items="[[categories]]">
-          <div class="category">
-            <p class="cat-name">Category: <span>[[item.name]]</span></p>
-            <div class="tags-container layout horizontal wrap">
-              <template is="dom-repeat" items="[[item.tags]]">
-                <span class="tag layout vertical center-center">[[item]]</span>
-              </template>
-            </div>
-          </div>
-        </template>
-      </div>
-    </template>
-    <script>
-
-    function tagsGenerator() {
-      var tags = ["special1", "special2", "special3", "special4", "special5"];
-      ["l1", "l2", "l3", "l4", "l5"].forEach(function(l) {
-        ["foo", "bar", "baz", "boink", "zod", "specialx"].forEach(function(x) {
-          tags.push(l + "/" + x);
-        });
-      });
-      return tags;
-    }
-
-    Polymer({
-      is: "x-demo",
-      properties: {
-        tags: { type: Array, value: tagsGenerator },
-      },
-    });
-    </script>
-  </dom-module>
-
-  <x-demo id="demo"></x-demo>
- </body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer.html
deleted file mode 100644
index f09eb03582..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer.html
+++ /dev/null
@@ -1,63 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
-<link rel="import" href="../tf-imports/d3.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../vz-sorting/vz-sorting.html">
-<link rel="import" href="tf-regex-group.html">
-<link rel="import" href="tensorboard-color.html">
-
-<!--
-`tf-categorizer` turns an array of tags into an array of categories
-
-The transformation from tags to categories is controlled by the user, through
-interacting with the categorizer widget.
-
-(See type signatures in categorizer.ts)
-
-Example:
-  <tf-categorizer tags="[[tags]]" categories="{{categories}}"></tf-categorizer>
-
-Public Properties:
-`tags` - Array of strings that are the tags to categorize. Should be one-way bound downward.
-`categories` - Array of Categorizer.Category objects that are generated by the Categorizer.
-  Are readOnly and notify: True. Expected to be one-way bound upward.
-
-The categorizer provides inputs for adding regular expression rules and toggling whether
-categories are exclusive.
--->
-<dom-module id="tf-categorizer">
-  <template>
-    <div class="inputs">
-      <tf-regex-group id="regexGroup" regexes="{{regexes}}"></tf-regex-group>
-    </div>
-    <style>
-      :host {
-        display: block;
-        padding-bottom: 5px;
-      }
-      paper-checkbox {
-        --paper-checkbox-checked-color: var(--paper-grey-600);
-        --paper-checkbox-unchecked-color: var(--paper-grey-600);
-        font-size: 14px;
-      }
-    </style>
-  </template>
-  <script src="tf-categorizer.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer.ts b/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer.ts
deleted file mode 100644
index 0eaf852ff1..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-categorizer.ts
+++ /dev/null
@@ -1,189 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {compareTagNames} from '../vz-sorting/sorting';
-
-/**
- * This module contains methods that allow sorting tags into 'categories'.
- * A category contains a name and a list of tags.
- * The sorting strategy is defined by a 'CustomCategorization', which contains
- * 'categoryDefinitions' which are regex rules used to construct a category.
- * E.g. the regex rule 'xent' will create a category called 'xent' that
- * contains values whose tags match the regex.
- *
- * After custom categories are evaluated, the tags are sorted by a hardcoded
- * fallback categorizer, which may, for example, group tags into categories
- * based on their top namespace.
- */
-
-export interface Category {
-  // Categories that data is sorted into
-  name: string;
-  tags: string[];
-}
-
-export interface CustomCategorization {
-  // Defines a categorization strategy
-  categoryDefinitions: string[];
-  fallbackCategorizer: string;
-  /* {'TopLevelNamespaceCategorizer',
-      'LegacyUnderscoreCategorizer'} */
-}
-
-export interface Categorizer {
-  // Function that generates categories
-  (tags: string[]): Category[];
-}
-
-/* Canonical TensorFlow ops are namespaced using forward slashes.
- * This fallback categorizer categorizes by the top-level namespace.
- */
-export var topLevelNamespaceCategorizer: Categorizer = splitCategorizer(/\//);
-
-export function fallbackCategorizer(s: string): Categorizer {
-  switch (s) {
-    case 'TopLevelNamespaceCategorizer':
-      return topLevelNamespaceCategorizer;
-    default:
-      throw new Error('Unrecognized categorization strategy: ' + s);
-  }
-}
-
-/* An 'extractor' is a function that takes a tag name, and 'extracts' a
- * category name.
- * This function takes an extractor, and produces a categorizer.
- * Currently, it is just used for the fallbackCategorizer, but we may want to
- * refactor the general categorization logic to use the concept of extractors.
- */
-function extractorToCategorizer(extractor: (s: string) => string): Categorizer {
-  return (tags: string[]): Category[] => {
-    if (tags.length === 0) {
-      return [];
-    }
-
-    // Maps between top-level name and category. We use the mapping to avoid
-    // duplicating categories per run.
-    const categoryMapping: {[key: string]: Category} = {};
-
-    tags.forEach((t: string) => {
-      const topLevel = extractor(t);
-      if (!categoryMapping[topLevel]) {
-        const newCategory = {
-          name: topLevel,
-          tags: [],
-        };
-        categoryMapping[topLevel] = newCategory;
-      }
-
-      categoryMapping[topLevel].tags.push(t);
-    });
-
-    // Sort categories into alphabetical order.
-    const categories =
-        _.map(_.keys(categoryMapping).sort(), key => categoryMapping[key]);
-    _.forEach(categories, (category) => {
-      // Sort the tags within each category.
-      category.tags.sort(compareTagNames);
-    });
-    return categories;
-  };
-}
-
-function splitCategorizer(r: RegExp): Categorizer {
-  let extractor = (t: string) => {
-    return t.split(r)[0];
-  };
-  return extractorToCategorizer(extractor);
-}
-
-export interface CategoryDefinition {
-  name: string;
-  matches: (t: string) => boolean;
-}
-
-export function defineCategory(ruledef: string): CategoryDefinition {
-  let r = new RegExp(ruledef);
-  let f = function(tag: string): boolean {
-    return r.test(tag);
-  };
-  return {name: ruledef, matches: f};
-}
-
-export function _categorizer(
-    rules: CategoryDefinition[], fallback: Categorizer) {
-  return function(tags: string[]): Category[] {
-    let remaining: d3.Set = d3.set(tags);
-    let userSpecified = rules.map((def: CategoryDefinition) => {
-      let tags: string[] = [];
-      remaining.each((t: string) => {
-        if (def.matches(t)) {
-          tags.push(t);
-        }
-      });
-      let cat = {name: def.name, tags: tags.sort(compareTagNames)};
-      return cat;
-    });
-    let defaultCategories = fallback(remaining.values());
-    return userSpecified.concat(defaultCategories);
-  };
-}
-
-export function categorizer(s: CustomCategorization): Categorizer {
-  let rules = s.categoryDefinitions.map(defineCategory);
-  let fallback = fallbackCategorizer(s.fallbackCategorizer);
-  return _categorizer(rules, fallback);
-};
-
-Polymer({
-  is: 'tf-categorizer',
-  properties: {
-    regexes: {type: Array},
-    tags: {type: Array},
-    categoriesAreExclusive: {type: Boolean, value: true},
-    fallbackCategorizer: {
-      type: String,
-      value: 'TopLevelNamespaceCategorizer',
-    },
-    categorizer: {
-      type: Object,
-      computed:
-          'computeCategorization(regexes.*, categoriesAreExclusive, fallbackCategorizer)',
-    },
-    categories: {
-      type: Array,
-      value: function() {
-        return [];
-      },
-      notify: true,
-      readOnly: true
-    },
-  },
-  observers: ['recategorize(tags.*, categorizer)'],
-  computeCategorization: function(
-      regexes, categoriesAreExclusive, fallbackCategorizer) {
-    var categorizationStrategy = {
-      categoryDefinitions: regexes.base,
-      categoriesAreExclusive: categoriesAreExclusive,
-      fallbackCategorizer: fallbackCategorizer,
-    };
-    return categorizer(categorizationStrategy);
-  },
-  recategorize: function() {
-    this.debounce('tf-categorizer-recategorize', function() {
-      var categories = this.categorizer(this.tags);
-      this._setCategories(categories);
-    })
-  },
-});
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-chart-scaffold.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-chart-scaffold.html
deleted file mode 100644
index d337111363..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-chart-scaffold.html
+++ /dev/null
@@ -1,146 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-
-<!--
-tf-chart-scaffold is responsible for providing data from TensorBoard to charts.
-It has the following settable properties:
-tag: (required, string) - the name of the tag to load for this chart
-visibleSeries: (required, string[]) - the names of the series the chart should
-    display.
-dataProvider: (required, VZ.ChartHelpers.DataFn) - function that takes (tag,
-    run) and returns a promise containing an array of VZ.ChartHelpers.Datum,
-    compatible with TF.Backend.Datum.
-
-It exposes the following methods:
-chart() - Returns the underlying chart element.
-reload() - Reloads the data and sends it to the underlying chart.
-
-This element should have a compatible chart plugin element as it's content. The
-plugin is required to implement two functions:
-- setVisibleSeries(names: string[]): a function that receives an array of series
-    names as the first parameter, responsible for changing the series currently
-    being displayed to only the series in this array.
-- setSeriesData(name: string, data: VZ.ChartHelpers.Datum[]): sets the data of
-    the series with the given name to the data given in the second parameter.
--->
-<dom-module id="tf-chart-scaffold">
-  <template>
-    <content></content>
-    <style>
-      :host {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        display: flex;
-        flex-direction: column;
-        flex-grow: 1;
-        flex-shrink: 1;
-        position: relative;
-      }
-    </style>
-  </template>
-  <script>
-    "use strict";
-    import {Canceller} from "../tf-backend/canceller";
-
-    Polymer({
-      is: "tf-chart-scaffold",
-      properties: {
-        tag: String,
-        dataProvider: Function,
-        visibleSeries: Array,
-        _attached: {
-          type: Boolean,
-          value: false
-        },
-        _canceller: {
-          type: Object,
-          value: function() {
-            return new Canceller();
-          },
-        },
-      },
-      observers: [
-        "reload(tag, dataProvider)",
-        "_changeSeries(visibleSeries.*)"
-      ],
-      ready: function() {
-        this.fire('ready');
-      },
-      attached: function() {
-        this._attached = true;
-        this._changeSeries();
-      },
-      detached: function() {
-        this._attached = false;
-      },
-      reload: function() {
-        if (!this._attached) {
-          return;
-        }
-        else if (!this.dataProvider) {
-          throw new Error('tf-chart-scaffold requires a dataProvider.');
-        }
-        else if (!this.tag) {
-          throw new Error('tf-chart-scaffold requires a tag.');
-        }
-
-        // TODO(chizeng): At this point, notify effective children that the
-        // previous data has been invalidated. For instance, the image
-        // dashboard may want to clear its images. Today, the chart
-        // scaffold only informs children when the new image URLs response
-        // finishes loading.
-        this._canceller.cancelAll();  // prevent stale data
-        this.visibleSeries.forEach(function(name) {
-          var promise = this.dataProvider(this.tag, name);
-          promise.then(this._canceller.cancellable(function(result) {
-            if (result.cancelled) {
-              return;
-            }
-            var data = result.value;
-            this.chart().setSeriesData(name, data);
-          }.bind(this)));
-        }.bind(this));
-      },
-      _changeSeries: function() {
-        if (!this._attached) {
-           return;
-        }
-        else if (!this.visibleSeries) {
-          throw new Error('tf-chart-scaffold requires a visibleSeries.');
-        }
-
-        this.chart().setVisibleSeries(this.visibleSeries);
-        this.reload();
-      },
-      chart: function() {
-        var children = this.getEffectiveChildren();
-        if (!children.length) {
-          throw new Error('tf-chart-scaffold has no children');
-        }
-
-        var child = children[0];
-        if (!child.setVisibleSeries || !child.setSeriesData) {
-          throw new Error("tf-chart-scaffold's content doesn't implement the " +
-              "required interface");
-        }
-        return child;
-      }
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-collapsable-pane-demo.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-collapsable-pane-demo.html
deleted file mode 100644
index efa990b11c..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-collapsable-pane-demo.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
- <head>
-   <link rel="import" href="tf-collapsable-pane.html">
-   
- </head>
- <body>
-  <style>
-  </style>
-  <tf-collapsable-pane name="foo">
-    <h1>This is content inside the pane.</h1>
-  </tf-collapsable-pane>
- </body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-collapsable-pane.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-collapsable-pane.html
deleted file mode 100644
index e82540127f..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-collapsable-pane.html
+++ /dev/null
@@ -1,109 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-
-<dom-module id="tf-collapsable-pane">
-  <template>
-    <button
-      class="heading"
-      on-tap="togglePane"
-      open-button$="[[opened]]"
-    >
-    <span class="name">[[name]]</span>
-    <span class="count">
-      <span>[[count]]</span>
-    </span>
-  </button>
-    <iron-collapse opened="[[opened]]">
-      <div class="content">
-        <template is="dom-if" if="[[opened]]" restamp="[[restamp]]">
-          <content></content>
-        </template>
-      </div>
-    </iron-collapse>
-    <style>
-      :host {
-        display: block;
-        margin: 0 5px 1px 10px;
-      }
-
-      :host:first-of-type {
-        margin-top: 20px;
-      }
-      
-      :host:last-of-type {
-        margin-bottom: 20px;
-      }
-
-      .heading {
-        background-color: white;
-        border: none;
-        cursor: pointer;
-        width: 100%;
-        font-size: 15px;
-        line-height: 1;
-        box-shadow: 0 1px 5px rgba(0,0,0,0.2);
-        padding: 10px 15px;
-      }
-
-      .content {
-        padding: 15px;
-        border: 1px solid #dedede;
-        border-top: none;
-        border-bottom-left-radius: 2px;
-        border-bottom-right-radius: 2px;
-        background: white;
-      }
-
-      [open-button] {
-        border-bottom-left-radius: 0px !important;
-        border-bottom-right-radius: 0px !important;
-      }
-
-      .name {
-        float: left;
-      }
-
-      .count {
-        float: right;
-        margin-right: 5px;
-        font-size: 12px;
-        color: var(--paper-grey-500);
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-collapsable-pane",
-      properties: {
-        opened: {type: Boolean, value: false},
-        restamp: {type: Boolean, value: true},
-        name: {type: String, observer: "hide"},
-        count: {type: Number},
-      },
-      hide: function() {
-        this.opened = false;
-      },
-      togglePane: function() {
-        this.opened = !this.opened;
-      }
-    });
-  </script>
-
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-dashboard-layout.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-dashboard-layout.html
deleted file mode 100644
index e0e8a2b52c..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-dashboard-layout.html
+++ /dev/null
@@ -1,67 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="scrollbar-style.html">
-<link rel="import" href="tensorboard-color.html">
-
-<!--
-Generic layout for a dashboard.
--->
-<dom-module id="tf-dashboard-layout">
-  <template>
-    <div id="sidebar">
-      <content select=".sidebar"></content>
-    </div>
-
-    <div id="center" class="scrollbar">
-      <content select=".center"></content>
-    </div>
-    <style include="scrollbar-style"></style>
-    <style>
-      #sidebar {
-        width: inherit;
-        height: 100%;
-        overflow: ellipsis;
-        flex-grow: 0;
-        flex-shrink: 0;
-      }
-
-      #center {
-        height: 100%;
-        overflow-y: auto;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
-
-      .tf-graph-dashboard #center {
-        background: white;
-      }
-
-      :host {
-        display: flex;
-        flex-direction: row;
-        height: 100%;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-dashboard-layout",
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-dashboard.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-dashboard.html
deleted file mode 100644
index 9e2f6b9589..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-dashboard.html
+++ /dev/null
@@ -1,26 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="tf-dashboard-layout.html">
-<link rel="import" href="tensorboard-color.html">
-<link rel="import" href="dashboard-style.html">
-<link rel="import" href="tf-downloader.html">
-<link rel="import" href="tf-no-data-warning.html">
-
-<script src="dashboard-behavior.js"></script>
-<script src="reload-behavior.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-downloader.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-downloader.html
deleted file mode 100644
index 7191425959..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-downloader.html
+++ /dev/null
@@ -1,99 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
-<link rel="import" href="../paper-menu/paper-menu.html">
-<link rel="import" href="../paper-item/paper-item.html">
-
-<dom-module id="tf-downloader">
-  <template>
-    <paper-dropdown-menu
-      no-label-float="true"
-      label="run to download"
-      selected-item-label="{{_run}}"
-    >
-      <paper-menu class="dropdown-content">
-        <template is="dom-repeat" items="[[runs]]">
-          <paper-item no-label-float=true>[[item]]</paper-item>
-        </template>
-      </paper-menu>
-    </paper-dropdown-menu>
-    <div class="center">
-      <span>
-        <a
-          download="[[_csvName(_run)]]"
-          href="[[_csvUrl(_run, urlFn)]]"
-          >CSV</a>
-        <a
-          download="[[_jsonName(_run)]]"
-          href="[[_jsonUrl(_run, urlFn)]]"
-          >JSON</a>
-      </span>
-    </div>
-    <style>
-      :host {
-        display: flex;
-        height: 32px;
-      }
-      .center {
-        display: flex;
-        align-self: center;
-      }
-      paper-dropdown-menu {
-        width: 100px;
-        --paper-input-container-label: {
-          font-size: 10px;
-        }
-        --paper-input-container-input: {
-          font-size: 10px;
-        }
-      }
-      a {
-        font-size: 10px;
-        border-radius: 3px;
-        border: 1px solid #EEE;
-      }
-      paper-input {
-        font-size: 22px;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-downloader",
-      properties: {
-        _run: String,
-        runs: Array,
-        tag: String,
-        urlFn: Function,
-      },
-      _csvUrl: function(_run, urlFn) {
-        return urlFn(this.tag, _run) + "&format=csv";
-      },
-      _jsonUrl: function(_run, urlFn) {
-        return urlFn(this.tag, _run);
-      },
-      _csvName: function(_run) {
-        return "run_" + _run + ",tag_" + this.tag + ".csv";
-      },
-      _jsonName: function(_run) {
-        return "run-" + _run + "-tag-" + this.tag + ".json";
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox-demo.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox-demo.html
deleted file mode 100644
index 93898b152e..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox-demo.html
+++ /dev/null
@@ -1,168 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-<link rel="import" href="tf-multi-checkbox.html">
-
-</head>
-<body>
-<script>
-var seed = 1;
-function random() {
-  var x = Math.sin(seed++) * 10000;
-  return x - Math.floor(x);
-}
-</script>
-<style>
-</style>
-
-<dom-module id="mc-demo">
-  <template>
-    <tf-multi-checkbox
-      id="multiCheckbox"
-      names="[[names]]"
-      tooltips="[[_tooltips]]"
-      highlights="[[highlights]]"
-    ></tf-multi-checkbox>
-  <style>
-  </style>
-  </template>
-  <script>
-
-  function randomTooltip() {
-    var s = "";
-    while (random() < 0.8) {
-      s += String(10*random())[0];
-    }
-    return s;
-  }
-  Polymer({
-    is: "mc-demo",
-    properties: {
-      names: Array,
-      tooltips: Object,
-      autoGenerateTooltips: {value: true},
-      _tooltips: Object,
-      highlights: Array,
-    },
-    observers: [
-      'autogenerate(names, autoGenerateTooltips)',
-      'randomHighlights(names)'
-    ],
-    autogenerate: function(names, autoGenerateTooltips) {
-      if (autoGenerateTooltips) {
-        var tooltips = {};
-        names.forEach(function(n) {
-        if (random() > 0.5) {
-          tooltips[n] = randomTooltip();
-        }
-      });
-      this._tooltips = tooltips;
-      }
-    },
-    randomHighlights: function(names) {
-      var h = [];
-      names.forEach(function(n) {
-        if (random() > 0.6) {
-          h.push(n);
-        }
-      });
-      this.highlights = h;
-    }
-  });
-  </script>
-</dom-module>
-
-<dom-module id="x-demo">
-<style>
-.small {
-  width: 200px;
-  height: 500px;
-}
-.large {
-  width: 500px;
-  height: 900px;
-}
-html,body {
-  height: 100%;
-}
-mc-demo {
-  padding: 5px;
-  border: 1px solid var(--paper-red-500);
-  display: inline-block;
-}
-</style>
-<template>
-  <div class="demo-block">
-    <mc-demo id="demo1" class="small" names="[[long_names]]"></mc-demo>
-    <mc-demo class="small" names="[[many_names]]"></mc-demo>
-    <mc-demo class="small" names="[[many_long_names]]"></mc-demo>
-  </div>
-
-  <div class="demo-block">
-    <mc-demo class="large" names="[[long_names]]"></mc-demo>
-    <mc-demo class="large" names="[[many_names]]"></mc-demo>
-    <mc-demo class="large" names="[[many_long_names]]"></mc-demo>
-  </div>
-
-</template>
-<script>
-
-function long_names() {
-  return [
-    "foo_bar very long name with spaces",
-    "the quick brown fox jumped over the lazy dog",
-    "supercalifragilisticexpialodcious/bar/foo/zod/longer/longer",
-  ];
-}
-
-function many_names() {
-  var out = [];
-  for (var i=0; i<20; i++) {
-    out.push("foo_bar-" + i);
-    out.push("bar_zod_bing-" + i);
-    out.push("lol-" + i);
-  }
-  return out;
-}
-
-function many_long_names() {
-  var out = [];
-  for (var i=0; i<20; i++) {
-    out.push("foo_bar very very very long some spaces though-" + i);
-    out.push("bar_zod_bing_bas_womp_wub_wub_dub_wub_wub-" + i);
-    out.push("rightly_to_be_great_is_not_to_stir_without_great_argument_but_greatly_to_find_quarrel_in_a_straw_when_honors_at_the_stake-" + i);
-  }
-  return out;
-}
-
-Polymer({
-  is: "x-demo",
-  properties: {
-  long_names: {type: Array, value: long_names},
-  many_names: {type: Array, value: many_names},
-  many_long_names: {type: Array, value: many_long_names},
-},
-});
-</script>
-</dom-module>
-
-<x-demo id="demo"></x-demo>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.html
deleted file mode 100644
index fad4642963..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.html
+++ /dev/null
@@ -1,160 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-icons/iron-icons.html">
-<link rel="import" href="../paper-checkbox/paper-checkbox.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../tf-storage/tf-storage.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="scrollbar-style.html">
-<link rel="import" href="run-color-style.html">
-
-<!--
-tf-multi-checkbox creates a list of checkboxes that can be used to toggle on or off
-a large number of values. Each checkbox displays a name, and may also have an
-associated tooltip value. Checkboxes can be highlighted, hidden, and re-ordered.
-
-tf-multi-checkbox assumes that the names may be very long compared to the width
-of the checkbox, and the number of names may also be very large, and works to
-handle these situations gracefully.
--->
-<dom-module id="tf-multi-checkbox">
-  <style include="scrollbar-style"></style>
-  <style include="run-color-style"></style>
-
-  <template>
-      <paper-input
-        id="runs-regex"
-        no-label-float
-        label="Write a regex to filter runs"
-        value="[[regexInput]]"
-        on-bind-value-changed="_debouncedRegexChange"
-      ></paper-input>
-    <div id="outer-container" class="scrollbar">
-      <template
-        is="dom-repeat"
-        items="[[namesMatchingRegex]]"
-      >
-        <div
-          class="run-row"
-        >
-          <div class="icon-container checkbox-container vertical-align-container">
-            <paper-checkbox
-              class="checkbox vertical-align-center"
-              name="[[item]]"
-              checked$="[[_isChecked(item, runSelectionState.*)]]"
-              on-change="_checkboxChange"
-            ></paper-checkbox>
-
-          </div>
-          <div class="icon-container isolator-container vertical-align-container">
-            <paper-icon-button
-              icon="radio-button-unchecked"
-              class="isolator vertical-align-center"
-              on-tap="_isolateRun"
-              name="[[item]]"
-            ></paper-icon-button>
-          </div>
-          <div class="item-label-container">
-            <span>[[item]]</span>
-          </div>
-        </div>
-      </template>
-    </div>
-  <style>
-    paper-input {
-      --paper-input-container-focus-color: var(--tb-orange-strong);
-      --paper-input-container-input: {
-        font-size: 14px;
-      };
-      --paper-input-container-label: {
-        font-size: 14px;
-      };
-    }
-    :host {
-      display: flex;
-      flex-direction: column;
-      height: 100%;
-    }
-    #outer-container {
-      overflow-y: auto;
-      overflow-x: hidden;
-      width: 100%;
-      height: 0; /* Quirk to make firefox add scrolling instead of expand div */
-      flex-grow: 1;
-      flex-shrink: 1;
-      word-wrap: break-word;
-    }
-    .run-row {
-      padding-top: 5px;
-      padding-bottom: 5px;
-      display: flex;
-      flex-direction: row;
-      font-size: 13px;
-    }
-    .icon-container {
-      flex-grow: 0;
-      flex-shrink: 0;
-      padding-left: 2px;
-    }
-    .checkbox {
-      padding-left: 2px;
-      width: 18px;
-      height: 18px;
-    }
-    .isolator {
-      width: 18px;
-      height: 18px;
-      padding: 0px;
-    }
-    .isolator-container {
-      padding-left: 6px;
-      padding-right: 3px;
-    }
-    .checkbox-container {
-      padding-left: 2px;
-    }
-    .item-label-container {
-      padding-left: 5px;
-      flex-grow: 1;
-      flex-shrink: 1;
-      width: 0px; /* hack to get the flex-grow to work properly */
-    }
-    .tooltip-value-container {
-      display: flex;
-      justify-content: center;
-      flex-grow: 0;
-      flex-shrink: 0;
-      text-align:right;
-      padding-left: 2px;
-    }
-    .vertical-align-container {
-      display: flex;
-      justify-content: center;
-    }
-    .vertical-align-container .vertical-align-center {
-      align-self: center;
-    }
-    .vertical-align-container .vertical-align-top {
-      align-self: start;
-    }
-  </style>
-  </template>
-  <script src="tf-multi-checkbox.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.ts b/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.ts
deleted file mode 100644
index 453f2a4153..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-multi-checkbox.ts
+++ /dev/null
@@ -1,196 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {runsColorScale} from '../tf-color-scale/colorScale';
-import * as storage from '../tf-storage/storage';
-
-Polymer({
-  is: 'tf-multi-checkbox',
-  properties: {
-    names: {
-      type: Array,
-      value: function() {
-        return [];
-      },
-    },  // All the runs in consideration
-    regexInput: {
-      type: String,
-      value: storage.getStringInitializer('regexInput', ''),
-      observer: '_regexInputObserver',
-    },  // Regex for filtering the runs
-    regex: {type: Object, computed: '_makeRegex(regexInput)'},
-    namesMatchingRegex: {
-      type: Array,
-      computed: 'computeNamesMatchingRegex(names.*, regex)'
-    },  // Runs that match the regex
-    runSelectionState: {
-      // if a run is explicitly enabled, True, if explicitly disabled, False.
-      // if undefined, default value (enable for first k runs, disable after).
-      type: Object,
-      value: storage.getObjectInitializer('runSelectionState', {}),
-      observer: '_storeRunToIsCheckedMapping',
-    },
-    // (Allows state to persist across regex filtering)
-    outSelected: {
-      type: Array,
-      notify: true,
-      computed: 'computeOutSelected(namesMatchingRegex.*, runSelectionState.*)'
-    },
-    maxRunsToEnableByDefault: {
-      // When TB first loads, if it has k or fewer runs, they are all enabled
-      // by default. If there are more, then they are all disabled.
-      type: Number,
-      value: 40,
-    },
-    _debouncedRegexChange: {
-      type: Object,
-      // Updating the regex can be slow, because it involves updating styles
-      // on a large number of Polymer paper-checkboxes. We don't want to do
-      // this while the user is typing, as it may make a bad, laggy UI.
-      // So we debounce the updates that come from user typing.
-      value: function() {
-        const _this = this;
-        var debounced = _.debounce(function(r) {
-          _this.regexInput = r;
-        }, 150, {leading: false});
-        return function() {
-          var r = this.$$('#runs-regex').value;
-          if (r == '') {
-            // If the user cleared the field, they may be done typing, so
-            // update more quickly.
-            this.async(function() {
-              _this.regexInput = r;
-            }, 30);
-          } else {
-            debounced(r);
-          };
-        };
-      },
-    },
-  },
-  listeners: {
-    'dom-change': 'synchronizeColors',
-  },
-  observers: [
-    '_setIsolatorIcon(runSelectionState, names)',
-  ],
-  _storeRunToIsCheckedMapping:
-      storage.getObjectObserver('runSelectionState', {}),
-  _makeRegex: function(regex) {
-    try {
-      return new RegExp(regex)
-    } catch (e) {
-      return null;
-    }
-  },
-  _setIsolatorIcon: function() {
-    var runMap = this.runSelectionState;
-    var numChecked = _.filter(_.values(runMap)).length;
-    var buttons =
-        Array.prototype.slice.call(this.querySelectorAll('.isolator'));
-
-    buttons.forEach(function(b) {
-      if (numChecked === 1 && runMap[b.name]) {
-        b.icon = 'radio-button-checked';
-      } else {
-        b.icon = 'radio-button-unchecked';
-      }
-    });
-  },
-  computeNamesMatchingRegex: function(__, ___) {
-    var regex = this.regex;
-    return this.names.filter(function(n) {
-      return regex == null || regex.test(n);
-    });
-  },
-  computeOutSelected: function(__, ___) {
-    var runSelectionState = this.runSelectionState;
-    var num = this.maxRunsToEnableByDefault;
-    var allEnabled = this.namesMatchingRegex.length <= num;
-    return this.namesMatchingRegex.filter(function(n, i) {
-      return runSelectionState[n] == null ? allEnabled : runSelectionState[n];
-    });
-  },
-  synchronizeColors: function(e) {
-    this._setIsolatorIcon();
-
-    const checkboxes = this.querySelectorAll('paper-checkbox');
-    checkboxes.forEach(p => {
-      const color = runsColorScale(p.name);
-      p.customStyle['--paper-checkbox-checked-color'] = color;
-      p.customStyle['--paper-checkbox-checked-ink-color'] = color;
-      p.customStyle['--paper-checkbox-unchecked-color'] = color;
-      p.customStyle['--paper-checkbox-unchecked-ink-color'] = color;
-    });
-    const buttons = this.querySelectorAll('.isolator');
-    buttons.forEach(p => {
-      const color = runsColorScale(p.name);
-      p.style['color'] = color;
-    });
-    // The updateStyles call fails silently if the browser doesn't have focus,
-    // e.g. if TensorBoard was opened into a new tab that isn't visible.
-    // So we wait for requestAnimationFrame.
-    window.requestAnimationFrame(() => {
-      this.updateStyles();
-    });
-  },
-  _isolateRun: function(e) {
-    // If user clicks on the label for one run, enable it and disable all other
-    // runs.
-
-    var name = (Polymer.dom(e) as any).localTarget.name;
-    var selectionState = {};
-    this.names.forEach(function(n) {
-      selectionState[n] = n == name;
-    });
-    this.runSelectionState = selectionState;
-  },
-  _checkboxChange: function(e) {
-    var target = (Polymer.dom(e) as any).localTarget;
-    this.runSelectionState[target.name] = target.checked;
-    // n.b. notifyPath won't work because run names may have periods.
-    this.runSelectionState = _.clone(this.runSelectionState);
-  },
-  _isChecked: function(item, outSelectedChange) {
-    return this.outSelected.indexOf(item) != -1;
-  },
-  _regexInputObserver: storage.getStringObserver('regexInput', ''),
-  toggleAll: function() {
-    var _this = this;
-    var anyToggledOn = this.namesMatchingRegex.some(function(n) {
-      return _this.runSelectionState[n]
-    });
-
-
-    var runSelectionStateIsDefault =
-        Object.keys(this.runSelectionState).length == 0;
-
-    var defaultOff =
-        this.namesMatchingRegex.length > this.maxRunsToEnableByDefault;
-    // We have runs toggled either if some were explicitly toggled on, or if
-    // we are in the default state, and there are few enough that we default
-    // to toggling on.
-    anyToggledOn = anyToggledOn || runSelectionStateIsDefault && !defaultOff;
-
-    // If any are toggled on, we turn everything off. Or, if none are toggled
-    // on, we turn everything on.
-
-    var newRunsDisabled = {};
-    this.names.forEach(function(n) {
-      newRunsDisabled[n] = !anyToggledOn;
-    });
-    this.runSelectionState = newRunsDisabled;
-  },
-});
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-no-data-warning.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-no-data-warning.html
deleted file mode 100644
index c90efac1d6..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-no-data-warning.html
+++ /dev/null
@@ -1,129 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-
-<!--
-Display a warning when there is no data found.
--->
-<dom-module id="tf-no-data-warning">
-  <template>
-    <template is="dom-if" if="[[showWarning]]">
-      <div class="warning">
-        <template is="dom-if" if="[[_isGraph(dataType)]]">
-          <h3>
-            No graph definition files were found.
-          </h3>
-          <p>
-            To store a graph, create a
-            <code>tf.summary.FileWriter</code>
-            and pass the graph either via the constructor, or by calling its
-            <code>add_graph()</code> method.
-            You may want to check out the
-            <a href="https://www.tensorflow.org/get_started/graph_viz">
-              graph visualizer tutorial
-            </a>.
-          </p>
-        </template>
-        <template is="dom-if" if="[[_isProjector(dataType)]]">
-          <h3>
-            No checkpoint was found.
-          </h3>
-          <p>
-            Probable causes:
-            <ul>
-              <li>
-                No checkpoint has been saved yet. Please refresh the page periodically.
-              </li>
-              <li>
-                You are not saving any checkpoint. To save your model,
-                create a
-                <a href="https://www.tensorflow.org/api_docs/python/tf/train/Saver">
-                  <code>tf.train.Saver</code>
-                </a>
-                and save your model periodically
-                by calling <code>saver.save(session, LOG_DIR/model.ckpt, step)</code>.
-              </li>
-            </ul>
-          </p>
-        </template>
-        <template is="dom-if" if="[[_isOther(dataType)]]">
-          <h3>
-            No <span>[[dataType]]</span> data was found.
-          </h3>
-          <p>
-            Probable causes:
-            <ul>
-              <li>
-                You haven't written any <span>[[dataType]]</span> data
-                to your event files.
-              </li>
-              <li>
-                TensorBoard can't find your event files.
-              </li>
-            </ul>
-          </p>
-        </template>
-        <p>
-          If you're new to using TensorBoard, and want to find out how to add
-          data and set up your event files, check out the
-          <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/README.md">
-            README
-          </a>
-          and perhaps the
-          <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">
-            TensorBoard tutorial
-          </a>.
-        </p>
-
-        <p>
-          If you think TensorBoard is configured properly, please see the
-          <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">
-            section of the README devoted to missing data problems
-          </a>
-          and consider filing an issue on GitHub.
-        </p>
-
-      </div>
-    </template>
-    <style>
-      .warning {
-        max-width: 540px;
-        margin: 80px auto 0 auto;
-      }
-    </style>
-  </template>
-
-  <script>
-    Polymer({
-      is: "tf-no-data-warning",
-      properties: {
-        dataType: String,
-        showWarning: Boolean
-      },
-      _isGraph: function(dataType) {
-        return dataType === "graph";
-      },
-      _isProjector: function(dataType) {
-        return dataType === "projector";
-      },
-      _isOther: function(dataType) {
-        return !this._isGraph(dataType) && !this._isProjector(dataType);
-      }
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-option-selector.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-option-selector.html
deleted file mode 100644
index 547a558ad0..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-option-selector.html
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="tensorboard-color.html">
-
-<!--
-tf-option-selector is a simple component that has buttons as content and
-provides a "selectedId" property that is one of the IDs of the buttons inside it.
--->
-<dom-module id="tf-option-selector">
-  <template>
-    <div id="wrap">
-      <h3>[[name]]</h3>
-      <div class="content-wrapper"><content></content></div>
-    </div>
-    <style>
-      .content-wrapper ::content > * {
-        width: 30%;
-        font-size: 13px;
-        background: none;
-        margin-top: 10px;
-        color: var(--tb-ui-dark-accent);
-      }
-
-      .content-wrapper ::content :first-of-type {
-        margin-left: 0;
-      }
-
-      .content-wrapper ::content .selected {
-        background-color: var(--tb-ui-dark-accent);
-        color: white!important;
-      }
-
-      h3 {
-        color: var(--paper-grey-800);
-        margin: 0;
-        font-weight: normal;
-        font-size: 14px;
-        margin-bottom: 5px;
-        display: block;
-        pointer-events: none;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-option-selector",
-      properties: {
-        name: String,
-        selectedId: {
-          type: String,
-          notify: true,
-          observer: '_selectedIdChanged'
-        }
-      },
-      attached: function() {
-        this.async(function() {
-          this.getEffectiveChildren().forEach(function(node) {
-            this.listen(node, 'tap', '_selectTarget');
-          }.bind(this));
-        });
-      },
-      _selectTarget: function(e) {
-        this.selectedId = e.currentTarget.id;
-      },
-      _selectedIdChanged: function() {
-        var selected = this.queryEffectiveChildren('#' + this.selectedId);
-        if (!selected) {
-          return;
-        }
-
-        this.getEffectiveChildren().forEach(function(node) {
-          node.classList.remove("selected");
-        });
-        selected.classList.add("selected");
-      }
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-panes-helper.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-panes-helper.html
deleted file mode 100644
index 32d79c649c..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-panes-helper.html
+++ /dev/null
@@ -1,348 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="tf-collapsable-pane.html">
-<link rel="import" href="tf-no-data-warning.html">
-<link rel="import" href="tf-chart-scaffold.html">
-
-<!--
-tf-panes-helper is a component that renders the contents of TensorBoard pages.
-It renders a tf-collapsable-pane for each category. Inside each category, the
-provided content template is rendered repeatedly for each tag within that
-category.
-
-This helper also incorporates an expand button and data download utility for
-each card.
-
-To use it, just specify a template inside tf-panes-helper that contains the
-code that will be replicated for each tag.
-
-<tf-panes-helper
-  categories="[[categories]]"
-  data-type="type"
-  data-provider="[[provider]]"
-  run2tag="[[run2tag]]"
-  selected-runs="[[selectedRuns]]"
-  >
-  <template>
-    <Code instantiated for each card>
-  </template>
-</tf-panes-helper>
-
-If you want for the template to be replicated for each tag and run, not only for
-each tag, you can set the repeatForRuns property to true.
-
-You can also set the showDownloadLinks property, which will show a menu with
-options to download JSON and CSV data. For this, you must also set the
-downloadLinkUrlFunction property to an appropriate value.
-
-@element tf-panes-helper
--->
-<dom-module id="tf-panes-helper">
-  <template>
-    <content></content> <!-- User template will be put here -->
-    <tf-no-data-warning
-      data-type="[[dataType]]"
-      show-warning="[[dataNotFound]]"
-      ></tf-no-data-warning>
-
-    <template is="dom-repeat" items="[[categories]]" as="category">
-      <tf-collapsable-pane
-        name="[[category.name]]"
-        count="[[_count(category.tags, selectedRuns.*)]]"
-        >
-        <div class="layout horizontal wrap">
-          <template is="dom-repeat" items="[[_categoryCards(category, selectedRuns.*, run2tag.*)]]">
-              <div class="card">
-                <div class="card-title-container" style="border-color: [[_titleBorderColor(item.run)]]">
-                  <div class="card-title" inner-h-t-m-l="[[_break(item.tag)]]"></div>
-                  <template is="dom-if" if="[[repeatForRuns]]">
-                    <div class="card-subtitle" title="[[item.run]]">[[item.run]]</div>
-                  </template>
-                </div>
-                <div class="card-content">
-                  <tf-chart-scaffold
-                    tag="[[item.tag]]"
-                    data-provider="[[dataProvider]]"
-                    visible-series="[[item.runs]]"
-                    on-ready="_instantiateTemplate"
-                    >
-                    <!-- Instantiated template will be put here -->
-                  </tf-chart-scaffold>
-                </div>
-                <div class="card-bottom-row">
-                  <paper-icon-button
-                    class="expand-button"
-                    icon="fullscreen"
-                    on-tap="_toggleExpanded"
-                    ></paper-icon-button>
-                  <template is="dom-if" if="[[showDownloadLinks]]">
-                    <tf-downloader
-                      runs="[[item.runs]]"
-                      tag="[[item.tag]]"
-                      url-fn="[[downloadLinkUrlFunction]]"
-                      >
-                    </tf-downloader>
-                  </template>
-                </div>
-              </div>
-          </template>
-        </div>
-      </tf-collapsable-pane>
-    </template>
-
-    <style>
-      .card {
-        height: var(--card-height, 200px);
-        width: var(--card-width, 300px);
-        display: flex;
-        flex-direction: column;
-        margin: 5px;
-        padding: var(--card-padding, 0 30px 35px 0);
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        position: relative;
-      }
-
-      .card-expanded {
-        height: var(--card-expanded-height, 400px);
-        width: var(--card-expanded-width, 100%);
-      }
-
-      .card-title, .card-subtitle {
-        flex-grow: 0;
-        flex-shrink: 0;
-        font-size: 14px;
-        text-overflow: ellipsis;
-        overflow: hidden;
-      }
-
-      .card-subtitle {
-        font-size: 12px;
-      }
-
-      .card-content {
-        flex-grow: 1;
-        flex-shrink: 1;
-        display: flex;
-        margin-top: 10px;
-      }
-
-      .card-bottom-row {
-        position: absolute;
-        left: 0px;
-        bottom: 0px;
-        width: 100%;
-        display: flex;
-        flex-direction: row;
-        justify-content: space-between;
-        pointer-events: none;
-      }
-
-      .card-title-container {
-        border-left: 4px solid;
-        padding-left: 5px;
-      }
-
-      .expand-button {
-        color: #2196F3;
-        width: 32px;
-        height: 32px;
-        padding: 4px;
-        border-radius: 100%;
-        pointer-events: auto;
-        display: var(--show-expand-button, block);
-      }
-
-      .card-expanded .expand-button {
-        background: var(--tb-ui-light-accent);
-      }
-
-      tf-downloader {
-        margin-right: 30px;
-        pointer-events: auto;
-      }
-
-    </style>
-  </template>
-  <script>
-    import {runsColorScale} from '../tf-color-scale/colorScale';
-    Polymer({
-      is: "tf-panes-helper",
-      properties: {
-        /**
-         * Categories that separate the template instances. Each category will
-         * be given its own collapsible pane. The category must be an array of
-         * objects, each with a 'name' property and a 'tags' array of strings.
-         */
-        categories: Array,
-
-        /**
-         * The name of the data type that is used by this dashboard. This will
-         * be used to display what is missing when there is no data available.
-         */
-        dataType: String,
-
-        /**
-         * The function that requests and returns a promise with the data of the
-         * required type for the templates from the backend.
-         */
-        dataProvider: Object,
-
-        /**
-         * If false, instantiates one template for each tag and calls
-         * setVisibleSeries on the first element of the template with all valid
-         * runs the tag has. If true, instantiates one template for each run of
-         * each tag, and calls setVisibleSeries of the first element of the
-         * instantiated template with just the one run.
-         */
-        repeatForRuns: {
-          type: Boolean,
-          value: false
-        },
-
-        /**
-         * Map from runs to the valid tags that have them.
-         */
-        run2tag: Object,
-
-        /**
-         * Array with the runs that are selected by the user (i.e. valid to be
-         * displayed).
-         */
-        selectedRuns: Array,
-
-        /**
-         * If true, shows a menu with download links for the template data.
-         * If this is set to true, urlFn must also be provided.
-         */
-        showDownloadLinks: Boolean,
-
-        /**
-         * Function that returns the route to get data to download. Must be
-         * provided if showDownloadLinks is enabled.
-         */
-        downloadLinkUrlFunction: Function,
-        _contentTemplate: {
-          type: Object,
-          value: null
-        },
-        _stampedTemplates: {
-          type: Array,
-          value: function() { return [] }
-        }
-      },
-      behaviors: [
-        Polymer.Templatizer,
-      ],
-
-      /**
-       * Initializes the Polymer.Templatizer behavior with the template supplied
-       * by the user. With this, all calls to this.stamp() will produce an
-       * instance of the user template.
-       */
-      _initTemplatizer: function() {
-        if (!this._contentTemplate) {
-          // First template is used as the content.
-          this._contentTemplate = Polymer.dom(this).querySelector('template');
-          this.templatize(this._contentTemplate);
-        }
-      },
-
-      /**
-       * Called every time a tf-chart-scaffold is ready, stamps the user
-       * template inside the scaffold element (before it is attached) and
-       * stores the stamped template in an array to use for data binding
-       * (forwardParentProp/Path).
-       */
-      _instantiateTemplate: function(e) {
-        var scaffold = e.target;
-        this._initTemplatizer();
-        var instance = this.stamp();
-        this._stampedTemplates.push(instance);
-        Polymer.dom(scaffold).appendChild(instance.root);
-      },
-      _toggleExpanded: function(e) {
-        var currentTarget = Polymer.dom(e.currentTarget);
-        var card = currentTarget.node.closest('.card');
-        var scaffold = card.querySelector('tf-chart-scaffold');
-        card.classList.toggle('card-expanded');
-        scaffold.chart().redraw();
-      },
-      _count: function(tags) {
-        if (!this.repeatForRuns) {
-          return tags.length;
-        }
-
-        var targetTags = d3.set(tags);
-        var count = 0;
-        this.selectedRuns.forEach(function(r) {
-          this.run2tag[r].forEach(function(t) {
-            if (targetTags.has(t)) {
-              count++;
-            }
-          });
-        }.bind(this));
-        return count;
-      },
-      _categoryCards: function(category) {
-        var cards = [];
-        category.tags.forEach(function(tag) {
-          var runs = this.selectedRuns.filter(function(r) {
-            return this.run2tag[r] && this.run2tag[r].indexOf(tag) !== -1;
-          }.bind(this));
-
-          if (this.repeatForRuns) {
-            runs.forEach(function(run) {
-              cards.push({tag: tag, run: run, runs: [run]});
-            });
-          } else {
-            cards.push({tag: tag, runs: runs});
-          }
-        }.bind(this));
-
-        return cards;
-      },
-      _titleBorderColor: function(run) {
-        return this.repeatForRuns ? runsColorScale(run) : 'white';
-      },
-
-      /*
-       * Polymer data binding forwarding functions. Check the
-       * Polymer.Templatizer documentation for more information.
-       */
-
-      _forwardParentProp: function(property, value) {
-        this._stampedTemplates.forEach(function(instance) {
-          instance[property] = value;
-        });
-      },
-      _forwardParentPath: function(path, value) {
-        this._stampedTemplates.forEach(function(instance) {
-          instance.notifyPath(path, value, true);
-        });
-      },
-      // TODO(renatoutsch): implement the instance forwarding for two-way data
-      // binding.
-      // Add breaks to input so it will wrap nicely
-      _break: function(ipt) {
-        return ipt.replace(/([\/_-])/g, "$1<wbr>")
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group-demo.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group-demo.html
deleted file mode 100644
index 3565fec179..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group-demo.html
+++ /dev/null
@@ -1,45 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
- <head>
-   <link rel="import" href="tf-regex-group.html">
- </head>
- <body>
-  <style>
-  .container {
-    width: 255px;
-    padding: 10px;
-    border: 1px solid #3f51b5;
-    border-radius: 5px;
-  }
-  :host {
-    margin: 0px;
-  }
-  </style>
-  <template id="page-template" is="dom-bind">
-    <div class="container">
-      <tf-regex-group regexes="{{regexes}}" id="demo"></tf-regex-group>
-    </div>
-    <p> Regexes:</p>
-    <template is="dom-repeat" items="[[regexes]]">
-      <p>"<span>[[item]]</span>"</p>
-    </template>
-  </template>
- </body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group.html
deleted file mode 100644
index c1d3cf06ae..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group.html
+++ /dev/null
@@ -1,99 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../iron-icons/iron-icons.html">
-<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../tf-storage/tf-storage.html">
-
-<!--
-`tf-regex-group` provides an input component for a group of regular expressions.
-
-Example:
-  <tf-regex-group regexes="{{regexes}}"></tf-regex-group>
-
-It contains a series of regular expression input fields. From this, it computes
-`regexes', an array in which every element is either a string representing a
-valid, nonempty regular expression, or the value `null`
-
-Public Properties:
-`regexes` a readonly, notifying array of strings, where each string is a regex
-
-It maintains an invariant that the final regex should always be an empty string,
-so the user can easily add more regular expressions. It does this by adding
-a new empty regex when the final one is nonempty.
-
-Pressing "enter" moves focus to the next regex (or just blurs if there are no
-more regexes).
--->
-<dom-module id="tf-regex-group">
-  <template>
-    <div class="regex-list">
-      <template is="dom-repeat" items="{{rawRegexes}}">
-        <div class="regex-line">
-          <paper-input
-            id="text-input"
-            class="regex-input"
-            label="Write a regex to create a tag group"
-            no-label-float
-            value="{{item.regex}}"
-            invalid="[[!item.valid]]"
-            on-keyup="moveFocus"
-          ></paper-input>
-          <paper-icon-button
-            icon="close"
-            class="delete-button"
-            aria-label="Delete Regex"
-            tabindex="0"
-            on-tap="deleteRegex"
-          ></paper-icon-button>
-        </div>
-        <style>
-          .regex-input {
-            width: 250px;
-            display: inline-block;
-            margin-left: -3px;
-          }
-
-          .delete-button {
-            color: var(--paper-grey-700);
-            width: 40px;
-            height: 40px;
-            margin-right: -10px;
-          }
-
-          .regex-list {
-            margin-bottom: 10px;
-          }
-
-          paper-input {
-            --paper-input-container-focus-color: var(--tb-orange-strong);
-            --paper-input-container-input: {
-              font-size: 14px;
-            };
-            --paper-input-container-label: {
-              font-size: 14px;
-            };
-          }
-        </style>
-      </template>
-    </div>
-  </template>
-  <script src="tf-regex-group.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group.ts b/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group.ts
deleted file mode 100644
index 92a0eb6a0b..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-regex-group.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import * as storage from '../tf-storage/storage';
-
-Polymer({
-  is: 'tf-regex-group',
-  properties: {
-    rawRegexes: {
-      type: Array,
-      value: storage.getObjectInitializer(
-          'rawRegexes', [{regex: '', valid: true}]),
-    },
-    regexes:
-        {type: Array, computed: 'usableRegexes(rawRegexes.*)', notify: true},
-  },
-  observers: [
-    'addNewRegexIfNeeded(rawRegexes.*)',
-    'checkValidity(rawRegexes.*)',
-    '_uriStoreRegexes(rawRegexes.*)',
-  ],
-  _uriStoreRegexes:
-      storage.getObjectObserver('rawRegexes', [{regex: '', valid: true}]),
-  checkValidity: function(x) {
-    var match = x.path.match(/rawRegexes\.(\d+)\.regex/);
-    if (match) {
-      var idx = match[1];
-      this.set('rawRegexes.' + idx + '.valid', this.isValid(x.value));
-    }
-  },
-  isValid: function(s) {
-    try {
-      new RegExp(s);
-      return true;
-    } catch (e) {
-      return false;
-    }
-  },
-  usableRegexes: function(regexes) {
-    var isValid = this.isValid;
-    return regexes.base
-        .filter(function(r) {
-          // Checking validity here (rather than using the data property)
-          // is necessary because otherwise we might send invalid regexes due
-          // to the fact that this function can call before the observer does
-          return r.regex !== '' && isValid(r.regex);
-        })
-        .map(function(r) {
-          return r.regex;
-        });
-  },
-  addNewRegexIfNeeded: function() {
-    var last = this.rawRegexes[this.rawRegexes.length - 1];
-    if (last.regex !== '') {
-      this.push('rawRegexes', {regex: '', valid: true});
-    }
-  },
-  deleteRegex: function(e) {
-    if (this.rawRegexes.length > 1) {
-      this.splice('rawRegexes', e.model.index, 1);
-    }
-  },
-  moveFocus: function(e) {
-    if (e.keyCode === 13) {
-      var idx = e.model.index;
-      var inputs = Polymer.dom(this.root).querySelectorAll('.regex-input');
-      if (idx < this.rawRegexes.length - 1) {
-        (inputs[idx + 1] as any).$.input.focus();
-      } else {
-        (document.activeElement as HTMLElement).blur();
-      }
-    }
-  }
-});
diff --git a/tensorflow/tensorboard/components/tf_dashboard_common/tf-run-selector.html b/tensorflow/tensorboard/components/tf_dashboard_common/tf-run-selector.html
deleted file mode 100644
index 04eba058a1..0000000000
--- a/tensorflow/tensorboard/components/tf_dashboard_common/tf-run-selector.html
+++ /dev/null
@@ -1,185 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-dialog/paper-dialog.html">
-<link rel="import" href="tf-multi-checkbox.html">
-<link rel="import" href="scrollbar-style.html">
-
-<!--
-tf-run-selector creates a set of checkboxes to display which runs are selected.
-It also displays tooltips.
-
-Properties in:
-- runs: Array of strings representing the runs that may be selected
-
-Properties out:
-- outSelected: The array of run names that are currently checked by the user.
-
--->
-<dom-module id="tf-run-selector">
-  <template>
-    <paper-dialog with-backdrop id="logdir-dialog">
-      <h2>logdir</h2>
-      <div inner-h-t-m-l="{{_breakString(logdir)}}"></div>
-    </paper-dialog>
-    <div id="top-text">
-      <h3 id="tooltip-help" class="tooltip-container">
-        Runs
-      </h3>
-    </div>
-    <tf-multi-checkbox
-      id="multiCheckbox"
-      names="[[runs]]"
-      out-selected="{{outSelected}}"
-    ></tf-multi-checkbox>
-    <paper-button
-      class="x-button"
-      id="toggle-all"
-      on-tap="_toggleAll"
-    >
-    Toggle All Runs
-    </paper-button>
-    <template
-      is="dom-if"
-      if="[[logdir]]">
-      <div id="logdir">
-        <span id="clipped-logdir" inner-h-t-m-l="[[_clippedLogdir]]"></span><!--
-          We use HTML comments to remove spaces before the ellipsis.
-        --><template
-                     is="dom-if"
-                     if="[[_shouldShowExpandLogdirButton(logdir, _logdirClipLength)]]"><!--
-          --><a href="" on-click="_openLogdirDialog">…</a>
-        </template>
-      </div>
-    </template>
-    <style>
-      :host {
-        display: flex;
-        flex-direction: column;
-        padding-bottom: 10px;
-        box-sizing: border-box;
-      }
-      #top-text {
-        width: 100%;
-        flex-grow: 0;
-        flex-shrink: 0;
-        padding-right: 16px;
-        box-sizing: border-box;
-        color: var(--paper-grey-800);
-      }
-      tf-multi-checkbox {
-        display: flex;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
-      .x-button {
-        font-size: 13px;
-        background-color: var(--tb-ui-light-accent);
-        color: var(--tb-ui-dark-accent);
-      }
-      #tooltip-help {
-        color: var(--paper-grey-800);
-        margin: 0;
-        font-weight: normal;
-        font-size: 14px;
-        margin-bottom: 5px;
-      }
-      paper-button {
-        margin-left: 0;
-      }
-      #logdir {
-        color: var(--tb-ui-dark-accent);
-        font-size: 13px;
-        margin: 5px 0 0 0;
-        max-width: 288px;
-      }
-    </style>
-  </template>
-  <script>
-  Polymer({
-    is: "tf-run-selector",
-    properties: {
-      backend: Object,
-      outSelected: {type: Array, notify: true},
-      // runs: an array of strings, representing the run names that may be chosen
-      runs: Array,
-      logdir: {
-        type: String,
-        notify: true,
-      },
-      // This is the potentially clipped portion of the logdir we show at the bottom of the sidebar.
-      _clippedLogdir: {
-        type: String,
-      },
-      _logdirClipLength: {
-        type: Number,
-        value: 250,
-        readOnly: true,
-      },
-    },
-    observers: [
-      "_onBackendUpdate(backend)",
-      "_logdirSet(logdir)",
-    ],
-    _toggleAll: function() {
-      this.$.multiCheckbox.toggleAll();
-    },
-    // Break the string at natural points, including commas, equals, and slashes
-    _breakString: function(originalString) {
-      return originalString.replace(/([\/=\-_,])/g, "$1<wbr>");
-    },
-    _onBackendUpdate: function(backend) {
-      if (backend === undefined) {
-        return;
-      }
-
-      // When the backend is set, the selector can request the logdir.
-      backend.logdir().then(logdirObject => {
-        this.set('logdir', logdirObject.logdir);
-      }).catch(e => {
-        // Fetching the logdir failed. Prevent the exception from logging to
-        // console. The console already logs a 404 network event.
-      });
-    },
-    _logdirSet: function(logdir) {
-      if (logdir === undefined) {
-        // The logdir has not been set yet.
-        return;
-      }
-
-      var lineBrokenText;
-      if (logdir.length > this._logdirClipLength) {
-        // Clip the logdir to avoid blocking the runs selector. Let the user view a more full
-        // version of the logdir.
-        lineBrokenText = this._breakString(logdir.substring(0, this._logdirClipLength));
-      } else {
-        lineBrokenText = this._breakString(logdir);
-      }
-      this.set('_clippedLogdir', lineBrokenText);
-    },
-    _openLogdirDialog: function(event) {
-      event.preventDefault();
-      this.$$('#logdir-dialog').open();
-    },
-    _shouldShowExpandLogdirButton(logdir, _logdirClipLength) {
-      return logdir && logdir.length > _logdirClipLength;
-    },
-  });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/BUILD b/tensorflow/tensorboard/components/tf_distribution_dashboard/BUILD
deleted file mode 100644
index 2ccdc730d2..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/BUILD
+++ /dev/null
@@ -1,42 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_distribution_dashboard",
-    srcs = ["tf-distribution-dashboard.html"],
-    path = "/tf-distribution-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_runs_selector",
-        "//tensorflow/tensorboard/components/vz_distribution_chart",
-        "@org_polymer_iron_collapse",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-distribution-dashboard",
-    deps = [
-        ":tf_distribution_dashboard",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run1_tag_histo1.json b/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run1_tag_histo1.json
deleted file mode 100644
index a6765285b1..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run1_tag_histo1.json
+++ /dev/null
@@ -1,212 +0,0 @@
-[
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diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run2_tag_histo1.json b/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run2_tag_histo1.json
deleted file mode 100644
index 9e8a55b3f2..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run2_tag_histo1.json
+++ /dev/null
@@ -1,212 +0,0 @@
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-        300.0,
-        30,
-        [
-            [
-                0,
-                -3.360113978269659
-            ],
-            [
-                668,
-                -2.8293185004961043
-            ],
-            [
-                1587,
-                -1.5992540502266783
-            ],
-            [
-                3085,
-                0.14393860259807117
-            ],
-            [
-                5000,
-                1.47723448201245
-            ],
-            [
-                6915,
-                1.9510057389110733
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-            [
-                8413,
-                2.833176104473626
-            ],
-            [
-                9332,
-                4.142405216576347
-            ],
-            [
-                10000,
-                4.706937777668589
-            ]
-        ]
-    ],
-    [
-        400.0,
-        40,
-        [
-            [
-                0,
-                -2.599286228987632
-            ],
-            [
-                668,
-                -2.240365897443259
-            ],
-            [
-                1587,
-                -1.5992540502266783
-            ],
-            [
-                3085,
-                -0.9101893288861387
-            ],
-            [
-                5000,
-                0.7580548669750213
-            ],
-            [
-                6915,
-                1.6009864433919474
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-            [
-                8413,
-                2.3504002974280036
-            ],
-            [
-                9332,
-                2.7907805263353733
-            ],
-            [
-                10000,
-                3.5098048900144323
-            ]
-        ]
-    ]
-]
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run2_tag_histo2.json b/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run2_tag_histo2.json
deleted file mode 100644
index 7c8836f624..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/compressedHistograms_run_run2_tag_histo2.json
+++ /dev/null
@@ -1,212 +0,0 @@
-[
-    [
-        0.0,
-        0,
-        [
-            [
-                0,
-                -1.9291158122759586
-            ],
-            [
-                668,
-                -1.5970765333488954
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-                -1.0923120348519078
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-            [
-                3085,
-                -0.6688082872192093
-            ],
-            [
-                5000,
-                0.09312398815580714
-            ],
-            [
-                6915,
-                0.44532789251701854
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-            [
-                8413,
-                0.8238009655877649
-            ],
-            [
-                9332,
-                1.0357232383581656
-            ],
-            [
-                10000,
-                1.2741043689144438
-            ]
-        ]
-    ],
-    [
-        100.0,
-        10,
-        [
-            [
-                0,
-                -0.7780725642449806
-            ],
-            [
-                668,
-                -0.7138496178727424
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-                1587,
-                -0.5448932415735014
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-            [
-                3085,
-                -0.24370397454796228
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-                1.0451472255274825
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-    [
-        200.0,
-        20,
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-                668,
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-    [
-        300.0,
-        30,
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-                9332,
-                2.18821711651116
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-            [
-                10000,
-                2.2393193406467518
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-    ],
-    [
-        400.0,
-        40,
-        [
-            [
-                0,
-                -0.8286852465281818
-            ],
-            [
-                668,
-                -0.7815041529866706
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-            [
-                1587,
-                -0.3334896444053469
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-            [
-                3085,
-                0.21085213041026643
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-            [
-                5000,
-                0.5177616740489182
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-            [
-                6915,
-                1.077122434649409
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-            [
-                8413,
-                1.5898009703967424
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-            [
-                9332,
-                1.8859097291499742
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-            [
-                10000,
-                2.0954239138728523
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diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/logdir b/tensorflow/tensorboard/components/tf_distribution_dashboard/data/logdir
deleted file mode 100644
index b6362b45d7..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/logdir
+++ /dev/null
@@ -1 +0,0 @@
-{"logdir": "/foo/some/fake/logdir"}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/runs.json b/tensorflow/tensorboard/components/tf_distribution_dashboard/data/runs.json
deleted file mode 100644
index 739262a9fb..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/data/runs.json
+++ /dev/null
@@ -1,4 +0,0 @@
-{
-  "run1": {"compressedHistograms": ["histo1"]},
-  "run2": {"compressedHistograms": ["histo2", "histo1"]}
-}
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/index.html b/tensorflow/tensorboard/components/tf_distribution_dashboard/index.html
deleted file mode 100644
index fe899a0ba8..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/index.html
+++ /dev/null
@@ -1,69 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-<link rel="import" href="../paper-styles/typography.html">
-<link rel="import" href="tf-distribution-dashboard.html">
-
-<title>Distribution Dashboard Demo</title>
-<style>
-  #container {
-    height: 800px;
-    width: 100%;
-    display: block;
-  }
-
-  html, body {
-    margin: 0;
-    padding: 0;
-    font-family: "RobotoDraft","Roboto",sans-serif;
-  }
-
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="distribution-dash-demo">
-      <template>
-        <tf-distribution-dashboard id="demo" backend="[[backend]]"></tf-distribution-dashboard>
-      </template>
-      <script>
-        import {Backend} from "../../tf-backend/backend";
-        import {createRouter, setRouter} from "../../tf-backend/router";
-
-        Polymer({
-          is: "distribution-dash-demo",
-          properties: {
-            backend: {
-              type: Object,
-              value: function() {
-                return new Backend();
-              },
-            },
-          },
-          created: function() {
-            var path = "data";
-            var router = createRouter(path, true);
-            setRouter(router);
-          },
-        });
-      </script>
-    </dom-module>
-    <distribution-dash-demo id="container"></distribution-dash-demo>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html b/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
deleted file mode 100644
index 87ab99753a..0000000000
--- a/tensorflow/tensorboard/components/tf_distribution_dashboard/tf-distribution-dashboard.html
+++ /dev/null
@@ -1,134 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-dashboard-common/tf-option-selector.html">
-<link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../vz-distribution-chart/vz-distribution-chart.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-
-<!--
-tf-distribution-dashboard is a complete frontend that loads runs from a backend,
-and creates chart panes that display data for those runs.
-
-It provides a x type selector and the normal tf-sidebar-helper options, by
-which the user can customize how data is organized and displayed.
-
-Each chart has a button that can toggle whether it is "expanded"; expanded
-charts are larger.
-
-Organizationally, the #plumbing div contains components that have no concrete
-manifestation and just effect data bindings or data loading. The .sidebar div
-contains shared controls provided by tf-sidebar-helper. The .center div
-contains vz-distribution-charts embedded inside tf-panes-helper's.
--->
-<dom-module id="tf-distribution-dashboard">
-  <template>
-    <tf-dashboard-layout>
-      <div class="sidebar">
-        <div class="sidebar-section">
-          <tf-categorizer
-            id="categorizer"
-            tags="[[tags]]"
-            categories="{{_categories}}"
-          ></tf-categorizer>
-        </div>
-        <div class="sidebar-section">
-          <tf-option-selector
-            id="xTypeSelector"
-            name="Horizontal Axis"
-            selected-id="{{_xType}}"
-          >
-            <paper-button id="step">step</paper-button>
-            <paper-button id="relative">relative</paper-button>
-            <paper-button id="wall_time">wall</paper-button>
-          </tf-option-selector>
-        </div>
-        <div class="sidebar-section">
-          <tf-runs-selector selected-runs="{{_selectedRuns}}">
-          </tf-runs-selector>
-        </div>
-      </div>
-
-      <div class="center">
-        <tf-panes-helper
-          categories="[[_categories]]"
-          data-type="[[dataType]]"
-          data-provider="[[dataProvider]]"
-          data-not-found="[[dataNotFound]]"
-          run2tag="[[run2tag]]"
-          selected-runs="[[_selectedRuns]]"
-          repeat-for-runs
-          >
-          <template>
-            <vz-distribution-chart
-              x-type="[[_xType]]"
-              color-scale="[[_runsColorScale]]"
-            ></vz-distribution-chart>
-          </template>
-        </tf-panes-helper>
-      </div>
-    </tf-dashboard-layout>
-
-    <style include="dashboard-style"></style>
-  </template>
-
-  <script>
-    import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-    import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-    import {BackendBehavior} from "../tf-backend/behavior";
-    import {runsColorScale} from "../tf-color-scale/colorScale";
-
-    Polymer({
-      is: "tf-distribution-dashboard",
-      factoryImpl: function(backend) {
-        this.backend = backend;
-      },
-      behaviors: [
-        DashboardBehavior("distributions"),
-        ReloadBehavior("tf-chart-scaffold"),
-        BackendBehavior,
-      ],
-      properties: {
-        backend: Object,
-        _xType: {
-          type: String,
-          value: "step"
-        },
-        dataType: {
-          type: Object,
-          value: "compressedHistogram",
-        },
-        _runsColorScale: {
-          type: Object,
-          value: function() {
-            return {
-              scale: runsColorScale,
-            };
-          },
-          readOnly: true,
-        },
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_globals/BUILD b/tensorflow/tensorboard/components/tf_globals/BUILD
deleted file mode 100644
index c5b0cfbaa5..0000000000
--- a/tensorflow/tensorboard/components/tf_globals/BUILD
+++ /dev/null
@@ -1,27 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_globals",
-    srcs = [
-        "globals.ts",
-        "tf-globals.html",
-    ],
-    path = "/tf-globals",
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_globals"],
-    destdir = "tf-globals",
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_globals/globals.ts b/tensorflow/tensorboard/components/tf_globals/globals.ts
deleted file mode 100644
index fb6bb83b97..0000000000
--- a/tensorflow/tensorboard/components/tf_globals/globals.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// The names of TensorBoard tabs.
-export const TABS = [
-  'scalars', 'images', 'audio', 'graphs', 'distributions', 'histograms',
-  'embeddings', 'text'
-];
-
-// If true, TensorBoard stores its hash in the URI state.
-// If false, tab switching in TensorBoard will not update location hash,
-// because hash updates interfere with wct_tests.
-let _useHash = false;
-
-export function setUseHash(shouldUseHash: boolean): void {
-  _useHash = shouldUseHash;
-}
-
-export function useHash(): boolean {
-  return _useHash;
-}
-
-let _fakeHash = '';
-
-export function setFakeHash(h: string) {
-  _fakeHash = h;
-}
-
-export function getFakeHash() {
-  return _fakeHash;
-}
diff --git a/tensorflow/tensorboard/components/tf_globals/tf-globals.html b/tensorflow/tensorboard/components/tf_globals/tf-globals.html
deleted file mode 100644
index efb8e92e08..0000000000
--- a/tensorflow/tensorboard/components/tf_globals/tf-globals.html
+++ /dev/null
@@ -1,19 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="globals.js"></script>
-
diff --git a/tensorflow/tensorboard/components/tf_graph/BUILD b/tensorflow/tensorboard/components/tf_graph/BUILD
deleted file mode 100644
index 4c0894f192..0000000000
--- a/tensorflow/tensorboard/components/tf_graph/BUILD
+++ /dev/null
@@ -1,56 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph",
-    srcs = [
-        "tf-graph.html",
-        "tf-graph-minimap.html",
-        "tf-graph-scene.html",
-    ],
-    path = "/tf-graph",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "@org_polymer_iron_flex_layout",
-        "@org_polymer_iron_icons",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_dropdown_menu",
-        "@org_polymer_paper_input",
-        "@org_polymer_paper_menu",
-        "@org_polymer_paper_radio_group",
-        "@org_polymer_paper_toggle_button",
-        "@org_polymer_paper_tooltip",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph"],
-    destdir = "tf-graph",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_common:legacy",
-        "//third_party/javascript/polymer/v1/iron-flex-layout:lib",
-        "//third_party/javascript/polymer/v1/iron-icons:lib",
-        "//third_party/javascript/polymer/v1/paper-button:lib",
-        "//third_party/javascript/polymer/v1/paper-dropdown-menu:lib",
-        "//third_party/javascript/polymer/v1/paper-input:lib",
-        "//third_party/javascript/polymer/v1/paper-menu:lib",
-        "//third_party/javascript/polymer/v1/paper-radio-group:lib",
-        "//third_party/javascript/polymer/v1/paper-toggle-button:lib",
-        "//third_party/javascript/polymer/v1/paper-tooltip:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph/demo/BUILD b/tensorflow/tensorboard/components/tf_graph/demo/BUILD
deleted file mode 100644
index 02f3bf64bb..0000000000
--- a/tensorflow/tensorboard/components/tf_graph/demo/BUILD
+++ /dev/null
@@ -1,26 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-graph/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph",
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_graph_loader",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph/demo/data/graph.pbtxt b/tensorflow/tensorboard/components/tf_graph/demo/data/graph.pbtxt
deleted file mode 100644
index 30b2064534..0000000000
--- a/tensorflow/tensorboard/components/tf_graph/demo/data/graph.pbtxt
+++ /dev/null
@@ -1,4606 +0,0 @@
-node {
-  name: "GradientDescent/learning_rate"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_3"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_FLOAT
-        tensor_shape {
-        }
-        float_val: 0.1
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/Shape_1"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 1
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
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-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/MatMul_1"
-  op: "MatMul"
-  input: "Reshape"
-  input: "gradients/add_grad/tuple/control_dependency"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: -1
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "transpose_a"
-    value {
-      b: true
-    }
-  }
-  attr {
-    key: "transpose_b"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/MatMul"
-  op: "MatMul"
-  input: "gradients/add_grad/tuple/control_dependency"
-  input: "hidden_weights/read"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-          dim {
-            size: 784
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "transpose_a"
-    value {
-      b: false
-    }
-  }
-  attr {
-    key: "transpose_b"
-    value {
-      b: true
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/tuple/group_deps"
-  op: "NoOp"
-  input: "^gradients/MatMul_grad/MatMul"
-  input: "^gradients/MatMul_grad/MatMul_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-      }
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/tuple/control_dependency_1"
-  op: "Identity"
-  input: "gradients/MatMul_grad/MatMul_1"
-  input: "^gradients/MatMul_grad/tuple/group_deps"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@gradients/MatMul_grad/MatMul_1"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: -1
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "GradientDescent/update_hidden_weights/ApplyGradientDescent"
-  op: "ApplyGradientDescent"
-  input: "hidden_weights"
-  input: "GradientDescent/learning_rate"
-  input: "gradients/MatMul_grad/tuple/control_dependency_1"
-  device: "/job:localhost/replica:0/task:0/cpu:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@hidden_weights"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 784
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "use_locking"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "GradientDescent"
-  op: "NoOp"
-  input: "^GradientDescent/update_hidden_weights/ApplyGradientDescent"
-  input: "^GradientDescent/update_hidden_biases/ApplyGradientDescent"
-  input: "^GradientDescent/update_logits_weights/ApplyGradientDescent"
-  input: "^GradientDescent/update_logits_biases/ApplyGradientDescent"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_2"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-      }
-    }
-  }
-}
-node {
-  name: "Reshape_3"
-  op: "Reshape"
-  input: "SoftmaxCrossEntropyWithLogits"
-  input: "Slice_2"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "Tshape"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "Mean"
-  op: "Mean"
-  input: "Reshape_3"
-  input: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "Tidx"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "keep_dims"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "_send_Mean_0"
-  op: "_Send"
-  input: "Mean"
-  device: "/job:localhost/replica:0/task:0/cpu:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "client_terminated"
-    value {
-      b: true
-    }
-  }
-  attr {
-    key: "recv_device"
-    value {
-      s: "/job:localhost/replica:0/task:0/cpu:0"
-    }
-  }
-  attr {
-    key: "send_device"
-    value {
-      s: "/job:localhost/replica:0/task:0/cpu:0"
-    }
-  }
-  attr {
-    key: "send_device_incarnation"
-    value {
-      i: -5924635994370253548
-    }
-  }
-  attr {
-    key: "tensor_name"
-    value {
-      s: "Mean:0"
-    }
-  }
-}
-library {
-}
-versions {
-  producer: 21
-}
diff --git a/tensorflow/tensorboard/components/tf_graph/demo/index.html b/tensorflow/tensorboard/components/tf_graph/demo/index.html
deleted file mode 100644
index 52e2f0b934..0000000000
--- a/tensorflow/tensorboard/components/tf_graph/demo/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../tf-graph.html">
-<link rel="import" href="../../tf-graph-common/tf-graph-common.html">
-<link rel="import" href="../../tf-graph-loader/tf-graph-loader.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<title>TF Graph Demo</title>
-<style>
-  #demo-container {
-    border: 2px solid #808080;
-    width: 1000px;
-    height: 600px;
-  }
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="tf-graph-demo">
-      <template>
-        <!-- We first use the graph loader to load and parse a pbtxt file into a graph object. -->
-        <tf-graph-loader
-            id="loader"
-            datasets="[[_datasets]]"
-            selected-dataset="[[_selectedDataset]]"
-            out-graph="{{_graph}}">
-        </tf-graph-loader>
-
-        <!-- We color ops in the graph by XLA cluster. -->
-        <tf-graph id="graph" color-by="xla_cluster"></tf-graph>
-      </template>
-      <script>
-        "use strict";
-
-        Polymer({
-          is: "tf-graph-demo",
-          properties: {
-            // We tell the graph loader to load a specific pbtxt file.
-            _datasets: {
-              type: Array,
-              value: [{
-                "name": "Graph with XLA Clusters Specified",
-                "path": "data/graph.pbtxt"
-              }],
-            },
-            _selectedDataset: {
-              type: Number,
-              value: 0,
-            },
-
-            // This property will be updated by the graph loader.
-            _graph: {
-              type: Object,
-            },
-          },
-          observers: [
-            '_graphUpdated(_graph)',
-          ],
-          _graphUpdated: function(slimGraph) {
-            const tracker = tf.graph.util.getTracker(this.$.loader);
-            const hierarchyTracker = tf.graph.util.getSubtaskTracker(
-                tracker, 100, 'Namespace hierarchy');
-            const hierarchyOptions = {};
-            tf.graph.hierarchy.build(slimGraph, hierarchyOptions, hierarchyTracker).then(
-                function(graphHierarchy) {
-              // We have parsed and built the graph object from a pbtxt file. Render the graph.
-              this.$.graph.set('basicGraph', slimGraph);
-              this.$.graph.set('graphHierarchy', graphHierarchy);
-            }.bind(this));
-          },
-        });
-      </script>
-    </dom-module>
-    <div id='demo-container'>
-      <tf-graph-demo></tf-graph-demo>
-    </div>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph/tf-graph-minimap.html b/tensorflow/tensorboard/components/tf_graph/tf-graph-minimap.html
deleted file mode 100644
index 5fc16c0520..0000000000
--- a/tensorflow/tensorboard/components/tf_graph/tf-graph-minimap.html
+++ /dev/null
@@ -1,88 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<script src="../tf-graph-common/minimap.js"></script>
-
-<dom-module id="tf-graph-minimap">
-<template>
-<style>
-:host {
-  background-color:white;
-  transition: opacity .3s linear;
-  pointer-events: auto;
-}
-
-:host.hidden {
-  opacity: 0;
-  pointer-events: none;
-}
-
-canvas {
-  border: 1px solid #999;
-}
-
-rect {
-  fill: white;
-  stroke: #111111;
-  stroke-width: 1px;
-  fill-opacity: 0;
-  filter: url(#minimapDropShadow);
-  cursor: move;
-}
-
-svg {
-  position: absolute;
-}
-</style>
-<svg>
-  <defs>
-    <filter id="minimapDropShadow" x="-20%" y="-20%" width="150%" height="150%">
-      <feOffset result="offOut" in="SourceGraphic" dx="1" dy="1"></feOffset>
-      <feColorMatrix result="matrixOut" in="offOut" type="matrix" values="0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.5 0"></feColorMatrix>
-      <feGaussianBlur result="blurOut" in="matrixOut" stdDeviation="2"></feGaussianBlur>
-      <feBlend in="SourceGraphic" in2="blurOut" mode="normal"></feBlend>
-    </filter>
-  </defs>
-  <rect></rect>
-</svg>
-<canvas class="first"></canvas>
-<!-- Additional canvas to use as buffer to avoid flickering between updates -->
-<canvas class="second"></canvas>
-<canvas class="download"></canvas>
-</template>
-<script>
-Polymer({
-  is: 'tf-graph-minimap',
-
-  /**
-   * Initializes the minimap and returns a minimap object to notify when
-   * things update.
-   *
-   * @param svg The main svg element.
-   * @param zoomG The svg group used for panning and zooming the main svg.
-   * @param mainZoom The main zoom behavior.
-   * @param maxWandH The maximum width/height for the minimap.
-   * @param labelPadding Padding in pixels due to the main graph labels.
-   */
-  init: function(svg, zoomG, mainZoom, maxWAndH, labelPadding) {
-    return new tf.scene.Minimap(svg, zoomG, mainZoom, this, maxWAndH,
-        labelPadding);
-  }
-});
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html b/tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html
deleted file mode 100644
index 4777eddae0..0000000000
--- a/tensorflow/tensorboard/components/tf_graph/tf-graph-scene.html
+++ /dev/null
@@ -1,1080 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-<link rel="import" href="tf-graph-minimap.html">
-
-<!--
-  A module that takes a render hierarchy as input and produces an SVG DOM using
-  dagre and d3.
--->
-<dom-module id="tf-graph-scene">
-<template>
-<style>
-:host {
-  display: flex;
-  width: 100%;
-  font-size: 20px;
-}
-
-::content #svg {
-  overflow: hidden;
-  flex: 1;
-  height: 100%;
-  width: 100%;
-}
-
-::content #hidden {
-  position: fixed;
-  top: 0px;
-  visibility: hidden;
-}
-
-/* --- Node and annotation-node for Metanode --- */
-
-::content .meta > .nodeshape > rect,
-::content .meta > .annotation-node > rect {
-  cursor: pointer;
-  fill: hsl(0, 0%, 70%);
-}
-
-::content .node.meta.highlighted > .nodeshape > rect,
-::content .node.meta.highlighted > .annotation-node > rect {
-  stroke-width: 2;
-}
-
-::content .annotation.meta.highlighted > .nodeshape > rect,
-::content .annotation.meta.highlighted > .annotation-node > rect {
-  stroke-width: 1;
-}
-
-::content .meta.selected > .nodeshape > rect,
-::content .meta.selected > .annotation-node > rect {
-  stroke: red;
-  stroke-width: 2;
-}
-
-::content .node.meta.selected.expanded > .nodeshape > rect,
-::content .node.meta.selected.expanded > .annotation-node > rect {
-  stroke: red;
-  stroke-width: 3;
-}
-
-::content .annotation.meta.selected > .nodeshape > rect,
-::content .annotation.meta.selected > .annotation-node > rect {
-  stroke: red;
-  stroke-width: 2;
-}
-
-::content .node.meta.selected.expanded.highlighted > .nodeshape > rect,
-::content .node.meta.selected.expanded.highlighted > .annotation-node > rect {
-  stroke: red;
-  stroke-width: 4;
-}
-
-::content .faded,
-::content .faded rect,
-::content .faded ellipse,
-::content .faded path,
-::content .faded use,
-::content #rectHatch line,
-::content #ellipseHatch line {
-  color: #e0d4b3 !important;
-  fill: white;
-  stroke: #e0d4b3 !important;
-}
-
-
-::content .faded path {
-  stroke-width: 1px !important;
-}
-
-::content .faded rect {
-  fill: url(#rectHatch) !important;
-}
-
-::content .faded ellipse,
-::content .faded use {
-  fill: url(#ellipseHatch) !important;
-}
-
-::content .faded text {
-  opacity: 0;
-}
-
-/* Rules used for input-tracing. */
-::content .input-highlight > * > rect,
-::content .input-highlight > * > ellipse,
-::content .input-highlight > * > use
-{
-  fill: white;
-  stroke: #ff9800 !important;
-}
-
-/*  - Faded non-input styling */
-::content .non-input > * > rect,
-::content .non-input > * > ellipse,
-::content .non-input > * > use,
-/* For Const nodes. */
-::content .non-input > * > .constant:not([class*="input-highlight"]) >
-  .annotation-node > ellipse,
-/* For styling of annotation nodes of non-input nodes. */
-::content .non-input > g > .annotation > .annotation-node > rect {
-  stroke: #e0d4b3 !important;
-  stroke-width: inherit;
-  stroke-dasharray: inherit;
-}
-
-
-::content .non-input path {
-  visibility: hidden;
-}
-
-::content .non-input > .nodeshape > rect,
-::content .non-input > .annotation-node > rect,
-/* For styling of annotation nodes of non-input nodes. */
-::content .non-input > g > .annotation > .annotation-node > rect
-{
-  fill: url(#rectHatch) !important;
-}
-
-::content .non-input ellipse,
-::content .non-input use {
-  fill: url(#ellipseHatch) !important;
-}
-
-::content .non-input > text {
-  opacity: 0;
-}
-
-::content .non-input .annotation > .annotation-edge {
-  marker-end: url(#annotation-arrowhead-faded);
-}
-
-::content .non-input .annotation > .annotation-edge.refline {
-  marker-start: url(#ref-annotation-arrowhead-faded);
-}
-
-/* Input edges. */
-::content .input-edge-highlight > text {
-  fill: black !important;
-}
-::content .input-highlight > .in-annotations > .annotation > .annotation-edge,
-::content .input-highlight-selected > .in-annotations > .annotation >
-.annotation-edge {
-  stroke: #999 !important;
-}
-
-/* Non-input edges. */
-::content .non-input-edge-highlight,
-::content .non-input > g > .annotation > path,
-/* Annotation styles (label and edges respectively). */
-::content .non-input > g >
-.annotation:not(.input-highlight):not(.input-highlight-selected) >
-.annotation-label
-/*.annotation-edge*/
-{
-  visibility: hidden;
-}
-
-/* --- Op Node --- */
-
-::content .op > .nodeshape > ellipse,
-::content .op > .annotation-node > ellipse {
-  cursor: pointer;
-  fill: #fff;
-  stroke: #ccc;
-}
-
-::content .op.selected > .nodeshape > ellipse,
-::content .op.selected > .annotation-node > ellipse {
-  stroke: red;
-  stroke-width: 2;
-}
-
-::content .op.highlighted > .nodeshape > ellipse,
-::content .op.highlighted > .annotation-node > ellipse {
-  stroke-width: 2;
-}
-
-/* --- Series Node --- */
-
-/* By default, don't show the series background <rect>. */
-::content .series > .nodeshape > rect {
-  fill: hsl(0, 0%, 70%);
-  fill-opacity: 0;
-  stroke-dasharray: 5, 5;
-  stroke-opacity: 0;
-  cursor: pointer;
-}
-
-/* Once expanded, show the series background <rect> and hide the <use>. */
-::content .series.expanded > .nodeshape > rect {
-  fill-opacity: 0.15;
-  stroke: hsl(0, 0%, 70%);
-  stroke-opacity: 1;
-}
-::content .series.expanded > .nodeshape > use {
-  visibility: hidden;
-}
-
-/**
- * TODO(jimbo): Simplify this by applying a stable class name to all <g>
- * elements that currently have either the nodeshape or annotation-node classes.
- */
-::content .series > .nodeshape > use ,
-::content .series > .annotation-node > use {
-  stroke: #ccc;
-}
-::content .series.highlighted > .nodeshape > use ,
-::content .series.highlighted > .annotation-node > use {
-  stroke-width: 2;
-}
-::content .series.selected > .nodeshape > use ,
-::content .series.selected > .annotation-node > use {
-  stroke: red;
-  stroke-width: 2;
-}
-
-::content .series.selected > .nodeshape > rect {
-  stroke: red;
-  stroke-width: 2;
-}
-
-::content .annotation.series.selected > .annotation-node > use {
-  stroke: red;
-  stroke-width: 2;
-}
-
-/* --- Bridge Node --- */
-::content .bridge > .nodeshape > rect {
-  stroke: #f0f;
-  opacity: 0.2;
-  display: none;
-}
-
-/* --- Structural Elements --- */
-::content .edge > path.edgeline.structural {
-  stroke: #f0f;
-  opacity: 0.2;
-  display: none;
-}
-
-/* Reference Edge */
-::content .edge > path.edgeline.referenceedge {
-  stroke: #FFB74D;
-  opacity: 1;
-}
-
-/* --- Series Nodes --- */
-
-/* Hide the rect for a series' annotation. */
-::content .series > .annotation-node > rect {
-  display: none;
-}
-
-/* --- Node label --- */
-
-
-::content .node > text.nodelabel {
-  cursor: pointer;
-  fill: #444;
-}
-
-::content .meta.expanded > text.nodelabel {
-  font-size: 9px;
-}
-
-::content .series > text.nodelabel {
-  font-size: 8px;
-}
-
-::content .op > text.nodelabel {
-  font-size: 6px;
-}
-
-::content .bridge > text.nodelabel {
-  display: none;
-}
-
-::content .node.meta.expanded > text.nodelabel{
-  cursor: normal;
-}
-
-::content .annotation.meta.highlighted > text.annotation-label {
-  fill: #50A3F7;
-}
-
-::content .annotation.meta.selected > text.annotation-label {
-  fill: #4285F4;
-}
-
-/* --- Annotation --- */
-
-/* only applied for annotations that are not summary or constant.
-(.summary, .constant gets overridden below) */
-::content .annotation > .annotation-node > * {
-  stroke-width: 0.5;
-  stroke-dasharray: 1, 1;
-}
-
-::content .annotation.summary > .annotation-node > *,
-::content .annotation.constant > .annotation-node > * {
-  stroke-width: 1;
-  stroke-dasharray: none;
-}
-
-::content .annotation > .annotation-edge {
-  fill: none;
-  stroke: #aaa;
-  stroke-width: 0.5;
-  marker-end: url(#annotation-arrowhead);
-}
-
-::content .faded .annotation > .annotation-edge {
-  marker-end: url(#annotation-arrowhead-faded);
-}
-
-::content .annotation > .annotation-edge.refline {
-  marker-start: url(#ref-annotation-arrowhead);
-}
-
-::content .faded .annotation > .annotation-edge.refline {
-  marker-start: url(#ref-annotation-arrowhead-faded);
-}
-
-::content .annotation > .annotation-control-edge {
-  stroke-dasharray: 1, 1;
-}
-
-::content #annotation-arrowhead {
-  fill: #aaa;
-}
-
-::content #annotation-arrowhead-faded {
-  fill: #e0d4b3;
-}
-
-::content #ref-annotation-arrowhead {
-  fill: #aaa;
-}
-
-::content #ref-annotation-arrowhead-faded {
-  fill: #e0d4b3;
-}
-
-::content .annotation > .annotation-label {
-  font-size: 5px;
-  cursor: pointer;
-}
-::content .annotation > .annotation-label.annotation-ellipsis {
-  cursor: default;
-}
-
-/* Hide annotations on expanded meta nodes since they're redundant. */
-::content .expanded > .in-annotations,
-::content .expanded > .out-annotations {
-  display: none;
-}
-
-/* --- Annotation: Constant --- */
-
-::content .constant > .annotation-node > ellipse {
-  cursor: pointer;
-  fill: white;
-  stroke: #848484;
-}
-
-::content .constant.selected > .annotation-node > ellipse {
-  fill: white;
-  stroke: red;
-}
-
-::content .constant.highlighted > .annotation-node > ellipse {
-  stroke-width: 1.5;
-}
-
-/* --- Annotation: Summary --- */
-
-::content .summary > .annotation-node > ellipse {
-  cursor: pointer;
-  fill: #DB4437;
-  stroke: #DB4437;
-}
-
-::content .summary.selected > .annotation-node > ellipse {
-  fill: #A52714;
-  stroke: #A52714;
-}
-
-::content .summary.highlighted > .annotation-node > ellipse {
-  stroke-width: 1.5;
-}
-
-/* --- Edge --- */
-
-::content .edge > path.edgeline {
-  fill: none;
-  stroke: #bbb;
-  stroke-linecap: round;
-  stroke-width: 0.75;
-}
-
-/* Labels showing tensor shapes on edges */
-::content .edge > text {
-  font-size: 3.5px;
-  fill: #666;
-}
-
-::content .dataflow-arrowhead {
-  fill: #bbb;
-}
-
-::content .reference-arrowhead {
-  fill: #FFB74D;
-}
-
-::content .edge .control-dep {
-  stroke-dasharray: 2, 2;
-}
-
-/* --- Group node expand/collapse button --- */
-
-/* Hides expand/collapse buttons when a node isn't expanded or highlighted. Using
-   incredibly small opacity so that the bounding box of the <g> parent still takes
-   this container into account even when it isn't visible */
-::content .node:not(.highlighted):not(.expanded) > .nodeshape > .buttoncontainer {
-  opacity: 0.01;
-}
-::content .node.highlighted > .nodeshape > .buttoncontainer {
-  cursor: pointer;
-}
-::content .buttoncircle {
-  fill: #E7811D;
-}
-::content .buttoncircle:hover {
-  fill: #B96717;
-}
-::content .expandbutton,
-::content .collapsebutton {
-  stroke: white;
-}
-/* Do not let the path elements in the button take pointer focus */
-::content .node > .nodeshape > .buttoncontainer > .expandbutton,
-::content .node > .nodeshape > .buttoncontainer > .collapsebutton {
-  pointer-events: none;
-}
-/* Only show the expand button when a node is collapsed and only show the
-   collapse button when a node is expanded. */
-::content .node.expanded > .nodeshape > .buttoncontainer > .expandbutton {
-  display: none;
-}
-::content .node:not(.expanded) > .nodeshape > .buttoncontainer > .collapsebutton {
-  display: none;
-}
-
-::content .health-pill-stats {
-  font-size: 4px;
-  text-anchor: middle;
-}
-
-::content .health-pill rect {
-  filter: url(#health-pill-shadow);
-  rx: 3;
-  ry: 3;
-}
-
-.titleContainer {
-  position: relative;
-  top: 20px;
-}
-
-.title {
-  position: absolute;
-}
-
-.auxTitle {
-  position: absolute;
-}
-
-#minimap {
-  position: absolute;
-  right: 20px;
-  bottom: 20px;
-}
-</style>
-<div class="titleContainer">
-  <div id="title" class="title">Main Graph</div>
-  <div id="auxTitle" class="auxTitle">Auxiliary Nodes</div>
-</div>
-<svg id="svg">
-  <defs>
-
-    <!-- Arrow heads for reference edge paths of different predefined sizes per color. -->
-    <path id="reference-arrowhead-path" d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"/>
-    <marker class="reference-arrowhead" id="reference-arrowhead-small" viewBox="0 0 10 10" markerWidth="5" markerHeight="5"
-      refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#reference-arrowhead-path" />
-    </marker>
-    <marker class="reference-arrowhead" id="reference-arrowhead-medium" viewBox="0 0 10 10" markerWidth="13" markerHeight="13"
-        refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#reference-arrowhead-path" />
-    </marker>
-    <marker class="reference-arrowhead" id="reference-arrowhead-large" viewBox="0 0 10 10" markerWidth="16" markerHeight="16"
-        refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#reference-arrowhead-path" />
-    </marker>
-    <marker class="reference-arrowhead" id="reference-arrowhead-xlarge" viewBox="0 0 10 10" markerWidth="20" markerHeight="20"
-        refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#reference-arrowhead-path" />
-    </marker>
-
-    <!-- Arrow heads for dataflow edge paths of different predefined sizes per color. -->
-    <path id="dataflow-arrowhead-path" d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"/>
-    <marker class="dataflow-arrowhead" id="dataflow-arrowhead-small" viewBox="0 0 10 10" markerWidth="5" markerHeight="5"
-      refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#dataflow-arrowhead-path" />
-    </marker>
-    <marker class="dataflow-arrowhead" id="dataflow-arrowhead-medium" viewBox="0 0 10 10" markerWidth="13" markerHeight="13"
-        refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#dataflow-arrowhead-path" />
-    </marker>
-    <marker class="dataflow-arrowhead" id="dataflow-arrowhead-large" viewBox="0 0 10 10" markerWidth="16" markerHeight="16"
-        refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#dataflow-arrowhead-path" />
-    </marker>
-    <marker class="dataflow-arrowhead" id="dataflow-arrowhead-xlarge" viewBox="0 0 10 10" markerWidth="20" markerHeight="20"
-        refX="2" refY="5" orient="auto-start-reverse" markerUnits="userSpaceOnUse">
-      <use xlink:href="#dataflow-arrowhead-path" />
-    </marker>
-
-    <!-- Arrow head for annotation edge paths. -->
-    <marker id="annotation-arrowhead" markerWidth="5" markerHeight="5"
-      refX="5" refY="2.5" orient="auto">
-      <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"/>
-    </marker>
-    <marker id="annotation-arrowhead-faded" markerWidth="5" markerHeight="5"
-      refX="5" refY="2.5" orient="auto">
-      <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"/>
-    </marker>
-    <marker id="ref-annotation-arrowhead" markerWidth="5" markerHeight="5"
-      refX="0" refY="2.5" orient="auto">
-      <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"/>
-    </marker>
-    <marker id="ref-annotation-arrowhead-faded" markerWidth="5" markerHeight="5"
-      refX="0" refY="2.5" orient="auto">
-      <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"/>
-    </marker>
-    <!-- Template for an Op node ellipse. -->
-    <ellipse id="op-node-stamp"
-        rx="7.5" ry="3" stroke="inherit" fill="inherit" />
-    <!-- Template for an Op node annotation ellipse (smaller). -->
-    <ellipse id="op-node-annotation-stamp"
-        rx="5" ry="2" stroke="inherit" fill="inherit" />
-    <!-- Vertically stacked series of Op nodes when unexpanded. -->
-    <g id="op-series-vertical-stamp">
-      <use xlink:href="#op-node-stamp" x="8" y="9" />
-      <use xlink:href="#op-node-stamp" x="8" y="6" />
-      <use xlink:href="#op-node-stamp" x="8" y="3" />
-    </g>
-    <!-- Horizontally stacked series of Op nodes when unexpanded. -->
-    <g id="op-series-horizontal-stamp">
-      <use xlink:href="#op-node-stamp" x="16" y="4" />
-      <use xlink:href="#op-node-stamp" x="12" y="4" />
-      <use xlink:href="#op-node-stamp" x="8" y="4" />
-    </g>
-    <!-- Horizontally stacked series of Op nodes for annotation. -->
-    <g id="op-series-annotation-stamp">
-      <use xlink:href="#op-node-annotation-stamp" x="9" y="2" />
-      <use xlink:href="#op-node-annotation-stamp" x="7" y="2" />
-      <use xlink:href="#op-node-annotation-stamp" x="5" y="2" />
-    </g>
-    <svg id="summary-icon" fill="#848484" height="12" viewBox="0 0 24 24" width="12">
-      <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" />
-    </svg>
-    <!--
-      Where the linearGradient for each node is stored. Used when coloring
-      by proportions of devices.
-    -->
-    <g id="linearGradients"></g>
-
-    <!-- Hatch patterns for faded out nodes. -->
-    <pattern id="rectHatch" patternTransform="rotate(45 0 0)" width="5" height="5" patternUnits="userSpaceOnUse">
-      <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"/>
-    </pattern>
-    <pattern id="ellipseHatch" patternTransform="rotate(45 0 0)" width="2" height="2" patternUnits="userSpaceOnUse">
-      <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"/>
-    </pattern>
-
-    <!-- A shadow for health pills. -->
-    <filter id="health-pill-shadow" x="-40%" y="-40%" width="180%" height="180%">
-      <feGaussianBlur in="SourceAlpha" stdDeviation="0.8"/>
-      <feOffset dx="0" dy="0" result="offsetblur"/>
-      <feFlood flood-color="#000000"/>
-      <feComposite in2="offsetblur" operator="in"/>
-      <feMerge>
-        <feMergeNode/>
-        <feMergeNode in="SourceGraphic"/>
-      </feMerge>
-    </filter>
-  </defs>
-  <!-- Make a large rectangle that fills the svg space so that
-  zoom events get captured on safari -->
-  <rect fill="white" width="10000" height="10000"></rect>
-  <g id="root"></g>
-</svg>
-<tf-graph-minimap id="minimap"></tf-graph-minimap>
-</template>
-<script>
-Polymer({
-  is: 'tf-graph-scene',
-  properties: {
-    renderHierarchy: Object,
-    name: String,
-    colorBy: String,
-
-    // For each render hierarchy, we only fit it to the viewport once (when the scene is attached to
-    // the DOM). We do not fit the hierarchy again (unless the user clicks the reset button). For
-    // instance, if the user enters a certain view in the graph, switches to another dashboard, and
-    // returns to the graph dashboard, the user expects the previous view. These properties enable
-    // that behavior.
-
-    /** Whether the scene has fit the current render hierarchy (to the viewport) at least once. */
-    _hasRenderHierarchyBeenFitOnce: Boolean,
-    /** Whether this scene element is currently attached to a parent element. */
-    _isAttached: Boolean,
-
-    /** @type {d3_zoom} d3 zoom object */
-    _zoom: Object,
-    highlightedNode: {
-      type: String,
-      observer: '_highlightedNodeChanged'
-    },
-    selectedNode: {
-      type: String,
-      observer: '_selectedNodeChanged'
-    },
-    /** Keeps track of if the graph has been zoomed/panned since loading */
-    _zoomed: {
-      type: Boolean,
-      observer: '_onZoomChanged',
-      value: false
-    },
-    /** Keeps track of the starting coordinates of a graph zoom/pan */
-    _zoomStartCoords: {
-      type: Object,
-      value: null
-    },
-    /** Keeps track of the current coordinates of a graph zoom/pan */
-    _zoomTransform: {
-      type: Object,
-      value: null
-    },
-    /** Maximum distance of a zoom event for it to be interpreted as a click */
-    _maxZoomDistanceForClick: {
-      type: Number,
-      value: 20
-    },
-    /**
-     * @type {d3.scale.ordinal}
-     * Scale mapping from template name to a number between 0 and N-1
-     * where N is the number of different template names. Used by
-     * tf.graph.scene.node when computing node color by structure.
-     */
-    templateIndex: Function,
-    /**
-     * @type {tf.scene.Minimap}
-     * A minimap object to notify for zoom events.
-     */
-    minimap: Object,
-    /*
-     * Dictionary for easily stylizing nodes when state changes.
-     * _nodeGroupIndex[nodeName] = d3_selection of the nodeGroup
-     */
-    _nodeGroupIndex: {
-      type: Object,
-      value: function() { return {}; }
-    },
-    /*
-     * Dictionary for easily stylizing annotation nodes when state changes.
-     * _annotationGroupIndex[nodeName][hostNodeName] =
-     *   d3_selection of the annotationGroup
-     */
-    _annotationGroupIndex: {
-      type: Object,
-      value: function() { return {}; }
-    },
-    /*
-     * Dictionary for easily stylizing edges when state changes.
-     * _edgeGroupIndex[edgeName] = d3_selection of the edgeGroup
-     */
-    _edgeGroupIndex: {
-      type: Object,
-      value: function() { return {}; }
-    },
-    /**
-     * Max font size for metanode label strings.
-     */
-    maxMetanodeLabelLengthFontSize: {
-      type: Number,
-      value: 9
-    },
-    /**
-     * Min font size for metanode label strings.
-     */
-    minMetanodeLabelLengthFontSize: {
-      type: Number,
-      value: 6
-    },
-    /**
-     * Metanode label strings longer than this are given smaller fonts.
-     */
-    maxMetanodeLabelLengthLargeFont: {
-      type: Number,
-      value: 11
-    },
-    /**
-     * Metanode label strings longer than this are truncated with ellipses.
-     */
-    maxMetanodeLabelLength: {
-      type: Number,
-      value: 18
-    },
-    progress: Object,
-    // A mapping between node name to the tf.graph.scene.HealthPill to render.
-    nodeNamesToHealthPills: Object,
-    // The step of health pills to show throughout the graph.
-    healthPillStepIndex: Number,
-  },
-  observers: [
-    '_colorByChanged(colorBy)',
-    '_renderHierarchyChanged(renderHierarchy)',
-    // Animation and fitting must come after the observer for the hierarchy changing because we must
-    // first build the render hierarchy.
-    '_animateAndFit(_isAttached, renderHierarchy)',
-    '_updateHealthPills(nodeNamesToHealthPills, healthPillStepIndex)',
-  ],
-  getNode: function(nodeName) {
-    return this.renderHierarchy.getRenderNodeByName(nodeName);
-  },
-  isNodeExpanded: function(node) {
-    return node.expanded;
-  },
-  setNodeExpanded: function(renderNode) {
-    this._build(this.renderHierarchy);
-    this._updateLabels(!this._zoomed);
-  },
-  /**
-   * Resets the state of the component. Called whenever the whole graph
-   * (dataset) changes.
-   */
-  _resetState: function() {
-    // Reset the state of the component.
-    this._nodeGroupIndex = {};
-    this._annotationGroupIndex = {};
-    this._edgeGroupIndex = {};
-    this._updateLabels(false);
-    // Remove all svg elements under the 'root' svg group.
-    d3.select(this.$.svg).select('#root').selectAll('*').remove();
-    // And the defs.
-    d3.select(this.$.svg).select('defs #linearGradients')
-        .selectAll('*').remove();
-  },
-  /** Main method for building the scene */
-  _build: function(renderHierarchy) {
-    this.templateIndex = renderHierarchy.hierarchy.getTemplateIndex();
-    tf.graph.util.time('tf-graph-scene (layout):', function() {
-      // layout the scene for this meta / series node
-      tf.graph.layout.layoutScene(renderHierarchy.root, this);
-    }.bind(this));
-
-    tf.graph.util.time('tf-graph-scene (build scene):', function() {
-      tf.graph.scene.buildGroup(d3.select(this.$.root), renderHierarchy.root, this);
-      tf.graph.scene.addGraphClickListener(this.$.svg, this);
-      tf.graph.scene.node.traceInputs(renderHierarchy);
-    }.bind(this));
-    // Update the minimap again when the graph is done animating.
-    setTimeout(function() {
-      this._updateHealthPills(this.nodeNamesToHealthPills, this.healthPillStepIndex);
-      this.minimap.update();
-    }.bind(this), tf.graph.layout.PARAMS.animation.duration);
-  },
-  ready: function() {
-    this._zoom = d3.zoom()
-      .on('end', function() {
-        if (this._zoomStartCoords) {
-          // Calculate the total distance dragged during the zoom event.
-          // If it is sufficiently small, then fire an event indicating
-          // that zooming has ended. Otherwise wait to fire the zoom end
-          // event, so that a mouse click registered as part of this zooming
-          // is ignored (as this mouse click was part of a zooming, and should
-          // not be used to indicate an actual click on the graph).
-          var dragDistance = Math.sqrt(
-            Math.pow(this._zoomStartCoords.x - this._zoomTransform.x, 2) +
-            Math.pow(this._zoomStartCoords.y - this._zoomTransform.y, 2));
-          if (dragDistance < this._maxZoomDistanceForClick) {
-            this._fireEnableClick();
-          } else {
-            setTimeout(this._fireEnableClick.bind(this), 50);
-          }
-        }
-        this._zoomStartCoords = null;
-      }.bind(this))
-      .on('zoom', function() {
-        // Store the coordinates of the zoom event.
-        this._zoomTransform = d3.event.transform;
-
-        // If this is the first zoom event after a zoom-end, then
-        // store the coordinates as the start coordinates as well,
-        // and fire an event to indicate that zooming has started.
-        // This doesn't use the zoomstart event, as d3 sends this
-        // event on mouse-down, even if there has been no dragging
-        // done to translate the graph around.
-        if (!this._zoomStartCoords) {
-          this._zoomStartCoords = this._zoomTransform;
-          this.fire('disable-click');
-        }
-        this._zoomed = true;
-        d3.select(this.$.root).attr('transform', d3.event.transform);
-        // Notify the minimap.
-        this.minimap.zoom(d3.event.transform);
-      }.bind(this));
-    d3.select(this.$.svg).call(this._zoom)
-      .on('dblclick.zoom', null);
-    d3.select(window).on('resize', function() {
-      // Notify the minimap that the user's window was resized.
-      // The minimap will figure out the new dimensions of the main svg
-      // and will use the existing translate and scale params.
-      this.minimap.zoom();
-    }.bind(this));
-    // Initialize the minimap.
-    this.minimap = this.$.minimap.init(this.$.svg, this.$.root, this._zoom,
-        tf.graph.layout.PARAMS.minimap.size,
-        tf.graph.layout.PARAMS.subscene.meta.labelHeight);
-  },
-  attached: function() {
-    this.set('_isAttached', true);
-  },
-  detached: function() {
-    this.set('_isAttached', false);
-  },
-  _renderHierarchyChanged: function(renderHierarchy) {
-    this._hasRenderHierarchyBeenFitOnce = false;
-    this._resetState();
-    this._build(renderHierarchy);
-  },
-  _animateAndFit: function(isAttached, renderHierarchy) {
-    if (this._hasRenderHierarchyBeenFitOnce || !isAttached) {
-      // Do not animate and fit if the scene has already fitted this render hierarchy once. Or if
-      // the graph dashboard is not attached (in which case the scene lacks DOM info for fitting).
-      return;
-    }
-
-    // Fit to screen after the graph is done animating.
-    setTimeout(this.fit.bind(this), tf.graph.layout.PARAMS.animation.duration);
-  },
-  _updateLabels: function(showLabels) {
-    var mainGraphTitleElement = this.getElementsByClassName('title')[0];
-    var titleStyle = mainGraphTitleElement.style;
-    var auxTitleStyle = this.getElementsByClassName('auxTitle')[0].style;
-    var core = d3.select("." + tf.graph.scene.Class.Scene.GROUP + ">." +
-      tf.graph.scene.Class.Scene.CORE).node();
-    // Only show labels if the graph is fully loaded.
-    if (showLabels && core && this.progress && this.progress.value === 100) {
-      var aux =
-        d3.select("." + tf.graph.scene.Class.Scene.GROUP + ">." +
-          tf.graph.scene.Class.Scene.INEXTRACT).node() ||
-        d3.select("." + tf.graph.scene.Class.Scene.GROUP + ">." +
-          tf.graph.scene.Class.Scene.OUTEXTRACT).node();
-      var coreX = core.getCTM().e;
-      var auxX = aux ? aux.getCTM().e : null;
-      titleStyle.display = 'inline';
-      titleStyle.left = coreX + 'px';
-      if (auxX !== null && auxX !== coreX) {
-        auxTitleStyle.display = 'inline';
-
-        // Make sure that the aux title is positioned rightwards enough so as to
-        // prevent overlap with the main graph title.
-        auxX = Math.max(
-            coreX + mainGraphTitleElement.getBoundingClientRect().width, auxX);
-
-        auxTitleStyle.left = auxX + 'px';
-      } else {
-        auxTitleStyle.display = 'none';
-      }
-    } else {
-      titleStyle.display='none';
-      auxTitleStyle.display = 'none';
-    }
-  },
-  /**
-    * Called whenever the user changed the 'color by' option in the
-    * UI controls.
-    */
-  _colorByChanged: function() {
-    if (this.renderHierarchy != null) {
-      // We iterate through each svg node and update its state.
-      _.each(this._nodeGroupIndex, function(nodeGroup, nodeName) {
-        this._updateNodeState(nodeName);
-      }, this);
-      // Notify also the minimap.
-      this.minimap.update();
-    }
-  },
-  fit: function() {
-    this._hasRenderHierarchyBeenFitOnce = true;
-    tf.graph.scene.fit(this.$.svg, this.$.root, this._zoom, function() {
-      this._zoomed = false;
-    }.bind(this));
-  },
-  isNodeSelected: function(n) {
-    return n === this.selectedNode;
-  },
-  isNodeHighlighted: function(n) {
-    return n === this.highlightedNode;
-  },
-  addAnnotationGroup: function(a, d, selection) {
-    var an = a.node.name;
-    this._annotationGroupIndex[an] = this._annotationGroupIndex[an] || {};
-    this._annotationGroupIndex[an][d.node.name] = selection;
-  },
-  getAnnotationGroupsIndex: function(a) {
-    return this._annotationGroupIndex[a];
-  },
-  removeAnnotationGroup: function(a, d) {
-    delete this._annotationGroupIndex[a.node.name][d.node.name];
-  },
-  addNodeGroup: function(n, selection) {
-    this._nodeGroupIndex[n] = selection;
-  },
-  getNodeGroup: function(n) {
-    return this._nodeGroupIndex[n];
-  },
-  removeNodeGroup: function(n) {
-    delete this._nodeGroupIndex[n];
-  },
-  addEdgeGroup: function(n, selection) {
-    this._edgeGroupIndex[n] = selection;
-  },
-  getEdgeGroup: function(e) {
-    return this._edgeGroupIndex[e];
-  },
-  _updateHealthPills: function(nodeNamesToHealthPills, healthPillStepIndex) {
-    tf.graph.scene.addHealthPills(
-        this.$.svg, nodeNamesToHealthPills, healthPillStepIndex);
-  },
-  /**
-   * Update node and annotation node of the given name.
-   * @param  {String} n node name
-   */
-  _updateNodeState: function(n) {
-    var node = this.getNode(n);
-    var nodeGroup = this.getNodeGroup(n);
-
-    if (nodeGroup) {
-      tf.graph.scene.node.stylize(nodeGroup, node, this);
-    }
-
-    var annotationGroupIndex = this.getAnnotationGroupsIndex(n);
-    _.each(annotationGroupIndex, function(aGroup, hostName) {
-      tf.graph.scene.node.stylize(aGroup, node, this,
-          tf.graph.scene.Class.Annotation.NODE);
-    }, this);
-  },
-
-  /**
-   * Handles new node selection. 1) Updates the selected-state of each node,
-   * 2) triggers input tracing.
-   * @param selectedNode {string} The name of the newly selected node.
-   * @param oldSelectedNode {string} The name of the previously selected node.
-   * @private
-   */
-  _selectedNodeChanged: function(selectedNode, oldSelectedNode) {
-    if (selectedNode === oldSelectedNode) {
-      return;
-    }
-
-    if (selectedNode) {
-      this._updateNodeState(selectedNode);
-    }
-    if (oldSelectedNode) {
-      this._updateNodeState(oldSelectedNode);
-    }
-
-    tf.graph.scene.node.traceInputs(this.renderHierarchy);
-
-    if (!selectedNode) {
-      return;
-    }
-
-
-    // Update the minimap to reflect the highlighted (selected) node.
-    this.minimap.update();
-    var node = this.renderHierarchy.hierarchy.node(selectedNode);
-    var nodeParents = [];
-    // Create list of all metanode parents of the selected node.
-    while (node.parentNode != null
-        && node.parentNode.name != tf.graph.ROOT_NAME) {
-      node = node.parentNode;
-      nodeParents.push(node.name);
-    }
-    // Ensure each parent metanode is built and expanded.
-    var topParentNodeToBeExpanded;
-    _.forEachRight(nodeParents, function(parentName) {
-      this.renderHierarchy.buildSubhierarchy(parentName);
-      var renderNode = this.renderHierarchy.getRenderNodeByName(parentName);
-      if (renderNode.node.isGroupNode && !renderNode.expanded) {
-        renderNode.expanded = true;
-        if (!topParentNodeToBeExpanded) {
-          topParentNodeToBeExpanded = renderNode;
-        }
-      }
-    }, this);
-    // If any expansion was needed to display this selected node, then
-    // inform the scene of the top-most expansion.
-    if (topParentNodeToBeExpanded) {
-      this.setNodeExpanded(topParentNodeToBeExpanded);
-      this._zoomed = true;
-    }
-
-    if (tf.graph.scene.panToNode(selectedNode, this.$.svg, this.$.root,
-        this._zoom)) {
-      this._zoomed = true;
-    }
-  },
-  _highlightedNodeChanged: function(highlightedNode, oldHighlightedNode) {
-    if (highlightedNode === oldHighlightedNode) {
-      return;
-    }
-
-    if (highlightedNode) {
-      this._updateNodeState(highlightedNode);
-    }
-    if (oldHighlightedNode) {
-      this._updateNodeState(oldHighlightedNode);
-    }
-  },
-  _onZoomChanged: function() {
-    this._updateLabels(!this._zoomed);
-  },
-  _fireEnableClick: function() {
-    this.fire('enable-click');
-  },
-});
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_graph/tf-graph.html b/tensorflow/tensorboard/components/tf_graph/tf-graph.html
deleted file mode 100644
index efbf065a40..0000000000
--- a/tensorflow/tensorboard/components/tf_graph/tf-graph.html
+++ /dev/null
@@ -1,316 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-flex-layout/iron-flex-layout.html">
-<link rel="import" href="../iron-icons/iron-icons.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
-<link rel="import" href="../tf-graph-common/tf-graph-common.html">
-<link rel="import" href="tf-graph-scene.html">
-
-<dom-module id="tf-graph">
-<template>
-<style>
-.container {
-  width: 100%;
-  height: 100%;
-  background: white;
-  box-shadow: 0 1px 5px rgba(0,0,0,0.2);
-}
-
-.vertical {
-  width:100%;
-  height:100%;
-  @apply(--layout-vertical);
-}
-
-.auto {
-  @apply(--layout-flex-auto);
-  @apply(--layout-vertical);
-}
-
-h2 {
-  text-align: center;
-}
-
-paper-button {
-  text-transform: none;
-}
-</style>
-<div class="container">
-  <div class="vertical">
-    <template is="dom-if" if="[[title]]">
-      <h2>[[title]]</h2>
-    </template>
-    <tf-graph-scene id="scene" class="auto"
-          render-hierarchy="[[renderHierarchy]]"
-          highlighted-node="[[_getVisible(highlightedNode)]]"
-          selected-node="{{selectedNode}}"
-          color-by="[[colorBy]]"
-          progress="[[progress]]"
-          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
-          health-pill-step-index="{{healthPillStepIndex}}"
-    ></tf-graph-scene>
-  </div>
-</div>
-</template>
-</dom-module>
-
-<script>
-Polymer({
-
-  is: 'tf-graph',
-
-  properties: {
-    graphHierarchy: {
-      type: Object,
-      notify: true,
-      observer: '_graphChanged'
-    },
-    basicGraph: Object,
-    stats: Object,
-    devicesForStats: Object,
-    hierarchyParams: Object,
-    progress: {
-      type: Object,
-      notify: true,
-    },
-    title: String,
-    selectedNode: {
-      type: String,
-      notify: true,
-    },
-    highlightedNode: {
-      type: String,
-      notify: true
-    },
-    /** What to color the nodes by (compute time, memory, device etc.) */
-    colorBy: String,
-    colorByParams: {
-      type: Object,
-      notify: true,
-      readOnly: true, // Produces and doesn't consume.
-    },
-    renderHierarchy: {
-      type: Object,
-      readOnly: true,
-      notify: true,
-    },
-    _renderDepth: {
-      type: Number,
-      value: 1
-    },
-    _allowGraphSelect: {
-      type: Boolean,
-      value: true
-    },
-    // A mapping between node name to the tf.graph.scene.HealthPill to render.
-    nodeNamesToHealthPills: Object,
-    // The step of health pills to show throughout the graph.
-    healthPillStepIndex: Number,
-  },
-  observers: [
-    '_statsChanged(stats, devicesForStats)',
-    '_buildRenderHierarchy(graphHierarchy)'
-  ],
-  _statsChanged: function(stats, devicesForStats) {
-    if (this.graphHierarchy) {
-      if (stats && devicesForStats) {
-        tf.graph.joinStatsInfoWithGraph(this.basicGraph, stats, devicesForStats);
-        tf.graph.hierarchy.joinAndAggregateStats(this.graphHierarchy, stats);
-      }
-      // Recompute the rendering information.
-      this._buildRenderHierarchy(this.graphHierarchy);
-    }
-  },
-  _buildRenderHierarchy: function(graphHierarchy) {
-    tf.graph.util.time('new tf.graph.render.Hierarchy', function() {
-      if (graphHierarchy.root.type !== tf.graph.NodeType.META) {
-        // root must be metanode but sometimes Polymer's dom-if has not
-        // remove tf-graph element yet in <tf-node-info>
-        // and thus mistakenly pass non-metanode to this module.
-        return;
-      }
-      var renderGraph = new tf.graph.render.RenderGraphInfo(
-          graphHierarchy, !!this.stats /** displayingStats */);
-      // Producing the 'color by' parameters to be consumed
-      // by the tf-graph-controls panel. It contains information about the
-      // min and max values and their respective colors, as well as list
-      // of devices with their respective colors.
-
-      function getColorParamsFromScale(scale) {
-        return {
-          minValue: scale.domain()[0],
-          maxValue: scale.domain()[1],
-          startColor: scale.range()[0],
-          endColor: scale.range()[1]
-        };
-      }
-
-      this._setColorByParams({
-        compute_time: getColorParamsFromScale(renderGraph.computeTimeScale),
-        memory: getColorParamsFromScale(renderGraph.memoryUsageScale),
-        device: _.map(renderGraph.deviceColorMap.domain(),
-            function(deviceName) {
-          return {
-            device: deviceName,
-            color: renderGraph.deviceColorMap(deviceName)
-          };
-        }),
-        xla_cluster: _.map(renderGraph.xlaClusterColorMap.domain(),
-            function(xlaClusterName) {
-          return {
-            xla_cluster: xlaClusterName,
-            color: renderGraph.xlaClusterColorMap(xlaClusterName)
-          };
-        }),
-      });
-      this._setRenderHierarchy(renderGraph);
-      this.async(function() {
-        this.fire("rendered");
-      });
-    }.bind(this));
-  },
-  _getVisible: function(name) {
-    if (!name) {
-      return name;
-    }
-    return this.renderHierarchy.getNearestVisibleAncestor(name);
-  },
-  listeners: {
-    'graph-select': '_graphSelected',
-    'disable-click': '_disableClick',
-    'enable-click': '_enableClick',
-    // Nodes
-    'node-toggle-expand': '_nodeToggleExpand',
-    'node-select': '_nodeSelected',
-    'node-highlight': '_nodeHighlighted',
-    'node-unhighlight': '_nodeUnhighlighted',
-    'node-toggle-extract': '_nodeToggleExtract',
-    'node-toggle-seriesgroup': '_nodeToggleSeriesGroup',
-
-    // Annotations
-
-    /* Note: currently highlighting/selecting annotation node has the same
-      * behavior as highlighting/selecting actual node so we point to the same
-      * set of event listeners.  However, we might redesign this to be a bit
-      * different.
-      */
-    'annotation-select': '_nodeSelected',
-    'annotation-highlight': '_nodeHighlighted',
-    'annotation-unhighlight': '_nodeUnhighlighted',
-  },
-  _graphChanged: function() {
-    // When a new graph is loaded, fire this event so that there is no
-    // info-card being displayed for the previously-loaded graph.
-    this.fire('graph-select');
-  },
-  _graphSelected: function(event) {
-    // Graph selection is not allowed during an active zoom event, as the
-    // click seen during a zoom/pan is part of the zooming and does not
-    // indicate a user desire to click on a specific section of the graph.
-    if (this._allowGraphSelect) {
-      this.set('selectedNode', null);
-    }
-    // Reset this variable as a bug in d3 zoom behavior can cause zoomend
-    // callback not to be called if a right-click happens during a zoom event.
-    this._allowGraphSelect = true;
-  },
-  _disableClick: function(event) {
-    this._allowGraphSelect = false;
-  },
-  _enableClick: function(event) {
-    this._allowGraphSelect = true;
-  },
-  _nodeSelected: function(event) {
-    if (this._allowGraphSelect) {
-      this.set('selectedNode', event.detail.name);
-    }
-    // Reset this variable as a bug in d3 zoom behavior can cause zoomend
-    // callback not to be called if a right-click happens during a zoom event.
-    this._allowGraphSelect = true;
-  },
-  _nodeHighlighted: function(event) {
-    this.set('highlightedNode', event.detail.name);
-  },
-  _nodeUnhighlighted: function(event) {
-    this.set('highlightedNode', null);
-  },
-  _nodeToggleExpand: function(event) {
-    // Immediately select the node that is about to be expanded.
-    this._nodeSelected(event);
-
-    // Compute the sub-hierarchy scene.
-    var nodeName = event.detail.name;
-    var renderNode = this.renderHierarchy.getRenderNodeByName(nodeName);
-    // Op nodes are not expandable.
-    if (renderNode.node.type === tf.graph.NodeType.OP) {
-      return;
-    }
-    this.renderHierarchy.buildSubhierarchy(nodeName);
-    renderNode.expanded = !renderNode.expanded;
-
-    // Expand the node with some delay so that the user can immediately see
-    // the visual effect of selecting that node, before the expansion is
-    // done.
-    this.async(function() {
-      this.querySelector('#scene').setNodeExpanded(renderNode);
-    }, 75);
-  },
-  _nodeToggleExtract: function(event) {
-    // Toggle the include setting of the specified node appropriately.
-    var nodeName = event.detail.name;
-    var renderNode = this.renderHierarchy.getRenderNodeByName(nodeName);
-    if (renderNode.node.include == tf.graph.InclusionType.INCLUDE) {
-      renderNode.node.include = tf.graph.InclusionType.EXCLUDE;
-    } else if (renderNode.node.include == tf.graph.InclusionType.EXCLUDE) {
-      renderNode.node.include = tf.graph.InclusionType.INCLUDE;
-    } else {
-      renderNode.node.include =
-       this.renderHierarchy.isNodeAuxiliary(renderNode)
-          ? tf.graph.InclusionType.INCLUDE : tf.graph.InclusionType.EXCLUDE;
-    }
-
-    // Rebuild the render hierarchy.
-    this._buildRenderHierarchy(this.graphHierarchy);
-  },
-  _nodeToggleSeriesGroup: function(event) {
-    // Toggle the group setting of the specified node appropriately.
-    var nodeName = event.detail.name;
-    tf.graph.toggleNodeSeriesGroup(this.hierarchyParams.seriesMap, nodeName);
-
-    // Rebuild the render hierarchy with the updated series grouping map.
-    this.set('progress', {
-      value: 0,
-      msg: ''
-    });
-    var tracker = tf.graph.util.getTracker(this);
-    var hierarchyTracker = tf.graph.util.getSubtaskTracker(tracker, 100,
-          'Namespace hierarchy');
-    tf.graph.hierarchy.build(this.basicGraph, this.hierarchyParams, hierarchyTracker)
-    .then(function(graphHierarchy) {
-      this.set('graphHierarchy', graphHierarchy);
-      this._buildRenderHierarchy(this.graphHierarchy);
-    }.bind(this));
-  },
-  not: function(x) {
-    return !x;
-  }
-});
-</script>
diff --git a/tensorflow/tensorboard/components/tf_graph_app/BUILD b/tensorflow/tensorboard/components/tf_graph_app/BUILD
deleted file mode 100644
index d0b6d79640..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_app/BUILD
+++ /dev/null
@@ -1,47 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_app",
-    srcs = [
-        "index.html",
-        "tf-graph-app.html",
-    ],
-    path = "/tf-graph-app",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_board",
-        "//tensorflow/tensorboard/components/tf_graph_controls",
-        "//tensorflow/tensorboard/components/tf_graph_loader",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_component_page",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_app"],
-    visibility = [
-        "//learning/brain/python/client/colab:__pkg__",
-        "//learning/vis/vz_elements/catalog:__pkg__",
-    ],
-    destdir = "tf-graph-app",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_board:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_controls:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_loader:legacy",
-        "//third_party/javascript/polymer/v1/iron-component-page:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-        "//third_party/javascript/polymer/v1/webcomponentsjs:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_app/demo/BUILD b/tensorflow/tensorboard/components/tf_graph_app/demo/BUILD
deleted file mode 100644
index 0205e2fd92..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_app/demo/BUILD
+++ /dev/null
@@ -1,23 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph_app/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-graph-app/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_app",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_app/demo/data/graph.pbtxt b/tensorflow/tensorboard/components/tf_graph_app/demo/data/graph.pbtxt
deleted file mode 100644
index 8b95b258df..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_app/demo/data/graph.pbtxt
+++ /dev/null
@@ -1,90 +0,0 @@
-node {
-  name: "life"
-  op: "Const"
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-        }
-        int_val: 2
-      }
-    }
-  }
-}
-node {
-  name: "universe"
-  op: "Const"
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-        }
-        int_val: 40
-      }
-    }
-  }
-}
-node {
-  name: "everything"
-  op: "Const"
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-        }
-        int_val: 0
-      }
-    }
-  }
-}
-node {
-  name: "Add"
-  op: "Add"
-  input: "life"
-  input: "universe"
-  attr {
-    key: "T"
-    value {
-      type: DT_INT32
-    }
-  }
-}
-node {
-  name: "answer"
-  op: "Add"
-  input: "Add"
-  input: "everything"
-  attr {
-    key: "T"
-    value {
-      type: DT_INT32
-    }
-  }
-}
-versions {
-  producer: 10
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_app/demo/index.html b/tensorflow/tensorboard/components/tf_graph_app/demo/index.html
deleted file mode 100644
index f71feea390..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_app/demo/index.html
+++ /dev/null
@@ -1,34 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<link rel="import" href="../tf-graph-app.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<style>
-  /** Make the graph app tall enough so the bottom legend does not overlap with the top. */
-  tf-graph-app, .container.tf-graph-app {
-    display: block;
-    height: 700px;
-  }
-</style>
-<h3>Answer to the Ultimate Question of Life, the Universe, and Everything</h3>
-<demo-snippet>
-  <template>
-    <tf-graph-app pbtxt-file-location="data/graph.pbtxt"></tf-graph-app>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph_app/index.html b/tensorflow/tensorboard/components/tf_graph_app/index.html
deleted file mode 100644
index c80fbf4f63..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_app/index.html
+++ /dev/null
@@ -1,30 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-  <head>
-    <title>vz-vega</title>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <script src="../webcomponentsjs/webcomponents-lite.js"></script>
-    <link rel="import" href="../iron-component-page/iron-component-page.html">
-  </head>
-  <body>
-    <iron-component-page src="tf-graph-app.html"></iron-component-page>
-  </body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_graph_app/tf-graph-app.html b/tensorflow/tensorboard/components/tf_graph_app/tf-graph-app.html
deleted file mode 100644
index 915b54a06a..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_app/tf-graph-app.html
+++ /dev/null
@@ -1,152 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-graph-board/tf-graph-board.html">
-<link rel="import" href="../tf-graph-loader/tf-graph-loader.html">
-<link rel="import" href="../tf-graph-controls/tf-graph-controls.html">
-
-<!--
-Stand alone element of tf-graph for embedding.
-
-The pbtxt format is the stringified version of the graphdef.
-
-    <tf-graph-app pbtxt="[[pbtxt]]"></tf-graph-app>
-
-    import tensorflow as tf
-    life = tf.constant(2, name='life')
-    universe = tf.constant(40, name='universe')
-    everything = tf.constant(0, name='everything')
-    lifeuniverse = tf.add(life, universe)
-    answer = tf.add(lifeuniverse, everything, name='answer')
-    open("graph.pbtxt", "w").write(str(tf.get_default_graph().as_graph_def()))
-
-@demo demo/index.html
--->
-<dom-module id="tf-graph-app">
-<template>
-<style>
-
-:host /deep/ {
-  font-family: 'Roboto', sans-serif;
-}
-
-.main {
-  position: absolute;
-  right: 0;
-  left: 250px;
-  height: 100%;
-}
-
-.side {
-  position: absolute;
-  left: 0;
-  width: 250px;
-  height: 100%;
-  border: 1px solid black;
-  box-sizing: border-box;
-}
-
-.all {
-  position: relative;
-  width: 100%;
-  height: 100%
-}
-
-.container {
-  height: 650px;
-}
-
-</style>
-<div class="container">
-  <div class="all">
-    <div class="side">
-      <tf-graph-controls
-          color-by-params="[[colorByParams]]"
-          stats="[[stats]]"
-          color-by="{{colorBy}}"
-          render-hierarchy="[[_renderHierarchy]]"
-      ></tf-graph-controls>
-      <tf-graph-loader id="loader"
-          out-graph-hierarchy="{{graphHierarchy}}"
-          out-graph="{{graph}}"
-          out-stats="{{stats}}"
-          progress="{{_progress}}"
-      ></tf-graph-loader>
-    </div>
-    <div class="main">
-      <tf-graph-board id="graphboard"
-          graph-hierarchy="[[graphHierarchy]]"
-          graph="[[graph]]"
-          stats="[[stats]]"
-          progress="[[_progress]]"
-          color-by="[[colorBy]]"
-          color-by-params="{{colorByParams}}"
-          render-hierarchy="{{_renderHierarchy}}"
-      ></tf-graph-board>
-    </div>
-  </div>
-</div>
-</template>
-</dom-module>
-
-<script>
-(function(){
-
-Polymer({
-  is: 'tf-graph-app',
-  properties: {
-    stats: Object,
-
-    // To use tf-graph-app, specify one of these 2 properties. Provide either
-    // 1. The path to a pbtxt file to load (pbtxtFileLocation). This option nicely makes the
-    //    progress bar include the time it takes to load the file across the network. The path could
-    //    be either a relative path or an absolute URL (of a resource that supports CORS).
-    // 2. The raw contents of a pbtxt file (pbtxt).
-    // Do not set both of these 2 properties.
-    pbtxtFileLocation: {
-      type: String,
-      observer: '_updateGraph',
-    },
-    pbtxt: {
-      type: String,
-      observer: '_updateGraph',
-    },
-
-    _renderHierarchy: Object,
-    _progress: Object,
-  },
-  _updateGraph: function() {
-    if (this.pbtxtFileLocation) {
-      // Fetch a pbtxt file. The fetching will be part of the loading sequence.
-      this.$.loader.datasets = [{
-        // Just name the dataset based on the file location.
-        "name": this.pbtxtFileLocation,
-        "path": this.pbtxtFileLocation,
-      }];
-      this.$.loader.set('selectedDataset', 0);
-    } else if (this.pbtxt) {
-      // Render the provided pbtxt.
-      var blob = new Blob([this.pbtxt]);
-
-      // TODO(chizeng): Find out why we call a private method here and do away with the call.
-      this.$.loader._parseAndConstructHierarchicalGraph(null, blob);
-    }
-  },
-});
-})();
-</script>
diff --git a/tensorflow/tensorboard/components/tf_graph_board/BUILD b/tensorflow/tensorboard/components/tf_graph_board/BUILD
deleted file mode 100644
index 866112e021..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_board/BUILD
+++ /dev/null
@@ -1,38 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_board",
-    srcs = ["tf-graph-board.html"],
-    path = "/tf-graph-board",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph",
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_graph_info",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "@org_polymer_paper_progress",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_board"],
-    destdir = "tf-graph-board",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_common:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_info:legacy",
-        "//third_party/javascript/polymer/v1/paper-progress:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_board/demo/BUILD b/tensorflow/tensorboard/components/tf_graph_board/demo/BUILD
deleted file mode 100644
index 07e8d43dbe..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_board/demo/BUILD
+++ /dev/null
@@ -1,26 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph_board/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-graph-board/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_board",
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_graph_loader",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_board/demo/data/graph.pbtxt b/tensorflow/tensorboard/components/tf_graph_board/demo/data/graph.pbtxt
deleted file mode 100644
index 30b2064534..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_board/demo/data/graph.pbtxt
+++ /dev/null
@@ -1,4606 +0,0 @@
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-  name: "GradientDescent/learning_rate"
-  op: "Const"
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-  attr {
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-    value {
-      s: "cluster_3"
-    }
-  }
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-    }
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-        float_val: 0.1
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-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-      s: "cluster_1"
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-  name: "gradients/add_grad/Shape"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-}
-node {
-  name: "gradients/add_grad/BroadcastGradientArgs"
-  op: "BroadcastGradientArgs"
-  input: "gradients/add_grad/Shape"
-  input: "gradients/add_grad/Shape_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
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-node {
-  name: "gradients/add_1_grad/Shape_1"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-  name: "gradients/add_1_grad/Shape"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
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-  name: "gradients/add_1_grad/BroadcastGradientArgs"
-  op: "BroadcastGradientArgs"
-  input: "gradients/add_1_grad/Shape"
-  input: "gradients/add_1_grad/Shape_1"
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-  op: "Const"
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-node {
-  name: "gradients/SoftmaxCrossEntropyWithLogits_grad/ExpandDims/dim"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
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-    }
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-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-  op: "Const"
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-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-  input: "gradients/Mean_grad/Shape_1"
-  input: "gradients/Mean_grad/Const_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-  op: "Maximum"
-  input: "gradients/Mean_grad/Prod_1"
-  input: "gradients/Mean_grad/Maximum/y"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
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-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
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-  attr {
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-node {
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-  op: "MatMul"
-  input: "gradients/add_grad/tuple/control_dependency"
-  input: "hidden_weights/read"
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-  op: "ApplyGradientDescent"
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diff --git a/tensorflow/tensorboard/components/tf_graph_board/demo/index.html b/tensorflow/tensorboard/components/tf_graph_board/demo/index.html
deleted file mode 100644
index 2563e1595e..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_board/demo/index.html
+++ /dev/null
@@ -1,98 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../tf-graph-board.html">
-<link rel="import" href="../../tf-graph-common/tf-graph-common.html">
-<link rel="import" href="../../tf-graph-loader/tf-graph-loader.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<title>TF Graph Board Demo</title>
-<style>
-  #demo-container {
-    border: 2px solid #808080;
-    width: 1000px;
-    height: 600px;
-  }
-
-  /** Make the graph take up the entire height of the demo container. */
-  tf-graph-board-demo, #board, #board > div {
-    display: block;
-    height: 100%;
-  }
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="tf-graph-board-demo">
-      <template>
-        <!-- We first use the graph loader to load and parse a pbtxt file into a graph object. -->
-        <tf-graph-loader
-            id="loader"
-            datasets="[[_datasets]]"
-            selected-dataset="[[_selectedDataset]]"
-            out-graph="{{_graph}}">
-        </tf-graph-loader>
-
-        <!-- We color ops in the graph by XLA cluster. -->
-        <tf-graph-board id="board" color-by="xla_cluster"></tf-graph-board>
-      </template>
-      <script>
-        "use strict";
-
-        Polymer({
-          is: "tf-graph-board-demo",
-          properties: {
-            // We tell the graph loader to load a specific pbtxt file.
-            _datasets: {
-              type: Array,
-              value: [{
-                "name": "Graph with XLA Clusters Specified",
-                "path": "data/graph.pbtxt"
-              }],
-            },
-            _selectedDataset: {
-              type: Number,
-              value: 0,
-            },
-
-            // This property will be updated by the graph loader.
-            _graph: {
-              type: Object,
-            },
-          },
-          observers: [
-            '_graphUpdated(_graph)',
-          ],
-          _graphUpdated: function(slimGraph) {
-            const tracker = tf.graph.util.getTracker(this.$.loader);
-            const hierarchyTracker = tf.graph.util.getSubtaskTracker(
-                tracker, 100, 'Namespace hierarchy');
-            const hierarchyOptions = {};
-            tf.graph.hierarchy.build(slimGraph, hierarchyOptions, hierarchyTracker).then(
-                function(graphHierarchy) {
-              // We have parsed and built the graph object from a pbtxt file. Render the graph.
-              this.$.board.set('graph', slimGraph);
-              this.$.board.set('graphHierarchy', graphHierarchy);
-            }.bind(this));
-          },
-        });
-      </script>
-    </dom-module>
-    <div id='demo-container'>
-      <tf-graph-board-demo></tf-graph-board-demo>
-    </div>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph_board/tf-graph-board.html b/tensorflow/tensorboard/components/tf_graph_board/tf-graph-board.html
deleted file mode 100644
index 742bb63e04..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_board/tf-graph-board.html
+++ /dev/null
@@ -1,264 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-graph/tf-graph.html">
-<link rel="import" href="../tf-graph-common/tf-graph-common.html">
-<link rel="import" href="../tf-graph-info/tf-graph-info.html">
-<link rel="import" href="../paper-progress/paper-progress.html">
-
-<!--
-Element for putting tf-graph and tf-graph-info side by side.
-
-Example
-
-  <tf-graph-board graph=[[graph]]></tf-graph-board>
-
--->
-<dom-module id="tf-graph-board">
-<template>
-<style>
-::host {
-  display: block;
-}
-
-/deep/ .close {
-  position: absolute;
-  cursor: pointer;
-  left: 15px;
-  bottom: 15px;
-}
-
-.container {
-  width: 100%;
-  height: 100%;
-  opacity: 1;
-}
-
-.container.loading {
-  cursor: progress;
-  opacity: 0.1;
-}
-
-.container.loading.error {
-  cursor: auto;
-}
-
-#info {
-  position: absolute;
-  right: 5px;
-  top: 5px;
-  padding: 0px;
-  max-width: 380px;
-  min-width: 320px;
-  background-color: rgba(255,255,255,0.9);
-  @apply(--shadow-elevation-2dp);
-}
-
-#main {
-  width: 100%;
-  height: 100%;
-}
-
-#progress-bar {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  justify-content: center;
-  width: 100%;
-  position: absolute;
-  top: 40px;
-  left: 0;
-  font-size: 13px;
-}
-
-#progress-msg {
-  width: 400px;
-  margin-bottom: 5px;
-}
-
-paper-progress {
-  width: 400px;
-  --paper-progress-height: 6px;
-  --paper-progress-active-color: #f3913e;
-}
-
-.context-menu {
-  position: absolute;
-  display: none;
-  background-color: #e2e2e2;
-  border-radius: 2px;
-  font-size: 14px;
-  min-width: 150px;
-  border: 1px solid #d4d4d4;
-}
-
-/deep/ .context-menu ul {
-  list-style-type: none;
-  margin: 0;
-  padding: 0;
-  cursor: default;
-}
-
-/deep/ .context-menu ul li {
-  padding: 4px 16px;
-}
-
-/deep/ .context-menu ul li:hover {
-  background-color: #f3913e;
-  color: white;
-}
-</style>
-<template is="dom-if" if="[[_isNotComplete(progress)]]">
-  <div id="progress-bar">
-    <div id="progress-msg">[[progress.msg]]</div>
-    <paper-progress value="[[progress.value]]"></paper-progress>
-  </div>
-</template>
-<div class$="[[_getContainerClass(progress)]]">
-  <div id="main">
-    <tf-graph id="graph"
-              graph-hierarchy="{{graphHierarchy}}"
-              basic-graph="[[graph]]"
-              hierarchy-params="[[hierarchyParams]]"
-              render-hierarchy="{{renderHierarchy}}"
-              devices-for-stats="[[devicesForStats]]"
-              stats="[[stats]]"
-              selected-node="{{selectedNode}}"
-              highlighted-node="{{_highlightedNode}}"
-              color-by="[[colorBy]]"
-              color-by-params="{{colorByParams}}"
-              progress="{{progress}}"
-              node-names-to-health-pills="[[nodeNamesToHealthPills]]"
-              health-pill-step-index="[[healthPillStepIndex]]"
-    ></tf-graph>
-  </div>
-  <div id="info">
-    <tf-graph-info id="graph-info"
-              title="selected"
-              graph-hierarchy="[[graphHierarchy]]"
-              render-hierarchy="[[renderHierarchy]]"
-              graph="[[graph]]"
-              selected-node="{{selectedNode}}"
-              selected-node-include="{{_selectedNodeInclude}}"
-              highlighted-node="{{_highlightedNode}}"
-              color-by="[[colorBy]]"
-              color-by-params="[[colorByParams]]"
-              debugger-data-enabled="[[debuggerDataEnabled]]"
-              are-health-pills-loading="[[areHealthPillsLoading]]"
-              debugger-numeric-alerts="[[debuggerNumericAlerts]]"
-              node-names-to-health-pills="[[nodeNamesToHealthPills]]"
-              all-steps-mode-enabled="{{allStepsModeEnabled}}"
-              specific-health-pill-step="{{specificHealthPillStep}}"
-              health-pill-step-index="{{healthPillStepIndex}}"
-    ></tf-graph-info>
-  </div>
-  <div class="context-menu"></div>
-</div>
-</template>
-</dom-module>
-
-<script>
-Polymer({
-  is: 'tf-graph-board',
-  properties: {
-    // Public API.
-    graphHierarchy: Object,
-    graph: Object,
-    stats: Object,
-    /**
-     * A number between 0 and 100 denoting the % of progress
-     * for the progress bar and the displayed message.
-     * @type {{value: number, msg: string}}
-     */
-    progress: Object,
-    colorBy: String,
-    colorByParams: {
-      type: Object,
-      notify: true
-    },
-    renderHierarchy: {
-      type: Object,
-      notify: true
-    },
-    // Whether debugger data is enabled for this instance of Tensorboard.
-    debuggerDataEnabled: Boolean,
-    // Whether health pills are currently being loaded.
-    areHealthPillsLoading: Boolean,
-    // An array of alerts (in chronological order) provided by debugging libraries on when bad
-    // values (NaN, +/- Inf) appear.
-    debuggerNumericAlerts: {
-      type: Array,
-      notify: true,
-    },
-    // A mapping between node name to the tf.graph.scene.HealthPill to render.
-    nodeNamesToHealthPills: Object,
-    // Whether the user can request health pills for individual steps from the server. This can be
-    // slow compared the default of showing sampled health pills.
-    allStepsModeEnabled: {
-      type: Boolean,
-      notify: true,
-      value: false,
-    },
-    // Relevant if allStepsModeEnabled. The specific step for which to fetch health pills from the
-    // server for.
-    specificHealthPillStep: {
-      type: Number,
-      notify: true,
-      value: 0,
-    },
-    // The step of health pills to show throughout the graph.
-    healthPillStepIndex: Number,
-    // Private API: Data routing between child components.
-    selectedNode: {
-      type: String,
-      notify: true,
-    },
-    // The enum value of the include property of the selected node.
-    _selectedNodeInclude: Number,
-    _highlightedNode: String
-  },
-  listeners: {
-    'node-toggle-extract': '_nodeToggleExtract'
-  },
-  observers: [
-    '_updateNodeInclude(selectedNode)'
-  ],
-  /** True if the progress is not complete yet (< 100 %). */
-  _isNotComplete: function(progress) {
-    return progress.value < 100;
-  },
-  _getContainerClass: function(progress) {
-    var result = 'container';
-    if (progress.error) {
-      result += ' error';
-    }
-    if (this._isNotComplete(progress)) {
-      result += ' loading';
-    }
-    return result;
-  },
-  _updateNodeInclude: function(nodeName) {
-    var node = this.graphHierarchy.node(nodeName);
-    this.set("_selectedNodeInclude",
-      node ? node.include : tf.graph.InclusionType.UNSPECIFIED);
-  },
-  _nodeToggleExtract: function() {
-    this._updateNodeInclude(this.selectedNode);
-  }
-});
-</script>
diff --git a/tensorflow/tensorboard/components/tf_graph_common/BUILD b/tensorflow/tensorboard/components/tf_graph_common/BUILD
deleted file mode 100644
index e4e57149f3..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/BUILD
+++ /dev/null
@@ -1,54 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_common",
-    srcs = [
-        "annotation.ts",
-        "colors.ts",
-        "common.ts",
-        "contextmenu.ts",
-        "edge.ts",
-        "externs.ts",
-        "graph.ts",
-        "hierarchy.ts",
-        "layout.ts",
-        "minimap.ts",
-        "node.ts",
-        "parser.ts",
-        "proto.ts",
-        "render.ts",
-        "scene.ts",
-        "template.ts",
-        "tf-graph-common.html",
-        "util.ts",
-    ],
-    path = "/tf-graph-common",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:dagre",
-        "//tensorflow/tensorboard/components/tf_imports:graphlib",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_common"],
-    destdir = "tf-graph-common",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports_google:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_common/annotation.ts b/tensorflow/tensorboard/components/tf_graph_common/annotation.ts
deleted file mode 100644
index bde3829778..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/annotation.ts
+++ /dev/null
@@ -1,235 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph.scene.annotation {
-  /**
-   * Populate a given annotation container group
-   *
-   *     <g class='{in|out}-annotations'></g>
-   *
-   * with annotation group of the following structure:
-   *
-   * <g class='annotation'>
-   *   <g class='annotation-node'>
-   *   <!--
-   *   Content here determined by Scene.node.buildGroup.
-   *   -->
-   *   </g>
-   * </g>
-   *
-   * @param container selection of the container.
-   * @param annotationData node.{in|out}Annotations
-   * @param d node to build group for.
-   * @param sceneElement <tf-graph-scene> polymer element.
-   * @return selection of appended objects
-   */
-  export function buildGroup(
-      container, annotationData: render.AnnotationList,
-      d: render.RenderNodeInfo, sceneElement) {
-    // Select all children and join with data.
-    let annotationGroups =
-        container
-            .selectAll(function() {
-              // using d3's selector function
-              // See https://github.com/mbostock/d3/releases/tag/v2.0.0
-              // (It's not listed in the d3 wiki.)
-              return this.childNodes;
-            })
-            .data(annotationData.list, d => { return d.node.name; });
-
-    annotationGroups.enter()
-        .append('g')
-        .attr('data-name', a => { return a.node.name; })
-        .each(function(a) {
-          let aGroup = d3.select(this);
-
-          // Add annotation to the index in the scene
-          sceneElement.addAnnotationGroup(a, d, aGroup);
-          // Append annotation edge
-          let edgeType = Class.Annotation.EDGE;
-          let metaedge = a.renderMetaedgeInfo && a.renderMetaedgeInfo.metaedge;
-          if (metaedge && !metaedge.numRegularEdges) {
-            edgeType += ' ' + Class.Annotation.CONTROL_EDGE;
-          }
-          // If any edges are reference edges, add the reference edge class.
-          if (metaedge && metaedge.numRefEdges) {
-            edgeType += ' ' + Class.Edge.REF_LINE;
-          }
-          edge.appendEdge(aGroup, a, sceneElement, edgeType);
-
-          if (a.annotationType !== render.AnnotationType.ELLIPSIS) {
-            addAnnotationLabelFromNode(aGroup, a);
-            buildShape(aGroup, a);
-          } else {
-            addAnnotationLabel(
-                aGroup, a.node.name, a, Class.Annotation.ELLIPSIS);
-          }
-        }).merge(annotationGroups)
-        .attr(
-            'class',
-            a => {
-              return Class.Annotation.GROUP + ' ' +
-                  annotationToClassName(a.annotationType) + ' ' +
-                  node.nodeClass(a);
-            })
-        .each(function(a) {
-          let aGroup = d3.select(this);
-          update(aGroup, d, a, sceneElement);
-          if (a.annotationType !== render.AnnotationType.ELLIPSIS) {
-            addInteraction(aGroup, d, a, sceneElement);
-          }
-        });
-
-    annotationGroups.exit()
-        .each(function(a) {
-          let aGroup = d3.select(this);
-
-          // Remove annotation from the index in the scene
-          sceneElement.removeAnnotationGroup(a, d, aGroup);
-        })
-        .remove();
-    return annotationGroups;
-};
-
-/**
- * Maps an annotation enum to a class name used in css rules.
- */
-function annotationToClassName(annotationType: render.AnnotationType) {
-  return (render.AnnotationType[annotationType] || '').toLowerCase() || null;
-}
-
-function buildShape(aGroup, a: render.Annotation) {
-  if (a.annotationType === render.AnnotationType.SUMMARY) {
-    let summary = selectOrCreateChild(aGroup, 'use');
-    summary
-      .attr('class', 'summary')
-      .attr('xlink:href', '#summary-icon')
-      .attr('cursor', 'pointer');
-  } else {
-    let shape = node.buildShape(aGroup, a, Class.Annotation.NODE);
-    // add title tag to get native tooltips
-    selectOrCreateChild(shape, 'title').text(a.node.name);
-  }
-}
-
-function addAnnotationLabelFromNode(aGroup, a: render.Annotation) {
-  let namePath = a.node.name.split('/');
-  let text = namePath[namePath.length - 1];
-  return addAnnotationLabel(aGroup, text, a, null);
-}
-
-function addAnnotationLabel(
-    aGroup, label: string, a: render.Annotation, additionalClassNames) {
-  let classNames = Class.Annotation.LABEL;
-  if (additionalClassNames) {
-    classNames += ' ' + additionalClassNames;
-  }
-  let txtElement = aGroup.append('text')
-                       .attr('class', classNames)
-                       .attr('dy', '.35em')
-                       .attr('text-anchor', a.isIn ? 'end' : 'start')
-                       .text(label);
-
-  return tf.graph.scene.node.enforceLabelWidth(txtElement, -1);
-}
-
-function addInteraction(selection, d: render.RenderNodeInfo,
-    annotation: render.Annotation, sceneElement) {
-  selection
-      .on('mouseover',
-          a => {
-            sceneElement.fire(
-                'annotation-highlight',
-                {name: a.node.name, hostName: d.node.name});
-          })
-      .on('mouseout',
-          a => {
-            sceneElement.fire(
-                'annotation-unhighlight',
-                {name: a.node.name, hostName: d.node.name});
-          })
-      .on('click', a => {
-        // Stop this event's propagation so that it isn't also considered a
-        // graph-select.
-        (<Event>d3.event).stopPropagation();
-        sceneElement.fire(
-            'annotation-select', {name: a.node.name, hostName: d.node.name});
-      });
-  if (annotation.annotationType !== render.AnnotationType.SUMMARY &&
-      annotation.annotationType !== render.AnnotationType.CONSTANT) {
-    selection.on(
-        'contextmenu', contextmenu.getMenu(
-                           node.getContextMenu(annotation.node, sceneElement)));
-  }
-};
-
-/**
- * Adjust annotation's position.
- *
- * @param aGroup selection of a 'g.annotation' element.
- * @param d Host node data.
- * @param a annotation node data.
- * @param sceneElement <tf-graph-scene> polymer element.
- */
-function update(aGroup, d: render.RenderNodeInfo, a: render.Annotation,
-    sceneElement) {
-  let cx = layout.computeCXPositionOfNodeShape(d);
-  // Annotations that point to embedded nodes (constants,summary)
-  // don't have a render information attached so we don't stylize these.
-  // Also we don't stylize ellipsis annotations (the string '... and X more').
-  if (a.renderNodeInfo &&
-      a.annotationType !== render.AnnotationType.ELLIPSIS) {
-    node.stylize(aGroup, a.renderNodeInfo, sceneElement,
-      Class.Annotation.NODE);
-  }
-
-  if (a.annotationType === render.AnnotationType.SUMMARY) {
-    // Update the width of the annotation to give space for the image.
-    a.width += 10;
-  }
-
-  // label position
-  aGroup.select('text.' + Class.Annotation.LABEL).transition()
-    .attr('x', cx + a.dx + (a.isIn ? -1 : 1) * (a.width / 2 + a.labelOffset))
-    .attr('y', d.y + a.dy);
-
-  // Some annotations (such as summary) are represented using a 12x12 image tag.
-  // Purposely omitted units (e.g. pixels) since the images are vector graphics.
-  // If there is an image, we adjust the location of the image to be vertically
-  // centered with the node and horizontally centered between the arrow and the
-  // text label.
-  aGroup.select('use.summary').transition()
-    .attr('x', cx + a.dx - 3)
-    .attr('y', d.y + a.dy - 6);
-
-  // Node position (only one of the shape selection will be non-empty.)
-  positionEllipse(
-      aGroup.select('.' + Class.Annotation.NODE + ' ellipse'), cx + a.dx,
-      d.y + a.dy, a.width, a.height);
-  positionRect(
-      aGroup.select('.' + Class.Annotation.NODE + ' rect'), cx + a.dx,
-      d.y + a.dy, a.width, a.height);
-  positionRect(
-      aGroup.select('.' + Class.Annotation.NODE + ' use'), cx + a.dx,
-      d.y + a.dy, a.width, a.height);
-
-  // Edge position
-  aGroup.select('path.' + Class.Annotation.EDGE).transition().attr('d', a => {
-    // map relative position to absolute position
-    let points = a.points.map(p => { return {x: p.dx + cx, y: p.dy + d.y}; });
-    return edge.interpolate(points);
-  });
-};
-
-} // close module
diff --git a/tensorflow/tensorboard/components/tf_graph_common/colors.ts b/tensorflow/tensorboard/components/tf_graph_common/colors.ts
deleted file mode 100644
index 40f91f7d2d..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/colors.ts
+++ /dev/null
@@ -1,130 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-module tf {
-  /**
-   * Mapping from color palette name to color palette, which contains
-   * exact colors for multiple states of a single color palette.
-   */
-  export let COLORS = [
-    {
-      'name': 'Google Blue',
-      'color': '#4184f3',
-      'active': '#3a53c5',
-      'disabled': '#cad8fc'
-    },
-    {
-      'name': 'Google Red',
-      'color': '#db4437',
-      'active': '#8f2a0c',
-      'disabled': '#e8c6c1'
-    },
-    {
-      'name': 'Google Yellow',
-      'color': '#f4b400',
-      'active': '#db9200',
-      'disabled': '#f7e8b0'
-    },
-    {
-      'name': 'Google Green',
-      'color': '#0f9d58',
-      'active': '#488046',
-      'disabled': '#c2e1cc'
-    },
-    {
-      'name': 'Purple',
-      'color': '#aa46bb',
-      'active': '#5c1398',
-      'disabled': '#d7bce6'
-    },
-    {
-      'name': 'Teal',
-      'color': '#00abc0',
-      'active': '#47828e',
-      'disabled': '#c2eaf2'
-    },
-    {
-      'name': 'Deep Orange',
-      'color': '#ff6f42',
-      'active': '#ca4a06',
-      'disabled': '#f2cbba'
-    },
-    {
-      'name': 'Lime',
-      'color': '#9d9c23',
-      'active': '#7f771d',
-      'disabled': '#f1f4c2'
-    },
-    {
-      'name': 'Indigo',
-      'color': '#5b6abf',
-      'active': '#3e47a9',
-      'disabled': '#c5c8e8'
-    },
-    {
-      'name': 'Pink',
-      'color': '#ef6191',
-      'active': '#ca1c60',
-      'disabled': '#e9b9ce'
-    },
-    {
-      'name': 'Deep Teal',
-      'color': '#00786a',
-      'active': '#2b4f43',
-      'disabled': '#bededa'
-    },
-    {
-      'name': 'Deep Pink',
-      'color': '#c1175a',
-      'active': '#75084f',
-      'disabled': '#de8cae'
-    },
-    {
-      'name': 'Gray',
-      'color': '#9E9E9E',   // 500
-      'active': '#424242',  // 800
-      'disabled': 'F5F5F5'  // 100
-    }
-  ].reduce((m, c) => {
-    m[c.name] = c;
-    return m;
-  }, {});
-
-  /**
-   * Mapping from op category to color palette name
-   * e.g.,  OP_GROUP_COLORS['state_ops'] = 'Google Blue';
-   */
-  export let OP_GROUP_COLORS = [
-    {
-      color: 'Google Red',
-      groups: [
-        'gen_legacy_ops', 'legacy_ops', 'legacy_flogs_input',
-        'legacy_image_input', 'legacy_input_example_input',
-        'legacy_sequence_input', 'legacy_seti_input_input'
-      ]
-    },
-    {color: 'Deep Orange', groups: ['constant_ops']},
-    {color: 'Indigo', groups: ['state_ops']},
-    {color: 'Purple', groups: ['nn_ops', 'nn']},
-    {color: 'Google Green', groups: ['math_ops']},
-    {color: 'Lime', groups: ['array_ops']},
-    {color: 'Teal', groups: ['control_flow_ops', 'data_flow_ops']},
-    {color: 'Pink', groups: ['summary_ops']},
-    {color: 'Deep Pink', groups: ['io_ops']}
-  ].reduce((m, c) => {
-    c.groups.forEach(function(group) { m[group] = c.color; });
-    return m;
-  }, {});
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_common/common.ts b/tensorflow/tensorboard/components/tf_graph_common/common.ts
deleted file mode 100644
index e7eac54e58..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/common.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * @fileoverview Common interfaces for the tensorflow graph visualizer.
- */
-
-module tf {
-  /**
-   * Tracks task progress. Each task being passed a progress tracker needs
-   * to call the below-defined methods to notify the caller about the gradual
-   * progress of the task.
-   */
-  export interface ProgressTracker {
-    updateProgress(incrementValue: number): void;
-    setMessage(msg: string): void;
-    reportError(msg: string, err: Error): void;
-  }
-} // close module tf
diff --git a/tensorflow/tensorboard/components/tf_graph_common/contextmenu.ts b/tensorflow/tensorboard/components/tf_graph_common/contextmenu.ts
deleted file mode 100644
index 8121cf9f6d..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/contextmenu.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-module tf.graph.scene.contextmenu {
-
-/** Function that converts data to a title string. */
-export interface TitleFunction {
-  (data: any): string;
-}
-
-/** Function that takes action based on item clicked in the context menu. */
-export interface ActionFunction {
-  (elem: any, d: any, i: number): void;
-}
-
-/**
- * The interface for an item in the context menu
- */
-export interface ContextMenuItem {
-  title: TitleFunction;
-  action: ActionFunction;
-}
-
-/**
- * Returns the event listener, which can be used as an argument for the d3
- * selection.on function. Renders the context menu that is to be displayed
- * in response to the event.
- */
-export function getMenu(menu: ContextMenuItem[]) {
-  let menuSelection = d3.select('.context-menu');
-  // Close the menu when anything else is clicked.
-  d3.select('body').on(
-      'click.context', function() { menuSelection.style('display', 'none'); });
-
-  // Function called to populate the context menu.
-  return function(data, index: number): void {
-    // Position and display the menu.
-    let event = <MouseEvent>d3.event;
-    menuSelection
-      .style('display', 'block')
-      .style('left', (event.layerX + 1) + 'px')
-      .style('top', (event.layerY + 1) + 'px');
-
-    // Stop the event from propagating further.
-    event.preventDefault();
-    event.stopPropagation();
-
-    // Add provided items to the context menu.
-    menuSelection.html('');
-    let list = menuSelection.append('ul');
-    list.selectAll('li')
-        .data(menu)
-        .enter()
-        .append('li')
-        .html(function(d) { return d.title(data); })
-        .on('click', (d, i) => {
-          d.action(this, data, index);
-          menuSelection.style('display', 'none');
-        });
-  };
-};
-
-} // close module
diff --git a/tensorflow/tensorboard/components/tf_graph_common/edge.ts b/tensorflow/tensorboard/components/tf_graph_common/edge.ts
deleted file mode 100644
index 4a1182bb9f..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/edge.ts
+++ /dev/null
@@ -1,359 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph.scene.edge {
-
-/** Delimiter between dimensions when showing sizes of tensors. */
-const TENSOR_SHAPE_DELIM = '×';
-
-/** The minimum stroke width of an edge. */
-export const MIN_EDGE_WIDTH = 0.75;
-
-/** The maximum stroke width of an edge. */
-export const MAX_EDGE_WIDTH = 12;
-
-/** The exponent used in the power scale for edge thickness. */
-const EDGE_WIDTH_SCALE_EXPONENT = 0.3;
-
-/** The domain (min and max value) for the edge width. */
-const DOMAIN_EDGE_WIDTH_SCALE = [1, 5E6];
-
-export const EDGE_WIDTH_SCALE: d3.ScalePower<number, number> = d3.scalePow()
-      .exponent(EDGE_WIDTH_SCALE_EXPONENT)
-      .domain(DOMAIN_EDGE_WIDTH_SCALE)
-      .range([MIN_EDGE_WIDTH, MAX_EDGE_WIDTH])
-      .clamp(true);
-
-let arrowheadMap =
-    d3.scaleQuantize<String>().domain([MIN_EDGE_WIDTH, MAX_EDGE_WIDTH]).range([
-      'small', 'medium', 'large', 'xlarge'
-    ]);
-
-/** Minimum stroke width to put edge labels in the middle of edges */
-const CENTER_EDGE_LABEL_MIN_STROKE_WIDTH = 2.5;
-
-export type EdgeData = {v: string, w: string, label: render.RenderMetaedgeInfo};
-
-export function getEdgeKey(edgeObj: EdgeData) {
-  return edgeObj.v + EDGE_KEY_DELIM + edgeObj.w;
-}
-
-/**
- * Select or Create a 'g.edges' group to a given sceneGroup
- * and builds a number of 'g.edge' groups inside the group.
- *
- * Structure Pattern:
- *
- * <g class='edges'>
- *   <g class='edge'>
- *     <path class='edgeline'/>
- *   </g>
- *   ...
- * </g>
- *
- *
- * @param sceneGroup container
- * @param graph
- * @param sceneElement <tf-graph-scene> polymer element.
- * @return selection of the created nodeGroups
- */
-export function buildGroup(sceneGroup,
-    graph: graphlib.Graph<render.RenderNodeInfo, render.RenderMetaedgeInfo>,
-    sceneElement) {
-  let edges: EdgeData[] = [];
-  edges = _.reduce(graph.edges(), (edges, edgeObj) => {
-    let edgeLabel = graph.edge(edgeObj);
-    edges.push({
-      v: edgeObj.v,
-      w: edgeObj.w,
-      label: edgeLabel
-    });
-    return edges;
-  }, edges);
-
-  let container =
-      scene.selectOrCreateChild(sceneGroup, 'g', Class.Edge.CONTAINER);
-
-  // Select all children and join with data.
-  // (Note that all children of g.edges are g.edge)
-  let edgeGroups = (container as any).selectAll(function() {return this.childNodes;}).data(edges, getEdgeKey);
-
-  // Make edges a group to support rendering multiple lines for metaedge
-  edgeGroups.enter()
-      .append('g')
-      .attr('class', Class.Edge.GROUP)
-      .attr('data-edge', getEdgeKey)
-      .each(function(d: EdgeData) {
-        let edgeGroup = d3.select(this);
-        d.label.edgeGroup = edgeGroup;
-        // index node group for quick highlighting
-        sceneElement._edgeGroupIndex[getEdgeKey(d)] = edgeGroup;
-
-        // Add line during enter because we're assuming that type of line
-        // normally does not change.
-        appendEdge(edgeGroup, d, sceneElement);
-      })
-      .merge(edgeGroups)
-      .each(position)
-      .each(function(d) {
-    stylize(d3.select(this), d, sceneElement);
-  });
-
-  edgeGroups.exit()
-    .each(d => {
-      delete sceneElement._edgeGroupIndex[getEdgeKey(d)];
-    })
-    .remove();
-  return edgeGroups;
-};
-
-/**
- * Returns the label for the given base edge.
- * The label is the shape of the underlying tensor.
- */
-export function getLabelForBaseEdge(
-    baseEdge: BaseEdge, renderInfo: render.RenderGraphInfo): string {
-  let node = <OpNode>renderInfo.getNodeByName(baseEdge.v);
-  if (node.outputShapes == null || node.outputShapes.length === 0) {
-    return null;
-  }
-  let shape = node.outputShapes[baseEdge.outputTensorIndex];
-  if (shape == null) {
-    return null;
-  }
-  if (shape.length === 0) {
-    return 'scalar';
-  }
-  return shape.map(size => { return size === -1 ? '?' : size; })
-      .join(TENSOR_SHAPE_DELIM);
-}
-
-/**
- * Creates the label for the given metaedge. If the metaedge consists
- * of only 1 tensor, and it's shape is known, the label will contain that
- * shape. Otherwise, the label will say the number of tensors in the metaedge.
- */
-export function getLabelForEdge(metaedge: Metaedge,
-    renderInfo: render.RenderGraphInfo): string {
-  let isMultiEdge = metaedge.baseEdgeList.length > 1;
-  return isMultiEdge ?
-      metaedge.baseEdgeList.length + ' tensors' :
-      getLabelForBaseEdge(metaedge.baseEdgeList[0], renderInfo);
-}
-
-/**
- * Shortens the path enought such that the tip of the start/end marker will
- * point to the start/end of the path. The marker can be of arbitrary size.
- *
- * @param points Array of path control points.
- * @param marker D3 selection of the <marker> svg element.
- * @param isStart Is the marker a `start-marker`. If false, the marker is
- *     an `end-marker`.
- * @return The new array of control points.
- */
-function adjustPathPointsForMarker(points: render.Point[],
-    marker: d3.Selection<any, any, any, any>, isStart: boolean): render.Point[] {
-  let lineFunc = d3.line<render.Point>()
-    .x(d => d.x)
-    .y(d => d.y);
-  let path =
-      d3.select(document.createElementNS('http://www.w3.org/2000/svg', 'path'))
-          .attr('d', lineFunc(points));
-  let markerWidth = +marker.attr('markerWidth');
-  let viewBox = marker.attr('viewBox').split(' ').map(Number);
-  let viewBoxWidth = viewBox[2] - viewBox[0];
-  let refX = +marker.attr('refX');
-  let pathNode = <SVGPathElement> path.node();
-  if (isStart) {
-    // The edge flows downwards. Do not make the edge go the whole way, lest we
-    // clobber the arrowhead.
-    const fractionStickingOut = 1 - refX / viewBoxWidth;
-    const length = markerWidth * fractionStickingOut;
-    const point = pathNode.getPointAtLength(length);
-    // Figure out how many segments of the path we need to remove in order
-    // to shorten the path.
-    const segIndex = pathNode.getPathSegAtLength(length);
-    // Update the very first segment.
-    points[segIndex - 1] = {x: point.x, y: point.y};
-    // Ignore every point before segIndex - 1.
-    return points.slice(segIndex - 1);
-  } else {
-    // The edge flows upwards. Do not make the edge go the whole way, lest we
-    // clobber the arrowhead.
-    const fractionStickingOut = 1 - refX / viewBoxWidth;
-    const length =
-        pathNode.getTotalLength() - markerWidth * fractionStickingOut;
-    const point = pathNode.getPointAtLength(length);
-    // Figure out how many segments of the path we need to remove in order
-    // to shorten the path.
-    const segIndex = pathNode.getPathSegAtLength(length);
-    // Update the very last segment.
-    points[segIndex] = {x: point.x, y: point.y};
-    // Ignore every point after segIndex.
-    return points.slice(0, segIndex + 1);
-  }
-}
-
-/**
- * For a given d3 selection and data object, create a path to represent the
- * edge described in d.label.
- *
- * If d.label is defined, it will be a RenderMetaedgeInfo instance. It
- * will sometimes be undefined, for example for some Annotation edges for which
- * there is no underlying Metaedge in the hierarchical graph.
- */
-export function appendEdge(edgeGroup, d: EdgeData,
-    sceneElement: {renderHierarchy: render.RenderGraphInfo},
-    edgeClass?: string) {
-  let size = 1;
-  if (d.label != null && d.label.metaedge != null) {
-    // There is an underlying Metaedge.
-    size = d.label.metaedge.totalSize;
-  }
-  edgeClass = edgeClass || Class.Edge.LINE; // set default type
-
-  if (d.label && d.label.structural) {
-    edgeClass += ' ' + Class.Edge.STRUCTURAL;
-  }
-  if (d.label && d.label.metaedge && d.label.metaedge.numRefEdges) {
-    edgeClass += ' ' + Class.Edge.REFERENCE_EDGE;
-  }
-  // Give the path a unique id, which will be used to link
-  // the textPath (edge label) to this path.
-  let pathId = 'path_' + getEdgeKey(d);
-  let strokeWidth = sceneElement.renderHierarchy.edgeWidthScale(size);
-
-  let path = edgeGroup.append('path')
-                 .attr('id', pathId)
-                 .attr('class', edgeClass)
-                 .style('stroke-width', strokeWidth + 'px');
-
-  // Check if there is a reference edge and add an arrowhead of the right size.
-  if (d.label && d.label.metaedge) {
-    if (d.label.metaedge.numRefEdges) {
-      // We have a reference edge.
-      const markerId = `reference-arrowhead-${arrowheadMap(strokeWidth)}`;
-      path.style('marker-start', `url(#${markerId})`);
-      d.label.startMarkerId = markerId;
-    } else {
-      // We have a dataflow edge.
-      const markerId = `dataflow-arrowhead-${arrowheadMap(strokeWidth)}`;
-      path.style('marker-end', `url(#${markerId})`);
-      d.label.endMarkerId = markerId;
-    }
-  }
-
-  if (d.label == null || d.label.metaedge == null) {
-    // There is no associated metaedge, thus no text.
-    // This happens for annotation edges.
-    return;
-  }
-  let labelForEdge = getLabelForEdge(d.label.metaedge,
-      sceneElement.renderHierarchy);
-  if (labelForEdge == null) {
-    // We have no information to show on this edge.
-    return;
-  }
-
-  // Put edge label in the middle of edge only if the edge is thick enough.
-  let baseline = strokeWidth > CENTER_EDGE_LABEL_MIN_STROKE_WIDTH ?
-      'central' :
-      'text-after-edge';
-
-  edgeGroup.append('text')
-      .append('textPath')
-        .attr('xlink:href', '#' + pathId)
-        .attr('startOffset', '50%')
-        .attr('text-anchor', 'middle')
-        .attr('dominant-baseline', 'central')
-      .text(labelForEdge);
-};
-
-export let interpolate: d3.Line<{x: number, y: number}> = d3.line<{x: number, y: number}>()
-                             .curve(d3.curveBasis)
-                             .x((d) => { return d.x;})
-                             .y((d) => { return d.y;});
-
-/**
- * Returns a tween interpolator for the endpoint of an edge path.
- */
-function getEdgePathInterpolator(d: EdgeData, i: number, a: string) {
-  let renderMetaedgeInfo = <render.RenderMetaedgeInfo> d.label;
-  let adjoiningMetaedge = renderMetaedgeInfo.adjoiningMetaedge;
-  let points = renderMetaedgeInfo.points;
-
-  // Adjust the path so that start/end markers point to the end
-  // of the path.
-  if (d.label.startMarkerId) {
-    points = adjustPathPointsForMarker(
-        points, d3.select('#' + d.label.startMarkerId), true);
-  }
-  if (d.label.endMarkerId) {
-    points = adjustPathPointsForMarker(
-        points, d3.select('#' + d.label.endMarkerId), false);
-  }
-
-  if (!adjoiningMetaedge) {
-    return d3.interpolate(a, interpolate(points));
-  }
-
-  let renderPath = this;
-
-  // Get the adjoining path that matches the adjoining metaedge.
-  let adjoiningPath =
-    <SVGPathElement>((<HTMLElement>adjoiningMetaedge.edgeGroup.node())
-      .firstChild);
-
-  // Find the desired SVGPoint along the adjoining path, then convert those
-  // coordinates into the space of the renderPath using its Current
-  // Transformation Matrix (CTM).
-  let inbound = renderMetaedgeInfo.metaedge.inbound;
-
-  return function(t) {
-    let adjoiningPoint = adjoiningPath
-      .getPointAtLength(inbound ? adjoiningPath.getTotalLength() : 0)
-      .matrixTransform(adjoiningPath.getCTM())
-      .matrixTransform(renderPath.getCTM().inverse());
-
-    // Update the relevant point in the renderMetaedgeInfo's points list, then
-    // re-interpolate the path.
-    let index = inbound ? 0 : points.length - 1;
-    points[index].x = adjoiningPoint.x;
-    points[index].y = adjoiningPoint.y;
-    let dPath = interpolate(points);
-    return dPath;
-  };
-}
-
-function position(d) {
-  d3.select(this)
-      .select('path.' + Class.Edge.LINE)
-      .transition()
-      .attrTween('d', getEdgePathInterpolator as any);
-};
-
-/**
- * For a given d3 selection and data object, mark the edge as a control
- * dependency if it contains only control edges.
- *
- * d's label property will be a RenderMetaedgeInfo object.
- */
-function stylize(edgeGroup, d: EdgeData, stylize) {
-  edgeGroup.classed('faded', d.label.isFadedOut);
-  let metaedge = d.label.metaedge;
-  edgeGroup.select('path.' + Class.Edge.LINE)
-      .classed('control-dep', metaedge && !metaedge.numRegularEdges);
-};
-
-} // close module
diff --git a/tensorflow/tensorboard/components/tf_graph_common/externs.ts b/tensorflow/tensorboard/components/tf_graph_common/externs.ts
deleted file mode 100644
index 7c0d168a42..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/externs.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * @fileoverview Extern declarations for tensorflow graph visualizer.
- *     This file contains compiler stubs for external dependencies whos
- *     implementations are defined at runtime.
- */
-
-declare module graphlib {
-  interface GraphOptions {
-    name?: string;
-    /**
-     * Direction for rank nodes. Can be TB, BT, LR, or RL, where T = top,
-     * B = bottom, L = left, and R = right.
-     */
-    rankdir?: string;
-    type?: string|number;
-    /** Number of pixels between each rank in the layout. */
-    ranksep?: number;
-    /** Number of pixels that separate nodes horizontally in the layout. */
-    nodesep?: number;
-    /** Number of pixels that separate edges horizontally in the layout */
-    edgesep?: number;
-  }
-
-  export interface EdgeObject {
-    v: string;
-    w: string;
-    name?: string;
-  }
-
-  export class Graph<N, E> {
-    constructor(opt?: Object);
-    setNode(name: string, value?: N): void;
-    hasNode(name: string): boolean;
-    setEdge(fromName: string, toName: string, value?: E): void;
-    hasEdge(fromName: string, toName: string): boolean;
-    edge(fromName: string, toName: string): E;
-    edge(edgeObject: EdgeObject): E;
-    removeEdge(v: string, w: string): void;
-    nodes(): string[];
-    node(name: string): N;
-    removeNode(name: string): void;
-    setGraph(graphOptions: GraphOptions): void;
-    graph(): GraphOptions;
-    nodeCount(): number;
-    neighbors(name: string): string[];
-    successors(name: string): string[];
-    predecessors(name: string): string[];
-    edges(): EdgeObject[];
-    outEdges(name: string): E[];
-    inEdges(name: string): E[];
-    /**
-     * Returns those nodes in the graph that have no in-edges.
-     * Takes O(|V|) time.
-     */
-    sources(): string[];
-    /**
-     * Remove the node with the id v in the graph or do nothing if
-     * the node is not in the graph. If the node was removed this
-     * function also removes any incident edges. Returns the graph,
-     * allowing this to be chained with other functions. Takes O(|E|) time.
-     */
-    removeNode(name: string): Graph<N, E>;
-    setParent(name: string, parentName: string): void;
-  }
-}
-
-/**
- * Declaring dagre var used for dagre layout.
- */
-declare var dagre: {layout(graph: graphlib.Graph<any, any>): void;};
diff --git a/tensorflow/tensorboard/components/tf_graph_common/graph.ts b/tensorflow/tensorboard/components/tf_graph_common/graph.ts
deleted file mode 100644
index 1b0abcfd85..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/graph.ts
+++ /dev/null
@@ -1,1257 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph {
-
-/** Delimiter used in node names to denote namespaces. */
-export const NAMESPACE_DELIM = '/';
-export const ROOT_NAME = '__root__';
-
-/** Attribute key used for storing attributes that are too large. */
-export const LARGE_ATTRS_KEY = '_too_large_attrs';
-/**
- * Maximum allowed size in bytes, before the attribute is considered large
- * and filtered out of the graph.
- */
-export const LIMIT_ATTR_SIZE = 1024;
-
-// Separator between the source and the destination name of the edge.
-export const EDGE_KEY_DELIM = '--';
-
-export enum GraphType {FULL, EMBEDDED, META, SERIES, CORE, SHADOW, BRIDGE,
-    EDGE};
-export enum NodeType {META, OP, SERIES, BRIDGE, ELLIPSIS};
-
-/** Indicates if a node is to be included in the main graph when rendered. */
-export enum InclusionType {INCLUDE, EXCLUDE, UNSPECIFIED};
-
-/** Indicates if a series is to be grouped in the graph when rendered. */
-export enum SeriesGroupingType {GROUP, UNGROUP};
-
-/** Attribute key reserved for the shapes of the output tensors. */
-const OUTPUT_SHAPES_KEY = '_output_shapes';
-
-/** Attribute key reserved for the XLA cluster that an op runs on. */
-const _XLA_CLUSTER_KEY = '_XlaCluster';
-
-/**
- * A BaseEdge is the label object (in the graphlib sense) for an edge in the
- * original, full graph produced after parsing. Subsequent graphs, like those
- * which belong to Metanodes, should not use BaseEdge objects, but instead
- * contain Metaedges (which in turn may contain any number of BaseEdges).
- */
-export interface BaseEdge extends graphlib.EdgeObject {
-  isControlDependency: boolean;
-  isReferenceEdge: boolean;
-  /** The index of the output tensor of the source node. */
-  outputTensorIndex: number;
-}
-
-/**
- * A SlimGraph is inspired by graphlib.Graph, but having only the functionality
- * that we need.
- */
-export class SlimGraph {
-  nodes: { [nodeName: string]: OpNode };
-  edges: BaseEdge[];
-
-  constructor() {
-    this.nodes = {};
-    this.edges = [];
-  }
-}
-
-export interface NormalizedInput {
-  name: string;
-  /** The index of the output tensor of the source node. */
-  outputTensorIndex: number;
-  isControlDependency: boolean;
-}
-
-export interface BuildParams {
-  enableEmbedding: boolean;
-  inEmbeddingTypes: string[];
-  outEmbeddingTypes: string[];
-  refEdges: { [inputEdge: string]: boolean };
-}
-
-/**
- * The most basic information about a node in the hierarchical graph.
- */
-export interface Node {
-  /** The name of the node, used frequently to look up nodes by name. */
-  name: string;
-  /** Which type of node this is. */
-  type: NodeType;
-  /**
-   * Whether this node is a type that may contain other nodes. Those types
-   * should extend from GroupNode.
-   *
-   * For an OpNode, isGroupNode will be false, even though it may have
-   * embeddings. These embedding Nodes will have their parentNode set to the
-   * OpNode. However, embeddings are later rendered as annotations, not as
-   * children to be made visible on expansion (like a Metanode or SeriesNode).
-   */
-  isGroupNode: boolean;
-  /**
-   * The number of nodes this node represents. For OpNodes, this will be 1, and
-   * for GroupNodes it will be a count of the total number of descendents it
-   * contains.
-   */
-  cardinality: number;
-  /**
-   * The Node which is this Node's parent. This is of type Node and not
-   * GroupNode because of embeddings, which will have a parent OpNode.
-   */
-  parentNode: Node;
-  /** Runtime execution stats for this node, if available */
-  stats: NodeStats;
-  /** If the node is to be included or excluded from the main graph when
-   *  rendered. Defaults to UNSPECIFIED, which means that the rendering
-   *  algorithm determines if it will be included or not. Then can be set to
-   *  INCLUDE or EXCLUDE manually by the user.
-   */
-  include: InclusionType;
-  /**
-   * Node attributes specify customizable visual aspects of a node and
-   * application-specific metadata associated with a node. The name
-   * 'nodeAttributes' is meant to avoid naming-conflicts with the 'attr' in
-   * subclasses of Node.
-   */
-  nodeAttributes: {[key: string]: any;};
-}
-
-export type TensorShape = number[];
-
-export interface OpNode extends Node {
-  op: string;
-  // The device on which the op ran. Null if it is unknown.
-  device: string;
-  attr: {key: string, value: any}[];
-  inputs: NormalizedInput[];
-  inEmbeddings: OpNode[];
-  outEmbeddings: OpNode[];
-  // The name of the SeriesNode that can contain this node in its series.
-  // If there is no such node, then this is null.
-  owningSeries: string;
-  /**
-   * Array of tensor shapes. Null if the number of output tensors is unknown,
-   * otherwise the length will equal the number of output tensors.
-   *
-   * Each tensor shape is an array of numbers, or null. Details:
-   * - null means unknown rank, and therefore entire shape is unknown.
-   * - [4, 2, 1] means rank-3 tensor of size 4x2x1.
-   * - [] means a scalar (rank-0 tensor).
-   * - [1] means rank-1 tensor of size 1 (not the same as scalar).
-   * - [5, -1, 3] means rank-3 tensor of shape is 5x?x3. The size
-   *       of the middle dimension is unknown (encoded as -1).
-   */
-  outputShapes: TensorShape[];
-  // The XLA Cluster on which the op ran. Null if it is unknown.
-  xlaCluster: string;
-}
-
-export interface BridgeNode extends Node {
-  /**
-   * Whether this bridge node represents edges coming into its parent node.
-   */
-  inbound: boolean;
-}
-
-/**
- * A node that is used when there are more than the maximum number of allowed
- * annotations hanging off of a node.  This node represents an ellipsis
- * annotation, indicating a number of additional annotations.
- */
-export interface EllipsisNode extends Node {
-  /**
-   * The number of nodes this ellipsis represents.
-   */
-  numMoreNodes: number;
-
-  /**
-   * Sets the number of nodes this ellipsis represents and changes the node
-   * name accordingly.
-   */
-  setNumMoreNodes(numNodes: number);
-}
-
-export interface GroupNode extends Node {
-  /**
-   * The metagraph contains nodes and metaedges between the immediate children
-   * of this group. The node label objects may be other GroupNodes (like
-   * SeriesNodes and Metanodes) or individual OpNodes. All edge label objects
-   * are Metaedges, each of which contains references to the original
-   * BaseEdge(s) from which it was created.
-   */
-  metagraph: graphlib.Graph<GroupNode|OpNode, Metaedge>;
-
-  /**
-   * The bridgegraph contains only edges which link immediate children of this
-   * group with nodes outside of the metagraph. As in the metagraph, all edge
-   * label objects are Metaedges which contain references to the original
-   * BaseEdge(s) that contribute to it.
-   *
-   * For a Metaedge in the bridgegraph, its external endpoint will be the same
-   * as the metagraph edge from which it came. This is most easily explained
-   * by example.
-   *
-   * Consider an original graph that contains a BaseEdge A/B/C->Z/Y/X.
-   *
-   *     +-------+    (BaseEdge)     +-------+
-   *     | A/B/C |>----------------->| Z/Y/X |
-   *     +-------+                   +-------+
-   *
-   * When we construct the Root's metagraph, it will contain nodes for A and Z,
-   * and a Metaedge A->Z. The A->Z Metaedge will contain the original BaseEdge
-   * A/B/C->Z/Y/X in its baseEdgeGraph. The Root's bridgegraph will always be
-   * empty.
-   *
-   *     +---+    (Root.metagraph edge)    +---+
-   *     | A |>--------------------------->| Z |
-   *     +---+                             +---+
-   *
-   * Now consider the Metanode A. Its metagraph will contain a Metanode for A/B
-   * and no edges. A's bridgegraph will have one Metaedge from A/B->Z, which
-   * was derived from the Root's Metaedge A->Z. That Metaedge will contain the
-   * original BaseEdge in its baseEdgeGraph.
-   *
-   *     +---------+
-   *     | A       |
-   *     |  +---+  |   (A.bridgegraph edge)    +---+
-   *     |  | B |>---------------------------->| Z |
-   *     |  +---+  |                           +---+
-   *     +---------+
-   *
-   * Finally, consider the Metanode A/B. Its metagraph will contain a Metanode
-   * for A/B/C and again no edges. A/B's bridgegraph will have one Metaedge
-   * from A/B/C->Z, which was derived from A's bridgegraph Metaedge A/B->Z.
-   * As before, the A/B/C->Z Metaedge will contain the original BaseEdge in its
-   * baseEdgeGraph.
-   *
-   *     +---------------+
-   *     | A             |
-   *     |  +---------+  |
-   *     |  | B       |  |
-   *     |  |  +---+  |  |   (A/B.bridgegraph edge)      +---+
-   *     |  |  | C |>----------------------------------->| Z |
-   *     |  |  +---+  |  |                               +---+
-   *     |  +---------+  |
-   *     +---------------+
-   *
-   * Likewise, under the Metanode Z and Z/Y, to compute the bridgegraph, we'll
-   * end up with Metaedges A->Z/Y and A->Z/Y/X respectively. So the original
-   * BaseEdge A/B/C->Z/Y/X becomes four different Metaedges in four different
-   * bridgegraphs:
-   *
-   *   + A/B->Z in GroupNode A's bridgegraph,
-   *   + A/B/C->Z in GroupNode A/B's bridgegraph,
-   *   + A->Z/Y in GroupNode Z's bridgegraph, and
-   *   + A->Z/Y/X in GroupNode Z/Y's bridgegraph.
-   *
-   * Considering any BaseEdge then, if N is the number of path segments in the
-   * source and M is the number of path segments in the destination, then the
-   * total number of bridgegraph edges you could create would be (N-1)(M-1).
-   *
-   * For this reason, it is computationally expensive to generate all the
-   * bridgegraphs for all the Metanodes, and instead they should be computed
-   * on demand as needed.
-   */
-  bridgegraph: graphlib.Graph<GroupNode|OpNode, Metaedge>;
-
-  /**
-   * Stores how many times each device name appears in its children
-   * op nodes. Used to color group nodes by devices.
-   */
-  deviceHistogram: {[device: string]: number};
-
-  /**
-   * Flag indicating whether this GroupNode's metagraph contains any edges that
-   * are not control edges. Used to quickly determine how to draw a collapsed
-   * series (vertically or horizontally).
-   */
-  hasNonControlEdges: boolean;
-}
-
-export interface Metanode extends GroupNode {
-  depth: number;
-  templateId: string;
-  opHistogram: {[op: string]: number};
-  getFirstChild(): GroupNode|OpNode;
-  getRootOp(): OpNode;
-  /** Return name of all leaves inside a metanode. */
-  leaves(): string[];
-}
-
-export interface SeriesNode extends GroupNode {
-  hasLoop: boolean;
-  prefix: string;
-  suffix: string;
-  clusterId: number;
-  ids: number[];
-  parent: string;
-}
-
-export class EllipsisNodeImpl implements EllipsisNode {
-  name: string;
-  numMoreNodes: number;
-  stats: NodeStats;
-  type: NodeType;
-  isGroupNode: boolean;
-  cardinality: number;
-  parentNode: Node;
-  include: InclusionType;
-  nodeAttributes: {[key: string]: any;};
-  /**
-   * Constructs a new ellipsis annotation node.
-   *
-   * @param numNodes The number of additional annotations this node represents.
-   */
-  constructor(numNodes: number) {
-    this.type = NodeType.ELLIPSIS;
-    this.isGroupNode = false;
-    this.cardinality = 1;
-    this.parentNode = null;
-    this.stats = null;
-    this.setNumMoreNodes(numNodes);
-    this.include = InclusionType.UNSPECIFIED;
-  }
-
-  setNumMoreNodes(numNodes: number) {
-    this.numMoreNodes = numNodes;
-    this.name = '... ' + numNodes + ' more';
-  }
-};
-
-/**
- * A label object for nodes in the full graph and leaf nodes in the render
- * graph.
- */
-export class OpNodeImpl implements OpNode {
-  name: string;
-  op: string;
-  device: string;
-  stats: NodeStats;
-  attr: {key: string, value: any}[];
-  inputs: NormalizedInput[];
-  type: NodeType;
-  isGroupNode: boolean;
-  cardinality: number;
-  inEmbeddings: OpNode[];
-  outEmbeddings: OpNode[];
-  parentNode: Node;
-  include: InclusionType;
-  owningSeries: string;
-  outputShapes: TensorShape[];
-  nodeAttributes: {[key: string]: any;};
-  xlaCluster: string;
-
-  /**
-   * Constructs a new Op node.
-   *
-   * @param rawNode The raw node.
-   */
-  constructor(rawNode: tf.graph.proto.NodeDef) {
-    this.op = rawNode.op;
-    this.name = rawNode.name;
-    this.device = rawNode.device;
-    this.attr = rawNode.attr;
-    // An array of normalized inputs that denote the incoming edges to
-    // the current node. Each input contains the normalized name of the
-    // source node, whether it has a number part and whether it is a
-    // control dependency.
-    this.inputs = normalizeInputs(rawNode.input);
-    this.outputShapes = extractOutputShapes(rawNode.attr);
-    this.xlaCluster = extractXlaCluster(rawNode.attr);
-    // additional properties
-    this.type = NodeType.OP;
-    this.isGroupNode = false;
-    this.cardinality = 1;
-    this.inEmbeddings = [];
-    this.outEmbeddings = [];
-    this.parentNode = null;
-    this.include = InclusionType.UNSPECIFIED;
-    this.owningSeries = null;
-  }
-};
-
-export function createMetanode(name: string, opt = {}): Metanode {
-  return new MetanodeImpl(name, opt);
-}
-
-/**
- * Joins the information from the stats file (memory, compute time) with the
- * graph information.
- */
-export function joinStatsInfoWithGraph(
-    graph: SlimGraph, stats: tf.graph.proto.StepStats,
-    devicesForStats?: {[device: string]: boolean}): void {
-  // Reset stats for each node.
-  _.each(graph.nodes, node => { node.stats = null; });
-
-  _.each(stats.dev_stats, devStats => {
-    // Ignore devices that are not selected.
-    if (devicesForStats && !devicesForStats[devStats.device]) {
-      return;
-    }
-    _.each(devStats.node_stats, nodeStats => {
-      // Lookup the node in the graph by its original name, e.g. A. If not
-      // found, lookup by the rewritten name A/(A) in case the name is both
-      // a namespace and a node name.
-      let nodeName = nodeStats.node_name in graph.nodes ? nodeStats.node_name :
-                                                          nodeStats.node_name +
-              NAMESPACE_DELIM + '(' + nodeStats.node_name + ')';
-
-      // Couldn't find a matching node.
-      if (!(nodeName in graph.nodes)) {
-        return;
-      }
-
-      // Compute the total bytes used.
-      let totalBytes = 0;
-      if (nodeStats.memory) {
-        _.each(nodeStats.memory, alloc => {
-        if (alloc.total_bytes) {
-            if (alloc.total_bytes > 0) {
-              totalBytes += Number(alloc.total_bytes);
-            } else {
-              /* tslint:disable */
-              console.log(
-                  'ignoring negative memory allocation for ' + nodeName);
-              /* tslint:enable */
-            }
-          }
-        });
-      }
-      let outputSize: number[][] = null;
-      if (nodeStats.output) {
-        outputSize = _.map(nodeStats.output, output => {
-          return _.map(output.tensor_description.shape.dim,
-              dim => Number(dim.size));
-        });
-      }
-      graph.nodes[nodeName].device = devStats.device;
-      if (graph.nodes[nodeName].stats == null) {
-        graph.nodes[nodeName].stats = new NodeStats(outputSize);
-      }
-      graph.nodes[nodeName].stats.addBytesAllocation(totalBytes);
-      if (nodeStats.all_end_rel_micros) {
-        if (nodeStats.all_end_rel_micros > 0) {
-          graph.nodes[nodeName].stats.addExecutionTime(
-              nodeStats.all_start_micros,
-              nodeStats.all_start_micros + nodeStats.all_end_rel_micros);
-        } else {
-          /* tslint:disable */
-          console.log('ignoring negative runtime for ' + nodeName);
-          /* tslint:enable */
-        }
-      }
-    });
-  });
-}
-
-/**
- * Execution stats for the node.
- */
-export class NodeStats {
-  constructor(outputSize: number[][]) { this.outputSize = outputSize; }
-
-  /**
-   * Add the start and end time for a particular kernel execution of this op.
-   * Ops can have multiple kernel executions within the same session run.
-   */
-  addExecutionTime(startTime: number, endTime: number) {
-    if (this.startTime != null) {
-      this.startTime = Math.min(this.startTime, startTime);
-    } else {
-      this.startTime = startTime;
-    }
-    if (this.endTime != null) {
-      this.endTime = Math.max(this.endTime, endTime);
-    } else {
-      this.endTime = endTime;
-    }
-  }
-
-  /**
-   * Add the bytes allocated for a particular kernel execution of this op.
-   * Ops can have multiple kernel executions within the same session run.
-   */
-  addBytesAllocation(totalBytes: number) {
-    if (this.totalBytes != null) {
-      this.totalBytes = Math.max(this.totalBytes, totalBytes);
-    } else {
-      this.totalBytes = totalBytes;
-    }
-  }
-
-  /**
-   * Absolute start time for the very first kernel execution of this op.
-   */
-  startTime: number;
-  /**
-   * Absolute end time for the very last kernel execution of this op.
-   */
-  endTime: number;
-  /**
-   * Total number of bytes used for the node. Sum of all children
-   * if it is a Group node.
-   */
-  totalBytes = 0;
-
-  /**
-   * The shape of each output tensors, if there are any.
-   * Empty if it is a Group node.
-   */
-  outputSize: number[][];
-
-  /**
-   * Combines the specified stats with the current stats.
-   * Modifies the current object. This method is used to
-   * compute aggregate stats for group nodes.
-   */
-  combine(stats: NodeStats): void {
-    if (stats.totalBytes != null) {
-      this.totalBytes += stats.totalBytes;
-    }
-    if (stats.getTotalMicros() != null) {
-      this.addExecutionTime(stats.startTime, stats.endTime);
-    }
-  }
-
-  /**
-   * Total number of compute time in microseconds used for the node.
-   * Sum of all children if it is a Group node. Null if it is unknown.
-   * This method can not be scaffolded under a getter attribute because
-   * ECMAScript 5 does not support getter attributes.
-   */
-  getTotalMicros(): number {
-    if (this.startTime == null || this.endTime == null) {
-      return null;
-    }
-    return this.endTime - this.startTime;
-  }
-}
-
-export class MetanodeImpl implements Metanode {
-  name: string;
-  stats: NodeStats;
-  type: NodeType;
-  depth: number;
-  isGroupNode: boolean;
-  cardinality: number;
-  metagraph: graphlib.Graph<GroupNode|OpNode, Metaedge>;
-  bridgegraph: graphlib.Graph<GroupNode|OpNode, Metaedge>;
-  templateId: string;
-  opHistogram: {[op: string]: number};
-  deviceHistogram: {[op: string]: number};
-  parentNode: Node;
-  hasNonControlEdges: boolean;
-  include: InclusionType;
-  nodeAttributes: {[key: string]: any;};
-
-  /** A label object for meta-nodes in the graph hierarchy */
-  constructor(name: string, opt = {}) {
-    this.name = name;
-    this.type = NodeType.META;
-    /** number of levels under this group */
-    this.depth = 1;
-    this.isGroupNode = true;
-    /** # of leaf nodes (including embedded ones) */
-    this.cardinality = 0;
-    /** graph contains metanodes, nodes, edges
-     * and metaedges for main items within this metanode
-     */
-    this.metagraph =
-      createGraph<GroupNode|OpNode, Metaedge>(name, GraphType.META, opt);
-    /** bridgegraph must be constructed lazily-see hierarchy.getBridgegraph() */
-    this.bridgegraph = null;
-    /**
-     * A dictionary that count ops type of nodes in this metanode
-     * (op type => count).
-     */
-    this.opHistogram = {};
-    this.deviceHistogram = {};
-    /** unique id for a metanode of similar subgraph */
-    this.templateId = null;
-    /** Metanode which contains this node, if any */
-    this.parentNode = null;
-    this.hasNonControlEdges = false;
-    this.include = InclusionType.UNSPECIFIED;
-  }
-
-  getFirstChild(): GroupNode|OpNode {
-    return this.metagraph.node(this.metagraph.nodes()[0]);
-  }
-
-  /**
-   * Returns the op node associated with the metanode.
-   * For example, if the metanode is 'sgd', the associated
-   * op node is sgd/(sgd).
-   */
-  getRootOp(): OpNode {
-    let nameSplit = this.name.split('/');
-    let rootOpName = this.name + '/(' + nameSplit[nameSplit.length - 1] + ')';
-    return <OpNode>this.metagraph.node(rootOpName);
-  }
-
-  /**
-   * Return an array of the names of all the leaves (non-GroupNodes) inside
-   * this metanode. This performs a breadth-first search of the tree, so
-   * immediate child leaves will appear earlier in the output array than
-   * descendant leaves.
-   */
-  leaves(): string[] {
-    let leaves = [];
-    let queue = [<Node> this];
-    let metagraph; // Defined here due to a limitation of ES6->5 compilation.
-    while (queue.length) {
-      let node = queue.shift();
-      if (node.isGroupNode) {
-        metagraph = (<GroupNode> node).metagraph;
-        _.each(metagraph.nodes(), name => queue.push(metagraph.node(name)));
-      } else {
-        leaves.push(node.name);
-      }
-    }
-    return leaves;
-  }
-};
-
-export interface Metaedge extends graphlib.EdgeObject {
-
-  /**
-   * Stores the original BaseEdges represented by this Metaedge.
-   */
-  baseEdgeList: BaseEdge[];
-
-  /**
-   * Whether this edge represents a relationship that is inbound (or outbound)
-   * to the object which contains this information. For example, in a Metanode's
-   * bridgegraph, each edge connects an immediate child to something outside
-   * the Metanode. If the destination of the edge is inside the Metanode, then
-   * its inbound property should be true. If the destination is outside the
-   * Metanode, then its inbound property should be false.
-   *
-   * The property is optional because not all edges can be described as
-   * inbound/outbound. For example, in a Metanode's metagraph, all of the edges
-   * connect immediate children of the Metanode. None should have an inbound
-   * property, or they should be null/undefined.
-   */
-  inbound?: boolean;
-
-  /**
-   * Number of regular edges (not control dependency edges).
-   */
-  numRegularEdges: number;
-
-  /**
-   * Number of control dependency edges.
-   */
-  numControlEdges: number;
-
-  /**
-   * Number of reference edges, which is an edge to an operation
-   * that takes a reference to its input and changes its value.
-   */
-  numRefEdges: number;
-
-  /**
-   * Total size (number of units) of all the tensors flowing through this edge.
-   */
-  totalSize: number;
-
-  addBaseEdge(edge: BaseEdge, h: hierarchy.Hierarchy): void;
-}
-
-export function createMetaedge(v: string, w: string): Metaedge {
-  return new MetaedgeImpl(v, w);
-}
-
-/**
- * A label object for edges between metanodes of subgraphs in the render graph.
- */
-export class MetaedgeImpl implements Metaedge {
-  v: string;
-  w: string;
-  baseEdgeList: BaseEdge[];
-  inbound: boolean;
-  numRegularEdges: number;
-  numControlEdges: number;
-  numRefEdges: number;
-  totalSize: number;
-
-  constructor(v: string, w: string) {
-    this.v = v;
-    this.w = w;
-    this.baseEdgeList = [];
-    this.inbound = null;
-    this.numRegularEdges = 0;
-    this.numControlEdges = 0;
-    this.numRefEdges = 0;
-    this.totalSize = 0;
-  }
-
-  addBaseEdge(edge: BaseEdge, h: hierarchy.Hierarchy): void {
-    this.baseEdgeList.push(edge);
-    if (edge.isControlDependency) {
-      this.numControlEdges += 1;
-    } else {
-      this.numRegularEdges += 1;
-    }
-    if (edge.isReferenceEdge) {
-      this.numRefEdges += 1;
-    }
-    // Compute the size of the tensor flowing through this
-    // base edge.
-    this.totalSize += MetaedgeImpl.computeSizeOfEdge(edge, h);
-    h.maxMetaEdgeSize = Math.max(h.maxMetaEdgeSize, this.totalSize);
-  }
-
-  private static computeSizeOfEdge(edge: BaseEdge, h: hierarchy.Hierarchy):
-      number {
-    let opNode = <OpNode> h.node(edge.v);
-    if (opNode.outputShapes == null) {
-      // No shape information. Asssume a single number. This gives
-      // a lower bound for the total size.
-      return 1;
-    }
-    h.hasShapeInfo = true;
-    // Sum the sizes of all output tensors.
-    return _(opNode.outputShapes).map(shape => {
-      // If the shape is unknown, treat it as 1 when computing
-      // total size. This gives a lower bound for the total size.
-      if (shape == null) {
-        return 1;
-      }
-      // Multiply all shapes to get the total size of the tensor.
-      // E.g. The total size of [4, 2, 1] is 4 * 2 * 1.
-      return _(shape).reduce((accumulated, currSize) => {
-        // If this particular dimension is unknown, treat
-        // it as 1 when computing total size. This gives a lower bound
-        // for the total size.
-        if (currSize === -1) {
-          currSize = 1;
-        }
-        return accumulated * currSize;
-      }, 1);
-    }).sum();
-  }
-}
-
-export function createSeriesNode(prefix: string, suffix: string,
-    parent: string, clusterId: number, name: string): SeriesNode {
-  return new SeriesNodeImpl(prefix, suffix, parent, clusterId, name);
-}
-
-export function getSeriesNodeName(prefix: string, suffix: string,
-    parent: string, startId?: number, endId?: number): string {
-  let numRepresentation =
-      (typeof startId !== 'undefined' && typeof endId !== 'undefined') ?
-      '[' + startId + '-' + endId + ']' :
-      '#';
-  let pattern = prefix + numRepresentation + suffix;
-  return (parent ? parent + '/' : '') + pattern;
-}
-
-class SeriesNodeImpl implements SeriesNode {
-  name: string;
-  type: NodeType;
-  stats: NodeStats;
-  hasLoop: boolean;
-  prefix: string;
-  suffix: string;
-  clusterId: number;
-  ids: number[];
-  parent: string;
-  isGroupNode: boolean;
-  cardinality: number;
-  metagraph: graphlib.Graph<GroupNode|OpNode, Metaedge>;
-  bridgegraph: graphlib.Graph<GroupNode|OpNode, Metaedge>;
-  parentNode: Node;
-  deviceHistogram: {[op: string]: number};
-  hasNonControlEdges: boolean;
-  include: InclusionType;
-  nodeAttributes: {[key: string]: any;};
-
-  constructor(prefix: string, suffix: string, parent: string,
-      clusterId: number, name: string) {
-    this.name = name || getSeriesNodeName(prefix, suffix, parent);
-    this.type = NodeType.SERIES;
-    this.hasLoop = false;
-    this.prefix = prefix;
-    this.suffix = suffix;
-    this.clusterId = clusterId;
-    this.ids = [];
-    this.parent = parent;
-    this.isGroupNode = true;
-    this.cardinality = 0;
-    this.metagraph = createGraph<Metanode, Metaedge>(name, GraphType.SERIES);
-    // bridgegraph must be constructed lazily-see hierarchy.getBridgegraph()
-    this.bridgegraph = null;
-    this.parentNode = null;
-    this.deviceHistogram = {};
-    this.hasNonControlEdges = false;
-    this.include = InclusionType.UNSPECIFIED;
-  }
-}
-
-/**
- * Extracts the shapes of the output tensors from the attr property in the
- * node proto.
- */
-// tslint:disable-next-line:no-any
-function extractOutputShapes(attr: Array<{key: string, value: any}>):
-    TensorShape[] {
-  let result = null;
-  // We don't know anything about the output tensors.
-  if (!attr) {
-    return null;
-  }
-  for (let i = 0; i < attr.length; i++) {
-    let {key, value} = attr[i];
-    if (key === OUTPUT_SHAPES_KEY) {
-      if (!value.list.shape) {
-        // The OUTPUT_SHAPES_KEY lacks a value. We know nothing about the shape.
-        return null;
-      }
-
-      // Map all output tensors into array of numbers denoting their shape.
-      let result = value.list.shape.map(shape => {
-        if (shape.unknown_rank) {
-          // This output tensor is of unknown rank. We don't know if it is a
-          // scalar, or a tensor, or of what shape it is.
-          return null;
-        }
-        if (shape.dim == null ||
-            (shape.dim.length === 1 && shape.dim[0].size == null)) {
-          // This output tensor is a scalar.
-          return [];
-        }
-        // This output tensor has a known rank. Map each dimension size
-        // into a number.
-        return shape.dim.map(dim => {
-          // Size can be -1 if this particular dimension is unknown.
-          return dim.size;
-        });
-      });
-      // Since we already processed it, remove the entry from the attribute
-      // list (saves memory).
-      attr.splice(i, 1);
-      return result;
-    }
-  }
-  // We didn't find OUTPUT_SHAPES_KEY in attributes, so we don't know anything
-  // about the output tensors.
-  return null;
-}
-
-/**
- * Extracts the XLA Cluster that an op runs on from the attrs of the OpNode.
- * @param attr The attr property.
- * @return A string that is the name of the cluster. Or null if it could not be
- *     determined.
- */
-// tslint:disable-next-line:no-any
-function extractXlaCluster(attr: Array<{key: string, value: any}>): string|
-    null {
-  if (!attr) {
-    return null;
-  }
-
-  // Find the attribute for XLA cluster if there is one.
-  for (let i = 0; i < attr.length; i++) {
-    if (attr[i].key === _XLA_CLUSTER_KEY) {
-      return attr[i].value['s'] || null;
-    }
-  }
-  return null;
-}
-
-/**
- * Normalizes the inputs and extracts associated metadata:
- * 1) Inputs can contain a colon followed by a number at the end
- *    (e.g. inputName:1) and we remove this from the input name, and take note
- *    that the input was numbered.
- * 2) Control dependency inputs contain caret at the beginning and we
- *    remove this and annotate the edge as a control dependency.
- * @param inputs Array of unnormalized names of input nodes.
- */
-function normalizeInputs(inputs: string[]): NormalizedInput[] {
-  let normalizedInputs: NormalizedInput[] = [];
-  _.each(inputs, inputName => {
-    let start = inputName[0] === '^';
-    let colon = inputName.lastIndexOf(':');
-    let end = colon !== -1 &&
-      inputName.length - colon > 1 &&
-      !(/\D/).test(inputName.substring(colon + 1)) ?
-      colon : inputName.length;
-    let name = inputName.substring(start ? 1 : 0, end);
-    if (normalizedInputs.length === 0 ||
-      name !== normalizedInputs[normalizedInputs.length - 1].name) {
-      normalizedInputs.push({
-        name: name,
-        outputTensorIndex:
-            end === inputName.length ? 0 : Number(inputName.slice(colon + 1)),
-        isControlDependency: start
-      });
-    }
-  });
-  return normalizedInputs;
-}
-
-function addEdgeToGraph(
-    graph: SlimGraph, inputName: string, outputNode: OpNode,
-    input: NormalizedInput, params: BuildParams, index: number) {
-  // Don't allow loops in the graph.
-  if (inputName === outputNode.name) {
-    return;
-  }
-  // Check if this op type and input number corresponds to a
-  // reference edge using the refEdges dictionary in the params.
-  let isRefEdge = params.refEdges[outputNode.op + ' ' + index] === true;
-  graph.edges.push({
-    v: inputName,
-    w: outputNode.name,
-    outputTensorIndex: input.outputTensorIndex,
-    isControlDependency: input.isControlDependency,
-    isReferenceEdge: isRefEdge
-  });
-}
-
-export function build(
-    rawNodes: tf.graph.proto.NodeDef[], params: BuildParams,
-    tracker: ProgressTracker): Promise<SlimGraph|void> {
-  /**
-   * A dictionary that maps each in-embedding node name to the node
-   * object.
-   */
-  let inEmbedding: {[nodeName: string]: OpNode} = {};
-  /**
-   * A dictionary that maps each out-embedding node name to the node
-   * object.
-   */
-  let outEmbedding: {[nodeName: string]: OpNode} = {};
-  /**
-   * A dictionary that maps each node name to an array of the node's
-   * out-embedding node label objects.
-   */
-  let outEmbeddings: {[inputName: string]: OpNode[]} = {};
-  let isInEmbeddedPred = getEmbedPredicate(params.inEmbeddingTypes);
-  let isOutEmbeddedPred = getEmbedPredicate(params.outEmbeddingTypes);
-  let embeddingNodeNames: string[] = [];
-  /**
-   * A list of all the non-embedding node names which appear in the processed
-   * list of raw nodes. Here we pre-allocate enough room for all the rawNodes,
-   * even though there will some number of embeddings. The excess array length
-   * is spliced off later.
-   *
-   * Experimentation shows that around 30% of the array will go unused, and
-   * even for very large networks that amounts to less than 10k spaces.
-   */
-  let nodeNames = new Array<string>(rawNodes.length);
-
-  return tf.graph.util
-      .runAsyncTask(
-          'Normalizing names', 30,
-          () => {
-            let opNodes = new Array<OpNode>(rawNodes.length);
-            let index = 0;
-            _.each(rawNodes, rawNode => {
-              let opNode = new OpNodeImpl(rawNode);
-              if (isInEmbeddedPred(opNode)) {
-                embeddingNodeNames.push(opNode.name);
-                inEmbedding[opNode.name] = opNode;
-                return;
-              }
-
-              if (isOutEmbeddedPred(opNode)) {
-                embeddingNodeNames.push(opNode.name);
-                outEmbedding[opNode.name] = opNode;
-                _.each(opNode.inputs, input => {
-                  let inputName = input.name;
-                  outEmbeddings[inputName] = outEmbeddings[inputName] || [];
-                  outEmbeddings[inputName].push(opNode);
-                });
-                return;
-              }
-              // The node is not an embedding, so add it to the names and nodes
-              // lists.
-              opNodes[index] = opNode;
-              nodeNames[index] = opNode.name;
-              index++;
-            });
-            opNodes.splice(index);
-            nodeNames.splice(index);
-            return opNodes;
-          },
-          tracker)
-      .then((opNodes) => {
-        // Create the graph data structure from the graphlib library.
-        return tf.graph.util.runAsyncTask(
-            'Building the data structure', 70, () => {
-              let normalizedNameDict =
-                  mapStrictHierarchy(nodeNames, embeddingNodeNames);
-              let graph = new SlimGraph;
-
-              // Add the nodes to the graph.
-              _.each(opNodes, opNode => {
-                let normalizedName =
-                    normalizedNameDict[opNode.name] || opNode.name;
-                graph.nodes[normalizedName] = opNode;
-                // Check if the node has out-embeddings. If yes, add them to the
-                // node.
-                if (opNode.name in outEmbeddings) {
-                  opNode.outEmbeddings = outEmbeddings[opNode.name];
-                  // Normalize the names of the out-embeddings.
-                  _.each(opNode.outEmbeddings, node => {
-                    node.name = normalizedNameDict[node.name] || node.name;
-                  });
-                }
-                // Update the name of the node.
-                opNode.name = normalizedName;
-              });
-
-              // Visit each node's inputs to add the edges to the graph. If the
-              // input
-              // is an in-embedding, then add it to the node's in-embeddings
-              // instead.
-              _.each(opNodes, opNode => {
-                _.each(opNode.inputs, (input, i) => {
-                  let inputName = input.name;
-                  if (inputName in inEmbedding) {
-                    let inEmbedNode = inEmbedding[inputName];
-                    opNode.inEmbeddings.push(inEmbedNode);
-                    // Move the inputs of the in-embedding node into incoming
-                    // edges of
-                    // the main node. E.g. the control dependency of a constant
-                    // node
-                    // should be moved to the op node where the constant is
-                    // embedded.
-                    for (let embedInput of inEmbedNode.inputs) {
-                      addEdgeToGraph(
-                          graph, normalizedNameDict[embedInput.name] ||
-                              embedInput.name,
-                          opNode, embedInput, params, i);
-                    }
-                  } else if (inputName in outEmbedding) {
-                    // Move the inputs of the out-embedding node into inputs of
-                    // the main node where the out-embedding points to.
-                    let outEmbedNode = outEmbedding[inputName];
-                    for (let embedInput of outEmbedNode.inputs) {
-                      addEdgeToGraph(
-                          graph, normalizedNameDict[embedInput.name] ||
-                              embedInput.name,
-                          opNode, input, params, i);
-                    }
-                  } else {
-                    addEdgeToGraph(
-                        graph, normalizedNameDict[inputName] || inputName,
-                        opNode, input, params, i);
-                  }
-                });
-              });
-
-              // Normalize the names of in-embeddings.
-              _.each(inEmbedding, (node, name) => {
-                node.name = normalizedNameDict[node.name] || node.name;
-              });
-
-              return graph;
-            }, tracker);
-      });
-};
-
-/**
- * Create a new graphlib.Graph() instance with default parameters
- */
-export function createGraph<N, E>(name: string, type, opt = {}):
-    graphlib.Graph<N, E> {
-  let graph = new graphlib.Graph<N, E>(opt);
-  graph.setGraph({
-    name: name,
-    rankdir: 'BT',  // BT,TB,LR,RL
-    type: type
-  });
-  return graph;
-};
-
-/**
- * Create a predicate for checking whether a node should be embedded based on
- * the specified types.
- */
-function getEmbedPredicate(types: string[]) {
-  return function(node: OpNode) {
-    // check types
-    for (let i = 0; i < types.length; i++) {
-      let regExp = new RegExp(types[i]);
-      if (node.op.match(regExp)) { return true; }
-    }
-    return false;
-  };
-};
-
-/**
- * Returns a strict node name (name => name/(name)) to avoid conflicts
- * where the node name is also a namespace.
- */
-export function getStrictName(name: string): string {
-  let parts = name.split(NAMESPACE_DELIM);
-  return name + NAMESPACE_DELIM + '(' + parts[parts.length - 1] + ')';
-}
-
-/**
- * For each op node (embedding or non-embedding), rename it if there is a
- * non-embedding node under its namespace. For example, assume node name 'A'.
- * If there is a non-embedding node under its namespace (e.g. 'A/B'), 'A' will
- * be renamed to 'A/(A)'. Then the namespace 'A' will contain 2 nodes: '(A)'
- * and 'B'. If all the nodes under 'A' are embedding nodes (e.g. constant and
- * summary), keep 'A' as an Op node and don't create a namespace.
- *
- * @param nodeNames An array of regular (non-embedding) node names.
- * @param embeddingNodeNames An array of embedding node names.
- * @return Dictionary object mapping names that need to be renamed to
- *     new names.
- */
-function mapStrictHierarchy(nodeNames: string[],
-    embeddingNodeNames: string[]): {[oldName: string]: string} {
-  /** Dictionary that maps the old new to the new name */
-  let newNameDictionary: {[oldName: string]: string} = {};
-  /** Set used to store all namespaces. */
-  let namespaceSet: {[namespace: string]: boolean} = {};
-  // sort the nodes to make prefix check faster
-  nodeNames.sort();
-  // look for nodes with a prefix a,a/b -> a/(a),a/b
-  for (let i = 0; i < nodeNames.length - 1; ++i) {
-    let a = nodeNames[i];
-    // Get all the parent namespaces of the current node
-    // and add them in the namespace set.
-    _.each(getHierarchicalPath(a).slice(0, -1), ns => {
-      namespaceSet[ns] = true;
-    });
-    for (let j = i + 1; j < nodeNames.length; ++j) {
-      let b = nodeNames[j];
-      if (_.startsWith(b, a)) {
-        if (b.length > a.length && b.charAt(a.length) === NAMESPACE_DELIM) {
-          newNameDictionary[a] = getStrictName(a);
-          break;
-        }
-      } else {
-        break;
-      }
-    }
-  }
-  // Go through all the embedding node names and rename them in case they
-  // collide with namespaces.
-  _.each(embeddingNodeNames, embeddingName => {
-    if (embeddingName in namespaceSet) {
-      // Rename to follow strict hierarchy.
-      newNameDictionary[embeddingName] = getStrictName(embeddingName);
-    }
-  });
-  return newNameDictionary;
-};
-
-/**
- * Returns a list of the degrees of each node in the graph.
- */
-function degreeSequence(graph: graphlib.Graph<any, any>): number[] {
-  let degrees = graph.nodes().map(function(name) {
-    return graph.neighbors(name).length;
-  });
-  degrees.sort();
-  return degrees;
-};
-
-/**
- * Returns if the degree sequence of the two graphs is the same.
- */
-export function hasSimilarDegreeSequence(graph1: graphlib.Graph<any, any>,
-    graph2: graphlib.Graph<any, any>): boolean {
-  let dg1 = degreeSequence(graph1);
-  let dg2 = degreeSequence(graph2);
-
-  for (let i = 0; i < dg1.length; i++) {
-    if (dg1[i] !== dg2[i]) {
-      return false;
-    }
-  }
-  return true;
-};
-
-/**
- * Returns the hierarchical path of the current node, based on the node's name.
- * For example, if the name is 'a/b/c', the returned path is
- * ['a', 'a/b', 'a/b/c'].
- */
-export function getHierarchicalPath(name: string,
-  seriesNames?: { [name: string]: string }): string[] {
-  let path: string[] = [];
-  let i = name.indexOf(NAMESPACE_DELIM);
-  // Push all parent portions of the path.
-  while (i >= 0) {
-    path.push(name.substring(0, i));
-    i = name.indexOf(NAMESPACE_DELIM, i + 1);
-  }
-  // If the node's path is under a series, then add the series node name to the
-  // hierarchical path as the parent of the leaf.
-  if (seriesNames) {
-    let seriesName = seriesNames[name];
-    if (seriesName) {
-      path.push(seriesName);
-    }
-  }
-  // Push the leaf of the path.
-  path.push(name);
-  return path;
-};
-
-/**
- * Returns the string for the node inclusion toggle button, dependant
- * on the provided current InclusionType.
- */
-export function getIncludeNodeButtonString(include: InclusionType) {
-  if (include === tf.graph.InclusionType.EXCLUDE) {
-    return 'Add to main graph';
-  } else {
-    return 'Remove from main graph';
-  }
-};
-
-/**
- * Returns the string for the series node grouping toggle button, dependant
- * on the provided current SeriesGroupingType.
- */
-export function getGroupSeriesNodeButtonString(group: SeriesGroupingType) {
-  if (group === tf.graph.SeriesGroupingType.GROUP) {
-    return 'Ungroup this series of nodes';
-  } else {
-    return 'Group this series of nodes';
-  }
-};
-
-/**
- * Toggle the node series grouping option in the provided map, setting it
- * to ungroup if the series is not already in the map.
- */
-export function toggleNodeSeriesGroup(
-  map: { [name: string]: tf.graph.SeriesGroupingType }, name: string) {
-  if (!(name in map) || map[name] === tf.graph.SeriesGroupingType.GROUP) {
-    map[name] = tf.graph.SeriesGroupingType.UNGROUP;
-  } else {
-    map[name] = tf.graph.SeriesGroupingType.GROUP;
-  }
-};
-
-} // close module tf.graph
diff --git a/tensorflow/tensorboard/components/tf_graph_common/hierarchy.ts b/tensorflow/tensorboard/components/tf_graph_common/hierarchy.ts
deleted file mode 100644
index 889607ac50..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/hierarchy.ts
+++ /dev/null
@@ -1,807 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-/**
- * Package for the Graph Hierarchy for TensorFlow graph.
- */
-module tf.graph.hierarchy {
-
-/**
- * Class used as output for getPredecessors and getSuccessors methods
- */
-export interface Edges {
-  control: Metaedge[];
-  regular: Metaedge[];
-}
-
-export interface Hierarchy {
-  root: Metanode;
-  templates: {[templateId: string]: string[]};
-  /** List of all device names */
-  devices: string[];
-  /** List of all XLA cluster names */
-  xlaClusters: string[];
-  /** True if at least one tensor in the graph has shape information */
-  hasShapeInfo: boolean;
-  /** The maximum size across all meta edges. Used for scaling thickness. */
-  maxMetaEdgeSize: number;
-  getNodeMap(): {[nodeName: string]: GroupNode|OpNode};
-  node(name: string): GroupNode|OpNode;
-  setNode(name: string, node: GroupNode|OpNode): void;
-  getBridgegraph(nodeName: string): graphlib.Graph<GroupNode|OpNode, Metaedge>;
-  getPredecessors(nodeName: string): Edges;
-  getSuccessors(nodeName: string): Edges;
-  getTopologicalOrdering(nodeName: string): { [childName: string]: number };
-  getTemplateIndex(): (string) => number;
-}
-
-/**
- * Class for the Graph Hierarchy for TensorFlow graph.
- */
-class HierarchyImpl implements Hierarchy {
-  root: Metanode;
-  templates: {[templateId: string]: string[]};
-  private index: {[nodeName: string]: GroupNode|OpNode};
-  devices: string[];
-  xlaClusters: string[];
-  hasShapeInfo = false;
-  maxMetaEdgeSize = 1;
-  orderings: { [nodeName: string]: { [childName: string]: number } };
-
-  constructor() {
-    this.root = createMetanode(ROOT_NAME, {compound: true});
-    this.templates = null;
-    this.devices = null;
-    /**
-     * @type {Object} Dictionary object that maps node name to the node
-     * (could be op-node, metanode, or series-node)
-     */
-    this.index = {};
-    this.index[ROOT_NAME] = this.root;
-    this.orderings = {};
-  }
-
-  getNodeMap(): {[nodeName: string]: GroupNode|OpNode} {
-    return this.index;
-  }
-
-  node(name: string): GroupNode|OpNode {
-    return this.index[name];
-  }
-
-  setNode(name: string, node: GroupNode|OpNode): void {
-    this.index[name] = node;
-  }
-
-  /**
-   * Given the name of a node in this hierarchy, get its bridgegraph, creating
-   * it on the fly if necessary. If the node is not a GroupNode, then this
-   * method returns null. If the provided name does not map to a node in the
-   * hierarchy, an error will be thrown.
-   */
-  getBridgegraph(nodeName: string): graphlib.Graph<GroupNode|OpNode, Metaedge> {
-    let node = this.index[nodeName];
-    if (!node) {
-      throw Error('Could not find node in hierarchy: ' + nodeName);
-    }
-    if (!('metagraph' in node)) {
-      return null;
-    }
-    let groupNode = <GroupNode> node;
-    if (groupNode.bridgegraph) {
-      return groupNode.bridgegraph;
-    }
-    let bridgegraph = groupNode.bridgegraph =
-        createGraph<GroupNode|OpNode, Metaedge>(
-            'BRIDGEGRAPH', GraphType.BRIDGE);
-    if (!node.parentNode || !('metagraph' in node.parentNode)) {
-      return bridgegraph;
-    }
-
-    let parentNode = <GroupNode>node.parentNode;
-    let parentMetagraph = parentNode.metagraph;
-    let parentBridgegraph = this.getBridgegraph(parentNode.name);
-
-    // For each of the parent node's two Metaedge containing graphs, process
-    // each Metaedge involving this node.
-    _.each([parentMetagraph, parentBridgegraph], parentGraph => {
-      _(parentGraph.edges())
-        .filter(e => e.v === nodeName || e.w === nodeName)
-        .each(parentEdgeObj => {
-
-          let inbound = parentEdgeObj.w === nodeName;
-          let parentMetaedge = parentGraph.edge(parentEdgeObj);
-
-          // The parent's Metaedge represents some number of underlying
-          // BaseEdges from the original full graph. For each of those, we need
-          // to determine which immediate child is involved and make sure
-          // there's a Metaedge in the bridgegraph that covers it.
-          _.each(parentMetaedge.baseEdgeList, baseEdge => {
-
-            // Based on the direction, figure out which is the descendant node
-            // and which is the 'other' node (sibling of parent or ancestor).
-            let [descendantName, otherName] =
-              inbound ?
-                [baseEdge.w, parentEdgeObj.v] :
-                [baseEdge.v, parentEdgeObj.w];
-
-            // Determine the immediate child containing this descendant node.
-            let childName = this.getChildName(nodeName, descendantName);
-
-            // Look for an existing Metaedge in the bridgegraph (or create a
-            // new one) that covers the relationship between child and other.
-            let bridgeEdgeObj = <graphlib.EdgeObject> {
-              v: inbound ? otherName : childName,
-              w: inbound ? childName : otherName,
-            };
-            let bridgeMetaedge = bridgegraph.edge(bridgeEdgeObj);
-            if (!bridgeMetaedge) {
-              bridgeMetaedge = createMetaedge(bridgeEdgeObj.v, bridgeEdgeObj.w);
-              bridgeMetaedge.inbound = inbound;
-              bridgegraph.setEdge(bridgeEdgeObj.v, bridgeEdgeObj.w,
-                  bridgeMetaedge);
-            }
-
-            // Copy the BaseEdge from the parent's Metaedge into this
-            // bridgegraph Metaedge.
-            bridgeMetaedge.addBaseEdge(baseEdge, this);
-          });
-        })
-        .value(); // force lodash chain execution.
-    });
-
-    return bridgegraph;
-  }
-
-  /**
-   * Utility function for determining the name of the immediate child under a
-   * node for a given descendant path. If the descendant corresponds to no
-   * immediate child, an error is thrown.
-   */
-  getChildName(nodeName: string, descendantName: string): string {
-    // Walk up the hierarchy from the descendant to find the child.
-    let currentNode: Node = this.index[descendantName];
-    while (currentNode) {
-      if (currentNode.parentNode && currentNode.parentNode.name === nodeName) {
-        return currentNode.name;
-      }
-      currentNode = currentNode.parentNode;
-    }
-    throw Error(
-        'Could not find immediate child for descendant: ' + descendantName);
-  };
-
-  /** Given the name of a node, return its incoming metaedges. */
-  getPredecessors(nodeName: string): Edges {
-    let node = this.index[nodeName];
-    if (!node) {
-      throw Error('Could not find node with name: ' + nodeName);
-    }
-
-    let predecessors = this.getOneWayEdges(node, true);
-    // Add embedded predecessors, such as constants.
-    if (!node.isGroupNode) {
-      _.each((<OpNode>node).inEmbeddings, embeddedNode => {
-        _.each((<OpNode>node).inputs, input => {
-          if (input.name === embeddedNode.name) {
-            // Make a new metaedge holding the edge between the
-            // node and the in-embedding.
-            let metaedge = new MetaedgeImpl(embeddedNode.name, nodeName);
-            metaedge.addBaseEdge(
-                {
-                  isControlDependency: input.isControlDependency,
-                  outputTensorIndex: input.outputTensorIndex,
-                  isReferenceEdge: false,
-                  v: embeddedNode.name,
-                  w: nodeName
-                },
-                this);
-            predecessors.regular.push(metaedge);
-          }
-        });
-      });
-    }
-    return predecessors;
-  }
-
-  /**
-   * Given the name of a node, return its outgoing metaedges.
-   *
-   * This is the inverse of getPredecessors(). See that method's documentation
-   * for an in-depth example.
-   */
-  getSuccessors(nodeName: string): Edges {
-    let node = this.index[nodeName];
-    if (!node) {
-      throw Error('Could not find node with name: ' + nodeName);
-    }
-
-    let successors = this.getOneWayEdges(node, false);
-
-    // Add embedded successors, such as summaries.
-    if (!node.isGroupNode) {
-      _.each((<OpNode>node).outEmbeddings, embeddedNode => {
-        _.each(embeddedNode.inputs, input => {
-          if (input.name === nodeName) {
-            // Make a new metaedge holding the edge between the
-            // node and the out-embedding.
-            let metaedge = new MetaedgeImpl(nodeName, embeddedNode.name);
-            metaedge.addBaseEdge(
-                {
-                  isControlDependency: input.isControlDependency,
-                  outputTensorIndex: input.outputTensorIndex,
-                  isReferenceEdge: false,
-                  v: nodeName,
-                  w: embeddedNode.name
-                },
-                this);
-            successors.regular.push(metaedge);
-          }
-        });
-      });
-    }
-    return successors;
-  }
-
-  /** Helper method for getPredecessors and getSuccessors */
-  getOneWayEdges(node: GroupNode|OpNode, inEdges: boolean) {
-    let edges: Edges = {control: [], regular: []};
-    // A node with no parent cannot have any edges.
-    if (!node.parentNode || !node.parentNode.isGroupNode) {
-      return edges;
-    }
-    let parentNode = <GroupNode> node.parentNode;
-    let metagraph = parentNode.metagraph;
-    let bridgegraph = this.getBridgegraph(parentNode.name);
-    findEdgeTargetsInGraph(metagraph, node, inEdges, edges);
-    findEdgeTargetsInGraph(bridgegraph, node, inEdges, edges);
-    return edges;
-  }
-
-  /**
-   * For a given GroupNode, get or calculate an object which describes a
-   * topological ordering of child nodes within that GroupNode's metagraph.
-   *
-   * This ordering is used when rendering bridge control edges which are
-   * sometimes backwards relative to the dataflow.
-   *
-   * For example, say we have a graph with two edges A->B and A->C, and we're
-   * interested in the ordering under ROOT. In this case, any of the following
-   * would be legitimate return values:
-   *
-   *  - { 'A': 0, 'B': 1, 'C': 2 } -- most likely
-   *  - { 'A': 0, 'B': 2, 'C': 1 } -- less likely
-   *  - { 'A': 12, 'B': 100, 'C': 99 } -- unlikely, but still OK
-   *
-   * The algorithm does not guarantee that all numbers from 0-N (where N is
-   * the number of nodes) appear exactly once. Rather it guarantees that if
-   * there is a path between two nodes, the earlier one will have a lower
-   * number in the ordering hash.
-   *
-   * When generating the ordering, we ignore control Metaedges (those which
-   * represent only BaseEdges that have isControlDependency set to true).
-   *
-   * If there is no node with the specified name, an error is thrown. If the
-   * node with the specified name is not a group node, null is returned.
-   */
-  getTopologicalOrdering(nodeName: string): { [childName: string]: number } {
-    let node = this.index[nodeName];
-    if (!node) {
-      throw Error('Could not find node with name: ' + nodeName);
-    }
-    if (!node.isGroupNode) {
-      return null;
-    }
-    if (nodeName in this.orderings) {
-      return this.orderings[nodeName];
-    }
-
-    // Mapping of a child node names to lists of their successors.
-    let successors: { [childName: string]: string[] } = {};
-
-    // Set of node names which have appeared as a destination.
-    let destinations: { [childName: string]: boolean } = {};
-
-    let metagraph = (<GroupNode> node).metagraph;
-    _.each(metagraph.edges(), (e: graphlib.EdgeObject) => {
-      if (!metagraph.edge(e).numRegularEdges) {
-        return; // Skip control edges.
-      }
-
-      // Keep track of successors and destinations.
-      if (!(e.v in successors)) {
-        successors[e.v] = [];
-      }
-      successors[e.v].push(e.w);
-      destinations[e.w] = true;
-    });
-
-    // Seed the queue with true sources (those that are not destinations).
-    let queue: string[] =
-      _.difference(_.keys(successors), _.keys(destinations));
-
-    // Produce an ordering by traversing the graph breadth first.
-    let ordering = this.orderings[nodeName] = {};
-    let index = 0;
-    while (queue.length) {
-      let childName = queue.shift();
-      ordering[childName] = index++;
-      _.each(successors[childName], succName => queue.push(succName));
-      delete successors[childName]; // Prevent cycles from infinite looping.
-    }
-    return ordering;
-  }
-
-  /**
-   * Returns a d3 Ordinal function that can be used to look up the index of
-   * a node based on its template id.
-   */
-  getTemplateIndex(): (string) => number {
-    let templateNames = d3.keys(this.templates);
-    let templateIndex = d3.scaleOrdinal()
-        .domain(templateNames)
-        .range(d3.range(0, templateNames.length));
-    return (templateId: string) => <number>templateIndex(templateId);
-  }
-}
-
-/**
- * Internal utility function - given a graph (should be either a metagraph or a
- * bridgegraph) and a node which is known to be in that graph, determine
- * the other ends of edges that involve that node in the direction specified
- * by whether it's inbound.
- *
- * For example if you wanted to find the predecessors of a node, you'd call
- * this method for the parent's metagraph and bridgegraph, specifying inbound
- * as true (look at the source of inbound edges to the specified node).
- *
- * Discovered target names are appended to the targets array.
- */
-function findEdgeTargetsInGraph(
-    graph: graphlib.Graph<GroupNode|OpNode, Metaedge>,
-    node: Node, inbound: boolean, targets: Edges): void {
-  let edges = inbound ? graph.inEdges(node.name) : graph.outEdges(node.name);
-  _.each(edges, e => {
-    let metaedge = graph.edge(e);
-    let targetList =
-        metaedge.numRegularEdges ? targets.regular : targets.control;
-    targetList.push(metaedge);
-  });
-}
-
-export interface HierarchyParams {
-  verifyTemplate: boolean;
-  seriesNodeMinSize: number;
-  seriesMap: { [name: string]: tf.graph.SeriesGroupingType };
-}
-
-/**
- * @param graph The raw graph.
- * @param params Parameters used when building a hierarchy.
- */
-export function build(graph: tf.graph.SlimGraph, params: HierarchyParams,
-    tracker: ProgressTracker): Promise<Hierarchy|void> {
-  let h = new HierarchyImpl();
-  let seriesNames: { [name: string]: string } = {};
-  return tf.graph.util
-      .runAsyncTask(
-          'Adding nodes', 20,
-          () => {
-            // Get all the possible device and XLA cluster names.
-            let deviceNames = {};
-            let xlaClusterNames = {};
-            _.each(graph.nodes, (node, nodeName) => {
-              if (node.device) {
-                deviceNames[node.device] = true;
-              }
-
-              if (node.xlaCluster) {
-                xlaClusterNames[node.xlaCluster] = true;
-              }
-            });
-
-            h.devices = _.keys(deviceNames);
-            h.xlaClusters = _.keys(xlaClusterNames);
-
-            addNodes(h, graph);
-          },
-          tracker)
-      .then(() => {
-        return tf.graph.util.runAsyncTask('Detect series', 20, () => {
-          if (params.seriesNodeMinSize > 0) {
-            groupSeries(
-                h.root, h, seriesNames, params.seriesNodeMinSize,
-                params.seriesMap);
-          }
-        }, tracker);
-      })
-      .then(() => {
-        return tf.graph.util.runAsyncTask('Adding edges', 30, () => {
-          addEdges(h, graph, seriesNames);
-        }, tracker);
-      })
-      .then(() => {
-        return tf.graph.util.runAsyncTask(
-            'Finding similar subgraphs', 30, () => {
-              h.templates = template.detect(h, params.verifyTemplate);
-            }, tracker);
-      })
-      .then(() => {
-        return h;
-      });
-};
-
-export function joinAndAggregateStats(
-    h: Hierarchy, stats: tf.graph.proto.StepStats) {
-  // Get all the possible device names.
-  let deviceNames = {};
-  _.each(h.root.leaves(), nodeName => {
-    let leaf = <OpNode> h.node(nodeName);
-    if (leaf.device != null) {
-      deviceNames[leaf.device] = true;
-    }
-  });
-  h.devices = _.keys(deviceNames);
-
-  // Reset stats for each group node.
-  _.each(h.getNodeMap(), (node, nodeName) => {
-    if (node.isGroupNode) {
-      node.stats = new NodeStats(null);
-      (<GroupNode>node).deviceHistogram = {};
-    }
-  });
-
-  // Bubble-up the stats and device distribution from leaves to parents.
-  _.each(h.root.leaves(), nodeName => {
-    let leaf = <OpNode> h.node(nodeName);
-    let node = <GroupNode|OpNode> leaf;
-    while (node.parentNode != null) {
-      if (leaf.device != null) {
-        let deviceHistogram = (<GroupNode>node.parentNode).deviceHistogram;
-        deviceHistogram[leaf.device] = (deviceHistogram[leaf.device] || 0) + 1;
-      }
-      if (leaf.stats != null) {
-        node.parentNode.stats.combine(leaf.stats);
-      }
-      node = <GroupNode> node.parentNode;
-    }
-  });
-}
-
-/**
- * Creates the metanodes in the hierarchical graph and assigns parent-child
- * relationship between them.
- */
-function addNodes(h: Hierarchy, graph: SlimGraph) {
-  _.each(graph.nodes, (node, nodeName) => {
-    let path = getHierarchicalPath(node.name);
-    let parent: Metanode = h.root;
-
-    parent.depth = Math.max(path.length, parent.depth);
-
-    // Create parent metanodes for each depth. For example if the node name
-    // is 'a/b/c', then create metanodes 'a' and 'a/b', where 'a/b' is a child
-    // of a.
-    for (let i = 0; i < path.length; i++) {
-      parent.depth = Math.max(parent.depth, path.length - i);
-      parent.cardinality += node.cardinality;
-      parent.opHistogram[node.op] = (parent.opHistogram[node.op] || 0) + 1;
-      if (node.device != null) {
-        parent.deviceHistogram[node.device] =
-            (parent.deviceHistogram[node.device] || 0) + 1;
-      }
-      if (i === path.length - 1) { break; }
-      let name = path[i];
-      let child = <Metanode>h.node(name);
-      if (!child) {
-        child = createMetanode(name);
-        child.parentNode = parent;
-        h.setNode(name, child);
-        parent.metagraph.setNode(name, child);
-      }
-      parent = child;
-    }
-    // Assuming node name is 'a/b/c', assign the OpNode as a child of the
-    // metanode 'a/b'.
-    h.setNode(node.name, node);
-    node.parentNode = parent;
-    parent.metagraph.setNode(node.name, node);
-
-    // Add each of the in-embeddings and out-embeddings in the hierarchy.
-    _.each(node.inEmbeddings, function(embedding) {
-      h.setNode(embedding.name, embedding);
-      embedding.parentNode = node;
-    });
-    _.each(node.outEmbeddings, function(embedding) {
-      h.setNode(embedding.name, embedding);
-      embedding.parentNode = node;
-    });
-  });
-};
-
-/**
- * For each metanode in the hierarchical graph, this method adds:
- * the edges in the metagraph. These are edges between nodes
- * that share the same parent.
- */
-function addEdges(h: Hierarchy, graph: SlimGraph,
-    seriesNames: { [name: string]: string }) {
-
-  let nodeIndex = h.getNodeMap();
-
-  // Ancestor paths for the source and destination nodes of an edge. These are
-  // reused for each edge rather than allocating new ones. It's about 10% faster
-  // than allocating new ones on each pass through the loop.
-  let sourcePath: string[] = [];
-  let destPath: string[] = [];
-
-  // Insert the ancestor path for a node into the provided array, including the
-  // node itself. Return the index of the last node inserted (always ROOT).
-  let getPath = (node: Node, path: string[]): number => {
-    let i = 0;
-    while (node) {
-      path[i++] = node.name;
-      node = node.parentNode;
-    }
-    return i - 1;
-  };
-
-  _.each(graph.edges, baseEdge => {
-
-    // Get the hierarchical paths for the source and destination of the edge.
-    let sourceAncestorIndex = getPath(graph.nodes[baseEdge.v], sourcePath);
-    let destAncestorIndex = getPath(graph.nodes[baseEdge.w], destPath);
-
-    // If the hierarchical path cannot be found for either endpoint, then we
-    // cannot create the edge. This happens for example when a node has a
-    // control dependency on a summary node, which are embedded.
-    if (sourceAncestorIndex === -1 || destAncestorIndex === -1) {
-      return;
-    }
-
-    // Find the lowest shared ancestor between source and dest by looking for
-    // the highest nodes that differ between their ancestor paths.
-    while (sourcePath[sourceAncestorIndex] === destPath[destAncestorIndex]) {
-      sourceAncestorIndex--;
-      destAncestorIndex--;
-      if (sourceAncestorIndex < 0 || destAncestorIndex < 0) {
-        // This would only occur if the two nodes were the same (a cycle in the
-        // graph), or if one endpoint was a strict ancestor of the other. The
-        // latter shouldn't happen because we rename nodes which are both
-        // metanodes and op nodes. E.g. 'A/B' becomes 'A/B/(B)'.
-        throw Error('No difference found between ancestor paths.');
-      }
-    }
-
-    let sharedAncestorNode =
-      <GroupNode>nodeIndex[sourcePath[sourceAncestorIndex + 1]];
-    let sourceAncestorName = sourcePath[sourceAncestorIndex];
-    let destAncestorName = destPath[destAncestorIndex];
-
-    // Find or create the Metaedge which should contain this BaseEdge inside
-    // the shared ancestor.
-    let metaedge =
-      sharedAncestorNode.metagraph.edge(sourceAncestorName, destAncestorName);
-    if (!metaedge) {
-      metaedge = createMetaedge(sourceAncestorName, destAncestorName);
-      sharedAncestorNode.metagraph
-        .setEdge(sourceAncestorName, destAncestorName, metaedge);
-    }
-    if (!sharedAncestorNode.hasNonControlEdges &&
-        !baseEdge.isControlDependency) {
-      sharedAncestorNode.hasNonControlEdges = true;
-    }
-    metaedge.addBaseEdge(baseEdge, h);
-  });
-};
-
-/**
- * Using the hierarchy template information, detect series in the provided
- * metanode.  For each detected series, create a new SeriesNode
- * and remove series members from the metanode's metagraph and move them to
- * the new series node's metagraph.
- *
- * @param metanode
- * @param hierarchy
- * @param seriesNames Map of node names to their series they are contained in.
- *     This should be provided empty and is populated by this method.
- * @param threshold If the series has this many nodes or more, then group them
- *     into a series.
- * @param map Map of series names to their series grouping type, if one has
- *     been set.
- * @return A dictionary from node name to series node name that contains the
- *     node.
- */
-function groupSeries(metanode: Metanode, hierarchy: Hierarchy,
-    seriesNames: { [name: string]: string }, threshold: number,
-    map: { [name: string]: tf.graph.SeriesGroupingType }) {
-  let metagraph = metanode.metagraph;
-  _.each(metagraph.nodes(), n => {
-    let child = metagraph.node(n);
-    if (child.type === tf.graph.NodeType.META) {
-      groupSeries(<Metanode>child, hierarchy, seriesNames, threshold, map);
-    }
-  });
-
-  let clusters = clusterNodes(metagraph);
-  let seriesDict = detectSeries(clusters, metagraph);
-
-  // Add each series node to the graph and add its grouped children to its own
-  // metagraph.
-  _.each(seriesDict, function(seriesNode: SeriesNode, seriesName: string) {
-    let nodeMemberNames = seriesNode.metagraph.nodes();
-    _.each(nodeMemberNames, n => {
-      let child = <OpNode>metagraph.node(n);
-      if (!child.owningSeries) {
-        child.owningSeries = seriesName;
-      }
-    });
-    // If the series contains less than the threshold number of nodes and
-    // this series has not been adding to the series map, then set this
-    // series to be shown ungrouped in the map.
-    if (nodeMemberNames.length < threshold && !(seriesNode.name in map)) {
-      map[seriesNode.name] = tf.graph.SeriesGroupingType.UNGROUP;
-    }
-    // If the series is in the map as ungrouped then do not group the series.
-    if (seriesNode.name in map
-      && map[seriesNode.name] === tf.graph.SeriesGroupingType.UNGROUP) {
-      return;
-    }
-    hierarchy.setNode(seriesName, seriesNode); // add to the index
-    metagraph.setNode(seriesName, seriesNode);
-    _.each(nodeMemberNames, n => {
-      let child = <OpNode> metagraph.node(n);
-      seriesNode.metagraph.setNode(n, child);
-      seriesNode.parentNode = child.parentNode;
-      seriesNode.cardinality++;
-      if (child.device != null) {
-        seriesNode.deviceHistogram[child.device] =
-            (seriesNode.deviceHistogram[child.device] || 0) + 1;
-      }
-      child.parentNode = seriesNode;
-      seriesNames[n] = seriesName;
-      // Remove now-grouped node from its original parent's metagraph.
-      metagraph.removeNode(n);
-    });
-  });
-};
-
-/** cluster op-nodes with similar op */
-function clusterNodes(metagraph: graphlib.Graph<GroupNode|OpNode, Metaedge>):
-    {[clusterId: string]: string[]} {
-  let result: {[clusterId: string]: string[]} = {};
-  return  _.reduce(metagraph.nodes(),
-      (clusters: {[clusterId: string]: string[]}, n: string) => {
-    let child = metagraph.node(n);
-    if (child.type === NodeType.META) {
-      // skip metanodes
-      return clusters;
-    }
-    let template = (<OpNode>child).op;
-    if (template) {
-      clusters[template] = clusters[template] || [];
-      clusters[template].push(child.name);
-    }
-    return clusters;
-  }, result);
-}
-
-/**
- * For each cluster of op-nodes based op type, try to detect groupings.
- * Infer series name using by trying to find pattern '<number>' in the node
- * name.
- *
- * @param clusters Dictionary output from clusterNodes().
- * @param metagraph
- * @return A dictionary from series name => seriesNode
- */
-function detectSeries(clusters: {[clusterId: string]: string[]},
-     metagraph: graphlib.Graph<GroupNode|OpNode, Metaedge>):
-     {[seriesName: string]: SeriesNode} {
-  let seriesDict: {[seriesName: string]: SeriesNode} = {};
-  _.each(clusters, function(members, clusterId: string) {
-    if (members.length <= 1) { return; } // isolated clusters can't make series
-
-    /** @type {Object}  A dictionary mapping seriesName to seriesInfoArray,
-     * which is an array that contains objects with name, id, prefix, suffix,
-     * and parent properties.
-     */
-    let candidatesDict: {[seriesName: string]: SeriesNode[]} = {};
-
-    // Group all nodes that have the same name, with the exception of a
-    // number at the end of the name after an underscore, which is allowed to
-    // vary.
-    _.each(members, function(name: string) {
-      let isGroup = name.charAt(name.length - 1) === '*';
-      let namepath = name.split('/');
-      let leaf = namepath[namepath.length - 1];
-      let parent = namepath.slice(0, namepath.length - 1).join('/');
-      let matches = leaf.match(/^(\D*)_(\d+)$/);
-
-      let prefix;
-      let id;
-      let suffix = '';
-      if (matches) {         // if found '<number>' in the name, assign id.
-        prefix = matches[1]; // the front non-numeric characters
-        id = matches[2]; // the digits
-      } else {  // for node without '_<number>', make them zero-th items.
-        prefix = isGroup ? leaf.substr(0, leaf.length - 1) : leaf;
-        id = 0;
-        suffix = isGroup ? '*' : '';
-      }
-      let seriesName = getSeriesNodeName(prefix, suffix, parent);
-      candidatesDict[seriesName] = candidatesDict[seriesName] || [];
-      let seriesNode = createSeriesNode(prefix, suffix, parent, +id, name);
-      candidatesDict[seriesName].push(seriesNode);
-    });
-
-    // In each group of nodes, group nodes in bunches that have monotonically
-    // increasing numbers in their names.  Each of these bunches is a series.
-    _.each(candidatesDict, function(seriesInfoArray: SeriesNode[], seriesName) {
-      if (seriesInfoArray.length < 2) {
-        return;
-      }
-      seriesInfoArray.sort(function(a, b) {
-        return (+a.clusterId) - (+b.clusterId);
-      });
-
-      // Loop through the nodes sorted by its detected series number, grouping
-      // all nodes with monotonically-increasing series numbers.
-      let seriesNodes = [seriesInfoArray[0]];
-      for (let index = 1; index < seriesInfoArray.length; index++) {
-        let nextNode = seriesInfoArray[index];
-        if (nextNode.clusterId === seriesNodes[seriesNodes.length - 1].clusterId
-            + 1) {
-          seriesNodes.push(nextNode);
-          continue;
-        }
-        addSeriesToDict(seriesNodes, seriesDict, +clusterId, metagraph);
-        seriesNodes = [nextNode];
-      }
-      addSeriesToDict(seriesNodes, seriesDict, +clusterId, metagraph);
-    });
-  });
-  return seriesDict;
-}
-
-/**
- * Add a series to the provided dictionary mapping series names to series.
- *
- * @param seriesNodes the nodes in the series. Contains
- *     name, id, prefix, suffix and parent properties of the node.
- * @param seriesDict the dictionary of series
- * @param clusterId ID of the template of the nodes of the series
- * @param metagraph
- */
-function addSeriesToDict(seriesNodes: SeriesNode[],
-    seriesDict: {[seriesName: string]: SeriesNode},
-    clusterId: number,
-    metagraph: graphlib.Graph<GroupNode|OpNode, Metaedge>) {
-  if (seriesNodes.length > 1) {
-    let curSeriesName = getSeriesNodeName(
-      seriesNodes[0].prefix, seriesNodes[0].suffix,
-      seriesNodes[0].parent, seriesNodes[0].clusterId,
-      seriesNodes[seriesNodes.length - 1].clusterId);
-    let curSeriesNode = createSeriesNode(seriesNodes[0].prefix,
-      seriesNodes[0].suffix, seriesNodes[0].parent, clusterId,
-      curSeriesName);
-    _.each(seriesNodes, function(node) {
-      curSeriesNode.ids.push(node.clusterId);
-      curSeriesNode.metagraph.setNode(node.name, metagraph.node(node.name));
-    });
-    seriesDict[curSeriesName] = curSeriesNode;
-  }
-}
-
-} // close module tf.graph.hierarchy
diff --git a/tensorflow/tensorboard/components/tf_graph_common/layout.ts b/tensorflow/tensorboard/components/tf_graph_common/layout.ts
deleted file mode 100644
index 1019e4f269..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/layout.ts
+++ /dev/null
@@ -1,760 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph.layout {
-
-/** Set of parameters that define the look and feel of the graph. */
-export const PARAMS = {
-  animation: {
-    /** Default duration for graph animations in ms. */
-    duration: 250
-  },
-  graph: {
-    /** Graph parameter for metanode. */
-    meta: {
-      /**
-       * Dagre's nodesep param - number of pixels that
-       * separate nodes horizontally in the layout.
-       *
-       * See https://github.com/cpettitt/dagre/wiki#configuring-the-layout
-       */
-      nodeSep: 5,
-      /**
-       * Dagre's ranksep param - number of pixels
-       * between each rank in the layout.
-       *
-       * See https://github.com/cpettitt/dagre/wiki#configuring-the-layout
-       */
-      rankSep: 25,
-      /**
-       * Dagre's edgesep param - number of pixels that separate
-       * edges horizontally in the layout.
-       */
-      edgeSep: 5,
-    },
-    /** Graph parameter for metanode. */
-    series: {
-      /**
-       * Dagre's nodesep param - number of pixels that
-       * separate nodes horizontally in the layout.
-       *
-       * See https://github.com/cpettitt/dagre/wiki#configuring-the-layout
-       */
-      nodeSep: 5,
-      /**
-       * Dagre's ranksep param - number of pixels
-       * between each rank in the layout.
-       *
-       * See https://github.com/cpettitt/dagre/wiki#configuring-the-layout
-       */
-      rankSep: 25,
-      /**
-       * Dagre's edgesep param - number of pixels that separate
-       * edges horizontally in the layout.
-       */
-      edgeSep: 5
-    },
-    /**
-     * Padding is used to correctly position the graph SVG inside of its parent
-     * element. The padding amounts are applied using an SVG transform of X and
-     * Y coordinates.
-     */
-    padding: {paddingTop: 40, paddingLeft: 20}
-  },
-  subscene: {
-    meta: {
-      paddingTop: 10,
-      paddingBottom: 10,
-      paddingLeft: 10,
-      paddingRight: 10,
-      /**
-       * Used to leave room for the label on top of the highest node in
-       * the core graph.
-       */
-      labelHeight: 20,
-      /** X-space between each extracted node and the core graph. */
-      extractXOffset: 15,
-      /** Y-space between each extracted node. */
-      extractYOffset: 20
-    },
-    series: {
-      paddingTop: 10,
-      paddingBottom: 10,
-      paddingLeft: 10,
-      paddingRight: 10,
-      labelHeight: 10
-    }
-  },
-  nodeSize: {
-    /** Size of meta nodes. */
-    meta: {
-      radius: 5,
-      width: 60,
-      maxLabelWidth: 52,
-      /** A scale for the node's height based on number of nodes inside */
-      // Hack - set this as an any type to avoid issues in exporting a type
-      // from an external module.
-      height: (d3 as any).scaleLinear().domain([1, 200]).range([15, 60]).clamp(true),
-      /** The radius of the circle denoting the expand button. */
-      expandButtonRadius: 3
-    },
-    /** Size of op nodes. */
-    op: {
-      width: 15,
-      height: 6,
-      radius: 3,  // for making annotation touching ellipse
-      labelOffset: -8,
-      maxLabelWidth: 30
-    },
-    /** Size of series nodes. */
-    series: {
-      expanded: {
-        // For expanded series nodes, width and height will be
-        // computed to account for the subscene.
-        radius: 10,
-        labelOffset: 0,
-      },
-      vertical: {
-        // When unexpanded, series whose underlying metagraphs contain
-        // one or more non-control edges will show as a vertical stack
-        // of ellipses.
-        width: 16,
-        height: 13,
-        labelOffset: -13,
-      },
-      horizontal: {
-        // When unexpanded, series whose underlying metagraphs contain
-        // no non-control edges will show as a horizontal stack of
-        // ellipses.
-        width: 24,
-        height: 8,
-        radius: 10,  // Forces annotations to center line.
-        labelOffset: -10,
-      },
-    },
-    /** Size of bridge nodes. */
-    bridge: {
-      // NOTE: bridge nodes will normally be invisible, but they must
-      // take up some space so that the layout step leaves room for
-      // their edges.
-      width: 20,
-      height: 20,
-      radius: 2,
-      labelOffset: 0
-    }
-  },
-  shortcutSize: {
-    /** Size of shortcuts for op nodes */
-    op: {width: 10, height: 4},
-    /** Size of shortcuts for meta nodes */
-    meta: {width: 12, height: 4, radius: 1},
-    /** Size of shortcuts for series nodes */
-    series: {
-      width: 14,
-      height: 4,
-    }
-  },
-  annotations: {
-    /** Maximum possible width of the bounding box for in annotations */
-    inboxWidth: 50,
-    /** Maximum possible width of the bounding box for out annotations */
-    outboxWidth: 50,
-    /** X-space between the shape and each annotation-node. */
-    xOffset: 10,
-    /** Y-space between each annotation-node. */
-    yOffset: 3,
-    /** X-space between each annotation-node and its label. */
-    labelOffset: 2,
-    /** Defines the max width for annotation label */
-    maxLabelWidth: 120
-  },
-  constant: {size: {width: 4, height: 4}},
-  series: {
-    /** Maximum number of repeated item for unexpanded series node. */
-    maxStackCount: 3,
-    /**
-     * Positioning offset ratio for collapsed stack
-     * of parallel series (series without edges between its members).
-     */
-    parallelStackOffsetRatio: 0.2,
-    /**
-     * Positioning offset ratio for collapsed stack
-     * of tower series (series with edges between its members).
-     */
-    towerStackOffsetRatio: 0.5
-  },
-  minimap: {
-    /** The maximum width/height the minimap can have. */
-    size: 150
-  }
-};
-
-/** Calculate layout for a scene of a group node. */
-export function layoutScene(renderNodeInfo: render.RenderGroupNodeInfo): void {
-  // Update layout, size, and annotations of its children nodes and edges.
-  if (renderNodeInfo.node.isGroupNode) {
-    layoutChildren(renderNodeInfo);
-  }
-
-  // Update position of its children nodes and edges
-  if (renderNodeInfo.node.type === NodeType.META) {
-    layoutMetanode(renderNodeInfo);
-  } else if (renderNodeInfo.node.type === NodeType.SERIES) {
-    layoutSeriesNode(renderNodeInfo);
-  }
-};
-
-/**
- * Updates the total width of an unexpanded node which includes the size of its
- * in and out annotations.
- */
-function updateTotalWidthOfNode(renderInfo: render.RenderNodeInfo): void {
-  renderInfo.inboxWidth = renderInfo.inAnnotations.list.length > 0 ?
-      PARAMS.annotations.inboxWidth : 0;
-  renderInfo.outboxWidth = renderInfo.outAnnotations.list.length > 0 ?
-      PARAMS.annotations.outboxWidth : 0;
-  // Assign the width of the core box (the main shape of the node).
-  renderInfo.coreBox.width = renderInfo.width;
-  renderInfo.coreBox.height = renderInfo.height;
-  // TODO(jimbo): Account for font width rather than using a magic number.
-  let labelLength = renderInfo.node.name.length -
-      renderInfo.node.name.lastIndexOf(NAMESPACE_DELIM) - 1;
-  let charWidth = 3; // 3 pixels per character.
-  // Compute the total width of the node.
-  renderInfo.width = Math.max(renderInfo.coreBox.width +
-      renderInfo.inboxWidth + renderInfo.outboxWidth,
-      labelLength * charWidth);
-
-}
-
-/**
- * Update layout, size, and annotations of its children nodes and edges.
- */
-function layoutChildren(renderNodeInfo: render.RenderGroupNodeInfo): void {
-  let children = renderNodeInfo.coreGraph.nodes().map(n => {
-    return renderNodeInfo.coreGraph.node(n);
-  }).concat(renderNodeInfo.isolatedInExtract,
-      renderNodeInfo.isolatedOutExtract);
-
-  _.each(children, childNodeInfo => {
-    // Set size of each child
-    switch (childNodeInfo.node.type) {
-      case NodeType.OP:
-        _.extend(childNodeInfo, PARAMS.nodeSize.op);
-        break;
-      case NodeType.BRIDGE:
-        _.extend(childNodeInfo, PARAMS.nodeSize.bridge);
-        break;
-      case NodeType.META:
-        if (!childNodeInfo.expanded) {
-          // Set fixed width and scalable height based on cardinality
-          _.extend(childNodeInfo, PARAMS.nodeSize.meta);
-          childNodeInfo.height =
-              PARAMS.nodeSize.meta.height(childNodeInfo.node.cardinality);
-        } else {
-          let childGroupNodeInfo =
-            <render.RenderGroupNodeInfo>childNodeInfo;
-          layoutScene(childGroupNodeInfo); // Recursively layout its subscene.
-        }
-        break;
-      case NodeType.SERIES:
-        if (childNodeInfo.expanded) {
-          _.extend(childNodeInfo, PARAMS.nodeSize.series.expanded);
-          let childGroupNodeInfo =
-            <render.RenderGroupNodeInfo>childNodeInfo;
-          layoutScene(childGroupNodeInfo); // Recursively layout its subscene.
-        } else {
-          let childGroupNodeInfo =
-            <render.RenderGroupNodeInfo>childNodeInfo;
-          let seriesParams =
-            childGroupNodeInfo.node.hasNonControlEdges ?
-              PARAMS.nodeSize.series.vertical :
-              PARAMS.nodeSize.series.horizontal;
-          _.extend(childNodeInfo, seriesParams);
-        }
-        break;
-      default:
-        throw Error('Unrecognized node type: ' + childNodeInfo.node.type);
-    }
-    // Compute total width of un-expanded nodes. Width of expanded nodes
-    // has already been computed.
-    if (!childNodeInfo.expanded) {
-      updateTotalWidthOfNode(childNodeInfo);
-    }
-    // Layout each child's annotations
-    layoutAnnotation(childNodeInfo);
-  });
-}
-
-/**
- * Calculate layout for a graph using dagre
- * @param graph the graph to be laid out
- * @param params layout parameters
- * @return width and height of the core graph
- */
-function dagreLayout(
-    graph: graphlib.Graph<render.RenderNodeInfo, render.RenderMetaedgeInfo>,
-    params): {height: number, width: number} {
-  _.extend(graph.graph(), {
-    nodesep: params.nodeSep,
-    ranksep: params.rankSep,
-    edgesep: params.edgeSep
-  });
-  let bridgeNodeNames = [];
-  let nonBridgeNodeNames = [];
-
-  // Split out nodes into bridge and non-bridge nodes, and calculate the total
-  // width we should use for bridge nodes.
-  _.each(graph.nodes(), nodeName => {
-    let nodeInfo = graph.node(nodeName);
-    if (nodeInfo.node.type === NodeType.BRIDGE) {
-      bridgeNodeNames.push(nodeName);
-    } else {
-      nonBridgeNodeNames.push(nodeName);
-    }
-  });
-
-  // If there are no non-bridge nodes, then the graph has zero size.
-  if (!nonBridgeNodeNames.length) {
-    return {
-      width: 0,
-      height: 0,
-    };
-  }
-  dagre.layout(graph);
-
-  // Calculate the true bounding box of the graph by iterating over nodes and
-  // edges rather than accepting dagre's word for it. In particular, we should
-  // ignore the extra-wide bridge nodes and bridge edges, and allow for
-  // annotation boxes and labels.
-  let minX = Infinity;
-  let minY = Infinity;
-  let maxX = -Infinity;
-  let maxY = -Infinity;
-  _.each(nonBridgeNodeNames, nodeName => {
-    let nodeInfo = graph.node(nodeName);
-    let w = 0.5 * nodeInfo.width;
-    let x1 = nodeInfo.x - w;
-    let x2 = nodeInfo.x + w;
-    minX = x1 < minX ? x1 : minX;
-    maxX = x2 > maxX ? x2 : maxX;
-    // TODO(jimbo): Account for the height of labels above op nodes here.
-    let h = 0.5 * nodeInfo.height;
-    let y1 = nodeInfo.y - h;
-    let y2 = nodeInfo.y + h;
-    minY = y1 < minY ? y1 : minY;
-    maxY = y2 > maxY ? y2 : maxY;
-  });
-  _.each(graph.edges(), edgeObj => {
-    let edgeInfo = graph.edge(edgeObj);
-    if (edgeInfo.structural) {
-      return; // Skip structural edges from min/max calculations.
-    }
-
-    // Since the node size passed to dagre includes the in and out
-    // annotations, the endpoints of the edge produced by dagre may not
-    // point to the actual node shape (rectangle, ellipse). We correct the
-    // end-points by finding the intersection of a line between the
-    // next-to-last (next-to-first) point and the destination (source)
-    // rectangle.
-    let sourceNode = graph.node(edgeInfo.metaedge.v);
-    let destNode = graph.node(edgeInfo.metaedge.w);
-
-    // Straight 3-points edges are special case, since they are curved after
-    // our default correction. To keep them straight, we remove the mid point
-    // and correct the first and the last point to be the center of the
-    // source and destination node respectively.
-    if (edgeInfo.points.length === 3 && isStraightLine(edgeInfo.points)) {
-      if (sourceNode != null) {
-        let cxSource = sourceNode.expanded ?
-            sourceNode.x : computeCXPositionOfNodeShape(sourceNode);
-        edgeInfo.points[0].x = cxSource;
-      }
-      if (destNode != null) {
-        let cxDest = destNode.expanded ?
-            destNode.x : computeCXPositionOfNodeShape(destNode);
-        edgeInfo.points[2].x = cxDest;
-      }
-      // Remove the middle point so the edge doesn't curve.
-      edgeInfo.points = [edgeInfo.points[0], edgeInfo.points[1]];
-    }
-    // Correct the destination endpoint of the edge.
-    let nextToLastPoint = edgeInfo.points[edgeInfo.points.length - 2];
-    // The destination node might be null if this is a bridge edge.
-    if (destNode != null) {
-      edgeInfo.points[edgeInfo.points.length - 1] =
-          intersectPointAndNode(nextToLastPoint, destNode);
-    }
-    // Correct the source endpoint of the edge.
-    let secondPoint = edgeInfo.points[1];
-    // The source might be null if this is a bridge edge.
-    if (sourceNode != null) {
-      edgeInfo.points[0] = intersectPointAndNode(secondPoint, sourceNode);
-    }
-
-    _.each(edgeInfo.points, (point: render.Point) => {
-        minX = point.x < minX ? point.x : minX;
-        maxX = point.x > maxX ? point.x : maxX;
-        minY = point.y < minY ? point.y : minY;
-        maxY = point.y > maxY ? point.y : maxY;
-      });
-  });
-
-  // Shift all nodes and edge points to account for the left-padding amount,
-  // and the invisible bridge nodes.
-  _.each(graph.nodes(), nodeName => {
-    let nodeInfo = graph.node(nodeName);
-    nodeInfo.x -= minX;
-    nodeInfo.y -= minY;
-  });
-  _.each(graph.edges(), edgeObj => {
-    _.each(graph.edge(edgeObj).points, (point: render.Point) => {
-        point.x -= minX;
-        point.y -= minY;
-      });
-  });
-
-  return {
-    width: maxX - minX,
-    height: maxY - minY
-  };
-}
-
-/** Layout a metanode. Only called for an expanded node. */
-function layoutMetanode(renderNodeInfo: render.RenderGroupNodeInfo): void {
-  // First, copy params specific to meta nodes onto this render info object.
-  let params = PARAMS.subscene.meta;
-  _.extend(renderNodeInfo, params);
-  // Invoke dagre.layout() on the core graph and record the bounding box
-  // dimensions.
-  _.extend(renderNodeInfo.coreBox,
-      dagreLayout(renderNodeInfo.coreGraph, PARAMS.graph.meta));
-
-  // Calculate the position of nodes in isolatedInExtract relative to the
-  // top-left corner of inExtractBox (the bounding box for all inExtract nodes)
-  // and calculate the size of the inExtractBox.
-  let maxInExtractWidth = _.max(renderNodeInfo.isolatedInExtract,
-      renderNode => renderNode.width).width;
-  renderNodeInfo.inExtractBox.width = maxInExtractWidth != null ?
-      maxInExtractWidth : 0;
-
-  renderNodeInfo.inExtractBox.height =
-    _.reduce(renderNodeInfo.isolatedInExtract, (height, child, i) => {
-      let yOffset = i > 0 ? params.extractYOffset : 0;
-      // use width/height here to avoid overlaps between extracts
-      child.x = 0;
-      child.y = height + yOffset + child.height / 2;
-      return height + yOffset + child.height;
-    }, 0);
-
-  // Calculate the position of nodes in isolatedOutExtract relative to the
-  // top-left corner of outExtractBox (the bounding box for all outExtract
-  // nodes) and calculate the size of the outExtractBox.
-  let maxOutExtractWidth = _.max(renderNodeInfo.isolatedOutExtract,
-      renderNode => renderNode.width).width;
-  renderNodeInfo.outExtractBox.width = maxOutExtractWidth != null ?
-      maxOutExtractWidth : 0;
-
-  renderNodeInfo.outExtractBox.height =
-    _.reduce(renderNodeInfo.isolatedOutExtract, (height, child, i) => {
-      let yOffset = i > 0 ? params.extractYOffset : 0;
-      // use width/height here to avoid overlaps between extracts
-      child.x = 0;
-      child.y = height + yOffset + child.height / 2;
-      return height + yOffset + child.height;
-    }, 0);
-
-  // Compute the total padding between the core graph, in-extract and
-  // out-extract boxes.
-  let numParts = 0;
-  if (renderNodeInfo.isolatedInExtract.length > 0) {
-    numParts++;
-  }
-  if (renderNodeInfo.isolatedOutExtract.length > 0) {
-    numParts++;
-  }
-  if (renderNodeInfo.coreGraph.nodeCount() > 0) {
-    numParts++;
-  }
-  let offset = PARAMS.subscene.meta.extractXOffset;
-  let padding = numParts <= 1 ? 0 : (numParts  <= 2 ? offset : 2 * offset);
-
-  // Add the in-extract and out-extract width to the core box width.
-  renderNodeInfo.coreBox.width += renderNodeInfo.inExtractBox.width +
-      renderNodeInfo.outExtractBox.width + padding;
-  renderNodeInfo.coreBox.height =
-    params.labelHeight +
-    Math.max(
-      renderNodeInfo.inExtractBox.height,
-      renderNodeInfo.coreBox.height,
-      renderNodeInfo.outExtractBox.height
-  );
-  // Determine the whole metanode's width (from left to right).
-  renderNodeInfo.width = renderNodeInfo.coreBox.width +
-      params.paddingLeft + params.paddingRight;
-
-  // Determine the whole metanode's height (from top to bottom).
-  renderNodeInfo.height =
-      renderNodeInfo.paddingTop +
-      renderNodeInfo.coreBox.height +
-      renderNodeInfo.paddingBottom;
-}
-
-/**
- * Calculate layout for series node's core graph. Only called for an expanded
- * series.
- */
-function layoutSeriesNode(node: render.RenderGroupNodeInfo): void {
-  let graph = node.coreGraph;
-
-  let params = PARAMS.subscene.series;
-  _.extend(node, params);
-
-  // Layout the core.
-  _.extend(node.coreBox, dagreLayout(node.coreGraph, PARAMS.graph.series));
-
-  _.each(graph.nodes(), nodeName => {
-    graph.node(nodeName).excluded = false;
-  });
-
-  // Series do not have in/outExtractBox so no need to include them here.
-  node.width = node.coreBox.width + params.paddingLeft + params.paddingRight;
-  node.height = node.coreBox.height + params.paddingTop + params.paddingBottom;
-}
-
-/**
- * Calculate layout for annotations of a given node.
- * This will modify positions of the given node and its annotations.
- *
- * @see tf.graph.render.Node and tf.graph.render.Annotation
- * for description of each property of each render node.
- *
- */
-function layoutAnnotation(renderNodeInfo: render.RenderNodeInfo): void {
-  // If the render node is an expanded metanode, then its annotations will not
-  // be visible and we should skip the annotation calculations.
-  if (renderNodeInfo.expanded) {
-    return;
-  }
-
-  let inAnnotations = renderNodeInfo.inAnnotations.list;
-  let outAnnotations = renderNodeInfo.outAnnotations.list;
-
-  // Calculate size for in-annotations
-  _.each(inAnnotations, a => sizeAnnotation(a));
-
-  // Calculate size for out-annotations
-  _.each(outAnnotations, a => sizeAnnotation(a));
-
-  let params = PARAMS.annotations;
-
-  // Calculate annotation node position (a.dx, a.dy)
-  // and total height for in-annotations
-  // After this chunk of code:
-  // inboxHeight = sum of annotation heights+ (annotation.length - 1 * yOffset)
-  let inboxHeight = _.reduce(inAnnotations,
-      (height, a, i) => {
-        let yOffset = i > 0 ? params.yOffset : 0;
-        a.dx = -(renderNodeInfo.coreBox.width + a.width) / 2 - params.xOffset;
-        a.dy = height + yOffset + a.height / 2;
-        return height + yOffset + a.height;
-      }, 0);
-
-  _.each(inAnnotations, a => {
-    a.dy -= inboxHeight / 2;
-
-    a.labelOffset = params.labelOffset;
-  });
-
-  // Calculate annotation node position (a.dx, a.dy)
-  // and total height for out-annotations
-  // After this chunk of code:
-  // outboxHeight = sum of annotation heights +
-  //                (annotation.length - 1 * yOffset)
-  let outboxHeight = _.reduce(outAnnotations,
-      (height, a, i) => {
-        let yOffset = i > 0 ? params.yOffset : 0;
-        a.dx = (renderNodeInfo.coreBox.width + a.width) / 2 + params.xOffset;
-        a.dy = height + yOffset + a.height / 2;
-        return height + yOffset + a.height;
-      }, 0);
-
-  _.each(outAnnotations, a => {
-    // adjust by (half of ) the total height
-    // so dy is relative to the host node's center.
-    a.dy -= outboxHeight / 2;
-
-    a.labelOffset = params.labelOffset;
-  });
-
-  // Creating scales for touch point between the in-annotation edges
-  // and their hosts.
-
-  let inTouchHeight =
-      Math.min(renderNodeInfo.height / 2 - renderNodeInfo.radius,
-          inboxHeight / 2);
-  inTouchHeight = inTouchHeight < 0 ? 0 : inTouchHeight;
-
-  let inY = d3.scaleLinear()
-    .domain([0, inAnnotations.length - 1])
-    .range([-inTouchHeight, inTouchHeight]);
-
-  // Calculate annotation edge position
-  _.each(inAnnotations, (a, i) => {
-    a.points = [
-      // The annotation node end
-      {
-        dx: a.dx + a.width / 2,
-        dy: a.dy
-      },
-
-      // The host node end
-      {
-        dx: - renderNodeInfo.coreBox.width / 2,
-        // only use scale if there are more than one,
-        // otherwise center it vertically
-        dy: inAnnotations.length > 1 ? inY(i) : 0
-      }
-    ];
-  });
-
-  // Creating scales for touch point between the out-annotation edges
-  // and their hosts.
-  let outTouchHeight =
-      Math.min(renderNodeInfo.height / 2 - renderNodeInfo.radius,
-          outboxHeight / 2);
-  outTouchHeight = outTouchHeight < 0 ? 0 : outTouchHeight;
-  let outY = d3.scaleLinear()
-    .domain([0, outAnnotations.length - 1])
-    .range([-outTouchHeight, outTouchHeight]);
-
-  _.each(outAnnotations, (a, i) => {
-    // Add point from the border of the annotation node
-    a.points = [
-      // The host node end
-      {
-        dx: renderNodeInfo.coreBox.width / 2,
-        // only use scale if there are more than one,
-        // otherwise center it vertically
-        dy: outAnnotations.length > 1 ? outY(i) : 0
-      },
-      // The annotation node end
-      {
-        dx: a.dx - a.width / 2,
-        dy: a.dy
-      }
-    ];
-  });
-
-  renderNodeInfo.height =
-      Math.max(renderNodeInfo.height, inboxHeight, outboxHeight);
-}
-
-/**
- * Set size of an annotation node.
- */
-function sizeAnnotation(a: render.Annotation): void {
-  switch (a.annotationType) {
-    case render.AnnotationType.CONSTANT:
-      _.extend(a, PARAMS.constant.size);
-      break;
-    case render.AnnotationType.SHORTCUT:
-      if (a.node.type === NodeType.OP) {
-        _.extend(a, PARAMS.shortcutSize.op);
-      } else if (a.node.type === NodeType.META) {
-        _.extend(a, PARAMS.shortcutSize.meta);
-      } else if (a.node.type === NodeType.SERIES) {
-        _.extend(a, PARAMS.shortcutSize.series);
-      } else {
-        throw Error('Invalid node type: ' + a.node.type);
-      }
-      break;
-    case render.AnnotationType.SUMMARY:
-      _.extend(a, PARAMS.constant.size);
-      break;
-  }
-}
-
-/**
- * Determines the center position of the node's shape. The position depends
- * on if the node has in and out-annotations.
- */
-export function computeCXPositionOfNodeShape(renderInfo: render.RenderNodeInfo):
-    number {
-  if (renderInfo.expanded) {
-    return renderInfo.x;
-  }
-  let dx = renderInfo.inAnnotations.list.length ? renderInfo.inboxWidth : 0;
-  return renderInfo.x - renderInfo.width / 2 + dx +
-      renderInfo.coreBox.width / 2;
-}
-
-/** Returns the angle (in degrees) between two points. */
-function angleBetweenTwoPoints(a: render.Point, b: render.Point): number {
-  let dx = b.x - a.x;
-  let dy = b.y - a.y;
-  return 180 * Math.atan(dy / dx) / Math.PI;
-}
-
-/**
- * Returns if a line going through the specified points is a straight line.
- */
-function isStraightLine(points: render.Point[]) {
-  let angle = angleBetweenTwoPoints(points[0], points[1]);
-  for (let i = 1; i < points.length - 1; i++) {
-    let newAngle = angleBetweenTwoPoints(points[i], points[i + 1]);
-    // Have a tolerance of 1 degree.
-    if (Math.abs(newAngle - angle) > 1) {
-      return false;
-    }
-    angle = newAngle;
-  }
-  return true;
-}
-
-/**
- * Returns the intersection of a line between the provided point
- * and the provided rectangle.
- */
-function intersectPointAndNode(
-    point: render.Point, node: render.RenderNodeInfo): render.Point {
-  // cx and cy are the center of the rectangle.
-  let cx = node.expanded ?
-     node.x : computeCXPositionOfNodeShape(node);
-  let cy = node.y;
-  // Calculate the slope
-  let dx = point.x - cx;
-  let dy = point.y - cy;
-  let w = node.expanded ? node.width : node.coreBox.width;
-  let h = node.expanded ? node.height : node.coreBox.height;
-  let deltaX, deltaY;
-  if (Math.abs(dy) * w / 2  > Math.abs(dx) * h / 2) {
-    // The intersection is above or below the rectangle.
-    if (dy < 0) {
-      h = -h;
-    }
-    deltaX = dy === 0 ? 0 : h / 2 * dx / dy;
-    deltaY = h / 2;
-  } else {
-    // The intersection is left or right of the rectangle.
-    if (dx < 0) {
-      w = -w;
-    }
-    deltaX = w / 2;
-    deltaY = dx === 0 ? 0 : w / 2 * dy / dx;
-  }
-  return {x: cx + deltaX, y: cy + deltaY};
-}
-
-} // close module
diff --git a/tensorflow/tensorboard/components/tf_graph_common/minimap.ts b/tensorflow/tensorboard/components/tf_graph_common/minimap.ts
deleted file mode 100644
index 8129df3a42..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/minimap.ts
+++ /dev/null
@@ -1,328 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.scene {
-
-/** Show minimap when the viewpoint area is less than X% of the whole area. */
-const FRAC_VIEWPOINT_AREA: number = 0.8;
-
-export class Minimap {
-  /** The minimap container. */
-  private minimap: HTMLElement;
-  /** The canvas used for drawing the mini version of the svg. */
-  private canvas: HTMLCanvasElement;
-  /** A buffer canvas used for temporary drawing to avoid flickering. */
-  private canvasBuffer: HTMLCanvasElement;
-  private download: HTMLLinkElement;
-  private downloadCanvas: HTMLCanvasElement;
-
-  /** The minimap svg used for holding the viewpoint rectangle. */
-  private minimapSvg: SVGSVGElement;
-  /** The rectangle showing the current viewpoint. */
-  private viewpoint: SVGRectElement;
-  /**
-   * The scale factor for the minimap. The factor is determined automatically
-   * so that the minimap doesn't violate the maximum width/height specified
-   * in the constructor. The minimap maintains the same aspect ratio as the
-   * original svg.
-   */
-  private scaleMinimap: number;
-  /** The main svg element. */
-  private svg: SVGSVGElement;
-  /** The svg group used for panning and zooming the main svg. */
-  private zoomG: SVGGElement;
-  /** The zoom behavior of the main svg. */
-  private mainZoom: d3.ZoomBehavior<any, any>;
-  /** The maximum width and height for the minimap. */
-  private maxWandH: number;
-  /** The last translation vector used in the main svg. */
-  private translate: [number, number];
-  /** The last scaling factor used in the main svg. */
-  private scaleMain: number;
-  /** The coordinates of the viewpoint rectangle. */
-  private viewpointCoord: {x: number, y: number};
-  /** The current size of the minimap */
-  private minimapSize: {width: number, height: number};
-  /** Padding (px) due to the main labels of the graph. */
-  private labelPadding: number;
-  /**
-   * Constructs a new minimap.
-   *
-   * @param svg The main svg element.
-   * @param zoomG The svg group used for panning and zooming the main svg.
-   * @param mainZoom The main zoom behavior.
-   * @param minimap The minimap container.
-   * @param maxWandH The maximum width/height for the minimap.
-   * @param labelPadding Padding in pixels due to the main graph labels.
-   */
-  constructor(svg: SVGSVGElement, zoomG: SVGGElement,
-      mainZoom: d3.ZoomBehavior<any, any>, minimap: HTMLElement,
-      maxWandH: number, labelPadding: number) {
-    this.svg = svg;
-    this.labelPadding = labelPadding;
-    this.zoomG = zoomG;
-    this.mainZoom = mainZoom;
-    this.maxWandH = maxWandH;
-    let $minimap = d3.select(minimap);
-    // The minimap will have 2 main components: the canvas showing the content
-    // and an svg showing a rectangle of the currently zoomed/panned viewpoint.
-    let $minimapSvg = $minimap.select('svg');
-
-    // Make the viewpoint rectangle draggable.
-    let $viewpoint = $minimapSvg.select('rect');
-    let dragmove = (d) => {
-      this.viewpointCoord.x = (<DragEvent>d3.event).x;
-      this.viewpointCoord.y = (<DragEvent>d3.event).y;
-      this.updateViewpoint();
-    };
-    this.viewpointCoord = {x: 0, y: 0};
-    let drag = d3.drag().subject(Object).on('drag', dragmove);
-    $viewpoint.datum(this.viewpointCoord as any).call(drag);
-
-    // Make the minimap clickable.
-    $minimapSvg.on('click', () => {
-      if ((<Event>d3.event).defaultPrevented) {
-        // This click was part of a drag event, so suppress it.
-        return;
-      }
-      // Update the coordinates of the viewpoint.
-      let width = Number($viewpoint.attr('width'));
-      let height = Number($viewpoint.attr('height'));
-      let clickCoords = d3.mouse($minimapSvg.node() as any);
-      this.viewpointCoord.x = clickCoords[0] - width / 2;
-      this.viewpointCoord.y = clickCoords[1] - height / 2;
-      this.updateViewpoint();
-    });
-    this.viewpoint = <SVGRectElement>$viewpoint.node();
-    this.minimapSvg = <SVGSVGElement>$minimapSvg.node();
-    this.minimap = minimap;
-    this.canvas = <HTMLCanvasElement>$minimap.select('canvas.first').node();
-    this.canvasBuffer =
-        <HTMLCanvasElement>$minimap.select('canvas.second').node();
-    this.downloadCanvas =
-        <HTMLCanvasElement>$minimap.select('canvas.download').node();
-    d3.select(this.downloadCanvas).style('display', 'none');
-    this.update();
-  }
-
-  /**
-   * Updates the position and the size of the viewpoint rectangle.
-   * It also notifies the main svg about the new panned position.
-   */
-  private updateViewpoint(): void {
-    // Update the coordinates of the viewpoint rectangle.
-    d3.select(this.viewpoint)
-        .attr('x', this.viewpointCoord.x)
-        .attr('y', this.viewpointCoord.y);
-    // Update the translation vector of the main svg to reflect the
-    // new viewpoint.
-    let mainX = - this.viewpointCoord.x * this.scaleMain / this.scaleMinimap;
-    let mainY = - this.viewpointCoord.y * this.scaleMain / this.scaleMinimap;
-    d3.select(this.svg).call(
-        this.mainZoom.transform,
-        d3.zoomIdentity.translate(mainX, mainY).scale(this.scaleMain));
-  }
-
-  /**
-   * Redraws the minimap. Should be called whenever the main svg
-   * was updated (e.g. when a node was expanded).
-   */
-  update(): void {
-    let sceneSize = null;
-    try {
-      // Get the size of the entire scene.
-      sceneSize = this.zoomG.getBBox();
-      if (sceneSize.width === 0) {
-        // There is no scene anymore. We have been detached from the dom.
-        return;
-      }
-    } catch (e) {
-      // Firefox produced NS_ERROR_FAILURE if we have been
-      // detached from the dom.
-      return;
-    }
-    let $download = d3.select('#graphdownload');
-    this.download = <HTMLLinkElement>$download.node();
-    $download.on('click', d => {
-      this.download.href = this.downloadCanvas.toDataURL('image/png');
-    });
-
-    let $svg = d3.select(this.svg);
-    // Read all the style rules in the document and embed them into the svg.
-    // The svg needs to be self contained, i.e. all the style rules need to be
-    // embedded so the canvas output matches the origin.
-    let stylesText = '';
-    for (let k = 0; k < document.styleSheets.length; k++) {
-      try {
-        let cssRules = (<any>document.styleSheets[k]).cssRules ||
-          (<any>document.styleSheets[k]).rules;
-        if (cssRules == null) {
-          continue;
-        }
-        for (let i = 0; i < cssRules.length; i++) {
-          // Remove tf-* selectors from the styles.
-          stylesText +=
-              cssRules[i].cssText.replace(/ ?tf-[\w-]+ ?/g, '') + '\n';
-        }
-      } catch (e) {
-        if (e.name !== 'SecurityError') {
-          throw e;
-        }
-      }
-    }
-
-    // Temporarily add the css rules to the main svg.
-    let svgStyle = $svg.append('style');
-    svgStyle.text(stylesText);
-
-    // Temporarily remove the zoom/pan transform from the main svg since we
-    // want the minimap to show a zoomed-out and centered view.
-    let $zoomG = d3.select(this.zoomG);
-    let zoomTransform = $zoomG.attr('transform');
-    $zoomG.attr('transform', null);
-
-    // Since we add padding, account for that here.
-    sceneSize.height += this.labelPadding * 2;
-    sceneSize.width += this.labelPadding * 2;
-
-    // Temporarily assign an explicit width/height to the main svg, since
-    // it doesn't have one (uses flex-box), but we need it for the canvas
-    // to work.
-    $svg
-      .attr('width', sceneSize.width)
-      .attr('height', sceneSize.height);
-
-    // Since the content inside the svg changed (e.g. a node was expanded),
-    // the aspect ratio have also changed. Thus, we need to update the scale
-    // factor of the minimap. The scale factor is determined such that both
-    // the width and height of the minimap are <= maximum specified w/h.
-    this.scaleMinimap =
-        this.maxWandH / Math.max(sceneSize.width, sceneSize.height);
-
-    this.minimapSize = {
-      width: sceneSize.width * this.scaleMinimap,
-      height: sceneSize.height * this.scaleMinimap
-    };
-
-    // Update the size of the minimap's svg, the buffer canvas and the
-    // viewpoint rect.
-    d3.select(this.minimapSvg).attr(<any>this.minimapSize);
-    d3.select(this.canvasBuffer).attr(<any>this.minimapSize);
-
-    // Download canvas width and height are multiples of the style width and
-    // height in order to increase pixel density of the PNG for clarity.
-    d3.select(this.downloadCanvas).style(
-      <any>{ width: sceneSize.width, height: sceneSize.height });
-    d3.select(this.downloadCanvas).attr(
-      <any>{ width: sceneSize.width * 3, height: sceneSize.height * 3 });
-
-    if (this.translate != null && this.zoom != null) {
-      // Update the viewpoint rectangle shape since the aspect ratio of the
-      // map has changed.
-      requestAnimationFrame(() => this.zoom());
-    }
-
-    // Serialize the main svg to a string which will be used as the rendering
-    // content for the canvas.
-    let svgXml = (new XMLSerializer()).serializeToString(this.svg);
-
-    // Now that the svg is serialized for rendering, remove the temporarily
-    // assigned styles, explicit width and height and bring back the pan/zoom
-    // transform.
-    svgStyle.remove();
-    $svg.attr('width', null).attr('height', null);
-
-    $zoomG.attr('transform', zoomTransform);
-    let image = new Image();
-    image.onload = () => {
-      // Draw the svg content onto the buffer canvas.
-      let context = this.canvasBuffer.getContext('2d');
-      context.clearRect(0, 0, this.canvasBuffer.width,
-          this.canvasBuffer.height);
-      context.drawImage(image, 0, 0,
-        this.minimapSize.width, this.minimapSize.height);
-      requestAnimationFrame(() => {
-        // Hide the old canvas and show the new buffer canvas.
-        d3.select(this.canvasBuffer).style('display', null);
-        d3.select(this.canvas).style('display', 'none');
-        // Swap the two canvases.
-        [this.canvas, this.canvasBuffer] = [this.canvasBuffer, this.canvas];
-      });
-      let downloadContext = this.downloadCanvas.getContext('2d');
-      downloadContext.clearRect(0, 0, this.downloadCanvas.width,
-        this.downloadCanvas.height);
-      downloadContext.drawImage(image, 0, 0,
-        this.downloadCanvas.width, this.downloadCanvas.height);
-    };
-    image.onerror = () => {
-      let blob = new Blob([svgXml], {type: 'image/svg+xml;charset=utf-8'});
-      image.src = URL.createObjectURL(blob);
-    };
-    image.src =
-        'data:image/svg+xml;charset=utf-8,' + encodeURIComponent(svgXml);
-  }
-
-  /**
-   * Handles changes in zooming/panning. Should be called from the main svg
-   * to notify that a zoom/pan was performed and this minimap will update it's
-   * viewpoint rectangle.
-   *
-   * @param translate The translate vector, or none to use the last used one.
-   * @param scale The scaling factor, or none to use the last used one.
-   */
-  zoom(transform?: d3.ZoomTransform): void {
-    if (this.scaleMinimap == null) {
-      // Scene is not ready yet.
-      return;
-    }
-    // Update the new translate and scale params, only if specified.
-    if (transform) {
-      this.translate = [transform.x, transform.y];
-      this.scaleMain = transform.k;
-    }
-
-    // Update the location of the viewpoint rectangle.
-    let svgRect = this.svg.getBoundingClientRect();
-    let $viewpoint = d3.select(this.viewpoint);
-    this.viewpointCoord.x = -this.translate[0] * this.scaleMinimap /
-        this.scaleMain;
-    this.viewpointCoord.y = -this.translate[1] * this.scaleMinimap /
-        this.scaleMain;
-    let viewpointWidth = svgRect.width * this.scaleMinimap / this.scaleMain;
-    let viewpointHeight = svgRect.height * this.scaleMinimap / this.scaleMain;
-    $viewpoint
-      .attr('x', this.viewpointCoord.x)
-      .attr('y', this.viewpointCoord.y)
-      .attr('width', viewpointWidth)
-      .attr('height', viewpointHeight);
-    // Show/hide the minimap depending on the viewpoint area as fraction of the
-    // whole minimap.
-    let mapWidth = this.minimapSize.width;
-    let mapHeight = this.minimapSize.height;
-    let x = this.viewpointCoord.x;
-    let y = this.viewpointCoord.y;
-    let w = Math.min(Math.max(0, x + viewpointWidth), mapWidth) -
-        Math.min(Math.max(0, x), mapWidth);
-    let h = Math.min(Math.max(0, y + viewpointHeight), mapHeight) -
-        Math.min(Math.max(0, y), mapHeight);
-    let fracIntersect = (w * h) / (mapWidth * mapHeight);
-    if (fracIntersect < FRAC_VIEWPOINT_AREA) {
-      this.minimap.classList.remove('hidden');
-    } else {
-      this.minimap.classList.add('hidden');
-    }
-  }
-}
-
-} // close module tf.scene
diff --git a/tensorflow/tensorboard/components/tf_graph_common/node.ts b/tensorflow/tensorboard/components/tf_graph_common/node.ts
deleted file mode 100644
index f090a51fc4..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/node.ts
+++ /dev/null
@@ -1,1072 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph.scene.node {
-  import RenderNodeInfo = tf.graph.render.RenderNodeInfo;
-  /**
-   * Select or Create a 'g.nodes' group to a given sceneGroup
-   * and builds a number of 'g.node' groups inside the group.
-   *
-   * Structure Pattern:
-   *
-   * <g class='nodes'>
-   *   <g class='node'>
-   *     <g class='in-annotations'>
-   *       ...
-   *     </g>
-   *     <g class='out-annotations'>
-   *       ...
-   *     </g>
-   *     <g class='nodeshape'>
-   *      <!--
-   *      Content of the node shape should be for the node itself. For example a
-   *      Metanode would have a <rect> with rounded edges, an op would have an
-   *      <ellipse>. More complex nodes like series may contain multiple
-   *      elements which are conditionally visible based on whether the node is
-   *      expanded.
-   *      -->
-   *     </g>
-   *     <text class='label'>node name</text>
-   *     <g class='subscene'>
-   *       <!--
-   *       Content of  the subscene (only for metanode and series node).
-   *
-   *       Subscene is a svg group that contains content of the
-   *       metanode's metagraph that is recursively generated by Scene.build().
-   *
-   *       When the graph is expanded multiple times, a subscene can contain
-   *       nested subscenes inside.
-   *       -->
-   *     </g>
-   *   </g>
-   *   ...
-   * </g>
-   *
-   *
-   * @param sceneGroup selection of the container
-   * @param nodeData array of render node information to map
-   * @param sceneElement <tf-graph-scene> polymer element
-   * @return selection of the created nodeGroups
-   */
-  export function buildGroup(
-      sceneGroup, nodeData: render.RenderNodeInfo[], sceneElement) {
-    let container =
-        scene.selectOrCreateChild(sceneGroup, 'g', Class.Node.CONTAINER);
-    // Select all children and join with data.
-    // (Note that all children of g.nodes are g.node)
-    let nodeGroups =
-        (container as any).selectAll(function() {return this.childNodes;})
-            .data(nodeData, (d) => {
-              // make sure that we don't have to swap shape type
-              return d.node.name + ':' + d.node.type;
-            });
-
-    // ENTER
-    nodeGroups.enter()
-        .append('g')
-        .attr('data-name', d => { return d.node.name; })
-        .each(function(d) {
-          let nodeGroup = d3.select(this);
-          // index node group for quick stylizing
-          sceneElement.addNodeGroup(d.node.name, nodeGroup);
-        })
-        .merge(nodeGroups)
-        // ENTER + UPDATE
-        .attr('class', d => { return Class.Node.GROUP + ' ' + nodeClass(d); })
-        .each(function(d) {
-          let nodeGroup = d3.select(this);
-          // Add g.in-annotations (always add -- to keep layer order
-          // consistent.)
-          let inAnnotationBox =
-              scene.selectOrCreateChild(nodeGroup, 'g', Class.Annotation.INBOX);
-          annotation.buildGroup(
-              inAnnotationBox, d.inAnnotations, d, sceneElement);
-
-          // Add g.out-annotations  (always add -- to keep layer order
-          // consistent.)
-          let outAnnotationBox = scene.selectOrCreateChild(
-              nodeGroup, 'g', Class.Annotation.OUTBOX);
-          annotation.buildGroup(
-              outAnnotationBox, d.outAnnotations, d, sceneElement);
-
-          // Build .shape first (background of the node).
-          let shape = buildShape(nodeGroup, d, Class.Node.SHAPE);
-          if (d.node.isGroupNode) {
-            addButton(shape, d, sceneElement);
-          }
-          addInteraction(shape, d, sceneElement);
-
-          // Build subscene on the top.
-          subsceneBuild(nodeGroup, <render.RenderGroupNodeInfo>d, sceneElement);
-
-          // Build label last. Should be on top of everything else.
-          let label = labelBuild(nodeGroup, d, sceneElement);
-          // Do not add interaction to metanode labels as they live inside the
-          // metanode shape which already has the same interactions.
-          addInteraction(label, d, sceneElement, d.node.type === NodeType.META);
-
-          stylize(nodeGroup, d, sceneElement);
-          position(nodeGroup, d);
-        });
-
-    // EXIT
-    nodeGroups.exit()
-        .each(function(d) {
-          // remove all indices on remove
-          sceneElement.removeNodeGroup(d.node.name);
-
-          let nodeGroup = d3.select(this);
-          if (d.inAnnotations.list.length > 0) {
-            nodeGroup.select('.' + Class.Annotation.INBOX)
-                .selectAll('.' + Class.Annotation.GROUP)
-                .each(a => { sceneElement.removeAnnotationGroup(a, d); });
-          }
-          if (d.outAnnotations.list.length > 0) {
-            nodeGroup.select('.' + Class.Annotation.OUTBOX)
-                .selectAll('.' + Class.Annotation.GROUP)
-                .each(a => { sceneElement.removeAnnotationGroup(a, d); });
-          }
-        })
-        .remove();
-    return nodeGroups;
-};
-
-/**
- * Update or remove the subscene of a render group node depending on whether it
- * is a expanded. If the node is not a group node, this method has no effect.
- *
- * @param nodeGroup selection of the container
- * @param renderNodeInfo the render information for the node.
- * @param sceneElement <tf-graph-scene> polymer element.
- * @return Selection of the subscene group, or null if node group does not have
- *        a subscene. Op nodes, bridge nodes and unexpanded group nodes will
- *        not have a subscene.
- */
-function subsceneBuild(nodeGroup,
-    renderNodeInfo: render.RenderGroupNodeInfo, sceneElement) {
-  if (renderNodeInfo.node.isGroupNode) {
-    if (renderNodeInfo.expanded) {
-      // Recursively build the subscene.
-      return scene.buildGroup(nodeGroup, renderNodeInfo, sceneElement,
-        Class.Subscene.GROUP);
-    }
-    // Clean out existing subscene if the node is not expanded.
-    scene.selectChild(nodeGroup, 'g', Class.Subscene.GROUP).remove();
-  }
-  return null;
-};
-
-/**
- * Translate the subscene of the given node group
- */
-function subscenePosition(nodeGroup, d: render.RenderNodeInfo) {
-  let x0 = d.x - d.width / 2.0 + d.paddingLeft;
-  let y0 = d.y - d.height / 2.0 + d.paddingTop;
-
-  let subscene = scene.selectChild(nodeGroup, 'g', Class.Subscene.GROUP);
-  scene.translate(subscene, x0, y0);
-};
-
-/**
- * Add an expand/collapse button to a group node
- *
- * @param selection The group node selection.
- * @param d Info about the node being rendered.
- * @param sceneElement <tf-graph-scene> polymer element.
- */
-function addButton(selection, d: render.RenderNodeInfo, sceneElement) {
-  let group =
-      scene.selectOrCreateChild(selection, 'g', Class.Node.BUTTON_CONTAINER);
-  scene.selectOrCreateChild(group, 'circle', Class.Node.BUTTON_CIRCLE);
-  scene.selectOrCreateChild(group, 'path', Class.Node.EXPAND_BUTTON)
-      .attr('d', 'M0,-2.2 V2.2 M-2.2,0 H2.2');
-  scene.selectOrCreateChild(group, 'path', Class.Node.COLLAPSE_BUTTON)
-      .attr('d', 'M-2.2,0 H2.2');
-  (group as any).on('click', (d: any) => {
-    // Stop this event's propagation so that it isn't also considered a
-    // node-select.
-    (<Event>d3.event).stopPropagation();
-    sceneElement.fire('node-toggle-expand', {name: d.node.name});
-  });
-  scene.positionButton(group, d);
-};
-
-/**
- * Fire node-* events when the selection is interacted.
- *
- * @param disableInteraction When true, have the provided selection
- * ignore all pointer events. Used for text labels inside of metanodes, which
- * don't need interaction as their surrounding shape has interaction, and if
- * given interaction would cause conflicts with the expand/collapse button.
- */
-function addInteraction(selection, d: render.RenderNodeInfo,
-    sceneElement, disableInteraction?: boolean) {
-  if (disableInteraction) {
-    selection.attr('pointer-events', 'none');
-    return;
-  }
-
-  let contextMenuFunction = contextmenu.getMenu(
-    getContextMenu(d.node, sceneElement));
-  selection
-      .on('dblclick',
-          d => {
-            sceneElement.fire('node-toggle-expand', {name: d.node.name});
-          })
-      .on('mouseover',
-          d => {
-            // don't send mouseover over expanded group,
-            // otherwise it is causing too much glitches
-            if (sceneElement.isNodeExpanded(d)) {
-              return;
-            }
-
-            sceneElement.fire('node-highlight', {name: d.node.name});
-          })
-      .on('mouseout',
-          d => {
-            // don't send mouseover over expanded group,
-            // otherwise it is causing too much glitches
-            if (sceneElement.isNodeExpanded(d)) {
-              return;
-            }
-
-            sceneElement.fire('node-unhighlight', {name: d.node.name});
-          })
-      .on('click',
-          d => {
-            // Stop this event's propagation so that it isn't also considered
-            // a graph-select.
-            (<Event>d3.event).stopPropagation();
-            sceneElement.fire('node-select', {name: d.node.name});
-          })
-      .on('contextmenu', (d, i) => {
-        sceneElement.fire('node-select', {name: d.node.name});
-        contextMenuFunction.call(d, i);
-      });
-};
-
-/**
- * Returns the d3 context menu specification for the provided node.
- */
-export function getContextMenu(node: Node, sceneElement) {
-  let menu = [{
-    title: (d): string => {
-      return getIncludeNodeButtonString(node.include);
-    },
-    action: (elm, d, i) => {
-      sceneElement.fire('node-toggle-extract', {name: node.name});
-    }
-  }];
-  if (canBeInSeries(node)) {
-    menu.push({
-      title: d => { return getGroupSettingLabel(node); },
-      action: (elm, d, i) => {
-        sceneElement.fire(
-            'node-toggle-seriesgroup', {name: getSeriesName(node)});
-      }
-    });
-  }
-  return menu;
-}
-
-/** Returns if a node can be part of a grouped series */
-export function canBeInSeries(node: Node) {
-  return getSeriesName(node) !== null;
-}
-
-/**
- * Returns the name of the possible grouped series containing this node.
- * Returns null if the node cannot be part of a grouped series of nodes.
- */
-export function getSeriesName(node: Node) {
-  if (!node) {
-    return null;
-  }
-  if (node.type === NodeType.SERIES) {
-    return node.name;
-  }
-  if (node.type === NodeType.OP) {
-    let op = <OpNode>node;
-    return op.owningSeries;
-  }
-  return null;
-}
-
-/**
- * Returns the SeriesNode that represents the series that the provided node
- * is contained in (or itself if the provided node is itself a SeriesNode).
- * Returns null if the node is not rendered as part of a series.
- */
-function getContainingSeries(node: Node) {
-  let s: SeriesNode = null;
-  if (!node) {
-    return null;
-  } else if (node.type === NodeType.SERIES) {
-    s = <SeriesNode>node;
-  } else if (node.parentNode && node.parentNode.type === NodeType.SERIES) {
-    s = <SeriesNode>node.parentNode;
-  }
-  return s;
-}
-
-/**
- * Returns the label for a button to toggle the group setting of the provided
- * node.
- */
-export function getGroupSettingLabel(node: Node) {
-  return tf.graph.getGroupSeriesNodeButtonString(
-    getContainingSeries(node) !== null ? tf.graph.SeriesGroupingType.GROUP :
-     tf.graph.SeriesGroupingType.UNGROUP);
-}
-
-/**
- * Append svg text for label and assign data.
- * @param nodeGroup
- * @param renderNodeInfo The render node information for the label.
- * @param sceneElement <tf-graph-scene> polymer element.
- */
-function labelBuild(nodeGroup, renderNodeInfo: render.RenderNodeInfo,
-    sceneElement) {
-  let namePath = renderNodeInfo.node.name.split('/');
-  let text = namePath[namePath.length - 1];
-
-  // Truncate long labels for unexpanded Metanodes.
-  let useFontScale = renderNodeInfo.node.type === NodeType.META &&
-    !renderNodeInfo.expanded;
-
-  let label = scene.selectOrCreateChild(nodeGroup, 'text', Class.Node.LABEL);
-
-  // Make sure the label is visually on top among its siblings.
-  let labelNode = <HTMLElement> label.node();
-  labelNode.parentNode.appendChild(labelNode);
-
-  label.attr('dy', '.35em').attr('text-anchor', 'middle');
-  if (useFontScale) {
-    if (text.length > sceneElement.maxMetanodeLabelLength) {
-      text = text.substr(0, sceneElement.maxMetanodeLabelLength - 2) + '...';
-    }
-    let scale = getLabelFontScale(sceneElement);
-    label.attr('font-size', scale(text.length) + 'px');
-  }
-
-  let txtElement = <d3.Selection<any, any, any, any>>label.text(text);
-  enforceLabelWidth(txtElement, renderNodeInfo.node.type, renderNodeInfo);
-  return label;
-}
-/**
- * This function shortens text which would exceed the maximum pixel width of
- * a label.
- *
- * @param txtElementSelection The text element containing the label's text as d3
- * selection.
- * @param nodeType The type of the node the label belongs to. If the node is
- * an annotation, the value is -1. Label widths are defined in
- * layout.PARAMS.nodeSize.{meta|op|...}.maxLabelWidth for nodes and
- * layout.PARAMS.annotations.labelWidth for annotations.
- * @param renderNodeInfo The render information about the node, required to
- * determine whether META nodes are collapsed or expanded.
- */
-export function enforceLabelWidth(
-    txtElementSelection: d3.Selection<any, any, any, any>, nodeType: NodeType | number,
-    renderNodeInfo?: render.RenderNodeInfo): any {
-  // Get text element itself and its on-screen width.
-  let txtNode = <SVGTextElement>txtElementSelection.node();
-  let computedTxtLength = txtNode.getComputedTextLength();
-  let labelContent = txtNode.textContent;
-
-  // Get maximum length from settings.
-  let maxLength = null;
-  switch (nodeType) {
-    case NodeType.META:
-      if (renderNodeInfo && !renderNodeInfo.expanded) {  // Only trim text if
-        // node expanded.
-        maxLength = layout.PARAMS.nodeSize.meta.maxLabelWidth;
-      }
-      break;
-
-    case NodeType.OP:
-      maxLength = layout.PARAMS.nodeSize.op.maxLabelWidth;
-      break;
-
-    case -1:
-      maxLength = layout.PARAMS.annotations.maxLabelWidth;
-      break;
-
-    default:
-      break;
-  }
-
-  // Return if no max length provided for node type, or current label length is
-  // less than or equal to the provided length limit.
-  if (maxLength === null || computedTxtLength <= maxLength) {
-    return;
-  }
-
-  // Find the index of the character which exceeds the width.
-  // getSubStringLength performs far better than getComputedTextLength, and
-  // results in a 3x speed-up on average.
-  let index = 1;
-  while (txtNode.getSubStringLength(0, index) < maxLength) {
-    index++;
-  }
-
-  // Shorten the label starting at the string length known to be one
-  // character above max pixel length.
-  // When shortened the original label's substring is concatenated with
-  // '...', baseText contains the substring not including the '...'.
-  let baseText = <string>txtNode.textContent.substr(0, index);
-  do {
-    baseText = baseText.substr(0, baseText.length - 1);
-
-    // Recompute text length.
-    txtNode.textContent = baseText + '...';
-    computedTxtLength = txtNode.getComputedTextLength();
-  } while (computedTxtLength > maxLength && baseText.length > 0);
-
-  // Add tooltip with full name and return.
-  return txtElementSelection.append('title').text(labelContent);
-}
-
-/**
- * d3 scale used for sizing font of labels, used by labelBuild,
- * initialized once by getLabelFontScale.
- */
-let fontScale = null;
-function getLabelFontScale(sceneElement) {
-  if (!fontScale) {
-    fontScale = d3.scaleLinear()
-      .domain([sceneElement.maxMetanodeLabelLengthLargeFont,
-        sceneElement.maxMetanodeLabelLength])
-      .range([sceneElement.maxMetanodeLabelLengthFontSize,
-        sceneElement.minMetanodeLabelLengthFontSize]).clamp(true);
-  }
-  return fontScale;
-}
-
-/**
- * Set label position of a given node group
- */
-function labelPosition(nodeGroup, cx: number, cy: number,
-    yOffset: number) {
-  scene.selectChild(nodeGroup, 'text', Class.Node.LABEL)
-      .transition()
-      .attr('x', cx)
-      .attr('y', cy + yOffset);
-};
-
-/**
- * Select or append/insert shape for a node and assign renderNode
- * as the shape's data.
- *
- * @param nodeGroup
- * @param d Render node information.
- * @param nodeClass class for the element.
- * @return Selection of the shape.
- */
-export function buildShape(nodeGroup, d, nodeClass: string): d3.Selection<any, any, any, any> {
-  // Create a group to house the underlying visual elements.
-  let shapeGroup = scene.selectOrCreateChild(nodeGroup, 'g', nodeClass);
-  // TODO(jimbo): DOM structure should be templated in HTML somewhere, not JS.
-  switch (d.node.type) {
-    case NodeType.OP:
-      scene.selectOrCreateChild(shapeGroup, 'ellipse', Class.Node.COLOR_TARGET);
-      break;
-    case NodeType.SERIES:
-      // Choose the correct stamp to use to represent this series.
-      let stampType = 'annotation';
-      let groupNodeInfo = <render.RenderGroupNodeInfo>d;
-      if (groupNodeInfo.coreGraph) {
-        stampType =
-            groupNodeInfo.node.hasNonControlEdges ? 'vertical' : 'horizontal';
-      }
-      let classList = [Class.Node.COLOR_TARGET];
-      if (groupNodeInfo.isFadedOut) {
-        classList.push('faded-ellipse');
-      }
-      scene.selectOrCreateChild(shapeGroup, 'use', classList)
-          .attr('xlink:href', '#op-series-' + stampType + '-stamp');
-      scene.selectOrCreateChild(shapeGroup, 'rect', Class.Node.COLOR_TARGET)
-          .attr('rx', d.radius).attr('ry', d.radius);
-      break;
-    case NodeType.BRIDGE:
-      scene.selectOrCreateChild(shapeGroup, 'rect', Class.Node.COLOR_TARGET)
-          .attr('rx', d.radius).attr('ry', d.radius);
-      break;
-    case NodeType.META:
-      scene.selectOrCreateChild(shapeGroup, 'rect', Class.Node.COLOR_TARGET)
-          .attr('rx', d.radius).attr('ry', d.radius);
-      break;
-    default:
-      throw Error('Unrecognized node type: ' + d.node.type);
-  }
-  return shapeGroup;
-};
-
-export function nodeClass(d: render.RenderNodeInfo) {
-  switch (d.node.type) {
-    case NodeType.OP:
-      return Class.OPNODE;
-    case NodeType.META:
-      return Class.METANODE;
-    case NodeType.SERIES:
-      return Class.SERIESNODE;
-    case NodeType.BRIDGE:
-      return Class.BRIDGENODE;
-    case NodeType.ELLIPSIS:
-      return Class.ELLIPSISNODE;
-  };
-  throw Error('Unrecognized node type: ' + d.node.type);
-};
-
-/** Modify node and its subscene and its label's positional attributes */
-function position(nodeGroup, d: render.RenderNodeInfo) {
-  let shapeGroup = scene.selectChild(nodeGroup, 'g', Class.Node.SHAPE);
-  let cx = layout.computeCXPositionOfNodeShape(d);
-  switch (d.node.type) {
-    case NodeType.OP: {
-      // position shape
-      let shape = scene.selectChild(shapeGroup, 'ellipse');
-      scene.positionEllipse(shape, cx, d.y, d.coreBox.width, d.coreBox.height);
-      labelPosition(nodeGroup, cx, d.y, d.labelOffset);
-      break;
-    }
-    case NodeType.META: {
-      // position shape
-      let shape = scene.selectChild(shapeGroup, 'rect');
-      if (d.expanded) {
-        scene.positionRect(shape, d.x, d.y, d.width, d.height);
-        subscenePosition(nodeGroup, d);
-        // put label on top
-        labelPosition(nodeGroup, cx, d.y,
-          - d.height / 2 + d.labelHeight / 2);
-      } else {
-        scene.positionRect(shape, cx, d.y, d.coreBox.width, d.coreBox.height);
-        labelPosition(nodeGroup, cx, d.y, 0);
-      }
-      break;
-    }
-    case NodeType.SERIES: {
-      let shape = scene.selectChild(shapeGroup, 'use');
-      if (d.expanded) {
-        scene.positionRect(shape, d.x, d.y, d.width, d.height);
-        subscenePosition(nodeGroup, d);
-        // put label on top
-        labelPosition(nodeGroup, cx, d.y,
-          - d.height / 2 + d.labelHeight / 2);
-      } else {
-        scene.positionRect(shape, cx, d.y, d.coreBox.width, d.coreBox.height);
-        labelPosition(nodeGroup, cx, d.y, d.labelOffset);
-      }
-      break;
-    }
-    case NodeType.BRIDGE: {
-      // position shape
-      // NOTE: In reality, these will not be visible, but it helps to put them
-      // in the correct position for debugging purposes.
-      let shape = scene.selectChild(shapeGroup, 'rect');
-      scene.positionRect(shape, d.x, d.y, d.width, d.height);
-      break;
-    }
-    default: { throw Error('Unrecognized node type: ' + d.node.type); }
-  }
-};
-
-/** Enum specifying the options to color nodes by */
-export enum ColorBy {STRUCTURE, DEVICE, XLA_CLUSTER, COMPUTE_TIME, MEMORY}
-;
-
-/**
- * Returns the fill color for the node given its state and the 'color by'
- * option.
- */
-export function getFillForNode(templateIndex, colorBy,
-    renderInfo: render.RenderNodeInfo, isExpanded: boolean): string {
-  let colorParams = render.MetanodeColors;
-  switch (colorBy) {
-    case ColorBy.STRUCTURE:
-      if (renderInfo.node.type === NodeType.META) {
-        let tid = (<Metanode>renderInfo.node).templateId;
-        return tid === null ?
-          colorParams.UNKNOWN :
-          colorParams.STRUCTURE_PALETTE(templateIndex(tid), isExpanded);
-      } else if (renderInfo.node.type === NodeType.SERIES) {
-        // If expanded, we're showing the background rect, which we want to
-        // appear gray. Otherwise we're showing a stack of ellipses which we
-        // want to show white.
-        return isExpanded ? colorParams.EXPANDED_COLOR : 'white';
-      } else if (renderInfo.node.type === NodeType.BRIDGE) {
-        return renderInfo.structural ?
-            '#f0e' :
-            (<BridgeNode>renderInfo.node).inbound ? '#0ef' : '#fe0';
-      } else {
-        // Op nodes are white.
-        return 'white';
-      }
-    case ColorBy.DEVICE:
-      if (renderInfo.deviceColors == null) {
-        // Return the hue for unknown device.
-        return colorParams.UNKNOWN;
-      }
-      let id = renderInfo.node.name;
-      let escapedId = tf.graph.util.escapeQuerySelector(id);
-      let gradientDefs = d3.select('svg#svg defs #linearGradients');
-      let linearGradient = gradientDefs.select('linearGradient#' + escapedId);
-      // If the linear gradient is not there yet, create it.
-      if (linearGradient.size() === 0) {
-        linearGradient = gradientDefs.append('linearGradient').attr('id', id);
-        // Re-create the stops of the linear gradient.
-        linearGradient.selectAll('*').remove();
-        let cumulativeProportion = 0;
-        // For each device, create a stop using the proportion of that device.
-        _.each(renderInfo.deviceColors, d => {
-          let color = d.color;
-          linearGradient.append('stop')
-              .attr('offset', cumulativeProportion)
-              .attr('stop-color', color);
-          linearGradient.append('stop')
-              .attr('offset', cumulativeProportion + d.proportion)
-              .attr('stop-color', color);
-          cumulativeProportion += d.proportion;
-        });
-      }
-      return isExpanded ? colorParams.EXPANDED_COLOR : `url(#${escapedId})`;
-    case ColorBy.XLA_CLUSTER:
-      return isExpanded ? colorParams.EXPANDED_COLOR :
-                          renderInfo.xlaClusterColor || colorParams.UNKNOWN;
-    case ColorBy.COMPUTE_TIME:
-      return isExpanded ?
-        colorParams.EXPANDED_COLOR : renderInfo.computeTimeColor ||
-        colorParams.UNKNOWN;
-    case ColorBy.MEMORY:
-      return isExpanded ?
-        colorParams.EXPANDED_COLOR : renderInfo.memoryColor ||
-        colorParams.UNKNOWN;
-    default:
-      throw new Error('Unknown case to color nodes by');
-  }
-}
-
-/**
- * Modify node style by toggling class and assign attributes (only for things
- * that can't be done in css).
- */
-export function stylize(nodeGroup, renderInfo: render.RenderNodeInfo,
-    sceneElement, nodeClass?) {
-  nodeClass = nodeClass || Class.Node.SHAPE;
-  let isHighlighted = sceneElement.isNodeHighlighted(renderInfo.node.name);
-  let isSelected = sceneElement.isNodeSelected(renderInfo.node.name);
-  let isExtract = renderInfo.isInExtract || renderInfo.isOutExtract;
-  let isExpanded = renderInfo.expanded;
-  let isFadedOut = renderInfo.isFadedOut;
-  nodeGroup.classed('highlighted', isHighlighted);
-  nodeGroup.classed('selected', isSelected);
-  nodeGroup.classed('extract', isExtract);
-  nodeGroup.classed('expanded', isExpanded);
-  nodeGroup.classed('faded', isFadedOut);
-
-  // Main node always exists here and it will be reached before subscene,
-  // so d3 selection is fine here.
-  let node = nodeGroup.select('.' + nodeClass + ' .' + Class.Node.COLOR_TARGET);
-  let fillColor = getFillForNode(sceneElement.templateIndex,
-    ColorBy[sceneElement.colorBy.toUpperCase()],
-    renderInfo, isExpanded);
-  node.style('fill', fillColor);
-
-  // Choose outline to be darker version of node color if the node is a single
-  // color and is not selected.
-  node.style('stroke', isSelected ? null : getStrokeForFill(fillColor));
-};
-
-/**
- * Given a node's fill color/gradient, determine the stroke for the node.
- */
-export function getStrokeForFill(fill: string) {
-  // If node is colored by a gradient, then use a dark gray outline.
-  return fill.substring(0, 3) === 'url' ?
-      render.MetanodeColors.GRADIENT_OUTLINE :
-      d3.rgb(fill).darker().toString();
-}
-
-/**
- * Finds selected node and highlights all nodes which are providing direct
- * or indirect input to the node and all edges connecting these nodes
- * together and to the selected node.
- *
- * @param renderGraphInfo Information on the rendered state of the graph.
- */
-export function traceInputs(renderGraphInfo: tf.graph.render.RenderGraphInfo) {
-  // Reset all styling.
-  d3.selectAll('.input-highlight').classed('input-highlight', false);
-  d3.selectAll('.non-input').classed('non-input', false);
-  d3.selectAll('.input-parent').classed('input-parent', false);
-  d3.selectAll('.input-child').classed('input-child', false);
-  d3.selectAll('.input-edge-highlight').classed('input-edge-highlight', false);
-  d3.selectAll('.non-input-edge-highlight')
-      .classed('non-input-edge-highlight', false);
-  d3.selectAll('.input-highlight-selected')
-      .classed('input-highlight-selected', false);
-
-  // Extract currently selected node. Return if input tracing disabled or no
-  // node is selected.
-  const selectedNodeSelectorString = 'g.node.selected,g.op.selected';
-  const nodeSelection = d3.select(selectedNodeSelectorString);
-  let currentNode = undefined;
-  if (renderGraphInfo && renderGraphInfo.traceInputs &&
-      nodeSelection.nodes().length) {
-    currentNode = nodeSelection.nodes()[0];
-  } else {
-    return;
-  }
-  let nodeName = currentNode.getAttribute('data-name');
-  let opNodes = _getAllContainedOpNodes(nodeName, renderGraphInfo);
-  let allTracedNodes = {};
-  _.each(opNodes, function(nodeInstance) {
-    allTracedNodes =
-        traceAllInputsOfOpNode(renderGraphInfo, nodeInstance, allTracedNodes);
-  });
-
-  d3.selectAll(selectedNodeSelectorString)
-      // Remove the input-highlight from the selected node.
-      .classed('input-highlight', false)
-      // Add input-highlight-selected class to selected node, which allows
-      // treating the selected not as a special case of an input node.
-      .classed('input-highlight-selected', true);
-
-  // Highlight all parent nodes of each OpNode as input parent to allow
-  // specific highlighting.
-  let highlightedNodes = Object.keys(allTracedNodes);
-  let visibleNodes =
-      _findVisibleParentsFromOpNodes(renderGraphInfo, highlightedNodes);
-  _markParentsOfNodes(visibleNodes);
-
-  // Attach class to all non-input nodes and edges for styling.
-  d3.selectAll(
-        'g.node:not(.selected):not(.input-highlight)' +
-        ':not(.input-parent):not(.input-children)')
-      .classed('non-input', true)
-      .each(function(d: RenderNodeInfo) {
-        // Mark all nodes with the specified name as non-inputs. This
-        // results in Annotation nodes which are attached to inputs to be
-        // tagged as well.
-        let nodeName = d.node.name;
-        d3.selectAll(`[data-name="${nodeName}"]`).classed('non-input', true);
-      });
-  d3.selectAll('g.edge:not(.input-edge-highlight)')
-      .classed('non-input-edge-highlight', true);
-}
-
-/**
- * Recursively find all op nodes contained by the node identified by the
- * provided name.
- * @param nodeName The meta or op node of which the OpNode instances are
- * required.
- * @param renderGraphInfo The rendered graph information object.
- * @returns {Array} An array of OpNodeImpl instances.
- */
-export function _getAllContainedOpNodes(
-    nodeName: string, renderGraphInfo: tf.graph.render.RenderGraphInfo) {
-  let opNodes = [];
-
-  // Get current node.
-  let node = renderGraphInfo.getNodeByName(nodeName) as tf.graph.GroupNode |
-      tf.graph.OpNode;
-
-  // If node is already OpNode then return the node plus its input embeddings.
-  if (node instanceof tf.graph.OpNodeImpl) {
-    return [node].concat(node.inEmbeddings);
-  }
-
-  // Otherwise, make recursive call for each node contained by the GroupNode.
-  let childNodeNames = (node as tf.graph.GroupNode).metagraph.nodes();
-  _.each(childNodeNames, function(childNodeName) {
-    opNodes =
-        opNodes.concat(_getAllContainedOpNodes(childNodeName, renderGraphInfo));
-  });
-
-  return opNodes;
-}
-
-/**
- * When resolving inputs of a node the visible parent node of each input
- * node (i.e. the first parent which is rendered to the screen) needs to be
- * found, and since such a node may contain several input OpNodes a map
- * of the visible parent to all the input OpNodes it contains is provided by
- * opNodes.
- */
-interface VisibleParent {
-  visibleParent: Node;
-  opNodes: OpNode[];
-}
-
-export function traceAllInputsOfOpNode(
-    renderGraphInfo: tf.graph.render.RenderGraphInfo, startNode: OpNode,
-    allTracedNodes: Object) {
-  // To prevent infinite loops due to cyclical relationships and improving
-  // performance by tracing OpNode which is input to 2+ nodes only once.
-  if (allTracedNodes[startNode.name]) {
-    return allTracedNodes;
-  } else {
-    allTracedNodes[startNode.name] = true;
-  }
-  // Extract the inputs.
-  let inputs = startNode.inputs;
-  // Get visible parent.
-  let currentVisibleParent = getVisibleParent(renderGraphInfo, startNode);
-  // Mark as input node.
-  d3.select(`.node[data-name="${currentVisibleParent.name}"]`)
-      .classed('input-highlight', true);
-
-  // Find the visible parent of each input.
-  let visibleInputs = {};
-  _.each(inputs, function(nodeInstance) {
-    let resolvedNode = renderGraphInfo.getNodeByName(nodeInstance.name);
-    if (resolvedNode === undefined) {
-      // Node could not be found in rendered Hierarchy, which happens when
-      // tracing inputs of a SummaryNode.
-      return;
-    }
-    // Ensure node is resolved to OpNode if name collision with Metanode exists.
-    if (resolvedNode instanceof MetanodeImpl) {
-      let resolvedNodeName = tf.graph.getStrictName(resolvedNode.name);
-      resolvedNode = renderGraphInfo.getNodeByName(resolvedNodeName) as OpNode;
-    }
-
-    let visibleParent = getVisibleParent(renderGraphInfo, resolvedNode);
-
-    // Append OpNode to visible parent entry.
-    let visibleInputsEntry = visibleInputs[visibleParent.name];
-    if (visibleInputsEntry) {
-      visibleInputsEntry.opNodes.push(resolvedNode);
-    } else {  // Create new entry.
-      visibleInputs[visibleParent.name] = {
-        visibleParent: visibleParent,
-        opNodes: [resolvedNode]
-      } as VisibleParent;
-    }
-  });
-
-  // Find all parents of the start node.
-  let startNodeParents = {};
-  let indexedStartNodeParents = [currentVisibleParent];
-  startNodeParents[currentVisibleParent.name] = {
-    traced: false,
-    index: 0,
-    connectionEndpoints: []
-  };
-
-  let currentNode = currentVisibleParent as Node;
-  for (let index = 1; currentNode.name !== tf.graph.ROOT_NAME; index++) {
-    currentNode = currentNode.parentNode;
-    startNodeParents[currentNode.name] = {
-      traced: false,
-      index: index,
-      connectionEndpoints: []
-    };
-    indexedStartNodeParents[index] = currentNode;
-  }
-
-  // Find first mutual parent of each input node and highlight connection.
-  _.forOwn(visibleInputs, function(visibleParentInfo: VisibleParent, key) {
-    let nodeInstance = visibleParentInfo.visibleParent;
-    // Make recursive call for each input-OpNode contained by the visible
-    // parent.
-    _.each(visibleParentInfo.opNodes, function(opNode: OpNode) {
-      allTracedNodes =
-          traceAllInputsOfOpNode(renderGraphInfo, opNode, allTracedNodes);
-    });
-
-    if (nodeInstance.name !== currentVisibleParent.name) {
-      _createVisibleTrace(
-          nodeInstance, startNodeParents, indexedStartNodeParents);
-    }
-  });
-
-  return allTracedNodes;
-}
-
-/**
- * Colors the edges to connect the passed node to the start node. This is
- * done by:
- *
- * a) Finding the first (visible) common parent in the rendered
- * hierarchy.
- * NB: There are 2 types of connections:
- * 1) Direct connections between node A
- * and B, marked below as II,
- * 2) Connections from any node A to its parent, A'. Marked below as I and III.
- * For type 2 connection you need to know the inner-nested node, the
- * direct parent, and the ultimate destination of the connection.
- *
- *  A_parent      B_parent
- * +--------+    +---------+
- * |        |    |         |
- * |  +--+ I| II |III+--+  |
- * |  |A +---------->+B |  |
- * |  +--+  |    |   +--+  |
- * |        |    |         |
- * +--------+    +---------+
- *
- *
- * b) Highlighting the direct connection between the parents of A and B,
- * called A_parent and B_parent, s.t. A_parent and B_parent are children of the
- * mutual parent of A and B found in a), marked above as II.
- *
- * c) Highlighting the connection from A to A_parent and B to B_parent
- * (through all layers of parents between A and A_parent and B and B_parent,
- * respectively). Marked above as I and III.
- *
- * @param nodeInstance The instance of the node to use as destination node, B.
- * @param startNodeParents Map of startNodeParent names to information objects
- * about the parent.
- * @param indexedStartNodeParents An array of all parents of the start node.
- * This is required to find the child of the mutual parent which is a parent
- * of the start node.
- * @private
- */
-function _createVisibleTrace(
-    nodeInstance: Node, startNodeParents, indexedStartNodeParents: Node[]) {
-  let currentNode = nodeInstance;
-  let previousNode = nodeInstance;
-
-  // Ascend through parents until a mutual parent is found with the start
-  // node.
-  let destinationParentPairs = [];
-  while (!startNodeParents[currentNode.name]) {
-    if (previousNode.name !== currentNode.name) {
-      destinationParentPairs.push([previousNode, currentNode]);
-    }
-    previousNode = currentNode;
-    currentNode = currentNode.parentNode;
-  }
-
-  // Connection between nodes is drawn between the parents of each
-  // respective node, both of which share the mutual parent.
-  let startNodeIndex = startNodeParents[currentNode.name].index;
-  let startNodeName =
-      indexedStartNodeParents[Math.max(startNodeIndex - 1, 0)].name;
-
-  let startNodeTopParentName = startNodeName;
-  let targetNodeTopParentName = previousNode.name;
-
-  let endNodeName = previousNode.name;
-  d3.selectAll(`[data-edge="${endNodeName}--${startNodeName}"]`)
-      .classed('input-edge-highlight', true);
-
-  // Trace up the parents of the input.
-  _.each(destinationParentPairs, function(value) {
-    let inner = value[0];
-    let outer = value[1];
-    let edgeSelector = `[data-edge="${inner.name}--${startNodeTopParentName}` +
-        `~~${outer.name}~~OUT"]`;
-    d3.selectAll(edgeSelector).classed('input-edge-highlight', true);
-  });
-
-  // Trace up the parents of the start node.
-  for (let index = 1; index < startNodeIndex; index++) {
-    let inner = indexedStartNodeParents[index - 1];
-    let outer = indexedStartNodeParents[index];
-    let edgeSelector = `[data-edge="${targetNodeTopParentName}~~${outer.name}` +
-        `~~IN--${inner.name}"]`;
-    d3.selectAll(edgeSelector).classed('input-edge-highlight', true);
-  }
-}
-
-/**
- * Creates map { [name: string] -> Node } of all visible / rendered parents
- * of the nodes identified by the node names passed in.
- *
- * @param renderGraphInfo The information on the rendered graph.
- * @param nodeNames String array of node names.
- * @returns {[nodeName: string]: Node}
- * @private
- */
-function _findVisibleParentsFromOpNodes(renderGraphInfo, nodeNames: string[]) {
-  let visibleParents: {[nodeName: string]: Node} = {};
-  _.each(nodeNames, function(nodeName) {
-    let currentNode = renderGraphInfo.getNodeByName(nodeName);
-    let visibleParent = getVisibleParent(renderGraphInfo, currentNode);
-    visibleParents[visibleParent.name] = visibleParent;
-  });
-
-  return visibleParents;
-}
-
-/**
- * Traverse through the parents of all nodes in the list and mark each
- * encountered node as input-parent.
- * @param visibleNodes Map of input nodes, have to be visible/rendered when
- * called.
- * @private
- */
-function _markParentsOfNodes(visibleNodes: {[nodeName: string]: Node}) {
-  _.forOwn(visibleNodes, function(nodeInstance: Node) {
-    // Mark all parents of the node as input-parents.
-    let currentNode = nodeInstance;
-
-    while (currentNode.name !== tf.graph.ROOT_NAME) {
-      const renderedElementSelection =
-          d3.select(`.node[data-name="${currentNode.name}"]`);
-      // Only mark the element as a parent node to an input if it is not
-      // marked as input node itself.
-      if (renderedElementSelection.nodes().length &&
-          !renderedElementSelection.classed('input-highlight') &&
-          !renderedElementSelection.classed('selected') &&
-          // OpNode only parent if start node is embedded node, in which case
-          // the OpNode should be faded as well.
-          !renderedElementSelection.classed('op')) {
-        renderedElementSelection.classed('input-parent', true);
-      }
-      currentNode = currentNode.parentNode;
-    }
-  });
-}
-
-/**
- * Find the parent of the passed in op node which is expanded. This is done
- * by going through all parents until the parent's parent is expanded, thus
- * finding the first unexpanded parent which is rendered on the screen.
- * @param renderGraphInfo The graph info object used to gain access to the
- * render info of the parents.
- * @param currentNode The node whose parent is to be found.
- * @returns Node
- */
-export function getVisibleParent(
-    renderGraphInfo: tf.graph.render.RenderGraphInfo,
-    currentNode: tf.graph.Node) {
-  let found = false;
-  let currentParent = currentNode;
-
-  while (!found) {
-    // Get parent element, to extract name.
-    currentNode = currentParent;
-    currentParent = currentNode.parentNode;
-
-    if (currentParent === undefined) {
-      found = true;
-    } else {
-      let renderNode = renderGraphInfo.getRenderNodeByName(currentParent.name);
-      // Found if node is rendered on the screen (renderNode truthy), and
-      // the parent is either expanded (i.e. it is a metanode or seriesnode)
-      // or the parent is an OpNode in which case currentNode is an embedded
-      // node which has another OpNode as parent.
-      if (renderNode &&
-          (renderNode.expanded || currentParent instanceof graph.OpNodeImpl)) {
-        found = true;
-      }
-    }
-  }  // Close while loop.
-  return currentNode;
-}
-}  // Close module.
diff --git a/tensorflow/tensorboard/components/tf_graph_common/parser.ts b/tensorflow/tensorboard/components/tf_graph_common/parser.ts
deleted file mode 100644
index 04d879ef91..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/parser.ts
+++ /dev/null
@@ -1,284 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph.parser {
-
-/**
- * Parses a native js value, which can be either a string, boolean or number.
- *
- * @param value The value to be parsed.
- */
-function parseValue(value: string): string|number|boolean {
-  if (value === 'true') {
-    return true;
-  }
-  if (value === 'false') {
-    return false;
-  }
-  let firstChar = value[0];
-  if (firstChar === '"') {
-    return value.substring(1, value.length - 1);
-  }
-  let num = parseFloat(value);
-  return isNaN(num) ? value : num;
-}
-
-/**
- * Fetches a text file and returns a promise of the result.
- */
-export function fetchPbTxt(filepath: string): Promise<ArrayBuffer> {
-  return new Promise<ArrayBuffer>(function(resolve, reject) {
-    const request = new XMLHttpRequest();
-    request.open('GET', filepath);
-    request.responseType = 'arraybuffer';
-
-    request.onerror = () => reject(request.status);
-    request.onload = () => resolve(request.response);
-
-    request.send(null);
-  });
-}
-
-/**
- * Fetches the metadata file, parses it and returns a promise of the result.
- */
-export function fetchAndParseMetadata(path: string, tracker: ProgressTracker) {
-  return tf.graph.util
-      .runTask(
-          'Reading metadata pbtxt', 40,
-          () => {
-            if (path == null) {
-              return Promise.resolve(null);
-            }
-            return fetchPbTxt(path);
-          },
-          tracker)
-      .then((arrayBuffer: ArrayBuffer) => {
-        return tf.graph.util.runAsyncPromiseTask(
-            'Parsing metadata.pbtxt', 60, () => {
-              return arrayBuffer != null ? parseStatsPbTxt(arrayBuffer) :
-                                           Promise.resolve(null);
-            }, tracker);
-      });
-}
-
-/**
- * Fetches the graph file, parses it and returns a promise of the result. The
- * result will be undefined if the graph is empty.
- */
-export function fetchAndParseGraphData(path: string, pbTxtFile: Blob,
-    tracker: ProgressTracker) {
-  return tf.graph.util
-      .runTask(
-          'Reading graph pbtxt', 40,
-          () => {
-            if (pbTxtFile) {
-              return new Promise<ArrayBuffer>(function(resolve, reject) {
-                let fileReader = new FileReader();
-                fileReader.onload = () => resolve(fileReader.result);
-                fileReader.onerror = () => reject(fileReader.error);
-                fileReader.readAsArrayBuffer(pbTxtFile);
-              });
-            } else {
-              return fetchPbTxt(path);
-            }
-          },
-          tracker)
-      .then((arrayBuffer: ArrayBuffer) => {
-        return tf.graph.util.runTask('Parsing graph.pbtxt', 60, () => {
-          return parseGraphPbTxt(arrayBuffer);
-        }, tracker);
-      });
-}
-
-/**
- * Parse a file object in a streaming fashion line by line (or custom delim).
- * Can handle very large files.
- * @param input The file object as an array buffer.
- * @param callback The callback called on each line
- * @param chunkSize The size of each read chunk. (optional)
- * @param delim The delimiter used to split a line. (optional)
- * @returns A promise for when it is finished.
- */
-export function streamParse(
-    arrayBuffer: ArrayBuffer, callback: (string) => void,
-    chunkSize: number = 1000000, delim: string = '\n'): Promise<boolean> {
-  return new Promise<boolean>(function(resolve, reject) {
-    let offset = 0;
-    let bufferSize = arrayBuffer.byteLength - 1;
-    let data = '';
-
-    function readHandler(str) {
-      offset += chunkSize;
-      let parts = str.split(delim);
-      let first = data + parts[0];
-      if (parts.length === 1) {
-        data = first;
-        readChunk(offset, chunkSize);
-        return;
-      }
-      data = parts[parts.length - 1];
-      callback(first);
-      for (let i = 1; i < parts.length - 1; i++) {
-        callback(parts[i]);
-      }
-      if (offset >= bufferSize) {
-        if (data) {
-          callback(data);
-        }
-        resolve(true);
-        return;
-      }
-      readChunk(offset, chunkSize);
-    }
-
-    function readChunk(offset: number, size: number) {
-      const arrayBufferChunk = arrayBuffer.slice(offset, offset + size);
-
-      const blob = new Blob([arrayBufferChunk]);
-      const file = new FileReader();
-      file.onload = (e: any) => readHandler(e.target.result);
-      file.readAsText(blob);
-    }
-
-    readChunk(offset, chunkSize);
-  });
-}
-
-/**
- * Since proto-txt doesn't explicitly say whether an attribute is repeated
- * (an array) or not, we keep a hard-coded list of attributes that are known
- * to be repeated. This list is used in parsing time to convert repeated
- * attributes into arrays even when the attribute only shows up once in the
- * object.
- */
-const GRAPH_REPEATED_FIELDS: {[attrPath: string]: boolean} = {
-  'node': true,
-  'node.input': true,
-  'node.attr': true,
-  'node.attr.value.list.type': true,
-  'node.attr.value.shape.dim': true,
-  'node.attr.value.tensor.string_val': true,
-  'node.attr.value.tensor.tensor_shape.dim': true,
-  'node.attr.value.list.shape': true,
-  'node.attr.value.list.shape.dim': true,
-  'node.attr.value.list.s': true
-};
-
-const METADATA_REPEATED_FIELDS: {[attrPath: string]: boolean} = {
-  'step_stats.dev_stats': true,
-  'step_stats.dev_stats.node_stats': true,
-  'step_stats.dev_stats.node_stats.output': true,
-  'step_stats.dev_stats.node_stats.memory': true,
-  'step_stats.dev_stats.node_stats.output.tensor_description.shape.dim': true
-};
-
-/**
- * Parses an ArrayBuffer of a proto txt file into a raw Graph object.
- */
-export function parseGraphPbTxt(input: ArrayBuffer):
-    Promise<tf.graph.proto.NodeDef[]> {
-  return parsePbtxtFile(input, GRAPH_REPEATED_FIELDS).then(obj => obj['node']);
-}
-
-/**
- * Parses an ArrayBuffer of a proto txt file into a StepStats object.
- */
-export function parseStatsPbTxt(input: ArrayBuffer):
-    Promise<tf.graph.proto.StepStats> {
-  return parsePbtxtFile(input, METADATA_REPEATED_FIELDS)
-      .then(obj => obj['step_stats']);
-}
-
-/**
- * Parses a ArrayBuffer of a proto txt file into javascript object.
- *
- * @param input The ArrayBuffer or file object implementing slice.
- * @param repeatedFields Map (Set) of all the repeated fields, since you can't
- *   tell directly from the pbtxt if a field is repeated or not.
- * @returns The parsed object.
- */
-function parsePbtxtFile(
-    input: ArrayBuffer,
-    repeatedFields: {[attrPath: string]: boolean}): Promise<Object> {
-  let output: { [name: string]: any; } = {};
-  let stack = [];
-  let path: string[] = [];
-  let current: { [name: string]: any; } = output;
-
-  function splitNameAndValueInAttribute(line: string) {
-    let colonIndex = line.indexOf(':');
-    let name = line.substring(0, colonIndex).trim();
-    let value = parseValue(line.substring(colonIndex + 2).trim());
-    return {
-      name: name,
-      value: value
-    };
-  }
-
-  /**
-   * Adds a value, given the attribute name and the host object. If the
-   * attribute already exists, but is not an array, it will convert it to an
-   * array of values.
-   *
-   * @param obj The host object that holds the attribute.
-   * @param name The attribute name (key).
-   * @param value The attribute value.
-   * @param path A path that identifies the attribute. Used to check if
-   *     an attribute is an array or not.
-   */
-  function addAttribute(obj: Object, name: string,
-      value: Object|string|number|boolean, path: string[]): void {
-    // We treat 'node' specially since it is done so often.
-    let existingValue = obj[name];
-    if (existingValue == null) {
-      obj[name] = path.join('.') in repeatedFields ? [value] : value;
-    } else if (Array.isArray(existingValue)) {
-      existingValue.push(value);
-    } else {
-      obj[name] = [existingValue, value];
-    }
-  }
-
-  // Run through the file a line at a time.
-  return streamParse(input, function(line: string) {
-    if (!line) {
-      return;
-    }
-    line = line.trim();
-
-    switch (line[line.length - 1]) {
-      case '{':  // create new object
-        let name = line.substring(0, line.length - 2).trim();
-        let newValue: { [name: string]: any; } = {};
-        stack.push(current);
-        path.push(name);
-        addAttribute(current, name, newValue, path);
-        current = newValue;
-        break;
-      case '}':
-        current = stack.pop();
-        path.pop();
-        break;
-      default:
-        let x = splitNameAndValueInAttribute(line);
-        addAttribute(current, x.name, x.value, path.concat(x.name));
-        break;
-    }
-  }).then(function() {
-    return output;
-  });
-}
-
-} // Close module tf.graph.parser.
diff --git a/tensorflow/tensorboard/components/tf_graph_common/proto.ts b/tensorflow/tensorboard/components/tf_graph_common/proto.ts
deleted file mode 100644
index eda73e45c3..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/proto.ts
+++ /dev/null
@@ -1,143 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * @fileoverview Interfaces that parallel proto definitions in
- * third_party/tensorflow/core/framework/...
- *     graph.proto
- *     step_stats.proto
- * These should stay in sync.
- */
-module tf.graph.proto {
-  /**
-   * TensorFlow node definition as defined in the graph.proto file.
-   */
-  export interface NodeDef {
-    /** Name of the node */
-    name: string;
-    /** List of nodes that are inputs for this node. */
-    input: string[];
-    /** The name of the device where the computation will run. */
-    device: string;
-    /** The name of the operation associated with this node. */
-    op: string;
-    /** List of attributes that describe/modify the operation. */
-    attr: {key: string, value: Object}[];
-  }
-
-  /**
-   * Generic graph as defined in the graph_explorer.proto file.
-   */
-  export interface GenericGraph {
-    /** List of nodes in the graph */
-    node: GenericNode[];
-    /** List of nodes in the graph */
-    edge: GenericEdge[];
-    /** List of attributes that describe/modify the operation. */
-    attr: Array<{[key: string]: any}>;
-  }
-
-  /**
-   * GenericEdge corresponds to the Edge message in graph_explorer.proto.
-   */
-  export interface GenericEdge {
-    /** Name of the source node. */
-    source: string;
-    /** Name of the target node. */
-    target: string;
-    /** Attributes of the edge. */
-    edge_attr: Array<{[key: string]: any}>;
-  }
-
-  /**
-   * GenericNode corresponds to the Node message in graph_explorer.proto.
-   */
-  export interface GenericNode {
-    /** Name of the node */
-    name: string;
-    /** Attributes of a leaf node or leaf nodes within a metanode. */
-    node_attr: Array<{[key: string]: any}>;
-    /** Attributes of a metanode. */
-    metanode_attr: Array<{[key: string]: any}>;
-  }
-
-  /**
-   * TensorFlow stats file definition as defined in the stats proto file.
-   */
-  export interface StepStats {
-    dev_stats: {device: string, node_stats: NodeExecStats[]}[];
-  }
-
-  /**
-   * TensorFlow stats for a node as defined in the step_stats proto file.
-   */
-  export interface NodeExecStats {
-    node_name: string;
-    // The next 4 properties are currently stored as string in json
-    // and must be parsed.
-    all_start_micros: number;
-    op_start_rel_micros: number;
-    op_end_rel_micros: number;
-    all_end_rel_micros: number;
-    memory: {
-      allocator_name: string;
-      total_bytes: number;  // Stored as string in json and should be parsed.
-      peak_bytes: number;   // Stored as string in json and should be parsed.
-    }[];
-    /** Output sizes recorded for a single execution of a graph node */
-    output: NodeOutput[];
-    timeline_label: string;
-    scheduled_micros: string;
-    thread_id: string;
-  }
-
-  /**
-   * Description for the output tensor(s) of an operation in the graph as
-   * defined in the step_stats.proto file.
-   */
-  export interface NodeOutput {
-    slot: number;  // Stored as string in json and should be parsed.
-    tensor_description: {
-      /** Data type of tensor elements */
-      dtype: string;
-      /** Shape of the tensor */
-      shape: {
-        /**
-         * Dimensions of the tensor, such as [{name: 'input', size: 30},
-         * {name: 'output', size: 40}] for a 30 x 40 2D tensor.  The names
-         * are optional. The order of entries in 'dim' matters: It indicates
-         * the layout of the values in the tensor in-memory representation.
-         */
-        dim: {
-          /** Size of the tensor in that dimension */
-          size: number,  // Stored as string in json and should be parsed.
-          /** Optional name of the tensor dimension */
-          name?: string
-        }[];
-      };
-      /** Information about the size and allocator used for the data */
-      allocation_description: {
-        // The next 2 properties are stored as string in json and
-        // should be parsed.
-        /** Total number of bytes requested */
-        requested_bytes: number;
-        /** Total number of bytes allocated, if known */
-        allocated_bytes?: number;
-        /** Name of the allocator used */
-        allocator_name: string;
-      };
-    };
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_common/render.ts b/tensorflow/tensorboard/components/tf_graph_common/render.ts
deleted file mode 100644
index 4f28af481d..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/render.ts
+++ /dev/null
@@ -1,1673 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-/**
- * Package for the Render Hierarchy for TensorFlow graph.
- */
-module tf.graph.render {
-
-export type Point = {x: number, y: number};
-
-/**
- * Color parameters for op nodes.
- */
-export let OpNodeColors = {DEFAULT_FILL: 'white', DEFAULT_STROKE: '#b2b2b2'};
-
-/**
- * Color parameters for node encoding.
- * @type {Object}
- */
-export let MetanodeColors = {
-  /**
-   * Default fill and stroke to use when no other information is available.
-   */
-  DEFAULT_FILL: '#d9d9d9',
-  DEFAULT_STROKE: '#a6a6a6',
-  SATURATION: 0.6,
-  LIGHTNESS: 0.85,
-  /**
-   * Neutral color to use when the node is expanded (used when coloring by
-   * compute time, memory and device).
-   */
-  EXPANDED_COLOR: '#f0f0f0',
-  /**
-   * Standard hue values for node color palette.
-   */
-  HUES: [220, 100, 180, 40, 20, 340, 260, 300, 140, 60],
-  STRUCTURE_PALETTE(id: number, lightened?: boolean) {
-    // The code below is a flexible way to computationally create a set
-    // of colors that go well together.
-    let hues = MetanodeColors.HUES;
-    let n = hues.length;
-    let hue = hues[id % n];
-    let m = Math.sin(hue * Math.PI / 360);
-    let sat = lightened ? 30 : 90 - 60 * m;
-    let light = lightened ? 95 : 80;
-    return d3.hsl(hue, .01 * sat, .01 * light).toString();
-  },
-  DEVICE_PALETTE(index: number): string {
-    return MetanodeColors.STRUCTURE_PALETTE(index);
-  },
-  XLA_CLUSTER_PALETTE(index: number): string {
-    return MetanodeColors.STRUCTURE_PALETTE(index);
-  },
-  UNKNOWN: '#eee',
-  GRADIENT_OUTLINE: '#888'
-};
-
-/**
- * Color parameters for op nodes.
- */
-export let SeriesNodeColors = {
-  DEFAULT_FILL: 'white',
-  DEFAULT_STROKE: '#b2b2b2'
-};
-
-/**
- * Parameters that affect how the graph is rendered on the screen.
- */
-const PARAMS = {
-  /**
-   * Whether to extract high degree nodes from the core part of the graph.
-   */
-  enableExtraction: true,
-  /**
-   * The minimum number of nodes for a graph to have in order for high in and
-   * out degree nodes to be extracted in auxiliary. The aim here is to prevent
-   * nodes from being extracted from small graphs.
-   */
-  minNodeCountForExtraction: 15,
-  /**
-   * The minimum in or out degree a node must have in order to be possibly
-   * extracted.
-   */
-  minDegreeForExtraction: 5,
-  /**
-   * Maximum number of control edges a node can have before they aren't
-   * displayed.
-   */
-  maxControlDegree: 4,
-  /**
-   * Maximum in (for outbound bridge paths) or out (for inbound bridge paths)
-   * degree of a node allowed for a bridge path to be rendered to it from a
-   * subhierarchy of nodes. Having a max prevents having too many nodes emanate
-   * from a subhierarchy and crowding up.
-   */
-  maxBridgePathDegree: 4,
-  /**
-   * Types patterns for predefined out-extract nodes, which are
-   * sink-like nodes that will be extracted from the main graph.
-   */
-  outExtractTypes: [
-    'NoOp'  // NoOps are sink-like used for managing control dependencies.
-  ],
-
-  /**
-   * Types patterns for predefined in-extract nodes, which are
-   * source-like nodes that will be extracted from the main graph.
-   */
-  inExtractTypes: [],
-
-  /**
-   * When removing edges from a high degree node, remove all of its edges if
-   * detachAllEdgesForHighDegree is true.  Otherwise remove all in-edges if
-   * the node has high in-degree, or all out-edges if the node has high
-   * out-degree.
-   */
-  detachAllEdgesForHighDegree: true,
-
-  /**
-   * After extracting high in/out degree nodes and predefined
-   * source-like/sink-like, extract isolated nodes to the side
-   * if this extractIsolatedNodesWithAnnotationsOnOneSide is true.
-   */
-  extractIsolatedNodesWithAnnotationsOnOneSide: true,
-
-  /**
-   * Whether to add bridge nodes and edges to the core when building the
-   * subhierarchy of an expanded metanode. See buildSubhierarchy().
-   */
-  enableBridgegraph: true,
-
-  /**
-   * 2 colors, for the minimum and maximum value respectively, whenever we
-   * have a gradient scale.
-   */
-  minMaxColors: ['#fff5f0', '#fb6a4a'],
-
-  /**
-   * Maximum number of annotations to be displayed on a node before an
-   * ellipsis is used.
-   */
-  maxAnnotations: 5
-};
-
-/**
- * Stores the rendering information, such as x and y coordinates,
- * for each node in the graph.
- */
-export class RenderGraphInfo {
-  hierarchy: hierarchy.Hierarchy;
-  private displayingStats: boolean;
-  private index: {[nodeName: string]: RenderNodeInfo};
-  private renderedOpNames: string[];
-  private deviceColorMap: d3.ScaleOrdinal<string, string>;
-  private xlaClusterColorMap: d3.ScaleOrdinal<string, string>;
-  private memoryUsageScale: d3.ScaleLinear<string, string>;
-  private computeTimeScale: d3.ScaleLinear<string, string>;
-  /** Scale for the thickness of edges when there is no shape information. */
-  edgeWidthScale:
-      d3.ScaleLinear<number, number> | d3.ScalePower<number, number>;
-  // Since the rendering information for each node is constructed lazily,
-  // upon node's expansion by the user, we keep a map between the node's name
-  // and whether the rendering information was already constructed for that
-  // node.
-  private hasSubhierarchy: {[nodeName: string]: boolean};
-  root: RenderGroupNodeInfo;
-  traceInputs: Boolean;
-
-  constructor(hierarchy: hierarchy.Hierarchy, displayingStats: boolean) {
-    this.hierarchy = hierarchy;
-    this.displayingStats = displayingStats;
-    this.index = {};
-    this.renderedOpNames = [];
-
-    this.computeScales();
-    // Maps node name to whether the rendering hierarchy was already
-    // constructed.
-    this.hasSubhierarchy = {};
-    this.root = new RenderGroupNodeInfo(hierarchy.root);
-    this.index[hierarchy.root.name] = this.root;
-    this.renderedOpNames.push(hierarchy.root.name);
-    this.buildSubhierarchy(hierarchy.root.name);
-    this.root.expanded = true;
-    this.traceInputs = false;
-  }
-
-  computeScales() {
-    this.deviceColorMap = d3.scaleOrdinal<string>()
-        .domain(this.hierarchy.devices)
-        .range(_.map(d3.range(this.hierarchy.devices.length),
-                     MetanodeColors.DEVICE_PALETTE));
-
-    this.xlaClusterColorMap =
-        d3.scaleOrdinal<string>()
-            .domain(this.hierarchy.xlaClusters)
-            .range(_.map(
-                d3.range(this.hierarchy.xlaClusters.length),
-                MetanodeColors.XLA_CLUSTER_PALETTE));
-
-    let topLevelGraph = this.hierarchy.root.metagraph;
-    // Find the maximum and minimum memory usage.
-    let memoryExtent = d3.extent(topLevelGraph.nodes(),
-        (nodeName, index) => {
-      let node = topLevelGraph.node(nodeName);
-      // Some ops don't have stats at all.
-      if (node.stats != null) {
-        return node.stats.totalBytes;
-      }
-    });
-    this.memoryUsageScale = d3.scaleLinear<string, string>()
-        .domain(memoryExtent)
-        .range(PARAMS.minMaxColors);
-
-    // Find also the minimum and maximum compute time.
-    let computeTimeExtent = d3.extent(topLevelGraph.nodes(),
-        (nodeName, index) => {
-      let node = topLevelGraph.node(nodeName);
-      // Some ops don't have stats at all.
-      if (node.stats != null) {
-        return node.stats.getTotalMicros();
-      }
-    });
-    this.computeTimeScale = d3.scaleLinear<string, string>()
-        .domain(computeTimeExtent)
-        .range(PARAMS.minMaxColors);
-
-    this.edgeWidthScale = this.hierarchy.hasShapeInfo ?
-      scene.edge.EDGE_WIDTH_SCALE :
-      d3.scaleLinear()
-        .domain([1, this.hierarchy.maxMetaEdgeSize])
-        .range([scene.edge.MIN_EDGE_WIDTH, scene.edge.MAX_EDGE_WIDTH]);
-  }
-
-  /**
-   * Get a previously created RenderNodeInfo by its node name.
-   */
-  getRenderNodeByName(nodeName: string): RenderNodeInfo {
-    return this.index[nodeName];
-  }
-
-  /**
-   * Get the underlying node in the hierarchical graph by its name.
-   */
-  getNodeByName(nodeName: string): Node {
-    return this.hierarchy.node(nodeName);
-  }
-
-  /**
-   * Get a previously created RenderNodeInfo for the specified node name,
-   * or create one if it hasn't been created yet.
-   */
-  getOrCreateRenderNodeByName(nodeName: string): RenderNodeInfo {
-    // Polymer may invoke this with null.
-    if (!nodeName) {
-      return null;
-    }
-
-    if (nodeName in this.index) {
-      return this.index[nodeName];
-    }
-
-    let node = this.hierarchy.node(nodeName);
-    // Exit early if the node does not exist in the hierarchy. This can happen
-    // when a graph is reloaded while the infocard points to a node not visible
-    // at the top-level.
-    if (!node) {
-      return null;
-    }
-    let renderInfo = node.isGroupNode ?
-        new RenderGroupNodeInfo(<GroupNode>node) :
-        new RenderNodeInfo(node);
-    this.index[nodeName] = renderInfo;
-    this.renderedOpNames.push(nodeName);
-
-    if (node.stats) {
-      renderInfo.memoryColor = this.memoryUsageScale(node.stats.totalBytes);
-      renderInfo.computeTimeColor =
-          this.computeTimeScale(node.stats.getTotalMicros());
-    }
-
-    if (!node.isGroupNode) {
-      let clusterName = (node as OpNode).xlaCluster;
-      if (clusterName) {
-        renderInfo.xlaClusterColor = this.xlaClusterColorMap(clusterName);
-      }
-    }
-
-    // We only fade nodes when we're displaying stats.
-    renderInfo.isFadedOut = this.displayingStats &&
-        !tf.graph.util.hasDisplayableNodeStats(node.stats);
-
-    if (node.isGroupNode) {
-      // Make a list of tuples (device, proportion), where proportion
-      // is the fraction of op nodes that have that device.
-      let pairs = _.pairs((<GroupNode>node).deviceHistogram);
-      if (pairs.length > 0) {
-        // Compute the total # of devices.
-        let numDevices = _.sum(pairs, _.last);
-        renderInfo.deviceColors = _.map(pairs, pair => ({
-              color: this.deviceColorMap(pair[0]),
-              // Normalize to a proportion of total # of devices.
-              proportion: pair[1] / numDevices
-            }));
-      }
-    } else {
-      let device = (<OpNode>renderInfo.node).device;
-      if (device) {
-        renderInfo.deviceColors = [{
-          color: this.deviceColorMap(device),
-          proportion: 1.0
-        }];
-      }
-    }
-
-    return this.index[nodeName];
-  }
-
-  /**
-   * Return the nearest ancestor node, including itself, that is visible
-   * in the visualization. This method is used so that we can select
-   * (highlight) a node that isn't drawn yet, by selecting (highlighting)
-   * its nearest ancestor that has been drawn.
-   */
-  getNearestVisibleAncestor(name: string): string {
-    let path = getHierarchicalPath(name);
-    for (let i = 0; i < path.length; i++) {
-      let nodeName = path[i];
-      // Op nodes have expanded set to false by default.
-      if (!this.getRenderNodeByName(nodeName).expanded) {
-        return nodeName;
-      }
-    }
-    // Fallthrough. If everything was expanded return the node.
-    return name;
-  }
-
-  // TODO(jimbo): Delete this an any code it touches (all deprecated).
-  setDepth(depth: number): void {
-    setGroupNodeDepth(this.root, +depth);
-  }
-
-  /**
-   * Returns true if the renderNode is an isolated node within its parent node.
-   */
-  isNodeAuxiliary(renderNode: RenderNodeInfo): boolean {
-    let parentNode = <RenderGroupNodeInfo>this.getRenderNodeByName(
-      renderNode.node.parentNode.name);
-    let found = _.find(parentNode.isolatedInExtract, node => {
-      return node.node.name === renderNode.node.name;
-    });
-    if (found) {
-      return true;
-    }
-    found = _.find(parentNode.isolatedOutExtract, node => {
-      return node.node.name === renderNode.node.name;
-    });
-    return !!found;
-  }
-
-  /**
-   * Returns a list of ops that have been rendered so far for this graph. More
-   * ops may later be rendered if the user expands nodes for instance. The list
-   * returned here can only stay the same size or grow on successive calls.
-   */
-  getNamesOfRenderedOps(): string[] {
-    return this.renderedOpNames;
-  }
-
-  buildSubhierarchy(nodeName: string): void {
-    // Terminate if the rendering hierarchy was already constructed
-    // for this node.
-    if (nodeName in this.hasSubhierarchy) {
-      return;
-    }
-
-    let renderNodeInfo = this.index[nodeName];
-
-    // If it is not a meta node or a series node, don't do anything.
-    if (renderNodeInfo.node.type !== NodeType.META &&
-        renderNodeInfo.node.type !== NodeType.SERIES) {
-      return;
-    }
-
-    // At this point we know the rendering information is about a group node.
-    let renderGroupNodeInfo = <RenderGroupNodeInfo> renderNodeInfo;
-    let metagraph = renderGroupNodeInfo.node.metagraph;
-    let coreGraph = renderGroupNodeInfo.coreGraph;
-
-    // Create render nodes to represent each child from the metagraph. Although
-    // these will initially be added to the coreGraph, they may later be
-    // extracted. Also, due to extraction, the coreGraph may contain disjoint
-    // groups between which there is no visible path (other than annotations).
-    _.each(metagraph.nodes(), childName => {
-
-      let childRenderInfo = this.getOrCreateRenderNodeByName(childName);
-      let childNode = childRenderInfo.node;
-
-      coreGraph.setNode(childName, childRenderInfo);
-
-      if (!childNode.isGroupNode) {
-        _.each((<OpNode>childNode).inEmbeddings, embedding => {
-          let renderMetaedgeInfo = new RenderMetaedgeInfo(null);
-          addInAnnotation(childRenderInfo, embedding, null, renderMetaedgeInfo,
-              AnnotationType.CONSTANT);
-          this.index[embedding.name] = new RenderNodeInfo(embedding);
-        });
-        _.each((<OpNode>childNode).outEmbeddings, embedding => {
-          let renderMetaedgeInfo = new RenderMetaedgeInfo(null);
-          addOutAnnotation(childRenderInfo, embedding, null, renderMetaedgeInfo,
-              AnnotationType.SUMMARY);
-          this.index[embedding.name] = new RenderNodeInfo(embedding);
-        });
-      }
-
-    });
-
-    // Add render metaedge info for edges in the metagraph.
-    _.each(metagraph.edges(), edgeObj => {
-      let metaedge = metagraph.edge(edgeObj);
-      let renderMetaedgeInfo = new RenderMetaedgeInfo(metaedge);
-      renderMetaedgeInfo.isFadedOut =
-          this.index[edgeObj.v].isFadedOut || this.index[edgeObj.w].isFadedOut;
-      coreGraph.setEdge(edgeObj.v, edgeObj.w, renderMetaedgeInfo);
-    });
-
-    if (PARAMS.enableExtraction &&
-        renderGroupNodeInfo.node.type === NodeType.META) {
-      extractHighDegrees(renderGroupNodeInfo);
-    }
-
-    // Record that we constructed the rendering hierarchy for this node, so we
-    // don't construct it another time.
-    this.hasSubhierarchy[nodeName] = true;
-
-    // Look up the parent node's render information and short circuit if none.
-    let parentNode = renderGroupNodeInfo.node.parentNode;
-    if (!parentNode) {
-      return;
-    }
-    let parentNodeInfo =
-      <RenderGroupNodeInfo> this.index[parentNode.name];
-
-    // Utility function for computing the name of a bridge node.
-    let getBridgeNodeName = (inbound, ...rest) =>
-        rest.concat([inbound ? 'IN' : 'OUT']).join('~~');
-
-    // Build out the bridgegraph.
-    let bridgegraph = this.hierarchy.getBridgegraph(nodeName);
-
-    // Look for popular nodes so we can make annotations instead of paths.
-    let otherCounts = {
-      // Counts of edges coming INTO other nodes by name (outgoing from self).
-      in: <{[nodeName: string]: number}> {},
-      // Counts of edges going OUT from other nodes by name (coming into self).
-      out: <{[nodeName: string]: number}> {},
-      // Counts of all control edges involving other nodes by name.
-      control: <{[nodeName: string]: number}> {},
-    };
-    _.each(bridgegraph.edges(), e => {
-      // An edge is inbound if its destination node is in the metagraph.
-      let inbound = !!metagraph.node(e.w);
-      let otherName = inbound ? e.v : e.w;
-      let metaedge = bridgegraph.edge(e);
-      if (!metaedge.numRegularEdges) {
-        otherCounts.control[otherName] =
-          (otherCounts.control[otherName] || 0) + 1;
-      } else if (inbound) {
-        otherCounts.out[otherName] = (otherCounts.out[otherName] || 0) + 1;
-      } else {
-        otherCounts.in[otherName] = (otherCounts.in[otherName] || 0) + 1;
-      }
-    });
-
-    // Add annotations and edges for bridgegraph relationships.
-    let hierarchyNodeMap = this.hierarchy.getNodeMap();
-    _.each(bridgegraph.edges(), bridgeEdgeObj => {
-      let bridgeMetaedge = bridgegraph.edge(bridgeEdgeObj);
-
-      // Determine whether this bridge edge is incoming by checking the
-      // metagraph for a node that matches the destination end.
-      let inbound = !!metagraph.node(bridgeEdgeObj.w);
-
-      // Based on the direction of the edge, one endpoint will be an immediate
-      // child of this renderNodeInfo, and the other endpoint will be a sibling
-      // of the parent (or an ancestor further up).
-      let [childName, otherName] =
-        inbound ?
-          [bridgeEdgeObj.w, bridgeEdgeObj.v] :
-          [bridgeEdgeObj.v, bridgeEdgeObj.w];
-
-      let childRenderInfo = this.index[childName];
-      let otherRenderInfo = this.index[otherName];
-      let otherNode =
-        otherRenderInfo ?
-          otherRenderInfo.node :
-          hierarchyNodeMap[otherName];
-
-      // Determine whether this edge is a control edge between nodes where
-      // either node is high-degree with respect to control edges. This will
-      // be a signal to show it as an annotation instead of a bridge edge.
-      let isHighDegreeControlEdge = !bridgeMetaedge.numRegularEdges &&
-        otherCounts.control[otherName] > PARAMS.maxControlDegree;
-
-      let [, childAnnotations] =
-        inbound ?
-          [renderNodeInfo.inAnnotations, childRenderInfo.inAnnotations] :
-          [renderNodeInfo.outAnnotations, childRenderInfo.outAnnotations];
-
-      // Don't render a bridge path if the other node has in or out degree above
-      // a threshold, lest bridge paths emanating out of a metagraph crowd up,
-      // as was the case for the Fatcat LSTM lstm_1 > lstm_1 metagraph.
-      let otherDegreeCount =
-          (inbound ? otherCounts.out : otherCounts.in)[otherName];
-      let isOtherHighDegree = otherDegreeCount > PARAMS.maxBridgePathDegree;
-
-      // The adjoining render metaedge info from the parent's coreGraph, if any.
-      // It will either be a Metaedge involving this node directly, if it
-      // previously came from a metagraph, or it'll be a Metaedge involving
-      // a previously created bridge node standing in for the other node.
-      let adjoiningMetaedge = null;
-
-      // We can only hope to render a bridge path if:
-      //  - bridgegraph paths are enabled,
-      //  - the other node is not too high-degree,
-      //  - the child is in the core (not extracted for being high-degree), and
-      //  - there's a path (in the traversal sense) between child and other.
-      let canDrawBridgePath = false;
-      if (PARAMS.enableBridgegraph &&
-          !isOtherHighDegree &&
-          !isHighDegreeControlEdge &&
-          childRenderInfo.isInCore()) {
-
-        // Utility function for finding an adjoining metaedge.
-        let findAdjoiningMetaedge = targetName => {
-          let adjoiningEdgeObj: graphlib.EdgeObject =
-            inbound ?
-              { v: targetName, w: nodeName } :
-              { v: nodeName, w: targetName };
-          return <RenderMetaedgeInfo>
-            parentNodeInfo.coreGraph.edge(adjoiningEdgeObj);
-        };
-
-        adjoiningMetaedge = findAdjoiningMetaedge(otherName);
-        if (!adjoiningMetaedge) {
-          adjoiningMetaedge = findAdjoiningMetaedge(
-              getBridgeNodeName(inbound, otherName, parentNode.name));
-        }
-
-        canDrawBridgePath = !!adjoiningMetaedge;
-      }
-
-      // Although dataflow edges are acyclic, control dependency edges may
-      // actually point 'backwards' in the graph. If this bridgeMetaedge is
-      // a control dependency, we need to determine whether it's backwards
-      // pointing so that we render it appropriately.
-      //
-      // For instance, say we're rendering a graph with nodes named A/B and Z/Y,
-      // and we're currently rendering the bridgegraph for A. Further, let's say
-      // that there was an original BaseEdge from A/B->Z/Y and a CONTROL EDGE
-      // from Z/Y=>A/B.
-      //
-      //     +----------------+
-      //     | A              |
-      //     |  +-----+       |         +------+
-      //     |  | B   |>----->|>------->| Z    |
-      //     |  |     |       |         |      |
-      //     |  |     |   *   |         |      |
-      //     |  |     |<=====<|<=======<|      |
-      //     |  +-----+       |         +------+
-      //     +----------------+
-      //
-      // When we render the subhierarchy for Metanode A, we'll come across a
-      // control-only Metaedge in the bridgegraph from Z=>A/B (*). The question
-      // is whether this edge is backwards.
-      //
-      // To answer that question, we follow the chain of adjoining metaedges
-      // until we reach the topmost one. In this case, that's the control-only
-      // Metaedge Z=>A in the ROOT's metagraph. We determine that this edge
-      // is backwards by looking at the topological ordering of ROOT's metagraph
-      // (which ignores control edges) and seeing that Z comes AFTER A.
-      //
-      // The property of being backwards is independent of whether the edge
-      // is inbound or outbound. In the preceding example, if we were building
-      // the subhierarchy for Z, we'd find bridge edge Z/Y=>A, walk to its
-      // topmost adjoining metaedge Z=>A and discover that it's backwards.
-      let backwards = false;
-      if (adjoiningMetaedge && !bridgeMetaedge.numRegularEdges) {
-        // Find the top-most adjoining render metaedge information, and the
-        // GroupNode whose metagraph must contain the associated metaedge.
-        let topAdjoiningMetaedge = adjoiningMetaedge;
-        let topGroupNode = parentNodeInfo.node;
-        while (topAdjoiningMetaedge.adjoiningMetaedge) {
-          topAdjoiningMetaedge = topAdjoiningMetaedge.adjoiningMetaedge;
-          topGroupNode = <GroupNode>topGroupNode.parentNode;
-        }
-
-        // Check against the topological ordering for the top node. The current
-        // bridge metaedge we're evaluating is backwards if its source comes
-        // after its destination.
-        let ordering = this.hierarchy.getTopologicalOrdering(topGroupNode.name);
-        let e = topAdjoiningMetaedge.metaedge;
-        backwards = ordering[e.v] > ordering[e.w];
-      }
-
-      // Render backwards control edges as annotations.
-      canDrawBridgePath = canDrawBridgePath && !backwards;
-
-      // If we can't make a bridge path for any reason, then we add an
-      // annotation instead.
-      if (!canDrawBridgePath) {
-        childAnnotations.push(new Annotation(
-            otherNode,
-            otherRenderInfo,
-            new RenderMetaedgeInfo(bridgeMetaedge),
-            AnnotationType.SHORTCUT,
-            inbound));
-        return;
-      }
-
-      // At this point, all conditions have been met for drawing a bridge path.
-
-      // Find or create the IN/OUT node representing otherNode.
-      let bridgeContainerName = getBridgeNodeName(inbound, nodeName);
-      let bridgeNodeName = getBridgeNodeName(inbound, otherName, nodeName);
-      let bridgeNodeRenderInfo = coreGraph.node(bridgeNodeName);
-      if (!bridgeNodeRenderInfo) {
-
-        // Find or create the directional container for the bridge node.
-        let bridgeContainerInfo = coreGraph.node(bridgeContainerName);
-        if (!bridgeContainerInfo) {
-          let bridgeContainerNode: BridgeNode = {
-            // Important node properties.
-            name: bridgeContainerName,
-            type: NodeType.BRIDGE,
-            // Unused node properties.
-            isGroupNode: false,
-            cardinality: 0,
-            parentNode: null,
-            stats: null,
-            include: InclusionType.UNSPECIFIED,
-            // BridgeNode properties.
-            inbound: inbound,
-            nodeAttributes: {},
-          };
-          bridgeContainerInfo =
-            new RenderNodeInfo(bridgeContainerNode);
-          this.index[bridgeContainerName] = bridgeContainerInfo;
-          coreGraph.setNode(bridgeContainerName, bridgeContainerInfo);
-        }
-
-        let bridgeNode: BridgeNode = {
-          // Important node properties.
-          name: bridgeNodeName,
-          type: NodeType.BRIDGE,
-          // Unimportant node properties.
-          isGroupNode: false,
-          cardinality: 1,
-          parentNode: null,
-          stats: null,
-          include: InclusionType.UNSPECIFIED,
-          // BridgeNode properties.
-          inbound: inbound,
-          nodeAttributes: {},
-        };
-        bridgeNodeRenderInfo = new RenderNodeInfo(bridgeNode);
-        this.index[bridgeNodeName] = bridgeNodeRenderInfo;
-        coreGraph.setNode(bridgeNodeName, bridgeNodeRenderInfo);
-
-        // Set bridgeNode to be a graphlib child of the container node.
-        coreGraph.setParent(bridgeNodeName, bridgeContainerName);
-        bridgeContainerInfo.node.cardinality++;
-      }
-
-      // Create and add a bridge render metaedge.
-      let bridgeRenderMetaedge =
-        new RenderMetaedgeInfo(bridgeMetaedge);
-      bridgeRenderMetaedge.adjoiningMetaedge = adjoiningMetaedge;
-      inbound ?
-        coreGraph.setEdge(bridgeNodeName, childName, bridgeRenderMetaedge) :
-        coreGraph.setEdge(childName, bridgeNodeName, bridgeRenderMetaedge);
-
-    }); // End _.each(bridgegraph.edges).
-
-    // For each bridge container (IN and/or OUT), add structural edges between
-    // terminal nodes and that container. A terminal node is one which has no
-    // non-bridge edges in the direction of the container.
-    //
-    // For example, consider a Metanode A which contains two child nodes A/B
-    // and A/C. Let's say it has one edge in the metagraph from A/B->A/C, and
-    // one edge in the bridgegraph from Z->A/C.
-    //
-    // At this point, we've added a container bridge node IN to house all
-    // incoming bridge nodes. We've also added a bridge node Z' (with parent IN)
-    // to A, and a bridge edge from Z'->C.
-    //
-    //     +----------------------+
-    //     | A          +---+     |
-    //     |    +------>| C |     |
-    //     |    |       +---+     |
-    //     |    |         ^       |
-    //     |    |         |       |
-    //     |    |    +----|----+  |
-    //     |    |    | IN |    |  |
-    //     |  +---+  |  +---+  |  |
-    //     |  | B |  |  | Z'|  |  |
-    //     |  +---+  |  +---+  |  |
-    //     |         +---------+  |
-    //     +----------------------+
-    //
-    // With no other help, dagre would lay out B and Z' on the same level,
-    // because both of them have no incoming edges. In other words, B is a
-    // terminal node in the INCOMING direction.
-    //
-    // But we want to force dagre to lay out Z' (and everything in IN) lower
-    // than all non-bridge nodes, so that there's enough room for the bridge
-    // edges after they've been adjusted to meet up with paths coming in from
-    // outside.
-    //
-    // To force Z' (and all other bridge nodes) to be lowest in the graph, we
-    // identify terminal nodes like B and give them structural edges to
-    // a new structural bridge node S which we add to IN.
-    //
-    //     +----------------------+
-    //     | A          +---+     |
-    //     |       +--->| C |     |
-    //     |       |    +---+     |
-    //     |     +---+    ^       |
-    //     |     | B |    |       |
-    //     |     +---+    |       |
-    //     |       ^      |       |
-    //     |       |      |       |
-    //     |  +----|------|----+  |
-    //     |  |IN  |      |    |  |
-    //     |  |  +---+  +---+  |  |
-    //     |  |  | S |  | Z'|  |  |
-    //     |  |  +---+  +---+  |  |
-    //     |  +----------------+  |
-    //     +----------------------+
-    //
-    // This ensures that dagre will lay out the bridge containers strictly at
-    // the ends of the graph. The structural edges will never be seen in the
-    // visualization except as a debugging aid.
-    _.each([true, false], inbound => {
-      let bridgeContainerName = getBridgeNodeName(inbound, nodeName);
-      let bridgeContainerInfo = coreGraph.node(bridgeContainerName);
-      if (!bridgeContainerInfo) {
-        return;
-      }
-      _.each(coreGraph.nodes(), childName => {
-        // Short-circuit if this child is a bridge node or it's not a terminal
-        // node in the direction we're interested in.
-        let childNodeInfo = coreGraph.node(childName);
-        if (childNodeInfo.node.type === NodeType.BRIDGE) {
-          return;
-        }
-        let isTerminal = inbound ?
-          !coreGraph.predecessors(childName).length :
-          !coreGraph.successors(childName).length;
-        if (!isTerminal) {
-          return;
-        }
-
-        // Find or create a bridge node in the container for all structural
-        // metaedges. It would have been nice to skip this step and simply
-        // set a metaedge between the terminal node and the container node, but
-        // in that case, something about the graph upsets dagre.layout()'s
-        // longestPath algorithm (was getting errors due to an undefined).
-        let structuralNodeName =
-            getBridgeNodeName(inbound, nodeName, 'STRUCTURAL_TARGET');
-        let structuralRenderInfo = coreGraph.node(structuralNodeName);
-        if (!structuralRenderInfo) {
-          let bridgeNode: BridgeNode = {
-            // Important Node properties.
-            name: structuralNodeName,
-            type: NodeType.BRIDGE,
-            // Unimportant Node properties.
-            isGroupNode: false,
-            cardinality: 1,
-            parentNode: null,
-            stats: null,
-            include: InclusionType.UNSPECIFIED,
-            // BridgeNode properties.
-            inbound: inbound,
-            nodeAttributes: {},
-          };
-          structuralRenderInfo = new RenderNodeInfo(bridgeNode);
-          structuralRenderInfo.structural = true;
-          this.index[structuralNodeName] = structuralRenderInfo;
-          coreGraph.setNode(structuralNodeName, structuralRenderInfo);
-          bridgeContainerInfo.node.cardinality++;
-          coreGraph.setParent(structuralNodeName, bridgeContainerName);
-        }
-
-        // Create the structural Metaedge and insert it.
-        let structuralMetaedgeInfo = new RenderMetaedgeInfo(null);
-        structuralMetaedgeInfo.structural = true;
-        structuralMetaedgeInfo.weight--; // Reduce weight for dagre layout.
-        inbound ?
-          coreGraph.setEdge(
-              structuralNodeName, childName, structuralMetaedgeInfo) :
-          coreGraph.setEdge(
-              childName, structuralNodeName, structuralMetaedgeInfo);
-      });
-    });
-  }
-}
-
-/**
- * A class for rendering annotation object which contains label
- * about the node embedded as annotation, type of annotation and the location
- * of both the annotation's node and edge.
- *
- * Annotation objects include embedded constants, embedded summary, and
- * edge shortcuts.
- */
-export class Annotation {
-  node: Node;
-  renderNodeInfo: RenderNodeInfo;
-  renderMetaedgeInfo: RenderMetaedgeInfo;
-  annotationType: AnnotationType;
-  /**
-   * Center position of annotation relative to the host
-   * node's center x.
-   */
-  dx: number;
-  /**
-   * Center position of annotation relative to the host
-   * node's center y.
-   */
-  dy: number;
-  width: number;
-  height: number;
-  /**
-   * The names of nodes on either side of this edge.
-   */
-  v: string;
-  w: string;
-  /**
-   * A flag whether it is an in-annotation (if true) or
-   * out-annotation  (if false).
-   */
-  isIn: boolean;
-  /** Label horizontal offset from the end of the node shape */
-  labelOffset: number;
-  /**
-   * Array of points for edges from the annotation to its host
-   * node. Each point contains the point location, relative to
-   * the host node's center.
-   */
-  points: {dx: number, dy: number}[];
-
-  /**
-   * Creates a new Annotation.
-   *
-   * @param node The underlying node this annotation points to.
-   * @param renderNodeInfo The render information for the underlying node
-   *     this annotation points to. This can be null if the annotation
-   *     denotes an embedding (constant, summary), in which case we
-   *     use the node property.
-   * @param renderMetaedgeInfo The render information for the edge associated
-   *     with the annotation.
-   * @param type The type of the annotation.
-   * @param isIn True if it is an in-annotation. False if it is an
-   *     out-annotation.
-   */
-  constructor(node: Node, renderNodeInfo: RenderNodeInfo,
-      renderMetaedgeInfo: RenderMetaedgeInfo, type: AnnotationType,
-      isIn: boolean) {
-    this.node = node;
-    this.renderNodeInfo = renderNodeInfo;
-    this.renderMetaedgeInfo = renderMetaedgeInfo;
-    this.annotationType = type;
-    // Properties specified by layout
-    this.dx = 0;
-    this.dy = 0;
-    this.width = 0;
-    this.height = 0;
-    // Properties needed for generating an ID for the edge's path element if
-    // this annotation is associated with a metaedge.
-    if (renderMetaedgeInfo && renderMetaedgeInfo.metaedge) {
-      this.v = renderMetaedgeInfo.metaedge.v;
-      this.w = renderMetaedgeInfo.metaedge.w;
-    }
-
-    this.isIn = isIn;
-    this.points = [];
-  }
-};
-
-export enum AnnotationType {SHORTCUT, CONSTANT, SUMMARY, ELLIPSIS};
-
-/**
- * Manages a list of annotations. Two will be used for each
- * RenderNodeInfo, one for in annotations and one for out annotations.
- */
-export class AnnotationList {
-  /**
-   * List of visually drawable annotations, may include an ellipses annotation
-   * if the number added exceeds the number specified by maxAnnotations.
-   */
-  list: Annotation[];
-
-  /**
-   * Set of nodes which have been added as annotations to this list, so we can
-   * prevent duplicates.
-   */
-  nodeNames: { [nodeName: string]: boolean };
-
-  constructor() {
-    this.list = [];
-    this.nodeNames = {};
-  }
-
-  /**
-   * Append an annotation to the list, or a stand-in ellipsis annotation instead
-   * if this would make it too many.
-   */
-  push(annotation: Annotation): void {
-    if (annotation.node.name in this.nodeNames) {
-      return; // Skip duplicate annotation.
-    }
-    this.nodeNames[annotation.node.name] = true;
-
-    if (this.list.length < PARAMS.maxAnnotations) {
-      this.list.push(annotation);
-      return;
-    }
-
-    let lastAnnotation = this.list[this.list.length - 1];
-    if (lastAnnotation.annotationType === AnnotationType.ELLIPSIS) {
-      let ellipsisNode = <EllipsisNode>lastAnnotation.node;
-      ellipsisNode.setNumMoreNodes(++ellipsisNode.numMoreNodes);
-      return;
-    }
-
-    let ellipsisNode = new tf.graph.EllipsisNodeImpl(1);
-    this.list.push(new Annotation(ellipsisNode,
-        new RenderNodeInfo(ellipsisNode), null,
-        AnnotationType.ELLIPSIS, annotation.isIn));
-  }
-}
-
-/**
- * Contains rendering information about a node in the hierarchical graph.
- */
-export class RenderNodeInfo {
-  /** Reference to the original underlying Node from the hierarchical graph. */
-  node: Node;
-  /** Whether the node is expanded or not. */
-  expanded: boolean;
-  /**
-   * List of rendering information about in-annotations like constants and
-   * shortcuts to high-degree nodes.
-   */
-  inAnnotations: AnnotationList;
-  /**
-   * List of rendering information about out-annotations (e.g. summary nodes)
-   */
-  outAnnotations: AnnotationList;
-
-  // --- Params specified by layout --- //
-
-  /** Center x position */
-  x: number;
-  /** Center y position */
-  y: number;
-  /**
-   * Total width of the node's shape, including in- and out-annotations. This
-   * property is used by dagre to layout the graph.
-   */
-  width: number;
-  /**
-   * Total height of the node's shape, including in- and out-annotations. This
-   * property is used by dagre to layout the graph.
-   */
-  height: number;
-  /**
-   * Size of the main box of the node, excluding in- and out-annotations. This
-   * property is used to draw the rectangle/ellipse shape denoting the node.
-   */
-  coreBox: {
-    width: number,
-    height: number,
-  };
-
-  /** Width of the bounding box for all in-annotations. */
-  inboxWidth: number;
-  /** Width of the bounding box for all out-annotations. */
-  outboxWidth: number;
-  /**
-   * Whether the node should be excluded from the scene.
-   * This is only used when there are too many items in a series so we only
-   * want to include top N ones.
-   */
-  // TODO(jimbo): Now that series rendering is non-recursive, remove this and
-  // all its uses from the code base.
-  excluded: boolean;
-
-  // --- Params used in drawing the bridge paths --- //
-
-  /**
-   * All bridge nodes are meant to be invisible, but whereas most represent a
-   * relationship from the underlying graph hierarchy, some exist solely for
-   * layout reasons. Specifically, those bridge nodes which have only structural
-   * rendering metaedges.
-   */
-  structural: boolean;
-
-  // --- Params for the size of the node box --- //
-
-  /** Label vertical offset from the center of node shape */
-  labelOffset: number;
-  /** Rectangle radius (for making rounded rectangle) */
-  radius: number;
-
-  // --- Params for expanded node --- //
-
-  /** Label height for expanded node. */
-  labelHeight: number;
-  // Paddings between inner subscene and the border of the expanded node.
-  paddingTop: number;
-  paddingLeft: number;
-  paddingRight: number;
-  paddingBottom: number;
-
-  /**
-   * Whether a node is extracted as source-like (having high out-degree or
-   * matching predefined in-extract pattern.)
-   */
-  isInExtract: boolean;
-  /**
-   * Whether a node is extracted as sink-like (having high in-degree or matching
-   * predefined out-extract pattern.)
-   */
-  isOutExtract: boolean;
-
-  /**
-   * List of (color, proportion) tuples based on the proportion of devices of
-   * its children. If this node is an op node, this list will have only one
-   * color with proportion 1.0.
-   */
-  deviceColors: Array<{color: string, proportion: number}>;
-
-  /**
-   * Color according to the XLA cluster of this node.
-   */
-  xlaClusterColor: string;
-
-  /**
-   * Color according to the memory usage of this node.
-   */
-  memoryColor: string;
-
-  /**
-   * Color according to the compute time of this node.
-   */
-  computeTimeColor: string;
-
-  /**
-   * Whether this node is faded out. Used when displaying stats.
-   */
-  isFadedOut: boolean;
-
-  constructor(node: Node) {
-    this.node = node;
-    this.expanded = false;
-    this.inAnnotations = new AnnotationList();
-    this.outAnnotations = new AnnotationList();
-    // Params specified by layout
-    this.x = 0;
-    this.y = 0;
-    this.width = 0;
-    this.height = 0;
-    this.inboxWidth = 0;
-    this.outboxWidth = 0;
-
-    this.excluded = false;
-
-    // Params for bridge paths.
-    this.structural = false;
-
-    // Params for node box.
-    this.labelOffset = 0;
-    this.radius = 0;
-
-    // Params for expanded node
-    this.labelHeight = 0;
-    this.paddingTop = 0;
-    this.paddingLeft = 0;
-    this.paddingRight = 0;
-    this.paddingBottom = 0;
-    this.isInExtract = false;
-    this.isOutExtract = false;
-    this.coreBox = {width: 0, height: 0};
-
-    // By default, we don't fade nodes out. Default to false for safety.
-    this.isFadedOut = false;
-  }
-
-  isInCore(): boolean {
-    return !this.isInExtract && !this.isOutExtract;
-  }
-}
-
-/**
- * Contains rendering information about a Metaedge from the underlying
- * hierarchical graph. It may be from either a metagraph or a bridgegraph.
- */
-export class RenderMetaedgeInfo {
-  /**
-   * Reference to the original underlying Metaedge from the hierarchical graph,
-   * if any. This will be null for the edges which connect OpNodes to their
-   * embeddings, for example.
-   */
-  metaedge: Metaedge;
-
-  /**
-   * Reference to the adjoining RenderMetaedgeInfo from the parent's
-   * coreGraph. This is used during layout to determine the point at which this
-   * edge should touch the node's bounding box. This property will be null for
-   * edges which terminate at a node on both ends (all non-bridge edges).
-   */
-  adjoiningMetaedge: RenderMetaedgeInfo;
-
-  /**
-   * Most of the time, a RenderMetaedgeInfo object represents a real
-   * edge between nodes in the underlying graph structure. But sometimes, an
-   * edge only exists for layout purposes. These structural edges are added
-   * during buildSubhierarchy() to force dagre.layout() to put bridge nodes
-   * at the ends of the flow.
-   * @see buildSubhierarchy()
-   */
-  structural: boolean;
-
-  /**
-   * Weight of the edge, used by dagre when deciding how important an edge is.
-   * Edges with higher weight are made shorter and straighter. The default
-   * dagre uses is 1.
-   */
-  weight: number;
-
-  /**
-   * X and Y coordinate pairs of the points in the path of the edge.
-   * @see tf.graph.node.subsceneAdjustPaths
-   */
-  points: Point[];
-
-  /**
-   * D3 selection of the group containing the path that displays this edge.
-   */
-  edgeGroup: d3.Selection<RenderMetaedgeInfo & any, any, any, any>;
-
-  /** Id of the <marker> used as a start-marker for the edge path. */
-  startMarkerId: string;
-
-  /** Id of the <marker> used as an end-marker for the edge path. */
-  endMarkerId: string;
-
-  /**
-   * Whether this edge is faded out. Used for fading out unused edges when
-   * displaying run statistics.
-   */
-  isFadedOut: boolean;
-
-  constructor(metaedge: Metaedge) {
-    this.metaedge = metaedge;
-    this.adjoiningMetaedge = null;
-    this.structural = false;
-    this.weight = 1;
-    this.isFadedOut = false;
-  }
-}
-
-function addInAnnotation(node: RenderNodeInfo, predecessor: Node,
-    predecessorRenderInfo: RenderNodeInfo,
-    edge: RenderMetaedgeInfo, type: AnnotationType): void {
-  let annotation = new Annotation(predecessor, predecessorRenderInfo, edge,
-      type, true);
-  node.inAnnotations.push(annotation);
-}
-
-function addOutAnnotation(node: RenderNodeInfo, successor: Node,
-    successorRenderInfo: RenderNodeInfo, edge: RenderMetaedgeInfo,
-    type: AnnotationType): void {
-  let annotation = new Annotation(successor, successorRenderInfo, edge,
-      type, false);
-  node.outAnnotations.push(annotation);
-}
-
-function setGraphDepth(graph: graphlib.Graph<RenderNodeInfo, any>,
-    depth: number) {
-  _.each(graph.nodes(), nodeName => {
-    let child = graph.node(nodeName);
-    child.expanded = depth > 1; // set all child of depth 1 to collapsed
-    if (depth > 0) {
-      switch (child.node.type) {
-        case NodeType.META:
-        case NodeType.SERIES:
-          setGroupNodeDepth(<RenderGroupNodeInfo>child, depth - 1);
-          break;
-        // Do nothing for leaf
-      }
-    }
-  });
-};
-
-export class RenderGroupNodeInfo extends RenderNodeInfo {
-  node: GroupNode;
-  /**
-   * The core graph is derived from the underlying node's metagraph, minus
-   * the extracted source-like and sink-like nodes.
-   */
-  coreGraph: graphlib.Graph<RenderNodeInfo, RenderMetaedgeInfo>;
-  /** Size of the bounding box for a metanode's isolated in-extract children. */
-  inExtractBox: {width: number, height: number};
-  /**
-   * Size of the bounding box for a metanode's isolated out-extract children.
-   */
-  outExtractBox: {width: number, height: number};
-  /** Array of isolated in-extract nodes. */
-  isolatedInExtract: RenderNodeInfo[];
-  /** Array of isolated out-extract nodes. */
-  isolatedOutExtract: RenderNodeInfo[];
-
-  constructor(groupNode: GroupNode) {
-    super(groupNode);
-    let metagraph = groupNode.metagraph;
-    let gl = metagraph.graph();
-    this.coreGraph =
-        createGraph<RenderNodeInfo, RenderMetaedgeInfo>(
-            gl.name, GraphType.CORE, { compound: true });
-    this.inExtractBox = {width: 0, height: 0};
-    this.outExtractBox = {width: 0, height: 0};
-    this.isolatedInExtract = [];
-    this.isolatedOutExtract = [];
-  }
-}
-
-function setGroupNodeDepth(renderInfo: RenderGroupNodeInfo,
-    depth: number): void {
-  if (renderInfo.coreGraph) {
-    setGraphDepth(renderInfo.coreGraph, depth);
-  }
-}
-
-/**
- * Remove an edge from the graph and add annotations to both ends of the edge.
- *
- * @param The core graph.
- * @param v Source name.
- * @param w Sink name.
- */
-function createShortcut(
-    graph: graphlib.Graph<RenderNodeInfo, RenderMetaedgeInfo>,
-    v: string, w: string) {
-  let src = graph.node(v);
-  let sink = graph.node(w);
-  let edge = graph.edge(v, w);
-
-  // If either of the nodes is explicitly included in the main graph and
-  // both nodes are in the main graph then do not create the shortcut
-  // and instead keep the real edge.
-  if ((src.node.include === InclusionType.INCLUDE ||
-       sink.node.include === InclusionType.INCLUDE) &&
-      src.node.include !== InclusionType.EXCLUDE &&
-      sink.node.include !== InclusionType.EXCLUDE) {
-    return;
-  }
-
-  // Add each annotation.
-  addOutAnnotation(src, sink.node, sink, edge, AnnotationType.SHORTCUT);
-  addInAnnotation(sink, src.node, src, edge, AnnotationType.SHORTCUT);
-
-  // Remove the edge from the core graph.
-  graph.removeEdge(v, w);
-}
-
-/**
- * Remove edges from a node, and set its isOutExtract property to true,
- * and remove the node and move it to isolatedOutExtract.
- *
- * If detachAllEdgesForHighDegree or forceDetach is true, extract all of its
- * edges. Otherwise, only extract all in-edges.
- */
-function makeOutExtract(renderNode: RenderGroupNodeInfo, n: string,
-    forceDetach?: boolean) {
-  let graph = renderNode.coreGraph;
-  let child = graph.node(n);
-  child.isOutExtract = true;
-
-  _.each(graph.predecessors(n), (p, index) => {
-    createShortcut(graph, p, n);
-  });
-
-  if (PARAMS.detachAllEdgesForHighDegree || forceDetach) {
-    _.each(graph.successors(n), (s, index) => {
-      createShortcut(graph, n, s);
-    });
-  }
-
-  // Remove the node from the core graph if it no longer has neighbors.
-  if (graph.neighbors(n).length === 0) {
-    child.node.include = InclusionType.EXCLUDE;
-    renderNode.isolatedOutExtract.push(child);
-    graph.removeNode(n);
-  }
-}
-
-/**
- * Remove edges from a node, set its isInExtract property to true,
- * and remove the node and move it to isolatedInExtract.
- *
- * If detachAllEdgesForHighDegree or forceDetach is true, extract all of its
- * edges. Otherwise, only remove all out-edges.
- */
-export function makeInExtract(renderNode: RenderGroupNodeInfo, n: string,
-    forceDetach?: boolean) {
-  let graph = renderNode.coreGraph;
-  let child = graph.node(n);
-  child.isInExtract = true;
-
-  _.each(graph.successors(n), (s, index) => {
-    createShortcut(graph, n, s);
-  });
-
-  if (PARAMS.detachAllEdgesForHighDegree || forceDetach) {
-    _.each(graph.predecessors(n), (p, index) => {
-      createShortcut(graph, p, n);
-    });
-  }
-
-  // Remove the node from the core graph if it no longer has neighbors.
-  if (graph.neighbors(n).length === 0) {
-    child.node.include = InclusionType.EXCLUDE;
-    renderNode.isolatedInExtract.push(child);
-    graph.removeNode(n);
-  }
-}
-
-/**
- * Check whether the node's type is a member of the given list of types.
- *
- * @param node Node.
- * @param types List of type to match.
- */
-function hasTypeIn(node: Node, types: string[]): boolean {
-  if (node.type === NodeType.OP) {
-    for (let i = 0; i < types.length; i++) {
-      if ((<OpNode>node).op === types[i]) { return true; }
-    }
-  } else if (node.type === NodeType.META) {
-    let rootOpNode = (<Metanode>node).getRootOp();
-    if (rootOpNode) {
-      for (let i = 0; i < types.length; i++) {
-        if (rootOpNode.op === types[i]) { return true; }
-      }
-    }
-  }
-  return false;
-}
-
-/** Move nodes that are specified to be excluded out of the core graph. */
-function extractSpecifiedNodes(renderNode: RenderGroupNodeInfo) {
-  let graph = renderNode.coreGraph;
-  _.each(graph.nodes(), n => {
-    let renderInfo = graph.node(n);
-    if (renderInfo.node.include === InclusionType.EXCLUDE) {
-      if (renderNode.coreGraph.outEdges(n).length >
-          renderNode.coreGraph.inEdges(n).length) {
-        makeOutExtract(renderNode, n, true);
-      } else {
-        makeInExtract(renderNode, n, true);
-      }
-    }
-  });
-}
-
-/** Remove edges from pre-defined out-extract patterns */
-function extractPredefinedSink(renderNode: RenderGroupNodeInfo) {
-  let graph = renderNode.coreGraph;
-  _.each(graph.nodes(), n => {
-    let renderInfo = graph.node(n);
-    if (renderInfo.node.include !== InclusionType.UNSPECIFIED) {
-      return;
-    }
-    if (hasTypeIn(renderInfo.node, PARAMS.outExtractTypes)) {
-      makeOutExtract(renderNode, n);
-    }
-  });
-}
-
-/** Remove edges from pre-defined in-extract patterns */
-function extractPredefinedSource(renderNode) {
-  let graph = renderNode.coreGraph;
-  _.each(graph.nodes(), n => {
-    let renderInfo = graph.node(n);
-    if (renderInfo.node.include !== InclusionType.UNSPECIFIED) {
-      return;
-    }
-    if (hasTypeIn(renderInfo.node, PARAMS.inExtractTypes)) {
-      makeInExtract(renderNode, n);
-    }
-  });
-}
-
-/** Extract nodes deemed to have either high in-degree or high out-degree. */
-function extractHighInOrOutDegree(renderNode: RenderGroupNodeInfo) {
-  let graph = renderNode.coreGraph;
-
-  // Create mappings from node to in and out degrees. Count the number of valid
-  // nodes along the way.
-  let nodeToInDegree = {};
-  let nodeToOutDegree = {};
-  let validNodeCount = 0;
-  _.each(graph.nodes(), currentNode => {
-    if (graph.node(currentNode).node.include !== InclusionType.UNSPECIFIED) {
-      // This node is not included in the first place.
-      return;
-    }
-
-    // Count the in and out degrees based on only regular edges, unless there
-    // are no regular edges, in which case use the number of control edges.
-    // This is done so that control edges don't affect if nodes are extracted
-    // from the core graph, unless the node is only used for control.
-    let inDegree =
-        _.reduce(graph.predecessors(currentNode), (inDegree, pred) => {
-          let metaedge = graph.edge(pred, currentNode).metaedge;
-          return inDegree + (metaedge.numRegularEdges ? 1 : 0);
-        }, 0);
-    if (inDegree === 0 && graph.predecessors(currentNode).length > 0) {
-      inDegree = graph.predecessors(currentNode).length;
-    }
-
-    let outDegree =
-        _.reduce(graph.successors(currentNode), (outDegree, succ) => {
-          let metaedge = graph.edge(currentNode, succ).metaedge;
-          return outDegree + (metaedge.numRegularEdges ? 1 : 0);
-        }, 0);
-    if (outDegree === 0 && graph.successors(currentNode).length > 0) {
-      outDegree = graph.successors(currentNode).length;
-    }
-
-    // Store the in and out degrees of this node to avoid recomputing.
-    nodeToInDegree[currentNode] = inDegree;
-    nodeToOutDegree[currentNode] = outDegree;
-    validNodeCount++;
-  });
-
-  if (validNodeCount < PARAMS.minNodeCountForExtraction) {
-    // This graph has few nodes. Do not extract any nodes.
-    return;
-  }
-
-  // We only extract if the node has a min in or out degree greater than this.
-  let minUpperBound = PARAMS.minDegreeForExtraction - 1;
-
-  // Mark for extraction nodes with in-degree > Q3 + (Q3 - Q1).
-  let q3Index = Math.round(validNodeCount * 0.75);
-  let q1Index = Math.round(validNodeCount * 0.25);
-  let sortedByInDegree = Object.keys(nodeToInDegree).sort((node0, node1) => {
-    return nodeToInDegree[node0] - nodeToInDegree[node1];
-  });
-  let inDegreeQ3 = nodeToInDegree[sortedByInDegree[q3Index]];
-  let inDegreeQ1 = nodeToInDegree[sortedByInDegree[q1Index]];
-  let inDegreeUpperBound = inDegreeQ3 + inDegreeQ3 - inDegreeQ1;
-  // Only extract if the upper bound is high enough.
-  inDegreeUpperBound = Math.max(inDegreeUpperBound, minUpperBound);
-  for (let i = validNodeCount - 1;
-       nodeToInDegree[sortedByInDegree[i]] > inDegreeUpperBound; i--) {
-    // Extract a high in-degree node.
-    makeInExtract(renderNode, sortedByInDegree[i]);
-  }
-
-  // Mark for extraction nodes with out-degree > Q3 + (Q3 - Q1) * 4.
-  let sortedByOutDegree = Object.keys(nodeToOutDegree).sort((node0, node1) => {
-    return nodeToOutDegree[node0] - nodeToOutDegree[node1];
-  });
-  let outDegreeQ3 = nodeToOutDegree[sortedByOutDegree[q3Index]];
-  let outDegreeQ1 = nodeToOutDegree[sortedByOutDegree[q1Index]];
-  // The upper bound for extracting out-degree nodes is higher than that for
-  // extracting in-degree ones (Note the "* 4") because, in practice, some
-  // graphs look worse with a smaller out-degree bound. For instance, a smaller
-  // out-degree bound removes the convolution nodes from cifar 10 train's graph.
-  let outDegreeUpperBound = outDegreeQ3 + (outDegreeQ3 - outDegreeQ1) * 4;
-  // Only extract if the upper bound is high enough.
-  outDegreeUpperBound = Math.max(outDegreeUpperBound, minUpperBound);
-  for (let i = validNodeCount - 1;
-       nodeToOutDegree[sortedByOutDegree[i]] > outDegreeUpperBound; i--) {
-    let node = graph.node(sortedByOutDegree[i]);
-    if (!node || node.isInExtract) {
-      // This node has already been extracted due to high in-degree. It might
-      // have been removed from the graph in general (during in-degree
-      // extraction) due to a lack of neighbors. Do not extract this node twice.
-      continue;
-    }
-
-    // Extract a high out-degree node that has not already been extracted.
-    makeOutExtract(renderNode, sortedByOutDegree[i]);
-  }
-}
-
-/** Remove control edges from nodes that have too many control edges */
-function removeControlEdges(renderNode: RenderGroupNodeInfo) {
-  let graph = renderNode.coreGraph;
-
-  // Collect control edges into a map by node name.
-  let map = <{[nodeName: string]: graphlib.EdgeObject[]}>{};
-  _.each(graph.edges(), e => {
-    if (!graph.edge(e).metaedge.numRegularEdges) {
-      (map[e.v] = map[e.v] || []).push(e);
-      (map[e.w] = map[e.w] || []).push(e);
-    }
-  });
-
-  // For each node with too many control edges, turn them into annotations.
-  _.each(map, (edges, nodeName) => {
-    if (edges.length > PARAMS.maxControlDegree) {
-      _.each(edges, e => createShortcut(graph, e.v, e.w));
-    }
-  });
-}
-
-/**
- * Given an integer, picks a hue that is far apart from other colors.
- * The formula for picking color that avoid collision is:
- *     hue = (color range * golden ratio * index) % color range
- */
-export function mapIndexToHue(id: number): number {
-  let GOLDEN_RATIO = 1.61803398875;
-  // Hue of 0 is reserved for the gray nodes.
-  let MIN_HUE = 1;
-  let MAX_HUE = 359;
-  let COLOR_RANGE = MAX_HUE - MIN_HUE;
-  return MIN_HUE + ((COLOR_RANGE * GOLDEN_RATIO * id) % COLOR_RANGE);
-};
-
-/**
- * Remove edges and add to annotation instead.
- *
- * For root node, consider predefined types for source and sink.
- * We do not extract predefined type from non-root so that Variables and the
- * sgd node (op type = 'NoOp') do not get extract from inside own group.
- *
- * The order of extraction is important here as swapping the order can totally
- * screw up the graph layout.
- *
- * @param {Render.Node} renderNode Node to manipulate.
- */
-function extractHighDegrees(renderNode: RenderGroupNodeInfo) {
-
-  extractSpecifiedNodes(renderNode);
-
-  if (PARAMS.outExtractTypes) {
-    extractPredefinedSink(renderNode);
-  }
-
-  // This has to come before extract high in-degree to protect the core part
-  // that takes many variables.
-  if (PARAMS.inExtractTypes) {
-    extractPredefinedSource(renderNode);
-  }
-
-  extractHighInOrOutDegree(renderNode);
-
-  if (PARAMS.maxControlDegree) {
-    removeControlEdges(renderNode);
-  }
-
-  // Extract isolated nodes, which can be
-  // (1) source-like and sink-like nodes that are not originally isolated but
-  //     become isolated after further removal.
-  // (2) isolated nodes with annotations on one-side.  These might be either
-  //     - nodes that originally have high out-degree but because we remove
-  //       high in-degree nodes first, they no longer have high in-degree when
-  //       we check.  (Detecting all high-degree before removing also leads to
-  //       another problem.)
-  //     - nodes that do not have high degree, but their neighbors are all
-  //       extracted, so it might make sense to extract them too.
-
-  let graph = renderNode.coreGraph;
-  _.each(graph.nodes(), n => {
-    let child = graph.node(n);
-    let degree = graph.neighbors(n).length;
-    if (child.node.include !== InclusionType.UNSPECIFIED) {
-      return;
-    }
-    if (degree === 0) {
-      let hasOutAnnotations = child.outAnnotations.list.length > 0;
-      let hasInAnnotations = child.inAnnotations.list.length > 0;
-
-      if (child.isInExtract) { // Is source-like.
-        // This case only happens if detachAllEdgesForHighDegree is false.
-        // (Otherwise all source-like nodes are all isolated already.)
-        renderNode.isolatedInExtract.push(child);
-        child.node.include = InclusionType.EXCLUDE;
-        graph.removeNode(n);
-      } else if (child.isOutExtract) { // Is sink-like.
-        // This case only happens if detachAllEdgesForHighDegree is false.
-        // // (Otherwise all sink-like nodes are all isolated already.)
-        renderNode.isolatedOutExtract.push(child);
-        child.node.include = InclusionType.EXCLUDE;
-        graph.removeNode(n);
-      } else if (PARAMS.extractIsolatedNodesWithAnnotationsOnOneSide) {
-        if (hasOutAnnotations && !hasInAnnotations) {
-          child.isInExtract = true; // for ones with high out-annotations
-          renderNode.isolatedInExtract.push(child);
-          child.node.include = InclusionType.EXCLUDE;
-          graph.removeNode(n);
-        } else if (hasInAnnotations && !hasOutAnnotations) {
-          child.isOutExtract = true; // for ones with high in-annotations
-          renderNode.isolatedOutExtract.push(child);
-          child.node.include = InclusionType.EXCLUDE;
-          graph.removeNode(n);
-        } else {
-          // if a low degree node has both in- & out- annotations, do nothing
-          // because it is unclear which side it should go to.
-        }
-      }
-    }
-  });
-}
-
-/**
- * Expands nodes in the graph until the desired node is visible.
- *
- * @param scene The scene polymer component.
- * @param renderHierarchy The render hierarchy.
- * @param tensorName The name of a tensor.
- * @return A string that is the name of the node representing the given tensor.
- *     Note that the original tensor name might differ from this returned node
- *     name. Specifically, for instance, the tensor name usually ends with an
- *     output slot index (such as :0), while the node name lacks that suffix.
- */
-export function expandUntilNodeIsShown(
-    scene, renderHierarchy, tensorName: string) {
-  const splitTensorName = tensorName.split('/');
-
-  // Graph names do not take into account the output slot. Strip it.
-  const lastNodeNameMatch =
-      splitTensorName[splitTensorName.length - 1].match(/(.*):\d+/);
-  if (lastNodeNameMatch.length === 2) {
-    splitTensorName[splitTensorName.length - 1] = lastNodeNameMatch[1];
-  }
-
-  let nodeName = splitTensorName[0];
-  let renderNode = renderHierarchy.getRenderNodeByName(nodeName);
-  for (let i = 1; i < splitTensorName.length; i++) {
-    // Op nodes are not expandable.
-    if (renderNode.node.type === tf.graph.NodeType.OP) {
-      break;
-    }
-    renderHierarchy.buildSubhierarchy(nodeName);
-    renderNode.expanded = true;
-    scene.setNodeExpanded(renderNode);
-    nodeName += '/' + splitTensorName[i];
-    renderNode = renderHierarchy.getRenderNodeByName(nodeName);
-  }
-
-  return renderNode.node.name;
-}
-
-} // close module tf.graph.render
diff --git a/tensorflow/tensorboard/components/tf_graph_common/scene.ts b/tensorflow/tensorboard/components/tf_graph_common/scene.ts
deleted file mode 100644
index 4c70eacdea..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/scene.ts
+++ /dev/null
@@ -1,750 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph.scene {
-  const svgNamespace = 'http://www.w3.org/2000/svg';
-
-  /** Enums element class of objects in the scene */
-  export let Class = {
-    Node: {
-      // <g> element that contains nodes.
-      CONTAINER: 'nodes',
-      // <g> element that contains detail about a node.
-      GROUP: 'node',
-      // <g> element that contains visual elements (like rect, ellipse).
-      SHAPE: 'nodeshape',
-      // <*> element(s) under SHAPE that should receive color updates.
-      COLOR_TARGET: 'nodecolortarget',
-      // <text> element showing the node's label.
-      LABEL: 'nodelabel',
-      // <g> element that contains all visuals for the expand/collapse
-      // button for expandable group nodes.
-      BUTTON_CONTAINER: 'buttoncontainer',
-      // <circle> element that surrounds expand/collapse buttons.
-      BUTTON_CIRCLE: 'buttoncircle',
-      // <path> element of the expand button.
-      EXPAND_BUTTON: 'expandbutton',
-      // <path> element of the collapse button.
-      COLLAPSE_BUTTON: 'collapsebutton'
-    },
-    Edge: {
-      CONTAINER: 'edges',
-      GROUP: 'edge',
-      LINE: 'edgeline',
-      REFERENCE_EDGE: 'referenceedge',
-      REF_LINE: 'refline',
-      STRUCTURAL: 'structural'
-    },
-    Annotation: {
-      OUTBOX: 'out-annotations',
-      INBOX: 'in-annotations',
-      GROUP: 'annotation',
-      NODE: 'annotation-node',
-      EDGE: 'annotation-edge',
-      CONTROL_EDGE: 'annotation-control-edge',
-      LABEL: 'annotation-label',
-      ELLIPSIS: 'annotation-ellipsis'
-    },
-    Scene: {
-      GROUP: 'scene',
-      CORE: 'core',
-      INEXTRACT: 'in-extract',
-      OUTEXTRACT: 'out-extract'
-    },
-    Subscene: {GROUP: 'subscene'},
-    OPNODE: 'op',
-    METANODE: 'meta',
-    SERIESNODE: 'series',
-    BRIDGENODE: 'bridge',
-    ELLIPSISNODE: 'ellipsis'
-  };
-
-  /**
-   * A health pill encapsulates an overview of tensor element values. The value
-   * field is a list of 12 numbers that shed light on the status of the tensor.
-   * Visualized in health pills are the 3rd through 8th (inclusive) numbers of
-   * health pill values. Those 6 numbers are counts of tensor elements that fall
-   * under -Inf, negative, 0, positive, +Inf, NaN (in that order).
-   *
-   * Please keep this interface consistent with HealthPillDatum within
-   * backend.ts.
-   */
-  export interface HealthPill {
-    device_name: string;
-    node_name: string;
-    output_slot: number;
-    dtype: string;
-    shape: number[];
-    value: number[];
-    wall_time: number;
-    step: number;
-  }
-
-  interface HealthPillNumericStats {
-    min: number;
-    max: number;
-    mean: number;
-    stddev: number;
-  }
-
-  /**
-   * Encapsulates how to render a single entry in a health pill. Each entry
-   * corresponds to a category of tensor element values.
-   */
-  export interface HealthPillEntry {
-    background_color: string;
-    label: string;
-  }
-  ;
-  export let healthPillEntries: HealthPillEntry[] = [
-    {
-      background_color: '#CC2F2C',
-      label: 'NaN',
-    },
-    {
-      background_color: '#FF8D00',
-      label: '-∞',
-    },
-    {
-      background_color: '#EAEAEA',
-      label: '-',
-    },
-    {
-      background_color: '#A5A5A5',
-      label: '0',
-    },
-    {
-      background_color: '#262626',
-      label: '+',
-    },
-    {
-      background_color: '#003ED4',
-      label: '+∞',
-    },
-  ];
-
-  /**
-   * Helper method for fitting the graph in the svg view.
-   *
-   * @param svg The main svg.
-   * @param zoomG The svg group used for panning and zooming.
-   * @param d3zoom The zoom behavior.
-   * @param callback Called when the fitting is done.
-   */
-  export function fit(svg, zoomG, d3zoom, callback) {
-    let svgRect = svg.getBoundingClientRect();
-    let sceneSize = null;
-    try {
-      sceneSize = zoomG.getBBox();
-      if (sceneSize.width === 0) {
-        // There is no scene anymore. We have been detached from the dom.
-        return;
-      }
-    } catch (e) {
-      // Firefox produced NS_ERROR_FAILURE if we have been
-      // detached from the dom.
-      return;
-    }
-    let scale = 0.9 *
-        Math.min(
-            svgRect.width / sceneSize.width, svgRect.height / sceneSize.height,
-            2);
-    let params = layout.PARAMS.graph;
-    const transform = d3.zoomIdentity
-        .scale(scale)
-        .translate(params.padding.paddingLeft, params.padding.paddingTop);
-
-    d3.select(svg)
-        .transition()
-        .duration(500)
-        .call(d3zoom.transform, transform)
-        .on('end.fitted', () => {
-          // Remove the listener for the zoomend event,
-          // so we don't get called at the end of regular zoom events,
-          // just those that fit the graph to screen.
-          d3zoom.on('end.fitted', null);
-          callback();
-        });
-};
-
-/**
- * Helper method for panning the graph to center on the provided node,
- * if the node is currently off-screen.
- *
- * @param nodeName The node to center the graph on
- * @param svg The root SVG element for the graph
- * @param zoomG The svg group used for panning and zooming.
- * @param d3zoom The zoom behavior.
- * @return True if the graph had to be panned to display the
- *            provided node.
- */
-export function panToNode(nodeName: String, svg, zoomG, d3zoom): boolean {
-  let node = <SVGAElement>d3
-                 .select('[data-name="' + nodeName + '"].' + Class.Node.GROUP)
-                 .node();
-  if (!node) {
-    return false;
-  }
-
-  // Check if the selected node is off-screen in either
-  // X or Y dimension in either direction.
-  let nodeBox = node.getBBox();
-  let nodeCtm = node.getScreenCTM();
-  let pointTL = svg.createSVGPoint();
-  let pointBR = svg.createSVGPoint();
-  pointTL.x = nodeBox.x;
-  pointTL.y = nodeBox.y;
-  pointBR.x = nodeBox.x + nodeBox.width;
-  pointBR.y = nodeBox.y + nodeBox.height;
-  pointTL = pointTL.matrixTransform(nodeCtm);
-  pointBR = pointBR.matrixTransform(nodeCtm);
-  let isOutsideOfBounds = (start, end, bound) => {
-    return end < 0 || start > bound;
-  };
-  let svgRect = svg.getBoundingClientRect();
-  if (isOutsideOfBounds(pointTL.x, pointBR.x, svgRect.width) ||
-      isOutsideOfBounds(pointTL.y, pointBR.y, svgRect.height)) {
-    // Determine the amount to translate the graph in both X and Y dimensions in
-    // order to center the selected node. This takes into account the position
-    // of the node, the size of the svg scene, the amount the scene has been
-    // scaled by through zooming, and any previous transforms already performed
-    // by this logic.
-    let centerX = (pointTL.x + pointBR.x) / 2;
-    let centerY = (pointTL.y + pointBR.y) / 2;
-    let dx = ((svgRect.width / 2) - centerX);
-    let dy = ((svgRect.height / 2) - centerY);
-
-    // We translate by this amount. We divide the X and Y translations by the
-    // scale to undo how translateBy scales the translations (in d3 v4).
-    const svgTransform = d3.zoomTransform(svg);
-    d3.select(svg).transition().duration(500).call(
-        d3zoom.translateBy, dx / svgTransform.k, dy / svgTransform.k);
-
-    return true;
-  }
-  return false;
-};
-
-/**
- * Given a container d3 selection, select a child svg element of a given tag
- * and class if exists or append / insert one otherwise.  If multiple children
- * matches the tag and class name, returns only the first one.
- *
- * @param container
- * @param tagName tag name.
- * @param className (optional) Class name or a list of class names.
- * @param before (optional) reference DOM node for insertion.
- * @return selection of the element
- */
-export function selectOrCreateChild(
-    container, tagName: string, className?: string | string[], before?): d3.Selection<any, any, any, any> {
-  let child = selectChild(container, tagName, className);
-  if (!child.empty()) {
-    return child;
-  }
-  let newElement =
-      document.createElementNS('http://www.w3.org/2000/svg', tagName);
-
-  if (className instanceof Array) {
-    for (let i = 0; i < className.length; i++) {
-      newElement.classList.add(className[i]);
-    }
-  } else {
-    newElement.classList.add(className);
-  }
-
-  if (before) { // if before exists, insert
-    container.node().insertBefore(newElement, before);
-  } else { // otherwise, append
-    container.node().appendChild(newElement);
-  }
-  return d3.select(newElement)
-           // need to bind data to emulate d3_selection.append
-           .datum(container.datum());
-};
-
-/**
- * Given a container d3 selection, select a child element of a given tag and
- * class. If multiple children matches the tag and class name, returns only
- * the first one.
- *
- * @param container
- * @param tagName tag name.
- * @param className (optional) Class name or list of class names.
- * @return selection of the element, or an empty selection
- */
-export function selectChild(
-    container, tagName: string, className?: string | string[]): d3.Selection<any, any, any, any> {
-  let children = container.node().childNodes;
-  for (let i = 0; i < children.length; i++) {
-    let child = children[i];
-    if (child.tagName === tagName) {
-      if (className instanceof Array) {
-        let hasAllClasses = true;
-        for (let j = 0; j < className.length; j++) {
-          hasAllClasses =
-              hasAllClasses && child.classList.contains(className[j]);
-        }
-        if (hasAllClasses) {
-          return d3.select(child);
-        }
-      } else if ((!className || child.classList.contains(className))) {
-        return d3.select(child);
-      }
-    }
-  }
-  return d3.select(null);
-};
-
-/**
- * Select or create a sceneGroup and build/update its nodes and edges.
- *
- * Structure Pattern:
- *
- * <g class='scene'>
- *   <g class='core'>
- *     <g class='edges'>
- *       ... stuff from tf.graph.scene.edges.build ...
- *     </g>
- *     <g class='nodes'>
- *       ... stuff from tf.graph.scene.nodes.build ...
- *     </g>
- *   </g>
- *   <g class='in-extract'>
- *     <g class='nodes'>
- *       ... stuff from tf.graph.scene.nodes.build ...
- *     </g>
- *   </g>
- *   <g class='out-extract'>
- *     <g class='nodes'>
- *       ... stuff from tf.graph.scene.nodes.build ...
- *     </g>
- *   </g>
- * </g>
- *
- * @param container D3 selection of the parent.
- * @param renderNode render node of a metanode or series node.
- * @param sceneElement <tf-graph-scene> polymer element.
- * @param sceneClass class attribute of the scene (default='scene').
- */
-export function buildGroup(container,
-    renderNode: render.RenderGroupNodeInfo,
-    sceneElement,
-    sceneClass: string): d3.Selection<any, any, any, any> {
-  sceneClass = sceneClass || Class.Scene.GROUP;
-  let isNewSceneGroup = selectChild(container, 'g', sceneClass).empty();
-  let sceneGroup = selectOrCreateChild(container, 'g', sceneClass);
-
-  // core
-  let coreGroup = selectOrCreateChild(sceneGroup, 'g', Class.Scene.CORE);
-  let coreNodes = _.reduce(renderNode.coreGraph.nodes(), (nodes, name) => {
-                    let node = renderNode.coreGraph.node(name);
-                    if (!node.excluded) {
-                      nodes.push(node);
-                    }
-                    return nodes;
-                  }, []);
-
-  if (renderNode.node.type === NodeType.SERIES) {
-    // For series, we want the first item on top, so reverse the array so
-    // the first item in the series becomes last item in the top, and thus
-    // is rendered on the top.
-    coreNodes.reverse();
-  }
-
-  // Create the layer of edges for this scene (paths).
-  edge.buildGroup(coreGroup, renderNode.coreGraph, sceneElement);
-
-  // Create the layer of nodes for this scene (ellipses, rects etc).
-  node.buildGroup(coreGroup, coreNodes, sceneElement);
-
-  // In-extract
-  if (renderNode.isolatedInExtract.length > 0) {
-    let inExtractGroup =
-        selectOrCreateChild(sceneGroup, 'g', Class.Scene.INEXTRACT);
-    node.buildGroup(inExtractGroup, renderNode.isolatedInExtract,
-        sceneElement);
-  } else {
-    selectChild(sceneGroup, 'g', Class.Scene.INEXTRACT).remove();
-  }
-
-  // Out-extract
-  if (renderNode.isolatedOutExtract.length > 0) {
-    let outExtractGroup =
-        selectOrCreateChild(sceneGroup, 'g', Class.Scene.OUTEXTRACT);
-    node.buildGroup(outExtractGroup, renderNode.isolatedOutExtract,
-        sceneElement);
-  } else {
-    selectChild(sceneGroup, 'g', Class.Scene.OUTEXTRACT).remove();
-  }
-
-  position(sceneGroup, renderNode);
-
-  // Fade in the scene group if it didn't already exist.
-  if (isNewSceneGroup) {
-    sceneGroup.attr('opacity', 0).transition().attr('opacity', 1);
-  }
-
-  return sceneGroup;
-};
-
-/**
- * Given a scene's svg group, set  g.in-extract, g.coreGraph, g.out-extract svg
- * groups' position relative to the scene.
- *
- * @param sceneGroup
- * @param renderNode render node of a metanode or series node.
- */
-function position(sceneGroup, renderNode: render.RenderGroupNodeInfo) {
-  // Translate scenes down by the label height so that when showing graphs in
-  // expanded metanodes, the graphs are below the labels.  Do not shift them
-  // down for series nodes as series nodes don't have labels inside of their
-  // bounding boxes.
-  let yTranslate = renderNode.node.type === NodeType.SERIES ?
-    0 : layout.PARAMS.subscene.meta.labelHeight;
-
-  // core
-  translate(selectChild(sceneGroup, 'g', Class.Scene.CORE), 0, yTranslate);
-
-  // in-extract
-  let hasInExtract = renderNode.isolatedInExtract.length > 0;
-  let hasOutExtract = renderNode.isolatedOutExtract.length > 0;
-
-  if (hasInExtract) {
-    let offset = layout.PARAMS.subscene.meta.extractXOffset;
-    let inExtractX = renderNode.coreBox.width -
-      renderNode.inExtractBox.width / 2 - renderNode.outExtractBox.width -
-          (hasOutExtract ? offset : 0);
-    translate(
-        selectChild(sceneGroup, 'g', Class.Scene.INEXTRACT), inExtractX,
-        yTranslate);
-  }
-
-  // out-extract
-  if (hasOutExtract) {
-    let outExtractX = renderNode.coreBox.width -
-      renderNode.outExtractBox.width / 2;
-    translate(
-        selectChild(sceneGroup, 'g', Class.Scene.OUTEXTRACT), outExtractX,
-        yTranslate);
-  }
-};
-
-/** Adds a click listener to a group that fires a graph-select event */
-export function addGraphClickListener(graphGroup, sceneElement) {
-  d3.select(graphGroup).on('click', () => {
-    sceneElement.fire('graph-select');
-  });
-};
-
-/** Helper for adding transform: translate(x0, y0) */
-export function translate(selection, x0: number, y0: number) {
-  // If it is already placed on the screen, make it a transition.
-  if (selection.attr('transform') != null) {
-    selection = selection.transition('position');
-  }
-  selection.attr('transform', 'translate(' + x0 + ',' + y0 + ')');
-};
-
-/**
- * Helper for setting position of a svg rect
- * @param rect rect to set position of.
- * @param cx Center x.
- * @param cy Center x.
- * @param width Width to set.
- * @param height Height to set.
- */
-export function positionRect(rect, cx: number, cy: number, width: number,
-    height: number) {
-  rect.transition()
-    .attr('x', cx - width / 2)
-    .attr('y', cy - height / 2)
-    .attr('width', width)
-    .attr('height', height);
-};
-
-/**
- * Helper for setting position of a svg expand/collapse button
- * @param button container group
- * @param renderNode the render node of the group node to position
- *        the button on.
- */
-export function positionButton(button, renderNode: render.RenderNodeInfo) {
-  let cx = layout.computeCXPositionOfNodeShape(renderNode);
-  // Position the button in the top-right corner of the group node,
-  // with space given the draw the button inside of the corner.
-  let width = renderNode.expanded ?
-      renderNode.width : renderNode.coreBox.width;
-  let height = renderNode.expanded ?
-      renderNode.height : renderNode.coreBox.height;
-  let x = cx + width / 2 - 6;
-  let y = renderNode.y - height / 2 + 6;
-  // For unexpanded series nodes, the button has special placement due
-  // to the unique visuals of this group node.
-  if (renderNode.node.type === NodeType.SERIES && !renderNode.expanded) {
-    x += 10;
-    y -= 2;
-  }
-  let translateStr = 'translate(' + x + ',' + y + ')';
-  button.selectAll('path').transition().attr('transform', translateStr);
-  button.select('circle').transition().attr(
-      {cx: x, cy: y, r: layout.PARAMS.nodeSize.meta.expandButtonRadius});
-};
-
-/**
- * Helper for setting position of a svg ellipse
- * @param ellipse ellipse to set position of.
- * @param cx Center x.
- * @param cy Center x.
- * @param width Width to set.
- * @param height Height to set.
- */
-export function positionEllipse(ellipse, cx: number, cy: number,
-    width: number, height: number) {
-  ellipse.transition()
-    .attr('cx', cx)
-    .attr('cy', cy)
-    .attr('rx', width / 2)
-    .attr('ry', height / 2);
-};
-
-/**
- * @param {number} stat A stat for a health pill (such as mean or variance).
- * @param {boolean} shouldRoundOnesDigit Whether to round this number to the
- *     ones digit. Useful for say int, uint, and bool output types.
- * @return {string} A human-friendly string representation of that stat.
- */
-export function humanizeHealthPillStat(stat, shouldRoundOnesDigit) {
-  if (shouldRoundOnesDigit) {
-    return stat.toFixed(0);
-  }
-
-  if (Math.abs(stat) >= 1) {
-    return stat.toFixed(1);
-  }
-  return stat.toExponential(1);
-}
-
-/**
- * Get text content describing a health pill.
- */
-function _getHealthPillTextContent(healthPill: HealthPill,
-                                   totalCount: number,
-                                   elementsBreakdown: number[],
-                                   numericStats: HealthPillNumericStats) {
-  let text = 'Device: ' + healthPill.device_name + '\n';
-  text += 'dtype: ' + healthPill.dtype + '\n';
-
-  let shapeStr = '(scalar)';
-  if (healthPill.shape.length > 0) {
-    shapeStr = '(' + healthPill.shape.join(',') + ')';
-  }
-  text += '\nshape: ' + shapeStr + '\n\n';
-
-  text += '#(elements): ' + totalCount + '\n';
-  const breakdownItems = [];
-  for (let i = 0; i < elementsBreakdown.length; i++) {
-    if (elementsBreakdown[i] > 0) {
-      breakdownItems.push(
-          '#(' + healthPillEntries[i].label + '): ' + elementsBreakdown[i]);
-    }
-  }
-  text += breakdownItems.join(', ') + '\n\n';
-
-  // In some cases (e.g., size-0 tensors; all elements are nan or inf) the
-  // min/max and mean/stddev stats are meaningless.
-  if (numericStats.max >= numericStats.min) {
-    text += 'min: ' + numericStats.min + ', max: ' + numericStats.max + '\n';
-    text += 'mean: ' + numericStats.mean + ', stddev: ' + numericStats.stddev;
-  }
-
-  return text;
-}
-
-/**
- * Renders a health pill for an op atop a node.
- * nodeGroupElement: The SVG element in which to render.
- * healthPill: A list of backend.HealthPill objects.
- * nodeInfo: Info on the associated node.
- * healthPillId: A unique numeric ID assigned to this health pill.
- */
-function _addHealthPill(
-    nodeGroupElement: SVGElement, healthPill: HealthPill,
-    nodeInfo: render.RenderNodeInfo, healthPillId: number) {
-  // Check if text already exists at location.
-  d3.select(nodeGroupElement.parentNode as any).selectAll('.health-pill').remove();
-
-  if (!nodeInfo || !healthPill) {
-    return;
-  }
-
-  const lastHealthPillData = healthPill.value;
-
-  // For now, we only visualize the 6 values that summarize counts of tensor
-  // elements of various categories: -Inf, negative, 0, positive, Inf, and NaN.
-  const lastHealthPillElementsBreakdown = lastHealthPillData.slice(2, 8);
-  let totalCount = lastHealthPillData[1];
-  const numericStats: HealthPillNumericStats = {
-      min: lastHealthPillData[8],
-      max: lastHealthPillData[9],
-      mean: lastHealthPillData[10],
-      stddev: Math.sqrt(lastHealthPillData[11])
-  };
-
-  let healthPillWidth = 60;
-  let healthPillHeight = 10;
-  if (nodeInfo.node.type === tf.graph.NodeType.OP) {
-    // Use a smaller health pill for op nodes (rendered as smaller ellipses).
-    healthPillWidth /= 2;
-    healthPillHeight /= 2;
-  }
-
-  let healthPillGroup = document.createElementNS(svgNamespace, 'g');
-  healthPillGroup.classList.add('health-pill');
-
-  // Define the gradient for the health pill.
-  let healthPillDefs = document.createElementNS(svgNamespace, 'defs');
-  healthPillGroup.appendChild(healthPillDefs);
-  let healthPillGradient =
-      document.createElementNS(svgNamespace, 'linearGradient');
-
-  // Every element in a web page must have a unique ID.
-  const healthPillGradientId = 'health-pill-gradient-' + healthPillId;
-  healthPillGradient.setAttribute('id', healthPillGradientId);
-
-  let cumulativeCount = 0;
-  let previousOffset = '0%';
-  for (let i = 0; i < lastHealthPillElementsBreakdown.length; i++) {
-    if (!lastHealthPillElementsBreakdown[i]) {
-      // Exclude empty categories.
-      continue;
-    }
-    cumulativeCount += lastHealthPillElementsBreakdown[i];
-
-    // Create a color interval using 2 stop elements.
-    let stopElement0 = document.createElementNS(svgNamespace, 'stop');
-    stopElement0.setAttribute('offset', previousOffset);
-    stopElement0.setAttribute(
-        'stop-color', healthPillEntries[i].background_color);
-    healthPillGradient.appendChild(stopElement0);
-
-    let stopElement1 = document.createElementNS(svgNamespace, 'stop');
-    let percent = (cumulativeCount * 100 / totalCount) + '%';
-    stopElement1.setAttribute('offset', percent);
-    stopElement1.setAttribute(
-        'stop-color', healthPillEntries[i].background_color);
-    healthPillGradient.appendChild(stopElement1);
-    previousOffset = percent;
-  }
-  healthPillDefs.appendChild(healthPillGradient);
-
-  // Create the rectangle for the health pill.
-  let rect = document.createElementNS(svgNamespace, 'rect');
-  rect.setAttribute('fill', 'url(#' + healthPillGradientId + ')');
-  rect.setAttribute('width', String(healthPillWidth));
-  rect.setAttribute('height', String(healthPillHeight));
-  healthPillGroup.appendChild(rect);
-
-  // Show a title with specific counts on hover.
-  let titleSvg = document.createElementNS(svgNamespace, 'title');
-  titleSvg.textContent = _getHealthPillTextContent(
-      healthPill, totalCount, lastHealthPillElementsBreakdown, numericStats);
-  healthPillGroup.appendChild(titleSvg);
-  // TODO(cais): Make the tooltip content prettier.
-
-  // Center this health pill just right above the node for the op.
-  let healthPillX = nodeInfo.x - healthPillWidth / 2;
-  let healthPillY = nodeInfo.y - healthPillHeight - nodeInfo.height / 2 - 2;
-  if (nodeInfo.labelOffset < 0) {
-    // The label is positioned above the node. Do not occlude the label.
-    healthPillY += nodeInfo.labelOffset;
-  }
-
-  if (lastHealthPillElementsBreakdown[2] ||
-      lastHealthPillElementsBreakdown[3] ||
-      lastHealthPillElementsBreakdown[4]) {
-    // At least 1 "non-Inf and non-NaN" value exists (a -, 0, or + value). Show
-    // stats on tensor values.
-
-    // Determine if we should display the output range as integers.
-    let shouldRoundOnesDigit = false;
-    let node = nodeInfo.node as OpNode;
-    let attributes = node.attr;
-    if (attributes && attributes.length) {
-      // Find the attribute for output type if there is one.
-      for (let i = 0; i < attributes.length; i++) {
-        if (attributes[i].key === 'T') {
-          // Note whether the output type is an integer.
-          let outputType = attributes[i].value['type'];
-          shouldRoundOnesDigit =
-              outputType && /^DT_(BOOL|INT|UINT)/.test(outputType);
-          break;
-        }
-      }
-    }
-
-    let statsSvg = document.createElementNS(svgNamespace, 'text');
-    const minString =
-        humanizeHealthPillStat(numericStats.min, shouldRoundOnesDigit);
-    const maxString =
-        humanizeHealthPillStat(numericStats.max, shouldRoundOnesDigit);
-    if (totalCount > 1) {
-      statsSvg.textContent = minString + ' ~ ' + maxString;
-    } else {
-      statsSvg.textContent = minString;
-    }
-    statsSvg.classList.add('health-pill-stats');
-    statsSvg.setAttribute('x', String(healthPillWidth / 2));
-    statsSvg.setAttribute('y', '-2');
-    healthPillGroup.appendChild(statsSvg);
-  }
-
-  healthPillGroup.setAttribute(
-      'transform', 'translate(' + healthPillX + ', ' + healthPillY + ')');
-
-  Polymer.dom(nodeGroupElement.parentNode).appendChild(healthPillGroup);
-}
-
-/**
- * Adds health pills (which visualize tensor summaries) to a graph group.
- * @param svgRoot The root SVG element of the graph to add heath pills to.
- * @param nodeNamesToHealthPills An object mapping node name to health pill.
- * @param colors A list of colors to use.
- */
-export function addHealthPills(
-    svgRoot: SVGElement, nodeNamesToHealthPills: {[key: string]: HealthPill[]},
-    healthPillStepIndex: number) {
-  if (!nodeNamesToHealthPills) {
-    // No health pill information available.
-    return;
-  }
-
-  // We generate a unique ID for each health pill because the ID of each element
-  // in a web page must be unique, and each health pill generates a gradient
-  // that its code later refers to.
-  let healthPillId = 1;
-
-  let svgRootSelection = d3.select(svgRoot);
-  svgRootSelection.selectAll('g.nodeshape')
-      .each(function(nodeInfo: render.RenderNodeInfo) {
-        // Only show health pill data for this node if it is available.
-        const healthPills = nodeNamesToHealthPills[nodeInfo.node.name];
-        const healthPill =
-            healthPills ? healthPills[healthPillStepIndex] : null;
-        _addHealthPill(
-            (this as SVGElement), healthPill, nodeInfo, healthPillId++);
-      });
-};
-
-} // close module
diff --git a/tensorflow/tensorboard/components/tf_graph_common/template.ts b/tensorflow/tensorboard/components/tf_graph_common/template.ts
deleted file mode 100644
index 7800d46029..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/template.ts
+++ /dev/null
@@ -1,305 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-module tf.graph.template {
-
-/**
- * Detect repeating patterns of subgraphs.
- * Assign templateId to each subgraph if it belongs to a template.
- * Returns clusters of similar subgraphs .
- *
- * @param graph
- * @param verifyTemplate whether to run the template verification algorithm
- * @return a dict (template id => Array of node names)
- */
-export function detect(h, verifyTemplate): {[templateId: string]: string[]} {
-  // In any particular subgraph, there are either
-  // - leaf nodes (which do not have subgraph)
-  // - metanode nodes - some of them have only one member (singular metanode)
-  //                    and some have multiple members (non-singular metanode)
-
-  // First, generate a nearest neighbor hash of metanode nodes.
-  let nnGroups = clusterSimilarSubgraphs(h);
-
-  // For each metanode, compare its subgraph (starting from shallower groups)
-  // and assign template id.
-  let templates = groupTemplateAndAssignId(nnGroups, verifyTemplate);
-
-  // Sort the templates by minimum level in the graph at which they appear,
-  // as this leads to optimal setting of the colors of each template for
-  // maximum differentiation.
-  return <{[templateId: string]: string[]}>_(templates)
-      .pairs()
-      .sortBy(function(pair: {level: number, nodes: string[]}[]) {
-        return pair[1].level;
-      })
-      .map(function(pair: {level: number, nodes: string[]}[]) {
-        return [pair[0], pair[1].nodes];
-      })
-      .object()
-      .value();
-};
-
-/**
- * @return Unique string for a metanode based on depth, |V|, |E| and
- * op type histogram.
- */
-function getSignature(metanode) {
-  // depth=<number> |V|=<number> |E|=<number>
-  let props = _.map(
-                   {
-                     'depth': metanode.depth,
-                     '|V|': metanode.metagraph.nodes().length,
-                     '|E|': metanode.metagraph.edges().length
-                   },
-                   function(v, k) { return k + '=' + v; })
-                  .join(' ');
-
-  // optype1=count1,optype2=count2
-  let ops = _.map(metanode.opHistogram, function(count, op) {
-               return op + '=' + count;
-             }).join(',');
-
-  return props + ' [ops] ' + ops;
-}
-
-/**
- * Generate a nearest neighbor hash of metanodes
- * based on depth, |V|, |E|, and opHistogram of their subgraph
- * (excluding leaf nodes and singular metanodes).
- * @param graph The graph
- * @return Array of pairs of [signature,
- *   Object with min level of the template and an Array of tf.graph.Group]
- *   sort by ascending order of minimum depth at which metanode appears.
- */
-function clusterSimilarSubgraphs(h: hierarchy.Hierarchy) {
-  /** a dict from metanode.signature() => Array of tf.graph.Groups */
-  let hashDict = _(h.getNodeMap()).reduce(
-      (hash, node: OpNode|Metanode, name) => {
-    if (node.type !== NodeType.META) {
-        return hash;
-    }
-    let levelOfMetaNode = name.split('/').length - 1;
-    let signature = getSignature(node);
-    let templateInfo = hash[signature] ||
-      {nodes: [], level: levelOfMetaNode};
-    hash[signature] = templateInfo;
-    templateInfo.nodes.push(node);
-    if (templateInfo.level > levelOfMetaNode) {
-      templateInfo.level = levelOfMetaNode;
-    }
-    return hash;
-  }, {});
-
-  return _(hashDict)
-      .pairs()
-      // filter nn metanode with only one member
-      .filter(function(pair: {level: number, nodes: string[]}) {
-        return pair[1].nodes.length > 1;
-      })
-      .sortBy(function(pair: {level: number, nodes: string[]}) {
-        // sort by depth
-        // (all members in the same nnGroup has equal depth)
-        return pair[1].nodes[0].depth;
-      })
-      .value();
-}
-
-function groupTemplateAndAssignId(nnGroups, verifyTemplate) {
-  // For each metanode, compare its subgraph (starting from shallower groups)
-  // and assign template id.
-  let result: {[templateId: string]: {level: number, nodes: string[]}} = {};
-  return _.reduce(nnGroups, function(templates, nnGroupPair) {
-    let signature = nnGroupPair[0],
-      nnGroup = nnGroupPair[1].nodes,
-      clusters = [];
-
-    nnGroup.forEach(function(metanode) {
-      // check with each existing cluster
-      for (let i = 0; i < clusters.length; i++) {
-        let similar = !verifyTemplate ||
-                      isSimilarSubgraph(
-                        clusters[i].metanode.metagraph,
-                        metanode.metagraph
-                      );
-        // if similar, just add this metanode to the cluster
-        if (similar) {
-          // get template from the first one
-          metanode.templateId = clusters[i].metanode.templateId;
-          clusters[i].members.push(metanode.name);
-          return;
-        }
-      }
-      // otherwise create a new cluster with id 'signature [count] '
-      metanode.templateId = signature + '[' + clusters.length + ']';
-      clusters.push({
-        metanode: metanode,
-        members: [metanode.name]
-      });
-    });
-
-    clusters.forEach(function(c) {
-      templates[c.metanode.templateId] = {
-        level: nnGroupPair[1].level,
-        nodes: c.members
-      };
-    });
-    return templates;
-  }, result);
-}
-
-function sortNodes(names: string[],
-    graph: graphlib.Graph<Metanode|OpNode, Metaedge>, prefix: string) {
-  return _.sortByAll(names,
-    function(name) {
-      let node = graph.node(name);
-      return (<OpNode>node).op;
-    },
-    function(name) {
-      let node = graph.node(name);
-      return (<Metanode>node).templateId;
-    },
-    function(name) {
-      return graph.neighbors(name).length;
-    },
-    function(name) {
-      return graph.predecessors(name).length;
-    },
-    function(name) {
-      return graph.successors(name).length;
-    },
-    function(name) {
-      return name.substr(prefix.length);
-    });
-}
-
-function isSimilarSubgraph(g1: graphlib.Graph<any, any>,
-    g2: graphlib.Graph<any, any>) {
-  if (!tf.graph.hasSimilarDegreeSequence(g1, g2)) {
-      return false;
-  }
-
-  // if we want to skip, just return true here.
-  // return true;
-
-  // Verify sequence by running DFS
-  let g1prefix = g1.graph().name;
-  let g2prefix = g2.graph().name;
-
-  let visited1 = {};
-  let visited2 = {};
-  let stack = [];
-
-  /**
-   * push sources or successors into the stack
-   * if the visiting pattern has been similar.
-   */
-  function stackPushIfNotDifferent(n1, n2) {
-    let sub1 = n1.substr(g1prefix.length),
-      sub2 = n2.substr(g2prefix.length);
-
-    /* tslint:disable */
-    if (visited1[sub1] ^ visited2[sub1]) {
-      console.warn(
-          'different visit pattern', '[' + g1prefix + ']', sub1,
-          '[' + g2prefix + ']', sub2);
-      return true;
-    }
-    /* tslint:enable */
-    if (!visited1[sub1]) { // implied && !visited2[sub2]
-      visited1[sub1] = visited2[sub2] = true;
-      stack.push({n1: n1, n2: n2});
-    }
-
-    return false;
-  }
-
-  // check if have same # of sources then sort and push
-  let sources1 = g1.sources();
-  let sources2 = g2.sources();
-  if (sources1.length !== sources2.length) {
-    /* tslint:disable */
-    console.log('different source length');
-    /* tslint:enable */
-    return false;
-  }
-  sources1 = sortNodes(sources1, g1, g1prefix);
-  sources2 = sortNodes(sources2, g2, g2prefix);
-
-  for (let i = 0; i < sources1.length; i++) {
-    let different = stackPushIfNotDifferent(sources1[i], sources2[i]);
-    if (different) {
-        return false;
-    }
-  }
-
-  while (stack.length > 0) {
-    let cur = stack.pop();
-
-    // check node
-    let similar = isSimilarNode(g1.node(cur.n1), g2.node(cur.n2));
-    if (!similar) {
-        return false;
-    }
-
-    // check if have same # of successors then sort and push
-    let succ1 = g1.successors(cur.n1), succ2 = g2.successors(cur.n2);
-    if (succ1.length !== succ2.length) {
-      /* tslint:disable */
-      console.log('# of successors mismatch', succ1, succ2);
-      /* tslint:enable */
-      return false;
-    }
-    succ1 = sortNodes(succ1, g1, g1prefix);
-    succ2 = sortNodes(succ2, g2, g2prefix);
-
-    for (let j = 0; j < succ1.length; j++) {
-      let different = stackPushIfNotDifferent(succ1[j], succ2[j]);
-      if (different) {
-          return false;
-      }
-    }
-  }
-
-  return true;
-}
-
-/**
- * Returns if two nodes have identical structure.
- */
-function isSimilarNode(n1: OpNode|Metanode|SeriesNode,
-    n2: OpNode|Metanode|SeriesNode): boolean {
-  if (n1.type === NodeType.META) {
-    // compare metanode
-    let metanode1 = <Metanode> n1;
-    let metanode2 = <Metanode> n2;
-    return metanode1.templateId && metanode2.templateId &&
-        metanode1.templateId === metanode2.templateId;
-  } else if (n1.type === NodeType.OP && n2.type === NodeType.OP) {
-    // compare leaf node
-    return (<OpNode>n1).op === (<OpNode>n2).op;
-  } else if (n1.type === NodeType.SERIES && n2.type === NodeType.SERIES) {
-    // compare series node sizes and operations
-    // (only need to check one op as all op nodes are identical in series)
-    let sn1 = <SeriesNode> n1;
-    let sn2 = <SeriesNode> n2;
-    let seriesnode1Count = sn1.metagraph.nodeCount();
-    return (seriesnode1Count === sn2.metagraph.nodeCount() &&
-      (seriesnode1Count === 0 ||
-      ((<OpNode>sn1.metagraph.node(sn1.metagraph.nodes()[0])).op ===
-          (<OpNode>sn2.metagraph.node(sn2.metagraph.nodes()[0])).op)));
-  }
-  return false;
-}
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_common/test/graph-test.ts b/tensorflow/tensorboard/components/tf_graph_common/test/graph-test.ts
deleted file mode 100644
index af3030197e..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/test/graph-test.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-suite('graph', () => {
-  let assert = chai.assert;
-
-  test('graphlib exists', () => { assert.isTrue(graphlib != null); });
-
-  test('simple graph contruction', done => {
-    let pbtxt = tf.graph.test.util.stringToArrayBuffer(`
-      node {
-        name: "Q"
-        op: "Input"
-      }
-      node {
-        name: "W"
-        op: "Input"
-      }
-      node {
-        name: "X"
-        op: "MatMul"
-        input: "Q:2"
-        input: "W"
-      }`);
-    let statsPbtxt = tf.graph.test.util.stringToArrayBuffer(`step_stats {
-      dev_stats {
-        device: "cpu"
-        node_stats {
-          node_name: "Q"
-          all_start_micros: 10
-          all_end_rel_micros: 4
-        }
-        node_stats {
-          node_name: "Q"
-          all_start_micros: 12
-          all_end_rel_micros: 4
-        }
-      }
-    }`);
-
-    let buildParams: tf.graph.BuildParams = {
-      enableEmbedding: true,
-      inEmbeddingTypes: ['Const'],
-      outEmbeddingTypes: ['^[a-zA-Z]+Summary$'],
-      refEdges: {}
-    };
-    let dummyTracker =
-        tf.graph.util.getTracker({set: () => { return; }, progress: 0});
-    tf.graph.parser.parseGraphPbTxt(pbtxt).then(nodes => {
-      tf.graph.build(nodes, buildParams, dummyTracker)
-          .then((slimGraph: tf.graph.SlimGraph) => {
-            assert.isTrue(slimGraph.nodes['X'] != null);
-            assert.isTrue(slimGraph.nodes['W'] != null);
-            assert.isTrue(slimGraph.nodes['Q'] != null);
-
-            let firstInputOfX = slimGraph.nodes['X'].inputs[0];
-            assert.equal(firstInputOfX.name, 'Q');
-            assert.equal(firstInputOfX.outputTensorIndex, 2);
-
-            let secondInputOfX = slimGraph.nodes['X'].inputs[1];
-            assert.equal(secondInputOfX.name, 'W');
-            assert.equal(secondInputOfX.outputTensorIndex, 0);
-
-            tf.graph.parser.parseStatsPbTxt(statsPbtxt).then(stepStats => {
-              tf.graph.joinStatsInfoWithGraph(slimGraph, stepStats);
-              assert.equal(slimGraph.nodes['Q'].stats.getTotalMicros(), 6);
-              done();
-            });
-          });
-    });
-  });
-
-  test('health pill numbers round correctly', () => {
-    // Integers are rounded to the ones place.
-    assert.equal(tf.graph.scene.humanizeHealthPillStat(42.0, true), '42');
-
-    // Numbers with magnitude >= 1 are rounded to the tenths place.
-    assert.equal(tf.graph.scene.humanizeHealthPillStat(1, false), '1.0');
-    assert.equal(tf.graph.scene.humanizeHealthPillStat(42.42, false), '42.4');
-    assert.equal(tf.graph.scene.humanizeHealthPillStat(-42.42, false), '-42.4');
-
-    // Numbers with magnitude < 1 are written in scientific notation rounded to
-    // the tenths place.
-    assert.equal(tf.graph.scene.humanizeHealthPillStat(0, false), '0.0e+0');
-    assert.equal(tf.graph.scene.humanizeHealthPillStat(0.42, false), '4.2e-1');
-    assert.equal(
-        tf.graph.scene.humanizeHealthPillStat(-0.042, false), '-4.2e-2');
-  });
-
-  // TODO(bp): write tests.
-});
diff --git a/tensorflow/tensorboard/components/tf_graph_common/test/hierarchy-test.ts b/tensorflow/tensorboard/components/tf_graph_common/test/hierarchy-test.ts
deleted file mode 100644
index fa62ffe2c7..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/test/hierarchy-test.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-suite('graph', () => {
-  let assert = chai.assert;
-
-  test('graphlib exists', () => { assert.isTrue(graphlib != null); });
-
-  // TODO(bp): write tests.
-
-});
diff --git a/tensorflow/tensorboard/components/tf_graph_common/test/index.html b/tensorflow/tensorboard/components/tf_graph_common/test/index.html
deleted file mode 100644
index 7564167129..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/test/index.html
+++ /dev/null
@@ -1,34 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-  <meta charset="utf-8">
-  <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-  <script src="../../web-component-tester/browser.js"></script>
-  <link rel="import" href="../tf-graph-common.html">
-</head>
-<body>
-  <script src="parser-test.js"></script>
-  <script src="graph-test.js"></script>
-  <script src="hierarchy-test.js"></script>
-  <script src="layout-test.js"></script>
-  <script src="util-test.js"></script>
-  <script src="util.js"></script>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_graph_common/test/layout-test.ts b/tensorflow/tensorboard/components/tf_graph_common/test/layout-test.ts
deleted file mode 100644
index b4884413c9..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/test/layout-test.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-suite('layout', () => {
-  let assert = chai.assert;
-
-  test('dagre exists', () => { assert.isTrue(dagre != null); });
-
-  // TODO(bp): write tests.
-
-});
diff --git a/tensorflow/tensorboard/components/tf_graph_common/test/parser-test.ts b/tensorflow/tensorboard/components/tf_graph_common/test/parser-test.ts
deleted file mode 100644
index 7c73178c1c..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/test/parser-test.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-suite('parser', () => {
-  let assert = chai.assert;
-
-  test('simple pbtxt', done => {
-    let pbtxt = tf.graph.test.util.stringToArrayBuffer(`node {
-       name: "Q"
-       op: "Input"
-     }
-     node {
-       name: "W"
-       op: "Input"
-     }
-     node {
-       name: "X"
-       op: "MatMul"
-       input: "Q"
-       input: "W"
-     }`);
-    tf.graph.parser.parseGraphPbTxt(pbtxt).then(nodes => {
-      assert.isTrue(nodes != null && nodes.length === 3);
-
-      assert.equal('Q', nodes[0].name);
-      assert.equal('Input', nodes[0].op);
-
-      assert.equal('W', nodes[1].name);
-      assert.equal('Input', nodes[1].op);
-
-      assert.equal('X', nodes[2].name);
-      assert.equal('MatMul', nodes[2].op);
-      assert.equal('Q', nodes[2].input[0]);
-      assert.equal('W', nodes[2].input[1]);
-
-      done();
-    });
-  });
-
-  test('stats pbtxt parsing', done => {
-    let statsPbtxt = tf.graph.test.util.stringToArrayBuffer(`step_stats {
-      dev_stats {
-        device: "cpu"
-        node_stats {
-          node_name: "Q"
-          all_start_micros: 10
-          all_end_rel_micros: 4
-        }
-        node_stats {
-          node_name: "Q"
-          all_start_micros: 12
-          all_end_rel_micros: 4
-        }
-      }
-    }`);
-    tf.graph.parser.parseStatsPbTxt(statsPbtxt).then(stepStats => {
-      assert.equal(stepStats.dev_stats.length, 1);
-      assert.equal(stepStats.dev_stats[0].device, 'cpu');
-      assert.equal(stepStats.dev_stats[0].node_stats.length, 2);
-      assert.equal(stepStats.dev_stats[0].node_stats[0].all_start_micros, 10);
-      assert.equal(stepStats.dev_stats[0].node_stats[1].node_name, 'Q');
-      assert.equal(stepStats.dev_stats[0].node_stats[1].all_end_rel_micros, 4);
-      done();
-    });
-  });
-
-  test('d3 exists', () => { assert.isTrue(d3 != null); });
-
-  // TODO(nsthorat): write tests.
-
-});
diff --git a/tensorflow/tensorboard/components/tf_graph_common/test/util-test.ts b/tensorflow/tensorboard/components/tf_graph_common/test/util-test.ts
deleted file mode 100644
index 4535d24888..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/test/util-test.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-suite('util', () => {
-  let assert = chai.assert;
-
-  test('remove common prefix', () => {
-
-    // Empty array.
-    let result = tf.graph.util.removeCommonPrefix([]);
-    assert.deepEqual(result, []);
-
-    // No common prefix.
-    result = tf.graph.util.removeCommonPrefix(['a', 'b', 'c']);
-    assert.deepEqual(result, ['a', 'b', 'c']);
-
-    // One of the elements is empty string.
-    result = tf.graph.util.removeCommonPrefix(['a/b', '', 'a/c']);
-    assert.deepEqual(result, ['a/b', '', 'a/c']);
-
-    // Only one string.
-    result = tf.graph.util.removeCommonPrefix(['a/b/c']);
-    assert.deepEqual(result, ['a/b/c']);
-
-    // `q/w/` is the common prefix. Expect `q/w/` to be removed.
-    result = tf.graph.util.removeCommonPrefix(['q/w/a', 'q/w/b', 'q/w/c/f']);
-    assert.deepEqual(result, ['a', 'b', 'c/f']);
-
-    // `q/w/` is the common prefix and also an element. Expect nothing to be
-    // removed since the common prefix is also an element in the array.
-    result = tf.graph.util.removeCommonPrefix(['q/w/', 'q/w/b', 'q/w/c/f']);
-    assert.deepEqual(result, ['q/w/', 'q/w/b', 'q/w/c/f']);
-  });
-
-  test('query params', () => {
-    // Starts with question mark.
-    let queryParams = tf.graph.util.getQueryParams('?foo=1&bar=2');
-    assert.deepEqual(queryParams, {'foo': '1', 'bar': '2'});
-
-    // No question mark.
-    queryParams = tf.graph.util.getQueryParams('foo=1&bar=2');
-    assert.deepEqual(queryParams, {'foo': '1', 'bar': '2'});
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_graph_common/test/util.ts b/tensorflow/tensorboard/components/tf_graph_common/test/util.ts
deleted file mode 100644
index bc73b735ed..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/test/util.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-/* tslint:disable:no-namespace */
-module tf.graph.test.util {
-  /**
-   * Converts a utf-8 string to an ArrayBuffer.
-   */
-  export function stringToArrayBuffer(str): ArrayBuffer {
-    let buf = new ArrayBuffer(str.length);
-    let bufView = new Uint8Array(buf);
-    for (let i = 0, strLen = str.length; i < strLen; i++) {
-      bufView[i] = str.charCodeAt(i);
-    }
-    return buf;
-  }
-
-}  // module
diff --git a/tensorflow/tensorboard/components/tf_graph_common/tf-graph-common.html b/tensorflow/tensorboard/components/tf_graph_common/tf-graph-common.html
deleted file mode 100644
index a460072a38..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/tf-graph-common.html
+++ /dev/null
@@ -1,38 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../tf-imports/d3.html">
-<link rel="import" href="../tf-imports/dagre.html">
-<link rel="import" href="../tf-imports/graphlib.html">
-<link rel="import" href="../tf-imports/lodash.html">
-
-<script src="colors.js"></script>
-<script src="common.js"></script>
-<script src="externs.js"></script>
-<script src="graph.js"></script>
-<script src="hierarchy.js"></script>
-<script src="layout.js"></script>
-<script src="parser.js"></script>
-<script src="proto.js"></script>
-<script src="render.js"></script>
-<script src="annotation.js"></script>
-<script src="contextmenu.js"></script>
-<script src="edge.js"></script>
-<script src="node.js"></script>
-<script src="scene.js"></script>
-<script src="template.js"></script>
-<script src="util.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_graph_common/util.ts b/tensorflow/tensorboard/components/tf_graph_common/util.ts
deleted file mode 100644
index 0b2df6545c..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_common/util.ts
+++ /dev/null
@@ -1,316 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * @fileoverview Utility functions for the tensorflow graph visualizer.
- */
-
-module tf.graph.util {
-  /**
-   * Recommended delay (ms) when running an expensive task asynchronously
-   * that gives enough time for the progress bar to update its UI.
-   */
-  const ASYNC_TASK_DELAY = 20;
-
-  export function time<T>(msg: string, task: () => T) {
-    let start = Date.now();
-    let result = task();
-    /* tslint:disable */
-    console.log(msg, ':', Date.now() - start, 'ms');
-    /* tslint:enable */
-    return result;
-  }
-
-  /**
-   * Creates a tracker that sets the progress property of the
-   * provided polymer component. The provided component must have
-   * a property called 'progress' that is not read-only. The progress
-   * property is an object with a numerical 'value' property and a
-   * string 'msg' property.
-   */
-  export function getTracker(polymerComponent: any) {
-    return {
-      setMessage: function(msg) {
-        polymerComponent.set(
-            'progress', {value: polymerComponent.progress.value, msg: msg});
-      },
-      updateProgress: function(value) {
-        polymerComponent.set('progress', {
-          value: polymerComponent.progress.value + value,
-          msg: polymerComponent.progress.msg
-        });
-      },
-      reportError: function(msg: string, err) {
-        // Log the stack trace in the console.
-        console.error(err.stack);
-        // And send a user-friendly message to the UI.
-        polymerComponent.set(
-            'progress',
-            {value: polymerComponent.progress.value, msg: msg, error: true});
-      },
-    };
-  }
-
-  /**
-   * Creates a tracker for a subtask given the parent tracker, the total
-   * progress
-   * of the subtask and the subtask message. The parent task should pass a
-   * subtracker to its subtasks. The subtask reports its own progress which
-   * becomes relative to the main task.
-   */
-  export function getSubtaskTracker(
-      parentTracker: ProgressTracker, impactOnTotalProgress: number,
-      subtaskMsg: string): ProgressTracker {
-    return {
-      setMessage: function(progressMsg) {
-        // The parent should show a concatenation of its message along with
-        // its subtask tracker message.
-        parentTracker.setMessage(subtaskMsg + ': ' + progressMsg);
-      },
-      updateProgress: function(incrementValue) {
-        // Update the parent progress relative to the child progress.
-        // For example, if the sub-task progresses by 30%, and the impact on the
-        // total progress is 50%, then the task progresses by 30% * 50% = 15%.
-        parentTracker.updateProgress(
-            incrementValue * impactOnTotalProgress / 100);
-      },
-      reportError: function(msg: string, err: Error) {
-        // The parent should show a concatenation of its message along with
-        // its subtask error message.
-        parentTracker.reportError(subtaskMsg + ': ' + msg, err);
-      }
-    };
-  }
-
-  /**
-   * Runs an expensive task and return the result.
-   */
-  export function runTask<T>(
-      msg: string, incProgressValue: number, task: () => T,
-      tracker: ProgressTracker): T {
-    // Update the progress message to say the current running task.
-    tracker.setMessage(msg);
-    // Run the expensive task with a delay that gives enough time for the
-    // UI to update.
-    try {
-      let result = tf.graph.util.time(msg, task);
-      // Update the progress value.
-      tracker.updateProgress(incProgressValue);
-      // Return the result to be used by other tasks.
-      return result;
-    } catch (e) {
-      // Errors that happen inside asynchronous tasks are
-      // reported to the tracker using a user-friendly message.
-      tracker.reportError('Failed ' + msg, e);
-    }
-  }
-
-  /**
-   * Runs an expensive task asynchronously and returns a promise of the result.
-   */
-  export function runAsyncTask<T>(
-      msg: string, incProgressValue: number, task: () => T,
-      tracker: ProgressTracker): Promise<T> {
-    return new Promise((resolve, reject) => {
-      // Update the progress message to say the current running task.
-      tracker.setMessage(msg);
-      // Run the expensive task with a delay that gives enough time for the
-      // UI to update.
-      setTimeout(function() {
-        try {
-          let result = tf.graph.util.time(msg, task);
-          // Update the progress value.
-          tracker.updateProgress(incProgressValue);
-          // Return the result to be used by other tasks.
-          resolve(result);
-        } catch (e) {
-          // Errors that happen inside asynchronous tasks are
-          // reported to the tracker using a user-friendly message.
-          tracker.reportError('Failed ' + msg, e);
-        }
-      }, ASYNC_TASK_DELAY);
-    });
-  }
-
-  /**
-   * Asynchronously runs an expensive task that returns a promise. Updates the
-   * tracker's progress after the promise resolves. Returns a new promise that
-   * resolves after the progress is updated.
-   */
-  export function runAsyncPromiseTask<T>(
-      msg: string, incProgressValue: number, task: () => Promise<T>,
-      tracker: ProgressTracker): Promise<T> {
-    return new Promise((resolve, reject) => {
-      let handleError = function(e) {
-        // Errors that happen inside asynchronous tasks are
-        // reported to the tracker using a user-friendly message.
-        tracker.reportError('Failed ' + msg, e);
-        reject(e);
-      };
-
-      // Update the progress message to say the current running task.
-      tracker.setMessage(msg);
-      // Run the expensive task with a delay that gives enough time for the
-      // UI to update.
-      setTimeout(function() {
-        try {
-          let start = Date.now();
-          task()
-              .then(function(value) {
-                /* tslint:disable */
-                console.log(msg, ':', Date.now() - start, 'ms');
-                // Update the progress value.
-                tracker.updateProgress(incProgressValue);
-                // Return the result to be used by other tasks.
-                resolve(value);
-              })
-              .catch(handleError);
-        } catch (e) {
-          handleError(e);
-        }
-      }, ASYNC_TASK_DELAY);
-    });
-  }
-
-  /**
-   * Returns a query selector with escaped special characters that are not
-   * allowed in a query selector.
-   */
-  export function escapeQuerySelector(querySelector: string): string {
-    return querySelector.replace(/([:.\[\],/\\\(\)])/g, '\\$1');
-  }
-
-  // For unit conversion.
-  export const MEMORY_UNITS = [
-    // Atomic unit.
-    {symbol: 'B'},
-    // numUnits specifies how many previous units this unit contains.
-    {symbol: 'KB', numUnits: 1024}, {symbol: 'MB', numUnits: 1024},
-    {symbol: 'GB', numUnits: 1024}, {symbol: 'TB', numUnits: 1024},
-    {symbol: 'PB', numUnits: 1024}
-  ];
-  export const TIME_UNITS = [
-    // Atomic unit. Finest granularity in TensorFlow stat collection.
-    {symbol: 'µs'},
-    // numUnits specifies how many previous units this unit contains.
-    {symbol: 'ms', numUnits: 1000}, {symbol: 's', numUnits: 1000},
-    {symbol: 'min', numUnits: 60}, {symbol: 'hr', numUnits: 60},
-    {symbol: 'days', numUnits: 24}
-  ];
-
-  /**
-   * Returns the human readable version of the unit.
-   * (e.g. 1.35 GB, 23 MB, 34 ms, 6.53 min etc).
-   */
-  export function convertUnitsToHumanReadable(value, units, unitIndex) {
-    unitIndex = unitIndex == null ? 0 : unitIndex;
-    if (unitIndex + 1 < units.length &&
-        value >= units[unitIndex + 1].numUnits) {
-      return tf.graph.util.convertUnitsToHumanReadable(
-          value / units[unitIndex + 1].numUnits, units, unitIndex + 1);
-    }
-    // toPrecision() has the tendency to return a number in scientific
-    // notation and (number - 0) brings it back to normal notation.
-    return (value.toPrecision(3) - 0) + ' ' + units[unitIndex].symbol;
-  }
-
-  export function hasDisplayableNodeStats(stats: NodeStats) {
-    if (stats &&
-        (stats.totalBytes > 0 || stats.getTotalMicros() > 0 ||
-         stats.outputSize)) {
-      return true;
-    }
-    return false;
-  }
-
-  /**
-   * Given a list of strings, it returns a new list of strings with the longest
-   * common prefix removed. If the common prefix is one of the strings in the
-   * list, it returns the original strings.
-   */
-  export function removeCommonPrefix(strings: string[]) {
-    if (strings.length < 2) {
-      return strings;
-    }
-
-    let index = 0;
-    let largestIndex = 0;
-    // Find the shortest name across all strings.
-    let minLength = _.min(_.map(strings, str => str.length));
-    while (true) {
-      index++;
-      let prefixes = _.map(strings, str => str.substring(0, index));
-      let allTheSame = prefixes.every((prefix, i) => {
-        return (i === 0 ? true : prefix === prefixes[i - 1]);
-      });
-      if (allTheSame) {
-        if (index >= minLength) {
-          // There is a string whose whole name is a prefix to other string.
-          // In this case, we return the original list of string.
-          return strings;
-        }
-        largestIndex = index;
-      } else {
-        break;
-      }
-    }
-    return _.map(strings, str => str.substring(largestIndex));
-  }
-
-  /**
-   * Given a queryString, aka ?foo=1&bar=2, return the object representation.
-   */
-  export function getQueryParams(queryString: string) {
-    if (queryString.charAt(0) === '?') {
-      queryString = queryString.slice(1);
-    }
-
-    let queryParams = _.chain(queryString.split('&'))
-                          .map((item) => {
-                            if (item) {
-                              return item.split('=');
-                            }
-                          })
-                          .compact()
-                          .value();
-
-    return _.object(queryParams);
-  }
-
-  /**
-   * Given a timestamp in microseconds, return a human-friendly string denoting
-   * how long ago the timestamp was.
-   */
-  export function computeHumanFriendlyTime(timeInMicroseconds: number) {
-    var timeDifferenceInMs =
-        +(new Date()) - +(new Date(timeInMicroseconds / 1e3));
-    if (timeDifferenceInMs < 30000) {
-      return 'just now';
-    } else if (timeDifferenceInMs < 60000) {
-      return Math.floor(timeDifferenceInMs / 1000) + ' seconds ago';
-    } else if (timeDifferenceInMs < 120000) {
-      return 'a minute ago';
-    } else if (timeDifferenceInMs < 3600000) {
-      return Math.floor(timeDifferenceInMs / 60000) + ' minutes ago';
-    } else if (Math.floor(timeDifferenceInMs / 3600000) == 1) {
-      return 'an hour ago';
-    } else if (timeDifferenceInMs < 86400000) {
-      return Math.floor(timeDifferenceInMs / 3600000) + ' hours ago';
-    } else if (timeDifferenceInMs < 172800000) {
-      return 'yesterday';
-    }
-    return Math.floor(timeDifferenceInMs / 86400000) + ' days ago';
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_controls/BUILD b/tensorflow/tensorboard/components/tf_graph_controls/BUILD
deleted file mode 100644
index ecca2ba4cb..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_controls/BUILD
+++ /dev/null
@@ -1,46 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_controls",
-    srcs = ["tf-graph-controls.html"],
-    path = "/tf-graph-controls",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_dropdown_menu",
-        "@org_polymer_paper_menu",
-        "@org_polymer_paper_radio_group",
-        "@org_polymer_paper_toggle_button",
-        "@org_polymer_paper_tooltip",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_controls"],
-    destdir = "tf-graph-controls",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_common:legacy",
-        "//third_party/javascript/polymer/v1/paper-button:lib",
-        "//third_party/javascript/polymer/v1/paper-dropdown-menu:lib",
-        "//third_party/javascript/polymer/v1/paper-menu:lib",
-        "//third_party/javascript/polymer/v1/paper-radio-group:lib",
-        "//third_party/javascript/polymer/v1/paper-toggle-button:lib",
-        "//third_party/javascript/polymer/v1/paper-tooltip:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_controls/demo/BUILD b/tensorflow/tensorboard/components/tf_graph_controls/demo/BUILD
deleted file mode 100644
index 0e12054213..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_controls/demo/BUILD
+++ /dev/null
@@ -1,24 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph_controls/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"],
-    path = "/tf-graph-controls/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_controls",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_controls/demo/index.html b/tensorflow/tensorboard/components/tf_graph_controls/demo/index.html
deleted file mode 100644
index 8b12641b28..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_controls/demo/index.html
+++ /dev/null
@@ -1,49 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../tf-graph-controls.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<title>TF Graph Controls Demo</title>
-<style>
-  #demo-container {
-    border: 2px solid #808080;
-    width: 1000px;
-    height: 700px;
-    position: relative;
-  }
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="tf-graph-controls-demo">
-      <template>
-        <tf-graph-controls
-            id="controls"
-            color-by="structure"
-        ></tf-graph-controls>
-      </template>
-      <script>
-        Polymer({
-          is: "tf-graph-controls-demo",
-        });
-      </script>
-    </dom-module>
-    <div id="demo-container">
-      <tf-graph-controls-demo></tf-graph-controls-demo>
-    </div>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph_controls/tf-graph-controls.html b/tensorflow/tensorboard/components/tf_graph_controls/tf-graph-controls.html
deleted file mode 100644
index 6d89635748..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_controls/tf-graph-controls.html
+++ /dev/null
@@ -1,919 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-menu/paper-menu.html">
-<link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
-<link rel="import" href="../paper-radio-group/paper-radio-group.html">
-<link rel="import" href="../paper-tooltip/paper-tooltip.html">
-<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-<link rel="import" href="../tf-graph-common/tf-graph-common.html">
-
-<dom-module id="tf-graph-controls">
-<template>
-<style>
-:host {
-  font-size: 12px;
-  color: gray;
-  --paper-font-subhead: {
-    font-size: 14px;
-    color: gray;
-  };
-  --paper-dropdown-menu-icon: {
-    width: 15px;
-    height: 15px;
-  };
-  --paper-dropdown-menu-button: {
-    padding: 0;
-  };
-  --paper-dropdown-menu-input: {
-    padding: 0;
-  };
-  --paper-item-min-height: 30px;
-}
-
-paper-button[raised].keyboard-focus {
-  font-weight: normal;
-}
-
-.run-dropdown {
-  --paper-input-container: {
-    padding: 9px 0 0 25px;
-  };
-}
-
-.color-dropdown {
-  --paper-input-container: {
-    padding: 9px 0 0 13px;
-  };
-}
-
-table {
-  border-collapse: collapse;
-  border-spacing: 0;
-}
-
-table td {
-  padding: 0;
-  margin: 0;
-}
-
-.allcontrols {
-  width: 188px;
-  padding: 0 30px;
-}
-
-.legend-holder {
-  position: absolute;
-  bottom: 0;
-  padding-bottom: 10px;
-}
-
-paper-radio-button {
-  display: block;
-  padding: 5px;
-}
-svg.icon {
-  width: 60px;
-  height: 18px;
-}
-.icon ellipse {
-  rx: 10px;
-  ry: 5px;
-  stroke: #CCC;
-  stroke-width: 1px;
-  fill: #FFFFFF;
-  cy: 10px;
-}
-.icon rect {
-  height: 14px;
-  width: 35px;
-  rx: 5px;
-  ry: 5px;
-  stroke: #CCC;
-  stroke-width: 2px;
-  fill: #D9D9D9;
-}
-.domainValues {
-  margin-bottom: 10px;
-  width: 165px;
-}
-.domainStart {
-  float: left;
-}
-.domainEnd {
-  float: right;
-}
-.colorBox {
-  width: 20px;
-}
-
-.image-icon {
-  width: 24px;
-  height: 24px;
-}
-
-.help-icon {
-  height: 15px;
-  margin: 0;
-  padding: 0;
-}
-
-.gray {
-  color: #666;
-}
-
-.title {
-  font-size: 16px;
-  margin: 8px 5px 8px 0;
-  color: black;
-}
-.title small {
-  font-weight: normal;
-}
-.deviceList, .xlaClusterList {
-  max-height: 200px;
-  overflow-y: auto;
-}
-
-#file {
-  padding: 8px 0;
-}
-
-.color-legend-row {
-  clear: both;
-  height: 20px;
-  margin-top: 5px;
-  position: relative;
-}
-
-.color-legend-row svg {
-  position: absolute;
-  top: -1px;
-  width: 40px;
-}
-
-.color-legend-row span.color-legend-value {
-  margin-left: 60px;
-}
-
-#grey-rect {
-  fill: #eee;
-  stroke: #a6a6a6;
-}
-
-#faded-rect {
-  fill: url(#rectHatch);
-  stroke: var(--tb-graph-faded);
-}
-
-.button-text {
-  text-transform: none;
-  padding: 8px 18px 0 18px;
-  font-size: 14px
-}
-
-.upload-button {
-  width: 165px;
-  height: 25px;
-  text-transform: none;
-  margin-top: 4px;
-}
-
-.iconbutton {
-  padding: 2px;
-  width: 30px;
-  height: 30px;
-  color: var(--paper-orange-500);
-}
-
-.hidden-input {
-  height: 0px;
-  width: 0px;
-  overflow:hidden;
-}
-
-.allcontrols .control-holder {
-  display: flex;
-  clear: both;
-}
-
-.allcontrols .control-holder paper-radio-group {
-  margin-top: 5px;
-}
-
-span.counter {
-  font-size: 13px;
-  color: gray;
-}
-
-.runs paper-item {
-  --paper-item: {
-    white-space: nowrap;
-  }
-}
-
-table.control-holder {
-  border: 0;
-  border-collapse: collapse;
-}
-
-table.tf-graph-controls td.input-element-table-data {
-  padding: 0 0 0 20px;
-}
-
-/** Override inline styles that suppress pointer events for disabled buttons. Otherwise, the */
-/*  tooltips do not appear. */
-#color-by-radio-group paper-radio-button {
-  pointer-events: auto !important;
-}
-
-.legend-clarifier {
-  color: #266236;
-  cursor: help;
-  display: inline-block;
-  text-decoration: underline;
-}
-
-.legend-clarifier paper-tooltip {
-  width: 150px;
-}
-</style>
-<svg width="0" height="0">
-  <defs>
-    <g id="legend-rect">
-      <rect x="1" y="1" stroke-width="2px" height="14" width="35" rx="5" ry="5"></rect>
-    </g>
-    <g id="grey-rect">
-       <use xmlns:xlink="http://www.w3.org/1999/xlink"
-            xlink:href="#legend-rect"/>
-     </g>
-     <g id="faded-rect">
-       <use xmlns:xlink="http://www.w3.org/1999/xlink"
-            xlink:href="#legend-rect"/>
-     </g>
-  </defs>
-</svg>
-<div class="allcontrols">
-  <div class="control-holder">
-    <paper-icon-button icon="aspect-ratio" class="iconbutton" on-click="fit" alt="Fit to screen">
-    </paper-icon-button>
-    <paper-button class="button-text" on-click="fit">Fit to screen
-    </paper-button>
-  </div>
-  <div class="control-holder">
-    <paper-icon-button icon="file-download" class="iconbutton" on-click="download" alt="Download PNG">
-    </paper-icon-button>
-    <paper-button class="button-text" on-click="download">Download PNG
-    </paper-button>
-    <a href="#" id="graphdownload" class="title" download="graph.png">
-    </a>
-  </div>
-  <div class="control-holder runs">
-    <div class="title">Run <span class="counter">([[datasets.length]])</span></div>
-    <paper-dropdown-menu no-label-float no-animations noink class="run-dropdown">
-      <paper-menu id="select" class="dropdown-content" selected="{{selectedDataset}}">
-        <template is="dom-repeat" items="[[datasets]]">
-          <paper-item>[[item.name]]</paper-item>
-        </template>
-      </paper-menu>
-    </paper-dropdown-menu>
-  </div>
-  <template is="dom-if" if="[[showSessionRunsDropdown]]">
-    <div class="control-holder">
-      <div class="title">Session runs <span class="counter">([[_numSessionRuns(metadataTags)]])</span></div>
-      <paper-dropdown-menu no-label-float no-animations noink class="run-dropdown">
-        <paper-menu id="select" class="dropdown-content" selected="{{selectedMetadataTag}}">
-          <template is="dom-repeat" items="[[metadataTags]]">
-            <paper-item>[[item.tag]]</paper-item>
-          </template>
-          <paper-item>None</paper-item>
-        </paper-menu>
-      </paper-dropdown-menu>
-    </div>
-  </template>
-  <template is="dom-if" if="[[showUploadButton]]">
-    <div class="control-holder">
-      <div class="title">Upload</div>
-      <paper-button raised class="text-button upload-button"
-          on-click="_getFile">Choose File</paper-button>
-      <div class="hidden-input">
-        <input type="file" id="file" name="file" on-change="_updateFileInput" />
-      </div>
-    </div>
-  </template>
-  <table class="control-holder">
-    <tr>
-      <td class="title">Trace inputs</td>
-      <td class="input-element-table-data">
-        <paper-toggle-button id="trace-inputs"></paper-toggle-button>
-      </td>
-    </tr>
-    <template is="dom-if" if="[[healthPillsFeatureEnabled]]">
-      <tr>
-        <td class="title">Show health pills</td>
-        <td class="input-element-table-data">
-          <paper-toggle-button checked="{{healthPillsToggledOn}}"></paper-toggle-button>
-        </td>
-      </tr>
-    </template>
-  </table>
-  <div class="control-holder">
-    <div class="title">Color</div>
-    <paper-radio-group id="color-by-radio-group" selected="{{colorBy}}">
-      <paper-radio-button name="structure">Structure</paper-radio-button>
-
-      <paper-radio-button name="device">Device</paper-radio-button>
-
-      <paper-radio-button id="xla-cluster-radio-button"
-                          name="xla_cluster"
-                          disabled="[[!_xlaClustersProvided(renderHierarchy)]]">
-        XLA Cluster
-      </paper-radio-button>
-      <paper-tooltip animation-delay="0" for="xla-cluster-radio-button" position="right">
-        Coloring by XLA cluster is only enabled if at least 1 op specifies an XLA cluster.
-      </paper-tooltip>
-
-      <paper-radio-button id="compute-time-radio-button"
-                          name="compute_time"
-                          disabled="[[!stats]]">
-        Compute time
-      </paper-radio-button>
-      <paper-tooltip animation-delay="0" for="compute-time-radio-button" position="right">
-        Coloring by compute time is only enabled if the RunMetadata proto is passed to the
-        FileWriter when a specific session is run.
-      </paper-tooltip>
-
-      <paper-radio-button id="memory-radio-button"
-                          name="memory"
-                          disabled="[[!stats]]">
-        Memory
-      </paper-radio-button>
-      <paper-tooltip animation-delay="0" for="memory-radio-button" position="right">
-        Coloring by memory is only enabled if the RunMetadata proto is passed to the
-        FileWriter when a specific session is run.
-      </paper-tooltip>
-    </paper-radio-group>
-  </div>
-  <div>
-    <template is="dom-if" if="[[_isGradientColoring(stats, colorBy)]]">
-      <svg width="140" height="20" style="margin: 0 5px" class="color-text">
-        <defs>
-          <linearGradient id="linearGradient" x1="0%" y1="0%" x2="100%" y2="0%">
-            <stop class="start" offset="0%"
-                stop-color$="[[_currentGradientParams.startColor]]"/>
-            <stop class="end" offset="100%"
-                stop-color$="[[_currentGradientParams.endColor]]"/>
-          </linearGradient>
-        </defs>
-        <rect x="0" y="0" width="135" height="20" fill="url(#linearGradient)"
-            stroke="black" />
-      </svg>
-      <div class="domainValues color-text">
-        <div class="domainStart">[[_currentGradientParams.minValue]]</div>
-        <div class="domainEnd">[[_currentGradientParams.maxValue]]</div>
-      </div>
-      <br style="clear: both">
-      <div>Devices included in stats:</div>
-      <div class="deviceList">
-        <table>
-        <template is="dom-repeat" items="[[_getDevices(devicesForStats)]]">
-          <tr>
-            <td>
-              <input type="checkbox" value$="[[item.device]]" checked$="[[item.used]]" on-click="_deviceCheckboxClicked"/>
-            </td>
-            <td>
-              <div>
-                <span>[[item.suffix]]</span>
-                <template is="dom-if" if="[[item.ignoredMsg]]">
-                  <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-                  <paper-tooltip position="right" animation-delay="0">[[item.ignoredMsg]]</paper-tooltip>
-                </template>
-              </div>
-            </td>
-          </tr>
-        </template>
-        </table>
-      </div>
-    </template>
-    <template is="dom-if" if="[[_equals(colorBy, 'structure')]]">
-      <div class="color-text">
-        <div class="color-legend-row">
-          <div style="position: absolute;">
-            colors
-          </div>
-          <span class="color-legend-value">same substructure</span>
-        </div>
-        <div class="color-legend-row">
-          <svg>
-            <use xmlns:xlink="http://www.w3.org/1999/xlink"
-                 xlink:href="#grey-rect" x="0" y="0"/>
-          </svg>
-          <span class="color-legend-value">unique substructure</span>
-        </div>
-      </div>
-    </template>
-    <template is="dom-if" if="[[_equals(colorBy, 'device')]]">
-      <div class="color-text">
-        <div class="deviceList">
-          <table>
-          <template is="dom-repeat" items="[[colorByParams.device]]">
-            <tr>
-              <td style$="[[_getBackgroundColor(item.color)]]">
-                <div class="colorBox"></div>
-              </td>
-              <td>
-                <div>[[item.device]]</div>
-              </td>
-            </tr>
-          </template>
-          </table>
-        </div>
-        <br/>
-        <div class="color-legend-row">
-          <svg>
-            <use xmlns:xlink="http://www.w3.org/1999/xlink"
-                 xlink:href="#grey-rect" x="0" y="0"/>
-          </svg>
-          <span class="color-legend-value">unknown device</span>
-        </div>
-      </div>
-    </template>
-    <template is="dom-if" if="[[_equals(colorBy, 'xla_cluster')]]">
-      <div class="color-text">
-        <div class="xlaClusterList">
-          <table>
-          <template is="dom-repeat" items="[[colorByParams.xla_cluster]]">
-            <tr>
-              <td style$="[[_getBackgroundColor(item.color)]]">
-                <div class="colorBox"></div>
-              </td>
-              <td>
-                <div>[[item.xla_cluster]]</div>
-              </td>
-            </tr>
-          </template>
-          </table>
-        </div>
-        <br/>
-        <div class="color-legend-row">
-          <svg>
-            <use xmlns:xlink="http://www.w3.org/1999/xlink"
-                 xlink:href="#grey-rect" x="0" y="0"/>
-          </svg>
-          <span class="color-legend-value">unknown XLA cluster</span>
-        </div>
-      </div>
-    </template>
-    <template is="dom-if" if="[[_statsNotNull(stats)]]">
-      <div class="color-legend-row">
-        <svg>
-          <use xmlns:xlink="http://www.w3.org/1999/xlink"
-                xlink:href="#faded-rect" x="0" y="0"/>
-        </svg>
-        <span class="color-legend-value">unused substructure</span>
-      </div>
-    </template>
-  </div>
-  <!--
-    Due to limited vertical space on the left sidebar, hide the legend whenever
-    we show a list of devices to include in stats.
-  -->
-  <template is="dom-if" if="[[!_isGradientColoring(stats, colorBy)]]">
-    <div class="legend-holder">
-      <table>
-        <tr>
-          <td><div class="title">Graph</div></td>
-          <td>(* = expandable)</td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon">
-              <rect transform="translate(3, 1)" height="14" width="35"
-                  rx="5" ry="5"/>
-            </svg>
-          </td>
-          <td>
-            Namespace<span class="gray">*</span>
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Encapsulates a set of nodes. Namespace is hierarchical and based on scope.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon" preserveAspectRatio="xMinYMid meet"
-                viewBox="0 0 10 10">
-              <use xlink:href="#op-node-stamp" fill="white" stroke="#ccc" x="9.5"
-                y="6" />
-            </svg>
-          </td>
-          <td>
-            OpNode
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Node that performs an operation. These nodes cannot expand.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon" height="15px" preserveAspectRatio="xMinYMid meet"
-                viewBox="0 0 12 12">
-              <use xlink:href="#op-series-horizontal-stamp" fill="white"
-                  stroke="#ccc" x="2" y="2"/>
-            </svg>
-          </td>
-          <td>
-            Unconnected series<span class="gray">*</span>
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Sequence of numbered nodes that are not connected to each other.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon" height="15px"
-                preserveAspectRatio="xMinYMid meet" viewBox="0 0 15 15">
-              <use xlink:href="#op-series-vertical-stamp"
-                  fill="white" stroke="#ccc" x="2" y="2"/>
-            </svg>
-          </td>
-          <td>
-            Connected series<span class="gray">*</span>
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Sequence of numbered nodes that are connected to each other.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon">
-              <circle fill="white" stroke="#848484" cx="10" cy="10" r="5"/>
-            </svg>
-          </td>
-          <td>
-            Constant
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Node that outputs a constant value.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="image-icon" viewBox="0 0 12 12" width="24" height="24">
-              <use x="0" y="0" class="image-icon" xlink:href="#summary-icon"/>
-            </svg>
-          </td>
-          <td>
-            Summary
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Node that collects data for visualization within TensorBoard.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon" height="15px"
-                preserveAspectRatio="xMinYMid meet" viewBox="0 0 15 15">
-              <defs>
-                <marker id="dataflow-arrowhead-legend" fill="#bbb" markerWidth="10"
-                    markerHeight="10" refX="9" refY="5" orient="auto-start-reverse">
-                  <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"/>
-                </marker>
-              </defs>
-              <path marker-end="url(#dataflow-arrowhead-legend)"
-                    stroke="#bbb" d="M2 9 l 29 0"
-                    stroke-linecap="round" />
-            </svg>
-          </td>
-          <td>
-            Dataflow edge
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Edge showing the data flow between operations. Edges flow upwards unless arrowheads specify otherwise.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon" height="15px"
-                preserveAspectRatio="xMinYMid meet" viewBox="0 0 15 15">
-              <path stroke="#bbb"
-                d="M2 9 l 29 0" stroke-linecap="round" stroke-dasharray="2, 2" />
-            </svg>
-          </td>
-          <td>
-            Control dependency edge
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Edge showing the control dependency between operations.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-        <tr>
-          <td>
-            <svg class="icon" height="15px"
-                preserveAspectRatio="xMinYMid meet" viewBox="0 0 15 15">
-              <defs>
-                <marker id="reference-arrowhead-legend" fill="#FFB74D" markerWidth="10"
-                    markerHeight="10" refX="9" refY="5" orient="auto-start-reverse">
-                  <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"/>
-                </marker>
-              </defs>
-              <path marker-end="url(#reference-arrowhead-legend)"
-                    stroke="#FFB74D" d="M2 9 l 29 0"
-                    stroke-linecap="round" />
-            </svg>
-          </td>
-          <td>
-            Reference edge
-            <div class="legend-clarifier">
-              <span>?</span>
-              <paper-tooltip animation-delay="0" position="right">
-                Edge showing that the outgoing operation node can mutate the incoming tensor.
-              </paper-tooltip>
-            </div>
-          </td>
-        </tr>
-      </table>
-    </div>
-  </template>
-  </div>
-</template>
-</dom-module>
-
-<script>
-(function() { // Private scope.
-/**
- * Stats from device names that match these regexes will be excluded by default.
- * The user can still turn on a device by selecting the checkbox in the device list.
- * See b/29089982 for context.
- */
-var DEVICE_NAMES_EXCLUDE = [
-  {
-    regex: /gpu:[0-9]+$/,
-    msg: 'Excluded by default since this is a CPU thread setting up GPU kernels.'
-  }
-];
-
-Polymer({
-  is: 'tf-graph-controls',
-  properties: {
-    // Public API.
-    stats: {
-      value: null,
-      type: Object,
-      observer: '_statsChanged'
-    },
-    devicesForStats: {
-      value: null,
-      type: Object,
-      notify: true,
-      readonly: true,
-    },
-    colorBy: {
-      type: String,
-      value: 'structure',
-      notify: true,
-      readonly: true
-    },
-    colorByParams: Object,
-    datasets: {
-      type: Array,
-      observer: '_datasetsChanged'
-    },
-    renderHierarchy: {
-      type: Object,
-      notify: true,
-    },
-    metadataTags: {
-      type: Array,
-      computed: '_getMetadataTags(selectedDataset, datasets)'
-    },
-    selectedDataset: {
-      type: Number,
-      notify: true,
-      value: 0,
-      observer: '_selectedDatasetChanged'
-    },
-    selectedFile: {
-      type: Object,
-      notify: true
-    },
-    selectedMetadataTag: {
-      type: Number,
-      notify: true,
-      value: -1
-    },
-    _currentGradientParams: {
-      type: Object,
-      computed: '_getCurrentGradientParams(colorByParams, colorBy)'
-    },
-    showSessionRunsDropdown: {
-      type: Boolean,
-      value: true
-    },
-    showUploadButton: {
-      type: Boolean,
-      value: true
-    },
-    // This stores whether the feature for showing health pills is enabled in the first place.
-    healthPillsFeatureEnabled: Boolean,
-    // This stores whether to show health pills. Only relevant if healthPillsFeatureEnabled. The
-    // user can toggle this value.
-    healthPillsToggledOn: {
-      type: Boolean,
-      notify: true,
-    },
-  },
-  listeners: {
-    'trace-inputs.change': '_traceInputToggleChanged'
-  },
-  _traceInputToggleChanged: function(event) {
-    // Flip the state of the trace inputs flag.
-    this.renderHierarchy.traceInputs = event.target.active;
-    tf.graph.scene.node.traceInputs(this.renderHierarchy);
-  },
-  _xlaClustersProvided: function(renderHierarchy) {
-    return renderHierarchy &&
-        renderHierarchy.hierarchy &&
-        renderHierarchy.hierarchy.xlaClusters.length > 0;
-  },
-  _statsChanged: function(stats) {
-    if (stats == null) {
-      return;
-    }
-    var devicesForStats = {};
-    var devices = _.each(stats.dev_stats, function(d) {
-      // Avoid device names that are ignored by default.
-      var exclude = _.some(DEVICE_NAMES_EXCLUDE, function(rule) {
-        return rule.regex.test(d.device);
-      });
-      if (!exclude) {
-        devicesForStats[d.device] = true;
-      }
-    });
-    this.set('devicesForStats', devicesForStats);
-  },
-  _getDevices: function(devicesForStats) {
-    var devices = _.map(this.stats.dev_stats, function(d) {
-      return d.device;
-    });
-    // Devices names can be long so we remove the longest common prefix
-    // before showing the devices in a list.
-    var suffixes = tf.graph.util.removeCommonPrefix(devices);
-    return _.map(devices, function(device, i) {
-      var ignoredMsg = null;
-      _.each(DEVICE_NAMES_EXCLUDE, function(rule) {
-        if (rule.regex.test(device)) {
-          ignoredMsg = rule.msg;
-        }
-      });
-      return {
-        device: device,
-        suffix: suffixes[i],
-        used: devicesForStats[device],
-        ignoredMsg: ignoredMsg
-      };
-    });
-  },
-  _deviceCheckboxClicked: function(checkbox) {
-    // Update the device map.
-    var devicesForStats = _.extend({}, this.devicesForStats);
-    var device = checkbox.target.value;
-    if (checkbox.target.checked) {
-      devicesForStats[device] = true;
-    } else {
-      delete devicesForStats[device];
-    }
-    this.set('devicesForStats', devicesForStats);
-  },
-  _numSessionRuns: function(metadataTags) {
-    return metadataTags != null ? metadataTags.length : 0;
-  },
-  _getBackgroundColor: function(color) {
-    return 'background-color:' + color;
-  },
-  fit: function() {
-    document.querySelector('#scene').fit();
-  },
-  _isGradientColoring: function(stats, colorBy) {
-    return ["compute_time", "memory"].indexOf(colorBy) !== -1
-        && stats != null;
-  },
-  _equals: function(a, b) {
-    return a === b;
-  },
-  _getCurrentGradientParams: function(colorByParams, colorBy) {
-    if (!this._isGradientColoring(this.stats, colorBy)) {
-      return;
-    }
-    var params = colorByParams[colorBy];
-    var minValue = params.minValue;
-    var maxValue = params.maxValue;
-    if (colorBy === 'memory') {
-      minValue = tf.graph.util.convertUnitsToHumanReadable(
-          minValue, tf.graph.util.MEMORY_UNITS);
-      maxValue = tf.graph.util.convertUnitsToHumanReadable(
-          maxValue, tf.graph.util.MEMORY_UNITS);
-    } else if (colorBy === 'compute_time') {
-      minValue = tf.graph.util.convertUnitsToHumanReadable(
-          minValue, tf.graph.util.TIME_UNITS);
-      maxValue = tf.graph.util.convertUnitsToHumanReadable(
-          maxValue, tf.graph.util.TIME_UNITS);
-    }
-    return {
-      minValue: minValue,
-      maxValue: maxValue,
-      startColor: params.startColor,
-      endColor: params.endColor
-    };
-  },
-  download: function() {
-    this.$.graphdownload.click();
-  },
-  _updateFileInput: function(e) {
-    var file = e.target.files[0];
-    if (!file) {
-      return;
-    }
-    this._setDownloadFilename(file.name);
-    this.set('selectedFile', e);
-  },
-  _datasetsChanged: function(newDatasets, oldDatasets) {
-    if (oldDatasets != null || this.selected == null) {
-      // Select the first dataset by default.
-      this.set('selectedDataset', 0);
-      this._setDownloadFilename(this.datasets[this.selectedDataset].path);
-    }
-  },
-  _getMetadataTags: function(selectedDataset, datasets) {
-    return this.datasets[selectedDataset].runMetadata;
-  },
-  _selectedDatasetChanged: function(newDataset, oldDataset) {
-    if (this.datasets) {
-      this.set('selectedMetadataTag', -1);
-      this.set('colorBy', 'structure');
-      this.$['trace-inputs'].active = false; // Set trace input to off-state.
-      this._setDownloadFilename(this.datasets[newDataset].path);
-    }
-  },
-  _getFile: function() {
-    this.$$("#file").click();
-  },
-  _setDownloadFilename: function(graphPath) {
-    // Strip off everything before the last "/" and strip off the file
-    // extension in order to get the name of the PNG for the graph.
-    var dotIndex = graphPath.lastIndexOf('.');
-    if (dotIndex) {
-      graphPath = graphPath.substring(0, dotIndex);
-    }
-    var slashIndex = graphPath.lastIndexOf('/');
-    if (slashIndex) {
-      graphPath = graphPath.substring(slashIndex + 1);
-    }
-    this.$.graphdownload.setAttribute('download', graphPath + '.png');
-  },
-  _statsNotNull: function(stats) {
-    return stats !== null;
-  },
-});
-})(); // Closing private scope.
-</script>
diff --git a/tensorflow/tensorboard/components/tf_graph_dashboard/BUILD b/tensorflow/tensorboard/components/tf_graph_dashboard/BUILD
deleted file mode 100644
index c69a780903..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_dashboard/BUILD
+++ /dev/null
@@ -1,44 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_dashboard",
-    srcs = ["tf-graph-dashboard.html"],
-    path = "/tf-graph-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_graph",
-        "//tensorflow/tensorboard/components/tf_graph_board",
-        "//tensorflow/tensorboard/components/tf_graph_controls",
-        "//tensorflow/tensorboard/components/tf_graph_loader",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/vz_sorting",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_dashboard"],
-    destdir = "tf-graph-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend:legacy",
-        "//tensorflow/tensorboard/components/tf_dashboard_common:legacy",
-        "//tensorflow/tensorboard/components/tf_graph:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_board:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_controls:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_loader:legacy",
-        "//tensorflow/tensorboard/components/vz_sorting:legacy",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/BUILD b/tensorflow/tensorboard/components/tf_graph_dashboard/demo/BUILD
deleted file mode 100644
index 66a37b8978..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/BUILD
+++ /dev/null
@@ -1,24 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph_dashboard/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-graph-dashboard/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_dashboard",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/data/graph_run_run1.pbtxt b/tensorflow/tensorboard/components/tf_graph_dashboard/demo/data/graph_run_run1.pbtxt
deleted file mode 100644
index 30b2064534..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/data/graph_run_run1.pbtxt
+++ /dev/null
@@ -1,4606 +0,0 @@
-node {
-  name: "GradientDescent/learning_rate"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_3"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_FLOAT
-        tensor_shape {
-        }
-        float_val: 0.1
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/Shape_1"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 1
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
-        int_val: 100
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/Shape"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 2
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 2
-          }
-        }
-        tensor_content: "\310\000\000\000d\000\000\000"
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/BroadcastGradientArgs"
-  op: "BroadcastGradientArgs"
-  input: "gradients/add_grad/Shape"
-  input: "gradients/add_grad/Shape_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_INT32
-    }
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-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-          dim {
-            size: 784
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "transpose_a"
-    value {
-      b: false
-    }
-  }
-  attr {
-    key: "transpose_b"
-    value {
-      b: true
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/tuple/group_deps"
-  op: "NoOp"
-  input: "^gradients/MatMul_grad/MatMul"
-  input: "^gradients/MatMul_grad/MatMul_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-      }
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/tuple/control_dependency_1"
-  op: "Identity"
-  input: "gradients/MatMul_grad/MatMul_1"
-  input: "^gradients/MatMul_grad/tuple/group_deps"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@gradients/MatMul_grad/MatMul_1"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: -1
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "GradientDescent/update_hidden_weights/ApplyGradientDescent"
-  op: "ApplyGradientDescent"
-  input: "hidden_weights"
-  input: "GradientDescent/learning_rate"
-  input: "gradients/MatMul_grad/tuple/control_dependency_1"
-  device: "/job:localhost/replica:0/task:0/cpu:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@hidden_weights"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 784
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "use_locking"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "GradientDescent"
-  op: "NoOp"
-  input: "^GradientDescent/update_hidden_weights/ApplyGradientDescent"
-  input: "^GradientDescent/update_hidden_biases/ApplyGradientDescent"
-  input: "^GradientDescent/update_logits_weights/ApplyGradientDescent"
-  input: "^GradientDescent/update_logits_biases/ApplyGradientDescent"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_2"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-      }
-    }
-  }
-}
-node {
-  name: "Reshape_3"
-  op: "Reshape"
-  input: "SoftmaxCrossEntropyWithLogits"
-  input: "Slice_2"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "Tshape"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "Mean"
-  op: "Mean"
-  input: "Reshape_3"
-  input: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "Tidx"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "keep_dims"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "_send_Mean_0"
-  op: "_Send"
-  input: "Mean"
-  device: "/job:localhost/replica:0/task:0/cpu:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "client_terminated"
-    value {
-      b: true
-    }
-  }
-  attr {
-    key: "recv_device"
-    value {
-      s: "/job:localhost/replica:0/task:0/cpu:0"
-    }
-  }
-  attr {
-    key: "send_device"
-    value {
-      s: "/job:localhost/replica:0/task:0/cpu:0"
-    }
-  }
-  attr {
-    key: "send_device_incarnation"
-    value {
-      i: -5924635994370253548
-    }
-  }
-  attr {
-    key: "tensor_name"
-    value {
-      s: "Mean:0"
-    }
-  }
-}
-library {
-}
-versions {
-  producer: 21
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/data/runs.json b/tensorflow/tensorboard/components/tf_graph_dashboard/demo/data/runs.json
deleted file mode 100644
index 0429aa71f8..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/data/runs.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "run1": {
-    "graph": true,
-    "scalars": ["foo/sin"]
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/index.html b/tensorflow/tensorboard/components/tf_graph_dashboard/demo/index.html
deleted file mode 100644
index ae84c547b4..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_dashboard/demo/index.html
+++ /dev/null
@@ -1,62 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<link rel="import" href="../tf-graph-dashboard.html">
-<link rel="import" href="../../paper-styles/typography.html">
-
-<title>Graph Dashboard Demo</title>
-<style>
-  #demo-container {
-    display: block;
-    height: 900px;
-    position: relative;
-    width: 100%;
-  }
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="graph-dashboard-demo">
-      <template>
-        <tf-graph-dashboard backend="[[backend]]"></tf-graph-dashboard>
-      </template>
-      <script>
-        import {Backend} from "../../tf-backend/backend";
-        import {createRouter, setRouter} from "../../tf-backend/router";
-
-        Polymer({
-          is: "graph-dashboard-demo",
-          properties: {
-            backend: {
-              type: Object,
-              value: function() {
-                return new Backend();
-              },
-            },
-          },
-          created: function() {
-            var router = createRouter("data", true);
-            setRouter(router);
-          },
-        });
-      </script>
-    </dom-module>
-    <graph-dashboard-demo id="demo-container"></graph-dashboard-demo>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph_dashboard/tf-graph-dashboard.html b/tensorflow/tensorboard/components/tf_graph_dashboard/tf-graph-dashboard.html
deleted file mode 100644
index ba69882a23..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_dashboard/tf-graph-dashboard.html
+++ /dev/null
@@ -1,321 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-graph-loader/tf-graph-loader.html">
-<link rel="import" href="../tf-graph-board/tf-graph-board.html">
-<link rel="import" href="../tf-graph-controls/tf-graph-controls.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../vz-sorting/vz-sorting.html">
-
-<!--
-tf-graph-dashboard displays a graph from a TensorFlow run.
-
-It has simple behavior: Creates a url-generator and run-generator
-to talk to the backend, and then passes the runsWithGraph (list of runs with
-associated graphs) along with the url generator into tf-graph-board for display.
-
-If there are multiple runs with graphs, the first run's graph is shown
-by default. The user can select a different run from a dropdown menu.
--->
-<dom-module id="tf-graph-dashboard">
-<template>
-<tf-no-data-warning
-  data-type="graph"
-  show-warning="[[_datasetsEmpty(_datasets)]]"
-></tf-no-data-warning>
-<template is="dom-if" if="[[!_datasetsEmpty(_datasets)]]">
-<tf-dashboard-layout>
-<div class="sidebar">
-  <tf-graph-controls id="controls"
-        devices-for-stats="{{_devicesForStats}}"
-        color-by-params="[[_colorByParams]]"
-        stats="[[_stats]]"
-        color-by="{{_colorBy}}"
-        datasets="[[_datasets]]"
-        render-hierarchy="[[_renderHierarchy]]"
-        selected-dataset="{{_selectedDataset}}"
-        selected-file="{{_selectedFile}}"
-        selected-metadata-tag="{{_selectedMetadataTag}}"
-        health-pills-feature-enabled="[[debuggerDataEnabled]]"
-        health-pills-toggled-on="{{healthPillsToggledOn}}"
-  ></tf-graph-controls>
-  <tf-graph-loader id="loader"
-        datasets="[[_datasets]]"
-        selected-dataset="[[_selectedDataset]]"
-        selected-metadata-tag="[[_selectedMetadataTag]]"
-        selected-file="[[_selectedFile]]"
-        out-graph-hierarchy="{{_graphHierarchy}}"
-        out-graph="{{_graph}}"
-        out-stats="{{_stats}}"
-        progress="{{_progress}}"
-out-hierarchy-params="{{_hierarchyParams}}"
-  ></tf-graph-loader>
-</div>
-<div class="center">
-    <tf-graph-board id="graphboard"
-        devices-for-stats="[[_devicesForStats]]"
-        color-by="[[_colorBy]]"
-        color-by-params="{{_colorByParams}}"
-        graph-hierarchy="[[_graphHierarchy]]"
-        graph="[[_graph]]"
-        hierarchy-params="[[_hierarchyParams]]"
-        progress="[[_progress]]"
-        debugger-data-enabled="[[debuggerDataEnabled]]"
-        are-health-pills-loading="[[_areHealthPillsLoading]]"
-        debugger-numeric-alerts="[[_debuggerNumericAlerts]]"
-        node-names-to-health-pills="[[_nodeNamesToHealthPills]]"
-        all-steps-mode-enabled="{{allStepsModeEnabled}}"
-        specific-health-pill-step="{{specificHealthPillStep}}"
-        health-pill-step-index="[[_healthPillStepIndex]]"
-        render-hierarchy="{{_renderHierarchy}}"
-        selected-node="{{_selectedNode}}"
-        stats="[[_stats]]"
-    ></tf-graph-board>
-</div>
-</tf-dashboard-layout>
-</template>
-<style>
-
-:host /deep/ {
-  font-family: 'Roboto', sans-serif;
-}
-
-.center {
-  position: relative;
-  height: 100%;
-}
-
-</style>
-</template>
-</dom-module>
-
-<script>
-import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-import {BackendBehavior} from "../tf-backend/behavior";
-import {compareTagNames} from "../vz-sorting/sorting";
-
-Polymer({
-  is: 'tf-graph-dashboard',
-  factoryImpl: function(backend, debuggerDataEnabled) {
-    this.backend = backend;
-    this.debuggerDataEnabled = debuggerDataEnabled;
-  },
-  behaviors: [
-    DashboardBehavior("graphs"),
-    ReloadBehavior("tf-graph-dashboard"),
-    BackendBehavior,
-  ],
-  properties: {
-    _datasets: Object,
-    _renderHierarchy: {type: Object, observer: '_renderHierarchyChanged'},
-    backend: Object,
-    debuggerDataEnabled: Boolean,
-    allStepsModeEnabled: Boolean,
-    specificHealthPillStep: {type: Number, value: 0},
-    healthPillsToggledOn: {type: Boolean, value: true, observer: '_healthPillsToggledOnChanged'},
-    _selectedNode: Object,
-    _isAttached: Boolean,
-    // Whether this dashboard is initialized. This dashboard should only be initialized once.
-    _initialized: Boolean,
-    // Whether health pills are currently being loaded, in which case we may want to say show a
-    // spinner.
-    _areHealthPillsLoading: Boolean,
-    // An array of alerts (in chronological order) provided by debugging libraries on when bad
-    // values (NaN, +/- Inf) appear.
-    _debuggerNumericAlerts: {
-      type: Array,
-      value: [],
-      notify: true,
-    },
-    // Maps the names of nodes to an array of health pills (HealthPillDatums).
-    _nodeNamesToHealthPills: {
-      type: Object,
-      value: {},
-    },
-    _healthPillStepIndex: Number,
-    // A strictly increasing ID. Each request for health pills has a unique ID. This helps us
-    // identify stale requests.
-    _healthPillRequestId: {type: Number, value: 1},
-    // The setTimeout ID for the pending request for health pills at a specific step.
-    _healthPillStepRequestTimerId: Number,
-    // The request for health pills at a specific step (as opposed to all sampled health pills) may
-    // involve slow disk reads. Hence, we throttle to 1 of those requests every this many ms.
-    _healthPillStepRequestTimerDelay: {
-      type: Number,
-      value: 500,
-      readOnly: true,
-    },
-    runs: Array,
-  },
-  listeners: {
-    'node-toggle-expand': '_handleNodeToggleExpand',
-  },
-  observers: [
-    '_maybeFetchHealthPills(allStepsModeEnabled, specificHealthPillStep, _selectedNode)',
-    '_maybeInitializeDashboard(backend, _isAttached)',
-  ],
-  attached: function() {
-    this.set('_isAttached', true);
-  },
-  detached: function() {
-    this.set('_isAttached', false);
-  },
-  reload: function() {
-    this._maybeFetchHealthPills();
-  },
-  _shouldRequestHealthPills: function() {
-    // Do not load debugger data if the feature is disabled, if the user toggled off the feature,
-    // or if the graph itself has not loaded yet. We need the graph to load so that we know which
-    // nodes to request health pills for.
-    return this.debuggerDataEnabled &&
-        this.healthPillsToggledOn &&
-        this._renderHierarchy &&
-        !this._datasetsEmpty(this._datasets);
-  },
-  _maybeInitializeDashboard: function(backend, isAttached) {
-    if (this._initialized || !backend || !isAttached) {
-      // Either this dashboard is already initialized ... or we are not yet ready to initialize.
-      return;
-    }
-    if (typeof ga !== 'undefined' && ga != null) {
-      ga('send', {hitType: 'pageview', page: '/v/graph'});
-    }
-    // Set this to true so we only initialize once.
-    this._initialized = true;
-    Promise.all([backend.graphRuns(), backend.runMetadataTags()])
-      .then(function(result) {
-        var runsWithGraph = result[0].sort(compareTagNames);
-        var runToMetadata = result[1];
-        var datasets = _.map(runsWithGraph, function(runName) {
-          return {
-            name: runName,
-            path: backend.graphUrl(
-                runName, tf.graph.LIMIT_ATTR_SIZE, tf.graph.LARGE_ATTRS_KEY),
-            runMetadata: runToMetadata[runName] ? _.map(
-              runToMetadata[runName].sort(compareTagNames), function(tag) {
-                return {
-                  tag: tag,
-                  path: backend.runMetadataUrl(tag, runName)
-                };
-              }, this) : []
-          };
-        }, this);
-        this.set('_datasets', datasets);
-      }.bind(this));
-  },
-  _requestHealthPills: function() {
-    this.set('_areHealthPillsLoading', true);
-    var requestId = ++this._healthPillRequestId;
-
-    if (this._healthPillStepRequestTimerId !== null) {
-      // A request for health pills is already scheduled to be initiated. Clear it, and schedule a
-      // new request.
-      window.clearTimeout(this._healthPillStepRequestTimerId);
-      this._healthPillStepRequestTimerId = null;
-    }
-
-    if (this.allStepsModeEnabled) {
-      // This path may be slow. Schedule network requests to start some time later. If another
-      // request is scheduled in the mean time, drop this current request.
-      this._healthPillStepRequestTimerId = setTimeout(function() {
-        this._healthPillStepRequestTimerId = null;
-        this._initiateNetworkRequestForHealthPills(requestId);
-      }.bind(this), this._healthPillStepRequestTimerDelay);
-    } else {
-      // The user is fetching sampled steps. This path is fast, so no need to throttle. Directly
-      // fetch the health pills across the network.
-      this._initiateNetworkRequestForHealthPills(requestId);
-    }
-  },
-  // Initiates the network request for health pills. Do not directly call this method - network
-  // requests may be throttled. Instead, call _requestHealthPills, which uses this method.
-  _initiateNetworkRequestForHealthPills: function(requestId) {
-    if (this._healthPillRequestId !== requestId) {
-      // This possibly scheduled request was outdated before it was even sent across the network. Do
-      // not bother initiating it.
-      return;
-    }
-
-    var specificStep = this.allStepsModeEnabled ? this.specificHealthPillStep : undefined;
-
-    var healthPillsPromise = this.backend.healthPills(
-        this._renderHierarchy.getNamesOfRenderedOps(), specificStep);
-    var alertsPromise = this.backend.debuggerNumericsAlerts();
-
-    Promise.all([healthPillsPromise, alertsPromise]).then(
-        function(result) {
-      var healthPillsResult = result[0];
-      var alertsResult = result[1];
-
-      if (!this.healthPillsToggledOn) {
-        // The user has opted to hide health pills via the toggle button.
-        return;
-      }
-
-      if (requestId !== this._healthPillRequestId) {
-        // This response is no longer relevant.
-        return;
-      }
-
-      // Set the index for which step to show for the health pills. By default, show the last step.
-      // A precondition we assume (that Tensorboard's reservoir sampling guarantees) is that all
-      // node names should be mapped to the same number of steps.
-      for (var nodeName in healthPillsResult) {
-        this.set('_healthPillStepIndex', healthPillsResult[nodeName].length - 1);
-        break;
-      }
-
-      this.set('_debuggerNumericAlerts', alertsResult);
-      this.set('_nodeNamesToHealthPills', healthPillsResult);
-      this.set('_areHealthPillsLoading', false);
-      this.set('_healthPillStepRequestTimerId', null);
-    }.bind(this));
-  },
-  _datasetsEmpty: function(datasets) {
-    return !datasets || !datasets.length;
-  },
-  _renderHierarchyChanged: function(renderHierarchy) {
-    // Reload any data on the graph when the render hierarchy (which determines which nodes are
-    // rendered) changes.
-    this.reload();
-  },
-  _handleNodeToggleExpand: function() {
-    // Nodes were toggled. We may need to request health pills for more nodes.
-    this._maybeFetchHealthPills();
-  },
-  _healthPillsToggledOnChanged: function(healthPillsToggledOn) {
-    if (healthPillsToggledOn) {
-      // Load health pills.
-      this.reload();
-    } else {
-      // Remove all health pills by setting an empty mapping.
-      this.set('_nodeNamesToHealthPills', {});
-    }
-  },
-  // Fetch health pills for a specific step if applicable.
-  _maybeFetchHealthPills: function() {
-    if (!this._shouldRequestHealthPills()) {
-      return;
-    }
-
-    this._requestHealthPills();
-  },
-});
-</script>
diff --git a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/BUILD b/tensorflow/tensorboard/components/tf_graph_debugger_data_card/BUILD
deleted file mode 100644
index c0d2bd5a46..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/BUILD
+++ /dev/null
@@ -1,44 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_debugger_data_card",
-    srcs = [
-        "tf-graph-debugger-data-card.html",
-    ],
-    path = "/tf-graph-debugger-data-card",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_spinner",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_debugger_data_card"],
-    destdir = "tf-graph-debugger-data-card",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_common:legacy",
-        "//third_party/javascript/polymer/v1/iron-collapse:lib",
-        "//third_party/javascript/polymer/v1/iron-list:lib",
-        "//third_party/javascript/polymer/v1/paper-icon-button:lib",
-        "//third_party/javascript/polymer/v1/paper-item:lib",
-        "//third_party/javascript/polymer/v1/paper-slider:lib",
-        "//third_party/javascript/polymer/v1/paper-spinner:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo/BUILD b/tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo/BUILD
deleted file mode 100644
index 66cb115618..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo/BUILD
+++ /dev/null
@@ -1,24 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-graph-debugger-data-card/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_debugger_data_card",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo/index.html b/tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo/index.html
deleted file mode 100644
index 934e4f86a8..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/demo/index.html
+++ /dev/null
@@ -1,36 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../tf-graph-debugger-data-card.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<title>TF Graph Info Demo</title>
-<style>
-  #demo-container {
-    border: 2px solid #808080;
-    width: 1000px;
-    height: 600px;
-  }
-</style>
-<demo-snippet>
-  <template>
-    <div id='demo-container'>
-      <!-- This simple demo starts up a page with a health pill legend. -->
-      <tf-graph-debugger-data-card></tf-graph-debugger-data-card>
-    </div>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/tf-graph-debugger-data-card.html b/tensorflow/tensorboard/components/tf_graph_debugger_data_card/tf-graph-debugger-data-card.html
deleted file mode 100644
index 6cc99a327c..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_debugger_data_card/tf-graph-debugger-data-card.html
+++ /dev/null
@@ -1,560 +0,0 @@
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-slider/paper-slider.html">
-<link rel="import" href="../paper-spinner/paper-spinner-lite.html">
-<link rel="import" href="../tf-graph-common/tf-graph-common.html">
-
-<dom-module id="tf-graph-debugger-data-card">
-<template>
-<style>
-:host {
-  font-size: 12px;
-  margin: 0;
-  padding: 0;
-  display: block;
-}
-
-h2 {
-  padding: 0;
-  text-align: center;
-  margin: 0;
-}
-
-.health-pill-legend {
-  padding: 15px;
-}
-
-.health-pill-legend h2 {
-  text-align: left;
-}
-
-.health-pill-entry {
-  margin: 10px 10px 10px 0;
-}
-
-.health-pill-entry .color-preview {
-  width: 26px;
-  height: 26px;
-  border-radius: 3px;
-  display: inline-block;
-  margin: 0 10px 0 0;
-}
-
-.health-pill-entry .color-label, .health-pill-entry .tensor-count {
-  color: #777;
-  display: inline-block;
-  height: 26px;
-  font-size: 22px;
-  line-height: 26px;
-  vertical-align: top;
-}
-
-.health-pill-entry .tensor-count {
-  float: right;
-}
-
-#health-pill-step-slider {
-  width: 100%;
-  margin: 0 0 0 -15px;
-  /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
-   * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
-   * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2.
-   * Apparently, the paper-slider lacks a mixin for those padding values. */
-  width: calc(100% + 31px);
-}
-
-#health-pills-loading-spinner {
-  width: 20px;
-  height: 20px;
-  vertical-align: top;
-}
-
-#health-pill-step-number-input {
-  text-align: center;
-  vertical-align: top;
-}
-
-#numeric-alerts-table-container {
-  max-height: 400px;
-  overflow-x: hidden;
-  overflow-y: auto;
-}
-
-#numeric-alerts-table {
-  text-align: left;
-}
-
-#numeric-alerts-table td {
-  vertical-align: top;
-}
-
-#numeric-alerts-table .first-offense-td {
-  display: inline-block;
-}
-
-.first-offense-td {
-  width: 80px;
-}
-
-.tensor-device-td {
-  max-width: 140px;
-  word-wrap : break-word;
-}
-
-.tensor-section-within-table {
-  color: #266236;
-  cursor: pointer;
-  opacity: 0.8;
-  text-decoration: underline;
-}
-
-.tensor-section-within-table:hover {
-  opacity: 1;
-}
-
-.device-section-within-table {
-  color: #666;
-}
-
-.mini-health-pill {
-  width: 130px;
-}
-
-.mini-health-pill > div {
-  height: 100%;
-  width: 60px;
-  border-radius: 3px;
-}
-
-#event-counts-th {
-  padding: 0 0 0 10px;
-}
-
-.negative-inf-mini-health-pill-section {
-  background: rgb(255, 141, 0);
-  width: 20px;
-}
-
-.positive-inf-mini-health-pill-section {
-  background: rgb(0, 62, 212);
-  width: 20px;
-}
-
-.nan-mini-health-pill-section {
-  background: rgb(204, 47, 44);
-  width: 20px;
-}
-
-.negative-inf-mini-health-pill-section,
-.positive-inf-mini-health-pill-section,
-.nan-mini-health-pill-section {
-  color: #fff;
-  display: inline-block;
-  height: 100%;
-  line-height: 20px;
-  margin: 0 0 0 10px;
-  text-align: center;
-}
-
-.no-numeric-alerts-notification {
-  margin: 0;
-}
-</style>
-<paper-material elevation="1" class="card health-pill-legend">
-  <div class="title">
-    Enable all (not just sampled) steps. Requires slow disk read.
-  </div>
-  <paper-toggle-button id="enableAllStepsModeToggle" checked="{{allStepsModeEnabled}}">
-  </paper-toggle-button>
-  <h2>
-    Step of Health Pills:
-    <template is="dom-if" if="[[allStepsModeEnabled]]">
-      <input type="number"
-             id="health-pill-step-number-input"
-             min="0"
-             max="[[_biggestStepEverSeen]]"
-             value="{{specificHealthPillStep::input}}">
-    </template>
-    <template is="dom-if" if="[[!allStepsModeEnabled]]">
-      [[_currentStepDisplayValue]]
-    </template>
-    <paper-spinner-lite active
-                        hidden$=[[!areHealthPillsLoading]]
-                        id="health-pills-loading-spinner"></paper-spinner-lite>
-  </h2>
-  <template is="dom-if" if="[[allStepsModeEnabled]]">
-    <paper-slider
-          id="health-pill-step-slider"
-          immediate-value="{{specificHealthPillStep}}"
-          max="[[_biggestStepEverSeen]]"
-          snaps
-          step="1"
-          value="{{specificHealthPillStep}}"></paper-slider>
-  </template>
-  <template is="dom-if" if="[[!allStepsModeEnabled]]">
-    <template is="dom-if" if="[[_maxStepIndex]]">
-      <paper-slider
-            id="health-pill-step-slider"
-            immediate-value="{{healthPillStepIndex}}"
-            max="[[_maxStepIndex]]"
-            snaps
-            step="1"
-            value="{{healthPillStepIndex}}"></paper-slider>
-    </template>
-  </template>
-  <h2>
-    Health Pill
-    <template is="dom-if" if="[[healthPillValuesForSelectedNode]]">
-      Counts for Selected Node
-    </template>
-    <template is="dom-if" if="[[!healthPillValuesForSelectedNode]]">
-      Legend
-    </template>
-  </h2>
-  <template is="dom-repeat" items="[[healthPillEntries]]">
-    <div class="health-pill-entry">
-      <div class="color-preview" style="background:[[item.background_color]]"></div>
-      <div class="color-label">[[item.label]]</div>
-      <div class="tensor-count">
-        [[_computeTensorCountString(healthPillValuesForSelectedNode, index)]]
-      </div>
-    </div>
-  </template>
-  <div hidden$="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
-    <h2 id="numeric-alerts-header">Numeric Alerts</h2>
-    <p>
-      Alerts are sorted from top to bottom by increasing timestamp.
-    </p>
-    <div id='numeric-alerts-table-container'>
-      <table id="numeric-alerts-table">
-        <thead>
-          <tr>
-            <th>First Offense</th>
-            <th>Tensor (Device)</th>
-            <th id='event-counts-th'>Event Counts</th>
-          </tr>
-        </thead>
-        <tbody id="numeric-alerts-body">
-        </tbody>
-      </table>
-    </div>
-  </div>
-  <template is="dom-if" if="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
-    <p class="no-numeric-alerts-notification">
-      No numeric alerts so far. That is likely good. Alerts indicate the presence of NaN
-      or (+/-) Infinity values, which may be concerning.
-    </p>
-  </template>
-</paper-material>
-</template>
-<script>
-"use strict";
-
-(function() {
-  Polymer({
-    is: 'tf-graph-debugger-data-card',
-
-    properties: {
-      renderHierarchy: Object,
-      debuggerNumericAlerts: {
-        type: Array,
-        notify: true,
-      },
-      nodeNamesToHealthPills: Object,
-      healthPillStepIndex: {
-        type: Number,
-        notify: true,
-      },
-      // Only relevant if we are in all steps mode, in which case the user may want to view health
-      // pills for a specific step.
-      specificHealthPillStep: {
-        type: Number,
-        value: 0,
-        notify: true,
-      },
-      // Two-ways
-      selectedNode: {
-        type: String,
-        notify: true
-      },
-      highlightedNode: {
-        type: String,
-        notify: true
-      },
-      // The enum value of the include property of the selected node.
-      selectedNodeInclude: {
-        type: Number,
-        notify: true
-      },
-      // Whether health pills are currently being loaded, in which case we show a spinner (and the
-      // current health pills shown might be out of date).
-      areHealthPillsLoading: Boolean,
-      healthPillEntries: {
-        type: Array,
-        value: tf.graph.scene.healthPillEntries,
-        readOnly: true,
-      },
-      healthPillValuesForSelectedNode: {
-        type: Array,
-        computed: '_computeHealthPillForNode(nodeNamesToHealthPills, healthPillStepIndex, selectedNode, allStepsModeEnabled, areHealthPillsLoading)',
-      },
-      // When all-steps mode is enabled, the user can request health pills for any step. In this
-      // mode, Tensorboard makes a request every time the user drags the slider to a different step.
-      allStepsModeEnabled: {
-        type: Boolean,
-        notify: true,
-      },
-      // The biggest step value ever seen. Used to determine what steps of health pills to let the
-      // user fetch in all steps mode.
-      _biggestStepEverSeen: {
-        type: Number,
-        computed: '_computeBiggestStepEverSeen(nodeNamesToHealthPills)',
-      },
-      _maxStepIndex: {
-        type: Number,
-        computed: '_computeMaxStepIndex(nodeNamesToHealthPills)',
-      },
-      _currentStepDisplayValue: {
-        type: String,
-        computed: '_computeCurrentStepDisplayValue(nodeNamesToHealthPills, healthPillStepIndex, allStepsModeEnabled, specificHealthPillStep, areHealthPillsLoading)',
-      },
-    },
-    observers: [
-      '_updateAlertsList(debuggerNumericAlerts)',
-    ],
-    ready: function() {
-      var mainContainer = document.getElementById('mainContainer');
-      var scrollbarContainer = document.querySelector('tf-dashboard-layout .scrollbar');
-      if (mainContainer && scrollbarContainer) {
-        // If this component is being used inside of TensorBoard's dashboard layout, it may easily
-        // cause the dashboard layout element to overflow, giving the user 2 scroll bars. Prevent
-        // that by hiding whatever content overflows - the user will have to expand the viewport to
-        // use this debugging card.
-        mainContainer.style.overflow = 'hidden';
-        scrollbarContainer.style.overflow = 'hidden';
-      }
-    },
-    _healthPillsAvailable: function(debuggerDataEnabled, nodeNamesToHealthPills) {
-      // So long as there is a mapping (even if empty) from node name to health pills, show the
-      // legend and slider. We do that because, even if no health pills exist at the current step,
-      // the user may desire to change steps, and the slider must show for the user to do that.
-      return debuggerDataEnabled && nodeNamesToHealthPills;
-    },
-    _computeTensorCountString: function(healthPillValuesForSelectedNode, valueIndex) {
-      if (!healthPillValuesForSelectedNode) {
-        // No health pill data is available.
-        return '';
-      }
-
-      return healthPillValuesForSelectedNode[valueIndex].toFixed(0);
-    },
-    _computeHealthPillForNode: function(
-        nodeNamesToHealthPills, healthPillStepIndex, selectedNode, allStepsModeEnabled, areHealthPillsLoading) {
-      if (areHealthPillsLoading) {
-        // Health pills are loading. Do not render data that is out of date.
-        return null;
-      }
-
-      if (!selectedNode) {
-        // No node is selected.
-        return null;
-      }
-
-      const healthPills = nodeNamesToHealthPills[selectedNode];
-      if (!healthPills) {
-        // This node lacks a health pill.
-        return null;
-      }
-
-      // If all steps mode is enabled, we use the first health pill in the list because the JSON
-      // response from the server is a mapping between node name and a list of 1 health pill.
-      const healthPill = healthPills[allStepsModeEnabled ? 0 : healthPillStepIndex];
-      if (!healthPill) {
-        // This node lacks a health pill at the current step.
-        return null;
-      }
-
-      // The health pill count values start at 2. Each health pill contains 6 values.
-      return healthPill.value.slice(2, 8);
-    },
-    _computeCurrentStepDisplayValue: function(
-        nodeNamesToHealthPills,
-        healthPillStepIndex,
-        allStepsModeEnabled,
-        specificHealthPillStep,
-        areHealthPillsLoading) {
-      if (allStepsModeEnabled) {
-        // The user seeks health pills for specific step from the server.
-        return specificHealthPillStep.toFixed(0);
-      }
-
-      if (areHealthPillsLoading) {
-        // The current step is undefined.
-        return 0;
-      }
-
-      for (let nodeName in nodeNamesToHealthPills) {
-        // All nodes have the same number of steps stored, so only examine 1 node. We cannot
-        // directly index into the nodeNamesToHealthPills object because we do not have a key.
-        // If all steps mode is enabled, we only have 1 step to show.
-        return nodeNamesToHealthPills[nodeName][healthPillStepIndex].step.toFixed(0);
-      }
-
-      // The current step could not be computed.
-      return 0;
-    },
-    _computeBiggestStepEverSeen: function(nodeNamesToHealthPills) {
-      for (let nodeName in nodeNamesToHealthPills) {
-        // All nodes have the same number of steps stored, so only examine 1 node.
-        // The index is 1 less than the count. Tensorboard backend logic guarantees that the length
-        // of the array will be greater than 1.
-        var healthPills = nodeNamesToHealthPills[nodeName];
-        return Math.max(this._biggestStepEverSeen, healthPills[healthPills.length - 1].step);
-      }
-
-      // No steps seen so far. Default to 0.
-      return this._biggestStepEverSeen || 0;
-    },
-    _computeMaxStepIndex: function(nodeNamesToHealthPills) {
-      for (let nodeName in nodeNamesToHealthPills) {
-        // All nodes have the same number of steps stored, so only examine 1 node.
-        // The index is 1 less than the count. Tensorboard backend logic guarantees that the length
-        // of the array will be greater than 1.
-        return nodeNamesToHealthPills[nodeName].length - 1;
-      }
-
-      // Return a falsy value. The slider should be hidden.
-      return 0;
-    },
-    _hasDebuggerNumericAlerts: function(debuggerNumericAlerts) {
-      return debuggerNumericAlerts && debuggerNumericAlerts.length;
-    },
-    _updateAlertsList: function(debuggerNumericAlerts) {
-      var alertBody = this.$$('#numeric-alerts-body');
-      if (!alertBody) {
-        return;
-      }
-
-      alertBody.innerHTML = '';
-
-      for (var i = 0; i < debuggerNumericAlerts.length; i++) {
-        var alert = debuggerNumericAlerts[i];
-        var tableRow = document.createElement('tr');
-
-        var timestampTd = document.createElement('td');
-        timestampTd.innerHTML = tf.graph.util.computeHumanFriendlyTime(alert.first_timestamp);
-        timestampTd.classList.add('first-offense-td');
-        tableRow.appendChild(timestampTd);
-
-        var tensorDeviceTd = document.createElement('td');
-        tensorDeviceTd.classList.add('tensor-device-td')
-
-        var tensorSection = document.createElement('div');
-        tensorSection.classList.add('tensor-section-within-table');
-        tensorSection.innerHTML = alert.tensor_name;
-        this._addOpExpansionListener(tensorSection, alert.tensor_name);
-        tensorDeviceTd.appendChild(tensorSection);
-
-        var deviceSection = document.createElement('div');
-        deviceSection.classList.add('device-section-within-table');
-        deviceSection.innerHTML = '(' + alert.device_name + ')';
-        tensorDeviceTd.appendChild(deviceSection);
-        tableRow.appendChild(tensorDeviceTd);
-
-        var miniHealthPill = document.createElement('div');
-        miniHealthPill.classList.add('mini-health-pill');
-
-        var miniHealthPillTd = document.createElement('td');
-        miniHealthPillTd.classList.add('mini-health-pill-td');
-        miniHealthPillTd.appendChild(miniHealthPill);
-        tableRow.appendChild(miniHealthPillTd);
-
-        if (alert.neg_inf_event_count) {
-          var negativeInfCountSection = document.createElement('div');
-          negativeInfCountSection.classList.add('negative-inf-mini-health-pill-section');
-          negativeInfCountSection.innerHTML = alert.neg_inf_event_count;
-          negativeInfCountSection.setAttribute(
-              'title', alert.neg_inf_event_count + ' events with -∞')
-          miniHealthPill.appendChild(negativeInfCountSection);
-        }
-
-        if (alert.pos_inf_event_count) {
-          var positiveInfCountSection = document.createElement('div');
-          positiveInfCountSection.classList.add('positive-inf-mini-health-pill-section');
-          positiveInfCountSection.innerHTML = alert.pos_inf_event_count;
-          positiveInfCountSection.setAttribute(
-              'title', alert.pos_inf_event_count + ' events with +∞')
-          miniHealthPill.appendChild(positiveInfCountSection);
-        }
-
-        if (alert.nan_event_count) {
-          var nanCountSection = document.createElement('div');
-          nanCountSection.classList.add('nan-mini-health-pill-section');
-          nanCountSection.innerHTML = alert.nan_event_count;
-          nanCountSection.setAttribute(
-              'title', alert.nan_event_count + ' events with NaN')
-          miniHealthPill.appendChild(nanCountSection);
-        }
-
-        Polymer.dom(alertBody).appendChild(tableRow);
-      }
-    },
-    // Adds a listener to an element, so that when that element is clicked, the tensor with
-    // tensorName expands.
-    _addOpExpansionListener: function(clickableElement, tensorName) {
-      clickableElement.addEventListener('click', () => {
-        // When the user clicks on a tensor name, expand all nodes until the user can see the
-        // associated node.
-        var nameOfNodeToSelect = tf.graph.render.expandUntilNodeIsShown(
-            document.getElementById('scene'), this.renderHierarchy, tensorName);
-
-        // Store the current scroll of the graph info card. Node selection alters that scroll, and
-        // we restore the scroll later.
-        var previousScrollFromBottom;
-        var graphInfoCard = document.querySelector('tf-graph-info#graph-info');
-        if (graphInfoCard) {
-          previousScrollFromBottom = graphInfoCard.scrollHeight - graphInfoCard.scrollTop;
-        }
-
-        // Update the selected node within graph logic.
-        var previousSelectedNode = this.selectedNode;
-        this.set('selectedNode', nameOfNodeToSelect);
-
-        // Scroll the graph info card back down if necessary so that user can see the alerts section
-        // again. Selecting the node causes the info card to scroll to the top, which may mean the
-        // user no longer sees the list of alerts.
-        var scrollToOriginalLocation = () => {
-          graphInfoCard.scrollTop = graphInfoCard.scrollHeight - previousScrollFromBottom;
-        };
-        if (graphInfoCard) {
-          // This component is used within an info card. Restore the original scroll.
-          if (previousSelectedNode) {
-            // The card for the selected node has already opened. Immediately restore the scroll.
-            scrollToOriginalLocation();
-          } else {
-            // Give some time for the DOM of the info card to be created before scrolling down.
-            window.setTimeout(scrollToOriginalLocation, 20);
-          }
-        }
-      });
-    },
-  });
-})();
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_graph_info/BUILD b/tensorflow/tensorboard/components/tf_graph_info/BUILD
deleted file mode 100644
index 22e886d881..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/BUILD
+++ /dev/null
@@ -1,53 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_info",
-    srcs = [
-        "tf-graph-icon.html",
-        "tf-graph-info.html",
-        "tf-node-info.html",
-        "tf-node-list-item.html",
-    ],
-    path = "/tf-graph-info",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_graph_debugger_data_card",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "@org_polymer_iron_collapse",
-        "@org_polymer_iron_list",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_item",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_spinner",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_info"],
-    destdir = "tf-graph-info",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_common:legacy",
-        "//tensorflow/tensorboard/components/tf_graph_debugger_data_card:legacy",
-        "//third_party/javascript/polymer/v1/iron-collapse:lib",
-        "//third_party/javascript/polymer/v1/iron-list:lib",
-        "//third_party/javascript/polymer/v1/paper-icon-button:lib",
-        "//third_party/javascript/polymer/v1/paper-item:lib",
-        "//third_party/javascript/polymer/v1/paper-slider:lib",
-        "//third_party/javascript/polymer/v1/paper-spinner:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_info/demo/BUILD b/tensorflow/tensorboard/components/tf_graph_info/demo/BUILD
deleted file mode 100644
index 2f1f7bf276..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/demo/BUILD
+++ /dev/null
@@ -1,26 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph_info/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-graph-info/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_graph_info",
-        "//tensorflow/tensorboard/components/tf_graph_loader",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_info/demo/data/graph.pbtxt b/tensorflow/tensorboard/components/tf_graph_info/demo/data/graph.pbtxt
deleted file mode 100644
index 30b2064534..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/demo/data/graph.pbtxt
+++ /dev/null
@@ -1,4606 +0,0 @@
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-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "keep_dims"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/Reshape"
-  op: "Reshape"
-  input: "gradients/add_grad/Sum"
-  input: "gradients/add_grad/Shape"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "Tshape"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/tuple/group_deps"
-  op: "NoOp"
-  input: "^gradients/add_grad/Reshape"
-  input: "^gradients/add_grad/Reshape_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/tuple/control_dependency_1"
-  op: "Identity"
-  input: "gradients/add_grad/Reshape_1"
-  input: "^gradients/add_grad/tuple/group_deps"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@gradients/add_grad/Reshape_1"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "GradientDescent/update_hidden_biases/ApplyGradientDescent"
-  op: "ApplyGradientDescent"
-  input: "hidden_biases"
-  input: "GradientDescent/learning_rate"
-  input: "gradients/add_grad/tuple/control_dependency_1"
-  device: "/job:localhost/replica:0/task:0/cpu:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@hidden_biases"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "use_locking"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/tuple/control_dependency"
-  op: "Identity"
-  input: "gradients/add_grad/Reshape"
-  input: "^gradients/add_grad/tuple/group_deps"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@gradients/add_grad/Reshape"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/MatMul_1"
-  op: "MatMul"
-  input: "Reshape"
-  input: "gradients/add_grad/tuple/control_dependency"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: -1
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "transpose_a"
-    value {
-      b: true
-    }
-  }
-  attr {
-    key: "transpose_b"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/MatMul"
-  op: "MatMul"
-  input: "gradients/add_grad/tuple/control_dependency"
-  input: "hidden_weights/read"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-          dim {
-            size: 784
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "transpose_a"
-    value {
-      b: false
-    }
-  }
-  attr {
-    key: "transpose_b"
-    value {
-      b: true
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/tuple/group_deps"
-  op: "NoOp"
-  input: "^gradients/MatMul_grad/MatMul"
-  input: "^gradients/MatMul_grad/MatMul_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-      }
-    }
-  }
-}
-node {
-  name: "gradients/MatMul_grad/tuple/control_dependency_1"
-  op: "Identity"
-  input: "gradients/MatMul_grad/MatMul_1"
-  input: "^gradients/MatMul_grad/tuple/group_deps"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@gradients/MatMul_grad/MatMul_1"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: -1
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "GradientDescent/update_hidden_weights/ApplyGradientDescent"
-  op: "ApplyGradientDescent"
-  input: "hidden_weights"
-  input: "GradientDescent/learning_rate"
-  input: "gradients/MatMul_grad/tuple/control_dependency_1"
-  device: "/job:localhost/replica:0/task:0/cpu:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "_class"
-    value {
-      list {
-        s: "loc:@hidden_weights"
-      }
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 784
-          }
-          dim {
-            size: 100
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "use_locking"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "GradientDescent"
-  op: "NoOp"
-  input: "^GradientDescent/update_hidden_weights/ApplyGradientDescent"
-  input: "^GradientDescent/update_hidden_biases/ApplyGradientDescent"
-  input: "^GradientDescent/update_logits_weights/ApplyGradientDescent"
-  input: "^GradientDescent/update_logits_biases/ApplyGradientDescent"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_2"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-      }
-    }
-  }
-}
-node {
-  name: "Reshape_3"
-  op: "Reshape"
-  input: "SoftmaxCrossEntropyWithLogits"
-  input: "Slice_2"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "Tshape"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 200
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "Mean"
-  op: "Mean"
-  input: "Reshape_3"
-  input: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "Tidx"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "keep_dims"
-    value {
-      b: false
-    }
-  }
-}
-node {
-  name: "_send_Mean_0"
-  op: "_Send"
-  input: "Mean"
-  device: "/job:localhost/replica:0/task:0/cpu:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "client_terminated"
-    value {
-      b: true
-    }
-  }
-  attr {
-    key: "recv_device"
-    value {
-      s: "/job:localhost/replica:0/task:0/cpu:0"
-    }
-  }
-  attr {
-    key: "send_device"
-    value {
-      s: "/job:localhost/replica:0/task:0/cpu:0"
-    }
-  }
-  attr {
-    key: "send_device_incarnation"
-    value {
-      i: -5924635994370253548
-    }
-  }
-  attr {
-    key: "tensor_name"
-    value {
-      s: "Mean:0"
-    }
-  }
-}
-library {
-}
-versions {
-  producer: 21
-}
diff --git a/tensorflow/tensorboard/components/tf_graph_info/demo/index.html b/tensorflow/tensorboard/components/tf_graph_info/demo/index.html
deleted file mode 100644
index f7d2ef7ee5..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/demo/index.html
+++ /dev/null
@@ -1,94 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../tf-graph-info.html">
-<link rel="import" href="../../tf-graph-common/tf-graph-common.html">
-<link rel="import" href="../../tf-graph-loader/tf-graph-loader.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<title>TF Graph Info Demo</title>
-<style>
-  #demo-container {
-    border: 2px solid #808080;
-    width: 1000px;
-    height: 600px;
-  }
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="tf-graph-info-demo">
-      <template>
-        <!-- We first use the graph loader to load and parse a pbtxt file into a graph object. -->
-        <tf-graph-loader
-            id="loader"
-            datasets="[[_datasets]]"
-            selected-dataset="[[_selectedDataset]]"
-            out-graph="{{_graph}}">
-        </tf-graph-loader>
-
-        <tf-graph-info id="info" title="selected"></tf-graph-info>
-      </template>
-      <script>
-        "use strict";
-
-        Polymer({
-          is: "tf-graph-info-demo",
-          properties: {
-            // We tell the graph loader to load a specific pbtxt file.
-            _datasets: {
-              type: Array,
-              value: [{
-                "name": "Graph with XLA Clusters Specified",
-                "path": "data/graph.pbtxt"
-              }],
-            },
-            _selectedDataset: {
-              type: Number,
-              value: 0,
-            },
-
-            // This property will be updated by the graph loader.
-            _graph: {
-              type: Object,
-            },
-          },
-          observers: [
-            '_graphUpdated(_graph)',
-          ],
-          _graphUpdated: function(slimGraph) {
-            const tracker = tf.graph.util.getTracker(this.$.loader);
-            const hierarchyTracker = tf.graph.util.getSubtaskTracker(
-                tracker, 100, 'Namespace hierarchy');
-            const hierarchyOptions = {};
-            tf.graph.hierarchy.build(slimGraph, hierarchyOptions, hierarchyTracker).then(
-                function(graphHierarchy) {
-              // We have parsed and built the graph object from a pbtxt file. Show info.
-              this.$.info.set('graph', slimGraph);
-              this.$.info.set('graphHierarchy', graphHierarchy);
-
-              // Select a node within that graph.
-              this.$.info.set('selectedNode', 'GradientDescent/learning_rate');
-            }.bind(this));
-          },
-        });
-      </script>
-    </dom-module>
-    <div id='demo-container'>
-      <tf-graph-info-demo></tf-graph-info-demo>
-    </div>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph_info/tf-graph-icon.html b/tensorflow/tensorboard/components/tf_graph_info/tf-graph-icon.html
deleted file mode 100644
index a3e9dc59c5..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/tf-graph-icon.html
+++ /dev/null
@@ -1,296 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-
-<dom-module id="tf-graph-icon">
-  <style>
-    .faded-rect {
-      fill: url(#rectHatch);
-    }
-
-    .faded-ellipse {
-      fill: url(#ellipseHatch);
-    }
-
-    .faded-rect, .faded-ellipse, .faded-series {
-      stroke:   var(--tb-graph-faded) !important;
-    }
-  </style>
-  <template>
-    <template is="dom-if" if="[[_isType(node, type, 'OP')]]">
-      <template is="dom-if" if="[[_isConst(node, const)]]">
-        <svg height$="[[height]]"
-            preserveAspectRatio="xMinYMid meet" viewBox="0 0 10 10">
-          <circle cx="5" cy="5" r="3"
-              fill$="[[_getFill(_computedFill, 'OP')]]"
-              stroke$="[[_getStroke(_computedFill, 'OP')]]" />
-        </svg>
-      </template>
-      <template is="dom-if" if="[[_isSummary(node, summary)]]">
-        <svg width$="[[height]]" height$="[[height]]" viewBox="0 0 12 12">
-          <use x="0" y="0" xlink:href="#summary-icon" />
-        </svg>
-      </template>
-      <template is="dom-if" if="[[_isRegularOp(node, const, summary)]]">
-        <svg height$="[[height]]"
-            preserveAspectRatio="xMinYMid meet" viewBox="0 0 16 8">
-          <use xmlns:xlink="http://www.w3.org/1999/xlink"
-              xlink:href="#op-node-stamp"
-              fill$="[[_getFill(_computedFill, 'OP')]]"
-              stroke$="[[_getStroke(_computedFill, 'OP')]]"
-              class$="{{_fadedClass(renderInfo, 'ellipse')}}"
-              x="8" y="4" />
-        </svg>
-      </template>
-    </template>
-    <template is="dom-if" if="[[_isType(node, type, 'META')]]">
-      <svg height$="[[height]]"
-            preserveAspectRatio="xMinYMid meet" viewBox="0 0 37 16">
-        <rect x="1" y="1"
-            fill$="[[_getFill(_computedFill, 'META')]]"
-            stroke$="[[_getStroke(_computedFill, 'META')]]"
-            class$="{{_fadedClass(renderInfo, 'rect')}}"
-            stroke-width="2px"
-            height="14" width="35"
-            rx="5" ry="5"/>
-      </svg>
-    </template>
-    <template is="dom-if" if="[[_isType(node, type, 'SERIES')]]">
-      <template is="dom-if" if="[[_isVertical(node, vertical)]]">
-        <svg height$="[[height]]"
-            preserveAspectRatio="xMinYMid meet" viewBox="0 0 16 15">
-          <use xmlns:xlink="http://www.w3.org/1999/xlink"
-              xlink:href="#op-series-vertical-stamp"
-              fill$="[[_getFill(_computedFill, 'SERIES')]]"
-              stroke$="[[_getStroke(_computedFill, 'SERIES')]]"
-              class$="{{_fadedClass(renderInfo, 'series')}}"
-              x="0" y="2" />
-        </svg>
-      </template>
-      <template is="dom-if" if="[[!_isVertical(node, vertical)]]">
-        <svg height$="[[height]]"
-            preserveAspectRatio="xMinYMid meet" viewBox="0 0 24 10">
-          <use xmlns:xlink="http://www.w3.org/1999/xlink"
-              xlink:href="#op-series-horizontal-stamp"
-              fill$="[[_getFill(_computedFill, 'SERIES')]]"
-              stroke$="[[_getStroke(_computedFill, 'SERIES')]]"
-              class$="{{_fadedClass(renderInfo, 'series')}}"
-              x="0" y="1" />
-        </svg>
-      </template>
-    </template>
-  </template>
-
-  <script>
-    (function() {
-      Polymer({
-        is: 'tf-graph-icon',
-
-        properties: {
-          /**
-           * Node to represent with an icon. Optional, but if specified, its
-           * properties override those defined in the type, vertical, const and
-           * summary properties.
-           * @type {tf.graph.Node}
-           */
-          node: {
-            type: Object,
-            value: null
-          },
-
-          /**
-           * Render node information associated with this node. Optional. If
-           * specified, this is only used when computing the fill of the icon
-           * element.
-           * @type {tf.graph.render.RenderNodeInfo}
-           */
-          renderInfo: {
-            type: Object,
-            value: null
-          },
-
-          /**
-           * String indicating the type of coloring to use for this node, used
-           * only for determining the fill.
-           */
-          colorBy: {
-            type: Object,
-            value: "structural"
-          },
-
-          /**
-           * Function used by structural coloring algorithm to determine which
-           * color to use based on the template ID of the node. Optional.
-           */
-          templateIndex: {
-            type: Function,
-            value: null
-          },
-
-          /** Type of node to draw (ignored if node is set). */
-          type: {
-            type: String,
-            value: null
-          },
-
-          /** Direction for series (ignored for other types). */
-          vertical: {
-            type: Boolean,
-            value: false
-          },
-
-          /** Whether the op is Const (ignored for non-ops). */
-          const: {
-            type: Boolean,
-            value: false
-          },
-
-          /** Whether the op is a Summary (ignored for non-ops). */
-          summary: {
-            type: Boolean,
-            value: false
-          },
-
-          /**
-           * Fill for the icon, optional. If fill is specified and node is not
-           * specified, then this value will override the default for the
-           * element. However, if node is specified, this value will be ignored.
-           */
-          fill: {
-            type: String,
-            value: null
-          },
-
-          /** Height of the SVG element in pixels, used for scaling. */
-          height: {
-            type: Number,
-            value: 20
-          },
-
-          /** The computed fill for the node. **/
-          _computedFill: {
-            type: String,
-            computed:
-              "_getComputedFill(node, renderInfo, colorBy, templateIndex, fill)"
-          }
-
-        },
-
-        /**
-         * Get the computed fill value for the element.
-         */
-        _getComputedFill: function(inputNode, inputRenderInfo, inputColorBy,
-            inputTemplateIndex, inputFill) {
-          if (inputNode && inputRenderInfo &&
-              inputColorBy && inputTemplateIndex) {
-            var ns = tf.graph.scene.node;
-            var colorBy = ns.ColorBy[inputColorBy.toUpperCase()];
-            return ns.getFillForNode(inputTemplateIndex, colorBy,
-                inputRenderInfo, false);
-          }
-          return inputFill;
-        },
-
-        /**
-         * Get the fill value for the element, or if that's not possible, return
-         * the default fill value for the node type.
-         */
-        _getFill: function(inputComputedFill, inputNodeType) {
-          return inputComputedFill || ({
-            OP: tf.graph.render.OpNodeColors.DEFAULT_FILL,
-            META: tf.graph.render.MetanodeColors.DEFAULT_FILL,
-            SERIES: tf.graph.render.SeriesNodeColors.DEFAULT_FILL
-          })[inputNodeType];
-        },
-
-        /**
-         * Get the stroke value for the element, or if that's not possible,
-         * return the default stroke value for the node type.
-         */
-        _getStroke: function(inputComputedFill, inputNodeType) {
-          return inputComputedFill ?
-            tf.graph.scene.node.getStrokeForFill(inputComputedFill) :
-            ({
-              OP: tf.graph.render.OpNodeColors.DEFAULT_STROKE,
-              META: tf.graph.render.MetanodeColors.DEFAULT_STROKE,
-              SERIES: tf.graph.render.SeriesNodeColors.DEFAULT_STROKE
-            })[inputNodeType];
-        },
-
-        /**
-         * Test whether the specified node's type, or the literal type string,
-         * match a particular other type.
-         */
-        _isType: function(inputNode, inputType, targetType) {
-          if (inputNode) {
-            return tf.graph.NodeType[inputNode.type] === targetType;
-          }
-          return inputType === targetType;
-        },
-
-        /**
-         * Test whether the specified node should be represented as a vertical
-         * series. Defaults to the value of the vertical property if node is
-         * not specified.
-         */
-        _isVertical: function(inputNode, inputVertical) {
-          if (inputNode) {
-            return inputNode.hasNonControlEdges;
-          }
-          return !!inputVertical;
-        },
-
-        /**
-         * Test whether the specified node is a constant. Defaults to the value
-         * of the const property if node is not specified.
-         */
-        _isConst: function(inputNode, inputConst) {
-          if (inputNode) {
-            return inputNode.op === 'Const';
-          }
-          return !!inputConst;
-        },
-
-        /**
-         * Test whether the specified node is a summary. Defaults to the value
-         * of the summary property if node is not specified.
-         */
-        _isSummary: function(inputNode, inputSummary) {
-          if (inputNode) {
-            return this._isType(inputNode, null, 'OP') &&
-                inputNode.op.substr(-7) === 'Summary';
-          }
-          return !!inputSummary;
-        },
-
-        /**
-         * Test whether the op node is a regular non-summary non-const node.
-         */
-        _isRegularOp: function(inputNode, inputConst, inputSummary) {
-          return !this._isConst(inputNode, inputConst) &&
-              !this._isSummary(inputNode, inputSummary);
-        },
-
-        _fadedClass: function(itemRenderInfo, shape) {
-          return itemRenderInfo && itemRenderInfo.isFadedOut ? 'faded-' + shape : '';
-        }
-      });
-    })();
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_graph_info/tf-graph-info.html b/tensorflow/tensorboard/components/tf_graph_info/tf-graph-info.html
deleted file mode 100644
index bac25b67f7..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/tf-graph-info.html
+++ /dev/null
@@ -1,130 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-slider/paper-slider.html">
-<link rel="import" href="../paper-spinner/paper-spinner-lite.html">
-<link rel="import" href="../tf-graph-common/tf-graph-common.html">
-<link rel="import" href="../tf-graph-debugger-data-card/tf-graph-debugger-data-card.html">
-<link rel="import" href="tf-node-info.html">
-
-<dom-module id="tf-graph-info">
-<template>
-<style>
-:host {
-  font-size: 12px;
-  margin: 0;
-  padding: 0;
-  display: block;
-  max-height: 650px;
-  overflow-x: hidden;
-  overflow-y: auto;
-}
-
-h2 {
-  padding: 0;
-  text-align: center;
-  margin: 0;
-}
-</style>
-<template is="dom-if" if="{{selectedNode}}">
-  <paper-material elevation="1" class="card">
-    <tf-node-info graph-hierarchy="[[graphHierarchy]]"
-                  render-hierarchy="[[renderHierarchy]]"
-                  flat-graph="[[graph]]"
-                  node-name="[[selectedNode]]"
-                  node-include="[[selectedNodeInclude]]"
-                  highlighted-node="{{highlightedNode}}"
-                  color-by="[[colorBy]]">
-    </tf-node-info>
-  </paper-material>
-</template>
-<template is="dom-if" if="[[_healthPillsAvailable(debuggerDataEnabled, nodeNamesToHealthPills)]]">
-  <tf-graph-debugger-data-card render-hierarchy="[[renderHierarchy]]"
-                               debugger-numeric-alerts="[[debuggerNumericAlerts]]"
-                               node-names-to-health-pills="[[nodeNamesToHealthPills]]"
-                               render-hierarchy="[[renderHierarchy]]"
-                               selected-node="{{selectedNode}}"
-                               highlighted-node="{{highlightedNode}}"
-                               are-health-pills-loading="[[areHealthPillsLoading]]"
-                               all-steps-mode-enabled="{{allStepsModeEnabled}}"
-                               specific-health-pill-step="{{specificHealthPillStep}}"
-                               health-pill-step-index="{{healthPillStepIndex}}">
-  </tf-graph-debugger-data-card>
-</template>
-</template>
-<script>
-"use strict";
-
-(function() {
-  Polymer({
-    is: 'tf-graph-info',
-
-    properties: {
-      title: String,
-      graphHierarchy: Object,
-      graph: Object,
-      renderHierarchy: Object,
-      nodeNamesToHealthPills: Object,
-      healthPillStepIndex: {
-        type: Number,
-        notify: true,
-      },
-      colorBy: String,
-      // Two-ways
-      selectedNode: {
-        type: String,
-        notify: true
-      },
-      highlightedNode: {
-        type: String,
-        notify: true
-      },
-      // The enum value of the include property of the selected node.
-      selectedNodeInclude: {
-        type: Number,
-        notify: true
-      },
-      // Whether debugger data is enabled for this instance of Tensorboard.
-      debuggerDataEnabled: Boolean,
-      // Whether health pills are currently being loaded, in which case we show a spinner (and the
-      // current health pills shown might be out of date).
-    },
-    listeners: {
-      'node-list-item-click': '_nodeListItemClicked',
-      'node-list-item-mouseover': '_nodeListItemMouseover',
-      'node-list-item-mouseout': '_nodeListItemMouseout'
-    },
-    _nodeListItemClicked: function(event) {
-      this.selectedNode = event.detail.nodeName;
-    },
-    _nodeListItemMouseover: function(event) {
-      this.highlightedNode = event.detail.nodeName;
-    },
-    _nodeListItemMouseout: function() {
-      this.highlightedNode = null;
-    },
-    _healthPillsAvailable: function(debuggerDataEnabled, nodeNamesToHealthPills) {
-      // So long as there is a mapping (even if empty) from node name to health pills, show the
-      // legend and slider. We do that because, even if no health pills exist at the current step,
-      // the user may desire to change steps, and the slider must show for the user to do that.
-      return debuggerDataEnabled && nodeNamesToHealthPills;
-    },
-  });
-})();
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_graph_info/tf-node-info.html b/tensorflow/tensorboard/components/tf_graph_info/tf-node-info.html
deleted file mode 100644
index 66a3034b5b..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/tf-node-info.html
+++ /dev/null
@@ -1,652 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../iron-list/iron-list.html">
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-item/paper-item.html">
-<link rel="import" href="../paper-item/paper-item-body.html">
-<link rel="import" href="../tf-graph-common/tf-graph-common.html">
-<link rel="import" href="tf-graph-icon.html">
-<link rel="import" href="tf-node-list-item.html">
-
-<dom-module id="tf-node-info">
-  <style>
-  .sub-list-group {
-    font-weight: 500;
-    font-size: 12pt;
-    padding-bottom: 8px;
-    width: 100%;
-  }
-
-  .sub-list {
-    max-height: 300px;
-    overflow-y: scroll;
-  }
-
-  .attr-left {
-    float: left;
-    width: 30%;
-    word-wrap: break-word;
-    color: #565656;
-    font-size: 11pt;
-    font-weight: 400;
-  }
-
-  .attr-right {
-    margin-left: 30%;
-    word-wrap: break-word;
-    color: #565656;
-    font-weight: 400;
-  }
-
-  .sub-list-table {
-    display: table;
-    width: 100%;
-  }
-
-  .sub-list-table-row {
-    display: table-row;
-  }
-
-  .sub-list-table-row .sub-list-table-cell:last-child {
-    text-align: right;
-  }
-
-  .sub-list-table-cell {
-    color: #565656;
-    display: table-cell;
-    font-size: 11pt;
-    font-weight: 400;
-    max-width: 200px;
-    padding: 0 4px;
-  }
-
-  paper-item {
-    padding: 0;
-    background: #e9e9e9;
-  }
-
-  paper-item-body[two-line] {
-    min-height: 0;
-    padding: 8px 12px 4px;
-  }
-
-  .expandedInfo {
-    padding: 8px 12px;
-  }
-
-  .controlDeps {
-    padding: 0 0 0 8px;
-  }
-
-  .node-name {
-    white-space: normal;
-    word-wrap: break-word;
-    font-size: 14pt;
-    font-weight: 500;
-  }
-
-  .node-icon {
-    float: right;
-  }
-
-  .subtitle {
-    font-size: 12pt;
-    color: #5e5e5e;
-  }
-
-  .controlLine {
-    font-size: 11pt;
-    font-weight: 400;
-  }
-
-  .toggle-button {
-    float: right;
-    max-height: 20px;
-    max-width: 20px;
-    padding: 0;
-  }
-
-  .control-toggle-button {
-    float: left;
-    max-height: 20px;
-    max-width: 20px;
-    padding: 0;
-  }
-
-  .toggle-include-group {
-    padding-top: 4px;
-  }
-
-  .toggle-include {
-    margin: 5px 6px;
-    text-transform: none;
-    padding: 4px 6px;
-    font-size: 10pt;
-    background-color: #fafafa;
-    color: #666;
-  }
-
-  .toggle-include:hover {
-    background-color: var(--google-yellow-100);
-  }
-
-  .non-control-list-item {
-    padding-left: 10px;
-  }
-  </style>
-  <template>
-    <paper-item>
-      <paper-item-body two-line>
-        <div>
-          <paper-icon-button
-            icon="{{_getToggleIcon(_expanded)}}"
-            on-click="_toggleExpanded"
-            class="toggle-button">
-          </paper-icon-button>
-          <div class="node-name" id="nodetitle"></div>
-        </div>
-        <div secondary>
-          <tf-graph-icon class="node-icon" node="[[_node]]"
-              render-info="[[_getRenderInfo(nodeName, renderHierarchy)]]"
-              color-by="[[colorBy]]"
-              template-index="[[_templateIndex]]"
-              ></tf-graph-icon>
-          <template is="dom-if" if="{{_node.op}}">
-            <div class="subtitle">
-              Operation:
-              <span>[[_node.op]]</span>
-            </div>
-          </template>
-          <template is="dom-if" if="{{_node.metagraph}}">
-            <div class="subtitle">
-              Subgraph:
-              <span>[[_node.cardinality]]</span> nodes
-            </div>
-          </template>
-        </div>
-      </paper-item-body>
-    </paper-item>
-    <iron-collapse opened="{{_expanded}}">
-    <template is="dom-if" if="{{_expanded}}" restamp="true">
-      <div class="expandedInfo">
-        <div class="sub-list-group attributes">
-          Attributes
-          (<span>[[_attributes.length]]</span>)
-          <iron-list class="sub-list" id ="attributesList"
-                    items="[[_attributes]]">
-            <template>
-              <div>
-                <div class="attr-left">[[item.key]]</div>
-                <div class="attr-right">[[item.value]]</div>
-              </div>
-            </template>
-          </iron-list>
-        </div>
-
-        <template is="dom-if" if="{{_device}}">
-          <div class="sub-list-group device">
-            <div class="attr-left">Device</div>
-            <div class="attr-right">[[_device]]</div>
-          </div>
-        </template>
-
-        <div class="sub-list-group predecessors">
-          Inputs
-          (<span>[[_totalPredecessors]]</span>)
-          <iron-list class="sub-list" id ="inputsList"
-                    items="[[_predecessors.regular]]">
-            <template>
-              <tf-node-list-item
-                  class="non-control-list-item"
-                  card-node="[[_node]]"
-                  item-node="[[item.node]]"
-                  edge-label="[[item.edgeLabel]]"
-                  item-render-info="[[item.renderInfo]]"
-                  name="[[item.name]]"
-                  item-type="predecessors"
-                  color-by="[[colorBy]]"
-                  template-index="[[_templateIndex]]">
-              </tf-node-list-item>
-            </template>
-          </iron-list>
-          <template is="dom-if" if="[[_predecessors.control.length]]">
-            <div class="controlDeps">
-              <div class="controlLine">
-                <paper-icon-button
-                  icon="{{_getToggleIcon(_openedControlPred)}}"
-                  on-click="_toggleControlPred"
-                  class="control-toggle-button">
-                </paper-icon-button>
-                Control dependencies
-              </div>
-              <iron-collapse opened="{{_openedControlPred}}" no-animation>
-                <template is="dom-if" if="{{_openedControlPred}}" restamp="true">
-                  <iron-list class="sub-list" items="[[_predecessors.control]]">
-                    <template>
-                      <tf-node-list-item
-                          card-node="[[_node]]"
-                          item-node="[[item.node]]"
-                          item-render-info="[[item.renderInfo]]"
-                          name="[[item.name]]"
-                          item-type="predecessors"
-                          color-by="[[colorBy]]"
-                          template-index="[[_templateIndex]]">
-                      </tf-node-list-item>
-                    </template>
-                  </iron-list>
-                </template>
-              </iron-collapse>
-            </div>
-          </template>
-        </div>
-
-        <div class="sub-list-group successors">
-          Outputs
-          (<span>[[_totalSuccessors]]</span>)
-          <iron-list class="sub-list" id ="outputsList"
-                    items="[[_successors.regular]]">
-            <template>
-              <tf-node-list-item
-                  class="non-control-list-item"
-                  card-node="[[_node]]"
-                  item-node="[[item.node]]"
-                  edge-label="[[item.edgeLabel]]"
-                  item-render-info="[[item.renderInfo]]"
-                  name="[[item.name]]"
-                  item-type="successor"
-                  color-by="[[colorBy]]"
-                  template-index="[[_templateIndex]]">
-              </tf-node-list-item>
-            </template>
-          </iron-list>
-          <template is="dom-if" if="[[_successors.control.length]]">
-            <div class="controlDeps">
-              <div class="controlLine">
-                <paper-icon-button
-                  icon="{{_getToggleIcon(_openedControlSucc)}}"
-                  on-click="_toggleControlSucc"
-                  class="control-toggle-button">
-                </paper-icon-button>
-                Control dependencies
-              </div>
-              <iron-collapse opened="{{_openedControlSucc}}" no-animation>
-                <template is="dom-if" if="{{_openedControlSucc}}" restamp="true">
-                  <iron-list class="sub-list" items="[[_successors.control]]">
-                    <template>
-                      <tf-node-list-item
-                          card-node="[[_node]]"
-                          item-node="[[item.node]]"
-                          item-render-info="[[item.renderInfo]]"
-                          name="[[item.name]]"
-                          item-type="successors"
-                          color-by="[[colorBy]]"
-                          template-index="[[_templateIndex]]">
-                      </tf-node-list-item>
-                    </template>
-                  </iron-list>
-                </template>
-              </iron-collapse>
-            </div>
-          </template>
-        </div>
-        <template is="dom-if" if="{{_hasDisplayableNodeStats}}">
-          <div class="sub-list-group node-stats">
-            Node Stats
-            <div class="sub-list-table">
-              <template is="dom-if" if="{{_nodeStats.totalBytes}}">
-                <div class="sub-list-table-row">
-                  <div class="sub-list-table-cell">Memory</div>
-                  <div class="sub-list-table-cell">[[_nodeStatsFormattedBytes]]</div>
-                </div>
-              </template>
-              <template is="dom-if" if="{{_getTotalMicros(_nodeStats)}}">
-                <div class="sub-list-table-row">
-                  <div class="sub-list-table-cell">Compute Time</div>
-                  <div class="sub-list-table-cell">[[_nodeStatsFormattedComputeTime]]</div>
-                </div>
-              </template>
-              <template is="dom-if" if="{{_nodeStats.outputSize}}">
-                <div class="sub-list-table-row">
-                  <div class="sub-list-table-cell">Tensor Output Sizes</div>
-                  <div class="sub-list-table-cell">
-                    <template is="dom-repeat" items="{{_nodeStatsFormattedOutputSizes}}">
-                      [[item]] <br/>
-                    </template>
-                  </div>
-                </div>
-              </template>
-            </div>
-          </div>
-        </template>
-        <div class="toggle-include-group">
-          <paper-button raised class="toggle-include" on-click="_toggleInclude">
-            <span>[[_auxButtonText]]</span>
-          </paper-button>
-        </div>
-        <template is="dom-if" if="{{_isInSeries(_node)}}">
-          <div class="toggle-include-group">
-            <paper-button raised class="toggle-include" on-click="_toggleGroup">
-              <span>[[_groupButtonText]]</span>
-            </paper-button>
-          </div>
-        </template>
-      </div>
-    </template>
-    </iron-collapse>
-  </template>
-
-  <script>
-    (function() {
-      Polymer({
-        is: 'tf-node-info',
-
-        properties: {
-          nodeName: String,
-          graphHierarchy: Object,
-          renderHierarchy: Object,
-          /** What to color the nodes by (compute time, memory, device etc.) */
-          colorBy: String,
-          _templateIndex: {
-            type: Function,
-            computed: '_getTemplateIndex(graphHierarchy)'
-          },
-          _node: {
-            type: Object,
-            computed: '_getNode(nodeName, graphHierarchy)',
-            observer: '_resetState'
-          },
-          _nodeStats: {
-            type: Object,
-            computed: '_getNodeStats(nodeName, graphHierarchy)',
-            observer: '_resetState'
-          },
-          _hasDisplayableNodeStats: {
-            type: Object,
-            computed: '_getHasDisplayableNodeStats(_nodeStats)',
-          },
-          _nodeStatsFormattedBytes: {
-            type: String,
-            computed: '_getNodeStatsFormattedBytes(_nodeStats)',
-          },
-          _nodeStatsFormattedComputeTime: {
-            type: String,
-            computed: '_getNodeStatsFormattedComputeTime(_nodeStats)',
-          },
-          _nodeStatsFormattedOutputSizes: {
-            type: Array,
-            computed: '_getNodeStatsFormattedOutputSizes(_nodeStats)',
-          },
-          // The enum value of the include property of the selected node.
-          nodeInclude: {
-            type: Number,
-            observer: '_nodeIncludeStateChanged'
-          },
-          _attributes: {
-            type: Array,
-            computed: '_getAttributes(_node)'
-          },
-          _device: {
-            type: String,
-            computed: '_getDevice(_node)'
-          },
-          _successors: {
-            type: Object,
-            computed: '_getSuccessors(_node, graphHierarchy)'
-          },
-          _predecessors: {
-            type: Object,
-            computed: '_getPredecessors(_node, graphHierarchy)'
-          },
-          _subnodes: {
-            type: Array,
-            computed: '_getSubnodes(_node)'
-          },
-          _expanded: {
-            type: Boolean,
-            value: true
-          },
-          _totalPredecessors: {
-            type: Number,
-            computed: '_getTotalPred(_predecessors)'
-          },
-          _totalSuccessors: {
-            type: Number,
-            computed: '_getTotalSucc(_successors)'
-          },
-          _openedControlPred: {
-            type: Boolean,
-            value: false
-          },
-          _openedControlSucc: {
-            type: Boolean,
-            value: false
-          },
-          _auxButtonText: String,
-          _groupButtonText: String
-        },
-        expandNode: function() {
-          this.fire('_node.expand', this.node);
-        },
-        _getTemplateIndex: function(graphHierarchy) {
-          return graphHierarchy.getTemplateIndex();
-        },
-        _getNode: function(nodeName, graphHierarchy) {
-          return graphHierarchy.node(nodeName);
-        },
-        _getNodeStats: function(nodeName, graphHierarchy) {
-          var node = this._getNode(nodeName, graphHierarchy);
-          if (node) {
-            return node.stats;
-          }
-          return null;
-        },
-        _getTotalMicros: function(stats) {
-          return stats.getTotalMicros();
-        },
-        _getHasDisplayableNodeStats: function(stats) {
-          return tf.graph.util.hasDisplayableNodeStats(stats);
-        },
-        _getNodeStatsFormattedBytes: function(stats) {
-          if (!stats || !stats.totalBytes) {
-            return;
-          }
-
-          return tf.graph.util.convertUnitsToHumanReadable(
-              stats.totalBytes, tf.graph.util.MEMORY_UNITS);
-        },
-        _getNodeStatsFormattedComputeTime: function(stats) {
-          if (!stats || !stats.getTotalMicros()) {
-            return;
-          }
-
-          return tf.graph.util.convertUnitsToHumanReadable(
-              stats.getTotalMicros(), tf.graph.util.TIME_UNITS);
-        },
-        _getNodeStatsFormattedOutputSizes: function(stats) {
-          if (!stats || !stats.outputSize || !stats.outputSize.length) {
-            return;
-          }
-
-          return _.map(stats.outputSize, function(shape) {
-            if (shape.length === 0) {
-              return "scalar";
-            }
-            return "[" + shape.join(", ") + "]";
-          });
-        },
-        _getPrintableHTMLNodeName: function(nodeName) {
-          // Insert an optional line break before each slash so that
-          // long node names wrap cleanly at path boundaries.
-          return (nodeName || '').replace(/\//g, '<wbr>/');
-        },
-        _getRenderInfo: function(nodeName, renderHierarchy) {
-          return this.renderHierarchy.getOrCreateRenderNodeByName(nodeName);
-        },
-        _getAttributes: function(node) {
-          this.async(this._resizeList.bind(this, "#attributesList"));
-          if (!node || !node.attr) {
-            return [];
-          }
-          var attrs = [];
-          _.each(node.attr, function(entry) {
-            // Unpack the "too large" attributes into separate attributes
-            // in the info card, with values "too large to show".
-            if (entry.key === tf.graph.LARGE_ATTRS_KEY) {
-              attrs = attrs.concat(entry.value.list.s.map(function(key) {
-                return {key: key, value: "Too large to show..."};
-              }));
-            } else {
-              attrs.push({
-                key: entry.key,
-                value: JSON.stringify(entry.value)
-              });
-            }
-          });
-          return attrs;
-        },
-        _getDevice: function(node) {
-          return node ? node.device : null;
-        },
-        _getSuccessors: function(node, hierarchy) {
-          this.async(this._resizeList.bind(this, "#inputsList"));
-          if (!node) {
-            return {regular: [], control: []}
-          }
-          return this._convertEdgeListToEdgeInfoList(
-            hierarchy.getSuccessors(node.name), false, node.isGroupNode);
-        },
-        _getPredecessors: function(node, hierarchy) {
-          this.async(this._resizeList.bind(this, "#outputsList"));
-          if (!node) {
-            return {regular: [], control: []}
-          }
-          return this._convertEdgeListToEdgeInfoList(
-            hierarchy.getPredecessors(node.name), true, node.isGroupNode);
-        },
-        _convertEdgeListToEdgeInfoList: function(list, isPredecessor, isGroupNode) {
-
-          /**
-           * Unpacks the metaedge into a list of base edge information
-           * that can be rendered.
-           */
-          var unpackMetaedge = function(metaedge) {
-            return _.map(metaedge.baseEdgeList, function(baseEdge) {
-              var name = isPredecessor ? baseEdge.v : baseEdge.w;
-              return {
-                name: name,
-                node: this._getNode(name, this.graphHierarchy),
-                edgeLabel: tf.graph.scene.edge.getLabelForBaseEdge(baseEdge,
-                    this.renderHierarchy),
-                renderInfo: this._getRenderInfo(name, this.renderHierarchy)
-              };
-            }, this);
-          }.bind(this);
-
-          /**
-           * Converts a list of metaedges to a list of edge information
-           * that can be rendered.
-           */
-          var toEdgeInfoList = function(edges) {
-            var edgeInfoList = [];
-            _.each(edges, function(metaedge) {
-              var name = isPredecessor ? metaedge.v : metaedge.w;
-              // Enumerate all the base edges if the node is an OpNode, or the
-              // metaedge has only 1 edge in it.
-              if (!isGroupNode || metaedge.baseEdgeList.length == 1) {
-                edgeInfoList = edgeInfoList.concat(unpackMetaedge(metaedge));
-              } else {
-                edgeInfoList.push({
-                  name: name,
-                  node: this._getNode(name, this.graphHierarchy),
-                  edgeLabel: tf.graph.scene.edge.getLabelForEdge(metaedge,
-                      this.renderHierarchy),
-                  renderInfo: this._getRenderInfo(name, this.renderHierarchy)
-                });
-              }
-            }, this);
-            return edgeInfoList;
-          }.bind(this);
-
-          return {
-            regular: toEdgeInfoList(list.regular),
-            control: toEdgeInfoList(list.control)
-          };
-        },
-        _getSubnodes: function(node) {
-          return node && node.metagraph ? node.metagraph.nodes() : null;
-        },
-        _getTotalPred: function(predecessors) {
-          return predecessors.regular.length + predecessors.control.length;
-        },
-        _getTotalSucc: function(successors) {
-          return successors.regular.length + successors.control.length;
-        },
-        _toggleControlPred: function() {
-          this._openedControlPred = !this._openedControlPred;
-        },
-        _toggleControlSucc: function() {
-          this._openedControlSucc = !this._openedControlSucc;
-        },
-        _toggleExpanded: function() {
-          this._expanded = !this._expanded;
-        },
-        _getToggleIcon: function(expanded) {
-          return expanded ? "expand-less" : "expand-more";
-        },
-        _resetState: function() {
-          this._openedControlPred = false;
-          this._openedControlSucc = false;
-
-          this.set("_groupButtonText",
-            tf.graph.scene.node.getGroupSettingLabel(this._node));
-
-          if (this._node) {
-            Polymer.dom(this.$.nodetitle).innerHTML =
-              this._getPrintableHTMLNodeName(this._node.name);
-          }
-        },
-        _resizeList: function(selector) {
-          var list = document.querySelector(selector);
-          if (list) {
-            list.fire('iron-resize');
-          }
-        },
-        _toggleInclude: function() {
-          var graphElem = document.querySelector("#graph");
-          graphElem.fire("node-toggle-extract", { name: this.nodeName });
-          var graphBoardElem = document.querySelector("#graphboard");
-          graphBoardElem.fire("node-toggle-extract");
-        },
-        _nodeIncludeStateChanged: function(include, oldInclude) {
-          this.set("_auxButtonText",
-            tf.graph.getIncludeNodeButtonString(include));
-        },
-        _toggleGroup: function() {
-          var graphElem = document.querySelector("#graph");
-          var seriesName = tf.graph.scene.node.getSeriesName(this._node);
-          graphElem.fire("node-toggle-seriesgroup", { name: seriesName });
-        },
-        _isInSeries: function(node) {
-          return tf.graph.scene.node.canBeInSeries(node);
-        }
-      });
-    })();
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_graph_info/tf-node-list-item.html b/tensorflow/tensorboard/components/tf_graph_info/tf-node-list-item.html
deleted file mode 100644
index c15478d126..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_info/tf-node-list-item.html
+++ /dev/null
@@ -1,138 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-<link rel="import" href="tf-graph-icon.html">
-
-<dom-module id="tf-node-list-item">
-  <style>
-  #list-item {
-    width: 100%;
-    color: #565656;
-    font-size: 11pt;
-    font-weight: 400;
-    position: relative;
-    display: inline-block;
-  }
-
-  #list-item:hover {
-    background-color: var(--google-yellow-100);
-  }
-
-  .clickable {
-    cursor: pointer;
-  }
-
-  #list-item span {
-    margin-left: 40px;
-  }
-
-  #list-item.excluded span {
-    color: #999;
-  }
-
-  #list-item span.edge-label {
-    float: right;
-    font-size: 10px;
-    margin-left: 3px;
-    margin-right: 5px;
-  }
-
-  .node-icon {
-    position: absolute;
-    top: 1px;
-    left: 2px;
-  }
-
-  .faded span {
-    color: var(--tb-graph-faded);
-  }
-  </style>
-  <template>
-    <div id="list-item"
-         on-mouseover="_nodeListener"
-         on-mouseout="_nodeListener"
-         on-click="_nodeListener">
-      <div class$="{{_fadedClass(itemRenderInfo)}}">
-        <tf-graph-icon class="node-icon" height="12"
-            color-by="[[colorBy]]" color-by-params="[[colorByParams]]"
-            node="[[itemNode]]" render-info="[[itemRenderInfo]]"
-            template-index="[[templateIndex]]"></tf-graph-icon>
-        <span title$="[[name]]">[[name]]</span>
-        <span class="edge-label">[[edgeLabel]]</span>
-      </div>
-    </div>
-  </template>
-
-  <script>
-    (function() {
-      Polymer({
-        is: 'tf-node-list-item',
-
-        properties: {
-          /**
-           * The Node for the card itself, on which this item is being drawn.
-           * @type {tf.graph.Node}
-           */
-          cardNode: Object,
-          /**
-           * The Node for the item within the card, somehow related to cardNode.
-           * @type {tf.graph.Node}
-           */
-          itemNode: Object,
-          /** The edge label associated with this item. */
-          edgeLabel: String,
-          /**
-           * The render node information for the item node. Used by the graph
-           * icon in determining fill color.
-           */
-          itemRenderInfo: Object,
-          name: String,
-          itemType: {
-            type: String,
-            observer: '_itemTypeChanged'
-          },
-          colorBy: String,
-          colorByParams: Object,
-          templateIndex: Function
-        },
-
-        _itemTypeChanged: function() {
-          if (this.itemType !== 'subnode') {
-            this.$['list-item'].classList.add('clickable');
-          } else {
-            this.$['list-item'].classList.remove('clickable');
-          }
-        },
-
-        _nodeListener: function(event) {
-          // fire node.click/mouseover/mouseout
-          this.fire('node-list-item-' + event.type, {
-            cardNode: this.cardNode.name,
-            nodeName: this.name,
-            type: this.itemType
-          });
-        },
-
-        _fadedClass: function(itemRenderInfo) {
-          return itemRenderInfo && itemRenderInfo.isFadedOut ? 'faded' : '';
-        }
-      });
-    })();
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_graph_loader/BUILD b/tensorflow/tensorboard/components/tf_graph_loader/BUILD
deleted file mode 100644
index 41fbfb8ee8..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_loader/BUILD
+++ /dev/null
@@ -1,32 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_graph_loader",
-    srcs = ["tf-graph-loader.html"],
-    path = "/tf-graph-loader",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_common",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_graph_loader"],
-    destdir = "tf-graph-loader",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_common:legacy",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_loader/demo/BUILD b/tensorflow/tensorboard/components/tf_graph_loader/demo/BUILD
deleted file mode 100644
index f109a19163..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_loader/demo/BUILD
+++ /dev/null
@@ -1,24 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# bazel run //third_party/tensorflow/tensorboard/components/tf_graph_loader/demo
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-graph-loader/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_graph_loader",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_graph_loader/demo/data/graph.pbtxt b/tensorflow/tensorboard/components/tf_graph_loader/demo/data/graph.pbtxt
deleted file mode 100644
index 30b2064534..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_loader/demo/data/graph.pbtxt
+++ /dev/null
@@ -1,4606 +0,0 @@
-node {
-  name: "GradientDescent/learning_rate"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_3"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_FLOAT
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_FLOAT
-        tensor_shape {
-        }
-        float_val: 0.1
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/Shape_1"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 1
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
-        int_val: 100
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/Shape"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 2
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 2
-          }
-        }
-        tensor_content: "\310\000\000\000d\000\000\000"
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_grad/BroadcastGradientArgs"
-  op: "BroadcastGradientArgs"
-  input: "gradients/add_grad/Shape"
-  input: "gradients/add_grad/Shape_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: -1
-          }
-        }
-        shape {
-          dim {
-            size: -1
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_1_grad/Shape_1"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 1
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
-        int_val: 10
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_1_grad/Shape"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 2
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 2
-          }
-        }
-        tensor_content: "\310\000\000\000\n\000\000\000"
-      }
-    }
-  }
-}
-node {
-  name: "gradients/add_1_grad/BroadcastGradientArgs"
-  op: "BroadcastGradientArgs"
-  input: "gradients/add_1_grad/Shape"
-  input: "gradients/add_1_grad/Shape_1"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "T"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: -1
-          }
-        }
-        shape {
-          dim {
-            size: -1
-          }
-        }
-      }
-    }
-  }
-}
-node {
-  name: "gradients/Reshape_1_grad/Shape"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 2
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 2
-          }
-        }
-        tensor_content: "\310\000\000\000\n\000\000\000"
-      }
-    }
-  }
-}
-node {
-  name: "gradients/SoftmaxCrossEntropyWithLogits_grad/ExpandDims/dim"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-        }
-        int_val: -1
-      }
-    }
-  }
-}
-node {
-  name: "gradients/Reshape_3_grad/Shape"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-          dim {
-            size: 1
-          }
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
-        int_val: 200
-      }
-    }
-  }
-}
-node {
-  name: "gradients/Mean_grad/Maximum/y"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
-        shape {
-        }
-      }
-    }
-  }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
-  attr {
-    key: "value"
-    value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-        }
-        int_val: 1
-      }
-    }
-  }
-}
-node {
-  name: "gradients/Mean_grad/Const_1"
-  op: "Const"
-  device: "/job:localhost/replica:0/task:0/device:XLA_CPU:0"
-  attr {
-    key: "_XlaCluster"
-    value {
-      s: "cluster_1"
-    }
-  }
-  attr {
-    key: "_output_shapes"
-    value {
-      list {
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-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../tf-graph-loader.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<title>TF Graph Loader Demo</title>
-<demo-snippet>
-  <template>
-    <dom-module id="tf-graph-loader-demo">
-      <template>
-        <tf-graph-loader
-            id="loader"
-            datasets="[[_datasets]]"
-            selected-dataset="[[_selectedDataset]]"
-            progress="{{_progress}}">
-        </tf-graph-loader>
-      </template>
-      <script>
-        "use strict";
-
-        Polymer({
-          is: "tf-graph-loader-demo",
-          properties: {
-            // We tell the graph loader to load a specific pbtxt file.
-            _datasets: {
-              type: Array,
-              value: [{
-                "name": "Graph with XLA Clusters Specified",
-                "path": "data/graph.pbtxt"
-              }],
-            },
-            _selectedDataset: {
-              type: Number,
-              value: 0,
-            },
-
-            // This property will be updated by the graph loader.
-            _progress: {
-              type: Object,
-            },
-          },
-          observers: [
-            '_progressUpdated(_progress)',
-          ],
-          _progressUpdated(progress) {
-            // console.log the progress.
-            console.log('Progress updated.', progress);
-
-            // The graph has loaded. console.log it.
-            if (progress.value == 100) {
-              console.log('graph', this.$.loader.outGraph);
-            }
-          },
-        });
-      </script>
-    </dom-module>
-    <!-- The graph loader lacks visual elements. -->
-    <tf-graph-loader-demo></tf-graph-loader-demo>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_graph_loader/test/index.html b/tensorflow/tensorboard/components/tf_graph_loader/test/index.html
deleted file mode 100644
index c8e2027f42..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_loader/test/index.html
+++ /dev/null
@@ -1,30 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-  <meta charset="utf-8">
-  <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-  <script src="../../web-component-tester/browser.js"></script>
-  <link rel="import" href="../tf-graph-loader.html">
-</head>
-<body>
-  <tf-graph-loader id="loader"></tf-graph-loader>
-  <script src="loader.js"></script>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_graph_loader/test/loader.ts b/tensorflow/tensorboard/components/tf_graph_loader/test/loader.ts
deleted file mode 100644
index fcd9f7b529..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_loader/test/loader.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-suite('graph loader', () => {
-  let assert = chai.assert;
-
-  test('loader exists', () => {
-    assert.isTrue(document.getElementById('loader') != null);
-  });
-
-  // TODO(bp): write tests.
-
-});
diff --git a/tensorflow/tensorboard/components/tf_graph_loader/tf-graph-loader.html b/tensorflow/tensorboard/components/tf_graph_loader/tf-graph-loader.html
deleted file mode 100644
index 8d59cbd2aa..0000000000
--- a/tensorflow/tensorboard/components/tf_graph_loader/tf-graph-loader.html
+++ /dev/null
@@ -1,184 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-graph-common/tf-graph-common.html">
-
-<!--
-An element which provides a filter parsing for pbtxt to graph output.
--->
-<dom-module id="tf-graph-loader">
-</dom-module>
-
-<script>
-Polymer({
-
-  is: 'tf-graph-loader',
-
-  properties: {
-    /**
-     * @type {value: number, msg: string}
-     *
-     * A number between 0 and 100 denoting the % of progress
-     * for the progress bar and the displayed message.
-     */
-    progress: {
-      type: Object,
-      notify: true,
-    },
-    datasets: Array,
-    selectedDataset: Number,
-    selectedFile: {
-      type: Object,
-      observer: '_selectedFileChanged'
-    },
-    outGraphHierarchy: {
-      type: Object,
-      readOnly: true, //readonly so outsider can't change this via binding
-      notify: true
-    },
-    outGraph: {
-      type: Object,
-      readOnly: true, //readonly so outsider can't change this via binding
-      notify: true
-    },
-    outHierarchyParams: {
-      type: Object,
-      readOnly: true,
-      notify: true
-    },
-    outStats: {
-      type: Object,
-      readOnly: true, // This property produces data.
-      notify: true
-    }
-  },
-  observers: [
-    '_selectedDatasetChanged(selectedDataset, datasets)',
-    '_readAndParseMetadata(selectedMetadataTag)'
-  ],
-  _readAndParseMetadata: function(metadataIndex) {
-    if (metadataIndex == -1 || this.datasets[this.selectedDataset] == null ||
-        this.datasets[this.selectedDataset].runMetadata == null ||
-        this.datasets[this.selectedDataset].runMetadata[metadataIndex] == null) {
-      this._setOutStats(null);
-      return;
-    }
-    var path = this.datasets[this.selectedDataset].runMetadata[metadataIndex].path;
-    // Reset the progress bar to 0.
-    this.set('progress', {
-      value: 0,
-      msg: ''
-    });
-    var tracker = tf.graph.util.getTracker(this);
-    tf.graph.parser.fetchAndParseMetadata(path, tracker)
-    .then(function(stats) {
-      this._setOutStats(stats);
-    }.bind(this));
-  },
-  _parseAndConstructHierarchicalGraph: function(path, pbTxtFile) {
-    // Reset the progress bar to 0.
-    this.set('progress', {
-      value: 0,
-      msg: ''
-    });
-    var tracker = tf.graph.util.getTracker(this);
-    var hierarchyParams = {
-      verifyTemplate: true,
-      // If a set of numbered op nodes has at least this number of nodes
-      // then group them into a series node.
-      seriesNodeMinSize: 5,
-      // A map of series node names to series grouping settings, to indicate
-      // if a series is to be rendered as grouped or ungrouped.
-      // Starts out empty which allows the renderer to decide which series
-      // are initially rendered grouped and which aren't.
-      seriesMap: {},
-    };
-    this._setOutHierarchyParams(hierarchyParams);
-    var dataTracker = tf.graph.util.getSubtaskTracker(tracker, 30, 'Data');
-    tf.graph.parser.fetchAndParseGraphData(path, pbTxtFile, dataTracker)
-    .then(function(graph) {
-      if (!graph) {
-        throw 'The graph is empty. Make sure that the graph is passed to the ' +
-            'SummaryWriter after the graph is defined.';
-      }
-
-      // Build the flat graph (consists only of Op nodes).
-
-      // This is the whitelist of inputs on op types that are considered
-      // reference edges. "Assign 0" indicates that the first input to
-      // an OpNode with operation type "Assign" is a reference edge.
-      var refEdges = {};
-      refEdges["Assign 0"] = true;
-      refEdges["AssignAdd 0"] = true;
-      refEdges["AssignSub 0"] = true;
-      refEdges["assign 0"] = true;
-      refEdges["assign_add 0"] = true;
-      refEdges["assign_sub 0"] = true;
-      refEdges["count_up_to 0"] = true;
-      refEdges["ScatterAdd 0"] = true;
-      refEdges["ScatterSub 0"] = true;
-      refEdges["ScatterUpdate 0"] = true;
-      refEdges["scatter_add 0"] = true;
-      refEdges["scatter_sub 0"] = true;
-      refEdges["scatter_update 0"] = true;
-      var buildParams = {
-        enableEmbedding: true,
-        inEmbeddingTypes: ['Const'],
-        outEmbeddingTypes: ['^[a-zA-Z]+Summary$'],
-        refEdges: refEdges
-      };
-      var graphTracker = tf.graph.util.getSubtaskTracker(tracker, 20, 'Graph');
-      return tf.graph.build(graph, buildParams, graphTracker);
-    })
-    .then(function(graph) {
-      this._setOutGraph(graph);
-      var hierarchyTracker = tf.graph.util.getSubtaskTracker(tracker, 50,
-          'Namespace hierarchy');
-      return tf.graph.hierarchy.build(graph, hierarchyParams, hierarchyTracker);
-    }.bind(this))
-    .then(function(graphHierarchy) {
-      // Update the properties which notify the parent with the
-      // graph hierarchy and whether the data has live stats or not.
-      this._setOutGraphHierarchy(graphHierarchy);
-    }.bind(this))
-    .catch(function(e) {
-      // Generic error catch, for errors that happened outside
-      // asynchronous tasks.
-      tracker.reportError("Graph visualization failed: " + e, e);
-    });
-  },
-  _selectedDatasetChanged: function(datasetIndex, datasets) {
-    this._parseAndConstructHierarchicalGraph(datasets[datasetIndex].path);
-  },
-  _selectedFileChanged: function(e) {
-    if (!e) {
-      return;
-    }
-    var file = e.target.files[0];
-    if (!file) {
-      return;
-    }
-
-    // Clear out the value of the file chooser. This ensures that if the user
-    // selects the same file, we'll re-read it.
-    e.target.value = '';
-
-    this._parseAndConstructHierarchicalGraph(null, file);
-  }
-});
-</script>
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/BUILD b/tensorflow/tensorboard/components/tf_histogram_dashboard/BUILD
deleted file mode 100644
index e3c5f94caa..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/BUILD
+++ /dev/null
@@ -1,42 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_histogram_dashboard",
-    srcs = ["tf-histogram-dashboard.html"],
-    path = "/tf-histogram-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_runs_selector",
-        "//tensorflow/tensorboard/components/vz_histogram_timeseries",
-        "@org_polymer_iron_collapse",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-histogram-dashboard",
-    deps = [
-        ":tf_histogram_dashboard",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run1_tag_histo1.json b/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run1_tag_histo1.json
deleted file mode 100644
index a5600a356e..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run1_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[400.0, 40, [-0.3584790755077172, 3.0267252195784047, 20.0, 24.012225532303315, 48.29045006426564, [-0.35363819004775493, -0.29226296698161564, -0.19961953895336082, 0.3214892636797772, 0.5177616740489182, 0.56953784145381, 0.6264916255991911, 0.7580548669750213, 0.8338603536725235, 1.220854943811942, 1.3429404381931362, 1.47723448201245, 1.624957930213695, 1.7874537232350647, 1.9661990955585713, 2.379100905625872, 2.6170109961884593, 3.1665833053880363], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run2_tag_histo1.json b/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run2_tag_histo1.json
deleted file mode 100644
index 407c375d2f..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run2_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[400.0, 40, [-2.599286228987632, 3.5098048900144323, 20.0, 10.792285491200078, 66.66796979177158, [-2.379100905625872, -1.9661990955585713, -1.624957930213695, -1.47723448201245, -1.109868130738129, -1.0089710279437536, -0.42790220995778355, -0.2195814928486969, 0.47069243095356195, 0.7580548669750213, 0.917246389039776, 1.3429404381931362, 1.624957930213695, 1.7874537232350647, 2.1628190051144287, 2.6170109961884593, 2.8787120958073054, 3.8315657995195243], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run2_tag_histo2.json b/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run2_tag_histo2.json
deleted file mode 100644
index 752b621ab0..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/histograms_run_run2_tag_histo2.json
+++ /dev/null
@@ -1 +0,0 @@
-[[400.0, 40, [-0.8286852465281818, 2.0954239138728523, 20.0, 13.546880465642861, 24.14836803774091, [-0.7580548669750213, -0.38900200905253046, -0.06996543062044111, 0.07696197368248522, 0.19961953895336082, 0.2656936063469233, 0.29226296698161564, 0.5177616740489182, 0.7580548669750213, 0.917246389039776, 1.109868130738129, 1.220854943811942, 1.624957930213695, 2.1628190051144287], [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 2.0, 3.0]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/logdir b/tensorflow/tensorboard/components/tf_histogram_dashboard/data/logdir
deleted file mode 100644
index b6362b45d7..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/logdir
+++ /dev/null
@@ -1 +0,0 @@
-{"logdir": "/foo/some/fake/logdir"}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/runs.json b/tensorflow/tensorboard/components/tf_histogram_dashboard/data/runs.json
deleted file mode 100644
index cbe657af6b..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/data/runs.json
+++ /dev/null
@@ -1,4 +0,0 @@
-{
-	"run1": {"histograms": ["histo1"]}, 
-	"run2": {"histograms": ["histo2", "histo1"]}
-}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/index.html b/tensorflow/tensorboard/components/tf_histogram_dashboard/index.html
deleted file mode 100644
index 7f1e2f9ff8..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/index.html
+++ /dev/null
@@ -1,67 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-<link rel="import" href="../paper-styles/typography.html">
-<link rel="import" href="tf-histogram-dashboard.html">
-
-<title>Distribution Dashboard Demo</title>
-<style>
-  #container {
-    height: 800px;
-    display: block;
-  }
-
-  html, body {
-    margin: 0;
-    padding: 0;
-    font-family: "RobotoDraft","Roboto",sans-serif;
-  }
-
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="histogram-dash-demo">
-      <template>
-        <tf-histogram-dashboard id="demo" backend="[[backend]]"></tf-histogram-dashboard>
-      </template>
-      <script>
-        import {Backend} from "../../tf-backend/backend";
-        import {createRouter, setRouter} from "../../tf-backend/router";
-
-        Polymer({
-          is: "histogram-dash-demo",
-          properties: {
-            backend: {
-              type: Object,
-              value: function() {
-                return new Backend();
-              },
-            },
-          },
-          created: function() {
-            var router = createRouter("data", true);
-            setRouter(router);
-          },
-        });
-      </script>
-    </dom-module>
-    <histogram-dash-demo id="container"></histogram-dash-demo>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html b/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
deleted file mode 100644
index b3e1bd3e41..0000000000
--- a/tensorflow/tensorboard/components/tf_histogram_dashboard/tf-histogram-dashboard.html
+++ /dev/null
@@ -1,166 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
-<link rel="import" href="../tf-dashboard-common/tf-categorizer.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-dashboard-common/tf-option-selector.html">
-<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../vz-histogram-timeseries/vz-histogram-timeseries.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-
-<!--
-tf-histogram-dashboard is a complete frontend that loads runs from a backend,
-and creates chart panes that display data for those runs.
-
-It provides a mode and time property selector, together with the selectors
-provided by tf-sidebar-helper, by which the user can customize how data is
-organized and displayed.
-
-Each chart has a button that can toggle whether it is "selected"; selectedRuns
-charts are larger.
-
-Organizationally, the #plumbing div contains components that have no concrete
-manifestation and just effect data bindings or data loading. The .sidebar div
-contains shared controls provided by tf-sidebar-helper. The .center div
-contains vz-histogram-timeseries embedded inside tf-panes-helper's.
--->
-<dom-module id="tf-histogram-dashboard">
-  <template>
-    <tf-dashboard-layout>
-      <div class="sidebar">
-        <div class="sidebar-section">
-          <tf-categorizer
-            id="categorizer"
-            tags="[[tags]]"
-            categories="{{_categories}}"
-          ></tf-categorizer>
-        </div>
-        <div class="sidebar-section">
-          <tf-option-selector
-            id="histogramModeSelector"
-            name="Histogram Mode"
-            selected-id="{{_histogramMode}}"
-            >
-            <paper-button id="overlay">overlay</paper-button>
-            <paper-button id="offset">offset</paper-button>
-          </tf-option-selector>
-        </div>
-        <div class="sidebar-section">
-          <tf-option-selector
-            id="timePropertySelector"
-            name="Offset Time Axis"
-            selected-id="{{_timeProperty}}"
-            >
-            <paper-button id="step">step</paper-button>
-            <paper-button id="relative">relative</paper-button>
-            <paper-button id="wall_time">wall</paper-button>
-          </tf-option-selector>
-        </div>
-        <div class="sidebar-section">
-          <tf-runs-selector selected-runs="{{_selectedRuns}}">
-          </tf-runs-selector>
-        </div>
-       </div>
-      </div>
-
-      <div class="center">
-        <tf-panes-helper
-          categories="[[_categories]]"
-          data-type="[[dataType]]"
-          data-provider="[[dataProvider]]"
-          data-not-found="[[dataNotFound]]"
-          run2tag="[[run2tag]]"
-          selected-runs="[[_selectedRuns]]"
-          repeat-for-runs
-          >
-          <template>
-            <vz-histogram-timeseries
-              time-property="[[_timeProperty]]"
-              mode="[[_histogramMode]]"
-              color-scale="[[_colorScaleFunction]]"
-              ></vz-histogram-timeseries>
-          </template>
-        </tf-panes-helper>
-      </div>
-    </tf-dashboard-layout>
-
-    <style include="dashboard-style"></style>
-    <style>
-      tf-panes-helper {
-        --card-expanded-height: 500px;
-        --card-expanded-width: 700px;
-      }
-    </style>
-  </template>
-
-  <script>
-    import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-    import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-    import {BackendBehavior} from "../tf-backend/behavior";
-    import {runsColorScale} from "../tf-color-scale/colorScale";
-
-    Polymer({
-      is: "tf-histogram-dashboard",
-      factoryImpl: function(backend) {
-        this.backend = backend;
-      },
-      behaviors: [
-        DashboardBehavior("histograms"),
-        ReloadBehavior("tf-chart-scaffold"),
-        BackendBehavior,
-      ],
-      properties: {
-        backend: Object,
-        dataType: {
-          type: String,
-          value: "histogram"
-        },
-        _histogramMode: {
-          type: String,
-          value: "offset"
-        },
-        _timeProperty: {
-          type: String,
-          value: "step"
-        },
-        _colorScaleFunction: {
-          type: Function,
-          value: function() {
-            // NOTE: Because the result is a function, we can't just write
-            //
-            //     value: runsColorScale,
-            //
-            // above. If we did, Polymer would try to invoke _that_ to
-            // get an initial property value.
-            return runsColorScale;
-          },
-        },
-      },
-      attached: function() {
-        this.async(function() {
-          this.fire("rendered");
-        });
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/BUILD b/tensorflow/tensorboard/components/tf_image_dashboard/BUILD
deleted file mode 100644
index 84d7bf9049..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/BUILD
+++ /dev/null
@@ -1,45 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_image_dashboard",
-    srcs = [
-        "tf-image-dashboard.html",
-        "tf-image-loader.html",
-    ],
-    path = "/tf-image-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_runs_selector",
-        "@org_polymer_paper_dialog",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_spinner",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-image-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_image_dashboard",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run1_tag_im1_2Fimage_2F0.json b/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run1_tag_im1_2Fimage_2F0.json
deleted file mode 100644
index 3dec432213..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run1_tag_im1_2Fimage_2F0.json
+++ /dev/null
@@ -1,9 +0,0 @@
-[
-  {
-    "wall_time":1459200389.088045,
-    "width":4,
-    "height":4,
-    "step":0,
-    "query":"tag=im1%2Fimage%2F0&index=0&run=run1"
-  }
-]
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run1_tag_im2_2Fimage_2F0.json b/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run1_tag_im2_2Fimage_2F0.json
deleted file mode 100644
index 16152b8626..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run1_tag_im2_2Fimage_2F0.json
+++ /dev/null
@@ -1,9 +0,0 @@
-[
-  {
-    "wall_time":1459200389.093653,
-    "width":4,
-    "height":4,
-    "step":0,
-    "query":"tag=im2%2Fimage%2F0&index=0&run=run1"
-  }
-]
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run2_tag_im1_2Fimage_2F0.json b/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run2_tag_im1_2Fimage_2F0.json
deleted file mode 100644
index a717b79c5d..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/data/images_run_run2_tag_im1_2Fimage_2F0.json
+++ /dev/null
@@ -1,9 +0,0 @@
-[
-  {
-    "wall_time":1459200389.117463,
-    "width":4,
-    "height":4,
-    "step":0,
-    "query":"tag=im1%2Fimage%2F0&index=0&run=run2"
-  }
-]
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/individualImage_tag_im1_2Fimage_2F0_index_0_run_run1.png b/tensorflow/tensorboard/components/tf_image_dashboard/data/individualImage_tag_im1_2Fimage_2F0_index_0_run_run1.png
deleted file mode 100644
index 346fd0076be28b9338152c4d49a32fc5ed685e44..0000000000000000000000000000000000000000
GIT binary patch
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WHS6x#YB4Y{FnGH9xvX<aXaWGmYbV_R

diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/individualImage_tag_im1_2Fimage_2F0_index_0_run_run2.png b/tensorflow/tensorboard/components/tf_image_dashboard/data/individualImage_tag_im1_2Fimage_2F0_index_0_run_run2.png
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GIT binary patch
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diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/individualImage_tag_im2_2Fimage_2F0_index_0_run_run1.png b/tensorflow/tensorboard/components/tf_image_dashboard/data/individualImage_tag_im2_2Fimage_2F0_index_0_run_run1.png
deleted file mode 100644
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GIT binary patch
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Yvqj>ciqM=K1_lNOPgg&ebxsLQ0F@UfEC2ui

diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/logdir b/tensorflow/tensorboard/components/tf_image_dashboard/data/logdir
deleted file mode 100644
index c7d82022cc..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/data/logdir
+++ /dev/null
@@ -1 +0,0 @@
-{"logdir": "/some/fake/logdir"}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/data/runs.json b/tensorflow/tensorboard/components/tf_image_dashboard/data/runs.json
deleted file mode 100644
index b75de5b661..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/data/runs.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-   "run1":{
-      "images":[
-         "im1/image/0",
-         "im2/image/0"
-      ]
-   },
-   "run2":{
-      "images":[
-         "im1/image/0"
-      ]
-   }
-}
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/index.html b/tensorflow/tensorboard/components/tf_image_dashboard/index.html
deleted file mode 100644
index 27a31d5ad5..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/index.html
+++ /dev/null
@@ -1,72 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-  <head>
-    <script src="../webcomponentsjs/webcomponents-lite.min.js"></script>
-    <link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-    <link rel="import" href="tf-image-dashboard.html">
-    <title>Image Dashboard Demo</title>
-    <style>
-      #container{
-        width: 1000px;
-        height: 800px;
-        border: 2px solid grey;
-      }
-      html,body {
-        height: 100%;
-      }
-    </style>
-  </head>
-  <body>
-    <demo-snippet>
-      <template>
-        <dom-module id="image-dash-demo">
-          <template>
-            <tf-image-dashboard id="demo" backend="[[backend]]">
-            </tf-image-dashboard>
-          </template>
-          <script>
-            import {Backend} from "../../tf-backend/backend";
-            import {createRouter, setRouter} from "../../tf-backend/router";
-
-            Polymer({
-              is: "image-dash-demo",
-              properties: {
-                backend: {
-                  type: Object,
-                  value: function() {
-                    return new Backend();
-                  },
-                },
-              },
-              created: function() {
-                var path = "data";
-                var router = createRouter(path, true);
-                setRouter(router);
-              },
-            });
-          </script>
-        </dom-module>
-        <div id="container">
-          <image-dash-demo></image-dash-demo>
-        </div>
-      </template>
-    </demo-snippet>
-  </body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
deleted file mode 100644
index 0e27a61150..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-dashboard.html
+++ /dev/null
@@ -1,154 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-dialog/paper-dialog.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
-<link rel="import" href="tf-image-loader.html">
-
-<!--
-tf-image-dashboard displays a dashboard that loads images from a TensorFlow run.
--->
-<dom-module id="tf-image-dashboard">
-  <template>
-    <paper-dialog with-backdrop id="actual-image-size-dialog"></paper-dialog>
-    <tf-dashboard-layout>
-      <div class="sidebar">
-        <div class="sidebar-section">
-          <tf-categorizer
-            id="categorizer"
-            tags="[[tags]]"
-            categories="{{_categories}}"
-          ></tf-categorizer>
-        </div>
-        <div class="sidebar-section">
-          <tf-runs-selector
-            id="runs-selector"
-            selected-runs="{{_selectedRuns}}"
-          ></tf-runs-selector>
-        </div>
-      </div>
-      <div class="center">
-        <tf-panes-helper
-          categories="[[_categories]]"
-          data-type="[[dataType]]"
-          data-provider="[[dataProvider]]"
-          data-not-found="[[dataNotFound]]"
-          run2tag="[[run2tag]]"
-          selected-runs="[[_selectedRuns]]"
-          repeat-for-runs
-          >
-          <template>
-            <tf-image-loader></tf-image-loader>
-            <paper-icon-button
-              class="actual-size-button"
-              icon="aspect-ratio"
-              on-tap="_showActualSize"
-              title="Show the image at its true pixel size"
-              ></paper-icon-button>
-          </template>
-        </tf-panes-helper>
-      </div>
-    </tf-dashboard-layout>
-    <style include="dashboard-style"></style>
-    <style>
-      tf-panes-helper {
-        --card-width: 340px;
-        --card-height: auto;
-        --card-expanded-width: 700px;
-        --card-expanded-height: auto;
-      }
-
-      .actual-size-button {
-        background: #fff;
-        border-radius: 100%;
-        bottom: -35px;
-        color: #2196f3;
-        height: 32px;
-        left: 35px;
-        padding: 4px;
-        pointer-events: auto;
-        position: absolute;
-        width: 32px;
-      }
-
-      .actual-size-button-selected {
-        background: var(--tb-ui-light-accent);
-      }
-
-      #actual-image-size-dialog {
-        overflow: auto;
-      }
-    </style>
-  </template>
-  <script>
-    import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-    import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-    import {BackendBehavior} from "../tf-backend/behavior";
-
-    Polymer({
-      is: "tf-image-dashboard",
-      factoryImpl: function(backend) {
-        this.backend = backend;
-      },
-      properties: {
-        backend: Object,
-        dataType: {
-          type: String,
-          value: "image"
-        },
-      },
-      behaviors: [
-        DashboardBehavior("images"),
-        ReloadBehavior("tf-chart-scaffold"),
-        BackendBehavior,
-      ],
-      attached: function() {
-        this.async(function() {
-          this.fire("rendered");
-        });
-      },
-      _showActualSize: function(e) {
-        var currentTarget = Polymer.dom(e.currentTarget);
-        var card = currentTarget.node.closest('.card');
-
-        // Create a full-size copy of the image.
-        var newImage = card.querySelector('#img').cloneNode();
-        newImage.style.height = 'auto';
-        newImage.style.width = 'auto';
-        newImage.style.margin = 0;
-        newImage.style.padding = 0;
-        newImage.classList.add("actual-size-image");
-
-        // When the user clicks on the image, empty and close the dialog.
-        var dialog = this.$$('#actual-image-size-dialog');
-        newImage.addEventListener('click', function() {
-          dialog.close();
-        });
-
-        // Update dialog content. Show the dialog.
-        dialog.innerHTML = '';
-        dialog.appendChild(newImage);
-        dialog.open();
-      }
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-loader.html b/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-loader.html
deleted file mode 100644
index b9ae236421..0000000000
--- a/tensorflow/tensorboard/components/tf_image_dashboard/tf-image-loader.html
+++ /dev/null
@@ -1,235 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-slider/paper-slider.html">
-<link rel="import" href="../paper-spinner/paper-spinner-lite.html">
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../tf-imports/d3.html">
-
-<!--
-tf-image-loader loads an individual image from the TensorBoard backend.
-
-Right now it always loads the most recent image. We should add support in the
-future for loading older images.
--->
-<dom-module id="tf-image-loader">
-  <template>
-    <div id="image-annotation">
-      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
-        step
-        <span class="step-value">
-          [[_stepValue]]
-        </span>
-        <template is="dom-if" if="[[_currentWallTime]]">
-          ([[_currentWallTime]])
-        </template>
-        <paper-spinner-lite active hidden$=[[!_isImageLoading]]></paper-spinner-lite>
-      </template>
-      <template is="dom-if" if="[[_hasMultipleSteps]]">
-        <paper-slider
-          id="steps"
-          immediate-value="{{_stepIndex}}"
-          max="[[_maxStepIndex]]"
-          max-markers="[[_maxStepIndex]]"
-          snaps
-          step="1"
-          value="{{_stepIndex}}"></paper-slider>
-      </template>
-    </div>
-
-    <div id="main-image-container"></div>
-
-    <style>
-      :host {
-        display: block;
-        width: 100%;
-        height: auto;
-        position: relative;
-        --step-slider-knob-color: #424242;
-      }
-
-      #image-annotation {
-        border-left: 4px solid;
-        padding-left: 5px;
-        font-size: 12px;
-        margin: -10px 0 10px 0;
-      }
-
-      #image-annotation .step-value {
-        font-weight: bold;
-      }
-
-      #image-annotation paper-spinner-lite {
-        width: 14px;
-        height: 14px;
-        vertical-align: text-bottom;
-        --paper-spinner-color: var(--tb-orange-strong)
-      }
-
-      #steps {
-        height: 15px;
-        margin: 0 0 0 -15px;
-        /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
-         * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
-         * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2. */
-        width: calc(100% + 31px);
-        --paper-slider-active-color: var(--step-slider-knob-color);
-        --paper-slider-knob-color: var(--step-slider-knob-color);
-        --paper-slider-pin-color: var(--step-slider-knob-color);
-        --paper-slider-knob-start-color: var(--step-slider-knob-color);
-        --paper-slider-knob-start-border-color: var(--step-slider-knob-color);
-        --paper-slider-pin-start-color: var(--step-slider-knob-color);
-      }
-
-      #main-image-container img {
-        border: 1px solid #f5f5f5;
-        image-rendering: -moz-crisp-edges;
-        image-rendering: pixelated;
-        display: block;
-        width: 100%;
-        height: auto;
-        max-height: 500px;
-      }
-    </style>
-  </template>
-  <script>
-    "use strict";
-    import {runsColorScale} from "../tf-color-scale/colorScale";
-
-    Polymer({
-      is: "tf-image-loader",
-      properties: {
-        run: String,
-        // This is an array of Tensorboard Image&Datum objects (See backend.ts for details). The
-        // properties of objects in this array are
-        // {
-        //   width: number,
-        //   height: number,
-        //   wall_time: Date,
-        //   step: number,
-        //   url: string,
-        // }
-        _steps: {
-          type: Array,
-          value: [],
-          notify: true,
-        },
-        _stepIndex: {
-          type: Number,
-          notify: true,
-        },
-        _hasAtLeastOneStep: {
-          type: Boolean,
-          computed: "_computeHasAtLeastOneStep(_steps)",
-        },
-        _hasMultipleSteps: {
-          type: Boolean,
-          computed: "_computeHasMultipleSteps(_steps)",
-        },
-        _stepValue: {
-          type: Number,
-          computed: "_computeStepValue(_stepIndex)",
-        },
-        _currentWallTime: {
-          type: Number,
-          computed: "_computeCurrentWallTime(_stepIndex)",
-        },
-        _maxStepIndex: {
-          type: Number,
-          computed: "_computeMaxStepIndex(_steps)",
-        },
-        // We use a strictly increasing index to make sure that we don't settle on a stale image.
-        _currentImageLoadIndex: {
-          type: Number,
-          value: 1,
-        },
-        _isImageLoading: {
-          type: Boolean,
-          value: false,
-        },
-      },
-      observers: [
-        "_updateImageUrl(_steps, _stepIndex)",
-      ],
-      redraw: function() {
-        // Other dashboards logic requires a redraw method to be defined. redraw is called at
-        // various places such as when the image is expanded.
-        this.setSeriesData(this.run, this._steps);
-      },
-      setVisibleSeries: function(runs) {
-        // Do nothing.
-      },
-      setSeriesData: function(run, steps) {
-        this.set("run", run);
-        this.set("_steps", steps);
-        this.set("_stepIndex", steps.length - 1);
-
-        // Update the border color based on the run.
-        var color = runsColorScale(run);
-        this.$$("#image-annotation").style.borderColor = color;
-      },
-      _updateImageUrl: function(steps, stepIndex) {
-        // We manually change the image URL (instead of binding to the image's src attribute)
-        // because we would like to manage what happens when the image starts to / finishes loading.
-        if (!steps.length) {
-          return;
-        }
-
-        let img = new Image();
-        img.id = "img"; // '#img' used to select the image in tf-image-dashboard.
-
-        const loadIndex = ++this._currentImageLoadIndex;
-        img.onload = img.onerror = (function() {
-          if (loadIndex != this._currentImageLoadIndex) {
-            // This load is no longer relevant.
-            return;
-          }
-
-          // The new image has finished loading. Remove the old image. Add the new one.
-          let mainImageContainer = this.$$("#main-image-container");
-          mainImageContainer.innerHTML = "";
-          Polymer.dom(mainImageContainer).appendChild(img);
-
-          // The image has finished loading (or has erred and failed to load).
-          this.set("_isImageLoading", false);
-        }).bind(this);
-
-        // Load the new image.
-        this.set("_isImageLoading", true);
-        img.src = steps[stepIndex].url;
-      },
-      _computeHasAtLeastOneStep: function(steps) {
-        return !!steps && steps.length > 0;
-      },
-      _computeHasMultipleSteps: function(steps) {
-        return !!steps && steps.length > 1;
-      },
-      _computeStepValue: function(stepIndex) {
-        return this._steps[stepIndex].step;
-      },
-      _computeCurrentWallTime: function(stepIndex) {
-        return this._steps[stepIndex].wall_time.toString();
-      },
-      _computeMaxStepIndex: function(steps) {
-        return steps.length - 1;
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_imports/BUILD b/tensorflow/tensorboard/components/tf_imports/BUILD
deleted file mode 100644
index 84b46bf005..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/BUILD
+++ /dev/null
@@ -1,499 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:hacks.bzl", "tensorboard_typescript_bundle")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "webcomponentsjs",
-    srcs = ["@org_definitelytyped//:webcomponents.js.d.ts"],
-    path = "/webcomponentsjs",
-    visibility = ["//visibility:public"],
-    exports = ["@org_polymer_webcomponentsjs"],
-)
-
-ts_web_library(
-    name = "polymer",
-    srcs = ["@org_definitelytyped//:polymer.d.ts"],
-    path = "/polymer",
-    visibility = ["//visibility:public"],
-    exports = ["@org_polymer"],
-    deps = [":webcomponentsjs"],
-)
-
-ts_web_library(
-    name = "lodash",
-    srcs = [
-        "lodash.html",
-        "@org_definitelytyped//:lodash.d.ts",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-    deps = ["@com_lodash"],
-)
-
-ts_web_library(
-    name = "threejs",
-    srcs = [
-        "threejs.html",
-        "@org_definitelytyped//:three.d.ts",
-        "@org_threejs//:OrbitControls.js",
-        "@org_threejs//:three.js",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-)
-
-ts_web_library(
-    name = "numericjs",
-    srcs = [
-        "numericjs.html",
-        "@com_numericjs//:numeric.js",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-)
-
-ts_web_library(
-    name = "weblas",
-    srcs = [
-        "weblas.html",
-        "@io_github_waylonflinn_weblas//:weblas.js",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-)
-
-ts_web_library(
-    name = "graphlib",
-    srcs = [
-        "graphlib.html",
-        "@io_github_cpettitt_graphlib//:graphlib.core.js",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-    deps = [":lodash"],
-)
-
-ts_web_library(
-    name = "dagre",
-    srcs = [
-        "dagre.html",
-        "@io_github_cpettitt_dagre//:dagre.core.js",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":graphlib",
-        ":lodash",
-    ],
-)
-
-ts_web_library(
-    name = "d3",
-    srcs = [
-        "d3.d.ts",
-        "d3.html",
-        "@org_d3js//:d3.min.js",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-)
-
-ts_web_library(
-    name = "plottable",
-    srcs = [
-        "plottable.d.ts",
-        "plottable.html",
-    ],
-    path = "/tf-imports",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":d3",
-        ":plottable_js_css",
-    ],
-)
-
-ts_web_library(
-    name = "plottable_js_css",
-    srcs = [
-        "@com_palantir_plottable//:package/plottable.css",
-        "@com_palantir_plottable//:package/plottable.js",
-    ],
-    path = "/tf-imports",
-    strip_prefix = "package",
-    visibility = ["//visibility:private"],
-)
-
-ts_web_library(
-    name = "web_component_tester",
-    testonly = 1,
-    visibility = ["//visibility:public"],
-    exports = [
-        ":chai_typings",
-        ":mocha_typings",
-        ":sinon_typings",
-        "@org_npmjs_registry_web_component_tester",
-    ],
-)
-
-ts_web_library(
-    name = "chai_typings",
-    testonly = 1,
-    srcs = ["@org_definitelytyped//:chai.d.ts"],
-    path = "/chai",
-    visibility = ["//visibility:private"],
-)
-
-ts_web_library(
-    name = "mocha_typings",
-    testonly = 1,
-    srcs = ["@org_definitelytyped//:mocha.d.ts"],
-    path = "/mocha",
-    visibility = ["//visibility:private"],
-)
-
-ts_web_library(
-    name = "sinon_typings",
-    testonly = 1,
-    srcs = ["@org_definitelytyped//:sinon.d.ts"],
-    path = "/sinonjs",
-    visibility = ["//visibility:private"],
-)
-
-# Generate single TypeScript typings file for d3.js with no ES6 imports.
-#
-# The DefinitelyTyped definition of d3 v4 was written under the assumption that
-# we want to use d3 in a modularized way. We don't want to do that because its
-# import statements use NodeJS namespaces, and the Web Compiler only supports
-# W3C, ECMA, and IETF standards.
-tensorboard_typescript_bundle(
-    name = "d3_typings",
-    out = "d3.d.ts",
-    namespace_srcs = {"d3": [
-        "d3-transition.d.ts",
-        "@org_definitelytyped_types_d3_path//:index.d.ts",
-        "@org_definitelytyped_types_d3_time//:index.d.ts",
-        "@org_definitelytyped_types_d3_dsv//:index.d.ts",
-        "@org_definitelytyped_types_d3_color//:index.d.ts",
-        "@org_definitelytyped_types_d3_selection//:index.d.ts",
-        "@org_definitelytyped_types_d3_shape//:index.d.ts",
-        "@org_definitelytyped_types_d3_scale//:index.d.ts",
-        "@org_definitelytyped_types_d3_request//:index.d.ts",
-        "@org_definitelytyped_types_d3_interpolate//:index.d.ts",
-        "@org_definitelytyped_types_d3_drag//:index.d.ts",
-        "@org_definitelytyped_types_d3_brush//:index.d.ts",
-        "@org_definitelytyped_types_d3_axis//:index.d.ts",
-        "@org_definitelytyped_types_d3_zoom//:index.d.ts",
-        "@org_definitelytyped_types_d3_array//:index.d.ts",
-        "@org_definitelytyped_types_d3_chord//:index.d.ts",
-        "@org_definitelytyped_types_d3_collection//:index.d.ts",
-        "@org_definitelytyped_types_d3_dispatch//:index.d.ts",
-        "@org_definitelytyped_types_d3_ease//:index.d.ts",
-        "@org_definitelytyped_types_d3_force//:index.d.ts",
-        "@org_definitelytyped_types_d3_format//:index.d.ts",
-        "@org_definitelytyped_types_d3_hierarchy//:index.d.ts",
-        "@org_definitelytyped_types_d3_polygon//:index.d.ts",
-        "@org_definitelytyped_types_d3_quadtree//:index.d.ts",
-        "@org_definitelytyped_types_d3_queue//:index.d.ts",
-        "@org_definitelytyped_types_d3_random//:index.d.ts",
-        "@org_definitelytyped_types_d3_timer//:index.d.ts",
-        "@org_definitelytyped_types_d3_voronoi//:index.d.ts",
-    ]},
-)
-
-# It would be nice if Plottable released a .d.ts file for plottable.js like
-# they did for previous versions.
-tensorboard_typescript_bundle(
-    name = "plottable_typings",
-    out = "plottable.d.ts",
-    namespace_srcs = {
-        "Plottable": [
-            "@com_palantir_plottable//:package/build/src/core/dataset.d.ts",
-            "@com_palantir_plottable//:package/build/src/core/interfaces.d.ts",
-            "@com_palantir_plottable//:package/build/src/core/version.d.ts",
-        ],
-        "Plottable.Animators": [
-            "@com_palantir_plottable//:package/build/src/animators/animator.d.ts",
-            "@com_palantir_plottable//:package/build/src/animators/easingAnimator.d.ts",
-            "@com_palantir_plottable//:package/build/src/animators/nullAnimator.d.ts",
-        ],
-        "Plottable.Axes": [
-            "@com_palantir_plottable//:package/build/src/axes/axis.d.ts",
-            "@com_palantir_plottable//:package/build/src/axes/categoryAxis.d.ts",
-            "@com_palantir_plottable//:package/build/src/axes/numericAxis.d.ts",
-            "@com_palantir_plottable//:package/build/src/axes/timeAxis.d.ts",
-        ],
-        "Plottable.Components": [
-            "@com_palantir_plottable//:package/build/src/components/component.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/componentContainer.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/dragBoxLayer.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/dragLineLayer.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/gridlines.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/group.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/guideLineLayer.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/interpolatedColorLegend.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/label.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/legend.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/plotGroup.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/selectionBoxLayer.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/table.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/xDragBoxLayer.d.ts",
-            "@com_palantir_plottable//:package/build/src/components/yDragBoxLayer.d.ts",
-        ],
-        "Plottable.Configs": [
-            "@com_palantir_plottable//:package/build/src/core/config.d.ts",
-        ],
-        "Plottable.Formatters": [
-            "@com_palantir_plottable//:package/build/src/core/formatters.d.ts",
-        ],
-        "Plottable.RenderController": [
-            "@com_palantir_plottable//:package/build/src/core/renderController.d.ts",
-        ],
-        "Plottable.RenderPolicies": [
-            "@com_palantir_plottable//:package/build/src/core/renderPolicy.d.ts",
-        ],
-        "Plottable.SymbolFactories": [
-            "@com_palantir_plottable//:package/build/src/core/symbolFactories.d.ts",
-        ],
-        "Plottable.Dispatchers": [
-            "@com_palantir_plottable//:package/build/src/dispatchers/dispatcher.d.ts",
-            "@com_palantir_plottable//:package/build/src/dispatchers/keyDispatcher.d.ts",
-            "@com_palantir_plottable//:package/build/src/dispatchers/mouseDispatcher.d.ts",
-            "@com_palantir_plottable//:package/build/src/dispatchers/touchDispatcher.d.ts",
-        ],
-        "Plottable.Drawers": [
-            "@com_palantir_plottable//:package/build/src/drawers/arcDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/arcOutlineDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/areaDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/canvasBuffer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/canvasDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/drawStep.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/drawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/lineDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/rectangleDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/segmentDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/svgDrawer.d.ts",
-            "@com_palantir_plottable//:package/build/src/drawers/symbolDrawer.d.ts",
-        ],
-        "Plottable.Interactions": [
-            "@com_palantir_plottable//:package/build/src/interactions/clickInteraction.d.ts",
-            "@com_palantir_plottable//:package/build/src/interactions/dragInteraction.d.ts",
-            "@com_palantir_plottable//:package/build/src/interactions/interaction.d.ts",
-            "@com_palantir_plottable//:package/build/src/interactions/keyInteraction.d.ts",
-            "@com_palantir_plottable//:package/build/src/interactions/panZoomInteraction.d.ts",
-            "@com_palantir_plottable//:package/build/src/interactions/pointerInteraction.d.ts",
-        ],
-        "Plottable.Plots": [
-            "@com_palantir_plottable//:package/build/src/plots/areaPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/barPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/clusteredBarPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/commons.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/linePlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/piePlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/plot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/rectanglePlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/scatterPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/segmentPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/stackedAreaPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/stackedBarPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/waterfallPlot.d.ts",
-            "@com_palantir_plottable//:package/build/src/plots/xyPlot.d.ts",
-        ],
-        "Plottable.Scales": [
-            "@com_palantir_plottable//:package/build/src/scales/index.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/categoryScale.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/colorScale.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/interpolatedColorScale.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/linearScale.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/modifiedLogScale.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/quantitativeScale.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/scale.d.ts",
-            "@com_palantir_plottable//:package/build/src/scales/timeScale.d.ts",
-        ],
-        "Plottable.Scales.TickGenerators": [
-            "@com_palantir_plottable//:package/build/src/scales/tickGenerators.d.ts",
-        ],
-        "Plottable.Utils": [
-            "@com_palantir_plottable//:package/build/src/utils/addD3SelectionMulti.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/bucket.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/callbackSet.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/coerceD3.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/entityStore.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/makeEnum.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/map.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/set.d.ts",
-            "@com_palantir_plottable//:package/build/src/utils/transformAwareTranslator.d.ts",
-        ],
-        "Plottable.Utils.Array": [
-            "@com_palantir_plottable//:package/build/src/utils/arrayUtils.d.ts",
-        ],
-        "Plottable.Utils.Color": [
-            "@com_palantir_plottable//:package/build/src/utils/colorUtils.d.ts",
-        ],
-        "Plottable.Utils.DOM": [
-            "@com_palantir_plottable//:package/build/src/utils/domUtils.d.ts",
-        ],
-        "Plottable.Utils.Math": [
-            "@com_palantir_plottable//:package/build/src/utils/mathUtils.d.ts",
-        ],
-        "Plottable.Utils.Stacking": [
-            "@com_palantir_plottable//:package/build/src/utils/stackingUtils.d.ts",
-        ],
-        "Plottable.Utils.Window": [
-            "@com_palantir_plottable//:package/build/src/utils/windowUtils.d.ts",
-        ],
-    },
-    namespace_symbol_aliases = {
-        "Plottable.Animators": {
-            "AttributeToAppliedProjector": "Plottable.AttributeToAppliedProjector",
-            "SimpleSelection": "Plottable.SimpleSelection",
-        },
-        "Plottable.Axes": {
-            "Component": "Plottable.Components.Component",
-            "Formatter": "Plottable.Formatters.Formatter",
-            "Point": "Plottable.Point",
-            "QuantitativeScale": "Plottable.Scales.QuantitativeScale",
-            "Scale": "Plottable.Scales.Scale",
-            "Scales": "Plottable.Scales",
-            "SimpleSelection": "Plottable.SimpleSelection",
-            "SpaceRequest": "Plottable.SpaceRequest",
-        },
-        "Plottable.Components": {
-            "Bounds": "Plottable.Bounds",
-            "Formatter": "Plottable.Formatters.Formatter",
-            "IEntity": "Plottable.IEntity",
-            "Interactions": "Plottable.Interactions",
-            "Plots": "Plottable.Plots",
-            "Point": "Plottable.Point",
-            "QuantitativeScale": "Plottable.Scales.QuantitativeScale",
-            "Scales": "Plottable.Scales",
-            "SimpleSelection": "Plottable.SimpleSelection",
-            "SpaceRequest": "Plottable.SpaceRequest",
-            "SymbolFactory": "Plottable.SymbolFactories.SymbolFactory",
-        },
-        "Plottable.RenderController": {
-            "Component": "Plottable.Components.Component",
-            "RenderPolicies": "Plottable.RenderPolicies",
-        },
-        "Plottable.SymbolFactories": {
-            "d3Shape": "d3",
-        },
-        "Plottable.Dispatchers": {
-            "Component": "Plottable.Components.Component",
-            "Dispatchers": "Plottable.Dispatchers",
-            "Point": "Plottable.Point",
-        },
-        "Plottable.Drawers": {
-            "AttributeToAppliedProjector": "Plottable.AttributeToAppliedProjector",
-            "AttributeToProjector": "Plottable.AttributeToProjector",
-            "Dataset": "Plottable.Dataset",
-            "IAccessor": "Plottable.IAccessor",
-            "IAnimator": "Plottable.Animators.IAnimator",
-            "SimpleSelection": "Plottable.SimpleSelection",
-            "SymbolFactory": "Plottable.SymbolFactories.SymbolFactory",
-        },
-        "Plottable.Interactions": {
-            "Component": "Plottable.Components.Component",
-            "Point": "Plottable.Point",
-            "TransformableScale": "Plottable.Scales.TransformableScale",
-        },
-        "Plottable.Plots": {
-            "AppliedDrawStep": "Plottable.Drawers.AppliedDrawStep",
-            "AttributeToProjector": "Plottable.AttributeToProjector",
-            "Bounds": "Plottable.Bounds",
-            "Component": "Plottable.Components.Component",
-            "Dataset": "Plottable.Dataset",
-            "DrawStep": "Plottable.Drawers.DrawStep",
-            "Drawers": "Plottable.Drawers",
-            "Formatter": "Plottable.Formatters.Formatter",
-            "IAccessor": "Plottable.IAccessor",
-            "IAnimator": "Plottable.Animators.IAnimator",
-            "IDrawer": "Plottable.Drawers.IDrawer",
-            "IEntity": "Plottable.IEntity",
-            "IScaleCallback": "Plottable.Scales.IScaleCallback",
-            "Plots": "Plottable.Plots",
-            "Point": "Plottable.Point",
-            "Projector": "Plottable.Projector",
-            "ProxyDrawer": "Plottable.Drawers.ProxyDrawer",
-            "QuantitativeScale": "Plottable.Scales.QuantitativeScale",
-            "Range": "Plottable.Range",
-            "Scale": "Plottable.Scales.Scale",
-            "SimpleSelection": "Plottable.SimpleSelection",
-            "SymbolFactory": "Plottable.SymbolFactories.SymbolFactory",
-            "TransformableScale": "Plottable.Scales.TransformableScale",
-            "Utils": "Plottable.Utils",
-            "d3Shape": "d3",
-        },
-        "Plottable.Scales": {
-            "Dataset": "Plottable.Dataset",
-            "Scales": "Plottable.Scales",
-        },
-        "Plottable.Scales.TickGenerators": {
-            "QuantitativeScale": "Plottable.Scales.QuantitativeScale",
-        },
-        "Plottable.Utils": {
-            "Bounds": "Plottable.Bounds",
-            "Component": "Plottable.Components.Component",
-            "Dataset": "Plottable.Dataset",
-            "IAccessor": "Plottable.IAccessor",
-            "Point": "Plottable.Point",
-            "Range": "Plottable.Range",
-            "SimpleSelection": "Plottable.SimpleSelection",
-            "Utils": "Plottable.Utils",
-        },
-    },
-    namespace_symbol_aliases_public = {
-        "Plottable": {
-            "Axis": "Plottable.Axes.Axis",
-            "AxisOrientation": "Plottable.Axes.AxisOrientation",
-            "ClickCallback": "Plottable.Interactions.ClickCallback",
-            "Component": "Plottable.Components.Component",
-            "ComponentCallback": "Plottable.Components.ComponentCallback",
-            "ComponentContainer": "Plottable.Components.ComponentContainer",
-            "Dispatcher": "Plottable.Dispatchers.Dispatcher",
-            "DragBoxCallback": "Plottable.Components.DragBoxCallback",
-            "DragCallback": "Plottable.Interactions.DragCallback",
-            "EaseFn": "Plottable.Animators.EaseFn",
-            "EaseName": "Plottable.Animators.EaseName",
-            "Easing": "Plottable.Animators.Easing",
-            "Formatter": "Plottable.Formatters.Formatter",
-            "IAnimator": "Plottable.Animators.IAnimator",
-            "IDragLineCallback": "Plottable.Components.IDragLineCallback",
-            "IDrawer": "Plottable.Drawers.IDrawer",
-            "IResizeHandler": "Plottable.Components.IResizeHandler",
-            "IScaleCallback": "Plottable.Scales.IScaleCallback",
-            "Interaction": "Plottable.Interactions.Interaction",
-            "Key": "Plottable.Interactions.Key",
-            "KeyCallback": "Plottable.Interactions.KeyCallback",
-            "Null": "Plottable.Animators.Null",
-            "Plot": "Plottable.Plots.Plot",
-            "PointerCallback": "Plottable.Interactions.PointerCallback",
-            "ProxyDrawer": "Plottable.Drawers.ProxyDrawer",
-            "QuantitativeScale": "Plottable.Scales.QuantitativeScale",
-            "Renderer": "Plottable.Plots.Renderer",
-            "Scale": "Plottable.Scales.Scale",
-            "SymbolFactory": "Plottable.SymbolFactories.SymbolFactory",
-            "TimeInterval": "Plottable.Axes.TimeInterval",
-            "TransformableScale": "Plottable.Scales.TransformableScale",
-            "XAlignment": "Plottable.Components.XAlignment",
-            "XYPlot": "Plottable.Plots.XYPlot",
-            "YAlignment": "Plottable.Components.YAlignment",
-        },
-    },
-)
-
-# Removes the 'declare module' block inside this file, but keeps its content.
-genrule(
-    name = "kludge_d3_transition",
-    srcs = ["@org_definitelytyped_types_d3_transition//:index.d.ts"],
-    outs = ["d3-transition.d.ts"],
-    cmd = "sed '/^declare module/d' $< | awk '/^}$$/ && !p {p++;next}1' >$@",
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_imports/README.md b/tensorflow/tensorboard/components/tf_imports/README.md
deleted file mode 100644
index b1cabc61b9..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-This file acts as import routers for third party javascript libraries,
-e.g. Plottable and D3.
diff --git a/tensorflow/tensorboard/components/tf_imports/d3.html b/tensorflow/tensorboard/components/tf_imports/d3.html
deleted file mode 100644
index 76ca302709..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/d3.html
+++ /dev/null
@@ -1,50 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<!--
-@license
-d3
-Copyright 2010-2017 Mike Bostock
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without modification,
-are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice, this
-  list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice,
-  this list of conditions and the following disclaimer in the documentation
-  and/or other materials provided with the distribution.
-
-* Neither the name of the author nor the names of contributors may be used to
-  endorse or promote products derived from this software without specific prior
-  written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
-ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
--->
-
-<script jscomp-nocompile src="d3.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/dagre.html b/tensorflow/tensorboard/components/tf_imports/dagre.html
deleted file mode 100644
index 49522db38a..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/dagre.html
+++ /dev/null
@@ -1,45 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<!--
-@license
-Dagre
-Copyright (c) 2012-2014 Chris Pettitt
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
--->
-
-<link rel="import" href="lodash.html">
-<link rel="import" href="graphlib.html">
-
-<script jscomp-minify src="dagre.core.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/graphlib.html b/tensorflow/tensorboard/components/tf_imports/graphlib.html
deleted file mode 100644
index aa868b00b4..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/graphlib.html
+++ /dev/null
@@ -1,20 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="lodash.html">
-
-<script jscomp-minify src="graphlib.core.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/lodash.html b/tensorflow/tensorboard/components/tf_imports/lodash.html
deleted file mode 100644
index 192e61cc99..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/lodash.html
+++ /dev/null
@@ -1,18 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script jscomp-minify src="../lodash/lodash.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/numericjs.html b/tensorflow/tensorboard/components/tf_imports/numericjs.html
deleted file mode 100644
index 69e948eb99..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/numericjs.html
+++ /dev/null
@@ -1,43 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<!--
-@license
-Numeric Javascript
-Copyright (C) 2011 by Sébastien Loisel
-Copyright (c) 2011 Alberto Santini <albertosantini@gmail.com>
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
--->
-
-<script jscomp-minify src="numeric.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/plottable.html b/tensorflow/tensorboard/components/tf_imports/plottable.html
deleted file mode 100644
index 77ad544d5a..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/plottable.html
+++ /dev/null
@@ -1,44 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<!--
-@license
-Plottable.js
-Copyright (c) 2014-2017 Palantir Technologies, Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
--->
-
-<link rel="import" href="d3.html">
-<script jscomp-suppress src="plottable.js"></script>
-<link rel="stylesheet" href="plottable.css">
diff --git a/tensorflow/tensorboard/components/tf_imports/threejs.html b/tensorflow/tensorboard/components/tf_imports/threejs.html
deleted file mode 100644
index 7f4233b571..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/threejs.html
+++ /dev/null
@@ -1,43 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<!--
-@license
-three.js
-Copyright (c) 2010-2013 three.js authors
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
--->
-
-<script jscomp-suppress src="three.js"></script>
-<script jscomp-suppress src="OrbitControls.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_imports/weblas.html b/tensorflow/tensorboard/components/tf_imports/weblas.html
deleted file mode 100644
index c07020598f..0000000000
--- a/tensorflow/tensorboard/components/tf_imports/weblas.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<!--
-@license
-weblas
-Copyright (c) 2015 Waylon Flinn
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
--->
-
-<script jscomp-nocompile src="weblas.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_option_selector/BUILD b/tensorflow/tensorboard/components/tf_option_selector/BUILD
deleted file mode 100644
index 3f7eed25cb..0000000000
--- a/tensorflow/tensorboard/components/tf_option_selector/BUILD
+++ /dev/null
@@ -1,21 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_option_selector",
-    srcs = ["tf-option-selector.html"],
-    path = "/tf-option-selector",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_option_selector/tf-option-selector.html b/tensorflow/tensorboard/components/tf_option_selector/tf-option-selector.html
deleted file mode 100644
index d6fc9d6861..0000000000
--- a/tensorflow/tensorboard/components/tf_option_selector/tf-option-selector.html
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-
-<!--
-tf-option-selector is a simple component that has buttons as content and
-provides a "selectedId" property that is one of the IDs of the buttons inside it.
--->
-<dom-module id="tf-option-selector">
-  <template>
-    <div id="wrap">
-      <h3>[[name]]</h3>
-      <div class="content-wrapper"><content></content></div>
-    </div>
-    <style>
-      .content-wrapper ::content > * {
-        width: 30%;
-        font-size: 13px;
-        background: none;
-        margin-top: 10px;
-        color: var(--tb-ui-dark-accent);
-      }
-
-      .content-wrapper ::content :first-of-type {
-        margin-left: 0;
-      }
-
-      .content-wrapper ::content .selected {
-        background-color: var(--tb-ui-dark-accent);
-        color: white!important;
-      }
-
-      h3 {
-        color: var(--paper-grey-800);
-        margin: 0;
-        font-weight: normal;
-        font-size: 14px;
-        margin-bottom: 5px;
-        display: block;
-        pointer-events: none;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-option-selector",
-      properties: {
-        name: String,
-        selectedId: {
-          type: String,
-          notify: true,
-          observer: '_selectedIdChanged'
-        }
-      },
-      attached: function() {
-        this.async(function() {
-          this.getEffectiveChildren().forEach(function(node) {
-            this.listen(node, 'tap', '_selectTarget');
-          }.bind(this));
-        });
-      },
-      _selectTarget: function(e) {
-        this.selectedId = e.currentTarget.id;
-      },
-      _selectedIdChanged: function() {
-        var selected = this.queryEffectiveChildren('#' + this.selectedId);
-        if (!selected) {
-          return;
-        }
-
-        this.getEffectiveChildren().forEach(function(node) {
-          node.classList.remove("selected");
-        });
-        selected.classList.add("selected");
-      }
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/BUILD b/tensorflow/tensorboard/components/tf_profile_dashboard/BUILD
deleted file mode 100644
index 5d04618a54..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/BUILD
+++ /dev/null
@@ -1,25 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_profile_dashboard",
-    srcs = [
-        "tf-profile-dashboard.html",
-    ],
-    path = "/tf-profile-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_graph_controls",
-        "@org_polymer",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/BUILD b/tensorflow/tensorboard/components/tf_profile_dashboard/demo/BUILD
deleted file mode 100644
index 3cc20ba352..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/BUILD
+++ /dev/null
@@ -1,24 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-profile-dashboard/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_profile_dashboard",
-        "//tensorflow/tensorboard/components/tf_trace_viewer:demo",
-        "@org_polymer",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_webcomponentsjs",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/logdir b/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/logdir
deleted file mode 100644
index ecaaa8ac75..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/logdir
+++ /dev/null
@@ -1 +0,0 @@
-{"logdir": "/some/fake/logdir"}
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_bar_tag_trace_viewer.json b/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_bar_tag_trace_viewer.json
deleted file mode 100644
index bc1a08b535..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_bar_tag_trace_viewer.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-  "traceEvents": [
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 826, "ph": "C",
-        "name": "counter", "args": {"value": 10}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 826, "ph": "B",
-        "name": "A long name that doesnt fit but is exceedingly informative",
-        "args": {"name_false": false, "value_true": true}},
-    {"cat": "PERF", "pid": 22630, "ts": 835, "ph": "I", "s": "p",
-        "name": "ProcessWideEvent1", "args": {}}
-  ],
-  "stackFrames": {
-    "1": {
-      "category": "m1",
-      "name": "main"
-    },
-    "7": {
-      "category": "m2",
-      "name": "frame7",
-      "parent": "1"
-    },
-    "8": {
-      "category": "m2",
-      "name": "frame8",
-      "parent": "1"
-    }
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_bar_tag_unsupported.json b/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_bar_tag_unsupported.json
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_foo_tag_trace_viewer.json b/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_foo_tag_trace_viewer.json
deleted file mode 100644
index e1d57394e3..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/data_run_foo_tag_trace_viewer.json
+++ /dev/null
@@ -1,105 +0,0 @@
-{
-  "traceEvents": [
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 826, "ph": "C",
-        "name": "counter", "args": {"value": 10}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 826, "ph": "B",
-        "name": "A long name that doesnt fit but is exceedingly informative",
-        "args": {"name_false": false, "value_true": true}},
-    {"cat": "PERF", "pid": 22630, "ts": 835, "ph": "I", "s": "p",
-        "name": "ProcessWideEvent1", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 827, "ph": "B",
-        "name": "Asub with a name that wont fit", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 828, "ph": "E",
-        "name": "Asub", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 829, "ph": "B",
-        "name": "Asub", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 15, "ts": 820, "ph": "X",
-        "name": "Long X type", "args": {}, "sf": 7, "esf": 8},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 832, "ph": "E",
-        "name": "Asub", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 2, "ts": 818, "ph": "X",
-        "name": "X1", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 2, "ts": 818, "ph": "X",
-        "name": "X same ts and dur as X1", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 832, "ph": "C",
-        "name": "counter", "args": {"value": 1}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 833, "ph": "E",
-        "name": "", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 835, "ph": "I",
-        "name": "ThreadLevelI1", "args": {}},
-
-    {"cat": "PERF", "ts": 880, "ph": "I", "s": "g", "name": "GlobalEvent1",
-        "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 837, "ph": "I",
-        "name": "ThreadLevelI2", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 839, "ph": "C",
-        "name": "counter", "args": {"value": 5}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 840, "ph": "B",
-        "name": "A not as long a name", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 848, "ph": "E",
-        "name": "A not as long a name", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 848, "ph": "C",
-        "name": "counter", "args": {"value": 1}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 854, "ph": "C",
-        "name": "counter", "args": {"value": 10}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 850, "ph": "B",
-        "name": "B", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 854, "ph": "E",
-        "name": "B", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 827, "ph": "B",
-        "name": "A", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 835, "ph": "I",
-        "name": "ThreadLevelImmediate Three", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 845, "ph": "I",
-        "name": "ThreadLevelImmediate4", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 854, "ph": "E",
-        "name": "A", "args": {}},
-
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 860, "ph": "B",
-        "name": "B/E over X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 10, "ts": 860, "ph": "X",
-        "name": "X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 860, "ph": "B",
-        "name": "B/E under X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 870, "ph": "E",
-        "name": "B/E under X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 870, "ph": "E",
-        "name": "B/E over X", "args": {}},
-
-    {"cat": "SAMPLE", "pid": 22630, "tid": 22631, "ts": 870, "ph": "P",
-        "name": "SampleA", "args": {}},
-    {"cat": "SAMPLE", "pid": 22630, "tid": 22631, "ts": 875, "ph": "P",
-        "name": "SampleB", "args": {}},
-    {"cat": "SAMPLE", "pid": 22630, "tid": 22631, "ts": 878, "ph": "P",
-        "name": "SampleC", "args": {}, "sf": 8},
-
-    {"cat": "__metadata", "pid": 22630, "tid": 22630, "ts": 0, "ph": "M",
-        "name": "thread_name", "args": {"name": "threadA"}},
-    {"cat": "__metadata", "pid": 22630, "tid": 22631, "ts": 0, "ph": "M",
-        "name": "thread_name", "args": {"name": "threadB"}},
-    {"cat": "__metadata", "pid": 22630, "tid": 22632, "ts": 0, "ph": "M",
-        "name": "thread_name", "args": {"name": "threadC"}}
-  ],
-  "stackFrames": {
-    "1": {
-      "category": "m1",
-      "name": "main"
-    },
-    "7": {
-      "category": "m2",
-      "name": "frame7",
-      "parent": "1"
-    },
-    "8": {
-      "category": "m2",
-      "name": "frame8",
-      "parent": "1"
-    }
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/tags.json b/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/tags.json
deleted file mode 100644
index 12ef5bf8b2..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/data/plugin/profile/tags.json
+++ /dev/null
@@ -1 +0,0 @@
-{"foo": ["trace_viewer"], "bar": ["unsupported", "trace_viewer"]}
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/index.html b/tensorflow/tensorboard/components/tf_profile_dashboard/demo/index.html
deleted file mode 100644
index 15064a54f8..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/demo/index.html
+++ /dev/null
@@ -1,75 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-  <head>
-    <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-    <link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-    <link rel="import" href="../tf-profile-dashboard.html">
-    <title>Profile Dashboard Demo</title>
-    <style>
-      #container{
-        height: 800px;
-        border: 2px solid grey;
-      }
-      html, body {
-        margin: 0;
-        padding: 0;
-        height: 100%;
-        font-family: "RobotoDraft","Roboto",sans-serif;
-      }
-    </style>
-  </head>
-  <body>
-    <demo-snippet>
-      <template>
-        <dom-module id="profile-dash-demo">
-          <template>
-            <tf-profile-dashboard
-              backend="[[_backend]]",
-              trace-viewer-base-url="../../tf-trace-viewer/demo.html">
-            </tf-profile-dashboard>
-          </template>
-          <script>
-            import {Backend} from "../../tf-backend/backend";
-            import {createRouter, setRouter} from "../../tf-backend/router";
-
-            Polymer({
-              is: "profile-dash-demo",
-              properties: {
-                _backend: {
-                  type: Object,
-                  value: function() {
-                    return new Backend();
-                  },
-                },
-              },
-              created: function() {
-                var router = createRouter("data", true);
-                setRouter(router);
-              },
-            });
-          </script>
-        </dom-module>
-        <div id="container">
-          <profile-dash-demo></profile-dash-demo>
-        </div>
-      </template>
-    </demo-snippet>
-  </body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_profile_dashboard/tf-profile-dashboard.html b/tensorflow/tensorboard/components/tf_profile_dashboard/tf-profile-dashboard.html
deleted file mode 100644
index 4028f0e0f0..0000000000
--- a/tensorflow/tensorboard/components/tf_profile_dashboard/tf-profile-dashboard.html
+++ /dev/null
@@ -1,222 +0,0 @@
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-graph-controls/tf-graph-controls.html">
-
-<!--
-tf-profile-dashboard displays profiling information for different tools.
-
-In the profile dashboard, a "run" is a profile run and "tag" is a tool name. A
-profile run can have multiple tools that present the performance profile as different visualizations
-(e.g. Catapult TraceViewer).
--->
-
-<dom-module id="tf-profile-dashboard">
-<template>
-<tf-dashboard-layout>
-<div class="sidebar">
-  <div class="allcontrols">
-    <div class="sidebar-section">
-      <div class="title">Runs <span class="counter">([[_datasets.length]])</span></div>
-      <paper-dropdown-menu no-label-float no-animations noink class="run-dropdown">
-        <paper-menu id="select" class="dropdown-content" selected="{{selectedDataset}}">
-          <template is="dom-repeat" items="[[_datasets]]">
-            <paper-item>[[item.name]]</paper-item>
-          </template>
-        </paper-menu>
-      </paper-dropdown-menu>
-    </div>
-    <div class="sidebar-section">
-      <div class="title">Tools <span class="counter">([[_activeTools.length]])</span></div>
-      <paper-dropdown-menu no-label-float no-animations noink class="run-dropdown">
-        <paper-menu id="select" class="dropdown-content" selected="{{selectedTool}}">
-          <template is="dom-repeat" items="[[_activeTools]]">
-            <paper-item>[[item]]</paper-item>
-          </template>
-          <paper-item>None</paper-item>
-        </paper-menu>
-      </paper-dropdown-menu>
-    </div>
-    <div class="sidebar-section"></div>
-  </div>
-</div>
-<div class="center">
-  <template is="dom-if" if="[[_toolIsTraceViewer(_currentTool)]]">
-    <iframe
-      id="tv_iframe"
-      height="100%"
-      width="100%"
-      src="[[_traceDataUrl]]">
-    </iframe>
-  </template>
-</div>
-</tf-dashboard-layout>
-<style include="dashboard-style"></style>
-
-<style>
-  .center {
-    position: relative;
-    height: 100%;
-  }
-  iframe {
-    position: absolute;
-    width: 100%;
-    height: 100%;
-    box-sizing: border-box;
-  }
-</style>
-
-</template>
-<script>
-  "use strict";
-
-  import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-  import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-  import {BackendBehavior} from "../tf-backend/behavior";
-  import {getRouter} from '../tf-backend/router';
-
-  Polymer({
-    is: "tf-profile-dashboard",
-    factoryImpl: function(backend) {
-      this.backend = backend;
-    },
-    behaviors: [
-      DashboardBehavior("profile"),
-      ReloadBehavior("tf-profile-dashboard"),
-      BackendBehavior,
-    ],
-    properties: {
-      backend: Object,
-      _isAttached: Boolean,
-      // Whether this dashboard is initialized. This dashboard should only be
-      // initialized once.
-      _initialized: Boolean,
-      // The endpoint that serves trace viewer app.
-      traceViewerBaseUrl: {
-        type: String,
-        value: function() {
-          return getRouter().pluginRoute("profile", "/trace_viewer");
-        },
-      },
-      // The URL for the trace data being display.
-      _traceDataUrl: {
-        type: String,
-        value: "",
-      },
-      _datasets: {
-        type: Array,
-        observer: '_datasetsChanged'
-      },
-      _activeTools: {
-        type: Array,
-        computed: '_getActiveTools(selectedDataset, _datasets)'
-      },
-      selectedDataset: {
-        type: Number,
-        notify: true,
-        value: 0,
-        observer: '_selectedDatasetChanged'
-      },
-      _currentTool: {
-        type: String,
-        computed: '_getCurrentToolName(selectedTool, _activeTools)',
-        notify: true,
-      },
-      selectedTool: {
-        type: Number,
-        notify: true,
-        value: 0,
-      },
-    },
-    reload: function() {
-    },
-    ready: function() {
-    },
-    observers: [
-      '_maybeInitializeDashboard(backend, _isAttached)',
-      '_maybeUpdateTool(_datasets, selectedDataset, _currentTool)',
-    ],
-    attached: function() {
-      this.set('_isAttached', true);
-    },
-    detached: function() {
-      this.set('_isAttached', false);
-    },
-    _maybeInitializeDashboard: function(backend, isAttached) {
-      if (this._initialized || !backend || !isAttached) {
-        // Either this dashboard is already initialized ... or we are not yet
-        // ready to initialize.
-        return;
-      }
-      // Set this to true so we only initialize once.
-      this._initialized = true;
-      backend.profileTags().then((runToTool) => {
-        var datasets = _.map(runToTool, function(tools, run) {
-          return {
-            name: run,
-            activeTools: tools,
-          };
-        }, this);
-        this.set('_datasets', datasets);
-      });
-    },
-    _maybeUpdateTool: function(datasets, selectedDataset, currentTool) {
-      if (currentTool == "trace_viewer") {
-        var trace_data_url = (getRouter().pluginRunTagRoute('profile', '/data')(
-            'trace_viewer', datasets[selectedDataset].name));
-        // Make the trace data url relative to the root.
-        if (trace_data_url[0] != '/') {
-          trace_data_url = '/' + trace_data_url;
-        }
-        this._traceDataUrl = this.traceViewerBaseUrl + "?trace_data_url=" +
-                            encodeURIComponent(trace_data_url);
-        return;
-      }
-    },
-    _datasetsChanged: function(newDatasets, oldDatasets) {
-      if (oldDatasets != null || this.selected == null) {
-        // Select the first dataset by default.
-        this.set('selectedDataset', 0);
-      }
-    },
-    _selectedDatasetChanged: function(newDataset, oldDataset) {
-      if (this._datasets) {
-        // Display the first tool by default when switching to another run.
-        this.set('selectedTool', 0);
-      }
-    },
-    _getCurrentToolName: function(selectedToolIndex, activeTools) {
-      if (selectedToolIndex >= 0 && selectedToolIndex < activeTools.length) {
-        return activeTools[selectedToolIndex];
-      }
-      return null;
-    },
-    _getActiveTools: function(selectedDataset, datasets) {
-      if (datasets && selectedDataset < datasets.length) {
-        return datasets[selectedDataset].activeTools;
-      }
-      return [];
-    },
-    _toolIsTraceViewer: function(toolName) {
-      return toolName == 'trace_viewer';
-    },
-  });
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_runs_selector/BUILD b/tensorflow/tensorboard/components/tf_runs_selector/BUILD
deleted file mode 100644
index 30265c8d29..0000000000
--- a/tensorflow/tensorboard/components/tf_runs_selector/BUILD
+++ /dev/null
@@ -1,27 +0,0 @@
-package(default_visibility = ["//tensorflow:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_runs_selector",
-    srcs = [
-        "tf-runs-selector.html",
-    ],
-    path = "/tf-runs-selector",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_dialog",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_runs_selector/tf-runs-selector.html b/tensorflow/tensorboard/components/tf_runs_selector/tf-runs-selector.html
deleted file mode 100644
index 6964bb076d..0000000000
--- a/tensorflow/tensorboard/components/tf_runs_selector/tf-runs-selector.html
+++ /dev/null
@@ -1,195 +0,0 @@
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-dialog/paper-dialog.html">
-<link rel="import" href="../tf-dashboard-common/tf-multi-checkbox.html">
-<link rel="import" href="../tf-dashboard-common/scrollbar-style.html">
-
-<!--
-tf-runs-selector creates a set of checkboxes to display which runs are
-selected. It also displays tooltips.
-
-Properties in: none.
-Properties out:
-- selectedRuns: The array of run names that are currently checked by the user.
-
--->
-<dom-module id="tf-runs-selector">
-  <template>
-    <div id="plumbing">
-      <tf-color-scale
-        runs="[[runs]]"
-        out-color-scale="{{colorScale}}"
-      ></tf-color-scale>
-    </div>
-    <paper-dialog with-backdrop id="logdir-dialog">
-      <h2>logdir</h2>
-      <div inner-h-t-m-l="{{_breakString(logdir)}}"></div>
-    </paper-dialog>
-    <div id="top-text">
-      <h3 id="tooltip-help" class="tooltip-container">
-        Runs<!--
-        - TODO(wchargin): Remove the "new" notice when we remove the old
-        - selector. This is just so that we can tell them apart.
-        -->&#x2003;<span
-          style="text-transform:uppercase;color:red;font-size:smaller"
-        >new</span>
-      </h3>
-    </div>
-    <tf-multi-checkbox
-      id="multiCheckbox"
-      names="[[runs]]"
-      out-selected="{{selectedRuns}}"
-      color-scale="[[colorScale]]"
-    ></tf-multi-checkbox>
-    <paper-button
-      class="x-button"
-      id="toggle-all"
-      on-tap="_toggleAll"
-    >
-    Toggle All Runs
-    </paper-button>
-    <template
-      is="dom-if"
-      if="[[logdir]]">
-      <div id="logdir">
-        <span id="clipped-logdir" inner-h-t-m-l="[[_clippedLogdir]]"></span><!--
-          We use HTML comments to remove spaces before the ellipsis.
-        --><template
-                     is="dom-if"
-                     if="[[_shouldShowExpandLogdirButton(logdir, _logdirClipLength)]]"><!--
-          --><a href="" on-click="_openLogdirDialog">…</a>
-        </template>
-      </div>
-    </template>
-    <style>
-      :host {
-        display: flex;
-        flex-direction: column;
-        padding-bottom: 10px;
-        box-sizing: border-box;
-      }
-      #top-text {
-        width: 100%;
-        flex-grow: 0;
-        flex-shrink: 0;
-        padding-right: 16px;
-        box-sizing: border-box;
-        color: var(--paper-grey-800);
-      }
-      tf-multi-checkbox {
-        display: flex;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
-      .x-button {
-        font-size: 13px;
-        background-color: var(--tb-ui-light-accent);
-        color: var(--tb-ui-dark-accent);
-      }
-      #tooltip-help {
-        color: var(--paper-grey-800);
-        margin: 0;
-        font-weight: normal;
-        font-size: 14px;
-        margin-bottom: 5px;
-      }
-      paper-button {
-        margin-left: 0;
-      }
-      #logdir {
-        color: var(--tb-ui-dark-accent);
-        font-size: 13px;
-        margin: 5px 0 0 0;
-        max-width: 288px;
-      }
-    </style>
-  </template>
-  <script>
-  import {RequestManager} from "../tf-backend/requestManager";
-  import {getRouter} from "../tf-backend/router";
-  import * as RunsStore from "../tf-backend/runsStore";
-
-  var requestManager = new RequestManager();
-  Polymer({
-    is: "tf-runs-selector",
-    properties: {
-      selectedRuns: {type: Array, notify: true},
-      // runs: an array of strings, representing the run names that may be chosen
-      runs: Array,
-      logdir: {
-        type: String,
-        notify: true,
-      },
-      // This is the potentially clipped portion of the logdir we show at the bottom of the sidebar.
-      _clippedLogdir: {
-        type: String,
-        computed: '_getClippedLogdir(logdir, _logdirClipLength)',
-      },
-      _logdirClipLength: {
-        type: Number,
-        value: 250,
-        readOnly: true,
-      },
-    },
-    ready: function() {
-      var updateRuns = function() {
-        this.set("runs", RunsStore.getRuns());
-      }.bind(this);
-      RunsStore.addListener(updateRuns);
-      updateRuns();
-
-      requestManager.request(getRouter().logdir()).then(logdirObject => {
-        this.set('logdir', logdirObject.logdir);
-      }).catch(e => {
-        // Fetching the logdir failed. Prevent the exception from logging to
-        // console. The console already logs a 404 network event.
-      });
-    },
-    _toggleAll: function() {
-      this.$.multiCheckbox.toggleAll();
-    },
-    // Break the string at natural points, including commas, equals, and slashes
-    _breakString: function(originalString) {
-      return originalString.replace(/([\/=\-_,])/g, "$1<wbr>");
-    },
-    _getClippedLogdir: function(logdir, logdirClipLength) {
-      if (logdir === undefined) {
-        // The logdir has not been set yet.
-        return undefined;
-      }
-
-      if (logdir.length > logdirClipLength) {
-        // Clip the logdir to avoid blocking the runs selector. Let the user view a more full
-        // version of the logdir.
-        return this._breakString(logdir.substring(0, logdirClipLength));
-      } else {
-        return this._breakString(logdir);
-      }
-    },
-    _openLogdirDialog: function(event) {
-      event.preventDefault();
-      this.$$('#logdir-dialog').open();
-    },
-    _shouldShowExpandLogdirButton(logdir, _logdirClipLength) {
-      return logdir && logdir.length > _logdirClipLength;
-    },
-  });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/BUILD b/tensorflow/tensorboard/components/tf_scalar_dashboard/BUILD
deleted file mode 100644
index 7cc192b464..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/BUILD
+++ /dev/null
@@ -1,38 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_scalar_dashboard",
-    srcs = [
-        "tf-scalar-dashboard.html",
-        "tf-smoothing-input.html",
-    ],
-    path = "/tf-scalar-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_runs_selector",
-        "//tensorflow/tensorboard/components/vz_line_chart",
-        "@org_polymer_iron_collapse",
-        "@org_polymer_paper_checkbox",
-        "@org_polymer_paper_dropdown_menu",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_input",
-        "@org_polymer_paper_item",
-        "@org_polymer_paper_menu",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/BUILD b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/BUILD
deleted file mode 100644
index 0e892b1aa3..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/BUILD
+++ /dev/null
@@ -1,27 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"],
-    path = "/tf-scalar-dashboard/demo",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "//tensorflow/tensorboard/components/tf_scalar_dashboard",
-        "//tensorflow/tensorboard/demo:demo_data",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/logdir b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/logdir
deleted file mode 100644
index b6362b45d7..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/logdir
+++ /dev/null
@@ -1 +0,0 @@
-{"logdir": "/foo/some/fake/logdir"}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/runs.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/runs.json
deleted file mode 100644
index d45f530763..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/runs.json
+++ /dev/null
@@ -1,4 +0,0 @@
-{
-  "run1": {"scalars": ["foo/sin", "foo/cos", "foo/square", "bar/square"]},
-  "run2": {"scalars": ["foo/cos", "foo/square", "bar/square"]}
-}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars.json
deleted file mode 100644
index bc269395b6..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars.json
+++ /dev/null
@@ -1 +0,0 @@
-{"run2": {"foo/cos": [[0.0, 0, 2.0], [10.0, 1, 1.0806045532226562], [20.0, 2, -0.832293689250946], [30.0, 3, -1.979984998703003], [40.0, 4, -1.3072872161865234]], "bar/square": [[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]], "foo/square": [[0.0, 0, 0.0], [10.0, 1, 2.0], [20.0, 2, 8.0], [30.0, 3, 18.0], [40.0, 4, 32.0]]}, "run1": {"foo/sin": [[0.0, 0, 0.0], [10.0, 1, 0.8414709568023682], [20.0, 2, 0.9092974066734314], [30.0, 3, 0.14112000167369843], [40.0, 4, -0.756802499294281]], "foo/cos": [[0.0, 0, 1.0], [10.0, 1, 0.5403022766113281], [20.0, 2, -0.416146844625473], [30.0, 3, -0.9899924993515015], [40.0, 4, -0.6536436080932617]], "bar/square": [[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]], "foo/square": [[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]}}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_bar_2Fsquare.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_bar_2Fsquare.json
deleted file mode 100644
index 6d584fb4a9..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_bar_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fcos.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fcos.json
deleted file mode 100644
index 025eaa16e9..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fcos.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 1.0], [10.0, 1, 0.5403022766113281], [20.0, 2, -0.416146844625473], [30.0, 3, -0.9899924993515015], [40.0, 4, -0.6536436080932617]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fsin.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fsin.json
deleted file mode 100644
index eae69dd78f..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fsin.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 0.8414709568023682], [20.0, 2, 0.9092974066734314], [30.0, 3, 0.14112000167369843], [40.0, 4, -0.756802499294281]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fsquare.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fsquare.json
deleted file mode 100644
index 6d584fb4a9..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run1_tag_foo_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_bar_2Fsquare.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_bar_2Fsquare.json
deleted file mode 100644
index 6d584fb4a9..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_bar_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_foo_2Fcos.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_foo_2Fcos.json
deleted file mode 100644
index dd3593f9d1..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_foo_2Fcos.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 2.0], [10.0, 1, 1.0806045532226562], [20.0, 2, -0.832293689250946], [30.0, 3, -1.979984998703003], [40.0, 4, -1.3072872161865234]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_foo_2Fsquare.json b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_foo_2Fsquare.json
deleted file mode 100644
index 0ff9ef0551..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/data/scalars_run_run2_tag_foo_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 2.0], [20.0, 2, 8.0], [30.0, 3, 18.0], [40.0, 4, 32.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/index.html b/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/index.html
deleted file mode 100644
index 78f657b410..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/demo/index.html
+++ /dev/null
@@ -1,70 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../../polymer/polymer.html">
-<link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
-<link rel="import" href="../tf-scalar-dashboard.html">
-<link rel="import" href="../../paper-styles/typography.html">
-<link rel="import" href="../../tf-backend/tf-backend.html">
-
-<title>Scalar Dashboard Demo</title>
-<style>
-  #container {
-    height: 900px;
-    width: 100%;
-    display: block;
-  }
-
-  html, body {
-    margin: 0;
-    padding: 0;
-    font-family: "RobotoDraft","Roboto",sans-serif;
-  }
-
-</style>
-<demo-snippet>
-  <template>
-    <dom-module id="scalar-dash-demo">
-      <template>
-        <tf-scalar-dashboard id="demo" backend="[[backend]]"></tf-scalar-dashboard>
-      </template>
-      <script>
-        import {Backend} from "../tf-backend/backend";
-        import {createRouter, setRouter} from "../tf-backend/router";
-
-        Polymer({
-          is: "scalar-dash-demo",
-          properties: {
-            backend: {
-              type: Object,
-              value: function() {
-                return new Backend();
-              },
-            },
-            created: function() {
-              var router = createRouter("/data", true);
-              setRouter(router);
-            },
-          },
-        });
-      </script>
-    </dom-module>
-    <scalar-dash-demo id="container"></scalar-dash-demo>
-  </template>
-</demo-snippet>
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html b/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
deleted file mode 100644
index e2de568b4a..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-scalar-dashboard.html
+++ /dev/null
@@ -1,265 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="tf-smoothing-input.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-dashboard-common/tf-option-selector.html">
-<link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../vz-line-chart/vz-line-chart.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-menu/paper-menu.html">
-<link rel="import" href="../paper-item/paper-item.html">
-
-<!--
-tf-scalar-dashboard is a complete frontend that loads runs from a backend,
-and creates chart panes that display data for those runs.
-
-It provides a categorizer, run selector, and x type selector, by which the user
-can customize how data is organized and displayed.
-
-Each chart has a button that can toggle whether it is "expanded"; expanded
-charts are larger.
--->
-<dom-module id="tf-scalar-dashboard">
-  <template>
-    <tf-dashboard-layout>
-      <div class="sidebar">
-        <div class="sidebar-section">
-          <tf-categorizer tags="[[tags]]" categories="{{_categories}}">
-          </tf-categorizer>
-          <div class="line-item">
-            <paper-checkbox
-            id="download-option"
-            checked="{{_showDownloadLinks}}"
-            >Show data download links</paper-checkbox>
-          </div>
-          <div class="line-item">
-            <paper-checkbox
-            id="outliersCheckbox"
-            checked="{{_ignoreYOutliers}}"
-            >Ignore outliers in chart scaling</paper-checkbox>
-          </div>
-          <div id="tooltip-sorting">
-            <div id="tooltip-sorting-label">Tooltip sorting method:</div>
-            <paper-dropdown-menu
-              no-label-float
-              selected-item-label="{{_tooltipSortingMethod}}"
-              >
-              <paper-menu class="dropdown-content" selected="0">
-                <paper-item>default</paper-item>
-                <paper-item>descending</paper-item>
-                <paper-item>ascending</paper-item>
-                <paper-item>nearest</paper-item>
-              </paper-menu>
-            </paper-dropdown-menu>
-          </div>
-        </div>
-        <div class="sidebar-section">
-          <tf-smoothing-input
-            weight="{{_smoothingWeight}}"
-            step="0.001"
-            min="0"
-            max="1"
-            ></tf-smoothing-input>
-        </div>
-        <div class="sidebar-section">
-          <tf-option-selector
-            id="xTypeSelector"
-            name="Horizontal Axis"
-            selected-id="{{_xType}}"
-            >
-            <paper-button id="step">step</paper-button>
-            <paper-button id="relative">relative</paper-button>
-            <paper-button id="wall_time">wall</paper-button>
-          </tf-option-selector>
-        </div>
-        <div class="sidebar-section">
-          <tf-runs-selector selected-runs="{{_selectedRuns}}">
-          </tf-runs-selector>
-        </div>
-      </div>
-      <div class="center">
-        <tf-panes-helper
-          categories="[[_categories]]"
-          data-type="[[dataType]]"
-          data-provider="[[dataProvider]]"
-          data-not-found="[[dataNotFound]]"
-          run2tag="[[run2tag]]"
-          selected-runs="[[_selectedRuns]]"
-          show-download-links="[[_showDownloadLinks]]"
-          download-link-url-function="[[scalarUrl]]"
-        >
-          <template>
-            <vz-line-chart
-              x-type="[[_xType]]"
-              color-scale="[[_runsColorScale]]"
-              smoothing-enabled="[[_smoothingEnabled]]"
-              smoothing-weight="[[_smoothingWeight]]"
-              tooltip-sorting-method="[[_tooltipSortingMethod]]"
-              ignore-y-outliers="[[_ignoreYOutliers]]"
-              ></vz-line-chart>
-            <paper-icon-button
-              class="log-button"
-              icon="line-weight"
-              on-tap="toggleLogScale"
-              title="Toggle y-axis log scale"
-              ></paper-icon-button>
-          </template>
-        </tf-panes-helper>
-      </div>
-    </tf-dashboard-layout>
-
-    <style include="dashboard-style"></style>
-    <style>
-      .log-button {
-        position: absolute;
-        left: 35px;
-        bottom: -35px;
-        color: #2196F3;
-        background: #fff;
-        width: 32px;
-        height: 32px;
-        padding: 4px;
-        border-radius: 100%;
-      }
-
-      .log-button-selected {
-        background: var(--tb-ui-light-accent);
-      }
-
-      #tooltip-sorting {
-        display: flex;
-        font-size: 14px;
-        margin-top: 5px;
-      }
-
-      #tooltip-sorting-label {
-        margin-top: 13px;
-        margin-left: 28px;
-      }
-
-      #tooltip-sorting paper-dropdown-menu {
-        margin-left: 10px;
-        --paper-input-container-focus-color: var(--tb-orange-strong);
-        width: 105px;
-      }
-      .line-item {
-        display: block;
-        padding-top: 5px;
-      }
-
-      .sidebar-section {
-        border-top: solid 1px rgba(0, 0, 0, 0.12);
-        padding: 20px 0px 20px 30px;
-      }
-    </style>
-  </template>
-
-  <script>
-    import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-    import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-    import {BackendBehavior} from "../tf-backend/behavior";
-    import {getRouter} from "../tf-backend/router";
-    import {runsColorScale} from "../tf-color-scale/colorScale";
-    import * as storage from "../tf-storage/storage";
-
-    Polymer({
-      is: "tf-scalar-dashboard",
-      behaviors: [
-        DashboardBehavior("scalars"),
-        ReloadBehavior("tf-chart-scaffold"),
-        BackendBehavior,
-      ],
-      properties: {
-        backend: Object,
-        dataType: {
-          type: String,
-          value: "scalar"
-        },
-        scalarUrl: {
-          type: Function,
-          value: function() {
-            return getRouter().pluginRunTagRoute('scalars', '/scalars');
-          },
-        },
-        _showDownloadLinks: {
-          type: Boolean,
-          notify: true,
-          value: storage.getBooleanInitializer('_showDownloadLinks', false, true),
-          observer: '_showDownloadLinksObserver'
-        },
-        _smoothingWeight: {
-          type: Number,
-          notify: true,
-          value: storage.getNumberInitializer('_smoothingWeight', 0.6),
-          observer: '_smoothingWeightObserver'
-        },
-        _smoothingEnabled: {
-          type: Boolean,
-          computed: '_computeSmoothingEnabled(_smoothingWeight)'
-        },
-        _ignoreYOutliers: {
-          type: Boolean,
-          value: storage.getBooleanInitializer('_ignoreYOutliers', true, true),
-          observer: '_ignoreYOutliersObserver',
-        },
-        _xType: {
-          type: String,
-          value: "step"
-        },
-        _runsColorScale: {
-          type: Object,
-          value: function() {
-            return {
-              scale: runsColorScale,
-            };
-          },
-        },
-      },
-      attached: function() {
-        this.async(function() {
-          this.fire("rendered");
-        });
-      },
-      _showDownloadLinksObserver: storage.getBooleanObserver(
-          '_showDownloadLinks', /*default=*/ false, /*useLocalStorage=*/ true),
-      _smoothingWeightObserver: storage.getNumberObserver(
-          '_smoothingWeight', 0.6),
-      _ignoreYOutliersObserver: storage.getBooleanObserver(
-          '_ignoreYOutliers', /*default=*/ true, /*useLocalStorage=*/true),
-      _computeSmoothingEnabled: function(_smoothingWeight) {
-        return _smoothingWeight > 0;
-      },
-      toggleLogScale: function(e) {
-        var currentTarget = Polymer.dom(e.currentTarget);
-        var button = currentTarget.parentNode.querySelector('.log-button');
-        var chart = currentTarget.parentNode.querySelector('vz-line-chart');
-
-        button.classList.toggle("log-button-selected");
-        chart.yScaleType = chart.yScaleType === 'log' ? 'linear' : 'log';
-        chart.redraw();
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-smoothing-input.html b/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-smoothing-input.html
deleted file mode 100644
index a076033000..0000000000
--- a/tensorflow/tensorboard/components/tf_scalar_dashboard/tf-smoothing-input.html
+++ /dev/null
@@ -1,138 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-slider/paper-slider.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../paper-checkbox/paper-checkbox.html">
-<link rel="import" href="../tf-imports/lodash.html">
-
-<!--
-tf-smoothing-input creates an input component for exponential smoothing.
--->
-<dom-module id="tf-smoothing-input">
-  <template>
-    <h3 class="title">Smoothing</h3>
-    <div class="smoothing-block">
-      <paper-slider
-        id="slider"
-        value="{{weight}}"
-        immediate-value="{{_immediateWeightNumberForPaperSlider}}"
-        type="number"
-        step="[[step]]"
-        min="[[min]]"
-        max="[[max]]"
-        ></paper-slider>
-      <paper-input
-        id="input"
-        label="weight"
-        no-label-float
-        value="{{_inputWeightStringForPaperInput}}"
-        type="number"
-        step="[[step]]"
-        min="[[min]]"
-        max="[[max]]"
-        ></paper-input>
-    </div>
-    <style>
-      .title {
-        color: var(--paper-grey-800);
-        margin: 0;
-        font-weight: normal;
-        font-size: 14px;
-        margin-bottom: 5px;
-      }
-
-      .smoothing-block {
-        display: flex;
-      }
-
-      paper-slider {
-        margin-left: 12px;
-        --paper-slider-knob-color: var(--tb-orange-strong);
-        --paper-slider-active-color: var(--tb-orange-strong);
-        flex-grow: 2;
-      }
-
-      paper-input {
-        --paper-input-container-focus-color: var(--tb-orange-strong);
-        --paper-input-container-input: {
-          font-size: 14px;
-        };
-        --paper-input-container-label: {
-          font-size: 14px;
-        };
-        width: 60px;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-smoothing-input",
-
-      properties: {
-        step: Number,
-        max: Number,
-        min: Number,
-
-        weight: {
-          type: Number,
-          value: 0.6,
-          notify: true
-        },
-
-        _immediateWeightNumberForPaperSlider: {
-          type: Number,
-          notify: true,
-          observer: '_immediateWeightNumberForPaperSliderChanged'
-        },
-
-        // Paper input treats values as strings even if you specify them as
-        // numbers.
-        _inputWeightStringForPaperInput: {
-          type: String,
-          notify: true,
-          observer: '_inputWeightStringForPaperInputChanged'
-        }
-      },
-
-      _updateWeight: _.debounce(function(val) {
-        this.weight = val;
-      }, 250),
-
-      _immediateWeightNumberForPaperSliderChanged: function() {
-        this._inputWeightStringForPaperInput =
-            this._immediateWeightNumberForPaperSlider.toString();
-        this._updateWeight.call(this, this._immediateWeightNumberForPaperSlider);
-      },
-
-      _inputWeightStringForPaperInputChanged: function() {
-        if (+this._inputWeightStringForPaperInput < 0) {
-          this._inputWeightStringForPaperInput = '0';
-        }
-        else if (+this._inputWeightStringForPaperInput > 1) {
-          this._inputWeightStringForPaperInput = '1';
-        }
-
-        var d = +this._inputWeightStringForPaperInput;
-        if (!isNaN(d)) {
-          this._updateWeight.call(this, d);
-        }
-      }
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_storage/BUILD b/tensorflow/tensorboard/components/tf_storage/BUILD
deleted file mode 100644
index 197e0ae73d..0000000000
--- a/tensorflow/tensorboard/components/tf_storage/BUILD
+++ /dev/null
@@ -1,36 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_storage",
-    srcs = [
-        "storage.ts",
-        "tf-storage.html",
-    ],
-    path = "/tf-storage",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_globals",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":tf_storage"],
-    destdir = "tf-storage",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_globals:legacy",
-        "//tensorflow/tensorboard/components/tf_imports_google:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_storage/storage.ts b/tensorflow/tensorboard/components/tf_storage/storage.ts
deleted file mode 100644
index 873bc483a0..0000000000
--- a/tensorflow/tensorboard/components/tf_storage/storage.ts
+++ /dev/null
@@ -1,400 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {getFakeHash, setFakeHash, TABS, useHash} from '../tf-globals/globals';
-
-
-/* tslint:disable:no-namespace variable-name */
-/**
- * The Storage Module provides storage for URL parameters, and an API for
- * getting and setting TensorBoard's stateful URI.
- *
- * It generates URI components like: events&runPrefix=train*
- * which TensorBoard uses after like localhost:8000/#events&runPrefix=train*
- * to store state in the URI.
- *
- * It also allows saving the values to localStorage for long-term persistence.
- */
-type StringDict = {[key: string]: string};
-
-/**
- * A key that users cannot use, since TensorBoard uses this to store info
- * about the active tab.
- */
-export let TAB = '__tab__';
-
-/**
- * The name of the property for users to set on a Polymer component
- * in order for its stored properties to be stored in the URI unambiguously.
- * (No need to set this if you want multiple instances of the component to
- * share URI state)
- *
- * Example:
- * <my-component disambiguator="0"></my-component>
- *
- * The disambiguator should be set to any unique value so that multiple
- * instances of the component can store properties in URI storage.
- *
- * Because it's hard to dereference this variable in HTML property bindings,
- * it is NOT safe to change the disambiguator string without find+replace
- * across the codebase.
- */
-export let DISAMBIGUATOR = 'disambiguator';
-
-/**
- * Return a string stored in URI or localStorage.
- * Undefined if not found.
- */
-export function getString(key: string, useLocalStorage: boolean): string {
-  if (useLocalStorage) {
-    return window.localStorage.getItem(key);
-  } else {
-    return _componentToDict(_readComponent())[key];
-  }
-}
-
-/**
- * Set a string in URI or localStorage.
- */
-export function setString(
-    key: string, value: string, useLocalStorage: boolean) {
-  if (useLocalStorage) {
-    window.localStorage.setItem(key, value);
-  } else {
-    const items = _componentToDict(_readComponent());
-    items[key] = value;
-    _writeComponent(_dictToComponent(items));
-  }
-}
-
-/**
- * Return a boolean stored in stored in URI or localStorage.
- * Undefined if not found.
- */
-export function getBoolean(key: string, useLocalStorage: boolean): boolean {
-  const item = getString(key, useLocalStorage);
-  return item === 'true' ? true : item === 'false' ? false : undefined;
-}
-
-/**
- * Store a boolean in URI or localStorage.
- */
-export function setBoolean(
-    key: string, value: boolean, useLocalStorage = false) {
-  setString(key, value.toString(), useLocalStorage);
-}
-
-/**
- * Return a number stored in stored in URI or localStorage.
- * Undefined if not found.
- */
-export function getNumber(key: string, useLocalStorage: boolean): number {
-  const item = getString(key, useLocalStorage);
-  return item === undefined ? undefined : +item;
-}
-
-/**
- * Store a number in URI or localStorage.
- */
-export function setNumber(
-    key: string, value: number, useLocalStorage: boolean) {
-  setString(key, '' + value, useLocalStorage);
-}
-
-/**
- * Return an object stored in stored in URI or localStorage.
- * Undefined if not found.
- */
-export function getObject(key: string, useLocalStorage: boolean): {} {
-  const item = getString(key, useLocalStorage);
-  return item === undefined ? undefined : JSON.parse(atob(item));
-}
-
-/**
- * Store an object in URI or localStorage.
- */
-export function setObject(key: string, value: {}, useLocalStorage: boolean) {
-  setString(key, btoa(JSON.stringify(value)), useLocalStorage);
-}
-
-/**
- * Get a unique storage name for a (Polymer component, propertyName) tuple.
- *
- * DISAMBIGUATOR must be set on the component, if other components use the
- * same propertyName.
- */
-export function getURIStorageName(
-    component: {}, propertyName: string): string {
-  const d = component[DISAMBIGUATOR];
-  const components = d == null ? [propertyName] : [d, propertyName];
-  return components.join('.');
-}
-
-/**
- * Return a function that:
- * (1) Initializes a Polymer boolean property with a default value, if its
- *     value is not already set
- * (2) Sets up listener that updates Polymer property on hash change.
- */
-export function getBooleanInitializer(
-    propertyName: string, defaultVal: boolean,
-    useLocalStorage = false): Function {
-  return _getInitializer(
-      getBoolean, propertyName, defaultVal, useLocalStorage);
-}
-
-/**
- * Return a function that:
- * (1) Initializes a Polymer string property with a default value, if its
- *     value is not already set
- * (2) Sets up listener that updates Polymer property on hash change.
- */
-export function getStringInitializer(
-    propertyName: string, defaultVal: string,
-    useLocalStorage = false): Function {
-  return _getInitializer(
-      getString, propertyName, defaultVal, useLocalStorage);
-}
-
-/**
- * Return a function that:
- * (1) Initializes a Polymer number property with a default value, if its
- *     value is not already set
- * (2) Sets up listener that updates Polymer property on hash change.
- */
-export function getNumberInitializer(
-    propertyName: string, defaultVal: number,
-    useLocalStorage = false): Function {
-  return _getInitializer(
-      getNumber, propertyName, defaultVal, useLocalStorage);
-}
-
-/**
- * Return a function that:
- * (1) Initializes a Polymer Object property with a default value, if its
- *     value is not already set
- * (2) Sets up listener that updates Polymer property on hash change.
- *
- * Generates a deep clone of the defaultVal to avoid mutation issues.
- */
-export function getObjectInitializer(
-    propertyName: string, defaultVal: {}, useLocalStorage = false): Function {
-  return _getInitializer(
-      getObject, propertyName, defaultVal, useLocalStorage);
-}
-
-/**
- * Return a function that updates URIStorage when a string property changes.
- */
-export function getBooleanObserver(
-    propertyName: string, defaultVal: boolean,
-    useLocalStorage = false): Function {
-  return _getObserver(
-      getBoolean, setBoolean, propertyName, defaultVal, useLocalStorage);
-}
-
-/**
- * Return a function that updates URIStorage when a string property changes.
- */
-export function getStringObserver(
-    propertyName: string, defaultVal: string,
-    useLocalStorage = false): Function {
-  return _getObserver(
-      getString, setString, propertyName, defaultVal, useLocalStorage);
-}
-
-/**
- * Return a function that updates URIStorage when a number property changes.
- */
-export function getNumberObserver(
-    propertyName: string, defaultVal: number,
-    useLocalStorage = false): Function {
-  return _getObserver(
-      getNumber, setNumber, propertyName, defaultVal, useLocalStorage);
-}
-
-/**
- * Return a function that updates URIStorage when an object property changes.
- * Generates a deep clone of the defaultVal to avoid mutation issues.
- */
-export function getObjectObserver(
-    propertyName: string, defaultVal: {}, useLocalStorage = false): Function {
-  const clone = _.cloneDeep(defaultVal);
-  return _getObserver(
-      getObject, setObject, propertyName, clone, useLocalStorage);
-}
-
-/**
- * Read component from URI (e.g. returns "events&runPrefix=train*").
- */
-function _readComponent(): string {
-  return useHash() ? window.location.hash.slice(1) : getFakeHash();
-}
-
-/**
- * Write component to URI.
- */
-function _writeComponent(component: string) {
-  if (useHash()) {
-    window.location.hash = component;
-  } else {
-    setFakeHash(component);
-  }
-}
-
-/**
- * Convert dictionary of strings into a URI Component.
- * All key value entries get added as key value pairs in the component,
- * with the exception of a key with the TAB value, which if present
- * gets prepended to the URI Component string for backwards compatibility
- * reasons.
- */
-function _dictToComponent(items: StringDict): string {
-  let component = '';
-
-  // Add the tab name e.g. 'events', 'images', 'histograms' as a prefix
-  // for backwards compatbility.
-  if (items[TAB] !== undefined) {
-    component += items[TAB];
-  }
-
-  // Join other strings with &key=value notation
-  const nonTab = _.pairs(items)
-                   .filter((pair) =>  pair[0] !== TAB)
-                   .map((pair) => {
-                     return encodeURIComponent(pair[0]) + '=' +
-                         encodeURIComponent(pair[1]);
-                   })
-                   .join('&');
-
-  return nonTab.length > 0 ? (component + '&' + nonTab) : component;
-}
-
-/**
- * Convert a URI Component into a dictionary of strings.
- * Component should consist of key-value pairs joined by a delimiter
- * with the exception of the tabName.
- * Returns dict consisting of all key-value pairs and
- * dict[TAB] = tabName
- */
-function _componentToDict(component: string): StringDict {
-  const items = {} as StringDict;
-
-  const tokens = component.split('&');
-  tokens.forEach((token) => {
-    const kv = token.split('=');
-    // Special backwards compatibility for URI components like #events
-    if (kv.length === 1 && _.contains(TABS, kv[0])) {
-      items[TAB] = kv[0];
-    } else if (kv.length === 2) {
-      items[decodeURIComponent(kv[0])] = decodeURIComponent(kv[1]);
-    }
-  });
-  return items;
-}
-
-/**
- * Return a function that:
- * (1) Initializes a Polymer property with a default value, if its
- *     value is not already set
- * (2) Sets up listener that updates Polymer property on hash change.
- */
-function _getInitializer<T>(
-    get: (name: string, useLocalStorage: boolean) => T, propertyName: string,
-    defaultVal: T, useLocalStorage): Function {
-  return function() {
-    const URIStorageName = getURIStorageName(this, propertyName);
-    // setComponentValue will be called every time the hash changes, and is
-    // responsible for ensuring that new state in the hash will be propagated
-    // to the component with that property.
-    // It is important that this function does not re-assign needlessly,
-    // to avoid Polymer observer churn.
-    const setComponentValue = () => {
-      const uriValue = get(URIStorageName, false);
-      const currentValue = this[propertyName];
-      // if uriValue is undefined, we will ensure that the property has the
-      // default value
-      if (uriValue === undefined) {
-        let valueToSet: T;
-        // if we are using localStorage, we will set the value to the value
-        // from localStorage. Then, the corresponding observer will proxy
-        // the localStorage value into URI storage.
-        // in this way, localStorage takes precedence over the default val
-        // but not over the URI value.
-        if (useLocalStorage) {
-          const useLocalStorageValue = get(URIStorageName, true);
-          valueToSet = useLocalStorageValue === undefined ?
-              defaultVal :
-              useLocalStorageValue;
-        } else {
-          valueToSet = defaultVal;
-        }
-        if (!_.isEqual(currentValue, valueToSet)) {
-          // If we don't have an explicit URI value, then we need to ensure
-          // the property value is equal to the default value.
-          // We will assign a clone rather than the canonical default, because
-          // the component receiving this property may mutate it, and we need
-          // to keep a pristine copy of the default.
-          this[propertyName] = _.clone(valueToSet);
-        }
-        // In this case, we have an explicit URI value, so we will ensure that
-        // the component has an equivalent value.
-      } else {
-        if (!_.isEqual(uriValue, currentValue)) {
-          this[propertyName] = uriValue;
-        }
-      }
-    };
-    // Set the value on the property.
-    setComponentValue();
-    // Update it when the hashchanges.
-    window.addEventListener('hashchange', setComponentValue);
-  };
-}
-
-/**
- * Return a function that updates URIStorage when a property changes.
- */
-function _getObserver<T>(
-    get: (name: string, useLocalStorage: boolean) => T,
-    set: (name: string, newVal: T, useLocalStorage: boolean) => void,
-    propertyName: string, defaultVal: T, useLocalStorage: boolean): Function {
-  return function() {
-    const URIStorageName = getURIStorageName(this, propertyName);
-    const newVal = this[propertyName];
-    // if this is a localStorage property, we always synchronize the value
-    // in localStorage to match the one currently in the URI.
-    if (useLocalStorage) {
-      set(URIStorageName, newVal, true);
-    }
-    if (!_.isEqual(newVal, get(URIStorageName, false))) {
-      if (_.isEqual(newVal, defaultVal)) {
-        _unsetFromURI(URIStorageName);
-      } else {
-        set(URIStorageName, newVal, false);
-      }
-    }
-  };
-}
-
-/**
- * Delete a key from the URI.
- */
-function _unsetFromURI(key) {
-  const items = _componentToDict(_readComponent());
-  delete items[key];
-  _writeComponent(_dictToComponent(items));
-}
-
diff --git a/tensorflow/tensorboard/components/tf_storage/test/BUILD b/tensorflow/tensorboard/components/tf_storage/test/BUILD
deleted file mode 100644
index 32399ba7cb..0000000000
--- a/tensorflow/tensorboard/components/tf_storage/test/BUILD
+++ /dev/null
@@ -1,30 +0,0 @@
-package(
-    default_testonly = True,
-    default_visibility = ["//tensorflow/tensorboard:internal"],
-)
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "test",
-    srcs = [
-        "storageTests.ts",
-        "tests.html",
-    ],
-    path = "/tf-storage/test",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:web_component_tester",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "//tensorflow/tensorboard/components/tf_storage",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    testonly = 0,
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_storage/test/storageTests.ts b/tensorflow/tensorboard/components/tf_storage/test/storageTests.ts
deleted file mode 100644
index 82dc51f05d..0000000000
--- a/tensorflow/tensorboard/components/tf_storage/test/storageTests.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import {TAB, getString, getNumber, getObject, setString, setNumber, setObject} from '../storage';
-import {TABS} from '../../tf-globals/globals';
-
-/* tslint:disable:no-namespace */
-describe('URIStorage', () => {
-  it('get/setString', () => {
-    setString('key_a', 'hello', false);
-    setString('key_b', 'there', false);
-    chai.assert.equal('hello', getString('key_a', false));
-    chai.assert.equal('there', getString('key_b', false));
-    chai.assert.equal(null, getString('key_c', false));
-  });
-
-  it('get/setNumber', () => {
-    setNumber('key_a', 12, false);
-    setNumber('key_b', 3.4, false);
-    chai.assert.equal(12, getNumber('key_a', false));
-    chai.assert.equal(3.4, getNumber('key_b', false));
-    chai.assert.equal(null, getNumber('key_c', false));
-  });
-
-  it('get/setObject', () => {
-    const obj = {'foo': 2.3, 'bar': 'barstr'};
-    setObject('key_a', obj, false);
-    chai.assert.deepEqual(obj, getObject('key_a', false));
-  });
-
-  it('get/setWeirdValues', () => {
-    setNumber('key_a', NaN, false);
-    chai.assert.deepEqual(NaN, getNumber('key_a', false));
-
-    setNumber('key_a', +Infinity, false);
-    chai.assert.equal(+Infinity, getNumber('key_a', false));
-
-    setNumber('key_a', -Infinity, false);
-    chai.assert.equal(-Infinity, getNumber('key_a', false));
-
-    setNumber('key_a', 1 / 3, false);
-    chai.assert.equal(1 / 3, getNumber('key_a', false));
-
-    setNumber('key_a', -0, false);
-    chai.assert.equal(-0, getNumber('key_a', false));
-  });
-
-  it('set/getTab', () => {
-    setString(TAB, TABS[0], false);
-    chai.assert.equal(TABS[0], getString(TAB, false));
-  });
-});
-
diff --git a/tensorflow/tensorboard/components/tf_storage/test/tests.html b/tensorflow/tensorboard/components/tf_storage/test/tests.html
deleted file mode 100644
index 4668b119d2..0000000000
--- a/tensorflow/tensorboard/components/tf_storage/test/tests.html
+++ /dev/null
@@ -1,25 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<script src="../../web-component-tester/browser.js"></script>
-<link rel="import" href="../../polymer/polymer.html">
-<link rel="import" href="../tf-storage.html">
-<body>
-<script src="storageTests.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_storage/tf-storage.html b/tensorflow/tensorboard/components/tf_storage/tf-storage.html
deleted file mode 100644
index ff3f7b0ad4..0000000000
--- a/tensorflow/tensorboard/components/tf_storage/tf-storage.html
+++ /dev/null
@@ -1,21 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../tf-globals/tf-globals.html">
-<link rel="import" href="../tf-imports/lodash.html">
-
-<script src="storage.js"></script>
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/BUILD b/tensorflow/tensorboard/components/tf_tensorboard/BUILD
deleted file mode 100644
index 95fb8b7a88..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/BUILD
+++ /dev/null
@@ -1,65 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-load("//tensorflow/tensorboard/defs:vulcanize.bzl", "tensorboard_html_binary")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_tensorboard",
-    srcs = [
-        "autoReloadBehavior.ts",
-        "style.html",
-        "tf-tensorboard.html",
-    ],
-    path = "/tf-tensorboard",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow/tensorboard/components/tf_audio_dashboard",
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_distribution_dashboard",
-        "//tensorflow/tensorboard/components/tf_globals",
-        "//tensorflow/tensorboard/components/tf_graph_dashboard",
-        "//tensorflow/tensorboard/components/tf_histogram_dashboard",
-        "//tensorflow/tensorboard/components/tf_image_dashboard",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_scalar_dashboard",
-        "//tensorflow/tensorboard/components/tf_storage",
-        "//tensorflow/tensorboard/components/tf_text_dashboard",
-        "//tensorflow/tensorboard/components/vz_projector",
-        "@org_polymer_font_roboto",
-        "@org_polymer_iron_icons",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_checkbox",
-        "@org_polymer_paper_dialog",
-        "@org_polymer_paper_header_panel",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_tabs",
-        "@org_polymer_paper_toolbar",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["demo.html"],
-    path = "/tf-tensorboard",
-    deps = [
-        ":tf_tensorboard",
-        "//tensorflow/tensorboard/demo:demo_data",
-    ],
-)
-
-tensorboard_html_binary(
-    name = "devserver",
-    testonly = 1,
-    input_path = "/tf-tensorboard/demo.html",
-    output_path = "/index.html",
-    deps = [":demo"],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/autoReloadBehavior.ts b/tensorflow/tensorboard/components/tf_tensorboard/autoReloadBehavior.ts
deleted file mode 100644
index 54df16f5b5..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/autoReloadBehavior.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export var AUTORELOAD_LOCALSTORAGE_KEY = 'TF.TensorBoard.autoReloadEnabled';
-
-var getAutoReloadFromLocalStorage: () => boolean = () => {
-  var val = window.localStorage.getItem(AUTORELOAD_LOCALSTORAGE_KEY);
-  return val === 'true' || val == null;  // defaults to true
-};
-
-/**
- * @polymerBehavior
- */
-export var AutoReloadBehavior = {
-  properties: {
-    autoReloadEnabled: {
-      type: Boolean,
-      observer: '_autoReloadObserver',
-      value: getAutoReloadFromLocalStorage,
-    },
-    _autoReloadId: {
-      type: Number,
-    },
-    autoReloadIntervalSecs: {
-      type: Number,
-      value: 30,
-    },
-  },
-  detached: function() {
-    window.clearTimeout(this._autoReloadId);
-  },
-  _autoReloadObserver: function(autoReload) {
-    window.localStorage.setItem(AUTORELOAD_LOCALSTORAGE_KEY, autoReload);
-    if (autoReload) {
-      var _this = this;
-      this._autoReloadId = window.setTimeout(
-          this._doAutoReload.bind(this), this.autoReloadIntervalSecs * 1000);
-    } else {
-      window.clearTimeout(this._autoReloadId);
-    }
-  },
-  _doAutoReload: function() {
-    if (this.reload == null) {
-      throw new Error('AutoReloadBehavior requires a reload method');
-    }
-    this.reload();
-    this._autoReloadId = window.setTimeout(
-        this._doAutoReload.bind(this), this.autoReloadIntervalSecs * 1000);
-  }
-};
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/demo.html b/tensorflow/tensorboard/components/tf_tensorboard/demo.html
deleted file mode 100644
index f691f6211b..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/demo.html
+++ /dev/null
@@ -1,24 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<title>TensorBoard Demo</title>
-<link rel="import" href="style.html">
-<link rel="import" href="tf-tensorboard.html">
-<body>
-<tf-tensorboard demo-dir="/data" use-hash></tf-tensorboard>
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/style.html b/tensorflow/tensorboard/components/tf_tensorboard/style.html
deleted file mode 100644
index 575e89e398..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/style.html
+++ /dev/null
@@ -1,28 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../font-roboto/roboto.html">
-
-<style>
-  html,
-  body {
-    margin: 0;
-    padding: 0;
-    height: 100%;
-    font-family: "RobotoDraft", "Roboto", sans-serif;
-  }
-</style>
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/autoReloadTests.ts b/tensorflow/tensorboard/components/tf_tensorboard/test/autoReloadTests.ts
deleted file mode 100644
index b68fd8c943..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/autoReloadTests.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {AUTORELOAD_LOCALSTORAGE_KEY, AutoReloadBehavior} from '../autoReloadBehavior';
-
-declare function fixture(id: string): void;
-
-window.HTMLImports.whenReady(() => {
-  Polymer({
-    is: 'autoreload-test-element',
-    behaviors: [AutoReloadBehavior],
-  });
-
-  describe('autoReload-behavior', function() {
-    let testElement;
-    const ls = window.localStorage;
-    const key = AUTORELOAD_LOCALSTORAGE_KEY;
-    let clock;
-    let callCount: number;
-
-    beforeEach(function() {
-      ls.setItem(key, 'false');  // start it turned off so we can mutate fns
-      testElement = fixture('autoReloadFixture');
-      callCount = 0;
-      testElement.reload = function() { callCount++; };
-    });
-
-    before(function() { clock = sinon.useFakeTimers(); });
-
-    after(function() { clock.restore(); });
-
-    it('reads and writes autoReload state from localStorage', function() {
-      ls.removeItem(key);
-      testElement = fixture('autoReloadFixture');
-      chai.assert.isTrue(
-          testElement.autoReloadEnabled, 'autoReload defaults to true');
-      chai.assert.equal(ls.getItem(key), 'true', 'autoReload setting saved');
-      testElement = fixture('autoReloadFixture');
-      chai.assert.isTrue(
-          testElement.autoReloadEnabled, 'read true from localStorage');
-      testElement.autoReloadEnabled = false;
-      chai.assert.equal(ls.getItem(key), 'false', 'autoReload setting saved');
-      testElement = fixture('autoReloadFixture');
-      chai.assert.isFalse(
-          testElement.autoReloadEnabled, 'read false setting properly');
-      testElement.autoReloadEnabled = true;
-      chai.assert.equal(ls.getItem(key), 'true', 'saved true setting');
-    });
-
-    it('reloads every interval secs when autoReloading', function() {
-      testElement.autoReloadIntervalSecs = 1;
-      testElement.autoReloadEnabled = true;
-      clock.tick(1000);
-      chai.assert.equal(callCount, 1, 'ticking clock triggered call');
-      clock.tick(20 * 1000);
-      chai.assert.equal(callCount, 21, 'ticking clock 20s triggered 20 calls');
-    });
-
-    it('can cancel pending autoReload', function() {
-      testElement.autoReloadIntervalSecs = 10;
-      testElement.autoReloadEnabled = true;
-      clock.tick(5 * 1000);
-      testElement.autoReloadEnabled = false;
-      clock.tick(20 * 1000);
-      chai.assert.equal(callCount, 0, 'callCount is 0');
-    });
-
-    it('throws an error in absence of reload method', function() {
-      testElement.reload = undefined;
-      testElement.autoReloadIntervalSecs = 1;
-      testElement.autoReloadEnabled = true;
-      chai.assert.throws(function() {
-        clock.tick(5000);
-      });
-    });
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/e2eTests.html b/tensorflow/tensorboard/components/tf_tensorboard/test/e2eTests.html
deleted file mode 100644
index 5efc02ef98..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/e2eTests.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-  <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-  <script src="../../web-component-tester/browser.js"></script>
-  <link rel="import" href="../../tf-imports/d3.html">
-  <link rel="import" href="../tf-tensorboard.html">
-  <link rel="stylesheet" type="text/css" href="../../../lib/css/global.css">
-</head>
-<body>
-  <tf-tensorboard demo-dir="data/"></tf-tensorboard>
-  <script src="e2eTests.js"></script>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/e2eTests.ts b/tensorflow/tensorboard/components/tf_tensorboard/test/e2eTests.ts
deleted file mode 100644
index a00027963b..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/e2eTests.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {TABS} from '../../tf-globals/globals';
-
-describe('end-to-end test', () => {
-  window.HTMLImports.whenReady(() => {
-    let tb = d3.select('tf-tensorboard');
-    var tabs = (<any>tb.node()).$.tabs;
-
-    function testTab(tabIndex: number) {
-      it(`selecting ${TABS[tabIndex]} tab`, done => {
-        // Every dashboard emits a rendered event when it is done rendering.
-        tb.on('rendered', () => done());
-        tabs.set('selected', tabIndex);
-      });
-    }
-    // Listen for when the default tab has rendered and test other tabs after.
-    tb.on('rendered', () => {
-      // The default tab already rendered. Test everything else.
-      // If a bug happened while rendering the default tab, the test would
-      // have failed. Re-selecting the default tab and listening for
-      // "rendered" event won't work since the content is not re-stamped.
-      let selected = +tabs.get('selected');
-      for (let i = 0; i < TABS.length; i++) {
-        if (i !== selected) {
-          testTab(i);
-        }
-      }
-    });
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/fastTabSwitch.html b/tensorflow/tensorboard/components/tf_tensorboard/test/fastTabSwitch.html
deleted file mode 100644
index 88bb6edc48..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/fastTabSwitch.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-  <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-  <script src="../../web-component-tester/browser.js"></script>
-  <link rel="import" href="../../tf-imports/d3.html">
-  <link rel="import" href="../tf-tensorboard.html">
-  <link rel="stylesheet" type="text/css" href="../../../lib/css/global.css">
-</head>
-<body>
-  <tf-tensorboard demo-dir="data/"></tf-tensorboard>
-  <script src="fastTabSwitch.js"></script>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/fastTabSwitch.ts b/tensorflow/tensorboard/components/tf_tensorboard/test/fastTabSwitch.ts
deleted file mode 100644
index 905ed4ee4a..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/fastTabSwitch.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {TABS} from '../../tf-globals/globals';
-
-describe('fast tab switch', () => {
-  window.HTMLImports.whenReady(() => {
-    let tb = d3.select('tf-tensorboard');
-    // tslint:disable-next-line:no-any be quiet tsc
-    var tabs = (<any>tb.node()).$.tabs;
-
-    // This test will select the events tab. Once the events tab
-    // renders, will select the graph tab, and immediately select
-    // the images tab wihout waiting for the graph tab to finish
-    // rendering. Finally, it finishes when the images tab
-    // has rendered and no errors were thrown.
-    const eventsTabIndex = TABS.indexOf('events');
-    const imagesTabIndex = TABS.indexOf('images');
-    const graphTabIndex = TABS.indexOf('graphs');
-
-    // Listen for when the events tab rendered.
-    tb.on('rendered', () => {
-      it('switching to graph tab and immediately to images', done => {
-        // Select the graph tab.
-        tabs.set('selected', graphTabIndex);
-        // Interrupt graph rendering by immediately selecting the images tab
-        // and finish when the images tab has rendered.
-        tb.on('rendered', () => done());
-        tabs.set('selected', imagesTabIndex);
-      });
-    });
-    // Select the events tab.
-    tabs.set('selected', eventsTabIndex);
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/index.html b/tensorflow/tensorboard/components/tf_tensorboard/test/index.html
deleted file mode 100644
index 8806f36fad..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/index.html
+++ /dev/null
@@ -1,35 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-  <meta charset="utf-8">
-  <script src="../../web-component-tester/browser.js"></script>
-</head>
-<body>
-<script>
-// Run the tests for each main component in tensorboard.
-WCT.loadSuites([
-  'tensorboardTests.html',
-  // TODO: re-enable or remove. b/30163860
-  // 'e2eTests.html',
-  'fastTabSwitch.html'
-]);
-</script>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/tensorboardTests.html b/tensorflow/tensorboard/components/tf_tensorboard/test/tensorboardTests.html
deleted file mode 100644
index 2122cb79b1..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/tensorboardTests.html
+++ /dev/null
@@ -1,44 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-<head>
-  <link rel="import" href="../../polymer/polymer.html">
-  <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-  <script src="../../web-component-tester/browser.js"></script>
-  <link rel="import" href="../tf-tensorboard.html">
-  <link rel="stylesheet" type="text/css" href="../../../lib/css/global.css">
-</head>
-<body>
-  <test-fixture id="tensorboardFixture">
-    <template>
-      <tf-tensorboard>
-        <span id="inject-me">Injected content should be rendered by the element.</span>
-      </tf-tensorboard>
-    </template>
-  </test-fixture>
-
-  <test-fixture id="autoReloadFixture">
-    <template>
-      <autoreload-test-element></autoreload-test-element>
-    </template>
-  </test-fixture>
-  <script src="tensorboardTests.js"></script>
-  <script src="autoReloadTests.js"></script>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/test/tensorboardTests.ts b/tensorflow/tensorboard/components/tf_tensorboard/test/tensorboardTests.ts
deleted file mode 100644
index 06ff446f18..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/test/tensorboardTests.ts
+++ /dev/null
@@ -1,132 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {TABS} from '../../tf-globals/globals';
-
-describe('tf-tensorboard tests', () => {
-  window.HTMLImports.whenReady(() => {
-    let tensorboard: any;
-    beforeEach(function() {
-      tensorboard = fixture('tensorboardFixture');
-      tensorboard.demoDir = 'data';
-      tensorboard.autoReloadEnabled = false;
-    });
-
-    it('specified tabs are correct', function(done) {
-      setTimeout(function() {
-        let tabs = tensorboard.$.tabs.getElementsByTagName('paper-tab');
-        let tabMode = Array.prototype.map.call(tabs, (x) => x.dataMode);
-        chai.assert.deepEqual(tabMode, TABS, 'mode is correct');
-        let tabText =
-            Array.prototype.map.call(tabs, (x) => x.innerText.toLowerCase());
-        chai.assert.deepEqual(tabText, TABS, 'text is correct');
-        done();
-      });
-    });
-
-    it('renders injected content', function() {
-      let injected = tensorboard.querySelector('#inject-me');
-      chai.assert.isNotNull(injected);
-    });
-
-    describe('reloading the selected dashboard', function() {
-      TABS.forEach((name, tabIndex) => {
-        // These tabs do not support reload mode.
-        if (name === 'graphs' || name === 'projections') {
-          return;
-        }
-        it(`${name}: calling reload reloads dashboard`, function(done) {
-          tensorboard.$.tabs.set('selected', tabIndex);
-          setTimeout(function() {
-            let called = false;
-            tensorboard.selectedDashboard().reload = function() {
-              called = true;
-            };
-            tensorboard.reload();
-            chai.assert.isFalse(
-                tensorboard.$$('#reload-button').disabled,
-                'reload button not disabled');
-            chai.assert.isTrue(called, `reload was called`);
-            done();
-          });
-        });
-      });
-    });
-
-    it('reload is disabled for graph dashboard', function(done) {
-      const idx = TABS.indexOf('graphs');
-      chai.assert.notEqual(idx, -1, 'graphs was found');
-      tensorboard.$.tabs.set('selected', idx);
-      setTimeout(
-          function() {  // async so that the queued tab change will happen
-            let called = false;
-            tensorboard.selectedDashboard().reload = function() {
-              called = true;
-            };
-            tensorboard.reload();
-            chai.assert.isTrue(
-                tensorboard.$$('#reload-button').disabled,
-                'reload button disabled');
-            chai.assert.isFalse(called, `reload was not called`);
-            done();
-          });
-    });
-
-    describe('top right global icons', function() {
-      it('Clicking the reload button will call reload', function() {
-        let called = false;
-        tensorboard.reload = function() { called = true; };
-        tensorboard.$$('#reload-button').click();
-        chai.assert.isTrue(called);
-      });
-
-      it('settings pane is hidden', function() {
-        chai.assert.equal(tensorboard.$.settings.style['display'], 'none');
-      });
-
-      it('settings icon button opens the settings pane', function(done) {
-        tensorboard.$$('#settings-button').click();
-        // This test is a little hacky since we depend on polymer's
-        // async behavior, which is difficult to predict.
-
-        // keep checking until the panel is visible. error with a timeout if it
-        // is broken.
-        function verify() {
-          if (tensorboard.$.settings.style['display'] !== 'none') {
-            done();
-          } else {
-            setTimeout(verify, 3);  // wait and see if it becomes true
-          }
-        }
-        verify();
-      });
-
-      it('Autoreload checkbox toggle works', function() {
-        let checkbox = tensorboard.$$('#auto-reload-checkbox');
-        chai.assert.equal(checkbox.checked, tensorboard.autoReloadEnabled);
-        let oldValue = checkbox.checked;
-        checkbox.click();
-        chai.assert.notEqual(oldValue, checkbox.checked);
-        chai.assert.equal(checkbox.checked, tensorboard.autoReloadEnabled);
-      });
-
-      it('Autoreload checkbox contains correct interval info', function() {
-        let checkbox = tensorboard.$$('#auto-reload-checkbox');
-        let timeInSeconds = tensorboard.autoReloadIntervalSecs + 's';
-        chai.assert.include(checkbox.innerText, timeInSeconds);
-      });
-    });
-  });
-});
diff --git a/tensorflow/tensorboard/components/tf_tensorboard/tf-tensorboard.html b/tensorflow/tensorboard/components/tf_tensorboard/tf-tensorboard.html
deleted file mode 100644
index 26b742996a..0000000000
--- a/tensorflow/tensorboard/components/tf_tensorboard/tf-tensorboard.html
+++ /dev/null
@@ -1,361 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-icons/iron-icons.html">
-<link rel="import" href="../paper-tabs/paper-tabs.html">
-<link rel="import" href="../paper-dialog/paper-dialog.html">
-<link rel="import" href="../paper-checkbox/paper-checkbox.html">
-<link rel="import" href="../paper-toolbar/paper-toolbar.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-header-panel/paper-header-panel.html">
-<link rel="import" href="../tf-globals/tf-globals.html">
-<link rel="import" href="../tf-scalar-dashboard/tf-scalar-dashboard.html">
-<link rel="import" href="../tf-distribution-dashboard/tf-distribution-dashboard.html">
-<link rel="import" href="../tf-histogram-dashboard/tf-histogram-dashboard.html">
-<link rel="import" href="../tf-image-dashboard/tf-image-dashboard.html">
-<link rel="import" href="../tf-audio-dashboard/tf-audio-dashboard.html">
-<link rel="import" href="../tf-graph-dashboard/tf-graph-dashboard.html">
-<link rel="import" href="../tf-text-dashboard/tf-text-dashboard.html">
-<link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-storage/tf-storage.html">
-<link rel="import" href="../vz-projector/vz-projector-dashboard.html">
-
-<!--
-tf-tensorboard is the frontend entry point for TensorBoard.
-
-It implements a toolbar (via paper-header-panel and paper-toolbar) that
-allows the user to toggle between various dashboards.
--->
-<dom-module id="tf-tensorboard">
-  <template>
-    <paper-dialog with-backdrop id="settings">
-      <h2>Settings</h2>
-      <paper-checkbox id="auto-reload-checkbox" checked="{{autoReloadEnabled}}">
-        Reload data every <span>[[autoReloadIntervalSecs]]</span>s.
-      </paper-checkbox>
-    </paper-dialog>
-    <paper-header-panel>
-      <paper-toolbar id="toolbar">
-        <div id="toolbar-content">
-          <div class="toolbar-title">TensorBoard</div>
-          <paper-tabs selected="{{modeIndex}}" noink class="tabs" id="tabs">
-            <template is="dom-repeat" items="[[tabs]]">
-              <template is="dom-if" if="[[_isTabEnabled(item)]]">
-                <paper-tab data-mode="[[item]]">[[item]]</paper-tab>
-              </template>
-            </template>
-          </paper-tabs>
-          <div class="global-actions">
-            <paper-icon-button
-              icon="refresh"
-              on-tap="reload"
-              disabled$="[[_isReloadDisabled(mode)]]"
-              id="reload-button"
-            ></paper-icon-button>
-            <paper-icon-button
-              icon="settings"
-              on-tap="openSettings"
-              id="settings-button"
-            ></paper-icon-button>
-            <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/README.md" tabindex="-1">
-              <paper-icon-button icon="help-outline"></paper-icon-button>
-            </a>
-          </div>
-        </div>
-      </paper-toolbar>
-
-      <div id="content" class="fit">
-        <content id="injected-overview"></content>
-
-        <template is="dom-if" if="[[_modeIsScalars(mode)]]">
-          <tf-scalar-dashboard
-            id="scalars"
-            backend="[[_backend]]"
-          ></tf-scalar-dashboard>
-        </template>
-
-        <template is="dom-if" if="[[_modeIsImages(mode)]]">
-          <tf-image-dashboard
-            id="images"
-            backend="[[_backend]]"
-          ></tf-image-dashboard>
-        </template>
-
-        <template is="dom-if" if="[[_modeIsAudio(mode)]]">
-          <tf-audio-dashboard
-            id="audio"
-            backend="[[_backend]]"
-          ></tf-audio-dashboard>
-        </template>
-
-        <template is="dom-if" if="[[_modeIsGraphs(mode)]]">
-          <tf-graph-dashboard
-            id="graphs"
-            backend="[[_backend]]"
-            debugger-data-enabled="[[_debuggerDataEnabled]]"
-          ></tf-graph-dashboard>
-        </template>
-
-        <template is="dom-if" if="[[_modeIsDistributions(mode)]]">
-          <tf-distribution-dashboard
-            id="distributions"
-            backend="[[_backend]]"
-          ></tf-distribution-dashboard>
-        </template>
-
-        <template is="dom-if" if="[[_modeIsHistograms(mode)]]">
-          <tf-histogram-dashboard
-            id="histograms"
-            backend="[[_backend]]"
-          ></tf-histogram-dashboard>
-        </template>
-
-        <template is="dom-if" if="[[_modeIsEmbeddings(mode)]]">
-          <vz-projector-dashboard
-            id="projector"
-            route-prefix="/data/plugin/projector">
-          </vz-projector-dashboard>
-        </template>
-
-        <template is="dom-if" if="[[_modeIsText(mode)]]">
-          <tf-text-dashboard
-            id="text"
-            backend="[[_backend]]">
-          </tf-text-dashboard>
-        </template>
-      </div>
-    </paper-header-panel>
-
-    <style>
-      :host {
-        height: 100%;
-        display: block;
-        background-color: var(--paper-grey-100);
-      }
-
-      #toolbar {
-        background-color: var(--tb-toolbar-background-color, --tb-orange-strong);
-        -webkit-font-smoothing: antialiased;
-      }
-
-      .toolbar-title {
-        font-size: 20px;
-        margin-left: 10px;
-        text-rendering: optimizeLegibility;
-        letter-spacing: -0.025em;
-        font-weight: 500;
-        flex-grow: 2;
-        display: var(--tb-toolbar-title-display, block);
-      }
-
-      .tabs {
-        flex-grow: 1;
-        text-transform: uppercase;
-        height: 100%;
-      }
-
-      paper-tabs {
-        --paper-tabs-selection-bar-color: white;
-      }
-
-      .global-actions {
-        flex-grow: 2;
-        display: inline-flex; /* Ensure that icons stay aligned */
-        justify-content: flex-end;
-        text-align: right;
-        color: white;
-      }
-
-      .global-actions a {
-        color: white;
-      }
-
-      #toolbar-content {
-        width: 100%;
-        height: 100%;
-        display: flex;
-        flex-direction: row;
-        justify-content: space-between;
-        align-items: center;
-      }
-
-      #content {
-        height: 100%;
-      }
-
-      [disabled] {
-        opacity: 0.2;
-        color: white;
-      }
-
-    </style>
-  </template>
-  <script src="autoReloadBehavior.js"></script>
-  <script>
-    import {AutoReloadBehavior} from "./autoReloadBehavior";
-    import {Backend} from "../tf-backend/backend";
-    import {TABS, setUseHash} from "../tf-globals/globals";
-    import {getString, setString, TAB} from "../tf-storage/storage";
-    import {setRouter, createRouter} from "../tf-backend/router";
-    import {fetchRuns} from "../tf-backend/runsStore";
-
-    Polymer({
-      is: "tf-tensorboard",
-      behaviors: [AutoReloadBehavior],
-      properties: {
-        router: {
-          type: Object,  // only to trigger an observer
-          observer: '_updateRouter',
-        },
-        _backend: {
-          type: Object,
-          computed: "_makeBackend(demoDir)",
-        },
-        _debuggerDataEnabled: {
-          type: Boolean,
-          value: function() {
-            // For now, Tensorboard only shows debugger data if the debugger_data GET param is set
-            // to enabled.
-            let match = window.location.href.match(/[&\?]debugger_data=enabled/);
-            return match && match.length == 1;
-          },
-        },
-        // Which tab is selected (scalars, graph, images etc).
-        mode: {
-          type: String,
-          computed: '_getModeFromIndex(modeIndex)',
-          notify: true,
-        },
-        tabs: {
-          type: Array,
-          readOnly: true,
-          value: TABS,
-        },
-        // If this is set to a string, TensorBoard will switch to "demo mode"
-        // and attempt to load serialized json data from that directory. You can
-        // generate conformant json using
-        // tensorboard/scripts/serialize_tensorboard.py
-        demoDir: {
-          type: String,
-          value: null,
-        },
-        // Set this to true to store state in URI hash. Should be true for all non-test purposes.
-        useHash: {
-          type: Boolean,
-          value: false,
-        },
-        disabledTabs: String,
-      },
-      _isTabEnabled: function(tab) {
-        if (this.disabledTabs != null &&
-            this.disabledTabs.split(',').indexOf(tab) >= 0) {
-          return false;
-        }
-        return true;
-      },
-      _getModeFromIndex: function(modeIndex) {
-        var mode = this.tabs[modeIndex];
-        setString(TAB, mode);
-        return mode;
-      },
-      _makeBackend: function(demoDir) {
-        // If the user has provided a router, we'll always use that.
-        // Otherwise, if the user has provided a demoDir, we'll use that
-        // to create a router.
-        if (demoDir != null && this.router == null) {
-          var router = createRouter(demoDir, true);
-          setRouter(router);
-        }
-        return new Backend();
-      },
-      _isReloadDisabled: function(mode) {
-        return !this._debuggerDataEnabled && this._modeIsGraphs(mode);
-      },
-      _modeIsScalars: function(mode) {
-        return mode === "scalars";
-      },
-      _modeIsImages: function(mode) {
-        return mode === "images";
-      },
-      _modeIsAudio: function(mode) {
-        return mode === "audio";
-      },
-      _modeIsGraphs: function(mode) {
-        return mode === "graphs";
-      },
-      _modeIsEmbeddings: function(mode) {
-        return mode === "embeddings";
-      },
-      _modeIsDistributions: function(mode) {
-        return mode === "distributions";
-      },
-      _modeIsHistograms: function(mode) {
-        return mode === "histograms";
-      },
-      _modeIsText: function(mode) {
-        return mode === "text";
-      },
-      selectedDashboard: function() {
-        var dashboard = this.$$("#" + this.mode);
-        if (dashboard == null) {
-          throw new Error(`Unable to find dashboard for mode: ${this.mode}`);
-        }
-        return dashboard;
-      },
-      ready: function() {
-        setUseHash(this.useHash);
-
-        this._getModeFromHash();
-        window.addEventListener('hashchange', function() {
-          this._getModeFromHash();
-        }.bind(this));
-        fetchRuns();
-      },
-      _getModeFromHash: function() {
-        var tabName = getString(TAB);
-        var modeIndex = this.tabs.indexOf(tabName);
-        if (modeIndex == -1 && this.modeIndex == null) {
-          // Select the first tab as default.
-          this.set('modeIndex', 0);
-        }
-        if (modeIndex != -1 && modeIndex != this.modeIndex) {
-          this.set('modeIndex', modeIndex);
-        }
-      },
-      _updateRouter: function(router) {
-        setRouter(router);
-      },
-      reload: function() {
-        if (this._modeIsEmbeddings(this.mode)) {
-          return;
-        }
-        if (!this._debuggerDataEnabled && this._modeIsGraphs(this.mode)) {
-          return;
-        }
-        fetchRuns().then(function() {
-          this.selectedDashboard().reload();
-        }.bind(this));
-      },
-      openSettings: function() {
-        this.$.settings.open();
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/BUILD b/tensorflow/tensorboard/components/tf_text_dashboard/BUILD
deleted file mode 100644
index ea606f5d58..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/BUILD
+++ /dev/null
@@ -1,46 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_text_dashboard",
-    srcs = [
-        "tf-text-dashboard.html",
-        "tf-text-loader.html",
-    ],
-    path = "/tf-text-dashboard",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_backend",
-        "//tensorflow/tensorboard/components/tf_color_scale",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_runs_selector",
-        "@org_polymer_paper_dialog",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_material",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_spinner",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"] + glob(["data/**"]),
-    path = "/tf-text-dashboard",
-    deps = [
-        ":tf_text_dashboard",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/data/logdir b/tensorflow/tensorboard/components/tf_text_dashboard/data/logdir
deleted file mode 100644
index c7d82022cc..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/data/logdir
+++ /dev/null
@@ -1 +0,0 @@
-{"logdir": "/some/fake/logdir"}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/data/runs.json b/tensorflow/tensorboard/components/tf_text_dashboard/data/runs.json
deleted file mode 100644
index aea7de5f91..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/data/runs.json
+++ /dev/null
@@ -1 +0,0 @@
-{"fry": ["message", "markdown"], "leela": ["message"]}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_fry_tag_markdown.json b/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_fry_tag_markdown.json
deleted file mode 100644
index 94183ae13d..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_fry_tag_markdown.json
+++ /dev/null
@@ -1,32 +0,0 @@
-[
-  {
-    "wall_time": 1489715207.593146,
-    "step": 0,
-    "text": "<p><em>Italics1</em> <em>Italics2</em> <strong>bold1</strong> <strong>bold2</strong></p>"
-  },
-  {
-    "wall_time": 1489715207.593801,
-    "step": 1,
-    "text": "<ol>\n<li>List item one.</li>\n<li>List item two.</li>\n<li>Sublist</li>\n<li>Sublist2</li>\n<li>List continues.</li>\n</ol>"
-  },
-  {
-    "wall_time": 1489715207.594842,
-    "step": 2,
-    "text": "<table>\n<thead>\n<tr>\n<th>An</th>\n<th>Example</th>\n<th>Table</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>A</td>\n<td>B</td>\n<td>C</td>\n</tr>\n<tr>\n<td>1</td>\n<td>2</td>\n<td>3</td>\n</tr>\n</tbody>\n</table>"
-  },
-  {
-    "wall_time": 1489715207.595761,
-    "step": 3,
-    "text": "<p>hello <a><em>you</em></a></p>"
-  },
-  {
-    "wall_time": 1489715207.595761,
-    "step": 4,
-    "text": "<p><a href=\"http://tensorflow.org\">TensorFlow</a></p>"
-  },
-  {
-    "wall_time": 1489715207.595761,
-    "step": 530234352,
-    "text": "&lt;script&gt;alert('xss')&lt;/script&gt;"
-  }
-]
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_fry_tag_message.json b/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_fry_tag_message.json
deleted file mode 100644
index e8cc006c0d..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_fry_tag_message.json
+++ /dev/null
@@ -1,22 +0,0 @@
-[
-  {
-    "wall_time": 1489715207.593146,
-    "step": 0,
-    "text": "fry loves garnet"
-  },
-  {
-    "wall_time": 1489715207.593801,
-    "step": 1,
-    "text": "fry loves amethyst"
-  },
-  {
-    "wall_time": 1489715207.594842,
-    "step": 2,
-    "text": "fry loves pearl"
-  },
-  {
-    "wall_time": 1489715207.595761,
-    "step": 3,
-    "text": "fry loves steven"
-  }
-]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_leela_tag_message.json b/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_leela_tag_message.json
deleted file mode 100644
index 5a6d259893..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/data/text_run_leela_tag_message.json
+++ /dev/null
@@ -1,22 +0,0 @@
-[
-  {
-    "step": 0,
-    "wall_time": 1489715207.607792,
-    "text": "leela loves garnet and feels strongly about various issues of the day including the two-cent titanium tax and whether nixon's head contributes to greenhouse gas emissions"
-  },
-  {
-    "step": 1,
-    "wall_time": 1489715207.609011,
-    "text": "leela loves amethyst"
-  },
-  {
-    "step": 2,
-    "wall_time": 1489715207.610028,
-    "text": "leela loves pearl"
-  },
-  {
-    "step": 3,
-    "wall_time": 1489715207.611142,
-    "text": "leela loves someverylongwordwithoutanybreaksorspacessowecanseehowthatishandledbythefrontend"
-  }
-]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/index.html b/tensorflow/tensorboard/components/tf_text_dashboard/index.html
deleted file mode 100644
index 55ec4d79cf..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/index.html
+++ /dev/null
@@ -1,74 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-  <head>
-    <script src="../webcomponentsjs/webcomponents-lite.min.js"></script>
-    <link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-    <link rel="import" href="tf-text-dashboard.html">
-    <title>text Dashboard Demo</title>
-    <style>
-      #container{
-        height: 800px;
-        border: 2px solid grey;
-      }
-      html, body {
-        margin: 0;
-        padding: 0;
-        height: 100%;
-        font-family: "RobotoDraft","Roboto",sans-serif;
-      }
-    </style>
-  </head>
-  <body>
-    <demo-snippet>
-      <template>
-        <dom-module id="text-dash-demo">
-          <template>
-            <tf-text-dashboard id="demo" backend="[[backend]]">
-            </tf-text-dashboard>
-          </template>
-          <script>
-            import * as backend_backend from '../tf-backend/backend';
-            import {createRouter, setRouter} from '../tf-backend/router';
-
-            Polymer({
-              is: "text-dash-demo",
-              properties: {
-                backend: {
-                  type: Object,
-                  value: function() {
-                    return new backend_backend.Backend();
-                  },
-                },
-              },
-              created: function() {
-                var path = "data";
-                var router = createRouter(path, true);
-                setRouter(router);
-              },
-            });
-          </script>
-        </dom-module>
-        <div id="container">
-          <text-dash-demo></text-dash-demo>
-        </div>
-      </template>
-    </demo-snippet>
-  </body>
-</html>
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
deleted file mode 100644
index 14953a276a..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-dashboard.html
+++ /dev/null
@@ -1,104 +0,0 @@
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-dialog/paper-dialog.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../tf-backend/tf-backend.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-dashboard-common/tf-panes-helper.html">
-<link rel="import" href="../tf-runs-selector/tf-runs-selector.html">
-<link rel="import" href="tf-text-loader.html">
-
-<!--
-tf-text-dashboard displays a dashboard that loads texts from a TensorFlow run.
--->
-<dom-module id="tf-text-dashboard">
-  <template>
-    <paper-dialog with-backdrop id="actual-text-size-dialog"></paper-dialog>
-    <tf-dashboard-layout>
-      <div class="sidebar">
-        <div class="sidebar-section">
-          <tf-categorizer
-            id="categorizer"
-            tags="[[tags]]"
-            categories="{{_categories}}"
-          ></tf-categorizer>
-        </div>
-        <div class="sidebar-section">
-          <tf-runs-selector selected-runs="{{_selectedRuns}}">
-          </tf-runs-selector>
-        </div>
-      </div>
-      <div class="center">
-        <tf-panes-helper
-          categories="[[_categories]]"
-          data-type="[[dataType]]"
-          data-provider="[[dataProvider]]"
-          data-not-found="[[dataNotFound]]"
-          run2tag="[[run2tag]]"
-          selected-runs="[[_selectedRuns]]"
-          repeat-for-runs
-          >
-          <template>
-            <tf-text-loader></tf-text-loader>
-          </template>
-        </tf-panes-helper>
-      </div>
-    </tf-dashboard-layout>
-    <style include="dashboard-style"></style>
-    <style>
-      tf-panes-helper {
-        --card-width: 100%;
-        --card-height: auto;
-        --card-expanded-width: 100%;
-        --card-expanded-height: 1000px;
-        --card-padding: 0 5px 5px 5px;
-        --show-expand-button: none;
-      }
-    </style>
-  </template>
-  <script>
-    import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-    import {ReloadBehavior} from "../tf-dashboard-common/reload-behavior";
-    import {BackendBehavior} from "../tf-backend/behavior";
-
-    Polymer({
-      is: "tf-text-dashboard",
-      factoryImpl: function(backend) {
-        this.backend = backend;
-      },
-      properties: {
-        backend: Object,
-        dataType: {
-          type: String,
-          value: "text"
-        },
-      },
-      behaviors: [
-        DashboardBehavior("text"),
-        ReloadBehavior("tf-chart-scaffold"),
-        BackendBehavior,
-      ],
-      attached: function() {
-        this.async(function() {
-          this.fire("rendered");
-        });
-      },
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-loader.html b/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-loader.html
deleted file mode 100644
index 95723a0b88..0000000000
--- a/tensorflow/tensorboard/components/tf_text_dashboard/tf-text-loader.html
+++ /dev/null
@@ -1,144 +0,0 @@
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-material/paper-material.html">
-<link rel="import" href="../tf-color-scale/tf-color-scale.html">
-<link rel="import" href="../tf-dashboard-common/scrollbar-style.html">
-<link rel="import" href="../tf-imports/d3.html">
-
-<!--
-tf-text-loader displays markdown text data from the Text plugin.
--->
-
-<style>
-  tf-text-loader p {
-    margin: 0.3em 0;
-  }
-
-  tf-text-loader table {
-    border-collapse: collapse;
-  }
-
-  tf-text-loader table th {
-    font-weight: 600;
-  }
-
-  tf-text-loader table th,
-  tf-text-loader table td {
-    padding: 6px 13px;
-    border: 1px solid #dfe2e5;
-  }
-
-  tf-text-loader table tr {
-    background-color: #fff;
-    border-top: 1px solid #c6cbd1;
-  }
-
-</style>
-<dom-module id="tf-text-loader">
-
-  <!-- Set the innerHTML with the textual content, so we can render the
-   html generated by our markdown parser. Note this content is always
-   sanitized by the backend, so xss attacks are not possible.
-  -->
-  <template>
-    <style include="scrollbar-style"></style>
-    <paper-material elevation="1" id="outer" class="container scrollbar">
-      <template id="repeater" is="dom-repeat" items="[[_texts]]">
-      <paper-material elevation="1" class="step-container">
-        step <span class="step-value">[[_numfmt(item.step)]]</span>
-      </paper-material>
-      <paper-material elevation="1" inner-h-t-m-l="[[item.text]]" class="text">
-        </paper-material>
-      </template>
-    </paper-material>
-
-
-    <style>
-      #outer {
-        display: block;
-        overflow: auto;
-        max-height: 500px;
-        position: relative;
-        border-radius: 3px;
-        border: 2px solid black;
-      }
-      .text {
-        margin: 0 10px 10px 10px;
-        border-radius: 0 3px 3px 3px;
-        background-color: white;
-        padding: 5px;
-        word-break: break-word;
-      }
-      .step-container {
-        border-left: 1px solid #ccc;
-        border-right: 1px solid #ccc;
-        border-top: 1px solid #ccc;
-        border-radius: 3px 3px 0 0;
-        font-style: italic;
-        margin-top: 10px;
-        background-color: var(--tb-ui-light-accent);
-        display: inline-block;
-        margin-left: 9px;
-        padding: 3px;
-        font-size: 12px;
-      }
-
-    </style>
-
-  </template>
-  <script>
-    import {runsColorScale} from "../tf-color-scale/colorScale";
-    Polymer({
-      is: "tf-text-loader",
-      properties: {
-        run: String,
-        // This is an array of Tensorboard Text&Datum objects (See backend.ts for details). The
-        // properties of objects in this array are
-        // {
-        //   wall_time: Date,
-        //   step: number,
-        //   text: string,
-        // }
-        // they are ordered from most recent to oldest
-        _texts: {
-          type: Array,
-          value: [],
-        },
-
-      },
-      redraw: function() {
-        // Other dashboards logic requires a redraw method to be defined.
-      },
-      setVisibleSeries: function(runs) {
-        // Do nothing.
-      },
-      setSeriesData: function(run, texts) {
-        this.set("run", run);
-        this.set("_texts", texts.reverse());
-
-        // Update the border color based on the run.
-        var color = runsColorScale(run);
-        this.$$("#outer").style.borderColor = color;
-      },
-      _numfmt: function(n) {
-        return d3.format(",")(n);
-      }
-    });
-  </script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/tf_trace_viewer/BUILD b/tensorflow/tensorboard/components/tf_trace_viewer/BUILD
deleted file mode 100644
index 9f582329f1..0000000000
--- a/tensorflow/tensorboard/components/tf_trace_viewer/BUILD
+++ /dev/null
@@ -1,30 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "tf_trace_viewer",
-    srcs = [
-        "tf-trace-viewer.html",
-        "@org_chromium_catapult_vulcanized_trace_viewer//:trace_viewer_full.html",
-    ],
-    path = "/tf-trace-viewer",
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["demo.html"],
-    path = "/tf-trace-viewer",
-    deps = [
-        ":tf_trace_viewer",
-        "//tensorflow/tensorboard/components/tf_trace_viewer/data",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_trace_viewer/data/BUILD b/tensorflow/tensorboard/components/tf_trace_viewer/data/BUILD
deleted file mode 100644
index c295d38258..0000000000
--- a/tensorflow/tensorboard/components/tf_trace_viewer/data/BUILD
+++ /dev/null
@@ -1,17 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("@io_bazel_rules_closure//closure:defs.bzl", "web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-web_library(
-    name = "data",
-    srcs = glob(["*.json"]),
-    path = "/tf-trace-viewer/data/plugin/profile",
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/tf_trace_viewer/data/trace.json b/tensorflow/tensorboard/components/tf_trace_viewer/data/trace.json
deleted file mode 100644
index e1d57394e3..0000000000
--- a/tensorflow/tensorboard/components/tf_trace_viewer/data/trace.json
+++ /dev/null
@@ -1,105 +0,0 @@
-{
-  "traceEvents": [
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 826, "ph": "C",
-        "name": "counter", "args": {"value": 10}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 826, "ph": "B",
-        "name": "A long name that doesnt fit but is exceedingly informative",
-        "args": {"name_false": false, "value_true": true}},
-    {"cat": "PERF", "pid": 22630, "ts": 835, "ph": "I", "s": "p",
-        "name": "ProcessWideEvent1", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 827, "ph": "B",
-        "name": "Asub with a name that wont fit", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 828, "ph": "E",
-        "name": "Asub", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 829, "ph": "B",
-        "name": "Asub", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 15, "ts": 820, "ph": "X",
-        "name": "Long X type", "args": {}, "sf": 7, "esf": 8},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 832, "ph": "E",
-        "name": "Asub", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 2, "ts": 818, "ph": "X",
-        "name": "X1", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 2, "ts": 818, "ph": "X",
-        "name": "X same ts and dur as X1", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 832, "ph": "C",
-        "name": "counter", "args": {"value": 1}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 833, "ph": "E",
-        "name": "", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 835, "ph": "I",
-        "name": "ThreadLevelI1", "args": {}},
-
-    {"cat": "PERF", "ts": 880, "ph": "I", "s": "g", "name": "GlobalEvent1",
-        "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 837, "ph": "I",
-        "name": "ThreadLevelI2", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 839, "ph": "C",
-        "name": "counter", "args": {"value": 5}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 840, "ph": "B",
-        "name": "A not as long a name", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 848, "ph": "E",
-        "name": "A not as long a name", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 848, "ph": "C",
-        "name": "counter", "args": {"value": 1}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 854, "ph": "C",
-        "name": "counter", "args": {"value": 10}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 850, "ph": "B",
-        "name": "B", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22630, "ts": 854, "ph": "E",
-        "name": "B", "args": {}},
-
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 827, "ph": "B",
-        "name": "A", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 835, "ph": "I",
-        "name": "ThreadLevelImmediate Three", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 845, "ph": "I",
-        "name": "ThreadLevelImmediate4", "args": {}},
-    {"cat": "PERF", "pid": 22630, "tid": 22631, "ts": 854, "ph": "E",
-        "name": "A", "args": {}},
-
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 860, "ph": "B",
-        "name": "B/E over X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "dur": 10, "ts": 860, "ph": "X",
-        "name": "X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 860, "ph": "B",
-        "name": "B/E under X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 870, "ph": "E",
-        "name": "B/E under X", "args": {}},
-    {"cat": "PREF", "pid": 22630, "tid": 22630, "ts": 870, "ph": "E",
-        "name": "B/E over X", "args": {}},
-
-    {"cat": "SAMPLE", "pid": 22630, "tid": 22631, "ts": 870, "ph": "P",
-        "name": "SampleA", "args": {}},
-    {"cat": "SAMPLE", "pid": 22630, "tid": 22631, "ts": 875, "ph": "P",
-        "name": "SampleB", "args": {}},
-    {"cat": "SAMPLE", "pid": 22630, "tid": 22631, "ts": 878, "ph": "P",
-        "name": "SampleC", "args": {}, "sf": 8},
-
-    {"cat": "__metadata", "pid": 22630, "tid": 22630, "ts": 0, "ph": "M",
-        "name": "thread_name", "args": {"name": "threadA"}},
-    {"cat": "__metadata", "pid": 22630, "tid": 22631, "ts": 0, "ph": "M",
-        "name": "thread_name", "args": {"name": "threadB"}},
-    {"cat": "__metadata", "pid": 22630, "tid": 22632, "ts": 0, "ph": "M",
-        "name": "thread_name", "args": {"name": "threadC"}}
-  ],
-  "stackFrames": {
-    "1": {
-      "category": "m1",
-      "name": "main"
-    },
-    "7": {
-      "category": "m2",
-      "name": "frame7",
-      "parent": "1"
-    },
-    "8": {
-      "category": "m2",
-      "name": "frame8",
-      "parent": "1"
-    }
-  }
-}
diff --git a/tensorflow/tensorboard/components/tf_trace_viewer/demo.html b/tensorflow/tensorboard/components/tf_trace_viewer/demo.html
deleted file mode 100644
index dd0029e967..0000000000
--- a/tensorflow/tensorboard/components/tf_trace_viewer/demo.html
+++ /dev/null
@@ -1,30 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="tf-trace-viewer.html">
-<title>Trace Viewer Demo</title>
-<style>
-  #container{
-    height: 800px;
-    border: 2px solid grey;
-  }
-</style>
-<div id="container">
-  <tf-trace-viewer trace-data-url="data/plugin/profile/trace.json">
-  </tf-trace-viewer>
-</div>
diff --git a/tensorflow/tensorboard/components/tf_trace_viewer/tf-trace-viewer.html b/tensorflow/tensorboard/components/tf_trace_viewer/tf-trace-viewer.html
deleted file mode 100644
index cd65d729a4..0000000000
--- a/tensorflow/tensorboard/components/tf_trace_viewer/tf-trace-viewer.html
+++ /dev/null
@@ -1,134 +0,0 @@
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="trace_viewer_full.html">
-
-<!--
-tf-trace-viewer is the frontend entry point for Trace Viewer on TensorBoard.
-
-The server serves the trace viewer app at a separate endpoint. TensorBoard
-dashboard would integrate trace viewer app using iframe.
--->
-<style>
-  tr-ui-timeline-view {
-    position: absolute;
-    width: 100%;
-    height: 100%;
-  }
-</style>
-<script>
-  "use strict";
-
-  Polymer({
-    is: "tf-trace-viewer",
-    properties: {
-      // The URL of trace data. Provided by caller via URL parameter.
-      traceDataUrl: {
-        type: String,
-        value: null,
-      },
-      _traceData: {
-        type: Object,
-        observer: "_traceDataChanged"
-      },
-      _traceViewer: Object,
-      _traceContainer: Object,
-      _traceModel: Object,
-    },
-    ready: function() {
-      // Initiate the trace viewer app.
-      this._traceContainer = document.createElement("track-view-container");
-      this._traceContainer.id = "track_view_container";
-
-      this._traceViewer = document.createElement("tr-ui-timeline-view");
-      this._traceViewer.track_view_container = this._traceContainer;
-      this._traceViewer.appendChild(this._traceContainer);
-
-      this._traceViewer.id = 'trace-viewer';
-      this._traceViewer.globalMode = true;
-
-      Polymer.dom(this.root).appendChild(this._traceViewer);
-
-      // Retrieve the URL of trace data.
-      var queryString = window.location.href.split("?")[1];
-      if (queryString) {
-        var parts = queryString.split('&')
-        for (var i=0; i<parts.length; i++) {
-          var components = parts[i].split('=');
-          if (components[0] == "trace_data_url") {
-            this.traceDataUrl = decodeURIComponent(components[1]);
-            break;
-          }
-        }
-      }
-
-      this._loadTrace();
-    },
-    _loadTrace : function() {
-      if (!this.traceDataUrl) {
-        this._displayOverlay("Trace data URL is not provided.", "Trace Viewer");
-        return null;
-      }
-      // Send HTTP request to get the trace data.
-      var req = new XMLHttpRequest();
-      var is_binary = / [.] gz$ /.test(this.traceDataUrl) ||
-                      / [.] zip$ /.test(this.traceDataUrl);
-      req.overrideMimeType('text/plain; charset=x-user-defined');
-      req.open('GET', this.traceDataUrl, true);
-      if (is_binary) {
-        req.responseType = 'arraybuffer';
-      }
-
-      req.onreadystatechange = function(event) {
-        if (req.readyState !== 4) {
-          return;
-        }
-        window.setTimeout(function() {
-          if (req.status === 200) {
-            this.set("_traceData", is_binary ? req.response : req.responseText);
-          } else {
-            this._displayOverlay(req.status, "Failed to fetch data");
-          }
-        }.bind(this), 0);
-      }.bind(this);
-      req.send(null);
-    },
-    _traceDataChanged: function(data) {
-      if (!data) {
-        this._displayOverlay("Trace Viewer", "No trace to display...");
-        return;
-      }
-      // Feed the trace data into the trace viewer app.
-      this._traceModel = new tr.Model();
-      var i = new tr.importer.Import(this._traceModel);
-      var p = i.importTracesWithProgressDialog([data]);
-      p.then(() => {
-        this._traceViewer.model = this._traceModel;
-        this._traceViewer.viewTitle = "Trace View";
-      }).catch((err) => {
-        this._displayOverlay(
-            'Import error', tr.b.normalizeException(err).message);
-      });
-    },
-    _displayOverlay: function(title, content) {
-      var overlay = new tr.ui.b.Overlay();
-      overlay.textContent = content;
-      overlay.title = title;
-      overlay.visible = true;
-    },
-  });
-</script>
diff --git a/tensorflow/tensorboard/components/trace_viewer.html b/tensorflow/tensorboard/components/trace_viewer.html
deleted file mode 100644
index 773bc548ca..0000000000
--- a/tensorflow/tensorboard/components/trace_viewer.html
+++ /dev/null
@@ -1,29 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<html>
-<head>
-  <title>Trace Viewer</title>
-  <link rel="import" href="tf-trace-viewer/tf-trace-viewer.html" jscomp-nocompile="true">
-  <style>body { margin: 0 }</style>
-</head>
-<body>
-  <tf-trace-viewer></tf-trace-viewer>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/vz_distribution_chart/BUILD b/tensorflow/tensorboard/components/vz_distribution_chart/BUILD
deleted file mode 100644
index 6645805d0c..0000000000
--- a/tensorflow/tensorboard/components/vz_distribution_chart/BUILD
+++ /dev/null
@@ -1,39 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "vz_distribution_chart",
-    srcs = [
-        "vz-distribution-chart.html",
-        "vz-distribution-chart.ts",
-    ],
-    path = "/vz-distribution-chart",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:plottable",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/vz_line_chart",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"],
-    path = "/vz-distribution-chart",
-    deps = [
-        ":vz_distribution_chart",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/vz_distribution_chart/index.html b/tensorflow/tensorboard/components/vz_distribution_chart/index.html
deleted file mode 100644
index 39db09354b..0000000000
--- a/tensorflow/tensorboard/components/vz_distribution_chart/index.html
+++ /dev/null
@@ -1,61 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-  <head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>vz-distribution chart demo</title>
-    <script src="../webcomponentsjs/webcomponents-lite.min.js"></script>
-    <link rel="import" href="vz-distribution-chart.html">
-    <link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-    <link rel="import" href="../paper-styles/typography.html">
-    <style type="text/css">
-      body {
-        font-family: "Roboto";
-      }
-
-      vz-distribution-chart {
-        height: 400px;
-      }
-    </style>
-  </head>
-  <body>
-    <h3>Simple distribution chart</h3>
-    <script>
-      var data = [[1716.20,0,[[0,0.10],[668,0.10],[1587,0.10],[3085,0.10],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.10]]],[1720.60,10,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.10]]],[1724.90,20,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.10]]],[1729.18,30,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.10]]],[1733.55,40,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1737.84,50,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.10]]],[1742.35,60,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1746.75,70,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1751.15,80,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.10]]],[1755.44,90,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.10]]],[1759.99,100,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1764.40,110,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1768.70,120,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1773.00,130,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1777.57,140,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.10],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1781.96,150,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.10],[10000,0.11]]],[1786.34,160,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1790.67,170,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1794.96,180,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1799.29,190,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1803.68,200,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1808.88,210,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.11],[9332,0.11],[10000,0.11]]],[1813.33,220,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1817.66,230,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1821.95,240,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1826.97,250,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.11],[9332,0.11],[10000,0.11]]],[1831.64,260,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.10],[9332,0.11],[10000,0.11]]],[1836.01,270,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.11],[9332,0.11],[10000,0.11]]],[1840.31,280,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.11],[9332,0.11],[10000,0.11]]],[1844.63,290,[[0,0.09],[668,0.09],[1587,0.09],[3085,0.09],[5000,0.09],[6915,0.10],[8413,0.11],[9332,0.11],[10000,0.11]]]];
-    </script>
-    <demo-snippet>
-      <template>
-        <vz-distribution-chart id="demo"></vz-distribution-chart>
-        <script>
-          var elem = document.querySelector('#demo');
-          elem.setVisibleSeries(['demo']);
-          // The data format is strange. We don't expect you to use this chart.
-          var xform = data.map(function(x) {
-            var out = x[2];
-            out.wall_time = x[0];
-            out.step = x[1];
-            return out;
-          });
-          elem.setSeriesData('demo', xform);
-        </script>
-      </template>
-    </demo-snippet>
-  </body>
-</html>
diff --git a/tensorflow/tensorboard/components/vz_distribution_chart/vz-distribution-chart.html b/tensorflow/tensorboard/components/vz_distribution_chart/vz-distribution-chart.html
deleted file mode 100644
index 1f1fdda919..0000000000
--- a/tensorflow/tensorboard/components/vz_distribution_chart/vz-distribution-chart.html
+++ /dev/null
@@ -1,45 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-imports/plottable.html">
-<link rel="import" href="../vz-line-chart/vz-line-chart.html">
-
-<dom-module id="vz-distribution-chart">
-  <template>
-    <div id="chartdiv"></div>
-    <style>
-      :host {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        display: flex;
-        flex-direction: column;
-        flex-grow: 1;
-        flex-shrink: 1;
-        position: relative;
-      }
-      #chartdiv {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
-
-    </style>
-  </template>
-  <script src="vz-distribution-chart.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_distribution_chart/vz-distribution-chart.ts b/tensorflow/tensorboard/components/vz_distribution_chart/vz-distribution-chart.ts
deleted file mode 100644
index f3911d301d..0000000000
--- a/tensorflow/tensorboard/components/vz_distribution_chart/vz-distribution-chart.ts
+++ /dev/null
@@ -1,237 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import * as ChartHelpers from '../vz-line-chart/vz-chart-helpers';
-
-export class DistributionChart {
-  private run2datasets: {[run: string]: Plottable.Dataset};
-  protected runs: string[];
-
-  protected xAccessor: Plottable.IAccessor<number|Date>;
-  protected xScale: Plottable.QuantitativeScale<number|Date>;
-  protected yScale: Plottable.QuantitativeScale<number>;
-  protected gridlines: Plottable.Components.Gridlines;
-  protected center: Plottable.Components.Group;
-  protected xAxis: Plottable.Axes.Numeric|Plottable.Axes.Time;
-  protected yAxis: Plottable.Axes.Numeric;
-  protected xLabel: Plottable.Components.AxisLabel;
-  protected yLabel: Plottable.Components.AxisLabel;
-  protected outer: Plottable.Components.Table;
-  protected colorScale: Plottable.Scales.Color;
-  private plots: Plottable.XYPlot<number|Date, number>[];
-
-  private targetSVG: d3.Selection<any, any, any, any>;
-
-  constructor(xType: string, colorScale: Plottable.Scales.Color) {
-    this.run2datasets = {};
-    this.colorScale = colorScale;
-    this.buildChart(xType);
-  }
-
-  protected getDataset(run: string) {
-    if (this.run2datasets[run] === undefined) {
-      this.run2datasets[run] = new Plottable.Dataset([], {run: run});
-    }
-    return this.run2datasets[run];
-  }
-
-  protected buildChart(xType: string) {
-    if (this.outer) {
-      this.outer.destroy();
-    }
-    let xComponents = ChartHelpers.getXComponents(xType);
-    this.xAccessor = xComponents.accessor;
-    this.xScale = xComponents.scale;
-    this.xAxis = xComponents.axis;
-    this.xAxis.margin(0).tickLabelPadding(3);
-    this.yScale = new Plottable.Scales.Linear();
-    this.yAxis = new Plottable.Axes.Numeric(this.yScale, 'left');
-    let yFormatter = ChartHelpers.multiscaleFormatter(
-        ChartHelpers.Y_AXIS_FORMATTER_PRECISION);
-    this.yAxis.margin(0).tickLabelPadding(5).formatter(yFormatter);
-    this.yAxis.usesTextWidthApproximation(true);
-
-    let center = this.buildPlot(this.xAccessor, this.xScale, this.yScale);
-
-    this.gridlines =
-        new Plottable.Components.Gridlines(this.xScale, this.yScale);
-
-    this.center = new Plottable.Components.Group([this.gridlines, center]);
-    this.outer = new Plottable.Components.Table(
-        [[this.yAxis, this.center], [null, this.xAxis]]);
-  }
-
-  protected buildPlot(xAccessor, xScale, yScale): Plottable.Component {
-    let percents = [0, 228, 1587, 3085, 5000, 6915, 8413, 9772, 10000];
-    let opacities = _.range(percents.length - 1)
-                        .map((i) => (percents[i + 1] - percents[i]) / 2500);
-    let accessors = percents.map((p, i) => (datum) => datum[i][1]);
-    let median = 4;
-    let medianAccessor = accessors[median];
-
-    let plots = _.range(accessors.length - 1).map((i) => {
-      let p = new Plottable.Plots.Area<number|Date>();
-      p.x(xAccessor, xScale);
-
-      let y0 = i > median ? accessors[i] : accessors[i + 1];
-      let y = i > median ? accessors[i + 1] : accessors[i];
-      p.y(y, yScale);
-      p.y0(y0);
-      p.attr(
-          'fill',
-          (d: any, i: number, dataset: Plottable.Dataset) =>
-              this.colorScale.scale(dataset.metadata().run));
-      p.attr(
-          'stroke',
-          (d: any, i: number, dataset: Plottable.Dataset) =>
-              this.colorScale.scale(dataset.metadata().run));
-      p.attr('stroke-weight', (d: any, i: number, m: any) => '0.5px');
-      p.attr('stroke-opacity', () => opacities[i]);
-      p.attr('fill-opacity', () => opacities[i]);
-      return p;
-    });
-
-    let medianPlot = new Plottable.Plots.Line<number|Date>();
-    medianPlot.x(xAccessor, xScale);
-    medianPlot.y(medianAccessor, yScale);
-    medianPlot.attr(
-        'stroke', (d: any, i: number, m: any) => this.colorScale.scale(m.run));
-
-    this.plots = plots;
-    return new Plottable.Components.Group(plots);
-  }
-
-  public setVisibleSeries(runs: string[]) {
-    this.runs = runs;
-    let datasets = runs.map((r) => this.getDataset(r));
-    this.plots.forEach((p) => p.datasets(datasets));
-  }
-
-  /**
-   * Set the data of a series on the chart.
-   */
-  public setSeriesData(name: string, data: any) {
-    this.getDataset(name).data(data);
-  }
-
-  public renderTo(targetSVG: d3.Selection<any, any, any, any>) {
-    this.targetSVG = targetSVG;
-    this.outer.renderTo(targetSVG);
-  }
-
-  public redraw() {
-    this.outer.redraw();
-  }
-
-  protected destroy() {
-    this.outer.destroy();
-  }
-}
-
-
-Polymer({
-  is: 'vz-distribution-chart',
-  properties: {
-    /**
-     * Scale that maps series names to colors. The default colors are from
-     * d3.d3.schemeCategory10. Use this property to replace the default
-     * line colors with colors of your own choice.
-     * @type {Plottable.Scales.Color}
-     * @required
-     */
-    colorScale: {
-      type: Object,
-      value: function() {
-        return new Plottable.Scales.Color().range(d3.schemeCategory10);
-      }
-    },
-    /**
-     * The way to display the X values. Allows:
-     * - "step" - Linear scale using the  "step" property of the datum.
-     * - "wall_time" - Temporal scale using the "wall_time" property of the
-     * datum.
-     * - "relative" - Temporal scale using the "relative" property of the
-     * datum if it is present or calculating from "wall_time" if it isn't.
-     */
-    xType: {type: String, value: 'step'},
-    _attached: Boolean,
-    _chart: Object,
-    _visibleSeriesCache: {
-      type: Array,
-      value: function() {
-        return []
-      }
-    },
-    _seriesDataCache: {
-      type: Object,
-      value: function() {
-        return {}
-      }
-    },
-    _makeChartAsyncCallbackId: {type: Number, value: null}
-  },
-  observers: [
-    '_makeChart(xType, colorScale, _attached)',
-    '_reloadFromCache(_chart)',
-  ],
-  setVisibleSeries: function(names) {
-    this._visibleSeriesCache = names;
-    if (this._chart) {
-      this._chart.setVisibleSeries(names);
-      this.redraw();
-    }
-  },
-  setSeriesData: function(name, data) {
-    this._seriesDataCache[name] = data;
-    if (this._chart) {
-      this._chart.setSeriesData(name, data);
-    }
-  },
-  redraw: function() {
-    this._chart.redraw();
-  },
-  ready: function() {
-    this.scopeSubtree(this.$.chartdiv, true);
-  },
-  _makeChart: function(xType, colorScale, _attached) {
-    if (this._makeChartAsyncCallbackId === null) {
-      this.cancelAsync(this._makeChartAsyncCallbackId);
-    }
-
-    this._makeChartAsyncCallbackId = this.async(function() {
-      this._makeChartAsyncCallbackId = null;
-      if (!_attached) return;
-      if (this._chart) this._chart.destroy();
-      var chart = new DistributionChart(xType, colorScale);
-      var svg = d3.select(this.$.chartdiv);
-      chart.renderTo(svg);
-      this._chart = chart;
-    }, 350);
-  },
-  _reloadFromCache: function() {
-    if (this._chart) {
-      this._chart.setVisibleSeries(this._visibleSeriesCache);
-      this._visibleSeriesCache.forEach(function(name) {
-        this._chart.setSeriesData(name, this._seriesDataCache[name] || []);
-      }.bind(this));
-    }
-  },
-  attached: function() {
-    this._attached = true;
-  },
-  detached: function() {
-    this._attached = false;
-  }
-});
diff --git a/tensorflow/tensorboard/components/vz_histogram_timeseries/BUILD b/tensorflow/tensorboard/components/vz_histogram_timeseries/BUILD
deleted file mode 100644
index 6f6c8d94c3..0000000000
--- a/tensorflow/tensorboard/components/vz_histogram_timeseries/BUILD
+++ /dev/null
@@ -1,46 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "vz_histogram_timeseries",
-    srcs = ["vz-histogram-timeseries.html"],
-    path = "/vz-histogram-timeseries",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"],
-    path = "/vz-histogram-timeseries",
-    deps = [
-        ":vz_histogram_timeseries",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":vz_histogram_timeseries"],
-    visibility = ["//learning/vis/vz_elements/catalog:__pkg__"],
-    destdir = "vz-histogram-timeseries",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports_google:lib",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/vz_histogram_timeseries/index.html b/tensorflow/tensorboard/components/vz_histogram_timeseries/index.html
deleted file mode 100644
index 42efa83eb0..0000000000
--- a/tensorflow/tensorboard/components/vz_histogram_timeseries/index.html
+++ /dev/null
@@ -1,84 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-  <head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>vz-histogram-timeseries demo</title>
-    <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-    <link rel="import" href="vz-histogram-timeseries.html">
-    <link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-    <link rel="import" href="../paper-styles/typography.html">
-    <link rel="import" href="../paper-button/paper-button.html">
-    <style type="text/css">
-      body {
-        font-family: "Roboto";
-      }
-
-      vz-histogram-timeseries {
-        height: 300px;
-        width: 500px;
-      }
-    </style>
-  </head>
-  <body>
-    <h3>vz-histogram-timeseries mode</h3>
-    <demo-snippet>
-      <template>
-        <paper-button id="offsetButton">Offset</paper-button>
-        <paper-button id="overlayButton">Overlay</paper-button>
-        <vz-histogram-timeseries id="histo1"></vz-histogram-timeseries>
-        <script>
-          var histo1 = document.querySelector('#histo1'),
-            offsetButton = document.querySelector('#offsetButton'),
-            overlayButton = document.querySelector('#overlayButton');
-
-          histo1.setSeriesData('data', data);
-          offsetButton.addEventListener('click', function() { histo1.mode = 'offset' });
-          overlayButton.addEventListener('click', function() { histo1.mode = 'overlay' });
-        </script>
-      </template>
-    </demo-snippet>
-
-    <h3>vz-histogram-timeseries axis</h3>
-    <demo-snippet>
-      <template>
-        <paper-button id="stepButton">Step</paper-button>
-        <paper-button id="relativeButton">Relative</paper-button>
-        <paper-button id="wallTimeButton">Wall Time</paper-button>
-        <vz-histogram-timeseries id="histo2"></vz-histogram-timeseries>
-        <script>
-          var histo2 = document.querySelector('#histo2'),
-            stepButton = document.querySelector('#stepButton'),
-            relativeButton = document.querySelector('#relativeButton'),
-            wallTimeButton = document.querySelector('#wallTimeButton');
-
-          histo2.setSeriesData('data', data);
-          stepButton.addEventListener('click', function() { histo2.timeProperty = 'step' });
-          relativeButton.addEventListener('click', function() { histo2.timeProperty = 'relative' });
-          wallTimeButton.addEventListener('click', function() { histo2.timeProperty = 'wall_time' });
-        </script>
-      </template>
-    </demo-snippet>
-
-    <script>
-      var data = [{"wall_time":new Date("2016-06-24T04:13:11.455Z"),"step":28,"bins":[{"x":0.0537,"dx":0.0054,"y":0},{"x":0.0591,"dx":0.0054,"y":0},{"x":0.0645,"dx":0.0054,"y":0},{"x":0.0698,"dx":0.0054,"y":0},{"x":0.0752,"dx":0.0054,"y":3.2512},{"x":0.0805,"dx":0.0054,"y":19.1043},{"x":0.0859,"dx":0.0054,"y":66.4758},{"x":0.0913,"dx":0.0054,"y":89.9105},{"x":0.0966,"dx":0.0054,"y":102.4476},{"x":0.102,"dx":0.0054,"y":83.8924},{"x":0.1073,"dx":0.0054,"y":81.9883},{"x":0.1127,"dx":0.0054,"y":25.21},{"x":0.1181,"dx":0.0054,"y":25.21},{"x":0.1234,"dx":0.0054,"y":2.51},{"x":0.1288,"dx":0.0054,"y":0},{"x":0.1341,"dx":0.0054,"y":0},{"x":0.1395,"dx":0.0054,"y":0},{"x":0.1449,"dx":0.0054,"y":0},{"x":0.1502,"dx":0.0054,"y":0},{"x":0.1556,"dx":0.0054,"y":0}]},{"wall_time":new 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Date("2016-06-24T04:20:20.058Z"),"step":974,"bins":[{"x":0.0537,"dx":0.0054,"y":3.5385},{"x":0.0591,"dx":0.0054,"y":9.0258},{"x":0.0645,"dx":0.0054,"y":12.6399},{"x":0.0698,"dx":0.0054,"y":17.4814},{"x":0.0752,"dx":0.0054,"y":30.9491},{"x":0.0805,"dx":0.0054,"y":39.7415},{"x":0.0859,"dx":0.0054,"y":46.8496},{"x":0.0913,"dx":0.0054,"y":42.9501},{"x":0.0966,"dx":0.0054,"y":40.8639},{"x":0.102,"dx":0.0054,"y":51.8154},{"x":0.1073,"dx":0.0054,"y":52.8153},{"x":0.1127,"dx":0.0054,"y":41.8581},{"x":0.1181,"dx":0.0054,"y":41.8581},{"x":0.1234,"dx":0.0054,"y":22.8571},{"x":0.1288,"dx":0.0054,"y":20.7561},{"x":0.1341,"dx":0.0054,"y":11.0964},{"x":0.1395,"dx":0.0054,"y":4.3242},{"x":0.1449,"dx":0.0054,"y":4.22},{"x":0.1502,"dx":0.0054,"y":1.9569},{"x":0.1556,"dx":0.0054,"y":1.9569}]},{"wall_time":new Date("2016-06-24T04:20:29.637Z"),"step":999,"bins":[{"x":0.0537,"dx":0.0054,"y":5.2083},{"x":0.0591,"dx":0.0054,"y":6.5532},{"x":0.0645,"dx":0.0054,"y":11.7972},{"x":0.0698,"dx":0.0054,"y":18.9477},{"x":0.0752,"dx":0.0054,"y":30.5305},{"x":0.0805,"dx":0.0054,"y":39.1091},{"x":0.0859,"dx":0.0054,"y":48.7489},{"x":0.0913,"dx":0.0054,"y":39.4876},{"x":0.0966,"dx":0.0054,"y":34.5329},{"x":0.102,"dx":0.0054,"y":55.0967},{"x":0.1073,"dx":0.0054,"y":57},{"x":0.1127,"dx":0.0054,"y":45.6633},{"x":0.1181,"dx":0.0054,"y":45.6633},{"x":0.1234,"dx":0.0054,"y":18.5636},{"x":0.1288,"dx":0.0054,"y":15.567},{"x":0.1341,"dx":0.0054,"y":10.3444},{"x":0.1395,"dx":0.0054,"y":6.6828},{"x":0.1449,"dx":0.0054,"y":6.4532},{"x":0.1502,"dx":0.0054,"y":1.4677},{"x":0.1556,"dx":0.0054,"y":1.4677}]}];
-    </script>
-  </body>
-</html>
diff --git a/tensorflow/tensorboard/components/vz_histogram_timeseries/vz-histogram-timeseries.html b/tensorflow/tensorboard/components/vz_histogram_timeseries/vz-histogram-timeseries.html
deleted file mode 100644
index bdba230077..0000000000
--- a/tensorflow/tensorboard/components/vz_histogram_timeseries/vz-histogram-timeseries.html
+++ /dev/null
@@ -1,707 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-imports/d3.html">
-
-<!--
-vz-histogram-timeseries creates an element that draws beautiful histograms for
-displaying how data is distributed over time.
-
-This histogram supports changing the time axis type and different modes of
-visualization.
-
-@element vz-histogram-timeseries
-@demo demo/index.html
--->
-<dom-module id="vz-histogram-timeseries">
-    <template>
-      <div id="tooltip"><span></span></div>
-      <svg id="svg">
-        <g>
-          <g class="axis x"></g>
-          <g class="axis y"></g>
-          <g class="axis y slice"></g>
-          <g class="stage">
-            <rect class="background"></rect>
-          </g>
-          <g class="x-axis-hover"></g>
-          <g class="y-axis-hover"></g>
-          <g class="y-slice-axis-hover"></g>
-        </g>
-      </svg>
-
-      <style>
-        :host {
-          display: flex;
-          flex-direction: column;
-          flex-grow: 1;
-          flex-shrink: 1;
-          position: relative;
-        }
-
-        svg {
-          font-family: roboto, sans-serif;
-          overflow: visible;
-          display: block;
-          width: 100%;
-          flex-grow: 1;
-          flex-shrink: 1;
-        }
-
-        #tooltip {
-          position: absolute;
-          display: block;
-          opacity: 0;
-          font-weight: bold;
-          font-size: 11px;
-        }
-
-        .background {
-          fill-opacity: 0;
-          fill: red;
-        }
-
-        .histogram {
-          pointer-events: none;
-        }
-
-        .hover {
-          font-size: 9px;
-          dominant-baseline: middle;
-          opacity: 0;
-        }
-
-        .hover circle {
-          stroke: white;
-          stroke-opacity: 0.5;
-          stroke-width: 1px;
-        }
-
-        .hover text {
-          fill: black;
-          opacity: 0;
-        }
-
-        .hover.hover-closest circle {
-          fill: black!important;
-        }
-
-        .hover.hover-closest text {
-          opacity: 1;
-        }
-
-        .baseline {
-          stroke: black;
-          stroke-opacity: 0.1;
-        }
-
-        .outline {
-          fill: none;
-          stroke: white;
-          stroke-opacity: 0.5;
-        }
-
-        .outline.outline-hover {
-          stroke: black!important;
-          stroke-opacity: 1;
-        }
-
-        .x-axis-hover,
-        .y-axis-hover,
-        .y-slice-axis-hover {
-          pointer-events: none;
-        }
-
-        .x-axis-hover .label,
-        .y-axis-hover .label,
-        .y-slice-axis-hover .label {
-          opacity: 0;
-          font-weight: bold;
-          font-size: 11px;
-          text-anchor: end;
-        }
-
-        .x-axis-hover text {
-          text-anchor: middle;
-        }
-
-        .y-axis-hover text,
-        .y-slice-axis-hover text {
-          text-anchor: start;
-        }
-
-        .x-axis-hover line,
-        .y-axis-hover line,
-        .y-slice-axis-hover line {
-          stroke: black;
-        }
-
-        .x-axis-hover rect,
-        .y-axis-hover rect,
-        .y-slice-axis-hover rect {
-          fill: white;
-        }
-
-        .axis {
-          font-size: 11px;
-        }
-
-        .axis path.domain {
-          fill: none;
-        }
-
-        .axis .tick line {
-          stroke: #ddd;
-        }
-
-        .axis.slice {
-          opacity: 0;
-        }
-
-        .axis.slice .tick line {
-          stroke-dasharray: 2;
-        }
-
-        .small .axis text { display: none; }
-        .small .axis .tick:first-of-type text { display: block; }
-        .small .axis .tick:last-of-type text { display: block; }
-        .medium .axis text { display: none; }
-        .medium .axis .tick:nth-child(2n + 1) text { display: block; }
-        .large .axis text { display: none; }
-        .large .axis .tick:nth-child(2n + 1) text { display: block; }
-
-      </style>
-    </template>
-
-    <script>
-    Polymer({
-      is: "vz-histogram-timeseries",
-      properties: {
-        /**
-         * Defines which view mode is being used by the chart. Supported values
-         * are:
-         * - "offset" - Offset view of the data showing all timesteps.
-         * - "overlay" - Overlays all timesteps into one 2D view, with the
-         * brighter lines representing the newer timesteps.
-         */
-        mode: {
-          type: String,
-          value: "offset"
-        },
-
-        /*
-         * The name of the datum's property that contains the time values.
-         * Allows:
-         * - "step" - Linear scale using the "step" property of the datum.
-         * - "wall_time" - Temporal scale using the "wall_time" property of the
-         * datum.
-         * - "relative" - Temporal scale starting at 0 created by using
-         * the "wall_time" property of the datum.
-         */
-        timeProperty: {
-          type: String,
-          value: "step"
-        },
-
-        /**
-         * The name of the data's property that contains the bins.
-         */
-        bins: {
-          type: String,
-          value: "bins"
-        },
-
-        /**
-         * The name of the datum's property that contains the x values.
-         */
-        x: {
-          type: String,
-          value: "x"
-        },
-
-        /**
-         * The name of the datum's property that contains the bin width values.
-         */
-        dx: {
-          type: String,
-          value: "dx"
-        },
-
-        /**
-         * The name of the datum's property that contains the bin height.
-         */
-        y: {
-          type: String,
-          value: "y"
-        },
-
-        /**
-         * Scale that maps series names to colors. The default colors are from
-         * d3.schemeCategory10() scale. Use this property to replace the default
-         * line colors with colors of your own choice.
-         */
-        colorScale: {
-          type: Object,
-          value: function() {
-            return d3.scaleOrdinal(d3.schemeCategory10);
-          }
-        },
-
-        /**
-         * Duration of the transition between histogram modes.
-         */
-        modeTransitionDuration: {
-          type: Number,
-          value: 500
-        },
-
-        _attached: Boolean,
-        _name: { type: String, value: null },
-        _data: { type: Array, value: null },
-      },
-      observers: [
-        'redraw(timeProperty, _attached)',
-        '_modeRedraw(mode)'
-      ],
-      ready: function() {
-        // Polymer's way of scoping styles on nodes that d3 created
-        this.scopeSubtree(this.$.svg, true);
-      },
-      attached: function() {
-        this._attached = true;
-      },
-      detached: function() {
-        this._attached = false;
-      },
-      setVisibleSeries: function(names) {
-        // Do nothing.
-      },
-      setSeriesData: function(name, data) {
-        this._name = name;
-        this._data = data;
-        this.redraw();
-      },
-
-      /**
-       * Redraws the chart. This is only called if the chart is attached to the
-       * screen and if the chart has data.
-       */
-      redraw: function() {
-        this._draw(0);
-      },
-
-      _modeRedraw: function() {
-        this._draw(this.modeTransitionDuration);
-      },
-
-      _draw: function(duration) {
-        if (!this._attached || !this._data) {
-          return;
-        }
-
-        //
-        // Data verification
-        //
-        if (duration === undefined) throw(new Error("vz-histogram-timeseries _draw needs duration"));
-        if (this._data.length <= 0) throw(new Error("Not enough steps in the data"));
-        if (!this._data[0].hasOwnProperty(this.bins)) throw(new Error("No bins property of '" + this.bins + "' in data"));
-        if (this._data[0][this.bins].length <= 0) throw(new Error("Must have at least one bin in bins in data"));
-        if (!this._data[0][this.bins][0].hasOwnProperty(this.x)) throw(new Error("No x property '" + this.x + "' on bins data"));
-        if (!this._data[0][this.bins][0].hasOwnProperty(this.dx)) throw(new Error("No dx property '" + this.dx + "' on bins data"));
-        if (!this._data[0][this.bins][0].hasOwnProperty(this.y)) throw(new Error("No y property '" + this.y + "' on bins data"));
-
-        //
-        // Initialization
-        //
-        var timeProp = this.timeProperty;
-        var xProp = this.x;
-        var binsProp = this.bins;
-        var dxProp = this.dx;
-        var yProp = this.y;
-
-        var data = this._data;
-        var name = this._name;
-        var mode = this.mode;
-        var color = d3.hcl(this.colorScale(name));
-        var tooltip = d3.select(this.$.tooltip);
-
-        var xAccessor = function(d) { return d[xProp] };
-        var yAccessor = function(d) { return d[yProp] };
-        var dxAccessor = function(d) { return d[dxProp] };
-        var xRightAccessor = function(d) { return d[xProp] + d[dxProp] };
-        var timeAccessor = function(d) { return d[timeProp] };
-
-        if (timeProp === "relative") {
-          timeAccessor = function(d) { return d.wall_time - data[0].wall_time };
-        }
-
-        var brect = this.$.svg.getBoundingClientRect();
-        var outerWidth = brect.width,
-            outerHeight = brect.height;
-
-        var sliceHeight,
-            margin = {top: 5, right: 60, bottom: 20, left: 24};
-
-        if (mode === "offset") {
-          sliceHeight = outerHeight / 2.5;
-          margin.top = sliceHeight + 5;
-        } else {
-          sliceHeight = outerHeight - margin.top - margin.bottom;
-        }
-
-        var width = outerWidth - margin.left - margin.right,
-            height = outerHeight - margin.top - margin.bottom;
-
-        var leftMin = d3.min(data, xAccessor),
-            rightMax = d3.max(data, xRightAccessor);
-
-        //
-        // Text formatters
-        //
-        var format = d3.format(".3n");
-        var yAxisFormat = d3.format(".0f");
-
-        if (timeProp === "wall_time") {
-          yAxisFormat = d3.timeFormat("%m/%d %X");
-        } else if (timeProp === "relative") {
-          yAxisFormat = function(d) {
-            return d3.format(".1r")(d / 3.6e6) + 'h'; // Convert to hours.
-          };
-        }
-
-        //
-        // Calculate the extents
-        //
-        var xExtents = data.map(function(d, i) {
-          return [
-            d3.min(d[binsProp], xAccessor),
-            d3.max(d[binsProp], xRightAccessor)
-          ];
-        });
-        var yExtents = data.map(function(d) {
-          return d3.extent(d[binsProp], yAccessor);
-        });
-
-        //
-        // Scales and axis
-        //
-        var outlineCanvasSize = 500;
-
-        var extent = d3.extent(data, timeAccessor);
-
-        var yScale = (timeProp === "wall_time" ? d3.scaleTime() : d3.scaleLinear())
-            .domain(extent)
-            .range([0, (mode === "offset" ? height : 0)]);
-
-        var ySliceScale = d3.scaleLinear()
-            .domain([0, d3.max(data, function(d, i) { return yExtents[i][1]; })])
-            .range([sliceHeight, 0]);
-
-        var yLineScale = d3.scaleLinear()
-            .domain(ySliceScale.domain())
-            .range([outlineCanvasSize, 0]);
-
-        var xScale = d3.scaleLinear()
-            .domain([
-              d3.min(data, function(d, i) { return xExtents[i][0]; }),
-              d3.max(data, function(d, i) { return xExtents[i][1]; })
-            ])
-            .nice()
-            .range([0, width]);
-
-        var xLineScale = d3.scaleLinear()
-            .domain(xScale.domain())
-            .range([0, outlineCanvasSize]);
-
-        var outlineColor = d3.scaleLinear()
-            .domain(d3.extent(data, timeAccessor))
-            .range([color.darker(), color.brighter()])
-            .interpolate(d3.interpolateHcl);
-
-        var xAxis = d3.axisBottom(xScale).ticks(Math.max(2, width / 20));
-
-        var yAxis = d3.axisRight(yScale)
-            .ticks(Math.max(2, height / 15))
-            .tickFormat(yAxisFormat);
-
-
-
-        var ySliceAxis = d3.axisRight(ySliceScale)
-            .ticks(Math.max(2, height / 15))
-            .tickSize(width + 5)
-            .tickFormat(format);
-
-        var xBinCentroid = function(d) {
-          return d[xProp] + d[dxProp] / 2;
-        };
-
-        var linePath = d3.line()
-            .x(function(d) { return xLineScale(xBinCentroid(d)); })
-            .y(function(d) { return yLineScale(d[yProp]); });
-
-        var path = function(d) {
-          // Draw a line from 0 to the first point and from the last point to 0.
-          return 'M' + xLineScale(xBinCentroid(d[0])) + ',' + yLineScale(0) +
-              'L' + linePath(d).slice(1) +
-              "L" + xLineScale(xBinCentroid(d[d.length - 1])) + "," + yLineScale(0);
-        };
-
-        //
-        // Render
-        //
-        var svgNode = this.$.svg;
-
-        var svg = d3.select(svgNode)
-
-        var svgTransition = svg.transition().duration(duration);
-
-        var g = svg.select("g")
-            .classed("small", function() { return (width > 0 && width <= 150); })
-            .classed("medium", function() { return (width > 150 && width <= 300); })
-            .classed("large", function() { return (width > 300); })
-
-        var gTransition = svgTransition.select("g")
-            .attr("transform", "translate(" + margin.left + "," + margin.top + ")");
-
-        var bisect = d3.bisector(xRightAccessor).left;
-        var stage = g.select(".stage")
-            .on("mouseover", function() {
-              hoverUpdate.style("opacity", 1);
-              xAxisHoverUpdate.style("opacity", 1);
-              yAxisHoverUpdate.style("opacity", 1);
-              ySliceAxisHoverUpdate.style("opacity", 1);
-              tooltip.style("opacity", 1);
-            })
-            .on("mouseout", function() {
-              hoverUpdate.style("opacity", 0);
-              xAxisHoverUpdate.style("opacity", 0);
-              yAxisHoverUpdate.style("opacity", 0);
-              ySliceAxisHoverUpdate.style("opacity", 0);
-              hoverUpdate.classed("hover-closest", false);
-              outlineUpdate.classed("outline-hover", false);
-              tooltip.style("opacity", 0);
-            })
-            .on("mousemove", onMouseMove);
-
-        var background = stage.select(".background")
-            .attr("transform", "translate(" + -margin.left + "," + -margin.top + ")")
-            .attr("width", outerWidth)
-            .attr("height", outerHeight);
-
-        var histogram = stage.selectAll(".histogram").data(data),
-            histogramExit = histogram.exit().remove(),
-            histogramEnter = histogram.enter().append("g").attr("class", "histogram"),
-            histogramUpdate = histogramEnter.merge(histogram)
-                .sort(function(a, b) { return timeAccessor(a) - timeAccessor(b); }),
-            histogramTransition = gTransition.selectAll(".histogram")
-                .attr("transform", function(d) {
-                  return "translate(0, " +
-                    (mode === "offset" ? (yScale(timeAccessor(d)) - sliceHeight) : 0) + ")";
-                });
-
-        var baselineEnter = histogramEnter.append("line").attr("class", "baseline"),
-            baselineUpdate = histogramTransition.select(".baseline")
-                .style("stroke-opacity", function(d) { return (mode === "offset" ? 0.1 : 0); })
-                .attr("y1", sliceHeight)
-                .attr("y2", sliceHeight)
-                .attr("x2", width);
-
-        var outlineEnter = histogramEnter.append("path").attr("class", "outline"),
-            outlineUpdate = histogramUpdate.select(".outline")
-                .attr("vector-effect", "non-scaling-stroke")
-                .attr("d", function(d) { return path(d[binsProp]); })
-                .style("stroke-width", 1),
-            outlineTransition = histogramTransition.select(".outline")
-                .attr("transform", "scale(" + width / outlineCanvasSize + ", " +
-                      sliceHeight / outlineCanvasSize + ")")
-                .style("stroke", function(d) {
-                  return (mode === "offset" ? "white" : outlineColor(timeAccessor(d)));
-                })
-                .style("fill-opacity", function(d) { return (mode === "offset" ? 1 : 0); })
-                .style("fill", function(d) { return outlineColor(timeAccessor(d)); });
-
-        var hoverEnter = histogramEnter.append("g")
-                .attr("class", "hover")
-                .style("fill", function(d) { return outlineColor(timeAccessor(d)); }),
-            hoverUpdate = histogramUpdate.select(".hover");
-
-        hoverEnter.append("circle")
-            .attr("r", 2);
-
-        hoverEnter.append("text")
-            .style("display", "none")
-            .attr("dx", 4);
-
-        var xAxisHover = g.select(".x-axis-hover").selectAll(".label").data(["x"]),
-            xAxisHoverEnter = xAxisHover.enter().append("g").attr("class", "label"),
-            xAxisHoverUpdate = xAxisHover.merge(xAxisHoverEnter);
-
-        xAxisHoverEnter.append("rect")
-            .attr("x", -20)
-            .attr("y", 6)
-            .attr("width", 40)
-            .attr("height", 14)
-
-        xAxisHoverEnter.append("line")
-            .attr("x1", 0)
-            .attr("x2", 0)
-            .attr("y1", 0)
-            .attr("y2", 6);
-
-        xAxisHoverEnter.append("text")
-            .attr("dy", 18);
-
-        var yAxisHover = g.select(".y-axis-hover").selectAll(".label").data(["y"]),
-            yAxisHoverEnter = yAxisHover.enter().append("g").attr("class", "label"),
-            yAxisHoverUpdate = yAxisHover.merge(yAxisHoverEnter);
-
-        yAxisHoverEnter.append("rect")
-            .attr("x", 8)
-            .attr("y", -6)
-            .attr("width", 40)
-            .attr("height", 14)
-
-        yAxisHoverEnter.append("line")
-            .attr("x1", 0)
-            .attr("x2", 6)
-            .attr("y1", 0)
-            .attr("y2", 0);
-
-        yAxisHoverEnter.append("text")
-            .attr("dx", 8)
-            .attr("dy", 4);
-
-        var ySliceAxisHover = g.select(".y-slice-axis-hover").selectAll(".label").data(["y"]),
-            ySliceAxisHoverEnter = ySliceAxisHover.enter().append("g").attr("class", "label"),
-            ySliceAxisHoverUpdate = ySliceAxisHover.merge(ySliceAxisHoverEnter);
-
-        ySliceAxisHoverEnter.append("rect")
-            .attr("x", 8)
-            .attr("y", -6)
-            .attr("width", 40)
-            .attr("height", 14)
-
-        ySliceAxisHoverEnter.append("line")
-            .attr("x1", 0)
-            .attr("x2", 6)
-            .attr("y1", 0)
-            .attr("y2", 0);
-
-        ySliceAxisHoverEnter.append("text")
-            .attr("dx", 8)
-            .attr("dy", 4);
-
-        gTransition.select(".y.axis.slice")
-            .style("opacity", mode === "offset" ? 0 : 1)
-            .attr("transform", "translate(0, " + (mode === "offset" ? -sliceHeight : 0) + ")")
-            .call(ySliceAxis);
-
-        gTransition.select(".x.axis")
-            .attr("transform", "translate(0, " + height + ")")
-            .call(xAxis);
-
-        gTransition.select(".y.axis")
-            .style("opacity", mode === "offset" ? 1 : 0)
-            .attr("transform", "translate(" + width + ", " + (mode === "offset" ? 0 : height) + ")")
-            .call(yAxis);
-
-        gTransition.selectAll(".tick text")
-            .attr("fill", "#aaa");
-        gTransition.selectAll(".axis path.domain").attr("stroke", "none");
-
-
-        function onMouseMove() {
-          var m = d3.mouse(this),
-              v = xScale.invert(m[0]),
-              t = yScale.invert(m[1]);
-
-          function hoverXIndex(d) {
-            return Math.min(d[binsProp].length - 1, bisect(d[binsProp], v));
-          }
-          var closestSliceData;
-          var closestSliceDistance = Infinity;
-          var lastSliceData;
-          hoverUpdate
-            .attr("transform", function(d, i) {
-              var index = hoverXIndex(d);
-              lastSliceData = d;
-              var x = xScale(d[binsProp][index][xProp] + d[binsProp][index][dxProp] / 2);
-              var y = ySliceScale(d[binsProp][index][yProp]);
-              var globalY = (mode === "offset" ? yScale(timeAccessor(d)) - (sliceHeight - y) : y);
-              var dist = Math.abs(m[1] - globalY);
-              if (dist < closestSliceDistance) {
-                closestSliceDistance = dist;
-                closestSliceData = d;
-              }
-              return "translate(" + x + "," + y + ")";
-            });
-          hoverUpdate.select("text").text(function(d) {
-            var index = hoverXIndex(d);
-            return d[binsProp][index][yProp];
-          })
-          hoverUpdate.classed("hover-closest", function(d) { return d === closestSliceData; });
-          outlineUpdate.classed("outline-hover", function(d) { return d === closestSliceData; });
-
-          var index = hoverXIndex(lastSliceData);
-
-          xAxisHoverUpdate
-              .attr("transform", function(d) {
-                return "translate(" +
-                  xScale(lastSliceData[binsProp][index][xProp] +
-                         lastSliceData[binsProp][index][dxProp] / 2) + ", " +
-                  height + ")";
-              })
-            .select("text")
-              .text(function(d) { return format(lastSliceData[binsProp][index][xProp] +
-                                                lastSliceData[binsProp][index][dxProp] / 2); });
-
-          var fy = yAxis.tickFormat();
-          yAxisHoverUpdate
-              .attr("transform", function(d) {
-                return "translate(" + width + ", " +
-                  (mode === "offset" ? yScale(timeAccessor(closestSliceData)) : 0) + ")";
-              })
-              .style("display", mode === "offset" ? "" : "none")
-            .select("text")
-              .text(function(d) { return fy(timeAccessor(closestSliceData));});
-
-          var fsy = ySliceAxis.tickFormat();
-          ySliceAxisHoverUpdate
-              .attr("transform", function(d) {
-                return "translate(" + width + ", " +
-                  (mode === "offset" ? 0 : ySliceScale(closestSliceData[binsProp][index][yProp])) +
-                  ")";
-              })
-              .style("display", mode === "offset" ? "none" : "")
-            .select("text")
-              .text(function(d) { return fsy(closestSliceData[binsProp][index][yProp]); });
-
-          var svgMouse = d3.mouse(svgNode);
-          tooltip.style("transform", "translate(" + (svgMouse[0] + 15) + "px," +
-              (svgMouse[1] - 15) + "px)")
-            .select('span')
-            .text(mode === "offset" ?
-                fsy(closestSliceData[binsProp][index][yProp]) :
-                (timeProp === "step" ? "step " : "") +
-                fy(timeAccessor(closestSliceData)));
-        }
-      }
-    });
-    </script>
-
-  </dom-module>
diff --git a/tensorflow/tensorboard/components/vz_line_chart/BUILD b/tensorflow/tensorboard/components/vz_line_chart/BUILD
deleted file mode 100644
index 8bbf8a24d3..0000000000
--- a/tensorflow/tensorboard/components/vz_line_chart/BUILD
+++ /dev/null
@@ -1,53 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "vz_line_chart",
-    srcs = [
-        "dragZoomInteraction.ts",
-        "vz-chart-helpers.ts",
-        "vz-line-chart.html",
-        "vz-line-chart.ts",
-    ],
-    path = "/vz-line-chart",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:lodash",
-        "//tensorflow/tensorboard/components/tf_imports:plottable",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-    ],
-)
-
-ts_web_library(
-    name = "demo",
-    srcs = ["index.html"],
-    path = "/vz-line-chart",
-    deps = [
-        ":vz_line_chart",
-        "@org_polymer_iron_demo_helpers",
-        "@org_polymer_paper_styles",
-    ],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":vz_line_chart"],
-    visibility = ["//learning/vis/vz_elements/catalog:__pkg__"],
-    destdir = "vz-line-chart",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports_google:lib",
-        "//tensorflow/tensorboard/components/vz_sorting:legacy",
-        "//third_party/javascript/polymer/v1/polymer:lib",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/vz_line_chart/dragZoomInteraction.ts b/tensorflow/tensorboard/components/vz_line_chart/dragZoomInteraction.ts
deleted file mode 100644
index c7f1f30e76..0000000000
--- a/tensorflow/tensorboard/components/vz_line_chart/dragZoomInteraction.ts
+++ /dev/null
@@ -1,200 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export class DragZoomLayer extends Plottable.Components.SelectionBoxLayer {
-  private _dragInteraction: Plottable.Interactions.Drag;
-  private _doubleClickInteraction: Plottable.Interactions.Click;
-  private isZoomed = false;
-  private easeFn: (t: number) => number = d3.easeCubicInOut;
-  private _animationTime = 750;
-  private onStart: Function;
-  private onEnd: Function;
-  private unzoomMethod: Function;
-
-  /**
-   * Constructs a SelectionBoxLayer with an attached DragInteraction and
-   * ClickInteraction. On drag, it triggers an animated zoom into the box
-   * that was dragged. On double click, it zooms back out to the original
-   * view, before any zooming.
-   * The zoom animation uses an easing function (default
-   * d3.ease('cubic-in-out')) and is customizable.
-   * Usage: Construct the selection box layer and attach x and y scales,
-   * and then add the layer over the plot you are zooming on using a
-   * Component Group.
-   * TODO(danmane) - merge this into Plottable
-   */
-  constructor(
-      xScale: Plottable.QuantitativeScale<number|{valueOf(): number}>,
-      yScale: Plottable.QuantitativeScale<number|{valueOf(): number}>,
-      unzoomMethod: Function) {
-    super();
-    this.xScale(xScale);
-    this.yScale(yScale);
-    this._dragInteraction = new Plottable.Interactions.Drag();
-    this._dragInteraction.attachTo(this);
-    this._doubleClickInteraction = new Plottable.Interactions.Click();
-    this._doubleClickInteraction.attachTo(this);
-    this.setupCallbacks();
-    this.unzoomMethod = unzoomMethod;
-  }
-
-  /**
-   * Register a method that calls when the DragZoom interaction starts.
-   */
-  public interactionStart(cb: Function) {
-    this.onStart = cb;
-  }
-
-  /**
-   * Register a method that calls when the DragZoom interaction ends.
-   */
-  public interactionEnd(cb: Function) {
-    this.onEnd = cb;
-  }
-
-  private setupCallbacks() {
-    let dragging = false;
-    this._dragInteraction.onDragStart((startPoint: Plottable.Point) => {
-      this.bounds({
-        topLeft: startPoint,
-        bottomRight: startPoint,
-      });
-      this.onStart();
-    });
-    this._dragInteraction.onDrag((startPoint, endPoint) => {
-      this.bounds({topLeft: startPoint, bottomRight: endPoint});
-      this.boxVisible(true);
-      dragging = true;
-    });
-    this._dragInteraction.onDragEnd((startPoint, endPoint) => {
-      this.boxVisible(false);
-      this.bounds({topLeft: startPoint, bottomRight: endPoint});
-      if (dragging) {
-        this.zoom();
-      } else {
-        this.onEnd();
-      }
-      dragging = false;
-    });
-
-    this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this));
-  }
-
-  /* Set the time (in ms) over which the zoom will interpolate.
-   * 0 implies no interpolation. (ie zoom is instant)
-   */
-  public animationTime(): number;
-  public animationTime(animationTime: number): DragZoomLayer;
-  public animationTime(animationTime?: number): any {
-    if (animationTime == null) {
-      return this._animationTime;
-    }
-    if (animationTime < 0) {
-      throw new Error('animationTime cannot be negative');
-    }
-    this._animationTime = animationTime;
-    return this;
-  }
-
-  /**
-   * Set the easing function, which determines how the zoom interpolates
-   * over time.
-   */
-  public ease(fn: (t: number) => number): DragZoomLayer {
-    if (typeof(fn) !== 'function') {
-      throw new Error('ease function must be a function');
-    }
-    if (fn(0) !== 0 || fn(1) !== 1) {
-      Plottable.Utils.Window.warn(
-          'Easing function does not maintain invariant ' +
-          'f(0)==0 && f(1)==1. Bad behavior may result.');
-    }
-    this.easeFn = fn;
-    return this;
-  }
-
-  // Zoom into extent of the selection box bounds
-  private zoom() {
-    let x0: number = this.xExtent()[0].valueOf();
-    let x1: number = this.xExtent()[1].valueOf();
-    let y0: number = this.yExtent()[1].valueOf();
-    let y1: number = this.yExtent()[0].valueOf();
-
-    if (x0 === x1 || y0 === y1) {
-      return;
-    }
-
-    if (!this.isZoomed) {
-      this.isZoomed = true;
-    }
-    this.interpolateZoom(x0, x1, y0, y1);
-  }
-
-  // Restore the scales to their state before any zoom
-  private unzoom() {
-    if (!this.isZoomed) {
-      return;
-    }
-    this.isZoomed = false;
-    let xScale = this.xScale() as any;
-    xScale._domainMin = null;
-    xScale._domainMax = null;
-    let xDomain = xScale._getExtent();
-    this.xScale().domain(xDomain);
-    this.unzoomMethod();
-  }
-
-  // If we are zooming, disable interactions, to avoid contention
-  private isZooming(isZooming: boolean) {
-    this._dragInteraction.enabled(!isZooming);
-    this._doubleClickInteraction.enabled(!isZooming);
-  }
-
-  private interpolateZoom(x0f: number, x1f: number, y0f: number, y1f: number) {
-    let x0s: number = this.xScale().domain()[0].valueOf();
-    let x1s: number = this.xScale().domain()[1].valueOf();
-    let y0s: number = this.yScale().domain()[0].valueOf();
-    let y1s: number = this.yScale().domain()[1].valueOf();
-
-    // Copy a ref to the ease fn, so that changing ease wont affect zooms in
-    // progress.
-    let ease = this.easeFn;
-    let interpolator = (a: number, b: number, p: number) =>
-        d3.interpolateNumber(a, b)(ease(p));
-
-    this.isZooming(true);
-    let start = Date.now();
-    let draw = () => {
-      let now = Date.now();
-      let passed = now - start;
-      let p = this._animationTime === 0 ?
-          1 :
-          Math.min(1, passed / this._animationTime);
-      let x0 = interpolator(x0s, x0f, p);
-      let x1 = interpolator(x1s, x1f, p);
-      let y0 = interpolator(y0s, y0f, p);
-      let y1 = interpolator(y1s, y1f, p);
-      this.xScale().domain([x0, x1]);
-      this.yScale().domain([y0, y1]);
-      if (p < 1) {
-        Plottable.Utils.DOM.requestAnimationFramePolyfill(draw);
-      } else {
-        this.onEnd();
-        this.isZooming(false);
-      }
-    };
-    draw();
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_line_chart/index.html b/tensorflow/tensorboard/components/vz_line_chart/index.html
deleted file mode 100644
index 856ab7d1ef..0000000000
--- a/tensorflow/tensorboard/components/vz_line_chart/index.html
+++ /dev/null
@@ -1,72 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<html>
-  <head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>vz-line-chart demo</title>
-    <link rel="import" href="vz-line-chart.html">
-    <link rel="import" href="../iron-demo-helpers/demo-snippet.html">
-    <link rel="import" href="../paper-styles/typography.html">
-    <style type="text/css">
-      body {
-        font-family: "Roboto";
-      }
-
-      vz-line-chart {
-        height: 400px;
-      }
-    </style>
-  </head>
-  <body>
-    <h3>Simple line chart</h3>
-    <demo-snippet>
-      <template>
-        <vz-line-chart id="simpleline"></vz-line-chart>
-        <script>
-          var elem = document.querySelector('#simpleline');
-          elem.setVisibleSeries(['test', 'train']);
-          elem.setSeriesData('test', data.test);
-          elem.setSeriesData('train', data.train);
-        </script>
-      </template>
-    </demo-snippet>
-
-    <h3>Exponential Smoothing enabled</h3>
-    <demo-snippet>
-      <template>
-        <vz-line-chart id="smoothedline"></vz-line-chart>
-        <script>
-          var elem = document.querySelector('#smoothedline');
-          elem.smoothingEnabled = true;
-          elem.setVisibleSeries(['test', 'train']);
-          elem.setSeriesData('test', data.test);
-          elem.setSeriesData('train', data.train);
-        </script>
-      </template>
-    </demo-snippet>
-
-    <script>
-      var data = {
-        "test": [{"scalar":0.07039999961853027,"wall_time":new Date("2016-06-24T04:13:01.295Z"),"step":0},{"scalar":0.6891000270843506,"wall_time":new Date("2016-06-24T04:13:05.909Z"),"step":10},{"scalar":0.8208000063896179,"wall_time":new Date("2016-06-24T04:13:10.318Z"),"step":20},{"scalar":0.8554999828338623,"wall_time":new Date("2016-06-24T04:13:14.794Z"),"step":30},{"scalar":0.8776000142097473,"wall_time":new Date("2016-06-24T04:13:19.166Z"),"step":40},{"scalar":0.8848999738693237,"wall_time":new Date("2016-06-24T04:13:23.603Z"),"step":50},{"scalar":0.8906000256538391,"wall_time":new Date("2016-06-24T04:13:27.931Z"),"step":60},{"scalar":0.8853999972343445,"wall_time":new Date("2016-06-24T04:13:32.281Z"),"step":70},{"scalar":0.8848999738693237,"wall_time":new Date("2016-06-24T04:13:36.636Z"),"step":80},{"scalar":0.8985999822616577,"wall_time":new Date("2016-06-24T04:13:41.070Z"),"step":90},{"scalar":0.9057000279426575,"wall_time":new Date("2016-06-24T04:13:45.475Z"),"step":100},{"scalar":0.9136000275611877,"wall_time":new Date("2016-06-24T04:13:49.947Z"),"step":110},{"scalar":0.919700026512146,"wall_time":new Date("2016-06-24T04:13:54.384Z"),"step":120},{"scalar":0.9182000160217285,"wall_time":new Date("2016-06-24T04:13:58.790Z"),"step":130},{"scalar":0.9282000064849854,"wall_time":new Date("2016-06-24T04:14:03.180Z"),"step":140},{"scalar":0.9218999743461609,"wall_time":new Date("2016-06-24T04:14:08.060Z"),"step":150},{"scalar":0.9294000267982483,"wall_time":new Date("2016-06-24T04:14:12.520Z"),"step":160},{"scalar":0.9261000156402588,"wall_time":new Date("2016-06-24T04:14:16.893Z"),"step":170},{"scalar":0.9236999750137329,"wall_time":new Date("2016-06-24T04:14:21.653Z"),"step":180},{"scalar":0.925000011920929,"wall_time":new Date("2016-06-24T04:14:26.065Z"),"step":190},{"scalar":0.9319000244140625,"wall_time":new Date("2016-06-24T04:14:30.430Z"),"step":200},{"scalar":0.933899998664856,"wall_time":new 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Date("2016-06-24T04:19:53.623Z"),"step":917},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:19:53.768Z"),"step":918},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:19:53.899Z"),"step":919},{"scalar":0.9399999976158142,"wall_time":new Date("2016-06-24T04:19:57.197Z"),"step":921},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:19:57.333Z"),"step":922},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:19:57.467Z"),"step":923},{"scalar":0.9300000071525574,"wall_time":new Date("2016-06-24T04:19:57.594Z"),"step":924},{"scalar":0.9900000095367432,"wall_time":new Date("2016-06-24T04:19:57.731Z"),"step":925},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:19:57.866Z"),"step":926},{"scalar":0.9399999976158142,"wall_time":new Date("2016-06-24T04:19:57.991Z"),"step":927},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:19:58.128Z"),"step":928},{"scalar":0.9900000095367432,"wall_time":new Date("2016-06-24T04:19:58.260Z"),"step":929},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:01.545Z"),"step":931},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:01.681Z"),"step":932},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:01.814Z"),"step":933},{"scalar":0.9300000071525574,"wall_time":new Date("2016-06-24T04:20:01.950Z"),"step":934},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:02.111Z"),"step":935},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:02.255Z"),"step":936},{"scalar":0.9300000071525574,"wall_time":new Date("2016-06-24T04:20:02.399Z"),"step":937},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:02.542Z"),"step":938},{"scalar":1,"wall_time":new Date("2016-06-24T04:20:02.672Z"),"step":939},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:06.030Z"),"step":941},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:06.172Z"),"step":942},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:06.314Z"),"step":943},{"scalar":0.9900000095367432,"wall_time":new Date("2016-06-24T04:20:06.436Z"),"step":944},{"scalar":0.9300000071525574,"wall_time":new Date("2016-06-24T04:20:06.572Z"),"step":945},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:06.704Z"),"step":946},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:06.833Z"),"step":947},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:06.966Z"),"step":948},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:07.105Z"),"step":949},{"scalar":0.9900000095367432,"wall_time":new Date("2016-06-24T04:20:10.389Z"),"step":951},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:10.522Z"),"step":952},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:10.660Z"),"step":953},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:10.806Z"),"step":954},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:10.939Z"),"step":955},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:11.073Z"),"step":956},{"scalar":0.9300000071525574,"wall_time":new Date("2016-06-24T04:20:11.204Z"),"step":957},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:11.339Z"),"step":958},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:11.465Z"),"step":959},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:14.750Z"),"step":961},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:14.879Z"),"step":962},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:15.017Z"),"step":963},{"scalar":0.9200000166893005,"wall_time":new Date("2016-06-24T04:20:15.152Z"),"step":964},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:15.288Z"),"step":965},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:15.415Z"),"step":966},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:15.550Z"),"step":967},{"scalar":0.9300000071525574,"wall_time":new Date("2016-06-24T04:20:15.679Z"),"step":968},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:15.815Z"),"step":969},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:19.650Z"),"step":971},{"scalar":0.9399999976158142,"wall_time":new Date("2016-06-24T04:20:19.786Z"),"step":972},{"scalar":0.9900000095367432,"wall_time":new Date("2016-06-24T04:20:19.922Z"),"step":973},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:20.058Z"),"step":974},{"scalar":0.949999988079071,"wall_time":new Date("2016-06-24T04:20:20.198Z"),"step":975},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:20.326Z"),"step":976},{"scalar":0.9300000071525574,"wall_time":new Date("2016-06-24T04:20:20.461Z"),"step":977},{"scalar":1,"wall_time":new Date("2016-06-24T04:20:20.588Z"),"step":978},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:20.718Z"),"step":979},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:24.015Z"),"step":981},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:24.151Z"),"step":982},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:24.300Z"),"step":983},{"scalar":0.9900000095367432,"wall_time":new Date("2016-06-24T04:20:24.447Z"),"step":984},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:24.576Z"),"step":985},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:24.701Z"),"step":986},{"scalar":0.9399999976158142,"wall_time":new Date("2016-06-24T04:20:24.830Z"),"step":987},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:24.977Z"),"step":988},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:25.134Z"),"step":989},{"scalar":0.9599999785423279,"wall_time":new Date("2016-06-24T04:20:28.551Z"),"step":991},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:28.681Z"),"step":992},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:28.812Z"),"step":993},{"scalar":0.9900000095367432,"wall_time":new Date("2016-06-24T04:20:28.949Z"),"step":994},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:29.086Z"),"step":995},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:29.219Z"),"step":996},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:29.351Z"),"step":997},{"scalar":0.9700000286102295,"wall_time":new Date("2016-06-24T04:20:29.497Z"),"step":998},{"scalar":0.9800000190734863,"wall_time":new Date("2016-06-24T04:20:29.637Z"),"step":999,"name":"train","relative":0.12446694444444445}]
-      };
-    </script>
-  </body>
-</html>
diff --git a/tensorflow/tensorboard/components/vz_line_chart/vz-chart-helpers.ts b/tensorflow/tensorboard/components/vz_line_chart/vz-chart-helpers.ts
deleted file mode 100644
index fa89e06ada..0000000000
--- a/tensorflow/tensorboard/components/vz_line_chart/vz-chart-helpers.ts
+++ /dev/null
@@ -1,219 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export interface Datum {
-  wall_time: Date;
-  step: number;
-}
-
-export interface Scalar {
-  scalar: number;
-  smoothed: number;
-}
-
-export type ScalarDatum = Datum & Scalar;
-
-export type DataFn = (run: string, tag: string) => Promise<Array<Datum>>;
-
-export let Y_TOOLTIP_FORMATTER_PRECISION = 4;
-export let STEP_FORMATTER_PRECISION = 4;
-export let Y_AXIS_FORMATTER_PRECISION = 3;
-export let TOOLTIP_Y_PIXEL_OFFSET = 20;
-export let TOOLTIP_CIRCLE_SIZE = 4;
-export let NAN_SYMBOL_SIZE = 6;
-
-export interface Point {
-  x: number;  // pixel space
-  y: number;  // pixel space
-  datum: ScalarDatum;
-  dataset: Plottable.Dataset;
-}
-
-/* Create a formatter function that will switch between exponential and
- * regular display depending on the scale of the number being formatted,
- * and show `digits` significant digits.
- */
-export function multiscaleFormatter(digits: number): ((v: number) => string) {
-  return (v: number) => {
-    let absv = Math.abs(v);
-    if (absv < 1E-15) {
-      // Sometimes zero-like values get an annoying representation
-      absv = 0;
-    }
-    let f: (x: number) => string;
-    if (absv >= 1E4) {
-      f = d3.format('.' + digits + 'e');
-    } else if (absv > 0 && absv < 0.01) {
-      f = d3.format('.' + digits + 'e');
-    } else {
-      f = d3.format('.' + digits + 'g');
-    }
-    return f(v);
-  };
-}
-
-/* Compute an appropriate domain given an array of all the values that are
- * going to be displayed. If ignoreOutliers is true, it will ignore the
- * lowest 10% and highest 10% of the data when computing a domain.
- * It has n log n performance when ignoreOutliers is true, as it needs to
- * sort the data.
- */
-export function computeDomain(values: number[], ignoreOutliers: boolean) {
-  // Don't include infinities and NaNs in the domain computation.
-  values = values.filter(z => isFinite(z));
-
-  if (values.length === 0) {
-    return [-0.1, 1.1];
-  }
-  let a: number;
-  let b: number;
-  if (ignoreOutliers) {
-    let sorted = _.sortBy(values);
-    a = d3.quantile(sorted, 0.05);
-    b = d3.quantile(sorted, 0.95);
-  } else {
-    a = d3.min(values);
-    b = d3.max(values);
-  }
-
-  let padding: number;
-  let span = b - a;
-  if (span === 0) {
-    // If b===a, we would create an empty range. We instead select the range
-    // [0, 2*a] if a > 0, or [-2*a, 0] if a < 0, plus a little bit of
-    // extra padding on the top and bottom of the plot.
-    padding = Math.abs(a) * 1.1 + 1.1;
-  } else {
-    padding = span * 0.2;
-  }
-
-  let lower: number;
-  if (a >= 0 && a < span) {
-    // We include the intercept (y = 0) if doing so less than doubles the span
-    // of the y-axis. (We actually select a lower bound that's slightly less
-    // than 0 so that 0.00 will clearly be written on the lower edge of the
-    // chart. The label on the lowest tick is often filtered out.)
-    lower = -0.1 * b;
-  } else {
-    lower = a - padding;
-  }
-
-
-  let domain = [lower, b + padding];
-  domain = d3.scaleLinear().domain(domain).nice().domain();
-  return domain;
-}
-
-export function accessorize(key: string): Plottable.IAccessor<number> {
-  // tslint:disable-next-line:no-any be quiet tsc
-  return (d: any, index: number, dataset: Plottable.Dataset) => d[key];
-}
-
-export interface XComponents {
-  /* tslint:disable */
-  scale: Plottable.Scales.Linear|Plottable.Scales.Time,
-      axis: Plottable.Axes.Numeric|Plottable.Axes.Time,
-      accessor: Plottable.IAccessor<number|Date>,
-  /* tslint:enable */
-}
-
-export let stepFormatter =
-    Plottable.Formatters.siSuffix(STEP_FORMATTER_PRECISION);
-export function stepX(): XComponents {
-  let scale = new Plottable.Scales.Linear();
-  let axis = new Plottable.Axes.Numeric(scale, 'bottom');
-  axis.formatter(stepFormatter);
-  return {
-    scale: scale,
-    axis: axis,
-    accessor: (d: Datum) => d.step,
-  };
-}
-
-export let timeFormatter = Plottable.Formatters.time('%a %b %e, %H:%M:%S');
-
-export function wallX(): XComponents {
-  let scale = new Plottable.Scales.Time();
-  return {
-    scale: scale,
-    axis: new Plottable.Axes.Time(scale, 'bottom'),
-    accessor: (d: Datum) => d.wall_time,
-  };
-}
-export let relativeAccessor =
-    // tslint:disable-next-line:no-any be quiet tsc
-    (d: any, index: number, dataset: Plottable.Dataset) => {
-      // We may be rendering the final-point datum for scatterplot.
-      // If so, we will have already provided the 'relative' property
-      if (d.relative != null) {
-        return d.relative;
-      }
-      let data = dataset.data();
-      // I can't imagine how this function would be called when the data is
-      // empty (after all, it iterates over the data), but lets guard just
-      // to be safe.
-      let first = data.length > 0 ? +data[0].wall_time : 0;
-      return (+d.wall_time - first) / (60 * 60 * 1000);  // ms to hours
-    };
-
-export let relativeFormatter = (n: number) => {
-  // we will always show 2 units of precision, e.g days and hours, or
-  // minutes and seconds, but not hours and minutes and seconds
-  let ret = '';
-  let days = Math.floor(n / 24);
-  n -= (days * 24);
-  if (days) {
-    ret += days + 'd ';
-  }
-  let hours = Math.floor(n);
-  n -= hours;
-  n *= 60;
-  if (hours || days) {
-    ret += hours + 'h ';
-  }
-  let minutes = Math.floor(n);
-  n -= minutes;
-  n *= 60;
-  if (minutes || hours || days) {
-    ret += minutes + 'm ';
-  }
-  let seconds = Math.floor(n);
-  return ret + seconds + 's';
-};
-export function relativeX(): XComponents {
-  let scale = new Plottable.Scales.Linear();
-  return {
-    scale: scale,
-    axis: new Plottable.Axes.Numeric(scale, 'bottom'),
-    accessor: relativeAccessor,
-  };
-}
-
-// a very literal definition of NaN: true for NaN for a non-number type
-// or null, etc. False for Infinity or -Infinity
-export let isNaN = (x) => +x !== x;
-
-export function getXComponents(xType: string): XComponents {
-  switch (xType) {
-    case 'step':
-      return stepX();
-    case 'wall_time':
-      return wallX();
-    case 'relative':
-      return relativeX();
-    default:
-      throw new Error('invalid xType: ' + xType);
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_line_chart/vz-line-chart.html b/tensorflow/tensorboard/components/vz_line_chart/vz-line-chart.html
deleted file mode 100644
index 38e0d7cb8d..0000000000
--- a/tensorflow/tensorboard/components/vz_line_chart/vz-line-chart.html
+++ /dev/null
@@ -1,131 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-imports/d3.html">
-<link rel="import" href="../tf-imports/lodash.html">
-<link rel="import" href="../tf-imports/plottable.html">
-
-<!--
-vz-line-chart creates an element that draws a line chart for
-displaying event values.
-
-This line chart supports drawing multiple lines at the same time, with features
-such as different X scales (linear and temporal), tooltips and smoothing.
-
-@element vz-line-chart
-@demo demo/index.html
--->
-<dom-module id="vz-line-chart">
-  <template>
-    <div id="tooltip">
-      <table>
-        <thead>
-          <tr>
-            <th></th>
-            <th>Name</th>
-            <template is="dom-if" if="{{smoothingEnabled}}">
-              <th>Smoothed</th>
-            </template>
-            <th>Value</th>
-            <th>Step</th>
-            <th>Time</th>
-            <th>Relative</th>
-          </tr>
-        </thead>
-        <tbody>
-        </tbody>
-      </table>
-    </div>
-    <div id="chartdiv"></div>
-    <style>
-      :host {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        display: flex;
-        flex-direction: column;
-        flex-grow: 1;
-        flex-shrink: 1;
-        position: relative;
-        outline: none;
-      }
-      div {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
-      td {
-        padding-left: 5px;
-        padding-right: 5px;
-        font-size: 13px;
-        opacity: 1;
-      }
-      #tooltip {
-        pointer-events: none;
-        position: absolute;
-        opacity: 0;
-        box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
-        font-size: 14px;
-        background: rgba(0, 0, 0, 0.8);
-        color: white;
-        border-radius: 4px;
-        line-height: 1.4em;
-        padding: 8px;
-        z-index: 5;
-        cursor: none;
-        margin-top: 10px;
-      }
-      .swatch {
-        border-radius: 50%;
-        width: 14px;
-        height: 14px;
-        display: block;
-        border: 2px solid rgba(0,0,0,0);
-      }
-      .closest .swatch {
-        border: 2px solid white;
-      }
-      th {
-        padding-left: 5px;
-        padding-right: 5px;
-        text-align: left;
-      }
-      .distant td {
-        opacity: 0.8;
-      }
-
-      .distant td.swatch {
-        opacity: 1;
-      }
-
-      .ghost {
-        opacity: 0.2;
-        stroke-width: 1px;
-      }
-
-      #chartdiv line.guide-line {
-        stroke: #999;
-        stroke-width: 1.5px;
-      }
-
-    </style>
-  </template>
-  <script src="vz-chart-helpers.js"></script>
-  <script src="dragZoomInteraction.js"></script>
-  <script src="vz-line-chart.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_line_chart/vz-line-chart.ts b/tensorflow/tensorboard/components/vz_line_chart/vz-line-chart.ts
deleted file mode 100644
index 5da6190ea2..0000000000
--- a/tensorflow/tensorboard/components/vz_line_chart/vz-line-chart.ts
+++ /dev/null
@@ -1,773 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-/* tslint:disable:no-namespace variable-name */
-
-import {DragZoomLayer} from './dragZoomInteraction'
-import * as ChartHelpers from './vz-chart-helpers'
-
-Polymer({
-  is: 'vz-line-chart',
-  properties: {
-    /**
-     * Scale that maps series names to colors. The default colors are from
-     * d3.schemeCategory10. Use this property to replace the default line
-     * colors with colors of your own choice.
-     * @type {Plottable.Scales.Color}
-     * @required
-     */
-    colorScale: {
-      type: Object,
-      value: function() {
-        return new Plottable.Scales.Color().range(d3.schemeCategory10);
-      }
-    },
-
-    /**
-     * Whether smoothing is enabled or not. If true, smoothed lines will be
-     * plotted in the chart while the unsmoothed lines will be ghosted in
-     * the background.
-     *
-     * The smoothing algorithm is a simple moving average, which, given a
-     * point p and a window w, replaces p with a simple average of the
-     * points in the [p - floor(w/2), p + floor(w/2)] range.  If there
-     * aren't enough points to cover the entire window to the left, the
-     * window is reduced to fit exactly the amount of elements available.
-     * This means that the smoothed line will be less in and gradually
-     * become more smooth until the desired window is reached. However when
-     * there aren't enough points on the right, the line stops being
-     * rendered at all.
-     */
-    smoothingEnabled: {type: Boolean, value: false},
-
-    /**
-     * Weight (between 0.0 and 1.0) of the smoothing. This weight controls
-     * the window size, and a weight of 1.0 means using 50% of the entire
-     * dataset as the window, while a weight of 0.0 means using a window of
-     * 0 (and thus replacing each point with themselves).
-     *
-     * The growth between 0.0 and 1.0 is not linear though. Because
-     * changing the window from 0% to 30% of the dataset smooths the line a
-     * lot more than changing the window from 70% to 100%, an exponential
-     * function is used instead: http://i.imgur.com/bDrhEZU.png. This
-     * function increases the size of the window slowly at the beginning
-     * and gradually speeds up the growth, but 0.0 still means a window of
-     * 0 and 1.0 still means a window of the dataset's length.
-     */
-    smoothingWeight: {type: Number, value: 0.6},
-
-    /**
-     * The way to display the X values. Allows:
-     * - "step" - Linear scale using the  "step" property of the datum.
-     * - "wall_time" - Temporal scale using the "wall_time" property of the
-     * datum.
-     * - "relative" - Temporal scale using the "relative" property of the
-     * datum if it is present or calculating from "wall_time" if it isn't.
-     */
-    xType: {type: String, value: 'step'},
-
-    /**
-     * The scale for the y-axis. Allows:
-     * - "linear" - linear scale (Plottable.Scales.Linear)
-     * - "log" - modified-log scale (Plottable.Scales.ModifiedLog)
-     */
-    yScaleType: {type: String, value: 'linear'},
-
-    /**
-     * Whether to ignore outlier data when computing the yScale domain.
-     */
-
-    ignoreYOutliers: {
-      type: Boolean,
-      value: false,
-    },
-
-    /**
-     * Change how the tooltip is sorted. Allows:
-     * - "default" - Sort the tooltip by input order.
-     * - "ascending" - Sort the tooltip by ascending value.
-     * - "descending" - Sort the tooltip by descending value.
-     * - "nearest" - Sort the tooltip by closest to cursor.
-     */
-    tooltipSortingMethod: {type: String, value: 'default'},
-
-    /**
-     * Change how the tooltip is positioned. Allows:
-     * - "bottom" - Position the tooltip on the bottom of the chart.
-     * - "right" - Position the tooltip to the right of the chart.
-     */
-    tooltipPosition: {type: String, value: 'bottom'},
-
-    _attached: Boolean,
-    _chart: Object,
-    _visibleSeriesCache: {
-      type: Array,
-      value: function() {
-        return []
-      }
-    },
-    _seriesDataCache: {
-      type: Object,
-      value: function() {
-        return {}
-      }
-    },
-    _makeChartAsyncCallbackId: {type: Number, value: null}
-  },
-  observers: [
-    '_makeChart(xType, yScaleType, colorScale, _attached)',
-    '_reloadFromCache(_chart)',
-    '_smoothingChanged(smoothingEnabled, smoothingWeight, _chart)',
-    '_tooltipSortingMethodChanged(tooltipSortingMethod, _chart)',
-    '_tooltipPositionChanged(tooltipPosition, _chart)',
-    '_outliersChanged(ignoreYOutliers, _chart)'
-  ],
-
-  /**
-   * Sets the series that the chart displays. Series with other names will
-   * not be displayed.
-   *
-   * @param {Array<String>} names Array with the names of the series to
-   * display.
-   */
-  setVisibleSeries: function(names) {
-    this._visibleSeriesCache = names;
-    if (this._chart) {
-      this._chart.setVisibleSeries(names);
-      this.redraw();
-    }
-  },
-
-  /**
-   * Sets the data of one of the series. Note that to display this series
-   * its name must be in the setVisibleSeries() array.
-   *
-   * @param {string} name Name of the series.
-   * @param {Array<ChartHelpers.ScalarDatum>} data Data of the series. This is
-   * an array of objects with at least the following properties:
-   * - step: (Number) - index of the datum.
-   * - wall_time: (Date) - Date object with the datum's time.
-   * - scalar: (Number) - Value of the datum.
-   */
-  setSeriesData: function(name, data) {
-    this._seriesDataCache[name] = data;
-    if (this._chart) {
-      this._chart.setSeriesData(name, data);
-    }
-  },
-
-  /**
-   * Re-renders the chart. Useful if e.g. the container size changed.
-   */
-  redraw: function() {
-    this._chart.redraw();
-  },
-  attached: function() {
-    this._attached = true;
-  },
-  detached: function() {
-    this._attached = false;
-  },
-  ready: function() {
-    this.scopeSubtree(this.$.tooltip, true);
-    this.scopeSubtree(this.$.chartdiv, true);
-  },
-  _makeChart: function(xType, yScaleType, colorScale, _attached) {
-    if (this._makeChartAsyncCallbackId !== null) {
-      this.cancelAsync(this._makeChartAsyncCallbackId);
-      this._makeChartAsyncCallbackId = null;
-    }
-
-    this._makeChartAsyncCallbackId = this.async(function() {
-      this._makeChartAsyncCallbackId = null;
-      if (!this._attached) return;
-      if (this._chart) this._chart.destroy();
-      var tooltip = d3.select(this.$.tooltip);
-      var chart = new LineChart(xType, yScaleType, colorScale, tooltip);
-      var div = d3.select(this.$.chartdiv);
-      chart.renderTo(div);
-      this._chart = chart;
-    }, 350);
-  },
-  _reloadFromCache: function() {
-    if (this._chart) {
-      this._chart.setVisibleSeries(this._visibleSeriesCache);
-      this._visibleSeriesCache.forEach(function(name) {
-        this._chart.setSeriesData(name, this._seriesDataCache[name] || []);
-      }.bind(this));
-    }
-  },
-  _smoothingChanged: function() {
-    if (!this._chart) {
-      return;
-    }
-    if (this.smoothingEnabled) {
-      this._chart.smoothingUpdate(this.smoothingWeight);
-    } else {
-      this._chart.smoothingDisable();
-    }
-  },
-  _outliersChanged: function() {
-    if (!this._chart) {
-      return;
-    }
-    this._chart.ignoreYOutliers(this.ignoreYOutliers);
-  },
-  _tooltipSortingMethodChanged: function() {
-    if (this._chart) {
-      this._chart.setTooltipSortingMethod(this.tooltipSortingMethod);
-    }
-  },
-  _tooltipPositionChanged: function() {
-    if (this._chart) {
-      this._chart.setTooltipPosition(this.tooltipPosition);
-    }
-  }
-});
-
-class LineChart {
-  private name2datasets: {[name: string]: Plottable.Dataset};
-  private seriesNames: string[];
-
-  private xAccessor: Plottable.IAccessor<number|Date>;
-  private xScale: Plottable.QuantitativeScale<number|Date>;
-  private yScale: Plottable.QuantitativeScale<number>;
-  private gridlines: Plottable.Components.Gridlines;
-  private center: Plottable.Components.Group;
-  private xAxis: Plottable.Axes.Numeric|Plottable.Axes.Time;
-  private yAxis: Plottable.Axes.Numeric;
-  private outer: Plottable.Components.Table;
-  private colorScale: Plottable.Scales.Color;
-  private tooltip: d3.Selection<any, any, any, any>;
-  private dzl: DragZoomLayer;
-
-  private linePlot: Plottable.Plots.Line<number|Date>;
-  private smoothLinePlot: Plottable.Plots.Line<number|Date>;
-  private scatterPlot: Plottable.Plots.Scatter<number|Date, Number>;
-  private nanDisplay: Plottable.Plots.Scatter<number|Date, Number>;
-  private scalarAccessor: Plottable.IAccessor<number>;
-  private smoothedAccessor: Plottable.IAccessor<number>;
-  private lastPointsDataset: Plottable.Dataset;
-  private datasets: Plottable.Dataset[];
-  private onDatasetChanged: (dataset: Plottable.Dataset) => void;
-  private nanDataset: Plottable.Dataset;
-  private smoothingWeight: number;
-  private smoothingEnabled: Boolean;
-  private tooltipSortingMethod: string;
-  private tooltipPosition: string;
-  private _ignoreYOutliers: boolean;
-
-  private targetSVG: d3.Selection<any, any, any, any>;
-
-  constructor(
-      xType: string, yScaleType: string, colorScale: Plottable.Scales.Color,
-      tooltip: d3.Selection<any, any, any, any>) {
-    this.seriesNames = [];
-    this.name2datasets = {};
-    this.colorScale = colorScale;
-    this.tooltip = tooltip;
-    this.datasets = [];
-    this._ignoreYOutliers = false;
-    // lastPointDataset is a dataset that contains just the last point of
-    // every dataset we're currently drawing.
-    this.lastPointsDataset = new Plottable.Dataset();
-    this.nanDataset = new Plottable.Dataset();
-    // need to do a single bind, so we can deregister the callback from
-    // old Plottable.Datasets. (Deregistration is done by identity checks.)
-    this.onDatasetChanged = this._onDatasetChanged.bind(this);
-    this.buildChart(xType, yScaleType);
-  }
-
-  private buildChart(xType: string, yScaleType: string) {
-    if (this.outer) {
-      this.outer.destroy();
-    }
-    let xComponents = ChartHelpers.getXComponents(xType);
-    this.xAccessor = xComponents.accessor;
-    this.xScale = xComponents.scale;
-    this.xAxis = xComponents.axis;
-    this.xAxis.margin(0).tickLabelPadding(3);
-    this.yScale = LineChart.getYScaleFromType(yScaleType);
-    this.yAxis = new Plottable.Axes.Numeric(this.yScale, 'left');
-    let yFormatter = ChartHelpers.multiscaleFormatter(
-        ChartHelpers.Y_AXIS_FORMATTER_PRECISION);
-    this.yAxis.margin(0).tickLabelPadding(5).formatter(yFormatter);
-    this.yAxis.usesTextWidthApproximation(true);
-
-    this.dzl = new DragZoomLayer(
-        this.xScale, this.yScale, this.updateSpecialDatasets.bind(this));
-
-    let center = this.buildPlot(this.xAccessor, this.xScale, this.yScale);
-
-    this.gridlines =
-        new Plottable.Components.Gridlines(this.xScale, this.yScale);
-
-    let xZeroLine = new Plottable.Components.GuideLineLayer('horizontal');
-    xZeroLine.scale(this.yScale).value(0);
-    let yZeroLine = new Plottable.Components.GuideLineLayer('vertical');
-    yZeroLine.scale(this.xScale).value(0);
-
-    this.center = new Plottable.Components.Group(
-        [this.gridlines, xZeroLine, yZeroLine, center, this.dzl]);
-    this.outer = new Plottable.Components.Table(
-        [[this.yAxis, this.center], [null, this.xAxis]]);
-  }
-
-  private buildPlot(xAccessor, xScale, yScale): Plottable.Component {
-    this.scalarAccessor = (d: ChartHelpers.ScalarDatum) => d.scalar;
-    this.smoothedAccessor = (d: ChartHelpers.ScalarDatum) => d.smoothed;
-    let linePlot = new Plottable.Plots.Line<number|Date>();
-    linePlot.x(xAccessor, xScale);
-    linePlot.y(this.scalarAccessor, yScale);
-    linePlot.attr(
-        'stroke',
-        (d: ChartHelpers.Datum, i: number, dataset: Plottable.Dataset) =>
-            this.colorScale.scale(dataset.metadata().name));
-    this.linePlot = linePlot;
-    let group = this.setupTooltips(linePlot);
-
-    let smoothLinePlot = new Plottable.Plots.Line<number|Date>();
-    smoothLinePlot.x(xAccessor, xScale);
-    smoothLinePlot.y(this.smoothedAccessor, yScale);
-    smoothLinePlot.attr(
-        'stroke',
-        (d: ChartHelpers.Datum, i: number, dataset: Plottable.Dataset) =>
-            this.colorScale.scale(dataset.metadata().name));
-    this.smoothLinePlot = smoothLinePlot;
-
-    // The scatterPlot will display the last point for each dataset.
-    // This way, if there is only one datum for the series, it is still
-    // visible. We hide it when tooltips are active to keep things clean.
-    let scatterPlot = new Plottable.Plots.Scatter<number|Date, number>();
-    scatterPlot.x(xAccessor, xScale);
-    scatterPlot.y(this.scalarAccessor, yScale);
-    scatterPlot.attr('fill', (d: any) => this.colorScale.scale(d.name));
-    scatterPlot.attr('opacity', 1);
-    scatterPlot.size(ChartHelpers.TOOLTIP_CIRCLE_SIZE * 2);
-    scatterPlot.datasets([this.lastPointsDataset]);
-    this.scatterPlot = scatterPlot;
-
-    let nanDisplay = new Plottable.Plots.Scatter<number|Date, number>();
-    nanDisplay.x(xAccessor, xScale);
-    nanDisplay.y((x) => x.displayY, yScale);
-    nanDisplay.attr('fill', (d: any) => this.colorScale.scale(d.name));
-    nanDisplay.attr('opacity', 1);
-    nanDisplay.size(ChartHelpers.NAN_SYMBOL_SIZE * 2);
-    nanDisplay.datasets([this.nanDataset]);
-    nanDisplay.symbol(Plottable.SymbolFactories.triangle);
-    this.nanDisplay = nanDisplay;
-
-    return new Plottable.Components.Group(
-        [nanDisplay, scatterPlot, smoothLinePlot, group]);
-  }
-
-  /** Updates the chart when a dataset changes. Called every time the data of
-   * a dataset changes to update the charts.
-   */
-  private _onDatasetChanged(dataset: Plottable.Dataset) {
-    if (this.smoothingEnabled) {
-      this.resmoothDataset(dataset);
-    }
-    this.updateSpecialDatasets();
-  }
-
-  public ignoreYOutliers(ignoreYOutliers: boolean) {
-    if (ignoreYOutliers !== this._ignoreYOutliers) {
-      this._ignoreYOutliers = ignoreYOutliers;
-      this.updateSpecialDatasets();
-    }
-  }
-
-  private updateSpecialDatasets() {
-    if (this.smoothingEnabled) {
-      this.updateSpecialDatasetsWithAccessor(this.smoothedAccessor);
-    } else {
-      this.updateSpecialDatasetsWithAccessor(this.scalarAccessor);
-    }
-  }
-
-  /** Constructs special datasets. Each special dataset contains exceptional
-   * values from all of the regular datasets, e.g. last points in series, or
-   * NaN values. Those points will have a `name` and `relative` property added
-   * (since usually those are context in the surrounding dataset).
-   * The accessor will point to the correct data to access.
-   */
-  private updateSpecialDatasetsWithAccessor(accessor:
-                                                Plottable.IAccessor<number>) {
-    let lastPointsData =
-        this.datasets
-            .map((d) => {
-              let datum = null;
-              // filter out NaNs to ensure last point is a clean one
-              let nonNanData =
-                  d.data().filter((x) => !isNaN(accessor(x, -1, d)));
-              if (nonNanData.length > 0) {
-                let idx = nonNanData.length - 1;
-                datum = nonNanData[idx];
-                datum.name = d.metadata().name;
-                datum.relative = ChartHelpers.relativeAccessor(datum, -1, d);
-              }
-              return datum;
-            })
-            .filter((x) => x != null);
-    this.lastPointsDataset.data(lastPointsData);
-
-    // Take a dataset, return an array of NaN data points
-    // the NaN points will have a "displayY" property which is the
-    // y-value of a nearby point that was not NaN (0 if all points are NaN)
-    let datasetToNaNData = (d: Plottable.Dataset) => {
-      let displayY = null;
-      let data = d.data();
-      let i = 0;
-      while (i < data.length && displayY == null) {
-        if (!isNaN(accessor(data[i], -1, d))) {
-          displayY = accessor(data[i], -1, d);
-        }
-        i++;
-      }
-      if (displayY == null) {
-        displayY = 0;
-      }
-      let nanData = [];
-      for (i = 0; i < data.length; i++) {
-        if (!isNaN(accessor(data[i], -1, d))) {
-          displayY = accessor(data[i], -1, d);
-        } else {
-          data[i].name = d.metadata().name;
-          data[i].displayY = displayY;
-          data[i].relative = ChartHelpers.relativeAccessor(data[i], -1, d);
-          nanData.push(data[i]);
-        }
-      }
-      return nanData;
-    };
-    let nanData = _.flatten(this.datasets.map(datasetToNaNData));
-    this.nanDataset.data(nanData);
-
-    let datasetToValues: (d: Plottable.Dataset) => number[] = (d) => {
-      return d.data().map((x) => accessor(x, -1, d));
-    };
-    let vals = _.flatten(this.datasets.map(datasetToValues));
-    vals = vals.filter((x) => x === x && x !== Infinity && x !== -Infinity);
-    let domain = ChartHelpers.computeDomain(vals, this._ignoreYOutliers);
-    this.yScale.domain(domain);
-  }
-
-  private setupTooltips(plot: Plottable.XYPlot<number|Date, number>):
-      Plottable.Components.Group {
-    let pi = new Plottable.Interactions.Pointer();
-    pi.attachTo(plot);
-    // PointsComponent is a Plottable Component that will hold the little
-    // circles we draw over the closest data points
-    let pointsComponent = new Plottable.Component();
-    let group = new Plottable.Components.Group([plot, pointsComponent]);
-
-    let hideTooltips = () => {
-      this.tooltip.style('opacity', 0);
-      this.scatterPlot.attr('opacity', 1);
-      pointsComponent.content().selectAll('.point').remove();
-    };
-
-    let enabled = true;
-    let disableTooltips = () => {
-      enabled = false;
-      hideTooltips();
-    };
-    let enableTooltips = () => {
-      enabled = true;
-    };
-
-    this.dzl.interactionStart(disableTooltips);
-    this.dzl.interactionEnd(enableTooltips);
-
-    pi.onPointerMove((p: Plottable.Point) => {
-      if (!enabled) {
-        return;
-      }
-      let target: ChartHelpers.Point = {
-        x: p.x,
-        y: p.y,
-        datum: null,
-        dataset: null,
-      };
-
-
-      let bbox: SVGRect = (<any>this.gridlines.content().node()).getBBox();
-
-      // pts is the closets point to the tooltip for each dataset
-      let pts = plot.datasets()
-                    .map((dataset) => this.findClosestPoint(target, dataset))
-                    .filter(x => x != null);
-      let intersectsBBox = Plottable.Utils.DOM.intersectsBBox;
-      // We draw tooltips for points that are NaN, or are currently visible
-      let ptsForTooltips = pts.filter(
-          (p) => intersectsBBox(p.x, p.y, bbox) || isNaN(p.datum.scalar));
-      // Only draw little indicator circles for the non-NaN points
-      let ptsToCircle = ptsForTooltips.filter((p) => !isNaN(p.datum.scalar));
-
-      let ptsSelection: any =
-          pointsComponent.content().selectAll('.point').data(
-              ptsToCircle,
-              (p: ChartHelpers.Point) => p.dataset.metadata().name);
-      if (pts.length !== 0) {
-        ptsSelection.enter().append('circle').classed('point', true);
-        ptsSelection.attr('r', ChartHelpers.TOOLTIP_CIRCLE_SIZE)
-            .attr('cx', (p) => p.x)
-            .attr('cy', (p) => p.y)
-            .style('stroke', 'none')
-            .attr(
-                'fill',
-                (p) => this.colorScale.scale(p.dataset.metadata().name));
-        ptsSelection.exit().remove();
-        this.drawTooltips(ptsForTooltips, target);
-      } else {
-        hideTooltips();
-      }
-    });
-
-    pi.onPointerExit(hideTooltips);
-
-    return group;
-  }
-
-  private drawTooltips(
-      points: ChartHelpers.Point[], target: ChartHelpers.Point) {
-    // Formatters for value, step, and wall_time
-    this.scatterPlot.attr('opacity', 0);
-    let valueFormatter = ChartHelpers.multiscaleFormatter(
-        ChartHelpers.Y_TOOLTIP_FORMATTER_PRECISION);
-
-    let dist = (p: ChartHelpers.Point) =>
-        Math.pow(p.x - target.x, 2) + Math.pow(p.y - target.y, 2);
-    let closestDist = _.min(points.map(dist));
-
-    let valueSortMethod = this.scalarAccessor;
-    if (this.smoothingEnabled) {
-      valueSortMethod = this.smoothedAccessor;
-    }
-
-    if (this.tooltipSortingMethod === 'ascending') {
-      points = _.sortBy(points, (d) => valueSortMethod(d.datum, -1, d.dataset));
-    } else if (this.tooltipSortingMethod === 'descending') {
-      points = _.sortBy(points, (d) => valueSortMethod(d.datum, -1, d.dataset))
-                   .reverse();
-    } else if (this.tooltipSortingMethod === 'nearest') {
-      points = _.sortBy(points, dist);
-    } else {
-      // The 'default' sorting method maintains the order of names passed to
-      // setVisibleSeries(). However we reverse that order when defining the
-      // datasets. So we must call reverse again to restore the order.
-      points = points.slice(0).reverse();
-    }
-
-    let rows = this.tooltip.select('tbody')
-                   .html('')
-                   .selectAll('tr')
-                   .data(points)
-                   .enter()
-                   .append('tr');
-    // Grey out the point if any of the following are true:
-    // - The cursor is outside of the x-extent of the dataset
-    // - The point's y value is NaN
-    rows.classed('distant', (d) => {
-      let firstPoint = d.dataset.data()[0];
-      let lastPoint = _.last(d.dataset.data());
-      let firstX = this.xScale.scale(this.xAccessor(firstPoint, 0, d.dataset));
-      let lastX = this.xScale.scale(this.xAccessor(lastPoint, 0, d.dataset));
-      let s = this.smoothingEnabled ? d.datum.smoothed : d.datum.scalar;
-      return target.x < firstX || target.x > lastX || isNaN(s);
-    });
-    rows.classed('closest', (p) => dist(p) === closestDist);
-    // It is a bit hacky that we are manually applying the width to the swatch
-    // and the nowrap property to the text here. The reason is as follows:
-    // the style gets updated asynchronously by Polymer scopeSubtree observer.
-    // Which means we would get incorrect sizing information since the text
-    // would wrap by default. However, we need correct measurements so that
-    // we can stop the text from falling off the edge of the screen.
-    // therefore, we apply the size-critical styles directly.
-    rows.style('white-space', 'nowrap');
-    rows.append('td')
-        .append('span')
-        .classed('swatch', true)
-        .style(
-            'background-color',
-            (d) => this.colorScale.scale(d.dataset.metadata().name));
-    rows.append('td').text((d) => d.dataset.metadata().name);
-    if (this.smoothingEnabled) {
-      rows.append('td').text(
-          (d) => isNaN(d.datum.smoothed) ? 'NaN' :
-                                           valueFormatter(d.datum.smoothed));
-    }
-    rows.append('td').text(
-        (d) => isNaN(d.datum.scalar) ? 'NaN' : valueFormatter(d.datum.scalar));
-    rows.append('td').text((d) => ChartHelpers.stepFormatter(d.datum.step));
-    rows.append('td').text(
-        (d) => ChartHelpers.timeFormatter(d.datum.wall_time));
-    rows.append('td').text(
-        (d) => ChartHelpers.relativeFormatter(
-            ChartHelpers.relativeAccessor(d.datum, -1, d.dataset)));
-
-    // compute left position
-    let documentWidth = document.body.clientWidth;
-    let node: any = this.tooltip.node();
-    let parentRect = node.parentElement.getBoundingClientRect();
-    let nodeRect = node.getBoundingClientRect();
-    // prevent it from falling off the right side of the screen
-    let left = documentWidth - parentRect.left - nodeRect.width - 60, top = 0;
-
-    if (this.tooltipPosition === 'right') {
-      left = Math.min(parentRect.width, left);
-    } else {  // 'bottom'
-      left = Math.min(0, left);
-      top = parentRect.height + ChartHelpers.TOOLTIP_Y_PIXEL_OFFSET;
-    }
-
-    this.tooltip.style('transform', 'translate(' + left + 'px,' + top + 'px)');
-    this.tooltip.style('opacity', 1);
-  }
-
-  private findClosestPoint(
-      target: ChartHelpers.Point,
-      dataset: Plottable.Dataset): ChartHelpers.Point {
-    let points: ChartHelpers.Point[] = dataset.data().map((d, i) => {
-      let x = this.xAccessor(d, i, dataset);
-      let y = this.smoothingEnabled ? this.smoothedAccessor(d, i, dataset) :
-                                      this.scalarAccessor(d, i, dataset);
-      return {
-        x: this.xScale.scale(x),
-        y: this.yScale.scale(y),
-        datum: d,
-        dataset: dataset,
-      };
-    });
-    let idx: number =
-        _.sortedIndex(points, target, (p: ChartHelpers.Point) => p.x);
-    if (idx === points.length) {
-      return points[points.length - 1];
-    } else if (idx === 0) {
-      return points[0];
-    } else {
-      let prev = points[idx - 1];
-      let next = points[idx];
-      let prevDist = Math.abs(prev.x - target.x);
-      let nextDist = Math.abs(next.x - target.x);
-      return prevDist < nextDist ? prev : next;
-    }
-  }
-
-  private resmoothDataset(dataset: Plottable.Dataset) {
-    let data = dataset.data();
-    const smoothingWeight = this.smoothingWeight;
-    let last = data.length > 0 ? data[0].scalar : NaN;
-    data.forEach((d) => {
-      if (!_.isFinite(last)) {
-        d.smoothed = d.scalar;
-      } else {
-        // 1st-order IIR low-pass filter to attenuate the higher-
-        // frequency components of the time-series.
-        d.smoothed = last * smoothingWeight + (1 - smoothingWeight) * d.scalar;
-      }
-      last = d.smoothed;
-    });
-  }
-
-  private getDataset(name: string) {
-    if (this.name2datasets[name] === undefined) {
-      this.name2datasets[name] = new Plottable.Dataset([], {name: name});
-    }
-    return this.name2datasets[name];
-  }
-
-  static getYScaleFromType(yScaleType: string):
-      Plottable.QuantitativeScale<number> {
-    if (yScaleType === 'log') {
-      return new Plottable.Scales.ModifiedLog();
-    } else if (yScaleType === 'linear') {
-      return new Plottable.Scales.Linear();
-    } else {
-      throw new Error('Unrecognized yScale type ' + yScaleType);
-    }
-  }
-
-  /**
-   * Update the selected series on the chart.
-   */
-  public setVisibleSeries(names: string[]) {
-    names = names.sort();
-    this.seriesNames = names;
-
-    names.reverse();  // draw first series on top
-    this.datasets.forEach((d) => d.offUpdate(this.onDatasetChanged));
-    this.datasets = names.map((r) => this.getDataset(r));
-    this.datasets.forEach((d) => d.onUpdate(this.onDatasetChanged));
-    this.linePlot.datasets(this.datasets);
-
-    if (this.smoothingEnabled) {
-      this.smoothLinePlot.datasets(this.datasets);
-    }
-    this.updateSpecialDatasets();
-  }
-
-  /**
-   * Set the data of a series on the chart.
-   */
-  public setSeriesData(name: string, data: ChartHelpers.ScalarDatum[]) {
-    this.getDataset(name).data(data);
-  }
-
-  public smoothingUpdate(weight: number) {
-    this.smoothingWeight = weight;
-    this.datasets.forEach((d) => this.resmoothDataset(d));
-
-    if (!this.smoothingEnabled) {
-      this.linePlot.addClass('ghost');
-      this.scatterPlot.y(this.smoothedAccessor, this.yScale);
-      this.smoothingEnabled = true;
-      this.smoothLinePlot.datasets(this.datasets);
-    }
-
-    this.updateSpecialDatasetsWithAccessor(this.smoothedAccessor);
-  }
-
-  public smoothingDisable() {
-    if (this.smoothingEnabled) {
-      this.linePlot.removeClass('ghost');
-      this.scatterPlot.y(this.scalarAccessor, this.yScale);
-      this.smoothLinePlot.datasets([]);
-      this.smoothingEnabled = false;
-      this.updateSpecialDatasetsWithAccessor(this.scalarAccessor);
-    }
-  }
-
-  public setTooltipSortingMethod(method: string) {
-    this.tooltipSortingMethod = method;
-  }
-
-  public setTooltipPosition(position: string) {
-    this.tooltipPosition = position;
-  }
-
-  public renderTo(targetSVG: d3.Selection<any, any, any, any>) {
-    this.targetSVG = targetSVG;
-    this.outer.renderTo(targetSVG);
-  }
-
-  public redraw() {
-    this.outer.redraw();
-  }
-
-  public destroy() {
-    this.outer.destroy();
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/BUILD b/tensorflow/tensorboard/components/vz_projector/BUILD
deleted file mode 100644
index acc1312a94..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/BUILD
+++ /dev/null
@@ -1,110 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "vz_projector",
-    srcs = [
-        "analyticsLogger.ts",
-        "bundle.html",
-        "data.ts",
-        "data-provider.ts",
-        "data-provider-demo.ts",
-        "data-provider-proto.ts",
-        "data-provider-server.ts",
-        "external.d.ts",
-        "knn.ts",
-        "label.ts",
-        "logging.ts",
-        "projectorEventContext.ts",
-        "projectorScatterPlotAdapter.ts",
-        "renderContext.ts",
-        "scatterPlot.ts",
-        "scatterPlotRectangleSelector.ts",
-        "scatterPlotVisualizer.ts",
-        "scatterPlotVisualizer3DLabels.ts",
-        "scatterPlotVisualizerCanvasLabels.ts",
-        "scatterPlotVisualizerPolylines.ts",
-        "scatterPlotVisualizerSprites.ts",
-        "styles.html",
-        "util.ts",
-        "vector.ts",
-        "vz-projector.html",
-        "vz-projector.ts",
-        "vz-projector-app.html",
-        "vz-projector-bookmark-panel.html",
-        "vz-projector-bookmark-panel.ts",
-        "vz-projector-colab.html",
-        "vz-projector-dashboard.html",
-        "vz-projector-data-panel.html",
-        "vz-projector-data-panel.ts",
-        "vz-projector-input.html",
-        "vz-projector-input.ts",
-        "vz-projector-inspector-panel.html",
-        "vz-projector-inspector-panel.ts",
-        "vz-projector-legend.html",
-        "vz-projector-legend.ts",
-        "vz-projector-metadata-card.html",
-        "vz-projector-metadata-card.ts",
-        "vz-projector-projections-panel.html",
-        "vz-projector-projections-panel.ts",
-        "vz-projector-util.ts",
-    ],
-    path = "/vz-projector",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":bh_tsne",
-        ":heap",
-        ":sptree",
-        "//tensorflow/tensorboard/components/tf_dashboard_common",
-        "//tensorflow/tensorboard/components/tf_imports:d3",
-        "//tensorflow/tensorboard/components/tf_imports:numericjs",
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:threejs",
-        "//tensorflow/tensorboard/components/tf_imports:weblas",
-        "@org_polymer_iron_collapse",
-        "@org_polymer_iron_icons",
-        "@org_polymer_paper_button",
-        "@org_polymer_paper_checkbox",
-        "@org_polymer_paper_dialog",
-        "@org_polymer_paper_dialog_scrollable",
-        "@org_polymer_paper_dropdown_menu",
-        "@org_polymer_paper_icon_button",
-        "@org_polymer_paper_input",
-        "@org_polymer_paper_item",
-        "@org_polymer_paper_listbox",
-        "@org_polymer_paper_slider",
-        "@org_polymer_paper_spinner",
-        "@org_polymer_paper_styles",
-        "@org_polymer_paper_toast",
-        "@org_polymer_paper_toggle_button",
-        "@org_polymer_paper_tooltip",
-    ],
-)
-
-ts_web_library(
-    name = "heap",
-    srcs = ["heap.ts"],
-    path = "/vz-projector",
-)
-
-ts_web_library(
-    name = "sptree",
-    srcs = ["sptree.ts"],
-    path = "/vz-projector",
-)
-
-ts_web_library(
-    name = "bh_tsne",
-    srcs = ["bh_tsne.ts"],
-    path = "/vz-projector",
-    deps = [":sptree"],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/vz_projector/analyticsLogger.ts b/tensorflow/tensorboard/components/vz_projector/analyticsLogger.ts
deleted file mode 100644
index aa1f86927d..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/analyticsLogger.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import {ProjectionType} from './data';
-
-export class AnalyticsLogger {
-  private eventLogging: boolean;
-  private pageViewLogging: boolean;
-
-  /**
-   * Constructs an event logger using Google Analytics. It assumes there is a
-   * Google Analytics script added to the page elsewhere. If there is no such
-   * script, the logger acts as a no-op.
-   *
-   * @param pageViewLogging Whether to log page views.
-   * @param eventLogging Whether to log user interaction.
-   */
-  constructor(pageViewLogging: boolean, eventLogging: boolean) {
-    if (typeof ga === 'undefined' || ga == null) {
-      this.eventLogging = false;
-      this.pageViewLogging = false;
-      return;
-    }
-    this.eventLogging = eventLogging;
-    this.pageViewLogging = pageViewLogging;
-  }
-
-  logPageView(pageTitle: string) {
-    if (this.pageViewLogging) {
-      // Always send a page view.
-      ga('send', {hitType: 'pageview', page: `/v/${pageTitle}`});
-    }
-  }
-
-  logProjectionChanged(projection: ProjectionType) {
-    if (this.eventLogging) {
-      ga('send', {
-        hitType: 'event',
-        eventCategory: 'Projection',
-        eventAction: 'click',
-        eventLabel: projection
-      });
-    }
-  }
-
-  logWebGLDisabled() {
-    if (this.eventLogging) {
-      ga('send', {
-        hitType: 'event',
-        eventCategory: 'Error',
-        eventAction: 'PageLoad',
-        eventLabel: 'WebGL_disabled'
-      });
-    }
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/bh_tsne.ts b/tensorflow/tensorboard/components/vz_projector/bh_tsne.ts
deleted file mode 100644
index 063d57ec40..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/bh_tsne.ts
+++ /dev/null
@@ -1,473 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * This is a fork of the Karpathy's TSNE.js (original license below).
- * This fork implements Barnes-Hut approximation and runs in O(NlogN)
- * time, as opposed to the Karpathy's O(N^2) version.
- *
- * @author smilkov@google.com (Daniel Smilkov)
- */
-
-/**
- * @license
- * The MIT License (MIT)
- * Copyright (c) 2015 Andrej Karpathy
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-
-import {SPNode, SPTree} from './sptree';
-
-type AugmSPNode = SPNode&{numCells: number, yCell: number[], rCell: number};
-
-/**
- * Barnes-hut approximation level. Higher means more approximation and faster
- * results. Recommended value mentioned in the paper is 0.8.
- */
-const THETA = 0.8;
-
-const MIN_POSSIBLE_PROB = 1E-9;
-
-// Variables used for memorizing the second random number since running
-// gaussRandom() generates two random numbers at the cost of 1 atomic
-// computation. This optimization results in 2X speed-up of the generator.
-let return_v = false;
-let v_val = 0.0;
-
-/** Returns the square euclidean distance between two vectors. */
-export function dist2(a: number[], b: number[]): number {
-  if (a.length !== b.length) {
-    throw new Error('Vectors a and b must be of same length');
-  }
-
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    let diff = a[i] - b[i];
-    result += diff * diff;
-  }
-  return result;
-}
-
-/** Returns the square euclidean distance between two 2D points. */
-export function dist2_2D(a: number[], b: number[]): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  return dX * dX + dY * dY;
-}
-
-/** Returns the square euclidean distance between two 3D points. */
-export function dist2_3D(a: number[], b: number[]): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  let dZ = a[2] - b[2];
-  return dX * dX + dY * dY + dZ * dZ;
-}
-
-function gaussRandom(rng: () => number): number {
-  if (return_v) {
-    return_v = false;
-    return v_val;
-  }
-  let u = 2 * rng() - 1;
-  let v = 2 * rng() - 1;
-  let r = u * u + v * v;
-  if (r === 0 || r > 1) {
-    return gaussRandom(rng);
-  }
-  let c = Math.sqrt(-2 * Math.log(r) / r);
-  v_val = v * c;  // cache this for next function call for efficiency
-  return_v = true;
-  return u * c;
-};
-
-// return random normal number
-function randn(rng: () => number, mu: number, std: number) {
-  return mu + gaussRandom(rng) * std;
-};
-
-// utilitity that creates contiguous vector of zeros of size n
-function zeros(n: number): Float64Array {
-  return new Float64Array(n);
-};
-
-// utility that returns a matrix filled with random numbers
-// generated by the provided generator.
-function randnMatrix(n: number, d: number, rng: () => number) {
-  let nd = n * d;
-  let x = zeros(nd);
-  for (let i = 0; i < nd; ++i) {
-    x[i] = randn(rng, 0.0, 1E-4);
-  }
-  return x;
-};
-
-// utility that returns a matrix filled with the provided value.
-function arrayofs(n: number, d: number, val: number) {
-  let x: number[][] = [];
-  for (let i = 0; i < n; ++i) {
-    x.push(d === 3 ? [val, val, val] : [val, val]);
-  }
-  return x;
-};
-
-// compute (p_{i|j} + p_{j|i})/(2n)
-function nearest2P(
-    nearest: {index: number, dist: number}[][], perplexity: number,
-    tol: number) {
-  let N = nearest.length;
-  let Htarget = Math.log(perplexity);  // target entropy of distribution
-  let P = zeros(N * N);                // temporary probability matrix
-  let K = nearest[0].length;
-  let pRow: number[] = new Array(K);  // pij[].
-
-  for (let i = 0; i < N; ++i) {
-    let neighbors = nearest[i];
-    let betaMin = -Infinity;
-    let betaMax = Infinity;
-    let beta = 1;  // initial value of precision
-    let maxTries = 50;
-
-    // perform binary search to find a suitable precision beta
-    // so that the entropy of the distribution is appropriate
-    let numTries = 0;
-    while (true) {
-      // compute entropy and kernel row with beta precision
-      let psum = 0.0;
-      for (let k = 0; k < neighbors.length; ++k) {
-        let neighbor = neighbors[k];
-        let pij = (i === neighbor.index) ? 0 : Math.exp(-neighbor.dist * beta);
-        pij = Math.max(pij, MIN_POSSIBLE_PROB);
-        pRow[k] = pij;
-        psum += pij;
-      }
-      // normalize p and compute entropy
-      let Hhere = 0.0;
-      for (let k = 0; k < pRow.length; ++k) {
-        pRow[k] /= psum;
-        let pij = pRow[k];
-        if (pij > 1E-7) {
-          Hhere -= pij * Math.log(pij);
-        };
-      }
-
-      // adjust beta based on result
-      if (Hhere > Htarget) {
-        // entropy was too high (distribution too diffuse)
-        // so we need to increase the precision for more peaky distribution
-        betaMin = beta;  // move up the bounds
-        if (betaMax === Infinity) {
-          beta = beta * 2;
-        } else {
-          beta = (beta + betaMax) / 2;
-        }
-
-      } else {
-        // converse case. make distrubtion less peaky
-        betaMax = beta;
-        if (betaMin === -Infinity) {
-          beta = beta / 2;
-        } else {
-          beta = (beta + betaMin) / 2;
-        }
-      }
-      numTries++;
-      // stopping conditions: too many tries or got a good precision
-      if (numTries >= maxTries || Math.abs(Hhere - Htarget) < tol) {
-        break;
-      }
-    }
-
-    // copy over the final prow to P at row i
-    for (let k = 0; k < pRow.length; ++k) {
-      let pij = pRow[k];
-      let j = neighbors[k].index;
-      P[i * N + j] = pij;
-    }
-  }  // end loop over examples i
-
-  // symmetrize P and normalize it to sum to 1 over all ij
-  let N2 = N * 2;
-  for (let i = 0; i < N; ++i) {
-    for (let j = i + 1; j < N; ++j) {
-      let i_j = i * N + j;
-      let j_i = j * N + i;
-      let value = (P[i_j] + P[j_i]) / N2;
-      P[i_j] = value;
-      P[j_i] = value;
-    }
-  }
-  return P;
-};
-
-// helper function
-function sign(x: number) {
-  return x > 0 ? 1 : x < 0 ? -1 : 0;
-}
-
-function computeForce_2d(
-    force: number[], mult: number, pointA: number[], pointB: number[]) {
-  force[0] += mult * (pointA[0] - pointB[0]);
-  force[1] += mult * (pointA[1] - pointB[1]);
-}
-
-function computeForce_3d(
-    force: number[], mult: number, pointA: number[], pointB: number[]) {
-  force[0] += mult * (pointA[0] - pointB[0]);
-  force[1] += mult * (pointA[1] - pointB[1]);
-  force[2] += mult * (pointA[2] - pointB[2]);
-}
-
-export interface TSNEOptions {
-  /** How many dimensions. */
-  dim: number;
-  /** Roughly how many neighbors each point influences. */
-  perplexity?: number;
-  /** Learning rate. */
-  epsilon?: number;
-  /** A random number generator. */
-  rng?: () => number;
-}
-
-export class TSNE {
-  private perplexity: number;
-  private epsilon: number;
-  /** Random generator */
-  private rng: () => number;
-  private iter = 0;
-  private Y: Float64Array;
-  private N: number;
-  private P: Float64Array;
-  private gains: number[][];
-  private ystep: number[][];
-  private nearest: {index: number, dist: number}[][];
-  private dim: number;
-  private dist2: (a: number[], b: number[]) => number;
-  private computeForce:
-      (force: number[], mult: number, pointA: number[],
-       pointB: number[]) => void;
-
-  constructor(opt: TSNEOptions) {
-    opt = opt || {dim: 2};
-    this.perplexity = opt.perplexity || 30;
-    this.epsilon = opt.epsilon || 10;
-    this.rng = opt.rng || Math.random;
-    this.dim = opt.dim;
-    if (opt.dim === 2) {
-      this.dist2 = dist2_2D;
-      this.computeForce = computeForce_2d;
-    } else if (opt.dim === 3) {
-      this.dist2 = dist2_3D;
-      this.computeForce = computeForce_3d;
-    } else {
-      throw new Error('Only 2D and 3D is supported');
-    }
-  }
-
-  // this function takes a fattened distance matrix and creates
-  // matrix P from them.
-  // D is assumed to be provided as an array of size N^2.
-  initDataDist(nearest: {index: number, dist: number}[][]) {
-    let N = nearest.length;
-    this.nearest = nearest;
-    this.P = nearest2P(nearest, this.perplexity, 1E-4);
-    this.N = N;
-    this.initSolution();  // refresh this
-  }
-
-  // (re)initializes the solution to random
-  initSolution() {
-    // generate random solution to t-SNE
-    this.Y = randnMatrix(this.N, this.dim, this.rng);  // the solution
-    this.gains = arrayofs(this.N, this.dim, 1.0);      // step gains
-    // to accelerate progress in unchanging directions
-    this.ystep = arrayofs(this.N, this.dim, 0.0);  // momentum accumulator
-    this.iter = 0;
-  }
-
-  // return pointer to current solution
-  getSolution() { return this.Y; }
-
-  // perform a single step of optimization to improve the embedding
-  step() {
-    this.iter += 1;
-    let N = this.N;
-
-    let grad = this.costGrad(this.Y);  // evaluate gradient
-
-    // perform gradient step
-    let ymean = this.dim === 3 ? [0, 0, 0] : [0, 0];
-    for (let i = 0; i < N; ++i) {
-      for (let d = 0; d < this.dim; ++d) {
-        let gid = grad[i][d];
-        let sid = this.ystep[i][d];
-        let gainid = this.gains[i][d];
-
-        // compute gain update
-        let newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
-        if (newgain < 0.01) {
-          newgain = 0.01;  // clamp
-        }
-        this.gains[i][d] = newgain;  // store for next turn
-
-        // compute momentum step direction
-        let momval = this.iter < 250 ? 0.5 : 0.8;
-        let newsid = momval * sid - this.epsilon * newgain * grad[i][d];
-        this.ystep[i][d] = newsid;  // remember the step we took
-
-        // step!
-        let i_d = i * this.dim + d;
-        this.Y[i_d] += newsid;
-        ymean[d] += this.Y[i_d];  // accumulate mean so that we
-                                  // can center later
-      }
-    }
-
-    // reproject Y to be zero mean
-    for (let i = 0; i < N; ++i) {
-      for (let d = 0; d < this.dim; ++d) {
-        this.Y[i * this.dim + d] -= ymean[d] / N;
-      }
-    }
-  }
-
-  // return cost and gradient, given an arrangement
-  costGrad(Y: Float64Array): number[][] {
-    let N = this.N;
-    let P = this.P;
-
-    // Trick that helps with local optima.
-    let alpha = this.iter < 100 ? 4 : 1;
-
-    // Make data for the SP tree.
-    let points: number[][] = new Array(N);  // (x, y)[]
-    for (let i = 0; i < N; ++i) {
-      let iTimesD = i * this.dim;
-      let row = new Array(this.dim);
-      for (let d = 0; d < this.dim; ++d) {
-        row[d] = Y[iTimesD + d];
-      }
-      points[i] = row;
-    }
-
-    // Make a tree.
-    let tree = new SPTree(points);
-    let root = tree.root as AugmSPNode;
-    // Annotate the tree.
-
-    let annotateTree =
-        (node: AugmSPNode): {numCells: number, yCell: number[]} => {
-          let numCells = 1;
-          if (node.children == null) {
-            // Update the current node and tell the parent.
-            node.numCells = numCells;
-            node.yCell = node.point;
-            return {numCells, yCell: node.yCell};
-          }
-          // node.point is a 2 or 3-dim number[], so slice() makes a copy.
-          let yCell = node.point.slice();
-          for (let i = 0; i < node.children.length; ++i) {
-            let child = node.children[i];
-            if (child == null) {
-              continue;
-            }
-            let result = annotateTree(child as AugmSPNode);
-            numCells += result.numCells;
-            for (let d = 0; d < this.dim; ++d) {
-              yCell[d] += result.yCell[d];
-            }
-          }
-          // Update the node and tell the parent.
-          node.numCells = numCells;
-          node.yCell = yCell.map(v => v / numCells);
-          return {numCells, yCell};
-        };
-
-    // Augment the tree with more info.
-    annotateTree(root);
-    tree.visit((node: AugmSPNode, low: number[], high: number[]) => {
-      node.rCell = high[0] - low[0];
-      return false;
-    });
-    // compute current Q distribution, unnormalized first
-    let grad: number[][] = [];
-    let Z = 0;
-    let forces: [number[], number[]][] = new Array(N);
-    for (let i = 0; i < N; ++i) {
-      let pointI = points[i];
-      // Compute the positive forces for the i-th node.
-      let Fpos = this.dim === 3 ? [0, 0, 0] : [0, 0];
-      let neighbors = this.nearest[i];
-      for (let k = 0; k < neighbors.length; ++k) {
-        let j = neighbors[k].index;
-        let pij = P[i * N + j];
-        let pointJ = points[j];
-        let squaredDistItoJ = this.dist2(pointI, pointJ);
-        let premult = pij / (1 + squaredDistItoJ);
-        this.computeForce(Fpos, premult, pointI, pointJ);
-      }
-      // Compute the negative forces for the i-th node.
-      let FnegZ = this.dim === 3 ? [0, 0, 0] : [0, 0];
-      tree.visit((node: AugmSPNode) => {
-        let squaredDistToCell = this.dist2(pointI, node.yCell);
-        // Squared distance from point i to cell.
-        if (node.children == null ||
-            (squaredDistToCell > 0 &&
-             node.rCell / Math.sqrt(squaredDistToCell) < THETA)) {
-          let qijZ = 1 / (1 + squaredDistToCell);
-          let dZ = node.numCells * qijZ;
-          Z += dZ;
-          dZ *= qijZ;
-          this.computeForce(FnegZ, dZ, pointI, node.yCell);
-          return true;
-        }
-        // Cell is too close to approximate.
-        let squaredDistToPoint = this.dist2(pointI, node.point);
-        let qijZ = 1 / (1 + squaredDistToPoint);
-        Z += qijZ;
-        qijZ *= qijZ;
-        this.computeForce(FnegZ, qijZ, pointI, node.point);
-        return false;
-      }, true);
-      forces[i] = [Fpos, FnegZ];
-    }
-    // Normalize the negative forces and compute the gradient.
-    const A = 4 * alpha;
-    const B = 4 / Z;
-    for (let i = 0; i < N; ++i) {
-      let [FPos, FNegZ] = forces[i];
-      let gsum = new Array(this.dim);
-      for (let d = 0; d < this.dim; ++d) {
-        gsum[d] = A * FPos[d] - B * FNegZ[d];
-      }
-      grad.push(gsum);
-    }
-    return grad;
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/bundle.html b/tensorflow/tensorboard/components/vz_projector/bundle.html
deleted file mode 100644
index f5a25230a0..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/bundle.html
+++ /dev/null
@@ -1,48 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-imports/d3.html">
-<link rel="import" href="../tf-imports/numericjs.html">
-<link rel="import" href="../tf-imports/threejs.html">
-<link rel="import" href="../tf-imports/weblas.html">
-
-<script src="heap.js"></script>
-<script src="label.js"></script>
-<script src="sptree.js"></script>
-<script src="bh_tsne.js"></script>
-<script src="logging.js"></script>
-<script src="renderContext.js"></script>
-<script src="scatterPlotRectangleSelector.js"></script>
-<script src="analyticsLogger.js"></script>
-<script src="util.js"></script>
-<script src="vector.js"></script>
-<script src="knn.js"></script>
-<script src="data.js"></script>
-<script src="data-provider.js"></script>
-<script src="data-provider-demo.js"></script>
-<script src="data-provider-proto.js"></script>
-<script src="data-provider-server.js"></script>
-<script src="projectorEventContext.js"></script>
-<script src="scatterPlot.js"></script>
-<script src="scatterPlotVisualizer3DLabels.js"></script>
-<script src="scatterPlotVisualizerCanvasLabels.js"></script>
-<script src="scatterPlotVisualizerPolylines.js"></script>
-<script src="scatterPlotVisualizerSprites.js"></script>
-<script src="scatterPlotVisualizer.js"></script>
-<script src="projectorScatterPlotAdapter.js"></script>
-<script src="vz-projector-util.js"></script>
diff --git a/tensorflow/tensorboard/components/vz_projector/data-provider-demo.ts b/tensorflow/tensorboard/components/vz_projector/data-provider-demo.ts
deleted file mode 100644
index 1410a84a8e..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/data-provider-demo.ts
+++ /dev/null
@@ -1,127 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataSet, SpriteAndMetadataInfo, State} from './data';
-import {ProjectorConfig, DataProvider, EmbeddingInfo, TENSORS_MSG_ID} from './data-provider';
-import * as dataProvider from './data-provider';
-import * as logging from './logging';
-
-const BYTES_EXTENSION = '.bytes';
-
-/** Data provider that loads data from a demo folder. */
-export class DemoDataProvider implements DataProvider {
-  private projectorConfigPath: string;
-  private projectorConfig: ProjectorConfig;
-
-  constructor(projectorConfigPath: string) {
-    this.projectorConfigPath = projectorConfigPath;
-  }
-
-  private getEmbeddingInfo(tensorName: string): EmbeddingInfo {
-    let embeddings = this.projectorConfig.embeddings;
-    for (let i = 0; i < embeddings.length; i++) {
-      let embedding = embeddings[i];
-      if (embedding.tensorName === tensorName) {
-        return embedding;
-      }
-    }
-    return null;
-  }
-
-  retrieveRuns(callback: (runs: string[]) => void): void {
-    callback(['Demo']);
-  }
-
-  retrieveProjectorConfig(run: string, callback: (d: ProjectorConfig) => void)
-      : void {
-    const msgId = logging.setModalMessage('Fetching projector config...');
-
-    const xhr = new XMLHttpRequest();
-    xhr.open('GET', this.projectorConfigPath);
-    xhr.onerror = (err) => {
-      let errorMessage = err.message;
-      // If the error is a valid XMLHttpResponse, it's possible this is a
-      // cross-origin error.
-      if (xhr.responseText != null) {
-        errorMessage = 'Cannot fetch projector config, possibly a ' +
-            'Cross-Origin request error.';
-      }
-      logging.setErrorMessage(errorMessage, 'fetching projector config');
-    };
-    xhr.onload = () => {
-      const projectorConfig = JSON.parse(xhr.responseText) as ProjectorConfig;
-      logging.setModalMessage(null, msgId);
-      this.projectorConfig = projectorConfig;
-      callback(projectorConfig);
-    };
-    xhr.send();
-  }
-
-  retrieveTensor(run: string, tensorName: string,
-      callback: (ds: DataSet) => void) {
-    let embedding = this.getEmbeddingInfo(tensorName);
-    let url = `${embedding.tensorPath}`;
-    if (embedding.tensorPath.substr(-1 * BYTES_EXTENSION.length) ===
-        BYTES_EXTENSION) {
-      dataProvider.retrieveTensorAsBytes(
-          this, this.getEmbeddingInfo(tensorName), run, tensorName, url,
-          callback);
-    } else {
-      logging.setModalMessage('Fetching tensors...', TENSORS_MSG_ID);
-      const request = new XMLHttpRequest();
-      request.open('GET', url);
-      request.responseType = 'arraybuffer';
-
-      request.onerror = () => {
-        logging.setErrorMessage(request.responseText, 'fetching tensors');
-      };
-      request.onload = () => {
-        dataProvider.parseTensors(request.response).then(points => {
-          callback(new DataSet(points));
-        });
-      };
-      request.send();
-    }
-  }
-
-  retrieveSpriteAndMetadata(run: string, tensorName: string,
-      callback: (r: SpriteAndMetadataInfo) => void) {
-    let embedding = this.getEmbeddingInfo(tensorName);
-    let spriteImagePath = null;
-    if (embedding.sprite && embedding.sprite.imagePath) {
-      spriteImagePath = embedding.sprite.imagePath;
-    }
-    dataProvider.retrieveSpriteAndMetadataInfo(
-        embedding.metadataPath, spriteImagePath, embedding.sprite, callback);
-  }
-
-  getBookmarks(
-      run: string, tensorName: string, callback: (r: State[]) => void) {
-    let embedding = this.getEmbeddingInfo(tensorName);
-    let msgId = logging.setModalMessage('Fetching bookmarks...');
-
-    const xhr = new XMLHttpRequest();
-    xhr.open('GET', embedding.bookmarksPath);
-    xhr.onerror = (err) => {
-      logging.setErrorMessage(xhr.responseText);
-    };
-    xhr.onload = () => {
-      const bookmarks = JSON.parse(xhr.responseText) as State[];
-      logging.setModalMessage(null, msgId);
-      callback(bookmarks);
-    };
-    xhr.send();
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/data-provider-proto.ts b/tensorflow/tensorboard/components/vz_projector/data-provider-proto.ts
deleted file mode 100644
index 67124a9232..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/data-provider-proto.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataPoint, DataProto, DataSet, SpriteAndMetadataInfo, PointMetadata, State} from './data';
-import {analyzeMetadata, ProjectorConfig, DataProvider} from './data-provider';
-
-
-export class ProtoDataProvider implements DataProvider {
-  private dataProto: DataProto;
-
-  constructor(dataProto: DataProto) {
-    this.dataProto = dataProto;
-  }
-
-  retrieveRuns(callback: (runs: string[]) => void): void {
-    callback(['proto']);
-  }
-
-  retrieveProjectorConfig(run: string, callback: (d: ProjectorConfig) => void) {
-    callback({
-      modelCheckpointPath: 'proto',
-      embeddings: [{
-        tensorName: 'proto',
-        tensorShape: this.dataProto.shape,
-        metadataPath: 'proto'
-      }]
-    });
-  }
-
-  retrieveTensor(run: string, tensorName: string,
-      callback: (ds: DataSet) => void) {
-    callback(this.flatArrayToDataset(this.dataProto.tensor));
-  }
-
-  retrieveSpriteAndMetadata(run: string, tensorName: string,
-      callback: (r: SpriteAndMetadataInfo) => void): void {
-    let columnNames = this.dataProto.metadata.columns.map(c => c.name);
-    let n = this.dataProto.shape[0];
-    let pointsMetadata: PointMetadata[] = new Array(n);
-    this.dataProto.metadata.columns.forEach(c => {
-      let values = c.numericValues || c.stringValues;
-      for (let i = 0; i < n; i++) {
-        pointsMetadata[i] = pointsMetadata[i] || {};
-        pointsMetadata[i][c.name] = values[i];
-      }
-    });
-    callback({
-      stats: analyzeMetadata(columnNames, pointsMetadata),
-      pointsInfo: pointsMetadata
-    });
-  }
-
-  getBookmarks(run: string, tensorName: string,
-      callback: (r: State[]) => void): void {
-    return callback([]);
-  }
-
-  private flatArrayToDataset(tensor: number[]): DataSet {
-    let points: DataPoint[] = [];
-    let n = this.dataProto.shape[0];
-    let d = this.dataProto.shape[1];
-    if (n * d !== tensor.length) {
-      throw 'The shape doesn\'t match the length of the flattened array';
-    }
-    for (let i = 0; i < n; i++) {
-      let offset = i * d;
-      points.push({
-        vector: new Float32Array(tensor.slice(offset, offset + d)),
-        metadata: {},
-        projections: null,
-        index: i
-      });
-    }
-    return new DataSet(points);
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/data-provider-server.ts b/tensorflow/tensorboard/components/vz_projector/data-provider-server.ts
deleted file mode 100644
index 02720ebf6a..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/data-provider-server.ts
+++ /dev/null
@@ -1,137 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataSet, SpriteAndMetadataInfo, State} from './data';
-import * as dataProvider from './data-provider';
-import {DataProvider, EmbeddingInfo, ProjectorConfig} from './data-provider';
-import * as logging from './logging';
-
-// Limit for the number of data points we receive from the server.
-export const LIMIT_NUM_POINTS = 100000;
-
-/**
- * Data provider that loads data provided by a python server (usually backed
- * by a checkpoint file).
- */
-export class ServerDataProvider implements DataProvider {
-  private routePrefix: string;
-  private runProjectorConfigCache: {[run: string]: ProjectorConfig} = {};
-
-  constructor(routePrefix: string) {
-    this.routePrefix = routePrefix;
-  }
-
-  private getEmbeddingInfo(run: string, tensorName: string,
-      callback: (e: EmbeddingInfo) => void): void {
-    this.retrieveProjectorConfig(run, config => {
-      const embeddings = config.embeddings;
-      for (let i = 0; i < embeddings.length; i++) {
-        const embedding = embeddings[i];
-        if (embedding.tensorName === tensorName) {
-          callback(embedding);
-          return;
-        }
-      }
-      callback(null);
-    });
-  }
-
-  retrieveRuns(callback: (runs: string[]) => void): void {
-    const msgId = logging.setModalMessage('Fetching runs...');
-
-    const xhr = new XMLHttpRequest();
-    xhr.open('GET', `${this.routePrefix}/runs`);
-    xhr.onerror = (err) => {
-      logging.setErrorMessage(xhr.responseText, 'fetching runs');
-    };
-    xhr.onload = () => {
-      const runs = JSON.parse(xhr.responseText);
-      logging.setModalMessage(null, msgId);
-      callback(runs);
-    };
-    xhr.send();
-  }
-
-  retrieveProjectorConfig(run: string, callback: (d: ProjectorConfig) => void)
-      : void {
-    if (run in this.runProjectorConfigCache) {
-      callback(this.runProjectorConfigCache[run]);
-      return;
-    }
-
-    const msgId = logging.setModalMessage('Fetching projector config...');
-
-    const xhr = new XMLHttpRequest();
-    xhr.open('GET', `${this.routePrefix}/info?run=${run}`);
-    xhr.onerror = (err) => {
-      logging.setErrorMessage(xhr.responseText, 'fetching projector config');
-    };
-    xhr.onload = () => {
-      const config = JSON.parse(xhr.responseText) as ProjectorConfig;
-      logging.setModalMessage(null, msgId);
-      this.runProjectorConfigCache[run] = config;
-      callback(config);
-    };
-    xhr.send();
-  }
-
-  retrieveTensor(run: string, tensorName: string,
-      callback: (ds: DataSet) => void) {
-    this.getEmbeddingInfo(run, tensorName, embedding => {
-      dataProvider.retrieveTensorAsBytes(
-          this, embedding, run, tensorName,
-          `${this.routePrefix}/tensor?run=${run}&name=${tensorName}` +
-              `&num_rows=${LIMIT_NUM_POINTS}`,
-          callback);
-    });
-  }
-
-  retrieveSpriteAndMetadata(run: string, tensorName: string,
-      callback: (r: SpriteAndMetadataInfo) => void) {
-    this.getEmbeddingInfo(run, tensorName, embedding => {
-      let metadataPath = null;
-      if (embedding.metadataPath) {
-        metadataPath =
-            `${this.routePrefix}/metadata?` +
-            `run=${run}&name=${tensorName}&num_rows=${LIMIT_NUM_POINTS}`;
-      }
-      let spriteImagePath = null;
-      if (embedding.sprite && embedding.sprite.imagePath) {
-        spriteImagePath =
-            `${this.routePrefix}/sprite_image?run=${run}&name=${tensorName}`;
-      }
-      dataProvider.retrieveSpriteAndMetadataInfo(metadataPath, spriteImagePath,
-          embedding.sprite, callback);
-    });
-  }
-
-  getBookmarks(
-      run: string, tensorName: string, callback: (r: State[]) => void) {
-    const msgId = logging.setModalMessage('Fetching bookmarks...');
-
-    const xhr = new XMLHttpRequest();
-    xhr.open(
-        'GET', `${this.routePrefix}/bookmarks?run=${run}&name=${tensorName}`);
-    xhr.onerror = (err) => {
-      logging.setErrorMessage(xhr.responseText, 'fetching bookmarks');
-    };
-    xhr.onload = () => {
-      logging.setModalMessage(null, msgId);
-      const bookmarks = JSON.parse(xhr.responseText);
-      callback(bookmarks);
-    };
-    xhr.send();
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/data-provider.ts b/tensorflow/tensorboard/components/vz_projector/data-provider.ts
deleted file mode 100644
index c8eede798c..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/data-provider.ts
+++ /dev/null
@@ -1,429 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {ColumnStats, DataPoint, DataSet, SpriteAndMetadataInfo, PointMetadata, State} from './data';
-import * as logging from './logging';
-import {runAsyncTask} from './util';
-
-/** Maximum number of colors supported in the color map. */
-const NUM_COLORS_COLOR_MAP = 50;
-const MAX_SPRITE_IMAGE_SIZE_PX = 8192;
-
-export const METADATA_MSG_ID = 'metadata';
-export const TENSORS_MSG_ID = 'tensors';
-
-/** Matches the json format of `projector_config.proto` */
-export interface SpriteMetadata {
-  imagePath: string;
-  singleImageDim: [number, number];
-}
-
-/** Matches the json format of `projector_config.proto` */
-export interface EmbeddingInfo {
-  /** Name of the tensor. */
-  tensorName: string;
-  /** The shape of the tensor. */
-  tensorShape: [number, number];
-  /**
-   * The path to the tensors TSV file. If empty, it is assumed that the tensor
-   * is stored in the checkpoint file.
-   */
-  tensorPath?: string;
-  /** The path to the metadata file associated with the tensor. */
-  metadataPath?: string;
-  /** The path to the bookmarks file associated with the tensor. */
-  bookmarksPath?: string;
-  sprite?: SpriteMetadata;
-}
-
-/**
- * Matches the json format of `projector_config.proto`
- * This should be kept in sync with the code in vz-projector-data-panel which
- * holds a template for users to build a projector config JSON object from the
- * projector UI.
- */
-export interface ProjectorConfig {
-  embeddings: EmbeddingInfo[];
-  modelCheckpointPath?: string;
-}
-
-export type ServingMode = 'demo' | 'server' | 'proto';
-
-/** Interface between the data storage and the UI. */
-export interface DataProvider {
-  /** Returns a list of run names that have embedding config files. */
-  retrieveRuns(callback: (runs: string[]) => void): void;
-
-  /**
-   * Returns the projector configuration: number of tensors, their shapes,
-   * and their associated metadata files.
-   */
-  retrieveProjectorConfig(run: string,
-      callback: (d: ProjectorConfig) => void): void;
-
-  /** Fetches and returns the tensor with the specified name. */
-  retrieveTensor(run: string, tensorName: string,
-      callback: (ds: DataSet) => void);
-
-  /**
-   * Fetches the metadata for the specified tensor.
-   */
-  retrieveSpriteAndMetadata(run: string, tensorName: string,
-      callback: (r: SpriteAndMetadataInfo) => void): void;
-
-  getBookmarks(run: string, tensorName: string, callback: (r: State[]) => void):
-      void;
-}
-
-export function retrieveTensorAsBytes(
-    dp: DataProvider, embedding: EmbeddingInfo, run: string, tensorName: string,
-    tensorsPath: string, callback: (ds: DataSet) => void) {
-  // Get the tensor.
-  logging.setModalMessage('Fetching tensor values...', TENSORS_MSG_ID);
-  let xhr = new XMLHttpRequest();
-  xhr.open('GET', tensorsPath);
-  xhr.responseType = 'arraybuffer';
-  xhr.onprogress = (ev) => {
-    if (ev.lengthComputable) {
-      let percent = (ev.loaded * 100 / ev.total).toFixed(1);
-      logging.setModalMessage(
-          'Fetching tensor values: ' + percent + '%', TENSORS_MSG_ID);
-    }
-  };
-  xhr.onload = () => {
-    if (xhr.status !== 200) {
-      let msg = String.fromCharCode.apply(null, new Uint8Array(xhr.response));
-      logging.setErrorMessage(msg, 'fetching tensors');
-      return;
-    }
-    let data: Float32Array;
-    try {
-      data = new Float32Array(xhr.response);
-    } catch (e) {
-      logging.setErrorMessage(e, 'parsing tensor bytes');
-      return;
-    }
-
-    let dim = embedding.tensorShape[1];
-    let N = data.length / dim;
-    if (embedding.tensorShape[0] > N) {
-      logging.setWarningMessage(
-          `Showing the first ${N.toLocaleString()}` +
-          ` of ${embedding.tensorShape[0].toLocaleString()} data points`);
-    }
-    parseTensorsFromFloat32Array(data, dim).then(dataPoints => {
-      callback(new DataSet(dataPoints));
-    });
-  };
-  xhr.send();
-}
-
-export function parseRawTensors(
-    content: ArrayBuffer, callback: (ds: DataSet) => void) {
-  parseTensors(content).then(data => {
-    callback(new DataSet(data));
-  });
-}
-
-export function parseRawMetadata(
-    contents: ArrayBuffer, callback: (r: SpriteAndMetadataInfo) => void) {
-  parseMetadata(contents).then(result => callback(result));
-}
-
-/**
- * Parse an ArrayBuffer in a streaming fashion line by line (or custom delim).
- * Can handle very large files.
- *
- * @param content The array buffer.
- * @param callback The callback called on each line.
- * @param chunkSize The size of each read chunk, defaults to ~1MB. (optional)
- * @param delim The delimiter used to split a line, defaults to '\n'. (optional)
- * @returns A promise for when it is finished.
- */
-function streamParse(
-    content: ArrayBuffer, callback: (line: string) => void, chunkSize = 1000000,
-    delim = '\n'): Promise<void> {
-  return new Promise<void>((resolve, reject) => {
-    let offset = 0;
-    let bufferSize = content.byteLength - 1;
-    let data = '';
-
-    function readHandler(str) {
-      offset += chunkSize;
-      let parts = str.split(delim);
-      let first = data + parts[0];
-      if (parts.length === 1) {
-        data = first;
-        readChunk(offset, chunkSize);
-        return;
-      }
-      data = parts[parts.length - 1];
-      callback(first);
-      for (let i = 1; i < parts.length - 1; i++) {
-        callback(parts[i]);
-      }
-      if (offset >= bufferSize) {
-        if (data) {
-          callback(data);
-        }
-        resolve();
-        return;
-      }
-      readChunk(offset, chunkSize);
-    }
-
-    function readChunk(offset: number, size: number) {
-      const contentChunk = content.slice(offset, offset + size);
-
-      const blob = new Blob([contentChunk]);
-      const file = new FileReader();
-      file.onload = (e: any) => readHandler(e.target.result);
-      file.readAsText(blob);
-    }
-
-    readChunk(offset, chunkSize);
-  });
-}
-
-/** Parses a tsv text file. */
-export function parseTensors(
-    content: ArrayBuffer, valueDelim = '\t'): Promise<DataPoint[]> {
-  logging.setModalMessage('Parsing tensors...', TENSORS_MSG_ID);
-
-  return new Promise<DataPoint[]>((resolve, reject) => {
-    const data: DataPoint[] = [];
-    let numDim: number;
-
-    streamParse(content, (line: string) => {
-      line = line.trim();
-      if (line === '') {
-        return;
-      }
-      const row = line.split(valueDelim);
-      const dataPoint: DataPoint = {
-        metadata: {},
-        vector: null,
-        index: data.length,
-        projections: null,
-      };
-      // If the first label is not a number, take it as the label.
-      if (isNaN(row[0] as any) || numDim === row.length - 1) {
-        dataPoint.metadata['label'] = row[0];
-        dataPoint.vector = new Float32Array(row.slice(1).map(Number));
-      } else {
-        dataPoint.vector = new Float32Array(row.map(Number));
-      }
-      data.push(dataPoint);
-      if (numDim == null) {
-        numDim = dataPoint.vector.length;
-      }
-      if (numDim !== dataPoint.vector.length) {
-        logging.setModalMessage(
-            'Parsing failed. Vector dimensions do not match');
-        throw Error('Parsing failed');
-      }
-      if (numDim <= 1) {
-        logging.setModalMessage(
-            'Parsing failed. Found a vector with only one dimension?');
-        throw Error('Parsing failed');
-      }
-    }).then(() => {
-      logging.setModalMessage(null, TENSORS_MSG_ID);
-      resolve(data);
-    });
-  });
-}
-
-/** Parses a tsv text file. */
-export function parseTensorsFromFloat32Array(data: Float32Array,
-    dim: number): Promise<DataPoint[]> {
-  return runAsyncTask('Parsing tensors...', () => {
-    const N = data.length / dim;
-    const dataPoints: DataPoint[] = [];
-    let offset = 0;
-    for (let i = 0; i < N; ++i) {
-      dataPoints.push({
-        metadata: {},
-        vector: data.subarray(offset, offset + dim),
-        index: i,
-        projections: null,
-      });
-      offset += dim;
-    }
-    return dataPoints;
-  }, TENSORS_MSG_ID).then(dataPoints => {
-    logging.setModalMessage(null, TENSORS_MSG_ID);
-    return dataPoints;
-  });
-}
-
-export function analyzeMetadata(
-    columnNames, pointsMetadata: PointMetadata[]): ColumnStats[] {
-  const columnStats: ColumnStats[] = columnNames.map(name => {
-    return {
-      name: name,
-      isNumeric: true,
-      tooManyUniqueValues: false,
-      min: Number.POSITIVE_INFINITY,
-      max: Number.NEGATIVE_INFINITY
-    };
-  });
-
-  const mapOfValues: [{[value: string]: number}] =
-      columnNames.map(() => new Object());
-
-  pointsMetadata.forEach(metadata => {
-    columnNames.forEach((name: string, colIndex: number) => {
-      const stats = columnStats[colIndex];
-      const map = mapOfValues[colIndex];
-      const value = metadata[name];
-
-      // Skip missing values.
-      if (value == null) {
-        return;
-      }
-
-      if (!stats.tooManyUniqueValues) {
-        if (value in map) {
-          map[value]++;
-        } else {
-          map[value] = 1;
-        }
-        if (Object.keys(map).length > NUM_COLORS_COLOR_MAP) {
-          stats.tooManyUniqueValues = true;
-        }
-      }
-      if (isNaN(value as any)) {
-        stats.isNumeric = false;
-      } else {
-        metadata[name] = +value;
-        stats.min = Math.min(stats.min, +value);
-        stats.max = Math.max(stats.max, +value);
-      }
-    });
-  });
-  columnStats.forEach((stats, colIndex) => {
-    stats.uniqueEntries = Object.keys(mapOfValues[colIndex]).map(label => {
-      return {label, count: mapOfValues[colIndex][label]};
-    });
-  });
-  return columnStats;
-}
-
-export function parseMetadata(content: ArrayBuffer):
-    Promise<SpriteAndMetadataInfo> {
-  logging.setModalMessage('Parsing metadata...', METADATA_MSG_ID);
-
-  return new Promise<SpriteAndMetadataInfo>((resolve, reject) => {
-    let pointsMetadata: PointMetadata[] = [];
-    let hasHeader = false;
-    let lineNumber = 0;
-    let columnNames = ['label'];
-    streamParse(content, (line: string) => {
-      if (line.trim().length === 0) {
-        return;
-      }
-      if (lineNumber === 0) {
-        hasHeader = line.indexOf('\t') >= 0;
-
-        // If the first row doesn't contain metadata keys, we assume that the
-        // values are labels.
-        if (hasHeader) {
-          columnNames = line.split('\t');
-          lineNumber++;
-          return;
-        }
-      }
-
-      lineNumber++;
-
-      let rowValues = line.split('\t');
-      let metadata: PointMetadata = {};
-      pointsMetadata.push(metadata);
-      columnNames.forEach((name: string, colIndex: number) => {
-        let value = rowValues[colIndex];
-        // Normalize missing values.
-        value = (value === '' ? null : value);
-        metadata[name] = value;
-      });
-    }).then(() => {
-      logging.setModalMessage(null, METADATA_MSG_ID);
-      resolve({
-        stats: analyzeMetadata(columnNames, pointsMetadata),
-        pointsInfo: pointsMetadata
-      });
-    });
-  });
-}
-
-export function fetchImage(url: string): Promise<HTMLImageElement> {
-  return new Promise<HTMLImageElement>((resolve, reject) => {
-    let image = new Image();
-    image.onload = () => resolve(image);
-    image.onerror = (err) => reject(err);
-    image.crossOrigin = '';
-    image.src = url;
-  });
-}
-
-export function retrieveSpriteAndMetadataInfo(metadataPath: string,
-    spriteImagePath: string, spriteMetadata: SpriteMetadata,
-    callback: (r: SpriteAndMetadataInfo) => void) {
-  let metadataPromise: Promise<SpriteAndMetadataInfo> = Promise.resolve({});
-  if (metadataPath) {
-    metadataPromise = new Promise<SpriteAndMetadataInfo>((resolve, reject) => {
-      logging.setModalMessage('Fetching metadata...', METADATA_MSG_ID);
-
-      const request = new XMLHttpRequest();
-      request.open('GET', metadataPath);
-      request.responseType = 'arraybuffer';
-
-      request.onerror = () => {
-        logging.setErrorMessage(request.responseText, 'fetching metadata');
-        reject();
-      };
-      request.onload = () => {
-        resolve(parseMetadata(request.response));
-      };
-      request.send(null);
-    });
-  }
-  let spriteMsgId = null;
-  let spritesPromise: Promise<HTMLImageElement> = null;
-  if (spriteImagePath) {
-    spriteMsgId = logging.setModalMessage('Fetching sprite image...');
-    spritesPromise = fetchImage(spriteImagePath);
-  }
-
-  // Fetch the metadata and the image in parallel.
-  Promise.all([metadataPromise, spritesPromise]).then(values => {
-    if (spriteMsgId) {
-      logging.setModalMessage(null, spriteMsgId);
-    }
-    const [metadata, spriteImage] = values;
-
-    if (spriteImage && (spriteImage.height > MAX_SPRITE_IMAGE_SIZE_PX ||
-                        spriteImage.width > MAX_SPRITE_IMAGE_SIZE_PX)) {
-      logging.setModalMessage(
-          `Error: Sprite image of dimensions ${spriteImage.width}px x ` +
-          `${spriteImage.height}px exceeds maximum dimensions ` +
-          `${MAX_SPRITE_IMAGE_SIZE_PX}px x ${MAX_SPRITE_IMAGE_SIZE_PX}px`);
-    } else {
-      metadata.spriteImage = spriteImage;
-      metadata.spriteMetadata = spriteMetadata;
-      callback(metadata);
-    }
-  });
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/data.ts b/tensorflow/tensorboard/components/vz_projector/data.ts
deleted file mode 100644
index c4e81985fc..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/data.ts
+++ /dev/null
@@ -1,547 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {TSNE} from './bh_tsne';
-import {SpriteMetadata} from './data-provider';
-import * as knn from './knn';
-import * as logging from './logging';
-import * as scatterPlot from './scatterPlot';
-import * as util from './util';
-import * as vector from './vector';
-
-export type DistanceFunction = (a: number[], b: number[]) => number;
-export type ProjectionComponents3D = [string, string, string];
-
-export interface PointMetadata { [key: string]: number|string; }
-
-export interface DataProto {
-  shape: [number, number];
-  tensor: number[];
-  metadata: {
-    columns: Array<
-        {name: string; stringValues: string[]; numericValues: number[];}>;
-  };
-}
-
-/** Statistics for a metadata column. */
-export interface ColumnStats {
-  name: string;
-  isNumeric: boolean;
-  tooManyUniqueValues: boolean;
-  uniqueEntries?: Array<{label: string, count: number}>;
-  min: number;
-  max: number;
-}
-
-export interface SpriteAndMetadataInfo {
-  stats?: ColumnStats[];
-  pointsInfo?: PointMetadata[];
-  spriteImage?: HTMLImageElement;
-  spriteMetadata?: SpriteMetadata;
-}
-
-/** A single collection of points which make up a sequence through space. */
-export interface Sequence {
-  /** Indices into the DataPoints array in the Data object. */
-  pointIndices: number[];
-}
-
-export interface DataPoint {
-  /** The point in the original space. */
-  vector: Float32Array;
-
-  /*
-   * Metadata for each point. Each metadata is a set of key/value pairs
-   * where the value can be a string or a number.
-   */
-  metadata: PointMetadata;
-
-  /** index of the sequence, used for highlighting on click */
-  sequenceIndex?: number;
-
-  /** index in the original data source */
-  index: number;
-
-  /** This is where the calculated projections space are cached */
-  projections: {[key: string]: number};
-}
-
-const IS_FIREFOX = navigator.userAgent.toLowerCase().indexOf('firefox') >= 0;
-/** Controls whether nearest neighbors computation is done on the GPU or CPU. */
-const KNN_GPU_ENABLED = util.hasWebGLSupport() && !IS_FIREFOX;
-
-export const TSNE_SAMPLE_SIZE = 10000;
-export const PCA_SAMPLE_SIZE = 50000;
-/** Number of dimensions to sample when doing approximate PCA. */
-export const PCA_SAMPLE_DIM = 200;
-/** Number of pca components to compute. */
-const NUM_PCA_COMPONENTS = 10;
-/**
- * Reserved metadata attributes used for sequence information
- * NOTE: Use "__seq_next__" as "__next__" is deprecated.
- */
-const SEQUENCE_METADATA_ATTRS = ['__next__', '__seq_next__'];
-
-function getSequenceNextPointIndex(pointMetadata: PointMetadata): number|null {
-  let sequenceAttr = null;
-  for (let metadataAttr of SEQUENCE_METADATA_ATTRS) {
-    if (metadataAttr in pointMetadata && pointMetadata[metadataAttr] !== '') {
-      sequenceAttr = pointMetadata[metadataAttr];
-      break;
-    }
-  }
-  if (sequenceAttr == null) {
-    return null;
-  }
-  return +sequenceAttr;
-}
-
-/**
- * Dataset contains a DataPoints array that should be treated as immutable. This
- * acts as a working subset of the original data, with cached properties
- * from computationally expensive operations. Because creating a subset
- * requires normalizing and shifting the vector space, we make a copy of the
- * data so we can still always create new subsets based on the original data.
- */
-export class DataSet {
-  points: DataPoint[];
-  sequences: Sequence[];
-
-  shuffledDataIndices: number[] = [];
-
-  /**
-   * This keeps a list of all current projections so you can easily test to see
-   * if it's been calculated already.
-   */
-  projections: {[projection: string]: boolean} = {};
-  nearest: knn.NearestEntry[][];
-  nearestK: number;
-  tSNEIteration: number = 0;
-  tSNEShouldStop = true;
-  dim: [number, number] = [0, 0];
-  hasTSNERun: boolean = false;
-  spriteAndMetadataInfo: SpriteAndMetadataInfo;
-  fracVariancesExplained: number[];
-
-  private tsne: TSNE;
-
-  /** Creates a new Dataset */
-  constructor(
-      points: DataPoint[], spriteAndMetadataInfo?: SpriteAndMetadataInfo) {
-    this.points = points;
-    this.shuffledDataIndices = util.shuffle(util.range(this.points.length));
-    this.sequences = this.computeSequences(points);
-    this.dim = [this.points.length, this.points[0].vector.length];
-    this.spriteAndMetadataInfo = spriteAndMetadataInfo;
-  }
-
-  private computeSequences(points: DataPoint[]) {
-    // Keep a list of indices seen so we don't compute sequences for a given
-    // point twice.
-    let indicesSeen = new Int8Array(points.length);
-    // Compute sequences.
-    let indexToSequence: {[index: number]: Sequence} = {};
-    let sequences: Sequence[] = [];
-    for (let i = 0; i < points.length; i++) {
-      if (indicesSeen[i]) {
-        continue;
-      }
-      indicesSeen[i] = 1;
-
-      // Ignore points without a sequence attribute.
-      let next = getSequenceNextPointIndex(points[i].metadata);
-      if (next == null) {
-        continue;
-      }
-      if (next in indexToSequence) {
-        let existingSequence = indexToSequence[next];
-        // Pushing at the beginning of the array.
-        existingSequence.pointIndices.unshift(i);
-        indexToSequence[i] = existingSequence;
-        continue;
-      }
-      // The current point is pointing to a new/unseen sequence.
-      let newSequence: Sequence = {pointIndices: []};
-      indexToSequence[i] = newSequence;
-      sequences.push(newSequence);
-      let currentIndex = i;
-      while (points[currentIndex]) {
-        newSequence.pointIndices.push(currentIndex);
-        let next = getSequenceNextPointIndex(points[currentIndex].metadata);
-        if (next != null) {
-          indicesSeen[next] = 1;
-          currentIndex = next;
-        } else {
-          currentIndex = -1;
-        }
-      }
-    }
-    return sequences;
-  }
-
-  projectionCanBeRendered(projection: ProjectionType): boolean {
-    if (projection !== 'tsne') {
-      return true;
-    }
-    return this.tSNEIteration > 0;
-  }
-
-  /**
-   * Returns a new subset dataset by copying out data. We make a copy because
-   * we have to modify the vectors by normalizing them.
-   *
-   * @param subset Array of indices of points that we want in the subset.
-   *
-   * @return A subset of the original dataset.
-   */
-  getSubset(subset?: number[]): DataSet {
-    const pointsSubset = ((subset != null) && (subset.length > 0)) ?
-        subset.map(i => this.points[i]) :
-        this.points;
-    let points = pointsSubset.map(dp => {
-      return {
-        metadata: dp.metadata,
-        index: dp.index,
-        vector: dp.vector.slice(),
-        projections: {} as {[key: string]: number}
-      };
-    });
-    return new DataSet(points, this.spriteAndMetadataInfo);
-  }
-
-  /**
-   * Computes the centroid, shifts all points to that centroid,
-   * then makes them all unit norm.
-   */
-  normalize() {
-    // Compute the centroid of all data points.
-    let centroid = vector.centroid(this.points, a => a.vector);
-    if (centroid == null) {
-      throw Error('centroid should not be null');
-    }
-    // Shift all points by the centroid and make them unit norm.
-    for (let id = 0; id < this.points.length; ++id) {
-      let dataPoint = this.points[id];
-      dataPoint.vector = vector.sub(dataPoint.vector, centroid);
-      vector.unit(dataPoint.vector);
-    }
-  }
-
-  /** Projects the dataset onto a given vector and caches the result. */
-  projectLinear(dir: vector.Vector, label: string) {
-    this.projections[label] = true;
-    this.points.forEach(dataPoint => {
-      dataPoint.projections[label] = vector.dot(dataPoint.vector, dir);
-    });
-  }
-
-  /** Projects the dataset along the top 10 principal components. */
-  projectPCA(): Promise<void> {
-    if (this.projections['pca-0'] != null) {
-      return Promise.resolve<void>(null);
-    }
-    return util.runAsyncTask('Computing PCA...', () => {
-      // Approximate pca vectors by sampling the dimensions.
-      let dim = this.points[0].vector.length;
-      let vectors = this.shuffledDataIndices.map(i => this.points[i].vector);
-      if (dim > PCA_SAMPLE_DIM) {
-        vectors = vector.projectRandom(vectors, PCA_SAMPLE_DIM);
-      }
-      let sampledVectors = vectors.slice(0, PCA_SAMPLE_SIZE);
-
-      let sigma = numeric.div(
-          numeric.dot(numeric.transpose(sampledVectors), sampledVectors),
-          sampledVectors.length);
-      let svd = numeric.svd(sigma);
-
-      let variances: number[] = svd.S;
-      let totalVariance = 0;
-      for (let i = 0; i < variances.length; ++i) {
-        totalVariance += variances[i];
-      }
-      for (let i = 0; i < variances.length; ++i) {
-        variances[i] /= totalVariance;
-      }
-      this.fracVariancesExplained = variances;
-
-      let U: number[][] = svd.U;
-      let pcaVectors = vectors.map(vector => {
-        let newV = new Float32Array(NUM_PCA_COMPONENTS);
-        for (let newDim = 0; newDim < NUM_PCA_COMPONENTS; newDim++) {
-          let dot = 0;
-          for (let oldDim = 0; oldDim < vector.length; oldDim++) {
-            dot += vector[oldDim] * U[oldDim][newDim];
-          }
-          newV[newDim] = dot;
-        }
-        return newV;
-      });
-      for (let d = 0; d < NUM_PCA_COMPONENTS; d++) {
-        let label = 'pca-' + d;
-        this.projections[label] = true;
-        for (let i = 0; i < pcaVectors.length; i++) {
-          let pointIndex = this.shuffledDataIndices[i];
-          this.points[pointIndex].projections[label] = pcaVectors[i][d];
-        }
-      }
-    });
-  }
-
-  /** Runs tsne on the data. */
-  projectTSNE(
-      perplexity: number, learningRate: number, tsneDim: number,
-      stepCallback: (iter: number) => void) {
-    this.hasTSNERun = true;
-    let k = Math.floor(3 * perplexity);
-    let opt = {epsilon: learningRate, perplexity: perplexity, dim: tsneDim};
-    this.tsne = new TSNE(opt);
-    this.tSNEShouldStop = false;
-    this.tSNEIteration = 0;
-
-    let sampledIndices = this.shuffledDataIndices.slice(0, TSNE_SAMPLE_SIZE);
-    let step = () => {
-      if (this.tSNEShouldStop) {
-        stepCallback(null);
-        this.tsne = null;
-        return;
-      }
-      this.tsne.step();
-      let result = this.tsne.getSolution();
-      sampledIndices.forEach((index, i) => {
-        let dataPoint = this.points[index];
-
-        dataPoint.projections['tsne-0'] = result[i * tsneDim + 0];
-        dataPoint.projections['tsne-1'] = result[i * tsneDim + 1];
-        if (tsneDim === 3) {
-          dataPoint.projections['tsne-2'] = result[i * tsneDim + 2];
-        }
-      });
-      this.tSNEIteration++;
-      stepCallback(this.tSNEIteration);
-      requestAnimationFrame(step);
-    };
-
-    // Nearest neighbors calculations.
-    let knnComputation: Promise<knn.NearestEntry[][]>;
-
-    if (this.nearest != null && k === this.nearestK) {
-      // We found the nearest neighbors before and will reuse them.
-      knnComputation = Promise.resolve(this.nearest);
-    } else {
-      let sampledData = sampledIndices.map(i => this.points[i]);
-      this.nearestK = k;
-      knnComputation = KNN_GPU_ENABLED ?
-          knn.findKNNGPUCosine(sampledData, k, (d => d.vector)) :
-          knn.findKNN(
-              sampledData, k, (d => d.vector),
-              (a, b, limit) => vector.cosDistNorm(a, b));
-    }
-    knnComputation.then(nearest => {
-      this.nearest = nearest;
-      util.runAsyncTask('Initializing T-SNE...', () => {
-            this.tsne.initDataDist(this.nearest);
-          }).then(step);
-    });
-  }
-
-  /**
-   * Merges metadata to the dataset and returns whether it succeeded.
-   */
-  mergeMetadata(metadata: SpriteAndMetadataInfo): boolean {
-    if (metadata.pointsInfo.length !== this.points.length) {
-      let errorMessage = `Number of tensors (${this.points.length}) do not` +
-          ` match the number of lines in metadata` +
-          ` (${metadata.pointsInfo.length}).`;
-
-      if (metadata.stats.length === 1 &&
-          this.points.length + 1 === metadata.pointsInfo.length) {
-        // If there is only one column of metadata and the number of points is
-        // exactly one less than the number of metadata lines, this is due to an
-        // unnecessary header line in the metadata and we can show a meaningful
-        // error.
-        logging.setErrorMessage(
-            errorMessage + ' Single column metadata should not have a header ' +
-                'row.',
-            'merging metadata');
-        return false;
-      } else if (
-          metadata.stats.length > 1 &&
-          this.points.length - 1 === metadata.pointsInfo.length) {
-        // If there are multiple columns of metadata and the number of points is
-        // exactly one greater than the number of lines in the metadata, this
-        // means there is a missing metadata header.
-        logging.setErrorMessage(
-            errorMessage + ' Multi-column metadata should have a header ' +
-                'row with column labels.',
-            'merging metadata');
-        return false;
-      }
-
-      logging.setWarningMessage(errorMessage);
-    }
-    this.spriteAndMetadataInfo = metadata;
-    metadata.pointsInfo.slice(0, this.points.length)
-        .forEach((m, i) => this.points[i].metadata = m);
-    return true;
-  }
-
-  stopTSNE() {
-    this.tSNEShouldStop = true;
-  }
-
-  /**
-   * Finds the nearest neighbors of the query point using a
-   * user-specified distance metric.
-   */
-  findNeighbors(pointIndex: number, distFunc: DistanceFunction, numNN: number):
-      knn.NearestEntry[] {
-    // Find the nearest neighbors of a particular point.
-    let neighbors = knn.findKNNofPoint(
-        this.points, pointIndex, numNN, (d => d.vector), distFunc);
-    // TODO(smilkov): Figure out why we slice.
-    let result = neighbors.slice(0, numNN);
-    return result;
-  }
-
-  /**
-   * Search the dataset based on a metadata field.
-   */
-  query(query: string, inRegexMode: boolean, fieldName: string): number[] {
-    let predicate = util.getSearchPredicate(query, inRegexMode, fieldName);
-    let matches: number[] = [];
-    this.points.forEach((point, id) => {
-      if (predicate(point)) {
-        matches.push(id);
-      }
-    });
-    return matches;
-  }
-}
-
-export type ProjectionType = 'tsne' | 'pca' | 'custom';
-
-export class Projection {
-  constructor(
-      public projectionType: ProjectionType,
-      public projectionComponents: ProjectionComponents3D,
-      public dimensionality: number, public dataSet: DataSet) {}
-}
-
-export interface ColorOption {
-  name: string;
-  desc?: string;
-  map?: (value: string|number) => string;
-  /** List of items for the color map. Defined only for categorical map. */
-  items?: {label: string, count: number}[];
-  /** Threshold values and their colors. Defined for gradient color map. */
-  thresholds?: {value: number, color: string}[];
-  isSeparator?: boolean;
-  tooManyUniqueValues?: boolean;
-}
-
-/**
- * An interface that holds all the data for serializing the current state of
- * the world.
- */
-export class State {
-  /** A label identifying this state. */
-  label: string = '';
-
-  /** Whether this State is selected in the bookmarks pane. */
-  isSelected: boolean = false;
-
-  /** The selected projection tab. */
-  selectedProjection: ProjectionType;
-
-  /** Dimensions of the DataSet. */
-  dataSetDimensions: [number, number];
-
-  /** t-SNE parameters */
-  tSNEIteration: number = 0;
-  tSNEPerplexity: number = 0;
-  tSNELearningRate: number = 0;
-  tSNEis3d: boolean = true;
-
-  /** PCA projection component dimensions */
-  pcaComponentDimensions: number[] = [];
-
-  /** Custom projection parameters */
-  customSelectedSearchByMetadataOption: string;
-  customXLeftText: string;
-  customXLeftRegex: boolean;
-  customXRightText: string;
-  customXRightRegex: boolean;
-  customYUpText: string;
-  customYUpRegex: boolean;
-  customYDownText: string;
-  customYDownRegex: boolean;
-
-  /** The computed projections of the tensors. */
-  projections: Array<{[key: string]: number}> = [];
-
-  /** Filtered dataset indices. */
-  filteredPoints: number[];
-
-  /** The indices of selected points. */
-  selectedPoints: number[] = [];
-
-  /** Camera state (2d/3d, position, target, zoom, etc). */
-  cameraDef: scatterPlot.CameraDef;
-
-  /** Color by option. */
-  selectedColorOptionName: string;
-  forceCategoricalColoring: boolean;
-
-  /** Label by option. */
-  selectedLabelOption: string;
-}
-
-export function getProjectionComponents(
-    projection: ProjectionType,
-    components: (number|string)[]): ProjectionComponents3D {
-  if (components.length > 3) {
-    throw new RangeError('components length must be <= 3');
-  }
-  const projectionComponents: [string, string, string] = [null, null, null];
-  const prefix = (projection === 'custom') ? 'linear' : projection;
-  for (let i = 0; i < components.length; ++i) {
-    if (components[i] == null) {
-      continue;
-    }
-    projectionComponents[i] = prefix + '-' + components[i];
-  }
-  return projectionComponents;
-}
-
-export function stateGetAccessorDimensions(state: State): Array<number|string> {
-  let dimensions: Array<number|string>;
-  switch (state.selectedProjection) {
-    case 'pca':
-      dimensions = state.pcaComponentDimensions.slice();
-      break;
-    case 'tsne':
-      dimensions = [0, 1];
-      if (state.tSNEis3d) {
-        dimensions.push(2);
-      }
-      break;
-    case 'custom':
-      dimensions = ['x', 'y'];
-      break;
-    default:
-      throw new Error('Unexpected fallthrough');
-  }
-  return dimensions;
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/external.d.ts b/tensorflow/tensorboard/components/vz_projector/external.d.ts
deleted file mode 100644
index cbc1512c21..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/external.d.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// TODO(smilkov): Split into weblas.d.ts and numeric.d.ts and write
-// typings for numeric.
-interface Tensor {
-  new(size: [number, number], data: Float32Array);
-  transfer(): Float32Array;
-  delete(): void;
-}
-
-interface Weblas {
-  sgemm(M: number, N: number, K: number, alpha: number,
-      A: Float32Array, B: Float32Array, beta: number, C: Float32Array):
-      Float32Array;
-  pipeline: {
-     Tensor: Tensor;
-     sgemm(alpha: number, A: Tensor, B: Tensor, beta: number,
-         C: Tensor): Tensor;
-  };
-  util: {
-    transpose(M: number, N: number, data: Float32Array): Tensor;
-  };
-
-}
-
-declare let numeric: any;
-declare let weblas: Weblas;
-
-interface AnalyticsEventType {
-  hitType: string;
-  page?: string;
-  eventCategory?: string;
-  eventAction?: string;
-  eventLabel?: string;
-  eventValue?: number;
-}
-
-declare let ga: (command: string, eventObj: AnalyticsEventType) => void;
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz_projector/heap.ts b/tensorflow/tensorboard/components/vz_projector/heap.ts
deleted file mode 100644
index ac3144e649..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/heap.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/** Min key heap. */
-export type HeapItem<T> = {
-  key: number,
-  value: T
-};
-
-/**
- * Min-heap data structure. Provides O(1) for peek, returning the smallest key.
- */
-// TODO(jart): Rename to Heap and use Comparator.
-export class MinHeap<T> {
-  private arr: HeapItem<T>[] = [];
-
-  /** Push an element with the provided key. */
-  push(key: number, value: T): void {
-    this.arr.push({key, value});
-    this.bubbleUp(this.arr.length - 1);
-  }
-
-  /** Pop the element with the smallest key. */
-  pop(): HeapItem<T> {
-    if (this.arr.length === 0) {
-      throw new Error('pop() called on empty binary heap');
-    }
-    let item = this.arr[0];
-    let last = this.arr.length - 1;
-    this.arr[0] = this.arr[last];
-    this.arr.pop();
-    if (last > 0) {
-      this.bubbleDown(0);
-    }
-    return item;
-  };
-
-  /** Returns, but doesn't remove the element with the smallest key */
-  peek(): HeapItem<T> { return this.arr[0]; }
-
-  /**
-   * Pops the element with the smallest key and at the same time
-   * adds the newly provided element. This is faster than calling
-   * pop() and push() separately.
-   */
-  popPush(key: number, value: T): HeapItem<T> {
-    if (this.arr.length === 0) {
-      throw new Error('pop() called on empty binary heap');
-    }
-    let item = this.arr[0];
-    this.arr[0] = {key, value};
-    if (this.arr.length > 0) {
-      this.bubbleDown(0);
-    }
-    return item;
-  }
-
-  /** Returns the number of elements in the heap. */
-  size(): number { return this.arr.length; }
-
-  /** Returns all the items in the heap. */
-  items(): HeapItem<T>[] { return this.arr; }
-
-  private swap(a: number, b: number) {
-    let temp = this.arr[a];
-    this.arr[a] = this.arr[b];
-    this.arr[b] = temp;
-  }
-
-  private bubbleDown(pos: number) {
-    let left = (pos << 1) + 1;
-    let right = left + 1;
-    let largest = pos;
-    if (left < this.arr.length && this.arr[left].key < this.arr[largest].key) {
-      largest = left;
-    }
-    if (right < this.arr.length &&
-        this.arr[right].key < this.arr[largest].key) {
-      largest = right;
-    }
-    if (largest !== pos) {
-      this.swap(largest, pos);
-      this.bubbleDown(largest);
-    }
-  }
-
-  private bubbleUp(pos: number) {
-    if (pos <= 0) {
-      return;
-    }
-    let parent = ((pos - 1) >> 1);
-    if (this.arr[pos].key < this.arr[parent].key) {
-      this.swap(pos, parent);
-      this.bubbleUp(parent);
-    }
-  }
-}
-
-/** List that keeps the K elements with the smallest keys. */
-export class KMin<T> {
-  private k: number;
-  private maxHeap = new MinHeap<T>();
-
-  /** Constructs a new k-min data structure with the provided k. */
-  constructor(k: number) { this.k = k; }
-
-  /** Adds an element to the list. */
-  add(key: number, value: T) {
-    if (this.maxHeap.size() < this.k) {
-      this.maxHeap.push(-key, value);
-      return;
-    }
-    let largest = this.maxHeap.peek();
-    // If the new element is smaller, replace the largest with the new element.
-    if (key < -largest.key) {
-      this.maxHeap.popPush(-key, value);
-    }
-  }
-
-  /** Returns the k items with the smallest keys. */
-  getMinKItems(): T[] {
-    let items = this.maxHeap.items();
-    items.sort((a, b) => b.key - a.key);
-    return items.map(a => a.value);
-  }
-
-  /** Returns the size of the list. */
-  getSize(): number { return this.maxHeap.size(); }
-
-  /** Returns the largest key in the list. */
-  getLargestKey(): number {
-    return this.maxHeap.size() === 0 ? null : -this.maxHeap.peek().key;
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/knn.ts b/tensorflow/tensorboard/components/vz_projector/knn.ts
deleted file mode 100644
index 906e077b5d..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/knn.ts
+++ /dev/null
@@ -1,235 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {runAsyncTask} from './util';
-import * as logging from './logging';
-import {KMin} from './heap';
-import {Vector} from './vector';
-import * as vector from './vector';
-
-export type NearestEntry = {
-  index: number,
-  dist: number
-};
-
-/**
- * Optimal size for the height of the matrix when doing computation on the GPU
- * using WebGL. This was found experimentally.
- *
- * This also guarantees that for computing pair-wise distance for up to 10K
- * vectors, no more than 40MB will be allocated in the GPU. Without the
- * allocation limit, we can freeze the graphics of the whole OS.
- */
-const OPTIMAL_GPU_BLOCK_SIZE = 256;
-/** Id of message box used for knn gpu progress bar. */
-const KNN_GPU_MSG_ID = 'knn-gpu';
-
-/**
- * Returns the K nearest neighbors for each vector where the distance
- * computation is done on the GPU (WebGL) using cosine distance.
- *
- * @param dataPoints List of data points, where each data point holds an
- *   n-dimensional vector.
- * @param k Number of nearest neighbors to find.
- * @param accessor A method that returns the vector, given the data point.
- */
-export function findKNNGPUCosine<T>(
-    dataPoints: T[], k: number,
-    accessor: (dataPoint: T) => Float32Array): Promise<NearestEntry[][]> {
-  let N = dataPoints.length;
-  let dim = accessor(dataPoints[0]).length;
-
-  // The goal is to compute a large matrix multiplication A*A.T where A is of
-  // size NxD and A.T is its transpose. This results in a NxN matrix which
-  // could be too big to store on the GPU memory. To avoid memory overflow, we
-  // compute multiple A*partial_A.T where partial_A is of size BxD (B is much
-  // smaller than N). This results in storing only NxB size matrices on the GPU
-  // at a given time.
-
-  // A*A.T will give us NxN matrix holding the cosine distance between every
-  // pair of points, which we sort using KMin data structure to obtain the
-  // K nearest neighbors for each point.
-  let typedArray = vector.toTypedArray(dataPoints, accessor);
-  let bigMatrix = new weblas.pipeline.Tensor([N, dim], typedArray);
-  let nearest: NearestEntry[][] = new Array(N);
-  let numPieces = Math.ceil(N / OPTIMAL_GPU_BLOCK_SIZE);
-  let M = Math.floor(N / numPieces);
-  let modulo = N % numPieces;
-  let offset = 0;
-  let progress = 0;
-  let progressDiff = 1 / (2 * numPieces);
-  let piece = 0;
-
-  function step(resolve: (result: NearestEntry[][]) => void) {
-    let progressMsg =
-        'Finding nearest neighbors: ' + (progress * 100).toFixed() + '%';
-    runAsyncTask(progressMsg, () => {
-      let B = piece < modulo ? M + 1 : M;
-      let typedB = new Float32Array(B * dim);
-      for (let i = 0; i < B; ++i) {
-        let vector = accessor(dataPoints[offset + i]);
-        for (let d = 0; d < dim; ++d) {
-          typedB[i * dim + d] = vector[d];
-        }
-      }
-      let partialMatrix = new weblas.pipeline.Tensor([B, dim], typedB);
-      // Result is N x B matrix.
-      let result =
-          weblas.pipeline.sgemm(1, bigMatrix, partialMatrix, null, null);
-      let partial = result.transfer();
-      partialMatrix.delete();
-      result.delete();
-      progress += progressDiff;
-      for (let i = 0; i < B; i++) {
-        let kMin = new KMin<NearestEntry>(k);
-        let iReal = offset + i;
-        for (let j = 0; j < N; j++) {
-          if (j === iReal) {
-            continue;
-          }
-          let cosDist = 1 - partial[j * B + i];  // [j, i];
-          kMin.add(cosDist, {index: j, dist: cosDist});
-        }
-        nearest[iReal] = kMin.getMinKItems();
-      }
-      progress += progressDiff;
-      offset += B;
-      piece++;
-    }, KNN_GPU_MSG_ID).then(() => {
-      if (piece < numPieces) {
-        step(resolve);
-      } else {
-        logging.setModalMessage(null, KNN_GPU_MSG_ID);
-        bigMatrix.delete();
-        resolve(nearest);
-      }
-    }, error => {
-      // GPU failed. Reverting back to CPU.
-      logging.setModalMessage(null, KNN_GPU_MSG_ID);
-      let distFunc = (a, b, limit) => vector.cosDistNorm(a, b);
-      findKNN(dataPoints, k, accessor, distFunc).then(nearest => {
-        resolve(nearest);
-      });
-    });
-  }
-  return new Promise<NearestEntry[][]>(resolve => step(resolve));
-}
-
-/**
- * Returns the K nearest neighbors for each vector where the distance
- * computation is done on the CPU using a user-specified distance method.
- *
- * @param dataPoints List of data points, where each data point holds an
- *   n-dimensional vector.
- * @param k Number of nearest neighbors to find.
- * @param accessor A method that returns the vector, given the data point.
- * @param dist Method that takes two vectors and a limit, and computes the
- *   distance between two vectors, with the ability to stop early if the
- *   distance is above the limit.
- */
-export function findKNN<T>(
-    dataPoints: T[], k: number, accessor: (dataPoint: T) => Float32Array,
-    dist: (a: Vector, b: Vector, limit: number) =>
-        number): Promise<NearestEntry[][]> {
-  return runAsyncTask<NearestEntry[][]>('Finding nearest neighbors...', () => {
-    let N = dataPoints.length;
-    let nearest: NearestEntry[][] = new Array(N);
-    // Find the distances from node i.
-    let kMin: KMin<NearestEntry>[] = new Array(N);
-    for (let i = 0; i < N; i++) {
-      kMin[i] = new KMin<NearestEntry>(k);
-    }
-    for (let i = 0; i < N; i++) {
-      let a = accessor(dataPoints[i]);
-      let kMinA = kMin[i];
-      for (let j = i + 1; j < N; j++) {
-        let kMinB = kMin[j];
-        let limitI = kMinA.getSize() === k ?
-            kMinA.getLargestKey() || Number.MAX_VALUE :
-            Number.MAX_VALUE;
-        let limitJ = kMinB.getSize() === k ?
-            kMinB.getLargestKey() || Number.MAX_VALUE :
-            Number.MAX_VALUE;
-        let limit = Math.max(limitI, limitJ);
-        let dist2ItoJ = dist(a, accessor(dataPoints[j]), limit);
-        if (dist2ItoJ >= 0) {
-          kMinA.add(dist2ItoJ, {index: j, dist: dist2ItoJ});
-          kMinB.add(dist2ItoJ, {index: i, dist: dist2ItoJ});
-        }
-      }
-    }
-    for (let i = 0; i < N; i++) {
-      nearest[i] = kMin[i].getMinKItems();
-    }
-    return nearest;
-  });
-}
-
-/** Calculates the minimum distance between a search point and a rectangle. */
-function minDist(
-    point: [number, number], x1: number, y1: number, x2: number, y2: number) {
-  let x = point[0];
-  let y = point[1];
-  let dx1 = x - x1;
-  let dx2 = x - x2;
-  let dy1 = y - y1;
-  let dy2 = y - y2;
-
-  if (dx1 * dx2 <= 0) {    // x is between x1 and x2
-    if (dy1 * dy2 <= 0) {  // (x,y) is inside the rectangle
-      return 0;            // return 0 as point is in rect
-    }
-    return Math.min(Math.abs(dy1), Math.abs(dy2));
-  }
-  if (dy1 * dy2 <= 0) {  // y is between y1 and y2
-    // We know it is already inside the rectangle
-    return Math.min(Math.abs(dx1), Math.abs(dx2));
-  }
-  let corner: [number, number];
-  if (x > x2) {
-    // Upper-right vs lower-right.
-    corner = y > y2 ? [x2, y2] : [x2, y1];
-  } else {
-    // Upper-left vs lower-left.
-    corner = y > y2 ? [x1, y2] : [x1, y1];
-  }
-  return Math.sqrt(vector.dist22D([x, y], corner));
-}
-
-/**
- * Returns the nearest neighbors of a particular point.
- *
- * @param dataPoints List of data points.
- * @param pointIndex The index of the point we need the nearest neighbors of.
- * @param k Number of nearest neighbors to search for.
- * @param accessor Method that maps a data point => vector (array of numbers).
- * @param distance Method that takes two vectors and returns their distance.
- */
-export function findKNNofPoint<T>(
-    dataPoints: T[], pointIndex: number, k: number,
-    accessor: (dataPoint: T) => Float32Array,
-    distance: (a: Vector, b: Vector) => number) {
-  let kMin = new KMin<NearestEntry>(k);
-  let a = accessor(dataPoints[pointIndex]);
-  for (let i = 0; i < dataPoints.length; ++i) {
-    if (i === pointIndex) {
-      continue;
-    }
-    let b = accessor(dataPoints[i]);
-    let dist = distance(a, b);
-    kMin.add(dist, {index: i, dist: dist});
-  }
-  return kMin.getMinKItems();
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/label.ts b/tensorflow/tensorboard/components/vz_projector/label.ts
deleted file mode 100644
index 67987f06ea..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/label.ts
+++ /dev/null
@@ -1,151 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export interface BoundingBox {
-  loX: number;
-  loY: number;
-  hiX: number;
-  hiY: number;
-}
-
-/**
- * Accelerates label placement by dividing the view into a uniform grid.
- * Labels only need to be tested for collision with other labels that overlap
- * the same grid cells. This is a fork of {@code amoeba.CollisionGrid}.
- */
-export class CollisionGrid {
-  private numHorizCells: number;
-  private numVertCells: number;
-  private grid: BoundingBox[][];
-  private bound: BoundingBox;
-  private cellWidth: number;
-  private cellHeight: number;
-
-  /**
-   * Constructs a new Collision grid.
-   *
-   * @param bound The bound of the grid. Labels out of bounds will be rejected.
-   * @param cellWidth Width of a cell in the grid.
-   * @param cellHeight Height of a cell in the grid.
-   */
-  constructor(bound: BoundingBox, cellWidth: number, cellHeight: number) {
-    /** The bound of the grid. Labels out of bounds will be rejected. */
-    this.bound = bound;
-
-    /** Width of a cell in the grid. */
-    this.cellWidth = cellWidth;
-
-    /** Height of a cell in the grid. */
-    this.cellHeight = cellHeight;
-
-    /** Number of grid cells along the x axis. */
-    this.numHorizCells = Math.ceil(this.boundWidth(bound) / cellWidth);
-
-    /** Number of grid cells along the y axis. */
-    this.numVertCells = Math.ceil(this.boundHeight(bound) / cellHeight);
-
-    /**
-     * The 2d grid (stored as a 1d array.) Each cell consists of an array of
-     * BoundingBoxes for objects that are in the cell.
-     */
-    this.grid = new Array(this.numHorizCells * this.numVertCells);
-  }
-
-  private boundWidth(bound: BoundingBox) { return bound.hiX - bound.loX; }
-
-  private boundHeight(bound: BoundingBox) { return bound.hiY - bound.loY; }
-
-  private boundsIntersect(a: BoundingBox, b: BoundingBox) {
-    return !(a.loX > b.hiX || a.loY > b.hiY || a.hiX < b.loX || a.hiY < b.loY);
-  }
-
-  /**
-   * Checks if a given bounding box has any conflicts in the grid and inserts it
-   * if none are found.
-   *
-   * @param bound The bound to insert.
-   * @param justTest If true, just test if it conflicts, without inserting.
-   * @return True if the bound was successfully inserted; false if it
-   *         could not be inserted due to a conflict.
-   */
-  insert(bound: BoundingBox, justTest = false): boolean {
-    // Reject if the label is out of bounds.
-    if ((bound.hiX < this.bound.loX) || (bound.loX > this.bound.hiX) ||
-        (bound.hiY < this.bound.loY) || (bound.loY > this.bound.hiY)) {
-      return false;
-    }
-
-    let minCellX = this.getCellX(bound.loX);
-    let maxCellX = this.getCellX(bound.hiX);
-    let minCellY = this.getCellY(bound.loY);
-    let maxCellY = this.getCellY(bound.hiY);
-
-    // Check all overlapped cells to verify that we can insert.
-    let baseIdx = minCellY * this.numHorizCells + minCellX;
-    let idx = baseIdx;
-    for (let j = minCellY; j <= maxCellY; j++) {
-      for (let i = minCellX; i <= maxCellX; i++) {
-        let cell = this.grid[idx++];
-        if (cell) {
-          for (let k = 0; k < cell.length; k++) {
-            if (this.boundsIntersect(bound, cell[k])) {
-              return false;
-            }
-          }
-        }
-      }
-      idx += this.numHorizCells - (maxCellX - minCellX + 1);
-    }
-
-    if (justTest) {
-      return true;
-    }
-
-    // Insert into the overlapped cells.
-    idx = baseIdx;
-    for (let j = minCellY; j <= maxCellY; j++) {
-      for (let i = minCellX; i <= maxCellX; i++) {
-        if (!this.grid[idx]) {
-          this.grid[idx] = [bound];
-        } else {
-          this.grid[idx].push(bound);
-        }
-        idx++;
-      }
-      idx += this.numHorizCells - (maxCellX - minCellX + 1);
-    }
-    return true;
-  }
-
-  /**
-   * Returns the x index of the grid cell where the given x coordinate falls.
-   *
-   * @param x the coordinate, in world space.
-   * @return the x index of the cell.
-   */
-  private getCellX(x: number) {
-    return Math.floor((x - this.bound.loX) / this.cellWidth);
-  };
-
-  /**
-   * Returns the y index of the grid cell where the given y coordinate falls.
-   *
-   * @param y the coordinate, in world space.
-   * @return the y index of the cell.
-   */
-  private getCellY(y: number) {
-    return Math.floor((y - this.bound.loY) / this.cellHeight);
-  };
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/logging.ts b/tensorflow/tensorboard/components/vz_projector/logging.ts
deleted file mode 100644
index 59f3720601..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/logging.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/** Duration in ms for showing warning messages to the user */
-const WARNING_DURATION_MS = 10000;
-
-let dom: HTMLElement = null;
-let msgId = 0;
-let numActiveMessages = 0;
-
-export function setDomContainer(domElement: HTMLElement) {
-  dom = domElement;
-}
-
-/**
- * Updates the user message with the provided id.
- *
- * @param msg The message shown to the user. If null, the message is removed.
- * @param id The id of an existing message. If no id is provided, a unique id
- *     is assigned.
- * @param title The title of the notification.
- * @param isErrorMsg If true, the message is error and the dialog will have a
- *                   close button.
- * @return The id of the message.
- */
-export function setModalMessage(
-    msg: string, id: string = null, title = null, isErrorMsg = false): string {
-  if (dom == null) {
-    console.warn('Can\'t show modal message before the dom is initialized');
-    return;
-  }
-  if (id == null) {
-    id = (msgId++).toString();
-  }
-  let dialog = dom.querySelector('#notification-dialog') as any;
-  dialog.querySelector('.close-button').style.display =
-      isErrorMsg ? null : 'none';
-  let spinner = dialog.querySelector('.progress');
-  spinner.style.display = isErrorMsg ? 'none' : null;
-  spinner.active = isErrorMsg ? null : true;
-  dialog.querySelector('#notification-title').innerHTML = title;
-  let msgsContainer = dialog.querySelector('#notify-msgs') as HTMLElement;
-  if (isErrorMsg) {
-    msgsContainer.innerHTML = '';
-  } else {
-    const errors = msgsContainer.querySelectorAll('.error');
-    for (let i = 0; i < errors.length; i++) {
-      msgsContainer.removeChild(errors[i]);
-    }
-  }
-  let divId = `notify-msg-${id}`;
-  let msgDiv = dialog.querySelector('#' + divId) as HTMLDivElement;
-  if (msgDiv == null) {
-    msgDiv = document.createElement('div');
-    msgDiv.className = 'notify-msg ' + (isErrorMsg ? 'error' : '');
-    msgDiv.id = divId;
-
-    msgsContainer.insertBefore(msgDiv, msgsContainer.firstChild);
-
-    if (!isErrorMsg) {
-      numActiveMessages++;
-    } else {
-      numActiveMessages = 0;
-    }
-  }
-  if (msg == null) {
-    numActiveMessages--;
-    if (numActiveMessages === 0) {
-      dialog.close();
-    }
-    msgDiv.remove();
-  } else {
-    msgDiv.innerText = msg;
-    dialog.open();
-  }
-  return id;
-}
-
-export function setErrorMessage(errMsg: string, task?: string) {
-  setModalMessage(errMsg, null, 'Error ' + (task != null ? task : ''), true);
-}
-
-/**
- * Shows a warning message to the user for a certain amount of time.
- */
-export function setWarningMessage(msg: string): void {
-  let toast = dom.querySelector('#toast') as any;
-  toast.text = msg;
-  toast.duration = WARNING_DURATION_MS;
-  toast.open();
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/projectorEventContext.ts b/tensorflow/tensorboard/components/vz_projector/projectorEventContext.ts
deleted file mode 100644
index 36f5c4c584..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/projectorEventContext.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DistanceFunction, Projection} from './data';
-import {NearestEntry} from './knn';
-
-export type HoverListener = (index: number) => void;
-export type SelectionChangedListener =
-    (selectedPointIndices: number[], neighborsOfFirstPoint: NearestEntry[]) =>
-        void;
-export type ProjectionChangedListener = (projection: Projection) => void;
-export type DistanceMetricChangedListener =
-    (distanceMetric: DistanceFunction) => void;
-export interface ProjectorEventContext {
-  /** Register a callback to be invoked when the mouse hovers over a point. */
-  registerHoverListener(listener: HoverListener);
-  /** Notify the hover system that a point is under the mouse. */
-  notifyHoverOverPoint(pointIndex: number);
-  /** Registers a callback to be invoked when the selection changes. */
-  registerSelectionChangedListener(listener: SelectionChangedListener);
-  /**
-   * Notify the selection system that a client has changed the selected point
-   * set.
-   */
-  notifySelectionChanged(newSelectedPointIndices: number[]);
-  /** Registers a callback to be invoked when the projection changes. */
-  registerProjectionChangedListener(listener: ProjectionChangedListener);
-  /** Notify listeners that a reprojection occurred. */
-  notifyProjectionChanged(projection: Projection);
-  registerDistanceMetricChangedListener(listener:
-                                            DistanceMetricChangedListener);
-  notifyDistanceMetricChanged(distMetric: DistanceFunction);
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/projectorScatterPlotAdapter.ts b/tensorflow/tensorboard/components/vz_projector/projectorScatterPlotAdapter.ts
deleted file mode 100644
index c0da952659..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/projectorScatterPlotAdapter.ts
+++ /dev/null
@@ -1,711 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataSet, DistanceFunction, Projection, ProjectionComponents3D, State} from './data';
-import {NearestEntry} from './knn';
-import {ProjectorEventContext} from './projectorEventContext';
-import {LabelRenderParams} from './renderContext';
-import {ScatterPlot} from './scatterPlot';
-import {ScatterPlotVisualizer3DLabels} from './scatterPlotVisualizer3DLabels';
-import {ScatterPlotVisualizerCanvasLabels} from './scatterPlotVisualizerCanvasLabels';
-import {ScatterPlotVisualizerPolylines} from './scatterPlotVisualizerPolylines';
-import {ScatterPlotVisualizerSprites} from './scatterPlotVisualizerSprites';
-import * as vector from './vector';
-
-const LABEL_FONT_SIZE = 10;
-const LABEL_SCALE_DEFAULT = 1.0;
-const LABEL_SCALE_LARGE = 2;
-const LABEL_FILL_COLOR_SELECTED = 0x000000;
-const LABEL_FILL_COLOR_HOVER = 0x000000;
-const LABEL_FILL_COLOR_NEIGHBOR = 0x000000;
-const LABEL_STROKE_COLOR_SELECTED = 0xFFFFFF;
-const LABEL_STROKE_COLOR_HOVER = 0xFFFFFF;
-const LABEL_STROKE_COLOR_NEIGHBOR = 0xFFFFFF;
-
-const POINT_COLOR_UNSELECTED = 0xE3E3E3;
-const POINT_COLOR_NO_SELECTION = 0x7575D9;
-const POINT_COLOR_SELECTED = 0xFA6666;
-const POINT_COLOR_HOVER = 0x760B4F;
-
-const POINT_SCALE_DEFAULT = 1.0;
-const POINT_SCALE_SELECTED = 1.2;
-const POINT_SCALE_NEIGHBOR = 1.2;
-const POINT_SCALE_HOVER = 1.2;
-
-const LABELS_3D_COLOR_UNSELECTED = 0xFFFFFF;
-const LABELS_3D_COLOR_NO_SELECTION = 0xFFFFFF;
-
-const SPRITE_IMAGE_COLOR_UNSELECTED = 0xFFFFFF;
-const SPRITE_IMAGE_COLOR_NO_SELECTION = 0xFFFFFF;
-
-const POLYLINE_START_HUE = 60;
-const POLYLINE_END_HUE = 360;
-const POLYLINE_SATURATION = 1;
-const POLYLINE_LIGHTNESS = .3;
-
-const POLYLINE_DEFAULT_OPACITY = .2;
-const POLYLINE_DEFAULT_LINEWIDTH = 2;
-const POLYLINE_SELECTED_OPACITY = .9;
-const POLYLINE_SELECTED_LINEWIDTH = 3;
-const POLYLINE_DESELECTED_OPACITY = .05;
-
-const SCATTER_PLOT_CUBE_LENGTH = 2;
-
-/** Color scale for nearest neighbors. */
-const NN_COLOR_SCALE =
-    d3.scaleLinear<string, string>()
-        .domain([1, 0.7, 0.4])
-        .range(['hsl(285, 80%, 40%)', 'hsl(0, 80%, 65%)', 'hsl(40, 70%, 60%)'])
-        .clamp(true);
-
-/**
- * Interprets projector events and assembes the arrays and commands necessary
- * to use the ScatterPlot to render the current projected data set.
- */
-export class ProjectorScatterPlotAdapter {
-  public scatterPlot: ScatterPlot;
-  private projection: Projection;
-  private hoverPointIndex: number;
-  private selectedPointIndices: number[];
-  private neighborsOfFirstSelectedPoint: NearestEntry[];
-  private renderLabelsIn3D: boolean = false;
-  private labelPointAccessor: string;
-  private legendPointColorer: (ds: DataSet, index: number) => string;
-  private distanceMetric: DistanceFunction;
-
-  private spriteVisualizer: ScatterPlotVisualizerSprites;
-  private labels3DVisualizer: ScatterPlotVisualizer3DLabels;
-  private canvasLabelsVisualizer: ScatterPlotVisualizerCanvasLabels;
-  private polylineVisualizer: ScatterPlotVisualizerPolylines;
-
-  constructor(
-      private scatterPlotContainer: HTMLElement,
-      projectorEventContext: ProjectorEventContext) {
-    this.scatterPlot =
-        new ScatterPlot(scatterPlotContainer, projectorEventContext);
-    projectorEventContext.registerProjectionChangedListener(projection => {
-      this.projection = projection;
-      this.updateScatterPlotWithNewProjection(projection);
-    });
-    projectorEventContext.registerSelectionChangedListener(
-        (selectedPointIndices, neighbors) => {
-          this.selectedPointIndices = selectedPointIndices;
-          this.neighborsOfFirstSelectedPoint = neighbors;
-          this.updateScatterPlotPositions();
-          this.updateScatterPlotAttributes();
-          this.scatterPlot.render();
-        });
-    projectorEventContext.registerHoverListener(hoverPointIndex => {
-      this.hoverPointIndex = hoverPointIndex;
-      this.updateScatterPlotAttributes();
-      this.scatterPlot.render();
-    });
-    projectorEventContext.registerDistanceMetricChangedListener(
-        distanceMetric => {
-          this.distanceMetric = distanceMetric;
-          this.updateScatterPlotAttributes();
-          this.scatterPlot.render();
-        });
-    this.createVisualizers(false);
-  }
-
-  notifyProjectionPositionsUpdated() {
-    this.updateScatterPlotPositions();
-    this.scatterPlot.render();
-  }
-
-  setDataSet(dataSet: DataSet) {
-    if (this.projection != null) {
-      // TODO(nicholsonc): setDataSet needs to go away, the projection is the
-      // atomic unit of update.
-      this.projection.dataSet = dataSet;
-    }
-    if (this.polylineVisualizer != null) {
-      this.polylineVisualizer.setDataSet(dataSet);
-    }
-    if (this.labels3DVisualizer != null) {
-      this.labels3DVisualizer.setLabelStrings(
-          this.generate3DLabelsArray(dataSet, this.labelPointAccessor));
-    }
-    if (this.spriteVisualizer == null) {
-      return;
-    }
-    this.spriteVisualizer.clearSpriteAtlas();
-    if ((dataSet == null) || (dataSet.spriteAndMetadataInfo == null)) {
-      return;
-    }
-    const metadata = dataSet.spriteAndMetadataInfo;
-    if ((metadata.spriteImage == null) || (metadata.spriteMetadata == null)) {
-      return;
-    }
-    const n = dataSet.points.length;
-    const spriteIndices = new Float32Array(n);
-    for (let i = 0; i < n; ++i) {
-      spriteIndices[i] = dataSet.points[i].index;
-    }
-    this.spriteVisualizer.setSpriteAtlas(
-        metadata.spriteImage, metadata.spriteMetadata.singleImageDim,
-        spriteIndices);
-  }
-
-  set3DLabelMode(renderLabelsIn3D: boolean) {
-    this.renderLabelsIn3D = renderLabelsIn3D;
-    this.createVisualizers(renderLabelsIn3D);
-    this.updateScatterPlotAttributes();
-    this.scatterPlot.render();
-  }
-
-  setLegendPointColorer(
-      legendPointColorer: (ds: DataSet, index: number) => string) {
-    this.legendPointColorer = legendPointColorer;
-  }
-
-  setLabelPointAccessor(labelPointAccessor: string) {
-    this.labelPointAccessor = labelPointAccessor;
-    if (this.labels3DVisualizer != null) {
-      const ds = (this.projection == null) ? null : this.projection.dataSet;
-      this.labels3DVisualizer.setLabelStrings(
-          this.generate3DLabelsArray(ds, labelPointAccessor));
-    }
-  }
-
-  resize() {
-    this.scatterPlot.resize();
-  }
-
-  populateBookmarkFromUI(state: State) {
-    state.cameraDef = this.scatterPlot.getCameraDef();
-  }
-
-  restoreUIFromBookmark(state: State) {
-    this.scatterPlot.setCameraParametersForNextCameraCreation(
-        state.cameraDef, false);
-  }
-
-  updateScatterPlotPositions() {
-    const ds = (this.projection == null) ? null : this.projection.dataSet;
-    const projectionComponents =
-        (this.projection == null) ? null : this.projection.projectionComponents;
-    const newPositions =
-        this.generatePointPositionArray(ds, projectionComponents);
-    this.scatterPlot.setPointPositions(newPositions);
-  }
-
-  updateScatterPlotAttributes() {
-    if (this.projection == null) {
-      return;
-    }
-    const dataSet = this.projection.dataSet;
-    const selectedSet = this.selectedPointIndices;
-    const hoverIndex = this.hoverPointIndex;
-    const neighbors = this.neighborsOfFirstSelectedPoint;
-    const pointColorer = this.legendPointColorer;
-
-    const pointColors = this.generatePointColorArray(
-        dataSet, pointColorer, this.distanceMetric, selectedSet, neighbors,
-        hoverIndex, this.renderLabelsIn3D, this.getSpriteImageMode());
-    const pointScaleFactors = this.generatePointScaleFactorArray(
-        dataSet, selectedSet, neighbors, hoverIndex);
-    const labels = this.generateVisibleLabelRenderParams(
-        dataSet, selectedSet, neighbors, hoverIndex);
-    const polylineColors =
-        this.generateLineSegmentColorMap(dataSet, pointColorer);
-    const polylineOpacities =
-        this.generateLineSegmentOpacityArray(dataSet, selectedSet);
-    const polylineWidths =
-        this.generateLineSegmentWidthArray(dataSet, selectedSet);
-
-    this.scatterPlot.setPointColors(pointColors);
-    this.scatterPlot.setPointScaleFactors(pointScaleFactors);
-    this.scatterPlot.setLabels(labels);
-    this.scatterPlot.setPolylineColors(polylineColors);
-    this.scatterPlot.setPolylineOpacities(polylineOpacities);
-    this.scatterPlot.setPolylineWidths(polylineWidths);
-  }
-
-  render() {
-    this.scatterPlot.render();
-  }
-
-  generatePointPositionArray(
-      ds: DataSet, projectionComponents: ProjectionComponents3D): Float32Array {
-    if (ds == null) {
-      return null;
-    }
-
-    const xScaler = d3.scaleLinear();
-    const yScaler = d3.scaleLinear();
-    let zScaler = null;
-    {
-      // Determine max and min of each axis of our data.
-      const xExtent = d3.extent(
-          ds.points,
-          (p, i) => ds.points[i].projections[projectionComponents[0]]);
-      const yExtent = d3.extent(
-          ds.points,
-          (p, i) => ds.points[i].projections[projectionComponents[1]]);
-
-      const range =
-          [-SCATTER_PLOT_CUBE_LENGTH / 2, SCATTER_PLOT_CUBE_LENGTH / 2];
-
-      xScaler.domain(xExtent).range(range);
-      yScaler.domain(yExtent).range(range);
-
-      if (projectionComponents[2] != null) {
-        const zExtent = d3.extent(
-            ds.points,
-            (p, i) => ds.points[i].projections[projectionComponents[2]]);
-        zScaler = d3.scaleLinear();
-        zScaler.domain(zExtent).range(range);
-      }
-    }
-
-    const positions = new Float32Array(ds.points.length * 3);
-    let dst = 0;
-
-    ds.points.forEach((d, i) => {
-      positions[dst++] =
-          xScaler(ds.points[i].projections[projectionComponents[0]]);
-      positions[dst++] =
-          yScaler(ds.points[i].projections[projectionComponents[1]]);
-      positions[dst++] = 0.0;
-    });
-
-    if (zScaler) {
-      dst = 2;
-      ds.points.forEach((d, i) => {
-        positions[dst] =
-            zScaler(ds.points[i].projections[projectionComponents[2]]);
-        dst += 3;
-      });
-    }
-
-    return positions;
-  }
-
-  generateVisibleLabelRenderParams(
-      ds: DataSet, selectedPointIndices: number[],
-      neighborsOfFirstPoint: NearestEntry[],
-      hoverPointIndex: number): LabelRenderParams {
-    if (ds == null) {
-      return null;
-    }
-
-    const selectedPointCount =
-        (selectedPointIndices == null) ? 0 : selectedPointIndices.length;
-    const neighborCount =
-        (neighborsOfFirstPoint == null) ? 0 : neighborsOfFirstPoint.length;
-    const n = selectedPointCount + neighborCount +
-        ((hoverPointIndex != null) ? 1 : 0);
-
-    const visibleLabels = new Uint32Array(n);
-    const scale = new Float32Array(n);
-    const opacityFlags = new Int8Array(n);
-    const fillColors = new Uint8Array(n * 3);
-    const strokeColors = new Uint8Array(n * 3);
-    const labelStrings: string[] = [];
-
-    scale.fill(LABEL_SCALE_DEFAULT);
-    opacityFlags.fill(1);
-
-    let dst = 0;
-
-    if (hoverPointIndex != null) {
-      labelStrings.push(
-          this.getLabelText(ds, hoverPointIndex, this.labelPointAccessor));
-      visibleLabels[dst] = hoverPointIndex;
-      scale[dst] = LABEL_SCALE_LARGE;
-      opacityFlags[dst] = 0;
-      const fillRgb = styleRgbFromHexColor(LABEL_FILL_COLOR_HOVER);
-      packRgbIntoUint8Array(
-          fillColors, dst, fillRgb[0], fillRgb[1], fillRgb[2]);
-      const strokeRgb = styleRgbFromHexColor(LABEL_STROKE_COLOR_HOVER);
-      packRgbIntoUint8Array(
-          strokeColors, dst, strokeRgb[0], strokeRgb[1], strokeRgb[1]);
-      ++dst;
-    }
-
-    // Selected points
-    {
-      const n = selectedPointCount;
-      const fillRgb = styleRgbFromHexColor(LABEL_FILL_COLOR_SELECTED);
-      const strokeRgb = styleRgbFromHexColor(LABEL_STROKE_COLOR_SELECTED);
-      for (let i = 0; i < n; ++i) {
-        const labelIndex = selectedPointIndices[i];
-        labelStrings.push(
-            this.getLabelText(ds, labelIndex, this.labelPointAccessor));
-        visibleLabels[dst] = labelIndex;
-        scale[dst] = LABEL_SCALE_LARGE;
-        opacityFlags[dst] = (n === 1) ? 0 : 1;
-        packRgbIntoUint8Array(
-            fillColors, dst, fillRgb[0], fillRgb[1], fillRgb[2]);
-        packRgbIntoUint8Array(
-            strokeColors, dst, strokeRgb[0], strokeRgb[1], strokeRgb[2]);
-        ++dst;
-      }
-    }
-
-    // Neighbors
-    {
-      const n = neighborCount;
-      const fillRgb = styleRgbFromHexColor(LABEL_FILL_COLOR_NEIGHBOR);
-      const strokeRgb = styleRgbFromHexColor(LABEL_STROKE_COLOR_NEIGHBOR);
-      for (let i = 0; i < n; ++i) {
-        const labelIndex = neighborsOfFirstPoint[i].index;
-        labelStrings.push(
-            this.getLabelText(ds, labelIndex, this.labelPointAccessor));
-        visibleLabels[dst] = labelIndex;
-        packRgbIntoUint8Array(
-            fillColors, dst, fillRgb[0], fillRgb[1], fillRgb[2]);
-        packRgbIntoUint8Array(
-            strokeColors, dst, strokeRgb[0], strokeRgb[1], strokeRgb[2]);
-        ++dst;
-      }
-    }
-
-    return new LabelRenderParams(
-        new Float32Array(visibleLabels), labelStrings, scale, opacityFlags,
-        LABEL_FONT_SIZE, fillColors, strokeColors);
-  }
-
-  generatePointScaleFactorArray(
-      ds: DataSet, selectedPointIndices: number[],
-      neighborsOfFirstPoint: NearestEntry[],
-      hoverPointIndex: number): Float32Array {
-    if (ds == null) {
-      return new Float32Array(0);
-    }
-
-    const scale = new Float32Array(ds.points.length);
-    scale.fill(POINT_SCALE_DEFAULT);
-
-    const selectedPointCount =
-        (selectedPointIndices == null) ? 0 : selectedPointIndices.length;
-    const neighborCount =
-        (neighborsOfFirstPoint == null) ? 0 : neighborsOfFirstPoint.length;
-
-    // Scale up all selected points.
-    {
-      const n = selectedPointCount;
-      for (let i = 0; i < n; ++i) {
-        const p = selectedPointIndices[i];
-        scale[p] = POINT_SCALE_SELECTED;
-      }
-    }
-
-    // Scale up the neighbor points.
-    {
-      const n = neighborCount;
-      for (let i = 0; i < n; ++i) {
-        const p = neighborsOfFirstPoint[i].index;
-        scale[p] = POINT_SCALE_NEIGHBOR;
-      }
-    }
-
-    // Scale up the hover point.
-    if (hoverPointIndex != null) {
-      scale[hoverPointIndex] = POINT_SCALE_HOVER;
-    }
-
-    return scale;
-  }
-
-  generateLineSegmentColorMap(
-      ds: DataSet, legendPointColorer: (ds: DataSet, index: number) => string):
-      {[polylineIndex: number]: Float32Array} {
-    let polylineColorArrayMap: {[polylineIndex: number]: Float32Array} = {};
-    if (ds == null) {
-      return polylineColorArrayMap;
-    }
-
-    for (let i = 0; i < ds.sequences.length; i++) {
-      let sequence = ds.sequences[i];
-      let colors = new Float32Array(2 * (sequence.pointIndices.length - 1) * 3);
-      let colorIndex = 0;
-
-      if (legendPointColorer) {
-        for (let j = 0; j < sequence.pointIndices.length - 1; j++) {
-          const c1 =
-              new THREE.Color(legendPointColorer(ds, sequence.pointIndices[j]));
-          const c2 = new THREE.Color(
-              legendPointColorer(ds, sequence.pointIndices[j + 1]));
-          colors[colorIndex++] = c1.r;
-          colors[colorIndex++] = c1.g;
-          colors[colorIndex++] = c1.b;
-          colors[colorIndex++] = c2.r;
-          colors[colorIndex++] = c2.g;
-          colors[colorIndex++] = c2.b;
-        }
-      } else {
-        for (let j = 0; j < sequence.pointIndices.length - 1; j++) {
-          const c1 =
-              getDefaultPointInPolylineColor(j, sequence.pointIndices.length);
-          const c2 = getDefaultPointInPolylineColor(
-              j + 1, sequence.pointIndices.length);
-          colors[colorIndex++] = c1.r;
-          colors[colorIndex++] = c1.g;
-          colors[colorIndex++] = c1.b;
-          colors[colorIndex++] = c2.r;
-          colors[colorIndex++] = c2.g;
-          colors[colorIndex++] = c2.b;
-        }
-      }
-
-      polylineColorArrayMap[i] = colors;
-    }
-
-    return polylineColorArrayMap;
-  }
-
-  generateLineSegmentOpacityArray(ds: DataSet, selectedPoints: number[]):
-      Float32Array {
-    if (ds == null) {
-      return new Float32Array(0);
-    }
-    const opacities = new Float32Array(ds.sequences.length);
-    const selectedPointCount =
-        (selectedPoints == null) ? 0 : selectedPoints.length;
-    if (selectedPointCount > 0) {
-      opacities.fill(POLYLINE_DESELECTED_OPACITY);
-      const i = ds.points[selectedPoints[0]].sequenceIndex;
-      opacities[i] = POLYLINE_SELECTED_OPACITY;
-    } else {
-      opacities.fill(POLYLINE_DEFAULT_OPACITY);
-    }
-    return opacities;
-  }
-
-  generateLineSegmentWidthArray(ds: DataSet, selectedPoints: number[]):
-      Float32Array {
-    if (ds == null) {
-      return new Float32Array(0);
-    }
-    const widths = new Float32Array(ds.sequences.length);
-    widths.fill(POLYLINE_DEFAULT_LINEWIDTH);
-    const selectedPointCount =
-        (selectedPoints == null) ? 0 : selectedPoints.length;
-    if (selectedPointCount > 0) {
-      const i = ds.points[selectedPoints[0]].sequenceIndex;
-      widths[i] = POLYLINE_SELECTED_LINEWIDTH;
-    }
-    return widths;
-  }
-
-  generatePointColorArray(
-      ds: DataSet, legendPointColorer: (ds: DataSet, index: number) => string,
-      distFunc: DistanceFunction, selectedPointIndices: number[],
-      neighborsOfFirstPoint: NearestEntry[], hoverPointIndex: number,
-      label3dMode: boolean, spriteImageMode: boolean): Float32Array {
-    if (ds == null) {
-      return new Float32Array(0);
-    }
-
-    const selectedPointCount =
-        (selectedPointIndices == null) ? 0 : selectedPointIndices.length;
-    const neighborCount =
-        (neighborsOfFirstPoint == null) ? 0 : neighborsOfFirstPoint.length;
-    const colors = new Float32Array(ds.points.length * 3);
-
-    let unselectedColor = POINT_COLOR_UNSELECTED;
-    let noSelectionColor = POINT_COLOR_NO_SELECTION;
-
-    if (label3dMode) {
-      unselectedColor = LABELS_3D_COLOR_UNSELECTED;
-      noSelectionColor = LABELS_3D_COLOR_NO_SELECTION;
-    }
-
-    if (spriteImageMode) {
-      unselectedColor = SPRITE_IMAGE_COLOR_UNSELECTED;
-      noSelectionColor = SPRITE_IMAGE_COLOR_NO_SELECTION;
-    }
-
-    // Give all points the unselected color.
-    {
-      const n = ds.points.length;
-      let dst = 0;
-      if (selectedPointCount > 0) {
-        const c = new THREE.Color(unselectedColor);
-        for (let i = 0; i < n; ++i) {
-          colors[dst++] = c.r;
-          colors[dst++] = c.g;
-          colors[dst++] = c.b;
-        }
-      } else {
-        if (legendPointColorer != null) {
-          for (let i = 0; i < n; ++i) {
-            const c = new THREE.Color(legendPointColorer(ds, i));
-            colors[dst++] = c.r;
-            colors[dst++] = c.g;
-            colors[dst++] = c.b;
-          }
-        } else {
-          const c = new THREE.Color(noSelectionColor);
-          for (let i = 0; i < n; ++i) {
-            colors[dst++] = c.r;
-            colors[dst++] = c.g;
-            colors[dst++] = c.b;
-          }
-        }
-      }
-    }
-
-    // Color the selected points.
-    {
-      const n = selectedPointCount;
-      const c = new THREE.Color(POINT_COLOR_SELECTED);
-      for (let i = 0; i < n; ++i) {
-        let dst = selectedPointIndices[i] * 3;
-        colors[dst++] = c.r;
-        colors[dst++] = c.g;
-        colors[dst++] = c.b;
-      }
-    }
-
-    // Color the neighbors.
-    {
-      const n = neighborCount;
-      let minDist = n > 0 ? neighborsOfFirstPoint[0].dist : 0;
-      for (let i = 0; i < n; ++i) {
-        const c = new THREE.Color(
-            dist2color(distFunc, neighborsOfFirstPoint[i].dist, minDist));
-        let dst = neighborsOfFirstPoint[i].index * 3;
-        colors[dst++] = c.r;
-        colors[dst++] = c.g;
-        colors[dst++] = c.b;
-      }
-    }
-
-    // Color the hover point.
-    if (hoverPointIndex != null) {
-      const c = new THREE.Color(POINT_COLOR_HOVER);
-      let dst = hoverPointIndex * 3;
-      colors[dst++] = c.r;
-      colors[dst++] = c.g;
-      colors[dst++] = c.b;
-    }
-
-    return colors;
-  }
-
-  generate3DLabelsArray(ds: DataSet, accessor: string) {
-    if ((ds == null) || (accessor == null)) {
-      return null;
-    }
-    let labels: string[] = [];
-    const n = ds.points.length;
-    for (let i = 0; i < n; ++i) {
-      labels.push(this.getLabelText(ds, i, accessor));
-    }
-    return labels;
-  }
-
-  private getLabelText(ds: DataSet, i: number, accessor: string) {
-    return ds.points[i].metadata[accessor].toString();
-  }
-
-  private updateScatterPlotWithNewProjection(projection: Projection) {
-    if (projection == null) {
-      this.createVisualizers(this.renderLabelsIn3D);
-      this.scatterPlot.render();
-      return;
-    }
-    this.setDataSet(projection.dataSet);
-    this.scatterPlot.setDimensions(projection.dimensionality);
-    if (projection.dataSet.projectionCanBeRendered(projection.projectionType)) {
-      this.updateScatterPlotAttributes();
-      this.notifyProjectionPositionsUpdated();
-    }
-    this.scatterPlot.setCameraParametersForNextCameraCreation(null, false);
-  }
-
-  private createVisualizers(inLabels3DMode: boolean) {
-    const ds = (this.projection == null) ? null : this.projection.dataSet;
-    const scatterPlot = this.scatterPlot;
-    scatterPlot.removeAllVisualizers();
-    this.labels3DVisualizer = null;
-    this.canvasLabelsVisualizer = null;
-    this.spriteVisualizer = null;
-    this.polylineVisualizer = null;
-    if (inLabels3DMode) {
-      this.labels3DVisualizer = new ScatterPlotVisualizer3DLabels();
-      this.labels3DVisualizer.setLabelStrings(
-          this.generate3DLabelsArray(ds, this.labelPointAccessor));
-    } else {
-      this.spriteVisualizer = new ScatterPlotVisualizerSprites();
-      scatterPlot.addVisualizer(this.spriteVisualizer);
-      this.canvasLabelsVisualizer =
-          new ScatterPlotVisualizerCanvasLabels(this.scatterPlotContainer);
-    }
-    this.polylineVisualizer = new ScatterPlotVisualizerPolylines();
-    this.setDataSet(ds);
-    if (this.spriteVisualizer) {
-      scatterPlot.addVisualizer(this.spriteVisualizer);
-    }
-    if (this.labels3DVisualizer) {
-      scatterPlot.addVisualizer(this.labels3DVisualizer);
-    }
-    if (this.canvasLabelsVisualizer) {
-      scatterPlot.addVisualizer(this.canvasLabelsVisualizer);
-    }
-    scatterPlot.addVisualizer(this.polylineVisualizer);
-  }
-
-  private getSpriteImageMode(): boolean {
-    if (this.projection == null) {
-      return false;
-    }
-    const ds = this.projection.dataSet;
-    if ((ds == null) || (ds.spriteAndMetadataInfo == null)) {
-      return false;
-    }
-    return ds.spriteAndMetadataInfo.spriteImage != null;
-  }
-}
-
-function packRgbIntoUint8Array(
-    rgbArray: Uint8Array, labelIndex: number, r: number, g: number, b: number) {
-  rgbArray[labelIndex * 3] = r;
-  rgbArray[labelIndex * 3 + 1] = g;
-  rgbArray[labelIndex * 3 + 2] = b;
-}
-
-function styleRgbFromHexColor(hex: number): [number, number, number] {
-  const c = new THREE.Color(hex);
-  return [(c.r * 255) | 0, (c.g * 255) | 0, (c.b * 255) | 0];
-}
-
-function getDefaultPointInPolylineColor(
-    index: number, totalPoints: number): THREE.Color {
-  let hue = POLYLINE_START_HUE +
-      (POLYLINE_END_HUE - POLYLINE_START_HUE) * index / totalPoints;
-
-  let rgb = d3.hsl(hue, POLYLINE_SATURATION, POLYLINE_LIGHTNESS).rgb();
-  return new THREE.Color(rgb.r / 255, rgb.g / 255, rgb.b / 255);
-}
-
-/**
- * Normalizes the distance so it can be visually encoded with color.
- * The normalization depends on the distance metric (cosine vs euclidean).
- */
-export function normalizeDist(
-    distFunc: DistanceFunction, d: number, minDist: number): number {
-  return (distFunc === vector.dist) ? (minDist / d) : (1 - d);
-}
-
-/** Normalizes and encodes the provided distance with color. */
-export function dist2color(
-    distFunc: DistanceFunction, d: number, minDist: number): string {
-  return NN_COLOR_SCALE(normalizeDist(distFunc, d, minDist));
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/renderContext.ts b/tensorflow/tensorboard/components/vz_projector/renderContext.ts
deleted file mode 100644
index 8d5232a804..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/renderContext.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http:www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * LabelRenderParams describes the set of points that should have labels
- * rendered next to them.
- */
-export class LabelRenderParams {
-  constructor(
-      public pointIndices: Float32Array, public labelStrings: string[],
-      public scaleFactors: Float32Array, public useSceneOpacityFlags: Int8Array,
-      public defaultFontSize: number, public fillColors: Uint8Array,
-      public strokeColors: Uint8Array) {}
-}
-
-/** Details about the camera projection being used to render the scene. */
-export enum CameraType {
-  Perspective,
-  Orthographic
-}
-
-/**
- * RenderContext contains all of the state required to color and render the data
- * set. ScatterPlot passes this to every attached visualizer as part of the
- * render callback.
- * TODO(nicholsonc): This should only contain the data that's changed between
- * each frame. Data like colors / scale factors / labels should be reapplied
- * only when they change.
- */
-export class RenderContext {
-  constructor(
-      public camera: THREE.Camera, public cameraType: CameraType,
-      public cameraTarget: THREE.Vector3, public screenWidth: number,
-      public screenHeight: number, public nearestCameraSpacePointZ: number,
-      public farthestCameraSpacePointZ: number, public backgroundColor: number,
-      public pointColors: Float32Array, public pointScaleFactors: Float32Array,
-      public labels: LabelRenderParams,
-      public polylineColors: {[polylineIndex: number]: Float32Array},
-      public polylineOpacities: Float32Array,
-      public polylineWidths: Float32Array) {}
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/scatterPlot.ts b/tensorflow/tensorboard/components/vz_projector/scatterPlot.ts
deleted file mode 100644
index 283b608e83..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/scatterPlot.ts
+++ /dev/null
@@ -1,723 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {ProjectorEventContext} from './projectorEventContext';
-import {CameraType, LabelRenderParams, RenderContext} from './renderContext';
-import {BoundingBox, ScatterPlotRectangleSelector} from './scatterPlotRectangleSelector';
-import {ScatterPlotVisualizer} from './scatterPlotVisualizer';
-import * as util from './util';
-import {Point2D, Point3D} from './vector';
-
-const BACKGROUND_COLOR = 0xffffff;
-
-/**
- * The length of the cube (diameter of the circumscribing sphere) where all the
- * points live.
- */
-const CUBE_LENGTH = 2;
-const MAX_ZOOM = 5 * CUBE_LENGTH;
-const MIN_ZOOM = 0.025 * CUBE_LENGTH;
-
-// Constants relating to the camera parameters.
-const PERSP_CAMERA_FOV_VERTICAL = 70;
-const PERSP_CAMERA_NEAR_CLIP_PLANE = 0.01;
-const PERSP_CAMERA_FAR_CLIP_PLANE = 100;
-const ORTHO_CAMERA_FRUSTUM_HALF_EXTENT = 1.2;
-
-// Key presses.
-const SHIFT_KEY = 16;
-const CTRL_KEY = 17;
-
-const START_CAMERA_POS_3D = new THREE.Vector3(0.45, 0.9, 1.6);
-const START_CAMERA_TARGET_3D = new THREE.Vector3(0, 0, 0);
-const START_CAMERA_POS_2D = new THREE.Vector3(0, 0, 4);
-const START_CAMERA_TARGET_2D = new THREE.Vector3(0, 0, 0);
-
-const ORBIT_MOUSE_ROTATION_SPEED = 1;
-const ORBIT_ANIMATION_ROTATION_CYCLE_IN_SECONDS = 7;
-
-export type OnCameraMoveListener =
-    (cameraPosition: THREE.Vector3, cameraTarget: THREE.Vector3) => void;
-
-/** Supported modes of interaction. */
-export enum MouseMode {
-  AREA_SELECT,
-  CAMERA_AND_CLICK_SELECT
-}
-
-/** Defines a camera, suitable for serialization. */
-export class CameraDef {
-  orthographic: boolean = false;
-  position: Point3D;
-  target: Point3D;
-  zoom: number;
-}
-
-/**
- * Maintains a three.js instantiation and context,
- * animation state, and all other logic that's
- * independent of how a 3D scatter plot is actually rendered. Also holds an
- * array of visualizers and dispatches application events to them.
- */
-export class ScatterPlot {
-  private visualizers: ScatterPlotVisualizer[] = [];
-
-  private onCameraMoveListeners: OnCameraMoveListener[] = [];
-
-  private height: number;
-  private width: number;
-
-  private mouseMode: MouseMode;
-  private backgroundColor: number = BACKGROUND_COLOR;
-
-  private dimensionality: number = 3;
-  private renderer: THREE.WebGLRenderer;
-
-  private scene: THREE.Scene;
-  private pickingTexture: THREE.WebGLRenderTarget;
-  private light: THREE.PointLight;
-
-  private cameraDef: CameraDef = null;
-  private camera: THREE.Camera;
-  private orbitAnimationOnNextCameraCreation: boolean = false;
-  private orbitCameraControls: any;
-  private orbitAnimationId: number;
-
-  private worldSpacePointPositions: Float32Array;
-  private pointColors: Float32Array;
-  private pointScaleFactors: Float32Array;
-  private labels: LabelRenderParams;
-  private polylineColors: {[polylineIndex: number]: Float32Array};
-  private polylineOpacities: Float32Array;
-  private polylineWidths: Float32Array;
-
-  private selecting = false;
-  private nearestPoint: number;
-  private mouseIsDown = false;
-  private isDragSequence = false;
-  private rectangleSelector: ScatterPlotRectangleSelector;
-
-  constructor(
-      private container: HTMLElement,
-      private projectorEventContext: ProjectorEventContext) {
-    this.getLayoutValues();
-
-    this.scene = new THREE.Scene();
-    this.renderer = new THREE.WebGLRenderer(
-        {alpha: true, premultipliedAlpha: false, antialias: false});
-    this.renderer.setClearColor(BACKGROUND_COLOR, 1);
-    this.container.appendChild(this.renderer.domElement);
-    this.light = new THREE.PointLight(0xFFECBF, 1, 0);
-    this.scene.add(this.light);
-
-    this.setDimensions(3);
-    this.recreateCamera(this.makeDefaultCameraDef(this.dimensionality));
-    this.renderer.render(this.scene, this.camera);
-
-    this.rectangleSelector = new ScatterPlotRectangleSelector(
-        this.container,
-        (boundingBox: BoundingBox) => this.selectBoundingBox(boundingBox));
-    this.addInteractionListeners();
-  }
-
-  private addInteractionListeners() {
-    this.container.addEventListener('mousemove', this.onMouseMove.bind(this));
-    this.container.addEventListener('mousedown', this.onMouseDown.bind(this));
-    this.container.addEventListener('mouseup', this.onMouseUp.bind(this));
-    this.container.addEventListener('click', this.onClick.bind(this));
-    window.addEventListener('keydown', this.onKeyDown.bind(this), false);
-    window.addEventListener('keyup', this.onKeyUp.bind(this), false);
-  }
-
-  private addCameraControlsEventListeners(cameraControls: any) {
-    // Start is called when the user stars interacting with
-    // controls.
-    cameraControls.addEventListener('start', () => {
-      this.stopOrbitAnimation();
-      this.onCameraMoveListeners.forEach(
-          l => l(this.camera.position, cameraControls.target));
-    });
-
-    // Change is called everytime the user interacts with the controls.
-    cameraControls.addEventListener('change', () => {
-      this.render();
-    });
-
-    // End is called when the user stops interacting with the
-    // controls (e.g. on mouse up, after dragging).
-    cameraControls.addEventListener('end', () => {});
-  }
-
-  private makeOrbitControls(
-      camera: THREE.Camera, cameraDef: CameraDef, cameraIs3D: boolean) {
-    if (this.orbitCameraControls != null) {
-      this.orbitCameraControls.dispose();
-    }
-    const occ =
-        new (THREE as any).OrbitControls(camera, this.renderer.domElement);
-    occ.target0 = new THREE.Vector3(
-        cameraDef.target[0], cameraDef.target[1], cameraDef.target[2]);
-    occ.position0 = new THREE.Vector3().copy(camera.position);
-    occ.zoom0 = cameraDef.zoom;
-    occ.enableRotate = cameraIs3D;
-    occ.autoRotate = false;
-    occ.rotateSpeed = ORBIT_MOUSE_ROTATION_SPEED;
-    if (cameraIs3D) {
-      occ.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
-      occ.mouseButtons.PAN = THREE.MOUSE.RIGHT;
-    } else {
-      occ.mouseButtons.ORBIT = null;
-      occ.mouseButtons.PAN = THREE.MOUSE.LEFT;
-    }
-    occ.reset();
-
-    this.camera = camera;
-    this.orbitCameraControls = occ;
-    this.addCameraControlsEventListeners(this.orbitCameraControls);
-  }
-
-  private makeCamera3D(cameraDef: CameraDef, w: number, h: number) {
-    let camera: THREE.PerspectiveCamera;
-    {
-      const aspectRatio = w / h;
-      camera = new THREE.PerspectiveCamera(
-          PERSP_CAMERA_FOV_VERTICAL, aspectRatio, PERSP_CAMERA_NEAR_CLIP_PLANE,
-          PERSP_CAMERA_FAR_CLIP_PLANE);
-      camera.position.set(
-          cameraDef.position[0], cameraDef.position[1], cameraDef.position[2]);
-      const at = new THREE.Vector3(
-          cameraDef.target[0], cameraDef.target[1], cameraDef.target[2]);
-      camera.lookAt(at);
-      camera.zoom = cameraDef.zoom;
-      camera.updateProjectionMatrix();
-    }
-    this.camera = camera;
-    this.makeOrbitControls(camera, cameraDef, true);
-  }
-
-  private makeCamera2D(cameraDef: CameraDef, w: number, h: number) {
-    let camera: THREE.OrthographicCamera;
-    const target = new THREE.Vector3(
-        cameraDef.target[0], cameraDef.target[1], cameraDef.target[2]);
-    {
-      const aspectRatio = w / h;
-      let left = -ORTHO_CAMERA_FRUSTUM_HALF_EXTENT;
-      let right = ORTHO_CAMERA_FRUSTUM_HALF_EXTENT;
-      let bottom = -ORTHO_CAMERA_FRUSTUM_HALF_EXTENT;
-      let top = ORTHO_CAMERA_FRUSTUM_HALF_EXTENT;
-      // Scale up the larger of (w, h) to match the aspect ratio.
-      if (aspectRatio > 1) {
-        left *= aspectRatio;
-        right *= aspectRatio;
-      } else {
-        top /= aspectRatio;
-        bottom /= aspectRatio;
-      }
-      camera =
-          new THREE.OrthographicCamera(left, right, top, bottom, -1000, 1000);
-      camera.position.set(
-          cameraDef.position[0], cameraDef.position[1], cameraDef.position[2]);
-      camera.up = new THREE.Vector3(0, 1, 0);
-      camera.lookAt(target);
-      camera.zoom = cameraDef.zoom;
-      camera.updateProjectionMatrix();
-    }
-    this.camera = camera;
-    this.makeOrbitControls(camera, cameraDef, false);
-  }
-
-  private makeDefaultCameraDef(dimensionality: number): CameraDef {
-    const def = new CameraDef();
-    def.orthographic = (dimensionality === 2);
-    def.zoom = 1.0;
-    if (def.orthographic) {
-      def.position =
-          [START_CAMERA_POS_2D.x, START_CAMERA_POS_2D.y, START_CAMERA_POS_2D.z];
-      def.target = [
-        START_CAMERA_TARGET_2D.x, START_CAMERA_TARGET_2D.y,
-        START_CAMERA_TARGET_2D.z
-      ];
-    } else {
-      def.position =
-          [START_CAMERA_POS_3D.x, START_CAMERA_POS_3D.y, START_CAMERA_POS_3D.z];
-      def.target = [
-        START_CAMERA_TARGET_3D.x, START_CAMERA_TARGET_3D.y,
-        START_CAMERA_TARGET_3D.z
-      ];
-    }
-    return def;
-  }
-
-  /** Recreate the scatter plot camera from a definition structure. */
-  recreateCamera(cameraDef: CameraDef) {
-    if (cameraDef.orthographic) {
-      this.makeCamera2D(cameraDef, this.width, this.height);
-    } else {
-      this.makeCamera3D(cameraDef, this.width, this.height);
-    }
-    this.orbitCameraControls.minDistance = MIN_ZOOM;
-    this.orbitCameraControls.maxDistance = MAX_ZOOM;
-    this.orbitCameraControls.update();
-    if (this.orbitAnimationOnNextCameraCreation) {
-      this.startOrbitAnimation();
-    }
-  }
-
-  private onClick(e?: MouseEvent, notify = true) {
-    if (e && this.selecting) {
-      return;
-    }
-    // Only call event handlers if the click originated from the scatter plot.
-    if (!this.isDragSequence && notify) {
-      const selection = (this.nearestPoint != null) ? [this.nearestPoint] : [];
-      this.projectorEventContext.notifySelectionChanged(selection);
-    }
-    this.isDragSequence = false;
-    this.render();
-  }
-
-  private onMouseDown(e: MouseEvent) {
-    this.isDragSequence = false;
-    this.mouseIsDown = true;
-    if (this.selecting) {
-      this.orbitCameraControls.enabled = false;
-      this.rectangleSelector.onMouseDown(e.offsetX, e.offsetY);
-      this.setNearestPointToMouse(e);
-    } else if (
-        !e.ctrlKey && this.sceneIs3D() &&
-        this.orbitCameraControls.mouseButtons.ORBIT === THREE.MOUSE.RIGHT) {
-      // The user happened to press the ctrl key when the tab was active,
-      // unpressed the ctrl when the tab was inactive, and now he/she
-      // is back to the projector tab.
-      this.orbitCameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
-      this.orbitCameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
-    } else if (
-        e.ctrlKey && this.sceneIs3D() &&
-        this.orbitCameraControls.mouseButtons.ORBIT === THREE.MOUSE.LEFT) {
-      // Similarly to the situation above.
-      this.orbitCameraControls.mouseButtons.ORBIT = THREE.MOUSE.RIGHT;
-      this.orbitCameraControls.mouseButtons.PAN = THREE.MOUSE.LEFT;
-    }
-  }
-
-  /** When we stop dragging/zooming, return to normal behavior. */
-  private onMouseUp(e: any) {
-    if (this.selecting) {
-      this.orbitCameraControls.enabled = true;
-      this.rectangleSelector.onMouseUp();
-      this.render();
-    }
-    this.mouseIsDown = false;
-  }
-
-  /**
-   * When the mouse moves, find the nearest point (if any) and send it to the
-   * hoverlisteners (usually called from embedding.ts)
-   */
-  private onMouseMove(e: MouseEvent) {
-    this.isDragSequence = this.mouseIsDown;
-    // Depending if we're selecting or just navigating, handle accordingly.
-    if (this.selecting && this.mouseIsDown) {
-      this.rectangleSelector.onMouseMove(e.offsetX, e.offsetY);
-      this.render();
-    } else if (!this.mouseIsDown) {
-      this.setNearestPointToMouse(e);
-      this.projectorEventContext.notifyHoverOverPoint(this.nearestPoint);
-    }
-  }
-
-  /** For using ctrl + left click as right click, and for circle select */
-  private onKeyDown(e: any) {
-    // If ctrl is pressed, use left click to orbit
-    if (e.keyCode === CTRL_KEY && this.sceneIs3D()) {
-      this.orbitCameraControls.mouseButtons.ORBIT = THREE.MOUSE.RIGHT;
-      this.orbitCameraControls.mouseButtons.PAN = THREE.MOUSE.LEFT;
-    }
-
-    // If shift is pressed, start selecting
-    if (e.keyCode === SHIFT_KEY) {
-      this.selecting = true;
-      this.container.style.cursor = 'crosshair';
-    }
-  }
-
-  /** For using ctrl + left click as right click, and for circle select */
-  private onKeyUp(e: any) {
-    if (e.keyCode === CTRL_KEY && this.sceneIs3D()) {
-      this.orbitCameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
-      this.orbitCameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
-    }
-
-    // If shift is released, stop selecting
-    if (e.keyCode === SHIFT_KEY) {
-      this.selecting = (this.getMouseMode() === MouseMode.AREA_SELECT);
-      if (!this.selecting) {
-        this.container.style.cursor = 'default';
-      }
-      this.render();
-    }
-  }
-
-  /**
-   * Returns a list of indices of points in a bounding box from the picking
-   * texture.
-   * @param boundingBox The bounding box to select from.
-   */
-  private getPointIndicesFromPickingTexture(boundingBox: BoundingBox):
-      number[] {
-    if (this.worldSpacePointPositions == null) {
-      return null;
-    }
-    const pointCount = this.worldSpacePointPositions.length / 3;
-    const dpr = window.devicePixelRatio || 1;
-    const x = Math.floor(boundingBox.x * dpr);
-    const y = Math.floor(boundingBox.y * dpr);
-    const width = Math.floor(boundingBox.width * dpr);
-    const height = Math.floor(boundingBox.height * dpr);
-
-    // Create buffer for reading all of the pixels from the texture.
-    let pixelBuffer = new Uint8Array(width * height * 4);
-
-    // Read the pixels from the bounding box.
-    this.renderer.readRenderTargetPixels(
-        this.pickingTexture, x, this.pickingTexture.height - y, width, height,
-        pixelBuffer);
-
-    // Keep a flat list of each point and whether they are selected or not. This
-    // approach is more efficient than using an object keyed by the index.
-    let pointIndicesSelection =
-        new Uint8Array(this.worldSpacePointPositions.length);
-    for (let i = 0; i < width * height; i++) {
-      const id = (pixelBuffer[i * 4] << 16) | (pixelBuffer[i * 4 + 1] << 8) |
-          pixelBuffer[i * 4 + 2];
-      if (id !== 0xffffff && (id < pointCount)) {
-        pointIndicesSelection[id] = 1;
-      }
-    }
-    let pointIndices: number[] = [];
-    for (let i = 0; i < pointIndicesSelection.length; i++) {
-      if (pointIndicesSelection[i] === 1) {
-        pointIndices.push(i);
-      }
-    }
-
-    return pointIndices;
-  }
-
-
-  private selectBoundingBox(boundingBox: BoundingBox) {
-    let pointIndices = this.getPointIndicesFromPickingTexture(boundingBox);
-    this.projectorEventContext.notifySelectionChanged(pointIndices);
-  }
-
-  private setNearestPointToMouse(e: MouseEvent) {
-    if (this.pickingTexture == null) {
-      this.nearestPoint = null;
-      return;
-    }
-    const boundingBox:
-        BoundingBox = {x: e.offsetX, y: e.offsetY, width: 1, height: 1};
-    const pointIndices = this.getPointIndicesFromPickingTexture(boundingBox);
-    this.nearestPoint = (pointIndices != null) ? pointIndices[0] : null;
-  }
-
-  private getLayoutValues(): Point2D {
-    this.width = this.container.offsetWidth;
-    this.height = Math.max(1, this.container.offsetHeight);
-    return [this.width, this.height];
-  }
-
-  private sceneIs3D(): boolean {
-    return this.dimensionality === 3;
-  }
-
-  private remove3dAxisFromScene(): THREE.Object3D {
-    const axes = this.scene.getObjectByName('axes');
-    if (axes != null) {
-      this.scene.remove(axes);
-    }
-    return axes;
-  }
-
-  private add3dAxis() {
-    const axes = new THREE.AxisHelper();
-    axes.name = 'axes';
-    this.scene.add(axes);
-  }
-
-  /** Set 2d vs 3d mode. */
-  setDimensions(dimensionality: number) {
-    if ((dimensionality !== 2) && (dimensionality !== 3)) {
-      throw new RangeError('dimensionality must be 2 or 3');
-    }
-    this.dimensionality = dimensionality;
-
-    const def = this.cameraDef || this.makeDefaultCameraDef(dimensionality);
-    this.recreateCamera(def);
-
-    this.remove3dAxisFromScene();
-    if (dimensionality === 3) {
-      this.add3dAxis();
-    }
-  }
-
-  /** Gets the current camera information, suitable for serialization. */
-  getCameraDef(): CameraDef {
-    const def = new CameraDef();
-    const pos = this.camera.position;
-    const tgt = this.orbitCameraControls.target;
-    def.orthographic = !this.sceneIs3D();
-    def.position = [pos.x, pos.y, pos.z];
-    def.target = [tgt.x, tgt.y, tgt.z];
-    def.zoom = (this.camera as any).zoom;
-    return def;
-  }
-
-  /** Sets parameters for the next camera recreation. */
-  setCameraParametersForNextCameraCreation(
-      def: CameraDef, orbitAnimation: boolean) {
-    this.cameraDef = def;
-    this.orbitAnimationOnNextCameraCreation = orbitAnimation;
-  }
-
-  /** Gets the current camera position. */
-  getCameraPosition(): Point3D {
-    const currPos = this.camera.position;
-    return [currPos.x, currPos.y, currPos.z];
-  }
-
-  /** Gets the current camera target. */
-  getCameraTarget(): Point3D {
-    let currTarget = this.orbitCameraControls.target;
-    return [currTarget.x, currTarget.y, currTarget.z];
-  }
-
-  /** Sets up the camera from given position and target coordinates. */
-  setCameraPositionAndTarget(position: Point3D, target: Point3D) {
-    this.stopOrbitAnimation();
-    this.camera.position.set(position[0], position[1], position[2]);
-    this.orbitCameraControls.target.set(target[0], target[1], target[2]);
-    this.orbitCameraControls.update();
-    this.render();
-  }
-
-  /** Starts orbiting the camera around its current lookat target. */
-  startOrbitAnimation() {
-    if (!this.sceneIs3D()) {
-      return;
-    }
-    if (this.orbitAnimationId != null) {
-      this.stopOrbitAnimation();
-    }
-    this.orbitCameraControls.autoRotate = true;
-    this.orbitCameraControls.rotateSpeed =
-        ORBIT_ANIMATION_ROTATION_CYCLE_IN_SECONDS;
-    this.updateOrbitAnimation();
-  }
-
-  private updateOrbitAnimation() {
-    this.orbitCameraControls.update();
-    this.orbitAnimationId =
-        requestAnimationFrame(() => this.updateOrbitAnimation());
-  }
-
-  /** Stops the orbiting animation on the camera. */
-  stopOrbitAnimation() {
-    this.orbitCameraControls.autoRotate = false;
-    this.orbitCameraControls.rotateSpeed = ORBIT_MOUSE_ROTATION_SPEED;
-    if (this.orbitAnimationId != null) {
-      cancelAnimationFrame(this.orbitAnimationId);
-      this.orbitAnimationId = null;
-    }
-  }
-
-  /** Adds a visualizer to the set, will start dispatching events to it */
-  addVisualizer(visualizer: ScatterPlotVisualizer) {
-    if (this.scene) {
-      visualizer.setScene(this.scene);
-    }
-    visualizer.onResize(this.width, this.height);
-    visualizer.onPointPositionsChanged(this.worldSpacePointPositions);
-    this.visualizers.push(visualizer);
-  }
-
-  /** Removes all visualizers attached to this scatter plot. */
-  removeAllVisualizers() {
-    this.visualizers.forEach(v => v.dispose());
-    this.visualizers = [];
-  }
-
-  /** Update scatter plot with a new array of packed xyz point positions. */
-  setPointPositions(worldSpacePointPositions: Float32Array) {
-    this.worldSpacePointPositions = worldSpacePointPositions;
-    this.visualizers.forEach(
-        v => v.onPointPositionsChanged(worldSpacePointPositions));
-  }
-
-  render() {
-    {
-      const lightPos = this.camera.position.clone();
-      lightPos.x += 1;
-      lightPos.y += 1;
-      this.light.position.set(lightPos.x, lightPos.y, lightPos.z);
-    }
-
-    const cameraType = (this.camera instanceof THREE.PerspectiveCamera) ?
-        CameraType.Perspective :
-        CameraType.Orthographic;
-
-    let cameraSpacePointExtents: [number, number] = [0, 0];
-    if (this.worldSpacePointPositions != null) {
-      cameraSpacePointExtents = util.getNearFarPoints(
-          this.worldSpacePointPositions, this.camera.position,
-          this.orbitCameraControls.target);
-    }
-
-    const rc = new RenderContext(
-        this.camera, cameraType, this.orbitCameraControls.target, this.width,
-        this.height, cameraSpacePointExtents[0], cameraSpacePointExtents[1],
-        this.backgroundColor, this.pointColors, this.pointScaleFactors,
-        this.labels, this.polylineColors, this.polylineOpacities,
-        this.polylineWidths);
-
-    // Render first pass to picking target. This render fills pickingTexture
-    // with colors that are actually point ids, so that sampling the texture at
-    // the mouse's current x,y coordinates will reveal the data point that the
-    // mouse is over.
-    this.visualizers.forEach(v => v.onPickingRender(rc));
-
-    {
-      const axes = this.remove3dAxisFromScene();
-      this.renderer.render(this.scene, this.camera, this.pickingTexture);
-      if (axes != null) {
-        this.scene.add(axes);
-      }
-    }
-
-    // Render second pass to color buffer, to be displayed on the canvas.
-    this.visualizers.forEach(v => v.onRender(rc));
-
-    this.renderer.render(this.scene, this.camera);
-  }
-
-  setMouseMode(mouseMode: MouseMode) {
-    this.mouseMode = mouseMode;
-    if (mouseMode === MouseMode.AREA_SELECT) {
-      this.selecting = true;
-      this.container.style.cursor = 'crosshair';
-    } else {
-      this.selecting = false;
-      this.container.style.cursor = 'default';
-    }
-  }
-
-  /** Set the colors for every data point. (RGB triplets) */
-  setPointColors(colors: Float32Array) {
-    this.pointColors = colors;
-  }
-
-  /** Set the scale factors for every data point. (scalars) */
-  setPointScaleFactors(scaleFactors: Float32Array) {
-    this.pointScaleFactors = scaleFactors;
-  }
-
-  /** Set the labels to rendered */
-  setLabels(labels: LabelRenderParams) {
-    this.labels = labels;
-  }
-
-  /** Set the colors for every data polyline. (RGB triplets) */
-  setPolylineColors(colors: {[polylineIndex: number]: Float32Array}) {
-    this.polylineColors = colors;
-  }
-
-  setPolylineOpacities(opacities: Float32Array) {
-    this.polylineOpacities = opacities;
-  }
-
-  setPolylineWidths(widths: Float32Array) {
-    this.polylineWidths = widths;
-  }
-
-  getMouseMode(): MouseMode {
-    return this.mouseMode;
-  }
-
-  resetZoom() {
-    this.recreateCamera(this.makeDefaultCameraDef(this.dimensionality));
-    this.render();
-  }
-
-  setDayNightMode(isNight: boolean) {
-    const canvases = this.container.querySelectorAll('canvas');
-    const filterValue = isNight ? 'invert(100%)' : null;
-    for (let i = 0; i < canvases.length; i++) {
-      canvases[i].style.filter = filterValue;
-    }
-  }
-
-  resize(render = true) {
-    const [oldW, oldH] = [this.width, this.height];
-    const [newW, newH] = this.getLayoutValues();
-
-    if (this.dimensionality === 3) {
-      const camera = (this.camera as THREE.PerspectiveCamera);
-      camera.aspect = newW / newH;
-      camera.updateProjectionMatrix();
-    } else {
-      const camera = (this.camera as THREE.OrthographicCamera);
-      // Scale the ortho frustum by however much the window changed.
-      const scaleW = newW / oldW;
-      const scaleH = newH / oldH;
-      const newCamHalfWidth = ((camera.right - camera.left) * scaleW) / 2;
-      const newCamHalfHeight = ((camera.top - camera.bottom) * scaleH) / 2;
-      camera.top = newCamHalfHeight;
-      camera.bottom = -newCamHalfHeight;
-      camera.left = -newCamHalfWidth;
-      camera.right = newCamHalfWidth;
-      camera.updateProjectionMatrix();
-    }
-
-    // Accouting for retina displays.
-    const dpr = window.devicePixelRatio || 1;
-    this.renderer.setPixelRatio(dpr);
-    this.renderer.setSize(newW, newH);
-
-    // the picking texture needs to be exactly the same as the render texture.
-    {
-      const renderCanvasSize = this.renderer.getSize();
-      const pixelRatio = this.renderer.getPixelRatio();
-      this.pickingTexture = new THREE.WebGLRenderTarget(
-          renderCanvasSize.width * pixelRatio,
-          renderCanvasSize.height * pixelRatio);
-      this.pickingTexture.texture.minFilter = THREE.LinearFilter;
-    }
-
-    this.visualizers.forEach(v => v.onResize(newW, newH));
-
-    if (render) {
-      this.render();
-    };
-  }
-
-  onCameraMove(listener: OnCameraMoveListener) {
-    this.onCameraMoveListeners.push(listener);
-  }
-
-  clickOnPoint(pointIndex: number) {
-    this.nearestPoint = pointIndex;
-    this.onClick(null, false);
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/scatterPlotRectangleSelector.ts b/tensorflow/tensorboard/components/vz_projector/scatterPlotRectangleSelector.ts
deleted file mode 100644
index a781877014..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/scatterPlotRectangleSelector.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-const FILL = '#dddddd';
-const FILL_OPACITY = .2;
-const STROKE = '#aaaaaa';
-const STROKE_WIDTH = 2;
-const STROKE_DASHARRAY = '10 5';
-
-export interface BoundingBox {
-  // The bounding box (x, y) position refers to the bottom left corner of the
-  // rect.
-  x: number;
-  y: number;
-  width: number;
-  height: number;
-}
-
-/**
- * A class that manages and renders a data selection rectangle.
- */
-export class ScatterPlotRectangleSelector {
-  private svgElement: SVGElement;
-  private rectElement: SVGRectElement;
-
-  private isMouseDown: boolean;
-  private startCoordinates: [number, number];
-  private lastBoundingBox: BoundingBox;
-
-  private selectionCallback: (boundingBox: BoundingBox) => void;
-
-  /**
-   * @param container The container HTML element that the selection SVG rect
-   *     will be a child of.
-   * @param selectionCallback The callback that accepts a bounding box to be
-   *     called when selection changes. Currently, we only call the callback on
-   *     mouseUp.
-   */
-  constructor(
-      container: HTMLElement,
-      selectionCallback: (boundingBox: BoundingBox) => void) {
-    this.svgElement = container.querySelector('#selector') as SVGElement;
-    this.rectElement =
-        document.createElementNS('http://www.w3.org/2000/svg', 'rect');
-    this.rectElement.style.stroke = STROKE;
-    this.rectElement.style.strokeDasharray = STROKE_DASHARRAY;
-    this.rectElement.style.strokeWidth = '' + STROKE_WIDTH;
-    this.rectElement.style.fill = FILL;
-    this.rectElement.style.fillOpacity = '' + FILL_OPACITY;
-    this.svgElement.appendChild(this.rectElement);
-
-    this.selectionCallback = selectionCallback;
-    this.isMouseDown = false;
-  }
-
-  onMouseDown(offsetX: number, offsetY: number) {
-    this.isMouseDown = true;
-    this.rectElement.style.display = 'block';
-
-    this.startCoordinates = [offsetX, offsetY];
-    this.lastBoundingBox = {
-      x: this.startCoordinates[0],
-      y: this.startCoordinates[1],
-      width: 1,
-      height: 1
-    };
-  }
-
-  onMouseMove(offsetX: number, offsetY: number) {
-    if (!this.isMouseDown) {
-      return;
-    }
-
-    this.lastBoundingBox.x = Math.min(offsetX, this.startCoordinates[0]);
-    this.lastBoundingBox.y = Math.max(offsetY, this.startCoordinates[1]);
-    this.lastBoundingBox.width =
-        Math.max(offsetX, this.startCoordinates[0]) - this.lastBoundingBox.x;
-    this.lastBoundingBox.height =
-        this.lastBoundingBox.y - Math.min(offsetY, this.startCoordinates[1]);
-
-    this.rectElement.setAttribute('x', '' + this.lastBoundingBox.x);
-    this.rectElement.setAttribute(
-        'y', '' + (this.lastBoundingBox.y - this.lastBoundingBox.height));
-    this.rectElement.setAttribute('width', '' + this.lastBoundingBox.width);
-    this.rectElement.setAttribute('height', '' + this.lastBoundingBox.height);
-  }
-
-  onMouseUp() {
-    this.isMouseDown = false;
-    this.rectElement.style.display = 'none';
-    this.rectElement.setAttribute('width', '0');
-    this.rectElement.setAttribute('height', '0');
-    this.selectionCallback(this.lastBoundingBox);
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizer.ts b/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizer.ts
deleted file mode 100644
index b0974a2053..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizer.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {RenderContext} from './renderContext';
-
-/**
- * ScatterPlotVisualizer is an interface used by ScatterPlotContainer
- * to manage and aggregate any number of concurrent visualization behaviors.
- * To add a new visualization to the 3D scatter plot, create a new class that
- * implements this interface and attach it to the ScatterPlotContainer.
- */
-export interface ScatterPlotVisualizer {
-  /** Called to initialize the visualizer with the primary scene. */
-  setScene(scene: THREE.Scene);
-  /**
-   * Called when the main scatter plot tears down the visualizer. Remove all
-   * objects from the scene, and dispose any heavy resources.
-   */
-  dispose();
-  /**
-   * Called when the positions of the scatter plot points have changed.
-   */
-  onPointPositionsChanged(newWorldSpacePointPositions: Float32Array);
-  /**
-   * Called immediately before the main scatter plot performs a picking
-   * (selection) render. Set up render state for any geometry to use picking IDs
-   * instead of visual colors.
-   */
-  onPickingRender(renderContext: RenderContext);
-  /**
-   * Called immediately before the main scatter plot performs a color (visual)
-   * render. Set up render state, lights, etc here.
-   */
-  onRender(renderContext: RenderContext);
-  /**
-   * Called when the canvas size changes.
-   */
-  onResize(newWidth: number, newHeight: number);
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizer3DLabels.ts b/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizer3DLabels.ts
deleted file mode 100644
index 7820af0d48..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizer3DLabels.ts
+++ /dev/null
@@ -1,367 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {RenderContext} from './renderContext';
-import {ScatterPlotVisualizer} from './scatterPlotVisualizer';
-import * as util from './util';
-
-const FONT_SIZE = 80;
-const ONE_OVER_FONT_SIZE = 1 / FONT_SIZE;
-const LABEL_SCALE = 2.2;  // at 1:1 texel/pixel ratio
-const LABEL_COLOR = 'black';
-const LABEL_BACKGROUND = 'white';
-const MAX_CANVAS_DIMENSION = 8192;
-const NUM_GLYPHS = 256;
-const RGB_ELEMENTS_PER_ENTRY = 3;
-const XYZ_ELEMENTS_PER_ENTRY = 3;
-const UV_ELEMENTS_PER_ENTRY = 2;
-const VERTICES_PER_GLYPH = 2 * 3;  // 2 triangles, 3 verts per triangle
-
-/**
- * Each label is made up of triangles (two per letter.) Each vertex, then, is
- * the corner of one of these triangles (and thus the corner of a letter
- * rectangle.)
- * Each has the following attributes:
- *    posObj: The (x, y) position of the vertex within the label, where the
- *            bottom center of the word is positioned at (0, 0);
- *    position: The position of the label in worldspace.
- *    vUv: The (u, v) coordinates that index into the glyphs sheet (range 0, 1.)
- *    color: The color of the label (matches the corresponding point's color.)
- *    wordShown: Boolean. Whether or not the label is visible.
- */
-
-const VERTEX_SHADER = `
-    attribute vec2 posObj;
-    attribute vec3 color;
-    varying vec2 vUv;
-    varying vec3 vColor;
-
-    void main() {
-      vUv = uv;
-      vColor = color;
-
-      // Rotate label to face camera.
-
-      vec4 vRight = vec4(
-        modelViewMatrix[0][0], modelViewMatrix[1][0], modelViewMatrix[2][0], 0);
-
-      vec4 vUp = vec4(
-        modelViewMatrix[0][1], modelViewMatrix[1][1], modelViewMatrix[2][1], 0);
-
-      vec4 vAt = -vec4(
-        modelViewMatrix[0][2], modelViewMatrix[1][2], modelViewMatrix[2][2], 0);
-
-      mat4 pointToCamera = mat4(vRight, vUp, vAt, vec4(0, 0, 0, 1));
-
-      vec2 scaledPos = posObj * ${ONE_OVER_FONT_SIZE} * ${LABEL_SCALE};
-
-      vec4 posRotated = pointToCamera * vec4(scaledPos, 0, 1);
-      vec4 mvPosition = modelViewMatrix * (vec4(position, 0) + posRotated);
-      gl_Position = projectionMatrix * mvPosition;
-    }`;
-
-const FRAGMENT_SHADER = `
-    uniform sampler2D texture;
-    uniform bool picking;
-    varying vec2 vUv;
-    varying vec3 vColor;
-
-    void main() {
-      if (picking) {
-        gl_FragColor = vec4(vColor, 1.0);
-      } else {
-        vec4 fromTexture = texture2D(texture, vUv);
-        gl_FragColor = vec4(vColor, 1.0) * fromTexture;
-      }
-    }`;
-
-type GlyphTexture = {
-  texture: THREE.Texture; lengths: Float32Array; offsets: Float32Array;
-};
-
-/**
- * Renders the text labels as 3d geometry in the world.
- */
-export class ScatterPlotVisualizer3DLabels implements ScatterPlotVisualizer {
-  private scene: THREE.Scene;
-  private labelStrings: string[];
-  private geometry: THREE.BufferGeometry;
-  private worldSpacePointPositions: Float32Array;
-  private pickingColors: Float32Array;
-  private renderColors: Float32Array;
-  private material: THREE.ShaderMaterial;
-  private uniforms: Object;
-  private labelsMesh: THREE.Mesh;
-  private positions: THREE.BufferAttribute;
-  private totalVertexCount: number;
-  private labelVertexMap: number[][];
-  private glyphTexture: GlyphTexture;
-
-  private createGlyphTexture(): GlyphTexture {
-    let canvas = document.createElement('canvas');
-    canvas.width = MAX_CANVAS_DIMENSION;
-    canvas.height = FONT_SIZE;
-    let ctx = canvas.getContext('2d');
-    ctx.font = 'bold ' + FONT_SIZE * 0.75 + 'px roboto';
-    ctx.textBaseline = 'top';
-    ctx.fillStyle = LABEL_BACKGROUND;
-    ctx.rect(0, 0, canvas.width, canvas.height);
-    ctx.fill();
-    ctx.fillStyle = LABEL_COLOR;
-    let spaceOffset = ctx.measureText(' ').width;
-    // For each letter, store length, position at the encoded index.
-    let glyphLengths = new Float32Array(NUM_GLYPHS);
-    let glyphOffset = new Float32Array(NUM_GLYPHS);
-    let leftCoord = 0;
-    for (let i = 0; i < NUM_GLYPHS; i++) {
-      let text = ' ' + String.fromCharCode(i);
-      let textLength = ctx.measureText(text).width;
-      glyphLengths[i] = textLength - spaceOffset;
-      glyphOffset[i] = leftCoord;
-      ctx.fillText(text, leftCoord - spaceOffset, 0);
-      leftCoord += textLength;
-    }
-    const tex = util.createTexture(canvas);
-    return {texture: tex, lengths: glyphLengths, offsets: glyphOffset};
-  }
-
-  private processLabelVerts(pointCount: number) {
-    let numTotalLetters = 0;
-    this.labelVertexMap = [];
-    for (let i = 0; i < pointCount; i++) {
-      const label = this.labelStrings[i];
-      let vertsArray: number[] = [];
-      for (let j = 0; j < label.length; j++) {
-        for (let k = 0; k < VERTICES_PER_GLYPH; k++) {
-          vertsArray.push(numTotalLetters * VERTICES_PER_GLYPH + k);
-        }
-        numTotalLetters++;
-      }
-      this.labelVertexMap.push(vertsArray);
-    }
-    this.totalVertexCount = numTotalLetters * VERTICES_PER_GLYPH;
-  }
-
-  private createColorBuffers(pointCount: number) {
-    this.pickingColors =
-        new Float32Array(this.totalVertexCount * RGB_ELEMENTS_PER_ENTRY);
-    this.renderColors =
-        new Float32Array(this.totalVertexCount * RGB_ELEMENTS_PER_ENTRY);
-    for (let i = 0; i < pointCount; i++) {
-      let color = new THREE.Color(i);
-      this.labelVertexMap[i].forEach((j) => {
-        this.pickingColors[RGB_ELEMENTS_PER_ENTRY * j] = color.r;
-        this.pickingColors[RGB_ELEMENTS_PER_ENTRY * j + 1] = color.g;
-        this.pickingColors[RGB_ELEMENTS_PER_ENTRY * j + 2] = color.b;
-        this.renderColors[RGB_ELEMENTS_PER_ENTRY * j] = 1.0;
-        this.renderColors[RGB_ELEMENTS_PER_ENTRY * j + 1] = 1.0;
-        this.renderColors[RGB_ELEMENTS_PER_ENTRY * j + 2] = 1.0;
-      });
-    }
-  }
-
-  private createLabels() {
-    if ((this.labelStrings == null) ||
-        (this.worldSpacePointPositions == null)) {
-      return;
-    }
-    const pointCount =
-        this.worldSpacePointPositions.length / XYZ_ELEMENTS_PER_ENTRY;
-    if (pointCount !== this.labelStrings.length) {
-      return;
-    }
-    this.glyphTexture = this.createGlyphTexture();
-
-    this.uniforms = {
-      texture: {type: 't'},
-      picking: {type: 'bool'},
-    };
-
-    this.material = new THREE.ShaderMaterial({
-      uniforms: this.uniforms,
-      transparent: true,
-      vertexShader: VERTEX_SHADER,
-      fragmentShader: FRAGMENT_SHADER,
-    });
-
-    this.processLabelVerts(pointCount);
-    this.createColorBuffers(pointCount);
-
-    let positionArray =
-        new Float32Array(this.totalVertexCount * XYZ_ELEMENTS_PER_ENTRY);
-    this.positions =
-        new THREE.BufferAttribute(positionArray, XYZ_ELEMENTS_PER_ENTRY);
-
-    let posArray =
-        new Float32Array(this.totalVertexCount * XYZ_ELEMENTS_PER_ENTRY);
-    let uvArray =
-        new Float32Array(this.totalVertexCount * UV_ELEMENTS_PER_ENTRY);
-    let colorsArray =
-        new Float32Array(this.totalVertexCount * RGB_ELEMENTS_PER_ENTRY);
-    let positionObject = new THREE.BufferAttribute(posArray, 2);
-    let uv = new THREE.BufferAttribute(uvArray, UV_ELEMENTS_PER_ENTRY);
-    let colors = new THREE.BufferAttribute(colorsArray, RGB_ELEMENTS_PER_ENTRY);
-
-    this.geometry = new THREE.BufferGeometry();
-    this.geometry.addAttribute('posObj', positionObject);
-    this.geometry.addAttribute('position', this.positions);
-    this.geometry.addAttribute('uv', uv);
-    this.geometry.addAttribute('color', colors);
-
-    let lettersSoFar = 0;
-    for (let i = 0; i < pointCount; i++) {
-      const label = this.labelStrings[i];
-      let leftOffset = 0;
-      // Determine length of word in pixels.
-      for (let j = 0; j < label.length; j++) {
-        let letterCode = label.charCodeAt(j);
-        leftOffset += this.glyphTexture.lengths[letterCode];
-      }
-      leftOffset /= -2;  // centers text horizontally around the origin
-      for (let j = 0; j < label.length; j++) {
-        let letterCode = label.charCodeAt(j);
-        let letterWidth = this.glyphTexture.lengths[letterCode];
-        let scale = FONT_SIZE;
-        let right = (leftOffset + letterWidth) / scale;
-        let left = (leftOffset) / scale;
-        let top = FONT_SIZE / scale;
-
-        // First triangle
-        positionObject.setXY(lettersSoFar * VERTICES_PER_GLYPH + 0, left, 0);
-        positionObject.setXY(lettersSoFar * VERTICES_PER_GLYPH + 1, right, 0);
-        positionObject.setXY(lettersSoFar * VERTICES_PER_GLYPH + 2, left, top);
-
-        // Second triangle
-        positionObject.setXY(lettersSoFar * VERTICES_PER_GLYPH + 3, left, top);
-        positionObject.setXY(lettersSoFar * VERTICES_PER_GLYPH + 4, right, 0);
-        positionObject.setXY(lettersSoFar * VERTICES_PER_GLYPH + 5, right, top);
-
-        // Set UVs based on letter.
-        let uLeft = (this.glyphTexture.offsets[letterCode]);
-        let uRight = (this.glyphTexture.offsets[letterCode] + letterWidth);
-        // Scale so that uvs lie between 0 and 1 on the texture.
-        uLeft /= MAX_CANVAS_DIMENSION;
-        uRight /= MAX_CANVAS_DIMENSION;
-        let vTop = 1;
-        let vBottom = 0;
-        uv.setXY(lettersSoFar * VERTICES_PER_GLYPH + 0, uLeft, vTop);
-        uv.setXY(lettersSoFar * VERTICES_PER_GLYPH + 1, uRight, vTop);
-        uv.setXY(lettersSoFar * VERTICES_PER_GLYPH + 2, uLeft, vBottom);
-        uv.setXY(lettersSoFar * VERTICES_PER_GLYPH + 3, uLeft, vBottom);
-        uv.setXY(lettersSoFar * VERTICES_PER_GLYPH + 4, uRight, vTop);
-        uv.setXY(lettersSoFar * VERTICES_PER_GLYPH + 5, uRight, vBottom);
-
-        lettersSoFar++;
-        leftOffset += letterWidth;
-      }
-    }
-
-    for (let i = 0; i < pointCount; i++) {
-      const p = util.vector3FromPackedArray(this.worldSpacePointPositions, i);
-      this.labelVertexMap[i].forEach((j) => {
-        this.positions.setXYZ(j, p.x, p.y, p.z);
-      });
-    };
-
-    this.labelsMesh = new THREE.Mesh(this.geometry, this.material);
-    this.labelsMesh.frustumCulled = false;
-    this.scene.add(this.labelsMesh);
-  }
-
-  private colorLabels(pointColors: Float32Array) {
-    if (this.labelStrings == null || this.geometry == null ||
-        pointColors == null) {
-      return;
-    }
-
-    const colors = this.geometry.getAttribute('color') as THREE.BufferAttribute;
-    colors.array = this.renderColors;
-
-    const n = pointColors.length / XYZ_ELEMENTS_PER_ENTRY;
-    let src = 0;
-    for (let i = 0; i < n; ++i) {
-      const c = new THREE.Color(
-          pointColors[src], pointColors[src + 1], pointColors[src + 2]);
-      const m = this.labelVertexMap[i].length;
-      for (let j = 0; j < m; ++j) {
-        colors.setXYZ(this.labelVertexMap[i][j], c.r, c.g, c.b);
-      }
-      src += RGB_ELEMENTS_PER_ENTRY;
-    }
-    colors.needsUpdate = true;
-  }
-
-  setScene(scene: THREE.Scene) {
-    this.scene = scene;
-  }
-
-  dispose() {
-    if (this.labelsMesh) {
-      if (this.scene) {
-        this.scene.remove(this.labelsMesh);
-      }
-      this.labelsMesh = null;
-    }
-    if (this.geometry) {
-      this.geometry.dispose();
-      this.geometry = null;
-    }
-    if ((this.glyphTexture != null) && (this.glyphTexture.texture != null)) {
-      this.glyphTexture.texture.dispose();
-      this.glyphTexture.texture = null;
-    }
-  }
-
-  onPickingRender(rc: RenderContext) {
-    if (this.geometry == null) {
-      this.createLabels();
-    }
-    if (this.geometry == null) {
-      return;
-    }
-    this.material.uniforms.texture.value = this.glyphTexture.texture;
-    this.material.uniforms.picking.value = true;
-    const colors = this.geometry.getAttribute('color') as THREE.BufferAttribute;
-    colors.array = this.pickingColors;
-    colors.needsUpdate = true;
-  }
-
-  onRender(rc: RenderContext) {
-    if (this.geometry == null) {
-      this.createLabels();
-    }
-    if (this.geometry == null) {
-      return;
-    }
-    this.colorLabels(rc.pointColors);
-    this.material.uniforms.texture.value = this.glyphTexture.texture;
-    this.material.uniforms.picking.value = false;
-    const colors = this.geometry.getAttribute('color') as THREE.BufferAttribute;
-    colors.array = this.renderColors;
-    colors.needsUpdate = true;
-  }
-
-  onPointPositionsChanged(newPositions: Float32Array) {
-    this.worldSpacePointPositions = newPositions;
-    this.dispose();
-  }
-
-  setLabelStrings(labelStrings: string[]) {
-    this.labelStrings = labelStrings;
-    this.dispose();
-  }
-
-  onResize(newWidth: number, newHeight: number) {}
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerCanvasLabels.ts b/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerCanvasLabels.ts
deleted file mode 100644
index 2f3146d213..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerCanvasLabels.ts
+++ /dev/null
@@ -1,186 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {BoundingBox, CollisionGrid} from './label';
-import {CameraType, RenderContext} from './renderContext';
-import {ScatterPlotVisualizer} from './scatterPlotVisualizer';
-import * as util from './util';
-
-const MAX_LABELS_ON_SCREEN = 10000;
-const LABEL_STROKE_WIDTH = 3;
-const LABEL_FILL_WIDTH = 6;
-
-/**
- * Creates and maintains a 2d canvas on top of the GL canvas. All labels, when
- * active, are rendered to the 2d canvas as part of the visible render pass.
- */
-export class ScatterPlotVisualizerCanvasLabels implements
-    ScatterPlotVisualizer {
-  private worldSpacePointPositions: Float32Array;
-  private gc: CanvasRenderingContext2D;
-  private canvas: HTMLCanvasElement;
-  private labelsActive: boolean = true;
-
-  constructor(container: HTMLElement) {
-    this.canvas = document.createElement('canvas');
-    container.appendChild(this.canvas);
-
-    this.gc = this.canvas.getContext('2d');
-    this.canvas.style.position = 'absolute';
-    this.canvas.style.left = '0';
-    this.canvas.style.top = '0';
-    this.canvas.style.pointerEvents = 'none';
-  }
-
-  private removeAllLabels() {
-    const pixelWidth = this.canvas.width * window.devicePixelRatio;
-    const pixelHeight = this.canvas.height * window.devicePixelRatio;
-    this.gc.clearRect(0, 0, pixelWidth, pixelHeight);
-  }
-
-  /** Render all of the non-overlapping visible labels to the canvas. */
-  private makeLabels(rc: RenderContext) {
-    if ((rc.labels == null) || (rc.labels.pointIndices.length === 0)) {
-      return;
-    }
-    if (this.worldSpacePointPositions == null) {
-      return;
-    }
-
-    const lrc = rc.labels;
-    const sceneIs3D: boolean = (rc.cameraType === CameraType.Perspective);
-    const labelHeight = parseInt(this.gc.font, 10);
-    const dpr = window.devicePixelRatio;
-
-    let grid: CollisionGrid;
-    {
-      const pixw = this.canvas.width * dpr;
-      const pixh = this.canvas.height * dpr;
-      const bb: BoundingBox = {loX: 0, hiX: pixw, loY: 0, hiY: pixh};
-      grid = new CollisionGrid(bb, pixw / 25, pixh / 50);
-    }
-
-    let opacityMap =
-        d3.scalePow()
-            .exponent(Math.E)
-            .domain([rc.farthestCameraSpacePointZ, rc.nearestCameraSpacePointZ])
-            .range([0.1, 1]);
-
-    const camPos = rc.camera.position;
-    const camToTarget = camPos.clone().sub(rc.cameraTarget);
-    let camToPoint = new THREE.Vector3();
-
-    this.gc.textBaseline = 'middle';
-    this.gc.miterLimit = 2;
-
-    // Have extra space between neighboring labels. Don't pack too tightly.
-    const labelMargin = 2;
-    // Shift the label to the right of the point circle.
-    const xShift = 4;
-
-    const n = Math.min(MAX_LABELS_ON_SCREEN, lrc.pointIndices.length);
-    for (let i = 0; i < n; ++i) {
-      let point: THREE.Vector3;
-      {
-        const pi = lrc.pointIndices[i];
-        point = util.vector3FromPackedArray(this.worldSpacePointPositions, pi);
-      }
-
-      // discard points that are behind the camera
-      camToPoint.copy(camPos).sub(point);
-      if (camToTarget.dot(camToPoint) < 0) {
-        continue;
-      }
-
-      let [x, y] = util.vector3DToScreenCoords(
-          rc.camera, rc.screenWidth, rc.screenHeight, point);
-      x += xShift;
-
-      // Computing the width of the font is expensive,
-      // so we assume width of 1 at first. Then, if the label doesn't
-      // conflict with other labels, we measure the actual width.
-      const textBoundingBox: BoundingBox = {
-        loX: x - labelMargin,
-        hiX: x + 1 + labelMargin,
-        loY: y - labelHeight / 2 - labelMargin,
-        hiY: y + labelHeight / 2 + labelMargin
-      };
-
-      if (grid.insert(textBoundingBox, true)) {
-        const text = lrc.labelStrings[i];
-        const fontSize = lrc.defaultFontSize * lrc.scaleFactors[i] * dpr;
-        this.gc.font = fontSize + 'px roboto';
-
-        // Now, check with properly computed width.
-        textBoundingBox.hiX += this.gc.measureText(text).width - 1;
-        if (grid.insert(textBoundingBox)) {
-          let opacity = 1;
-          if (sceneIs3D && (lrc.useSceneOpacityFlags[i] === 1)) {
-            opacity = opacityMap(camToPoint.length());
-          }
-          this.gc.fillStyle =
-              this.styleStringFromPackedRgba(lrc.fillColors, i, opacity);
-          this.gc.strokeStyle =
-              this.styleStringFromPackedRgba(lrc.strokeColors, i, opacity);
-          this.gc.lineWidth = LABEL_STROKE_WIDTH;
-          this.gc.strokeText(text, x, y);
-          this.gc.lineWidth = LABEL_FILL_WIDTH;
-          this.gc.fillText(text, x, y);
-        }
-      }
-    }
-  }
-
-  private styleStringFromPackedRgba(
-      packedRgbaArray: Uint8Array, colorIndex: number,
-      opacity: number): string {
-    const offset = colorIndex * 3;
-    const r = packedRgbaArray[offset];
-    const g = packedRgbaArray[offset + 1];
-    const b = packedRgbaArray[offset + 2];
-    return 'rgba(' + r + ',' + g + ',' + b + ',' + opacity + ')';
-  }
-
-  onResize(newWidth: number, newHeight: number) {
-    let dpr = window.devicePixelRatio;
-    this.canvas.width = newWidth * dpr;
-    this.canvas.height = newHeight * dpr;
-    this.canvas.style.width = newWidth + 'px';
-    this.canvas.style.height = newHeight + 'px';
-  }
-
-  dispose() {
-    this.removeAllLabels();
-    this.canvas = null;
-    this.gc = null;
-  }
-
-  onPointPositionsChanged(newPositions: Float32Array) {
-    this.worldSpacePointPositions = newPositions;
-    this.removeAllLabels();
-  }
-
-  onRender(rc: RenderContext) {
-    if (!this.labelsActive) {
-      return;
-    }
-
-    this.removeAllLabels();
-    this.makeLabels(rc);
-  }
-
-  setScene(scene: THREE.Scene) {}
-  onPickingRender(renderContext: RenderContext) {}
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerPolylines.ts b/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerPolylines.ts
deleted file mode 100644
index e6d4aeda28..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerPolylines.ts
+++ /dev/null
@@ -1,149 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataSet} from './data';
-import {RenderContext} from './renderContext';
-import {ScatterPlotVisualizer} from './scatterPlotVisualizer';
-import * as util from './util';
-
-const RGB_NUM_ELEMENTS = 3;
-const XYZ_NUM_ELEMENTS = 3;
-
-/**
- * Renders polylines that connect multiple points in the dataset.
- */
-export class ScatterPlotVisualizerPolylines implements ScatterPlotVisualizer {
-  private dataSet: DataSet;
-  private scene: THREE.Scene;
-  private polylines: THREE.Line[];
-  private polylinePositionBuffer:
-      {[polylineIndex: number]: THREE.BufferAttribute} = {};
-  private polylineColorBuffer:
-      {[polylineIndex: number]: THREE.BufferAttribute} = {};
-
-  private updateSequenceIndicesInDataSet(ds: DataSet) {
-    for (let i = 0; i < ds.sequences.length; i++) {
-      const sequence = ds.sequences[i];
-      for (let j = 0; j < sequence.pointIndices.length - 1; j++) {
-        ds.points[sequence.pointIndices[j]].sequenceIndex = i;
-        ds.points[sequence.pointIndices[j + 1]].sequenceIndex = i;
-      }
-    }
-  }
-
-  private createPolylines(scene: THREE.Scene) {
-    if (!this.dataSet || !this.dataSet.sequences) {
-      return;
-    }
-
-    this.updateSequenceIndicesInDataSet(this.dataSet);
-    this.polylines = [];
-
-    for (let i = 0; i < this.dataSet.sequences.length; i++) {
-      const geometry = new THREE.BufferGeometry();
-      geometry.addAttribute('position', this.polylinePositionBuffer[i]);
-      geometry.addAttribute('color', this.polylineColorBuffer[i]);
-
-      const material = new THREE.LineBasicMaterial({
-        linewidth: 1,  // unused default, overwritten by width array.
-        opacity: 1.0,  // unused default, overwritten by opacity array.
-        transparent: true,
-        vertexColors: THREE.VertexColors
-      });
-
-      const polyline = new THREE.LineSegments(geometry, material);
-      polyline.frustumCulled = false;
-      this.polylines.push(polyline);
-      scene.add(polyline);
-    }
-  }
-
-  dispose() {
-    if (this.polylines == null) {
-      return;
-    }
-    for (let i = 0; i < this.polylines.length; i++) {
-      this.scene.remove(this.polylines[i]);
-      this.polylines[i].geometry.dispose();
-    }
-    this.polylines = null;
-    this.polylinePositionBuffer = {};
-    this.polylineColorBuffer = {};
-  }
-
-  setScene(scene: THREE.Scene) {
-    this.scene = scene;
-  }
-
-  setDataSet(dataSet: DataSet) {
-    this.dataSet = dataSet;
-  }
-
-  onPointPositionsChanged(newPositions: Float32Array) {
-    if ((newPositions == null) || (this.polylines != null)) {
-      this.dispose();
-    }
-    if ((newPositions == null) || (this.dataSet == null)) {
-      return;
-    }
-    // Set up the position buffer arrays for each polyline.
-    for (let i = 0; i < this.dataSet.sequences.length; i++) {
-      let sequence = this.dataSet.sequences[i];
-      const vertexCount = 2 * (sequence.pointIndices.length - 1);
-
-      let polylines = new Float32Array(vertexCount * XYZ_NUM_ELEMENTS);
-      this.polylinePositionBuffer[i] =
-          new THREE.BufferAttribute(polylines, XYZ_NUM_ELEMENTS);
-
-      let colors = new Float32Array(vertexCount * RGB_NUM_ELEMENTS);
-      this.polylineColorBuffer[i] =
-          new THREE.BufferAttribute(colors, RGB_NUM_ELEMENTS);
-    }
-    for (let i = 0; i < this.dataSet.sequences.length; i++) {
-      const sequence = this.dataSet.sequences[i];
-      let src = 0;
-      for (let j = 0; j < sequence.pointIndices.length - 1; j++) {
-        const p1Index = sequence.pointIndices[j];
-        const p2Index = sequence.pointIndices[j + 1];
-        const p1 = util.vector3FromPackedArray(newPositions, p1Index);
-        const p2 = util.vector3FromPackedArray(newPositions, p2Index);
-        this.polylinePositionBuffer[i].setXYZ(src, p1.x, p1.y, p1.z);
-        this.polylinePositionBuffer[i].setXYZ(src + 1, p2.x, p2.y, p2.z);
-        src += 2;
-      }
-      this.polylinePositionBuffer[i].needsUpdate = true;
-    }
-
-    if (this.polylines == null) {
-      this.createPolylines(this.scene);
-    }
-  }
-
-  onRender(renderContext: RenderContext) {
-    if (this.polylines == null) {
-      return;
-    }
-    for (let i = 0; i < this.polylines.length; i++) {
-      this.polylines[i].material.opacity = renderContext.polylineOpacities[i];
-      (this.polylines[i].material as THREE.LineBasicMaterial).linewidth =
-          renderContext.polylineWidths[i];
-      this.polylineColorBuffer[i].array = renderContext.polylineColors[i];
-      this.polylineColorBuffer[i].needsUpdate = true;
-    }
-  }
-
-  onPickingRender(renderContext: RenderContext) {}
-  onResize(newWidth: number, newHeight: number) {}
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerSprites.ts b/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerSprites.ts
deleted file mode 100644
index be9c1703c7..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/scatterPlotVisualizerSprites.ts
+++ /dev/null
@@ -1,435 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {CameraType, RenderContext} from './renderContext';
-import {ScatterPlotVisualizer} from './scatterPlotVisualizer';
-import * as util from './util';
-
-const NUM_POINTS_FOG_THRESHOLD = 5000;
-const MIN_POINT_SIZE = 5.0;
-const IMAGE_SIZE = 30;
-
-// Constants relating to the indices of buffer arrays.
-const RGB_NUM_ELEMENTS = 3;
-const INDEX_NUM_ELEMENTS = 1;
-const XYZ_NUM_ELEMENTS = 3;
-
-const VERTEX_SHADER = `
-  // Index of the specific vertex (passed in as bufferAttribute), and the
-  // variable that will be used to pass it to the fragment shader.
-  attribute float spriteIndex;
-  attribute vec3 color;
-  attribute float scaleFactor;
-
-  varying vec2 xyIndex;
-  varying vec3 vColor;
-
-  uniform bool sizeAttenuation;
-  uniform float pointSize;
-  uniform float spritesPerRow;
-  uniform float spritesPerColumn;
-
-  void main() {
-    // Pass index and color values to fragment shader.
-    vColor = color;
-    xyIndex = vec2(mod(spriteIndex, spritesPerRow),
-              floor(spriteIndex / spritesPerColumn));
-
-    // Transform current vertex by modelViewMatrix (model world position and
-    // camera world position matrix).
-    vec4 cameraSpacePos = modelViewMatrix * vec4(position, 1.0);
-
-    // Project vertex in camera-space to screen coordinates using the camera's
-    // projection matrix.
-    gl_Position = projectionMatrix * cameraSpacePos;
-
-    // Create size attenuation (if we're in 3D mode) by making the size of
-    // each point inversly proportional to its distance to the camera.
-    float outputPointSize = pointSize;
-    if (sizeAttenuation) {
-      outputPointSize = -pointSize / cameraSpacePos.z;
-    }
-
-    gl_PointSize =
-      max(outputPointSize * scaleFactor, ${MIN_POINT_SIZE.toFixed(1)});
-  }`;
-
-const FRAGMENT_SHADER_POINT_TEST_CHUNK = `
-  bool point_in_unit_circle(vec2 spriteCoord) {
-    vec2 centerToP = spriteCoord - vec2(0.5, 0.5);
-    return dot(centerToP, centerToP) < (0.5 * 0.5);
-  }
-
-  bool point_in_unit_equilateral_triangle(vec2 spriteCoord) {
-    vec3 v0 = vec3(0, 1, 0);
-    vec3 v1 = vec3(0.5, 0, 0);
-    vec3 v2 = vec3(1, 1, 0);
-    vec3 p = vec3(spriteCoord, 0);
-    float p_in_v0_v1 = cross(v1 - v0, p - v0).z;
-    float p_in_v1_v2 = cross(v2 - v1, p - v1).z;
-    return (p_in_v0_v1 > 0.0) && (p_in_v1_v2 > 0.0);
-  }
-
-  bool point_in_unit_square(vec2 spriteCoord) {
-    return true;
-  }
-`;
-
-const FRAGMENT_SHADER = `
-  varying vec2 xyIndex;
-  varying vec3 vColor;
-
-  uniform sampler2D texture;
-  uniform float spritesPerRow;
-  uniform float spritesPerColumn;
-  uniform bool isImage;
-
-  ${THREE.ShaderChunk['common']}
-  ${THREE.ShaderChunk['fog_pars_fragment']}
-  ${FRAGMENT_SHADER_POINT_TEST_CHUNK}
-
-  void main() {
-    if (isImage) {
-      // Coordinates of the vertex within the entire sprite image.
-      vec2 coords =
-        (gl_PointCoord + xyIndex) / vec2(spritesPerRow, spritesPerColumn);
-      gl_FragColor = vec4(vColor, 1.0) * texture2D(texture, coords);
-    } else {
-      bool inside = point_in_unit_circle(gl_PointCoord);
-      if (!inside) {
-        discard;
-      }
-      gl_FragColor = vec4(vColor, 1);
-    }
-    ${THREE.ShaderChunk['fog_fragment']}
-  }`;
-
-const FRAGMENT_SHADER_PICKING = `
-  varying vec2 xyIndex;
-  varying vec3 vColor;
-  uniform bool isImage;
-
-  ${FRAGMENT_SHADER_POINT_TEST_CHUNK}
-
-  void main() {
-    xyIndex; // Silence 'unused variable' warning.
-    if (isImage) {
-      gl_FragColor = vec4(vColor, 1);
-    } else {
-      bool inside = point_in_unit_circle(gl_PointCoord);
-      if (!inside) {
-        discard;
-      }
-      gl_FragColor = vec4(vColor, 1);
-    }
-  }`;
-
-/**
- * Uses GL point sprites to render the dataset.
- */
-export class ScatterPlotVisualizerSprites implements ScatterPlotVisualizer {
-  private scene: THREE.Scene;
-  private fog: THREE.Fog;
-  private texture: THREE.Texture = null;
-  private standinTextureForPoints: THREE.Texture;
-  private spritesPerRow: number;
-  private spritesPerColumn: number;
-  private spriteDimensions: [number, number];
-  private spriteIndexBufferAttribute: THREE.BufferAttribute;
-  private renderMaterial: THREE.ShaderMaterial;
-  private pickingMaterial: THREE.ShaderMaterial;
-
-  private points: THREE.Points;
-  private worldSpacePointPositions: Float32Array;
-  private pickingColors: Float32Array;
-  private renderColors: Float32Array;
-
-  constructor() {
-    this.standinTextureForPoints =
-        util.createTexture(document.createElement('canvas'));
-    this.renderMaterial = this.createRenderMaterial(false);
-    this.pickingMaterial = this.createPickingMaterial(false);
-  }
-
-  private createTextureFromSpriteAtlas(
-      spriteAtlas: HTMLImageElement, spriteDimensions: [number, number],
-      spriteIndices: Float32Array) {
-    this.texture = util.createTexture(spriteAtlas);
-    this.spritesPerRow = spriteAtlas.width / spriteDimensions[0];
-    this.spritesPerColumn = spriteAtlas.height / spriteDimensions[1];
-    this.spriteDimensions = spriteDimensions;
-    this.spriteIndexBufferAttribute =
-        new THREE.BufferAttribute(spriteIndices, INDEX_NUM_ELEMENTS);
-
-    if (this.points != null) {
-      (this.points.geometry as THREE.BufferGeometry)
-          .addAttribute('spriteIndex', this.spriteIndexBufferAttribute);
-    }
-  }
-
-  private createUniforms(): any {
-    return {
-      texture: {type: 't'},
-      spritesPerRow: {type: 'f'},
-      spritesPerColumn: {type: 'f'},
-      fogColor: {type: 'c'},
-      fogNear: {type: 'f'},
-      fogFar: {type: 'f'},
-      isImage: {type: 'bool'},
-      sizeAttenuation: {type: 'bool'},
-      pointSize: {type: 'f'}
-    };
-  }
-
-  private createRenderMaterial(haveImage: boolean): THREE.ShaderMaterial {
-    const uniforms = this.createUniforms();
-    return new THREE.ShaderMaterial({
-      uniforms: uniforms,
-      vertexShader: VERTEX_SHADER,
-      fragmentShader: FRAGMENT_SHADER,
-      transparent: !haveImage,
-      depthTest: haveImage,
-      depthWrite: haveImage,
-      fog: true,
-      blending: THREE.MultiplyBlending,
-    });
-  }
-
-  private createPickingMaterial(haveImage: boolean): THREE.ShaderMaterial {
-    const uniforms = this.createUniforms();
-    return new THREE.ShaderMaterial({
-      uniforms: uniforms,
-      vertexShader: VERTEX_SHADER,
-      fragmentShader: FRAGMENT_SHADER_PICKING,
-      transparent: true,
-      depthTest: true,
-      depthWrite: true,
-      fog: false,
-      blending: THREE.NormalBlending,
-    });
-  }
-
-  /**
-   * Create points, set their locations and actually instantiate the
-   * geometry.
-   */
-  private createPointSprites(scene: THREE.Scene, positions: Float32Array) {
-    const pointCount =
-        (positions != null) ? (positions.length / XYZ_NUM_ELEMENTS) : 0;
-    const geometry = this.createGeometry(pointCount);
-
-    this.fog = new THREE.Fog(0xFFFFFF);  // unused value, gets overwritten.
-
-    this.points = new THREE.Points(geometry, this.renderMaterial);
-    this.points.frustumCulled = false;
-    if (this.spriteIndexBufferAttribute != null) {
-      (this.points.geometry as THREE.BufferGeometry)
-          .addAttribute('spriteIndex', this.spriteIndexBufferAttribute);
-    }
-    scene.add(this.points);
-  }
-
-  private calculatePointSize(sceneIs3D: boolean): number {
-    if (this.texture != null) {
-      return sceneIs3D ? IMAGE_SIZE : this.spriteDimensions[0];
-    }
-    const n = (this.worldSpacePointPositions != null) ?
-        (this.worldSpacePointPositions.length / XYZ_NUM_ELEMENTS) :
-        1;
-    const SCALE = 200;
-    const LOG_BASE = 8;
-    const DIVISOR = 1.5;
-    // Scale point size inverse-logarithmically to the number of points.
-    const pointSize = SCALE / Math.log(n) / Math.log(LOG_BASE);
-    return sceneIs3D ? pointSize : (pointSize / DIVISOR);
-  }
-
-  /**
-   * Set up buffer attributes to be used for the points/images.
-   */
-  private createGeometry(pointCount: number): THREE.BufferGeometry {
-    const n = pointCount;
-
-    // Fill pickingColors with each point's unique id as its color.
-    this.pickingColors = new Float32Array(n * RGB_NUM_ELEMENTS);
-    {
-      let dst = 0;
-      for (let i = 0; i < n; i++) {
-        const c = new THREE.Color(i);
-        this.pickingColors[dst++] = c.r;
-        this.pickingColors[dst++] = c.g;
-        this.pickingColors[dst++] = c.b;
-      }
-    }
-
-    const geometry = new THREE.BufferGeometry();
-    geometry.addAttribute(
-        'position', new THREE.BufferAttribute(null, XYZ_NUM_ELEMENTS));
-    geometry.addAttribute(
-        'color', new THREE.BufferAttribute(null, RGB_NUM_ELEMENTS));
-    geometry.addAttribute(
-        'scaleFactor', new THREE.BufferAttribute(null, INDEX_NUM_ELEMENTS));
-    return geometry;
-  }
-
-  private setFogDistances(
-      sceneIs3D: boolean, nearestPointZ: number, farthestPointZ: number) {
-    if (sceneIs3D) {
-      const n = this.worldSpacePointPositions.length / XYZ_NUM_ELEMENTS;
-      this.fog.near = nearestPointZ;
-      // If there are fewer points we want less fog. We do this
-      // by making the "far" value (that is, the distance from the camera to the
-      // far edge of the fog) proportional to the number of points.
-      let multiplier =
-          2 - Math.min(n, NUM_POINTS_FOG_THRESHOLD) / NUM_POINTS_FOG_THRESHOLD;
-      this.fog.far = farthestPointZ * multiplier;
-    } else {
-      this.fog.near = Infinity;
-      this.fog.far = Infinity;
-    }
-  }
-
-  dispose() {
-    this.disposeGeometry();
-    this.disposeTextureAtlas();
-  }
-
-  private disposeGeometry() {
-    if (this.points != null) {
-      this.scene.remove(this.points);
-      this.points.geometry.dispose();
-      this.points = null;
-      this.worldSpacePointPositions = null;
-    }
-  }
-
-  private disposeTextureAtlas() {
-    if (this.texture != null) {
-      this.texture.dispose();
-    }
-    this.texture = null;
-    this.renderMaterial = null;
-    this.pickingMaterial = null;
-  }
-
-  setScene(scene: THREE.Scene) {
-    this.scene = scene;
-  }
-
-  setSpriteAtlas(
-      spriteImage: HTMLImageElement, spriteDimensions: [number, number],
-      spriteIndices: Float32Array) {
-    this.disposeTextureAtlas();
-    this.createTextureFromSpriteAtlas(
-        spriteImage, spriteDimensions, spriteIndices);
-    this.renderMaterial = this.createRenderMaterial(true);
-    this.pickingMaterial = this.createPickingMaterial(true);
-  }
-
-  clearSpriteAtlas() {
-    this.disposeTextureAtlas();
-    this.renderMaterial = this.createRenderMaterial(false);
-    this.pickingMaterial = this.createPickingMaterial(false);
-  }
-
-  onPointPositionsChanged(newPositions: Float32Array) {
-    if ((newPositions == null) || (newPositions.length === 0)) {
-      this.dispose();
-      return;
-    }
-    if (this.points != null) {
-      if (this.worldSpacePointPositions.length !== newPositions.length) {
-        this.disposeGeometry();
-      }
-    }
-
-    this.worldSpacePointPositions = newPositions;
-
-    if (this.points == null) {
-      this.createPointSprites(this.scene, newPositions);
-    }
-
-    const positions = (this.points.geometry as THREE.BufferGeometry)
-                          .getAttribute('position') as THREE.BufferAttribute;
-    positions.array = newPositions;
-    positions.needsUpdate = true;
-  }
-
-  onPickingRender(rc: RenderContext) {
-    if (this.points == null) {
-      return;
-    }
-
-    const sceneIs3D: boolean = (rc.cameraType === CameraType.Perspective);
-
-    this.pickingMaterial.uniforms.spritesPerRow.value = this.spritesPerRow;
-    this.pickingMaterial.uniforms.spritesPerRow.value = this.spritesPerColumn;
-    this.pickingMaterial.uniforms.sizeAttenuation.value = sceneIs3D;
-    this.pickingMaterial.uniforms.pointSize.value =
-        this.calculatePointSize(sceneIs3D);
-    this.points.material = this.pickingMaterial;
-
-    let colors = (this.points.geometry as THREE.BufferGeometry)
-                     .getAttribute('color') as THREE.BufferAttribute;
-    colors.array = this.pickingColors;
-    colors.needsUpdate = true;
-
-    let scaleFactors =
-        (this.points.geometry as THREE.BufferGeometry)
-            .getAttribute('scaleFactor') as THREE.BufferAttribute;
-    scaleFactors.array = rc.pointScaleFactors;
-    scaleFactors.needsUpdate = true;
-  }
-
-  onRender(rc: RenderContext) {
-    if (!this.points) {
-      return;
-    }
-    const sceneIs3D: boolean = (rc.camera instanceof THREE.PerspectiveCamera);
-
-    this.setFogDistances(
-        sceneIs3D, rc.nearestCameraSpacePointZ, rc.farthestCameraSpacePointZ);
-
-    this.scene.fog = this.fog;
-    this.scene.fog.color = new THREE.Color(rc.backgroundColor);
-
-    this.renderMaterial.uniforms.fogColor.value = this.scene.fog.color;
-    this.renderMaterial.uniforms.fogNear.value = this.fog.near;
-    this.renderMaterial.uniforms.fogFar.value = this.fog.far;
-    this.renderMaterial.uniforms.spritesPerRow.value = this.spritesPerRow;
-    this.renderMaterial.uniforms.spritesPerColumn.value = this.spritesPerColumn;
-    this.renderMaterial.uniforms.isImage.value = (this.texture != null);
-    this.renderMaterial.uniforms.texture.value =
-        (this.texture != null) ? this.texture : this.standinTextureForPoints;
-    this.renderMaterial.uniforms.sizeAttenuation.value = sceneIs3D;
-    this.renderMaterial.uniforms.pointSize.value =
-        this.calculatePointSize(sceneIs3D);
-    this.points.material = this.renderMaterial;
-
-    let colors = (this.points.geometry as THREE.BufferGeometry)
-                     .getAttribute('color') as THREE.BufferAttribute;
-    this.renderColors = rc.pointColors;
-    colors.array = this.renderColors;
-    colors.needsUpdate = true;
-
-    let scaleFactors =
-        (this.points.geometry as THREE.BufferGeometry)
-            .getAttribute('scaleFactor') as THREE.BufferAttribute;
-    scaleFactors.array = rc.pointScaleFactors;
-    scaleFactors.needsUpdate = true;
-  }
-
-  onResize(newWidth: number, newHeight: number) {}
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/sptree.ts b/tensorflow/tensorboard/components/vz_projector/sptree.ts
deleted file mode 100644
index 991369a335..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/sptree.ts
+++ /dev/null
@@ -1,175 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/** N-dimensional point. Usually 2D or 3D. */
-export type Point = number[];
-
-export interface BBox {
-  center: Point;
-  halfDim: number;
-}
-
-/** A node in a space-partitioning tree. */
-export interface SPNode {
-  /** The children of this node. */
-  children?: SPNode[];
-  /** The bounding box of the region this node occupies. */
-  box: BBox;
-  /** One or more points this node has. */
-  point: Point;
-}
-
-/**
- * A Space-partitioning tree (https://en.wikipedia.org/wiki/Space_partitioning)
- * that recursively divides the space into regions of equal sizes. This data
- * structure can act both as a Quad tree and an Octree when the data is 2 or
- * 3 dimensional respectively. One usage is in t-SNE in order to do Barnes-Hut
- * approximation.
- */
-export class SPTree {
-  root: SPNode;
-
-  private masks: number[];
-  private dim: number;
-
-  /**
-   * Constructs a new tree with the provided data.
-   *
-   * @param data List of n-dimensional data points.
-   * @param capacity Number of data points to store in a single node.
-   */
-  constructor(data: Point[]) {
-    if (data.length < 1) {
-      throw new Error('There should be at least 1 data point');
-    }
-    // Make a bounding box based on the extent of the data.
-    this.dim = data[0].length;
-    // Each node has 2^d children, where d is the dimension of the space.
-    // Binary masks (e.g. 000, 001, ... 111 in 3D) are used to determine in
-    // which child (e.g. quadron in 2D) the new point is going to be assigned.
-    // For more details, see the insert() method and its comments.
-    this.masks = new Array(Math.pow(2, this.dim));
-    for (let d = 0; d < this.masks.length; ++d) {
-      this.masks[d] = (1 << d);
-    }
-    let min: Point = new Array(this.dim);
-    fillArray(min, Number.POSITIVE_INFINITY);
-    let max: Point = new Array(this.dim);
-    fillArray(max, Number.NEGATIVE_INFINITY);
-
-    for (let i = 0; i < data.length; ++i) {
-      // For each dim get the min and max.
-      // E.g. For 2-D, get the x_min, x_max, y_min, y_max.
-      for (let d = 0; d < this.dim; ++d) {
-        min[d] = Math.min(min[d], data[i][d]);
-        max[d] = Math.max(max[d], data[i][d]);
-      }
-    }
-    // Create a bounding box with the center of the largest span.
-    let center: Point = new Array(this.dim);
-    let halfDim = 0;
-    for (let d = 0; d < this.dim; ++d) {
-      let span = max[d] - min[d];
-      center[d] = min[d] + span / 2;
-      halfDim = Math.max(halfDim, span / 2);
-    }
-    this.root = {box: {center: center, halfDim: halfDim}, point: data[0]};
-    for (let i = 1; i < data.length; ++i) {
-      this.insert(this.root, data[i]);
-    }
-  }
-
-  /**
-   * Visits every node in the tree. Each node can store 1 or more points,
-   * depending on the node capacity provided in the constructor.
-   *
-   * @param accessor Method that takes the currently visited node, and the
-   * low and high point of the region that this node occupies. E.g. in 2D,
-   * the low and high points will be the lower-left corner and the upper-right
-   * corner.
-   */
-  visit(
-      accessor: (node: SPNode, lowPoint: Point, highPoint: Point) => boolean,
-      noBox = false) {
-    this.visitNode(this.root, accessor, noBox);
-  }
-
-  private visitNode(
-      node: SPNode,
-      accessor: (node: SPNode, lowPoint?: Point, highPoint?: Point) => boolean,
-      noBox: boolean) {
-    let skipChildren: boolean;
-    if (noBox) {
-      skipChildren = accessor(node);
-    } else {
-      let lowPoint = new Array(this.dim);
-      let highPoint = new Array(this.dim);
-      for (let d = 0; d < this.dim; ++d) {
-        lowPoint[d] = node.box.center[d] - node.box.halfDim;
-        highPoint[d] = node.box.center[d] + node.box.halfDim;
-      }
-      skipChildren = accessor(node, lowPoint, highPoint);
-    }
-    if (!node.children || skipChildren) {
-      return;
-    }
-    for (let i = 0; i < node.children.length; ++i) {
-      let child = node.children[i];
-      if (child) {
-        this.visitNode(child, accessor, noBox);
-      }
-    }
-  }
-
-  private insert(node: SPNode, p: Point) {
-    // Subdivide and then add the point to whichever node will accept it.
-    if (node.children == null) {
-      node.children = new Array(this.masks.length);
-    }
-
-    // Decide which child will get the new point by constructing a D-bits binary
-    // signature (D=3 for 3D) where the k-th bit is 1 if the point's k-th
-    // coordinate is greater than the node's k-th coordinate, 0 otherwise.
-    // Then the binary signature in decimal system gives us the index of the
-    // child where the new point should be.
-    let index = 0;
-    for (let d = 0; d < this.dim; ++d) {
-      if (p[d] > node.box.center[d]) {
-        index |= this.masks[d];
-      }
-    }
-    if (node.children[index] == null) {
-      this.makeChild(node, index, p);
-    } else {
-      this.insert(node.children[index], p);
-    }
-  }
-
-  private makeChild(node: SPNode, index: number, p: Point): void {
-    let oldC = node.box.center;
-    let h = node.box.halfDim / 2;
-    let newC: Point = new Array(this.dim);
-    for (let d = 0; d < this.dim; ++d) {
-      newC[d] = (index & (1 << d)) ? oldC[d] + h : oldC[d] - h;
-    }
-    node.children[index] = {box: {center: newC, halfDim: h}, point: p};
-  }
-}
-
-function fillArray<T>(arr: T[], value: T): void {
-  for (let i = 0; i < arr.length; ++i) {
-    arr[i] = value;
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/styles.html b/tensorflow/tensorboard/components/vz_projector/styles.html
deleted file mode 100644
index 32dc984b5d..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/styles.html
+++ /dev/null
@@ -1,185 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<dom-module id="vz-projector-styles">
-<template>
-<style>
-:host {
-  --paper-input-container-label: {
-    font-size: 14px;
-  };
-  --paper-input-container-input: {
-    font-size: 14px;
-  };
-  /* TODO: Figure out why this doesn't work */
-  --paper-dropdown-menu-input: {
-    font-size: 14px;
-  };
-}
-
-paper-button {
-  background: #e3e3e3;
-  margin-left: 0;
-  text-transform: none;
-}
-
-paper-dropdown-menu paper-item {
-  font-size: 13px;
-}
-
-paper-tooltip {
-  max-width: 200px;
-  --paper-tooltip: {
-    font-size: 12px;
-  };
-}
-
-paper-checkbox {
-  --paper-checkbox-checked-color: #880E4F;
-}
-
-paper-toggle-button {
-  --paper-toggle-button-checked-bar-color:  #880E4F;
-  --paper-toggle-button-checked-button-color:  #880E4F;
-  --paper-toggle-button-checked-ink-color: #880E4F;
-}
-
-paper-icon-button {
-  border-radius: 50%;
-}
-
-paper-icon-button[active] {
-  color: white;
-  background-color: #880E4F;
-}
-
-.slider {
-  display: flex;
-  align-items: center;
-  margin-bottom: 10px;
-  justify-content: space-between;
-}
-
-.slider span {
-  width: 35px;
-  text-align: right;
-}
-
-.slider label {
-  align-items: center;
-  display: flex;
-}
-
-.help-icon {
-  height: 15px;
-  left: 2px;
-  min-width: 15px;
-  min-height: 15px;
-  margin: 0;
-  padding: 0;
-  top: -2px;
-  width: 15px;
-}
-
-.ink-panel {
-  display: flex;
-  flex-direction: column;
-  font-size: 14px;
-}
-
-.ink-panel h4 {
-  border-bottom: 1px solid #ddd;
-  font-size: 14px;
-  font-weight: 500;
-  margin: 0;
-  margin-bottom: 10px;
-  padding-bottom: 5px;
-}
-
-.ink-panel-header {
-  border-bottom: 1px solid rgba(0, 0, 0, 0.1);
-  border-top: 1px solid rgba(0, 0, 0, 0.1);
-  height: 50px;
-}
-
-.ink-panel-content {
-  display: none;
-  height: 100%;
-}
-
-.ink-panel-content.active {
-  display: block;
-}
-
-.ink-panel-content h3 {
-  font-weight: 500;
-  font-size: 14px;
-  margin-top: 20px;
-  margin-bottom: 5px;
-  text-transform: uppercase;
-}
-
-.ink-panel-header h3 {
-  font-weight: 500;
-  font-size: 14px;
-  margin: 0;
-  padding: 0 24px;
-  text-transform: uppercase;
-}
-
-
-/* - Tabs */
-.ink-tab-group {
-  align-items: center;
-  box-sizing: border-box;
-  display: flex;
-  height: 100%;
-  justify-content: space-around;
-}
-
-.ink-tab-group .projection-tab {
-  color: rgba(0, 0, 0, 0.5);
-  cursor: pointer;
-  font-weight: 300;
-  line-height: 49px;
-  padding: 0 12px;
-  text-align: center;
-  text-transform: uppercase;
-}
-
-.ink-tab-group .projection-tab:hover {
-  color: black;
-}
-
-.ink-tab-group .projection-tab.active {
-  border-bottom: 2px solid black;
-  color: black;
-  font-weight: 500;
-}
-
-h4 {
-  margin: 30px 0 10px 0;
-}
-
-.dismiss-dialog-note {
-  margin-top: 25px;
-  font-size: 11px;
-  text-align: right;
-}
-</style>
-</template>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/test/BUILD b/tensorflow/tensorboard/components/vz_projector/test/BUILD
deleted file mode 100644
index fc8659f06a..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/BUILD
+++ /dev/null
@@ -1,36 +0,0 @@
-package(
-    default_testonly = True,
-    default_visibility = ["//tensorflow/tensorboard:internal"],
-)
-
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "test",
-    srcs = [
-        "assert.ts",
-        "data-provider_test.ts",
-        "data_test.ts",
-        "sptree_test.ts",
-        "tests.html",
-        "util_test.ts",
-        # "scatterPlotRectangleSelector_test.ts",
-        # "vz-projector-projections-panel_test.ts",
-    ],
-    path = "/vz-projector/test",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:polymer",
-        "//tensorflow/tensorboard/components/tf_imports:web_component_tester",
-        "//tensorflow/tensorboard/components/tf_imports:webcomponentsjs",
-        "//tensorflow/tensorboard/components/vz_projector",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    testonly = 0,
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/vz_projector/test/assert.ts b/tensorflow/tensorboard/components/vz_projector/test/assert.ts
deleted file mode 100644
index f489517a7f..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/assert.ts
+++ /dev/null
@@ -1,16 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-const assert = chai.assert;
diff --git a/tensorflow/tensorboard/components/vz_projector/test/data-provider_test.ts b/tensorflow/tensorboard/components/vz_projector/test/data-provider_test.ts
deleted file mode 100644
index 59a42ffbfd..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/data-provider_test.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataPoint, SpriteAndMetadataInfo} from '../data';
-import * as data_provider from '../data-provider';
-
-/**
- * Converts a string to an ArrayBuffer.
- */
-function stringToArrayBuffer(str: string): Promise<ArrayBuffer> {
-  return new Promise<ArrayBuffer>((resolve, reject) => {
-    let blob = new Blob([str]);
-    let file = new FileReader();
-    file.onload = (e: any) => {
-      resolve(e.target.result);
-    };
-    file.readAsArrayBuffer(blob);
-  });
-}
-
-/**
- * Converts an data array to TSV format.
- */
-function dataToTsv(data: string[][]|number[][]) {
-  let lines = [];
-  for (let i = 0; i < data.length; i++) {
-    lines.push(data[i].join('\t'));
-  }
-  return lines.join('\n');
-}
-
-describe('parse tensors', () => {
-  it('parseTensors', (doneFn) => {
-    let tensors = [[1.0, 2.0], [2.0, 3.0]];
-    stringToArrayBuffer(dataToTsv(tensors))
-        .then((tensorsArrayBuffer: ArrayBuffer) => {
-          data_provider.parseTensors(tensorsArrayBuffer)
-              .then((data: DataPoint[]) => {
-                assert.equal(2, data.length);
-
-                assert.deepEqual(new Float32Array(tensors[0]), data[0].vector);
-                assert.equal(0, data[0].index);
-                assert.isNull(data[0].projections);
-
-                assert.deepEqual(new Float32Array(tensors[1]), data[1].vector);
-                assert.equal(1, data[1].index);
-                assert.isNull(data[1].projections);
-                doneFn();
-              });
-        });
-  });
-  it('parseMetadata', (doneFn) => {
-    let metadata = [['label', 'fakecol'], ['Г', '0'], ['label1', '1']];
-
-    stringToArrayBuffer(dataToTsv(metadata))
-        .then((metadataArrayBuffer: ArrayBuffer) => {
-          data_provider.parseMetadata(metadataArrayBuffer)
-              .then((spriteAndMetadataInfo: SpriteAndMetadataInfo) => {
-                assert.equal(2, spriteAndMetadataInfo.stats.length);
-                assert.equal(metadata[0][0],
-                             spriteAndMetadataInfo.stats[0].name);
-                assert.isFalse(spriteAndMetadataInfo.stats[0].isNumeric);
-                assert.isFalse(
-                    spriteAndMetadataInfo.stats[0].tooManyUniqueValues);
-                assert.equal(metadata[0][1],
-                             spriteAndMetadataInfo.stats[1].name);
-                assert.isTrue(spriteAndMetadataInfo.stats[1].isNumeric);
-                assert.isFalse(
-                    spriteAndMetadataInfo.stats[1].tooManyUniqueValues);
-
-                assert.equal(2, spriteAndMetadataInfo.pointsInfo.length);
-                assert.equal(metadata[1][0],
-                             spriteAndMetadataInfo.pointsInfo[0]['label']);
-                assert.equal(+metadata[1][1],
-                             spriteAndMetadataInfo.pointsInfo[0]['fakecol']);
-                assert.equal(metadata[2][0],
-                             spriteAndMetadataInfo.pointsInfo[1]['label']);
-                assert.equal(+metadata[2][1],
-                             spriteAndMetadataInfo.pointsInfo[1]['fakecol']);
-                doneFn();
-              });
-        });
-  });
-});
diff --git a/tensorflow/tensorboard/components/vz_projector/test/data_test.ts b/tensorflow/tensorboard/components/vz_projector/test/data_test.ts
deleted file mode 100644
index 5e47c091c5..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/data_test.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataPoint, DataSet, State, stateGetAccessorDimensions} from '../data';
-
-/**
- * Helper method that makes a list of points given an array of
- * sequence indexes.
- *
- * @param sequences The i-th entry holds the 'next' attribute for the i-th
- * point.
- */
-function makePointsWithSequences(
-    sequences: number[], nextAttr = '__seq_next__') {
-  let points: DataPoint[] = [];
-  sequences.forEach((t, i) => {
-    let metadata: {[key: string]: any} = {};
-    metadata[nextAttr] = t >= 0 ? t : null;
-    points.push({
-      vector: new Float32Array(0),
-      metadata: metadata,
-      projections: {},
-      index: i
-    });
-  });
-  return points;
-}
-
-describe('constructor_with_sequences', () => {
-  it('Simple forward pointing sequences, __seq_next__ metadata format', () => {
-    // The input is: 0->2, 1->None, 2->3, 3->None. This should return
-    // one sequence 0->2->3.
-    const points = makePointsWithSequences([2, -1, 3, -1]);
-    let dataset = new DataSet(points);
-    assert.equal(1, dataset.sequences.length);
-    assert.deepEqual([0, 2, 3], dataset.sequences[0].pointIndices);
-  });
-
-  it('Simple forward pointing sequences, __next__ metadata format', () => {
-    // The input is: 0->2, 1->None, 2->3, 3->None. This should return
-    // one sequence 0->2->3.
-    const points = makePointsWithSequences([2, -1, 3, -1], '__next__');
-    let dataset = new DataSet(points);
-    assert.equal(1, dataset.sequences.length);
-    assert.deepEqual([0, 2, 3], dataset.sequences[0].pointIndices);
-  });
-
-  it('No sequences', () => {
-    let points = makePointsWithSequences([-1, -1, -1, -1]);
-    let dataset = new DataSet(points);
-    assert.equal(0, dataset.sequences.length);
-  });
-
-  it('A sequence that goes backwards and forward in the array', () => {
-    // The input is: 0->2, 1->0, 2->nothing, 3->1. This should return
-    // one sequence 3->1->0->2.
-    let points = makePointsWithSequences([2, 0, -1, 1]);
-    let dataset = new DataSet(points);
-    assert.equal(1, dataset.sequences.length);
-    assert.deepEqual([3, 1, 0, 2], dataset.sequences[0].pointIndices);
-  });
-});
-
-describe('stateGetAccessorDimensions', () => {
-  it('returns [0, 1] for 2d t-SNE', () => {
-    const state = new State();
-    state.selectedProjection = 'tsne';
-    state.tSNEis3d = false;
-    assert.deepEqual([0, 1], stateGetAccessorDimensions(state));
-  });
-
-  it('returns [0, 1, 2] for 3d t-SNE', () => {
-    const state = new State();
-    state.selectedProjection = 'tsne';
-    state.tSNEis3d = true;
-    assert.deepEqual([0, 1, 2], stateGetAccessorDimensions(state));
-  });
-
-  it('returns pca component dimensions array for pca', () => {
-    const state = new State();
-    state.selectedProjection = 'pca';
-    state.pcaComponentDimensions = [13, 12, 11, 10];
-    assert.deepEqual(state.pcaComponentDimensions,
-                     stateGetAccessorDimensions(state));
-  });
-
-  it('returns ["x", "y"] for custom projections', () => {
-    const state = new State();
-    state.selectedProjection = 'custom';
-    assert.deepEqual(['x', 'y'], stateGetAccessorDimensions(state));
-  });
-});
diff --git a/tensorflow/tensorboard/components/vz_projector/test/scatterPlotRectangleSelector_test.ts b/tensorflow/tensorboard/components/vz_projector/test/scatterPlotRectangleSelector_test.ts
deleted file mode 100644
index 0ee6cf620d..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/scatterPlotRectangleSelector_test.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {BoundingBox, ScatterPlotRectangleSelector} from '../scatterPlotRectangleSelector';
-
-describe('selector callbacks make bounding box start bottom left', () => {
-  let containerElement: HTMLElement;
-  let selectionCallback: (boundingBox: BoundingBox) => void;
-  let selection: ScatterPlotRectangleSelector;
-
-  beforeEach(() => {
-    containerElement = document.createElement('div');
-    const selector = document.createElement('svg');
-    selector.id = 'selector';
-    containerElement.appendChild(selector);
-
-    selectionCallback = jasmine.createSpy('selectionCallback');
-    selection =
-        new ScatterPlotRectangleSelector(containerElement, selectionCallback);
-  });
-
-  it('Simple mouse event starting top left', () => {
-    selection.onMouseDown(0, 0);
-    selection.onMouseMove(10, 10);
-    selection.onMouseUp();
-
-    expect(selectionCallback)
-        .toHaveBeenCalledWith({x: 0, y: 10, width: 10, height: 10});
-  });
-
-  it('Simple mouse event starting bottom left', () => {
-    selection.onMouseDown(0, 10);
-    selection.onMouseMove(10, 0);
-    selection.onMouseUp();
-
-    expect(selectionCallback)
-        .toHaveBeenCalledWith({x: 0, y: 10, width: 10, height: 10});
-  });
-
-  it('Simple mouse event starting top right', () => {
-    selection.onMouseDown(10, 0);
-    selection.onMouseMove(0, 10);
-    selection.onMouseUp();
-
-    expect(selectionCallback)
-        .toHaveBeenCalledWith({x: 0, y: 10, width: 10, height: 10});
-  });
-
-  it('Simple mouse event starting bottom right', () => {
-    selection.onMouseDown(10, 10);
-    selection.onMouseMove(0, 0);
-    selection.onMouseUp();
-
-    expect(selectionCallback)
-        .toHaveBeenCalledWith({x: 0, y: 10, width: 10, height: 10});
-  });
-});
diff --git a/tensorflow/tensorboard/components/vz_projector/test/sptree_test.ts b/tensorflow/tensorboard/components/vz_projector/test/sptree_test.ts
deleted file mode 100644
index 7e340ea62f..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/sptree_test.ts
+++ /dev/null
@@ -1,102 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {SPTree} from '../sptree';
-
-it('simple 2D data', () => {
-  let data = [
-    [0, 1],
-    [1, 0],
-    [1, 1],
-    [0, 0],
-  ];
-  let tree = new SPTree(data);
-  // Check that each point is within the bound.
-  tree.visit((node, low, high) => {
-    assert.equal(low.length, 2);
-    assert.equal(high.length, 2);
-    let point = node.point;
-    assert.equal(point.length, 2);
-    // Each point should be in the node's bounding box.
-    assert.equal(
-        point[0] >= low[0] && point[0] <= high[0] && point[1] >= low[1] &&
-            point[1] <= high[1],
-        true);
-    return false;
-  });
-});
-
-it('simple 3D data', () => {
-  let data = [
-    [0, 1, 0],
-    [1, 0.4, 2],
-    [1, 1, 3],
-    [0, 0, 5],
-  ];
-  let tree = new SPTree(data);
-  // Check that each point is within the bound.
-  tree.visit((node, low, high) => {
-    assert.equal(low.length, 3);
-    assert.equal(high.length, 3);
-    let point = node.point;
-    assert.equal(point.length, 3);
-    // Each point should be in the node's bounding box.
-    assert.equal(
-        point[0] >= low[0] && point[0] <= high[0] && point[1] >= low[1] &&
-            point[1] <= high[1] && point[2] >= low[2] && point[2] <= high[2],
-        true);
-    return false;
-  });
-});
-
-it('Only visit root', () => {
-  let data = [
-    [0, 1, 0],
-    [1, 0.4, 2],
-    [1, 1, 3],
-    [0, 0, 5],
-  ];
-  let tree = new SPTree(data);
-  let numVisits = 0;
-  tree.visit((node, low, high) => {
-    numVisits++;
-    return true;
-  });
-  assert.equal(numVisits, 1);
-});
-
-it('Search in random data', () => {
-  let N = 10000;
-  let data = new Array(N);
-  for (let i = 0; i < N; i++) {
-    data[i] = [Math.random(), Math.random()];
-  }
-  let tree = new SPTree(data);
-  let numVisits = 0;
-  let query = data[Math.floor(Math.random() * N)];
-  let found = false;
-  tree.visit((node, low, high) => {
-    numVisits++;
-    if (node.point === query) {
-      found = true;
-      return true;
-    }
-    let outOfBounds = query[0] < low[0] || query[0] > high[0] ||
-        query[1] < low[1] || query[1] > high[1];
-    return outOfBounds;
-  });
-  assert.equal(found, true);
-  assert.isBelow(numVisits, N / 4);
-});
diff --git a/tensorflow/tensorboard/components/vz_projector/test/tests.html b/tensorflow/tensorboard/components/vz_projector/test/tests.html
deleted file mode 100644
index a6843d0d6b..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/tests.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<script src="../../web-component-tester/browser.js"></script>
-<script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
-<link rel="import" href="../bundle.html">
-<body>
-<script src="assert.js"></script>
-<script src="sptree_test.js"></script>
-<script src="data_test.js"></script>
-<script src="data-provider_test.js"></script>
-<script src="util_test.js"></script>
-<!-- TODO(smilkov): Migrate these away from jasmine. -->
-<!-- <script src="scatterPlotRectangleSelector_test.js"></script>
-     <script src="vz-projector-projections-panel_test.js"></script> -->
diff --git a/tensorflow/tensorboard/components/vz_projector/test/util_test.ts b/tensorflow/tensorboard/components/vz_projector/test/util_test.ts
deleted file mode 100644
index c18db95eed..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/util_test.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import * as util from '../util';
-
-describe('getURLParams', () => {
-  it('search query with valid param returns correct object', () => {
-    let urlParams = util.getURLParams('?config=http://google.com/');
-    assert.deepEqual({'config': 'http://google.com/'}, urlParams);
-  });
-
-  it('search query with multiple valid params returns correct object', () => {
-    let urlParams = util.getURLParams('?config=http://google.com/&foo=bar');
-    assert.deepEqual({'config': 'http://google.com/', 'foo': 'bar'}, urlParams);
-  });
-
-  it('search query with valid param with URL encoded characters', () => {
-    let urlParams = util.getURLParams('?config=http://google.com/%20search');
-    assert.deepEqual({'config': 'http://google.com/ search'}, urlParams);
-  });
-
-  it('search query with pound sign', () => {
-    let urlParams = util.getURLParams('?config=http://google.com/#foo');
-    assert.deepEqual({'config': 'http://google.com/'}, urlParams);
-  });
-
-  it('no search query returns empty object', () => {
-    let urlParams = util.getURLParams('');
-    assert.deepEqual({}, urlParams);
-  });
-});
diff --git a/tensorflow/tensorboard/components/vz_projector/test/vz-projector-projections-panel_test.ts b/tensorflow/tensorboard/components/vz_projector/test/vz-projector-projections-panel_test.ts
deleted file mode 100644
index 2bf0c6eb48..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/test/vz-projector-projections-panel_test.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import {State} from '../data';
-import {ProjectionsPanel} from '../vz-projector-projections-panel';
-
-describe('restoreUIFromBookmark', () => {
-  let projectionsPanel: ProjectionsPanel;
-  beforeEach(() => {
-    projectionsPanel = document.createElement(ProjectionsPanel.prototype.is) as
-        ProjectionsPanel;
-
-    // Set up some of the UI so the elements are found in the production code.
-    const tsnePerplexityContainer = document.createElement('div');
-    tsnePerplexityContainer.className = 'tsne-perplexity';
-    const tsnePerplexity = document.createElement('span');
-    tsnePerplexityContainer.appendChild(tsnePerplexity);
-    projectionsPanel.appendChild(tsnePerplexityContainer);
-
-    const tsneLearningRateContainer = document.createElement('div');
-    tsneLearningRateContainer.className = 'tsne-learning-rate';
-    const tsneLearningRate = document.createElement('span');
-    tsneLearningRateContainer.appendChild(tsneLearningRate);
-    projectionsPanel.appendChild(tsneLearningRateContainer);
-  });
-
-  it('sets the pcaX/Y properties when setting 2D component values', () => {
-    spyOn(projectionsPanel, 'setZDropdownEnabled');
-
-    const s = new State();
-    s.pcaComponentDimensions = [0, 1];
-    projectionsPanel.restoreUIFromBookmark(s);
-
-    assert.equal(0, projectionsPanel.pcaX);
-    assert.equal(1, projectionsPanel.pcaY);
-
-    expect(projectionsPanel.setZDropdownEnabled).toHaveBeenCalledWith(false);
-  });
-
-  it('sets the pcaX/Y properties when setting 3D component values', () => {
-    spyOn(projectionsPanel, 'setZDropdownEnabled');
-
-    const s = new State();
-    s.pcaComponentDimensions = [0, 1, 2];
-    projectionsPanel.restoreUIFromBookmark(s);
-
-    assert.equal(0, projectionsPanel.pcaX);
-    assert.equal(1, projectionsPanel.pcaY);
-    assert.equal(2, projectionsPanel.pcaZ);
-
-    expect(projectionsPanel.setZDropdownEnabled).toHaveBeenCalledWith(true);
-  });
-});
-
-describe('populateBookmarkFromUI', () => {
-  let projectionsPanel: ProjectionsPanel;
-
-  beforeEach(() => {
-    projectionsPanel = document.createElement(ProjectionsPanel.prototype.is) as
-        ProjectionsPanel;
-
-    // Set up some of the UI so the elements are found in the production code.
-    const tsnePerplexityContainer = document.createElement('div');
-    tsnePerplexityContainer.className = 'tsne-perplexity';
-    const tsnePerplexity = document.createElement('span');
-    tsnePerplexityContainer.appendChild(tsnePerplexity);
-    projectionsPanel.appendChild(tsnePerplexityContainer);
-
-    const tsneLearningRateContainer = document.createElement('div');
-    tsneLearningRateContainer.className = 'tsne-learning-rate';
-    const tsneLearningRate = document.createElement('span');
-    tsneLearningRateContainer.appendChild(tsneLearningRate);
-    projectionsPanel.appendChild(tsneLearningRateContainer);
-  });
-
-  it('gets the PCA component UI values from a 2D PCA projection', () => {
-    projectionsPanel.pcaX = 0;
-    projectionsPanel.pcaY = 1;
-    projectionsPanel.pcaIs3d = false;
-
-    const s = new State();
-    projectionsPanel.populateBookmarkFromUI(s);
-    assert.deepEqual([0, 1], s.pcaComponentDimensions);
-  });
-
-  it('gets the PCA component UI values from a 3D PCA projection', () => {
-    projectionsPanel.pcaX = 0;
-    projectionsPanel.pcaY = 1;
-    projectionsPanel.pcaZ = 2;
-    projectionsPanel.pcaIs3d = true;
-
-    const s = new State();
-    projectionsPanel.populateBookmarkFromUI(s);
-    assert.deepEqual([0, 1, 2], s.pcaComponentDimensions);
-  });
-});
diff --git a/tensorflow/tensorboard/components/vz_projector/util.ts b/tensorflow/tensorboard/components/vz_projector/util.ts
deleted file mode 100644
index bd6df68b1a..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/util.ts
+++ /dev/null
@@ -1,252 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataPoint} from './data';
-import * as logging from './logging';
-import {Point2D} from './vector';
-
-/**
- * Delay for running expensive tasks, in milliseconds.
- * The duration was empirically found so that it leaves enough time for the
- * browser to update its UI state before starting an expensive UI-blocking task.
- */
-const TASK_DELAY_MS = 200;
-
-/** Shuffles the array in-place in O(n) time using Fisher-Yates algorithm. */
-export function shuffle<T>(array: T[]): T[] {
-  let m = array.length;
-  let t: T;
-  let i: number;
-
-  // While there remain elements to shuffle.
-  while (m) {
-    // Pick a remaining element
-    i = Math.floor(Math.random() * m--);
-    // And swap it with the current element.
-    t = array[m];
-    array[m] = array[i];
-    array[i] = t;
-  }
-  return array;
-}
-
-export function range(count: number): number[] {
-  const rangeOutput: number[] = [];
-  for (let i = 0; i < count; i++) {
-    rangeOutput.push(i);
-  }
-  return rangeOutput;
-}
-
-export function classed(
-    element: HTMLElement, className: string, enabled: boolean) {
-  const classNames = element.className.split(' ');
-  if (enabled) {
-    if (className in classNames) {
-      return;
-    } else {
-      classNames.push(className);
-    }
-  } else {
-    const index = classNames.indexOf(className);
-    if (index === -1) {
-      return;
-    }
-    classNames.splice(index, 1);
-  }
-  element.className = classNames.join(' ');
-}
-
-/** Projects a 3d point into screen space */
-export function vector3DToScreenCoords(
-    cam: THREE.Camera, w: number, h: number, v: THREE.Vector3): Point2D {
-  let dpr = window.devicePixelRatio;
-  let pv = new THREE.Vector3().copy(v).project(cam);
-
-  // The screen-space origin is at the middle of the screen, with +y up.
-  let coords: Point2D =
-      [((pv.x + 1) / 2 * w) * dpr, -((pv.y - 1) / 2 * h) * dpr];
-  return coords;
-}
-
-/** Loads 3 contiguous elements from a packed xyz array into a Vector3. */
-export function vector3FromPackedArray(
-    a: Float32Array, pointIndex: number): THREE.Vector3 {
-  const offset = pointIndex * 3;
-  return new THREE.Vector3(a[offset], a[offset + 1], a[offset + 2]);
-}
-
-/**
- * Gets the camera-space z coordinates of the nearest and farthest points.
- * Ignores points that are behind the camera.
- */
-export function getNearFarPoints(
-    worldSpacePoints: Float32Array, cameraPos: THREE.Vector3,
-    cameraTarget: THREE.Vector3): [number, number] {
-  let shortestDist: number = Infinity;
-  let furthestDist: number = 0;
-  const camToTarget = new THREE.Vector3().copy(cameraTarget).sub(cameraPos);
-  const camPlaneNormal = new THREE.Vector3().copy(camToTarget).normalize();
-  const n = worldSpacePoints.length / 3;
-  let src = 0;
-  let p = new THREE.Vector3();
-  let camToPoint = new THREE.Vector3();
-  for (let i = 0; i < n; i++) {
-    p.x = worldSpacePoints[src];
-    p.y = worldSpacePoints[src + 1];
-    p.z = worldSpacePoints[src + 2];
-    src += 3;
-
-    camToPoint.copy(p).sub(cameraPos);
-    const dist = camPlaneNormal.dot(camToPoint);
-    if (dist < 0) {
-      continue;
-    }
-    furthestDist = (dist > furthestDist) ? dist : furthestDist;
-    shortestDist = (dist < shortestDist) ? dist : shortestDist;
-  }
-  return [shortestDist, furthestDist];
-}
-
-/**
- * Generate a texture for the points/images and sets some initial params
- */
-export function createTexture(image: HTMLImageElement|
-                              HTMLCanvasElement): THREE.Texture {
-  let tex = new THREE.Texture(image);
-  tex.needsUpdate = true;
-  // Used if the texture isn't a power of 2.
-  tex.minFilter = THREE.LinearFilter;
-  tex.generateMipmaps = false;
-  tex.flipY = false;
-  return tex;
-}
-
-/**
- * Assert that the condition is satisfied; if not, log user-specified message
- * to the console.
- */
-export function assert(condition: boolean, message?: string) {
-  if (!condition) {
-    message = message || 'Assertion failed';
-    throw new Error(message);
-  }
-}
-
-export type SearchPredicate = (p: DataPoint) => boolean;
-
-export function getSearchPredicate(
-    query: string, inRegexMode: boolean, fieldName: string): SearchPredicate {
-  let predicate: SearchPredicate;
-  if (inRegexMode) {
-    let regExp = new RegExp(query, 'i');
-    predicate = p => regExp.test(p.metadata[fieldName].toString());
-  } else {
-    // Doing a case insensitive substring match.
-    query = query.toLowerCase();
-    predicate = p => {
-      let label = p.metadata[fieldName].toString().toLowerCase();
-      return label.indexOf(query) >= 0;
-    };
-  }
-  return predicate;
-}
-
-/**
- * Runs an expensive task asynchronously with some delay
- * so that it doesn't block the UI thread immediately.
- *
- * @param message The message to display to the user.
- * @param task The expensive task to run.
- * @param msgId Optional. ID of an existing message. If provided, will overwrite
- *     an existing message and won't automatically clear the message when the
- *     task is done.
- * @return The value returned by the task.
- */
-export function runAsyncTask<T>(
-    message: string, task: () => T, msgId: string = null): Promise<T> {
-  let autoClear = (msgId == null);
-  msgId = logging.setModalMessage(message, msgId);
-  return new Promise<T>((resolve, reject) => {
-    setTimeout(() => {
-      try {
-        let result = task();
-        // Clearing the old message.
-        if (autoClear) {
-          logging.setModalMessage(null, msgId);
-        }
-        resolve(result);
-      } catch (ex) {
-        reject(ex);
-      }
-      return true;
-    }, TASK_DELAY_MS);
-  });
-}
-
-
-/**
- * Parses the URL for query parameters, e.g. ?foo=1&bar=2 will return
- *   {'foo': '1', 'bar': '2'}.
- * @param url The URL to parse.
- * @return A map of queryParam key to its value.
- */
-export function getURLParams(url: string): {[key: string]: string} {
-  if (!url) {
-    return {};
-  }
-
-  let queryString = url.indexOf('?') !== -1 ? url.split('?')[1] : url;
-  if (queryString.indexOf('#')) {
-    queryString = queryString.split('#')[0];
-  }
-
-  const queryEntries = queryString.split('&');
-  let queryParams: {[key: string]: string} = {};
-  for (let i = 0; i < queryEntries.length; i++) {
-    let queryEntryComponents = queryEntries[i].split('=');
-    queryParams[queryEntryComponents[0].toLowerCase()] =
-        decodeURIComponent(queryEntryComponents[1]);
-  }
-  return queryParams;
-}
-
-/** List of substrings that auto generated tensors have in their name. */
-const SUBSTR_GEN_TENSORS = ['/Adagrad'];
-
-/** Returns true if the tensor was automatically generated by TF API calls. */
-export function tensorIsGenerated(tensorName: string): boolean {
-  for (let i = 0; i < SUBSTR_GEN_TENSORS.length; i++) {
-    if (tensorName.indexOf(SUBSTR_GEN_TENSORS[i]) >= 0) {
-      return true;
-    }
-  }
-  return false;
-}
-
-export function xor(cond1: boolean, cond2: boolean): boolean {
-  return (cond1 || cond2) && !(cond1 && cond2);
-}
-
-/** Checks to see if the browser supports webgl. */
-export function hasWebGLSupport(): boolean {
-  try {
-    let c = document.createElement('canvas');
-    let gl = c.getContext('webgl') || c.getContext('experimental-webgl');
-    return gl != null && typeof weblas !== 'undefined';
-  } catch (e) {
-    return false;
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/vector.ts b/tensorflow/tensorboard/components/vz_projector/vector.ts
deleted file mode 100644
index cab3048313..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vector.ts
+++ /dev/null
@@ -1,265 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {assert} from './util';
-
-/**
- * @fileoverview Useful vector utilities.
- */
-
-export type Vector = Float32Array | number[];
-export type Point2D = [number, number];
-export type Point3D = [number, number, number];
-
-/** Returns the dot product of two vectors. */
-export function dot(a: Vector, b: Vector): number {
-  assert(a.length === b.length, 'Vectors a and b must be of same length');
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    result += a[i] * b[i];
-  }
-  return result;
-}
-
-/** Sums all the elements in the vector */
-export function sum(a: Vector): number {
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    result += a[i];
-  }
-  return result;
-}
-
-/** Returns the sum of two vectors, i.e. a + b */
-export function add(a: Vector, b: Vector): Float32Array {
-  assert(a.length === b.length, 'Vectors a and b must be of same length');
-  let result = new Float32Array(a.length);
-  for (let i = 0; i < a.length; ++i) {
-    result[i] = a[i] + b[i];
-  }
-  return result;
-}
-
-/** Subtracts vector b from vector a, i.e. returns a - b */
-export function sub(a: Vector, b: Vector): Float32Array {
-  assert(a.length === b.length, 'Vectors a and b must be of same length');
-  let result = new Float32Array(a.length);
-  for (let i = 0; i < a.length; ++i) {
-    result[i] = a[i] - b[i];
-  }
-  return result;
-}
-
-/** Returns the square norm of the vector */
-export function norm2(a: Vector): number {
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    result += a[i] * a[i];
-  }
-  return result;
-}
-
-/** Returns the euclidean distance between two vectors. */
-export function dist(a: Vector, b: Vector): number {
-  return Math.sqrt(dist2(a, b));
-}
-
-/** Returns the square euclidean distance between two vectors. */
-export function dist2(a: Vector, b: Vector): number {
-  assert(a.length === b.length, 'Vectors a and b must be of same length');
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    let diff = a[i] - b[i];
-    result += diff * diff;
-  }
-  return result;
-}
-
-/** Returns the square euclidean distance between two 2D points. */
-export function dist2_2D(a: Vector, b: Vector): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  return dX * dX + dY * dY;
-}
-
-/** Returns the square euclidean distance between two 3D points. */
-export function dist2_3D(a: Vector, b: Vector): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  let dZ = a[2] - b[2];
-  return dX * dX + dY * dY + dZ * dZ;
-}
-
-/** Returns the euclidean distance between 2 3D points. */
-export function dist_3D(a: Vector, b: Vector): number {
-  return Math.sqrt(dist2_3D(a, b));
-}
-
-/**
- * Returns the square euclidean distance between two vectors, with an early
- * exit (returns -1) if the distance is >= to the provided limit.
- */
-export function dist2WithLimit(a: Vector, b: Vector, limit: number): number {
-  assert(a.length === b.length, 'Vectors a and b must be of same length');
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    let diff = a[i] - b[i];
-    result += diff * diff;
-    if (result >= limit) {
-      return -1;
-    }
-  }
-  return result;
-}
-
-/** Returns the square euclidean distance between two 2D points. */
-export function dist22D(a: Point2D, b: Point2D): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  return dX * dX + dY * dY;
-}
-
-/** Modifies the vector in-place to have unit norm. */
-export function unit(a: Vector): void {
-  let norm = Math.sqrt(norm2(a));
-  assert(norm >= 0, 'Norm of the vector must be > 0');
-  for (let i = 0; i < a.length; ++i) {
-    a[i] /= norm;
-  }
-}
-
-/**
- *  Projects the vectors to a lower dimension
- *
- * @param vectors Array of vectors to be projected.
- * @param newDim The resulting dimension of the vectors.
- */
-export function projectRandom(vectors: Float32Array[], newDim: number):
-    Float32Array[] {
-  let dim = vectors[0].length;
-  let N = vectors.length;
-  let newVectors: Float32Array[] = new Array(N);
-  for (let i = 0; i < N; ++i) {
-    newVectors[i] = new Float32Array(newDim);
-  }
-  // Make nDim projections.
-  for (let k = 0; k < newDim; ++k) {
-    let randomVector = rn(dim);
-    for (let i = 0; i < N; ++i) {
-      newVectors[i][k] = dot(vectors[i], randomVector);
-    }
-  }
-  return newVectors;
-}
-
-/**
- * Projects a vector onto a 2D plane specified by the two direction vectors.
- */
-export function project2d(a: Vector, dir1: Vector, dir2: Vector): Point2D {
-  return [dot(a, dir1), dot(a, dir2)];
-}
-
-/**
- * Computes the centroid of the data points. If the provided data points are not
- * vectors, an accessor function needs to be provided.
- */
-export function centroid<T>(dataPoints: T[], accessor?: (a: T) => Vector):
-    Vector {
-  if (dataPoints.length === 0) {
-    return null;
-  }
-  if (accessor == null) {
-    accessor = (a: T) => <any>a;
-  }
-  assert(dataPoints.length >= 0, '`vectors` must be of length >= 1');
-  let centroid = new Float32Array(accessor(dataPoints[0]).length);
-  for (let i = 0; i < dataPoints.length; ++i) {
-    let dataPoint = dataPoints[i];
-    let vector = accessor(dataPoint);
-    for (let j = 0; j < centroid.length; ++j) {
-      centroid[j] += vector[j];
-    }
-  }
-  for (let j = 0; j < centroid.length; ++j) {
-    centroid[j] /= dataPoints.length;
-  }
-  return centroid;
-}
-
-/**
- * Generates a vector of the specified size where each component is drawn from
- * a random (0, 1) gaussian distribution.
- */
-export function rn(size: number): Float32Array {
-  const normal = d3.randomNormal();
-  let result = new Float32Array(size);
-  for (let i = 0; i < size; ++i) {
-    result[i] = normal();
-  }
-  return result;
-}
-
-/**
- * Returns the cosine distance ([0, 2]) between two vectors
- * that have been normalized to unit norm.
- */
-export function cosDistNorm(a: Vector, b: Vector): number {
-  return 1 - dot(a, b);
-}
-
-/**
- * Returns the cosine distance ([0, 2]) between two vectors.
- */
-export function cosDist(a: Vector, b: Vector): number {
-  return 1 - cosSim(a, b);
-}
-
-/** Returns the cosine similarity ([-1, 1]) between two vectors. */
-export function cosSim(a: Vector, b: Vector): number {
-  return dot(a, b) / Math.sqrt(norm2(a) * norm2(b));
-}
-
-/**
- * Converts list of vectors (matrix) into a 1-dimensional
- * typed array with row-first order.
- */
-export function toTypedArray<T>(
-    dataPoints: T[], accessor: (dataPoint: T) => Float32Array): Float32Array {
-  let N = dataPoints.length;
-  let dim = accessor(dataPoints[0]).length;
-  let result = new Float32Array(N * dim);
-  for (let i = 0; i < N; ++i) {
-    let vector = accessor(dataPoints[i]);
-    for (let d = 0; d < dim; ++d) {
-      result[i * dim + d] = vector[d];
-    }
-  }
-  return result;
-}
-
-/**
- * Transposes an RxC matrix represented as a flat typed array
- * into a CxR matrix, again represented as a flat typed array.
- */
-export function transposeTypedArray(
-    r: number, c: number, typedArray: Float32Array) {
-  let result = new Float32Array(r * c);
-  for (let i = 0; i < r; ++i) {
-    for (let j = 0; j < c; ++j) {
-      result[j * r + i] = typedArray[i * c + j];
-    }
-  }
-  return result;
-}
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-app.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-app.html
deleted file mode 100644
index e19f0364c4..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-app.html
+++ /dev/null
@@ -1,105 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-tooltip/paper-tooltip.html">
-
-<link rel="import" href="vz-projector.html">
-<link rel="import" href="styles.html">
-
-<dom-module id="vz-projector-app">
-<template>
-<style include="vz-projector-styles"></style>
-<style>
-#appbar {
-  display: flex;
-  align-items: center;
-  justify-content: space-between;
-  padding: 0 24px;
-  height: 60px;
-  color: white;
-  background: #560731;
-}
-
-#appbar .logo {
-  font-size: 18px;
-  font-weight: 300;
-}
-
-.icons {
-  display: flex;
-}
-
-.icons a {
-  color: white;
-}
-
-vz-projector {
-  height: calc(100% - 60px);
-}
-
-#container {
-  height: 100%;
-}
-</style>
-
-<div id="container">
-  <div id="appbar">
-    <div>Embedding Projector</div>
-    <div class="icons">
-      <a title="Documentation" target="_blank" href="[[documentationLink]]">
-        <paper-icon-button icon="help-outline"></paper-icon-button>
-        <paper-tooltip position="bottom" animation-delay="0" fit-to-visible-bounds>
-          Open documentation
-        </paper-tooltip>
-      </a>
-      <a title="Report bug" target="_blank" href="[[bugReportLink]]">
-        <paper-icon-button icon="bug-report"></paper-icon-button>
-        <paper-tooltip position="bottom" animation-delay="0" fit-to-visible-bounds>
-          Report a bug
-        </paper-tooltip>
-      </a>
-    </div>
-  </div>
-  <vz-projector route-prefix="[[routePrefix]]"
-      serving-mode="[[servingMode]]"
-      projector-config-json-path="[[projectorConfigJsonPath]]"
-      page-view-logging="[[pageViewLogging]]"
-      event-logging="[[eventLogging]]">
-  </vz-projector>
-</div>
-</template>
-<!-- Google analytics -->
-<script jscomp-nocompile>
-  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-  ga('create', 'UA-46457317-5', 'auto');
-</script>
-<script>
-  Polymer({
-    is: 'vz-projector-app',
-    properties: {
-      pageViewLogging: {type: Boolean, value: false},
-      eventLogging: {type: Boolean, value: false}
-    }
-  });
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-bookmark-panel.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-bookmark-panel.html
deleted file mode 100644
index f3f3f59a94..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-bookmark-panel.html
+++ /dev/null
@@ -1,207 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../paper-input/paper-textarea.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-tooltip/paper-tooltip.html">
-<link rel="import" href="styles.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id="vz-projector-bookmark-panel">
-<template>
-<style include="vz-projector-styles"></style>
-<style>
-#title {
-  background-color: #fafafa;
-  color: black;
-  font-weight: 500;
-  left: 0;
-  line-height: 60px;
-  padding-left: 24px;
-  position: absolute;
-  width: 276px;
-}
-#bookmark-container {
-  background-color: #fafafa;
-}
-#icon-container {
-  line-height: 60px;
-  position: absolute;
-  right: 0;
-}
-#header {
-  border-top: 1px solid rgba(0, 0, 0, 0.1);
-  position: relative;
-}
-#panel {
-  background-color: #fafafa;
-  position: relative;
-  overflow-y: scroll;
-  top: 60px;
-  max-height: 50vh;
-}
-
-#save-container {
-  text-align: center;
-}
-
-.state-radio {
-  display: table-cell;
-  vertical-align: middle;
-  padding-top: 16px;
-}
-
-.state-label {
-  display: table-cell;
-  vertical-align: middle;
-  top: 14px;
-}
-
-.state-label-input {
-  width: 194px;
-}
-
-.state-clear {
-  display: table-cell;
-  vertical-align: middle;
-  padding-top: 20px;
-}
-#state-file {
-  display: none;
-}
-#no-bookmarks {
-  padding: 0 24px;
-}
-#action-buttons-container .add-icon-button {
-  background-color: #03a9f4;
-  color: white;
-  margin: 0 4px 4px auto;
-  right: 7px;
-  top: -4px;
-}
-.upload-download-icon-button {
-  padding: 0;
-}
-#action-buttons-container {
-  display: flex;
-  margin-left: 34px;
-  margin-top: 6px;
-}
-.ink-fab {
-  border-radius: 50%;
-  background: white;
-  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
-}
-paper-textarea {
-  --paper-input-container-input: {
-    font-size: 12px;
-  }
-  --paper-font-caption: {
-    display: none
-  }
-}
-</style>
-
-<!-- Bookmarking controls -->
-<div id="bookmark-container">
-  <div id="header">
-    <div id="title">
-      BOOKMARKS ([[savedStates.length]])
-      <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-      <paper-tooltip animation-delay="0" position="top" offset="0">
-        Open this drawer to save a set of views of the projection, including
-        selected points. A file containing the bookmarks can then be saved and
-        later loaded to view them.
-      </paper-tooltip>
-    </div>
-    <div id="icon-container">
-      <!-- Icons and event handlers are inverted because the tray expands upwards. -->
-      <paper-icon-button id="expand-more"
-          icon="expand-less"
-          on-tap="_expandMore"></paper-icon-button>
-      <paper-icon-button id="expand-less"
-          style="display: none"
-          icon="expand-more"
-          on-tap="_expandLess"></paper-icon-button>
-    </div>
-  </div>
-  <iron-collapse id="panel">
-    <!-- Saving state section -->
-    <div id="state-section">
-      <template is="dom-if" if="[[!savedStates.length]]">
-        <p id="no-bookmarks">
-            No bookmarks yet, upload a bookmarks file or add a new bookmark by clicking the "+" below.
-        </p>
-      </template>
-
-      <template is="dom-repeat" items="{{savedStates}}">
-        <div class="state-row">
-          <div class="state-radio">
-            <template is="dom-if" if="{{item.isSelected}}">
-              <paper-icon-button icon="radio-button-checked"></paper-icon-button>
-            </template>
-            <template is="dom-if" if="{{!item.isSelected}}">
-              <paper-icon-button
-                  icon="radio-button-unchecked"
-                  data-index$="{{index}}"
-                  on-tap="_radioButtonHandler"></paper-icon-button>
-            </template>
-          </div>
-          <div class="state-label">
-            <paper-textarea value="[[item.label]]"
-                class="state-label-input"
-                on-keyup="_labelChange"
-                data-index$="[[index]]"
-                autoresizing></paper-input>
-          </div>
-          <div class="state-clear">
-            <paper-icon-button
-                icon="clear"
-                data-index$="{{index}}"
-                on-tap="_clearButtonHandler"></paper-icon-button>
-          </div>
-        </div>
-      </template>
-
-      <div id="action-buttons-container">
-        <paper-icon-button
-            class="upload-download-icon-button"
-            icon="save"
-            title="Save bookmarks"
-            disabled="[[!hasStates]]"
-            on-tap="_downloadFile"></paper-icon-button>
-        <paper-icon-button
-            class="upload-download-icon-button"
-            icon="file-upload"
-            title="Load bookmarks"
-            on-tap="_uploadFile"></paper-icon-button>
-        <paper-icon-button
-            class="add-icon-button ink-fab"
-            icon="add"
-            title="Add bookmark"
-            on-tap="_addBookmark"></paper-icon-button>
-        <input type="file" id="state-file" name="state-file"/>
-      </div>
-    </div>
-  </iron-collapse>
-</div>
-
-</template>
-<script src="vz-projector-bookmark-panel.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-bookmark-panel.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-bookmark-panel.ts
deleted file mode 100644
index 53195fa47c..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-bookmark-panel.ts
+++ /dev/null
@@ -1,283 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-import {State} from './data';
-import {DataProvider, EmbeddingInfo} from './data-provider';
-import * as logging from './logging';
-import {ProjectorEventContext} from './projectorEventContext';
-import {Projector} from './vz-projector';
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-// tslint:disable-next-line
-export let BookmarkPanelPolymer = PolymerElement({
-  is: 'vz-projector-bookmark-panel',
-  properties: {
-    savedStates: Object,
-    // Keep a separate polymer property because the savedStates doesn't change
-    // when adding and removing states.
-    hasStates: {type: Boolean, value: false},
-    selectedState: Number
-  }
-});
-
-export class BookmarkPanel extends BookmarkPanelPolymer {
-  private projector: Projector;
-
-  // A list containing all of the saved states.
-  private savedStates: State[];
-  private hasStates = false;
-  private selectedState: number;
-  private ignoreNextProjectionEvent: boolean;
-
-  private expandLessButton: HTMLButtonElement;
-  private expandMoreButton: HTMLButtonElement;
-
-  ready() {
-    this.savedStates = [];
-    this.setupUploadButton();
-    this.ignoreNextProjectionEvent = false;
-    this.expandLessButton =
-        this.querySelector('#expand-less') as HTMLButtonElement;
-    this.expandMoreButton =
-        this.querySelector('#expand-more') as HTMLButtonElement;
-  }
-
-  initialize(
-      projector: Projector, projectorEventContext: ProjectorEventContext) {
-    this.projector = projector;
-    projectorEventContext.registerProjectionChangedListener(() => {
-      if (this.ignoreNextProjectionEvent) {
-        this.ignoreNextProjectionEvent = false;
-      } else {
-        this.clearStateSelection();
-      }
-    });
-  }
-
-  setSelectedTensor(
-      run: string, tensorInfo: EmbeddingInfo, dataProvider: DataProvider) {
-    // Clear any existing bookmarks.
-    this.addStates(null);
-    if (tensorInfo && tensorInfo.bookmarksPath) {
-      // Get any bookmarks that may come when the projector starts up.
-      dataProvider.getBookmarks(run, tensorInfo.tensorName, bookmarks => {
-        this.addStates(bookmarks);
-        this._expandMore();
-      });
-    } else {
-      this._expandLess();
-    }
-  }
-
-  /** Handles a click on show bookmarks tray button. */
-  _expandMore() {
-    this.$.panel.show();
-    this.expandMoreButton.style.display = 'none';
-    this.expandLessButton.style.display = '';
-  }
-
-  /** Handles a click on hide bookmarks tray button. */
-  _expandLess() {
-    this.$.panel.hide();
-    this.expandMoreButton.style.display = '';
-    this.expandLessButton.style.display = 'none';
-  }
-
-  /** Handles a click on the add bookmark button. */
-  _addBookmark() {
-    let currentState = this.projector.getCurrentState();
-    currentState.label = 'State ' + this.savedStates.length;
-    currentState.isSelected = true;
-
-    this.selectedState = this.savedStates.length;
-
-    for (let i = 0; i < this.savedStates.length; i++) {
-      this.savedStates[i].isSelected = false;
-      // We have to call notifyPath so that polymer knows this element was
-      // updated.
-      this.notifyPath('savedStates.' + i + '.isSelected', false, false);
-    }
-
-    this.push('savedStates', currentState as any);
-    this.updateHasStates();
-  }
-
-  /** Handles a click on the download bookmarks button. */
-  _downloadFile() {
-    let serializedState = this.serializeAllSavedStates();
-    let blob = new Blob([serializedState], {type: 'text/plain'});
-    let textFile = window.URL.createObjectURL(blob);
-
-    // Force a download.
-    let a = document.createElement('a');
-    document.body.appendChild(a);
-    a.style.display = 'none';
-    a.href = textFile;
-    (a as any).download = 'state';
-    a.click();
-
-    document.body.removeChild(a);
-    window.URL.revokeObjectURL(textFile);
-  }
-
-  /** Handles a click on the upload bookmarks button. */
-  _uploadFile() {
-    let fileInput = this.dom.select('#state-file');
-    (fileInput.node() as HTMLInputElement).click();
-  }
-
-  private setupUploadButton() {
-    // Show and setup the load view button.
-    const fileInput = this.querySelector('#state-file') as HTMLInputElement;
-    fileInput.onchange = () => {
-      const file: File = fileInput.files[0];
-      // Clear out the value of the file chooser. This ensures that if the user
-      // selects the same file, we'll re-read it.
-      fileInput.value = '';
-      const fileReader = new FileReader();
-      fileReader.onload = (evt) => {
-        const str: string = fileReader.result;
-        const savedStates = JSON.parse(str);
-
-        // Verify the bookmarks match.
-        if (this.savedStatesValid(savedStates)) {
-          this.addStates(savedStates);
-          this.loadSavedState(0);
-        } else {
-          logging.setWarningMessage(
-              `Unable to load bookmarks: wrong dataset, expected dataset ` +
-              `with shape (${savedStates[0].dataSetDimensions}).`);
-        }
-      };
-      fileReader.readAsText(file);
-    };
-  }
-
-  addStates(savedStates?: State[]) {
-    if (savedStates == null) {
-      this.savedStates = [];
-    } else {
-      for (let i = 0; i < savedStates.length; i++) {
-        savedStates[i].isSelected = false;
-        this.push('savedStates', savedStates[i] as any);
-      }
-    }
-    this.updateHasStates();
-  }
-
-  /** Deselects any selected state selection. */
-  clearStateSelection() {
-    for (let i = 0; i < this.savedStates.length; i++) {
-      this.setSelectionState(i, false);
-    }
-  }
-
-  /** Handles a radio button click on a saved state. */
-  _radioButtonHandler(evt: Event) {
-    const index = this.getParentDataIndex(evt);
-    this.loadSavedState(index);
-    this.setSelectionState(index, true);
-  }
-
-  loadSavedState(index: number) {
-    for (let i = 0; i < this.savedStates.length; i++) {
-      if (this.savedStates[i].isSelected) {
-        this.setSelectionState(i, false);
-      } else if (index === i) {
-        this.setSelectionState(i, true);
-        this.ignoreNextProjectionEvent = true;
-        this.projector.loadState(this.savedStates[i]);
-      }
-    }
-  }
-
-  private setSelectionState(stateIndex: number, selected: boolean) {
-    this.savedStates[stateIndex].isSelected = selected;
-    const path = 'savedStates.' + stateIndex + '.isSelected';
-    this.notifyPath(path, selected, false);
-  }
-
-  /**
-   * Crawls up the DOM to find an ancestor with a data-index attribute. This is
-   * used to match events to their bookmark index.
-   */
-  private getParentDataIndex(evt: Event) {
-    for (let i = 0; i < (evt as any).path.length; i++) {
-      let dataIndex = (evt as any).path[i].getAttribute('data-index');
-      if (dataIndex != null) {
-        return +dataIndex;
-      }
-    }
-    return -1;
-  }
-
-  /** Handles a clear button click on a bookmark. */
-  _clearButtonHandler(evt: Event) {
-    let index = this.getParentDataIndex(evt);
-    this.splice('savedStates', index, 1);
-    this.updateHasStates();
-  }
-
-  /** Handles a label change event on a bookmark. */
-  _labelChange(evt: Event) {
-    let index = this.getParentDataIndex(evt);
-    this.savedStates[index].label = (evt.target as any).value;
-  }
-
-  /**
-   * Used to determine whether to select the radio button for a given bookmark.
-   */
-  _isSelectedState(index: number) {
-    return index === this.selectedState;
-  }
-  _isNotSelectedState(index: number) {
-    return index !== this.selectedState;
-  }
-
-  /**
-   * Gets all of the saved states as a serialized string.
-   */
-  serializeAllSavedStates(): string {
-    return JSON.stringify(this.savedStates);
-  }
-
-  /**
-   * Loads all of the serialized states and shows them in the list of
-   * viewable states.
-   */
-  loadSavedStates(serializedStates: string) {
-    this.savedStates = JSON.parse(serializedStates);
-    this.updateHasStates();
-  }
-
-  /**
-   * Updates the hasState polymer property.
-   */
-  private updateHasStates() {
-    this.hasStates = (this.savedStates.length !== 0);
-  }
-
-  /** Sanity checks a State array to ensure it matches the current dataset. */
-  private savedStatesValid(states: State[]): boolean {
-    for (let i = 0; i < states.length; i++) {
-      if (states[i].dataSetDimensions[0] !== this.projector.dataSet.dim[0] ||
-          states[i].dataSetDimensions[1] !== this.projector.dataSet.dim[1]) {
-        return false;
-      }
-    }
-    return true;
-  }
-}
-document.registerElement(BookmarkPanel.prototype.is, BookmarkPanel);
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-colab.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-colab.html
deleted file mode 100644
index 2acb570b3c..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-colab.html
+++ /dev/null
@@ -1,32 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="vz-projector.html">
-<dom-module id="vz-projector-colab">
-<template>
-<vz-projector serving-mode="proto" data-proto="[[dataProto]]"></vz-projector>
-</template>
-<script>
-Polymer({
-  is: 'vz-projector-colab',
-  properties: {
-    dataProto: Object
-  }
-});
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-dashboard.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-dashboard.html
deleted file mode 100644
index 8223c503ec..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-dashboard.html
+++ /dev/null
@@ -1,79 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../tf-dashboard-common/tf-dashboard.html">
-<link rel="import" href="../tf-dashboard-common/tf-no-data-warning.html">
-<link rel="import" href="vz-projector.html">
-
-<dom-module id="vz-projector-dashboard">
-<template>
-  <tf-no-data-warning
-    data-type="projector"
-    show-warning="[[dataNotFound]]"
-  ></tf-no-data-warning>
-  <template is="dom-if" if="[[!dataNotFound]]">
-    <vz-projector
-      id="projector"
-      route-prefix="[[routePrefix]]"
-      serving-mode="server"
-      page-view-logging
-      event-logging
-    ></vz-projector>
-  </template>
-</template>
-<script>
-import {DashboardBehavior} from "../tf-dashboard-common/dashboard-behavior";
-
-Polymer({
-  is: 'vz-projector-dashboard',
-  factoryImpl: function(routePrefix) {
-    this.routePrefix = routePrefix;
-  },
-  properties: {
-    dataNotFound: Boolean,
-    routePrefix: String,
-    // Whether this dashboard is initialized. This dashboard should only be initialized once.
-    _initialized: Boolean,
-  },
-  behaviors: [
-    DashboardBehavior("embeddings"),
-  ],
-  reload: function() {
-    // Do not reload the embedding projector. Reloading could take a long time.
-  },
-  attached: function() {
-    if (this._initialized) {
-      return;
-    }
-    let xhr = new XMLHttpRequest();
-    xhr.open('GET', this.routePrefix + '/runs');
-    xhr.onload = () => {
-      // Set this to true so we only initialize once.
-      this._initialized = true;
-
-      let runs = JSON.parse(xhr.responseText);
-      this.set('dataNotFound', runs.length === 0);
-    };
-    xhr.onerror = () => {
-      this.set('dataNotFound', false);
-    };
-    xhr.send();
-  },
-});
-</script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-data-panel.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-data-panel.html
deleted file mode 100644
index d8dfd6e978..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-data-panel.html
+++ /dev/null
@@ -1,402 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../paper-input/paper-textarea.html">
-<link rel="import" href="../paper-item/paper-item.html">
-<link rel="import" href="../paper-listbox/paper-listbox.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-checkbox/paper-checkbox.html">
-<link rel="import" href="../paper-dialog/paper-dialog.html">
-<link rel="import" href="../paper-dialog-scrollable/paper-dialog-scrollable.html">
-<link rel="import" href="../paper-tooltip/paper-tooltip.html">
-<link rel="import" href="../tf-imports/d3.html">
-<link rel="import" href="vz-projector-legend.html">
-<link rel="import" href="styles.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id="vz-projector-data-panel">
-<template>
-<style include="vz-projector-styles"></style>
-<style>
-.container {
-  padding: 10px 20px 20px 20px;
-}
-
-input[type=file] {
-  display: none;
-}
-
-.file-name {
-  margin-right: 10px;
-}
-
-.dirs {
-  color: rgba(0, 0, 0, 0.7);
-  font-size: 12px;
-}
-
-.dirs table tr {
-  vertical-align: top;
-}
-
-.dirs table tr td {
-  padding-bottom: 10px;
-}
-
-paper-item {
-  --paper-item-disabled: {
-    border-bottom: 1px solid black;
-    justify-content: center;
-    font-size: 12px;
-    line-height: normal;
-    min-height: 0px;
-  };
-}
-
-.item-details {
-  margin-left: 5px;
-  color: gray;
-  font-size: 12px;
-}
-
-paper-dropdown-menu {
-  width: 100%;
-}
-
-paper-dropdown-menu paper-item {
-  justify-content: space-between;
-}
-
-.title {
-  align-items: center;
-  border-bottom: 1px solid rgba(0, 0, 0, 0.1);
-  color: black;
-  display: flex;
-  font-weight: 500;
-  height: 59px;
-  padding-left: 20px;
-}
-
-#normalize-data-checkbox {
-  margin: 10px 0;
-}
-
-#projector-config-template {
-  --paper-input-container-input: {
-    line-height: 13px;
-    font-family: monospace;
-    font-size: 12px;
-  };
-}
-
-#generate-share-url {
-  padding: 16px;
-  margin-left: 24px;
-}
-
-#projector-share-button-container {
-  margin: 10px 0;
-}
-
-.config-checkbox {
-  display: inline-block;
-  font-size: 11px;
-  margin-left: 10px;
-}
-
-.projector-config-options {
-  margin-top: 12px;
-}
-
-.projector-config-dialog-container {
-  padding: 24px;
-}
-
-.code {
-  background-color: #f7f7f7;
-  display: table;
-  font-family: monospace;
-  margin-top: 7px;
-  padding: 15px;
-}
-
-.delimiter {
-  color: #B71C1C;
-}
-
-.upload-step {
-  display: flex;
-  justify-content: space-between;
-  margin-bottom: 6px;
-}
-
-.upload-step paper-button {
-  margin-left: 30px;
-}
-
-.step-label {
-  color: rgb(38, 180, 226);
-}
-
-.scrollable-container {
-  margin-top: 0;
-  min-width: 400px;
-}
-
-#projectorConfigDialog p {
-  margin: 8px 0 8px;
-}
-
-.data-step {
-  margin-top: 40px;
-}
-
-.data-step-contents {
-  display: table;
-  width: 100%;
-}
-
-.data-step-contents-contents {
-  display: table-cell;
-  margin-top: 6px;
-}
-
-.data-step-contents-upload {
-  display: table-cell;
-  text-align: right;
-  vertical-align: bottom;
-}
-
-#demo-data-buttons-container {
-  display: none;
-}
-
-.colorby-container {
-  margin-bottom: 10px;
-}
-</style>
-<div class="title">DATA</div>
-<div class="container">
-  <!-- List of runs -->
-  <template is="dom-if" if="[[_hasChoices(runNames)]]">
-    <paper-dropdown-menu no-animations label="[[_getNumRunsLabel(runNames)]] found">
-      <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedRun}}">
-        <template is="dom-repeat" items="[[runNames]]">
-          <paper-item value="[[item]]" label="[[item]]">
-            [[item]]
-          </paper-item>
-        </template>
-      </paper-listbox>
-    </paper-dropdown-menu>
-  </template>
-
-  <template is="dom-if" if="[[tensorNames]]">
-    <!-- List of tensors in checkpoint -->
-    <paper-dropdown-menu no-animations label="[[_getNumTensorsLabel(tensorNames)]] found">
-      <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedTensor}}">
-        <template is="dom-repeat" items="[[tensorNames]]">
-          <paper-item value="[[item.name]]" label="[[item.name]]">
-            [[item.name]]
-            <span class="item-details">
-              [[item.shape.0]]x[[item.shape.1]]
-            </span>
-          </paper-item>
-        </template>
-      </paper-listbox>
-    </paper-dropdown-menu>
-  </template>
-  <!-- Label by -->
-  <template is="dom-if" if="[[_hasChoices(labelOptions)]]">
-    <paper-dropdown-menu no-animations label="Label by">
-      <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedLabelOption}}">
-        <template is="dom-repeat" items="[[labelOptions]]">
-          <paper-item value="[[item]]" label="[[item]]">
-            [[item]]
-          </paper-item>
-        </template>
-      </paper-listbox>
-    </paper-dropdown-menu>
-  </template>
-
-  <!-- Color by -->
-  <div hidden$="[[!_hasChoices(colorOptions)]]" class="colorby-container">
-    <paper-dropdown-menu id="colorby" no-animations label="Color by">
-      <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedColorOptionName}}">
-        <template is="dom-repeat" items="[[colorOptions]]">
-          <paper-item class$="[[getSeparatorClass(item.isSeparator)]]" value="[[item.name]]" label="[[item.name]]" disabled="[[item.isSeparator]]">
-            [[item.name]]
-            <span class="item-details">[[item.desc]]</span>
-          </paper-item>
-        </template>
-      </paper-listbox>
-    </paper-dropdown-menu>
-    <div hidden$="[[!showForceCategoricalColorsCheckbox]]">
-      <paper-checkbox id="force-categorical-checkbox"></paper-checkbox>
-      Use categorical coloring
-      <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-      <paper-tooltip position="bottom" animation-delay="0" fit-to-visible-bounds>
-        For metadata fields that have many unique values we use a gradient color map
-        by default. This checkbox allows you to force categorical coloring by a given
-        metadata field.
-      </paper-tooltip>
-    </div>
-    <template dom-if="[[colorLegendRenderInfo]]">
-      <vz-projector-legend render-info="[[colorLegendRenderInfo]]"></vz-projector-legend>
-    </template>
-  </div>
-  <paper-checkbox id="normalize-data-checkbox" checked="{{normalizeData}}">
-    Sphereize data
-    <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-    <paper-tooltip position="bottom" animation-delay="0" fit-to-visible-bounds>
-      The data is normalized by shifting each point by the centroid and making
-      it unit norm.
-    </paper-tooltip>
-  </paper-checkbox>
-  <p id="demo-data-buttons-container">
-    <span>
-      <paper-tooltip position="bottom" animation-delay="0" fit-to-visible-bounds>
-        Load data from your computer
-      </paper-tooltip>
-      <paper-button id="upload" class="ink-button" onclick="dataDialog.open()">Load data</paper-button>
-    </span>
-    <span id="publish-container">
-      <paper-tooltip position="bottom" animation-delay="0" fit-to-visible-bounds>
-        Publish your embedding visualization and data
-      </paper-tooltip>
-      <paper-button id="host-embedding" class="ink-button" onclick="projectorConfigDialog.open()">Publish</paper-button>
-    </span>
-  </p>
-  <div>
-    <paper-dialog id="dataDialog" with-backdrop>
-      <h2>Load data from your computer</h2>
-      <paper-dialog-scrollable class="scrollable-container">
-        <div class="data-step" id="upload-tensors-step-container">
-          <div class="upload-step">
-            <div>
-                <b><span class="step-label">Step 1:</span> Load a TSV file of vectors.</b>
-            </div>
-          </div>
-          <div class="data-step-contents">
-            <div class="data-step-contents-contents">
-              Example of 3 vectors with dimension 4:
-              <div class="code">
-                0.1<span class="delimiter">\t</span>0.2<span class="delimiter">\t</span>0.5<span class="delimiter">\t</span>0.9<br/>
-                0.2<span class="delimiter">\t</span>0.1<span class="delimiter">\t</span>5.0<span class="delimiter">\t</span>0.2<br/>
-                0.4<span class="delimiter">\t</span>0.1<span class="delimiter">\t</span>7.0<span class="delimiter">\t</span>0.8
-              </div>
-            </div>
-            <div class="data-step-contents-upload">
-              <paper-button id="upload-tensors" title="Choose a TSV tensor file">Choose file</paper-button>
-              <input type="file" id="file" name="file"/>
-            </div>
-          </div>
-        </div>
-        <div class="data-step">
-          <div class="upload-step">
-            <div>
-                <span class="step-label" id="upload-metadata-label"><b>Step 2</b> (optional):</span> <b>Load a TSV file of metadata.</b>
-            </div>
-          </div>
-          <div class="data-step-contents">
-            <div class="data-step-contents-contents">
-              Example of 3 data points and 2 columns.<br/>
-              <i>Note: If there is more than one column, the first row will be parsed as column labels.</i>
-              <div class="code">
-                <b>Pokémon<span class="delimiter">\t</span>Species</b><br/>
-                Wartortle<span class="delimiter">\t</span>Turtle<br/>
-                Venusaur<span class="delimiter">\t</span>Seed<br/>
-                Charmeleon<span class="delimiter">\t</span>Flame
-              </div>
-            </div>
-            <div class="data-step-contents-upload">
-              <paper-button id="upload-metadata" title="Choose a TSV metadata file" class="ink-button">Choose file</paper-button>
-              <input type="file" id="file-metadata" name="file-metadata"/>
-            </div>
-          </div>
-        </div>
-      </paper-dialog-scrollable>
-      <div class="dismiss-dialog-note">Click outside to dismiss.</div>
-    </paper-dialog>
-    <paper-dialog id="projectorConfigDialog" with-backdrop>
-      <h2>Publish your embedding visualization and data</h2>
-      <paper-dialog-scrollable class="scrollable-container">
-        <div>
-          <p>
-            If you'd like to share your visualization with the world, follow these simple steps.
-            See <a target=_blank href="https://www.tensorflow.org/get_started/embedding_viz">this tutorial</a> for more.
-          </p>
-          <h4><span class="step-label">Step 1:</span> Make data public</h4>
-          <p>
-            Host tensors, metadata, sprite image, and bookmarks TSV files <i>publicly</i> on the web.
-          </p>
-          <p>
-            One option is using a <a target=_blank href="https://gist.github.com/">github gist</a>.
-            If you choose this approach, make sure to link directly to the raw file.
-          </p>
-        </div>
-        <div>
-          <h4><span class="step-label">Step 2:</span> Projector config</h4>
-          <div class="projector-config-options">
-            <i>Optional:</i>
-            <div class="config-checkbox">
-              <paper-checkbox id="config-metadata-checkbox" checked>Metadata</paper-checkbox>
-            </div>
-            <div class="config-checkbox">
-              <paper-checkbox id="config-sprite-checkbox">Sprite</paper-checkbox>
-            </div>
-            <div class="config-checkbox">
-              <paper-checkbox id="config-bookmarks-checkbox">Bookmarks</paper-checkbox>
-            </div>
-          </div>
-        </div>
-        <paper-textarea id="projector-config-template" label="template_projector_config.json"></paper-textarea>
-        <div>
-          <h4><span class="step-label">Step 3:</span> Host projector config</h4>
-          After you have hosted the projector config JSON file you built above, paste the URL to the config below.
-        </div>
-        <paper-input id="projector-config-url" label="Path to projector config"></paper-input>
-        <paper-input id="projector-share-url" label="Your shareable URL" readonly></paper-input>
-        <div id="projector-share-button-container">
-          <a target=_blank id="projector-share-url-link">
-            <paper-button title="Test your shareable URL" class="ink-button">Test your shareable URL</paper-button>
-          </a>
-        </div>
-      </paper-dialog-scrollable>
-      <div class="dismiss-dialog-note">Click outside to dismiss.</div>
-    </paper-dialog>
-  </div>
-  <div class="dirs">
-    <table>
-      <tr>
-        <td>Checkpoint:</td>
-        <td><span id="checkpoint-file"></span></td>
-      </tr>
-      <tr>
-        <td>Metadata:</td>
-        <td><span id="metadata-file"></span></td>
-      </tr>
-    </table>
-  </div>
-</div>
-<!-- Closing global template -->
-</template>
-<script src="vz-projector-data-panel.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-data-panel.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-data-panel.ts
deleted file mode 100644
index a9b6f6c5a0..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-data-panel.ts
+++ /dev/null
@@ -1,496 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {ColorOption, ColumnStats, SpriteAndMetadataInfo} from './data';
-import {DataProvider, EmbeddingInfo, parseRawMetadata, parseRawTensors, ProjectorConfig} from './data-provider';
-import * as util from './util';
-import {Projector} from './vz-projector';
-import {ColorLegendRenderInfo, ColorLegendThreshold} from './vz-projector-legend';
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-export let DataPanelPolymer = PolymerElement({
-  is: 'vz-projector-data-panel',
-  properties: {
-    selectedTensor: {type: String, observer: '_selectedTensorChanged'},
-    selectedRun: {type: String, observer: '_selectedRunChanged'},
-    selectedColorOptionName: {
-      type: String,
-      notify: true,
-      observer: '_selectedColorOptionNameChanged'
-    },
-    selectedLabelOption:
-        {type: String, notify: true, observer: '_selectedLabelOptionChanged'},
-    normalizeData: Boolean,
-    showForceCategoricalColorsCheckbox: Boolean
-  }
-});
-
-export class DataPanel extends DataPanelPolymer {
-  selectedLabelOption: string;
-  selectedColorOptionName: string;
-  showForceCategoricalColorsCheckbox: boolean;
-
-  private normalizeData: boolean;
-  private labelOptions: string[];
-  private colorOptions: ColorOption[];
-  forceCategoricalColoring: boolean = false;
-
-  private selectedTensor: string;
-  private selectedRun: string;
-  private dataProvider: DataProvider;
-  private tensorNames: {name: string, shape: number[]}[];
-  private runNames: string[];
-  private projector: Projector;
-  private projectorConfig: ProjectorConfig;
-  private colorLegendRenderInfo: ColorLegendRenderInfo;
-  private spriteAndMetadata: SpriteAndMetadataInfo;
-  private metadataFile: string;
-
-  ready() {
-    this.normalizeData = true;
-  }
-
-  initialize(projector: Projector, dp: DataProvider) {
-    this.projector = projector;
-    this.dataProvider = dp;
-    this.setupUploadButtons();
-
-    // Tell the projector whenever the data normalization changes.
-    // Unknown why, but the polymer checkbox button stops working as soon as
-    // you do d3.select() on it.
-    this.querySelector('#normalize-data-checkbox')
-        .addEventListener('change', () => {
-          this.projector.setNormalizeData(this.normalizeData);
-        });
-
-    let forceCategoricalColoringCheckbox =
-        this.querySelector('#force-categorical-checkbox');
-    forceCategoricalColoringCheckbox.addEventListener('change', () => {
-      this.setForceCategoricalColoring(
-          (forceCategoricalColoringCheckbox as HTMLInputElement).checked);
-    });
-
-    // Get all the runs.
-    this.dataProvider.retrieveRuns(runs => {
-      this.runNames = runs;
-      // Choose the first run by default.
-      if (this.runNames.length > 0) {
-        this.selectedRun = runs[0];
-      }
-    });
-  }
-
-  setForceCategoricalColoring(forceCategoricalColoring: boolean) {
-    this.forceCategoricalColoring = forceCategoricalColoring;
-    (this.querySelector('#force-categorical-checkbox') as HTMLInputElement)
-        .checked = this.forceCategoricalColoring;
-
-    this.updateMetadataUI(this.spriteAndMetadata.stats, this.metadataFile);
-
-    // The selected color option name doesn't change when we switch to using
-    // categorical coloring for stats with too many unique values, so we
-    // manually call this polymer observer so that we update the UI.
-    this._selectedColorOptionNameChanged();
-  }
-
-  getSeparatorClass(isSeparator: boolean): string {
-    return isSeparator ? 'separator' : null;
-  }
-
-  metadataChanged(
-      spriteAndMetadata: SpriteAndMetadataInfo, metadataFile: string) {
-    this.spriteAndMetadata = spriteAndMetadata;
-    this.metadataFile = metadataFile;
-
-    this.updateMetadataUI(this.spriteAndMetadata.stats, this.metadataFile);
-    this.selectedColorOptionName = this.colorOptions[0].name;
-  }
-
-  private addWordBreaks(longString: string): string {
-    if (longString == null) {
-      return '';
-    }
-    return longString.replace(/([\/=-_,])/g, '$1<wbr>');
-  }
-
-  private updateMetadataUI(columnStats: ColumnStats[], metadataFile: string) {
-    const metadataFileElement =
-        this.querySelector('#metadata-file') as HTMLSpanElement;
-    metadataFileElement.innerHTML = this.addWordBreaks(metadataFile);
-    metadataFileElement.title = metadataFile;
-
-    // Label by options.
-    let labelIndex = -1;
-    this.labelOptions = columnStats.map((stats, i) => {
-      // Make the default label by the first non-numeric column.
-      if (!stats.isNumeric && labelIndex === -1) {
-        labelIndex = i;
-      }
-      return stats.name;
-    });
-    this.selectedLabelOption = this.labelOptions[Math.max(0, labelIndex)];
-
-    // Color by options.
-    const standardColorOption: ColorOption[] = [
-      {name: 'No color map'},
-      // TODO(smilkov): Implement this.
-      // {name: 'Distance of neighbors',
-      //    desc: 'How far is each point from its neighbors'}
-    ];
-    const metadataColorOption: ColorOption[] =
-        columnStats
-            .filter(stats => {
-              return !stats.tooManyUniqueValues || stats.isNumeric;
-            })
-            .map(stats => {
-              let map;
-              let items: {label: string, count: number}[];
-              let thresholds: ColorLegendThreshold[];
-              let isCategorical =
-                  this.forceCategoricalColoring || !stats.tooManyUniqueValues;
-              if (isCategorical) {
-                const scale = d3.scaleOrdinal(d3.schemeCategory20);
-                let range = scale.range();
-                // Re-order the range.
-                let newRange = range.map((color, i) => {
-                  let index = (i * 3) % range.length;
-                  return range[index];
-                });
-                items = stats.uniqueEntries;
-                scale.range(newRange).domain(items.map(x => x.label));
-                map = scale;
-              } else {
-                thresholds = [
-                  {color: '#ffffdd', value: stats.min},
-                  {color: '#1f2d86', value: stats.max}
-                ];
-                map = d3.scaleLinear<string, string>()
-                          .domain(thresholds.map(t => t.value))
-                          .range(thresholds.map(t => t.color));
-              }
-              let desc = !isCategorical ? 'gradient' :
-                                          stats.uniqueEntries.length +
-                      ((stats.uniqueEntries.length > 20) ? ' non-unique' : '') +
-                      ' colors';
-              return {
-                name: stats.name,
-                desc: desc,
-                map: map,
-                items: items,
-                thresholds: thresholds,
-                tooManyUniqueValues: stats.tooManyUniqueValues
-              };
-            });
-
-    if (metadataColorOption.length > 0) {
-      // Add a separator line between built-in color maps
-      // and those based on metadata columns.
-      standardColorOption.push({name: 'Metadata', isSeparator: true});
-    }
-    this.colorOptions = standardColorOption.concat(metadataColorOption);
-  }
-
-  setNormalizeData(normalizeData: boolean) {
-    this.normalizeData = normalizeData;
-  }
-
-  _selectedTensorChanged() {
-    this.projector.updateDataSet(null, null, null);
-    if (this.selectedTensor == null) {
-      return;
-    }
-    this.dataProvider.retrieveTensor(
-        this.selectedRun, this.selectedTensor, ds => {
-          let metadataFile =
-              this.getEmbeddingInfoByName(this.selectedTensor).metadataPath;
-          this.dataProvider.retrieveSpriteAndMetadata(
-              this.selectedRun, this.selectedTensor, metadata => {
-                this.projector.updateDataSet(ds, metadata, metadataFile);
-              });
-        });
-    this.projector.setSelectedTensor(
-        this.selectedRun, this.getEmbeddingInfoByName(this.selectedTensor));
-  }
-
-  _selectedRunChanged() {
-    this.dataProvider.retrieveProjectorConfig(this.selectedRun, info => {
-      this.projectorConfig = info;
-      let names =
-          this.projectorConfig.embeddings.map(e => e.tensorName)
-              .filter(name => {
-                let shape = this.getEmbeddingInfoByName(name).tensorShape;
-                return shape.length === 2 && shape[0] > 1 && shape[1] > 1;
-              })
-              .sort((a, b) => {
-                let embA = this.getEmbeddingInfoByName(a);
-                let embB = this.getEmbeddingInfoByName(b);
-
-                // Prefer tensors with metadata.
-                if (util.xor(!!embA.metadataPath, !!embB.metadataPath)) {
-                  return embA.metadataPath ? -1 : 1;
-                }
-
-                // Prefer non-generated tensors.
-                let isGenA = util.tensorIsGenerated(a);
-                let isGenB = util.tensorIsGenerated(b);
-                if (util.xor(isGenA, isGenB)) {
-                  return isGenB ? -1 : 1;
-                }
-
-                // Prefer bigger tensors.
-                let sizeA = embA.tensorShape[0];
-                let sizeB = embB.tensorShape[0];
-                if (sizeA !== sizeB) {
-                  return sizeB - sizeA;
-                }
-
-                // Sort alphabetically by tensor name.
-                return a <= b ? -1 : 1;
-              });
-      this.tensorNames = names.map(name => {
-        return {name, shape: this.getEmbeddingInfoByName(name).tensorShape};
-      });
-      const wordBreakablePath =
-          this.addWordBreaks(this.projectorConfig.modelCheckpointPath);
-      const checkpointFile =
-          this.querySelector('#checkpoint-file') as HTMLSpanElement;
-      checkpointFile.innerHTML = wordBreakablePath;
-      checkpointFile.title = this.projectorConfig.modelCheckpointPath;
-
-      // If in demo mode, let the order decide which tensor to load by default.
-      const defaultTensor = this.projector.servingMode === 'demo' ?
-          this.projectorConfig.embeddings[0].tensorName :
-          names[0];
-      if (this.selectedTensor === defaultTensor) {
-        // Explicitly call the observer. Polymer won't call it if the previous
-        // string matches the current string.
-        this._selectedTensorChanged();
-      } else {
-        this.selectedTensor = defaultTensor;
-      }
-    });
-  }
-
-  _selectedLabelOptionChanged() {
-    this.projector.setSelectedLabelOption(this.selectedLabelOption);
-  }
-
-  _selectedColorOptionNameChanged() {
-    let colorOption: ColorOption;
-    for (let i = 0; i < this.colorOptions.length; i++) {
-      if (this.colorOptions[i].name === this.selectedColorOptionName) {
-        colorOption = this.colorOptions[i];
-        break;
-      }
-    }
-    if (!colorOption) {
-      return;
-    }
-
-    this.showForceCategoricalColorsCheckbox = !!colorOption.tooManyUniqueValues;
-
-    if (colorOption.map == null) {
-      this.colorLegendRenderInfo = null;
-    } else if (colorOption.items) {
-      let items = colorOption.items.map(item => {
-        return {
-          color: colorOption.map(item.label),
-          label: item.label,
-          count: item.count
-        };
-      });
-      this.colorLegendRenderInfo = {items, thresholds: null};
-    } else {
-      this.colorLegendRenderInfo = {
-        items: null,
-        thresholds: colorOption.thresholds
-      };
-    }
-    this.projector.setSelectedColorOption(colorOption);
-  }
-
-  private tensorWasReadFromFile(rawContents: ArrayBuffer, fileName: string) {
-    parseRawTensors(rawContents, ds => {
-      const checkpointFile =
-          this.querySelector('#checkpoint-file') as HTMLSpanElement;
-      checkpointFile.innerText = fileName;
-      checkpointFile.title = fileName;
-      this.projector.updateDataSet(ds);
-    });
-  }
-
-  private metadataWasReadFromFile(rawContents: ArrayBuffer, fileName: string) {
-    parseRawMetadata(rawContents, metadata => {
-      this.projector.updateDataSet(this.projector.dataSet, metadata, fileName);
-    });
-  }
-
-  private getEmbeddingInfoByName(tensorName: string): EmbeddingInfo {
-    for (let i = 0; i < this.projectorConfig.embeddings.length; i++) {
-      const e = this.projectorConfig.embeddings[i];
-      if (e.tensorName === tensorName) {
-        return e;
-      }
-    }
-  }
-
-  private setupUploadButtons() {
-    // Show and setup the upload button.
-    const fileInput = this.querySelector('#file') as HTMLInputElement;
-    fileInput.onchange = () => {
-      const file: File = fileInput.files[0];
-      // Clear out the value of the file chooser. This ensures that if the user
-      // selects the same file, we'll re-read it.
-      fileInput.value = '';
-      const fileReader = new FileReader();
-      fileReader.onload = evt => {
-        const content: ArrayBuffer = fileReader.result;
-        this.tensorWasReadFromFile(content, file.name);
-      };
-      fileReader.readAsArrayBuffer(file);
-    };
-
-    const uploadButton =
-        this.querySelector('#upload-tensors') as HTMLButtonElement;
-    uploadButton.onclick = () => {
-      fileInput.click();
-    };
-
-    // Show and setup the upload metadata button.
-    const fileMetadataInput =
-        this.querySelector('#file-metadata') as HTMLInputElement;
-    fileMetadataInput.onchange = () => {
-      const file: File = fileMetadataInput.files[0];
-      // Clear out the value of the file chooser. This ensures that if the user
-      // selects the same file, we'll re-read it.
-      fileMetadataInput.value = '';
-      const fileReader = new FileReader();
-      fileReader.onload = evt => {
-        const contents: ArrayBuffer = fileReader.result;
-        this.metadataWasReadFromFile(contents, file.name);
-      };
-      fileReader.readAsArrayBuffer(file);
-    };
-
-    const uploadMetadataButton =
-        this.querySelector('#upload-metadata') as HTMLButtonElement;
-    uploadMetadataButton.onclick = () => {
-      fileMetadataInput.click();
-    };
-
-    if (this.projector.servingMode !== 'demo') {
-      (this.$$('#publish-container') as HTMLElement).style.display = 'none';
-      (this.$$('#upload-tensors-step-container') as HTMLElement).style.display =
-          'none';
-      (this.$$('#upload-metadata-label') as HTMLElement).style.display = 'none';
-    }
-
-    (this.$$('#demo-data-buttons-container') as HTMLElement).style.display =
-        'block';
-
-    // Fill out the projector config.
-    const projectorConfigTemplate =
-        this.$$('#projector-config-template') as HTMLTextAreaElement;
-    const projectorConfigTemplateJson: ProjectorConfig = {
-      embeddings: [{
-        tensorName: 'My tensor',
-        tensorShape: [1000, 50],
-        tensorPath: 'https://raw.githubusercontent.com/.../tensors.tsv',
-        metadataPath:
-            'https://raw.githubusercontent.com/.../optional.metadata.tsv',
-      }],
-    };
-    this.setProjectorConfigTemplateJson(
-        projectorConfigTemplate, projectorConfigTemplateJson);
-
-    // Set up optional field checkboxes.
-    const spriteFieldCheckbox =
-        this.$$('#config-sprite-checkbox') as HTMLInputElement;
-    spriteFieldCheckbox.onchange = () => {
-      if ((spriteFieldCheckbox as any).checked) {
-        projectorConfigTemplateJson.embeddings[0].sprite = {
-          imagePath: 'https://github.com/.../optional.sprite.png',
-          singleImageDim: [32, 32]
-        };
-      } else {
-        delete projectorConfigTemplateJson.embeddings[0].sprite;
-      }
-      this.setProjectorConfigTemplateJson(
-          projectorConfigTemplate, projectorConfigTemplateJson);
-    };
-    const bookmarksFieldCheckbox =
-        this.$$('#config-bookmarks-checkbox') as HTMLInputElement;
-    bookmarksFieldCheckbox.onchange = () => {
-      if ((bookmarksFieldCheckbox as any).checked) {
-        projectorConfigTemplateJson.embeddings[0].bookmarksPath =
-            'https://raw.githubusercontent.com/.../bookmarks.txt';
-      } else {
-        delete projectorConfigTemplateJson.embeddings[0].bookmarksPath;
-      }
-      this.setProjectorConfigTemplateJson(
-          projectorConfigTemplate, projectorConfigTemplateJson);
-    };
-    const metadataFieldCheckbox =
-        this.$$('#config-metadata-checkbox') as HTMLInputElement;
-    metadataFieldCheckbox.onchange = () => {
-      if ((metadataFieldCheckbox as HTMLInputElement).checked) {
-        projectorConfigTemplateJson.embeddings[0].metadataPath =
-            'https://raw.githubusercontent.com/.../optional.metadata.tsv';
-      } else {
-        delete projectorConfigTemplateJson.embeddings[0].metadataPath;
-      }
-      this.setProjectorConfigTemplateJson(
-          projectorConfigTemplate, projectorConfigTemplateJson);
-    };
-
-    // Update the link and the readonly shareable URL.
-    const projectorConfigUrlInput =
-        this.$$('#projector-config-url') as HTMLInputElement;
-    const projectorConfigDemoUrlInput = this.$$('#projector-share-url');
-    const projectorConfigDemoUrlLink = this.$$('#projector-share-url-link');
-    projectorConfigUrlInput.onchange = () => {
-      let projectorDemoUrl = location.protocol + '//' + location.host +
-          location.pathname +
-          '?config=' + (projectorConfigUrlInput as HTMLInputElement).value;
-
-      (projectorConfigDemoUrlInput as HTMLInputElement).value =
-          projectorDemoUrl;
-      (projectorConfigDemoUrlLink as HTMLLinkElement).href = projectorDemoUrl;
-    };
-  }
-
-  private setProjectorConfigTemplateJson(
-      projectorConfigTemplate: HTMLTextAreaElement, config: ProjectorConfig) {
-    projectorConfigTemplate.value =
-        JSON.stringify(config, null, /** replacer */ 2 /** white space */);
-  }
-
-  _getNumTensorsLabel(): string {
-    return this.tensorNames.length === 1 ? '1 tensor' :
-                                           this.tensorNames.length + ' tensors';
-  }
-
-  _getNumRunsLabel(): string {
-    return this.runNames.length === 1 ? '1 run' :
-                                        this.runNames.length + ' runs';
-  }
-
-  _hasChoices(choices: any[]): boolean {
-    return choices.length > 1;
-  }
-}
-
-document.registerElement(DataPanel.prototype.is, DataPanel);
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-input.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-input.html
deleted file mode 100644
index 0d7bf7cdda..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-input.html
+++ /dev/null
@@ -1,66 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-tooltip/paper-tooltip.html">
-<link rel="import" href="styles.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id="vz-projector-input">
-<template>
-<style include="vz-projector-styles"></style>
-<style>
-.info {
-  color: rgba(0, 0, 0, 0.5);
-  display: block;
-  font-size: 11px;
-}
-
-.toggle {
-  font-size: 12px;
-  height: 21px;
-  margin: 0px;
-  min-width: 0px;
-  min-height: 0px;
-  padding: 0;
-  width: 17px;
-}
-
-.toggle[active] {
-  background-color: #880E4F;
-  color: white;
-}
-</style>
-
-<paper-input label="[[label]]">
-  <div class="slash" prefix>/</div>
-  <div class="slash" suffix>/</div>
-  <div suffix>
-    <paper-button id="regex" toggles class="toggle">.*</paper-button>
-  </div>
-</paper-input>
-<paper-tooltip for="regex" position="bottom" animation-delay="0" fit-to-visible-bounds>
-  Enable/disable regex mode.
-</paper-tooltip>
-<span class="info">[[message]]</span>
-
-<!-- Closing global template -->
-</template>
-<script src="vz-projector-input.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-input.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-input.ts
deleted file mode 100644
index e11346d327..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-input.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-// tslint:disable-next-line
-export let PolymerClass = PolymerElement(
-    {is: 'vz-projector-input', properties: {label: String, message: String}});
-
-export interface InputChangedListener {
-  (value: string, inRegexMode: boolean): void;
-}
-
-/** Input control with custom capabilities (e.g. regex). */
-export class ProjectorInput extends PolymerClass {
-  private textChangedListeners: InputChangedListener[];
-  private paperInput: HTMLInputElement;
-  private inRegexModeButton: HTMLButtonElement;
-  private inRegexMode: boolean;
-
-  /** Message that will be displayed at the bottom of the input control. */
-  message: string;
-
-  /** Subscribe to be called everytime the input changes. */
-  registerInputChangedListener(listener: InputChangedListener) {
-    this.textChangedListeners.push(listener);
-  }
-
-  ready() {
-    this.inRegexMode = false;
-    this.textChangedListeners = [];
-    this.paperInput = this.querySelector('paper-input') as HTMLInputElement;
-    this.inRegexModeButton =
-        this.querySelector('paper-button') as HTMLButtonElement;
-    this.paperInput.setAttribute('error-message', 'Invalid regex');
-
-    this.paperInput.addEventListener('input', () => {
-      this.onTextChanged();
-    });
-
-    this.paperInput.addEventListener('keydown', event => {
-      event.stopPropagation();
-    });
-
-    this.inRegexModeButton.addEventListener(
-        'click', () => this.onClickRegexModeButton());
-    this.updateRegexModeDisplaySlashes();
-    this.onTextChanged();
-  }
-
-  private onClickRegexModeButton() {
-    this.inRegexMode = (this.inRegexModeButton as any).active;
-    this.updateRegexModeDisplaySlashes();
-    this.onTextChanged();
-  }
-
-  private notifyInputChanged(value: string, inRegexMode: boolean) {
-    this.textChangedListeners.forEach(l => l(value, inRegexMode));
-  }
-
-  private onTextChanged() {
-    try {
-      if (this.inRegexMode) {
-        new RegExp(this.paperInput.value);
-      }
-    } catch (invalidRegexException) {
-      this.paperInput.setAttribute('invalid', 'true');
-      this.message = '';
-      this.notifyInputChanged(null, true);
-      return;
-    }
-    this.paperInput.removeAttribute('invalid');
-    this.notifyInputChanged(this.paperInput.value, this.inRegexMode);
-  }
-
-  private updateRegexModeDisplaySlashes() {
-    const slashes = this.paperInput.querySelectorAll('.slash');
-    const display = this.inRegexMode ? '' : 'none';
-
-    for (let i = 0; i < slashes.length; i++) {
-      (slashes[i] as HTMLDivElement).style.display = display;
-    }
-  }
-
-  getValue(): string {
-    return this.paperInput.value;
-  }
-
-  getInRegexMode(): boolean {
-    return this.inRegexMode;
-  }
-
-  set(value: string, inRegexMode: boolean) {
-    (this.inRegexModeButton as any).active = inRegexMode;
-    this.paperInput.value = value;
-    this.onClickRegexModeButton();
-  }
-}
-
-document.registerElement(ProjectorInput.prototype.is, ProjectorInput);
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-inspector-panel.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-inspector-panel.html
deleted file mode 100644
index 1b81094776..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-inspector-panel.html
+++ /dev/null
@@ -1,241 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-slider/paper-slider.html">
-<link rel="import" href="vz-projector-input.html">
-<link rel="import" href="styles.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id="vz-projector-inspector-panel">
-<style include="vz-projector-styles"></style>
-<style>
-:host {
-   display: flex;
-   flex-direction: column;
-   /* Account for the bookmark pane at the bottom */
-   height: calc(100% - 55px);
-}
-
-.container {
-  display: block;
-  padding: 10px 20px 0 20px;
-}
-
-.buttons {
-  display: flex;
-  height: 60px;
-}
-
-.button {
-  margin-right: 10px;
-  border: none;
-  border-radius: 7px;
-  font-size: 13px;
-  padding: 10px;
-  background: #e3e3e3;
-}
-
-.button:last-child {
-  margin-right: 0;
-}
-
-.nn {
-  display: flex;
-  flex-direction: column;
-}
-
-.nn > * {
-  padding: 0 20px;
-}
-
-.nn-list {
-  overflow-y: auto;
-}
-
-.nn-list .neighbor {
-  font-size: 12px;
-  margin-bottom: 8px;
-}
-
-.nn-list .label-and-value {
-  display: flex;
-  justify-content: space-between;
-}
-
-.label {
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
-}
-
-.nn-list .value {
-  color: #666;
-  float: right;
-  font-weight: 300;
-  margin-left: 8px;
-}
-
-.nn-list .bar {
-  position: relative;
-  border-top: 1px solid rgba(0, 0, 0, 0.15);
-  margin: 2px 0;
-}
-
-.nn-list .bar .fill {
-  position: absolute;
-  top: -1px;
-  border-top: 1px solid white;
-}
-
-.nn-list .tick {
-  position: absolute;
-  top: 0px;
-  height: 3px;
-  border-left: 1px solid rgba(0, 0, 0, 0.15);
-}
-
-.nn-list .neighbor-link:hover {
-  cursor: pointer;
-}
-
-.search-by {
-  display: flex;
-}
-
-.search-by vz-projector-input {
-  width: 100%;
-}
-
-.search-by paper-dropdown-menu {
-  margin-left: 10px;
-  width: 100px;
-}
-
-.distance .options {
-  float: right;
-}
-
-.options a {
-  color: #727272;
-  font-size: 13px;
-  margin-left: 12px;
-  text-decoration: none;
-}
-
-.options a.selected {
-  color: #009EFE;
-}
-
-.neighbors {
-  margin-bottom: 30px;
-}
-
-.neighbors-options {
-  margin-top: 6px;
-}
-
-.neighbors-options .option-label, .distance .option-label {
-  color: #727272;
-  margin-right: 2px;
-  width: auto;
-}
-
-.num-neighbors-container {
-  display: inline-block;
-}
-
-#nn-slider {
-  margin: 0 -12px 0 10px;
-}
-
-.euclidean {
-  margin-right: 10px;
-}
-
-.matches-list {
-  padding: 0 20px;
-}
-
-.matches-list .row {
-  border-bottom: 1px solid #ddd;
-  cursor: pointer;
-  display: flex;
-  font-size: 12px;
-  margin: 5px 0;
-  padding: 4px 0;
-}
-
-.results {
-  display: flex;
-  flex-direction: column;
-}
-</style>
-<template>
-<div class="container">
-  <div class="buttons">
-    <button class="button reset-filter">Show All Data</button>
-    <button class="button set-filter">Isolate selection</button>
-    <button class="button clear-selection">Clear selection</button>
-  </div>
-  <div class="search-by">
-    <vz-projector-input id="search-box" label="Search"></vz-projector-input>
-    <paper-dropdown-menu no-animations label="by">
-      <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedMetadataField}}">
-        <template is="dom-repeat" items="[[metadataFields]]">
-          <paper-item value="[[item]]" label="[[item]]">
-            [[item]]
-          </paper-item>
-        </template>
-      </paper-listbox>
-    </paper-dropdown-menu>
-  </div>
-</div>
-<div class="results">
-  <div class="nn" style="display: none">
-    <div class="neighbors">
-      <div class="neighbors-options">
-        <div class="slider num-nn">
-          <span class="option-label">neighbors</span>
-          <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-          <paper-tooltip position="bottom" animation-delay="0" fit-to-visible-bounds>
-            The number of neighbors (in the original space) to show when clicking on a point.
-          </paper-tooltip>
-          <paper-slider id="nn-slider" pin min="5" max="1000" value="100"></paper-slider>
-          <span class="nn-count"></span>
-        </div>
-      </div>
-      <div class="distance">
-        <span class="option-label">distance</span>
-        <div class="options">
-          <a class="selected cosine" href="javascript:void(0);">COSINE</a>
-          <a class="euclidean" href="javascript:void(0);">EUCLIDEAN</a>
-        </div>
-      </div>
-    </div>
-    <p>Nearest points in the original space:
-    <div class="nn-list"></div>
-  </div>
-  <div class="matches-list" style="display: none">
-    <div class="list"></div>
-    <div class="limit-msg">Showing only the first 100 results...</div>
-  </div>
-</div>
-<!-- Closing global template -->
-</template>
-<script src="vz-projector-inspector-panel.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-inspector-panel.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-inspector-panel.ts
deleted file mode 100644
index 3ee2c2165f..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-inspector-panel.ts
+++ /dev/null
@@ -1,337 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DistanceFunction, SpriteAndMetadataInfo, State} from './data';
-import * as knn from './knn';
-import {ProjectorEventContext} from './projectorEventContext';
-import * as adapter from './projectorScatterPlotAdapter';
-import * as util from './util';
-import * as vector from './vector';
-import {Projector} from './vz-projector';
-import {ProjectorInput} from './vz-projector-input';
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-/** Limit the number of search results we show to the user. */
-const LIMIT_RESULTS = 100;
-
-// tslint:disable-next-line
-export let PolymerClass = PolymerElement({
-  is: 'vz-projector-inspector-panel',
-  properties: {selectedMetadataField: String, metadataFields: Array}
-});
-
-export class InspectorPanel extends PolymerClass {
-  distFunc: DistanceFunction;
-  numNN: number;
-
-  private projectorEventContext: ProjectorEventContext;
-
-  private selectedMetadataField: string;
-  private metadataFields: string[];
-  private projector: Projector;
-  private selectedPointIndices: number[];
-  private neighborsOfFirstPoint: knn.NearestEntry[];
-  private searchBox: ProjectorInput;
-
-  private resetFilterButton: HTMLButtonElement;
-  private setFilterButton: HTMLButtonElement;
-  private clearSelectionButton: HTMLButtonElement;
-  private limitMessage: HTMLDivElement;
-
-  ready() {
-    this.resetFilterButton =
-        this.querySelector('.reset-filter') as HTMLButtonElement;
-    this.setFilterButton =
-        this.querySelector('.set-filter') as HTMLButtonElement;
-    this.clearSelectionButton =
-        this.querySelector('.clear-selection') as HTMLButtonElement;
-    this.limitMessage = this.querySelector('.limit-msg') as HTMLDivElement;
-    this.searchBox = this.querySelector('#search-box') as ProjectorInput;
-    // https://www.polymer-project.org/1.0/docs/devguide/styling#scope-subtree
-    this.scopeSubtree(this, true);
-  }
-
-  initialize(
-      projector: Projector, projectorEventContext: ProjectorEventContext) {
-    this.projector = projector;
-    this.projectorEventContext = projectorEventContext;
-    this.setupUI(projector);
-    projectorEventContext.registerSelectionChangedListener(
-        (selection, neighbors) =>
-            this.updateInspectorPane(selection, neighbors));
-  }
-
-  /** Updates the nearest neighbors list in the inspector. */
-  private updateInspectorPane(
-      indices: number[], neighbors: knn.NearestEntry[]) {
-    this.neighborsOfFirstPoint = neighbors;
-    this.selectedPointIndices = indices;
-
-    this.updateFilterButtons(indices.length + neighbors.length);
-    this.updateNeighborsList(neighbors);
-    if (neighbors.length === 0) {
-      this.updateSearchResults(indices);
-    } else {
-      this.updateSearchResults([]);
-    }
-  }
-
-  private enableResetFilterButton(enabled: boolean) {
-    this.resetFilterButton.disabled = !enabled;
-  }
-
-  restoreUIFromBookmark(bookmark: State) {
-    this.enableResetFilterButton(bookmark.filteredPoints != null);
-  }
-
-  metadataChanged(spriteAndMetadata: SpriteAndMetadataInfo) {
-    let labelIndex = -1;
-    this.metadataFields = spriteAndMetadata.stats.map((stats, i) => {
-      if (!stats.isNumeric && labelIndex === -1) {
-        labelIndex = i;
-      }
-      return stats.name;
-    });
-    labelIndex = Math.max(0, labelIndex);
-    // Make the default label the first non-numeric column.
-    this.selectedMetadataField = spriteAndMetadata.stats[labelIndex].name;
-  }
-
-  datasetChanged() {
-    this.enableResetFilterButton(false);
-  }
-
-  private updateSearchResults(indices: number[]) {
-    const container = this.querySelector('.matches-list') as HTMLDivElement;
-    container.style.display = indices.length ? null : 'none';
-    const list = container.querySelector('.list') as HTMLDivElement;
-    list.innerHTML = '';
-    if (indices.length === 0) {
-      return;
-    }
-
-    this.limitMessage.style.display =
-        indices.length <= LIMIT_RESULTS ? 'none' : null;
-    indices = indices.slice(0, LIMIT_RESULTS);
-
-    for (let i = 0; i < indices.length; i++) {
-      const index = indices[i];
-
-      const row = document.createElement('div');
-      row.className = 'row';
-
-      const label = this.getLabelFromIndex(index);
-      const rowLink = document.createElement('a');
-      rowLink.className = 'label';
-      rowLink.title = label;
-      rowLink.innerText = label;
-
-      rowLink.onmouseenter = () => {
-        this.projectorEventContext.notifyHoverOverPoint(index);
-      };
-      rowLink.onmouseleave = () => {
-        this.projectorEventContext.notifyHoverOverPoint(null);
-      };
-      rowLink.onclick = () => {
-        this.projectorEventContext.notifySelectionChanged([index]);
-      };
-
-      row.appendChild(rowLink);
-      list.appendChild(row);
-    }
-  }
-
-  private getLabelFromIndex(pointIndex: number): string {
-    const point = this.projector.dataSet.points[pointIndex];
-    return point.metadata[this.selectedMetadataField].toString();
-  }
-
-  private updateNeighborsList(neighbors: knn.NearestEntry[]) {
-    const nnlist = this.querySelector('.nn-list') as HTMLDivElement;
-    nnlist.innerHTML = '';
-
-    (this.querySelector('.nn') as HTMLDivElement).style.display =
-        neighbors.length ? null : 'none';
-
-    if (neighbors.length === 0) {
-      return;
-    }
-
-    this.searchBox.message = '';
-    const minDist = neighbors.length > 0 ? neighbors[0].dist : 0;
-
-    for (let i = 0; i < neighbors.length; i++) {
-      const neighbor = neighbors[i];
-
-      const neighborElement = document.createElement('div');
-      neighborElement.className = 'neighbor';
-
-      const neighborElementLink = document.createElement('a');
-      neighborElementLink.className = 'neighbor-link';
-      neighborElementLink.title = this.getLabelFromIndex(neighbor.index);
-
-      const labelValueElement = document.createElement('div');
-      labelValueElement.className = 'label-and-value';
-
-      const labelElement = document.createElement('div');
-      labelElement.className = 'label';
-      labelElement.style.color =
-          adapter.dist2color(this.distFunc, neighbor.dist, minDist);
-      labelElement.innerText = this.getLabelFromIndex(neighbor.index);
-
-      const valueElement = document.createElement('div');
-      valueElement.className = 'value';
-      valueElement.innerText = neighbor.dist.toFixed(3);
-
-      labelValueElement.appendChild(labelElement);
-      labelValueElement.appendChild(valueElement);
-
-      const barElement = document.createElement('div');
-      barElement.className = 'bar';
-
-      const barFillElement = document.createElement('div');
-      barFillElement.className = 'fill';
-      barFillElement.style.borderTopColor =
-          adapter.dist2color(this.distFunc, neighbor.dist, minDist);
-      barFillElement.style.width =
-          adapter.normalizeDist(this.distFunc, neighbor.dist, minDist) * 100 +
-          '%';
-      barElement.appendChild(barFillElement);
-
-      for (let j = 1; j < 4; j++) {
-        const tickElement = document.createElement('div');
-        tickElement.className = 'tick';
-        tickElement.style.left = j * 100 / 4 + '%';
-        barElement.appendChild(tickElement);
-      }
-
-      neighborElementLink.appendChild(labelValueElement);
-      neighborElementLink.appendChild(barElement);
-      neighborElement.appendChild(neighborElementLink);
-      nnlist.appendChild(neighborElement);
-
-      neighborElementLink.onmouseenter = () => {
-        this.projectorEventContext.notifyHoverOverPoint(neighbor.index);
-      };
-      neighborElementLink.onmouseleave = () => {
-        this.projectorEventContext.notifyHoverOverPoint(null);
-      };
-      neighborElementLink.onclick = () => {
-        this.projectorEventContext.notifySelectionChanged([neighbor.index]);
-      };
-    }
-  }
-
-  private updateFilterButtons(numPoints: number) {
-    if (numPoints > 1) {
-      this.setFilterButton.innerText = `Isolate ${numPoints} points`;
-      this.setFilterButton.disabled = null;
-      this.clearSelectionButton.disabled = null;
-    } else {
-      this.setFilterButton.disabled = true;
-      this.clearSelectionButton.disabled = true;
-    }
-  }
-
-  private setupUI(projector: Projector) {
-    this.distFunc = vector.cosDist;
-    const eucDist =
-        this.querySelector('.distance a.euclidean') as HTMLLinkElement;
-    eucDist.onclick = () => {
-      const links = this.querySelectorAll('.distance a');
-      for (let i = 0; i < links.length; i++) {
-        util.classed(links[i] as HTMLElement, 'selected', false);
-      }
-      util.classed(eucDist as HTMLElement, 'selected', true);
-
-      this.distFunc = vector.dist;
-      this.projectorEventContext.notifyDistanceMetricChanged(this.distFunc);
-      const neighbors = projector.dataSet.findNeighbors(
-          this.selectedPointIndices[0], this.distFunc, this.numNN);
-      this.updateNeighborsList(neighbors);
-    };
-
-    const cosDist = this.querySelector('.distance a.cosine') as HTMLLinkElement;
-    cosDist.onclick = () => {
-      const links = this.querySelectorAll('.distance a');
-      for (let i = 0; i < links.length; i++) {
-        util.classed(links[i] as HTMLElement, 'selected', false);
-      }
-      util.classed(cosDist, 'selected', true);
-
-      this.distFunc = vector.cosDist;
-      this.projectorEventContext.notifyDistanceMetricChanged(this.distFunc);
-      const neighbors = projector.dataSet.findNeighbors(
-          this.selectedPointIndices[0], this.distFunc, this.numNN);
-      this.updateNeighborsList(neighbors);
-    };
-
-    // Called whenever the search text input changes.
-    const updateInput = (value: string, inRegexMode: boolean) => {
-      if (value == null || value.trim() === '') {
-        this.searchBox.message = '';
-        this.projectorEventContext.notifySelectionChanged([]);
-        return;
-      }
-      const indices = projector.dataSet.query(
-          value, inRegexMode, this.selectedMetadataField);
-      if (indices.length === 0) {
-        this.searchBox.message = '0 matches.';
-      } else {
-        this.searchBox.message = `${indices.length} matches.`;
-      }
-      this.projectorEventContext.notifySelectionChanged(indices);
-    };
-    this.searchBox.registerInputChangedListener((value, inRegexMode) => {
-      updateInput(value, inRegexMode);
-    });
-
-    // Nearest neighbors controls.
-    const numNNInput = this.$$('#nn-slider') as HTMLInputElement;
-    const updateNumNN = () => {
-      this.numNN = +numNNInput.value;
-      (this.querySelector('.num-nn .nn-count') as HTMLSpanElement).innerText =
-          '' + this.numNN;
-      if (this.selectedPointIndices != null) {
-        this.projectorEventContext.notifySelectionChanged(
-            [this.selectedPointIndices[0]]);
-      }
-    };
-    numNNInput.addEventListener('change', updateNumNN);
-    updateNumNN();
-
-    // Filtering dataset.
-    this.setFilterButton.onclick = () => {
-      const indices = this.selectedPointIndices.concat(
-          this.neighborsOfFirstPoint.map(n => n.index));
-      projector.filterDataset(indices);
-      this.enableResetFilterButton(true);
-      this.updateFilterButtons(0);
-    };
-
-    this.resetFilterButton.onclick = () => {
-      projector.resetFilterDataset();
-      this.enableResetFilterButton(false);
-    };
-
-    this.clearSelectionButton.onclick = () => {
-      projector.adjustSelectionAndHover([]);
-    };
-    this.enableResetFilterButton(false);
-  }
-}
-
-document.registerElement(InspectorPanel.prototype.is, InspectorPanel);
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-legend.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-legend.html
deleted file mode 100644
index 4b98d8bded..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-legend.html
+++ /dev/null
@@ -1,78 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="styles.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id='vz-projector-legend'>
-<template>
-<style include="vz-projector-styles"></style>
-<style>
-.item {
-  display: flex;
-  align-items: flex-start;
-  margin-bottom: 10px;
-}
-
-.shape {
-  width: 10px;
-  height: 10px;
-  margin-right: 10px;
-  margin-top: 5px;
-  border-radius: 50%;
-}
-
-.label {
-  flex-grow: 1;
-}
-
-.gradient {
-  width: 100%;
-  height: 10px;
-}
-
-.gradient-boundaries {
-  display: flex;
-  justify-content: space-between;
-}
-</style>
-
-<template is="dom-repeat" items="[[renderInfo.items]]">
-  <div class="item">
-    <div class="shape" style="background-color: [[item.color]];"></div>
-    <div class="label">[[item.label]]</div>
-    <div class="info" style="color: [[item.color]];">[[item.count]]</div>
-  </div>
-</template>
-
-<template is="dom-if" if="[[renderInfo.thresholds]]">
-  <svg class="gradient">
-    <defs>
-      <linearGradient id="gradient" x1="0%" y1="100%" x2="100%" y2="100%"></linearGradient>
-    </defs>
-    <rect height="10" style="fill: url('#gradient');"></rect>
-  </svg>
-  <div class="gradient-boundaries">
-    <div>[[renderInfo.thresholds.0.value]]</div>
-    <div>[[_getLastThreshold(renderInfo.thresholds)]]</div>
-  </div>
-</template>
-<!-- Closing global template -->
-</template>
-<script src="vz-projector-legend.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-legend.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-legend.ts
deleted file mode 100644
index 1c4ddf940d..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-legend.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-// tslint:disable-next-line
-export let LegendPolymer = PolymerElement({
-  is: 'vz-projector-legend',
-  properties: {renderInfo: {type: Object, observer: '_renderInfoChanged'}}
-});
-
-export interface ColorLegendRenderInfo {
-  // To be used for categorical map.
-  items: ColorLegendItem[];
-  // To be used for gradient map.
-  thresholds: ColorLegendThreshold[];
-}
-
-/** An item in the categorical color legend. */
-export interface ColorLegendItem {
-  color: string;
-  label: string;
-  count: number;
-}
-
-/** An item in the gradient color legend. */
-export interface ColorLegendThreshold {
-  color: string;
-  value: number;
-}
-
-export class Legend extends LegendPolymer {
-  renderInfo: ColorLegendRenderInfo;
-
-  _renderInfoChanged() {
-    if (this.renderInfo == null) {
-      return;
-    }
-    if (this.renderInfo.thresholds) {
-      // <linearGradient> is under dom-if so we should wait for it to be
-      // inserted in the dom tree using async().
-      this.async(() => this.setupLinearGradient());
-    }
-  }
-
-  _getLastThreshold(): number {
-    if (this.renderInfo == null || this.renderInfo.thresholds == null) {
-      return;
-    }
-    return this.renderInfo.thresholds[this.renderInfo.thresholds.length - 1]
-        .value;
-  }
-
-  private getOffset(value: number): string {
-    const min = this.renderInfo.thresholds[0].value;
-    const max =
-        this.renderInfo.thresholds[this.renderInfo.thresholds.length - 1].value;
-    return (100 * (value - min) / (max - min)).toFixed(2) + '%';
-  }
-
-  private setupLinearGradient() {
-    const linearGradient =
-        this.querySelector('#gradient') as SVGLinearGradientElement;
-
-    const width =
-        (this.querySelector('svg.gradient') as SVGElement).clientWidth;
-
-    // Set the svg <rect> to be the width of its <svg> parent.
-    (this.querySelector('svg.gradient rect') as SVGRectElement).style.width =
-        width + 'px';
-
-    // Remove all <stop> children from before.
-    linearGradient.innerHTML = '';
-
-    // Add a <stop> child in <linearGradient> for each gradient threshold.
-    this.renderInfo.thresholds.forEach(t => {
-      const stopElement =
-          document.createElementNS('http://www.w3.org/2000/svg', 'stop');
-      stopElement.setAttribute('offset', this.getOffset(t.value));
-      stopElement.setAttribute('stop-color', t.color);
-    });
-  }
-}
-
-document.registerElement(Legend.prototype.is, Legend);
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-metadata-card.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-metadata-card.html
deleted file mode 100644
index 4231a61ff3..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-metadata-card.html
+++ /dev/null
@@ -1,99 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id="vz-projector-metadata-card">
-<template>
-<style>
-#metadata-card {
-  background-color: rgba(255,255,255,0.9);
-  box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14),
-      0 1px 5px 0 rgba(0, 0, 0, 0.12), 0 3px 1px -2px rgba(0, 0, 0, 0.2);
-  width: 280px;
-}
-
-#header {
-  background: #e9e9e9;
-}
-
-#icon-container {
-  position: absolute;
-  right: 0;
-  top: 4px;
-}
-
-#metadata-label {
-  font-weight: 400;
-  font-size: 14px;
-  line-height: 24px;
-  padding: 12px 12px 8px;
-  width: 230px;
-}
-
-#metadata-table {
-  display: table;
-  padding: 8px 12px 4px;
-}
-
-.metadata-row {
-  display: table-row;
-}
-
-.metadata-key {
-  font-weight: bold;
-}
-
-.metadata-key, .metadata-value {
-  display: table-cell;
-  font-size: 12px;
-  padding: 3px 3px;
-}
-</style>
-
-<template is="dom-if" if="[[hasMetadata]]">
-  <div id="metadata-card">
-    <div id="icon-container">
-      <paper-icon-button id="expand-more"
-          style="display: none"
-          icon="expand-more"
-          on-tap="_expandMore"></paper-icon-button>
-      <paper-icon-button id="expand-less"
-          on-tap="_expandLess"
-          icon="expand-less"></paper-icon-button>
-    </div>
-    <div id="header">
-      <div id="metadata-label">[[label]]</div>
-    </div>
-    <iron-collapse id="metadata-container" opened>
-      <div id="metadata-table">
-        <template is="dom-repeat" items="[[metadata]]">
-          <div class="metadata-row">
-            <div class="metadata-key">[[item.key]]</div>
-            <div class="metadata-value">[[item.value]]</div>
-          </div>
-        </template>
-      </div>
-    </iron-collapse>
-  </div>
-</template>
-</template>
-<script src="vz-projector-metadata-card.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-metadata-card.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-metadata-card.ts
deleted file mode 100644
index 939300f387..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-metadata-card.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {PointMetadata} from './data';
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-// tslint:disable-next-line
-export let MetadataCardPolymer = PolymerElement({
-  is: 'vz-projector-metadata-card',
-  properties: {
-    hasMetadata: {type: Boolean, value: false},
-    metadata: {type: Array},
-    label: String
-  }
-});
-
-export class MetadataCard extends MetadataCardPolymer {
-  hasMetadata: boolean;
-  metadata: Array<{key: string, value: string}>;
-  label: string;
-
-  private labelOption: string;
-  private pointMetadata: PointMetadata;
-
-  private expandLessButton: HTMLButtonElement;
-  private expandMoreButton: HTMLButtonElement;
-
-  ready() {
-    this.expandLessButton =
-        this.querySelector('#expand-less') as HTMLButtonElement;
-    this.expandMoreButton =
-        this.querySelector('#expand-more') as HTMLButtonElement;
-  }
-  /** Handles a click on the expand more icon. */
-  _expandMore() {
-    (this.$$('#metadata-container') as any).toggle();
-
-    this.expandMoreButton.style.display = 'none';
-    this.expandLessButton.style.display = '';
-  }
-
-  /** Handles a click on the expand less icon. */
-  _expandLess() {
-    (this.$$('#metadata-container') as any).toggle();
-    this.expandMoreButton.style.display = '';
-    this.expandLessButton.style.display = 'none';
-  }
-
-  updateMetadata(pointMetadata?: PointMetadata) {
-    this.pointMetadata = pointMetadata;
-    this.hasMetadata = (pointMetadata != null);
-
-    if (pointMetadata) {
-      let metadata = [];
-      for (let metadataKey in pointMetadata) {
-        if (!pointMetadata.hasOwnProperty(metadataKey)) {
-          continue;
-        }
-        metadata.push({key: metadataKey, value: pointMetadata[metadataKey]});
-      }
-
-      this.metadata = metadata;
-      this.label = '' + this.pointMetadata[this.labelOption];
-    }
-  }
-
-  setLabelOption(labelOption: string) {
-    this.labelOption = labelOption;
-    if (this.pointMetadata) {
-      this.label = '' + this.pointMetadata[this.labelOption];
-    }
-  }
-}
-
-document.registerElement(MetadataCard.prototype.is, MetadataCard);
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-projections-panel.html b/tensorflow/tensorboard/components/vz_projector/vz-projector-projections-panel.html
deleted file mode 100644
index b82f3f520b..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-projections-panel.html
+++ /dev/null
@@ -1,316 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
-<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
-<link rel="import" href="../paper-listbox/paper-listbox.html">
-<link rel="import" href="../paper-item/paper-item.html">
-<link rel="import" href="../paper-checkbox/paper-checkbox.html">
-<link rel="import" href="../iron-icons/iron-icons.html">
-<link rel="import" href="../iron-icons/image-icons.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-tooltip/paper-tooltip.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-slider/paper-slider.html">
-<link rel="import" href="styles.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id="vz-projector-projections-panel">
-<template>
-<style include="vz-projector-styles"></style>
-<style>
-:host {
-  transition: height 0.2s;
-}
-
-.ink-button, ::shadow .ink-button {
-  border: none;
-  border-radius: 2px;
-  font-size: 13px;
-  padding: 10px;
-  min-width: 100px;
-  flex-shrink: 0;
-  background: #e3e3e3;
-}
-
-.ink-panel-buttons {
-  margin-bottom: 10px;
-}
-
-.two-way-toggle {
-  display: flex;
-  flex-direction: row;
-}
-
-.two-way-toggle span {
-  padding-right: 7px;
-}
-
-.has-border {
-  border: 1px solid rgba(0, 0, 0, 0.1);
-}
-
-.toggle {
-  min-width: 0px;
-  font-size: 12px;
-  width: 17px;
-  min-height: 0px;
-  height: 21px;
-  padding: 0;
-  margin: 0px;
-}
-
-.toggle[active] {
-  background-color: #880E4F;
-  color: white;
-}
-
-.two-columns {
-  display:flex;
-  justify-content: space-between;
-}
-
-.two-columns > :first-child {
-  margin-right: 15px;
-}
-
-.two-columns > div {
-  width: 50%;
-}
-
-.dropdown-item {
-  justify-content: space-between;
-  min-height: 35px;
-}
-
-#z-container {
-  display: flex;
-  align-items: center;
-  width: 50%;
-}
-
-#z-checkbox {
-  margin: 27px 0 0 5px;
-  width: 18px;
-}
-
-#z-dropdown {
-  flex-grow: 1;
-}
-
-.notice {
-  color: #880E4F;
-}
-
-.container {
-  padding: 20px;
-}
-
-.book-icon {
-  height: 20px;
-  color: rgba(0, 0, 0, 0.7);
-}
-
-.item-details {
-  color: gray;
-  font-size: 12px;
-  margin-left: 5px;
-}
-
-.pca-dropdown {
-  width: 100%;
-}
-
-.pca-dropdown paper-listbox {
-  width: 135px;
-}
-
-.dropdown-item.header {
-  border-bottom: 1px solid #aaa;
-  color: #333;
-  font-weight: bold;
-}
-
-#total-variance {
-  color: rgba(0, 0, 0, 0.7);
-}
-</style>
-<div id="main">
-  <div class="ink-panel-header">
-    <div class="ink-tab-group">
-
-      <div data-tab="tsne" id="tsne-tab" class="ink-tab projection-tab">t-SNE</div>
-      <paper-tooltip for="tsne-tab" position="bottom" animation-delay="0" fit-to-visible-bounds>
-        t-distributed stochastic neighbor embedding
-      </paper-tooltip>
-
-      <div data-tab="pca" id="pca-tab" class="ink-tab projection-tab">PCA</div>
-      <paper-tooltip for="pca-tab" position="bottom" animation-delay="0" fit-to-visible-bounds>
-        Principal component analysis
-      </paper-tooltip>
-
-      <div data-tab="custom" id="custom-tab" class="ink-tab projection-tab" title="Linear projection of two custom vectors">Custom</div>
-      <paper-tooltip for="custom-tab" position="bottom" animation-delay="0" fit-to-visible-bounds>
-        Search for two vectors upon which to project all points.
-      </paper-tooltip>
-
-    </div>
-  </div>
-  <div class="container">
-    <!-- TSNE Controls -->
-    <div data-panel="tsne" class="ink-panel-content">
-      <div class="slider">
-        <label>Dimension</label>
-        <div class="two-way-toggle">
-          <span>2D</span>
-          <paper-toggle-button id="tsne-toggle" checked="{{tSNEis3d}}">3D</paper-toggle-button>
-        </div>
-      </div>
-      <div class="slider tsne-perplexity">
-        <label>
-          Perplexity
-          <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-          <paper-tooltip position="right" animation-delay="0" fit-to-visible-bounds>
-            The most appropriate perplexity value depends on the density of the
-            data. Loosely speaking, a larger / denser dataset
-            requires a larger perplexity. Typical values for perplexity range
-            between 5 and 50.
-          </paper-tooltip>
-        </label>
-        <paper-slider id="perplexity-slider" pin min="2" max="100" value="30"></paper-slider>
-        <span></span>
-      </div>
-      <div class="slider tsne-learning-rate">
-        <label>
-          Learning rate
-          <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-          <paper-tooltip position="right" animation-delay="0" fit-to-visible-bounds>
-            The ideal learning rate often depends on the size of the data,
-            with smaller datasets requiring smaller learning rates.
-          </paper-tooltip>
-        </label>
-        <paper-slider id="learning-rate-slider" snaps min="-3" max="2" step="1"
-            value="1" max-markers="6">
-        </paper-slider>
-        <span></span>
-      </div>
-      <p>
-        <button class="run-tsne ink-button" title="Re-run t-SNE">Re-run</button>
-        <button class="stop-tsne ink-button" title="Stop t-SNE">Stop</button>
-      </p>
-      <p>Iteration: <span class="run-tsne-iter">0</span></p>
-      <p id="tsne-sampling" class="notice">
-        For fast results, the data will be sampled down to [[getTsneSampleSizeText()]] points.
-      </p>
-      <p>
-        <iron-icon icon="book" class="book-icon"></iron-icon>
-        <a target="_blank" href="http://distill.pub/2016/misread-tsne/">
-          How to use t-SNE effectively.
-        </a>
-      </p>
-    </div>
-    <!-- PCA Controls -->
-    <div data-panel="pca" class="ink-panel-content">
-      <div class="two-columns">
-        <div> <!-- Left column -->
-          <paper-dropdown-menu class="pca-dropdown" vertical-align="bottom" no-animations label="X">
-            <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{pcaX}}">
-              <paper-item disabled class="dropdown-item header">
-                  <div>#</div>
-                  <div>Variance (%)</div>
-              </paper-item>
-              <template is="dom-repeat" items="[[pcaComponents]]">
-                <paper-item class="dropdown-item" value="[[item.id]]"
-                            label="Component #[[item.componentNumber]]">
-                  <div>[[item.componentNumber]]</div>
-                  <div class="item-details">[[item.percVariance]]</div>
-                </paper-item>
-              </template>
-            </paper-listbox>
-          </paper-dropdown-menu>
-          <paper-dropdown-menu class="pca-dropdown" no-animations vertical-align="bottom" label="Z" disabled="[[!hasPcaZ]]" id="z-dropdown">
-            <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{pcaZ}}">
-              <paper-item disabled class="dropdown-item header">
-                  <div>#</div>
-                  <div>Variance (%)</div>
-              </paper-item>
-              <template is="dom-repeat" items="[[pcaComponents]]">
-                <paper-item class="dropdown-item" value="[[item.id]]"
-                            label="Component #[[item.componentNumber]]">
-                  <div>[[item.componentNumber]]</div>
-                  <div class="item-details">[[item.percVariance]]</div>
-                </paper-item>
-              </template>
-            </paper-listbox>
-          </paper-dropdown-menu>
-        </div>
-        <div> <!-- Right column -->
-          <paper-dropdown-menu class="pca-dropdown" vertical-align="bottom" no-animations label="Y">
-            <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{pcaY}}">
-              <paper-item disabled class="dropdown-item header">
-                  <div>#</div>
-                  <div>Variance (%)</div>
-              </paper-item>
-              <template is="dom-repeat" items="[[pcaComponents]]">
-                <paper-item class="dropdown-item" value="[[item.id]]"
-                            label="Component #[[item.componentNumber]]">
-                  <div>[[item.componentNumber]]</div>
-                  <div class="item-details">[[item.percVariance]]</div>
-                </paper-item>
-              </template>
-            </paper-listbox>
-          </paper-dropdown-menu>
-          <paper-checkbox id="z-checkbox" checked="{{pcaIs3d}}"></paper-checkbox>
-        </div>
-      </div>
-      <p id="pca-sampling" class="notice">
-        PCA is approximate.
-        <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
-      </p>
-      <div id="total-variance">Total variance</div>
-      <paper-tooltip for="pca-sampling" position="top" animation-delay="0" fit-to-visible-bounds>
-        For fast results, the data was sampled to [[getPcaSampleSizeText()]] points and randomly projected down to [[getPcaSampledDimText()]] dimensions.
-      </paper-tooltip>
-    </div>
-    <!-- Custom Controls -->
-    <div data-panel="custom" class="ink-panel-content">
-      <paper-dropdown-menu style="width: 100%" no-animations label="Search by">
-        <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{customSelectedSearchByMetadataOption}}">
-          <template is="dom-repeat" items="[[searchByMetadataOptions]]">
-            <paper-item class="dropdown-item" value="[[item]]" label="[[item]]">
-              [[item]]
-            </paper-item>
-          </template>
-        </paper-listbox>
-      </paper-dropdown-menu>
-      <div class="two-columns">
-        <vz-projector-input id="xLeft" label="Left"></vz-projector-input>
-        <vz-projector-input id="xRight" label="Right"></vz-projector-input>
-      </div>
-      <div class="two-columns">
-        <vz-projector-input id="yUp" label="Up"></vz-projector-input>
-        <vz-projector-input id="yDown" label="Down"></vz-projector-input>
-      </div>
-    </div>
-  </div>
-</div>
-</template>
-<script src="vz-projector-projections-panel.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-projections-panel.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-projections-panel.ts
deleted file mode 100644
index 377c6c11ad..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-projections-panel.ts
+++ /dev/null
@@ -1,589 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import * as data from './data';
-import {DataSet, Projection, ProjectionType, SpriteAndMetadataInfo, State} from './data';
-import * as util from './util';
-import * as vector from './vector';
-import {Vector} from './vector';
-import {Projector} from './vz-projector';
-import {ProjectorInput} from './vz-projector-input';
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-const NUM_PCA_COMPONENTS = 10;
-
-// tslint:disable-next-line
-export let ProjectionsPanelPolymer = PolymerElement({
-  is: 'vz-projector-projections-panel',
-  properties: {
-    pcaIs3d:
-        {type: Boolean, value: true, observer: '_pcaDimensionToggleObserver'},
-    tSNEis3d:
-        {type: Boolean, value: true, observer: '_tsneDimensionToggleObserver'},
-    // PCA projection.
-    pcaComponents: Array,
-    pcaX: {type: Number, value: 0, observer: 'showPCAIfEnabled'},
-    pcaY: {type: Number, value: 1, observer: 'showPCAIfEnabled'},
-    pcaZ: {type: Number, value: 2, observer: 'showPCAIfEnabled'},
-    // Custom projection.
-    customSelectedSearchByMetadataOption: {
-      type: String,
-      observer: '_customSelectedSearchByMetadataOptionChanged'
-    },
-  }
-});
-
-type InputControlName = 'xLeft'|'xRight'|'yUp'|'yDown';
-
-type CentroidResult = {
-  centroid?: Vector; numMatches?: number;
-};
-
-type Centroids = {
-  [key: string]: Vector; xLeft: Vector; xRight: Vector; yUp: Vector;
-  yDown: Vector;
-};
-
-/**
- * A polymer component which handles the projection tabs in the projector.
- */
-export class ProjectionsPanel extends ProjectionsPanelPolymer {
-  private projector: Projector;
-  private pcaComponents:
-      Array<{id: number, componentNumber: number, percVariance: string}>;
-  private currentProjection: ProjectionType;
-  private polymerChangesTriggerReprojection: boolean;
-  private dataSet: DataSet;
-  private originalDataSet: DataSet;
-  private dim: number;
-
-  /** T-SNE perplexity. Roughly how many neighbors each point influences. */
-  private perplexity: number;
-  /** T-SNE learning rate. */
-  private learningRate: number;
-
-  private searchByMetadataOptions: string[];
-
-  /** Centroids for custom projections. */
-  private centroidValues: any;
-  private centroids: Centroids;
-  /** The centroid across all points. */
-  private allCentroid: number[];
-
-  /** Polymer properties. */
-  // TODO(nsthorat): Move these to a separate view controller.
-  public tSNEis3d: boolean;
-  public pcaIs3d: boolean;
-  public pcaX: number;
-  public pcaY: number;
-  public pcaZ: number;
-  public customSelectedSearchByMetadataOption: string;
-
-  /** Polymer elements. */
-  private runTsneButton: HTMLButtonElement;
-  private stopTsneButton: HTMLButtonElement;
-  private perplexitySlider: HTMLInputElement;
-  private learningRateInput: HTMLInputElement;
-  private zDropdown: HTMLElement;
-  private iterationLabel: HTMLElement;
-
-  private customProjectionXLeftInput: ProjectorInput;
-  private customProjectionXRightInput: ProjectorInput;
-  private customProjectionYUpInput: ProjectorInput;
-  private customProjectionYDownInput: ProjectorInput;
-
-  initialize(projector: Projector) {
-    this.polymerChangesTriggerReprojection = true;
-    this.projector = projector;
-
-    // Set up TSNE projections.
-    this.perplexity = 30;
-    this.learningRate = 10;
-
-    // Setup Custom projections.
-    this.centroidValues = {xLeft: null, xRight: null, yUp: null, yDown: null};
-    this.clearCentroids();
-
-    this.setupUIControls();
-  }
-
-  ready() {
-    this.zDropdown = this.querySelector('#z-dropdown') as HTMLElement;
-    this.runTsneButton = this.querySelector('.run-tsne') as HTMLButtonElement;
-    this.stopTsneButton = this.querySelector('.stop-tsne') as HTMLButtonElement;
-    this.perplexitySlider =
-        this.querySelector('#perplexity-slider') as HTMLInputElement;
-    this.learningRateInput =
-        this.querySelector('#learning-rate-slider') as HTMLInputElement;
-    this.iterationLabel = this.querySelector('.run-tsne-iter') as HTMLElement;
-  }
-
-  disablePolymerChangesTriggerReprojection() {
-    this.polymerChangesTriggerReprojection = false;
-  }
-
-  enablePolymerChangesTriggerReprojection() {
-    this.polymerChangesTriggerReprojection = true;
-  }
-
-  private updateTSNEPerplexityFromSliderChange() {
-    if (this.perplexitySlider) {
-      this.perplexity = +this.perplexitySlider.value;
-    }
-    (this.querySelector('.tsne-perplexity span') as HTMLSpanElement).innerText =
-        '' + this.perplexity;
-  }
-
-  private updateTSNELearningRateFromUIChange() {
-    if (this.learningRateInput) {
-      this.learningRate = Math.pow(10, +this.learningRateInput.value);
-    }
-    (this.querySelector('.tsne-learning-rate span') as HTMLSpanElement)
-        .innerText = '' + this.learningRate;
-  }
-
-  private setupUIControls() {
-    {
-      const self = this;
-      const inkTabs = this.querySelectorAll('.ink-tab');
-      for (let i = 0; i < inkTabs.length; i++) {
-        inkTabs[i].addEventListener('click', function() {
-          let id = this.getAttribute('data-tab');
-          self.showTab(id);
-        });
-      }
-    }
-
-    this.runTsneButton.addEventListener('click', () => this.runTSNE());
-    this.stopTsneButton.addEventListener(
-        'click', () => this.dataSet.stopTSNE());
-
-    this.perplexitySlider.value = this.perplexity.toString();
-    this.perplexitySlider.addEventListener(
-        'change', () => this.updateTSNEPerplexityFromSliderChange());
-    this.updateTSNEPerplexityFromSliderChange();
-
-    this.learningRateInput.addEventListener(
-        'change', () => this.updateTSNELearningRateFromUIChange());
-    this.updateTSNELearningRateFromUIChange();
-
-    this.setupCustomProjectionInputFields();
-    // TODO: figure out why `--paper-input-container-input` css mixin didn't
-    // work.
-    const inputs =
-        this.querySelectorAll('paper-dropdown-menu paper-input input');
-    for (let i = 0; i < inputs.length; i++) {
-      (inputs[i] as HTMLElement).style.fontSize = '14px';
-    }
-  }
-
-  restoreUIFromBookmark(bookmark: State) {
-    this.disablePolymerChangesTriggerReprojection();
-
-    // PCA
-    this.pcaX = bookmark.pcaComponentDimensions[0];
-    this.pcaY = bookmark.pcaComponentDimensions[1];
-    if (bookmark.pcaComponentDimensions.length === 3) {
-      this.pcaZ = bookmark.pcaComponentDimensions[2];
-    }
-    this.pcaIs3d = (bookmark.pcaComponentDimensions.length === 3);
-
-    // t-SNE
-    if (this.perplexitySlider) {
-      this.perplexitySlider.value = bookmark.tSNEPerplexity.toString();
-    }
-    if (this.learningRateInput) {
-      this.learningRateInput.value = bookmark.tSNELearningRate.toString();
-    }
-    this.tSNEis3d = bookmark.tSNEis3d;
-
-    // custom
-    this.customSelectedSearchByMetadataOption =
-        bookmark.customSelectedSearchByMetadataOption;
-    if (this.customProjectionXLeftInput) {
-      this.customProjectionXLeftInput.set(
-          bookmark.customXLeftText, bookmark.customXLeftRegex);
-    }
-    if (this.customProjectionXRightInput) {
-      this.customProjectionXRightInput.set(
-          bookmark.customXRightText, bookmark.customXRightRegex);
-    }
-    if (this.customProjectionYUpInput) {
-      this.customProjectionYUpInput.set(
-          bookmark.customYUpText, bookmark.customYUpRegex);
-    }
-    if (this.customProjectionYDownInput) {
-      this.customProjectionYDownInput.set(
-          bookmark.customYDownText, bookmark.customYDownRegex);
-    }
-    this.computeAllCentroids();
-
-    this.setZDropdownEnabled(this.pcaIs3d);
-    this.updateTSNEPerplexityFromSliderChange();
-    this.updateTSNELearningRateFromUIChange();
-    if (this.iterationLabel) {
-      this.iterationLabel.innerText = bookmark.tSNEIteration.toString();
-    }
-    if (bookmark.selectedProjection != null) {
-      this.showTab(bookmark.selectedProjection);
-    }
-    this.enablePolymerChangesTriggerReprojection();
-  }
-
-  populateBookmarkFromUI(bookmark: State) {
-    this.disablePolymerChangesTriggerReprojection();
-
-    // PCA
-    bookmark.pcaComponentDimensions = [this.pcaX, this.pcaY];
-    if (this.pcaIs3d) {
-      bookmark.pcaComponentDimensions.push(this.pcaZ);
-    }
-
-    // t-SNE
-    if (this.perplexitySlider != null) {
-      bookmark.tSNEPerplexity = +this.perplexitySlider.value;
-    }
-    if (this.learningRateInput != null) {
-      bookmark.tSNELearningRate = +this.learningRateInput.value;
-    }
-    bookmark.tSNEis3d = this.tSNEis3d;
-
-    // custom
-    bookmark.customSelectedSearchByMetadataOption =
-        this.customSelectedSearchByMetadataOption;
-    if (this.customProjectionXLeftInput != null) {
-      bookmark.customXLeftText = this.customProjectionXLeftInput.getValue();
-      bookmark.customXLeftRegex =
-          this.customProjectionXLeftInput.getInRegexMode();
-    }
-    if (this.customProjectionXRightInput != null) {
-      bookmark.customXRightText = this.customProjectionXRightInput.getValue();
-      bookmark.customXRightRegex =
-          this.customProjectionXRightInput.getInRegexMode();
-    }
-    if (this.customProjectionYUpInput != null) {
-      bookmark.customYUpText = this.customProjectionYUpInput.getValue();
-      bookmark.customYUpRegex = this.customProjectionYUpInput.getInRegexMode();
-    }
-    if (this.customProjectionYDownInput != null) {
-      bookmark.customYDownText = this.customProjectionYDownInput.getValue();
-      bookmark.customYDownRegex =
-          this.customProjectionYDownInput.getInRegexMode();
-    }
-
-    this.enablePolymerChangesTriggerReprojection();
-  }
-
-  // This method is marked as public as it is used as the view method that
-  // abstracts DOM manipulation so we can stub it in a test.
-  // TODO(nsthorat): Move this to its own class as the glue between this class
-  // and the DOM.
-  setZDropdownEnabled(enabled: boolean) {
-    if (this.zDropdown) {
-      if (enabled) {
-        this.zDropdown.removeAttribute('disabled');
-      } else {
-        this.zDropdown.setAttribute('disabled', 'true');
-      }
-    }
-  }
-
-  dataSetUpdated(dataSet: DataSet, originalDataSet: DataSet, dim: number) {
-    this.dataSet = dataSet;
-    this.originalDataSet = originalDataSet;
-    this.dim = dim;
-    const pointCount = (dataSet == null) ? 0 : dataSet.points.length;
-    const perplexity = Math.max(5, Math.ceil(Math.sqrt(pointCount) / 4));
-    this.perplexitySlider.value = perplexity.toString();
-    this.updateTSNEPerplexityFromSliderChange();
-    this.clearCentroids();
-
-    (this.querySelector('#tsne-sampling') as HTMLElement).style.display =
-        pointCount > data.TSNE_SAMPLE_SIZE ? null : 'none';
-    const wasSampled =
-        (dataSet == null) ? false : (dataSet.dim[0] > data.PCA_SAMPLE_DIM ||
-                                     dataSet.dim[1] > data.PCA_SAMPLE_DIM);
-    (this.querySelector('#pca-sampling') as HTMLElement).style.display =
-        wasSampled ? null : 'none';
-    this.showTab('pca');
-  }
-
-  _pcaDimensionToggleObserver() {
-    this.setZDropdownEnabled(this.pcaIs3d);
-    this.beginProjection(this.currentProjection);
-  }
-
-  _tsneDimensionToggleObserver() {
-    this.beginProjection(this.currentProjection);
-  }
-
-  metadataChanged(spriteAndMetadata: SpriteAndMetadataInfo) {
-    // Project by options for custom projections.
-    let searchByMetadataIndex = -1;
-    this.searchByMetadataOptions = spriteAndMetadata.stats.map((stats, i) => {
-      // Make the default label by the first non-numeric column.
-      if (!stats.isNumeric && searchByMetadataIndex === -1) {
-        searchByMetadataIndex = i;
-      }
-      return stats.name;
-    });
-    this.customSelectedSearchByMetadataOption =
-        this.searchByMetadataOptions[Math.max(0, searchByMetadataIndex)];
-  }
-
-  public showTab(id: ProjectionType) {
-    this.currentProjection = id;
-
-    const tab =
-        this.querySelector('.ink-tab[data-tab="' + id + '"]') as HTMLElement;
-    const allTabs = this.querySelectorAll('.ink-tab');
-    for (let i = 0; i < allTabs.length; i++) {
-      util.classed(allTabs[i] as HTMLElement, 'active', false);
-    }
-
-    util.classed(tab, 'active', true);
-
-    const allTabContent = this.querySelectorAll('.ink-panel-content');
-    for (let i = 0; i < allTabContent.length; i++) {
-      util.classed(allTabContent[i] as HTMLElement, 'active', false);
-    }
-
-    util.classed(
-        this.querySelector('.ink-panel-content[data-panel="' + id + '"]') as
-            HTMLElement,
-        'active', true);
-
-    // guard for unit tests, where polymer isn't attached and $ doesn't exist.
-    if (this.$ != null) {
-      const main = this.$['main'];
-      // In order for the projections panel to animate its height, we need to
-      // set it explicitly.
-      requestAnimationFrame(() => {
-        this.style.height = main.clientHeight + 'px';
-      });
-    }
-
-    this.beginProjection(id);
-  }
-
-  private beginProjection(projection: ProjectionType) {
-    if (this.polymerChangesTriggerReprojection === false) {
-      return;
-    }
-    if (projection === 'pca') {
-      if (this.dataSet != null) {
-        this.dataSet.stopTSNE();
-      }
-      this.showPCA();
-    } else if (projection === 'tsne') {
-      this.showTSNE();
-    } else if (projection === 'custom') {
-      if (this.dataSet != null) {
-        this.dataSet.stopTSNE();
-      }
-      this.computeAllCentroids();
-      this.reprojectCustom();
-    }
-  }
-
-  private showTSNE() {
-    const dataSet = this.dataSet;
-    if (dataSet == null) {
-      return;
-    }
-    const accessors =
-        data.getProjectionComponents('tsne', [0, 1, this.tSNEis3d ? 2 : null]);
-    const dimensionality = this.tSNEis3d ? 3 : 2;
-    const projection =
-        new Projection('tsne', accessors, dimensionality, dataSet);
-    this.projector.setProjection(projection);
-
-    if (!this.dataSet.hasTSNERun) {
-      this.runTSNE();
-    } else {
-      this.projector.notifyProjectionPositionsUpdated();
-    }
-  }
-
-  private runTSNE() {
-    this.runTsneButton.disabled = true;
-    this.stopTsneButton.disabled = null;
-    this.dataSet.projectTSNE(
-        this.perplexity, this.learningRate, this.tSNEis3d ? 3 : 2,
-        (iteration: number) => {
-          if (iteration != null) {
-            this.iterationLabel.innerText = '' + iteration;
-            this.projector.notifyProjectionPositionsUpdated();
-          } else {
-            this.runTsneButton.disabled = null;
-            this.stopTsneButton.disabled = true;
-          }
-        });
-  }
-
-  // tslint:disable-next-line:no-unused-variable
-  private showPCAIfEnabled() {
-    if (this.polymerChangesTriggerReprojection) {
-      this.showPCA();
-    }
-  }
-
-  private updateTotalVarianceMessage() {
-    let variances = this.dataSet.fracVariancesExplained;
-    let totalVariance = variances[this.pcaX] + variances[this.pcaY];
-    let msg = 'Total variance described: ';
-    if (this.pcaIs3d) {
-      totalVariance += variances[this.pcaZ];
-    }
-    msg += (totalVariance * 100).toFixed(1) + '%.';
-    (this.querySelector('#total-variance') as HTMLElement).innerHTML = msg;
-  }
-
-  private showPCA() {
-    if (this.dataSet == null) {
-      return;
-    }
-    this.dataSet.projectPCA().then(() => {
-      // Polymer properties are 1-based.
-      const accessors = data.getProjectionComponents(
-          'pca', [this.pcaX, this.pcaY, this.pcaZ]);
-
-      const dimensionality = this.pcaIs3d ? 3 : 2;
-      const projection =
-          new Projection('pca', accessors, dimensionality, this.dataSet);
-      this.projector.setProjection(projection);
-      let numComponents = Math.min(NUM_PCA_COMPONENTS, this.dataSet.dim[1]);
-      this.updateTotalVarianceMessage();
-      this.pcaComponents = util.range(numComponents).map(i => {
-        let fracVariance = this.dataSet.fracVariancesExplained[i];
-        return {
-          id: i,
-          componentNumber: i + 1,
-          percVariance: (fracVariance * 100).toFixed(1)
-        };
-      });
-    });
-  }
-
-  private reprojectCustom() {
-    if (this.centroids == null || this.centroids.xLeft == null ||
-        this.centroids.xRight == null || this.centroids.yUp == null ||
-        this.centroids.yDown == null) {
-      return;
-    }
-    const xDir = vector.sub(this.centroids.xRight, this.centroids.xLeft);
-    this.dataSet.projectLinear(xDir, 'linear-x');
-
-    const yDir = vector.sub(this.centroids.yUp, this.centroids.yDown);
-    this.dataSet.projectLinear(yDir, 'linear-y');
-
-    const accessors = data.getProjectionComponents('custom', ['x', 'y']);
-    const projection = new Projection('custom', accessors, 2, this.dataSet);
-    this.projector.setProjection(projection);
-  }
-
-  clearCentroids(): void {
-    this.centroids = {xLeft: null, xRight: null, yUp: null, yDown: null};
-    this.allCentroid = null;
-  }
-
-  _customSelectedSearchByMetadataOptionChanged(newVal: string, oldVal: string) {
-    if (this.polymerChangesTriggerReprojection === false) {
-      return;
-    }
-    if (this.currentProjection === 'custom') {
-      this.computeAllCentroids();
-      this.reprojectCustom();
-    }
-  }
-
-  private setupCustomProjectionInputFields() {
-    this.customProjectionXLeftInput =
-        this.setupCustomProjectionInputField('xLeft');
-    this.customProjectionXRightInput =
-        this.setupCustomProjectionInputField('xRight');
-    this.customProjectionYUpInput = this.setupCustomProjectionInputField('yUp');
-    this.customProjectionYDownInput =
-        this.setupCustomProjectionInputField('yDown');
-  }
-
-  private computeAllCentroids() {
-    this.computeCentroid('xLeft');
-    this.computeCentroid('xRight');
-    this.computeCentroid('yUp');
-    this.computeCentroid('yDown');
-  }
-
-  private computeCentroid(name: InputControlName) {
-    const input = this.querySelector('#' + name) as ProjectorInput;
-    if (input == null) {
-      return;
-    }
-    const value = input.getValue();
-    if (value == null) {
-      return;
-    }
-    let inRegexMode = input.getInRegexMode();
-    let result = this.getCentroid(value, inRegexMode);
-    if (result.numMatches === 0) {
-      input.message = '0 matches. Using a random vector.';
-      result.centroid = vector.rn(this.dim);
-    } else {
-      input.message = `${result.numMatches} matches.`;
-    }
-    this.centroids[name] = result.centroid;
-    this.centroidValues[name] = value;
-  }
-
-  private setupCustomProjectionInputField(name: InputControlName):
-      ProjectorInput {
-    let input = this.querySelector('#' + name) as ProjectorInput;
-    input.registerInputChangedListener((input, inRegexMode) => {
-      if (this.polymerChangesTriggerReprojection) {
-        this.computeCentroid(name);
-        this.reprojectCustom();
-      }
-    });
-    return input;
-  }
-
-  private getCentroid(pattern: string, inRegexMode: boolean): CentroidResult {
-    if (pattern == null || pattern === '') {
-      return {numMatches: 0};
-    }
-    // Search by the original dataset since we often want to filter and project
-    // only the nearest neighbors of A onto B-C where B and C are not nearest
-    // neighbors of A.
-    let accessor = (i: number) => this.originalDataSet.points[i].vector;
-    let r = this.originalDataSet.query(
-        pattern, inRegexMode, this.customSelectedSearchByMetadataOption);
-    return {centroid: vector.centroid(r, accessor), numMatches: r.length};
-  }
-
-  getPcaSampledDimText() {
-    return data.PCA_SAMPLE_DIM.toLocaleString();
-  }
-
-  getPcaSampleSizeText() {
-    return data.PCA_SAMPLE_SIZE.toLocaleString();
-  }
-
-  getTsneSampleSizeText() {
-    return data.TSNE_SAMPLE_SIZE.toLocaleString();
-  }
-}
-
-document.registerElement(ProjectionsPanel.prototype.is, ProjectionsPanel);
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector-util.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector-util.ts
deleted file mode 100644
index 44062062a3..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector-util.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export type Spec = {
-  is: string; properties?: {
-    [key: string]:
-        (Function |
-         {
-           type: Function, value?: any;
-           readonly?: boolean;
-           notify?: boolean;
-           observer?: string;
-         })
-  };
-  observers?: string[];
-};
-
-export function PolymerElement(spec: Spec) {
-  return Polymer.Class(spec as any) as{new (): PolymerHTMLElement};
-}
-
-export interface PolymerHTMLElement extends HTMLElement, polymer.Base {}
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector.html b/tensorflow/tensorboard/components/vz_projector/vz-projector.html
deleted file mode 100644
index 438ea9f4e9..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector.html
+++ /dev/null
@@ -1,346 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../iron-collapse/iron-collapse.html">
-<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
-<link rel="import" href="../paper-listbox/paper-listbox.html">
-<link rel="import" href="../paper-item/paper-item.html">
-<link rel="import" href="../paper-checkbox/paper-checkbox.html">
-<link rel="import" href="../iron-icons/iron-icons.html">
-<link rel="import" href="../iron-icons/image-icons.html">
-<link rel="import" href="../paper-icon-button/paper-icon-button.html">
-<link rel="import" href="../paper-tooltip/paper-tooltip.html">
-<link rel="import" href="../paper-input/paper-input.html">
-<link rel="import" href="../paper-button/paper-button.html">
-<link rel="import" href="../paper-dialog/paper-dialog.html">
-<link rel="import" href="../paper-toast/paper-toast.html">
-<link rel="import" href="../paper-styles/typography.html">
-<link rel="import" href="../paper-spinner/paper-spinner-lite.html">
-<link rel="import" href="../paper-dialog-scrollable/paper-dialog-scrollable.html">
-<link rel="import" href="../tf-imports/threejs.html">
-
-<link rel="import" href="vz-projector-bookmark-panel.html">
-<link rel="import" href="vz-projector-data-panel.html">
-<link rel="import" href="vz-projector-inspector-panel.html">
-<link rel="import" href="vz-projector-input.html">
-<link rel="import" href="vz-projector-metadata-card.html">
-<link rel="import" href="vz-projector-projections-panel.html">
-<link rel="import" href="styles.html">
-<link rel="import" href="bundle.html">
-
-<dom-module id="vz-projector">
-<template>
-<style include="vz-projector-styles"></style>
-<style>
-:host {
-  display: flex;
-  width: 100%;
-  height: 100%;
-}
-
-#container {
-  display: flex;
-  width: 100%;
-  height: 100%;
-  overflow: hidden;
-}
-
-.hidden {
-  display: none !important;
-}
-
-/* Main */
-
-#main {
-  position: relative;
-  flex-grow: 2;
-}
-
-#main .stage {
-  position: relative;
-  flex-grow: 2;
-}
-
-#scatter {
-  position: absolute;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-}
-
-#selector {
-  display: none;
-  height: 100%;
-  position: absolute;
-  width: 100%;
-}
-
-#left-pane {
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  min-width: 312px;
-  width: 312px;
-  border-right: 1px solid rgba(0, 0, 0, 0.1);
-  background: #fafafa;
-}
-
-#right-pane {
-  border-left: 1px solid rgba(0, 0, 0, 0.1);
-  background: #fafafa;
-  display: flex;
-  height: 100%;
-  min-width: 300px;
-  width: 300px;
-}
-
-.file-name {
-  margin-right: 5px;
-}
-
-.control input[type=text]:focus {
-  outline: none;
-  border-bottom: 1px solid rgba(0, 0, 0, 1);
-}
-
-.control {
-  display: inline-block;
-  width: 45%;
-  vertical-align: top;
-  margin-right: 10px;
-  overflow-x: hidden;
-}
-
-.control.last {
-  margin-right: 0;
-}
-
-#notification-dialog {
-  width: 400px;
-  padding-bottom: 20px;
-}
-
-#notification-dialog paper-button {
-  background: none;
-  text-transform: uppercase;
-}
-
-#notification-dialog .progress {
-  --paper-spinner-color: #880E4F;
-  --paper-spinner-stroke-width: 2px;
-}
-
-#notify-msgs {
-  text-align: center;
-  display: block;
-}
-
-.notify-msg {
-  font-weight: 500;
-  margin: 0;
-  padding: 0;
-}
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-.notify-msg.error {
-  text-align: left;
-}
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-.brush .extent {
-  stroke: #fff;
-  fill-opacity: .125;
-  shape-rendering: crispEdges;
-}
-
-.origin text {
-  font-size: 12px;
-  font-weight: 500;
-}
-
-.origin line {
-  stroke: black;
-  stroke-opacity: 0.2;
-}
-
-/* Ink Framework */
-
-/* - Buttons */
-.ink-button, ::shadow .ink-button {
-  border: none;
-  border-radius: 2px;
-  font-size: 13px;
-  padding: 10px;
-  min-width: 100px;
-  flex-shrink: 0;
-  background: #e3e3e3;
-}
-
-.status-bar-panel {
-  display: flex;
-  align-items: center;
-}
-
-.status-bar-entry {
-  border-left: 1px solid rgba(0, 0, 0, 0.5);
-  margin-left: 5px;
-  padding-left: 5px;
-}
-
-/* - Menubar */
-
-.ink-panel-menubar {
-  align-items: center;
-  position: relative;
-  height: 60px;
-  border-bottom: solid 1px #eee;
-  padding: 0 24px;
-  display: flex;
-}
-
-.ink-panel-menubar .ink-fabs {
-  position: absolute;
-  right: 12px;
-  top: 40px;
-  z-index: 1;
-}
-
-#bookmark-panel {
-  bottom: 0;
-  position: absolute;
-  width: 300px;
-}
-#bookmark-panel-container {
-  bottom: 60px;
-  position: absolute;
-}
-
-.ink-fab {
-  margin-left: 8px;
-  border: 1px solid rgba(0, 0, 0, 0.02);
-  background: white;
-  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
-}
-
-#metadata-card {
-  position: absolute;
-  right: 5px;
-  top: 25px;
-}
-
-#help-3d-icon {
-  position: absolute;
-  top: 20px;
-  left: 20px;
-}
-
-#help3dDialog .main {
-  margin: 0;
-  padding: 20px;
-}
-
-#help3dDialog h3 {
-  margin-top: 20px;
-  margin-bottom: 5px;
-}
-
-#help3dDialog h3:first-child {
-  margin-top: 0;
-}
-
-#data-panel {
-  border-top: 1px solid rgba(0, 0, 0, 0.1);
-  overflow-y: auto;
-}
-
-#toast {
-  display: flex;
-  align-items: center;
-  --paper-toast-color: #eeff41;
-}
-</style>
-<paper-dialog id="notification-dialog" modal>
-  <h2 id="notification-title"></h2>
-  <paper-dialog-scrollable>
-    <div id="notify-msgs"></div>
-  </paper-dialog-scrollable>
-  <div style="text-align: center;"><paper-spinner-lite active class="progress"></paper-spinner-lite></div>
-  <div class="buttons">
-    <paper-button class="close-button" dialog-confirm autofocus>Close</paper-button>
-  </div>
-</paper-dialog>
-<div id="container">
-  <div id="left-pane" class="ink-panel">
-    <vz-projector-data-panel id="data-panel"></vz-projector-data-panel>
-    <vz-projector-projections-panel id="projections-panel"></vz-projector-projections-panel>
-  </div>
-  <div id="main" class="ink-panel">
-    <div class="ink-panel-menubar">
-      <paper-icon-button id="selectMode" alt="Bounding box selection" toggles icon="image:photo-size-select-small"></paper-icon-button>
-      <paper-tooltip for="selectMode" position="bottom" animation-delay="0" fit-to-visible-bounds>Bounding box selection</paper-tooltip>
-
-      <paper-icon-button id="nightDayMode" alt="Enable/disable night mode" toggles icon="image:brightness-2"></paper-icon-button>
-      <paper-tooltip for="nightDayMode" position="bottom" animation-delay="0" fit-to-visible-bounds>Enable/disable night mode</paper-tooltip>
-
-      <paper-icon-button id="labels3DMode" alt="Enable/disable 3D labels mode" toggles icon="font-download"></paper-icon-button>
-      <paper-tooltip for="labels3DMode" position="bottom" animation-delay="0" fit-to-visible-bounds>Enable/disable 3D labels mode</paper-tooltip>
-      <div class="status-bar-panel">
-        <div class="status-bar-entry">Points: <span class="numDataPoints">Loading...</span></div>
-        <div class="status-bar-entry">Dimension: <span class="dim">Loading...</span></div>
-        <div id="status-bar" class="status-bar-entry" style="display: none;"></div>
-      </div>
-      <div class="ink-fabs">
-        <paper-icon-button id="reset-zoom" class="ink-fab" alt="Reset zoom to fit all points" icon="home"></paper-icon-button>
-        <paper-tooltip for="reset-zoom" position="left" animation-delay="0">Reset zoom to fit all points</paper-tooltip>
-      </div>
-    </div>
-    <div class="stage">
-      <div id="scatter">
-        <svg id="selector"></svg>
-      </div>
-      <vz-projector-metadata-card id="metadata-card"></vz-projector-metadata-card>
-      <paper-icon-button raised onclick="help3dDialog.open()" icon="help-outline" id="help-3d-icon"></paper-icon-button>
-      <paper-tooltip animation-delay="0" for="help-3d-icon">Help with interaction controls.</paper-tooltip>
-      <paper-dialog id="help3dDialog" with-backdrop>
-        <div class="main" dialog-confirm autofocus>
-          <h3>3D controls</h3>
-            <b>Rotate</b> Mouse left click.<br/>
-            <b>Pan</b> Mouse right click.<br/>
-            <b>Zoom</b> Mouse wheel.<br/>
-            Holding <b>ctrl</b> reverses the mouse clicks.
-          <h3>2D controls</h3>
-            <b>Pan</b> Mouse left click.<br/>
-            <b>Zoom</b> Mouse wheel.
-          <div class="dismiss-dialog-note"> Click anywhere to dismiss.</div>
-        </div>
-      </paper-dialog>
-    </div>
-  </div>
-  <div id="right-pane" class="ink-panel">
-    <div class="ink-panel-content active">
-      <vz-projector-inspector-panel id="inspector-panel"></vz-projector-inspector-panel>
-    </div>
-    <div id="bookmark-panel-container">
-      <vz-projector-bookmark-panel id="bookmark-panel"></vz-projector-bookmark-panel>
-    </div>
-  </div>
-</div>
-<paper-toast id="toast" always-on-top></paper-toast>
-
-</template> <!-- global template -->
-<script src="vz-projector.js"></script>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz_projector/vz-projector.ts b/tensorflow/tensorboard/components/vz_projector/vz-projector.ts
deleted file mode 100644
index bf98a4d478..0000000000
--- a/tensorflow/tensorboard/components/vz_projector/vz-projector.ts
+++ /dev/null
@@ -1,570 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {AnalyticsLogger} from './analyticsLogger';
-import * as data from './data';
-import {ColorOption, ColumnStats, DataPoint, DataProto, DataSet, DistanceFunction, PointMetadata, Projection, SpriteAndMetadataInfo, State, stateGetAccessorDimensions} from './data';
-import {DataProvider, EmbeddingInfo, ServingMode} from './data-provider';
-import {DemoDataProvider} from './data-provider-demo';
-import {ProtoDataProvider} from './data-provider-proto';
-import {ServerDataProvider} from './data-provider-server';
-import * as knn from './knn';
-import * as logging from './logging';
-import {DistanceMetricChangedListener, HoverListener, ProjectionChangedListener, ProjectorEventContext, SelectionChangedListener} from './projectorEventContext';
-import {ProjectorScatterPlotAdapter} from './projectorScatterPlotAdapter';
-import {MouseMode} from './scatterPlot';
-import * as util from './util';
-import {BookmarkPanel} from './vz-projector-bookmark-panel';
-import {DataPanel} from './vz-projector-data-panel';
-import {InspectorPanel} from './vz-projector-inspector-panel';
-import {MetadataCard} from './vz-projector-metadata-card';
-import {ProjectionsPanel} from './vz-projector-projections-panel';
-// tslint:disable-next-line:no-unused-variable
-import {PolymerElement, PolymerHTMLElement} from './vz-projector-util';
-
-/**
- * The minimum number of dimensions the data should have to automatically
- * decide to normalize the data.
- */
-const THRESHOLD_DIM_NORMALIZE = 50;
-const POINT_COLOR_MISSING = 'black';
-
-export let ProjectorPolymer = PolymerElement({
-  is: 'vz-projector',
-  properties: {
-    routePrefix: String,
-    dataProto: {type: String, observer: '_dataProtoChanged'},
-    servingMode: String,
-    projectorConfigJsonPath: String,
-    pageViewLogging: Boolean,
-    eventLogging: Boolean
-  }
-});
-
-const INDEX_METADATA_FIELD = '__index__';
-
-export class Projector extends ProjectorPolymer implements
-    ProjectorEventContext {
-  // The working subset of the data source's original data set.
-  dataSet: DataSet;
-  servingMode: ServingMode;
-  // The path to the projector config JSON file for demo mode.
-  projectorConfigJsonPath: string;
-
-  private selectionChangedListeners: SelectionChangedListener[];
-  private hoverListeners: HoverListener[];
-  private projectionChangedListeners: ProjectionChangedListener[];
-  private distanceMetricChangedListeners: DistanceMetricChangedListener[];
-
-  private originalDataSet: DataSet;
-  private dataSetBeforeFilter: DataSet;
-  private projectorScatterPlotAdapter: ProjectorScatterPlotAdapter;
-  private dim: number;
-
-  private dataSetFilterIndices: number[];
-  private selectedPointIndices: number[];
-  private neighborsOfFirstPoint: knn.NearestEntry[];
-  private hoverPointIndex: number;
-
-  private dataProvider: DataProvider;
-  private inspectorPanel: InspectorPanel;
-
-  private selectedColorOption: ColorOption;
-  private selectedLabelOption: string;
-  private routePrefix: string;
-  private normalizeData: boolean;
-  private projection: Projection;
-
-  /** Polymer component panels */
-  private dataPanel: DataPanel;
-  private bookmarkPanel: BookmarkPanel;
-  private projectionsPanel: ProjectionsPanel;
-  private metadataCard: MetadataCard;
-
-  private statusBar: HTMLDivElement;
-  private analyticsLogger: AnalyticsLogger;
-  private eventLogging: boolean;
-  private pageViewLogging: boolean;
-
-  ready() {
-    logging.setDomContainer(this);
-
-    this.analyticsLogger =
-        new AnalyticsLogger(this.pageViewLogging, this.eventLogging);
-    this.analyticsLogger.logPageView('embeddings');
-
-    if (!util.hasWebGLSupport()) {
-      this.analyticsLogger.logWebGLDisabled();
-      logging.setErrorMessage(
-          'Your browser or device does not have WebGL enabled. Please enable ' +
-          'hardware acceleration, or use a browser that supports WebGL.');
-      return;
-    }
-
-    this.selectionChangedListeners = [];
-    this.hoverListeners = [];
-    this.projectionChangedListeners = [];
-    this.distanceMetricChangedListeners = [];
-    this.selectedPointIndices = [];
-    this.neighborsOfFirstPoint = [];
-
-    this.dataPanel = this.$['data-panel'] as DataPanel;
-    this.inspectorPanel = this.$['inspector-panel'] as InspectorPanel;
-    this.inspectorPanel.initialize(this, this as ProjectorEventContext);
-    this.projectionsPanel = this.$['projections-panel'] as ProjectionsPanel;
-    this.projectionsPanel.initialize(this);
-    this.bookmarkPanel = this.$['bookmark-panel'] as BookmarkPanel;
-    this.bookmarkPanel.initialize(this, this as ProjectorEventContext);
-    this.metadataCard = this.$['metadata-card'] as MetadataCard;
-    this.statusBar = this.querySelector('#status-bar') as HTMLDivElement;
-    this.scopeSubtree(this.$$('#notification-dialog'), true);
-    this.setupUIControls();
-    this.initializeDataProvider();
-  }
-
-  setSelectedLabelOption(labelOption: string) {
-    this.selectedLabelOption = labelOption;
-    this.metadataCard.setLabelOption(this.selectedLabelOption);
-    this.projectorScatterPlotAdapter.setLabelPointAccessor(labelOption);
-    this.projectorScatterPlotAdapter.updateScatterPlotAttributes();
-    this.projectorScatterPlotAdapter.render();
-  }
-
-  setSelectedColorOption(colorOption: ColorOption) {
-    this.selectedColorOption = colorOption;
-    this.projectorScatterPlotAdapter.setLegendPointColorer(
-        this.getLegendPointColorer(colorOption));
-    this.projectorScatterPlotAdapter.updateScatterPlotAttributes();
-    this.projectorScatterPlotAdapter.render();
-  }
-
-  setNormalizeData(normalizeData: boolean) {
-    this.normalizeData = normalizeData;
-    this.setCurrentDataSet(this.originalDataSet.getSubset());
-  }
-
-  updateDataSet(
-      ds: DataSet, spriteAndMetadata?: SpriteAndMetadataInfo,
-      metadataFile?: string) {
-    this.dataSetFilterIndices = null;
-    this.originalDataSet = ds;
-    if (ds != null) {
-      this.normalizeData =
-          this.originalDataSet.dim[1] >= THRESHOLD_DIM_NORMALIZE;
-      spriteAndMetadata = spriteAndMetadata || {};
-      if (spriteAndMetadata.pointsInfo == null) {
-        let [pointsInfo, stats] = this.makeDefaultPointsInfoAndStats(ds.points);
-        spriteAndMetadata.pointsInfo = pointsInfo;
-        spriteAndMetadata.stats = stats;
-      }
-      let metadataMergeSucceeded = ds.mergeMetadata(spriteAndMetadata);
-      if (!metadataMergeSucceeded) {
-        return;
-      }
-    }
-    if (this.projectorScatterPlotAdapter != null) {
-      if (ds == null) {
-        this.projectorScatterPlotAdapter.setLabelPointAccessor(null);
-        this.setProjection(null);
-      } else {
-        this.projectorScatterPlotAdapter.updateScatterPlotPositions();
-        this.projectorScatterPlotAdapter.updateScatterPlotAttributes();
-        this.projectorScatterPlotAdapter.resize();
-        this.projectorScatterPlotAdapter.render();
-      }
-    }
-    if (ds != null) {
-      this.dataPanel.setNormalizeData(this.normalizeData);
-      this.setCurrentDataSet(ds.getSubset());
-      this.projectorScatterPlotAdapter.setLabelPointAccessor(
-          this.selectedLabelOption);
-      this.inspectorPanel.datasetChanged();
-
-      this.inspectorPanel.metadataChanged(spriteAndMetadata);
-      this.projectionsPanel.metadataChanged(spriteAndMetadata);
-      this.dataPanel.metadataChanged(spriteAndMetadata, metadataFile);
-      // Set the container to a fixed height, otherwise in Colab the
-      // height can grow indefinitely.
-      const container = this.querySelector('#container') as HTMLDivElement;
-      container.style.height = container.clientHeight + 'px';
-    } else {
-      this.setCurrentDataSet(null);
-    }
-  }
-
-  setSelectedTensor(run: string, tensorInfo: EmbeddingInfo) {
-    this.bookmarkPanel.setSelectedTensor(run, tensorInfo, this.dataProvider);
-  }
-
-  /**
-   * Registers a listener to be called any time the selected point set changes.
-   */
-  registerSelectionChangedListener(listener: SelectionChangedListener) {
-    this.selectionChangedListeners.push(listener);
-  }
-
-  filterDataset(pointIndices: number[]) {
-    const selectionSize = this.selectedPointIndices.length;
-    if (this.dataSetBeforeFilter == null) {
-      this.dataSetBeforeFilter = this.dataSet;
-    }
-    this.setCurrentDataSet(this.dataSet.getSubset(pointIndices));
-    this.dataSetFilterIndices = pointIndices;
-    this.projectorScatterPlotAdapter.updateScatterPlotPositions();
-    this.projectorScatterPlotAdapter.updateScatterPlotAttributes();
-    this.adjustSelectionAndHover(util.range(selectionSize));
-  }
-
-  resetFilterDataset() {
-    const originalPointIndices = this.selectedPointIndices.map(
-        filteredIndex => this.dataSet.points[filteredIndex].index);
-    this.setCurrentDataSet(this.dataSetBeforeFilter);
-    if (this.projection != null) {
-      this.projection.dataSet = this.dataSetBeforeFilter;
-    }
-    this.dataSetBeforeFilter = null;
-    this.projectorScatterPlotAdapter.updateScatterPlotPositions();
-    this.projectorScatterPlotAdapter.updateScatterPlotAttributes();
-    this.dataSetFilterIndices = [];
-    this.adjustSelectionAndHover(originalPointIndices);
-  }
-
-  /**
-   * Used by clients to indicate that a selection has occurred.
-   */
-  notifySelectionChanged(newSelectedPointIndices: number[]) {
-    this.selectedPointIndices = newSelectedPointIndices;
-    let neighbors: knn.NearestEntry[] = [];
-
-    if (newSelectedPointIndices.length === 1) {
-      neighbors = this.dataSet.findNeighbors(
-          newSelectedPointIndices[0], this.inspectorPanel.distFunc,
-          this.inspectorPanel.numNN);
-      this.metadataCard.updateMetadata(
-          this.dataSet.points[newSelectedPointIndices[0]].metadata);
-    } else {
-      this.metadataCard.updateMetadata(null);
-    }
-
-    this.selectionChangedListeners.forEach(
-        l => l(this.selectedPointIndices, neighbors));
-  }
-
-  /**
-   * Registers a listener to be called any time the mouse hovers over a point.
-   */
-  registerHoverListener(listener: HoverListener) {
-    this.hoverListeners.push(listener);
-  }
-
-  /**
-   * Used by clients to indicate that a hover is occurring.
-   */
-  notifyHoverOverPoint(pointIndex: number) {
-    this.hoverListeners.forEach(l => l(pointIndex));
-  }
-
-  registerProjectionChangedListener(listener: ProjectionChangedListener) {
-    this.projectionChangedListeners.push(listener);
-  }
-
-  notifyProjectionChanged(projection: Projection) {
-    this.projectionChangedListeners.forEach(l => l(projection));
-  }
-
-  registerDistanceMetricChangedListener(l: DistanceMetricChangedListener) {
-    this.distanceMetricChangedListeners.push(l);
-  }
-
-  notifyDistanceMetricChanged(distMetric: DistanceFunction) {
-    this.distanceMetricChangedListeners.forEach(l => l(distMetric));
-  }
-
-  _dataProtoChanged(dataProtoString: string) {
-    let dataProto =
-        dataProtoString ? JSON.parse(dataProtoString) as DataProto : null;
-    this.initializeDataProvider(dataProto);
-  }
-
-  private makeDefaultPointsInfoAndStats(points: DataPoint[]):
-      [PointMetadata[], ColumnStats[]] {
-    let pointsInfo: PointMetadata[] = [];
-    points.forEach(p => {
-      let pointInfo: PointMetadata = {};
-      pointInfo[INDEX_METADATA_FIELD] = p.index;
-      pointsInfo.push(pointInfo);
-    });
-    let stats: ColumnStats[] = [{
-      name: INDEX_METADATA_FIELD,
-      isNumeric: false,
-      tooManyUniqueValues: true,
-      min: 0,
-      max: pointsInfo.length - 1
-    }];
-    return [pointsInfo, stats];
-  }
-
-  private initializeDataProvider(dataProto?: DataProto) {
-    if (this.servingMode === 'demo') {
-      let projectorConfigUrl: string;
-
-      // Only in demo mode do we allow the config being passed via URL.
-      let urlParams = util.getURLParams(window.location.search);
-      if ('config' in urlParams) {
-        projectorConfigUrl = urlParams['config'];
-      } else {
-        projectorConfigUrl = this.projectorConfigJsonPath;
-      }
-      this.dataProvider = new DemoDataProvider(projectorConfigUrl);
-    } else if (this.servingMode === 'server') {
-      if (!this.routePrefix) {
-        throw 'route-prefix is a required parameter';
-      }
-      this.dataProvider = new ServerDataProvider(this.routePrefix);
-    } else if (this.servingMode === 'proto' && dataProto != null) {
-      this.dataProvider = new ProtoDataProvider(dataProto);
-    }
-
-    this.dataPanel.initialize(this, this.dataProvider);
-  }
-
-  private getLegendPointColorer(colorOption: ColorOption):
-      (ds: DataSet, index: number) => string {
-    if ((colorOption == null) || (colorOption.map == null)) {
-      return null;
-    }
-    const colorer = (ds: DataSet, i: number) => {
-      let value = ds.points[i].metadata[this.selectedColorOption.name];
-      if (value == null) {
-        return POINT_COLOR_MISSING;
-      }
-      return colorOption.map(value);
-    };
-    return colorer;
-  }
-
-  private get3DLabelModeButton(): any {
-    return this.querySelector('#labels3DMode');
-  }
-
-  private get3DLabelMode(): boolean {
-    const label3DModeButton = this.get3DLabelModeButton();
-    return (label3DModeButton as any).active;
-  }
-
-  adjustSelectionAndHover(selectedPointIndices: number[], hoverIndex?: number) {
-    this.notifySelectionChanged(selectedPointIndices);
-    this.notifyHoverOverPoint(hoverIndex);
-    this.setMouseMode(MouseMode.CAMERA_AND_CLICK_SELECT);
-  }
-
-  private setMouseMode(mouseMode: MouseMode) {
-    let selectModeButton = this.querySelector('#selectMode');
-    (selectModeButton as any).active = (mouseMode === MouseMode.AREA_SELECT);
-    this.projectorScatterPlotAdapter.scatterPlot.setMouseMode(mouseMode);
-  }
-
-  private setCurrentDataSet(ds: DataSet) {
-    this.adjustSelectionAndHover([]);
-    if (this.dataSet != null) {
-      this.dataSet.stopTSNE();
-    }
-    if ((ds != null) && this.normalizeData) {
-      ds.normalize();
-    }
-    this.dim = (ds == null) ? 0 : ds.dim[1];
-    (this.querySelector('span.numDataPoints') as HTMLSpanElement).innerText =
-        (ds == null) ? '0' : '' + ds.dim[0];
-    (this.querySelector('span.dim') as HTMLSpanElement).innerText =
-        (ds == null) ? '0' : '' + ds.dim[1];
-
-    this.dataSet = ds;
-
-    this.projectionsPanel.dataSetUpdated(
-        this.dataSet, this.originalDataSet, this.dim);
-
-    this.projectorScatterPlotAdapter.setDataSet(this.dataSet);
-    this.projectorScatterPlotAdapter.scatterPlot
-        .setCameraParametersForNextCameraCreation(null, true);
-  }
-
-  private setupUIControls() {
-    // View controls
-    this.querySelector('#reset-zoom').addEventListener('click', () => {
-      this.projectorScatterPlotAdapter.scatterPlot.resetZoom();
-      this.projectorScatterPlotAdapter.scatterPlot.startOrbitAnimation();
-    });
-
-    let selectModeButton = this.querySelector('#selectMode');
-    selectModeButton.addEventListener('click', (event) => {
-      this.setMouseMode(
-          (selectModeButton as any).active ? MouseMode.AREA_SELECT :
-                                             MouseMode.CAMERA_AND_CLICK_SELECT);
-    });
-    let nightModeButton = this.querySelector('#nightDayMode');
-    nightModeButton.addEventListener('click', () => {
-      this.projectorScatterPlotAdapter.scatterPlot.setDayNightMode(
-          (nightModeButton as any).active);
-    });
-
-    const labels3DModeButton = this.get3DLabelModeButton();
-    labels3DModeButton.addEventListener('click', () => {
-      this.projectorScatterPlotAdapter.set3DLabelMode(this.get3DLabelMode());
-    });
-
-    window.addEventListener('resize', () => {
-      const container = this.querySelector('#container') as HTMLDivElement;
-      const parentHeight = (container.parentNode as HTMLElement).clientHeight;
-      container.style.height = parentHeight + 'px';
-      this.projectorScatterPlotAdapter.resize();
-    });
-
-    {
-      this.projectorScatterPlotAdapter = new ProjectorScatterPlotAdapter(
-          this.getScatterContainer(), this as ProjectorEventContext);
-      this.projectorScatterPlotAdapter.setLabelPointAccessor(
-          this.selectedLabelOption);
-    }
-
-    this.projectorScatterPlotAdapter.scatterPlot.onCameraMove(
-        (cameraPosition: THREE.Vector3, cameraTarget: THREE.Vector3) =>
-            this.bookmarkPanel.clearStateSelection());
-
-    this.registerHoverListener(
-        (hoverIndex: number) => this.onHover(hoverIndex));
-
-    this.registerSelectionChangedListener(
-        (selectedPointIndices: number[],
-         neighborsOfFirstPoint: knn.NearestEntry[]) =>
-            this.onSelectionChanged(
-                selectedPointIndices, neighborsOfFirstPoint));
-  }
-
-  private onHover(hoverIndex: number) {
-    this.hoverPointIndex = hoverIndex;
-    let hoverText = null;
-    if (hoverIndex != null) {
-      const point = this.dataSet.points[hoverIndex];
-      if (point.metadata[this.selectedLabelOption]) {
-        hoverText = point.metadata[this.selectedLabelOption].toString();
-      }
-    }
-    if (this.selectedPointIndices.length === 0) {
-      this.statusBar.style.display = hoverText ? null : 'none';
-      this.statusBar.innerText = hoverText;
-    }
-  }
-
-  private getScatterContainer(): HTMLDivElement {
-    return this.querySelector('#scatter') as HTMLDivElement;
-  }
-
-  private onSelectionChanged(
-      selectedPointIndices: number[],
-      neighborsOfFirstPoint: knn.NearestEntry[]) {
-    this.selectedPointIndices = selectedPointIndices;
-    this.neighborsOfFirstPoint = neighborsOfFirstPoint;
-    let totalNumPoints =
-        this.selectedPointIndices.length + neighborsOfFirstPoint.length;
-    this.statusBar.innerText = `Selected ${totalNumPoints} points`;
-    this.statusBar.style.display = totalNumPoints > 0 ? null : 'none';
-  }
-
-  setProjection(projection: Projection) {
-    this.projection = projection;
-    if (projection != null) {
-      this.analyticsLogger.logProjectionChanged(projection.projectionType);
-    }
-    this.notifyProjectionChanged(projection);
-  }
-
-  notifyProjectionPositionsUpdated() {
-    this.projectorScatterPlotAdapter.notifyProjectionPositionsUpdated();
-  }
-
-  /**
-   * Gets the current view of the embedding and saves it as a State object.
-   */
-  getCurrentState(): State {
-    const state = new State();
-
-    // Save the individual datapoint projections.
-    state.projections = [];
-    for (let i = 0; i < this.dataSet.points.length; i++) {
-      const point = this.dataSet.points[i];
-      const projections: {[key: string]: number} = {};
-      const keys = Object.keys(point.projections);
-      for (let j = 0; j < keys.length; ++j) {
-        projections[keys[j]] = point.projections[keys[j]];
-      }
-      state.projections.push(projections);
-    }
-    state.selectedProjection = this.projection.projectionType;
-    state.dataSetDimensions = this.dataSet.dim;
-    state.tSNEIteration = this.dataSet.tSNEIteration;
-    state.selectedPoints = this.selectedPointIndices;
-    state.filteredPoints = this.dataSetFilterIndices;
-    this.projectorScatterPlotAdapter.populateBookmarkFromUI(state);
-    state.selectedColorOptionName = this.dataPanel.selectedColorOptionName;
-    state.forceCategoricalColoring = this.dataPanel.forceCategoricalColoring;
-    state.selectedLabelOption = this.selectedLabelOption;
-    this.projectionsPanel.populateBookmarkFromUI(state);
-    return state;
-  }
-
-  /** Loads a State object into the world. */
-  loadState(state: State) {
-    this.setProjection(null);
-    {
-      this.projectionsPanel.disablePolymerChangesTriggerReprojection();
-      if (this.dataSetBeforeFilter != null) {
-        this.resetFilterDataset();
-      }
-      if (state.filteredPoints != null) {
-        this.filterDataset(state.filteredPoints);
-      }
-      this.projectionsPanel.enablePolymerChangesTriggerReprojection();
-    }
-    for (let i = 0; i < state.projections.length; i++) {
-      const point = this.dataSet.points[i];
-      const projection = state.projections[i];
-      const keys = Object.keys(projection);
-      for (let j = 0; j < keys.length; ++j) {
-        point.projections[keys[j]] = projection[keys[j]];
-      }
-    }
-    this.dataSet.hasTSNERun = (state.selectedProjection === 'tsne');
-    this.dataSet.tSNEIteration = state.tSNEIteration;
-    this.projectionsPanel.restoreUIFromBookmark(state);
-    this.inspectorPanel.restoreUIFromBookmark(state);
-    this.dataPanel.selectedColorOptionName = state.selectedColorOptionName;
-    this.dataPanel.setForceCategoricalColoring(
-        !!state.forceCategoricalColoring);
-    this.selectedLabelOption = state.selectedLabelOption;
-    this.projectorScatterPlotAdapter.restoreUIFromBookmark(state);
-    {
-      const dimensions = stateGetAccessorDimensions(state);
-      const components =
-          data.getProjectionComponents(state.selectedProjection, dimensions);
-      const projection = new Projection(
-          state.selectedProjection, components, dimensions.length,
-          this.dataSet);
-      this.setProjection(projection);
-    }
-    this.notifySelectionChanged(state.selectedPoints);
-  }
-}
-
-document.registerElement(Projector.prototype.is, Projector);
diff --git a/tensorflow/tensorboard/components/vz_sorting/BUILD b/tensorflow/tensorboard/components/vz_sorting/BUILD
deleted file mode 100644
index e06b8ae197..0000000000
--- a/tensorflow/tensorboard/components/vz_sorting/BUILD
+++ /dev/null
@@ -1,28 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "tensorboard_webcomponent_library")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "vz_sorting",
-    srcs = [
-        "sorting.ts",
-        "vz-sorting.html",
-    ],
-    path = "/vz-sorting",
-    visibility = ["//visibility:public"],
-)
-
-tensorboard_webcomponent_library(
-    name = "legacy",
-    srcs = [":vz_sorting"],
-    destdir = "vz-sorting",
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/vz_sorting/sorting.ts b/tensorflow/tensorboard/components/vz_sorting/sorting.ts
deleted file mode 100644
index 061184d24b..0000000000
--- a/tensorflow/tensorboard/components/vz_sorting/sorting.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * Compares tag names asciinumerically broken into components.
- *
- * <p>This is the comparison function used for sorting most string values in
- * TensorBoard. Unlike the standard asciibetical comparator, this function
- * knows that 'a10b' > 'a2b'. Fixed point and engineering notation are
- * supported. This function also splits the input by slash and underscore to
- * perform array comparison. Therefore it knows that 'a/a' < 'a+/a' even
- * though '+' < '/' in the ASCII table.
- */
-export function compareTagNames(a, b: string): number {
-  let ai = 0;
-  let bi = 0;
-  while (true) {
-    if (ai === a.length) {
-      return bi === b.length ? 0 : -1;
-    }
-    if (bi === b.length) {
-      return 1;
-    }
-    if (isDigit(a[ai]) && isDigit(b[bi])) {
-      const ais = ai;
-      const bis = bi;
-      ai = consumeNumber(a, ai + 1);
-      bi = consumeNumber(b, bi + 1);
-      const an = parseFloat(a.slice(ais, ai));
-      const bn = parseFloat(b.slice(bis, bi));
-      if (an < bn) {
-        return -1;
-      }
-      if (an > bn) {
-        return 1;
-      }
-      continue;
-    }
-    if (isBreak(a[ai])) {
-      if (!isBreak(b[bi])) {
-        return -1;
-      }
-    } else if (isBreak(b[bi])) {
-      return 1;
-    } else if (a[ai] < b[bi]) {
-      return -1;
-    } else if (a[ai] > b[bi]) {
-      return 1;
-    }
-    ai++;
-    bi++;
-  }
-}
-
-function consumeNumber(s: string, i: number): number {
-  enum State { NATURAL, REAL, EXPONENT_SIGN, EXPONENT }
-  let state = State.NATURAL;
-  for (; i < s.length; i++) {
-    if (state === State.NATURAL) {
-      if (s[i] === '.') {
-        state = State.REAL;
-      } else if (s[i] === 'e' || s[i] === 'E') {
-        state = State.EXPONENT_SIGN;
-      } else if (!isDigit(s[i])) {
-        break;
-      }
-    } else if (state === State.REAL) {
-      if (s[i] === 'e' || s[i] === 'E') {
-        state = State.EXPONENT_SIGN;
-      } else if (!isDigit(s[i])) {
-        break;
-      }
-    } else if (state === State.EXPONENT_SIGN) {
-      if (isDigit(s[i]) || s[i] === '+' || s[i] === '-') {
-        state = State.EXPONENT;
-      } else {
-        break;
-      }
-    } else if (state === State.EXPONENT) {
-      if (!isDigit(s[i])) {
-        break;
-      }
-    }
-  }
-  return i;
-}
-
-function isDigit(c: string): boolean {
-  return '0' <= c && c <= '9';
-}
-
-function isBreak(c: string): boolean {
-  // TODO(jart): Remove underscore when people stop using it like a slash.
-  return c === '/' || c === '_' || isDigit(c);
-}
diff --git a/tensorflow/tensorboard/components/vz_sorting/test/BUILD b/tensorflow/tensorboard/components/vz_sorting/test/BUILD
deleted file mode 100644
index 929e80d372..0000000000
--- a/tensorflow/tensorboard/components/vz_sorting/test/BUILD
+++ /dev/null
@@ -1,37 +0,0 @@
-package(
-    default_testonly = True,
-    default_visibility = ["//tensorflow/tensorboard:internal"],
-)
-
-load("//tensorflow/tensorboard/defs:vulcanize.bzl", "tensorboard_html_binary")
-load("//tensorflow/tensorboard/defs:web.bzl", "ts_web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-ts_web_library(
-    name = "test",
-    srcs = [
-        "sortingTests.ts",
-        "tests.html",
-    ],
-    path = "/vz-sorting/test",
-    deps = [
-        "//tensorflow/tensorboard/components/tf_imports:web_component_tester",
-        "//tensorflow/tensorboard/components/vz_sorting",
-    ],
-)
-
-tensorboard_html_binary(
-    name = "devserver",
-    compilation_level = "WHITESPACE_ONLY",
-    input_path = "/vz-sorting/test/tests.html",
-    output_path = "/vz-sorting/test/tests.html",
-    deps = [":test"],
-)
-
-filegroup(
-    name = "all_files",
-    testonly = 0,
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/components/vz_sorting/test/sortingTests.ts b/tensorflow/tensorboard/components/vz_sorting/test/sortingTests.ts
deleted file mode 100644
index 510685cb4b..0000000000
--- a/tensorflow/tensorboard/components/vz_sorting/test/sortingTests.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {compareTagNames} from '../sorting';
-
-describe('compareTagNames', () => {
-
-  const assert = chai.assert;
-  const sortTagNames = (a) => a.sort(compareTagNames);
-
-  it('is asciibetical', () => {
-    assert.deepEqual(sortTagNames(['a', 'b']), ['a', 'b']);
-    assert.deepEqual(sortTagNames(['a', 'B']), ['B', 'a']);
-  });
-
-  it('sorts integer portions', () => {
-    assert.deepEqual(['03', '1'].sort(), ['03', '1']);
-    assert.deepEqual(sortTagNames(['03', '1']), ['1', '03']);
-    assert.deepEqual(sortTagNames(['a03', 'a1']), ['a1', 'a03']);
-    assert.deepEqual(sortTagNames(['a03', 'b1']), ['a03', 'b1']);
-    assert.deepEqual(sortTagNames(['x0a03', 'x0a1']), ['x0a1', 'x0a03']);
-    assert.deepEqual(sortTagNames(['a/b/03', 'a/b/1']), ['a/b/1', 'a/b/03']);
-  });
-
-  it('sorts fixed point numbers', () => {
-    assert.deepEqual(sortTagNames(['a0.1', 'a0.01']), ['a0.01', 'a0.1']);
-  });
-
-  it('sorts engineering notation', () => {
-    assert.deepEqual(sortTagNames(['a1e9', 'a9e8']), ['a9e8', 'a1e9']);
-    assert.deepEqual(sortTagNames(['a1e+9', 'a9e+8']), ['a9e+8', 'a1e+9']);
-    assert.deepEqual(sortTagNames(['a1e+5', 'a9e-6']), ['a9e-6', 'a1e+5']);
-    assert.deepEqual(sortTagNames(['a1.0e9', 'a9.0e8']), ['a9.0e8', 'a1.0e9']);
-    assert.deepEqual(
-        sortTagNames(['a1.0e+9', 'a9.0e+8']), ['a9.0e+8', 'a1.0e+9']);
-  });
-
-  it('is componentized by slash', () => {
-    assert.deepEqual(['a+/a', 'a/a', 'ab/a'].sort(), ['a+/a', 'a/a', 'ab/a']);
-    assert.deepEqual(
-        sortTagNames(['a+/a', 'a/a', 'ab/a']), ['a/a', 'a+/a', 'ab/a']);
-  });
-
-  it('is componentized by underscore', () => {
-    assert.deepEqual(
-        sortTagNames(['a+_a', 'a_a', 'ab_a']), ['a_a', 'a+_a', 'ab_a']);
-    assert.deepEqual(
-        sortTagNames(['a+/a', 'a_a', 'ab_a']), ['a_a', 'a+/a', 'ab_a']);
-  });
-
-  it('is componentized by number boundaries', () => {
-    assert.deepEqual(
-        sortTagNames(['a+0a', 'a0a', 'ab0a']), ['a0a', 'a+0a', 'ab0a']);
-  });
-
-  it('empty comes first', () => {
-    assert.deepEqual(sortTagNames(['a', '//', '/', '']), ['', '/', '//', 'a']);
-  });
-
-  it('decimal parsed correctly', () => {
-    assert.deepEqual(sortTagNames(['0.2', '0.03']), ['0.03', '0.2']);
-    assert.deepEqual(sortTagNames(['0..2', '0..03']), ['0..2', '0..03']);
-    assert.deepEqual(sortTagNames(['.2', '.03']), ['.2', '.03']);
-  });
-});
diff --git a/tensorflow/tensorboard/components/vz_sorting/test/tests.html b/tensorflow/tensorboard/components/vz_sorting/test/tests.html
deleted file mode 100644
index f92c608cdb..0000000000
--- a/tensorflow/tensorboard/components/vz_sorting/test/tests.html
+++ /dev/null
@@ -1,23 +0,0 @@
-<!doctype html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<meta charset="utf-8">
-<script vulcanize-noinline src="../../web-component-tester/browser.js"></script>
-<link rel="import" href="../vz-sorting.html">
-<body>
-<script src="sortingTests.js"></script>
diff --git a/tensorflow/tensorboard/components/vz_sorting/vz-sorting.html b/tensorflow/tensorboard/components/vz_sorting/vz-sorting.html
deleted file mode 100644
index 5ff6f31158..0000000000
--- a/tensorflow/tensorboard/components/vz_sorting/vz-sorting.html
+++ /dev/null
@@ -1,18 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<script src="sorting.js"></script>
diff --git a/tensorflow/tensorboard/defs/BUILD b/tensorflow/tensorboard/defs/BUILD
deleted file mode 100644
index 92a2af3404..0000000000
--- a/tensorflow/tensorboard/defs/BUILD
+++ /dev/null
@@ -1,14 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-filegroup(
-    name = "ts_web_library_default_typings",
-    srcs = [
-        # Ordering probably matters.
-        "@com_microsoft_typescript//:lib.es6.d.ts",
-        "@io_angular_clutz//:src/resources/closure.lib.d.ts",
-        "clutz.d.ts",
-    ],
-    visibility = ["//visibility:public"],
-)
diff --git a/tensorflow/tensorboard/defs/clutz.d.ts b/tensorflow/tensorboard/defs/clutz.d.ts
deleted file mode 100644
index 47cf307d26..0000000000
--- a/tensorflow/tensorboard/defs/clutz.d.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//    http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// tslint:disable
-declare namespace ಠ_ಠ.clutz {
-  interface IteratorIterable<T> extends Iterator<T>, Iterable<T> {}
-  interface IIterableResult<T> extends IteratorResult<T> {}
-}
diff --git a/tensorflow/tensorboard/defs/defs.bzl b/tensorflow/tensorboard/defs/defs.bzl
deleted file mode 100644
index 94e2d7c540..0000000000
--- a/tensorflow/tensorboard/defs/defs.bzl
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-def tensorboard_webcomponent_library(**kwargs):
-  """Rules referencing this will be deleted from the codebase soon."""
-  pass
-
-def _legacy_js_impl(target, ctx):
-  return struct()
-
-legacy_js = aspect(
-    implementation=_legacy_js_impl,
-    attr_aspects=["exports"])
diff --git a/tensorflow/tensorboard/defs/hacks.bzl b/tensorflow/tensorboard/defs/hacks.bzl
deleted file mode 100644
index f1d4be7906..0000000000
--- a/tensorflow/tensorboard/defs/hacks.bzl
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TODO(jart): Merge this file into defs.bzl once that file is sync unified.
-
-def tensorboard_typescript_bundle(
-    name,
-    out,
-    namespace_srcs,
-    namespace_symbol_aliases={},
-    namespace_symbol_aliases_public={},
-    **kwargs):
-  """Rolls TypeScript ES6 modules into one vanilla source file without imports.
-
-  This is a genrule wrapper that concatenates TypeScripts sources inside
-  namespace blocks while removing ^import lines. Because the sources themselves
-  are not parsed, the structure of the modules must be passed to this macro as
-  a Skylark data structure.
-
-  Args:
-    name: Name of this build rule target.
-    out: Path of outputted TypeScript source file.
-    namespace_srcs: Multimap of namespace strings to build file targets. The
-        ordering of the dictionary and nested lists does not matter when
-        generating a typings file, but *does* matter when generating a source
-        file.
-    namespace_symbol_aliases: Map of namespace strings where each value is a
-        map of symbol names to fully qualified symbol names.
-    namespace_symbol_aliases_public: Same as namespace_symbol_aliases but the
-        symbol will be visible to other namespaces.
-  """
-  cmd = ["(", "echo // GENERATED BY TENSORBOARD_TYPESCRIPT_BUNDLE"]
-  inputs = set()
-  for namespace, srcs in namespace_srcs.items():
-    cmd.append("echo")
-    if out[-5:] == ".d.ts":
-      cmd.append("echo 'declare namespace %s {'" % namespace)
-    elif out[-3:] == ".ts":
-      cmd.append("echo 'module %s {'" % namespace)
-    else:
-      fail("'out' must end with .ts or .d.ts: " + out)
-    for symbol, canon in namespace_symbol_aliases.get(namespace, {}).items():
-      cmd.append("echo 'import %s = %s;'" % (symbol, canon))
-    for symbol, canon in namespace_symbol_aliases_public.get(namespace,
-                                                             {}).items():
-      cmd.append("echo 'export import %s = %s;'" % (symbol, canon))
-    inputs += srcs
-    for src in srcs:
-      cmd.append("for f in $(locations %s); do" % src)
-      cmd.append("  echo")
-      cmd.append("  echo /////////////////////////////////////////////////////")
-      cmd.append("  echo // " + namespace)
-      cmd.append("  echo // $$f")
-      cmd.append("  echo /////////////////////////////////////////////////////")
-      cmd.append("  echo")
-      cmd.append("  sed 's!^import !// import !' $$f \\")
-      cmd.append("    | sed 's!^export declare !export !' \\")
-      cmd.append("    | sed '/^export .* from /d' \\")
-      cmd.append("    | sed '/^export {.*};$$/d'")
-      cmd.append("done")
-    cmd.append("echo '}'")
-  cmd.append(") >$@")
-  native.genrule(
-      name = name,
-      srcs = list(inputs),
-      outs = [out],
-      cmd = "\n".join(cmd),
-      **kwargs
-  )
diff --git a/tensorflow/tensorboard/defs/protos.bzl b/tensorflow/tensorboard/defs/protos.bzl
deleted file mode 100644
index 6d1982e098..0000000000
--- a/tensorflow/tensorboard/defs/protos.bzl
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("@protobuf//:protobuf.bzl", "py_proto_library")
-
-def tb_proto_library(name, srcs = [], visibility = []):
-  py_proto_library(
-    name = name + "_py",
-    srcs = srcs,
-    srcs_version = "PY2AND3",
-    deps = ["@protobuf//:protobuf_python"],
-    protoc = "@protobuf//:protoc",
-    visibility = visibility,
-    default_runtime = "@protobuf//:protobuf_python",
-    testonly = 0,
-  )
\ No newline at end of file
diff --git a/tensorflow/tensorboard/defs/vulcanize.bzl b/tensorflow/tensorboard/defs/vulcanize.bzl
deleted file mode 100644
index 6ff49a35ed..0000000000
--- a/tensorflow/tensorboard/defs/vulcanize.bzl
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "legacy_js")
-load("@io_bazel_rules_closure//closure/private:defs.bzl", "collect_js", "unfurl", "long_path")
-load("//tensorflow/tensorboard/defs:web.bzl", "web_aspect")
-
-def _tensorboard_html_binary(ctx):
-  deps = unfurl(ctx.attr.deps, provider="webfiles")
-  manifests = set(order="topological")
-  files = set()
-  webpaths = set()
-  for dep in deps:
-    manifests += dep.webfiles.manifests
-    webpaths += dep.webfiles.webpaths
-    files += dep.data_runfiles.files
-  webpaths += [ctx.attr.output_path]
-  closure_js_library=collect_js(
-      ctx, unfurl(ctx.attr.deps, provider="closure_js_library"))
-
-  # vulcanize
-  jslibs = depset(ctx.files._jslibs) + closure_js_library.srcs
-  ctx.action(
-      inputs=list(manifests | files | jslibs),
-      outputs=[ctx.outputs.html],
-      executable=ctx.executable._Vulcanize,
-      arguments=([ctx.attr.compilation_level,
-                  "true" if ctx.attr.testonly else "false",
-                  ctx.attr.input_path,
-                  ctx.attr.output_path,
-                  ctx.outputs.html.path] +
-                 [f.path for f in jslibs] +
-                 [f.path for f in manifests]),
-      progress_message="Vulcanizing %s" % ctx.attr.input_path)
-
-  # webfiles manifest
-  manifest_srcs = [struct(path=ctx.outputs.html.path,
-                          longpath=long_path(ctx, ctx.outputs.html),
-                          webpath=ctx.attr.output_path)]
-  manifest = ctx.new_file(ctx.configuration.bin_dir,
-                          "%s.pbtxt" % ctx.label.name)
-  ctx.file_action(
-      output=manifest,
-      content=struct(
-          label=str(ctx.label),
-          src=manifest_srcs).to_proto())
-  manifests += [manifest]
-
-  # webfiles server
-  params = struct(
-      label=str(ctx.label),
-      bind="[::]:6006",
-      manifest=[long_path(ctx, man) for man in manifests],
-      external_asset=[struct(webpath=k, path=v)
-                      for k, v in ctx.attr.external_assets.items()])
-  params_file = ctx.new_file(ctx.configuration.bin_dir,
-                             "%s_server_params.pbtxt" % ctx.label.name)
-  ctx.file_action(output=params_file, content=params.to_proto())
-  ctx.file_action(
-      executable=True,
-      output=ctx.outputs.executable,
-      content="#!/bin/sh\nexec %s %s" % (
-          ctx.executable._WebfilesServer.short_path,
-          long_path(ctx, params_file)))
-
-  transitive_runfiles = depset()
-  transitive_runfiles += ctx.attr._WebfilesServer.data_runfiles.files
-  for dep in deps:
-    transitive_runfiles += dep.data_runfiles.files
-  return struct(
-      files=depset([ctx.outputs.html]),
-      webfiles=struct(
-          manifest=manifest,
-          manifests=manifests,
-          webpaths=webpaths,
-          dummy=ctx.outputs.html),
-      runfiles=ctx.runfiles(
-          files=ctx.files.data + [manifest,
-                                  params_file,
-                                  ctx.outputs.html,
-                                  ctx.outputs.executable],
-          transitive_files=transitive_runfiles))
-
-tensorboard_html_binary = rule(
-    implementation=_tensorboard_html_binary,
-    executable=True,
-    attrs={
-        "compilation_level": attr.string(default="ADVANCED"),
-        "input_path": attr.string(mandatory=True),
-        "output_path": attr.string(mandatory=True),
-        "data": attr.label_list(cfg="data", allow_files=True),
-        "deps": attr.label_list(
-            aspects=[
-                web_aspect,
-                legacy_js,
-            ],
-            mandatory=True),
-        "external_assets": attr.string_dict(default={"/_/runfiles": "."}),
-        "_jslibs": attr.label(
-            default=Label("//tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize:jslibs"),
-            allow_files=True),
-        "_Vulcanize": attr.label(
-            default=Label("//tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize:Vulcanize"),
-            executable=True,
-            cfg="host"),
-        "_WebfilesServer": attr.label(
-            default=Label(
-                "@io_bazel_rules_closure//java/io/bazel/rules/closure/webfiles/server:WebfilesServer"),
-            executable=True,
-            cfg="host"),
-    },
-    outputs={
-        "html": "%{name}.html",
-    })
diff --git a/tensorflow/tensorboard/defs/web.bzl b/tensorflow/tensorboard/defs/web.bzl
deleted file mode 100644
index 103942b0a2..0000000000
--- a/tensorflow/tensorboard/defs/web.bzl
+++ /dev/null
@@ -1,419 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Same as web_library but supports TypeScript."""
-
-load("//tensorflow/tensorboard/defs:defs.bzl", "legacy_js")
-
-load("//third_party:clutz.bzl",
-     "CLUTZ_ATTRIBUTES",
-     "CLUTZ_OUTPUTS",
-     "clutz_aspect",
-     "extract_dts_from_closure_libraries")
-
-load("@io_bazel_rules_closure//closure/private:defs.bzl",
-     "CLOSURE_LIBRARY_BASE_ATTR",
-     "CLOSURE_LIBRARY_DEPS_ATTR",
-     "collect_js",
-     "collect_runfiles",
-     "convert_path_to_es6_module_name",
-     "create_argfile",
-     "difference",
-     "long_path",
-     "unfurl")
-
-_ASPECT_SLURP_FILE_TYPE = FileType([
-    ".html", ".js", ".css", ".gss", ".png", ".jpg", ".gif", ".ico", ".svg"])
-
-_CLOSURE_WORKER = attr.label(
-    default=Label("@io_bazel_rules_closure//java/io/bazel/rules/closure:ClosureWorker"),
-    executable=True,
-    cfg="host")
-
-def _ts_web_library(ctx):
-  if not ctx.attr.srcs:
-    if ctx.attr.deps:
-      fail("deps can not be set when srcs is not")
-    if not ctx.attr.exports:
-      fail("exports must be set if srcs is not")
-  if ctx.attr.path:
-    if not ctx.attr.path.startswith("/"):
-      fail("webpath must start with /")
-    if ctx.attr.path != "/" and ctx.attr.path.endswith("/"):
-      fail("webpath must not end with / unless it is /")
-    if "//" in ctx.attr.path:
-      fail("webpath must not have //")
-  elif ctx.attr.srcs:
-    fail("path must be set when srcs is set")
-  if "*" in ctx.attr.suppress and len(ctx.attr.suppress) != 1:
-    fail("when \"*\" is suppressed no other items should be present")
-
-  # process what came before
-  deps = unfurl(ctx.attr.deps, provider="webfiles")
-  webpaths = depset()
-  ts_typings = depset(ctx.files._default_typings)
-  ts_typings_paths = depset(
-      [long_path(ctx, f) for f in ctx.files._default_typings])
-  ts_typings_execroots = depset()
-  aspect_runfiles = depset()
-  for dep in deps:
-    webpaths += dep.webfiles.webpaths
-    if hasattr(dep.webfiles, "ts_typings"):
-      ts_typings += dep.webfiles.ts_typings
-    if hasattr(dep.webfiles, "ts_typings_paths"):
-      ts_typings_paths += dep.webfiles.ts_typings_paths
-    if hasattr(dep.webfiles, "ts_typings_execroots"):
-      ts_typings_execroots += dep.webfiles.ts_typings_execroots
-    if hasattr(dep.webfiles, "aspect_runfiles"):
-      aspect_runfiles += dep.webfiles.aspect_runfiles
-
-  # process what comes now
-  manifest_srcs = []
-  new_webpaths = []
-  ts_inputs = depset()
-  ts_outputs = []
-  ts_files = list(ts_typings_paths)
-  new_typings = []
-  new_typings_paths = []
-  new_typings_execroot = struct(inputs=[])
-  execroot = struct(
-      inputs=[(long_path(ctx, f), f.path) for f in ctx.files._default_typings],
-      outputs=[],
-      program=[ctx.executable._tsc.path, "-p"])
-  web_srcs = []
-  path = ctx.attr.path
-  strip = _get_strip(ctx)
-  for src in ctx.files.srcs:
-    suffix = _get_path_relative_to_package(src)
-    if strip:
-      if not suffix.startswith(strip):
-        fail("Relative src path not start with '%s': %s" % (strip, suffix))
-      suffix = suffix[len(strip):]
-    webpath = "%s/%s" % ("" if path == "/" else path, suffix)
-    _add_webpath(ctx, src, webpath, webpaths, new_webpaths, manifest_srcs)
-    if suffix.endswith(".d.ts"):
-      web_srcs.append(src)
-      entry = (webpath[1:], src.path)
-      new_typings.append(src)
-      new_typings_paths.append(entry[0])
-      new_typings_execroot.inputs.append(entry)
-      ts_inputs += [src]
-      ts_files.append(entry[0])
-      execroot.inputs.append(entry)
-    elif suffix.endswith(".ts"):
-      noext = suffix[:-3]
-      js = ctx.new_file(ctx.bin_dir, "%s.js" % noext)
-      dts = ctx.new_file(ctx.bin_dir, "%s.d.ts" % noext)
-      webpath_js = webpath[:-3] + ".js"
-      webpath_dts = webpath[:-3] + ".d.ts"
-      _add_webpath(ctx, js, webpath_js, webpaths, new_webpaths, manifest_srcs)
-      _add_webpath(ctx, dts, webpath_dts, webpaths, new_webpaths, manifest_srcs)
-      ts_inputs += [src]
-      ts_outputs.append(js)
-      ts_outputs.append(dts)
-      web_srcs.append(dts)
-      web_srcs.append(js)
-      ts_files.append(webpath[1:])
-      execroot.inputs.append((webpath[1:], src.path))
-      execroot.outputs.append((webpath_js[1:], js.path))
-      execroot.outputs.append((webpath_dts[1:], dts.path))
-      new_typings.append(dts)
-      new_typings_paths.append(webpath_dts[1:])
-      new_typings_execroot.inputs.append((webpath_dts[1:], dts.path))
-    else:
-      web_srcs.append(src)
-
-  # get typings for closure code
-  clutz_dts = extract_dts_from_closure_libraries(ctx)
-  if clutz_dts:
-    entry = (long_path(ctx, clutz_dts), clutz_dts.path)
-    ts_inputs += [clutz_dts]
-    ts_files.append(entry[0])
-    execroot.inputs.append(entry)
-
-  # compile typescript
-  workspace = ""
-  if ctx.label.workspace_root:
-    workspace = "/" + ctx.label.workspace_root
-  if execroot.outputs:
-    ts_config = _new_file(ctx, "-tsc.json")
-    execroot.inputs.append(("tsconfig.json", ts_config.path))
-    ctx.file_action(
-        output=ts_config,
-        content=struct(
-            compilerOptions=struct(
-                baseUrl=".",
-                declaration=True,
-                inlineSourceMap=True,
-                inlineSources=True,
-                module="es6",
-                moduleResolution="node",
-                noResolve=True,
-                target="es5",
-            ),
-            files=ts_files,
-        ).to_json())
-    er_config = _new_file(ctx, "-tsc-execroot.json")
-    ctx.file_action(output=er_config, content=execroot.to_json())
-    ts_inputs += collect_runfiles([ctx.attr._tsc])
-    ts_inputs += ctx.files._tsc
-    ts_inputs += ts_typings
-    ts_inputs += ts_typings_execroots
-    ts_inputs += [ts_config, er_config]
-    ctx.action(
-        inputs=list(ts_inputs),
-        outputs=ts_outputs,
-        executable=ctx.executable._execrooter,
-        arguments=[er_config.path] + [f.path for f in ts_typings_execroots],
-        progress_message="Compiling %d TypeScript files %s" % (
-            len(ts_files), ctx.label))
-
-  # perform strict dependency checking
-  manifest = _make_manifest(ctx, manifest_srcs)
-  webpaths += new_webpaths
-  dummy, manifests = _run_webfiles_validator(ctx, web_srcs, deps, manifest)
-  web_srcs.append(dummy)
-
-  # define development web server that only applies to this transitive closure
-  params = struct(
-      label=str(ctx.label),
-      bind="[::]:6006",
-      manifest=[long_path(ctx, man) for man in manifests],
-      external_asset=[struct(webpath=k, path=v)
-                      for k, v in ctx.attr.external_assets.items()])
-  params_file = _new_file(ctx, "-params.pbtxt")
-  ctx.file_action(output=params_file, content=params.to_proto())
-  ctx.file_action(
-      executable=True,
-      output=ctx.outputs.executable,
-      content="#!/bin/sh\nexec %s %s" % (
-          ctx.executable._WebfilesServer.short_path,
-          long_path(ctx, params_file)))
-
-  if new_typings:
-    er_config = _new_file(ctx, "-typings-execroot.json")
-    ctx.file_action(output=er_config, content=new_typings_execroot.to_json())
-    ts_typings += new_typings
-    ts_typings_paths += new_typings_paths
-    ts_typings_execroots += [er_config]
-  else:
-    ts_typings = depset()
-    ts_typings_paths = depset()
-    ts_typings_execroots = depset()
-
-  # export data to parent rules
-  return struct(
-      files=depset(web_srcs + [dummy]),
-      exports=unfurl(ctx.attr.exports),
-      webfiles=struct(
-          manifest=manifest,
-          manifests=manifests,
-          webpaths=webpaths,
-          dummy=dummy,
-          ts_typings=ts_typings,
-          ts_typings_paths=ts_typings_paths,
-          ts_typings_execroots=ts_typings_execroots),
-      closure_js_library=collect_js(
-          ctx, unfurl(ctx.attr.deps, provider="closure_js_library")),
-      runfiles=ctx.runfiles(
-          files=ctx.files.srcs + ctx.files.data + ts_outputs + [
-              manifest,
-              params_file,
-              ctx.outputs.executable,
-              dummy],
-          transitive_files=(collect_runfiles([ctx.attr._WebfilesServer]) |
-                            collect_runfiles(deps) |
-                            collect_runfiles(ctx.attr.data) |
-                            aspect_runfiles)))
-
-def _web_aspect_impl(target, ctx):
-  if hasattr(target, "webfiles"):
-    return struct()
-  srcs = []
-  deps = []
-  if hasattr(ctx.rule.files, "srcs"):
-    srcs.extend(_ASPECT_SLURP_FILE_TYPE.filter(ctx.rule.files.srcs))
-  for attr in ("deps", "sticky_deps", "module_deps"):
-    value = getattr(ctx.rule.attr, attr, None)
-    if value:
-      deps.extend(value)
-  deps = unfurl(deps, provider="webfiles")
-  webpaths = depset()
-  aspect_runfiles = depset(srcs)
-  for dep in deps:
-    webpaths += dep.webfiles.webpaths
-    if hasattr(dep.webfiles, "aspect_runfiles"):
-      aspect_runfiles += dep.webfiles.aspect_runfiles
-  manifest_srcs = []
-  new_webpaths = []
-  for src in srcs:
-    webpath = "/" + long_path(ctx, src)
-    _add_webpath(ctx, src, webpath, webpaths, new_webpaths, manifest_srcs)
-  webpaths += new_webpaths
-  manifest = _make_manifest(ctx, manifest_srcs)
-  dummy, manifests = _run_webfiles_validator(ctx, srcs, deps, manifest)
-  aspect_runfiles += [dummy, manifest]
-  return struct(
-      webfiles=struct(
-          manifest=manifest,
-          manifests=manifests,
-          webpaths=webpaths,
-          dummy=dummy,
-          aspect_runfiles=aspect_runfiles))
-
-def _make_manifest(ctx, src_list):
-  manifest = _new_file(ctx, "-webfiles.pbtxt")
-  ctx.file_action(
-      output=manifest,
-      content=struct(
-          label=str(ctx.label),
-          src=src_list).to_proto())
-  return manifest
-
-def _run_webfiles_validator(ctx, srcs, deps, manifest):
-  dummy = _new_file(ctx, "-webfiles.ignoreme")
-  manifests = depset(order="topological")
-  for dep in deps:
-    manifests += dep.webfiles.manifests
-  if srcs:
-    args = ["WebfilesValidator",
-            "--dummy", dummy.path,
-            "--target", manifest.path]
-    if hasattr(ctx, "attr") and hasattr(ctx.attr, "suppress"):
-      for category in ctx.attr.suppress:
-        args.append("--suppress")
-        args.append(category)
-    inputs = [manifest]
-    inputs.extend(srcs)
-    direct_manifests = depset()
-    for dep in deps:
-      inputs.append(dep.webfiles.dummy)
-      for f in dep.files:
-        inputs.append(f)
-      direct_manifests += [dep.webfiles.manifest]
-      inputs.append(dep.webfiles.manifest)
-      args.append("--direct_dep")
-      args.append(dep.webfiles.manifest.path)
-    for man in difference(manifests, direct_manifests):
-      inputs.append(man)
-      args.append("--transitive_dep")
-      args.append(man.path)
-    argfile = _new_file(ctx, "-webfiles-checker-args.txt")
-    ctx.file_action(output=argfile, content="\n".join(args))
-    inputs.append(argfile)
-    ctx.action(
-        inputs=inputs,
-        outputs=[dummy],
-        executable=(getattr(ctx.executable, "_ClosureWorker", None) or
-                    getattr(ctx.executable, "_ClosureWorkerAspect", None)),
-        arguments=["@@" + argfile.path],
-        mnemonic="Closure",
-        execution_requirements={"supports-workers": "1"},
-        progress_message="Checking webfiles %s" % ctx.label)
-  else:
-    ctx.file_action(output=dummy, content="BOO!")
-  manifests += [manifest]
-  return dummy, manifests
-
-def _new_file(ctx, suffix):
-  return ctx.new_file(ctx.bin_dir, "%s%s" % (ctx.label.name, suffix))
-
-def _add_webpath(ctx, src, webpath, webpaths, new_webpaths, manifest_srcs):
-  if webpath in new_webpaths:
-    _fail(ctx, "multiple srcs within %s define the webpath %s " % (
-        ctx.label, webpath))
-  if webpath in webpaths:
-    _fail(ctx, "webpath %s was defined by %s when already defined by deps" % (
-        webpath, ctx.label))
-  new_webpaths.append(webpath)
-  manifest_srcs.append(struct(
-      path=src.path,
-      longpath=long_path(ctx, src),
-      webpath=webpath))
-
-def _fail(ctx, message):
-  if ctx.attr.suppress == ["*"]:
-    print(message)
-  else:
-    fail(message)
-
-def _get_path_relative_to_package(artifact):
-  """Returns file path relative to the package that declared it."""
-  path = artifact.path
-  for prefix in (artifact.root.path,
-                 artifact.owner.workspace_root if artifact.owner else '',
-                 artifact.owner.package if artifact.owner else ''):
-    if prefix:
-      prefix = prefix + "/"
-      if not path.startswith(prefix):
-        fail("Path %s doesn't start with %s" % (path, prefix))
-      path = path[len(prefix):]
-  return path
-
-def _get_strip(ctx):
-  strip = ctx.attr.strip_prefix
-  if strip:
-    if strip.startswith("/"):
-      _fail(ctx, "strip_prefix should not end with /")
-      strip = strip[1:]
-    if strip.endswith("/"):
-      _fail(ctx, "strip_prefix should not end with /")
-    else:
-      strip += "/"
-  return strip
-
-web_aspect = aspect(
-    implementation=_web_aspect_impl,
-    attr_aspects=["deps", "sticky_deps", "module_deps"],
-    attrs={"_ClosureWorkerAspect": _CLOSURE_WORKER})
-
-ts_web_library = rule(
-    implementation=_ts_web_library,
-    executable=True,
-    attrs=CLUTZ_ATTRIBUTES + {
-        "path": attr.string(),
-        "srcs": attr.label_list(allow_files=True),
-        "deps": attr.label_list(
-            aspects=[
-                web_aspect,
-                clutz_aspect,
-                legacy_js,
-            ]),
-        "exports": attr.label_list(),
-        "data": attr.label_list(cfg="data", allow_files=True),
-        "suppress": attr.string_list(),
-        "strip_prefix": attr.string(),
-        "external_assets": attr.string_dict(default={"/_/runfiles": "."}),
-        "clutz_entry_points": attr.string_list(),
-        "_execrooter": attr.label(
-            default=Label("//tensorflow/tensorboard/scripts:execrooter"),
-            executable=True,
-            cfg="host"),
-        "_tsc": attr.label(
-            default=Label("@com_microsoft_typescript//:tsc"),
-            allow_files=True,
-            executable=True,
-            cfg="host"),
-        "_default_typings": attr.label(
-            default=Label("//tensorflow/tensorboard:ts_web_library_default_typings"),
-            allow_files=True),
-        "_WebfilesServer": attr.label(
-            default=Label("@io_bazel_rules_closure//java/io/bazel/rules/closure/webfiles/server:WebfilesServer"),
-            executable=True,
-            cfg="host"),
-        "_ClosureWorker": _CLOSURE_WORKER,
-        "_closure_library_base": CLOSURE_LIBRARY_BASE_ATTR,
-        "_closure_library_deps": CLOSURE_LIBRARY_DEPS_ATTR,
-    },
-    outputs=CLUTZ_OUTPUTS)
diff --git a/tensorflow/tensorboard/defs/zipper.bzl b/tensorflow/tensorboard/defs/zipper.bzl
deleted file mode 100644
index e98309ec9a..0000000000
--- a/tensorflow/tensorboard/defs/zipper.bzl
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("@io_bazel_rules_closure//closure/private:defs.bzl", "unfurl", "long_path")
-
-def _tensorboard_zip_file(ctx):
-  deps = unfurl(ctx.attr.deps, provider="webfiles")
-  manifests = set(order="link")
-  files = set()
-  webpaths = set()
-  for dep in deps:
-    manifests += dep.webfiles.manifests
-    webpaths += dep.webfiles.webpaths
-    files += dep.data_runfiles.files
-  ctx.action(
-      inputs=list(manifests + files),
-      outputs=[ctx.outputs.zip],
-      executable=ctx.executable._Zipper,
-      arguments=([ctx.outputs.zip.path] +
-                 [m.path for m in manifests]),
-      progress_message="Zipping %d files" % len(webpaths))
-  transitive_runfiles = set()
-  for dep in deps:
-    transitive_runfiles += dep.data_runfiles.files
-  return struct(
-      files=set([ctx.outputs.zip]),
-      runfiles=ctx.runfiles(
-          files=ctx.files.data + [ctx.outputs.zip],
-          transitive_files=transitive_runfiles))
-
-tensorboard_zip_file = rule(
-    implementation=_tensorboard_zip_file,
-    attrs={
-        "data": attr.label_list(cfg="data", allow_files=True),
-        "deps": attr.label_list(providers=["webfiles"], mandatory=True),
-        "_Zipper": attr.label(
-            default=Label("//tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize:Zipper"),
-            executable=True,
-            cfg="host"),
-    },
-    outputs={
-        "zip": "%{name}.zip",
-    })
diff --git a/tensorflow/tensorboard/demo/BUILD b/tensorflow/tensorboard/demo/BUILD
deleted file mode 100644
index b253572ec5..0000000000
--- a/tensorflow/tensorboard/demo/BUILD
+++ /dev/null
@@ -1,20 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-load("@io_bazel_rules_closure//closure:defs.bzl", "web_library")
-
-licenses(["notice"])  # Apache 2.0
-
-# THIS PACKAGE HAS MOVED
-# See tensorflow/tensorboard/components/tf_tensorboard:demo
-
-web_library(
-    name = "demo_data",
-    srcs = glob(["data/**"]),
-    path = "/",
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/demo/data/audio_run_run1_tag_au1_2Faudio_2F0.json b/tensorflow/tensorboard/demo/data/audio_run_run1_tag_au1_2Faudio_2F0.json
deleted file mode 100644
index 7dfe32c711..0000000000
--- a/tensorflow/tensorboard/demo/data/audio_run_run1_tag_au1_2Faudio_2F0.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"query": "index=0&tag=au1%2Faudio%2F0&run=run1", "step": 0, "wall_time": 1461795049.203407, "content_type": "audio/wav"}]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/audio_run_run2_tag_au2_2Faudio_2F0.json b/tensorflow/tensorboard/demo/data/audio_run_run2_tag_au2_2Faudio_2F0.json
deleted file mode 100644
index 13f9c2de42..0000000000
--- a/tensorflow/tensorboard/demo/data/audio_run_run2_tag_au2_2Faudio_2F0.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"query": "index=0&tag=au2%2Faudio%2F0&run=run2", "step": 0, "wall_time": 1461795049.212815, "content_type": "audio/wav"}]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/compressedHistograms_run_run1_tag_histo1.json b/tensorflow/tensorboard/demo/data/compressedHistograms_run_run1_tag_histo1.json
deleted file mode 100644
index 6ae6fbf880..0000000000
--- a/tensorflow/tensorboard/demo/data/compressedHistograms_run_run1_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, [[0, -2.3150592308536755], [668, -2.0967547155036605], [1587, -1.4326244423655616], [3085, -0.8871306575801902], [5000, -0.09312398815580714], [6915, 0.2584093405812282], [8413, 0.8895470642005087], [9332, 1.3198979614453679], [10000, 1.6793308878855118]]], [100.0, 10, [[0, -1.3417572789138936], [668, -1.183563374619141], [1587, -0.48920418783271574], [3085, 0.29326906896076954], [5000, 0.56953784145381], [6915, 0.8684655583499333], [8413, 1.4133127368907181], [9332, 1.906140650457873], [10000, 2.135771998171255]]], [200.0, 20, [[0, -1.5066917525035333], [668, -1.3910909571770793], [1587, -0.902737218885874], [3085, -0.3807791904765027], [5000, 0.38900200905253046], [6915, 0.8209734209339482], [8413, 1.302385856695965], [9332, 1.9324626053521639], [10000, 2.957505317875451]]], [300.0, 30, [[0, -0.5430457051469562], [668, -0.4626161834245273], [1587, 0.21573949543027715], [3085, 0.37353741100174215], [5000, 0.6891407881591103], [6915, 1.0927156232630852], [8413, 1.2745337159550916], [9332, 1.4321116832891605], [10000, 2.1913774993059034]]], [400.0, 40, [[0, -0.3584790755077172], [668, -0.33301611509753215], [1587, -0.1089466072951948], [3085, 0.5792199847585249], [5000, 1.220854943811942], [6915, 1.759829438421432], [8413, 2.3072559906741614], [9332, 2.753036118353921], [10000, 3.0267252195784047]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/compressedHistograms_run_run2_tag_histo1.json b/tensorflow/tensorboard/demo/data/compressedHistograms_run_run2_tag_histo1.json
deleted file mode 100644
index 3ad520c568..0000000000
--- a/tensorflow/tensorboard/demo/data/compressedHistograms_run_run2_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, [[0, -3.6801669545044846], [668, -3.192188140974744], [1587, -2.3414678549368806], [3085, -0.9632173471995873], [5000, -0.3214892636797772], [6915, 0.11870794142185205], [8413, 0.8895470642005087], [9332, 1.183563374619141], [10000, 2.665663810418372]]], [100.0, 10, [[0, -3.564793583751807], [668, -3.376844436865802], [1587, -1.0366615731293798], [3085, -0.27318696312672563], [5000, 0.9718642422053263], [6915, 2.5765662807928194], [8413, 3.1415385101545126], [9332, 4.085981768607621], [10000, 4.623079406808927]]], [200.0, 20, [[0, -2.235172510433281], [668, -2.004569042815611], [1587, -1.2015432383370985], [3085, 0.11835464933202625], [5000, 0.56953784145381], [6915, 1.202844810963146], [8413, 2.689066032283515], [9332, 2.8494015726499944], [10000, 3.481377676013788]]], [300.0, 30, [[0, -3.360113978269659], [668, -2.8293185004961043], [1587, -1.5992540502266783], [3085, 0.14393860259807117], [5000, 1.47723448201245], [6915, 1.9510057389110733], [8413, 2.833176104473626], [9332, 4.142405216576347], [10000, 4.706937777668589]]], [400.0, 40, [[0, -2.599286228987632], [668, -2.240365897443259], [1587, -1.5992540502266783], [3085, -0.9101893288861387], [5000, 0.7580548669750213], [6915, 1.6009864433919474], [8413, 2.3504002974280036], [9332, 2.7907805263353733], [10000, 3.5098048900144323]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/compressedHistograms_run_run2_tag_histo2.json b/tensorflow/tensorboard/demo/data/compressedHistograms_run_run2_tag_histo2.json
deleted file mode 100644
index a3802ba236..0000000000
--- a/tensorflow/tensorboard/demo/data/compressedHistograms_run_run2_tag_histo2.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, [[0, -1.9291158122759586], [668, -1.5970765333488954], [1587, -1.0923120348519078], [3085, -0.6688082872192093], [5000, 0.09312398815580714], [6915, 0.44532789251701854], [8413, 0.8238009655877649], [9332, 1.0357232383581656], [10000, 1.2741043689144438]]], [100.0, 10, [[0, -0.7780725642449806], [668, -0.7138496178727424], [1587, -0.5448932415735014], [3085, -0.24370397454796228], [5000, 0.42790220995778355], [6915, 0.6191730643365096], [8413, 0.752059342118037], [9332, 1.0451472255274825], [10000, 2.5559479569222825]]], [200.0, 20, [[0, -1.3876904425996377], [668, -1.1464188862638496], [1587, -0.4049955219067526], [3085, 0.04721394862139682], [5000, 0.56953784145381], [6915, 1.3221859041483333], [8413, 1.6188495656305735], [9332, 1.7613953069723651], [10000, 2.3257482385477384]]], [300.0, 30, [[0, -1.600772629982185], [668, -1.1548516185367033], [1587, -0.260387173785447], [3085, 0.17416570914366614], [5000, 0.47069243095356195], [6915, 1.1559276581637614], [8413, 2.0474031182051404], [9332, 2.18821711651116], [10000, 2.2393193406467518]]], [400.0, 40, [[0, -0.8286852465281818], [668, -0.7815041529866706], [1587, -0.3334896444053469], [3085, 0.21085213041026643], [5000, 0.5177616740489182], [6915, 1.077122434649409], [8413, 1.5898009703967424], [9332, 1.8859097291499742], [10000, 2.0954239138728523]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/graph_run_run1.pbtxt b/tensorflow/tensorboard/demo/data/graph_run_run1.pbtxt
deleted file mode 100644
index 2a6af32840..0000000000
--- a/tensorflow/tensorboard/demo/data/graph_run_run1.pbtxt
+++ /dev/null
@@ -1,9 +0,0 @@
-node {
-  name: "a"
-  op: "matmul"
-}
-node {
-  name: "b"
-  op: "matmul"
-  input: "a:0"
-}
diff --git a/tensorflow/tensorboard/demo/data/graph_run_run2.pbtxt b/tensorflow/tensorboard/demo/data/graph_run_run2.pbtxt
deleted file mode 100644
index a5a4d65d5c..0000000000
--- a/tensorflow/tensorboard/demo/data/graph_run_run2.pbtxt
+++ /dev/null
@@ -1,15 +0,0 @@
-node {
-  name: "a"
-  op: "matmul"
-}
-node {
-  name: "b"
-  op: "matmul"
-  input: "a:0"
-}
-node {
-  name: "c"
-  op: "matmul"
-  input: "a:0"
-  input: "b:0"
-}
diff --git a/tensorflow/tensorboard/demo/data/histograms_run_run1_tag_histo1.json b/tensorflow/tensorboard/demo/data/histograms_run_run1_tag_histo1.json
deleted file mode 100644
index a5600a356e..0000000000
--- a/tensorflow/tensorboard/demo/data/histograms_run_run1_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[400.0, 40, [-0.3584790755077172, 3.0267252195784047, 20.0, 24.012225532303315, 48.29045006426564, [-0.35363819004775493, -0.29226296698161564, -0.19961953895336082, 0.3214892636797772, 0.5177616740489182, 0.56953784145381, 0.6264916255991911, 0.7580548669750213, 0.8338603536725235, 1.220854943811942, 1.3429404381931362, 1.47723448201245, 1.624957930213695, 1.7874537232350647, 1.9661990955585713, 2.379100905625872, 2.6170109961884593, 3.1665833053880363], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/histograms_run_run2_tag_histo1.json b/tensorflow/tensorboard/demo/data/histograms_run_run2_tag_histo1.json
deleted file mode 100644
index 407c375d2f..0000000000
--- a/tensorflow/tensorboard/demo/data/histograms_run_run2_tag_histo1.json
+++ /dev/null
@@ -1 +0,0 @@
-[[400.0, 40, [-2.599286228987632, 3.5098048900144323, 20.0, 10.792285491200078, 66.66796979177158, [-2.379100905625872, -1.9661990955585713, -1.624957930213695, -1.47723448201245, -1.109868130738129, -1.0089710279437536, -0.42790220995778355, -0.2195814928486969, 0.47069243095356195, 0.7580548669750213, 0.917246389039776, 1.3429404381931362, 1.624957930213695, 1.7874537232350647, 2.1628190051144287, 2.6170109961884593, 2.8787120958073054, 3.8315657995195243], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/histograms_run_run2_tag_histo2.json b/tensorflow/tensorboard/demo/data/histograms_run_run2_tag_histo2.json
deleted file mode 100644
index 752b621ab0..0000000000
--- a/tensorflow/tensorboard/demo/data/histograms_run_run2_tag_histo2.json
+++ /dev/null
@@ -1 +0,0 @@
-[[400.0, 40, [-0.8286852465281818, 2.0954239138728523, 20.0, 13.546880465642861, 24.14836803774091, [-0.7580548669750213, -0.38900200905253046, -0.06996543062044111, 0.07696197368248522, 0.19961953895336082, 0.2656936063469233, 0.29226296698161564, 0.5177616740489182, 0.7580548669750213, 0.917246389039776, 1.109868130738129, 1.220854943811942, 1.624957930213695, 2.1628190051144287], [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 2.0, 3.0]]]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/images_run_run1_tag_im1_2Fimage_2F0.json b/tensorflow/tensorboard/demo/data/images_run_run1_tag_im1_2Fimage_2F0.json
deleted file mode 100644
index 814b4193c6..0000000000
--- a/tensorflow/tensorboard/demo/data/images_run_run1_tag_im1_2Fimage_2F0.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"wall_time": 1459200389.088045, "width": 4, "height": 4, "step": 0, "query": "tag=im1%2Fimage%2F0&index=0&run=run1"}]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/images_run_run1_tag_im2_2Fimage_2F0.json b/tensorflow/tensorboard/demo/data/images_run_run1_tag_im2_2Fimage_2F0.json
deleted file mode 100644
index 0c2bdcfc79..0000000000
--- a/tensorflow/tensorboard/demo/data/images_run_run1_tag_im2_2Fimage_2F0.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"wall_time": 1459200389.093653, "width": 4, "height": 4, "step": 0, "query": "tag=im2%2Fimage%2F0&index=0&run=run1"}]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/images_run_run2_tag_im1_2Fimage_2F0.json b/tensorflow/tensorboard/demo/data/images_run_run2_tag_im1_2Fimage_2F0.json
deleted file mode 100644
index 3160aae366..0000000000
--- a/tensorflow/tensorboard/demo/data/images_run_run2_tag_im1_2Fimage_2F0.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"wall_time": 1459200389.117463, "width": 4, "height": 4, "step": 0, "query": "tag=im1%2Fimage%2F0&index=0&run=run2"}]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/individualAudio_index_0_tag_au1_2Faudio_2F0_run_run1.wav b/tensorflow/tensorboard/demo/data/individualAudio_index_0_tag_au1_2Faudio_2F0_run_run1.wav
deleted file mode 100644
index f1d24adc0cef5a734e07e8899b9abf8ae26fa228..0000000000000000000000000000000000000000
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diff --git a/tensorflow/tensorboard/demo/data/logdir b/tensorflow/tensorboard/demo/data/logdir
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index b6362b45d7..0000000000
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diff --git a/tensorflow/tensorboard/demo/data/runs.json b/tensorflow/tensorboard/demo/data/runs.json
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diff --git a/tensorflow/tensorboard/demo/data/scalars.json b/tensorflow/tensorboard/demo/data/scalars.json
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+++ /dev/null
@@ -1 +0,0 @@
-{"run2": {"foo/cos": [[0.0, 0, 2.0], [10.0, 1, 1.0806045532226562], [20.0, 2, -0.832293689250946], [30.0, 3, -1.979984998703003], [40.0, 4, -1.3072872161865234]], "bar/square": [[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]], "foo/square": [[0.0, 0, 0.0], [10.0, 1, 2.0], [20.0, 2, 8.0], [30.0, 3, 18.0], [40.0, 4, 32.0]]}, "run1": {"foo/sin": [[0.0, 0, 0.0], [10.0, 1, 0.8414709568023682], [20.0, 2, 0.9092974066734314], [30.0, 3, 0.14112000167369843], [40.0, 4, -0.756802499294281]], "foo/cos": [[0.0, 0, 1.0], [10.0, 1, 0.5403022766113281], [20.0, 2, -0.416146844625473], [30.0, 3, -0.9899924993515015], [40.0, 4, -0.6536436080932617]], "bar/square": [[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]], "foo/square": [[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]}}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_bar_2Fsquare.json b/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_bar_2Fsquare.json
deleted file mode 100644
index 6d584fb4a9..0000000000
--- a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_bar_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fcos.json b/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fcos.json
deleted file mode 100644
index 025eaa16e9..0000000000
--- a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fcos.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 1.0], [10.0, 1, 0.5403022766113281], [20.0, 2, -0.416146844625473], [30.0, 3, -0.9899924993515015], [40.0, 4, -0.6536436080932617]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsin.json b/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsin.json
deleted file mode 100644
index eae69dd78f..0000000000
--- a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsin.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 0.8414709568023682], [20.0, 2, 0.9092974066734314], [30.0, 3, 0.14112000167369843], [40.0, 4, -0.756802499294281]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsquare.json b/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsquare.json
deleted file mode 100644
index 6d584fb4a9..0000000000
--- a/tensorflow/tensorboard/demo/data/scalars_run_run1_tag_foo_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_bar_2Fsquare.json b/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_bar_2Fsquare.json
deleted file mode 100644
index 6d584fb4a9..0000000000
--- a/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_bar_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 1.0], [20.0, 2, 4.0], [30.0, 3, 9.0], [40.0, 4, 16.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fcos.json b/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fcos.json
deleted file mode 100644
index dd3593f9d1..0000000000
--- a/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fcos.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 2.0], [10.0, 1, 1.0806045532226562], [20.0, 2, -0.832293689250946], [30.0, 3, -1.979984998703003], [40.0, 4, -1.3072872161865234]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fsquare.json b/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fsquare.json
deleted file mode 100644
index 0ff9ef0551..0000000000
--- a/tensorflow/tensorboard/demo/data/scalars_run_run2_tag_foo_2Fsquare.json
+++ /dev/null
@@ -1 +0,0 @@
-[[0.0, 0, 0.0], [10.0, 1, 2.0], [20.0, 2, 8.0], [30.0, 3, 18.0], [40.0, 4, 32.0]]
\ No newline at end of file
diff --git a/tensorflow/tensorboard/http_api.md b/tensorflow/tensorboard/http_api.md
deleted file mode 100644
index c2885daf93..0000000000
--- a/tensorflow/tensorboard/http_api.md
+++ /dev/null
@@ -1,402 +0,0 @@
-# Tensorboard client-server HTTP API
-
-## Runs, Tags, and Tag Types
-
-TensorBoard data is organized around the concept of a `run`, which represents
-all the related data thrown off by a single execution of TensorFlow, a `tag`,
-which groups values of data that come from the same source within a TensorFlow
-run, and `tag types`, which are our way of distinguishing different types of
-data that have fundamentally different representations and should be processed
-on different code paths. For example, a "train" run may have a `scalars`
-tag that represents the learning rate, another `scalars` tag that
-represents the value of the objective function, a `histograms` tag that reveals
-information on weights in a particular layer over time, and an `images` tag that
-shows input images flowing into the system. The "eval" run might have an
-entirely different set of tag names, or some duplicated tag names.
-
-The currently supported tag types are `scalars`, `images`, `audio`,
-`histograms`, `graph` and `run_metadata`. Each tag type corresponds to a route
-(documented below) for retrieving tag data of that type.
-
-All of the data provided comes from TensorFlow events files ('\*.tfevents\*'),
-which are written using the SummaryWriter class
-(tensorflow/python/training/summary_writer.py), and the data is generated by
-summary ops (tensorflow/python/ops/summary_ops.py). The `scalars` come from the
-`ScalarSummary` op, the `histograms` from the `HistogramSummary`, the `audio`
-from the `AudioSummary`, and the `images` from `ImageSummary`. The tag type
-`graph` is special in that it is not a collection of tags of that type, but a
-boolean denoting if there is a graph definition associated with the run. The tag
-is provided to the summary op (usually as a constant).
-
-## `data/logdir`
-
-Returns a JSON object with a key "logdir" that maps to the `logdir` argument
-(string) with which Tensorboard started up. Example:
-`{logdir: '/foo/logdir/argument'}`
-
-The `logdir` argument is the path of the directory that contains events files.
-
-## `data/plugins_listing`
-
-Returns a dict mapping from plugin name to a boolean indicating whether the
-plugin is active. A plugin might be inactive, for instance, if it lacks relevant
-data. Every plugin has a key. This route helps the frontend avoid issuing
-requests to an inactive plugin - the routes of an inactive plugin do not work.
-
-## `data/runs`
-
-Returns an array containing the names of all the runs known to the
-TensorBoard backend at this time. Each entry is a string corresponding
-to a single run.
-
-We guarantee that as new runs are created in the log directory, they
-will always appear at the end of the list returned by this route. That
-is, the order of runs is persistent, and the result of this route is an
-&ldquo;append-only&rdquo; list.
-
-Example response:
-
-    ["train_run", "eval"]
-
-## `/data/plugin/scalars/tags`
-
-Returns a dictionary mapping from `run_name` (quoted string) to arrays of
-`tag_name` (quoted string), where each array contains the names of all
-scalar tags present in the corresponding run. Here is an example:
-
-    {
-      "train_run": ["xent", "loss", "learning_rate"],
-      "eval": ["precision", "recall"]
-    }
-
-Note that runs without any scalar tags are included as keys with value the
-empty array.
-
-## `/data/plugin/scalars/scalars?run=foo&tag=bar`
-
-Returns an array of event_accumulator.SimpleValueEvents ([wall_time, step,
-value]) for the given run and tag. wall_time is seconds since epoch.
-
-Example:
-
-    [
-      [1443856985.705543, 1448, 0.7461960315704346],  # wall_time, step, value
-      [1443857105.704628, 3438, 0.5427092909812927],
-      [1443857225.705133, 5417, 0.5457325577735901],
-      ...
-    ]
-
-If the format parameter is set to 'csv', the response will instead be in CSV
-format:
-
-    Wall time,step,value
-    1443856985.705543,1448,0.7461960315704346
-    1443857105.704628,3438,0.5427092909812927
-    1443857225.705133,5417,0.5457325577735901
-
-## `/data/plugin/histograms/tags`
-
-Returns a dictionary mapping from `run_name` (quoted string) to arrays of
-`tag_name` (quoted string), where each array contains the names of all
-histogram tags present in the corresponding run. Here is an example:
-
-    {
-      "train_run": ["foo_histogram", "bar_histogram"],
-      "eval": ["foo_histogram", "bar_histogram"]
-    }
-
-Note that runs without any histogram tags are included as keys with
-value the empty array.
-
-## `/data/plugin/histograms/histograms?run=foo&tag=bar`
-
-Returns an array of event_accumulator.HistogramEvents ([wall_time, step,
-HistogramValue]) for the given run and tag. A HistogramValue is [min, max, num,
-sum, sum_squares, bucket_limit, bucket]. wall_time is seconds since epoch.
-
-Annotated Example: (note - real data is higher precision)
-
-    [
-      [
-        1443871386.185149, # wall_time
-        235166,            # step
-        [
-          -0.66,           # minimum value
-          0.44,            # maximum value
-          8.0,             # number of items in the histogram
-          -0.80,           # sum of items in the histogram
-          0.73,            # sum of squares of items in the histogram
-          [-0.68, -0.62, -0.292, -0.26, -0.11, -0.10, -0.08, -0.07, -0.05,
-          -0.0525, -0.0434, -0.039, -0.029, -0.026, 0.42, 0.47, 1.8e+308],
-                          # the right edge of each bucket
-        [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0,
-          1.0, 0.0]        # the number of elements within each bucket
-        ]
-      ]
-    ]
-
-## `/data/plugin/distributions/tags`
-
-Returns a dictionary mapping from `run_name` (quoted string) to arrays of
-`tag_name` (quoted string), where each array contains the names of all
-distribution tags present in the corresponding run. Here is an example:
-
-    {
-      "train_run": ["foo_histogram", "bar_histogram"],
-      "eval": ["foo_histogram", "bar_histogram"]
-    }
-
-Note that runs without any distribution tags are included as keys with
-value the empty array.
-
-## `/data/plugin/distributions/distributions?run=foo&tag=bar`
-
-Returns an array of event_accumulator.CompressedHistogramEvents ([wall_time,
-step, CompressedHistogramValues]) for the given run and tag.
-
-CompressedHistogramValues is a list of namedtuples with each tuple specifying
-a basis point (bps) as well as an interpolated value of the histogram value
-at that basis point. A basis point is 1/100 of a percent.
-
-The current compression strategy is to choose basis points that correspond to
-the median and bands of 1SD, 2SD, and 3SDs around the median. Note that the
-current compression strategy does not work well for representing multimodal
-data -- this is something that will be improved in a later iteration.
-
-Annotated Example: (note - real data is higher precision)
-
-    [
-      [
-        1441154832.580509,   # wall_time
-        5,                   # step
-        [ [0, -3.67],        # CompressedHistogramValue for 0th percentile
-          [2500, -4.19],     # CompressedHistogramValue for 25th percentile
-          [5000, 6.29],
-          [7500, 1.64],
-          [10000, 3.67]
-        ]
-      ],
-      ...
-    ]
-
-## `/data/plugin/images/images?run=foo&tag=bar`
-
-Gets a sample of ImageMetadatas for the given run and tag.
-
-Returns an array of objects containing information about available images,
-crucially including the query parameter that may be used to retrieve that image.
-(See /data/plugin/images/individualImage for details.)
-
-For example:
-
-      {
-        "width": 28,                 # width in pixels
-        "height": 28,                # height in pixels
-        "wall_time": 1440210599.246, # time in seconds since epoch
-        "step": 63702821,            # number of steps that have passed
-        "query": "index=0&tagname=input%2Fimage%2F2&run=train"
-                                     # param for /individualImage
-      }
-
-## `/data/plugin/images/individualImage?{{query}}`
-
-Retrieves an individual image. The image query should not be generated by the
-frontend, but instead acquired from calling the /images route (the image
-metadata objects contain the query to use). The response is the image itself
-with mime-type 'image/png'.
-
-Note that the query is not guaranteed to always refer to the same image even
-within a single run, as images may be removed from the sampling reservoir and
-replaced with other images. (See Notes for details on the reservoir sampling.)
-
-An example call to this route would look like this:
-/data/plugin/images/individualImage?index=0&tagname=input%2Fimage%2F2&run=train
-
-## `/data/plugin/images/tags`
-
-Returns a dictionary mapping from `run_name` (quoted string) to arrays of
-`tag_name` (quoted string), where each array contains the names of all image
-tags present in the corresponding run. Here is an example:
-
-    {
-      "train": ["foo_image", "bar_image"],
-      "eval": ["foo_image", "bar_image"]
-    }
-
-Note that runs without any image tags are included as keys with value the empty
-array.
-
-## `/data/plugin/audio/audio?run=foo&tag=bar`
-
-Gets a sample of AudioMetadatas for the given run and tag.
-
-Returns an array of objects containing information about available audio,
-crucially including the query parameter that may be used to retrieve that audio.
-(See /data/plugin/audio/individualAudio for details.)
-
-For example:
-
-      {
-        "wall_time": 1440210599.246, # time in seconds since epoch
-        "step": 63702821,            # number of steps that have passed
-        "content_type": "audio/wav"  # the MIME-type of the audio
-        "query": "index=0&tagname=input%2Faudio%2F2&run=train"
-                                     # param for /individualAudio
-      }
-
-## `/data/plugin/audio/individualAudio?{{query}}`
-
-Retrieves an individual audio clip. The audio query should not be generated by
-the frontend, but instead acquired from calling the /audio route (the audio
-metadata objects contain the query to use). The response is the audio itself
-with an appropriate Content-Type header set.
-
-Note that the query is not guaranteed to always refer to the same clip even
-within a single run, as audio may be removed from the sampling reservoir and
-replaced with other clips. (See Notes for details on the reservoir sampling.)
-
-An example call to this route would look like this:
-/individualAudio?index=0&tagname=input%2Faudio%2F2&run=train
-
-## `/data/plugin/audio/tags`
-
-Returns a dictionary mapping from `run_name` (quoted string) to arrays of
-`tag_name` (quoted string), where each array contains the names of all audio
-tags present in the corresponding run. Here is an example:
-
-    {
-      "train": ["foo_audio", "bar_audio"],
-      "eval": ["foo_audio", "bar_audio"],
-    }
-
-Note that runs without any audio tags are included as keys with value the empty
-array.
-
-## `/data/plugin/graphs/runs`
-
-Returns a list of runs that have associated graphs.
-
-For example:
-
-    ["train"]
-
-## `/data/plugin/graphs/graph?run=foo&limit_attr_size=1024&large_attrs_key=key`
-
-Returns the graph definition for the given run in pbtxt format. The
-graph is composed of a list of nodes, where each node is a specific
-TensorFlow operation which takes as inputs other nodes (operations).
-
-The query parameters `limit_attr_size` and `large_attrs_key` are optional.
-
-`limit_attr_size` specifies the maximum allowed size in bytes, before the
-attribute is considered large and filtered out of the graph. If specified,
-it must be an int and > 0. If not specified, no filtering is applied.
-
-`large_attrs_key` is the attribute key that will be used for storing
-attributes that are too large. The value of this key (list of strings)
-should be used by the client in order to determine which attributes
-have been filtered. Must be specified if `limit_attr_size` is specified.
-
-For the query
-
-    /data/plugin/graphs/graph?run=foo&limit_attr_size=1024&large_attrs_key=_too_large,
-
-here is an example pbtxt response of a graph with 3 nodes, where the second
-node had two large attributes "a" and "b" that were filtered out (size > 1024):
-
-    node {
-      op: "Input"
-      name: "A"
-    }
-    node {
-      op: "Input"
-      name: "B"
-      attr {
-        key: "small_attr"
-        value: {
-          s: "some string"
-        }
-      }
-      attr {
-        key: "_too_large"
-        value {
-          list {
-            s: "a"
-            s: "b"
-          }
-        }
-      }
-    }
-    node {
-      op: "MatMul"
-      name: "C"
-      input: "A"
-      input: "B"
-    }
-
-Prior to filtering, the original node "B" had the following content:
-
-    node {
-      op: "Input"
-      name: "B"
-      attr {
-        key: "small_attr"
-        value: {
-          s: "some string"
-        }
-      }
-      attr {
-        key: "a"
-        value { Very large object... }
-      }
-      attr {
-        key: "b"
-        value { Very large object... }
-      }
-    }
-
-## `/data/run_metadata?run=foo&tag=bar`
-
-Given a run and tag, returns the metadata of a particular
-`session.run()` as a gzipped, pbtxt serialized [`RunMetadata`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto)
-proto. For example:
-
-    step_stats {
-      dev_stats {
-        device: "/job:localhost/replica:0/task:0/cpu:0"
-        node_stats {
-          node_name: "_SOURCE"
-          all_start_micros: 1458337695775395
-          op_start_rel_micros: 11
-          op_end_rel_micros: 12
-          all_end_rel_micros: 38
-          memory {
-            allocator_name: "cpu"
-          }
-          timeline_label: "_SOURCE = NoOp()"
-          scheduled_micros: 1458337695775363
-        }
-      }
-    }
-
-## Notes
-
-All returned values, histograms, audio, and images are returned in the order
-they were written by TensorFlow (which should correspond to increasing
-`wall_time` order, but may not necessarily correspond to increasing step count
-if the process had to restart from a previous checkpoint).
-
-The returned values may be downsampled using reservoir sampling, which is
-configurable by the TensorBoard server. When downsampling occurs, the server
-guarantees that different tags will all sample at the same sequence of indices,
-so that if there are two tags `A` and `B` which are related so that `A[i] ~
-B[i]` for all `i`, then `D(A)[i] ~ D(B)[i]` for all `i`, where `D` represents
-the downsampling operation.
-
-The reservoir sampling puts an upper bound on the number of items that will be
-returned for a given run-tag combination, and guarantees that all items are
-equally likely to be in the final sample (ie it is a uniform distribution over
-the values), with the proviso that the most recent individual item is always
-included in the sample.
-
-The reservoir sizes are configurable on a per-tag type basis.
diff --git a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/BUILD b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/BUILD
deleted file mode 100644
index f1f7746ff8..0000000000
--- a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/BUILD
+++ /dev/null
@@ -1,56 +0,0 @@
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-java_binary(
-    name = "Vulcanize",
-    srcs = ["Vulcanize.java"],
-    jvm_flags = [
-        "-Xss20m",  # JSCompiler needs big stacks for recursive parsing
-        "-XX:+UseParallelGC",  # Best GC when app isn't latency sensitive
-        "-Djava.util.logging.SimpleFormatter.format='%1$$tY-%1$$tm-%1$$td %1$$tH:%1$$tM:%1$$tS.%1$$tL %4$$-6s %5$$s%6$$s%n'",  # Less log spam
-    ],
-    visibility = ["//visibility:public"],
-    deps = [
-        "@com_google_guava",
-        "@com_google_protobuf_java",
-        "@io_bazel_rules_closure//closure/compiler",
-        "@io_bazel_rules_closure//java/io/bazel/rules/closure:webpath",
-        "@io_bazel_rules_closure//java/io/bazel/rules/closure/webfiles:build_info_java_proto",
-        "@io_bazel_rules_closure//java/org/jsoup/nodes",
-        "@org_jsoup",
-    ],
-)
-
-java_binary(
-    name = "Zipper",
-    srcs = ["Zipper.java"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "@com_google_guava",
-        "@com_google_protobuf_java",
-        "@io_bazel_rules_closure//java/io/bazel/rules/closure/webfiles",
-        "@io_bazel_rules_closure//java/io/bazel/rules/closure/webfiles:build_info_java_proto",
-    ],
-)
-
-# These JS files are always taken into consideration by the Closure Compiler
-# when vulcanizing, per vulcanize.bzl.
-filegroup(
-    name = "jslibs",
-    srcs = [
-        # Ordering probably matters
-        "@com_google_javascript_closure_compiler_externs",
-        "@com_google_javascript_closure_compiler_externs_polymer",
-        "externs.js",
-        "@com_google_javascript_closure_library//:closure/goog/base.js",
-        "@com_google_javascript_closure_library//:closure/goog/deps.js",
-    ],
-    visibility = ["//visibility:public"],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    tags = ["notsan"],
-)
diff --git a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
deleted file mode 100644
index a4366d7cfa..0000000000
--- a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Vulcanize.java
+++ /dev/null
@@ -1,583 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-package org.tensorflow.tensorboard.vulcanize;
-
-import static com.google.common.base.Preconditions.checkNotNull;
-import static com.google.common.base.Verify.verify;
-import static com.google.common.base.Verify.verifyNotNull;
-import static java.nio.charset.StandardCharsets.UTF_8;
-
-import com.google.common.base.CharMatcher;
-import com.google.common.base.Joiner;
-import com.google.common.base.Optional;
-import com.google.common.base.Splitter;
-import com.google.common.collect.HashMultimap;
-import com.google.common.collect.ImmutableList;
-import com.google.common.collect.ImmutableMultimap;
-import com.google.common.collect.ImmutableSet;
-import com.google.common.collect.Iterables;
-import com.google.common.collect.Lists;
-import com.google.common.collect.Multimap;
-import com.google.javascript.jscomp.BasicErrorManager;
-import com.google.javascript.jscomp.CheckLevel;
-import com.google.javascript.jscomp.CompilationLevel;
-import com.google.javascript.jscomp.Compiler;
-import com.google.javascript.jscomp.CompilerOptions;
-import com.google.javascript.jscomp.DiagnosticGroup;
-import com.google.javascript.jscomp.DiagnosticGroups;
-import com.google.javascript.jscomp.DiagnosticType;
-import com.google.javascript.jscomp.JSError;
-import com.google.javascript.jscomp.ModuleIdentifier;
-import com.google.javascript.jscomp.PropertyRenamingPolicy;
-import com.google.javascript.jscomp.Result;
-import com.google.javascript.jscomp.SourceFile;
-import com.google.javascript.jscomp.WarningsGuard;
-import com.google.protobuf.TextFormat;
-import io.bazel.rules.closure.Webpath;
-import io.bazel.rules.closure.webfiles.BuildInfo.Webfiles;
-import io.bazel.rules.closure.webfiles.BuildInfo.WebfilesSource;
-import java.io.ByteArrayInputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.nio.file.StandardOpenOption;
-import java.util.ArrayDeque;
-import java.util.ArrayList;
-import java.util.Collection;
-import java.util.Deque;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.LinkedHashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Set;
-import java.util.regex.Matcher;
-import java.util.regex.Pattern;
-import java.util.stream.Collectors;
-import org.jsoup.Jsoup;
-import org.jsoup.nodes.Attribute;
-import org.jsoup.nodes.Comment;
-import org.jsoup.nodes.DataNode;
-import org.jsoup.nodes.Document;
-import org.jsoup.nodes.Element;
-import org.jsoup.nodes.Html5Printer;
-import org.jsoup.nodes.Node;
-import org.jsoup.nodes.TextNode;
-import org.jsoup.parser.Parser;
-import org.jsoup.parser.Tag;
-
-/** Simple one-off solution for TensorBoard vulcanization. */
-public final class Vulcanize {
-
-  private static final Pattern IGNORE_PATHS_PATTERN =
-      Pattern.compile("/(?:polymer|marked-element)/.*");
-
-  private static final ImmutableSet<String> EXTRA_JSDOC_TAGS =
-      ImmutableSet.of("attribute", "hero", "group", "required");
-
-  private static final Pattern WEBPATH_PATTERN = Pattern.compile("//~~WEBPATH~~([^\n]+)");
-
-  private static final Parser parser = Parser.htmlParser();
-  private static final Map<Webpath, Path> webfiles = new HashMap<>();
-  private static final Set<Webpath> alreadyInlined = new HashSet<>();
-  private static final Set<String> legalese = new HashSet<>();
-  private static final List<String> licenses = new ArrayList<>();
-  private static final List<Webpath> stack = new ArrayList<>();
-  private static final List<SourceFile> externs = new ArrayList<>();
-  private static final List<SourceFile> sourcesFromJsLibraries = new ArrayList<>();
-  private static final Map<Webpath, String> sourcesFromScriptTags = new LinkedHashMap<>();
-  private static final Map<Webpath, Node> sourceTags = new LinkedHashMap<>();
-  private static final Multimap<Webpath, String> suppressions = HashMultimap.create();
-  private static CompilationLevel compilationLevel;
-  private static Webpath outputPath;
-  private static Node firstCompiledScript;
-  private static Node licenseComment;
-  private static int insideDemoSnippet;
-  private static boolean testOnly;
-
-  public static void main(String[] args) throws IOException {
-    compilationLevel = CompilationLevel.fromString(args[0]);
-    testOnly = args[1].equals("true");
-    Webpath inputPath = Webpath.get(args[2]);
-    outputPath = Webpath.get(args[3]);
-    Path output = Paths.get(args[4]);
-    for (int i = 5; i < args.length; i++) {
-      if (args[i].endsWith(".js")) {
-        String code = new String(Files.readAllBytes(Paths.get(args[i])), UTF_8);
-        SourceFile sourceFile = SourceFile.fromCode(args[i], code);
-        if (code.contains("@externs")) {
-          externs.add(sourceFile);
-        } else {
-          sourcesFromJsLibraries.add(sourceFile);
-        }
-        continue;
-      }
-      if (!args[i].endsWith(".pbtxt")) {
-        continue;
-      }
-      Webfiles manifest = loadWebfilesPbtxt(Paths.get(args[i]));
-      for (WebfilesSource src : manifest.getSrcList()) {
-        webfiles.put(Webpath.get(src.getWebpath()), Paths.get(src.getPath()));
-      }
-    }
-    stack.add(inputPath);
-    Document document = parse(Files.readAllBytes(webfiles.get(inputPath)));
-    transform(document);
-    compile();
-    if (licenseComment != null) {
-      licenseComment.attr("comment", String.format("\n%s\n", Joiner.on("\n\n").join(licenses)));
-    }
-    Files.write(
-        output,
-        Html5Printer.stringify(document).getBytes(UTF_8),
-        StandardOpenOption.WRITE,
-        StandardOpenOption.CREATE,
-        StandardOpenOption.TRUNCATE_EXISTING);
-  }
-
-  private static void transform(Node root) throws IOException {
-    Node node = checkNotNull(root);
-    Node newNode;
-    while (true) {
-      newNode = enterNode(node);
-      if (node.equals(root)) {
-        root = newNode;
-      }
-      node = newNode;
-      if (node.childNodeSize() > 0) {
-        node = node.childNode(0);
-      } else {
-        while (true) {
-          newNode = leaveNode(node);
-          if (node.equals(root)) {
-            root = newNode;
-          }
-          node = newNode;
-          if (node.equals(root)) {
-            return;
-          }
-          Node next = node.nextSibling();
-          if (next == null) {
-            if (node.parentNode() == null) {
-              return;
-            }
-            node = verifyNotNull(node.parentNode(), "unexpected root: %s", node);
-          } else {
-            node = next;
-            break;
-          }
-        }
-      }
-    }
-  }
-
-  private static Node enterNode(Node node) throws IOException {
-    if (node.nodeName().equals("demo-snippet")) {
-      insideDemoSnippet++;
-    }
-    if (insideDemoSnippet > 0) {
-      return node;
-    }
-    if (node instanceof Element) {
-      if (!getAttrTransitive(node, "vulcanize-noinline").isPresent()) {
-        if (node.nodeName().equals("link") && node.attr("rel").equals("import")) {
-          // Inline HTML.
-          node = visitHtmlImport(node);
-        } else if (node.nodeName().equals("script")
-            && !shouldIgnoreUri(node.attr("src"))
-            && !node.hasAttr("jscomp-ignore")) {
-          node = visitScript(node);
-        } else if (node.nodeName().equals("link")
-            && node.attr("rel").equals("stylesheet")
-            && !node.attr("href").isEmpty()
-            && !shouldIgnoreUri(node.attr("href"))) {
-          node = visitStylesheet(node);
-        }
-      }
-      rootifyAttribute(node, "href");
-      rootifyAttribute(node, "src");
-      rootifyAttribute(node, "action");
-      rootifyAttribute(node, "assetpath");
-    } else if (node instanceof Comment) {
-      String text = ((Comment) node).getData();
-      if (text.contains("@license")) {
-        handleLicense(text);
-        if (licenseComment == null) {
-          licenseComment = node;
-        } else {
-          node = replaceNode(node, new TextNode("", node.baseUri()));
-        }
-      } else {
-        node = replaceNode(node, new TextNode("", node.baseUri()));
-      }
-    }
-    return node;
-  }
-
-  private static Node leaveNode(Node node) {
-    if (node instanceof Document) {
-      stack.remove(stack.size() - 1);
-    } else if (node.nodeName().equals("demo-snippet")) {
-      insideDemoSnippet--;
-    }
-    return node;
-  }
-
-  private static Node visitHtmlImport(Node node) throws IOException {
-    Webpath href = me().lookup(Webpath.get(node.attr("href")));
-    if (alreadyInlined.add(href)) {
-      stack.add(href);
-      Document subdocument = parse(Files.readAllBytes(getWebfile(href)));
-      for (Attribute attr : node.attributes()) {
-        subdocument.attr(attr.getKey(), attr.getValue());
-      }
-      return replaceNode(node, subdocument);
-    } else {
-      return replaceNode(node, new TextNode("", node.baseUri()));
-    }
-  }
-
-  private static Node visitScript(Node node) throws IOException {
-    Webpath path;
-    String script;
-    if (node.attr("src").isEmpty()) {
-      path = makeSyntheticName(".js");
-      script = getInlineScriptFromNode(node);
-    } else {
-      path = me().lookup(Webpath.get(node.attr("src")));
-      script = new String(Files.readAllBytes(getWebfile(path)), UTF_8);
-    }
-    boolean wantsMinify = getAttrTransitive(node, "jscomp-minify").isPresent();
-    if (node.attr("src").endsWith(".min.js")
-        || getAttrTransitive(node, "jscomp-nocompile").isPresent()
-        || wantsMinify) {
-      if (wantsMinify) {
-        script = minify(path, script);
-      }
-      Node newScript =
-          new Element(Tag.valueOf("script"), node.baseUri(), node.attributes())
-              .appendChild(new DataNode(script, node.baseUri()))
-              .removeAttr("src")
-              .removeAttr("jscomp-minify")
-              .removeAttr("jscomp-nocompile");
-      if (firstCompiledScript != null) {
-        firstCompiledScript.before(newScript);
-        return replaceNode(node, new TextNode("", node.baseUri()));
-      } else {
-        return replaceNode(node, newScript);
-      }
-    } else {
-      if (firstCompiledScript == null) {
-        firstCompiledScript = node;
-      }
-      sourcesFromScriptTags.put(path, script);
-      sourceTags.put(path, node);
-      Optional<String> suppress = getAttrTransitive(node, "jscomp-suppress");
-      if (suppress.isPresent()) {
-        if (suppress.get().isEmpty()) {
-          suppressions.put(path, "*");
-        } else {
-          suppressions.putAll(path, Splitter.on(' ').split(suppress.get()));
-        }
-      }
-      return node;
-    }
-  }
-
-  private static Node visitStylesheet(Node node) throws IOException {
-    Webpath href = me().lookup(Webpath.get(node.attr("href")));
-    return replaceNode(
-        node,
-        new Element(Tag.valueOf("style"), node.baseUri(), node.attributes())
-            .appendChild(
-                new DataNode(
-                    new String(Files.readAllBytes(getWebfile(href)), UTF_8), node.baseUri()))
-            .removeAttr("rel")
-            .removeAttr("href"));
-  }
-
-  private static Optional<String> getAttrTransitive(Node node, String attr) {
-    while (node != null) {
-      if (node.hasAttr(attr)) {
-        return Optional.of(node.attr(attr));
-      }
-      node = node.parent();
-    }
-    return Optional.absent();
-  }
-
-  private static Node replaceNode(Node oldNode, Node newNode) {
-    oldNode.replaceWith(newNode);
-    return newNode;
-  }
-
-  private static Path getWebfile(Webpath path) {
-    return verifyNotNull(webfiles.get(path), "Bad ref: %s -> %s", me(), path);
-  }
-
-  private static void compile() {
-    if (sourcesFromScriptTags.isEmpty()) {
-      return;
-    }
-
-    CompilerOptions options = new CompilerOptions();
-    compilationLevel.setOptionsForCompilationLevel(options);
-
-    // Nice options.
-    options.setColorizeErrorOutput(true);
-    options.setContinueAfterErrors(true);
-    options.setLanguageIn(CompilerOptions.LanguageMode.ECMASCRIPT_2016);
-    options.setLanguageOut(CompilerOptions.LanguageMode.ECMASCRIPT5);
-    options.setGenerateExports(true);
-    options.setStrictModeInput(false);
-    options.setExtraAnnotationNames(EXTRA_JSDOC_TAGS);
-
-    // So we can chop JS binary back up into the original script tags.
-    options.setPrintInputDelimiter(true);
-    options.setInputDelimiter("//~~WEBPATH~~%name%");
-
-    // Optimizations that are too advanced for us right now.
-    options.setPropertyRenaming(PropertyRenamingPolicy.OFF);
-    options.setCheckGlobalThisLevel(CheckLevel.OFF);
-    options.setRemoveUnusedPrototypeProperties(false);
-    options.setRemoveUnusedPrototypePropertiesInExterns(false);
-    options.setRemoveUnusedClassProperties(false);
-
-    // Dependency management.
-    options.setClosurePass(true);
-    options.setManageClosureDependencies(true);
-    options.getDependencyOptions().setDependencyPruning(true);
-    options.getDependencyOptions().setDependencySorting(true);
-    options.getDependencyOptions().setMoocherDropping(false);
-    options.getDependencyOptions()
-        .setEntryPoints(
-            sourceTags
-                .keySet()
-                .stream()
-                .map(Webpath::toString)
-                .map(ModuleIdentifier::forFile)
-                .collect(Collectors.toList()));
-
-    // Polymer pass.
-    options.setPolymerVersion(1);
-
-    // Debug flags.
-    if (testOnly) {
-      options.setPrettyPrint(true);
-      options.setGeneratePseudoNames(true);
-      options.setExportTestFunctions(true);
-    }
-
-    // Don't print warnings from <script jscomp-suppress="group1 group2" ...> tags.
-    ImmutableMultimap<DiagnosticType, String> diagnosticGroups = initDiagnosticGroups();
-    options.addWarningsGuard(
-        new WarningsGuard() {
-          @Override
-          public CheckLevel level(JSError error) {
-            if (error.sourceName == null) {
-              return null;
-            }
-            if (error.sourceName.startsWith("javascript/externs")
-                || error.sourceName.contains("com_google_javascript_closure_compiler_externs")) {
-              // TODO(jart): Figure out why these "mismatch of the removeEventListener property on
-              //             type" warnings are showing up.
-              //             https://github.com/google/closure-compiler/pull/1959
-              return CheckLevel.OFF;
-            }
-            if (IGNORE_PATHS_PATTERN.matcher(error.sourceName).matches()) {
-              return CheckLevel.OFF;
-            }
-            if (error.sourceName.startsWith("/tf-graph")
-                && error.getType().key.equals("JSC_VAR_MULTIPLY_DECLARED_ERROR")) {
-              return CheckLevel.OFF; // TODO(jart): Remove when tf-graph is ES6 modules.
-            }
-            if (error.getType().key.equals("JSC_POLYMER_UNQUALIFIED_BEHAVIOR")
-                || error.getType().key.equals("JSC_POLYMER_UNANNOTATED_BEHAVIOR")) {
-              return CheckLevel.OFF; // TODO(jart): What is wrong with this thing?
-            }
-            Collection<String> codes = suppressions.get(Webpath.get(error.sourceName));
-            if (codes.contains("*") || codes.contains(error.getType().key)) {
-              return CheckLevel.OFF;
-            }
-            for (String group : diagnosticGroups.get(error.getType())) {
-              if (codes.contains(group)) {
-                return CheckLevel.OFF;
-              }
-            }
-            return null;
-          }
-        });
-
-    // Get reverse topological script tags and their web paths, which js_library stuff first.
-    List<SourceFile> sauce = Lists.newArrayList(sourcesFromJsLibraries);
-    for (Map.Entry<Webpath, String> source : sourcesFromScriptTags.entrySet()) {
-      sauce.add(SourceFile.fromCode(source.getKey().toString(), source.getValue()));
-    }
-
-    // Compile everything into a single script.
-    Compiler compiler = new Compiler();
-    compiler.disableThreads();
-    Result result = compiler.compile(externs, sauce, options);
-    if (!result.success) {
-      System.exit(1);
-    }
-    String jsBlob = compiler.toSource();
-
-    // Split apart the JS blob and put it back in the original <script> locations.
-    Deque<Map.Entry<Webpath, Node>> tags = new ArrayDeque<>();
-    tags.addAll(sourceTags.entrySet());
-    Matcher matcher = WEBPATH_PATTERN.matcher(jsBlob);
-    verify(matcher.find(), "Nothing found in compiled JS blob!");
-    Webpath path = Webpath.get(matcher.group(1));
-    int start = 0;
-    while (matcher.find()) {
-      if (sourceTags.containsKey(path)) {
-        swapScript(tags, path, jsBlob.substring(start, matcher.start()));
-        start = matcher.start();
-      }
-      path = Webpath.get(matcher.group(1));
-    }
-    swapScript(tags, path, jsBlob.substring(start));
-    verify(tags.isEmpty(), "<script> wasn't compiled: %s", tags);
-  }
-
-  private static void swapScript(
-      Deque<Map.Entry<Webpath, Node>> tags, Webpath path, String script) {
-    verify(!tags.isEmpty(), "jscomp compiled %s after last <script>?!", path);
-    Webpath want = tags.getFirst().getKey();
-    verify(path.equals(want), "<script> tag for %s should come before %s", path, want);
-    Node tag = tags.removeFirst().getValue();
-    tag.replaceWith(
-        new Element(Tag.valueOf("script"), tag.baseUri())
-            .appendChild(new DataNode(script, tag.baseUri())));
-  }
-
-  private static String minify(Webpath path, String script) {
-    CompilerOptions options = new CompilerOptions();
-    options.skipAllCompilerPasses();
-    options.setLanguageIn(CompilerOptions.LanguageMode.ECMASCRIPT_2016);
-    options.setLanguageOut(CompilerOptions.LanguageMode.ECMASCRIPT5);
-    options.setContinueAfterErrors(true);
-    CompilationLevel.SIMPLE_OPTIMIZATIONS.setOptionsForCompilationLevel(options);
-    if (testOnly) {
-      options.setPrettyPrint(true);
-      options.setGeneratePseudoNames(true);
-    }
-    Compiler compiler = new Compiler(new JsPrintlessErrorManager());
-    compiler.disableThreads();
-    compiler.compile(
-        ImmutableList.<SourceFile>of(),
-        ImmutableList.of(SourceFile.fromCode(path.toString(), script)),
-        options);
-    return compiler.toSource();
-  }
-
-  private static void handleLicense(String text) {
-    if (legalese.add(CharMatcher.whitespace().removeFrom(text))) {
-      licenses.add(CharMatcher.anyOf("\r\n").trimFrom(text));
-    }
-  }
-
-  private static Webpath me() {
-    return Iterables.getLast(stack);
-  }
-
-  private static Webpath makeSyntheticName(String extension) {
-    String me = me().toString();
-    Webpath result = Webpath.get(me + extension);
-    int n = 2;
-    while (sourcesFromScriptTags.containsKey(result)) {
-      result = Webpath.get(String.format("%s-%d%s", me, n++, extension));
-    }
-    return result;
-  }
-
-  private static void rootifyAttribute(Node node, String attribute) {
-    String value = node.attr(attribute);
-    if (value.isEmpty()) {
-      return;
-    }
-    Webpath uri = Webpath.get(value);
-    if (webfiles.containsKey(uri)) {
-      node.attr(attribute, outputPath.getParent().relativize(uri).toString());
-    }
-  }
-
-  private static String getInlineScriptFromNode(Node node) {
-    StringBuilder sb = new StringBuilder();
-    for (Node child : node.childNodes()) {
-      if (child instanceof DataNode) {
-        sb.append(((DataNode) child).getWholeData());
-      }
-    }
-    return sb.toString();
-  }
-
-  private static Document parse(byte[] bytes) {
-    return parse(new ByteArrayInputStream(bytes));
-  }
-
-  private static Document parse(InputStream input) {
-    Document document;
-    try {
-      document = Jsoup.parse(input, null, "", parser);
-    } catch (IOException e) {
-      throw new AssertionError("I/O error when parsing byte array D:", e);
-    }
-    document.outputSettings().indentAmount(0);
-    document.outputSettings().prettyPrint(false);
-    return document;
-  }
-
-  private static Webfiles loadWebfilesPbtxt(Path path) throws IOException {
-    verify(path.toString().endsWith(".pbtxt"), "Not a pbtxt file: %s", path);
-    Webfiles.Builder build = Webfiles.newBuilder();
-    TextFormat.getParser().merge(new String(Files.readAllBytes(path), UTF_8), build);
-    return build.build();
-  }
-
-  private static boolean shouldIgnoreUri(String uri) {
-    return uri.startsWith("#")
-        || uri.endsWith("/")
-        || uri.contains("//")
-        || uri.startsWith("data:")
-        || uri.startsWith("javascript:")
-        // The following are intended to filter out URLs with Polymer variables.
-        || (uri.contains("[[") && uri.contains("]]"))
-        || (uri.contains("{{") && uri.contains("}}"));
-  }
-
-  private static ImmutableMultimap<DiagnosticType, String> initDiagnosticGroups() {
-    DiagnosticGroups groups = new DiagnosticGroups();
-    Multimap<DiagnosticType, String> builder = HashMultimap.create();
-    for (Map.Entry<String, DiagnosticGroup> group : groups.getRegisteredGroups().entrySet()) {
-      for (DiagnosticType type : group.getValue().getTypes()) {
-        builder.put(type, group.getKey());
-      }
-    }
-    return ImmutableMultimap.copyOf(builder);
-  }
-
-  private static final class JsPrintlessErrorManager extends BasicErrorManager {
-
-    @Override
-    public void println(CheckLevel level, JSError error) {}
-
-    @Override
-    public void printSummary() {}
-  }
-}
diff --git a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Zipper.java b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Zipper.java
deleted file mode 100644
index 31b3aa195e..0000000000
--- a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/Zipper.java
+++ /dev/null
@@ -1,73 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-package org.tensorflow.tensorboard.vulcanize;
-
-import static java.nio.charset.StandardCharsets.UTF_8;
-
-import com.google.protobuf.TextFormat;
-import io.bazel.rules.closure.webfiles.BuildInfo.WebfileInfo;
-import io.bazel.rules.closure.webfiles.BuildInfo.Webfiles;
-import io.bazel.rules.closure.webfiles.BuildInfo.WebfilesSource;
-import io.bazel.rules.closure.webfiles.WebfilesWriter;
-import java.io.IOException;
-import java.io.InputStream;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.nio.file.StandardOpenOption;
-import java.util.HashSet;
-import java.util.Set;
-import java.util.zip.Deflater;
-
-/**
- * Simple one-off solution for TensorBoard zipping of web_library rules.
- *
- * <p>This is intended to collect static assets for production web server deployment. The paths of
- * files inside the zip will be web paths, with the prefix slash removed. These files will be
- * topologically ordered, i.e. web files higher up in the build tree come first.
- */
-public final class Zipper {
-
-  public static void main(String[] args) throws IOException {
-    Set<String> alreadyZipped = new HashSet<>();
-    try (WebfilesWriter writer =
-        new WebfilesWriter(
-            Files.newByteChannel(
-                Paths.get(args[0]),
-                StandardOpenOption.WRITE,
-                StandardOpenOption.CREATE,
-                StandardOpenOption.TRUNCATE_EXISTING),
-            Deflater.BEST_SPEED)) {
-      for (int i = 1; i < args.length; i++) {
-        Webfiles manifest = loadWebfilesPbtxt(Paths.get(args[i]));
-        for (WebfilesSource src : manifest.getSrcList()) {
-          if (!alreadyZipped.add(src.getWebpath())) {
-            continue;
-          }
-          try (InputStream input = Files.newInputStream(Paths.get(src.getPath()))) {
-            writer.writeWebfile(
-                WebfileInfo.newBuilder().setWebpath(src.getWebpath()).build(), input);
-          }
-        }
-      }
-    }
-  }
-
-  private static Webfiles loadWebfilesPbtxt(Path path) throws IOException {
-    Webfiles.Builder build = Webfiles.newBuilder();
-    TextFormat.getParser().merge(new String(Files.readAllBytes(path), UTF_8), build);
-    return build.build();
-  }
-}
diff --git a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js b/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js
deleted file mode 100644
index 85d0ea80c0..0000000000
--- a/tensorflow/tensorboard/java/org/tensorflow/tensorboard/vulcanize/externs.js
+++ /dev/null
@@ -1,49 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-/**
- * @fileoverview Miscellaneous JSCompiler externs needed for TensorBoard.
- * @externs
- */
-
-/** @type {!Object} */ var _;
-/** @type {!Object} */ var d3;
-/** @type {!Object} */ var dagre;
-/** @type {!Object} */ var numeric;
-/** @type {!Object} */ var weblas;
-/** @type {!Object} */ var graphlib;
-/** @type {!Object} */ var Plottable;
-/** @type {!Object} */ var GroupEffect;
-/** @type {!Function|undefined} */ var ga;
-/** @type {!Function|undefined} */ var KeyframeEffect;
-
-/**
- * Some weird webcomponents-lite.js thing.
- * @type {!Function|undefined}
- */
-var wrap;
-
-/**
- * Some weird webcomponents-lite.js thing.
- * @type {!Function|undefined}
- */
-window.wrap;
-
-var HTMLImports;
-
-/**
- * @param {function()} callback
- * @param {!HTMLDocument=} opt_doc
- */
-HTMLImports.whenReady = function(callback, opt_doc) {};
diff --git a/tensorflow/tensorboard/main.py b/tensorflow/tensorboard/main.py
deleted file mode 100644
index 764fee94d8..0000000000
--- a/tensorflow/tensorboard/main.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Serve TensorFlow summary data to a web frontend.
-
-This is a simple web server to proxy data from the event_loader to the web, and
-serve static web files.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import logging as base_logging
-import os
-import socket
-import sys
-
-import tensorflow as tf
-from werkzeug import serving
-
-
-from tensorflow.tensorboard.backend import application
-from tensorflow.tensorboard.backend.event_processing import event_file_inspector as efi
-from tensorflow.tensorboard.plugins.audio import audio_plugin
-from tensorflow.tensorboard.plugins.distributions import distributions_plugin
-from tensorflow.tensorboard.plugins.graphs import graphs_plugin
-from tensorflow.tensorboard.plugins.histograms import histograms_plugin
-from tensorflow.tensorboard.plugins.images import images_plugin
-from tensorflow.tensorboard.plugins.projector import projector_plugin
-from tensorflow.tensorboard.plugins.scalars import scalars_plugin
-from tensorflow.tensorboard.plugins.text import text_plugin
-
-# TensorBoard flags
-
-tf.flags.DEFINE_string('logdir', '', """logdir specifies the directory where
-TensorBoard will look to find TensorFlow event files that it can display.
-TensorBoard will recursively walk the directory structure rooted at logdir,
-looking for .*tfevents.* files.
-
-You may also pass a comma separated list of log directories, and TensorBoard
-will watch each directory. You can also assign names to individual log
-directories by putting a colon between the name and the path, as in
-
-tensorboard --logdir=name1:/path/to/logs/1,name2:/path/to/logs/2
-""")
-
-tf.flags.DEFINE_string(
-    'host', '', 'What host to listen to. Defaults to '
-    'serving on all interfaces, set to 127.0.0.1 (localhost) to'
-    'disable remote access (also quiets security warnings).')
-
-tf.flags.DEFINE_integer('port', 6006, 'What port to serve TensorBoard on.')
-
-tf.flags.DEFINE_boolean(
-    'purge_orphaned_data', True, 'Whether to purge data that '
-    'may have been orphaned due to TensorBoard restarts. '
-    'Disabling purge_orphaned_data can be used to debug data '
-    'disappearance.')
-
-tf.flags.DEFINE_integer('reload_interval', 5,
-                        'How often the backend should load '
-                        'more data.')
-
-# Inspect Mode flags
-
-tf.flags.DEFINE_boolean('inspect', False, """Use this flag to print out a digest
-of your event files to the command line, when no data is shown on TensorBoard or
-the data shown looks weird.
-
-Example usages:
-tensorboard --inspect --event_file=myevents.out
-tensorboard --inspect --event_file=myevents.out --tag=loss
-tensorboard --inspect --logdir=mylogdir
-tensorboard --inspect --logdir=mylogdir --tag=loss
-
-See tensorflow/python/summary/event_file_inspector.py for more info and
-detailed usage.
-""")
-tf.flags.DEFINE_string(
-    'tag', '',
-    'The particular tag to query for. Only used if --inspect is present')
-tf.flags.DEFINE_string(
-    'event_file', '',
-    'The particular event file to query for. Only used if --inspect is present '
-    'and --logdir is not specified.')
-
-FLAGS = tf.flags.FLAGS
-
-
-def create_tb_app(plugins):
-  """Read the flags, and create a TensorBoard WSGI application.
-
-  Args:
-    plugins: A list of constructor functions for TBPlugin subclasses.
-
-  Raises:
-    ValueError: if a logdir is not specified.
-
-  Returns:
-    A new TensorBoard WSGI application.
-  """
-  if not FLAGS.logdir:
-    raise ValueError('A logdir must be specified. Run `tensorboard --help` for '
-                     'details and examples.')
-
-  logdir = os.path.expanduser(FLAGS.logdir)
-  return application.standard_tensorboard_wsgi(
-      logdir=logdir,
-      purge_orphaned_data=FLAGS.purge_orphaned_data,
-      reload_interval=FLAGS.reload_interval,
-      plugins=plugins)
-
-
-def make_simple_server(tb_app, host, port):
-  """Create an HTTP server for TensorBoard.
-
-  Args:
-    tb_app: The TensorBoard WSGI application to create a server for.
-    host: Indicates the interfaces to bind to ('::' or '0.0.0.0' for all
-        interfaces, '::1' or '127.0.0.1' for localhost). A blank value ('')
-        indicates protocol-agnostic all interfaces.
-    port: The port to bind to (0 indicates an unused port selected by the
-        operating system).
-  Returns:
-    A tuple of (server, url):
-      server: An HTTP server object configured to host TensorBoard.
-      url: A best guess at a URL where TensorBoard will be accessible once the
-        server has been started.
-  Raises:
-    socket.error: If a server could not be constructed with the host and port
-      specified. Also logs an error message.
-  """
-  # Mute the werkzeug logging.
-  base_logging.getLogger('werkzeug').setLevel(base_logging.WARNING)
-
-  try:
-    if host:
-      # The user gave us an explicit host
-      server = serving.make_server(host, port, tb_app, threaded=True)
-      if ':' in host and not host.startswith('['):
-        # Display IPv6 addresses as [::1]:80 rather than ::1:80
-        final_host = '[{}]'.format(host)
-      else:
-        final_host = host
-    else:
-      # We've promised to bind to all interfaces on this host. However, we're
-      # not sure whether that means IPv4 or IPv6 interfaces.
-      try:
-        # First try passing in a blank host (meaning all interfaces). This,
-        # unfortunately, defaults to IPv4 even if no IPv4 interface is available
-        # (yielding a socket.error).
-        server = serving.make_server(host, port, tb_app, threaded=True)
-      except socket.error:
-        # If a blank host didn't work, we explicitly request IPv6 interfaces.
-        server = serving.make_server('::', port, tb_app, threaded=True)
-      final_host = socket.gethostname()
-    server.daemon_threads = True
-  except socket.error as socket_error:
-    if port == 0:
-      msg = 'TensorBoard unable to find any open port'
-    else:
-      msg = (
-          'TensorBoard attempted to bind to port %d, but it was already in use'
-          % FLAGS.port)
-    tf.logging.error(msg)
-    print(msg)
-    raise socket_error
-
-  final_port = server.socket.getsockname()[1]
-  tensorboard_url = 'http://%s:%d' % (final_host, final_port)
-  return server, tensorboard_url
-
-
-def run_simple_server(tb_app):
-  """Run a TensorBoard HTTP server, and print some messages to the console."""
-  try:
-    server, url = make_simple_server(tb_app, FLAGS.host, FLAGS.port)
-  except socket.error:
-    # An error message was already logged
-    # TODO(jart): Remove log and throw anti-pattern.
-    sys.exit(-1)
-  msg = 'Starting TensorBoard %s at %s' % (tb_app.tag, url)
-  print(msg)
-  tf.logging.info(msg)
-  print('(Press CTRL+C to quit)')
-  sys.stdout.flush()
-
-  server.serve_forever()
-
-
-def main(unused_argv=None):
-  if FLAGS.inspect:
-    tf.logging.info('Not bringing up TensorBoard, but inspecting event files.')
-    event_file = os.path.expanduser(FLAGS.event_file)
-    efi.inspect(FLAGS.logdir, event_file, FLAGS.tag)
-    return 0
-  else:
-    plugins = [
-        scalars_plugin.ScalarsPlugin,
-        images_plugin.ImagesPlugin,
-        audio_plugin.AudioPlugin,
-        graphs_plugin.GraphsPlugin,
-        distributions_plugin.DistributionsPlugin,
-        histograms_plugin.HistogramsPlugin,
-        projector_plugin.ProjectorPlugin,
-        text_plugin.TextPlugin,
-    ]
-    tb = create_tb_app(plugins)
-    run_simple_server(tb)
-
-if __name__ == '__main__':
-  tf.app.run()
diff --git a/tensorflow/tensorboard/plugins/BUILD b/tensorflow/tensorboard/plugins/BUILD
deleted file mode 100644
index 7d3a96c0e1..0000000000
--- a/tensorflow/tensorboard/plugins/BUILD
+++ /dev/null
@@ -1,20 +0,0 @@
-# Description:
-# A plugin system for TensorBoard
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-py_library(
-    name = "base_plugin",
-    srcs = ["base_plugin.py"],
-    srcs_version = "PY2AND3",
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/audio/BUILD b/tensorflow/tensorboard/plugins/audio/BUILD
deleted file mode 100644
index daabd322e1..0000000000
--- a/tensorflow/tensorboard/plugins/audio/BUILD
+++ /dev/null
@@ -1,57 +0,0 @@
-# Description:
-# TensorBoard plugin for audio
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-py_library(
-    name = "audio_plugin",
-    srcs = ["audio_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "audio_plugin_test",
-    size = "small",
-    srcs = ["audio_plugin_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":audio_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_binary(
-    name = "audio_demo",
-    srcs = ["audio_demo.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "@six_archive//:six",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/audio/audio_demo.py b/tensorflow/tensorboard/plugins/audio/audio_demo.py
deleted file mode 100644
index b89310d3a8..0000000000
--- a/tensorflow/tensorboard/plugins/audio/audio_demo.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Sample data exhibiting audio summaries, via a waveform generator."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import math
-import os.path
-
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-
-FLAGS = tf.flags.FLAGS
-
-tf.flags.DEFINE_string('logdir', '/tmp/audio_demo',
-                       'Directory into which to write TensorBoard data.')
-
-tf.flags.DEFINE_integer('steps', 500,
-                        'Number of frequencies of each waveform to generate.')
-
-# Parameters for the audio output.
-tf.flags.DEFINE_integer('sample_rate', 44100, 'Sample rate, in Hz.')
-tf.flags.DEFINE_float('duration', 2.0, 'Duration of each waveform, in s.')
-
-
-def _samples():
-  """Compute how many samples should be included in each waveform."""
-  return int(FLAGS.sample_rate * FLAGS.duration)
-
-
-def run(logdir, run_name, wave_name, wave_constructor):
-  """Generate wave data of the given form.
-
-  The provided function `wave_constructor` should accept a scalar tensor
-  of type float32, representing the frequency (in Hz) at which to
-  construct a wave, and return a tensor of shape [1, _samples(), `n`]
-  representing audio data (for some number of channels `n`).
-
-  Waves will be generated at frequencies ranging from A4 to A5.
-
-  Arguments:
-    logdir: the top-level directory into which to write summary data
-    run_name: the name of this run; will be created as a subdirectory
-      under logdir
-    wave_name: the name of the wave being generated
-    wave_constructor: see above
-  """
-  tf.reset_default_graph()
-  tf.set_random_seed(0)
-
-  # On each step `i`, we'll set this placeholder to `i`. This allows us
-  # to know "what time it is" at each step.
-  step_placeholder = tf.placeholder(tf.float32, shape=[])
-
-  # We want to linearly interpolate a frequency between A4 (440 Hz) and
-  # A5 (880 Hz).
-  f_min = 440.0
-  f_max = 880.0
-  t = step_placeholder / (FLAGS.steps - 1)
-  frequency = f_min * (1.0 - t) + f_max * t
-
-  # Let's log this frequency, just so that we can make sure that it's as
-  # expected.
-  tf.summary.scalar('frequency', frequency)
-
-  # Now, we pass this to the wave constructor to get our waveform. Doing
-  # so within a name scope means that any summaries that the wave
-  # constructor produces will be namespaced.
-  with tf.name_scope(wave_name):
-    waveform = wave_constructor(frequency)
-
-  # Here's the crucial piece: we interpret this result as audio.
-  tf.summary.audio('waveform', waveform, FLAGS.sample_rate)
-
-  # Now, we can collect up all the summaries and begin the run.
-  summ = tf.summary.merge_all()
-
-  sess = tf.Session()
-  writer = tf.summary.FileWriter(os.path.join(logdir, run_name))
-  writer.add_graph(sess.graph)
-  sess.run(tf.global_variables_initializer())
-  for step in xrange(FLAGS.steps):
-    s = sess.run(summ, feed_dict={step_placeholder: float(step)})
-    writer.add_summary(s, global_step=step)
-  writer.close()
-
-
-# Now, let's take a look at the kinds of waves that we can generate.
-
-
-def sine_wave(frequency):
-  """Emit a sine wave at the given frequency."""
-  xs = tf.reshape(tf.range(_samples(), dtype=tf.float32), [1, _samples(), 1])
-  ts = xs / FLAGS.sample_rate
-  return tf.sin(2 * math.pi * frequency * ts)
-
-
-def square_wave(frequency):
-  """Emit a square wave at the given frequency."""
-  # The square is just the sign of the sine!
-  return tf.sign(sine_wave(frequency))
-
-
-def triangle_wave(frequency):
-  """Emit a triangle wave at the given frequency."""
-  xs = tf.reshape(tf.range(_samples(), dtype=tf.float32), [1, _samples(), 1])
-  ts = xs / FLAGS.sample_rate
-  #
-  # A triangle wave looks like this:
-  #
-  #      /\      /\
-  #     /  \    /  \
-  #         \  /    \  /
-  #          \/      \/
-  #
-  # If we look at just half a period (the first four slashes in the
-  # diagram above), we can see that it looks like a transformed absolute
-  # value function.
-  #
-  # Let's start by computing the times relative to the start of each
-  # half-wave pulse (each individual "mountain" or "valley", of which
-  # there are four in the above diagram).
-  half_pulse_index = ts * (frequency * 2)
-  half_pulse_angle = half_pulse_index % 1.0  # in [0, 1]
-  #
-  # Now, we can see that each positive half-pulse ("mountain") has
-  # amplitude given by A(z) = 0.5 - abs(z - 0.5), and then normalized:
-  absolute_amplitude = (0.5 - tf.abs(half_pulse_angle - 0.5)) / 0.5
-  #
-  # But every other half-pulse is negative, so we should invert these.
-  half_pulse_parity = tf.sign(1 - (half_pulse_index % 2.0))
-  amplitude = half_pulse_parity * absolute_amplitude
-  #
-  # This is precisely the desired result, so we're done!
-  return amplitude
-
-
-# If we want to get fancy, we can use our above waves as primitives to
-# build more interesting waves.
-
-
-def bisine_wave(frequency):
-  """Emit two sine waves, in stereo at different octaves."""
-  #
-  # We can first our existing sine generator to generate two different
-  # waves.
-  f_hi = frequency
-  f_lo = frequency / 2.0
-  with tf.name_scope('hi'):
-    sine_hi = sine_wave(f_hi)
-  with tf.name_scope('lo'):
-    sine_lo = sine_wave(f_lo)
-  #
-  # Now, we have two tensors of shape [1, _samples(), 1]. By concatenating
-  # them along axis 2, we get a tensor of shape [1, _samples(), 2]---a
-  # stereo waveform.
-  return tf.concat([sine_lo, sine_hi], axis=2)
-
-
-def bisine_wahwah_wave(frequency):
-  """Emit two sine waves with balance oscillating left and right."""
-  #
-  # This is clearly intended to build on the bisine wave defined above,
-  # so we can start by generating that.
-  waves_a = bisine_wave(frequency)
-  #
-  # Then, by reversing axis 2, we swap the stereo channels. By mixing
-  # this with `waves_a`, we'll be able to create the desired effect.
-  waves_b = tf.reverse(waves_a, axis=[2])
-  #
-  # Let's have the balance oscillate from left to right four times.
-  iterations = 4
-  #
-  # Now, we compute the balance for each sample: `ts` has values
-  # in [0, 1] that indicate how much we should use `waves_a`.
-  xs = tf.reshape(tf.range(_samples(), dtype=tf.float32), [1, _samples(), 1])
-  thetas = xs / _samples() * iterations
-  ts = (tf.sin(math.pi * 2 * thetas) + 1) / 2
-  #
-  # Finally, we can mix the two together, and we're done.
-  return ts * waves_a + (1.0 - ts) * waves_b
-
-
-def run_all(logdir, verbose=False):
-  """Generate waves of the shapes defined above.
-
-  Arguments:
-    logdir: the directory into which to store all the runs' data
-    verbose: if true, print out each run's name as it begins
-  """
-  waves = [sine_wave, square_wave, triangle_wave,
-           bisine_wave, bisine_wahwah_wave]
-  for (i, wave_constructor) in enumerate(waves):
-    wave_name = wave_constructor.__name__
-    run_name = 'wave:%02d,%s' % (i + 1, wave_name)
-    if verbose:
-      print('--- Running: %s' % run_name)
-    run(logdir, run_name, wave_name, wave_constructor)
-
-
-def main(unused_argv):
-  print('Saving output to %s.' % FLAGS.logdir)
-  run_all(FLAGS.logdir, verbose=True)
-  print('Done. Output saved to %s.' % FLAGS.logdir)
-
-
-if __name__ == '__main__':
-  tf.app.run()
diff --git a/tensorflow/tensorboard/plugins/audio/audio_plugin.py b/tensorflow/tensorboard/plugins/audio/audio_plugin.py
deleted file mode 100644
index fbf3eb4b06..0000000000
--- a/tensorflow/tensorboard/plugins/audio/audio_plugin.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The TensorBoard Audio plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from six.moves import urllib
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.plugins import base_plugin
-
-_PLUGIN_PREFIX_ROUTE = event_accumulator.AUDIO
-
-
-class AudioPlugin(base_plugin.TBPlugin):
-  """Audio Plugin for TensorBoard."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates AudioPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self._multiplexer = context.multiplexer
-
-  def get_plugin_apps(self):
-    return {
-        '/audio': self._serve_audio_metadata,
-        '/individualAudio': self._serve_individual_audio,
-        '/tags': self._serve_tags,
-    }
-
-  def is_active(self):
-    """The audio plugin is active iff any run has at least one relevant tag."""
-    return bool(self._multiplexer) and any(self._index_impl().values())
-
-  def _index_impl(self):
-    return {
-        run_name: run_data[event_accumulator.AUDIO]
-        for (run_name, run_data) in self._multiplexer.Runs().items()
-        if event_accumulator.AUDIO in run_data
-    }
-
-  @wrappers.Request.application
-  def _serve_audio_metadata(self, request):
-    """Given a tag and list of runs, serve a list of metadata for audio.
-
-    Note that the audio themselves are not sent; instead, we respond with URLs
-    to the audio. The frontend should treat these URLs as opaque and should not
-    try to parse information about them or generate them itself, as the format
-    may change.
-
-    Args:
-      request: A werkzeug.wrappers.Request object.
-
-    Returns:
-      A werkzeug.Response application.
-    """
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-
-    audio_list = self._multiplexer.Audio(run, tag)
-    response = self._audio_response_for_run(audio_list, run, tag)
-    return http_util.Respond(request, response, 'application/json')
-
-  def _audio_response_for_run(self, run_audio, run, tag):
-    """Builds a JSON-serializable object with information about run_audio.
-
-    Args:
-      run_audio: A list of event_accumulator.AudioValueEvent objects.
-      run: The name of the run.
-      tag: The name of the tag the audio entries all belong to.
-
-    Returns:
-      A list of dictionaries containing the wall time, step, URL, width, and
-      height for each audio entry.
-    """
-    response = []
-    for index, run_audio_clip in enumerate(run_audio):
-      response.append({
-          'wall_time': run_audio_clip.wall_time,
-          'step': run_audio_clip.step,
-          'content_type': run_audio_clip.content_type,
-          'query': self._query_for_individual_audio(run, tag, index)
-      })
-    return response
-
-  def _query_for_individual_audio(self, run, tag, index):
-    """Builds a URL for accessing the specified audio.
-
-    This should be kept in sync with _serve_audio_metadata. Note that the URL is
-    *not* guaranteed to always return the same audio, since audio may be
-    unloaded from the reservoir as new audio entries come in.
-
-    Args:
-      run: The name of the run.
-      tag: The tag.
-      index: The index of the audio entry. Negative values are OK.
-
-    Returns:
-      A string representation of a URL that will load the index-th sampled audio
-      in the given run with the given tag.
-    """
-    query_string = urllib.parse.urlencode({
-        'run': run,
-        'tag': tag,
-        'index': index
-    })
-    return query_string
-
-  @wrappers.Request.application
-  def _serve_individual_audio(self, request):
-    """Serves an individual audio entry."""
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-    index = int(request.args.get('index'))
-    audio = self._multiplexer.Audio(run, tag)[index]
-    return http_util.Respond(
-        request, audio.encoded_audio_string, audio.content_type)
-
-  @wrappers.Request.application
-  def _serve_tags(self, request):
-    index = self._index_impl()
-    return http_util.Respond(request, index, 'application/json')
diff --git a/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py b/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py
deleted file mode 100644
index 8f42838edb..0000000000
--- a/tensorflow/tensorboard/plugins/audio/audio_plugin_test.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests the Tensorboard audio plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import json
-import os
-import shutil
-import tempfile
-
-import numpy
-from six.moves import urllib
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-from werkzeug import test as werkzeug_test
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import application
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.audio import audio_plugin
-
-
-class AudioPluginTest(tf.test.TestCase):
-
-  def setUp(self):
-    self.log_dir = tempfile.mkdtemp()
-
-    # We use numpy.random to generate audio. We seed to avoid non-determinism
-    # in this test.
-    numpy.random.seed(42)
-
-    # Create audio summaries for run foo.
-    tf.reset_default_graph()
-    sess = tf.Session()
-    placeholder = tf.placeholder(tf.float32)
-    tf.summary.audio(name="baz", tensor=placeholder, sample_rate=44100)
-    merged_summary_op = tf.summary.merge_all()
-    foo_directory = os.path.join(self.log_dir, "foo")
-    writer = tf.summary.FileWriter(foo_directory)
-    writer.add_graph(sess.graph)
-    for step in xrange(2):
-      # The floats (sample data) range from -1 to 1.
-      writer.add_summary(sess.run(merged_summary_op, feed_dict={
-          placeholder: numpy.random.rand(42, 22050) * 2 - 1
-      }), global_step=step)
-    writer.close()
-
-    # Create audio summaries for run bar.
-    tf.reset_default_graph()
-    sess = tf.Session()
-    placeholder = tf.placeholder(tf.float32)
-    tf.summary.audio(name="quux", tensor=placeholder, sample_rate=44100)
-    merged_summary_op = tf.summary.merge_all()
-    bar_directory = os.path.join(self.log_dir, "bar")
-    writer = tf.summary.FileWriter(bar_directory)
-    writer.add_graph(sess.graph)
-    for step in xrange(2):
-      # The floats (sample data) range from -1 to 1.
-      writer.add_summary(sess.run(merged_summary_op, feed_dict={
-          placeholder: numpy.random.rand(42, 11025) * 2 - 1
-      }), global_step=step)
-    writer.close()
-
-    # Start a server with the plugin.
-    multiplexer = event_multiplexer.EventMultiplexer({
-        "foo": foo_directory,
-        "bar": bar_directory,
-    })
-    context = base_plugin.TBContext(
-        logdir=self.log_dir, multiplexer=multiplexer)
-    self.plugin = audio_plugin.AudioPlugin(context)
-    wsgi_app = application.TensorBoardWSGIApp(
-        self.log_dir, [self.plugin], multiplexer, reload_interval=0)
-    self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
-
-  def tearDown(self):
-    shutil.rmtree(self.log_dir, ignore_errors=True)
-
-  def _DeserializeResponse(self, byte_content):
-    """Deserializes byte content that is a JSON encoding.
-
-    Args:
-      byte_content: The byte content of a response.
-
-    Returns:
-      The deserialized python object decoded from JSON.
-    """
-    return json.loads(byte_content.decode("utf-8"))
-
-  def testRoutesProvided(self):
-    """Tests that the plugin offers the correct routes."""
-    routes = self.plugin.get_plugin_apps()
-    self.assertIsInstance(routes["/audio"], collections.Callable)
-    self.assertIsInstance(routes["/individualAudio"], collections.Callable)
-    self.assertIsInstance(routes["/tags"], collections.Callable)
-
-  def testAudioRoute(self):
-    """Tests that the /audio routes returns with the correct data."""
-    response = self.server.get(
-        "/data/plugin/audio/audio?run=foo&tag=baz/audio/0")
-    self.assertEqual(200, response.status_code)
-
-    # Verify that the correct entries are returned.
-    entries = self._DeserializeResponse(response.get_data())
-    self.assertEqual(2, len(entries))
-
-    # Verify that the 1st entry is correct.
-    entry = entries[0]
-    self.assertEqual(0, entry["step"])
-    parsed_query = urllib.parse.parse_qs(entry["query"])
-    self.assertListEqual(["0"], parsed_query["index"])
-    self.assertListEqual(["foo"], parsed_query["run"])
-    self.assertListEqual(["baz/audio/0"], parsed_query["tag"])
-
-    # Verify that the 2nd entry is correct.
-    entry = entries[1]
-    self.assertEqual(1, entry["step"])
-    parsed_query = urllib.parse.parse_qs(entry["query"])
-    self.assertListEqual(["1"], parsed_query["index"])
-    self.assertListEqual(["foo"], parsed_query["run"])
-    self.assertListEqual(["baz/audio/0"], parsed_query["tag"])
-
-  def testIndividualAudioRoute(self):
-    """Tests fetching an individual audio."""
-    response = self.server.get(
-        "/data/plugin/audio/individualAudio?run=bar&tag=quux/audio/0&index=0")
-    self.assertEqual(200, response.status_code)
-    self.assertEqual("audio/wav", response.headers.get("content-type"))
-
-  def testRunsRoute(self):
-    """Tests that the /runs route offers the correct run to tag mapping."""
-    response = self.server.get("/data/plugin/audio/tags")
-    self.assertEqual(200, response.status_code)
-    run_to_tags = self._DeserializeResponse(response.get_data())
-    self.assertItemsEqual(("foo", "bar"), run_to_tags.keys())
-    self.assertItemsEqual(
-        ["baz/audio/0", "baz/audio/1", "baz/audio/2"], run_to_tags["foo"])
-    self.assertItemsEqual(
-        ["quux/audio/0", "quux/audio/1", "quux/audio/2"], run_to_tags["bar"])
-
-
-if __name__ == "__main__":
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/base_plugin.py b/tensorflow/tensorboard/plugins/base_plugin.py
deleted file mode 100644
index 35414626c8..0000000000
--- a/tensorflow/tensorboard/plugins/base_plugin.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""TensorBoard Plugin abstract base class.
-
-Every plugin in TensorBoard must extend and implement the abstract methods of
-this base class.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-
-from abc import ABCMeta
-from abc import abstractmethod
-
-
-class TBPlugin(object):
-  """TensorBoard plugin interface. Every plugin must extend from this class.
-
-  Subclasses must have a constructor that takes a TBContext argument.
-  """
-  __metaclass__ = ABCMeta
-
-  # The plugin_name will also be a prefix in the http handlers generated by
-  # the plugin, e.g. `data/plugins/$PLUGIN_NAME/$HANDLER`
-  # The plugin name must be unique for each registered plugin, or
-  # a ValueError will be thrown when the application is constructed. The
-  # plugin name must only contain characters among [A-Za-z0-9_.-], and
-  # must be nonempty, or a ValueError will similarly be thrown.
-  plugin_name = None
-
-  @abstractmethod
-  def get_plugin_apps(self):
-    """Returns a set of WSGI applications that the plugin implements.
-
-    Each application gets registered with the tensorboard app and is served
-    under a prefix path that includes the name of the plugin.
-
-    Returns:
-      A dict mapping route paths to WSGI applications. Each route path
-      should include a leading slash.
-    """
-    raise NotImplementedError()
-
-  @abstractmethod
-  def is_active(self):
-    """Determines whether this plugin is active.
-
-    A plugin may not be active for instance if it lacks relevant data. If a
-    plugin is inactive, the frontend may avoid issuing requests to its routes.
-
-    Returns:
-      A boolean value. Whether this plugin is active.
-    """
-    raise NotImplementedError()
-
-
-class TBContext(collections.namedtuple('TBContext', ('logdir', 'multiplexer'))):
-  """Magic container of information passed from TensorBoard core to plugins.
-
-  A TBContext instance is passed to the constructor of a TBPlugin class.
-
-  Fields:
-    logdir: The string logging directory TensorBoard was started with.
-    multiplexer: An EventMultiplexer with underlying TB data, or None if SQL
-        mode is being used.
-  """
-  __slots__ = ()  # Enforces use of only tuple fields.
diff --git a/tensorflow/tensorboard/plugins/distributions/BUILD b/tensorflow/tensorboard/plugins/distributions/BUILD
deleted file mode 100644
index a172e61666..0000000000
--- a/tensorflow/tensorboard/plugins/distributions/BUILD
+++ /dev/null
@@ -1,68 +0,0 @@
-# Description:
-# TensorBoard plugin for distributions
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-## Distributions Plugin ##
-py_library(
-    name = "distributions_plugin",
-    srcs = ["distributions_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "distributions_plugin_test",
-    size = "small",
-    srcs = ["distributions_plugin_test.py"],
-    main = "distributions_plugin_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":distributions_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_library(
-    name = "compressor",
-    srcs = ["compressor.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-    deps = ["//third_party/py/numpy"],
-)
-
-py_test(
-    name = "compressor_test",
-    size = "small",
-    srcs = ["compressor_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":compressor",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/distributions/compressor.py b/tensorflow/tensorboard/plugins/distributions/compressor.py
deleted file mode 100644
index dd9d00f5cd..0000000000
--- a/tensorflow/tensorboard/plugins/distributions/compressor.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Package for histogram compression."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-
-import numpy as np
-
-# Normal CDF for std_devs: (-Inf, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, Inf)
-# naturally gives bands around median of width 1 std dev, 2 std dev, 3 std dev,
-# and then the long tail.
-NORMAL_HISTOGRAM_BPS = (0, 668, 1587, 3085, 5000, 6915, 8413, 9332, 10000)
-
-
-CompressedHistogramValue = collections.namedtuple('CompressedHistogramValue',
-                                                  ['basis_point', 'value'])
-
-
-def CompressHistogram(histo, bps=NORMAL_HISTOGRAM_BPS):
-  """Creates fixed size histogram by adding compression to accumulated state.
-
-  This routine transforms a histogram at a particular step by linearly
-  interpolating its variable number of buckets to represent their cumulative
-  weight at a constant number of compression points. This significantly reduces
-  the size of the histogram and makes it suitable for a two-dimensional area
-  plot where the output of this routine constitutes the ranges for a single x
-  coordinate.
-
-  Args:
-    histo: A HistogramProto object.
-    bps: Compression points represented in basis points, 1/100ths of a percent.
-        Defaults to normal distribution.
-
-  Returns:
-    List of values for each basis point.
-  """
-  # See also: Histogram::Percentile() in core/lib/histogram/histogram.cc
-  if not histo.num:
-    return [CompressedHistogramValue(b, 0.0) for b in bps]
-  bucket = np.array(histo.bucket)
-  bucket_limit = list(histo.bucket_limit)
-  weights = (bucket * bps[-1] / (bucket.sum() or 1.0)).cumsum()
-  values = []
-  j = 0
-  while j < len(bps):
-    i = np.searchsorted(weights, bps[j], side='right')
-    while i < len(weights):
-      cumsum = weights[i]
-      cumsum_prev = weights[i - 1] if i > 0 else 0.0
-      if cumsum == cumsum_prev:  # prevent remap divide by zero
-        i += 1
-        continue
-      if not i or not cumsum_prev:
-        lhs = histo.min
-      else:
-        lhs = max(bucket_limit[i - 1], histo.min)
-      rhs = min(bucket_limit[i], histo.max)
-      weight = _Remap(bps[j], cumsum_prev, cumsum, lhs, rhs)
-      values.append(CompressedHistogramValue(bps[j], weight))
-      j += 1
-      break
-    else:
-      break
-  while j < len(bps):
-    values.append(CompressedHistogramValue(bps[j], histo.max))
-    j += 1
-  return values
-
-
-def _Remap(x, x0, x1, y0, y1):
-  """Linearly map from [x0, x1] unto [y0, y1]."""
-  return y0 + (x - x0) * float(y1 - y0) / (x1 - x0)
diff --git a/tensorflow/tensorboard/plugins/distributions/compressor_test.py b/tensorflow/tensorboard/plugins/distributions/compressor_test.py
deleted file mode 100644
index 92e6408954..0000000000
--- a/tensorflow/tensorboard/plugins/distributions/compressor_test.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-from tensorflow.tensorboard.plugins.distributions import compressor
-
-
-def _make_expected_value(*values):
-  return [compressor.CompressedHistogramValue(bp, val) for bp, val in values]
-
-
-class HistcompTest(tf.test.TestCase):
-
-  def testExample(self):
-    bps = (0, 2500, 5000, 7500, 10000)
-    proto = tf.HistogramProto(
-        min=1,
-        max=2,
-        num=3,
-        sum=4,
-        sum_squares=5,
-        bucket_limit=[1, 2, 3],
-        bucket=[0, 3, 0])
-    self.assertEqual(
-        _make_expected_value(
-            (0, 1.0),
-            (2500, 1.25),
-            (5000, 1.5),
-            (7500, 1.75),
-            (10000, 2.0)),
-        compressor.CompressHistogram(proto, bps))
-
-  def testAnotherExample(self):
-    bps = (0, 2500, 5000, 7500, 10000)
-    proto = tf.HistogramProto(
-        min=-2,
-        max=3,
-        num=4,
-        sum=5,
-        sum_squares=6,
-        bucket_limit=[2, 3, 4],
-        bucket=[1, 3, 0])
-    self.assertEqual(
-        _make_expected_value(
-            (0, -2),
-            (2500, 2),
-            (5000, 2 + 1 / 3),
-            (7500, 2 + 2 / 3),
-            (10000, 3)),
-        compressor.CompressHistogram(proto, bps))
-
-  def testEmpty(self):
-    bps = (0, 2500, 5000, 7500, 10000)
-    proto = tf.HistogramProto(
-        min=None,
-        max=None,
-        num=0,
-        sum=0,
-        sum_squares=0,
-        bucket_limit=[1, 2, 3],
-        bucket=[0, 0, 0])
-    self.assertEqual(
-        _make_expected_value(
-            (0, 0),
-            (2500, 0),
-            (5000, 0),
-            (7500, 0),
-            (10000, 0)),
-        compressor.CompressHistogram(proto, bps))
-
-  def testUgly(self):
-    bps = (0, 668, 1587, 3085, 5000, 6915, 8413, 9332, 10000)
-    proto = tf.HistogramProto(
-        min=0.0,
-        max=1.0,
-        num=960.0,
-        sum=64.0,
-        sum_squares=64.0,
-        bucket_limit=[0.0, 1e-12, 0.917246389039776, 1.0089710279437536,
-                      1.7976931348623157e+308],
-        bucket=[0.0, 896.0, 0.0, 64.0, 0.0])
-    vals = compressor.CompressHistogram(proto, bps)
-    self.assertEquals(tuple(v.basis_point for v in vals), bps)
-    self.assertAlmostEqual(vals[0].value, 0.0)
-    self.assertAlmostEqual(vals[1].value, 7.157142857142856e-14)
-    self.assertAlmostEqual(vals[2].value, 1.7003571428571426e-13)
-    self.assertAlmostEqual(vals[3].value, 3.305357142857143e-13)
-    self.assertAlmostEqual(vals[4].value, 5.357142857142857e-13)
-    self.assertAlmostEqual(vals[5].value, 7.408928571428571e-13)
-    self.assertAlmostEqual(vals[6].value, 9.013928571428571e-13)
-    self.assertAlmostEqual(vals[7].value, 9.998571428571429e-13)
-    self.assertAlmostEqual(vals[8].value, 1.0)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/distributions/distributions_plugin.py b/tensorflow/tensorboard/plugins/distributions/distributions_plugin.py
deleted file mode 100644
index 716ee799fb..0000000000
--- a/tensorflow/tensorboard/plugins/distributions/distributions_plugin.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The TensorBoard Distributions (a.k.a. compressed histograms) plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.plugins import base_plugin
-
-_PLUGIN_PREFIX_ROUTE = event_accumulator.COMPRESSED_HISTOGRAMS
-
-
-class DistributionsPlugin(base_plugin.TBPlugin):
-  """Distributions Plugin for TensorBoard."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates DistributionsPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self._multiplexer = context.multiplexer
-
-  def get_plugin_apps(self):
-    return {
-        '/distributions': self.distributions_route,
-        '/tags': self.tags_route,
-    }
-
-  def is_active(self):
-    """This plugin is active iff any run has at least one relevant tag."""
-    return bool(self._multiplexer) and any(self.index_impl().values())
-
-  def index_impl(self):
-    return {
-        run_name: run_data[event_accumulator.COMPRESSED_HISTOGRAMS]
-        for (run_name, run_data) in self._multiplexer.Runs().items()
-        if event_accumulator.COMPRESSED_HISTOGRAMS in run_data
-    }
-
-  def distributions_impl(self, tag, run):
-    """Result of the form `(body, mime_type)`."""
-    values = self._multiplexer.CompressedHistograms(run, tag)
-    return (values, 'application/json')
-
-  @wrappers.Request.application
-  def tags_route(self, request):
-    index = self.index_impl()
-    return http_util.Respond(request, index, 'application/json')
-
-  @wrappers.Request.application
-  def distributions_route(self, request):
-    """Given a tag and single run, return array of compressed histograms."""
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-    (body, mime_type) = self.distributions_impl(tag, run)
-    return http_util.Respond(request, body, mime_type)
diff --git a/tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py b/tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py
deleted file mode 100644
index 5212281f38..0000000000
--- a/tensorflow/tensorboard/plugins/distributions/distributions_plugin_test.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Integration tests for the Distributions Plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import os.path
-
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.distributions import distributions_plugin
-
-
-class DistributionsPluginTest(tf.test.TestCase):
-
-  _STEPS = 99
-
-  _DISTRIBUTION_TAG = 'my-favorite-distribution'
-  _SCALAR_TAG = 'my-boring-scalars'
-
-  _RUN_WITH_DISTRIBUTION = '_RUN_WITH_DISTRIBUTION'
-  _RUN_WITH_SCALARS = '_RUN_WITH_SCALARS'
-
-  def set_up_with_runs(self, run_names):
-    self.logdir = self.get_temp_dir()
-    for run_name in run_names:
-      self.generate_run(run_name)
-    multiplexer = event_multiplexer.EventMultiplexer(size_guidance={
-        # don't truncate my test data, please
-        event_accumulator.COMPRESSED_HISTOGRAMS:
-            self._STEPS,
-    })
-    multiplexer.AddRunsFromDirectory(self.logdir)
-    multiplexer.Reload()
-    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
-    self.plugin = distributions_plugin.DistributionsPlugin(context)
-
-  def testRoutesProvided(self):
-    """Tests that the plugin offers the correct routes."""
-    self.set_up_with_runs([self._RUN_WITH_SCALARS])
-    routes = self.plugin.get_plugin_apps()
-    self.assertIsInstance(routes['/distributions'], collections.Callable)
-    self.assertIsInstance(routes['/tags'], collections.Callable)
-
-  def generate_run(self, run_name):
-    if run_name == self._RUN_WITH_DISTRIBUTION:
-      (use_distributions, use_scalars) = (True, False)
-    elif run_name == self._RUN_WITH_SCALARS:
-      (use_distributions, use_scalars) = (False, True)
-    else:
-      assert False, 'Invalid run name: %r' % run_name
-    tf.reset_default_graph()
-    sess = tf.Session()
-    placeholder = tf.placeholder(tf.float32, shape=[3])
-    if use_distributions:
-      tf.summary.histogram(self._DISTRIBUTION_TAG, placeholder)
-    if use_scalars:
-      tf.summary.scalar(self._SCALAR_TAG, tf.reduce_mean(placeholder))
-    summ = tf.summary.merge_all()
-
-    subdir = os.path.join(self.logdir, run_name)
-    writer = tf.summary.FileWriter(subdir)
-    writer.add_graph(sess.graph)
-    for step in xrange(self._STEPS):
-      feed_dict = {placeholder: [1 + step, 2 + step, 3 + step]}
-      s = sess.run(summ, feed_dict=feed_dict)
-      writer.add_summary(s, global_step=step)
-    writer.close()
-
-  def test_index(self):
-    self.set_up_with_runs([self._RUN_WITH_DISTRIBUTION,
-                           self._RUN_WITH_SCALARS])
-    self.assertEqual({
-        self._RUN_WITH_DISTRIBUTION: [self._DISTRIBUTION_TAG],
-        self._RUN_WITH_SCALARS: [],
-    }, self.plugin.index_impl())
-
-  def _test_distributions_json(self, run_name, should_have_distributions):
-    self.set_up_with_runs([self._RUN_WITH_DISTRIBUTION,
-                           self._RUN_WITH_SCALARS])
-    if should_have_distributions:
-      (data, mime_type) = self.plugin.distributions_impl(
-          self._DISTRIBUTION_TAG, run_name)
-      self.assertEqual('application/json', mime_type)
-      self.assertEqual(len(data), self._STEPS)
-      for i in xrange(self._STEPS):
-        self.assertEqual(i, data[i].step)
-    else:
-      with self.assertRaises(KeyError):
-        self.plugin.distributions_impl(
-            self._DISTRIBUTION_TAG, run_name)
-
-  def test_distributions_json_with_scalars(self):
-    self._test_distributions_json(self._RUN_WITH_DISTRIBUTION, True)
-
-  def test_distributions_json_with_histogram(self):
-    self._test_distributions_json(self._RUN_WITH_SCALARS, False)
-
-  def test_active_with_distribution(self):
-    self.set_up_with_runs([self._RUN_WITH_DISTRIBUTION])
-    self.assertTrue(self.plugin.is_active())
-
-  def test_active_with_scalars(self):
-    self.set_up_with_runs([self._RUN_WITH_SCALARS])
-    self.assertFalse(self.plugin.is_active())
-
-  def test_active_with_both(self):
-    self.set_up_with_runs([self._RUN_WITH_DISTRIBUTION,
-                           self._RUN_WITH_SCALARS])
-    self.assertTrue(self.plugin.is_active())
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/graphs/BUILD b/tensorflow/tensorboard/plugins/graphs/BUILD
deleted file mode 100644
index 4ac041bdf7..0000000000
--- a/tensorflow/tensorboard/plugins/graphs/BUILD
+++ /dev/null
@@ -1,50 +0,0 @@
-# Description:
-# TensorBoard plugin for graphs
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-## Graphs Plugin ##
-py_library(
-    name = "graphs_plugin",
-    srcs = ["graphs_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/backend:process_graph",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "graphs_plugin_test",
-    size = "small",
-    srcs = ["graphs_plugin_test.py"],
-    main = "graphs_plugin_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":graphs_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/graphs/graphs_plugin.py b/tensorflow/tensorboard/plugins/graphs/graphs_plugin.py
deleted file mode 100644
index 1c77905e83..0000000000
--- a/tensorflow/tensorboard/plugins/graphs/graphs_plugin.py
+++ /dev/null
@@ -1,147 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The TensorBoard Graphs plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.backend import process_graph
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.plugins import base_plugin
-
-_PLUGIN_PREFIX_ROUTE = 'graphs'
-
-
-class GraphsPlugin(base_plugin.TBPlugin):
-  """Graphs Plugin for TensorBoard."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates GraphsPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self._multiplexer = context.multiplexer
-
-  def get_plugin_apps(self):
-    return {
-        '/graph': self.graph_route,
-        '/runs': self.runs_route,
-        '/run_metadata': self.run_metadata_route,
-        '/run_metadata_tags': self.run_metadata_tags_route,
-    }
-
-  def is_active(self):
-    """The graphs plugin is active iff any run has a graph."""
-    return bool(self._multiplexer and self.index_impl())
-
-  def index_impl(self):
-    """Returns a list of all runs that have a graph."""
-    return [run_name
-            for (run_name, run_data) in self._multiplexer.Runs().items()
-            if run_data.get(event_accumulator.GRAPH)]
-
-  def run_metadata_index_impl(self):
-    """Returns a run-to-tag mapping for metadata."""
-    return {
-        run_name: run_data[event_accumulator.RUN_METADATA]
-        for (run_name, run_data) in self._multiplexer.Runs().items()
-        if event_accumulator.RUN_METADATA in run_data
-    }
-
-  def graph_impl(self, run, limit_attr_size=None, large_attrs_key=None):
-    """Result of the form `(body, mime_type)`, or `None` if no graph exists."""
-    try:
-      graph = self._multiplexer.Graph(run)
-    except ValueError:
-      return None
-    # This next line might raise a ValueError if the limit parameters
-    # are invalid (size is negative, size present but key absent, etc.).
-    process_graph.prepare_graph_for_ui(graph, limit_attr_size, large_attrs_key)
-    return (str(graph), 'text/x-protobuf')  # pbtxt
-
-  def run_metadata_impl(self, run, tag):
-    """Result of the form `(body, mime_type)`, or `None` if no data exists."""
-    try:
-      run_metadata = self._multiplexer.RunMetadata(run, tag)
-    except ValueError:
-      return None
-    return (str(run_metadata), 'text/x-protobuf')  # pbtxt
-
-  @wrappers.Request.application
-  def runs_route(self, request):
-    index = self.index_impl()
-    return http_util.Respond(request, index, 'application/json')
-
-  @wrappers.Request.application
-  def run_metadata_tags_route(self, request):
-    index = self.run_metadata_index_impl()
-    return http_util.Respond(request, index, 'application/json')
-
-  @wrappers.Request.application
-  def graph_route(self, request):
-    """Given a single run, return the graph definition in protobuf format."""
-    run = request.args.get('run')
-    if run is None:
-      return http_util.Respond(
-          request, 'query parameter "run" is required', 'text/plain', 400)
-
-    limit_attr_size = request.args.get('limit_attr_size', None)
-    if limit_attr_size is not None:
-      try:
-        limit_attr_size = int(limit_attr_size)
-      except ValueError:
-        return http_util.Respond(
-            request, 'query parameter `limit_attr_size` must be an integer',
-            'text/plain', 400)
-
-    large_attrs_key = request.args.get('large_attrs_key', None)
-
-    try:
-      result = self.graph_impl(run, limit_attr_size, large_attrs_key)
-    except ValueError as e:
-      return http_util.Respond(request, e.message, 'text/plain', code=400)
-    else:
-      if result is not None:
-        (body, mime_type) = result  # pylint: disable=unpacking-non-sequence
-        return http_util.Respond(request, body, mime_type)
-      else:
-        return http_util.Respond(request, '404 Not Found', 'text/plain',
-                                 code=404)
-
-  @wrappers.Request.application
-  def run_metadata_route(self, request):
-    """Given a tag and a run, return the session.run() metadata."""
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-    if tag is None:
-      return http_util.Respond(
-          request, 'query parameter "tag" is required', 'text/plain', 400)
-    if run is None:
-      return http_util.Respond(
-          request, 'query parameter "run" is required', 'text/plain', 400)
-    result = self.run_metadata_impl(run, tag)
-    if result is not None:
-      (body, mime_type) = result  # pylint: disable=unpacking-non-sequence
-      return http_util.Respond(request, body, mime_type)
-    else:
-      return http_util.Respond(request, '404 Not Found', 'text/plain',
-                               code=404)
diff --git a/tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py b/tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py
deleted file mode 100644
index f4e2c20f7a..0000000000
--- a/tensorflow/tensorboard/plugins/graphs/graphs_plugin_test.py
+++ /dev/null
@@ -1,153 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Integration tests for the Graphs Plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import math
-import os.path
-
-import tensorflow as tf
-
-from google.protobuf import text_format
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.graphs import graphs_plugin
-
-
-class GraphsPluginTest(tf.test.TestCase):
-
-  _RUN_WITH_GRAPH = '_RUN_WITH_GRAPH'
-  _RUN_WITHOUT_GRAPH = '_RUN_WITHOUT_GRAPH'
-
-  _METADATA_TAG = 'secret-stats'
-  _MESSAGE_PREFIX_LENGTH_LOWER_BOUND = 1024
-
-  def generate_run(self, run_name, include_graph):
-    """Create a run with a text summary, metadata, and optionally a graph."""
-    tf.reset_default_graph()
-    k1 = tf.constant(math.pi, name='k1')
-    k2 = tf.constant(math.e, name='k2')
-    result = (k1 ** k2) - k1
-    expected = tf.constant(20.0, name='expected')
-    error = tf.abs(result - expected, name='error')
-    message_prefix_value = 'error ' * 1000
-    true_length = len(message_prefix_value)
-    assert true_length > self._MESSAGE_PREFIX_LENGTH_LOWER_BOUND, true_length
-    message_prefix = tf.constant(message_prefix_value, name='message_prefix')
-    error_message = tf.string_join([message_prefix,
-                                    tf.as_string(error, name='error_string')],
-                                   name='error_message')
-    summary_message = tf.summary.text('summary_message', error_message)
-
-    sess = tf.Session()
-    writer = tf.summary.FileWriter(os.path.join(self.logdir, run_name))
-    if include_graph:
-      writer.add_graph(sess.graph)
-    options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
-    run_metadata = tf.RunMetadata()
-    s = sess.run(summary_message, options=options, run_metadata=run_metadata)
-    writer.add_summary(s)
-    writer.add_run_metadata(run_metadata, self._METADATA_TAG)
-    writer.close()
-
-  def set_up_with_runs(self, with_graph=True, without_graph=True):
-    self.logdir = self.get_temp_dir()
-    if with_graph:
-      self.generate_run(self._RUN_WITH_GRAPH, include_graph=True)
-    if without_graph:
-      self.generate_run(self._RUN_WITHOUT_GRAPH, include_graph=False)
-    multiplexer = event_multiplexer.EventMultiplexer()
-    multiplexer.AddRunsFromDirectory(self.logdir)
-    multiplexer.Reload()
-    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
-    self.plugin = graphs_plugin.GraphsPlugin(context)
-
-  def testRoutesProvided(self):
-    """Tests that the plugin offers the correct routes."""
-    self.set_up_with_runs(with_graph=True, without_graph=False)
-    routes = self.plugin.get_plugin_apps()
-    self.assertIsInstance(routes['/graph'], collections.Callable)
-    self.assertIsInstance(routes['/runs'], collections.Callable)
-    self.assertIsInstance(routes['/run_metadata'], collections.Callable)
-    self.assertIsInstance(routes['/run_metadata_tags'], collections.Callable)
-
-  def test_index(self):
-    self.set_up_with_runs()
-    self.assertItemsEqual([self._RUN_WITH_GRAPH], self.plugin.index_impl())
-
-  def test_run_metadata_index(self):
-    self.set_up_with_runs()
-    self.assertDictEqual({
-        self._RUN_WITH_GRAPH: [self._METADATA_TAG],
-        self._RUN_WITHOUT_GRAPH: [self._METADATA_TAG],
-    }, self.plugin.run_metadata_index_impl())
-
-  def _get_graph(self, *args, **kwargs):
-    """Set up runs, then fetch and return the graph as a proto."""
-    self.set_up_with_runs()
-    (graph_pbtxt, mime_type) = self.plugin.graph_impl(
-        self._RUN_WITH_GRAPH, *args, **kwargs)
-    self.assertEqual(mime_type, 'text/x-protobuf')
-    return text_format.Parse(graph_pbtxt, tf.GraphDef())
-
-  def test_graph_simple(self):
-    graph = self._get_graph()
-    node_names = set(node.name for node in graph.node)
-    self.assertEqual({'k1', 'k2', 'pow', 'sub', 'expected', 'sub_1', 'error',
-                      'message_prefix', 'error_string', 'error_message',
-                      'summary_message'},
-                     node_names)
-
-  def test_graph_large_attrs(self):
-    key = 'o---;;-;'
-    graph = self._get_graph(
-        limit_attr_size=self._MESSAGE_PREFIX_LENGTH_LOWER_BOUND,
-        large_attrs_key=key)
-    large_attrs = {
-        node.name: list(node.attr[key].list.s)
-        for node in graph.node
-        if key in node.attr
-    }
-    self.assertEqual({'message_prefix': [b'value']},
-                     large_attrs)
-
-  def test_run_metadata(self):
-    self.set_up_with_runs()
-    (metadata_pbtxt, mime_type) = self.plugin.run_metadata_impl(
-        self._RUN_WITH_GRAPH, self._METADATA_TAG)
-    self.assertEqual(mime_type, 'text/x-protobuf')
-    text_format.Parse(metadata_pbtxt, tf.RunMetadata())
-    # If it parses, we're happy.
-
-  def test_is_active_with_graph(self):
-    self.set_up_with_runs(with_graph=True, without_graph=False)
-    self.assertTrue(self.plugin.is_active())
-
-  def test_is_active_without_graph(self):
-    self.set_up_with_runs(with_graph=False, without_graph=True)
-    self.assertFalse(self.plugin.is_active())
-
-  def test_is_active_with_both(self):
-    self.set_up_with_runs(with_graph=True, without_graph=True)
-    self.assertTrue(self.plugin.is_active())
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/histograms/BUILD b/tensorflow/tensorboard/plugins/histograms/BUILD
deleted file mode 100644
index 6537e2cdbd..0000000000
--- a/tensorflow/tensorboard/plugins/histograms/BUILD
+++ /dev/null
@@ -1,49 +0,0 @@
-# Description:
-# TensorBoard plugin for histograms
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-## Histograms Plugin ##
-py_library(
-    name = "histograms_plugin",
-    srcs = ["histograms_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "histograms_plugin_test",
-    size = "small",
-    srcs = ["histograms_plugin_test.py"],
-    main = "histograms_plugin_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":histograms_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/histograms/histograms_plugin.py b/tensorflow/tensorboard/plugins/histograms/histograms_plugin.py
deleted file mode 100644
index 4f28e73b66..0000000000
--- a/tensorflow/tensorboard/plugins/histograms/histograms_plugin.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The TensorBoard Histograms plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.plugins import base_plugin
-
-_PLUGIN_PREFIX_ROUTE = event_accumulator.HISTOGRAMS
-
-
-class HistogramsPlugin(base_plugin.TBPlugin):
-  """Histograms Plugin for TensorBoard."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates HistogramsPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self._multiplexer = context.multiplexer
-
-  def get_plugin_apps(self):
-    return {
-        '/histograms': self.histograms_route,
-        '/tags': self.tags_route,
-    }
-
-  def is_active(self):
-    """This plugin is active iff any run has at least one histograms tag."""
-    return bool(self._multiplexer) and any(self.index_impl().values())
-
-  def index_impl(self):
-    return {
-        run_name: run_data[event_accumulator.HISTOGRAMS]
-        for (run_name, run_data) in self._multiplexer.Runs().items()
-        if event_accumulator.HISTOGRAMS in run_data
-    }
-
-  def histograms_impl(self, tag, run):
-    """Result of the form `(body, mime_type)`."""
-    values = self._multiplexer.Histograms(run, tag)
-    return (values, 'application/json')
-
-  @wrappers.Request.application
-  def tags_route(self, request):
-    index = self.index_impl()
-    return http_util.Respond(request, index, 'application/json')
-
-  @wrappers.Request.application
-  def histograms_route(self, request):
-    """Given a tag and single run, return array of histogram values."""
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-    (body, mime_type) = self.histograms_impl(tag, run)
-    return http_util.Respond(request, body, mime_type)
diff --git a/tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py b/tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py
deleted file mode 100644
index 9371b4a593..0000000000
--- a/tensorflow/tensorboard/plugins/histograms/histograms_plugin_test.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Integration tests for the Histograms Plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import os.path
-
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.histograms import histograms_plugin
-
-
-class HistogramsPluginTest(tf.test.TestCase):
-
-  _STEPS = 99
-
-  _HISTOGRAM_TAG = 'my-favorite-histogram'
-  _SCALAR_TAG = 'my-boring-scalars'
-
-  _RUN_WITH_HISTOGRAM = '_RUN_WITH_HISTOGRAM'
-  _RUN_WITH_SCALARS = '_RUN_WITH_SCALARS'
-
-  def set_up_with_runs(self, run_names):
-    self.logdir = self.get_temp_dir()
-    for run_name in run_names:
-      self.generate_run(run_name)
-    multiplexer = event_multiplexer.EventMultiplexer(size_guidance={
-        # don't truncate my test data, please
-        event_accumulator.HISTOGRAMS:
-            self._STEPS,
-    })
-    multiplexer.AddRunsFromDirectory(self.logdir)
-    multiplexer.Reload()
-    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
-    self.plugin = histograms_plugin.HistogramsPlugin(context)
-
-  def generate_run(self, run_name):
-    if run_name == self._RUN_WITH_HISTOGRAM:
-      (use_histogram, use_scalars) = (True, False)
-    elif run_name == self._RUN_WITH_SCALARS:
-      (use_histogram, use_scalars) = (False, True)
-    else:
-      assert False, 'Invalid run name: %r' % run_name
-    tf.reset_default_graph()
-    sess = tf.Session()
-    placeholder = tf.placeholder(tf.float32, shape=[3])
-    if use_histogram:
-      tf.summary.histogram(self._HISTOGRAM_TAG, placeholder)
-    if use_scalars:
-      tf.summary.scalar(self._SCALAR_TAG, tf.reduce_mean(placeholder))
-    summ = tf.summary.merge_all()
-
-    subdir = os.path.join(self.logdir, run_name)
-    writer = tf.summary.FileWriter(subdir)
-    writer.add_graph(sess.graph)
-    for step in xrange(self._STEPS):
-      feed_dict = {placeholder: [1 + step, 2 + step, 3 + step]}
-      s = sess.run(summ, feed_dict=feed_dict)
-      writer.add_summary(s, global_step=step)
-    writer.close()
-
-  def testRoutesProvided(self):
-    """Tests that the plugin offers the correct routes."""
-    self.set_up_with_runs([self._RUN_WITH_SCALARS])
-    routes = self.plugin.get_plugin_apps()
-    self.assertIsInstance(routes['/histograms'], collections.Callable)
-    self.assertIsInstance(routes['/tags'], collections.Callable)
-
-  def test_index(self):
-    self.set_up_with_runs([self._RUN_WITH_HISTOGRAM, self._RUN_WITH_SCALARS])
-    self.assertEqual({
-        self._RUN_WITH_HISTOGRAM: [self._HISTOGRAM_TAG],
-        self._RUN_WITH_SCALARS: [],
-    }, self.plugin.index_impl())
-
-  def _test_histograms(self, run_name, should_have_histogram):
-    self.set_up_with_runs([self._RUN_WITH_HISTOGRAM, self._RUN_WITH_SCALARS])
-    if should_have_histogram:
-      (data, mime_type) = self.plugin.histograms_impl(self._HISTOGRAM_TAG,
-                                                      run_name)
-      self.assertEqual('application/json', mime_type)
-      self.assertEqual(len(data), self._STEPS)
-      for i in xrange(self._STEPS):
-        frame = data[i]
-        self.assertEqual(i, frame.step)
-        self.assertEqual(1 + i, frame.histogram_value.min)
-        self.assertEqual(3 + i, frame.histogram_value.max)
-        self.assertAlmostEqual(
-            3,  # three items across all buckets
-            sum(frame.histogram_value.bucket))
-    else:
-      with self.assertRaises(KeyError):
-        self.plugin.histograms_impl(self._HISTOGRAM_TAG, run_name)
-
-  def test_histograms_with_scalars(self):
-    self._test_histograms(self._RUN_WITH_HISTOGRAM, True)
-
-  def test_histograms_with_histogram(self):
-    self._test_histograms(self._RUN_WITH_SCALARS, False)
-
-  def test_active_with_histogram(self):
-    self.set_up_with_runs([self._RUN_WITH_HISTOGRAM])
-    self.assertTrue(self.plugin.is_active())
-
-  def test_active_with_scalars(self):
-    self.set_up_with_runs([self._RUN_WITH_SCALARS])
-    self.assertFalse(self.plugin.is_active())
-
-  def test_active_with_both(self):
-    self.set_up_with_runs([self._RUN_WITH_HISTOGRAM, self._RUN_WITH_SCALARS])
-    self.assertTrue(self.plugin.is_active())
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/images/BUILD b/tensorflow/tensorboard/plugins/images/BUILD
deleted file mode 100644
index 34951cc6fa..0000000000
--- a/tensorflow/tensorboard/plugins/images/BUILD
+++ /dev/null
@@ -1,47 +0,0 @@
-# Description:
-# TensorBoard plugin for images
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-py_library(
-    name = "images_plugin",
-    srcs = ["images_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "images_plugin_test",
-    size = "small",
-    srcs = ["images_plugin_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":images_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/images/images_plugin.py b/tensorflow/tensorboard/plugins/images/images_plugin.py
deleted file mode 100644
index f1c4b75b13..0000000000
--- a/tensorflow/tensorboard/plugins/images/images_plugin.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The TensorBoard Images plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import imghdr
-
-from six.moves import urllib
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.plugins import base_plugin
-
-_PLUGIN_PREFIX_ROUTE = event_accumulator.IMAGES
-
-_IMGHDR_TO_MIMETYPE = {
-    'bmp': 'image/bmp',
-    'gif': 'image/gif',
-    'jpeg': 'image/jpeg',
-    'png': 'image/png'
-}
-
-_DEFAULT_IMAGE_MIMETYPE = 'application/octet-stream'
-
-
-class ImagesPlugin(base_plugin.TBPlugin):
-  """Images Plugin for TensorBoard."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates ImagesPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self._multiplexer = context.multiplexer
-
-  def get_plugin_apps(self):
-    return {
-        '/images': self._serve_image_metadata,
-        '/individualImage': self._serve_individual_image,
-        '/tags': self._serve_tags,
-    }
-
-  def is_active(self):
-    """The images plugin is active iff any run has at least one relevant tag."""
-    return bool(self._multiplexer) and any(self._index_impl().values())
-
-  def _index_impl(self):
-    return {
-        run_name: run_data[event_accumulator.IMAGES]
-        for (run_name, run_data) in self._multiplexer.Runs().items()
-        if event_accumulator.IMAGES in run_data
-    }
-
-  @wrappers.Request.application
-  def _serve_image_metadata(self, request):
-    """Given a tag and list of runs, serve a list of metadata for images.
-
-    Note that the images themselves are not sent; instead, we respond with URLs
-    to the images. The frontend should treat these URLs as opaque and should not
-    try to parse information about them or generate them itself, as the format
-    may change.
-
-    Args:
-      request: A werkzeug.wrappers.Request object.
-
-    Returns:
-      A werkzeug.Response application.
-    """
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-
-    images = self._multiplexer.Images(run, tag)
-    response = self._image_response_for_run(images, run, tag)
-    return http_util.Respond(request, response, 'application/json')
-
-  def _image_response_for_run(self, run_images, run, tag):
-    """Builds a JSON-serializable object with information about run_images.
-
-    Args:
-      run_images: A list of event_accumulator.ImageValueEvent objects.
-      run: The name of the run.
-      tag: The name of the tag the images all belong to.
-
-    Returns:
-      A list of dictionaries containing the wall time, step, URL, width, and
-      height for each image.
-    """
-    response = []
-    for index, run_image in enumerate(run_images):
-      response.append({
-          'wall_time': run_image.wall_time,
-          'step': run_image.step,
-          # We include the size so that the frontend can add that to the <img>
-          # tag so that the page layout doesn't change when the image loads.
-          'width': run_image.width,
-          'height': run_image.height,
-          'query': self._query_for_individual_image(run, tag, index)
-      })
-    return response
-
-  def _query_for_individual_image(self, run, tag, index):
-    """Builds a URL for accessing the specified image.
-
-    This should be kept in sync with _serve_image_metadata. Note that the URL is
-    *not* guaranteed to always return the same image, since images may be
-    unloaded from the reservoir as new images come in.
-
-    Args:
-      run: The name of the run.
-      tag: The tag.
-      index: The index of the image. Negative values are OK.
-
-    Returns:
-      A string representation of a URL that will load the index-th sampled image
-      in the given run with the given tag.
-    """
-    query_string = urllib.parse.urlencode({
-        'run': run,
-        'tag': tag,
-        'index': index
-    })
-    return query_string
-
-  @wrappers.Request.application
-  def _serve_individual_image(self, request):
-    """Serves an individual image."""
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-    index = int(request.args.get('index'))
-    image = self._multiplexer.Images(run, tag)[index]
-    image_type = imghdr.what(None, image.encoded_image_string)
-    content_type = _IMGHDR_TO_MIMETYPE.get(image_type, _DEFAULT_IMAGE_MIMETYPE)
-    return http_util.Respond(request, image.encoded_image_string, content_type)
-
-  @wrappers.Request.application
-  def _serve_tags(self, request):
-    index = self._index_impl()
-    return http_util.Respond(request, index, 'application/json')
diff --git a/tensorflow/tensorboard/plugins/images/images_plugin_test.py b/tensorflow/tensorboard/plugins/images/images_plugin_test.py
deleted file mode 100644
index f24ef90d0f..0000000000
--- a/tensorflow/tensorboard/plugins/images/images_plugin_test.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests the Tensorboard images plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import json
-import os
-import shutil
-import tempfile
-
-import numpy
-from six.moves import urllib
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-from werkzeug import test as werkzeug_test
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import application
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.images import images_plugin
-
-
-class ImagesPluginTest(tf.test.TestCase):
-
-  def setUp(self):
-    self.log_dir = tempfile.mkdtemp()
-
-    # We use numpy.random to generate images. We seed to avoid non-determinism
-    # in this test.
-    numpy.random.seed(42)
-
-    # Create image summaries for run foo.
-    tf.reset_default_graph()
-    sess = tf.Session()
-    placeholder = tf.placeholder(tf.uint8)
-    tf.summary.image(name="baz", tensor=placeholder)
-    merged_summary_op = tf.summary.merge_all()
-    foo_directory = os.path.join(self.log_dir, "foo")
-    writer = tf.summary.FileWriter(foo_directory)
-    writer.add_graph(sess.graph)
-    for step in xrange(2):
-      writer.add_summary(sess.run(merged_summary_op, feed_dict={
-          placeholder: (numpy.random.rand(1, 16, 42, 3) * 255).astype(
-              numpy.uint8)
-      }), global_step=step)
-    writer.close()
-
-    # Create image summaries for run bar.
-    tf.reset_default_graph()
-    sess = tf.Session()
-    placeholder = tf.placeholder(tf.uint8)
-    tf.summary.image(name="quux", tensor=placeholder)
-    merged_summary_op = tf.summary.merge_all()
-    bar_directory = os.path.join(self.log_dir, "bar")
-    writer = tf.summary.FileWriter(bar_directory)
-    writer.add_graph(sess.graph)
-    for step in xrange(2):
-      writer.add_summary(sess.run(merged_summary_op, feed_dict={
-          placeholder: (numpy.random.rand(1, 6, 8, 3) * 255).astype(
-              numpy.uint8)
-      }), global_step=step)
-    writer.close()
-
-    # Start a server with the plugin.
-    multiplexer = event_multiplexer.EventMultiplexer({
-        "foo": foo_directory,
-        "bar": bar_directory,
-    })
-    context = base_plugin.TBContext(
-        logdir=self.log_dir, multiplexer=multiplexer)
-    plugin = images_plugin.ImagesPlugin(context)
-    wsgi_app = application.TensorBoardWSGIApp(
-        self.log_dir, [plugin], multiplexer, reload_interval=0)
-    self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
-    self.routes = plugin.get_plugin_apps()
-
-  def tearDown(self):
-    shutil.rmtree(self.log_dir, ignore_errors=True)
-
-  def _DeserializeResponse(self, byte_content):
-    """Deserializes byte content that is a JSON encoding.
-
-    Args:
-      byte_content: The byte content of a response.
-
-    Returns:
-      The deserialized python object decoded from JSON.
-    """
-    return json.loads(byte_content.decode("utf-8"))
-
-  def testRoutesProvided(self):
-    """Tests that the plugin offers the correct routes."""
-    self.assertIsInstance(self.routes["/images"], collections.Callable)
-    self.assertIsInstance(self.routes["/individualImage"], collections.Callable)
-    self.assertIsInstance(self.routes["/tags"], collections.Callable)
-
-  def testImagesRoute(self):
-    """Tests that the /images routes returns with the correct data."""
-    response = self.server.get(
-        "/data/plugin/images/images?run=foo&tag=baz/image/0")
-    self.assertEqual(200, response.status_code)
-
-    # Verify that the correct entries are returned.
-    entries = self._DeserializeResponse(response.get_data())
-    self.assertEqual(2, len(entries))
-
-    # Verify that the 1st entry is correct.
-    entry = entries[0]
-    self.assertEqual(42, entry["width"])
-    self.assertEqual(16, entry["height"])
-    self.assertEqual(0, entry["step"])
-    parsed_query = urllib.parse.parse_qs(entry["query"])
-    self.assertListEqual(["0"], parsed_query["index"])
-    self.assertListEqual(["foo"], parsed_query["run"])
-    self.assertListEqual(["baz/image/0"], parsed_query["tag"])
-
-    # Verify that the 2nd entry is correct.
-    entry = entries[1]
-    self.assertEqual(42, entry["width"])
-    self.assertEqual(16, entry["height"])
-    self.assertEqual(1, entry["step"])
-    parsed_query = urllib.parse.parse_qs(entry["query"])
-    self.assertListEqual(["1"], parsed_query["index"])
-    self.assertListEqual(["foo"], parsed_query["run"])
-    self.assertListEqual(["baz/image/0"], parsed_query["tag"])
-
-  def testIndividualImageRoute(self):
-    """Tests fetching an individual image."""
-    response = self.server.get(
-        "/data/plugin/images/individualImage?run=bar&tag=quux/image/0&index=0")
-    self.assertEqual(200, response.status_code)
-    self.assertEqual("image/png", response.headers.get("content-type"))
-
-  def testRunsRoute(self):
-    """Tests that the /runs route offers the correct run to tag mapping."""
-    response = self.server.get("/data/plugin/images/tags")
-    self.assertEqual(200, response.status_code)
-    self.assertDictEqual({
-        "foo": ["baz/image/0"],
-        "bar": ["quux/image/0"]
-    }, self._DeserializeResponse(response.get_data()))
-
-
-if __name__ == "__main__":
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/projector/BUILD b/tensorflow/tensorboard/plugins/projector/BUILD
deleted file mode 100644
index dff480e632..0000000000
--- a/tensorflow/tensorboard/plugins/projector/BUILD
+++ /dev/null
@@ -1,59 +0,0 @@
-# Embedding Projector plugin.
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-load("//tensorflow/tensorboard/defs:protos.bzl", "tb_proto_library")
-
-py_library(
-    name = "projector_plugin",
-    srcs = ["projector_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//learning/vis/projector:__subpackages__",
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        ":protos_all_py",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "//third_party/py/numpy",
-        "@org_pocoo_werkzeug//:werkzeug",
-    ],
-)
-
-py_test(
-    name = "projector_plugin_test",
-    size = "small",
-    srcs = ["projector_plugin_test.py"],
-    main = "projector_plugin_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":projector_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "//third_party/py/numpy",
-        "@org_pocoo_werkzeug//:werkzeug",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
-
-tb_proto_library(
-    name = "protos_all",
-    srcs = glob(["*.proto"]),
-    visibility = [
-        "//tensorflow:internal",
-        "//tensorflow/tensorboard:internal",
-    ],
-)
diff --git a/tensorflow/tensorboard/plugins/projector/projector_plugin.py b/tensorflow/tensorboard/plugins/projector/projector_plugin.py
deleted file mode 100644
index 304140cee4..0000000000
--- a/tensorflow/tensorboard/plugins/projector/projector_plugin.py
+++ /dev/null
@@ -1,645 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The Embedding Projector plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import imghdr
-import math
-import os
-import numpy as np
-import tensorflow as tf
-from werkzeug import wrappers
-
-from google.protobuf import json_format
-from google.protobuf import text_format
-from tensorflow.tensorboard.backend.http_util import Respond
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.projector import projector_config_pb2
-
-# The prefix of routes provided by this plugin.
-_PLUGIN_PREFIX_ROUTE = 'projector'
-
-# FYI - the PROJECTOR_FILENAME is hardcoded in the visualize_embeddings
-# method in tf.contrib.tensorboard.plugins.projector module.
-# TODO(dandelion): Fix duplication when we find a permanent home for the
-# projector module.
-PROJECTOR_FILENAME = 'projector_config.pbtxt'
-_PLUGIN_NAME = 'org_tensorflow_tensorboard_projector'
-_PLUGINS_DIR = 'plugins'
-
-# Number of tensors in the LRU cache.
-_TENSOR_CACHE_CAPACITY = 1
-
-# HTTP routes.
-CONFIG_ROUTE = '/info'
-TENSOR_ROUTE = '/tensor'
-METADATA_ROUTE = '/metadata'
-RUNS_ROUTE = '/runs'
-BOOKMARKS_ROUTE = '/bookmarks'
-SPRITE_IMAGE_ROUTE = '/sprite_image'
-
-_IMGHDR_TO_MIMETYPE = {
-    'bmp': 'image/bmp',
-    'gif': 'image/gif',
-    'jpeg': 'image/jpeg',
-    'png': 'image/png'
-}
-_DEFAULT_IMAGE_MIMETYPE = 'application/octet-stream'
-
-
-class LRUCache(object):
-  """LRU cache. Used for storing the last used tensor."""
-
-  def __init__(self, size):
-    if size < 1:
-      raise ValueError('The cache size must be >=1')
-    self._size = size
-    self._dict = collections.OrderedDict()
-
-  def get(self, key):
-    try:
-      value = self._dict.pop(key)
-      self._dict[key] = value
-      return value
-    except KeyError:
-      return None
-
-  def set(self, key, value):
-    if value is None:
-      raise ValueError('value must be != None')
-    try:
-      self._dict.pop(key)
-    except KeyError:
-      if len(self._dict) >= self._size:
-        self._dict.popitem(last=False)
-    self._dict[key] = value
-
-
-class EmbeddingMetadata(object):
-  """Metadata container for an embedding.
-
-  The metadata holds different columns with values used for visualization
-  (color by, label by) in the "Embeddings" tab in TensorBoard.
-  """
-
-  def __init__(self, num_points):
-    """Constructs a metadata for an embedding of the specified size.
-
-    Args:
-      num_points: Number of points in the embedding.
-    """
-    self.num_points = num_points
-    self.column_names = []
-    self.name_to_values = {}
-
-  def add_column(self, column_name, column_values):
-    """Adds a named column of metadata values.
-
-    Args:
-      column_name: Name of the column.
-      column_values: 1D array/list/iterable holding the column values. Must be
-          of length `num_points`. The i-th value corresponds to the i-th point.
-
-    Raises:
-      ValueError: If `column_values` is not 1D array, or of length `num_points`,
-          or the `name` is already used.
-    """
-    # Sanity checks.
-    if isinstance(column_values, list) and isinstance(column_values[0], list):
-      raise ValueError('"column_values" must be a flat list, but we detected '
-                       'that its first entry is a list')
-
-    if isinstance(column_values, np.ndarray) and column_values.ndim != 1:
-      raise ValueError('"column_values" should be of rank 1, '
-                       'but is of rank %d' % column_values.ndim)
-    if len(column_values) != self.num_points:
-      raise ValueError('"column_values" should be of length %d, but is of '
-                       'length %d' % (self.num_points, len(column_values)))
-    if column_name in self.name_to_values:
-      raise ValueError('The column name "%s" is already used' % column_name)
-
-    self.column_names.append(column_name)
-    self.name_to_values[column_name] = column_values
-
-
-def _read_tensor_tsv_file(fpath):
-  with tf.gfile.GFile(fpath, 'r') as f:
-    tensor = []
-    for line in f:
-      if line:
-        tensor.append(list(map(float, line.rstrip('\n').split('\t'))))
-  return np.array(tensor, dtype='float32')
-
-
-def _assets_dir_to_logdir(assets_dir):
-  sub_path = os.path.sep + _PLUGINS_DIR + os.path.sep
-  if sub_path in assets_dir:
-    two_parents_up = os.pardir + os.path.sep + os.pardir
-    return os.path.abspath(os.path.join(assets_dir, two_parents_up))
-  return assets_dir
-
-
-def _latest_checkpoints_changed(configs, run_path_pairs):
-  """Returns true if the latest checkpoint has changed in any of the runs."""
-  for run_name, assets_dir in run_path_pairs:
-    if run_name not in configs:
-      config = projector_config_pb2.ProjectorConfig()
-      config_fpath = os.path.join(assets_dir, PROJECTOR_FILENAME)
-      if tf.gfile.Exists(config_fpath):
-        with tf.gfile.GFile(config_fpath, 'r') as f:
-          file_content = f.read()
-        text_format.Merge(file_content, config)
-    else:
-      config = configs[run_name]
-
-    # See if you can find a checkpoint file in the logdir.
-    logdir = _assets_dir_to_logdir(assets_dir)
-    ckpt_path = _find_latest_checkpoint(logdir)
-    if not ckpt_path:
-      continue
-    if config.model_checkpoint_path != ckpt_path:
-      return True
-  return False
-
-
-def _parse_positive_int_param(request, param_name):
-  """Parses and asserts a positive (>0) integer query parameter.
-
-  Args:
-    request: The Werkzeug Request object
-    param_name: Name of the parameter.
-
-  Returns:
-    Param, or None, or -1 if parameter is not a positive integer.
-  """
-  param = request.args.get(param_name)
-  if not param:
-    return None
-  try:
-    param = int(param)
-    if param <= 0:
-      raise ValueError()
-    return param
-  except ValueError:
-    return -1
-
-
-def _rel_to_abs_asset_path(fpath, config_fpath):
-  fpath = os.path.expanduser(fpath)
-  if not os.path.isabs(fpath):
-    return os.path.join(os.path.dirname(config_fpath), fpath)
-  return fpath
-
-
-class ProjectorPlugin(base_plugin.TBPlugin):
-  """Embedding projector."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates ProjectorPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self.multiplexer = context.multiplexer
-    self.logdir = context.logdir
-    self._handlers = None
-    self.readers = {}
-    self.run_paths = None
-    self._configs = None
-    self.old_num_run_paths = None
-    self.tensor_cache = LRUCache(_TENSOR_CACHE_CAPACITY)
-    self.run_paths = None
-    if self.multiplexer:
-      self.run_paths = self.multiplexer.RunPaths()
-
-  def get_plugin_apps(self):
-    self._handlers = {
-        RUNS_ROUTE: self._serve_runs,
-        CONFIG_ROUTE: self._serve_config,
-        TENSOR_ROUTE: self._serve_tensor,
-        METADATA_ROUTE: self._serve_metadata,
-        BOOKMARKS_ROUTE: self._serve_bookmarks,
-        SPRITE_IMAGE_ROUTE: self._serve_sprite_image
-    }
-    return self._handlers
-
-  def is_active(self):
-    """Determines whether this plugin is active.
-
-    This plugin is only active if any run has an embedding.
-
-    Returns:
-      A boolean. Whether this plugin is active.
-    """
-    return bool(self.multiplexer and self.configs)
-
-  @property
-  def configs(self):
-    """Returns a map of run paths to `ProjectorConfig` protos."""
-    run_path_pairs = list(self.run_paths.items())
-    self._append_plugin_asset_directories(run_path_pairs)
-    # If there are no summary event files, the projector should still work,
-    # treating the `logdir` as the model checkpoint directory.
-    if not run_path_pairs:
-      run_path_pairs.append(('.', self.logdir))
-    if (self._run_paths_changed() or
-        _latest_checkpoints_changed(self._configs, run_path_pairs)):
-      self.readers = {}
-      self._configs, self.config_fpaths = self._read_latest_config_files(
-          run_path_pairs)
-      self._augment_configs_with_checkpoint_info()
-    return self._configs
-
-  def _run_paths_changed(self):
-    num_run_paths = len(list(self.run_paths.keys()))
-    if num_run_paths != self.old_num_run_paths:
-      self.old_num_run_paths = num_run_paths
-      return True
-    return False
-
-  def _augment_configs_with_checkpoint_info(self):
-    for run, config in self._configs.items():
-      for embedding in config.embeddings:
-        # Normalize the name of the embeddings.
-        if embedding.tensor_name.endswith(':0'):
-          embedding.tensor_name = embedding.tensor_name[:-2]
-        # Find the size of embeddings associated with a tensors file.
-        if embedding.tensor_path and not embedding.tensor_shape:
-          fpath = _rel_to_abs_asset_path(embedding.tensor_path,
-                                         self.config_fpaths[run])
-          tensor = self.tensor_cache.get(embedding.tensor_name)
-          if tensor is None:
-            tensor = _read_tensor_tsv_file(fpath)
-            self.tensor_cache.set(embedding.tensor_name, tensor)
-          embedding.tensor_shape.extend([len(tensor), len(tensor[0])])
-
-      reader = self._get_reader_for_run(run)
-      if not reader:
-        continue
-      # Augment the configuration with the tensors in the checkpoint file.
-      special_embedding = None
-      if config.embeddings and not config.embeddings[0].tensor_name:
-        special_embedding = config.embeddings[0]
-        config.embeddings.remove(special_embedding)
-      var_map = reader.get_variable_to_shape_map()
-      for tensor_name, tensor_shape in var_map.items():
-        if len(tensor_shape) != 2:
-          continue
-        embedding = self._get_embedding(tensor_name, config)
-        if not embedding:
-          embedding = config.embeddings.add()
-          embedding.tensor_name = tensor_name
-          if special_embedding:
-            embedding.metadata_path = special_embedding.metadata_path
-            embedding.bookmarks_path = special_embedding.bookmarks_path
-        if not embedding.tensor_shape:
-          embedding.tensor_shape.extend(tensor_shape)
-
-    # Remove configs that do not have any valid (2D) tensors.
-    runs_to_remove = []
-    for run, config in self._configs.items():
-      if not config.embeddings:
-        runs_to_remove.append(run)
-    for run in runs_to_remove:
-      del self._configs[run]
-      del self.config_fpaths[run]
-
-  def _read_latest_config_files(self, run_path_pairs):
-    """Reads and returns the projector config files in every run directory."""
-    configs = {}
-    config_fpaths = {}
-    for run_name, assets_dir in run_path_pairs:
-      config = projector_config_pb2.ProjectorConfig()
-      config_fpath = os.path.join(assets_dir, PROJECTOR_FILENAME)
-      if tf.gfile.Exists(config_fpath):
-        with tf.gfile.GFile(config_fpath, 'r') as f:
-          file_content = f.read()
-        text_format.Merge(file_content, config)
-      has_tensor_files = False
-      for embedding in config.embeddings:
-        if embedding.tensor_path:
-          if not embedding.tensor_name:
-            embedding.tensor_name = os.path.basename(embedding.tensor_path)
-          has_tensor_files = True
-          break
-
-      if not config.model_checkpoint_path:
-        # See if you can find a checkpoint file in the logdir.
-        logdir = _assets_dir_to_logdir(assets_dir)
-        ckpt_path = _find_latest_checkpoint(logdir)
-        if not ckpt_path and not has_tensor_files:
-          continue
-        if ckpt_path:
-          config.model_checkpoint_path = ckpt_path
-
-      # Sanity check for the checkpoint file.
-      if (config.model_checkpoint_path and
-          not tf.train.checkpoint_exists(config.model_checkpoint_path)):
-        tf.logging.warning('Checkpoint file "%s" not found',
-                           config.model_checkpoint_path)
-        continue
-      configs[run_name] = config
-      config_fpaths[run_name] = config_fpath
-    return configs, config_fpaths
-
-  def _get_reader_for_run(self, run):
-    if run in self.readers:
-      return self.readers[run]
-
-    config = self._configs[run]
-    reader = None
-    if config.model_checkpoint_path:
-      try:
-        reader = tf.pywrap_tensorflow.NewCheckpointReader(
-            config.model_checkpoint_path)
-      except Exception:  # pylint: disable=broad-except
-        tf.logging.warning('Failed reading "%s"', config.model_checkpoint_path)
-    self.readers[run] = reader
-    return reader
-
-  def _get_metadata_file_for_tensor(self, tensor_name, config):
-    embedding_info = self._get_embedding(tensor_name, config)
-    if embedding_info:
-      return embedding_info.metadata_path
-    return None
-
-  def _get_bookmarks_file_for_tensor(self, tensor_name, config):
-    embedding_info = self._get_embedding(tensor_name, config)
-    if embedding_info:
-      return embedding_info.bookmarks_path
-    return None
-
-  def _canonical_tensor_name(self, tensor_name):
-    if ':' not in tensor_name:
-      return tensor_name + ':0'
-    else:
-      return tensor_name
-
-  def _get_embedding(self, tensor_name, config):
-    if not config.embeddings:
-      return None
-    for info in config.embeddings:
-      if (self._canonical_tensor_name(info.tensor_name) ==
-          self._canonical_tensor_name(tensor_name)):
-        return info
-    return None
-
-  def _append_plugin_asset_directories(self, run_path_pairs):
-    for run, assets in self.multiplexer.PluginAssets(_PLUGIN_NAME).items():
-      if PROJECTOR_FILENAME not in assets:
-        continue
-      assets_dir = os.path.join(self.run_paths[run], _PLUGINS_DIR, _PLUGIN_NAME)
-      assets_path_pair = (run, os.path.abspath(assets_dir))
-      run_path_pairs.append(assets_path_pair)
-
-  @wrappers.Request.application
-  def _serve_runs(self, request):
-    """Returns a list of runs that have embeddings."""
-    return Respond(request, list(self.configs.keys()), 'application/json')
-
-  @wrappers.Request.application
-  def _serve_config(self, request):
-    run = request.args.get('run')
-    if run is None:
-      return Respond(request, 'query parameter "run" is required', 'text/plain',
-                     400)
-    if run not in self.configs:
-      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)
-
-    config = self.configs[run]
-    return Respond(request,
-                   json_format.MessageToJson(config), 'application/json')
-
-  @wrappers.Request.application
-  def _serve_metadata(self, request):
-    run = request.args.get('run')
-    if run is None:
-      return Respond(request, 'query parameter "run" is required', 'text/plain',
-                     400)
-
-    name = request.args.get('name')
-    if name is None:
-      return Respond(request, 'query parameter "name" is required',
-                     'text/plain', 400)
-
-    num_rows = _parse_positive_int_param(request, 'num_rows')
-    if num_rows == -1:
-      return Respond(request, 'query parameter num_rows must be integer > 0',
-                     'text/plain', 400)
-
-    if run not in self.configs:
-      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)
-
-    config = self.configs[run]
-    fpath = self._get_metadata_file_for_tensor(name, config)
-    if not fpath:
-      return Respond(
-          request,
-          'No metadata file found for tensor "%s" in the config file "%s"' %
-          (name, self.config_fpaths[run]), 'text/plain', 400)
-    fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
-    if not tf.gfile.Exists(fpath) or tf.gfile.IsDirectory(fpath):
-      return Respond(request, '"%s" not found, or is not a file' % fpath,
-                     'text/plain', 400)
-
-    num_header_rows = 0
-    with tf.gfile.GFile(fpath, 'r') as f:
-      lines = []
-      # Stream reading the file with early break in case the file doesn't fit in
-      # memory.
-      for line in f:
-        lines.append(line)
-        if len(lines) == 1 and '\t' in lines[0]:
-          num_header_rows = 1
-        if num_rows and len(lines) >= num_rows + num_header_rows:
-          break
-    return Respond(request, ''.join(lines), 'text/plain')
-
-  @wrappers.Request.application
-  def _serve_tensor(self, request):
-    run = request.args.get('run')
-    if run is None:
-      return Respond(request, 'query parameter "run" is required', 'text/plain',
-                     400)
-
-    name = request.args.get('name')
-    if name is None:
-      return Respond(request, 'query parameter "name" is required',
-                     'text/plain', 400)
-
-    num_rows = _parse_positive_int_param(request, 'num_rows')
-    if num_rows == -1:
-      return Respond(request, 'query parameter num_rows must be integer > 0',
-                     'text/plain', 400)
-
-    if run not in self.configs:
-      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)
-
-    config = self.configs[run]
-
-    tensor = self.tensor_cache.get(name)
-    if tensor is None:
-      # See if there is a tensor file in the config.
-      embedding = self._get_embedding(name, config)
-
-      if embedding and embedding.tensor_path:
-        fpath = _rel_to_abs_asset_path(embedding.tensor_path,
-                                       self.config_fpaths[run])
-        if not tf.gfile.Exists(fpath):
-          return Respond(request,
-                         'Tensor file "%s" does not exist' % fpath,
-                         'text/plain', 400)
-        tensor = _read_tensor_tsv_file(fpath)
-      else:
-        reader = self._get_reader_for_run(run)
-        if not reader or not reader.has_tensor(name):
-          return Respond(request,
-                         'Tensor "%s" not found in checkpoint dir "%s"' %
-                         (name, config.model_checkpoint_path), 'text/plain',
-                         400)
-        try:
-          tensor = reader.get_tensor(name)
-        except tf.errors.InvalidArgumentError as e:
-          return Respond(request, str(e), 'text/plain', 400)
-
-      self.tensor_cache.set(name, tensor)
-
-    if num_rows:
-      tensor = tensor[:num_rows]
-    if tensor.dtype != 'float32':
-      tensor = tensor.astype(dtype='float32', copy=False)
-    data_bytes = tensor.tobytes()
-    return Respond(request, data_bytes, 'application/octet-stream')
-
-  @wrappers.Request.application
-  def _serve_bookmarks(self, request):
-    run = request.args.get('run')
-    if not run:
-      return Respond(request, 'query parameter "run" is required', 'text/plain',
-                     400)
-
-    name = request.args.get('name')
-    if name is None:
-      return Respond(request, 'query parameter "name" is required',
-                     'text/plain', 400)
-
-    if run not in self.configs:
-      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)
-
-    config = self.configs[run]
-    fpath = self._get_bookmarks_file_for_tensor(name, config)
-    if not fpath:
-      return Respond(
-          request,
-          'No bookmarks file found for tensor "%s" in the config file "%s"' %
-          (name, self.config_fpaths[run]), 'text/plain', 400)
-    fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
-    if not tf.gfile.Exists(fpath) or tf.gfile.IsDirectory(fpath):
-      return Respond(request, '"%s" not found, or is not a file' % fpath,
-                     'text/plain', 400)
-
-    bookmarks_json = None
-    with tf.gfile.GFile(fpath, 'rb') as f:
-      bookmarks_json = f.read()
-    return Respond(request, bookmarks_json, 'application/json')
-
-  @wrappers.Request.application
-  def _serve_sprite_image(self, request):
-    run = request.args.get('run')
-    if not run:
-      return Respond(request, 'query parameter "run" is required', 'text/plain',
-                     400)
-
-    name = request.args.get('name')
-    if name is None:
-      return Respond(request, 'query parameter "name" is required',
-                     'text/plain', 400)
-
-    if run not in self.configs:
-      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)
-
-    config = self.configs[run]
-    embedding_info = self._get_embedding(name, config)
-
-    if not embedding_info or not embedding_info.sprite.image_path:
-      return Respond(
-          request,
-          'No sprite image file found for tensor "%s" in the config file "%s"' %
-          (name, self.config_fpaths[run]), 'text/plain', 400)
-
-    fpath = os.path.expanduser(embedding_info.sprite.image_path)
-    fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
-    if not tf.gfile.Exists(fpath) or tf.gfile.IsDirectory(fpath):
-      return Respond(request, '"%s" does not exist or is directory' % fpath,
-                     'text/plain', 400)
-    f = tf.gfile.GFile(fpath, 'rb')
-    encoded_image_string = f.read()
-    f.close()
-    image_type = imghdr.what(None, encoded_image_string)
-    mime_type = _IMGHDR_TO_MIMETYPE.get(image_type, _DEFAULT_IMAGE_MIMETYPE)
-    return Respond(request, encoded_image_string, mime_type)
-
-
-def _find_latest_checkpoint(dir_path):
-  try:
-    ckpt_path = tf.train.latest_checkpoint(dir_path)
-    if not ckpt_path:
-      # Check the parent directory.
-      ckpt_path = tf.train.latest_checkpoint(os.path.join(dir_path, os.pardir))
-    return ckpt_path
-  except tf.errors.NotFoundError:
-    return None
-
-
-def _make_sprite_image(thumbnails, thumbnail_dim):
-  """Constructs a sprite image from thumbnails and returns the png bytes."""
-  if len(thumbnails) < 1:
-    raise ValueError('The length of "thumbnails" must be >= 1')
-
-  if isinstance(thumbnails, np.ndarray) and thumbnails.ndim != 4:
-    raise ValueError('"thumbnails" should be of rank 4, '
-                     'but is of rank %d' % thumbnails.ndim)
-  if isinstance(thumbnails, list):
-    if not isinstance(thumbnails[0], np.ndarray) or thumbnails[0].ndim != 3:
-      raise ValueError('Each element of "thumbnails" must be a 3D `ndarray`')
-    thumbnails = np.array(thumbnails)
-
-  with tf.Graph().as_default():
-    s = tf.Session()
-    resized_images = tf.image.resize_images(thumbnails, thumbnail_dim).eval(
-        session=s)
-    images_per_row = int(math.ceil(math.sqrt(len(thumbnails))))
-    thumb_height = thumbnail_dim[0]
-    thumb_width = thumbnail_dim[1]
-    master_height = images_per_row * thumb_height
-    master_width = images_per_row * thumb_width
-    num_channels = thumbnails.shape[3]
-    master = np.zeros([master_height, master_width, num_channels])
-    for idx, image in enumerate(resized_images):
-      left_idx = idx % images_per_row
-      top_idx = int(math.floor(idx / images_per_row))
-      left_start = left_idx * thumb_width
-      left_end = left_start + thumb_width
-      top_start = top_idx * thumb_height
-      top_end = top_start + thumb_height
-      master[top_start:top_end, left_start:left_end, :] = image
-
-    return tf.image.encode_png(master).eval(session=s)
diff --git a/tensorflow/tensorboard/plugins/projector/projector_plugin_test.py b/tensorflow/tensorboard/plugins/projector/projector_plugin_test.py
deleted file mode 100644
index acdba3f3d4..0000000000
--- a/tensorflow/tensorboard/plugins/projector/projector_plugin_test.py
+++ /dev/null
@@ -1,351 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Integration tests for the Embedding Projector."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import gzip
-import io
-import json
-import os
-import numpy as np
-import tensorflow as tf
-
-from werkzeug import test as werkzeug_test
-from werkzeug import wrappers
-
-from google.protobuf import text_format
-
-from tensorflow.tensorboard.backend import application
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.projector import projector_config_pb2
-from tensorflow.tensorboard.plugins.projector import projector_plugin
-
-
-class ProjectorAppTest(tf.test.TestCase):
-
-  def setUp(self):
-    self.log_dir = self.get_temp_dir()
-
-  def testRunsWithValidCheckpoint(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-    run_json = self._GetJson('/data/plugin/projector/runs')
-    self.assertTrue(run_json)
-
-  def testRunsWithNoCheckpoint(self):
-    self._SetupWSGIApp()
-    run_json = self._GetJson('/data/plugin/projector/runs')
-    self.assertEqual(run_json, [])
-
-  def testRunsWithInvalidModelCheckpointPath(self):
-    checkpoint_file = os.path.join(self.log_dir, 'checkpoint')
-    f = open(checkpoint_file, 'w')
-    f.write('model_checkpoint_path: "does_not_exist"\n')
-    f.write('all_model_checkpoint_paths: "does_not_exist"\n')
-    f.close()
-    self._SetupWSGIApp()
-
-    run_json = self._GetJson('/data/plugin/projector/runs')
-    self.assertEqual(run_json, [])
-
-  def testRunsWithInvalidModelCheckpointPathInConfig(self):
-    config_path = os.path.join(self.log_dir, 'projector_config.pbtxt')
-    config = projector_config_pb2.ProjectorConfig()
-    config.model_checkpoint_path = 'does_not_exist'
-    embedding = config.embeddings.add()
-    embedding.tensor_name = 'var1'
-    with tf.gfile.GFile(config_path, 'w') as f:
-      f.write(text_format.MessageToString(config))
-    self._SetupWSGIApp()
-
-    run_json = self._GetJson('/data/plugin/projector/runs')
-    self.assertEqual(run_json, [])
-
-  def testInfoWithValidCheckpointNoEventsData(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    info_json = self._GetJson('/data/plugin/projector/info?run=.')
-    self.assertItemsEqual(info_json['embeddings'], [{
-        'tensorShape': [1, 2],
-        'tensorName': 'var1',
-        'bookmarksPath': 'bookmarks.json'
-    }, {
-        'tensorShape': [10, 10],
-        'tensorName': 'var2'
-    }, {
-        'tensorShape': [100, 100],
-        'tensorName': 'var3'
-    }])
-
-  def testInfoWithValidCheckpointAndEventsData(self):
-    self._GenerateProjectorTestData()
-    self._GenerateEventsData()
-    self._SetupWSGIApp()
-
-    run_json = self._GetJson('/data/plugin/projector/runs')
-    self.assertTrue(run_json)
-    run = run_json[0]
-    info_json = self._GetJson('/data/plugin/projector/info?run=%s' % run)
-    self.assertItemsEqual(info_json['embeddings'], [{
-        'tensorShape': [1, 2],
-        'tensorName': 'var1',
-        'bookmarksPath': 'bookmarks.json'
-    }, {
-        'tensorShape': [10, 10],
-        'tensorName': 'var2'
-    }, {
-        'tensorShape': [100, 100],
-        'tensorName': 'var3'
-    }])
-
-  def testTensorWithValidCheckpoint(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    url = '/data/plugin/projector/tensor?run=.&name=var1'
-    tensor_bytes = self._Get(url).data
-    expected_tensor = np.array([[6, 6]], dtype=np.float32)
-    self._AssertTensorResponse(tensor_bytes, expected_tensor)
-
-  def testBookmarksRequestMissingRunAndName(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    url = '/data/plugin/projector/bookmarks'
-    self.assertEqual(self._Get(url).status_code, 400)
-
-  def testBookmarksRequestMissingName(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    url = '/data/plugin/projector/bookmarks?run=.'
-    self.assertEqual(self._Get(url).status_code, 400)
-
-  def testBookmarksRequestMissingRun(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    url = '/data/plugin/projector/bookmarks?name=var1'
-    self.assertEqual(self._Get(url).status_code, 400)
-
-  def testBookmarksUnknownRun(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    url = '/data/plugin/projector/bookmarks?run=unknown&name=var1'
-    self.assertEqual(self._Get(url).status_code, 400)
-
-  def testBookmarksUnknownName(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    url = '/data/plugin/projector/bookmarks?run=.&name=unknown'
-    self.assertEqual(self._Get(url).status_code, 400)
-
-  def testBookmarks(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    url = '/data/plugin/projector/bookmarks?run=.&name=var1'
-    bookmark = self._GetJson(url)
-    self.assertEqual(bookmark, {'a': 'b'})
-
-  def testEndpointsNoAssets(self):
-    g = tf.Graph()
-
-    fw = tf.summary.FileWriter(self.log_dir, graph=g)
-    fw.close()
-
-    self._SetupWSGIApp()
-    run_json = self._GetJson('/data/plugin/projector/runs')
-    self.assertEqual(run_json, [])
-
-  def _AssertTensorResponse(self, tensor_bytes, expected_tensor):
-    tensor = np.reshape(np.fromstring(tensor_bytes, dtype=np.float32),
-                        expected_tensor.shape)
-    self.assertTrue(np.array_equal(tensor, expected_tensor))
-
-  def testPluginIsActive(self):
-    self._GenerateProjectorTestData()
-    self._SetupWSGIApp()
-
-    # Embedding data is available.
-    self.assertTrue(self.plugin.is_active())
-
-  def testPluginIsNotActive(self):
-    self._SetupWSGIApp()
-
-    # Embedding data is not available.
-    self.assertFalse(self.plugin.is_active())
-
-  def _SetupWSGIApp(self):
-    multiplexer = event_multiplexer.EventMultiplexer(
-        size_guidance=application.DEFAULT_SIZE_GUIDANCE,
-        purge_orphaned_data=True)
-    context = base_plugin.TBContext(
-        logdir=self.log_dir, multiplexer=multiplexer)
-    self.plugin = projector_plugin.ProjectorPlugin(context)
-    wsgi_app = application.TensorBoardWSGIApp(
-        self.log_dir, [self.plugin], multiplexer, reload_interval=0)
-    self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
-
-  def _Get(self, path):
-    return self.server.get(path)
-
-  def _GetJson(self, path):
-    response = self.server.get(path)
-    data = response.data
-    if response.headers.get('Content-Encoding') == 'gzip':
-      data = gzip.GzipFile('', 'rb', 9, io.BytesIO(data)).read()
-    return json.loads(data.decode('utf-8'))
-
-  def _GenerateEventsData(self):
-    fw = tf.summary.FileWriter(self.log_dir)
-    event = tf.Event(
-        wall_time=1,
-        step=1,
-        summary=tf.Summary(value=[tf.Summary.Value(tag='s1', simple_value=0)]))
-    fw.add_event(event)
-    fw.close()
-
-  def _GenerateProjectorTestData(self):
-    config_path = os.path.join(self.log_dir, 'projector_config.pbtxt')
-    config = projector_config_pb2.ProjectorConfig()
-    embedding = config.embeddings.add()
-    # Add an embedding by its canonical tensor name.
-    embedding.tensor_name = 'var1:0'
-
-    with tf.gfile.GFile(os.path.join(self.log_dir, 'bookmarks.json'), 'w') as f:
-      f.write('{"a": "b"}')
-    embedding.bookmarks_path = 'bookmarks.json'
-
-    config_pbtxt = text_format.MessageToString(config)
-    with tf.gfile.GFile(config_path, 'w') as f:
-      f.write(config_pbtxt)
-
-    # Write a checkpoint with some dummy variables.
-    with tf.Graph().as_default():
-      sess = tf.Session()
-      checkpoint_path = os.path.join(self.log_dir, 'model')
-      tf.get_variable('var1', [1, 2], initializer=tf.constant_initializer(6.0))
-      tf.get_variable('var2', [10, 10])
-      tf.get_variable('var3', [100, 100])
-      sess.run(tf.global_variables_initializer())
-      saver = tf.train.Saver(write_version=tf.train.SaverDef.V1)
-      saver.save(sess, checkpoint_path)
-
-
-class MetadataColumnsTest(tf.test.TestCase):
-
-  def testLengthDoesNotMatch(self):
-    metadata = projector_plugin.EmbeddingMetadata(10)
-
-    with self.assertRaises(ValueError):
-      metadata.add_column('Labels', [''] * 11)
-
-  def testValuesNot1D(self):
-    metadata = projector_plugin.EmbeddingMetadata(3)
-    values = np.array([[1, 2, 3]])
-
-    with self.assertRaises(ValueError):
-      metadata.add_column('Labels', values)
-
-  def testMultipleColumnsRetrieval(self):
-    metadata = projector_plugin.EmbeddingMetadata(3)
-    metadata.add_column('Sizes', [1, 2, 3])
-    metadata.add_column('Labels', ['a', 'b', 'c'])
-    self.assertEqual(metadata.column_names, ['Sizes', 'Labels'])
-    self.assertEqual(metadata.name_to_values['Labels'], ['a', 'b', 'c'])
-    self.assertEqual(metadata.name_to_values['Sizes'], [1, 2, 3])
-
-  def testValuesAreListofLists(self):
-    metadata = projector_plugin.EmbeddingMetadata(3)
-    values = [[1, 2, 3], [4, 5, 6]]
-    with self.assertRaises(ValueError):
-      metadata.add_column('Labels', values)
-
-  def testStringListRetrieval(self):
-    metadata = projector_plugin.EmbeddingMetadata(3)
-    metadata.add_column('Labels', ['a', 'B', 'c'])
-    self.assertEqual(metadata.name_to_values['Labels'], ['a', 'B', 'c'])
-    self.assertEqual(metadata.column_names, ['Labels'])
-
-  def testNumericListRetrieval(self):
-    metadata = projector_plugin.EmbeddingMetadata(3)
-    metadata.add_column('Labels', [1, 2, 3])
-    self.assertEqual(metadata.name_to_values['Labels'], [1, 2, 3])
-
-  def testNumericNdArrayRetrieval(self):
-    metadata = projector_plugin.EmbeddingMetadata(3)
-    metadata.add_column('Labels', np.array([1, 2, 3]))
-    self.assertEqual(metadata.name_to_values['Labels'].tolist(), [1, 2, 3])
-
-  def testStringNdArrayRetrieval(self):
-    metadata = projector_plugin.EmbeddingMetadata(2)
-    metadata.add_column('Labels', np.array(['a', 'b']))
-    self.assertEqual(metadata.name_to_values['Labels'].tolist(), ['a', 'b'])
-
-  def testDuplicateColumnName(self):
-    metadata = projector_plugin.EmbeddingMetadata(2)
-    metadata.add_column('Labels', np.array(['a', 'b']))
-    with self.assertRaises(ValueError):
-      metadata.add_column('Labels', np.array(['a', 'b']))
-
-
-class LRUCacheTest(tf.test.TestCase):
-
-  def testInvalidSize(self):
-    with self.assertRaises(ValueError):
-      projector_plugin.LRUCache(0)
-
-  def testSimpleGetAndSet(self):
-    cache = projector_plugin.LRUCache(1)
-    value = cache.get('a')
-    self.assertIsNone(value)
-    cache.set('a', 10)
-    self.assertEqual(cache.get('a'), 10)
-
-  def testErrorsWhenSettingNoneAsValue(self):
-    cache = projector_plugin.LRUCache(1)
-    with self.assertRaises(ValueError):
-      cache.set('a', None)
-
-  def testLRUReplacementPolicy(self):
-    cache = projector_plugin.LRUCache(2)
-    cache.set('a', 1)
-    cache.set('b', 2)
-    cache.set('c', 3)
-    self.assertIsNone(cache.get('a'))
-    self.assertEqual(cache.get('b'), 2)
-    self.assertEqual(cache.get('c'), 3)
-
-    # Make 'b' the most recently used.
-    cache.get('b')
-    cache.set('d', 4)
-
-    # Make sure 'c' got replaced with 'd'.
-    self.assertIsNone(cache.get('c'))
-    self.assertEqual(cache.get('b'), 2)
-    self.assertEqual(cache.get('d'), 4)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/scalars/BUILD b/tensorflow/tensorboard/plugins/scalars/BUILD
deleted file mode 100644
index 917cc1425b..0000000000
--- a/tensorflow/tensorboard/plugins/scalars/BUILD
+++ /dev/null
@@ -1,59 +0,0 @@
-# Description:
-# TensorBoard plugin for scalars
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-## Scalars Plugin ##
-py_library(
-    name = "scalars_plugin",
-    srcs = ["scalars_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/backend/event_processing:event_accumulator",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "scalars_plugin_test",
-    size = "small",
-    srcs = ["scalars_plugin_test.py"],
-    main = "scalars_plugin_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":scalars_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-py_binary(
-    name = "scalars_demo",
-    srcs = ["scalars_demo.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "@six_archive//:six",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/scalars/scalars_demo.py b/tensorflow/tensorboard/plugins/scalars/scalars_demo.py
deleted file mode 100644
index f3195fd849..0000000000
--- a/tensorflow/tensorboard/plugins/scalars/scalars_demo.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Sample data exhibiting scalar summaries, via a temperature simulation."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os.path
-
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-# Directory into which to write tensorboard data.
-LOGDIR = '/tmp/scalars_demo'
-
-# Duration of the simulation.
-STEPS = 1000
-
-
-def run(logdir, run_name,
-        initial_temperature, ambient_temperature, heat_coefficient):
-  """Run a temperature simulation.
-
-  This will simulate an object at temperature `initial_temperature`
-  sitting at rest in a large room at temperature `ambient_temperature`.
-  The object has some intrinsic `heat_coefficient`, which indicates
-  how much thermal conductivity it has: for instance, metals have high
-  thermal conductivity, while the thermal conductivity of water is low.
-
-  Over time, the object's temperature will adjust to match the
-  temperature of its environment. We'll track the object's temperature,
-  how far it is from the room's temperature, and how much it changes at
-  each time step.
-
-  Arguments:
-    logdir: the top-level directory into which to write summary data
-    run_name: the name of this run; will be created as a subdirectory
-      under logdir
-    initial_temperature: float; the object's initial temperature
-    ambient_temperature: float; the temperature of the enclosing room
-    heat_coefficient: float; a measure of the object's thermal
-      conductivity
-  """
-  tf.reset_default_graph()
-  tf.set_random_seed(0)
-
-  with tf.name_scope('temperature'):
-    # Create a mutable variable to hold the object's temperature, and
-    # create a scalar summary to track its value over time. The name of
-    # the summary will appear as "temperature/current" due to the
-    # name-scope above.
-    temperature = tf.Variable(tf.constant(initial_temperature),
-                              name='temperature')
-    tf.summary.scalar('current', temperature)
-
-    # Compute how much the object's temperature differs from that of its
-    # environment, and track this, too: likewise, as
-    # "temperature/difference_to_ambient".
-    ambient_difference = temperature - ambient_temperature
-    tf.summary.scalar('difference_to_ambient', ambient_difference)
-
-  # Newton suggested that the rate of change of the temperature of an
-  # object is directly proportional to this `ambient_difference` above,
-  # where the proportionality constant is what we called the heat
-  # coefficient. But in real life, not everything is quite so clean, so
-  # we'll add in some noise. (The value of 50 is arbitrary, chosen to
-  # make the data look somewhat interesting. :-) )
-  noise = 50 * tf.random_normal([])
-  delta = -heat_coefficient * (ambient_difference + noise)
-  tf.summary.scalar('delta', delta)
-
-  # Now, augment the current temperature by this delta that we computed.
-  update_step = temperature.assign_add(delta)
-
-  # Collect all the scalars that we want to keep track of.
-  summ = tf.summary.merge_all()
-
-  sess = tf.Session()
-  writer = tf.summary.FileWriter(os.path.join(logdir, run_name))
-  writer.add_graph(sess.graph)
-  sess.run(tf.global_variables_initializer())
-  for step in xrange(STEPS):
-    # By asking TensorFlow to compute the update step, we force it to
-    # change the value of the temperature variable. We don't actually
-    # care about this value, so we discard it; instead, we grab the
-    # summary data computed along the way.
-    (s, _) = sess.run([summ, update_step])
-    writer.add_summary(s, global_step=step)
-  writer.close()
-
-
-def run_all(logdir, verbose=False):
-  """Run simulations on a reasonable set of parameters.
-
-  Arguments:
-    logdir: the directory into which to store all the runs' data
-    verbose: if true, print out each run's name as it begins
-  """
-  for initial_temperature in [270.0, 310.0, 350.0]:
-    for final_temperature in [270.0, 310.0, 350.0]:
-      for heat_coefficient in [0.001, 0.005]:
-        run_name = 'temperature:t0=%g,tA=%g,kH=%g' % (
-            initial_temperature, final_temperature, heat_coefficient)
-        if verbose:
-          print('--- Running: %s' % run_name)
-        run(logdir, run_name,
-            initial_temperature, final_temperature, heat_coefficient)
-
-
-def main(unused_argv):
-  print('Saving output to %s.' % LOGDIR)
-  run_all(LOGDIR, verbose=True)
-  print('Done. Output saved to %s.' % LOGDIR)
-
-
-if __name__ == '__main__':
-  tf.app.run()
diff --git a/tensorflow/tensorboard/plugins/scalars/scalars_plugin.py b/tensorflow/tensorboard/plugins/scalars/scalars_plugin.py
deleted file mode 100644
index cbdca9aa53..0000000000
--- a/tensorflow/tensorboard/plugins/scalars/scalars_plugin.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The TensorBoard Scalars plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import csv
-
-from six import StringIO
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.backend.event_processing import event_accumulator
-from tensorflow.tensorboard.plugins import base_plugin
-
-_PLUGIN_PREFIX_ROUTE = event_accumulator.SCALARS
-
-
-class OutputFormat(object):
-  """An enum used to list the valid output formats for API calls."""
-  JSON = 'json'
-  CSV = 'csv'
-
-
-class ScalarsPlugin(base_plugin.TBPlugin):
-  """Scalars Plugin for TensorBoard."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates ScalarsPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self._multiplexer = context.multiplexer
-
-  def get_plugin_apps(self):
-    return {
-        '/scalars': self.scalars_route,
-        '/tags': self.tags_route,
-    }
-
-  def is_active(self):
-    """The scalars plugin is active iff any run has at least one scalar tag."""
-    return bool(self._multiplexer) and any(self.index_impl().values())
-
-  def index_impl(self):
-    return {
-        run_name: run_data[event_accumulator.SCALARS]
-        for (run_name, run_data) in self._multiplexer.Runs().items()
-        if event_accumulator.SCALARS in run_data
-    }
-
-  def scalars_impl(self, tag, run, output_format):
-    """Result of the form `(body, mime_type)`."""
-    values = self._multiplexer.Scalars(run, tag)
-    if output_format == OutputFormat.CSV:
-      string_io = StringIO()
-      writer = csv.writer(string_io)
-      writer.writerow(['Wall time', 'Step', 'Value'])
-      writer.writerows(values)
-      return (string_io.getvalue(), 'text/csv')
-    else:
-      return (values, 'application/json')
-
-  @wrappers.Request.application
-  def tags_route(self, request):
-    index = self.index_impl()
-    return http_util.Respond(request, index, 'application/json')
-
-  @wrappers.Request.application
-  def scalars_route(self, request):
-    """Given a tag and single run, return array of ScalarEvents."""
-    # TODO(cassandrax): return HTTP status code for malformed requests
-    tag = request.args.get('tag')
-    run = request.args.get('run')
-    output_format = request.args.get('format')
-    (body, mime_type) = self.scalars_impl(tag, run, output_format)
-    return http_util.Respond(request, body, mime_type)
diff --git a/tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py b/tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py
deleted file mode 100644
index 7d4ad5a046..0000000000
--- a/tensorflow/tensorboard/plugins/scalars/scalars_plugin_test.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Integration tests for the Scalars Plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import csv
-import os.path
-
-from six import StringIO
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.scalars import scalars_plugin
-
-
-class ScalarsPluginTest(tf.test.TestCase):
-
-  _STEPS = 99
-
-  _SCALAR_TAG = 'simple-values'
-  _HISTOGRAM_TAG = 'complicated-values'
-
-  _RUN_WITH_SCALARS = '_RUN_WITH_SCALARS'
-  _RUN_WITH_HISTOGRAM = '_RUN_WITH_HISTOGRAM'
-
-  def set_up_with_runs(self, run_names):
-    self.logdir = self.get_temp_dir()
-    for run_name in run_names:
-      self.generate_run(run_name)
-    multiplexer = event_multiplexer.EventMultiplexer()
-    multiplexer.AddRunsFromDirectory(self.logdir)
-    multiplexer.Reload()
-    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
-    self.plugin = scalars_plugin.ScalarsPlugin(context)
-
-  def testRoutesProvided(self):
-    """Tests that the plugin offers the correct routes."""
-    self.set_up_with_runs([self._RUN_WITH_SCALARS])
-    routes = self.plugin.get_plugin_apps()
-    self.assertIsInstance(routes['/scalars'], collections.Callable)
-    self.assertIsInstance(routes['/tags'], collections.Callable)
-
-  def generate_run(self, run_name):
-    if run_name == self._RUN_WITH_SCALARS:
-      (use_scalars, use_histogram) = (True, False)
-    elif run_name == self._RUN_WITH_HISTOGRAM:
-      (use_scalars, use_histogram) = (False, True)
-    else:
-      assert False, 'Invalid run name: %r' % run_name
-    tf.reset_default_graph()
-    sess = tf.Session()
-    if use_scalars:
-      scalar_placeholder = tf.placeholder(tf.int64)
-      tf.summary.scalar(self._SCALAR_TAG, scalar_placeholder)
-    if use_histogram:
-      histogram_placeholder = tf.placeholder(tf.float32, shape=[3])
-      tf.summary.histogram(self._HISTOGRAM_TAG, histogram_placeholder)
-    summ = tf.summary.merge_all()
-
-    subdir = os.path.join(self.logdir, run_name)
-    writer = tf.summary.FileWriter(subdir)
-    writer.add_graph(sess.graph)
-    for step in xrange(self._STEPS):
-      feed_dict = {}
-      if use_scalars:
-        feed_dict[scalar_placeholder] = int((43**step) % 47)
-      if use_histogram:
-        feed_dict[histogram_placeholder] = [1 + step, 2 + step, 3 + step]
-      s = sess.run(summ, feed_dict=feed_dict)
-      writer.add_summary(s, global_step=step)
-    writer.close()
-
-  def test_index(self):
-    self.set_up_with_runs([self._RUN_WITH_SCALARS, self._RUN_WITH_HISTOGRAM])
-    self.assertEqual({
-        self._RUN_WITH_SCALARS: [self._SCALAR_TAG],
-        self._RUN_WITH_HISTOGRAM: [],
-    }, self.plugin.index_impl())
-
-  def _test_scalars_json(self, run_name, should_have_scalars):
-    self.set_up_with_runs([self._RUN_WITH_SCALARS, self._RUN_WITH_HISTOGRAM])
-    if should_have_scalars:
-      (data, mime_type) = self.plugin.scalars_impl(
-          self._SCALAR_TAG, run_name, scalars_plugin.OutputFormat.JSON)
-      self.assertEqual('application/json', mime_type)
-      self.assertEqual(len(data), self._STEPS)
-    else:
-      with self.assertRaises(KeyError):
-        self.plugin.scalars_impl(self._SCALAR_TAG, run_name,
-                                 scalars_plugin.OutputFormat.JSON)
-
-  def _test_scalars_csv(self, run_name, should_have_scalars):
-    self.set_up_with_runs([self._RUN_WITH_SCALARS, self._RUN_WITH_HISTOGRAM])
-    if should_have_scalars:
-      (data, mime_type) = self.plugin.scalars_impl(
-          self._SCALAR_TAG, run_name, scalars_plugin.OutputFormat.CSV)
-      self.assertEqual('text/csv', mime_type)
-      s = StringIO(data)
-      reader = csv.reader(s)
-      self.assertEqual(['Wall time', 'Step', 'Value'], next(reader))
-      self.assertEqual(len(list(reader)), self._STEPS)
-    else:
-      with self.assertRaises(KeyError):
-        self.plugin.scalars_impl(self._SCALAR_TAG, run_name,
-                                 scalars_plugin.OutputFormat.CSV)
-
-  def test_scalars_json_with_scalars(self):
-    self._test_scalars_json(self._RUN_WITH_SCALARS, True)
-
-  def test_scalars_json_with_histogram(self):
-    self._test_scalars_json(self._RUN_WITH_HISTOGRAM, False)
-
-  def test_scalars_csv_with_scalars(self):
-    self._test_scalars_csv(self._RUN_WITH_SCALARS, True)
-
-  def test_scalars_csv_with_histogram(self):
-    self._test_scalars_csv(self._RUN_WITH_HISTOGRAM, False)
-
-  def test_active_with_scalars(self):
-    self.set_up_with_runs([self._RUN_WITH_SCALARS])
-    self.assertTrue(self.plugin.is_active())
-
-  def test_active_with_histogram(self):
-    self.set_up_with_runs([self._RUN_WITH_HISTOGRAM])
-    self.assertFalse(self.plugin.is_active())
-
-  def test_active_with_both(self):
-    self.set_up_with_runs([self._RUN_WITH_SCALARS, self._RUN_WITH_HISTOGRAM])
-    self.assertTrue(self.plugin.is_active())
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/plugins/text/BUILD b/tensorflow/tensorboard/plugins/text/BUILD
deleted file mode 100644
index 5e18328b4b..0000000000
--- a/tensorflow/tensorboard/plugins/text/BUILD
+++ /dev/null
@@ -1,50 +0,0 @@
-# Description:
-# TensorBoard plugin for the Text
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-## Text Plugin ##
-py_library(
-    name = "text_plugin",
-    srcs = ["text_plugin.py"],
-    srcs_version = "PY2AND3",
-    visibility = [
-        "//tensorflow/tensorboard:internal",
-    ],
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:http_util",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_mozilla_bleach",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@org_pythonhosted_markdown",
-        "@six_archive//:six",
-    ],
-)
-
-py_test(
-    name = "text_plugin_test",
-    size = "small",
-    srcs = ["text_plugin_test.py"],
-    main = "text_plugin_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":text_plugin",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/tensorboard/backend:application",
-        "//tensorflow/tensorboard/backend/event_processing:event_multiplexer",
-        "//tensorflow/tensorboard/plugins:base_plugin",
-        "@org_pocoo_werkzeug//:werkzeug",
-        "@six_archive//:six",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["**"]),
-    visibility = ["//tensorflow:__pkg__"],
-)
diff --git a/tensorflow/tensorboard/plugins/text/text_plugin.py b/tensorflow/tensorboard/plugins/text/text_plugin.py
deleted file mode 100644
index 50779caa86..0000000000
--- a/tensorflow/tensorboard/plugins/text/text_plugin.py
+++ /dev/null
@@ -1,313 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The TensorBoard Text plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import textwrap
-
-# pylint: disable=g-bad-import-order
-# Necessary for an internal test with special behavior for numpy.
-import numpy as np
-# pylint: enable=g-bad-import-order
-
-import bleach
-# pylint: disable=g-bad-import-order
-# Google-only: import markdown_freewisdom
-import markdown
-import six
-# pylint: enable=g-bad-import-order
-import tensorflow as tf
-from werkzeug import wrappers
-
-from tensorflow.tensorboard.backend import http_util
-from tensorflow.tensorboard.plugins import base_plugin
-
-# The prefix of routes provided by this plugin.
-_PLUGIN_PREFIX_ROUTE = 'text'
-
-# HTTP routes
-RUNS_ROUTE = '/runs'
-TEXT_ROUTE = '/text'
-
-ALLOWED_TAGS = [
-    'ul',
-    'ol',
-    'li',
-    'p',
-    'pre',
-    'code',
-    'blockquote',
-    'h1',
-    'h2',
-    'h3',
-    'h4',
-    'h5',
-    'h6',
-    'hr',
-    'br',
-    'strong',
-    'em',
-    'a',
-    'img',
-    'table',
-    'thead',
-    'tbody',
-    'td',
-    'tr',
-    'th',
-]
-
-ALLOWED_ATTRIBUTES = {'a': ['href', 'title'], 'img': ['src', 'title', 'alt']}
-
-WARNING_TEMPLATE = textwrap.dedent("""\
-  **Warning:** This text summary contained data of dimensionality %d, but only \
-  2d tables are supported. Showing a 2d slice of the data instead.""")
-
-
-def markdown_and_sanitize(markdown_string):
-  """Takes a markdown string and converts it into sanitized html.
-
-  It uses the table extension; while that's not a part of standard
-  markdown, it is sure to be useful for TensorBoard users.
-
-  The sanitizer uses the allowed_tags and attributes specified above. Mostly,
-  we ensure that our standard use cases like tables and links are supported.
-
-  Args:
-    markdown_string: Markdown string to sanitize
-
-  Returns:
-    a string containing sanitized html for input markdown
-  """
-  # Convert to utf-8 whenever we have a binary input.
-  if isinstance(markdown_string, six.binary_type):
-    markdown_string = markdown_string.decode('utf-8')
-
-  string_html = markdown.markdown(
-      markdown_string, extensions=['markdown.extensions.tables'])
-  string_sanitized = bleach.clean(
-      string_html, tags=ALLOWED_TAGS, attributes=ALLOWED_ATTRIBUTES)
-  return string_sanitized
-
-
-def make_table_row(contents, tag='td'):
-  """Given an iterable of string contents, make a table row.
-
-  Args:
-    contents: An iterable yielding strings.
-    tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
-
-  Returns:
-    A string containing the content strings, organized into a table row.
-
-  Example: make_table_row(['one', 'two', 'three']) == '''
-  <tr>
-  <td>one</td>
-  <td>two</td>
-  <td>three</td>
-  </tr>'''
-  """
-  columns = ('<%s>%s</%s>\n' % (tag, s, tag) for s in contents)
-  return '<tr>\n' + ''.join(columns) + '</tr>\n'
-
-
-def make_table(contents, headers=None):
-  """Given a numpy ndarray of strings, concatenate them into a html table.
-
-  Args:
-    contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
-      table is laid out vertically (i.e. row-major).
-    headers: A np.ndarray or list of string header names for the table.
-
-  Returns:
-    A string containing all of the content strings, organized into a table.
-
-  Raises:
-    ValueError: If contents is not a np.ndarray.
-    ValueError: If contents is not 1d or 2d.
-    ValueError: If contents is empty.
-    ValueError: If headers is present and not a list, tuple, or ndarray.
-    ValueError: If headers is not 1d.
-    ValueError: If number of elements in headers does not correspond to number
-      of columns in contents.
-  """
-  if not isinstance(contents, np.ndarray):
-    raise ValueError('make_table contents must be a numpy ndarray')
-
-  if contents.ndim not in [1, 2]:
-    raise ValueError('make_table requires a 1d or 2d numpy array, was %dd' %
-                     contents.ndim)
-
-  if headers:
-    if isinstance(headers, list) or isinstance(headers, tuple):
-      headers = np.array(headers)
-    if not isinstance(headers, np.ndarray):
-      raise ValueError('Could not convert headers %s into np.ndarray' % headers)
-    if headers.ndim != 1:
-      raise ValueError('Headers must be 1d, is %dd' % headers.ndim)
-    expected_n_columns = contents.shape[1] if contents.ndim == 2 else 1
-    if headers.shape[0] != expected_n_columns:
-      raise ValueError('Number of headers %d must match number of columns %d' %
-                       (headers.shape[0], expected_n_columns))
-    header = '<thead>\n%s</thead>\n' % make_table_row(headers, tag='th')
-  else:
-    header = ''
-
-  n_rows = contents.shape[0]
-  if contents.ndim == 1:
-    # If it's a vector, we need to wrap each element in a new list, otherwise
-    # we would turn the string itself into a row (see test code)
-    rows = (make_table_row([contents[i]]) for i in range(n_rows))
-  else:
-    rows = (make_table_row(contents[i, :]) for i in range(n_rows))
-
-  return '<table>\n%s<tbody>\n%s</tbody>\n</table>' % (header, ''.join(rows))
-
-
-def reduce_to_2d(arr):
-  """Given a np.npdarray with nDims > 2, reduce it to 2d.
-
-  It does this by selecting the zeroth coordinate for every dimension greater
-  than two.
-
-  Args:
-    arr: a numpy ndarray of dimension at least 2.
-
-  Returns:
-    A two-dimensional subarray from the input array.
-
-  Raises:
-    ValueError: If the argument is not a numpy ndarray, or the dimensionality
-      is too low.
-  """
-  if not isinstance(arr, np.ndarray):
-    raise ValueError('reduce_to_2d requires a numpy.ndarray')
-
-  ndims = len(arr.shape)
-  if ndims < 2:
-    raise ValueError('reduce_to_2d requires an array of dimensionality >=2')
-  # slice(None) is equivalent to `:`, so we take arr[0,0,...0,:,:]
-  slices = ([0] * (ndims - 2)) + [slice(None), slice(None)]
-  return arr[slices]
-
-
-def text_array_to_html(text_arr):
-  """Take a numpy.ndarray containing strings, and convert it into html.
-
-  If the ndarray contains a single scalar string, that string is converted to
-  html via our sanitized markdown parser. If it contains an array of strings,
-  the strings are individually converted to html and then composed into a table
-  using make_table. If the array contains dimensionality greater than 2,
-  all but two of the dimensions are removed, and a warning message is prefixed
-  to the table.
-
-  Args:
-    text_arr: A numpy.ndarray containing strings.
-
-  Returns:
-    The array converted to html.
-  """
-  if not text_arr.shape:
-    # It is a scalar. No need to put it in a table, just apply markdown
-    return markdown_and_sanitize(text_arr.astype(np.dtype(str)).tostring())
-  warning = ''
-  if len(text_arr.shape) > 2:
-    warning = markdown_and_sanitize(WARNING_TEMPLATE % len(text_arr.shape))
-    text_arr = reduce_to_2d(text_arr)
-
-  html_arr = [markdown_and_sanitize(x) for x in text_arr.reshape(-1)]
-  html_arr = np.array(html_arr).reshape(text_arr.shape)
-
-  return warning + make_table(html_arr)
-
-
-def process_string_tensor_event(event):
-  """Convert a TensorEvent into a JSON-compatible response."""
-  string_arr = tf.make_ndarray(event.tensor_proto)
-  html = text_array_to_html(string_arr)
-  return {
-      'wall_time': event.wall_time,
-      'step': event.step,
-      'text': html,
-  }
-
-
-class TextPlugin(base_plugin.TBPlugin):
-  """Text Plugin for TensorBoard."""
-
-  plugin_name = _PLUGIN_PREFIX_ROUTE
-
-  def __init__(self, context):
-    """Instantiates TextPlugin via TensorBoard core.
-
-    Args:
-      context: A base_plugin.TBContext instance.
-    """
-    self._multiplexer = context.multiplexer
-
-  def index_impl(self):
-    run_to_series = {}
-    name = 'tensorboard_text'
-    run_to_assets = self._multiplexer.PluginAssets(name)
-
-    for run, assets in run_to_assets.items():
-      if 'tensors.json' in assets:
-        tensors_json = self._multiplexer.RetrievePluginAsset(
-            run, name, 'tensors.json')
-        tensors = json.loads(tensors_json)
-        run_to_series[run] = tensors
-      else:
-        run_to_series[run] = []
-    return run_to_series
-
-  @wrappers.Request.application
-  def runs_route(self, request):
-    index = self.index_impl()
-    return http_util.Respond(request, index, 'application/json')
-
-  def text_impl(self, run, tag):
-    try:
-      text_events = self._multiplexer.Tensors(run, tag)
-    except KeyError:
-      text_events = []
-    responses = [process_string_tensor_event(ev) for ev in text_events]
-    return responses
-
-  @wrappers.Request.application
-  def text_route(self, request):
-    run = request.args.get('run')
-    tag = request.args.get('tag')
-    response = self.text_impl(run, tag)
-    return http_util.Respond(request, response, 'application/json')
-
-  def get_plugin_apps(self):
-    return {
-        RUNS_ROUTE: self.runs_route,
-        TEXT_ROUTE: self.text_route,
-    }
-
-  def is_active(self):
-    """Determines whether this plugin is active.
-
-    This plugin is only active if TensorBoard sampled any text summaries.
-
-    Returns:
-      Whether this plugin is active.
-    """
-    return bool(self._multiplexer and self.index_impl())
diff --git a/tensorflow/tensorboard/plugins/text/text_plugin_test.py b/tensorflow/tensorboard/plugins/text/text_plugin_test.py
deleted file mode 100644
index 0460d9777c..0000000000
--- a/tensorflow/tensorboard/plugins/text/text_plugin_test.py
+++ /dev/null
@@ -1,415 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Integration tests for the Text Plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import os
-import textwrap
-import numpy as np
-import tensorflow as tf
-
-from tensorflow.tensorboard.backend.event_processing import event_multiplexer
-from tensorflow.tensorboard.plugins import base_plugin
-from tensorflow.tensorboard.plugins.text import text_plugin
-
-GEMS = ['garnet', 'amethyst', 'pearl', 'steven']
-
-
-class TextPluginTest(tf.test.TestCase):
-
-  def setUp(self):
-    self.logdir = self.get_temp_dir()
-    self.generate_testdata()
-    multiplexer = event_multiplexer.EventMultiplexer()
-    multiplexer.AddRunsFromDirectory(self.logdir)
-    multiplexer.Reload()
-    context = base_plugin.TBContext(logdir=self.logdir, multiplexer=multiplexer)
-    self.plugin = text_plugin.TextPlugin(context)
-
-  def testRoutesProvided(self):
-    routes = self.plugin.get_plugin_apps()
-    self.assertIsInstance(routes['/runs'], collections.Callable)
-    self.assertIsInstance(routes['/text'], collections.Callable)
-
-  def assertConverted(self, actual, expected):
-    expected_html = text_plugin.markdown_and_sanitize(expected)
-    self.assertEqual(actual, expected_html)
-
-  def generate_testdata(self):
-    tf.reset_default_graph()
-    sess = tf.Session()
-    placeholder = tf.placeholder(tf.string)
-    summary_tensor = tf.summary.text('message', placeholder)
-
-    vector_summary = tf.summary.text('vector', placeholder)
-
-    run_names = ['fry', 'leela']
-    for run_name in run_names:
-      subdir = os.path.join(self.logdir, run_name)
-      writer = tf.summary.FileWriter(subdir)
-      writer.add_graph(sess.graph)
-
-      step = 0
-      for gem in GEMS:
-        message = run_name + ' *loves* ' + gem
-        feed_dict = {placeholder: message}
-        summ = sess.run(summary_tensor, feed_dict=feed_dict)
-        writer.add_summary(summ, global_step=step)
-        step += 1
-
-      vector_message = ['one', 'two', 'three', 'four']
-      summ = sess.run(vector_summary, feed_dict={placeholder: vector_message})
-      writer.add_summary(summ)
-      writer.close()
-
-  def testIndex(self):
-    index = self.plugin.index_impl()
-    self.assertEqual(index, {
-        'fry': ['message', 'vector'],
-        'leela': ['message', 'vector'],
-    })
-
-  def testText(self):
-    fry = self.plugin.text_impl('fry', 'message')
-    leela = self.plugin.text_impl('leela', 'message')
-    self.assertEqual(len(fry), 4)
-    self.assertEqual(len(leela), 4)
-    for i in range(4):
-      self.assertEqual(fry[i]['step'], i)
-      self.assertConverted(fry[i]['text'], 'fry *loves* ' + GEMS[i])
-      self.assertEqual(leela[i]['step'], i)
-      self.assertConverted(leela[i]['text'], 'leela *loves* ' + GEMS[i])
-
-    table = self.plugin.text_impl('fry', 'vector')[0]['text']
-    self.assertEqual(table,
-                     textwrap.dedent("""\
-      <table>
-      <tbody>
-      <tr>
-      <td><p>one</p></td>
-      </tr>
-      <tr>
-      <td><p>two</p></td>
-      </tr>
-      <tr>
-      <td><p>three</p></td>
-      </tr>
-      <tr>
-      <td><p>four</p></td>
-      </tr>
-      </tbody>
-      </table>"""))
-
-  def assertTextConverted(self, actual, expected):
-    self.assertEqual(text_plugin.markdown_and_sanitize(actual), expected)
-
-  def testMarkdownConversion(self):
-    emphasis = '*Italics1* _Italics2_ **bold1** __bold2__'
-    emphasis_converted = ('<p><em>Italics1</em> <em>Italics2</em> '
-                          '<strong>bold1</strong> <strong>bold2</strong></p>')
-
-    self.assertEqual(
-        text_plugin.markdown_and_sanitize(emphasis), emphasis_converted)
-
-    md_list = textwrap.dedent("""\
-    1. List item one.
-    2. List item two.
-      * Sublist
-      * Sublist2
-    1. List continues.
-    """)
-    md_list_converted = textwrap.dedent("""\
-    <ol>
-    <li>List item one.</li>
-    <li>List item two.</li>
-    <li>Sublist</li>
-    <li>Sublist2</li>
-    <li>List continues.</li>
-    </ol>""")
-    self.assertEqual(
-        text_plugin.markdown_and_sanitize(md_list), md_list_converted)
-
-    link = '[TensorFlow](http://tensorflow.org)'
-    link_converted = '<p><a href="http://tensorflow.org">TensorFlow</a></p>'
-    self.assertEqual(text_plugin.markdown_and_sanitize(link), link_converted)
-
-    table = textwrap.dedent("""\
-    An | Example | Table
-    --- | --- | ---
-    A | B | C
-    1 | 2 | 3
-    """)
-
-    table_converted = textwrap.dedent("""\
-    <table>
-    <thead>
-    <tr>
-    <th>An</th>
-    <th>Example</th>
-    <th>Table</th>
-    </tr>
-    </thead>
-    <tbody>
-    <tr>
-    <td>A</td>
-    <td>B</td>
-    <td>C</td>
-    </tr>
-    <tr>
-    <td>1</td>
-    <td>2</td>
-    <td>3</td>
-    </tr>
-    </tbody>
-    </table>""")
-
-    self.assertEqual(text_plugin.markdown_and_sanitize(table), table_converted)
-
-  def testSanitization(self):
-    dangerous = "<script>alert('xss')</script>"
-    sanitized = "&lt;script&gt;alert('xss')&lt;/script&gt;"
-    self.assertEqual(text_plugin.markdown_and_sanitize(dangerous), sanitized)
-
-    dangerous = textwrap.dedent("""\
-    hello <a name='n'
-    href='javascript:alert('xss')'>*you*</a>""")
-    sanitized = '<p>hello <a><em>you</em></a></p>'
-    self.assertEqual(text_plugin.markdown_and_sanitize(dangerous), sanitized)
-
-  def testTableGeneration(self):
-    array2d = np.array([['one', 'two'], ['three', 'four']])
-    expected_table = textwrap.dedent("""\
-    <table>
-    <tbody>
-    <tr>
-    <td>one</td>
-    <td>two</td>
-    </tr>
-    <tr>
-    <td>three</td>
-    <td>four</td>
-    </tr>
-    </tbody>
-    </table>""")
-    self.assertEqual(text_plugin.make_table(array2d), expected_table)
-
-    expected_table_with_headers = textwrap.dedent("""\
-    <table>
-    <thead>
-    <tr>
-    <th>c1</th>
-    <th>c2</th>
-    </tr>
-    </thead>
-    <tbody>
-    <tr>
-    <td>one</td>
-    <td>two</td>
-    </tr>
-    <tr>
-    <td>three</td>
-    <td>four</td>
-    </tr>
-    </tbody>
-    </table>""")
-
-    actual_with_headers = text_plugin.make_table(array2d, headers=['c1', 'c2'])
-    self.assertEqual(actual_with_headers, expected_table_with_headers)
-
-    array_1d = np.array(['one', 'two', 'three', 'four', 'five'])
-    expected_1d = textwrap.dedent("""\
-    <table>
-    <tbody>
-    <tr>
-    <td>one</td>
-    </tr>
-    <tr>
-    <td>two</td>
-    </tr>
-    <tr>
-    <td>three</td>
-    </tr>
-    <tr>
-    <td>four</td>
-    </tr>
-    <tr>
-    <td>five</td>
-    </tr>
-    </tbody>
-    </table>""")
-    self.assertEqual(text_plugin.make_table(array_1d), expected_1d)
-
-    expected_1d_with_headers = textwrap.dedent("""\
-    <table>
-    <thead>
-    <tr>
-    <th>X</th>
-    </tr>
-    </thead>
-    <tbody>
-    <tr>
-    <td>one</td>
-    </tr>
-    <tr>
-    <td>two</td>
-    </tr>
-    <tr>
-    <td>three</td>
-    </tr>
-    <tr>
-    <td>four</td>
-    </tr>
-    <tr>
-    <td>five</td>
-    </tr>
-    </tbody>
-    </table>""")
-    actual_1d_with_headers = text_plugin.make_table(array_1d, headers=['X'])
-    self.assertEqual(actual_1d_with_headers, expected_1d_with_headers)
-
-  def testMakeTableExceptions(self):
-    # Verify that contents is being type-checked and shape-checked.
-    with self.assertRaises(ValueError):
-      text_plugin.make_table([])
-
-    with self.assertRaises(ValueError):
-      text_plugin.make_table('foo')
-
-    with self.assertRaises(ValueError):
-      invalid_shape = np.full((3, 3, 3), 'nope', dtype=np.dtype('S3'))
-      text_plugin.make_table(invalid_shape)
-
-    # Test headers exceptions in 2d array case.
-    test_array = np.full((3, 3), 'foo', dtype=np.dtype('S3'))
-    with self.assertRaises(ValueError):
-      # Headers is wrong type.
-      text_plugin.make_table(test_array, headers='foo')
-    with self.assertRaises(ValueError):
-      # Too many headers.
-      text_plugin.make_table(test_array, headers=['foo', 'bar', 'zod', 'zoink'])
-    with self.assertRaises(ValueError):
-      # headers is 2d
-      text_plugin.make_table(test_array, headers=test_array)
-
-    # Also make sure the column counting logic works in the 1d array case.
-    test_array = np.array(['foo', 'bar', 'zod'])
-    with self.assertRaises(ValueError):
-      # Too many headers.
-      text_plugin.make_table(test_array, headers=test_array)
-
-  def test_reduce_to_2d(self):
-
-    def make_range_array(dim):
-      """Produce an incrementally increasing multidimensional array.
-
-      Args:
-        dim: the number of dimensions for the array
-
-      Returns:
-        An array of increasing integer elements, with dim dimensions and size
-        two in each dimension.
-
-      Example: rangeArray(2) results in [[0,1],[2,3]].
-      """
-      return np.array(range(2**dim)).reshape([2] * dim)
-
-    for i in range(2, 5):
-      actual = text_plugin.reduce_to_2d(make_range_array(i))
-      expected = make_range_array(2)
-      np.testing.assert_array_equal(actual, expected)
-
-  def test_text_array_to_html(self):
-
-    convert = text_plugin.text_array_to_html
-    scalar = np.array('foo')
-    scalar_expected = '<p>foo</p>'
-    self.assertEqual(convert(scalar), scalar_expected)
-
-    vector = np.array(['foo', 'bar'])
-    vector_expected = textwrap.dedent("""\
-      <table>
-      <tbody>
-      <tr>
-      <td><p>foo</p></td>
-      </tr>
-      <tr>
-      <td><p>bar</p></td>
-      </tr>
-      </tbody>
-      </table>""")
-    self.assertEqual(convert(vector), vector_expected)
-
-    d2 = np.array([['foo', 'bar'], ['zoink', 'zod']])
-    d2_expected = textwrap.dedent("""\
-      <table>
-      <tbody>
-      <tr>
-      <td><p>foo</p></td>
-      <td><p>bar</p></td>
-      </tr>
-      <tr>
-      <td><p>zoink</p></td>
-      <td><p>zod</p></td>
-      </tr>
-      </tbody>
-      </table>""")
-    self.assertEqual(convert(d2), d2_expected)
-
-    d3 = np.array([[['foo', 'bar'], ['zoink', 'zod']], [['FOO', 'BAR'],
-                                                        ['ZOINK', 'ZOD']]])
-
-    warning = text_plugin.markdown_and_sanitize(text_plugin.WARNING_TEMPLATE %
-                                                3)
-    d3_expected = warning + textwrap.dedent("""\
-      <table>
-      <tbody>
-      <tr>
-      <td><p>foo</p></td>
-      <td><p>bar</p></td>
-      </tr>
-      <tr>
-      <td><p>zoink</p></td>
-      <td><p>zod</p></td>
-      </tr>
-      </tbody>
-      </table>""")
-    self.assertEqual(convert(d3), d3_expected)
-
-  def testPluginIsActive(self):
-    multiplexer = event_multiplexer.EventMultiplexer()
-    context = base_plugin.TBContext(logdir=None, multiplexer=multiplexer)
-    plugin = text_plugin.TextPlugin(context)
-
-    # The plugin is inactive because text summaries are not available.
-    self.assertFalse(plugin.is_active())
-
-    multiplexer.AddRunsFromDirectory(self.logdir)
-    multiplexer.Reload()
-
-    # The plugin is active because text summaries are available.
-    self.assertTrue(self.plugin.is_active())
-
-  def testUnicode(self):
-    self.assertConverted(u'<p>Iñtërnâtiônàlizætiøn⚡💩</p>',
-                         'Iñtërnâtiônàlizætiøn⚡💩')
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tensorflow/tensorboard/scripts/BUILD b/tensorflow/tensorboard/scripts/BUILD
deleted file mode 100644
index 05425ee61d..0000000000
--- a/tensorflow/tensorboard/scripts/BUILD
+++ /dev/null
@@ -1,32 +0,0 @@
-# Description:
-# Some useful scripts that are bundled with TensorBoard.
-
-package(default_visibility = ["//tensorflow/tensorboard:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-py_binary(
-    name = "generate_testdata",
-    srcs = ["generate_testdata.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//third_party/py/numpy",
-        "@six_archive//:six",
-    ],
-)
-
-py_binary(
-    name = "execrooter",
-    srcs = ["execrooter.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(["*"]),
-    visibility = ["//tensorflow:__subpackages__"],
-)
diff --git a/tensorflow/tensorboard/scripts/__init__.py b/tensorflow/tensorboard/scripts/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/tensorflow/tensorboard/scripts/execrooter.py b/tensorflow/tensorboard/scripts/execrooter.py
deleted file mode 100644
index 65569b9151..0000000000
--- a/tensorflow/tensorboard/scripts/execrooter.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Utility for running programs in a symlinked execroot."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import os
-import shutil
-import subprocess
-import sys
-import tempfile
-
-
-def run(inputs, program, outputs):
-  """Creates temp symlink tree, runs program, and copies back outputs.
-
-  Args:
-    inputs: List of fake paths to real paths, which are used for symlink tree.
-    program: List containing real path of program and its arguments. The
-        execroot directory will be appended as the last argument.
-    outputs: List of fake outputted paths to copy back to real paths.
-  Returns:
-    0 if succeeded or nonzero if failed.
-  """
-  root = tempfile.mkdtemp()
-  try:
-    cwd = os.getcwd()
-    for fake, real in inputs:
-      parent = os.path.join(root, os.path.dirname(fake))
-      if not os.path.exists(parent):
-        os.makedirs(parent)
-      os.symlink(os.path.join(cwd, real), os.path.join(root, fake))
-    if subprocess.call(program + [root]) != 0:
-      return 1
-    for fake, real in outputs:
-      shutil.copyfile(os.path.join(root, fake), real)
-    return 0
-  finally:
-    shutil.rmtree(root)
-
-
-def main(args):
-  """Invokes run function using a JSON file config.
-
-  Args:
-    args: CLI args, which can be a JSON file containing an object whose
-        attributes are the parameters to the run function. If multiple JSON
-        files are passed, their contents are concatenated.
-  Returns:
-    0 if succeeded or nonzero if failed.
-  Raises:
-    Exception: If input data is missing.
-  """
-  if not args:
-    raise Exception('Please specify at least one JSON config path')
-  inputs = []
-  program = []
-  outputs = []
-  for arg in args:
-    with open(arg) as fd:
-      config = json.load(fd)
-    inputs.extend(config.get('inputs', []))
-    program.extend(config.get('program', []))
-    outputs.extend(config.get('outputs', []))
-  if not program:
-    raise Exception('Please specify a program')
-  return run(inputs, program, outputs)
-
-
-if __name__ == '__main__':
-  sys.exit(main(sys.argv[1:]))
diff --git a/tensorflow/tensorboard/scripts/generate_testdata.py b/tensorflow/tensorboard/scripts/generate_testdata.py
deleted file mode 100644
index f191d16a82..0000000000
--- a/tensorflow/tensorboard/scripts/generate_testdata.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Generate some standard test data for debugging TensorBoard.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import bisect
-import math
-import os
-import os.path
-import random
-import shutil
-
-import numpy as np
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-
-tf.flags.DEFINE_string("target", None, """The directory where serialized data
-will be written""")
-
-tf.flags.DEFINE_boolean("overwrite", False, """Whether to remove and overwrite
-TARGET if it already exists.""")
-
-FLAGS = tf.flags.FLAGS
-
-# Hardcode a start time and reseed so script always generates the same data.
-_start_time = 0
-random.seed(0)
-
-
-def _MakeHistogramBuckets():
-  v = 1E-12
-  buckets = []
-  neg_buckets = []
-  while v < 1E20:
-    buckets.append(v)
-    neg_buckets.append(-v)
-    v *= 1.1
-  # Should include DBL_MAX, but won't bother for test data.
-  return neg_buckets[::-1] + [0] + buckets
-
-
-def _MakeHistogram(values):
-  """Convert values into a histogram proto using logic from histogram.cc."""
-  limits = _MakeHistogramBuckets()
-  counts = [0] * len(limits)
-  for v in values:
-    idx = bisect.bisect_left(limits, v)
-    counts[idx] += 1
-
-  limit_counts = [(limits[i], counts[i]) for i in xrange(len(limits))
-                  if counts[i]]
-  bucket_limit = [lc[0] for lc in limit_counts]
-  bucket = [lc[1] for lc in limit_counts]
-  sum_sq = sum(v * v for v in values)
-  return tf.HistogramProto(
-      min=min(values),
-      max=max(values),
-      num=len(values),
-      sum=sum(values),
-      sum_squares=sum_sq,
-      bucket_limit=bucket_limit,
-      bucket=bucket)
-
-
-def WriteScalarSeries(writer, tag, f, n=5):
-  """Write a series of scalar events to writer, using f to create values."""
-  step = 0
-  wall_time = _start_time
-  for i in xrange(n):
-    v = f(i)
-    value = tf.Summary.Value(tag=tag, simple_value=v)
-    summary = tf.Summary(value=[value])
-    event = tf.Event(wall_time=wall_time, step=step, summary=summary)
-    writer.add_event(event)
-    step += 1
-    wall_time += 10
-
-
-def WriteHistogramSeries(writer, tag, mu_sigma_tuples, n=20):
-  """Write a sequence of normally distributed histograms to writer."""
-  step = 0
-  wall_time = _start_time
-  for [mean, stddev] in mu_sigma_tuples:
-    data = [random.normalvariate(mean, stddev) for _ in xrange(n)]
-    histo = _MakeHistogram(data)
-    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=histo)])
-    event = tf.Event(wall_time=wall_time, step=step, summary=summary)
-    writer.add_event(event)
-    step += 10
-    wall_time += 100
-
-
-def WriteImageSeries(writer, tag, n_images=1):
-  """Write a few dummy images to writer."""
-  step = 0
-  session = tf.Session()
-  p = tf.placeholder("uint8", (1, 4, 4, 3))
-  s = tf.summary.image(tag, p)
-  for _ in xrange(n_images):
-    im = np.random.random_integers(0, 255, (1, 4, 4, 3))
-    summ = session.run(s, feed_dict={p: im})
-    writer.add_summary(summ, step)
-    step += 20
-  session.close()
-
-
-def WriteAudioSeries(writer, tag, n_audio=1):
-  """Write a few dummy audio clips to writer."""
-  step = 0
-  session = tf.Session()
-
-  min_frequency_hz = 440
-  max_frequency_hz = 880
-  sample_rate = 4000
-  duration_frames = sample_rate // 2  # 0.5 seconds.
-  frequencies_per_run = 1
-  num_channels = 2
-
-  p = tf.placeholder("float32", (frequencies_per_run, duration_frames,
-                                 num_channels))
-  s = tf.summary.audio(tag, p, sample_rate)
-
-  for _ in xrange(n_audio):
-    # Generate a different frequency for each channel to show stereo works.
-    frequencies = np.random.random_integers(
-        min_frequency_hz,
-        max_frequency_hz,
-        size=(frequencies_per_run, num_channels))
-    tiled_frequencies = np.tile(frequencies, (1, duration_frames))
-    tiled_increments = np.tile(
-        np.arange(0, duration_frames),
-        (num_channels, 1)).T.reshape(1, duration_frames * num_channels)
-    tones = np.sin(2.0 * np.pi * tiled_frequencies * tiled_increments /
-                   sample_rate)
-    tones = tones.reshape(frequencies_per_run, duration_frames, num_channels)
-
-    summ = session.run(s, feed_dict={p: tones})
-    writer.add_summary(summ, step)
-    step += 20
-  session.close()
-
-
-def GenerateTestData(path):
-  """Generates the test data directory."""
-  run1_path = os.path.join(path, "run1")
-  os.makedirs(run1_path)
-  writer1 = tf.summary.FileWriter(run1_path)
-  WriteScalarSeries(writer1, "foo/square", lambda x: x * x)
-  WriteScalarSeries(writer1, "bar/square", lambda x: x * x)
-  WriteScalarSeries(writer1, "foo/sin", math.sin)
-  WriteScalarSeries(writer1, "foo/cos", math.cos)
-  WriteHistogramSeries(writer1, "histo1", [[0, 1], [0.3, 1], [0.5, 1], [0.7, 1],
-                                           [1, 1]])
-  WriteImageSeries(writer1, "im1")
-  WriteImageSeries(writer1, "im2")
-  WriteAudioSeries(writer1, "au1")
-
-  run2_path = os.path.join(path, "run2")
-  os.makedirs(run2_path)
-  writer2 = tf.summary.FileWriter(run2_path)
-  WriteScalarSeries(writer2, "foo/square", lambda x: x * x * 2)
-  WriteScalarSeries(writer2, "bar/square", lambda x: x * x * 3)
-  WriteScalarSeries(writer2, "foo/cos", lambda x: math.cos(x) * 2)
-  WriteHistogramSeries(writer2, "histo1", [[0, 2], [0.3, 2], [0.5, 2], [0.7, 2],
-                                           [1, 2]])
-  WriteHistogramSeries(writer2, "histo2", [[0, 1], [0.3, 1], [0.5, 1], [0.7, 1],
-                                           [1, 1]])
-  WriteImageSeries(writer2, "im1")
-  WriteAudioSeries(writer2, "au2")
-
-  graph_def = tf.GraphDef()
-  node1 = graph_def.node.add()
-  node1.name = "a"
-  node1.op = "matmul"
-  node2 = graph_def.node.add()
-  node2.name = "b"
-  node2.op = "matmul"
-  node2.input.extend(["a:0"])
-
-  writer1.add_graph(graph_def)
-  node3 = graph_def.node.add()
-  node3.name = "c"
-  node3.op = "matmul"
-  node3.input.extend(["a:0", "b:0"])
-  writer2.add_graph(graph_def)
-  writer1.close()
-  writer2.close()
-
-
-def main(unused_argv=None):
-  target = FLAGS.target
-  if not target:
-    print("The --target flag is required.")
-    return -1
-  if os.path.exists(target):
-    if FLAGS.overwrite:
-      if os.path.isdir(target):
-        shutil.rmtree(target)
-      else:
-        os.remove(target)
-    else:
-      print("Refusing to overwrite target %s without --overwrite" % target)
-      return -2
-  GenerateTestData(target)
-
-
-if __name__ == "__main__":
-  tf.app.run()
diff --git a/tensorflow/tools/ci_build/Dockerfile.tensorboard b/tensorflow/tools/ci_build/Dockerfile.tensorboard
deleted file mode 100644
index 9795872e2c..0000000000
--- a/tensorflow/tools/ci_build/Dockerfile.tensorboard
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM ubuntu:14.04
-
-MAINTAINER Jan Prach <jendap@google.com>
-
-# Copy and run the install scripts.
-COPY install/*.sh /install/
-RUN /install/install_bootstrap_deb_packages.sh
-RUN add-apt-repository -y ppa:openjdk-r/ppa && \
-    add-apt-repository -y ppa:george-edison55/cmake-3.x
-RUN /install/install_deb_packages.sh
-RUN /install/install_tensorboard_packages.sh
diff --git a/tensorflow/tools/ci_build/builds/tensorboard.sh b/tensorflow/tools/ci_build/builds/tensorboard.sh
deleted file mode 100755
index 77bd29c09f..0000000000
--- a/tensorflow/tools/ci_build/builds/tensorboard.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-export LAUNCHPAD_CHROME=${LAUNCHPAD_CHROME:-$(which chromium-browser)}
-
-cd tensorflow/tensorboard
-
-# Install all js dependencies (tooling via npm, frontend assets via bower)
-npm run prepare
-
-npm run compile
-
-# Run wct in headless chrome using xvfb
-xvfb-run ./node_modules/web-component-tester/bin/wct --skip-plugin=sauce
diff --git a/tensorflow/tools/ci_build/install/install_tensorboard_packages.sh b/tensorflow/tools/ci_build/install/install_tensorboard_packages.sh
deleted file mode 100755
index ca5092cd47..0000000000
--- a/tensorflow/tools/ci_build/install/install_tensorboard_packages.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-# Install dependencies from ubuntu deb repository.
-apt-get update
-apt-get install -y --no-install-recommends \
-    chromium-browser \
-    nodejs \
-    nodejs-legacy \
-    npm \
-    python-numpy \
-    xvfb
-apt-get clean
-rm -rf /var/lib/apt/lists/*
diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
index e5f4a22f7a..6574fd144a 100755
--- a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
+++ b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
@@ -36,5 +36,4 @@ bazel test --test_tag_filters=-gpu,-benchmark-test,-nomac \
     --test_timeout 300,450,1200,3600 \
     --test_size_filters=small,medium \
     --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \
-    //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/... \
-    -//tensorflow/tensorboard/...
+    //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/update_version.sh b/tensorflow/tools/ci_build/update_version.sh
index 682f5329f5..b707ee338a 100755
--- a/tensorflow/tools/ci_build/update_version.sh
+++ b/tensorflow/tools/ci_build/update_version.sh
@@ -130,12 +130,6 @@ if [[ ${OLD_MAJOR} != ${MAJOR} ]] || [[ ${OLD_MINOR} != ${MINOR} ]]; then
   echo "Detected Major.Minor change. "\
 "Updating pattern ${OLD_R_MAJOR_MINOR} to ${R_MAJOR_MINOR} in additional files"
 
-  # Update tensorflow/tensorboard/README.md
-  TENSORBOARD_README_MD="${TF_SRC_DIR}/tensorboard/README.md"
-  check_existence file "${TENSORBOARD_README_MD}"
-  sed -i -r -e "s/${OLD_R_MAJOR_MINOR}/${R_MAJOR_MINOR}/g" \
-      "${TENSORBOARD_README_MD}"
-
   # Update dockerfiles
   DEVEL_DOCKERFILE="${TF_SRC_DIR}/tools/docker/Dockerfile.devel"
   check_existence file "${DEVEL_DOCKERFILE}"
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 9cfadf93b0..451ecb6dec 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -75,10 +75,6 @@ py_binary(
         "//tensorflow/python/saved_model",
         "//tensorflow/python/tools:tools_pip",
         # These targets don't build on Windows yet. Exclude them for now.
-        # rules_closure currently doesn't build on Windows due to
-        # https://github.com/bazelbuild/rules_closure/pull/206
-        # Since tensorboard dependes on rules_closure, exclude tensorboard until it's fixed.
-        # "//tensorflow/tensorboard",
         # "//tensorflow/contrib/ndlstm",
         # "//tensorflow/contrib/slim",
         # "//tensorflow/contrib/slim/python/slim/nets:nets_pip",
@@ -113,10 +109,6 @@ filegroup(
         "@lmdb//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
         "@nanopb_git//:LICENSE.txt",
-        "@org_html5lib//:LICENSE",
-        "@org_mozilla_bleach//:LICENSE",
-        "@org_pocoo_werkzeug//:LICENSE",
-        "@org_pythonhosted_markdown//:LICENSE.md",
         "@png_archive//:LICENSE",
         "@protobuf//:LICENSE",
         "@six_archive//:LICENSE",
@@ -163,7 +155,6 @@ sh_binary(
             "//tensorflow/python/debug:debug_pip",
             "//tensorflow/python/saved_model:saved_model",
             "//tensorflow/python/tools:tools_pip",
-            "//tensorflow/tensorboard",
         ],
     }) + if_mkl(["//third_party/mkl:intel_binary_blob"]),
 )
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 3bbc900042..073988765c 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -35,11 +35,8 @@ REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
     'six >= 1.10.0',
     'protobuf >= 3.2.0',
-    'werkzeug >= 0.11.10',
-    'html5lib == 0.9999999',  # identical to 1.0b8
-    'markdown == 2.2.0',
-    'bleach == 1.5.0',
     'backports.weakref == 1.0rc1',
+    'tensorflow-tensorboard',
 ]
 
 project_name = 'tensorflow'
@@ -59,7 +56,6 @@ else:
 
 # pylint: disable=line-too-long
 CONSOLE_SCRIPTS = [
-    'tensorboard = tensorflow.tensorboard.tensorboard:main',
     'saved_model_cli = tensorflow.python.tools.saved_model_cli:main',
 ]
 # pylint: enable=line-too-long
@@ -191,8 +187,6 @@ setup(
     package_data={
         'tensorflow': [
             EXTENSION_NAME,
-            'tensorboard/components/index.html',
-            'tensorboard/TAG',
         ] + matches,
     },
     zip_safe=False,
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 01f12ee8db..5fd08d4f4c 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -4,14 +4,8 @@ load("//third_party/gpus:cuda_configure.bzl", "cuda_configure")
 load("//third_party/sycl:sycl_configure.bzl", "sycl_configure")
 load("@io_bazel_rules_closure//closure/private:java_import_external.bzl", "java_import_external")
 load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external")
-load("@io_bazel_rules_closure//closure:defs.bzl", "web_library_external")
 load("//third_party/py:python_configure.bzl", "python_configure")
 
-load("//third_party:polymer.bzl", "tensorboard_polymer_workspace")
-load("//third_party:python.bzl", "tensorboard_python_workspace")
-load("//third_party:js.bzl", "tensorboard_js_workspace")
-load("//third_party:typings.bzl", "tensorboard_typings_workspace")
-
 
 def _is_windows(repository_ctx):
   """Returns true if the host operating system is windows."""
@@ -150,12 +144,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
     print("path_prefix was specified to tf_workspace but is no longer used " +
           "and will be removed in the future.")
 
-  # TODO(dandelion): Take these out when TB exits TF
-  tensorboard_polymer_workspace()
-  tensorboard_python_workspace()
-  tensorboard_typings_workspace()
-  tensorboard_js_workspace()
-
   native.new_http_archive(
       name = "eigen_archive",
       urls = [
diff --git a/third_party/bleach.BUILD b/third_party/bleach.BUILD
deleted file mode 100644
index 1bf75b84a7..0000000000
--- a/third_party/bleach.BUILD
+++ /dev/null
@@ -1,20 +0,0 @@
-# Description:
-#   Build file for Bleach.
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-py_library(
-    name = "org_mozilla_bleach",
-    srcs = [
-        "bleach/__init__.py",
-        "bleach/callbacks.py",
-        "bleach/encoding.py",
-        "bleach/sanitizer.py",
-        "bleach/version.py",
-    ],
-    srcs_version = "PY2AND3",
-    deps = ["@org_html5lib"],
-)
diff --git a/third_party/clutz.BUILD b/third_party/clutz.BUILD
deleted file mode 100644
index 593b70366a..0000000000
--- a/third_party/clutz.BUILD
+++ /dev/null
@@ -1,44 +0,0 @@
-# Description:
-#   Build tool for making TypeScript .d.ts files from Closure JavaScript.
-
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"])  # MIT
-
-exports_files([
-    "LICENSE",
-    "src/resources/closure.lib.d.ts",
-])
-
-JVM_FLAGS = [
-    "-Xss20m",  # JSCompiler needs big stacks for recursive parsing
-    "-XX:+UseParallelGC",  # Best GC when app isn't latency sensitive
-]
-
-java_binary(
-    name = "clutz",
-    srcs = glob(["src/main/java/com/google/javascript/clutz/**/*.java"]),
-    jvm_flags = JVM_FLAGS,
-    main_class = "com.google.javascript.clutz.DeclarationGenerator",
-    deps = [
-        "@args4j",
-        "@com_google_code_findbugs_jsr305",
-        "@com_google_code_gson",
-        "@com_google_guava",
-        "@com_google_javascript_closure_compiler",
-    ],
-)
-
-java_binary(
-    name = "gents",
-    srcs = glob(["src/main/java/com/google/javascript/gents/**/*.java"]),
-    jvm_flags = JVM_FLAGS,
-    main_class = "com.google.javascript.gents.TypeScriptGenerator",
-    deps = [
-        "@args4j",
-        "@com_google_code_findbugs_jsr305",
-        "@com_google_code_gson",
-        "@com_google_guava",
-        "@com_google_javascript_closure_compiler",
-    ],
-)
diff --git a/third_party/clutz.bzl b/third_party/clutz.bzl
deleted file mode 100644
index f273c78c79..0000000000
--- a/third_party/clutz.bzl
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Build definitions for TypeScript from Closure JavaScript libraries."""
-
-load("@io_bazel_rules_closure//closure/private:defs.bzl",
-     "JS_FILE_TYPE",
-     "collect_js",
-     "unfurl")
-
-CLUTZ_ATTRIBUTES = {
-    "_clutz": attr.label(
-        default=Label("@io_angular_clutz//:clutz"),
-        executable=True,
-        cfg="host"),
-    "_clutz_externs": attr.label(
-        default=Label("@com_google_javascript_closure_compiler_externs"),
-        allow_files=True),
-}
-
-def extract_dts_from_closure_libraries(ctx):
-  """Extracts type definitions from closure dependencies.
-
-  This just generates one big .d.ts file for all transitive Closure sources,
-  and does not pass it down. That means each rule has to duplicate the effort,
-  but on the other hand allows transitive dependencies on shared rules without
-  causing duplicate definition errors.
-
-  Args:
-      ctx: A Skylark context.
-  Returns:
-      The generated Clutz typings file, or None if there were no JS deps.
-  """
-  deps = unfurl(ctx.attr.deps, provider="closure_js_library")
-  js = collect_js(ctx, deps)
-  if not js.srcs:
-    return None
-  js_typings = ctx.new_file(ctx.bin_dir, "%s-js-typings.d.ts" % ctx.label.name)
-  srcs = depset(JS_FILE_TYPE.filter(ctx.files._clutz_externs)) + js.srcs
-  args = ["-o", js_typings.path]
-  for src in srcs:
-    args.append(src.path)
-  if getattr(ctx.attr, "clutz_entry_points", None):
-    args.append("--closure_entry_points")
-    args.extend(ctx.attr.clutz_entry_points)
-  ctx.action(
-      inputs=list(srcs),
-      outputs=[js_typings],
-      executable=ctx.executable._clutz,
-      arguments=args,
-      mnemonic="Clutz",
-      progress_message="Running Clutz on %d JS files %s" % (
-          len(srcs), ctx.label))
-  return js_typings
-
-################################################################################
-# The following definitions are for API compatibility with internal clutz.bzl
-
-CLUTZ_OUTPUTS = {}
-
-def _clutz_aspect_impl(target, ctx):
-  return struct()
-
-clutz_aspect = aspect(
-    implementation=_clutz_aspect_impl,
-    attr_aspects=["exports"])
diff --git a/third_party/html5lib.BUILD b/third_party/html5lib.BUILD
deleted file mode 100644
index 63aac14f15..0000000000
--- a/third_party/html5lib.BUILD
+++ /dev/null
@@ -1,17 +0,0 @@
-# Description:
-# Import of html5lib library.
-
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"])  # BSD-like notice-style license, see LICENSE file
-
-exports_files(["LICENSE"])
-
-py_library(
-    name = "org_html5lib",
-    srcs = glob(["html5lib/**/*.py"]),
-    srcs_version = "PY2AND3",
-    deps = [
-        "@six_archive//:six",
-    ],
-)
diff --git a/third_party/js.bzl b/third_party/js.bzl
deleted file mode 100644
index b5395a0b0c..0000000000
--- a/third_party/js.bzl
+++ /dev/null
@@ -1,424 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TensorBoard external JS dependencies (both infrastructure and frontend libs)
-load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external")
-load("@io_bazel_rules_closure//closure:defs.bzl", "web_library_external")
-
-
-def tensorboard_js_workspace():
-
-  ##############################################################################
-  # TensorBoard Build Tools
-
-  filegroup_external(
-      name = "org_nodejs",
-      # MIT with portions licensed:
-      # - MIT
-      # - Old MIT
-      # - 2-Clause-BSD
-      # - 3-Clause-BSD
-      # - ISC
-      # - Unicode
-      # - zlib
-      # - Artistic 2.0
-      licenses = ["notice"],
-      sha256_urls_extract_macos = {
-          "47109a00cac344d80296c195451bb5eee7c21727fcef1594384ddfe1f852957a": [
-              "http://mirror.bazel.build/nodejs.org/dist/v4.3.2/node-v4.3.2-darwin-x64.tar.xz",
-              "http://nodejs.org/dist/v4.3.2/node-v4.3.2-darwin-x64.tar.xz",
-          ],
-      },
-      sha256_urls_windows = {
-          "3d4cfca9dcec556a077a2324bf5bd165ea3e6e64a2bfd7fc6e7a1f0dc4eb552b": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/nodejs/node/v4.3.2/LICENSE",
-              "https://raw.githubusercontent.com/nodejs/node/v4.3.2/LICENSE",
-          ],
-          "606c44c42d17866c017c50c0afadad411d9492ac4281d2431b937f881911614e": [
-              "http://mirror.bazel.build/nodejs.org/dist/v4.3.2/win-x64/node.exe",
-              "http://nodejs.org/dist/v4.3.2/win-x64/node.exe",
-          ],
-          "451a40570099a95488d6438f175813629e0430f87f23c8659bc18dc42494820a": [
-              "http://mirror.bazel.build/nodejs.org/dist/v4.3.2/win-x64/node.lib",
-              "http://nodejs.org/dist/v4.3.2/win-x64/node.lib",
-          ],
-      },
-      sha256_urls_extract = {
-          "4350d0431b49697517c6cca5d66adf5f74eb9101c52f52ae959fa94225822d44": [
-              "http://mirror.bazel.build/nodejs.org/dist/v4.3.2/node-v4.3.2-linux-x64.tar.xz",
-              "http://nodejs.org/dist/v4.3.2/node-v4.3.2-linux-x64.tar.xz",
-          ],
-      },
-      strip_prefix = {
-          "node-v4.3.2-darwin-x64.tar.xz": "node-v4.3.2-darwin-x64",
-          "node-v4.3.2-linux-x64.tar.xz": "node-v4.3.2-linux-x64",
-      },
-      executable = [
-          "node",
-          "node.exe",
-      ],
-  )
-
-  filegroup_external(
-      name = "com_microsoft_typescript",
-      licenses = ["notice"],  # Apache 2.0
-      sha256_urls = {
-          "a7d00bfd54525bc694b6e32f64c7ebcf5e6b7ae3657be5cc12767bce74654a47": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/Microsoft/TypeScript/v2.3.1/LICENSE.txt",
-              "https://raw.githubusercontent.com/Microsoft/TypeScript/v2.3.1/LICENSE.txt",
-          ],
-          "8465342c318f9c4cf0a29b109fa63ee3742dd4dc7080d05d9fd8f604814d04cf": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/Microsoft/TypeScript/v2.3.1/lib/tsc.js",
-              "https://raw.githubusercontent.com/Microsoft/TypeScript/v2.3.1/lib/tsc.js",
-          ],
-          "a67e36da3029d232e4e938e61a0a3302f516d71e7100d54dbf5362ad8618e994": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/Microsoft/TypeScript/v2.3.1/lib/lib.es6.d.ts",
-              "https://raw.githubusercontent.com/Microsoft/TypeScript/v2.3.1/lib/lib.es6.d.ts",
-          ],
-      },
-      extra_build_file_content = "\n".join([
-          "sh_binary(",
-          "    name = \"tsc\",",
-          "    srcs = [\"tsc.sh\"],",
-          "    data = [",
-          "        \"tsc.js\",",
-          "        \"@org_nodejs\",",
-          "    ],",
-          ")",
-          "",
-          "genrule(",
-          "    name = \"tsc_sh\",",
-          "    outs = [\"tsc.sh\"],",
-          "    cmd = \"cat >$@ <<'EOF'\\n\" +",
-          "          \"#!/bin/bash\\n\" +",
-          "          \"NODE=external/org_nodejs/bin/node\\n\" +",
-          "          \"if [[ -e external/org_nodejs/node.exe ]]; then\\n\" +",
-          "          \"  NODE=external/org_nodejs/node.exe\\n\" +",
-          "          \"fi\\n\" +",
-          "          \"exec $${NODE} external/com_microsoft_typescript/tsc.js \\\"$$@\\\"\\n\" +",
-          "          \"EOF\",",
-          "    executable = True,",
-          ")",
-      ]),
-  )
-
-
-  native.new_http_archive(
-      name = "io_angular_clutz",
-      build_file = "//third_party:clutz.BUILD",
-      sha256 = "2981de41d1ff4774b544423da9a2cd8beb3be649e95aef2ef2fd83957300b3fe",
-      strip_prefix = "clutz-b0db5ade9bb535d387f05292316c422790c9848e",
-      urls = [
-          "http://mirror.bazel.build/github.com/angular/clutz/archive/b0db5ade9bb535d387f05292316c422790c9848e.tar.gz",  # 2017-05-22
-          "https://github.com/angular/clutz/archive/b0db5ade9bb535d387f05292316c422790c9848e.tar.gz",
-      ],
-  )
-
-  filegroup_external(
-      name = "com_google_javascript_closure_compiler_externs",
-      licenses = ["notice"],  # Apache 2.0
-      sha256_urls_extract = {
-          "0f515a6ebfa138490b3c5ea9f3591ea1a7e4a930d3074f18b3eca86084ad9b66": [
-              "http://mirror.bazel.build/github.com/google/closure-compiler/archive/b37e6000001b0a6bf4c0be49024ebda14a8711d9.tar.gz",  # 2017-06-02
-              "https://github.com/google/closure-compiler/archive/b37e6000001b0a6bf4c0be49024ebda14a8711d9.tar.gz",
-          ],
-      },
-      strip_prefix = {"b37e6000001b0a6bf4c0be49024ebda14a8711d9.tar.gz": "closure-compiler-b37e6000001b0a6bf4c0be49024ebda14a8711d9/externs"},
-  )
-
-  filegroup_external(
-      name = "com_google_javascript_closure_compiler_externs_polymer",
-      licenses = ["notice"],  # Apache 2.0
-      sha256_urls = {
-          "23baad9a200a717a821c6df504c84d3a893d7ea9102b14876eb80097e3b94292": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/google/closure-compiler/0e8dc5597a295ee259e3fecd98d6535dc621232f/contrib/externs/polymer-1.0.js",  # 2017-05-27
-              "https://raw.githubusercontent.com/google/closure-compiler/0e8dc5597a295ee259e3fecd98d6535dc621232f/contrib/externs/polymer-1.0.js",
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_threejs",
-      # no @license header
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "881cc79c84c34a1f61f8c8af0ee3f237d83a2eda3868720fdcb47bcacf8da44a": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/mrdoob/three.js/r77/build/three.js",
-              "https://raw.githubusercontent.com/mrdoob/three.js/r77/build/three.js",
-          ],
-          "98b8b5954901025a98033c8bdd65969be1f30b59e11f823ec864253bb72f768d": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/mrdoob/three.js/r77/examples/js/controls/OrbitControls.js",
-              "https://raw.githubusercontent.com/mrdoob/three.js/r77/examples/js/controls/OrbitControls.js",
-          ],
-      },
-  )
-
-  ##############################################################################
-  # TensorBoard JavaScript Production Dependencies
-
-  web_library_external(
-      name = "com_lodash",
-      licenses = ["notice"],  # MIT
-      sha256 = "0e88207e5f90af4ce8790d6e1e7d09d2702d81bce0bafdc253d18c0a5bf7661e",
-      urls = [
-          "http://mirror.bazel.build/github.com/lodash/lodash/archive/3.10.1.tar.gz",
-          "https://github.com/lodash/lodash/archive/3.10.1.tar.gz",
-      ],
-      strip_prefix = "lodash-3.10.1",
-      path = "/lodash",
-      srcs = ["lodash.js"],
-  )
-
-  filegroup_external(
-      name = "com_numericjs",
-      # no @license header
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "0e94aada97f12dee6118064add9170484c55022f5d53206ee4407143cd36ddcd": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/sloisel/numeric/v1.2.6/license.txt",
-              "https://raw.githubusercontent.com/sloisel/numeric/v1.2.6/license.txt",
-          ],
-          "5dcaba2016fd237091e3a17b0dc272fb21f0e2b15d7628f95a0ad0cd4cdf4020": [
-              "http://mirror.bazel.build/www.numericjs.com/lib/numeric-1.2.6.js",
-              "http://www.numericjs.com/lib/numeric-1.2.6.js",
-          ],
-      },
-      rename = {"numeric-1.2.6.js": "numeric.js"},
-  )
-
-  filegroup_external(
-      name = "com_palantir_plottable",
-      # no @license header
-      licenses = ["notice"],  # MIT
-      sha256_urls_extract = {
-          # Plottable doesn't have a release tarball on GitHub. Using the
-          # sources directly from git also requires running Node tooling
-          # beforehand to generate files. NPM is the only place to get it.
-          "e3159beb279391c47433789f22b32bac88488cfcad6c0b6ec8605ce6b0081b0d": [
-              "http://mirror.bazel.build/registry.npmjs.org/plottable/-/plottable-3.1.0.tgz",
-              "https://registry.npmjs.org/plottable/-/plottable-3.1.0.tgz",
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "io_github_cpettitt_dagre",
-      # no @license header
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "6a349742a6cb219d5a2fc8d0844f6d89a6efc62e20c664450d884fc7ff2d6015": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/cpettitt/dagre/v0.7.4/LICENSE",
-              "https://raw.githubusercontent.com/cpettitt/dagre/v0.7.4/LICENSE",
-          ],
-          "7323829ddd77924a69e2b1235ded3eac30acd990da0f037e0fbd3c8e9035b50d": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/cpettitt/dagre/v0.7.4/dist/dagre.core.js",
-              "https://raw.githubusercontent.com/cpettitt/dagre/v0.7.4/dist/dagre.core.js",
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "io_github_cpettitt_graphlib",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "6a349742a6cb219d5a2fc8d0844f6d89a6efc62e20c664450d884fc7ff2d6015": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/cpettitt/graphlib/v1.0.7/LICENSE",
-              "https://raw.githubusercontent.com/cpettitt/graphlib/v1.0.7/LICENSE",
-          ],
-          "772045d412b1513b549be991c2e1846c38019429d43974efcae943fbe83489bf": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/cpettitt/graphlib/v1.0.7/dist/graphlib.core.js",
-              "https://raw.githubusercontent.com/cpettitt/graphlib/v1.0.7/dist/graphlib.core.js",
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "io_github_waylonflinn_weblas",
-      # no @license header
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "633f2861a9a862b9cd7967e841e14dd3527912f209d6563595774fa31e3d84cb": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/waylonflinn/weblas/v0.9.0/LICENSE",
-              "https://raw.githubusercontent.com/waylonflinn/weblas/v0.9.0/LICENSE",
-          ],
-          "f138fce57f673ca8a633f4aee5ae5b6fcb6ad0de59069a42a74e996fd04d8fcc": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/waylonflinn/weblas/v0.9.0/dist/weblas.js",
-              "https://raw.githubusercontent.com/waylonflinn/weblas/v0.9.0/dist/weblas.js",
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_d3js",
-      # no @license header
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256_urls_extract = {
-          "d858c0878af36bd00e2af6029029106328d408c2bff0a60a9d78c4e27f47b99a": [
-              "http://mirror.bazel.build/github.com/d3/d3/releases/download/v4.9.1/d3.zip",
-              "https://github.com/d3/d3/releases/download/v4.9.1/d3.zip",
-          ],
-      },
-      # TODO(jart): Use srcs=["d3.js"] instead of this once supported.
-      generated_rule_name = "all_files",
-      extra_build_file_content = "\n".join([
-          "filegroup(",
-          "    name = \"org_d3js\",",
-          "    srcs = [\"d3.js\"],",
-          ")",
-      ]),
-  )
-
-  filegroup_external(
-      name = "org_chromium_catapult_vulcanized_trace_viewer",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256_urls = {
-          "f0df289ba9d03d857ad1c2f5918861376b1510b71588ffc60eff5c7a7bfedb09": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/catapult-project/catapult/2f7ee994984f3ebd3dd3dc3e05777bf180ec2ee8/LICENSE",
-              "https://raw.githubusercontent.com/catapult-project/catapult/2f7ee994984f3ebd3dd3dc3e05777bf180ec2ee8/LICENSE",
-          ],
-          "9e99e79439ea5a1471bd4dd325bd6733e133bcb3da4df4b878ed6d2aec7c8d86": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/catapult-project/catapult/2f7ee994984f3ebd3dd3dc3e05777bf180ec2ee8/trace_viewer_full.html",
-              "https://raw.githubusercontent.com/catapult-project/catapult/2f7ee994984f3ebd3dd3dc3e05777bf180ec2ee8/trace_viewer_full.html"
-          ],
-      },
-  )
-
-  ##############################################################################
-  # TensorBoard Testing Dependencies
-
-  web_library_external(
-      name = "org_npmjs_registry_accessibility_developer_tools",
-      licenses = ["notice"],  # Apache License 2.0
-      sha256 = "1d6a72f401c9d53f68238c617dd43a05cd85ca5aa2e676a5b3c352711448e093",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/accessibility-developer-tools/-/accessibility-developer-tools-2.10.0.tgz",
-          "https://registry.npmjs.org/accessibility-developer-tools/-/accessibility-developer-tools-2.10.0.tgz",
-      ],
-      strip_prefix = "package",
-      path = "/accessibility-developer-tools",
-      suppress = ["strictDependencies"],
-  )
-
-  web_library_external(
-      name = "org_npmjs_registry_async",
-      licenses = ["notice"],  # MIT
-      sha256 = "08655255ae810bf4d1cb1642df57658fcce823776d3ba8f4b46f4bbff6c87ece",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/async/-/async-1.5.0.tgz",
-          "https://registry.npmjs.org/async/-/async-1.5.0.tgz",
-      ],
-      strip_prefix = "package",
-      path = "/async",
-  )
-
-  web_library_external(
-      name = "org_npmjs_registry_chai",
-      licenses = ["notice"],  # MIT
-      sha256 = "aca8137bed5bb295bd7173325b7ad604cd2aeb341d739232b4f9f0b26745be90",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/chai/-/chai-3.5.0.tgz",
-          "https://registry.npmjs.org/chai/-/chai-3.5.0.tgz",
-      ],
-      strip_prefix = "package",
-      path = "/chai",
-  )
-
-  web_library_external(
-      name = "org_npmjs_registry_mocha",
-      licenses = ["notice"],  # MIT
-      sha256 = "13ef37a071196a2fba680799b906555d3f0ab61e80a7e8f73f93e77914590dd4",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/mocha/-/mocha-2.5.3.tgz",
-          "https://registry.npmjs.org/mocha/-/mocha-2.5.3.tgz",
-      ],
-      suppress = ["strictDependencies"],
-      strip_prefix = "package",
-      path = "/mocha",
-  )
-
-  web_library_external(
-      name = "org_npmjs_registry_sinon",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "49edb057695fc9019aae992bf7e677a07de7c6ce2bf9f9facde4a245045d1532",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/sinon/-/sinon-1.17.4.tgz",
-          "https://registry.npmjs.org/sinon/-/sinon-1.17.4.tgz",
-      ],
-      strip_prefix = "package/lib",
-      path = "/sinonjs",
-  )
-
-  web_library_external(
-      name = "org_npmjs_registry_sinon_chai",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "b85fc56f713832960b56fe9269ee4bb2cd41edd2ceb130b0936e5bdbed5dea63",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/sinon-chai/-/sinon-chai-2.8.0.tgz",
-          "https://registry.npmjs.org/sinon-chai/-/sinon-chai-2.8.0.tgz",
-      ],
-      strip_prefix = "package",
-      path = "/sinon-chai",
-  )
-
-  web_library_external(
-      name = "org_npmjs_registry_stacky",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "c659e60f7957d9d80c23a7aacc4d71b19c6421a08f91174c0062de369595acae",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/stacky/-/stacky-1.3.1.tgz",
-          "https://registry.npmjs.org/stacky/-/stacky-1.3.1.tgz",
-      ],
-      strip_prefix = "package",
-      path = "/stacky",
-  )
-
-  web_library_external(
-      name = "org_npmjs_registry_web_component_tester",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "9d4ebd4945df8a936916d4d32b7f280f2a3afa35f79e7ca8ad3ed0a42770c537",
-      urls = [
-          "http://mirror.bazel.build/registry.npmjs.org/web-component-tester/-/web-component-tester-4.3.6.tgz",
-          "https://registry.npmjs.org/web-component-tester/-/web-component-tester-4.3.6.tgz",
-      ],
-      strip_prefix = "package",
-      path = "/web-component-tester",
-      suppress = [
-          "absolutePaths",
-          "strictDependencies",
-      ],
-      deps = [
-          "@com_lodash",
-          "@org_npmjs_registry_accessibility_developer_tools",
-          "@org_npmjs_registry_async",
-          "@org_npmjs_registry_chai",
-          "@org_npmjs_registry_mocha",
-          "@org_npmjs_registry_sinon",
-          "@org_npmjs_registry_sinon_chai",
-          "@org_npmjs_registry_stacky",
-          "@org_polymer_test_fixture",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_test_fixture",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "59d6cfb1187733b71275becfea181fe0aa1f734df5ff77f5850c806bbbf9a0d9",
-      strip_prefix = "test-fixture-2.0.1",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/test-fixture/archive/v2.0.1.tar.gz",
-          "https://github.com/PolymerElements/test-fixture/archive/v2.0.1.tar.gz",
-      ],
-      path = "/test-fixture",
-      exclude = ["test/**"],
-  )
diff --git a/third_party/markdown.BUILD b/third_party/markdown.BUILD
deleted file mode 100644
index fa3e85d530..0000000000
--- a/third_party/markdown.BUILD
+++ /dev/null
@@ -1,15 +0,0 @@
-# Description:
-#   Markdown processor
-
-package(default_visibility = ["//visibility:public"])
-
-# This software says they use a BSD license.
-licenses(["notice"])
-
-exports_files(["LICENSE.md"])
-
-py_library(
-    name = "org_pythonhosted_markdown",
-    srcs = glob(["markdown/**/*.py"]),
-    srcs_version = "PY2AND3",
-)
diff --git a/third_party/polymer.bzl b/third_party/polymer.bzl
deleted file mode 100644
index 812afbf3f7..0000000000
--- a/third_party/polymer.bzl
+++ /dev/null
@@ -1,1337 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TensorBoard Polymer Dependencies
-
-load("@io_bazel_rules_closure//closure:defs.bzl", "web_library_external")
-
-def tensorboard_polymer_workspace():
-  web_library_external(
-      name = "org_polymer",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "2c38cf73bdd09e0f80a4b7210fd58f88a6bdd8624da73fb3e028e66a84c9e095",
-      strip_prefix = "polymer-1.8.1",
-      urls = [
-          "http://mirror.bazel.build/github.com/polymer/polymer/archive/v1.8.1.tar.gz",
-          "https://github.com/polymer/polymer/archive/v1.8.1.tar.gz",
-      ],
-      path = "/polymer",
-      srcs = [
-          "polymer.html",
-          "polymer-micro.html",
-          "polymer-mini.html",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_font_roboto",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "fae51429b56a4a4c15f1f0c23b733c7095940cc9c04c275fa7adb3bf055b23b3",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/font-roboto/archive/v1.0.1.tar.gz",
-          "https://github.com/PolymerElements/font-roboto/archive/v1.0.1.tar.gz",
-      ],
-      strip_prefix = "font-roboto-1.0.1",
-      path = "/font-roboto",
-      srcs = ["roboto.html"],
-  )
-
-  web_library_external(
-      name = "org_polymer_hydrolysis",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "703b50f6b00f9e0546b5a3451da57bb20f77a166e27e4967923b9e835bab9b80",
-      urls = [
-          "http://mirror.bazel.build/github.com/Polymer/polymer-analyzer/archive/v1.19.3.tar.gz",
-          "https://github.com/Polymer/polymer-analyzer/archive/v1.19.3.tar.gz",
-      ],
-      strip_prefix = "polymer-analyzer-1.19.3",
-      path = "/hydrolysis",
-      srcs = [
-          "hydrolysis-analyzer.html",
-          "hydrolysis.html",
-          "hydrolysis.js",
-      ],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_a11y_announcer",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "53114ceb57d9f33a7a8058488cf06450e48502e5d033adf51c91330f61620353",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-a11y-announcer/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/iron-a11y-announcer/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "iron-a11y-announcer-2.0.0",
-      path = "/iron-a11y-announcer",
-      srcs = ["iron-a11y-announcer.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_a11y_keys_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "09274155c8d537f8bb567b3be5e747253ef760995a59ee06cb0ab38e704212fb",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-a11y-keys-behavior/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/iron-a11y-keys-behavior/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "iron-a11y-keys-behavior-2.0.0",
-      path = "/iron-a11y-keys-behavior",
-      srcs = ["iron-a11y-keys-behavior.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_ajax",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "9162d8af4611e911ac3ebbfc08bb7038ac04f6e79a9287b1476fe36ad6770bc5",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-ajax/archive/v1.2.0.tar.gz",
-          "https://github.com/PolymerElements/iron-ajax/archive/v1.2.0.tar.gz",
-      ],
-      strip_prefix = "iron-ajax-1.2.0",
-      path = "/iron-ajax",
-      srcs = [
-          "iron-ajax.html",
-          "iron-request.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_promise_polyfill",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_autogrow_textarea",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "50bbb901d2c8f87462e3552e3d671a552faa12c37c485e548d7a234ebffbc427",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-autogrow-textarea/archive/v1.0.12.tar.gz",
-          "https://github.com/PolymerElements/iron-autogrow-textarea/archive/v1.0.12.tar.gz",
-      ],
-      strip_prefix = "iron-autogrow-textarea-1.0.12",
-      path = "/iron-autogrow-textarea",
-      srcs = ["iron-autogrow-textarea.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_form_element_behavior",
-          "@org_polymer_iron_validatable_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_behaviors",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "a1e8d4b7a13f3d36beba9c2a6b186ed33a53e6af2e79f98c1fcc7e85e7b53f89",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-behaviors/archive/v1.0.17.tar.gz",
-          "https://github.com/PolymerElements/iron-behaviors/archive/v1.0.17.tar.gz",
-      ],
-      strip_prefix = "iron-behaviors-1.0.17",
-      path = "/iron-behaviors",
-      srcs = [
-          "iron-button-state.html",
-          "iron-control-state.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_checked_element_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "539a0e1c4df0bc702d3bd342388e4e56c77ec4c2066cce69e41426a69f92e8bd",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-checked-element-behavior/archive/v1.0.4.tar.gz",
-          "https://github.com/PolymerElements/iron-checked-element-behavior/archive/v1.0.4.tar.gz",
-      ],
-      strip_prefix = "iron-checked-element-behavior-1.0.4",
-      path = "/iron-checked-element-behavior",
-      srcs = ["iron-checked-element-behavior.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_form_element_behavior",
-          "@org_polymer_iron_validatable_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_component_page",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "3636e8b9a1f229fc33b5aad3933bd02a9825f66e679a0be31855d7c8245c4b4b",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-component-page/archive/v1.1.4.tar.gz",
-          "https://github.com/PolymerElements/iron-component-page/archive/v1.1.4.tar.gz",
-      ],
-      strip_prefix = "iron-component-page-1.1.4",
-      path = "/iron-component-page",
-      srcs = ["iron-component-page.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_hydrolysis",
-          "@org_polymer_iron_ajax",
-          "@org_polymer_iron_doc_viewer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_icons",
-          "@org_polymer_iron_selector",
-          "@org_polymer_paper_header_panel",
-          "@org_polymer_paper_styles",
-          "@org_polymer_paper_toolbar",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_collapse",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "eb72f459a2a5adbcd922327eea02ed909e8056ad72fd8a32d04a14ce54b2e480",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-collapse/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/iron-collapse/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "iron-collapse-2.0.0",
-      path = "/iron-collapse",
-      srcs = ["iron-collapse.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_resizable_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_demo_helpers",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "e196985cb7e50108283c3fc189a3a018e697b4648107d597df71a5c17f5ee907",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-demo-helpers/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/iron-demo-helpers/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "iron-demo-helpers-2.0.0",
-      path = "/iron-demo-helpers",
-      srcs = [
-          "demo-pages-shared-styles.html",
-          "demo-snippet.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_font_roboto",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_icons",
-          "@org_polymer_marked_element",
-          "@org_polymer_paper_icon_button",
-          "@org_polymer_paper_styles",
-          "@org_polymer_prism_element",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_doc_viewer",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "5d487c99dd0cf626c800ae8667b0c8c88095f4482a68e837a1d3f58484ca8fb4",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-doc-viewer/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/iron-doc-viewer/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "iron-doc-viewer-2.0.0",
-      path = "/iron-doc-viewer",
-      srcs = [
-          "iron-doc-property-styles.html",
-          "iron-doc-property.html",
-          "iron-doc-viewer-styles.html",
-          "iron-doc-viewer.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_marked_element",
-          "@org_polymer_paper_button",
-          "@org_polymer_paper_styles",
-          "@org_polymer_prism_element",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_dropdown",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "db9d6598157f8b114f1be1e6ed1c74917d4c37e660b2dda1e31f6873f1a33b80",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-dropdown/archive/v1.5.5.tar.gz",
-          "https://github.com/PolymerElements/iron-dropdown/archive/v1.5.5.tar.gz",
-      ],
-      strip_prefix = "iron-dropdown-1.5.5",
-      path = "/iron-dropdown",
-      srcs = [
-          "iron-dropdown.html",
-          "iron-dropdown-scroll-manager.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_overlay_behavior",
-          "@org_polymer_iron_resizable_behavior",
-          "@org_polymer_neon_animation",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_fit_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "10132a2ea309a37c4c07b8fead71f64abc588ee6107931e34680f5f36dd8291e",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-fit-behavior/archive/v1.2.5.tar.gz",
-          "https://github.com/PolymerElements/iron-fit-behavior/archive/v1.2.5.tar.gz",
-      ],
-      strip_prefix = "iron-fit-behavior-1.2.5",
-      path = "/iron-fit-behavior",
-      srcs = ["iron-fit-behavior.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_flex_layout",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "79287f6ca1c2d4e003f68b88fe19d03a1b6a0011e2b4cae579fe4d1474163a2e",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-flex-layout/archive/v1.3.0.tar.gz",
-          "https://github.com/PolymerElements/iron-flex-layout/archive/v1.3.0.tar.gz",
-      ],
-      strip_prefix = "iron-flex-layout-1.3.0",
-      path = "/iron-flex-layout",
-      srcs = [
-          "classes/iron-flex-layout.html",
-          "classes/iron-shadow-flex-layout.html",
-          "iron-flex-layout.html",
-          "iron-flex-layout-classes.html",
-      ],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_form_element_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "1dd9371c638e5bc2ecba8a64074aa680dfb8712198e9612f9ed24d387efc8f26",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-form-element-behavior/archive/v1.0.6.tar.gz",
-          "https://github.com/PolymerElements/iron-form-element-behavior/archive/v1.0.6.tar.gz",
-      ],
-      strip_prefix = "iron-form-element-behavior-1.0.6",
-      path = "/iron-form-element-behavior",
-      srcs = ["iron-form-element-behavior.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_icon",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "9ed58a69159a02c07a6050d242e6d4e585a29f3245b8c8c390cfd52ddb786dc4",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-icon/archive/v1.0.11.tar.gz",
-          "https://github.com/PolymerElements/iron-icon/archive/v1.0.11.tar.gz",
-      ],
-      strip_prefix = "iron-icon-1.0.11",
-      path = "/iron-icon",
-      srcs = ["iron-icon.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_meta",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_icons",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "3b18542c147c7923dc3a36b1a51984a73255d610f297d43c9aaccc52859bd0d0",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-icons/archive/v1.1.3.tar.gz",
-          "https://github.com/PolymerElements/iron-icons/archive/v1.1.3.tar.gz",
-      ],
-      strip_prefix = "iron-icons-1.1.3",
-      path = "/iron-icons",
-      srcs = [
-          "av-icons.html",
-          "communication-icons.html",
-          "device-icons.html",
-          "editor-icons.html",
-          "hardware-icons.html",
-          "image-icons.html",
-          "iron-icons.html",
-          "maps-icons.html",
-          "notification-icons.html",
-          "places-icons.html",
-          "social-icons.html",
-      ],
-      deps = [
-          "@org_polymer_iron_icon",
-          "@org_polymer_iron_iconset_svg",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_iconset_svg",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "75cfb41e78f86ef6cb5d201ad12021785ef9e192b490ad46dcc15a9c19bdf71a",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-iconset-svg/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/iron-iconset-svg/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "iron-iconset-svg-2.0.0",
-      path = "/iron-iconset-svg",
-      srcs = ["iron-iconset-svg.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_meta",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_input",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "c505101ead08ab25526b1f49baecc8c28b4221b92a65e7334c783bdc81553c36",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-input/archive/1.0.10.tar.gz",
-          "https://github.com/PolymerElements/iron-input/archive/1.0.10.tar.gz",
-      ],
-      strip_prefix = "iron-input-1.0.10",
-      path = "/iron-input",
-      srcs = ["iron-input.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_announcer",
-          "@org_polymer_iron_validatable_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_list",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "72a6530b9f0ad5557f5d287845792a0ada74d8b159198e27f940e226313dc116",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-list/archive/v1.3.9.tar.gz",
-          "https://github.com/PolymerElements/iron-list/archive/v1.3.9.tar.gz",
-      ],
-      strip_prefix = "iron-list-1.3.9",
-      path = "/iron-list",
-      srcs = ["iron-list.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_resizable_behavior",
-          "@org_polymer_iron_scroll_target_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_menu_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "35d33d1ae55c6efaa0c3744ebe8a06cc0a8b2af9286dd8d36e20726a8540a11a",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-menu-behavior/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/iron-menu-behavior/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "iron-menu-behavior-2.0.0",
-      path = "/iron-menu-behavior",
-      srcs = [
-          "iron-menu-behavior.html",
-          "iron-menubar-behavior.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_selector",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_meta",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "fb05e6031bae6b4effe5f15d44b3f548d5807f9e3b3aa2442ba17cf4b8b84361",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-meta/archive/v1.1.1.tar.gz",
-          "https://github.com/PolymerElements/iron-meta/archive/v1.1.1.tar.gz",
-      ],
-      strip_prefix = "iron-meta-1.1.1",
-      path = "/iron-meta",
-      srcs = ["iron-meta.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_overlay_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "3df5b54ff2e0510c87a2aff8c9d730d3fe83d3d11277cc1a49fa29b549acb46c",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-overlay-behavior/archive/v1.10.1.tar.gz",
-          "https://github.com/PolymerElements/iron-overlay-behavior/archive/v1.10.1.tar.gz",
-      ],
-      strip_prefix = "iron-overlay-behavior-1.10.1",
-      path = "/iron-overlay-behavior",
-      srcs = [
-          "iron-focusables-helper.html",
-          "iron-overlay-backdrop.html",
-          "iron-overlay-behavior.html",
-          "iron-overlay-manager.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_fit_behavior",
-          "@org_polymer_iron_resizable_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_range_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "b2f2b6d52284542330bd30b586e217926eb0adec5e13934a3cef557717c22dc2",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-range-behavior/archive/v1.0.4.tar.gz",
-          "https://github.com/PolymerElements/iron-range-behavior/archive/v1.0.4.tar.gz",
-      ],
-      strip_prefix = "iron-range-behavior-1.0.4",
-      path = "/iron-range-behavior",
-      srcs = ["iron-range-behavior.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_resizable_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "a87a78ee9223c2f6afae7fc94a3ff91cbce6f7e2a7ed3f2979af7945c9281616",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-resizable-behavior/archive/v1.0.3.tar.gz",
-          "https://github.com/PolymerElements/iron-resizable-behavior/archive/v1.0.3.tar.gz",
-      ],
-      strip_prefix = "iron-resizable-behavior-1.0.3",
-      path = "/iron-resizable-behavior",
-      srcs = ["iron-resizable-behavior.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_scroll_target_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "7c6614c07d354375666ee96eea9f6d485dbf7898146a444ec26094a70d4e0afa",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-scroll-target-behavior/archive/v1.1.1.tar.gz",
-          "https://github.com/PolymerElements/iron-scroll-target-behavior/archive/v1.1.1.tar.gz",
-      ],
-      strip_prefix = "iron-scroll-target-behavior-1.1.1",
-      path = "/iron-scroll-target-behavior",
-      srcs = ["iron-scroll-target-behavior.html"],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_selector",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "ba28a47443bad3b744611c9d7a79fb21dbdf2e35edc5ef8f812e2dcd72b16747",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-selector/archive/v1.5.2.tar.gz",
-          "https://github.com/PolymerElements/iron-selector/archive/v1.5.2.tar.gz",
-      ],
-      strip_prefix = "iron-selector-1.5.2",
-      path = "/iron-selector",
-      srcs = [
-          "iron-multi-selectable.html",
-          "iron-selectable.html",
-          "iron-selection.html",
-          "iron-selector.html",
-      ],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_iron_validatable_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "aef4901e68043824f36104799269573dd345ffaac494186e466fdc79c06fdb63",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/iron-validatable-behavior/archive/v1.1.1.tar.gz",
-          "https://github.com/PolymerElements/iron-validatable-behavior/archive/v1.1.1.tar.gz",
-      ],
-      strip_prefix = "iron-validatable-behavior-1.1.1",
-      path = "/iron-validatable-behavior",
-      srcs = ["iron-validatable-behavior.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_meta",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_marked",
-      licenses = ["notice"],  # MIT
-      sha256 = "93d30bd593736ca440938d77808b7ef5972da0f3fcfe4ae63ae7b4ce117da2cb",
-      urls = [
-          "http://mirror.bazel.build/github.com/chjj/marked/archive/v0.3.2.zip",
-          "https://github.com/chjj/marked/archive/v0.3.2.zip",
-      ],
-      strip_prefix = "marked-0.3.2",
-      path = "/marked",
-      srcs = ["lib/marked.js"],
-  )
-
-  web_library_external(
-      name = "org_polymer_marked_element",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "2ac1f7fae0c1b656e671b2772492809714d836f27c9efa7f2c5fe077ee760f3c",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/marked-element/archive/v2.1.0.tar.gz",
-          "https://github.com/PolymerElements/marked-element/archive/v2.1.0.tar.gz",
-      ],
-      strip_prefix = "marked-element-2.1.0",
-      path = "/marked-element",
-      srcs = [
-          "marked-element.html",
-          "marked-import.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_marked",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_neon_animation",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "8800c314a76b2da190a2b203259c1091f6d38e0057ed37c2a3d0b734980fa9a5",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/neon-animation/archive/v1.2.2.tar.gz",
-          "https://github.com/PolymerElements/neon-animation/archive/v1.2.2.tar.gz",
-      ],
-      strip_prefix = "neon-animation-1.2.2",
-      path = "/neon-animation",
-      srcs = [
-          "animations/cascaded-animation.html",
-          "animations/fade-in-animation.html",
-          "animations/fade-out-animation.html",
-          "animations/hero-animation.html",
-          "animations/opaque-animation.html",
-          "animations/reverse-ripple-animation.html",
-          "animations/ripple-animation.html",
-          "animations/scale-down-animation.html",
-          "animations/scale-up-animation.html",
-          "animations/slide-down-animation.html",
-          "animations/slide-from-bottom-animation.html",
-          "animations/slide-from-left-animation.html",
-          "animations/slide-from-right-animation.html",
-          "animations/slide-from-top-animation.html",
-          "animations/slide-left-animation.html",
-          "animations/slide-right-animation.html",
-          "animations/slide-up-animation.html",
-          "animations/transform-animation.html",
-          "neon-animatable.html",
-          "neon-animatable-behavior.html",
-          "neon-animated-pages.html",
-          "neon-animation.html",
-          "neon-animation-behavior.html",
-          "neon-animation-runner-behavior.html",
-          "neon-animations.html",
-          "neon-shared-element-animatable-behavior.html",
-          "neon-shared-element-animation-behavior.html",
-          "web-animations.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_meta",
-          "@org_polymer_iron_resizable_behavior",
-          "@org_polymer_iron_selector",
-          "@org_polymer_web_animations_js",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_behaviors",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "7cfcb9082ef9909da262df6b5c120bc62dbeaff278cb563e8fc60465ddd387e5",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-behaviors/archive/v1.0.12.tar.gz",
-          "https://github.com/PolymerElements/paper-behaviors/archive/v1.0.12.tar.gz",
-      ],
-      strip_prefix = "paper-behaviors-1.0.12",
-      path = "/paper-behaviors",
-      srcs = [
-          "paper-button-behavior.html",
-          "paper-checked-element-behavior.html",
-          "paper-inky-focus-behavior.html",
-          "paper-ripple-behavior.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_checked_element_behavior",
-          "@org_polymer_paper_ripple",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_button",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "896c0a7e34bfcce63fc23c63e105ed9c4d62fa3a6385b7161e1e5cd4058820a6",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-button/archive/v1.0.11.tar.gz",
-          "https://github.com/PolymerElements/paper-button/archive/v1.0.11.tar.gz",
-      ],
-      strip_prefix = "paper-button-1.0.11",
-      path = "/paper-button",
-      srcs = ["paper-button.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_material",
-          "@org_polymer_paper_ripple",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_checkbox",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "0a291d0c64de1b6b807d66697bead9c66c0d7bc3c68b8037e6667f3d66a5904c",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-checkbox/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/paper-checkbox/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "paper-checkbox-2.0.0",
-      path = "/paper-checkbox",
-      srcs = ["paper-checkbox.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_dialog",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "cf60d3aa6ad57ba4eb8b1c16713e65057735eed94b009aeebdbcf3436c95a161",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-dialog/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/paper-dialog/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "paper-dialog-2.0.0",
-      path = "/paper-dialog",
-      srcs = ["paper-dialog.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_neon_animation",
-          "@org_polymer_paper_dialog_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_dialog_behavior",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "d78e4f7d008c22537a9255ccda1e919fddae5cc125ef26a66eb2c47f648c20ab",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-dialog-behavior/archive/v1.2.7.tar.gz",
-          "https://github.com/PolymerElements/paper-dialog-behavior/archive/v1.2.7.tar.gz",
-      ],
-      strip_prefix = "paper-dialog-behavior-1.2.7",
-      path = "/paper-dialog-behavior",
-      srcs = [
-          "paper-dialog-behavior.html",
-          "paper-dialog-common.css",
-          "paper-dialog-shared-styles.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_overlay_behavior",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_dialog_scrollable",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "a2e69283e7674f782c44d811387a0f8da2d01fac0172743d1add65e253e6b5ff",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-dialog-scrollable/archive/1.1.5.tar.gz",
-          "https://github.com/PolymerElements/paper-dialog-scrollable/archive/1.1.5.tar.gz",
-      ],
-      strip_prefix = "paper-dialog-scrollable-1.1.5",
-      path = "/paper-dialog-scrollable",
-      srcs = ["paper-dialog-scrollable.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_paper_dialog_behavior",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_dropdown_menu",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "9d88f654ec03ee9be211df9e69bede9e8a22b51bf1dbcc63b79762e4256d81ad",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-dropdown-menu/archive/v1.4.0.tar.gz",
-          "https://github.com/PolymerElements/paper-dropdown-menu/archive/v1.4.0.tar.gz",
-      ],
-      strip_prefix = "paper-dropdown-menu-1.4.0",
-      path = "/paper-dropdown-menu",
-      srcs = [
-          "paper-dropdown-menu.html",
-          "paper-dropdown-menu-icons.html",
-          "paper-dropdown-menu-light.html",
-          "paper-dropdown-menu-shared-styles.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_form_element_behavior",
-          "@org_polymer_iron_icon",
-          "@org_polymer_iron_iconset_svg",
-          "@org_polymer_iron_validatable_behavior",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_input",
-          "@org_polymer_paper_menu_button",
-          "@org_polymer_paper_ripple",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_header_panel",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "0db4bd8a4bf6f20dcd0dffb4f907b31c93a8647c9c021344239cf30b40b87075",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-header-panel/archive/v1.1.4.tar.gz",
-          "https://github.com/PolymerElements/paper-header-panel/archive/v1.1.4.tar.gz",
-      ],
-      strip_prefix = "paper-header-panel-1.1.4",
-      path = "/paper-header-panel",
-      srcs = ["paper-header-panel.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_icon_button",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "9cba5bcfd6aeb4c41581c1392c678cf2278d360e9d122f4d9db54a9ebb404496",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-icon-button/archive/v1.1.3.tar.gz",
-          "https://github.com/PolymerElements/paper-icon-button/archive/v1.1.3.tar.gz",
-      ],
-      strip_prefix = "paper-icon-button-1.1.3",
-      path = "/paper-icon-button",
-      srcs = [
-          "paper-icon-button.html",
-          "paper-icon-button-light.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_icon",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_input",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "17c3dea9bb1c2026cc61324696c6c774214a0dc37686b91ca214a6af550994db",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-input/archive/v1.1.18.tar.gz",
-          "https://github.com/PolymerElements/paper-input/archive/v1.1.18.tar.gz",
-      ],
-      strip_prefix = "paper-input-1.1.18",
-      path = "/paper-input",
-      srcs = [
-          "paper-input.html",
-          "paper-input-addon-behavior.html",
-          "paper-input-behavior.html",
-          "paper-input-char-counter.html",
-          "paper-input-container.html",
-          "paper-input-error.html",
-          "paper-textarea.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_autogrow_textarea",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_form_element_behavior",
-          "@org_polymer_iron_input",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_item",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "12ee0dcb61b0d5721c5988571f6974d7b2211e97724f4195893fbcc9058cdac8",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-item/archive/v1.1.4.tar.gz",
-          "https://github.com/PolymerElements/paper-item/archive/v1.1.4.tar.gz",
-      ],
-      strip_prefix = "paper-item-1.1.4",
-      path = "/paper-item",
-      srcs = [
-          "paper-icon-item.html",
-          "paper-item.html",
-          "paper-item-behavior.html",
-          "paper-item-body.html",
-          "paper-item-shared-styles.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_listbox",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "674992d882b18a0618fa697180f196dbc052fb2f5d9ce4e19026a918b568ffd6",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-listbox/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/paper-listbox/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "paper-listbox-2.0.0",
-      path = "/paper-listbox",
-      srcs = ["paper-listbox.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_menu_behavior",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_material",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "913e9c63cf5c8286b0fab817079d7dc900a343d2c05809995d8d9ba0e41f8a29",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-material/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/paper-material/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "paper-material-2.0.0",
-      path = "/paper-material",
-      srcs = [
-          "paper-material.html",
-          "paper-material-shared-styles.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_menu",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "a3cee220926e315f7412236b3628288774694447c0da4428345f36d0f127ba3b",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-menu/archive/v1.2.2.tar.gz",
-          "https://github.com/PolymerElements/paper-menu/archive/v1.2.2.tar.gz",
-      ],
-      strip_prefix = "paper-menu-1.2.2",
-      path = "/paper-menu",
-      srcs = [
-          "paper-menu.html",
-          "paper-menu-shared-styles.html",
-          "paper-submenu.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_collapse",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_menu_behavior",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_menu_button",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "be3290c288a2bd4f9887213db22c75add99cc29ff4d088100c0bc4eb0e57997b",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-menu-button/archive/v1.5.1.tar.gz",
-          "https://github.com/PolymerElements/paper-menu-button/archive/v1.5.1.tar.gz",
-      ],
-      strip_prefix = "paper-menu-button-1.5.1",
-      path = "/paper-menu-button",
-      srcs = [
-          "paper-menu-button.html",
-          "paper-menu-button-animations.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_dropdown",
-          "@org_polymer_neon_animation",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_progress",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "2b6776b2f023c1f344feea17ba29b58d879e46f8ed43b7256495054b5183fff6",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-progress/archive/v1.0.9.tar.gz",
-          "https://github.com/PolymerElements/paper-progress/archive/v1.0.9.tar.gz",
-      ],
-      strip_prefix = "paper-progress-1.0.9",
-      path = "/paper-progress",
-      srcs = ["paper-progress.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_range_behavior",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_radio_button",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "6e911d0c308aa388136b3af79d1bdcbe5a1f4159cbc79d71efb4ff3b6c0b4e91",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-radio-button/archive/v1.1.2.tar.gz",
-          "https://github.com/PolymerElements/paper-radio-button/archive/v1.1.2.tar.gz",
-      ],
-      strip_prefix = "paper-radio-button-1.1.2",
-      path = "/paper-radio-button",
-      srcs = ["paper-radio-button.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_radio_group",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "7885ad1f81e9dcc03dcea4139b54a201ff55c18543770cd44f94530046c9e163",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-radio-group/archive/v1.0.9.tar.gz",
-          "https://github.com/PolymerElements/paper-radio-group/archive/v1.0.9.tar.gz",
-      ],
-      strip_prefix = "paper-radio-group-1.0.9",
-      path = "/paper-radio-group",
-      srcs = ["paper-radio-group.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_selector",
-          "@org_polymer_paper_radio_button",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_ripple",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "ba76bfb1c737260a8a103d3ca97faa1f7c3288c7db9b2519f401b7a782147c09",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-ripple/archive/v1.0.5.tar.gz",
-          "https://github.com/PolymerElements/paper-ripple/archive/v1.0.5.tar.gz",
-      ],
-      strip_prefix = "paper-ripple-1.0.5",
-      path = "/paper-ripple",
-      srcs = ["paper-ripple.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_slider",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "8bb4db532e8b11b11f78006d9aefa217841392c1aeb449ee2a12e3b56748b774",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-slider/archive/v1.0.14.tar.gz",
-          "https://github.com/PolymerElements/paper-slider/archive/v1.0.14.tar.gz",
-      ],
-      strip_prefix = "paper-slider-1.0.14",
-      path = "/paper-slider",
-      srcs = ["paper-slider.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_keys_behavior",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_form_element_behavior",
-          "@org_polymer_iron_range_behavior",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_input",
-          "@org_polymer_paper_progress",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_spinner",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "6a752907fab7899cbeed15b478e7b9299047c15fbf9d1561d6eb4d204bdbd178",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-spinner/archive/v1.1.1.tar.gz",
-          "https://github.com/PolymerElements/paper-spinner/archive/v1.1.1.tar.gz",
-      ],
-      strip_prefix = "paper-spinner-1.1.1",
-      path = "/paper-spinner",
-      srcs = [
-          "paper-spinner.html", "paper-spinner-behavior.html",
-          "paper-spinner-lite.html", "paper-spinner-styles.html"
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_styles",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "6d26b0a4c286402098853dc7388f6b22f30dfb7a74e47b34992ac03380144bb2",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-styles/archive/v1.1.4.tar.gz",
-          "https://github.com/PolymerElements/paper-styles/archive/v1.1.4.tar.gz",
-      ],
-      strip_prefix = "paper-styles-1.1.4",
-      path = "/paper-styles",
-      srcs = [
-          "classes/global.html",
-          "classes/shadow.html",
-          "classes/shadow-layout.html",
-          "classes/typography.html",
-          "color.html",
-          "default-theme.html",
-          "demo.css",
-          "demo-pages.html",
-          "paper-styles.html",
-          "paper-styles-classes.html",
-          "shadow.html",
-          "typography.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_font_roboto",
-          "@org_polymer_iron_flex_layout",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_tabs",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "c23b6a5221db35e5b1ed3eb8e8696b952572563e285adaec96aba1e3134db825",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-tabs/archive/v1.7.0.tar.gz",
-          "https://github.com/PolymerElements/paper-tabs/archive/v1.7.0.tar.gz",
-      ],
-      strip_prefix = "paper-tabs-1.7.0",
-      path = "/paper-tabs",
-      srcs = [
-          "paper-tab.html",
-          "paper-tabs.html",
-          "paper-tabs-icons.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_behaviors",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_iron_icon",
-          "@org_polymer_iron_iconset_svg",
-          "@org_polymer_iron_menu_behavior",
-          "@org_polymer_iron_resizable_behavior",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_icon_button",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_toast",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "b1c677e1681ef8d3f688a83da8f7b263902f757f395a9354a1c35f93b9125b60",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-toast/archive/v2.0.0.tar.gz",
-          "https://github.com/PolymerElements/paper-toast/archive/v2.0.0.tar.gz",
-      ],
-      strip_prefix = "paper-toast-2.0.0",
-      path = "/paper-toast",
-      srcs = ["paper-toast.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_a11y_announcer",
-          "@org_polymer_iron_overlay_behavior",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_toggle_button",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "4aa7cf0396fa2994a8bc2ac6e8428f48b07b945bb7c41bd52041ef5827b45de3",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-toggle-button/archive/v1.2.0.tar.gz",
-          "https://github.com/PolymerElements/paper-toggle-button/archive/v1.2.0.tar.gz",
-      ],
-      strip_prefix = "paper-toggle-button-1.2.0",
-      path = "/paper-toggle-button",
-      srcs = ["paper-toggle-button.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_paper_behaviors",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_toolbar",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "dbddffc0654d9fb5fb48843087eebe16bf7a134902495a664c96c11bf8a2c63d",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-toolbar/archive/v1.1.4.tar.gz",
-          "https://github.com/PolymerElements/paper-toolbar/archive/v1.1.4.tar.gz",
-      ],
-      strip_prefix = "paper-toolbar-1.1.4",
-      path = "/paper-toolbar",
-      srcs = ["paper-toolbar.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_iron_flex_layout",
-          "@org_polymer_paper_styles",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_paper_tooltip",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "4c6667acf01f73da14c3cbc0aa574bf14280304567987ee0314534328377d2ad",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/paper-tooltip/archive/v1.1.2.tar.gz",
-          "https://github.com/PolymerElements/paper-tooltip/archive/v1.1.2.tar.gz",
-      ],
-      strip_prefix = "paper-tooltip-1.1.2",
-      path = "/paper-tooltip",
-      srcs = ["paper-tooltip.html"],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_neon_animation",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_prism",
-      licenses = ["notice"],  # MIT
-      sha256 = "e06eb54f2a80e6b3cd0bd4d59f900423bcaee53fc03998a056df63740c684683",
-      urls = [
-          "http://mirror.bazel.build/github.com/PrismJS/prism/archive/abee2b7587f1925e57777044270e2a1860810994.tar.gz",
-          "https://github.com/PrismJS/prism/archive/abee2b7587f1925e57777044270e2a1860810994.tar.gz",
-      ],
-      strip_prefix = "prism-abee2b7587f1925e57777044270e2a1860810994",
-      path = "/prism",
-      srcs = [
-          "prism.js",
-          "themes/prism.css",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_prism_element",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "c5c03c17520d4992a3576e397aa7a375e64c7f6794ec2af58031f47eef458945",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerElements/prism-element/archive/v1.2.0.tar.gz",
-          "https://github.com/PolymerElements/prism-element/archive/v1.2.0.tar.gz",
-      ],
-      strip_prefix = "prism-element-1.2.0",
-      path = "/prism-element",
-      srcs = [
-          "prism-highlighter.html",
-          "prism-import.html",
-          "prism-theme-default.html",
-      ],
-      deps = [
-          "@org_polymer",
-          "@org_polymer_prism",
-      ],
-  )
-
-  web_library_external(
-      name = "org_polymer_promise_polyfill",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "4495450e5d884c3e16b537b43afead7f84d17c7dc061bcfcbf440eac083e4ef5",
-      strip_prefix = "promise-polyfill-1.0.0",
-      urls = [
-          "http://mirror.bazel.build/github.com/PolymerLabs/promise-polyfill/archive/v1.0.0.tar.gz",
-          "https://github.com/PolymerLabs/promise-polyfill/archive/v1.0.0.tar.gz",
-      ],
-      path = "/promise-polyfill",
-      srcs = [
-          "Promise.js",
-          "Promise-Statics.js",
-          "promise-polyfill.html",
-          "promise-polyfill-lite.html"
-      ],
-      deps = ["@org_polymer"],
-  )
-
-  web_library_external(
-      name = "org_polymer_web_animations_js",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "f8bd760cbdeba131f6790bd5abe170bcbf7b1755ff58ed16d0b82fa8a7f34a7f",
-      urls = [
-          "http://mirror.bazel.build/github.com/web-animations/web-animations-js/archive/2.2.1.tar.gz",
-          "https://github.com/web-animations/web-animations-js/archive/2.2.1.tar.gz",
-      ],
-      strip_prefix = "web-animations-js-2.2.1",
-      path = "/web-animations-js",
-      srcs = ["web-animations-next-lite.min.js"],
-  )
-
-  web_library_external(
-      name = "org_polymer_webcomponentsjs",
-      licenses = ["notice"],  # BSD-3-Clause
-      sha256 = "1f58decac693deb926e6b62b5dbd459fb7c2e961f7241e6e646d1cd9a60281d2",
-      urls = [
-          "http://mirror.bazel.build/github.com/webcomponents/webcomponentsjs/archive/v0.7.23.tar.gz",
-          "https://github.com/webcomponents/webcomponentsjs/archive/v0.7.23.tar.gz",
-      ],
-      strip_prefix = "webcomponentsjs-0.7.23",
-      path = "/webcomponentsjs",
-      srcs = [
-          "CustomElements.js",
-          "CustomElements.min.js",
-          "HTMLImports.js",
-          "HTMLImports.min.js",
-          "MutationObserver.js",
-          "MutationObserver.min.js",
-          "ShadowDOM.js",
-          "ShadowDOM.min.js",
-          "webcomponents.js",
-          "webcomponents.min.js",
-          "webcomponents-lite.js",
-          "webcomponents-lite.min.js",
-      ],
-  )
diff --git a/third_party/python.bzl b/third_party/python.bzl
deleted file mode 100644
index 25c2ae3e78..0000000000
--- a/third_party/python.bzl
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TensorBoard external dependencies that are used on the python side.
-# Protobuf and six were deliberately left in the top-level workspace, as they
-# are used in TensorFlow as well.
-
-def tensorboard_python_workspace():
-  native.new_http_archive(
-      name = "org_pythonhosted_markdown",
-      urls = [
-          "http://mirror.bazel.build/pypi.python.org/packages/1d/25/3f6d2cb31ec42ca5bd3bfbea99b63892b735d76e26f20dd2dcc34ffe4f0d/Markdown-2.6.8.tar.gz",
-          "https://pypi.python.org/packages/1d/25/3f6d2cb31ec42ca5bd3bfbea99b63892b735d76e26f20dd2dcc34ffe4f0d/Markdown-2.6.8.tar.gz",
-      ],
-      strip_prefix = "Markdown-2.6.8",
-      sha256 = "0ac8a81e658167da95d063a9279c9c1b2699f37c7c4153256a458b3a43860e33",
-      build_file = str(Label("//third_party:markdown.BUILD")),
-  )
-
-  native.new_http_archive(
-      name = "org_html5lib",
-      urls = [
-          "http://mirror.bazel.build/github.com/html5lib/html5lib-python/archive/0.9999999.tar.gz",
-          "https://github.com/html5lib/html5lib-python/archive/0.9999999.tar.gz",  # identical to 1.0b8
-      ],
-      sha256 = "184257f98539159a433e2a2197309657ae1283b4c44dbd9c87b2f02ff36adce8",
-      strip_prefix = "html5lib-python-0.9999999",
-      build_file = str(Label("//third_party:html5lib.BUILD")),
-  )
-
-  native.new_http_archive(
-      name = "org_mozilla_bleach",
-      urls = [
-          "http://mirror.bazel.build/github.com/mozilla/bleach/archive/v1.5.tar.gz",
-          "https://github.com/mozilla/bleach/archive/v1.5.tar.gz",
-      ],
-      strip_prefix = "bleach-1.5",
-      sha256 = "0d68713d02ba4148c417ab1637dd819333d96929a34401d0233947bec0881ad8",
-      build_file = str(Label("//third_party:bleach.BUILD")),
-  )
-  
-  native.new_http_archive(
-      name = "org_pocoo_werkzeug",
-      urls = [
-          "http://mirror.bazel.build/pypi.python.org/packages/b7/7f/44d3cfe5a12ba002b253f6985a4477edfa66da53787a2a838a40f6415263/Werkzeug-0.11.10.tar.gz",
-          "https://pypi.python.org/packages/b7/7f/44d3cfe5a12ba002b253f6985a4477edfa66da53787a2a838a40f6415263/Werkzeug-0.11.10.tar.gz",
-      ],
-      strip_prefix = "Werkzeug-0.11.10",
-      sha256 = "cc64dafbacc716cdd42503cf6c44cb5a35576443d82f29f6829e5c49264aeeee",
-      build_file = str(Label("//third_party:werkzeug.BUILD")),
-  )
\ No newline at end of file
diff --git a/third_party/typings.bzl b/third_party/typings.bzl
deleted file mode 100644
index e3931fa68a..0000000000
--- a/third_party/typings.bzl
+++ /dev/null
@@ -1,361 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TensorBoard typing dependencies
-
-load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external")
-
-def tensorboard_typings_workspace():
-  filegroup_external(
-      name = "org_definitelytyped",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "b7da645f6e5555feb7aeede73775da0023ce2257df9c8e76c9159266035a9c0d": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/ebc69904eb78f94030d5d517b42db20867f679c0/chai/chai.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/ebc69904eb78f94030d5d517b42db20867f679c0/chai/chai.d.ts",
-          ],
-          "e4cd3d5de0eb3bc7b1063b50d336764a0ac82a658b39b5cf90511f489ffdee60": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/efd40e67ff323f7147651bdbef03c03ead7b1675/lodash/lodash.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/efd40e67ff323f7147651bdbef03c03ead7b1675/lodash/lodash.d.ts",
-          ],
-          "695a03dd2ccb238161d97160b239ab841562710e5c4e42886aefd4ace2ce152e": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/ebc69904eb78f94030d5d517b42db20867f679c0/mocha/mocha.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/ebc69904eb78f94030d5d517b42db20867f679c0/mocha/mocha.d.ts",
-          ],
-          "513ccd9ee1c708881120eeacd56788fc3b3da8e5c6172b20324cebbe858803fe": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/708609e0764daeb5eb64104af7aca50c520c4e6e/sinon/sinon.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/708609e0764daeb5eb64104af7aca50c520c4e6e/sinon/sinon.d.ts",
-          ],
-          "44eba36339bd1c0792072b7b204ee926fe5ffe1e9e2da916e67ac55548e3668a": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/a872802c0c84ba98ff207d5e673a1fa867c67fd6/polymer/polymer.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/a872802c0c84ba98ff207d5e673a1fa867c67fd6/polymer/polymer.d.ts",  # 2016-09-22
-          ],
-          "7ce67447146eb2b9e9cdaaf8bf45b3209865378022cc8acf86616d3be84f6481": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/8cb9ee3fdfe352cfef672bdfdb5f9c428f915e9f/threejs/three.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/8cb9ee3fdfe352cfef672bdfdb5f9c428f915e9f/threejs/three.d.ts",  # r74 @ 2016-04-06
-          ],
-          "691756a6eb455f340c9e834de0d49fff269e7b8c1799c2454465dcd6a4435b80": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/46719185c564694c5583c4b7ad94dbb786ecad46/webcomponents.js/webcomponents.js.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/46719185c564694c5583c4b7ad94dbb786ecad46/webcomponents.js/webcomponents.js.d.ts",
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_array",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "61e7abb7b1f01fbcb0cab8cf39003392f422566209edd681fbd070eaa84ca000": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-array/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-array/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_axis",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "95f75c8dcc89850b2e72581d96a7b5f46ea4ac852f828893f141f14a597421f9": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-axis/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-axis/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_brush",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "a2738e693ce8a8640c2d29001e77582c9c361fd23bda44db471629866b60ada7": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-brush/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-brush/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_chord",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "c54d24756eb6d744b31e538ad9bab3a75f6d54e2288b29cc72338d4a057d3e83": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-chord/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-chord/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_collection",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "39e8599a768f45f80aa70ca3032f026111da50d409c7e39a2ef091667cc343d9": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-collection/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-collection/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_color",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "6dd19edd11276476c5d535279237d1a009c1a733611cc44621a88fda1ca04377": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-color/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-color/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_dispatch",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "af1474301e594fcb4bbdb134361fb6d26c7b333386c3213821532acde59e61a3": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dispatch/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dispatch/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_drag",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "2f8248ae2bf33fb1d61bb1ea4271cb4bacfd9a9939dc8d7bde7ec8b66d4441ed": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-drag/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-drag/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_dsv",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "62594d00cf9e4bb895339c8e56f64330e202a5eb2a0fa580a1f6e6336f2c93ce": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dsv/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-dsv/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_ease",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "d5a9be5316b2d1823a3faa7f75de1e2c2efda5c75f0631b44a0f7b69e11f3a90": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-ease/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-ease/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_force",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "288421e2008668d2076a4684657dd3d29b992832ef02c552981eb94a91042553": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-force/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-force/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_format",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "b42cb17e580c1fd0b64d478f7bd80ca806efaefda24426a833cf1f30a7275bca": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-format/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-format/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_hierarchy",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "a5683f5835d8716c6b89c075235078438cfab5897023ed720bfa492e244e969e": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-hierarchy/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-hierarchy/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_interpolate",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "effeefea9ac02539def43d7b9aa2f39e8672c03aac9b407a61b09563ff141fad": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-interpolate/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-interpolate/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_path",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "deea4ab3654925d365dd1ffab69a2140808c6173e7f23c461ded2852c309eb9c": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-path/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-path/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_polygon",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "ec7a42affe79c87066f14173fcbc8d8b5747f54bfbe0e60111e2786ee4d227bf": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-polygon/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-polygon/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_quadtree",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "2908631a7da3bfb0096e3b89f464b45390bbb31ec798d1b6c0898ff82e344560": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-quadtree/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-quadtree/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_queue",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "4fc0503e3558d136b855335f36ea8984937ab63a2a28b8c7b293d35825388615": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-queue/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-queue/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_random",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "5130e803ba26d2dc931ddd0fa574b5abbb0fc4486e7975f97a83c01630763676": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-random/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-random/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_request",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "fc2b7c2c05498011eb039825aab76a7916698fb3e7133e278fc92ae529ae99f0": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-request/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-request/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_scale",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "ff3e2d2033a37d698c3bd2896ffd9dd4ceab1903d96aa90d388a6a2d14d8ee05": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-scale/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-scale/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_selection",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "47fae7c4bc425101490daae067727b74ee09e6c830331a4cf333cdb532a5d108": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-selection/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-selection/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_shape",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "7fec580ba54bc29417dc9030bb3731c9756a65c5e57dcce5a4f183fff7180cd8": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-shape/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-shape/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_time",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "4b68f2a4ee428f21f2e7d706c0a64f628f0ff5f130cd9f023ab23a04a8fe31de": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-time/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-time/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_timer",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "a196f42560be9fa1a77d473c0180f9f2f8d570ed0eee616aad0da94d90ef3661": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-timer/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-timer/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_transition",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "10c6cf259d6f965014e75a63925f302911c5afb8581d6d63b0597544fe104bd7": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-transition/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-transition/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_voronoi",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "411482515e2ccda4659f7b3d2fbd3a7ef5ea2c7053eec62c95a174b68ad60c3d": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-voronoi/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-voronoi/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
-
-  filegroup_external(
-      name = "org_definitelytyped_types_d3_zoom",
-      licenses = ["notice"],  # MIT
-      sha256_urls = {
-          "df0bedbb7711366a43418d6a3b47c4688ccb02a3d8ad0c2468cafcb6c2faa346": [
-              "http://mirror.bazel.build/raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-zoom/index.d.ts",
-              "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/dc27c3788c00d279ae5ff61e8e2dfd568aae5e8e/types/d3-zoom/index.d.ts",  # 2017-06-08
-          ],
-      },
-  )
diff --git a/third_party/werkzeug.BUILD b/third_party/werkzeug.BUILD
deleted file mode 100644
index 72a1402030..0000000000
--- a/third_party/werkzeug.BUILD
+++ /dev/null
@@ -1,14 +0,0 @@
-# Description:
-#   Werkzeug provides utilities for making WSGI applications
-
-licenses(["notice"])  # BSD 3-Clause
-
-exports_files(["LICENSE"])
-
-# Note: this library includes test code. Consider creating a testonly target.
-py_library(
-    name = "werkzeug",
-    srcs = glob(["werkzeug/*.py"]),
-    srcs_version = "PY2AND3",
-    visibility = ["//visibility:public"],
-)
-- 
GitLab


From 56b36d88fce295c151458f42a120fcdcac7a3ca2 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 15 Jun 2017 19:04:59 -0700
Subject: [PATCH 0131/1375] [TF:XLA] Add no-op implementation of
 TensorArrayCloseV3 to the XLA bridge.

PiperOrigin-RevId: 159185414
---
 .../compiler/tests/tensor_array_ops_test.py   | 34 +++++++++----------
 .../tf2xla/kernels/tensor_array_ops.cc        | 14 ++++++++
 2 files changed, 30 insertions(+), 18 deletions(-)

diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index 27a2977305..00a7358130 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -573,13 +573,12 @@ class TensorArrayTest(xla_test.XLATestCase):
                            [2000.0, -2000.0]],
                           grad_vals[0])
 
-  # TODO(phawkins): implement TensorArrayClose
-  # def testCloseTensorArray(self):
-  #   with self.test_session() as session, self.test_scope():
-  #     ta = tensor_array_ops.TensorArray(
-  #         dtype=dtypes.float32, tensor_array_name="foo", size=3)
-  #     c1 = ta.close()
-  #     session.run(c1)
+  def testCloseTensorArray(self):
+    with self.test_session() as session, self.test_scope():
+      ta = tensor_array_ops.TensorArray(
+          dtype=dtypes.float32, tensor_array_name="foo", size=3)
+      c1 = ta.close()
+      session.run(c1)
 
   def testSizeTensorArray(self):
     with self.test_session(), self.test_scope():
@@ -588,17 +587,16 @@ class TensorArrayTest(xla_test.XLATestCase):
       s = ta.size()
       self.assertAllEqual(3, s.eval())
 
-  # TODO(phawkins): implement TensorArrayClose
-  # def testWriteCloseTensorArray(self):
-  #   with self.test_session(), self.test_scope():
-  #     ta = tensor_array_ops.TensorArray(
-  #         dtype=dtypes.float32,
-  #         tensor_array_name="foo",
-  #         size=3,
-  #         infer_shape=False)
-  #     w0 = ta.write(0, [[4.0, 5.0]])
-  #     w1 = w0.write(1, [3.0])
-  #     w1.close().run()  # Expected to run without problems
+  def testWriteCloseTensorArray(self):
+    with self.test_session(), self.test_scope():
+      ta = tensor_array_ops.TensorArray(
+          dtype=dtypes.float32,
+          tensor_array_name="foo",
+          size=3,
+          infer_shape=False)
+      w0 = ta.write(0, [[4.0, 5.0]])
+      w1 = w0.write(1, [3.0])
+      w1.close().run()  # Expected to run without problems
 
   # TODO(phawkins): implement while loops.
   # def _testWhileLoopWritePackGradients(self, dynamic_size, dtype):
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index de542d55e8..c7510bf3d2 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -534,5 +534,19 @@ class TensorArrayGradOp : public XlaOpKernel {
 
 REGISTER_XLA_OP(Name("TensorArrayGradV3"), TensorArrayGradOp);
 
+class TensorArrayCloseOp : public XlaOpKernel {
+ public:
+  explicit TensorArrayCloseOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    // Do nothing; XLA handles resource management.
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(TensorArrayCloseOp);
+};
+
+REGISTER_XLA_OP(Name("TensorArrayCloseV3"), TensorArrayCloseOp);
+
 }  // anonymous namespace
 }  // namespace tensorflow
-- 
GitLab


From dbde3987ca1f8451cb58dee1b23aee20e60a702a Mon Sep 17 00:00:00 2001
From: Taehoon Lee <taehoonlee@snu.ac.kr>
Date: Fri, 16 Jun 2017 11:31:49 +0900
Subject: [PATCH 0132/1375] Fix typos

---
 tensorflow/compiler/xla/service/compile_only_service.h      | 2 +-
 .../python/ops/vector_laplace_linear_operator.py            | 2 +-
 tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py  | 2 +-
 tensorflow/core/framework/graph_def_util.h                  | 2 +-
 tensorflow/core/grappler/optimizers/constant_folding.h      | 2 +-
 tensorflow/core/grappler/optimizers/layout_optimizer.cc     | 2 +-
 tensorflow/python/ops/lookup_ops.py                         | 6 +++---
 7 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h
index b19f4bd592..3358305c03 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.h
+++ b/tensorflow/compiler/xla/service/compile_only_service.h
@@ -55,7 +55,7 @@ class CompileOnlyService : public Service {
 
   // Override Service methods that require or imply the existence of an
   // execute backend.  Note that this does not include TransferToClient, as
-  // computing contants produces global data that we may wish to transfer.
+  // computing constants produces global data that we may wish to transfer.
   tensorflow::Status Execute(const ExecuteRequest* arg,
                              ExecuteResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
index fd2c46d94d..fdee57695e 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Vectorized Laplace distribution class, directly using LinearOpeartor."""
+"""Vectorized Laplace distribution class, directly using LinearOperator."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index b659988a56..fbbfb25aea 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -344,7 +344,7 @@ class LuongAttention(_BaseAttentionMechanism):
       #   [batch_size, 1, depth] . [batch_size, depth, max_time]
       # resulting in an output shape of:
       #   [batch_time, 1, max_time].
-      # we then squeee out the center singleton dimension.
+      # we then squeeze out the center singleton dimension.
       score = math_ops.matmul(query, self.keys, transpose_b=True)
       score = array_ops.squeeze(score, [1])
 
diff --git a/tensorflow/core/framework/graph_def_util.h b/tensorflow/core/framework/graph_def_util.h
index 56355eaf36..950737c39a 100644
--- a/tensorflow/core/framework/graph_def_util.h
+++ b/tensorflow/core/framework/graph_def_util.h
@@ -62,7 +62,7 @@ Status AddDefaultAttrsToGraphDef(GraphDef* graph_def,
 // attr with a default was added). Note that this will not affect
 // attrs with non-default values, so you must run a
 // ValidateGraphDef...() function to see if the result is in fact
-// compatible. If not nulllptr, the op/attr pairs that were removed
+// compatible. If not nullptr, the op/attr pairs that were removed
 // are added to '*op_attr_removed'.
 //
 // Expected usage, for a producer that wants to prepare a graph for
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index dca8f17468..36c9ad2c64 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -28,7 +28,7 @@ namespace grappler {
 const char kConstantFoldingConst[] = "ConstantFolding";
 const char kConstantFoldingCtrl[] = "ConstantFoldingCtrl";
 
-// Contant folding optimization for a graph.
+// Constant folding optimization for a graph.
 class ConstantFolding : public GraphOptimizer {
  public:
   ConstantFolding();
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 0a802fa0f5..5ac42c2aba 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -927,7 +927,7 @@ struct TuningConfig {
   // Conv2DBackpropFilter will use a specialized GEMM implementation, which is
   // usually faster than the NCHW implementation. The downside is that this
   // might result in more non-cancellable layout conversion nodes (implemented
-  // by the Tranpose op).
+  // by the Transpose op).
   bool no_gemm;
 };
 
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index 43af95e278..3ae9316fee 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -988,9 +988,9 @@ def index_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   table = tf.contrib.lookup.index_table_from_tensor(
-      vocabulary_list=vocabulary_list, num_oov_buckets=1, default_value=-1)
+      mapping=vocabulary_list, num_oov_buckets=1, default_value=-1)
   features = tf.constant(["emerson", "lake", "and", "palmer"])
   ids = table.lookup(features)
   ...
@@ -1160,7 +1160,7 @@ def index_to_string_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       vocabulary_list, default_value="UNKNOWN")
-- 
GitLab


From 442dd54df79a72e9213023ab4baee7b788576556 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 20:08:29 -0700
Subject: [PATCH 0133/1375] Further BUILD cleanup

PiperOrigin-RevId: 159188501
---
 tensorflow/cc/BUILD                 | 12 ------------
 tensorflow/compiler/jit/ops/BUILD   | 20 ++++----------------
 tensorflow/compiler/tests/BUILD     |  1 +
 tensorflow/compiler/tf2xla/BUILD    |  4 ----
 tensorflow/compiler/xla/tests/BUILD |  1 +
 5 files changed, 6 insertions(+), 32 deletions(-)

diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index b86731b118..a884f11d48 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -61,7 +61,6 @@ cc_library(
         ":gradients",
         ":ops",
         ":scope",
-        "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
@@ -274,10 +273,6 @@ cc_library(
     deps = [
         ":cc_ops",
         ":grad_op_registry",
-        ":ops",
-        ":scope",
-        "//tensorflow/core:core_cpu",
-        "//tensorflow/core:framework",
     ],
 )
 
@@ -305,10 +300,6 @@ cc_library(
         ":cc_ops",
         ":cc_ops_internal",
         ":grad_op_registry",
-        ":ops",
-        ":scope",
-        "//tensorflow/core:core_cpu",
-        "//tensorflow/core:framework",
     ],
 )
 
@@ -514,7 +505,6 @@ cc_library(
     deps = [
         ":coordinator",
         "//tensorflow/core:core_cpu",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
@@ -547,8 +537,6 @@ cc_library(
     srcs = ["training/coordinator.cc"],
     hdrs = ["training/coordinator.h"],
     deps = [
-        "//tensorflow/core:core_cpu",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/compiler/jit/ops/BUILD b/tensorflow/compiler/jit/ops/BUILD
index 8d1fa03cc0..e5787ca4c8 100644
--- a/tensorflow/compiler/jit/ops/BUILD
+++ b/tensorflow/compiler/jit/ops/BUILD
@@ -1,32 +1,20 @@
 licenses(["notice"])  # Apache 2.0
 
 package(
-    default_visibility = [
-        "//tensorflow/compiler/tf2xla:internal",
-    ],
+    default_visibility = ["//tensorflow/compiler/tf2xla:internal"],
 )
 
 cc_library(
     name = "xla_ops",
-    srcs = [
-        "xla_ops.cc",
-    ],
-    deps = [
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:protos_all_cc",
-    ],
+    srcs = ["xla_ops.cc"],
+    deps = ["//tensorflow/core:framework"],
     alwayslink = 1,
 )
 
 cc_library(
     name = "parallel_check_op",
     srcs = ["parallel_check_op.cc"],
-    deps = [
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:protos_all_cc",
-    ],
+    deps = ["//tensorflow/core:framework"],
     alwayslink = 1,
 )
 
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index ef19e23858..8d5f880ede 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -40,6 +40,7 @@ py_library(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:variables",
     ],
 )
diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD
index a1c8c9209e..f4d3bc9635 100644
--- a/tensorflow/compiler/tf2xla/BUILD
+++ b/tensorflow/compiler/tf2xla/BUILD
@@ -153,7 +153,6 @@ cc_library(
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
-        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -166,13 +165,10 @@ cc_test(
         "//tensorflow/cc:cc_ops",
         "//tensorflow/cc:function_ops",
         "//tensorflow/cc:ops",
-        "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
-        "//tensorflow/core:framework",
         "//tensorflow/core:ops",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
-        "//tensorflow/core:testlib",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 55a9c430e5..c095983cd5 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -176,6 +176,7 @@ cc_library(
     deps = [
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:backend",
         "//tensorflow/compiler/xla/service:compiler",
         "//tensorflow/compiler/xla/service:executable",
-- 
GitLab


From 8bb8099697c3104ef00ae90027f1d17e3b6e992b Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Thu, 15 Jun 2017 20:28:41 -0700
Subject: [PATCH 0134/1375] Support optimizers in cost analyzer tool.

PiperOrigin-RevId: 159189380
---
 .../python/grappler/cost_analyzer_tool.py     | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py
index 5c1114a409..146bb4311c 100644
--- a/tensorflow/python/grappler/cost_analyzer_tool.py
+++ b/tensorflow/python/grappler/cost_analyzer_tool.py
@@ -21,8 +21,12 @@ from __future__ import print_function
 import argparse
 import sys
 
+from google.protobuf import text_format
+
 from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.grappler import cost_analyzer
+from tensorflow.python.grappler import tf_optimizer
 from tensorflow.python.platform import app
 from tensorflow.python.platform import gfile
 
@@ -32,6 +36,12 @@ def main(_):
     metagraph = meta_graph_pb2.MetaGraphDef()
     metagraph.ParseFromString(input_file.read())
 
+  if FLAGS.rewriter_config is not None:
+    rewriter_config = rewriter_config_pb2.RewriterConfig()
+    text_format.Merge(FLAGS.rewriter_config, rewriter_config)
+    optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, metagraph)
+    metagraph.graph_def.CopyFrom(optimized_graph)
+
   report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report)
   print(report)
 
@@ -40,6 +50,15 @@ if __name__ == "__main__":
   parser = argparse.ArgumentParser()
   parser.add_argument(
       "--input", type=str, default=None, help="Input .meta file path.")
+  parser.add_argument(
+      "--rewriter_config",
+      type=str,
+      default=None,
+      help="Configuration for the grappler optimizers, described as a "
+      "RewriterConfig protocol buffer. Usage example 1: "
+      "--rewriter_config='optimize_tensor_layout: true "
+      "disable_model_pruning: true'. Usage example 2: "
+      "--rewriter_config='optimizers: \"constfold\" optimizers: \"layout\"'")
   parser.add_argument(
       "--per_node_report",
       action="store_true",
-- 
GitLab


From 3ca692ef01038c05b02c87236eaa405159dae6ed Mon Sep 17 00:00:00 2001
From: Liangliang He <lliang.he@gmail.com>
Date: Fri, 16 Jun 2017 11:48:34 +0800
Subject: [PATCH 0135/1375] Fix alloc attributes

---
 tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
index 62a152ff90..9ea696589a 100644
--- a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
+++ b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
@@ -102,10 +102,6 @@ void RdmaRemoteRendezvous::RecvFromRemoteAsync(
                     RdmaMessage::kTensorBufferStartIndex;
       bool can_memcpy = DataTypeCanUseMemcpy(rm.data_type_);
       if (can_memcpy) {
-        AllocatorAttributes host_alloc_attrs;
-        host_alloc_attrs.set_gpu_compatible(true);
-        host_alloc_attrs.set_on_host(true);
-
         if (dst_dev->tensorflow_gpu_device_info() &&
             (!recv_args.alloc_attrs.on_host())) {
           CHECK(recv_args.device_context)
@@ -115,13 +111,16 @@ void RdmaRemoteRendezvous::RecvFromRemoteAsync(
           Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
           memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
 
-          Allocator* dst_alloc = dst_dev->GetAllocator(host_alloc_attrs);
+          Allocator* dst_alloc = dst_dev->GetAllocator(recv_args.alloc_attrs);
           Tensor gpu_copy(dst_alloc, rm.data_type_, rm.tensor_shape_);
           s = VerbsUtil::CopyCPUTensorToGPUSync(&copy, recv_args.device_context,
                                                 dst_dev, &gpu_copy);
           CHECK(s.ok()) << "copy tensor to gpu sync";
           val = std::move(gpu_copy);
         } else {
+          AllocatorAttributes host_alloc_attrs;
+          host_alloc_attrs.set_gpu_compatible(true);
+          host_alloc_attrs.set_on_host(true);
           Allocator* alloc = dst_dev->GetAllocator(host_alloc_attrs);
           Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
           memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
-- 
GitLab


From 7fffdb236ecaf7a2f50f3363e947b19e2a5a327a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 15 Jun 2017 22:06:08 -0700
Subject: [PATCH 0136/1375] Automated g4 rollback of changelist 158990733

PiperOrigin-RevId: 159194246
---
 .../core/kernels/depthwise_conv_op_gpu.cu.cc  | 541 +++++++++++-------
 .../kernel_tests/depthwise_conv_op_test.py    |  19 +-
 2 files changed, 342 insertions(+), 218 deletions(-)

diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
index 319dbb68e6..038c596207 100644
--- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
@@ -38,8 +38,8 @@ using Eigen::GpuDevice;
 // performed using the faster ('Small') variant of the kernel.
 EIGEN_DEVICE_FUNC bool CanLaunchDepthwiseConv2dGPUSmall(
     const DepthwiseArgs args) {
-  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 16 &&
-         args.in_cols <= 16 && args.in_rows == args.out_rows &&
+  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 32 &&
+         args.in_cols <= 32 && args.in_rows == args.out_rows &&
          args.in_cols == args.out_cols && args.pad_rows >= 0 &&
          args.pad_rows < args.filter_rows && args.pad_cols >= 0 &&
          args.pad_cols < args.filter_cols &&
@@ -51,8 +51,8 @@ EIGEN_DEVICE_FUNC bool CanLaunchDepthwiseConv2dGPUSmall(
 // using the faster ('Small') variant of the kernel.
 EIGEN_DEVICE_FUNC bool CanLaunchDepthwiseConv2dBackpropFilterGPUSmall(
     const DepthwiseArgs args, const int block_rows) {
-  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 16 &&
-         args.in_cols <= 16 && args.in_rows == args.out_rows &&
+  return args.depth_multiplier == 1 && args.stride == 1 && args.in_rows <= 32 &&
+         args.in_cols <= 32 && args.in_rows == args.out_rows &&
          args.in_cols == args.out_cols && args.pad_rows >= 0 &&
          args.pad_rows < args.filter_rows && args.pad_cols >= 0 &&
          args.pad_cols < args.filter_cols && block_rows <= args.in_rows &&
@@ -142,14 +142,14 @@ __global__ void __launch_bounds__(1024, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution forward pass in NHWC format,
-// tailored for small images up to 16x16. Stride and depth multiplier must be 1.
+// tailored for small images up to 32x32. Stride and depth multiplier must be 1.
 // Padding must be 'SAME', which allows to reuse the index computation. Only
 // use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input and filter tensors are loaded into shared memory before
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          bool kKnownEvenRows>
+          int kBlockSlices, bool kKnownEvenRows>
 __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
   assert(CanLaunchDepthwiseConv2dGPUSmall(args));
@@ -168,25 +168,23 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
   const int pad_rows = args.pad_rows;
   const int pad_cols = args.pad_cols;
 
-  // Fixed blockDim.x, corresponding to Pascal's global load granularity of 32B.
-  const int block_slices = 8;
   const int block_rows = blockDim.z;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
-  const int block_size = block_rows * in_cols * block_slices;
+  const int block_size = block_rows * in_cols * kBlockSlices;
   const int in_row_size = in_cols * in_depth;
   const int in_size = in_rows * in_row_size;
-  const int in_increment = (in_cols - 1) * block_slices;
+  const int in_increment = (in_cols - 1) * kBlockSlices;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
-  const int tile_row_size = tile_cols * block_slices;
+  const int tile_row_size = tile_cols * kBlockSlices;
   const int tile_size = tile_rows * tile_row_size;
   const int tile_offset = block_rows * tile_row_size;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int batch_blocks = (in_depth + block_slices - 1) / block_slices;
+  const int batch_blocks = (in_depth + kBlockSlices - 1) / kBlockSlices;
   const int in_blocks = batch_blocks * batches;
   const int tensor_offset =
       kKnownEvenRows ? in_size / 2 : block_rows * in_row_size;
@@ -197,7 +195,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
 
   // Position in block.
   const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_pix * block_slices + thread_depth;
+  const int thread_idx = thread_pix * kBlockSlices + thread_depth;
 
   // Initialize tile, in particular the padding.
   for (int i = thread_idx; i < tile_size; i += block_size) {
@@ -210,11 +208,11 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
 
   // Position in (padded) shared memory.
   const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = data_pix * block_slices + thread_depth;
+  const int data_idx = data_pix * kBlockSlices + thread_depth;
 
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_pix = data_pix + pad_offset;
-  const int tile_idx = tile_pix * block_slices + thread_depth;
+  const int tile_idx = tile_pix * kBlockSlices + thread_depth;
 
   const int max_depth = in_depth - thread_depth;
   const int filter_write_offset =
@@ -227,7 +225,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
     const int batch = b / batch_blocks;
     const int stack = b - batch * batch_blocks;
 
-    const int start_depth = stack * block_slices;
+    const int start_depth = stack * kBlockSlices;
     const int filter_offset = tensor_idx + start_depth;
     const int inout_offset = batch * in_size + filter_offset;
     const bool depth_in_range = start_depth < max_depth;
@@ -259,8 +257,8 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
-          shared_offset += block_slices;
-          filter_ptr += block_slices;
+          shared_offset += kBlockSlices;
+          filter_ptr += kBlockSlices;
         }
         shared_offset += in_increment;
       }
@@ -404,14 +402,14 @@ __global__ void __launch_bounds__(1024, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution forward pass in NCHW format,
-// tailored for small images up to 16x16. Stride and depth multiplier must be 1.
+// tailored for small images up to 32x32. Stride and depth multiplier must be 1.
 // Padding must be 'SAME', which allows to reuse the index computation. Only
 // use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input and filter tensors are loaded into shared memory before
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          bool kKnownEvenRows>
+          int kBlockSlices, bool kKnownEvenRows>
 __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
   assert(CanLaunchDepthwiseConv2dGPUSmall(args));
@@ -432,12 +430,11 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
 
   // Fixed blockDim.z, tailored for maximum grid size for images of size 16x16.
   const int block_rows = blockDim.y;
-  const int block_slices = 8;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
   const int block_pixels = in_cols * block_rows;
-  const int block_size = block_pixels * block_slices;
+  const int block_size = block_pixels * kBlockSlices;
   const int in_pixels = in_cols * in_rows;
   const int in_increment = in_cols - 1;
   const int filter_pixels = filter_rows * filter_cols;
@@ -445,11 +442,11 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
   const int tile_pixels = tile_cols * tile_rows;
-  const int tile_size = tile_pixels * block_slices;
+  const int tile_size = tile_pixels * kBlockSlices;
   const int tile_offset = block_rows * tile_cols;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
   const int in_slices = in_depth * batches;
-  const int in_blocks = (in_slices + block_slices - 1) / block_slices;
+  const int in_blocks = (in_slices + kBlockSlices - 1) / kBlockSlices;
 
   const int thread_col = threadIdx.x;
   const int thread_row = threadIdx.y;
@@ -476,8 +473,8 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
   const int tile_idx = data_idx + pad_offset;
 
   // Filter is always in HWCK format, irrespective of the input/output format.
-  const int filter_pix = thread_idx / block_slices;
-  const int filter_depth = thread_idx % block_slices;
+  const int filter_pix = thread_idx / kBlockSlices;
+  const int filter_depth = thread_idx % kBlockSlices;
   const int filter_idx = filter_pix * in_depth;
 
   const int max_slice = in_slices - thread_depth;
@@ -488,7 +485,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
   for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int slice = b * block_slices;
+    const int slice = b * kBlockSlices;
 
     const int inout_offset = slice * in_pixels + tensor_idx;
     const bool slice_in_range = slice < max_slice;
@@ -522,7 +519,7 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
           ++shared_offset;
-          filter_ptr += block_slices;
+          filter_ptr += kBlockSlices;
         }
         shared_offset += in_increment;
       }
@@ -539,42 +536,43 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          bool kKnownEvenRows>
+          int kBlockSlices, bool kKnownEvenRows>
 void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
                                    const T* input, const T* filter, T* output,
                                    TensorFormat data_format) {
   const int block_rows = (args.in_rows + 1) / 2;
-  const int block_slices = 8;
   const int tile_cols = args.in_cols + args.filter_cols - 1;
   const int tile_rows = block_rows * 2 + args.filter_rows - 1;
   const int tile_pixels = tile_rows * tile_cols;
   const int filter_pixels = args.filter_rows * args.filter_cols;
 
   const int shared_memory_size =
-      block_slices * (tile_pixels + filter_pixels) * sizeof(T);
+      kBlockSlices * (tile_pixels + filter_pixels) * sizeof(T);
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
 
   if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(block_slices, args.in_cols, block_rows);
+    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dGPUKernelNHWCSmall<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, kKnownEvenRows>,
+                                          kKnownFilterHeight, kBlockSlices,
+                                          kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dGPUKernelNHWCSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                      kKnownEvenRows>
+                                      kBlockSlices, kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, input, filter, output);
   } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, block_slices);
+    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dGPUKernelNCHWSmall<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, kKnownEvenRows>,
+                                          kKnownFilterHeight, kBlockSlices,
+                                          kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dGPUKernelNCHWSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                      kKnownEvenRows>
+                                      kBlockSlices, kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, input, filter, output);
   } else {
@@ -582,17 +580,37 @@ void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
+          int kBlockSlices>
 void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
                                    const T* input, const T* filter, T* output,
                                    TensorFormat data_format) {
   if (args.in_rows & 1) {
     LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                  /*kKnownEvenRows=*/false>(
-        d, args, input, filter, output, data_format);
+                                  kBlockSlices, false>(d, args, input, filter,
+                                                       output, data_format);
   } else {
     LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                  /*kKnownEvenRows=*/true>(
+                                  kBlockSlices, true>(d, args, input, filter,
+                                                      output, data_format);
+  }
+}
+
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
+                                   const T* input, const T* filter, T* output,
+                                   TensorFormat data_format) {
+  // Maximize (power of two) kBlockSlices while keeping a block within 1024
+  // threads (2 pixels per thread).
+  const int block_pixels = (args.in_rows + 1) / 2 * args.in_cols;
+  if (block_pixels > 256) {
+    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 2>(
+        d, args, input, filter, output, data_format);
+  } else if (block_pixels > 128) {
+    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 4>(
+        d, args, input, filter, output, data_format);
+  } else {
+    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 8>(
         d, args, input, filter, output, data_format);
   }
 }
@@ -602,11 +620,6 @@ template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
 void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
                               const T* input, const T* filter, T* output,
                               TensorFormat data_format) {
-  if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight>(
-        d, args, input, filter, output, data_format);
-    return;
-  }
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
   // The compile-time constant version runs faster with a single block.
@@ -641,18 +654,36 @@ void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
   }
 }
 
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
+                              const T* input, const T* filter, T* output,
+                              TensorFormat data_format) {
+  if (args.depth_multiplier == 1) {
+    if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
+      LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight>(
+          d, args, input, filter, output, data_format);
+      return;
+    }
+
+    LaunchDepthwiseConv2dGPU<T, kKnownFilterWidth, kKnownFilterHeight, 1>(
+        d, args, input, filter, output, data_format);
+  } else {
+    LaunchDepthwiseConv2dGPU<T, kKnownFilterWidth, kKnownFilterHeight, -1>(
+        d, args, input, filter, output, data_format);
+  }
+}
+
 // A simple launch pad to launch the Cuda kernel for depthwise convolution.
 template <typename T>
 struct DepthwiseConv2dGPULaunch {
   static void Run(const GpuDevice& d, const DepthwiseArgs args, const T* input,
                   const T* filter, T* output, TensorFormat data_format) {
-    if (args.filter_rows == 3 && args.filter_cols == 3 &&
-        args.depth_multiplier == 1) {
-      LaunchDepthwiseConv2dGPU<T, 3, 3, 1>(d, args, input, filter, output,
-                                           data_format);
+    if (args.filter_rows == 3 && args.filter_cols == 3) {
+      LaunchDepthwiseConv2dGPU<T, 3, 3>(d, args, input, filter, output,
+                                        data_format);
     } else {
-      LaunchDepthwiseConv2dGPU<T, -1, -1, -1>(d, args, input, filter, output,
-                                              data_format);
+      LaunchDepthwiseConv2dGPU<T, -1, -1>(d, args, input, filter, output,
+                                          data_format);
     }
   }
 };
@@ -726,7 +757,7 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. input in
-// NHWC format, tailored for small images up to 16x16. Stride and depth
+// NHWC format, tailored for small images up to 32x32. Stride and depth
 // multiplier must be 1. Padding must be 'SAME', which allows to reuse the index
 // computation. Only use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args)
 // returns true.
@@ -736,7 +767,7 @@ __global__ void __launch_bounds__(640, 2)
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          bool kKnownEvenRows>
+          int kBlockSlices, bool kKnownEvenRows>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
@@ -757,24 +788,23 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
   const int pad_cols = args.pad_cols;
 
   // Fixed blockDim.x, corresponding to Pascal's global load granularity of 32B.
-  const int block_slices = 8;
   const int block_rows = blockDim.z;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
-  const int block_size = block_rows * in_cols * block_slices;
+  const int block_size = block_rows * in_cols * kBlockSlices;
   const int in_row_size = in_cols * in_depth;
   const int in_size = in_rows * in_row_size;
-  const int in_increment = (in_cols - 1) * block_slices;
+  const int in_increment = (in_cols - 1) * kBlockSlices;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
-  const int tile_row_size = tile_cols * block_slices;
+  const int tile_row_size = tile_cols * kBlockSlices;
   const int tile_size = tile_rows * tile_row_size;
   const int tile_offset = block_rows * tile_row_size;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int batch_blocks = (in_depth + block_slices - 1) / block_slices;
+  const int batch_blocks = (in_depth + kBlockSlices - 1) / kBlockSlices;
   const int in_blocks = batch_blocks * batches;
   const int tensor_offset =
       kKnownEvenRows ? in_size / 2 : block_rows * in_row_size;
@@ -785,7 +815,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
 
   // Position in block.
   const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_pix * block_slices + thread_depth;
+  const int thread_idx = thread_pix * kBlockSlices + thread_depth;
 
   // Initialize tile, in particular the padding.
   for (int i = thread_idx; i < tile_size; i += block_size) {
@@ -798,17 +828,17 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
 
   // Position in (padded) shared memory.
   const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = data_pix * block_slices + thread_depth;
+  const int data_idx = data_pix * kBlockSlices + thread_depth;
 
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_pix = data_pix + pad_offset;
-  const int tile_idx = tile_pix * block_slices + thread_depth;
+  const int tile_idx = tile_pix * kBlockSlices + thread_depth;
 
   const int max_depth = in_depth - thread_depth;
   const int filter_write_offset =
       thread_pix < filter_pixels ? tile_size + thread_idx : 0;
   const int filter_read_offset =
-      tile_size + filter_pixels * block_slices + thread_depth;
+      tile_size + filter_pixels * kBlockSlices + thread_depth;
   const bool skip_second =
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
@@ -816,7 +846,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
     const int batch = b / batch_blocks;
     const int stack = b - batch * batch_blocks;
 
-    const int start_depth = stack * block_slices;
+    const int start_depth = stack * kBlockSlices;
     const int filter_offset = tensor_idx + start_depth;
     const int inout_offset = batch * in_size + filter_offset;
     const bool depth_in_range = start_depth < max_depth;
@@ -844,12 +874,12 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
       const T* filter_ptr = filter_read_offset + shared_data;
       UNROLL for (int r = 0; r < filter_rows; ++r) {
         UNROLL for (int c = 0; c < filter_cols; ++c) {
-          filter_ptr -= block_slices;
+          filter_ptr -= kBlockSlices;
           const T filter_value = *filter_ptr;
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
-          shared_offset += block_slices;
+          shared_offset += kBlockSlices;
         }
         shared_offset += in_increment;
       }
@@ -937,7 +967,7 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. input in
-// NHWC format, tailored for small images up to 16x16. Stride and depth
+// NHWC format, tailored for small images up to 32x32. Stride and depth
 // multiplier must be 1. Padding must be 'SAME', which allows to reuse the index
 // computation. Only use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args)
 // returns true.
@@ -947,7 +977,7 @@ __global__ void __launch_bounds__(640, 2)
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          bool kKnownEvenRows>
+          int kBlockSlices, bool kKnownEvenRows>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
@@ -969,12 +999,11 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
 
   // Fixed blockDim.z, tailored for maximum grid size for images of size 16x16.
   const int block_rows = blockDim.y;
-  const int block_slices = 8;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
   const int block_pixels = in_cols * block_rows;
-  const int block_size = block_pixels * block_slices;
+  const int block_size = block_pixels * kBlockSlices;
   const int in_pixels = in_cols * in_rows;
   const int in_increment = in_cols - 1;
   const int filter_pixels = filter_rows * filter_cols;
@@ -982,11 +1011,11 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
   const int even_rows = kKnownEvenRows || (1 & ~in_rows);
   const int tile_rows = in_rows + filter_rows - even_rows;
   const int tile_pixels = tile_cols * tile_rows;
-  const int tile_size = tile_pixels * block_slices;
+  const int tile_size = tile_pixels * kBlockSlices;
   const int tile_offset = block_rows * tile_cols;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
   const int in_slices = in_depth * batches;
-  const int in_blocks = (in_slices + block_slices - 1) / block_slices;
+  const int in_blocks = (in_slices + kBlockSlices - 1) / kBlockSlices;
 
   const int thread_col = threadIdx.x;
   const int thread_row = threadIdx.y;
@@ -1013,20 +1042,20 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
   const int tile_idx = data_idx + pad_offset;
 
   // Filter is always in HWCK format, irrespective of the input/output format.
-  const int filter_pix = thread_idx / block_slices;
-  const int filter_depth = thread_idx % block_slices;
+  const int filter_pix = thread_idx / kBlockSlices;
+  const int filter_depth = thread_idx % kBlockSlices;
   const int filter_idx = filter_pix * in_depth;
 
   const int max_slice = in_slices - thread_depth;
   const int filter_write_offset =
       filter_pix < filter_pixels ? tile_size + thread_idx : 0;
   const int filter_read_offset =
-      tile_size + filter_pixels * block_slices + thread_depth;
+      tile_size + filter_pixels * kBlockSlices + thread_depth;
   const bool skip_second =
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
   for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int slice = b * block_slices;
+    const int slice = b * kBlockSlices;
 
     const int inout_offset = slice * in_pixels + tensor_idx;
     const bool slice_in_range = slice < max_slice;
@@ -1055,7 +1084,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
       const T* filter_ptr = filter_read_offset + shared_data;
       UNROLL for (int r = 0; r < filter_rows; ++r) {
         UNROLL for (int c = 0; c < filter_cols; ++c) {
-          filter_ptr -= block_slices;
+          filter_ptr -= kBlockSlices;
           const T filter_value = *filter_ptr;
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
@@ -1077,44 +1106,45 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          bool kKnownEvenRows>
+          int kBlockSlices, bool kKnownEvenRows>
 void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
                                                 const DepthwiseArgs args,
                                                 const T* out_backprop,
                                                 const T* filter, T* in_backprop,
                                                 TensorFormat data_format) {
   const int block_rows = (args.in_rows + 1) / 2;
-  const int block_slices = 8;
   const int tile_cols = args.in_cols + args.filter_cols - 1;
   const int tile_rows = block_rows * 2 + args.filter_rows - 1;
   const int tile_pixels = tile_rows * tile_cols;
   const int filter_pixels = args.filter_rows * args.filter_cols;
 
   const int shared_memory_size =
-      block_slices * (tile_pixels + filter_pixels) * sizeof(T);
+      kBlockSlices * (tile_pixels + filter_pixels) * sizeof(T);
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
 
   if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(block_slices, args.in_cols, block_rows);
+    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dBackpropInputGPUKernelNHWCSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
+            kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropInputGPUKernelNHWCSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, filter, in_backprop);
   } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, block_slices);
+    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_outputs, d,
         DepthwiseConv2dBackpropInputGPUKernelNCHWSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
+            kKnownEvenRows>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropInputGPUKernelNCHWSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownEvenRows>
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kKnownEvenRows>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, filter, in_backprop);
   } else {
@@ -1122,25 +1152,48 @@ void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
+          int kBlockSlices>
 void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
                                                 const DepthwiseArgs args,
                                                 const T* out_backprop,
                                                 const T* filter, T* in_backprop,
                                                 TensorFormat data_format) {
   if (args.in_rows & 1) {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight,
-                                               /*kKnownEvenRows=*/false>(
+    LaunchDepthwiseConv2dBackpropInputGPUSmall<
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, false>(
         d, args, out_backprop, filter, in_backprop, data_format);
   } else {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight,
-                                               /*kKnownEvenRows=*/true>(
+    LaunchDepthwiseConv2dBackpropInputGPUSmall<
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, true>(
         d, args, out_backprop, filter, in_backprop, data_format);
   }
 }
 
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
+                                                const DepthwiseArgs args,
+                                                const T* input, const T* filter,
+                                                T* output,
+                                                TensorFormat data_format) {
+  // Maximize (power of two) kBlockSlices while keeping a block within 1024
+  // threads (2 pixels per thread).
+  const int block_pixels = (args.in_rows + 1) / 2 * args.in_cols;
+  if (block_pixels > 256) {
+    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
+                                               kKnownFilterHeight, 2>(
+        d, args, input, filter, output, data_format);
+  } else if (block_pixels > 128) {
+    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
+                                               kKnownFilterHeight, 4>(
+        d, args, input, filter, output, data_format);
+  } else {
+    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
+                                               kKnownFilterHeight, 8>(
+        d, args, input, filter, output, data_format);
+  }
+}
+
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
           int kKnownDepthMultiplier>
 void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
@@ -1148,12 +1201,6 @@ void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
                                            const T* out_backprop,
                                            const T* filter, T* in_backprop,
                                            TensorFormat data_format) {
-  if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight>(
-        d, args, out_backprop, filter, in_backprop, data_format);
-    return;
-  }
   const int num_in_backprop =
       args.batch * args.in_rows * args.in_cols * args.in_depth;
   if (data_format == FORMAT_NHWC) {
@@ -1181,22 +1228,41 @@ void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
   }
 }
 
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
+                                           const DepthwiseArgs args,
+                                           const T* out_backprop,
+                                           const T* filter, T* in_backprop,
+                                           TensorFormat data_format) {
+  if (args.depth_multiplier == 1) {
+    if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
+      LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
+                                                 kKnownFilterHeight>(
+          d, args, out_backprop, filter, in_backprop, data_format);
+      return;
+    }
+
+    LaunchDepthwiseConv2dBackpropInputGPU<T, kKnownFilterWidth,
+                                          kKnownFilterHeight, 1>(
+        d, args, out_backprop, filter, in_backprop, data_format);
+  } else {
+    LaunchDepthwiseConv2dBackpropInputGPU<T, kKnownFilterWidth,
+                                          kKnownFilterHeight, -1>(
+        d, args, out_backprop, filter, in_backprop, data_format);
+  }
+}
+
 // A simple launch pad to launch the Cuda kernel for depthwise convolution.
 template <typename T>
 struct DepthwiseConv2dBackpropInputGPULaunch {
   static void Run(const GpuDevice& d, const DepthwiseArgs args,
                   const T* out_backprop, const T* filter, T* in_backprop,
                   TensorFormat data_format) {
-    if (args.depth_multiplier == 1) {
-      if (args.filter_rows == 3 && args.filter_cols == 3) {
-        LaunchDepthwiseConv2dBackpropInputGPU<T, 3, 3, 1>(
-            d, args, out_backprop, filter, in_backprop, data_format);
-      } else {
-        LaunchDepthwiseConv2dBackpropInputGPU<T, -1, -1, 1>(
-            d, args, out_backprop, filter, in_backprop, data_format);
-      }
+    if (args.filter_rows == 3 && args.filter_cols == 3) {
+      LaunchDepthwiseConv2dBackpropInputGPU<T, 3, 3>(
+          d, args, out_backprop, filter, in_backprop, data_format);
     } else {
-      LaunchDepthwiseConv2dBackpropInputGPU<T, -1, -1, -1>(
+      LaunchDepthwiseConv2dBackpropInputGPU<T, -1, -1>(
           d, args, out_backprop, filter, in_backprop, data_format);
     }
   }
@@ -1300,19 +1366,20 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. filter in
-// NHWC format, tailored for small images up to 16x16. Stride and depth
+// NHWC format, tailored for small images up to 32x32. Stride and depth
 // multiplier must be 1. Padding must be 'SAME'. Only use this kernel if
 // CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input tensor are loaded into shared memory before performing the
 // convolution. Per iteration and filter element, each thread first performs
 // a partial convolution for two elements, one each in the lower and upper half
-// of a tile. The intermediate result of 4 consecutive columns are then
+// of a tile. The intermediate result of all pixels of a warp are then
 // accumulated and written to shared memory. Finally, the values in shared
 // memory are warp-accumulated (in chunks of kAccumPixels elements) and summed
 // up in global memory using atomics.
+// Requirements: threads per block must be multiple of 32 and <= launch_bounds,
+// kAccumPixels * 64 >= args.in_rows * args.in_cols * kBlockSlices.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          // Requirement: kAccumPixels * 8 >= args.in_rows * args.in_cols
-          int kAccumPixels>
+          int kBlockSlices, int kAccumPixels>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
     const DepthwiseArgs args, const T* output, const T* input, T* filter) {
@@ -1332,29 +1399,29 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
   const int pad_rows = args.pad_rows;
   const int pad_cols = args.pad_cols;
 
-  // Fixed blockDim.x, corresponding to Pascal's global load granularity of 32B.
-  const int block_slices = 8;
   const int block_rows = blockDim.z;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
-  const int block_size = block_rows * in_cols * block_slices;
+  const int block_size = block_rows * in_cols * kBlockSlices;
+  assert((block_size & 31) == 0);
   const int in_row_size = in_cols * in_depth;
   const int in_size = in_rows * in_row_size;
-  const int in_increment = (in_cols - 1) * block_slices;
+  const int in_increment = (in_cols - 1) * kBlockSlices;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int tile_rows = 2 * block_rows + filter_rows - 1;
-  const int tile_row_size = tile_cols * block_slices;
+  const int tile_row_size = tile_cols * kBlockSlices;
   const int tile_size = tile_rows * tile_row_size;
   const int tile_offset = block_rows * tile_row_size;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int batch_blocks = (in_depth + block_slices - 1) / block_slices;
+  const int batch_blocks = (in_depth + kBlockSlices - 1) / kBlockSlices;
   const int in_blocks = batch_blocks * batches;
   const int tensor_offset = block_rows * in_row_size;
   // The accumulator has a fixed number of pixels that can be reduced by one
-  // warp. Pixels beyond block_pixels/4 are never written.
-  const int accum_increment = kAccumPixels * block_slices;
+  // warp. Pixels beyond ceil(in_pixels * kBlockSlices / 64) are never written.
+  assert(kAccumPixels * 64 >= in_rows * in_cols * kBlockSlices);
+  const int accum_increment = kAccumPixels * kBlockSlices;
   const int accum_size = filter_pixels * accum_increment;
 
   const int thread_depth = threadIdx.x;
@@ -1363,7 +1430,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
 
   // Position in block.
   const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_pix * block_slices + thread_depth;
+  const int thread_idx = thread_pix * kBlockSlices + thread_depth;
 
   // Initialize tile, in particular the padding and accumulator.
   for (int i = thread_idx; i < tile_size + accum_size; i += block_size) {
@@ -1376,14 +1443,14 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
 
   // Position in (padded) shared memory.
   const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = data_pix * block_slices + thread_depth;
+  const int data_idx = data_pix * kBlockSlices + thread_depth;
 
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_pix = data_pix + pad_offset;
-  const int tile_idx = tile_pix * block_slices + thread_depth;
+  const int tile_idx = tile_pix * kBlockSlices + thread_depth;
 
-  // Position in accumulator (1 per 4 threads, depth major).
-  const int accum_pix = thread_pix / 4;
+  // Position in accumulator (kBlockSlices per warp, depth major).
+  const int accum_pix = thread_pix / (32 / kBlockSlices);
   const int accum_idx = thread_depth * kAccumPixels + accum_pix;
 
   const int max_depth = in_depth - thread_depth;
@@ -1394,7 +1461,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
     const int batch = b / batch_blocks;
     const int stack = b - batch * batch_blocks;
 
-    const int start_depth = stack * block_slices;
+    const int start_depth = stack * kBlockSlices;
     const int filter_offset = tensor_idx + start_depth;
     const int inout_offset = batch * in_size + filter_offset;
     const bool depth_in_range = start_depth < max_depth;
@@ -1421,13 +1488,14 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
         UNROLL for (int c = 0; c < filter_cols; ++c) {
           const T* const tile_ptr = shared_offset + shared_data;
           T val = out1 * tile_ptr[0] + out2 * tile_ptr[tile_offset];
-          // Sum up 4 block_pixels of the same depth and write to accumulator.
-          val += CudaShuffleDown(val, 16);
-          val += CudaShuffleDown(val, 8);
-          if (!(thread_idx & 24) /* i.e. 'lane_idx < 8' */) {
+          // Warp-accumulate pixels of the same depth and write to accumulator.
+          for (int delta = 16; delta >= kBlockSlices; delta /= 2) {
+            val += CudaShuffleDown(val, delta);
+          }
+          if (!(thread_idx & 32 - kBlockSlices) /* lane_idx < kBlockSlices */) {
             *accum_ptr = val;
           }
-          shared_offset += block_slices;
+          shared_offset += kBlockSlices;
           accum_ptr += accum_increment;
         }
         shared_offset += in_increment;
@@ -1440,8 +1508,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
     const T* const accum_data = tile_size + shared_data;
     for (int i = thread_idx; i < accum_size; i += block_size) {
       const int filter_idx = i / kAccumPixels;
-      const int filter_pix = filter_idx / block_slices;
-      const int filter_depth = filter_idx % block_slices + start_depth;
+      const int filter_pix = filter_idx / kBlockSlices;
+      const int filter_depth = filter_idx % kBlockSlices + start_depth;
       const int filter_offset = filter_pix * in_depth + filter_depth;
       if (filter_depth < in_depth) {
         T val = accum_data[i];
@@ -1558,19 +1626,20 @@ __global__ void __launch_bounds__(640, 2)
 }
 
 // CUDA kernel to compute the depthwise convolution backward w.r.t. filter in
-// NCHW format, tailored for small images up to 16x16. Stride and depth
+// NCHW format, tailored for small images up to 32x32. Stride and depth
 // multiplier must be 1. Padding must be 'SAME'. Only use this kernel if
 // CanLaunchDepthwiseConv2dGPUSmall(args) returns true.
 // Tiles of the input tensor are loaded into shared memory before performing the
 // convolution. Per iteration and filter element, each thread first performs
 // a partial convolution for two elements, one each in the lower and upper half
-// of a tile. The intermediate result of 4 consecutive columns are then
+// of a tile. The intermediate result of all pixels of a warp are then
 // accumulated and written to shared memory. Finally, the values in shared
 // memory are warp-accumulated (in chunks of kAccumPixels elements) and summed
 // up in global memory using atomics.
+// Requirements: threads per block must be multiple of 32 and <= launch_bounds,
+// kAccumPixels * 64 >= args.in_rows * args.in_cols * kBlockSlices.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          // Requirement: kAccumPixels * 8 >= args.in_rows * args.in_cols
-          int kAccumPixels>
+          int kBlockSlices, int kAccumPixels>
 __global__
 __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
     const DepthwiseArgs args, const T* output, const T* input, T* filter) {
@@ -1590,28 +1659,28 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
   const int pad_rows = args.pad_rows;
   const int pad_cols = args.pad_cols;
 
-  // Fixed blockDim.z, corresponding to Pascal's global load granularity of 32B.
   const int block_rows = blockDim.y;
-  const int block_slices = 8;
 
   // These values are the same for all threads and could
   // be precomputed on the CPU.
   const int block_pixels = in_cols * block_rows;
-  const int block_size = block_pixels * block_slices;
+  const int block_size = block_pixels * kBlockSlices;
+  assert((block_size & 31) == 0);
   const int in_pixels = in_cols * in_rows;
   const int in_increment = in_cols - 1;
   const int filter_pixels = filter_rows * filter_cols;
   const int tile_cols = in_cols + filter_cols - 1;
   const int tile_rows = 2 * block_rows + filter_rows - 1;
   const int tile_pixels = tile_cols * tile_rows;
-  const int tile_size = tile_pixels * block_slices;
+  const int tile_size = tile_pixels * kBlockSlices;
   const int tile_offset = block_rows * tile_cols;
   const int pad_offset = pad_rows * tile_cols + pad_cols;
   const int in_slices = in_depth * batches;
-  const int in_blocks = (in_slices + block_slices - 1) / block_slices;
+  const int in_blocks = (in_slices + kBlockSlices - 1) / kBlockSlices;
   // The accumulator has a fixed number of pixels that can be reduced by one
-  // warp. Pixels beyond block_pixels/4 are never written.
-  const int accum_increment = kAccumPixels * block_slices;
+  // warp. Pixels beyond ceil(in_pixels * kBlockSlices / 64) are never written.
+  assert(kAccumPixels * 64 >= in_rows * in_cols * kBlockSlices);
+  const int accum_increment = kAccumPixels * kBlockSlices;
   const int accum_size = filter_pixels * accum_increment;
 
   const int thread_col = threadIdx.x;
@@ -1638,8 +1707,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
   // Position in shared memory, offset by pad_rows / pad_cols.
   const int tile_idx = data_idx + pad_offset;
 
-  // Position in accumulator (1 per 4 threads, depth major).
-  const int accum_pix = thread_pix / 4;
+  // Position in accumulator (kBlockSlices per warp, depth major).
+  const int accum_pix = thread_pix / (32 / kBlockSlices);
   const int accum_idx = thread_depth * kAccumPixels + accum_pix;
 
   const int max_slice = in_slices - thread_depth;
@@ -1647,7 +1716,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
   const bool skip_second = block_rows + thread_row >= in_rows;
 
   for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int slice = b * block_slices;
+    const int slice = b * kBlockSlices;
 
     const int inout_offset = slice * in_pixels + tensor_idx;
     const bool slice_in_range = slice < max_slice;
@@ -1674,10 +1743,11 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
         UNROLL for (int c = 0; c < filter_cols; ++c) {
           const T* const tile_ptr = shared_offset + shared_data;
           T val = out1 * tile_ptr[0] + out2 * tile_ptr[tile_offset];
-          // Sum up 4 block_pixels of the same depth and write to accumulator.
-          val += CudaShuffleDown(val, 2);
-          val += CudaShuffleDown(val, 1);
-          if (!(thread_idx & 3)) {
+          // Warp-accumulate pixels of the same depth and write to accumulator.
+          for (int delta = 16 / kBlockSlices; delta > 0; delta /= 2) {
+            val += CudaShuffleDown(val, delta);
+          }
+          if (!(thread_idx & 32 / kBlockSlices - 1)) {
             *accum_ptr = val;
           }
           ++shared_offset;
@@ -1693,8 +1763,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
     const T* const accum_data = tile_size + shared_data;
     for (int i = thread_idx; i < accum_size; i += block_size) {
       const int filter_idx = i / kAccumPixels;
-      const int filter_pix = filter_idx / block_slices;
-      const int filter_depth = (slice + filter_idx % block_slices) % in_depth;
+      const int filter_pix = filter_idx / kBlockSlices;
+      const int filter_depth = (slice + filter_idx % kBlockSlices) % in_depth;
       const int filter_offset = filter_pix * in_depth + filter_depth;
       if (filter_depth < in_depth) {
         T val = accum_data[i];
@@ -1711,87 +1781,121 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kAccumPixels>
-void LaunchDepthwiseConv2dBackpropFilterGPUSmall(
-    const GpuDevice& d, const DepthwiseArgs args, int block_rows,
-    int shared_memory_size, const T* out_backprop, const T* input,
-    T* filter_backprop, TensorFormat data_format) {
-  const int block_slices = 8;
+          int kBlockSlices, int kAccumPixels>
+bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
+    const GpuDevice& d, const DepthwiseArgs args, const int block_rows,
+    const T* out_backprop, const T* input, T* filter_backprop,
+    TensorFormat data_format) {
+  const int tile_cols = args.in_cols + args.filter_cols - 1;
+  const int tile_rows = block_rows * 2 + args.filter_rows - 1;
+  const int tile_pixels = tile_rows * tile_cols;
+  const int filter_pixels = args.filter_rows * args.filter_cols;
+  const int shared_memory_size =
+      kBlockSlices * (tile_pixels + filter_pixels * kAccumPixels) * sizeof(T);
+  if (shared_memory_size > d.sharedMemPerBlock()) {
+    return false;
+  }
+
   const int num_out_backprop =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
   if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(block_slices, args.in_cols, block_rows);
+    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_out_backprop, d,
         DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
+            kAccumPixels>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, input, filter_backprop);
   } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, block_slices);
+    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
     CudaLaunchConfig config = GetCudaLaunchConfig(
         num_out_backprop, d,
         DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>,
+            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
+            kAccumPixels>,
         shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
     DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kAccumPixels>
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>
         <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
             args, out_backprop, input, filter_backprop);
   } else {
     assert(false && "Incorrect data format");
   }
+  return true;
+}
+
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
+          int kBlockSlices>
+bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
+    const GpuDevice& d, const DepthwiseArgs args, const int block_rows,
+    const T* out_backprop, const T* input, T* filter_backprop,
+    TensorFormat data_format) {
+  // Minimize (power of two) kAccumPixels, while satisfying
+  // kAccumPixels * 32 >= block_rows * in_cols * kBlockSlices.
+  const int block_pixels = block_rows * args.in_cols * kBlockSlices;
+  if (block_pixels > 512) {
+    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, 32>(
+        d, args, block_rows, out_backprop, input, filter_backprop, data_format);
+  } else if (block_pixels > 256) {
+    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, 16>(
+        d, args, block_rows, out_backprop, input, filter_backprop, data_format);
+  } else {
+    return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, 8>(
+        d, args, block_rows, out_backprop, input, filter_backprop, data_format);
+  }
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
 bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
     const GpuDevice& d, const DepthwiseArgs args, const T* out_backprop,
     const T* input, T* filter_backprop, TensorFormat data_format) {
-  // args.in_cols * blocks_rows (block_pixels) must be multiple of 4.
-  const int lookup_table[] = {0, 3, 1, 3};
-  const int rows_mask = lookup_table[args.in_cols & 3];
-  const int block_rows = (args.in_rows + 1) / 2 + rows_mask & ~rows_mask;
-  if (!CanLaunchDepthwiseConv2dBackpropFilterGPUSmall(args, block_rows)) {
-    return false;
-  }
-
-  const int in_pixels = args.in_rows * args.in_cols;
-  int accum_pixels = 8;
-  while (accum_pixels * 8 < in_pixels) {
-    accum_pixels *= 2;
+  // Maximize (power of two) kBlockSlices while keeping a block within 1024
+  // threads (2 pixels per thread).
+  int block_slices = 8;
+  int block_rows = (args.in_rows + 1) / 2;
+  int round_mask = 1;
+  for (; block_slices > 1; block_slices /= 2) {
+    // args.in_cols * block_rows * kBlockSlices must be multiple of 32.
+    for (; block_rows * args.in_cols * block_slices & 31;
+         round_mask = round_mask * 2 + 1) {
+      block_rows = block_rows + round_mask & ~round_mask;
+    }
+    int block_size = block_rows * args.in_cols * block_slices;
+    if (block_size <= 1024) {
+      break;
+    }
   }
 
-  const int block_slices = 8;
-  const int tile_cols = args.in_cols + args.filter_cols - 1;
-  const int tile_rows = block_rows * 2 + args.filter_rows - 1;
-  const int tile_pixels = tile_rows * tile_cols;
-  const int filter_pixels = args.filter_rows * args.filter_cols;
-  const int shared_memory_size =
-      block_slices * (tile_pixels + filter_pixels * accum_pixels) * sizeof(T);
-  if (shared_memory_size > d.sharedMemPerBlock()) {
+  if (!CanLaunchDepthwiseConv2dBackpropFilterGPUSmall(args, block_rows)) {
     return false;
   }
 
-  if (accum_pixels == 8) {
-    LaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
-                                                kKnownFilterHeight, 8>(
-        d, args, block_rows, shared_memory_size, out_backprop, input,
-        filter_backprop, data_format);
-  } else if (accum_pixels == 16) {
-    LaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
-                                                kKnownFilterHeight, 16>(
-        d, args, block_rows, shared_memory_size, out_backprop, input,
-        filter_backprop, data_format);
-  } else {
-    LaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
-                                                kKnownFilterHeight, 32>(
-        d, args, block_rows, shared_memory_size, out_backprop, input,
-        filter_backprop, data_format);
+  switch (block_slices) {
+    case 8:
+      return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
+          T, kKnownFilterWidth, kKnownFilterHeight, 8>(
+          d, args, block_rows, out_backprop, input, filter_backprop,
+          data_format);
+    case 4:
+      return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
+          T, kKnownFilterWidth, kKnownFilterHeight, 4>(
+          d, args, block_rows, out_backprop, input, filter_backprop,
+          data_format);
+    case 2:
+      return TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<
+          T, kKnownFilterWidth, kKnownFilterHeight, 2>(
+          d, args, block_rows, out_backprop, input, filter_backprop,
+          data_format);
+    default:
+      return false;
   }
-  return true;
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
@@ -1801,11 +1905,6 @@ void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& d,
                                             const T* out_backprop,
                                             const T* input, T* filter_backprop,
                                             TensorFormat data_format) {
-  if (TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
-                                                     kKnownFilterHeight>(
-          d, args, out_backprop, input, filter_backprop, data_format)) {
-    return;
-  }
   const int num_out_backprop =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
   if (data_format == FORMAT_NHWC) {
@@ -1833,18 +1932,40 @@ void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& d,
   }
 }
 
+template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& d,
+                                            const DepthwiseArgs args,
+                                            const T* out_backprop,
+                                            const T* input, T* filter_backprop,
+                                            TensorFormat data_format) {
+  if (args.depth_multiplier == 1) {
+    if (TryLaunchDepthwiseConv2dBackpropFilterGPUSmall<T, kKnownFilterWidth,
+                                                       kKnownFilterHeight>(
+            d, args, out_backprop, input, filter_backprop, data_format)) {
+      return;
+    }
+
+    LaunchDepthwiseConv2dBackpropFilterGPU<T, kKnownFilterWidth,
+                                           kKnownFilterHeight, 1>(
+        d, args, out_backprop, input, filter_backprop, data_format);
+  } else {
+    LaunchDepthwiseConv2dBackpropFilterGPU<T, kKnownFilterWidth,
+                                           kKnownFilterHeight, -1>(
+        d, args, out_backprop, input, filter_backprop, data_format);
+  }
+}
+
 // A simple launch pad to launch the Cuda kernel for depthwise convolution.
 template <typename T>
 struct DepthwiseConv2dBackpropFilterGPULaunch {
   static void Run(const GpuDevice& d, const DepthwiseArgs args,
                   const T* out_backprop, const T* input, T* filter_backprop,
                   TensorFormat data_format) {
-    if (args.filter_rows == 3 && args.filter_cols == 3 &&
-        args.depth_multiplier == 1) {
-      LaunchDepthwiseConv2dBackpropFilterGPU<T, 3, 3, 1>(
+    if (args.filter_rows == 3 && args.filter_cols == 3) {
+      LaunchDepthwiseConv2dBackpropFilterGPU<T, 3, 3>(
           d, args, out_backprop, input, filter_backprop, data_format);
     } else {
-      LaunchDepthwiseConv2dBackpropFilterGPU<T, -1, -1, -1>(
+      LaunchDepthwiseConv2dBackpropFilterGPU<T, -1, -1>(
           d, args, out_backprop, input, filter_backprop, data_format);
     }
   }
diff --git a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py
index 8ba9d0efff..3298092fbe 100644
--- a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py
+++ b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py
@@ -37,18 +37,21 @@ def ConfigsToTest():
     Tuple (input_size, filter_size, out_size, stride, padding), the depthwise
     convolution parameters.
   """
-  input_sizes = [[4, 5, 5, 48], [4, 8, 8, 84], [4, 17, 17, 48], [4, 35, 35, 2],
-                 [4, 147, 147, 2], [3, 299, 299, 3], [5, 183, 183, 1]]
-  filter_sizes = [[1, 1, 48, 2], [1, 3, 84, 1], [3, 1, 48, 4], [5, 5, 2, 1],
-                  [3, 3, 2, 8], [2, 2, 3, 8], [5, 5, 1, 2]]
-  out_sizes = [[4, 5, 5, 96], [4, 8, 8, 84], [4, 17, 17, 192], [4, 35, 35, 2],
-               [4, 49, 49, 16], [3, 150, 150, 24], [5, 92, 92, 2]]
-  strides = [1, 1, 1, 1, 3, 2, 2]
+  input_sizes = [[4, 5, 5, 48], [4, 8, 8, 84], [4, 17, 17, 48], [4, 9, 27, 8],
+                 [4, 31, 31, 7], [4, 35, 35, 2], [4, 147, 147, 2],
+                 [3, 299, 299, 3], [5, 183, 183, 1]]
+  filter_sizes = [[1, 1, 48, 2], [1, 3, 84, 1], [3, 1, 48, 4], [3, 3, 8, 1],
+                  [3, 3, 7, 1], [5, 5, 2, 1], [3, 3, 2, 8], [2, 2, 3,
+                                                             8], [5, 5, 1, 2]]
+  out_sizes = [[4, 5, 5, 96], [4, 8, 8, 84], [4, 17, 17, 192], [4, 9, 27, 8],
+               [4, 31, 31, 7], [4, 35, 35, 2], [4, 49, 49, 16],
+               [3, 150, 150, 24], [5, 92, 92, 2]]
+  strides = [1, 1, 1, 1, 1, 1, 3, 2, 2]
   # pylint: disable=invalid-name
   VALID = "VALID"
   SAME = "SAME"
   # pylint: enable=invalid-name
-  paddings = [SAME, SAME, SAME, SAME, VALID, SAME, SAME, SAME]
+  paddings = [SAME, SAME, SAME, SAME, SAME, SAME, VALID, SAME, SAME, SAME]
   for i, f, o, s, p in zip(input_sizes, filter_sizes, out_sizes, strides,
                            paddings):
     yield i, f, o, s, p
-- 
GitLab


From 1e8d1fe6c3c1221f16c05dd1883f893f291c94d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 00:49:37 -0700
Subject: [PATCH 0137/1375] Use the new SummaryMetadata field of the Value
 proto to pass per-plugin information.

This will let TensorBoard figure out which plugins should handle which types of summaries.

PiperOrigin-RevId: 159202469
---
 tensorflow/core/debug/BUILD                   | 10 ++++
 tensorflow/core/debug/debug_grpc_testlib.cc   | 23 ++++++--
 tensorflow/core/debug/debug_io_utils.cc       | 29 +++++++++-
 tensorflow/core/debug/debug_io_utils_test.cc  | 33 ++++++++++-
 .../core/debug/debugger_event_metadata.proto  |  9 +++
 .../core/kernels/summary_tensor_op_test.cc    |  9 ++-
 tensorflow/python/ops/summary_ops.py          |  1 -
 tensorflow/python/summary/summary.py          |  2 +
 tensorflow/python/summary/text_summary.py     | 28 +++++++--
 .../python/summary/text_summary_test.py       | 10 +---
 tensorflow/python/summary/writer/writer.py    | 26 +++++++++
 .../python/summary/writer/writer_test.py      | 57 +++++++++++++++++++
 12 files changed, 215 insertions(+), 22 deletions(-)
 create mode 100644 tensorflow/core/debug/debugger_event_metadata.proto

diff --git a/tensorflow/core/debug/BUILD b/tensorflow/core/debug/BUILD
index 2fc49d4412..30824a9072 100644
--- a/tensorflow/core/debug/BUILD
+++ b/tensorflow/core/debug/BUILD
@@ -123,6 +123,7 @@ tf_cuda_library(
     linkstatic = 1,
     deps = [
         ":debug_service_proto_cc",
+        ":debugger_event_metadata_proto_cc",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -144,6 +145,7 @@ tf_cuda_library(
         ":debug_graph_utils",
         ":debug_io_utils",
         ":debug_service_proto_cc",
+        ":debugger_event_metadata_proto_cc",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
@@ -194,6 +196,7 @@ tf_cc_test(
     deps = [
         ":debug_grpc_testlib",
         ":debug_io_utils",
+        ":debugger_event_metadata_proto_cc",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
@@ -204,6 +207,7 @@ tf_cc_test(
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
+        "//tensorflow/core/platform/default/build_config:platformlib",
     ],
 )
 
@@ -247,6 +251,12 @@ tf_cc_test(
     ],
 )
 
+tf_proto_library_cc(
+    name = "debugger_event_metadata_proto",
+    srcs = ["debugger_event_metadata.proto"],
+    cc_api_version = 2,
+)
+
 # TODO(cais): Add the following back in when tfdbg is supported on Android.
 # filegroup(
 #     name = "android_srcs",
diff --git a/tensorflow/core/debug/debug_grpc_testlib.cc b/tensorflow/core/debug/debug_grpc_testlib.cc
index c19842a2f6..7317aa0372 100644
--- a/tensorflow/core/debug/debug_grpc_testlib.cc
+++ b/tensorflow/core/debug/debug_grpc_testlib.cc
@@ -16,10 +16,12 @@ limitations under the License.
 #include "tensorflow/core/debug/debug_grpc_testlib.h"
 
 #include "tensorflow/core/debug/debug_graph_utils.h"
+#include "tensorflow/core/debug/debugger_event_metadata.pb.h"
 #include "tensorflow/core/framework/summary.pb.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/tracing.h"
 
 namespace tensorflow {
@@ -44,8 +46,6 @@ namespace test {
           tensorflow::str_util::Split(val.node_name(), ':');
 
       const string node_name = name_items[0];
-      int32 output_slot = 0;
-      tensorflow::strings::safe_strto32(name_items[1], &output_slot);
       const string debug_op = name_items[2];
 
       const TensorProto& tensor_proto = val.tensor();
@@ -54,9 +54,24 @@ namespace test {
         return ::grpc::Status::CANCELLED;
       }
 
-      device_names.push_back(val.tag());
+      // Obtain the device name, which is encoded in JSON.
+      third_party::tensorflow::core::debug::DebuggerEventMetadata metadata;
+      for (int i = 0; i < val.metadata().plugin_data_size(); i++) {
+        if (val.metadata().plugin_data(i).plugin_name() != "debugger") {
+          // This plugin data was meant for another plugin.
+          continue;
+        }
+        auto status = tensorflow::protobuf::util::JsonStringToMessage(
+            val.metadata().plugin_data(i).content(), &metadata);
+        if (status.ok()) {
+          // The device name has been determined.
+          break;
+        }
+      }
+
+      device_names.push_back(metadata.device());
       node_names.push_back(node_name);
-      output_slots.push_back(output_slot);
+      output_slots.push_back(metadata.output_slot());
       debug_ops.push_back(debug_op);
       debug_tensors.push_back(tensor);
     }
diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index 88dffb934c..69fc367789 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -26,11 +26,13 @@ limitations under the License.
 #pragma comment(lib,"Ws2_32.lib")
 #endif
 
+#include "tensorflow/core/debug/debugger_event_metadata.pb.h"
 #include "tensorflow/core/framework/summary.pb.h"
 #include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/util/event.pb.h"
 
 #define GRPC_OSS_UNIMPLEMENTED_ERROR \
@@ -55,7 +57,32 @@ Event WrapTensorAsEvent(const DebugNodeKey& debug_node_key,
   // "DebugIdentity", the debug node_name in the Summary proto will be
   // "foo/node_a:0:DebugIdentity".
   summ_val->set_node_name(debug_node_key.debug_node_name);
-  summ_val->set_tag(debug_node_key.device_name);
+
+  // Tag by the node name. This allows TensorBoard to quickly fetch data per op.
+  summ_val->set_tag(debug_node_key.node_name);
+
+  // Store data within debugger metadata to be stored for each event.
+  third_party::tensorflow::core::debug::DebuggerEventMetadata metadata;
+  metadata.set_device(debug_node_key.device_name);
+  metadata.set_output_slot(debug_node_key.output_slot);
+
+  // Encode the data in JSON.
+  string json_output;
+  tensorflow::protobuf::util::JsonPrintOptions json_options;
+  json_options.always_print_primitive_fields = true;
+  auto status = tensorflow::protobuf::util::MessageToJsonString(
+      metadata, &json_output, json_options);
+  if (status.ok()) {
+    // Store summary metadata. Set the plugin to use this data as "debugger".
+    SummaryMetadata::PluginData* plugin_data =
+        summ_val->mutable_metadata()->add_plugin_data();
+    plugin_data->set_plugin_name("debugger");
+    plugin_data->set_content(json_output);
+  } else {
+    LOG(WARNING) << "Failed to convert DebuggerEventMetadata proto to JSON. "
+                 << "The debug_node_name is " << debug_node_key.debug_node_name
+                 << ".";
+  }
 
   if (tensor.dtype() == DT_STRING) {
     // Treat DT_STRING specially, so that tensor_util.MakeNdarray can convert
diff --git a/tensorflow/core/debug/debug_io_utils_test.cc b/tensorflow/core/debug/debug_io_utils_test.cc
index 35c95fb98c..08ef4001bc 100644
--- a/tensorflow/core/debug/debug_io_utils_test.cc
+++ b/tensorflow/core/debug/debug_io_utils_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/debug/debug_io_utils.h"
 
+#include "tensorflow/core/debug/debugger_event_metadata.pb.h"
 #include "tensorflow/core/framework/summary.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/lib/core/notification.h"
@@ -124,10 +125,18 @@ TEST_F(DebugIOUtilsTest, DumpStringTensorToFileSunnyDay) {
 
   ASSERT_GE(wall_time, event.wall_time());
   ASSERT_EQ(1, event.summary().value().size());
-  ASSERT_EQ(kDebugNodeKey.device_name, event.summary().value(0).tag());
+  ASSERT_EQ(kDebugNodeKey.node_name, event.summary().value(0).tag());
   ASSERT_EQ(kDebugNodeKey.debug_node_name,
             event.summary().value(0).node_name());
 
+  // Determine and validate some information from the metadata.
+  third_party::tensorflow::core::debug::DebuggerEventMetadata metadata;
+  auto status = tensorflow::protobuf::util::JsonStringToMessage(
+      event.summary().value(0).metadata().plugin_data(0).content(), &metadata);
+  ASSERT_TRUE(status.ok());
+  ASSERT_EQ(kDebugNodeKey.device_name, metadata.device());
+  ASSERT_EQ(kDebugNodeKey.output_slot, metadata.output_slot());
+
   Tensor b_prime(DT_STRING);
   ASSERT_TRUE(b_prime.FromProto(event.summary().value(0).tensor()));
 
@@ -229,10 +238,19 @@ TEST_F(DebugIOUtilsTest, PublishTensorToMultipleFileURLs) {
 
     ASSERT_GE(wall_time, event.wall_time());
     ASSERT_EQ(1, event.summary().value().size());
-    ASSERT_EQ(kDebugNodeKey.device_name, event.summary().value(0).tag());
+    ASSERT_EQ(kDebugNodeKey.node_name, event.summary().value(0).tag());
     ASSERT_EQ(kDebugNodeKey.debug_node_name,
               event.summary().value(0).node_name());
 
+    // Determine and validate some information from the metadata.
+    third_party::tensorflow::core::debug::DebuggerEventMetadata metadata;
+    auto status = tensorflow::protobuf::util::JsonStringToMessage(
+        event.summary().value(0).metadata().plugin_data(0).content(),
+        &metadata);
+    ASSERT_TRUE(status.ok());
+    ASSERT_EQ(kDebugNodeKey.device_name, metadata.device());
+    ASSERT_EQ(kDebugNodeKey.output_slot, metadata.output_slot());
+
     Tensor a_prime(DT_FLOAT);
     ASSERT_TRUE(a_prime.FromProto(event.summary().value(0).tensor()));
 
@@ -333,10 +351,19 @@ TEST_F(DebugIOUtilsTest, PublishTensorConcurrentlyToPartiallyOverlappingPaths) {
 
       ASSERT_GE(wall_time, event.wall_time());
       ASSERT_EQ(1, event.summary().value().size());
-      ASSERT_EQ(kDebugNodeKey.device_name, event.summary().value(0).tag());
+      ASSERT_EQ(kDebugNodeKey.node_name, event.summary().value(0).tag());
       ASSERT_EQ(kDebugNodeKey.debug_node_name,
                 event.summary().value(0).node_name());
 
+      // Determine and validate some information from the metadata.
+      third_party::tensorflow::core::debug::DebuggerEventMetadata metadata;
+      auto status = tensorflow::protobuf::util::JsonStringToMessage(
+          event.summary().value(0).metadata().plugin_data(0).content(),
+          &metadata);
+      ASSERT_TRUE(status.ok());
+      ASSERT_EQ(kDebugNodeKey.device_name, metadata.device());
+      ASSERT_EQ(kDebugNodeKey.output_slot, metadata.output_slot());
+
       Tensor a_prime(DT_FLOAT);
       ASSERT_TRUE(a_prime.FromProto(event.summary().value(0).tensor()));
 
diff --git a/tensorflow/core/debug/debugger_event_metadata.proto b/tensorflow/core/debug/debugger_event_metadata.proto
new file mode 100644
index 0000000000..44ef305f5a
--- /dev/null
+++ b/tensorflow/core/debug/debugger_event_metadata.proto
@@ -0,0 +1,9 @@
+syntax = "proto3";
+
+package third_party.tensorflow.core.debug;
+
+// Encapsulates per-event data related to debugging.
+message DebuggerEventMetadata {
+  string device = 1;
+  int32 output_slot = 2;
+};
diff --git a/tensorflow/core/kernels/summary_tensor_op_test.cc b/tensorflow/core/kernels/summary_tensor_op_test.cc
index 0006a71bd7..010ff443fa 100644
--- a/tensorflow/core/kernels/summary_tensor_op_test.cc
+++ b/tensorflow/core/kernels/summary_tensor_op_test.cc
@@ -85,8 +85,15 @@ TEST_F(SummaryTensorOpV2Test, BasicPluginData) {
   ASSERT_EQ(0, out_tensor->dims());
   Summary summary;
   ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
-
   ASSERT_EQ(1, summary.value_size());
+
+  // Check the content of the tensor stored in the summary.
+  Tensor string_content_tensor;
+  CHECK(string_content_tensor.FromProto(summary.value(0).tensor()));
+  ASSERT_EQ("some string tensor content",
+            string_content_tensor.scalar<string>()());
+
+  // Check plugin-related data.
   ASSERT_EQ("tag_foo", summary.value(0).tag());
   ASSERT_EQ(2, summary.value(0).metadata().plugin_data_size());
   ASSERT_EQ("foo", summary.value(0).metadata().plugin_data(0).plugin_name());
diff --git a/tensorflow/python/ops/summary_ops.py b/tensorflow/python/ops/summary_ops.py
index 4ad0862dcc..3d19bc2526 100644
--- a/tensorflow/python/ops/summary_ops.py
+++ b/tensorflow/python/ops/summary_ops.py
@@ -131,7 +131,6 @@ def _tensor_summary_v2(  # pylint: disable=invalid-name
     val = gen_logging_ops._tensor_summary_v2(
         tensor=tensor,
         tag=tag,
-        description="",
         name=scope,
         serialized_summary_metadata=serialized_summary_metadata)
     summary_op_util.collect(val, collections, [ops.GraphKeys.SUMMARIES])
diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py
index 7ff01a51f3..f3600793a6 100644
--- a/tensorflow/python/summary/summary.py
+++ b/tensorflow/python/summary/summary.py
@@ -20,6 +20,7 @@ See the @{$python/summary} guide.
 @@FileWriter
 @@FileWriterCache
 @@tensor_summary
+@@_tensor_summary_v2
 @@scalar
 @@histogram
 @@audio
@@ -28,6 +29,7 @@ See the @{$python/summary} guide.
 @@merge
 @@merge_all
 @@get_summary_description
+@@PluginAsset
 @@get_plugin_asset
 @@get_all_plugin_assets
 """
diff --git a/tensorflow/python/summary/text_summary.py b/tensorflow/python/summary/text_summary.py
index 52bc913b2a..2132dc6eb8 100644
--- a/tensorflow/python/summary/text_summary.py
+++ b/tensorflow/python/summary/text_summary.py
@@ -23,13 +23,26 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from collections import namedtuple
 import json
 
+from tensorflow.core.framework import summary_pb2
 from tensorflow.python.framework import dtypes
-from tensorflow.python.ops.summary_ops import tensor_summary
+from tensorflow.python.ops.summary_ops import _tensor_summary_v2
 from tensorflow.python.summary import plugin_asset
+from tensorflow.python.util import deprecation
 
+PLUGIN_NAME = "text"
 
+# Contains event-related data specific to the text plugin.
+_TextPluginData = namedtuple("_TextPluginData", [])
+
+
+@deprecation.deprecated_args(
+    "2017-06-13",
+    "collections is deprecated. Instead of using collections to associate "
+    "plugins to events, add a PluginData field to the SummaryMetadata of a "
+    "Value proto.", "collections")
 def text_summary(name, tensor, collections=None):
   """Summarizes textual data.
 
@@ -60,9 +73,16 @@ def text_summary(name, tensor, collections=None):
     raise ValueError("Expected tensor %s to have dtype string, got %s" %
                      (tensor.name, tensor.dtype))
 
-  t_summary = tensor_summary(name, tensor, collections=collections)
-  text_assets = plugin_asset.get_plugin_asset(TextSummaryPluginAsset)
-  text_assets.register_tensor(t_summary.op.name)
+  summary_metadata = summary_pb2.SummaryMetadata()
+  text_plugin_data = _TextPluginData()
+  data_dict = text_plugin_data._asdict()  # pylint: disable=protected-access
+  summary_metadata.plugin_data.add(
+      plugin_name=PLUGIN_NAME, content=json.dumps(data_dict))
+  t_summary = _tensor_summary_v2(
+      name=name,
+      tensor=tensor,
+      summary_metadata=summary_metadata,
+      collections=collections)
   return t_summary
 
 
diff --git a/tensorflow/python/summary/text_summary_test.py b/tensorflow/python/summary/text_summary_test.py
index 31009702ca..4d357918f6 100644
--- a/tensorflow/python/summary/text_summary_test.py
+++ b/tensorflow/python/summary/text_summary_test.py
@@ -17,7 +17,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.framework import ops as framework_ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import googletest
@@ -43,16 +42,11 @@ class TextPluginTest(test_util.TensorFlowTestCase):
       # The API accepts vectors.
       arr = array_ops.constant(["one", "two", "three"])
       summ = text_summary.text_summary("foo", arr)
-      self.assertEqual(summ.op.type, "TensorSummary")
+      self.assertEqual(summ.op.type, "TensorSummaryV2")
 
       # the API accepts scalars
       summ = text_summary.text_summary("foo", array_ops.constant("one"))
-      self.assertEqual(summ.op.type, "TensorSummary")
-
-  def testTextSummaryCollections(self):
-    text_summary.text_summary("bar", array_ops.constant("2"), collections=[])
-    summaries = framework_ops.get_collection(framework_ops.GraphKeys.SUMMARIES)
-    self.assertEqual(len(summaries), 0)
+      self.assertEqual(summ.op.type, "TensorSummaryV2")
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py
index 05f97fb284..8ce49d623d 100644
--- a/tensorflow/python/summary/writer/writer.py
+++ b/tensorflow/python/summary/writer/writer.py
@@ -86,6 +86,14 @@ class SummaryToEventTransformer(object):
           meta_graph.create_meta_graph_def(graph_def=graph_def or
                                            maybe_graph_as_def))
 
+    # This set contains tags of Summary Values that have been encountered
+    # already. The motivation here is that the SummaryWriter only keeps the
+    # metadata property (which is a SummaryMetadata proto) of the first Summary
+    # Value encountered for each tag. The SummaryWriter strips away the
+    # SummaryMetadata for all subsequent Summary Values with tags seen
+    # previously. This saves space.
+    self._seen_summary_tags = set()
+
   def add_summary(self, summary, global_step=None):
     """Adds a `Summary` protocol buffer to the event file.
 
@@ -108,6 +116,24 @@ class SummaryToEventTransformer(object):
       summ = summary_pb2.Summary()
       summ.ParseFromString(summary)
       summary = summ
+
+    # We strip metadata from values with tags that we have seen before in order
+    # to save space - we just store the metadata on the first value with a
+    # specific tag.
+    for value in summary.value:
+      if not value.metadata:
+        continue
+
+      if value.tag in self._seen_summary_tags:
+        # This tag has been encountered before. Strip the metadata.
+        value.ClearField("metadata")
+        continue
+
+      # We encounter a value with a tag we have not encountered previously. And
+      # it has metadata. Remember to strip metadata from future values with this
+      # tag string.
+      self._seen_summary_tags.add(value.tag)
+
     event = event_pb2.Event(summary=summary)
     self._add_event(event, global_step)
 
diff --git a/tensorflow/python/summary/writer/writer_test.py b/tensorflow/python/summary/writer/writer_test.py
index 8c34eb82e3..3d27b11cb9 100644
--- a/tensorflow/python/summary/writer/writer_test.py
+++ b/tensorflow/python/summary/writer/writer_test.py
@@ -317,6 +317,63 @@ class SummaryWriterTestCase(test.TestCase):
     # We should be done.
     self.assertRaises(StopIteration, lambda: next(rr))
 
+  def testPluginMetadataStrippedFromSubsequentEvents(self):
+    test_dir = self._CleanTestDir("basics")
+    sw = writer.FileWriter(test_dir)
+
+    sw.add_session_log(event_pb2.SessionLog(status=SessionLog.START), 1)
+
+    # We add 2 summaries with the same tags. They both have metadata. The writer
+    # should strip the metadata from the second one.
+    value = summary_pb2.Summary.Value(tag="foo", simple_value=10.0)
+    value.metadata.plugin_data.add(plugin_name="bar", content="... content ...")
+    sw.add_summary(summary_pb2.Summary(value=[value]), 10)
+    value = summary_pb2.Summary.Value(tag="foo", simple_value=10.0)
+    value.metadata.plugin_data.add(plugin_name="bar", content="... content ...")
+    sw.add_summary(summary_pb2.Summary(value=[value]), 10)
+
+    sw.close()
+    rr = self._EventsReader(test_dir)
+
+    # The first event should list the file_version.
+    ev = next(rr)
+    self._assertRecent(ev.wall_time)
+    self.assertEquals("brain.Event:2", ev.file_version)
+
+    # The next event should be the START message.
+    ev = next(rr)
+    self._assertRecent(ev.wall_time)
+    self.assertEquals(1, ev.step)
+    self.assertEquals(SessionLog.START, ev.session_log.status)
+
+    # This is the first event with tag foo. It should contain SummaryMetadata.
+    ev = next(rr)
+    self.assertProtoEquals("""
+      value {
+        tag: "foo"
+        simple_value: 10.0
+        metadata {
+          plugin_data {
+            plugin_name: "bar"
+            content: "... content ..."
+          }
+        }
+      }
+      """, ev.summary)
+
+    # This is the second event with tag foo. It should lack SummaryMetadata
+    # because the file writer should have stripped it.
+    ev = next(rr)
+    self.assertProtoEquals("""
+      value {
+        tag: "foo"
+        simple_value: 10.0
+      }
+      """, ev.summary)
+
+    # We should be done.
+    self.assertRaises(StopIteration, lambda: next(rr))
+
   def testFileWriterWithSuffix(self):
     test_dir = self._CleanTestDir("test_suffix")
     sw = writer.FileWriter(test_dir, filename_suffix="_test_suffix")
-- 
GitLab


From d1630d4d15407aac5fe428e7193f4b4dc5edc191 Mon Sep 17 00:00:00 2001
From: John Lawson <john@codeplay.com>
Date: Wed, 14 Jun 2017 12:18:26 +0100
Subject: [PATCH 0138/1375] [OpenCL] Fixes grpc test failure for SYCL devices

The test constructs a graph containing an IdentityOp on strings,
which doesn't exist on CUDA and SYCL devices. The test expects to fail
for CUDA devices, so add the same expectation for SYCL devices.
---
 tensorflow/core/debug/grpc_session_debug_test.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/debug/grpc_session_debug_test.cc b/tensorflow/core/debug/grpc_session_debug_test.cc
index 3827596a67..d6f35fe24c 100644
--- a/tensorflow/core/debug/grpc_session_debug_test.cc
+++ b/tensorflow/core/debug/grpc_session_debug_test.cc
@@ -279,9 +279,12 @@ TEST_F(GrpcSessionDebugTest, MultiDevices_String) {
 
         DeleteDumpDir();
       } else {
+        // CUDA and SYCL devices do not have an Identity op for strings
         LOG(ERROR) << "Error: " << s;
         ASSERT_TRUE((a_dev.device_type() == DEVICE_GPU) ||
-                    (b_dev.device_type() == DEVICE_GPU));
+                    (a_dev.device_type() == DEVICE_SYCL) ||
+                    (b_dev.device_type() == DEVICE_GPU) ||
+                    (b_dev.device_type() == DEVICE_SYCL));
         ASSERT_FALSE(s.ok());
       }
     }
-- 
GitLab


From 6c4735a07e15cc3a32d82637c33d82c0536bab0c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 07:34:45 -0700
Subject: [PATCH 0139/1375] Add fixed-grid ODE integration routines.

I'd like to have lighter-weight ODE integration ops available since doing backprop through integration with adaptive step size control is much more time consuming.

Also:
* Ran g4 fix on odes/odes_test.
* Reordered some of the code in odes.py. (E.g. IMO better to have public functions first.)
PiperOrigin-RevId: 159225626
---
 .../contrib/integrate/python/ops/odes.py      | 272 +++++++++++++-----
 .../contrib/integrate/python/ops/odes_test.py |  57 +++-
 2 files changed, 242 insertions(+), 87 deletions(-)

diff --git a/tensorflow/contrib/integrate/python/ops/odes.py b/tensorflow/contrib/integrate/python/ops/odes.py
index d13844d613..b4a99867ed 100644
--- a/tensorflow/contrib/integrate/python/ops/odes.py
+++ b/tensorflow/contrib/integrate/python/ops/odes.py
@@ -12,47 +12,55 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """ODE solvers for TensorFlow."""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import abc
 import collections
 
+import six
+
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import functional_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import tensor_array_ops
 
-
-_ButcherTableau = collections.namedtuple(
-    '_ButcherTableau', 'alpha beta c_sol c_mid c_error')
+_ButcherTableau = collections.namedtuple('_ButcherTableau',
+                                         'alpha beta c_sol c_mid c_error')
 
 # Parameters from Shampine (1986), section 4.
 _DORMAND_PRINCE_TABLEAU = _ButcherTableau(
-    alpha=[1/5, 3/10, 4/5, 8/9, 1., 1.],
-    beta=[[1/5],
-          [3/40, 9/40],
-          [44/45, -56/15, 32/9],
-          [19372/6561, -25360/2187, 64448/6561, -212/729],
-          [9017/3168, -355/33, 46732/5247, 49/176, -5103/18656],
-          [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84]],
-    c_sol=[35/384, 0, 500/1113, 125/192, -2187/6784, 11/84, 0],
-    c_mid=[6025192743/30085553152 / 2, 0, 51252292925/65400821598 / 2,
-           -2691868925/45128329728 / 2, 187940372067/1594534317056 / 2,
-           -1776094331/19743644256 / 2, 11237099/235043384 / 2],
-    c_error=[1951/21600 - 35/384,
-             0,
-             22642/50085 - 500/1113,
-             451/720 - 125/192,
-             -12231/42400 - -2187/6784,
-             649/6300 - 11/84,
-             1/60],
-)
+    alpha=[1 / 5, 3 / 10, 4 / 5, 8 / 9, 1., 1.],
+    beta=[
+        [1 / 5],
+        [3 / 40, 9 / 40],
+        [44 / 45, -56 / 15, 32 / 9],
+        [19372 / 6561, -25360 / 2187, 64448 / 6561, -212 / 729],
+        [9017 / 3168, -355 / 33, 46732 / 5247, 49 / 176, -5103 / 18656],
+        [35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84],
+    ],
+    c_sol=[35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84, 0],
+    c_mid=[
+        6025192743 / 30085553152 / 2, 0, 51252292925 / 65400821598 / 2,
+        -2691868925 / 45128329728 / 2, 187940372067 / 1594534317056 / 2,
+        -1776094331 / 19743644256 / 2, 11237099 / 235043384 / 2
+    ],
+    c_error=[
+        1951 / 21600 - 35 / 384,
+        0,
+        22642 / 50085 - 500 / 1113,
+        451 / 720 - 125 / 192,
+        -12231 / 42400 - -2187 / 6784,
+        649 / 6300 - 11 / 84,
+        1 / 60,
+    ],)
 
 
 def _possibly_nonzero(x):
@@ -64,9 +72,10 @@ def _scaled_dot_product(scale, xs, ys, name=None):
   with ops.name_scope(name, 'scaled_dot_product', [scale, xs, ys]) as scope:
     # Some of the parameters in our Butcher tableau include zeros. Using
     # _possibly_nonzero lets us avoid wasted computation.
-    return math_ops.add_n([(scale * x) * y for x, y in zip(xs, ys)
-                           if _possibly_nonzero(x) or _possibly_nonzero(y)],
-                          name=scope)
+    return math_ops.add_n(
+        [(scale * x) * y for x, y in zip(xs, ys)
+         if _possibly_nonzero(x) or _possibly_nonzero(y)],
+        name=scope)
 
 
 def _dot_product(xs, ys, name=None):
@@ -75,7 +84,12 @@ def _dot_product(xs, ys, name=None):
     return math_ops.add_n([x * y for x, y in zip(xs, ys)], name=scope)
 
 
-def _runge_kutta_step(func, y0, f0, t0, dt, tableau=_DORMAND_PRINCE_TABLEAU,
+def _runge_kutta_step(func,
+                      y0,
+                      f0,
+                      t0,
+                      dt,
+                      tableau=_DORMAND_PRINCE_TABLEAU,
                       name=None):
   """Take an arbitrary Runge-Kutta step and estimate error.
 
@@ -115,8 +129,8 @@ def _runge_kutta_step(func, y0, f0, t0, dt, tableau=_DORMAND_PRINCE_TABLEAU,
 
     y1 = array_ops.identity(yi, name='%s/y1' % scope)
     f1 = array_ops.identity(k[-1], name='%s/f1' % scope)
-    y1_error = _scaled_dot_product(dt_cast, tableau.c_error, k,
-                                   name='%s/y1_error' % scope)
+    y1_error = _scaled_dot_product(
+        dt_cast, tableau.c_error, k, name='%s/y1_error' % scope)
     return (y1, f1, y1_error, k)
 
 
@@ -208,15 +222,15 @@ def _optimal_step_size(last_step,
                        order=5,
                        name=None):
   """Calculate the optimal size for the next Runge-Kutta step."""
-  with ops.name_scope(
-      name, 'optimal_step_size', [last_step, error_ratio]) as scope:
+  with ops.name_scope(name, 'optimal_step_size', [last_step,
+                                                  error_ratio]) as scope:
     error_ratio = math_ops.cast(error_ratio, last_step.dtype)
     exponent = math_ops.cast(1 / order, last_step.dtype)
     # this looks more complex than necessary, but importantly it keeps
     # error_ratio in the numerator so we can't divide by zero:
-    factor = math_ops.maximum(
-        1 / ifactor,
-        math_ops.minimum(error_ratio ** exponent / safety, 1 / dfactor))
+    factor = math_ops.maximum(1 / ifactor,
+                              math_ops.minimum(error_ratio**exponent / safety,
+                                               1 / dfactor))
     return math_ops.div(last_step, factor, name=scope)
 
 
@@ -232,8 +246,9 @@ def _ta_append(tensor_array, value):
   return tensor_array.write(tensor_array.size(), value)
 
 
-class _RungeKuttaState(collections.namedtuple(
-    '_RungeKuttaState', 'y1, f1, t0, t1, dt, interp_coeff')):
+class _RungeKuttaState(
+    collections.namedtuple('_RungeKuttaState',
+                           'y1, f1, t0, t1, dt, interp_coeff')):
   """Saved state of the Runge Kutta solver.
 
   Attributes:
@@ -247,8 +262,8 @@ class _RungeKuttaState(collections.namedtuple(
   """
 
 
-class _History(collections.namedtuple(
-    '_History', 'integrate_points, error_ratio')):
+class _History(
+    collections.namedtuple('_History', 'integrate_points, error_ratio')):
   """Saved integration history for use in `info_dict`.
 
   Attributes:
@@ -258,6 +273,20 @@ class _History(collections.namedtuple(
   """
 
 
+def _assert_increasing(t):
+  assert_increasing = control_flow_ops.Assert(
+      math_ops.reduce_all(t[1:] > t[:-1]), ['`t` must be monotonic increasing'])
+  return ops.control_dependencies([assert_increasing])
+
+
+def _check_input_types(t, y0):
+  if not (y0.dtype.is_floating or y0.dtype.is_complex):
+    raise TypeError('`y0` must have a floating point or complex floating '
+                    'point dtype')
+  if not t.dtype.is_floating:
+    raise TypeError('`t` must have a floating point dtype')
+
+
 def _dopri5(func,
             y0,
             t,
@@ -277,24 +306,24 @@ def _dopri5(func,
     # automatically
     first_step = 1.0
 
-  with ops.name_scope(
-      name, 'dopri5',
-      [y0, t, rtol, atol, safety, ifactor, dfactor, max_num_steps]) as scope:
+  with ops.name_scope(name, 'dopri5', [
+      y0, t, rtol, atol, safety, ifactor, dfactor, max_num_steps
+  ]) as scope:
 
-    first_step = ops.convert_to_tensor(first_step, dtype=t.dtype,
-                                       name='first_step')
+    first_step = ops.convert_to_tensor(
+        first_step, dtype=t.dtype, name='first_step')
     safety = ops.convert_to_tensor(safety, dtype=t.dtype, name='safety')
     ifactor = ops.convert_to_tensor(ifactor, dtype=t.dtype, name='ifactor')
     dfactor = ops.convert_to_tensor(dfactor, dtype=t.dtype, name='dfactor')
-    max_num_steps = ops.convert_to_tensor(max_num_steps, dtype=dtypes.int32,
-                                          name='max_num_steps')
+    max_num_steps = ops.convert_to_tensor(
+        max_num_steps, dtype=dtypes.int32, name='max_num_steps')
 
     def adaptive_runge_kutta_step(rk_state, history, n_steps):
       """Take an adaptive Runge-Kutta step to integrate the ODE."""
       y0, f0, _, t0, dt, interp_coeff = rk_state
       with ops.name_scope('assertions'):
-        check_underflow = control_flow_ops.Assert(
-            t0 + dt > t0, ['underflow in dt', dt])
+        check_underflow = control_flow_ops.Assert(t0 + dt > t0,
+                                                  ['underflow in dt', dt])
         check_max_num_steps = control_flow_ops.Assert(
             n_steps < max_num_steps, ['max_num_steps exceeded'])
         check_numerics = control_flow_ops.Assert(
@@ -320,16 +349,16 @@ def _dopri5(func,
         f_next = control_flow_ops.cond(accept_step, lambda: f1, lambda: f0)
         t_next = control_flow_ops.cond(accept_step, lambda: t0 + dt, lambda: t0)
         interp_coeff = control_flow_ops.cond(
-            accept_step,
-            lambda: _interp_fit_rk(y0, y1, k, dt),
+            accept_step, lambda: _interp_fit_rk(y0, y1, k, dt),
             lambda: interp_coeff)
         dt_next = _optimal_step_size(dt, error_ratio, safety, ifactor, dfactor)
-        rk_state = _RungeKuttaState(
-            y_next, f_next, t0, t_next, dt_next, interp_coeff)
+        rk_state = _RungeKuttaState(y_next, f_next, t0, t_next, dt_next,
+                                    interp_coeff)
 
       with ops.name_scope('update/history'):
-        history = _History(_ta_append(history.integrate_points, t0 + dt),
-                           _ta_append(history.error_ratio, error_ratio))
+        history = _History(
+            _ta_append(history.integrate_points, t0 + dt),
+            _ta_append(history.error_ratio, error_ratio))
       return rk_state, history, n_steps + 1
 
     def interpolate(solution, history, rk_state, i):
@@ -337,18 +366,14 @@ def _dopri5(func,
       with ops.name_scope('interpolate'):
         rk_state, history, _ = control_flow_ops.while_loop(
             lambda rk_state, *_: t[i] > rk_state.t1,
-            adaptive_runge_kutta_step,
-            (rk_state, history, 0),
+            adaptive_runge_kutta_step, (rk_state, history, 0),
             name='integrate_loop')
-        y = _interp_evaluate(
-            rk_state.interp_coeff, rk_state.t0, rk_state.t1, t[i])
+        y = _interp_evaluate(rk_state.interp_coeff, rk_state.t0, rk_state.t1,
+                             t[i])
         solution = solution.write(i, y)
         return solution, history, rk_state, i + 1
 
-    assert_increasing = control_flow_ops.Assert(
-        math_ops.reduce_all(t[1:] > t[:-1]),
-        ['`t` must be monotonic increasing'])
-    with ops.control_dependencies([assert_increasing]):
+    with _assert_increasing(t):
       num_times = array_ops.size(t)
 
     solution = tensor_array_ops.TensorArray(
@@ -363,8 +388,7 @@ def _dopri5(func,
 
     solution, history, _, _ = control_flow_ops.while_loop(
         lambda _, __, ___, i: i < num_times,
-        interpolate,
-        (solution, history, rk_state, 1),
+        interpolate, (solution, history, rk_state, 1),
         name='interpolate_loop')
 
     y = solution.stack(name=scope)
@@ -373,9 +397,11 @@ def _dopri5(func,
       return y
     else:
       integrate_points = history.integrate_points.stack()
-      info_dict = {'num_func_evals': 6 * array_ops.size(integrate_points) + 1,
-                   'integrate_points': integrate_points,
-                   'error_ratio': history.error_ratio.stack()}
+      info_dict = {
+          'num_func_evals': 6 * array_ops.size(integrate_points) + 1,
+          'integrate_points': integrate_points,
+          'error_ratio': history.error_ratio.stack()
+      }
       return (y, info_dict)
 
 
@@ -390,7 +416,7 @@ def odeint(func,
            name=None):
   """Integrate a system of ordinary differential equations.
 
-  Solves the initial value problem for a non-stiff system of first order ode-s:
+  Solves the initial value problem for a non-stiff system of first order ODEs:
 
     ```
     dy/dt = func(y, t), y(t[0]) = y0
@@ -483,21 +509,109 @@ def odeint(func,
     # arbitrarily nested tuple. This will help performance and usability by
     # avoiding the need to pack/unpack in user functions.
     y0 = ops.convert_to_tensor(y0, name='y0')
-    if not (y0.dtype.is_floating or y0.dtype.is_complex):
-      raise TypeError('`y0` must have a floating point or complex floating '
-                      'point dtype')
-
     t = ops.convert_to_tensor(t, preferred_dtype=dtypes.float64, name='t')
-    if not t.dtype.is_floating:
-      raise TypeError('`t` must have a floating point dtype')
+    _check_input_types(t, y0)
 
     error_dtype = abs(y0).dtype
     rtol = ops.convert_to_tensor(rtol, dtype=error_dtype, name='rtol')
     atol = ops.convert_to_tensor(atol, dtype=error_dtype, name='atol')
 
-    return _dopri5(func, y0, t,
-                   rtol=rtol,
-                   atol=atol,
-                   full_output=full_output,
-                   name=scope,
-                   **options)
+    return _dopri5(
+        func,
+        y0,
+        t,
+        rtol=rtol,
+        atol=atol,
+        full_output=full_output,
+        name=scope,
+        **options)
+
+
+class _FixedGridIntegrator(six.with_metaclass(abc.ABCMeta)):
+  """Base class for fixed-grid ODE integrators."""
+
+  def integrate(self, evol_func, y0, time_grid):
+    time_delta_grid = time_grid[1:] - time_grid[:-1]
+
+    scan_func = self._make_scan_func(evol_func)
+
+    y_grid = functional_ops.scan(scan_func, (time_grid[:-1], time_delta_grid),
+                                 y0)
+    return array_ops.concat([[y0], y_grid], axis=0)
+
+  def _make_scan_func(self, evol_func):
+
+    def scan_func(y, t_and_dt):
+      t, dt = t_and_dt
+      dy = self._step_func(evol_func, t, dt, y)
+      dy = math_ops.cast(dy, dtype=y.dtype)
+      return y + dy
+
+    return scan_func
+
+  @abc.abstractmethod
+  def _step_func(self, evol_func, t, dt, y):
+    pass
+
+
+class _MidpointFixedGridIntegrator(_FixedGridIntegrator):
+
+  def _step_func(self, evol_func, t, dt, y):
+    dt_cast = math_ops.cast(dt, y.dtype)
+    # yn1 = yn + h * f(tn + h/2, yn + f(tn, yn) * h/2)
+    return dt_cast * evol_func(y + evol_func(y, t) * dt_cast / 2, t + dt / 2)
+
+
+class _RK4FixedGridIntegrator(_FixedGridIntegrator):
+
+  def _step_func(self, evol_func, t, dt, y):
+    k1 = evol_func(y, t)
+    half_step = t + dt / 2
+    dt_cast = math_ops.cast(dt, y.dtype)
+
+    k2 = evol_func(y + dt_cast * k1 / 2, half_step)
+    k3 = evol_func(y + dt_cast * k2 / 2, half_step)
+    k4 = evol_func(y + dt_cast * k3, t + dt)
+    return math_ops.add_n([k1, 2 * k2, 2 * k3, k4]) * (dt_cast / 6)
+
+
+def odeint_fixed(func, y0, t, method='rk4', name=None):
+  """ODE integration on a fixed grid (with no step size control).
+
+  Useful in certain scenarios to avoid the overhead of adaptive step size
+  control, e.g. when differentiation of the integration result is desired and/or
+  the time grid is known a priori to be sufficient.
+
+  Args:
+    func: Function that maps a Tensor holding the state `y` and a scalar Tensor
+      `t` into a Tensor of state derivatives with respect to time.
+    y0: N-D Tensor giving starting value of `y` at time point `t[0]`.
+    t: 1-D Tensor holding a sequence of time points for which to solve for
+      `y`. The initial time point should be the first element of this sequence,
+      and each time must be larger than the previous time. May have any floating
+      point dtype.
+    method: One of 'midpoint' or 'rk4'.
+    name: Optional name for the resulting operation.
+
+  Returns:
+    y: (N+1)-D tensor, where the first dimension corresponds to different
+      time points. Contains the solved value of y for each desired time point in
+      `t`, with the initial value `y0` being the first element along the first
+      dimension.
+
+  Raises:
+    ValueError: Upon caller errors.
+  """
+  with ops.name_scope(name, 'odeint_fixed', [y0, t]):
+    t = ops.convert_to_tensor(t, preferred_dtype=dtypes.float64, name='t')
+    y0 = ops.convert_to_tensor(y0, name='y0')
+    _check_input_types(t, y0)
+
+    with _assert_increasing(t):
+      with ops.name_scope(method):
+        if method == 'midpoint':
+          return _MidpointFixedGridIntegrator().integrate(func, y0, t)
+        elif method == 'rk4':
+          return _RK4FixedGridIntegrator().integrate(func, y0, t)
+        else:
+          raise ValueError('method not supported: {!s}'.format(method))
diff --git a/tensorflow/contrib/integrate/python/ops/odes_test.py b/tensorflow/contrib/integrate/python/ops/odes_test.py
index 009e1d1f77..3ec01212d2 100644
--- a/tensorflow/contrib/integrate/python/ops/odes_test.py
+++ b/tensorflow/contrib/integrate/python/ops/odes_test.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Tests for ODE solvers."""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -165,11 +166,9 @@ class OdeIntTest(test.TestCase):
 
     with self.test_session() as sess:
       y_solved_0, info_0 = sess.run(
-          odes.odeint(
-              self.func, self.y0, times0, full_output=True))
+          odes.odeint(self.func, self.y0, times0, full_output=True))
       y_solved_1, info_1 = sess.run(
-          odes.odeint(
-              self.func, self.y0, times1, full_output=True))
+          odes.odeint(self.func, self.y0, times1, full_output=True))
 
     self.assertAllClose(y_solved_0, y_solved_1[::10])
     self.assertEqual(info_0['num_func_evals'], info_1['num_func_evals'])
@@ -182,11 +181,9 @@ class OdeIntTest(test.TestCase):
         full_output=True, method='dopri5', options=dict(max_num_steps=2000))
     with self.test_session() as sess:
       _, info_0 = sess.run(
-          odes.odeint(
-              self.func, self.y0, t, rtol=0, atol=1e-6, **kwargs))
+          odes.odeint(self.func, self.y0, t, rtol=0, atol=1e-6, **kwargs))
       _, info_1 = sess.run(
-          odes.odeint(
-              self.func, self.y0, t, rtol=0, atol=1e-9, **kwargs))
+          odes.odeint(self.func, self.y0, t, rtol=0, atol=1e-9, **kwargs))
     self.assertAllClose(
         info_0['integrate_points'].size * 1000**0.2,
         float(info_1['integrate_points'].size),
@@ -243,5 +240,49 @@ class InterpolationTest(test.TestCase):
         sess.run(y_invalid)
 
 
+class OdeIntFixedTest(test.TestCase):
+
+  def _test_integrate_sine(self, method):
+
+    def evol_func(y, t):
+      del t
+      return array_ops.stack([y[1], -y[0]])
+
+    y0 = [0., 1.]
+    time_grid = np.linspace(0., 10., 200)
+    y_grid = odes.odeint_fixed(evol_func, y0, time_grid, method=method)
+
+    with self.test_session() as sess:
+      y_grid_array = sess.run(y_grid)
+
+    np.testing.assert_allclose(
+        y_grid_array[:, 0], np.sin(time_grid), rtol=1e-2, atol=1e-2)
+
+  def _test_integrate_gaussian(self, method):
+
+    def evol_func(y, t):
+      return -math_ops.cast(t, dtype=y.dtype) * y[0]
+
+    y0 = [1.]
+    time_grid = np.linspace(0., 2., 100)
+    y_grid = odes.odeint_fixed(evol_func, y0, time_grid, method=method)
+
+    with self.test_session() as sess:
+      y_grid_array = sess.run(y_grid)
+
+    np.testing.assert_allclose(
+        y_grid_array[:, 0], np.exp(-time_grid**2 / 2), rtol=1e-2, atol=1e-2)
+
+  def _test_everything(self, method):
+    self._test_integrate_sine(method)
+    self._test_integrate_gaussian(method)
+
+  def test_midpoint(self):
+    self._test_everything('midpoint')
+
+  def test_rk4(self):
+    self._test_everything('rk4')
+
+
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From 7d3901a91caacfabf2925ad5123a65a04efef652 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 07:40:02 -0700
Subject: [PATCH 0140/1375] Add num_features to TensorForest parameter proto.

PiperOrigin-RevId: 159226019
---
 .../contrib/tensor_forest/proto/tensor_forest_params.proto       | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto b/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
index 3813bbce62..49b19e0b62 100644
--- a/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
+++ b/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
@@ -120,6 +120,7 @@ message TensorForestParams {
   // --------- Parameters that can't change by definition --------------- //
   int32 num_trees = 6;
   int32 max_nodes = 7;
+  int32 num_features = 21;
 
   decision_trees.InequalityTest.Type inequality_test_type = 19;
 
-- 
GitLab


From 452aa0dc7d66c5661b3da7c8fbdff92e0df33590 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 08:25:54 -0700
Subject: [PATCH 0141/1375] Implements Debug Estimators.

Debug Estimators are simple bias-only estimators that can be used as a baseline or to facilitate end-to-end development.

PiperOrigin-RevId: 159229816
---
 tensorflow/contrib/learn/BUILD                |  22 +
 .../learn/python/learn/estimators/debug.py    | 321 +++++++
 .../python/learn/estimators/debug_test.py     | 840 ++++++++++++++++++
 3 files changed, 1183 insertions(+)
 create mode 100644 tensorflow/contrib/learn/python/learn/estimators/debug.py
 create mode 100644 tensorflow/contrib/learn/python/learn/estimators/debug_test.py

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 0ab39e35d0..980f971c50 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -780,6 +780,28 @@ py_test(
     ],
 )
 
+py_test(
+    name = "debug_test",
+    size = "medium",
+    srcs = ["python/learn/estimators/debug_test.py"],
+    shard_count = 4,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":learn",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/contrib/learn/python/learn/datasets",
+        "//tensorflow/contrib/metrics:metrics_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
 py_test(
     name = "composable_model_test",
     size = "medium",
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug.py b/tensorflow/contrib/learn/python/learn/estimators/debug.py
new file mode 100644
index 0000000000..010448be12
--- /dev/null
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug.py
@@ -0,0 +1,321 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Debug estimators.
+
+Debug estimators are bias-only estimators that can be used for debugging
+and as simple baselines.
+
+Example:
+
+```
+# Build DebugClassifier
+classifier = DebugClassifier()
+
+# Input builders
+def input_fn_train: # returns x, y (where y represents label's class index).
+  pass
+
+def input_fn_eval: # returns x, y (where y represents label's class index).
+  pass
+
+# Fit model.
+classifier.fit(input_fn=input_fn_train)
+
+# Evaluate cross entropy between the test and train labels.
+loss = classifier.evaluate(input_fn=input_fn_eval)["loss"]
+
+# predict_classes outputs the most commonly seen class in training.
+predicted_label = classifier.predict_classes(new_samples)
+
+# predict_proba outputs the class distribution from training.
+label_distribution = classifier.predict_proba(new_samples)
+```
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.layers.python.layers import optimizers
+from tensorflow.contrib.learn.python.learn.estimators import estimator
+from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
+from tensorflow.contrib.learn.python.learn.estimators import prediction_key
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+
+
+def _get_feature_dict(features):
+  if isinstance(features, dict):
+    return features
+  return {"": features}
+
+
+def debug_model_fn(features, labels, mode, params, config=None):
+  """Model_fn for debug models.
+
+  Args:
+    features: `Tensor` or dict of `Tensor` (depends on data passed to `fit`).
+    labels: Labels that are compatible with the `_Head` instance in `params`.
+    mode: Defines whether this is training, evaluation or prediction.
+      See `ModeKeys`.
+    params: A dict of hyperparameters containing:
+      * head: A `_Head` instance.
+    config: `RunConfig` object to configure the runtime settings.
+
+  Raises:
+    KeyError: If weight column is specified but not present.
+    ValueError: If features is an empty dictionary.
+
+  Returns:
+    A `ModelFnOps` instance.
+  """
+  del config  # Unused.
+
+  features = _get_feature_dict(features)
+  if not features:
+    raise ValueError("Features cannot be empty.")
+
+  head = params["head"]
+  size_checks = []
+  batch_size = None
+
+  # The first dimension is assumed to be a batch size and must be consistent
+  # among all of the features.
+  for feature in features.values():
+    first_dim = array_ops.shape(feature)[0]
+    if batch_size is None:
+      batch_size = first_dim
+    else:
+      size_checks.append(check_ops.assert_equal(batch_size, first_dim))
+
+  with ops.control_dependencies(size_checks):
+    logits = array_ops.zeros([batch_size, head.logits_dimension])
+
+  def train_op_fn(loss):
+    return optimizers.optimize_loss(
+        loss, global_step=None, learning_rate=0.3, optimizer="Adagrad")
+
+  return head.create_model_fn_ops(
+      features=features,
+      labels=labels,
+      mode=mode,
+      train_op_fn=train_op_fn,
+      logits=logits)
+
+
+class DebugClassifier(estimator.Estimator):
+  """A classifier for TensorFlow Debug models.
+
+  Example:
+
+  ```python
+
+  # Build DebugClassifier
+  classifier = DebugClassifier()
+
+  # Input builders
+  def input_fn_train: # returns x, y (where y represents label's class index).
+    pass
+
+  def input_fn_eval: # returns x, y (where y represents label's class index).
+    pass
+
+  # Fit model.
+  classifier.fit(input_fn=input_fn_train)
+
+  # Evaluate cross entropy between the test and train labels.
+  loss = classifier.evaluate(input_fn=input_fn_eval)["loss"]
+
+  # predict_class outputs the most commonly seen class in training.
+  predicted_label = classifier.predict_class(new_samples)
+
+  # predict_proba outputs the class distribution from training.
+  label_distribution = classifier.predict_proba(new_samples)
+  ```
+
+  Input of `fit` and `evaluate` should have following features,
+    otherwise there will be a `KeyError`:
+
+  * if `weight_column_name` is not `None`, a feature with
+     `key=weight_column_name` whose value is a `Tensor`.
+  """
+
+  def __init__(self,
+               model_dir=None,
+               n_classes=2,
+               weight_column_name=None,
+               config=None,
+               feature_engineering_fn=None):
+    """Initializes a DebugClassifier instance.
+
+    Args:
+      model_dir: Directory to save model parameters, graph and etc. This can
+        also be used to load checkpoints from the directory into a estimator to
+        continue training a previously saved model.
+      n_classes: number of label classes. Default is binary classification.
+        It must be greater than 1. Note: Class labels are integers representing
+        the class index (i.e. values from 0 to n_classes-1). For arbitrary
+        label values (e.g. string labels), convert to class indices first.
+      weight_column_name: A string defining feature column name representing
+        weights. It is used to down weight or boost examples during training. It
+        will be multiplied by the loss of the example.
+      config: `RunConfig` object to configure the runtime settings.
+      feature_engineering_fn: Feature engineering function. Takes features and
+                        labels which are the output of `input_fn` and returns
+                        features and labels which will be fed into the model.
+    Returns:
+      A `DebugClassifier` estimator.
+
+    Raises:
+      ValueError: If `n_classes` < 2.
+    """
+    params = {"head":
+              head_lib._multi_class_head(  # pylint: disable=protected-access
+                  n_classes=n_classes,
+                  weight_column_name=weight_column_name,
+                  enable_centered_bias=True)}
+
+    super(DebugClassifier, self).__init__(
+        model_fn=debug_model_fn,
+        model_dir=model_dir,
+        config=config,
+        params=params,
+        feature_engineering_fn=feature_engineering_fn)
+
+  def predict_classes(self, input_fn=None, batch_size=None):
+    """Returns predicted classes for given features.
+
+    Args:
+      input_fn: Input function.
+      batch_size: Override default batch size.
+
+    Returns:
+      An iterable of predicted classes. Each predicted class is represented by
+      its class index (i.e. integer from 0 to n_classes-1).
+    """
+    key = prediction_key.PredictionKey.CLASSES
+    preds = self.predict(
+        input_fn=input_fn, batch_size=batch_size, outputs=[key])
+    return (pred[key] for pred in preds)
+
+  def predict_proba(self,
+                    input_fn=None,
+                    batch_size=None):
+    """Returns prediction probabilities for given features.
+
+    Args:
+      input_fn: Input function.
+      batch_size: Override default batch size.
+
+    Returns:
+      An iterable of predicted probabilities with shape [batch_size, n_classes].
+    """
+    key = prediction_key.PredictionKey.PROBABILITIES
+    preds = self.predict(
+        input_fn=input_fn,
+        batch_size=batch_size,
+        outputs=[key])
+    return (pred[key] for pred in preds)
+
+
+class DebugRegressor(estimator.Estimator):
+  """A regressor for TensorFlow Debug models.
+
+  Example:
+
+  ```python
+
+  # Build DebugRegressor
+  regressor = DebugRegressor()
+
+  # Input builders
+  def input_fn_train: # returns x, y (where y represents label's class index).
+    pass
+
+  def input_fn_eval: # returns x, y (where y represents label's class index).
+    pass
+
+  # Fit model.
+  regressor.fit(input_fn=input_fn_train)
+
+  # Evaluate squared-loss between the test and train targets.
+  loss = regressor.evaluate(input_fn=input_fn_eval)["loss"]
+
+  # predict_scores outputs mean value seen during training.
+  predicted_targets = regressor.predict_scores(new_samples)
+  ```
+
+  Input of `fit` and `evaluate` should have following features,
+    otherwise there will be a `KeyError`:
+
+  * if `weight_column_name` is not `None`, a feature with
+     `key=weight_column_name` whose value is a `Tensor`.
+  """
+
+  def __init__(self,
+               model_dir=None,
+               label_dimension=1,
+               weight_column_name=None,
+               config=None,
+               feature_engineering_fn=None):
+    """Initializes a DebugRegressor instance.
+
+    Args:
+      model_dir: Directory to save model parameters, graph and etc. This can
+        also be used to load checkpoints from the directory into a estimator to
+        continue training a previously saved model.
+      label_dimension: Number of regression targets per example. This is the
+        size of the last dimension of the labels and logits `Tensor` objects
+        (typically, these have shape `[batch_size, label_dimension]`).
+      weight_column_name: A string defining feature column name representing
+        weights. It is used to down weight or boost examples during training. It
+        will be multiplied by the loss of the example.
+      config: `RunConfig` object to configure the runtime settings.
+      feature_engineering_fn: Feature engineering function. Takes features and
+                        labels which are the output of `input_fn` and returns
+                        features and labels which will be fed into the model.
+    Returns:
+      A `DebugRegressor` estimator.
+    """
+
+    params = {
+        "head":
+            head_lib._regression_head(  # pylint: disable=protected-access
+                weight_column_name=weight_column_name,
+                label_dimension=label_dimension,
+                enable_centered_bias=True)
+    }
+
+    super(DebugRegressor, self).__init__(
+        model_fn=debug_model_fn,
+        model_dir=model_dir,
+        config=config,
+        params=params,
+        feature_engineering_fn=feature_engineering_fn)
+
+  def predict_scores(self, input_fn=None, batch_size=None):
+    """Returns predicted scores for given features.
+
+    Args:
+      input_fn: Input function.
+      batch_size: Override default batch size.
+
+    Returns:
+      An iterable of predicted scores.
+    """
+    key = prediction_key.PredictionKey.SCORES
+    preds = self.predict(
+        input_fn=input_fn, batch_size=batch_size, outputs=[key])
+    return (pred[key] for pred in preds)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
new file mode 100644
index 0000000000..935e66ee8c
--- /dev/null
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
@@ -0,0 +1,840 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Debug estimators."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import functools
+import operator
+import tempfile
+
+import numpy as np
+
+from tensorflow.contrib.layers.python.layers import feature_column
+from tensorflow.contrib.layers.python.layers import feature_column_ops
+from tensorflow.contrib.learn.python.learn import experiment
+from tensorflow.contrib.learn.python.learn.datasets import base
+from tensorflow.contrib.learn.python.learn.estimators import _sklearn
+from tensorflow.contrib.learn.python.learn.estimators import debug
+from tensorflow.contrib.learn.python.learn.estimators import estimator_test_utils
+from tensorflow.contrib.learn.python.learn.estimators import run_config
+from tensorflow.contrib.learn.python.learn.estimators import test_data
+from tensorflow.contrib.learn.python.learn.metric_spec import MetricSpec
+from tensorflow.contrib.metrics.python.ops import metric_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+from tensorflow.python.training import input as input_lib
+
+
+NUM_EXAMPLES = 100
+N_CLASSES = 5  #  Cardinality of multiclass labels.
+LABEL_DIMENSION = 3  #  Dimensionality of regression labels.
+
+
+def _train_test_split(features_and_labels):
+  features, labels = features_and_labels
+  train_set = (features[:len(features) / 2], labels[:len(features) / 2])
+  test_set = (features[len(features) / 2:], labels[len(features) / 2:])
+  return train_set, test_set
+
+
+def _input_fn_builder(features, labels):
+
+  def input_fn():
+    feature_dict = {'features': constant_op.constant(features)}
+    my_labels = labels
+    if my_labels is not None:
+      my_labels = constant_op.constant(my_labels)
+    return feature_dict, my_labels
+
+  return input_fn
+
+
+class DebugClassifierTest(test.TestCase):
+
+  def setUp(self):
+    np.random.seed(100)
+    self.features = np.random.rand(NUM_EXAMPLES, 5)
+    self.labels = np.random.choice(
+        range(N_CLASSES), p=[0.1, 0.3, 0.4, 0.1, 0.1], size=NUM_EXAMPLES)
+    self.binary_labels = np.random.choice(
+        range(2), p=[0.2, 0.8], size=NUM_EXAMPLES)
+    self.binary_float_labels = np.random.choice(
+        range(2), p=[0.2, 0.8], size=NUM_EXAMPLES)
+
+  def testPredict(self):
+    """Tests that DebugClassifier outputs the majority class."""
+    (train_features, train_labels), (test_features,
+                                     test_labels) = _train_test_split(
+                                         [self.features, self.labels])
+    majority_class, _ = max(collections.Counter(train_labels).items(),
+                            key=operator.itemgetter(1))
+    expected_prediction = np.vstack(
+        [[majority_class] for _ in range(test_labels.shape[0])])
+
+    classifier = debug.DebugClassifier(n_classes=N_CLASSES)
+    classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
+                   steps=50)
+
+    pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features,
+                                                                 None))
+    self.assertAllEqual(expected_prediction, np.vstack(pred))
+
+  def testPredictBinary(self):
+    """Same as above for binary predictions."""
+    (train_features, train_labels), (test_features,
+                                     test_labels) = _train_test_split(
+                                         [self.features, self.binary_labels])
+
+    majority_class, _ = max(collections.Counter(train_labels).items(),
+                            key=operator.itemgetter(1))
+    expected_prediction = np.vstack(
+        [[majority_class] for _ in range(test_labels.shape[0])])
+
+    classifier = debug.DebugClassifier(n_classes=2)
+    classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
+                   steps=50)
+
+    pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features,
+                                                                 None))
+    self.assertAllEqual(expected_prediction, np.vstack(pred))
+
+    (train_features, train_labels), (
+        test_features, test_labels) = _train_test_split(
+            [self.features, self.binary_float_labels])
+
+    majority_class, _ = max(collections.Counter(train_labels).items(),
+                            key=operator.itemgetter(1))
+    expected_prediction = np.vstack(
+        [[majority_class] for _ in range(test_labels.shape[0])])
+
+    classifier = debug.DebugClassifier(n_classes=2)
+    classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
+                   steps=50)
+
+    pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features,
+                                                                 None))
+    self.assertAllEqual(expected_prediction, np.vstack(pred))
+
+  def testPredictProba(self):
+    """Tests that DebugClassifier outputs observed class distribution."""
+    (train_features, train_labels), (test_features,
+                                     test_labels) = _train_test_split(
+                                         [self.features, self.labels])
+
+    class_distribution = np.zeros((1, N_CLASSES))
+    for label in train_labels:
+      class_distribution[0, label] += 1
+    class_distribution /= len(train_labels)
+
+    expected_prediction = np.vstack(
+        [class_distribution for _ in range(test_labels.shape[0])])
+
+    classifier = debug.DebugClassifier(n_classes=N_CLASSES)
+    classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
+                   steps=50)
+
+    pred = classifier.predict_proba(
+        input_fn=_input_fn_builder(test_features, None))
+
+    self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
+
+  def testPredictProbaBinary(self):
+    """Same as above but for binary classification."""
+    (train_features, train_labels), (test_features,
+                                     test_labels) = _train_test_split(
+                                         [self.features, self.binary_labels])
+
+    class_distribution = np.zeros((1, 2))
+    for label in train_labels:
+      class_distribution[0, label] += 1
+    class_distribution /= len(train_labels)
+
+    expected_prediction = np.vstack(
+        [class_distribution for _ in range(test_labels.shape[0])])
+
+    classifier = debug.DebugClassifier(n_classes=2)
+    classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
+                   steps=50)
+
+    pred = classifier.predict_proba(
+        input_fn=_input_fn_builder(test_features, None))
+
+    self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
+
+    (train_features, train_labels), (
+        test_features, test_labels) = _train_test_split(
+            [self.features, self.binary_float_labels])
+
+    class_distribution = np.zeros((1, 2))
+    for label in train_labels:
+      class_distribution[0, int(label)] += 1
+    class_distribution /= len(train_labels)
+
+    expected_prediction = np.vstack(
+        [class_distribution for _ in range(test_labels.shape[0])])
+
+    classifier = debug.DebugClassifier(n_classes=2)
+    classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
+                   steps=50)
+
+    pred = classifier.predict_proba(
+        input_fn=_input_fn_builder(test_features, None))
+
+    self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
+
+  def testExperimentIntegration(self):
+    exp = experiment.Experiment(
+        estimator=debug.DebugClassifier(n_classes=3),
+        train_input_fn=test_data.iris_input_multiclass_fn,
+        eval_input_fn=test_data.iris_input_multiclass_fn)
+    exp.test()
+
+  def _assertInRange(self, expected_min, expected_max, actual):
+    self.assertLessEqual(expected_min, actual)
+    self.assertGreaterEqual(expected_max, actual)
+
+  def testEstimatorContract(self):
+    estimator_test_utils.assert_estimator_contract(self, debug.DebugClassifier)
+
+  def testLogisticRegression_MatrixData(self):
+    """Tests binary classification using matrix data as input."""
+    classifier = debug.DebugClassifier(
+        config=run_config.RunConfig(tf_random_seed=1))
+    input_fn = test_data.iris_input_logistic_fn
+    classifier.fit(input_fn=input_fn, steps=5)
+    scores = classifier.evaluate(input_fn=input_fn, steps=1)
+    self._assertInRange(0.0, 1.0, scores['accuracy'])
+    self.assertIn('loss', scores)
+
+  def testLogisticRegression_MatrixData_Labels1D(self):
+    """Same as the last test, but label shape is [100] instead of [100, 1]."""
+
+    def _input_fn():
+      iris = test_data.prepare_iris_data_for_logistic_regression()
+      return {
+          'feature': constant_op.constant(
+              iris.data, dtype=dtypes.float32)
+      }, constant_op.constant(
+          iris.target, shape=[100], dtype=dtypes.int32)
+
+    classifier = debug.DebugClassifier(config=run_config.RunConfig(
+        tf_random_seed=1))
+    classifier.fit(input_fn=_input_fn, steps=5)
+    scores = classifier.evaluate(input_fn=_input_fn, steps=1)
+    self.assertIn('loss', scores)
+
+  def testLogisticRegression_NpMatrixData(self):
+    """Tests binary classification using numpy matrix data as input."""
+    iris = test_data.prepare_iris_data_for_logistic_regression()
+    train_x = iris.data
+    train_y = iris.target
+    classifier = debug.DebugClassifier(
+        config=run_config.RunConfig(tf_random_seed=1))
+    classifier.fit(x=train_x, y=train_y, steps=5)
+    scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
+    self._assertInRange(0.0, 1.0, scores['accuracy'])
+
+  def _assertBinaryPredictions(self, expected_len, predictions):
+    self.assertEqual(expected_len, len(predictions))
+    for prediction in predictions:
+      self.assertIn(prediction, (0, 1))
+
+  def _assertProbabilities(self, expected_batch_size, expected_n_classes,
+                           probabilities):
+    self.assertEqual(expected_batch_size, len(probabilities))
+    for b in range(expected_batch_size):
+      self.assertEqual(expected_n_classes, len(probabilities[b]))
+      for i in range(expected_n_classes):
+        self._assertInRange(0.0, 1.0, probabilities[b][i])
+
+  def testLogisticRegression_TensorData(self):
+    """Tests binary classification using tensor data as input."""
+
+    def _input_fn(num_epochs=None):
+      features = {
+          'age':
+              input_lib.limit_epochs(
+                  constant_op.constant([[.8], [0.2], [.1]]),
+                  num_epochs=num_epochs),
+          'language':
+              sparse_tensor.SparseTensor(
+                  values=input_lib.limit_epochs(
+                      ['en', 'fr', 'zh'], num_epochs=num_epochs),
+                  indices=[[0, 0], [0, 1], [2, 0]],
+                  dense_shape=[3, 2])
+      }
+      return features, constant_op.constant([[1], [0], [0]], dtype=dtypes.int32)
+
+    classifier = debug.DebugClassifier(n_classes=2)
+
+    classifier.fit(input_fn=_input_fn, steps=50)
+
+    scores = classifier.evaluate(input_fn=_input_fn, steps=1)
+    self._assertInRange(0.0, 1.0, scores['accuracy'])
+    self.assertIn('loss', scores)
+    predict_input_fn = functools.partial(_input_fn, num_epochs=1)
+    predictions = list(classifier.predict_classes(input_fn=predict_input_fn))
+    self._assertBinaryPredictions(3, predictions)
+
+  def testLogisticRegression_FloatLabel(self):
+    """Tests binary classification with float labels."""
+
+    def _input_fn_float_label(num_epochs=None):
+      features = {
+          'age':
+              input_lib.limit_epochs(
+                  constant_op.constant([[50], [20], [10]]),
+                  num_epochs=num_epochs),
+          'language':
+              sparse_tensor.SparseTensor(
+                  values=input_lib.limit_epochs(
+                      ['en', 'fr', 'zh'], num_epochs=num_epochs),
+                  indices=[[0, 0], [0, 1], [2, 0]],
+                  dense_shape=[3, 2])
+      }
+      labels = constant_op.constant([[0.8], [0.], [0.2]], dtype=dtypes.float32)
+      return features, labels
+
+    classifier = debug.DebugClassifier(n_classes=2)
+
+    classifier.fit(input_fn=_input_fn_float_label, steps=50)
+
+    predict_input_fn = functools.partial(_input_fn_float_label, num_epochs=1)
+    predictions = list(classifier.predict_classes(input_fn=predict_input_fn))
+    self._assertBinaryPredictions(3, predictions)
+    predictions_proba = list(
+        classifier.predict_proba(input_fn=predict_input_fn))
+    self._assertProbabilities(3, 2, predictions_proba)
+
+  def testMultiClass_MatrixData(self):
+    """Tests multi-class classification using matrix data as input."""
+    classifier = debug.DebugClassifier(n_classes=3)
+
+    input_fn = test_data.iris_input_multiclass_fn
+    classifier.fit(input_fn=input_fn, steps=200)
+    scores = classifier.evaluate(input_fn=input_fn, steps=1)
+    self._assertInRange(0.0, 1.0, scores['accuracy'])
+    self.assertIn('loss', scores)
+
+  def testMultiClass_MatrixData_Labels1D(self):
+    """Same as the last test, but label shape is [150] instead of [150, 1]."""
+
+    def _input_fn():
+      iris = base.load_iris()
+      return {
+          'feature': constant_op.constant(
+              iris.data, dtype=dtypes.float32)
+      }, constant_op.constant(
+          iris.target, shape=[150], dtype=dtypes.int32)
+
+    classifier = debug.DebugClassifier(n_classes=3)
+
+    classifier.fit(input_fn=_input_fn, steps=200)
+    scores = classifier.evaluate(input_fn=_input_fn, steps=1)
+    self._assertInRange(0.0, 1.0, scores['accuracy'])
+
+  def testMultiClass_NpMatrixData(self):
+    """Tests multi-class classification using numpy matrix data as input."""
+    iris = base.load_iris()
+    train_x = iris.data
+    train_y = iris.target
+    classifier = debug.DebugClassifier(n_classes=3)
+    classifier.fit(x=train_x, y=train_y, steps=200)
+    scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
+    self._assertInRange(0.0, 1.0, scores['accuracy'])
+
+  def testLoss(self):
+    """Tests loss calculation."""
+
+    def _input_fn_train():
+      # Create 4 rows, one of them (y = x), three of them (y=Not(x))
+      # The logistic prediction should be (y = 0.25).
+      labels = constant_op.constant([[1], [0], [0], [0]])
+      features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
+      return features, labels
+
+    classifier = debug.DebugClassifier(n_classes=2)
+
+    classifier.fit(input_fn=_input_fn_train, steps=5)
+    scores = classifier.evaluate(input_fn=_input_fn_train, steps=1)
+    self.assertIn('loss', scores)
+
+  def testLossWithWeights(self):
+    """Tests loss calculation with weights."""
+
+    def _input_fn_train():
+      # 4 rows with equal weight, one of them (y = x), three of them (y=Not(x))
+      # The logistic prediction should be (y = 0.25).
+      labels = constant_op.constant([[1.], [0.], [0.], [0.]])
+      features = {
+          'x': array_ops.ones(
+              shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[1.], [1.], [1.], [1.]])
+      }
+      return features, labels
+
+    def _input_fn_eval():
+      # 4 rows, with different weights.
+      labels = constant_op.constant([[1.], [0.], [0.], [0.]])
+      features = {
+          'x': array_ops.ones(
+              shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[7.], [1.], [1.], [1.]])
+      }
+      return features, labels
+
+    classifier = debug.DebugClassifier(
+        weight_column_name='w',
+        n_classes=2,
+        config=run_config.RunConfig(tf_random_seed=1))
+
+    classifier.fit(input_fn=_input_fn_train, steps=5)
+    scores = classifier.evaluate(input_fn=_input_fn_eval, steps=1)
+    self.assertIn('loss', scores)
+
+  def testTrainWithWeights(self):
+    """Tests training with given weight column."""
+
+    def _input_fn_train():
+      # Create 4 rows, one of them (y = x), three of them (y=Not(x))
+      # First row has more weight than others. Model should fit (y=x) better
+      # than (y=Not(x)) due to the relative higher weight of the first row.
+      labels = constant_op.constant([[1], [0], [0], [0]])
+      features = {
+          'x': array_ops.ones(
+              shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[100.], [3.], [2.], [2.]])
+      }
+      return features, labels
+
+    def _input_fn_eval():
+      # Create 4 rows (y = x)
+      labels = constant_op.constant([[1], [1], [1], [1]])
+      features = {
+          'x': array_ops.ones(
+              shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[1.], [1.], [1.], [1.]])
+      }
+      return features, labels
+
+    classifier = debug.DebugClassifier(weight_column_name='w')
+
+    classifier.fit(input_fn=_input_fn_train, steps=5)
+    scores = classifier.evaluate(input_fn=_input_fn_eval, steps=1)
+    self._assertInRange(0.0, 1.0, scores['accuracy'])
+
+  def testCustomMetrics(self):
+    """Tests custom evaluation metrics."""
+
+    def _input_fn(num_epochs=None):
+      # Create 4 rows, one of them (y = x), three of them (y=Not(x))
+      labels = constant_op.constant([[1], [0], [0], [0]])
+      features = {
+          'x':
+              input_lib.limit_epochs(
+                  array_ops.ones(
+                      shape=[4, 1], dtype=dtypes.float32),
+                  num_epochs=num_epochs),
+      }
+      return features, labels
+
+    def _my_metric_op(predictions, labels):
+      # For the case of binary classification, the 2nd column of "predictions"
+      # denotes the model predictions.
+      labels = math_ops.to_float(labels)
+      predictions = array_ops.strided_slice(
+          predictions, [0, 1], [-1, 2], end_mask=1)
+      labels = math_ops.cast(labels, predictions.dtype)
+      return math_ops.reduce_sum(math_ops.multiply(predictions, labels))
+
+    classifier = debug.DebugClassifier(
+        config=run_config.RunConfig(tf_random_seed=1))
+
+    classifier.fit(input_fn=_input_fn, steps=5)
+    scores = classifier.evaluate(
+        input_fn=_input_fn,
+        steps=5,
+        metrics={
+            'my_accuracy':
+                MetricSpec(
+                    metric_fn=metric_ops.streaming_accuracy,
+                    prediction_key='classes'),
+            'my_precision':
+                MetricSpec(
+                    metric_fn=metric_ops.streaming_precision,
+                    prediction_key='classes'),
+            'my_metric':
+                MetricSpec(
+                    metric_fn=_my_metric_op, prediction_key='probabilities')
+        })
+    self.assertTrue(
+        set(['loss', 'my_accuracy', 'my_precision', 'my_metric']).issubset(
+            set(scores.keys())))
+    predict_input_fn = functools.partial(_input_fn, num_epochs=1)
+    predictions = np.array(
+        list(classifier.predict_classes(input_fn=predict_input_fn)))
+    self.assertEqual(
+        _sklearn.accuracy_score([1, 0, 0, 0], predictions),
+        scores['my_accuracy'])
+
+    # Test the case where the 2nd element of the key is neither "classes" nor
+    # "probabilities".
+    with self.assertRaisesRegexp(KeyError, 'bad_type'):
+      classifier.evaluate(
+          input_fn=_input_fn,
+          steps=5,
+          metrics={
+              'bad_name':
+                  MetricSpec(
+                      metric_fn=metric_ops.streaming_auc,
+                      prediction_key='bad_type')
+          })
+
+  def testTrainSaveLoad(self):
+    """Tests that insures you can save and reload a trained model."""
+
+    def _input_fn(num_epochs=None):
+      features = {
+          'age':
+              input_lib.limit_epochs(
+                  constant_op.constant([[.8], [.2], [.1]]),
+                  num_epochs=num_epochs),
+          'language':
+              sparse_tensor.SparseTensor(
+                  values=input_lib.limit_epochs(
+                      ['en', 'fr', 'zh'], num_epochs=num_epochs),
+                  indices=[[0, 0], [0, 1], [2, 0]],
+                  dense_shape=[3, 2])
+      }
+      return features, constant_op.constant([[1], [0], [0]], dtype=dtypes.int32)
+
+    model_dir = tempfile.mkdtemp()
+    classifier = debug.DebugClassifier(
+        model_dir=model_dir,
+        n_classes=3,
+        config=run_config.RunConfig(tf_random_seed=1))
+
+    classifier.fit(input_fn=_input_fn, steps=5)
+    predict_input_fn = functools.partial(_input_fn, num_epochs=1)
+    predictions1 = classifier.predict_classes(input_fn=predict_input_fn)
+    del classifier
+
+    classifier2 = debug.DebugClassifier(
+        model_dir=model_dir,
+        n_classes=3,
+        config=run_config.RunConfig(tf_random_seed=1))
+    predictions2 = classifier2.predict_classes(input_fn=predict_input_fn)
+    self.assertEqual(list(predictions1), list(predictions2))
+
+  def testExport(self):
+    """Tests export model for servo."""
+
+    def input_fn():
+      return {
+          'age':
+              constant_op.constant([1]),
+          'language':
+              sparse_tensor.SparseTensor(
+                  values=['english'], indices=[[0, 0]], dense_shape=[1, 1])
+      }, constant_op.constant([[1]])
+
+    language = feature_column.sparse_column_with_hash_bucket('language', 100)
+    feature_columns = [
+        feature_column.real_valued_column('age'),
+        feature_column.embedding_column(
+            language, dimension=1)
+    ]
+
+    classifier = debug.DebugClassifier(config=run_config.RunConfig(
+        tf_random_seed=1))
+    classifier.fit(input_fn=input_fn, steps=5)
+
+    def default_input_fn(unused_estimator, examples):
+      return feature_column_ops.parse_feature_columns_from_examples(
+          examples, feature_columns)
+
+    export_dir = tempfile.mkdtemp()
+    classifier.export(export_dir, input_fn=default_input_fn)
+
+
+class DebugRegressorTest(test.TestCase):
+
+  def setUp(self):
+    np.random.seed(100)
+    self.features = np.random.rand(NUM_EXAMPLES, 5)
+    self.targets = np.random.rand(NUM_EXAMPLES, LABEL_DIMENSION)
+
+  def testPredictScores(self):
+    """Tests that DebugRegressor outputs the mean target."""
+    (train_features, train_labels), (test_features,
+                                     test_labels) = _train_test_split(
+                                         [self.features, self.targets])
+    mean_target = np.mean(train_labels, 0)
+    expected_prediction = np.vstack(
+        [mean_target for _ in range(test_labels.shape[0])])
+
+    classifier = debug.DebugRegressor(label_dimension=LABEL_DIMENSION)
+    classifier.fit(
+        input_fn=_input_fn_builder(train_features, train_labels), steps=50)
+
+    pred = classifier.predict_scores(input_fn=_input_fn_builder(test_features,
+                                                                None))
+    self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
+
+  def testExperimentIntegration(self):
+    exp = experiment.Experiment(
+        estimator=debug.DebugRegressor(),
+        train_input_fn=test_data.iris_input_logistic_fn,
+        eval_input_fn=test_data.iris_input_logistic_fn)
+    exp.test()
+
+  def testEstimatorContract(self):
+    estimator_test_utils.assert_estimator_contract(self, debug.DebugRegressor)
+
+  def testRegression_MatrixData(self):
+    """Tests regression using matrix data as input."""
+    regressor = debug.DebugRegressor(
+        config=run_config.RunConfig(tf_random_seed=1))
+    input_fn = test_data.iris_input_logistic_fn
+    regressor.fit(input_fn=input_fn, steps=200)
+    scores = regressor.evaluate(input_fn=input_fn, steps=1)
+    self.assertIn('loss', scores)
+
+  def testRegression_MatrixData_Labels1D(self):
+    """Same as the last test, but label shape is [100] instead of [100, 1]."""
+
+    def _input_fn():
+      iris = test_data.prepare_iris_data_for_logistic_regression()
+      return {
+          'feature': constant_op.constant(iris.data, dtype=dtypes.float32)
+      }, constant_op.constant(
+          iris.target, shape=[100], dtype=dtypes.int32)
+
+    regressor = debug.DebugRegressor(
+        config=run_config.RunConfig(tf_random_seed=1))
+
+    regressor.fit(input_fn=_input_fn, steps=200)
+    scores = regressor.evaluate(input_fn=_input_fn, steps=1)
+    self.assertIn('loss', scores)
+
+  def testRegression_NpMatrixData(self):
+    """Tests binary classification using numpy matrix data as input."""
+    iris = test_data.prepare_iris_data_for_logistic_regression()
+    train_x = iris.data
+    train_y = iris.target
+    regressor = debug.DebugRegressor(
+        config=run_config.RunConfig(tf_random_seed=1))
+    regressor.fit(x=train_x, y=train_y, steps=200)
+    scores = regressor.evaluate(x=train_x, y=train_y, steps=1)
+    self.assertIn('loss', scores)
+
+  def testRegression_TensorData(self):
+    """Tests regression using tensor data as input."""
+
+    def _input_fn(num_epochs=None):
+      features = {
+          'age':
+              input_lib.limit_epochs(
+                  constant_op.constant([[.8], [.15], [0.]]),
+                  num_epochs=num_epochs),
+          'language':
+              sparse_tensor.SparseTensor(
+                  values=input_lib.limit_epochs(
+                      ['en', 'fr', 'zh'], num_epochs=num_epochs),
+                  indices=[[0, 0], [0, 1], [2, 0]],
+                  dense_shape=[3, 2])
+      }
+      return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
+
+    regressor = debug.DebugRegressor(
+        config=run_config.RunConfig(tf_random_seed=1))
+
+    regressor.fit(input_fn=_input_fn, steps=200)
+
+    scores = regressor.evaluate(input_fn=_input_fn, steps=1)
+    self.assertIn('loss', scores)
+
+  def testLoss(self):
+    """Tests loss calculation."""
+
+    def _input_fn_train():
+      # Create 4 rows, one of them (y = x), three of them (y=Not(x))
+      # The algorithm should learn (y = 0.25).
+      labels = constant_op.constant([[1.], [0.], [0.], [0.]])
+      features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
+      return features, labels
+
+    regressor = debug.DebugRegressor(
+        config=run_config.RunConfig(tf_random_seed=1))
+
+    regressor.fit(input_fn=_input_fn_train, steps=5)
+    scores = regressor.evaluate(input_fn=_input_fn_train, steps=1)
+    self.assertIn('loss', scores)
+
+  def testLossWithWeights(self):
+    """Tests loss calculation with weights."""
+
+    def _input_fn_train():
+      # 4 rows with equal weight, one of them (y = x), three of them (y=Not(x))
+      # The algorithm should learn (y = 0.25).
+      labels = constant_op.constant([[1.], [0.], [0.], [0.]])
+      features = {
+          'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[1.], [1.], [1.], [1.]])
+      }
+      return features, labels
+
+    def _input_fn_eval():
+      # 4 rows, with different weights.
+      labels = constant_op.constant([[1.], [0.], [0.], [0.]])
+      features = {
+          'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[7.], [1.], [1.], [1.]])
+      }
+      return features, labels
+
+    regressor = debug.DebugRegressor(
+        weight_column_name='w', config=run_config.RunConfig(tf_random_seed=1))
+
+    regressor.fit(input_fn=_input_fn_train, steps=5)
+    scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
+    self.assertIn('loss', scores)
+
+  def testTrainWithWeights(self):
+    """Tests training with given weight column."""
+
+    def _input_fn_train():
+      # Create 4 rows, one of them (y = x), three of them (y=Not(x))
+      # First row has more weight than others. Model should fit (y=x) better
+      # than (y=Not(x)) due to the relative higher weight of the first row.
+      labels = constant_op.constant([[1.], [0.], [0.], [0.]])
+      features = {
+          'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[100.], [3.], [2.], [2.]])
+      }
+      return features, labels
+
+    def _input_fn_eval():
+      # Create 4 rows (y = x)
+      labels = constant_op.constant([[1.], [1.], [1.], [1.]])
+      features = {
+          'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
+          'w': constant_op.constant([[1.], [1.], [1.], [1.]])
+      }
+      return features, labels
+
+    regressor = debug.DebugRegressor(
+        weight_column_name='w', config=run_config.RunConfig(tf_random_seed=1))
+
+    regressor.fit(input_fn=_input_fn_train, steps=5)
+    scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
+    self.assertIn('loss', scores)
+
+  def testCustomMetrics(self):
+    """Tests custom evaluation metrics."""
+
+    def _input_fn(num_epochs=None):
+      # Create 4 rows, one of them (y = x), three of them (y=Not(x))
+      labels = constant_op.constant([[1.], [0.], [0.], [0.]])
+      features = {
+          'x':
+              input_lib.limit_epochs(
+                  array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
+                  num_epochs=num_epochs),
+      }
+      return features, labels
+
+    def _my_metric_op(predictions, labels):
+      return math_ops.reduce_sum(math_ops.multiply(predictions, labels))
+
+    regressor = debug.DebugRegressor(
+        config=run_config.RunConfig(tf_random_seed=1))
+
+    regressor.fit(input_fn=_input_fn, steps=5)
+    scores = regressor.evaluate(
+        input_fn=_input_fn,
+        steps=1,
+        metrics={
+            'my_error':
+                MetricSpec(
+                    metric_fn=metric_ops.streaming_mean_squared_error,
+                    prediction_key='scores'),
+            'my_metric':
+                MetricSpec(metric_fn=_my_metric_op, prediction_key='scores')
+        })
+    self.assertIn('loss', set(scores.keys()))
+    self.assertIn('my_error', set(scores.keys()))
+    self.assertIn('my_metric', set(scores.keys()))
+    predict_input_fn = functools.partial(_input_fn, num_epochs=1)
+    predictions = np.array(
+        list(regressor.predict_scores(input_fn=predict_input_fn)))
+    self.assertAlmostEqual(
+        _sklearn.mean_squared_error(np.array([1, 0, 0, 0]), predictions),
+        scores['my_error'])
+
+    # Tests the case where the prediction_key is not "scores".
+    with self.assertRaisesRegexp(KeyError, 'bad_type'):
+      regressor.evaluate(
+          input_fn=_input_fn,
+          steps=1,
+          metrics={
+              'bad_name':
+                  MetricSpec(
+                      metric_fn=metric_ops.streaming_auc,
+                      prediction_key='bad_type')
+          })
+
+  def testTrainSaveLoad(self):
+    """Tests that insures you can save and reload a trained model."""
+
+    def _input_fn(num_epochs=None):
+      features = {
+          'age':
+              input_lib.limit_epochs(
+                  constant_op.constant([[0.8], [0.15], [0.]]),
+                  num_epochs=num_epochs),
+          'language':
+              sparse_tensor.SparseTensor(
+                  values=input_lib.limit_epochs(
+                      ['en', 'fr', 'zh'], num_epochs=num_epochs),
+                  indices=[[0, 0], [0, 1], [2, 0]],
+                  dense_shape=[3, 2])
+      }
+      return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
+
+    model_dir = tempfile.mkdtemp()
+    regressor = debug.DebugRegressor(
+        model_dir=model_dir, config=run_config.RunConfig(tf_random_seed=1))
+
+    regressor.fit(input_fn=_input_fn, steps=5)
+    predict_input_fn = functools.partial(_input_fn, num_epochs=1)
+    predictions = list(regressor.predict_scores(input_fn=predict_input_fn))
+    del regressor
+
+    regressor2 = debug.DebugRegressor(
+        model_dir=model_dir, config=run_config.RunConfig(tf_random_seed=1))
+    predictions2 = list(regressor2.predict_scores(input_fn=predict_input_fn))
+    self.assertAllClose(predictions, predictions2)
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 571424a44f0e03b744fbc156382b2522311f6c3a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 08:36:01 -0700
Subject: [PATCH 0142/1375] Updates input.fn doc and corresponding boston.py
 tutorial.

PiperOrigin-RevId: 159230680
---
 tensorflow/docs_src/get_started/input_fn.md   | 190 +++++++++++-------
 .../examples/tutorials/input_fn/boston.py     |  38 ++--
 2 files changed, 140 insertions(+), 88 deletions(-)

diff --git a/tensorflow/docs_src/get_started/input_fn.md b/tensorflow/docs_src/get_started/input_fn.md
index cb5792a6d3..2ef050d9ad 100644
--- a/tensorflow/docs_src/get_started/input_fn.md
+++ b/tensorflow/docs_src/get_started/input_fn.md
@@ -1,6 +1,6 @@
 # Building Input Functions with tf.contrib.learn
 
-This tutorial introduces you to creating input functions in tf.contrib.learn.
+This tutorial introduces you to creating input functions in tf.estimator.
 You'll get an overview of how to construct an `input_fn` to preprocess and feed
 data into your models. Then, you'll implement an `input_fn` that feeds training,
 evaluation, and prediction data into a neural network regressor for predicting
@@ -8,27 +8,25 @@ median house values.
 
 ## Custom Input Pipelines with input_fn
 
-When training a neural network using tf.contrib.learn, it's possible to pass
-your feature and target data directly into your `fit`, `evaluate`, or `predict`
-operations. Here's an example taken from the
-@{$estimator$tf.estimator quickstart tutorial}:
+The `input_fn` is used to pass feature and target data to the `train`,
+`evaluate`, and `predict` methods of the `Estimator`.
+The user can do feature engineering or pre-processing inside the `input_fn`.
+Here's an example taken from the @{$estimator$tf.estimator Quickstart tutorial}:
 
 ```python
+import numpy as np
+
 training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
     filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32)
-test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
-    filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float32)
-...
 
-classifier.fit(x=training_set.data,
-               y=training_set.target,
-               steps=2000)
-```
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": np.array(training_set.data)},
+    y=np.array(training_set.target),
+    num_epochs=None,
+    shuffle=True)
 
-This approach works well when little to no manipulation of source data is
-required. But in cases where more feature engineering is needed,
-`tf.contrib.learn` supports using a custom input function (`input_fn`) to
-encapsulate the logic for preprocessing and piping data into your models.
+classifier.train(input_fn=train_input_fn, steps=2000)
+```
 
 ### Anatomy of an input_fn
 
@@ -44,8 +42,9 @@ def my_input_fn():
     return feature_cols, labels
 ```
 
-The body of the input function contains the specific logic for preprocessing your
-input data, such as scrubbing out bad examples or [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling).
+The body of the input function contains the specific logic for preprocessing
+your input data, such as scrubbing out bad examples or
+[feature scaling](https://en.wikipedia.org/wiki/Feature_scaling).
 
 Input functions must return the following two values containing the final
 feature and label data to be fed into your model (as shown in the above code
@@ -62,15 +61,27 @@ data.</dd>
 
 ### Converting Feature Data to Tensors
 
-If your feature/label data is stored in [_pandas_](http://pandas.pydata.org/)
-dataframes or [numpy](http://www.numpy.org/) arrays, you'll need to convert it
-to `Tensor`s before returning it from your `input_fn`.
+If your feature/label data is a python array or stored in
+[_pandas_](http://pandas.pydata.org/) dataframes or
+[numpy](http://www.numpy.org/) arrays, you can use the following methods to
+construct `input_fn`:
 
-For continuous data, you can create and populate a `Tensor` using `tf.constant`:
+```python
+import numpy as np
+# numpy input_fn.
+my_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": np.array(x_data)},
+    y=np.array(y_data),
+    ...)
+```
 
 ```python
-feature_column_data = [1, 2.4, 0, 9.9, 3, 120]
-feature_tensor = tf.constant(feature_column_data)
+import pandas as pd
+# pandas input_fn.
+my_input_fn = tf.estimator.inputs.pandas_input_fn(
+    x=pd.DataFrame({"x": x_data}),
+    y=pd.Series(y_data),
+    ...)
 ```
 
 For [sparse, categorical data](https://en.wikipedia.org/wiki/Sparse_matrix)
@@ -104,33 +115,26 @@ This corresponds to the following dense tensor:
  [0, 0, 0, 0, 0.5]]
 ```
 
-For more on `SparseTensor`, see the
-@{tf.SparseTensor}.
+For more on `SparseTensor`, see @{tf.SparseTensor}.
 
 ### Passing input_fn Data to Your Model
 
 To feed data to your model for training, you simply pass the input function
-you've created to your `fit` operation as the value of the `input_fn` parameter,
-e.g.:
+you've created to your `train` operation as the value of the `input_fn`
+parameter, e.g.:
 
 ```python
-classifier.fit(input_fn=my_input_fn, steps=2000)
+classifier.train(input_fn=my_input_fn, steps=2000)
 ```
 
-Note that the `input_fn` is responsible for supplying both feature and label
-data to the model, and replaces both the `x` and `y` parameters in `fit`. If you
-supply an `input_fn` value to `fit` that is not `None` in conjunction with
-either an `x` or `y` parameter that is not `None`, it will result in a
-`ValueError`.
-
-Also note that the `input_fn` parameter must receive a function object (i.e.,
+Note that the `input_fn` parameter must receive a function object (i.e.,
 `input_fn=my_input_fn`), not the return value of a function call
-(`input_fn=my_input_fn()`). This means that if you try to pass parameters to the input
-function in your `fit` call, as in the following code, it will result in a
+(`input_fn=my_input_fn()`). This means that if you try to pass parameters to the
+`input_fn` in your `train` call, as in the following code, it will result in a
 `TypeError`:
 
 ```python
-classifier.fit(input_fn=my_input_fn(training_set), steps=2000)
+classifier.train(input_fn=my_input_fn(training_set), steps=2000)
 ```
 
 However, if you'd like to be able to parameterize your input function, there are
@@ -139,29 +143,33 @@ arguments as your `input_fn` and use it to invoke your input function
 with the desired parameters. For example:
 
 ```python
-def my_input_function_training_set():
-  return my_input_function(training_set)
+def my_input_fn(data_set):
+  ...
 
-classifier.fit(input_fn=my_input_fn_training_set, steps=2000)
+def my_input_fn_training_set():
+  return my_input_fn(training_set)
+
+classifier.train(input_fn=my_input_fn_training_set, steps=2000)
 ```
 
 Alternatively, you can use Python's [`functools.partial`](https://docs.python.org/2/library/functools.html#functools.partial)
 function to construct a new function object with all parameter values fixed:
 
 ```python
-classifier.fit(input_fn=functools.partial(my_input_function,
-                                          data_set=training_set), steps=2000)
+classifier.train(
+    input_fn=functools.partial(my_input_fn, data_set=training_set),
+    steps=2000)
 ```
 
-A third option is to wrap your input_fn invocation in a
+A third option is to wrap your `input_fn` invocation in a
 [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions)
 and pass it to the `input_fn` parameter:
 
 ```python
-classifier.fit(input_fn=lambda: my_input_fn(training_set), steps=2000)
+classifier.train(input_fn=lambda: my_input_fn(training_set), steps=2000)
 ```
 
-One big advantage of architecting your input pipeline as shown above—to accept a
+One big advantage of designing your input pipeline as shown above—to accept a
 parameter for data set—is that you can pass the same `input_fn` to `evaluate`
 and `predict` operations by just changing the data set argument, e.g.:
 
@@ -169,9 +177,36 @@ and `predict` operations by just changing the data set argument, e.g.:
 classifier.evaluate(input_fn=lambda: my_input_fn(test_set), steps=2000)
 ```
 
-This approach enhances code maintainability: no need to capture `x` and `y`
-values in separate variables (e.g., `x_train`, `x_test`, `y_train`, `y_test`)
-for each type of operation.
+This approach enhances code maintainability: no need to define multiple
+`input_fn` (e.g. `input_fn_train`, `input_fn_test`, `input_fn_predict`) for each
+type of operation.
+
+Finally, you can use the methods in `tf.estimator.inputs` to create `input_fn`
+from numpy or pandas data sets. The additional benefit is that you can use
+more arguments, such as `num_epochs` and `shuffle` to control how the `input_fn`
+iterates over the data:
+
+```python
+import pandas as pd
+
+def get_input_fn_from_pandas(data_set, num_epochs=None, shuffle=True):
+  return tf.estimator.inputs.pandas_input_fn(
+      x=pdDataFrame(...),
+      y=pd.Series(...),
+      num_epochs=num_epochs,
+      shuffle=shuffle)
+```
+
+```python
+import numpy as np
+
+def get_input_fn_from_numpy(data_set, num_epochs=None, shuffle=True):
+  return tf.estimator.inputs.numpy_input_fn(
+      x={...},
+      y=np.array(...),
+      num_epochs=num_epochs,
+      shuffle=shuffle)
+```
 
 ### A Neural Network Model for Boston House Values
 
@@ -260,8 +295,7 @@ housing data set contain continuous values, you can create their
 `FeatureColumn`s using the `tf.contrib.layers.real_valued_column()` function:
 
 ```python
-feature_cols = [tf.contrib.layers.real_valued_column(k)
-                  for k in FEATURES]
+feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]
 ```
 
 NOTE: For a more in-depth overview of feature columns, see
@@ -276,36 +310,47 @@ with 10 nodes each), and `feature_columns`, containing the list of
 `FeatureColumns` you just defined:
 
 ```python
-regressor = tf.contrib.learn.DNNRegressor(feature_columns=feature_cols,
-                                          hidden_units=[10, 10],
-                                          model_dir="/tmp/boston_model")
+regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
+                                      hidden_units=[10, 10],
+                                      model_dir="/tmp/boston_model")
 ```
 
 ### Building the input_fn
 
-To pass input data into the `regressor`, create an input function, which will
-accept a _pandas_ `Dataframe` and return feature column and label values as
-`Tensor`s:
+To pass input data into the `regressor`, write a factory method that accepts a
+_pandas_ `Dataframe` and returns an `input_fn`:
 
 ```python
-def input_fn(data_set):
-  feature_cols = {k: tf.constant(data_set[k].values)
-                  for k in FEATURES}
-  labels = tf.constant(data_set[LABEL].values)
-  return feature_cols, labels
+def get_input_fn(data_set, num_epochs=None, shuffle=True):
+  return tf.estimator.inputs.pandas_input_fn(
+      x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
+      y = pd.Series(data_set[LABEL].values),
+      num_epochs=num_epochs,
+      shuffle=shuffle)
 ```
 
 Note that the input data is passed into `input_fn` in the `data_set` argument,
 which means the function can process any of the `DataFrame`s you've imported:
 `training_set`, `test_set`, and `prediction_set`.
 
+Two additional arguments are provided:
+* `num_epochs`: controls the number of
+  epochs to iterate over data. For training, set this to `None`, so the
+  `input_fn` keeps returning data until the required number of train steps is
+  reached. For evaluate and predict, set this to 1, so the `input_fn` will
+  iterate over the data once and then raise `OutOfRangeError`. That error will
+  signal the `Estimator` to stop evaluate or predict.
+* `shuffle`: Whether to shuffle the data. For evaluate and predict, set this to
+  `False`, so the `input_fn` iterates over the data sequentially. For train,
+  set this to `True`.
+
 ### Training the Regressor
 
-To train the neural network regressor, run `fit` with the `training_set` passed
-to the `input_fn` as follows:
+To train the neural network regressor, run `train` with the `training_set`
+passed to the `input_fn` as follows:
 
 ```python
-regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000)
+regressor.train(input_fn=get_input_fn(training_set), steps=5000)
 ```
 
 You should see log output similar to the following, which reports training loss
@@ -331,7 +376,8 @@ Next, see how the trained model performs against the test data set. Run
 `evaluate`, and this time pass the `test_set` to the `input_fn`:
 
 ```python
-ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
+ev = regressor.evaluate(
+    input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))
 ```
 
 Retrieve the loss from the `ev` results and print it to output:
@@ -355,10 +401,12 @@ Finally, you can use the model to predict median house values for the
 `prediction_set`, which contains feature data but no labels for six examples:
 
 ```python
-y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
-# .predict() returns an iterator; convert to a list and print predictions
-predictions = list(itertools.islice(y, 6))
-print ("Predictions: {}".format(str(predictions)))
+y = regressor.predict(
+    input_fn=get_input_fn(prediction_set, num_epochs=1, shuffle=False))
+# .predict() returns an iterator of dicts; convert to a list and print
+# predictions
+predictions = list(p["predictions"] for p in itertools.islice(y, 6))
+print("Predictions: {}".format(str(predictions)))
 ```
 
 Your results should contain six house-value predictions in thousands of dollars,
diff --git a/tensorflow/examples/tutorials/input_fn/boston.py b/tensorflow/examples/tutorials/input_fn/boston.py
index c7fb7e2316..34f350e9ac 100644
--- a/tensorflow/examples/tutorials/input_fn/boston.py
+++ b/tensorflow/examples/tutorials/input_fn/boston.py
@@ -31,10 +31,12 @@ FEATURES = ["crim", "zn", "indus", "nox", "rm",
 LABEL = "medv"
 
 
-def input_fn(data_set):
-  feature_cols = {k: tf.constant(data_set[k].values) for k in FEATURES}
-  labels = tf.constant(data_set[LABEL].values)
-  return feature_cols, labels
+def get_input_fn(data_set, num_epochs=None, shuffle=True):
+  return tf.estimator.inputs.pandas_input_fn(
+      x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
+      y=pd.Series(data_set[LABEL].values),
+      num_epochs=num_epochs,
+      shuffle=shuffle)
 
 
 def main(unused_argv):
@@ -49,26 +51,28 @@ def main(unused_argv):
                                skiprows=1, names=COLUMNS)
 
   # Feature cols
-  feature_cols = [tf.contrib.layers.real_valued_column(k)
-                  for k in FEATURES]
+  feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]
 
   # Build 2 layer fully connected DNN with 10, 10 units respectively.
-  regressor = tf.contrib.learn.DNNRegressor(feature_columns=feature_cols,
-                                            hidden_units=[10, 10],
-                                            model_dir="/tmp/boston_model")
+  regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
+                                        hidden_units=[10, 10],
+                                        model_dir="/tmp/boston_model")
 
-  # Fit
-  regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000)
+  # Train
+  regressor.train(input_fn=get_input_fn(training_set), steps=5000)
 
-  # Score accuracy
-  ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
+  # Evaluate loss over one epoch of test_set.
+  ev = regressor.evaluate(
+      input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))
   loss_score = ev["loss"]
   print("Loss: {0:f}".format(loss_score))
 
-  # Print out predictions
-  y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
-  # .predict() returns an iterator; convert to a list and print predictions
-  predictions = list(itertools.islice(y, 6))
+  # Print out predictions over a slice of prediction_set.
+  y = regressor.predict(
+      input_fn=get_input_fn(prediction_set, num_epochs=1, shuffle=False))
+  # .predict() returns an iterator of dicts; convert to a list and print
+  # predictions
+  predictions = list(p["predictions"] for p in itertools.islice(y, 6))
   print("Predictions: {}".format(str(predictions)))
 
 if __name__ == "__main__":
-- 
GitLab


From 60af0b18d15625753327835b79f4efa66c4b7bf7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 09:07:21 -0700
Subject: [PATCH 0143/1375] Add C++ shape fn for QueueDequeueManyV2 and
 QueueDequeueUpToV2.

PiperOrigin-RevId: 159233695
---
 .../framework/shape_inference_testutil.cc     | 23 ++++-
 .../core/framework/shape_inference_testutil.h |  3 +
 tensorflow/core/ops/data_flow_ops.cc          | 37 +++++++-
 tensorflow/core/ops/data_flow_ops_test.cc     | 95 +++++++++++++++++++
 4 files changed, 153 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/framework/shape_inference_testutil.cc b/tensorflow/core/framework/shape_inference_testutil.cc
index 7b3cd07429..b4765ab0b2 100644
--- a/tensorflow/core/framework/shape_inference_testutil.cc
+++ b/tensorflow/core/framework/shape_inference_testutil.cc
@@ -43,9 +43,26 @@ Status ShapeInferenceTestutil::InferShapes(ShapeInferenceTestOp op,
     in_shapes.push_back(shape);
   }
 
-  shape_inference::InferenceContext c(op.graph_def_version, &op.node_def,
-                                      op_reg_data->op_def, in_shapes,
-                                      op.input_tensors, {}, {});
+  std::vector<std::unique_ptr<std::vector<shape_inference::ShapeAndType>>>
+      input_resource_handle_shapes_and_types;
+  for (const auto p : op.input_resource_handle_shapes_and_types) {
+    if (p == nullptr) {
+      input_resource_handle_shapes_and_types.push_back(nullptr);
+    } else {
+      std::unique_ptr<std::vector<ShapeAndType>> v(
+          new std::vector<ShapeAndType>());
+      for (const auto& shape_and_type : *p) {
+        ShapeHandle shape;
+        TF_RETURN_IF_ERROR(
+            MakeShapeFromString(&manager, shape_and_type.first, &shape));
+        v->emplace_back(shape, shape_and_type.second);
+      }
+      input_resource_handle_shapes_and_types.emplace_back(v.release());
+    }
+  }
+  shape_inference::InferenceContext c(
+      op.graph_def_version, &op.node_def, op_reg_data->op_def, in_shapes,
+      op.input_tensors, {}, std::move(input_resource_handle_shapes_and_types));
   TF_RETURN_IF_ERROR(c.construction_status());
   if (op_reg_data->shape_inference_fn == nullptr) {
     return errors::InvalidArgument(
diff --git a/tensorflow/core/framework/shape_inference_testutil.h b/tensorflow/core/framework/shape_inference_testutil.h
index 996281e70e..6bd2cd4291 100644
--- a/tensorflow/core/framework/shape_inference_testutil.h
+++ b/tensorflow/core/framework/shape_inference_testutil.h
@@ -31,10 +31,13 @@ class NodeDef;
 class Tensor;
 
 struct ShapeInferenceTestOp {
+  typedef std::pair<string, DataType> ShapeAndType;
   explicit ShapeInferenceTestOp(StringPiece name) : name(name.ToString()) {}
   string name;
   NodeDef node_def;
   std::vector<const Tensor*> input_tensors;
+  std::vector<std::vector<ShapeAndType>*>
+      input_resource_handle_shapes_and_types;
   int graph_def_version = TF_GRAPH_DEF_VERSION;
 };
 
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index f0fcd02835..4bf3d24324 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -24,6 +24,25 @@ using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
 using shape_inference::ShapeHandle;
 
+namespace {
+
+Status DequeueManyV2Shape(InferenceContext* c, ShapeHandle n_shape) {
+  auto* t = c->input_handle_shapes_and_types(0);
+  if (t != nullptr && t->size() == c->num_outputs()) {
+    for (int i = 0; i < c->num_outputs(); ++i) {
+      ShapeHandle combined_shape;
+      TF_RETURN_IF_ERROR(
+          c->Concatenate(n_shape, (*t)[i].shape, &combined_shape));
+      c->set_output(i, combined_shape);
+    }
+    return Status::OK();
+  } else {
+    return shape_inference::UnknownShape(c);
+  }
+}
+
+}  // namespace
+
 // --------------------------------------------------------------------------
 
 REGISTER_OP("DynamicPartition")
@@ -711,7 +730,19 @@ REGISTER_OP("QueueDequeueManyV2")
     .Output("components: component_types")
     .Attr("component_types: list(type) >= 1")
     .Attr("timeout_ms: int = -1")
-    .SetShapeFn(shape_inference::UnknownShape)
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle n_shape;
+      if (c->input_tensor(1) == nullptr) {
+        n_shape = c->Vector(InferenceContext::kUnknownDim);
+      } else {
+        const int32 n = c->input_tensor(1)->scalar<int32>()();
+        if (n < 0) {
+          return errors::InvalidArgument("Input 'n' must be >= 0, but is ", n);
+        }
+        n_shape = c->Vector(n);
+      }
+      return DequeueManyV2Shape(c, n_shape);
+    })
     .Doc(R"doc(
 Dequeues `n` tuples of one or more tensors from the given queue.
 
@@ -781,7 +812,9 @@ REGISTER_OP("QueueDequeueUpToV2")
     .Output("components: component_types")
     .Attr("component_types: list(type) >= 1")
     .Attr("timeout_ms: int = -1")
-    .SetShapeFn(shape_inference::UnknownShape)
+    .SetShapeFn([](InferenceContext* c) {
+      return DequeueManyV2Shape(c, c->Vector(InferenceContext::kUnknownDim));
+    })
     .Doc(R"doc(
 Dequeues `n` tuples of one or more tensors from the given queue.
 
diff --git a/tensorflow/core/ops/data_flow_ops_test.cc b/tensorflow/core/ops/data_flow_ops_test.cc
index 53c843eb60..6f59db3a1b 100644
--- a/tensorflow/core/ops/data_flow_ops_test.cc
+++ b/tensorflow/core/ops/data_flow_ops_test.cc
@@ -149,4 +149,99 @@ TEST(DataFlowOpsTest, TensorArrayV3) {
   INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[2]");
 }
 
+TEST(DataFlowOpsTest, QueueDequeueV2ShapeFn) {
+  ShapeInferenceTestOp op("QueueDequeueV2");
+  TF_ASSERT_OK(NodeDefBuilder("test", op.name)
+                   .Input("handle", 0, DT_RESOURCE)
+                   .Attr("component_types", {DT_FLOAT, DT_INT32})
+                   .Finalize(&op.node_def));
+
+  INFER_OK(op, "?", "?;?");
+
+  std::vector<ShapeInferenceTestOp::ShapeAndType> shapes_and_types;
+  op.input_resource_handle_shapes_and_types.push_back(&shapes_and_types);
+  INFER_OK(op, "?", "?;?");
+
+  // Wrong number of shapes provided by handle.
+  shapes_and_types.emplace_back("[1,?,3]", DT_FLOAT);
+  INFER_OK(op, "?", "?;?");
+
+  // Correct number of shapes provided by handle.
+  shapes_and_types.emplace_back("[?,2]", DT_FLOAT);
+  INFER_OK(op, "?", "[1,?,3];[?,2]");
+}
+
+TEST(DataFlowOpsTest, QueueDequeueManyV2ShapeFn) {
+  ShapeInferenceTestOp op("QueueDequeueManyV2");
+  TF_ASSERT_OK(NodeDefBuilder("test", op.name)
+                   .Input("handle", 0, DT_RESOURCE)
+                   .Input("n", 0, DT_INT32)
+                   .Attr("component_types", {DT_FLOAT, DT_INT32})
+                   .Finalize(&op.node_def));
+
+  ////////////////////////////
+  // Input n is not a constant.
+  INFER_OK(op, "?;?", "?;?");
+  std::vector<ShapeInferenceTestOp::ShapeAndType> shapes_and_types;
+  op.input_resource_handle_shapes_and_types.push_back(&shapes_and_types);
+  op.input_resource_handle_shapes_and_types.push_back(nullptr);
+  // Wrong number of shapes provided by handle.
+  shapes_and_types.emplace_back("[1,?,3]", DT_FLOAT);
+  INFER_OK(op, "?;?", "?;?");
+  // Correct number of shapes provided by handle.
+  shapes_and_types.emplace_back("[?,2]", DT_FLOAT);
+  INFER_OK(op, "?;?", "[?,1,?,3];[?,?,2]");
+
+  ////////////////////////////
+  // Input n is a constant. (set up test and repeat the cases from above).
+  Tensor n_tensor = test::AsScalar(12);
+  op.input_tensors.push_back(nullptr);
+  op.input_tensors.push_back(&n_tensor);
+  op.input_resource_handle_shapes_and_types.clear();
+  shapes_and_types.clear();
+
+  INFER_OK(op, "?;?", "?;?");
+  op.input_resource_handle_shapes_and_types.push_back(&shapes_and_types);
+  op.input_resource_handle_shapes_and_types.push_back(nullptr);
+  // Wrong number of shapes provided by handle.
+  shapes_and_types.emplace_back("[1,?,3]", DT_FLOAT);
+  INFER_OK(op, "?;?", "?;?");
+  // Correct number of shapes provided by handle.
+  shapes_and_types.emplace_back("[?,2]", DT_FLOAT);
+  INFER_OK(op, "?;?", "[12,1,?,3];[12,?,2]");
+
+  n_tensor = test::AsScalar<int32>(-1);  // invalid value of n.
+  INFER_ERROR("must be >= 0", op, "?;?");
+}
+
+TEST(DataFlowOpsTest, QueueDequeueUpToV2ShapeFn) {
+  // Results are the same regardless of what value is passed for n.
+  for (int pass = 0; pass < 2; ++pass) {
+    ShapeInferenceTestOp op("QueueDequeueUpToV2");
+    TF_ASSERT_OK(NodeDefBuilder("test", op.name)
+                     .Input("handle", 0, DT_RESOURCE)
+                     .Input("n", 0, DT_INT32)
+                     .Attr("component_types", {DT_FLOAT, DT_INT32})
+                     .Finalize(&op.node_def));
+
+    Tensor n_tensor = test::AsScalar(12);
+    if (pass == 1) {
+      // Second pass, pass value of <n> as a constant.
+      op.input_tensors.push_back(nullptr);
+      op.input_tensors.push_back(&n_tensor);
+    }
+
+    INFER_OK(op, "?;?", "?;?");
+    std::vector<ShapeInferenceTestOp::ShapeAndType> shapes_and_types;
+    op.input_resource_handle_shapes_and_types.push_back(&shapes_and_types);
+    op.input_resource_handle_shapes_and_types.push_back(nullptr);
+    // Wrong number of shapes provided by handle.
+    shapes_and_types.emplace_back("[1,?,3]", DT_FLOAT);
+    INFER_OK(op, "?;?", "?;?");
+    // Correct number of shapes provided by handle.
+    shapes_and_types.emplace_back("[?,2]", DT_FLOAT);
+    INFER_OK(op, "?;?", "[?,1,?,3];[?,?,2]");
+  }
+}
+
 }  // end namespace tensorflow
-- 
GitLab


From a6000da882aef2d8631c6c22bcfaa0db01d5336f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 09:48:47 -0700
Subject: [PATCH 0144/1375] Further BUILD cleanup

PiperOrigin-RevId: 159238874
---
 tensorflow/core/BUILD                         | 20 +++++++++++++++----
 tensorflow/core/distributed_runtime/BUILD     |  1 -
 tensorflow/core/distributed_runtime/rpc/BUILD |  2 ++
 tensorflow/core/grappler/clusters/BUILD       |  1 -
 tensorflow/core/grappler/costs/BUILD          |  3 +--
 tensorflow/core/grappler/inputs/BUILD         |  2 --
 tensorflow/core/grappler/optimizers/BUILD     |  5 +----
 tensorflow/core/util/ctc/BUILD                |  1 -
 tensorflow/core/util/tensor_bundle/BUILD      |  1 -
 9 files changed, 20 insertions(+), 16 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 1f0b100bbb..6276b50931 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -329,6 +329,7 @@ cc_library(
     deps = [
         ":lib",
         ":lib_internal",
+        ":protos_all_cc",
         "//tensorflow/core/platform/default/build_config:gtest",
     ] + tf_additional_test_deps(),
 )
@@ -422,6 +423,7 @@ cc_library(
     hdrs = ["util/overflow.h"],
     deps = [
         ":framework_lite",
+        ":lib",
     ],
 )
 
@@ -433,6 +435,7 @@ cc_library(
     deps = [
         ":framework",
         ":lib",
+        ":protos_all_cc",
     ],
 )
 
@@ -460,6 +463,10 @@ cc_library(
     name = "session_options",
     hdrs = ["public/session_options.h"],
     visibility = ["//visibility:public"],
+    deps = [
+        ":lib",
+        ":protos_all_cc",
+    ],
 )
 
 cc_library(
@@ -1130,7 +1137,6 @@ cc_library(
         ":framework_internal",
         ":lib",
         ":lib_internal",
-        ":proto_text",
         ":protos_all_cc",
         ":stream_executor",
     ],
@@ -1348,6 +1354,7 @@ cc_library(
     }),
     deps = [
         ":lib",
+        ":lib_internal",
         "//tensorflow/core/platform/default/build_config:gif",
     ],
 )
@@ -1370,6 +1377,7 @@ cc_library(
     }),
     deps = [
         ":lib",
+        ":lib_internal",
         "//tensorflow/core/platform/default/build_config:jpeg",
     ],
 )
@@ -1733,6 +1741,7 @@ cc_library(
     linkstatic = 1,
     visibility = ["//visibility:public"],
     deps = [
+        ":core_cpu",
         ":core_cpu_internal",
         ":framework",
         ":lib",
@@ -1845,7 +1854,7 @@ cc_library(
         ":framework_internal",
         ":lib",
         ":lib_internal",
-        ":protos_all_cc",
+        ":proto_text",
         "//third_party/eigen3",
         "@local_config_sycl//sycl:sycl",
     ],
@@ -1866,7 +1875,10 @@ cc_library(
         "lib/random/philox_random_test_utils.h",
         "platform/snappy.h",
     ],
-    deps = [":lib_internal"],
+    deps = [
+        ":lib",
+        ":lib_internal",
+    ],
 )
 
 cc_library(
@@ -1892,6 +1904,7 @@ cc_library(
         ":framework",
         ":lib",
         ":lib_internal",
+        ":protos_all_cc",
     ],
 )
 
@@ -2833,7 +2846,6 @@ cc_test(
         ":testlib",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/core/kernels:example_parsing_ops",
-        "//tensorflow/core/kernels:ops_util",
     ],
 )
 
diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD
index efc08e4c9d..f59e5f4dc2 100644
--- a/tensorflow/core/distributed_runtime/BUILD
+++ b/tensorflow/core/distributed_runtime/BUILD
@@ -399,7 +399,6 @@ cc_library(
     srcs = ["server_lib.cc"],
     hdrs = ["server_lib.h"],
     deps = [
-        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index 3ebc11614d..c0918ef445 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -119,6 +119,7 @@ cc_library(
     hdrs = ["grpc_call.h"],
     deps = [
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "@grpc//:grpc++_unsecure",
     ],
 )
@@ -381,6 +382,7 @@ cc_library(
         "//tensorflow/core/distributed_runtime:call_options",
         "//tensorflow/core/distributed_runtime:local_master",
         "//tensorflow/core/distributed_runtime:master_interface",
+        "//tensorflow/core/distributed_runtime:message_wrappers",
     ],
     alwayslink = 1,
 )
diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index 10df765e13..3cdddeb38f 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -77,7 +77,6 @@ cc_test(
     srcs = ["virtual_cluster_test.cc"],
     deps = [
         ":virtual_cluster",
-        "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD
index 2b30facd84..39dc9e071d 100644
--- a/tensorflow/core/grappler/costs/BUILD
+++ b/tensorflow/core/grappler/costs/BUILD
@@ -160,7 +160,7 @@ cc_test(
     srcs = ["virtual_placer_test.cc"],
     deps = [
         ":virtual_placer",
-        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -264,7 +264,6 @@ cc_library(
         ":virtual_scheduler",
         "//tensorflow/core:core_cpu_base",
         "//tensorflow/core:lib",
-        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
     ],
 )
diff --git a/tensorflow/core/grappler/inputs/BUILD b/tensorflow/core/grappler/inputs/BUILD
index 176b3e982f..5c70f40969 100644
--- a/tensorflow/core/grappler/inputs/BUILD
+++ b/tensorflow/core/grappler/inputs/BUILD
@@ -22,7 +22,6 @@ cc_library(
     ],
     visibility = ["//visibility:public"],
     deps = [
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
     ],
@@ -37,7 +36,6 @@ cc_test(
     deps = [
         ":utils",
         "//tensorflow/core:lib",
-        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index c83c218b54..3a705b85f7 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -59,7 +59,6 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         ":graph_optimizer",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:devices",
@@ -78,11 +77,11 @@ cc_test(
         ":auto_parallel",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:tensorflow",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:utils",
-        "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder",
     ],
 )
 
@@ -230,7 +229,6 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         ":graph_optimizer",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:devices",
@@ -255,7 +253,6 @@ cc_test(
         "//tensorflow/core:testlib",
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:utils",
-        "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder",
     ],
 )
 
diff --git a/tensorflow/core/util/ctc/BUILD b/tensorflow/core/util/ctc/BUILD
index 357b253551..c955b28014 100644
--- a/tensorflow/core/util/ctc/BUILD
+++ b/tensorflow/core/util/ctc/BUILD
@@ -102,5 +102,4 @@ cc_library(
     hdrs = [
         "ctc_loss_util.h",
     ],
-    deps = ["//tensorflow/core:lib"],
 )
diff --git a/tensorflow/core/util/tensor_bundle/BUILD b/tensorflow/core/util/tensor_bundle/BUILD
index bae00f7400..ca92713a62 100644
--- a/tensorflow/core/util/tensor_bundle/BUILD
+++ b/tensorflow/core/util/tensor_bundle/BUILD
@@ -39,7 +39,6 @@ cc_library(
         ":naming",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
-        "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
-- 
GitLab


From 1111e06d9cd691cbdfcb67cf9f234a504f4e0f6d Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Fri, 16 Jun 2017 10:15:27 -0700
Subject: [PATCH 0145/1375] In filter_dataset, if the element doesn't match,
 clear the output.

Prior to this change, the output tensors list wouldn't be cleared
if the element didn't match, so output_tensors would be appended to
on the next iteration, violating the number of inputs contract for
the filter function.  The new test exhibited this problem.  By
clearing the output tensors list when the element doesn't match, we
reset the state back to empty, maintaining the invariant at
the start of the loop.

Fixes #10725.

PiperOrigin-RevId: 159242269
---
 .../python/kernel_tests/filter_dataset_op_test.py     | 11 +++++++++++
 tensorflow/core/kernels/filter_dataset_op.cc          |  4 ++++
 2 files changed, 15 insertions(+)

diff --git a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py
index 3ea783ad89..19be94e174 100644
--- a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py
@@ -72,6 +72,17 @@ class FilterDatasetTest(test.TestCase):
       # Test an empty dataset.
       do_test(0, 1)
 
+  def testFilterRange(self):
+    dataset = dataset_ops.Dataset.range(100).filter(
+        lambda x: math_ops.not_equal(math_ops.mod(x, 3), 2))
+    iterator = dataset.make_one_shot_iterator()
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      self.assertEqual(0, sess.run(get_next))
+      self.assertEqual(1, sess.run(get_next))
+      self.assertEqual(3, sess.run(get_next))
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/core/kernels/filter_dataset_op.cc b/tensorflow/core/kernels/filter_dataset_op.cc
index 3503c45f9a..c0e909d73a 100644
--- a/tensorflow/core/kernels/filter_dataset_op.cc
+++ b/tensorflow/core/kernels/filter_dataset_op.cc
@@ -124,6 +124,10 @@ class FilterDatasetOp : public UnaryDatasetOpKernel {
                 "Filter predicate `f` must return a scalar bool.");
           }
           matched = result[0].scalar<bool>()();
+          if (!matched) {
+            // Clear the output tensor list since it didn't match.
+            out_tensors->clear();
+          }
         } while (!matched);
         *end_of_sequence = false;
         return Status::OK();
-- 
GitLab


From d06121593035687176d8b660b83bab568853deff Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 10:27:48 -0700
Subject: [PATCH 0146/1375] n/a (non-public changes only)

PiperOrigin-RevId: 159243869
---
 tensorflow/core/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 6276b50931..694512c161 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -672,6 +672,7 @@ tf_cuda_library(
     name = "core_cpu",
     hdrs = [
         "common_runtime/device.h",
+        "common_runtime/optimization_registry.h",
         "common_runtime/shape_refiner.h",
         "graph/algorithm.h",
         "graph/default_device.h",
-- 
GitLab


From a66de1eca225bc95e7972974a7089d84df8a8055 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Fri, 16 Jun 2017 10:32:29 -0700
Subject: [PATCH 0147/1375] [TF:XLA] Refactor handling of Resources (Variables
 and TensorArrays) in the XLA bridge.

* Rename "Variable" to "Resource" in many places where non-Variable resources might be used.
* Add kTensorArray to the XlaCompiler::Argument enum. Remove kUninitializedVariable and make "initialized" a separate boolean field.
* Add a kind field to XlaResource. Add checks that Variables are not used where TensorArrays are expected, and vice-versa.
* Clean ups to the TensorArray operators.

PiperOrigin-RevId: 159244478
---
 .../jit/kernels/xla_device_launch_op.cc       |   4 +-
 .../compiler/jit/xla_compilation_cache.cc     |   5 +-
 .../compiler/tests/tensor_array_ops_test.py   |   2 +-
 tensorflow/compiler/tf2xla/kernels/arg_op.cc  |  25 ++-
 .../tf2xla/kernels/tensor_array_ops.cc        | 205 +++++++++++-------
 .../compiler/tf2xla/kernels/while_op.cc       |  89 ++++----
 .../compiler/tf2xla/xla_compilation_device.h  |  35 +--
 tensorflow/compiler/tf2xla/xla_compiler.cc    |  73 ++++---
 tensorflow/compiler/tf2xla/xla_compiler.h     |  39 ++--
 tensorflow/compiler/tf2xla/xla_context.cc     |  20 +-
 tensorflow/compiler/tf2xla/xla_context.h      |  26 ++-
 tensorflow/compiler/tf2xla/xla_op_kernel.cc   |  39 ++--
 tensorflow/compiler/tf2xla/xla_op_kernel.h    |  20 +-
 13 files changed, 327 insertions(+), 255 deletions(-)

diff --git a/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
index 29c5ff7242..e786fdb16d 100644
--- a/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
+++ b/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
@@ -202,8 +202,8 @@ void XlaDeviceLaunchOp::Compute(OpKernelContext* ctx) {
 
   // Apply variable updates, if any.
   VLOG(2) << "Applying variable updates";
-  for (int i = 0; i < kernel->variable_updates.size(); ++i) {
-    const XlaCompiler::VariableUpdate& write = kernel->variable_updates[i];
+  for (int i = 0; i < kernel->resource_updates.size(); ++i) {
+    const XlaCompiler::ResourceUpdate& write = kernel->resource_updates[i];
     OP_REQUIRES(ctx,
                 write.input_index >= 0 && write.input_index < ctx->num_inputs(),
                 errors::Internal("Invalid input index for variable write."));
diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc
index 63ca77f9a9..2325217b97 100644
--- a/tensorflow/compiler/jit/xla_compilation_cache.cc
+++ b/tensorflow/compiler/jit/xla_compilation_cache.cc
@@ -182,17 +182,18 @@ Status BuildArguments(int num_constant_args,
     XlaCompiler::Argument& arg = (*args)[input_num];
 
     arg.name = variable_args[variable_id].name;
+    arg.kind = XlaCompiler::Argument::kVariable;
     if (variable_args[variable_id].present) {
       const Tensor& value = variable_args[variable_id].value;
-      arg.kind = XlaCompiler::Argument::kVariable;
       arg.type = value.dtype();
       arg.shape = value.shape();
+      arg.initialized = true;
     } else {
       // The values of uninitialized variables are not passed as inputs, since
       // they are meaningless. However, it is legal to assign to a resource
       // variable for the first time inside the XLA computation, so we do permit
       // uninitialized variables.
-      arg.kind = XlaCompiler::Argument::kUninitializedVariable;
+      arg.initialized = false;
       arg.type = DT_INVALID;
       arg.shape = TensorShape();
     }
diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index 00a7358130..b3067be51d 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -335,7 +335,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0_bad = gen_data_flow_ops._tensor_array_read_v3(
           handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow)
       with self.assertRaisesOpError(
-          "TensorArray dtype is float but Op requested dtype double."):
+          "TensorArray dtype is float but op has dtype double."):
         r0_bad.eval()
 
       # Test reading from a different index than the one we wrote to
diff --git a/tensorflow/compiler/tf2xla/kernels/arg_op.cc b/tensorflow/compiler/tf2xla/kernels/arg_op.cc
index 620fc84437..6ad72c6219 100644
--- a/tensorflow/compiler/tf2xla/kernels/arg_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/arg_op.cc
@@ -51,13 +51,26 @@ class ArgOp : public XlaOpKernel {
 
     XlaContext& xc = XlaContext::Get(ctx);
     const XlaContext::Argument& arg = xc.args()[index_];
-    if (arg.is_variable) {
+    if (arg.is_resource) {
+      XlaResource::Kind kind;
+      switch (arg.kind) {
+        case XlaCompiler::Argument::kVariable:
+          kind = XlaResource::kVariable;
+          break;
+        case XlaCompiler::Argument::kTensorArray:
+          kind = XlaResource::kTensorArray;
+          break;
+        default:
+          CHECK(false);
+      }
+
       // TODO(phawkins): this code assumes that variables do not alias.
-      XlaVariable* var;
-      OP_REQUIRES_OK(ctx, xc.CreateVariable(index_, arg.name, arg.value.type,
-                                            arg.value.handle, &var));
-      var->tensor_array_size = arg.tensor_array_size;
-      ctx->SetVariableOutput(0, var);
+      XlaResource* resource;
+      OP_REQUIRES_OK(ctx,
+                     xc.CreateResource(kind, index_, arg.name, arg.value.type,
+                                       arg.value.handle, &resource));
+      resource->tensor_array_size = arg.tensor_array_size;
+      ctx->SetResourceOutput(0, resource);
     } else if (arg.value.is_constant) {
       ctx->SetConstantOutput(0, arg.value.constant_value);
     } else {
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index c7510bf3d2..598b341002 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -41,32 +41,36 @@ namespace {
 // Since the element shape is not always provided to the TensorArrayV3 operator,
 // we must support lazily initialization of the TensorArray at the time of the
 // first write.
-// If a TensorArray `var` has not been initialized, constructs storage for the
-// TensorArray with elements of `elem_shape`. For both initialized and
+// If a TensorArray `resource` has not been initialized, constructs storage for
+// the TensorArray with elements of `elem_shape`. For both initialized and
 // uninitialized TensorArrays, checks that the tensor has a type compatible with
 // 'dtype' and shape compatible with 'elem_shape'.
 Status MaybeInitializeTensorArray(xla::ComputationBuilder* builder,
-                                  XlaVariable* var, DataType dtype,
+                                  XlaResource* resource, DataType dtype,
                                   const TensorShape& elem_shape) {
-  if (var->type != dtype) {
+  if (resource->kind != XlaResource::kTensorArray) {
+    return errors::InvalidArgument("Unexpected non-TensorArray resource");
+  }
+
+  if (resource->type != dtype) {
     return errors::InvalidArgument(
-        "TensorArray dtype is ", DataTypeString(var->type),
+        "TensorArray dtype is ", DataTypeString(resource->type),
         " but op has dtype ", DataTypeString(dtype), ".");
   }
 
-  TF_RET_CHECK(var->tensor_array_size >= 0)
-      << var->name << " size " << var->tensor_array_size;
+  TF_RET_CHECK(resource->tensor_array_size >= 0)
+      << resource->name << " size " << resource->tensor_array_size;
   TensorShape ta_shape;
-  ta_shape.AddDim(var->tensor_array_size);
+  ta_shape.AddDim(resource->tensor_array_size);
   ta_shape.AppendShape(elem_shape);
 
-  if (var->value.handle() == 0) {
+  if (resource->value.handle() == 0) {
     // TensorArray has not been initialized.
-    xla::ComputationDataHandle zero = XlaHelpers::Zero(builder, var->type);
-    var->value = builder->Broadcast(zero, ta_shape.dim_sizes());
+    xla::ComputationDataHandle zero = XlaHelpers::Zero(builder, resource->type);
+    resource->value = builder->Broadcast(zero, ta_shape.dim_sizes());
   } else {
     // Checks the elem_shape matches the TensorArray shape.
-    auto shape_or_status = builder->GetShape(var->value);
+    auto shape_or_status = builder->GetShape(resource->value);
     if (!shape_or_status.ok()) {
       return shape_or_status.status();
     }
@@ -80,6 +84,44 @@ Status MaybeInitializeTensorArray(xla::ComputationBuilder* builder,
   return Status::OK();
 }
 
+// Checks that the TensorArray 'resource' has been initialized, and has type
+// 'dtype'. Sets 'shape' to the shape
+Status CheckTensorArrayIsInitialized(const string& op_name,
+                                     const XlaResource* resource,
+                                     DataType dtype) {
+  if (resource->kind != XlaResource::kTensorArray) {
+    return errors::InvalidArgument(
+        "Unexpected non-TensorArray resource passed "
+        "to ",
+        op_name);
+  }
+  if (resource->value.handle() == 0) {
+    return errors::InvalidArgument("Uninitialized TensorArray passed to ",
+                                   op_name);
+  }
+  if (resource->type != dtype) {
+    return errors::InvalidArgument(
+        "TensorArray dtype is ", DataTypeString(resource->type),
+        " but op has dtype ", DataTypeString(dtype), ".");
+  }
+
+  return Status::OK();
+}
+
+Status GetTensorArrayShape(const XlaResource* resource,
+                           xla::ComputationBuilder* builder,
+                           TensorShape* shape) {
+  auto shape_or_status = builder->GetShape(resource->value);
+  if (!shape_or_status.ok()) {
+    return shape_or_status.status();
+  }
+  *shape = XLAShapeToTensorShape(*shape_or_status.ValueOrDie());
+  if (shape->dims() < 1) {
+    return errors::InvalidArgument("TensorArray rank must be >= 1");
+  }
+  return Status::OK();
+}
+
 // Pads 'x' with 'count' zero indices. 'x' must have 1 element.
 xla::ComputationDataHandle PadIndexWithZeros(
     xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x,
@@ -125,7 +167,6 @@ class TensorArrayOp : public XlaOpKernel {
                 errors::InvalidArgument("TensorArray size must be >= 0"));
 
     xla::ComputationBuilder* b = ctx->builder();
-    b->set_die_immediately_on_error(true);
 
     // Initializes the TensorArray value if we know the element shape.
     // Otherwise, defer initialization to the first write.
@@ -141,12 +182,13 @@ class TensorArrayOp : public XlaOpKernel {
     }
 
     XlaContext& xc = XlaContext::Get(ctx);
-    XlaVariable* var;
+    XlaResource* var;
     string name = strings::StrCat("TensorArray: ", tensor_array_name_);
-    OP_REQUIRES_OK(ctx,
-                   xc.CreateVariable(-1, std::move(name), dtype_, value, &var));
+    OP_REQUIRES_OK(
+        ctx, xc.CreateResource(XlaResource::kTensorArray, -1, std::move(name),
+                               dtype_, value, &var));
     var->tensor_array_size = size;
-    ctx->SetVariableOutput(0, var);
+    ctx->SetResourceOutput(0, var);
     ctx->SetConstantOutput(1, Tensor(DT_FLOAT));
   }
 
@@ -173,11 +215,12 @@ class TensorArrayWriteOp : public XlaOpKernel {
 
     // Initializes the TensorArray, if the element shape was not known at
     // construction time.
-    XlaVariable* var;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableInput(0, &var));
-    OP_REQUIRES_OK(ctx, MaybeInitializeTensorArray(b, var, dtype_, elem_shape));
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
+    OP_REQUIRES_OK(ctx,
+                   MaybeInitializeTensorArray(b, resource, dtype_, elem_shape));
 
-    xla::ComputationDataHandle ta = var->value;
+    xla::ComputationDataHandle ta = resource->value;
     xla::ComputationDataHandle index = ctx->Input(1);
     xla::ComputationDataHandle value = ctx->Input(2);
 
@@ -191,7 +234,7 @@ class TensorArrayWriteOp : public XlaOpKernel {
     xla::ComputationDataHandle written =
         DynamicAddSlice(b, ta, update, slice_shape.dim_sizes(), start_indices);
 
-    OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, written));
+    resource->value = written;
     ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
   }
 
@@ -210,20 +253,17 @@ class TensorArrayReadOp : public XlaOpKernel {
   }
 
   void Compile(XlaOpKernelContext* ctx) override {
-    DataType ta_type;
-    TensorShape ta_shape;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableTypeAndShape(0, &ta_type, &ta_shape));
-    OP_REQUIRES(ctx, ta_type == dtype_,
-                errors::InvalidArgument(
-                    "TensorArray dtype is ", DataTypeString(ta_type),
-                    " but Op requested dtype ", DataTypeString(dtype_), "."));
-    OP_REQUIRES(ctx, ta_shape.dims() >= 1,
-                errors::InvalidArgument("TensorArray rank must be >= 1"));
-
     xla::ComputationBuilder* b = ctx->builder();
 
-    xla::ComputationDataHandle ta;
-    OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, &ta));
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
+
+    OP_REQUIRES_OK(ctx,
+                   CheckTensorArrayIsInitialized(name(), resource, dtype_));
+    TensorShape ta_shape;
+    OP_REQUIRES_OK(ctx, GetTensorArrayShape(resource, b, &ta_shape));
+
+    xla::ComputationDataHandle ta = resource->value;
     xla::ComputationDataHandle index = ctx->Input(1);
 
     // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
@@ -255,13 +295,15 @@ class TensorArrayGatherOp : public XlaOpKernel {
   }
 
   void Compile(XlaOpKernelContext* ctx) override {
-    DataType ta_type;
+    xla::ComputationBuilder* b = ctx->builder();
+
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
+
+    OP_REQUIRES_OK(ctx,
+                   CheckTensorArrayIsInitialized(name(), resource, dtype_));
     TensorShape ta_shape;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableTypeAndShape(0, &ta_type, &ta_shape));
-    OP_REQUIRES(ctx, ta_type == dtype_,
-                errors::InvalidArgument("TensorArray type mismatch"));
-    OP_REQUIRES(ctx, ta_shape.dims() >= 1,
-                errors::InvalidArgument("TensorArray rank must be >= 1"));
+    OP_REQUIRES_OK(ctx, GetTensorArrayShape(resource, b, &ta_shape));
 
     const TensorShape indices_shape = ctx->InputShape(1);
     OP_REQUIRES(ctx, indices_shape.dims() >= 1,
@@ -269,10 +311,7 @@ class TensorArrayGatherOp : public XlaOpKernel {
     const int num_indices = indices_shape.dim_size(0);
     auto indices = ctx->Input(1);
 
-    xla::ComputationBuilder* b = ctx->builder();
-
-    xla::ComputationDataHandle ta;
-    OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, &ta));
+    xla::ComputationDataHandle ta = resource->value;
 
     // For each index in `indices`, add the corresponding slice to `slices`.
     std::vector<xla::ComputationDataHandle> slices(num_indices);
@@ -320,11 +359,12 @@ class TensorArrayScatterOp : public XlaOpKernel {
 
     const TensorShape value_shape = ctx->InputShape(2);
 
-    XlaVariable* var;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableInput(0, &var));
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
     TensorShape elem_shape = value_shape;
     elem_shape.RemoveDim(0);
-    OP_REQUIRES_OK(ctx, MaybeInitializeTensorArray(b, var, dtype_, elem_shape));
+    OP_REQUIRES_OK(ctx,
+                   MaybeInitializeTensorArray(b, resource, dtype_, elem_shape));
 
     const TensorShape indices_shape = ctx->InputShape(1);
     OP_REQUIRES(ctx, indices_shape.dims() >= 1,
@@ -332,7 +372,7 @@ class TensorArrayScatterOp : public XlaOpKernel {
     const int num_indices = indices_shape.dim_size(0);
     const xla::ComputationDataHandle indices = ctx->Input(1);
 
-    xla::ComputationDataHandle ta = var->value;
+    xla::ComputationDataHandle ta = resource->value;
     const xla::ComputationDataHandle value = ctx->Input(2);
 
     auto slice_dims = value_shape.dim_sizes();
@@ -355,7 +395,7 @@ class TensorArrayScatterOp : public XlaOpKernel {
       ta = DynamicAddSlice(b, ta, slice, slice_dims, start_indices);
     }
 
-    OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, ta));
+    resource->value = ta;
     ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
   }
 
@@ -374,18 +414,17 @@ class TensorArrayConcatOp : public XlaOpKernel {
   }
 
   void Compile(XlaOpKernelContext* ctx) override {
-    DataType ta_type;
-    TensorShape ta_shape;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableTypeAndShape(0, &ta_type, &ta_shape));
-    OP_REQUIRES(ctx, ta_type == dtype_,
-                errors::InvalidArgument("TensorArray type mismatch"));
-    OP_REQUIRES(ctx, ta_shape.dims() >= 1,
-                errors::InvalidArgument("TensorArray rank must be >= 1"));
-
     xla::ComputationBuilder* b = ctx->builder();
 
-    xla::ComputationDataHandle ta;
-    OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, &ta));
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
+
+    OP_REQUIRES_OK(ctx,
+                   CheckTensorArrayIsInitialized(name(), resource, dtype_));
+    TensorShape ta_shape;
+    OP_REQUIRES_OK(ctx, GetTensorArrayShape(resource, b, &ta_shape));
+
+    xla::ComputationDataHandle ta = resource->value;
 
     auto ta_dims = ta_shape.dim_sizes();
     std::vector<int64> shape(ta_dims.begin() + 1, ta_dims.end());
@@ -436,19 +475,20 @@ class TensorArraySplitOp : public XlaOpKernel {
     elem_shape.set_dim(0, length);
 
     xla::ComputationBuilder* b = ctx->builder();
-    XlaVariable* var;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableInput(0, &var));
-    OP_REQUIRES_OK(ctx, MaybeInitializeTensorArray(b, var, dtype_, elem_shape));
-    xla::ComputationDataHandle ta = var->value;
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
+    OP_REQUIRES_OK(ctx,
+                   MaybeInitializeTensorArray(b, resource, dtype_, elem_shape));
+    xla::ComputationDataHandle ta = resource->value;
 
     TensorShape ta_shape;
-    ta_shape.AddDim(var->tensor_array_size);
+    ta_shape.AddDim(resource->tensor_array_size);
     ta_shape.AppendShape(elem_shape);
 
-    OP_REQUIRES(ctx, lengths.size() == var->tensor_array_size,
+    OP_REQUIRES(ctx, lengths.size() == resource->tensor_array_size,
                 errors::InvalidArgument(
                     "TensorArray's size is not equal to the size of lengths (",
-                    lengths.size(), " vs. ", var->tensor_array_size, ")"));
+                    lengths.size(), " vs. ", resource->tensor_array_size, ")"));
 
     const xla::ComputationDataHandle value = ctx->Input(1);
 
@@ -457,8 +497,7 @@ class TensorArraySplitOp : public XlaOpKernel {
                                         value_shape.DebugString(), " vs. ",
                                         ta_shape.DebugString()));
 
-    ta = b->Add(ta, b->Reshape(value, ta_shape.dim_sizes()));
-    OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, ta));
+    resource->value = b->Add(ta, b->Reshape(value, ta_shape.dim_sizes()));
 
     ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
   }
@@ -476,8 +515,8 @@ class TensorArraySizeOp : public XlaOpKernel {
   explicit TensorArraySizeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
 
   void Compile(XlaOpKernelContext* ctx) override {
-    XlaVariable* var;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableInput(0, &var));
+    XlaResource* var;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &var));
     Tensor size_tensor(DT_INT32, {});
     size_tensor.scalar<int32>()() = static_cast<int32>(var->tensor_array_size);
     ctx->SetConstantOutput(0, size_tensor);
@@ -498,31 +537,31 @@ class TensorArrayGradOp : public XlaOpKernel {
   void Compile(XlaOpKernelContext* ctx) override {
     xla::ComputationBuilder* b = ctx->builder();
 
-    XlaVariable* var;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableInput(0, &var));
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
 
-    DataType ta_type;
+    OP_REQUIRES_OK(
+        ctx, CheckTensorArrayIsInitialized(name(), resource, resource->type));
     TensorShape ta_shape;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableTypeAndShape(0, &ta_type, &ta_shape));
-    OP_REQUIRES(ctx, ta_shape.dims() >= 1,
-                errors::InvalidArgument("TensorArray rank must be >= 1"));
+    OP_REQUIRES_OK(ctx, GetTensorArrayShape(resource, b, &ta_shape));
 
     // Finds or looks up the corresponding gradient TensorArray, which stores
     // gradients computed during backpropagation.
-    XlaVariable*& gradient = var->tensor_array_gradient[source_];
+    XlaResource*& gradient = resource->tensor_array_gradient[source_];
     if (!gradient) {
-      xla::ComputationDataHandle zero = XlaHelpers::Zero(b, ta_type);
+      xla::ComputationDataHandle zero = XlaHelpers::Zero(b, resource->type);
       xla::ComputationDataHandle value =
           b->Broadcast(zero, ta_shape.dim_sizes());
 
       XlaContext& xc = XlaContext::Get(ctx);
-      string name = strings::StrCat("TensorArrayGrad: ", var->name);
-      OP_REQUIRES_OK(ctx, xc.CreateVariable(-1, std::move(name), var->type,
-                                            value, &gradient));
-      gradient->tensor_array_size = var->tensor_array_size;
+      string name = strings::StrCat("TensorArrayGrad: ", resource->name);
+      OP_REQUIRES_OK(
+          ctx, xc.CreateResource(XlaResource::kTensorArray, -1, std::move(name),
+                                 resource->type, value, &gradient));
+      gradient->tensor_array_size = resource->tensor_array_size;
     }
 
-    ctx->SetVariableOutput(0, gradient);
+    ctx->SetResourceOutput(0, gradient);
     ctx->SetConstantOutput(1, Tensor(DT_FLOAT));
   }
 
diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc
index ae9a358e22..0caa9c5f37 100644
--- a/tensorflow/compiler/tf2xla/kernels/while_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc
@@ -42,29 +42,38 @@ Status MakeXlaCompilerArgumentsFromInputs(
             << " shape: " << ctx->InputShape(i).DebugString();
     XlaCompiler::Argument& arg = (*args)[i];
     DataType type = ctx->input_type(i);
-    // When reading a variable input, use the type and shape of the variable's
+    // When reading a resource input, use the type and shape of the resource's
     // current value.
     if (type == DT_RESOURCE) {
-      XlaVariable* var;
-      TF_RETURN_IF_ERROR(ctx->GetVariableInput(i, &var));
-
-      bool initialized = var->value.handle() > 0;
-      if (initialized) {
-        arg.kind = XlaCompiler::Argument::kVariable;
-        TF_RETURN_IF_ERROR(
-            ctx->GetVariableTypeAndShape(i, &arg.type, &arg.shape));
+      XlaResource* resource;
+      TF_RETURN_IF_ERROR(ctx->GetResourceInput(i, &resource));
+
+      arg.initialized = resource->value.handle() > 0;
+      switch (resource->kind) {
+        case XlaResource::kVariable:
+          arg.kind = XlaCompiler::Argument::kVariable;
+          break;
+        case XlaResource::kTensorArray:
+          arg.kind = XlaCompiler::Argument::kTensorArray;
+          break;
+        case XlaResource::kInvalid:
+          CHECK(false);
+      }
+      arg.type = resource->type;
+      if (arg.initialized) {
+        auto shape = ctx->builder()->GetShape(resource->value);
+        TF_RETURN_IF_ERROR(shape.status());
+        arg.shape = XLAShapeToTensorShape(*shape.ValueOrDie());
       } else {
-        arg.kind = XlaCompiler::Argument::kUninitializedVariable;
-        arg.type = var->type;
         *has_uninitialized_vars = true;
       }
-      arg.tensor_array_size = var->tensor_array_size;
-      arg.name = var->name;
+      arg.tensor_array_size = resource->tensor_array_size;
+      arg.name = resource->name;
       // TODO(phawkins): propagate TensorArray gradients into loops.
-      VLOG(2) << "    variable " << var->name
+      VLOG(2) << "    resource " << resource->name
               << " type: " << DataTypeString(arg.type)
               << " shape: " << arg.shape.DebugString()
-              << " initialized: " << initialized;
+              << " initialized: " << arg.initialized;
 
     } else {
       arg.kind = XlaCompiler::Argument::kParameter;
@@ -86,7 +95,7 @@ XlaWhileOp::XlaWhileOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
 }
 
 void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
-  VLOG(1) << "WhileOp::Compute";
+  VLOG(1) << "WhileOp::Compile";
 
   std::vector<XlaCompiler::Argument> arguments;
   bool has_uninitialized_vars;
@@ -100,16 +109,16 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
 
   VLOG(1) << "Compiling body";
 
-  // All resource variables that are inputs to the loop's body must also be
+  // All resource that are inputs to the loop's body must also be
   // present as loop body outputs; the signature of the loop's input and
   // output must match. We ensure this by asking the compiler to include the
-  // current values of all variables, even if they haven't been updated by the
+  // current values of all resources, even if they haven't been updated by the
   // computation.
   // TODO(phawkins): consider adding loop-invariant inputs to XLA's While()
   // operator.
   XlaCompiler::CompileOptions body_options;
   body_options.use_tuple_arg = use_tuple_arg;
-  body_options.return_updated_values_for_all_variables = true;
+  body_options.return_updated_values_for_all_resources = true;
   XlaCompiler::CompilationResult body;
   OP_REQUIRES_OK(ctx, compiler->CompileFunction(body_options, body_name_attr_,
                                                 arguments, &body));
@@ -118,26 +127,28 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
   // we may not know the shape of a TensorArray if it is first written inside
   // the loop. Ideally we would require the user to provide a static shape,
   // but this is not always easy.
-  // So if uninitialized variables are used by the loop body, we compile the
+  // So if uninitialized resource are used by the loop body, we compile the
   // body function twice:
-  // 1) once with uninitialized variable inputs. We discard the computation
-  //    but we assume variable shapes reach a fixpoint after one iteration.
-  //    So we can use the output shapes of the variables as the "true" shapes.
+  // 1) once with uninitialized resource inputs. We discard the computation
+  //    but we assume resource shapes reach a fixpoint after one iteration.
+  //    So we can use the output shapes of the resource as the "true" shapes.
   // 2) again with the "correct" input shapes determined by (1).
   if (has_uninitialized_vars) {
-    // Initializes any uninitialized variables with zero values of the
+    // Initializes any uninitialized resource with zero values of the
     // shape determined by the first compilation.
-    for (int i = 0; i < body.variable_updates.size(); ++i) {
-      const XlaCompiler::VariableUpdate& update = body.variable_updates[i];
+    for (int i = 0; i < body.resource_updates.size(); ++i) {
+      const XlaCompiler::ResourceUpdate& update = body.resource_updates[i];
       XlaCompiler::Argument& arg = arguments[update.input_index];
-      if (arg.kind == XlaCompiler::Argument::kUninitializedVariable) {
-        arg.kind = XlaCompiler::Argument::kVariable;
+      if (!arg.initialized) {
+        arg.initialized = true;
         arg.shape = update.shape;
 
+        XlaResource* resource;
+        OP_REQUIRES_OK(ctx,
+                       ctx->GetResourceInput(update.input_index, &resource));
+
         xla::ComputationDataHandle zero = XlaHelpers::Zero(builder, arg.type);
-        auto value = builder->Broadcast(zero, update.shape.dim_sizes());
-        OP_REQUIRES_OK(
-            ctx, ctx->AssignVariable(update.input_index, arg.type, value));
+        resource->value = builder->Broadcast(zero, update.shape.dim_sizes());
       }
     }
     // Recompile the body with the "correct" shapes.
@@ -191,7 +202,9 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
   for (int i = 0; i < num_inputs; ++i) {
     int input_num = body.input_mapping[i];
     if (ctx->input_type(input_num) == DT_RESOURCE) {
-      OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(input_num, &inputs[i]));
+      XlaResource* resource;
+      OP_REQUIRES_OK(ctx, ctx->GetResourceInput(input_num, &resource));
+      inputs[i] = resource->value;
     } else {
       inputs[i] = ctx->Input(i);
     }
@@ -225,16 +238,16 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
   }
 
   // Updates the values of any resource variables modified by the loop.
-  for (int i = 0; i < body.variable_updates.size(); ++i) {
-    const XlaCompiler::VariableUpdate& update = body.variable_updates[i];
+  for (int i = 0; i < body.resource_updates.size(); ++i) {
+    const XlaCompiler::ResourceUpdate& update = body.resource_updates[i];
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(update.input_index, &resource));
     if (update.modified) {
       int pos = body.outputs.size() + i;
-      OP_REQUIRES_OK(ctx, ctx->AssignVariable(update.input_index, update.type,
-                                              get_loop_output(pos)));
+      resource->value = get_loop_output(pos);
     }
     VLOG(2) << "Loop-carried variable: pos: " << update.input_index
-            << " name: " << ctx->VariableDebugString(update.input_index)
-            << " modified: " << update.modified
+            << " name: " << resource->name << " modified: " << update.modified
             << " type: " << DataTypeString(update.type)
             << " shape: " << update.shape.DebugString();
     // Copies the identity of the resource variable from input to output
diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.h b/tensorflow/compiler/tf2xla/xla_compilation_device.h
index 75630bee39..e4f43f1950 100644
--- a/tensorflow/compiler/tf2xla/xla_compilation_device.h
+++ b/tensorflow/compiler/tf2xla/xla_compilation_device.h
@@ -64,26 +64,35 @@ class XlaCompilationDevice : public LocalDevice {
   std::unique_ptr<XlaCompilationAllocator> allocator_;
 };
 
-struct XlaVariable {
-  // If this variable is visible externally, what was its argument number?
+// Represents a resource, such as a Variable or TensorArray.
+struct XlaResource {
+  enum Kind {
+    kInvalid,
+    kVariable,
+    kTensorArray,
+  };
+
+  Kind kind = kInvalid;
+
+  // If this resource is visible externally, what was its argument number?
   int arg_num = -1;
 
-  // A descriptive name for the variable, used in error messages.
+  // A descriptive name for the resource, used in error messages.
   string name;
 
-  // Current type and value of the variable. Uninitialized variables are
+  // Current type and value of the resource. Uninitialized resources are
   // represented by a default (zero) handle and type DT_INVALID.
-  // While the type of a variable is notionally fixed during execution, when
-  // a variable is first initialized we do not yet know its type, so we keep
+  // While the type of a resource is notionally fixed during execution, when
+  // a resource is first initialized we do not yet know its type, so we keep
   // track of its type dynamically.
   DataType type = DT_INVALID;
   xla::ComputationDataHandle value;
 
-  // Value of the variable at computation entry. Used to detect which
+  // Value of the resource at computation entry. Used to detect which
   // variables have new values that need to be written back.
   xla::ComputationDataHandle initial_value;
 
-  // We treat TensorArrays as a Variable with some extra metadata.
+  // TensorArray-specific fields
 
   // 'tensor_array_size' stores the expected size of the TensorArray. We need
   // to store this since sometimes TensorArrays must be initialized lazily since
@@ -91,10 +100,10 @@ struct XlaVariable {
   int64 tensor_array_size = -1;
 
   // 'tensor_array_gradient' is a map from TensorArrayGradV3 'source' attributes
-  // to an XlaVariable containing the gradient TensorArrays. We store a pointer
+  // to an XlaResource containing the gradient TensorArrays. We store a pointer
   // here since there should only be one gradient TensorArray per 'source'
   // string, irrespective of the number of calls to TensorArrayGrad.
-  std::unordered_map<string, XlaVariable*> tensor_array_gradient;
+  std::unordered_map<string, XlaResource*> tensor_array_gradient;
 };
 
 // A XlaExpression wraps an XLA computation. Each Tensor on an
@@ -115,8 +124,8 @@ class XlaExpression {
   bool has_constant_value() const { return has_constant_value_; }
   const Tensor& constant_value() const { return constant_value_; }
 
-  void set_variable(XlaVariable* variable) { variable_ = variable; }
-  XlaVariable* variable() const { return variable_; }
+  void set_resource(XlaResource* resource) { resource_ = resource; }
+  XlaResource* resource() const { return resource_; }
 
  private:
   // The XLA handle of the expression's computation.
@@ -128,7 +137,7 @@ class XlaExpression {
   bool has_constant_value_ = false;
   Tensor constant_value_;
 
-  XlaVariable* variable_ = nullptr;  // Not owned.
+  XlaResource* resource_ = nullptr;  // Not owned.
 
   TF_DISALLOW_COPY_AND_ASSIGN(XlaExpression);
 };
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index d7c92a710c..50b384997a 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -251,35 +251,36 @@ Status BuildArguments(const std::vector<XlaCompiler::Argument>& args,
                       std::vector<xla::Shape>* input_shapes) {
   context_args->resize(args.size());
 
-  // Argument numbers of arguments and variables that are to be passed to the
+  // Argument numbers of arguments and resources that are to be passed to the
   // XLA computation as runtime parameters.
-  std::vector<int> parameters, variables;
+  std::vector<int> parameters, resources;
   parameters.reserve(args.size());
-  variables.reserve(args.size());
+  resources.reserve(args.size());
 
   for (std::vector<XlaCompiler::Argument>::size_type i = 0; i < args.size();
        ++i) {
     XlaContext::Argument& context_arg = (*context_args)[i];
+    context_arg.kind = args[i].kind;
     context_arg.name = args[i].name;
     context_arg.value.constant_value = args[i].constant_value;
     context_arg.value.type = args[i].type;
 
     switch (args[i].kind) {
       case XlaCompiler::Argument::kVariable:
-        variables.push_back(i);
-        context_arg.is_variable = true;
-        context_arg.value.is_constant = false;
+      case XlaCompiler::Argument::kTensorArray:
+        context_arg.is_resource = true;
+        if (args[i].initialized) {
+          resources.push_back(i);
+          context_arg.value.is_constant = false;
+        } else {
+          context_arg.value.is_constant = true;
+        }
         context_arg.tensor_array_size = args[i].tensor_array_size;
         break;
       case XlaCompiler::Argument::kParameter:
         parameters.push_back(i);
         context_arg.value.is_constant = false;
         break;
-      case XlaCompiler::Argument::kUninitializedVariable:
-        context_arg.is_variable = true;
-        context_arg.value.is_constant = true;
-        context_arg.tensor_array_size = args[i].tensor_array_size;
-        break;
       case XlaCompiler::Argument::kConstant:
         context_arg.value.is_constant = true;
         break;
@@ -290,7 +291,7 @@ Status BuildArguments(const std::vector<XlaCompiler::Argument>& args,
 
   // Append parameters containing variable values after the other runtime
   // parameters.
-  parameters.insert(parameters.end(), variables.begin(), variables.end());
+  parameters.insert(parameters.end(), resources.begin(), resources.end());
   if (parameters.empty()) {
     return Status::OK();
   }
@@ -331,22 +332,22 @@ Status BuildArguments(const std::vector<XlaCompiler::Argument>& args,
 // variable states, generated by the symbolic evaluation.
 // If `has_side_effects` is true, the computation has side effects and should be
 // built even if it has no outputs.
-// If `return_updated_values_for_all_variables` is true, all variables will be
-// included in `variable_updates`, regardless of whether their value changed.
+// If `return_updated_values_for_all_resources` is true, all resources will be
+// included in `resource_updates`, regardless of whether their value changed.
 // Sets `*num_nonconst_outputs` to the number of outputs of the `computation`.
-// Sets `*variable_updates` to a description of variables whose values are
+// Sets `*resource_updates` to a description of resources whose values are
 // written by the computation; the variable writes are the last
-// `variable_updates.size()` return values from the computation. Each entry in
-// `variable_updates` is a (input_index, type) pair, where `input_index` is the
+// `resource_updates.size()` return values from the computation. Each entry in
+// `resource_updates` is a (input_index, type) pair, where `input_index` is the
 // index of a resource variable argument to the computation, and `type` is the
 // type of the final output.
 Status BuildComputation(
     const std::vector<XlaContext::HandleOrConstant>& retvals,
-    const std::vector<std::unique_ptr<XlaVariable>>& variables,
-    bool has_side_effects, bool return_updated_values_for_all_variables,
+    const std::vector<std::unique_ptr<XlaResource>>& resources,
+    bool has_side_effects, bool return_updated_values_for_all_resources,
     xla::ComputationBuilder* builder, xla::Computation* computation,
     int* num_nonconst_outputs,
-    std::vector<XlaCompiler::VariableUpdate>* variable_updates) {
+    std::vector<XlaCompiler::ResourceUpdate>* resource_updates) {
   std::vector<xla::ComputationDataHandle> elems;
   elems.reserve(retvals.size());
   for (const XlaContext::HandleOrConstant& retval : retvals) {
@@ -356,24 +357,24 @@ Status BuildComputation(
   }
   *num_nonconst_outputs = elems.size();
 
-  // Add return values for variables whose values have changed.
-  std::vector<const XlaVariable*> arg_vars;
-  arg_vars.reserve(variables.size());
-  for (const auto& var : variables) {
+  // Add return values for resources whose values have changed.
+  std::vector<const XlaResource*> arg_vars;
+  arg_vars.reserve(resources.size());
+  for (const auto& var : resources) {
     if (var->arg_num >= 0) {
       arg_vars.push_back(var.get());
     }
   }
   std::sort(arg_vars.begin(), arg_vars.end(),
-            [](const XlaVariable* a, const XlaVariable* b) {
+            [](const XlaResource* a, const XlaResource* b) {
               return a->arg_num < b->arg_num;
             });
 
-  for (const XlaVariable* var : arg_vars) {
+  for (const XlaResource* var : arg_vars) {
     bool modified = var->value.handle() != var->initial_value.handle();
-    if (return_updated_values_for_all_variables || modified) {
-      variable_updates->emplace_back();
-      XlaCompiler::VariableUpdate& update = variable_updates->back();
+    if (return_updated_values_for_all_resources || modified) {
+      resource_updates->emplace_back();
+      XlaCompiler::ResourceUpdate& update = resource_updates->back();
       update.input_index = var->arg_num;
       update.type = var->type;
       update.modified = modified;
@@ -439,10 +440,10 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
   int num_nonconst_outputs;
   result->computation = std::make_shared<xla::Computation>();
   TF_RETURN_IF_ERROR(BuildComputation(
-      context->retvals(), context->variables(), context->has_side_effects(),
-      options.return_updated_values_for_all_variables, &builder,
+      context->retvals(), context->resources(), context->has_side_effects(),
+      options.return_updated_values_for_all_resources, &builder,
       result->computation.get(), &num_nonconst_outputs,
-      &result->variable_updates));
+      &result->resource_updates));
 
   result->requires_runtime_context = context->has_context_parameter();
 
@@ -517,15 +518,15 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
     }
   }
 
-  for (std::vector<VariableUpdate>::size_type i = 0;
-       i < result->variable_updates.size(); ++i) {
+  for (std::vector<ResourceUpdate>::size_type i = 0;
+       i < result->resource_updates.size(); ++i) {
     if (num_computation_outputs > 1) {
-      result->variable_updates[i].shape =
+      result->resource_updates[i].shape =
           XLAShapeToTensorShape(xla::ShapeUtil::GetTupleElementShape(
               result->xla_output_shape, computation_output));
     } else {
       CHECK_EQ(0, computation_output);
-      result->variable_updates[i].shape =
+      result->resource_updates[i].shape =
           XLAShapeToTensorShape(result->xla_output_shape);
     }
     ++computation_output;
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h
index 6b9bf4159d..58e42c3474 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.h
+++ b/tensorflow/compiler/tf2xla/xla_compiler.h
@@ -85,14 +85,14 @@ class XlaCompiler {
       // Argument is a compile-time constant. No associated runtime parameter.
       kConstant,
 
-      // Argument is a variable that has not been initialized yet. No associated
-      // runtime parameter.
-      kUninitializedVariable,
-
-      // Argument is a variable that already has a value set. Expects a runtime
-      // parameter containing the current value.
+      // Argument is a variable resource. Has an associated runtime parameter
+      // iff `initialized` is true.
       kVariable,
 
+      // Argument is a TensorArray resource. Has an associated runtime parameter
+      // iff `initialized` is true.
+      kTensorArray,
+
       // Argument is a run-time parameter.
       kParameter,
     };
@@ -114,8 +114,11 @@ class XlaCompiler {
     // The name of this argument, used for debugging.
     string name;
 
-    // For a kVariable or kUninitializedVariable corresponding to a TensorArray,
-    // what is the tensor array's declared size?
+    // For a kVariable or kTensorArray, has this resource been initialized?
+    bool initialized = false;
+
+    // For a kTensorArray, what is the array's declared size? (Used for lazy
+    // initialization.)
     int64 tensor_array_size = -1;
 
     bool operator==(const Argument& other) const;
@@ -133,7 +136,7 @@ class XlaCompiler {
   };
 
   // Describes a variable write side effect of the computation.
-  struct VariableUpdate {
+  struct ResourceUpdate {
     // Index of the input that contains the variable resource to write to.
     int input_index;
 
@@ -142,14 +145,14 @@ class XlaCompiler {
     TensorShape shape;
 
     // Was the value of the variable modified by the computation?
-    // (Always true, unless `return_updated_values_for_all_variables` is true.)
+    // (Always true, unless `return_updated_values_for_all_resources` is true.)
     bool modified;
   };
 
   struct CompilationResult {
     // Vector that maps from the parameters of the XLA computation to their
     // original argument positions. To handle compile-time constant inputs and
-    // variables, the parameters to the XLA computation may be a subset of the
+    // resources, the parameters to the XLA computation may be a subset of the
     // original arguments, and are not necessarily in the same order.)
     std::vector<int> input_mapping;
 
@@ -172,10 +175,10 @@ class XlaCompiler {
     // containing both constant and non-constant results.
     std::vector<OutputDescription> outputs;
 
-    // Variables whose values were updated by the computation, ordered
-    // by return value position. Variable updates follow the non-constant
+    // Resources whose values were updated by the computation, ordered
+    // by return value position. Resource updates follow the non-constant
     // results in the outputs of XLA computation.
-    std::vector<VariableUpdate> variable_updates;
+    std::vector<ResourceUpdate> resource_updates;
 
     // The XLA computation built from the tensorflow subgraph. May be null
     // if the output consists solely of compile-time constants.
@@ -229,12 +232,12 @@ class XlaCompiler {
     // arguments; if false, each argument gets its own parameter.
     bool use_tuple_arg = false;
 
-    // If 'return_updated_values_for_all_variables' is true, then updated
-    // values of all resource variables arguments will be included in the
-    // 'variable_updates' of the computation, even if the variable was not
+    // If 'return_updated_values_for_all_resources' is true, then updated
+    // values of all resource resources arguments will be included in the
+    // 'resource_updates' of the computation, even if the resource was not
     // modified by the computation. Used when compiling loop bodies to ensure
     // the input and output signatures match.
-    bool return_updated_values_for_all_variables = false;
+    bool return_updated_values_for_all_resources = false;
   };
 
   // Compiles a Tensorflow function `fn_name_attrs` into an XLA computation.
diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc
index 4440b53069..1a37d61944 100644
--- a/tensorflow/compiler/tf2xla/xla_context.cc
+++ b/tensorflow/compiler/tf2xla/xla_context.cc
@@ -129,16 +129,18 @@ void XlaContext::AddSideEffects() {
 
 xla::ComputationBuilder* XlaContext::builder() { return builder_; }
 
-Status XlaContext::CreateVariable(int arg_num, string name, DataType type,
+Status XlaContext::CreateResource(XlaResource::Kind kind, int arg_num,
+                                  string name, DataType type,
                                   const xla::ComputationDataHandle& handle,
-                                  XlaVariable** variable) {
-  variables_.emplace_back(new XlaVariable);
-  *variable = variables_.back().get();
-  XlaVariable& var = **variable;
-  var.arg_num = arg_num;
-  var.name = std::move(name);
-  var.type = type;
-  var.initial_value = var.value = handle;
+                                  XlaResource** resource) {
+  resources_.emplace_back(new XlaResource);
+  *resource = resources_.back().get();
+  XlaResource& r = **resource;
+  r.kind = kind;
+  r.arg_num = arg_num;
+  r.name = std::move(name);
+  r.type = type;
+  r.initial_value = r.value = handle;
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h
index 3978baaf63..dbede52b5d 100644
--- a/tensorflow/compiler/tf2xla/xla_context.h
+++ b/tensorflow/compiler/tf2xla/xla_context.h
@@ -52,11 +52,13 @@ class XlaContext : public ResourceBase {
   };
 
   struct Argument {
-    // Descriptive name for the variable, for use in error messages.
+    XlaCompiler::Argument::Kind kind;
+
+    // Descriptive name for the resource, for use in error messages.
     string name;
 
-    // Is this a variable?
-    bool is_variable = false;
+    // Is this a resource?
+    bool is_resource = false;
 
     HandleOrConstant value;
 
@@ -106,15 +108,15 @@ class XlaContext : public ResourceBase {
 
   bool has_side_effects() const { return has_side_effects_; }
 
-  // Creates a variable with variable `variable_id` and initial type `type` and
+  // Creates a resource with resource `kind` and initial type `type` and
   // value `handle`. `name` is a descriptive name for use in error messages.
-  // Fails if the variable already exists.
-  Status CreateVariable(int arg_num, string name, DataType type,
-                        const xla::ComputationDataHandle& handle,
-                        XlaVariable** variable);
+  // Fails if the resource already exists.
+  Status CreateResource(XlaResource::Kind kind, int arg_num, string name,
+                        DataType type, const xla::ComputationDataHandle& handle,
+                        XlaResource** resource);
 
-  const std::vector<std::unique_ptr<XlaVariable>>& variables() {
-    return variables_;
+  const std::vector<std::unique_ptr<XlaResource>>& resources() {
+    return resources_;
   }
 
   // Get an XLA lambda to compute Max. This is cached in the
@@ -166,8 +168,8 @@ class XlaContext : public ResourceBase {
   // Does the computation have side effects, i.e., Send() calls?
   bool has_side_effects_ = false;
 
-  // Holds ownership of variables. The variables are not ordered.
-  std::vector<std::unique_ptr<XlaVariable>> variables_;
+  // Holds ownership of resources. The resources are not ordered.
+  std::vector<std::unique_ptr<XlaResource>> resources_;
 
   // Cache of prebuilt computations indexed by their type.
   using ComputationMap = std::map<DataType, xla::Computation>;
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
index 3272b1efa1..edb7e2a563 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
@@ -39,7 +39,7 @@ static const XlaExpression* CastExpressionFromTensor(const Tensor& tensor) {
   const XlaExpression* expression =
       reinterpret_cast<const XlaExpression*>(tensor.tensor_data().data());
   CHECK(expression->handle().handle() != 0 ||
-        expression->variable() != nullptr);
+        expression->resource() != nullptr);
   VLOG(1) << "Fetched T" << expression->handle().handle();
   return expression;
 }
@@ -252,8 +252,9 @@ Status XlaOpKernelContext::ReadVariableInput(
     int index, xla::ComputationDataHandle* value) {
   const Tensor& tensor = context_->input(index);
   const XlaExpression* expression = CastExpressionFromTensor(tensor);
-  XlaVariable* variable = expression->variable();
+  XlaResource* variable = expression->resource();
   TF_RET_CHECK(variable != nullptr);
+  TF_RET_CHECK(variable->kind == XlaResource::kVariable);
   if (variable->value.handle() == 0) {
     return errors::InvalidArgument("Read of uninitialized variable ",
                                    variable->name);
@@ -262,22 +263,13 @@ Status XlaOpKernelContext::ReadVariableInput(
   return Status::OK();
 }
 
-string XlaOpKernelContext::VariableDebugString(int index) {
-  const Tensor& tensor = context_->input(index);
-  const XlaExpression* expression = CastExpressionFromTensor(tensor);
-  XlaVariable* variable = expression->variable();
-  if (!variable) {
-    return "<invalid variable ID>";
-  }
-  return variable->name;
-}
-
 Status XlaOpKernelContext::GetVariableTypeAndShape(int index, DataType* type,
                                                    TensorShape* shape) const {
   const Tensor& tensor = context_->input(index);
   const XlaExpression* expression = CastExpressionFromTensor(tensor);
-  XlaVariable* variable = expression->variable();
+  XlaResource* variable = expression->resource();
   TF_RET_CHECK(variable != nullptr);
+  TF_RET_CHECK(variable->kind == XlaResource::kVariable);
   if (variable->value.handle() == 0) {
     return errors::InvalidArgument("Read of uninitialized variable ",
                                    variable->name);
@@ -337,33 +329,34 @@ void XlaOpKernelContext::SetConstantOutput(int index, const Tensor& constant) {
   expression->set_constant_value(constant);
 }
 
-void XlaOpKernelContext::SetVariableOutput(int index, XlaVariable* variable) {
+void XlaOpKernelContext::SetResourceOutput(int index, XlaResource* resource) {
   Tensor* output = nullptr;
-  // The shape of the output tensor is the shape of the variable resource
-  // (i.e., a scalar), not the shape of the variable's value.
+  // The shape of the output tensor is the shape of the resource itself
+  // (i.e., a scalar), not the shape of the resource's value.
   OP_REQUIRES_OK(context_,
                  context_->allocate_output(index, TensorShape(), &output));
   XlaExpression* expression = CastExpressionFromUninitializedTensor(output);
-  expression->set_variable(variable);
+  expression->set_resource(resource);
 }
 
-Status XlaOpKernelContext::GetVariableInput(int index, XlaVariable** variable) {
+Status XlaOpKernelContext::GetResourceInput(int index, XlaResource** resource) {
   const XlaExpression* expression =
       CastExpressionFromTensor(context_->input(index));
-  TF_RET_CHECK(expression->variable() != nullptr);
-  *variable = expression->variable();
+  TF_RET_CHECK(expression->resource() != nullptr);
+  *resource = expression->resource();
   return Status::OK();
 }
 
 Status XlaOpKernelContext::AssignVariable(
-    int index, DataType type, const xla::ComputationDataHandle& handle) {
+    int input_index, DataType type, const xla::ComputationDataHandle& handle) {
   TF_RET_CHECK(handle.handle() != 0);
   SetOpHasSideEffects();
 
   const XlaExpression* expression =
-      CastExpressionFromTensor(context_->input(index));
-  XlaVariable* variable = expression->variable();
+      CastExpressionFromTensor(context_->input(input_index));
+  XlaResource* variable = expression->resource();
   TF_RET_CHECK(variable != nullptr);
+  TF_RET_CHECK(variable->kind == XlaResource::kVariable);
   if (!((variable->type == DT_INVALID && type != DT_INVALID) ||
         (variable->type == type))) {
     return errors::InvalidArgument(
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h
index a25774c3a6..b151286217 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.h
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h
@@ -148,6 +148,12 @@ class XlaOpKernelContext {
 
   // Variables
 
+  // Sets '*resource' to the resource associated with input `index`.
+  Status GetResourceInput(int index, XlaResource** resource);
+
+  // Sets output 'index' to be a reference to resource 'resource'.
+  void SetResourceOutput(int index, XlaResource* resource);
+
   // Sets `*type` and `*shape` to the current type and shape of a variable's
   // value.
   Status GetVariableTypeAndShape(int index, DataType* type,
@@ -158,20 +164,10 @@ class XlaOpKernelContext {
   Status ReadVariableInput(int index, xla::ComputationDataHandle* value);
 
   // Assigns the value `handle` to the variable referenced by input
-  // `variable_index`. Marks the operator as having side effects.
-  Status AssignVariable(int variable_index, DataType type,
+  // `input_index`. Marks the operator as having side effects.
+  Status AssignVariable(int input_index, DataType type,
                         const xla::ComputationDataHandle& handle);
 
-  // Sets '*variable' to the variable associated with input `index`.
-  Status GetVariableInput(int index, XlaVariable** variable);
-
-  // Sets output 'index' to be a reference to variable 'variable'. Used
-  // to propagate resource variables through the compilation.
-  void SetVariableOutput(int index, XlaVariable* variable);
-
-  // Returns a human-readable debug string describing 'variable_index'.
-  string VariableDebugString(int variable_index);
-
   // Helper routines for the OP_REQUIRES macros
   void CtxFailure(Status s);
   void CtxFailureWithWarning(Status s);
-- 
GitLab


From 059828d177a7befee8d68d0015f5b4cd125969b4 Mon Sep 17 00:00:00 2001
From: Niranjan Hasabnis <niranjan.hasabnis@intel.com>
Date: Fri, 16 Jun 2017 10:49:34 -0700
Subject: [PATCH 0148/1375] Changing context search for BiasAddGrad rewrite
 from BFS to stricter check

---
 tensorflow/core/graph/mkl_layout_pass.cc      | 277 ++++++++++++------
 tensorflow/core/graph/mkl_layout_pass_test.cc | 252 +++++++++++++++-
 2 files changed, 439 insertions(+), 90 deletions(-)

diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc
index 94741a11ff..625780e7c9 100644
--- a/tensorflow/core/graph/mkl_layout_pass.cc
+++ b/tensorflow/core/graph/mkl_layout_pass.cc
@@ -247,16 +247,10 @@ namespace tensorflow {
 //
 //           P = Conv2DWithBiasBackpropBias(O, O_m)
 //
-// 'Distance' between input of BiasAddGrad and _MklConv2D in terms of hops is
-// the context matching depth. If _MklConv2DWithBias is not within the context
-// matching depth, then we do not rewrite BiasAddGrad.
-
-// How many hops do we search for matching node in the backward dataflow graph?
-// We use maxhop of 10 based on empirical observations. Also, these are
-// maxhops in backward data-flow graph. Since input of forward nodes (Conv2D)
-// directly goes to backward nodes, we do not expect the hop-distance
-// would be more than few nodes.
-static size_t kNodeMergeContextMaxDepth = 10;
+// Rewrite of BiasAddGrad into Conv2DWithBiasBackpropBias takes place depending
+// on the matching 'context'. The term context is loosely related to which
+// forward op is _associated_ to BiasAddGrad. If it is _MklConv2DWithBias then
+// we consider it Conv2D context; if it is MatMul, then it is MatMul context.
 
 class MklLayoutRewritePass : public GraphOptimizationPass {
  public:
@@ -280,6 +274,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     csinfo_.max_pool = "MaxPool";
     csinfo_.max_pool_grad = "MaxPoolGrad";
     csinfo_.mkl_conv2d = "_MklConv2D";
+    csinfo_.mkl_conv2d_grad_input = "_MklConv2DBackpropInput";
+    csinfo_.mkl_conv2d_grad_filter = "_MklConv2DBackpropFilter";
     csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias";
     csinfo_.mkl_conv2d_with_bias_backprop_bias =
                                    "_MklConv2DWithBiasBackpropBias";
@@ -360,16 +356,12 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     minfo_.push_back({csinfo_.mkl_conv2d, csinfo_.bias_add, 0,
                       csinfo_.mkl_conv2d_with_bias});
 
-    // We use maxhop of 10 based on empirical observations. Also, these are
-    // maxhops in backward data-flow graph. Since input of forward nodes
-    // (Conv2D) directly goes to backward nodes, we do not expect the
-    // hop-distance would be more than few nodes.
     biasaddgrad_matmul_context_ = {csinfo_.bias_add_grad, csinfo_.matmul,
-                                   kNodeMergeContextMaxDepth};
+                                   IsBiasAddGradInMatMulContext};
 
     biasaddgrad_conv2dwithbias_context_ = {csinfo_.bias_add_grad,
                                    csinfo_.mkl_conv2d_with_bias,
-                                   kNodeMergeContextMaxDepth};
+                                   IsBiasAddGradInConv2DWithBiasContext};
 
     cinfo_.push_back(&biasaddgrad_matmul_context_);
     cinfo_.push_back(&biasaddgrad_conv2dwithbias_context_);
@@ -392,9 +384,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string node;     // Name of the node to be rewritten
     string fwd;      // Name of the node in the forward pass that this node
                      // corresponds to
-    size_t max_hop;  // Maximum number of hops the fwd is located
-                     // from this node. If the fwd is farther than max_hop
-                     // then we do not rewrite the node.
+    std::function<bool(const Node*, const Node**, void* c)> context_match_fn;
   } ContextInfo;
 
   /// Structure to specify the name of an original node, its new name after
@@ -438,7 +428,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
 
   /// Structure to store all constant strings
   /// NOTE: names are alphabetically sorted.
-  struct {
+  typedef struct {
     string avg_pool;
     string avg_pool_grad;
     string bias_add;
@@ -457,13 +447,15 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string max_pool;
     string max_pool_grad;
     string mkl_conv2d;
+    string mkl_conv2d_grad_input;
+    string mkl_conv2d_grad_filter;
     string mkl_conv2d_with_bias;
     string mkl_conv2d_with_bias_backprop_bias;
     string relu;
     string relu_grad;
     string reshape;
     string split;
-  } csinfo_;
+  } ConstStringsInfo;
 
  private:
   /// Maintain info about nodes to rewrite
@@ -478,6 +470,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   /// Maintain info about nodes to rewrite
   static std::vector<ContextInfo*> cinfo_;
 
+  /// Maintain structure of constant strings
+  static ConstStringsInfo csinfo_;
+
   /// Context variables used in referencing rules
   static ContextInfo biasaddgrad_matmul_context_;
   static ContextInfo biasaddgrad_conv2dwithbias_context_;
@@ -629,6 +624,173 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     return false;
   }
 
+  // Is BiasAddGrad node in 'n' is associated with Conv2DWithBias node
+  // specified in contextinfo 'ci'. Function updates fwd_node to point
+  // to Conv2DWithBias node if 'n' is associated with Conv2DWithBias.
+  //
+  // Association checks for one of the following graphs:
+  //
+  // Graph A:
+  //
+  // _ = Conv2DWithBias(F, I, _)
+  // ..
+  // _ = Conv2DBackpropFilter(F, _, G)
+  // _ = Conv2DBackpropInput(_, I, G)
+  // _ = BiasAddGrad(G)
+  //
+  // OR
+  //
+  // Graph B:
+  //
+  // _ = Conv2DWithBias(F, _, _)
+  // ..
+  // _ = Conv2DBackpropFilter(F, _, G)
+  // _ = BiasAddGrad(G)
+  //
+  // Here F, G, and I are graph nodes; _ represents graph nodes that we
+  // don't care here.
+  //
+  // @return - true (if BiasAddGrad is associated with Conv2DWithBias);
+  //           false otherwise.
+  static bool IsBiasAddGradInConv2DWithBiasContext(const Node* n,
+                                                   const Node** fwd_node,
+                                                   void* ci) {
+    CHECK_NOTNULL(n);
+    CHECK_NOTNULL(fwd_node);
+    CHECK_NOTNULL(ci);
+    *fwd_node = nullptr;
+
+    CHECK_EQ(n->type_string(), csinfo_.bias_add_grad);
+
+    // Get the only 1 input of BiasAddGrad.
+    CHECK_EQ(n->num_inputs(), 1);
+    const Node* bias_add_grad_inp = nullptr;
+    TF_CHECK_OK(n->input_node(0, &bias_add_grad_inp));
+    CHECK_NOTNULL(bias_add_grad_inp);
+
+    // Check if this input also goes to BackpropFilter and BackpropInput
+    // as 3rd input.
+    bool found_backprop_input = false;
+    bool found_backprop_filter = false;
+    Node* backprop_filter_node = nullptr;
+    Node* backprop_input_node = nullptr;
+
+    for (const Edge* e : bias_add_grad_inp->out_edges()) {
+      Node* third_input = nullptr;
+      if (e->dst()->type_string() == csinfo_.conv2d_grad_input ||
+          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_input) {
+        // Third input (index 2) of BackpropInput
+        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
+        // Third input (index 2) of BackpropInput must be same as the input
+        // of BiasAddGrad.
+        if (third_input == bias_add_grad_inp) {
+          found_backprop_input = true;
+          backprop_input_node = e->dst();
+        }
+      }
+
+      if (e->dst()->type_string() == csinfo_.conv2d_grad_filter ||
+          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_filter) {
+        // Third input (index 2) of BackpropFilter
+        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
+        // Third input (index 2) of BackpropFilter must be same as the input
+        // of BiasAddGrad.
+        if (third_input == bias_add_grad_inp) {
+          found_backprop_filter = true;
+          backprop_filter_node = e->dst();
+        }
+      }
+
+      // If we found both the nodes, then we can stop the search.
+      if (found_backprop_input && found_backprop_filter) {
+        break;
+      }
+    }
+
+    // If BackpropFilter node is not found, then this is not
+    // Conv2DWithBias context. For 2nd graph in the example above, only
+    // BackpropFilter would be present.
+    if (!found_backprop_filter) {
+      return false;
+    }
+
+    // Otherwise, we found the nodes.
+    CHECK_NOTNULL(backprop_filter_node);
+    if (found_backprop_input) {
+      CHECK_NOTNULL(backprop_input_node);
+    }
+
+    // Now that we confirmed that this is Conv2DWithBias context, we need to
+    // get access to the forward node (Conv2DWithBias). 2nd input of
+    // Conv2DWithBias is same as the 2nd input of Conv2DBackpropInput; 1st
+    // input of Conv2DWithBias is same as the 1st input of Conv2DBackpropFilter
+    // (This comes from definition of gradient computation for Conv2D).
+    if (found_backprop_input) {
+      // Graph A in the example.
+      Node* second_inp_of_input = nullptr;
+      Node* first_inp_of_filter = nullptr;
+      TF_CHECK_OK(backprop_input_node->input_node(1, &second_inp_of_input));
+      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
+      CHECK_NOTNULL(second_inp_of_input);
+      CHECK_NOTNULL(first_inp_of_filter);
+
+      // Now we need to find out Conv2DWithBias node from these input nodes.
+      // Conv2DWithBias node is the node that accepts both the nodes
+      // second_inp_of_input and first_inp_of_filter in 2nd and 1st input slots.
+      for (const Edge* fe : first_inp_of_filter->out_edges()) {
+        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+            fe->dst_input() == 0) {
+          for (const Edge* ie : second_inp_of_input->out_edges()) {
+            if (ie->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+                ie->dst_input() == 1 && fe->dst() == ie->dst()) {
+              VLOG(1) << "MklLayoutRewritePass: found "
+                      << fe->dst()->DebugString()
+                      << " as the forward node for matching context, backward"
+                      << " node is: " << n->DebugString();
+              *fwd_node = fe->dst();
+              return true;
+            }
+          }
+        }
+      }
+    } else {
+      // We did not find BackpropInput, so we work with BackpropFilter only.
+      // Graph B in the example.
+      Node* first_inp_of_filter = nullptr;
+      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
+      CHECK_NOTNULL(first_inp_of_filter);
+
+      // Now we need to find out Conv2DWithBias node from first input of
+      // BackpropFIlter. Conv2DWithBias node is the node that accepts
+      // first_inp_of_filter in 1st input slot.
+      for (const Edge* fe : first_inp_of_filter->out_edges()) {
+        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+            fe->dst_input() == 0) {
+          VLOG(1) << "MklLayoutRewritePass: found "
+                  << fe->dst()->DebugString()
+                  << " as the forward node for matching context, backward"
+                  << " node is: " << n->DebugString();
+          *fwd_node = fe->dst();
+          return true;
+        }
+      }
+    }
+
+    return false;
+  }
+
+  // Is BiasAddGrad node in 'n' is associated with MatMul node
+  // specified in contextinfo 'ci'. Function does not update fwd_node.
+  //
+  // @return - true (if BiasAddGrad is associated with MatMul);
+  //           false otherwise.
+  static bool IsBiasAddGradInMatMulContext(const Node* n,
+                                           const Node** fwd_node,
+                                           void* ci) {
+    return (!IsBiasAddGradInConv2DWithBiasContext(n, fwd_node, ci));
+  }
+
+
   // Rewrite rule that uses context-information for matching,
   // used in scenario 2.
   //
@@ -639,8 +801,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   static bool ContextMatchRewrite(const Node* n, const ContextInfo* c);
 
   // Helper function that searches the matching contextinfo for the node.
-  // Implements depth-first search in the data dependence graph for the
-  // gradient op in the backward direction.
   //
   // @input n - Node (gradient op) whose contextinfo is to be searched,
   //        fwd_node - pointer to node from the forward pass that this node
@@ -788,6 +948,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
                                    Node* orig_node);
 };
 
+MklLayoutRewritePass::ConstStringsInfo MklLayoutRewritePass::csinfo_;
 MklLayoutRewritePass::ContextInfo
   MklLayoutRewritePass::biasaddgrad_conv2dwithbias_context_;
 MklLayoutRewritePass::ContextInfo
@@ -1667,12 +1828,12 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr<Graph>* g,
   const ContextInfo* ci = nullptr;
   bool is_context_based_rewrite = false;
   if ((ci = SearchMatchingContext(orig_node, &fwd_node)) != nullptr) {
-    CHECK_NOTNULL(fwd_node);
     is_context_based_rewrite = true;
 
     // Sanity checks for context-based rewrite (if any)
     if (orig_node->type_string() == csinfo_.bias_add_grad &&
         ri->new_name == csinfo_.mkl_conv2d_with_bias_backprop_bias) {
+      CHECK_NOTNULL(fwd_node);
       DataType orig_T, ctx_T;
       string orig_data_format, ctx_data_format;
       TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &orig_T));
@@ -1784,69 +1945,17 @@ MklLayoutRewritePass::SearchMatchingContext(const Node* n,
   CHECK_NOTNULL(fwd_node);
   *fwd_node = nullptr;
 
-  // Search for matching contextinfo based on node name.
-  // There could be more than one matching contextinfos.
-  bool is_matching_cinfo_found = false;
-  std::vector<const ContextInfo*> mci;
+  // Search for matching contextinfo based on node name and call
+  // callback function using matching contextinfo.
+  // There could be more than one matching contextinfos but whichever
+  // matches first is returned.
   for (auto ci = cinfo_.cbegin(); ci != cinfo_.cend(); ++ci) {
-    if (n->type_string() == (*ci)->node) {
-      mci.push_back(*ci);
-      is_matching_cinfo_found = true;
+    if (n->type_string() == (*ci)->node &&
+        (*ci)->context_match_fn(n, fwd_node, *ci)) {
+      VLOG(1) << "Found context as matching: " << (*ci)->fwd;
+      return *ci;
     }
   }
-  // If no matching contextinfo is found, return immediately.
-  if (!is_matching_cinfo_found) {
-    return nullptr;
-  }
-
-  VLOG(1) << "MklLayoutRewritePass: Searching graph for: " << n->type_string()
-          << " in backwards.";
-
-  // Now we will check for forward op name for context info in data
-  // flow graph. Get the max hops we should search for the fwd node.
-  // We are now going to search (breadth-first) backwards in data
-  // dependence graph (for up to max hops) from n for the node
-  // specified in fwd.
-  // queue to maintain nodes to be visited and depth info for
-  // breadth-first search
-  std::queue<std::pair<const Node*, int>> nqueue;
-  const Node* curr_node = n;
-  size_t curr_depth = 0;
-  nqueue.push(std::make_pair(curr_node, curr_depth));
-
-  while (curr_depth < kNodeMergeContextMaxDepth && !nqueue.empty()) {
-    std::pair<const Node*, int> curr_pair = nqueue.front();
-    nqueue.pop();
-
-    std::set<const Node*> visited_nodes;
-    curr_node = curr_pair.first;
-    curr_depth = curr_pair.second;
-    CHECK_NOTNULL(curr_node);
-
-    VLOG(1) << "MklLayoutRewritePass: Visiting node: "
-            << curr_node->type_string() << " at depth: " << curr_depth
-            << " for node: " << n->type_string();
-
-    // If we find a match, we return immediately.
-    for (const ContextInfo* ci : mci) {
-      if (curr_node->type_string() == ci->fwd) {
-        *fwd_node = curr_node;
-        return ci;
-      }
-    }
-
-    // Else we explore backward edges from current node.
-    // Add the source nodes of all incoming edges of the node to the queue.
-    for (const Edge* e : curr_node->in_edges()) {
-      // We do not visit already visited node.
-      if (visited_nodes.find(e->src()) == visited_nodes.end()) {
-        // Depth of these nodes is 1 more than the depth of current node.
-        nqueue.push(std::make_pair(e->src(), curr_depth + 1));
-        visited_nodes.insert(e->src());
-      }
-    }
-  } /* while */
-
   return nullptr;
 }
 
diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc
index 3c4a5263af..efbe2134e0 100644
--- a/tensorflow/core/graph/mkl_layout_pass_test.cc
+++ b/tensorflow/core/graph/mkl_layout_pass_test.cc
@@ -345,7 +345,8 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_AttrMismatch) {
 // Test set 2: _MklConv2D..BiasAddGrad -> _MklConv2DWithBiasBackpropBias
 // rewrite tests
 
-// D=_MklConv2D(A,M,B,N,C,O); E=Sub(D,A); F=BiasAddGrad(E)
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput
 TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
   InitGraph(
       "node { name: 'A' op: 'Input'}"
@@ -364,16 +365,255 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
       "node { name: 'E' op: 'Sub'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
       " input: ['D', 'A']}"
-      "node { name: 'F' op: 'BiasAddGrad'"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
       " attr { key: 'data_format'      value { s: 'NCHW' } }"
       " input: ['E'] }");
   EXPECT_EQ(DoMklLayoutOptimizationPass(),
             "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
-            "E(Sub);F(_MklConv2DWithBiasBackpropBias);M(_MklInput);"
-            "N(_MklInput);O(_MklInput)|A->D;A->E:1;B->D:1;C->D:2;D->E;"
-            "DMT/_0->F:1;E->F;E:control->DMT/_0:control;M->D:3;N->D:4;"
-            "O->D:5");
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(_MklConv2DWithBiasBackpropBias);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G;B->D:1;"
+            "B->I:1;C->D:2;D->E;DMT/_0->J:1;E->G:2;E->I:2;E->J;"
+            "E:control->DMT/_0:control;F->G:1;H->I;M->D:3;M->G:3;M->I:3;"
+            "N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput. But nodes do not match criteria for rewrite. So
+// rewrite should not happen.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative1) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
+            "B->I:1;C->D:2;D->E;E->G;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
+            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput. But nodes do not match criteria for rewrite. So
+// rewrite should not happen.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative2) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
+            "B->I:1;C->D:2;D->E;E->G:2;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
+            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Positive) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);"
+            "H(_MklConv2DWithBiasBackpropBias);M(_MklInput);N(_MklInput);"
+            "O(_MklInput)|A->D;A->E:1;A->G;B->D:1;C->D:2;D->E;DMT/_0->H:1;"
+            "E->G:2;E->H;E:control->DMT/_0:control;F->G:1;M->D:3;M->G:3;"
+            "N->D:4;N->G:4;O->D:5;O->G:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+// But BackpropFilter node inputs do not satisfy criteria for rewrite.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative1) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
+            "C->D:2;D->E;E->G;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
+            "O->G:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+// But BackpropFilter node inputs do not satisfy criteria for rewrite.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative2) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
+            "C->D:2;D->E;E->G:2;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
+            "O->G:5");
 }
 
 // No _MklConv2DWithBias in context, but _MklConv2D in context.
-- 
GitLab


From 76a0a15cb90c370e41766d124a7a11b28c18089a Mon Sep 17 00:00:00 2001
From: Andrew Selle <aselle@andyselle.com>
Date: Thu, 15 Jun 2017 14:56:36 -0700
Subject: [PATCH 0149/1375] Fix shape.go's Size function.

Fixes #10741.
---
 tensorflow/go/shape.go | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/go/shape.go b/tensorflow/go/shape.go
index 114ab5decb..8d000cb9de 100644
--- a/tensorflow/go/shape.go
+++ b/tensorflow/go/shape.go
@@ -59,7 +59,7 @@ func (s Shape) NumDimensions() int {
 //
 // REQUIRES: 0 <= dim < s.NumDimensions()
 func (s Shape) Size(dim int) int64 {
-	if dim < 0 || dim > s.NumDimensions() {
+	if dim < 0 || dim >= s.NumDimensions() {
 		return -1
 	}
 	return s.dims[dim]
-- 
GitLab


From e6acff2a05ac633c086e581bda96d0dd91a1e142 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Fri, 16 Jun 2017 14:16:37 +0200
Subject: [PATCH 0150/1375] Fix a bug in topk_op.cc

---
 tensorflow/core/kernels/topk_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/topk_op.cc b/tensorflow/core/kernels/topk_op.cc
index 630fcb76f3..5c89eaef5f 100644
--- a/tensorflow/core/kernels/topk_op.cc
+++ b/tensorflow/core/kernels/topk_op.cc
@@ -93,7 +93,7 @@ class TopK : public OpKernel {
       rows_by_one.set(0, num_rows);
 #else
       Eigen::array<int, 1> reduce_on_cols = {1};
-      Eigen::array<int, 1> rows_by_one = {static_cast<int>(num_rows), 1};
+      Eigen::array<int, 2> rows_by_one = {static_cast<int>(num_rows), 1};
 #endif
 
       values.device(d) =
-- 
GitLab


From ce717be4a2966a268e9a4831c39254b611f1c9e7 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Thu, 15 Jun 2017 17:37:18 -0700
Subject: [PATCH 0151/1375] Fix sanity status reporting.

---
 tensorflow/tools/ci_build/ci_sanity.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index ddc95f6900..e428766a40 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -463,7 +463,7 @@ while [[ ${COUNTER} -lt "${#SANITY_STEPS[@]}" ]]; do
     ((PASS_COUNTER++))
   fi
 
-  IFS=" " read -r -a STEP_EXIT_CODES <<< "${RESULT}"
+  STEP_EXIT_CODES+=(${RESULT})
 
   echo ""
   ((COUNTER++))
-- 
GitLab


From 12af8acf2cdf876ef000eb4fbb822f6f07179120 Mon Sep 17 00:00:00 2001
From: Johnson145 <Johnson145@users.noreply.github.com>
Date: Wed, 14 Jun 2017 17:24:47 +0200
Subject: [PATCH 0152/1375] add support for android sdk version < 18

Alternatively, the trace calls could be removed completely or replaced by simple Log calls.
---
 .../android/TensorFlowInferenceInterface.java | 23 ++++++++++++++-----
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
index 1f180429b2..04864365c8 100644
--- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
+++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
@@ -17,6 +17,7 @@ package org.tensorflow.contrib.android;
 
 import android.content.res.AssetManager;
 import android.os.Trace;
+import android.os.Build.VERSION;
 import android.text.TextUtils;
 import android.util.Log;
 import java.io.FileInputStream;
@@ -370,9 +371,12 @@ public class TensorFlowInferenceInterface {
   private void loadGraph(InputStream is, Graph g) throws IOException {
     final long startMs = System.currentTimeMillis();
 
-    Trace.beginSection("initializeTensorFlow");
+    if( VERSION.SDK_INT >= 18 ){
+      Trace.beginSection("initializeTensorFlow");
+
+      Trace.beginSection("readGraphDef");
+    }
 
-    Trace.beginSection("readGraphDef");
     // TODO(ashankar): Can we somehow mmap the contents instead of copying them?
     byte[] graphDef = new byte[is.available()];
     final int numBytesRead = is.read(graphDef);
@@ -383,17 +387,24 @@ public class TensorFlowInferenceInterface {
               + " of the graph, expected to read "
               + graphDef.length);
     }
-    Trace.endSection();
 
-    Trace.beginSection("importGraphDef");
+    if( VERSION.SDK_INT >= 18 ) {
+      Trace.endSection();
+
+      Trace.beginSection("importGraphDef");
+    }
+
     try {
       g.importGraphDef(graphDef);
     } catch (IllegalArgumentException e) {
       throw new IOException("Not a valid TensorFlow Graph serialization: " + e.getMessage());
     }
-    Trace.endSection();
 
-    Trace.endSection(); // initializeTensorFlow.
+    if( VERSION.SDK_INT >= 18 ) {
+      Trace.endSection();
+
+      Trace.endSection(); // initializeTensorFlow.
+    }
 
     final long endMs = System.currentTimeMillis();
     Log.i(
-- 
GitLab


From 20f0ab8de03fcc8f32166606908eab853fbe6729 Mon Sep 17 00:00:00 2001
From: Johnson145 <Johnson145@users.noreply.github.com>
Date: Thu, 15 Jun 2017 12:06:28 +0200
Subject: [PATCH 0153/1375] improve readability of trace calls

---
 .../android/TensorFlowInferenceInterface.java       | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
index 04864365c8..b1d18d2faf 100644
--- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
+++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
@@ -371,9 +371,8 @@ public class TensorFlowInferenceInterface {
   private void loadGraph(InputStream is, Graph g) throws IOException {
     final long startMs = System.currentTimeMillis();
 
-    if( VERSION.SDK_INT >= 18 ){
+    if (VERSION.SDK_INT >= 18) {
       Trace.beginSection("initializeTensorFlow");
-
       Trace.beginSection("readGraphDef");
     }
 
@@ -388,9 +387,8 @@ public class TensorFlowInferenceInterface {
               + graphDef.length);
     }
 
-    if( VERSION.SDK_INT >= 18 ) {
-      Trace.endSection();
-
+    if (VERSION.SDK_INT >= 18) {
+      Trace.endSection(); // readGraphDef.
       Trace.beginSection("importGraphDef");
     }
 
@@ -400,9 +398,8 @@ public class TensorFlowInferenceInterface {
       throw new IOException("Not a valid TensorFlow Graph serialization: " + e.getMessage());
     }
 
-    if( VERSION.SDK_INT >= 18 ) {
-      Trace.endSection();
-
+    if (VERSION.SDK_INT >= 18) {
+      Trace.endSection(); // importGraphDef.
       Trace.endSection(); // initializeTensorFlow.
     }
 
-- 
GitLab


From 147dc8dc7e836833906db0ff0dd9694987d0179c Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Mon, 12 Jun 2017 12:51:32 -0700
Subject: [PATCH 0154/1375] Upgrade to protobuf 3.3.1

---
 tensorflow/workspace.bzl | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index de5525b0f2..ec5922ada8 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -306,11 +306,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   patched_http_archive(
       name = "protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
       # TODO: remove patching when tensorflow stops linking same protos into
       #       multiple shared libraries loaded in runtime by python.
       #       This patch fixes a runtime crash when tensorflow is compiled
@@ -324,21 +324,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.http_archive(
       name = "com_google_protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
   )
 
   native.http_archive(
       name = "com_google_protobuf_cc",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
   )
 
   native.new_http_archive(
-- 
GitLab


From d603a253d521515c3d529f13209e7a7f3dab7b83 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 15 Jun 2017 12:01:19 -0700
Subject: [PATCH 0155/1375] Update all protobuf versions

---
 tensorflow/java/maven/proto/pom.xml                           | 2 +-
 tensorflow/java/maven/run_inside_container.sh                 | 2 +-
 tensorflow/tools/ci_build/install/install_pip_packages.sh     | 4 ++--
 tensorflow/tools/ci_build/install/install_proto3.sh           | 2 +-
 .../tools/ci_build/install/install_python3.5_pip_packages.sh  | 2 +-
 tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh  | 2 +-
 tensorflow/tools/pip_package/setup.py                         | 2 +-
 7 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index 6405daddef..aa2a472f94 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -16,7 +16,7 @@
     <dependency>
       <groupId>com.google.protobuf</groupId>
       <artifactId>protobuf-java</artifactId>
-      <version>3.2.0</version>
+      <version>3.3.1</version>
     </dependency>
   </dependencies>
 
diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh
index b5e2bfc3a6..218790c8f1 100644
--- a/tensorflow/java/maven/run_inside_container.sh
+++ b/tensorflow/java/maven/run_inside_container.sh
@@ -23,7 +23,7 @@ IS_SNAPSHOT="false"
 if [[ "${TF_VERSION}" == *"-SNAPSHOT" ]]; then
   IS_SNAPSHOT="true"
 fi
-PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip"
+PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.3.1/protoc-3.3.1-linux-x86_64.zip"
 
 set -ex
 
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index 8768852dc7..c9867796f3 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -44,8 +44,8 @@ pip2 install --upgrade markdown==2.6.8
 pip3 install --upgrade markdown==2.6.8
 
 # Install protobuf.
-pip2 install --upgrade protobuf==3.2.0
-pip3 install --upgrade protobuf==3.2.0
+pip2 install --upgrade protobuf==3.3.0
+pip3 install --upgrade protobuf==3.3.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/install/install_proto3.sh b/tensorflow/tools/ci_build/install/install_proto3.sh
index 773c89b70b..d6e7f42ffc 100755
--- a/tensorflow/tools/ci_build/install/install_proto3.sh
+++ b/tensorflow/tools/ci_build/install/install_proto3.sh
@@ -17,7 +17,7 @@
 # Install protobuf3.
 
 # Select protobuf version.
-PROTOBUF_VERSION="3.2.0"
+PROTOBUF_VERSION="3.3.1"
 protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g')
 local_protobuf_ver=$(protoc --version | awk '{print $2}')
 local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g')
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index edfc4e3a98..33b3bc104b 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -64,7 +64,7 @@ set -e
 pip3.5 install --upgrade six==1.10.0
 
 # Install protobuf.
-pip3.5 install --upgrade protobuf==3.2.0
+pip3.5 install --upgrade protobuf==3.3.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
index 59ba71f5df..3e31aa1ce1 100755
--- a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
+++ b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 
-PROTOBUF_VERSION="3.2.0"
+PROTOBUF_VERSION="3.3.1"
 PYTHON_BIN=${PYTHON_BIN:-python}
 DIR=${PWD}/protobuf
 
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 3bbc900042..a9d9911148 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -34,7 +34,7 @@ _VERSION = '1.2.0-rc2'
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
     'six >= 1.10.0',
-    'protobuf >= 3.2.0',
+    'protobuf >= 3.3.0',
     'werkzeug >= 0.11.10',
     'html5lib == 0.9999999',  # identical to 1.0b8
     'markdown == 2.2.0',
-- 
GitLab


From c3160293b12b77202be3388da994f6ef9f768051 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 15 Jun 2017 12:50:48 -0700
Subject: [PATCH 0156/1375] Fix install_proto3.sh

---
 tensorflow/tools/ci_build/install/install_proto3.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/tools/ci_build/install/install_proto3.sh b/tensorflow/tools/ci_build/install/install_proto3.sh
index d6e7f42ffc..7934002b2c 100755
--- a/tensorflow/tools/ci_build/install/install_proto3.sh
+++ b/tensorflow/tools/ci_build/install/install_proto3.sh
@@ -17,9 +17,9 @@
 # Install protobuf3.
 
 # Select protobuf version.
-PROTOBUF_VERSION="3.3.1"
+PROTOBUF_VERSION="3.3.0"
 protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g')
-local_protobuf_ver=$(protoc --version | awk '{print $2}')
+local_protobuf_ver=$(protoc --version)
 local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g')
 if [[ -z $local_protobuf_ver_flat ]]; then
   local_protobuf_ver_flat=0
@@ -30,7 +30,7 @@ if (( $local_protobuf_ver_flat < $protobuf_ver_flat )); then
   PROTOBUF_ZIP=$(basename "${PROTOBUF_URL}")
   UNZIP_DEST="google-protobuf"
 
-  wget -q "${PROTOBUF_URL}"
+  wget "${PROTOBUF_URL}"
   unzip "${PROTOBUF_ZIP}" -d "${UNZIP_DEST}"
   cp "${UNZIP_DEST}/bin/protoc" /usr/local/bin/
 
-- 
GitLab


From b4e4008ed5e2ee0bb1123c9422361ce189df8e4c Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 15 Jun 2017 12:52:10 -0700
Subject: [PATCH 0157/1375] Fix maven

---
 tensorflow/java/maven/run_inside_container.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh
index 218790c8f1..302ed96c12 100644
--- a/tensorflow/java/maven/run_inside_container.sh
+++ b/tensorflow/java/maven/run_inside_container.sh
@@ -23,7 +23,7 @@ IS_SNAPSHOT="false"
 if [[ "${TF_VERSION}" == *"-SNAPSHOT" ]]; then
   IS_SNAPSHOT="true"
 fi
-PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.3.1/protoc-3.3.1-linux-x86_64.zip"
+PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.3.0/protoc-3.3.0-linux-x86_64.zip"
 
 set -ex
 
-- 
GitLab


From 8f2bf53b4bc2a43d00f157f06545e04749ff35da Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 11:37:40 -0700
Subject: [PATCH 0158/1375] Add python support of remote fused graph ops to
 contrib

PiperOrigin-RevId: 159254265
---
 tensorflow/BUILD                              |  1 +
 tensorflow/contrib/BUILD                      |  1 +
 tensorflow/contrib/__init__.py                |  5 +-
 .../contrib/cmake/tf_core_kernels.cmake       |  3 +-
 tensorflow/contrib/cmake/tf_python.cmake      |  5 ++
 tensorflow/contrib/cmake/tf_tests.cmake       |  1 -
 .../contrib/remote_fused_graph/README.md      |  8 +++
 .../contrib/remote_fused_graph/pylib/BUILD    | 56 ++++++++++++++++
 .../remote_fused_graph/pylib/__init__.py      | 33 ++++++++++
 .../pylib/python/__init__.py                  | 19 ++++++
 .../pylib/python/ops/__init__.py              | 19 ++++++
 .../python/ops/remote_fused_graph_ops.py      | 66 +++++++++++++++++++
 .../python/ops/remote_fused_graph_ops_test.py | 66 +++++++++++++++++++
 tensorflow/core/BUILD                         |  1 +
 tensorflow/core/kernels/BUILD                 | 15 +----
 .../kernels/remote_fused_graph_execute_op.cc  |  6 ++
 tensorflow/core/ops/remote_fused_graph_ops.cc | 33 ++++++++--
 17 files changed, 314 insertions(+), 24 deletions(-)
 create mode 100644 tensorflow/contrib/remote_fused_graph/README.md
 create mode 100644 tensorflow/contrib/remote_fused_graph/pylib/BUILD
 create mode 100644 tensorflow/contrib/remote_fused_graph/pylib/__init__.py
 create mode 100644 tensorflow/contrib/remote_fused_graph/pylib/python/__init__.py
 create mode 100644 tensorflow/contrib/remote_fused_graph/pylib/python/ops/__init__.py
 create mode 100644 tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops.py
 create mode 100644 tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops_test.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index ca7df391d2..abc8cdc410 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -279,6 +279,7 @@ filegroup(
         "//tensorflow/contrib/nn:all_files",
         "//tensorflow/contrib/opt:all_files",
         "//tensorflow/contrib/predictor:all_files",
+        "//tensorflow/contrib/remote_fused_graph/pylib:all_files",
         "//tensorflow/contrib/rnn:all_files",
         "//tensorflow/contrib/saved_model:all_files",
         "//tensorflow/contrib/saved_model/cc/saved_model:all_files",
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index b99933ff9b..518f01a4b1 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -53,6 +53,7 @@ py_library(
         "//tensorflow/contrib/nn:nn_py",
         "//tensorflow/contrib/opt:opt_py",
         "//tensorflow/contrib/quantization:quantization_py",
+        "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py",
         "//tensorflow/contrib/rnn:rnn_py",
         "//tensorflow/contrib/saved_model:saved_model_py",
         "//tensorflow/contrib/seq2seq:seq2seq_py",
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index a94e809c13..ce29729506 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -68,11 +68,12 @@ from tensorflow.contrib import tfprof
 from tensorflow.contrib import training
 from tensorflow.contrib import util
 from tensorflow.contrib.ndlstm import python as ndlstm
+from tensorflow.contrib.remote_fused_graph import pylib as remote_fused_graph
 from tensorflow.contrib.specs import python as specs
 
 from tensorflow.python.util.lazy_loader import LazyLoader
-ffmpeg = LazyLoader("ffmpeg", globals(),
-                    "tensorflow.contrib.ffmpeg")
+ffmpeg = LazyLoader("ffmpeg",
+                    globals(), "tensorflow.contrib.ffmpeg")
 del LazyLoader
 
 del absolute_import
diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 500b917ac9..05f656cf3b 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -102,10 +102,11 @@ file(GLOB_RECURSE tf_core_kernels_exclude_srcs
    "${tensorflow_source_dir}/tensorflow/core/kernels/*test*.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*testutil.h"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*testutil.cc"
+   "${tensorflow_source_dir}/tensorflow/core/kernels/*test_utils.h"
+   "${tensorflow_source_dir}/tensorflow/core/kernels/*test_utils.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*main.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*.cu.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/hexagon/*"
-   "${tensorflow_source_dir}/tensorflow/core/kernels/remote_fused_graph_execute*.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/remote_fused_graph_rewriter_transform*.cc"
 )
 list(REMOVE_ITEM tf_core_kernels_srcs ${tf_core_kernels_exclude_srcs})
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 001f4b4049..f95d73ca43 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -443,6 +443,9 @@ add_python_module("tensorflow/contrib/pi_examples/label_image")
 add_python_module("tensorflow/contrib/pi_examples/label_image/data")
 add_python_module("tensorflow/contrib/quantization")
 add_python_module("tensorflow/contrib/quantization/python")
+add_python_module("tensorflow/contrib/remote_fused_graph/pylib")
+add_python_module("tensorflow/contrib/remote_fused_graph/pylib/python")
+add_python_module("tensorflow/contrib/remote_fused_graph/pylib/python/ops")
 add_python_module("tensorflow/contrib/rnn")
 add_python_module("tensorflow/contrib/rnn/kernels")
 add_python_module("tensorflow/contrib/rnn/ops")
@@ -604,6 +607,8 @@ GENERATE_PYTHON_OP_LIB("lookup_ops")
 GENERATE_PYTHON_OP_LIB("nn_ops")
 GENERATE_PYTHON_OP_LIB("parsing_ops")
 GENERATE_PYTHON_OP_LIB("random_ops")
+GENERATE_PYTHON_OP_LIB("remote_fused_graph_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/remote_fused_graph/pylib/python/ops/gen_remote_fused_graph_ops.py)
 GENERATE_PYTHON_OP_LIB("resource_variable_ops")
 GENERATE_PYTHON_OP_LIB("script_ops")
 GENERATE_PYTHON_OP_LIB("sdca_ops")
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 9d3dabcb60..d415749ac5 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -278,7 +278,6 @@ if (tensorflow_BUILD_CC_TESTS)
     "${tensorflow_source_dir}/tensorflow/cc/framework/gradients_test.cc"
     "${tensorflow_source_dir}/tensorflow/core/distributed_runtime/call_options_test.cc"
     "${tensorflow_source_dir}/tensorflow/core/distributed_runtime/tensor_coding_test.cc"
-    "${tensorflow_source_dir}/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc"
     "${tensorflow_source_dir}/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc"
     "${tensorflow_source_dir}/tensorflow/core/kernels/hexagon/graph_transferer_test.cc"
     "${tensorflow_source_dir}/tensorflow/core/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc"
diff --git a/tensorflow/contrib/remote_fused_graph/README.md b/tensorflow/contrib/remote_fused_graph/README.md
new file mode 100644
index 0000000000..267cfa1019
--- /dev/null
+++ b/tensorflow/contrib/remote_fused_graph/README.md
@@ -0,0 +1,8 @@
+# Remote Fused Graph
+
+## Description
+
+This module contains libraries for remote fused graph utilities
+
+Maintainers:
+- Satoshi Kataoka (satok@google.com, github.com/satok16)
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
new file mode 100644
index 0000000000..c7ed663131
--- /dev/null
+++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
@@ -0,0 +1,56 @@
+# Description:
+# Contains ops for remote fused graph
+
+package(default_visibility = ["//tensorflow:__subpackages__"])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
+
+tf_gen_op_wrapper_py(
+    name = "gen_remote_fused_graph_ops",
+    out = "python/ops/gen_remote_fused_graph_ops.py",
+    deps = [
+        "//tensorflow/core:remote_fused_graph_ops_op_lib",
+    ],
+)
+
+py_library(
+    name = "remote_fused_graph_ops_py",
+    srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_remote_fused_graph_ops",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+py_test(
+    name = "remote_fused_graph_ops_test",
+    size = "small",
+    srcs = ["python/ops/remote_fused_graph_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_windows"],
+    deps = [
+        ":remote_fused_graph_ops_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:nn_ops",
+        "//third_party/py/numpy",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/__init__.py b/tensorflow/contrib/remote_fused_graph/pylib/__init__.py
new file mode 100644
index 0000000000..4d23c38932
--- /dev/null
+++ b/tensorflow/contrib/remote_fused_graph/pylib/__init__.py
@@ -0,0 +1,33 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Remote fused graph ops python library.
+
+## This package provides classes for remote fused graph ops.
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import, line-too-long
+from tensorflow.contrib.remote_fused_graph.pylib.python.ops.remote_fused_graph_ops import *
+# pylint: enable=unused-import,wildcard-import,line-too-long
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = ['remote_fused_graph_execute']
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/python/__init__.py b/tensorflow/contrib/remote_fused_graph/pylib/python/__init__.py
new file mode 100644
index 0000000000..b66091f875
--- /dev/null
+++ b/tensorflow/contrib/remote_fused_graph/pylib/python/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Remote fused graph ops python library."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/python/ops/__init__.py b/tensorflow/contrib/remote_fused_graph/pylib/python/ops/__init__.py
new file mode 100644
index 0000000000..b66091f875
--- /dev/null
+++ b/tensorflow/contrib/remote_fused_graph/pylib/python/ops/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Remote fused graph ops python library."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops.py b/tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops.py
new file mode 100644
index 0000000000..2054367f0d
--- /dev/null
+++ b/tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops.py
@@ -0,0 +1,66 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Operations to execute a subgraph on a remote processor."""
+
+# pylint: disable=g-bad-name
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import, line-too-long
+from tensorflow.contrib.remote_fused_graph.pylib.python.ops import gen_remote_fused_graph_ops
+from tensorflow.core.framework import remote_fused_graph_execute_info_pb2 as info_pb2
+# pylint: enable=unused-import,wildcard-import,line-too-long
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+
+# RemoteFusedGraphExecute is not differenciable op.
+ops.NotDifferentiable("RemoteFusedGraphExecute")
+
+
+def remote_fused_graph_execute(inputs,
+                               output_types,
+                               graph_def,
+                               graph_input_node_names,
+                               graph_output_node_names,
+                               executor_name,
+                               serialized_executor_parameters,
+                               default_graph_input_tensor_type_shapes=None,
+                               default_graph_output_tensor_type_shapes=None):
+  """A wrapper for remote_fused_graph_execute."""
+  info_proto = info_pb2.RemoteFusedGraphExecuteInfo()
+  info_proto.remote_graph.CopyFrom(graph_def)
+  info_proto.graph_input_node_name.extend(graph_input_node_names)
+  info_proto.graph_output_node_name.extend(graph_output_node_names)
+  info_proto.executor_name = executor_name
+  info_proto.serialized_executor_parameters = serialized_executor_parameters
+  if default_graph_input_tensor_type_shapes:
+    for type_shape in default_graph_input_tensor_type_shapes:
+      type_shape_proto = info_proto.default_graph_input_tensor_shape.add()
+      type_shape_proto.dtype = int(dtypes.as_dtype(type_shape[0]))
+      for dim in type_shape[1]:
+        type_shape_proto.shape.dim.add().size = dim
+  if default_graph_output_tensor_type_shapes:
+    for type_shape in default_graph_output_tensor_type_shapes:
+      type_shape_proto = info_proto.default_graph_output_tensor_shape.add()
+      type_shape_proto.dtype = int(dtypes.as_dtype(type_shape[0]))
+      for dim in type_shape[1]:
+        type_shape_proto.shape.dim.add().size = dim
+
+  serialized_info = info_proto.SerializeToString()
+
+  return gen_remote_fused_graph_ops.remote_fused_graph_execute(
+      inputs, output_types, serialized_info)
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops_test.py b/tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops_test.py
new file mode 100644
index 0000000000..45df909148
--- /dev/null
+++ b/tensorflow/contrib/remote_fused_graph/pylib/python/ops/remote_fused_graph_ops_test.py
@@ -0,0 +1,66 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow.ops.remote_fused_graph_ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+# pylint: disable=unused-import,wildcard-import, line-too-long
+from tensorflow.contrib.remote_fused_graph.pylib.python.ops import remote_fused_graph_ops
+# pylint: enable=unused-import,wildcard-import,line-too-long
+
+from tensorflow.core.framework import graph_pb2
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import googletest
+
+
+class RemoteFusedGraphExecuteTest(test_util.TensorFlowTestCase):
+  """Tests for RemoteFusedGraphExecute op."""
+
+  def testBuild(self):
+    graph = graph_pb2.GraphDef()
+    node = graph.node.add()
+    node.name = "a"
+    node.op = "op0"
+    node = graph.node.add()
+    node.name = "b"
+    node.op = "op1"
+    inputs = [ops.convert_n_to_tensor([1], dtypes.int64)]
+    output_types = [np.int64, np.int64]
+    graph_input_node_names = ["a"]
+    graph_output_node_names = ["a", "b"]
+    executor_name = ""
+    serialized_executor_parameters = b""
+    default_graph_input_tensor_type_shapes = [[dtypes.int64, [1]]]
+    default_graph_output_tensor_type_shapes = [[dtypes.int64, [1]],
+                                               [dtypes.int64, [1]]]
+
+    output_nodes = remote_fused_graph_ops.remote_fused_graph_execute(
+        inputs, output_types, graph, graph_input_node_names,
+        graph_output_node_names, executor_name, serialized_executor_parameters,
+        default_graph_input_tensor_type_shapes,
+        default_graph_output_tensor_type_shapes)
+    self.assertEqual(2, len(output_nodes))
+    for output_node in output_nodes:
+      with self.test_session(use_gpu=False):
+        output_node.eval()
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 694512c161..18cac5f48b 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -597,6 +597,7 @@ cc_library(
         ":no_op_op_lib",
         ":parsing_ops_op_lib",
         ":random_ops_op_lib",
+        ":remote_fused_graph_ops_op_lib",
         ":script_ops_op_lib",
         ":sdca_ops_op_lib",
         ":sendrecv_ops_op_lib",
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 99530c9317..d393f94a18 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -4981,7 +4981,6 @@ tf_kernel_library(
     name = "remote_fused_graph_ops",
     prefix = "remote_fused_graph_execute_op",
     deps = [
-        ":remote_fused_graph_execute_op",
         ":remote_fused_graph_execute_utils",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -4991,18 +4990,6 @@ tf_kernel_library(
     ],
 )
 
-cc_library(
-    name = "remote_fused_graph_execute_op",
-    srcs = ["remote_fused_graph_execute_op.cc"],
-    deps = [
-        ":remote_fused_graph_execute_utils",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
-        "//tensorflow/core:protos_all_cc",
-    ],
-)
-
 cc_library(
     name = "remote_fused_graph_execute_utils",
     srcs = ["remote_fused_graph_execute_utils.cc"],
@@ -5071,9 +5058,9 @@ tf_cc_test(
     deps = [
         ":ops_testutil",
         ":ops_util",
-        ":remote_fused_graph_execute_op",
         ":remote_fused_graph_execute_op_test_utils",
         ":remote_fused_graph_execute_utils",
+        ":remote_fused_graph_ops",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/cc:ops",
         "//tensorflow/cc:scope",
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_op.cc b/tensorflow/core/kernels/remote_fused_graph_execute_op.cc
index aa3835ecc5..f0b2aa6e1f 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_op.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_op.cc
@@ -109,6 +109,12 @@ class RemoteFusedGraphExecuteOp : public OpKernel {
               TF_CHECK_OK(ctx->allocate_output(i, shape, &output));
               return output;
             });
+      } else {
+        // For compatibility purpose, returns an empty tensor with specified
+        // data type as output if no executor is used.
+        Tensor* output = nullptr;
+        TensorShape ts({});
+        TF_CHECK_OK(ctx->allocate_output(i, ts, &output));
       }
     }
   }
diff --git a/tensorflow/core/ops/remote_fused_graph_ops.cc b/tensorflow/core/ops/remote_fused_graph_ops.cc
index 6e9f37a615..85370e648c 100644
--- a/tensorflow/core/ops/remote_fused_graph_ops.cc
+++ b/tensorflow/core/ops/remote_fused_graph_ops.cc
@@ -19,19 +19,40 @@ limitations under the License.
 
 namespace tensorflow {
 
-// TODO(satok): Implement shape_inference
+namespace {
+using shape_inference::InferenceContext;
+
+Status RemoteFusedGraphExecuteShapeFn(InferenceContext* c) {
+  for (int i = 0; i < c->num_outputs(); ++i) {
+    c->set_output(i, c->UnknownShape());
+  }
+  return Status::OK();
+}
+}  // namespace
+
 REGISTER_OP("RemoteFusedGraphExecute")
     .Input("inputs: Tinputs")
     .Output("outputs: Toutputs")
     .Attr("Tinputs: list(type) >= 0")
     .Attr("Toutputs: list(type) >= 0")
     .Attr("serialized_remote_fused_graph_execute_info: string")
-    .SetShapeFn(shape_inference::UnknownShape)
+    .SetShapeFn(RemoteFusedGraphExecuteShapeFn)
     .Doc(R"doc(
-Execute a sub graph on a remote processor transferred by GraphTransferer.
-The graph specifications are serialized by protobuf as graph_transfer_info.
-The implementation / limitations may differ for each platform
-and each available peripheral.
+Execute a sub graph on a remote processor.
+
+The graph specifications(such as graph itself, input tensors and output names)
+are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo
+as serialized_remote_fused_graph_execute_info.
+The specifications will be passed to a dedicated registered
+remote fused graph executor.  The executor will send the graph specifications
+to a remote processor and execute that graph.  The execution results
+will be passed to consumer nodes as outputs of this node.
+
+inputs: Arbitrary number of tensors with arbitrary data types
+outputs: Arbitrary number of tensors with arbitrary data types
+serialized_remote_fused_graph_execute_info: Serialized protocol buffer
+of RemoteFusedGraphExecuteInfo which contains graph specifications.
+
 )doc");
 
 }  // namespace tensorflow
-- 
GitLab


From 8181addd149383b2b31f589bb530b676645e5c6e Mon Sep 17 00:00:00 2001
From: windead <windead@gmail.com>
Date: Thu, 15 Jun 2017 10:44:47 +0800
Subject: [PATCH 0159/1375] To fix link error

To fix link error when building //tensorflow/cc:tutorials_example_trainer
The local environment is centos release 6.8 (Final), gcc 4.9.2, bazel 0.5.0.
Error detail: /opt/rh/devtoolset-3/root/usr/bin/ld: bazel-out/local_linux-opt/bin/tensorflow/cc/_objs/tutorials_example_trainer/tensorflow/cc/tutorials/example_trainer.o: undefined reference to symbol 'clock_gettime@@GLIBC_2.2.5'
---
 tensorflow/cc/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index b86731b118..23d0535660 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -484,6 +484,7 @@ cc_binary(
     linkopts = [
         "-lpthread",
         "-lm",
+        "-lrt",
     ],
     deps = [
         ":cc_ops",
-- 
GitLab


From 214404282ccbb22f90f19d91a43b22f13e03690c Mon Sep 17 00:00:00 2001
From: windead <windead@gmail.com>
Date: Fri, 16 Jun 2017 17:36:34 +0800
Subject: [PATCH 0160/1375] merge the usage of linkopts select

merge the usage of linkopts select from other BUILD file in tf to deal with -lrt in different platform
---
 tensorflow/cc/BUILD | 22 +++++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)

diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index 23d0535660..f89cc6384b 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -481,11 +481,23 @@ cc_binary(
     name = "tutorials_example_trainer",
     srcs = ["tutorials/example_trainer.cc"],
     copts = tf_copts(),
-    linkopts = [
-        "-lpthread",
-        "-lm",
-        "-lrt",
-    ],
+    linkopts = select({
+        "//tensorflow:windows": [],
+        "//tensorflow:windows_msvc": [],
+        "//tensorflow:darwin": [
+            "-lm",
+            "-lpthread",
+        ],
+        "//tensorflow:ios": [
+            "-lm",
+            "-lpthread",
+        ],
+        "//conditions:default": [
+            "-lm",
+            "-lpthread",
+            "-lrt",
+        ],
+    }),
     deps = [
         ":cc_ops",
         "//tensorflow/core:core_cpu",
-- 
GitLab


From 721165bc33d44dd0e8414d0c505ee4a41268a4de Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 16 Jun 2017 15:14:39 -0400
Subject: [PATCH 0161/1375] Update docker rebuilding instructions (#10774)

* Update docker rebuilding instructions

* Update README.md
---
 tensorflow/tools/docker/README.md | 28 ++++++++++++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index 3e45ae362c..6d5a9bdc4c 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -54,6 +54,30 @@ for additional containers, such as release candidates or nightly builds.
 
 ## Rebuilding the containers
 
-Just pick the dockerfile corresponding to the container you want to build, and run
+Building TensorFlow Docker containers should be done through the
+[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/README.md)
+script. The raw Dockerfiles should not be used directly as they contain strings
+to be replaced by the script during the build.
 
-    $ docker build --pull -t $USER/tensorflow-suffix -f Dockerfile.suffix .
+To use the script, specify the container type (`CPU` vs. `GPU`), the desired
+Python version (`PYTHON2` vs. `PYTHON3`) and whether the developer Docker image
+is to be built (`NO` vs. `YES`). In addition, you need to specify the central
+location from where the pip package of TensorFlow will be downloaded.
+
+For example, to build a CPU-only non-developer Docker image for Python 2, using
+TensorFlow's nightly pip package:
+
+``` bash
+export TF_DOCKER_BUILD_IS_DEVEL=NO
+export TF_DOCKER_BUILD_TYPE=CPU
+export TF_DOCKER_BUILD_PYTHON_VERSION=PYTHON2
+
+export NIGHTLY_VERSION="1.head"
+export TF_DOCKER_BUILD_CENTRAL_PIP=$(echo ${TF_DOCKER_BUILD_PYTHON_VERSION} | sed s^PYTHON2^http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=${TF_DOCKER_BUILD_PYTHON_VERSION},label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp27-cp27mu-manylinux1_x86_64.whl^ | sed s^PYTHON3^http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp35-cp35m-manylinux1_x86_64.whl^)
+
+tensorflow/tools/docker/parameterized_docker_build.sh
+```
+
+If successful, the image will be tagged as `${USER}/tensorflow:latest` by default.
+
+Rebuilding GPU images requires [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).
-- 
GitLab


From eb0d49ace5b63c10fd50f7e1f554bc9fa880769e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 12:18:15 -0700
Subject: [PATCH 0162/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 159259595
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 25 ++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 30 +++++++++++++++++++
 2 files changed, 55 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 4b10e5b79e..d0098f3008 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -18798,6 +18798,31 @@ op {
     }
   }
 }
+op {
+  name: "RemoteFusedGraphExecute"
+  input_arg {
+    name: "inputs"
+    type_list_attr: "Tinputs"
+  }
+  output_arg {
+    name: "outputs"
+    type_list_attr: "Toutputs"
+  }
+  attr {
+    name: "Tinputs"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Toutputs"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "serialized_remote_fused_graph_execute_info"
+    type: "string"
+  }
+}
 op {
   name: "RepeatDataset"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 0334a4c990..78144485db 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -18567,6 +18567,36 @@ op {
   }
   summary: "Computes rectified linear gradients for a Relu operation."
 }
+op {
+  name: "RemoteFusedGraphExecute"
+  input_arg {
+    name: "inputs"
+    description: "Arbitrary number of tensors with arbitrary data types"
+    type_list_attr: "Tinputs"
+  }
+  output_arg {
+    name: "outputs"
+    description: "Arbitrary number of tensors with arbitrary data types"
+    type_list_attr: "Toutputs"
+  }
+  attr {
+    name: "Tinputs"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Toutputs"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "serialized_remote_fused_graph_execute_info"
+    type: "string"
+    description: "Serialized protocol buffer\nof RemoteFusedGraphExecuteInfo which contains graph specifications."
+  }
+  summary: "Execute a sub graph on a remote processor."
+  description: "The graph specifications(such as graph itself, input tensors and output names)\nare stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo\nas serialized_remote_fused_graph_execute_info.\nThe specifications will be passed to a dedicated registered\nremote fused graph executor.  The executor will send the graph specifications\nto a remote processor and execute that graph.  The execution results\nwill be passed to consumer nodes as outputs of this node."
+}
 op {
   name: "RepeatDataset"
   input_arg {
-- 
GitLab


From 1f7990e6d762a0444b1c0a94c7e70e7cf5c9d0a8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 12:25:53 -0700
Subject: [PATCH 0163/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 159260396
---
 tensorflow/go/op/wrappers.go | 806 ++++++++++++++++++-----------------
 1 file changed, 409 insertions(+), 397 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 8e6a24d40b..ee244f6b68 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -7495,85 +7495,6 @@ func RsqrtGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
-// Execute a sub graph on a remote processor transferred by GraphTransferer.
-//
-// The graph specifications are serialized by protobuf as graph_transfer_info.
-// The implementation / limitations may differ for each platform
-// and each available peripheral.
-func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info}
-	opspec := tf.OpSpec{
-		Type: "RemoteFusedGraphExecute",
-		Input: []tf.Input{
-			tf.OutputList(inputs),
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
-	}
-	var idx int
-	var err error
-	if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
-		scope.UpdateErr("RemoteFusedGraphExecute", err)
-		return
-	}
-	return outputs
-}
-
-// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2.
-type Conv3DBackpropFilterV2Attr func(optionalAttr)
-
-// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value.
-//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-//     [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-//     [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
-	return func(m optionalAttr) {
-		m["data_format"] = value
-	}
-}
-
-// Computes the gradients of 3-D convolution with respect to the filter.
-//
-// Arguments:
-//	input: Shape `[batch, depth, rows, cols, in_channels]`.
-//	filter_sizes: An integer vector representing the tensor shape of `filter`,
-// where `filter` is a 5-D
-// `[filter_depth, filter_height, filter_width, in_channels, out_channels]`
-// tensor.
-//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-//	strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-//	padding: The type of padding algorithm to use.
-func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"strides": strides, "padding": padding}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "Conv3DBackpropFilterV2",
-		Input: []tf.Input{
-			input, filter_sizes, out_backprop,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // MapClearAttr is an optional argument to MapClear.
 type MapClearAttr func(optionalAttr)
 
@@ -10482,288 +10403,196 @@ func AddSparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values
 	return op.Output(0)
 }
 
-// DecodeBmpAttr is an optional argument to DecodeBmp.
-type DecodeBmpAttr func(optionalAttr)
-
-// DecodeBmpChannels sets the optional channels attribute to value.
-// If not specified, defaults to 0
-func DecodeBmpChannels(value int64) DecodeBmpAttr {
-	return func(m optionalAttr) {
-		m["channels"] = value
-	}
-}
-
-// Decode the first frame of a BMP-encoded image to a uint8 tensor.
-//
-// The attr `channels` indicates the desired number of color channels for the
-// decoded image.
-//
-// Accepted values are:
+// Constructs a tensor by tiling a given tensor.
 //
-// *   0: Use the number of channels in the BMP-encoded image.
-// *   3: output an RGB image.
-// *   4: output an RGBA image.
+// This operation creates a new tensor by replicating `input` `multiples` times.
+// The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
+// and the values of `input` are replicated `multiples[i]` times along the 'i'th
+// dimension. For example, tiling `[a b c d]` by `[2]` produces
+// `[a b c d a b c d]`.
 //
 // Arguments:
-//	contents: 0-D.  The BMP-encoded image.
-//
-// Returns 3-D with shape `[height, width, channels]`. RGB order
-func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (image tf.Output) {
+//	input: 1-D or higher.
+//	multiples: 1-D. Length must be the same as the number of dimensions in `input`
+func Tile(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "DecodeBmp",
+		Type: "Tile",
 		Input: []tf.Input{
-			contents,
+			input, multiples,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes softmax activations.
-//
-// For each batch `i` and class `j` we have
+// Returns the element-wise min of two SparseTensors.
 //
-//     softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))
+// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
 //
 // Arguments:
-//	logits: 2-D with shape `[batch_size, num_classes]`.
+//	a_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, in the canonical lexicographic ordering.
+//	a_values: 1-D.  `N` non-empty values corresponding to `a_indices`.
+//	a_shape: 1-D.  Shape of the input SparseTensor.
+//	b_indices: counterpart to `a_indices` for the other operand.
+//	b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
+//	b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
 //
-// Returns Same shape as `logits`.
-func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) {
+// Returns 2-D.  The indices of the output SparseTensor.1-D.  The values of the output SparseTensor.
+func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Softmax",
+		Type: "SparseSparseMinimum",
 		Input: []tf.Input{
-			logits,
+			a_indices, a_values, a_shape, b_indices, b_values, b_shape,
 		},
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return op.Output(0), op.Output(1)
 }
 
-// RandomShuffleQueueV2Attr is an optional argument to RandomShuffleQueueV2.
-type RandomShuffleQueueV2Attr func(optionalAttr)
-
-// RandomShuffleQueueV2Shapes sets the optional shapes attribute to value.
-//
-// value: The shape of each component in a value. The length of this attr must
-// be either 0 or the same as the length of component_types. If the length of
-// this attr is 0, the shapes of queue elements are not constrained, and
-// only one element may be dequeued at a time.
-// If not specified, defaults to <>
+// Computes the gradient of the sigmoid of `x` wrt its input.
 //
-// REQUIRES: len(value) >= 0
-func RandomShuffleQueueV2Shapes(value []tf.Shape) RandomShuffleQueueV2Attr {
-	return func(m optionalAttr) {
-		m["shapes"] = value
+// Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
+// `dy` is the corresponding input gradient.
+func SigmoidGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
 	}
-}
-
-// RandomShuffleQueueV2Capacity sets the optional capacity attribute to value.
-//
-// value: The upper bound on the number of elements in this queue.
-// Negative numbers mean no limit.
-// If not specified, defaults to -1
-func RandomShuffleQueueV2Capacity(value int64) RandomShuffleQueueV2Attr {
-	return func(m optionalAttr) {
-		m["capacity"] = value
+	opspec := tf.OpSpec{
+		Type: "SigmoidGrad",
+		Input: []tf.Input{
+			x, y,
+		},
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// RandomShuffleQueueV2MinAfterDequeue sets the optional min_after_dequeue attribute to value.
+// Generate a sharded filename. The filename is printf formatted as
 //
-// value: Dequeue will block unless there would be this
-// many elements after the dequeue or the queue is closed. This
-// ensures a minimum level of mixing of elements.
-// If not specified, defaults to 0
-func RandomShuffleQueueV2MinAfterDequeue(value int64) RandomShuffleQueueV2Attr {
-	return func(m optionalAttr) {
-		m["min_after_dequeue"] = value
+//    %s-%05d-of-%05d, basename, shard, num_shards.
+func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ShardedFilename",
+		Input: []tf.Input{
+			basename, shard, num_shards,
+		},
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// RandomShuffleQueueV2Seed sets the optional seed attribute to value.
+// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2.
+type Conv3DBackpropFilterV2Attr func(optionalAttr)
+
+// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value.
 //
-// value: If either seed or seed2 is set to be non-zero, the random number
-// generator is seeded by the given seed.  Otherwise, a random seed is used.
-// If not specified, defaults to 0
-func RandomShuffleQueueV2Seed(value int64) RandomShuffleQueueV2Attr {
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+//     [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+//     [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
 	return func(m optionalAttr) {
-		m["seed"] = value
+		m["data_format"] = value
 	}
 }
 
-// RandomShuffleQueueV2Seed2 sets the optional seed2 attribute to value.
+// Computes the gradients of 3-D convolution with respect to the filter.
 //
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func RandomShuffleQueueV2Seed2(value int64) RandomShuffleQueueV2Attr {
-	return func(m optionalAttr) {
-		m["seed2"] = value
+// Arguments:
+//	input: Shape `[batch, depth, rows, cols, in_channels]`.
+//	filter_sizes: An integer vector representing the tensor shape of `filter`,
+// where `filter` is a 5-D
+// `[filter_depth, filter_height, filter_width, in_channels, out_channels]`
+// tensor.
+//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
+//	strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+//	padding: The type of padding algorithm to use.
+func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"strides": strides, "padding": padding}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Conv3DBackpropFilterV2",
+		Input: []tf.Input{
+			input, filter_sizes, out_backprop,
+		},
+		Attrs: attrs,
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// RandomShuffleQueueV2Container sets the optional container attribute to value.
+// Execute a sub graph on a remote processor.
 //
-// value: If non-empty, this queue is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func RandomShuffleQueueV2Container(value string) RandomShuffleQueueV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// RandomShuffleQueueV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this queue will be shared under the given name
-// across multiple sessions.
-// If not specified, defaults to ""
-func RandomShuffleQueueV2SharedName(value string) RandomShuffleQueueV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// A queue that randomizes the order of elements.
-//
-// Arguments:
-//	component_types: The type of each component in a value.
-//
-// Returns The handle to the queue.
-func RandomShuffleQueueV2(scope *Scope, component_types []tf.DataType, optional ...RandomShuffleQueueV2Attr) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"component_types": component_types}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "RandomShuffleQueueV2",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Outputs a `Summary` protocol buffer with scalar values.
-//
-// The input `tags` and `values` must have the same shape.  The generated summary
-// has a summary value for each tag-value pair in `tags` and `values`.
+// The graph specifications(such as graph itself, input tensors and output names)
+// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo
+// as serialized_remote_fused_graph_execute_info.
+// The specifications will be passed to a dedicated registered
+// remote fused graph executor.  The executor will send the graph specifications
+// to a remote processor and execute that graph.  The execution results
+// will be passed to consumer nodes as outputs of this node.
 //
 // Arguments:
-//	tags: Tags for the summary.
-//	values: Same shape as `tags.  Values for the summary.
-//
-// Returns Scalar.  Serialized `Summary` protocol buffer.
-func ScalarSummary(scope *Scope, tags tf.Output, values tf.Output) (summary tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "ScalarSummary",
-		Input: []tf.Input{
-			tags, values,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Constructs a tensor by tiling a given tensor.
+//	inputs: Arbitrary number of tensors with arbitrary data types
 //
-// This operation creates a new tensor by replicating `input` `multiples` times.
-// The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
-// and the values of `input` are replicated `multiples[i]` times along the 'i'th
-// dimension. For example, tiling `[a b c d]` by `[2]` produces
-// `[a b c d a b c d]`.
+//	serialized_remote_fused_graph_execute_info: Serialized protocol buffer
+// of RemoteFusedGraphExecuteInfo which contains graph specifications.
 //
-// Arguments:
-//	input: 1-D or higher.
-//	multiples: 1-D. Length must be the same as the number of dimensions in `input`
-func Tile(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
+// Returns Arbitrary number of tensors with arbitrary data types
+func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info}
 	opspec := tf.OpSpec{
-		Type: "Tile",
+		Type: "RemoteFusedGraphExecute",
 		Input: []tf.Input{
-			input, multiples,
+			tf.OutputList(inputs),
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Returns the element-wise min of two SparseTensors.
-//
-// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
-//
-// Arguments:
-//	a_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, in the canonical lexicographic ordering.
-//	a_values: 1-D.  `N` non-empty values corresponding to `a_indices`.
-//	a_shape: 1-D.  Shape of the input SparseTensor.
-//	b_indices: counterpart to `a_indices` for the other operand.
-//	b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
-//	b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
-//
-// Returns 2-D.  The indices of the output SparseTensor.1-D.  The values of the output SparseTensor.
-func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	opspec := tf.OpSpec{
-		Type: "SparseSparseMinimum",
-		Input: []tf.Input{
-			a_indices, a_values, a_shape, b_indices, b_values, b_shape,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1)
-}
-
-// Computes the gradient of the sigmoid of `x` wrt its input.
-//
-// Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
-// `dy` is the corresponding input gradient.
-func SigmoidGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
-	if scope.Err() != nil {
+	var idx int
+	var err error
+	if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
+		scope.UpdateErr("RemoteFusedGraphExecute", err)
 		return
 	}
-	opspec := tf.OpSpec{
-		Type: "SigmoidGrad",
-		Input: []tf.Input{
-			x, y,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return outputs
 }
 
-// Generate a sharded filename. The filename is printf formatted as
+// Computes numerical negative value element-wise.
 //
-//    %s-%05d-of-%05d, basename, shard, num_shards.
-func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) {
+// I.e., \\(y = -x\\).
+func Neg(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "ShardedFilename",
+		Type: "Neg",
 		Input: []tf.Input{
-			basename, shard, num_shards,
+			x,
 		},
 	}
 	op := scope.AddOperation(opspec)
@@ -11888,23 +11717,6 @@ func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64,
 	return op.Output(0)
 }
 
-// Computes numerical negative value element-wise.
-//
-// I.e., \\(y = -x\\).
-func Neg(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Neg",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // DestroyResourceOpAttr is an optional argument to DestroyResourceOp.
 type DestroyResourceOpAttr func(optionalAttr)
 
@@ -15253,97 +15065,6 @@ func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Out
 	return scope.AddOperation(opspec)
 }
 
-// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam.
-type ResourceApplyAdamAttr func(optionalAttr)
-
-// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value.
-//
-// value: If `True`, updating of the var, m, and v tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr {
-	return func(m optionalAttr) {
-		m["use_locking"] = value
-	}
-}
-
-// ResourceApplyAdamUseNesterov sets the optional use_nesterov attribute to value.
-//
-// value: If `True`, uses the nesterov update.
-// If not specified, defaults to false
-func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr {
-	return func(m optionalAttr) {
-		m["use_nesterov"] = value
-	}
-}
-
-// Update '*var' according to the Adam algorithm.
-//
-// lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
-// m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t
-// v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t
-// variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
-//
-// Arguments:
-//	var_: Should be from a Variable().
-//	m: Should be from a Variable().
-//	v: Should be from a Variable().
-//	beta1_power: Must be a scalar.
-//	beta2_power: Must be a scalar.
-//	lr: Scaling factor. Must be a scalar.
-//	beta1: Momentum factor. Must be a scalar.
-//	beta2: Momentum factor. Must be a scalar.
-//	epsilon: Ridge term. Must be a scalar.
-//	grad: The gradient.
-//
-// Returns the created operation.
-func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "ResourceApplyAdam",
-		Input: []tf.Input{
-			var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
-		},
-		Attrs: attrs,
-	}
-	return scope.AddOperation(opspec)
-}
-
-// 3D fast Fourier transform.
-//
-// Computes the 3-dimensional discrete Fourier transform over the inner-most 3
-// dimensions of `input`.
-//
-// Arguments:
-//	input: A complex64 tensor.
-//
-// Returns A complex64 tensor of the same shape as `input`. The inner-most 3
-//   dimensions of `input` are replaced with their 3D Fourier transform.
-//
-// @compatibility(numpy)
-// Equivalent to np.fft.fftn with 3 dimensions.
-// @end_compatibility
-func FFT3D(scope *Scope, input tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "FFT3D",
-		Input: []tf.Input{
-			input,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Output a fact about factorials.
 func Fact(scope *Scope) (fact tf.Output) {
 	if scope.Err() != nil {
@@ -18711,6 +18432,297 @@ func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
+// DecodeBmpAttr is an optional argument to DecodeBmp.
+type DecodeBmpAttr func(optionalAttr)
+
+// DecodeBmpChannels sets the optional channels attribute to value.
+// If not specified, defaults to 0
+func DecodeBmpChannels(value int64) DecodeBmpAttr {
+	return func(m optionalAttr) {
+		m["channels"] = value
+	}
+}
+
+// Decode the first frame of a BMP-encoded image to a uint8 tensor.
+//
+// The attr `channels` indicates the desired number of color channels for the
+// decoded image.
+//
+// Accepted values are:
+//
+// *   0: Use the number of channels in the BMP-encoded image.
+// *   3: output an RGB image.
+// *   4: output an RGBA image.
+//
+// Arguments:
+//	contents: 0-D.  The BMP-encoded image.
+//
+// Returns 3-D with shape `[height, width, channels]`. RGB order
+func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (image tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "DecodeBmp",
+		Input: []tf.Input{
+			contents,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes softmax activations.
+//
+// For each batch `i` and class `j` we have
+//
+//     softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))
+//
+// Arguments:
+//	logits: 2-D with shape `[batch_size, num_classes]`.
+//
+// Returns Same shape as `logits`.
+func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Softmax",
+		Input: []tf.Input{
+			logits,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// RandomShuffleQueueV2Attr is an optional argument to RandomShuffleQueueV2.
+type RandomShuffleQueueV2Attr func(optionalAttr)
+
+// RandomShuffleQueueV2Shapes sets the optional shapes attribute to value.
+//
+// value: The shape of each component in a value. The length of this attr must
+// be either 0 or the same as the length of component_types. If the length of
+// this attr is 0, the shapes of queue elements are not constrained, and
+// only one element may be dequeued at a time.
+// If not specified, defaults to <>
+//
+// REQUIRES: len(value) >= 0
+func RandomShuffleQueueV2Shapes(value []tf.Shape) RandomShuffleQueueV2Attr {
+	return func(m optionalAttr) {
+		m["shapes"] = value
+	}
+}
+
+// RandomShuffleQueueV2Capacity sets the optional capacity attribute to value.
+//
+// value: The upper bound on the number of elements in this queue.
+// Negative numbers mean no limit.
+// If not specified, defaults to -1
+func RandomShuffleQueueV2Capacity(value int64) RandomShuffleQueueV2Attr {
+	return func(m optionalAttr) {
+		m["capacity"] = value
+	}
+}
+
+// RandomShuffleQueueV2MinAfterDequeue sets the optional min_after_dequeue attribute to value.
+//
+// value: Dequeue will block unless there would be this
+// many elements after the dequeue or the queue is closed. This
+// ensures a minimum level of mixing of elements.
+// If not specified, defaults to 0
+func RandomShuffleQueueV2MinAfterDequeue(value int64) RandomShuffleQueueV2Attr {
+	return func(m optionalAttr) {
+		m["min_after_dequeue"] = value
+	}
+}
+
+// RandomShuffleQueueV2Seed sets the optional seed attribute to value.
+//
+// value: If either seed or seed2 is set to be non-zero, the random number
+// generator is seeded by the given seed.  Otherwise, a random seed is used.
+// If not specified, defaults to 0
+func RandomShuffleQueueV2Seed(value int64) RandomShuffleQueueV2Attr {
+	return func(m optionalAttr) {
+		m["seed"] = value
+	}
+}
+
+// RandomShuffleQueueV2Seed2 sets the optional seed2 attribute to value.
+//
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func RandomShuffleQueueV2Seed2(value int64) RandomShuffleQueueV2Attr {
+	return func(m optionalAttr) {
+		m["seed2"] = value
+	}
+}
+
+// RandomShuffleQueueV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this queue is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func RandomShuffleQueueV2Container(value string) RandomShuffleQueueV2Attr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// RandomShuffleQueueV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this queue will be shared under the given name
+// across multiple sessions.
+// If not specified, defaults to ""
+func RandomShuffleQueueV2SharedName(value string) RandomShuffleQueueV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// A queue that randomizes the order of elements.
+//
+// Arguments:
+//	component_types: The type of each component in a value.
+//
+// Returns The handle to the queue.
+func RandomShuffleQueueV2(scope *Scope, component_types []tf.DataType, optional ...RandomShuffleQueueV2Attr) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"component_types": component_types}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "RandomShuffleQueueV2",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Outputs a `Summary` protocol buffer with scalar values.
+//
+// The input `tags` and `values` must have the same shape.  The generated summary
+// has a summary value for each tag-value pair in `tags` and `values`.
+//
+// Arguments:
+//	tags: Tags for the summary.
+//	values: Same shape as `tags.  Values for the summary.
+//
+// Returns Scalar.  Serialized `Summary` protocol buffer.
+func ScalarSummary(scope *Scope, tags tf.Output, values tf.Output) (summary tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ScalarSummary",
+		Input: []tf.Input{
+			tags, values,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam.
+type ResourceApplyAdamAttr func(optionalAttr)
+
+// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var, m, and v tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// ResourceApplyAdamUseNesterov sets the optional use_nesterov attribute to value.
+//
+// value: If `True`, uses the nesterov update.
+// If not specified, defaults to false
+func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr {
+	return func(m optionalAttr) {
+		m["use_nesterov"] = value
+	}
+}
+
+// Update '*var' according to the Adam algorithm.
+//
+// lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
+// m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t
+// v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t
+// variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
+//
+// Arguments:
+//	var_: Should be from a Variable().
+//	m: Should be from a Variable().
+//	v: Should be from a Variable().
+//	beta1_power: Must be a scalar.
+//	beta2_power: Must be a scalar.
+//	lr: Scaling factor. Must be a scalar.
+//	beta1: Momentum factor. Must be a scalar.
+//	beta2: Momentum factor. Must be a scalar.
+//	epsilon: Ridge term. Must be a scalar.
+//	grad: The gradient.
+//
+// Returns the created operation.
+func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResourceApplyAdam",
+		Input: []tf.Input{
+			var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// 3D fast Fourier transform.
+//
+// Computes the 3-dimensional discrete Fourier transform over the inner-most 3
+// dimensions of `input`.
+//
+// Arguments:
+//	input: A complex64 tensor.
+//
+// Returns A complex64 tensor of the same shape as `input`. The inner-most 3
+//   dimensions of `input` are replaced with their 3D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.fftn with 3 dimensions.
+// @end_compatibility
+func FFT3D(scope *Scope, input tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "FFT3D",
+		Input: []tf.Input{
+			input,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Given a quantized tensor described by (input, input_min, input_max), outputs a
 //
 // range that covers the actual values present in that tensor.  This op is
-- 
GitLab


From 12efd3d0bbea953e52aee12eb5a3d5d2269ec16a Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Fri, 16 Jun 2017 12:37:05 -0700
Subject: [PATCH 0164/1375] [XLA] Remove gpu/gpu_backend-specific flags

Move useful flags into debug_options, and leave some less used flags out - they
can be propagated through debug_options if required (for now there's too much
duplication between them and what's already inside)

PiperOrigin-RevId: 159261661
---
 tensorflow/compiler/jit/kernels/BUILD         |  1 +
 .../jit/kernels/xla_device_launch_op.cc       |  3 +
 tensorflow/compiler/xla/legacy_flags/BUILD    | 24 -----
 .../xla/legacy_flags/debug_options_flags.cc   | 18 +++-
 .../xla/legacy_flags/gpu_backend_lib_flags.cc | 88 -------------------
 .../xla/legacy_flags/gpu_backend_lib_flags.h  | 55 ------------
 .../xla/legacy_flags/gpu_compiler_flags.cc    | 68 --------------
 .../xla/legacy_flags/gpu_compiler_flags.h     | 52 -----------
 tensorflow/compiler/xla/service/BUILD         |  1 +
 tensorflow/compiler/xla/service/gpu/BUILD     |  1 -
 .../compiler/xla/service/gpu/gpu_compiler.cc  | 14 +--
 .../compiler/xla/service/gpu/gpu_compiler.h   |  2 +-
 .../xla/service/gpu/llvm_gpu_backend/BUILD    |  1 -
 .../gpu/llvm_gpu_backend/gpu_backend_lib.cc   | 86 ++++--------------
 .../compiler/xla/service/local_service.cc     |  2 +
 .../xla/tests/check_execution_arity_test.cc   | 23 +++--
 tensorflow/compiler/xla/tests/client_test.cc  |  4 +-
 .../compiler/xla/tests/codegen_test_base.cc   |  4 +-
 .../compiler/xla/tests/codegen_test_base.h    |  4 +-
 .../xla/tests/compilation_cache_test.cc       | 11 ++-
 .../xla/tests/deconstruct_tuple_test.cc       |  3 +-
 tensorflow/compiler/xla/tests/prng_test.cc    |  8 +-
 tensorflow/compiler/xla/tests/replay_test.cc  |  9 +-
 tensorflow/compiler/xla/tests/reshape_test.cc |  4 +-
 tensorflow/compiler/xla/tests/while_test.cc   |  2 +-
 tensorflow/compiler/xla/xla.proto             |  6 ++
 26 files changed, 99 insertions(+), 395 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.h
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h

diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD
index c4116cb8b5..ed204b8182 100644
--- a/tensorflow/compiler/jit/kernels/BUILD
+++ b/tensorflow/compiler/jit/kernels/BUILD
@@ -38,6 +38,7 @@ cc_library(
         "//tensorflow/compiler/tf2xla:xla_compiler",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
index e786fdb16d..bd051d06ae 100644
--- a/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
+++ b/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include "tensorflow/compiler/jit/xla_device.h"
 #include "tensorflow/compiler/jit/xla_device_context.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
 #include "tensorflow/core/framework/allocator.h"
@@ -149,6 +150,8 @@ void XlaDeviceLaunchOp::Compute(OpKernelContext* ctx) {
     xla::ExecutionOptions execution_options;
     *execution_options.mutable_shape_with_output_layout() =
         kernel->xla_output_shape;
+    *execution_options.mutable_debug_options() =
+        xla::legacy_flags::GetDebugOptionsFromFlags();
     Env* env = Env::Default();
     auto start_time = env->NowMicros();
     VLOG(1) << "Executing XLA Computation...";
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 09101708f2..abaccb81e5 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -114,30 +114,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "gpu_compiler_flags",
-    srcs = ["gpu_compiler_flags.cc"],
-    hdrs = ["gpu_compiler_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
-cc_library(
-    name = "gpu_backend_lib_flags",
-    srcs = ["gpu_backend_lib_flags.cc"],
-    hdrs = ["gpu_backend_lib_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "stream_assignment_flags",
     srcs = ["stream_assignment_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 52b048ec15..5f029a5f53 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -30,6 +30,10 @@ struct DebugOptionsFlags {
   int32 xla_backend_optimization_level;
   bool xla_embed_ir_in_executable;
   string xla_dump_debug_json_to;
+
+  string xla_gpu_cuda_data_dir;
+  bool xla_gpu_ftz;
+
   string xla_backend_extra_options;
 };
 
@@ -46,9 +50,11 @@ void AllocateFlags() {
   flag_values->xla_generate_hlo_graph = "";
   flag_values->xla_disable_hlo_passes = "";
   flag_values->xla_enable_fast_math = true;
-  flag_values->xla_backend_optimization_level = 2;
+  flag_values->xla_backend_optimization_level = 3;
   flag_values->xla_embed_ir_in_executable = false;
   flag_values->xla_dump_debug_json_to = "";
+  flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
+  flag_values->xla_gpu_ftz = false;
   flag_values->xla_backend_extra_options = "";
 
   flag_objects = new std::vector<tensorflow::Flag>(
@@ -72,6 +78,14 @@ void AllocateFlags() {
        tensorflow::Flag("xla_embed_ir_in_executable",
                         &flag_values->xla_embed_ir_in_executable,
                         "Embed the compiler IR as a string in the executable."),
+       tensorflow::Flag("xla_gpu_cuda_data_dir",
+                        &flag_values->xla_gpu_cuda_data_dir,
+                        "If non-empty, speficies a local directory containing "
+                        "ptxas and nvvm libdevice files; otherwise we use "
+                        "those from runfile directories."),
+       tensorflow::Flag("xla_gpu_ftz", &flag_values->xla_gpu_ftz,
+                        "If true, flush-to-zero semantics are enabled in the "
+                        "code generated for GPUs."),
        tensorflow::Flag(
            "xla_dump_debug_json_to", &flag_values->xla_dump_debug_json_to,
            "Dump compilation artifacts as JSON into this directory."),
@@ -110,6 +124,8 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
   options.set_xla_embed_ir_in_executable(
       flag_values->xla_embed_ir_in_executable);
   options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
+  options.set_xla_gpu_cuda_data_dir(flag_values->xla_gpu_cuda_data_dir);
+  options.set_xla_gpu_ftz(flag_values->xla_gpu_ftz);
 
   std::vector<string> extra_options_parts =
       tensorflow::str_util::Split(flag_values->xla_backend_extra_options, ',');
diff --git a/tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.cc b/tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.cc
deleted file mode 100644
index f8f6ea26b1..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.cc
+++ /dev/null
@@ -1,88 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's gpu_backend_lib module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static GpuBackendLibFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new GpuBackendLibFlags;
-  flags->dump_temp_products_to = "";
-  flags->ftz = false;
-  flags->fma = true;
-  flags->verbose_ptx_asm = false;
-  flags->kernel = "";
-  flags->llvm_dump_passes = false;
-  flags->llvm_cl_opts = "";
-  flags->dump_ir_before_passes = false;
-  flags->opt_level = 3;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("dump_temp_products_to", &flags->dump_temp_products_to,
-                       "dump temporary compilation products to this directory. "
-                       "If empty, no dump is produced"),
-      tensorflow::Flag("ftz", &flags->ftz, "flush to zero semantics"),
-      tensorflow::Flag("fma", &flags->fma, "use FMA synthesis"),
-      tensorflow::Flag("verbose_ptx_asm", &flags->verbose_ptx_asm,
-                       "emit PTX assembly with extra comments"),
-      tensorflow::Flag("kernel", &flags->kernel,
-                       "only emit the IR and PTX for this kernel"),
-      tensorflow::Flag("llvm_dump_passes", &flags->llvm_dump_passes,
-                       "dump the passes LLVM runs to stderr"),
-      tensorflow::Flag(
-          "llvm_cl_opts", &flags->llvm_cl_opts,
-          "comma-separated list of command line options to pass to "
-          "LLVM.  For example, --llvm_cl_opts=--print-before=loop-unroll"),
-      tensorflow::Flag("dump_ir_before_passes", &flags->dump_ir_before_passes,
-                       "dump the IR before each optimization pass in "
-                       "sequentially-named files."),
-      tensorflow::Flag("opt_level", &flags->opt_level,
-                       "optimization level (default to 3)"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's gpu_backend_lib
-// module.
-void AppendGpuBackendLibFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the GpuBackendLibFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-GpuBackendLibFlags* GetGpuBackendLibFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.h b/tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.h
deleted file mode 100644
index 31cb50e9da..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_GPU_BACKEND_LIB_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_GPU_BACKEND_LIB_FLAGS_H_
-
-// Legacy flags for XLA's gpu_backend_lib module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's gpu_backend_lib
-// module.
-void AppendGpuBackendLibFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's gpu_backend_lib module.
-typedef struct {
-  string dump_temp_products_to;  // temporary compilation products dir
-  bool ftz;                      // flush to zero semantics
-  bool fma;                      // use FMA synthesis
-  bool verbose_ptx_asm;          // emit PTX assembly with extra comments
-  string kernel;                 // only emit the IR and PTX for this kernel
-  bool llvm_dump_passes;         // dump the passes LLVM runs to stderr
-  string llvm_cl_opts;           // comma-separated list of LLVM options
-  bool dump_ir_before_passes;    // dump IR before each pass
-  int32 opt_level;               // optimization level
-} GpuBackendLibFlags;
-
-// Return a pointer to the GpuBackendLibFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-GpuBackendLibFlags* GetGpuBackendLibFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_GPU_BACKEND_LIB_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc b/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc
deleted file mode 100644
index a8af794494..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's gpu_compiler module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static GpuCompilerFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new GpuCompilerFlags;
-  flags->xla_cuda_data_dir = "./cuda_sdk_lib";
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_cuda_data_dir", &flags->xla_cuda_data_dir,
-          "If non-empty, specifies a local directory containing ptxas and "
-          "nvvm libdevice files. Otherwise, by default, we use those from "
-          "runfile directories."),
-      tensorflow::Flag("xla_ptxas_path", &flags->xla_ptxas_path,
-                       "The path to ptxas. Required to log stats of the ptx."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's gpu_compiler
-// module.
-void AppendGpuCompilerFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the GpuCompilerFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-GpuCompilerFlags* GetGpuCompilerFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h b/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h
deleted file mode 100644
index 0b0f186b25..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_GPU_COMPILER_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_GPU_COMPILER_FLAGS_H_
-
-// Legacy flags for XLA's gpu_compiler module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's gpu_compiler
-// module.
-void AppendGpuCompilerFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's gpu_compiler module.
-typedef struct {
-  string xla_cuda_data_dir;  // If non-empty, specifies a local directory
-                             // containing ptxas and nvvm libdevice files.
-                             // Otherwise, by default, we use those from runfile
-                             // directories.
-  string xla_ptxas_path;     // The path to ptxas.  Required to log stats of
-                             // the ptx.
-} GpuCompilerFlags;
-
-// Return a pointer to the GpuCompilerFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-GpuCompilerFlags* GetGpuCompilerFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_GPU_COMPILER_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index f4551b0a71..20d8a5d0e6 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -419,6 +419,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index de71006791..52b4a13296 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -417,7 +417,6 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/legacy_flags:gpu_compiler_flags",
         "//tensorflow/compiler/xla/service:algebraic_simplifier",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:buffer_liveness",
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 85962f7795..7ce8b8290a 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/DiagnosticPrinter.h"
 #include "external/llvm/include/llvm/IR/LLVMContext.h"
 #include "external/llvm/include/llvm/IR/Module.h"
-#include "tensorflow/compiler/xla/legacy_flags/gpu_compiler_flags.h"
 #include "tensorflow/compiler/xla/protobuf_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
@@ -95,11 +94,9 @@ constexpr int64 kMemoryAlignment = 256;
 // called in GpuCompiler's constructor, so can't return an error. But
 // GpuCompiler::Compile will return an error when the wanted libdevice file
 // doesn't exist in the folder this function returns.
-string GetLibdeviceDir() {
+string GetLibdeviceDir(const HloModuleConfig& config) {
   std::vector<string> potential_libdevice_dirs;
-  // Flag xla_cuda_data_dir specified by the user.
-  legacy_flags::GpuCompilerFlags* flags = legacy_flags::GetGpuCompilerFlags();
-  const string datadir = flags->xla_cuda_data_dir;
+  const string datadir = config.debug_options().xla_gpu_cuda_data_dir();
   if (!datadir.empty()) {
     potential_libdevice_dirs.push_back(datadir);
   }
@@ -230,8 +227,7 @@ void DumpPtxasInfo(const string& ptx) {
 }  // namespace
 
 GpuCompiler::GpuCompiler()
-    : libdevice_dir_(GetLibdeviceDir()),
-      pointer_size_(llvm::DataLayout(kDataLayout).getPointerSize()) {}
+    : pointer_size_(llvm::DataLayout(kDataLayout).getPointerSize()) {}
 
 StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
     std::unique_ptr<HloModule> module, HloDumper dump_hlo,
@@ -316,6 +312,10 @@ StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
     cc_major = 2;
     cc_minor = 0;
   }
+  if (libdevice_dir_.empty()) {
+    // Compute libdevice_dir_ just once and cache it in this member.
+    libdevice_dir_ = GetLibdeviceDir(module->config());
+  }
   TF_ASSIGN_OR_RETURN(*ptx, CompileToPtx(&llvm_module, {cc_major, cc_minor},
                                          module->config(), libdevice_dir_));
 
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
index da52f5ab1f..68dddcdc19 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
@@ -65,7 +65,7 @@ class GpuCompiler : public Compiler {
 
  private:
   // The parent directory of libdevice IR libraries.
-  const string libdevice_dir_;
+  string libdevice_dir_;
 
   // The list of PTX strings generated by this GpuCompiler. We let GpuCompiler
   // to own them because they need to be alive across the life span of the
diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD
index c6dd67dc18..1d1e5bee54 100644
--- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD
@@ -32,7 +32,6 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/legacy_flags:gpu_backend_lib_flags",
         "//tensorflow/compiler/xla/service:hlo_module_config",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
index e03571a967..16fe4455db 100644
--- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
+++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include <string>
 #include <utility>
 
-#include "tensorflow/compiler/xla/legacy_flags/gpu_backend_lib_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/dump_ir_pass.h"
 #include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/utils.h"
@@ -134,13 +133,8 @@ static string GetSmName(std::pair<int, int> compute_capability) {
 // from the input filename.
 string MakeNameForTempProduct(const std::string& input_filename,
                               tensorflow::StringPiece extension) {
-  legacy_flags::GpuBackendLibFlags* flags =
-      legacy_flags::GetGpuBackendLibFlags();
-  return tensorflow::io::JoinPath(
-      flags->dump_temp_products_to,
-      ReplaceFilenameExtension(
-          tensorflow::io::Basename(llvm_ir::AsString(input_filename)),
-          extension));
+  return ReplaceFilenameExtension(
+      tensorflow::io::Basename(llvm_ir::AsString(input_filename)), extension);
 }
 
 // Initializes LLVM passes. Uses the PassRegistry mechanism.
@@ -177,20 +171,16 @@ std::unique_ptr<llvm::TargetMachine> GetTargetMachine(
           .xla_enable_fast_math(),
       &target_options);
 
-  // Enable FMA synthesis if desired.
-  legacy_flags::GpuBackendLibFlags* flags =
-      legacy_flags::GetGpuBackendLibFlags();
-  if (flags->fma) {
-    target_options.AllowFPOpFusion = FPOpFusion::Fast;
-  }
+  // Enable FMA synthesis.
+  target_options.AllowFPOpFusion = FPOpFusion::Fast;
 
   // Set the verbose assembly options.
-  target_options.MCOptions.AsmVerbose = flags->verbose_ptx_asm;
+  target_options.MCOptions.AsmVerbose = false;
 
   // The selection of codegen optimization level is copied from function
   // GetCodeGenOptLevel in //external/llvm/tools/opt/opt.cpp.
   CodeGenOpt::Level codegen_opt_level;
-  switch (flags->opt_level) {
+  switch (hlo_module_config.debug_options().xla_backend_optimization_level()) {
     case 1:
       codegen_opt_level = CodeGenOpt::Less;
       break;
@@ -262,12 +252,10 @@ string EmitModuleToPTX(Module* module, llvm::TargetMachine* target_machine) {
     // The extension is stripped by IrDumpingPassManager, so we need to
     // get creative to add a suffix.
     string module_id(llvm_ir::AsString(module->getModuleIdentifier()));
-    legacy_flags::GpuBackendLibFlags* flags =
-        legacy_flags::GetGpuBackendLibFlags();
     IrDumpingPassManager codegen_passes(
         ReplaceFilenameExtension(tensorflow::io::Basename(module_id),
                                  "-nvptx.dummy"),
-        flags->dump_temp_products_to, flags->dump_ir_before_passes);
+        "", false);
     codegen_passes.add(new llvm::TargetLibraryInfoWrapperPass(
         llvm::Triple(module->getTargetTriple())));
 
@@ -345,36 +333,19 @@ StatusOr<string> CompileModuleToPtx(llvm::Module* module,
   TF_RETURN_IF_ERROR(
       LinkLibdeviceIfNecessary(module, compute_capability, libdevice_dir_path));
 
-  legacy_flags::GpuBackendLibFlags* flags =
-      legacy_flags::GetGpuBackendLibFlags();
-  if (!flags->dump_temp_products_to.empty()) {
-    string linked_filename =
-        MakeNameForTempProduct(module->getModuleIdentifier(), "linked.bc");
-    LOG(INFO) << "dumping bitcode after linking libdevice to: "
-              << linked_filename;
-    EmitBitcodeToFile(*module, linked_filename);
-  }
-
   // Set the flush-denormals-to-zero flag on the module so the NVVM reflect pass
   // can access it.
-  module->addModuleFlag(llvm::Module::Override, "nvvm-reflect-ftz", flags->ftz);
+  module->addModuleFlag(llvm::Module::Override, "nvvm-reflect-ftz",
+                        hlo_module_config.debug_options().xla_gpu_ftz());
 
   // If ftz is enabled, set it as an attribute on every function in the module.
-  if (flags->ftz) {
+  if (hlo_module_config.debug_options().xla_gpu_ftz()) {
     for (llvm::Function& fn : *module) {
       fn.addFnAttr("nvptx-f32ftz", "true");
     }
   }
 
-  // Run IR-level optimizations.
-  if (flags->dump_ir_before_passes && flags->dump_temp_products_to.empty()) {
-    LOG(FATAL) << "--dump_ir_before_passes must be specified with "
-                  "--dump_temp_products_to";
-  }
-
-  IrDumpingPassManager module_passes(module->getModuleIdentifier(),
-                                     flags->dump_temp_products_to,
-                                     flags->dump_ir_before_passes);
+  IrDumpingPassManager module_passes(module->getModuleIdentifier(), "", false);
 
   // Add an appropriate TargetLibraryInfo pass for the module's triple.
   llvm::TargetLibraryInfoWrapperPass* tliwp =
@@ -406,8 +377,13 @@ StatusOr<string> CompileModuleToPtx(llvm::Module* module,
   // too.
   llvm::legacy::FunctionPassManager function_passes(module);
 
-  AddOptimizationPasses(flags->opt_level, /*size_level=*/0,
-                        target_machine.get(), &module_passes, &function_passes);
+  int32 opt_level =
+      hlo_module_config.debug_options().xla_backend_optimization_level();
+
+  AddOptimizationPasses(opt_level,
+                        /*size_level=*/0, target_machine.get(), &module_passes,
+                        &function_passes);
+
   // Loop unrolling exposes more opportunities for SROA. Therefore, we run SROA
   // again after the standard optimization passes [http://b/13329423].
   // TODO(jingyue): SROA may further expose more optimization opportunities, such
@@ -415,7 +391,7 @@ StatusOr<string> CompileModuleToPtx(llvm::Module* module,
   // the inlining cost of a function). For now, running SROA already emits good
   // enough code for the evaluated benchmarks. We may want to run more
   // optimizations later.
-  if (flags->opt_level > 0) {
+  if (opt_level > 0) {
     // LLVM's optimizer turns on SROA when the optimization level is greater
     // than 0. We mimic this behavior here.
     module_passes.add(llvm::createSROAPass());
@@ -433,14 +409,6 @@ StatusOr<string> CompileModuleToPtx(llvm::Module* module,
   function_passes.doFinalization();
   module_passes.run(*module);
 
-  if (!flags->dump_temp_products_to.empty()) {
-    string optimized_filename =
-        MakeNameForTempProduct(module->getModuleIdentifier(), "optimized.bc");
-    LOG(INFO) << "dumping bitcode after optimizations to: "
-              << optimized_filename;
-    EmitBitcodeToFile(*module, optimized_filename);
-  }
-
   // Finally, produce PTX.
   return EmitModuleToPTX(module, target_machine.get());
 }
@@ -473,22 +441,6 @@ void GPUBackendInit() {
   // between those loads.
   FeedLLVMWithFlags({"-memdep-block-scan-limit=500"});
 
-  legacy_flags::GpuBackendLibFlags* flags =
-      legacy_flags::GetGpuBackendLibFlags();
-  if (!flags->llvm_cl_opts.empty()) {
-    std::vector<string> opts =
-        tensorflow::str_util::Split(flags->llvm_cl_opts, ',');
-    FeedLLVMWithFlags(opts);
-  }
-
-  if (flags->llvm_dump_passes) {
-    // Enable LLVM pass debugging dump. LLVM dumps this information when a pass
-    // manager is initialized for execution. It's done to stderr (this is
-    // hardcoded within LLVM to the dbgs() stream, we can't change it from the
-    // outside).
-    FeedLLVMWithFlags({"-debug-pass=Arguments"});
-  }
-
   // Initialize the NVPTX target; it's the only target we link with, so call its
   // specific initialization functions instead of the catch-all InitializeAll*.
   LLVMInitializeNVPTXTarget();
diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index 748124ebc7..ffc775e564 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/backend.h"
@@ -153,6 +154,7 @@ StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable(
   auto module_config = MakeUnique<HloModuleConfig>(*program_shape);
   module_config->set_has_hybrid_result(has_hybrid_result);
   module_config->set_replica_count(execute_backend_->replica_count());
+  module_config->set_debug_options(legacy_flags::GetDebugOptionsFromFlags());
   legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
   if (flags->xla_hlo_profile) {
     module_config->enable_hlo_profiling(true);
diff --git a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
index eb580693b8..b2433f03d3 100644
--- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
+++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
@@ -54,18 +54,20 @@ TEST_F(CheckExecutionArityTest, TwoParamComputationNumArguments) {
 
   // The arity of the UserComputation is 2 arguments. Execution will succeed
   // with 2 arguments, but fail with a different number.
-  auto result_two_args =
-      client_->Execute(computation, {param0_data.get(), param1_data.get()});
+  auto result_two_args = client_->Execute(
+      computation, {param0_data.get(), param1_data.get()}, &execution_options_);
   ASSERT_IS_OK(result_two_args.status());
 
-  auto result_one_arg = client_->Execute(computation, {param0_data.get()});
+  auto result_one_arg =
+      client_->Execute(computation, {param0_data.get()}, &execution_options_);
   ASSERT_FALSE(result_one_arg.ok());
   ASSERT_EQ(result_one_arg.status().code(),
             tensorflow::error::INVALID_ARGUMENT);
   ASSERT_THAT(result_one_arg.status().error_message(),
               ContainsRegex("takes 2"));
 
-  auto result_zero_args = client_->Execute(computation, {});
+  auto result_zero_args =
+      client_->Execute(computation, {}, &execution_options_);
   ASSERT_FALSE(result_zero_args.ok());
   ASSERT_EQ(result_zero_args.status().code(),
             tensorflow::error::INVALID_ARGUMENT);
@@ -93,26 +95,29 @@ XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) {
   auto u8_4_data = client_->TransferToServer(*u8_4_literal).ConsumeValueOrDie();
 
   // Match
-  auto status =
-      client_->Execute(computation, {f32_data.get(), f32_4_data.get()});
+  auto status = client_->Execute(
+      computation, {f32_data.get(), f32_4_data.get()}, &execution_options_);
   ASSERT_IS_OK(status.status());
 
   // Shape mismatch in parameter 0
-  status = client_->Execute(computation, {f32_4_data.get(), f32_4_data.get()});
+  status = client_->Execute(computation, {f32_4_data.get(), f32_4_data.get()},
+                            &execution_options_);
   ASSERT_FALSE(status.ok());
   ASSERT_EQ(status.status().code(), tensorflow::error::INVALID_ARGUMENT);
   ASSERT_THAT(status.status().error_message(),
               ContainsRegex("expects parameter 0"));
 
   // Shape mismatch in parameter 1 (rank)
-  status = client_->Execute(computation, {f32_data.get(), f32_data.get()});
+  status = client_->Execute(computation, {f32_data.get(), f32_data.get()},
+                            &execution_options_);
   ASSERT_FALSE(status.ok());
   ASSERT_EQ(status.status().code(), tensorflow::error::INVALID_ARGUMENT);
   ASSERT_THAT(status.status().error_message(),
               ContainsRegex("expects parameter 1"));
 
   // Shape mismatch in parameter 1 (element type)
-  status = client_->Execute(computation, {f32_data.get(), u8_4_data.get()});
+  status = client_->Execute(computation, {f32_data.get(), u8_4_data.get()},
+                            &execution_options_);
   ASSERT_FALSE(status.ok());
   ASSERT_EQ(status.status().code(), tensorflow::error::INVALID_ARGUMENT);
   ASSERT_THAT(status.status().error_message(),
diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc
index 99162ef2e5..e84a6ce710 100644
--- a/tensorflow/compiler/xla/tests/client_test.cc
+++ b/tensorflow/compiler/xla/tests/client_test.cc
@@ -46,7 +46,7 @@ TEST_F(ClientTest, ExecuteWithLayout) {
       auto computation = b.Build();
       ASSERT_TRUE(computation.ok()) << computation.status();
 
-      ExecutionOptions execution_options;
+      ExecutionOptions execution_options = execution_options_;
       *execution_options.mutable_shape_with_output_layout() =
           ShapeUtil::MakeShapeWithLayout(S32, /*dimensions=*/{2, 2},
                                          execute_layout);
@@ -76,7 +76,7 @@ TEST_F(ClientTest, ExecuteWithTupleLayout) {
   auto computation = b.Build();
   ASSERT_TRUE(computation.ok()) << computation.status();
 
-  ExecutionOptions execution_options;
+  ExecutionOptions execution_options = execution_options_;
   // Create a result shape with one element column major and the other row
   // major.
   *execution_options.mutable_shape_with_output_layout() =
diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.cc b/tensorflow/compiler/xla/tests/codegen_test_base.cc
index a7562b5484..f48dc50708 100644
--- a/tensorflow/compiler/xla/tests/codegen_test_base.cc
+++ b/tensorflow/compiler/xla/tests/codegen_test_base.cc
@@ -33,10 +33,12 @@ limitations under the License.
 
 namespace xla {
 
-std::unique_ptr<HloModule> CodegenTestBase::CreateNewModuleWithEmbeddedIr() {
+std::unique_ptr<HloModule> CodegenTestBase::CreateNewModuleWithEmbeddedIr(
+    bool ftz) {
   HloModuleConfig config;
   auto debug_options = legacy_flags::GetDebugOptionsFromFlags();
   debug_options.set_xla_embed_ir_in_executable(true);
+  debug_options.set_xla_gpu_ftz(ftz);
   config.set_debug_options(debug_options);
   return MakeUnique<HloModule>(TestName(), VersionedComputationHandle(),
                                config);
diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.h b/tensorflow/compiler/xla/tests/codegen_test_base.h
index 033077c014..fa073cd91e 100644
--- a/tensorflow/compiler/xla/tests/codegen_test_base.h
+++ b/tensorflow/compiler/xla/tests/codegen_test_base.h
@@ -30,7 +30,9 @@ class CodegenTestBase : public HloTestBase {
  protected:
   // Like HloTestBase::CreateNewModule, but also sets the "embed ir in
   // executable" flag to true, since this is needed for codegen tests.
-  std::unique_ptr<HloModule> CreateNewModuleWithEmbeddedIr();
+  // The optional ftz flags configures whether these modules have their ftz
+  // option turned on.
+  std::unique_ptr<HloModule> CreateNewModuleWithEmbeddedIr(bool ftz = false);
 
   // Returns the embedded LLVM IR from the given executable. Codegen tests must
   // override this method, but execution tests do not have to because they do
diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
index 0d92e67447..3439be46fd 100644
--- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc
+++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
@@ -47,7 +47,7 @@ class CompilationCacheTest : public ClientLibraryTestBase {
     std::unique_ptr<Literal> result =
         client_
             ->ExecuteAndTransfer(computation, arguments,
-                                 /*execution_options=*/nullptr,
+                                 /*execution_options=*/&execution_options_,
                                  &execution_profile)
             .ConsumeValueOrDie();
     LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR0<float>(expected_result),
@@ -61,11 +61,10 @@ class CompilationCacheTest : public ClientLibraryTestBase {
       std::initializer_list<std::initializer_list<float>> expected_result,
       bool expect_cache_hit) {
     ExecutionProfile execution_profile;
-    auto data_handle =
-        client_
-            ->Execute(computation, arguments, /*execution_options=*/nullptr,
-                      &execution_profile)
-            .ConsumeValueOrDie();
+    auto data_handle = client_
+                           ->Execute(computation, arguments,
+                                     &execution_options_, &execution_profile)
+                           .ConsumeValueOrDie();
     std::unique_ptr<Literal> result =
         client_->Transfer(*data_handle).ConsumeValueOrDie();
     LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR2<float>(expected_result),
diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
index 4dcf82b9c4..23f9c8e880 100644
--- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
@@ -47,7 +47,8 @@ class DeconstructTupleTest : public ClientLibraryTestBase {
       tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
     Computation computation = builder->Build().ConsumeValueOrDie();
     auto global_data =
-        client_->Execute(computation, arguments).ConsumeValueOrDie();
+        client_->Execute(computation, arguments, &execution_options_)
+            .ConsumeValueOrDie();
     TF_CHECK_OK(client_->Transfer(*global_data).status());
     return global_data;
   }
diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc
index 46f8e12e16..74346342e0 100644
--- a/tensorflow/compiler/xla/tests/prng_test.cc
+++ b/tensorflow/compiler/xla/tests/prng_test.cc
@@ -70,7 +70,7 @@ void PrngTest::BernoulliTest(float p, tensorflow::gtl::ArraySlice<int64> dims) {
   builder.RngBernoulli(builder.ConstantR0<float>(p), shape);
 
   TF_ASSIGN_OR_ASSERT_OK(auto computation, builder.Build());
-  ExecutionOptions execution_options;
+  ExecutionOptions execution_options = execution_options_;
   execution_options.set_seed(42);
   TF_ASSIGN_OR_ASSERT_OK(
       auto actual,
@@ -179,7 +179,7 @@ XLA_TEST_F(PrngTest, MapUsingRng) {
 
   TF_ASSIGN_OR_ASSERT_OK(auto computation, builder.Build());
 
-  ExecutionOptions execution_options;
+  ExecutionOptions execution_options = execution_options_;
   execution_options.set_seed(125);
   TF_ASSIGN_OR_ASSERT_OK(
       auto actual,
@@ -208,10 +208,10 @@ XLA_TEST_F(PrngTest, PassInGlobalRngSeed) {
     return builder.Build();
   };
 
-  ExecutionOptions execution_options1;
+  ExecutionOptions execution_options1 = execution_options_;
   execution_options1.set_seed(42);
 
-  ExecutionOptions execution_options2;
+  ExecutionOptions execution_options2 = execution_options_;
   execution_options2.set_seed(65);
 
   std::unique_ptr<Literal> result1;
diff --git a/tensorflow/compiler/xla/tests/replay_test.cc b/tensorflow/compiler/xla/tests/replay_test.cc
index 70ef1bc710..4dea470477 100644
--- a/tensorflow/compiler/xla/tests/replay_test.cc
+++ b/tensorflow/compiler/xla/tests/replay_test.cc
@@ -60,7 +60,8 @@ TEST_F(ReplayTest, TwoPlusTwoReplay) {
 
   // Run it.
   std::unique_ptr<Literal> literal =
-      client_->ExecuteAndTransfer(replayed, /*arguments=*/{})
+      client_
+          ->ExecuteAndTransfer(replayed, /*arguments=*/{}, &execution_options_)
           .ConsumeValueOrDie();
 
   // Expect 4.
@@ -99,7 +100,8 @@ XLA_TEST_F(ReplayTest, XPlusYReplayWithParameters) {
   std::unique_ptr<Literal> literal =
       client_
           ->ExecuteAndTransfer(replayed,
-                               /*arguments=*/{x_data.get(), y_data.get()})
+                               /*arguments=*/{x_data.get(), y_data.get()},
+                               &execution_options_)
           .ConsumeValueOrDie();
 
   // Expect 5.
@@ -140,7 +142,8 @@ TEST_F(ReplayTest, MapPlusTwoOverR1) {
 
   // Run it.
   std::unique_ptr<Literal> literal =
-      client_->ExecuteAndTransfer(replayed, /*arguments=*/{})
+      client_
+          ->ExecuteAndTransfer(replayed, /*arguments=*/{}, &execution_options_)
           .ConsumeValueOrDie();
 
   // Expect result.
diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc
index f1fff09ecf..096ba8deb4 100644
--- a/tensorflow/compiler/xla/tests/reshape_test.cc
+++ b/tensorflow/compiler/xla/tests/reshape_test.cc
@@ -466,7 +466,7 @@ XLA_TEST_F(ReshapeTest, R4Dim0MinorLayoutToR2Dim0MajorLayout) {
   });
 
   Computation computation = builder.Build().ConsumeValueOrDie();
-  ExecutionOptions execution_options;
+  ExecutionOptions execution_options = execution_options_;
   *execution_options.mutable_shape_with_output_layout() =
       ShapeUtil::MakeShapeWithLayout(F32, {2, 8}, {1, 0});
   std::unique_ptr<Literal> actual =
@@ -625,7 +625,7 @@ XLA_TEST_F(ReshapeTest, NoopReshape) {
                   /*new_sizes=*/{7, 2, 3, 5});
   Computation computation = builder.Build().ConsumeValueOrDie();
 
-  ExecutionOptions execution_options;
+  ExecutionOptions execution_options = execution_options_;
   *execution_options.mutable_shape_with_output_layout() =
       ShapeUtil::MakeShapeWithLayout(F32, {7, 2, 3, 5}, {2, 3, 0, 1});
   std::unique_ptr<Literal> output_literal =
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index f0a0e494ec..3aeeb29c1f 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -587,7 +587,7 @@ TEST_F(WhileTest, WhileWithPrngScalarResult) {
   for (int i = 1; i < 4; ++i) {
     TF_ASSIGN_OR_ASSERT_OK(auto computation, while_loop(i));
 
-    ExecutionOptions execution_options;
+    ExecutionOptions execution_options = execution_options_;
     execution_options.set_seed(65);
     TF_ASSIGN_OR_ASSERT_OK(
         auto result,
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index f98fc2f5a4..b6289f8e1c 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -51,6 +51,12 @@ message DebugOptions {
   // Dump compilation artifacts as JSON into this directory.
   string xla_dump_debug_json_to = 6;
 
+  // Path to directory with cuda/ptx tools and libraries.
+  string xla_gpu_cuda_data_dir = 7;
+
+  // Enable flush-to-zero semantics in the GPU backend.
+  bool xla_gpu_ftz = 8;
+
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
   map<string, string> xla_backend_extra_options = 500;
-- 
GitLab


From a94a333762b4d2c3fcae0a9e610d654bba6980de Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 12:59:56 -0700
Subject: [PATCH 0165/1375] Add a bitwise module with bitwise_and, bitwise_or,
 bitwise_xor, and invert functions.

PiperOrigin-RevId: 159264147
---
 tensorflow/contrib/cmake/tf_core_ops.cmake    |  1 +
 tensorflow/contrib/cmake/tf_python.cmake      |  1 +
 tensorflow/contrib/makefile/tf_op_files.txt   |  4 +
 tensorflow/core/BUILD                         |  2 +
 tensorflow/core/kernels/BUILD                 |  6 +-
 .../core/kernels/cwise_op_bitwise_and.cc      | 42 +++++++++++
 .../core/kernels/cwise_op_bitwise_or.cc       | 42 +++++++++++
 .../core/kernels/cwise_op_bitwise_xor.cc      | 42 +++++++++++
 .../kernels/cwise_op_gpu_bitwise_and.cu.cc    | 26 +++++++
 .../kernels/cwise_op_gpu_bitwise_or.cu.cc     | 26 +++++++
 .../kernels/cwise_op_gpu_bitwise_xor.cu.cc    | 26 +++++++
 .../core/kernels/cwise_op_gpu_invert.cu.cc    | 26 +++++++
 tensorflow/core/kernels/cwise_op_invert.cc    | 32 ++++++++
 tensorflow/core/kernels/cwise_ops.h           | 47 ++++++++++++
 tensorflow/core/ops/bitwise_ops.cc            | 64 ++++++++++++++++
 tensorflow/python/BUILD                       | 36 +++++++++
 tensorflow/python/__init__.py                 |  2 +
 tensorflow/python/ops/bitwise_ops.py          | 40 ++++++++++
 tensorflow/python/ops/bitwise_ops_test.py     | 74 +++++++++++++++++++
 .../tools/api/golden/tensorflow.bitwise.pbtxt | 19 +++++
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  4 +
 21 files changed, 561 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/core/kernels/cwise_op_bitwise_and.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_bitwise_or.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_bitwise_xor.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_bitwise_and.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_bitwise_or.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_bitwise_xor.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_invert.cc
 create mode 100644 tensorflow/core/ops/bitwise_ops.cc
 create mode 100644 tensorflow/python/ops/bitwise_ops.py
 create mode 100644 tensorflow/python/ops/bitwise_ops_test.py
 create mode 100644 tensorflow/tools/api/golden/tensorflow.bitwise.pbtxt

diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index 3c2f89c6c8..6cc1b82238 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -14,6 +14,7 @@
 # ==============================================================================
 set(tf_op_lib_names
     "array_ops"
+    "bitwise_ops"
     "candidate_sampling_ops"
     "control_flow_ops"
     "ctc_ops"
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index f95d73ca43..532d31216b 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -591,6 +591,7 @@ function(GENERATE_PYTHON_OP_LIB tf_python_op_lib_name)
 endfunction()
 
 GENERATE_PYTHON_OP_LIB("array_ops")
+GENERATE_PYTHON_OP_LIB("bitwise_ops")
 GENERATE_PYTHON_OP_LIB("math_ops")
 GENERATE_PYTHON_OP_LIB("functional_ops")
 GENERATE_PYTHON_OP_LIB("candidate_sampling_ops")
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index c73ec0305b..541d13aed1 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -138,6 +138,7 @@ tensorflow/core/kernels/cwise_op_logical_and.cc
 tensorflow/core/kernels/cwise_op_log.cc
 tensorflow/core/kernels/cwise_op_less.cc
 tensorflow/core/kernels/cwise_op_isfinite.cc
+tensorflow/core/kernels/cwise_op_invert.cc
 tensorflow/core/kernels/cwise_op_greater_equal.cc
 tensorflow/core/kernels/cwise_op_greater.cc
 tensorflow/core/kernels/cwise_op_floor_div.cc
@@ -146,6 +147,9 @@ tensorflow/core/kernels/cwise_op_exp.cc
 tensorflow/core/kernels/cwise_op_equal_to_2.cc
 tensorflow/core/kernels/cwise_op_equal_to_1.cc
 tensorflow/core/kernels/cwise_op_div.cc
+tensorflow/core/kernels/cwise_op_bitwise_xor.cc
+tensorflow/core/kernels/cwise_op_bitwise_or.cc
+tensorflow/core/kernels/cwise_op_bitwise_and.cc
 tensorflow/core/kernels/cwise_op_add_2.cc
 tensorflow/core/kernels/cwise_op_add_1.cc
 tensorflow/core/kernels/cwise_op_abs.cc
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 18cac5f48b..27544c9961 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -502,6 +502,7 @@ cc_library(
 tf_gen_op_libs(
     op_lib_names = [
         "array_ops",
+        "bitwise_ops",
         "candidate_sampling_ops",
         "control_flow_ops",
         "ctc_ops",
@@ -580,6 +581,7 @@ cc_library(
     deps = [
         ":array_ops_op_lib",
         ":audio_ops_op_lib",
+        ":bitwise_ops_op_lib",
         ":candidate_sampling_ops_op_lib",
         ":control_flow_ops_op_lib",
         ":ctc_ops_op_lib",
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index d393f94a18..2bfbad5dc4 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -2346,7 +2346,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "cwise_op",
     prefix = "cwise_op",
-    deps = MATH_DEPS,
+    deps = MATH_DEPS + ["//tensorflow/core:bitwise_ops_op_lib"],
 )
 
 tf_kernel_library(
@@ -4162,6 +4162,9 @@ filegroup(
         "cwise_op_abs.cc",
         "cwise_op_add_1.cc",
         "cwise_op_add_2.cc",
+        "cwise_op_bitwise_and.cc",
+        "cwise_op_bitwise_or.cc",
+        "cwise_op_bitwise_xor.cc",
         "cwise_op_div.cc",
         "cwise_op_equal_to_1.cc",
         "cwise_op_equal_to_2.cc",
@@ -4170,6 +4173,7 @@ filegroup(
         "cwise_op_floor_div.cc",
         "cwise_op_greater.cc",
         "cwise_op_greater_equal.cc",
+        "cwise_op_invert.cc",
         "cwise_op_isfinite.cc",
         "cwise_op_less.cc",
         "cwise_op_log.cc",
diff --git a/tensorflow/core/kernels/cwise_op_bitwise_and.cc b/tensorflow/core/kernels/cwise_op_bitwise_and.cc
new file mode 100644
index 0000000000..017a2182dc
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_bitwise_and.cc
@@ -0,0 +1,42 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER6(BinaryOp, CPU, "BitwiseAnd", functor::bitwise_and, int8, int16, int32,
+          int64, uint8, uint16);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                      \
+  REGISTER_KERNEL_BUILDER(                                              \
+      Name("BitwiseAnd").Device(DEVICE_SYCL).TypeConstraint<TYPE>("T"), \
+      BinaryOp<SYCLDevice, functor::bitwise_and<TYPE>>);
+REGISTER_SYCL_KERNEL(int8);
+REGISTER_SYCL_KERNEL(int16);
+REGISTER_SYCL_KERNEL(int32);
+REGISTER_SYCL_KERNEL(int64);
+REGISTER_SYCL_KERNEL(uint8);
+REGISTER_SYCL_KERNEL(uint16);
+#undef REGISTER_SYCL_KERNEL
+
+#endif  // TENSORFLOW_USE_SYCL
+
+#if GOOGLE_CUDA
+REGISTER6(BinaryOp, GPU, "BitwiseAnd", functor::bitwise_and, int8, int16, int32,
+          int64, uint8, uint16);
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_bitwise_or.cc b/tensorflow/core/kernels/cwise_op_bitwise_or.cc
new file mode 100644
index 0000000000..36f45fe92d
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_bitwise_or.cc
@@ -0,0 +1,42 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER6(BinaryOp, CPU, "BitwiseOr", functor::bitwise_or, int8, int16, int32,
+          int64, uint8, uint16);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                     \
+  REGISTER_KERNEL_BUILDER(                                             \
+      Name("BitwiseOr").Device(DEVICE_SYCL).TypeConstraint<TYPE>("T"), \
+      BinaryOp<SYCLDevice, functor::bitwise_or<TYPE>>);
+REGISTER_SYCL_KERNEL(int8);
+REGISTER_SYCL_KERNEL(int16);
+REGISTER_SYCL_KERNEL(int32);
+REGISTER_SYCL_KERNEL(int64);
+REGISTER_SYCL_KERNEL(uint8);
+REGISTER_SYCL_KERNEL(uint16);
+#undef REGISTER_SYCL_KERNEL
+
+#endif  // TENSORFLOW_USE_SYCL
+
+#if GOOGLE_CUDA
+REGISTER6(BinaryOp, GPU, "BitwiseOr", functor::bitwise_or, int8, int16, int32,
+          int64, uint8, uint16);
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_bitwise_xor.cc b/tensorflow/core/kernels/cwise_op_bitwise_xor.cc
new file mode 100644
index 0000000000..36432d851d
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_bitwise_xor.cc
@@ -0,0 +1,42 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER6(BinaryOp, CPU, "BitwiseXor", functor::bitwise_xor, int8, int16, int32,
+          int64, uint8, uint16);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                      \
+  REGISTER_KERNEL_BUILDER(                                              \
+      Name("BitwiseXor").Device(DEVICE_SYCL).TypeConstraint<TYPE>("T"), \
+      BinaryOp<SYCLDevice, functor::bitwise_xor<TYPE>>);
+REGISTER_SYCL_KERNEL(int8);
+REGISTER_SYCL_KERNEL(int16);
+REGISTER_SYCL_KERNEL(int32);
+REGISTER_SYCL_KERNEL(int64);
+REGISTER_SYCL_KERNEL(uint8);
+REGISTER_SYCL_KERNEL(uint16);
+#undef REGISTER_SYCL_KERNEL
+
+#endif  // TENSORFLOW_USE_SYCL
+
+#if GOOGLE_CUDA
+REGISTER6(BinaryOp, GPU, "BitwiseXor", functor::bitwise_xor, int8, int16, int32,
+          int64, uint8, uint16);
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_gpu_bitwise_and.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_bitwise_and.cu.cc
new file mode 100644
index 0000000000..27f973c90d
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_bitwise_and.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_BINARY6(bitwise_and, int8, int16, int32, int64, uint8, uint16);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_bitwise_or.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_bitwise_or.cu.cc
new file mode 100644
index 0000000000..a34c3a52cd
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_bitwise_or.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_BINARY6(bitwise_or, int8, int16, int32, int64, uint8, uint16);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_bitwise_xor.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_bitwise_xor.cu.cc
new file mode 100644
index 0000000000..a4531ab7c6
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_bitwise_xor.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_BINARY6(bitwise_xor, int8, int16, int32, int64, uint8, uint16);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc
new file mode 100644
index 0000000000..62f33612db
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY6(invert, int8, int16, int32, int64, uint8, uint16);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc
new file mode 100644
index 0000000000..c84ee6894e
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_invert.cc
@@ -0,0 +1,32 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64,
+          uint8, uint16);
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
+         uint8, uint16);
+#endif  // TENSORFLOW_USE_SYCL
+
+#if GOOGLE_CUDA
+REGISTER6(UnaryOp, GPU, "Invert", functor::invert, int8, int16, int32, int64,
+          uint8, uint16);
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h
index 423307fd4c..97bdc5e878 100644
--- a/tensorflow/core/kernels/cwise_ops.h
+++ b/tensorflow/core/kernels/cwise_ops.h
@@ -522,6 +522,17 @@ struct atan : base<T, Eigen::internal::scalar_atan_op<T> > {};
 struct logical_not : base<bool, Eigen::internal::scalar_boolean_not_op<bool> > {
 };
 
+// Flip all bits. Named invert to be consistent with numpy.
+template <typename T>
+struct invert_op {
+  EIGEN_EMPTY_STRUCT_CTOR(invert_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
+    return ~a;
+  }
+};
+
+template <typename T>
+struct invert : base<T, invert_op<T>> {};
 
 // NOTE: std::isinf, std::isnan, std::isfinite are plain function.
 // Therefore we need to wrap them in functors to be used with Eigen's
@@ -702,6 +713,42 @@ struct logical_and : base<bool, Eigen::internal::scalar_boolean_and_op> {};
 
 struct logical_or : base<bool, Eigen::internal::scalar_boolean_or_op> {};
 
+template <typename T>
+struct bitwise_and_op {
+  EIGEN_EMPTY_STRUCT_CTOR(bitwise_and_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& x,
+                                                           const T& y) const {
+    return x & y;
+  }
+};
+
+template <typename T>
+struct bitwise_or_op {
+  EIGEN_EMPTY_STRUCT_CTOR(bitwise_or_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& x,
+                                                           const T& y) const {
+    return x | y;
+  }
+};
+
+template <typename T>
+struct bitwise_xor_op {
+  EIGEN_EMPTY_STRUCT_CTOR(bitwise_xor_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& x,
+                                                           const T& y) const {
+    return x ^ y;
+  }
+};
+
+template <typename T>
+struct bitwise_and : base<T, bitwise_and_op<T>> {};
+
+template <typename T>
+struct bitwise_or : base<T, bitwise_or_op<T>> {};
+
+template <typename T>
+struct bitwise_xor : base<T, bitwise_xor_op<T>> {};
+
 template <typename T>
 struct make_complex_func {
   typedef std::complex<T> result_type;
diff --git a/tensorflow/core/ops/bitwise_ops.cc b/tensorflow/core/ops/bitwise_ops.cc
new file mode 100644
index 0000000000..2005d5e102
--- /dev/null
+++ b/tensorflow/core/ops/bitwise_ops.cc
@@ -0,0 +1,64 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+REGISTER_OP("Invert")
+    .Input("x: T")
+    .Output("y: T")
+    .Attr("T: {int8, int16, int32, int64, uint8, uint16}")
+    .SetShapeFn(shape_inference::UnchangedShape)
+    .Doc(R"doc(
+Flips all bits elementwise.
+
+The result will have exactly those bits set, that are not set in `x`. The
+computation is performed on the underlying representation of x.
+)doc");
+
+#define BINARY_BITWISE()                                     \
+  Input("x: T")                                              \
+      .Input("y: T")                                         \
+      .Output("z: T")                                        \
+      .SetIsCommutative()                                    \
+      .Attr("T: {int8, int16, int32, int64, uint8, uint16}") \
+      .SetShapeFn(shape_inference::UnchangedShape)
+
+REGISTER_OP("BitwiseAnd").BINARY_BITWISE().Doc(R"doc(
+Elementwise computes the bitwise AND of `x` and `y`.
+
+The result will have those bits set, that are set in both `x` and `y`. The
+computation is performed on the underlying representations of `x` and `y`.
+)doc");
+
+REGISTER_OP("BitwiseOr").BINARY_BITWISE().Doc(R"doc(
+Elementwise computes the bitwise OR of `x` and `y`.
+
+The result will have those bits set, that are set in `x`, `y` or both. The
+computation is performed on the underlying representations of `x` and `y`.
+)doc");
+
+REGISTER_OP("BitwiseXor").BINARY_BITWISE().Doc(R"doc(
+Elementwise computes the bitwise XOR of `x` and `y`.
+
+The result will have those bits set, that are different in `x` and `y`. The
+computation is performed on the underlying representations of `x` and `y`.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 64b052839f..0566a7d019 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -47,6 +47,7 @@ py_library(
     deps = [
         ":tf_optimizer",
         ":array_ops",
+        ":bitwise_ops",
         ":check_ops",
         ":client",
         ":client_testlib",
@@ -1055,6 +1056,17 @@ tf_gen_op_wrapper_private_py(
     ],
 )
 
+tf_gen_op_wrapper_private_py(
+    name = "bitwise_ops_gen",
+    require_shape_functions = True,
+    visibility = [
+        "//learning/brain/python/ops:__pkg__",
+        "//tensorflow/compiler/tests:__pkg__",
+        "//tensorflow/contrib/quantization:__pkg__",
+        "//tensorflow/python/kernel_tests:__pkg__",
+    ],
+)
+
 tf_gen_op_wrapper_private_py(
     name = "candidate_sampling_ops_gen",
     require_shape_functions = True,
@@ -1263,6 +1275,17 @@ py_library(
     ],
 )
 
+py_library(
+    name = "bitwise_ops",
+    srcs = ["ops/bitwise_ops.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":bitwise_ops_gen",
+        ":framework",
+        ":util",
+    ],
+)
+
 py_library(
     name = "sets",
     srcs = [
@@ -1423,6 +1446,7 @@ py_library(
     deps = [
         ":array_grad",
         ":array_ops",
+        ":bitwise_ops",
         ":control_flow_grad",
         ":control_flow_ops",
         ":framework",
@@ -2127,6 +2151,18 @@ py_library(
     ],
 )
 
+cuda_py_test(
+    name = "bitwise_ops_test",
+    size = "small",
+    srcs = ["ops/bitwise_ops_test.py"],
+    additional_deps = [
+        ":bitwise_ops",
+        ":constant_op",
+        ":dtypes",
+        ":framework_test_lib",
+    ],
+)
+
 cuda_py_test(
     name = "control_flow_ops_test",
     size = "small",
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index e11c5e44a9..db2c5b1fd9 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -77,6 +77,7 @@ from tensorflow.python.ops.standard_ops import *
 from tensorflow.python.estimator import estimator_lib as estimator
 from tensorflow.python.feature_column import feature_column_lib as feature_column
 from tensorflow.python.layers import layers
+from tensorflow.python.ops import bitwise_ops as bitwise
 from tensorflow.python.ops import image_ops as image
 from tensorflow.python.ops import metrics
 from tensorflow.python.ops import nn
@@ -209,6 +210,7 @@ _allowed_symbols.extend([
 # Export modules and constants.
 _allowed_symbols.extend([
     'app',
+    'bitwise',
     'compat',
     'errors',
     'estimator',
diff --git a/tensorflow/python/ops/bitwise_ops.py b/tensorflow/python/ops/bitwise_ops.py
new file mode 100644
index 0000000000..cbabc3ed9b
--- /dev/null
+++ b/tensorflow/python/ops/bitwise_ops.py
@@ -0,0 +1,40 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Operations for manipulating the binary representations of integers.
+
+@@bitwise_and
+@@bitwise_or
+@@bitwise_xor
+@@invert
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import
+from tensorflow.python.ops.gen_bitwise_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.python.util.all_util import remove_undocumented
+
+ops.NotDifferentiable("BitwiseAnd")
+ops.NotDifferentiable("BitwiseOr")
+ops.NotDifferentiable("BitwiseXor")
+ops.NotDifferentiable("Invert")
+
+remove_undocumented(__name__)
diff --git a/tensorflow/python/ops/bitwise_ops_test.py b/tensorflow/python/ops/bitwise_ops_test.py
new file mode 100644
index 0000000000..904cf99a5a
--- /dev/null
+++ b/tensorflow/python/ops/bitwise_ops_test.py
@@ -0,0 +1,74 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for bitwise operations."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import bitwise_ops
+from tensorflow.python.platform import googletest
+
+
+class BitwiseOpTest(test_util.TensorFlowTestCase):
+
+  def __init__(self, method_name="runTest"):
+    super(BitwiseOpTest, self).__init__(method_name)
+
+  def testBinaryOps(self):
+    dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
+                  dtypes.uint8, dtypes.uint16]
+
+    with self.test_session(use_gpu=True) as sess:
+      for dtype in dtype_list:
+        lhs = constant_op.constant([0, 5, 3, 14], dtype=dtype)
+        rhs = constant_op.constant([5, 0, 7, 11], dtype=dtype)
+        and_result, or_result, xor_result = sess.run(
+            [bitwise_ops.bitwise_and(lhs, rhs),
+             bitwise_ops.bitwise_or(lhs, rhs),
+             bitwise_ops.bitwise_xor(lhs, rhs)])
+        self.assertAllEqual(and_result, [0, 0, 3, 10])
+        self.assertAllEqual(or_result, [5, 5, 7, 15])
+        self.assertAllEqual(xor_result, [5, 5, 4, 5])
+
+  def testInvertOp(self):
+    dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
+                  dtypes.uint8, dtypes.uint16]
+    inputs = [0, 5, 3, 14]
+    with self.test_session(use_gpu=True) as sess:
+      for dtype in dtype_list:
+        # Because of issues with negative numbers, let's test this indirectly.
+        # 1. invert(a) and a = 0
+        # 2. invert(a) or a = invert(0)
+        input_tensor = constant_op.constant(inputs, dtype=dtype)
+        not_a_and_a, not_a_or_a, not_0 = sess.run(
+            [bitwise_ops.bitwise_and(
+                input_tensor, bitwise_ops.invert(input_tensor)),
+             bitwise_ops.bitwise_or(
+                 input_tensor, bitwise_ops.invert(input_tensor)),
+             bitwise_ops.invert(constant_op.constant(0, dtype=dtype))])
+        self.assertAllEqual(not_a_and_a, [0, 0, 0, 0])
+        self.assertAllEqual(not_a_or_a, [not_0] * 4)
+        # For unsigned dtypes let's also check the result directly.
+        if dtype.is_unsigned:
+          inverted = sess.run(bitwise_ops.invert(input_tensor))
+          expected = [dtype.max - x for x in inputs]
+          self.assertAllEqual(inverted, expected)
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/tools/api/golden/tensorflow.bitwise.pbtxt b/tensorflow/tools/api/golden/tensorflow.bitwise.pbtxt
new file mode 100644
index 0000000000..1e4d333cc0
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.bitwise.pbtxt
@@ -0,0 +1,19 @@
+path: "tensorflow.bitwise"
+tf_module {
+  member_method {
+    name: "bitwise_and"
+    argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "bitwise_or"
+    argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "bitwise_xor"
+    argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "invert"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 5708c9f7ad..57f4ec6007 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -256,6 +256,10 @@ tf_module {
     name: "bfloat16"
     mtype: "<class \'tensorflow.python.framework.dtypes.DType\'>"
   }
+  member {
+    name: "bitwise"
+    mtype: "<type \'module\'>"
+  }
   member {
     name: "bool"
     mtype: "<class \'tensorflow.python.framework.dtypes.DType\'>"
-- 
GitLab


From d5985d7ff784eec41cdfe69c71d00de16984dc2e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dandelion=20Man=C3=A9?= <danmane@gmail.com>
Date: Fri, 16 Jun 2017 12:02:57 -0700
Subject: [PATCH 0166/1375] Link to TensorBoard repo in ISSUES_TEMPLATE.md

Any new TensorBoard issues should be filed against the TensorBoard repository, not on core TensorFlow.
---
 ISSUE_TEMPLATE.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 6f4c048ce8..7741ed939a 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -9,6 +9,8 @@ If you open a GitHub issue, here is our policy:
 
 **Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
 
+TensorBoard issues should be filed at the TensorBoard repository: https://github.com/tensorflow/tensorboard
+
 ------------------------
 
 ### System information
-- 
GitLab


From 7847f2a4f050be75fa427d5c58a1902067d0684b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dandelion=20Man=C3=A9?= <danmane@gmail.com>
Date: Fri, 16 Jun 2017 12:40:21 -0700
Subject: [PATCH 0167/1375] Update ISSUE_TEMPLATE.md

---
 ISSUE_TEMPLATE.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 7741ed939a..3a818225ca 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -6,11 +6,10 @@ If you open a GitHub issue, here is our policy:
 
 1. It must be a bug or a feature request.
 2. The form below must be filled out.
+3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorflow/issues)
 
 **Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
 
-TensorBoard issues should be filed at the TensorBoard repository: https://github.com/tensorflow/tensorboard
-
 ------------------------
 
 ### System information
-- 
GitLab


From c710161df0678e8cc335e7921238ddb824811f51 Mon Sep 17 00:00:00 2001
From: Martin Wicke <martin.wicke@gmail.com>
Date: Fri, 16 Jun 2017 13:04:32 -0700
Subject: [PATCH 0168/1375] Update ISSUE_TEMPLATE.md

---
 ISSUE_TEMPLATE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 3a818225ca..5b37028c50 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -6,7 +6,7 @@ If you open a GitHub issue, here is our policy:
 
 1. It must be a bug or a feature request.
 2. The form below must be filled out.
-3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorflow/issues)
+3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorflow/issues).
 
 **Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
 
-- 
GitLab


From f63b724bf30bb0ff5de47e4811452862643a3475 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 13:03:40 -0700
Subject: [PATCH 0169/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 159264617
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 115 ++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 123 ++++++++++++++++++
 2 files changed, 238 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index d0098f3008..edc21b6029 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -3725,6 +3725,96 @@ op {
     }
   }
 }
+op {
+  name: "BitwiseAnd"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  is_commutative: true
+}
+op {
+  name: "BitwiseOr"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  is_commutative: true
+}
+op {
+  name: "BitwiseXor"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  is_commutative: true
+}
 op {
   name: "BroadcastArgs"
   input_arg {
@@ -10428,6 +10518,31 @@ op {
     version: 17
   }
 }
+op {
+  name: "Invert"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+}
 op {
   name: "InvertPermutation"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 78144485db..ddaaed4396 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -3697,6 +3697,102 @@ op {
   summary: "Bitcasts a tensor from one type to another without copying data."
   description: "Given a tensor `input`, this operation returns a tensor that has the same buffer\ndata as `input` with datatype `type`.\n\nIf the input datatype `T` is larger than the output datatype `type` then the\nshape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)].\n\nIf `T` is smaller than `type`, the operator requires that the rightmost\ndimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from\n[..., sizeof(`type`)/sizeof(`T`)] to [...].\n\n*NOTE*: Bitcast is implemented as a low-level cast, so machines with different\nendian orderings will give different results."
 }
+op {
+  name: "BitwiseAnd"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Elementwise computes the bitwise AND of `x` and `y`."
+  description: "The result will have those bits set, that are set in both `x` and `y`. The\ncomputation is performed on the underlying representations of `x` and `y`."
+  is_commutative: true
+}
+op {
+  name: "BitwiseOr"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Elementwise computes the bitwise OR of `x` and `y`."
+  description: "The result will have those bits set, that are set in `x`, `y` or both. The\ncomputation is performed on the underlying representations of `x` and `y`."
+  is_commutative: true
+}
+op {
+  name: "BitwiseXor"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Elementwise computes the bitwise XOR of `x` and `y`."
+  description: "The result will have those bits set, that are different in `x` and `y`. The\ncomputation is performed on the underlying representations of `x` and `y`."
+  is_commutative: true
+}
 op {
   name: "BroadcastArgs"
   input_arg {
@@ -9909,6 +10005,33 @@ op {
     explanation: "Use ReciprocalGrad"
   }
 }
+op {
+  name: "Invert"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Flips all bits elementwise."
+  description: "The result will have exactly those bits set, that are not set in `x`. The\ncomputation is performed on the underlying representation of x."
+}
 op {
   name: "InvertPermutation"
   input_arg {
-- 
GitLab


From bceba028aa18871d06a2874e2d48171ee006e00f Mon Sep 17 00:00:00 2001
From: "karl@kubx.ca" <karl@kubx.ca>
Date: Wed, 14 Jun 2017 08:08:26 -0400
Subject: [PATCH 0170/1375] Add input interfaces required by the Java Ops API.

---
 .../src/main/java/org/tensorflow/Input.java   | 61 +++++++++++++++++++
 .../src/main/java/org/tensorflow/Output.java  | 10 ++-
 .../java/org/tensorflow/VariableInput.java    | 35 +++++++++++
 3 files changed, 105 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/Input.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/VariableInput.java

diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
new file mode 100644
index 0000000000..60050b634e
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/Input.java
@@ -0,0 +1,61 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow;
+
+/**
+ * A tensor that can be used as an operand for operation wrappers.
+ *
+ * <p>The {@code Input} interface is at the base of the Java Ops API by allowing any object that
+ * wraps an input tensor to be passed as an operand to an operation.
+ *
+ * <pre>{@code
+ * // Output implements Input.
+ * Output image = ops.image().decodeJpeg(...).image();
+ * ops.math().cast(image, DataType.FLOAT);
+ *
+ * // DecodeJpeg operation also implements Input, returning the image as its output.
+ * ops.math().cast(ops.image().decodeJpeg(...), DataType.FLOAT);
+ * }</pre>
+ *
+ * <p>Additionally, objects implementing {@code Iterable<Input>} can be passed in input to
+ * operations handling a list of tensors as one of its operand.
+ *
+ * <pre>{@code
+ * // List<Output> extends from Iterable<Input>
+ * List<Output> outputs = ops.array().split(...).output();
+ * ops.array().concat(0, outputs);
+ *
+ * // Split implements Iterable<Input>, returning an iterator on the split tensors.
+ * ops.arrays().concat(0, ops.array().split(...));
+ * }</pre>
+ *
+ * <p>The handle of the tensor to be passed in input is encapsulated by an instance of {@code
+ * Output}, which could be retrieved by calling {@link #asOutput()}. Therefore, the {@code Output}
+ * class and the {@code Input} interface are complementary to represent a tensor being passed
+ * between two nodes of the graph.
+ */
+public interface Input {
+
+  /**
+   * Returns the symbolic handle of a tensor.
+   *
+   * <p>This method is called by the Java Ops API to retrieve the symbolic handle of a tensor added
+   * in input to an operation.
+   *
+   * @see {@link OperationBuilder#addInput(Output)}.
+   */
+  Output asOutput();
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index ab128c2b30..687ea413dc 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -20,8 +20,11 @@ package org.tensorflow;
  *
  * <p>An Output is a symbolic handle to a tensor. The value of the Tensor is computed by executing
  * the {@link Operation} in a {@link Session}.
+ *
+ * <p>By implementing the {@link Input} interface, instances of this class could also be passed
+ * directly in input to an operation wrapper when using the Java Ops API.
  */
-public final class Output {
+public final class Output implements Input {
 
   /** Handle to the idx-th output of the Operation {@code op}. */
   public Output(Operation op, int idx) {
@@ -49,6 +52,11 @@ public final class Output {
     return operation.dtype(index);
   }
 
+  @Override
+  public Output asOutput() {
+    return this;
+  }
+
   private final Operation operation;
   private final int index;
 }
diff --git a/tensorflow/java/src/main/java/org/tensorflow/VariableInput.java b/tensorflow/java/src/main/java/org/tensorflow/VariableInput.java
new file mode 100644
index 0000000000..47b7481ff2
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/VariableInput.java
@@ -0,0 +1,35 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow;
+
+/**
+ * A tensor that can be used as an operand for operation wrappers that require variables.
+ *
+ * <p>This interface allows operation wrappers that require a variable tensor enforce it at
+ * compile-time by requiring this interface as an operand. Note that a {@code VariableInput} can
+ * also be used as an {@code Input}, but not the other way around.
+ *
+ * <pre>{@code
+ * VariableInput var = ....;
+ * ops.math.mean(var, ...); // will work
+ * ops.training.applyGradientDescent(var, ...); // will work
+ *
+ * Input input = ...;
+ * ops.math.mean(input, ...); // will work
+ * ops.training.applyGradientDescent(input, ...); // won't work
+ * }</pre>
+ */
+public interface VariableInput extends Input {}
-- 
GitLab


From 7ee712214764dcf21c363b34a5612edb15ea6dab Mon Sep 17 00:00:00 2001
From: "karl@kubx.ca" <karl@kubx.ca>
Date: Fri, 16 Jun 2017 15:16:53 -0400
Subject: [PATCH 0171/1375] Add input interface required by the Ops API (first
 review)

---
 .../src/main/java/org/tensorflow/Input.java   | 39 +++++++------------
 .../src/main/java/org/tensorflow/Output.java  |  2 +-
 .../java/org/tensorflow/VariableInput.java    | 35 -----------------
 3 files changed, 14 insertions(+), 62 deletions(-)
 delete mode 100644 tensorflow/java/src/main/java/org/tensorflow/VariableInput.java

diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
index 60050b634e..dff3a45463 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Input.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Input.java
@@ -16,44 +16,31 @@ limitations under the License.
 package org.tensorflow;
 
 /**
- * A tensor that can be used as an operand for operation wrappers.
+ * Interface implemented by operands of a TensorFlow operation.
  *
- * <p>The {@code Input} interface is at the base of the Java Ops API by allowing any object that
- * wraps an input tensor to be passed as an operand to an operation.
+ * <p>Example usage:
  *
  * <pre>{@code
- * // Output implements Input.
- * Output image = ops.image().decodeJpeg(...).image();
- * ops.math().cast(image, DataType.FLOAT);
+ * // The "decodeJpeg" operation can be used as input to the "cast" operation
+ * Input decodeJpeg = ops.image().decodeJpeg(...);
+ * ops.math().cast(decodeJpeg, DataType.FLOAT);
  *
- * // DecodeJpeg operation also implements Input, returning the image as its output.
- * ops.math().cast(ops.image().decodeJpeg(...), DataType.FLOAT);
- * }</pre>
- *
- * <p>Additionally, objects implementing {@code Iterable<Input>} can be passed in input to
- * operations handling a list of tensors as one of its operand.
+ * // The output "y" of the "unique" operation can be used as input to the "cast" operation
+ * Output y = ops.array().unique(...).y();
+ * ops.math().cast(y, DataType.FLOAT);
  *
- * <pre>{@code
- * // List<Output> extends from Iterable<Input>
- * List<Output> outputs = ops.array().split(...).output();
- * ops.array().concat(0, outputs);
- *
- * // Split implements Iterable<Input>, returning an iterator on the split tensors.
- * ops.arrays().concat(0, ops.array().split(...));
+ * // The "split" operation can be used as input list to the "concat" operation
+ * Iterable<? extends Input> split = ops.array().split(...);
+ * ops.array().concat(0, split);
  * }</pre>
- *
- * <p>The handle of the tensor to be passed in input is encapsulated by an instance of {@code
- * Output}, which could be retrieved by calling {@link #asOutput()}. Therefore, the {@code Output}
- * class and the {@code Input} interface are complementary to represent a tensor being passed
- * between two nodes of the graph.
  */
 public interface Input {
 
   /**
    * Returns the symbolic handle of a tensor.
    *
-   * <p>This method is called by the Java Ops API to retrieve the symbolic handle of a tensor added
-   * in input to an operation.
+   * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
+   * used to obtain a symbolic handle that represents the computation of the input.
    *
    * @see {@link OperationBuilder#addInput(Output)}.
    */
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index 687ea413dc..2e3f8d4eac 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -22,7 +22,7 @@ package org.tensorflow;
  * the {@link Operation} in a {@link Session}.
  *
  * <p>By implementing the {@link Input} interface, instances of this class could also be passed
- * directly in input to an operation wrapper when using the Java Ops API.
+ * directly in input to an operation.
  */
 public final class Output implements Input {
 
diff --git a/tensorflow/java/src/main/java/org/tensorflow/VariableInput.java b/tensorflow/java/src/main/java/org/tensorflow/VariableInput.java
deleted file mode 100644
index 47b7481ff2..0000000000
--- a/tensorflow/java/src/main/java/org/tensorflow/VariableInput.java
+++ /dev/null
@@ -1,35 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-package org.tensorflow;
-
-/**
- * A tensor that can be used as an operand for operation wrappers that require variables.
- *
- * <p>This interface allows operation wrappers that require a variable tensor enforce it at
- * compile-time by requiring this interface as an operand. Note that a {@code VariableInput} can
- * also be used as an {@code Input}, but not the other way around.
- *
- * <pre>{@code
- * VariableInput var = ....;
- * ops.math.mean(var, ...); // will work
- * ops.training.applyGradientDescent(var, ...); // will work
- *
- * Input input = ...;
- * ops.math.mean(input, ...); // will work
- * ops.training.applyGradientDescent(input, ...); // won't work
- * }</pre>
- */
-public interface VariableInput extends Input {}
-- 
GitLab


From 14a71e5ce516aada1be746f466be9ff1530fc718 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 13:10:42 -0700
Subject: [PATCH 0172/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 159265397
---
 tensorflow/go/op/wrappers.go | 72 ++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index ee244f6b68..9e3a4666b9 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -2320,6 +2320,42 @@ func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (aud
 	return op.Output(0), op.Output(1)
 }
 
+// Elementwise computes the bitwise XOR of `x` and `y`.
+//
+// The result will have those bits set, that are different in `x` and `y`. The
+// computation is performed on the underlying representations of `x` and `y`.
+func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "BitwiseXor",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Elementwise computes the bitwise AND of `x` and `y`.
+//
+// The result will have those bits set, that are set in both `x` and `y`. The
+// computation is performed on the underlying representations of `x` and `y`.
+func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "BitwiseAnd",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // AllCandidateSamplerAttr is an optional argument to AllCandidateSampler.
 type AllCandidateSamplerAttr func(optionalAttr)
 
@@ -10675,6 +10711,24 @@ func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_value
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// Elementwise computes the bitwise OR of `x` and `y`.
+//
+// The result will have those bits set, that are set in `x`, `y` or both. The
+// computation is performed on the underlying representations of `x` and `y`.
+func BitwiseOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "BitwiseOr",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
 //
 // This Op does not require `a_indices` be sorted in standard lexicographic order.
@@ -13190,6 +13244,24 @@ func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Outp
 	return scope.AddOperation(opspec)
 }
 
+// Flips all bits elementwise.
+//
+// The result will have exactly those bits set, that are not set in `x`. The
+// computation is performed on the underlying representation of x.
+func Invert(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Invert",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the mean along segments of a tensor.
 //
 // Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-- 
GitLab


From f8cbcc087ef69a20ab85dc55af2496e4db5cc9ae Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Fri, 16 Jun 2017 13:27:18 -0700
Subject: [PATCH 0173/1375] Increase test sizes to combat strange timeouts.

I ran both these tests with 50 repetitions and they never get close to the 1m
limit; but on TAP they go just over a minute (by a few seconds), so setting
their size to medium should help.

PiperOrigin-RevId: 159267430
---
 tensorflow/compiler/tests/BUILD | 2 +-
 tensorflow/contrib/rnn/BUILD    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 8d5f880ede..044857d422 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -324,7 +324,7 @@ tf_xla_py_test(
 
 tf_xla_py_test(
     name = "reverse_ops_test",
-    size = "small",
+    size = "medium",
     srcs = ["reverse_ops_test.py"],
     deps = [
         ":xla_test",
diff --git a/tensorflow/contrib/rnn/BUILD b/tensorflow/contrib/rnn/BUILD
index 8c71977d5a..42db1ad01a 100644
--- a/tensorflow/contrib/rnn/BUILD
+++ b/tensorflow/contrib/rnn/BUILD
@@ -65,7 +65,7 @@ tf_custom_op_py_library(
 
 cuda_py_tests(
     name = "rnn_cell_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/rnn_cell_test.py"],
     additional_deps = [
         ":rnn_py",
-- 
GitLab


From 940d968347eccd4afded06603b4d63eedc721a16 Mon Sep 17 00:00:00 2001
From: Kaarthik Sivashanmugam <ksivas@microsoft.com>
Date: Fri, 16 Jun 2017 14:20:40 -0700
Subject: [PATCH 0174/1375] fix incorrect usage of num_gpus & num_workers
 (#7312)

---
 tensorflow/tools/dist_test/python/mnist_replica.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tensorflow/tools/dist_test/python/mnist_replica.py b/tensorflow/tools/dist_test/python/mnist_replica.py
index f7dbfea7fb..d21385e384 100644
--- a/tensorflow/tools/dist_test/python/mnist_replica.py
+++ b/tensorflow/tools/dist_test/python/mnist_replica.py
@@ -123,8 +123,6 @@ def main(unused_argv):
 
   is_chief = (FLAGS.task_index == 0)
   if FLAGS.num_gpus > 0:
-    if FLAGS.num_gpus < num_workers:
-      raise ValueError("number of gpus is less than number of workers")
     # Avoid gpu allocation conflict: now allocate task_num -> #gpu 
     # for each worker in the corresponding machine
     gpu = (FLAGS.task_index % FLAGS.num_gpus)
-- 
GitLab


From 2a31e4dc8b3e4ae305b10bbd1aac024761f19c6e Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Fri, 16 Jun 2017 14:55:06 -0700
Subject: [PATCH 0175/1375] Reverting chances to non_max_suppression.

---
 .../core/kernels/non_max_suppression_op.cc    | 142 ++++++++++-----
 .../kernels/non_max_suppression_op_test.cc    | 170 +++++++++++++++++-
 2 files changed, 259 insertions(+), 53 deletions(-)

diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index 4d4851c70c..dc95f67ff0 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 
 namespace tensorflow {
+namespace {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 
@@ -89,6 +90,63 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
   return intersection_area / (area_i + area_j - intersection_area);
 }
 
+void DoNonMaxSuppressionOp(OpKernelContext* context,
+                           const Tensor& boxes,
+                           const Tensor& scores,
+                           const Tensor& max_output_size,
+                           const float iou_threshold) {
+  OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
+      errors::InvalidArgument("iou_threshold must be in [0, 1]"));
+  
+  int num_boxes = 0;
+  ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
+  if (!context->status().ok()) {
+    return;
+  }
+
+  const int output_size =
+      std::min(max_output_size.scalar<int>()(), num_boxes);
+  typename TTypes<float, 2>::ConstTensor boxes_data =
+      boxes.tensor<float, 2>();
+
+  std::vector<float> scores_data(num_boxes);
+  std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
+  std::vector<int> sorted_indices;
+  DecreasingArgSort(scores_data, &sorted_indices);
+
+  std::vector<bool> active(num_boxes, true);
+  std::vector<int> selected;
+  int num_active = active.size();
+  for (int i = 0; i < num_boxes; ++i) {
+    if (num_active == 0 || selected.size() >= output_size) break;
+    if (active[i]) {
+      selected.push_back(sorted_indices[i]);
+    } else {
+      continue;
+    }
+    for (int j = i + 1; j < num_boxes; ++j) {
+      if (active[j]) {
+        float iou =
+          ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
+        if (iou > iou_threshold) {
+          active[j] = false;
+          num_active--;
+        }
+      }
+    }
+  }
+
+  // Allocate output tensor
+  Tensor* output = nullptr;
+  TensorShape output_shape({static_cast<int>(selected.size())});
+  OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
+  typename TTypes<int, 1>::Tensor selected_indices_data =
+      output->tensor<int, 1>();
+  std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
+}
+
+} // namespace
+
 template <typename Device>
 class NonMaxSuppressionOp : public OpKernel {
  public:
@@ -98,9 +156,6 @@ class NonMaxSuppressionOp : public OpKernel {
   }
 
   void Compute(OpKernelContext* context) override {
-    OP_REQUIRES(context, iou_threshold_ >= 0 && iou_threshold_ <= 1,
-                errors::InvalidArgument("iou_threshold must be in [0, 1]"));
-
     // boxes: [num_boxes, 4]
     const Tensor& boxes = context->input(0);
     // scores: [num_boxes]
@@ -112,59 +167,48 @@ class NonMaxSuppressionOp : public OpKernel {
         errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                 max_output_size.shape().DebugString()));
 
-    int num_boxes = 0;
-    ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
-    if (!context->status().ok()) {
-      return;
-    }
-
-    const int output_size =
-        std::min(max_output_size.scalar<int>()(), num_boxes);
-    typename TTypes<float, 2>::ConstTensor boxes_data =
-        boxes.tensor<float, 2>();
-
-    std::vector<float> scores_data(num_boxes);
-    std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
-    std::vector<int> sorted_indices;
-    DecreasingArgSort(scores_data, &sorted_indices);
-
-    std::vector<bool> active(num_boxes, true);
-    std::vector<int> selected;
-    int num_active = active.size();
-    for (int i = 0; i < num_boxes; ++i) {
-      if (num_active == 0 || selected.size() >= output_size) break;
-      if (active[i]) {
-        selected.push_back(sorted_indices[i]);
-      } else {
-        continue;
-      }
-      for (int j = i + 1; j < num_boxes; ++j) {
-        if (active[j]) {
-          float iou =
-              ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
-          if (iou > iou_threshold_) {
-            active[j] = false;
-            num_active--;
-          }
-        }
-      }
-    }
-
-    // Allocate output tensor
-    Tensor* output = nullptr;
-    TensorShape output_shape({static_cast<int>(selected.size())});
-    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
-    typename TTypes<int, 1>::Tensor selected_indices_data =
-        output->tensor<int, 1>();
-    std::copy_n(selected.begin(), selected.size(),
-                selected_indices_data.data());
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_);
   }
 
  private:
   float iou_threshold_;
 };
 
+template <typename Device>
+class NonMaxSuppressionV2Op : public OpKernel {
+ public:
+  explicit NonMaxSuppressionV2Op(OpKernelConstruction* context)
+      : OpKernel(context) {
+  }
+
+  void Compute(OpKernelContext* context) override {
+    // boxes: [num_boxes, 4]
+    const Tensor& boxes = context->input(0);
+    // scores: [num_boxes]
+    const Tensor& scores = context->input(1);
+    // max_output_size: scalar
+    const Tensor& max_output_size = context->input(2);
+    OP_REQUIRES(
+        context, TensorShapeUtils::IsScalar(max_output_size.shape()),
+        errors::InvalidArgument("max_output_size must be 0-D, got shape ",
+                                max_output_size.shape().DebugString()));
+    // iou_threshold: scalar
+    const Tensor& iou_threshold = context->input(3);
+    OP_REQUIRES(
+        context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
+        errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
+                                iou_threshold.shape().DebugString()));
+
+    const float iou_threshold_val = iou_threshold.scalar<float>()();
+
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_val);
+  }
+};
+
 REGISTER_KERNEL_BUILDER(Name("NonMaxSuppression").Device(DEVICE_CPU),
                         NonMaxSuppressionOp<CPUDevice>);
 
+REGISTER_KERNEL_BUILDER(Name("NonMaxSuppressionV2").Device(DEVICE_CPU),
+                        NonMaxSuppressionV2Op<CPUDevice>);
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/non_max_suppression_op_test.cc b/tensorflow/core/kernels/non_max_suppression_op_test.cc
index 36bd8e2d14..e0e8c87f95 100644
--- a/tensorflow/core/kernels/non_max_suppression_op_test.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op_test.cc
@@ -141,6 +141,166 @@ TEST_F(NonMaxSuppressionOpTest, TestInconsistentBoxAndScoreShapes) {
   AddInputFromArray<int>(TensorShape({}), {30});
   Status s = RunOpKernel();
 
+  ASSERT_FALSE(s.ok());
+  EXPECT_TRUE(
+              StringPiece(s.ToString()).contains("scores has incompatible shape"))
+    << s;
+}
+
+TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
+  MakeOp(1.2);
+  AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
+  AddInputFromArray<float>(TensorShape({1}), {.9f});
+  AddInputFromArray<int>(TensorShape({}), {3});
+  Status s = RunOpKernel();
+
+  ASSERT_FALSE(s.ok());
+  EXPECT_TRUE(
+              StringPiece(s.ToString()).contains("iou_threshold must be in [0, 1]"))
+    << s;
+}
+
+TEST_F(NonMaxSuppressionOpTest, TestEmptyInput) {
+  MakeOp(.5);
+  AddInputFromArray<float>(TensorShape({0, 4}), {});
+  AddInputFromArray<float>(TensorShape({0}), {});
+  AddInputFromArray<int>(TensorShape({}), {30});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_INT32, TensorShape({0}));
+  test::FillValues<int>(&expected, {});
+  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
+}
+
+//
+// NonMaxSuppressionV2Op Tests
+//
+
+class NonMaxSuppressionV2OpTest : public OpsTestBase {
+ protected:
+  void MakeOp() {
+    TF_EXPECT_OK(NodeDefBuilder("non_max_suppression_op", "NonMaxSuppressionV2")
+                     .Input(FakeInput(DT_FLOAT))
+                     .Input(FakeInput(DT_FLOAT))
+                     .Input(FakeInput(DT_INT32))
+                     .Input(FakeInput(DT_FLOAT))
+                     .Finalize(node_def()));
+    TF_EXPECT_OK(InitOp());
+  }
+};
+
+TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromThreeClusters) {
+  MakeOp();
+  AddInputFromArray<float>(
+      TensorShape({6, 4}),
+      {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
+       0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,   1, 101});
+  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
+  AddInputFromArray<int>(TensorShape({}), {3});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_INT32, TensorShape({3}));
+  test::FillValues<int>(&expected, {3, 0, 5});
+  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
+}
+
+TEST_F(NonMaxSuppressionV2OpTest,
+       TestSelectFromThreeClustersFlippedCoordinates) {
+  MakeOp();
+  AddInputFromArray<float>(TensorShape({6, 4}),
+                           {1, 1,  0, 0,  0, 0.1f,  1, 1.1f,  0, .9f, 1, -0.1f,
+                            0, 10, 1, 11, 1, 10.1f, 0, 11.1f, 1, 101, 0, 100});
+  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
+  AddInputFromArray<int>(TensorShape({}), {3});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_INT32, TensorShape({3}));
+  test::FillValues<int>(&expected, {3, 0, 5});
+  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
+}
+
+TEST_F(NonMaxSuppressionV2OpTest, TestSelectAtMostTwoBoxesFromThreeClusters) {
+  MakeOp();
+  AddInputFromArray<float>(
+      TensorShape({6, 4}),
+      {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
+       0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,   1, 101});
+  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
+  AddInputFromArray<int>(TensorShape({}), {2});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_INT32, TensorShape({2}));
+  test::FillValues<int>(&expected, {3, 0});
+  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
+}
+
+TEST_F(NonMaxSuppressionV2OpTest,
+       TestSelectAtMostThirtyBoxesFromThreeClusters) {
+  MakeOp();
+  AddInputFromArray<float>(
+      TensorShape({6, 4}),
+      {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
+       0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,   1, 101});
+  AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
+  AddInputFromArray<int>(TensorShape({}), {30});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_INT32, TensorShape({3}));
+  test::FillValues<int>(&expected, {3, 0, 5});
+  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
+}
+
+TEST_F(NonMaxSuppressionV2OpTest, TestSelectSingleBox) {
+  MakeOp();
+  AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
+  AddInputFromArray<float>(TensorShape({1}), {.9f});
+  AddInputFromArray<int>(TensorShape({}), {3});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_INT32, TensorShape({1}));
+  test::FillValues<int>(&expected, {0});
+  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
+}
+
+TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromTenIdenticalBoxes) {
+  MakeOp();
+
+  int num_boxes = 10;
+  std::vector<float> corners(num_boxes * 4);
+  std::vector<float> scores(num_boxes);
+  for (int i = 0; i < num_boxes; ++i) {
+    corners[i * 4 + 0] = 0;
+    corners[i * 4 + 1] = 0;
+    corners[i * 4 + 2] = 1;
+    corners[i * 4 + 3] = 1;
+    scores[i] = .9;
+  }
+  AddInputFromArray<float>(TensorShape({num_boxes, 4}), corners);
+  AddInputFromArray<float>(TensorShape({num_boxes}), scores);
+  AddInputFromArray<int>(TensorShape({}), {3});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_INT32, TensorShape({1}));
+  test::FillValues<int>(&expected, {0});
+  test::ExpectTensorEqual<int>(expected, *GetOutput(0));
+}
+
+TEST_F(NonMaxSuppressionV2OpTest, TestInconsistentBoxAndScoreShapes) {
+  MakeOp();
+  AddInputFromArray<float>(
+      TensorShape({6, 4}),
+      {0, 0,  1, 1,  0, 0.1f,  1, 1.1f,  0, -0.1f, 1, 0.9f,
+       0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100,   1, 101});
+  AddInputFromArray<float>(TensorShape({5}), {.9f, .75f, .6f, .95f, .5f});
+  AddInputFromArray<int>(TensorShape({}), {30});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
+  Status s = RunOpKernel();
 
   ASSERT_FALSE(s.ok());
   EXPECT_TRUE(
@@ -148,11 +308,12 @@ TEST_F(NonMaxSuppressionOpTest, TestInconsistentBoxAndScoreShapes) {
       << s;
 }
 
-TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
-  MakeOp(1.2);
+TEST_F(NonMaxSuppressionV2OpTest, TestInvalidIOUThreshold) {
+  MakeOp();
   AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
   AddInputFromArray<float>(TensorShape({1}), {.9f});
   AddInputFromArray<int>(TensorShape({}), {3});
+  AddInputFromArray<float>(TensorShape({}), {1.2f});
   Status s = RunOpKernel();
 
   ASSERT_FALSE(s.ok());
@@ -161,11 +322,12 @@ TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
       << s;
 }
 
-TEST_F(NonMaxSuppressionOpTest, TestEmptyInput) {
-  MakeOp(.5);
+TEST_F(NonMaxSuppressionV2OpTest, TestEmptyInput) {
+  MakeOp();
   AddInputFromArray<float>(TensorShape({0, 4}), {});
   AddInputFromArray<float>(TensorShape({0}), {});
   AddInputFromArray<int>(TensorShape({}), {30});
+  AddInputFromArray<float>(TensorShape({}), {.5f});
   TF_ASSERT_OK(RunOpKernel());
 
   Tensor expected(allocator(), DT_INT32, TensorShape({0}));
-- 
GitLab


From f0ff37b9073f60fae052545796e93fa82dc4604c Mon Sep 17 00:00:00 2001
From: gunan <gunan@google.com>
Date: Fri, 16 Jun 2017 15:23:42 -0700
Subject: [PATCH 0176/1375] Fix #9392 (#10782)

---
 tensorflow/core/platform/cloud/retrying_utils.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/platform/cloud/retrying_utils.cc b/tensorflow/core/platform/cloud/retrying_utils.cc
index 096c77c6e3..99691ecfb9 100644
--- a/tensorflow/core/platform/cloud/retrying_utils.cc
+++ b/tensorflow/core/platform/cloud/retrying_utils.cc
@@ -89,7 +89,7 @@ Status RetryingUtils::DeleteWithRetries(
   bool is_retried = false;
   return RetryingUtils::CallWithRetries(
       [delete_func, &is_retried]() {
-        const auto& status = delete_func();
+        const Status status = delete_func();
         if (is_retried && status.code() == error::NOT_FOUND) {
           return Status::OK();
         }
-- 
GitLab


From b0cd3cfeec5970e987b0946376148b18948ef95f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 15:26:03 -0700
Subject: [PATCH 0177/1375] Fix check for buffer interference with While
 instructions.

The attached test case fails because
`AddWhileSetToColocatedBufferSets` does not take into account the
enforced aliasing between the input value and the result for the While
instruction it wants to add to the co-located set.  It does take care
of the enforced aliasing for the While instructions already present in
the co-located set though, so just do that for the new while
instruction we're try to co-locate as well.

PiperOrigin-RevId: 159284279
---
 .../compiler/xla/service/buffer_assignment.cc | 46 ++++++---
 .../compiler/xla/service/buffer_assignment.h  |  3 +-
 .../xla/service/buffer_assignment_test.cc     | 98 +++++++++++++++++++
 3 files changed, 131 insertions(+), 16 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index f91eb0207a..44b4f4e3d8 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -1074,7 +1074,8 @@ void BufferAssigner::AddSetToColocatedBufferSets(
 // different while instructions.
 void BufferAssigner::AddWhileSetToColocatedBufferSets(
     const std::vector<const LogicalBuffer*>& colocated_set,
-    const LogicalBuffer* while_init_buffer, const HloInstruction* while_hlo,
+    const LogicalBuffer* while_init_buffer,
+    const LogicalBuffer* while_result_buffer, const HloInstruction* while_hlo,
     const HloComputation& computation, const BufferLiveness& buffer_liveness,
     const LogicalBuffer::SizeFunction& buffer_size,
     std::vector<ColocatedBufferSet>* colocated_buffer_sets) {
@@ -1137,16 +1138,30 @@ void BufferAssigner::AddWhileSetToColocatedBufferSets(
       continue;
     }
 
-    // Skip predecessor set if the live range of any predecessor buffers
-    // overlaps with 'while_init_buffer'. Note that tuple element buffer
-    // forwarding can cause the same buffer to appear on both sides of the
-    // interference comparison below.
-    if (std::any_of(
-            predecessor_while_buffers.begin(), predecessor_while_buffers.end(),
-            [while_init_buffer, &buffer_liveness](const LogicalBuffer* buffer) {
-              return while_init_buffer->id() != buffer->id() &&
-                     buffer_liveness.MayInterfere(*while_init_buffer, *buffer);
-            })) {
+    // Skip predecessor set if the live range of any predecessor
+    // buffers overlaps with 'while_init_buffer' or
+    // 'while_result_buffer' (we need to check both since they're
+    // aliased together, but the points-to analysis is unaware of this
+    // aliasing). Note that tuple element buffer forwarding can cause
+    // the same buffer to appear on both sides of the interference
+    // comparison below.
+    auto may_interfere_with_init_or_result = [&](const LogicalBuffer* buffer) {
+      if (while_init_buffer->id() != buffer->id() &&
+          buffer_liveness.MayInterfere(*while_init_buffer, *buffer)) {
+        return true;
+      }
+
+      if (while_result_buffer->id() != buffer->id() &&
+          buffer_liveness.MayInterfere(*while_result_buffer, *buffer)) {
+        return true;
+      }
+
+      return false;
+    };
+
+    if (std::any_of(predecessor_while_buffers.begin(),
+                    predecessor_while_buffers.end(),
+                    may_interfere_with_init_or_result)) {
       continue;
     }
 
@@ -1209,8 +1224,8 @@ void BufferAssigner::BuildColocatedBufferSets(
                   AddBufferToColocatedSet(while_hlo->operand(0), index,
                                           points_to_analysis, &colocated_set);
               // Add while.result.
-              AddBufferToColocatedSet(while_hlo, index, points_to_analysis,
-                                      &colocated_set);
+              auto* result_buffer = AddBufferToColocatedSet(
+                  while_hlo, index, points_to_analysis, &colocated_set);
               // Add while.cond.parameter.
               AddBufferToColocatedSet(
                   while_hlo->while_condition()->parameter_instruction(0), index,
@@ -1224,8 +1239,9 @@ void BufferAssigner::BuildColocatedBufferSets(
                   while_hlo->while_body()->root_instruction(), index,
                   points_to_analysis, &colocated_set);
               AddWhileSetToColocatedBufferSets(
-                  colocated_set, init_buffer, while_hlo, *computation,
-                  buffer_liveness, buffer_size, colocated_buffer_sets);
+                  colocated_set, init_buffer, result_buffer, while_hlo,
+                  *computation, buffer_liveness, buffer_size,
+                  colocated_buffer_sets);
             });
       } else if (opcode == HloOpcode::kCall) {
         const HloInstruction* call_hlo = instruction;
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h
index b3933f11c1..dd84b06b77 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.h
+++ b/tensorflow/compiler/xla/service/buffer_assignment.h
@@ -511,7 +511,8 @@ class BufferAssigner {
   // colocated buffers for while instructions.
   void AddWhileSetToColocatedBufferSets(
       const std::vector<const LogicalBuffer*>& colocated_set,
-      const LogicalBuffer* while_init_buffer, const HloInstruction* while_hlo,
+      const LogicalBuffer* while_init_buffer,
+      const LogicalBuffer* while_result_buffer, const HloInstruction* while_hlo,
       const HloComputation& computation, const BufferLiveness& buffer_liveness,
       const LogicalBuffer::SizeFunction& buffer_size,
       std::vector<ColocatedBufferSet>* colocated_buffer_sets);
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 566fadecc4..77ea39b24b 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -1565,6 +1565,104 @@ TEST_F(BufferAssignmentTest, TwoCalls) {
   EXPECT_TRUE(BuffersDistinct({call1}, {call2}, *assignment));
 }
 
+static bool IsPostOrderTraversal(
+    const std::vector<const HloInstruction*>& sequence) {
+  tensorflow::gtl::FlatSet<const HloInstruction*> seen_so_far;
+  auto has_not_been_seen_yet = [&](const HloInstruction* instruction) {
+    return seen_so_far.count(instruction) == 0;
+  };
+
+  for (auto instruction : sequence) {
+    if (std::any_of(instruction->operands().begin(),
+                    instruction->operands().end(), has_not_been_seen_yet) ||
+        std::any_of(instruction->control_predecessors().begin(),
+                    instruction->control_predecessors().end(),
+                    has_not_been_seen_yet)) {
+      return false;  // Not a post order.
+    }
+    if (!seen_so_far.insert(instruction).second) {
+      return false;  // Not a "traversal".
+    }
+  }
+
+  return true;
+}
+
+TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) {
+  auto module = MakeUnique<HloModule>(TestName());
+  auto builder = HloComputation::Builder(TestName());
+
+  auto zero = builder.AddInstruction(
+      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0)));
+  auto one = builder.AddInstruction(
+      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+
+  auto input0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, data_shape_, "input0"));
+  auto weights0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, data_shape_, "weights0"));
+  auto output0 = builder.AddInstruction(
+      HloInstruction::CreateBroadcast(data_shape_, zero, {1}));
+
+  auto input1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(2, data_shape_, "input1"));
+  auto weights1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(3, data_shape_, "weights1"));
+  auto output1 = builder.AddInstruction(
+      HloInstruction::CreateBroadcast(data_shape_, one, {1}));
+
+  auto cond =
+      module->AddEmbeddedComputation(BuildWhileConditionComputation("cond"));
+  auto body = module->AddEmbeddedComputation(BuildWhileBodyComputation("body"));
+
+  auto tuple0 = builder.AddInstruction(
+      HloInstruction::CreateTuple({input0, weights0, output0}));
+  auto tuple1 = builder.AddInstruction(
+      HloInstruction::CreateTuple({input1, weights1, output1}));
+
+  auto while0 = builder.AddInstruction(
+      HloInstruction::CreateWhile(loop_state_shape_, cond, body, tuple0));
+  auto while1 = builder.AddInstruction(
+      HloInstruction::CreateWhile(loop_state_shape_, cond, body, tuple1));
+
+  auto root_add = builder.AddInstruction(HloInstruction::CreateBinary(
+      while0->shape(), HloOpcode::kAdd, while0, while1));
+  module->AddEntryComputation(builder.Build());
+
+  RunCopyInsertion(module.get());
+
+  {
+    FlattenCallGraph flatten;
+    TF_ASSIGN_OR_ASSERT_OK(bool result, flatten.Run(module.get()));
+    EXPECT_TRUE(result);
+  }
+
+  auto sequence =
+      CreateMemoryMinimizingSequence(*module, ByteSizeOf).ConsumeValueOrDie();
+
+  // To trigger b/38494731, we want a specific Hlo sequence for the
+  // root computation, so we overwrite that entry with a manually
+  // crafted sequence.
+  std::vector<const HloInstruction*> sequence_for_buffer_assigment = {
+      input1,   weights1, one,     output1, tuple1, while1,  input0,
+      weights0, zero,     output0, tuple0,  while0, root_add};
+
+  // If this CHECK fails, we constructed a bogus sequence above and
+  // this test itself is buggy.
+  ASSERT_TRUE(IsPostOrderTraversal(sequence_for_buffer_assigment));
+
+  sequence[module->entry_computation()] =
+      std::move(sequence_for_buffer_assigment);
+
+  auto assignment = BufferAssigner::Run(module.get(),
+                                        MakeUnique<SequentialHloOrdering>(
+                                            module.get(), sequence),
+                                        ByteSizeOf, 1)
+                        .ConsumeValueOrDie();
+
+  EXPECT_TRUE(BuffersDistinct({while0}, {while1}, *assignment));
+}
+
 // Test buffer assignment for while nodes with multiple uses.
 // TODO(b/37245345): Fix buffer assignment for this case.
 TEST_F(WhileBufferAssignmentTest, DISABLED_TwoWhiles) {
-- 
GitLab


From 1fe430cd91ba0df45c99d142c4e57b71ffc124ca Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Fri, 16 Jun 2017 15:53:33 -0700
Subject: [PATCH 0178/1375] Reverting Jonathan's commit for the remaining 4
 files in his merge.

---
 tensorflow/core/ops/image_ops.cc              | 47 +++++++++++++++++--
 tensorflow/python/ops/image_ops.py            |  1 +
 tensorflow/python/ops/image_ops_impl.py       |  1 +
 .../tools/api/golden/tensorflow.image.pbtxt   |  4 ++
 4 files changed, 49 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index 0f60677713..68f4863026 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -1030,11 +1030,50 @@ method: A string specifying the interpolation method. Only 'bilinear' is
 // --------------------------------------------------------------------------
 
 REGISTER_OP("NonMaxSuppression")
+  .Input("boxes: float")
+  .Input("scores: float")
+  .Input("max_output_size: int32")
+  .Output("selected_indices: int32")
+  .Attr("iou_threshold: float = 0.5")
+  .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+  .Doc(R"doc(
+Greedily selects a subset of bounding boxes in descending order of score,
+pruning away boxes that have high intersection-over-union (IOU) overlap
+with previously selected boxes.  Bounding boxes are supplied as
+[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+diagonal pair of box corners and the coordinates can be provided as normalized
+(i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
+is agnostic to where the origin is in the coordinate system.  Note that this
+algorithm is invariant to orthogonal transformations and translations
+of the coordinate system; thus translating or reflections of the coordinate
+system result in the same boxes being selected by the algorithm.
+The output of this operation is a set of integers indexing into the input
+collection of bounding boxes representing the selected boxes.  The bounding
+box coordinates corresponding to the selected indices can then be obtained
+using the `tf.gather operation`.  For example:
+  selected_indices = tf.image.non_max_suppression(
+      boxes, scores, max_output_size, iou_threshold)
+  selected_boxes = tf.gather(boxes, selected_indices)
+boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
+scores: A 1-D float tensor of shape `[num_boxes]` representing a single
+  score corresponding to each box (each row of boxes).
+max_output_size: A scalar integer tensor representing the maximum number of
+  boxes to be selected by non max suppression.
+iou_threshold: A float representing the threshold for deciding whether boxes
+  overlap too much with respect to IOU.
+selected_indices: A 1-D integer tensor of shape `[M]` representing the selected
+  indices from the boxes tensor, where `M <= max_output_size`.
+)doc");
+
+REGISTER_OP("NonMaxSuppressionV2")
     .Input("boxes: float")
     .Input("scores: float")
     .Input("max_output_size: int32")
+    .Input("iou_threshold: float")
     .Output("selected_indices: int32")
-    .Attr("iou_threshold: float = 0.5")
     .SetShapeFn([](InferenceContext* c) {
       c->set_output(0, c->Vector(c->UnknownDim()));
       return Status::OK();
@@ -1056,7 +1095,7 @@ collection of bounding boxes representing the selected boxes.  The bounding
 box coordinates corresponding to the selected indices can then be obtained
 using the `tf.gather operation`.  For example:
 
-  selected_indices = tf.image.non_max_suppression(
+  selected_indices = tf.image.non_max_suppression_v2(
       boxes, scores, max_output_size, iou_threshold)
   selected_boxes = tf.gather(boxes, selected_indices)
 
@@ -1065,8 +1104,8 @@ scores: A 1-D float tensor of shape `[num_boxes]` representing a single
   score corresponding to each box (each row of boxes).
 max_output_size: A scalar integer tensor representing the maximum number of
   boxes to be selected by non max suppression.
-iou_threshold: A float representing the threshold for deciding whether boxes
-  overlap too much with respect to IOU.
+iou_threshold: A 0-D float tensor representing the threshold for deciding whether
+  boxes overlap too much with respect to IOU.
 selected_indices: A 1-D integer tensor of shape `[M]` representing the selected
   indices from the boxes tensor, where `M <= max_output_size`.
 )doc");
diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py
index 75c67dcb3c..51d0276140 100644
--- a/tensorflow/python/ops/image_ops.py
+++ b/tensorflow/python/ops/image_ops.py
@@ -60,6 +60,7 @@ See the @{$python/image} guide.
 @@per_image_standardization
 @@draw_bounding_boxes
 @@non_max_suppression
+@@non_max_suppression_v2
 @@sample_distorted_bounding_box
 @@total_variation
 """
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 73e2caf23e..65a1399c5b 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -52,6 +52,7 @@ ops.NotDifferentiable('SampleDistortedBoundingBox')
 # latent bugs here.
 ops.NotDifferentiable('ExtractGlimpse')
 ops.NotDifferentiable('NonMaxSuppression')
+ops.NotDifferentiable('NonMaxSuppressionV2')
 
 
 def _assert(cond, ex_type, msg):
diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
index 8f7790f299..93257c84a1 100644
--- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
@@ -96,6 +96,10 @@ tf_module {
     name: "non_max_suppression"
     argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
+  member_method {
+    name: "non_max_suppression_v2"
+    argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "pad_to_bounding_box"
     argspec: "args=[\'image\', \'offset_height\', \'offset_width\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None"
-- 
GitLab


From 6a2a767c1e1ae7b4162e91fafccbddf6581bf750 Mon Sep 17 00:00:00 2001
From: Toby Boyd <tobyboyd@google.com>
Date: Fri, 16 Jun 2017 16:03:42 -0700
Subject: [PATCH 0179/1375] Update performance_guide.md (#10781)

* Update performance_guide.md

* Update performance_guide.md
---
 tensorflow/docs_src/performance/performance_guide.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index 07c5d3087f..f8e247b8bc 100644
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -52,7 +52,8 @@ bazel build -c opt --copt=-march="broadwell" --config=cuda //tensorflow/tools/pi
     (pascal): 6.2, Titan X (maxwell): 5.2, and K80: 3.7.
 *   Install the latest CUDA platform and cuDNN libraries.
 *   Make sure to use a version of gcc that supports all of the optimizations of
-    the target CPU. The recommended minimum gcc version is 4.8.3.
+    the target CPU. The recommended minimum gcc version is 4.8.3.  On OS X upgrade
+    to the latest Xcode version and use the version of clang that comes with Xcode.
 *   TensorFlow checks on startup whether it has been compiled with the
     optimizations available on the CPU. If the optimizations are not included,
     TensorFlow will emit warnings, e.g. AVX, AVX2, and FMA instructions not
-- 
GitLab


From 8d29be57f7058e8f750d3ef7f65e0576fb2fde9f Mon Sep 17 00:00:00 2001
From: Toby Boyd <tobyboyd@google.com>
Date: Fri, 16 Jun 2017 16:03:54 -0700
Subject: [PATCH 0180/1375] Update performance_guide.md (#10783)

---
 tensorflow/docs_src/performance/performance_guide.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index f8e247b8bc..a5508ac23e 100644
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -123,6 +123,11 @@ format.
 The best practice is to build models that work with both `NCHW` and `NHWC` as it
 is common to train using `NCHW` on GPU, and then do inference with NHWC on CPU.
 
+There are edge cases where `NCHW` can be slower on GPU than `NHWC`. One
+[case](https://github.com/tensorflow/tensorflow/issues/7551#issuecomment-280421351)
+is using non-fused batch norm on WRN-16-4 without dropout. In that case using
+fused batch norm, which is also recommended, is the optimal solution.
+
 The very brief history of these two formats is that TensorFlow started by using
 `NHWC` because it was a little faster on CPUs. Then the TensorFlow team
 discovered that `NCHW` performs better when using the NVIDIA cuDNN library.  The
-- 
GitLab


From c81484b5dad8327e1158ae48c9e2cc8526a27313 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 16 Jun 2017 16:02:39 -0700
Subject: [PATCH 0181/1375] tfdbg: prevent curses UI from crashing during rapid
 resizing

Under rapid resizing, pad.refresh() and addstr() sometimes throws curses.error.

PiperOrigin-RevId: 159288309
---
 tensorflow/python/debug/cli/curses_ui.py | 42 +++++++++++++++---------
 1 file changed, 26 insertions(+), 16 deletions(-)

diff --git a/tensorflow/python/debug/cli/curses_ui.py b/tensorflow/python/debug/cli/curses_ui.py
index 6a571c097e..498e346393 100644
--- a/tensorflow/python/debug/cli/curses_ui.py
+++ b/tensorflow/python/debug/cli/curses_ui.py
@@ -513,6 +513,18 @@ class CursesUI(base_ui.BaseUI):
   def get_help(self):
     return self._command_handler_registry.get_help()
 
+  def _addstr(self, *args):
+    try:
+      self._stdscr.addstr(*args)
+    except curses.error:
+      pass
+
+  def _refresh_pad(self, pad, *args):
+    try:
+      pad.refresh(*args)
+    except curses.error:
+      pass
+
   def _screen_create_command_textbox(self, existing_command=None):
     """Create command textbox on screen.
 
@@ -522,8 +534,8 @@ class CursesUI(base_ui.BaseUI):
     """
 
     # Display the tfdbg prompt.
-    self._stdscr.addstr(self._max_y - self._command_textbox_height, 0,
-                        self.CLI_PROMPT, curses.A_BOLD)
+    self._addstr(self._max_y - self._command_textbox_height, 0,
+                 self.CLI_PROMPT, curses.A_BOLD)
     self._stdscr.refresh()
 
     self._command_window.clear()
@@ -948,7 +960,7 @@ class CursesUI(base_ui.BaseUI):
     color_pair = (self._default_color_pair if color is None else
                   self._color_pairs[color])
 
-    self._stdscr.addstr(row, 0, line, color_pair | attr)
+    self._addstr(row, 0, line, color_pair | attr)
     self._screen_refresh()
 
   def _screen_new_output_pad(self, rows, cols):
@@ -1235,10 +1247,9 @@ class CursesUI(base_ui.BaseUI):
   def _screen_scroll_output_pad(self, pad, viewport_top, viewport_left,
                                 screen_location_top, screen_location_left,
                                 screen_location_bottom, screen_location_right):
-    pad.refresh(viewport_top, viewport_left, screen_location_top,
-                screen_location_left, screen_location_bottom,
-                screen_location_right)
-
+    self._refresh_pad(pad, viewport_top, viewport_left, screen_location_top,
+                      screen_location_left, screen_location_bottom,
+                      screen_location_right)
     self._scroll_bar = ScrollBar(
         self._max_x - 2,
         3,
@@ -1249,9 +1260,9 @@ class CursesUI(base_ui.BaseUI):
 
     (scroll_pad, _, _) = self._display_lines(
         self._scroll_bar.layout(), self._output_num_rows - 1)
-    scroll_pad.refresh(
-        0, 0, self._output_top_row + 1, self._max_x - 2,
-        self._output_num_rows + 1, self._max_x - 1)
+    self._refresh_pad(scroll_pad, 0, 0, self._output_top_row + 1,
+                      self._max_x - 2, self._output_num_rows + 1,
+                      self._max_x - 1)
 
   def _scroll_output(self, direction, line_index=None):
     """Scroll the output pad.
@@ -1332,15 +1343,14 @@ class CursesUI(base_ui.BaseUI):
 
   def _screen_render_nav_bar(self):
     if self._nav_bar_pad:
-      self._nav_bar_pad.refresh(0, 0, self._nav_bar_row, 0,
-                                self._output_pad_screen_location.top,
-                                self._max_x)
+      self._refresh_pad(self._nav_bar_pad, 0, 0, self._nav_bar_row, 0,
+                        self._output_pad_screen_location.top, self._max_x)
 
   def _screen_render_menu_pad(self):
     if self._main_menu_pad:
-      self._main_menu_pad.refresh(0, 0, self._output_pad_screen_location.top, 0,
-                                  self._output_pad_screen_location.top,
-                                  self._max_x)
+      self._refresh_pad(
+          self._main_menu_pad, 0, 0, self._output_pad_screen_location.top, 0,
+          self._output_pad_screen_location.top, self._max_x)
 
   def _compile_ui_status_summary(self):
     """Compile status summary about this Curses UI instance.
-- 
GitLab


From 5d906eaa86781da4e0ddfeeb0f69a78a5574b390 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 16:09:12 -0700
Subject: [PATCH 0182/1375] VirtualCluster constructor takes a node_estimator
 pointer.

PiperOrigin-RevId: 159289065
---
 tensorflow/core/grappler/clusters/virtual_cluster.cc | 6 ++++++
 tensorflow/core/grappler/clusters/virtual_cluster.h  | 2 ++
 2 files changed, 8 insertions(+)

diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc
index 30eaccb801..32d9be9aee 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster.cc
+++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc
@@ -29,6 +29,12 @@ VirtualCluster::VirtualCluster(
   devices_ = devices;
 }
 
+VirtualCluster::VirtualCluster(
+    const std::unordered_map<string, DeviceProperties>& devices,
+    OpLevelCostEstimator* node_estimator)
+    : Cluster(0), node_estimator_(node_estimator) {
+  devices_ = devices;
+}
 VirtualCluster::~VirtualCluster() {}
 
 Status VirtualCluster::Provision() { return Status::OK(); }
diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.h b/tensorflow/core/grappler/clusters/virtual_cluster.h
index 452c6f7c8e..a74911cb23 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster.h
+++ b/tensorflow/core/grappler/clusters/virtual_cluster.h
@@ -30,6 +30,8 @@ namespace grappler {
 class VirtualCluster : public Cluster {
  public:
   VirtualCluster(const std::unordered_map<string, DeviceProperties>& devices);
+  VirtualCluster(const std::unordered_map<string, DeviceProperties>& devices,
+                 OpLevelCostEstimator* node_estimator);
 
   ~VirtualCluster() override;
 
-- 
GitLab


From 226b193e709bd513400f2d74020b02d90cd3a0a0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 16:09:35 -0700
Subject: [PATCH 0183/1375] Fix a typo in a comment; NFC

PiperOrigin-RevId: 159289106
---
 tensorflow/compiler/xla/service/buffer_assignment_test.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 77ea39b24b..ddfb847c5d 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -1647,8 +1647,8 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) {
       input1,   weights1, one,     output1, tuple1, while1,  input0,
       weights0, zero,     output0, tuple0,  while0, root_add};
 
-  // If this CHECK fails, we constructed a bogus sequence above and
-  // this test itself is buggy.
+  // If this ASSERT_TRUE fails, we constructed a bogus sequence above
+  // and this test itself is buggy.
   ASSERT_TRUE(IsPostOrderTraversal(sequence_for_buffer_assigment));
 
   sequence[module->entry_computation()] =
-- 
GitLab


From df609c9de4ea0cae0fb1d41893b8071d67bd6bb2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 16:14:33 -0700
Subject: [PATCH 0184/1375] Allow explict "gpu" in backends without failing if
 CUDA is not enabled.

PiperOrigin-RevId: 159289583
---
 tensorflow/compiler/xla/tests/build_defs.bzl | 32 ++++++++++++++------
 1 file changed, 23 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/build_defs.bzl b/tensorflow/compiler/xla/tests/build_defs.bzl
index 1f61743451..50edd8ea5b 100644
--- a/tensorflow/compiler/xla/tests/build_defs.bzl
+++ b/tensorflow/compiler/xla/tests/build_defs.bzl
@@ -2,11 +2,25 @@
 
 load("@local_config_cuda//cuda:build_defs.bzl", "cuda_is_configured")
 
-def all_backends():
+all_backends = ["cpu", "cpu_parallel", "gpu"]
+
+def filter_backends(backends):
+  """Removes "gpu" from a backend list if CUDA is not enabled.
+
+  This allows us to simply hardcode lists including "gpu" here and in the
+  BUILD file, without causing failures when CUDA isn't enabled.'
+
+  Args:
+    backends: A list of backends to filter.
+
+  Returns:
+    The filtered list of backends.
+  """
   if cuda_is_configured():
-    return ["cpu", "cpu_parallel", "gpu"]
+    return backends
   else:
-    return ["cpu", "cpu_parallel"]
+    return [backend for backend in backends if backend != "gpu"]
+
 
 def xla_test(name,
              srcs,
@@ -81,7 +95,7 @@ def xla_test(name,
   """
   test_names = []
   if not backends:
-    backends = all_backends()
+    backends = all_backends
 
   native.cc_library(
       name="%s_lib" % name,
@@ -91,7 +105,7 @@ def xla_test(name,
       deps=deps + ["//tensorflow/compiler/xla/tests:test_macros_header"],
   )
 
-  for backend in backends:
+  for backend in filter_backends(backends):
     test_name = "%s_%s" % (name, backend)
     this_backend_tags = ["xla_%s" % backend]
     this_backend_copts = []
@@ -127,16 +141,16 @@ def xla_test(name,
 
 def generate_backend_suites(backends=[]):
   if not backends:
-    backends = all_backends()
-  for backend in backends:
+    backends = all_backends
+  for backend in filter_backends(backends):
     native.test_suite(name="%s_tests" % backend,
                       tags = ["xla_%s" % backend])
 
 
 def generate_backend_test_macros(backends=[]):
   if not backends:
-    backends = all_backends()
-  for backend in backends:
+    backends = all_backends
+  for backend in filter_backends(backends):
     native.cc_library(
         name="test_macros_%s" % backend,
         testonly = True,
-- 
GitLab


From 2473505b70f03838fef4ae6108387ba66d443d62 Mon Sep 17 00:00:00 2001
From: Blake Hechtman <blakehechtman@google.com>
Date: Fri, 16 Jun 2017 16:18:44 -0700
Subject: [PATCH 0185/1375] Adjust rematerialization cost function to only be
 the inverse of its benefit (total_memory/saved_memory).

PiperOrigin-RevId: 159290105
---
 .../xla/service/hlo_rematerialization.cc      | 57 +++++--------------
 .../xla/service/hlo_rematerialization.h       |  6 +-
 2 files changed, 15 insertions(+), 48 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
index 2c1b0fff4e..fb6d8674b6 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
@@ -58,9 +58,8 @@ bool IsRematerializable(const HloInstruction* instruction) {
     return false;
   }
 
-  // Don't rematerialize instructions with side effects, those with a cost that
-  // might not be captured by HloCostAnalysis, or instructions which cannot be
-  // cloned safely.
+  // Don't rematerialize instructions with side effects or instructions which
+  // cannot be cloned safely.
   switch (instruction->opcode()) {
     case HloOpcode::kCall:
     case HloOpcode::kConstant:
@@ -802,23 +801,14 @@ bool MemoryUsageTracker::Check() const {
 // Computes and returns the cost of rematerializing the given instruction.
 // Cost per rematerialized instruction is defined as:
 //
-// (flop_count + transcendental_count + element_count) / memory_reduced
+// memory_limit_bytes / memory_reduced
 //
-//   flop_count: from HloCostAnalysis
-//   transcendental_count: from HloCostAnalysis
-//   element_count: number of elements accessed in operands and output of
-//     instruction
-//   memory_reduced: The memory usage reduced by rematerializing the
-//     instruction.
-//
-// This is a rough estimate of the extra execution time per byte saved by
-// rematerializing this instruction for its remaining uses. In general, we
-// want the most memory saving for the least latency penalty which is captured
-// by this heuristic.
+// The idea is to choose the operation that will save the most memory for
+// rematerialization and do not worry about how much the compute costs since
+// running out of memory is more harmful than taking longer to get the answer.
 int64 RematerializationCost(const HloInstruction* instruction,
                             const MemoryUsageTracker& memory_tracker,
-                            const HloCostAnalysis& cost_analysis,
-                            int64 memory_reduced) {
+                            int64 memory_reduced, int64 memory_limit_bytes) {
   // If none of the users of 'instruction' have been placed in the sequence (as
   // tracked by memory_tracker), then rematerialization of 'instruction' is a
   // zero-cost move of 'instruction' in the sequence.
@@ -830,22 +820,8 @@ int64 RematerializationCost(const HloInstruction* instruction,
   }
 
   CHECK_GT(memory_reduced, 0);
-  const int64 bytes_accessed = cost_analysis.bytes_accessed(*instruction);
-  const int64 elements_accessed =
-      ShapeUtil::IsTuple(instruction->shape())
-          ? bytes_accessed
-          : bytes_accessed / ShapeUtil::ByteSizeOfPrimitiveType(
-                                 instruction->shape().element_type());
-
-  // Multiply by 256 to improve precision of cost. Without this factor,
-  // many instructions such as many elementwise instructions would have
-  // zero cost because the bytes reduced can be several times greater than
-  // the element count.
-  return 256 *
-         (cost_analysis.flop_count(*instruction) +
-          cost_analysis.transcendental_count(*instruction) +
-          elements_accessed) /
-         memory_reduced;
+  // Return the inverse of the benefit of rematerialization.
+  return memory_limit_bytes / memory_reduced;
 }
 
 // Selects and returns the best candidate instruction for rematerialization.
@@ -856,8 +832,8 @@ int64 RematerializationCost(const HloInstruction* instruction,
 HloInstruction* PickRematerializationCandidate(
     const MemoryUsageTracker& memory_tracker,
     const InstructionList& instruction_list,
-    const HloCostAnalysis& cost_analysis,
-    const tensorflow::gtl::FlatSet<const HloInstruction*>& blacklist) {
+    const tensorflow::gtl::FlatSet<const HloInstruction*>& blacklist,
+    int64 memory_limit_bytes) {
   HloInstruction* best = nullptr;
   int64 best_cost = 0;
 
@@ -891,12 +867,12 @@ HloInstruction* PickRematerializationCandidate(
 
     if (memory_reduced <= 0) {
       VLOG(5) << "candidate " << candidate->name()
-              << " memory reduced = " << memory_reduced << " <= 0";
+              << " memory reduced = " << memory_reduced << " <=  0";
       continue;
     }
 
     const int cost = RematerializationCost(candidate, memory_tracker,
-                                           cost_analysis, memory_reduced);
+                                           memory_reduced, memory_limit_bytes);
 
     VLOG(5) << "candidate " << candidate->name() << ", memory reduced "
             << memory_reduced << ", cost per byte " << cost;
@@ -1011,7 +987,7 @@ StatusOr<bool> HloRematerialization::RematerializeComputation(
               << ", limit is " << HumanReadableNumBytes(memory_limit_bytes);
 
       HloInstruction* best = PickRematerializationCandidate(
-          memory_tracker, instruction_list, cost_analysis_, blacklist);
+          memory_tracker, instruction_list, blacklist, memory_limit_bytes);
 
       if (best == nullptr) {
         VLOG(3) << "Unable to find rematerialization candidate at program "
@@ -1211,11 +1187,6 @@ StatusOr<bool> HloRematerialization::Run(
   VLOG(1) << "Peak memory usage of module (before): "
           << HumanReadableNumBytes(before_peak_memory);
 
-  // Run cost analysis. Operation cost is used in the heuristic for selecting
-  // instructions for rematerialization.
-  TF_RETURN_IF_ERROR(
-      module->entry_computation()->root_instruction()->Accept(&cost_analysis_));
-
   // Subcomputations called by the entry computation will also be
   // rematerialized.
   TF_ASSIGN_OR_RETURN(bool changed, RematerializeComputation(
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.h b/tensorflow/compiler/xla/service/hlo_rematerialization.h
index 1693f93183..42c279d440 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization.h
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization.h
@@ -18,7 +18,6 @@
 #include "tensorflow/compiler/xla/service/buffer_liveness.h"
 #include "tensorflow/compiler/xla/service/call_graph.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
-#include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
@@ -61,7 +60,7 @@ class HloRematerialization {
 
  protected:
   HloRematerialization(const ShapeSizeFunction& size_function)
-      : size_function_(size_function), cost_analysis_(size_function_) {}
+      : size_function_(size_function) {}
   ~HloRematerialization() {}
 
   // Runs rematerialization on the given module. Returns whether the module was
@@ -100,9 +99,6 @@ class HloRematerialization {
   // Call graph of the hlo_module.
   std::unique_ptr<CallGraph> call_graph_;
 
-  // Analysis used for computing the rematerialization cost of instructions.
-  HloCostAnalysis cost_analysis_;
-
   // The peak memory usage of each computation. The map contains only those
   // computations called from sequential context
   // (CallContext::kSequential). These values are updated as rematerialization
-- 
GitLab


From 8e7c2c180c4b75768c707d66c9080ee06ee72773 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 16:34:56 -0700
Subject: [PATCH 0186/1375] Cleanup and build fix of hexagon controller

PiperOrigin-RevId: 159291812
---
 .../src_impl/graph_functions_wrapper.c        | 119 +++++++-----------
 .../src_impl/hexagon_controller.c             | 119 ++++++++----------
 .../src_impl/include/hexagon_controller.h     |   2 -
 .../src_log/include/tfm_log.h                 |   5 +
 4 files changed, 107 insertions(+), 138 deletions(-)

diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c
index d83b58dc6b..f6a38fe8a9 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/graph_functions_wrapper.c
@@ -52,21 +52,21 @@ static enum InceptionVersion s_inception_version = INCEPTION_V3;
 /////////////////////////////////////////////////
 // file local functions
 
-static const char *ConvertGraphInfoIdToName(unsigned int id) {
+static const char* ConvertGraphInfoIdToName(unsigned int id) {
   // TODO(satok): implement
   return "?";
 }
 
-static const char *ConvertGraphInfoIdToOpName(unsigned int id) {
+static const char* ConvertGraphInfoIdToOpName(unsigned int id) {
   // TODO(satok): implement
   return "?";
 }
 
 /////////////////////////////////////////////////
 // file local utilities
-static uint32_t FindMaxIdxWithExcludeList(
-    const float *data, uint32_t entries, const int exclude_size,
-    const int* exclude_idx) {
+static uint32_t FindMaxIdxWithExcludeList(const float* data, uint32_t entries,
+                                          const int exclude_size,
+                                          const int* exclude_idx) {
   int i;
   float maxval = data[0];
   int maxidx = 0;
@@ -94,7 +94,7 @@ static uint32_t FindMaxIdx(const float* data, uint32_t entries) {
 }
 
 void hexagon_controller_PrintMaxNIdx(const float* data, const uint32_t entries,
-                         const int n, int* out_ranking) {
+                                     const int n, int* out_ranking) {
   if (DUMP_OUTPUT) {
     for (int i = 0; i < entries; ++i) {
       TFMLOGD("%d: val = %f", i, data[i]);
@@ -123,9 +123,9 @@ static inline unsigned long long int GetCounter(hexagon_nn_perfinfo s) {
   return ret;
 }
 
-static int CompareCycle(const void *va, const void *vb) {
-  const hexagon_nn_perfinfo *a = va;
-  const hexagon_nn_perfinfo *b = vb;
+static int CompareCycle(const void* va, const void* vb) {
+  const hexagon_nn_perfinfo* a = va;
+  const hexagon_nn_perfinfo* b = vb;
   unsigned long long int acount = GetCounter(*a);
   unsigned long long int bcount = GetCounter(*b);
   if (acount < bcount) {
@@ -142,8 +142,6 @@ static int CompareCycle(const void *va, const void *vb) {
 
 uint32_t hexagon_controller_InstantiateGraph() {
   const uint32_t nn_id = hexagon_nn_init();
-  // set debug level to 99 for now
-  //hexagon_nn_set_debug_level(nn_id, 99);
   // TODO(satok): make this as argument
   hexagon_nn_set_debug_level(nn_id, 0);
   return nn_id;
@@ -170,7 +168,7 @@ bool hexagon_controller_ConstructGraph(uint32_t nn_id) {
   int err;
   if ((err = hexagon_nn_prepare(nn_id)) != 0) {
     TFMLOGE("Prepare failed! returned 0x%x\n", err);
-    hexagon_controller_PrintLog(nn_id);
+    DumpNNId(nn_id);
     return false;
   } else {
     TFMLOGD("Prepare success!\n");
@@ -178,7 +176,7 @@ bool hexagon_controller_ConstructGraph(uint32_t nn_id) {
   }
 }
 
-uint32_t hexagon_controller_SetupGraph(int version)  {
+uint32_t hexagon_controller_SetupGraph(int version) {
   const uint32_t nn_id = hexagon_controller_InstantiateGraph();
   hexagon_controller_InitGraph(version, nn_id);
   hexagon_controller_ConstructGraph(nn_id);
@@ -200,7 +198,7 @@ bool hexagon_controller_ExecuteGraphWithMultipleInOut(
     if (DBG_EXECUTION) {
       LogDHexagon("Execution failed!");
       TFMLOGE("execute got err: %d\n", err);
-      hexagon_controller_PrintLog(nn_id);
+      DumpNNId(nn_id);
     }
     return false;
   } else {
@@ -212,24 +210,15 @@ bool hexagon_controller_ExecuteGraphWithMultipleInOut(
 }
 
 bool hexagon_controller_ExecuteGraph(
-    const uint32_t nn_id,
-    const uint32_t batches,
-    const uint32_t height,
-    const uint32_t width,
-    const uint32_t depth,
-    uint8_t* int_data,
-    const uint32_t int_data_size,
-    uint32_t* out_batches,
-    uint32_t* out_height,
-    uint32_t* out_width,
-    uint32_t* out_depth,
-    uint8_t* out_vals,
-    const uint32_t output_val_byte_size,
-    uint32_t* out_data_byte_size) {
+    const uint32_t nn_id, const uint32_t batches, const uint32_t height,
+    const uint32_t width, const uint32_t depth, uint8_t* int_data,
+    const uint32_t int_data_size, uint32_t* out_batches, uint32_t* out_height,
+    uint32_t* out_width, uint32_t* out_depth, uint8_t* out_vals,
+    const uint32_t output_val_byte_size, uint32_t* out_data_byte_size) {
   if (DBG_EXECUTION) {
     TFMLOGD("Preparing to execute...");
-    TFMLOGD("Input: %d, %d, %d, %d, %d, %d",
-            batches, height, width, depth, int_data[0], int_data_size);
+    TFMLOGD("Input: %d, %d, %d, %d, %d, %d", batches, height, width, depth,
+            int_data[0], int_data_size);
     TFMLOGD("Output: %d, %p", output_val_byte_size, out_vals);
     LogDHexagon("Execute graph!");
   }
@@ -273,27 +262,21 @@ bool hexagon_controller_ExecuteInceptionDummyData(uint32_t nn_id) {
   const bool success = hexagon_controller_ExecuteGraph(
       nn_id, INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V3,
       INCEPTION_PARAM_WIDTH_V3, INCEPTION_PARAM_DEPTH,
-      (uint8_t *)inception_dummy_int_data_299x299,
+      (uint8_t*)inception_dummy_int_data_299x299,
       INCEPTION_PARAM_HEIGHT_V3 * INCEPTION_PARAM_WIDTH_V3 *
-      INCEPTION_PARAM_DEPTH,
+          INCEPTION_PARAM_DEPTH,
       &out_batches, &out_height, &out_width, &out_depth,
-      (uint8_t *)s_output_values, sizeof(s_output_values),
-      &out_data_size);
+      (uint8_t*)s_output_values, sizeof(s_output_values), &out_data_size);
   if (success) {
     int out_ranking[OUT_RANKING_SIZE];
     hexagon_controller_PrintMaxNIdx(
-        s_output_values,
-        out_batches * out_height * out_width * out_depth,
+        s_output_values, out_batches * out_height * out_width * out_depth,
         OUT_RANKING_SIZE, out_ranking);
-    TFMLOGD("%d x %d x %d x %d, size = %d\n",
-            out_batches,
-            out_height,
-            out_width,
-            out_depth,
-            out_data_size);
-    TFMLOGD("max idx: %d\n", FindMaxIdx(
-        s_output_values,
-        out_batches * out_height * out_width * out_depth));
+    TFMLOGD("%d x %d x %d x %d, size = %d\n", out_batches, out_height,
+            out_width, out_depth, out_data_size);
+    TFMLOGD("max idx: %d\n",
+            FindMaxIdx(s_output_values,
+                       out_batches * out_height * out_width * out_depth));
     if (out_ranking[0] == 169 && out_ranking[1] == 7) {
       return true;
     } else {
@@ -317,25 +300,22 @@ void hexagon_controller_DumpPerf(uint32_t nn_id) {
     TFMLOGE("perf info failure");
     return;
   }
-  TFMLOGD("Total %d nodes.",n_nodes);
-  qsort(info,n_nodes,sizeof(info[0]), CompareCycle);
+  TFMLOGD("Total %d nodes.", n_nodes);
+  qsort(info, n_nodes, sizeof(info[0]), CompareCycle);
   for (i = 0; i < n_nodes; i++) {
     total_cycles += GetCounter(info[i]);
   }
-  TFMLOGD("Total %lld cycles.",total_cycles);
+  TFMLOGD("Total %lld cycles.", total_cycles);
   for (i = 0; i < n_nodes; i++) {
     counter = GetCounter(info[i]);
     cum_cycles += counter;
-    TFMLOGD("node,0x%x,%s,%s,executions,%d,cycles,%lld,%f %%,"
-            "cum_cycles,%lld,%f %%\n",
-           info[i].node_id,
-           ConvertGraphInfoIdToName(info[i].node_id),
-           ConvertGraphInfoIdToOpName(info[i].node_id),
-           info[i].executions,
-           counter,
-           100*((double)counter)/total_cycles,
-           cum_cycles,
-           100*((double)cum_cycles)/total_cycles);
+    TFMLOGD(
+        "node,0x%x,%s,%s,executions,%d,cycles,%lld,%f %%,"
+        "cum_cycles,%lld,%f %%\n",
+        info[i].node_id, ConvertGraphInfoIdToName(info[i].node_id),
+        ConvertGraphInfoIdToOpName(info[i].node_id), info[i].executions,
+        counter, 100 * ((double)counter) / total_cycles, cum_cycles,
+        100 * ((double)cum_cycles) / total_cycles);
   }
 #ifdef ENABLE_HVX_FULL_DEBUG
   DumpAllPerf(nn_id);
@@ -356,7 +336,7 @@ void hexagon_controller_DumpNodeName(uint32_t nn_id) {
     TFMLOGD("perf info failure");
     return;
   }
-  TFMLOGD("Total %d nodes.",node_count);
+  TFMLOGD("Total %d nodes.", node_count);
   qsort(info, node_count, sizeof(info[0]), CompareCycle);
   for (i = 0; i < node_count; i++) {
     total_cycles += GetCounter(info[i]);
@@ -365,19 +345,14 @@ void hexagon_controller_DumpNodeName(uint32_t nn_id) {
   for (i = 0; i < node_count; i++) {
     counter = GetCounter(info[i]);
     cum_cycles += counter;
-    TFMLOGD("node,0x%x,%s,%s,executions,%d,cycles,%lld,%f %%,"
-            "cum_cycles,%lld,%f %%",
-            info[i].node_id,
-            ConvertGraphInfoIdToName(info[i].node_id),
-            ConvertGraphInfoIdToOpName(info[i].node_id),
-            info[i].executions,
-            counter,
-            100*((double)counter)/total_cycles,
-            cum_cycles,
-            100*((double)cum_cycles)/total_cycles);
+    TFMLOGD(
+        "node,0x%x,%s,%s,executions,%d,cycles,%lld,%f %%,"
+        "cum_cycles,%lld,%f %%",
+        info[i].node_id, ConvertGraphInfoIdToName(info[i].node_id),
+        ConvertGraphInfoIdToOpName(info[i].node_id), info[i].executions,
+        counter, 100 * ((double)counter) / total_cycles, cum_cycles,
+        100 * ((double)cum_cycles) / total_cycles);
   }
 }
 
-void hexagon_controller_Teardown(uint32_t nn_id) {
-  hexagon_nn_teardown(nn_id);
-}
+void hexagon_controller_Teardown(uint32_t nn_id) { hexagon_nn_teardown(nn_id); }
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c
index ba50da6abd..6a5d982dc8 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c
@@ -24,7 +24,8 @@ limitations under the License.
 
 #include "adspmsgd.h"
 #include "dspCV.h"
-#include "rpcmem.h"    // helper API's for shared buffer allocation
+#include "node_data_float.h"
+#include "rpcmem.h"  // helper API's for shared buffer allocation
 #include "soc_interface.h"
 #include "tfm_log.h"
 
@@ -86,7 +87,8 @@ static bool SetInputTensorDef(int port, hexagon_nn_tensordef* tensordef) {
     TFMLOGE("Error exceeds input count.");
     return false;
   }
-  NodeDataFloat* input_node_data_buffer = &s_input_node_data_buffer[port];
+  struct NodeDataFloat* input_node_data_buffer =
+      &s_input_node_data_buffer[port];
   tensordef->batches = input_node_data_buffer->x;
   tensordef->height = input_node_data_buffer->y;
   tensordef->width = input_node_data_buffer->z;
@@ -142,19 +144,19 @@ void hexagon_controller_InitInputNodeDataToInceptionDummyData(int version) {
     }
     hexagon_controller_CopyByteNodeData(
         0, INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V1,
-        INCEPTION_PARAM_WIDTH_V1, INCEPTION_PARAM_DEPTH,
-        1, inception_dummy_int_data_224x224);
+        INCEPTION_PARAM_WIDTH_V1, INCEPTION_PARAM_DEPTH, 1,
+        inception_dummy_int_data_224x224);
   } else if (version == 3) {
     if (USE_FLOAT_DATA) {
       hexagon_controller_CopyByteNodeData(
           0, INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V3,
-          INCEPTION_PARAM_WIDTH_V3, INCEPTION_PARAM_DEPTH,
-          sizeof(float), (uint8_t*)inception_dummy_float_data_299x299);
+          INCEPTION_PARAM_WIDTH_V3, INCEPTION_PARAM_DEPTH, sizeof(float),
+          (uint8_t*)inception_dummy_float_data_299x299);
     } else {
       hexagon_controller_CopyByteNodeData(
           0, INCEPTION_PARAM_BATCHES, INCEPTION_PARAM_HEIGHT_V3,
-          INCEPTION_PARAM_WIDTH_V3, INCEPTION_PARAM_DEPTH,
-          1, inception_dummy_int_data_299x299);
+          INCEPTION_PARAM_WIDTH_V3, INCEPTION_PARAM_DEPTH, 1,
+          inception_dummy_int_data_299x299);
     }
   }
 }
@@ -188,7 +190,7 @@ bool hexagon_controller_ExecuteGraphWithBuffer(uint32_t nn_id,
 
   if (!success) {
     TFMLOGE("Execution failed");
-    hexagon_controller_PrintLog(nn_id);
+    DumpNNId(nn_id);
     return false;
   } else if (!show_ranking) {
     return true;
@@ -217,9 +219,7 @@ bool hexagon_controller_ExecuteGraphWithBuffer(uint32_t nn_id,
   return true;
 }
 
-uint32_t hexagon_controller_GetTargetGraphId() {
-  return s_target_graph_id;
-}
+uint32_t hexagon_controller_GetTargetGraphId() { return s_target_graph_id; }
 
 void hexagon_controller_SetTargetGraphId(uint32_t graph_id) {
   s_target_graph_id = graph_id;
@@ -305,7 +305,8 @@ bool hexagon_controller_AllocateNodeDataBuffers(int input_size,
 }
 
 bool hexagon_controller_ReleaseInputNodeDataBuffersWithPort(int port) {
-  NodeDataFloat* input_node_data_buffer = &s_input_node_data_buffer[port];
+  struct NodeDataFloat* input_node_data_buffer =
+      &s_input_node_data_buffer[port];
   if (input_node_data_buffer->max_buf_byte_size == 0) {
     TFMLOGE("ERROR! input buffer has not been allocated yet!!");
     return false;
@@ -346,15 +347,14 @@ bool hexagon_controller_CopyByteNodeData(int port, int x, int y, int z, int d,
   int array_byte_size = x * y * z * d * type_byte_size;
   TFMLOGD("--- %d, %d, %d, %d, %d, %d", x, y, z, d, type_byte_size,
           array_byte_size);
-  NodeDataFloat* input_node_data_buffer = s_input_node_data_buffer[0];
+  struct NodeDataFloat* input_node_data_buffer = &s_input_node_data_buffer[0];
 
   if (input_node_data_buffer->max_buf_byte_size < array_byte_size) {
     TFMLOGE("ERROR! input buffer size is too small! %d < %d",
             input_node_data_buffer->max_buf_byte_size, array_byte_size);
     return false;
   }
-  memcpy(input_node_data_buffer->byte_array_data, array_data,
-         array_byte_size);
+  memcpy(input_node_data_buffer->byte_array_data, array_data, array_byte_size);
   input_node_data_buffer->array_byte_size = array_byte_size;
   input_node_data_buffer->x = x;
   input_node_data_buffer->y = y;
@@ -363,8 +363,9 @@ bool hexagon_controller_CopyByteNodeData(int port, int x, int y, int z, int d,
   return true;
 }
 
-int hexagon_controller_InitHexagonWithMaxAttributes(
-    int enable_dcvs, int bus_usage, int version) {
+int hexagon_controller_InitHexagonWithMaxAttributes(int enable_dcvs,
+                                                    int bus_usage,
+                                                    int version) {
   TFMLOGI("Init hexagon with max attributes (Controller version = %d)",
           HEXAGON_CONTROLLER_VERSION);
   const int MCPS = 1000;
@@ -373,17 +374,17 @@ int hexagon_controller_InitHexagonWithMaxAttributes(
   adspmsgd_start(0, RPCMEM_HEAP_DEFAULT, 4096);
 
   dspCV_Attribute attrib[] = {
-    // The below values will result in the maximum aDSP performance,
-    // at Turbo voltage.
-    // Slightly more MCPS than are available on current targets
-    {DSP_TOTAL_MCPS, MCPS},
-    // drive the clock to MAX on known targets
-    {DSP_MCPS_PER_THREAD, MCPS / 2},
-    // 12 GB/sec is slightly higher than the max realistic
-    // max BW on existing targets.
-    {PEAK_BUS_BANDWIDTH_MBPS, MBPS},
-    // This app is non-real time, and constantly reading/writing memory
-    {BUS_USAGE_PERCENT, bus_usage},
+      // The below values will result in the maximum aDSP performance,
+      // at Turbo voltage.
+      // Slightly more MCPS than are available on current targets
+      {DSP_TOTAL_MCPS, MCPS},
+      // drive the clock to MAX on known targets
+      {DSP_MCPS_PER_THREAD, MCPS / 2},
+      // 12 GB/sec is slightly higher than the max realistic
+      // max BW on existing targets.
+      {PEAK_BUS_BANDWIDTH_MBPS, MBPS},
+      // This app is non-real time, and constantly reading/writing memory
+      {BUS_USAGE_PERCENT, bus_usage},
   };
   int retval = 0;
   if (!enable_dcvs) {
@@ -414,9 +415,7 @@ int hexagon_controller_DeInitHexagon() {
   return retval;
 }
 
-void hexagon_controller_GrowMemorySize() {
-  hexagon_nn_config();
-}
+void hexagon_controller_GrowMemorySize() { hexagon_nn_config(); }
 
 struct NodeDataFloat* hexagon_controller_GetInputNodeDataBuffer(int port) {
   if (port >= GetInputNodeCount()) {
@@ -435,16 +434,17 @@ uint8_t* hexagon_controller_GetOutputNodeDataBuffer(int port,
 }
 
 // Append const node to the graph
-int hexagon_controller_AppendConstNode(
-    const char* const name, int graph_id, int node_id,
-    int batch, int height, int width, int depth,
-    const uint8_t* const data, int data_length) {
+int hexagon_controller_AppendConstNode(const char* const name, int graph_id,
+                                       int node_id, int batch, int height,
+                                       int width, int depth,
+                                       const uint8_t* const data,
+                                       int data_length) {
   if (DBG_SHOW_ID) {
-    TFMLOGV("---(CONST) %s, %d, %d, %d, %d, %d, %d",
-            name, node_id, batch, height, width, depth, data_length);
+    TFMLOGV("---(CONST) %s, %d, %d, %d, %d, %d, %d", name, node_id, batch,
+            height, width, depth, data_length);
   } else {
-    TFMLOGV("---(CONST) %s, %d, %d, %d, %d, %d",
-            name, batch, height, width, depth, data_length);
+    TFMLOGV("---(CONST) %s, %d, %d, %d, %d, %d", name, batch, height, width,
+            depth, data_length);
   }
   const int retval = hexagon_nn_append_const_node(
       graph_id, node_id, batch, height, width, depth, data, data_length);
@@ -456,11 +456,12 @@ int hexagon_controller_AppendConstNode(
 }
 
 // Append node to the graph
-int hexagon_controller_AppendNode(
-    const char* const name, int graph_id, int node_id, int ops_id,
-    int padding_id, const hexagon_nn_input* const inputs,
-    int inputs_count, const hexagon_nn_output* const outputs,
-    int outputs_count) {
+int hexagon_controller_AppendNode(const char* const name, int graph_id,
+                                  int node_id, int ops_id, int padding_id,
+                                  const hexagon_nn_input* const inputs,
+                                  int inputs_count,
+                                  const hexagon_nn_output* const outputs,
+                                  int outputs_count) {
   char input_param_buf[OUTPUT_PARAM_MAX_LINE_SIZE];
   memset(input_param_buf, 0, OUTPUT_PARAM_MAX_LINE_SIZE);
   int pos = 0;
@@ -470,8 +471,8 @@ int hexagon_controller_AppendNode(
       pos += snprintf(&input_param_buf[pos], 500, "(%d, %d), ",
                       inputs[i].src_id, inputs[i].output_idx);
     } else {
-      pos += snprintf(&input_param_buf[pos], 500, "(%d), ",
-                      inputs[i].output_idx);
+      pos +=
+          snprintf(&input_param_buf[pos], 500, "(%d), ", inputs[i].output_idx);
     }
   }
 
@@ -484,18 +485,16 @@ int hexagon_controller_AppendNode(
   }
 
   if (DBG_SHOW_ID) {
-    TFMLOGV("---(OP) %s, %d, %d, %d, %d, %d, %s, %s", name, node_id,
-            ops_id, padding_id, inputs_count, outputs_count, input_param_buf,
+    TFMLOGV("---(OP) %s, %d, %d, %d, %d, %d, %s, %s", name, node_id, ops_id,
+            padding_id, inputs_count, outputs_count, input_param_buf,
             output_param_buf);
   } else {
-    TFMLOGV("---(OP) %s, %d, %d, %d, %d, %s, %s", name,
-            ops_id, padding_id, inputs_count, outputs_count, input_param_buf,
-            output_param_buf);
+    TFMLOGV("---(OP) %s, %d, %d, %d, %d, %s, %s", name, ops_id, padding_id,
+            inputs_count, outputs_count, input_param_buf, output_param_buf);
   }
-  const int retval = hexagon_nn_append_node(
-      graph_id, node_id, ops_id, padding_id,
-      inputs, inputs_count,
-      outputs, outputs_count);
+  const int retval =
+      hexagon_nn_append_node(graph_id, node_id, ops_id, padding_id, inputs,
+                             inputs_count, outputs, outputs_count);
   if (retval != 0) {
     TFMLOGE("Failed to append const node %d", node_id);
     return retval;
@@ -510,11 +509,3 @@ void hexagon_controller_EnableDbgUseInceptionDummyData(bool enable) {
 bool hexagon_controller_IsDbgUseInceptionDummyDataEnabled() {
   return s_dbg_use_inception_dummy_data;
 }
-
-void hexagon_controller_PrintLog(uint32_t nn_id) {
-  unsigned char* buf = NULL;
-  posix_memalign((void**)&buf, 128, PRINT_BUFSIZE);
-  hexagon_nn_getlog(nn_id, buf, PRINT_BUFSIZE);
-  TFMLOGE("DUMP HEXAGON LOG: %s", buf);
-  free(buf);
-}
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h b/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h
index d23048c116..fc921ff8b9 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/include/hexagon_controller.h
@@ -142,8 +142,6 @@ void hexagon_controller_EnableDbgUseInceptionDummyData(bool enable);
 
 bool hexagon_controller_IsDbgUseInceptionDummyDataEnabled();
 
-void hexagon_controller_PrintLog(uint32_t nn_id);
-
 #ifdef __cplusplus
 }
 #endif  // __cplusplus
diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h b/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h
index 91bd15d383..8d11ee4a34 100644
--- a/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h
+++ b/tensorflow/contrib/hvx/hexagon_controller/src_log/include/tfm_log.h
@@ -74,4 +74,9 @@ static inline void LogDHexagon(const char* fmt, ...) {
   va_end(ap);
 }
 
+static inline void DumpNNId(uint32_t nn_id) {
+  // TODO(satok): Dump more information
+  TFMLOGI("NN Id = %d", nn_id);
+}
+
 #endif
-- 
GitLab


From 9d2a432ce74eab4c439fe8c60389e4da9d6c92b2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 17:15:50 -0700
Subject: [PATCH 0187/1375] Add plumbing for a ReducePrecision operation.

This CL is the first part of a series that adds a ReducePrecision operation for experimenting with the effects of reduced-precision storage of intermediate values.  ReducePrecision is a Unary operation parameterized on floating-point exponent and mantissa bit sizes, and rounds the input data as if it were converted to a floating-point value with the given bit sizes and then converted back to "normal" F32 data.

Using arbitrary parameterized values to describe the lower-precision value type, rather than hardcoding this as a reduction to IEEE f16, allows us to do more flexible experiments -- e.g., "Is this training error due to the reduced mantissa precision, or due to the reduced exponent range?" or "Is this a smooth degradation with reduced precision or is there a sudden drop at some value?" -- which may suggest software mitigations for the effects.

This version of the CL adds the kReducePrecision instruction opcode, and the overall plumbing to support the operation.  To allow testing, it includes an exceptionally simple implementation of the actual operation that returns "unimplemented" except for the exponent and mantissa bit sizes where it is a complete no-op.

PiperOrigin-RevId: 159295615
---
 .../xla/client/computation_builder.cc         | 22 +++++++++
 .../compiler/xla/client/computation_builder.h |  5 ++
 .../compiler/xla/service/dfs_hlo_visitor.h    |  5 ++
 .../xla/service/elemental_ir_emitter.cc       | 26 ++++++++++
 .../xla/service/elemental_ir_emitter.h        |  3 ++
 .../compiler/xla/service/hlo_cost_analysis.cc |  5 ++
 .../compiler/xla/service/hlo_cost_analysis.h  |  2 +
 .../compiler/xla/service/hlo_graph_dumper.cc  |  5 ++
 .../compiler/xla/service/hlo_instruction.cc   | 25 ++++++++++
 .../compiler/xla/service/hlo_instruction.h    | 27 +++++++++++
 tensorflow/compiler/xla/service/hlo_opcode.cc |  2 +
 tensorflow/compiler/xla/service/hlo_opcode.h  |  1 +
 .../xla/service/instruction_fusion.cc         |  1 +
 tensorflow/compiler/xla/service/service.cc    |  5 ++
 .../compiler/xla/service/shape_inference.cc   | 24 ++++++++++
 .../compiler/xla/service/shape_inference.h    |  6 +++
 .../compiler/xla/service/user_computation.cc  | 47 +++++++++++++++++++
 .../compiler/xla/service/user_computation.h   |  4 ++
 .../xla/tests/array_elementwise_ops_test.cc   | 23 +++++++++
 tensorflow/compiler/xla/xla_data.proto        |  9 +++-
 20 files changed, 246 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index cefa4af23c..49b0e164b0 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -1514,6 +1514,28 @@ ComputationDataHandle ComputationBuilder::SelectAndScatterWithGeneralPadding(
   return ParseOpResponse(s, &response);
 }
 
+ComputationDataHandle ComputationBuilder::ReducePrecision(
+    const ComputationDataHandle& operand, const int exponent_bits,
+    const int mantissa_bits) {
+  if (!first_error_.ok() || !PrepareComputation().ok()) {
+    return ComputationDataHandle();
+  }
+
+  ReducePrecisionRequest request;
+  *request.mutable_operand() = operand;
+  request.set_exponent_bits(exponent_bits);
+  request.set_mantissa_bits(mantissa_bits);
+  OpRequest op_request;
+  *op_request.mutable_computation() = computation_.handle();
+  *op_request.mutable_reduce_precision_request() = request;
+  AddOpMetadata(&op_request);
+  OpResponse response;
+
+  VLOG(2) << "making reduce-precision request";
+  Status s = client_->stub()->Op(&op_request, &response);
+  return ParseOpResponse(s, &response);
+}
+
 void ComputationBuilder::Send(const ComputationDataHandle& operand,
                               const ChannelHandle& handle) {
   if (!first_error_.ok() || !PrepareComputation().ok()) {
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 13b44a71a5..6a87784f6a 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -598,6 +598,11 @@ class ComputationBuilder {
                               const Computation& body,
                               const ComputationDataHandle& init);
 
+  // Enqueues a ReducePrecision node onto the computation.
+  ComputationDataHandle ReducePrecision(const ComputationDataHandle& operand,
+                                        const int exponent_bits,
+                                        const int mantissa_bits);
+
   // Enqueues a Send node onto the computation, to send the given operand to
   // a Recv instruction that shares the same channel handle.
   void Send(const ComputationDataHandle& operand, const ChannelHandle& handle);
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 40ff037e73..1f58562ac2 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -171,6 +171,11 @@ class DfsHloVisitor {
                                  HloInstruction* lhs, HloInstruction* rhs) {
     return HandleElementwiseBinary(logical_or, HloOpcode::kLogicalOr, lhs, rhs);
   }
+  virtual Status HandleReducePrecision(HloInstruction* reduce_precision,
+                                       HloInstruction* operand) {
+    return HandleElementwiseUnary(reduce_precision, HloOpcode::kReducePrecision,
+                                  operand);
+  }
 
   virtual Status HandleInfeed(HloInstruction* infeed) = 0;
   virtual Status HandleOutfeed(HloInstruction* outfeed) = 0;
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index c99ebceb45..bea1329d40 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -385,6 +385,24 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitErfcInv(
   return EmitErfInv(prim_type, ir_builder_->CreateFSub(one, value));
 }
 
+StatusOr<llvm::Value*> ElementalIrEmitter::EmitReducePrecision(
+    const HloInstruction* hlo, llvm::Value* x) const {
+  if (hlo->operand(0)->shape().element_type() != F32) {
+    return Unimplemented("reduce-precision only implemented for F32");
+  }
+  // As a preliminary implementation, we only implement this for the case
+  // where it is a no-op -- that is, where the exponent and mantissa bit
+  // counts are equal to the (IEEE f32) bit counts for the input values.
+  if (hlo->exponent_bits() != 8) {
+    return Unimplemented("reduce-precision requires 8 exponent bits");
+  }
+  if (hlo->mantissa_bits() != 23) {
+    return Unimplemented("reduce-precision requires 23 mantissa bits");
+  }
+
+  return x;
+}
+
 StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerBinaryOp(
     const HloInstruction* op, llvm::Value* lhs_value, llvm::Value* rhs_value,
     bool is_signed) const {
@@ -742,6 +760,14 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
                                 ElementwiseSourceIndex(index, *hlo, 2)));
         return EmitFloatMin(max_value, EmitFloatMax(min_value, arg_value));
       };
+    case HloOpcode::kReducePrecision:
+      return [this, hlo, &operand_to_generator](
+                 const IrArray::Index& index) -> StatusOr<llvm::Value*> {
+        TF_ASSIGN_OR_RETURN(llvm::Value * operand_value,
+                            operand_to_generator.at(hlo->operand(0))(
+                                ElementwiseSourceIndex(index, *hlo, 0)));
+        return EmitReducePrecision(hlo, operand_value);
+      };
     case HloOpcode::kConcatenate:
       return [this, hlo, &operand_to_generator](
                  const IrArray::Index target_index) -> StatusOr<llvm::Value*> {
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h
index 2576d3823e..bb9117ca61 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h
@@ -84,6 +84,9 @@ class ElementalIrEmitter {
   virtual StatusOr<llvm::Value*> EmitErfcInv(PrimitiveType prim_type,
                                              llvm::Value* value) const;
 
+  virtual StatusOr<llvm::Value*> EmitReducePrecision(const HloInstruction* hlo,
+                                                     llvm::Value* x) const;
+
   // A helper method for MakeElementGenerator. Given an elementwise op `hlo` and
   // the target array index, computes the source array index of its
   // `operand_no`-th operand.
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index 7f88474a27..cbabf00913 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -100,6 +100,11 @@ Status HloCostAnalysis::HandleClamp(HloInstruction* clamp,
   return HandleElementwiseOp(clamp);
 }
 
+Status HloCostAnalysis::HandleReducePrecision(HloInstruction* hlo,
+                                              HloInstruction* operand) {
+  return HandleElementwiseOp(hlo);
+}
+
 Status HloCostAnalysis::HandleParameter(HloInstruction* parameter) {
   current_bytes_accessed_ = 0;
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index 30f553a81f..f14baf6da2 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -58,6 +58,8 @@ class HloCostAnalysis : public DfsHloVisitor {
                        HloInstruction* lhs, HloInstruction* rhs) override;
   Status HandleClamp(HloInstruction* clamp, HloInstruction* min,
                      HloInstruction* arg, HloInstruction* max) override;
+  Status HandleReducePrecision(HloInstruction* hlo,
+                               HloInstruction* operand) override;
   Status HandleConcatenate(
       HloInstruction* concatenate,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) override;
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 48c33d62c5..9fe4d85f8b 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -318,6 +318,11 @@ string InstructionSequenceGraph(
         StrAppend(&name, "<br/>",
                   "custom_call_target=", instruction->custom_call_target());
         break;
+      case HloOpcode::kReducePrecision:
+        // Make ReducePrecision ops a bit more visible, since typically they
+        // will be inserted as modifications to an existing graph.
+        color = kDarkRed;
+        break;
     }
 
     // Create instruction node with appropriate label, shape, and color.
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 6bb9e9a9e6..33bb29e16d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -227,6 +227,19 @@ HloInstruction::CreateGetTupleElement(const Shape& shape,
   return instruction;
 }
 
+/* static */ std::unique_ptr<HloInstruction>
+HloInstruction::CreateReducePrecision(const Shape& shape,
+                                      HloInstruction* operand,
+                                      const int exponent_bits,
+                                      const int mantissa_bits) {
+  auto instruction =
+      WrapUnique(new HloInstruction(HloOpcode::kReducePrecision, shape));
+  instruction->AppendOperand(operand);
+  instruction->exponent_bits_ = exponent_bits;
+  instruction->mantissa_bits_ = mantissa_bits;
+  return instruction;
+}
+
 /* static */ std::unique_ptr<HloInstruction>
 HloInstruction::CreateCrossReplicaSum(const Shape& shape,
                                       HloInstruction* operand) {
@@ -796,6 +809,10 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
     case HloOpcode::kConvert:
       CHECK_EQ(new_operands.size(), 1);
       return CreateConvert(shape, new_operands[0]);
+    case HloOpcode::kReducePrecision:
+      CHECK_EQ(new_operands.size(), 1);
+      return CreateReducePrecision(shape, new_operands[0], exponent_bits_,
+                                   mantissa_bits_);
     case HloOpcode::kConvolution:
       CHECK_EQ(new_operands.size(), 2);
       return CreateConvolve(shape, new_operands[0], new_operands[1], *window_,
@@ -1171,6 +1188,11 @@ bool HloInstruction::Identical(
     case HloOpcode::kConvert:
       return shape().element_type() == other.shape().element_type();
 
+    // A reduce-precision operation is determined by the bit sizes.
+    case HloOpcode::kReducePrecision:
+      return exponent_bits() == other.exponent_bits() &&
+             mantissa_bits() == other.mantissa_bits();
+
     // Convolution has a window and dimensions.
     case HloOpcode::kConvolution:
       return protobuf_util::ProtobufEquals(window(), other.window()) &&
@@ -1855,6 +1877,8 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
       return visitor->HandleTranspose(this);
     case HloOpcode::kReverse:
       return visitor->HandleReverse(this, operands_[0]);
+    case HloOpcode::kReducePrecision:
+      return visitor->HandleReducePrecision(this, operands_[0]);
     case HloOpcode::kSlice:
       return visitor->HandleSlice(this, operands_[0]);
     case HloOpcode::kDynamicSlice:
@@ -2106,6 +2130,7 @@ bool HloInstruction::IsElementwise() const {
     case HloOpcode::kLog:
     case HloOpcode::kLogicalNot:
     case HloOpcode::kNegate:
+    case HloOpcode::kReducePrecision:
     case HloOpcode::kSign:
     case HloOpcode::kTanh:
       return true;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index cb19c84814..fb80ca86af 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -131,6 +131,13 @@ class HloInstruction {
       const Window& window,
       const ConvolutionDimensionNumbers& dimension_numbers);
 
+  // Creates a reduce-precision op, where operand is the data to reduce in
+  // precision, and exponent_bits and mantissa_bits describe the precision to
+  // reduce it to.
+  static std::unique_ptr<HloInstruction> CreateReducePrecision(
+      const Shape& shape, HloInstruction* operand, const int exponent_bits,
+      const int mantissa_bits);
+
   // Creates a cross replica sum op.
   static std::unique_ptr<HloInstruction> CreateCrossReplicaSum(
       const Shape& shape, HloInstruction* operand);
@@ -668,6 +675,22 @@ class HloInstruction {
     return dynamic_slice_sizes_;
   }
 
+  // Returns the number of exponent bits for a reduce-precision node.
+  //
+  // Precondition: opcode() == HloOpcode::kReducePrecision
+  int32 exponent_bits() const {
+    CHECK_EQ(HloOpcode::kReducePrecision, opcode_);
+    return exponent_bits_;
+  }
+
+  // Returns the number of mantissa bits for a reduce-precision node.
+  //
+  // Precondition: opcode() == HloOpcode::kReducePrecision
+  int32 mantissa_bits() const {
+    CHECK_EQ(HloOpcode::kReducePrecision, opcode_);
+    return mantissa_bits_;
+  }
+
   // Returns data on the window in a windowed operation such as
   // convolution.
   const Window& window() const {
@@ -864,6 +887,10 @@ class HloInstruction {
   std::vector<int64> slice_starts_;
   std::vector<int64> slice_limits_;
 
+  // The bit sizes for a reduce-precision operation.
+  int32 exponent_bits_;
+  int32 mantissa_bits_;
+
   // Describes the [start, start + size) range size for a dynamic slice
   // ('start' is specified dynamically in the second operand of the operation).
   std::vector<int64> dynamic_slice_sizes_;
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.cc b/tensorflow/compiler/xla/service/hlo_opcode.cc
index 342c43dc5a..4d68d0d088 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.cc
+++ b/tensorflow/compiler/xla/service/hlo_opcode.cc
@@ -116,6 +116,8 @@ string HloOpcodeString(HloOpcode opcode) {
       return "recv";
     case HloOpcode::kReduce:
       return "reduce";
+    case HloOpcode::kReducePrecision:
+      return "reduce-precision";
     case HloOpcode::kReduceWindow:
       return "reduce-window";
     case HloOpcode::kRemainder:
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index 8e0fa7b4f1..d1263219c0 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -76,6 +76,7 @@ enum class HloOpcode {
   kPower,
   kRecv,
   kReduce,
+  kReducePrecision,
   kReduceWindow,
   kRemainder,
   kReshape,
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 9bace7edaa..52da222ab9 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -65,6 +65,7 @@ namespace xla {
     case HloOpcode::kNegate:
     case HloOpcode::kOutfeed:
     case HloOpcode::kPad:
+    case HloOpcode::kReducePrecision:
     case HloOpcode::kReshape:
     case HloOpcode::kReverse:
     case HloOpcode::kSelect:
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 5812d3e487..2c3a0a1a25 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -1307,6 +1307,11 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
           computation->AddReduceInstruction(arg->reduce_request(), *to_apply);
       break;
     }
+    case OpRequest::kReducePrecisionRequest: {
+      handle_status = computation->AddReducePrecisionInstruction(
+          arg->reduce_precision_request());
+      break;
+    }
     case OpRequest::kReduceWindowRequest: {
       TF_ASSIGN_OR_RETURN(UserComputation * to_apply,
                           computation_tracker_.Resolve(
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 2508f4c13d..88fbfdf33c 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -298,6 +298,30 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
   return ShapeUtil::ChangeElementType(operand_shape, new_element_type);
 }
 
+/* static */ StatusOr<Shape> ShapeInference::InferReducePrecisionShape(
+    const Shape& operand_shape, const int exponent_bits,
+    const int mantissa_bits) {
+  if (!ShapeUtil::ElementIsFloating(operand_shape)) {
+    return InvalidArgument(
+        "expected element type in shape to be floating point for "
+        "ReducePrecision operation; got %s",
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+  if (exponent_bits < 1) {
+    // One exponent bit is necessary to distinguish 0 from infinity.  Having
+    // no exponent bits doesn't produce a sensible number, so we require at
+    // least one.
+    return InvalidArgument("expected exponent_bits >= 1; got %d",
+                           exponent_bits);
+  }
+  if (mantissa_bits < 0) {
+    // A number with no mantissa bits is still meaningful, however.
+    return InvalidArgument("expected non-negative mantissa_bits; got %d",
+                           mantissa_bits);
+  }
+  return operand_shape;
+}
+
 /* static */ StatusOr<Shape> ShapeInference::InferPadShape(
     const Shape& operand_shape, const Shape& padding_value_shape,
     const PaddingConfig& padding_config) {
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index 8bd3585133..55c60e149d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -171,6 +171,12 @@ class ShapeInference {
   static StatusOr<Shape> InferConvertShape(const Shape& operand_shape,
                                            PrimitiveType new_element_type);
 
+  // Helper that validates the input data type for a reduce-precision operation,
+  // and returns the result shape.
+  static StatusOr<Shape> InferReducePrecisionShape(const Shape& operand_shape,
+                                                   const int exponent_bits,
+                                                   const int mantissa_bits);
+
   // Helper that infers the shape produced by a pad operation based on the
   // padding configuration.
   static StatusOr<Shape> InferPadShape(const Shape& operand_shape,
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 3e7942075c..8014c64953 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -881,6 +881,34 @@ StatusOr<ComputationDataHandle> UserComputation::AddConvertInstruction(
   return handle;
 }
 
+StatusOr<ComputationDataHandle> UserComputation::AddReducePrecisionInstruction(
+    const ReducePrecisionRequest& reduce_precision_request) {
+  tensorflow::mutex_lock lock(mutex_);
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* operand,
+                      LookUpRequest(reduce_precision_request.operand()));
+
+  TF_ASSIGN_OR_RETURN(
+      Shape new_shape,
+      ShapeInference::InferReducePrecisionShape(
+          operand->output_shape(), reduce_precision_request.exponent_bits(),
+          reduce_precision_request.mantissa_bits()));
+
+  ComputationDataHandle handle = CreateComputationDataHandle();
+
+  OperationRequest& request =
+      (*session_computation_.mutable_requests())[handle.handle()];
+  *request.mutable_output_handle() = handle;
+  *request.mutable_output_shape() = new_shape;
+  *request.mutable_request()->mutable_reduce_precision_request() =
+      reduce_precision_request;
+
+  VLOG(1) << "AddReducePrecisionInstruction (" << GetVersionedHandleInternal()
+          << "), data handle " << handle.handle() << ": "
+          << reduce_precision_request.ShortDebugString();
+  return handle;
+}
+
 StatusOr<ComputationDataHandle> UserComputation::AddConvolveInstruction(
     const ConvolveRequest& convolve_request) {
   tensorflow::mutex_lock lock(mutex_);
@@ -2180,6 +2208,13 @@ static void ForEachOperand(
       break;
     }
 
+    case OpRequest::kReducePrecisionRequest: {
+      const ReducePrecisionRequest& reduce_precision_request =
+          request.request().reduce_precision_request();
+      apply(reduce_precision_request.operand());
+      break;
+    }
+
     case OpRequest::kTraceRequest: {
       const TraceRequest& trace_request = request.request().trace_request();
       apply(trace_request.operand());
@@ -2767,6 +2802,18 @@ void ComputationLowerer::Visit(
       break;
     }
 
+    case OpRequest::kReducePrecisionRequest: {
+      const ReducePrecisionRequest& reduce_precision_request =
+          request.request().reduce_precision_request();
+      HloInstruction* operand =
+          lookup_instruction(reduce_precision_request.operand());
+      auto exponent_bits = reduce_precision_request.exponent_bits();
+      auto mantissa_bits = reduce_precision_request.mantissa_bits();
+      hlo_instruction = add_instruction(HloInstruction::CreateReducePrecision(
+          request.output_shape(), operand, exponent_bits, mantissa_bits));
+      break;
+    }
+
     case OpRequest::kTraceRequest: {
       const TraceRequest& trace_request = request.request().trace_request();
       HloInstruction* operand = lookup_instruction(trace_request.operand());
diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h
index a8bedf20b5..9bb7bf491a 100644
--- a/tensorflow/compiler/xla/service/user_computation.h
+++ b/tensorflow/compiler/xla/service/user_computation.h
@@ -116,6 +116,10 @@ class UserComputation {
       const MapRequest& map_request,
       const UserComputation& to_apply_computation);
 
+  // Enqueues a reduce-precision instruction onto this user computation.
+  StatusOr<ComputationDataHandle> AddReducePrecisionInstruction(
+      const ReducePrecisionRequest& reduce_precision_request);
+
   // Enqueues a convolution instruction onto this user computation.
   StatusOr<ComputationDataHandle> AddConvolveInstruction(
       const ConvolveRequest& convolve_request);
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index b6088f8d08..f43d4b6f57 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1864,6 +1864,29 @@ INSTANTIATE_TEST_CASE_P(ArrayElementwiseOpTestParamCount,
                         ArrayElementwiseOpTestParamCount,
                         ::testing::Values(127, 128, 129, 17 * 4096));
 
+XLA_TEST_F(ArrayElementwiseOpTest, ReducePrecisionNoOpF32) {
+  ComputationBuilder builder(client_, TestName());
+  auto a = builder.ConstantR1<float>({-2.5f, 25.5f});
+  auto reduce_precision = builder.ReducePrecision(a, 8, 23);
+
+  ComputeAndCompareR1<float>(&builder, {-2.5f, 25.5f}, {});
+}
+
+XLA_TEST_F(ArrayElementwiseOpTest, ReducePrecisionNoOpParamF32) {
+  ComputationBuilder builder(client_, TestName());
+
+  std::vector<float> a_values = {-2.5f, 25.5f};
+
+  std::unique_ptr<Literal> a_literal = LiteralUtil::CreateR1<float>({a_values});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a_param = builder.Parameter(0, a_literal->shape(), "a_param");
+
+  auto reduce_precision = builder.ReducePrecision(a_param, 8, 23);
+
+  ComputeAndCompareR1<float>(&builder, {-2.5f, 25.5f}, {a_data.get()});
+}
+
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index a95ac968dd..95c1f0995b 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -743,6 +743,12 @@ message VariadicOpRequest {
   repeated ComputationDataHandle operands = 3;
 }
 
+message ReducePrecisionRequest {
+  ComputationDataHandle operand = 1;
+  int32 exponent_bits = 2;
+  int32 mantissa_bits = 3;
+}
+
 message SendRequest {
   ComputationDataHandle operand = 1;
   ChannelHandle channel_handle = 2;
@@ -774,6 +780,7 @@ message OpRequest {
     MapRequest map_request = 15;
     PadRequest pad_request = 16;
     ParameterRequest parameter_request = 17;
+    ReducePrecisionRequest reduce_precision_request = 36;
     ReduceRequest reduce_request = 18;
     ReduceWindowRequest reduce_window_request = 19;
     ReshapeRequest reshape_request = 20;
@@ -791,7 +798,7 @@ message OpRequest {
     RecvRequest recv_request = 31;
     OutfeedRequest outfeed_request = 32;
     BatchNormTrainingRequest batch_norm_training_request = 35;
-    // Next: 36
+    // Next: 37
   }
 }
 
-- 
GitLab


From 348812b4a56ca460d2006abf1032f9ad9c86a084 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Jun 2017 17:49:58 -0700
Subject: [PATCH 0188/1375] Use new C API for Session.partial_run and its setup

PiperOrigin-RevId: 159298135
---
 tensorflow/c/c_api.cc                         |  14 +--
 tensorflow/c/c_api.h                          |   4 +-
 tensorflow/python/client/session.py           |  40 +++++--
 .../python/client/session_partial_run_test.py | 105 ++++++++++--------
 tensorflow/python/client/session_test.py      |   3 +-
 tensorflow/python/client/tf_session.i         |  43 +++++--
 tensorflow/python/client/tf_session_helper.cc |  61 +++++++---
 tensorflow/python/client/tf_session_helper.h  |  38 ++++++-
 .../python/client/tf_sessionrun_wrapper.i     |  10 +-
 9 files changed, 216 insertions(+), 102 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index 77faa475ed..b1568c5f63 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -628,7 +628,7 @@ void TF_PRunSetup(TF_DeprecatedSession* s,
                   // Target nodes
                   const char** c_target_oper_names, int ntargets,
                   const char** handle, TF_Status* status) {
-  status->status = Status::OK();
+  *handle = nullptr;
 
   std::vector<tensorflow::string> input_names(ninputs);
   std::vector<tensorflow::string> output_names(noutputs);
@@ -643,16 +643,12 @@ void TF_PRunSetup(TF_DeprecatedSession* s,
     target_oper_names[i] = c_target_oper_names[i];
   }
   tensorflow::string new_handle;
-  Status result;
-  result = s->session->PRunSetup(input_names, output_names, target_oper_names,
-                                 &new_handle);
-  if (result.ok()) {
+  status->status = s->session->PRunSetup(input_names, output_names,
+                                         target_oper_names, &new_handle);
+  if (status->status.ok()) {
     char* buf = new char[new_handle.size() + 1];
     memcpy(buf, new_handle.c_str(), new_handle.size() + 1);
     *handle = buf;
-  } else {
-    *handle = nullptr;
-    status->status = result;
   }
 }
 
@@ -2326,6 +2322,8 @@ void TF_SessionPRunSetup(TF_Session* session, const TF_Output* inputs,
                          int ninputs, const TF_Output* outputs, int noutputs,
                          const TF_Operation* const* target_opers, int ntargets,
                          const char** handle, TF_Status* status) {
+  *handle = nullptr;
+
   if (!ExtendSessionGraphHelper(session, status)) {
     return;
   }
diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h
index 15139a47ac..3aeafb4685 100644
--- a/tensorflow/c/c_api.h
+++ b/tensorflow/c/c_api.h
@@ -1101,8 +1101,7 @@ TF_CAPI_EXPORT extern void TF_SessionRun(
 // needed.
 //
 // On failure, out_status contains a tensorflow::Status with an error
-// message.
-// NOTE: This is EXPERIMENTAL and subject to change.
+// message. *handle is set to nullptr.
 TF_CAPI_EXPORT extern void TF_SessionPRunSetup(
     TF_Session*,
     // Input names
@@ -1118,7 +1117,6 @@ TF_CAPI_EXPORT extern void TF_SessionPRunSetup(
 
 // Continue to run the graph with additional feeds and fetches. The
 // execution state is uniquely identified by the handle.
-// NOTE: This is EXPERIMENTAL and subject to change.
 TF_CAPI_EXPORT extern void TF_SessionPRun(
     TF_Session*, const char* handle,
     // Input tensors
diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index ad2ee13db5..849f3cc5e1 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -971,7 +971,6 @@ class BaseSession(SessionInterface):
       TypeError: If `fetches` or `feed_dict` keys are of an inappropriate type.
       tf.errors.OpError: Or one of its subclasses if a TensorFlow error happens.
     """
-    assert not self._created_with_new_api, 'Partial runs don\'t work with C API'
 
     def _feed_fn(feed):
       for tensor_type, _, _, feed_fn in _REGISTERED_EXPANSIONS:
@@ -999,7 +998,12 @@ class BaseSession(SessionInterface):
         try:
           subfeed_t = self.graph.as_graph_element(subfeed, allow_tensor=True,
                                                   allow_operation=False)
-          feed_list.append(compat.as_bytes(subfeed_t.name))
+          if self._created_with_new_api:
+            # pylint: disable=protected-access
+            feed_list.append(subfeed_t._as_tf_output())
+            # pylint: enable=protected-access
+          else:
+            feed_list.append(compat.as_bytes(subfeed_t.name))
         except Exception as e:
           e.message = ('Cannot interpret feed_list key as Tensor: '
                        + e.message)
@@ -1014,12 +1018,24 @@ class BaseSession(SessionInterface):
     def _setup_fn(session, feed_list, fetch_list, target_list):
       self._extend_graph()
       with errors.raise_exception_on_not_ok_status() as status:
-        return tf_session.TF_PRunSetup(session, feed_list, fetch_list,
-                                       target_list, status)
+        if self._created_with_new_api:
+          return tf_session.TF_SessionPRunSetup_wrapper(
+              session, feed_list, fetch_list, target_list, status)
+        else:
+          return tf_session.TF_PRunSetup(session, feed_list, fetch_list,
+                                         target_list, status)
+
+    if self._created_with_new_api:
+      # pylint: disable=protected-access
+      final_fetches = [t._as_tf_output() for t in fetch_handler.fetches()]
+      final_targets = [op._c_op for op in fetch_handler.targets()]
+      # pylint: enable=protected-access
+    else:
+      final_fetches = _name_list(fetch_handler.fetches())
+      final_targets = _name_list(fetch_handler.targets())
 
-    return self._do_call(_setup_fn, self._session, feed_list,
-                         _name_list(fetch_handler.fetches()),
-                         _name_list(fetch_handler.targets()))
+    return self._do_call(_setup_fn, self._session, feed_list, final_fetches,
+                         final_targets)
 
   def _run(self, handle, fetches, feed_dict, options, run_metadata):
     """Perform either run or partial_run, depending the presence of `handle`."""
@@ -1248,13 +1264,15 @@ class BaseSession(SessionInterface):
                                    status, run_metadata)
 
     def _prun_fn(session, handle, feed_dict, fetch_list):
-      assert not self._created_with_new_api, ('Partial runs don\'t work with '
-                                              'C API')
       if target_list:
         raise RuntimeError('partial_run() requires empty target_list.')
       with errors.raise_exception_on_not_ok_status() as status:
-        return tf_session.TF_PRun(session, handle, feed_dict, fetch_list,
-                                  status)
+        if self._created_with_new_api:
+          return tf_session.TF_SessionPRun_wrapper(session, handle, feed_dict,
+                                                   fetch_list, status)
+        else:
+          return tf_session.TF_PRun(session, handle, feed_dict, fetch_list,
+                                    status)
 
     if handle is None:
       return self._do_call(_run_fn, self._session, feeds, fetches, targets,
diff --git a/tensorflow/python/client/session_partial_run_test.py b/tensorflow/python/client/session_partial_run_test.py
index 9e0eca2089..33b90e6156 100644
--- a/tensorflow/python/client/session_partial_run_test.py
+++ b/tensorflow/python/client/session_partial_run_test.py
@@ -33,25 +33,15 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import googletest
 from tensorflow.python.training import server_lib
 
-ops._USE_C_API = True
 
 # NOTE(mrry): Dummy shape registration for ops used in the tests, since they
 # don't have C++ op registrations on which to attach C++ shape fns.
 ops.RegisterShape('ConstructionFails')(common_shapes.unknown_shape)
 
 
-class PartialRunTest(test_util.TensorFlowTestCase):
+class PartialRunTestMethods(object):
 
-  def setUp(self):
-    # Partial runs don't work with C API
-    ops._USE_C_API = False
-    super(PartialRunTest, self).setUp()
-
-  def tearDown(self):
-    ops._USE_C_API = True
-    super(PartialRunTest, self).tearDown()
-
-  def runTestPartialRun(self, sess):
+  def RunTestPartialRun(self, sess):
     a = array_ops.placeholder(dtypes.float32, shape=[])
     b = array_ops.placeholder(dtypes.float32, shape=[])
     c = array_ops.placeholder(dtypes.float32, shape=[])
@@ -73,7 +63,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
     res = sess.partial_run(h2, r2, feed_dict={c: temp})
     self.assertEqual(162, res)
 
-  def runTestPartialRunIncomplete(self, sess):
+  def RunTestPartialRunIncomplete(self, sess):
     a = array_ops.placeholder(dtypes.float32, shape=[])
     b = array_ops.placeholder(dtypes.float32, shape=[])
     c = array_ops.placeholder(dtypes.float32, shape=[])
@@ -84,7 +74,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
     res = sess.partial_run(h, r1, feed_dict={a: 1, b: 2})
     self.assertEqual(3, res)
 
-  def runTestConcurrentPartialRun(self, sess):
+  def RunTestConcurrentPartialRun(self, sess):
     a = array_ops.placeholder(dtypes.float32, shape=[])
     b = array_ops.placeholder(dtypes.float32, shape=[])
     c = array_ops.placeholder(dtypes.float32, shape=[])
@@ -101,7 +91,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
     res = sess.partial_run(h2, r2, feed_dict={c: 7})
     self.assertEqual(462, res)
 
-  def runTestManyPartialRun(self, sess):
+  def RunTestManyPartialRun(self, sess):
     steps = 200
     inputs = []
     outputs = []
@@ -123,7 +113,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
     self.assertEqual(steps, len(res))
     self.assertEqual(2.0, res[-1])
 
-  def runTestRunAndPartialRun(self, sess):
+  def RunTestRunAndPartialRun(self, sess):
     a = constant_op.constant(2.0, dtypes.float32)
     b = a * 2
     c = b * 3
@@ -132,7 +122,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
     r2 = sess.partial_run(h, [b, c])
     self.assertEqual(r1, r2)
 
-  def runTestPartialRunMissingPlaceholderFeedException(self, sess):
+  def RunTestPartialRunMissingPlaceholderFeedException(self, sess):
     x = array_ops.placeholder(dtypes.float32, shape=())
     fetches = [x * 2, x * 3]
     handle = sess.partial_run_setup(fetches=fetches, feeds=[])
@@ -140,7 +130,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
                                  'You must feed a value for placeholder'):
       sess.partial_run(handle, fetches[0])
 
-  def runTestPartialRunUnspecifiedFeed(self, sess):
+  def RunTestPartialRunUnspecifiedFeed(self, sess):
     a = array_ops.placeholder(dtypes.float32, shape=[])
     b = array_ops.placeholder(dtypes.float32, shape=[])
     c = array_ops.placeholder(dtypes.float32, shape=[])
@@ -151,7 +141,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
                                  'was not specified in partial_run_setup.$'):
       sess.partial_run(h, r1, feed_dict={a: 1, b: 2, c: 3})
 
-  def runTestPartialRunUnspecifiedFetch(self, sess):
+  def RunTestPartialRunUnspecifiedFetch(self, sess):
     a = array_ops.placeholder(dtypes.float32, shape=[])
     b = array_ops.placeholder(dtypes.float32, shape=[])
     c = array_ops.placeholder(dtypes.float32, shape=[])
@@ -163,7 +153,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
                                  'was not specified in partial_run_setup.$'):
       sess.partial_run(h, r2, feed_dict={a: 1, c: 3})
 
-  def runTestPartialRunAlreadyFed(self, sess):
+  def RunTestPartialRunAlreadyFed(self, sess):
     a = array_ops.placeholder(dtypes.float32, shape=[])
     b = array_ops.placeholder(dtypes.float32, shape=[])
     c = array_ops.placeholder(dtypes.float32, shape=[])
@@ -176,7 +166,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
                                  'has already been fed.$'):
       sess.partial_run(h, r2, feed_dict={a: 1, c: 3})
 
-  def runTestPartialRunAlreadyFetched(self, sess):
+  def RunTestPartialRunAlreadyFetched(self, sess):
     a = array_ops.placeholder(dtypes.float32, shape=[])
     b = array_ops.placeholder(dtypes.float32, shape=[])
     c = array_ops.placeholder(dtypes.float32, shape=[])
@@ -189,7 +179,7 @@ class PartialRunTest(test_util.TensorFlowTestCase):
                                  'has already been fetched.$'):
       sess.partial_run(h, r1, feed_dict={c: 3})
 
-  def runTestPartialRunEmptyFetches(self, sess):
+  def RunTestPartialRunEmptyFetches(self, sess):
     a = array_ops.placeholder(dtypes.float32)
     b = a * 2.0
 
@@ -207,82 +197,109 @@ class PartialRunTest(test_util.TensorFlowTestCase):
       sess.partial_run_setup(fetches=[], feeds=[x])
 
   def testPartialRunDirect(self):
-    self.runTestPartialRun(session.Session())
+    self.RunTestPartialRun(session.Session())
 
   def testPartialRunIncompleteDirect(self):
-    self.runTestPartialRunIncomplete(session.Session())
+    self.RunTestPartialRunIncomplete(session.Session())
 
   def testConcurrentPartialRunDirect(self):
-    self.runTestConcurrentPartialRun(session.Session())
+    self.RunTestConcurrentPartialRun(session.Session())
 
   def testManyPartialRunDirect(self):
-    self.runTestManyPartialRun(session.Session())
+    self.RunTestManyPartialRun(session.Session())
 
   def testRunAndPartialRunDirect(self):
-    self.runTestRunAndPartialRun(session.Session())
+    self.RunTestRunAndPartialRun(session.Session())
 
   def testPartialRunMissingPlaceholderFeedExceptionDirect(self):
-    self.runTestPartialRunMissingPlaceholderFeedException(session.Session())
+    self.RunTestPartialRunMissingPlaceholderFeedException(session.Session())
 
   def testPartialRunUnspecifiedFeedDirect(self):
-    self.runTestPartialRunUnspecifiedFeed(session.Session())
+    self.RunTestPartialRunUnspecifiedFeed(session.Session())
 
   def testPartialRunUnspecifiedFetchDirect(self):
-    self.runTestPartialRunUnspecifiedFetch(session.Session())
+    self.RunTestPartialRunUnspecifiedFetch(session.Session())
 
   def testPartialRunAlreadyFedDirect(self):
-    self.runTestPartialRunAlreadyFed(session.Session())
+    self.RunTestPartialRunAlreadyFed(session.Session())
 
   def testPartialRunAlreadyFetchedDirect(self):
-    self.runTestPartialRunAlreadyFetched(session.Session())
+    self.RunTestPartialRunAlreadyFetched(session.Session())
 
   def testPartialRunEmptyFetchesDirect(self):
-    self.runTestPartialRunEmptyFetches(session.Session())
+    self.RunTestPartialRunEmptyFetches(session.Session())
 
   def testPartialRunDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRun(session.Session(server.target))
+    self.RunTestPartialRun(session.Session(server.target))
 
   def testPartialRunIncompleteDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRunIncomplete(session.Session(server.target))
+    self.RunTestPartialRunIncomplete(session.Session(server.target))
 
   def testConcurrentPartialRunDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestConcurrentPartialRun(session.Session(server.target))
+    self.RunTestConcurrentPartialRun(session.Session(server.target))
 
   def testManyPartialRunDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestManyPartialRun(session.Session(server.target))
+    self.RunTestManyPartialRun(session.Session(server.target))
 
   def testRunAndPartialRunDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestRunAndPartialRun(session.Session(server.target))
+    self.RunTestRunAndPartialRun(session.Session(server.target))
 
   def testPartialRunMissingPlaceholderFeedExceptionDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRunMissingPlaceholderFeedException(
+    self.RunTestPartialRunMissingPlaceholderFeedException(
         session.Session(server.target))
 
   def testPartialRunUnspecifiedFeedDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRunUnspecifiedFeed(session.Session(server.target))
+    self.RunTestPartialRunUnspecifiedFeed(session.Session(server.target))
 
   def testPartialRunUnspecifiedFetchDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRunUnspecifiedFetch(session.Session(server.target))
+    self.RunTestPartialRunUnspecifiedFetch(session.Session(server.target))
 
   def testPartialRunAlreadyFedDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRunAlreadyFed(session.Session(server.target))
+    self.RunTestPartialRunAlreadyFed(session.Session(server.target))
 
   def testPartialRunAlreadyFetchedDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRunAlreadyFetched(session.Session(server.target))
+    self.RunTestPartialRunAlreadyFetched(session.Session(server.target))
 
   def testPartialRunEmptyFetchesDist(self):
     server = server_lib.Server.create_local_server()
-    self.runTestPartialRunEmptyFetches(session.Session(server.target))
+    self.RunTestPartialRunEmptyFetches(session.Session(server.target))
+
+
+class PartialRunTest(PartialRunTestMethods, test_util.TensorFlowTestCase):
+  """Test case that invokes test methods with _USE_C_API=False."""
+
+  def setUp(self):
+    self.prev_use_c_api = ops._USE_C_API
+    ops._USE_C_API = False
+    super(PartialRunTest, self).setUp()
+
+  def tearDown(self):
+    ops._USE_C_API = self.prev_use_c_api
+    super(PartialRunTest, self).tearDown()
+
+
+class PartialRunWithCApiTest(PartialRunTestMethods,
+                             test_util.TensorFlowTestCase):
+  """Test case that invokes test methods with _USE_C_API=True."""
+
+  def setUp(self):
+    self.prev_use_c_api = ops._USE_C_API
+    ops._USE_C_API = True
+    super(PartialRunWithCApiTest, self).setUp()
+
+  def tearDown(self):
+    ops._USE_C_API = self.prev_use_c_api
+    super(PartialRunWithCApiTest, self).tearDown()
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index a1f98059cd..ef8f25c873 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -140,7 +140,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       with self.assertRaisesOpError(lambda e: e.op == a.op):
         a.eval()
 
-  @test_util.disable_c_api  # Partial runs don't work with C API
   def testErrorCodeWithNoNodeDef(self):
     with session.Session() as s:
       a = array_ops.placeholder(dtypes.float32, shape=[])
@@ -1525,7 +1524,7 @@ class SessionTest(test_util.TensorFlowTestCase):
         sess.run(enqueue_op)
       self.assertEqual(sess.run(q.size()), num_epochs * 2)
 
-  @test_util.disable_c_api  # Partial runs don't work with C API
+  @test_util.disable_c_api  # set_device does not work with C API
   def testRegisterFetchAndFeedConversionFunctions(self):
     class SquaredTensor(object):
       def __init__(self, tensor):
diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index 4a62a96935..e46ba021dc 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -112,6 +112,8 @@ tensorflow::ImportNumpy();
     tensorflow::PyObjectVector temp) {
   $1 = &temp;
 }
+// TODO(iga): move this and the corresponding typemap(argout) to
+// tf_sessionrun_wrapper.i once we get rid of this code for DeprecatedSession.
 %typemap(in, numinputs=0) char** out_handle (
     char* temp) {
   $1 = &temp;
@@ -142,7 +144,7 @@ tensorflow::ImportNumpy();
 %#else
   $result = PyUnicode_FromStringAndSize(
 %#endif
-    *$1, strlen(*$1));
+    *$1, *$1 == nullptr ? 0 : strlen(*$1));
   delete[] *$1;
 }
 
@@ -163,29 +165,37 @@ tensorflow::ImportNumpy();
 // Helper function to convert a Python list of Tensors to a C++ vector of
 // TF_Outputs.
 //
-// Caller should have already checked that `py_tensor_list` is a list (this
-// isn't done in this function to allow for function-specific error messages)
-void PyTensorListToVector(PyObject* py_tensor_list,
-                          std::vector<TF_Output>* vec) {
+// Returns true if successful. Otherwise, returns false and sets error_msg.
+bool PyTensorListToVector(PyObject* py_tensor_list,
+                          std::vector<TF_Output>* vec,
+                          string* error_msg) {
+  if (!PyList_Check(py_tensor_list)) {
+    *error_msg = "expected Python list.";
+    return false;
+  }
   size_t size = PyList_Size(py_tensor_list);
   for (int i = 0; i < size; ++i) {
     PyObject* item = PyList_GetItem(py_tensor_list, i);
     TF_Output* input_ptr;
-    SWIG_ConvertPtr(item, reinterpret_cast<void**>(&input_ptr),
-                    SWIGTYPE_p_TF_Output, 0);
+    if (!SWIG_IsOK(SWIG_ConvertPtr(item, reinterpret_cast<void**>(&input_ptr),
+                                   SWIGTYPE_p_TF_Output, 0))) {
+      *error_msg = "expected Python list of wrapped TF_Output objects. "
+          "Found python list of something else.";
+      return false;
+    }
     vec->push_back(*input_ptr);
   }
+  return true;
 }
 %}
 
 // Converts input Python list of wrapped TF_Outputs into a single array
 %typemap(in) (const TF_Output* inputs, int num_inputs)
     (std::vector<TF_Output> inputs) {
-  if (!PyList_Check($input)) {
-    SWIG_exception_fail(
-        SWIG_TypeError, "$symname: expected Python list of wrapped TF_Outputs");
+  string error_msg;
+  if (!PyTensorListToVector($input, &inputs, &error_msg)) {
+    SWIG_exception_fail(SWIG_TypeError, ("$symname: " + error_msg).c_str());
   }
-  PyTensorListToVector($input, &inputs);
   $1 = inputs.data();
   $2 = inputs.size();
 }
@@ -211,6 +221,17 @@ void PyTensorListToVector(PyObject* py_tensor_list,
 // PyArray_Return, maybe others).
 %noexception TF_SessionRun_wrapper;
 
+// We use TF_SessionPRunSetup_wrapper instead of TF_SessionPRunSetup
+%ignore TF_SessionPRunSetup;
+%unignore TF_SessionPRunSetup_wrapper;
+// See comment for "%noexception TF_SessionRun_wrapper;"
+%noexception TF_SessionPRunSetup_wrapper;
+
+// We use TF_SessionPRun_wrapper instead of TF_SessionPRun
+%ignore TF_SessionPRun;
+%unignore TF_SessionPRun_wrapper;
+// See comment for "%noexception TF_SessionRun_wrapper;"
+%noexception TF_SessionPRun_wrapper;
 
 %rename("_TF_SetTarget") TF_SetTarget;
 %rename("_TF_SetConfig") TF_SetConfig;
diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc
index 86088d0ab4..7ebb1a7fe4 100644
--- a/tensorflow/python/client/tf_session_helper.cc
+++ b/tensorflow/python/client/tf_session_helper.cc
@@ -594,14 +594,6 @@ void TF_PRunSetup_wrapper(TF_DeprecatedSession* session,
       const_cast<const char**>(output_names.data()), output_names.size(),
       const_cast<const char**>(target_nodes.data()), target_nodes.size(),
       out_handle, out_status);
-  // TF_PRunSetup leaves out_handle undefined if it fails, but SWIG will call
-  // free(out_handle) on the returned handle regardless. Thus, must make sure it
-  // is valid.
-  if (TF_GetCode(out_status) != TF_OK) {
-    char* tmp = new char[1];
-    tmp[0] = '\0';
-    *out_handle = tmp;
-  }
   Py_END_ALLOW_THREADS;
 }
 
@@ -623,7 +615,7 @@ void TF_Reset_wrapper(const TF_SessionOptions* opt,
            out_status);
 }
 
-void TF_SessionRun_wrapper_helper(TF_Session* session,
+void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
                                   const TF_Buffer* run_options,
                                   const std::vector<TF_Output>& inputs,
                                   const std::vector<PyObject*>& input_ndarrays,
@@ -678,10 +670,16 @@ void TF_SessionRun_wrapper_helper(TF_Session* session,
 
   // Call TF_SessionRun() (and release GIL during execution)
   Py_BEGIN_ALLOW_THREADS;
-  TF_SessionRun(session, run_options, inputs.data(), input_vals.data(),
-                inputs.size(), outputs.data(), output_vals.data(),
-                outputs.size(), targets.data(), targets.size(), run_metadata,
-                out_status);
+  if (handle == nullptr) {
+    TF_SessionRun(session, run_options, inputs.data(), input_vals.data(),
+                  inputs.size(), outputs.data(), output_vals.data(),
+                  outputs.size(), targets.data(), targets.size(), run_metadata,
+                  out_status);
+  } else {
+    TF_SessionPRun(session, handle, inputs.data(), input_vals.data(),
+                   inputs.size(), outputs.data(), output_vals.data(),
+                   outputs.size(), targets.data(), targets.size(), out_status);
+  }
   Py_END_ALLOW_THREADS;
 
   // Create scoped containers for output tensors
@@ -716,9 +714,9 @@ void TF_SessionRun_wrapper(TF_Session* session, const TF_Buffer* run_options,
                            const std::vector<TF_Operation*>& targets,
                            TF_Buffer* run_metadata, TF_Status* out_status,
                            std::vector<PyObject*>* py_outputs) {
-  TF_SessionRun_wrapper_helper(session, run_options, inputs, input_ndarrays,
-                               outputs, targets, run_metadata, out_status,
-                               py_outputs);
+  TF_SessionRun_wrapper_helper(session, nullptr, run_options, inputs,
+                               input_ndarrays, outputs, targets, run_metadata,
+                               out_status, py_outputs);
   // Release any unused ndarray references (see memory management comment in
   // TF_SessionRun_wrapper_helper)
   ClearDecrefCache();
@@ -737,4 +735,35 @@ string EqualGraphDefWrapper(const string& actual, const string& expected) {
   return EqualGraphDef(actual_def, expected_def, &diff) ? "" : diff;
 }
 
+void TF_SessionPRunSetup_wrapper(TF_Session* session,
+                                 const std::vector<TF_Output>& inputs,
+                                 const std::vector<TF_Output>& outputs,
+                                 const std::vector<TF_Operation*>& targets,
+                                 const char** out_handle,
+                                 TF_Status* out_status) {
+  // Call TF_SessionPRunSetup() (and release GIL during execution)
+  Py_BEGIN_ALLOW_THREADS;
+  TF_SessionPRunSetup(session, inputs.data(), inputs.size(), outputs.data(),
+                      outputs.size(), targets.data(), targets.size(),
+                      out_handle, out_status);
+  Py_END_ALLOW_THREADS;
+}
+
+void TF_SessionPRun_wrapper(TF_Session* session, const char* handle,
+                            const std::vector<TF_Output>& inputs,
+                            const std::vector<PyObject*>& input_ndarrays,
+                            const std::vector<TF_Output>& outputs,
+                            TF_Status* out_status,
+                            std::vector<PyObject*>* py_outputs) {
+  const std::vector<TF_Operation*> targets;
+  TF_SessionRun_wrapper_helper(session, handle,
+                               nullptr,  // run_options
+                               inputs, input_ndarrays, outputs, targets,
+                               nullptr,  // run_metadata
+                               out_status, py_outputs);
+  // Release any unused ndarray references (see memory management comment in
+  // TF_SessionRun_wrapper_helper)
+  ClearDecrefCache();
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h
index 727e8ade52..9937b6aeeb 100644
--- a/tensorflow/python/client/tf_session_helper.h
+++ b/tensorflow/python/client/tf_session_helper.h
@@ -81,8 +81,6 @@ void TF_Run_wrapper(TF_DeprecatedSession* session, const TF_Buffer* run_options,
 //
 // On failure, out_status contains a tensorflow::Status with an error
 // message.
-//
-// NOTE: This is EXPERIMENTAL and subject to change.
 void TF_PRunSetup_wrapper(TF_DeprecatedSession* session,
                           const NameVector& input_names,
                           const NameVector& output_names,
@@ -101,8 +99,6 @@ void TF_PRunSetup_wrapper(TF_DeprecatedSession* session,
 //
 // On failure,  out_status contains a tensorflow::Status with an error
 // message.
-//
-// NOTE: This is EXPERIMENTAL and subject to change.
 void TF_PRun_wrapper(TF_DeprecatedSession* session, const char* handle,
                      PyObject* feed_dict, const NameVector& output_names,
                      TF_Status* out_status, PyObjectVector* out_values);
@@ -128,6 +124,40 @@ void TF_SessionRun_wrapper(TF_Session* session, const TF_Buffer* run_options,
                            const std::vector<TF_Operation*>& targets,
                            TF_Buffer* run_metadata, TF_Status* out_status,
                            std::vector<PyObject*>* py_outputs);
+
+// Set up the graph with the intended feeds (inputs) and fetches (output) for
+// a sequence of partial run calls.
+//
+// On success, returns a handle that can be used for subsequent PRun calls. The
+// handle is owned by the caller and should be deleted with TF_DeletePRunHandle
+// when it is no longer needed.
+//
+// On failure, out_status contains a tensorflow::Status with an error
+// message.
+void TF_SessionPRunSetup_wrapper(TF_Session* session,
+                                 const std::vector<TF_Output>& inputs,
+                                 const std::vector<TF_Output>& outputs,
+                                 const std::vector<TF_Operation*>& targets,
+                                 const char** out_handle,
+                                 TF_Status* out_status);
+
+// Continue to run the graph with additional feeds and fetches. The
+// execution state is uniquely identified by the handle.
+//
+// On success, `py_outputs` is populated with a numpy ndarray for each output
+// (the caller must decref these ndarrays, although this will likely be handled
+// by the Python gc). `session`, `handle`, `out_status`, and `py_outputs` must
+// be non-null. `py_outputs` should be empty.
+//
+// On failure, out_status contains a tensorflow::Status with an error
+// message.
+void TF_SessionPRun_wrapper(TF_Session* session, const char* handle,
+                            const std::vector<TF_Output>& inputs,
+                            const std::vector<PyObject*>& input_ndarrays,
+                            const std::vector<TF_Output>& outputs,
+                            TF_Status* out_status,
+                            std::vector<PyObject*>* py_outputs);
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
diff --git a/tensorflow/python/client/tf_sessionrun_wrapper.i b/tensorflow/python/client/tf_sessionrun_wrapper.i
index 289792fef2..473bc3ccc5 100644
--- a/tensorflow/python/client/tf_sessionrun_wrapper.i
+++ b/tensorflow/python/client/tf_sessionrun_wrapper.i
@@ -73,13 +73,17 @@ tensorflow::ImportNumpy();
 // $input is a Python list of wrapped TF_Outputs
 %typemap(in) (const std::vector<TF_Output>& outputs)
     (std::vector<TF_Output> outputs) {
-  if (!PyList_Check($input)) {
-    SWIG_exception_fail(SWIG_TypeError, "$symname: expected list");
+  string error_msg;
+  if (!PyTensorListToVector($input, &outputs, &error_msg)) {
+    SWIG_exception_fail(SWIG_TypeError, ("$symname: " + error_msg).c_str());
   }
-  PyTensorListToVector($input, &outputs);
   $1 = &outputs;
 }
 
+// Apply the typemap above to inputs as well
+%typemap(in) (const std::vector<TF_Output>& inputs) =
+             (const std::vector<TF_Output>& outputs);
+
 // Create temporary py_outputs_vec variable to store return value
 %typemap(in, numinputs=0) (std::vector<PyObject*>* py_outputs)
     (std::vector<PyObject*> py_outputs_vec) {
-- 
GitLab


From 8560a4be26ca8fe4ef036230b2e77ae2956ca188 Mon Sep 17 00:00:00 2001
From: Adriano Carmezim <Carmezim.filho@gmail.com>
Date: Sat, 17 Jun 2017 04:02:49 +0100
Subject: [PATCH 0189/1375] Explicitly state Python targeting 64-bit

Reference: https://github.com/tensorflow/tensorflow/issues/10786#issuecomment-309187925
---
 tensorflow/docs_src/install/install_windows.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 9462a096dd..8282afaab4 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -74,7 +74,7 @@ Use that package at your own risk.
 If the following version of Python is not installed on your machine,
 install it now:
 
-  * [Python 3.5.x from python.org](https://www.python.org/downloads/release/python-352/)
+  * [Python 3.5.x 64-bit from python.org](https://www.python.org/downloads/release/python-352/)
 
 TensorFlow only supports version 3.5.x of Python on Windows.
 Note that Python 3.5.x comes with the pip3 package manager, which is the
-- 
GitLab


From 817c4db19e9b562681c0230796b9463fe46148f8 Mon Sep 17 00:00:00 2001
From: Sahil Dua <sahildua2305@gmail.com>
Date: Sat, 17 Jun 2017 18:52:03 +0200
Subject: [PATCH 0190/1375] Tutorial: Pass train_file.name instead of
 train_file

Change train_file to train_file.name in tutorial for Linear model.
---
 tensorflow/docs_src/tutorials/wide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index b87efedb80..24c866eee5 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -69,8 +69,8 @@ COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
            "marital_status", "occupation", "relationship", "race", "gender",
            "capital_gain", "capital_loss", "hours_per_week", "native_country",
            "income_bracket"]
-df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
-df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1)
+df_train = pd.read_csv(train_file.name, names=COLUMNS, skipinitialspace=True)
+df_test = pd.read_csv(test_file.name, names=COLUMNS, skipinitialspace=True, skiprows=1)
 ```
 
 Since the task is a binary classification problem, we'll construct a label
-- 
GitLab


From 360bcbecf9b807e89dfc1200b1d8cb7fcd53b077 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sun, 18 Jun 2017 14:25:07 -0700
Subject: [PATCH 0191/1375] Selected BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 159373397
---
 tensorflow/contrib/bayesflow/BUILD        |  2 +-
 tensorflow/contrib/cloud/BUILD            |  4 ++--
 tensorflow/contrib/cluster_resolver/BUILD |  2 +-
 tensorflow/contrib/crf/BUILD              |  3 ++-
 tensorflow/contrib/ffmpeg/BUILD           |  3 +--
 tensorflow/contrib/framework/BUILD        | 10 ++++------
 tensorflow/contrib/graph_editor/BUILD     | 22 ++++++----------------
 tensorflow/contrib/grid_rnn/BUILD         |  1 +
 tensorflow/contrib/input_pipeline/BUILD   |  7 ++++---
 tensorflow/contrib/nn/BUILD               |  5 ++++-
 tensorflow/contrib/quantization/BUILD     |  5 +----
 tensorflow/contrib/rnn/BUILD              |  7 +++++++
 12 files changed, 34 insertions(+), 37 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 18df14d336..3edcb6bc20 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -16,7 +16,6 @@ py_library(
     srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
@@ -26,6 +25,7 @@ py_library(
         "//tensorflow/python:training",
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
+        "//tensorflow/python/ops/distributions",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/contrib/cloud/BUILD b/tensorflow/contrib/cloud/BUILD
index 840997223f..eec2beddc4 100644
--- a/tensorflow/contrib/cloud/BUILD
+++ b/tensorflow/contrib/cloud/BUILD
@@ -49,9 +49,9 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":gen_bigquery_reader_ops",
-        "//tensorflow/contrib/util:util_py",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:platform",
+        "//tensorflow/python:io_ops",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD
index 6792ebd615..0dfc5a81d5 100644
--- a/tensorflow/contrib/cluster_resolver/BUILD
+++ b/tensorflow/contrib/cluster_resolver/BUILD
@@ -29,7 +29,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:framework",
+        "//tensorflow/python:training",
     ],
 )
 
diff --git a/tensorflow/contrib/crf/BUILD b/tensorflow/contrib/crf/BUILD
index e82d2cf6f8..7aad4abdb9 100644
--- a/tensorflow/contrib/crf/BUILD
+++ b/tensorflow/contrib/crf/BUILD
@@ -15,11 +15,12 @@ py_library(
     srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/rnn:rnn_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:rnn",
+        "//tensorflow/python:rnn_cell",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//third_party/py/numpy",
     ],
diff --git a/tensorflow/contrib/ffmpeg/BUILD b/tensorflow/contrib/ffmpeg/BUILD
index 31a286939b..e495ab4880 100644
--- a/tensorflow/contrib/ffmpeg/BUILD
+++ b/tensorflow/contrib/ffmpeg/BUILD
@@ -124,10 +124,9 @@ py_library(
         ":decode_audio_op_py",
         ":encode_audio_op_py",
         "//tensorflow/contrib/util:util_py",
-        "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index 61fe729fd7..a953c04c1a 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -178,7 +178,6 @@ py_test(
     deps = [
         ":framework_py",
         "//tensorflow/python:client_testlib",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -198,7 +197,6 @@ py_test(
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -211,7 +209,6 @@ py_test(
         ":framework_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -223,9 +220,9 @@ py_test(
     deps = [
         ":framework_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:tensor_array_ops",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -241,7 +238,6 @@ py_test(
         ":framework_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:platform",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -255,8 +251,8 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
@@ -308,6 +304,7 @@ py_test(
     tags = ["no_pip"],
     deps = [
         ":framework_py",
+        ":gen_checkpoint_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
@@ -316,6 +313,7 @@ py_test(
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:partitioned_variables",
+        "//tensorflow/python:platform",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
diff --git a/tensorflow/contrib/graph_editor/BUILD b/tensorflow/contrib/graph_editor/BUILD
index d570a6d702..bdb58fb543 100644
--- a/tensorflow/contrib/graph_editor/BUILD
+++ b/tensorflow/contrib/graph_editor/BUILD
@@ -25,6 +25,7 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "//tensorflow/python:util",
         "@six_archive//:six",
     ],
 )
@@ -54,7 +55,11 @@ py_library(
     name = "match",
     srcs = ["tests/match.py"],
     srcs_version = "PY2AND3",
-    deps = [":graph_editor_py"],
+    deps = [
+        ":graph_editor_py",
+        "//tensorflow/python:framework_ops",
+        "@six_archive//:six",
+    ],
 )
 
 py_test(
@@ -66,9 +71,7 @@ py_test(
         ":graph_editor_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -81,9 +84,7 @@ py_test(
         ":graph_editor_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -93,13 +94,10 @@ py_test(
     srcs = ["tests/match_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":graph_editor_py",
         ":match",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -112,9 +110,7 @@ py_test(
         ":graph_editor_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -128,9 +124,7 @@ py_test(
         ":match",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -144,9 +138,7 @@ py_test(
         ":match",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -163,9 +155,7 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
diff --git a/tensorflow/contrib/grid_rnn/BUILD b/tensorflow/contrib/grid_rnn/BUILD
index 73473becf9..7fbb9f024c 100644
--- a/tensorflow/contrib/grid_rnn/BUILD
+++ b/tensorflow/contrib/grid_rnn/BUILD
@@ -20,6 +20,7 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
+        "//tensorflow/python:platform",
         "//tensorflow/python:variable_scope",
     ],
 )
diff --git a/tensorflow/contrib/input_pipeline/BUILD b/tensorflow/contrib/input_pipeline/BUILD
index 9aa5763efc..bb7857eb99 100644
--- a/tensorflow/contrib/input_pipeline/BUILD
+++ b/tensorflow/contrib/input_pipeline/BUILD
@@ -82,9 +82,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":input_pipeline_py",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:variables",
     ],
diff --git a/tensorflow/contrib/nn/BUILD b/tensorflow/contrib/nn/BUILD
index 13a05bf378..dbac049d83 100644
--- a/tensorflow/contrib/nn/BUILD
+++ b/tensorflow/contrib/nn/BUILD
@@ -17,7 +17,10 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
-    deps = ["//tensorflow/python:nn"],
+    deps = [
+        "//tensorflow/python:nn",
+        "//tensorflow/python:util",
+    ],
 )
 
 filegroup(
diff --git a/tensorflow/contrib/quantization/BUILD b/tensorflow/contrib/quantization/BUILD
index b1d12cc510..c19a31afb2 100644
--- a/tensorflow/contrib/quantization/BUILD
+++ b/tensorflow/contrib/quantization/BUILD
@@ -35,13 +35,10 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:array_ops",
         "//tensorflow/python:array_ops_gen",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:common_shapes",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:math_ops",
         "//tensorflow/python:math_ops_gen",
-        "//tensorflow/python:nn_ops",
         "//tensorflow/python:nn_ops_gen",
     ],
 )
diff --git a/tensorflow/contrib/rnn/BUILD b/tensorflow/contrib/rnn/BUILD
index 42db1ad01a..835f7df8b2 100644
--- a/tensorflow/contrib/rnn/BUILD
+++ b/tensorflow/contrib/rnn/BUILD
@@ -360,7 +360,10 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:client",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:platform",
+        "//tensorflow/python:pywrap_tensorflow",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
     ],
@@ -374,6 +377,10 @@ py_test(
     tags = ["no_pip"],
     deps = [
         ":checkpoint_convert",
+        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
     ],
 )
-- 
GitLab


From 4db617d50b7af82e67ed03a41a26a5bc10f36f78 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sun, 18 Jun 2017 14:48:11 -0700
Subject: [PATCH 0192/1375] Don't eliminate placeholders during constant
 subexpression elimination optimization.

PiperOrigin-RevId: 159374219
---
 tensorflow/core/graph/optimizer_cse.cc | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/tensorflow/core/graph/optimizer_cse.cc b/tensorflow/core/graph/optimizer_cse.cc
index a22a9b3fa3..54cfd10cdf 100644
--- a/tensorflow/core/graph/optimizer_cse.cc
+++ b/tensorflow/core/graph/optimizer_cse.cc
@@ -187,6 +187,12 @@ bool OptimizerCSE::Optimize(
   for (Node* n : order) {
     if (!n->IsOp()) continue;
 
+    // Don't prune placeholder nodes.
+    if (n->def().op() == "Placeholder" || n->def().op() == "PlaceholderV2" ||
+        n->def().op() == "PlaceholderWithDefault") {
+      continue;
+    }
+
     // See if we should consider this node at all
     if (consider_fn != nullptr && !consider_fn(n)) continue;
 
@@ -204,6 +210,7 @@ bool OptimizerCSE::Optimize(
       for (const Edge* e : n->out_edges()) {
         g_->AddEdge(*candidate, e->src_output(), e->dst(), e->dst_input());
       }
+
       g_->RemoveNode(n);
       changed = true;
     }
-- 
GitLab


From 19216d7b433c20c0c85b6d7e38cc97e28c833e8e Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Sun, 18 Jun 2017 21:50:17 -0700
Subject: [PATCH 0193/1375] Add missing dep for tf_optimizer to
 cost_analyzer_tool.py

PiperOrigin-RevId: 159388465
---
 tensorflow/python/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 0566a7d019..64ebbc603d 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3904,6 +3904,7 @@ py_binary(
     deps = [
         ":cost_analyzer",
         ":framework_for_generated_wrappers",
+        ":tf_optimizer",
         "//tensorflow/core:protos_all_py",
     ],
 )
-- 
GitLab


From c26146a70667ff4ceec30fbcf583f551feab351b Mon Sep 17 00:00:00 2001
From: Mark Neumann <markn@allenai.org>
Date: Sun, 18 Jun 2017 23:04:38 -0700
Subject: [PATCH 0194/1375] add log_softmax and test

---
 tensorflow/cc/gradients/nn_grad.cc      | 13 +++++++++++++
 tensorflow/cc/gradients/nn_grad_test.cc |  7 +++++++
 2 files changed, 20 insertions(+)

diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc
index 5e5203d090..952b2015ed 100644
--- a/tensorflow/cc/gradients/nn_grad.cc
+++ b/tensorflow/cc/gradients/nn_grad.cc
@@ -46,6 +46,19 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad);
 
+Status LogSoftmaxGrad(const Scope& scope, const Operation& op,
+                   const std::vector<Output>& grad_inputs,
+                   std::vector<Output>* grad_outputs) {
+
+  auto softmax = Exp(scope, op.output(0));
+  auto sum = Sum(scope, grad_inputs[0], {1}, Sum::KeepDims(true));
+  auto mul = Mul(scope, sum, softmax);
+  auto dx = Sub(scope, grad_inputs[0], mul);
+  grad_outputs->push_back(dx);
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("LogSoftmax", LogSoftmaxGrad);
+
 Status ReluGradHelper(const Scope& scope, const Operation& op,
                       const std::vector<Output>& grad_inputs,
                       std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc
index 70c9bd4e08..de6b614037 100644
--- a/tensorflow/cc/gradients/nn_grad_test.cc
+++ b/tensorflow/cc/gradients/nn_grad_test.cc
@@ -57,6 +57,13 @@ TEST_F(NNGradTest, SoftmaxGrad) {
   RunTest(x, shape, y, shape);
 }
 
+TEST_F(NNGradTest, LogSoftmaxGrad) {
+  TensorShape shape({32, 10});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
+  auto y = LogSoftmax(scope_, x);
+  RunTest(x, shape, y, shape);
+}
+
 TEST_F(NNGradTest, ReluGrad) {
   TensorShape shape({5, 2});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
-- 
GitLab


From 824f13801e0653d517c7f6b083295967c4c2dee8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 02:45:48 -0700
Subject: [PATCH 0195/1375] In the CUDA path of depthwise_conv2d, merge
 'Forward' and 'BackpropInput' variants of the optimized 'Small' kernels as
 their implementations are almost equal. Reduce kernel launch boiler plate
 code.

PiperOrigin-RevId: 159405089
---
 .../core/kernels/depthwise_conv_op_gpu.cu.cc  | 647 ++++--------------
 1 file changed, 135 insertions(+), 512 deletions(-)

diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
index 038c596207..f63a99a730 100644
--- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
@@ -59,6 +59,10 @@ EIGEN_DEVICE_FUNC bool CanLaunchDepthwiseConv2dBackpropFilterGPUSmall(
          args.filter_rows * args.filter_cols <= args.in_cols * block_rows;
 }
 
+// The DepthwiseConv2dGPUKernels perform either forward or backprop input
+// convolution depending on a template argument of this enum.
+enum DepthwiseConv2dDirection { DIRECTION_FORWARD, DIRECTION_BACKWARD };
+
 // A Cuda kernel to compute the depthwise convolution forward pass
 // in NHWC format.
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
@@ -148,8 +152,11 @@ __global__ void __launch_bounds__(1024, 2)
 // Tiles of the input and filter tensors are loaded into shared memory before
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+// Backprop input direction is the same as forward direction with the filter
+// rotated by 180°.
+template <typename T, DepthwiseConv2dDirection kDirection,
+          int kKnownFilterWidth, int kKnownFilterHeight, int kBlockSlices,
+          bool kKnownEvenRows>
 __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
   assert(CanLaunchDepthwiseConv2dGPUSmall(args));
@@ -217,7 +224,9 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
   const int max_depth = in_depth - thread_depth;
   const int filter_write_offset =
       thread_pix < filter_pixels ? tile_size + thread_idx : 0;
-  const int filter_read_offset = tile_size + thread_depth;
+  const int filter_read_offset =
+      tile_size + thread_depth +
+      (kDirection == DIRECTION_FORWARD ? 0 : filter_pixels * kBlockSlices);
   const bool skip_second =
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
@@ -253,12 +262,17 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
       const T* filter_ptr = filter_read_offset + shared_data;
       UNROLL for (int r = 0; r < filter_rows; ++r) {
         UNROLL for (int c = 0; c < filter_cols; ++c) {
+          if (kDirection == DIRECTION_BACKWARD) {
+            filter_ptr -= kBlockSlices;
+          }
           const T filter_value = *filter_ptr;
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
           shared_offset += kBlockSlices;
-          filter_ptr += kBlockSlices;
+          if (kDirection == DIRECTION_FORWARD) {
+            filter_ptr += kBlockSlices;
+          }
         }
         shared_offset += in_increment;
       }
@@ -408,8 +422,11 @@ __global__ void __launch_bounds__(1024, 2)
 // Tiles of the input and filter tensors are loaded into shared memory before
 // performing the convolution. Each thread handles two elements per iteration,
 // one each in the lower and upper half of a tile.
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+// Backprop input direction is the same as forward direction with the filter
+// rotated by 180°.
+template <typename T, DepthwiseConv2dDirection kDirection,
+          int kKnownFilterWidth, int kKnownFilterHeight, int kBlockSlices,
+          bool kKnownEvenRows>
 __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
     const DepthwiseArgs args, const T* input, const T* filter, T* output) {
   assert(CanLaunchDepthwiseConv2dGPUSmall(args));
@@ -480,7 +497,9 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
   const int max_slice = in_slices - thread_depth;
   const int filter_write_offset =
       filter_pix < filter_pixels ? tile_size + thread_idx : 0;
-  const int filter_read_offset = tile_size + thread_depth;
+  const int filter_read_offset =
+      tile_size + thread_depth +
+      (kDirection == DIRECTION_FORWARD ? 0 : filter_pixels * kBlockSlices);
   const bool skip_second =
       !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
 
@@ -514,12 +533,17 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
       const T* filter_ptr = filter_read_offset + shared_data;
       UNROLL for (int r = 0; r < filter_rows; ++r) {
         UNROLL for (int c = 0; c < filter_cols; ++c) {
+          if (kDirection == DIRECTION_BACKWARD) {
+            filter_ptr -= kBlockSlices;
+          }
           const T filter_value = *filter_ptr;
           const T* const tile_ptr = shared_offset + shared_data;
           sum1 += filter_value * tile_ptr[0];
           sum2 += filter_value * tile_ptr[tile_offset];
           ++shared_offset;
-          filter_ptr += kBlockSlices;
+          if (kDirection == DIRECTION_FORWARD) {
+            filter_ptr += kBlockSlices;
+          }
         }
         shared_offset += in_increment;
       }
@@ -535,68 +559,62 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
+template <typename T, DepthwiseConv2dDirection kDirection,
+          int kKnownFilterWidth, int kKnownFilterHeight, int kBlockSlices,
+          bool kKnownEvenRows>
 void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
                                    const T* input, const T* filter, T* output,
                                    TensorFormat data_format) {
   const int block_rows = (args.in_rows + 1) / 2;
+  dim3 block_dim;
+  void (*kernel)(const DepthwiseArgs, const T*, const T*, T*);
+  if (data_format == FORMAT_NHWC) {
+    block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
+    kernel = DepthwiseConv2dGPUKernelNHWCSmall<T, kDirection, kKnownFilterWidth,
+                                               kKnownFilterHeight, kBlockSlices,
+                                               kKnownEvenRows>;
+  } else if (data_format == FORMAT_NCHW) {
+    block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
+    kernel = DepthwiseConv2dGPUKernelNCHWSmall<T, kDirection, kKnownFilterWidth,
+                                               kKnownFilterHeight, kBlockSlices,
+                                               kKnownEvenRows>;
+  } else {
+    assert(false && "Incorrect data format");
+    return;
+  }
   const int tile_cols = args.in_cols + args.filter_cols - 1;
   const int tile_rows = block_rows * 2 + args.filter_rows - 1;
   const int tile_pixels = tile_rows * tile_cols;
   const int filter_pixels = args.filter_rows * args.filter_cols;
-
   const int shared_memory_size =
       kBlockSlices * (tile_pixels + filter_pixels) * sizeof(T);
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
-
-  if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_outputs, d,
-        DepthwiseConv2dGPUKernelNHWCSmall<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, kBlockSlices,
-                                          kKnownEvenRows>,
-        shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
-    DepthwiseConv2dGPUKernelNHWCSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                      kBlockSlices, kKnownEvenRows>
-        <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
-            args, input, filter, output);
-  } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_outputs, d,
-        DepthwiseConv2dGPUKernelNCHWSmall<T, kKnownFilterWidth,
-                                          kKnownFilterHeight, kBlockSlices,
-                                          kKnownEvenRows>,
-        shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
-    DepthwiseConv2dGPUKernelNCHWSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                      kBlockSlices, kKnownEvenRows>
-        <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
-            args, input, filter, output);
-  } else {
-    assert(false && "Incorrect data format");
-  }
+  CudaLaunchConfig config =
+      GetCudaLaunchConfig(num_outputs, d, kernel, shared_memory_size,
+                          block_dim.x * block_dim.y * block_dim.z);
+  kernel<<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
+      args, input, filter, output);
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices>
+template <typename T, DepthwiseConv2dDirection kDirection,
+          int kKnownFilterWidth, int kKnownFilterHeight, int kBlockSlices>
 void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
                                    const T* input, const T* filter, T* output,
                                    TensorFormat data_format) {
   if (args.in_rows & 1) {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                  kBlockSlices, false>(d, args, input, filter,
-                                                       output, data_format);
+    LaunchDepthwiseConv2dGPUSmall<T, kDirection, kKnownFilterWidth,
+                                  kKnownFilterHeight, kBlockSlices, false>(
+        d, args, input, filter, output, data_format);
   } else {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight,
-                                  kBlockSlices, true>(d, args, input, filter,
-                                                      output, data_format);
+    LaunchDepthwiseConv2dGPUSmall<T, kDirection, kKnownFilterWidth,
+                                  kKnownFilterHeight, kBlockSlices, true>(
+        d, args, input, filter, output, data_format);
   }
 }
 
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
+template <typename T, DepthwiseConv2dDirection kDirection,
+          int kKnownFilterWidth, int kKnownFilterHeight>
 void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
                                    const T* input, const T* filter, T* output,
                                    TensorFormat data_format) {
@@ -604,14 +622,17 @@ void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& d, const DepthwiseArgs args,
   // threads (2 pixels per thread).
   const int block_pixels = (args.in_rows + 1) / 2 * args.in_cols;
   if (block_pixels > 256) {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 2>(
-        d, args, input, filter, output, data_format);
+    LaunchDepthwiseConv2dGPUSmall<T, kDirection, kKnownFilterWidth,
+                                  kKnownFilterHeight, 2>(d, args, input, filter,
+                                                         output, data_format);
   } else if (block_pixels > 128) {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 4>(
-        d, args, input, filter, output, data_format);
+    LaunchDepthwiseConv2dGPUSmall<T, kDirection, kKnownFilterWidth,
+                                  kKnownFilterHeight, 4>(d, args, input, filter,
+                                                         output, data_format);
   } else {
-    LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight, 8>(
-        d, args, input, filter, output, data_format);
+    LaunchDepthwiseConv2dGPUSmall<T, kDirection, kKnownFilterWidth,
+                                  kKnownFilterHeight, 8>(d, args, input, filter,
+                                                         output, data_format);
   }
 }
 
@@ -620,38 +641,30 @@ template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
 void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
                               const T* input, const T* filter, T* output,
                               TensorFormat data_format) {
+  void (*kernel)(const DepthwiseArgs, const T*, const T*, T*, int);
+  if (data_format == FORMAT_NHWC) {
+    kernel =
+        DepthwiseConv2dGPUKernelNHWC<T, kKnownFilterWidth, kKnownFilterHeight,
+                                     kKnownDepthMultiplier>;
+  } else if (data_format == FORMAT_NCHW) {
+    kernel =
+        DepthwiseConv2dGPUKernelNCHW<T, kKnownFilterWidth, kKnownFilterHeight,
+                                     kKnownDepthMultiplier>;
+  } else {
+    assert(false && "Incorrect data format");
+    return;
+  }
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
+  CudaLaunchConfig config = GetCudaLaunchConfig(num_outputs, d, kernel, 0, 0);
   // The compile-time constant version runs faster with a single block.
   const int max_block_count = kKnownFilterWidth < 0 || kKnownFilterHeight < 0 ||
                                       kKnownDepthMultiplier < 0
                                   ? std::numeric_limits<int>::max()
                                   : d.getNumCudaMultiProcessors();
-  if (data_format == FORMAT_NHWC) {
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_outputs, d,
-        DepthwiseConv2dGPUKernelNHWC<T, kKnownFilterWidth, kKnownFilterHeight,
-                                     kKnownDepthMultiplier>,
-        0, 0);
-    DepthwiseConv2dGPUKernelNHWC<T, kKnownFilterWidth, kKnownFilterHeight,
-                                 kKnownDepthMultiplier>
-        <<<std::min(max_block_count, config.block_count),
+  kernel<<<std::min(max_block_count, config.block_count),
            config.thread_per_block, 0, d.stream()>>>(args, input, filter,
                                                      output, num_outputs);
-  } else if (data_format == FORMAT_NCHW) {
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_outputs, d,
-        DepthwiseConv2dGPUKernelNCHW<T, kKnownFilterWidth, kKnownFilterHeight,
-                                     kKnownDepthMultiplier>,
-        0, 0);
-    DepthwiseConv2dGPUKernelNCHW<T, kKnownFilterWidth, kKnownFilterHeight,
-                                 kKnownDepthMultiplier>
-        <<<std::min(max_block_count, config.block_count),
-           config.thread_per_block, 0, d.stream()>>>(args, input, filter,
-                                                     output, num_outputs);
-  } else {
-    assert(false && "Incorrect data format");
-  }
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
@@ -660,8 +673,9 @@ void LaunchDepthwiseConv2dGPU(const GpuDevice& d, const DepthwiseArgs args,
                               TensorFormat data_format) {
   if (args.depth_multiplier == 1) {
     if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
-      LaunchDepthwiseConv2dGPUSmall<T, kKnownFilterWidth, kKnownFilterHeight>(
-          d, args, input, filter, output, data_format);
+      LaunchDepthwiseConv2dGPUSmall<T, DIRECTION_FORWARD, kKnownFilterWidth,
+                                    kKnownFilterHeight>(d, args, input, filter,
+                                                        output, data_format);
       return;
     }
 
@@ -756,145 +770,6 @@ __global__ void __launch_bounds__(640, 2)
   }
 }
 
-// CUDA kernel to compute the depthwise convolution backward w.r.t. input in
-// NHWC format, tailored for small images up to 32x32. Stride and depth
-// multiplier must be 1. Padding must be 'SAME', which allows to reuse the index
-// computation. Only use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args)
-// returns true.
-// Implementation is the same as the forward pass, except that the filter is
-// rotate by 180°, see filter_read_offset and filter_ptr.
-// Tiles of the input and filter tensors are loaded into shared memory before
-// performing the convolution. Each thread handles two elements per iteration,
-// one each in the lower and upper half of a tile.
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
-__global__
-__launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNHWCSmall(
-    const DepthwiseArgs args, const T* input, const T* filter, T* output) {
-  assert(CanLaunchDepthwiseConv2dGPUSmall(args));
-  // Holds block plus halo and filter data for blockDim.x depths.
-  extern __shared__ __align__(sizeof(T)) unsigned char shared_memory[];
-  T* const shared_data = reinterpret_cast<T*>(shared_memory);
-
-  const int batches = args.batch;
-  const int in_rows = args.in_rows;
-  const int in_cols = args.in_cols;
-  const int in_depth = args.in_depth;
-  const int filter_rows =
-      kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight;
-  const int filter_cols =
-      kKnownFilterWidth < 0 ? args.filter_cols : kKnownFilterWidth;
-  const int pad_rows = args.pad_rows;
-  const int pad_cols = args.pad_cols;
-
-  // Fixed blockDim.x, corresponding to Pascal's global load granularity of 32B.
-  const int block_rows = blockDim.z;
-
-  // These values are the same for all threads and could
-  // be precomputed on the CPU.
-  const int block_size = block_rows * in_cols * kBlockSlices;
-  const int in_row_size = in_cols * in_depth;
-  const int in_size = in_rows * in_row_size;
-  const int in_increment = (in_cols - 1) * kBlockSlices;
-  const int filter_pixels = filter_rows * filter_cols;
-  const int tile_cols = in_cols + filter_cols - 1;
-  const int even_rows = kKnownEvenRows || (1 & ~in_rows);
-  const int tile_rows = in_rows + filter_rows - even_rows;
-  const int tile_row_size = tile_cols * kBlockSlices;
-  const int tile_size = tile_rows * tile_row_size;
-  const int tile_offset = block_rows * tile_row_size;
-  const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int batch_blocks = (in_depth + kBlockSlices - 1) / kBlockSlices;
-  const int in_blocks = batch_blocks * batches;
-  const int tensor_offset =
-      kKnownEvenRows ? in_size / 2 : block_rows * in_row_size;
-
-  const int thread_depth = threadIdx.x;
-  const int thread_col = threadIdx.y;
-  const int thread_row = threadIdx.z;
-
-  // Position in block.
-  const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_pix * kBlockSlices + thread_depth;
-
-  // Initialize tile, in particular the padding.
-  for (int i = thread_idx; i < tile_size; i += block_size) {
-    shared_data[i] = T(0);
-  }
-  __syncthreads();
-
-  // Position in tensors.
-  const int tensor_idx = thread_pix * in_depth + thread_depth;
-
-  // Position in (padded) shared memory.
-  const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = data_pix * kBlockSlices + thread_depth;
-
-  // Position in shared memory, offset by pad_rows / pad_cols.
-  const int tile_pix = data_pix + pad_offset;
-  const int tile_idx = tile_pix * kBlockSlices + thread_depth;
-
-  const int max_depth = in_depth - thread_depth;
-  const int filter_write_offset =
-      thread_pix < filter_pixels ? tile_size + thread_idx : 0;
-  const int filter_read_offset =
-      tile_size + filter_pixels * kBlockSlices + thread_depth;
-  const bool skip_second =
-      !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
-
-  for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int batch = b / batch_blocks;
-    const int stack = b - batch * batch_blocks;
-
-    const int start_depth = stack * kBlockSlices;
-    const int filter_offset = tensor_idx + start_depth;
-    const int inout_offset = batch * in_size + filter_offset;
-    const bool depth_in_range = start_depth < max_depth;
-
-    if (depth_in_range) {
-      const T* const in_ptr = inout_offset + input;
-      T* const tile_ptr = tile_idx + shared_data;
-      tile_ptr[0] = ldg(in_ptr);
-      if (!skip_second) {
-        tile_ptr[tile_offset] = ldg(tensor_offset + in_ptr);
-      }
-
-      if (filter_write_offset != 0) {
-        shared_data[filter_write_offset] = ldg(filter_offset + filter);
-      }
-    }
-
-    // Note: the condition to reach this is uniform across the entire block.
-    __syncthreads();
-
-    if (depth_in_range) {
-      T sum1 = 0;
-      T sum2 = 0;
-      int shared_offset = data_idx;
-      const T* filter_ptr = filter_read_offset + shared_data;
-      UNROLL for (int r = 0; r < filter_rows; ++r) {
-        UNROLL for (int c = 0; c < filter_cols; ++c) {
-          filter_ptr -= kBlockSlices;
-          const T filter_value = *filter_ptr;
-          const T* const tile_ptr = shared_offset + shared_data;
-          sum1 += filter_value * tile_ptr[0];
-          sum2 += filter_value * tile_ptr[tile_offset];
-          shared_offset += kBlockSlices;
-        }
-        shared_offset += in_increment;
-      }
-      T* const out_ptr = inout_offset + output;
-      out_ptr[0] = sum1;
-      if (!skip_second) {
-        out_ptr[tensor_offset] = sum2;
-      }
-    }
-
-    // Note: the condition to reach this is uniform across the entire block.
-    __syncthreads();
-  }
-}
-
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
           int kKnownDepthMultiplier>
 __global__ void __launch_bounds__(640, 2)
@@ -966,234 +841,6 @@ __global__ void __launch_bounds__(640, 2)
   }
 }
 
-// CUDA kernel to compute the depthwise convolution backward w.r.t. input in
-// NHWC format, tailored for small images up to 32x32. Stride and depth
-// multiplier must be 1. Padding must be 'SAME', which allows to reuse the index
-// computation. Only use this kernel if CanLaunchDepthwiseConv2dGPUSmall(args)
-// returns true.
-// Implementation is the same as the forward pass, except that the filter is
-// rotate by 180°, see filter_read_offset and filter_ptr.
-// Tiles of the input and filter tensors are loaded into shared memory before
-// performing the convolution. Each thread handles two elements per iteration,
-// one each in the lower and upper half of a tile.
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
-__global__
-__launch_bounds__(1024, 2) void DepthwiseConv2dBackpropInputGPUKernelNCHWSmall(
-    const DepthwiseArgs args, const T* input, const T* filter, T* output) {
-  assert(CanLaunchDepthwiseConv2dGPUSmall(args));
-  // Holds block plus halo and filter data for blockDim.z depths.
-  extern __shared__ __align__(sizeof(T)) unsigned char shared_memory[];
-  T* const shared_data = reinterpret_cast<T*>(shared_memory);
-
-  const int batches = args.batch;
-  const int in_rows = args.in_rows;
-  const int in_cols = args.in_cols;
-  const int in_depth = args.in_depth;
-  const int filter_rows =
-      kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight;
-  const int filter_cols =
-      kKnownFilterWidth < 0 ? args.filter_cols : kKnownFilterWidth;
-  const int pad_rows = args.pad_rows;
-  const int pad_cols = args.pad_cols;
-
-  // Fixed blockDim.z, tailored for maximum grid size for images of size 16x16.
-  const int block_rows = blockDim.y;
-
-  // These values are the same for all threads and could
-  // be precomputed on the CPU.
-  const int block_pixels = in_cols * block_rows;
-  const int block_size = block_pixels * kBlockSlices;
-  const int in_pixels = in_cols * in_rows;
-  const int in_increment = in_cols - 1;
-  const int filter_pixels = filter_rows * filter_cols;
-  const int tile_cols = in_cols + filter_cols - 1;
-  const int even_rows = kKnownEvenRows || (1 & ~in_rows);
-  const int tile_rows = in_rows + filter_rows - even_rows;
-  const int tile_pixels = tile_cols * tile_rows;
-  const int tile_size = tile_pixels * kBlockSlices;
-  const int tile_offset = block_rows * tile_cols;
-  const int pad_offset = pad_rows * tile_cols + pad_cols;
-  const int in_slices = in_depth * batches;
-  const int in_blocks = (in_slices + kBlockSlices - 1) / kBlockSlices;
-
-  const int thread_col = threadIdx.x;
-  const int thread_row = threadIdx.y;
-  const int thread_depth = threadIdx.z;
-
-  // Position in block.
-  const int thread_pix = thread_row * in_cols + thread_col;
-  const int thread_idx = thread_depth * block_pixels + thread_pix;
-
-  // Initialize tile, in particular the padding.
-  for (int i = thread_idx; i < tile_size; i += block_size) {
-    shared_data[i] = T(0);
-  }
-  __syncthreads();
-
-  // Position in tensors.
-  const int tensor_idx = thread_depth * in_pixels + thread_pix;
-
-  // Position in (padded) shared memory.
-  const int data_pix = thread_row * tile_cols + thread_col;
-  const int data_idx = thread_depth * tile_pixels + data_pix;
-
-  // Position in shared memory, offset by pad_rows / pad_cols.
-  const int tile_idx = data_idx + pad_offset;
-
-  // Filter is always in HWCK format, irrespective of the input/output format.
-  const int filter_pix = thread_idx / kBlockSlices;
-  const int filter_depth = thread_idx % kBlockSlices;
-  const int filter_idx = filter_pix * in_depth;
-
-  const int max_slice = in_slices - thread_depth;
-  const int filter_write_offset =
-      filter_pix < filter_pixels ? tile_size + thread_idx : 0;
-  const int filter_read_offset =
-      tile_size + filter_pixels * kBlockSlices + thread_depth;
-  const bool skip_second =
-      !kKnownEvenRows && thread_row + (in_rows & 1) == block_rows;
-
-  for (int b = blockIdx.x; b < in_blocks; b += gridDim.x) {
-    const int slice = b * kBlockSlices;
-
-    const int inout_offset = slice * in_pixels + tensor_idx;
-    const bool slice_in_range = slice < max_slice;
-
-    if (slice_in_range) {
-      const T* const in_ptr = inout_offset + input;
-      T* const tile_ptr = tile_idx + shared_data;
-      tile_ptr[0] = ldg(in_ptr);
-      if (!skip_second) {
-        tile_ptr[tile_offset] = ldg(block_pixels + in_ptr);
-      }
-    }
-
-    if (filter_write_offset != 0) {
-      const int filter_offset = filter_idx + (slice + filter_depth) % in_depth;
-      shared_data[filter_write_offset] = ldg(filter_offset + filter);
-    }
-
-    // Note: the condition to reach this is uniform across the entire block.
-    __syncthreads();
-
-    if (slice_in_range) {
-      T sum1 = 0;
-      T sum2 = 0;
-      int shared_offset = data_idx;
-      const T* filter_ptr = filter_read_offset + shared_data;
-      UNROLL for (int r = 0; r < filter_rows; ++r) {
-        UNROLL for (int c = 0; c < filter_cols; ++c) {
-          filter_ptr -= kBlockSlices;
-          const T filter_value = *filter_ptr;
-          const T* const tile_ptr = shared_offset + shared_data;
-          sum1 += filter_value * tile_ptr[0];
-          sum2 += filter_value * tile_ptr[tile_offset];
-          ++shared_offset;
-        }
-        shared_offset += in_increment;
-      }
-      T* const out_ptr = inout_offset + output;
-      out_ptr[0] = sum1;
-      if (!skip_second) {
-        out_ptr[block_pixels] = sum2;
-      }
-    }
-
-    // Note: the condition to reach this is uniform across the entire block.
-    __syncthreads();
-  }
-}
-
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices, bool kKnownEvenRows>
-void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
-                                                const DepthwiseArgs args,
-                                                const T* out_backprop,
-                                                const T* filter, T* in_backprop,
-                                                TensorFormat data_format) {
-  const int block_rows = (args.in_rows + 1) / 2;
-  const int tile_cols = args.in_cols + args.filter_cols - 1;
-  const int tile_rows = block_rows * 2 + args.filter_rows - 1;
-  const int tile_pixels = tile_rows * tile_cols;
-  const int filter_pixels = args.filter_rows * args.filter_cols;
-
-  const int shared_memory_size =
-      kBlockSlices * (tile_pixels + filter_pixels) * sizeof(T);
-  const int num_outputs =
-      args.batch * args.out_rows * args.out_cols * args.out_depth;
-
-  if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_outputs, d,
-        DepthwiseConv2dBackpropInputGPUKernelNHWCSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kKnownEvenRows>,
-        shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
-    DepthwiseConv2dBackpropInputGPUKernelNHWCSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kKnownEvenRows>
-        <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
-            args, out_backprop, filter, in_backprop);
-  } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_outputs, d,
-        DepthwiseConv2dBackpropInputGPUKernelNCHWSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kKnownEvenRows>,
-        shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
-    DepthwiseConv2dBackpropInputGPUKernelNCHWSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kKnownEvenRows>
-        <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
-            args, out_backprop, filter, in_backprop);
-  } else {
-    assert(false && "Incorrect data format");
-  }
-}
-
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
-          int kBlockSlices>
-void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
-                                                const DepthwiseArgs args,
-                                                const T* out_backprop,
-                                                const T* filter, T* in_backprop,
-                                                TensorFormat data_format) {
-  if (args.in_rows & 1) {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, false>(
-        d, args, out_backprop, filter, in_backprop, data_format);
-  } else {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, true>(
-        d, args, out_backprop, filter, in_backprop, data_format);
-  }
-}
-
-template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-void LaunchDepthwiseConv2dBackpropInputGPUSmall(const GpuDevice& d,
-                                                const DepthwiseArgs args,
-                                                const T* input, const T* filter,
-                                                T* output,
-                                                TensorFormat data_format) {
-  // Maximize (power of two) kBlockSlices while keeping a block within 1024
-  // threads (2 pixels per thread).
-  const int block_pixels = (args.in_rows + 1) / 2 * args.in_cols;
-  if (block_pixels > 256) {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight, 2>(
-        d, args, input, filter, output, data_format);
-  } else if (block_pixels > 128) {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight, 4>(
-        d, args, input, filter, output, data_format);
-  } else {
-    LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                               kKnownFilterHeight, 8>(
-        d, args, input, filter, output, data_format);
-  }
-}
-
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight,
           int kKnownDepthMultiplier>
 void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
@@ -1201,31 +848,23 @@ void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
                                            const T* out_backprop,
                                            const T* filter, T* in_backprop,
                                            TensorFormat data_format) {
-  const int num_in_backprop =
-      args.batch * args.in_rows * args.in_cols * args.in_depth;
+  void (*kernel)(const DepthwiseArgs, const T*, const T*, T*, int);
   if (data_format == FORMAT_NHWC) {
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_in_backprop, d,
-        DepthwiseConv2dBackpropInputGPUKernelNHWC<
-            T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>,
-        0, 0);
-    DepthwiseConv2dBackpropInputGPUKernelNHWC<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>
-        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
-            args, out_backprop, filter, in_backprop, num_in_backprop);
+    kernel = DepthwiseConv2dBackpropInputGPUKernelNHWC<
+        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
   } else if (data_format == FORMAT_NCHW) {
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_in_backprop, d,
-        DepthwiseConv2dBackpropInputGPUKernelNCHW<
-            T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>,
-        0, 0);
-    DepthwiseConv2dBackpropInputGPUKernelNCHW<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>
-        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
-            args, out_backprop, filter, in_backprop, num_in_backprop);
+    kernel = DepthwiseConv2dBackpropInputGPUKernelNCHW<
+        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
   } else {
     assert(false && "Incorrect data format");
+    return;
   }
+  const int num_in_backprop =
+      args.batch * args.in_rows * args.in_cols * args.in_depth;
+  CudaLaunchConfig config =
+      GetCudaLaunchConfig(num_in_backprop, d, kernel, 0, 0);
+  kernel<<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+      args, out_backprop, filter, in_backprop, num_in_backprop);
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
@@ -1236,8 +875,8 @@ void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& d,
                                            TensorFormat data_format) {
   if (args.depth_multiplier == 1) {
     if (CanLaunchDepthwiseConv2dGPUSmall(args)) {
-      LaunchDepthwiseConv2dBackpropInputGPUSmall<T, kKnownFilterWidth,
-                                                 kKnownFilterHeight>(
+      LaunchDepthwiseConv2dGPUSmall<T, DIRECTION_BACKWARD, kKnownFilterWidth,
+                                    kKnownFilterHeight>(
           d, args, out_backprop, filter, in_backprop, data_format);
       return;
     }
@@ -1796,35 +1435,27 @@ bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
     return false;
   }
 
-  const int num_out_backprop =
-      args.batch * args.out_rows * args.out_cols * args.out_depth;
+  dim3 block_dim;
+  void (*kernel)(const DepthwiseArgs, const T*, const T*, T*);
   if (data_format == FORMAT_NHWC) {
-    dim3 block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_out_backprop, d,
-        DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kAccumPixels>,
-        shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
-    DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>
-        <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
-            args, out_backprop, input, filter_backprop);
+    block_dim = dim3(kBlockSlices, args.in_cols, block_rows);
+    kernel = DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>;
   } else if (data_format == FORMAT_NCHW) {
-    dim3 block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_out_backprop, d,
-        DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
-            T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices,
-            kAccumPixels>,
-        shared_memory_size, block_dim.x * block_dim.y * block_dim.z);
-    DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>
-        <<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
-            args, out_backprop, input, filter_backprop);
+    block_dim = dim3(args.in_cols, block_rows, kBlockSlices);
+    kernel = DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
+        T, kKnownFilterWidth, kKnownFilterHeight, kBlockSlices, kAccumPixels>;
   } else {
     assert(false && "Incorrect data format");
+    return false;
   }
+  const int num_out_backprop =
+      args.batch * args.out_rows * args.out_cols * args.out_depth;
+  CudaLaunchConfig config =
+      GetCudaLaunchConfig(num_out_backprop, d, kernel, shared_memory_size,
+                          block_dim.x * block_dim.y * block_dim.z);
+  kernel<<<config.block_count, block_dim, shared_memory_size, d.stream()>>>(
+      args, out_backprop, input, filter_backprop);
   return true;
 }
 
@@ -1905,31 +1536,23 @@ void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& d,
                                             const T* out_backprop,
                                             const T* input, T* filter_backprop,
                                             TensorFormat data_format) {
-  const int num_out_backprop =
-      args.batch * args.out_rows * args.out_cols * args.out_depth;
+  void (*kernel)(const DepthwiseArgs, const T*, const T*, T*, int);
   if (data_format == FORMAT_NHWC) {
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_out_backprop, d,
-        DepthwiseConv2dBackpropFilterGPUKernelNHWC<
-            T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>,
-        0, 0);
-    DepthwiseConv2dBackpropFilterGPUKernelNHWC<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>
-        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
-            args, out_backprop, input, filter_backprop, num_out_backprop);
+    kernel = DepthwiseConv2dBackpropFilterGPUKernelNHWC<
+        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
   } else if (data_format == FORMAT_NCHW) {
-    CudaLaunchConfig config = GetCudaLaunchConfig(
-        num_out_backprop, d,
-        DepthwiseConv2dBackpropFilterGPUKernelNCHW<
-            T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>,
-        0, 0);
-    DepthwiseConv2dBackpropFilterGPUKernelNCHW<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>
-        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
-            args, out_backprop, input, filter_backprop, num_out_backprop);
+    kernel = DepthwiseConv2dBackpropFilterGPUKernelNCHW<
+        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
   } else {
     assert(false && "Incorrect data format");
+    return;
   }
+  const int num_out_backprop =
+      args.batch * args.out_rows * args.out_cols * args.out_depth;
+  CudaLaunchConfig config =
+      GetCudaLaunchConfig(num_out_backprop, d, kernel, 0, 0);
+  kernel<<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+      args, out_backprop, input, filter_backprop, num_out_backprop);
 }
 
 template <typename T, int kKnownFilterWidth, int kKnownFilterHeight>
-- 
GitLab


From 18890a070b94f5a1c38ad9720844e2ca0fac7d83 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 05:00:02 -0700
Subject: [PATCH 0196/1375] Removed unused shape creation.

PiperOrigin-RevId: 159413535
---
 tensorflow/compiler/xla/service/user_computation.cc | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 8014c64953..2387e02f03 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -1905,7 +1905,6 @@ Status UserComputation::CheckParametersAreContiguous(
     }
   }
 
-  auto program_shape = MakeUnique<ProgramShape>();
   for (int64 i = 0; i < parameter_requests.size(); ++i) {
     auto it = parameter_requests.find(i);
     if (it == parameter_requests.end()) {
-- 
GitLab


From ae2647715797d6f589356964a259c372323b8362 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=BB=84=E7=92=9E?= <awsomekde@gmail.com>
Date: Mon, 19 Jun 2017 20:24:55 +0800
Subject: [PATCH 0197/1375] Variable name typo

In the "Working example: Tracking Multiple Metrics", the variable name "name_to_updates" --> "names_to_updates"
---
 tensorflow/contrib/slim/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index d37c632be7..4bde661698 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -836,7 +836,7 @@ with tf.Session() as sess:
   for batch_id in range(num_batches):
     sess.run(names_to_updates.values())
 
-  metric_values = sess.run(name_to_values.values())
+  metric_values = sess.run(names_to_values.values())
   for metric, value in zip(names_to_values.keys(), metric_values):
     print('Metric %s has value: %f' % (metric, value))
 ```
-- 
GitLab


From 06fc77c5e264813a9e2effddad8307bbf7efae43 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 10:05:27 -0700
Subject: [PATCH 0198/1375] Have 3 times: exec_micros, accelerator_exec_micros,
 cpu_exec_micros. Test RunMetadata format main file cleanup.

PiperOrigin-RevId: 159440662
---
 .../contrib/tfprof/python/tools/tfprof/BUILD  |  15 +-
 .../tfprof/python/tools/tfprof/internal/BUILD |  14 +
 .../internal/print_model_analysis_test.py     |  54 +++-
 .../tfprof/internal/run_metadata_test.py      | 111 ++++++++
 .../tools/tfprof/model_analyzer_test.py       |  20 +-
 tensorflow/tools/tfprof/g3doc/options.md      |   4 +-
 tensorflow/tools/tfprof/internal/BUILD        |   1 +
 .../advisor/accelerator_utilization_checker.h |   2 +-
 .../internal/advisor/operation_checker.h      |   2 +-
 .../tools/tfprof/internal/tfprof_code.cc      |  12 +-
 .../tools/tfprof/internal/tfprof_node.cc      |  76 +++---
 .../tools/tfprof/internal/tfprof_node.h       |  89 +++++--
 .../tools/tfprof/internal/tfprof_node_show.cc |  41 ++-
 tensorflow/tools/tfprof/internal/tfprof_op.cc |  80 ++++--
 .../tools/tfprof/internal/tfprof_options.h    |   8 +-
 .../tools/tfprof/internal/tfprof_show.cc      |  30 ++-
 .../tools/tfprof/internal/tfprof_show.h       |  30 +++
 .../tfprof/internal/tfprof_show_multi.cc      |  31 ++-
 .../tools/tfprof/internal/tfprof_show_multi.h |  13 +-
 .../tools/tfprof/internal/tfprof_show_test.cc |  98 +++++--
 .../tfprof/internal/tfprof_stats_test.cc      | 133 ++++++----
 .../tfprof/internal/tfprof_tensor_test.cc     |  28 +-
 .../tools/tfprof/internal/tfprof_timeline.cc  |   3 -
 .../tfprof/internal/tfprof_timeline_test.cc   |   5 +-
 .../tools/tfprof/internal/tfprof_utils.cc     |  20 +-
 .../tools/tfprof/internal/tfprof_utils.h      |  24 +-
 tensorflow/tools/tfprof/tfprof_main.cc        | 246 +++++++++---------
 tensorflow/tools/tfprof/tfprof_output.proto   |  12 +
 28 files changed, 873 insertions(+), 329 deletions(-)
 create mode 100644 tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
index 8040c791ee..32403c3af4 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
@@ -2,6 +2,7 @@ package(default_visibility = ["//visibility:public"])
 
 licenses(["notice"])  # Apache 2.0
 
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_py_test")
 
@@ -16,12 +17,10 @@ py_library(
     ],
 )
 
-py_test(
+cuda_py_test(
     name = "model_analyzer_test",
     srcs = ["model_analyzer_test.py"],
-    srcs_version = "PY2AND3",
-    tags = ["no_pip"],
-    deps = [
+    additional_deps = [
         ":model_analyzer",
         "//tensorflow/contrib/tfprof/python/tools/tfprof/internal:model_analyzer_testlib",
         "//tensorflow/python:client",
@@ -30,14 +29,13 @@ py_test(
         "//tensorflow/python:platform",
         "//tensorflow/python:variables",
     ],
+    tags = ["no_pip"],
 )
 
-py_test(
+cuda_py_test(
     name = "profiler_test",
     srcs = ["profiler_test.py"],
-    srcs_version = "PY2AND3",
-    tags = ["no_pip"],
-    deps = [
+    additional_deps = [
         ":model_analyzer",
         "//tensorflow/contrib/tfprof/python/tools/tfprof/internal:model_analyzer_testlib",
         "//tensorflow/python:client",
@@ -46,6 +44,7 @@ py_test(
         "//tensorflow/python:platform",
         "//tensorflow/python:variables",
     ],
+    tags = ["no_pip"],
 )
 
 py_library(
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
index 3fa5b7867d..e844046811 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
@@ -2,6 +2,7 @@ package(default_visibility = ["//tensorflow/contrib/tfprof/python/tools/tfprof:_
 
 licenses(["notice"])  # Apache 2.0
 
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_py_test")
 load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc")
@@ -60,6 +61,19 @@ py_test(
     ],
 )
 
+cuda_py_test(
+    name = "run_metadata_test",
+    srcs = ["run_metadata_test.py"],
+    additional_deps = [
+        "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/tools/tfprof:protos_all_py",
+    ],
+)
+
 # -----------------------------------------------------------------------------
 # Google-internal targets.  These must be at the end for syncrepo.
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
index 76e7d627ce..091fcdef88 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
@@ -153,6 +153,10 @@ class PrintModelAnalysisTest(test.TestCase):
                 }
               }
             }
+            accelerator_exec_micros: 0
+            cpu_exec_micros: 0
+            total_accelerator_exec_micros: 0
+            total_cpu_exec_micros: 0
           }
           children {
             name: "DW"
@@ -205,6 +209,10 @@ class PrintModelAnalysisTest(test.TestCase):
                   }
                 }
               }
+              accelerator_exec_micros: 0
+              cpu_exec_micros: 0
+              total_accelerator_exec_micros: 0
+              total_cpu_exec_micros: 0
             }
             children {
               name: "DW/Initializer"
@@ -237,6 +245,10 @@ class PrintModelAnalysisTest(test.TestCase):
                       }
                     }
                   }
+                  accelerator_exec_micros: 0
+                  cpu_exec_micros: 0
+                  total_accelerator_exec_micros: 0
+                  total_cpu_exec_micros: 0
                 }
                 children {
                   name: "DW/Initializer/random_normal/mean"
@@ -247,6 +259,10 @@ class PrintModelAnalysisTest(test.TestCase):
                   total_parameters: 0
                   float_ops: 0
                   total_float_ops: 0
+                  accelerator_exec_micros: 0
+                  cpu_exec_micros: 0
+                  total_accelerator_exec_micros: 0
+                  total_cpu_exec_micros: 0
                 }
                 children {
                   name: "DW/Initializer/random_normal/mul"
@@ -282,6 +298,10 @@ class PrintModelAnalysisTest(test.TestCase):
                       }
                     }
                   }
+                  accelerator_exec_micros: 0
+                  cpu_exec_micros: 0
+                  total_accelerator_exec_micros: 0
+                  total_cpu_exec_micros: 0
                 }
                 children {
                   name: "DW/Initializer/random_normal/shape"
@@ -292,6 +312,10 @@ class PrintModelAnalysisTest(test.TestCase):
                   total_parameters: 0
                   float_ops: 0
                   total_float_ops: 0
+                  accelerator_exec_micros: 0
+                  cpu_exec_micros: 0
+                  total_accelerator_exec_micros: 0
+                  total_cpu_exec_micros: 0
                 }
                 children {
                   name: "DW/Initializer/random_normal/stddev"
@@ -302,6 +326,10 @@ class PrintModelAnalysisTest(test.TestCase):
                   total_parameters: 0
                   float_ops: 0
                   total_float_ops: 0
+                  accelerator_exec_micros: 0
+                  cpu_exec_micros: 0
+                  total_accelerator_exec_micros: 0
+                  total_cpu_exec_micros: 0
                 }
                 float_ops: 0
                 total_float_ops: 0
@@ -330,9 +358,17 @@ class PrintModelAnalysisTest(test.TestCase):
                     }
                   }
                 }
+                accelerator_exec_micros: 0
+                cpu_exec_micros: 0
+                total_accelerator_exec_micros: 0
+                total_cpu_exec_micros: 0
               }
               float_ops: 0
               total_float_ops: 0
+              accelerator_exec_micros: 0
+              cpu_exec_micros: 0
+              total_accelerator_exec_micros: 0
+              total_cpu_exec_micros: 0
             }
             children {
               name: "DW/read"
@@ -360,9 +396,17 @@ class PrintModelAnalysisTest(test.TestCase):
                   }
                 }
               }
+              accelerator_exec_micros: 0
+              cpu_exec_micros: 0
+              total_accelerator_exec_micros: 0
+              total_cpu_exec_micros: 0
             }
             float_ops: 0
             total_float_ops: 0
+            accelerator_exec_micros: 0
+            cpu_exec_micros: 0
+            total_accelerator_exec_micros: 0
+            total_cpu_exec_micros: 0
           }
           children {
             name: "zeros"
@@ -373,9 +417,17 @@ class PrintModelAnalysisTest(test.TestCase):
             total_parameters: 0
             float_ops: 0
             total_float_ops: 0
+            accelerator_exec_micros: 0
+            cpu_exec_micros: 0
+            total_accelerator_exec_micros: 0
+            total_cpu_exec_micros: 0
           }
           float_ops: 0
-          total_float_ops: 0""", expected_pb)
+          total_float_ops: 0
+          accelerator_exec_micros: 0
+          cpu_exec_micros: 0
+          total_accelerator_exec_micros: 0
+          total_cpu_exec_micros: 0""", expected_pb)
       self.assertEqual(expected_pb, tfprof_pb)
 
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
new file mode 100644
index 0000000000..feb20dc0f4
--- /dev/null
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -0,0 +1,111 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""test the RunMetadata proto."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import defaultdict
+
+import six
+
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.platform import test
+
+# pylint: disable=g-bad-import-order
+# XXX: this depends on pywrap_tensorflow and must come later
+from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
+
+SIZE = 1300
+
+
+def _extract_node(run_meta, node_name):
+  ret = defaultdict(list)
+  for dev_stat in run_meta.step_stats.dev_stats:
+    dev = dev_stat.device
+    for node_stat in dev_stat.node_stats:
+      if node_stat.node_name == node_name:
+        ret[dev].append(node_stat)
+  return ret
+
+
+def _run_model():
+  x = random_ops.random_normal(shape=[1, SIZE])
+  w = random_ops.random_normal(shape=[SIZE, 2 * SIZE])
+  y = math_ops.matmul(x, w)
+
+  with session.Session() as sess:
+    run_metadata = config_pb2.RunMetadata()
+    opts = model_analyzer.PRINT_ALL_TIMING_MEMORY
+    opts['min_micros'] = 0
+    opts['min_bytes'] = 0
+    _ = sess.run(y,
+                 options=config_pb2.RunOptions(
+                     trace_level=config_pb2.RunOptions.FULL_TRACE),
+                 run_metadata=run_metadata)
+    tfprof_node = model_analyzer.print_model_analysis(
+        sess.graph,
+        run_meta=run_metadata,
+        tfprof_options=opts)
+
+    return tfprof_node, run_metadata
+
+
+class RunMetadataTest(test.TestCase):
+
+  def testGPU(self):
+    if not test.is_gpu_available():
+      return
+
+    with ops.device('/gpu:0'):
+      tfprof_node, run_meta = _run_model()
+
+      self.assertEqual(tfprof_node.children[0].name, 'MatMul')
+      self.assertGreater(tfprof_node.children[0].exec_micros, 10)
+
+    ret = _extract_node(run_meta, 'MatMul')
+    self.assertEqual(len(ret), 1)
+    self.assertTrue('/job:localhost/replica:0/task:0/gpu:0' in ret)
+
+    ret = _extract_node(run_meta, 'MatMul:MatMul')
+    self.assertEqual(len(ret), 2)
+    has_all_stream = False
+    for k, _ in six.iteritems(ret):
+      self.assertTrue('gpu:0/stream' in k)
+      if 'gpu:0/stream:all' in k:
+        has_all_stream = True
+    self.assertTrue(has_all_stream)
+
+  def testCPU(self):
+    with ops.device('/cpu:0'):
+      tfprof_node, run_meta = _run_model()
+
+      self.assertEqual(tfprof_node.children[0].name, 'MatMul')
+      self.assertGreater(tfprof_node.children[0].exec_micros, 10)
+
+    ret = _extract_node(run_meta, 'MatMul')
+    self.assertEqual(len(ret), 1)
+    self.assertTrue('/job:localhost/replica:0/task:0/cpu:0' in ret)
+
+    ret = _extract_node(run_meta, 'MatMul:MatMul')
+    self.assertEqual(len(ret), 0)
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index 913971afaf..1b5041441f 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -38,7 +38,7 @@ class PrintModelAnalysisTest(test.TestCase):
     outfile = os.path.join(test.get_temp_dir(), 'dump')
     opts['output'] = 'file:outfile=' + outfile
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       _ = lib.BuildSmallModel()
       model_analyzer.print_model_analysis(sess.graph, tfprof_options=opts)
 
@@ -57,7 +57,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['output'] = 'file:outfile=' + outfile
     opts['account_type_regexes'] = ['.*']
     opts['select'] = [
-        'bytes', 'params', 'float_ops', 'occurrence', 'device', 'op_types',
+        'params', 'float_ops', 'occurrence', 'device', 'op_types',
         'input_shapes'
     ]
 
@@ -77,7 +77,7 @@ class PrintModelAnalysisTest(test.TestCase):
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
         self.assertEqual(
-            'node name | # parameters | # float_ops | output bytes | assigned devices | op types | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, --/5.28KB, _kTFScopeParent, )\n  Conv2D (0/0 params, 5.83k/5.83k flops, 432B/432B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x6x6x3|1:3x3x3x6)\n  Conv2D_1 (0/0 params, 4.61k/4.61k flops, 384B/384B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x3x3x6|1:2x2x6x12)\n  DW (3x3x3x6, 162/162 params, 0/0 flops, 648B/1.30KB, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW/Assign (0/0 params, 0/0 flops, 0B/0B, Assign, 0:3x3x3x6|1:3x3x3x6)\n    DW/Initializer (0/0 params, 0/0 flops, 0B/0B, _kTFScopeParent, )\n      DW/Initializer/random_normal (0/0 params, 0/0 flops, 0B/0B, Add, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, 0B/0B, RandomStandardNormal, 0:4)\n        DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, 0B/0B, Const, )\n        DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, 0B/0B, Mul, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, 0B/0B, Const, )\n        DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, 0B/0B, Const, )\n    DW/read (0/0 params, 0/0 flops, 648B/648B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:3x3x3x6)\n  DW2 (2x2x6x12, 288/288 params, 0/0 flops, 1.15KB/2.30KB, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW2/Assign (0/0 params, 0/0 flops, 0B/0B, Assign, 0:2x2x6x12|1:2x2x6x12)\n    DW2/Initializer (0/0 params, 0/0 flops, 0B/0B, _kTFScopeParent, )\n      DW2/Initializer/random_normal (0/0 params, 0/0 flops, 0B/0B, Add, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, 0B/0B, RandomStandardNormal, 0:4)\n        DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, 0B/0B, Const, )\n        DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, 0B/0B, Mul, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, 0B/0B, Const, )\n        DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, 0B/0B, Const, )\n    DW2/read (0/0 params, 0/0 flops, 1.15KB/1.15KB, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:2x2x6x12)\n  ScalarW (1, 1/1 params, 0/0 flops, 0B/0B, VariableV2|_trainable_variables, )\n    ScalarW/Assign (0/0 params, 0/0 flops, 0B/0B, Assign, 0:1|1:1)\n    ScalarW/Initializer (0/0 params, 0/0 flops, 0B/0B, _kTFScopeParent, )\n      ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, 0B/0B, Add, 0:1|1:1)\n        ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, 0B/0B, RandomStandardNormal, 0:0)\n        ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, 0B/0B, Const, )\n        ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, 0B/0B, Mul, 0:1|1:1)\n        ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, 0B/0B, Const, )\n        ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, 0B/0B, Const, )\n    ScalarW/read (0/0 params, 0/0 flops, 0B/0B, Identity, 0:1)\n  init (0/0 params, 0/0 flops, 0B/0B, NoOp, 0:1|1:3x3x3x6|2:2x2x6x12)\n  zeros (0/0 params, 0/0 flops, 864B/864B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, )\n',
+            'node name | # parameters | # float_ops | assigned devices | op types | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, _kTFScopeParent, )\n  Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x6x6x3|1:3x3x3x6)\n  Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x3x3x6|1:2x2x6x12)\n  DW (3x3x3x6, 162/162 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW/Assign (0/0 params, 0/0 flops, Assign, 0:3x3x3x6|1:3x3x3x6)\n    DW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      DW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:4)\n        DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:3x3x3x6)\n  DW2 (2x2x6x12, 288/288 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW2/Assign (0/0 params, 0/0 flops, Assign, 0:2x2x6x12|1:2x2x6x12)\n    DW2/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      DW2/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:4)\n        DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:2x2x6x12)\n  ScalarW (1, 1/1 params, 0/0 flops, VariableV2|_trainable_variables, )\n    ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0:1|1:1)\n    ScalarW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:1|1:1)\n        ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:0)\n        ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:1|1:1)\n        ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    ScalarW/read (0/0 params, 0/0 flops, Identity, 0:1)\n  init (0/0 params, 0/0 flops, NoOp, 0:1|1:3x3x3x6|2:2x2x6x12)\n  zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, )\n',
             f.read())
         # pylint: enable=line-too-long
 
@@ -96,7 +96,7 @@ class PrintModelAnalysisTest(test.TestCase):
         'input_shapes'
     ]
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildSmallModel()
 
       sess.run(variables.global_variables_initializer())
@@ -177,7 +177,7 @@ class PrintModelAnalysisTest(test.TestCase):
         'bytes', 'params', 'float_ops', 'device'
     ]
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildSmallModel()
 
       sess.run(variables.global_variables_initializer())
@@ -205,7 +205,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['max_depth'] = 100000
     opts['step'] = 0
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -233,7 +233,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['select'] = ['params', 'micros', 'occurrence', 'input_shapes']
     opts['order_by'] = 'occurrence'
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -247,9 +247,11 @@ class PrintModelAnalysisTest(test.TestCase):
           sess.graph, run_meta, tfprof_cmd='op', tfprof_options=opts)
 
       with gfile.Open(outfile, 'r') as f:
+        # pylint: disable=line-too-long
         self.assertEqual(
-            'nodename|executiontime|#parameters|opoccurrence|inputshapes\n',
-            f.read().replace('\t', '').replace(' ', '')[0:60])
+            'nodename|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence|input',
+            f.read().replace('\t', '').replace(' ', '')[0:100])
+        # pylint: enable=line-too-long
 
       total_children = 0
       last_occurrence = 1e32
diff --git a/tensorflow/tools/tfprof/g3doc/options.md b/tensorflow/tools/tfprof/g3doc/options.md
index 78c72bf5ed..57f67c66fa 100644
--- a/tensorflow/tools/tfprof/g3doc/options.md
+++ b/tensorflow/tools/tfprof/g3doc/options.md
@@ -49,7 +49,7 @@ In graph view, in means the number of hops in the <b>graph</b>.
 
 `-step`: Show the stats of the this step when multiple steps of RunMetadata were added. By default, show the average of all steps."
 
-`-order_by`: Order the results by [name|depth|bytes|micros|params|float_ops|occurrence]
+`-order_by`: Order the results by [name|depth|bytes|micros|accelerator_micros|cpu_micros|params|float_ops|occurrence]
 
 `-account_type_regexes`: Account and display the ops whose types match one of the type regexes specified. tfprof allow user to define extra op types for ops through tensorflow.tfprof.OpLog proto. regexes are comma-sperated.
 
@@ -73,7 +73,7 @@ as long as they match the `-account_xxx` options.
 `-account_displayed_op_only`: If True, only account the statistics of ops eventually displayed. If False, account all op statistics matching -account_type_regexes recursively.
 
 `-select`: Comma-separated list of metrics to show:
-[bytes|micros|params|float_ops|occurrence|tensor_value|device|op_types|input_shapes].
+[bytes|micros|accelerator_micros|cpu_micros|params|float_ops|occurrence|tensor_value|device|op_types|input_shapes].
 
 `-output`: Output results as stdout, file or timeline.
 The format is ```output_type:key=value,key=value```.
diff --git a/tensorflow/tools/tfprof/internal/BUILD b/tensorflow/tools/tfprof/internal/BUILD
index 9b77b0fb3f..c47008b069 100644
--- a/tensorflow/tools/tfprof/internal/BUILD
+++ b/tensorflow/tools/tfprof/internal/BUILD
@@ -185,6 +185,7 @@ cc_library(
         ":tfprof_node_show",
         ":tfprof_options",
         ":tfprof_scope",
+        ":tfprof_show",
         ":tfprof_tensor",
         ":tfprof_timeline",
         ":tfprof_utils",
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
index 8f256584f7..fb7f65d7dc 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
@@ -76,7 +76,7 @@ class AcceleratorUtilizationChecker : public Checker {
     if (execs.empty()) {
       return;
     }
-    if (!IsAcceleratorDevice(node->canonical_device())) {
+    if (!IsPlacedOnAccelerator(node->canonical_device())) {
       return;
     }
 
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
index 78132e3a46..2a05f9bfd0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
@@ -47,7 +47,7 @@ class OperationChecker : public Checker {
       if (node->op_attrs().find("data_format") != node->op_attrs().end()) {
         const AttrValue* attr_val = node->op_attrs().at("data_format");
         if (attr_val->s() == "NHWC" &&
-            IsAcceleratorDevice(node->canonical_device())) {
+            IsPlacedOnAccelerator(node->canonical_device())) {
           recommend_nchw = true;
         }
       }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_code.cc b/tensorflow/tools/tfprof/internal/tfprof_code.cc
index f328e3b0cd..70e341e8d4 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_code.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_code.cc
@@ -264,15 +264,9 @@ string TFCode::FormatNode(CodeNode* node, const Options& opts, int64 indent) {
     }
     attrs.push_back(memory);
   }
-  if (opts.select.find(kShown[1]) != opts.select.end()) {
-    string time = FormatTime(node->proto().total_exec_micros());
-    if (node->account) {
-      time = FormatTime(node->proto().exec_micros()) + "/" + time;
-    } else {
-      time = "--/" + time;
-    }
-    attrs.push_back(time);
-  }
+  std::vector<string> time_attrs = FormatTimes(node, opts);
+  attrs.insert(attrs.end(), time_attrs.begin(), time_attrs.end());
+
   if (opts.select.find(kShown[5]) != opts.select.end() &&
       !node->node->devices().empty()) {
     attrs.push_back(str_util::Join(node->node->devices(), "|"));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.cc b/tensorflow/tools/tfprof/internal/tfprof_node.cc
index 6353813a26..9039926201 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_node.cc
@@ -19,6 +19,18 @@ limitations under the License.
 
 namespace tensorflow {
 namespace tfprof {
+namespace {
+bool CountAsAcceleratorTime(const string& device) {
+  return device.find("stream:all") != device.npos;
+}
+
+bool CountAsCPUTime(const string& device) {
+  return RE2::FullMatch(device, ".*/(gpu|cpu):\\d+");
+}
+
+bool IsCanonicalDevice(const string& device) { return CountAsCPUTime(device); }
+
+}  // namespace
 // Notes about start and end time from the NodeExecStats proto:
 // For GPU, there is no difference between op_end_rel_micros and
 // all_end_rel_micros. All are kernel times.
@@ -52,13 +64,17 @@ void ExecStep::AddTimeStats(const string& dev, const NodeExecStats& step_stat) {
     latest_end_micros_ = std::max(
         latest_end_micros_, step_stat.all_start_micros() + op_end_rel_micros);
 
-    op_execs_[dev].push_back(
-        std::make_pair(step_stat.all_start_micros(), op_end_rel_micros));
-
-    // TODO(xpan): Can a stream only in stream:all or doesn't in stream at all?
-    if (dev.find("stream") != dev.npos && dev.find("stream:all") == dev.npos) {
-      gpu_kernel_execs_[dev].push_back(
-          std::make_pair(step_stat.all_start_micros(), op_end_rel_micros));
+    const std::pair<int64, int64> pair =
+        std::make_pair(step_stat.all_start_micros(), op_end_rel_micros);
+    if (CountAsAcceleratorTime(dev)) {
+      accelerator_execs_[dev].push_back(pair);
+      op_execs_[dev].push_back(pair);
+    } else if (CountAsCPUTime(dev)) {
+      if (!cpu_execs_.empty()) {
+        fprintf(stderr, "Multiple CPU execution of 1 op, please file bug.\n");
+      }
+      cpu_execs_[dev].push_back(pair);
+      op_execs_[dev].push_back(pair);
     }
   }
 }
@@ -113,8 +129,11 @@ void TFGraphNode::AddStepStat(int64 step, const string& device,
                               const NodeExecStats& step_stat) {
   string dev = str_util::Lowercase(device);
 
-  // TODO(xpan): Test it.
-  if (RE2::FullMatch(dev, "/job:.*/replica:\\d+/task:\\d+/[a-z]+:\\d+")) {
+  // TODO(xpan): Make this more robust?
+  // See run_metadata_test.py
+  // It can be /job:0/replica:0/xxxx/gpu:0, or simply /gpu:0.
+  // It can has some ad-hoc suffix, such as /stream:xx or /memcpy:xx.
+  if (IsCanonicalDevice(device)) {
     if (!canonical_device_.empty()) {
       if (canonical_device_ != dev) {
         fprintf(stderr, "Unexpected: graph node changed device: %s->%s.\n",
@@ -143,16 +162,15 @@ void TFGraphNode::AddStepStat(int64 step, const string& device,
 }
 
 int64 ExecStep::exec_micros() const {
-  int64 total = accelerator_exec_micros();
-  if (total > 0) return total;
-
-  // If there is no gpu kernel time, fall back to assume it runs on cpu.
-  // TODO(xpan): No way to track CPU async op timing accurately?
-  if (op_execs_.size() > 1) {
-    fprintf(stderr, "Op: %s has over 1 no-gpu assignment\n",
-            node->name().c_str());
-  }
-  for (const auto& execs : op_execs_) {
+  return accelerator_exec_micros() + cpu_exec_micros();
+}
+
+int64 ExecStep::accelerator_exec_micros() const {
+  int64 total = 0;
+  // Normally, an op should only be scheduled on 1 accelerator device.
+  // Hence there should generally be 1 element in accelerator_execs_.
+  for (const auto& execs : accelerator_execs_) {
+    // A op can fire multiple kernel runs hence multiple elements here.
     for (const auto& exec : execs.second) {
       total += exec.second;
     }
@@ -160,9 +178,15 @@ int64 ExecStep::exec_micros() const {
   return total;
 }
 
-int64 ExecStep::accelerator_exec_micros() const {
+int64 ExecStep::cpu_exec_micros() const {
   int64 total = 0;
-  for (const auto& execs : gpu_kernel_execs_) {
+  // Here we use for loop just for consistent appearence with
+  // accelerator_execs.
+  // We only expect cpu_execs_ to have 1 element because an
+  // op can only be scheduled on 1 device.
+  for (const auto& execs : cpu_execs_) {
+    // We only expect exec to have 1 element because an op
+    // can only be schedule once.
     for (const auto& exec : execs.second) {
       total += exec.second;
     }
@@ -170,14 +194,6 @@ int64 ExecStep::accelerator_exec_micros() const {
   return total;
 }
 
-bool IsCombinedGPUStream(const string& device) {
-  return device.find("stream:all") != device.npos;
-}
-
-bool IsCPUDevice(const string& device) {
-  return device.find("cpu:0") != device.npos;
-}
-
 std::vector<int64> ShapeProtoToVec(const TensorShapeProto& shape_pb) {
   std::vector<int64> shape_vec;
   if (shape_pb.dim_size() == 0 && !shape_pb.unknown_rank()) {
@@ -203,7 +219,7 @@ TensorShapeProto VecToShapeProto(const std::vector<int64> shape_vec) {
   return shape_pb;
 }
 
-bool IsAcceleratorDevice(const string& device) {
+bool IsPlacedOnAccelerator(const string& device) {
   return device.find("gpu") != device.npos;
 }
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.h b/tensorflow/tools/tfprof/internal/tfprof_node.h
index d788f2acf4..6fd65c1ee7 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_node.h
@@ -63,9 +63,10 @@ class ExecStep {
   // The execution time of an op. If it runs on accelerator, then it's
   // accelerator_exec_micros(). Otherwise, it's CPU time.
   int64 exec_micros() const;
-
-  // The execution time of an op. 0 if it runs on cpu.
+  // The accelerator execution time of an op. 0 if not run on accelerator.
   int64 accelerator_exec_micros() const;
+  // The cpu execution time of an op.
+  int64 cpu_exec_micros() const;
 
   const std::map<string, std::vector<std::pair<int64, int64>>>& op_execs()
       const {
@@ -92,10 +93,13 @@ class ExecStep {
   int64 all_start_micros_;
   int64 latest_end_micros_;
   // device -> vector of {op_start_micros, op_exec_micros} pairs.
-  // For accelerator op, op_start_micros and op_exec_micros are kernel time.
-  // For cpu op, op_start_micros and op_exec_micros are scheduling time. (
-  // might include compute time if it's sync op).
-  std::map<string, std::vector<std::pair<int64, int64>>> gpu_kernel_execs_;
+  // accelerator_execs: gpu:id/stream:all -> {op_start_micros, op_exec_micros}
+  // For accelerator, vector size can be larger than 1, multiple kernel fires.
+  std::map<string, std::vector<std::pair<int64, int64>>> accelerator_execs_;
+  // cpu_execs: cpu/gpu:id -> {op_start_micros, op_exec_micros}
+  // For cpu, normally vector size is 1, that is only one run.
+  std::map<string, std::vector<std::pair<int64, int64>>> cpu_execs_;
+  // combines accelerator_execs_ and cpu_execs_.
   std::map<string, std::vector<std::pair<int64, int64>>> op_execs_;
   // All devices the op is associated with (e.g. gpu:0 (scheduling),
   // gpu:0:stream:xx (kernel exec), cpu:0 host)
@@ -184,8 +188,10 @@ class TFGraphNode {
     return src_output_idx_;
   }
 
-  // This is time spent in kernel execution.
-  int64 kernel_exec_micros(int64 step) const {
+  // This is overall computation time, including both cpu and accelerator.
+  // Note, cpu and accelerator might or might not run in parallel.
+  int64 exec_micros(int64 step) const {
+    // Empty when no RunMetadata is provided.
     if (execs_.empty()) {
       return 0;
     }
@@ -202,6 +208,46 @@ class TFGraphNode {
     return total_micros / execs_.size();
   }
 
+  // This is accelerator computation time of a step, or average of
+  // multiple step, when step < 0.
+  int64 accelerator_exec_micros(int64 step) const {
+    // Empty when no RunMetadata is provided.
+    if (execs_.empty()) {
+      return 0;
+    }
+    if (step >= 0) {
+      auto exec = execs_.find(step);
+      CHECK(exec != execs_.end());
+      return exec->second.accelerator_exec_micros();
+    }
+
+    int64 total_micros = 0;
+    for (const auto& exec : execs_) {
+      total_micros += exec.second.accelerator_exec_micros();
+    }
+    return total_micros / execs_.size();
+  }
+
+  // This is cpu computation time of a step, or average of
+  // multiple step, when step < 0.
+  int64 cpu_exec_micros(int64 step) const {
+    // Empty when no RunMetadata is provided.
+    if (execs_.empty()) {
+      return 0;
+    }
+    if (step >= 0) {
+      auto exec = execs_.find(step);
+      CHECK(exec != execs_.end());
+      return exec->second.cpu_exec_micros();
+    }
+
+    int64 total_micros = 0;
+    for (const auto& exec : execs_) {
+      total_micros += exec.second.cpu_exec_micros();
+    }
+    return total_micros / execs_.size();
+  }
+
   int64 requested_bytes(int64 step) const {
     if (execs_.empty()) {
       return 0;
@@ -325,12 +371,17 @@ class TFMultiGraphNode {
  public:
   TFMultiGraphNode(const string& name)
       : name_(name),
-        kernel_exec_micros_(0),
+        exec_micros_(0),
+        accelerator_exec_micros_(0),
+        cpu_exec_micros_(0),
         requested_bytes_(0),
         float_ops_(0) {}
 
   bool SnapshotNodes(int64 step, const std::vector<string>& type_regexes) {
-    kernel_exec_micros_ = 0;
+    exec_micros_ = 0;
+    accelerator_exec_micros_ = 0;
+    cpu_exec_micros_ = 0;
+
     requested_bytes_ = 0;
     float_ops_ = 0;
     op_types_.clear();
@@ -347,7 +398,10 @@ class TFMultiGraphNode {
     for (const TFGraphNode* node : nodes) {
       op_types_.insert(node->op_types().begin(), node->op_types().end());
 
-      kernel_exec_micros_ += node->kernel_exec_micros(step);
+      exec_micros_ += node->exec_micros(step);
+      accelerator_exec_micros_ += node->accelerator_exec_micros(step);
+      cpu_exec_micros_ += node->cpu_exec_micros(step);
+
       requested_bytes_ += node->requested_bytes(step);
       float_ops_ += node->float_ops();
       if (node->shape().size() > 0) {
@@ -382,7 +436,9 @@ class TFMultiGraphNode {
 
   const string& name() const { return name_; }
 
-  int64 kernel_exec_micros() const { return kernel_exec_micros_; }
+  int64 exec_micros() const { return exec_micros_; }
+  int64 accelerator_exec_micros() const { return accelerator_exec_micros_; }
+  int64 cpu_exec_micros() const { return cpu_exec_micros_; }
 
   int64 requested_bytes() const { return requested_bytes_; }
 
@@ -424,7 +480,10 @@ class TFMultiGraphNode {
   const string name_;
   // Snapshot based on type_regexes
   std::set<string> op_types_;
-  int64 kernel_exec_micros_;
+  int64 exec_micros_;
+  int64 accelerator_exec_micros_;
+  int64 cpu_exec_micros_;
+
   int64 requested_bytes_;
   int64 float_ops_;
   std::set<string> devices_;
@@ -436,9 +495,7 @@ class TFMultiGraphNode {
   std::map<string, std::unique_ptr<TFMultiGraphNode>> children_;
 };
 
-bool IsCombinedGPUStream(const string& device);
-bool IsCPUDevice(const string& device);
-bool IsAcceleratorDevice(const string& device);
+bool IsPlacedOnAccelerator(const string& device);
 }  // namespace tfprof
 }  // namespace tensorflow
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc b/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
index 7b604e091a..7b22e88e07 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
@@ -31,7 +31,11 @@ void ShowNode::ReInit(int64 step) {
   if (!node->canonical_device().empty()) {
     mutable_proto()->add_devices(node->canonical_device());
   }
-  mutable_proto()->set_exec_micros(node->kernel_exec_micros(step));
+  mutable_proto()->set_exec_micros(node->exec_micros(step));
+  mutable_proto()->set_accelerator_exec_micros(
+      node->accelerator_exec_micros(step));
+  mutable_proto()->set_cpu_exec_micros(node->cpu_exec_micros(step));
+
   mutable_proto()->set_requested_bytes(node->requested_bytes(step));
   mutable_proto()->set_float_ops(node->float_ops());
 
@@ -69,6 +73,12 @@ void ShowNode::AggregateTotalStats(ShowNode* node) {
   TFGraphNodeProto* node_pb = node->mutable_proto();
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          node_pb->total_exec_micros());
+  mutable_proto()->set_total_accelerator_exec_micros(
+      proto().total_accelerator_exec_micros() +
+      node_pb->total_accelerator_exec_micros());
+  mutable_proto()->set_total_cpu_exec_micros(proto().total_cpu_exec_micros() +
+                                             node_pb->total_cpu_exec_micros());
+
   mutable_proto()->set_total_requested_bytes(proto().total_requested_bytes() +
                                              node_pb->total_requested_bytes());
   mutable_proto()->set_total_parameters(proto().total_parameters() +
@@ -80,6 +90,12 @@ void ShowNode::AggregateTotalStats(ShowNode* node) {
 void ShowNode::AddSelfToTotalStats() {
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          proto().exec_micros());
+  mutable_proto()->set_total_accelerator_exec_micros(
+      proto().total_accelerator_exec_micros() +
+      proto().accelerator_exec_micros());
+  mutable_proto()->set_total_cpu_exec_micros(proto().total_cpu_exec_micros() +
+                                             proto().cpu_exec_micros());
+
   mutable_proto()->set_total_requested_bytes(proto().total_requested_bytes() +
                                              proto().requested_bytes());
   mutable_proto()->set_total_parameters(proto().total_parameters() +
@@ -90,6 +106,9 @@ void ShowNode::AddSelfToTotalStats() {
 
 void ShowNode::ResetTotalStats() {
   mutable_proto()->set_total_exec_micros(0);
+  mutable_proto()->set_total_accelerator_exec_micros(0);
+  mutable_proto()->set_total_cpu_exec_micros(0);
+
   mutable_proto()->set_total_requested_bytes(0);
   mutable_proto()->set_total_parameters(0);
   mutable_proto()->set_total_float_ops(0);
@@ -116,7 +135,10 @@ bool ShowMultiNode::ReInit(int64 step,
   }
 
   mutable_proto()->set_name(name());
-  mutable_proto()->set_exec_micros(node->kernel_exec_micros());
+  mutable_proto()->set_exec_micros(node->exec_micros());
+  mutable_proto()->set_accelerator_exec_micros(node->accelerator_exec_micros());
+  mutable_proto()->set_cpu_exec_micros(node->cpu_exec_micros());
+
   mutable_proto()->set_requested_bytes(node->requested_bytes());
   mutable_proto()->set_float_ops(node->float_ops());
 
@@ -151,6 +173,12 @@ void ShowMultiNode::AggregateTotalStats(ShowMultiNode* node) {
   TFMultiGraphNodeProto* node_pb = node->mutable_proto();
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          node_pb->total_exec_micros());
+  mutable_proto()->set_total_accelerator_exec_micros(
+      proto().total_accelerator_exec_micros() +
+      node_pb->total_accelerator_exec_micros());
+  mutable_proto()->set_total_cpu_exec_micros(proto().total_cpu_exec_micros() +
+                                             node_pb->total_cpu_exec_micros());
+
   mutable_proto()->set_total_requested_bytes(proto().total_requested_bytes() +
                                              node_pb->total_requested_bytes());
   mutable_proto()->set_total_parameters(proto().total_parameters() +
@@ -162,6 +190,12 @@ void ShowMultiNode::AggregateTotalStats(ShowMultiNode* node) {
 void ShowMultiNode::AddSelfToTotalStats() {
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          proto().exec_micros());
+  mutable_proto()->set_total_accelerator_exec_micros(
+      proto().total_accelerator_exec_micros() +
+      proto().accelerator_exec_micros());
+  mutable_proto()->set_total_cpu_exec_micros(proto().total_cpu_exec_micros() +
+                                             proto().cpu_exec_micros());
+
   mutable_proto()->set_total_requested_bytes(proto().total_requested_bytes() +
                                              proto().requested_bytes());
   mutable_proto()->set_total_parameters(proto().total_parameters() +
@@ -172,6 +206,9 @@ void ShowMultiNode::AddSelfToTotalStats() {
 
 void ShowMultiNode::ResetTotalStats() {
   mutable_proto()->set_total_exec_micros(0);
+  mutable_proto()->set_total_accelerator_exec_micros(0);
+  mutable_proto()->set_total_cpu_exec_micros(0);
+
   mutable_proto()->set_total_requested_bytes(0);
   mutable_proto()->set_total_parameters(0);
   mutable_proto()->set_total_float_ops(0);
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index 655569f1a2..ac702320b3 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -26,6 +26,60 @@ limitations under the License.
 
 namespace tensorflow {
 namespace tfprof {
+namespace {
+string FormatToalExecTime(const ShowMultiNode* node,
+                          const ShowMultiNode* root) {
+  double accu_pct = 0.0;
+  double pct = 0.0;
+  if (node->proto().total_exec_micros() > 0) {
+    accu_pct = 100.0 * node->proto().total_exec_micros() /
+               root->proto().total_exec_micros();
+    pct =
+        100.0 * node->proto().exec_micros() / root->proto().total_exec_micros();
+  }
+
+  return strings::Printf(
+      "%30s", strings::Printf("%s (%.2f%%, %.2f%%)",
+                              FormatTime(node->proto().exec_micros()).c_str(),
+                              accu_pct, pct)
+                  .c_str());
+}
+string FormatCPUExecTime(const ShowMultiNode* node, const ShowMultiNode* root) {
+  double accu_pct = 0.0;
+  double pct = 0.0;
+  if (node->proto().total_cpu_exec_micros() > 0) {
+    accu_pct = 100.0 * node->proto().total_cpu_exec_micros() /
+               root->proto().total_cpu_exec_micros();
+    pct = 100.0 * node->proto().cpu_exec_micros() /
+          root->proto().total_cpu_exec_micros();
+  }
+
+  return strings::Printf(
+      "%30s",
+      strings::Printf("%s (%.2f%%, %.2f%%)",
+                      FormatTime(node->proto().cpu_exec_micros()).c_str(),
+                      accu_pct, pct)
+          .c_str());
+}
+string FormatAcceleratorExecTime(const ShowMultiNode* node,
+                                 const ShowMultiNode* root) {
+  double accu_pct = 0.0;
+  double pct = 0.0;
+  if (node->proto().total_accelerator_exec_micros() > 0) {
+    accu_pct = 100.0 * node->proto().total_accelerator_exec_micros() /
+               root->proto().total_accelerator_exec_micros();
+    pct = 100.0 * node->proto().accelerator_exec_micros() /
+          root->proto().total_accelerator_exec_micros();
+  }
+
+  return strings::Printf(
+      "%30s", strings::Printf(
+                  "%s (%.2f%%, %.2f%%)",
+                  FormatTime(node->proto().accelerator_exec_micros()).c_str(),
+                  accu_pct, pct)
+                  .c_str());
+}
+}  // namespace
 
 void TFOp::AddNode(TFGraphNode* node) {
   const string& op = node->op();
@@ -168,22 +222,18 @@ string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
   }
 
   if (opts.select.find(kShown[1]) != opts.select.end()) {
-    double accu_pct = 0.0;
-    double pct = 0.0;
-    if (node->proto().total_exec_micros() > 0) {
-      accu_pct = 100.0 * node->proto().total_exec_micros() /
-          root->proto().total_exec_micros();
-      pct = 100.0 * node->proto().exec_micros() /
-          root->proto().total_exec_micros();
-    }
-
-    attrs.push_back(strings::Printf(
-        "%30s", strings::Printf("%s (%.2f%%, %.2f%%)",
-                                FormatTime(node->proto().exec_micros()).c_str(),
-                                accu_pct, pct)
-                    .c_str()));
+    attrs.push_back(FormatToalExecTime(node, root));
+    attrs.push_back(FormatAcceleratorExecTime(node, root));
+    attrs.push_back(FormatCPUExecTime(node, root));
+  }
+  if (opts.select.find(kShown[9]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    attrs.push_back(FormatAcceleratorExecTime(node, root));
+  }
+  if (opts.select.find(kShown[10]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    attrs.push_back(FormatCPUExecTime(node, root));
   }
-
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     double accu_pct = 0.0;
     double pct = 0.0;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/tools/tfprof/internal/tfprof_options.h
index d8c172e0a2..6c9db24342 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_options.h
@@ -46,15 +46,17 @@ static const char* const kOptions[] = {
 };
 
 static const char* const kOrderBy[] = {
-    "name", "bytes", "micros", "params", "float_ops", "occurrence",
+    "name",       "bytes",  "micros",    "accelerator_micros",
+    "cpu_micros", "params", "float_ops", "occurrence",
 };
 
 // Append Only.
 // TODO(xpan): As we are adding more fields to be selected, we
 // need to have a way to tell users what fields are available in which view.
 static const char* const kShown[] = {
-    "bytes",  "micros",   "params",     "float_ops",   "tensor_value",
-    "device", "op_types", "occurrence", "input_shapes"};
+    "bytes",     "micros",   "params",     "float_ops",    "tensor_value",
+    "device",    "op_types", "occurrence", "input_shapes", "accelerator_micros",
+    "cpu_micros"};
 
 static const char* const kCmds[] = {
     "scope", "graph", "code", "op", "set", "help",
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index 517a09f0c7..eaab5b9608 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -156,13 +156,17 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) {
     info.push_back(memory);
   }
   if (opts.select.find(kShown[1]) != opts.select.end()) {
-    string time = FormatTime(node->proto().total_exec_micros());
-    if (node->account) {
-      time = FormatTime(node->proto().exec_micros()) + "/" + time;
-    } else {
-      time = "--/" + time;
-    }
-    info.push_back(time);
+    info.push_back(FormatTotalExecTime(node, opts));
+    info.push_back(FormatAcceleratorExecTime(node, opts));
+    info.push_back(FormatCPUExecTime(node, opts));
+  }
+  if (opts.select.find(kShown[9]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    info.push_back(FormatAcceleratorExecTime(node, opts));
+  }
+  if (opts.select.find(kShown[10]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    info.push_back(FormatCPUExecTime(node, opts));
   }
   if (opts.select.find(kShown[5]) != opts.select.end()) {
     if (node->proto().devices_size() > 0) {
@@ -202,7 +206,17 @@ string TFShow::FormatLegend(const Options& opts) {
     legends.push_back("output bytes");
   }
   if (opts.select.find(kShown[1]) != opts.select.end()) {
-    legends.push_back("execution time");
+    legends.push_back("total execution time");
+    legends.push_back("accelerator execution time");
+    legends.push_back("cpu execution time");
+  }
+  if (opts.select.find(kShown[9]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    legends.push_back("accelerator execution time");
+  }
+  if (opts.select.find(kShown[10]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    legends.push_back("cpu execution time");
   }
   if (opts.select.find(kShown[5]) != opts.select.end()) {
     legends.push_back("assigned devices");
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/tools/tfprof/internal/tfprof_show.h
index a337a584f7..2c61b4fd73 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.h
@@ -101,6 +101,36 @@ class TFShow {
   checkpoint::CheckpointReader* ckpt_reader_;
 };
 
+template <typename T>
+string FormatTotalExecTime(const T* node, const Options& opts) {
+  string time = FormatTime(node->proto().total_exec_micros());
+  if (node->account) {
+    time = FormatTime(node->proto().exec_micros()) + "/" + time;
+  } else {
+    time = "--/" + time;
+  }
+  return time;
+}
+template <typename T>
+string FormatCPUExecTime(const T* node, const Options& opts) {
+  string time = FormatTime(node->proto().total_cpu_exec_micros());
+  if (node->account) {
+    time = FormatTime(node->proto().cpu_exec_micros()) + "/" + time;
+  } else {
+    time = "--/" + time;
+  }
+  return time;
+}
+template <typename T>
+string FormatAcceleratorExecTime(const T* node, const Options& opts) {
+  string time = FormatTime(node->proto().total_accelerator_exec_micros());
+  if (node->account) {
+    time = FormatTime(node->proto().accelerator_exec_micros()) + "/" + time;
+  } else {
+    time = "--/" + time;
+  }
+  return time;
+}
 }  // namespace tfprof
 }  // namespace tensorflow
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 4714ffc33a..97f204d25b 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -108,7 +108,17 @@ string TFMultiShow::FormatLegend(const Options& opts) {
     legends.push_back("output bytes");
   }
   if (opts.select.find(kShown[1]) != opts.select.end()) {
-    legends.push_back("execution time");
+    legends.push_back("total execution time");
+    legends.push_back("accelerator execution time");
+    legends.push_back("cpu execution time");
+  }
+  if (opts.select.find(kShown[9]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    legends.push_back("accelerator execution time");
+  }
+  if (opts.select.find(kShown[10]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    legends.push_back("cpu execution time");
   }
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     legends.push_back("# parameters");
@@ -179,5 +189,24 @@ string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
   return str_util::Join(input_types, "\n");
 }
 
+std::vector<string> TFMultiShow::FormatTimes(const ShowMultiNode* node,
+                                             const Options& opts) {
+  std::vector<string> attrs;
+  if (opts.select.find(kShown[1]) != opts.select.end()) {
+    attrs.push_back(FormatTotalExecTime(node, opts));
+    attrs.push_back(FormatAcceleratorExecTime(node, opts));
+    attrs.push_back(FormatCPUExecTime(node, opts));
+  }
+  if (opts.select.find(kShown[9]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    attrs.push_back(FormatAcceleratorExecTime(node, opts));
+  }
+  if (opts.select.find(kShown[10]) != opts.select.end() &&
+      opts.select.find(kShown[1]) == opts.select.end()) {
+    attrs.push_back(FormatCPUExecTime(node, opts));
+  }
+  return attrs;
+}
+
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
index 1181e45ee1..e6faf1231d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_node.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_node_show.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
+#include "tensorflow/tools/tfprof/internal/tfprof_show.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_tensor.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_timeline.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
@@ -67,6 +68,8 @@ class TFMultiShow {
 
   string FormatLegend(const Options& opts);
   string FormatInputShapes(const TFMultiGraphNodeProto& proto);
+  std::vector<string> FormatTimes(const ShowMultiNode* node,
+                                  const Options& opts);
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
@@ -88,12 +91,18 @@ class TFMultiShow {
                   return n1->proto().total_exec_micros() >
                          n2->proto().total_exec_micros();
                 } else if (opts.order_by == kOrderBy[3]) {
+                  return n1->proto().total_accelerator_exec_micros() >
+                         n2->proto().total_accelerator_exec_micros();
+                } else if (opts.order_by == kOrderBy[4]) {
+                  return n1->proto().total_cpu_exec_micros() >
+                         n2->proto().total_cpu_exec_micros();
+                } else if (opts.order_by == kOrderBy[5]) {
                   return n1->proto().total_parameters() >
                          n2->proto().total_parameters();
-                } else if (opts.order_by == kOrderBy[4]) {
+                } else if (opts.order_by == kOrderBy[6]) {
                   return n1->proto().total_float_ops() >
                          n2->proto().total_float_ops();
-                } else if (opts.order_by == kOrderBy[5]) {
+                } else if (opts.order_by == kOrderBy[7]) {
                   return n1->node->graph_nodes().size() >
                          n2->node->graph_nodes().size();
                 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index 498477de0a..478e269f87 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -38,7 +38,8 @@ class TFProfShowTest : public ::testing::Test {
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "tools/tfprof/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
-    TF_CHECK_OK(ReadGraphDef(Env::Default(), graph_path, graph_pb.get()));
+    TF_CHECK_OK(
+        ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
 
     std::unique_ptr<tensorflow::RunMetadata> run_meta_pb(
         new tensorflow::RunMetadata());
@@ -46,7 +47,7 @@ class TFProfShowTest : public ::testing::Test {
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "tools/tfprof/internal/testdata/run_meta");
     TF_CHECK_OK(
-        ReadBinaryProto(Env::Default(), run_meta_path, run_meta_pb.get()));
+        ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
     std::unique_ptr<OpLog> op_log_pb(new OpLog());
     string op_log_path =
@@ -81,14 +82,73 @@ TEST_F(TFProfShowTest, DumpScopeMode) {
   string dump_str;
   TF_CHECK_OK(ReadFileToString(Env::Default(), dump_file, &dump_str));
   EXPECT_EQ(
-      "node name | # parameters | # float_ops | output bytes | execution "
-      "time\n_TFProfRoot (--/370 params, --/0 flops, --/1.48KB, --/5us)\n  "
-      "conv2d (--/140 params, --/0 flops, --/560B, --/2us)\n    conv2d/bias "
-      "(5, 5/5 params, 0/0 flops, 20B/20B, 1us/1us)\n    conv2d/kernel "
-      "(3x3x3x5, 135/135 params, 0/0 flops, 540B/540B, 1us/1us)\n  conv2d_1 "
-      "(--/230 params, --/0 flops, --/920B, --/3us)\n    conv2d_1/bias (5, 5/5 "
-      "params, 0/0 flops, 20B/20B, 1us/1us)\n    conv2d_1/kernel (3x3x5x5, "
-      "225/225 params, 0/0 flops, 900B/900B, 2us/2us)\n",
+      "node name | # parameters | # float_ops | output bytes | total execution "
+      "time | accelerator execution time | cpu execution time\n_TFProfRoot "
+      "(--/370 params, --/0 flops, --/1.48KB, --/5us, --/0us, --/5us)\n  "
+      "conv2d (--/140 params, --/0 flops, --/560B, --/2us, --/0us, --/2us)\n   "
+      " conv2d/bias (5, 5/5 params, 0/0 flops, 20B/20B, 1us/1us, 0us/0us, "
+      "1us/1us)\n    conv2d/kernel (3x3x3x5, 135/135 params, 0/0 flops, "
+      "540B/540B, 1us/1us, 0us/0us, 1us/1us)\n  conv2d_1 (--/230 params, --/0 "
+      "flops, --/920B, --/3us, --/0us, --/3us)\n    conv2d_1/bias (5, 5/5 "
+      "params, 0/0 flops, 20B/20B, 1us/1us, 0us/0us, 1us/1us)\n    "
+      "conv2d_1/kernel (3x3x5x5, 225/225 params, 0/0 flops, 900B/900B, "
+      "2us/2us, 0us/0us, 2us/2us)\n",
+      dump_str);
+}
+
+TEST_F(TFProfShowTest, DumpAcceleratorAndCPUMicros) {
+  string dump_file = io::JoinPath(testing::TmpDir(), "dump");
+  Options opts(
+      5, 0, 0, 0, 0, 0, -1, "cpu_micros", {".*"},  // accout_type_regexes
+      {".*"}, {""}, {".*"}, {""}, false, {"accelerator_micros", "cpu_micros"},
+      "file", {{"outfile", dump_file}});
+  tf_stats_->ShowGraphNode("scope", opts);
+
+  string dump_str;
+  TF_CHECK_OK(ReadFileToString(Env::Default(), dump_file, &dump_str));
+  EXPECT_EQ(
+      "node name | accelerator execution time | cpu execution "
+      "time\n_TFProfRoot (--/0us, --/97us)\n  conv2d (0us/0us, 0us/76us)\n    "
+      "conv2d/convolution (0us/0us, 60us/60us)\n      conv2d/convolution/Shape "
+      "(0us/0us, 0us/0us)\n      conv2d/convolution/dilation_rate (0us/0us, "
+      "0us/0us)\n    conv2d/BiasAdd (0us/0us, 12us/12us)\n    conv2d/bias "
+      "(0us/0us, 1us/2us)\n      conv2d/bias/Assign (0us/0us, 0us/0us)\n      "
+      "conv2d/bias/Initializer (0us/0us, 0us/0us)\n        "
+      "conv2d/bias/Initializer/Const (0us/0us, 0us/0us)\n      "
+      "conv2d/bias/read (0us/0us, 1us/1us)\n    conv2d/kernel (0us/0us, "
+      "1us/2us)\n      conv2d/kernel/Assign (0us/0us, 0us/0us)\n      "
+      "conv2d/kernel/Initializer (0us/0us, 0us/0us)\n        "
+      "conv2d/kernel/Initializer/random_uniform (0us/0us, 0us/0us)\n      "
+      "conv2d/kernel/read (0us/0us, 1us/1us)\n  conv2d_2 (0us/0us, 0us/15us)\n "
+      "   conv2d_2/convolution (0us/0us, 13us/13us)\n      "
+      "conv2d_2/convolution/Shape (0us/0us, 0us/0us)\n      "
+      "conv2d_2/convolution/dilation_rate (0us/0us, 0us/0us)\n    "
+      "conv2d_2/BiasAdd (0us/0us, 2us/2us)\n  conv2d_1 (0us/0us, 0us/5us)\n    "
+      "conv2d_1/bias (0us/0us, 1us/2us)\n      conv2d_1/bias/Assign (0us/0us, "
+      "0us/0us)\n      conv2d_1/bias/Initializer (0us/0us, 0us/0us)\n        "
+      "conv2d_1/bias/Initializer/Const (0us/0us, 0us/0us)\n      "
+      "conv2d_1/bias/read (0us/0us, 1us/1us)\n    conv2d_1/kernel (0us/0us, "
+      "2us/3us)\n      conv2d_1/kernel/Assign (0us/0us, 0us/0us)\n      "
+      "conv2d_1/kernel/Initializer (0us/0us, 0us/0us)\n        "
+      "conv2d_1/kernel/Initializer/random_uniform (0us/0us, 0us/0us)\n      "
+      "conv2d_1/kernel/read (0us/0us, 1us/1us)\n  init (0us/0us, 0us/0us)\n  "
+      "save (0us/0us, 0us/0us)\n    save/Assign (0us/0us, 0us/0us)\n    "
+      "save/Assign_1 (0us/0us, 0us/0us)\n    save/Assign_2 (0us/0us, "
+      "0us/0us)\n    save/Assign_3 (0us/0us, 0us/0us)\n    save/Const "
+      "(0us/0us, 0us/0us)\n    save/RestoreV2 (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2/shape_and_slices (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2/tensor_names (0us/0us, 0us/0us)\n    save/RestoreV2_1 "
+      "(0us/0us, 0us/0us)\n      save/RestoreV2_1/shape_and_slices (0us/0us, "
+      "0us/0us)\n      save/RestoreV2_1/tensor_names (0us/0us, 0us/0us)\n    "
+      "save/RestoreV2_2 (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2_2/shape_and_slices (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2_2/tensor_names (0us/0us, 0us/0us)\n    save/RestoreV2_3 "
+      "(0us/0us, 0us/0us)\n      save/RestoreV2_3/shape_and_slices (0us/0us, "
+      "0us/0us)\n      save/RestoreV2_3/tensor_names (0us/0us, 0us/0us)\n    "
+      "save/SaveV2 (0us/0us, 0us/0us)\n      save/SaveV2/shape_and_slices "
+      "(0us/0us, 0us/0us)\n      save/SaveV2/tensor_names (0us/0us, 0us/0us)\n "
+      "   save/control_dependency (0us/0us, 0us/0us)\n    save/restore_all "
+      "(0us/0us, 0us/0us)\n  zeros (0us/0us, 1us/1us)\n",
       dump_str);
 }
 
@@ -104,14 +164,16 @@ TEST_F(TFProfShowTest, DumpOpMode) {
   string dump_str;
   TF_CHECK_OK(ReadFileToString(Env::Default(), dump_file, &dump_str));
   EXPECT_EQ(
-      "nodename|outputbytes|executiontime|#parameters|#float_ops|opoccurrence|"
-      "inputshapes\nVariableV21.48KB(100.00%,17.10%),5us(100.00%,5.15%),"
-      "370params(100.00%,100.00%),0float_ops(100.00%,0.00%),4\n\ninput_type:\t("
-      "*4)\texec_time:5us\n\nAssign0B(0.00%,0.00%),0us(94.85%,0.00%),0params(0."
-      "00%,0.00%),0float_ops(100.00%,0.00%),8\n\ninput_type:0:unknown,\t1:"
-      "unknown\t(*8)\texec_time:0us\n\nConst1.54KB(58.87%,17.74%),1us(80.41%,1."
-      "03%),0params(0.00%,0.00%),0float_ops(98.49%,0.00%),24\n\ninput_type:\t(*"
-      "24)\texec_time:1us\n\n",
+      "nodename|outputbytes|totalexecutiontime|acceleratorexecutiontime|"
+      "cpuexecutiontime|#parameters|#float_ops|opoccurrence|"
+      "inputshapes\nVariableV21.48KB(100.00%,17.10%),5us(100.00%,5.15%),0us(0."
+      "00%,0.00%),5us(100.00%,5.15%),370params(100.00%,100.00%),0float_ops(100."
+      "00%,0.00%),4\n\ninput_type:\t(*4)\texec_time:5us\n\nAssign0B(0.00%,0.00%"
+      "),0us(94.85%,0.00%),0us(0.00%,0.00%),0us(94.85%,0.00%),0params(0.00%,0."
+      "00%),0float_ops(100.00%,0.00%),8\n\ninput_type:0:unknown,\t1:unknown\t(*"
+      "8)\texec_time:0us\n\nConst1.54KB(58.87%,17.74%),1us(80.41%,1.03%),0us(0."
+      "00%,0.00%),1us(80.41%,1.03%),0params(0.00%,0.00%),0float_ops(98.49%,0."
+      "00%),24\n\ninput_type:\t(*24)\texec_time:1us\n\n",
       StringReplace(dump_str, " ", ""));
 }
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index a1e500f949..948ce49df4 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -39,7 +39,8 @@ class TFProfStatsTest : public ::testing::Test {
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "tools/tfprof/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
-    TF_CHECK_OK(ReadGraphDef(Env::Default(), graph_path, graph_pb.get()));
+    TF_CHECK_OK(
+        ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
 
     std::unique_ptr<tensorflow::RunMetadata> run_meta_pb(
         new tensorflow::RunMetadata());
@@ -47,7 +48,7 @@ class TFProfStatsTest : public ::testing::Test {
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "tools/tfprof/internal/testdata/run_meta");
     TF_CHECK_OK(
-        ReadBinaryProto(Env::Default(), run_meta_path, run_meta_pb.get()));
+        ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
     std::unique_ptr<OpLog> op_log_pb(new OpLog());
     string op_log_path =
@@ -88,26 +89,40 @@ TEST_F(TFProfStatsTest, CustomOpType) {
       "total_exec_micros: 1\n    total_requested_bytes: 20\n    "
       "total_parameters: 5\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  children {\n    name: \"conv2d/kernel\"\n    "
-      "exec_micros: 1\n    requested_bytes: 540\n    parameters: 135\n    "
-      "total_exec_micros: 1\n    total_requested_bytes: 540\n    "
-      "total_parameters: 135\n    devices: "
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "\"conv2d/kernel\"\n    exec_micros: 1\n    requested_bytes: 540\n    "
+      "parameters: 135\n    total_exec_micros: 1\n    total_requested_bytes: "
+      "540\n    total_parameters: 135\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  float_ops: 0\n  total_float_ops: "
-      "0\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "2\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 3\n  total_requested_bytes: "
       "920\n  total_parameters: 230\n  children {\n    name: "
       "\"conv2d_1/bias\"\n    exec_micros: 1\n    requested_bytes: 20\n    "
       "parameters: 5\n    total_exec_micros: 1\n    total_requested_bytes: "
       "20\n    total_parameters: 5\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  children {\n    name: \"conv2d_1/kernel\"\n  "
-      "  exec_micros: 2\n    requested_bytes: 900\n    parameters: 225\n    "
-      "total_exec_micros: 2\n    total_requested_bytes: 900\n    "
-      "total_parameters: 225\n    devices: "
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "\"conv2d_1/kernel\"\n    exec_micros: 2\n    requested_bytes: 900\n    "
+      "parameters: 225\n    total_exec_micros: 2\n    total_requested_bytes: "
+      "900\n    total_parameters: 225\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  float_ops: 0\n  total_float_ops: "
-      "0\n}\nfloat_ops: 0\ntotal_float_ops: 0\n",
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 2\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 2\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "3\n}\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
+      "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
+      "0\ntotal_cpu_exec_micros: 5\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -130,26 +145,40 @@ TEST_F(TFProfStatsTest, CheckPointOpType) {
       "total_exec_micros: 1\n    total_requested_bytes: 20\n    "
       "total_parameters: 5\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  children {\n    name: \"conv2d/kernel\"\n    "
-      "exec_micros: 1\n    requested_bytes: 540\n    parameters: 135\n    "
-      "total_exec_micros: 1\n    total_requested_bytes: 540\n    "
-      "total_parameters: 135\n    devices: "
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "\"conv2d/kernel\"\n    exec_micros: 1\n    requested_bytes: 540\n    "
+      "parameters: 135\n    total_exec_micros: 1\n    total_requested_bytes: "
+      "540\n    total_parameters: 135\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  float_ops: 0\n  total_float_ops: "
-      "0\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "2\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 3\n  total_requested_bytes: "
       "920\n  total_parameters: 230\n  children {\n    name: "
       "\"conv2d_1/bias\"\n    exec_micros: 1\n    requested_bytes: 20\n    "
       "parameters: 5\n    total_exec_micros: 1\n    total_requested_bytes: "
       "20\n    total_parameters: 5\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  children {\n    name: \"conv2d_1/kernel\"\n  "
-      "  exec_micros: 2\n    requested_bytes: 900\n    parameters: 225\n    "
-      "total_exec_micros: 2\n    total_requested_bytes: 900\n    "
-      "total_parameters: 225\n    devices: "
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "\"conv2d_1/kernel\"\n    exec_micros: 2\n    requested_bytes: 900\n    "
+      "parameters: 225\n    total_exec_micros: 2\n    total_requested_bytes: "
+      "900\n    total_parameters: 225\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
-      "total_float_ops: 0\n  }\n  float_ops: 0\n  total_float_ops: "
-      "0\n}\nfloat_ops: 0\ntotal_float_ops: 0\n",
+      "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 2\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 2\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "3\n}\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
+      "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
+      "0\ntotal_cpu_exec_micros: 5\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -163,10 +192,11 @@ TEST_F(TFProfStatsTest, TestGraph) {
 
   TFGraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
-      "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: 0\n"
-      "total_exec_micros: 97\ntotal_requested_bytes: "
-      "8656\ntotal_parameters: 370\nfloat_ops: "
-      "0\ntotal_float_ops: 34360\n",
+      "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
+      "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
+      "8656\ntotal_parameters: 370\nfloat_ops: 0\ntotal_float_ops: "
+      "34360\naccelerator_exec_micros: 0\ncpu_exec_micros: "
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 97\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -186,28 +216,39 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n  float_ops: 360\n  "
       "total_float_ops: 360\n  input_shapes {\n    key: 0\n    value {\n      "
       "unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    value "
-      "{\n      unknown_rank: true\n    }\n  }\n}\nchildren {\n  name: "
+      "{\n      unknown_rank: true\n    }\n  }\n  accelerator_exec_micros: 0\n "
+      " cpu_exec_micros: 12\n  total_accelerator_exec_micros: 0\n  "
+      "total_cpu_exec_micros: 12\n}\nchildren {\n  name: "
       "\"conv2d/convolution\"\n  exec_micros: 60\n  requested_bytes: 1440\n  "
       "total_exec_micros: 60\n  total_requested_bytes: 1440\n  "
       "total_parameters: 0\n  devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n  float_ops: 19440\n  "
       "total_float_ops: 19440\n  input_shapes {\n    key: 0\n    value {\n     "
       " unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    "
-      "value {\n      unknown_rank: true\n    }\n  }\n}\nchildren {\n  name: "
-      "\"conv2d_2/BiasAdd\"\n  exec_micros: 2\n  requested_bytes: 640\n  "
-      "total_exec_micros: 2\n  total_requested_bytes: 640\n  total_parameters: "
-      "0\n  devices: \"/job:localhost/replica:0/task:0/cpu:0\"\n  float_ops: "
-      "160\n  total_float_ops: 160\n  input_shapes {\n    key: 0\n    value "
-      "{\n      unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n "
-      "   value {\n      unknown_rank: true\n    }\n  }\n}\nchildren {\n  "
-      "name: \"conv2d_2/convolution\"\n  exec_micros: 13\n  requested_bytes: "
-      "640\n  total_exec_micros: 13\n  total_requested_bytes: 640\n  "
+      "value {\n      unknown_rank: true\n    }\n  }\n  "
+      "accelerator_exec_micros: 0\n  cpu_exec_micros: 60\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "60\n}\nchildren {\n  name: \"conv2d_2/BiasAdd\"\n  exec_micros: 2\n  "
+      "requested_bytes: 640\n  total_exec_micros: 2\n  total_requested_bytes: "
+      "640\n  total_parameters: 0\n  devices: "
+      "\"/job:localhost/replica:0/task:0/cpu:0\"\n  float_ops: 160\n  "
+      "total_float_ops: 160\n  input_shapes {\n    key: 0\n    value {\n      "
+      "unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    value "
+      "{\n      unknown_rank: true\n    }\n  }\n  accelerator_exec_micros: 0\n "
+      " cpu_exec_micros: 2\n  total_accelerator_exec_micros: 0\n  "
+      "total_cpu_exec_micros: 2\n}\nchildren {\n  name: "
+      "\"conv2d_2/convolution\"\n  exec_micros: 13\n  requested_bytes: 640\n  "
+      "total_exec_micros: 13\n  total_requested_bytes: 640\n  "
       "total_parameters: 0\n  devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n  float_ops: 14400\n  "
       "total_float_ops: 14400\n  input_shapes {\n    key: 0\n    value {\n     "
       " unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    "
-      "value {\n      unknown_rank: true\n    }\n  }\n}\nfloat_ops: "
-      "0\ntotal_float_ops: 34360\n",
+      "value {\n      unknown_rank: true\n    }\n  }\n  "
+      "accelerator_exec_micros: 0\n  cpu_exec_micros: 13\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "13\n}\nfloat_ops: 0\ntotal_float_ops: 34360\naccelerator_exec_micros: "
+      "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
+      "0\ntotal_cpu_exec_micros: 97\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -223,7 +264,9 @@ TEST_F(TFProfStatsTest, TestAccountShownNameOnly) {
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 0\ntotal_requested_bytes: 0\ntotal_parameters: "
-      "0\nfloat_ops: 0\ntotal_float_ops: 0\n",
+      "0\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
+      "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
+      "0\ntotal_cpu_exec_micros: 0\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -238,7 +281,9 @@ TEST_F(TFProfStatsTest, TestShowTensorValue) {
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
-      "8656\ntotal_parameters: 370\nfloat_ops: 0\ntotal_float_ops: 34360\n",
+      "8656\ntotal_parameters: 370\nfloat_ops: 0\ntotal_float_ops: "
+      "34360\naccelerator_exec_micros: 0\ncpu_exec_micros: "
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 97\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index 3dd721cbcc..698738c23c 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -34,7 +34,8 @@ class TFProfTensorTest : public ::testing::Test {
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "tools/tfprof/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
-    TF_CHECK_OK(ReadGraphDef(Env::Default(), graph_path, graph_pb.get()));
+    TF_CHECK_OK(
+        ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
 
     std::unique_ptr<tensorflow::RunMetadata> run_meta_pb;
     std::unique_ptr<OpLog> op_log_pb;
@@ -72,7 +73,9 @@ TEST_F(TFProfTensorTest, Basics) {
       "total_parameters: 5\n    float_ops: 0\n    total_float_ops: 0\n    "
       "tensor_value {\n      dtype: DT_FLOAT\n      value_double: 0\n      "
       "value_double: 0\n      value_double: 0\n      value_double: 0\n      "
-      "value_double: 0\n    }\n  }\n  children {\n    name: "
+      "value_double: 0\n    }\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 0\n  }\n  children {\n    name: "
       "\"conv2d/kernel\"\n    exec_micros: 0\n    requested_bytes: 0\n    "
       "parameters: 135\n    total_exec_micros: 0\n    total_requested_bytes: "
       "0\n    total_parameters: 135\n    float_ops: 0\n    total_float_ops: "
@@ -143,7 +146,11 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0.19068\n      value_double: 0.220352\n      "
       "value_double: -0.255741\n      value_double: 0.110853\n      "
       "value_double: 0.146625\n      value_double: 0.167754\n      "
-      "value_double: 0.249554\n    }\n  }\n  float_ops: 0\n  total_float_ops: "
+      "value_double: 0.249554\n    }\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 0\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
       "0\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 0\n  total_requested_bytes: "
       "0\n  total_parameters: 230\n  children {\n    name: \"conv2d_1/bias\"\n "
@@ -152,7 +159,9 @@ TEST_F(TFProfTensorTest, Basics) {
       "total_parameters: 5\n    float_ops: 0\n    total_float_ops: 0\n    "
       "tensor_value {\n      dtype: DT_FLOAT\n      value_double: 0\n      "
       "value_double: 0\n      value_double: 0\n      value_double: 0\n      "
-      "value_double: 0\n    }\n  }\n  children {\n    name: "
+      "value_double: 0\n    }\n    accelerator_exec_micros: 0\n    "
+      "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
+      "total_cpu_exec_micros: 0\n  }\n  children {\n    name: "
       "\"conv2d_1/kernel\"\n    exec_micros: 0\n    requested_bytes: 0\n    "
       "parameters: 225\n    total_exec_micros: 0\n    total_requested_bytes: "
       "0\n    total_parameters: 225\n    float_ops: 0\n    total_float_ops: "
@@ -268,9 +277,14 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0.237298\n      value_double: -0.0896481\n      "
       "value_double: -0.0605349\n      value_double: 0.231679\n      "
       "value_double: -0.123842\n      value_double: 0.0858642\n      "
-      "value_double: 0.23111\n      value_double: 0.0491742\n    }\n  }\n  "
-      "float_ops: 0\n  total_float_ops: 0\n}\nfloat_ops: 0\ntotal_float_ops: "
-      "0\n",
+      "value_double: 0.23111\n      value_double: 0.0491742\n    }\n    "
+      "accelerator_exec_micros: 0\n    cpu_exec_micros: 0\n    "
+      "total_accelerator_exec_micros: 0\n    total_cpu_exec_micros: 0\n  }\n  "
+      "float_ops: 0\n  total_float_ops: 0\n  accelerator_exec_micros: 0\n  "
+      "cpu_exec_micros: 0\n  total_accelerator_exec_micros: 0\n  "
+      "total_cpu_exec_micros: 0\n}\nfloat_ops: 0\ntotal_float_ops: "
+      "0\naccelerator_exec_micros: 0\ncpu_exec_micros: "
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 0\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline.cc
index c98aa940c8..c835da8162 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline.cc
@@ -192,9 +192,6 @@ void Timeline::AllocateTimeNodes(GraphNode* gnode) {
     const TFGraphNode* node = gnode->node;
     for (const auto& kernel_execs : node->op_execs(step_)) {
       const string& device = kernel_execs.first;
-      if (!IsCombinedGPUStream(device) && !IsCPUDevice(device)) {
-        continue;
-      }
 
       if (process_.find(device) == process_.end()) {
         int64 pid = AllocatePID();
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
index bcf2bf0594..0e9bb9658c 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
@@ -39,7 +39,8 @@ class TFProfTimelineTest : public ::testing::Test {
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "tools/tfprof/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
-    TF_CHECK_OK(ReadGraphDef(Env::Default(), graph_path, graph_pb.get()));
+    TF_CHECK_OK(
+        ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
 
     std::unique_ptr<tensorflow::RunMetadata> run_meta_pb(
         new tensorflow::RunMetadata());
@@ -47,7 +48,7 @@ class TFProfTimelineTest : public ::testing::Test {
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "tools/tfprof/internal/testdata/run_meta");
     TF_CHECK_OK(
-        ReadBinaryProto(Env::Default(), run_meta_path, run_meta_pb.get()));
+        ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 nullptr, nullptr));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_utils.cc b/tensorflow/tools/tfprof/internal/tfprof_utils.cc
index 0bc1217012..8f208b75d3 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_utils.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_utils.cc
@@ -72,19 +72,6 @@ string StringReplace(const string& str, const string& oldsub,
   return out;
 }
 
-Status ReadGraphDef(Env* env, const string& fname, GraphDef* graph_def) {
-  string out;
-  Status s = ReadFileToString(env, fname, &out);
-  if (!s.ok()) return s;
-  if (protobuf::TextFormat::ParseFromString(out, graph_def)) {
-    return Status();
-  } else if (ReadBinaryProto(tensorflow::Env::Default(), fname, graph_def)
-                 .ok()) {
-    return Status();
-  }
-  return errors::InvalidArgument("Cannot parse proto string.");
-}
-
 namespace {
 string StripQuote(const string& s) {
   int start = s.find_first_not_of("\"\'");
@@ -301,8 +288,8 @@ void PrintHelp() {
       "of times. Only available in op view.\n\n"
       "  -step: Show the stats of a step when multiple steps of "
       "RunMetadata were added. By default (-1), show the average of all steps."
-      "  -order_by: Order the results by [name|depth|bytes|micros|params|"
-      "float_ops]\n\n"
+      "  -order_by: Order the results by [name|depth|bytes|micros"
+      "|accelerator_micros|cpu_micros|params|float_ops]\n\n"
       "  -account_type_regexes: Account and display the ops whose types match "
       "one of the type regexes specified. tfprof "
       "allow user to define extra op types for ops "
@@ -333,7 +320,8 @@ void PrintHelp() {
       "ops eventually displayed. If False, account all "
       "op statistics matching -account_type_regexes recursively.\n\n"
       "  -select: Comma-separated list of metrics to show: [bytes|micros|"
-      "params|float_ops|tensor_value|device|op_types]."
+      "accelerator_micros|cpu_micros|params|float_ops|tensor_value|device|"
+      "op_types|input_shapes]."
       "\n\n"
       "  -dump_to_file: Dump the output to a file, instead of terminal.\n\n"
       ""
diff --git a/tensorflow/tools/tfprof/internal/tfprof_utils.h b/tensorflow/tools/tfprof/internal/tfprof_utils.h
index afa7a58acd..6130325bab 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_utils.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_utils.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 
 namespace tensorflow {
@@ -40,7 +41,28 @@ tensorflow::Status ParseCmdLine(const string& line, string* cmd,
 string StringReplace(const string& str, const string& oldsub,
                      const string& newsub);
 
-Status ReadGraphDef(Env* env, const string& fname, GraphDef* graph_def);
+template <typename T>
+Status ReadProtoFile(Env* env, const string& fname, T* proto,
+                     bool binary_first) {
+  string out;
+  Status s = ReadFileToString(env, fname, &out);
+  if (!s.ok()) return s;
+
+  if (binary_first) {
+    if (ReadBinaryProto(tensorflow::Env::Default(), fname, proto).ok()) {
+      return Status();
+    } else if (protobuf::TextFormat::ParseFromString(out, proto)) {
+      return Status();
+    }
+  } else {
+    if (protobuf::TextFormat::ParseFromString(out, proto)) {
+      return Status();
+    } else if (ReadBinaryProto(tensorflow::Env::Default(), fname, proto).ok()) {
+      return Status();
+    }
+  }
+  return errors::InvalidArgument("Cannot parse proto file.");
+}
 
 void PrintHelp();
 
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/tools/tfprof/tfprof_main.cc
index ae02b52634..7a4e7e85ff 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/tools/tfprof/tfprof_main.cc
@@ -38,121 +38,114 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
 #include "tensorflow/tools/tfprof/tfprof_log.pb.h"
 
-using tensorflow::str_util::Split;
-
+namespace tensorflow {
+namespace tfprof {
 void completion(const char* buf, linenoiseCompletions* lc) {
-  tensorflow::string buf_str = buf;
+  string buf_str = buf;
   if (buf_str.find(" ") == buf_str.npos) {
-    for (const char* opt : tensorflow::tfprof::kCmds) {
-      if (tensorflow::string(opt).find(buf_str) == 0) {
+    for (const char* opt : kCmds) {
+      if (string(opt).find(buf_str) == 0) {
         linenoiseAddCompletion(lc, opt);
       }
     }
     return;
   }
 
-  tensorflow::string prefix;
+  string prefix;
   int last_dash = buf_str.find_last_of(' ');
-  if (last_dash != tensorflow::string::npos) {
+  if (last_dash != string::npos) {
     prefix = buf_str.substr(0, last_dash + 1);
-    buf_str = buf_str.substr(last_dash + 1, tensorflow::kint32max);
+    buf_str = buf_str.substr(last_dash + 1, kint32max);
   }
-  for (const char* opt : tensorflow::tfprof::kOptions) {
-    if (tensorflow::string(opt).find(buf_str) == 0) {
+  for (const char* opt : kOptions) {
+    if (string(opt).find(buf_str) == 0) {
       linenoiseAddCompletion(lc, (prefix + opt).c_str());
     }
   }
 }
 
-int main(int argc, char** argv) {
-  tensorflow::string FLAGS_graph_path = "";
-  tensorflow::string FLAGS_run_meta_path = "";
-  tensorflow::string FLAGS_op_log_path = "";
-  tensorflow::string FLAGS_checkpoint_path = "";
-  tensorflow::int32 FLAGS_max_depth = 10;
-  tensorflow::int64 FLAGS_min_bytes = 0;
-  tensorflow::int64 FLAGS_min_micros = 0;
-  tensorflow::int64 FLAGS_min_params = 0;
-  tensorflow::int64 FLAGS_min_float_ops = 0;
-  tensorflow::int64 FLAGS_min_occurrence = 0;
-  tensorflow::int64 FLAGS_step = -1;
-  tensorflow::string FLAGS_order_by = "name";
-  tensorflow::string FLAGS_account_type_regexes = ".*";
-  tensorflow::string FLAGS_start_name_regexes = ".*";
-  tensorflow::string FLAGS_trim_name_regexes = "";
-  tensorflow::string FLAGS_show_name_regexes = ".*";
-  tensorflow::string FLAGS_hide_name_regexes;
+int Run(int argc, char** argv) {
+  string FLAGS_graph_path = "";
+  string FLAGS_run_meta_path = "";
+  string FLAGS_op_log_path = "";
+  string FLAGS_checkpoint_path = "";
+  int32 FLAGS_max_depth = 10;
+  int64 FLAGS_min_bytes = 0;
+  int64 FLAGS_min_micros = 0;
+  int64 FLAGS_min_params = 0;
+  int64 FLAGS_min_float_ops = 0;
+  int64 FLAGS_min_occurrence = 0;
+  int64 FLAGS_step = -1;
+  string FLAGS_order_by = "name";
+  string FLAGS_account_type_regexes = ".*";
+  string FLAGS_start_name_regexes = ".*";
+  string FLAGS_trim_name_regexes = "";
+  string FLAGS_show_name_regexes = ".*";
+  string FLAGS_hide_name_regexes;
   bool FLAGS_account_displayed_op_only = false;
-  tensorflow::string FLAGS_select = "params";
-  tensorflow::string FLAGS_output = "";
+  string FLAGS_select = "micros";
+  string FLAGS_output = "";
   for (int i = 0; i < argc; i++) {
     fprintf(stderr, "%s\n", argv[i]);
   }
 
-  std::vector<tensorflow::Flag> flag_list = {
-      tensorflow::Flag("graph_path", &FLAGS_graph_path,
-                       "GraphDef proto text file name"),
-      tensorflow::Flag("run_meta_path", &FLAGS_run_meta_path,
-                       "Comma-separated list of RunMetadata proto binary "
-                       "files. Each file is given step number 0,1,2,etc"),
-      tensorflow::Flag("op_log_path", &FLAGS_op_log_path,
-                       "tensorflow::tfprof::OpLog proto binary file name"),
-      tensorflow::Flag("checkpoint_path", &FLAGS_checkpoint_path,
-                       "TensorFlow Checkpoint file name"),
-      tensorflow::Flag("max_depth", &FLAGS_max_depth, "max depth"),
-      tensorflow::Flag("min_bytes", &FLAGS_min_bytes, "min_bytes"),
-      tensorflow::Flag("min_micros", &FLAGS_min_micros, "min micros"),
-      tensorflow::Flag("min_params", &FLAGS_min_params, "min params"),
-      tensorflow::Flag("min_float_ops", &FLAGS_min_float_ops, "min float ops"),
-      tensorflow::Flag("min_occurrence", &FLAGS_min_occurrence,
-                       "min occurrence"),
-      tensorflow::Flag("step", &FLAGS_step,
-                       "The stats of which step to use. By default average"),
-      tensorflow::Flag("order_by", &FLAGS_order_by, "order by"),
-      tensorflow::Flag("account_type_regexes", &FLAGS_start_name_regexes,
-                       "start name regexes"),
-      tensorflow::Flag("trim_name_regexes", &FLAGS_trim_name_regexes,
-                       "trim name regexes"),
-      tensorflow::Flag("show_name_regexes", &FLAGS_show_name_regexes,
-                       "show name regexes"),
-      tensorflow::Flag("hide_name_regexes", &FLAGS_hide_name_regexes,
-                       "hide name regexes"),
-      tensorflow::Flag("account_displayed_op_only",
-                       &FLAGS_account_displayed_op_only,
-                       "account displayed op only"),
-      tensorflow::Flag("select", &FLAGS_select, "select"),
-      tensorflow::Flag("output", &FLAGS_output, "output"),
+  std::vector<Flag> flag_list = {
+      Flag("graph_path", &FLAGS_graph_path, "GraphDef proto text file name"),
+      Flag("run_meta_path", &FLAGS_run_meta_path,
+           "Comma-separated list of RunMetadata proto binary "
+           "files. Each file is given step number 0,1,2,etc"),
+      Flag("op_log_path", &FLAGS_op_log_path,
+           "tensorflow::tfprof::OpLog proto binary file name"),
+      Flag("checkpoint_path", &FLAGS_checkpoint_path,
+           "TensorFlow Checkpoint file name"),
+      Flag("max_depth", &FLAGS_max_depth, "max depth"),
+      Flag("min_bytes", &FLAGS_min_bytes, "min_bytes"),
+      Flag("min_micros", &FLAGS_min_micros, "min micros"),
+      Flag("min_params", &FLAGS_min_params, "min params"),
+      Flag("min_float_ops", &FLAGS_min_float_ops, "min float ops"),
+      Flag("min_occurrence", &FLAGS_min_occurrence, "min occurrence"),
+      Flag("step", &FLAGS_step,
+           "The stats of which step to use. By default average"),
+      Flag("order_by", &FLAGS_order_by, "order by"),
+      Flag("account_type_regexes", &FLAGS_start_name_regexes,
+           "start name regexes"),
+      Flag("trim_name_regexes", &FLAGS_trim_name_regexes, "trim name regexes"),
+      Flag("show_name_regexes", &FLAGS_show_name_regexes, "show name regexes"),
+      Flag("hide_name_regexes", &FLAGS_hide_name_regexes, "hide name regexes"),
+      Flag("account_displayed_op_only", &FLAGS_account_displayed_op_only,
+           "account displayed op only"),
+      Flag("select", &FLAGS_select, "select"),
+      Flag("output", &FLAGS_output, "output"),
   };
-  tensorflow::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
-  bool parse_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  string usage = Flags::Usage(argv[0], flag_list);
+  bool parse_ok = Flags::Parse(&argc, argv, flag_list);
   if (!parse_ok) {
     printf("%s", usage.c_str());
     return (2);
   }
-  tensorflow::port::InitMain(argv[0], &argc, &argv);
+  port::InitMain(argv[0], &argc, &argv);
 
   fprintf(stderr, "%s\n", FLAGS_graph_path.c_str());
 
-  std::vector<tensorflow::string> account_type_regexes =
-      Split(FLAGS_account_type_regexes, ',', tensorflow::str_util::SkipEmpty());
-  std::vector<tensorflow::string> start_name_regexes =
-      Split(FLAGS_start_name_regexes, ',', tensorflow::str_util::SkipEmpty());
-  std::vector<tensorflow::string> trim_name_regexes =
-      Split(FLAGS_trim_name_regexes, ',', tensorflow::str_util::SkipEmpty());
-  std::vector<tensorflow::string> show_name_regexes =
-      Split(FLAGS_show_name_regexes, ',', tensorflow::str_util::SkipEmpty());
-  std::vector<tensorflow::string> hide_name_regexes =
-      Split(FLAGS_hide_name_regexes, ',', tensorflow::str_util::SkipEmpty());
-  std::vector<tensorflow::string> select =
-      Split(FLAGS_select, ',', tensorflow::str_util::SkipEmpty());
+  std::vector<string> account_type_regexes =
+      str_util::Split(FLAGS_account_type_regexes, ',', str_util::SkipEmpty());
+  std::vector<string> start_name_regexes =
+      str_util::Split(FLAGS_start_name_regexes, ',', str_util::SkipEmpty());
+  std::vector<string> trim_name_regexes =
+      str_util::Split(FLAGS_trim_name_regexes, ',', str_util::SkipEmpty());
+  std::vector<string> show_name_regexes =
+      str_util::Split(FLAGS_show_name_regexes, ',', str_util::SkipEmpty());
+  std::vector<string> hide_name_regexes =
+      str_util::Split(FLAGS_hide_name_regexes, ',', str_util::SkipEmpty());
+  std::vector<string> select =
+      str_util::Split(FLAGS_select, ',', str_util::SkipEmpty());
 
-  tensorflow::string output_type;
-  std::map<tensorflow::string, tensorflow::string> output_options;
-  tensorflow::Status s = tensorflow::tfprof::ParseOutput(
-      FLAGS_output, &output_type, &output_options);
+  string output_type;
+  std::map<string, string> output_options;
+  Status s = ParseOutput(FLAGS_output, &output_type, &output_options);
   CHECK(s.ok()) << s.ToString();
 
-  tensorflow::string cmd = "";
+  string cmd = "";
   if (argc == 1 && FLAGS_graph_path.empty()) {
     printf("1) go/tfprof: Tutorial.\n");
     printf("2) tfprof help: Detail help information.\n");
@@ -168,44 +161,40 @@ int main(int argc, char** argv) {
         "Profiling everything!\n");
     return 0;
   } else if (argc > 1) {
-    if (tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[5]) {
-      tensorflow::tfprof::PrintHelp();
+    if (string(argv[1]) == kCmds[5]) {
+      PrintHelp();
       return 0;
     }
-    if (tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[0] ||
-        tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[1] ||
-        tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[2] ||
-        tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[3]) {
+    if (string(argv[1]) == kCmds[0] || string(argv[1]) == kCmds[1] ||
+        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3]) {
       cmd = argv[1];
     }
   }
 
   printf("Reading Files...\n");
-  std::unique_ptr<tensorflow::GraphDef> graph(new tensorflow::GraphDef());
-  TF_CHECK_OK(tensorflow::tfprof::ReadGraphDef(tensorflow::Env::Default(),
-                                               FLAGS_graph_path, graph.get()));
+  std::unique_ptr<GraphDef> graph(new GraphDef());
+  TF_CHECK_OK(
+      ReadProtoFile(Env::Default(), FLAGS_graph_path, graph.get(), false));
 
-  std::unique_ptr<tensorflow::tfprof::OpLog> op_log(
-      new tensorflow::tfprof::OpLog());
+  std::unique_ptr<OpLog> op_log(new OpLog());
   if (!FLAGS_op_log_path.empty()) {
-    tensorflow::string op_log_str;
-    s = tensorflow::ReadFileToString(tensorflow::Env::Default(),
-                                     FLAGS_op_log_path, &op_log_str);
+    string op_log_str;
+    s = ReadFileToString(Env::Default(), FLAGS_op_log_path, &op_log_str);
     if (!s.ok()) {
       fprintf(stderr, "Failed to read op_log_path: %s\n", s.ToString().c_str());
       return 1;
     }
-    if (!tensorflow::ParseProtoUnlimited(op_log.get(), op_log_str)) {
+    if (!ParseProtoUnlimited(op_log.get(), op_log_str)) {
       fprintf(stderr, "Failed to parse op_log_path\n");
       return 1;
     }
   }
 
-  std::unique_ptr<tensorflow::checkpoint::CheckpointReader> ckpt_reader;
+  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader;
   TF_Status* status = TF_NewStatus();
   if (!FLAGS_checkpoint_path.empty()) {
-    ckpt_reader.reset(new tensorflow::checkpoint::CheckpointReader(
-        FLAGS_checkpoint_path, status));
+    ckpt_reader.reset(
+        new checkpoint::CheckpointReader(FLAGS_checkpoint_path, status));
     if (TF_GetCode(status) != TF_OK) {
       fprintf(stderr, "%s\n", TF_Message(status));
       TF_DeleteStatus(status);
@@ -214,16 +203,14 @@ int main(int argc, char** argv) {
     TF_DeleteStatus(status);
   }
 
-  tensorflow::tfprof::TFStats tf_stat(
-      std::move(graph), nullptr, std::move(op_log), std::move(ckpt_reader));
+  TFStats tf_stat(std::move(graph), nullptr, std::move(op_log),
+                  std::move(ckpt_reader));
 
-  std::vector<tensorflow::string> run_meta_files =
-      Split(FLAGS_run_meta_path, ',', tensorflow::str_util::SkipEmpty());
+  std::vector<string> run_meta_files =
+      str_util::Split(FLAGS_run_meta_path, ',', str_util::SkipEmpty());
   for (int i = 0; i < run_meta_files.size(); ++i) {
-    std::unique_ptr<tensorflow::RunMetadata> run_meta(
-        new tensorflow::RunMetadata());
-    s = ReadBinaryProto(tensorflow::Env::Default(), run_meta_files[i],
-                        run_meta.get());
+    std::unique_ptr<RunMetadata> run_meta(new RunMetadata());
+    s = ReadProtoFile(Env::Default(), run_meta_files[i], run_meta.get(), true);
     if (!s.ok()) {
       fprintf(stderr, "Failed to read run_meta_path %s. Status: %s\n",
               run_meta_files[i].c_str(), s.ToString().c_str());
@@ -232,19 +219,17 @@ int main(int argc, char** argv) {
     tf_stat.ParseRunMeta(i, std::move(run_meta));
   }
 
-  tensorflow::tfprof::Options opts(
-      FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros, FLAGS_min_params,
-      FLAGS_min_float_ops, FLAGS_min_occurrence, FLAGS_step, FLAGS_order_by,
-      account_type_regexes, start_name_regexes, trim_name_regexes,
-      show_name_regexes, hide_name_regexes, FLAGS_account_displayed_op_only,
-      select, output_type, output_options);
+  Options opts(FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros,
+               FLAGS_min_params, FLAGS_min_float_ops, FLAGS_min_occurrence,
+               FLAGS_step, FLAGS_order_by, account_type_regexes,
+               start_name_regexes, trim_name_regexes, show_name_regexes,
+               hide_name_regexes, FLAGS_account_displayed_op_only, select,
+               output_type, output_options);
 
-  if (cmd == tensorflow::tfprof::kCmds[2] ||
-      cmd == tensorflow::tfprof::kCmds[3]) {
+  if (cmd == kCmds[2] || cmd == kCmds[3]) {
     tf_stat.ShowMultiGraphNode(cmd, opts);
     return 0;
-  } else if (cmd == tensorflow::tfprof::kCmds[0] ||
-             cmd == tensorflow::tfprof::kCmds[1]) {
+  } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
     tf_stat.ShowGraphNode(cmd, opts);
     return 0;
   }
@@ -253,7 +238,7 @@ int main(int argc, char** argv) {
   linenoiseHistoryLoad(".tfprof_history.txt");
 
   for (char* line = nullptr; (line = linenoise("tfprof> ")) != nullptr;) {
-    tensorflow::string line_s = line;
+    string line_s = line;
     free(line);
 
     if (line_s.empty()) {
@@ -263,24 +248,25 @@ int main(int argc, char** argv) {
     linenoiseHistoryAdd(line_s.c_str());
     linenoiseHistorySave(".tfprof_history.txt");
 
-    tensorflow::tfprof::Options new_opts = opts;
-    tensorflow::Status s =
-        tensorflow::tfprof::ParseCmdLine(line_s, &cmd, &new_opts);
+    Options new_opts = opts;
+    Status s = ParseCmdLine(line_s, &cmd, &new_opts);
     if (!s.ok()) {
       fprintf(stderr, "E: %s\n", s.ToString().c_str());
       continue;
     }
-    if (cmd == tensorflow::tfprof::kCmds[4]) {
+    if (cmd == kCmds[4]) {
       opts = new_opts;
-    } else if (cmd == tensorflow::tfprof::kCmds[5]) {
-      tensorflow::tfprof::PrintHelp();
-    } else if (cmd == tensorflow::tfprof::kCmds[2] ||
-               cmd == tensorflow::tfprof::kCmds[3]) {
+    } else if (cmd == kCmds[5]) {
+      PrintHelp();
+    } else if (cmd == kCmds[2] || cmd == kCmds[3]) {
       tf_stat.ShowMultiGraphNode(cmd, new_opts);
-    } else if (cmd == tensorflow::tfprof::kCmds[0] ||
-               cmd == tensorflow::tfprof::kCmds[1]) {
+    } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
       tf_stat.ShowGraphNode(cmd, new_opts);
     }
   }
   return 0;
 }
+}  // namespace tfprof
+}  // namespace tensorflow
+
+int main(int argc, char** argv) { return tensorflow::tfprof::Run(argc, argv); }
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index d00e93c939..1ea956152c 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -22,6 +22,9 @@ message TFGraphNodeProto {
   optional TFProfTensorProto tensor_value = 15;
   // op execution time.
   optional int64 exec_micros = 2;
+  optional int64 accelerator_exec_micros = 17;
+  optional int64 cpu_exec_micros = 18;
+
   // Total requested bytes by the op.
   optional int64 requested_bytes = 3;
   // Number of parameters if available.
@@ -36,6 +39,9 @@ message TFGraphNodeProto {
   // the node itself. The actual children depend on the data structure used
   // (scope, graph).
   optional int64 total_exec_micros = 6;
+  optional int64 total_accelerator_exec_micros = 19;
+  optional int64 total_cpu_exec_micros = 20;
+
   optional int64 total_requested_bytes = 7;
   optional int64 total_parameters = 8;
   optional int64 total_float_ops = 14;
@@ -64,6 +70,9 @@ message TFMultiGraphNodeProto {
 
   // code execution time.
   optional int64 exec_micros = 2;
+  optional int64 accelerator_exec_micros = 12;
+  optional int64 cpu_exec_micros = 13;
+
   // Total requested bytes by the code.
   optional int64 requested_bytes = 3;
   // Number of parameters if available.
@@ -74,6 +83,9 @@ message TFMultiGraphNodeProto {
   // The following are the aggregated stats from descendants.
   // The actual descendants depend on the data structure used.
   optional int64 total_exec_micros = 6;
+  optional int64 total_accelerator_exec_micros = 14;
+  optional int64 total_cpu_exec_micros = 15;
+
   optional int64 total_requested_bytes = 7;
   optional int64 total_parameters = 8;
   optional int64 total_float_ops = 9;
-- 
GitLab


From 5479240a00e904b0f05fa4ae3760cb4deedb864c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 10:18:35 -0700
Subject: [PATCH 0199/1375] Update input_fn to use tf.estimator.inputs in
 tf.estimator tutorial.

PiperOrigin-RevId: 159442285
---
 tensorflow/docs_src/get_started/estimator.md | 102 ++++++++++---------
 1 file changed, 52 insertions(+), 50 deletions(-)

diff --git a/tensorflow/docs_src/get_started/estimator.md b/tensorflow/docs_src/get_started/estimator.md
index c4dcac0189..a2c2a1ece5 100644
--- a/tensorflow/docs_src/get_started/estimator.md
+++ b/tensorflow/docs_src/get_started/estimator.md
@@ -64,7 +64,7 @@ def main():
       features_dtype=np.float32)
 
   # Specify that all features have real-value data
-  feature_columns = [tf.feature_column.numeric_column("", shape=[4])]
+  feature_columns = [tf.feature_column.numeric_column("x", shape=[4])]
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
   classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
@@ -72,35 +72,37 @@ def main():
                                           n_classes=3,
                                           model_dir="/tmp/iris_model")
   # Define the training inputs
-  def get_train_inputs():
-    x = tf.constant(training_set.data)
-    y = tf.constant(training_set.target)
-
-    return x, y
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": np.array(training_set.data)},
+      y=np.array(training_set.target),
+      num_epochs=None,
+      shuffle=True)
 
   # Train model.
-  classifier.train(input_fn=get_train_inputs, steps=2000)
+  classifier.train(input_fn=train_input_fn, steps=2000)
 
   # Define the test inputs
-  def get_test_inputs():
-    x = tf.constant(test_set.data)
-    y = tf.constant(test_set.target)
-
-    return x, y
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": np.array(test_set.data)},
+      y=np.array(test_set.target),
+      num_epochs=1,
+      shuffle=False)
 
   # Evaluate accuracy.
-  accuracy_score = classifier.evaluate(input_fn=get_test_inputs,
-                                       steps=1)["accuracy"]
+  accuracy_score = classifier.evaluate(input_fn=test_input_fn)["accuracy"]
 
   print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
 
   # Classify two new flower samples.
-  def new_samples():
-    return np.array(
+  new_samples = np.array(
       [[6.4, 3.2, 4.5, 1.5],
        [5.8, 3.1, 5.0, 1.7]], dtype=np.float32)
+  predict_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": new_samples},
+      num_epochs=1,
+      shuffle=False)
 
-  predictions = list(classifier.predict(input_fn=new_samples))
+  predictions = list(classifier.predict(input_fn=predict_input_fn))
   predicted_classes = [p["classes"] for p in predictions]
 
   print(
@@ -247,7 +249,7 @@ data. Using tf.estimator, you can instantiate your
 
 ```python
 # Specify that all features have real-value data
-feature_columns = [tf.feature_column.numeric_column("", shape=[4])]
+feature_columns = [tf.feature_column.numeric_column("x", shape=[4])]
 
 # Build 3 layer DNN with 10, 20, 10 units respectively.
 classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
@@ -278,29 +280,28 @@ Then, the code creates a `DNNClassifier` model using the following arguments:
 ## Describe the training input pipeline {#train-input}
 
 The `tf.estimator` API uses input functions, which create the TensorFlow
-operations that generate data for the model. In this case, the data is small
-enough that it can be stored in @{tf.constant$TensorFlow constants}. The
-following code produces the simplest possible input pipeline:
+operations that generate data for the model.
+We can use `tf.estimator.inputs.numpy_input_fn` to produce the input pipeline:
 
 ```python
 # Define the training inputs
-def get_train_inputs():
-  x = tf.constant(training_set.data)
-  y = tf.constant(training_set.target)
-
-  return x, y
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": np.array(training_set.data)},
+    y=np.array(training_set.target),
+    num_epochs=None,
+    shuffle=True)
 ```
 
 ## Fit the DNNClassifier to the Iris Training Data {#fit-dnnclassifier}
 
 Now that you've configured your DNN `classifier` model, you can fit it to the
 Iris training data using the @{tf.estimator.Estimator.train$`train`} method.
-Pass `get_train_inputs` as the `input_fn`, and the number of steps to train
+Pass `train_input_fn` as the `input_fn`, and the number of steps to train
 (here, 2000):
 
 ```python
 # Train model.
-classifier.train(input_fn=get_train_inputs, steps=2000)
+classifier.train(input_fn=train_input_fn, steps=2000)
 ```
 
 The state of the model is preserved in the `classifier`, which means you can
@@ -308,8 +309,8 @@ train iteratively if you like. For example, the above is equivalent to the
 following:
 
 ```python
-classifier.train(input_fn=get_train_inputs, steps=1000)
-classifier.train(input_fn=get_train_inputs, steps=1000)
+classifier.train(input_fn=train_input_fn, steps=1000)
+classifier.train(input_fn=train_input_fn, steps=1000)
 ```
 
 However, if you're looking to track the model while it trains, you'll likely
@@ -324,30 +325,28 @@ You've trained your `DNNClassifier` model on the Iris training data; now, you
 can check its accuracy on the Iris test data using the
 @{tf.estimator.Estimator.evaluate$`evaluate`} method. Like `train`,
 `evaluate` takes an input function that builds its input pipeline. `evaluate`
-returns a `dict` with the evaluation results. The following code passes the Iris
-test data&mdash;`test_set.data` and `test_set.target`&mdash;to `evaluate` and
-prints the `accuracy` from the results:
+returns a `dict`s with the evaluation results. The following code passes the
+Iris test data&mdash;`test_set.data` and `test_set.target`&mdash;to `evaluate`
+and prints the `accuracy` from the results:
 
 ```python
 # Define the test inputs
-def get_test_inputs():
-  x = tf.constant(test_set.data)
-  y = tf.constant(test_set.target)
-
-  return x, y
+test_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": np.array(test_set.data)},
+    y=np.array(test_set.target),
+    num_epochs=1,
+    shuffle=False)
 
 # Evaluate accuracy.
-accuracy_score = classifier.evaluate(input_fn=get_test_inputs,
-                                     steps=1)["accuracy"]
+accuracy_score = classifier.evaluate(input_fn=test_input_fn)["accuracy"]
 
 print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
 ```
 
-Note: The `steps` argument to `evaluate` is important here.
-@{tf.estimator.Estimator.evaluate$`evaluate`} normally runs until it reaches
-the end of the input. This is perfect for evaluating over a set of files, but
-the constants being used here will never throw the `OutOfRangeError` or
-`StopIteration` that it is expecting.
+Note: The `num_epochs=1` argument to `numpy_input_fn` is important here.
+`test_input_fn` will iterate over the data once, and then raise
+`OutOfRangeError`. This error signals the classifier to stop evaluating, so it
+will evaluate over the input once.
 
 When you run the full script, it will print something close to:
 
@@ -369,17 +368,20 @@ Sepal Length | Sepal Width | Petal Length | Petal Width
 5.8          | 3.1         | 5.0          | 1.7
 
 You can predict their species using the `predict()` method. `predict` returns a
-generator, which can easily be converted to a list. The following code retrieves
-and prints the class predictions:
+generator of dicts, which can easily be converted to a list. The following code
+retrieves and prints the class predictions:
 
 ```python
 # Classify two new flower samples.
-def new_samples():
-  return np.array(
+new_samples = np.array(
     [[6.4, 3.2, 4.5, 1.5],
      [5.8, 3.1, 5.0, 1.7]], dtype=np.float32)
+predict_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": new_samples},
+    num_epochs=1,
+    shuffle=False)
 
-predictions = list(classifier.predict(input_fn=new_samples))
+predictions = list(classifier.predict(input_fn=predict_input_fn))
 predicted_classes = [p["classes"] for p in predictions]
 
 print(
-- 
GitLab


From affc8b084284125ac366d53b63dda7f57eb2d12a Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Mon, 19 Jun 2017 10:27:47 -0700
Subject: [PATCH 0200/1375] Fix warnings.

Fixes #9138.
* lstm_ops: use_peephole checked twice, merged "if" bodies.
* unpack_path_op: cast to 1LL for shift to operate on full range.
* sample_input_ops: add CHECK to ensure that sparse_input_start is
initialized.
* function: make sure we deref "e" after checking for nullptr
* master_session: remove unused variable
* encode_jpeg_op: remove double assignment
* resize_area_op: use unused variable
* strcat: remove double parentheses

PiperOrigin-RevId: 159443547
---
 tensorflow/contrib/rnn/kernels/lstm_ops.h                   | 3 ---
 .../contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc | 2 +-
 .../contrib/tensor_forest/kernels/sample_inputs_op.cc       | 2 ++
 tensorflow/core/common_runtime/function.cc                  | 6 ++++--
 tensorflow/core/distributed_runtime/master_session.cc       | 2 --
 tensorflow/core/kernels/encode_jpeg_op.cc                   | 2 --
 tensorflow/core/kernels/resize_area_op.cc                   | 5 ++---
 tensorflow/core/lib/strings/strcat.cc                       | 2 +-
 8 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/tensorflow/contrib/rnn/kernels/lstm_ops.h b/tensorflow/contrib/rnn/kernels/lstm_ops.h
index d9ed9e3ab7..6317f32ac3 100644
--- a/tensorflow/contrib/rnn/kernels/lstm_ops.h
+++ b/tensorflow/contrib/rnn/kernels/lstm_ops.h
@@ -279,9 +279,6 @@ struct LSTMBlockCellBprop : public LSTMBlockCell {
           cs_prev_grad +
           di * wci.reshape(p_shape).broadcast(p_broadcast_shape) +
           df * wcf.reshape(p_shape).broadcast(p_broadcast_shape);
-    }
-
-    if (use_peephole) {
       wci_grad.device(d) = (di * cs_prev).sum(Eigen::array<int, 1>({0}));
       wcf_grad.device(d) = (df * cs_prev).sum(Eigen::array<int, 1>({0}));
       wco_grad.device(d) = (do_ * cs).sum(Eigen::array<int, 1>({0}));
diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc
index 555674ca69..9d5e1400a5 100644
--- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc
+++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc
@@ -52,7 +52,7 @@ REGISTER_OP("UnpackPath")
       auto tree_depth = c->Dim(params, 1);
       int64 num_nodes = InferenceContext::kUnknownDim;
       if (c->ValueKnown(tree_depth)) {
-        num_nodes = (1 << c->Value(tree_depth)) - 1;
+        num_nodes = (static_cast<int64>(1) << c->Value(tree_depth)) - 1;
       }
 
       c->set_output(0, c->Matrix(num_points, num_nodes));
diff --git a/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc b/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc
index 6bfc29d96f..3ddc72f216 100644
--- a/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc
@@ -284,6 +284,8 @@ class SampleInputs : public OpKernel {
             index = rand_feature;
             val = inputs(*it, rand_feature);
           } else {
+            CHECK(sparse_input) << rand_feature << " selected, and dense is "
+                                << input_spec_.dense_features_size();
             const auto indices = sparse_input_indices.matrix<int64>();
             const auto values = sparse_input_values.vec<float>();
             const int32 sparse_index = sparse_input_start + rand_feature -
diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index 320e214117..b0b834b66a 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -1047,10 +1047,12 @@ void ToGraphDef(const Graph* g, GraphDef* gdef, bool pretty) {
     // to be unique and stable after optimization rewrites. Therefore,
     // we use "n<node id>" instead.
     for (const Edge* e : inputs) {
-      const string srcname = NewName(e->src(), pretty);
       if (e == nullptr) {
         ndef->add_input("unknown");
-      } else if (!e->src()->IsOp()) {
+        continue;
+      }
+      const string srcname = NewName(e->src(), pretty);
+      if (!e->src()->IsOp()) {
       } else if (e->IsControlEdge()) {
         ndef->add_input(strings::StrCat("^", srcname));
       } else if (e->src_output() == 0) {
diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc
index 94fec4f6d0..c79f68a068 100644
--- a/tensorflow/core/distributed_runtime/master_session.cc
+++ b/tensorflow/core/distributed_runtime/master_session.cc
@@ -1165,7 +1165,6 @@ WorkerCacheInterface* MasterSession::get_worker_cache() const {
 Status MasterSession::StartStep(const BuildGraphOptions& opts, int64* count,
                                 ReffedClientGraph** rcg, bool is_partial) {
   const uint64 hash = HashBuildGraphOptions(opts);
-  ReffedClientGraph* to_unref = nullptr;
   {
     mutex_lock l(mu_);
     // Keep track of how many times this subgraph has been executed in
@@ -1196,7 +1195,6 @@ Status MasterSession::StartStep(const BuildGraphOptions& opts, int64* count,
     *rcg = iter->second;
     (*rcg)->Ref();
   }
-  if (to_unref) to_unref->Unref();
   return Status::OK();
 }
 
diff --git a/tensorflow/core/kernels/encode_jpeg_op.cc b/tensorflow/core/kernels/encode_jpeg_op.cc
index 8e021b9256..4fcae25aa6 100644
--- a/tensorflow/core/kernels/encode_jpeg_op.cc
+++ b/tensorflow/core/kernels/encode_jpeg_op.cc
@@ -55,8 +55,6 @@ class EncodeJpegOp : public OpKernel {
         context, context->GetAttr("optimize_size", &flags_.optimize_jpeg_size));
     OP_REQUIRES_OK(context, context->GetAttr("chroma_downsampling",
                                              &flags_.chroma_downsampling));
-    OP_REQUIRES_OK(context, context->GetAttr("chroma_downsampling",
-                                             &flags_.chroma_downsampling));
 
     string density_unit;
     OP_REQUIRES_OK(context, context->GetAttr("density_unit", &density_unit));
diff --git a/tensorflow/core/kernels/resize_area_op.cc b/tensorflow/core/kernels/resize_area_op.cc
index ad94de89db..ada50dfb70 100644
--- a/tensorflow/core/kernels/resize_area_op.cc
+++ b/tensorflow/core/kernels/resize_area_op.cc
@@ -33,7 +33,6 @@ namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
 
 namespace {
-
 struct CachedInterpolation {
   int64 start;
   int64 end;
@@ -41,7 +40,7 @@ struct CachedInterpolation {
   float end_minus_one_scale;
   bool needs_bounding;
 };
-};
+}  // namespace
 
 template <typename Device, typename T>
 class ResizeAreaOp : public OpKernel {
@@ -170,7 +169,7 @@ class ResizeAreaOp : public OpKernel {
                    : (v + 1 > in_x1 ? in_x1 - v : 1.0);
 
       v = ceil(in_x1);
-      x_interp.end = ceil(in_x1);
+      x_interp.end = v;
       v = x_interp.end - 1;
       x_interp.end_minus_one_scale =
           v < in_x ? (v + 1 > in_x1 ? st.width_scale : v + 1 - in_x)
diff --git a/tensorflow/core/lib/strings/strcat.cc b/tensorflow/core/lib/strings/strcat.cc
index 3e864c4f28..46a45a6678 100644
--- a/tensorflow/core/lib/strings/strcat.cc
+++ b/tensorflow/core/lib/strings/strcat.cc
@@ -38,7 +38,7 @@ AlphaNum::AlphaNum(Hex hex) {
   // We accomplish minimum width by OR'ing in 0x10000 to the user's value,
   // where 0x10000 is the smallest hex number that is as wide as the user
   // asked for.
-  uint64 mask = ((static_cast<uint64>(1) << (width - 1) * 4)) | value;
+  uint64 mask = (static_cast<uint64>(1) << (width - 1) * 4) | value;
   static const char hexdigits[] = "0123456789abcdef";
   do {
     *--writer = hexdigits[value & 0xF];
-- 
GitLab


From 7f968a43c51a6668055e1552b38c65fd2a799aa3 Mon Sep 17 00:00:00 2001
From: Sukriti Ramesh <sukritiramesh@google.com>
Date: Mon, 19 Jun 2017 10:35:20 -0700
Subject: [PATCH 0201/1375] Add a constant for "assets.extra" in SavedModel.

PiperOrigin-RevId: 159444552
---
 tensorflow/cc/saved_model/constants.h | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/cc/saved_model/constants.h b/tensorflow/cc/saved_model/constants.h
index 94a3b3cf46..c940df8a87 100644
--- a/tensorflow/cc/saved_model/constants.h
+++ b/tensorflow/cc/saved_model/constants.h
@@ -21,6 +21,9 @@ namespace tensorflow {
 /// SavedModel assets directory.
 constexpr char kSavedModelAssetsDirectory[] = "assets";
 
+/// SavedModel assets.extra directory.
+constexpr char kSavedModelAssetsExtraDirectory[] = "assets.extra";
+
 /// SavedModel assets key for graph collection-def.
 constexpr char kSavedModelAssetsKey[] = "saved_model_assets";
 
-- 
GitLab


From 448f23bd57de5e94385f2bd01e7fb19e6aec2611 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 10:36:33 -0700
Subject: [PATCH 0202/1375] Fix typo in contrib.image documentation.

PiperOrigin-RevId: 159444685
---
 tensorflow/contrib/image/python/ops/image_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/image/python/ops/image_ops.py b/tensorflow/contrib/image/python/ops/image_ops.py
index b396dcea21..aef3e385b5 100644
--- a/tensorflow/contrib/image/python/ops/image_ops.py
+++ b/tensorflow/contrib/image/python/ops/image_ops.py
@@ -136,7 +136,7 @@ def transform(images, transforms, interpolation="NEAREST"):
        `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
        where `k = c0 x + c1 y + 1`. The transforms are *inverted* compared to
        the transform mapping input points to output points.
-     interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
+    interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
 
   Returns:
     Image(s) with the same type and shape as `images`, with the given
-- 
GitLab


From 09d090282667e28ed04547ee0dcf1b6faba75659 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Mon, 19 Jun 2017 10:47:37 -0700
Subject: [PATCH 0203/1375] Fix a bug with sharing Keras models that feature
 internal losses.

PiperOrigin-RevId: 159446295
---
 .../keras/python/keras/engine/topology.py     |  1 +
 .../keras/python/keras/integration_test.py    | 67 +++++++++++++++++++
 .../contrib/keras/python/keras/models.py      |  1 +
 3 files changed, 69 insertions(+)

diff --git a/tensorflow/contrib/keras/python/keras/engine/topology.py b/tensorflow/contrib/keras/python/keras/engine/topology.py
index 07d708ada3..637d0c5a48 100644
--- a/tensorflow/contrib/keras/python/keras/engine/topology.py
+++ b/tensorflow/contrib/keras/python/keras/engine/topology.py
@@ -1176,6 +1176,7 @@ class Container(Layer):
     # The following properties are not actually used by Keras;
     # they exist for compatibility with TF.
     self._updates = []
+    self._losses = []
     self._scope = None
     self._reuse = None
     self._base_name = name
diff --git a/tensorflow/contrib/keras/python/keras/integration_test.py b/tensorflow/contrib/keras/python/keras/integration_test.py
index bcd844201c..0f6db097d1 100644
--- a/tensorflow/contrib/keras/python/keras/integration_test.py
+++ b/tensorflow/contrib/keras/python/keras/integration_test.py
@@ -161,6 +161,73 @@ class KerasIntegrationTest(test.TestCase):
                           verbose=2)
       self.assertGreater(history.history['val_acc'][-1], 0.70)
 
+  def test_vector_classification_shared_sequential(self):
+    # Test that Sequential models that feature internal updates
+    # and internal losses can be shared.
+    with self.test_session():
+      np.random.seed(1337)
+      (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
+          train_samples=200,
+          test_samples=100,
+          input_shape=(10,),
+          num_classes=2)
+      y_train = keras.utils.to_categorical(y_train)
+      y_test = keras.utils.to_categorical(y_test)
+
+      base_model = keras.models.Sequential([
+          keras.layers.Dense(16,
+                             activation='relu',
+                             kernel_regularizer=keras.regularizers.l2(1e-5),
+                             bias_regularizer=keras.regularizers.l2(1e-5),
+                             input_shape=x_train.shape[1:]),
+          keras.layers.BatchNormalization(),
+      ])
+      x = keras.layers.Input(x_train.shape[1:])
+      y = base_model(x)
+      y = keras.layers.Dense(y_train.shape[-1], activation='softmax')(y)
+      model = keras.models.Model(x, y)
+      model.compile(loss='categorical_crossentropy',
+                    optimizer='rmsprop',
+                    metrics=['accuracy'])
+      history = model.fit(x_train, y_train, epochs=10, batch_size=16,
+                          validation_data=(x_test, y_test),
+                          verbose=2)
+      self.assertGreater(history.history['val_acc'][-1], 0.85)
+
+  def test_vector_classification_shared_model(self):
+    # Test that functional models that feature internal updates
+    # and internal losses can be shared.
+    with self.test_session():
+      np.random.seed(1337)
+      (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
+          train_samples=200,
+          test_samples=100,
+          input_shape=(10,),
+          num_classes=2)
+      y_train = keras.utils.to_categorical(y_train)
+      y_test = keras.utils.to_categorical(y_test)
+
+      inputs = keras.layers.Input(x_train.shape[1:])
+      x = keras.layers.Dense(16,
+                             activation='relu',
+                             kernel_regularizer=keras.regularizers.l2(1e-5),
+                             bias_regularizer=keras.regularizers.l2(1e-5),
+                             input_shape=x_train.shape[1:])(inputs)
+      x = keras.layers.BatchNormalization()(x)
+      base_model = keras.models.Model(inputs, x)
+
+      x = keras.layers.Input(x_train.shape[1:])
+      y = base_model(x)
+      y = keras.layers.Dense(y_train.shape[-1], activation='softmax')(y)
+      model = keras.models.Model(x, y)
+      model.compile(loss='categorical_crossentropy',
+                    optimizer='rmsprop',
+                    metrics=['accuracy'])
+      history = model.fit(x_train, y_train, epochs=10, batch_size=16,
+                          validation_data=(x_test, y_test),
+                          verbose=2)
+      self.assertGreater(history.history['val_acc'][-1], 0.85)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/keras/python/keras/models.py b/tensorflow/contrib/keras/python/keras/models.py
index 0ae373da3c..8786e0b97a 100644
--- a/tensorflow/contrib/keras/python/keras/models.py
+++ b/tensorflow/contrib/keras/python/keras/models.py
@@ -436,6 +436,7 @@ class Sequential(Model):
     # The following properties are not actually used by Keras;
     # they exist for compatibility with TF's variable scoping mechanism.
     self._updates = []
+    self._losses = []
     self._scope = None
     self._reuse = None
     self._base_name = name
-- 
GitLab


From 46737e4e81314f7482bfd6a710f126a27f5d7975 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 10:48:05 -0700
Subject: [PATCH 0204/1375] Remove class xla::LiteralUtil.  NFC (mind-numbingly
 so).

This patch removes class xla::LiteralUtil and rewrites every call to use class
xla::Literal instead.
PiperOrigin-RevId: 159446373
---
 tensorflow/compiler/jit/xla_device_context.cc |   2 +-
 .../tf2xla/kernels/batchtospace_op.cc         |   4 +-
 .../compiler/tf2xla/kernels/bcast_ops.cc      |   2 +-
 .../compiler/tf2xla/kernels/concat_op.cc      |  12 +-
 .../tf2xla/kernels/dynamic_stitch_op.cc       |   5 +-
 tensorflow/compiler/tf2xla/kernels/fill_op.cc |   2 +-
 .../compiler/tf2xla/kernels/gather_op.cc      |   6 +-
 .../compiler/tf2xla/kernels/index_ops.cc      |   9 +-
 tensorflow/compiler/tf2xla/kernels/pad_op.cc  |   4 +-
 .../compiler/tf2xla/kernels/reduction_ops.cc  |   4 +-
 .../tf2xla/kernels/reduction_ops_common.cc    |   4 +-
 .../compiler/tf2xla/kernels/reshape_op.cc     |   2 +-
 .../compiler/tf2xla/kernels/sequence_ops.cc   |  12 +-
 .../tf2xla/kernels/spacetobatch_op.cc         |   4 +-
 .../compiler/tf2xla/kernels/split_op.cc       |   8 +-
 .../compiler/tf2xla/kernels/tile_ops.cc       |   2 +-
 tensorflow/compiler/tf2xla/literal_util.cc    |   6 +-
 .../compiler/tf2xla/literal_util_test.cc      |   4 +-
 .../compiler/tf2xla/xla_compiler_test.cc      |  19 +-
 tensorflow/compiler/tf2xla/xla_helpers.cc     |  26 +-
 tensorflow/compiler/tf2xla/xla_op_kernel.cc   |   8 +-
 .../compiler/xla/client/computation_builder.h |  41 +-
 tensorflow/compiler/xla/client/lib/testing.cc |   2 +-
 tensorflow/compiler/xla/literal_util.h        | 533 ------------------
 tensorflow/compiler/xla/literal_util_test.cc  | 524 ++++++++---------
 .../compiler/xla/packed_literal_reader.cc     |   3 +-
 .../compiler/xla/reference_util_test.cc       |  26 +-
 .../xla/service/algebraic_simplifier.cc       |  24 +-
 .../xla/service/algebraic_simplifier_test.cc  |  68 ++-
 .../xla/service/buffer_assignment_test.cc     |  47 +-
 .../xla/service/buffer_liveness_test.cc       |  24 +-
 .../compiler/xla/service/call_graph_test.cc   |   2 +-
 .../xla/service/copy_insertion_test.cc        |  74 ++-
 .../service/cpu/conv_canonicalization_test.cc |   8 +-
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   2 +-
 .../xla/service/cpu/sample_harness.cc         |   9 +-
 .../xla/service/cpu_transfer_manager.cc       |   3 +-
 .../xla/service/flatten_call_graph_test.cc    |   6 +-
 .../xla/service/generic_transfer_manager.cc   |  13 +-
 .../xla/service/gpu/elemental_ir_emitter.cc   |   2 +-
 .../xla/service/gpu/fusion_merger_test.cc     |   6 +-
 .../xla/service/gpu/ir_emitter_unnested.cc    |   2 +-
 .../compiler/xla/service/gpu/pad_insertion.cc |  10 +-
 .../xla/service/gpu/while_transformer.cc      |   9 +-
 .../xla/service/gpu/while_transformer_test.cc |  16 +-
 .../xla/service/gpu_transfer_manager.cc       |   3 +-
 .../compiler/xla/service/graphviz_example.cc  |   4 +-
 .../xla/service/heap_simulator_test.cc        |   2 +-
 .../xla/service/hlo_alias_analysis_test.cc    |  58 +-
 .../xla/service/hlo_computation_test.cc       |  40 +-
 .../xla/service/hlo_constant_folding_test.cc  |  32 +-
 .../xla/service/hlo_cost_analysis_test.cc     |  11 +-
 tensorflow/compiler/xla/service/hlo_cse.cc    |   2 +-
 .../compiler/xla/service/hlo_cse_test.cc      |  50 +-
 .../xla/service/hlo_dataflow_analysis_test.cc |  88 +--
 .../compiler/xla/service/hlo_dce_test.cc      |   8 +-
 .../compiler/xla/service/hlo_evaluator.cc     |  89 ++-
 .../xla/service/hlo_evaluator_test.cc         |  92 ++-
 .../compiler/xla/service/hlo_graph_dumper.cc  |   3 +-
 .../compiler/xla/service/hlo_instruction.cc   |   8 +-
 .../xla/service/hlo_instruction_test.cc       |  37 +-
 .../compiler/xla/service/hlo_module_test.cc   |   2 +-
 .../compiler/xla/service/hlo_ordering_test.cc |   2 +-
 tensorflow/compiler/xla/service/hlo_query.cc  |   2 +-
 .../xla/service/hlo_rematerialization_test.cc |   8 +-
 .../hlo_subcomputation_unification_test.cc    |   6 +-
 .../xla/service/hlo_tfgraph_builder.cc        |   3 +-
 .../xla/service/hlo_tfgraph_builder_test.cc   |   2 +-
 .../compiler/xla/service/inliner_test.cc      |  16 +-
 .../xla/service/instruction_fusion_test.cc    |   8 +-
 .../xla/service/layout_assignment_test.cc     |   4 +-
 .../xla/service/liveness_util_test.cc         |  24 +-
 .../compiler/xla/service/llvm_ir/llvm_util.cc |  32 +-
 .../xla/service/reshape_mover_test.cc         |  15 +-
 .../xla/service/transfer_manager_test.cc      |   6 +-
 .../xla/service/transpose_folding_test.cc     |  10 +-
 .../service/tuple_points_to_analysis_test.cc  |  59 +-
 .../compiler/xla/service/user_computation.cc  |   2 +-
 .../xla/service/user_computation_test.cc      |   6 +-
 .../xla/tests/array_elementwise_ops_test.cc   |  30 +-
 .../xla/tests/batch_normalization_test.cc     |   2 +-
 .../xla/tests/broadcast_simple_test.cc        | 100 ++--
 .../compiler/xla/tests/broadcast_test.cc      |  64 +--
 tensorflow/compiler/xla/tests/call_test.cc    |  14 +-
 .../xla/tests/check_execution_arity_test.cc   |   8 +-
 .../xla/tests/client_library_test_base.cc     |  10 +-
 .../xla/tests/client_library_test_base.h      |  28 +-
 .../xla/tests/compilation_cache_test.cc       |  10 +-
 .../xla/tests/compute_constant_test.cc        |   6 +-
 tensorflow/compiler/xla/tests/concat_test.cc  |  16 +-
 .../compiler/xla/tests/constants_test.cc      |  14 +-
 .../convolution_dimension_numbers_test.cc     |   3 +-
 .../compiler/xla/tests/convolution_test.cc    |  35 +-
 .../xla/tests/convolution_variants_test.cc    |  27 +-
 tensorflow/compiler/xla/tests/copy_test.cc    |  31 +-
 .../compiler/xla/tests/custom_call_test.cc    |   6 +-
 .../xla/tests/deconstruct_tuple_test.cc       |   2 +-
 .../compiler/xla/tests/dot_operation_test.cc  |  12 +-
 .../compiler/xla/tests/dynamic_ops_test.cc    |   8 +-
 tensorflow/compiler/xla/tests/fusion_test.cc  |  95 ++--
 .../compiler/xla/tests/literal_test_util.cc   |  86 ++-
 .../compiler/xla/tests/literal_test_util.h    |  34 +-
 .../xla/tests/literal_test_util_test.cc       |  25 +-
 tensorflow/compiler/xla/tests/map_test.cc     |  46 +-
 .../xla/tests/matrix_ops_simple_test.cc       |  22 +-
 tensorflow/compiler/xla/tests/pad_test.cc     |  20 +-
 tensorflow/compiler/xla/tests/params_test.cc  |  44 +-
 tensorflow/compiler/xla/tests/prng_test.cc    |  21 +-
 tensorflow/compiler/xla/tests/reduce_test.cc  |  42 +-
 .../compiler/xla/tests/reduce_window_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/replay_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/reshape_test.cc |  70 +--
 .../tests/round_trip_packed_literal_test.cc   |  20 +-
 .../xla/tests/round_trip_transfer_test.cc     |  48 +-
 .../xla/tests/scalar_computations_test.cc     |  24 +-
 tensorflow/compiler/xla/tests/test_utils.h    |   8 +-
 tensorflow/compiler/xla/tests/tuple_test.cc   |  56 +-
 tensorflow/compiler/xla/tests/while_test.cc   |  12 +-
 .../compiler/xla/text_literal_reader.cc       |   6 +-
 .../compiler/xla/text_literal_reader_test.cc  |  12 +-
 .../compiler/xla/text_literal_writer.cc       |   6 +-
 .../compiler/xla/text_literal_writer_test.cc  |   2 +-
 .../compiler/xla/tools/replay_computation.cc  |   4 +-
 tensorflow/compiler/xla/tools/show_literal.cc |   2 +-
 .../compiler/xla/tools/show_text_literal.cc   |   2 +-
 125 files changed, 1405 insertions(+), 2069 deletions(-)

diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc
index f329e83e14..0ab81ebd5f 100644
--- a/tensorflow/compiler/jit/xla_device_context.cc
+++ b/tensorflow/compiler/jit/xla_device_context.cc
@@ -137,7 +137,7 @@ void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor,
       done(result.status());
       return;
     }
-    const void* src_ptr = xla::LiteralUtil::InternalData(*result.ValueOrDie());
+    const void* src_ptr = result.ValueOrDie()->InternalData();
     void* dst_ptr = DMAHelper::base(cpu_tensor);
     size_t total_bytes = cpu_tensor->TotalBytes();
     memcpy(dst_ptr, src_ptr, total_bytes);
diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
index eb4bd47ee5..47d2d747e6 100644
--- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
@@ -126,8 +126,8 @@ void BatchToSpace(XlaOpKernelContext* ctx,
   std::vector<int64> start_indices(input_rank, 0);
   std::vector<int64> end_indices = reshaped_permuted_shape;
   for (int i = 0; i < block_rank; ++i) {
-    int64 crop_start = xla::LiteralUtil::Get<int64>(crops, {i, 0});
-    int64 crop_end = xla::LiteralUtil::Get<int64>(crops, {i, 1});
+    int64 crop_start = crops.Get<int64>({i, 0});
+    int64 crop_end = crops.Get<int64>({i, 1});
     OP_REQUIRES(ctx, crop_start >= 0 && crop_end >= 0,
                 errors::InvalidArgument("Crops must be non-negative"));
     start_indices[1 + i] = crop_start;
diff --git a/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc b/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc
index b0fee5e4bc..bc2cd31230 100644
--- a/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc
@@ -55,7 +55,7 @@ class BCastGradArgsOp : public XlaOpKernel {
 
       BCast::Vec vec;
       for (int64 i = 0; i < in_shape.num_elements(); ++i) {
-        vec.push_back(xla::LiteralUtil::Get<int>(literal, {i}));
+        vec.push_back(literal.Get<int>({i}));
       }
       shapes.push_back(vec);
     }
diff --git a/tensorflow/compiler/tf2xla/kernels/concat_op.cc b/tensorflow/compiler/tf2xla/kernels/concat_op.cc
index e2eacb3839..73a4740e29 100644
--- a/tensorflow/compiler/tf2xla/kernels/concat_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/concat_op.cc
@@ -52,7 +52,7 @@ class ConcatBaseOp : public XlaOpKernel {
     xla::Literal literal;
     OP_REQUIRES_OK(ctx, ctx->ConstantInput(axis_index_, &literal));
     // TODO(annarev): add a helper to support int64 input.
-    const int32 concat_dim = xla::LiteralUtil::Get<int>(literal, {});
+    const int32 concat_dim = literal.Get<int>({});
 
     std::vector<xla::ComputationDataHandle> values;
     std::vector<TensorShape> shapes;
@@ -163,7 +163,7 @@ class ConcatOffsetOp : public XlaOpKernel {
 
     xla::Literal concat_dim_literal;
     OP_REQUIRES_OK(ctx, ctx->ConstantInput(0, &concat_dim_literal));
-    const int64 cdim = xla::LiteralUtil::Get<int>(concat_dim_literal, {});
+    const int64 cdim = concat_dim_literal.Get<int>({});
 
     VLOG(1) << "ConcatOffset " << cdim << "," << dims;
     int32 axis = cdim < 0 ? cdim + dims : cdim;
@@ -185,12 +185,10 @@ class ConcatOffsetOp : public XlaOpKernel {
       for (int64 j = 0; j < dims; ++j) {
         if (j == axis) {
           out_vec(j) = offset;
-          offset += xla::LiteralUtil::Get<int>(inp_literal, {j});
+          offset += inp_literal.Get<int>({j});
         } else {
-          const int32 inp0_element =
-              xla::LiteralUtil::Get<int>(inp0_literal, {j});
-          const int32 inp_element =
-              xla::LiteralUtil::Get<int>(inp_literal, {j});
+          const int32 inp0_element = inp0_literal.Get<int>({j});
+          const int32 inp_element = inp_literal.Get<int>({j});
           OP_REQUIRES(
               ctx, (inp0_element == inp_element),
               errors::InvalidArgument("input[", i, ",", j, "] mismatch: ",
diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
index 2d1a056719..faa7ef0ef9 100644
--- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
@@ -103,8 +103,7 @@ class DynamicStitchOp : public XlaOpKernel {
     int max_index = -1;
     for (int input_num = 0; input_num < indices.size(); input_num++) {
       for (int i = 0; i < indices[input_num].shape().dimensions(0); ++i) {
-        max_index = std::max(
-            max_index, xla::LiteralUtil::Get<int>(indices[input_num], {i}));
+        max_index = std::max(max_index, indices[input_num].Get<int>({i}));
       }
     }
     int number_of_indices = max_index + 1;
@@ -118,7 +117,7 @@ class DynamicStitchOp : public XlaOpKernel {
     int index_used_count = 0;
     for (int input_num = 0; input_num < indices.size(); input_num++) {
       for (int i = 0; i < indices[input_num].shape().dimensions(0); ++i) {
-        int index = xla::LiteralUtil::Get<int>(indices[input_num], {i});
+        int index = indices[input_num].Get<int>({i});
         src_input_vector[index] = input_num;
         src_slice_vector[index] = i;
         if (!src_index_used[index]) {
diff --git a/tensorflow/compiler/tf2xla/kernels/fill_op.cc b/tensorflow/compiler/tf2xla/kernels/fill_op.cc
index 1e1d2a1b4b..9e090fe01c 100644
--- a/tensorflow/compiler/tf2xla/kernels/fill_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/fill_op.cc
@@ -52,7 +52,7 @@ class FillOp : public XlaOpKernel {
     std::vector<int64> broadcast;
     broadcast.reserve(dims_literal.shape().dimensions(0));
     for (int i = 0; i < dims_literal.shape().dimensions(0); ++i) {
-      broadcast.push_back(xla::LiteralUtil::Get<int>(dims_literal, {i}));
+      broadcast.push_back(dims_literal.Get<int>({i}));
     }
     // Look up the value input, reshaping to a scalar if it was a
     // 'legacy' scalar (secretly a vector).
diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc
index 49eadaf9d1..3c1cdef5f8 100644
--- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc
@@ -66,10 +66,10 @@ class GatherOp : public XlaOpKernel {
     std::vector<xla::ComputationDataHandle> args;
     args.push_back(tc.GetOrCreateRuntimeContextParameter());
     args.push_back(b.ConstantLiteral(
-        *xla::LiteralUtil::CreateR0<int64>(indices_shape.num_elements())));
+        *xla::Literal::CreateR0<int64>(indices_shape.num_elements())));
     args.push_back(b.ConstantLiteral(
-        *xla::LiteralUtil::CreateR0<int64>(params_shape.dim_size(0))));
-    args.push_back(b.ConstantLiteral(*xla::LiteralUtil::CreateR0<int64>(
+        *xla::Literal::CreateR0<int64>(params_shape.dim_size(0))));
+    args.push_back(b.ConstantLiteral(*xla::Literal::CreateR0<int64>(
         params_shape.num_elements() / params_shape.dim_size(0))));
     args.push_back(ctx->Input(0));
     args.push_back(ctx->Input(1));
diff --git a/tensorflow/compiler/tf2xla/kernels/index_ops.cc b/tensorflow/compiler/tf2xla/kernels/index_ops.cc
index df002dddd0..6be66cf66e 100644
--- a/tensorflow/compiler/tf2xla/kernels/index_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/index_ops.cc
@@ -69,7 +69,7 @@ class ArgMaxOp : public XlaOpKernel {
     // XLA op would have the same requirement.
     xla::Literal literal;
     OP_REQUIRES_OK(ctx, ctx->ConstantInput(1, &literal));
-    const int32 dim = xla::LiteralUtil::Get<int32>(literal, {});
+    const int32 dim = literal.Get<int32>({});
     OP_REQUIRES(ctx, dim >= 0, errors::InvalidArgument("dim must be >= 0"));
     OP_REQUIRES(
         ctx, dim < input_shape.dims(),
@@ -97,14 +97,13 @@ class ArgMaxOp : public XlaOpKernel {
     std::vector<xla::ComputationDataHandle> args;
     args.push_back(ctx->Input(0));
     args.push_back(b.ConstantLiteral(
-        *xla::LiteralUtil::CreateR1<int64>(input_shape.dim_sizes())));
+        *xla::Literal::CreateR1<int64>(input_shape.dim_sizes())));
     if (input_shape.dims() > 1) {
       // Don't bother passing the output shape and dim for the 1d case, since
       // the shape is always a scalar and the dim is always 0.
       args.push_back(b.ConstantLiteral(
-          *xla::LiteralUtil::CreateR1<int64>(output_shape.dim_sizes())));
-      args.push_back(
-          b.ConstantLiteral(*xla::LiteralUtil::CreateR0<int32>(dim)));
+          *xla::Literal::CreateR1<int64>(output_shape.dim_sizes())));
+      args.push_back(b.ConstantLiteral(*xla::Literal::CreateR0<int32>(dim)));
     }
 
     xla::Shape xla_shape =
diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc
index 22476f4a0c..cc13ab0203 100644
--- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc
@@ -60,8 +60,8 @@ class PadOp : public XlaOpKernel {
     xla::PaddingConfig config;
     for (int i = 0; i < fixed_dims; ++i) {
       auto* dim = config.add_dimensions();
-      int before = xla::LiteralUtil::Get<int32>(pad_literal, {i, 0});
-      int after = xla::LiteralUtil::Get<int32>(pad_literal, {i, 1});
+      int before = pad_literal.Get<int32>({i, 0});
+      int after = pad_literal.Get<int32>({i, 1});
       OP_REQUIRES(ctx, before >= 0 && after >= 0,
                   errors::InvalidArgument("Paddings must be non-negative: ",
                                           before, " ", after));
diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc
index 518a9372c4..dae2eb9d2a 100644
--- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc
@@ -63,7 +63,7 @@ class MinOp : public XlaReductionOp {
       xla::ComputationBuilder* builder) override {
     xla::PrimitiveType type;
     TF_CHECK_OK(DataTypeToPrimitiveType(input_type(0), &type));
-    return builder->ConstantLiteral(xla::LiteralUtil::MaxValue(type));
+    return builder->ConstantLiteral(xla::Literal::MaxValue(type));
   }
 
   void BuildReducer(xla::ComputationBuilder* builder,
@@ -83,7 +83,7 @@ class MaxOp : public XlaReductionOp {
       xla::ComputationBuilder* builder) override {
     xla::PrimitiveType type;
     TF_CHECK_OK(DataTypeToPrimitiveType(input_type(0), &type));
-    return builder->ConstantLiteral(xla::LiteralUtil::MinValue(type));
+    return builder->ConstantLiteral(xla::Literal::MinValue(type));
   }
 
   void BuildReducer(xla::ComputationBuilder* builder,
diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc
index 8798c80ad5..4b5d09eb9f 100644
--- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc
+++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc
@@ -66,13 +66,13 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) {
                      1, {axes_tensor_shape.num_elements()}, &axes_literal));
 
   VLOG(1) << "data shape: " << data_shape.DebugString();
-  VLOG(1) << "axes      : " << xla::LiteralUtil::ToString(axes_literal);
+  VLOG(1) << "axes      : " << axes_literal.ToString();
 
   gtl::InlinedVector<bool, 4> bitmap(data_shape.dims(), false);
   std::vector<int64> xla_axes;
   int64 num_elements_reduced = 1LL;
   for (int64 i = 0; i < axes_tensor_shape.num_elements(); ++i) {
-    int32 index = xla::LiteralUtil::Get<int>(axes_literal, {i});
+    int32 index = axes_literal.Get<int>({i});
     OP_REQUIRES(ctx,
                 !(index < -data_shape.dims() || index >= data_shape.dims()),
                 errors::InvalidArgument("Invalid reduction dimension (", index,
diff --git a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc
index df542350b4..5952e75272 100644
--- a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc
@@ -50,7 +50,7 @@ class ReshapeOp : public XlaOpKernel {
     int64 product = 1;
     int unknown_index = -1;
     for (int d = 0; d < num_dims; ++d) {
-      const int32 size = xla::LiteralUtil::Get<int>(literal, {d});
+      const int32 size = literal.Get<int>({d});
       if (size == -1) {
         OP_REQUIRES(
             ctx, unknown_index == -1,
diff --git a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc
index 5b6fa64fa8..c2b0e1bb4c 100644
--- a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc
@@ -32,7 +32,7 @@ template <typename T>
 Status GetValue(int index, XlaOpKernelContext* ctx, T* value) {
   xla::Literal literal;
   TF_RETURN_IF_ERROR(ctx->ConstantInput(index, &literal));
-  *value = xla::LiteralUtil::Get<T>(literal, {});
+  *value = literal.Get<T>({});
   return Status::OK();
 }
 
@@ -41,10 +41,10 @@ Status GetIntValue(int index, XlaOpKernelContext* ctx, int64* value) {
   TF_RETURN_IF_ERROR(ctx->ConstantInput(index, &literal));
   switch (literal.shape().element_type()) {
     case xla::S32:
-      *value = xla::LiteralUtil::Get<int32>(literal, {});
+      *value = literal.Get<int32>({});
       break;
     case xla::S64:
-      *value = xla::LiteralUtil::Get<int64>(literal, {});
+      *value = literal.Get<int64>({});
       break;
     default:
       return errors::InvalidArgument("Invalid argument type for argument",
@@ -58,9 +58,9 @@ template <typename T>
 Status CreateRangeTensor(const xla::Literal& start_literal,
                          const xla::Literal& limit_literal,
                          const xla::Literal& delta_literal, Tensor* output) {
-  T start = xla::LiteralUtil::Get<T>(start_literal, {});
-  T limit = xla::LiteralUtil::Get<T>(limit_literal, {});
-  T delta = xla::LiteralUtil::Get<T>(delta_literal, {});
+  T start = start_literal.Get<T>({});
+  T limit = limit_literal.Get<T>({});
+  T delta = delta_literal.Get<T>({});
 
   if (delta == 0) {
     return errors::InvalidArgument("Requires delta != 0: ", delta);
diff --git a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc
index f15b354cb2..83a87f19a7 100644
--- a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc
@@ -56,8 +56,8 @@ void SpaceToBatch(XlaOpKernelContext* ctx,
   padding_config.add_dimensions();  // Don't pad the batch dimension.
   for (int i = 0; i < block_rank; ++i) {
     auto* dim = padding_config.add_dimensions();
-    int64 pad_start = xla::LiteralUtil::Get<int64>(paddings, {i, 0});
-    int64 pad_end = xla::LiteralUtil::Get<int64>(paddings, {i, 1});
+    int64 pad_start = paddings.Get<int64>({i, 0});
+    int64 pad_end = paddings.Get<int64>({i, 1});
     OP_REQUIRES(ctx, pad_start >= 0 && pad_end >= 0,
                 errors::InvalidArgument("Paddings must be non-negative"));
     dim->set_edge_padding_low(pad_start);
diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc
index f3cec5c3ca..017f3a110e 100644
--- a/tensorflow/compiler/tf2xla/kernels/split_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc
@@ -39,7 +39,7 @@ class SplitOp : public XlaOpKernel {
 
     int32 split_dim;
     if (index_shape.dims() == 0) {
-      split_dim = xla::LiteralUtil::Get<int>(literal_index, {});
+      split_dim = literal_index.Get<int>({});
     } else {
       OP_REQUIRES(
           ctx, index_shape.dims() == 1,
@@ -49,7 +49,7 @@ class SplitOp : public XlaOpKernel {
           ctx, index_shape.dim_size(0) == 1,
           errors::InvalidArgument("split_index input to Split Op must be a "
                                   "scalar or a vector with 1 element"));
-      split_dim = xla::LiteralUtil::Get<int>(literal_index, {0});
+      split_dim = literal_index.Get<int>({0});
     }
     const int32 num_split = num_outputs();
     const TensorShape input_shape = ctx->InputShape(1);
@@ -115,7 +115,7 @@ class SplitVOp : public XlaOpKernel {
     OP_REQUIRES(ctx, index_shape.dims() == 0,
                 errors::InvalidArgument("split_dim input to Split Op must be a "
                                         "scalar"));
-    split_dim = xla::LiteralUtil::Get<int>(literal_index, {});
+    split_dim = literal_index.Get<int>({});
 
     xla::ComputationDataHandle input = ctx->Input(0);
     const TensorShape input_shape = ctx->InputShape(0);
@@ -152,7 +152,7 @@ class SplitVOp : public XlaOpKernel {
 
     for (int i = 0; i < num_split; ++i) {
       int slice_size;
-      slice_size = xla::LiteralUtil::Get<int>(split_size_literal, {i});
+      slice_size = split_size_literal.Get<int>({i});
       if (slice_size == -1) {
         OP_REQUIRES(
             ctx, neg_one_dim == -1,
diff --git a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc
index 4cc2eb8f87..9ee6bd8925 100644
--- a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc
@@ -68,7 +68,7 @@ class TileOp : public XlaOpKernel {
     bool all_multiples_are_one = true;
     bool one_dimension_is_broadcasted_without_multiple = true;
     for (int i = 0; i < input_dims; ++i) {
-      int multiple = xla::LiteralUtil::Get<int>(literal, {i});
+      int multiple = literal.Get<int>({i});
       OP_REQUIRES(ctx, multiple,
                   errors::InvalidArgument("Expected multiples[", i,
                                           "] >= 0, but got ", multiple));
diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc
index 1f2bc01cf4..e166e8a9b0 100644
--- a/tensorflow/compiler/tf2xla/literal_util.cc
+++ b/tensorflow/compiler/tf2xla/literal_util.cc
@@ -27,13 +27,13 @@ Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal) {
   TF_RETURN_IF_ERROR(TensorShapeToXLAShape(
       host_tensor.dtype(), host_tensor.shape(), literal->mutable_shape()));
 
-  xla::LiteralUtil::Reserve(host_tensor.NumElements(), literal);
+  literal->Reserve(host_tensor.NumElements());
 
   // memcpy over the payload ...
   // TODO(phawkins): handle string types.
   size_t total_bytes = host_tensor.TotalBytes();
   if (total_bytes > 0) {
-    void* dst_ptr = xla::LiteralUtil::MutableInternalData(literal);
+    void* dst_ptr = literal->MutableInternalData();
     const void* src_ptr = DMAHelper::base(&host_tensor);
     memcpy(dst_ptr, src_ptr, total_bytes);
   }
@@ -55,7 +55,7 @@ Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type,
   *host_tensor = Tensor(target_type, shape);
   size_t total_bytes = host_tensor->TotalBytes();
   if (total_bytes > 0) {
-    const void* src_ptr = xla::LiteralUtil::InternalData(literal);
+    const void* src_ptr = literal.InternalData();
     void* dst_ptr = DMAHelper::base(host_tensor);
     memcpy(dst_ptr, src_ptr, total_bytes);
   }
diff --git a/tensorflow/compiler/tf2xla/literal_util_test.cc b/tensorflow/compiler/tf2xla/literal_util_test.cc
index 56993bc585..f3d6787daa 100644
--- a/tensorflow/compiler/tf2xla/literal_util_test.cc
+++ b/tensorflow/compiler/tf2xla/literal_util_test.cc
@@ -27,7 +27,7 @@ TEST(LiteralUtil, LiteralToHostTensor) {
   {
     std::vector<int64> int64_values = {1, 2, 3};
     std::unique_ptr<xla::Literal> int64_values_literal =
-        xla::LiteralUtil::CreateR1(gtl::ArraySlice<int64>(int64_values));
+        xla::Literal::CreateR1(gtl::ArraySlice<int64>(int64_values));
     Tensor host_tensor;
     EXPECT_EQ("Cannot convert literal of type S64 to tensor of type int32",
               LiteralToHostTensor(*int64_values_literal, DT_INT32, &host_tensor)
@@ -48,7 +48,7 @@ TEST(LiteralUtil, LiteralToHostTensor) {
     Tensor host_tensor;
     std::vector<int32> int32_values = {10, 11};
     std::unique_ptr<xla::Literal> int32_values_literal =
-        xla::LiteralUtil::CreateR1(gtl::ArraySlice<int32>(int32_values));
+        xla::Literal::CreateR1(gtl::ArraySlice<int32>(int32_values));
     EXPECT_TRUE(
         LiteralToHostTensor(*int32_values_literal, DT_INT32, &host_tensor)
             .ok());
diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
index 58d74057d1..427b14534f 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
@@ -163,9 +163,9 @@ TEST_F(XlaCompilerTest, Simple) {
 
   // Tests that the generated computation works.
   std::unique_ptr<xla::Literal> param0_literal =
-      xla::LiteralUtil::CreateR1<int32>({7, 42});
+      xla::Literal::CreateR1<int32>({7, 42});
   std::unique_ptr<xla::Literal> param1_literal =
-      xla::LiteralUtil::CreateR1<int32>({-3, 101});
+      xla::Literal::CreateR1<int32>({-3, 101});
   std::unique_ptr<xla::GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
   std::unique_ptr<xla::GlobalData> param1_data =
@@ -179,7 +179,7 @@ TEST_F(XlaCompilerTest, Simple) {
       client_->Transfer(*actual).ConsumeValueOrDie();
 
   std::unique_ptr<xla::Literal> expected_literal =
-      xla::LiteralUtil::CreateR1<int32>({4, 143});
+      xla::Literal::CreateR1<int32>({4, 143});
   xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
 }
 
@@ -225,7 +225,7 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
 
     // Tests that the generated computation works.
     std::unique_ptr<xla::Literal> param0_literal =
-        xla::LiteralUtil::CreateR1<int32>({7, 42});
+        xla::Literal::CreateR1<int32>({7, 42});
     std::unique_ptr<xla::GlobalData> param0_data =
         client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -236,7 +236,7 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
         client_->Transfer(*actual).ConsumeValueOrDie();
 
     std::unique_ptr<xla::Literal> expected_literal =
-        xla::LiteralUtil::CreateR1<int32>({-7, -42});
+        xla::Literal::CreateR1<int32>({-7, -42});
     xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
   }
 
@@ -260,7 +260,7 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
 
     // Tests that the generated computation works.
     std::unique_ptr<xla::Literal> param0_literal =
-        xla::LiteralUtil::CreateR1<int32>({7, 42});
+        xla::Literal::CreateR1<int32>({7, 42});
     std::unique_ptr<xla::GlobalData> param0_data =
         client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -270,12 +270,11 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
     std::unique_ptr<xla::Literal> actual_literal =
         client_->Transfer(*actual).ConsumeValueOrDie();
 
-    std::unique_ptr<xla::Literal> expected0 =
-        xla::LiteralUtil::CreateR0<int32>(7);
+    std::unique_ptr<xla::Literal> expected0 = xla::Literal::CreateR0<int32>(7);
     std::unique_ptr<xla::Literal> expected1 =
-        xla::LiteralUtil::CreateR1<int32>({-7, -42});
+        xla::Literal::CreateR1<int32>({-7, -42});
     std::unique_ptr<xla::Literal> expected =
-        xla::LiteralUtil::MakeTuple({expected0.get(), expected1.get()});
+        xla::Literal::MakeTuple({expected0.get(), expected1.get()});
     xla::LiteralTestUtil::ExpectEqual(*expected, *actual_literal);
   }
 }
diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc
index f060f8f2f1..2366c02dd2 100644
--- a/tensorflow/compiler/tf2xla/xla_helpers.cc
+++ b/tensorflow/compiler/tf2xla/xla_helpers.cc
@@ -30,28 +30,28 @@ xla::ComputationDataHandle XlaHelpers::MinValue(xla::ComputationBuilder* b,
                                                 DataType data_type) {
   xla::PrimitiveType type;
   TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type));
-  return b->ConstantLiteral(xla::LiteralUtil::MinValue(type));
+  return b->ConstantLiteral(xla::Literal::MinValue(type));
 }
 
 xla::ComputationDataHandle XlaHelpers::MaxValue(xla::ComputationBuilder* b,
                                                 DataType data_type) {
   xla::PrimitiveType type;
   TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type));
-  return b->ConstantLiteral(xla::LiteralUtil::MaxValue(type));
+  return b->ConstantLiteral(xla::Literal::MaxValue(type));
 }
 
 xla::ComputationDataHandle XlaHelpers::Zero(xla::ComputationBuilder* b,
                                             DataType data_type) {
   xla::PrimitiveType type;
   TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type));
-  return b->ConstantLiteral(xla::LiteralUtil::Zero(type));
+  return b->ConstantLiteral(xla::Literal::Zero(type));
 }
 
 xla::ComputationDataHandle XlaHelpers::One(xla::ComputationBuilder* b,
                                            DataType data_type) {
   xla::PrimitiveType type;
   TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type));
-  return b->ConstantLiteral(xla::LiteralUtil::One(type));
+  return b->ConstantLiteral(xla::Literal::One(type));
 }
 
 xla::ComputationDataHandle XlaHelpers::IntegerLiteral(
@@ -61,28 +61,28 @@ xla::ComputationDataHandle XlaHelpers::IntegerLiteral(
   TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type));
   switch (type) {
     case xla::U8:
-      literal = *xla::LiteralUtil::CreateR0<uint8>(value);
+      literal = *xla::Literal::CreateR0<uint8>(value);
       break;
     case xla::U32:
-      literal = *xla::LiteralUtil::CreateR0<uint32>(value);
+      literal = *xla::Literal::CreateR0<uint32>(value);
       break;
     case xla::U64:
-      literal = *xla::LiteralUtil::CreateR0<uint64>(value);
+      literal = *xla::Literal::CreateR0<uint64>(value);
       break;
     case xla::S8:
-      literal = *xla::LiteralUtil::CreateR0<int8>(value);
+      literal = *xla::Literal::CreateR0<int8>(value);
       break;
     case xla::S32:
-      literal = *xla::LiteralUtil::CreateR0<int32>(value);
+      literal = *xla::Literal::CreateR0<int32>(value);
       break;
     case xla::S64:
-      literal = *xla::LiteralUtil::CreateR0<int64>(value);
+      literal = *xla::Literal::CreateR0<int64>(value);
       break;
     case xla::F32:
-      literal = *xla::LiteralUtil::CreateR0<float>(value);
+      literal = *xla::Literal::CreateR0<float>(value);
       break;
     case xla::F64:
-      literal = *xla::LiteralUtil::CreateR0<double>(value);
+      literal = *xla::Literal::CreateR0<double>(value);
       break;
     case xla::PRED:
       LOG(FATAL) << "pred element type is not integral";
@@ -91,7 +91,7 @@ xla::ComputationDataHandle XlaHelpers::IntegerLiteral(
       LOG(FATAL) << "u16/s16 literals not yet implemented";
     case xla::F16:
       literal =
-          *xla::LiteralUtil::CreateR0<xla::half>(static_cast<xla::half>(value));
+          *xla::Literal::CreateR0<xla::half>(static_cast<xla::half>(value));
       break;
     case xla::TUPLE:
       LOG(FATAL) << "tuple element type is not integral";
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
index edb7e2a563..d606b32931 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
@@ -144,9 +144,9 @@ static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) {
     return errors::InvalidArgument("value is not a scalar");
   }
   if (literal.shape().element_type() == xla::S32) {
-    *out = xla::LiteralUtil::Get<int32>(literal, {});
+    *out = literal.Get<int32>({});
   } else if (literal.shape().element_type() == xla::S64) {
-    *out = xla::LiteralUtil::Get<int64>(literal, {});
+    *out = literal.Get<int64>({});
   } else {
     return errors::InvalidArgument("value must be either int32 or int64");
   }
@@ -168,11 +168,11 @@ static Status LiteralToInt64Vector(const xla::Literal& literal,
   int64 size = xla::ShapeUtil::ElementsIn(literal.shape());
   if (literal.shape().element_type() == xla::S32) {
     for (int64 i = 0; i < size; ++i) {
-      out->push_back(xla::LiteralUtil::Get<int32>(literal, {i}));
+      out->push_back(literal.Get<int32>({i}));
     }
   } else if (literal.shape().element_type() == xla::S64) {
     for (int64 i = 0; i < size; ++i) {
-      out->push_back(xla::LiteralUtil::Get<int64>(literal, {i}));
+      out->push_back(literal.Get<int64>({i}));
     }
   } else {
     return errors::InvalidArgument("value must be either int32 or int64");
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 6a87784f6a..5dceb03281 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -826,87 +826,80 @@ class ComputationBuilder {
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR0(NativeT value) {
-  return ConstantOp(
-      [value](Literal* literal) { LiteralUtil::PopulateR0(value, literal); });
+  return ConstantOp([value](Literal* literal) { literal->PopulateR0(value); });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR1(
     tensorflow::gtl::ArraySlice<NativeT> values) {
-  return ConstantOp([&values](Literal* literal) {
-    LiteralUtil::PopulateR1(values, literal);
-  });
+  return ConstantOp(
+      [&values](Literal* literal) { literal->PopulateR1(values); });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR1(int64 length,
                                                      NativeT value) {
   return ConstantOp([length, value](Literal* literal) {
-    LiteralUtil::PopulateWithValue(value, {length}, literal);
+    literal->PopulateWithValue(value, {length});
   });
 }
 
 inline ComputationDataHandle ComputationBuilder::ConstantR1(
     const tensorflow::core::Bitmap& values) {
-  return ConstantOp([&values](Literal* literal) {
-    LiteralUtil::PopulateR1(values, literal);
-  });
+  return ConstantOp(
+      [&values](Literal* literal) { literal->PopulateR1(values); });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR2(
     std::initializer_list<std::initializer_list<NativeT>> values) {
-  return ConstantOp([&values](Literal* literal) {
-    LiteralUtil::PopulateR2(values, literal);
-  });
+  return ConstantOp(
+      [&values](Literal* literal) { literal->PopulateR2(values); });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR2FromArray2DWithLayout(
     const Array2D<NativeT>& values, const Layout& layout) {
   return ConstantOp([&values, &layout](Literal* literal) {
-    LiteralUtil::PopulateR2FromArray2DWithLayout(values, layout, literal);
+    literal->PopulateR2FromArray2DWithLayout(values, layout);
   });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR2FromArray2D(
     const Array2D<NativeT>& values) {
-  return ConstantOp([&values](Literal* literal) {
-    LiteralUtil::PopulateR2FromArray2D(values, literal);
-  });
+  return ConstantOp(
+      [&values](Literal* literal) { literal->PopulateR2FromArray2D(values); });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR3FromArray3DWithLayout(
     const Array3D<NativeT>& values, const Layout& layout) {
   return ConstantOp([&values, &layout](Literal* literal) {
-    LiteralUtil::PopulateR3FromArray3DWithLayout(values, layout, literal);
+    literal->PopulateR3FromArray3DWithLayout(values, layout);
   });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR3FromArray3D(
     const Array3D<NativeT>& values) {
-  return ConstantOp([&values](Literal* literal) {
-    LiteralUtil::PopulateR3FromArray3D(values, literal);
-  });
+  return ConstantOp(
+      [&values](Literal* literal) { literal->PopulateR3FromArray3D(values); });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR4FromArray4DWithLayout(
     const Array4D<NativeT>& values, const Layout& layout) {
   return ConstantOp([&values, &layout](Literal* literal) {
-    LiteralUtil::PopulateR4FromArray4DWithLayout(values, layout, literal);
+    literal->PopulateR4FromArray4DWithLayout(values, layout);
   });
 }
 
 template <typename NativeT>
 ComputationDataHandle ComputationBuilder::ConstantR4FromArray4D(
     const Array4D<NativeT>& values) {
-  return ConstantOp([&values](Literal* literal) {
-    LiteralUtil::PopulateR4FromArray4D(values, literal);
-  });
+  return ConstantOp(
+      [&values](Literal* literal) { literal->PopulateR4FromArray4D(values); });
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc
index daa1557df0..ffdc7dd943 100644
--- a/tensorflow/compiler/xla/client/lib/testing.cc
+++ b/tensorflow/compiler/xla/client/lib/testing.cc
@@ -34,7 +34,7 @@ std::unique_ptr<GlobalData> MakeFakeDataOrDie(const Shape& shape,
       client,
       tensorflow::strings::StrCat("make_fake_", ShapeUtil::HumanString(shape)));
   // TODO(b/26811613): Replace this when RNG is supported on all backends.
-  b.Broadcast(b.ConstantLiteral(LiteralUtil::One(shape.element_type())),
+  b.Broadcast(b.ConstantLiteral(Literal::One(shape.element_type())),
               AsInt64Slice(shape.dimensions()));
   Computation computation = b.Build().ConsumeValueOrDie();
 
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 64e58e32fb..caee6cc0a0 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -646,539 +646,6 @@ class Literal {
   std::vector<Literal> tuple_literals_;
 };
 
-// Utility class for dealing with XLA literal values.  Most methods are
-// templated by native (host) type which corresponds to a unique XLA
-// PrimitiveType. See ComputationBuilder for details.  Not all primitive types
-// defined in xla_data.proto have a corresponding native type or even have a
-// storage location in the Literal proto yet (for example, primitive type F16).
-//
-// TODO(dnovillo) - All functions in this class simply redirect to the
-// corresponding function in class Literal. Remove this class after converting
-// all user code to use Literal directly.
-class LiteralUtil {
- public:
-  // Creates new literal of a given rank. To minimize ambiguity (for users and
-  // the compiler) these CreateR[0-2] methods should explicitly specify the
-  // native type. For example:
-  //
-  //  CreateR1<float>({1.0, 42.0});
-  //  CreateR2<uint32>({{1, 2}, {3, 4}});
-  //
-  // The variants not ending with WithLayout use the default XLA layout for the
-  // literal's linear representation in memory.
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR0(NativeT value) {
-    return Literal::CreateR0(value);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR1(
-      tensorflow::gtl::ArraySlice<NativeT> values) {
-    return Literal::CreateR1(values);
-  }
-
-  static std::unique_ptr<Literal> CreateR1(
-      const tensorflow::core::Bitmap& values) {
-    return Literal::CreateR1(values);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR2(
-      std::initializer_list<std::initializer_list<NativeT>> values) {
-    return Literal::CreateR2(values);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR2WithLayout(
-      std::initializer_list<std::initializer_list<NativeT>> values,
-      const Layout& layout) {
-    return Literal::CreateR2WithLayout(values, layout);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR3(
-      std::initializer_list<
-          std::initializer_list<std::initializer_list<NativeT>>>
-          values) {
-    return Literal::CreateR3(values);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR3WithLayout(
-      std::initializer_list<
-          std::initializer_list<std::initializer_list<NativeT>>>
-          values,
-      const Layout& layout) {
-    return Literal::CreateR3WithLayout(values, layout);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR4(
-      std::initializer_list<std::initializer_list<
-          std::initializer_list<std::initializer_list<NativeT>>>>
-          values) {
-    return Literal::CreateR4(values);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR4WithLayout(
-      std::initializer_list<std::initializer_list<
-          std::initializer_list<std::initializer_list<NativeT>>>>
-          values,
-      const Layout& layout) {
-    return Literal::CreateR4WithLayout(values, layout);
-  }
-
-  // Creates a new Literal object with the shape specified as parameter.
-  // The content of the literal values is the default value of the primitive
-  // type of literal itself (0 for numeric types, and false for predicates).
-  static std::unique_ptr<Literal> CreateFromShape(const Shape& shape) {
-    return Literal::CreateFromShape(shape);
-  }
-
-  // Creates a new Literal object with its values havings the primitive_type
-  // type, and with dimensions defined by the dimensions parameter.
-  // The content of the literal values is the default value of the primitive
-  // type of literal itself (0 for numeric types, and false for predicates).
-  static std::unique_ptr<Literal> CreateFromDimensions(
-      PrimitiveType primitive_type,
-      tensorflow::gtl::ArraySlice<int64> dimensions) {
-    return Literal::CreateFromDimensions(primitive_type, dimensions);
-  }
-
-  // Copies the values from src_literal, starting at src_base shape indexes,
-  // to dest_literal, starting at dest_base, where the copy size in each
-  // dimension is specified by copy_size.
-  //
-  // The src_literal and dest_literal must have the same primitive type,
-  // src_base+copy_size must fit the source literal dimensions, as well as
-  // dest_base+copy_size must fit the destination literal dimensions.
-  static Status Copy(const Literal& src_literal,
-                     tensorflow::gtl::ArraySlice<int64> src_base,
-                     Literal* dest_literal,
-                     tensorflow::gtl::ArraySlice<int64> dest_base,
-                     tensorflow::gtl::ArraySlice<int64> copy_size) {
-    return dest_literal->Copy(src_literal, src_base, dest_base, copy_size);
-  }
-
-  // Creates a new value that has the equivalent value as literal, but conforms
-  // to new_layout; e.g. a literal matrix that was in {0, 1} minor-to-major
-  // dimension layout can be re-laid-out as {1, 0} minor-to-major dimension
-  // layout and the value in the cell at any given logical index (i0, i1) will
-  // be the same.
-  //
-  // Note: this is useful when the client wants to ensure that a value placed in
-  // the XLA allocation tracker has a particular layout; for efficiency
-  // purposes or avoiding unimplemented operation/layout combinations.
-  static std::unique_ptr<Literal> Relayout(const Literal& literal,
-                                           const Layout& new_layout) {
-    return literal.Relayout(new_layout);
-  }
-
-  // Reshapes literal 'input' to have 'shape'. Both the original shape and
-  // 'shape' must contain the same number of elements. The implementation
-  // currently only supports monotonic dim0-major layouts.
-  static StatusOr<std::unique_ptr<Literal>> Reshape(
-      const xla::Literal& input, tensorflow::gtl::ArraySlice<int64> shape) {
-    return input.Reshape(shape);
-  }
-
-  // Creates a new literal by reordering the dimensions of the original literal.
-  // The given `permutation` must be a permutation of the dimension numbers
-  // in the original literal, and it specifies the order of the new dimensions
-  // in the result literal (i.e., new_order[i] = old_order[permutation[i]]).
-  // For example, a transpose call on a literal of shape [3 x 8 x 4] and
-  // `permutation` = {2, 0, 1} returns a new literal of shape [4 x 3 x 8].
-  static std::unique_ptr<Literal> Transpose(
-      const Literal& literal, tensorflow::gtl::ArraySlice<int64> permutation) {
-    return literal.Transpose(permutation);
-  }
-
-  // Creates a sub-array from the given literal by extracting the indices
-  // [start_index, limit_index) of each dimension. The result literal has the
-  // same rank and layout as for the given literal. The number of indices in
-  // start_indices and limit_indices must be the rank of the literal, and the
-  // indices follow the order of the dimensions.
-  static std::unique_ptr<Literal> Slice(
-      const Literal& literal, tensorflow::gtl::ArraySlice<int64> start_indices,
-      tensorflow::gtl::ArraySlice<int64> limit_indices) {
-    return literal.Slice(start_indices, limit_indices);
-  }
-
-  // Creates a literal with a prepended dimension with bound "times"; e.g. a
-  // f32[3x2] with times=4 will produce a f32[4x3x2] with the 3x2 from the input
-  // literal replicated four times.
-  template <typename NativeT>
-  static std::unique_ptr<Literal> Replicate(const Literal& input, int64 times) {
-    return input.Replicate<NativeT>(times);
-  }
-
-  // Creates a literal by converting each element in an original literal to a
-  // new type.
-  template <typename NativeSrcT, typename NativeDestT>
-  static std::unique_ptr<Literal> Convert(const Literal& literal) {
-    return literal.Convert<NativeSrcT, NativeDestT>();
-  }
-
-  // Creates a literal value zero of the given primitive type.
-  static Literal Zero(PrimitiveType primitive_type) {
-    return Literal::Zero(primitive_type);
-  }
-
-  // Creates a literal value one of the given primitive type.
-  static Literal One(PrimitiveType primitive_type) {
-    return Literal::One(primitive_type);
-  }
-
-  // Creates a literal value containing the minimum value of the given
-  // primitive type. For floating-point types, returns -inf.
-  static Literal MinValue(PrimitiveType primitive_type) {
-    return Literal::MinValue(primitive_type);
-  }
-
-  // Creates a literal value containing the maximum value of the given
-  // primitive type. For floating-point types, returns inf.
-  static Literal MaxValue(PrimitiveType primitive_type) {
-    return Literal::MaxValue(primitive_type);
-  }
-
-  // Creates a literal of the given shape where each element is `value`.
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateFullWithMonotonicDim0MajorLayout(
-      tensorflow::gtl::ArraySlice<int64> dimensions, NativeT value) {
-    return Literal::CreateFullWithMonotonicDim0MajorLayout(dimensions, value);
-  }
-
-  // Creates a new literal from an array. The variants not ending with
-  // WithLayout use the default XLA layout for the literal's linear
-  // representation in memory.
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR2FromArray2D(
-      const Array2D<NativeT>& values) {
-    return Literal::CreateR2FromArray2D(values);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR2FromArray2DWithLayout(
-      const Array2D<NativeT>& values, const Layout& layout) {
-    return Literal::CreateR2FromArray2DWithLayout(values, layout);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR3FromArray3D(
-      const Array3D<NativeT>& values) {
-    return Literal::CreateR3FromArray3D(values);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR3FromArray3DWithLayout(
-      const Array3D<NativeT>& values, const Layout& layout) {
-    return Literal::CreateR3FromArray3DWithLayout(values, layout);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR4FromArray4D(
-      const Array4D<NativeT>& values) {
-    return Literal::CreateR4FromArray4D(values);
-  }
-
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR4FromArray4DWithLayout(
-      const Array4D<NativeT>& values, const Layout& layout) {
-    return Literal::CreateR4FromArray4DWithLayout(values, layout);
-  }
-
-  // Creates a new vector of U8s literal value from a string.
-  static std::unique_ptr<Literal> CreateR1U8(tensorflow::StringPiece value) {
-    return Literal::CreateR1U8(value);
-  }
-
-  // Creates a linspace-populated literal with the given number of rows and
-  // columns.
-  static std::unique_ptr<Literal> CreateR2F32Linspace(float from, float to,
-                                                      int64 rows, int64 cols) {
-    return Literal::CreateR2F32Linspace(from, to, rows, cols);
-  }
-
-  // Creates a literal that projects the (x, y) dimensions given in values into
-  // the z dimension given by "projection".
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR3Projected(
-      std::initializer_list<std::initializer_list<NativeT>> values,
-      int64 projection) {
-    return Literal::CreateR3Projected(values, projection);
-  }
-
-  // Creates a literal that projects the (x, y) dimensions given in values into
-  // the z and p dimensions given.
-  template <typename NativeT>
-  static std::unique_ptr<Literal> CreateR4Projected(
-      std::initializer_list<std::initializer_list<NativeT>> values,
-      int64 projection_p, int64 projection_z) {
-    return Literal::CreateR4Projected(values, projection_p, projection_z);
-  }
-
-  // Clones literal into an owned unique_ptr version.
-  static std::unique_ptr<Literal> CloneToUnique(const Literal& literal) {
-    return literal.CloneToUnique();
-  }
-
-  // Returns the linear index of the given index within the literal's
-  // element_type repeated field.
-  static int64 LinearIndex(const Literal& literal,
-                           tensorflow::gtl::ArraySlice<int64> multi_index) {
-    return literal.LinearIndex(multi_index);
-  }
-
-  // Gets or sets an element in the literal at the given index. The index is
-  // CHECKed against the dimension sizes.
-  template <typename NativeT>
-  static NativeT Get(const Literal& literal,
-                     tensorflow::gtl::ArraySlice<int64> multi_index) {
-    return literal.Get<NativeT>(multi_index);
-  }
-
-  template <typename NativeT>
-  static void Set(Literal* literal,
-                  tensorflow::gtl::ArraySlice<int64> multi_index,
-                  NativeT value) {
-    literal->Set(multi_index, value);
-  }
-
-  // Retrieves the mutable array slice interface which can be used to manipulate
-  // pre-allocated literal values.
-  template <typename NativeT>
-  static tensorflow::gtl::MutableArraySlice<NativeT> GetMutableArraySlice(
-      Literal* literal) {
-    return literal->GetMutableArraySlice<NativeT>();
-  }
-
-  // Returns the element value at index (0, ..., 0), however many zeroes are
-  // required for that index.
-  template <typename NativeT>
-  static NativeT GetFirstElement(const Literal& literal) {
-    return literal.GetFirstElement<NativeT>();
-  }
-
-  // As Get(), but determines the correct type and converts the value
-  // into text.
-  static string GetAsString(const Literal& literal,
-                            tensorflow::gtl::ArraySlice<int64> multi_index) {
-    return literal.GetAsString(multi_index);
-  }
-
-  // Returns an identity matrix (rank 2) with the given row and column count.
-  template <typename NativeT>
-  static std::unique_ptr<Literal> MakeIdentityR2(int64 size) {
-    return Literal::MakeIdentityR2<NativeT>(size);
-  }
-
-  // Returns a tuple literal composed of given literals.
-  static std::unique_ptr<Literal> MakeTuple(
-      tensorflow::gtl::ArraySlice<const Literal*> elements) {
-    return Literal::MakeTuple(elements);
-  }
-
-  // Validates that the data payload of the literal matches the literal shape;
-  // if it does not, an appropriate status is returned.
-  static tensorflow::Status ValidateLiteral(const Literal& literal) {
-    return literal.ValidateLiteral();
-  }
-
-  // Returns a string representation of the literal value.
-  static string ToString(const Literal& literal) { return literal.ToString(); }
-
-  // Invokes the "per cell" callback for each element in the provided
-  // literal with the element's indices and a string representation of
-  // the element's value.
-  //
-  // This function is useful if you want a polymorphic representation
-  // of the tensor's elements (turning it to a string for something
-  // like representation in a protobuf).
-  static void EachCellAsString(
-      const Literal& literal,
-      const std::function<void(tensorflow::gtl::ArraySlice<int64> indices,
-                               const string& value)>& per_cell) {
-    literal.EachCellAsString(per_cell);
-  }
-
-  template <typename NativeT>
-  static void EachCell(
-      const Literal& literal,
-      std::function<void(tensorflow::gtl::ArraySlice<int64> indices,
-                         NativeT value)>
-          per_cell) {
-    literal.EachCell<NativeT>(per_cell);
-  }
-
-  // Templated methods which populate the given repeated field in the Literal
-  // proto with the given value(s). The Shape field of the Literal proto is set
-  // to match the array dimensions and type. Examples:
-  //
-  //   // Populate with floats.
-  //   Array2D<float> float_values = ...
-  //   PopulateR2FromArray2D(values, literal);
-  //
-  //   // Populate with int32s.
-  //   PopulateR2({{1, 2}, {3, 4}}, literal);
-  //
-  template <typename NativeT>
-  static void PopulateR0(NativeT values, Literal* literal) {
-    literal->PopulateR0(values);
-  }
-
-  template <typename NativeT>
-  static void PopulateR1(tensorflow::gtl::ArraySlice<NativeT> values,
-                         Literal* literal) {
-    literal->PopulateR1(values);
-  }
-
-  static void PopulateR1(const tensorflow::core::Bitmap& values,
-                         Literal* literal) {
-    literal->PopulateR1(values);
-  }
-
-  template <typename NativeT>
-  static void PopulateR2(
-      std::initializer_list<std::initializer_list<NativeT>> values,
-      Literal* literal) {
-    literal->PopulateR2(values);
-  }
-
-  template <typename NativeT>
-  static void PopulateR2WithLayout(
-      std::initializer_list<std::initializer_list<NativeT>> values,
-      const Layout& layout, Literal* literal) {
-    literal->PopulateR2WithLayout(values, layout);
-  }
-
-  template <typename NativeT>
-  static void PopulateR2FromArray2D(const Array2D<NativeT>& values,
-                                    Literal* literal) {
-    literal->PopulateR2FromArray2D(values);
-  }
-
-  template <typename NativeT>
-  static void PopulateR2FromArray2DWithLayout(const Array2D<NativeT>& values,
-                                              const Layout& layout,
-                                              Literal* literal) {
-    literal->PopulateR2FromArray2DWithLayout(values, layout);
-  }
-
-  template <typename NativeT>
-  static void PopulateR3FromArray3D(const Array3D<NativeT>& values,
-                                    Literal* literal) {
-    literal->PopulateR3FromArray3D(values);
-  }
-
-  template <typename NativeT>
-  static void PopulateR3FromArray3DWithLayout(const Array3D<NativeT>& values,
-                                              const Layout& layout,
-                                              Literal* literal) {
-    literal->PopulateR3FromArray3DWithLayout(values, layout);
-  }
-
-  template <typename NativeT>
-  static void PopulateR4FromArray4D(const Array4D<NativeT>& values,
-                                    Literal* literal) {
-    literal->PopulateR4FromArray4D(values);
-  }
-
-  template <typename NativeT>
-  static void PopulateR4FromArray4DWithLayout(const Array4D<NativeT>& values,
-                                              const Layout& layout,
-                                              Literal* literal) {
-    literal->PopulateR4FromArray4DWithLayout(values, layout);
-  }
-
-  // Populates literal values by calling the generator function for every cell
-  // in the literal object.
-  template <typename NativeT>
-  static Status Populate(
-      Literal* literal,
-      const std::function<NativeT(tensorflow::gtl::ArraySlice<int64> indexes)>&
-          generator) {
-    return literal->Populate(generator);
-  }
-
-  // Creates a Literal of the given dimensions with all elements set to the
-  // given value.
-  template <typename NativeT>
-  static void PopulateWithValue(NativeT value,
-                                tensorflow::gtl::ArraySlice<int64> dimensions,
-                                Literal* literal) {
-    return literal->PopulateWithValue(value, dimensions);
-  }
-
-  // Returns a pointer to the underlying vector containing the array data. Use
-  // with care.
-  static const void* InternalData(const Literal& literal) {
-    return literal.InternalData();
-  }
-
-  static void* MutableInternalData(Literal* literal) {
-    return literal->MutableInternalData();
-  }
-
-  // Allocates space in the underlying vector of the literal sufficient to hold
-  // num_elements of the literal's primitive type. Values in the vector are set
-  // to zero. num_elements must equal the number of elements in the literals
-  // shape.
-  static void Reserve(int64 num_elements, Literal* literal) {
-    literal->Reserve(num_elements);
-  }
-
-  // Allocates space in the underlying vector of the literal sufficient to hold
-  // num_elements of the literal's primitive type and sets each element in the
-  // literal to the given value. num_elements must equal the number of elements
-  // in the literals shape.
-  template <typename NativeT>
-  static void Resize(int64 num_elements, NativeT value, Literal* literal) {
-    literal->Resize(num_elements, value);
-  }
-
-  // Returns true if the two given literals have the same shape and
-  // values. Layout is not considered in the comparison.
-  static bool Equal(const Literal& literal1, const Literal& literal2) {
-    return literal1.Equal(literal2);
-  }
-
-  // Returns whether every element in the given literal is equal to value.
-  //
-  // value is an int8 because we expect this to be called with small
-  // compile-time constants (0, -1, etc.) and so that whatever value you pass
-  // can be represented exactly by floating-point types as small as 16 bits.
-  //
-  // If value doesn't fit in literal's type, returns false.  Values of 1/0 are
-  // considered equal to true/false; other values are not considered equal to
-  // true.
-  static bool IsAll(const Literal& literal, int8 value) {
-    return literal.IsAll(value);
-  }
-
-  // Like IsAll(const Literal&, int8), except we check whether the literal is
-  // equal to a particular floating-point number.
-  //
-  // If the literal is not a floating-point value, this always returns false.
-  //
-  // This casts value to the type of literal, then compares using ==.  The usual
-  // admonishments about floating-point equality checks apply.  We expect you to
-  // use this to check for values that can be expressed precisely as a float,
-  // e.g. -0.5.
-  static bool IsAllFloat(const Literal& literal, float value) {
-    return literal.IsAllFloat(value);
-  }
-
-  // Returns whether the literal is zero at the specified index. The literal
-  // must be an array.
-  static bool IsZero(const Literal& literal,
-                     tensorflow::gtl::ArraySlice<int64> indices) {
-    return literal.IsZero(indices);
-  }
-
-  TF_DISALLOW_COPY_AND_ASSIGN(LiteralUtil);
-};
-
 // Declarations of template specializations for GetArraySlice and
 // GetMutableArraySlice. The specializations map native type to XLA primitive
 // type.
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 50ea286b53..d52e6eb5e2 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -72,11 +72,11 @@ class LiteralUtilTest : public ::testing::Test {
     layout_r4_dim0minor_ = LayoutUtil::MakeLayout({0, 1, 2, 3});
 
     literal_r4_2x2x3x3_dim0major_ =
-        LiteralUtil::CreateR4FromArray4DWithLayout<float>(arr4d,
-                                                          layout_r4_dim0major_);
+        Literal::CreateR4FromArray4DWithLayout<float>(arr4d,
+                                                      layout_r4_dim0major_);
     literal_r4_2x2x3x3_dim0minor_ =
-        LiteralUtil::CreateR4FromArray4DWithLayout<float>(arr4d,
-                                                          layout_r4_dim0minor_);
+        Literal::CreateR4FromArray4DWithLayout<float>(arr4d,
+                                                      layout_r4_dim0minor_);
   }
 
   Layout layout_r2_dim0major_;
@@ -90,43 +90,42 @@ class LiteralUtilTest : public ::testing::Test {
 };
 
 TEST_F(LiteralUtilTest, LiteralScalarToString) {
-  auto true_lit = LiteralUtil::CreateR0<bool>(true);
-  ASSERT_EQ("true", LiteralUtil::ToString(*true_lit));
+  auto true_lit = Literal::CreateR0<bool>(true);
+  ASSERT_EQ("true", true_lit->ToString());
 
-  auto false_lit = LiteralUtil::CreateR0<bool>(false);
-  ASSERT_EQ("false", LiteralUtil::ToString(*false_lit));
+  auto false_lit = Literal::CreateR0<bool>(false);
+  ASSERT_EQ("false", false_lit->ToString());
 
-  auto u32_lit = LiteralUtil::CreateR0<uint32>(42);
-  ASSERT_EQ("42", LiteralUtil::ToString(*u32_lit));
+  auto u32_lit = Literal::CreateR0<uint32>(42);
+  ASSERT_EQ("42", u32_lit->ToString());
 
-  auto s32_lit = LiteralUtil::CreateR0<int32>(-999);
-  ASSERT_EQ("-999", LiteralUtil::ToString(*s32_lit));
+  auto s32_lit = Literal::CreateR0<int32>(-999);
+  ASSERT_EQ("-999", s32_lit->ToString());
 
-  auto f32_lit = LiteralUtil::CreateR0<float>(3.14f);
-  ASSERT_EQ("3.14", LiteralUtil::ToString(*f32_lit));
+  auto f32_lit = Literal::CreateR0<float>(3.14f);
+  ASSERT_EQ("3.14", f32_lit->ToString());
 
-  auto f16_lit = LiteralUtil::CreateR0<half>(static_cast<half>(0.5f));
-  ASSERT_EQ("0.5", LiteralUtil::ToString(*f16_lit));
+  auto f16_lit = Literal::CreateR0<half>(static_cast<half>(0.5f));
+  ASSERT_EQ("0.5", f16_lit->ToString());
 }
 
 TEST_F(LiteralUtilTest, LiteralVectorToString) {
-  auto pred_vec = LiteralUtil::CreateR1<bool>({true, false, true});
-  ASSERT_EQ("{101}", LiteralUtil::ToString(*pred_vec));
+  auto pred_vec = Literal::CreateR1<bool>({true, false, true});
+  ASSERT_EQ("{101}", pred_vec->ToString());
 }
 
 TEST_F(LiteralUtilTest, R2ToString) {
-  const auto literal = LiteralUtil::CreateR2({{1, 2}, {3, 4}, {5, 6}});
+  const auto literal = Literal::CreateR2({{1, 2}, {3, 4}, {5, 6}});
   const string expected = R"(s32[3,2] {
   { 1, 2 },
   { 3, 4 },
   { 5, 6 },
 })";
-  ASSERT_EQ(expected, LiteralUtil::ToString(*literal));
+  ASSERT_EQ(expected, literal->ToString());
 }
 
 TEST_F(LiteralUtilTest, R3ToString) {
-  const auto literal =
-      LiteralUtil::CreateR3({{{1}, {2}}, {{3}, {4}}, {{5}, {6}}});
+  const auto literal = Literal::CreateR3({{{1}, {2}}, {{3}, {4}}, {{5}, {6}}});
   const string expected = R"(s32[3,2,1] {
 { { 1 },
   { 2 } },
@@ -135,13 +134,13 @@ TEST_F(LiteralUtilTest, R3ToString) {
 { { 5 },
   { 6 } }
 })";
-  ASSERT_EQ(expected, LiteralUtil::ToString(*literal));
+  ASSERT_EQ(expected, literal->ToString());
 }
 
 TEST_F(LiteralUtilTest, TupleToString) {
-  auto scalar = LiteralUtil::CreateR0<float>(1.0);
-  auto matrix = LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
-  auto tuple = LiteralUtil::MakeTuple({scalar.get(), matrix.get()});
+  auto scalar = Literal::CreateR0<float>(1.0);
+  auto matrix = Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
+  auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()});
   const string expected = R"((f32[], f32[2,2]) (
 1,
 f32[2,2] {
@@ -149,7 +148,7 @@ f32[2,2] {
   { 3, 4 },
 },
 ))";
-  ASSERT_EQ(expected, LiteralUtil::ToString(*tuple));
+  ASSERT_EQ(expected, tuple->ToString());
 }
 
 TEST_F(LiteralUtilTest, CreateR3FromArray3d) {
@@ -164,9 +163,9 @@ TEST_F(LiteralUtilTest, CreateR3FromArray3d) {
   });
   // clang-format on
 
-  auto literal = LiteralUtil::CreateR3FromArray3D(array_3d);
+  auto literal = Literal::CreateR3FromArray3D(array_3d);
   EXPECT_THAT(literal->shape().dimensions(), ElementsAre(2, 3, 2));
-  string result = LiteralUtil::ToString(*literal);
+  string result = literal->ToString();
   const string expected = R"(f32[2,3,2] {
 { { 1, 2 },
   { 3, 4 },
@@ -180,14 +179,14 @@ TEST_F(LiteralUtilTest, CreateR3FromArray3d) {
 
 TEST_F(LiteralUtilTest, LiteralR4F32ProjectedStringifies) {
   // clang-format off
-  auto literal = LiteralUtil::CreateR4Projected<float>({
+  auto literal = Literal::CreateR4Projected<float>({
     {1, 2},
     {1001, 1002},
     {2001, 2002},
   }, /*projection_p=*/1, /*projection_z=*/2);
   // clang-format on
   EXPECT_THAT(literal->shape().dimensions(), ElementsAre(1, 2, 3, 2));
-  string result = LiteralUtil::ToString(*literal);
+  string result = literal->ToString();
   const string expected = R"(f32[1,2,3,2] {
   {  // i0=0
     {  // i1=0
@@ -208,7 +207,7 @@ TEST_F(LiteralUtilTest, LiteralR4F32ProjectedStringifies) {
 TEST_F(LiteralUtilTest, LiteralR4F32Stringifies) {
   EXPECT_THAT(literal_r4_2x2x3x3_dim0major_->shape().dimensions(),
               ElementsAre(2, 2, 3, 3));
-  string result = LiteralUtil::ToString(*literal_r4_2x2x3x3_dim0major_);
+  string result = literal_r4_2x2x3x3_dim0major_->ToString();
   const string expected = R"(f32[2,2,3,3] {
   {  // i0=0
     {  // i1=0
@@ -240,14 +239,13 @@ TEST_F(LiteralUtilTest, LiteralR4F32Stringifies) {
 
 TEST_F(LiteralUtilTest, EachCellR2F32) {
   // clang-format off
-  auto literal = LiteralUtil::CreateR2<float>({
+  auto literal = Literal::CreateR2<float>({
     {3.1f, 4.2f},
     {9.3f, 12.4f},
   });
   // clang-format on
   std::vector<std::tuple<int64, int64, string>> seen;
-  LiteralUtil::EachCellAsString(
-      *literal,
+  literal->EachCellAsString(
       [&seen](tensorflow::gtl::ArraySlice<int64> indices, const string& value) {
         seen.emplace_back(indices[0], indices[1], value);
       });
@@ -259,176 +257,161 @@ TEST_F(LiteralUtilTest, EachCellR2F32) {
 }
 
 TEST_F(LiteralUtilTest, ScalarEquality) {
-  // Test LiteralUtil::Equal with scalars.
-  auto f32_42 = LiteralUtil::CreateR0<float>(42.0);
-  auto f32_42_clone = LiteralUtil::CreateR0<float>(42.0);
+  // Test Literal::Equal with scalars.
+  auto f32_42 = Literal::CreateR0<float>(42.0);
+  auto f32_42_clone = Literal::CreateR0<float>(42.0);
 
-  EXPECT_TRUE(LiteralUtil::Equal(*f32_42, *f32_42));
-  EXPECT_TRUE(LiteralUtil::Equal(*f32_42, *f32_42_clone));
+  EXPECT_TRUE(f32_42->Equal(*f32_42));
+  EXPECT_TRUE(f32_42->Equal(*f32_42_clone));
 
-  auto f32_123 = LiteralUtil::CreateR0<float>(123.0);
-  EXPECT_FALSE(LiteralUtil::Equal(*f32_42, *f32_123));
+  auto f32_123 = Literal::CreateR0<float>(123.0);
+  EXPECT_FALSE(f32_42->Equal(*f32_123));
 
-  auto f64_42 = LiteralUtil::CreateR0<double>(42.0);
-  EXPECT_FALSE(LiteralUtil::Equal(*f32_42, *f64_42));
+  auto f64_42 = Literal::CreateR0<double>(42.0);
+  EXPECT_FALSE(f32_42->Equal(*f64_42));
 }
 
 TEST_F(LiteralUtilTest, NonScalarEquality) {
-  // Test LiteralUtil::Equal with nonscalars.
-  auto matrix = LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
-  auto matrix_clone = LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
-  auto matrix_different =
-      LiteralUtil::CreateR2<float>({{4.0, 3.0}, {1.0, 2.0}});
-  auto vector_literal = LiteralUtil::CreateR1<float>({1.0, 2.0, 3.0, 4.0});
-  auto scalar = LiteralUtil::CreateR0<float>(1.0);
+  // Test Literal::Equal with nonscalars.
+  auto matrix = Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
+  auto matrix_clone = Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
+  auto matrix_different = Literal::CreateR2<float>({{4.0, 3.0}, {1.0, 2.0}});
+  auto vector_literal = Literal::CreateR1<float>({1.0, 2.0, 3.0, 4.0});
+  auto scalar = Literal::CreateR0<float>(1.0);
 
-  EXPECT_TRUE(LiteralUtil::Equal(*matrix, *matrix));
-  EXPECT_TRUE(LiteralUtil::Equal(*matrix, *matrix_clone));
-  EXPECT_FALSE(LiteralUtil::Equal(*matrix, *matrix_different));
-  EXPECT_FALSE(LiteralUtil::Equal(*matrix, *vector_literal));
-  EXPECT_FALSE(LiteralUtil::Equal(*matrix, *scalar));
+  EXPECT_TRUE(matrix->Equal(*matrix));
+  EXPECT_TRUE(matrix->Equal(*matrix_clone));
+  EXPECT_FALSE(matrix->Equal(*matrix_different));
+  EXPECT_FALSE(matrix->Equal(*vector_literal));
+  EXPECT_FALSE(matrix->Equal(*scalar));
 }
 
 TEST_F(LiteralUtilTest, DifferentLayoutEquality) {
-  // Test LiteralUtil::Equal with literals which have different layouts.
+  // Test Literal::Equal with literals which have different layouts.
   auto colmajor = MakeUnique<Literal>();
   *colmajor->mutable_shape() = ShapeUtil::MakeShape(F32, {2, 2});
   *colmajor->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1});
-  LiteralUtil::Reserve(4, colmajor.get());
-  LiteralUtil::Set<float>(colmajor.get(), {0, 0}, 1.0);
-  LiteralUtil::Set<float>(colmajor.get(), {0, 1}, 2.0);
-  LiteralUtil::Set<float>(colmajor.get(), {1, 0}, 3.0);
-  LiteralUtil::Set<float>(colmajor.get(), {1, 1}, 4.0);
+  colmajor.get()->Reserve(4);
+  colmajor.get()->Set<float>({0, 0}, 1.0);
+  colmajor.get()->Set<float>({0, 1}, 2.0);
+  colmajor.get()->Set<float>({1, 0}, 3.0);
+  colmajor.get()->Set<float>({1, 1}, 4.0);
 
   auto rowmajor = MakeUnique<Literal>();
   *rowmajor->mutable_shape() = ShapeUtil::MakeShape(F32, {2, 2});
   *rowmajor->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({1, 0});
-  LiteralUtil::Reserve(4, rowmajor.get());
-  LiteralUtil::Set<float>(rowmajor.get(), {0, 0}, 1.0);
-  LiteralUtil::Set<float>(rowmajor.get(), {0, 1}, 2.0);
-  LiteralUtil::Set<float>(rowmajor.get(), {1, 0}, 3.0);
-  LiteralUtil::Set<float>(rowmajor.get(), {1, 1}, 4.0);
+  rowmajor.get()->Reserve(4);
+  rowmajor.get()->Set<float>({0, 0}, 1.0);
+  rowmajor.get()->Set<float>({0, 1}, 2.0);
+  rowmajor.get()->Set<float>({1, 0}, 3.0);
+  rowmajor.get()->Set<float>({1, 1}, 4.0);
 
-  EXPECT_TRUE(LiteralUtil::Equal(*rowmajor, *colmajor));
+  EXPECT_TRUE(rowmajor->Equal(*colmajor));
 }
 
 TEST_F(LiteralUtilTest, TupleEquality) {
-  // Test LiteralUtil::Equal with tuples.
-  auto scalar = LiteralUtil::CreateR0<float>(1.0);
-  auto matrix = LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
-  auto tuple1 = LiteralUtil::MakeTuple({scalar.get(), matrix.get()});
+  // Test Literal::Equal with tuples.
+  auto scalar = Literal::CreateR0<float>(1.0);
+  auto matrix = Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
+  auto tuple1 = Literal::MakeTuple({scalar.get(), matrix.get()});
 
   // Tuple with the same elements. One element is shared with the original
   // tuple, the other is a clone of the element in the original tuple.
-  auto scalar_clone = LiteralUtil::CreateR0<float>(1.0);
-  auto tuple2 = LiteralUtil::MakeTuple({scalar_clone.get(), matrix.get()});
-  EXPECT_TRUE(LiteralUtil::Equal(*tuple1, *tuple2));
+  auto scalar_clone = Literal::CreateR0<float>(1.0);
+  auto tuple2 = Literal::MakeTuple({scalar_clone.get(), matrix.get()});
+  EXPECT_TRUE(tuple1->Equal(*tuple2));
 
   // Tuple with elements reversed.
-  auto reversed_tuple = LiteralUtil::MakeTuple({matrix.get(), scalar.get()});
-  EXPECT_FALSE(LiteralUtil::Equal(*tuple1, *reversed_tuple));
+  auto reversed_tuple = Literal::MakeTuple({matrix.get(), scalar.get()});
+  EXPECT_FALSE(tuple1->Equal(*reversed_tuple));
 
   // Tuple with different value.
-  auto scalar_42 = LiteralUtil::CreateR0<float>(42.0);
-  auto different_tuple =
-      LiteralUtil::MakeTuple({scalar_42.get(), matrix.get()});
-  EXPECT_FALSE(LiteralUtil::Equal(*tuple1, *different_tuple));
+  auto scalar_42 = Literal::CreateR0<float>(42.0);
+  auto different_tuple = Literal::MakeTuple({scalar_42.get(), matrix.get()});
+  EXPECT_FALSE(tuple1->Equal(*different_tuple));
 }
 
 TEST_F(LiteralUtilTest, IsAllTuple) {
-  auto element1 = LiteralUtil::CreateR0<float>(0.0);
-  auto element2 = LiteralUtil::CreateR2<float>({{0.0, 0.0}, {0.0, 0.0}});
-  auto tuple = LiteralUtil::MakeTuple({element1.get(), element1.get()});
+  auto element1 = Literal::CreateR0<float>(0.0);
+  auto element2 = Literal::CreateR2<float>({{0.0, 0.0}, {0.0, 0.0}});
+  auto tuple = Literal::MakeTuple({element1.get(), element1.get()});
 
   // Tuples should always return false for IsAll.
-  EXPECT_FALSE(LiteralUtil::IsAll(*tuple, 0));
-  EXPECT_FALSE(LiteralUtil::IsAll(*tuple, 1));
+  EXPECT_FALSE(tuple->IsAll(0));
+  EXPECT_FALSE(tuple->IsAll(1));
 }
 
 TEST_F(LiteralUtilTest, IsAll) {
-  EXPECT_TRUE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<bool>(false), 0));
-  EXPECT_TRUE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<bool>(true), 1));
-  EXPECT_FALSE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<bool>(false), 1));
-  EXPECT_FALSE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<bool>(false), 2));
-  EXPECT_FALSE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<bool>(true), 0));
-  EXPECT_FALSE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<bool>(true), 2));
-  EXPECT_FALSE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<bool>(true), -1));
+  EXPECT_TRUE(Literal::CreateR0<bool>(false)->IsAll(0));
+  EXPECT_TRUE(Literal::CreateR0<bool>(true)->IsAll(1));
+  EXPECT_FALSE(Literal::CreateR0<bool>(false)->IsAll(1));
+  EXPECT_FALSE(Literal::CreateR0<bool>(false)->IsAll(2));
+  EXPECT_FALSE(Literal::CreateR0<bool>(true)->IsAll(0));
+  EXPECT_FALSE(Literal::CreateR0<bool>(true)->IsAll(2));
+  EXPECT_FALSE(Literal::CreateR0<bool>(true)->IsAll(-1));
 
   // We shouldn't reinterpret int8_min as an unsigned type and then decide that
   // it is equal to 255.
   auto int8_min = std::numeric_limits<int8>::min();
-  EXPECT_FALSE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR0<uint8>(255), int8_min));
+  EXPECT_FALSE(Literal::CreateR0<uint8>(255)->IsAll(int8_min));
 
-  EXPECT_TRUE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<float>(42.0), 42));
-  EXPECT_FALSE(LiteralUtil::IsAll(*LiteralUtil::CreateR0<float>(42.0001), 42));
+  EXPECT_TRUE(Literal::CreateR0<float>(42.0)->IsAll(42));
+  EXPECT_FALSE(Literal::CreateR0<float>(42.0001)->IsAll(42));
 
-  EXPECT_TRUE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR1<int>({100, 100, 100}), 100));
-  EXPECT_FALSE(LiteralUtil::IsAll(
-      *LiteralUtil::CreateR1<double>({100, 100, 100.001}), 100));
+  EXPECT_TRUE(Literal::CreateR1<int>({100, 100, 100})->IsAll(100));
+  EXPECT_FALSE(Literal::CreateR1<double>({100, 100, 100.001})->IsAll(100));
 
-  EXPECT_TRUE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR2<uint64>({{8, 8}, {8, 8}}), 8));
-  EXPECT_FALSE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR2<uint64>({{8, 8}, {8, 9}}), 8));
-  EXPECT_FALSE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR2<uint64>({{9, 8}, {8, 8}}), 8));
+  EXPECT_TRUE(Literal::CreateR2<uint64>({{8, 8}, {8, 8}})->IsAll(8));
+  EXPECT_FALSE(Literal::CreateR2<uint64>({{8, 8}, {8, 9}})->IsAll(8));
+  EXPECT_FALSE(Literal::CreateR2<uint64>({{9, 8}, {8, 8}})->IsAll(8));
 
   half h8(8.0f);
   half h9(9.0f);
-  EXPECT_TRUE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR2<half>({{h8}, {h8}}), 8));
-  EXPECT_FALSE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR2<half>({{h8}, {h9}}), 8));
-  EXPECT_FALSE(
-      LiteralUtil::IsAll(*LiteralUtil::CreateR2<half>({{h9}, {h8}}), 8));
+  EXPECT_TRUE(Literal::CreateR2<half>({{h8}, {h8}})->IsAll(8));
+  EXPECT_FALSE(Literal::CreateR2<half>({{h8}, {h9}})->IsAll(8));
+  EXPECT_FALSE(Literal::CreateR2<half>({{h9}, {h8}})->IsAll(8));
 
   auto uint64_max = std::numeric_limits<uint64>::max();
-  EXPECT_FALSE(LiteralUtil::IsAll(
-      *LiteralUtil::CreateR2<uint64>(
-          {{uint64_max, uint64_max}, {uint64_max, uint64_max}}),
-      -1));
+  EXPECT_FALSE(Literal::CreateR2<uint64>(
+                   {{uint64_max, uint64_max}, {uint64_max, uint64_max}})
+                   ->IsAll(-1));
 }
 
 TEST_F(LiteralUtilTest, IsAllFloat) {
   // IsAllFloat always returns false when the literal is not floating-point.
-  EXPECT_FALSE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<bool>(false), 0));
-  EXPECT_FALSE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<int8>(0), 0));
-  EXPECT_FALSE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<uint8>(0), 0));
-  EXPECT_FALSE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<int>(0), 0));
-
-  EXPECT_TRUE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<float>(0), 0));
-  EXPECT_TRUE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<float>(.5), .5));
-  EXPECT_TRUE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<float>(-.5), -.5));
+  EXPECT_FALSE(Literal::CreateR0<bool>(false)->IsAllFloat(0));
+  EXPECT_FALSE(Literal::CreateR0<int8>(0)->IsAllFloat(0));
+  EXPECT_FALSE(Literal::CreateR0<uint8>(0)->IsAllFloat(0));
+  EXPECT_FALSE(Literal::CreateR0<int>(0)->IsAllFloat(0));
+
+  EXPECT_TRUE(Literal::CreateR0<float>(0)->IsAllFloat(0));
+  EXPECT_TRUE(Literal::CreateR0<float>(.5)->IsAllFloat(.5));
+  EXPECT_TRUE(Literal::CreateR0<float>(-.5)->IsAllFloat(-.5));
+  EXPECT_FALSE(Literal::CreateR0<float>(-.5)->IsAllFloat(-.49));
   EXPECT_FALSE(
-      LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<float>(-.5), -.49));
-  EXPECT_FALSE(LiteralUtil::IsAllFloat(
-      *LiteralUtil::CreateR2<float>({{0, 0, 0}, {0, .1, 0}}), 0));
-  EXPECT_TRUE(LiteralUtil::IsAllFloat(
-      *LiteralUtil::CreateR2<float>({{.5, .5, .5}, {.5, .5, .5}}), .5));
-
-  EXPECT_TRUE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<double>(0), 0));
-  EXPECT_TRUE(LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<double>(.5), .5));
+      Literal::CreateR2<float>({{0, 0, 0}, {0, .1, 0}})->IsAllFloat(0));
   EXPECT_TRUE(
-      LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<double>(-.5), -.5));
+      Literal::CreateR2<float>({{.5, .5, .5}, {.5, .5, .5}})->IsAllFloat(.5));
+
+  EXPECT_TRUE(Literal::CreateR0<double>(0)->IsAllFloat(0));
+  EXPECT_TRUE(Literal::CreateR0<double>(.5)->IsAllFloat(.5));
+  EXPECT_TRUE(Literal::CreateR0<double>(-.5)->IsAllFloat(-.5));
+  EXPECT_FALSE(Literal::CreateR0<double>(-.5)->IsAllFloat(-.49));
   EXPECT_FALSE(
-      LiteralUtil::IsAllFloat(*LiteralUtil::CreateR0<double>(-.5), -.49));
-  EXPECT_FALSE(LiteralUtil::IsAllFloat(
-      *LiteralUtil::CreateR2<double>({{0, 0, 0}, {0, .1, 0}}), 0));
+      Literal::CreateR2<double>({{0, 0, 0}, {0, .1, 0}})->IsAllFloat(0));
 }
 
 TEST_F(LiteralUtilTest, IsZero) {
-  auto scalar_zero = LiteralUtil::CreateR0<float>(0.0f);
-  auto scalar_one = LiteralUtil::CreateR0<float>(1.0f);
-  EXPECT_TRUE(LiteralUtil::IsZero(*scalar_zero, {}));
-  EXPECT_FALSE(LiteralUtil::IsZero(*scalar_one, {}));
+  auto scalar_zero = Literal::CreateR0<float>(0.0f);
+  auto scalar_one = Literal::CreateR0<float>(1.0f);
+  EXPECT_TRUE(scalar_zero->IsZero({}));
+  EXPECT_FALSE(scalar_one->IsZero({}));
 
-  auto array = LiteralUtil::CreateR2<uint32>({{1, 2, 0, 3}, {1, 0, 1, 2}});
-  EXPECT_FALSE(LiteralUtil::IsZero(*array, {0, 1}));
-  EXPECT_TRUE(LiteralUtil::IsZero(*array, {0, 2}));
-  EXPECT_TRUE(LiteralUtil::IsZero(*array, {1, 1}));
-  EXPECT_FALSE(LiteralUtil::IsZero(*array, {1, 2}));
+  auto array = Literal::CreateR2<uint32>({{1, 2, 0, 3}, {1, 0, 1, 2}});
+  EXPECT_FALSE(array->IsZero({0, 1}));
+  EXPECT_TRUE(array->IsZero({0, 2}));
+  EXPECT_TRUE(array->IsZero({1, 1}));
+  EXPECT_FALSE(array->IsZero({1, 2}));
 }
 
 template <typename T>
@@ -440,127 +423,122 @@ TYPED_TEST_CASE(LiteralUtilTestTemplated, TestedTypes);
 TYPED_TEST(LiteralUtilTestTemplated, Relayout2x2) {
   // Make a non-integer for floating point types.
   TypeParam half = TypeParam(1) / TypeParam(2);
-  auto data = LiteralUtil::CreateR2<TypeParam>({{half, 2}, {3, 4}});
+  auto data = Literal::CreateR2<TypeParam>({{half, 2}, {3, 4}});
   const Layout layout01 = LayoutUtil::MakeLayout({0, 1});
   const Layout layout10 = LayoutUtil::MakeLayout({1, 0});
 
-  auto data01 = LiteralUtil::Relayout(*data, layout01);
+  auto data01 = data->Relayout(layout01);
   EXPECT_TRUE(LayoutUtil::Equal(data01->shape().layout(), layout01));
-  EXPECT_TRUE(LiteralUtil::Equal(*data, *data01));
+  EXPECT_TRUE(data->Equal(*data01));
 
-  auto data10 = LiteralUtil::Relayout(*data, layout10);
+  auto data10 = data->Relayout(layout10);
   EXPECT_TRUE(LayoutUtil::Equal(data10->shape().layout(), layout10));
-  EXPECT_TRUE(LiteralUtil::Equal(*data, *data10));
+  EXPECT_TRUE(data->Equal(*data10));
 }
 
 TEST_F(LiteralUtilTest, ReshapeR0) {
-  auto original = LiteralUtil::CreateR0<float>(1.7f);
-  auto reshape =
-      LiteralUtil::Reshape(*original, /*shape=*/{}).ConsumeValueOrDie();
-  EXPECT_TRUE(LiteralUtil::Equal(*original, *reshape));
+  auto original = Literal::CreateR0<float>(1.7f);
+  auto reshape = original->Reshape(/*shape=*/{}).ConsumeValueOrDie();
+  EXPECT_TRUE(original->Equal(*reshape));
 }
 
 TEST_F(LiteralUtilTest, ReshapeR4) {
   // clang-format off
   // F32[1x3x2x4]
-  auto original = LiteralUtil::CreateR4WithLayout<float>({{
+  auto original = Literal::CreateR4WithLayout<float>({{
      {{10, 11, 12, 13}, {14, 15, 16, 17}},
      {{18, 19, 20, 21}, {22, 23, 24, 25}},
      {{26, 27, 28, 29}, {30, 31, 32, 33}},
   }}, layout_r4_dim0major_);
   // F32[1x3x4x2]
-  auto expected = LiteralUtil::CreateR3WithLayout<float>({
+  auto expected = Literal::CreateR3WithLayout<float>({
     {{10, 11}, {12, 13}, {14, 15}, {16, 17}},
     {{18, 19}, {20, 21}, {22, 23}, {24, 25}},
     {{26, 27}, {28, 29}, {30, 31}, {32, 33}},
   }, layout_r3_dim0major_);
   // clang-format on
-  auto reshape = LiteralUtil::Reshape(*original, {3, 4, 2}).ConsumeValueOrDie();
+  auto reshape = original->Reshape({3, 4, 2}).ConsumeValueOrDie();
 
-  EXPECT_TRUE(LiteralUtil::Equal(*expected, *reshape));
+  EXPECT_TRUE(expected->Equal(*reshape));
 }
 
 TEST_F(LiteralUtilTest, ReshapeR4Dim0Minor) {
   // clang-format off
   // F32[1x3x2x4]
-  auto original = LiteralUtil::CreateR4WithLayout<float>({{
+  auto original = Literal::CreateR4WithLayout<float>({{
      {{10, 11, 12, 13}, {14, 15, 16, 17}},
      {{18, 19, 20, 21}, {22, 23, 24, 25}},
      {{26, 27, 28, 29}, {30, 31, 32, 33}},
   }}, layout_r4_dim0minor_);
   // F32[1x3x4x2]
-  auto expected = LiteralUtil::CreateR3WithLayout<float>({
+  auto expected = Literal::CreateR3WithLayout<float>({
     {{10, 11}, {12, 13}, {14, 15}, {16, 17}},
     {{18, 19}, {20, 21}, {22, 23}, {24, 25}},
     {{26, 27}, {28, 29}, {30, 31}, {32, 33}},
   }, layout_r3_dim0major_);
   // clang-format on
-  auto reshape = LiteralUtil::Reshape(*original, {3, 4, 2}).ConsumeValueOrDie();
+  auto reshape = original->Reshape({3, 4, 2}).ConsumeValueOrDie();
 
-  EXPECT_TRUE(LiteralUtil::Equal(*expected, *reshape));
+  EXPECT_TRUE(expected->Equal(*reshape));
 }
 
 TEST_F(LiteralUtilTest, TransposeR0) {
-  auto original = LiteralUtil::CreateR0<float>(1.7f);
-  auto reshape = LiteralUtil::Transpose(*original, /*permutation=*/{});
-  EXPECT_TRUE(LiteralUtil::Equal(*original, *reshape));
+  auto original = Literal::CreateR0<float>(1.7f);
+  auto reshape = original->Transpose(/*permutation=*/{});
+  EXPECT_TRUE(original->Equal(*reshape));
 }
 
 TEST_F(LiteralUtilTest, TransposeR4) {
   // clang-format off
   // F32[1x3x2x4]
-  auto original = LiteralUtil::CreateR4<float>({{
+  auto original = Literal::CreateR4<float>({{
      {{10, 11, 12, 13}, {14, 15, 16, 17}},
      {{18, 19, 20, 21}, {22, 23, 24, 25}},
      {{26, 27, 28, 29}, {30, 31, 32, 33}},
   }});
   // clang-format on
-  auto reshape =
-      LiteralUtil::Transpose(*original, /*permutation=*/{2, 3, 0, 1});
-
-  LiteralUtil::EachCell<float>(
-      *reshape, [&](tensorflow::gtl::ArraySlice<int64> indices, float value) {
-        EXPECT_EQ(value,
-                  LiteralUtil::Get<float>(*original, {indices[2], indices[3],
-                                                      indices[0], indices[1]}));
+  auto reshape = original->Transpose(/*permutation=*/{2, 3, 0, 1});
+
+  reshape->EachCell<float>(
+      [&](tensorflow::gtl::ArraySlice<int64> indices, float value) {
+        EXPECT_EQ(value, original->Get<float>(
+                             {indices[2], indices[3], indices[0], indices[1]}));
       });
 }
 
 TEST_F(LiteralUtilTest, TestR4RelayoutEquivalence) {
   // Tests that using Relayout on an array is equivalent to creating it in the
   // target layout in the first place.
-  auto dim0minor_relaid_to_dim0major = LiteralUtil::Relayout(
-      *literal_r4_2x2x3x3_dim0minor_, layout_r4_dim0major_);
-  EXPECT_TRUE(LiteralUtil::Equal(*literal_r4_2x2x3x3_dim0major_,
-                                 *dim0minor_relaid_to_dim0major));
+  auto dim0minor_relaid_to_dim0major =
+      literal_r4_2x2x3x3_dim0minor_->Relayout(layout_r4_dim0major_);
+  EXPECT_TRUE(
+      literal_r4_2x2x3x3_dim0major_->Equal(*dim0minor_relaid_to_dim0major));
 
-  auto dim0major_relaid_to_dim0minor = LiteralUtil::Relayout(
-      *literal_r4_2x2x3x3_dim0major_, layout_r4_dim0minor_);
-  EXPECT_TRUE(LiteralUtil::Equal(*literal_r4_2x2x3x3_dim0minor_,
-                                 *dim0major_relaid_to_dim0minor));
+  auto dim0major_relaid_to_dim0minor =
+      literal_r4_2x2x3x3_dim0major_->Relayout(layout_r4_dim0minor_);
+  EXPECT_TRUE(
+      literal_r4_2x2x3x3_dim0minor_->Equal(*dim0major_relaid_to_dim0minor));
 }
 
 TEST_F(LiteralUtilTest, TestR2LinearLayout) {
   // Test expected memory layout of R2 dim0-minor (column-major) literal.
-  auto mat_dim0minor = LiteralUtil::CreateR2WithLayout<int>(
-      {{1, 2, 3}, {4, 5, 6}}, layout_r2_dim0minor_);
+  auto mat_dim0minor = Literal::CreateR2WithLayout<int>({{1, 2, 3}, {4, 5, 6}},
+                                                        layout_r2_dim0minor_);
   EXPECT_EQ(mat_dim0minor->s32s_size(), 6);
   EXPECT_THAT(mat_dim0minor->s32s(), ElementsAre(1, 4, 2, 5, 3, 6));
 
   // Test expected memory layout when using Relayout to row major.
-  auto relaid_mat_to_dim0major =
-      LiteralUtil::Relayout(*mat_dim0minor, layout_r2_dim0major_);
+  auto relaid_mat_to_dim0major = mat_dim0minor->Relayout(layout_r2_dim0major_);
   EXPECT_THAT(relaid_mat_to_dim0major->s32s(), ElementsAre(1, 2, 3, 4, 5, 6));
 
   // Test expected memory layout of R2 created with dim0-major (row-major).
-  auto mat_dim0major = LiteralUtil::CreateR2WithLayout<int>(
-      {{1, 2, 3}, {4, 5, 6}}, layout_r2_dim0major_);
+  auto mat_dim0major = Literal::CreateR2WithLayout<int>({{1, 2, 3}, {4, 5, 6}},
+                                                        layout_r2_dim0major_);
   EXPECT_EQ(mat_dim0major->s32s_size(), 6);
   EXPECT_THAT(mat_dim0major->s32s(), ElementsAre(1, 2, 3, 4, 5, 6));
 
   // Test expected memory layout when using Relayout to column major.
-  auto relaid_mat_to_dim0minor =
-      LiteralUtil::Relayout(*mat_dim0major, layout_r2_dim0minor_);
+  auto relaid_mat_to_dim0minor = mat_dim0major->Relayout(layout_r2_dim0minor_);
   EXPECT_THAT(relaid_mat_to_dim0minor->s32s(), ElementsAre(1, 4, 2, 5, 3, 6));
 }
 
@@ -578,8 +556,8 @@ TEST_F(LiteralUtilTest, TestR3LinearLayout) {
             {10, 11, 12},
           },
       });  // clang-format on
-  auto lit_dim0minor = LiteralUtil::CreateR3FromArray3DWithLayout<int>(
-      arr3d, layout_r3_dim0minor_);
+  auto lit_dim0minor =
+      Literal::CreateR3FromArray3DWithLayout<int>(arr3d, layout_r3_dim0minor_);
 
   EXPECT_EQ(lit_dim0minor->s32s_size(), 12);
   std::vector<int> expected_dim0minor{1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12};
@@ -587,122 +565,120 @@ TEST_F(LiteralUtilTest, TestR3LinearLayout) {
               testing::ElementsAreArray(expected_dim0minor));
 
   // Test expected memory layout when using Relayout to row major.
-  auto relaid_lit_to_dim0major =
-      LiteralUtil::Relayout(*lit_dim0minor, layout_r3_dim0major_);
+  auto relaid_lit_to_dim0major = lit_dim0minor->Relayout(layout_r3_dim0major_);
   std::vector<int> expected_dim0major{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
   EXPECT_THAT(relaid_lit_to_dim0major->s32s(),
               testing::ElementsAreArray(expected_dim0major));
 
   // Test expected memory layout of R3 created with dim0-major (row-major).
-  auto lit_dim0major = LiteralUtil::CreateR3FromArray3DWithLayout<int>(
-      arr3d, layout_r3_dim0major_);
+  auto lit_dim0major =
+      Literal::CreateR3FromArray3DWithLayout<int>(arr3d, layout_r3_dim0major_);
   EXPECT_EQ(lit_dim0major->s32s_size(), 12);
   EXPECT_THAT(lit_dim0major->s32s(),
               testing::ElementsAreArray(expected_dim0major));
 
   // Test expected memory layout when using Relayout to column major.
-  auto relaid_lit_to_dim0minor =
-      LiteralUtil::Relayout(*lit_dim0major, layout_r3_dim0minor_);
+  auto relaid_lit_to_dim0minor = lit_dim0major->Relayout(layout_r3_dim0minor_);
   EXPECT_THAT(relaid_lit_to_dim0minor->s32s(),
               testing::ElementsAreArray(expected_dim0minor));
 }
 
 TEST_F(LiteralUtilTest, SliceR0S32) {
-  auto input = LiteralUtil::CreateR0<int32>(1);
-  auto result = LiteralUtil::Slice(*input, {}, {});
-  EXPECT_TRUE(LiteralUtil::Equal(*input, *result));
+  auto input = Literal::CreateR0<int32>(1);
+  auto result = input->Slice({}, {});
+  EXPECT_TRUE(input->Equal(*result));
 }
 
 TEST_F(LiteralUtilTest, SliceR1F32) {
-  auto input = LiteralUtil::CreateR1<float>({1.0, 2.0, 3.0, 4.0, 5.0});
-  auto result = LiteralUtil::Slice(*input, {3}, {4});
-  auto expected = LiteralUtil::CreateR1<float>({4.0});
-  EXPECT_TRUE(LiteralUtil::Equal(*expected, *result));
+  auto input = Literal::CreateR1<float>({1.0, 2.0, 3.0, 4.0, 5.0});
+  auto result = input->Slice({3}, {4});
+  auto expected = Literal::CreateR1<float>({4.0});
+  EXPECT_TRUE(expected->Equal(*result));
 }
 
 TEST_F(LiteralUtilTest, SliceR2U32) {
-  auto input_3x4 = LiteralUtil::CreateR2<uint32>(
-      {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}});
-  auto result = LiteralUtil::Slice(*input_3x4, {0, 2}, {2, 4});
-  auto expected = LiteralUtil::CreateR2<uint32>({{3, 4}, {7, 8}});
-  EXPECT_TRUE(LiteralUtil::Equal(*expected, *result));
+  auto input_3x4 =
+      Literal::CreateR2<uint32>({{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}});
+  auto result = input_3x4->Slice({0, 2}, {2, 4});
+  auto expected = Literal::CreateR2<uint32>({{3, 4}, {7, 8}});
+  EXPECT_TRUE(expected->Equal(*result));
 }
 
 TEST_F(LiteralUtilTest, SliceR3U32Full) {
-  auto input_2x3x2 = LiteralUtil::CreateR3<uint32>(
+  auto input_2x3x2 = Literal::CreateR3<uint32>(
       {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}});
-  auto result = LiteralUtil::Slice(*input_2x3x2, {0, 0, 0}, {2, 3, 2});
-  EXPECT_TRUE(LiteralUtil::Equal(*input_2x3x2, *result));
+  auto result = input_2x3x2->Slice({0, 0, 0}, {2, 3, 2});
+  EXPECT_TRUE(input_2x3x2->Equal(*result));
 }
 
 TEST_F(LiteralUtilTest, PopulateR1S64) {
   Literal output;
-  LiteralUtil::PopulateR1<int64>({77}, &output);
-  auto expected = LiteralUtil::CreateR1<int64>({77});
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateR1<int64>({77});
+  auto expected = Literal::CreateR1<int64>({77});
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, PopulateR2U64) {
   Literal output;
-  LiteralUtil::PopulateR1<uint64>({{77, 88}}, &output);
-  auto expected = LiteralUtil::CreateR1<uint64>({{77, 88}});
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateR1<uint64>({{77, 88}});
+  auto expected = Literal::CreateR1<uint64>({{77, 88}});
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, PopulateWithValueR0F32) {
   Literal output;
-  LiteralUtil::PopulateWithValue<float>(2.5f, {}, &output);
-  auto expected = LiteralUtil::CreateR0<float>(2.5f);
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateWithValue<float>(2.5f, {});
+  auto expected = Literal::CreateR0<float>(2.5f);
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, PopulateWithValueR1S64) {
   Literal output;
-  LiteralUtil::PopulateWithValue<int64>(-7, {3}, &output);
-  auto expected = LiteralUtil::CreateR1<int64>({-7, -7, -7});
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateWithValue<int64>(-7, {3});
+  auto expected = Literal::CreateR1<int64>({-7, -7, -7});
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, PopulateWithValueR2U64) {
   Literal output;
-  LiteralUtil::PopulateWithValue<uint64>(42, {2, 2}, &output);
-  auto expected = LiteralUtil::CreateR2<uint64>({{42, 42}, {42, 42}});
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateWithValue<uint64>(42, {2, 2});
+  auto expected = Literal::CreateR2<uint64>({{42, 42}, {42, 42}});
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, PopulateWithValueR0F16) {
   Literal output;
   half h(0.25f);
-  LiteralUtil::PopulateWithValue<half>(h, {}, &output);
-  auto expected = LiteralUtil::CreateR0<half>(h);
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateWithValue<half>(h, {});
+  auto expected = Literal::CreateR0<half>(h);
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, PopulateWithValueR1F16) {
   Literal output;
   half h(0.5f);
-  LiteralUtil::PopulateWithValue<half>(h, {3}, &output);
-  auto expected = LiteralUtil::CreateR1<half>({h, h, h});
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateWithValue<half>(h, {3});
+  auto expected = Literal::CreateR1<half>({h, h, h});
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, PopulateWithValueR2F16) {
   Literal output;
   half h(2.0f);
-  LiteralUtil::PopulateWithValue<half>(h, {2, 2}, &output);
-  auto expected = LiteralUtil::CreateR2<half>({{h, h}, {h, h}});
-  EXPECT_TRUE(LiteralUtil::Equal(output, *expected));
+  output.PopulateWithValue<half>(h, {2, 2});
+  auto expected = Literal::CreateR2<half>({{h, h}, {h, h}});
+  EXPECT_TRUE(output.Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, ReplicateR2U32) {
-  auto input = LiteralUtil::CreateR2<uint32>(
-      {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}});
-  auto output = LiteralUtil::Replicate<uint32>(*input, 3);
-  auto expected = LiteralUtil::CreateR3<uint32>(
+  auto input =
+      Literal::CreateR2<uint32>({{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}});
+  auto output = input->Replicate<uint32>(3);
+  auto expected = Literal::CreateR3<uint32>(
       {{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}},
        {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}},
        {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}});
-  EXPECT_TRUE(LiteralUtil::Equal(*output, *expected));
+  EXPECT_TRUE(output->Equal(*expected));
 }
 
 TEST_F(LiteralUtilTest, Copy) {
@@ -712,13 +688,13 @@ TEST_F(LiteralUtilTest, Copy) {
   for (const auto& layout : layouts) {
     Shape shape = ShapeUtil::MakeShapeWithLayout(
         primitive_util::NativeToPrimitiveType<uint32>(), dimensions, layout);
-    auto blank = LiteralUtil::CreateFromShape(shape);
-    auto source = LiteralUtil::CreateFromShape(shape);
+    auto blank = Literal::CreateFromShape(shape);
+    auto source = Literal::CreateFromShape(shape);
     const int64 zero_base[] = {0, 0, 0, 0};
     const int64 step[] = {1, 1, 1, 1};
     uint32 seqnr = 0;
     auto init_proc = [&](const std::vector<int64>& indexes) {
-      LiteralUtil::Set(source.get(), indexes, ++seqnr);
+      source.get()->Set(indexes, ++seqnr);
       return true;
     };
 
@@ -729,8 +705,7 @@ TEST_F(LiteralUtilTest, Copy) {
     const int64 dest_base[] = {6, 4, 12, 2};
     const int64 copy_size[] = {7, 8, 11, 9};
 
-    TF_EXPECT_OK(LiteralUtil::Copy(*source, src_base, blank.get(), dest_base,
-                                   copy_size));
+    TF_EXPECT_OK(blank.get()->Copy(*source, src_base, dest_base, copy_size));
     std::vector<int64> source_indexes(TF_ARRAYSIZE(dimensions), 0);
     std::vector<int64> blank_indexes(TF_ARRAYSIZE(dimensions), 0);
     bool matched = true;
@@ -741,9 +716,8 @@ TEST_F(LiteralUtilTest, Copy) {
       std::copy(indexes.begin(), indexes.end(), blank_indexes.begin());
       std::transform(blank_indexes.begin(), blank_indexes.end(), dest_base,
                      blank_indexes.begin(), std::plus<int64>());
-      auto bval = LiteralUtil::Get<uint32>(*blank, blank_indexes);
-      matched = (bval != 0 &&
-                 bval == LiteralUtil::Get<uint32>(*source, source_indexes));
+      auto bval = blank->Get<uint32>(blank_indexes);
+      matched = (bval != 0 && bval == source->Get<uint32>(source_indexes));
       return matched;
     };
     ShapeUtil::ForEachIndex(source->shape(), zero_base, copy_size, step,
@@ -753,25 +727,25 @@ TEST_F(LiteralUtilTest, Copy) {
 }
 
 TEST_F(LiteralUtilTest, CopyScalars) {
-  auto zero = LiteralUtil::CreateR0<uint32>(0);
-  auto nine = LiteralUtil::CreateR0<uint32>(9);
-  TF_EXPECT_OK(LiteralUtil::Copy(*nine, {}, zero.get(), {}, {}));
-  EXPECT_TRUE(LiteralUtil::Equal(*zero, *nine));
+  auto zero = Literal::CreateR0<uint32>(0);
+  auto nine = Literal::CreateR0<uint32>(9);
+  TF_EXPECT_OK(zero.get()->Copy(*nine, {}, {}, {}));
+  EXPECT_TRUE(zero->Equal(*nine));
 
-  auto vect = LiteralUtil::CreateR1<uint32>({3, 4, 9, 12, 5, 17, 21});
-  TF_EXPECT_OK(LiteralUtil::Copy(*vect, {5}, zero.get(), {}, {}));
-  EXPECT_EQ(LiteralUtil::Get<uint32>(*zero, {}), 17);
-  TF_EXPECT_OK(LiteralUtil::Copy(*zero, {}, vect.get(), {4}, {}));
-  EXPECT_EQ(LiteralUtil::Get<uint32>(*vect, {4}), 17);
+  auto vect = Literal::CreateR1<uint32>({3, 4, 9, 12, 5, 17, 21});
+  TF_EXPECT_OK(zero.get()->Copy(*vect, {5}, {}, {}));
+  EXPECT_EQ(zero->Get<uint32>({}), 17);
+  TF_EXPECT_OK(vect.get()->Copy(*zero, {}, {4}, {}));
+  EXPECT_EQ(vect->Get<uint32>({4}), 17);
 }
 
 TEST_F(LiteralUtilTest, F16) {
   // Verify that the internal data views are consistent and that they
   // are in little endian format
   // TODO - modify if we make the data format machine endianess dependent
-  auto m1 = LiteralUtil::CreateFromShape(ShapeUtil::MakeShape(F16, {2, 2}));
+  auto m1 = Literal::CreateFromShape(ShapeUtil::MakeShape(F16, {2, 2}));
   Literal* l1 = m1.get();
-  const char* d1 = static_cast<const char*>(LiteralUtil::InternalData(*l1));
+  const char* d1 = static_cast<const char*>(l1->InternalData());
   EXPECT_EQ(d1[0], 0);
   EXPECT_EQ(d1[1], 0);
   EXPECT_EQ(d1[2], 0);
@@ -780,14 +754,13 @@ TEST_F(LiteralUtilTest, F16) {
   EXPECT_EQ(d1[5], 0);
   EXPECT_EQ(d1[6], 0);
   EXPECT_EQ(d1[7], 0);
-  EXPECT_EQ(LiteralUtil::InternalData(*l1),
-            LiteralUtil::MutableInternalData(l1));
+  EXPECT_EQ(l1->InternalData(), l1->MutableInternalData());
 
   half h1(1.0f);
   half h2(2.0f);
-  auto m2 = LiteralUtil::CreateR2<half>({{h1, h2}, {h2, h1}});
+  auto m2 = Literal::CreateR2<half>({{h1, h2}, {h2, h1}});
   Literal* l2 = m2.get();
-  const char* d2 = static_cast<const char*>(LiteralUtil::InternalData(*l2));
+  const char* d2 = static_cast<const char*>(l2->InternalData());
   EXPECT_EQ(d2[0], 0);
   EXPECT_EQ(d2[1], 0x3C);
   EXPECT_EQ(d2[2], 0);
@@ -796,8 +769,7 @@ TEST_F(LiteralUtilTest, F16) {
   EXPECT_EQ(d2[5], 0x40);
   EXPECT_EQ(d2[6], 0);
   EXPECT_EQ(d2[7], 0x3C);
-  EXPECT_EQ(LiteralUtil::InternalData(*l2),
-            LiteralUtil::MutableInternalData(l2));
+  EXPECT_EQ(l2->InternalData(), l2->MutableInternalData());
 }
 
 TEST_F(LiteralUtilTest, Populate) {
@@ -818,19 +790,19 @@ TEST_F(LiteralUtilTest, Populate) {
     Shape shape = ShapeUtil::MakeShapeWithLayout(
         primitive_util::NativeToPrimitiveType<uint32>(), data.dimensions,
         data.layout);
-    auto literal = LiteralUtil::CreateFromShape(shape);
+    auto literal = Literal::CreateFromShape(shape);
     auto generator = [&](tensorflow::gtl::ArraySlice<int64> indexes) -> uint32 {
       // Offsets from linear index just to avoid R0 literals to be initialized
       // with zero.
-      return LiteralUtil::LinearIndex(*literal, indexes) + 17;
+      return literal->LinearIndex(indexes) + 17;
     };
-    TF_EXPECT_OK(LiteralUtil::Populate<uint32>(literal.get(), generator));
+    TF_EXPECT_OK(literal.get()->Populate<uint32>(generator));
 
     std::vector<int64> zero_base(data.dimensions.size(), 0);
     std::vector<int64> step(data.dimensions.size(), 1);
     bool matched = true;
     auto check_function = [&](const std::vector<int64>& indexes) {
-      auto value = LiteralUtil::Get<uint32>(*literal, indexes);
+      auto value = literal->Get<uint32>(indexes);
       matched = matched && (value == generator(indexes));
       return matched;
     };
@@ -842,20 +814,20 @@ TEST_F(LiteralUtilTest, Populate) {
 
 TEST_F(LiteralUtilTest, ConvertR4) {
   // clang-format off
-  auto original = LiteralUtil::CreateR4WithLayout<int8>({{
+  auto original = Literal::CreateR4WithLayout<int8>({{
      {{10, 11, 12, 13}, {14, 15, 16, 17}},
      {{18, 19, 20, 21}, {22, 23, 24, 25}},
      {{26, 27, 28, 29}, {30, 31, 32, 33}},
   }}, layout_r4_dim0major_);
-  auto expected = LiteralUtil::CreateR4WithLayout<uint32>({{
+  auto expected = Literal::CreateR4WithLayout<uint32>({{
      {{10, 11, 12, 13}, {14, 15, 16, 17}},
      {{18, 19, 20, 21}, {22, 23, 24, 25}},
      {{26, 27, 28, 29}, {30, 31, 32, 33}},
   }}, layout_r4_dim0major_);
   // clang-format on
-  auto converted = LiteralUtil::Convert<int8, uint32>(*original);
+  auto converted = original->Convert<int8, uint32>();
 
-  EXPECT_TRUE(LiteralUtil::Equal(*expected, *converted));
+  EXPECT_TRUE(expected->Equal(*converted));
 }
 
 TEST_F(LiteralUtilTest, CopyFromProto_Bool) {
diff --git a/tensorflow/compiler/xla/packed_literal_reader.cc b/tensorflow/compiler/xla/packed_literal_reader.cc
index d488830a6c..1187079906 100644
--- a/tensorflow/compiler/xla/packed_literal_reader.cc
+++ b/tensorflow/compiler/xla/packed_literal_reader.cc
@@ -58,8 +58,7 @@ StatusOr<std::unique_ptr<Literal>> PackedLiteralReader::Read(
   }
 
   int64 elements = ShapeUtil::ElementsIn(shape);
-  LiteralUtil::Resize(elements, std::numeric_limits<float>::quiet_NaN(),
-                      result.get());
+  result.get()->Resize(elements, std::numeric_limits<float>::quiet_NaN());
   std::vector<float>* field = result->mutable_f32s();
   char* data = tensorflow::bit_cast<char*>(field->data());
   uint64 bytes = elements * sizeof(float);
diff --git a/tensorflow/compiler/xla/reference_util_test.cc b/tensorflow/compiler/xla/reference_util_test.cc
index f839ac019d..215f220258 100644
--- a/tensorflow/compiler/xla/reference_util_test.cc
+++ b/tensorflow/compiler/xla/reference_util_test.cc
@@ -52,7 +52,7 @@ class ReferenceUtilTest : public ::testing::Test {
 
 TEST_F(ReferenceUtilTest, TransposeArray2D) {
   auto result = ReferenceUtil::TransposeArray2D(*matrix_);
-  auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result);
+  auto actual_literal = Literal::CreateR2FromArray2D(*result);
   LiteralTestUtil::ExpectR2Near<float>({{1.f, 4.f}, {2.f, 5.f}, {3.f, 6.f}},
                                        *actual_literal, ErrorSpec(0.0001));
 }
@@ -62,7 +62,7 @@ TEST_F(ReferenceUtilTest, MatmulArray2D) {
       {7.f, 8.f}, {9.f, 10.f}, {11.f, 12.f},
   });
   auto result = ReferenceUtil::MatmulArray2D(*matrix_, rhs);
-  auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result);
+  auto actual_literal = Literal::CreateR2FromArray2D(*result);
   LiteralTestUtil::ExpectR2Near<float>({{58.f, 64.f}, {139.f, 154.f}},
                                        *actual_literal, ErrorSpec(0.0001));
 }
@@ -70,7 +70,7 @@ TEST_F(ReferenceUtilTest, MatmulArray2D) {
 TEST_F(ReferenceUtilTest, ReduceToColArray2D) {
   auto add = [](float lhs, float rhs) { return lhs + rhs; };
   auto result = ReferenceUtil::ReduceToColArray2D(*matrix_, 0.0f, add);
-  auto actual_literal = LiteralUtil::CreateR1<float>(*result);
+  auto actual_literal = Literal::CreateR1<float>(*result);
   LiteralTestUtil::ExpectR1Near<float>({6.f, 15.f}, *actual_literal,
                                        ErrorSpec(0.0001));
 }
@@ -78,7 +78,7 @@ TEST_F(ReferenceUtilTest, ReduceToColArray2D) {
 TEST_F(ReferenceUtilTest, ReduceToRowArray2D) {
   auto add = [](float lhs, float rhs) { return lhs + rhs; };
   auto result = ReferenceUtil::ReduceToRowArray2D(*matrix_, 0.0f, add);
-  auto actual_literal = LiteralUtil::CreateR1<float>(*result);
+  auto actual_literal = Literal::CreateR1<float>(*result);
   LiteralTestUtil::ExpectR1Near<float>({5.f, 7.f, 9.f}, *actual_literal,
                                        ErrorSpec(0.0001));
 }
@@ -86,7 +86,7 @@ TEST_F(ReferenceUtilTest, ReduceToRowArray2D) {
 TEST_F(ReferenceUtilTest, MapArray2D) {
   auto identity = [](float value) { return log(exp(value)); };
   auto result = ReferenceUtil::MapArray2D(*matrix_, identity);
-  auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result);
+  auto actual_literal = Literal::CreateR2FromArray2D(*result);
   LiteralTestUtil::ExpectR2NearArray2D(*matrix_, *actual_literal,
                                        ErrorSpec(0.0001));
 }
@@ -96,7 +96,7 @@ TEST_F(ReferenceUtilTest, MapWithIndexArray2D) {
     return value + row + col;
   };
   auto result = ReferenceUtil::MapWithIndexArray2D(*matrix_, add_index);
-  auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result);
+  auto actual_literal = Literal::CreateR2FromArray2D(*result);
   LiteralTestUtil::ExpectR2Near<float>({{1.f, 3.f, 5.f}, {5.f, 7.f, 9.f}},
                                        *actual_literal, ErrorSpec(0.0001));
 }
@@ -107,7 +107,7 @@ TEST_F(ReferenceUtilTest, MapArray4D) {
   input->FillWithMultiples(1.0f);
   auto multiply_by_two = [](float value) { return 2 * value; };
   auto result = ReferenceUtil::MapArray4D(*input, multiply_by_two);
-  auto actual_literal = LiteralUtil::CreateR4FromArray4D(*result);
+  auto actual_literal = Literal::CreateR4FromArray4D(*result);
 
   Array4D<float> expected(/*planes=*/2, /*depth=*/3, /*height=*/4, /*width=*/5);
   expected.FillWithMultiples(2.0f);
@@ -124,7 +124,7 @@ TEST_F(ReferenceUtilTest, MapWithIndexArray4D) {
     return value - (3 * 4 * 5 * plane + 4 * 5 * depth + 5 * height + width);
   };
   auto result = ReferenceUtil::MapWithIndexArray4D(*input, subtract_index);
-  auto actual_literal = LiteralUtil::CreateR4FromArray4D(*result);
+  auto actual_literal = Literal::CreateR4FromArray4D(*result);
 
   Array4D<float> expected(/*planes=*/2, /*depth=*/3, /*height=*/4, /*width=*/5);
   expected.Fill(0.0f);
@@ -161,7 +161,7 @@ TEST_F(ReferenceUtilTest, ConvWithSamePadding) {
   }));
   // clang-format on
 
-  auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual);
+  auto actual_literal = Literal::CreateR4FromArray4D(*actual);
 
   LiteralTestUtil::ExpectR4NearArray4D<float>(expected, *actual_literal,
                                               ErrorSpec(0.0001));
@@ -195,7 +195,7 @@ TEST_F(ReferenceUtilTest, ConvWithValidPadding) {
   }));
   // clang-format on
 
-  auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual);
+  auto actual_literal = Literal::CreateR4FromArray4D(*actual);
 
   LiteralTestUtil::ExpectR4NearArray4D<float>(expected, *actual_literal,
                                               ErrorSpec(0.0001));
@@ -247,7 +247,7 @@ TEST_F(ReferenceUtilTest, ConvGeneralDimensionsWithSamePadding) {
   }});
   // clang-format on
 
-  auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual);
+  auto actual_literal = Literal::CreateR4FromArray4D(*actual);
 
   LiteralTestUtil::ExpectR4NearArray4D<float>(expected, *actual_literal,
                                               ErrorSpec(0.0001));
@@ -296,7 +296,7 @@ TEST_F(ReferenceUtilTest, ConvGeneralDimensionsWithValidPadding) {
   Array4D<float> expected({{{{2514, 2685}}}});
   // clang-format on
 
-  auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual);
+  auto actual_literal = Literal::CreateR4FromArray4D(*actual);
 
   LiteralTestUtil::ExpectR4NearArray4D<float>(expected, *actual_literal,
                                               ErrorSpec(0.0001));
@@ -309,7 +309,7 @@ TEST_F(ReferenceUtilTest, ApplyElementwise2D) {
 
   auto actual = ReferenceUtil::ApplyElementwise2D(
       [](float x, float y, float z) { return 100 * x + 10 * y + z; }, a, b, c);
-  auto actual_literal = LiteralUtil::CreateR2FromArray2D(*actual);
+  auto actual_literal = Literal::CreateR2FromArray2D(*actual);
   LiteralTestUtil::ExpectR2Near({{300.f, 600.f}, {900.f, 1200.f}},
                                 *actual_literal, ErrorSpec(0.0001));
 }
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 0fd3cd2ccc..1302026ccf 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -48,7 +48,7 @@ namespace {
 // Returns whether operand is a literal with the given value.
 bool IsLiteralWithValue(const HloInstruction* operand, int8 value) {
   return operand->opcode() == HloOpcode::kConstant &&
-         LiteralUtil::IsAll(operand->literal(), value);
+         operand->literal().IsAll(value);
 }
 
 bool IsAll(const HloInstruction* op, int8 value) {
@@ -469,7 +469,7 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot,
       ShapeUtil::HasZeroElements(lhs->shape()) ||
       ShapeUtil::HasZeroElements(rhs->shape())) {
     auto zero = computation_->AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0(0.0f)));
     return ReplaceWithNewInstruction(
         dot, HloInstruction::CreateBroadcast(dot->shape(), zero, {}));
   }
@@ -507,7 +507,7 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot,
     HloComputation* add_reduce_computation = CreateScalarBinaryComputation(
         computation_->parent(), F32, HloOpcode::kAdd);
     auto zero = computation_->AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0(0.0f)));
     auto reduce = computation_->AddInstruction(HloInstruction::CreateReduce(
         ShapeUtil::MakeShape(dot->shape().element_type(), {}), multiply, zero,
         {0}, add_reduce_computation));
@@ -531,7 +531,7 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot,
     HloComputation* add_reduce_computation = CreateScalarBinaryComputation(
         computation_->parent(), F32, HloOpcode::kAdd);
     auto zero = computation_->AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0(0.0f)));
     HloInstruction* reduce;
     if (ShapeUtil::Rank(rhs->shape()) == 1) {
       auto multiply = computation_->AddInstruction(HloInstruction::CreateBinary(
@@ -571,7 +571,7 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot,
     HloComputation* add_reduce_computation = CreateScalarBinaryComputation(
         computation_->parent(), F32, HloOpcode::kAdd);
     auto zero = computation_->AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0(0.0f)));
     auto reduce = computation_->AddInstruction(HloInstruction::CreateReduce(
         ShapeUtil::MakeShape(dot->shape().element_type(),
                              {lhs->shape().dimensions(0)}),
@@ -894,8 +894,8 @@ Status AlgebraicSimplifierVisitor::HandlePower(HloInstruction* power,
                                                HloInstruction* rhs) {
   VLOG(10) << "trying transform [pow(A, 0) => 1]: " << power->ToString();
   if (IsAll(rhs, 0)) {
-    auto one = HloInstruction::CreateConstant(LiteralUtil::CloneToUnique(
-        LiteralUtil::One(power->shape().element_type())));
+    auto one = HloInstruction::CreateConstant(
+        Literal::One(power->shape().element_type()).CloneToUnique());
     std::unique_ptr<HloInstruction> ones;
     if (ShapeUtil::IsScalar(power->shape())) {
       ones = std::move(one);
@@ -920,9 +920,8 @@ Status AlgebraicSimplifierVisitor::HandlePower(HloInstruction* power,
 
   VLOG(10) << "trying transform [pow(A, -1) => 1/A]: " << power->ToString();
   if (IsAll(rhs, -1)) {
-    auto* one = computation_->AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CloneToUnique(
-            LiteralUtil::One(rhs->shape().element_type()))));
+    auto* one = computation_->AddInstruction(HloInstruction::CreateConstant(
+        Literal::One(rhs->shape().element_type()).CloneToUnique()));
     return ReplaceWithNewInstruction(
         power, HloInstruction::CreateBinary(power->shape(), HloOpcode::kDivide,
                                             one, lhs));
@@ -1005,7 +1004,7 @@ Status AlgebraicSimplifierVisitor::HandleReshape(HloInstruction* reshape) {
   // dimension.
   if (ShapeUtil::HasZeroElements(reshape->shape())) {
     auto empty_constant = HloInstruction::CreateConstant(
-        LiteralUtil::CreateFromShape(reshape->shape()));
+        Literal::CreateFromShape(reshape->shape()));
 
     return ReplaceWithNewInstruction(reshape, std::move(empty_constant));
   }
@@ -1205,8 +1204,7 @@ Status AlgebraicSimplifierVisitor::HandleReduceWindow(
     // try to get more fancy about proving equivalence in cases beyond that.
     if (pad_value->opcode() != HloOpcode::kConstant ||
         reduce_init_value->opcode() != HloOpcode::kConstant ||
-        !LiteralUtil::Equal(pad_value->literal(),
-                            reduce_init_value->literal())) {
+        !pad_value->literal().Equal(reduce_init_value->literal())) {
       VLOG(10) << "Not folding pad into reduce-window due to different pad "
                   "values.";
       return Status::OK();
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index cc1e868bc3..0792006ddb 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -55,7 +55,7 @@ TEST_F(AlgebraicSimplifierTest, AddZero) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param0, zero));
 
@@ -76,7 +76,7 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR0Operand) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r2f32, "param0"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   HloInstruction* bcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(r2f32, zero, {0, 1}));
   builder.AddInstruction(
@@ -99,7 +99,7 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR1Operand) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r2f32, "param0"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({0, 0, 0})));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({0, 0, 0})));
   HloInstruction* bcast =
       builder.AddInstruction(HloInstruction::CreateBroadcast(r2f32, zero, {1}));
   builder.AddInstruction(
@@ -123,7 +123,7 @@ TEST_F(AlgebraicSimplifierTest, SubZero) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(r0f32, HloOpcode::kSubtract, param0, zero));
 
@@ -145,7 +145,7 @@ TEST_F(AlgebraicSimplifierTest, DivOneScalar) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   HloInstruction* div = builder.AddInstruction(
       HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, one));
 
@@ -167,7 +167,7 @@ TEST_F(AlgebraicSimplifierTest, DivOneArray) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r2f32, "param0"));
   HloInstruction* one = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 1.0}, {1.0, 1.0}})));
+      Literal::CreateR2<float>({{1.0, 1.0}, {1.0, 1.0}})));
   HloInstruction* div = builder.AddInstruction(
       HloInstruction::CreateBinary(r2f32, HloOpcode::kDivide, param0, one));
 
@@ -300,7 +300,7 @@ TEST_F(AlgebraicSimplifierTest, Pow0Scalar) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, zero));
 
@@ -315,7 +315,7 @@ TEST_F(AlgebraicSimplifierTest, Pow0Scalar) {
 
   HloInstruction* root = computation->root_instruction();
   EXPECT_THAT(root, op::Constant());
-  EXPECT_EQ(LiteralUtil::GetFirstElement<float>(root->literal()), 1);
+  EXPECT_EQ(root->literal().GetFirstElement<float>(), 1);
 }
 
 // Test that pow(A, 0) where A is not a scalar is simplified to broadcast(1).
@@ -325,7 +325,7 @@ TEST_F(AlgebraicSimplifierTest, Pow0Vector) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r1f32, "param0"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, param0, zero));
 
@@ -344,8 +344,7 @@ TEST_F(AlgebraicSimplifierTest, Pow0Vector) {
       << ShapeUtil::HumanString(root->shape());
   EXPECT_EQ(root->dimensions().size(), 0);
   EXPECT_TRUE(ShapeUtil::IsScalar(root->operand(0)->shape()));
-  EXPECT_EQ(LiteralUtil::GetFirstElement<float>(root->operand(0)->literal()),
-            1);
+  EXPECT_EQ(root->operand(0)->literal().GetFirstElement<float>(), 1);
 }
 
 // Test that pow(A, 1) is simplified to A.
@@ -355,7 +354,7 @@ TEST_F(AlgebraicSimplifierTest, Pow1) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, one));
 
@@ -378,7 +377,7 @@ TEST_F(AlgebraicSimplifierTest, Pow2) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* two = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, two));
 
@@ -401,7 +400,7 @@ TEST_F(AlgebraicSimplifierTest, PowNegative1) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* negative_one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(-1)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(-1)));
   builder.AddInstruction(HloInstruction::CreateBinary(r0f32, HloOpcode::kPower,
                                                       param0, negative_one));
 
@@ -416,8 +415,7 @@ TEST_F(AlgebraicSimplifierTest, PowNegative1) {
 
   HloInstruction* root = computation->root_instruction();
   EXPECT_THAT(root, op::Divide(op::Constant(), param0));
-  EXPECT_EQ(LiteralUtil::GetFirstElement<float>(root->operand(0)->literal()),
-            1);
+  EXPECT_EQ(root->operand(0)->literal().GetFirstElement<float>(), 1);
 }
 
 TEST_F(AlgebraicSimplifierTest, ReshapeBroadcast) {
@@ -451,7 +449,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeBroadcast) {
 TEST_F(AlgebraicSimplifierTest, ConvertBetweenSameType) {
   HloComputation::Builder builder(TestName());
   HloInstruction* input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   builder.AddInstruction(
       HloInstruction::CreateConvert(ShapeUtil::MakeShape(F32, {}), input));
 
@@ -519,7 +517,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) {
   HloInstruction* param1 = builder.AddInstruction(
       HloInstruction::CreateParameter(1, r1f32, "param1"));
   HloInstruction* empty_literal = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({})));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
           ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}));
@@ -550,7 +548,7 @@ TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r1f32, "param0"));
   HloInstruction* empty_literal = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({})));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
           ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}));
@@ -735,7 +733,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeAfterEffectiveUnary) {
       builder.AddInstruction(HloInstruction::CreateReshape(
           ShapeUtil::MakeShape(F32, {1, 2, 3, 4, 5}), param));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(ShapeUtil::MakeShape(F32, {1, 2, 3, 4, 5}),
                                    HloOpcode::kMaximum, movable_reshape, zero));
@@ -1035,7 +1033,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopPad) {
       builder.AddInstruction(HloInstruction::CreateParameter(
           0, ShapeUtil::MakeShape(F32, {2, 2}), "param"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   PaddingConfig no_padding;
   for (int i = 0; i < 2; ++i) {
     auto dimension = no_padding.add_dimensions();
@@ -1066,7 +1064,7 @@ TEST_F(AlgebraicSimplifierTest, NegativePadding) {
       builder.AddInstruction(HloInstruction::CreateParameter(
           0, ShapeUtil::MakeShape(F32, {10, 10}), "param"));
   HloInstruction* zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   PaddingConfig padding;
   int64 low_padding[2] = {-1, -2};
   int64 high_padding[2] = {2, -3};
@@ -1376,9 +1374,9 @@ TEST_F(AlgebraicSimplifierTest, MaxMinToClamp) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* min_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   HloInstruction* max_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   HloInstruction* min = builder.AddInstruction(HloInstruction::CreateBinary(
       r0f32, HloOpcode::kMinimum, param0, min_value));
   builder.AddInstruction(
@@ -1406,9 +1404,9 @@ TEST_F(AlgebraicSimplifierTest, MinMaxToClamp) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* min_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   HloInstruction* max_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   HloInstruction* max = builder.AddInstruction(HloInstruction::CreateBinary(
       r0f32, HloOpcode::kMaximum, param0, max_value));
   builder.AddInstruction(
@@ -1437,9 +1435,9 @@ TEST_F(AlgebraicSimplifierTest, MinMaxWithBroadcastToClamp) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r1f32, "param0"));
   HloInstruction* min_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   HloInstruction* max_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   HloInstruction* max = builder.AddInstruction(HloInstruction::CreateBinary(
       r1f32, HloOpcode::kMaximum, param0, max_value));
   builder.AddInstruction(
@@ -1497,9 +1495,9 @@ TEST_F(AlgebraicSimplifierTest, MinEquationWithMaxNotToClamp) {
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, r0f32, "param0"));
   HloInstruction* min_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   HloInstruction* max_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   HloInstruction* max = builder.AddInstruction(HloInstruction::CreateBinary(
       r0f32, HloOpcode::kMaximum, param0, max_value));
   HloInstruction* fmax = builder.AddInstruction(
@@ -1566,7 +1564,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) {
 TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) {
   HloComputation::Builder builder(TestName());
   HloInstruction* forty_two = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
 
   Shape broadcast_shape = ShapeUtil::MakeShape(F32, {4, 5, 6});
   HloInstruction* broadcast =
@@ -1614,7 +1612,7 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) {
   padding.mutable_dimensions(3)->set_edge_padding_high(2);
 
   HloInstruction* pad_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(5.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(5.0f)));
   HloInstruction* pad = builder.AddInstruction(HloInstruction::CreatePad(
       ShapeUtil::MakeShape(F32, {1, 3, 3, 5}), operand, pad_value, padding));
 
@@ -1645,7 +1643,7 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) {
   const Shape reduce_window_shape =
       ShapeUtil::MakeShape(F32, {111, 113, 113, 115});
   HloInstruction* reduce_init_value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(5.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(5.0f)));
   HloInstruction* reduce_window =
       builder.AddInstruction(HloInstruction::CreateReduceWindow(
           reduce_window_shape, pad, reduce_init_value, window,
@@ -1714,9 +1712,9 @@ TEST_F(AlgebraicSimplifierTest, IteratorInvalidation) {
 
   HloComputation::Builder call_builder(TestName() + ".Call");
   HloInstruction* zero = call_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({0.0f})));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({0.0f})));
   HloInstruction* one = call_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({1.0f})));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({1.0f})));
   builder.AddInstruction(
       HloInstruction::CreateCall(r1f32, {zero, one}, dot_computation.get()));
 
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index ddfb847c5d..10021b2513 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -105,7 +105,7 @@ class BufferAssignmentTest : public HloTestBase {
     auto param =
         builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "x"));
     auto value = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
     builder.AddInstruction(
         HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, param, value));
     return builder.Build();
@@ -122,7 +122,7 @@ class BufferAssignmentTest : public HloTestBase {
       const string& name) {
     auto builder = HloComputation::Builder(name);
     auto const4 = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int>(4)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int>(4)));
     auto param = builder.AddInstruction(
         HloInstruction::CreateParameter(0, t_s32_f32v4_, "x"));
     auto index = builder.AddInstruction(
@@ -147,9 +147,9 @@ class BufferAssignmentTest : public HloTestBase {
       const string& name) {
     auto builder = HloComputation::Builder(name);
     auto const1 = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int>(1)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int>(1)));
     auto constv = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
+        Literal::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
     auto param = builder.AddInstruction(
         HloInstruction::CreateParameter(0, t_s32_f32v4_, "x"));
     auto indexc = builder.AddInstruction(
@@ -264,7 +264,7 @@ static bool BuffersDistinct(const std::vector<const HloInstruction*>& a,
 TEST_F(BufferAssignmentTest, ScalarConstant) {
   auto builder = HloComputation::Builder(TestName());
   auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
@@ -278,9 +278,9 @@ TEST_F(BufferAssignmentTest, BufferForConst) {
   // no buffers assigned, and their consumer has a buffer.
   auto builder = HloComputation::Builder(TestName());
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
+      Literal::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
   auto const1 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({4.1f, 4.2f, 4.3f, 4.4f})));
+      Literal::CreateR1<float>({4.1f, 4.2f, 4.3f, 4.4f})));
   auto add = builder.AddInstruction(
       HloInstruction::CreateBinary(f32vec4_, HloOpcode::kAdd, const0, const1));
   auto module = CreateNewModule();
@@ -298,7 +298,7 @@ TEST_F(BufferAssignmentTest, BufferForOutputConst) {
   // This computation copies a constant to output.
   auto builder = HloComputation::Builder(TestName());
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
+      Literal::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
   auto copy = builder.AddInstruction(
       HloInstruction::CreateUnary(const0->shape(), HloOpcode::kCopy, const0));
   auto module = CreateNewModule();
@@ -586,7 +586,7 @@ TEST_F(BufferAssignmentTest, CannotReuseInputBufferOfReduce) {
   auto exp2 = builder.AddInstruction(
       HloInstruction::CreateUnary(f32a100x10_, HloOpcode::kExp, exp1));
   auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   auto reduce = builder.AddInstruction(HloInstruction::CreateReduce(
       /*shape=*/f32vec10_,
       /*operand=*/exp2,
@@ -634,9 +634,9 @@ TEST_F(BufferAssignmentTest, ExampleWhile) {
   // Creates the main kernel and verifies instruction counts.
   auto builder = HloComputation::Builder(TestName());
   auto const3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int>(0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int>(0)));
   auto const4 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
+      Literal::CreateR1<float>({1.1f, 2.2f, 3.3f, 4.4f})));
   auto tuple =
       builder.AddInstruction(HloInstruction::CreateTuple({const3, const4}));
   auto while_op = builder.AddInstruction(HloInstruction::CreateWhile(
@@ -1075,9 +1075,8 @@ TEST_F(BufferAssignmentTest, DISABLED_TupleConstantAsOutput) {
   // Test that a tuple constant which is forwarded to the computation output is
   // properly handled.
   auto builder = HloComputation::Builder(TestName());
-  builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::MakeTuple({LiteralUtil::CreateR0<int64>(0).get(),
-                              LiteralUtil::CreateR0<int64>(1).get()})));
+  builder.AddInstruction(HloInstruction::CreateConstant(Literal::MakeTuple(
+      {Literal::CreateR0<int64>(0).get(), Literal::CreateR0<int64>(1).get()})));
 
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
@@ -1369,9 +1368,9 @@ class WhileBufferAssignmentTest : public HloTestBase {
     builder.AddInstruction(
         HloInstruction::CreateParameter(0, loop_state_shape_, "loop_state"));
     auto zero = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int>(0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int>(0)));
     auto ten = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int>(10)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int>(10)));
     builder.AddInstruction(HloInstruction::CreateBinary(
         ShapeUtil::MakeShape(PRED, {}), HloOpcode::kLt, zero, ten));
     return builder.Build();
@@ -1429,7 +1428,7 @@ TEST_F(WhileBufferAssignmentTest, TwoForwardWhileLoops) {
       HloInstruction::CreateParameter(2, data_shape_, "weights1"));
 
   auto zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0)));
   auto output0 = builder.AddInstruction(
       HloInstruction::CreateBroadcast(data_shape_, zero, {1}));
   auto output1 = builder.AddInstruction(
@@ -1484,7 +1483,7 @@ TEST_F(WhileBufferAssignmentTest, OneForwardBackwardWhileLoopSet) {
       HloInstruction::CreateParameter(1, data_shape_, "weights0"));
 
   auto zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0)));
   auto output0 = builder.AddInstruction(
       HloInstruction::CreateBroadcast(data_shape_, zero, {1}));
   auto output1 = builder.AddInstruction(
@@ -1532,16 +1531,16 @@ TEST_F(BufferAssignmentTest, TwoCalls) {
     auto param = builder.AddInstruction(
         HloInstruction::CreateParameter(0, r0f32, "param"));
     auto constant1 = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
     auto add = builder.AddInstruction(
         HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param, constant1));
     sub_computation = module->AddEmbeddedComputation(builder.Build(add));
   }
   auto builder = HloComputation::Builder(TestName());
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto call1 = builder.AddInstruction(
       HloInstruction::CreateCall(r0f32, {constant2}, sub_computation));
   auto call2 = builder.AddInstruction(
@@ -1593,9 +1592,9 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) {
   auto builder = HloComputation::Builder(TestName());
 
   auto zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0)));
   auto one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
 
   auto input0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, data_shape_, "input0"));
@@ -1675,7 +1674,7 @@ TEST_F(WhileBufferAssignmentTest, DISABLED_TwoWhiles) {
       HloInstruction::CreateParameter(1, data_shape_, "weights0"));
 
   auto zero = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0)));
   auto output0 = builder.AddInstruction(
       HloInstruction::CreateBroadcast(data_shape_, zero, {1}));
 
diff --git a/tensorflow/compiler/xla/service/buffer_liveness_test.cc b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
index c5c24e2d48..a31e9b1782 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
@@ -397,13 +397,11 @@ TEST_F(BufferLivenessTest, TupleConstantLiveOut) {
   // computation. The buffer containing {0, 1} is copied by GetTupleElement, and
   // the buffers containing {3} and 3 are dead.
   auto builder = HloComputation::Builder(TestName());
-  auto inner_tuple0 =
-      LiteralUtil::MakeTuple({LiteralUtil::CreateR0<int64>(0).get(),
-                              LiteralUtil::CreateR0<int64>(1).get()});
-  auto inner_tuple1 =
-      LiteralUtil::MakeTuple({LiteralUtil::CreateR0<int64>(3).get()});
+  auto inner_tuple0 = Literal::MakeTuple(
+      {Literal::CreateR0<int64>(0).get(), Literal::CreateR0<int64>(1).get()});
+  auto inner_tuple1 = Literal::MakeTuple({Literal::CreateR0<int64>(3).get()});
   auto tuple_constant = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::MakeTuple({inner_tuple0.get(), inner_tuple1.get()})));
+      Literal::MakeTuple({inner_tuple0.get(), inner_tuple1.get()})));
   builder.AddInstruction(HloInstruction::CreateGetTupleElement(
       inner_tuple0->shape(), tuple_constant, 0));
 
@@ -450,7 +448,7 @@ TEST_F(BufferLivenessTest, IndependentTupleElements) {
       builder.AddInstruction(HloInstruction::CreateGetTupleElement(
           tuple_element0_shape, tuple_param0, 0));
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
+      Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
   auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
       tuple_element0_shape, HloOpcode::kAdd, tuple_element0, const0));
 
@@ -462,7 +460,7 @@ TEST_F(BufferLivenessTest, IndependentTupleElements) {
       builder.AddInstruction(HloInstruction::CreateGetTupleElement(
           tuple_element1_shape, tuple_param0, 1));
   auto const1 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f})));
+      Literal::CreateR1<float>({2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f})));
   auto add1 = builder.AddInstruction(HloInstruction::CreateBinary(
       tuple_element1_shape, HloOpcode::kAdd, tuple_element1, const1));
 
@@ -513,7 +511,7 @@ TEST_F(BufferLivenessTest, DependentTupleElements) {
       builder.AddInstruction(HloInstruction::CreateGetTupleElement(
           tuple_element0_shape, tuple_param0, 0));
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
+      Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
   auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
       tuple_element0_shape, HloOpcode::kAdd, tuple_element0, const0));
 
@@ -585,7 +583,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
         HloInstruction::CreateGetTupleElement(data_shape, tuple_param0, 1));
 
     auto update = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR1<float>({2.f, 2.f, 2.f})));
+        Literal::CreateR1<float>({2.f, 2.f, 2.f})));
     HloInstruction* slice = nullptr;
     if (update_uses_tuple_element1) {
       // Create a slice instruction as an additional user of 'gte1'.
@@ -596,7 +594,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
     }
     // Create a DynamicUpdateSlice instruction of tuple element 1 with 'update'.
     auto starts = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<int32>({2})));
+        HloInstruction::CreateConstant(Literal::CreateR1<int32>({2})));
     auto dynamic_update_slice =
         builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice(
             data_shape, gte1, update, starts));
@@ -715,7 +713,7 @@ class DynamicUpdateSliceLivenessTest : public BufferLivenessTest {
         HloInstruction::CreateGetTupleElement(data_shape, tuple_param0, 1));
 
     auto update = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR1<float>({2.f, 2.f, 2.f})));
+        Literal::CreateR1<float>({2.f, 2.f, 2.f})));
 
     if (tuple_element1_has_two_uses) {
       // Add 'gte0' and 'gte1' to create another user of 'gte1'.
@@ -724,7 +722,7 @@ class DynamicUpdateSliceLivenessTest : public BufferLivenessTest {
     }
     // Create a DynamicUpdateSlice instruction of tuple element 1 with 'update'.
     auto starts = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<int32>({2})));
+        HloInstruction::CreateConstant(Literal::CreateR1<int32>({2})));
     auto dynamic_update_slice =
         builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice(
             data_shape, gte1, update, starts));
diff --git a/tensorflow/compiler/xla/service/call_graph_test.cc b/tensorflow/compiler/xla/service/call_graph_test.cc
index e276473c90..bbf67c9803 100644
--- a/tensorflow/compiler/xla/service/call_graph_test.cc
+++ b/tensorflow/compiler/xla/service/call_graph_test.cc
@@ -81,7 +81,7 @@ class CallGraphTest : public HloTestBase {
     HloInstruction* param0 = builder.AddInstruction(
         HloInstruction::CreateParameter(0, kScalarShape, "param0"));
     HloInstruction* zero = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
     builder.AddInstruction(HloInstruction::CreateBinary(
         ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, param0, zero));
     return builder.Build();
diff --git a/tensorflow/compiler/xla/service/copy_insertion_test.cc b/tensorflow/compiler/xla/service/copy_insertion_test.cc
index cc77339bb6..026be75757 100644
--- a/tensorflow/compiler/xla/service/copy_insertion_test.cc
+++ b/tensorflow/compiler/xla/service/copy_insertion_test.cc
@@ -87,7 +87,7 @@ TEST_F(CopyInsertionTest, SingleParameter) {
 TEST_F(CopyInsertionTest, SingleConstant) {
   auto builder = HloComputation::Builder(TestName());
   HloInstruction* constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   HloInstruction* tuple =
       builder.AddInstruction(HloInstruction::CreateTuple({constant}));
 
@@ -110,9 +110,9 @@ TEST_F(CopyInsertionTest, MultipleConstantsAndParameters) {
   auto builder = HloComputation::Builder(TestName());
 
   HloInstruction* constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   HloInstruction* constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
 
   HloInstruction* x = builder.AddInstruction(
       HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(F32, {}), "x"));
@@ -140,11 +140,11 @@ TEST_F(CopyInsertionTest, AmbiguousPointsToSet) {
   // the computation result. Verify that copies are added properly.
   auto builder = HloComputation::Builder(TestName());
   HloInstruction* constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   HloInstruction* constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   HloInstruction* constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
 
   HloInstruction* tuple1 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
@@ -152,7 +152,7 @@ TEST_F(CopyInsertionTest, AmbiguousPointsToSet) {
       HloInstruction::CreateTuple({constant3, constant2}));
 
   HloInstruction* pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   builder.AddInstruction(HloInstruction::CreateTernary(
       tuple1->shape(), HloOpcode::kSelect, pred, tuple1, tuple2));
 
@@ -196,9 +196,8 @@ TEST_F(CopyInsertionTest, BitcastConstant) {
   // The output of a bitcast is its operand (same buffer), so a bitcast
   // constant feeding the result must have a copy added.
   auto builder = HloComputation::Builder(TestName());
-  HloInstruction* constant =
-      builder.AddInstruction(HloInstruction::CreateConstant(
-          LiteralUtil::CreateR1<float>({1.0, 42.0})));
+  HloInstruction* constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({1.0, 42.0})));
   HloInstruction* bitcast = builder.AddInstruction(HloInstruction::CreateUnary(
       ShapeUtil::MakeShape(F32, {2, 2}), HloOpcode::kBitcast, constant));
 
@@ -308,9 +307,9 @@ TEST_F(CopyInsertionTest, AmbiguousTopLevelRoot) {
   // copy is added.
   auto builder = HloComputation::Builder(TestName());
   HloInstruction* constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   HloInstruction* constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
 
   HloInstruction* tuple1 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
@@ -318,7 +317,7 @@ TEST_F(CopyInsertionTest, AmbiguousTopLevelRoot) {
       HloInstruction::CreateTuple({constant2, constant1}));
 
   HloInstruction* pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloInstruction* select = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple1->shape(), HloOpcode::kSelect, pred, tuple1, tuple2));
   HloInstruction* gte =
@@ -350,7 +349,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
       bool nested = false) {
     auto builder = HloComputation::Builder(TestName() + ".Condition");
     auto limit_const = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(10)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(10)));
     const Shape& loop_state_shape =
         nested ? nested_loop_state_shape_ : loop_state_shape_;
     auto loop_state = builder.AddInstruction(
@@ -381,7 +380,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
         builder.AddInstruction(HloInstruction::CreateGetTupleElement(
             induction_variable_shape_, loop_state, 0));
     auto inc = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(1)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(1)));
     auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
         induction_variable->shape(), HloOpcode::kAdd, induction_variable, inc));
     // Update data GTE(1).
@@ -419,7 +418,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
         builder.AddInstruction(HloInstruction::CreateGetTupleElement(
             induction_variable_shape_, loop_state, 0));
     auto inc = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(1)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(1)));
 
     // add0 = Add(in0, 1)
     auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
@@ -488,7 +487,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
         builder.AddInstruction(HloInstruction::CreateGetTupleElement(
             induction_variable_shape_, loop_state, 0));
     auto inc = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(1)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(1)));
     // add0 = Add(in0, 1)
     auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
         induction_variable->shape(), HloOpcode::kAdd, induction_variable, inc));
@@ -503,9 +502,8 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
       data = builder.AddInstruction(
           HloInstruction::CreateGetTupleElement(data_shape_, loop_state, 1));
     }
-    auto update = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(
-            {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
+    auto update = builder.AddInstruction(HloInstruction::CreateConstant(
+        Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
     // add1 = Add(in1, {1, 1, 1, 1, 1, 1, 1, 1})
     auto add1 = builder.AddInstruction(HloInstruction::CreateBinary(
         data_shape_, HloOpcode::kAdd, data, update));
@@ -538,7 +536,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
     auto gte0 = builder.AddInstruction(HloInstruction::CreateGetTupleElement(
         induction_variable_shape_, loop_state, 0));
     auto inc = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(1)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(1)));
     auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
         gte0->shape(), HloOpcode::kAdd, gte0, inc));
 
@@ -548,9 +546,8 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
     // GTE(GTE(loop_state, 1), 0) -> Add
     auto gte10 = builder.AddInstruction(
         HloInstruction::CreateGetTupleElement(data_shape_, gte1, 0));
-    auto update10 = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(
-            {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
+    auto update10 = builder.AddInstruction(HloInstruction::CreateConstant(
+        Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
     auto add10 = builder.AddInstruction(HloInstruction::CreateBinary(
         data_shape_, HloOpcode::kAdd, gte10, update10));
 
@@ -574,11 +571,10 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
                                         bool nested = false) {
     auto builder = HloComputation::Builder(TestName() + ".While");
     auto induction_var_init = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(0)));
 
-    auto data_init = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(
-            {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f})));
+    auto data_init = builder.AddInstruction(HloInstruction::CreateConstant(
+        Literal::CreateR1<float>({0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f})));
 
     if (nested) {
       auto inner_init = builder.AddInstruction(
@@ -601,9 +597,8 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
 
   HloInstruction* BuildWhileInstruction_InitPointsToConstant() {
     auto builder = HloComputation::Builder(TestName() + ".While");
-    auto data_init = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(
-            {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f})));
+    auto data_init = builder.AddInstruction(HloInstruction::CreateConstant(
+        Literal::CreateR1<float>({0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f})));
     return BuildWhileInstructionWithCustomInit(loop_state_shape_, data_init,
                                                &builder);
   }
@@ -620,11 +615,11 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
     auto builder = HloComputation::Builder(TestName() + ".While");
 
     auto one = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
     auto v1 = builder.AddInstruction(
         HloInstruction::CreateBroadcast(data_shape_, one, {1}));
     auto zero = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
     auto v2 = builder.AddInstruction(
         HloInstruction::CreateBroadcast(data_shape_, zero, {1}));
 
@@ -632,7 +627,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
     auto tuple2 = builder.AddInstruction(HloInstruction::CreateTuple({v2, v1}));
 
     auto pred = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+        HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
     auto data_init = builder.AddInstruction(HloInstruction::CreateTernary(
         nested_tuple_shape_, HloOpcode::kSelect, pred, tuple1, tuple2));
 
@@ -644,7 +639,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
     auto builder = HloComputation::Builder(TestName() + ".While");
 
     auto one = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
     auto one_vec = builder.AddInstruction(
         HloInstruction::CreateBroadcast(data_shape_, one, {1}));
     auto data_init =
@@ -657,12 +652,11 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
   HloInstruction* BuildWhileInstruction_InitPointsToInterfering() {
     auto builder = HloComputation::Builder(TestName() + ".While");
     auto one = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
     auto data_init = builder.AddInstruction(
         HloInstruction::CreateBroadcast(data_shape_, one, {1}));
-    auto one_vec = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(
-            {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
+    auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant(
+        Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f})));
     // Take a reference to 'data_init' to make it interfere with while result.
     builder.AddInstruction(HloInstruction::CreateBinary(
         data_shape_, HloOpcode::kAdd, data_init, one_vec));
@@ -677,7 +671,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest {
     const bool nested =
         ShapeUtil::Equal(loop_state_shape, nested_loop_state_shape_);
     auto induction_var_init = builder->AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(0)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(0)));
     auto condition =
         module_->AddEmbeddedComputation(BuildConditionComputation(nested));
     auto body = module_->AddEmbeddedComputation(
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc
index f5ad431277..ec992f15e6 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc
@@ -59,11 +59,11 @@ TEST_F(ConvCanonicalizationTest, NonCanonicalToCanonical) {
   auto builder = HloComputation::Builder(TestName());
   // The input dimensions are in CNHW order.
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR4FromArray4D(Array4D<float>(
+      Literal::CreateR4FromArray4D(Array4D<float>(
           kInputFeatureCount, kBatchSize, kInputSize, kInputSize))));
   // The kernel dimensions are in OIHW order.
   auto kernel = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR4FromArray4D(Array4D<float>(
+      Literal::CreateR4FromArray4D(Array4D<float>(
           kOutputFeatureCount, kInputFeatureCount, kWindowSize, kWindowSize))));
 
   ConvolutionDimensionNumbers dnums;
@@ -113,11 +113,11 @@ TEST_F(ConvCanonicalizationTest, CanonicalStaysTheSame) {
   auto builder = HloComputation::Builder(TestName());
   // The input dimensions are in NHWC order.
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR4FromArray4D(Array4D<float>(
+      Literal::CreateR4FromArray4D(Array4D<float>(
           kBatchSize, kInputSize, kInputSize, kInputFeatureCount))));
   // The kernel dimensions are in HWIO order.
   auto kernel = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR4FromArray4D(Array4D<float>(
+      Literal::CreateR4FromArray4D(Array4D<float>(
           kWindowSize, kWindowSize, kInputFeatureCount, kOutputFeatureCount))));
 
   ConvolutionDimensionNumbers dnums;
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 9c3463d103..4a73bda05e 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -406,7 +406,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
       if (instruction->opcode() == HloOpcode::kConstant) {
         // Copy the constant out of the ProtocolBuffer so that we can give it a
         // higher alignment.
-        const void* data = LiteralUtil::InternalData(instruction->literal());
+        const void* data = instruction->literal().InternalData();
         int64 size = CpuExecutable::ShapeSizeBytes(instruction->shape());
         auto iter = aligned_constants.emplace(
             instruction, MakeUnique<unsigned char[]>(size));
diff --git a/tensorflow/compiler/xla/service/cpu/sample_harness.cc b/tensorflow/compiler/xla/service/cpu/sample_harness.cc
index 8f1ce82d49..b3f4609d46 100644
--- a/tensorflow/compiler/xla/service/cpu/sample_harness.cc
+++ b/tensorflow/compiler/xla/service/cpu/sample_harness.cc
@@ -38,13 +38,12 @@ int main(int argc, char** argv) {
 
   // Transfer parameters.
   std::unique_ptr<xla::Literal> param0_literal =
-      xla::LiteralUtil::CreateR1<float>({1.1f, 2.2f, 3.3f, 5.5f});
+      xla::Literal::CreateR1<float>({1.1f, 2.2f, 3.3f, 5.5f});
   std::unique_ptr<xla::GlobalData> param0_data =
       client->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
-  std::unique_ptr<xla::Literal> param1_literal =
-      xla::LiteralUtil::CreateR2<float>(
-          {{3.1f, 4.2f, 7.3f, 9.5f}, {1.1f, 2.2f, 3.3f, 4.4f}});
+  std::unique_ptr<xla::Literal> param1_literal = xla::Literal::CreateR2<float>(
+      {{3.1f, 4.2f, 7.3f, 9.5f}, {1.1f, 2.2f, 3.3f, 4.4f}});
   std::unique_ptr<xla::GlobalData> param1_data =
       client->TransferToServer(*param1_literal).ConsumeValueOrDie();
 
@@ -69,7 +68,7 @@ int main(int argc, char** argv) {
 
   LOG(INFO) << tensorflow::strings::Printf("computation took %lldns",
                                            profile.compute_time_ns());
-  LOG(INFO) << xla::LiteralUtil::ToString(*actual);
+  LOG(INFO) << actual->ToString();
 
   return 0;
 }
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
index 5e431687c4..1d553cab1a 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
@@ -81,8 +81,7 @@ Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
                          ShapeUtil::HumanString(literal.shape()).c_str(), size);
   }
 
-  return TransferBufferToInfeed(executor, size,
-                                LiteralUtil::InternalData(literal));
+  return TransferBufferToInfeed(executor, size, literal.InternalData());
 }
 
 Status CpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor,
diff --git a/tensorflow/compiler/xla/service/flatten_call_graph_test.cc b/tensorflow/compiler/xla/service/flatten_call_graph_test.cc
index bb4712c86f..a08506d84d 100644
--- a/tensorflow/compiler/xla/service/flatten_call_graph_test.cc
+++ b/tensorflow/compiler/xla/service/flatten_call_graph_test.cc
@@ -80,7 +80,7 @@ class FlattenCallGraphTest : public HloTestBase {
     HloInstruction* param0 = builder.AddInstruction(
         HloInstruction::CreateParameter(0, kScalarShape, "param0"));
     HloInstruction* zero = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
     builder.AddInstruction(HloInstruction::CreateBinary(
         ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, param0, zero));
     return builder.Build();
@@ -157,7 +157,7 @@ TEST_F(FlattenCallGraphTest, SharedWhileConditionAndBody) {
         builder.AddInstruction(HloInstruction::CreateParameter(
             0, ShapeUtil::MakeShape(PRED, {}), "param0"));
     HloInstruction* false_constant = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+        HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
     builder.AddInstruction(
         HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}),
                                      HloOpcode::kEq, param0, false_constant));
@@ -168,7 +168,7 @@ TEST_F(FlattenCallGraphTest, SharedWhileConditionAndBody) {
   {
     HloComputation::Builder builder(TestName() + ".entry");
     HloInstruction* false_constant = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+        HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
     builder.AddInstruction(HloInstruction::CreateWhile(
         ShapeUtil::MakeShape(PRED, {}), cond_computation, cond_computation,
         false_constant));
diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
index b1c7eadf6a..476b2b8d6f 100644
--- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
@@ -82,13 +82,12 @@ Status GenericTransferManager::TransferLiteralFromDevice(
   }
 
   *literal->mutable_shape() = device_shape;
-  LiteralUtil::Reserve(ShapeUtil::ElementsIn(device_shape), literal);
+  literal->Reserve(ShapeUtil::ElementsIn(device_shape));
   TF_RETURN_IF_ERROR(TransferBufferFromDevice(
       executor, source, /*size=*/ShapeUtil::ByteSizeOf(device_shape),
-      /*destination=*/LiteralUtil::MutableInternalData(literal)));
+      /*destination=*/literal->MutableInternalData()));
   if (!ShapeUtil::Equal(literal_shape, device_shape)) {
-    literal->Swap(
-        LiteralUtil::Relayout(*literal, literal_shape.layout()).get());
+    literal->Swap(literal->Relayout(literal_shape.layout()).get());
   }
   TF_RET_CHECK(ShapeUtil::Equal(literal_shape, literal->shape()));
   return Status::OK();
@@ -152,9 +151,9 @@ Status GenericTransferManager::TransferLiteralToDevice(
         tuple_elements_on_device.data(), destination);
   }
 
-  return TransferBufferToDevice(
-      executor, /*size=*/GetByteSizeRequirement(shape),
-      /*source=*/LiteralUtil::InternalData(literal), destination);
+  return TransferBufferToDevice(executor,
+                                /*size=*/GetByteSizeRequirement(shape),
+                                /*source=*/literal.InternalData(), destination);
 }
 
 Status GenericTransferManager::TransferLiteralToInfeed(
diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
index 2987c8913d..c2dec7ed6a 100644
--- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
@@ -55,7 +55,7 @@ using tensorflow::strings::StrAppend;
 // Returns whether operand is a floating-point literal with the given value.
 bool IsFPLiteralWithValue(const HloInstruction* operand, float value) {
   return operand->opcode() == HloOpcode::kConstant &&
-         LiteralUtil::IsAllFloat(operand->literal(), value);
+         operand->literal().IsAllFloat(value);
 }
 
 GpuElementalIrEmitter::GpuElementalIrEmitter(
diff --git a/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc b/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc
index 8afc32dea9..242c32936d 100644
--- a/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc
+++ b/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc
@@ -59,7 +59,7 @@ class FusionMergerTest : public HloTestBase {
 
     // Create const vector of ones to be used in element-wise computations.
     auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
+        Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
 
     // Create simple fusable computation for tuple element 0 (wont get merged).
     auto out0 = builder.AddInstruction(HloInstruction::CreateBinary(
@@ -138,7 +138,7 @@ class FusionMergerTest : public HloTestBase {
 
     // Create two sub-computations, both of which are users of 'mul0'.
     auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
+        Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
 
     // First sub-computation: out0 = Mul(Add(mul0, one_vec), one_vec)
     auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
@@ -209,7 +209,7 @@ class FusionMergerTest : public HloTestBase {
     // Create two fusable sub-computations which are dependent on shared
     // computation 'reduce_out'.
     auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
+        Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
 
     // First sub-computation: out0 = Mul(Add(reduce_out, one_vec), one_vec)
     auto add2 = builder.AddInstruction(HloInstruction::CreateBinary(
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
index e44b645342..ab04d1736e 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
@@ -1648,7 +1648,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildCopyThunk(
   const HloInstruction* operand = inst->operand(0);
   CHECK_EQ(HloOpcode::kConstant, operand->opcode());
   return MakeUnique<CopyThunk>(
-      /*source_address=*/LiteralUtil::InternalData(operand->literal()),
+      /*source_address=*/operand->literal().InternalData(),
       /*destination_buffer=*/GetAllocationSlice(*inst),
       /*mem_size=*/
       llvm_ir::ByteSizeOf(operand->shape(),
diff --git a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
index c645e84aa4..4e130de311 100644
--- a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
+++ b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
@@ -61,7 +61,7 @@ HloInstruction* MaybePaddedAndSlicedInput(
     PrimitiveType element_type = input->shape().element_type();
     HloInstruction* padding =
         computation->AddInstruction(HloInstruction::CreateConstant(
-            MakeUnique<Literal>(LiteralUtil::Zero(element_type))));
+            MakeUnique<Literal>(Literal::Zero(element_type))));
     input = computation->AddInstruction(HloInstruction::CreatePad(
         ShapeInference::InferPadShape(
             /*operand_shape=*/input->shape(),
@@ -126,7 +126,7 @@ HloInstruction* MaybePaddedKernel(const Window& conv_window,
   PrimitiveType element_type = kernel->shape().element_type();
   HloInstruction* padding =
       computation->AddInstruction(HloInstruction::CreateConstant(
-          MakeUnique<Literal>(LiteralUtil::Zero(element_type))));
+          MakeUnique<Literal>(Literal::Zero(element_type))));
   return computation->AddInstruction(HloInstruction::CreatePad(
       ShapeInference::InferPadShape(
           /*operand_shape=*/kernel->shape(),
@@ -241,9 +241,9 @@ bool PadInsertion::CanonicalizeBackwardFilterConvolution(
   // Create a new backward convolution replacing the old one.
   HloComputation* computation = backward_conv->parent();
   HloInstruction* output = backward_conv->mutable_operand(1);
-  HloInstruction* padding = computation->AddInstruction(
-      HloInstruction::CreateConstant(MakeUnique<Literal>(
-          LiteralUtil::Zero(input->shape().element_type()))));
+  HloInstruction* padding =
+      computation->AddInstruction(HloInstruction::CreateConstant(
+          MakeUnique<Literal>(Literal::Zero(input->shape().element_type()))));
   HloInstruction* padded_input =
       computation->AddInstruction(HloInstruction::CreatePad(
           ShapeInference::InferPadShape(input->shape(), padding->shape(),
diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer.cc b/tensorflow/compiler/xla/service/gpu/while_transformer.cc
index 06b01d311d..3034ed06b7 100644
--- a/tensorflow/compiler/xla/service/gpu/while_transformer.cc
+++ b/tensorflow/compiler/xla/service/gpu/while_transformer.cc
@@ -37,8 +37,8 @@ namespace {
 // patterns to match.
 //
 // Each ExprTree node is comprised of an HloOpcode, and a set of operands (each
-// of type ExprTree). Operands can be added by specifying the index and HloOpcode
-// of the operand.
+// of type ExprTree). Operands can be added by specifying the index and
+// HloOpcode of the operand.
 //
 // For example, the following computation:
 //
@@ -197,10 +197,9 @@ class MatcherBase {
       return InvalidArgument("Must use S32 or S64 integral types.");
     }
     if (type == S32) {
-      *const_value =
-          static_cast<int64>(LiteralUtil::GetFirstElement<int32>(literal));
+      *const_value = static_cast<int64>(literal.GetFirstElement<int32>());
     } else if (type == S64) {
-      *const_value = LiteralUtil::GetFirstElement<int64>(literal);
+      *const_value = literal.GetFirstElement<int64>();
     }
     return tensorflow::Status::OK();
   }
diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc b/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc
index e82491fd6f..51d38f8421 100644
--- a/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc
+++ b/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc
@@ -41,7 +41,7 @@ class WhileTransformerTest : public HloTestBase {
       const int64 tuple_index, const int64 limit) {
     auto builder = HloComputation::Builder(TestName() + ".Condition");
     auto limit_const = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(limit)));
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(limit)));
     auto loop_state = builder.AddInstruction(
         HloInstruction::CreateParameter(0, loop_state_shape_, "loop_state"));
     auto induction_variable =
@@ -64,8 +64,8 @@ class WhileTransformerTest : public HloTestBase {
     auto induction_variable =
         builder.AddInstruction(HloInstruction::CreateGetTupleElement(
             induction_variable_shape_, loop_state, ind_var_tuple_index));
-    auto inc = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR0<int32>(increment)));
+    auto inc = builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(increment)));
     auto add0 = builder.AddInstruction(HloInstruction::CreateBinary(
         induction_variable->shape(), HloOpcode::kAdd, induction_variable, inc));
     // Update data GTE(data_tuple_index).
@@ -88,12 +88,10 @@ class WhileTransformerTest : public HloTestBase {
                                         const int64 ind_var_tuple_index,
                                         const int64 ind_var_init) {
     auto builder = HloComputation::Builder(TestName() + ".While");
-    auto induction_var_init =
-        builder.AddInstruction(HloInstruction::CreateConstant(
-            LiteralUtil::CreateR0<int32>(ind_var_init)));
-    auto data_init = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(
-            {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f})));
+    auto induction_var_init = builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(ind_var_init)));
+    auto data_init = builder.AddInstruction(HloInstruction::CreateConstant(
+        Literal::CreateR1<float>({0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f})));
     auto loop_state_init =
         ind_var_tuple_index == 0
             ? builder.AddInstruction(
diff --git a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
index 3e4e590d3b..cd1b182b22 100644
--- a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
@@ -113,8 +113,7 @@ GpuTransferManager::TransferLiteralToInfeedInternal(
                          ShapeUtil::HumanString(literal.shape()).c_str());
   }
 
-  return TransferBufferToInfeedInternal(executor, size,
-                                        LiteralUtil::InternalData(literal));
+  return TransferBufferToInfeedInternal(executor, size, literal.InternalData());
 }
 
 StatusOr<gpu::InfeedBuffer*> GpuTransferManager::TransferBufferToInfeedInternal(
diff --git a/tensorflow/compiler/xla/service/graphviz_example.cc b/tensorflow/compiler/xla/service/graphviz_example.cc
index cd00a41a03..d194b3a310 100644
--- a/tensorflow/compiler/xla/service/graphviz_example.cc
+++ b/tensorflow/compiler/xla/service/graphviz_example.cc
@@ -47,7 +47,7 @@ HloComputation* AddScalarConstantComputation(int64 addend, HloModule* module) {
   auto x_value = builder.AddInstruction(HloInstruction::CreateParameter(
       0, ShapeUtil::MakeShape(F32, {}), "x_value"));
   auto half = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.5)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.5)));
   builder.AddInstruction(HloInstruction::CreateBinary(
       half->shape(), HloOpcode::kAdd, x_value, half));
   return module->AddEmbeddedComputation(builder.Build());
@@ -118,7 +118,7 @@ std::unique_ptr<HloModule> MakeBigGraph() {
   auto rng = builder.AddInstruction(
       HloInstruction::CreateRng(vshape, RNG_UNIFORM, {param_m, param_m}));
   auto one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto add_computation = ScalarSumComputation(module.get());
   builder.AddInstruction(
       HloInstruction::CreateReduce(vshape, rng, one, {1}, add_computation));
diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc
index 60a0768a86..fefc4c6a0f 100644
--- a/tensorflow/compiler/xla/service/heap_simulator_test.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc
@@ -173,7 +173,7 @@ class HeapSimulatorTest : public HloTestBase {
 TEST_F(HeapSimulatorTest, ScalarConstant) {
   auto builder = HloComputation::Builder(TestName());
   auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
 
   // Constants aren't assigned.  See b/32248867
   HeapSimulatorTracker tracker(TestName(), builder.Build(), {const0});
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
index 24c467d411..a869689b46 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
@@ -87,9 +87,9 @@ TEST_F(HloAliasAnalysisTest, BinaryOperation) {
   // Test the analysis on a single binary operation (Add).
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto add = builder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, constant1, constant2));
   module_.AddEntryComputation(builder.Build());
@@ -196,9 +196,9 @@ TEST_F(HloAliasAnalysisTest, SingleCall) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, called_computation));
   module_.AddEntryComputation(builder.Build());
@@ -233,9 +233,9 @@ TEST_F(HloAliasAnalysisTest, ComputationCalledTwice) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call1 = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, called_computation));
   auto call2 = builder.AddInstruction(HloInstruction::CreateCall(
@@ -310,15 +310,15 @@ TEST_F(HloAliasAnalysisTest, SingleWhile) {
   auto cond_param = cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while = builder.AddInstruction(
@@ -398,15 +398,15 @@ TEST_F(HloAliasAnalysisTest, SequentialWhiles) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while0 = builder.AddInstruction(
@@ -453,7 +453,7 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
@@ -491,9 +491,9 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto entry_while = builder.AddInstruction(
@@ -554,17 +554,17 @@ TEST_F(HloAliasAnalysisTest, SwizzlingWhile) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   auto cond_constant = cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2, constant3}));
   auto xla_while = builder.AddInstruction(
@@ -600,15 +600,15 @@ TEST_F(HloAliasAnalysisTest, TupleSelect) {
   // instruction.
   auto builder = HloComputation::Builder(TestName());
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto constant4 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(4.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(4.0)));
   auto tuple1 =
       builder.AddInstruction(HloInstruction::CreateTuple({constant1}));
   auto tuple2 =
@@ -694,16 +694,16 @@ TEST_F(HloAliasAnalysisTest, TupleSelectToWhile) {
   auto cond_param = cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple1 =
       builder.AddInstruction(HloInstruction::CreateTuple({constant1}));
   auto tuple2 =
@@ -742,7 +742,7 @@ TEST_F(HloAliasAnalysisTest, Bitcast) {
   // Bitcasting a value should not produce a new buffer.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary(
       scalar_shape_, HloOpcode::kBitcast, constant));
 
diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc
index 5d49c83e2d..057d1ce09b 100644
--- a/tensorflow/compiler/xla/service/hlo_computation_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc
@@ -110,7 +110,7 @@ TEST_F(HloComputationTest, PostOrderSingleton) {
   // Test GetInstructionPostOrder for a computation with one instruction.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto computation = builder.Build();
 
   EXPECT_THAT(computation->MakeInstructionPostOrder(), ElementsAre(constant));
@@ -121,7 +121,7 @@ TEST_F(HloComputationTest, PostOrderSimple) {
   // instructions.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto negate1 = builder.AddInstruction(
       HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant));
   auto negate2 = builder.AddInstruction(
@@ -136,7 +136,7 @@ TEST_F(HloComputationTest, PostOrderTrace) {
   // Test GetInstructionPostOrder for a computation with a trace instruction.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto negate1 = builder.AddInstruction(
       HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant));
   auto trace =
@@ -155,13 +155,13 @@ TEST_F(HloComputationTest, PostOrderDisconnectedInstructions) {
   // which are not connected.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant4 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto computation = builder.Build();
 
   EXPECT_THAT(computation->MakeInstructionPostOrder(),
@@ -173,11 +173,11 @@ TEST_F(HloComputationTest, PostOrderWithMultipleRoots) {
   // which are not connected.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto add1 = builder.AddInstruction(HloInstruction::CreateBinary(
       r0f32_, HloOpcode::kAdd, constant1, constant2));
   auto add2 = builder.AddInstruction(HloInstruction::CreateBinary(
@@ -197,11 +197,11 @@ TEST_F(HloComputationTest, VisitWithMultipleRoots) {
   // computation has multiple roots (dead code).
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   // Add three disconnected add expressions.
   builder.AddInstruction(HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd,
                                                       constant1, constant2));
@@ -248,7 +248,7 @@ TEST_F(HloComputationTest, DeepCopyArray) {
   // Test that DeepCopyInstruction properly copies an array.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.0, 2.0, 3.0})));
+      Literal::CreateR1<float>({1.0, 2.0, 3.0})));
   auto computation = builder.Build();
 
   auto copy = computation->DeepCopyInstruction(constant).ValueOrDie();
@@ -260,9 +260,9 @@ TEST_F(HloComputationTest, DeepCopyTuple) {
   // Test that DeepCopyInstruction properly copies a tuple.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.0, 2.0, 3.0})));
+      Literal::CreateR1<float>({1.0, 2.0, 3.0})));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
 
@@ -280,7 +280,7 @@ TEST_F(HloComputationTest, CycleDetection) {
   // Test whether the visitor can detect cycles in the graph.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant));
   auto add = builder.AddInstruction(
@@ -303,7 +303,7 @@ TEST_F(HloComputationTest, RemoveInstructionWithDuplicateOperand) {
   // twice.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto dead_negate = builder.AddInstruction(
       HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant));
   auto dead_add = builder.AddInstruction(HloInstruction::CreateBinary(
@@ -326,9 +326,9 @@ TEST_F(HloComputationTest, RemoveInstructionWithDuplicateOperand) {
 TEST_F(HloComputationTest, CloneWithControlDependency) {
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0f)));
   auto add = builder.AddInstruction(HloInstruction::CreateBinary(
       r0f32_, HloOpcode::kAdd, constant1, constant2));
 
diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
index 04ab02995b..a643bc4076 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
@@ -41,7 +41,7 @@ using HloConstantFoldingTest = HloTestBase;
 TEST_F(HloConstantFoldingTest, ConvertF32ToS64) {
   HloComputation::Builder builder(TestName());
   HloInstruction* input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   builder.AddInstruction(
       HloInstruction::CreateConvert(ShapeUtil::MakeShape(S64, {}), input));
 
@@ -55,15 +55,14 @@ TEST_F(HloConstantFoldingTest, ConvertF32ToS64) {
   EXPECT_TRUE(result);
 
   EXPECT_THAT(computation->root_instruction(), op::Constant());
-  EXPECT_EQ(LiteralUtil::GetFirstElement<int64>(
-                computation->root_instruction()->literal()),
+  EXPECT_EQ(computation->root_instruction()->literal().GetFirstElement<int64>(),
             42);
 }
 
 TEST_F(HloConstantFoldingTest, ConvertS64ToF32) {
   HloComputation::Builder builder(TestName());
   HloInstruction* input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int64>(42)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int64>(42)));
   builder.AddInstruction(
       HloInstruction::CreateConvert(ShapeUtil::MakeShape(F32, {}), input));
 
@@ -77,15 +76,14 @@ TEST_F(HloConstantFoldingTest, ConvertS64ToF32) {
   EXPECT_TRUE(result);
 
   EXPECT_THAT(computation->root_instruction(), op::Constant());
-  EXPECT_EQ(LiteralUtil::GetFirstElement<float>(
-                computation->root_instruction()->literal()),
+  EXPECT_EQ(computation->root_instruction()->literal().GetFirstElement<float>(),
             42.0f);
 }
 
 TEST_F(HloConstantFoldingTest, ConvertF32ArrayToS64Array) {
   HloComputation::Builder builder(TestName());
-  HloInstruction* input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({42.0f, 19.0f})));
+  HloInstruction* input = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({42.0f, 19.0f})));
   builder.AddInstruction(
       HloInstruction::CreateConvert(ShapeUtil::MakeShape(S64, {2}), input));
 
@@ -99,12 +97,8 @@ TEST_F(HloConstantFoldingTest, ConvertF32ArrayToS64Array) {
   EXPECT_TRUE(result);
 
   EXPECT_THAT(computation->root_instruction(), op::Constant());
-  EXPECT_EQ(
-      LiteralUtil::Get<int64>(computation->root_instruction()->literal(), {0}),
-      42);
-  EXPECT_EQ(
-      LiteralUtil::Get<int64>(computation->root_instruction()->literal(), {1}),
-      19);
+  EXPECT_EQ(computation->root_instruction()->literal().Get<int64>({0}), 42);
+  EXPECT_EQ(computation->root_instruction()->literal().Get<int64>({1}), 19);
 }
 
 TEST_F(HloConstantFoldingTest, Concatenate) {
@@ -126,7 +120,7 @@ TEST_F(HloConstantFoldingTest, Concatenate) {
     for (auto csize : test_config.concat_sizes) {
       dimensions[test_config.concat_dimension] = csize;
       concat_size += csize;
-      auto literal = LiteralUtil::CreateFromDimensions(F32, dimensions);
+      auto literal = Literal::CreateFromDimensions(F32, dimensions);
       HloInstruction* insn = builder.AddInstruction(
           HloInstruction::CreateConstant(std::move(literal)));
       operands.push_back(insn);
@@ -179,7 +173,7 @@ TEST_F(HloConstantFoldingTest, TransposeConstantFold) {
   TF_ASSIGN_OR_ASSERT_OK(auto literal,
                          LiteralTestUtil::CreateRandomLiteral<F32>(
                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
-  auto literal_clone = LiteralUtil::CloneToUnique(*literal);
+  auto literal_clone = literal->Literal::CloneToUnique();
   HloInstruction* literal_instruction = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
   Shape shape = ShapeUtil::MakeShape(F32, {8, 7, 11, 9, 5});
@@ -199,12 +193,10 @@ TEST_F(HloConstantFoldingTest, TransposeConstantFold) {
 
   using NativeT = typename primitive_util::PrimitiveTypeToNative<F32>::type;
   bool matched = true;
-  LiteralUtil::EachCell<NativeT>(
-      root->literal(),
+  root->literal().EachCell<NativeT>(
       [&](tensorflow::gtl::ArraySlice<int64> indices, NativeT value) {
         std::vector<int64> rindexes = Permute(permutation, indices);
-        matched = matched && (value == LiteralUtil::Get<NativeT>(*literal_clone,
-                                                                 rindexes));
+        matched = matched && (value == literal_clone->Get<NativeT>(rindexes));
       });
   EXPECT_TRUE(matched);
 }
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
index b74c7eb4e0..5c71056bb5 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
@@ -342,11 +342,11 @@ TEST_F(FusionCostAnalysis, LoopFusion) {
   //   mul = Mul(exp, C3)
   //   sub = Sub(mul, clamp)
   //   tuple = Tuple({sub, sub, mul, C1})
-  auto c1 = HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace(
+  auto c1 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
       /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2));
-  auto c2 = HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace(
+  auto c2 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
       /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2));
-  auto c3 = HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace(
+  auto c3 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
       /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2));
 
   auto add =
@@ -383,9 +383,8 @@ TEST_F(FusionCostAnalysis, NoLayout) {
   shape_without_layout.clear_layout();
 
   auto c1 = HloInstruction::CreateConstant(
-      LiteralUtil::CreateR4FromArray4D(Array4D<float>(2, 3, 4, 5)));
-  auto c2 =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({1, 2, 3}));
+      Literal::CreateR4FromArray4D(Array4D<float>(2, 3, 4, 5)));
+  auto c2 = HloInstruction::CreateConstant(Literal::CreateR1<float>({1, 2, 3}));
 
   auto broadcast =
       HloInstruction::CreateBroadcast(shape_without_layout, c2.get(), {1});
diff --git a/tensorflow/compiler/xla/service/hlo_cse.cc b/tensorflow/compiler/xla/service/hlo_cse.cc
index 4c6af5c40f..0fef89a06d 100644
--- a/tensorflow/compiler/xla/service/hlo_cse.cc
+++ b/tensorflow/compiler/xla/service/hlo_cse.cc
@@ -68,7 +68,7 @@ bool CombineConstants(HloComputation* computation, bool is_layout_sensitive) {
       auto range = constants.equal_range(shape_string);
       HloInstruction* match = nullptr;
       for (auto it = range.first; it != range.second; ++it) {
-        if (LiteralUtil::Equal(instruction->literal(), it->second->literal())) {
+        if (instruction->literal().Equal(it->second->literal())) {
           match = it->second;
           break;
         }
diff --git a/tensorflow/compiler/xla/service/hlo_cse_test.cc b/tensorflow/compiler/xla/service/hlo_cse_test.cc
index cc39c3ac20..8b0b9c8bbd 100644
--- a/tensorflow/compiler/xla/service/hlo_cse_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_cse_test.cc
@@ -51,9 +51,9 @@ TEST_F(HloCseTest, CombineTwoConstants) {
   // Test that two identical constants are commoned.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   builder.AddInstruction(HloInstruction::CreateBinary(
       constant1->shape(), HloOpcode::kAdd, constant1, constant2));
 
@@ -67,10 +67,10 @@ TEST_F(HloCseTest, CombineTwoConstants) {
 
   EXPECT_EQ(2, computation->instruction_count());
   HloInstruction* constant = computation->instructions().begin()->get();
-  EXPECT_EQ(42.0f, LiteralUtil::Get<float>(constant->literal(), {}));
+  EXPECT_EQ(42.0f, constant->literal().Get<float>({}));
 
   auto result = ExecuteAndTransfer(std::move(module), {});
-  auto expected = LiteralUtil::CreateR0<float>(84.0);
+  auto expected = Literal::CreateR0<float>(84.0);
   LiteralTestUtil::ExpectNear(*expected, *result, ErrorSpec(1e-4));
 }
 
@@ -102,7 +102,7 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndInsensitive) {
   EXPECT_THAT(add, op::Add(first_operand, first_operand));
 
   auto result = ExecuteAndTransfer(std::move(module), {});
-  auto expected = LiteralUtil::CreateR2<float>({{2.0, 4.0}, {6.0, 8.0}});
+  auto expected = Literal::CreateR2<float>({{2.0, 4.0}, {6.0, 8.0}});
   LiteralTestUtil::ExpectNear(*expected, *result, ErrorSpec(1e-4));
 }
 
@@ -132,7 +132,7 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndSensitive) {
   EXPECT_THAT(add, op::Add(constant1, constant2));
 
   auto result = ExecuteAndTransfer(std::move(module), {});
-  auto expected = LiteralUtil::CreateR2<float>({{2.0, 4.0}, {6.0, 8.0}});
+  auto expected = Literal::CreateR2<float>({{2.0, 4.0}, {6.0, 8.0}});
   LiteralTestUtil::ExpectNear(*expected, *result, ErrorSpec(1e-4));
 }
 
@@ -141,20 +141,20 @@ TEST_F(HloCseTest, ConstantsSameValueDifferentType) {
   // commoned.
   auto builder = HloComputation::Builder(TestName());
   builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<uint32>(42)));
+      HloInstruction::CreateConstant(Literal::CreateR0<uint32>(42)));
   builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(42)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(42)));
   builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<uint64>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<uint64>(42.0)));
   builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int64>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int64>(42.0)));
   builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<double>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<double>(42.0)));
   builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   // Duplicate the float constant to verify something happens.
   builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
 
   auto module = CreateNewModule();
   auto computation = module->AddEntryComputation(builder.Build());
@@ -171,13 +171,13 @@ TEST_F(HloCseTest, NonscalarConstants) {
   // Test that identical nonscalar constants are merged.
   auto builder = HloComputation::Builder(TestName());
   auto common_constant1 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
   auto common_constant2 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
   // Create a constant which has the same shape but a different value.
   auto uncommon_constant =
       builder.AddInstruction(HloInstruction::CreateConstant(
-          LiteralUtil::CreateR2<float>({{2.0, 4.0}, {6.0, 8.0}})));
+          Literal::CreateR2<float>({{2.0, 4.0}, {6.0, 8.0}})));
 
   // Tie the constants together with a tuple. This makes it easier to refer to
   // the constant instructions via their use.
@@ -206,7 +206,7 @@ TEST_F(HloCseTest, IdenticalInstructions) {
   // Test that three identical instructions are commoned.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0)));
   auto exp1 = builder.AddInstruction(HloInstruction::CreateUnary(
       constant->shape(), HloOpcode::kExp, constant));
   auto exp2 = builder.AddInstruction(HloInstruction::CreateUnary(
@@ -236,7 +236,7 @@ TEST_F(HloCseTest, IdenticalInstructionsDifferentLayoutsSensitive) {
   // commoned if the pass is layout sensitive.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
 
   auto exp1 = builder.AddInstruction(HloInstruction::CreateUnary(
       constant->shape(), HloOpcode::kExp, constant));
@@ -267,7 +267,7 @@ TEST_F(HloCseTest, IdenticalInstructionsDifferentLayoutsInsensitive) {
   // the pass is layout insensitive.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
 
   auto exp1 = builder.AddInstruction(HloInstruction::CreateUnary(
       constant->shape(), HloOpcode::kExp, constant));
@@ -311,7 +311,7 @@ TEST_F(HloCseTest, IdenticalExpressions) {
   // The *1 instructions should be merged with the *2 instructions.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0)));
 
   auto negate1 = builder.AddInstruction(HloInstruction::CreateUnary(
       constant->shape(), HloOpcode::kNegate, constant));
@@ -349,9 +349,9 @@ TEST_F(HloCseTest, DoNotCombineRng) {
   // Test that two RNG ops are not commoned.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   auto rng1 = builder.AddInstruction(HloInstruction::CreateRng(
       ShapeUtil::MakeShape(F32, {}), RandomDistribution::RNG_UNIFORM,
       {constant1, constant2}));
@@ -392,9 +392,9 @@ TEST_F(HloCseTest, DISABLED_DoNotCombineCallsToImpureFunctions) {
     Shape scalar_shape = ShapeUtil::MakeShape(F32, {});
     auto builder = HloComputation::Builder(TestName() + "_rng_fun");
     auto constant1 = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
     auto constant2 = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
     auto rng = builder.AddInstruction(HloInstruction::CreateRng(
         scalar_shape, RandomDistribution::RNG_UNIFORM, {constant1, constant2}));
     auto param = builder.AddInstruction(HloInstruction::CreateParameter(
@@ -409,7 +409,7 @@ TEST_F(HloCseTest, DISABLED_DoNotCombineCallsToImpureFunctions) {
   {
     auto builder = HloComputation::Builder(TestName());
     auto constant = builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({5.0f})));
+        HloInstruction::CreateConstant(Literal::CreateR1<float>({5.0f})));
     auto rng1 = builder.AddInstruction(
         HloInstruction::CreateMap(constant->shape(), {constant}, rng_function));
     auto rng2 = builder.AddInstruction(
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
index 21344af5f2..a97620cd0d 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
@@ -73,9 +73,9 @@ TEST_P(HloDataflowAnalysisTest, BinaryOperation) {
   // Test the dataflow for a simple binary operation (Add).
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto add = builder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, constant1, constant2));
   module_.AddEntryComputation(builder.Build());
@@ -176,9 +176,9 @@ TEST_P(HloDataflowAnalysisTest, NestedTuple) {
   //
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto nested_tuple = builder.AddInstruction(
@@ -240,9 +240,9 @@ TEST_P(HloDataflowAnalysisTest, SingleCall) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, called_computation));
   module_.AddEntryComputation(builder.Build());
@@ -289,9 +289,9 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithSameArguments) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call1 = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, called_computation));
   auto call2 = builder.AddInstruction(HloInstruction::CreateCall(
@@ -343,9 +343,9 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithDifferentArguments) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call1 = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, called_computation));
   auto call2 = builder.AddInstruction(HloInstruction::CreateCall(
@@ -407,9 +407,9 @@ TEST_P(HloDataflowAnalysisTest, NestedCalls) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, outer_computation));
   module_.AddEntryComputation(builder.Build());
@@ -474,15 +474,15 @@ TEST_P(HloDataflowAnalysisTest, SingleWhile) {
   auto cond_param = cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while = builder.AddInstruction(
@@ -571,15 +571,15 @@ TEST_P(HloDataflowAnalysisTest, SequentialWhiles) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while0 = builder.AddInstruction(
@@ -630,7 +630,7 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
@@ -668,9 +668,9 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) {
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto entry_while = builder.AddInstruction(
@@ -757,15 +757,15 @@ TEST_P(HloDataflowAnalysisTest, SwizzlingWhile) {
   auto cond_param = cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while = builder.AddInstruction(
@@ -817,11 +817,11 @@ TEST_P(HloDataflowAnalysisTest, ArraySelect) {
   // Test a kSelect of an array value.
   auto builder = HloComputation::Builder(TestName());
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       scalar_shape_, HloOpcode::kSelect, pred, constant1, constant2));
 
@@ -841,15 +841,15 @@ TEST_P(HloDataflowAnalysisTest, TupleSelect) {
   // instruction.
   auto builder = HloComputation::Builder(TestName());
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto constant4 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(4.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(4.0)));
   auto tuple1 =
       builder.AddInstruction(HloInstruction::CreateTuple({constant1}));
   auto tuple2 =
@@ -913,17 +913,17 @@ TEST_P(HloDataflowAnalysisTest, NestedTupleSelect) {
   // Test kSelect of a nested tuple.
   auto builder = HloComputation::Builder(TestName());
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto constant4 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(4.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(4.0)));
   auto constant5 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(5.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(5.0)));
   auto inner_tuple1 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant2, constant3}));
   auto tuple1 = builder.AddInstruction(
@@ -999,18 +999,18 @@ TEST_P(HloDataflowAnalysisTest, TupleSelectToWhile) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
       module_.AddEmbeddedComputation(cond_builder.Build());
 
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto tuple1 =
       builder.AddInstruction(HloInstruction::CreateTuple({constant1}));
   auto tuple2 =
@@ -1062,7 +1062,7 @@ TEST_P(HloDataflowAnalysisTest, BitcastDefinesValue) {
   // Test the bitcast_defines_value flag to the dataflow analysis.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary(
       scalar_shape_, HloOpcode::kBitcast, constant));
 
diff --git a/tensorflow/compiler/xla/service/hlo_dce_test.cc b/tensorflow/compiler/xla/service/hlo_dce_test.cc
index 10cd7ca7c0..704b8dfca7 100644
--- a/tensorflow/compiler/xla/service/hlo_dce_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_dce_test.cc
@@ -45,9 +45,9 @@ TEST_F(HloDceTest, NoDeadCode) {
   // Verify that no dead code is removed from a computation with no dead code.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(123.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(123.0f)));
   builder.AddInstruction(HloInstruction::CreateBinary(
       constant1->shape(), HloOpcode::kAdd, constant1, constant2));
 
@@ -98,9 +98,9 @@ TEST_F(HloDceTest, ControlDependencies) {
   // Verify that instructions with control dependencies are not removed.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(123.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(123.0f)));
 
   // Create two dead instructions: a negate and an add.
   auto dead_negate = builder.AddInstruction(HloInstruction::CreateUnary(
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 48eefd92ae..a42289590b 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -89,11 +89,11 @@ StatusOr<std::unique_ptr<Literal>> Compare(const Shape& shape, HloOpcode opcode,
                  << HloOpcodeString(opcode);
   }
 
-  auto result = LiteralUtil::CreateFromShape(shape);
-  TF_RETURN_IF_ERROR(LiteralUtil::Populate<bool>(
-      result.get(), [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
-        return compare_op(LiteralUtil::Get<OperandT>(lhs_literal, multi_index),
-                          LiteralUtil::Get<OperandT>(rhs_literal, multi_index));
+  auto result = Literal::CreateFromShape(shape);
+  TF_RETURN_IF_ERROR(result.get()->Populate<bool>(
+      [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+        return compare_op(lhs_literal.Get<OperandT>(multi_index),
+                          rhs_literal.Get<OperandT>(multi_index));
       }));
 
   return std::move(result);
@@ -117,12 +117,11 @@ StatusOr<std::unique_ptr<Literal>> ElementWiseUnaryOpImpl(
         ShapeUtil::HumanString(operand->shape()).c_str());
   }
 
-  auto result = LiteralUtil::CreateFromShape(shape);
+  auto result = Literal::CreateFromShape(shape);
 
-  TF_RETURN_IF_ERROR(LiteralUtil::Populate<ReturnT>(
-      result.get(), [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
-        return unary_op(
-            LiteralUtil::Get<NativeT>(operand_literal, multi_index));
+  TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+      [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+        return unary_op(operand_literal.Get<NativeT>(multi_index));
       }));
   return std::move(result);
 }
@@ -170,19 +169,18 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
   Status HandleBroadcast(HloInstruction* broadcast) override {
     parent_->evaluated_[broadcast] =
-        LiteralUtil::CreateFromShape(broadcast->shape());
+        Literal::CreateFromShape(broadcast->shape());
     auto output = parent_->evaluated_[broadcast].get();
     auto operand_to_broadcast =
         parent_->GetEvaluatedLiteralFor(broadcast->operand(0));
     std::vector<int64> broadcast_indices(
         ShapeUtil::Rank(broadcast->operand(0)->shape()), 0);
-    return LiteralUtil::Populate<ReturnT>(
-        output, [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+    return output->Populate<ReturnT>(
+        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
           for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
             broadcast_indices[i] = multi_index[broadcast->dimensions(i)];
           }
-          return LiteralUtil::Get<ReturnT>(operand_to_broadcast,
-                                           broadcast_indices);
+          return operand_to_broadcast.Get<ReturnT>(broadcast_indices);
         });
   }
 
@@ -205,10 +203,9 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
   template <PrimitiveType src_type, PrimitiveType dest_type>
   std::unique_ptr<Literal> ConvertIfTypesMatch(const Literal& src_literal) {
     DCHECK_EQ(src_type, src_literal.shape().element_type());
-    return LiteralUtil::Convert<
+    return src_literal.Convert<
         typename primitive_util::PrimitiveTypeToNative<src_type>::type,
-        typename primitive_util::PrimitiveTypeToNative<dest_type>::type>(
-        src_literal);
+        typename primitive_util::PrimitiveTypeToNative<dest_type>::type>();
   }
 
   Status HandleConvert(HloInstruction* convert,
@@ -218,9 +215,9 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     switch (operand->shape().element_type()) {
 #define CONVERT_IF_TYPES_MATCH(src_type)                                \
   case (src_type):                                                      \
-    parent_->evaluated_[convert] = LiteralUtil::Convert<                \
+    parent_->evaluated_[convert] = operand_literal.Convert<             \
         typename primitive_util::PrimitiveTypeToNative<src_type>::type, \
-        ReturnT>(operand_literal);                                      \
+        ReturnT>();                                                     \
     break;
       CONVERT_IF_TYPES_MATCH(PRED)
       CONVERT_IF_TYPES_MATCH(S8)
@@ -464,12 +461,12 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
     const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
 
-    auto result = LiteralUtil::CreateFromShape(shape);
+    auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(LiteralUtil::Populate<ReturnT>(
-        result.get(), [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
-          return binary_op(LiteralUtil::Get<ReturnT>(lhs_literal, multi_index),
-                           LiteralUtil::Get<ReturnT>(rhs_literal, multi_index));
+    TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+          return binary_op(lhs_literal.Get<ReturnT>(multi_index),
+                           rhs_literal.Get<ReturnT>(multi_index));
         }));
     return std::move(result);
   }
@@ -501,14 +498,13 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
     const Literal& ehs_literal = parent_->GetEvaluatedLiteralFor(ehs);
 
-    auto result = LiteralUtil::CreateFromShape(shape);
+    auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(LiteralUtil::Populate<ReturnT>(
-        result.get(), [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
-          return ternary_op(
-              LiteralUtil::Get<LhsType>(lhs_literal, multi_index),
-              LiteralUtil::Get<RhsType>(rhs_literal, multi_index),
-              LiteralUtil::Get<EhsType>(ehs_literal, multi_index));
+    TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+          return ternary_op(lhs_literal.Get<LhsType>(multi_index),
+                            rhs_literal.Get<RhsType>(multi_index),
+                            ehs_literal.Get<EhsType>(multi_index));
         }));
 
     return std::move(result);
@@ -570,7 +566,7 @@ StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate(
     if (operand->opcode() == HloOpcode::kParameter) {
       const Literal* input_literal = arg_literals_[operand->parameter_number()];
       VLOG(2) << "Parameter operand evaluated to: "
-              << LiteralUtil::ToString(*input_literal);
+              << input_literal->ToString();
       TF_RET_CHECK(ShapeUtil::Equal(operand->shape(), input_literal->shape()));
 
       evaluated_[operand] = MakeUnique<Literal>(*input_literal);
@@ -607,8 +603,7 @@ std::unique_ptr<Literal> HloEvaluator::TryEvaluate(
 Status HloEvaluator::HandleParameter(HloInstruction* parameter) {
   VLOG(2) << "HandleParameter: " << parameter->ToString();
   const Literal* input_literal = arg_literals_[parameter->parameter_number()];
-  VLOG(2) << "Parameter evaluated to: "
-          << LiteralUtil::ToString(*input_literal);
+  VLOG(2) << "Parameter evaluated to: " << input_literal->ToString();
   DCHECK(ShapeUtil::Equal(parameter->shape(), input_literal->shape()));
 
   evaluated_[parameter] = MakeUnique<Literal>(*input_literal);
@@ -624,14 +619,14 @@ Status HloEvaluator::HandleConstant(HloInstruction* constant,
 Status HloEvaluator::HandleReshape(HloInstruction* reshape) {
   TF_ASSIGN_OR_RETURN(
       evaluated_[reshape],
-      LiteralUtil::Reshape(GetEvaluatedLiteralFor(reshape->operand(0)),
-                           AsInt64Slice(reshape->shape().dimensions())));
+      GetEvaluatedLiteralFor(reshape->operand(0))
+          .Reshape(AsInt64Slice(reshape->shape().dimensions())));
   return Status::OK();
 }
 
 Status HloEvaluator::HandleTranspose(HloInstruction* transpose) {
-  evaluated_[transpose] = LiteralUtil::Transpose(
-      GetEvaluatedLiteralFor(transpose->operand(0)), transpose->dimensions());
+  evaluated_[transpose] = GetEvaluatedLiteralFor(transpose->operand(0))
+                              .Transpose(transpose->dimensions());
   return Status::OK();
 }
 
@@ -659,16 +654,16 @@ Status HloEvaluator::HandleConcatenate(
         ShapeUtil::GetDimension(operand_shape, concat_dim);
   }
 
-  auto result_literal = LiteralUtil::CreateFromDimensions(
+  auto result_literal = Literal::CreateFromDimensions(
       reference_shape.element_type(), concat_dimensions);
   DimensionVector source_indices(rank, 0);
   DimensionVector dest_indices(concat_dimensions.size(), 0);
 
   for (auto operand : operands) {
     const Shape& operand_shape = operand->shape();
-    TF_RETURN_IF_ERROR(LiteralUtil::Copy(
-        GetEvaluatedLiteralFor(operand), source_indices, result_literal.get(),
-        dest_indices, AsInt64Slice(operand_shape.dimensions())));
+    TF_RETURN_IF_ERROR(result_literal.get()->Copy(
+        GetEvaluatedLiteralFor(operand), source_indices, dest_indices,
+        AsInt64Slice(operand_shape.dimensions())));
     dest_indices[concat_dim] +=
         ShapeUtil::GetDimension(operand_shape, concat_dim);
   }
@@ -793,14 +788,14 @@ Status HloEvaluator::HandleCompare(HloInstruction* compare, HloOpcode opcode,
 Status HloEvaluator::HandleSlice(HloInstruction* slice,
                                  HloInstruction* operand) {
   const Shape& shape = slice->shape();
-  auto literal = LiteralUtil::CreateFromDimensions(
+  auto literal = Literal::CreateFromDimensions(
       shape.element_type(), AsInt64Slice(shape.dimensions()));
 
   DimensionVector dest_indices(slice->slice_starts().size(), 0);
 
-  TF_RETURN_IF_ERROR(LiteralUtil::Copy(
-      GetEvaluatedLiteralFor(operand), slice->slice_starts(), literal.get(),
-      dest_indices, AsInt64Slice(shape.dimensions())));
+  TF_RETURN_IF_ERROR(literal.get()->Copy(GetEvaluatedLiteralFor(operand),
+                                         slice->slice_starts(), dest_indices,
+                                         AsInt64Slice(shape.dimensions())));
 
   evaluated_[slice] = std::move(literal);
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index 03aa6f7f8c..0db05bf64c 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -44,9 +44,9 @@ class HloEvaluatorTest : public ::testing::Test {
 // Verifies that HloEvaluator evaluates a HLO instruction that performs clamp
 // with 3 operands.
 TEST_F(HloEvaluatorTest, DoesClamp) {
-  auto low = LiteralUtil::CreateR2<float>({{0.f, 2.f}, {2.f, 4.f}});
-  auto high = LiteralUtil::CreateR2<float>({{2.f, 4.f}, {4.f, 4.f}});
-  auto value = LiteralUtil::CreateR2<float>({{0.f, 5.f}, {0.f, 4.f}});
+  auto low = Literal::CreateR2<float>({{0.f, 2.f}, {2.f, 4.f}});
+  auto high = Literal::CreateR2<float>({{2.f, 4.f}, {4.f, 4.f}});
+  auto value = Literal::CreateR2<float>({{0.f, 5.f}, {0.f, 4.f}});
 
   Shape shape = low->shape();
   auto c1 = HloInstruction::CreateConstant(std::move(low));
@@ -58,17 +58,17 @@ TEST_F(HloEvaluatorTest, DoesClamp) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(instruction.get(), {}).ConsumeValueOrDie();
 
-  auto expected = LiteralUtil::CreateR2<float>({{0, 4}, {2, 4}});
+  auto expected = Literal::CreateR2<float>({{0, 4}, {2, 4}});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs select
 // with 3 operands.
 TEST_F(HloEvaluatorTest, DoesSelect) {
-  auto pred = LiteralUtil::CreateR2<bool>({{true, false}, {false, true}});
-  auto on_true = LiteralUtil::CreateR2<float>({{2.f, 4.f}, {4.f, 4.f}});
-  auto on_false = LiteralUtil::CreateR2<float>({{0.f, 5.f}, {0.f, 4.f}});
+  auto pred = Literal::CreateR2<bool>({{true, false}, {false, true}});
+  auto on_true = Literal::CreateR2<float>({{2.f, 4.f}, {4.f, 4.f}});
+  auto on_false = Literal::CreateR2<float>({{0.f, 5.f}, {0.f, 4.f}});
 
   Shape shape = on_true->shape();
   auto c1 = HloInstruction::CreateConstant(std::move(pred));
@@ -80,16 +80,16 @@ TEST_F(HloEvaluatorTest, DoesSelect) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(instruction.get(), {}).ConsumeValueOrDie();
 
-  auto expected = LiteralUtil::CreateR2<float>({{2, 5}, {0, 4}});
+  auto expected = Literal::CreateR2<float>({{2, 5}, {0, 4}});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
 // element-wise addition with 2 operands.
 TEST_F(HloEvaluatorTest, DoesAdd) {
-  auto lhs = LiteralUtil::CreateR2<int64>({{1, 0}, {-100, 4}});
-  auto rhs = LiteralUtil::CreateR2<int64>({{2, 4}, {4, 4}});
+  auto lhs = Literal::CreateR2<int64>({{1, 0}, {-100, 4}});
+  auto rhs = Literal::CreateR2<int64>({{2, 4}, {4, 4}});
 
   Shape shape = ShapeUtil::MakeShape(S64, {2, 2});
   auto c1 = HloInstruction::CreateConstant(std::move(lhs));
@@ -100,16 +100,16 @@ TEST_F(HloEvaluatorTest, DoesAdd) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(instruction.get(), {}).ConsumeValueOrDie();
 
-  auto expected = LiteralUtil::CreateR2<int64>({{3, 4}, {-96, 8}});
+  auto expected = Literal::CreateR2<int64>({{3, 4}, {-96, 8}});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
 // element-wise divide with 2 operands.
 TEST_F(HloEvaluatorTest, DoesDivide) {
-  auto lhs_s64 = LiteralUtil::CreateR2<int64>({{1, 0}, {-100, 4}});
-  auto rhs_s64 = LiteralUtil::CreateR2<int64>({{2, 4}, {4, 4}});
+  auto lhs_s64 = Literal::CreateR2<int64>({{1, 0}, {-100, 4}});
+  auto rhs_s64 = Literal::CreateR2<int64>({{2, 4}, {4, 4}});
 
   Shape shape_s64 = ShapeUtil::MakeShape(S64, {2, 2});
   auto c1_s64 = HloInstruction::CreateConstant(std::move(lhs_s64));
@@ -120,12 +120,12 @@ TEST_F(HloEvaluatorTest, DoesDivide) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(instruction.get(), {}).ConsumeValueOrDie();
 
-  auto expected = LiteralUtil::CreateR2<int64>({{0, 0}, {-25, 1}});
+  auto expected = Literal::CreateR2<int64>({{0, 0}, {-25, 1}});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 
-  auto lhs_f64 = LiteralUtil::CreateR2<double>({{1.0, 0.0}, {-100.0, 4.0}});
-  auto rhs_f64 = LiteralUtil::CreateR2<double>({{2.2, 4.0}, {4.0, 4.0}});
+  auto lhs_f64 = Literal::CreateR2<double>({{1.0, 0.0}, {-100.0, 4.0}});
+  auto rhs_f64 = Literal::CreateR2<double>({{2.2, 4.0}, {4.0, 4.0}});
 
   Shape shape_f64 = ShapeUtil::MakeShape(F64, {2, 2});
   auto c1_f64 = HloInstruction::CreateConstant(std::move(lhs_f64));
@@ -135,16 +135,15 @@ TEST_F(HloEvaluatorTest, DoesDivide) {
 
   result = evaluator_->Evaluate(instruction.get(), {}).ConsumeValueOrDie();
 
-  expected =
-      LiteralUtil::CreateR2<double>({{0.45454545454545453, 0}, {-25, 1}});
+  expected = Literal::CreateR2<double>({{0.45454545454545453, 0}, {-25, 1}});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
 // element-wise abs op with 1 operand.
 TEST_F(HloEvaluatorTest, DoesAbs) {
-  auto operand = LiteralUtil::CreateR2<int64>({{1, -20}, {-100, 4}});
+  auto operand = Literal::CreateR2<int64>({{1, -20}, {-100, 4}});
   const Shape& shape = ShapeUtil::MakeShape(S64, {2, 2});
   auto c1 = HloInstruction::CreateConstant(std::move(operand));
   auto instruction =
@@ -153,31 +152,31 @@ TEST_F(HloEvaluatorTest, DoesAbs) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(instruction.get(), {}).ConsumeValueOrDie();
 
-  auto expected = LiteralUtil::CreateR2<int64>({{1, 20}, {100, 4}});
+  auto expected = Literal::CreateR2<int64>({{1, 20}, {100, 4}});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 
   // For R0 literal.
   const Shape& r0 = ShapeUtil::MakeShape(F32, {});
-  operand = LiteralUtil::CreateR0<float>(-1.0f);
+  operand = Literal::CreateR0<float>(-1.0f);
   c1 = HloInstruction::CreateConstant(std::move(operand));
   instruction = HloInstruction::CreateUnary(r0, HloOpcode::kAbs, c1.get());
   result = evaluator_->Evaluate(instruction.get()).ConsumeValueOrDie();
-  expected = LiteralUtil::CreateR0<float>(1.0f);
+  expected = Literal::CreateR0<float>(1.0f);
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 
   // For R1 literal with dimension of size 0.
   Shape empty_r1 = ShapeUtil::MakeShape(F32, {0});
-  operand = LiteralUtil::CreateR1<float>({});
+  operand = Literal::CreateR1<float>({});
   c1 = HloInstruction::CreateConstant(std::move(operand));
   instruction =
       HloInstruction::CreateUnary(empty_r1, HloOpcode::kAbs, c1.get());
 
   result = evaluator_->Evaluate(instruction.get()).ConsumeValueOrDie();
-  expected = LiteralUtil::CreateR1<float>({});
+  expected = Literal::CreateR1<float>({});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 }  // namespace
 
 // Verifies that HloEvaluator evaluates a HLO Computation with non-parameter nor
@@ -186,9 +185,9 @@ TEST_F(HloEvaluatorTest, DoesTraveseInstructions) {
   HloComputation::Builder builder(
       ::testing::UnitTest::GetInstance()->current_test_info()->name());
 
-  auto lhs = LiteralUtil::CreateR2<int64>({{1, 0}, {-100, 4}});
-  auto rhs = LiteralUtil::CreateR2<int64>({{2, 4}, {4, 4}});
-  auto rhs2 = LiteralUtil::CreateR2<int64>({{1, -20}, {-100, 4}});
+  auto lhs = Literal::CreateR2<int64>({{1, 0}, {-100, 4}});
+  auto rhs = Literal::CreateR2<int64>({{2, 4}, {4, 4}});
+  auto rhs2 = Literal::CreateR2<int64>({{1, -20}, {-100, 4}});
   std::vector<const Literal*> args = {lhs.get(), rhs.get(), rhs2.get()};
 
   Shape shape = ShapeUtil::MakeShape(S64, {2, 2});
@@ -206,9 +205,9 @@ TEST_F(HloEvaluatorTest, DoesTraveseInstructions) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(builder.Build().get(), args).ConsumeValueOrDie();
 
-  auto expected = LiteralUtil::CreateR2<int64>({{4, -16}, {-196, 12}});
+  auto expected = Literal::CreateR2<int64>({{4, -16}, {-196, 12}});
 
-  EXPECT_TRUE(LiteralUtil::Equal(*result, *expected));
+  EXPECT_TRUE(result->Equal(*expected));
 }
 
 // Verifies Reshape operation is correctly evaluated.
@@ -220,7 +219,7 @@ TEST_F(HloEvaluatorTest, DoesReshape) {
   TF_ASSIGN_OR_ASSERT_OK(auto literal,
                          LiteralTestUtil::CreateRandomLiteral<F32>(
                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
-  auto literal_clone = LiteralUtil::CloneToUnique(*literal);
+  auto literal_clone = literal->CloneToUnique();
   HloInstruction* literal_instruction = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
 
@@ -233,11 +232,10 @@ TEST_F(HloEvaluatorTest, DoesReshape) {
       evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
 
   using NativeT = typename primitive_util::PrimitiveTypeToNative<F32>::type;
-  LiteralUtil::EachCell<NativeT>(
-      *result, [&](tensorflow::gtl::ArraySlice<int64> indices, NativeT value) {
+  result->EachCell<NativeT>(
+      [&](tensorflow::gtl::ArraySlice<int64> indices, NativeT value) {
         std::vector<int64> rindexes = Permute(permutation, indices);
-        EXPECT_TRUE(value ==
-                    LiteralUtil::Get<NativeT>(*literal_clone, rindexes));
+        EXPECT_TRUE(value == literal_clone->Get<NativeT>(rindexes));
       });
 }
 
@@ -246,8 +244,8 @@ TEST_F(HloEvaluatorTest, DoesBroadcast) {
   HloComputation::Builder builder(
       ::testing::UnitTest::GetInstance()->current_test_info()->name());
 
-  auto input_literal = LiteralUtil::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}});
-  auto output_literal = LiteralUtil::CreateR3<int32>(
+  auto input_literal = Literal::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}});
+  auto output_literal = Literal::CreateR3<int32>(
       {{{1, 2}, {3, 4}, {5, 6}}, {{1, 2}, {3, 4}, {5, 6}}});
   HloInstruction* literal_instruction = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(input_literal)));
@@ -258,9 +256,9 @@ TEST_F(HloEvaluatorTest, DoesBroadcast) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
 
-  LiteralUtil::EachCell<int32>(
-      *result, [&](tensorflow::gtl::ArraySlice<int64> indices, int32 value) {
-        EXPECT_TRUE(value == LiteralUtil::Get<int32>(*output_literal, indices));
+  result->EachCell<int32>(
+      [&](tensorflow::gtl::ArraySlice<int64> indices, int32 value) {
+        EXPECT_TRUE(value == output_literal->Get<int32>(indices));
       });
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 9fe4d85f8b..dffb53320c 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -335,8 +335,7 @@ string InstructionSequenceGraph(
         ShapeUtil::IsEffectiveScalar(instruction->shape())) {
       auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
           instruction->shape(), /*linear_index=*/0);
-      StrAppend(&label, " = {",
-                LiteralUtil::GetAsString(instruction->literal(), elem_idx),
+      StrAppend(&label, " = {", instruction->literal().GetAsString(elem_idx),
                 "}");
     }
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 33bb29e16d..fe6233205c 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -870,7 +870,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CreateWhile(shape, while_condition(), while_body(),
                          new_operands[0]);
     case HloOpcode::kConstant:
-      return CreateConstant(LiteralUtil::CloneToUnique(*literal_));
+      return CreateConstant(literal_->CloneToUnique());
     case HloOpcode::kFusion:
       return CloneFusionWithNewOperands(shape, new_operands);
     case HloOpcode::kParameter:
@@ -1181,7 +1181,7 @@ bool HloInstruction::Identical(
 
     // A constant is defined by the value in the literal.
     case HloOpcode::kConstant:
-      return LiteralUtil::Equal(literal(), other.literal());
+      return literal().Equal(other.literal());
 
     // A convert result is determined by the primitive type that the operand is
     // converted into.
@@ -1482,9 +1482,9 @@ string HloInstruction::ToString(bool compact_operands,
   if (opcode() == HloOpcode::kConstant) {
     // For constants, show the actual value in place of an empty operand list.
     if (ShapeUtil::ElementsIn(shape()) <= 10) {
-      // LiteralUtil::ToString emits multidimensional arrays over multiple
+      // Literal::ToString emits multidimensional arrays over multiple
       // lines. Compact this into one line by stripping out white space.
-      string tmp = LiteralUtil::ToString(literal());
+      string tmp = literal().ToString();
       std::replace(tmp.begin(), tmp.end(), '\n', ' ');
       std::vector<string> v = tensorflow::str_util::Split(tmp, ' ');
       bool first = true;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index bcf81cd8dd..bb1b477e13 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -232,7 +232,7 @@ TEST_F(HloInstructionTest, MultipleUsersAndOperands) {
   //                 -------
   auto param0 = HloInstruction::CreateParameter(0, r0f32_, "param0");
   auto param1 = HloInstruction::CreateParameter(1, r0f32_, "param1");
-  auto c0 = HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+  auto c0 = HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto addleft = HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd,
                                               param0.get(), c0.get());
   auto addright = HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd,
@@ -271,7 +271,7 @@ TEST_F(HloInstructionTest, MultipleUsersAndOperandsWithUnaryOps) {
   //                 -------
   auto param0 = HloInstruction::CreateParameter(0, r0f32_, "param0");
   auto param1 = HloInstruction::CreateParameter(1, r0f32_, "param1");
-  auto c0 = HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+  auto c0 = HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto neg1 = HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, c0.get());
   auto addleft = HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd,
                                               param0.get(), neg1.get());
@@ -307,7 +307,7 @@ TEST_F(HloInstructionTest, TrivialMap) {
   auto param =
       builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32, "x"));
   auto value = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   builder.AddInstruction(
       HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param, value));
   auto add_f32 = builder.Build();
@@ -349,9 +349,8 @@ TEST_F(HloInstructionTest, TrivialReduce) {
 
   // Builds a parameter and an initial value and feeds them to the reduce.
   auto param0 = HloInstruction::CreateParameter(0, f32a100x10, "");
-  auto const0 =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(0.0f));
-  auto c0 = HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+  auto const0 = HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f));
+  auto c0 = HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto reduce =
       HloInstruction::CreateReduce(f32v100, param0.get(), const0.get(),
                                    /*dimensions_to_reduce=*/{1}, add_f32.get());
@@ -560,7 +559,7 @@ TEST_F(HloInstructionTest, PostProcessAllVisitedNodes) {
 TEST_F(HloInstructionTest, SingletonFusionOp) {
   // Create a fusion instruction containing a single unary operation.
   auto constant =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto exp =
       HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant.get());
 
@@ -574,9 +573,9 @@ TEST_F(HloInstructionTest, SingletonFusionOp) {
 TEST_F(HloInstructionTest, BinaryFusionOp) {
   // Create a fusion instruction containing a single binary operation.
   auto constant1 =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto constant2 =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.1f));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.1f));
   auto add = HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd,
                                           constant1.get(), constant2.get());
 
@@ -594,7 +593,7 @@ TEST_F(HloInstructionTest, BinaryFusionOp) {
 TEST_F(HloInstructionTest, ChainFusionOp) {
   // Create a chain of fused unary ops.
   auto constant =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto exp1 =
       HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant.get());
   auto exp2 = HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp1.get());
@@ -613,7 +612,7 @@ TEST_F(HloInstructionTest, ChainFusionOp) {
 TEST_F(HloInstructionTest, PreserveMetadataInFusionAndClone) {
   // Create a chain of fused unary ops.
   auto constant =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto exp1 =
       HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant.get());
   auto exp2 = HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp1.get());
@@ -644,7 +643,7 @@ TEST_F(HloInstructionTest, FusionOpWithCalledComputations) {
   std::unique_ptr<HloComputation> computation_y = make_map_computation();
 
   auto constant =
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
   auto map_1_x =
       HloInstruction::CreateMap(scalar_shape, {constant.get()},
                                 computation_x.get(), /*static_operands=*/{});
@@ -681,9 +680,9 @@ TEST_F(HloInstructionTest, ComplexFusionOp) {
   //
   // Notable complexities are repeated operands in a same instruction, different
   // shapes, use of value in different expressions.
-  auto c1 = HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.1f));
-  auto c2 = HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.1f));
-  auto c3 = HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(9.0f));
+  auto c1 = HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
+  auto c2 = HloInstruction::CreateConstant(Literal::CreateR0<float>(2.1f));
+  auto c3 = HloInstruction::CreateConstant(Literal::CreateR0<float>(9.0f));
 
   auto add =
       HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, c1.get(), c2.get());
@@ -732,11 +731,11 @@ TEST_F(HloInstructionTest, IdenticalInstructions) {
   // Create a set of random constant operands to use below. Make them matrices
   // so dimensions are interesting.
   auto operand1 = HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}}));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}}));
   auto operand2 = HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{10.0, 20.0}, {30.0, 40.0}}));
-  auto vector_operand = HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({42.0, 123.0}));
+      Literal::CreateR2<float>({{10.0, 20.0}, {30.0, 40.0}}));
+  auto vector_operand =
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({42.0, 123.0}));
   Shape shape = operand1->shape();
 
   // Convenient short names for the operands.
diff --git a/tensorflow/compiler/xla/service/hlo_module_test.cc b/tensorflow/compiler/xla/service/hlo_module_test.cc
index 870bc729ae..58173bca07 100644
--- a/tensorflow/compiler/xla/service/hlo_module_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_test.cc
@@ -38,7 +38,7 @@ class HloModuleTest : public HloTestBase {
   std::unique_ptr<HloComputation> CreateConstantComputation() {
     auto builder = HloComputation::Builder("Constant");
     builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
     return builder.Build();
   }
 
diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
index 21d852a51d..d36784e67d 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
@@ -101,7 +101,7 @@ TEST_F(HloOrderingTest, InstructionsInDifferentComputations) {
 
   auto builder_c = HloComputation::Builder("C");
   HloInstruction* c = builder_c.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   HloComputation* computation_c =
       module->AddEmbeddedComputation(builder_c.Build());
 
diff --git a/tensorflow/compiler/xla/service/hlo_query.cc b/tensorflow/compiler/xla/service/hlo_query.cc
index a153d73dbd..d45038f1f4 100644
--- a/tensorflow/compiler/xla/service/hlo_query.cc
+++ b/tensorflow/compiler/xla/service/hlo_query.cc
@@ -25,7 +25,7 @@ namespace hlo_query {
 bool IsConstantR0F32(HloInstruction* instruction, float* out) {
   if (instruction->opcode() == HloOpcode::kConstant &&
       ShapeUtil::IsScalarF32(instruction->shape())) {
-    *out = LiteralUtil::Get<float>(instruction->literal(), {});
+    *out = instruction->literal().Get<float>({});
     return true;
   }
 
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
index d9c2a5f0ac..8a1e705711 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
@@ -122,7 +122,7 @@ class HloRematerializationTest : public HloTestBase {
     builder.AddInstruction(
         HloInstruction::CreateParameter(0, vec1_shape_, "param"));
     builder.AddInstruction(
-        HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(true)));
+        HloInstruction::CreateConstant(Literal::CreateR0<bool>(true)));
     return builder.Build();
   }
 
@@ -211,7 +211,7 @@ TEST_F(HloRematerializationTest, RematerializeAroundWhile) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, vec1_shape_, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(true)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(true)));
   HloComputation* while_cond =
       module->AddEmbeddedComputation(cond_builder.Build());
 
@@ -250,7 +250,7 @@ TEST_F(HloRematerializationTest, RematerializeEntryAndWhileBody) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, vec1_shape_, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(true)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(true)));
   HloComputation* while_cond =
       module->AddEmbeddedComputation(cond_builder.Build());
 
@@ -285,7 +285,7 @@ TEST_F(HloRematerializationTest, RematerializeNestedComputations) {
   cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, vec1_shape_, "param"));
   cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(true)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(true)));
   HloComputation* while_cond =
       module->AddEmbeddedComputation(cond_builder.Build());
 
diff --git a/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc b/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc
index 867ebc7f61..c98856b192 100644
--- a/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc
@@ -75,7 +75,7 @@ TEST_F(HloSubcomputationUnificationTest, UnifyIdentities) {
       module->AddEmbeddedComputation(CreateR0S32IdentityComputation());
 
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(5)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(5)));
   auto x = builder.AddInstruction(
       HloInstruction::CreateCall(r0s32_, {constant}, callee1));
   auto y = builder.AddInstruction(
@@ -110,9 +110,9 @@ TEST_F(HloSubcomputationUnificationTest, UnifyAdditions) {
       module->AddEmbeddedComputation(CreateR0S32AdditionComputation());
 
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(5)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(5)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(3)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(3)));
   auto x = builder.AddInstruction(
       HloInstruction::CreateCall(r0s32_, {constant1, constant2}, callee1));
   auto y = builder.AddInstruction(
diff --git a/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc b/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc
index 6707b02c5c..76177462aa 100644
--- a/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc
+++ b/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc
@@ -171,8 +171,7 @@ void HloTfGraphBuilder::SetNodeAttrs(const HloInstruction* instruction,
       break;
     case HloOpcode::kConstant:
       if (ShapeUtil::IsScalar(instruction->shape())) {
-        attrs["value"].set_s(
-            LiteralUtil::GetAsString(instruction->literal(), {}));
+        attrs["value"].set_s(instruction->literal().GetAsString({}));
       }
       break;
     case HloOpcode::kCustomCall:
diff --git a/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc b/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc
index c2718ea800..8e9d93e367 100644
--- a/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc
@@ -91,7 +91,7 @@ TEST_F(HloTfGraphBuilderTest, CheckConcatenateDimsAndShapes) {
 TEST_F(HloTfGraphBuilderTest, CheckScalarValue) {
   auto builder = HloComputation::Builder("Const");
   HloInstruction *instruction = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0(123)));
+      HloInstruction::CreateConstant(Literal::CreateR0(123)));
   OpMetadata metadata;
   metadata.set_op_name("x");
   metadata.set_op_type("y");
diff --git a/tensorflow/compiler/xla/service/inliner_test.cc b/tensorflow/compiler/xla/service/inliner_test.cc
index 2887a8a0a0..84bfbb30c3 100644
--- a/tensorflow/compiler/xla/service/inliner_test.cc
+++ b/tensorflow/compiler/xla/service/inliner_test.cc
@@ -51,10 +51,10 @@ TEST_F(InlinerTest, MapMax) {
   auto max_f32 = max_builder.Build();
 
   auto builder = HloComputation::Builder("MapMaxFunction");
-  auto lhs = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1, 2, 3, 4})));
-  auto rhs = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({4, 3, 2, 1})));
+  auto lhs = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({1, 2, 3, 4})));
+  auto rhs = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({4, 3, 2, 1})));
   builder.AddInstruction(
       HloInstruction::CreateMap(lhs->shape(), {lhs, rhs}, max_f32.get()));
 
@@ -70,7 +70,7 @@ TEST_F(InlinerTest, MapMax) {
 
   // Verify execution on CPU.
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
-  auto expected = LiteralUtil::CreateR1<float>({4, 3, 3, 4});
+  auto expected = Literal::CreateR1<float>({4, 3, 3, 4});
   LiteralTestUtil::ExpectEqual(*result, *expected);
 }
 
@@ -83,12 +83,12 @@ TEST_F(InlinerTest, MapConstant) {
       HloInstruction::CreateParameter(0, r0f32, "x"));
   (void)param1;
   const2_builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0f)));
   auto const2_f32 = const2_builder.Build();
 
   auto builder = HloComputation::Builder("MapConstFunction");
   auto lhs = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1, 2, 3, 4}, {5, 6, 7, 8}})));
+      Literal::CreateR2<float>({{1, 2, 3, 4}, {5, 6, 7, 8}})));
   builder.AddInstruction(
       HloInstruction::CreateMap(lhs->shape(), {lhs}, const2_f32.get()));
 
@@ -104,7 +104,7 @@ TEST_F(InlinerTest, MapConstant) {
 
   // Verify execution on CPU.
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
-  auto expected = LiteralUtil::CreateR2<float>({{2, 2, 2, 2}, {2, 2, 2, 2}});
+  auto expected = Literal::CreateR2<float>({{2, 2, 2, 2}, {2, 2, 2, 2}});
   LiteralTestUtil::ExpectEqual(*result, *expected);
 }
 
diff --git a/tensorflow/compiler/xla/service/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/instruction_fusion_test.cc
index a2e6c2ae00..b3e0007dcc 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion_test.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion_test.cc
@@ -28,7 +28,7 @@ TEST_F(InstructionFusionTest,
        CostlyProducerAndOperandElementReusingConsumerNotFused) {
   HloComputation::Builder builder(TestName());
   HloInstruction* const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0(5)));
+      HloInstruction::CreateConstant(Literal::CreateR0(5)));
   HloInstruction* exp1 = builder.AddInstruction(HloInstruction::CreateUnary(
       ShapeUtil::MakeShape(S32, {}), HloOpcode::kExp, const0));
   HloInstruction* broadcast2 =
@@ -49,7 +49,7 @@ TEST_F(InstructionFusionTest,
        NonCostlyProducerAndOperandElementReusingConsumerFused) {
   HloComputation::Builder builder(TestName());
   HloInstruction* const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0(5)));
+      HloInstruction::CreateConstant(Literal::CreateR0(5)));
   HloInstruction* negate1 = builder.AddInstruction(HloInstruction::CreateUnary(
       ShapeUtil::MakeShape(S32, {}), HloOpcode::kNegate, const0));
   HloInstruction* broadcast2 =
@@ -70,7 +70,7 @@ TEST_F(InstructionFusionTest,
        CostlyProducerAndNonOperandElementReusingConsumerFused_Reshape) {
   HloComputation::Builder builder(TestName());
   HloInstruction* const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0(5)));
+      HloInstruction::CreateConstant(Literal::CreateR0(5)));
   HloInstruction* exp1 = builder.AddInstruction(HloInstruction::CreateUnary(
       ShapeUtil::MakeShape(S32, {}), HloOpcode::kExp, const0));
   HloInstruction* reshape2 = builder.AddInstruction(
@@ -90,7 +90,7 @@ TEST_F(InstructionFusionTest,
        CostlyProducerAndNonOperandElementReusingConsumerFused_Transpose) {
   HloComputation::Builder builder(TestName());
   HloInstruction* const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0(5)));
+      HloInstruction::CreateConstant(Literal::CreateR0(5)));
   HloInstruction* exp1 = builder.AddInstruction(HloInstruction::CreateUnary(
       ShapeUtil::MakeShape(S32, {}), HloOpcode::kExp, const0));
   HloInstruction* transpose2 = builder.AddInstruction(
diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index 6d818cdea0..a83466d807 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -230,7 +230,7 @@ TEST_F(LayoutAssignmentTest, TupleSelect) {
       HloInstruction::CreateTuple({constant0, constant1}));
 
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(true)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(true)));
 
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple0->shape(), HloOpcode::kSelect, pred, tuple0, tuple1));
@@ -264,7 +264,7 @@ TEST_F(LayoutAssignmentTest, ConflictingLayoutTuple) {
   // tuple and assigning the layouts of the copied arrays as needed.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
   auto inner_tuple =
       builder.AddInstruction(HloInstruction::CreateTuple({constant}));
   auto nested_tuple = builder.AddInstruction(
diff --git a/tensorflow/compiler/xla/service/liveness_util_test.cc b/tensorflow/compiler/xla/service/liveness_util_test.cc
index bad4be149a..14d831f289 100644
--- a/tensorflow/compiler/xla/service/liveness_util_test.cc
+++ b/tensorflow/compiler/xla/service/liveness_util_test.cc
@@ -85,9 +85,9 @@ TEST_F(DoesNotUseOperandBufferTest, FusedDynamicUpdateSlice) {
 
   // Create a DynamicUpdateSlice instruction of tuple element 1.
   auto starts = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<int32>({2})));
+      HloInstruction::CreateConstant(Literal::CreateR1<int32>({2})));
   auto update = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({2.f, 2.f, 2.f})));
+      Literal::CreateR1<float>({2.f, 2.f, 2.f})));
   auto dynamic_update_slice =
       builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice(
           data_shape, gte1, update, starts));
@@ -180,9 +180,9 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDynamicUpdateSlice) {
 
   // Create a DynamicUpdateSlice instruction of tuple element 1.
   auto starts = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<int32>({2})));
+      HloInstruction::CreateConstant(Literal::CreateR1<int32>({2})));
   auto update = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({2.f, 2.f, 2.f})));
+      Literal::CreateR1<float>({2.f, 2.f, 2.f})));
   auto dynamic_update_slice =
       builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice(
           data_shape, gte1, update, starts));
@@ -234,15 +234,15 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) {
   Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2});
 
   auto a = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 0.0}, {0.0, 1.0}})));
+      Literal::CreateR2<float>({{1.0, 0.0}, {0.0, 1.0}})));
   auto b = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{2.0, 2.0}, {2.0, 2.0}})));
+      Literal::CreateR2<float>({{2.0, 2.0}, {2.0, 2.0}})));
 
   auto dot = builder.AddInstruction(
       HloInstruction::CreateBinary(data_shape, HloOpcode::kDot, a, b));
 
   auto one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto add_operand = builder.AddInstruction(
       HloInstruction::CreateBroadcast(data_shape, one, {1}));
 
@@ -264,9 +264,9 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedTransposeDotAdd) {
   Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2});
 
   auto a = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 0.0}, {0.0, 1.0}})));
+      Literal::CreateR2<float>({{1.0, 0.0}, {0.0, 1.0}})));
   auto b = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{2.0, 2.0}, {2.0, 2.0}})));
+      Literal::CreateR2<float>({{2.0, 2.0}, {2.0, 2.0}})));
   auto b_t = builder.AddInstruction(
       HloInstruction::CreateTranspose(data_shape, b, {1, 0}));
 
@@ -274,7 +274,7 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedTransposeDotAdd) {
       HloInstruction::CreateBinary(data_shape, HloOpcode::kDot, a, b_t));
 
   auto one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto add_operand = builder.AddInstruction(
       HloInstruction::CreateBroadcast(data_shape, one, {1}));
 
@@ -300,7 +300,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) {
   Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2});
 
   auto one = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto operand = builder.AddInstruction(
       HloInstruction::CreateBroadcast(data_shape, one, {1}));
 
@@ -308,7 +308,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) {
       HloInstruction::CreateReverse(data_shape, operand, {0, 1}));
 
   auto two = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{2.0, 2.0}, {2.0, 2.0}})));
+      Literal::CreateR2<float>({{2.0, 2.0}, {2.0, 2.0}})));
 
   auto add = builder.AddInstruction(
       HloInstruction::CreateBinary(data_shape, HloOpcode::kAdd, reverse, two));
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index 99ad107a90..d9e233af2a 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -163,36 +163,36 @@ llvm::Constant* LiteralToConstant(const Literal& literal, int64 dimension_index,
     llvm::Constant* value;
     switch (shape.element_type()) {
       case PRED:
-        value = llvm::ConstantInt::get(
-            ir_element_type, LiteralUtil::Get<bool>(literal, *multi_index));
+        value = llvm::ConstantInt::get(ir_element_type,
+                                       literal.Get<bool>(*multi_index));
         break;
       case U8:
-        value = llvm::ConstantInt::get(
-            ir_element_type, LiteralUtil::Get<uint8>(literal, *multi_index));
+        value = llvm::ConstantInt::get(ir_element_type,
+                                       literal.Get<uint8>(*multi_index));
         break;
       case S32:
-        value = llvm::ConstantInt::get(
-            ir_element_type, LiteralUtil::Get<int32>(literal, *multi_index));
+        value = llvm::ConstantInt::get(ir_element_type,
+                                       literal.Get<int32>(*multi_index));
         break;
       case U32:
-        value = llvm::ConstantInt::get(
-            ir_element_type, LiteralUtil::Get<uint32>(literal, *multi_index));
+        value = llvm::ConstantInt::get(ir_element_type,
+                                       literal.Get<uint32>(*multi_index));
         break;
       case S64:
-        value = llvm::ConstantInt::get(
-            ir_element_type, LiteralUtil::Get<int64>(literal, *multi_index));
+        value = llvm::ConstantInt::get(ir_element_type,
+                                       literal.Get<int64>(*multi_index));
         break;
       case U64:
-        value = llvm::ConstantInt::get(
-            ir_element_type, LiteralUtil::Get<uint64>(literal, *multi_index));
+        value = llvm::ConstantInt::get(ir_element_type,
+                                       literal.Get<uint64>(*multi_index));
         break;
       case F32:
-        value = llvm::ConstantFP::get(
-            ir_element_type, LiteralUtil::Get<float>(literal, *multi_index));
+        value = llvm::ConstantFP::get(ir_element_type,
+                                      literal.Get<float>(*multi_index));
         break;
       case F64:
-        value = llvm::ConstantFP::get(
-            ir_element_type, LiteralUtil::Get<double>(literal, *multi_index));
+        value = llvm::ConstantFP::get(ir_element_type,
+                                      literal.Get<double>(*multi_index));
         break;
       default:
         LOG(FATAL) << "unsupported type " << shape.element_type();
diff --git a/tensorflow/compiler/xla/service/reshape_mover_test.cc b/tensorflow/compiler/xla/service/reshape_mover_test.cc
index 9becdb2bed..49c1755520 100644
--- a/tensorflow/compiler/xla/service/reshape_mover_test.cc
+++ b/tensorflow/compiler/xla/service/reshape_mover_test.cc
@@ -84,7 +84,7 @@ TEST_F(ReshapeMoverTest, 1ConstantAnd1ReshapesOnRngNotMoved) {
       builder.AddInstruction(HloInstruction::CreateReshape(root_shape, rng0));
 
   auto const1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateFromShape(root_shape)));
+      HloInstruction::CreateConstant(Literal::CreateFromShape(root_shape)));
 
   builder.AddInstruction(HloInstruction::CreateBinary(
       root_shape, HloOpcode::kAdd, reshape0, const1));
@@ -179,9 +179,8 @@ TEST_F(ReshapeMoverTest, EquivalentReshapesMoved) {
 TEST_F(ReshapeMoverTest, 1ConstantAnd2ReshapesMoved) {
   HloComputation::Builder builder(TestName());
   auto root_shape = ShapeUtil::MakeShape(F32, {2, 3});
-  auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR2<bool>(
-          {{true, true, false}, {false, false, true}})));
+  auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
+      Literal::CreateR2<bool>({{true, true, false}, {false, false, true}})));
 
   auto param1 = builder.AddInstruction(HloInstruction::CreateParameter(
       0, ShapeUtil::MakeShape(F32, {1, 3, 1, 2}), "param1"));
@@ -263,12 +262,12 @@ TEST_F(ReshapeMoverTest, 2TrivialConstantReshapeNotMoved) {
   HloComputation::Builder builder(TestName());
   auto root_shape = ShapeUtil::MakeShape(F32, {2, 3});
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1, 2, 3}, {4, 5, 6}})));
+      Literal::CreateR2<float>({{1, 2, 3}, {4, 5, 6}})));
   auto reshape0 =
       builder.AddInstruction(HloInstruction::CreateReshape(root_shape, const0));
 
   auto const1 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1, 2, 3}, {4, 5, 6}})));
+      Literal::CreateR2<float>({{1, 2, 3}, {4, 5, 6}})));
   auto reshape1 =
       builder.AddInstruction(HloInstruction::CreateReshape(root_shape, const1));
 
@@ -318,7 +317,7 @@ TEST_F(ReshapeMoverTest, 1NonTrivialReshapeMoved) {
   auto param0 = builder.AddInstruction(HloInstruction::CreateParameter(
       0, ShapeUtil::MakeShape(F32, {1, 3, 1, 2}), "param0"));
   auto const1 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1, 2, 3}, {4, 5, 6}})));
+      Literal::CreateR2<float>({{1, 2, 3}, {4, 5, 6}})));
   auto reshape0 =
       builder.AddInstruction(HloInstruction::CreateReshape(root_shape, param0));
   builder.AddInstruction(HloInstruction::CreateBinary(
@@ -464,7 +463,7 @@ TEST_F(ReshapeMoverTest, ImplicitlyBroadcastReshapeIsNotMovedBug37787999) {
   auto reshape = builder.AddInstruction(HloInstruction::CreateReshape(
       ShapeUtil::MakeShape(F32, {128, 1}), param0));
   Array2D<float> a(128, 1024);
-  auto literal = LiteralUtil::CreateR2FromArray2D<float>(a);
+  auto literal = Literal::CreateR2FromArray2D<float>(a);
   auto constant = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
   auto multiply = builder.AddInstruction(HloInstruction::CreateBinary(
diff --git a/tensorflow/compiler/xla/service/transfer_manager_test.cc b/tensorflow/compiler/xla/service/transfer_manager_test.cc
index ca38601d91..29ecef9510 100644
--- a/tensorflow/compiler/xla/service/transfer_manager_test.cc
+++ b/tensorflow/compiler/xla/service/transfer_manager_test.cc
@@ -55,7 +55,7 @@ class CpuTransferManagerTest : public ::testing::Test {
 TEST_F(CpuTransferManagerTest, TransferR0U32ToDevice) {
   std::vector<uint8> storage(sizeof(uint32), '\x00');
   se::DeviceMemoryBase memptr(storage.data(), storage.size());
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR0<uint32>(42);
+  std::unique_ptr<Literal> literal = Literal::CreateR0<uint32>(42);
   TF_CHECK_OK(transfer_manager_.TransferLiteralToDevice(stream_exec_, *literal,
                                                         &memptr));
 
@@ -66,7 +66,7 @@ TEST_F(CpuTransferManagerTest, TransferR1F32ToDevice) {
   std::vector<uint8> storage(4 * sizeof(float), '\x00');
   se::DeviceMemoryBase memptr(storage.data(), storage.size());
   std::unique_ptr<Literal> literal =
-      LiteralUtil::CreateR1<float>({1.25f, 2.5f, -17.0f, -20.125f});
+      Literal::CreateR1<float>({1.25f, 2.5f, -17.0f, -20.125f});
   TF_CHECK_OK(transfer_manager_.TransferLiteralToDevice(stream_exec_, *literal,
                                                         &memptr));
 
@@ -80,7 +80,7 @@ TEST_F(CpuTransferManagerTest, TransferR1U8ToDevice) {
   std::vector<uint8> storage(16, '\x00');
   se::DeviceMemoryBase memptr(storage.data(), storage.size());
   const char* str = "0123456789abcdef";
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR1U8(str);
+  std::unique_ptr<Literal> literal = Literal::CreateR1U8(str);
   TF_CHECK_OK(transfer_manager_.TransferLiteralToDevice(stream_exec_, *literal,
                                                         &memptr));
 
diff --git a/tensorflow/compiler/xla/service/transpose_folding_test.cc b/tensorflow/compiler/xla/service/transpose_folding_test.cc
index c72d127ea8..9520c42d28 100644
--- a/tensorflow/compiler/xla/service/transpose_folding_test.cc
+++ b/tensorflow/compiler/xla/service/transpose_folding_test.cc
@@ -92,11 +92,11 @@ TEST_F(TransposeFoldingTest, FoldDotTransposeConstant) {
   auto builder = HloComputation::Builder("entry_computation");
   // 2x1
   HloInstruction* const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR2<float>({{1}, {2}})));
+      HloInstruction::CreateConstant(Literal::CreateR2<float>({{1}, {2}})));
   // 3x2
   HloInstruction* const1 =
       builder.AddInstruction(HloInstruction::CreateConstant(
-          LiteralUtil::CreateR2<float>({{1, 2}, {3, 4}, {5, 6}})));
+          Literal::CreateR2<float>({{1, 2}, {3, 4}, {5, 6}})));
   HloInstruction* transpose0 =
       builder.AddInstruction(HloInstruction::CreateTranspose(
           ShapeUtil::MakeShape(F32, {1, 2}), const0, {1, 0}));
@@ -130,11 +130,11 @@ TEST_F(TransposeFoldingTest, FuseDotWithConstantOperands) {
   auto builder = HloComputation::Builder("entry");
   // (1.0 + 2.0) * (2.0 - 3.0)
   HloInstruction* const1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   HloInstruction* const2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   HloInstruction* const3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   HloInstruction* add = builder.AddInstruction(HloInstruction::CreateBinary(
       const1->shape(), HloOpcode::kAdd, const1, const2));
   HloInstruction* sub = builder.AddInstruction(HloInstruction::CreateBinary(
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
index f75487dd74..d25e5adee3 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
@@ -124,9 +124,9 @@ class TuplePointsToAnalysisTest : public HloTestBase {
 TEST_F(TuplePointsToAnalysisTest, SimpleTuple) {
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
 
@@ -177,14 +177,14 @@ TEST_F(TuplePointsToAnalysisTest, NestedTuple) {
   // tuple.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto inner_tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
 
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({inner_tuple, constant3}));
 
@@ -238,14 +238,14 @@ TEST_F(TuplePointsToAnalysisTest, GetTupleElement) {
   // tuple.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto inner_tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
 
   auto constant3 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(3.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(3.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({inner_tuple, constant3}));
 
@@ -270,7 +270,7 @@ TEST_F(TuplePointsToAnalysisTest, DuplicatedElement) {
   // Create a tuple which contains duplicate elements.
   auto builder = HloComputation::Builder(TestName());
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant, constant, constant}));
 
@@ -291,9 +291,9 @@ TEST_F(TuplePointsToAnalysisTest, TupleCopy) {
   // the same.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto copy = builder.AddInstruction(
@@ -318,16 +318,16 @@ TEST_F(TuplePointsToAnalysisTest, TupleSelect) {
   // set containing the union of both sides.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple1 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto tuple2 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant2, constant2}));
 
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple1->shape(), HloOpcode::kSelect, pred, tuple1, tuple2));
 
@@ -356,7 +356,7 @@ TEST_F(TuplePointsToAnalysisTest, SelectTupleParameters) {
   auto param1 = builder.AddInstruction(
       HloInstruction::CreateParameter(1, tuple_shape, "param1"));
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple_shape, HloOpcode::kSelect, pred, param0, param1));
   auto copy = builder.AddInstruction(
@@ -396,16 +396,16 @@ TEST_F(TuplePointsToAnalysisTest, UnambiguousTupleSelect) {
   // Select from two identical tuples. The result should not be ambiguous.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto tuple1 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto tuple2 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
 
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple1->shape(), HloOpcode::kSelect, pred, tuple1, tuple2));
 
@@ -427,9 +427,9 @@ TEST_F(TuplePointsToAnalysisTest, NestedTupleSelect) {
   // the right values.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto inner_tuple1 = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto inner_tuple2 = builder.AddInstruction(
@@ -441,7 +441,7 @@ TEST_F(TuplePointsToAnalysisTest, NestedTupleSelect) {
       builder.AddInstruction(HloInstruction::CreateTuple({inner_tuple2}));
 
   auto pred = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<bool>(false)));
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple1->shape(), HloOpcode::kSelect, pred, tuple1, tuple2));
 
@@ -474,9 +474,9 @@ TEST_F(TuplePointsToAnalysisTest, TupleWithBitcast) {
   // have the operand of the bitcast in its points-to set.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary(
       constant2->shape(), HloOpcode::kBitcast, constant2));
   auto tuple =
@@ -510,10 +510,9 @@ TEST_F(TuplePointsToAnalysisTest, PointsToTupleConstantElements) {
   // Construct a tuple constant and kCopy it. Verify the points-to set of the
   // copy correctly correctly points into the nested elements of the constant.
   auto builder = HloComputation::Builder(TestName());
-  auto tuple_constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::MakeTuple(
-          {LiteralUtil::CreateR2<float>({{1.0}, {2.0}}).get(),
-           LiteralUtil::CreateR1<float>({2.0, 42}).get()})));
+  auto tuple_constant = builder.AddInstruction(HloInstruction::CreateConstant(
+      Literal::MakeTuple({Literal::CreateR2<float>({{1.0}, {2.0}}).get(),
+                          Literal::CreateR1<float>({2.0, 42}).get()})));
   auto copy = builder.AddInstruction(HloInstruction::CreateUnary(
       tuple_constant->shape(), HloOpcode::kCopy, tuple_constant));
 
@@ -533,9 +532,9 @@ TEST_F(TuplePointsToAnalysisTest, BufferAliases) {
   // times. Verify buffer alias sets.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(2.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto inner_tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto tuple = builder.AddInstruction(
@@ -574,7 +573,7 @@ class FusionPointsToAnalysisTest : public TuplePointsToAnalysisTest {
     auto tuple_element1 = builder.AddInstruction(
         HloInstruction::CreateGetTupleElement(update_shape, tuple_param0, 1));
     auto ones = builder.AddInstruction(HloInstruction::CreateConstant(
-        LiteralUtil::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
+        Literal::CreateR1<float>({1.f, 1.f, 1.f, 1.f})));
     // Create 'update' = Add(GetTupleElement(tuple_param0, 1), ones)
     auto update = builder.AddInstruction(HloInstruction::CreateBinary(
         update_shape, HloOpcode::kAdd, tuple_element1, ones));
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 2387e02f03..1f6e789379 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -2373,7 +2373,7 @@ void ComputationLowerer::Visit(
       const ConstantRequest& constant_request =
           request.request().constant_request();
       hlo_instruction = add_instruction(HloInstruction::CreateConstant(
-          LiteralUtil::CloneToUnique(Literal(constant_request.literal()))));
+          Literal(constant_request.literal()).CloneToUnique()));
       break;
     }
 
diff --git a/tensorflow/compiler/xla/service/user_computation_test.cc b/tensorflow/compiler/xla/service/user_computation_test.cc
index ea69120126..41bb641f43 100644
--- a/tensorflow/compiler/xla/service/user_computation_test.cc
+++ b/tensorflow/compiler/xla/service/user_computation_test.cc
@@ -50,7 +50,7 @@ TEST_F(UserComputationTest, SimpleComputation) {
 
   ConstantRequest constant_request;
   *constant_request.mutable_literal() =
-      LiteralUtil::CreateR1<float>({123.0f, 42.0f})->ToProto();
+      Literal::CreateR1<float>({123.0f, 42.0f})->ToProto();
   TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle constant_handle,
                          computation.AddConstantInstruction(constant_request));
 
@@ -161,12 +161,12 @@ TEST_F(UserComputationTest, EliminateScalarBroadcast) {
 
   ConstantRequest a_request;
   *a_request.mutable_literal() =
-      LiteralUtil::CreateR1<float>({123.0f, 42.0f})->ToProto();
+      Literal::CreateR1<float>({123.0f, 42.0f})->ToProto();
   TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle a_handle,
                          computation.AddConstantInstruction(a_request));
 
   ConstantRequest b_request;
-  *b_request.mutable_literal() = LiteralUtil::CreateR0<float>(1.0f)->ToProto();
+  *b_request.mutable_literal() = Literal::CreateR0<float>(1.0f)->ToProto();
   TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle b_handle,
                          computation.AddConstantInstruction(b_request));
 
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index f43d4b6f57..25d9fbf8c4 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -157,13 +157,13 @@ TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) {
     b_values.push_back(2 * i / static_cast<float>(count + 2));
   }
 
-  std::unique_ptr<Literal> a_literal = LiteralUtil::CreateR1<float>({a_values});
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({a_values});
   std::unique_ptr<GlobalData> a_data =
       client_->TransferToServer(*a_literal).ConsumeValueOrDie();
   auto a_constant = builder.ConstantR1<float>(a_values);
   auto a_param = builder.Parameter(0, a_literal->shape(), "a_param");
 
-  std::unique_ptr<Literal> b_literal = LiteralUtil::CreateR1<float>({b_values});
+  std::unique_ptr<Literal> b_literal = Literal::CreateR1<float>({b_values});
   std::unique_ptr<GlobalData> b_data =
       client_->TransferToServer(*b_literal).ConsumeValueOrDie();
   auto b_constant = builder.Parameter(1, a_literal->shape(), "b_param");
@@ -803,7 +803,7 @@ TEST_F(ArrayElementwiseOpTest, PowSpecialF32) {
   std::vector<float> values = {1.0f, 2.0f, 3.2f, -4.0f};
   std::vector<float> exponents = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
 
-  std::unique_ptr<Literal> param_literal = LiteralUtil::CreateR1<float>(values);
+  std::unique_ptr<Literal> param_literal = Literal::CreateR1<float>(values);
   std::unique_ptr<GlobalData> param_data =
       client_->TransferToServer(*param_literal).ConsumeValueOrDie();
 
@@ -1240,12 +1240,12 @@ TEST_F(ArrayElementwiseOpTest, AddTwoParametersF32s) {
   ComputationBuilder builder(client_, TestName());
 
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({1.1f, 2.2f, 3.3f, 5.5f});
+      Literal::CreateR1<float>({1.1f, 2.2f, 3.3f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
   std::unique_ptr<Literal> param1_literal =
-      LiteralUtil::CreateR1<float>({7.2f, 2.3f, 3.4f, 5.6f});
+      Literal::CreateR1<float>({7.2f, 2.3f, 3.4f, 5.6f});
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*param1_literal).ConsumeValueOrDie();
 
@@ -1262,12 +1262,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoParametersZeroElementF32s) {
   ComputationBuilder builder(client_, TestName());
 
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR3FromArray3D<float>(Array3D<float>(0, 7, 0));
+      Literal::CreateR3FromArray3D<float>(Array3D<float>(0, 7, 0));
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
   std::unique_ptr<Literal> param1_literal =
-      LiteralUtil::CreateR3FromArray3D<float>(Array3D<float>(0, 7, 0));
+      Literal::CreateR3FromArray3D<float>(Array3D<float>(0, 7, 0));
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*param1_literal).ConsumeValueOrDie();
 
@@ -1284,7 +1284,7 @@ TEST_F(ArrayElementwiseOpTest, AddParameterToConstantF32s) {
   ComputationBuilder builder(client_, TestName());
 
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({1.1f, 2.2f, 3.3f, 5.5f});
+      Literal::CreateR1<float>({1.1f, 2.2f, 3.3f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -1455,9 +1455,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Eq) {
   auto cmp_dim_1 = builder.Eq(v, m, /*broadcast_dimensions=*/{0});
   auto result = builder.Tuple({cmp_dim_0, cmp_dim_1});
 
-  auto expected = LiteralUtil::MakeTuple(
-      {LiteralUtil::CreateR2<bool>({{true, true}, {true, false}}).get(),
-       LiteralUtil::CreateR2<bool>({{true, false}, {false, false}}).get()});
+  auto expected = Literal::MakeTuple(
+      {Literal::CreateR2<bool>({{true, true}, {true, false}}).get(),
+       Literal::CreateR2<bool>({{true, false}, {false, false}}).get()});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -1810,7 +1810,7 @@ TEST_F(ArrayElementwiseOpTest, R4_16x16x2x2_Plus_R1_16) {
   std::iota(r1.begin(), r1.end(), 1.0);
 
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> a_literal = LiteralUtil::CreateR4FromArray4D(r4);
+  std::unique_ptr<Literal> a_literal = Literal::CreateR4FromArray4D(r4);
   *a_literal->mutable_shape()->mutable_layout() =
       LayoutUtil::MakeLayout({0, 1, 2, 3});
   auto a = builder.ConstantLiteral(*a_literal);
@@ -1846,8 +1846,8 @@ TEST_F(ArrayElementwiseOpTest, CannotAddOpaques) {
 // broadcast.
 TEST_F(ArrayElementwiseOpTest, ImplictBroadcastInFusedExpressions) {
   ComputationBuilder builder(client_, TestName());
-  auto x_literal = LiteralUtil::CreateR1<float>({1, 2, 3});
-  auto y_literal = LiteralUtil::CreateR1<float>({4, 5});
+  auto x_literal = Literal::CreateR1<float>({1, 2, 3});
+  auto y_literal = Literal::CreateR1<float>({4, 5});
   auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie();
   auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie();
 
@@ -1877,7 +1877,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, ReducePrecisionNoOpParamF32) {
 
   std::vector<float> a_values = {-2.5f, 25.5f};
 
-  std::unique_ptr<Literal> a_literal = LiteralUtil::CreateR1<float>({a_values});
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({a_values});
   std::unique_ptr<GlobalData> a_data =
       client_->TransferToServer(*a_literal).ConsumeValueOrDie();
   auto a_param = builder.Parameter(0, a_literal->shape(), "a_param");
diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index b9b051b78c..9f9f541230 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -47,7 +47,7 @@ class BatchNormalizationTest : public ClientLibraryTestBase {
         {5.0f, 4.4f},   // p2
     });
     input_array_.FillWithPZ(pz);
-    input_literal_ = *LiteralUtil::CreateR4FromArray4D(input_array_);
+    input_literal_ = *Literal::CreateR4FromArray4D(input_array_);
     CHECK_EQ(kSamples, input_array_.planes());
     CHECK_EQ(kZ, input_array_.depth());
     CHECK_EQ(kY, input_array_.height());
diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
index b8c35cad47..aab2c74634 100644
--- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
@@ -62,9 +62,8 @@ class BroadcastSimpleTest : public ClientLibraryTestBase {
       Array3D<float>* r3_array, float start, float end, int seed) {
     *r3_shape = ShapeUtil::MakeShapeWithLayout(F32, bounds, minor_to_major);
     r3_array->FillRandom(start, end, seed);
-    auto r3_data =
-        LiteralUtil::Relayout(*LiteralUtil::CreateR3FromArray3D(*r3_array),
-                              LayoutUtil::MakeLayout(minor_to_major));
+    auto r3_data = Literal::CreateR3FromArray3D(*r3_array)->Relayout(
+        LayoutUtil::MakeLayout(minor_to_major));
     std::unique_ptr<GlobalData> r3_global_data =
         client_->TransferToServer(*r3_data).ConsumeValueOrDie();
     return r3_global_data;
@@ -76,9 +75,8 @@ class BroadcastSimpleTest : public ClientLibraryTestBase {
       Array2D<float>* r2_array, float start, float end, int seed) {
     *r2_shape = ShapeUtil::MakeShapeWithLayout(F32, bounds, minor_to_major);
     r2_array->FillRandom(start, end, seed);
-    auto r2_data =
-        LiteralUtil::Relayout(*LiteralUtil::CreateR2FromArray2D(*r2_array),
-                              LayoutUtil::MakeLayout(minor_to_major));
+    auto r2_data = Literal::CreateR2FromArray2D(*r2_array)->Relayout(
+        LayoutUtil::MakeLayout(minor_to_major));
     std::unique_ptr<GlobalData> r2_global_data =
         client_->TransferToServer(*r2_data).ConsumeValueOrDie();
     return r2_global_data;
@@ -216,13 +214,13 @@ XLA_TEST_F(BroadcastSimpleTest, InDimensionAndDegenerateBroadcasting) {
   ComputationBuilder b(client_, TestName());
 
   b.Add(b.ConstantR2<float>({{1.0, 5.0}}),
-        b.ConstantLiteral(*LiteralUtil::CreateR3<float>(
+        b.ConstantLiteral(*Literal::CreateR3<float>(
             {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})),
         /*broadcast_dimensions=*/{1, 2});
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{3.0, 7.0}, {4.0, 8.0}, {5.0, 9.0}},
-                                    {{6.0, 10.0}, {7.0, 11.0}, {8.0, 12.0}}});
+      Literal::CreateR3<float>({{{3.0, 7.0}, {4.0, 8.0}, {5.0, 9.0}},
+                                {{6.0, 10.0}, {7.0, 11.0}, {8.0, 12.0}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
@@ -291,7 +289,7 @@ XLA_TEST_P(BroadcastR3ImplicitTest, Doit) {
       }
     }
   }
-  auto expected = LiteralUtil::CreateR3FromArray3D(expected_array);
+  auto expected = Literal::CreateR3FromArray3D(expected_array);
   ComputeAndCompareLiteral(
       &builder, *expected,
       {r3_implicit_global_data.get(), r3_global_data.get()},
@@ -316,7 +314,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1_2) {
   b.Add(r3h, r1h);
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{2, 3}, {4, 5}}, {{7, 8}, {9, 10}}});
+      Literal::CreateR3<float>({{{2, 3}, {4, 5}}, {{7, 8}, {9, 10}}});
 
   ComputeAndCompareLiteral(&b, *expected, {r3.get(), r1.get()},
                            ErrorSpec(0.0001));
@@ -324,81 +322,79 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1_2) {
 
 XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_1) {
   ComputationBuilder b(client_, TestName());
-  auto r1 = b.ConstantLiteral(*LiteralUtil::CreateR3<float>({{{1, 2}}}));
+  auto r1 = b.ConstantLiteral(*Literal::CreateR3<float>({{{1, 2}}}));
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r3, r1);
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{2, 4}, {4, 6}}, {{6, 8}, {8, 10}}});
+      Literal::CreateR3<float>({{{2, 4}, {4, 6}}, {{6, 8}, {8, 10}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_2) {
   ComputationBuilder b(client_, TestName());
-  auto r1 = b.ConstantLiteral(*LiteralUtil::CreateR3<float>({{{1}, {2}}}));
+  auto r1 = b.ConstantLiteral(*Literal::CreateR3<float>({{{1}, {2}}}));
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r3, r1);
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{2, 3}, {5, 6}}, {{6, 7}, {9, 10}}});
+      Literal::CreateR3<float>({{{2, 3}, {5, 6}}, {{6, 7}, {9, 10}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0) {
   ComputationBuilder b(client_, TestName());
-  auto r1 =
-      b.ConstantLiteral(*LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}}));
+  auto r1 = b.ConstantLiteral(*Literal::CreateR3<float>({{{1, 2}, {3, 4}}}));
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r3, r1);
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{2, 4}, {6, 8}}, {{6, 8}, {10, 12}}});
+      Literal::CreateR3<float>({{{2, 4}, {6, 8}}, {{6, 8}, {10, 12}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1) {
   ComputationBuilder b(client_, TestName());
-  auto r1 =
-      b.ConstantLiteral(*LiteralUtil::CreateR3<float>({{{1, 2}}, {{3, 4}}}));
+  auto r1 = b.ConstantLiteral(*Literal::CreateR3<float>({{{1, 2}}, {{3, 4}}}));
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r3, r1);
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{2, 4}, {4, 6}}, {{8, 10}, {10, 12}}});
+      Literal::CreateR3<float>({{{2, 4}, {4, 6}}, {{8, 10}, {10, 12}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_2) {
   ComputationBuilder b(client_, TestName());
-  auto r1 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1}, {2}}, {{3}, {4}}}));
+  auto r1 =
+      b.ConstantLiteral(*Literal::CreateR3<float>({{{1}, {2}}, {{3}, {4}}}));
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r3, r1);
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{2, 3}, {5, 6}}, {{8, 9}, {11, 12}}});
+      Literal::CreateR3<float>({{{2, 3}, {5, 6}}, {{8, 9}, {11, 12}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_1_2) {
   ComputationBuilder b(client_, TestName());
-  auto r1 = b.ConstantLiteral(*LiteralUtil::CreateR3<float>({{{1}}}));
+  auto r1 = b.ConstantLiteral(*Literal::CreateR3<float>({{{1}}}));
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r3, r1);
 
   auto expected =
-      LiteralUtil::CreateR3<float>({{{2, 3}, {4, 5}}, {{6, 7}, {8, 9}}});
+      Literal::CreateR3<float>({{{2, 3}, {4, 5}}, {{6, 7}, {8, 9}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
@@ -540,7 +536,7 @@ XLA_TEST_P(BroadcastR2ImplicitTest, Doit) {
     *v = ApplyOpToFloats(spec.op2, tmp, v3);
   });
 
-  auto expected = LiteralUtil::CreateR2FromArray2D(expected_array);
+  auto expected = Literal::CreateR2FromArray2D(expected_array);
   ComputeAndCompareLiteral(
       &builder, *expected,
       {r2_implicit_global_data1.get(), r2_global_data.get(),
@@ -554,22 +550,22 @@ INSTANTIATE_TEST_CASE_P(BroadcastR2ImplicitTestInstances,
 
 XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_0) {
   ComputationBuilder b(client_, TestName());
-  auto r1 = b.ConstantLiteral(*LiteralUtil::CreateR2<float>({{1, 2}}));
-  auto r2 = b.ConstantLiteral(*LiteralUtil::CreateR2<float>({{1, 2}, {3, 4}}));
+  auto r1 = b.ConstantLiteral(*Literal::CreateR2<float>({{1, 2}}));
+  auto r2 = b.ConstantLiteral(*Literal::CreateR2<float>({{1, 2}, {3, 4}}));
   b.Add(r2, r1);
 
-  auto expected = LiteralUtil::CreateR2<float>({{2, 4}, {4, 6}});
+  auto expected = Literal::CreateR2<float>({{2, 4}, {4, 6}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_1) {
   ComputationBuilder b(client_, TestName());
-  auto r1 = b.ConstantLiteral(*LiteralUtil::CreateR2<float>({{1}, {2}}));
-  auto r2 = b.ConstantLiteral(*LiteralUtil::CreateR2<float>({{1, 2}, {3, 4}}));
+  auto r1 = b.ConstantLiteral(*Literal::CreateR2<float>({{1}, {2}}));
+  auto r2 = b.ConstantLiteral(*Literal::CreateR2<float>({{1, 2}, {3, 4}}));
   b.Add(r2, r1);
 
-  auto expected = LiteralUtil::CreateR2<float>({{2, 3}, {5, 6}});
+  auto expected = Literal::CreateR2<float>({{2, 3}, {5, 6}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
@@ -578,11 +574,11 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim0) {
   ComputationBuilder b(client_, TestName());
   auto r1 = b.ConstantR1<float>({10, 20});
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r3, r1, {0});
 
-  auto expected = LiteralUtil::CreateR3<float>(
-      {{{11, 12}, {13, 14}}, {{25, 26}, {27, 28}}});
+  auto expected =
+      Literal::CreateR3<float>({{{11, 12}, {13, 14}}, {{25, 26}, {27, 28}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
@@ -591,11 +587,11 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim1) {
   ComputationBuilder b(client_, TestName());
   auto r1 = b.ConstantR1<float>({10, 20});
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r1, r3, {1});
 
-  auto expected = LiteralUtil::CreateR3<float>(
-      {{{11, 12}, {23, 24}}, {{15, 16}, {27, 28}}});
+  auto expected =
+      Literal::CreateR3<float>({{{11, 12}, {23, 24}}, {{15, 16}, {27, 28}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
@@ -604,11 +600,11 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim2) {
   ComputationBuilder b(client_, TestName());
   auto r1 = b.ConstantR1<float>({10, 20});
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   b.Add(r1, r3, {2});
 
-  auto expected = LiteralUtil::CreateR3<float>(
-      {{{11, 22}, {13, 24}}, {{15, 26}, {17, 28}}});
+  auto expected =
+      Literal::CreateR3<float>({{{11, 22}, {13, 24}}, {{15, 26}, {17, 28}}});
 
   ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001));
 }
@@ -619,7 +615,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAll) {
   auto r1_1 = b.ConstantR1<float>({100, 200});
   auto r1_2 = b.ConstantR1<float>({10, 20});
   auto r3 = b.ConstantLiteral(
-      *LiteralUtil::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
+      *Literal::CreateR3<float>({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}));
   for (int i = 0; i < 3; ++i) {
     r3 = b.Add(r1_0, r3, {0});
     r3 = b.Add(r3, r1_1, {1});
@@ -627,7 +623,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAll) {
   }
   r3 = b.Mul(r3, b.ConstantR0<float>(-2));
 
-  auto expected = LiteralUtil::CreateR3<float>(
+  auto expected = Literal::CreateR3<float>(
       {{{-6 * 1110 - 2, -6 * 1120 - 4}, {-6 * 1210 - 6, -6 * 1220 - 8}},
        {{-6 * 2110 - 10, -6 * 2120 - 12}, {-6 * 2210 - 14, -6 * 2220 - 16}}});
 
@@ -648,7 +644,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAllWithScalarBroadcast) {
   }
   r3 = b.Mul(r3, b.ConstantR0<float>(-1));
 
-  auto expected = LiteralUtil::CreateR3<float>(
+  auto expected = Literal::CreateR3<float>(
       {{{-3 * 1110 - 3, -3 * 1120 - 3}, {-3 * 1210 - 3, -3 * 1220 - 3}},
        {{-3 * 2110 - 3, -3 * 2120 - 3}, {-3 * 2210 - 3, -3 * 2220 - 3}}});
 
@@ -661,7 +657,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidBinaryAndDegenerateBroadcasting) {
   ComputationBuilder b(client_, TestName());
 
   b.Add(b.ConstantR2<float>({{1.0, 5.0}, {1.0, 5.0}}),
-        b.ConstantLiteral(*LiteralUtil::CreateR3<float>(
+        b.ConstantLiteral(*Literal::CreateR3<float>(
             {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})),
         /*broadcast_dimensions=*/{1, 2});
 
diff --git a/tensorflow/compiler/xla/tests/broadcast_test.cc b/tensorflow/compiler/xla/tests/broadcast_test.cc
index 820f0ab6f3..dc1443f536 100644
--- a/tensorflow/compiler/xla/tests/broadcast_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_test.cc
@@ -38,7 +38,7 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarToScalar) {
   // Test degenerate case of broadcasting a scalar into a scalar.
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0)));
   builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {}), input, {}));
 
@@ -47,14 +47,14 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarToScalar) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR0<float>(42.0), *result,
+  LiteralTestUtil::ExpectNear(*Literal::CreateR0<float>(42.0), *result,
                               error_spec_);
 }
 
 XLA_TEST_F(BroadcastTest, BroadcastScalarTo2D) {
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0)));
   builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {2, 2}), input, {}));
 
@@ -64,14 +64,14 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarTo2D) {
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
   LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR2<float>({{42.0, 42.0}, {42.0, 42.0}}), *result,
+      *Literal::CreateR2<float>({{42.0, 42.0}, {42.0, 42.0}}), *result,
       error_spec_);
 }
 
 XLA_TEST_F(BroadcastTest, BroadcastVectorTo2D) {
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.0, 2.0, 3.0})));
+      Literal::CreateR1<float>({1.0, 2.0, 3.0})));
 
   // Broadcast vector in both dimension 0 and dimension 1. Join them in a tuple
   // to enable testing of the results.
@@ -87,18 +87,18 @@ XLA_TEST_F(BroadcastTest, BroadcastVectorTo2D) {
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
   LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR2<float>({{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}}),
+      *Literal::CreateR2<float>({{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}}),
       result->tuple_literals(0), error_spec_);
 
   LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR2<float>({{1.0, 2.0, 3.0}, {1.0, 2.0, 3.0}}),
+      *Literal::CreateR2<float>({{1.0, 2.0, 3.0}, {1.0, 2.0, 3.0}}),
       result->tuple_literals(1), error_spec_);
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast2DTo2D) {
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
   builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {2, 2}), input, {0, 1}));
 
@@ -108,7 +108,7 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2D) {
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
   LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}}), *result,
+      *Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}}), *result,
       error_spec_);
 }
 
@@ -117,7 +117,7 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2DTranspose) {
   // the dimensions, ie transpose.
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
   builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {2, 2}), input, {1, 0}));
 
@@ -127,14 +127,14 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2DTranspose) {
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
   LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR2<float>({{1.0, 3.0}, {2.0, 4.0}}), *result,
+      *Literal::CreateR2<float>({{1.0, 3.0}, {2.0, 4.0}}), *result,
       error_spec_);
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast2DTo3D) {
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}})));
   builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {2, 3, 2}), input, {0, 2}));
 
@@ -144,15 +144,15 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo3D) {
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
   LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR3<float>({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}},
-                                     {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}}),
+      *Literal::CreateR3<float>({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}},
+                                 {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}}),
       *result, error_spec_);
 }
 
 TEST_F(BroadcastTest, Broadcast_R1_2_To_R4_2x2x3x3) {
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({1.0, 2.0})));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({1.0, 2.0})));
 
   // Broadcast vector in dimension 1.
   builder.AddInstruction(HloInstruction::CreateBroadcast(
@@ -167,8 +167,8 @@ TEST_F(BroadcastTest, Broadcast_R1_2_To_R4_2x2x3x3) {
   Array2D<float> pz({{1, 2}, {1, 2}});
   expected.FillWithPZ(pz);
 
-  LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR4FromArray4D<float>(expected), *result, error_spec_);
+  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D<float>(expected),
+                              *result, error_spec_);
 }
 
 TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) {
@@ -177,7 +177,7 @@ TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) {
   int64 r1_size = input_data.size();
   std::iota(input_data.begin(), input_data.end(), 0.0f);
   auto input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(input_data)));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>(input_data)));
 
   // Broadcast vector in dimension 3.
   builder.AddInstruction(HloInstruction::CreateBroadcast(
@@ -197,8 +197,8 @@ TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) {
   }
   expected.FillWithYX(yx);
 
-  LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR4FromArray4D<float>(expected), *result, error_spec_);
+  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D<float>(expected),
+                              *result, error_spec_);
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) {
@@ -208,7 +208,7 @@ XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) {
   std::vector<float> r1_array(64, 42.0);
 
   auto input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>(r1_array)));
+      HloInstruction::CreateConstant(Literal::CreateR1<float>(r1_array)));
 
   // Broadcast vector in dimension 1.
   builder.AddInstruction(HloInstruction::CreateBroadcast(
@@ -219,14 +219,14 @@ XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR4FromArray4D(r4_array),
-                              *result, error_spec_);
+  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D(r4_array), *result,
+                              error_spec_);
 }
 
 TEST_F(BroadcastTest, Broadcast_R0_to_R4_64x64x3x3) {
   auto builder = HloComputation::Builder(TestName());
   auto input = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
   builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {64, 64, 3, 3}), input, {}));
 
@@ -239,15 +239,15 @@ TEST_F(BroadcastTest, Broadcast_R0_to_R4_64x64x3x3) {
   Array4D<float> expected(64, 64, 3, 3);
   expected.Fill(1.0f);
 
-  LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR4FromArray4D<float>(expected), *result, error_spec_);
+  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D<float>(expected),
+                              *result, error_spec_);
 }
 
 TEST_F(BroadcastTest, Broadcast_R2_2x2_To_R4_3x3x2x2) {
   auto builder = HloComputation::Builder(TestName());
   Array2D<float> to_broadcast({{1.0f, 2.0f}, {3.0f, 4.0f}});
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2FromArray2D<float>(to_broadcast)));
+      Literal::CreateR2FromArray2D<float>(to_broadcast)));
 
   // Broadcast vector in dimensions 2 and 3.
   builder.AddInstruction(HloInstruction::CreateBroadcast(
@@ -261,8 +261,8 @@ TEST_F(BroadcastTest, Broadcast_R2_2x2_To_R4_3x3x2x2) {
   Array4D<float> expected(3, 3, 2, 2);
   expected.FillWithYX(to_broadcast);
 
-  LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR4FromArray4D<float>(expected), *result, error_spec_);
+  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D<float>(expected),
+                              *result, error_spec_);
 }
 
 TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) {
@@ -281,7 +281,7 @@ TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) {
     }
   }
   auto input = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR3FromArray3D<float>(input_vals)));
+      Literal::CreateR3FromArray3D<float>(input_vals)));
 
   // Broadcast vector in dimensions 2 and 3.
   builder.AddInstruction(HloInstruction::CreateBroadcast(
@@ -292,8 +292,8 @@ TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR4FromArray4D<float>(expected), *result, error_spec_);
+  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D<float>(expected),
+                              *result, error_spec_);
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc
index 0d587a4964..086199fda1 100644
--- a/tensorflow/compiler/xla/tests/call_test.cc
+++ b/tensorflow/compiler/xla/tests/call_test.cc
@@ -77,7 +77,7 @@ class CallOpTest : public ClientLibraryTestBase {
 XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR0F32IdentityScalar)) {
   ComputationBuilder builder(client_, TestName());
   Computation callee = CreateR0F32IdentityComputation();
-  auto constant = builder.ConstantLiteral(*LiteralUtil::CreateR0<float>(42.0));
+  auto constant = builder.ConstantLiteral(*Literal::CreateR0<float>(42.0));
   builder.Call(callee, {constant});
 
   ComputeAndCompareR0<float>(&builder, 42.0, {}, ErrorSpec(0.01f));
@@ -86,8 +86,8 @@ XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR0F32IdentityScalar)) {
 XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR1S0F32AddArray)) {
   ComputationBuilder builder(client_, TestName());
   Computation callee = CreateR1S0F32AdditionComputation();
-  auto x = builder.ConstantLiteral(*LiteralUtil::CreateR1<float>({}));
-  auto y = builder.ConstantLiteral(*LiteralUtil::CreateR1<float>({}));
+  auto x = builder.ConstantLiteral(*Literal::CreateR1<float>({}));
+  auto y = builder.ConstantLiteral(*Literal::CreateR1<float>({}));
   builder.Call(callee, {x, y});
 
   ComputeAndCompareR1<float>(&builder, {}, {}, ErrorSpec(0.01f));
@@ -96,8 +96,8 @@ XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR1S0F32AddArray)) {
 XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR1S2F32AddArray)) {
   ComputationBuilder builder(client_, TestName());
   Computation callee = CreateR1S2F32AdditionComputation();
-  auto x = builder.ConstantLiteral(*LiteralUtil::CreateR1<float>({1.0f, 2.0f}));
-  auto y = builder.ConstantLiteral(*LiteralUtil::CreateR1<float>({2.0f, 3.0f}));
+  auto x = builder.ConstantLiteral(*Literal::CreateR1<float>({1.0f, 2.0f}));
+  auto y = builder.ConstantLiteral(*Literal::CreateR1<float>({2.0f, 3.0f}));
   builder.Call(callee, {x, y});
 
   ComputeAndCompareR1<float>(&builder, {3.0f, 5.0f}, {}, ErrorSpec(0.01f));
@@ -106,8 +106,8 @@ XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR1S2F32AddArray)) {
 XLA_TEST_F(CallOpTest, DISABLED_ON_GPU(CallR0F32Tuple)) {
   ComputationBuilder builder(client_, TestName());
   Computation callee = CreateR0F32TupleComputation();
-  auto elem = LiteralUtil::CreateR0<float>(42.0);
-  auto tuple = LiteralUtil::MakeTuple({elem.get()});
+  auto elem = Literal::CreateR0<float>(42.0);
+  auto tuple = Literal::MakeTuple({elem.get()});
   builder.Call(callee, {builder.ConstantLiteral(*elem)});
 
   ComputeAndCompareTuple(&builder, *tuple, {}, ErrorSpec(0.01f));
diff --git a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
index b2433f03d3..2f4ad22f5b 100644
--- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
+++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc
@@ -37,7 +37,7 @@ class CheckExecutionArityTest : public ClientLibraryTestBase {};
 
 TEST_F(CheckExecutionArityTest, TwoParamComputationNumArguments) {
   ComputationBuilder builder(client_, "add_two_params");
-  auto param_literal = LiteralUtil::CreateR1<float>({1.1f, 2.2f});
+  auto param_literal = Literal::CreateR1<float>({1.1f, 2.2f});
 
   auto p0 = builder.Parameter(0, param_literal->shape(), "param0");
   auto p1 = builder.Parameter(1, param_literal->shape(), "param1");
@@ -86,12 +86,12 @@ XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) {
   ASSERT_IS_OK(computation_status.status());
   auto computation = computation_status.ConsumeValueOrDie();
 
-  auto f32_literal = LiteralUtil::CreateR0<float>(1.1f);
+  auto f32_literal = Literal::CreateR0<float>(1.1f);
   auto f32_data = client_->TransferToServer(*f32_literal).ConsumeValueOrDie();
-  auto f32_4_literal = LiteralUtil::CreateR1<float>({1.0f, 2.0f, 3.0f, 4.0f});
+  auto f32_4_literal = Literal::CreateR1<float>({1.0f, 2.0f, 3.0f, 4.0f});
   auto f32_4_data =
       client_->TransferToServer(*f32_4_literal).ConsumeValueOrDie();
-  auto u8_4_literal = LiteralUtil::CreateR1U8("hola");
+  auto u8_4_literal = Literal::CreateR1U8("hola");
   auto u8_4_data = client_->TransferToServer(*u8_4_literal).ConsumeValueOrDie();
 
   // Match
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index b96bb8f846..6094a31231 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -113,14 +113,14 @@ string ClientLibraryTestBase::ExecuteToString(
   if (!result.ok()) {
     return result.status().ToString();
   } else {
-    return LiteralUtil::ToString(*result.ValueOrDie());
+    return result.ValueOrDie()->ToString();
   }
 }
 
 void ClientLibraryTestBase::ComputeAndCompareR1(
     ComputationBuilder* builder, const tensorflow::core::Bitmap& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
-  std::unique_ptr<Literal> expected_literal = LiteralUtil::CreateR1(expected);
+  std::unique_ptr<Literal> expected_literal = Literal::CreateR1(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments);
 }
@@ -179,10 +179,10 @@ void ClientLibraryTestBase::ComputeAndCompareR1U8(
   auto actual = actual_status.ConsumeValueOrDie();
 
   // Turn the expected value into a literal.
-  std::unique_ptr<Literal> expected_literal = LiteralUtil::CreateR1U8(expected);
+  std::unique_ptr<Literal> expected_literal = Literal::CreateR1U8(expected);
 
-  VLOG(1) << "expected: " << LiteralUtil::ToString(*expected_literal);
-  VLOG(1) << "actual:   " << LiteralUtil::ToString(*actual);
+  VLOG(1) << "expected: " << expected_literal->ToString();
+  VLOG(1) << "actual:   " << actual->ToString();
 
   EXPECT_EQ(expected, actual->u8s_string());
 }
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index f9e1082ebb..a5cb74bdfa 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -278,7 +278,7 @@ void ClientLibraryTestBase::ComputeAndCompareR0(
     ComputationBuilder* builder, NativeT expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR0<NativeT>(expected);
+      Literal::CreateR0<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments);
 }
@@ -291,7 +291,7 @@ void ClientLibraryTestBase::ComputeAndCompareR0(
                     std::is_same<NativeT, double>::value,
                 "Floating point type required when specifying an ErrorSpec");
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR0<NativeT>(expected);
+      Literal::CreateR0<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments, error);
 }
@@ -301,7 +301,7 @@ void ClientLibraryTestBase::ComputeAndCompareR1(
     ComputationBuilder* builder, tensorflow::gtl::ArraySlice<NativeT> expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR1<NativeT>(expected);
+      Literal::CreateR1<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments);
 }
@@ -314,7 +314,7 @@ void ClientLibraryTestBase::ComputeAndCompareR1(
                     std::is_same<NativeT, double>::value,
                 "Floating point type required when specifying an ErrorSpec");
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR1<NativeT>(expected);
+      Literal::CreateR1<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments, error);
 }
@@ -324,7 +324,7 @@ void ClientLibraryTestBase::ComputeAndCompareR2(
     ComputationBuilder* builder, const Array2D<NativeT>& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR2FromArray2D<NativeT>(expected);
+      Literal::CreateR2FromArray2D<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments);
 }
@@ -337,7 +337,7 @@ void ClientLibraryTestBase::ComputeAndCompareR2(
                     std::is_same<NativeT, double>::value,
                 "Floating point type required when specifying an ErrorSpec");
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR2FromArray2D<NativeT>(expected);
+      Literal::CreateR2FromArray2D<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments, error);
 }
@@ -347,7 +347,7 @@ void ClientLibraryTestBase::ComputeAndCompareR3(
     ComputationBuilder* builder, const Array3D<NativeT>& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR3FromArray3D<NativeT>(expected);
+      Literal::CreateR3FromArray3D<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments);
 }
@@ -360,7 +360,7 @@ void ClientLibraryTestBase::ComputeAndCompareR3(
                     std::is_same<NativeT, double>::value,
                 "Floating point type required when specifying an ErrorSpec");
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR3FromArray3D<NativeT>(expected);
+      Literal::CreateR3FromArray3D<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments, error);
 }
@@ -370,7 +370,7 @@ void ClientLibraryTestBase::ComputeAndCompareR4(
     ComputationBuilder* builder, const Array4D<NativeT>& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR4FromArray4D<NativeT>(expected);
+      Literal::CreateR4FromArray4D<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments);
 }
@@ -383,7 +383,7 @@ void ClientLibraryTestBase::ComputeAndCompareR4(
                     std::is_same<NativeT, double>::value,
                 "Floating point type required when specifying an ErrorSpec");
   std::unique_ptr<Literal> expected_literal =
-      LiteralUtil::CreateR4FromArray4D<NativeT>(expected);
+      Literal::CreateR4FromArray4D<NativeT>(expected);
   ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
                                                   arguments, error);
 }
@@ -392,7 +392,7 @@ template <typename NativeT>
 std::unique_ptr<GlobalData> ClientLibraryTestBase::CreateR0Parameter(
     NativeT value, int64 parameter_number, const string& name,
     ComputationBuilder* builder, ComputationDataHandle* data_handle) {
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR0(value);
+  std::unique_ptr<Literal> literal = Literal::CreateR0(value);
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
   *data_handle = builder->Parameter(parameter_number, literal->shape(), name);
@@ -404,7 +404,7 @@ std::unique_ptr<GlobalData> ClientLibraryTestBase::CreateR1Parameter(
     tensorflow::gtl::ArraySlice<NativeT> values, int64 parameter_number,
     const string& name, ComputationBuilder* builder,
     ComputationDataHandle* data_handle) {
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR1(values);
+  std::unique_ptr<Literal> literal = Literal::CreateR1(values);
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
   *data_handle = builder->Parameter(parameter_number, literal->shape(), name);
@@ -416,7 +416,7 @@ std::unique_ptr<GlobalData> ClientLibraryTestBase::CreateR2Parameter(
     const Array2D<NativeT>& array_2d, int64 parameter_number,
     const string& name, ComputationBuilder* builder,
     ComputationDataHandle* data_handle) {
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR2FromArray2D(array_2d);
+  std::unique_ptr<Literal> literal = Literal::CreateR2FromArray2D(array_2d);
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
   *data_handle = builder->Parameter(parameter_number, literal->shape(), name);
@@ -428,7 +428,7 @@ std::unique_ptr<GlobalData> ClientLibraryTestBase::CreateR3Parameter(
     const Array3D<NativeT>& array_3d, int64 parameter_number,
     const string& name, ComputationBuilder* builder,
     ComputationDataHandle* data_handle) {
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR3FromArray3D(array_3d);
+  std::unique_ptr<Literal> literal = Literal::CreateR3FromArray3D(array_3d);
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
   *data_handle = builder->Parameter(parameter_number, literal->shape(), name);
diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
index 3439be46fd..7038afc5b1 100644
--- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc
+++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
@@ -50,7 +50,7 @@ class CompilationCacheTest : public ClientLibraryTestBase {
                                  /*execution_options=*/&execution_options_,
                                  &execution_profile)
             .ConsumeValueOrDie();
-    LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR0<float>(expected_result),
+    LiteralTestUtil::ExpectNear(*Literal::CreateR0<float>(expected_result),
                                 *result, error_spec_);
     EXPECT_EQ(expect_cache_hit, execution_profile.compilation_cache_hit());
   }
@@ -67,7 +67,7 @@ class CompilationCacheTest : public ClientLibraryTestBase {
                            .ConsumeValueOrDie();
     std::unique_ptr<Literal> result =
         client_->Transfer(*data_handle).ConsumeValueOrDie();
-    LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR2<float>(expected_result),
+    LiteralTestUtil::ExpectNear(*Literal::CreateR2<float>(expected_result),
                                 *result, error_spec_);
     EXPECT_EQ(expect_cache_hit, execution_profile.compilation_cache_hit());
   }
@@ -87,13 +87,13 @@ XLA_TEST_F(CompilationCacheTest, ComputationCalledMultipleTimes) {
 
 XLA_TEST_F(CompilationCacheTest, ComputationCalledWithDifferentParameters) {
   std::unique_ptr<GlobalData> data_42 =
-      client_->TransferToServer(*LiteralUtil::CreateR0<float>(42.0f))
+      client_->TransferToServer(*Literal::CreateR0<float>(42.0f))
           .ConsumeValueOrDie();
   std::unique_ptr<GlobalData> data_123 =
-      client_->TransferToServer(*LiteralUtil::CreateR0<float>(123.0f))
+      client_->TransferToServer(*Literal::CreateR0<float>(123.0f))
           .ConsumeValueOrDie();
   std::unique_ptr<GlobalData> data_456 =
-      client_->TransferToServer(*LiteralUtil::CreateR0<float>(456.0f))
+      client_->TransferToServer(*Literal::CreateR0<float>(456.0f))
           .ConsumeValueOrDie();
 
   ComputationBuilder builder(client_, TestName());
diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc
index 264ee9e1ca..4384c9b314 100644
--- a/tensorflow/compiler/xla/tests/compute_constant_test.cc
+++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc
@@ -85,7 +85,7 @@ class ComputeConstantTest : public ::testing::Test {
                                          ComputationBuilder* builder) {
     TF_ASSIGN_OR_RETURN(auto literal,
                         ComputeConstantLiteral(client, operand, builder));
-    return LiteralUtil::Get<Scalar>(*literal, {});
+    return literal->Get<Scalar>({});
   }
 
   bool IsConstant(const ComputationDataHandle& operand,
@@ -210,7 +210,7 @@ TEST_F(ComputeConstantTest, NonScalarAdd) {
     auto computed = ComputeConstantLiteral(client, computation, &b);
     ASSERT_TRUE(computed.ok()) << computed.status();
     std::unique_ptr<Literal> expected_literal =
-        LiteralUtil::CreateR1<int32>({4, 6});
+        Literal::CreateR1<int32>({4, 6});
     LiteralTestUtil::ExpectEqual(*expected_literal, *computed.ValueOrDie());
   }
 }
@@ -224,7 +224,7 @@ TEST_F(ComputeConstantTest, IntegerDivide) {
 
     auto computed = ComputeConstantLiteral(client, computation, &b);
     ASSERT_TRUE(computed.ok()) << computed.status();
-    std::unique_ptr<Literal> expected_literal = LiteralUtil::CreateR0<int32>(5);
+    std::unique_ptr<Literal> expected_literal = Literal::CreateR0<int32>(5);
     LiteralTestUtil::ExpectEqual(*expected_literal, *computed.ValueOrDie());
   }
 }
diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc
index d47144ae4b..c5d88ad6a0 100644
--- a/tensorflow/compiler/xla/tests/concat_test.cc
+++ b/tensorflow/compiler/xla/tests/concat_test.cc
@@ -517,8 +517,8 @@ TEST_P(ConcatR2BinaryTest, DoIt) {
 //     concat
 XLA_TEST_F(ConcatTest, ConcatOperandsOfSameOperand) {
   auto f32_scalar = ShapeUtil::MakeShape(xla::F32, {});
-  auto x_literal = LiteralUtil::CreateR0<float>(2.f);
-  auto y_literal = LiteralUtil::CreateR0<float>(3.f);
+  auto x_literal = Literal::CreateR0<float>(2.f);
+  auto y_literal = Literal::CreateR0<float>(3.f);
   auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie();
   auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie();
 
@@ -539,9 +539,9 @@ XLA_TEST_F(ConcatTest, ConcatOperandsOfSameOperand) {
 // produces the correct result in rank 1.
 XLA_TEST_F(ConcatTest, ConcatBroadcastArgument) {
   auto f32_scalar = ShapeUtil::MakeShape(xla::F32, {});
-  auto x_literal = LiteralUtil::CreateR1<float>({2.0f, 3.0f, 5.0f, 6.0f});
-  auto y_literal = LiteralUtil::CreateR0<float>(1.5f);
-  auto z_literal = LiteralUtil::CreateR0<float>(5.5f);
+  auto x_literal = Literal::CreateR1<float>({2.0f, 3.0f, 5.0f, 6.0f});
+  auto y_literal = Literal::CreateR0<float>(1.5f);
+  auto z_literal = Literal::CreateR0<float>(5.5f);
   auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie();
   auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie();
   auto z_data = client_->TransferToServer(*z_literal).ConsumeValueOrDie();
@@ -567,9 +567,9 @@ XLA_TEST_F(ConcatTest, ConcatBroadcastArgument) {
 XLA_TEST_F(ConcatTest, ConcatBroadcastArgumentR3) {
   auto f32_scalar = ShapeUtil::MakeShape(xla::F32, {});
   Array3D<float> x3d(3, 5, 7, 3.14f);
-  auto x_literal = LiteralUtil::CreateR3FromArray3D<float>(x3d);
-  auto y_literal = LiteralUtil::CreateR0<float>(1.5f);
-  auto z_literal = LiteralUtil::CreateR0<float>(5.5f);
+  auto x_literal = Literal::CreateR3FromArray3D<float>(x3d);
+  auto y_literal = Literal::CreateR0<float>(1.5f);
+  auto z_literal = Literal::CreateR0<float>(5.5f);
   auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie();
   auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie();
   auto z_data = client_->TransferToServer(*z_literal).ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc
index 356d4a7161..7c276c8c8d 100644
--- a/tensorflow/compiler/xla/tests/constants_test.cc
+++ b/tensorflow/compiler/xla/tests/constants_test.cc
@@ -112,7 +112,7 @@ TEST_F(ConstantsTest, Small_2x2) {
 TEST_F(ConstantsTest, Empty_3x0x2) {
   ComputationBuilder builder(client_, TestName());
   auto constant = builder.ConstantLiteral(
-      *LiteralUtil::CreateR3FromArray3D<float>(Array3D<float>(3, 0, 2)));
+      *Literal::CreateR3FromArray3D<float>(Array3D<float>(3, 0, 2)));
 
   ComputeAndCompareR3<float>(&builder, Array3D<float>(3, 0, 2), {});
 }
@@ -127,8 +127,8 @@ TEST_F(ConstantsTest, Small_2x2x2) {
       {{5.f, 6.f},   // y0
        {7.f, 8.f}},  // y1
   });
-  auto constant = builder.ConstantLiteral(
-      *LiteralUtil::CreateR3FromArray3D<float>(array3d));
+  auto constant =
+      builder.ConstantLiteral(*Literal::CreateR3FromArray3D<float>(array3d));
 
   ComputeAndCompareR3<float>(&builder, array3d, {});
 }
@@ -142,7 +142,7 @@ TEST_F(ConstantsTest, Small_3x2x1x1) {
       {5.0f, 4.4f},   // p2
   });
   input_array.FillWithPZ(pz);
-  Literal input_literal = *LiteralUtil::CreateR4FromArray4D(input_array);
+  Literal input_literal = *Literal::CreateR4FromArray4D(input_array);
 
   {
     ComputationBuilder builder(client_, TestName());
@@ -160,9 +160,9 @@ TEST_F(ConstantsTest, Small_3x2x1x1) {
 // TODO(b/29263943): Support tuple constants.
 TEST_F(ConstantsTest, DISABLED_TupleConstant) {
   ComputationBuilder builder(client_, TestName());
-  builder.ConstantLiteral(*LiteralUtil::MakeTuple(
-      {LiteralUtil::CreateR2<float>({{1.0}, {2.0}}).get(),
-       LiteralUtil::CreateR1<float>({2.0, 42}).get()}));
+  builder.ConstantLiteral(
+      *Literal::MakeTuple({Literal::CreateR2<float>({{1.0}, {2.0}}).get(),
+                           Literal::CreateR1<float>({2.0, 42}).get()}));
 
   std::unique_ptr<Literal> result = ExecuteAndTransferOrDie(&builder, {});
 
diff --git a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
index 34c8ef4cee..fb50d9b0eb 100644
--- a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
@@ -62,8 +62,7 @@ XLA_TEST_F(ConvolutionDimensionNumbersTest,
   auto weight_array = MakeUnique<Array4D<float>>(4, 3, 1, 1);
   weight_array->FillWithMultiples(0.2);
   auto weight_data =
-      client_
-          ->TransferToServer(*LiteralUtil::CreateR4FromArray4D(*weight_array))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(*weight_array))
           .ConsumeValueOrDie();
 
   ComputationBuilder builder(client_, TestName());
diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc
index 8a6c5adb8a..a110082f9a 100644
--- a/tensorflow/compiler/xla/tests/convolution_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_test.cc
@@ -114,10 +114,10 @@ TEST_F(ConvolutionTest, Convolve_1x1x1x2_1x1x1x2_Valid) {
       ReferenceUtil::ConvArray4D(input, filter, {1, 1}, Padding::kValid);
 
   auto input_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(input))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(input))
           .ConsumeValueOrDie();
   auto filter_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(filter))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(filter))
           .ConsumeValueOrDie();
 
   ComputeAndCompareR4<float>(&builder, *aexpected,
@@ -157,10 +157,10 @@ TEST_F(ConvolutionTest, Convolve_1x1x4x4_1x1x2x2_Valid) {
       ReferenceUtil::ConvArray4D(input, filter, {1, 1}, Padding::kValid);
 
   auto input_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(input))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(input))
           .ConsumeValueOrDie();
   auto filter_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(filter))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(filter))
           .ConsumeValueOrDie();
 
   ComputeAndCompareR4<float>(&builder, *aexpected,
@@ -200,10 +200,10 @@ TEST_F(ConvolutionTest, Convolve_1x1x4x4_1x1x2x2_Same) {
       ReferenceUtil::ConvArray4D(input, filter, {1, 1}, Padding::kSame);
 
   auto input_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(input))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(input))
           .ConsumeValueOrDie();
   auto filter_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(filter))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(filter))
           .ConsumeValueOrDie();
 
   ComputeAndCompareR4<float>(&builder, *aexpected,
@@ -245,10 +245,10 @@ TEST_F(ConvolutionTest, Convolve_1x1x4x4_1x1x3x3_Same) {
       ReferenceUtil::ConvArray4D(input, filter, {1, 1}, Padding::kSame);
 
   auto input_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(input))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(input))
           .ConsumeValueOrDie();
   auto filter_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR4FromArray4D(filter))
+      client_->TransferToServer(*Literal::CreateR4FromArray4D(filter))
           .ConsumeValueOrDie();
 
   ComputeAndCompareR4<float>(&builder, *aexpected,
@@ -272,10 +272,10 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_Valid) {
   Array3D<float> expected({{{510, 610, 710, 810}}});
 
   auto input_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(input))
+      client_->TransferToServer(*Literal::CreateR3FromArray3D(input))
           .ConsumeValueOrDie();
   auto filter_literal =
-      client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(filter))
+      client_->TransferToServer(*Literal::CreateR3FromArray3D(filter))
           .ConsumeValueOrDie();
 
   ComputeAndCompareR3<float>(&builder, expected,
@@ -312,21 +312,18 @@ XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) {
 
   std::vector<float> input_elems(ShapeUtil::ElementsIn(input_shape));
   std::iota(input_elems.begin(), input_elems.end(), 1.0f);
-  auto input_r1 = LiteralUtil::CreateR1<float>(input_elems);
-  auto input_r5 =
-      LiteralUtil::Reshape(*input_r1, input_dims).ConsumeValueOrDie();
+  auto input_r1 = Literal::CreateR1<float>(input_elems);
+  auto input_r5 = input_r1->Reshape(input_dims).ConsumeValueOrDie();
 
   std::vector<float> filter_elems(ShapeUtil::ElementsIn(filter_shape));
   std::iota(filter_elems.begin(), filter_elems.end(), 1.0f);
-  auto filter_r1 = LiteralUtil::CreateR1<float>(filter_elems);
-  auto filter_r5 =
-      LiteralUtil::Reshape(*filter_r1, filter_dims).ConsumeValueOrDie();
+  auto filter_r1 = Literal::CreateR1<float>(filter_elems);
+  auto filter_r5 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie();
 
-  auto expected_r1 = LiteralUtil::CreateR1<float>(
+  auto expected_r1 = Literal::CreateR1<float>(
       {19554, 19962, 20370, 22110, 22590, 23070, 34890, 35730, 36570, 37446,
        38358, 39270, 50226, 51498, 52770, 52782, 54126, 55470});
-  auto expected_r5 =
-      LiteralUtil::Reshape(*expected_r1, {1, 3, 1, 2, 3}).ConsumeValueOrDie();
+  auto expected_r5 = expected_r1->Reshape({1, 3, 1, 2, 3}).ConsumeValueOrDie();
 
   auto input_literal = client_->TransferToServer(*input_r5).ConsumeValueOrDie();
   auto filter_literal =
diff --git a/tensorflow/compiler/xla/tests/convolution_variants_test.cc b/tensorflow/compiler/xla/tests/convolution_variants_test.cc
index dfaa023e33..c8e74aa01a 100644
--- a/tensorflow/compiler/xla/tests/convolution_variants_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_variants_test.cc
@@ -1311,20 +1311,19 @@ TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding1D) {
 TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding3D) {
   ComputationBuilder builder(client_, TestName());
 
-  auto gradients_flat = LiteralUtil::CreateR1<float>({1});
+  auto gradients_flat = Literal::CreateR1<float>({1});
   auto gradients_literal =
-      LiteralUtil::Reshape(*gradients_flat, {1, 1, 1, 1, 1})
-          .ConsumeValueOrDie();
+      gradients_flat->Reshape({1, 1, 1, 1, 1}).ConsumeValueOrDie();
   auto gradients = builder.ConstantLiteral(*gradients_literal);
 
-  auto weights_flat = LiteralUtil::CreateR1<float>({1, 10, 100});
+  auto weights_flat = Literal::CreateR1<float>({1, 10, 100});
   auto weights_literal =
-      LiteralUtil::Reshape(*weights_flat, {1, 1, 1, 1, 3}).ConsumeValueOrDie();
+      weights_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie();
   auto weights = builder.ConstantLiteral(*weights_literal);
 
-  auto expected_flat = LiteralUtil::CreateR1<float>({10});
+  auto expected_flat = Literal::CreateR1<float>({10});
   auto expected_literal =
-      LiteralUtil::Reshape(*expected_flat, {1, 1, 1, 1, 1}).ConsumeValueOrDie();
+      expected_flat->Reshape({1, 1, 1, 1, 1}).ConsumeValueOrDie();
 
   auto mirrored_weights = builder.Rev(weights, {2, 3, 4});
   builder.ConvWithGeneralPadding(gradients, mirrored_weights,
@@ -1336,21 +1335,19 @@ TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding3D) {
 TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding3D) {
   ComputationBuilder builder(client_, TestName());
 
-  auto activations_flat = LiteralUtil::CreateR1<float>({1, 2, 3, 4});
+  auto activations_flat = Literal::CreateR1<float>({1, 2, 3, 4});
   auto activations_literal =
-      LiteralUtil::Reshape(*activations_flat, {1, 1, 1, 1, 4})
-          .ConsumeValueOrDie();
+      activations_flat->Reshape({1, 1, 1, 1, 4}).ConsumeValueOrDie();
   auto activations = builder.ConstantLiteral(*activations_literal);
 
-  auto gradients_flat = LiteralUtil::CreateR1<float>({100, 10, 1});
+  auto gradients_flat = Literal::CreateR1<float>({100, 10, 1});
   auto gradients_literal =
-      LiteralUtil::Reshape(*gradients_flat, {1, 1, 1, 1, 3})
-          .ConsumeValueOrDie();
+      gradients_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie();
   auto gradients = builder.ConstantLiteral(*gradients_literal);
 
-  auto expected_flat = LiteralUtil::CreateR1<float>({13, 24, 130});
+  auto expected_flat = Literal::CreateR1<float>({13, 24, 130});
   auto expected_literal =
-      LiteralUtil::Reshape(*expected_flat, {1, 1, 1, 1, 3}).ConsumeValueOrDie();
+      expected_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie();
 
   auto forward_conv = builder.ConvGeneralDilated(
       activations, gradients,
diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc
index 47bcc96c19..76ae280f1a 100644
--- a/tensorflow/compiler/xla/tests/copy_test.cc
+++ b/tensorflow/compiler/xla/tests/copy_test.cc
@@ -57,39 +57,34 @@ class CopyOpTest : public HloTestBase {
                                 tensorflow::gtl::ArraySlice<int64> permutation);
 };
 
-TEST_F(CopyOpTest, CopyR0Bool) {
-  TestCopyOp(*LiteralUtil::CreateR0<bool>(true));
-}
+TEST_F(CopyOpTest, CopyR0Bool) { TestCopyOp(*Literal::CreateR0<bool>(true)); }
 
-TEST_F(CopyOpTest, CopyR1S0U32) {
-  TestCopyOp(*LiteralUtil::CreateR1<uint32>({}));
-}
+TEST_F(CopyOpTest, CopyR1S0U32) { TestCopyOp(*Literal::CreateR1<uint32>({})); }
 
 TEST_F(CopyOpTest, CopyR1S3U32) {
-  TestCopyOp(*LiteralUtil::CreateR1<uint32>({1, 2, 3}));
+  TestCopyOp(*Literal::CreateR1<uint32>({1, 2, 3}));
 }
 
 TEST_F(CopyOpTest, CopyR3F32_2x2x3) {
-  TestCopyOp(
-      *LiteralUtil::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}},
-                              {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}));
+  TestCopyOp(*Literal::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}},
+                                 {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}));
 }
 
 TEST_F(CopyOpTest, CopyR4S32_2x2x3x2) {
-  TestCopyOp(*LiteralUtil::CreateR4(
+  TestCopyOp(*Literal::CreateR4(
       {{{{1, -2}, {-4, 5}, {6, 7}}, {{8, 9}, {10, 11}, {12, 13}}},
        {{{10, 3}, {7, -2}, {3, 6}}, {{2, 5}, {-11, 5}, {-2, -5}}}}));
 }
 
 TEST_F(CopyOpTest, CopyR4S32_0x2x3x2) {
-  TestCopyOp(*LiteralUtil::CreateR4FromArray4D(Array4D<int32>(0, 2, 3, 2)));
+  TestCopyOp(*Literal::CreateR4FromArray4D(Array4D<int32>(0, 2, 3, 2)));
 }
 
 TEST_F(CopyOpTest, CopyParameterScalar) {
   auto builder = HloComputation::Builder(TestName());
 
   // Copy literal to device to use as parameter.
-  auto literal = LiteralUtil::CreateR0<float>(42.0);
+  auto literal = Literal::CreateR0<float>(42.0);
   Shape shape = literal->shape();
   auto constant_device_base = TransferToDevice(*literal);
 
@@ -111,7 +106,7 @@ TEST_F(CopyOpTest, CopyParameterScalar) {
 TEST_F(CopyOpTest, CopyConstantR2Twice) {
   auto builder = HloComputation::Builder(TestName());
 
-  auto literal = LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
+  auto literal = Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
   auto constant = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
 
@@ -133,7 +128,7 @@ TEST_F(CopyOpTest, CopyConstantR2DifferentLayouts) {
   HloComputation::Builder builder(TestName());
 
   std::unique_ptr<Literal> literal =
-      LiteralUtil::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}});
   // Reverse the minor-to-major order of the literal.
   Layout* literal_layout = literal->mutable_shape()->mutable_layout();
   ASSERT_EQ(2, literal_layout->minor_to_major_size());
@@ -169,7 +164,7 @@ void CopyOpTest::TestCopyConstantLayout021(size_t n1, size_t n2, size_t n3) {
 
   HloComputation::Builder builder(TestName());
 
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR3FromArray3D(a);
+  std::unique_ptr<Literal> literal = Literal::CreateR3FromArray3D(a);
 
   HloInstruction* constant = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
@@ -203,7 +198,7 @@ void CopyOpTest::TestCopyConstantLayoutR4(
 
   HloComputation::Builder builder(TestName());
 
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR4FromArray4D(a);
+  std::unique_ptr<Literal> literal = Literal::CreateR4FromArray4D(a);
 
   HloInstruction* constant = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
@@ -246,7 +241,7 @@ using CopyOpClientTest = ClientLibraryTestBase;
 XLA_TEST_F(CopyOpClientTest, Copy0x0) {
   Shape in_shape = ShapeUtil::MakeShapeWithLayout(F32, {0, 0}, {0, 1});
   Shape out_shape = ShapeUtil::MakeShapeWithLayout(F32, {0, 0}, {1, 0});
-  auto empty = LiteralUtil::CreateFromShape(in_shape);
+  auto empty = Literal::CreateFromShape(in_shape);
 
   ComputationBuilder builder(client_, TestName());
   auto param0 = builder.Parameter(0, in_shape, "input");
diff --git a/tensorflow/compiler/xla/tests/custom_call_test.cc b/tensorflow/compiler/xla/tests/custom_call_test.cc
index 903c01f1c6..73772fdec0 100644
--- a/tensorflow/compiler/xla/tests/custom_call_test.cc
+++ b/tensorflow/compiler/xla/tests/custom_call_test.cc
@@ -67,7 +67,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR0F32Add2)) {
   auto builder = HloComputation::Builder(TestName());
 
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f)));
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.0f)));
   builder.AddInstruction(
       HloInstruction::CreateCustomCall(r0f32_, {constant}, "R0F32Add2"));
 
@@ -88,7 +88,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR2F32Reduce)) {
   array(1, 1) = 4.0f;
 
   auto constant = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR2FromArray2D(array)));
+      HloInstruction::CreateConstant(Literal::CreateR2FromArray2D(array)));
   builder.AddInstruction(
       HloInstruction::CreateCustomCall(r0f32_, {constant}, "R2F32ReduceSum"));
 
@@ -104,7 +104,7 @@ XLA_TEST_F(CustomCallTest,
   auto b = HloComputation::Builder(TestName());
 
   auto input = b.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR2FromArray2D(
+      HloInstruction::CreateConstant(Literal::CreateR2FromArray2D(
           Array2D<float>{{1.0f, 2.0f}, {3.0f, 4.0f}})));
   auto incremented = b.AddInstruction(HloInstruction::CreateCustomCall(
       ShapeUtil::MakeShape(F32, {1, 2, 2}), {input}, "Add1ToValues"));
diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
index 23f9c8e880..3d6a995a24 100644
--- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
@@ -173,7 +173,7 @@ TEST_F(DeconstructTupleTest, DeconstructNonTuple) {
 XLA_TEST_F(DeconstructTupleTest, DeconstructTupleFromParam) {
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({3.14f, -100.25f});
+      Literal::CreateR1<float>({3.14f, -100.25f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
   auto p = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2}), "param0");
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 1581e15d7b..b06b5c5f47 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -185,14 +185,14 @@ void DotOperationTest::TestMatrixDot(int M, int K, int N, bool lhs_row_major,
                                      bool rhs_row_major) {
   std::unique_ptr<Array2D<float>> lhs_data =
       MakeLinspaceArray2D(0.0, 1.0, M, K);
-  std::unique_ptr<Literal> lhs_lit = LiteralUtil::CreateR2FromArray2DWithLayout(
+  std::unique_ptr<Literal> lhs_lit = Literal::CreateR2FromArray2DWithLayout(
       *lhs_data,
       LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(lhs_row_major)));
   auto lhs_handle = client_->TransferToServer(*lhs_lit).ConsumeValueOrDie();
 
   std::unique_ptr<Array2D<float>> rhs_data =
       MakeLinspaceArray2D(0.0, 1.0, K, N);
-  std::unique_ptr<Literal> rhs_lit = LiteralUtil::CreateR2FromArray2DWithLayout(
+  std::unique_ptr<Literal> rhs_lit = Literal::CreateR2FromArray2DWithLayout(
       *rhs_data,
       LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(rhs_row_major)));
   auto rhs_handle = client_->TransferToServer(*rhs_lit).ConsumeValueOrDie();
@@ -379,12 +379,12 @@ XLA_TEST_F(DotOperationTest, BatchMatMul) {
   builder.Reshape(out_flat, {0, 1, 2}, {2, 2, 2, 2});
 
   auto x_data = client_
-                    ->TransferToServer(*LiteralUtil::CreateR4<float>(
+                    ->TransferToServer(*Literal::CreateR4<float>(
                         {{{{1000, 100}, {10, 1}}, {{2000, 200}, {20, 2}}},
                          {{{3000, 300}, {30, 3}}, {{4000, 400}, {40, 4}}}}))
                     .ConsumeValueOrDie();
   auto y_data = client_
-                    ->TransferToServer(*LiteralUtil::CreateR4<float>(
+                    ->TransferToServer(*Literal::CreateR4<float>(
                         {{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}},
                          {{{11, 22}, {33, 44}}, {{55, 66}, {77, 88}}}}))
                     .ConsumeValueOrDie();
@@ -415,14 +415,14 @@ TEST_F(DotOperationTest, TransposeFolding) {
         auto lhs_handle =
             client_
                 ->TransferToServer(
-                    *LiteralUtil::CreateR2FromArray2DWithLayout<float>(
+                    *Literal::CreateR2FromArray2DWithLayout<float>(
                         *lhs, LayoutUtil::MakeLayout(
                                   MinorToMajorForIsRowMajor(row_major))))
                 .ConsumeValueOrDie();
         auto rhs_handle =
             client_
                 ->TransferToServer(
-                    *LiteralUtil::CreateR2FromArray2DWithLayout<float>(
+                    *Literal::CreateR2FromArray2DWithLayout<float>(
                         *rhs, LayoutUtil::MakeLayout(
                                   MinorToMajorForIsRowMajor(row_major))))
                 .ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
index d86998b708..f653766f39 100644
--- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
@@ -388,8 +388,8 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase {
   template <typename NativeT>
   void DumpArray(const string& name, const Array3D<NativeT> values) {
     std::unique_ptr<Literal> literal =
-        LiteralUtil::CreateR3FromArray3D<NativeT>(values);
-    LOG(INFO) << name << ":" << LiteralUtil::ToString(*literal);
+        Literal::CreateR3FromArray3D<NativeT>(values);
+    LOG(INFO) << name << ":" << literal->ToString();
   }
 };
 
@@ -469,7 +469,7 @@ void BM_DynamicSlice(int num_iters) {
   ComputationBuilder builder(client, "DynamicSlice");
 
   // Create input as a constant: shape [1, 2, 3, 4]
-  auto input_literal = LiteralUtil::CreateR4(
+  auto input_literal = Literal::CreateR4(
       {{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}},
         {{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}});
   auto input = builder.ConstantLiteral(*input_literal);
@@ -487,7 +487,7 @@ void BM_DynamicSlice(int num_iters) {
                                                            &allocator, 0)
                     .ConsumeValueOrDie();
 
-  auto start_indices_literal = LiteralUtil::CreateR1<int32>({0, 1, 2, 3});
+  auto start_indices_literal = Literal::CreateR1<int32>({0, 1, 2, 3});
   ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice(
       executors[device_ordinal], *start_indices_literal,
       buffer->mutable_buffer({})));
diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index 0e4fc33579..3546e9a1e8 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -80,7 +80,7 @@ class FusionTest : public HloTestBase {
     HloInstruction* hlos[4];
     for (int i = 0; i < Arity; ++i) {
       hlos[i + 1] = builder.AddInstruction(HloInstruction::CreateConstant(
-          LiteralUtil::CreateR2FromArray2D(operand_data[i])));
+          Literal::CreateR2FromArray2D(operand_data[i])));
     }
     auto answer_shape =
         ShapeUtil::MakeShape(prim_type, {test_width, test_height});
@@ -106,7 +106,7 @@ class FusionTest : public HloTestBase {
             ArraySlice<HloInstruction*>(hlos, 0, Arity + 1),
             HloInstruction::FusionKind::kLoop);
 
-    auto expected = LiteralUtil::CreateR2FromArray2D(answer_data);
+    auto expected = Literal::CreateR2FromArray2D(answer_data);
     auto actual = ExecuteAndTransfer(std::move(hlo_module), {});
     if (primitive_util::IsFloatingPointType(prim_type)) {
       LiteralTestUtil::ExpectNear(*expected, *actual, ErrorSpec(1e-4));
@@ -177,28 +177,27 @@ XLA_TEST_F(FusionTest, Test) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0}, {2.0}, {3.0}})));
+      Literal::CreateR2<float>({{1.0}, {2.0}, {3.0}})));
   auto const1 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{-1.0}, {-1.0}, {-1.0}})));
+      Literal::CreateR2<float>({{-1.0}, {-1.0}, {-1.0}})));
   auto add2 = builder.AddInstruction(HloInstruction::CreateBinary(
       ShapeUtil::MakeShape(F32, {3, 1}), HloOpcode::kAdd, const0, const1));
   auto reshape3 = builder.AddInstruction(HloInstruction::CreateTranspose(
       ShapeUtil::MakeShape(F32, {1, 3}), add2, {1, 0}));
   auto const4 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.62, 2.72, 3.14}})));
+      Literal::CreateR2<float>({{1.62, 2.72, 3.14}})));
   auto concat5 = builder.AddInstruction(HloInstruction::CreateConcatenate(
       ShapeUtil::MakeShape(F32, {2, 3}), {reshape3, const4}, 0));
   auto const6 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 1.0, 1.0}, {0.0, 0.0, 0.0}})));
+      Literal::CreateR2<float>({{1.0, 1.0, 1.0}, {0.0, 0.0, 0.0}})));
   auto negate7 = builder.AddInstruction(HloInstruction::CreateUnary(
       ShapeUtil::MakeShape(F32, {2, 3}), HloOpcode::kNegate, const6));
   auto add8 = builder.AddInstruction(HloInstruction::CreateBinary(
       ShapeUtil::MakeShape(F32, {2, 3}), HloOpcode::kAdd, concat5, negate7));
   auto const9 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{0.5, 0.5, 0.5}, {0.5, 0.5, 0.5}})));
-  auto const10 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR2<bool>(
-          {{true, false, true}, {false, true, false}})));
+      Literal::CreateR2<float>({{0.5, 0.5, 0.5}, {0.5, 0.5, 0.5}})));
+  auto const10 = builder.AddInstruction(HloInstruction::CreateConstant(
+      Literal::CreateR2<bool>({{true, false, true}, {false, true, false}})));
   auto select11 = builder.AddInstruction(
       HloInstruction::CreateTernary(ShapeUtil::MakeShape(F32, {2, 3}),
                                     HloOpcode::kSelect, const10, add8, const9));
@@ -213,7 +212,7 @@ XLA_TEST_F(FusionTest, Test) {
            const4, reshape3, add2, const1, const0},
           HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR2<float>({{0.5}, {2.72}}),
+  LiteralTestUtil::ExpectNear(*Literal::CreateR2<float>({{0.5}, {2.72}}),
                               *ExecuteAndTransfer(std::move(hlo_module), {}),
                               ErrorSpec(1e-4));
 }
@@ -225,11 +224,11 @@ XLA_TEST_F(FusionTest, Parameter) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{1.0, 2.0, 3.0}})));
+      Literal::CreateR2<float>({{1.0, 2.0, 3.0}})));
   auto copy1 = builder.AddInstruction(HloInstruction::CreateUnary(
       ShapeUtil::MakeShape(F32, {1, 3}), HloOpcode::kCopy, const0));
   auto const2 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{-2.0, -2.0, -2.0}})));
+      Literal::CreateR2<float>({{-2.0, -2.0, -2.0}})));
   // add3 = copy1 + const2 = const0 + const2 = {1,2,3} + {-2,-2,-2} = {-1,0,+1}
   auto add3 = builder.AddInstruction(HloInstruction::CreateBinary(
       ShapeUtil::MakeShape(F32, {1, 3}), HloOpcode::kAdd, copy1, const2));
@@ -239,7 +238,7 @@ XLA_TEST_F(FusionTest, Parameter) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{add3, const2},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectNear(*LiteralUtil::CreateR2<float>({{-1.0, 0.0, 1.0}}),
+  LiteralTestUtil::ExpectNear(*Literal::CreateR2<float>({{-1.0, 0.0, 1.0}}),
                               *ExecuteAndTransfer(std::move(hlo_module), {}),
                               ErrorSpec(1e-4));
 }
@@ -248,9 +247,9 @@ XLA_TEST_F(FusionTest, BroadcastIntoBinaryOp) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const_vector = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<float>({1.0, 2.0, 3.0})));
+      Literal::CreateR1<float>({1.0, 2.0, 3.0})));
   auto const_array = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<float>({{-1.0, -2.0, -4.0}, {10.0, 20.0, 30.0}})));
+      Literal::CreateR2<float>({{-1.0, -2.0, -4.0}, {10.0, 20.0, 30.0}})));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(const_array->shape(), const_vector, {1}));
   // add2 = broadcast(const_vector) + const_array
@@ -264,7 +263,7 @@ XLA_TEST_F(FusionTest, BroadcastIntoBinaryOp) {
                                 HloInstruction::FusionKind::kLoop);
 
   LiteralTestUtil::ExpectNear(
-      *LiteralUtil::CreateR2<float>({{0.0, 0.0, -1.0}, {11.0, 22.0, 33.0}}),
+      *Literal::CreateR2<float>({{0.0, 0.0, -1.0}, {11.0, 22.0, 33.0}}),
       *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4));
 }
 
@@ -272,13 +271,13 @@ XLA_TEST_F(FusionTest, ReshapeToScalar) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto single_element_array = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR2<int32>({{5}})));
+      HloInstruction::CreateConstant(Literal::CreateR2<int32>({{5}})));
   auto reshape = builder.AddInstruction(HloInstruction::CreateReshape(
       ShapeUtil::MakeShape(S32, {}), single_element_array));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*LiteralUtil::CreateR0<int32>(5),
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR0<int32>(5),
                                *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -286,14 +285,14 @@ XLA_TEST_F(FusionTest, Reshape_3by2_1by2by3) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}})));
+      Literal::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}})));
   auto reshape1 = builder.AddInstruction(HloInstruction::CreateReshape(
       ShapeUtil::MakeShape(S32, {1, 2, 3}), const0));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
   LiteralTestUtil::ExpectEqual(
-      *LiteralUtil::CreateR3<int32>({{{1, 2, 3}, {4, 5, 6}}}),
+      *Literal::CreateR3<int32>({{{1, 2, 3}, {4, 5, 6}}}),
       *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -301,14 +300,14 @@ XLA_TEST_F(FusionTest, Reshape_1by2by3_3by2) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR3<int32>({{{1, 2, 3}, {4, 5, 6}}})));
+      Literal::CreateR3<int32>({{{1, 2, 3}, {4, 5, 6}}})));
   auto reshape1 = builder.AddInstruction(
       HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {3, 2}), const0));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
   LiteralTestUtil::ExpectEqual(
-      *LiteralUtil::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}}),
+      *Literal::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}}),
       *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -316,13 +315,13 @@ XLA_TEST_F(FusionTest, Reshape_1by1by1_) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR3<int32>({{{7}}})));
+      HloInstruction::CreateConstant(Literal::CreateR3<int32>({{{7}}})));
   auto reshape1 = builder.AddInstruction(
       HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {}), const0));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*LiteralUtil::CreateR0<int32>(7),
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR0<int32>(7),
                                *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -330,13 +329,13 @@ XLA_TEST_F(FusionTest, Reshape__1by1by1) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(7)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(7)));
   auto reshape1 = builder.AddInstruction(HloInstruction::CreateReshape(
       ShapeUtil::MakeShape(S32, {1, 1, 1}), const0));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*LiteralUtil::CreateR3<int32>({{{7}}}),
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR3<int32>({{{7}}}),
                                *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -344,13 +343,13 @@ XLA_TEST_F(FusionTest, Reshape__) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(7)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(7)));
   auto reshape1 = builder.AddInstruction(
       HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {}), const0));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*LiteralUtil::CreateR0<int32>(7),
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR0<int32>(7),
                                *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -358,14 +357,14 @@ XLA_TEST_F(FusionTest, Reshape_3by3_3by3) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}})));
+      Literal::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}})));
   auto reshape1 = builder.AddInstruction(
       HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {3, 3}), const0));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
   LiteralTestUtil::ExpectEqual(
-      *LiteralUtil::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}),
+      *Literal::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}),
       *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -373,14 +372,14 @@ XLA_TEST_F(FusionTest, Transpose_2by3) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}})));
+      Literal::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}})));
   auto reshape1 = builder.AddInstruction(HloInstruction::CreateTranspose(
       ShapeUtil::MakeShape(S32, {3, 2}), const0, {1, 0}));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
   LiteralTestUtil::ExpectEqual(
-      *LiteralUtil::CreateR2<int32>({{1, 4}, {2, 5}, {3, 6}}),
+      *Literal::CreateR2<int32>({{1, 4}, {2, 5}, {3, 6}}),
       *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -388,14 +387,14 @@ XLA_TEST_F(FusionTest, Transpose_3by3) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}})));
+      Literal::CreateR2<int32>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}})));
   auto reshape1 = builder.AddInstruction(HloInstruction::CreateTranspose(
       ShapeUtil::MakeShape(S32, {3, 3}), const0, {1, 0}));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
   LiteralTestUtil::ExpectEqual(
-      *LiteralUtil::CreateR2<int32>({{1, 4, 7}, {2, 5, 8}, {3, 6, 9}}),
+      *Literal::CreateR2<int32>({{1, 4, 7}, {2, 5, 8}, {3, 6, 9}}),
       *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -403,14 +402,14 @@ XLA_TEST_F(FusionTest, Reverse) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR1<int32>({1, 2, 3})));
+      HloInstruction::CreateConstant(Literal::CreateR1<int32>({1, 2, 3})));
   auto reverse1 = builder.AddInstruction(HloInstruction::CreateReverse(
       ShapeUtil::MakeShape(S32, {3}), const0, {0}));
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reverse1},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*LiteralUtil::CreateR1<int32>({3, 2, 1}),
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR1<int32>({3, 2, 1}),
                                *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -429,10 +428,10 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(Reduce)) {
   auto hlo_module = CreateNewModule();
 
   auto builder = HloComputation::Builder(TestName());
-  auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<int32>({1, 2, 4, 8})));
+  auto const0 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<int32>({1, 2, 4, 8})));
   auto const1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(0)));
   auto reduce2 = builder.AddInstruction(HloInstruction::CreateReduce(
       ShapeUtil::MakeShape(S32, {}), const0, const1, {0},
       hlo_module->AddEmbeddedComputation(MakeReduceTestComputation())));
@@ -440,7 +439,7 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(Reduce)) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reduce2},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*LiteralUtil::CreateR0<int32>(15),
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR0<int32>(15),
                                *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -448,10 +447,10 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceImplicitBroadcast)) {
   auto hlo_module = CreateNewModule();
 
   auto builder = HloComputation::Builder(TestName());
-  auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR1<int32>({1, 2, 4, 8})));
+  auto const0 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<int32>({1, 2, 4, 8})));
   auto const1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(0)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(0)));
   auto reduce2 = builder.AddInstruction(HloInstruction::CreateReduce(
       ShapeUtil::MakeShape(S32, {}), const0, const1, {0},
       hlo_module->AddEmbeddedComputation(MakeReduceTestComputation())));
@@ -461,7 +460,7 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceImplicitBroadcast)) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate3, reduce2},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*LiteralUtil::CreateR1<int32>({-15}),
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR1<int32>({-15}),
                                *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
@@ -469,9 +468,9 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceWindow)) {
   auto builder = HloComputation::Builder(TestName());
   auto hlo_module = CreateNewModule();
   auto const0 = builder.AddInstruction(HloInstruction::CreateConstant(
-      LiteralUtil::CreateR2<int32>({{2, 3, 5}, {7, 11, 13}, {17, 19, 23}})));
+      Literal::CreateR2<int32>({{2, 3, 5}, {7, 11, 13}, {17, 19, 23}})));
   auto const1 = builder.AddInstruction(
-      HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(1)));
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(1)));
   Window window;
   ASSERT_TRUE(
       tensorflow::protobuf::TextFormat::ParseFromString("dimensions:{\n"
@@ -511,7 +510,7 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceWindow)) {
                                 HloInstruction::FusionKind::kLoop);
 
   LiteralTestUtil::ExpectEqual(
-      *LiteralUtil::CreateR2<int32>({{462, 2145}, {24871, 62491}}),
+      *Literal::CreateR2<int32>({{462, 2145}, {24871, 62491}}),
       *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index f3a49059c9..69c12cc437 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -133,8 +133,8 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
                          tensorflow::gtl::MutableArraySlice<int64> multi_index,
                          int64 dimension) {
   if (dimension == expected.shape().dimensions_size()) {
-    NativeT expected_value = LiteralUtil::Get<NativeT>(expected, multi_index);
-    NativeT actual_value = LiteralUtil::Get<NativeT>(actual, multi_index);
+    NativeT expected_value = expected.Get<NativeT>(multi_index);
+    NativeT actual_value = actual.Get<NativeT>(multi_index);
     ::testing::AssertionResult result =
         CompareEqual<NativeT>(expected_value, actual_value);
     return result;  // Defines implicit coersion to bool.
@@ -153,10 +153,10 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
 
 /* static */ void LiteralTestUtil::ExpectEqual(const Literal& expected,
                                                const Literal& actual) {
-  EXPECT_TRUE(Equal(expected, actual)) << "expected:\n"
-                                       << LiteralUtil::ToString(expected)
-                                       << "\n\tvs actual:\n"
-                                       << LiteralUtil::ToString(actual);
+  EXPECT_TRUE(Equal(expected, actual))
+      << "expected:\n"
+      << expected.ToString() << "\n\tvs actual:\n"
+      << actual.ToString();
 }
 
 /* static */ void LiteralTestUtil::ExpectNotEqual(const Literal& expected,
@@ -166,8 +166,8 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
 
 /* static */ ::testing::AssertionResult LiteralTestUtil::Equal(
     const Literal& expected, const Literal& actual) {
-  VLOG(1) << "expected: " << LiteralUtil::ToString(expected);
-  VLOG(1) << "actual:   " << LiteralUtil::ToString(actual);
+  VLOG(1) << "expected: " << expected.ToString();
+  VLOG(1) << "actual:   " << actual.ToString();
 
   AssertEqualShapes(expected.shape(), actual.shape());
   std::vector<int64> multi_index(expected.shape().dimensions_size(), 0);
@@ -215,8 +215,8 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
   ::testing::AssertionResult result = ::testing::AssertionSuccess();
   if (!match) {
     result = ::testing::AssertionFailure()
-             << "expected: " << LiteralUtil::ToString(expected)
-             << "\nactual:   " << LiteralUtil::ToString(actual);
+             << "expected: " << expected.ToString()
+             << "\nactual:   " << actual.ToString();
     VLOG(1) << result.message();
   }
   return result;
@@ -224,8 +224,8 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
 
 /* static */ void LiteralTestUtil::ExpectEqualTuple(const Literal& expected,
                                                     const Literal& actual) {
-  VLOG(1) << "expected: " << LiteralUtil::ToString(expected);
-  VLOG(1) << "actual:   " << LiteralUtil::ToString(actual);
+  VLOG(1) << "expected: " << expected.ToString();
+  VLOG(1) << "actual:   " << actual.ToString();
 
   ASSERT_TRUE(ShapeUtil::IsTuple(expected.shape()));
   ASSERT_TRUE(ShapeUtil::IsTuple(actual.shape()));
@@ -252,8 +252,8 @@ class NearComparator {
   // within the error bound. Emits useful log messages and dumps literals to
   // temporary files on failure. Returns true if  literals match.
   bool ExpectNear(const Literal& expected, const Literal& actual) {
-    VLOG(1) << "expected: " << LiteralUtil::ToString(expected);
-    VLOG(1) << "actual:   " << LiteralUtil::ToString(actual);
+    VLOG(1) << "expected: " << expected.ToString();
+    VLOG(1) << "actual:   " << actual.ToString();
 
     LiteralTestUtil::AssertEqualShapes(expected.shape(), actual.shape());
 
@@ -287,9 +287,9 @@ class NearComparator {
     if (num_miscompares_ > 0) {
       if (!VLOG_IS_ON(1)) {
         LOG(INFO) << "expected: " << ShapeUtil::HumanString(expected.shape())
-                  << " " << LiteralUtil::ToString(expected);
+                  << " " << expected.ToString();
         LOG(INFO) << "actual:   " << ShapeUtil::HumanString(actual.shape())
-                  << " " << LiteralUtil::ToString(actual);
+                  << " " << actual.ToString();
       }
       EXPECT_TRUE(num_miscompares_ == 0)
           << "\nmax relative mismatch at index "
@@ -374,10 +374,9 @@ class NearComparator {
   void ExpectLiteralsNear(const Literal& expected, const Literal& actual,
                           int64 dimension) {
     if (dimension == expected.shape().dimensions_size()) {
-      bool near =
-          ExpectValuesNear(LiteralUtil::Get<NativeT>(expected, multi_index_),
-                           LiteralUtil::Get<NativeT>(actual, multi_index_));
-      LiteralUtil::Set<bool>(&miscompares_, multi_index_, !near);
+      bool near = ExpectValuesNear(expected.Get<NativeT>(multi_index_),
+                                   actual.Get<NativeT>(multi_index_));
+      miscompares_.Set<bool>(multi_index_, !near);
     } else {
       for (int64 i = 0; i < expected.shape().dimensions(dimension); ++i) {
         multi_index_[dimension] = i;
@@ -442,8 +441,8 @@ class NearComparator {
 
 /* static */ ::testing::AssertionResult LiteralTestUtil::NearTuple(
     const Literal& expected, const Literal& actual, const ErrorSpec& error) {
-  VLOG(1) << "expected: " << LiteralUtil::ToString(expected);
-  VLOG(1) << "actual:   " << LiteralUtil::ToString(actual);
+  VLOG(1) << "expected: " << expected.ToString();
+  VLOG(1) << "actual:   " << actual.ToString();
 
   if (!ShapeUtil::IsTuple(expected.shape()) ||
       !ShapeUtil::IsTuple(actual.shape())) {
@@ -509,8 +508,7 @@ class NearComparator {
   *shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout(minor_to_major);
 
   // Allocate space in the new literal.
-  LiteralUtil::Reserve(ShapeUtil::ElementsIn(literal.shape()),
-                       new_literal.get());
+  new_literal.get()->Reserve(ShapeUtil::ElementsIn(literal.shape()));
 
   // Copy data into new literal, element-by-element.
   for (int64 i = 0; i < ShapeUtil::ElementsIn(literal.shape()); ++i) {
@@ -520,44 +518,36 @@ class NearComparator {
         IndexUtil::LinearIndexToMultidimensionalIndex(shape_with_layout, i);
     switch (literal.shape().element_type()) {
       case PRED:
-        LiteralUtil::Set<bool>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<bool>(literal, from_multi_index));
+        new_literal.get()->Set<bool>(to_multi_index,
+                                     literal.Get<bool>(from_multi_index));
         break;
       case U8:
-        LiteralUtil::Set<uint8>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<uint8>(literal, from_multi_index));
+        new_literal.get()->Set<uint8>(to_multi_index,
+                                      literal.Get<uint8>(from_multi_index));
         break;
       case U32:
-        LiteralUtil::Set<uint32>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<uint32>(literal, from_multi_index));
+        new_literal.get()->Set<uint32>(to_multi_index,
+                                       literal.Get<uint32>(from_multi_index));
         break;
       case S32:
-        LiteralUtil::Set<int32>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<int32>(literal, from_multi_index));
+        new_literal.get()->Set<int32>(to_multi_index,
+                                      literal.Get<int32>(from_multi_index));
         break;
       case U64:
-        LiteralUtil::Set<uint64>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<uint64>(literal, from_multi_index));
+        new_literal.get()->Set<uint64>(to_multi_index,
+                                       literal.Get<uint64>(from_multi_index));
         break;
       case S64:
-        LiteralUtil::Set<int64>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<int64>(literal, from_multi_index));
+        new_literal.get()->Set<int64>(to_multi_index,
+                                      literal.Get<int64>(from_multi_index));
         break;
       case F32:
-        LiteralUtil::Set<float>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<float>(literal, from_multi_index));
+        new_literal.get()->Set<float>(to_multi_index,
+                                      literal.Get<float>(from_multi_index));
         break;
       case F64:
-        LiteralUtil::Set<double>(
-            new_literal.get(), to_multi_index,
-            LiteralUtil::Get<double>(literal, from_multi_index));
+        new_literal.get()->Set<double>(to_multi_index,
+                                       literal.Get<double>(from_multi_index));
         break;
       default:
         LOG(FATAL) << "Unhandled primitive element type: "
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h
index a8b07a2c5d..0def25f34e 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.h
+++ b/tensorflow/compiler/xla/tests/literal_test_util.h
@@ -210,20 +210,20 @@ class LiteralTestUtil {
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR0Equal(NativeT expected,
                                                  const Literal& actual) {
-  ExpectEqual(*LiteralUtil::CreateR0<NativeT>(expected), actual);
+  ExpectEqual(*Literal::CreateR0<NativeT>(expected), actual);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR1Equal(
     tensorflow::gtl::ArraySlice<NativeT> expected, const Literal& actual) {
-  ExpectEqual(*LiteralUtil::CreateR1<NativeT>(expected), actual);
+  ExpectEqual(*Literal::CreateR1<NativeT>(expected), actual);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR2Equal(
     std::initializer_list<std::initializer_list<NativeT>> expected,
     const Literal& actual) {
-  ExpectEqual(*LiteralUtil::CreateR2<NativeT>(expected), actual);
+  ExpectEqual(*Literal::CreateR2<NativeT>(expected), actual);
 }
 
 template <typename NativeT>
@@ -231,46 +231,46 @@ template <typename NativeT>
     std::initializer_list<std::initializer_list<std::initializer_list<NativeT>>>
         expected,
     const Literal& actual) {
-  ExpectEqual(*LiteralUtil::CreateR3<NativeT>(expected), actual);
+  ExpectEqual(*Literal::CreateR3<NativeT>(expected), actual);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR2EqualArray2D(
     const Array2D<NativeT>& expected, const Literal& actual) {
-  ExpectEqual(*LiteralUtil::CreateR2FromArray2D(expected), actual);
+  ExpectEqual(*Literal::CreateR2FromArray2D(expected), actual);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR3EqualArray3D(
     const Array3D<NativeT>& expected, const Literal& actual) {
-  ExpectEqual(*LiteralUtil::CreateR3FromArray3D(expected), actual);
+  ExpectEqual(*Literal::CreateR3FromArray3D(expected), actual);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR4EqualArray4D(
     const Array4D<NativeT>& expected, const Literal& actual) {
-  ExpectEqual(*LiteralUtil::CreateR4FromArray4D(expected), actual);
+  ExpectEqual(*Literal::CreateR4FromArray4D(expected), actual);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR0Near(NativeT expected,
                                                 const Literal& actual,
                                                 const ErrorSpec& error) {
-  ExpectNear(*LiteralUtil::CreateR0<NativeT>(expected), actual, error);
+  ExpectNear(*Literal::CreateR0<NativeT>(expected), actual, error);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR1Near(
     tensorflow::gtl::ArraySlice<NativeT> expected, const Literal& actual,
     const ErrorSpec& error) {
-  ExpectNear(*LiteralUtil::CreateR1<NativeT>(expected), actual, error);
+  ExpectNear(*Literal::CreateR1<NativeT>(expected), actual, error);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR2Near(
     std::initializer_list<std::initializer_list<NativeT>> expected,
     const Literal& actual, const ErrorSpec& error) {
-  ExpectNear(*LiteralUtil::CreateR2<NativeT>(expected), actual, error);
+  ExpectNear(*Literal::CreateR2<NativeT>(expected), actual, error);
 }
 
 template <typename NativeT>
@@ -278,28 +278,28 @@ template <typename NativeT>
     std::initializer_list<std::initializer_list<std::initializer_list<NativeT>>>
         expected,
     const Literal& actual, const ErrorSpec& error) {
-  ExpectNear(*LiteralUtil::CreateR3<NativeT>(expected), actual, error);
+  ExpectNear(*Literal::CreateR3<NativeT>(expected), actual, error);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR2NearArray2D(
     const Array2D<NativeT>& expected, const Literal& actual,
     const ErrorSpec& error) {
-  ExpectNear(*LiteralUtil::CreateR2FromArray2D(expected), actual, error);
+  ExpectNear(*Literal::CreateR2FromArray2D(expected), actual, error);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR3NearArray3D(
     const Array3D<NativeT>& expected, const Literal& actual,
     const ErrorSpec& error) {
-  ExpectNear(*LiteralUtil::CreateR3FromArray3D(expected), actual, error);
+  ExpectNear(*Literal::CreateR3FromArray3D(expected), actual, error);
 }
 
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR4NearArray4D(
     const Array4D<NativeT>& expected, const Literal& actual,
     const ErrorSpec& error) {
-  ExpectNear(*LiteralUtil::CreateR4FromArray4D(expected), actual, error);
+  ExpectNear(*Literal::CreateR4FromArray4D(expected), actual, error);
 }
 
 template <PrimitiveType type, typename T>
@@ -309,9 +309,9 @@ LiteralTestUtil::CreateRandomLiteral(
     const std::function<T(tensorflow::gtl::ArraySlice<int64>)>& generator) {
   using NativeT = typename primitive_util::PrimitiveTypeToNative<type>::type;
   TF_RET_CHECK(shape.element_type() == type);
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateFromShape(shape);
-  TF_RETURN_IF_ERROR(LiteralUtil::Populate<NativeT>(
-      literal.get(), [&](tensorflow::gtl::ArraySlice<int64> indexes) {
+  std::unique_ptr<Literal> literal = Literal::CreateFromShape(shape);
+  TF_RETURN_IF_ERROR(literal.get()->Populate<NativeT>(
+      [&](tensorflow::gtl::ArraySlice<int64> indexes) {
         return generator(indexes);
       }));
   return std::move(literal);
diff --git a/tensorflow/compiler/xla/tests/literal_test_util_test.cc b/tensorflow/compiler/xla/tests/literal_test_util_test.cc
index a94f45f73b..2acf27ed39 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util_test.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util_test.cc
@@ -31,9 +31,8 @@ namespace xla {
 namespace {
 
 TEST(LiteralTestUtilTest, ComparesEqualTuplesEqual) {
-  std::unique_ptr<Literal> literal = LiteralUtil::MakeTuple({
-      LiteralUtil::CreateR0<int32>(42).get(),
-      LiteralUtil::CreateR0<int32>(64).get(),
+  std::unique_ptr<Literal> literal = Literal::MakeTuple({
+      Literal::CreateR0<int32>(42).get(), Literal::CreateR0<int32>(64).get(),
   });
   LiteralTestUtil::ExpectEqual(*literal, *literal);
 }
@@ -43,13 +42,11 @@ TEST(LiteralTestUtilTest, ComparesUnequalTuplesUnequal) {
   // un-fail an assertion failure. The CHECK-failure is death, so we can make a
   // death assertion.
   auto unequal_things_are_equal = [] {
-    std::unique_ptr<Literal> lhs = LiteralUtil::MakeTuple({
-        LiteralUtil::CreateR0<int32>(42).get(),
-        LiteralUtil::CreateR0<int32>(64).get(),
+    std::unique_ptr<Literal> lhs = Literal::MakeTuple({
+        Literal::CreateR0<int32>(42).get(), Literal::CreateR0<int32>(64).get(),
     });
-    std::unique_ptr<Literal> rhs = LiteralUtil::MakeTuple({
-        LiteralUtil::CreateR0<int32>(64).get(),
-        LiteralUtil::CreateR0<int32>(42).get(),
+    std::unique_ptr<Literal> rhs = Literal::MakeTuple({
+        Literal::CreateR0<int32>(64).get(), Literal::CreateR0<int32>(42).get(),
     });
     CHECK(LiteralTestUtil::Equal(*lhs, *rhs)) << "LHS and RHS are unequal";
   };
@@ -58,8 +55,8 @@ TEST(LiteralTestUtilTest, ComparesUnequalTuplesUnequal) {
 
 TEST(LiteralTestUtilTest, ExpectNearFailurePlacesResultsInTemporaryDirectory) {
   auto dummy_lambda = [] {
-    auto two = LiteralUtil::CreateR0<float>(2);
-    auto four = LiteralUtil::CreateR0<float>(4);
+    auto two = Literal::CreateR0<float>(2);
+    auto four = Literal::CreateR0<float>(4);
     ErrorSpec error(0.001);
     CHECK(LiteralTestUtil::Near(*two, *four, error)) << "two is not near four";
   };
@@ -88,11 +85,11 @@ TEST(LiteralTestUtilTest, ExpectNearFailurePlacesResultsInTemporaryDirectory) {
                                             &literal_proto));
     Literal literal(literal_proto);
     if (result.find("expected") != string::npos) {
-      EXPECT_EQ("2", LiteralUtil::ToString(literal));
+      EXPECT_EQ("2", literal.ToString());
     } else if (result.find("actual") != string::npos) {
-      EXPECT_EQ("4", LiteralUtil::ToString(literal));
+      EXPECT_EQ("4", literal.ToString());
     } else if (result.find("miscompares") != string::npos) {
-      EXPECT_EQ("true", LiteralUtil::ToString(literal));
+      EXPECT_EQ("true", literal.ToString());
     } else {
       FAIL() << "unknown file in temporary directory: " << result;
     }
diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc
index d613d79993..ffa87348a0 100644
--- a/tensorflow/compiler/xla/tests/map_test.cc
+++ b/tensorflow/compiler/xla/tests/map_test.cc
@@ -169,7 +169,7 @@ class MapTest : public ClientLibraryTestBase {
 TEST_F(MapTest, MapEachElemPlusOneR0) {
   // Applies lambda (x) (+ x 1)) to an input scalar.
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR0<float>(42.0);
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR0<float>(42.0);
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -183,7 +183,7 @@ TEST_F(MapTest, MapEachElemPlusOneR0) {
 XLA_TEST_F(MapTest, MapEachElemPlusOneR1S0) {
   // Maps (lambda (x) (+ x 1)) onto an input R1F32 vector of length 0.
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR1<float>({});
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR1<float>({});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -198,7 +198,7 @@ TEST_F(MapTest, MapEachElemPlusOneR1S4) {
   // Maps (lambda (x) (+ x 1)) onto an input R1F32 vector of length 4.
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -212,7 +212,7 @@ TEST_F(MapTest, MapEachElemPlusOneR1S4) {
 TEST_F(MapTest, MapEachF32ElementToS32Constant) {
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -225,7 +225,7 @@ TEST_F(MapTest, MapEachF32ElementToS32Constant) {
 TEST_F(MapTest, MapEachF32ElementToU32Constant) {
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -239,7 +239,7 @@ TEST_F(MapTest, MapEachElemLongerChainR1) {
   // Maps (lambda (x) (* (+ x 1) x)) onto an input R1F32 vector.
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.6f, -5.1f, 0.1f, 0.2f, 999.0f, 255.5f});
+      Literal::CreateR1<float>({2.6f, -5.1f, 0.1f, 0.2f, 999.0f, 255.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -255,7 +255,7 @@ XLA_TEST_F(MapTest, MapMultipleMapsR1S0) {
   // Maps (lambda (x) (+ x 1)) onto an input R1F32 vector of length 0, and then
   // maps (lambda (x) (* x 2)) on the result.
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR1<float>({});
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR1<float>({});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -272,7 +272,7 @@ TEST_F(MapTest, MapMultipleMapsR1S4) {
   // maps (lambda (x) (* x 2)) on the result.
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -287,7 +287,7 @@ TEST_F(MapTest, MapMultipleMapsR1S4) {
 TEST_F(MapTest, MapEachElemPlusOneR2) {
   // Maps (lambda (x) (+ x 1)) onto an input R2F32 vector.
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR2<float>(
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR2<float>(
       {{13.25f, 14.0f}, {-7.1f, -7.2f}, {-8.8f, 8.8f}});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
@@ -384,11 +384,11 @@ TEST_F(MapTest, MapBinaryAdder) {
   // Maps (lambda (x y) (+ x y)) onto two R1F32 vectors.
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
   std::unique_ptr<Literal> param1_literal =
-      LiteralUtil::CreateR1<float>({5.1f, 4.4f, -0.1f, -5.5f});
+      Literal::CreateR1<float>({5.1f, 4.4f, -0.1f, -5.5f});
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*param1_literal).ConsumeValueOrDie();
 
@@ -433,12 +433,12 @@ XLA_TEST_F(MapTest, AddWithMixedLayouts) {
 XLA_TEST_F(MapTest, AddR3_3x0x2) {
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR3FromArray3D<int32>(Array3D<int32>(3, 0, 2));
+      Literal::CreateR3FromArray3D<int32>(Array3D<int32>(3, 0, 2));
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
   std::unique_ptr<Literal> param1_literal =
-      LiteralUtil::CreateR3FromArray3D<int32>(Array3D<int32>(3, 0, 2));
+      Literal::CreateR3FromArray3D<int32>(Array3D<int32>(3, 0, 2));
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*param1_literal).ConsumeValueOrDie();
 
@@ -455,15 +455,15 @@ TEST_F(MapTest, MapTernaryAdder) {
   // Maps (lambda (x y z) (+ x y z)) onto three R1F32 vectors.
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
   std::unique_ptr<Literal> param1_literal =
-      LiteralUtil::CreateR1<float>({5.1f, 4.4f, -0.1f, -5.5f});
+      Literal::CreateR1<float>({5.1f, 4.4f, -0.1f, -5.5f});
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*param1_literal).ConsumeValueOrDie();
   std::unique_ptr<Literal> param2_literal =
-      LiteralUtil::CreateR1<float>({-10.0f, -100.0f, -900.0f, -400.0f});
+      Literal::CreateR1<float>({-10.0f, -100.0f, -900.0f, -400.0f});
   std::unique_ptr<GlobalData> param2_data =
       client_->TransferToServer(*param2_literal).ConsumeValueOrDie();
 
@@ -516,11 +516,11 @@ TEST_F(MapTest, MapOperantionWithBuildError) {
   auto error_add = sub_builder->BuildAndNoteError();
 
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 3.3f, 4.4f, 5.5f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
   std::unique_ptr<Literal> param1_literal =
-      LiteralUtil::CreateR1<float>({5.1f, 4.4f, -0.1f, -5.5f});
+      Literal::CreateR1<float>({5.1f, 4.4f, -0.1f, -5.5f});
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*param1_literal).ConsumeValueOrDie();
 
@@ -553,8 +553,8 @@ TEST_F(MapTestWithFullOpt, MapScalarPower) {
   sub_builder->Pow(x, y);
   auto power = sub_builder->BuildAndNoteError();
 
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR0<float>(2.0f);
-  std::unique_ptr<Literal> param1_literal = LiteralUtil::CreateR0<float>(5.0f);
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR0<float>(2.0f);
+  std::unique_ptr<Literal> param1_literal = Literal::CreateR0<float>(5.0f);
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
   std::unique_ptr<GlobalData> param1_data =
@@ -580,8 +580,8 @@ TEST_F(MapTestWithFullOpt, MapSubtractOppositeOrder) {
   sub_builder->Sub(y, x);  // note that this is y - x, not x - y
   auto sub_opposite = sub_builder->BuildAndNoteError();
 
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR0<float>(2.0f);
-  std::unique_ptr<Literal> param1_literal = LiteralUtil::CreateR0<float>(5.0f);
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR0<float>(2.0f);
+  std::unique_ptr<Literal> param1_literal = Literal::CreateR0<float>(5.0f);
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
   std::unique_ptr<GlobalData> param1_data =
@@ -605,7 +605,7 @@ TEST_F(MapTestWithFullOpt, MapSquare) {
   sub_builder->Mul(x, x);
   auto square = sub_builder->BuildAndNoteError();
 
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR0<float>(10.0f);
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR0<float>(10.0f);
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
index 883ec2277b..717e9cd494 100644
--- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
@@ -87,8 +87,8 @@ TEST_F(MatOpsSimpleTest, ExpTwoByTwoValues) {
   builder.Exp(data);
 
   std::unique_ptr<Literal> expected =
-      LiteralUtil::CreateR2<float>({{2.71828, 1.00000},    // row 0
-                                    {0.36788, 1.64872}});  // row 1
+      Literal::CreateR2<float>({{2.71828, 1.00000},    // row 0
+                                {0.36788, 1.64872}});  // row 1
 
   ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
 }
@@ -115,8 +115,8 @@ TEST_F(MatOpsSimpleTest, MapTwoByTwo) {
   auto map = builder.Map({data}, add_half);
 
   std::unique_ptr<Literal> expected =
-      LiteralUtil::CreateR2<float>({{1.5, 0.5},     // row 0
-                                    {-0.5, 1.0}});  // row 1
+      Literal::CreateR2<float>({{1.5, 0.5},     // row 0
+                                {-0.5, 1.0}});  // row 1
   ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
 }
 
@@ -133,8 +133,8 @@ TEST_F(MatOpsSimpleTest, MaxTwoByTwoValues) {
   auto max = builder.Max(lhs, rhs);
 
   std::unique_ptr<Literal> expected =
-      LiteralUtil::CreateR2<float>({{7.0, 6.0},     // row 0
-                                    {3.0, -4.0}});  // row 1
+      Literal::CreateR2<float>({{7.0, 6.0},     // row 0
+                                {3.0, -4.0}});  // row 1
   ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6));
 }
 
@@ -180,14 +180,12 @@ TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2) {
 
   TF_ASSIGN_OR_ASSERT_OK(
       auto lhs_handle,
-      client_->TransferToServer(
-          *LiteralUtil::CreateR2FromArray2DWithLayout<float>(
-              lhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
+      client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<float>(
+          lhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
   TF_ASSIGN_OR_ASSERT_OK(
       auto rhs_handle,
-      client_->TransferToServer(
-          *LiteralUtil::CreateR2FromArray2DWithLayout<float>(
-              rhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
+      client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<float>(
+          rhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
 
   ComputationBuilder builder(client_, TestName());
   auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs");
diff --git a/tensorflow/compiler/xla/tests/pad_test.cc b/tensorflow/compiler/xla/tests/pad_test.cc
index 73f4d539d9..e270a0477f 100644
--- a/tensorflow/compiler/xla/tests/pad_test.cc
+++ b/tensorflow/compiler/xla/tests/pad_test.cc
@@ -182,8 +182,8 @@ TEST_F(PadTest, Pad4DFloatArrayMinorFirstSmall) {
 
   const float pad_value = -5.123f;
   Array4D<float> input_array(1, 1, 2, 3, {1, 2, 3, 4, 5, 6});
-  auto input = LiteralUtil::CreateR4FromArray4D<float>(input_array);
-  input = LiteralUtil::Relayout(*input, layout);
+  auto input = Literal::CreateR4FromArray4D<float>(input_array);
+  input = input->Relayout(layout);
 
   b.Pad(b.ConstantLiteral(*input), b.ConstantR0(pad_value), padding_config);
 
@@ -227,8 +227,8 @@ XLA_TEST_F(PadTest, Pad4DFloatArrayMinorFirstNonTrivialMinorDimensions) {
   input_array(0, 0, 0, 0) = 1.0f;
   input_array(0, 24, 6, 6) = 2.0f;
   input_array(0, 17, 2, 5) = 3.0f;
-  auto input = LiteralUtil::CreateR4FromArray4D<float>(input_array);
-  input = LiteralUtil::Relayout(*input, layout);
+  auto input = Literal::CreateR4FromArray4D<float>(input_array);
+  input = input->Relayout(layout);
 
   b.Pad(b.ConstantLiteral(*input), b.ConstantR0(pad_value), padding_config);
 
@@ -307,7 +307,7 @@ XLA_TEST_F(PadTest, Large2DPad) {
 
   auto ones = MakeUnique<Array2D<float>>(4, 4);
   ones->Fill(1.0f);
-  auto input_literal = LiteralUtil::CreateR2FromArray2D<float>(*ones);
+  auto input_literal = Literal::CreateR2FromArray2D<float>(*ones);
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -333,7 +333,7 @@ XLA_TEST_F(PadTest, AllTypes2DPad) {
 
   auto operand = MakeUnique<Array2D<float>>(in_rows, in_cols);
   operand->FillUnique(0.0f);
-  auto input_literal = LiteralUtil::CreateR2FromArray2D<float>(*operand);
+  auto input_literal = Literal::CreateR2FromArray2D<float>(*operand);
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -364,7 +364,7 @@ XLA_TEST_F(PadTest, High2DPad) {
 
   auto operand = MakeUnique<Array2D<float>>(in_rows, in_cols);
   operand->FillUnique(1.0f);
-  auto input_literal = LiteralUtil::CreateR2FromArray2D<float>(*operand);
+  auto input_literal = Literal::CreateR2FromArray2D<float>(*operand);
   auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f);
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -396,7 +396,7 @@ XLA_TEST_F(PadTest, NegativePadding2D) {
 
   auto operand = MakeUnique<Array2D<float>>(in_rows, in_cols);
   operand->FillUnique(1.0f);
-  auto input_literal = LiteralUtil::CreateR2FromArray2D<float>(*operand);
+  auto input_literal = Literal::CreateR2FromArray2D<float>(*operand);
   auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f);
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -428,7 +428,7 @@ XLA_TEST_F(PadTest, NegativeAndInteriorPadding2D) {
 
   auto operand = MakeUnique<Array2D<float>>(in_rows, in_cols);
   operand->FillUnique(1.0f);
-  auto input_literal = LiteralUtil::CreateR2FromArray2D<float>(*operand);
+  auto input_literal = Literal::CreateR2FromArray2D<float>(*operand);
   auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f);
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -452,7 +452,7 @@ XLA_TEST_F(PadTest, ReducePad) {
 
   auto ones = MakeUnique<Array4D<float>>(2, 2, 2, 2);
   ones->Fill(1.0);
-  auto input_literal = LiteralUtil::CreateR4FromArray4D<float>(*ones);
+  auto input_literal = Literal::CreateR4FromArray4D<float>(*ones);
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc
index 03b8392a82..2065e9e813 100644
--- a/tensorflow/compiler/xla/tests/params_test.cc
+++ b/tensorflow/compiler/xla/tests/params_test.cc
@@ -43,8 +43,7 @@ class ParamsTest : public ClientLibraryTestBase {};
 
 XLA_TEST_F(ParamsTest, ConstantR0F32Param) {
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR0<float>(3.14159f);
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR0<float>(3.14159f);
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -56,7 +55,7 @@ XLA_TEST_F(ParamsTest, ConstantR0F32Param) {
 
 XLA_TEST_F(ParamsTest, ConstantR1S0F32Param) {
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR1<float>({});
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR1<float>({});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -69,7 +68,7 @@ XLA_TEST_F(ParamsTest, ConstantR1S0F32Param) {
 XLA_TEST_F(ParamsTest, ConstantR1S2F32Param) {
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({3.14f, -100.25f});
+      Literal::CreateR1<float>({3.14f, -100.25f});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -82,7 +81,7 @@ XLA_TEST_F(ParamsTest, ConstantR1S2F32Param) {
 XLA_TEST_F(ParamsTest, ConstantR1U8Param) {
   ComputationBuilder builder(client_, TestName());
   string str("hello world");
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR1U8(str);
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR1U8(str);
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -95,7 +94,7 @@ XLA_TEST_F(ParamsTest, ConstantR1U8Param) {
 XLA_TEST_F(ParamsTest, ConstantR2_3x0_F32Param) {
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR2FromArray2D<float>(Array2D<float>(3, 0));
+      Literal::CreateR2FromArray2D<float>(Array2D<float>(3, 0));
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -107,7 +106,7 @@ XLA_TEST_F(ParamsTest, ConstantR2_3x0_F32Param) {
 
 XLA_TEST_F(ParamsTest, ConstantR2F32Param) {
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR2<float>(
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR2<float>(
       {{3.14f, -100.25f}, {7e8f, 7e-9f}, {30.3f, -100.0f}});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
@@ -123,12 +122,12 @@ XLA_TEST_F(ParamsTest, ConstantR2F32Param) {
 XLA_TEST_F(ParamsTest, TwoParameters) {
   ComputationBuilder builder(client_, TestName());
 
-  std::unique_ptr<Literal> literal0 = LiteralUtil::CreateR1<float>({1, 2});
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>({1, 2});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*literal0).ConsumeValueOrDie();
   auto param0 = builder.Parameter(0, literal0->shape(), "param0");
 
-  std::unique_ptr<Literal> literal1 = LiteralUtil::CreateR1<float>({10, 20});
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>({10, 20});
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*literal1).ConsumeValueOrDie();
   auto param1 = builder.Parameter(1, literal1->shape(), "param1");
@@ -154,7 +153,7 @@ XLA_TEST_F(ParamsTest, TwoParameters) {
 XLA_TEST_F(ParamsTest, MissingParameter) {
   // Test that an error is returned when a computation with an incomplete set of
   // parameters (parameter numbers not contiguous from 0) is executed.
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR0<float>(3.14159f);
+  std::unique_ptr<Literal> literal = Literal::CreateR0<float>(3.14159f);
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
 
@@ -172,12 +171,12 @@ XLA_TEST_F(ParamsTest, MissingParameter) {
 XLA_TEST_F(ParamsTest, UnusedParameter) {
   ComputationBuilder builder(client_, TestName());
 
-  std::unique_ptr<Literal> literal0 = LiteralUtil::CreateR1<float>({1, 2});
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>({1, 2});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*literal0).ConsumeValueOrDie();
   auto param0 = builder.Parameter(0, literal0->shape(), "param0");
 
-  std::unique_ptr<Literal> literal1 = LiteralUtil::CreateR1<float>({10, 20});
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>({10, 20});
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*literal1).ConsumeValueOrDie();
   auto param1 = builder.Parameter(1, literal1->shape(), "param1");
@@ -192,12 +191,11 @@ XLA_TEST_F(ParamsTest, UnusedParametersInUnusedExpression) {
   // unused expression.
   ComputationBuilder builder(client_, TestName());
 
-  std::unique_ptr<Literal> literal0 = LiteralUtil::CreateR1<float>({1, 2});
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>({1, 2});
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*literal0).ConsumeValueOrDie();
 
-  std::unique_ptr<Literal> literal1 =
-      LiteralUtil::CreateR1<float>({10, 20, 30});
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>({10, 20, 30});
   std::unique_ptr<GlobalData> param1_data =
       client_->TransferToServer(*literal1).ConsumeValueOrDie();
 
@@ -237,7 +235,7 @@ XLA_TEST_F(ParamsTest, HundredLargeR1Parameters) {
 
     std::vector<float> sum_value = {{entry0, entry1}};
     sum_value.resize(size);
-    std::unique_ptr<Literal> literal = LiteralUtil::CreateR1<float>(sum_value);
+    std::unique_ptr<Literal> literal = Literal::CreateR1<float>(sum_value);
     param_data_owner.push_back(
         client_->TransferToServer(*literal).ConsumeValueOrDie());
     ComputationDataHandle param =
@@ -267,9 +265,9 @@ XLA_TEST_F(ParamsTest,
 
   std::unique_ptr<GlobalData> data =
       client_
-          ->TransferToServer(*LiteralUtil::MakeTuple({
-              LiteralUtil::CreateR1<float>({1, 2, 3}).get(),
-              LiteralUtil::CreateR1<float>({4, 5, 6}).get(),
+          ->TransferToServer(*Literal::MakeTuple({
+              Literal::CreateR1<float>({1, 2, 3}).get(),
+              Literal::CreateR1<float>({4, 5, 6}).get(),
           }))
           .ConsumeValueOrDie();
 
@@ -281,7 +279,7 @@ XLA_TEST_F(ParamsTest,
 // Verifies that passing a 2x2 with {0, 1} layout returns the same value back
 // when (transferred to the server and) passed through a parameter.
 XLA_TEST_F(ParamsTest, R2_2x2_Layout_01) {
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR2<float>({
+  std::unique_ptr<Literal> literal = Literal::CreateR2<float>({
       {1, 2}, {3, 4},
   });
   *literal->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1});
@@ -295,7 +293,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_Layout_01) {
 
 // As above, but for {1, 0} layout.
 XLA_TEST_F(ParamsTest, R2_2x2_Layout_10) {
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR2<float>({
+  std::unique_ptr<Literal> literal = Literal::CreateR2<float>({
       {1, 3}, {2, 4},
   });
   *literal->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({1, 0});
@@ -308,7 +306,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_Layout_10) {
 }
 
 XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) {
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR2<float>({
+  std::unique_ptr<Literal> literal = Literal::CreateR2<float>({
       {1, 3}, {2, 4},
   });
   const Shape original = literal->shape();
@@ -321,7 +319,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) {
     std::reverse(original_layout.begin(), original_layout.end());
     *literal->mutable_shape()->mutable_layout() =
         LayoutUtil::MakeLayout(original_layout);
-    ASSERT_EQ(2, LiteralUtil::Get<float>(*literal, {0, 1}));
+    ASSERT_EQ(2, literal->Get<float>({0, 1}));
   }
   // Use the original shape in building the computation.
   ComputationBuilder builder(client_, TestName());
diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc
index 74346342e0..57e390bff9 100644
--- a/tensorflow/compiler/xla/tests/prng_test.cc
+++ b/tensorflow/compiler/xla/tests/prng_test.cc
@@ -57,11 +57,10 @@ void PrngTest::UniformTest(T a, T b, tensorflow::gtl::ArraySlice<int64> dims) {
   SetSeed(42);
   auto actual = ExecuteAndTransferOrDie(&builder, /*arguments=*/{});
   EXPECT_THAT(dims, ::testing::ElementsAreArray(actual->shape().dimensions()));
-  LiteralUtil::EachCell<T>(*actual,
-                           [=](tensorflow::gtl::ArraySlice<int64>, T value) {
-                             EXPECT_LE(a, value);
-                             EXPECT_LT(value, b);
-                           });
+  actual->EachCell<T>([=](tensorflow::gtl::ArraySlice<int64>, T value) {
+    EXPECT_LE(a, value);
+    EXPECT_LT(value, b);
+  });
 }
 
 void PrngTest::BernoulliTest(float p, tensorflow::gtl::ArraySlice<int64> dims) {
@@ -78,8 +77,8 @@ void PrngTest::BernoulliTest(float p, tensorflow::gtl::ArraySlice<int64> dims) {
                                   &execution_options));
   EXPECT_THAT(dims, ::testing::ElementsAreArray(actual->shape().dimensions()));
   int32 sum = 0;
-  LiteralUtil::EachCell<uint32>(
-      *actual, [&sum](tensorflow::gtl::ArraySlice<int64>, uint32 value) {
+  actual->EachCell<uint32>(
+      [&sum](tensorflow::gtl::ArraySlice<int64>, uint32 value) {
         EXPECT_TRUE(value == 0 || value == 1);
         sum += value;
       });
@@ -123,10 +122,8 @@ double PrngTest::UniformChiSquared(int32 range_size, int32 expected_count) {
   SetSeed(42);
   auto actual = ExecuteAndTransferOrDie(&builder, /*arguments=*/{});
   std::vector<int32> counts(range_size, 0);
-  LiteralUtil::EachCell<int32>(
-      *actual, [&counts](tensorflow::gtl::ArraySlice<int64>, int32 value) {
-        ++counts[value];
-      });
+  actual->EachCell<int32>([&counts](tensorflow::gtl::ArraySlice<int64>,
+                                    int32 value) { ++counts[value]; });
   int64 sum = 0;
   for (int32 i = 0; i < range_size; ++i) {
     sum += Square(static_cast<int64>(counts[i] - expected_count));
@@ -169,7 +166,7 @@ XLA_TEST_F(PrngTest, MapUsingRng) {
 
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR1<float>({2.2f, 5.3f, 4.4f, 5.5f});
+      Literal::CreateR1<float>({2.2f, 5.3f, 4.4f, 5.5f});
   TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> param0_data,
                          client_->TransferToServer(*param0_literal));
 
diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc
index 12072b6c95..ac65a47afa 100644
--- a/tensorflow/compiler/xla/tests/reduce_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_test.cc
@@ -63,12 +63,12 @@ class ReduceTest : public ClientLibraryTestBase {
   ReduceTest() {
     // Implementation note: laid out z >> y >> x by default.
     // clang-format off
-    literal_2d_ = LiteralUtil::CreateR2<float>({
+    literal_2d_ = Literal::CreateR2<float>({
       // x0   x1   x2
       { 1.f, 2.f, 3.f},  // y0
       { 4.f, 5.f, 6.f},  // y1
     });
-    literal_3d_ = LiteralUtil::CreateR3Projected<float>({
+    literal_3d_ = Literal::CreateR3Projected<float>({
       // x0   x1   x2
       { 1.f, 2.f, 3.f},  // y0
       { 4.f, 5.f, 6.f},  // y1
@@ -97,7 +97,7 @@ class ReduceTest : public ClientLibraryTestBase {
       }
     }
     std::unique_ptr<Literal> input_literal =
-        LiteralUtil::CreateR1(AsSlice(input_data));
+        Literal::CreateR1(AsSlice(input_data));
     std::unique_ptr<GlobalData> input_global_data =
         client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -129,7 +129,7 @@ class ReduceTest : public ClientLibraryTestBase {
     builder.Reduce(pred_values, init_value, reduce,
                    /*dimensions_to_reduce=*/{0});
 
-    std::unique_ptr<Literal> input_literal = LiteralUtil::CreateR1(input_data);
+    std::unique_ptr<Literal> input_literal = Literal::CreateR1(input_data);
     std::unique_ptr<GlobalData> input_global_data =
         client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -156,9 +156,9 @@ class ReduceTest : public ClientLibraryTestBase {
     Array2D<float> input_data(rows, cols);
     input_data.FillRandom(3.14f, 0.04);
     std::unique_ptr<Literal> input_literal =
-        LiteralUtil::CreateR2FromArray2D(input_data);
-    input_literal = LiteralUtil::Relayout(
-        *input_literal, LayoutUtil::MakeLayout({minor, major}));
+        Literal::CreateR2FromArray2D(input_data);
+    input_literal =
+        input_literal->Relayout(LayoutUtil::MakeLayout({minor, major}));
     std::unique_ptr<GlobalData> input_global_data =
         client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -184,9 +184,9 @@ class ReduceTest : public ClientLibraryTestBase {
     Array2D<float> input_data(rows, cols);
     input_data.FillRandom(3.14f, 0.04);
     std::unique_ptr<Literal> input_literal =
-        LiteralUtil::CreateR2FromArray2D(input_data);
-    input_literal = LiteralUtil::Relayout(
-        *input_literal, LayoutUtil::MakeLayout({minor, major}));
+        Literal::CreateR2FromArray2D(input_data);
+    input_literal =
+        input_literal->Relayout(LayoutUtil::MakeLayout({minor, major}));
     std::unique_ptr<GlobalData> input_global_data =
         client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -305,9 +305,8 @@ XLA_TEST_F(ReduceTest, ReduceElementwiseR2_111x50_To_R1) {
   Array2D<float> input_data(rows, cols);
   input_data.FillRandom(3.14f, 0.04);
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR2FromArray2D(input_data);
-  input_literal =
-      LiteralUtil::Relayout(*input_literal, LayoutUtil::MakeLayout({0, 1}));
+      Literal::CreateR2FromArray2D(input_data);
+  input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({0, 1}));
   std::unique_ptr<GlobalData> input_global_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -338,9 +337,8 @@ XLA_TEST_F(ReduceTest, TransposeAndReduceElementwiseR2_111x50_To_R1) {
   Array2D<float> input_data(rows, cols);
   input_data.FillRandom(3.14f, 0.04);
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR2FromArray2D(input_data);
-  input_literal =
-      LiteralUtil::Relayout(*input_literal, LayoutUtil::MakeLayout({0, 1}));
+      Literal::CreateR2FromArray2D(input_data);
+  input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({0, 1}));
   std::unique_ptr<GlobalData> input_global_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -371,7 +369,7 @@ XLA_TEST_F(ReduceTest, Reshape_111x2x25Reduce_111x50_To_R1) {
   Array3D<float> input_data(rows, 2, cols / 2);
   input_data.FillRandom(3.14f, 0.04);
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR3FromArray3D(input_data);
+      Literal::CreateR3FromArray3D(input_data);
   std::unique_ptr<GlobalData> input_global_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
@@ -434,7 +432,7 @@ XLA_TEST_F(ReduceTest, MaxReduce2DToR0) {
   auto max = CreateScalarMaxComputation(F32, &builder);
   Array2D<float> input(300, 250);
   input.FillRandom(214.0f);
-  auto input_literal = LiteralUtil::CreateR2FromArray2D(input);
+  auto input_literal = Literal::CreateR2FromArray2D(input);
   builder.Reduce(builder.ConstantLiteral(*input_literal),
                  builder.ConstantR0<float>(FLT_MIN), max, {0, 1});
   auto input_max = FLT_MIN;
@@ -449,7 +447,7 @@ XLA_TEST_F(ReduceTest, MinReduce2DToR0) {
   auto min = CreateScalarMinComputation(F32, &builder);
   Array2D<float> input(150, 130);
   input.FillRandom(214.0f);
-  auto input_literal = LiteralUtil::CreateR2FromArray2D(input);
+  auto input_literal = Literal::CreateR2FromArray2D(input);
   builder.Reduce(builder.ConstantLiteral(*input_literal),
                  builder.ConstantR0<float>(FLT_MAX), min, {0, 1});
 
@@ -579,9 +577,9 @@ XLA_TEST_P(ReduceR3ToR2Test, ReduceR3ToR2) {
   Array3D<float> input_array(bounds[0], bounds[1], bounds[2]);
   input_array.FillRandom(3.14f, 0.05);
 
-  auto input_literal = LiteralUtil::CreateR3FromArray3D(input_array);
-  input_literal = LiteralUtil::Relayout(
-      *input_literal, LayoutUtil::MakeLayout(GetParam().layout));
+  auto input_literal = Literal::CreateR3FromArray3D(input_array);
+  input_literal =
+      input_literal->Relayout(LayoutUtil::MakeLayout(GetParam().layout));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
 
diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index ee3a837e16..6b4bceb437 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -57,7 +57,7 @@ class ReduceWindowTest : public ClientLibraryTestBase {
                        tensorflow::gtl::ArraySlice<int64> window_strides,
                        Padding padding) {
     builder_.ReduceWindow(
-        input, builder_.ConstantLiteral(LiteralUtil::MinValue(F32)),
+        input, builder_.ConstantLiteral(Literal::MinValue(F32)),
         CreateScalarMax(), window_dimensions, window_strides, padding);
   }
 
@@ -66,7 +66,7 @@ class ReduceWindowTest : public ClientLibraryTestBase {
                        tensorflow::gtl::ArraySlice<int64> window_strides,
                        Padding padding) {
     builder_.ReduceWindow(input,
-                          builder_.ConstantLiteral(LiteralUtil::MaxValue(F32)),
+                          builder_.ConstantLiteral(Literal::MaxValue(F32)),
                           CreateScalarMinComputation(F32, &builder_),
                           window_dimensions, window_strides, padding);
   }
diff --git a/tensorflow/compiler/xla/tests/replay_test.cc b/tensorflow/compiler/xla/tests/replay_test.cc
index 4dea470477..cb7f54ea01 100644
--- a/tensorflow/compiler/xla/tests/replay_test.cc
+++ b/tensorflow/compiler/xla/tests/replay_test.cc
@@ -92,10 +92,10 @@ XLA_TEST_F(ReplayTest, XPlusYReplayWithParameters) {
 
   // Run it.
   std::unique_ptr<GlobalData> x_data =
-      client_->TransferToServer(*LiteralUtil::CreateR0<int32>(2))
+      client_->TransferToServer(*Literal::CreateR0<int32>(2))
           .ConsumeValueOrDie();
   std::unique_ptr<GlobalData> y_data =
-      client_->TransferToServer(*LiteralUtil::CreateR0<int32>(3))
+      client_->TransferToServer(*Literal::CreateR0<int32>(3))
           .ConsumeValueOrDie();
   std::unique_ptr<Literal> literal =
       client_
diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc
index 096ba8deb4..6748d196c1 100644
--- a/tensorflow/compiler/xla/tests/reshape_test.cc
+++ b/tensorflow/compiler/xla/tests/reshape_test.cc
@@ -70,7 +70,7 @@ XLA_TEST_F(ReshapeTest, SingleElementArrayToScalar) {
 XLA_TEST_F(ReshapeTest, ScalarToSingleElementArray) {
   ComputationBuilder builder(client_, TestName());
 
-  std::unique_ptr<Literal> param0_literal = LiteralUtil::CreateR0<float>(1.0f);
+  std::unique_ptr<Literal> param0_literal = Literal::CreateR0<float>(1.0f);
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -98,7 +98,7 @@ XLA_TEST_F(ReshapeTest, DISABLED_ON_GPU(Trivial0x3WithParameter)) {
   ComputationBuilder builder(client_, TestName());
 
   std::unique_ptr<Literal> param0_literal =
-      LiteralUtil::CreateR2FromArray2D<float>(Array2D<float>(0, 3));
+      Literal::CreateR2FromArray2D<float>(Array2D<float>(0, 3));
   std::unique_ptr<GlobalData> param0_data =
       client_->TransferToServer(*param0_literal).ConsumeValueOrDie();
 
@@ -402,7 +402,7 @@ XLA_TEST_F(ReshapeTest, FullyConnectedCollapseDesugared) {
 XLA_TEST_F(ReshapeTest, ToScalar) {
   for (int rank = 0; rank < 8; ++rank) {
     ComputationBuilder b(client_, TestName());
-    auto input = LiteralUtil::CreateR1<float>({83.0f});
+    auto input = Literal::CreateR1<float>({83.0f});
     std::vector<int64> ones(rank, 1);  // this is {1, ..., 1}.
     std::vector<int64> dimensions(rank);
     std::iota(dimensions.begin(), dimensions.end(), 0);
@@ -434,7 +434,7 @@ XLA_TEST_F(ReshapeTest, R4Dim0MinorLayoutToR2Dim0MajorLayout) {
   builder.Reshape(a, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{2, 8});
 
   // clang-format off
-  auto literal = LiteralUtil::CreateR4FromArray4DWithLayout(Array4D<float>{
+  auto literal = Literal::CreateR4FromArray4DWithLayout(Array4D<float>{
     {
       {
         {0, 1},
@@ -474,12 +474,12 @@ XLA_TEST_F(ReshapeTest, R4Dim0MinorLayoutToR2Dim0MajorLayout) {
           ->ExecuteAndTransfer(computation, {input.get()}, &execution_options)
           .ConsumeValueOrDie();
   std::unique_ptr<Literal> expected =
-      LiteralUtil::CreateR2FromArray2D<float>(expected_array);
+      Literal::CreateR2FromArray2D<float>(expected_array);
   LiteralTestUtil::ExpectEqual(*expected, *actual);
 }
 
 XLA_TEST_F(ReshapeTest, R2ToR4_3x8_To_3x2x1x4) {
-  std::unique_ptr<Literal> input = LiteralUtil::CreateR2<float>({
+  std::unique_ptr<Literal> input = Literal::CreateR2<float>({
       {0, 1, 2, 3, 4, 5, 6, 7},
       {100, 101, 102, 103, 104, 105, 106, 107},
       {200, 201, 202, 203, 204, 205, 206, 207},
@@ -507,7 +507,7 @@ XLA_TEST_F(ReshapeTest, R2ToR4_3x8_To_3x2x1x4) {
 
 // Tests R2->R4 reshape with the reshape dimensions {1, 0}.
 XLA_TEST_F(ReshapeTest, R2ToR4_3x8_To_3x2x1x4_Dimensions_10) {
-  std::unique_ptr<Literal> input = LiteralUtil::CreateR2<float>({
+  std::unique_ptr<Literal> input = Literal::CreateR2<float>({
       {0, 1, 2, 3, 4, 5, 6, 7},
       {100, 101, 102, 103, 104, 105, 106, 107},
       {200, 201, 202, 203, 204, 205, 206, 207},
@@ -541,7 +541,7 @@ XLA_TEST_F(ReshapeTest, R4ToR2_2x1x1x1_To_2x1) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({3, 2, 1, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -564,7 +564,7 @@ XLA_TEST_F(ReshapeTest, R4ToR2_2x1x4x1_To_4x2) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({3, 2, 1, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -588,7 +588,7 @@ XLA_TEST_F(ReshapeTest, R4ToR2_5x10x2x3_To_5x60_Dimensions_0213) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({3, 2, 1, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -602,7 +602,7 @@ XLA_TEST_F(ReshapeTest, R4ToR2_5x10x2x3_To_5x60_Dimensions_0213) {
     expected_array(indices[0], indices[2] * 30 + indices[1] * 3 + indices[3]) =
         *cell;
   });
-  auto expected = LiteralUtil::CreateR2FromArray2D(expected_array);
+  auto expected = Literal::CreateR2FromArray2D(expected_array);
   ComputeAndCompareLiteral(&builder, *expected, {input_data.get()});
 }
 
@@ -614,7 +614,7 @@ XLA_TEST_F(ReshapeTest, NoopReshape) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input_array, LayoutUtil::MakeLayout({1, 2, 3, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -641,7 +641,7 @@ XLA_TEST_F(ReshapeTest, NoopReshape) {
 }
 
 XLA_TEST_F(ReshapeTest, R4ToR4Reshape_Trivial) {
-  auto literal_1x2x3x4 = LiteralUtil::CreateR4(
+  auto literal_1x2x3x4 = Literal::CreateR4(
       {{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}},
         {{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}});
 
@@ -654,7 +654,7 @@ XLA_TEST_F(ReshapeTest, R4ToR4Reshape_Trivial) {
 }
 
 XLA_TEST_F(ReshapeTest, R4ToR4Reshape) {
-  auto literal_1x2x3x4 = LiteralUtil::CreateR4(
+  auto literal_1x2x3x4 = Literal::CreateR4(
       {{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}},
         {{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}});
 
@@ -664,7 +664,7 @@ XLA_TEST_F(ReshapeTest, R4ToR4Reshape) {
                   /*new_sizes=*/{2, 4, 3, 1});
 
   // clang-format off
-  auto expected_2x4x3x1 = LiteralUtil::CreateR4(
+  auto expected_2x4x3x1 = Literal::CreateR4(
       {{{{1}, {5}, {9}},
         {{2}, {6}, {10}},
         {{3}, {7}, {11}},
@@ -688,7 +688,7 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeSimple) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({3, 2, 1, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -697,9 +697,9 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeSimple) {
   auto a = builder.Parameter(0, input_literal->shape(), "a");
   builder.Reshape(a, /*dimensions=*/{0, 1, 3, 2}, /*new_sizes=*/new_bounds);
 
-  std::unique_ptr<Literal> expected = LiteralUtil::Relayout(
-      *LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal),
-      LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  std::unique_ptr<Literal> expected =
+      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+          ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
   // actually corresponds to a two minor transpose.
@@ -717,7 +717,7 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeMajorFirstEffectiveR2) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({3, 2, 1, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -726,9 +726,9 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeMajorFirstEffectiveR2) {
   auto a = builder.Parameter(0, input_literal->shape(), "a");
   builder.Reshape(a, /*dimensions=*/{0, 1, 3, 2}, /*new_sizes=*/new_bounds);
 
-  std::unique_ptr<Literal> expected = LiteralUtil::Relayout(
-      *LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal),
-      LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  std::unique_ptr<Literal> expected =
+      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+          ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
   // actually corresponds to a two minor transpose.
@@ -746,7 +746,7 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({3, 2, 1, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -755,9 +755,9 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1) {
   auto a = builder.Parameter(0, input_literal->shape(), "a");
   builder.Reshape(a, /*dimensions=*/{0, 1, 3, 2}, /*new_sizes=*/new_bounds);
 
-  std::unique_ptr<Literal> expected = LiteralUtil::Relayout(
-      *LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal),
-      LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  std::unique_ptr<Literal> expected =
+      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+          ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
   // actually corresponds to a two minor transpose.
@@ -776,7 +776,7 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1InR2) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({3, 2, 1, 0}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -785,9 +785,9 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1InR2) {
   auto a = builder.Parameter(0, input_literal->shape(), "a");
   builder.Reshape(a, /*dimensions=*/{0, 1, 3, 2}, /*new_sizes=*/new_bounds);
 
-  std::unique_ptr<Literal> expected = LiteralUtil::Relayout(
-      *LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal),
-      LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  std::unique_ptr<Literal> expected =
+      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+          ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
   // actually corresponds to a two minor transpose.
@@ -805,7 +805,7 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeTrivialR2) {
       [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */,
                             float* cell) { *cell = distribution(rng); });
   std::unique_ptr<Literal> input_literal =
-      LiteralUtil::CreateR4FromArray4DWithLayout(
+      Literal::CreateR4FromArray4DWithLayout(
           input, LayoutUtil::MakeLayout({0, 1, 2, 3}));
   std::unique_ptr<GlobalData> input_data =
       client_->TransferToServer(*input_literal).ConsumeValueOrDie();
@@ -814,9 +814,9 @@ XLA_TEST_F(ReshapeTest, R4TwoMinorTransposeTrivialR2) {
   auto a = builder.Parameter(0, input_literal->shape(), "a");
   builder.Reshape(a, /*dimensions=*/{1, 0, 2, 3}, /*new_sizes=*/new_bounds);
 
-  std::unique_ptr<Literal> expected = LiteralUtil::Relayout(
-      *LiteralTestUtil::Reshape(new_bounds, {1, 0, 2, 3}, *input_literal),
-      input_literal->shape().layout());
+  std::unique_ptr<Literal> expected =
+      LiteralTestUtil::Reshape(new_bounds, {1, 0, 2, 3}, *input_literal)
+          ->Relayout(input_literal->shape().layout());
 
   // Specify the requested output shape explicitly to ensure that this reshape
   // actually corresponds to a two minor transpose.
diff --git a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
index 54d1d76ec9..5b4c05c673 100644
--- a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
+++ b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
@@ -65,8 +65,8 @@ TEST_F(RoundTripPackedLiteralTest, RoundTripsR1F32Length2) {
       reader.Read(ShapeUtil::MakeShape(F32, {2})).ConsumeValueOrDie();
   EXPECT_TRUE(reader.IsExhausted());
 
-  EXPECT_EQ(42.0, LiteralUtil::Get<float>(*actual, {0}));
-  EXPECT_EQ(24.0, LiteralUtil::Get<float>(*actual, {1}));
+  EXPECT_EQ(42.0, actual->Get<float>({0}));
+  EXPECT_EQ(24.0, actual->Get<float>({1}));
 }
 
 TEST_F(RoundTripPackedLiteralTest, RoundTripsR2F32Size2x2Dim0Minor) {
@@ -95,10 +95,10 @@ TEST_F(RoundTripPackedLiteralTest, RoundTripsR2F32Size2x2Dim0Minor) {
           .ConsumeValueOrDie();
   EXPECT_TRUE(reader.IsExhausted());
 
-  EXPECT_EQ(42.0f, LiteralUtil::Get<float>(*actual, {0, 0}));
-  EXPECT_EQ(24.0f, LiteralUtil::Get<float>(*actual, {0, 1}));
-  EXPECT_EQ(64.0f, LiteralUtil::Get<float>(*actual, {1, 0}));
-  EXPECT_EQ(46.0f, LiteralUtil::Get<float>(*actual, {1, 1}));
+  EXPECT_EQ(42.0f, actual->Get<float>({0, 0}));
+  EXPECT_EQ(24.0f, actual->Get<float>({0, 1}));
+  EXPECT_EQ(64.0f, actual->Get<float>({1, 0}));
+  EXPECT_EQ(46.0f, actual->Get<float>({1, 1}));
 
   std::unique_ptr<Literal> round_tripped = RoundTripToServer(*actual);
   LiteralTestUtil::ExpectEqual(*round_tripped, *actual);
@@ -130,10 +130,10 @@ TEST_F(RoundTripPackedLiteralTest, RoundTripsR2F32Size2x2Dim1Minor) {
           .ConsumeValueOrDie();
   EXPECT_TRUE(reader.IsExhausted());
 
-  EXPECT_EQ(42.0f, LiteralUtil::Get<float>(*actual, {0, 0}));
-  EXPECT_EQ(24.0f, LiteralUtil::Get<float>(*actual, {1, 0}));
-  EXPECT_EQ(64.0f, LiteralUtil::Get<float>(*actual, {0, 1}));
-  EXPECT_EQ(46.0f, LiteralUtil::Get<float>(*actual, {1, 1}));
+  EXPECT_EQ(42.0f, actual->Get<float>({0, 0}));
+  EXPECT_EQ(24.0f, actual->Get<float>({1, 0}));
+  EXPECT_EQ(64.0f, actual->Get<float>({0, 1}));
+  EXPECT_EQ(46.0f, actual->Get<float>({1, 1}));
 
   std::unique_ptr<Literal> round_tripped = RoundTripToServer(*actual);
   LiteralTestUtil::ExpectEqual(*round_tripped, *actual);
diff --git a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
index 71af695ae3..e6a6b7b37a 100644
--- a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
+++ b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
@@ -47,62 +47,61 @@ class RoundTripTransferTest : public ClientLibraryTestBase {
 };
 
 TEST_F(RoundTripTransferTest, R0S32) {
-  RoundTripTest(*LiteralUtil::CreateR0<int32>(42));
+  RoundTripTest(*Literal::CreateR0<int32>(42));
 }
 
 TEST_F(RoundTripTransferTest, R0F32) {
-  RoundTripTest(*LiteralUtil::CreateR0<float>(42.0));
+  RoundTripTest(*Literal::CreateR0<float>(42.0));
 }
 
 TEST_F(RoundTripTransferTest, R1F32_Len0) {
-  RoundTripTest(*LiteralUtil::CreateR1<float>({}));
+  RoundTripTest(*Literal::CreateR1<float>({}));
 }
 
 TEST_F(RoundTripTransferTest, R1F32_Len2) {
-  RoundTripTest(*LiteralUtil::CreateR1<float>({42.0, 64.0}));
+  RoundTripTest(*Literal::CreateR1<float>({42.0, 64.0}));
 }
 
 TEST_F(RoundTripTransferTest, R1F32_Len256) {
   std::vector<float> values(256);
   std::iota(values.begin(), values.end(), 1.0);
-  RoundTripTest(*LiteralUtil::CreateR1<float>(values));
+  RoundTripTest(*Literal::CreateR1<float>(values));
 }
 
 TEST_F(RoundTripTransferTest, R1F32_Len1024) {
   std::vector<float> values(1024);
   std::iota(values.begin(), values.end(), 1.0);
-  RoundTripTest(*LiteralUtil::CreateR1<float>(values));
+  RoundTripTest(*Literal::CreateR1<float>(values));
 }
 
 TEST_F(RoundTripTransferTest, R1F32_Len1025) {
   std::vector<float> values(1025);
   std::iota(values.begin(), values.end(), 1.0);
-  RoundTripTest(*LiteralUtil::CreateR1<float>(values));
+  RoundTripTest(*Literal::CreateR1<float>(values));
 }
 
 TEST_F(RoundTripTransferTest, R1F32_Len4096) {
   std::vector<float> values(4096);
   std::iota(values.begin(), values.end(), 1.0);
-  RoundTripTest(*LiteralUtil::CreateR1<float>(values));
+  RoundTripTest(*Literal::CreateR1<float>(values));
 }
 
 TEST_F(RoundTripTransferTest, R2F32_Len10x0) {
-  RoundTripTest(
-      *LiteralUtil::CreateR2FromArray2D<float>(Array2D<float>(10, 0)));
+  RoundTripTest(*Literal::CreateR2FromArray2D<float>(Array2D<float>(10, 0)));
 }
 
 TEST_F(RoundTripTransferTest, R2F32_Len2x2) {
-  RoundTripTest(*LiteralUtil::CreateR2<float>({{42.0, 64.0}, {77.0, 88.0}}));
+  RoundTripTest(*Literal::CreateR2<float>({{42.0, 64.0}, {77.0, 88.0}}));
 }
 
 TEST_F(RoundTripTransferTest, R3F32) {
   RoundTripTest(
-      *LiteralUtil::CreateR3<float>({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}},
-                                     {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}}));
+      *Literal::CreateR3<float>({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}},
+                                 {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}}));
 }
 
 TEST_F(RoundTripTransferTest, R4F32) {
-  RoundTripTest(*LiteralUtil::CreateR4<float>({{
+  RoundTripTest(*Literal::CreateR4<float>({{
       {{10, 11, 12, 13}, {14, 15, 16, 17}},
       {{18, 19, 20, 21}, {22, 23, 24, 25}},
       {{26, 27, 28, 29}, {30, 31, 32, 33}},
@@ -110,36 +109,33 @@ TEST_F(RoundTripTransferTest, R4F32) {
 }
 
 TEST_F(RoundTripTransferTest, EmptyTuple) {
-  RoundTripTest(*LiteralUtil::MakeTuple({}));
+  RoundTripTest(*Literal::MakeTuple({}));
 }
 
 TEST_F(RoundTripTransferTest, TupleOfR1F32) {
-  RoundTripTest(
-      *LiteralUtil::MakeTuple({LiteralUtil::CreateR1<float>({1, 2}).get(),
-                               LiteralUtil::CreateR1<float>({3, 4}).get()}));
+  RoundTripTest(*Literal::MakeTuple({Literal::CreateR1<float>({1, 2}).get(),
+                                     Literal::CreateR1<float>({3, 4}).get()}));
 }
 
 TEST_F(RoundTripTransferTest, TupleOfR1F32_Len0_Len2) {
-  RoundTripTest(
-      *LiteralUtil::MakeTuple({LiteralUtil::CreateR1<float>({}).get(),
-                               LiteralUtil::CreateR1<float>({3, 4}).get()}));
+  RoundTripTest(*Literal::MakeTuple({Literal::CreateR1<float>({}).get(),
+                                     Literal::CreateR1<float>({3, 4}).get()}));
 }
 
 TEST_F(RoundTripTransferTest, TupleOfR0F32AndR1S32) {
-  RoundTripTest(
-      *LiteralUtil::MakeTuple({LiteralUtil::CreateR0<float>(1.0).get(),
-                               LiteralUtil::CreateR1<int>({2, 3}).get()}));
+  RoundTripTest(*Literal::MakeTuple({Literal::CreateR0<float>(1.0).get(),
+                                     Literal::CreateR1<int>({2, 3}).get()}));
 }
 
 // Below two tests are added to identify the cost of large data transfers.
 TEST_F(RoundTripTransferTest, R2F32_Large) {
-  RoundTripTest(*LiteralUtil::CreateR2F32Linspace(-1.0f, 1.0f, 512, 512));
+  RoundTripTest(*Literal::CreateR2F32Linspace(-1.0f, 1.0f, 512, 512));
 }
 
 TEST_F(RoundTripTransferTest, R4F32_Large) {
   Array4D<float> array4d(2, 2, 256, 256);
   array4d.FillWithMultiples(1.0f);
-  RoundTripTest(*LiteralUtil::CreateR4FromArray4D<float>(array4d));
+  RoundTripTest(*Literal::CreateR4FromArray4D<float>(array4d));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index 7b32bb93fd..07bd00f015 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -211,9 +211,9 @@ TEST_F(ScalarComputationsTest, MulThreeScalarsS32) {
 
 TEST_F(ScalarComputationsTest, MulThreeScalarsF32Params) {
   ComputationBuilder builder(client_, TestName());
-  std::unique_ptr<Literal> a_literal = LiteralUtil::CreateR0<float>(2.1f);
-  std::unique_ptr<Literal> b_literal = LiteralUtil::CreateR0<float>(5.5f);
-  std::unique_ptr<Literal> c_literal = LiteralUtil::CreateR0<float>(0.5f);
+  std::unique_ptr<Literal> a_literal = Literal::CreateR0<float>(2.1f);
+  std::unique_ptr<Literal> b_literal = Literal::CreateR0<float>(5.5f);
+  std::unique_ptr<Literal> c_literal = Literal::CreateR0<float>(0.5f);
 
   std::unique_ptr<GlobalData> a_data =
       client_->TransferToServer(*a_literal).ConsumeValueOrDie();
@@ -360,8 +360,8 @@ TEST_F(ScalarComputationsTest, DivU32s) {
   for (uint32 divisor : vals) {
     if (divisor != 0) {
       for (uint32 dividend : vals) {
-        auto dividend_literal = LiteralUtil::CreateR0<uint32>(dividend);
-        auto divisor_literal = LiteralUtil::CreateR0<uint32>(divisor);
+        auto dividend_literal = Literal::CreateR0<uint32>(dividend);
+        auto divisor_literal = Literal::CreateR0<uint32>(divisor);
         TF_ASSIGN_OR_ASSERT_OK(auto dividend_data,
                                client_->TransferToServer(*dividend_literal));
         TF_ASSIGN_OR_ASSERT_OK(auto divisor_data,
@@ -372,8 +372,7 @@ TEST_F(ScalarComputationsTest, DivU32s) {
                                      {dividend_data.get(), divisor_data.get()},
                                      &execution_options_)
                 .ConsumeValueOrDie();
-        auto expected_literal =
-            LiteralUtil::CreateR0<uint32>(dividend / divisor);
+        auto expected_literal = Literal::CreateR0<uint32>(dividend / divisor);
         LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
       }
     }
@@ -402,8 +401,8 @@ TEST_F(ScalarComputationsTest, RemU32s) {
   for (uint32 divisor : vals) {
     if (divisor != 0) {
       for (uint32 dividend : vals) {
-        auto dividend_literal = LiteralUtil::CreateR0<uint32>(dividend);
-        auto divisor_literal = LiteralUtil::CreateR0<uint32>(divisor);
+        auto dividend_literal = Literal::CreateR0<uint32>(dividend);
+        auto divisor_literal = Literal::CreateR0<uint32>(divisor);
         TF_ASSIGN_OR_ASSERT_OK(auto dividend_data,
                                client_->TransferToServer(*dividend_literal));
         TF_ASSIGN_OR_ASSERT_OK(auto divisor_data,
@@ -414,8 +413,7 @@ TEST_F(ScalarComputationsTest, RemU32s) {
                                      {dividend_data.get(), divisor_data.get()},
                                      &execution_options_)
                 .ConsumeValueOrDie();
-        auto expected_literal =
-            LiteralUtil::CreateR0<uint32>(dividend % divisor);
+        auto expected_literal = Literal::CreateR0<uint32>(dividend % divisor);
         LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
       }
     }
@@ -427,7 +425,7 @@ TEST_F(ScalarComputationsTest, RemainderTwoScalarsNonConstDividendS32) {
   auto x = builder.Parameter(0, ShapeUtil::MakeShape(S32, {}), "x");
   builder.Rem(x, builder.ConstantR0<int32>(80000));
 
-  std::unique_ptr<Literal> literal = LiteralUtil::CreateR0<int32>(87919);
+  std::unique_ptr<Literal> literal = Literal::CreateR0<int32>(87919);
   TF_ASSIGN_OR_ASSERT_OK(auto input_data, client_->TransferToServer(*literal));
   ComputeAndCompareR0<int32>(&builder, 7919, {input_data.get()});
 }
@@ -763,7 +761,7 @@ TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionS32) {
 
 TEST_F(ScalarComputationsTest, SqrtF320) {
   ComputationBuilder builder(client_, TestName());
-  Literal zero_literal = LiteralUtil::Zero(PrimitiveType::F32);
+  Literal zero_literal = Literal::Zero(PrimitiveType::F32);
 
   std::unique_ptr<GlobalData> zero_data =
       client_->TransferToServer(zero_literal).ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/tests/test_utils.h b/tensorflow/compiler/xla/tests/test_utils.h
index 6a23df4d3c..f3a522b05e 100644
--- a/tensorflow/compiler/xla/tests/test_utils.h
+++ b/tensorflow/compiler/xla/tests/test_utils.h
@@ -61,7 +61,7 @@ std::unique_ptr<Literal> CreateR2LiteralWithLayout(
   auto literal = MakeUnique<Literal>();
   const int64 d0 = values.size();
   const int64 d1 = values.begin()->size();
-  LiteralUtil::PopulateWithValue<NativeT>(0, {d0, d1}, literal.get());
+  literal.get()->PopulateWithValue<NativeT>(0, {d0, d1});
   *literal->mutable_shape()->mutable_layout() =
       LayoutUtil::MakeLayout(minor_to_major);
   TF_CHECK_OK(ShapeUtil::ValidateShape(literal->shape()));
@@ -70,7 +70,7 @@ std::unique_ptr<Literal> CreateR2LiteralWithLayout(
   for (auto inner_list : values) {
     int64 dim1 = 0;
     for (auto value : inner_list) {
-      LiteralUtil::Set(literal.get(), {dim0, dim1}, value);
+      literal.get()->Set({dim0, dim1}, value);
       ++dim1;
     }
     ++dim0;
@@ -88,7 +88,7 @@ std::unique_ptr<Literal> CreateR3LiteralWithLayout(
   const int64 d0 = values.size();
   const int64 d1 = values.begin()->size();
   const int64 d2 = values.begin()->begin()->size();
-  LiteralUtil::PopulateWithValue<NativeT>(0, {d0, d1, d2}, literal.get());
+  literal.get()->PopulateWithValue<NativeT>(0, {d0, d1, d2});
   *literal->mutable_shape()->mutable_layout() =
       LayoutUtil::MakeLayout(minor_to_major);
   TF_CHECK_OK(ShapeUtil::ValidateShape(literal->shape()));
@@ -99,7 +99,7 @@ std::unique_ptr<Literal> CreateR3LiteralWithLayout(
     for (auto inner_inner_list : inner_list) {
       int64 dim2 = 0;
       for (auto value : inner_inner_list) {
-        LiteralUtil::Set(literal.get(), {dim0, dim1, dim2}, value);
+        literal.get()->Set({dim0, dim1, dim2}, value);
         ++dim2;
       }
       ++dim1;
diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc
index e9d523a31e..a4d96646a1 100644
--- a/tensorflow/compiler/xla/tests/tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/tuple_test.cc
@@ -53,10 +53,10 @@ XLA_TEST_F(TupleTest, TupleCreate) {
                                builder.ConstantR1<float>(constant_vector),
                                builder.ConstantR2<float>(constant_matrix)});
 
-  auto expected = LiteralUtil::MakeTuple(
-      {LiteralUtil::CreateR0<float>(constant_scalar).get(),
-       LiteralUtil::CreateR1<float>(constant_vector).get(),
-       LiteralUtil::CreateR2<float>(constant_matrix).get()});
+  auto expected =
+      Literal::MakeTuple({Literal::CreateR0<float>(constant_scalar).get(),
+                          Literal::CreateR1<float>(constant_vector).get(),
+                          Literal::CreateR2<float>(constant_matrix).get()});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -67,9 +67,8 @@ XLA_TEST_F(TupleTest, TupleCreateWithZeroElementEntry) {
   auto result = builder.Tuple(
       {builder.ConstantR0<float>(7.0), builder.ConstantR1<float>({})});
 
-  auto expected =
-      LiteralUtil::MakeTuple({LiteralUtil::CreateR0<float>(7.0).get(),
-                              LiteralUtil::CreateR1<float>({}).get()});
+  auto expected = Literal::MakeTuple({Literal::CreateR0<float>(7.0).get(),
+                                      Literal::CreateR1<float>({}).get()});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -77,7 +76,7 @@ XLA_TEST_F(TupleTest, TupleCreateWithZeroElementEntry) {
 XLA_TEST_F(TupleTest, EmptyTupleCreate) {
   ComputationBuilder builder(client_, TestName());
   auto result = builder.Tuple({});
-  auto expected = LiteralUtil::MakeTuple({});
+  auto expected = Literal::MakeTuple({});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -146,9 +145,9 @@ XLA_TEST_F(TupleTest, TupleGTEToTuple) {
                                    builder.ConstantR2<float>(constant_matrix)});
   auto new_tuple = builder.Tuple({builder.GetTupleElement(tuple_data, 1),
                                   builder.GetTupleElement(tuple_data, 0)});
-  auto expected = LiteralUtil::MakeTuple(
-      {LiteralUtil::CreateR2<float>(constant_matrix).get(),
-       LiteralUtil::CreateR1<float>(constant_vector).get()});
+  auto expected =
+      Literal::MakeTuple({Literal::CreateR2<float>(constant_matrix).get(),
+                          Literal::CreateR1<float>(constant_vector).get()});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -212,9 +211,8 @@ XLA_TEST_F(TupleTest, DISABLED_ON_CPU_PARALLEL(SelectBetweenTuplesOnFalse)) {
 
   auto select =
       builder.Select(builder.ConstantR0<bool>(false), tuple12, tuple21);
-  auto expected =
-      LiteralUtil::MakeTuple({LiteralUtil::CreateR1<float>(vec2).get(),
-                              LiteralUtil::CreateR1<float>(vec1).get()});
+  auto expected = Literal::MakeTuple({Literal::CreateR1<float>(vec2).get(),
+                                      Literal::CreateR1<float>(vec1).get()});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -258,9 +256,8 @@ XLA_TEST_F(TupleTest, DISABLED_ON_CPU_PARALLEL(SelectBetweenTuplesOnTrue)) {
 
   auto select =
       builder.Select(builder.ConstantR0<bool>(true), tuple12, tuple21);
-  auto expected =
-      LiteralUtil::MakeTuple({LiteralUtil::CreateR1<float>(vec1).get(),
-                              LiteralUtil::CreateR1<float>(vec2).get()});
+  auto expected = Literal::MakeTuple({Literal::CreateR1<float>(vec1).get(),
+                                      Literal::CreateR1<float>(vec2).get()});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -339,9 +336,8 @@ XLA_TEST_F(TupleTest,
 
   auto select =
       builder.Select(builder.ConstantR0<bool>(false), tuple12, tuple21);
-  auto expected =
-      LiteralUtil::MakeTuple({LiteralUtil::CreateR1<float>(vec2).get(),
-                              LiteralUtil::CreateR1<float>(vec1).get()});
+  auto expected = Literal::MakeTuple({Literal::CreateR1<float>(vec2).get(),
+                                      Literal::CreateR1<float>(vec1).get()});
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
@@ -352,13 +348,13 @@ XLA_TEST_F(TupleTest, NestedTuples) {
   auto outer_tuple =
       builder.Tuple({inner_tuple, builder.ConstantR1<float>({22.0, 44.0})});
 
-  auto expected_v1 = LiteralUtil::CreateR1<float>({1.0, 2.0});
-  auto expected_s = LiteralUtil::CreateR0<float>(42.0);
+  auto expected_v1 = Literal::CreateR1<float>({1.0, 2.0});
+  auto expected_s = Literal::CreateR0<float>(42.0);
   auto expected_inner_tuple =
-      LiteralUtil::MakeTuple({expected_v1.get(), expected_s.get()});
-  auto expected_v2 = LiteralUtil::CreateR1<float>({22.0, 44.0});
+      Literal::MakeTuple({expected_v1.get(), expected_s.get()});
+  auto expected_v2 = Literal::CreateR1<float>({22.0, 44.0});
   auto expected =
-      LiteralUtil::MakeTuple({expected_inner_tuple.get(), expected_v2.get()});
+      Literal::MakeTuple({expected_inner_tuple.get(), expected_v2.get()});
 
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
@@ -378,14 +374,14 @@ XLA_TEST_F(TupleTest, GetTupleElementOfNestedTuple) {
 
   std::unique_ptr<GlobalData> data =
       client_
-          ->TransferToServer(*LiteralUtil::MakeTuple({
-              LiteralUtil::MakeTuple(
+          ->TransferToServer(*Literal::MakeTuple({
+              Literal::MakeTuple(
                   {
-                      LiteralUtil::CreateR1<float>({1.0, 2.0, 3.0}).get(),
-                      LiteralUtil::CreateR1<float>({4.0, 5.0, 6.0}).get(),
+                      Literal::CreateR1<float>({1.0, 2.0, 3.0}).get(),
+                      Literal::CreateR1<float>({4.0, 5.0, 6.0}).get(),
                   })
                   .get(),
-              LiteralUtil::CreateR1<float>({7.0, 8.0, 9.0}).get(),
+              Literal::CreateR1<float>({7.0, 8.0, 9.0}).get(),
           }))
           .ConsumeValueOrDie();
 
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index 3aeeb29c1f..2df9197428 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -239,11 +239,11 @@ TEST_F(WhileTest, WhileWithTupleResult) {
   VLOG(2) << "while = " << ShapeUtil::HumanString(
                                *builder.GetShape(result).ConsumeValueOrDie());
 
-  auto expected_counter = LiteralUtil::CreateR0<int32>(5);
-  auto expected_data = LiteralUtil::CreateR1<float>(
+  auto expected_counter = Literal::CreateR0<int32>(5);
+  auto expected_data = Literal::CreateR1<float>(
       {5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f});
   auto expected =
-      LiteralUtil::MakeTuple({expected_counter.get(), expected_data.get()});
+      Literal::MakeTuple({expected_counter.get(), expected_data.get()});
   VLOG(2) << "expected = " << ShapeUtil::HumanString(expected->shape());
   ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001));
 }
@@ -524,11 +524,11 @@ XLA_TEST_F(WhileTest, WhileWithDynamicUpdateSlice) {
           << ShapeUtil::HumanString(
                  *builder.GetShape(result).ConsumeValueOrDie());
 
-  auto expected_counter = LiteralUtil::CreateR0<int32>(5);
-  auto expected_data = LiteralUtil::CreateR1<float>(
+  auto expected_counter = Literal::CreateR0<int32>(5);
+  auto expected_data = Literal::CreateR1<float>(
       {1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f});
   auto expected =
-      LiteralUtil::MakeTuple({expected_counter.get(), expected_data.get()});
+      Literal::MakeTuple({expected_counter.get(), expected_data.get()});
   VLOG(2) << "expected = " << ShapeUtil::HumanString(expected->shape());
   ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001));
 }
diff --git a/tensorflow/compiler/xla/text_literal_reader.cc b/tensorflow/compiler/xla/text_literal_reader.cc
index 7876272467..afdc6726f1 100644
--- a/tensorflow/compiler/xla/text_literal_reader.cc
+++ b/tensorflow/compiler/xla/text_literal_reader.cc
@@ -104,8 +104,8 @@ StatusOr<std::unique_ptr<Literal>> TextLiteralReader::ReadAllLines() {
 
   auto result = MakeUnique<Literal>();
   const float fill = std::numeric_limits<float>::quiet_NaN();
-  LiteralUtil::PopulateWithValue<float>(fill, AsInt64Slice(shape.dimensions()),
-                                        result.get());
+  result.get()->PopulateWithValue<float>(fill,
+                                         AsInt64Slice(shape.dimensions()));
   std::vector<tensorflow::StringPiece> pieces;
   std::vector<tensorflow::StringPiece> coordinates;
   std::vector<int64> coordinate_values;
@@ -147,7 +147,7 @@ StatusOr<std::unique_ptr<Literal>> TextLiteralReader::ReadAllLines() {
           "\"%s\"",
           shape.dimensions_size(), coordinate_values.size(), line.c_str());
     }
-    LiteralUtil::Set<float>(result.get(), coordinate_values, value);
+    result.get()->Set<float>(coordinate_values, value);
   }
   return std::move(result);
 }
diff --git a/tensorflow/compiler/xla/text_literal_reader_test.cc b/tensorflow/compiler/xla/text_literal_reader_test.cc
index a167d80f73..23070b6638 100644
--- a/tensorflow/compiler/xla/text_literal_reader_test.cc
+++ b/tensorflow/compiler/xla/text_literal_reader_test.cc
@@ -46,12 +46,12 @@ TEST(TextLiteralReaderTest, ReadsR3File) {
       TextLiteralReader::ReadPath(fname).ConsumeValueOrDie();
   EXPECT_TRUE(
       ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {1, 2, 3}), literal->shape()));
-  EXPECT_EQ(42.5, LiteralUtil::Get<float>(*literal, {0, 0, 0}));
-  EXPECT_EQ(43.5, LiteralUtil::Get<float>(*literal, {0, 0, 1}));
-  EXPECT_EQ(44.5, LiteralUtil::Get<float>(*literal, {0, 0, 2}));
-  EXPECT_EQ(45.5, LiteralUtil::Get<float>(*literal, {0, 1, 0}));
-  EXPECT_EQ(46.5, LiteralUtil::Get<float>(*literal, {0, 1, 1}));
-  EXPECT_EQ(47.5, LiteralUtil::Get<float>(*literal, {0, 1, 2}));
+  EXPECT_EQ(42.5, literal->Get<float>({0, 0, 0}));
+  EXPECT_EQ(43.5, literal->Get<float>({0, 0, 1}));
+  EXPECT_EQ(44.5, literal->Get<float>({0, 0, 2}));
+  EXPECT_EQ(45.5, literal->Get<float>({0, 1, 0}));
+  EXPECT_EQ(46.5, literal->Get<float>({0, 1, 1}));
+  EXPECT_EQ(47.5, literal->Get<float>({0, 1, 2}));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/text_literal_writer.cc b/tensorflow/compiler/xla/text_literal_writer.cc
index a5097e41cb..3fee467594 100644
--- a/tensorflow/compiler/xla/text_literal_writer.cc
+++ b/tensorflow/compiler/xla/text_literal_writer.cc
@@ -45,9 +45,9 @@ namespace xla {
 
   tensorflow::Status status;
   tensorflow::WritableFile* f_ptr = f.get();
-  LiteralUtil::EachCellAsString(
-      literal, [f_ptr, &status](tensorflow::gtl::ArraySlice<int64> indices,
-                                const string& value) {
+  literal.EachCellAsString(
+      [f_ptr, &status](tensorflow::gtl::ArraySlice<int64> indices,
+                       const string& value) {
         if (!status.ok()) {
           return;
         }
diff --git a/tensorflow/compiler/xla/text_literal_writer_test.cc b/tensorflow/compiler/xla/text_literal_writer_test.cc
index 177ae4ea03..70cf2fb1b8 100644
--- a/tensorflow/compiler/xla/text_literal_writer_test.cc
+++ b/tensorflow/compiler/xla/text_literal_writer_test.cc
@@ -30,7 +30,7 @@ namespace xla {
 namespace {
 
 TEST(TextLiteralWriterTest, WritesFloatLiteral) {
-  auto literal = LiteralUtil::CreateR2<float>({
+  auto literal = Literal::CreateR2<float>({
       {3.14, 2.17}, {1.23, 4.56},
   });
   string path =
diff --git a/tensorflow/compiler/xla/tools/replay_computation.cc b/tensorflow/compiler/xla/tools/replay_computation.cc
index 3a75bf6495..6228ca34c0 100644
--- a/tensorflow/compiler/xla/tools/replay_computation.cc
+++ b/tensorflow/compiler/xla/tools/replay_computation.cc
@@ -98,11 +98,11 @@ void RealMain(tensorflow::gtl::ArraySlice<char*> args, bool use_fake_data) {
     std::unique_ptr<Literal> result = result_status.ConsumeValueOrDie();
     fprintf(stdout, "%s: %s :: %s:%s\n", arg, module.entry().name().c_str(),
             ShapeUtil::HumanString(result->shape()).c_str(),
-            LiteralUtil::ToString(*result).c_str());
+            result->ToString().c_str());
     if (module.has_result()) {
       fprintf(stdout, "was %s:%s\n",
               ShapeUtil::HumanString(module.result().shape()).c_str(),
-              LiteralUtil::ToString(Literal(module.result())).c_str());
+              Literal(module.result()).ToString().c_str());
     }
   }
 }
diff --git a/tensorflow/compiler/xla/tools/show_literal.cc b/tensorflow/compiler/xla/tools/show_literal.cc
index b6538f5de0..b50cb5e28e 100644
--- a/tensorflow/compiler/xla/tools/show_literal.cc
+++ b/tensorflow/compiler/xla/tools/show_literal.cc
@@ -42,5 +42,5 @@ int main(int argc, char **argv) {
                                           &literal_proto));
   xla::Literal literal(literal_proto);
   LOG(INFO) << "literal: " << literal_proto.ShortDebugString();
-  fprintf(stderr, "%s\n", xla::LiteralUtil::ToString(literal).c_str());
+  fprintf(stderr, "%s\n", literal.ToString().c_str());
 }
diff --git a/tensorflow/compiler/xla/tools/show_text_literal.cc b/tensorflow/compiler/xla/tools/show_text_literal.cc
index 2d983b407c..bbe9902aa1 100644
--- a/tensorflow/compiler/xla/tools/show_text_literal.cc
+++ b/tensorflow/compiler/xla/tools/show_text_literal.cc
@@ -40,7 +40,7 @@ int main(int argc, char **argv) {
       xla::TextLiteralReader::ReadPath(argv[1]).ConsumeValueOrDie();
 
   LOG(INFO) << "literal: " << literal->ShortDebugString();
-  fprintf(stderr, "%s\n", xla::LiteralUtil::ToString(*literal).c_str());
+  fprintf(stderr, "%s\n", literal->ToString().c_str());
   if (literal->shape().element_type() == xla::F32) {
     float min =
         *std::min_element(literal->f32s().begin(), literal->f32s().end());
-- 
GitLab


From 24f49d0af7284ad17070184b1eeb73ee392d2394 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 11:23:10 -0700
Subject: [PATCH 0205/1375] Shard test case that was causing timeouts in
 non-fastbuild.

PiperOrigin-RevId: 159451808
---
 tensorflow/compiler/tests/spacetobatch_op_test.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/tests/spacetobatch_op_test.py b/tensorflow/compiler/tests/spacetobatch_op_test.py
index 9c3b86c84b..c013f4b50a 100644
--- a/tensorflow/compiler/tests/spacetobatch_op_test.py
+++ b/tensorflow/compiler/tests/spacetobatch_op_test.py
@@ -228,34 +228,40 @@ class SpaceToBatchNDTest(XLATestCase):
         outputs=[[[0, 0], [2, 21]], [[0, 0], [5, 51]], [[1, 11], [3, 31]],
                  [[4, 41], [6, 61]]])
 
-  def testDirect(self):
+  def testDirect0(self):
     # Test with zero-size remaining dimension.
     self._testDirect(
         input_shape=[3, 1, 2, 0], block_shape=[3], paddings=[[0, 2]])
 
+  def testDirect1(self):
     # Test with zero-size blocked dimension.
     self._testDirect(
         input_shape=[3, 0, 2, 5], block_shape=[3], paddings=[[0, 0]])
 
+  def testDirect2(self):
     # Test with padding up from zero size.
     self._testDirect(
         input_shape=[3, 0, 2, 5], block_shape=[3], paddings=[[1, 2]])
 
+  def testDirect3(self):
     self._testDirect(
         input_shape=[3, 3, 4, 5, 2],
         block_shape=[3, 4, 2],
         paddings=[[1, 2], [0, 0], [3, 0]])
 
+  def testDirect4(self):
     self._testDirect(
         input_shape=[3, 3, 4, 5, 2],
         block_shape=[3, 4, 2, 2],
         paddings=[[1, 2], [0, 0], [3, 0], [0, 0]])
 
+  def testDirect5(self):
     self._testDirect(
         input_shape=[3, 2, 2, 3, 4, 5, 2, 5],
         block_shape=[1, 1, 3, 4, 2, 2],
         paddings=[[0, 0], [0, 0], [1, 2], [0, 0], [3, 0], [0, 0]])
 
+  def testDirect6(self):
     self._testDirect(
         input_shape=[3, 2, 2, 3, 4, 5, 2, 5],
         block_shape=[1, 1, 3, 4, 2, 2, 1],
-- 
GitLab


From 1e2c062e4a612ce9afafe1685c7ed1ec09c7be1e Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Mon, 19 Jun 2017 11:41:47 -0700
Subject: [PATCH 0206/1375] Add new GPU kernel to improve small dimension
 transpose performance.

PiperOrigin-RevId: 159454657
---
 tensorflow/core/kernels/conv_ops_gpu_3.cu.cc | 112 ++++++++++++++-
 tensorflow/python/BUILD                      |  20 +++
 tensorflow/python/ops/conv2d_benchmark.py    | 141 +++++++++++++++++++
 tensorflow/python/ops/transpose_benchmark.py |  64 ++++++---
 4 files changed, 312 insertions(+), 25 deletions(-)
 create mode 100644 tensorflow/python/ops/conv2d_benchmark.py

diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
index 2307c2de0e..b2bbc5831c 100644
--- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
+++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
@@ -272,6 +272,75 @@ __global__ void SwapDimension1And2InTensor3UsingTiles(const T* input,
   }
 }
 
+// Use shared memory tiles to swap dimension-1 and dimension-2 of a 3D tensor
+// when only one of the dimension sizes is smaller than 16,
+// where dimensions are zero-based: output[i][j][k] = input[i][k][j].
+//
+// small_dim = the_smaller_dimension_size
+// large_dim = the_larger_dimension_size
+// tile_num_per_block = blockDim.x
+// kTileLength = small_dim
+//
+// Each thread block operates on a single rectangle tile, where its width is
+// kTileLength (we currently set it to 64) and its height is small_dim,
+// We set the thread block's X dimension to be tile_num_per_block, and its Y
+// and Z to be one.
+template <typename T, bool SmallDim2>
+__global__ void SwapDimension1And2InTensor3SmallDim(const T* input,
+                                                    Dimension<3> input_dims,
+                                                    T* output) {
+  // TODO(yangzihao) avoid share memory bank conflict.
+  extern __shared__ __align__(sizeof(T)) unsigned char shmem[];
+  T* shared_memory_tile = reinterpret_cast<T*>(shmem);
+
+  eigen_assert(blockDim.y == 1);
+  eigen_assert(blockDim.z == 1);
+  eigen_assert(gridDim.z == 1);
+
+  int x = threadIdx.x;
+  int tile_height = blockDim.x;
+
+  // Get tile height, width, and thread/block origin indices.
+  int small_dim = SmallDim2 ? input_dims[2] : input_dims[1];
+  int large_dim = SmallDim2 ? input_dims[1] : input_dims[2];
+  int block_origin_idx = small_dim * large_dim * blockIdx.y;
+  int block_offset = blockIdx.x * blockDim.x;
+  int thread_origin_idx =
+      block_origin_idx + (SmallDim2 ? block_offset * small_dim : block_offset) +
+      x;
+
+  if (block_offset + blockDim.x > large_dim) {
+    tile_height = large_dim - block_offset;
+  }
+
+  // Load a continous memory region to shared memory tile.
+  if (x < tile_height) {
+    for (int y = 0; y < small_dim; y++) {
+      int shmem_index = SmallDim2 ? (x + y * tile_height) : (x * small_dim + y);
+      shared_memory_tile[shmem_index] =
+          ldg(input + thread_origin_idx +
+              y * (SmallDim2 ? tile_height : large_dim));
+    }
+  }
+
+  __syncthreads();
+
+  // Get block origin index for output array.
+  int output_block_offset = block_origin_idx;
+  int output_block_idx = SmallDim2 ? block_offset : block_offset * small_dim;
+  int output_block_origin_idx = output_block_offset + output_block_idx;
+
+  // Store the tranposed memory region in shared memory to device.
+  if (x < tile_height) {
+    for (int y = 0; y < small_dim; y++) {
+      int output_idx = output_block_origin_idx + x +
+                       y * (SmallDim2 ? large_dim : tile_height);
+      int shmem_index = SmallDim2 ? (x * small_dim + y) : (x + y * tile_height);
+      output[output_idx] = shared_memory_tile[shmem_index];
+    }
+  }
+}
+
 // A Cuda custom kernel that convert input to output, given proper padding on
 // the left and the top. The padded value is zero.
 template <typename T, int NDIMS>
@@ -420,25 +489,60 @@ template <typename T>
 void RunSwapDimension1And2InTensor3(const GPUDevice& d, const T* input,
                                     const Dimension<3>& input_dims, T* output) {
   // If both dimensions are not trivial, use tiles for the actual swapping.
+  // If one dimension is trivial, use SmallDim kernel for swapping.
   // Otherwise, the trivial swapping relying on the ldg cache is more efficient.
   static const int kMinDimensionToUseTiles = 16;
   bool use_tiles = (input_dims[1] >= kMinDimensionToUseTiles &&
                     input_dims[2] >= kMinDimensionToUseTiles);
+  bool use_small_dim = ((input_dims[1] >= kMinDimensionToUseTiles &&
+                         input_dims[2] < kMinDimensionToUseTiles)) ||
+                       ((input_dims[1] < kMinDimensionToUseTiles &&
+                         input_dims[2] >= kMinDimensionToUseTiles));
+
+  static const int NumSubTiles = 8;
   if (use_tiles) {
-    // We get best performance when TileSize is the number of threads in a warp
-    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
-    // threads.
     static const int TileSize = 32;
-    static const int NumSubTiles = 8;
     Dimension<3> input_dims_in_tiles = {
         input_dims[0], (input_dims[1] + TileSize - 1) / TileSize,
         (input_dims[2] + TileSize - 1) / TileSize,
     };
     int total_tiles_count = input_dims_in_tiles[0] * input_dims_in_tiles[1] *
                             input_dims_in_tiles[2];
+    // We get best performance when TileSize is the number of threads in a warp
+    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
+    // threads.
     SwapDimension1And2InTensor3UsingTiles<T, TileSize, NumSubTiles><<<
         total_tiles_count, dim3(TileSize, NumSubTiles), 0, d.stream()>>>(
         input, input_dims, output);
+  } else if (use_small_dim) {
+    // When only one of the dimensions is smaller than kMinDimensionToUseTiles,
+    // we use one block to process a rectangle region with the size of
+    // kTileLength * small_dim. We found that when set kTileLength to 64 on
+    // TitanX Maxwell GPU, it achieves the best performance.
+    //              large_dim
+    //            +---------------...--------+
+    //            |            |        |    |
+    // small_dim  |            |  ...   |    |
+    //            |            |        |    |
+    //            +--------------...---------+
+    //            \----- ------/         \- -/
+    //                  V                  V
+    //    kTileLength(tile_height)    tile_height
+    static const int kTileLength = 64;
+    int small_dim = std::min(input_dims[2], input_dims[1]);
+    int large_dim = std::max(input_dims[2], input_dims[1]);
+    int tile_num_per_block = (large_dim + kTileLength - 1) / kTileLength;
+    if (input_dims[2] < input_dims[1]) {
+      SwapDimension1And2InTensor3SmallDim<T, true>
+          <<<dim3(tile_num_per_block, input_dims[0]), kTileLength,
+             kTileLength * small_dim * sizeof(T), d.stream()>>>(
+              input, input_dims, output);
+    } else {
+      SwapDimension1And2InTensor3SmallDim<T, false>
+          <<<dim3(tile_num_per_block, input_dims[0]), kTileLength,
+             kTileLength * small_dim * sizeof(T), d.stream()>>>(
+              input, input_dims, output);
+    }
   } else {
     int total_element_count = input_dims[0] * input_dims[1] * input_dims[2];
     CudaLaunchConfig config = GetCudaLaunchConfig(total_element_count, d);
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 64ebbc603d..d8d51a9d0c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3768,6 +3768,26 @@ cuda_py_test(
     main = "ops/concat_benchmark.py",
 )
 
+cuda_py_test(
+    name = "conv2d_benchmark",
+    size = "large",
+    srcs = ["ops/conv2d_benchmark.py"],
+    additional_deps = [
+        ":client",
+        ":client_testlib",
+        ":control_flow_ops",
+        ":framework_for_generated_wrappers",
+        ":nn_ops",
+        ":platform",
+        ":platform_benchmark",
+        ":random_ops",
+        ":variables",
+        "//third_party/py/numpy",
+        "//tensorflow/core:protos_all_py",
+    ],
+    main = "ops/conv2d_benchmark.py",
+)
+
 cuda_py_test(
     name = "split_benchmark",
     srcs = ["ops/split_benchmark.py"],
diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py
new file mode 100644
index 0000000000..c6352b2a98
--- /dev/null
+++ b/tensorflow/python/ops/conv2d_benchmark.py
@@ -0,0 +1,141 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Benchmark for Conv2D op."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+import time
+
+from tensorflow.python.client import session as session_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+def build_graph(device, input_shape, filter_shape, strides, padding, num_iters):
+  """builds a graph containing a sequence of conv2d operations.
+
+  Args:
+    device: String, the device to run on.
+    input_shape: Shape of the input tensor.
+    filter_shape: Shape of the filter tensor.
+    strides: A list of ints. 1-D of length 4. The stride of sliding
+             window for each dimension of input.
+    padding: A string from: "SAME", "VALID". The type of padding
+             algorithm to use.
+    num_iters: number of iterations to run conv2d.
+
+  Returns:
+    An array of tensors to run()
+  """
+  with ops.device("/%s:0" % device):
+    inp = variables.Variable(random_ops.truncated_normal(input_shape))
+    filt = variables.Variable(random_ops.truncated_normal(filter_shape))
+
+    outputs = []
+    conv2d_op = nn_ops.conv2d(inp, filt, strides, padding, data_format="NHWC")
+    outputs.append(conv2d_op)
+    for _ in range(1, num_iters):
+      with ops.control_dependencies([conv2d_op]):
+        conv2d_op = nn_ops.conv2d(
+            inp, filt, strides, padding, data_format="NHWC")
+        outputs.append(conv2d_op)
+    return control_flow_ops.group(*outputs)
+
+
+class Conv2DBenchmark(test.Benchmark):
+  """Benchmark conv2d!"""
+
+  def _run_graph(self, device, input_shape, filter_shape, strides, padding,
+                 num_iters):
+    """runs the graph and print its execution time.
+
+    Args:
+      device: String, the device to run on.
+      input_shape: Shape of the input tensor.
+      filter_shape: Shape of the filter tensor.
+      strides: A list of ints. 1-D of length 4. The stride of sliding
+               window for each dimension of input.
+      padding: A string from: "SAME", "VALID". The type of padding
+               algorithm to use.  num_iters: Number of iterations to run the
+                 benchmark.
+      num_iters: number of iterations to run conv2d.
+
+    Returns:
+      The duration of the run in seconds.
+    """
+    graph = ops.Graph()
+    with graph.as_default():
+      outputs = build_graph(device, input_shape, filter_shape, strides, padding,
+                            num_iters)
+      with session_lib.Session(graph=graph) as session:
+        variables.global_variables_initializer().run()
+        # warmup runs
+        session.run(outputs)
+
+        start_time = time.time()
+        session.run(outputs)
+        duration = (time.time() - start_time) / num_iters
+
+        print("%s inputshape:%s filtershape:%s strides:%s padding:%s "
+              "%d iters: %.8f sec" %
+              (device, str(input_shape).replace(" ", ""),
+               str(filter_shape).replace(" ", ""),
+               str(strides).replace(" ", ""), padding, num_iters, duration))
+
+    name_template = (
+        "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_"
+        "strides_{strides}_padding_{padding}")
+
+    self.report_benchmark(
+        name=name_template.format(
+            device=device,
+            inputshape=str(input_shape).replace(" ", ""),
+            filtershape=str(filter_shape).replace(" ", ""),
+            strides=str(strides).replace(" ", ""),
+            padding=padding).replace(" ", ""),
+        iters=num_iters,
+        wall_time=duration / num_iters)
+
+    return duration
+
+  def benchmark_conv2d(self):
+    print("conv2d benchmark:")
+
+    h = 1000
+    w = 1000
+    fh = 3
+    fw = 3
+    input_shapes = []
+    filter_shapes = []
+    for b, c in itertools.product([4, 16, 32], [i for i in range(3, 16)]):
+      input_shapes += [[b, h, w, c]]
+      filter_shapes += [[fh, fw, c, b]]
+    strides = [[1, 2, 2, 1]]
+    paddings = ["VALID", "SAME"]
+    for ishape, fshape in zip(input_shapes, filter_shapes):
+      for stride in strides:
+        for padding in paddings:
+          self._run_graph("gpu", ishape, fshape, stride, padding, 80)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/ops/transpose_benchmark.py b/tensorflow/python/ops/transpose_benchmark.py
index 6bd3fe5e5a..cddefacf2e 100644
--- a/tensorflow/python/ops/transpose_benchmark.py
+++ b/tensorflow/python/ops/transpose_benchmark.py
@@ -1,4 +1,4 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -32,7 +32,7 @@ from tensorflow.python.platform import test
 
 
 def build_graph(device, input_shape, perm, datatype, num_iters):
-  """Build a graph containing a sequence of conv2d operations.
+  """builds a graph containing a sequence of conv2d operations.
 
   Args:
     device: String, the device to run on.
@@ -50,10 +50,12 @@ def build_graph(device, input_shape, perm, datatype, num_iters):
     t = constant_op.constant(inp, shape=input_shape)
 
     outputs = []
-    outputs.append(array_ops.transpose(t, perm))
-    for i in range(1, num_iters):
-      with ops.control_dependencies([outputs[i - 1]]):
-        outputs.append(array_ops.transpose(t, perm))
+    transpose_op = array_ops.transpose(t, perm)
+    outputs.append(transpose_op)
+    for _ in range(1, num_iters):
+      with ops.control_dependencies([transpose_op]):
+        transpose_op = array_ops.transpose(t, perm)
+        outputs.append(transpose_op)
     return control_flow_ops.group(*outputs)
 
 
@@ -61,7 +63,7 @@ class TransposeBenchmark(test.Benchmark):
   """Benchmark transpose!"""
 
   def _run_graph(self, device, input_shape, perm, num_iters, datatype):
-    """Run the graph and print its execution time.
+    """runs the graph and print its execution time.
 
     Args:
       device: String, the device to run on.
@@ -82,9 +84,11 @@ class TransposeBenchmark(test.Benchmark):
         session.run(outputs)
         start_time = time.time()
         session.run(outputs)
+
         duration = (time.time() - start_time) / num_iters
-        throughput = np.prod(np.array(
-            input_shape)) * datatype().itemsize * 2 / duration / 1e9
+        throughput = np.prod(
+            np.array(input_shape)) * datatype().itemsize * 2 / duration / 1e9
+
         print("%s %s inputshape:%s perm:%s %d %.6fsec, %.4fGB/s." %
               (device, str(datatype), str(input_shape).replace(" ", ""),
                str(perm).replace(" ", ""), num_iters, duration, throughput))
@@ -108,19 +112,19 @@ class TransposeBenchmark(test.Benchmark):
 
     datatypes = [np.complex128, np.float64, np.float32, np.float16, np.int8]
 
-    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2 + [[
-        2, 100, 100, 16
-    ], [2, 16, 100, 100]] * 2 + [[2, 5000, 16], [2, 16, 5000]] * 2
-    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
-        [0, 3, 1, 2], [0, 2, 3, 1]
-    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2
+    small_shapes += [[2, 100, 100, 16], [2, 16, 100, 100]] * 2
+    small_shapes += [[2, 5000, 16], [2, 16, 5000]] * 2
+    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
+    small_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
+    small_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
-    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2 + [[
-        2, 1000, 1000, 32
-    ], [2, 1000, 1000, 64]] * 2 + [[2, 1000000, 32], [2, 1000000, 64]] * 2
-    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
-        [0, 3, 1, 2], [0, 2, 3, 1]
-    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2
+    large_shapes += [[2, 1000, 1000, 32], [2, 1000, 1000, 64]] * 2
+    large_shapes += [[2, 1000000, 32], [2, 1000000, 64]] * 2
+    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
+    large_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
+    large_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
     huge_shapes = [[2, 100, 100, 100, 128], [2, 1000, 1000, 128],
                    [2, 1000000, 128]] * 2
@@ -143,5 +147,23 @@ class TransposeBenchmark(test.Benchmark):
             for ishape, perm in zip(huge_shapes, huge_perms):
               self._run_graph("gpu", ishape, perm, num_iters, datatype)
 
+    small_dim_large_shapes = [[2, 1000000, 3], [2, 3, 1000000], [2, 1000000, 8],
+                              [2, 8, 1000000]]
+    small_dim_small_shapes = [[2, 5000, 3], [2, 3, 5000], [2, 5000, 8],
+                              [2, 8, 5000]]
+    small_dim_perms = [[0, 2, 1]] * 4
+
+    num_iters = 320
+    small_dim_large_shape_datatypes = [np.float64, np.float32, np.int8]
+    for datatype in small_dim_large_shape_datatypes:
+      for ishape, perm in zip(small_dim_large_shapes, small_dim_perms):
+        self._run_graph("gpu", ishape, perm, num_iters, datatype)
+
+    small_dim_small_shape_datatypes = [np.complex128, np.float16]
+    for datatype in small_dim_small_shape_datatypes:
+      for ishape, perm in zip(small_dim_small_shapes, small_dim_perms):
+        self._run_graph("gpu", ishape, perm, num_iters, datatype)
+
+
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 2706ed82c5c1867fcdb0036bb98bdeedcc3ac930 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 12:16:05 -0700
Subject: [PATCH 0207/1375] Minor cleanup

PiperOrigin-RevId: 159459438
---
 tensorflow/compiler/xla/service/BUILD             | 1 -
 tensorflow/contrib/tensor_forest/kernels/v4/BUILD | 4 +---
 tensorflow/core/kernels/lmdb_reader_op.cc         | 4 ++--
 3 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 20d8a5d0e6..2b176d1270 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1422,7 +1422,6 @@ cc_library(
         ":call_graph",
         ":flatten_call_graph",
         ":hlo",
-        ":hlo_cost_analysis",
         ":hlo_dce",
         ":hlo_ordering",
         ":liveness_util",
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index da87b051a9..0542508a8e 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -25,7 +25,6 @@ cc_library(
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
@@ -56,7 +55,6 @@ cc_library(
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
         "//tensorflow/contrib/tensor_forest:tree_utils",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
@@ -80,6 +78,7 @@ cc_library(
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
         "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
     ],
 )
 
@@ -143,7 +142,6 @@ cc_library(
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
diff --git a/tensorflow/core/kernels/lmdb_reader_op.cc b/tensorflow/core/kernels/lmdb_reader_op.cc
index 61035cb43d..3bb07301b5 100755
--- a/tensorflow/core/kernels/lmdb_reader_op.cc
+++ b/tensorflow/core/kernels/lmdb_reader_op.cc
@@ -13,12 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "lmdb.h"
 #include "tensorflow/core/framework/reader_op_kernel.h"
 #include "tensorflow/core/framework/reader_base.h"
 #include "tensorflow/core/lib/core/errors.h"
 
 #include <sys/stat.h>
+#include "lmdb.h"
 
 namespace tensorflow {
 
@@ -129,4 +129,4 @@ class LMDBReaderOp : public ReaderOpKernel {
 REGISTER_KERNEL_BUILDER(Name("LMDBReader").Device(DEVICE_CPU),
                         LMDBReaderOp);
 
-}
+}  // namespace tensorflow
-- 
GitLab


From 6457f0df532033114adc1768b375a5e168543ad4 Mon Sep 17 00:00:00 2001
From: Chirag Bhatia <chirag64@users.noreply.github.com>
Date: Tue, 20 Jun 2017 02:13:53 +0530
Subject: [PATCH 0208/1375] Documentation fix: Referencing correct pip in
 installation examples (#10831)

* Documentation fix: Referencing correct pip in installation example

* Documentation fix: Referencing correct binary packages in installation examples
---
 tensorflow/docs_src/install/install_linux.md | 10 +++++-----
 tensorflow/docs_src/install/install_mac.md   |  4 ++--
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index d1c6c9d36e..99f27d7b85 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -171,8 +171,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
-     <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     <pre>(tensorflow)$ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -276,8 +276,8 @@ take the following steps:
      the following command:
 
      <pre>
-     $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl</b>
+     $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index ab833626cf..8ff0fb872f 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -108,7 +108,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip3 install --upgrade \
+     <pre> $ <b>pip install --upgrade \
      https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
@@ -229,7 +229,7 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-     <pre> $ <b>sudo pip3 install --upgrade \
+     <pre> $ <b>sudo pip install --upgrade \
      https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
-- 
GitLab


From 3b41352a3177c2fe8a1329e8981b285bb6aacf8b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 14:03:56 -0700
Subject: [PATCH 0209/1375] [XLA:CPU] Thread-parallel CPU backend (work in
 progress). *) Partitions HLO instructions along outer dimensions, based on
 simple cost model. *) Emits loop nests with dynamic outer loop bounds (for
 partitions), leaves inner loop bounds static (for optimizations). *)
 Dispatches parallel tasks on thread pool for execution.

Simple element-wise fusion benchmark:

CPU: Intel Sandybridge with HyperThreading (16 cores) dL1:32KB dL2:256KB dL3:20MB
Benchmark              Time(ns)       CPU(ns)   Iterations
----------------------------------------------------------
BM_ParallelFusion/T1   16821490       16740939   100  237.791MB/s
BM_ParallelFusion/T2    9175467       17826232   100  435.945MB/s
BM_ParallelFusion/T4    5106019       18875761   100  783.389MB/s
BM_ParallelFusion/T8    2833598       19624622   233  1.379GB/s
BM_ParallelFusion/T16   1995259       26541594   344  1.958GB/s

Performance on some select model benchmarks (more work needed is needed here, but wanted to get this CL in and iterate).
Benchmark runs with 16 threads and wall time reported in seconds.

InceptionResnetV2.inception_resnet_v2_200x200x20x1000_inference_xla_cpu
wall_time(old): 7.97818803787
wall_time(new): 4.328297019

InceptionV3.inception_v3_200x200x20x1000_inference_xla_cpu
wall_time(old): 2.96792650223
wall_time(new): 1.21296644211

InceptionResnetV2.inception_resnet_v2_200x200x20x1000_training_xla_cpu
wall_time(old): 42.0342495441
wall_time(new): 17.9182584286

InceptionV3.inception_v3_200x200x20x1000_training_xla_cpu
wall_time(old): 6.99778497219
wall_time(new): 3.95318603516

BenchmarkRNN.rnn_basic_lstm_64x512_4x20_xla_cpu_forward
wall_time(old): 11.869822979
wall_time(new): 7.89778208733

BenchmarkRNN.rnn_basic_lstm_64x512_4x20_xla_cpu_forward_backward
wall_time(old): 38.1911079884
wall_time(new): 29.8181960583

PiperOrigin-RevId: 159474444
---
 tensorflow/compiler/tf2xla/xla_op_registry.cc |  21 +-
 tensorflow/compiler/xla/service/cpu/BUILD     |  38 ++-
 .../compiler/xla/service/cpu/cpu_compiler.cc  |  12 +-
 .../compiler/xla/service/cpu/cpu_compiler.h   |   2 +-
 .../cpu/cpu_parallelization_preparation.cc    | 147 +++++++-
 .../cpu/cpu_parallelization_preparation.h     |  40 ++-
 .../compiler/xla/service/cpu/ir_emitter.cc    | 101 +++++-
 .../compiler/xla/service/cpu/ir_emitter.h     |  18 +
 .../service/cpu/parallel_cpu_executable.cc    | 317 +++++++++++++-----
 .../xla/service/cpu/shape_partition.cc        | 160 +++++++++
 .../xla/service/cpu/shape_partition.h         | 105 ++++++
 .../xla/service/cpu/shape_partition_test.cc   | 248 ++++++++++++++
 .../compiler/xla/service/hlo_instruction.cc   |   5 +
 .../compiler/xla/service/hlo_instruction.h    |  15 +
 .../compiler/xla/service/hlo_module_config.cc |   4 +
 .../compiler/xla/service/hlo_module_config.h  |  13 +
 .../compiler/xla/service/local_service.cc     |   4 +
 tensorflow/compiler/xla/tests/BUILD           |   6 +
 tensorflow/compiler/xla/tests/fusion_test.cc  |  64 ++++
 19 files changed, 1219 insertions(+), 101 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/cpu/shape_partition.cc
 create mode 100644 tensorflow/compiler/xla/service/cpu/shape_partition.h
 create mode 100644 tensorflow/compiler/xla/service/cpu/shape_partition_test.cc

diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc
index 1bb0d85289..aaef27f16d 100644
--- a/tensorflow/compiler/tf2xla/xla_op_registry.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc
@@ -34,11 +34,18 @@ const char* const DEVICE_GPU_XLA_JIT = "XLA_GPU_JIT";
 const char* const DEVICE_XLA_CPU = "XLA_CPU";
 const char* const DEVICE_XLA_GPU = "XLA_GPU";
 
-// Is platform 'id' supported by XLA?
-static bool IsPlatformSupported(perftools::gputools::Platform::Id id) {
-  auto platform = perftools::gputools::MultiPlatformManager::PlatformWithId(id);
-  if (!platform.ok()) return false;
-  return xla::ClientLibrary::GetOrCreateLocalClient(platform.ValueOrDie()).ok();
+static Status LaunchOpHasKernelForDevice(const DeviceType& device_type) {
+  const OpDef* op_def;
+  TF_RETURN_IF_ERROR(OpRegistry::Global()->LookUpOpDef("_XlaLaunch", &op_def));
+  NodeDef node_def;
+  node_def.set_name("_XlaLaunch-op");
+  node_def.set_op("_XlaLaunch");
+  string kernel_class_name;
+  TF_RETURN_IF_ERROR(FindKernelDef(device_type, node_def, /*KernelDef*/ nullptr,
+                                   &kernel_class_name));
+  VLOG(1) << "LaunchOpHasKernelForDevice"
+          << " kernel_class_name: " << kernel_class_name;
+  return Status::OK();
 }
 
 XlaOpRegistry::XlaOpRegistry() = default;
@@ -75,7 +82,7 @@ XlaOpRegistry::~XlaOpRegistry() = default;
   // GetCompilationDevice is called.
   static void* registration_init = [&registry]() {
     mutex_lock lock(registry.mutex_);
-    if (IsPlatformSupported(perftools::gputools::host::kHostPlatformId)) {
+    if (LaunchOpHasKernelForDevice(DeviceType(DEVICE_CPU)).ok()) {
       DeviceRegistration& registration =
           registry.compilation_devices_[DEVICE_CPU];
       registration.compilation_device_name = DEVICE_CPU_XLA_JIT;
@@ -83,7 +90,7 @@ XlaOpRegistry::~XlaOpRegistry() = default;
       registration.enable_jit_by_default = false;
       registration.compile_resource_ops = false;
     }
-    if (IsPlatformSupported(perftools::gputools::cuda::kCudaPlatformId)) {
+    if (LaunchOpHasKernelForDevice(DeviceType(DEVICE_GPU)).ok()) {
       DeviceRegistration& registration =
           registry.compilation_devices_[DEVICE_GPU];
       registration.compilation_device_name = DEVICE_GPU_XLA_JIT;
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 5108fda4a9..de6660e3b5 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -150,9 +150,12 @@ cc_library(
 cc_library(
     name = "parallel_cpu_executable",
     srcs = ["parallel_cpu_executable.cc"],
-    hdrs = ["parallel_cpu_executable.h"],
+    hdrs = [
+        "parallel_cpu_executable.h",
+    ],
     deps = [
         ":cpu_runtime",
+        ":shape_partition",
         ":simple_orc_jit",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
@@ -176,12 +179,15 @@ cc_library(
 cc_library(
     name = "ir_emitter",
     srcs = ["ir_emitter.cc"],
-    hdrs = ["ir_emitter.h"],
+    hdrs = [
+        "ir_emitter.h",
+    ],
     deps = [
         ":cpu_runtime",
         ":dot_op_emitter",
         ":elemental_ir_emitter",
         ":ir_emission_utils",
+        ":shape_partition",
         ":simple_orc_jit",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
@@ -438,10 +444,15 @@ cc_library(
 cc_library(
     name = "cpu_parallelization_preparation",
     srcs = ["cpu_parallelization_preparation.cc"],
-    hdrs = ["cpu_parallelization_preparation.h"],
+    hdrs = [
+        "cpu_parallelization_preparation.h",
+    ],
     deps = [
+        ":ir_emission_utils",
+        ":shape_partition",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:hlo_cost_analysis",
         "//tensorflow/compiler/xla/service:hlo_pass",
         "//tensorflow/compiler/xla/service:logical_buffer",
         "//tensorflow/compiler/xla/service:tuple_points_to_analysis",
@@ -521,6 +532,27 @@ cc_test(
     ],
 )
 
+cc_library(
+    name = "shape_partition",
+    srcs = ["shape_partition.cc"],
+    hdrs = ["shape_partition.h"],
+    deps = [
+        "//tensorflow/compiler/xla/service:hlo",
+    ],
+)
+
+cc_test(
+    name = "shape_partition_test",
+    srcs = ["shape_partition_test.cc"],
+    deps = [
+        ":shape_partition",
+        "//tensorflow/compiler/xla:test_helpers",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/core:test_main",
+    ],
+)
 # -----------------------------------------------------------------------------
 
 filegroup(
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 4a73bda05e..4786e75fa7 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -284,8 +284,13 @@ Status CpuCompiler::RunHloPasses(HloModule* module, HloDumper dump_hlo) {
       /*enable_dot_simplification=*/false);
   pipeline.AddPass<HloCSE>(/*is_layout_sensitive=*/true);
   // Outline ops in the entry computation into calls to subcomputations.
+  const int max_parallelism =
+      module->config().intra_op_parallelism_threads() > 0
+          ? module->config().intra_op_parallelism_threads()
+          : tensorflow::port::NumSchedulableCPUs();
   if (CpuParallelBackendRequested(module->config())) {
-    pipeline.AddPass<ParallelizationPreparation>();
+    pipeline.AddPass<ParallelizationPreparation>(max_parallelism,
+                                                 ShapeSizeBytesFunction());
   }
   // Copy insertion should be performed immediately before IR emission to avoid
   // inserting unnecessary copies (later pass adds an instruction which
@@ -298,7 +303,8 @@ Status CpuCompiler::RunHloPasses(HloModule* module, HloDumper dump_hlo) {
   if (CpuParallelBackendRequested(module->config())) {
     // Re-run the outlining, in case any copies were inserted into the entry
     // computation.
-    pipeline.AddPass<ParallelizationPreparation>();
+    pipeline.AddPass<ParallelizationPreparation>(max_parallelism,
+                                                 ShapeSizeBytesFunction());
   }
   pipeline.AddPass<HloDCE>();
   pipeline.AddPass<FlattenCallGraph>();
@@ -425,6 +431,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 
     IrEmitter ir_emitter(*module, *assignment, llvm_module.get(),
                          &hlo_to_profile_idx);
+
     std::unique_ptr<std::map<HloInstruction*, string>> function_names(
         new std::map<HloInstruction*, string>());
     for (auto embedded_computation :
@@ -496,6 +503,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     // before a caller computation.
     IrEmitter ir_emitter(*module, *assignment, llvm_module.get(),
                          &hlo_to_profile_idx);
+
     for (auto embedded_computation :
          computation->MakeEmbeddedComputationsList()) {
       TF_RETURN_IF_ERROR(
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
index 29fa4eac61..3b130a614e 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
@@ -132,7 +132,7 @@ class CpuCompiler : public Compiler {
 
   // Runs the HLO passes which are necessary for both optimizations and
   // correctness.
-  Status RunHloPasses(HloModule* hlo_module, HloDumper dump_hlo);
+  Status RunHloPasses(HloModule* module, HloDumper dump_hlo);
 
   TF_DISALLOW_COPY_AND_ASSIGN(CpuCompiler);
 };
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc
index f6b1dcae75..af931f7b01 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc
@@ -15,19 +15,28 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h"
 
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
+#include "tensorflow/compiler/xla/service/cpu/shape_partition.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 
 namespace xla {
 namespace cpu {
 
 StatusOr<bool> ParallelizationPreparation::Run(HloModule* module) {
+  XLA_VLOG_LINES(2, "ParallelizationPreparation ENTRY");
+  XLA_VLOG_LINES(2, module->ToString());
+
   bool changed = false;
+  TF_ASSIGN_OR_RETURN(changed, RunParallelTaskAssignment(module));
+
   HloComputation* entry_computation = module->entry_computation();
   std::unordered_set<HloInstruction*> outlined;
   std::vector<HloInstruction*> instructions_to_outline;
@@ -44,13 +53,21 @@ StatusOr<bool> ParallelizationPreparation::Run(HloModule* module) {
         instruction->opcode() == HloOpcode::kConstant) {
       continue;
     }
+
+    // Outline 'instruction' in isolation if it was assigned parallel tasks.
+    if (OutlineParallelizableInstruction(instruction)) {
+      outlined.insert(instruction);
+      changed = true;
+      continue;
+    }
+
     instructions_to_outline.clear();
     HloInstruction* outline_candidate = instruction;
     instructions_to_outline.push_back(outline_candidate);
     bool all_bitcasts = outline_candidate->opcode() == HloOpcode::kBitcast;
 
     // Outline sole users with the current instruction.
-    while (outline_candidate->users().size() == 1) {
+    while (CanOutlineWithUser(outline_candidate)) {
       HloInstruction* prior_candidate = outline_candidate;
       outline_candidate = *outline_candidate->users().begin();
       all_bitcasts |= outline_candidate->opcode() == HloOpcode::kBitcast;
@@ -120,8 +137,136 @@ StatusOr<bool> ParallelizationPreparation::Run(HloModule* module) {
       changed = true;
     }
   }
+
+  XLA_VLOG_LINES(2, "ParallelizationPreparation EXIT");
+  XLA_VLOG_LINES(2, module->ToString());
   return changed;
 }
 
+StatusOr<bool> ParallelizationPreparation::RunParallelTaskAssignment(
+    HloModule* module) {
+  VLOG(1) << "RunParallelTaskAssignment max_parallelism_: " << max_parallelism_;
+  bool changed = false;
+  // Run cost analysis on entry computation.
+  HloCostAnalysis cost_analysis(shape_size_);
+  HloComputation* computation = module->entry_computation();
+  Status cost_status = computation->root_instruction()->Accept(&cost_analysis);
+  for (auto& instruction : computation->instructions()) {
+    // Currently, we do not assign parallel tasks to instructions with at least
+    // one of the following properties:
+    // *) Internal threading (library calls to kConv, kDot, and kCustomCall).
+    // *) Emit custom loops (kSelectAndScatter, FusionKind::kTransposeDot).
+    // *) Tuple-shaped.
+    // TODO(b/27458679) Parallelize instructions which are skipped here.
+    if (instruction->opcode() == HloOpcode::kParameter ||
+        instruction->opcode() == HloOpcode::kConstant ||
+        instruction->opcode() == HloOpcode::kCall ||
+        instruction->opcode() == HloOpcode::kCustomCall ||
+        instruction->opcode() == HloOpcode::kSelectAndScatter ||
+        (instruction->opcode() == HloOpcode::kConvolution &&
+         PotentiallyImplementedAsEigenConvolution(*instruction)) ||
+        PotentiallyImplementedAsEigenDot(*instruction) ||
+        (instruction->opcode() == HloOpcode::kFusion &&
+         instruction->fusion_kind() != HloInstruction::FusionKind::kLoop) ||
+        ShapeUtil::IsTuple(instruction->shape())) {
+      continue;
+    }
+
+    // Calculate target parallel task count in [1, max_parallelism_].
+    const int64 target_parallel_task_count = GetTargetParallelTaskCount(
+        cost_status.ok() ? &cost_analysis : nullptr, instruction.get());
+    if (target_parallel_task_count == 1) {
+      continue;
+    }
+
+    // Assign feasible dimension partitions (based on actual dimension sizes).
+    auto dim_partition_counts = ShapePartitionAssigner(instruction->shape())
+                                    .Run(target_parallel_task_count);
+    const int64 total_partition_count =
+        ShapePartitionAssigner::GetTotalPartitionCount(dim_partition_counts);
+    if (total_partition_count <= 1) {
+      // Feasible partition calculation resulting in no partitioning, so skip.
+      continue;
+    }
+    VLOG(2) << "Assigning parallel task count: " << total_partition_count
+            << " to instruction: " << instruction->name();
+    // Map 'instruction' to assigned dimension partitioning.
+    instruction->set_outer_dimension_partitions(dim_partition_counts);
+  }
+
+  return changed;
+}
+
+int64 ParallelizationPreparation::GetTargetParallelTaskCount(
+    const HloCostAnalysis* cost_analysis, HloInstruction* instruction) {
+  // Default to a simple cost model based on hlo size and typical L2 cache size.
+  // Note that 'cost_analysis' can be 'nullptr' if HloCostAnalysis returns an
+  // error status (likely because HLOs like CustomCall are not yet implemented
+  // in the HloCostAnalysis).
+  int64 instruction_cost = shape_size_(instruction->shape());
+  int64 min_cost_per_thread = 256LL << 10;  // 256KB L2 Cache size.
+  if (cost_analysis != nullptr) {
+    // Calculate the instruction cost in cycles.
+    // TODO(29630486) Improve on this linear cost model.
+    // Consider making 'min_cost_per_thread' be a function of the target
+    // bandwidth limit for instructions with low arithmetic complexity.
+    instruction_cost = 1 * cost_analysis->flop_count(*instruction) +
+                       2 * cost_analysis->transcendental_count(*instruction) +
+                       10 * cost_analysis->bytes_accessed(*instruction);
+    // Minimum per-thread cost is 100us of work on a 2GHz core.
+    min_cost_per_thread = 100000;
+  }
+  // Return target parallel task count in [1, max_parallelism_].
+  return std::min(max_parallelism_,
+                  std::max(1LL, instruction_cost / min_cost_per_thread));
+}
+
+bool ParallelizationPreparation::OutlineParallelizableInstruction(
+    HloInstruction* instruction) {
+  if (instruction->outer_dimension_partitions().empty()) {
+    return false;
+  }
+  // Store dimension partition counts before outlining (which clones
+  // 'instruction').
+  std::vector<int64> dim_partition_counts =
+      instruction->outer_dimension_partitions();
+  // Outline 'instruction' in its own sub-computation.
+  HloModule* module = instruction->parent()->parent();
+  auto* call = module->OutlineExpressionFromComputation(
+      {instruction}, tensorflow::strings::StrCat("pp_", instruction->name()),
+      module->entry_computation());
+  // Map previously assigned 'dim_partition_counts' to cloned root instruction.
+  VLOG(1) << "Outlining parallelizable"
+          << " caller: " << call->name()
+          << " callee: " << call->to_apply()->root_instruction()->name();
+  call->to_apply()->root_instruction()->set_outer_dimension_partitions(
+      dim_partition_counts);
+  return true;
+}
+
+bool ParallelizationPreparation::CanOutlineWithUser(
+    HloInstruction* instruction) {
+  if (instruction->users().size() != 1) {
+    // Do not outline 'instruction' with multiple users.
+    return false;
+  }
+  if (AssignedParallelTasks(instruction) ||
+      AssignedParallelTasks(*instruction->users().begin())) {
+    // Do not outline if 'instruction' (or user) were assigned parallel tasks.
+    return false;
+  }
+  return true;
+}
+
+bool ParallelizationPreparation::AssignedParallelTasks(
+    HloInstruction* instruction) {
+  return !instruction->outer_dimension_partitions().empty() ||
+         (instruction->opcode() == HloOpcode::kCall &&
+          !instruction->to_apply()
+               ->root_instruction()
+               ->outer_dimension_partitions()
+               .empty());
+}
+
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
index 62999f5686..70e3492934 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_PARALLELIZATION_PREPARATION_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_PARALLELIZATION_PREPARATION_H_
 
+#include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
@@ -32,14 +33,51 @@ namespace cpu {
 // handle While constructs.
 class ParallelizationPreparation : public HloPassInterface {
  public:
+  // 'max_parallelism': the maximum parallel task count per instruction.
+  // 'shape_size': shape size function used by HloCostAnalysis during parallel
+  //               task assignment.
+  ParallelizationPreparation(
+      const int64 max_parallelism,
+      const HloCostAnalysis::ShapeSizeFunction& shape_size)
+      : max_parallelism_(max_parallelism), shape_size_(shape_size) {}
   ~ParallelizationPreparation() override {}
+
   tensorflow::StringPiece name() const override {
     return "cpu-parallel-prepare";
   }
 
-  // Run instruction fusion on the given computation. Returns whether the
+  // Run parallel preparation on the given computation. Returns whether the
   // computation was changed.
   StatusOr<bool> Run(HloModule* module) override;
+
+ private:
+  // Assigns parallel task partitions to conformant instructions in 'module'.
+  // Returns true on success or error status otherwise.
+  StatusOr<bool> RunParallelTaskAssignment(HloModule* module);
+
+  // Returns the target parallel task count for 'instruction'.
+  // Utilizes 'cost_analysis' if non-null.
+  // Otherwise defaults to a simple HLO output size-based cost model.
+  int64 GetTargetParallelTaskCount(const HloCostAnalysis* cost_analysis,
+                                   HloInstruction* instruction);
+
+  // Outlines 'instruction' from entry computation, if it had
+  // been assigned parallel tasks in an earlier pass through the computation.
+  // Returns true if 'instruction' was succesfully outlined, false otherwise.
+  bool OutlineParallelizableInstruction(HloInstruction* instruction);
+
+  // Returns true if 'instruction' can be outlined into the same sub-computation
+  // with its single user (parallelizable instructions are not outlined with
+  // each other). Returns false otherwise.
+  bool CanOutlineWithUser(HloInstruction* instruction);
+
+  // Returns true if 'instruction' (or the root of the sub-computation that
+  // 'instruction' calls) has had parallel tasks assigned in earlier pass.
+  // Returns false otherwise.
+  bool AssignedParallelTasks(HloInstruction* instruction);
+
+  const int64 max_parallelism_;
+  const HloCostAnalysis::ShapeSizeFunction shape_size_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 5225e8b96a..421618d819 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -55,6 +55,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
 
 namespace xla {
 
@@ -84,6 +85,12 @@ StatusOr<llvm::Function*> IrEmitter::EmitComputation(
     std::vector<const HloInstruction*>* instruction_order) {
   string function_name = name_uniquer_.GetUniqueName(function_name_prefix);
   VLOG(2) << "Emitting IR for CPU function [" << function_name_prefix << "]";
+  num_dynamic_loop_bounds_ = 0;
+  if (!computation->root_instruction()->outer_dimension_partitions().empty()) {
+    num_dynamic_loop_bounds_ =
+        computation->root_instruction()->outer_dimension_partitions().size();
+  }
+
   InitializeIrFunction(function_name, is_entry_computation);
   // The rdtscp instruction is x86 specific.  We will fallback to LLVM's generic
   // readcyclecounter if it is unavailable.
@@ -112,7 +119,7 @@ void IrEmitter::InitializeIrFunction(const string& function_name,
                                      bool is_entry_computation) {
   // The function signature is:
   //   void function(i8* retval, i8* run_options, i8** params, i8** temps,
-  //                 i64* prof_counters)
+  //                 i64* dynamic_loop_bounds, i64* prof_counters)
   //
   // retval: points to the returned value.
   // params: address of an array with pointers to parameters.
@@ -152,6 +159,10 @@ void IrEmitter::InitializeIrFunction(const string& function_name,
   //                     | temp  0 |  | temp  1 |         | temp  N-1 |
   //                     \---------/  \---------/         \-----------/
   //
+  //                        /--------------------------------------------\
+  // dynamic loop bounds -> | outer_dim0_start | outer_dim0_limit | .....|
+  //  (elided for aot)      \--------------------------------------------/
+  //
   //                     /---------------------------------------------\
   //   prof counters ->  | counter 0 | counter 1 | ..... | counter N-1 |
   //  (elided for aot)   \---------------------------------------------/
@@ -164,6 +175,9 @@ void IrEmitter::InitializeIrFunction(const string& function_name,
   llvm::Type* i64_ptr_type = llvm::Type::getInt64PtrTy(module_->getContext());
   std::vector<llvm::Type*> compute_function_params(
       {i8_ptr_type, i8_ptr_type, i8_ptr_ptr_type, i8_ptr_ptr_type});
+  if (num_dynamic_loop_bounds_ > 0) {
+    compute_function_params.push_back(i64_ptr_type);
+  }
   if (hlo_to_profile_idx_) {
     compute_function_params.push_back(i64_ptr_type);
   }
@@ -190,6 +204,9 @@ void IrEmitter::InitializeIrFunction(const string& function_name,
   (++arg_iter)->setName("run_options");
   (++arg_iter)->setName("params");
   (++arg_iter)->setName("temps");
+  if (num_dynamic_loop_bounds_ > 0) {
+    (++arg_iter)->setName("dynamic_loop_bounds");
+  }
   if (hlo_to_profile_idx_) {
     (++arg_iter)->setName("prof_counters");
   }
@@ -1831,13 +1848,24 @@ llvm::Argument* IrEmitter::GetResultArgument() {
 }
 
 llvm::Argument* IrEmitter::GetProfileCountersArgument() {
-  return hlo_to_profile_idx_ ? GetArg(compute_function_, 4) : nullptr;
+  const int64 arg_index = num_dynamic_loop_bounds_ > 0 ? 5 : 4;
+  return hlo_to_profile_idx_ ? GetArg(compute_function_, arg_index) : nullptr;
 }
 
 llvm::Value* IrEmitter::GetTempBuffersArgument() {
   return GetArg(compute_function_, 3);
 }
 
+llvm::Value* IrEmitter::GetDynamicLoopBound(const int64 offset) {
+  CHECK_GT(num_dynamic_loop_bounds_, 0);
+  CHECK_LT(offset, num_dynamic_loop_bounds_ * 2);
+  llvm::Argument* loop_bounds_arg = GetArg(compute_function_, 4);
+  string name = tensorflow::strings::StrCat("dynamic_loop_bound_", offset);
+  return ir_builder_.CreateLoad(
+      ir_builder_.CreateGEP(loop_bounds_arg, ir_builder_.getInt64(offset),
+                            llvm_ir::AsStringRef(name)));
+}
+
 llvm::Value* IrEmitter::GetExecutableRunOptionsArgument() {
   return GetArg(compute_function_, 1);
 }
@@ -2001,13 +2029,76 @@ Status IrEmitter::EmitTargetElementLoop(
   llvm_ir::IrArray target_array(target_address, target_shape);
   AddAliasingInformationToIrArray(*target_op, &target_array);
 
-  TF_RETURN_IF_ERROR(
-      llvm_ir::LoopEmitter(element_generator, target_array, &ir_builder_)
-          .EmitLoop());
+  if (num_dynamic_loop_bounds_ > 0 &&
+      target_op == target_op->parent()->root_instruction()) {
+    // Emit parallel loop for root instruction if dynamic outer-dimension loop
+    // bounds were specified.
+    TF_RETURN_IF_ERROR(EmitParallelTargetElementLoop(
+        target_shape, element_generator, &target_array));
+  } else {
+    TF_RETURN_IF_ERROR(
+        llvm_ir::LoopEmitter(element_generator, target_array, &ir_builder_)
+            .EmitLoop());
+  }
+
   emitted_value_[target_op] = target_address;
   return Status::OK();
 }
 
+Status IrEmitter::EmitParallelTargetElementLoop(
+    const Shape& target_shape,
+    const llvm_ir::ElementGenerator& element_generator,
+    llvm_ir::IrArray* target_array) {
+  CHECK(!ShapeUtil::IsTuple(target_shape));
+  CHECK(!ShapeUtil::IsScalar(target_shape));
+
+  // Emit code to read dynamic loop bounds from function argument 4.
+  std::vector<llvm::Value*> dynamic_loop_bounds(2 * num_dynamic_loop_bounds_);
+  for (int i = 0; i < 2 * num_dynamic_loop_bounds_; ++i) {
+    dynamic_loop_bounds[i] = GetDynamicLoopBound(i);
+  }
+
+  llvm_ir::ForLoopNest loop_nest(&ir_builder_);
+  const int64 num_dims = target_shape.dimensions_size();
+  llvm_ir::IrArray::Index array_index(num_dims);
+
+  // Add loops from outer-most to inner-most dimensions.
+  for (int i = target_shape.layout().minor_to_major_size() - 1; i >= 0; --i) {
+    const int64 dimension = target_shape.layout().minor_to_major(i);
+    const int bounds_index = num_dims - 1 - i;
+    if (bounds_index < num_dynamic_loop_bounds_) {
+      // Emit dynamic loop bounds for this dimension. Dynamic loop bounds
+      // are read from ir function dynamic loop bounds argument.
+      llvm::Value* start_index = dynamic_loop_bounds[bounds_index * 2 + 0];
+      llvm::Value* end_index = dynamic_loop_bounds[bounds_index * 2 + 1];
+
+      std::unique_ptr<llvm_ir::ForLoop> loop = loop_nest.AddLoop(
+          /*suffix=*/tensorflow::strings::Printf("dim.%lld", dimension),
+          start_index, end_index);
+      array_index[dimension] = loop->GetIndVarValue();
+    } else {
+      // Emit static loop bounds for this dimension.
+      std::unique_ptr<llvm_ir::ForLoop> loop = loop_nest.AddLoop(
+          /*start_index=*/0,
+          /*end_index=*/target_shape.dimensions(dimension),
+          /*suffix=*/tensorflow::strings::Printf("dim.%lld", dimension));
+      array_index[dimension] = loop->GetIndVarValue();
+    }
+  }
+  // Point IR builder at inner loop BB.
+  SetToFirstInsertPoint(loop_nest.GetInnerLoopBodyBasicBlock(), &ir_builder_);
+
+  // Emit loop body.
+  TF_ASSIGN_OR_RETURN(llvm::Value * target_element,
+                      element_generator(array_index));
+  target_array->EmitWriteArrayElement(array_index, target_element,
+                                      &ir_builder_);
+  // Point IR builder at outer loop exit BB.
+  SetToFirstInsertPoint(loop_nest.GetOuterLoopExitBasicBlock(), &ir_builder_);
+
+  return Status::OK();
+}
+
 Status IrEmitter::EmitMemcpy(const HloInstruction& source,
                              const HloInstruction& destination) {
   llvm::Value* source_value = GetEmittedValueFor(&source);
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 4fb86434a1..1a8c91efd4 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -193,6 +193,11 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   // computation function being emitted by this emitter.
   llvm::Value* GetTempBuffersArgument();
 
+  // Emit ir to read and return the ir value for the dynamic loop bound at
+  // 'offset' from the "dynamic_loop_bounds" argument of the computation
+  // function being emitted by this emitter.
+  llvm::Value* GetDynamicLoopBound(const int64 offset);
+
   // Emits code that computes the address of the given temporary buffer to the
   // function. target_shape is the shape of this temporary buffer.
   // The returned Value's type is a pointer to element_type.
@@ -263,6 +268,15 @@ class IrEmitter : public DfsHloVisitorWithDefault {
       HloInstruction* target_op,
       const llvm_ir::ElementGenerator& element_generator);
 
+  // Emit IR to perform a computation for every element in a partition/slice of
+  // 'target_shape'. The loop bounds for the outer-dimension partitions are
+  // passed into the compute function as a runtime argument (accessible from
+  // GetDynamicLoopBound).
+  Status EmitParallelTargetElementLoop(
+      const Shape& target_shape,
+      const llvm_ir::ElementGenerator& element_generator,
+      llvm_ir::IrArray* target_array);
+
   // Emits a memcpy from the source instruction's result value to the
   // destination's.  Both source and destination must have an entry in the
   // emitted_value_ table.
@@ -320,6 +334,10 @@ class IrEmitter : public DfsHloVisitorWithDefault {
 
   llvm_ir::AliasAnalysis alias_analysis_;
 
+  // The number of root instruction outer dimensions used in parallel loop
+  // emission (EmitParallelTargetElementLoop).
+  int64 num_dynamic_loop_bounds_ = 0;
+
   // This struct contains all the state needed to emit instructions for
   // profiling a computation.
   class ProfilingState {
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
index bdddca99c2..19909f4bed 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
+#include "tensorflow/compiler/xla/service/cpu/shape_partition.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -70,7 +71,7 @@ ParallelCpuExecutable::ParallelCpuExecutable(
 
 // Type of the computation function we expect in the JIT.
 using ComputeFunctionType = void (*)(void*, const void*, const void**, void**,
-                                     uint64*);
+                                     int64*, uint64*);
 
 // Given a pointer to an output buffer (following the CPU JIT calling
 // conventions), mark addresses that are "live". The initial pointer itself is
@@ -95,6 +96,232 @@ static void MarkLiveAddressesInOutput(
   }
 }
 
+namespace {
+
+// Executor manages the concurrent execution of 'functions' for instructions
+// in 'pending' on 'thread_pool' (storing resulting data in 'results').
+class Executor {
+ public:
+  Executor(const std::map<HloInstruction*, ComputeFunctionType>& functions,
+           const ServiceExecutableRunOptions* run_options,
+           std::list<HloInstruction*>* pending,
+           std::map<HloInstruction*, const void*>* results, void** temps_array,
+           uint64* profile_counters_array, BufferAssignment* assignment)
+      : functions_(functions),
+        run_options_(run_options),
+        pending_(pending),
+        results_(results),
+        temps_array_(temps_array),
+        profile_counters_array_(profile_counters_array),
+        thread_pool_(CHECK_NOTNULL(run_options_->xla_intra_op_thread_pool())),
+        assignment_(assignment) {}
+
+  // Executes pending list of instructions on thread pool.
+  // Returns OK status on success, error status otherwise.
+  Status Run();
+
+ private:
+  // Schedules a parallel invocation of compute function for 'instruction' on
+  // 'thread_pool_', storing result in 'result_buffer'.
+  // If 'partition_buffers' is non-null, parallel task will be invoked on
+  // per-dimension partition [start, limit) values stored in
+  // 'partition_buffers'.
+  void Schedule(HloInstruction* instruction, int64* partition_buffers,
+                void* result_buffer);
+
+  // Returns true if 'instruction' has been assigned parallel tasks (returns
+  // false otherwise).
+  bool HasParallelTasks(HloInstruction* instruction);
+
+  // Returns in 'partition_buffers' the partition [size, limit) for each
+  // dimension.
+  int64* GetPartitionBuffers(
+      const std::vector<std::pair<int64, int64>>& partition);
+
+  // Returns array of result buffers for all operands in 'instruction'.
+  const void** GetOperandBuffers(HloInstruction* instruction);
+
+  // Arguments passed into Executor.
+  const std::map<HloInstruction*, ComputeFunctionType>& functions_;
+  const ServiceExecutableRunOptions* run_options_;
+  std::list<HloInstruction*>* pending_;
+  std::map<HloInstruction*, const void*>* results_;
+  void** temps_array_;
+  uint64* profile_counters_array_;
+  tensorflow::thread::ThreadPool* thread_pool_;
+  BufferAssignment* assignment_;
+
+  // Members used to manage instruction execution.
+  tensorflow::mutex completion_queue_lock_;
+  tensorflow::condition_variable completion_queue_cv_;
+  std::deque<HloInstruction*> completion_queue_;
+  int64 instructions_in_flight_ = 0;
+  std::unordered_map<const HloInstruction*, int64> tasks_in_flight_;
+};
+
+Status Executor::Run() {
+  while (!pending_->empty() || instructions_in_flight_ > 0) {
+    auto pending_it = pending_->begin();
+    while (pending_it != pending_->end()) {
+      HloInstruction* instruction = *pending_it;
+      // Skip pending instructions whose operands aren't ready.
+      if (std::any_of(instruction->operands().begin(),
+                      instruction->operands().end(),
+                      [&](HloInstruction* operand) {
+                        return !ContainsKey(*results_, operand);
+                      })) {
+        ++pending_it;
+        continue;
+      }
+
+      // Get 'result_buffer' reference to result buffer for 'instruction'.
+      TF_ASSIGN_OR_RETURN(const BufferAllocation::Slice result_slice,
+                          assignment_->GetUniqueTopLevelSlice(instruction));
+      void* result_buffer =
+          static_cast<char*>(temps_array_[result_slice.index()]) +
+          result_slice.offset();
+
+      if (HasParallelTasks(instruction)) {
+        // 'instruction' has been assigned parallel task partitions.
+        CHECK_EQ(HloOpcode::kCall, instruction->opcode());
+        HloInstruction* root = instruction->to_apply()->root_instruction();
+
+        // Create ShapePartitionIterator to iterate through all outer dimension
+        // partitions of 'instruction'.
+        ShapePartitionIterator partition_iterator(
+            root->shape(), root->outer_dimension_partitions());
+
+        const int64 partition_count =
+            partition_iterator.GetTotalPartitionCount();
+
+        // Record total parallel task count for 'instruction' before dispatch.
+        {
+          tensorflow::mutex_lock l(completion_queue_lock_);
+          tasks_in_flight_.insert(std::make_pair(instruction, partition_count));
+          VLOG(2) << "Schedule PARALLEL"
+                  << " instruction: " << instruction->name()
+                  << " instruction.callee: "
+                  << instruction->to_apply()->root_instruction()->name()
+                  << " partition_count: " << partition_count;
+        }
+
+        for (int64 i = 0; i < partition_count; ++i) {
+          // Get partition [start, limit) for each dimension.
+          auto partition_buffers =
+              GetPartitionBuffers(partition_iterator.GetPartition(i));
+          Schedule(instruction, partition_buffers, result_buffer);
+        }
+
+      } else {
+        // Set tasks in-flight to '1' for sequential instruction execution.
+        {
+          tensorflow::mutex_lock l(completion_queue_lock_);
+          tasks_in_flight_.insert(std::make_pair(instruction, 1));
+          VLOG(2) << "Schedule SEQUENTIAL"
+                  << " instruction: " << instruction->name()
+                  << " instruction.callee: "
+                  << instruction->to_apply()->root_instruction()->name();
+        }
+        Schedule(instruction, nullptr, result_buffer);
+      }
+
+      ++instructions_in_flight_;
+      pending_it = pending_->erase(pending_it);
+    }
+    // Wait for a completed HLO instruction to be present in the queue.  We will
+    // pop it out of the queue and make the result available to its users.
+    HloInstruction* instruction;
+    do {
+      tensorflow::mutex_lock l(completion_queue_lock_);
+      if (completion_queue_.empty()) {
+        completion_queue_cv_.wait(l);
+      }
+      if (!completion_queue_.empty()) {
+        instruction = completion_queue_.front();
+        completion_queue_.pop_front();
+        break;
+      }
+    } while (1);
+    TF_ASSIGN_OR_RETURN(const BufferAllocation::Slice result_slice,
+                        assignment_->GetUniqueTopLevelSlice(instruction));
+    void* result_buffer =
+        static_cast<char*>(temps_array_[result_slice.index()]) +
+        result_slice.offset();
+    InsertOrDie(results_, instruction, result_buffer);
+    --instructions_in_flight_;
+  }
+  return Status::OK();
+}
+
+void Executor::Schedule(HloInstruction* instruction, int64* partition_buffers,
+                        void* result_buffer) {
+  // The thread pool entry takes ownership of |operand_buffers|.
+  auto operand_buffers = GetOperandBuffers(instruction);
+
+  auto function = FindOrDie(functions_, instruction);
+  const auto* exec_run_options = &run_options_->run_options();
+  thread_pool_->Schedule([this, instruction, result_buffer, operand_buffers,
+                          partition_buffers, exec_run_options, function]() {
+    function(result_buffer, exec_run_options, operand_buffers, temps_array_,
+             partition_buffers, profile_counters_array_);
+
+    delete[] operand_buffers;
+    delete[] partition_buffers;
+    // Push the completed HLO instruction on the queue, the main
+    // thread will pop it off and potentially launch more work which
+    // uses the result.
+    // TODO(b/27458679) Consider alternative task scheduling and synchronization
+    // schemes. For example, we could avoid the overhead associate with the
+    // condvar here if the thread just dequed the next instruction to execute
+    // on completion.
+    {
+      tensorflow::mutex_lock l(completion_queue_lock_);
+      // Decrement in-flight task count for this completion.
+      if (--FindOrDie(tasks_in_flight_, instruction) == 0) {
+        completion_queue_.push_back(instruction);
+        completion_queue_cv_.notify_all();
+        tasks_in_flight_.erase(instruction);
+      }
+    }
+  });
+}
+
+int64* Executor::GetPartitionBuffers(
+    const std::vector<std::pair<int64, int64>>& partition) {
+  // Return in 'partition_buffers' partition [size, limit) for each dimension.
+  auto partition_buffers = new int64[partition.size() * 2];
+  for (int i = 0; i < partition.size(); ++i) {
+    partition_buffers[2 * i + 0] = partition[i].first;
+    partition_buffers[2 * i + 1] = partition[i].first + partition[i].second;
+  }
+  return partition_buffers;
+}
+
+bool Executor::HasParallelTasks(HloInstruction* instruction) {
+  return instruction->opcode() == HloOpcode::kCall &&
+         !instruction->to_apply()
+              ->root_instruction()
+              ->outer_dimension_partitions()
+              .empty();
+}
+
+const void** Executor::GetOperandBuffers(HloInstruction* instruction) {
+  // We cannot use a move-only RAII type like std::unique_ptr because the
+  // list of operands is allocated on the main thread and transferred to the
+  // worker via the lambda passed to enqueue_function.  In order for the
+  // lambda to take ownership, we would need to use generalized lambda
+  // capture which is a feature new to C++14.
+  // TODO(b/27458679) Avoid dynamic allocations in Executor.
+  auto operand_buffers = new const void*[instruction->operand_count()];
+  std::transform(instruction->operands().begin(), instruction->operands().end(),
+                 operand_buffers, [this](HloInstruction* operand) {
+                   return FindOrDie(*results_, operand);
+                 });
+  return operand_buffers;
+}
+
+}  // namespace
+
 Status ParallelCpuExecutable::AllocateBuffers(
     DeviceMemoryAllocator* memory_allocator, int device_ordinal,
     std::vector<perftools::gputools::DeviceMemoryBase>* buffers) {
@@ -210,88 +437,16 @@ Status ParallelCpuExecutable::ExecuteComputeFunctions(
     }
   }
 
-  void** temps_array = buffer_pointers.data();
-  uint64* profile_counters_array = profile_counters.data();
-  auto* thread_pool = CHECK_NOTNULL(run_options->xla_intra_op_thread_pool());
-  tensorflow::mutex completion_queue_lock;
-  tensorflow::condition_variable completion_queue_cv;
-  std::deque<HloInstruction*> completion_queue;
-  int64 instructions_in_flight = 0;
-  while (!pending.empty() || instructions_in_flight > 0) {
-    auto pending_it = pending.begin();
-    while (pending_it != pending.end()) {
-      HloInstruction* instruction = *pending_it;
-      // Skip pending instructions whose operands aren't ready.
-      if (std::any_of(instruction->operands().begin(),
-                      instruction->operands().end(),
-                      [&](HloInstruction* operand) {
-                        return !ContainsKey(results, operand);
-                      })) {
-        ++pending_it;
-        continue;
-      }
+  // TODO(b/27458679) Manage scheduling based on in-flight concurrency limits.
+  // For example, if we expect a library conv/matmul call to run at max
+  // concurrency, we should not dispatch runnable instructions until the
+  // libary call is finished (to avoid expensive cache invalidation).
+  Executor executor(functions, run_options, &pending, &results,
+                    buffer_pointers.data(), profile_counters.data(),
+                    assignment_.get());
 
-      TF_ASSIGN_OR_RETURN(const BufferAllocation::Slice result_slice,
-                          assignment_->GetUniqueTopLevelSlice(instruction));
-      void* result_buffer =
-          static_cast<char*>(temps_array[result_slice.index()]) +
-          result_slice.offset();
-      // We cannot use a move-only RAII type like std::unique_ptr because the
-      // list of operands is allocated on the main thread and transferred to the
-      // worker via the lambda passed to enqueue_function.  In order for the
-      // lambda to take ownership, we would need to use generalized lambda
-      // capture which is a feature new to C++14.
-      auto operand_buffers = new const void*[instruction->operand_count()];
-      std::transform(instruction->operands().begin(),
-                     instruction->operands().end(), operand_buffers,
-                     [&results](HloInstruction* operand) {
-                       return FindOrDie(results, operand);
-                     });
-      auto function = FindOrDie(functions, instruction);
-      // The thread pool entry takes ownership of |operand_buffers|.
-      const auto* exec_run_options = &run_options->run_options();
-      thread_pool->Schedule([instruction, &completion_queue,
-                             &completion_queue_lock, &completion_queue_cv,
-                             result_buffer, exec_run_options, operand_buffers,
-                             temps_array, profile_counters_array, function] {
-        function(result_buffer, exec_run_options, operand_buffers, temps_array,
-                 profile_counters_array);
-        delete[] operand_buffers;
-        // Push the completed HLO instruction on the queue, the main thread
-        // will pop it off and potentially launch more work which uses the
-        // result.
-        {
-          tensorflow::mutex_lock l(completion_queue_lock);
-          completion_queue.push_back(instruction);
-          completion_queue_cv.notify_all();
-        }
-      });
+  TF_RETURN_IF_ERROR(executor.Run());
 
-      ++instructions_in_flight;
-      pending_it = pending.erase(pending_it);
-    }
-    // Wait for a completed HLO instruction to be present in the queue.  We will
-    // pop it out of the queue and make the result available to its users.
-    HloInstruction* instruction;
-    do {
-      tensorflow::mutex_lock l(completion_queue_lock);
-      if (completion_queue.empty()) {
-        completion_queue_cv.wait(l);
-      }
-      if (!completion_queue.empty()) {
-        instruction = completion_queue.front();
-        completion_queue.pop_front();
-        break;
-      }
-    } while (1);
-    TF_ASSIGN_OR_RETURN(const BufferAllocation::Slice result_slice,
-                        assignment_->GetUniqueTopLevelSlice(instruction));
-    void* result_buffer =
-        static_cast<char*>(temps_array[result_slice.index()]) +
-        result_slice.offset();
-    InsertOrDie(&results, instruction, result_buffer);
-    --instructions_in_flight;
-  }
   uint64 end_micros = tensorflow::Env::Default()->NowMicros();
 
   {
diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition.cc b/tensorflow/compiler/xla/service/cpu/shape_partition.cc
new file mode 100644
index 0000000000..e27ff13edd
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition.cc
@@ -0,0 +1,160 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/cpu/shape_partition.h"
+
+namespace xla {
+namespace cpu {
+
+std::vector<int64> ShapePartitionAssigner::Run(int64 target_partition_count) {
+  // Gather outer-most dims where dim_size >= 'target_partition_count'.
+  // Note: always leave inner-dim static for vectorization/optimzations.
+  std::vector<int64> outer_dims;
+  int64 outer_dim_size = 1;
+  // TODO(b/27458679) Consider reserving enough minor dimensions (based on
+  // target vector register width) to enable vector instructions.
+  for (int i = shape_.layout().minor_to_major_size() - 1; i >= 1; --i) {
+    const int64 dimension = shape_.layout().minor_to_major(i);
+    outer_dims.push_back(dimension);
+    outer_dim_size *= shape_.dimensions(dimension);
+    if (outer_dim_size >= target_partition_count) {
+      break;
+    }
+  }
+
+  // Clip target partition count if outer dim size is insufficient to cover.
+  target_partition_count = std::min(outer_dim_size, target_partition_count);
+
+  // Calculate the target number of partitions per-dimension, by factoring
+  // 'target_partition_count' into 'num_outer_dims' equal terms.
+  // EX:
+  // *) target_partition_count = 16
+  // *) out_dim_count = 2
+  // *) target_dim_partition_count = 16 ^ (1.0 / 2) == 4
+  const int64 target_dim_partition_count = std::pow(
+      static_cast<double>(target_partition_count), 1.0 / outer_dims.size());
+
+  // Assign feasible dimension partitions based on 'target_dim_partition_count'
+  // and actual dimension sizes from 'shape_'.
+  std::vector<int64> dimension_partition_counts(outer_dims.size());
+  for (int64 i = 0; i < outer_dims.size(); ++i) {
+    dimension_partition_counts[i] =
+        std::min(static_cast<int64>(shape_.dimensions(outer_dims[i])),
+                 target_dim_partition_count);
+  }
+
+  // Check if total partition count is below 'target_partition_count'.
+  // This can occur if some dimensions in 'shape_' are below the
+  // 'target_dim_partition_count' threshold.
+  if (GetTotalPartitionCount(dimension_partition_counts) <
+      target_partition_count) {
+    // Assign additional partitions (greedily to outer dimensions), if doing
+    // so would keep the total number of partitions <= 'target_partition_count',
+    // using one pass over 'dimension_partition_counts'.
+    for (int64 i = 0; i < dimension_partition_counts.size(); ++i) {
+      const int64 current_dim_partition_count = dimension_partition_counts[i];
+      const int64 other_dims_partition_count =
+          GetTotalPartitionCount(dimension_partition_counts) /
+          current_dim_partition_count;
+      // Constraint: (current + additional) * other <= target
+      // Calculate: additional = target / other - current
+      int64 additional_partition_count =
+          target_partition_count / other_dims_partition_count -
+          current_dim_partition_count;
+      // Clip 'additional_partition_count' by current dimension size.
+      additional_partition_count = std::min(
+          shape_.dimensions(outer_dims[i]) - dimension_partition_counts[i],
+          additional_partition_count);
+      if (additional_partition_count > 0) {
+        dimension_partition_counts[i] += additional_partition_count;
+      }
+    }
+  }
+
+  return dimension_partition_counts;
+}
+
+int64 ShapePartitionAssigner::GetTotalPartitionCount(
+    const std::vector<int64>& dimension_partition_counts) {
+  int64 total_partition_count = 1;
+  for (int64 dim_partition_count : dimension_partition_counts) {
+    total_partition_count *= dim_partition_count;
+  }
+  return total_partition_count;
+}
+
+ShapePartitionIterator::ShapePartitionIterator(
+    const Shape& shape, const std::vector<int64>& dimension_partition_counts)
+    : shape_(shape),
+      dimension_partition_counts_(dimension_partition_counts),
+      dimensions_(dimension_partition_counts_.size()),
+      dimension_partition_sizes_(dimension_partition_counts_.size()),
+      dimension_partition_strides_(dimension_partition_counts_.size()) {
+  // Store partitioned outer dimensions from 'shape_'.
+  for (int i = 0; i < dimensions_.size(); ++i) {
+    dimensions_[i] = shape_.layout().minor_to_major(
+        shape_.layout().minor_to_major_size() - 1 - i);
+  }
+
+  // Calculate partition size for each dimension (note that the size of
+  // the last partition in each dimension may be different if the dimension
+  // size is not a multiple of partition size).
+  for (int i = 0; i < dimension_partition_sizes_.size(); ++i) {
+    const int64 dim_size = shape_.dimensions(dimensions_[i]);
+    dimension_partition_sizes_[i] =
+        std::max(1LL, dim_size / dimension_partition_counts_[i]);
+  }
+
+  // Calculate the partition strides for each dimension.
+  dimension_partition_strides_[dimension_partition_strides_.size() - 1] = 1;
+  for (int i = dimension_partition_strides_.size() - 2; i >= 0; --i) {
+    dimension_partition_strides_[i] = dimension_partition_strides_[i + 1] *
+                                      dimension_partition_counts_[i + 1];
+  }
+}
+
+std::vector<std::pair<int64, int64>> ShapePartitionIterator::GetPartition(
+    int64 index) const {
+  // Calculate and return the partition for 'index'.
+  // Returns for each dimension: (partition_start, partition_size).
+  std::vector<std::pair<int64, int64>> partition(dimensions_.size());
+  for (int64 i = 0; i < partition.size(); ++i) {
+    // Calculate the index for dimension 'i'.
+    const int64 partition_index = index / dimension_partition_strides_[i];
+    // Calculate dimension partition start at 'partition_index'.
+    partition[i].first = partition_index * dimension_partition_sizes_[i];
+    // Calculate dimension partition size (note that the last partition size
+    // may be adjusted if dimension size is not a multiple of partition size).
+    if (partition_index == dimension_partition_counts_[i] - 1) {
+      // Last partition in this dimension.
+      partition[i].second =
+          shape_.dimensions(dimensions_[i]) - partition[i].first;
+    } else {
+      partition[i].second = dimension_partition_sizes_[i];
+    }
+    CHECK_GT(partition[i].second, 0);
+    // Update index to remove conribution from current dimension.
+    index -= partition_index * dimension_partition_strides_[i];
+  }
+  return partition;
+}
+
+int64 ShapePartitionIterator::GetTotalPartitionCount() const {
+  return ShapePartitionAssigner::GetTotalPartitionCount(
+      dimension_partition_counts_);
+}
+
+}  // namespace cpu
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition.h b/tensorflow/compiler/xla/service/cpu/shape_partition.h
new file mode 100644
index 0000000000..bdbcb874c1
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition.h
@@ -0,0 +1,105 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_COMPILER_XLA_SERVICE_CPU_SHAPE_PARTITION_H_
+#define THIRD_PARTY_TENSORFLOW_COMPILER_XLA_SERVICE_CPU_SHAPE_PARTITION_H_
+
+#include <vector>
+
+#include "tensorflow/compiler/xla/shape_util.h"
+
+namespace xla {
+namespace cpu {
+
+// ShapePartitionAssigner partitions the most-major dimensions of 'shape' such
+// that the total partition count <= 'target_partition_count'.
+//
+// Example 1:
+//
+//   Let 'shape' = [8, 16, 32] and 'target_partition_count' = 6.
+//
+//   Because the most-major dimension size is <= 'target_partition_count', we
+//   can generate our target number of partitions by partition the most-major
+//   dimensions.
+//
+//   This will result in the following partitions of the most-major dimension:
+//
+//     [0, 1), [1, 2), [2, 3), [3, 4), [4, 5) [5, 8)
+//
+//   Note that the last parition has residule because the dimension size is
+//   not a multiple of the partition count.
+//
+//
+// Example 2:
+//
+//   Let 'shape' = [8, 16, 32] and 'target_partition_count' = 16.
+//
+//   Because the most-major dimension only has size 8, we must also partition
+//   the next most-major dimension to generate the target of 16 partitions.
+//   We factor 'target_partition_count' by the number of most-major dimensions
+//   we need to partition, to get a per-dimension target partition count:
+//
+//     target_dimension_partition_count = 16 ^ (1 / 2) == 4
+//
+//   This will result in the following partitions of the most-major dimension:
+//
+//     [0, 2), [2, 4), [4, 6), [6, 8)
+//
+//   This will result in the following partitions of the second most-major
+//   dimension:
+//
+//     [0, 4), [4, 8), [8, 12), [12, 16)
+//
+class ShapePartitionAssigner {
+ public:
+  ShapePartitionAssigner(const Shape& shape) : shape_(shape) {}
+
+  // Returns dimension partition counts (starting at outer-most dimension).
+  std::vector<int64> Run(int64 target_partition_count);
+
+  // Returns the total partition count based on 'dimension_partition_counts'.
+  static int64 GetTotalPartitionCount(
+      const std::vector<int64>& dimension_partition_counts);
+
+ private:
+  const Shape& shape_;
+};
+
+// ShapePartitionIterator iterates through outer-dimension partitions of
+// 'shape' as specified by 'dimension_partition_counts'.
+class ShapePartitionIterator {
+ public:
+  ShapePartitionIterator(const Shape& shape,
+                         const std::vector<int64>& dimension_partition_counts);
+
+  // Returns a partition [start, size] for each dimension.
+  // Partitions are listed starting from outer-most dimension first.
+  std::vector<std::pair<int64, int64>> GetPartition(int64 index) const;
+
+  int64 GetTotalPartitionCount() const;
+
+ private:
+  const Shape& shape_;
+  const std::vector<int64> dimension_partition_counts_;
+
+  std::vector<int64> dimensions_;
+  std::vector<int64> dimension_partition_sizes_;
+  std::vector<int64> dimension_partition_strides_;
+};
+
+}  // namespace cpu
+}  // namespace xla
+
+#endif  // THIRD_PARTY_TENSORFLOW_COMPILER_XLA_SERVICE_CPU_SHAPE_PARTITION_H_
diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition_test.cc b/tensorflow/compiler/xla/service/cpu/shape_partition_test.cc
new file mode 100644
index 0000000000..6cc6e3fe85
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition_test.cc
@@ -0,0 +1,248 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/cpu/shape_partition.h"
+
+#include <algorithm>
+#include <random>
+
+#include "tensorflow/compiler/xla/test_helpers.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/util.h"
+
+namespace xla {
+namespace cpu {
+namespace {
+
+class ShapePartitionAssignerTest : public HloTestBase {
+ protected:
+  typedef std::vector<int64> Vec;
+
+  void RunR2Test(const Shape& shape, const int64 expected_max_partition_count) {
+    ShapePartitionAssigner assigner(shape);
+    // Check all partitions of outer dimension.
+    for (int64 i = 1; i <= expected_max_partition_count; ++i) {
+      EXPECT_TRUE(ContainersEqual(Vec({i}),
+                                  assigner.Run(/*target_partition_count=*/i)));
+    }
+    // Check target_partition_count > outer dimension size.
+    EXPECT_TRUE(ContainersEqual(
+        Vec({expected_max_partition_count}),
+        assigner.Run(
+            /*target_partition_count=*/expected_max_partition_count + 1)));
+  }
+};
+
+TEST_F(ShapePartitionAssignerTest, Shape13WithLayout10) {
+  RunR2Test(ShapeUtil::MakeShapeWithLayout(F32, {1, 3}, {1, 0}), 1);
+}
+
+TEST_F(ShapePartitionAssignerTest, Shape31WithLayout01) {
+  RunR2Test(ShapeUtil::MakeShapeWithLayout(F32, {3, 1}, {0, 1}), 1);
+}
+
+TEST_F(ShapePartitionAssignerTest, Shape53WithLayout10) {
+  RunR2Test(ShapeUtil::MakeShapeWithLayout(F32, {5, 3}, {1, 0}), 5);
+}
+
+TEST_F(ShapePartitionAssignerTest, Shape53WithLayout01) {
+  RunR2Test(ShapeUtil::MakeShapeWithLayout(F32, {5, 3}, {0, 1}), 3);
+}
+
+TEST_F(ShapePartitionAssignerTest, Shape532WithLayout210) {
+  Shape shape = ShapeUtil::MakeShapeWithLayout(F32, {5, 3, 2}, {2, 1, 0});
+  ShapePartitionAssigner assigner(shape);
+
+  for (int64 i = 1; i <= 5; ++i) {
+    EXPECT_TRUE(ContainersEqual(Vec({i}), assigner.Run(
+                                              /*target_partition_count=*/i)));
+  }
+
+  EXPECT_TRUE(
+      ContainersEqual(Vec({3, 2}), assigner.Run(/*target_partition_count=*/6)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({3, 2}), assigner.Run(/*target_partition_count=*/7)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({4, 2}), assigner.Run(/*target_partition_count=*/8)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({3, 3}), assigner.Run(/*target_partition_count=*/9)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 3}),
+                              assigner.Run(/*target_partition_count=*/10)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 3}),
+                              assigner.Run(/*target_partition_count=*/11)));
+  EXPECT_TRUE(ContainersEqual(Vec({4, 3}),
+                              assigner.Run(/*target_partition_count=*/12)));
+  EXPECT_TRUE(ContainersEqual(Vec({4, 3}),
+                              assigner.Run(/*target_partition_count=*/13)));
+  EXPECT_TRUE(ContainersEqual(Vec({4, 3}),
+                              assigner.Run(/*target_partition_count=*/14)));
+  EXPECT_TRUE(ContainersEqual(Vec({5, 3}),
+                              assigner.Run(/*target_partition_count=*/15)));
+  EXPECT_TRUE(ContainersEqual(Vec({5, 3}),
+                              assigner.Run(/*target_partition_count=*/16)));
+}
+
+TEST_F(ShapePartitionAssignerTest, Shape532WithLayout201) {
+  Shape shape = ShapeUtil::MakeShapeWithLayout(F32, {5, 3, 2}, {2, 0, 1});
+  ShapePartitionAssigner assigner(shape);
+
+  for (int64 i = 1; i <= 3; ++i) {
+    EXPECT_TRUE(ContainersEqual(Vec({i}), assigner.Run(
+                                              /*target_partition_count=*/i)));
+  }
+
+  EXPECT_TRUE(
+      ContainersEqual(Vec({2, 2}), assigner.Run(/*target_partition_count=*/4)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({2, 2}), assigner.Run(/*target_partition_count=*/5)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({3, 2}), assigner.Run(/*target_partition_count=*/6)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({3, 2}), assigner.Run(/*target_partition_count=*/7)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({3, 2}), assigner.Run(/*target_partition_count=*/8)));
+  EXPECT_TRUE(
+      ContainersEqual(Vec({3, 3}), assigner.Run(/*target_partition_count=*/9)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 3}),
+                              assigner.Run(/*target_partition_count=*/10)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 3}),
+                              assigner.Run(/*target_partition_count=*/11)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 4}),
+                              assigner.Run(/*target_partition_count=*/12)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 4}),
+                              assigner.Run(/*target_partition_count=*/13)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 4}),
+                              assigner.Run(/*target_partition_count=*/14)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 5}),
+                              assigner.Run(/*target_partition_count=*/15)));
+  EXPECT_TRUE(ContainersEqual(Vec({3, 5}),
+                              assigner.Run(/*target_partition_count=*/16)));
+}
+
+class ShapePartitionIteratorTest : public HloTestBase {
+ protected:
+  typedef std::vector<std::pair<int64, int64>> Partition;
+};
+
+TEST_F(ShapePartitionIteratorTest, Shape53WithLayout10) {
+  Shape shape = ShapeUtil::MakeShapeWithLayout(F32, {5, 3}, {1, 0});
+
+  {
+    ShapePartitionIterator iterator(shape, {1});
+    EXPECT_EQ(1, iterator.GetTotalPartitionCount());
+    EXPECT_TRUE(ContainersEqual(Partition({{0, 5}}), iterator.GetPartition(0)));
+  }
+
+  {
+    ShapePartitionIterator iterator(shape, {2});
+    EXPECT_EQ(2, iterator.GetTotalPartitionCount());
+    EXPECT_TRUE(ContainersEqual(Partition({{0, 2}}), iterator.GetPartition(0)));
+    EXPECT_TRUE(ContainersEqual(Partition({{2, 3}}), iterator.GetPartition(1)));
+  }
+
+  {
+    ShapePartitionIterator iterator(shape, {3});
+    EXPECT_EQ(3, iterator.GetTotalPartitionCount());
+    EXPECT_TRUE(ContainersEqual(Partition({{0, 1}}), iterator.GetPartition(0)));
+    EXPECT_TRUE(ContainersEqual(Partition({{1, 1}}), iterator.GetPartition(1)));
+    EXPECT_TRUE(ContainersEqual(Partition({{2, 3}}), iterator.GetPartition(2)));
+  }
+}
+
+TEST_F(ShapePartitionIteratorTest, Shape532WithLayout210) {
+  Shape shape = ShapeUtil::MakeShapeWithLayout(F32, {5, 3, 2}, {2, 1, 0});
+
+  {
+    ShapePartitionIterator iterator(shape, {1, 1});
+    EXPECT_EQ(1, iterator.GetTotalPartitionCount());
+    EXPECT_TRUE(
+        ContainersEqual(Partition({{0, 5}, {0, 3}}), iterator.GetPartition(0)));
+  }
+
+  {
+    ShapePartitionIterator iterator(shape, {2, 2});
+    EXPECT_EQ(4, iterator.GetTotalPartitionCount());
+    EXPECT_TRUE(
+        ContainersEqual(Partition({{0, 2}, {0, 1}}), iterator.GetPartition(0)));
+    EXPECT_TRUE(
+        ContainersEqual(Partition({{0, 2}, {1, 2}}), iterator.GetPartition(1)));
+    EXPECT_TRUE(
+        ContainersEqual(Partition({{2, 3}, {0, 1}}), iterator.GetPartition(2)));
+    EXPECT_TRUE(
+        ContainersEqual(Partition({{2, 3}, {1, 2}}), iterator.GetPartition(3)));
+  }
+}
+
+class RandomShapePartitionIteratorTest : public HloTestBase {
+ protected:
+  typedef std::vector<std::pair<int64, int64>> Partition;
+  RandomShapePartitionIteratorTest()
+      : generator_(rd_()), distribution_(1, 10) {}
+
+  std::vector<int64> RandR4Dims() { return {Rand(), Rand(), Rand(), Rand()}; }
+
+  int64 Rand() { return distribution_(generator_); }
+
+  std::random_device rd_;
+  std::mt19937 generator_;
+  std::uniform_int_distribution<int> distribution_;
+};
+
+TEST_F(RandomShapePartitionIteratorTest, RandomShapeAndPartitions) {
+  // Choose random dimensions for R4 shape.
+  Shape shape = ShapeUtil::MakeShapeWithLayout(F32, RandR4Dims(), {3, 2, 1, 0});
+  // Choose random number of outer dimensions to partition.
+  const int num_outer_dims_to_partiton = 1 + (Rand() % 3);
+  // Choose random outer dimension partiton counts.
+  std::vector<int64> dim_sizes(num_outer_dims_to_partiton);
+  std::vector<int64> dim_partition_counts(num_outer_dims_to_partiton);
+  int64 total_dim_size = 1;
+  for (int i = 0; i < num_outer_dims_to_partiton; ++i) {
+    const int64 dimension = shape.layout().minor_to_major(
+        shape.layout().minor_to_major_size() - 1 - i);
+    dim_sizes[i] = shape.dimensions(dimension);
+    total_dim_size *= dim_sizes[i];
+    // Choose dimension partition count in [1, dim_size]
+    const int64 dim_partition_count = 1 + Rand() % dim_sizes[i];
+    dim_partition_counts[i] = dim_partition_count;
+  }
+  // Iterate through all partition: for each partition record covered
+  // index ranges by dimension.
+  std::vector<std::map<int64, int64>> ranges(num_outer_dims_to_partiton);
+  ShapePartitionIterator partition_iterator(shape, dim_partition_counts);
+  const int64 partition_count = partition_iterator.GetTotalPartitionCount();
+  for (int64 i = 0; i < partition_count; ++i) {
+    const auto& dim_partition = partition_iterator.GetPartition(i);
+    for (int dim = 0; dim < dim_partition.size(); ++dim) {
+      ranges[dim].insert(
+          std::make_pair(dim_partition[dim].first,
+                         dim_partition[dim].first + dim_partition[dim].second));
+    }
+  }
+  // Check that partitions cover entire dimension size range (for each
+  // partitioned dimension).
+  for (int i = 0; i < ranges.size(); ++i) {
+    int64 expected_index = 0;
+    for (auto& r : ranges[i]) {
+      EXPECT_EQ(expected_index, r.first);
+      expected_index = r.second;
+    }
+    EXPECT_EQ(expected_index, dim_sizes[i]);
+  }
+}
+
+}  // namespace
+}  // namespace cpu
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index fe6233205c..c49c00bac0 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -2413,4 +2413,9 @@ void HloInstruction::UniquifyName(NameUniquer* name_uniquer) {
   name_ = name_uniquer->GetUniqueName(name_);
 }
 
+void HloInstruction::set_outer_dimension_partitions(
+    const std::vector<int64>& outer_dimension_partitions) {
+  outer_dimension_partitions_ = outer_dimension_partitions;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index fb80ca86af..8d1bc5e632 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -812,6 +812,17 @@ class HloInstruction {
     parent_fusion_instruction_ = fusion_instruction;
   }
 
+  // Get/Set the number of partitions per outer dimension (in order, starting
+  // with outer-most dimension first). Currently used by the parallel cpu
+  // backend to partition HLOs into parallel tasks.
+  // TODO(b/62783254) Replace these methods with a more general way to
+  // annotate HLOs with backend-specific information.
+  const std::vector<int64>& outer_dimension_partitions() const {
+    return outer_dimension_partitions_;
+  }
+  void set_outer_dimension_partitions(
+      const std::vector<int64>& outer_dimension_partitions);
+
  private:
   enum class UseKind { kNoUse, kReuse, kUsePermutingElements, kUse };
 
@@ -985,6 +996,10 @@ class HloInstruction {
   // Metadata for debugging.
   OpMetadata metadata_;
 
+  // The number of partitions per outer dimension (listed in order from
+  // outer-most dimension first).
+  std::vector<int64> outer_dimension_partitions_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(HloInstruction);
 };
 
diff --git a/tensorflow/compiler/xla/service/hlo_module_config.cc b/tensorflow/compiler/xla/service/hlo_module_config.cc
index a2235a2682..8974deb530 100644
--- a/tensorflow/compiler/xla/service/hlo_module_config.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_config.cc
@@ -58,6 +58,10 @@ string HloModuleConfig::compilation_cache_key() const {
     StrAppend(&key, "::replica_count=", replica_count());
   }
   StrAppend(&key, debug_options_.DebugString());
+  if (intra_op_parallelism_threads() > 0) {
+    StrAppend(&key, "::intra_op_parallelism_threads=",
+              intra_op_parallelism_threads());
+  }
   return key;
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_module_config.h b/tensorflow/compiler/xla/service/hlo_module_config.h
index ee32ab9bc4..2299200b5b 100644
--- a/tensorflow/compiler/xla/service/hlo_module_config.h
+++ b/tensorflow/compiler/xla/service/hlo_module_config.h
@@ -92,6 +92,15 @@ class HloModuleConfig {
     debug_options_ = debug_options;
   }
 
+  // Sets/returns the number of intra op threads for this module.
+  void set_intra_op_parallelism_threads(
+      const int intra_op_parallelism_threads) {
+    intra_op_parallelism_threads_ = intra_op_parallelism_threads;
+  }
+  int64 intra_op_parallelism_threads() const {
+    return intra_op_parallelism_threads_;
+  }
+
  private:
   // If you add new members, be sure to update compilation_cache_key.
 
@@ -116,6 +125,10 @@ class HloModuleConfig {
   // The number of replicas to compile this binary for.
   int64 replica_count_ = 1;
 
+  // The target maximum parallelism at which to partition HLOs for parallel
+  // execution on the CPU backend.
+  int64 intra_op_parallelism_threads_ = -1;
+
   DebugOptions debug_options_;
 };
 
diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index ffc775e564..c656f77b7c 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -155,6 +155,10 @@ StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable(
   module_config->set_has_hybrid_result(has_hybrid_result);
   module_config->set_replica_count(execute_backend_->replica_count());
   module_config->set_debug_options(legacy_flags::GetDebugOptionsFromFlags());
+  if (execute_backend_->eigen_intra_op_thread_pool() != nullptr) {
+    module_config->set_intra_op_parallelism_threads(
+        execute_backend_->eigen_intra_op_thread_pool()->NumThreads());
+  }
   legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
   if (flags->xla_hlo_profile) {
     module_config->enable_hlo_profiling(true);
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index c095983cd5..bf7c106832 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1329,8 +1329,14 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/client:client_library",
+        "//tensorflow/compiler/xla/client:computation",
+        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:platform_util",
+        "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index 3546e9a1e8..c8b91eafc7 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -20,6 +20,9 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/compiler/xla/array2d.h"
+#include "tensorflow/compiler/xla/client/client_library.h"
+#include "tensorflow/compiler/xla/client/computation.h"
+#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
@@ -28,7 +31,9 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/platform_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/compiler/xla/tests/test_macros.h"
@@ -36,10 +41,13 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/test_benchmark.h"
 #include "tensorflow/core/platform/types.h"
 
 using tensorflow::gtl::ArraySlice;
 
+namespace se = ::perftools::gputools;
+
 namespace xla {
 namespace {
 
@@ -566,6 +574,61 @@ XLA_TEST_F(FusionTest, Clamp2D) {
   TestElementwise2D<float, 3>(HloOpcode::kClamp);
 }
 
+void BM_ParallelFusion(int num_iters) {
+  // Simple element-wise computation to benchmark parallel task partitioning.
+  tensorflow::testing::StopTiming();
+
+  se::Platform* platform = PlatformUtil::GetDefaultPlatform().ValueOrDie();
+  auto executors = PlatformUtil::GetStreamExecutors(platform).ValueOrDie();
+  StreamExecutorMemoryAllocator allocator(platform, executors);
+
+  const int64 intra_op_parallelism_threads = 16;
+  xla::LocalClientOptions client_options;
+  client_options.set_platform(platform);
+  client_options.set_intra_op_parallelism_threads(intra_op_parallelism_threads);
+  auto client =
+      ClientLibrary::GetOrCreateLocalClient(client_options).ValueOrDie();
+
+  const int64 dim_size = 1024;
+  // Create a simple fusable elementwise computation.
+  ComputationBuilder builder(client, "ParallelFusion");
+  Shape input_shape = ShapeUtil::MakeShape(F32, {dim_size, dim_size});
+  auto input0 = builder.Broadcast(builder.ConstantR0<float>(1.5f),
+                                  AsInt64Slice(input_shape.dimensions()));
+  auto input1 = builder.Broadcast(builder.ConstantR0<float>(2.0f),
+                                  AsInt64Slice(input_shape.dimensions()));
+  auto input2 = builder.Broadcast(builder.ConstantR0<float>(3.0f),
+                                  AsInt64Slice(input_shape.dimensions()));
+  auto x = builder.Mul(input0, input1);
+  auto y = builder.Add(x, input2);
+  auto computation = builder.Build().ConsumeValueOrDie();
+
+  std::unique_ptr<LocalExecutable> executable =
+      client->Compile(computation, {}, ExecutableBuildOptions())
+          .ConsumeValueOrDie();
+
+  // Run some warm-up executions.
+  ExecutableRunOptions options;
+  options.set_allocator(&allocator);
+  const int kWarmups = 2;
+  for (int i = 0; i < kWarmups; ++i) {
+    auto result = executable->Run({}, options);
+    ASSERT_TRUE(result.ok());
+  }
+
+  // Run benchmark.
+  tensorflow::testing::BytesProcessed(static_cast<int64>(num_iters) * dim_size *
+                                      dim_size * sizeof(float));
+  tensorflow::testing::UseRealTime();
+  tensorflow::testing::StartTiming();
+  for (int i = 0; i < num_iters; ++i) {
+    auto result = executable->Run({}, options);
+    ASSERT_TRUE(result.ok());
+  }
+}
+
+BENCHMARK(BM_ParallelFusion);
+
 }  // namespace
 }  // namespace xla
 
@@ -583,5 +646,6 @@ int main(int argc, char** argv) {
     LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
     return 2;
   }
+  tensorflow::testing::RunBenchmarks();
   return RUN_ALL_TESTS();
 }
-- 
GitLab


From ba7b03f26f60a428c440c9042adf2643d01f73e5 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Mon, 19 Jun 2017 14:23:24 -0700
Subject: [PATCH 0210/1375] Install codegen in bazel workspace to allow docs
 tests to run.

PiperOrigin-RevId: 159477794
---
 tensorflow/tools/docs/BUILD                |  6 +--
 tensorflow/tools/docs/build_docs_test.py   | 29 +++++++++--
 tensorflow/tools/docs/generate_lib.py      |  3 ++
 tensorflow/tools/docs/generate_lib_test.py | 19 -------
 tensorflow/tools/docs/parser_test.py       | 59 +++++++++++++++++++++-
 tensorflow/workspace.bzl                   | 11 ++++
 third_party/codegen.BUILD                  | 16 ++++++
 7 files changed, 114 insertions(+), 29 deletions(-)
 create mode 100644 third_party/codegen.BUILD

diff --git a/tensorflow/tools/docs/BUILD b/tensorflow/tools/docs/BUILD
index 8e27b133c2..45722ec9eb 100644
--- a/tensorflow/tools/docs/BUILD
+++ b/tensorflow/tools/docs/BUILD
@@ -37,6 +37,7 @@ py_library(
     srcs = ["parser.py"],
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
+    deps = ["@com_github_andreif_codegen"],
 )
 
 py_test(
@@ -44,7 +45,6 @@ py_test(
     size = "small",
     srcs = ["parser_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["manual"],
     deps = [
         ":parser",
         "//tensorflow/python:platform_test",
@@ -78,13 +78,10 @@ py_test(
     size = "small",
     srcs = ["generate_lib_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["manual"],
     deps = [
         ":generate_lib",
         ":parser",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/python:platform_test",
-        "//tensorflow/python/debug:debug_py",
     ],
 )
 
@@ -105,7 +102,6 @@ py_test(
     srcs = ["build_docs_test.py"],
     data = ["//tensorflow:docs_src"],
     srcs_version = "PY2AND3",
-    tags = ["manual"],
     deps = [
         ":generate_lib",
         "//tensorflow:tensorflow_py",
diff --git a/tensorflow/tools/docs/build_docs_test.py b/tensorflow/tools/docs/build_docs_test.py
index d28dd93b9a..ae293f6576 100644
--- a/tensorflow/tools/docs/build_docs_test.py
+++ b/tensorflow/tools/docs/build_docs_test.py
@@ -19,6 +19,8 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+import sys
+import textwrap
 
 import tensorflow as tf
 from tensorflow.python import debug as tf_debug
@@ -29,19 +31,40 @@ from tensorflow.tools.docs import generate_lib
 
 class Flags(object):
   resource_root = resource_loader.get_root_dir_with_all_resources()
-  src_dir = os.path.join(resource_root, 'third_party/tensorflow/docs_src')
-  base_dir = os.path.join(resource_root, 'third_party/tensorflow/')
+  src_dir = os.path.join(resource_root, 'tensorflow/docs_src')
+  base_dir = os.path.join(resource_root, 'tensorflow/')
   output_dir = googletest.GetTempDir()
 
 
 class BuildDocsTest(googletest.TestCase):
 
   def testBuildDocs(self):
+    if sys.version_info >= (3, 0):
+      print('Warning: Doc generation is not supported from python3.')
+      return
+
     doc_generator = generate_lib.DocGenerator()
 
     doc_generator.set_py_modules([('tf', tf), ('tfdbg', tf_debug)])
 
-    status = doc_generator.build(Flags())
+    try:
+      status = doc_generator.build(Flags())
+    except RuntimeError as e:
+      if not e.args[0].startswith('Modules nested too deep'):
+        raise
+
+      msg = textwrap.dedent("""\
+          %s
+
+          ****************************************************************
+          If this test fails here, you have most likely introduced an
+          unsealed module. Make sure to use `remove_undocumented` or similar
+          utilities to avoid leaking symbols. See above for more information
+          on the exact point of failure.
+          ****************************************************************
+          """ % e.args[0])
+
+      raise RuntimeError(msg)
 
     if status:
       self.fail('Found %s Errors!' % status)
diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index 99872e1d84..67a4ad0ec9 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import argparse
 import os
+import sys
 
 import six
 
@@ -415,6 +416,8 @@ class DocGenerator(object):
   """Main entry point for generating docs."""
 
   def __init__(self):
+    if sys.version_info >= (3, 0):
+      print('Warning: Doc generation is not supported from python3.')
     self.argument_parser = argparse.ArgumentParser()
     self._py_modules = None
     self._private_map = _get_default_private_map()
diff --git a/tensorflow/tools/docs/generate_lib_test.py b/tensorflow/tools/docs/generate_lib_test.py
index 6e5deb6a36..ea6d28a02b 100644
--- a/tensorflow/tools/docs/generate_lib_test.py
+++ b/tensorflow/tools/docs/generate_lib_test.py
@@ -21,9 +21,6 @@ from __future__ import print_function
 import os
 import sys
 
-import tensorflow as tf
-
-from tensorflow.python import debug as tf_debug
 from tensorflow.python.platform import googletest
 from tensorflow.tools.docs import generate_lib
 from tensorflow.tools.docs import parser
@@ -54,22 +51,6 @@ class DummyVisitor(object):
 
 class GenerateTest(googletest.TestCase):
 
-  def test_extraction(self):
-    py_modules = [('tf', tf), ('tfdbg', tf_debug)]
-
-    try:
-      generate_lib.extract(py_modules,
-                           generate_lib._get_default_private_map(),
-                           generate_lib._get_default_do_not_descend_map())
-    except RuntimeError:
-      print('*****************************************************************')
-      print('If this test fails, you have most likely introduced an unsealed')
-      print('module. Make sure to use remove_undocumented or similar utilities')
-      print('to avoid leaking symbols. See below for more information on the')
-      print('failure.')
-      print('*****************************************************************')
-      raise
-
   def test_write(self):
     module = sys.modules[__name__]
 
diff --git a/tensorflow/tools/docs/parser_test.py b/tensorflow/tools/docs/parser_test.py
index 3e02160130..862f0acfa9 100644
--- a/tensorflow/tools/docs/parser_test.py
+++ b/tensorflow/tools/docs/parser_test.py
@@ -491,13 +491,13 @@ Returns:
 
 class TestParseFunctionDetails(googletest.TestCase):
 
-  def testParseFunctionDetails(self):
+  def test_parse_function_details(self):
     docstring, function_details = parser._parse_function_details(RELU_DOC)
 
     self.assertEqual(len(function_details), 2)
     args = function_details[0]
     self.assertEqual(args.keyword, 'Args')
-    self.assertEmpty(args.header)
+    self.assertEqual(len(args.header), 0)
     self.assertEqual(len(args.items), 2)
     self.assertEqual(args.items[0][0], 'features')
     self.assertEqual(args.items[1][0], 'name')
@@ -515,5 +515,60 @@ class TestParseFunctionDetails(googletest.TestCase):
         docstring + ''.join(str(detail) for detail in function_details))
 
 
+class TestGenerateSignature(googletest.TestCase):
+
+  def test_known_object(self):
+    if sys.version_info >= (3, 0):
+      print('Warning: Doc generation is not supported from python3.')
+      return
+
+    known_object = object()
+    reverse_index = {id(known_object): 'location.of.object.in.api'}
+
+    def example_fun(arg=known_object):  # pylint: disable=unused-argument
+      pass
+
+    sig = parser._generate_signature(example_fun, reverse_index)
+    self.assertEqual(sig, ['arg=location.of.object.in.api'])
+
+  def test_literals(self):
+    if sys.version_info >= (3, 0):
+      print('Warning: Doc generation is not supported from python3.')
+      return
+
+    def example_fun(a=5, b=5.0, c=None, d=True, e='hello', f=(1, (2, 3))):  # pylint: disable=g-bad-name, unused-argument
+      pass
+
+    sig = parser._generate_signature(example_fun, reverse_index={})
+    self.assertEqual(
+        sig, ['a=5', 'b=5.0', 'c=None', 'd=True', "e='hello'", 'f=(1, (2, 3))'])
+
+  def test_dotted_name(self):
+    if sys.version_info >= (3, 0):
+      print('Warning: Doc generation is not supported from python3.')
+      return
+
+    # pylint: disable=g-bad-name
+    class a(object):
+
+      class b(object):
+
+        class c(object):
+
+          class d(object):
+
+            def __init__(self, *args):
+              pass
+    # pylint: enable=g-bad-name
+
+    e = {'f': 1}
+
+    def example_fun(arg1=a.b.c.d, arg2=a.b.c.d(1, 2), arg3=e['f']):  # pylint: disable=unused-argument
+      pass
+
+    sig = parser._generate_signature(example_fun, reverse_index={})
+    self.assertEqual(sig, ['arg1=a.b.c.d', 'arg2=a.b.c.d(1, 2)', "arg3=e['f']"])
+
+
 if __name__ == '__main__':
   googletest.main()
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 5fd08d4f4c..6dd1752705 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -297,6 +297,17 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       build_file = str(Label("//third_party:backports_weakref.BUILD")),
   )
 
+  native.new_http_archive(
+      name = "com_github_andreif_codegen",
+      urls = [
+          "http://mirror.bazel.build/github.com/andreif/codegen/archive/1.0.tar.gz",
+          "https://github.com/andreif/codegen/archive/1.0.tar.gz",
+      ],
+      sha256 = "2dadd04a2802de27e0fe5a19b76538f6da9d39ff244036afa00c1bba754de5ee",
+      strip_prefix = "codegen-1.0",
+      build_file = str(Label("//third_party:codegen.BUILD")),
+  )
+
   filegroup_external(
       name = "org_python_license",
       licenses = ["notice"],  # Python 2.0
diff --git a/third_party/codegen.BUILD b/third_party/codegen.BUILD
new file mode 100644
index 0000000000..df436c8163
--- /dev/null
+++ b/third_party/codegen.BUILD
@@ -0,0 +1,16 @@
+# -*- mode: python; -*-
+#
+# Description:
+#   Extension to ast that allow ast -> python code generation.
+
+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])  # New BSD
+
+exports_files(["LICENSE"])
+
+py_library(
+    name = "com_github_andreif_codegen",
+    srcs = glob(["codegen.py"]),
+    srcs_version = "PY2AND3",
+)
-- 
GitLab


From 90e0df261ebc66736c1c7eb93039444b6835816e Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Mon, 19 Jun 2017 14:52:33 -0700
Subject: [PATCH 0211/1375] .trace() added to LinearOperator base class.
 Default implementation simply returns the sum of .diag_part.  Specializations
 to Identity/ScaledIdentity/Circulant as well.

PiperOrigin-RevId: 159482540
---
 .../linalg/python/ops/linear_operator.py      |  17 +++
 .../python/ops/linear_operator_identity.py    | 140 +++++++++++-------
 .../python/ops/linear_operator_test_util.py   |  17 +++
 3 files changed, 117 insertions(+), 57 deletions(-)

diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator.py b/tensorflow/contrib/linalg/python/ops/linear_operator.py
index 6cdfa86189..a4f8722951 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator.py
@@ -893,6 +893,23 @@ class LinearOperator(object):
     with self._name_scope(name):
       return self._diag_part()
 
+  def _trace(self):
+    return math_ops.reduce_sum(self.diag_part(), axis=-1)
+
+  def trace(self, name="trace"):
+    """Trace of the linear operator, equal to sum of `self.diag_part()`.
+
+    If the operator is square, this is also the sum of the eigenvalues.
+
+    Args:
+      name:  A name for this `Op`.
+
+    Returns:
+      Shape `[B1,...,Bb]` `Tensor` of same `dtype` as `self`.
+    """
+    with self._name_scope(name):
+      return self._trace()
+
   def _add_to_tensor(self, x):
     # Override if a more efficient implementation is available.
     return self._get_cached_dense_matrix() + x
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py b/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py
index acba1c7035..553a5471f1 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py
@@ -44,17 +44,15 @@ class BaseLinearOperatorIdentity(linear_operator.LinearOperator):
     """Static check of init arg `num_rows`, possibly add asserts."""
     # Possibly add asserts.
     if self._assert_proper_shapes:
-      self._num_rows = control_flow_ops.with_dependencies(
-          [
-              check_ops.assert_rank(
-                  self._num_rows,
-                  0,
-                  message="Argument num_rows must be a 0-D Tensor."),
-              check_ops.assert_non_negative(
-                  self._num_rows,
-                  message="Argument num_rows must be non-negative."),
-          ],
-          self._num_rows)
+      self._num_rows = control_flow_ops.with_dependencies([
+          check_ops.assert_rank(
+              self._num_rows,
+              0,
+              message="Argument num_rows must be a 0-D Tensor."),
+          check_ops.assert_non_negative(
+              self._num_rows,
+              message="Argument num_rows must be non-negative."),
+      ], self._num_rows)
 
     # Static checks.
     if not self._num_rows.dtype.is_integer:
@@ -74,15 +72,26 @@ class BaseLinearOperatorIdentity(linear_operator.LinearOperator):
       raise ValueError("Argument num_rows must be non-negative.  Found:"
                        " %s" % num_rows_static)
 
+  def _min_matrix_dim(self):
+    """Minimum of domain/range dimension, if statically available, else None."""
+    domain_dim = self.domain_dimension.value
+    range_dim = self.range_dimension.value
+    if domain_dim is None or range_dim is None:
+      return None
+    return min(domain_dim, range_dim)
+
+  def _min_matrix_dim_tensor(self):
+    """Minimum of domain/range dimension, as a tensor."""
+    return math_ops.reduce_min(self.shape_tensor()[-2:])
+
   def _ones_diag(self):
     """Returns the diagonal of this operator as all ones."""
     if self.shape.is_fully_defined():
-      d_shape = self.batch_shape.concatenate(
-          [min(self.domain_dimension.value, self.range_dimension.value)])
+      d_shape = self.batch_shape.concatenate([self._min_matrix_dim()])
     else:
       d_shape = array_ops.concat(
           [self.batch_shape_tensor(),
-           [math_ops.reduce_min(self.shape_tensor()[-2:])]], axis=0)
+           [self._min_matrix_dim_tensor()]], axis=0)
 
     return array_ops.ones(shape=d_shape, dtype=self.dtype)
 
@@ -276,8 +285,8 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
         self._check_batch_shape_possibly_add_asserts()
 
   def _shape(self):
-    matrix_shape = tensor_shape.TensorShape(
-        (self._num_rows_static, self._num_rows_static))
+    matrix_shape = tensor_shape.TensorShape((self._num_rows_static,
+                                             self._num_rows_static))
     if self._batch_shape_arg is None:
       return matrix_shape
 
@@ -285,8 +294,7 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
     return batch_shape.concatenate(matrix_shape)
 
   def _shape_tensor(self):
-    matrix_shape = array_ops.stack(
-        (self._num_rows, self._num_rows), axis=0)
+    matrix_shape = array_ops.stack((self._num_rows, self._num_rows), axis=0)
     if self._batch_shape_arg is None:
       return matrix_shape
 
@@ -338,8 +346,7 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
     # Note that adjoint has no effect since this matrix is self-adjoint.
     x = linear_operator_util.matrix_adjoint(x) if adjoint_arg else x
     if self._assert_proper_shapes:
-      aps = linear_operator_util.assert_compatible_matrix_dimensions(
-          self, x)
+      aps = linear_operator_util.assert_compatible_matrix_dimensions(self, x)
       x = control_flow_ops.with_dependencies([aps], x)
     return self._possibly_broadcast_batch_shape(x)
 
@@ -352,6 +359,20 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
   def _solve(self, rhs, adjoint=False, adjoint_arg=False):
     return self._matmul(rhs, adjoint_arg=adjoint_arg)
 
+  def _trace(self):
+    # Get Tensor of all ones of same shape as self.batch_shape.
+    if self.batch_shape.is_fully_defined():
+      batch_of_ones = array_ops.ones(shape=self.batch_shape, dtype=self.dtype)
+    else:
+      batch_of_ones = array_ops.ones(
+          shape=self.batch_shape_tensor(), dtype=self.dtype)
+
+    if self._min_matrix_dim() is not None:
+      return self._min_matrix_dim() * batch_of_ones
+    else:
+      return (math_ops.cast(self._min_matrix_dim_tensor(), self.dtype) *
+              batch_of_ones)
+
   def _diag_part(self):
     return self._ones_diag()
 
@@ -375,17 +396,15 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
     """Static check of init arg `num_rows`, possibly add asserts."""
     # Possibly add asserts.
     if self._assert_proper_shapes:
-      self._num_rows = control_flow_ops.with_dependencies(
-          [
-              check_ops.assert_rank(
-                  self._num_rows,
-                  0,
-                  message="Argument num_rows must be a 0-D Tensor."),
-              check_ops.assert_non_negative(
-                  self._num_rows,
-                  message="Argument num_rows must be non-negative."),
-          ],
-          self._num_rows)
+      self._num_rows = control_flow_ops.with_dependencies([
+          check_ops.assert_rank(
+              self._num_rows,
+              0,
+              message="Argument num_rows must be a 0-D Tensor."),
+          check_ops.assert_non_negative(
+              self._num_rows,
+              message="Argument num_rows must be non-negative."),
+      ], self._num_rows)
 
     # Static checks.
     if not self._num_rows.dtype.is_integer:
@@ -412,17 +431,15 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
 
     # Possibly add asserts
     if self._assert_proper_shapes:
-      self._batch_shape_arg = control_flow_ops.with_dependencies(
-          [
-              check_ops.assert_rank(
-                  self._batch_shape_arg,
-                  1,
-                  message="Argument batch_shape must be a 1-D Tensor."),
-              check_ops.assert_non_negative(
-                  self._batch_shape_arg,
-                  message="Argument batch_shape must be non-negative."),
-          ],
-          self._batch_shape_arg)
+      self._batch_shape_arg = control_flow_ops.with_dependencies([
+          check_ops.assert_rank(
+              self._batch_shape_arg,
+              1,
+              message="Argument batch_shape must be a 1-D Tensor."),
+          check_ops.assert_non_negative(
+              self._batch_shape_arg,
+              message="Argument batch_shape must be non-negative."),
+      ], self._batch_shape_arg)
 
     # Static checks
     if not self._batch_shape_arg.dtype.is_integer:
@@ -585,8 +602,7 @@ class LinearOperatorScaledIdentity(BaseLinearOperatorIdentity):
       # Shape [B1,...Bb, 1, 1]
       self._multiplier_matrix = array_ops.expand_dims(
           array_ops.expand_dims(self.multiplier, -1), -1)
-      self._multiplier_matrix_conj = math_ops.conj(
-          self._multiplier_matrix)
+      self._multiplier_matrix_conj = math_ops.conj(self._multiplier_matrix)
       self._abs_multiplier = math_ops.abs(self.multiplier)
 
       self._num_rows = linear_operator_util.shape_tensor(
@@ -594,27 +610,25 @@ class LinearOperatorScaledIdentity(BaseLinearOperatorIdentity):
       self._num_rows_static = tensor_util.constant_value(self._num_rows)
       self._check_num_rows_possibly_add_asserts()
       self._num_rows_cast_to_dtype = math_ops.cast(self._num_rows, self.dtype)
-      self._num_rows_cast_to_real_dtype = math_ops.cast(
-          self._num_rows, self.dtype.real_dtype)
+      self._num_rows_cast_to_real_dtype = math_ops.cast(self._num_rows,
+                                                        self.dtype.real_dtype)
 
   def _shape(self):
-    matrix_shape = tensor_shape.TensorShape(
-        (self._num_rows_static, self._num_rows_static))
+    matrix_shape = tensor_shape.TensorShape((self._num_rows_static,
+                                             self._num_rows_static))
 
     batch_shape = self.multiplier.get_shape()
     return batch_shape.concatenate(matrix_shape)
 
   def _shape_tensor(self):
-    matrix_shape = array_ops.stack(
-        (self._num_rows, self._num_rows), axis=0)
+    matrix_shape = array_ops.stack((self._num_rows, self._num_rows), axis=0)
 
     batch_shape = array_ops.shape(self.multiplier)
     return array_ops.concat((batch_shape, matrix_shape), 0)
 
   def _assert_non_singular(self):
     return check_ops.assert_positive(
-        math_ops.abs(self.multiplier),
-        message="LinearOperator was singular")
+        math_ops.abs(self.multiplier), message="LinearOperator was singular")
 
   def _assert_positive_definite(self):
     return check_ops.assert_positive(
@@ -635,13 +649,12 @@ class LinearOperatorScaledIdentity(BaseLinearOperatorIdentity):
     else:
       matrix = self._multiplier_matrix
     if self._assert_proper_shapes:
-      aps = linear_operator_util.assert_compatible_matrix_dimensions(
-          self, x)
+      aps = linear_operator_util.assert_compatible_matrix_dimensions(self, x)
       x = control_flow_ops.with_dependencies([aps], x)
     return x * matrix
 
   def _determinant(self):
-    return self.multiplier ** self._num_rows_cast_to_dtype
+    return self.multiplier**self._num_rows_cast_to_dtype
 
   def _log_abs_determinant(self):
     return self._num_rows_cast_to_real_dtype * math_ops.log(
@@ -654,11 +667,24 @@ class LinearOperatorScaledIdentity(BaseLinearOperatorIdentity):
     else:
       matrix = self._multiplier_matrix
     if self._assert_proper_shapes:
-      aps = linear_operator_util.assert_compatible_matrix_dimensions(
-          self, rhs)
+      aps = linear_operator_util.assert_compatible_matrix_dimensions(self, rhs)
       rhs = control_flow_ops.with_dependencies([aps], rhs)
     return rhs / matrix
 
+  def _trace(self):
+    # Get Tensor of all ones of same shape as self.batch_shape.
+    if self.batch_shape.is_fully_defined():
+      batch_of_ones = array_ops.ones(shape=self.batch_shape, dtype=self.dtype)
+    else:
+      batch_of_ones = array_ops.ones(
+          shape=self.batch_shape_tensor(), dtype=self.dtype)
+
+    if self._min_matrix_dim() is not None:
+      return self.multiplier * self._min_matrix_dim() * batch_of_ones
+    else:
+      return (self.multiplier * math_ops.cast(self._min_matrix_dim_tensor(),
+                                              self.dtype) * batch_of_ones)
+
   def _diag_part(self):
     return self._ones_diag() * self.multiplier[..., array_ops.newaxis]
 
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
index b2d7b10157..3d316450d8 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
@@ -263,6 +263,23 @@ class LinearOperatorDerivedClassTest(test.TestCase):
                                                    feed_dict=feed_dict)
                 self.assertAC(op_solve_v, mat_solve_v)
 
+  def test_trace(self):
+    self._skip_if_tests_to_skip_contains("trace")
+    for use_placeholder in False, True:
+      for shape in self._shapes_to_test:
+        for dtype in self._dtypes_to_test:
+          with self.test_session(graph=ops.Graph()) as sess:
+            sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
+            operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
+                shape, dtype, use_placeholder=use_placeholder)
+            op_trace = operator.trace()
+            mat_trace = math_ops.trace(mat)
+            if not use_placeholder:
+              self.assertAllEqual(op_trace.get_shape(), mat_trace.get_shape())
+            op_trace_v, mat_trace_v = sess.run([op_trace, mat_trace],
+                                               feed_dict=feed_dict)
+            self.assertAC(op_trace_v, mat_trace_v)
+
   def test_add_to_tensor(self):
     self._skip_if_tests_to_skip_contains("add_to_tensor")
     for use_placeholder in False, True:
-- 
GitLab


From ee0e62b0d998904d6c779c16f1cdfb80a34a31b0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 15:08:13 -0700
Subject: [PATCH 0212/1375] Mute an error message until we address duplicated
 node name in loop

PiperOrigin-RevId: 159485025
---
 tensorflow/tools/tfprof/internal/tfprof_node.cc | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.cc b/tensorflow/tools/tfprof/internal/tfprof_node.cc
index 9039926201..72f74902b5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_node.cc
@@ -70,9 +70,8 @@ void ExecStep::AddTimeStats(const string& dev, const NodeExecStats& step_stat) {
       accelerator_execs_[dev].push_back(pair);
       op_execs_[dev].push_back(pair);
     } else if (CountAsCPUTime(dev)) {
-      if (!cpu_execs_.empty()) {
-        fprintf(stderr, "Multiple CPU execution of 1 op, please file bug.\n");
-      }
+      // TODO(xpan): A while-loop can has multiple nodes sharing the
+      // same name. They shouldn't be counted in one node.
       cpu_execs_[dev].push_back(pair);
       op_execs_[dev].push_back(pair);
     }
-- 
GitLab


From 5a66d68fa1b794e37d45dfd9a7ba92e0c14f17bc Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 19 Jun 2017 15:20:53 -0700
Subject: [PATCH 0213/1375] Improved folding of merge nodes

PiperOrigin-RevId: 159486992
---
 .../grappler/optimizers/constant_folding.cc   | 89 +++++++++++++++++-
 .../optimizers/constant_folding_test.cc       | 91 +++++++++++++++++++
 2 files changed, 179 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index ac04be6d33..4fccd4e19b 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -283,15 +283,26 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
 
+  // We can only fold nodes if all their inputs are known statically, except in
+  // the case of a merge node that propagate the first inputs that becomes
+  // available, and therefore only requires a single constant input to be
+  // foldable.
+  bool has_constant_input = false;
+  const bool is_merge = IsMerge(node);
   for (const auto& input : node.input()) {
     if (IsControlInput(input)) {
       continue;
     }
     bool is_const = IsConstant(*node_map_->GetNode(input));
-    if (!is_const) {
+    if (!is_const && !is_merge) {
       return false;
     }
+    has_constant_input |= is_const;
   }
+  if (is_merge) {
+    return has_constant_input;
+  }
+
   return true;
 }
 
@@ -387,6 +398,82 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
 }
 
 Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
+  if (IsMerge(node)) {
+    // Merge nodes are special, in the sense that they execute as soon as one of
+    // their input is ready. We can therefore fold a merge node iff it has at
+    // least one constant input without control dependency.
+    // We still need to ensure that the nodes in the fanin of the merge node are
+    // scheduled. We'll therefore add a control dependency from the merge node
+    // to the folded constant. We end up with:
+    //  * the merge node and its inputs are preserved as is
+    //  * a new constant node C1, driven by the merge node through a control
+    //  dependency, initialized to the value of the folded input
+    //  * a new constant node C2, driven by the merge node through a control
+    //  dependency, initialized to the index of the folded input
+    //  * the fanout of the merge nodes is rewired to be driven by either C1 or
+    //  C2.
+    for (int input_index = 0; input_index < node.input_size(); ++input_index) {
+      const auto& input = node.input(input_index);
+      if (IsControlInput(input)) {
+        // Try the next input.
+        continue;
+      }
+      NodeDef* input_node = node_map_->GetNode(input);
+      if (!IsConstant(*input_node)) {
+        continue;
+      }
+      bool valid_input = true;
+      for (const string& fanin_of_input : input_node->input()) {
+        if (IsControlInput(fanin_of_input)) {
+          valid_input = false;
+          break;
+        }
+      }
+      if (!valid_input) {
+        // Try the next input
+        continue;
+      }
+      NodeDef* const_out = output->add_node();
+      *const_out = *input_node;
+      const_out->set_name(
+          AddPrefixToNodeName(node.name(), kConstantFoldingConst));
+      *const_out->add_input() = AsControlDependency(node);
+      node_map_->AddNode(const_out->name(), const_out);
+
+      NodeDef* const_index = output->add_node();
+      const_index->set_op("Const");
+      Tensor index(DT_INT32, TensorShape({}));
+      index.flat<int32>()(0) = input_index;
+      (*const_index->mutable_attr())["dtype"].set_type(DT_INT32);
+      index.AsProtoTensorContent(
+          (*const_index->mutable_attr())["value"].mutable_tensor());
+      const_index->set_name(AddPrefixToNodeName(
+          strings::StrCat(node.name(), "_index"), kConstantFoldingConst));
+      *const_index->add_input() = AsControlDependency(node);
+      node_map_->AddNode(const_index->name(), const_index);
+
+      auto outputs = node_map_->GetOutputs(node.name());
+      for (auto& output : outputs) {
+        for (int i = 0; i < output->input_size(); i++) {
+          int position;
+          string node_name = ParseNodeName(output->input(i), &position);
+          if (node_name == node.name()) {
+            if (position == 0) {
+              *output->mutable_input(i) = const_out->name();
+            } else if (position == 1) {
+              *output->mutable_input(i) = const_index->name();
+            } else {
+              // This is a control dependency (or an invalid edge since the
+              // merge node has only 2 inputs): preserve them.
+            }
+          }
+        }
+      }
+      return Status::OK();
+    }
+    return Status::OK();
+  }
+
   std::vector<NodeDef> const_nodes;
   TF_RETURN_IF_ERROR(EvaluateOneFoldable(node, &const_nodes));
 
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 566d3cd9a3..cb29ccc341 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -383,6 +383,97 @@ TEST_F(ConstantFoldingTest, SwitchNodes) {
   }
 }
 
+TEST_F(ConstantFoldingTest, MergeNodes) {
+  tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
+
+  Output x =
+      ops::RandomNormal(scope.WithOpName("x"), {3, 5}, DataType::DT_FLOAT);
+  Output y =
+      ops::RandomNormal(scope.WithOpName("y"), {3, 5}, DataType::DT_FLOAT);
+  Output const1 =
+      ops::Const(scope.WithOpName("const1").WithControlDependencies(x), 2.7f,
+                 TensorShape({3, 5}));
+  Output const2 =
+      ops::Const(scope.WithOpName("const2"), 3.14f, TensorShape({3, 5}));
+  Output const3 =
+      ops::Const(scope.WithOpName("const3").WithControlDependencies(x), 3.14f,
+                 TensorShape({3, 5}));
+
+  // Create 3 merge nodes: m1 is foldable, m2 and m3 aren't.
+  ops::Merge m1(scope.WithOpName("m1"), {x, const1, const2});
+  ops::Merge m2(scope.WithOpName("m2"), {const1, const3});
+  ops::Merge m3(scope.WithOpName("m3"), {x, y});
+
+  ops::Identity out1(scope.WithOpName("out1"), m1.output);
+  ops::Identity idx1(scope.WithOpName("idx1"), m1.value_index);
+  ops::Identity out2(scope.WithOpName("out2"), m2.output);
+  ops::Identity idx2(scope.WithOpName("idx2"), m2.value_index);
+  ops::Identity out3(scope.WithOpName("out3"), m3.output);
+  ops::Identity idx3(scope.WithOpName("idx3"), m3.value_index);
+
+  GrapplerItem item;
+  item.fetch.push_back("out1, idx1, out2, idx2, out3, idx3");
+  TF_CHECK_OK(scope.ToGraphDef(&item.graph));
+
+  ConstantFolding fold;
+  GraphDef output;
+  Status status = fold.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  int found_nodes = 0;
+  for (const auto& node : output.node()) {
+    if (node.name() == "out1") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("ConstantFolding/m1", node.input(0));
+      ++found_nodes;
+    } else if (node.name() == "idx1") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("ConstantFolding/m1_index", node.input(0));
+      ++found_nodes;
+    } else if (node.name() == "ConstantFolding/m1") {
+      EXPECT_EQ("Const", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("^m1", node.input(0));
+      ++found_nodes;
+    } else if (node.name() == "ConstantFolding/m1_index") {
+      EXPECT_EQ("Const", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("^m1", node.input(0));
+      ++found_nodes;
+    } else if (node.name() == "out2") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("m2", node.input(0));
+      ++found_nodes;
+    } else if (node.name() == "idx2") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("m2:1", node.input(0));
+      ++found_nodes;
+    } else if (node.name() == "out3") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("m3", node.input(0));
+      ++found_nodes;
+    } else if (node.name() == "idx3") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("m3:1", node.input(0));
+      ++found_nodes;
+    }
+  }
+  // Make sure the graph contains all the nodes we're expecting.
+  EXPECT_EQ(8, found_nodes);
+
+  std::vector<string> fetch = {"out1", "idx1"};
+  auto tensors = EvaluateNodes(output, fetch);
+  EXPECT_EQ(2, tensors.size());
+  const Tensor& out_value = tensors[0];
+  EXPECT_EQ(3 * 5, out_value.NumElements());
+  for (int i = 0; i < 3 * 5; ++i) {
+    EXPECT_EQ(3.14f, out_value.flat<float>()(i));
+  }
+  const Tensor& out_idx = tensors[1];
+  EXPECT_EQ(1, out_idx.NumElements());
+  EXPECT_EQ(2, out_idx.flat<int32>()(0));
+}
+
 TEST_F(ConstantFoldingTest, NoOpReduction) {
   // Build a simple graph with a reduction that can be reduced to the identity.
   tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
-- 
GitLab


From 76ee2cf584269c782961a7d835e9febd15522188 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 16:23:20 -0700
Subject: [PATCH 0214/1375] Remove operand parameters from
 HandleElementwiseUnary and HandleElementwiseBinary functions.

This allows incremental cleanup of the individual unary and binary operators.

PiperOrigin-RevId: 159495454
---
 .../compiler/xla/service/dfs_hlo_visitor.cc   |  7 +--
 .../compiler/xla/service/dfs_hlo_visitor.h    | 60 +++++++++----------
 .../service/dfs_hlo_visitor_with_default.h    |  9 ++-
 .../compiler/xla/service/hlo_cost_analysis.cc |  7 +--
 .../compiler/xla/service/hlo_cost_analysis.h  |  8 +--
 5 files changed, 39 insertions(+), 52 deletions(-)

diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
index b9a496be43..5121d36866 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
@@ -24,16 +24,13 @@ limitations under the License.
 namespace xla {
 
 Status DfsHloVisitor::HandleElementwiseUnary(HloInstruction* hlo,
-                                             HloOpcode opcode,
-                                             HloInstruction* operand) {
+                                             HloOpcode opcode) {
   return Unimplemented("DfsHloVisitor::HandleElementwiseUnary: %s",
                        HloOpcodeString(opcode).c_str());
 }
 
 Status DfsHloVisitor::HandleElementwiseBinary(HloInstruction* hlo,
-                                              HloOpcode opcode,
-                                              HloInstruction* lhs,
-                                              HloInstruction* rhs) {
+                                              HloOpcode opcode) {
   return Unimplemented("DfsHloVisitor::HandleElementwiseBinary: %s",
                        HloOpcodeString(opcode).c_str());
 }
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 1f58562ac2..ea7c22737f 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -65,11 +65,8 @@ class DfsHloVisitor {
   // These routines are self-descriptive, see class comment for usage
   // information.
 
-  virtual Status HandleElementwiseUnary(HloInstruction* hlo, HloOpcode opcode,
-                                        HloInstruction* operand);
-  virtual Status HandleElementwiseBinary(HloInstruction* hlo, HloOpcode opcode,
-                                         HloInstruction* lhs,
-                                         HloInstruction* rhs);
+  virtual Status HandleElementwiseUnary(HloInstruction* hlo, HloOpcode opcode);
+  virtual Status HandleElementwiseBinary(HloInstruction* hlo, HloOpcode opcode);
   virtual Status HandleClamp(HloInstruction* clamp, HloInstruction* min,
                              HloInstruction* arg, HloInstruction* max) = 0;
   virtual Status HandleSelect(HloInstruction* select, HloInstruction* pred,
@@ -77,31 +74,31 @@ class DfsHloVisitor {
                               HloInstruction* on_false) = 0;
   virtual Status HandleMaximum(HloInstruction* maximum, HloInstruction* lhs,
                                HloInstruction* rhs) {
-    return HandleElementwiseBinary(maximum, HloOpcode::kMaximum, lhs, rhs);
+    return HandleElementwiseBinary(maximum, HloOpcode::kMaximum);
   }
   virtual Status HandleMinimum(HloInstruction* minimum, HloInstruction* lhs,
                                HloInstruction* rhs) {
-    return HandleElementwiseBinary(minimum, HloOpcode::kMinimum, lhs, rhs);
+    return HandleElementwiseBinary(minimum, HloOpcode::kMinimum);
   }
   virtual Status HandleConcatenate(
       HloInstruction* concatenate,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) = 0;
   virtual Status HandleConvert(HloInstruction* convert,
                                HloInstruction* operand) {
-    return HandleElementwiseUnary(convert, HloOpcode::kConvert, operand);
+    return HandleElementwiseUnary(convert, HloOpcode::kConvert);
   }
   virtual Status HandleCopy(HloInstruction* copy, HloInstruction* operand) {
-    return HandleElementwiseUnary(copy, HloOpcode::kCopy, operand);
+    return HandleElementwiseUnary(copy, HloOpcode::kCopy);
   }
   virtual Status HandleMultiply(HloInstruction* multiply, HloInstruction* lhs,
                                 HloInstruction* rhs) {
-    return HandleElementwiseBinary(multiply, HloOpcode::kMultiply, lhs, rhs);
+    return HandleElementwiseBinary(multiply, HloOpcode::kMultiply);
   }
   virtual Status HandleDot(HloInstruction* dot, HloInstruction* lhs,
                            HloInstruction* rhs) = 0;
   virtual Status HandlePower(HloInstruction* power, HloInstruction* lhs,
                              HloInstruction* rhs) {
-    return HandleElementwiseBinary(power, HloOpcode::kPower, lhs, rhs);
+    return HandleElementwiseBinary(power, HloOpcode::kPower);
   }
   virtual Status HandleConvolution(HloInstruction* convolution,
                                    HloInstruction* lhs, HloInstruction* rhs,
@@ -109,72 +106,71 @@ class DfsHloVisitor {
   virtual Status HandleCrossReplicaSum(HloInstruction* crs) = 0;
   virtual Status HandleCompare(HloInstruction* compare, HloOpcode opcode,
                                HloInstruction* lhs, HloInstruction* rhs) {
-    return HandleElementwiseBinary(compare, opcode, lhs, rhs);
+    return HandleElementwiseBinary(compare, opcode);
   }
   virtual Status HandleAdd(HloInstruction* add, HloInstruction* lhs,
                            HloInstruction* rhs) {
-    return HandleElementwiseBinary(add, HloOpcode::kAdd, lhs, rhs);
+    return HandleElementwiseBinary(add, HloOpcode::kAdd);
   }
   virtual Status HandleDivide(HloInstruction* divide, HloInstruction* lhs,
                               HloInstruction* rhs) {
-    return HandleElementwiseBinary(divide, HloOpcode::kDivide, lhs, rhs);
+    return HandleElementwiseBinary(divide, HloOpcode::kDivide);
   }
   virtual Status HandleRemainder(HloInstruction* remainder, HloInstruction* lhs,
                                  HloInstruction* rhs) {
-    return HandleElementwiseBinary(remainder, HloOpcode::kRemainder, lhs, rhs);
+    return HandleElementwiseBinary(remainder, HloOpcode::kRemainder);
   }
   virtual Status HandleSubtract(HloInstruction* subtract, HloInstruction* lhs,
                                 HloInstruction* rhs) {
-    return HandleElementwiseBinary(subtract, HloOpcode::kSubtract, lhs, rhs);
+    return HandleElementwiseBinary(subtract, HloOpcode::kSubtract);
   }
   virtual Status HandleAbs(HloInstruction* abs, HloInstruction* operand) {
-    return HandleElementwiseUnary(abs, HloOpcode::kAbs, operand);
+    return HandleElementwiseUnary(abs, HloOpcode::kAbs);
   }
   virtual Status HandleSign(HloInstruction* sign, HloInstruction* operand) {
-    return HandleElementwiseUnary(sign, HloOpcode::kSign, operand);
+    return HandleElementwiseUnary(sign, HloOpcode::kSign);
   }
   virtual Status HandleNegate(HloInstruction* negate, HloInstruction* operand) {
-    return HandleElementwiseUnary(negate, HloOpcode::kNegate, operand);
+    return HandleElementwiseUnary(negate, HloOpcode::kNegate);
   }
   virtual Status HandleExp(HloInstruction* exp, HloInstruction* operand) {
-    return HandleElementwiseUnary(exp, HloOpcode::kExp, operand);
+    return HandleElementwiseUnary(exp, HloOpcode::kExp);
   }
   virtual Status HandleFloor(HloInstruction* floor, HloInstruction* operand) {
-    return HandleElementwiseUnary(floor, HloOpcode::kFloor, operand);
+    return HandleElementwiseUnary(floor, HloOpcode::kFloor);
   }
   virtual Status HandleCeil(HloInstruction* ceil, HloInstruction* operand) {
-    return HandleElementwiseUnary(ceil, HloOpcode::kCeil, operand);
+    return HandleElementwiseUnary(ceil, HloOpcode::kCeil);
   }
   virtual Status HandleLog(HloInstruction* log, HloInstruction* operand) {
-    return HandleElementwiseUnary(log, HloOpcode::kLog, operand);
+    return HandleElementwiseUnary(log, HloOpcode::kLog);
   }
   virtual Status HandleCos(HloInstruction* cos, HloInstruction* operand) {
-    return HandleElementwiseUnary(cos, HloOpcode::kCos, operand);
+    return HandleElementwiseUnary(cos, HloOpcode::kCos);
   }
   virtual Status HandleTanh(HloInstruction* tanh, HloInstruction* operand) {
-    return HandleElementwiseUnary(tanh, HloOpcode::kTanh, operand);
+    return HandleElementwiseUnary(tanh, HloOpcode::kTanh);
   }
   virtual Status HandleIsFinite(HloInstruction* is_finite,
                                 HloInstruction* operand) {
-    return HandleElementwiseUnary(is_finite, HloOpcode::kIsFinite, operand);
+    return HandleElementwiseUnary(is_finite, HloOpcode::kIsFinite);
   }
   virtual Status HandleLogicalAnd(HloInstruction* logical_and,
                                   HloInstruction* lhs, HloInstruction* rhs) {
-    return HandleElementwiseBinary(logical_and, HloOpcode::kLogicalAnd, lhs,
-                                   rhs);
+    return HandleElementwiseBinary(logical_and, HloOpcode::kLogicalAnd);
   }
   virtual Status HandleLogicalNot(HloInstruction* logical_not,
                                   HloInstruction* operand) {
-    return HandleElementwiseUnary(logical_not, HloOpcode::kLogicalNot, operand);
+    return HandleElementwiseUnary(logical_not, HloOpcode::kLogicalNot);
   }
   virtual Status HandleLogicalOr(HloInstruction* logical_or,
                                  HloInstruction* lhs, HloInstruction* rhs) {
-    return HandleElementwiseBinary(logical_or, HloOpcode::kLogicalOr, lhs, rhs);
+    return HandleElementwiseBinary(logical_or, HloOpcode::kLogicalOr);
   }
   virtual Status HandleReducePrecision(HloInstruction* reduce_precision,
                                        HloInstruction* operand) {
-    return HandleElementwiseUnary(reduce_precision, HloOpcode::kReducePrecision,
-                                  operand);
+    return HandleElementwiseUnary(reduce_precision,
+                                  HloOpcode::kReducePrecision);
   }
 
   virtual Status HandleInfeed(HloInstruction* infeed) = 0;
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
index 1bcc03bae1..75910b8cbb 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
@@ -41,13 +41,12 @@ class DfsHloVisitorWithDefault : public DfsHloVisitor {
   // Default action performed on HloInstruction.
   virtual Status DefaultAction(HloInstruction* hlo_instruction) = 0;
 
-  Status HandleElementwiseUnary(HloInstruction* hlo, HloOpcode opcode,
-                                HloInstruction* operand) override {
+  Status HandleElementwiseUnary(HloInstruction* hlo,
+                                HloOpcode opcode) override {
     return DefaultAction(hlo);
   }
-  Status HandleElementwiseBinary(HloInstruction* hlo, HloOpcode opcode,
-                                 HloInstruction* lhs,
-                                 HloInstruction* rhs) override {
+  Status HandleElementwiseBinary(HloInstruction* hlo,
+                                 HloOpcode opcode) override {
     return DefaultAction(hlo);
   }
 
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index cbabf00913..46ca316fe6 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -75,15 +75,12 @@ Status HloCostAnalysis::HandleElementwiseOp(HloInstruction* hlo_instruction) {
 }
 
 Status HloCostAnalysis::HandleElementwiseUnary(HloInstruction* hlo,
-                                               HloOpcode opcode,
-                                               HloInstruction* operand) {
+                                               HloOpcode opcode) {
   return HandleElementwiseOp(hlo);
 }
 
 Status HloCostAnalysis::HandleElementwiseBinary(HloInstruction* hlo,
-                                                HloOpcode opcode,
-                                                HloInstruction* lhs,
-                                                HloInstruction* rhs) {
+                                                HloOpcode opcode) {
   return HandleElementwiseOp(hlo);
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index f14baf6da2..6538266864 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -42,11 +42,9 @@ class HloCostAnalysis : public DfsHloVisitor {
   explicit HloCostAnalysis(const ShapeSizeFunction& shape_size)
       : shape_size_(shape_size) {}
 
-  Status HandleElementwiseUnary(HloInstruction* hlo, HloOpcode opcode,
-                                HloInstruction* operand) override;
-  Status HandleElementwiseBinary(HloInstruction* hlo, HloOpcode opcode,
-                                 HloInstruction* lhs,
-                                 HloInstruction* rhs) override;
+  Status HandleElementwiseUnary(HloInstruction* hlo, HloOpcode opcode) override;
+  Status HandleElementwiseBinary(HloInstruction* hlo,
+                                 HloOpcode opcode) override;
   Status HandleConstant(HloInstruction* constant,
                         const Literal& literal) override;
   Status HandleGetTupleElement(HloInstruction* get_tuple_element,
-- 
GitLab


From ab9b4ba1b1713c2d5932b7b8166bd00642e9451a Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Fri, 19 May 2017 16:59:25 -0700
Subject: [PATCH 0215/1375] Add support for sparse_reduce_max and
 sparse_reduce_max_sparse

This commit tries to address the issue raised in 10002 to have
the support for sparse_reduce_max and sparse_reduce_max_sparse.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 .../core/kernels/sparse_reduce_sum_op.cc      | 76 +++++++++++-----
 tensorflow/core/ops/sparse_ops.cc             | 69 +++++++++++++++
 .../python/kernel_tests/sparse_ops_test.py    | 46 +++++++---
 tensorflow/python/ops/sparse_ops.py           | 86 +++++++++++++++++++
 4 files changed, 245 insertions(+), 32 deletions(-)

diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op.cc b/tensorflow/core/kernels/sparse_reduce_sum_op.cc
index 074aab9f9e..aec0ae6757 100644
--- a/tensorflow/core/kernels/sparse_reduce_sum_op.cc
+++ b/tensorflow/core/kernels/sparse_reduce_sum_op.cc
@@ -130,10 +130,30 @@ Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
   return Status::OK();
 }
 
-template <typename T>
-class SparseReduceSumOp : public OpKernel {
+struct SumOp {
+  template <typename T>
+  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
+      s.device(ctx->eigen_cpu_device()) = v.sum();
+  }
+  static const char * Name() {
+      return "sum";
+  }
+};
+
+struct MaxOp {
+  template <typename T>
+  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
+      s.device(ctx->eigen_cpu_device()) = v.maximum();
+  }
+  static const char * Name() {
+      return "max";
+  }
+};
+
+template <typename T, typename Op>
+class SparseReduceOp : public OpKernel {
  public:
-  explicit SparseReduceSumOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+  explicit SparseReduceOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
   }
 
@@ -163,10 +183,10 @@ class SparseReduceSumOp : public OpKernel {
     auto out_flat = out_values->flat<T>();
     out_flat.setZero();
 
-    Tensor tmp_group_sum;
+    Tensor tmp_reduced_val;
     OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_group_sum));
-    auto group_sum = tmp_group_sum.scalar<T>();
+                                           TensorShape({}), &tmp_reduced_val));
+    auto reduced_val = tmp_reduced_val.scalar<T>();
 
     // Compute strides, and use it to convert coords to flat index.  The
     // coordinates returned by .group() have the same ndims as group_by_dims.
@@ -196,11 +216,12 @@ class SparseReduceSumOp : public OpKernel {
     // g.group() provides the coordinates of a particular reduced value.
     sp.Reorder<T>(reduction.reorder_dims);
     for (const auto &g : sp.group(reduction.group_by_dims)) {
-      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
+      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
       const int64 idx = CoordinatesToFlatIndex(g.group(), output_strides);
-      out_flat(idx) = group_sum();
+      out_flat(idx) = reduced_val();
       VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; idx: " << idx << "; group sum: " << group_sum();
+              << "; idx: " << idx << "; group " << Op::Name() << ": "
+              << reduced_val();
     }
   }
 
@@ -212,14 +233,21 @@ class SparseReduceSumOp : public OpKernel {
 #define REGISTER_KERNELS(T)                                              \
   REGISTER_KERNEL_BUILDER(                                               \
       Name("SparseReduceSum").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSumOp<T>)
+      SparseReduceOp<T, SumOp>)
 TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
 
-template <typename T>
-class SparseReduceSumSparseOp : public OpKernel {
+#define REGISTER_KERNELS(T)                                              \
+  REGISTER_KERNEL_BUILDER(                                               \
+      Name("SparseReduceMax").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceOp<T, MaxOp>)
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+template <typename T, typename Op>
+class SparseReduceSparseOp : public OpKernel {
  public:
-  explicit SparseReduceSumSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+  explicit SparseReduceSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
   }
 
@@ -260,13 +288,13 @@ class SparseReduceSumSparseOp : public OpKernel {
                    ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
     auto out_flat = out_values_t->flat<T>();
 
-    Tensor tmp_group_sum;
+    Tensor tmp_reduced_val;
     OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_group_sum));
-    auto group_sum = tmp_group_sum.scalar<T>();
+                                           TensorShape({}), &tmp_reduced_val));
+    auto reduced_val = tmp_reduced_val.scalar<T>();
     int64 i = 0;
     for (const auto &g : sp.group(reduction.group_by_dims)) {
-      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
+      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
       std::vector<int64> group = g.group();
       for (int64 j = 0; j < group.size(); j++) {
         if (keep_dims_) {
@@ -275,10 +303,11 @@ class SparseReduceSumSparseOp : public OpKernel {
           out_indices_mat(i, j) = group[j];
         }
       }
-      out_flat(i) = group_sum();
+      out_flat(i) = reduced_val();
       i++;
       VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; group sum: " << group_sum();
+              << "; group " << Op::Name() << ": "
+              << reduced_val();
     }
 
     Tensor *out_shape_t;
@@ -298,8 +327,15 @@ class SparseReduceSumSparseOp : public OpKernel {
 #define REGISTER_KERNELS(T)                                                    \
   REGISTER_KERNEL_BUILDER(                                                     \
       Name("SparseReduceSumSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSumSparseOp<T>)
+      SparseReduceSparseOp<T, SumOp>)
 TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
 
+#define REGISTER_KERNELS(T)                                                    \
+  REGISTER_KERNEL_BUILDER(                                                     \
+      Name("SparseReduceMaxSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceSparseOp<T, MaxOp>)
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 9722f0ee9a..6aca2c3b01 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -710,6 +710,75 @@ a_shape: 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
 b: `ndims`-D Tensor.  With shape `a_shape`.
 )doc");
 
+REGISTER_OP("SparseReduceMax")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output: T")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+instead of a sparse one.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+output: `R-K`-D.  The reduced Tensor.
+)doc");
+
+REGISTER_OP("SparseReduceMaxSparse")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output_indices: int64")
+    .Output("output_values: T")
+    .Output("output_shape: int64")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
+SparseTensor.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+)doc");
+
 REGISTER_OP("SparseReduceSum")
     .Input("input_indices: int64")
     .Input("input_values: T")
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index aff9753eb8..14eb2cba68 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -545,21 +545,24 @@ class SparseFillEmptyRowsTest(test_util.TensorFlowTestCase):
       self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool))
 
 
-class SparseReduceSumTest(test_util.TensorFlowTestCase):
+class SparseReduceTest(test_util.TensorFlowTestCase):
 
-  # [[1, ?, 1]
-  #  [?, 1, ?]]
+  # [[1, ?, 2]
+  #  [?, 3, ?]]
   # where ? is implictly-zero.
   ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64)
   vals = np.array([1, 1, 1]).astype(np.int32)
   dense_shape = np.array([2, 3]).astype(np.int64)
 
-  def _compare(self, sp_t, reduction_axes, ndims, keep_dims):
+  def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
     densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval()
 
     np_ans = densified
     if reduction_axes is None:
-      np_ans = np.sum(np_ans, keepdims=keep_dims)
+      if do_sum:
+        np_ans = np.sum(np_ans, keepdims=keep_dims)
+      else:
+        np_ans = np.max(np_ans, keepdims=keep_dims)
     else:
       if not isinstance(reduction_axes, list):  # Single scalar.
         reduction_axes = [reduction_axes]
@@ -569,15 +572,28 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
       # Loop below depends on sorted.
       reduction_axes.sort()
       for ra in reduction_axes.ravel()[::-1]:
-        np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
+        if do_sum:
+          np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
+        else:
+          np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
 
     with self.test_session():
-      tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
-                                                  keep_dims)
+      if do_sum:
+        tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
+                                                    keep_dims)
+      else:
+        tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
+                                                    keep_dims)
       out_dense = tf_dense_ans.eval()
 
-      tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t, reduction_axes,
-                                                          keep_dims)
+      if do_sum:
+        tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t,
+                                                            reduction_axes,
+                                                            keep_dims)
+      else:
+        tf_sparse_ans = sparse_ops.sparse_reduce_max_sparse(sp_t,
+                                                            reduction_axes,
+                                                            keep_dims)
       # Convert to dense for comparison purposes.
       out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans).eval()
 
@@ -585,8 +601,10 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
     self.assertAllClose(np_ans, out_sparse)
 
   def _compare_all(self, sp_t, reduction_axes, ndims):
-    self._compare(sp_t, reduction_axes, ndims, False)
-    self._compare(sp_t, reduction_axes, ndims, True)
+    self._compare(sp_t, reduction_axes, ndims, False, False)
+    self._compare(sp_t, reduction_axes, ndims, False, True)
+    self._compare(sp_t, reduction_axes, ndims, True, False)
+    self._compare(sp_t, reduction_axes, ndims, True, True)
 
   @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
@@ -623,6 +641,10 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
         sparse_ops.sparse_reduce_sum(sp_t, -3).eval()
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_sum(sp_t, 2).eval()
+      with self.assertRaisesOpError("Invalid reduction dimension -3"):
+        sparse_ops.sparse_reduce_max(sp_t, -3).eval()
+      with self.assertRaisesOpError("Invalid reduction dimension 2"):
+        sparse_ops.sparse_reduce_max(sp_t, 2).eval()
 
   @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index b52610661f..42f02e83a6 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -30,6 +30,8 @@
 @@sparse_reset_shape
 @@sparse_fill_empty_rows
 @@sparse_transpose
+@@sparse_reduce_max
+@@sparse_reduce_max_sparse
 @@sparse_reduce_sum
 @@sparse_reduce_sum_sparse
 @@sparse_add
@@ -710,6 +712,90 @@ def sparse_to_dense(sparse_indices,
       name=name)
 
 
+def sparse_reduce_max(sp_input, axis=None, keep_dims=False,
+                      reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+  instead of a sparse one.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  similar to the indexing rules in Python.
+
+  For example:
+
+  ```python
+  # 'x' represents [[1, ?, 2]
+  #                 [?, 3, ?]]
+  # where ? is implicitly-zero.
+  tf.sparse_reduce_max(x) ==> 3
+  tf.sparse_reduce_max(x, 0) ==> [1, 3, 2]
+  tf.sparse_reduce_max(x, 1) ==> [2, 3]  # Can also use -1 as the axis.
+  tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]]
+  tf.sparse_reduce_max(x, [0, 1]) ==> 3
+  ```
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis.
+
+  Returns:
+    The reduced Tensor.
+  """
+  return gen_sparse_ops.sparse_reduce_max(
+      sp_input.indices, sp_input.values,
+      sp_input.dense_shape,
+      math_ops._ReductionDims(sp_input, axis, reduction_axes),
+      keep_dims)
+
+
+def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False,
+                             reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In contrast to SparseReduceSum, this Op returns a
+  SparseTensor.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  which are interpreted according to the indexing rules in Python.
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis
+
+  Returns:
+    The reduced SparseTensor.
+  """
+  output_ind, output_val, output_shape = (
+      gen_sparse_ops.sparse_reduce_max_sparse(
+          sp_input.indices, sp_input.values,
+          sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis,
+                                                        reduction_axes),
+          keep_dims))
+
+  return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
+
+
 def sparse_reduce_sum(sp_input, axis=None, keep_dims=False,
                       reduction_axes=None):
   """Computes the sum of elements across dimensions of a SparseTensor.
-- 
GitLab


From 4ee2ee18b1fefda382bd930166e1597d12c93a53 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Wed, 24 May 2017 08:04:08 -0700
Subject: [PATCH 0216/1375] Move sparse_reduce_sum_op.cc to sparse_reduce_op.cc

This commit moves sparse_reduce_sum_op.cc to sparse_reduce_op.cc
so that both `sparse_reduce_sum` and `sparse_reduce_max` share
the same code base.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/core/kernels/BUILD                             | 8 ++++----
 .../{sparse_reduce_sum_op.cc => sparse_reduce_op.cc}      | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)
 rename tensorflow/core/kernels/{sparse_reduce_sum_op.cc => sparse_reduce_op.cc} (99%)

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index b8370a96a8..8384eaf00a 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -2468,7 +2468,7 @@ tf_cc_tests(
         ":ops_util",
         ":sparse_add_op",
         ":sparse_dense_binary_op_shared",
-        ":sparse_reduce_sum_op",
+        ":sparse_reduce_op",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -3211,7 +3211,7 @@ cc_library(
         ":sparse_cross_op",
         ":sparse_dense_binary_op_shared",
         ":sparse_fill_empty_rows_op",
-        ":sparse_reduce_sum_op",
+        ":sparse_reduce_op",
         ":sparse_reorder_op",
         ":sparse_reshape_op",
         ":sparse_softmax",
@@ -3267,8 +3267,8 @@ tf_kernel_library(
 )
 
 tf_kernel_library(
-    name = "sparse_reduce_sum_op",
-    prefix = "sparse_reduce_sum_op",
+    name = "sparse_reduce_op",
+    prefix = "sparse_reduce_op",
     deps = SPARSE_DEPS,
 )
 
diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op.cc b/tensorflow/core/kernels/sparse_reduce_op.cc
similarity index 99%
rename from tensorflow/core/kernels/sparse_reduce_sum_op.cc
rename to tensorflow/core/kernels/sparse_reduce_op.cc
index aec0ae6757..9e60791f97 100644
--- a/tensorflow/core/kernels/sparse_reduce_sum_op.cc
+++ b/tensorflow/core/kernels/sparse_reduce_op.cc
@@ -135,7 +135,7 @@ struct SumOp {
   static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
       s.device(ctx->eigen_cpu_device()) = v.sum();
   }
-  static const char * Name() {
+  static StringPiece Name() {
       return "sum";
   }
 };
@@ -145,7 +145,7 @@ struct MaxOp {
   static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
       s.device(ctx->eigen_cpu_device()) = v.maximum();
   }
-  static const char * Name() {
+  static StringPiece Name() {
       return "max";
   }
 };
-- 
GitLab


From ac47dc166f290d631c156846039ac78f30f362af Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Mon, 19 Jun 2017 16:32:34 -0700
Subject: [PATCH 0217/1375] [XLA] Properly propagate backend optimization
 options to XLA in more places.

With the flag changes, backend optimization options become critical because
without optimization the CPU code runs very slow and GPU code won't compile
PTX->AS properly in many cases.

Therefore in this CL we add a more helpful fatal CHECK in the GPU backend,
along with a convenient way to get default execution options from flags, which
clients of XLA should use.

PiperOrigin-RevId: 159496597
---
 tensorflow/compiler/xla/BUILD                 | 15 ++++++++++
 tensorflow/compiler/xla/client/lib/BUILD      |  1 +
 tensorflow/compiler/xla/client/lib/testing.cc |  3 +-
 .../compiler/xla/execution_options_util.cc    | 27 +++++++++++++++++
 .../compiler/xla/execution_options_util.h     | 29 +++++++++++++++++++
 .../gpu/llvm_gpu_backend/gpu_backend_lib.cc   |  3 ++
 tensorflow/compiler/xla/tests/BUILD           |  2 +-
 .../xla/tests/client_library_test_base.cc     | 22 ++++++++------
 .../xla/tests/client_library_test_base.h      |  8 ++++-
 .../compiler/xla/tests/deallocation_test.cc   |  3 +-
 tensorflow/compiler/xla/tests/prng_test.cc    |  6 ++--
 11 files changed, 104 insertions(+), 15 deletions(-)
 create mode 100644 tensorflow/compiler/xla/execution_options_util.cc
 create mode 100644 tensorflow/compiler/xla/execution_options_util.h

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index cde790c0ae..c508071f8c 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -46,6 +46,21 @@ xla_proto_library(
     ],
 )
 
+cc_library(
+    name = "execution_options_util",
+    srcs = [
+        "execution_options_util.cc",
+    ],
+    hdrs = [
+        "execution_options_util.h",
+    ],
+    visibility = [":friends"],
+    deps = [
+        ":xla_proto",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
+    ],
+)
+
 cc_library(
     name = "test",
     testonly = 1,
diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD
index 86b16be62f..edd971e114 100644
--- a/tensorflow/compiler/xla/client/lib/BUILD
+++ b/tensorflow/compiler/xla/client/lib/BUILD
@@ -32,6 +32,7 @@ cc_library(
     srcs = ["testing.cc"],
     hdrs = ["testing.h"],
     deps = [
+        "//tensorflow/compiler/xla:execution_options_util",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc
index ffdc7dd943..d8bfc94580 100644
--- a/tensorflow/compiler/xla/client/lib/testing.cc
+++ b/tensorflow/compiler/xla/client/lib/testing.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/execution_options_util.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -38,7 +39,7 @@ std::unique_ptr<GlobalData> MakeFakeDataOrDie(const Shape& shape,
               AsInt64Slice(shape.dimensions()));
   Computation computation = b.Build().ConsumeValueOrDie();
 
-  ExecutionOptions execution_options;
+  auto execution_options = CreateDefaultExecutionOptions();
   *execution_options.mutable_shape_with_output_layout() = shape;
   return client->Execute(computation, /*arguments=*/{}, &execution_options)
       .ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/execution_options_util.cc b/tensorflow/compiler/xla/execution_options_util.cc
new file mode 100644
index 0000000000..e83ff7cddd
--- /dev/null
+++ b/tensorflow/compiler/xla/execution_options_util.cc
@@ -0,0 +1,27 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/compiler/xla/execution_options_util.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
+
+namespace xla {
+
+ExecutionOptions CreateDefaultExecutionOptions() {
+  ExecutionOptions execution_options;
+  *(execution_options.mutable_debug_options()) =
+      legacy_flags::GetDebugOptionsFromFlags();
+  return execution_options;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/execution_options_util.h b/tensorflow/compiler/xla/execution_options_util.h
new file mode 100644
index 0000000000..562da78e83
--- /dev/null
+++ b/tensorflow/compiler/xla/execution_options_util.h
@@ -0,0 +1,29 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_COMPILER_XLA_EXECUTION_OPTIONS_UTIL_H_
+#define THIRD_PARTY_TENSORFLOW_COMPILER_XLA_EXECUTION_OPTIONS_UTIL_H_
+
+#include "tensorflow/compiler/xla/xla.pb.h"
+
+namespace xla {
+
+// Create a default ExecutionOptions proto; this proto has its debug options
+// popupated to the default values taken from flags.
+ExecutionOptions CreateDefaultExecutionOptions();
+
+}  // namespace xla
+
+#endif  // THIRD_PARTY_TENSORFLOW_COMPILER_XLA_EXECUTION_OPTIONS_UTIL_H_
diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
index 16fe4455db..881522a029 100644
--- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
+++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
@@ -380,6 +380,9 @@ StatusOr<string> CompileModuleToPtx(llvm::Module* module,
   int32 opt_level =
       hlo_module_config.debug_options().xla_backend_optimization_level();
 
+  CHECK_GE(opt_level, 2)
+      << "The XLA GPU backend doesn't support unoptimized code generation";
+
   AddOptimizationPasses(opt_level,
                         /*size_level=*/0, target_machine.get(), &module_passes,
                         &function_passes);
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index bf7c106832..a11ac0bec6 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -144,6 +144,7 @@ cc_library(
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:array4d",
+        "//tensorflow/compiler/xla:execution_options_util",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
@@ -156,7 +157,6 @@ cc_library(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index 6094a31231..4e2e0c7776 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -20,7 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
+#include "tensorflow/compiler/xla/execution_options_util.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -45,10 +45,8 @@ Client* GetOrCreateLocalClientOrDie(se::Platform* platform) {
 }  // namespace
 
 ClientLibraryTestBase::ClientLibraryTestBase(se::Platform* platform)
-    : client_(GetOrCreateLocalClientOrDie(platform)) {
-  *(execution_options_.mutable_debug_options()) =
-      legacy_flags::GetDebugOptionsFromFlags();
-
+    : client_(GetOrCreateLocalClientOrDie(platform)),
+      execution_options_(CreateDefaultExecutionOptions()) {
   // Disabling constant_folding so that tests (usually written using Constants)
   // will exercise the intended code paths, instead of being constant folded.
   //
@@ -72,12 +70,9 @@ StatusOr<std::unique_ptr<GlobalData>> ClientLibraryTestBase::Execute(
 }
 
 StatusOr<std::unique_ptr<Literal>> ClientLibraryTestBase::ExecuteAndTransfer(
-    ComputationBuilder* builder,
+    const Computation& computation,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments,
     const Shape* shape_with_output_layout) {
-  // Build the computation, as a convenience.
-  TF_ASSIGN_OR_RETURN(auto computation, builder->Build());
-
   ExecutionOptions execution_options = execution_options_;
   if (shape_with_output_layout != nullptr) {
     *execution_options.mutable_shape_with_output_layout() =
@@ -87,6 +82,15 @@ StatusOr<std::unique_ptr<Literal>> ClientLibraryTestBase::ExecuteAndTransfer(
                                      &execution_options);
 }
 
+StatusOr<std::unique_ptr<Literal>> ClientLibraryTestBase::ExecuteAndTransfer(
+    ComputationBuilder* builder,
+    tensorflow::gtl::ArraySlice<GlobalData*> arguments,
+    const Shape* shape_with_output_layout) {
+  // Build the computation, as a convenience.
+  TF_ASSIGN_OR_RETURN(auto computation, builder->Build());
+  return ExecuteAndTransfer(computation, arguments, shape_with_output_layout);
+}
+
 std::unique_ptr<GlobalData> ClientLibraryTestBase::ExecuteOrDie(
     ComputationBuilder* builder,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index a5cb74bdfa..763ff09965 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -66,7 +66,9 @@ class ClientLibraryTestBase : public ::testing::Test {
 
   // TODO(b/25566808): Add helper that populates a literal from a testdata file.
 
-  // Convenience methods for building and running a computation from a builder.
+  // Convenience methods for building and running a computation with the member
+  // execution options. Modify execution_options_ in your test if you want to
+  // customize the options.
   StatusOr<std::unique_ptr<GlobalData>> Execute(
       ComputationBuilder* builder,
       tensorflow::gtl::ArraySlice<GlobalData*> arguments);
@@ -74,6 +76,10 @@ class ClientLibraryTestBase : public ::testing::Test {
       ComputationBuilder* builder,
       tensorflow::gtl::ArraySlice<GlobalData*> arguments,
       const Shape* shape_with_output_layout = nullptr);
+  StatusOr<std::unique_ptr<Literal>> ExecuteAndTransfer(
+      const Computation& computation,
+      tensorflow::gtl::ArraySlice<GlobalData*> arguments,
+      const Shape* shape_with_output_layout = nullptr);
 
   // Convenience OrDie variants of above methods.
   std::unique_ptr<GlobalData> ExecuteOrDie(
diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc
index 9952ff3186..0c7c3a8ff6 100644
--- a/tensorflow/compiler/xla/tests/deallocation_test.cc
+++ b/tensorflow/compiler/xla/tests/deallocation_test.cc
@@ -41,7 +41,8 @@ class DeallocationTest : public ClientLibraryTestBase {
       tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
     Computation computation = builder->Build().ConsumeValueOrDie();
     auto global_data =
-        client_->Execute(computation, arguments).ConsumeValueOrDie();
+        client_->Execute(computation, arguments, &execution_options_)
+            .ConsumeValueOrDie();
     TF_CHECK_OK(client_->Transfer(*global_data).status());
     return global_data;
   }
diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc
index 57e390bff9..ed994fda45 100644
--- a/tensorflow/compiler/xla/tests/prng_test.cc
+++ b/tensorflow/compiler/xla/tests/prng_test.cc
@@ -243,9 +243,11 @@ XLA_TEST_F(PrngTest, PassInGlobalRngSeed) {
         client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                     &execution_options2));
     TF_ASSIGN_OR_ASSERT_OK(
-        result5, client_->ExecuteAndTransfer(computation, /*arguments=*/{}));
+        result5, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
+                                             &execution_options_));
     TF_ASSIGN_OR_ASSERT_OK(
-        result6, client_->ExecuteAndTransfer(computation, /*arguments=*/{}));
+        result6, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
+                                             &execution_options_));
   }
 
   LiteralTestUtil::ExpectEqual(*result1, *result2);
-- 
GitLab


From f9a6477826c567a799cbc54111b7cbe5c49c38a0 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 19 Jun 2017 16:39:29 -0700
Subject: [PATCH 0218/1375] Dont fold constant string.

PiperOrigin-RevId: 159497483
---
 tensorflow/core/grappler/optimizers/constant_folding.cc | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 4fccd4e19b..325ce8f5fe 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -293,10 +293,16 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     if (IsControlInput(input)) {
       continue;
     }
-    bool is_const = IsConstant(*node_map_->GetNode(input));
+    const NodeDef* input_node = node_map_->GetNode(input);
+    bool is_const = IsConstant(*input_node);
     if (!is_const && !is_merge) {
       return false;
     }
+    // Don't fold strings constants for now since this causes problems with
+    // checkpointing.
+    if (is_const && input_node->attr().at("dtype").type() == DT_STRING) {
+      return false;
+    }
     has_constant_input |= is_const;
   }
   if (is_merge) {
-- 
GitLab


From 4689fe74085701895b7b4b0be2e54edb4951bf08 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 19 Jun 2017 16:47:48 -0700
Subject: [PATCH 0219/1375] Don't assert when attempting to create more than
 one virtual cluster at a time.

PiperOrigin-RevId: 159498430
---
 tensorflow/core/grappler/clusters/cluster.cc        | 12 ------------
 tensorflow/core/grappler/clusters/single_machine.cc | 13 +++++++++++++
 2 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/grappler/clusters/cluster.cc b/tensorflow/core/grappler/clusters/cluster.cc
index 8690d9f24a..dec51842e4 100644
--- a/tensorflow/core/grappler/clusters/cluster.cc
+++ b/tensorflow/core/grappler/clusters/cluster.cc
@@ -14,27 +14,15 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/clusters/cluster.h"
-#include <atomic>
 
 namespace tensorflow {
 namespace grappler {
 
-static std::atomic<bool> already_created(false);
-
 Cluster::Cluster(int timeout_s) : timeout_s_(timeout_s) {
-  // This is really ugly: to avoid leaking variables, we need to reset the tf
-  // session every time we're done processing a grappler item. However,
-  // variables are global, and therefore we can't have more than 1 session alive
-  // at a time. This check detects when more that one cluster is created.
-  CHECK(!already_created);
-  already_created = true;
-
   DisableDetailedStats(false);
 }
 
 Cluster::~Cluster() {
-  CHECK(already_created);
-  already_created = false;
 }
 
 void Cluster::AllowSoftPlacement(bool soft_placement_state) {
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index b9ed7c0590..58b9fc6429 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/clusters/single_machine.h"
 
+#include <atomic>
 #include <memory>
 
 #include "tensorflow/cc/training/queue_runner.h"
@@ -31,11 +32,20 @@ limitations under the License.
 namespace tensorflow {
 namespace grappler {
 
+static std::atomic<bool> already_created(false);
+
 SingleMachine::SingleMachine(int timeout_s, int num_cpu_cores, int num_gpus)
     : Cluster(timeout_s),
       num_gpus_(num_gpus),
       expected_init_time_s_(0),
       closing_(false) {
+  // This is really ugly: to avoid leaking variables, we need to reset the tf
+  // session every time we're done processing a grappler item. However,
+  // variables are global, and therefore we can't have more than 1 session alive
+  // at a time. This check detects when more that one cluster is created.
+  CHECK(!already_created);
+  already_created = true;
+
   VLOG(1) << "Number of CPU cores: " << num_cpu_cores
           << " Number of GPUs: " << num_gpus;
   thread_pool_.reset(new thread::ThreadPool(
@@ -64,6 +74,9 @@ SingleMachine::~SingleMachine() {
   thread_pool_.reset();
 
   Reset(options_, {}).IgnoreError();
+
+  CHECK(already_created);
+  already_created = false;
 }
 
 Status SingleMachine::Provision() {
-- 
GitLab


From 34336a3df564b1264ec9fd52c852253fba7a8f59 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 16:49:24 -0700
Subject: [PATCH 0220/1375] Added LIFOManager to use LIFO approach of returning
 the next ready node.

PiperOrigin-RevId: 159498617
---
 .../core/grappler/costs/virtual_scheduler.cc  |   6 +-
 .../core/grappler/costs/virtual_scheduler.h   |  38 +++-
 .../grappler/costs/virtual_scheduler_test.cc  | 187 +++++++++++++++++-
 3 files changed, 217 insertions(+), 14 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index e5073d6301..75f485b414 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -58,8 +58,10 @@ Costs CombineCosts(const Costs& left, const Costs& right) {
 VirtualScheduler::VirtualScheduler(const GrapplerItem* grappler_item,
                                    const bool use_static_shapes,
                                    Cluster* cluster)
-    :  // TODO(dyoon): Use a better way than FIFO.
-      ready_nodes_(new FIFOManager()),
+    :  // Use LIFO instead of FIFO. LIFO allows an output node of an node to
+       // follow it in execution, saving addition memory time from having to
+       // write and read.
+      ready_nodes_(new LIFOManager()),
       graph_costs_(Costs::ZeroCosts()),
       graph_properties_(*grappler_item),
       cluster_(cluster),
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index b6c4907523..8b9eccbd43 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -127,7 +127,7 @@ class ReadyNodeManager {
   ReadyNodeManager() {}
   virtual ~ReadyNodeManager() {}
   virtual void AddNode(const NodeDef* node) = 0;
-  virtual const NodeDef* GetCurrNode() const = 0;
+  virtual const NodeDef* GetCurrNode() = 0;
   virtual void RemoveCurrNode() = 0;
   virtual bool Empty() const = 0;
 };
@@ -137,7 +137,7 @@ class FIFOManager : public ReadyNodeManager {
   FIFOManager() : ReadyNodeManager() {}
   ~FIFOManager() override {}
   void AddNode(const NodeDef* node) override { nodes_.push_back(node); }
-  const NodeDef* GetCurrNode() const override { return nodes_.front(); }
+  const NodeDef* GetCurrNode() override { return nodes_.front(); }
   void RemoveCurrNode() override { nodes_.pop_front(); }
   bool Empty() const override { return nodes_.empty(); }
 
@@ -145,6 +145,40 @@ class FIFOManager : public ReadyNodeManager {
   std::list<const NodeDef*> nodes_;
 };
 
+// The LIFOManager schedules nodes by returning the last one added to the
+// scheduler. A node is executed and then its ready outputs are newly added to
+// the scheduler, so the LIFOManager will return outputs to a node following
+// that node's execution.
+class LIFOManager : public ReadyNodeManager {
+ public:
+  LIFOManager() : ReadyNodeManager() {}
+  ~LIFOManager() override {}
+  void AddNode(const NodeDef* node) override { nodes_.push_back(node); }
+  const NodeDef* GetCurrNode() override {
+    curr_pos_ = nodes_.end();
+    curr_pos_--;
+    return nodes_.back();
+  }
+  void RemoveCurrNode() override {
+    if (curr_pos_ != nodes_.end()) {
+      nodes_.erase(curr_pos_);
+    } else if (!nodes_.empty()) {
+      nodes_.pop_back();
+    }
+    curr_pos_ = nodes_.end();
+    curr_pos_--;
+  }
+  bool Empty() const override { return nodes_.empty(); }
+
+ private:
+  std::list<const NodeDef*> nodes_;
+  // Keep track of the current node being executed by saving its position.
+  // Necessary because nodes may be added to the end of the list while a node is
+  // executing, and we want to remove the correct node (the one that is
+  // executing) rather than the new ones being added.
+  std::list<const NodeDef*>::iterator curr_pos_ = nodes_.end();
+};
+
 // A wrapper struct to OpInfo proto.
 // TODO(dyoon): once we extend OpInfo or implement a better interface, and  then
 // delete this wrapper struct.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index c630f60311..caf76bbc8f 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -38,6 +38,8 @@ class TestVirtualScheduler : public VirtualScheduler {
 
 class VirtualSchedulerTest : public ::testing::Test {
  protected:
+  NodeDef node1_, node2_, node3_, node4_, node5_, node6_;
+
   const string kCPU0 = "/job:localhost/replica:0/task:0/cpu:0";
 
   DeviceProperties GetDummyCPUDevice() {
@@ -53,6 +55,14 @@ class VirtualSchedulerTest : public ::testing::Test {
   }
 
   void SetUp() override {
+    // Initializes nodes for manager
+    node1_.set_name("Node1");
+    node2_.set_name("Node2");
+    node3_.set_name("Node3");
+    node4_.set_name("Node4");
+    node5_.set_name("Node5");
+    node6_.set_name("Node6");
+
     // Initializes cluster_ and placer_.
     std::unordered_map<string, DeviceProperties> devices;
 
@@ -118,18 +128,21 @@ class VirtualSchedulerTest : public ::testing::Test {
 
   void CreateGrapplerItemWithMatmulChain() {
     tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
+    // Add control dependencies to ensure tests do not rely on specific
+    // manager and the order remains consistent for the test.
     auto a = tensorflow::ops::RandomUniform(s.WithOpName("a"), {3200, 3200},
                                             DT_FLOAT);
-    auto b = tensorflow::ops::RandomUniform(s.WithOpName("b"), {3200, 3200},
-                                            DT_FLOAT);
-    auto c = tensorflow::ops::RandomUniform(s.WithOpName("c"), {3200, 3200},
-                                            DT_FLOAT);
-    auto d = tensorflow::ops::RandomUniform(s.WithOpName("d"), {3200, 3200},
-                                            DT_FLOAT);
-    auto e = tensorflow::ops::RandomUniform(s.WithOpName("e"), {3200, 3200},
-                                            DT_FLOAT);
-
-    auto ab = tensorflow::ops::MatMul(s.WithOpName("ab"), a, b);
+    auto b = tensorflow::ops::RandomUniform(
+        s.WithOpName("b").WithControlDependencies(a), {3200, 3200}, DT_FLOAT);
+    auto c = tensorflow::ops::RandomUniform(
+        s.WithOpName("c").WithControlDependencies(b), {3200, 3200}, DT_FLOAT);
+    auto d = tensorflow::ops::RandomUniform(
+        s.WithOpName("d").WithControlDependencies(c), {3200, 3200}, DT_FLOAT);
+    auto e = tensorflow::ops::RandomUniform(
+        s.WithOpName("e").WithControlDependencies(d), {3200, 3200}, DT_FLOAT);
+
+    auto ab = tensorflow::ops::MatMul(
+        s.WithOpName("ab").WithControlDependencies(e), a, b);
     auto abc = tensorflow::ops::MatMul(s.WithOpName("abc"), ab, c);
     auto abcd = tensorflow::ops::MatMul(s.WithOpName("abcd"), abc, d);
     auto abcde = tensorflow::ops::MatMul(s.WithOpName("abcde"), abcd, e);
@@ -374,6 +387,160 @@ class VirtualSchedulerTest : public ::testing::Test {
   const int depth_out_ = 16;
 };
 
+// Test that FIFOManager correctly returns the current node with only 1 node.
+TEST_F(VirtualSchedulerTest, GetSingleNodeFIFOManager) {
+  // Init.
+  FIFOManager manager = FIFOManager();
+
+  // Add the node to FIFOManager.
+  manager.AddNode(&node1_);
+  EXPECT_EQ("Node1", manager.GetCurrNode()->name());
+}
+
+// Test that FIFOManager removes the only node contained within.
+TEST_F(VirtualSchedulerTest, RemoveSingleNodeFIFOManager) {
+  // Init.
+  FIFOManager manager = FIFOManager();
+
+  // Add the node to FIFOManager.
+  manager.AddNode(&node1_);
+
+  // Remove the only node in FIFOManager.
+  manager.RemoveCurrNode();
+  EXPECT_TRUE(manager.Empty());
+}
+
+// Test that FIFOManager can remove multiple nodes and returns the current node
+// in the right order
+TEST_F(VirtualSchedulerTest, GetAndRemoveMultipleFIFOManager) {
+  // Init.
+  FIFOManager manager = FIFOManager();
+
+  // Add the nodes to FIFOManager.
+  manager.AddNode(&node1_);
+  manager.AddNode(&node2_);
+  manager.AddNode(&node3_);
+  manager.AddNode(&node4_);
+
+  // Keep checking current node while removing nodes from manager.
+  EXPECT_EQ("Node1", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node2", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node3", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node4", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_TRUE(manager.Empty());
+}
+
+// Test that FIFOManager can remove multiple nodes and add more nodes, still
+// returning the current node in the right order
+TEST_F(VirtualSchedulerTest, AddAndRemoveMultipleFIFOManager) {
+  // Init.
+  FIFOManager manager = FIFOManager();
+
+  // Add the nodes to FIFOManager.
+  manager.AddNode(&node1_);
+  manager.AddNode(&node2_);
+  manager.AddNode(&node3_);
+  manager.AddNode(&node4_);
+
+  // Keep checking current node as nodes are removed and added.
+  EXPECT_EQ("Node1", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node2", manager.GetCurrNode()->name());
+  manager.AddNode(&node5_);
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node3", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node4", manager.GetCurrNode()->name());
+  manager.AddNode(&node6_);
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node5", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node6", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_TRUE(manager.Empty());
+}
+
+// Test that LIFOManager correctly returns the current node with only 1 node.
+TEST_F(VirtualSchedulerTest, GetSingleNodeLIFOManager) {
+  // Init.
+  LIFOManager manager = LIFOManager();
+
+  // Add the node to LIFOManager.
+  manager.AddNode(&node1_);
+  EXPECT_EQ("Node1", manager.GetCurrNode()->name());
+}
+
+// Test that LIFOManager removes the only node contained within.
+TEST_F(VirtualSchedulerTest, RemoveSingleNodeLIFOManager) {
+  // Init.
+  LIFOManager manager = LIFOManager();
+
+  // Add the node to LIFOManager.
+  manager.AddNode(&node1_);
+
+  // Remove the only node in LIFOManager.
+  manager.RemoveCurrNode();
+  EXPECT_TRUE(manager.Empty());
+}
+
+// Test that LIFOManager can remove multiple nodes and returns the current node
+// in the right order
+TEST_F(VirtualSchedulerTest, GetAndRemoveMultipleLIFOManager) {
+  // Init.
+  LIFOManager manager = LIFOManager();
+
+  // Add the nodes to LIFOManager.
+  manager.AddNode(&node1_);
+  manager.AddNode(&node2_);
+  manager.AddNode(&node3_);
+  manager.AddNode(&node4_);
+
+  // Keep checking current node while removing nodes from manager.
+  EXPECT_EQ("Node4", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node3", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node2", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node1", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_TRUE(manager.Empty());
+}
+
+// Test that LIFOManager can remove multiple nodes (must be removing the current
+// node) and add more nodes, still returning the current node in the right order
+TEST_F(VirtualSchedulerTest, AddAndRemoveMultipleLIFOManager) {
+  // Init.
+  LIFOManager manager = LIFOManager();
+
+  // Add the nodes to LIFOManager.
+  manager.AddNode(&node1_);
+  manager.AddNode(&node2_);
+  manager.AddNode(&node3_);
+  manager.AddNode(&node4_);
+
+  // Keep checking current node as nodes are removed and added.
+  EXPECT_EQ("Node4", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node3", manager.GetCurrNode()->name());
+  manager.AddNode(&node5_);
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node5", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node2", manager.GetCurrNode()->name());
+  manager.AddNode(&node6_);
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node6", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_EQ("Node1", manager.GetCurrNode()->name());
+  manager.RemoveCurrNode();
+  EXPECT_TRUE(manager.Empty());
+}
+
 // Create small graph, run predict costs on it, make sure the costs from the
 // summary match the hand-calculated costs.
 TEST_F(VirtualSchedulerTest, SummaryCostTest) {
-- 
GitLab


From 01823ffed824f3e6d0e894ec0d24432341859d90 Mon Sep 17 00:00:00 2001
From: Pierre <zins.pierre@gmail.com>
Date: Mon, 19 Jun 2017 19:59:46 -0400
Subject: [PATCH 0221/1375] Direct Session : Run() method, name correction
 parition_graph_defs to partition_graph_defs

---
 tensorflow/core/common_runtime/direct_session.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index 17d966f770..e771674a1e 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -618,11 +618,11 @@ Status DirectSession::Run(const RunOptions& run_options,
 
   // If requested via RunOptions, output the partition graphs.
   if (run_options.output_partition_graphs()) {
-    protobuf::RepeatedPtrField<GraphDef>* parition_graph_defs =
+    protobuf::RepeatedPtrField<GraphDef>* partition_graph_defs =
         run_metadata->mutable_partition_graphs();
     for (const PerPartitionExecutorsAndLib& exec_and_lib :
          executors_and_keys->items) {
-      GraphDef* partition_graph_def = parition_graph_defs->Add();
+      GraphDef* partition_graph_def = partition_graph_defs->Add();
       exec_and_lib.graph->ToGraphDef(partition_graph_def);
     }
   }
-- 
GitLab


From 0d0b62f32a0939ac75cebcf0057d295e3a194c8d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Jun 2017 17:39:44 -0700
Subject: [PATCH 0222/1375] Fix grappler's layout optimizer when the input to a
 conv2d doesn't have explicit output dimensions.

PiperOrigin-RevId: 159504189
---
 tensorflow/core/grappler/optimizers/layout_optimizer.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 0a802fa0f5..1bc7b6d44d 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -1158,7 +1158,7 @@ Status LayoutOptimizer::InferOutputShapes(GrapplerItem* item) {
     for (const auto& tensor_property : tensor_properties) {
       *attr_output_shape.mutable_list()->add_shape() = tensor_property.shape();
     }
-    node->mutable_attr()->insert({"_output_shapes", attr_output_shape});
+    (*node->mutable_attr())["_output_shapes"] = attr_output_shape;
   }
   return Status::OK();
 }
-- 
GitLab


From a36488d812780e78f869a3eb2b692cf3c236f1cc Mon Sep 17 00:00:00 2001
From: David Majnemer <majnemer@google.com>
Date: Mon, 19 Jun 2017 18:01:59 -0700
Subject: [PATCH 0223/1375] [XLA] Update hlo_cost_analysis to account for
 fusion nodes with multiple outputs

PiperOrigin-RevId: 159506400
---
 .../compiler/xla/service/hlo_cost_analysis.cc       | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index 46ca316fe6..abbbbfa02b 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -370,6 +370,19 @@ Status HloCostAnalysis::HandleFusion(HloInstruction* fusion) {
   HloCostAnalysis visitor([](const Shape&) { return 0; });
   TF_RETURN_IF_ERROR(fused_expression_root->Accept(&visitor));
 
+  // If a fusion node produces a tuple, it also produces the operands of that
+  // tuple.
+  current_bytes_accessed_ = 0;
+  ShapeUtil::ForEachSubshape(
+      fusion->shape(),
+      [this](const Shape& subshape, const ShapeIndex& /*shape_index*/) {
+        current_bytes_accessed_ += shape_size_(subshape);
+      });
+
+  for (const HloInstruction* operand : fusion->operands()) {
+    current_bytes_accessed_ += shape_size_(operand->shape());
+  }
+
   // Attribute the cost of the fused expression to the fusion node.
   current_transcendental_count_ = visitor.transcendental_count();
   current_flop_count_ = visitor.flop_count();
-- 
GitLab


From 0319d756c6e62b50cef4b1e60122c4ba51640190 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Tue, 20 Jun 2017 01:20:08 +0000
Subject: [PATCH 0224/1375] Update API goldens for API compatibility

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/tools/api/golden/tensorflow.pbtxt | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 342ee95f74..bdb441f4b6 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -1696,6 +1696,14 @@ tf_module {
     name: "sparse_placeholder"
     argspec: "args=[\'dtype\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
+  member_method {
+    name: "sparse_reduce_max"
+    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "sparse_reduce_max_sparse"
+    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
   member_method {
     name: "sparse_reduce_sum"
     argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
-- 
GitLab


From 5b6a203c5c759656b2b7018271219916ddd85cb6 Mon Sep 17 00:00:00 2001
From: Mark Heffernan <meheff@google.com>
Date: Mon, 19 Jun 2017 18:38:20 -0700
Subject: [PATCH 0225/1375] [XLA] Add live range interference querying to
 dataflow analysis. Add method MayInterfere to HloDataflowAnalysis which
 returns whether the live ranges of two values interfere. This will replace
 buffer_liveness.cc. The cl includes a few related changes:

(1) HloOrdering: Apply an order to the condition and body computations. Specifically, for the purposes of HLO ordering the condition is ordered before the body. This ensures that the live ranges of values in the condition do not interfere with the live ranges in the body.

(2) Add a Dominates method to CallGraph for determining whether a computation dominates another in the call graph.

(3) Tightened the definition of "use" in the dataflow analysis. Now an instruction which passes through a value without reading it is no longer considered a use of the value. This new definition is reflected in the HloUse objects returned by HloValue::uses().

PiperOrigin-RevId: 159509724
---
 tensorflow/compiler/xla/service/BUILD         |   1 +
 .../compiler/xla/service/buffer_liveness.cc   |   2 +-
 tensorflow/compiler/xla/service/call_graph.cc |  31 +
 tensorflow/compiler/xla/service/call_graph.h  |  21 +
 .../compiler/xla/service/call_graph_test.cc   |  31 +
 .../compiler/xla/service/heap_simulator.cc    |   2 +-
 .../xla/service/hlo_alias_analysis_test.cc    | 128 +++--
 .../xla/service/hlo_dataflow_analysis.cc      | 267 ++++++++-
 .../xla/service/hlo_dataflow_analysis.h       |  44 +-
 .../xla/service/hlo_dataflow_analysis_test.cc | 528 +++++++++++++++---
 .../compiler/xla/service/hlo_ordering.cc      |  14 +
 .../compiler/xla/service/hlo_ordering_test.cc |  62 ++
 .../compiler/xla/service/liveness_util.cc     |  39 +-
 .../compiler/xla/service/liveness_util.h      |  13 +-
 .../xla/service/liveness_util_test.cc         |  46 +-
 .../compiler/xla/tests/hlo_test_base.cc       |   1 +
 tensorflow/compiler/xla/tests/hlo_test_base.h |   2 +-
 17 files changed, 1018 insertions(+), 214 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 2b176d1270..ed1420665e 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1189,6 +1189,7 @@ cc_library(
     deps = [
         ":call_graph",
         ":hlo",
+        ":hlo_ordering",
         ":liveness_util",
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
diff --git a/tensorflow/compiler/xla/service/buffer_liveness.cc b/tensorflow/compiler/xla/service/buffer_liveness.cc
index 1b14c26340..56b8b5e80b 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness.cc
@@ -122,7 +122,7 @@ bool BufferLiveness::live_range_strictly_before(const LogicalBuffer& a,
     if (b.instruction()->IsUserOf(alias.instruction()) &&
         !CanShareOperandBufferWithUser(alias.instruction(), alias.index(),
                                        b.instruction(), b.index(),
-                                       points_to_analysis())) {
+                                       &points_to_analysis())) {
       return false;
     }
   }
diff --git a/tensorflow/compiler/xla/service/call_graph.cc b/tensorflow/compiler/xla/service/call_graph.cc
index fa7b2a3095..b450e0c400 100644
--- a/tensorflow/compiler/xla/service/call_graph.cc
+++ b/tensorflow/compiler/xla/service/call_graph.cc
@@ -133,6 +133,37 @@ CallGraphNode& CallGraph::GetNode(const HloComputation* computation) {
   return nodes_[it->second];
 }
 
+bool CallGraph::DominatesHelper(
+    const HloComputation* a, const HloComputation* b,
+    tensorflow::gtl::FlatSet<const HloComputation*>* visited) const {
+  if (a == b || ContainsKey(*visited, b)) {
+    // The call graph is guaranteed to be acyclic so any previously visited node
+    // we encounter was already determined to be dominated.
+    return true;
+  }
+
+  const CallGraphNode& b_node = GetNode(b);
+  if (b_node.callers().empty()) {
+    // We reached a root node without hitting 'a'. 'a' does not dominate 'b'.
+    return false;
+  }
+
+  // Walk up the callers of 'b' until we hit 'a' or a root node (no callers).
+  visited->insert(b);
+  for (const HloComputation* b_caller : b_node.callers()) {
+    if (!DominatesHelper(a, b_caller, visited)) {
+      return false;
+    }
+  }
+  return true;
+}
+
+bool CallGraph::Dominates(const HloComputation* a,
+                          const HloComputation* b) const {
+  tensorflow::gtl::FlatSet<const HloComputation*> visited;
+  return DominatesHelper(a, b, &visited);
+}
+
 namespace {
 
 // Returns the call context of a computation which is called from contexts 'a'
diff --git a/tensorflow/compiler/xla/service/call_graph.h b/tensorflow/compiler/xla/service/call_graph.h
index 7f9990f06d..a3297ff534 100644
--- a/tensorflow/compiler/xla/service/call_graph.h
+++ b/tensorflow/compiler/xla/service/call_graph.h
@@ -189,6 +189,20 @@ class CallGraph {
   Status VisitNodes(const VisitorFunction& visitor_func,
                     bool visit_unreachable_nodes = true) const;
 
+  // Returns true if 'a' dominates 'b' in the call graph. Computation 'a'
+  // dominates computation 'b' iff all callgraph paths in the caller-to-callee
+  // direction from a root computation to 'b' pass through computation
+  // 'a'. Trivially, a computation dominates itself.
+  bool Dominates(const HloComputation* a, const HloComputation* b) const;
+
+  // Returns whether 'instruction' is contained in 'computation' either directly
+  // ('instruction->parent' is 'computation') or indirectly ('computation'
+  // dominates 'instruction->parent' in the call graph).
+  bool InstructionIsNestedIn(const HloInstruction* instruction,
+                             const HloComputation* computation) const {
+    return Dominates(computation, instruction->parent());
+  }
+
   string ToString() const;
 
  private:
@@ -205,6 +219,13 @@ class CallGraph {
       const VisitorFunction& visitor_func, const CallGraphNode& node,
       tensorflow::gtl::FlatSet<const CallGraphNode*>* visited) const;
 
+  // Recursive helper for computing whether 'a' dominates 'b' in the call
+  // graph. 'b_ancestor' is the currently visited node (which starts at 'b'),
+  // and 'visited' is the set of computations which have been visited.
+  bool DominatesHelper(
+      const HloComputation* a, const HloComputation* b,
+      tensorflow::gtl::FlatSet<const HloComputation*>* visited) const;
+
   // The HLO module represented by this call graph.
   const HloModule* module_ = nullptr;
 
diff --git a/tensorflow/compiler/xla/service/call_graph_test.cc b/tensorflow/compiler/xla/service/call_graph_test.cc
index bbf67c9803..3c22871b3b 100644
--- a/tensorflow/compiler/xla/service/call_graph_test.cc
+++ b/tensorflow/compiler/xla/service/call_graph_test.cc
@@ -314,6 +314,37 @@ TEST_F(CallGraphTest, ComplexGraph) {
   EXPECT_LT(index_of(cond_computation), index_of(a_computation));
   EXPECT_LT(index_of(c_computation), index_of(b_computation));
   EXPECT_LT(index_of(b_computation), index_of(a_computation));
+
+  // Verify dominance relations between computation in the graph.
+
+  // Entry dominates everybody, and is dominated by no one except itself.
+  EXPECT_TRUE(call_graph->Dominates(entry_computation, entry_computation));
+  EXPECT_TRUE(call_graph->Dominates(entry_computation, a_computation));
+  EXPECT_TRUE(call_graph->Dominates(entry_computation, b_computation));
+  EXPECT_TRUE(call_graph->Dominates(entry_computation, c_computation));
+  EXPECT_TRUE(call_graph->Dominates(entry_computation, cond_computation));
+  EXPECT_FALSE(call_graph->Dominates(a_computation, entry_computation));
+  EXPECT_FALSE(call_graph->Dominates(b_computation, entry_computation));
+  EXPECT_FALSE(call_graph->Dominates(c_computation, entry_computation));
+  EXPECT_FALSE(call_graph->Dominates(cond_computation, entry_computation));
+
+  // 'a' only dominates 'b' and 'c'.
+  EXPECT_TRUE(call_graph->Dominates(a_computation, a_computation));
+  EXPECT_TRUE(call_graph->Dominates(a_computation, b_computation));
+  EXPECT_TRUE(call_graph->Dominates(a_computation, c_computation));
+  EXPECT_FALSE(call_graph->Dominates(b_computation, a_computation));
+  EXPECT_FALSE(call_graph->Dominates(c_computation, a_computation));
+  EXPECT_FALSE(call_graph->Dominates(a_computation, cond_computation));
+
+  EXPECT_TRUE(call_graph->Dominates(b_computation, b_computation));
+  EXPECT_FALSE(call_graph->Dominates(b_computation, c_computation));
+  EXPECT_FALSE(call_graph->Dominates(b_computation, cond_computation));
+
+  EXPECT_TRUE(call_graph->Dominates(c_computation, c_computation));
+  EXPECT_FALSE(call_graph->Dominates(c_computation, cond_computation));
+  EXPECT_FALSE(call_graph->Dominates(cond_computation, c_computation));
+
+  EXPECT_TRUE(call_graph->Dominates(cond_computation, cond_computation));
 }
 
 TEST_F(CallGraphTest, VisitSingletonComputation) {
diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc
index 86f62accd3..c662cec9c7 100644
--- a/tensorflow/compiler/xla/service/heap_simulator.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator.cc
@@ -187,7 +187,7 @@ Status HeapSimulator::RunComputation(
             buffer->instruction()->opcode() != HloOpcode::kCopy &&
             CanShareOperandBufferWithUser(
                 operand_buffer->instruction(), operand_buffer->index(),
-                buffer->instruction(), buffer->index(), points_to_analysis)) {
+                buffer->instruction(), buffer->index(), &points_to_analysis)) {
           ShareBuffer(buffer, operand_buffer, instruction);
           shared = true;
           break;
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
index a869689b46..442585772f 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
@@ -37,12 +37,12 @@ using ::testing::UnorderedElementsAre;
 
 class HloAliasAnalysisTest : public HloTestBase {
  protected:
-  HloAliasAnalysisTest() : module_(TestName()) {}
+  HloAliasAnalysisTest() : module_(CreateNewModule()) {}
 
   // Run alias analysis on the member module. For convenience returns a
   // reference to the generated analysis stored in analysis_.
   const HloAliasAnalysis& RunAnalysis() {
-    analysis_ = HloAliasAnalysis::Run(&module_).ConsumeValueOrDie();
+    analysis_ = HloAliasAnalysis::Run(module_.get()).ConsumeValueOrDie();
     return *analysis_;
   }
 
@@ -77,7 +77,31 @@ class HloAliasAnalysisTest : public HloTestBase {
     return analysis_->dataflow_analysis().GetValue(buffer.value_ids()[0]);
   }
 
-  HloModule module_;
+  // Returns true if any values held in the same buffer interfere. Generally, in
+  // the compiler pipeline copy-insertion will guarantee that this interference
+  // never occurs, but HLO graphs with interference can be explicitly
+  // constructed.
+  bool AnyValuesInSameBufferInterfere() {
+    DependencyHloOrdering ordering(module_.get());
+    for (const HloBuffer* buffer : analysis_->buffers()) {
+      for (HloValue::Id value_id_a : buffer->value_ids()) {
+        for (HloValue::Id value_id_b : buffer->value_ids()) {
+          if (value_id_a != value_id_b &&
+              analysis_->dataflow_analysis().MayInterfere(
+                  value_id_a, value_id_b, ordering)) {
+            VLOG(1) << analysis_->dataflow_analysis().GetValue(value_id_a)
+                    << " interferes with "
+                    << analysis_->dataflow_analysis().GetValue(value_id_b)
+                    << " in buffer: " << *buffer;
+            return true;
+          }
+        }
+      }
+    }
+    return false;
+  }
+
+  std::unique_ptr<HloModule> module_;
   std::unique_ptr<HloAliasAnalysis> analysis_;
 
   const Shape scalar_shape_ = ShapeUtil::MakeShape(F32, {});
@@ -92,7 +116,7 @@ TEST_F(HloAliasAnalysisTest, BinaryOperation) {
       HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto add = builder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, constant1, constant2));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -107,6 +131,8 @@ TEST_F(HloAliasAnalysisTest, BinaryOperation) {
 
   EXPECT_FALSE(analysis.GetInstructionBufferSet(add).IsAmbiguous());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(add).IsDistinct());
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, TupleAndGtes) {
@@ -124,7 +150,7 @@ TEST_F(HloAliasAnalysisTest, TupleAndGtes) {
       HloInstruction::CreateGetTupleElement(scalar_shape_, tuple, 1));
   builder.AddInstruction(
       HloInstruction::CreateBinary(scalar_shape_, HloOpcode::kAdd, gte0, gte1));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -156,6 +182,8 @@ TEST_F(HloAliasAnalysisTest, TupleAndGtes) {
 
   EXPECT_FALSE(analysis.GetInstructionBufferSet(tuple).IsAmbiguous());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(tuple).IsDistinct());
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, NondistinctTuple) {
@@ -168,7 +196,7 @@ TEST_F(HloAliasAnalysisTest, NondistinctTuple) {
   // param0 is included twice in the tuple.
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({param0, param1, param0}));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -179,6 +207,8 @@ TEST_F(HloAliasAnalysisTest, NondistinctTuple) {
 
   EXPECT_FALSE(analysis.GetInstructionBufferSet(tuple).IsAmbiguous());
   EXPECT_FALSE(analysis.GetInstructionBufferSet(tuple).IsDistinct());
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, SingleCall) {
@@ -192,7 +222,7 @@ TEST_F(HloAliasAnalysisTest, SingleCall) {
   auto add = subbuilder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, subparam0, subparam1));
   HloComputation* called_computation =
-      module_.AddEmbeddedComputation(subbuilder.Build());
+      module_->AddEmbeddedComputation(subbuilder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -201,7 +231,7 @@ TEST_F(HloAliasAnalysisTest, SingleCall) {
       HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, called_computation));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -217,6 +247,8 @@ TEST_F(HloAliasAnalysisTest, SingleCall) {
   EXPECT_THAT(
       analysis.GetUniqueBufferAt(add).locations(),
       UnorderedElementsAre(HloLocation{add, {}}, HloLocation{call, {}}));
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, ComputationCalledTwice) {
@@ -229,7 +261,7 @@ TEST_F(HloAliasAnalysisTest, ComputationCalledTwice) {
   auto add = subbuilder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, subparam0, subparam1));
   HloComputation* called_computation =
-      module_.AddEmbeddedComputation(subbuilder.Build());
+      module_->AddEmbeddedComputation(subbuilder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -240,7 +272,7 @@ TEST_F(HloAliasAnalysisTest, ComputationCalledTwice) {
       scalar_shape_, {constant1, constant2}, called_computation));
   auto call2 = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {call1, constant2}, called_computation));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -269,6 +301,8 @@ TEST_F(HloAliasAnalysisTest, ComputationCalledTwice) {
   EXPECT_FALSE(analysis.GetInstructionBufferSet(subparam1).IsAmbiguous());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(subparam0).IsDistinct());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(subparam1).IsDistinct());
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, SingleWhile) {
@@ -303,7 +337,7 @@ TEST_F(HloAliasAnalysisTest, SingleWhile) {
       scalar_shape_, HloOpcode::kAdd, body_element_0, body_element_1));
   auto body_tuple = body_builder.AddInstruction(
       HloInstruction::CreateTuple({body_element_0, add}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   // Condition computation trivially returns a constant "false".
   auto cond_builder = HloComputation::Builder("condition");
@@ -312,7 +346,7 @@ TEST_F(HloAliasAnalysisTest, SingleWhile) {
   cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -323,7 +357,7 @@ TEST_F(HloAliasAnalysisTest, SingleWhile) {
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, body, tuple));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -356,6 +390,8 @@ TEST_F(HloAliasAnalysisTest, SingleWhile) {
                            GetValueDefinedAt(body_param, {1}),
                            GetValueDefinedAt(cond_param, {1}),
                            GetValueDefinedAt(add)));
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, SequentialWhiles) {
@@ -392,7 +428,7 @@ TEST_F(HloAliasAnalysisTest, SequentialWhiles) {
       scalar_shape_, HloOpcode::kAdd, body_element_0, body_element_1));
   body_builder.AddInstruction(
       HloInstruction::CreateTuple({body_element_0, add}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   auto cond_builder = HloComputation::Builder("condition");
   cond_builder.AddInstruction(
@@ -400,7 +436,7 @@ TEST_F(HloAliasAnalysisTest, SequentialWhiles) {
   cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -415,7 +451,7 @@ TEST_F(HloAliasAnalysisTest, SequentialWhiles) {
       HloInstruction::CreateWhile(tuple_shape, condition, body, xla_while0));
   auto xla_while2 = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, body, xla_while1));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -449,13 +485,21 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
   const Shape tuple_shape =
       ShapeUtil::MakeTupleShape({scalar_shape_, scalar_shape_});
 
-  auto cond_builder = HloComputation::Builder("condition");
-  cond_builder.AddInstruction(
-      HloInstruction::CreateParameter(0, tuple_shape, "param"));
-  cond_builder.AddInstruction(
-      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
-  HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+  auto build_cond_computation = [&tuple_shape]() {
+    auto cond_builder = HloComputation::Builder("condition");
+    cond_builder.AddInstruction(
+        HloInstruction::CreateParameter(0, tuple_shape, "param"));
+    cond_builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
+    return cond_builder.Build();
+  };
+  // Build separate condition computations so the call graph is flat. The
+  // callgraph is always flattened in the compiler pipeline, and the flattened
+  // callgraph enables representive interference analysis.
+  HloComputation* condition1 =
+      module_->AddEmbeddedComputation(build_cond_computation());
+  HloComputation* condition2 =
+      module_->AddEmbeddedComputation(build_cond_computation());
 
   // Element 0 passes transparently through the body.
   auto inner_builder = HloComputation::Builder("inner_body");
@@ -470,7 +514,7 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
   inner_builder.AddInstruction(
       HloInstruction::CreateTuple({inner_element_0, add}));
   HloComputation* inner_body =
-      module_.AddEmbeddedComputation(inner_builder.Build());
+      module_->AddEmbeddedComputation(inner_builder.Build());
 
   // Element 1 passes transparently through the body.
   auto outer_builder = HloComputation::Builder("outer_body");
@@ -485,9 +529,9 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
   auto outer_tuple = outer_builder.AddInstruction(
       HloInstruction::CreateTuple({negate, outer_element_1}));
   auto nested_while = outer_builder.AddInstruction(HloInstruction::CreateWhile(
-      tuple_shape, condition, inner_body, outer_tuple));
+      tuple_shape, condition1, inner_body, outer_tuple));
   HloComputation* outer_body =
-      module_.AddEmbeddedComputation(outer_builder.Build());
+      module_->AddEmbeddedComputation(outer_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -497,8 +541,8 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
   auto tuple = builder.AddInstruction(
       HloInstruction::CreateTuple({constant1, constant2}));
   auto entry_while = builder.AddInstruction(
-      HloInstruction::CreateWhile(tuple_shape, condition, outer_body, tuple));
-  module_.AddEntryComputation(builder.Build());
+      HloInstruction::CreateWhile(tuple_shape, condition2, outer_body, tuple));
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -515,6 +559,8 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
             analysis.GetUniqueBufferAt(nested_while, /*index=*/{1}));
   EXPECT_EQ(analysis.GetUniqueBufferAt(constant2),
             analysis.GetUniqueBufferAt(inner_element_1));
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, SwizzlingWhile) {
@@ -548,7 +594,7 @@ TEST_F(HloAliasAnalysisTest, SwizzlingWhile) {
       HloInstruction::CreateGetTupleElement(scalar_shape_, body_param, 2));
   body_builder.AddInstruction(HloInstruction::CreateTuple(
       {body_element_1, body_element_2, body_element_0}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   auto cond_builder = HloComputation::Builder("condition");
   cond_builder.AddInstruction(
@@ -556,7 +602,7 @@ TEST_F(HloAliasAnalysisTest, SwizzlingWhile) {
   auto cond_constant = cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -569,7 +615,7 @@ TEST_F(HloAliasAnalysisTest, SwizzlingWhile) {
       HloInstruction::CreateTuple({constant1, constant2, constant3}));
   auto xla_while = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, body, tuple));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -593,6 +639,10 @@ TEST_F(HloAliasAnalysisTest, SwizzlingWhile) {
             analysis.GetUniqueBufferAt(constant2));
   EXPECT_EQ(analysis.GetUniqueBufferAt(constant1),
             analysis.GetUniqueBufferAt(constant3));
+
+  // All elements in of the loop state tuple are forced into the same buffer
+  // resulting liveness interference.
+  EXPECT_TRUE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, TupleSelect) {
@@ -627,7 +677,7 @@ TEST_F(HloAliasAnalysisTest, TupleSelect) {
   auto select1234 = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple_shape, HloOpcode::kSelect, pred, select12, select34));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -655,6 +705,8 @@ TEST_F(HloAliasAnalysisTest, TupleSelect) {
   EXPECT_TRUE(analysis.GetInstructionBufferSet(select12).IsDistinct());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(select34).IsDistinct());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(select1234).IsDistinct());
+
+  EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, TupleSelectToWhile) {
@@ -688,7 +740,7 @@ TEST_F(HloAliasAnalysisTest, TupleSelectToWhile) {
   auto negate = body_builder.AddInstruction(HloInstruction::CreateUnary(
       scalar_shape_, HloOpcode::kNegate, body_element));
   body_builder.AddInstruction(HloInstruction::CreateTuple({negate}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   auto cond_builder = HloComputation::Builder("condition");
   auto cond_param = cond_builder.AddInstruction(
@@ -696,7 +748,7 @@ TEST_F(HloAliasAnalysisTest, TupleSelectToWhile) {
   cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto pred = builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
@@ -713,7 +765,7 @@ TEST_F(HloAliasAnalysisTest, TupleSelectToWhile) {
   auto xla_while = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, body, select));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
@@ -736,6 +788,10 @@ TEST_F(HloAliasAnalysisTest, TupleSelectToWhile) {
 
   EXPECT_TRUE(analysis.GetInstructionBufferSet(select).IsDistinct());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(xla_while).IsDistinct());
+
+  // The two operands of the select get flattened into the same buffer resulting
+  // in liveness interference.
+  EXPECT_TRUE(AnyValuesInSameBufferInterfere());
 }
 
 TEST_F(HloAliasAnalysisTest, Bitcast) {
@@ -746,7 +802,7 @@ TEST_F(HloAliasAnalysisTest, Bitcast) {
   auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary(
       scalar_shape_, HloOpcode::kBitcast, constant));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
index d1b8725644..7e951721ba 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
@@ -76,7 +76,8 @@ HloValue::HloValue(HloValue::Id id, HloInstruction* instruction,
 }
 
 bool HloValue::operator==(const HloValue& other) const {
-  bool equal = instruction() == other.instruction() && index() == other.index();
+  bool equal = defining_instruction() == other.defining_instruction() &&
+               defining_index() == other.defining_index();
   // If the values are equal they most both be phi (or non phi).
   CHECK(!(equal && is_phi() != other.is_phi()));
   return equal;
@@ -87,10 +88,11 @@ bool HloValue::operator!=(const HloValue& other) const {
 }
 
 string HloValue::ToShortString() const {
-  string index_str =
-      ShapeUtil::IsTuple(instruction()->shape()) ? index().ToString() : "";
-  return StrCat(is_phi_ ? "PHI " : "", instruction()->FullyQualifiedName(),
-                index_str);
+  string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
+                         ? defining_index().ToString()
+                         : "";
+  return StrCat(is_phi_ ? "PHI " : "",
+                defining_instruction()->FullyQualifiedName(), index_str);
 }
 
 string HloValue::ToString(int indent) const {
@@ -106,6 +108,50 @@ string HloValue::ToString(int indent) const {
   return out;
 }
 
+namespace {
+
+// Returns true if the instruction 'user' may use the value at the given
+// ShapeIndex in the given operand. Generally, instruction which pass through
+// values transparently without reading the value are not considered to use the
+// value.
+bool MayUseOperandValue(int64 operand_number, const ShapeIndex& index,
+                        const HloInstruction* user) {
+  switch (user->opcode()) {
+    case HloOpcode::kGetTupleElement:
+    case HloOpcode::kCopy:
+      // These instructions only access the top-level values of their
+      // operand. Non-top-level (nested) values are passed through
+      // transparently.
+      CHECK_EQ(operand_number, 0);
+      return index.empty();
+    case HloOpcode::kSelect:
+      // Select does not use any nested elements of its selected-from operands
+      // (operand 1 and 2)
+      CHECK_GE(operand_number, 0);
+      CHECK_LE(operand_number, 2);
+      return operand_number == 0 || index.empty();
+
+    case HloOpcode::kCall:
+    case HloOpcode::kTuple:
+      // These instructions always pass through their operands transparently.
+      return false;
+
+    case HloOpcode::kWhile:
+      // Though the while instructions passes through its operands, we return
+      // true because in SSA form there may be a Phi at the parameter of the
+      // while which is considered a use of its incoming value because the Phi
+      // input values are not passed through into the body computation. Because
+      // this function is used in both SSA and non-SSA forms of the analysis
+      // conservatively return true.
+      return true;
+
+    default:
+      return true;
+  }
+}
+
+}  // namespace
+
 void HloValue::AddLocation(HloInstruction* instruction,
                            const ShapeIndex& index) {
   // The given location should not already exist in locations_.
@@ -118,7 +164,7 @@ void HloValue::AddLocation(HloInstruction* instruction,
   //  Update uses.
   for (HloInstruction* user : instruction->users()) {
     for (int64 operand_number : user->OperandIndices(instruction)) {
-      if (!DoesNotUseOperandBuffer(instruction, index, user)) {
+      if (MayUseOperandValue(operand_number, index, user)) {
         for (const HloUse& use : uses_) {
           // Verify that this use does not already exist.
           DCHECK(!(use.instruction == user &&
@@ -136,12 +182,16 @@ void HloValue::AddLocation(HloInstruction* instruction,
   if (instruction == module.entry_computation()->root_instruction()) {
     live_out_of_module_ = true;
   }
+
+  if (instruction == instruction->parent()->root_instruction()) {
+    live_out_of_computation_ = true;
+  }
 }
 
 void HloValue::RemoveLocation(HloInstruction* instruction,
                               const ShapeIndex& index) {
   // The defining location cannot be removed.
-  CHECK(!(instruction == this->instruction() && index == this->index()));
+  CHECK(!(instruction == defining_instruction() && index == defining_index()));
 
   int64 size_before = locations_.size();
   locations_.erase(
@@ -163,19 +213,27 @@ void HloValue::RemoveLocation(HloInstruction* instruction,
                              }),
               uses_.end());
 
-  const HloModule& module = *instruction->parent()->parent();
-  if (instruction == module.entry_computation()->root_instruction()) {
-    // Value has been removed from a location in the entry root instruction.
-    // Check if the value is still live out of the module by walking all
-    // remaining locations.
-    live_out_of_module_ = false;
+  // Returns whether this value is contained in the given instruction's output.
+  auto is_contained_in = [this](const HloInstruction* instruction) {
     for (const HloLocation& location : locations()) {
-      if (location.instruction ==
-          module.entry_computation()->root_instruction()) {
-        live_out_of_module_ = true;
-        break;
+      if (location.instruction == instruction) {
+        return true;
       }
     }
+    return false;
+  };
+
+  const HloModule& module = *instruction->parent()->parent();
+  if (instruction == module.entry_computation()->root_instruction()) {
+    // Value has been removed from a location in the entry root instruction.
+    live_out_of_module_ =
+        is_contained_in(module.entry_computation()->root_instruction());
+  }
+  if (instruction == defining_instruction()->parent()->root_instruction()) {
+    // Value has been removed from the root of the computation the value has
+    // been defined in.
+    live_out_of_computation_ =
+        is_contained_in(defining_instruction()->parent()->root_instruction());
   }
 }
 
@@ -259,7 +317,8 @@ bool HloDataflowAnalysis::ValueIsDefinedAt(const HloInstruction* instruction,
   if (value_set.value_ids().size() != 1) {
     return false;
   }
-  return GetValue(value_set.GetUniqueValueId()).instruction() == instruction;
+  return GetValue(value_set.GetUniqueValueId()).defining_instruction() ==
+         instruction;
 }
 
 const HloValue& HloDataflowAnalysis::GetValueDefinedAt(
@@ -468,8 +527,8 @@ void HloDataflowAnalysis::UpdateLocationsOfValuesAt(
             }
             // Don't remove the defining location of the value.
             HloValue& value = GetValue(value_id);
-            if (instruction == value.instruction()) {
-              CHECK_EQ(index, value.index());
+            if (instruction == value.defining_instruction()) {
+              CHECK_EQ(index, value.defining_index());
             } else {
               value.RemoveLocation(instruction, index);
             }
@@ -482,8 +541,8 @@ void HloDataflowAnalysis::UpdateLocationsOfValuesAt(
                           const HloValueSet& value_set) {
         for (HloValue::Id value_id : value_set.value_ids()) {
           HloValue& value = GetValue(value_id);
-          if (instruction == value.instruction()) {
-            CHECK_EQ(index, value.index());
+          if (instruction == value.defining_instruction()) {
+            CHECK_EQ(index, value.defining_index());
           } else {
             value.AddLocation(instruction, index);
           }
@@ -694,15 +753,24 @@ InstructionValueSet HloDataflowAnalysis::RecomputeParameterValueSet(
   std::vector<const InstructionValueSet*> inputs;
   bool called_from_while = false;
   for (const CallSite& callsite : call_graph_node.caller_callsites()) {
-    inputs.push_back(&GetInstructionValueSet(
-        callsite.instruction()->operand(parameter->parameter_number())));
-    if (callsite.instruction()->opcode() == HloOpcode::kWhile) {
-      // In a while instruction, the backedge is also a dataflow input to the
-      // parameter instruction. This code covers the case where the parameter is
-      // in the while body or the parameter is in the while condition.
+    if (callsite.instruction()->opcode() == HloOpcode::kCall) {
+      // The operand values of a call instruction are forwarded to the
+      // respective parameter instruction of the subcomputation.
+      inputs.push_back(&GetInstructionValueSet(
+          callsite.instruction()->operand(parameter->parameter_number())));
+    } else if (callsite.instruction()->opcode() == HloOpcode::kWhile) {
+      // In a while instruction, the while operand (ie, the init value) and the
+      // backedge are dataflow inputs to the parameter instruction. This is the
+      // case for parameters of both the body and condition computations.
+      CHECK_EQ(parameter->parameter_number(), 0);
+      inputs.push_back(
+          &GetInstructionValueSet(callsite.instruction()->operand(0)));
       inputs.push_back(&GetInstructionValueSet(
           callsite.instruction()->while_body()->root_instruction()));
       called_from_while = true;
+    } else {
+      LOG(FATAL) << "CallContext::kSequential computations should only be "
+                    "called from call or while instructions";
     }
   }
 
@@ -804,6 +872,149 @@ Status HloDataflowAnalysis::InitializeInstructionValueSets() {
   return Status::OK();
 }
 
+bool HloDataflowAnalysis::IsDefinedBefore(const HloValue& a, const HloValue& b,
+                                          const HloOrdering& ordering) const {
+  // If 'b' is an entry param then 'a' cannot be defined before 'b' because 'b'
+  // is live into the module.
+  if (b.defining_instruction()->parent() == module_->entry_computation() &&
+      b.defining_instruction()->opcode() == HloOpcode::kParameter) {
+    return false;
+  }
+
+  // Phi values require special handling. Because XLA does not have a phi
+  // instruction, the definition instruction of the phis values are
+  // placeholders: either the subcomputation parameter (body or condition) or
+  // the while instruction. However, the program point where these values are
+  // logically defined does not necessarily coincide exactly with program point
+  // of these place-holder instructions. So we explicitly define the following
+  // order for phi values:
+  //
+  //   body/condition parameter phi:
+  //     Defined before all values defined in its computation excepting other
+  //     phis.
+  //
+  //   while phi:
+  //     defined after all values defined in the condition or body.
+  //
+  auto is_body_or_condition_phi = [](const HloValue& v) {
+    return v.is_phi() &&
+           v.defining_instruction()->opcode() == HloOpcode::kParameter;
+  };
+  if (is_body_or_condition_phi(a) && !is_body_or_condition_phi(b) &&
+      call_graph_->InstructionIsNestedIn(b.defining_instruction(),
+                                         a.defining_instruction()->parent())) {
+    return true;
+  }
+  if (is_body_or_condition_phi(b) &&
+      call_graph_->InstructionIsNestedIn(a.defining_instruction(),
+                                         b.defining_instruction()->parent())) {
+    return false;
+  }
+
+  // If 'b' is a while phi and 'a' is in the body or condition, then 'a'
+  // executes before 'b'.
+  if (b.is_phi() && b.defining_instruction()->opcode() == HloOpcode::kWhile &&
+      (call_graph_->InstructionIsNestedIn(
+           a.defining_instruction(), b.defining_instruction()->while_body()) ||
+       call_graph_->InstructionIsNestedIn(
+           a.defining_instruction(),
+           b.defining_instruction()->while_condition()))) {
+    return true;
+  }
+
+  return ordering.ExecutesBefore(a.defining_instruction(),
+                                 b.defining_instruction());
+}
+
+bool HloDataflowAnalysis::UseIsBeforeValueDefinition(
+    const HloUse& use, const HloValue& value,
+    const HloOrdering& ordering) const {
+  if (ordering.ExecutesBefore(use.instruction, value.defining_instruction())) {
+    return true;
+  }
+
+  // If the use is at the instruction where the value is defined, then the use
+  // is before the def if the instruction allows buffer sharing (in place
+  // computation).
+  if (use.instruction == value.defining_instruction() &&
+      CanShareOperandBufferWithUser(
+          use.instruction->mutable_operand(use.operand_number),
+          use.operand_index, value.defining_instruction(),
+          value.defining_index())) {
+    return true;
+  }
+
+  // The use at a while is an input to a phi, and logically occurs before values
+  // are defined in the body or condition computations.
+  if (use.instruction->opcode() == HloOpcode::kWhile) {
+    const HloInstruction* xla_while = use.instruction;
+    if (call_graph_->InstructionIsNestedIn(value.defining_instruction(),
+                                           xla_while->while_body()) ||
+        call_graph_->InstructionIsNestedIn(value.defining_instruction(),
+                                           xla_while->while_condition())) {
+      return true;
+    }
+  }
+
+  // Similarly if the value is defined at a while, it logically occurs after any
+  // uses in the body or condition computations.
+  if (value.defining_instruction()->opcode() == HloOpcode::kWhile) {
+    CHECK(ssa_form_);
+    const HloInstruction* xla_while = value.defining_instruction();
+    if (call_graph_->InstructionIsNestedIn(use.instruction,
+                                           xla_while->while_body()) ||
+        call_graph_->InstructionIsNestedIn(use.instruction,
+                                           xla_while->while_condition())) {
+      return true;
+    }
+  }
+  return false;
+}
+
+bool HloDataflowAnalysis::LiveRangeStrictlyBefore(
+    const HloValue& a, const HloValue& b, const HloOrdering& ordering) const {
+  VLOG(4) << "LiveRangeStrictlyBefore(a = " << a.ToShortString()
+          << ", b = " << b.ToShortString() << ")";
+  if (!IsDefinedBefore(a, b, ordering)) {
+    VLOG(4) << "a not defined before b";
+    return false;
+  }
+
+  // Live-out values from the module can never have ranges strictly before any
+  // other value.
+  if (a.live_out_of_module()) {
+    VLOG(4) << "a is live out of module";
+    return false;
+  }
+
+  // Live-out values of computations can never have ranges strictly before any
+  // other value in the computation (including values nested in
+  // subcomputations).
+  if (a.live_out_of_computation() &&
+      call_graph_->InstructionIsNestedIn(b.defining_instruction(),
+                                         a.defining_instruction()->parent())) {
+    VLOG(4) << "a is live out of computation containing b";
+    return false;
+  }
+
+  // All uses of 'a' must be before 'b' is defined.
+  for (const HloUse& use : a.uses()) {
+    if (!UseIsBeforeValueDefinition(use, b, ordering)) {
+      VLOG(4) << "use of a (" << use << ") not before b is defined";
+      return false;
+    }
+  }
+
+  return true;
+}
+
+bool HloDataflowAnalysis::MayInterfere(const HloValue& a, const HloValue& b,
+                                       const HloOrdering& ordering) const {
+  // Buffers without disjoint liveness may interfere.
+  return !LiveRangeStrictlyBefore(a, b, ordering) &&
+         !LiveRangeStrictlyBefore(b, a, ordering);
+}
+
 /* static */
 StatusOr<std::unique_ptr<HloDataflowAnalysis>> HloDataflowAnalysis::Run(
     HloModule* module, bool ssa_form, bool bitcast_defines_value) {
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
index 2f9b0a64be..7087bd978d 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/call_graph.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_ordering.h"
 #include "tensorflow/compiler/xla/shape_tree.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status.h"
@@ -123,16 +124,16 @@ class HloValue {
   bool is_phi() const { return is_phi_; }
 
   // Return the location where this value is defined.
-  const HloLocation& DefinitionLocation() const { return locations_[0]; }
+  const HloLocation& defining_location() const { return locations_[0]; }
 
   // Return the instruction which defines this HloValue.
-  HloInstruction* instruction() const {
-    return DefinitionLocation().instruction;
+  HloInstruction* defining_instruction() const {
+    return defining_location().instruction;
   }
 
   // Return the shape index at which this HloValue is defined in the output of
-  // instruction().
-  const ShapeIndex& index() const { return DefinitionLocation().index; }
+  // its defining instruction.
+  const ShapeIndex& defining_index() const { return defining_location().index; }
 
   // Add or remove a location at which the HloValue appears. The definition
   // location can not be removed. The uses of the HloValue are updated.
@@ -145,9 +146,12 @@ class HloValue {
   // Return all uses of the HloValue.
   const std::vector<HloUse>& uses() const { return uses_; }
 
-  // Set/get whether this HloValue is live out of the module.
+  // Get whether this HloValue is live out of the module.
   bool live_out_of_module() const { return live_out_of_module_; }
 
+  // Get whether this HloValue is live out of the computation it is defined in.
+  bool live_out_of_computation() const { return live_out_of_computation_; }
+
   bool operator==(const HloValue& other) const;
   bool operator!=(const HloValue& other) const;
 
@@ -172,6 +176,9 @@ class HloValue {
 
   // Whether this value is live out of the HLO module.
   bool live_out_of_module_ = false;
+
+  // Whether this value is live out of its computation.
+  bool live_out_of_computation_ = false;
 };
 
 std::ostream& operator<<(std::ostream& out, const HloValue& hlo_value);
@@ -309,6 +316,17 @@ class HloDataflowAnalysis {
   const HloValue& GetValue(HloValue::Id value_id) const;
   HloValue& GetValue(HloValue::Id value_id);
 
+  // Returns whether the given values interfere assuming the given HLO
+  // ordering. Two values interfere if they may both be simultaneously live.
+  bool MayInterfere(const HloValue& a, const HloValue& b,
+                    const HloOrdering& ordering) const;
+
+  // Overload which takes HloValue:Ids.
+  bool MayInterfere(HloValue::Id a, HloValue::Id b,
+                    const HloOrdering& ordering) const {
+    return MayInterfere(GetValue(a), GetValue(b), ordering);
+  }
+
   // Return the total number of HloValues.
   int64 value_count() const { return values_.size(); }
 
@@ -374,6 +392,20 @@ class HloDataflowAnalysis {
       HloInstruction* instruction, const InstructionValueSet& new_value_set,
       const InstructionValueSet* prev_value_set = nullptr);
 
+  // Returns true if the live range of the given value 'a' is strictly before
+  // the live range of value 'b' using the given HLO ordering.
+  bool LiveRangeStrictlyBefore(const HloValue& a, const HloValue& b,
+                               const HloOrdering& ordering) const;
+
+  // Returns whether the value 'a' is defined before the value 'b' under the
+  // given ordering.
+  bool IsDefinedBefore(const HloValue& a, const HloValue& b,
+                       const HloOrdering& ordering) const;
+
+  // Returns whether the given use is before the given value definition.
+  bool UseIsBeforeValueDefinition(const HloUse& use, const HloValue& value,
+                                  const HloOrdering& ordering) const;
+
   HloModule* const module_;
   const bool ssa_form_;
   const bool bitcast_defines_value_;
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
index a97620cd0d..79edd0fcb5 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
@@ -39,14 +39,14 @@ using ::testing::UnorderedElementsAre;
 class HloDataflowAnalysisTest : public HloTestBase,
                                 public ::testing::WithParamInterface<bool> {
  protected:
-  HloDataflowAnalysisTest() : module_(TestName()) {}
+  HloDataflowAnalysisTest() : module_(CreateNewModule()) {}
 
   // Run dataflow analysis on the member module. For convenience returns a
   // reference to the generated analysis stored in analysis_.
   const HloDataflowAnalysis& RunAnalysis(bool ssa_form,
                                          bool bitcast_defines_value = false) {
     analysis_ =
-        HloDataflowAnalysis::Run(&module_, ssa_form, bitcast_defines_value)
+        HloDataflowAnalysis::Run(module_.get(), ssa_form, bitcast_defines_value)
             .ConsumeValueOrDie();
     return *analysis_;
   }
@@ -63,10 +63,22 @@ class HloDataflowAnalysisTest : public HloTestBase,
     return values;
   }
 
-  HloModule module_;
+  // Returns true if the top-level values for instructions 'a' and 'b' may
+  // interfere. Precondition: 'a' and 'b' define array-shaped values.
+  bool InstructionsMayInterfere(const HloOrdering& ordering,
+                                const HloInstruction* a,
+                                const HloInstruction* b) {
+    EXPECT_FALSE(ShapeUtil::IsTuple(a->shape()));
+    EXPECT_FALSE(ShapeUtil::IsTuple(b->shape()));
+    return analysis_->MayInterfere(analysis_->GetValueDefinedAt(a),
+                                   analysis_->GetValueDefinedAt(b), ordering);
+  }
+
+  std::unique_ptr<HloModule> module_;
   std::unique_ptr<HloDataflowAnalysis> analysis_;
 
   const Shape scalar_shape_ = ShapeUtil::MakeShape(F32, {});
+  const Shape vector_shape_ = ShapeUtil::MakeShape(F32, {42});
 };
 
 TEST_P(HloDataflowAnalysisTest, BinaryOperation) {
@@ -78,7 +90,7 @@ TEST_P(HloDataflowAnalysisTest, BinaryOperation) {
       HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto add = builder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, constant1, constant2));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -126,7 +138,7 @@ TEST_P(HloDataflowAnalysisTest, TupleAndGtes) {
       HloInstruction::CreateGetTupleElement(scalar_shape_, tuple, 1));
   auto add = builder.AddInstruction(
       HloInstruction::CreateBinary(scalar_shape_, HloOpcode::kAdd, gte0, gte1));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -158,22 +170,16 @@ TEST_P(HloDataflowAnalysisTest, TupleAndGtes) {
   // Verify uses. Of interest is that a GetTupleElement instruction is only a
   // use of the top-level value in the tuple operand.
   EXPECT_THAT(analysis.GetValueDefinedAt(param0).uses(),
-              UnorderedElementsAre(HloUse{tuple, 0, {}}, HloUse{add, 0, {}}));
+              UnorderedElementsAre(HloUse{add, 0, {}}));
   EXPECT_THAT(analysis.GetValueDefinedAt(param1).uses(),
-              UnorderedElementsAre(HloUse{tuple, 1, {}}, HloUse{add, 1, {}}));
+              UnorderedElementsAre(HloUse{add, 1, {}}));
   EXPECT_THAT(analysis.GetValueDefinedAt(tuple, /*index=*/{}).uses(),
               UnorderedElementsAre(HloUse{gte0, 0, {}}, HloUse{gte1, 0, {}}));
   EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_module());
 }
 
 TEST_P(HloDataflowAnalysisTest, NestedTuple) {
-  // Verify the dataflow through a nested tuple of the following form for two
-  // constants %constant1 and %constant2:
-  //
-  // %nested_tuple = {{%constant1, %constant2},
-  //                  {%constant1, %constant2},
-  //                  %constant1}
-  //
+  // Verify the dataflow through a nested tuple.
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
@@ -187,7 +193,7 @@ TEST_P(HloDataflowAnalysisTest, NestedTuple) {
       HloInstruction::CreateGetTupleElement(tuple->shape(), nested_tuple, 1));
   auto gte_out = builder.AddInstruction(
       HloInstruction::CreateGetTupleElement(scalar_shape_, gte_tuple, 0));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -202,18 +208,15 @@ TEST_P(HloDataflowAnalysisTest, NestedTuple) {
           HloLocation{nested_tuple, {0, 0}}, HloLocation{nested_tuple, {1, 0}},
           HloLocation{nested_tuple, {2}}, HloLocation{gte_tuple, {0}},
           HloLocation{gte_out, {}}));
-  EXPECT_THAT(analysis.GetValueDefinedAt(constant1).uses(),
-              UnorderedElementsAre(
-                  HloUse{tuple, 0, {}}, HloUse{nested_tuple, 0, {0}},
-                  HloUse{nested_tuple, 1, {0}}, HloUse{nested_tuple, 2, {}}));
-  EXPECT_THAT(
-      analysis.GetValueDefinedAt(constant2).uses(),
-      UnorderedElementsAre(HloUse{tuple, 1, {}}, HloUse{nested_tuple, 0, {1}},
-                           HloUse{nested_tuple, 1, {1}}));
+  // Constant values should have no uses though one is live out. The locations
+  // where they appear as operands are on instructions which do not use the
+  // values (eg, Tuple).
+  EXPECT_TRUE(analysis.GetValueDefinedAt(constant1).uses().empty());
+  EXPECT_TRUE(analysis.GetValueDefinedAt(constant2).uses().empty());
+
+  // The top-level tuple values are used in GTE instructions.
   EXPECT_THAT(analysis.GetValueDefinedAt(tuple, /*index=*/{}).uses(),
-              UnorderedElementsAre(HloUse{nested_tuple, 0, {}},
-                                   HloUse{nested_tuple, 1, {}},
-                                   HloUse{gte_out, 0, {}}));
+              UnorderedElementsAre(HloUse{gte_out, 0, {}}));
   EXPECT_THAT(analysis.GetValueDefinedAt(nested_tuple, /*index=*/{}).uses(),
               UnorderedElementsAre(HloUse{gte_tuple, 0, {}}));
 
@@ -236,7 +239,7 @@ TEST_P(HloDataflowAnalysisTest, SingleCall) {
   auto add = subbuilder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, subparam0, subparam1));
   HloComputation* called_computation =
-      module_.AddEmbeddedComputation(subbuilder.Build());
+      module_->AddEmbeddedComputation(subbuilder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -245,7 +248,7 @@ TEST_P(HloDataflowAnalysisTest, SingleCall) {
       HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
   auto call = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, called_computation));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -268,11 +271,12 @@ TEST_P(HloDataflowAnalysisTest, SingleCall) {
   EXPECT_EQ(analysis.GetUniqueValueAt(call), analysis.GetValueDefinedAt(add));
 
   EXPECT_THAT(analysis.GetValueDefinedAt(constant1).uses(),
-              UnorderedElementsAre(HloUse{add, 0, {}}, HloUse{call, 0, {}}));
+              UnorderedElementsAre(HloUse{add, 0, {}}));
   EXPECT_THAT(analysis.GetValueDefinedAt(constant2).uses(),
-              UnorderedElementsAre(HloUse{add, 1, {}}, HloUse{call, 1, {}}));
+              UnorderedElementsAre(HloUse{add, 1, {}}));
 
   EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_module());
+  EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_computation());
 }
 
 TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithSameArguments) {
@@ -285,7 +289,7 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithSameArguments) {
   auto add = subbuilder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, subparam0, subparam1));
   HloComputation* called_computation =
-      module_.AddEmbeddedComputation(subbuilder.Build());
+      module_->AddEmbeddedComputation(subbuilder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -298,7 +302,7 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithSameArguments) {
       scalar_shape_, {constant1, constant2}, called_computation));
   auto sub = builder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kSubtract, call1, call2));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -316,17 +320,18 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithSameArguments) {
   EXPECT_TRUE(analysis.ValueIsDefinedAt(sub));
 
   EXPECT_THAT(analysis.GetValueDefinedAt(constant1).uses(),
-              UnorderedElementsAre(HloUse{add, 0, {}}, HloUse{call1, 0, {}},
-                                   HloUse{call2, 0, {}}));
+              UnorderedElementsAre(HloUse{add, 0, {}}));
   EXPECT_THAT(analysis.GetValueDefinedAt(constant2).uses(),
-              UnorderedElementsAre(HloUse{add, 1, {}}, HloUse{call1, 1, {}},
-                                   HloUse{call2, 1, {}}));
+              UnorderedElementsAre(HloUse{add, 1, {}}));
   // The Add from the subcomputation is used as both operands of the Subtract.
   EXPECT_THAT(analysis.GetValueDefinedAt(add).uses(),
               UnorderedElementsAre(HloUse{sub, 0, {}}, HloUse{sub, 1, {}}));
 
   EXPECT_FALSE(analysis.GetValueDefinedAt(add).live_out_of_module());
+  EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_computation());
+
   EXPECT_TRUE(analysis.GetValueDefinedAt(sub).live_out_of_module());
+  EXPECT_TRUE(analysis.GetValueDefinedAt(sub).live_out_of_computation());
 }
 
 TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithDifferentArguments) {
@@ -339,7 +344,7 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithDifferentArguments) {
   auto add = subbuilder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, subparam0, subparam1));
   HloComputation* called_computation =
-      module_.AddEmbeddedComputation(subbuilder.Build());
+      module_->AddEmbeddedComputation(subbuilder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -350,7 +355,7 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithDifferentArguments) {
       scalar_shape_, {constant1, constant2}, called_computation));
   auto call2 = builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {call1, constant2}, called_computation));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -392,7 +397,7 @@ TEST_P(HloDataflowAnalysisTest, NestedCalls) {
   auto add = inner_builder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, inner_param0, inner_param1));
   HloComputation* inner_computation =
-      module_.AddEmbeddedComputation(inner_builder.Build());
+      module_->AddEmbeddedComputation(inner_builder.Build());
 
   auto outer_builder = HloComputation::Builder("OuterComputation");
   auto outer_param0 = outer_builder.AddInstruction(
@@ -400,19 +405,19 @@ TEST_P(HloDataflowAnalysisTest, NestedCalls) {
   auto outer_param1 = outer_builder.AddInstruction(
       HloInstruction::CreateParameter(1, scalar_shape_, "param1"));
   // Swizzle parameters.
-  auto nested_call = outer_builder.AddInstruction(HloInstruction::CreateCall(
+  outer_builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {outer_param1, outer_param0}, inner_computation));
   HloComputation* outer_computation =
-      module_.AddEmbeddedComputation(outer_builder.Build());
+      module_->AddEmbeddedComputation(outer_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
   auto constant2 = builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0)));
-  auto call = builder.AddInstruction(HloInstruction::CreateCall(
+  builder.AddInstruction(HloInstruction::CreateCall(
       scalar_shape_, {constant1, constant2}, outer_computation));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -423,14 +428,10 @@ TEST_P(HloDataflowAnalysisTest, NestedCalls) {
 
   // Verify that the uses of the constants are properly swizzled by parameter
   // permutation in nested_call.
-  EXPECT_THAT(
-      analysis.GetValueDefinedAt(constant1).uses(),
-      UnorderedElementsAre(HloUse{call, 0, {}}, HloUse{nested_call, 1, {}},
-                           HloUse{add, 1, {}}));
-  EXPECT_THAT(
-      analysis.GetValueDefinedAt(constant2).uses(),
-      UnorderedElementsAre(HloUse{call, 1, {}}, HloUse{nested_call, 0, {}},
-                           HloUse{add, 0, {}}));
+  EXPECT_THAT(analysis.GetValueDefinedAt(constant1).uses(),
+              UnorderedElementsAre(HloUse{add, 1, {}}));
+  EXPECT_THAT(analysis.GetValueDefinedAt(constant2).uses(),
+              UnorderedElementsAre(HloUse{add, 0, {}}));
 
   EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_module());
 }
@@ -465,18 +466,18 @@ TEST_P(HloDataflowAnalysisTest, SingleWhile) {
       HloInstruction::CreateGetTupleElement(scalar_shape_, body_param, 1));
   auto add = body_builder.AddInstruction(HloInstruction::CreateBinary(
       scalar_shape_, HloOpcode::kAdd, body_element_0, body_element_1));
-  auto body_tuple = body_builder.AddInstruction(
+  body_builder.AddInstruction(
       HloInstruction::CreateTuple({body_element_0, add}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   // Condition computation trivially returns a constant "false".
   auto cond_builder = HloComputation::Builder("condition");
   auto cond_param = cond_builder.AddInstruction(
       HloInstruction::CreateParameter(0, tuple_shape, "param"));
-  cond_builder.AddInstruction(
+  auto cond_constant = cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -487,11 +488,15 @@ TEST_P(HloDataflowAnalysisTest, SingleWhile) {
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, body, tuple));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
 
+  EXPECT_TRUE(
+      analysis.GetValueDefinedAt(cond_constant).live_out_of_computation());
+  EXPECT_FALSE(analysis.GetValueDefinedAt(cond_constant).live_out_of_module());
+
   if (ssa_form) {
     // Element 0 of the tuple passed through the body so no phi value is
     // defined.
@@ -507,15 +512,17 @@ TEST_P(HloDataflowAnalysisTest, SingleWhile) {
     EXPECT_TRUE(analysis.ValueIsDefinedAt(cond_param, /*index=*/{1}));
     EXPECT_TRUE(analysis.GetValueDefinedAt(cond_param, /*index=*/{1}).is_phi());
 
-    EXPECT_THAT(analysis.GetValueDefinedAt(constant1).uses(),
-                UnorderedElementsAre(HloUse{add, 0, {}}, HloUse{tuple, 0, {}},
-                                     HloUse{xla_while, 0, {0}},
-                                     HloUse{body_tuple, 0, {}}));
+    EXPECT_THAT(
+        analysis.GetValueDefinedAt(constant1).uses(),
+        UnorderedElementsAre(HloUse{add, 0, {}}, HloUse{xla_while, 0, {0}}));
 
     // Constant1 passes through the body and out of the module.
     EXPECT_TRUE(analysis.GetValueDefinedAt(constant1).live_out_of_module());
     EXPECT_TRUE(analysis.GetValueDefinedAt(xla_while, /*index=*/{1})
                     .live_out_of_module());
+
+    EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_computation());
+    EXPECT_FALSE(analysis.GetValueDefinedAt(add).live_out_of_module());
   } else {
     // While instruction and subcomputation parameters should not define values
     // in non-ssa form.
@@ -528,6 +535,7 @@ TEST_P(HloDataflowAnalysisTest, SingleWhile) {
 
     EXPECT_TRUE(analysis.GetValueDefinedAt(constant1).live_out_of_module());
     EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_module());
+    EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_computation());
   }
 }
 
@@ -565,7 +573,7 @@ TEST_P(HloDataflowAnalysisTest, SequentialWhiles) {
       scalar_shape_, HloOpcode::kAdd, body_element_0, body_element_1));
   body_builder.AddInstruction(
       HloInstruction::CreateTuple({body_element_0, add}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   auto cond_builder = HloComputation::Builder("condition");
   cond_builder.AddInstruction(
@@ -573,7 +581,7 @@ TEST_P(HloDataflowAnalysisTest, SequentialWhiles) {
   cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -588,7 +596,7 @@ TEST_P(HloDataflowAnalysisTest, SequentialWhiles) {
       HloInstruction::CreateWhile(tuple_shape, condition, body, xla_while0));
   auto xla_while2 = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, body, xla_while1));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -632,7 +640,7 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) {
   cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   // Element 0 passes transparently through the body.
   auto inner_builder = HloComputation::Builder("inner_body");
@@ -647,7 +655,7 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) {
   inner_builder.AddInstruction(
       HloInstruction::CreateTuple({inner_element_0, add}));
   HloComputation* inner_body =
-      module_.AddEmbeddedComputation(inner_builder.Build());
+      module_->AddEmbeddedComputation(inner_builder.Build());
 
   // Element 1 passes transparently through the body.
   auto outer_builder = HloComputation::Builder("outer_body");
@@ -664,7 +672,7 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) {
   auto nested_while = outer_builder.AddInstruction(HloInstruction::CreateWhile(
       tuple_shape, condition, inner_body, outer_tuple));
   HloComputation* outer_body =
-      module_.AddEmbeddedComputation(outer_builder.Build());
+      module_->AddEmbeddedComputation(outer_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -675,7 +683,7 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) {
       HloInstruction::CreateTuple({constant1, constant2}));
   auto entry_while = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, outer_body, tuple));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -751,7 +759,7 @@ TEST_P(HloDataflowAnalysisTest, SwizzlingWhile) {
       HloInstruction::CreateGetTupleElement(scalar_shape_, body_param, 1));
   body_builder.AddInstruction(
       HloInstruction::CreateTuple({body_element_1, body_element_0}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   auto cond_builder = HloComputation::Builder("condition");
   auto cond_param = cond_builder.AddInstruction(
@@ -759,7 +767,7 @@ TEST_P(HloDataflowAnalysisTest, SwizzlingWhile) {
   cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto builder = HloComputation::Builder(TestName());
   auto constant1 = builder.AddInstruction(
@@ -770,7 +778,7 @@ TEST_P(HloDataflowAnalysisTest, SwizzlingWhile) {
       HloInstruction::CreateTuple({constant1, constant2}));
   auto xla_while = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple_shape, condition, body, tuple));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -825,7 +833,7 @@ TEST_P(HloDataflowAnalysisTest, ArraySelect) {
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       scalar_shape_, HloOpcode::kSelect, pred, constant1, constant2));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -868,7 +876,7 @@ TEST_P(HloDataflowAnalysisTest, TupleSelect) {
   auto select1234 = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple_shape, HloOpcode::kSelect, pred, select12, select34));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -899,14 +907,16 @@ TEST_P(HloDataflowAnalysisTest, TupleSelect) {
                                    analysis.GetValueDefinedAt(constant4)));
 
   EXPECT_THAT(
-      analysis.GetValueDefinedAt(constant1).uses(),
-      UnorderedElementsAre(HloUse{tuple1, 0, {}}, HloUse{select11, 1, {0}},
-                           HloUse{select11, 2, {0}}, HloUse{select12, 1, {0}},
-                           HloUse{select1234, 1, {0}}));
-  EXPECT_THAT(
-      analysis.GetValueDefinedAt(constant2).uses(),
-      UnorderedElementsAre(HloUse{tuple2, 0, {}}, HloUse{select12, 2, {0}},
-                           HloUse{select1234, 1, {0}}));
+      analysis.GetValueDefinedAt(tuple1, /*index=*/{}).uses(),
+      UnorderedElementsAre(HloUse{select11, 1, {}}, HloUse{select11, 2, {}},
+                           HloUse{select12, 1, {}}));
+
+  // The two constant values just pass through the Selects and are not
+  // used. They are live out however.
+  EXPECT_TRUE(analysis.GetValueDefinedAt(constant1).uses().empty());
+  EXPECT_TRUE(analysis.GetValueDefinedAt(constant2).uses().empty());
+  EXPECT_TRUE(analysis.GetValueDefinedAt(constant1).live_out_of_module());
+  EXPECT_TRUE(analysis.GetValueDefinedAt(constant2).live_out_of_module());
 }
 
 TEST_P(HloDataflowAnalysisTest, NestedTupleSelect) {
@@ -935,7 +945,7 @@ TEST_P(HloDataflowAnalysisTest, NestedTupleSelect) {
   auto select = builder.AddInstruction(HloInstruction::CreateTernary(
       tuple1->shape(), HloOpcode::kSelect, pred, tuple1, tuple2));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -993,7 +1003,7 @@ TEST_P(HloDataflowAnalysisTest, TupleSelectToWhile) {
       scalar_shape_, HloOpcode::kAdd, body_element_0, body_element_1));
   body_builder.AddInstruction(
       HloInstruction::CreateTuple({body_element_0, add}));
-  HloComputation* body = module_.AddEmbeddedComputation(body_builder.Build());
+  HloComputation* body = module_->AddEmbeddedComputation(body_builder.Build());
 
   auto cond_builder = HloComputation::Builder("condition");
   cond_builder.AddInstruction(
@@ -1001,7 +1011,7 @@ TEST_P(HloDataflowAnalysisTest, TupleSelectToWhile) {
   cond_builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
   HloComputation* condition =
-      module_.AddEmbeddedComputation(cond_builder.Build());
+      module_->AddEmbeddedComputation(cond_builder.Build());
 
   auto pred = builder.AddInstruction(
       HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
@@ -1024,7 +1034,7 @@ TEST_P(HloDataflowAnalysisTest, TupleSelectToWhile) {
   auto xla_while = builder.AddInstruction(
       HloInstruction::CreateWhile(tuple->shape(), condition, body, tuple));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -1066,7 +1076,7 @@ TEST_P(HloDataflowAnalysisTest, BitcastDefinesValue) {
   auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary(
       scalar_shape_, HloOpcode::kBitcast, constant));
 
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   {
@@ -1102,7 +1112,7 @@ TEST_P(HloDataflowAnalysisTest, TupleCopy) {
       builder.AddInstruction(HloInstruction::CreateTuple({param0, param1}));
   auto copy = builder.AddInstruction(
       HloInstruction::CreateUnary(tuple->shape(), HloOpcode::kCopy, tuple));
-  module_.AddEntryComputation(builder.Build());
+  module_->AddEntryComputation(builder.Build());
 
   bool ssa_form = GetParam();
   const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
@@ -1126,6 +1136,352 @@ TEST_P(HloDataflowAnalysisTest, TupleCopy) {
       analysis.GetValueDefinedAt(copy, /*index=*/{}).live_out_of_module());
 }
 
+TEST_P(HloDataflowAnalysisTest, ElementwiseChainInterference) {
+  // A simple chain of elementwise operations. No values should interfere.
+  //
+  // param --> negate -> exp -> log
+  //
+  auto builder = HloComputation::Builder(TestName());
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, vector_shape_, "param"));
+  auto negate = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kNegate, param));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kExp, negate));
+  auto log = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kLog, exp));
+
+  module_->AddEntryComputation(builder.Build());
+  RunAnalysis(GetParam());
+
+  DependencyHloOrdering ordering(module_.get());
+
+  // No values should interfere.
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, exp));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, log));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, negate, exp));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, negate, log));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, exp, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, exp, log));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, log, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, log, exp));
+
+  // Values should interfere with itself.
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, exp, exp));
+}
+
+TEST_P(HloDataflowAnalysisTest, MultipleEntryParameters_Sequential) {
+  // Two entry params, which interfere with each other.
+  //
+  // param0 --> negate ---------------\
+  //                param1 --> exp --> add
+  auto builder = HloComputation::Builder(TestName());
+  auto param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, vector_shape_, "param0"));
+  auto param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, vector_shape_, "param1"));
+  auto negate = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kNegate, param0));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kExp, param1));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      vector_shape_, HloOpcode::kAdd, negate, exp));
+
+  auto entry = module_->AddEntryComputation(builder.Build());
+  RunAnalysis(GetParam());
+
+  SequentialHloOrdering::HloModuleSequence sequence;
+  sequence.insert({entry, {param0, negate, param1, exp, add}});
+  SequentialHloOrdering ordering(module_.get(), sequence);
+
+  // Entry parameters interfere as if they are defined simultaneously at
+  // the very beginning.
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, param0, param1));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param0, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param0, exp));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param0, add));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, param1, param0));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, param1, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param1, exp));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param1, add));
+
+  // Negate and exp still interfere.
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, negate, exp));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, exp, negate));
+
+  // But {negate, add} and {exp, add} don't interfere.
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, negate, add));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, add, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, exp, add));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, add, exp));
+}
+
+TEST_P(HloDataflowAnalysisTest, WhileParameters_Sequential) {
+  // Similar to MultipleEntryParameters_Sequential, but the parameter is of
+  // while body computation. Body computation in the sequential order:
+  //
+  //  %constant = Constant(...)
+  //  %exp = Exp(%constant)
+  //  %param = Param(0)
+  //  %add = Add(%param, %exp)  ;; Root of body
+  //  %dead_constant = Constant(...)
+  //  %dead_negate = Negate(%dead_constant)
+  //
+  // %constant and its only use %exp are ordered before 'param'. However, the
+  // %constant and %param values still interfere because the parameter is
+  // considered live into the while body.
+  //
+  // Similarly, %dead_constant and %dead_negate are ordered after the root of
+  // the body computation %add. However, %add is liveout of the computation so
+  // %dead_constant and %add interfere.
+  auto body_builder = HloComputation::Builder(TestName());
+  auto body_param = body_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, scalar_shape_, "body_param"));
+  auto constant = body_builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
+  auto exp = body_builder.AddInstruction(
+      HloInstruction::CreateUnary(scalar_shape_, HloOpcode::kExp, constant));
+  auto add = body_builder.AddInstruction(HloInstruction::CreateBinary(
+      scalar_shape_, HloOpcode::kAdd, exp, body_param));
+  auto dead_constant = body_builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
+  auto dead_negate = body_builder.AddInstruction(HloInstruction::CreateUnary(
+      scalar_shape_, HloOpcode::kNegate, dead_constant));
+  HloComputation* body = module_->AddEmbeddedComputation(
+      body_builder.Build(/*root_instruction=*/add));
+
+  auto cond_builder = HloComputation::Builder("condition");
+  auto cond_param = cond_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, scalar_shape_, "cond_param"));
+  auto cond_constant = cond_builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
+  HloComputation* condition =
+      module_->AddEmbeddedComputation(cond_builder.Build());
+
+  auto builder = HloComputation::Builder(TestName());
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, scalar_shape_, "param"));
+  auto xla_while = builder.AddInstruction(
+      HloInstruction::CreateWhile(scalar_shape_, condition, body, param));
+
+  auto entry = module_->AddEntryComputation(builder.Build());
+  bool ssa_form = GetParam();
+  const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form);
+
+  SequentialHloOrdering::HloModuleSequence sequence;
+  sequence.insert({entry, {param, xla_while}});
+  sequence.insert({condition, {cond_param, cond_constant}});
+  // Construct the order such that 'constant' and its use 'exp' are before
+  // body_param.
+  sequence.insert({body, {constant, exp, body_param, add}});
+
+  SequentialHloOrdering ordering(module_.get(), sequence);
+
+  // 'add' is the body root even though later instructions follow in the order
+  // like 'dead_negate'. Only 'add' should be live out of the computation.
+  EXPECT_TRUE(analysis.GetValueDefinedAt(add).live_out_of_computation());
+  EXPECT_FALSE(
+      analysis.GetValueDefinedAt(dead_negate).live_out_of_computation());
+
+  // 'add' is live out of the body and will interfere with an later instructions
+  // such as 'dead_constant' and 'dead_negate'.
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, add, dead_constant));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, add, dead_negate));
+
+  // The remaining checks test phi values defined by body and condition
+  // parameters which only occur in the SSA form of the analysis.
+  if (ssa_form) {
+    // Though the ordering suggests 'constant' and 'param' should not interfere,
+    // 'param' is live in and thus interferes with any earlier instruction of
+    // the computation in the order (eg 'constant')'
+    EXPECT_TRUE(InstructionsMayInterfere(ordering, body_param, constant));
+    EXPECT_TRUE(InstructionsMayInterfere(ordering, body_param, exp));
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, body_param, add));
+
+    // The following values end up in the same buffer:
+    //  (1) the init value: 'param'
+    //  (2) the body parameter: 'body_param'
+    //  (3) the condition parameter: 'cond_param'
+    //  (4) the root value of the while body: 'add'
+    //  (5) the while value: 'xla_while'
+    // None should interfere.
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, param, body_param));
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, param, cond_param));
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, param, add));
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, param, xla_while));
+
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, body_param, cond_param));
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, body_param, add));
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, body_param, xla_while));
+
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, cond_param, add));
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, cond_param, xla_while));
+
+    EXPECT_FALSE(InstructionsMayInterfere(ordering, add, xla_while));
+  }
+}
+
+TEST_P(HloDataflowAnalysisTest, NonElementwiseOperand) {
+  // A chain of operations with two elementwise and one non-elementwise. The
+  // elementwise op should not interfere with its operand, while the
+  // non-elementwise op should interfere. Entry params always interfere.
+  //
+  // param --> exp -> negate -> reverse
+  //
+  auto builder = HloComputation::Builder(TestName());
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, vector_shape_, "param"));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kExp, param));
+  auto negate = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kNegate, exp));
+  auto reverse = builder.AddInstruction(
+      HloInstruction::CreateReverse(vector_shape_, negate, {0}));
+
+  module_->AddEntryComputation(builder.Build());
+  RunAnalysis(GetParam());
+
+  DependencyHloOrdering ordering(module_.get());
+
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, exp));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, reverse));
+
+  // Negate is elementwise, so doesn't interfere with its operand.
+  // Reverse is non-elementwise, so does interfere with its operand.
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, exp, negate));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, negate, reverse));
+}
+
+TEST_P(HloDataflowAnalysisTest, OverlappedValues) {
+  // Verify simultaneously live values interfere (exp and negate).
+  //
+  // param --> negate -> add
+  //     \---> exp -----/
+  //
+  auto builder = HloComputation::Builder(TestName());
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, vector_shape_, "param"));
+  auto negate = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kNegate, param));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kExp, param));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      vector_shape_, HloOpcode::kAdd, negate, exp));
+
+  module_->AddEntryComputation(builder.Build());
+  RunAnalysis(GetParam());
+
+  DependencyHloOrdering ordering(module_.get());
+
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, param, negate));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, param, exp));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, add));
+
+  // Negate and exp interfere with each other, but not with add.
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, negate, exp));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, exp, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, negate, add));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, add, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, exp, add));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, add, exp));
+}
+
+TEST_P(HloDataflowAnalysisTest, OverlappedValuesSequentialOrder) {
+  // Identical to the test OverlappedValue but using a sequential ordering of
+  // HLO instructions.
+  //
+  // param --> negate -> add
+  //     \---> exp -----/
+  //
+  // Sequential order:
+  //  param, negate, exp, add
+  //
+  // Liveness is identical to the DependencyHloOrdering.
+  auto builder = HloComputation::Builder(TestName());
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, vector_shape_, "param"));
+  auto negate = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kNegate, param));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kExp, param));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      vector_shape_, HloOpcode::kAdd, negate, exp));
+
+  auto entry = module_->AddEntryComputation(builder.Build());
+  RunAnalysis(GetParam());
+
+  SequentialHloOrdering::HloModuleSequence sequence;
+  std::vector<const HloInstruction*> order = {param, negate, exp, add};
+  sequence.emplace(entry, order);
+
+  SequentialHloOrdering ordering(module_.get(), sequence);
+
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, param, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, exp));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, param, add));
+
+  // Negate and exp interfere with each other, but not with add.
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, negate, exp));
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, exp, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, negate, add));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, add, negate));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, exp, add));
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, add, exp));
+}
+
+TEST_P(HloDataflowAnalysisTest, EmbeddedComputationInterference) {
+  // Test MayInterfere() for embedded computation, specifically the interference
+  // of values in different computations.
+  //
+  // embedded_computation:
+  //   %embedded_param = Param(0)
+  //   %embedded_log = Log(%embedded_param)
+  //
+  // entry computation:
+  //   %param = Param(0)
+  //   %negate = Negate(%param)
+  //   %exp = Negate(%exp)
+  //   %call = Call(embedded_computation, {%exp})
+  //   %add = Add(%negate, %call)
+  //
+  // Note %negate is live across the call and should interfere with all values
+  // in the embedded computation.
+  auto embedded_builder = HloComputation::Builder(TestName() + "_embedded");
+  auto embedded_param = embedded_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, vector_shape_, "embedded_param"));
+  auto embedded_log =
+      embedded_builder.AddInstruction(HloInstruction::CreateUnary(
+          vector_shape_, HloOpcode::kLog, embedded_param));
+  auto embedded_computation =
+      module_->AddEmbeddedComputation(embedded_builder.Build());
+
+  auto builder = HloComputation::Builder(TestName());
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, vector_shape_, "param"));
+  auto negate = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kNegate, param));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(vector_shape_, HloOpcode::kExp, param));
+  auto call = builder.AddInstruction(
+      HloInstruction::CreateCall(vector_shape_, {exp}, embedded_computation));
+  builder.AddInstruction(HloInstruction::CreateBinary(
+      vector_shape_, HloOpcode::kAdd, negate, call));
+  module_->AddEntryComputation(builder.Build());
+  RunAnalysis(GetParam());
+
+  DependencyHloOrdering ordering(module_.get());
+
+  // Exp only use is the call so it should not interfere with values inside the
+  // embedded computation.
+  EXPECT_FALSE(InstructionsMayInterfere(ordering, exp, embedded_log));
+
+  // Negate is live across the call and should interfere with values in the
+  // embedded computation
+  EXPECT_TRUE(InstructionsMayInterfere(ordering, negate, embedded_log));
+}
+
 INSTANTIATE_TEST_CASE_P(HloDataflowAnalysisInstantiation,
                         HloDataflowAnalysisTest,
                         ::testing::Values(false, true));
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc
index 72911ae9f9..61e5efa5b6 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering.cc
@@ -113,6 +113,20 @@ bool HloOrdering::ExecutesBefore(const HloInstruction* a,
   // a_ancestor and b_ancestor must be either both null or both non-null.
   CHECK_NE(b_ancestor, nullptr);
   CHECK_EQ(a_ancestor->parent(), b_ancestor->parent());
+
+  // If the common ancestor is a while instruction there is an additional
+  // ordering criteria which may apply. The condition computation is considered
+  // to execute before the body computation so if 'a' is in the condition and
+  // 'b' is in the body, then 'a' executes before 'b'.
+  if (a_ancestor == b_ancestor && a_ancestor->opcode() == HloOpcode::kWhile) {
+    const HloComputation* body = a_ancestor->while_body();
+    const HloComputation* condition = a_ancestor->while_condition();
+    if (call_graph_->InstructionIsNestedIn(a, condition) &&
+        call_graph_->InstructionIsNestedIn(b, body)) {
+      return true;
+    }
+  }
+
   return ExecutesBeforeInSameComputation(a_ancestor, b_ancestor);
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
index d36784e67d..56e36bd705 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
@@ -155,6 +155,68 @@ TEST_F(HloOrderingTest, InstructionsInDifferentComputations) {
   EXPECT_FALSE(ordering.ExecutesBefore(y, c));
 }
 
+TEST_F(HloOrderingTest, InstructionsInWhileComputations) {
+  // Tests the ordering of instructions in the body and condition of a while
+  // instruction. HLO code:
+  //
+  // body(F32[]) %param):
+  //   %negate = Negate(%param)
+  //
+  // condition(F32[] %param):
+  //   %convert = Convert<PRED>(%param)
+  //
+  // entry:
+  //   %constant = Constant(1.0)
+  //   return While(%constant, body, condition)
+  //
+  auto module = CreateNewModule();
+  const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {});
+
+  auto body_builder = HloComputation::Builder("body");
+  auto body_param = body_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, scalar_shape, "body_param"));
+  auto negate = body_builder.AddInstruction(HloInstruction::CreateUnary(
+      scalar_shape, HloOpcode::kNegate, body_param));
+  HloComputation* body = module->AddEmbeddedComputation(body_builder.Build());
+
+  auto cond_builder = HloComputation::Builder("condition");
+  auto cond_param = cond_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, scalar_shape, "cond_param"));
+  auto convert = cond_builder.AddInstruction(HloInstruction::CreateConvert(
+      ShapeUtil::MakeShape(xla::PRED, {}), cond_param));
+  HloComputation* condition =
+      module->AddEmbeddedComputation(cond_builder.Build());
+
+  auto builder = HloComputation::Builder(TestName());
+  auto constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0)));
+  auto xla_while = builder.AddInstruction(
+      HloInstruction::CreateWhile(scalar_shape, condition, body, constant));
+  module->AddEntryComputation(builder.Build());
+
+  DependencyHloOrdering ordering(module.get());
+  EXPECT_TRUE(ordering.ExecutesBefore(constant, xla_while));
+  EXPECT_TRUE(ordering.ExecutesBefore(constant, cond_param));
+  EXPECT_TRUE(ordering.ExecutesBefore(constant, convert));
+  EXPECT_TRUE(ordering.ExecutesBefore(constant, body_param));
+  EXPECT_TRUE(ordering.ExecutesBefore(constant, negate));
+
+  // The while should be unordered relative to the body and condition
+  // instructions.
+  EXPECT_FALSE(ordering.ExecutesBefore(xla_while, body_param));
+  EXPECT_FALSE(ordering.ExecutesBefore(xla_while, cond_param));
+  EXPECT_FALSE(ordering.ExecutesBefore(body_param, xla_while));
+  EXPECT_FALSE(ordering.ExecutesBefore(cond_param, xla_while));
+
+  // Condition instructions should be ordered before body instructions.
+  EXPECT_TRUE(ordering.ExecutesBefore(cond_param, body_param));
+  EXPECT_TRUE(ordering.ExecutesBefore(convert, body_param));
+  EXPECT_TRUE(ordering.ExecutesBefore(cond_param, negate));
+  EXPECT_TRUE(ordering.ExecutesBefore(convert, negate));
+
+  EXPECT_FALSE(ordering.ExecutesBefore(body_param, cond_param));
+}
+
 class MinimumMemoryForSequenceTest : public HloTestBase {};
 
 TEST_F(MinimumMemoryForSequenceTest, MultiComputation) {
diff --git a/tensorflow/compiler/xla/service/liveness_util.cc b/tensorflow/compiler/xla/service/liveness_util.cc
index 682bf19807..9c80fb3adb 100644
--- a/tensorflow/compiler/xla/service/liveness_util.cc
+++ b/tensorflow/compiler/xla/service/liveness_util.cc
@@ -28,17 +28,6 @@ limitations under the License.
 
 namespace xla {
 
-bool DoesNotUseOperandBuffer(const HloInstruction* operand,
-                             const ShapeIndex& index,
-                             const HloInstruction* user) {
-  CHECK(user->IsUserOf(operand))
-      << "user: " << user->ToString() << " operand: " << operand->ToString();
-
-  // GetTupleElement instructions only access the top-level buffer of their
-  // operand.
-  return (user->opcode() == HloOpcode::kGetTupleElement && !index.empty());
-}
-
 bool DoesNotUseOperandBuffer(const HloInstruction* operand,
                              const ShapeIndex& index,
                              const HloInstruction* user,
@@ -149,18 +138,22 @@ bool HasUniqueFusedUseOfOperandAt(
 
 // User and operand can share buffers iff both instructions emit the same shape
 // and layout, and 'user' meets one of the following qualifications:
-// *) Is element-wise. Or...
-// *) Is a loop fusion instruction where the only use of 'operand' at 'index'
-//    in the set 'user.fused_instructions' is a DynamicUpdateSlice fused root
-//    at operand 0. Or...
-// *) Is a kDot -> kAdd (or fused kTransposeDot -> kAdd) output fusion
-//    instruction where the only use of 'operand' at 'index' in the set
-//    'user.fused_instructions' is a kAdd fused root at operand 0 or 1. Or...
-// *) The 'user' of 'operand' is DynamicUpdateSlice or While at operand index 0.
+//
+// (1) Is element-wise. Or...
+// (2) Is a loop fusion instruction where the only use of 'operand' at 'index'
+//     in the set 'user.fused_instructions' is a DynamicUpdateSlice fused root
+//     at operand 0. Or...
+// (3) Is a kDot -> kAdd (or fused kTransposeDot -> kAdd) output fusion
+//     instruction where the only use of 'operand' at 'index' in the set
+//     'user.fused_instructions' is a kAdd fused root at operand 0 or 1. Or...
+// (4) The 'user' of 'operand' is DynamicUpdateSlice or While at operand index
+//     0.
+//
+// (2) and (3) can only be determined if points-to analysis is available.
 bool CanShareOperandBufferWithUser(
     HloInstruction* operand, const ShapeIndex& operand_index,
     HloInstruction* user, const ShapeIndex& user_index,
-    const TuplePointsToAnalysis& points_to_analysis) {
+    const TuplePointsToAnalysis* points_to_analysis) {
   CHECK(user->IsUserOf(operand))
       << "user: " << user->ToString() << " operand: " << operand->ToString();
   Shape operand_subshape =
@@ -170,7 +163,7 @@ bool CanShareOperandBufferWithUser(
   if (!ShapeUtil::Equal(operand_subshape, user_subshape)) {
     return false;
   }
-  if (user->opcode() == HloOpcode::kFusion) {
+  if (points_to_analysis != nullptr && user->opcode() == HloOpcode::kFusion) {
     if (user->fusion_kind() == HloInstruction::FusionKind::kLoop &&
         user->fused_expression_root()->opcode() ==
             HloOpcode::kDynamicUpdateSlice) {
@@ -180,7 +173,7 @@ bool CanShareOperandBufferWithUser(
       // 'operand_index', and this singleton use is the fused root at operand
       // index 0.
       return HasUniqueFusedUseOfOperandAt(operand, operand_index, user, 0,
-                                          points_to_analysis);
+                                          *points_to_analysis);
     } else if (user->fusion_kind() == HloInstruction::FusionKind::kOutput &&
                user->fused_expression_root()->opcode() == HloOpcode::kAdd) {
       // Output fusion with kAdd fused root.
@@ -208,7 +201,7 @@ bool CanShareOperandBufferWithUser(
       // index 'other_add_operand_index').
       return HasUniqueFusedUseOfOperandAt(operand, operand_index, user,
                                           other_add_operand_index,
-                                          points_to_analysis);
+                                          *points_to_analysis);
     }
   }
   if (user->opcode() == HloOpcode::kDynamicUpdateSlice ||
diff --git a/tensorflow/compiler/xla/service/liveness_util.h b/tensorflow/compiler/xla/service/liveness_util.h
index 0b01223db7..c7799e5ab5 100644
--- a/tensorflow/compiler/xla/service/liveness_util.h
+++ b/tensorflow/compiler/xla/service/liveness_util.h
@@ -34,21 +34,16 @@ bool DoesNotUseOperandBuffer(const HloInstruction* operand,
                              const HloInstruction* user,
                              const TuplePointsToAnalysis& points_to_analysis);
 
-// Overload which does not require points-to analysis. The result is more
-// conservative (returns false more often).
-bool DoesNotUseOperandBuffer(const HloInstruction* operand,
-                             const ShapeIndex& index,
-                             const HloInstruction* user);
-
 // Returns true if 'user' (at 'user_index') can share a buffer with its operand
-// 'operand' (at 'operand_index').
-// Returns false otherwise.
+// 'operand' (at 'operand_index'). Returns false otherwise. Optionally takes a
+// points-to analysis argument. Without the analysis, the result is more
+// conservative (returns false more often).
 //
 // REQUIRES: 'operand' is an operand of 'user'.
 bool CanShareOperandBufferWithUser(
     HloInstruction* operand, const ShapeIndex& operand_index,
     HloInstruction* user, const ShapeIndex& user_index,
-    const TuplePointsToAnalysis& points_to_analysis);
+    const TuplePointsToAnalysis* points_to_analysis = nullptr);
 
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/service/liveness_util_test.cc b/tensorflow/compiler/xla/service/liveness_util_test.cc
index 14d831f289..6a4fde8761 100644
--- a/tensorflow/compiler/xla/service/liveness_util_test.cc
+++ b/tensorflow/compiler/xla/service/liveness_util_test.cc
@@ -122,10 +122,10 @@ TEST_F(CanShareOperandBufferWithUserTest, ElementWiseSameShape) {
 
   BuildModuleAndRunAnalysis(builder.Build());
 
-  EXPECT_TRUE(
-      CanShareOperandBufferWithUser(param, {}, exp, {}, *points_to_analysis_));
-  EXPECT_TRUE(
-      CanShareOperandBufferWithUser(exp, {}, log, {}, *points_to_analysis_));
+  EXPECT_TRUE(CanShareOperandBufferWithUser(param, {}, exp, {},
+                                            points_to_analysis_.get()));
+  EXPECT_TRUE(CanShareOperandBufferWithUser(exp, {}, log, {},
+                                            points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, ElementWiseDifferentShape) {
@@ -143,9 +143,9 @@ TEST_F(CanShareOperandBufferWithUserTest, ElementWiseDifferentShape) {
   BuildModuleAndRunAnalysis(builder.Build());
 
   EXPECT_FALSE(CanShareOperandBufferWithUser(param0, {}, result, {},
-                                             *points_to_analysis_));
+                                             points_to_analysis_.get()));
   EXPECT_FALSE(CanShareOperandBufferWithUser(param1, {}, result, {},
-                                             *points_to_analysis_));
+                                             points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, CopyShares) {
@@ -161,10 +161,10 @@ TEST_F(CanShareOperandBufferWithUserTest, CopyShares) {
 
   BuildModuleAndRunAnalysis(builder.Build());
 
-  EXPECT_TRUE(
-      CanShareOperandBufferWithUser(param, {}, exp, {}, *points_to_analysis_));
-  EXPECT_TRUE(
-      CanShareOperandBufferWithUser(exp, {}, copy, {}, *points_to_analysis_));
+  EXPECT_TRUE(CanShareOperandBufferWithUser(param, {}, exp, {},
+                                            points_to_analysis_.get()));
+  EXPECT_TRUE(CanShareOperandBufferWithUser(exp, {}, copy, {},
+                                            points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, FusedDynamicUpdateSlice) {
@@ -197,9 +197,9 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDynamicUpdateSlice) {
 
   // The fusion instruction can share with tuple element 1.
   EXPECT_FALSE(CanShareOperandBufferWithUser(tuple, {0}, fusion, {},
-                                             *points_to_analysis_));
+                                             points_to_analysis_.get()));
   EXPECT_TRUE(CanShareOperandBufferWithUser(tuple, {1}, fusion, {},
-                                            *points_to_analysis_));
+                                            points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, DynamicUpdateSliceCanShare) {
@@ -221,12 +221,12 @@ TEST_F(CanShareOperandBufferWithUserTest, DynamicUpdateSliceCanShare) {
 
   // The DynamicUpdateSlice instruction can share with the data operand, but not
   // with update or starts.
-  EXPECT_TRUE(
-      CanShareOperandBufferWithUser(data, {}, dus, {}, *points_to_analysis_));
-  EXPECT_FALSE(
-      CanShareOperandBufferWithUser(update, {}, dus, {}, *points_to_analysis_));
-  EXPECT_FALSE(
-      CanShareOperandBufferWithUser(starts, {}, dus, {}, *points_to_analysis_));
+  EXPECT_TRUE(CanShareOperandBufferWithUser(data, {}, dus, {},
+                                            points_to_analysis_.get()));
+  EXPECT_FALSE(CanShareOperandBufferWithUser(update, {}, dus, {},
+                                             points_to_analysis_.get()));
+  EXPECT_FALSE(CanShareOperandBufferWithUser(starts, {}, dus, {},
+                                             points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) {
@@ -256,7 +256,7 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) {
 
   // Output fused dot add should be able to share buffer with 'add_operand'.
   EXPECT_TRUE(CanShareOperandBufferWithUser(add_operand, {}, fusion, {},
-                                            *points_to_analysis_));
+                                            points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, FusedTransposeDotAdd) {
@@ -292,7 +292,7 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedTransposeDotAdd) {
 
   // Output fused transpose-dot-add should be share buffer with 'add_operand'.
   EXPECT_TRUE(CanShareOperandBufferWithUser(add_operand, {}, fusion, {},
-                                            *points_to_analysis_));
+                                            points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) {
@@ -320,7 +320,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) {
 
   // Output fused operand->reverse->add cannot alias operand buffer 'operand'.
   EXPECT_FALSE(CanShareOperandBufferWithUser(operand, {}, fusion, {},
-                                             *points_to_analysis_));
+                                             points_to_analysis_.get()));
 }
 
 TEST_F(CanShareOperandBufferWithUserTest, WhileCanShare) {
@@ -360,8 +360,8 @@ TEST_F(CanShareOperandBufferWithUserTest, WhileCanShare) {
   RunAnalysis();
 
   // The While instruction can share with the data operand.
-  EXPECT_TRUE(
-      CanShareOperandBufferWithUser(data, {}, whil, {}, *points_to_analysis_));
+  EXPECT_TRUE(CanShareOperandBufferWithUser(data, {}, whil, {},
+                                            points_to_analysis_.get()));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index 5f7b7aa434..354e4a84c5 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -77,6 +77,7 @@ HloTestBase::~HloTestBase() {
   }
 }
 
+/* static */
 std::unique_ptr<HloModule> HloTestBase::CreateNewModule() {
   HloModuleConfig config;
   config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags());
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h
index 98bc35ae52..2b6a2e9672 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.h
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.h
@@ -48,7 +48,7 @@ class HloTestBase : public ::testing::Test {
   // TestName() for its name; it will also automatically populate its debug
   // options from command-line flags. It's recommended to use this method to
   // create all HloModules for tests.
-  std::unique_ptr<HloModule> CreateNewModule();
+  static std::unique_ptr<HloModule> CreateNewModule();
 
   // Executes the given module and returns a global data handle.
   StatusOr<perftools::gputools::DeviceMemoryBase> Execute(
-- 
GitLab


From 0f17a54d4dc4a8b941f56772c6b4e0c6dbf617de Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Tue, 20 Jun 2017 02:45:07 +0100
Subject: [PATCH 0226/1375] [OpenCL] Registers stack op (#10324)

---
 tensorflow/core/kernels/stack_ops.cc | 68 +++++++++++++++++++++++++++-
 1 file changed, 66 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/stack_ops.cc b/tensorflow/core/kernels/stack_ops.cc
index b4698a8053..2db3e5ef77 100644
--- a/tensorflow/core/kernels/stack_ops.cc
+++ b/tensorflow/core/kernels/stack_ops.cc
@@ -40,6 +40,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif // TENSORFLOW_USE_SYCL
 
 class Stack : public ResourceBase {
  public:
@@ -182,6 +185,10 @@ class StackOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_CPU), StackOp);
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_GPU).HostMemory("handle"),
                         StackOp);
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_SYCL).HostMemory("handle"),
+                        StackOp);
+#endif // TENSORFLOW_USE_SYCL
 
 template <typename Device>
 class StackPushOp : public AsyncOpKernel {
@@ -213,7 +220,11 @@ class StackPushOp : public AsyncOpKernel {
     static constexpr int kCopyThreshold = 2048;
     static constexpr double kOccupancy = 0.7;
     if (swap_memory_ && !alloc_attrs.on_host() &&
-        std::is_same<Device, GPUDevice>::value &&
+        ( std::is_same<Device, GPUDevice>::value
+#ifdef TENSORFLOW_USE_SYCL
+          || std::is_same<Device, SYCLDevice>::value
+#endif // TENSORFLOW_USE_SYCL
+        ) &&
         tensor.TotalBytes() > kCopyThreshold && stack->IsUsefulToSwap(tensor)) {
       DeviceContext* device_ctxt = ctx->op_device_context();
       auto device = static_cast<tensorflow::Device*>(ctx->device());
@@ -289,6 +300,31 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                        \
+  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("handle")       \
+                              .TypeConstraint<type>("T"), \
+                          StackPushOp<SYCLDevice>);
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
+
+#define REGISTER_SYCL_HOST_KERNEL(type)                   \
+  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("handle")       \
+                              .HostMemory("elem")         \
+                              .HostMemory("output")       \
+                              .TypeConstraint<type>("T"), \
+                          StackPushOp<SYCLDevice>)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(bool);
+#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 class StackPopOp : public AsyncOpKernel {
  public:
   explicit StackPopOp(OpKernelConstruction* context) : AsyncOpKernel(context) {}
@@ -359,6 +395,31 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                                \
+  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
+                              .Device(DEVICE_SYCL)                \
+                              .HostMemory("handle")               \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp)
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
+
+#define REGISTER_SYCL_HOST_KERNEL(type)                           \
+  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
+                              .Device(DEVICE_SYCL)                \
+                              .HostMemory("handle")               \
+                              .HostMemory("elem")                 \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(bool);
+
+#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 class StackCloseOp : public OpKernel {
  public:
   explicit StackCloseOp(OpKernelConstruction* context) : OpKernel(context) {}
@@ -376,5 +437,8 @@ class StackCloseOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("StackClose").Device(DEVICE_CPU), StackCloseOp);
 REGISTER_KERNEL_BUILDER(
     Name("StackClose").Device(DEVICE_GPU).HostMemory("handle"), StackCloseOp);
-
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(
+    Name("StackClose").Device(DEVICE_SYCL).HostMemory("handle"), StackCloseOp);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
-- 
GitLab


From 38615cb8ad4c02dc6050c6b895b781e59e5609f1 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <taehoonlee@snu.ac.kr>
Date: Tue, 20 Jun 2017 11:03:30 +0900
Subject: [PATCH 0227/1375] Fix references

---
 tensorflow/contrib/keras/python/keras/layers/recurrent.py | 2 +-
 tensorflow/contrib/rnn/python/ops/rnn_cell.py             | 2 +-
 tensorflow/docs_src/tutorials/word2vec.md                 | 2 +-
 tensorflow/python/ops/rnn_cell_impl.py                    | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/layers/recurrent.py b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
index 5e8c23ed3e..cdef55f599 100644
--- a/tensorflow/contrib/keras/python/keras/layers/recurrent.py
+++ b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
@@ -985,7 +985,7 @@ class LSTM(Recurrent):
 
   References:
       - [Long short-term
-        memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf)
+        memory](http://www.bioinf.jku.at/publications/older/2604.pdf)
         (original 1997 paper)
       - [Supervised sequence labeling with recurrent neural
         networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index 9c5e9fec9d..c8552fc050 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -79,7 +79,7 @@ class CoupledInputForgetGateLSTMCell(rnn_cell_impl.RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+    http://www.bioinf.jku.at/publications/older/2604.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
diff --git a/tensorflow/docs_src/tutorials/word2vec.md b/tensorflow/docs_src/tutorials/word2vec.md
index d7a9089949..380763db1a 100644
--- a/tensorflow/docs_src/tutorials/word2vec.md
+++ b/tensorflow/docs_src/tutorials/word2vec.md
@@ -351,7 +351,7 @@ to evaluate embeddings is to directly use them to predict syntactic and semantic
 relationships like `king is to queen as father is to ?`. This is called
 *analogical reasoning* and the task was introduced by
 [Mikolov and colleagues
-](http://msr-waypoint.com/en-us/um/people/gzweig/Pubs/NAACL2013Regularities.pdf).
+](http://www.anthology.aclweb.org/N/N13/N13-1090.pdf).
 Download the dataset for this task from
 [download.tensorflow.org](http://download.tensorflow.org/data/questions-words.txt).
 
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index cc6528d1f5..c00949a322 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -401,7 +401,7 @@ class LSTMCell(RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+    http://www.bioinf.jku.at/publications/older/2604.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
-- 
GitLab


From fd1ba4a81dd9db89cd3800bc17260dfb91331474 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 19 Jun 2017 21:57:35 -0700
Subject: [PATCH 0228/1375] Made sure that the grappler optimizers preserved
 the graph version and library.

PiperOrigin-RevId: 159521370
---
 tensorflow/core/grappler/optimizers/auto_parallel.cc  |  4 +++-
 .../core/grappler/optimizers/constant_folding.cc      |  6 ++++++
 tensorflow/core/grappler/optimizers/meta_optimizer.cc | 11 +++++++++--
 tensorflow/core/grappler/optimizers/model_pruner.cc   |  5 +++++
 tensorflow/core/grappler/utils/topological_sort.cc    |  2 +-
 5 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.cc b/tensorflow/core/grappler/optimizers/auto_parallel.cc
index d4326a022f..42f2f1850f 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/grappler/clusters/cluster.h"
 #include "tensorflow/core/grappler/devices.h"
 #include "tensorflow/core/grappler/grappler_item.h"
@@ -248,7 +249,8 @@ void AutoParallel::BuildGraph(GraphDef* graph) {
   for (const auto& fetch : item_->fetch) {
     AddNodeControl(fetch, {control->name()}, graph);
   }
-  *(graph->mutable_library()) = item_->graph.library();
+  *graph->mutable_library() = item_->graph.library();
+  *graph->mutable_versions() = item_->graph.versions();
   LOG(INFO) << "Parallelized graph size: " << graph->node_size();
 }
 
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 325ce8f5fe..07d2d1e1f1 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -18,11 +18,13 @@ limitations under the License.
 #include "tensorflow/core/grappler/optimizers/constant_folding.h"
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/grappler/clusters/cluster.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
@@ -642,6 +644,10 @@ Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
   TF_RETURN_IF_ERROR(FoldGraph(output));
   TF_RETURN_IF_ERROR(SimplifyGraph(output));
   LOG(INFO) << "Optimized graph size: " << output->node_size();
+
+  *output->mutable_library() = item.graph.library();
+  *output->mutable_versions() = item.graph.versions();
+
   return Status::OK();
 }
 
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index aac432b3fb..4007c8802b 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/optimizers/meta_optimizer.h"
+#include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/grappler/optimizers/auto_parallel.h"
 #include "tensorflow/core/grappler/optimizers/constant_folding.h"
@@ -101,8 +102,14 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
     }
   }
   TopologicalSort(optimized_graph);
-  // Copy the graph version.
-  *optimized_graph->mutable_versions() = item.graph.versions();
+
+  // Make sure that the optimizers preserved the graph version and library.
+  DCHECK_GE(optimized_graph->library().function_size(),
+            item.graph.library().function_size());
+  DCHECK_GE(optimized_graph->library().gradient_size(),
+            item.graph.library().gradient_size());
+  DCHECK_EQ(optimized_graph->versions().producer(),
+            item.graph.versions().producer());
 
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index efa2163836..34fb5b92c7 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -15,7 +15,9 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/optimizers/model_pruner.h"
 #include <unordered_set>
+#include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/optimizers/graph_rewriter.h"
 #include "tensorflow/core/grappler/utils.h"
@@ -73,6 +75,9 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
           << " nodes from the graph. The graph now contains "
           << pruned_graph->node_size() << " nodes.";
 
+  *pruned_graph->mutable_library() = item.graph.library();
+  *pruned_graph->mutable_versions() = item.graph.versions();
+
   return Status::OK();
 }
 
diff --git a/tensorflow/core/grappler/utils/topological_sort.cc b/tensorflow/core/grappler/utils/topological_sort.cc
index 131756fc5c..fdf7fb0d3d 100644
--- a/tensorflow/core/grappler/utils/topological_sort.cc
+++ b/tensorflow/core/grappler/utils/topological_sort.cc
@@ -56,7 +56,7 @@ void TopologicalSort(GraphDef* graph) {
     ready_nodes.pop_front();
   }
   if (sorted_graph.node_size() == graph->node_size()) {
-    *graph = sorted_graph;
+    graph->mutable_node()->Swap(sorted_graph.mutable_node());
   }
 }
 
-- 
GitLab


From 0f802ef693c29267dc69394597af808cbd90e60a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 02:27:46 -0700
Subject: [PATCH 0229/1375] Remove operand parameters from
 Handle{Maximum,Minimum,Convert,Copy}.

PiperOrigin-RevId: 159537163
---
 .../xla/service/algebraic_simplifier.cc       | 39 +++++++------------
 .../compiler/xla/service/cpu/ir_emitter.cc    |  4 +-
 .../compiler/xla/service/cpu/ir_emitter.h     |  2 +-
 .../compiler/xla/service/dfs_hlo_visitor.h    | 11 ++----
 .../service/dfs_hlo_visitor_with_default.h    |  6 +--
 .../compiler/xla/service/gpu/ir_emitter.h     |  2 +-
 .../xla/service/gpu/ir_emitter_unnested.cc    | 10 ++---
 .../compiler/xla/service/hlo_cost_analysis.cc |  6 +--
 .../compiler/xla/service/hlo_cost_analysis.h  |  5 +--
 .../compiler/xla/service/hlo_evaluator.cc     | 12 +++---
 .../compiler/xla/service/hlo_instruction.cc   |  8 ++--
 .../xla/service/tuple_points_to_analysis.cc   |  5 +--
 .../xla/service/tuple_points_to_analysis.h    |  2 +-
 13 files changed, 46 insertions(+), 66 deletions(-)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 1302026ccf..0187c09d7b 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -126,10 +126,9 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault {
       HloInstruction* concatenate,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) override;
 
-  Status HandleCopy(HloInstruction* copy, HloInstruction* operand) override;
+  Status HandleCopy(HloInstruction* copy) override;
 
-  Status HandleConvert(HloInstruction* convert,
-                       HloInstruction* operand) override;
+  Status HandleConvert(HloInstruction* convert) override;
 
   Status HandleConvolution(HloInstruction* convolution, HloInstruction* lhs,
                            HloInstruction* rhs, const Window& window) override;
@@ -179,11 +178,8 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault {
   Status HandleSubtract(HloInstruction* sub, HloInstruction* lhs,
                         HloInstruction* rhs) override;
 
-  Status HandleMaximum(HloInstruction* maximum, HloInstruction* lhs,
-                       HloInstruction* rhs) override;
-
-  Status HandleMinimum(HloInstruction* minimum, HloInstruction* lhs,
-                       HloInstruction* rhs) override;
+  Status HandleMaximum(HloInstruction* maximum) override;
+  Status HandleMinimum(HloInstruction* minimum) override;
 
   // Returns whether algebraic simplification has occurred.
   const bool changed() const { return changed_; }
@@ -334,16 +330,16 @@ Status AlgebraicSimplifierVisitor::HandleAdd(HloInstruction* add,
   return Status::OK();
 }
 
-Status AlgebraicSimplifierVisitor::HandleCopy(HloInstruction* copy,
-                                              HloInstruction* operand) {
+Status AlgebraicSimplifierVisitor::HandleCopy(HloInstruction* copy) {
   // If a copy feeds a copy, make it a single copy.
-  if (operand->opcode() == HloOpcode::kCopy) {
+  if (copy->operand(0)->opcode() == HloOpcode::kCopy) {
     return ReplaceWithNewInstruction(
-        copy, HloInstruction::CreateUnary(copy->shape(), HloOpcode::kCopy,
-                                          operand->operands()[0]));
+        copy, HloInstruction::CreateUnary(
+                  copy->shape(), HloOpcode::kCopy,
+                  copy->mutable_operand(0)->mutable_operand(0)));
   }
   // All copies can be eliminated (assuming layout constraints are satisified).
-  ReplaceInstructionIfSameShape(copy, operand);
+  ReplaceInstructionIfSameShape(copy, copy->mutable_operand(0));
   return Status::OK();
 }
 
@@ -792,12 +788,11 @@ Status AlgebraicSimplifierVisitor::HandleBroadcast(HloInstruction* broadcast) {
 // A conversion to the same element type as the operand is a nop and can be
 // removed.  A conversion of a constant can be simplified by making a new
 // constant.
-Status AlgebraicSimplifierVisitor::HandleConvert(HloInstruction* convert,
-                                                 HloInstruction* operand) {
-  PrimitiveType src_type = operand->shape().element_type();
+Status AlgebraicSimplifierVisitor::HandleConvert(HloInstruction* convert) {
+  PrimitiveType src_type = convert->operand(0)->shape().element_type();
   PrimitiveType dest_type = convert->shape().element_type();
   if (src_type == dest_type) {
-    return ReplaceInstruction(convert, operand);
+    return ReplaceInstruction(convert, convert->mutable_operand(0));
   }
   return Status::OK();
 }
@@ -1391,9 +1386,7 @@ bool AlgebraicSimplifierVisitor::TransformToClampIfSameShape(
   return true;
 }
 
-Status AlgebraicSimplifierVisitor::HandleMaximum(HloInstruction* maximum,
-                                                 HloInstruction* lhs,
-                                                 HloInstruction* rhs) {
+Status AlgebraicSimplifierVisitor::HandleMaximum(HloInstruction* maximum) {
   // Match the following tree:
   //          min_operand     operand
   //                     \   /
@@ -1424,9 +1417,7 @@ Status AlgebraicSimplifierVisitor::HandleMaximum(HloInstruction* maximum,
   return Status::OK();
 }
 
-Status AlgebraicSimplifierVisitor::HandleMinimum(HloInstruction* minimum,
-                                                 HloInstruction* lhs,
-                                                 HloInstruction* rhs) {
+Status AlgebraicSimplifierVisitor::HandleMinimum(HloInstruction* minimum) {
   // Match the following tree:
   //          max_operand     operand
   //                     \   /
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 421618d819..fee5fd8830 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -259,12 +259,12 @@ Status IrEmitter::HandleConstant(HloInstruction* constant,
   return Status::OK();
 }
 
-Status IrEmitter::HandleCopy(HloInstruction* copy, HloInstruction* operand) {
+Status IrEmitter::HandleCopy(HloInstruction* copy) {
   if (ShapeUtil::IsTuple(copy->shape())) {
     // kCopy shallow copies a tuple so just memcpy the top-level buffer.
     TF_ASSIGN_OR_RETURN(llvm::Value * copy_value, EmitTargetAddressForOp(copy));
     emitted_value_[copy] = copy_value;
-    return EmitMemcpy(*operand, *copy);
+    return EmitMemcpy(*(copy->operand(0)), *copy);
   } else {
     // Use the elemental emitter for non-tuple shapes.
     return DefaultAction(copy);
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 1a8c91efd4..a1b7bd9e6d 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -96,7 +96,7 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   Status HandleBitcast(HloInstruction* bitcast) override;
   Status HandleConstant(HloInstruction* constant,
                         const Literal& literal) override;
-  Status HandleCopy(HloInstruction* copy, HloInstruction* operand) override;
+  Status HandleCopy(HloInstruction* copy) override;
   Status HandleGetTupleElement(HloInstruction* get_tuple_element,
                                HloInstruction* operand) override;
   Status HandleSelect(HloInstruction* select, HloInstruction* pred,
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index ea7c22737f..3f9b71cf2b 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -72,22 +72,19 @@ class DfsHloVisitor {
   virtual Status HandleSelect(HloInstruction* select, HloInstruction* pred,
                               HloInstruction* on_true,
                               HloInstruction* on_false) = 0;
-  virtual Status HandleMaximum(HloInstruction* maximum, HloInstruction* lhs,
-                               HloInstruction* rhs) {
+  virtual Status HandleMaximum(HloInstruction* maximum) {
     return HandleElementwiseBinary(maximum, HloOpcode::kMaximum);
   }
-  virtual Status HandleMinimum(HloInstruction* minimum, HloInstruction* lhs,
-                               HloInstruction* rhs) {
+  virtual Status HandleMinimum(HloInstruction* minimum) {
     return HandleElementwiseBinary(minimum, HloOpcode::kMinimum);
   }
   virtual Status HandleConcatenate(
       HloInstruction* concatenate,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) = 0;
-  virtual Status HandleConvert(HloInstruction* convert,
-                               HloInstruction* operand) {
+  virtual Status HandleConvert(HloInstruction* convert) {
     return HandleElementwiseUnary(convert, HloOpcode::kConvert);
   }
-  virtual Status HandleCopy(HloInstruction* copy, HloInstruction* operand) {
+  virtual Status HandleCopy(HloInstruction* copy) {
     return HandleElementwiseUnary(copy, HloOpcode::kCopy);
   }
   virtual Status HandleMultiply(HloInstruction* multiply, HloInstruction* lhs,
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
index 75910b8cbb..2970ba8cc4 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
@@ -64,12 +64,10 @@ class DfsHloVisitorWithDefault : public DfsHloVisitor {
       tensorflow::gtl::ArraySlice<HloInstruction*> /*operands*/) override {
     return DefaultAction(concatenate);
   }
-  Status HandleConvert(HloInstruction* convert,
-                       HloInstruction* /*operand*/) override {
+  Status HandleConvert(HloInstruction* convert) override {
     return DefaultAction(convert);
   }
-  Status HandleCopy(HloInstruction* copy,
-                    HloInstruction* /*operand*/) override {
+  Status HandleCopy(HloInstruction* copy) override {
     return DefaultAction(copy);
   }
   Status HandleSelect(HloInstruction* select, HloInstruction* /*pred*/,
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h
index 607a366ac6..de72ac738e 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h
@@ -231,7 +231,7 @@ class IrEmitterUnnested : public IrEmitter {
 
   // IrEmitterUnnested handles the following instructions differently from
   // IrEmitter.
-  Status HandleCopy(HloInstruction* copy, HloInstruction* operand) override;
+  Status HandleCopy(HloInstruction* copy) override;
   Status HandleConvolution(HloInstruction* convolution, HloInstruction* lhs,
                            HloInstruction* rhs, const Window& window) override;
   Status HandleDot(HloInstruction* dot, HloInstruction* lhs_instruction,
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
index ab04d1736e..ea71d92417 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
@@ -722,8 +722,7 @@ int64 EmitTranspose021Tiled(llvm_ir::IrArray input, llvm_ir::IrArray output,
 
 }  // namespace
 
-Status IrEmitterUnnested::HandleCopy(HloInstruction* copy,
-                                     HloInstruction* operand) {
+Status IrEmitterUnnested::HandleCopy(HloInstruction* copy) {
   if (ImplementedAsMemcpy(*copy)) {
     thunk_sequence_->emplace_back(BuildCopyThunk(copy));
     return Status::OK();
@@ -731,7 +730,7 @@ Status IrEmitterUnnested::HandleCopy(HloInstruction* copy,
   bool is_transpose_021;
   Shape reduced_input_shape, reduced_output_shape;
   std::tie(is_transpose_021, reduced_input_shape, reduced_output_shape) =
-      IsTranspose021(operand->shape(), copy->shape());
+      IsTranspose021(copy->operand(0)->shape(), copy->shape());
   if (is_transpose_021 &&
       reduced_input_shape.dimensions(1) >= kMinDimensionToTransposeTiled &&
       reduced_input_shape.dimensions(2) >= kMinDimensionToTransposeTiled) {
@@ -739,7 +738,8 @@ Status IrEmitterUnnested::HandleCopy(HloInstruction* copy,
     VLOG(3) << "Emitting tiled 0-2-1 transposition";
     constexpr int64 tile_size = 32;
     int64 num_tiles = EmitTranspose021Tiled(
-        GetIrArray(*operand).CastToShape(reduced_input_shape, &ir_builder_),
+        GetIrArray(*(copy->operand(0)))
+            .CastToShape(reduced_input_shape, &ir_builder_),
         GetIrArray(*copy).CastToShape(reduced_output_shape, &ir_builder_),
         tile_size, &ir_builder_);
     UpdateLaunchDimensions(LaunchDimensions(num_tiles, tile_size), LastThunk(),
@@ -747,7 +747,7 @@ Status IrEmitterUnnested::HandleCopy(HloInstruction* copy,
     return Status::OK();
   }
 
-  return IrEmitter::HandleCopy(copy, operand);
+  return IrEmitter::HandleCopy(copy);
 }
 
 Status IrEmitterUnnested::EmitColumnReduction(
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index abbbbfa02b..f3a6cd43c2 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -166,13 +166,11 @@ Status HloCostAnalysis::HandleConcatenate(
   return Status::OK();
 }
 
-Status HloCostAnalysis::HandleConvert(HloInstruction* convert,
-                                      HloInstruction* operand) {
+Status HloCostAnalysis::HandleConvert(HloInstruction* convert) {
   return HandleElementwiseOp(convert);
 }
 
-Status HloCostAnalysis::HandleCopy(HloInstruction* copy,
-                                   HloInstruction* operand) {
+Status HloCostAnalysis::HandleCopy(HloInstruction* copy) {
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index 6538266864..3f0dfcc619 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -63,9 +63,8 @@ class HloCostAnalysis : public DfsHloVisitor {
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) override;
   Status HandleSend(HloInstruction* send) override;
   Status HandleRecv(HloInstruction* recv) override;
-  Status HandleConvert(HloInstruction* convert,
-                       HloInstruction* operand) override;
-  Status HandleCopy(HloInstruction* copy, HloInstruction* operand) override;
+  Status HandleConvert(HloInstruction* convert) override;
+  Status HandleCopy(HloInstruction* copy) override;
   Status HandleDot(HloInstruction* dot, HloInstruction* lhs,
                    HloInstruction* rhs) override;
   Status HandleConvolution(HloInstruction* convolution, HloInstruction* lhs,
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index a42289590b..4936b823a2 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -192,7 +192,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   };
 
-  Status HandleCopy(HloInstruction* copy, HloInstruction* operand) override {
+  Status HandleCopy(HloInstruction* copy) override {
     TF_ASSIGN_OR_RETURN(parent_->evaluated_[copy],
                         ElementWiseUnaryOp(copy, [](ReturnT elem_operand) {
                           return elem_operand;
@@ -208,8 +208,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
         typename primitive_util::PrimitiveTypeToNative<dest_type>::type>();
   }
 
-  Status HandleConvert(HloInstruction* convert,
-                       HloInstruction* operand) override {
+  Status HandleConvert(HloInstruction* convert) override {
+    const HloInstruction* operand = convert->operand(0);
     auto operand_literal = parent_->GetEvaluatedLiteralFor(operand);
 
     switch (operand->shape().element_type()) {
@@ -337,8 +337,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   };
 
-  Status HandleMaximum(HloInstruction* maximum, HloInstruction* lhs,
-                       HloInstruction* rhs) override {
+  Status HandleMaximum(HloInstruction* maximum) override {
     TF_ASSIGN_OR_RETURN(
         parent_->evaluated_[maximum],
         ElementWiseBinaryOp(maximum, [](ReturnT lhs, ReturnT rhs) {
@@ -347,8 +346,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   };
 
-  Status HandleMinimum(HloInstruction* minimum, HloInstruction* lhs,
-                       HloInstruction* rhs) override {
+  Status HandleMinimum(HloInstruction* minimum) override {
     TF_ASSIGN_OR_RETURN(
         parent_->evaluated_[minimum],
         ElementWiseBinaryOp(minimum, [](ReturnT lhs_el, ReturnT rhs_el) {
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index c49c00bac0..99b73dea29 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1803,9 +1803,9 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
     case HloOpcode::kSubtract:
       return visitor->HandleSubtract(this, operands_[0], operands_[1]);
     case HloOpcode::kMaximum:
-      return visitor->HandleMaximum(this, operands_[0], operands_[1]);
+      return visitor->HandleMaximum(this);
     case HloOpcode::kMinimum:
-      return visitor->HandleMinimum(this, operands_[0], operands_[1]);
+      return visitor->HandleMinimum(this);
     case HloOpcode::kLogicalAnd:
       return visitor->HandleLogicalAnd(this, operands_[0], operands_[1]);
     case HloOpcode::kLogicalOr:
@@ -1813,9 +1813,9 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
     case HloOpcode::kConcatenate:
       return visitor->HandleConcatenate(this, operands_);
     case HloOpcode::kConvert:
-      return visitor->HandleConvert(this, operands_[0]);
+      return visitor->HandleConvert(this);
     case HloOpcode::kCopy:
-      return visitor->HandleCopy(this, operands_[0]);
+      return visitor->HandleCopy(this);
     case HloOpcode::kMultiply:
       return visitor->HandleMultiply(this, operands_[0], operands_[1]);
     case HloOpcode::kDot:
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
index ad6f015c70..8d68398450 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
@@ -243,12 +243,11 @@ Status TuplePointsToAnalysis::HandleGetTupleElement(
   return Status::OK();
 }
 
-Status TuplePointsToAnalysis::HandleCopy(HloInstruction* copy,
-                                         HloInstruction* operand) {
+Status TuplePointsToAnalysis::HandleCopy(HloInstruction* copy) {
   // A kCopy instruction performs a shallow copy of the operand. The top-level
   // buffer (index={}) is newly created, but all other buffers (in the case of a
   // tuple shape) come from the operand
-  PointsToSet& points_to_set = CreateCopiedPointsToSet(copy, operand);
+  PointsToSet& points_to_set = CreateCopiedPointsToSet(copy, copy->operand(0));
   points_to_set.mutable_element(/*index=*/{})->clear();
   points_to_set.AddPointedToBuffer(NewLogicalBuffer(copy, /*index=*/{}),
                                    /*index=*/{});
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
index 4d7fc7cbc9..bab4235a28 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
@@ -208,7 +208,7 @@ class TuplePointsToAnalysis : public DfsHloVisitorWithDefault {
   Status HandleGetTupleElement(HloInstruction* get_tuple_element,
                                HloInstruction* operand) override;
   Status HandleBitcast(HloInstruction* bitcast) override;
-  Status HandleCopy(HloInstruction* copy, HloInstruction* operand) override;
+  Status HandleCopy(HloInstruction* copy) override;
   Status HandleSelect(HloInstruction* select, HloInstruction* pred,
                       HloInstruction* on_true,
                       HloInstruction* on_false) override;
-- 
GitLab


From 73af9bfa58dc1ff9df09fe9495e9658f778f6dc7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 03:06:26 -0700
Subject: [PATCH 0230/1375] Improve layout assignment for reshape.

Check if the default layout can be used to reach a bitcast instead of checking if it is a bitcast with MajorToMinor layout.
Also avoid propagating padding. This fixes a bug where a copy which is a bitcast (except for padding) is not optimized away.

PiperOrigin-RevId: 159539895
---
 .../compiler/xla/service/layout_assignment.cc | 48 +++++++++----------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index e9e199226a..377f2432db 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -382,7 +382,17 @@ Status LayoutAssignment::AddMandatoryConstraints(
       // instruction.
       // TODO(b/31425034): Change infeeds to be more like parameters, with
       // shapes in the ComputationLayout.
-      shape_with_layout = &instruction->shape();
+      Shape infeed_shape = instruction->shape();
+      // TODO(b/62477016): When the infeed does not set padding anymore, this
+      // can be removed.
+      ShapeUtil::ForEachMutableSubshape(
+          &infeed_shape, [](Shape* subshape, const ShapeIndex& index) {
+            auto layout = subshape->mutable_layout();
+            layout->clear_padding_value();
+            layout->clear_padded_dimensions();
+          });
+      TF_RETURN_IF_ERROR(
+          constraints->SetInstructionLayout(infeed_shape, instruction.get()));
     } else if (instruction->opcode() == HloOpcode::kOutfeed) {
       // Constrain the input to the Outfeed instruction to be the expected
       // layout of the Outfeed.
@@ -729,23 +739,18 @@ std::unique_ptr<Layout> LayoutAssignment::ChooseOperandLayoutFromOutputLayout(
   if (instruction->opcode() == HloOpcode::kReshape) {
     // Prefer the operand layout that makes the reshape an bitcast. If any
     // dimension bound is 1 in the operand shape, there may be several such
-    // layouts. So if 'output_layout' is a MajorToMinor layout, try if the
+    // layouts. So if 'output_layout' is the default layout, try if the
     // reshape is a bitcast when using the same layout. This may avoid copy
     // operations.
     const Shape& output_shape = instruction->shape();
     Shape output_shape_with_layout = ShapeUtil::MakeShapeWithLayout(
         output_shape.element_type(), AsInt64Slice(output_shape.dimensions()),
         AsInt64Slice(output_layout.minor_to_major()));
-    const Shape& operand_shape = operand->shape();
-    if (LayoutUtil::IsMonotonicWithDim0Major(output_layout)) {
-      Shape operand_shape_with_layout =
-          ShapeUtil::MakeShapeWithMonotonicDim0MajorLayout(
-              operand_shape.element_type(),
-              AsInt64Slice(operand_shape.dimensions()));
-      if (ShapeUtil::ReshapeIsBitcast(operand_shape_with_layout,
-                                      output_shape_with_layout)) {
-        return MakeUnique<Layout>(operand_shape_with_layout.layout());
-      }
+    Shape operand_shape = operand->shape();
+    *operand_shape.mutable_layout() =
+        LayoutUtil::GetDefaultLayoutForShape(operand_shape);
+    if (ShapeUtil::ReshapeIsBitcast(operand_shape, output_shape_with_layout)) {
+      return MakeUnique<Layout>(operand_shape.layout());
     }
     auto aligned_operand_shape =
         ShapeUtil::AlignLayouts(output_shape_with_layout, operand_shape);
@@ -789,23 +794,18 @@ std::unique_ptr<Layout> LayoutAssignment::ChooseOutputLayoutFromOperandLayout(
   if (user->opcode() == HloOpcode::kReshape) {
     // Prefer the user layout that makes the reshape an bitcast. If any
     // dimension bound is 1 in the user shape, there may be several such
-    // layouts. So if 'operand_layout' is a MajorToMinor layout, try if the
+    // layouts. So if 'operand_layout' is the default layout, try if the
     // reshape is a bitcast when using the same layout. This may avoid copy
     // operations.
     Shape operand_shape_with_layout = ShapeUtil::MakeShapeWithLayout(
         operand->shape().element_type(),
         AsInt64Slice(operand->shape().dimensions()),
         AsInt64Slice(operand_layout.minor_to_major()));
-    const Shape& output_shape = user->shape();
-    if (LayoutUtil::IsMonotonicWithDim0Major(operand_layout)) {
-      Shape output_shape_with_layout =
-          ShapeUtil::MakeShapeWithMonotonicDim0MajorLayout(
-              output_shape.element_type(),
-              AsInt64Slice(output_shape.dimensions()));
-      if (ShapeUtil::ReshapeIsBitcast(output_shape_with_layout,
-                                      operand_shape_with_layout)) {
-        return MakeUnique<Layout>(output_shape_with_layout.layout());
-      }
+    Shape output_shape = user->shape();
+    *output_shape.mutable_layout() =
+        LayoutUtil::GetDefaultLayoutForShape(output_shape);
+    if (ShapeUtil::ReshapeIsBitcast(output_shape, operand_shape_with_layout)) {
+      return MakeUnique<Layout>(output_shape.layout());
     }
     auto aligned_user_shape =
         ShapeUtil::AlignLayouts(operand_shape_with_layout, output_shape);
@@ -926,7 +926,7 @@ Status LayoutAssignment::PropagateUseConstraintToDefs(
                 ShapeUtil::IsArray(buffer->shape())) {
               TF_RETURN_IF_ERROR(constraints->SetBufferLayout(
                   ShapeUtil::GetSubshape(shape_layout.shape(), index).layout(),
-                  *buffer));
+                  *buffer, /*mandatory=*/true));
             }
           }
         }
-- 
GitLab


From 9c88b5ee38e83c9b75bf6785ff99293a87691a79 Mon Sep 17 00:00:00 2001
From: Hauke Brammer <hauke.brammer@googlemail.com>
Date: Tue, 20 Jun 2017 14:59:25 +0200
Subject: [PATCH 0231/1375] Fix Java OperationBuilder documentation example

"Constant" is not a registered Op type, "Const" is.
---
 .../java/src/main/java/org/tensorflow/OperationBuilder.java     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
index 38ffa2a8e1..8f7559d39e 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
@@ -28,7 +28,7 @@ import java.nio.charset.Charset;
  * <pre>{@code
  * // g is a Graph instance.
  * try (Tensor c1 = Tensor.create(3.0f)) {
- *   g.opBuilder("Constant", "MyConst")
+ *   g.opBuilder("Const", "MyConst")
  *       .setAttr("dtype", c1.dataType())
  *       .setAttr("value", c1)
  *       .build();
-- 
GitLab


From dde378ae034784879d0d1a4ad16f2765fdd4a5fd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 06:15:01 -0700
Subject: [PATCH 0232/1375] Creating configuration artifacts to use Bazel
 remote execution service

PiperOrigin-RevId: 159552253
---
 third_party/toolchains/cpus/BUILD             |   80 +
 third_party/toolchains/cpus/CROSSTOOL         |  922 +++++++++++
 third_party/toolchains/cpus/py/BUILD          |  185 +++
 third_party/toolchains/gpus/crosstool/BUILD   |   52 +
 .../toolchains/gpus/crosstool/CROSSTOOL       |  302 ++++
 third_party/toolchains/gpus/cuda/BUILD        | 1362 +++++++++++++++++
 .../toolchains/gpus/cuda/build_defs.bzl       |   37 +
 .../toolchains/gpus/cuda/cuda/cuda_config.h   |   20 +
 8 files changed, 2960 insertions(+)
 create mode 100644 third_party/toolchains/cpus/BUILD
 create mode 100644 third_party/toolchains/cpus/CROSSTOOL
 create mode 100644 third_party/toolchains/cpus/py/BUILD
 create mode 100644 third_party/toolchains/gpus/crosstool/BUILD
 create mode 100644 third_party/toolchains/gpus/crosstool/CROSSTOOL
 create mode 100644 third_party/toolchains/gpus/cuda/BUILD
 create mode 100644 third_party/toolchains/gpus/cuda/build_defs.bzl
 create mode 100644 third_party/toolchains/gpus/cuda/cuda/cuda_config.h

diff --git a/third_party/toolchains/cpus/BUILD b/third_party/toolchains/cpus/BUILD
new file mode 100644
index 0000000000..c33dcd4e11
--- /dev/null
+++ b/third_party/toolchains/cpus/BUILD
@@ -0,0 +1,80 @@
+# A build file to configure cc toolchain for CPU build used with Bazel remote
+# execution service
+# DO NOT EDIT: automatically generated BUILD file
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "malloc",
+)
+
+cc_library(
+    name = "stl",
+)
+
+filegroup(
+    name = "empty",
+    srcs = [],
+)
+
+filegroup(
+    name = "cc_wrapper",
+    srcs = ["cc_wrapper.sh"],
+)
+
+# This is the entry point for --crosstool_top.  Toolchains are found
+# by lopping off the name of --crosstool_top and searching for
+# the "${CPU}" entry in the toolchains attribute.
+cc_toolchain_suite(
+    name = "toolchain",
+    toolchains = {
+        "k8|clang": ":cc-compiler-k8",
+        "armeabi-v7a|compiler": ":cc-compiler-armeabi-v7a",
+        "ios_x86_64|compiler": ":cc-compiler-ios_x86_64",
+    },
+)
+
+cc_toolchain(
+    name = "cc-compiler-k8",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "k8",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 1,
+)
+
+# Android tooling requires a default toolchain for the armeabi-v7a cpu.
+cc_toolchain(
+    name = "cc-compiler-armeabi-v7a",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "local",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 1,
+)
+
+# ios crosstool configuration requires a default toolchain for the
+# ios_x86_64 cpu.
+cc_toolchain(
+    name = "cc-compiler-ios_x86_64",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "local",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 1,
+)
diff --git a/third_party/toolchains/cpus/CROSSTOOL b/third_party/toolchains/cpus/CROSSTOOL
new file mode 100644
index 0000000000..0e2d0119af
--- /dev/null
+++ b/third_party/toolchains/cpus/CROSSTOOL
@@ -0,0 +1,922 @@
+# A crosstool configuration for CPU build used with Bazel remote
+# execution service
+# DO NOT EDIT: automatically generated file
+
+major_version: "local"
+minor_version: ""
+default_target_cpu: "same_as_host"
+
+default_toolchain {
+  cpu: "k8"
+  toolchain_identifier: "linux_gnu_x86"
+}
+
+default_toolchain {
+  cpu: "armeabi-v7a"
+  toolchain_identifier: "stub_armeabi-v7a"
+}
+
+default_toolchain {
+  cpu: "x64_windows_msvc"
+  toolchain_identifier: "msvc_x64"
+}
+
+default_toolchain {
+  cpu: "x64_windows_msys"
+  toolchain_identifier: "msys_x64"
+}
+
+default_toolchain {
+  cpu: "s390x"
+  toolchain_identifier: "linux_gnu_x86"
+}
+
+default_toolchain {
+  cpu: "ios_x86_64"
+  toolchain_identifier: "ios_x86_64"
+}
+
+# Android tooling requires a default toolchain for the armeabi-v7a cpu.
+toolchain {
+  abi_version: "armeabi-v7a"
+  abi_libc_version: "armeabi-v7a"
+  builtin_sysroot: ""
+  compiler: "compiler"
+  host_system_name: "armeabi-v7a"
+  needsPic: true
+  supports_gold_linker: false
+  supports_incremental_linker: false
+  supports_fission: false
+  supports_interface_shared_objects: false
+  supports_normalizing_ar: false
+  supports_start_end_lib: false
+  target_libc: "armeabi-v7a"
+  target_cpu: "armeabi-v7a"
+  target_system_name: "armeabi-v7a"
+  toolchain_identifier: "stub_armeabi-v7a"
+
+  tool_path { name: "ar" path: "/bin/false" }
+  tool_path { name: "compat-ld" path: "/bin/false" }
+  tool_path { name: "cpp" path: "/bin/false" }
+  tool_path { name: "dwp" path: "/bin/false" }
+  tool_path { name: "gcc" path: "/bin/false" }
+  tool_path { name: "gcov" path: "/bin/false" }
+  tool_path { name: "ld" path: "/bin/false" }
+
+  tool_path { name: "nm" path: "/bin/false" }
+  tool_path { name: "objcopy" path: "/bin/false" }
+  tool_path { name: "objdump" path: "/bin/false" }
+  tool_path { name: "strip" path: "/bin/false" }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  toolchain_identifier: "ios_x86_64"
+  host_system_name: "x86_64-apple-macosx"
+  target_system_name: "x86_64-apple-ios"
+  target_cpu: "ios_x86_64"
+  target_libc: "ios"
+  compiler: "compiler"
+  abi_version: "local"
+  abi_libc_version: "local"
+  supports_gold_linker: false
+  supports_incremental_linker: false
+  supports_fission: false
+  supports_interface_shared_objects: false
+  supports_normalizing_ar: false
+  supports_start_end_lib: false
+
+  tool_path { name: "ar" path: "/bin/false" }
+  tool_path { name: "compat-ld" path: "/bin/false" }
+  tool_path { name: "cpp" path: "/bin/false" }
+  tool_path { name: "dwp" path: "/bin/false" }
+  tool_path { name: "gcc" path: "/bin/false" }
+  tool_path { name: "gcov" path: "/bin/false" }
+  tool_path { name: "ld" path: "/bin/false" }
+
+  tool_path { name: "nm" path: "/bin/false" }
+  tool_path { name: "objcopy" path: "/bin/false" }
+  tool_path { name: "objdump" path: "/bin/false" }
+  tool_path { name: "strip" path: "/bin/false" }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  toolchain_identifier: "linux_gnu_x86"
+  abi_version: "clang"
+  abi_libc_version: "glibc_2.19"
+  builtin_sysroot: ""
+  compiler: "clang"
+  host_system_name: "i686-unknown-linux-gnu"
+  needsPic: true
+  supports_gold_linker: true
+  supports_incremental_linker: false
+  supports_fission: false
+  supports_interface_shared_objects: false
+  supports_normalizing_ar: false
+  supports_start_end_lib: true
+  target_libc: "glibc_2.19"
+  target_cpu: "k8"
+  target_system_name: "x86_64-unknown-linux-gnu"
+  cxx_flag: "-std=c++0x"
+  linker_flag: "-lstdc++"
+  linker_flag: "-lm"
+  linker_flag: "-fuse-ld=gold"
+  linker_flag: "-B/usr/local/bin"
+  linker_flag: "-B/usr/bin"
+  cxx_builtin_include_directory: "/usr/include/c++/4.9"
+  cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu/c++/4.9"
+  cxx_builtin_include_directory: "/usr/include/c++/4.9/backward"
+  cxx_builtin_include_directory: "/usr/local/include"
+  cxx_builtin_include_directory: "/usr/local/lib/clang/5.0.0/include"
+  cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu"
+  cxx_builtin_include_directory: "/usr/include"
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  unfiltered_cxx_flag: "-Wno-builtin-macro-redefined"
+  unfiltered_cxx_flag: "-D__DATE__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIMESTAMP__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIME__=\"redacted\""
+  compiler_flag: "-U_FORTIFY_SOURCE"
+  compiler_flag: "-fstack-protector"
+  compiler_flag: "-Wall"
+  compiler_flag: "-B/usr/local/bin"
+  compiler_flag: "-B/usr/bin"
+  compiler_flag: "-fcolor-diagnostics"
+  compiler_flag: "-fno-omit-frame-pointer"
+  tool_path {name: "ld" path: "/usr/bin/ld" }
+  tool_path {name: "cpp" path: "/usr/bin/cpp" }
+  tool_path {name: "dwp" path: "/usr/bin/dwp" }
+  tool_path {name: "gcov" path: "/usr/bin/gcov" }
+  tool_path {name: "nm" path: "/usr/bin/nm" }
+  tool_path {name: "objcopy" path: "/usr/bin/objcopy" }
+  tool_path {name: "objdump" path: "/usr/bin/objdump" }
+  tool_path {name: "strip" path: "/usr/bin/strip" }
+  tool_path {name: "gcc" path: "/usr/local/bin/clang" }
+  tool_path {name: "ar" path: "/usr/bin/ar" }
+
+  compilation_mode_flags {
+    mode: DBG
+    compiler_flag: "-g"
+  }
+  compilation_mode_flags {
+    mode: OPT
+    compiler_flag: "-g0"
+    compiler_flag: "-O2"
+    compiler_flag: "-D_FORTIFY_SOURCE=1"
+    compiler_flag: "-DNDEBUG"
+    compiler_flag: "-ffunction-sections"
+    compiler_flag: "-fdata-sections"
+    linker_flag: "-Wl,--gc-sections"
+  }
+  linking_mode_flags { mode: DYNAMIC }
+
+
+    feature {
+      name: 'coverage'
+      provides: 'profile'
+      flag_set {
+        action: 'preprocess-assemble'
+        action: 'c-compile'
+        action: 'c++-compile'
+        action: 'c++-header-parsing'
+        action: 'c++-header-preprocessing'
+        action: 'c++-module-compile'
+        flag_group {
+          flag: '-fprofile-arcs'
+          flag: '-ftest-coverage'
+        }
+      }
+      flag_set {
+        action: 'c++-link-interface-dynamic-library'
+        action: 'c++-link-dynamic-library'
+        action: 'c++-link-executable'
+        flag_group {
+          flag: '-lgcov'
+        }
+      }
+    }
+  
+}
+
+toolchain {
+  toolchain_identifier: "msvc_x64"
+  host_system_name: "local"
+  target_system_name: "local"
+
+  abi_version: "local"
+  abi_libc_version: "local"
+  target_cpu: "x64_windows"
+  compiler: "cl"
+  target_libc: "msvcrt140"
+  default_python_version: "python2.7"
+
+
+
+  tool_path {
+    name: "ar"
+    path: "wrapper/bin/msvc_link.bat"
+  }
+  tool_path {
+    name: "cpp"
+    path: "wrapper/bin/msvc_cl.bat"
+  }
+  tool_path {
+    name: "gcc"
+    path: "wrapper/bin/msvc_cl.bat"
+  }
+  tool_path {
+    name: "gcov"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "ld"
+    path: "wrapper/bin/msvc_link.bat"
+  }
+  tool_path {
+    name: "nm"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "objcopy"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "objdump"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "strip"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  supports_gold_linker: false
+  supports_start_end_lib: false
+  supports_interface_shared_objects: false
+  supports_incremental_linker: false
+  supports_normalizing_ar: true
+  needsPic: false
+
+  # TODO(pcloudy): Review those flags below, they should be defined by cl.exe
+  compiler_flag: "/DOS_WINDOWS=OS_WINDOWS"
+  compiler_flag: "/DCOMPILER_MSVC"
+
+  # Don't pollute with GDI macros in windows.h.
+  compiler_flag: "/DNOGDI"
+  # Don't define min/max macros in windows.h.
+  compiler_flag: "/DNOMINMAX"
+  compiler_flag: "/DPRAGMA_SUPPORTED"
+  # Platform defines.
+  compiler_flag: "/D_WIN32_WINNT=0x0600"
+  # Turn off warning messages.
+  compiler_flag: "/D_CRT_SECURE_NO_DEPRECATE"
+  compiler_flag: "/D_CRT_SECURE_NO_WARNINGS"
+  compiler_flag: "/D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS"
+  # Use math constants (M_PI, etc.) from the math library
+  compiler_flag: "/D_USE_MATH_DEFINES"
+
+  # Useful options to have on for compilation.
+  # Increase the capacity of object files to 2^32 sections.
+  compiler_flag: "/bigobj"
+  # Allocate 500MB for precomputed headers.
+  compiler_flag: "/Zm500"
+  # Use unsigned char by default.
+  compiler_flag: "/J"
+  # Use function level linking.
+  compiler_flag: "/Gy"
+  # Use string pooling.
+  compiler_flag: "/GF"
+  # Warning level 3 (could possibly go to 4 in the future).
+  compiler_flag: "/W3"
+  # Catch both asynchronous (structured) and synchronous (C++) exceptions.
+  compiler_flag: "/EHsc"
+
+  # Globally disabled warnings.
+  # Don't warn about elements of array being be default initialized.
+  compiler_flag: "/wd4351"
+  # Don't warn about no matching delete found.
+  compiler_flag: "/wd4291"
+  # Don't warn about diamond inheritance patterns.
+  compiler_flag: "/wd4250"
+  # Don't warn about insecure functions (e.g. non _s functions).
+  compiler_flag: "/wd4996"
+
+  linker_flag: "/MACHINE:X64"
+
+  linker_flag: "/SUBSYSTEM:CONSOLE"
+
+  # Suppress startup banner.
+  feature {
+    name: "nologo"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      action: "c++-module-compile"
+      action: "c++-module-codegen"
+      action: "c++-header-parsing"
+      action: "c++-header-preprocessing"
+      action: "assemble"
+      action: "preprocess-assemble"
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      action: "c++-link-static-library"
+      action: "c++-link-alwayslink-static-library"
+      action: "c++-link-pic-static-library"
+      action: "c++-link-alwayslink-pic-static-library"
+      flag_group {
+        flag: "/nologo"
+      }
+    }
+  }
+
+  feature {
+    name: "msvc_env"
+    env_set {
+      action: "c-compile"
+      action: "c++-compile"
+      action: "c++-module-compile"
+      action: "c++-module-codegen"
+      action: "c++-header-parsing"
+      action: "c++-header-preprocessing"
+      action: "assemble"
+      action: "preprocess-assemble"
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      action: "c++-link-static-library"
+      action: "c++-link-alwayslink-static-library"
+      action: "c++-link-pic-static-library"
+      action: "c++-link-alwayslink-pic-static-library"
+      env_entry {
+        key: "PATH"
+        value: ""
+      }
+      env_entry {
+        key: "INCLUDE"
+        value: ""
+      }
+      env_entry {
+        key: "LIB"
+        value: ""
+      }
+      env_entry {
+        key: "TMP"
+        value: ""
+      }
+    }
+  }
+
+  feature {
+    name: 'include_paths'
+    flag_set {
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-header-preprocessing'
+      action: 'c++-module-compile'
+      flag_group {
+        flag: '/I%{quote_include_paths}'
+      }
+      flag_group {
+        flag: '/I%{include_paths}'
+      }
+      flag_group {
+        flag: '/I%{system_include_paths}'
+      }
+    }
+  }
+
+  # Stop adding any flag for dotD file, Bazel knows how to parse the output of /showIncludes option
+  # TODO(bazel-team): Remove this empty feature. https://github.com/bazelbuild/bazel/issues/2868
+  feature {
+    name: 'dependency_file'
+  }
+
+  # Tell Bazel to parse the output of /showIncludes
+  feature {
+    name: 'parse_showincludes'
+    flag_set {
+      action: 'assemble'
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-module-compile'
+      action: 'c++-header-preprocessing'
+      action: 'c++-header-parsing'
+      flag_group {
+        flag: "/showIncludes"
+      }
+    }
+  }
+
+  # Stop passing -frandom-seed option
+  feature {
+    name: 'random_seed'
+  }
+
+  # This feature is just for enabling flag_set in action_config for -c and -o options during the transitional period
+  feature {
+    name: 'compile_action_flags_in_flag_set'
+  }
+
+  action_config {
+    config_name: 'c-compile'
+    action_name: 'c-compile'
+    tool {
+      tool_path: 'wrapper/bin/msvc_cl.bat'
+    }
+    flag_set {
+      flag_group {
+        flag: '/c'
+        flag: '%{source_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_object_file'
+      flag_group {
+        flag: '/Fo%{output_object_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_assembly_file'
+      flag_group {
+        flag: '/Fa%{output_assembly_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_preprocess_file'
+      flag_group {
+        flag: '/P'
+        flag: '/Fi%{output_preprocess_file}'
+      }
+    }
+    implies: 'nologo'
+    implies: 'msvc_env'
+    implies: 'parse_showincludes'
+  }
+
+  action_config {
+    config_name: 'c++-compile'
+    action_name: 'c++-compile'
+    tool {
+      tool_path: 'wrapper/bin/msvc_cl.bat'
+    }
+    flag_set {
+      flag_group {
+        flag: '/c'
+        flag: '%{source_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_object_file'
+      flag_group {
+        flag: '/Fo%{output_object_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_assembly_file'
+      flag_group {
+        flag: '/Fa%{output_assembly_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_preprocess_file'
+      flag_group {
+        flag: '/P'
+        flag: '/Fi%{output_preprocess_file}'
+      }
+    }
+    implies: 'nologo'
+    implies: 'msvc_env'
+    implies: 'parse_showincludes'
+  }
+
+  action_config {
+    config_name: 'c++-link-executable'
+    action_name: 'c++-link-executable'
+    tool {
+      tool_path: 'wrapper/bin/msvc_link.bat'
+    }
+    implies: 'nologo'
+    implies: 'strip_debug_symbols'
+    implies: 'linkstamps'
+    implies: 'output_execpath_flags'
+    implies: 'input_param_flags'
+    implies: 'legacy_link_flags'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  action_config {
+    config_name: 'c++-link-dynamic-library'
+    action_name: 'c++-link-dynamic-library'
+    tool {
+      tool_path: 'wrapper/bin/msvc_link.bat'
+    }
+    implies: 'nologo'
+    implies: 'strip_debug_symbols'
+    implies: 'shared_flag'
+    implies: 'linkstamps'
+    implies: 'output_execpath_flags'
+    implies: 'input_param_flags'
+    implies: 'has_configured_linker_path'
+    implies: 'legacy_link_flags'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  action_config {
+    config_name: 'c++-link-static-library'
+    action_name: 'c++-link-static-library'
+    tool {
+      tool_path: 'wrapper/bin/msvc_link.bat'
+    }
+    implies: 'nologo'
+    implies: 'input_param_flags'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  action_config {
+    config_name: 'c++-link-alwayslink-static-library'
+    action_name: 'c++-link-alwayslink-static-library'
+    tool {
+      tool_path: 'wrapper/bin/msvc_link.bat'
+    }
+    implies: 'nologo'
+    implies: 'input_param_flags'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  # TODO(pcloudy): The following action_config is listed in MANDATORY_LINK_TARGET_TYPES.
+  # But do we really need them on Windows?
+  action_config {
+    config_name: 'c++-link-pic-static-library'
+    action_name: 'c++-link-pic-static-library'
+    tool {
+      tool_path: 'wrapper/bin/msvc_link.bat'
+    }
+    implies: 'nologo'
+    implies: 'input_param_flags'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  action_config {
+    config_name: 'c++-link-alwayslink-pic-static-library'
+    action_name: 'c++-link-alwayslink-pic-static-library'
+    tool {
+      tool_path: 'wrapper/bin/msvc_link.bat'
+    }
+    implies: 'nologo'
+    implies: 'input_param_flags'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  action_config {
+    config_name: 'c++-link-interface-dynamic-library'
+    action_name: 'c++-link-interface-dynamic-library'
+    tool {
+      tool_path: 'wrapper/bin/msvc_link.bat'
+    }
+    implies: 'nologo'
+    implies: 'strip_debug_symbols'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  feature {
+    name: 'generate_pdb_file'
+    requires: {
+      feature: 'dbg'
+    }
+    requires: {
+      feature: 'fastbuild'
+    }
+  }
+
+  feature {
+    name: 'has_configured_linker_path'
+  }
+
+  feature {
+    name: 'strip_debug_symbols'
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: 'c++-link-interface-dynamic-library'
+      flag_group {
+        expand_if_all_available: 'strip_debug_symbols'
+        flag: '-Wl,-S'
+      }
+    }
+  }
+
+  feature {
+    name: 'shared_flag'
+    flag_set {
+      action: 'c++-link-dynamic-library'
+      flag_group {
+        flag: '/DLL'
+      }
+    }
+  }
+
+  feature {
+    name: 'linkstamps'
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      expand_if_all_available: 'linkstamp_paths'
+      flag_group {
+        flag: '%{linkstamp_paths}'
+      }
+    }
+  }
+
+  feature {
+    name: 'output_execpath_flags'
+    flag_set {
+      expand_if_all_available: 'output_execpath'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      flag_group {
+        flag: '/OUT:%{output_execpath}'
+      }
+    }
+  }
+
+  feature {
+    name: 'input_param_flags'
+    flag_set {
+      expand_if_all_available: 'library_search_directories'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: 'c++-link-static-library'
+      action: 'c++-link-alwayslink-static-library'
+      action: 'c++-link-pic-static-library'
+      action: 'c++-link-alwayslink-pic-static-library'
+      flag_group {
+        iterate_over: 'library_search_directories'
+        flag: "-L%{library_search_directories}"
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'libopts'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: 'c++-link-static-library'
+      action: 'c++-link-alwayslink-static-library'
+      action: 'c++-link-pic-static-library'
+      action: 'c++-link-alwayslink-pic-static-library'
+      flag_group {
+        flag: '%{libopts}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'libraries_to_link'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: 'c++-link-static-library'
+      action: 'c++-link-alwayslink-static-library'
+      action: 'c++-link-pic-static-library'
+      action: 'c++-link-alwayslink-pic-static-library'
+      flag_group {
+        iterate_over: 'libraries_to_link'
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'object_file_group'
+          }
+          iterate_over: 'libraries_to_link.object_files'
+          flag_group {
+            flag: '%{libraries_to_link.object_files}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'object_file'
+          }
+          flag_group {
+            flag: '%{libraries_to_link.name}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'interface_library'
+          }
+          flag_group {
+            expand_if_false: 'libraries_to_link.is_whole_archive'
+            flag: '%{libraries_to_link.name}'
+          }
+          flag_group {
+            expand_if_true: 'libraries_to_link.is_whole_archive'
+            flag: '/WHOLEARCHIVE:%{libraries_to_link.name}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'static_library'
+          }
+          flag_group {
+            expand_if_false: 'libraries_to_link.is_whole_archive'
+            flag: '%{libraries_to_link.name}'
+          }
+          flag_group {
+            expand_if_true: 'libraries_to_link.is_whole_archive'
+            flag: '/WHOLEARCHIVE:%{libraries_to_link.name}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'dynamic_library'
+          }
+          flag_group {
+            expand_if_false: 'libraries_to_link.is_whole_archive'
+            flag: '%{libraries_to_link.name}'
+          }
+          flag_group {
+            expand_if_true: 'libraries_to_link.is_whole_archive'
+            flag: '/WHOLEARCHIVE:%{libraries_to_link.name}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'versioned_dynamic_library'
+          }
+          flag_group {
+            expand_if_false: 'libraries_to_link.is_whole_archive'
+            flag: '%{libraries_to_link.name}'
+          }
+          flag_group {
+            expand_if_true: 'libraries_to_link.is_whole_archive'
+            flag: '/WHOLEARCHIVE:%{libraries_to_link.name}'
+          }
+        }
+      }
+    }
+  }
+
+  feature {
+    name: 'legacy_link_flags'
+    flag_set {
+      expand_if_all_available: 'legacy_link_flags'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      flag_group {
+        flag: '%{legacy_link_flags}'
+      }
+    }
+  }
+
+  feature {
+    name: 'linker_param_file'
+    flag_set {
+      expand_if_all_available: 'linker_param_file'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      flag_group {
+        flag: '-Wl,@%{linker_param_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'linker_param_file'
+      action: 'c++-link-static-library'
+      action: 'c++-link-alwayslink-static-library'
+      action: 'c++-link-pic-static-library'
+      action: 'c++-link-alwayslink-pic-static-library'
+      flag_group {
+        flag: '@%{linker_param_file}'
+      }
+    }
+  }
+
+  feature {
+    name: 'link_crt_library'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        # The flag is filled by cc_configure.
+        # The default option is /MT, set USE_DYNAMIC_CRT=1 to change it to /MD
+        flag: ""
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      flag_group {
+      # The flag is filled by cc_configure.
+        # The default value is libcmt.lib, set USE_DYNAMIC_CRT=1 to change it to msvcrt.lib
+        flag: "/DEFAULTLIB:"
+      }
+    }
+  }
+
+  feature {
+    name: 'link_crt_debug_library'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        # The flag is filled by cc_configure.
+        # The default option is /MTd, set USE_DYNAMIC_CRT=1 to change it to /MDd
+        flag: ""
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      flag_group {
+        # The flag is filled by cc_configure.
+        # The default value is libcmtd.lib, set USE_DYNAMIC_CRT=1 to change it to msvcrtd.lib
+        flag: "/DEFAULTLIB:"
+      }
+    }
+  }
+
+  feature {
+    name: 'dbg'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/Od"
+        flag: "/Z7"
+        # This will signal the wrapper that we are doing a debug build, which sets
+        # some internal state of the toolchain wrapper. It is intentionally a "-"
+        # flag to make this very obvious.
+        flag: "-g"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      flag_group {
+        flag: "/DEBUG:FULL"
+        flag: "/INCREMENTAL:NO"
+      }
+    }
+    implies: 'link_crt_debug_library'
+    implies: 'generate_pdb_file'
+  }
+
+  feature {
+    name: 'fastbuild'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/Od"
+        flag: "/Z7"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      flag_group {
+        flag: "/DEBUG:FASTLINK"
+        flag: "/INCREMENTAL:NO"
+      }
+    }
+    implies: 'link_crt_library'
+    implies: 'generate_pdb_file'
+  }
+
+  feature {
+    name: 'opt'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/O2"
+      }
+    }
+    implies: 'link_crt_library'
+  }
+
+  compilation_mode_flags {
+    mode: DBG
+    compiler_flag: "-Xcompilation-mode=dbg"
+    linker_flag: "-Xcompilation-mode=dbg"
+  }
+
+  compilation_mode_flags {
+    mode: FASTBUILD
+    compiler_flag: "-Xcompilation-mode=fastbuild"
+    linker_flag: "-Xcompilation-mode=fastbuild"
+  }
+
+  compilation_mode_flags {
+    mode: OPT
+    compiler_flag: "-Xcompilation-mode=opt"
+    linker_flag: "-Xcompilation-mode=opt"
+  }
+
+}
diff --git a/third_party/toolchains/cpus/py/BUILD b/third_party/toolchains/cpus/py/BUILD
new file mode 100644
index 0000000000..d54eebb8e7
--- /dev/null
+++ b/third_party/toolchains/cpus/py/BUILD
@@ -0,0 +1,185 @@
+# A build file to configure python remote repository used with Bazel remote
+# execution service
+# DO NOT EDIT: automatically generated BUILD file
+
+licenses(["restricted"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "python_headers",
+    hdrs = [":python_include"],
+    includes = ["python_include"],
+)
+
+cc_library(
+    name = "numpy_headers",
+    hdrs = [":numpy_include"],
+    includes = ["numpy_include"],
+)
+
+config_setting(
+    name = "windows",
+    values = {"cpu": "x64_windows"},
+    visibility = ["//visibility:public"],
+)
+
+genrule(
+    name = "python_include",
+    outs = [
+        "python_include/code.h",
+        "python_include/dtoa.h",
+        "python_include/tupleobject.h",
+        "python_include/object.h",
+        "python_include/ast.h",
+        "python_include/pymacconfig.h",
+        "python_include/errcode.h",
+        "python_include/frameobject.h",
+        "python_include/pgenheaders.h",
+        "python_include/cellobject.h",
+        "python_include/intobject.h",
+        "python_include/pythread.h",
+        "python_include/cStringIO.h",
+        "python_include/boolobject.h",
+        "python_include/modsupport.h",
+        "python_include/import.h",
+        "python_include/pymath.h",
+        "python_include/node.h",
+        "python_include/funcobject.h",
+        "python_include/eval.h",
+        "python_include/longintrepr.h",
+        "python_include/floatobject.h",
+        "python_include/rangeobject.h",
+        "python_include/pyfpe.h",
+        "python_include/pystrcmp.h",
+        "python_include/dictobject.h",
+        "python_include/pyarena.h",
+        "python_include/objimpl.h",
+        "python_include/bitset.h",
+        "python_include/memoryobject.h",
+        "python_include/bytearrayobject.h",
+        "python_include/pydebug.h",
+        "python_include/pyerrors.h",
+        "python_include/weakrefobject.h",
+        "python_include/grammar.h",
+        "python_include/symtable.h",
+        "python_include/longobject.h",
+        "python_include/structmember.h",
+        "python_include/enumobject.h",
+        "python_include/classobject.h",
+        "python_include/unicodeobject.h",
+        "python_include/sliceobject.h",
+        "python_include/pystrtod.h",
+        "python_include/genobject.h",
+        "python_include/pymactoolbox.h",
+        "python_include/compile.h",
+        "python_include/pyexpat.h",
+        "python_include/asdl.h",
+        "python_include/codecs.h",
+        "python_include/pyctype.h",
+        "python_include/sysmodule.h",
+        "python_include/methodobject.h",
+        "python_include/graminit.h",
+        "python_include/cobject.h",
+        "python_include/intrcheck.h",
+        "python_include/pyport.h",
+        "python_include/warnings.h",
+        "python_include/osdefs.h",
+        "python_include/fileobject.h",
+        "python_include/stringobject.h",
+        "python_include/timefuncs.h",
+        "python_include/traceback.h",
+        "python_include/ceval.h",
+        "python_include/bytes_methods.h",
+        "python_include/pyconfig.h",
+        "python_include/Python.h",
+        "python_include/moduleobject.h",
+        "python_include/pystate.h",
+        "python_include/descrobject.h",
+        "python_include/ucnhash.h",
+        "python_include/pygetopt.h",
+        "python_include/pymem.h",
+        "python_include/complexobject.h",
+        "python_include/structseq.h",
+        "python_include/datetime.h",
+        "python_include/pythonrun.h",
+        "python_include/numpy/oldnumeric.h",
+        "python_include/numpy/npy_1_7_deprecated_api.h",
+        "python_include/numpy/ufunc_api.txt",
+        "python_include/numpy/multiarray_api.txt",
+        "python_include/numpy/halffloat.h",
+        "python_include/numpy/npy_common.h",
+        "python_include/numpy/utils.h",
+        "python_include/numpy/npy_interrupt.h",
+        "python_include/numpy/npy_endian.h",
+        "python_include/numpy/__ufunc_api.h",
+        "python_include/numpy/_neighborhood_iterator_imp.h",
+        "python_include/numpy/ufuncobject.h",
+        "python_include/numpy/ndarraytypes.h",
+        "python_include/numpy/npy_math.h",
+        "python_include/numpy/noprefix.h",
+        "python_include/numpy/npy_3kcompat.h",
+        "python_include/numpy/arrayscalars.h",
+        "python_include/numpy/npy_os.h",
+        "python_include/numpy/ndarrayobject.h",
+        "python_include/numpy/npy_no_deprecated_api.h",
+        "python_include/numpy/arrayobject.h",
+        "python_include/numpy/_numpyconfig.h",
+        "python_include/numpy/__multiarray_api.h",
+        "python_include/numpy/npy_cpu.h",
+        "python_include/numpy/old_defines.h",
+        "python_include/numpy/numpyconfig.h",
+        "python_include/pycapsule.h",
+        "python_include/setobject.h",
+        "python_include/listobject.h",
+        "python_include/bytesobject.h",
+        "python_include/pgen.h",
+        "python_include/patchlevel.h",
+        "python_include/opcode.h",
+        "python_include/parsetok.h",
+        "python_include/marshal.h",
+        "python_include/token.h",
+        "python_include/iterobject.h",
+        "python_include/abstract.h",
+        "python_include/py_curses.h",
+        "python_include/metagrammar.h",
+        "python_include/bufferobject.h",
+        "python_include/Python-ast.h",
+    ],
+    cmd = """
+cp  "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp  "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp  "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp  "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp  "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp  "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp  "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp  "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp  "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp  "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp  "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp  "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp  "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp  "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp  "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp  "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp  "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp  "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp  "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp  "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp  "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp  "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp  "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp  "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp  "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp  "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp  "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp  "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp  "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp  "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp  "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp  "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp  "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp  "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" && cp  "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp  "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp  "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp  "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp  "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp  "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp  "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp  "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp  "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp  "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp  "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp  "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp  "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp  "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp  "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp  "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp  "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp  "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp  "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp  "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp  "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp  "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp  "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp  "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp  "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp  "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp  "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp  "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp  "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp  "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp  "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp  "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp  "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp  "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp  "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp  "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp  "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp  "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp  "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp  "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp  "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp  "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp  "/usr/include/python2.7/numpy/oldnumeric.h" "$(@D)/python_include/numpy/oldnumeric.h" && cp  "/usr/include/python2.7/numpy/npy_1_7_deprecated_api.h" "$(@D)/python_include/numpy/npy_1_7_deprecated_api.h" && cp  "/usr/include/python2.7/numpy/ufunc_api.txt" "$(@D)/python_include/numpy/ufunc_api.txt" && cp  "/usr/include/python2.7/numpy/multiarray_api.txt" "$(@D)/python_include/numpy/multiarray_api.txt" && cp  "/usr/include/python2.7/numpy/halffloat.h" "$(@D)/python_include/numpy/halffloat.h" && cp  "/usr/include/python2.7/numpy/npy_common.h" "$(@D)/python_include/numpy/npy_common.h" && cp  "/usr/include/python2.7/numpy/utils.h" "$(@D)/python_include/numpy/utils.h" && cp  "/usr/include/python2.7/numpy/npy_interrupt.h" "$(@D)/python_include/numpy/npy_interrupt.h" && cp  "/usr/include/python2.7/numpy/npy_endian.h" "$(@D)/python_include/numpy/npy_endian.h" && cp  "/usr/include/python2.7/numpy/__ufunc_api.h" "$(@D)/python_include/numpy/__ufunc_api.h" && cp  "/usr/include/python2.7/numpy/_neighborhood_iterator_imp.h" "$(@D)/python_include/numpy/_neighborhood_iterator_imp.h" && cp  "/usr/include/python2.7/numpy/ufuncobject.h" "$(@D)/python_include/numpy/ufuncobject.h" && cp  "/usr/include/python2.7/numpy/ndarraytypes.h" "$(@D)/python_include/numpy/ndarraytypes.h" && cp  "/usr/include/python2.7/numpy/npy_math.h" "$(@D)/python_include/numpy/npy_math.h" && cp  "/usr/include/python2.7/numpy/noprefix.h" "$(@D)/python_include/numpy/noprefix.h" && cp  "/usr/include/python2.7/numpy/npy_3kcompat.h" "$(@D)/python_include/numpy/npy_3kcompat.h" && cp  "/usr/include/python2.7/numpy/arrayscalars.h" "$(@D)/python_include/numpy/arrayscalars.h" && cp  "/usr/include/python2.7/numpy/npy_os.h" "$(@D)/python_include/numpy/npy_os.h" && cp  "/usr/include/python2.7/numpy/ndarrayobject.h" "$(@D)/python_include/numpy/ndarrayobject.h" && cp  "/usr/include/python2.7/numpy/npy_no_deprecated_api.h" "$(@D)/python_include/numpy/npy_no_deprecated_api.h" && cp  "/usr/include/python2.7/numpy/arrayobject.h" "$(@D)/python_include/numpy/arrayobject.h" && cp  "/usr/include/python2.7/numpy/_numpyconfig.h" "$(@D)/python_include/numpy/_numpyconfig.h" && cp  "/usr/include/python2.7/numpy/__multiarray_api.h" "$(@D)/python_include/numpy/__multiarray_api.h" && cp  "/usr/include/python2.7/numpy/npy_cpu.h" "$(@D)/python_include/numpy/npy_cpu.h" && cp  "/usr/include/python2.7/numpy/old_defines.h" "$(@D)/python_include/numpy/old_defines.h" && cp  "/usr/include/python2.7/numpy/numpyconfig.h" "$(@D)/python_include/numpy/numpyconfig.h" && cp  "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp  "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp  "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp  "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp  "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp  "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp  "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp  "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp  "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp  "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp  "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp  "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp  "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp  "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp  "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp  "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h"    """,
+)
+
+genrule(
+    name = "numpy_include",
+    outs = [
+        "numpy_include/numpy/oldnumeric.h",
+        "numpy_include/numpy/npy_1_7_deprecated_api.h",
+        "numpy_include/numpy/ufunc_api.txt",
+        "numpy_include/numpy/multiarray_api.txt",
+        "numpy_include/numpy/halffloat.h",
+        "numpy_include/numpy/npy_common.h",
+        "numpy_include/numpy/utils.h",
+        "numpy_include/numpy/npy_interrupt.h",
+        "numpy_include/numpy/npy_endian.h",
+        "numpy_include/numpy/__ufunc_api.h",
+        "numpy_include/numpy/_neighborhood_iterator_imp.h",
+        "numpy_include/numpy/ufuncobject.h",
+        "numpy_include/numpy/ndarraytypes.h",
+        "numpy_include/numpy/npy_math.h",
+        "numpy_include/numpy/noprefix.h",
+        "numpy_include/numpy/npy_3kcompat.h",
+        "numpy_include/numpy/arrayscalars.h",
+        "numpy_include/numpy/npy_os.h",
+        "numpy_include/numpy/ndarrayobject.h",
+        "numpy_include/numpy/npy_no_deprecated_api.h",
+        "numpy_include/numpy/arrayobject.h",
+        "numpy_include/numpy/_numpyconfig.h",
+        "numpy_include/numpy/__multiarray_api.h",
+        "numpy_include/numpy/npy_cpu.h",
+        "numpy_include/numpy/old_defines.h",
+        "numpy_include/numpy/numpyconfig.h",
+    ],
+    cmd = """
+cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h"    """,
+)
diff --git a/third_party/toolchains/gpus/crosstool/BUILD b/third_party/toolchains/gpus/crosstool/BUILD
new file mode 100644
index 0000000000..a8c6b0f029
--- /dev/null
+++ b/third_party/toolchains/gpus/crosstool/BUILD
@@ -0,0 +1,52 @@
+# A build file to configure cc toolchain for GPU build used with Bazel remote
+# execution service
+# DO NOT EDIT: automatically generated BUILD file
+
+licenses(["restricted"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_toolchain_suite(
+    name = "toolchain",
+    toolchains = {
+        "local|compiler": ":cc-compiler-local",
+        "darwin|compiler": ":cc-compiler-darwin",
+    },
+)
+
+cc_toolchain(
+    name = "cc-compiler-local",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "local",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    # To support linker flags that need to go to the start of command line
+    # we need the toolchain to support parameter files. Parameter files are
+    # last on the command line and contain all shared libraries to link, so all
+    # regular options will be left of them.
+    supports_param_files = 1,
+)
+
+cc_toolchain(
+    name = "cc-compiler-darwin",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "darwin",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 0,
+)
+
+filegroup(
+    name = "empty",
+    srcs = [],
+)
diff --git a/third_party/toolchains/gpus/crosstool/CROSSTOOL b/third_party/toolchains/gpus/crosstool/CROSSTOOL
new file mode 100644
index 0000000000..224b8912f6
--- /dev/null
+++ b/third_party/toolchains/gpus/crosstool/CROSSTOOL
@@ -0,0 +1,302 @@
+# A crosstool configuration for GPU build used with Bazel remote
+# execution service
+# DO NOT EDIT: automatically generated file
+
+major_version: "local"
+minor_version: ""
+default_target_cpu: "same_as_host"
+
+default_toolchain {
+  cpu: "k8"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "piii"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "arm"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "darwin"
+  toolchain_identifier: "local_darwin"
+}
+default_toolchain {
+  cpu: "ppc"
+  toolchain_identifier: "local_linux"
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  compiler: "compiler"
+  host_system_name: "local"
+  needsPic: true
+  target_libc: "local"
+  target_cpu: "local"
+  target_system_name: "local"
+  toolchain_identifier: "local_linux"
+
+  feature {
+    name: "c++11"
+    flag_set {
+      action: "c++-compile"
+      flag_group {
+        flag: "-std=c++11"
+      }
+    }
+  }
+
+  feature {
+    name: "stdlib"
+    flag_set {
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      flag_group {
+        flag: "-lstdc++"
+      }
+    }
+  }
+
+  feature {
+    name: "determinism"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        # Make C++ compilation deterministic. Use linkstamping instead of these
+        # compiler symbols.
+        flag: "-Wno-builtin-macro-redefined"
+        flag: "-D__DATE__=\"redacted\""
+        flag: "-D__TIMESTAMP__=\"redacted\""
+        flag: "-D__TIME__=\"redacted\""
+      }
+    }
+  }
+
+  feature {
+    name: "alwayslink"
+    flag_set {
+      action: "c++-link-dynamic-library"
+      action: "c++-link-executable"
+      flag_group {
+        flag: "-Wl,-no-as-needed"
+      }
+    }
+  }
+
+  # This feature will be enabled for builds that support pic by bazel.
+  feature {
+    name: "pic"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        expand_if_all_available: "pic"
+        flag: "-fPIC"
+      }
+      flag_group {
+        expand_if_none_available: "pic"
+        flag: "-fPIE"
+      }
+    }
+  }
+
+  # Security hardening on by default.
+  feature {
+    name: "hardening"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases.
+        # We need to undef it before redefining it as some distributions now
+        # have it enabled by default.
+        flag: "-U_FORTIFY_SOURCE"
+        flag: "-D_FORTIFY_SOURCE=1"
+        flag: "-fstack-protector"
+      }
+    }
+    flag_set {
+      action: "c++-link-dynamic-library"
+      flag_group {
+        flag: "-Wl,-z,relro,-z,now"
+      }
+    }
+    flag_set {
+      action: "c++-link-executable"
+      flag_group {
+        flag: "-pie"
+        flag: "-Wl,-z,relro,-z,now"
+      }
+    }
+  }
+
+  feature {
+    name: "warnings"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        # All warnings are enabled. Maybe enable -Werror as well?
+        flag: "-Wall"
+        # TODO(ngiraldo): Some parts of the codebase set -Werror and hit this 
+        # warning, so switch it off for now.
+        flag: "-Wno-invalid-partial-specialization"
+      }
+    }
+  }
+
+  # Keep stack frames for debugging, even in opt mode.
+  feature {
+    name: "frame-pointer"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        flag: "-fno-omit-frame-pointer"
+      }
+    }
+  }
+
+  feature {
+    name: "build-id"
+    flag_set {
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      flag_group {
+        # Stamp the binary with a unique identifier.
+        flag: "-Wl,--build-id=md5"
+        flag: "-Wl,--hash-style=gnu"
+      }
+    }
+  }
+
+  feature {
+    name: "no-canonical-prefixes"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      flag_group {
+        flag:"-no-canonical-prefixes"
+      }
+    }
+  }
+
+  feature {
+    name: "disable-assertions"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        flag: "-DNDEBUG"
+      }
+    }
+  }
+
+  feature {
+    name: "linker-bin-path"
+
+    flag_set {
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      flag_group {
+        flag: "-B/usr/bin/"
+      }
+    }
+  }
+
+  feature {
+    name: "common"
+    implies: "stdlib"
+    implies: "c++11"
+    implies: "determinism"
+    implies: "alwayslink"
+    implies: "hardening"
+    implies: "warnings"
+    implies: "frame-pointer"
+    implies: "build-id"
+    implies: "no-canonical-prefixes"
+    implies: "linker-bin-path"
+  }
+
+  feature {
+    name: "opt"
+    implies: "common"
+    implies: "disable-assertions"
+
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        # No debug symbols.
+        # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt
+        # or even generally? However, that can't happen here, as it requires
+        # special handling in Bazel.
+        flag: "-g0"
+
+        # Conservative choice for -O
+        # -O3 can increase binary size and even slow down the resulting binaries.
+        # Profile first and / or use FDO if you need better performance than this.
+        flag: "-O2"
+
+        # Removal of unused code and data at link time (can this increase binary size in some cases?).
+        flag: "-ffunction-sections"
+        flag: "-fdata-sections"
+      }
+    }
+    flag_set {
+      action: "c++-link-dynamic-library"
+      action: "c++-link-executable"
+      flag_group {
+        flag: "-Wl,--gc-sections"
+      }
+    }
+  }
+
+  feature {
+    name: "fastbuild"
+    implies: "common"
+  }
+
+  feature {
+    name: "dbg"
+    implies: "common"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      flag_group {
+        flag: "-g"
+      }
+    }
+  }
+
+  # Set clang as a C/C++ compiler.
+  tool_path { name: "gcc" path: "/usr/local/bin/clang" }
+
+  # Use the default system toolchain for everything else.
+  tool_path { name: "ar" path: "/usr/bin/ar" }
+  tool_path { name: "compat-ld" path: "/usr/bin/ld" }
+  tool_path { name: "cpp" path: "/usr/bin/cpp" }
+  tool_path { name: "dwp" path: "/usr/bin/dwp" }
+  tool_path { name: "gcov" path: "/usr/bin/gcov" }
+  tool_path { name: "ld" path: "/usr/bin/ld" }
+  tool_path { name: "nm" path: "/usr/bin/nm" }
+  tool_path { name: "objcopy" path: "/usr/bin/objcopy" }
+  tool_path { name: "objdump" path: "/usr/bin/objdump" }
+  tool_path { name: "strip" path: "/usr/bin/strip" }
+
+  # Enabled dynamic linking.
+  linking_mode_flags { mode: DYNAMIC }
+
+  cxx_builtin_include_directory: "/usr/include/c++/5.4.0"
+  cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu/c++/5.4.0"
+  cxx_builtin_include_directory: "/usr/include/c++/5.4.0/backward"
+  cxx_builtin_include_directory: "/usr/local/include"
+  cxx_builtin_include_directory: "/usr/local/lib/clang/5.0.0/include"
+  cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu"
+  cxx_builtin_include_directory: "/usr/include"
+}
diff --git a/third_party/toolchains/gpus/cuda/BUILD b/third_party/toolchains/gpus/cuda/BUILD
new file mode 100644
index 0000000000..9f8f8754ff
--- /dev/null
+++ b/third_party/toolchains/gpus/cuda/BUILD
@@ -0,0 +1,1362 @@
+# A build file to configure cuda remote repository used with Bazel remote
+# execution service
+# DO NOT EDIT: automatically generated BUILD file
+
+licenses(["restricted"])  # MPL2, portions GPL v3, LGPL v3, BSD-like
+
+package(default_visibility = ["//visibility:public"])
+
+config_setting(
+    name = "using_nvcc",
+    values = {
+        "define": "using_cuda_nvcc=true",
+    },
+)
+
+config_setting(
+    name = "using_clang",
+    values = {
+        "define": "using_cuda_clang=true",
+    },
+)
+
+# Equivalent to using_clang && -c opt.
+config_setting(
+    name = "using_clang_opt",
+    values = {
+        "define": "using_cuda_clang=true",
+        "compilation_mode": "opt",
+    },
+)
+
+config_setting(
+    name = "darwin",
+    values = {"cpu": "darwin"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "freebsd",
+    values = {"cpu": "freebsd"},
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cuda_headers",
+    hdrs = [
+        "cuda/cuda_config.h",
+        ":cuda-include",
+        ":cudnn-include",
+    ],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cudart_static",
+    srcs = ["cuda/lib/libcudart_static.a"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    linkopts = select({
+        ":freebsd": [],
+        "//conditions:default": ["-ldl"],
+    }) + [
+        "-lpthread",
+        "-lrt",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cuda_driver",
+    srcs = ["cuda/lib/libcuda.so"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cudart",
+    srcs = ["cuda/lib/libcudart.so.8.0"],
+    data = ["cuda/lib/libcudart.so.8.0"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cublas",
+    srcs = ["cuda/lib/libcublas.so.8.0"],
+    data = ["cuda/lib/libcublas.so.8.0"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cusolver",
+    srcs = ["cuda/lib/libcusolver.so.8.0"],
+    data = ["cuda/lib/libcusolver.so.8.0"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    linkopts = ["-lgomp"],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cudnn",
+    srcs = ["cuda/lib/libcudnn.so.5"],
+    data = ["cuda/lib/libcudnn.so.5"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cufft",
+    srcs = ["cuda/lib/libcufft.so.8.0"],
+    data = ["cuda/lib/libcufft.so.8.0"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "curand",
+    srcs = ["cuda/lib/libcurand.so.8.0"],
+    data = ["cuda/lib/libcurand.so.8.0"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cuda",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":cublas",
+        ":cuda_headers",
+        ":cudart",
+        ":cudnn",
+        ":cufft",
+        ":curand",
+    ],
+)
+
+cc_library(
+    name = "cupti_headers",
+    hdrs = [
+        "cuda/cuda_config.h",
+        ":cuda-extras",
+    ],
+    includes = [
+        ".",
+        "cuda/extras/CUPTI/include/",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cupti_dsos",
+    data = ["cuda/lib/libcupti.so.8.0"],
+    includes = [
+        ".",
+        "cuda/extras/CUPTI/include/",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "libdevice_root",
+    data = [":cuda-nvvm"],
+    includes = [
+        ".",
+        "cuda/extras/CUPTI/include/",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+genrule(
+    name = "cuda-include",
+    outs = [
+        "cuda/include/math_functions.hpp",
+        "cuda/include/cufft.h",
+        "cuda/include/nvgraph.h",
+        "cuda/include/curand_normal.h",
+        "cuda/include/curand_uniform.h",
+        "cuda/include/nppi_data_exchange_and_initialization.h",
+        "cuda/include/cuda_gl_interop.h",
+        "cuda/include/nppi_compression_functions.h",
+        "cuda/include/npp.h",
+        "cuda/include/cuda.h",
+        "cuda/include/nppi_statistics_functions.h",
+        "cuda/include/vector_functions.hpp",
+        "cuda/include/sm_32_intrinsics.hpp",
+        "cuda/include/sm_32_intrinsics.h",
+        "cuda/include/curand_discrete.h",
+        "cuda/include/cuda_runtime.h",
+        "cuda/include/cufftXt.h",
+        "cuda/include/sm_61_intrinsics.h",
+        "cuda/include/texture_fetch_functions.h",
+        "cuda/include/curand_mrg32k3a.h",
+        "cuda/include/host_defines.h",
+        "cuda/include/common_functions.h",
+        "cuda/include/nppi_support_functions.h",
+        "cuda/include/nppi_linear_transforms.h",
+        "cuda/include/device_double_functions.hpp",
+        "cuda/include/math_constants.h",
+        "cuda/include/nvToolsExtSync.h",
+        "cuda/include/npps_initialization.h",
+        "cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h",
+        "cuda/include/texture_indirect_functions.hpp",
+        "cuda/include/cudaProfiler.h",
+        "cuda/include/npps_filtering_functions.h",
+        "cuda/include/cusparse_v2.h",
+        "cuda/include/nppi.h",
+        "cuda/include/surface_indirect_functions.h",
+        "cuda/include/sm_30_intrinsics.h",
+        "cuda/include/device_double_functions.h",
+        "cuda/include/sm_35_intrinsics.h",
+        "cuda/include/cusolverSp.h",
+        "cuda/include/library_types.h",
+        "cuda/include/surface_indirect_functions.hpp",
+        "cuda/include/cudalibxt.h",
+        "cuda/include/channel_descriptor.h",
+        "cuda/include/device_functions_decls.h",
+        "cuda/include/curand_kernel.h",
+        "cuda/include/curand_mtgp32_host.h",
+        "cuda/include/nvToolsExtCuda.h",
+        "cuda/include/nvToolsExt.h",
+        "cuda/include/cuComplex.h",
+        "cuda/include/sm_32_atomic_functions.h",
+        "cuda/include/texture_indirect_functions.h",
+        "cuda/include/sm_32_atomic_functions.hpp",
+        "cuda/include/sm_20_intrinsics.hpp",
+        "cuda/include/device_launch_parameters.h",
+        "cuda/include/curand_mtgp32.h",
+        "cuda/include/texture_fetch_functions.hpp",
+        "cuda/include/cuda_occupancy.h",
+        "cuda/include/CL/opencl.h",
+        "cuda/include/CL/cl_platform.h",
+        "cuda/include/CL/cl_egl.h",
+        "cuda/include/CL/cl_gl.h",
+        "cuda/include/CL/cl.h",
+        "cuda/include/CL/cl_gl_ext.h",
+        "cuda/include/CL/cl_ext.h",
+        "cuda/include/CL/cl.hpp",
+        "cuda/include/host_config.h",
+        "cuda/include/cuda_surface_types.h",
+        "cuda/include/math_functions.h",
+        "cuda/include/nvToolsExtMeta.h",
+        "cuda/include/sm_20_atomic_functions.hpp",
+        "cuda/include/device_functions.h",
+        "cuda/include/device_types.h",
+        "cuda/include/npps_conversion_functions.h",
+        "cuda/include/curand_precalc.h",
+        "cuda/include/cusolverRf.h",
+        "cuda/include/sm_60_atomic_functions.hpp",
+        "cuda/include/cuviddec.h",
+        "cuda/include/curand_discrete2.h",
+        "cuda/include/device_functions.hpp",
+        "cuda/include/thrust/transform_scan.h",
+        "cuda/include/thrust/system_error.h",
+        "cuda/include/thrust/device_malloc.h",
+        "cuda/include/thrust/partition.h",
+        "cuda/include/thrust/unique.h",
+        "cuda/include/thrust/device_delete.h",
+        "cuda/include/thrust/execution_policy.h",
+        "cuda/include/thrust/adjacent_difference.h",
+        "cuda/include/thrust/sequence.h",
+        "cuda/include/thrust/merge.h",
+        "cuda/include/thrust/device_new.h",
+        "cuda/include/thrust/transform_reduce.h",
+        "cuda/include/thrust/device_vector.h",
+        "cuda/include/thrust/gather.h",
+        "cuda/include/thrust/sort.h",
+        "cuda/include/thrust/scan.h",
+        "cuda/include/thrust/detail/temporary_array.h",
+        "cuda/include/thrust/detail/util/align.h",
+        "cuda/include/thrust/detail/util/blocking.h",
+        "cuda/include/thrust/detail/transform.inl",
+        "cuda/include/thrust/detail/device_vector.inl",
+        "cuda/include/thrust/detail/binary_search.inl",
+        "cuda/include/thrust/detail/overlapped_copy.h",
+        "cuda/include/thrust/detail/vector_base.inl",
+        "cuda/include/thrust/detail/device_reference.inl",
+        "cuda/include/thrust/detail/functional/actor.h",
+        "cuda/include/thrust/detail/functional/value.h",
+        "cuda/include/thrust/detail/functional/operators.h",
+        "cuda/include/thrust/detail/functional/operators/logical_operators.h",
+        "cuda/include/thrust/detail/functional/operators/relational_operators.h",
+        "cuda/include/thrust/detail/functional/operators/assignment_operator.h",
+        "cuda/include/thrust/detail/functional/operators/bitwise_operators.h",
+        "cuda/include/thrust/detail/functional/operators/operator_adaptors.h",
+        "cuda/include/thrust/detail/functional/operators/arithmetic_operators.h",
+        "cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h",
+        "cuda/include/thrust/detail/functional/argument.h",
+        "cuda/include/thrust/detail/functional/placeholder.h",
+        "cuda/include/thrust/detail/functional/actor.inl",
+        "cuda/include/thrust/detail/functional/composite.h",
+        "cuda/include/thrust/detail/static_map.h",
+        "cuda/include/thrust/detail/type_traits/has_nested_type.h",
+        "cuda/include/thrust/detail/type_traits/is_call_possible.h",
+        "cuda/include/thrust/detail/type_traits/function_traits.h",
+        "cuda/include/thrust/detail/type_traits/pointer_traits.h",
+        "cuda/include/thrust/detail/type_traits/has_member_function.h",
+        "cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h",
+        "cuda/include/thrust/detail/type_traits/minimum_type.h",
+        "cuda/include/thrust/detail/type_traits/has_trivial_assign.h",
+        "cuda/include/thrust/detail/type_traits/is_metafunction_defined.h",
+        "cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h",
+        "cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h",
+        "cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h",
+        "cuda/include/thrust/detail/reference.h",
+        "cuda/include/thrust/detail/inner_product.inl",
+        "cuda/include/thrust/detail/use_default.h",
+        "cuda/include/thrust/detail/sequence.inl",
+        "cuda/include/thrust/detail/sort.inl",
+        "cuda/include/thrust/detail/equal.inl",
+        "cuda/include/thrust/detail/execution_policy.h",
+        "cuda/include/thrust/detail/integer_traits.h",
+        "cuda/include/thrust/detail/type_traits.h",
+        "cuda/include/thrust/detail/reverse.inl",
+        "cuda/include/thrust/detail/tabulate.inl",
+        "cuda/include/thrust/detail/unique.inl",
+        "cuda/include/thrust/detail/scatter.inl",
+        "cuda/include/thrust/detail/set_operations.inl",
+        "cuda/include/thrust/detail/device_malloc.inl",
+        "cuda/include/thrust/detail/copy_if.inl",
+        "cuda/include/thrust/detail/fill.inl",
+        "cuda/include/thrust/detail/temporary_array.inl",
+        "cuda/include/thrust/detail/transform_scan.inl",
+        "cuda/include/thrust/detail/minmax.h",
+        "cuda/include/thrust/detail/swap.inl",
+        "cuda/include/thrust/detail/pointer.inl",
+        "cuda/include/thrust/detail/transform_reduce.inl",
+        "cuda/include/thrust/detail/config.h",
+        "cuda/include/thrust/detail/distance.inl",
+        "cuda/include/thrust/detail/pair.inl",
+        "cuda/include/thrust/detail/allocator/temporary_allocator.h",
+        "cuda/include/thrust/detail/allocator/tagged_allocator.h",
+        "cuda/include/thrust/detail/allocator/destroy_range.inl",
+        "cuda/include/thrust/detail/allocator/destroy_range.h",
+        "cuda/include/thrust/detail/allocator/no_throw_allocator.h",
+        "cuda/include/thrust/detail/allocator/default_construct_range.inl",
+        "cuda/include/thrust/detail/allocator/fill_construct_range.inl",
+        "cuda/include/thrust/detail/allocator/tagged_allocator.inl",
+        "cuda/include/thrust/detail/allocator/malloc_allocator.h",
+        "cuda/include/thrust/detail/allocator/allocator_traits.h",
+        "cuda/include/thrust/detail/allocator/copy_construct_range.h",
+        "cuda/include/thrust/detail/allocator/allocator_traits.inl",
+        "cuda/include/thrust/detail/allocator/default_construct_range.h",
+        "cuda/include/thrust/detail/allocator/copy_construct_range.inl",
+        "cuda/include/thrust/detail/allocator/malloc_allocator.inl",
+        "cuda/include/thrust/detail/allocator/temporary_allocator.inl",
+        "cuda/include/thrust/detail/allocator/fill_construct_range.h",
+        "cuda/include/thrust/detail/temporary_buffer.h",
+        "cuda/include/thrust/detail/reduce.inl",
+        "cuda/include/thrust/detail/device_new.inl",
+        "cuda/include/thrust/detail/pointer.h",
+        "cuda/include/thrust/detail/for_each.inl",
+        "cuda/include/thrust/detail/generate.inl",
+        "cuda/include/thrust/detail/dispatch/is_trivial_copy.h",
+        "cuda/include/thrust/detail/adjacent_difference.inl",
+        "cuda/include/thrust/detail/tuple_meta_transform.h",
+        "cuda/include/thrust/detail/functional.inl",
+        "cuda/include/thrust/detail/remove.inl",
+        "cuda/include/thrust/detail/tuple_transform.h",
+        "cuda/include/thrust/detail/merge.inl",
+        "cuda/include/thrust/detail/extrema.inl",
+        "cuda/include/thrust/detail/trivial_sequence.h",
+        "cuda/include/thrust/detail/vector_base.h",
+        "cuda/include/thrust/detail/count.inl",
+        "cuda/include/thrust/detail/uninitialized_copy.inl",
+        "cuda/include/thrust/detail/function.h",
+        "cuda/include/thrust/detail/swap_ranges.inl",
+        "cuda/include/thrust/detail/device_delete.inl",
+        "cuda/include/thrust/detail/static_assert.h",
+        "cuda/include/thrust/detail/logical.inl",
+        "cuda/include/thrust/detail/seq.h",
+        "cuda/include/thrust/detail/mpl/math.h",
+        "cuda/include/thrust/detail/mismatch.inl",
+        "cuda/include/thrust/detail/internal_functional.h",
+        "cuda/include/thrust/detail/get_iterator_value.h",
+        "cuda/include/thrust/detail/copy.inl",
+        "cuda/include/thrust/detail/copy.h",
+        "cuda/include/thrust/detail/complex/catrigf.h",
+        "cuda/include/thrust/detail/complex/cpowf.h",
+        "cuda/include/thrust/detail/complex/csqrtf.h",
+        "cuda/include/thrust/detail/complex/ccoshf.h",
+        "cuda/include/thrust/detail/complex/csinhf.h",
+        "cuda/include/thrust/detail/complex/clogf.h",
+        "cuda/include/thrust/detail/complex/ccosh.h",
+        "cuda/include/thrust/detail/complex/arithmetic.h",
+        "cuda/include/thrust/detail/complex/csqrt.h",
+        "cuda/include/thrust/detail/complex/cpow.h",
+        "cuda/include/thrust/detail/complex/complex.inl",
+        "cuda/include/thrust/detail/complex/math_private.h",
+        "cuda/include/thrust/detail/complex/c99math.h",
+        "cuda/include/thrust/detail/complex/cproj.h",
+        "cuda/include/thrust/detail/complex/catrig.h",
+        "cuda/include/thrust/detail/complex/ctanhf.h",
+        "cuda/include/thrust/detail/complex/cexpf.h",
+        "cuda/include/thrust/detail/complex/csinh.h",
+        "cuda/include/thrust/detail/complex/stream.h",
+        "cuda/include/thrust/detail/complex/ctanh.h",
+        "cuda/include/thrust/detail/complex/cexp.h",
+        "cuda/include/thrust/detail/complex/clog.h",
+        "cuda/include/thrust/detail/range/head_flags.h",
+        "cuda/include/thrust/detail/range/tail_flags.h",
+        "cuda/include/thrust/detail/execute_with_allocator.h",
+        "cuda/include/thrust/detail/integer_math.h",
+        "cuda/include/thrust/detail/swap.h",
+        "cuda/include/thrust/detail/uninitialized_fill.inl",
+        "cuda/include/thrust/detail/scan.inl",
+        "cuda/include/thrust/detail/gather.inl",
+        "cuda/include/thrust/detail/reference_forward_declaration.h",
+        "cuda/include/thrust/detail/numeric_traits.h",
+        "cuda/include/thrust/detail/reference.inl",
+        "cuda/include/thrust/detail/cstdint.h",
+        "cuda/include/thrust/detail/device_free.inl",
+        "cuda/include/thrust/detail/copy_if.h",
+        "cuda/include/thrust/detail/partition.inl",
+        "cuda/include/thrust/detail/find.inl",
+        "cuda/include/thrust/detail/config/forceinline.h",
+        "cuda/include/thrust/detail/config/debug.h",
+        "cuda/include/thrust/detail/config/config.h",
+        "cuda/include/thrust/detail/config/host_device.h",
+        "cuda/include/thrust/detail/config/host_system.h",
+        "cuda/include/thrust/detail/config/compiler.h",
+        "cuda/include/thrust/detail/config/device_system.h",
+        "cuda/include/thrust/detail/config/compiler_fence.h",
+        "cuda/include/thrust/detail/config/exec_check_disable.h",
+        "cuda/include/thrust/detail/config/simple_defines.h",
+        "cuda/include/thrust/detail/config/global_workarounds.h",
+        "cuda/include/thrust/detail/replace.inl",
+        "cuda/include/thrust/detail/device_ptr.inl",
+        "cuda/include/thrust/detail/tuple.inl",
+        "cuda/include/thrust/detail/malloc_and_free.h",
+        "cuda/include/thrust/detail/host_vector.inl",
+        "cuda/include/thrust/detail/raw_pointer_cast.h",
+        "cuda/include/thrust/detail/advance.inl",
+        "cuda/include/thrust/detail/contiguous_storage.h",
+        "cuda/include/thrust/detail/raw_reference_cast.h",
+        "cuda/include/thrust/detail/contiguous_storage.inl",
+        "cuda/include/thrust/reverse.h",
+        "cuda/include/thrust/device_malloc_allocator.h",
+        "cuda/include/thrust/scatter.h",
+        "cuda/include/thrust/pair.h",
+        "cuda/include/thrust/advance.h",
+        "cuda/include/thrust/find.h",
+        "cuda/include/thrust/device_ptr.h",
+        "cuda/include/thrust/generate.h",
+        "cuda/include/thrust/uninitialized_fill.h",
+        "cuda/include/thrust/system/system_error.h",
+        "cuda/include/thrust/system/detail/bad_alloc.h",
+        "cuda/include/thrust/system/detail/adl/transform_scan.h",
+        "cuda/include/thrust/system/detail/adl/unique_by_key.h",
+        "cuda/include/thrust/system/detail/adl/partition.h",
+        "cuda/include/thrust/system/detail/adl/unique.h",
+        "cuda/include/thrust/system/detail/adl/adjacent_difference.h",
+        "cuda/include/thrust/system/detail/adl/sequence.h",
+        "cuda/include/thrust/system/detail/adl/merge.h",
+        "cuda/include/thrust/system/detail/adl/transform_reduce.h",
+        "cuda/include/thrust/system/detail/adl/gather.h",
+        "cuda/include/thrust/system/detail/adl/sort.h",
+        "cuda/include/thrust/system/detail/adl/scan.h",
+        "cuda/include/thrust/system/detail/adl/temporary_buffer.h",
+        "cuda/include/thrust/system/detail/adl/scan_by_key.h",
+        "cuda/include/thrust/system/detail/adl/reverse.h",
+        "cuda/include/thrust/system/detail/adl/assign_value.h",
+        "cuda/include/thrust/system/detail/adl/scatter.h",
+        "cuda/include/thrust/system/detail/adl/find.h",
+        "cuda/include/thrust/system/detail/adl/generate.h",
+        "cuda/include/thrust/system/detail/adl/uninitialized_fill.h",
+        "cuda/include/thrust/system/detail/adl/remove.h",
+        "cuda/include/thrust/system/detail/adl/tabulate.h",
+        "cuda/include/thrust/system/detail/adl/for_each.h",
+        "cuda/include/thrust/system/detail/adl/reduce_by_key.h",
+        "cuda/include/thrust/system/detail/adl/reduce.h",
+        "cuda/include/thrust/system/detail/adl/equal.h",
+        "cuda/include/thrust/system/detail/adl/copy.h",
+        "cuda/include/thrust/system/detail/adl/swap_ranges.h",
+        "cuda/include/thrust/system/detail/adl/uninitialized_copy.h",
+        "cuda/include/thrust/system/detail/adl/binary_search.h",
+        "cuda/include/thrust/system/detail/adl/set_operations.h",
+        "cuda/include/thrust/system/detail/adl/mismatch.h",
+        "cuda/include/thrust/system/detail/adl/extrema.h",
+        "cuda/include/thrust/system/detail/adl/count.h",
+        "cuda/include/thrust/system/detail/adl/replace.h",
+        "cuda/include/thrust/system/detail/adl/get_value.h",
+        "cuda/include/thrust/system/detail/adl/inner_product.h",
+        "cuda/include/thrust/system/detail/adl/copy_if.h",
+        "cuda/include/thrust/system/detail/adl/logical.h",
+        "cuda/include/thrust/system/detail/adl/iter_swap.h",
+        "cuda/include/thrust/system/detail/adl/malloc_and_free.h",
+        "cuda/include/thrust/system/detail/adl/fill.h",
+        "cuda/include/thrust/system/detail/adl/transform.h",
+        "cuda/include/thrust/system/detail/errno.h",
+        "cuda/include/thrust/system/detail/error_category.inl",
+        "cuda/include/thrust/system/detail/sequential/transform_scan.h",
+        "cuda/include/thrust/system/detail/sequential/unique_by_key.h",
+        "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h",
+        "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl",
+        "cuda/include/thrust/system/detail/sequential/stable_merge_sort.h",
+        "cuda/include/thrust/system/detail/sequential/sort.inl",
+        "cuda/include/thrust/system/detail/sequential/partition.h",
+        "cuda/include/thrust/system/detail/sequential/unique.h",
+        "cuda/include/thrust/system/detail/sequential/execution_policy.h",
+        "cuda/include/thrust/system/detail/sequential/adjacent_difference.h",
+        "cuda/include/thrust/system/detail/sequential/sequence.h",
+        "cuda/include/thrust/system/detail/sequential/merge.h",
+        "cuda/include/thrust/system/detail/sequential/transform_reduce.h",
+        "cuda/include/thrust/system/detail/sequential/gather.h",
+        "cuda/include/thrust/system/detail/sequential/sort.h",
+        "cuda/include/thrust/system/detail/sequential/copy_backward.h",
+        "cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl",
+        "cuda/include/thrust/system/detail/sequential/scan.h",
+        "cuda/include/thrust/system/detail/sequential/temporary_buffer.h",
+        "cuda/include/thrust/system/detail/sequential/scan_by_key.h",
+        "cuda/include/thrust/system/detail/sequential/reverse.h",
+        "cuda/include/thrust/system/detail/sequential/assign_value.h",
+        "cuda/include/thrust/system/detail/sequential/scatter.h",
+        "cuda/include/thrust/system/detail/sequential/find.h",
+        "cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl",
+        "cuda/include/thrust/system/detail/sequential/merge.inl",
+        "cuda/include/thrust/system/detail/sequential/generate.h",
+        "cuda/include/thrust/system/detail/sequential/uninitialized_fill.h",
+        "cuda/include/thrust/system/detail/sequential/general_copy.h",
+        "cuda/include/thrust/system/detail/sequential/insertion_sort.h",
+        "cuda/include/thrust/system/detail/sequential/remove.h",
+        "cuda/include/thrust/system/detail/sequential/tabulate.h",
+        "cuda/include/thrust/system/detail/sequential/for_each.h",
+        "cuda/include/thrust/system/detail/sequential/reduce_by_key.h",
+        "cuda/include/thrust/system/detail/sequential/reduce.h",
+        "cuda/include/thrust/system/detail/sequential/equal.h",
+        "cuda/include/thrust/system/detail/sequential/stable_radix_sort.h",
+        "cuda/include/thrust/system/detail/sequential/copy.inl",
+        "cuda/include/thrust/system/detail/sequential/copy.h",
+        "cuda/include/thrust/system/detail/sequential/swap_ranges.h",
+        "cuda/include/thrust/system/detail/sequential/uninitialized_copy.h",
+        "cuda/include/thrust/system/detail/sequential/binary_search.h",
+        "cuda/include/thrust/system/detail/sequential/set_operations.h",
+        "cuda/include/thrust/system/detail/sequential/mismatch.h",
+        "cuda/include/thrust/system/detail/sequential/extrema.h",
+        "cuda/include/thrust/system/detail/sequential/count.h",
+        "cuda/include/thrust/system/detail/sequential/trivial_copy.h",
+        "cuda/include/thrust/system/detail/sequential/replace.h",
+        "cuda/include/thrust/system/detail/sequential/get_value.h",
+        "cuda/include/thrust/system/detail/sequential/inner_product.h",
+        "cuda/include/thrust/system/detail/sequential/copy_if.h",
+        "cuda/include/thrust/system/detail/sequential/logical.h",
+        "cuda/include/thrust/system/detail/sequential/iter_swap.h",
+        "cuda/include/thrust/system/detail/sequential/malloc_and_free.h",
+        "cuda/include/thrust/system/detail/sequential/fill.h",
+        "cuda/include/thrust/system/detail/sequential/transform.h",
+        "cuda/include/thrust/system/detail/error_condition.inl",
+        "cuda/include/thrust/system/detail/internal/decompose.h",
+        "cuda/include/thrust/system/detail/error_code.inl",
+        "cuda/include/thrust/system/detail/generic/transform_scan.h",
+        "cuda/include/thrust/system/detail/generic/memory.inl",
+        "cuda/include/thrust/system/detail/generic/transform.inl",
+        "cuda/include/thrust/system/detail/generic/binary_search.inl",
+        "cuda/include/thrust/system/detail/generic/scan_by_key.inl",
+        "cuda/include/thrust/system/detail/generic/unique_by_key.h",
+        "cuda/include/thrust/system/detail/generic/inner_product.inl",
+        "cuda/include/thrust/system/detail/generic/select_system.h",
+        "cuda/include/thrust/system/detail/generic/sequence.inl",
+        "cuda/include/thrust/system/detail/generic/sort.inl",
+        "cuda/include/thrust/system/detail/generic/equal.inl",
+        "cuda/include/thrust/system/detail/generic/partition.h",
+        "cuda/include/thrust/system/detail/generic/unique.h",
+        "cuda/include/thrust/system/detail/generic/adjacent_difference.h",
+        "cuda/include/thrust/system/detail/generic/tag.h",
+        "cuda/include/thrust/system/detail/generic/unique_by_key.inl",
+        "cuda/include/thrust/system/detail/generic/sequence.h",
+        "cuda/include/thrust/system/detail/generic/type_traits.h",
+        "cuda/include/thrust/system/detail/generic/merge.h",
+        "cuda/include/thrust/system/detail/generic/reverse.inl",
+        "cuda/include/thrust/system/detail/generic/tabulate.inl",
+        "cuda/include/thrust/system/detail/generic/unique.inl",
+        "cuda/include/thrust/system/detail/generic/scatter.inl",
+        "cuda/include/thrust/system/detail/generic/set_operations.inl",
+        "cuda/include/thrust/system/detail/generic/copy_if.inl",
+        "cuda/include/thrust/system/detail/generic/transform_reduce.h",
+        "cuda/include/thrust/system/detail/generic/transform_scan.inl",
+        "cuda/include/thrust/system/detail/generic/gather.h",
+        "cuda/include/thrust/system/detail/generic/reduce_by_key.inl",
+        "cuda/include/thrust/system/detail/generic/transform_reduce.inl",
+        "cuda/include/thrust/system/detail/generic/sort.h",
+        "cuda/include/thrust/system/detail/generic/distance.inl",
+        "cuda/include/thrust/system/detail/generic/scan.h",
+        "cuda/include/thrust/system/detail/generic/temporary_buffer.h",
+        "cuda/include/thrust/system/detail/generic/reduce.inl",
+        "cuda/include/thrust/system/detail/generic/scan_by_key.h",
+        "cuda/include/thrust/system/detail/generic/reverse.h",
+        "cuda/include/thrust/system/detail/generic/temporary_buffer.inl",
+        "cuda/include/thrust/system/detail/generic/scatter.h",
+        "cuda/include/thrust/system/detail/generic/generate.inl",
+        "cuda/include/thrust/system/detail/generic/adjacent_difference.inl",
+        "cuda/include/thrust/system/detail/generic/remove.inl",
+        "cuda/include/thrust/system/detail/generic/advance.h",
+        "cuda/include/thrust/system/detail/generic/find.h",
+        "cuda/include/thrust/system/detail/generic/merge.inl",
+        "cuda/include/thrust/system/detail/generic/scalar/binary_search.inl",
+        "cuda/include/thrust/system/detail/generic/scalar/binary_search.h",
+        "cuda/include/thrust/system/detail/generic/extrema.inl",
+        "cuda/include/thrust/system/detail/generic/generate.h",
+        "cuda/include/thrust/system/detail/generic/uninitialized_fill.h",
+        "cuda/include/thrust/system/detail/generic/count.inl",
+        "cuda/include/thrust/system/detail/generic/remove.h",
+        "cuda/include/thrust/system/detail/generic/uninitialized_copy.inl",
+        "cuda/include/thrust/system/detail/generic/tabulate.h",
+        "cuda/include/thrust/system/detail/generic/for_each.h",
+        "cuda/include/thrust/system/detail/generic/distance.h",
+        "cuda/include/thrust/system/detail/generic/swap_ranges.inl",
+        "cuda/include/thrust/system/detail/generic/reduce_by_key.h",
+        "cuda/include/thrust/system/detail/generic/reduce.h",
+        "cuda/include/thrust/system/detail/generic/equal.h",
+        "cuda/include/thrust/system/detail/generic/mismatch.inl",
+        "cuda/include/thrust/system/detail/generic/copy.inl",
+        "cuda/include/thrust/system/detail/generic/copy.h",
+        "cuda/include/thrust/system/detail/generic/swap_ranges.h",
+        "cuda/include/thrust/system/detail/generic/uninitialized_copy.h",
+        "cuda/include/thrust/system/detail/generic/binary_search.h",
+        "cuda/include/thrust/system/detail/generic/set_operations.h",
+        "cuda/include/thrust/system/detail/generic/uninitialized_fill.inl",
+        "cuda/include/thrust/system/detail/generic/mismatch.h",
+        "cuda/include/thrust/system/detail/generic/scan.inl",
+        "cuda/include/thrust/system/detail/generic/gather.inl",
+        "cuda/include/thrust/system/detail/generic/extrema.h",
+        "cuda/include/thrust/system/detail/generic/count.h",
+        "cuda/include/thrust/system/detail/generic/replace.h",
+        "cuda/include/thrust/system/detail/generic/inner_product.h",
+        "cuda/include/thrust/system/detail/generic/copy_if.h",
+        "cuda/include/thrust/system/detail/generic/logical.h",
+        "cuda/include/thrust/system/detail/generic/partition.inl",
+        "cuda/include/thrust/system/detail/generic/memory.h",
+        "cuda/include/thrust/system/detail/generic/find.inl",
+        "cuda/include/thrust/system/detail/generic/replace.inl",
+        "cuda/include/thrust/system/detail/generic/advance.inl",
+        "cuda/include/thrust/system/detail/generic/fill.h",
+        "cuda/include/thrust/system/detail/generic/transform.h",
+        "cuda/include/thrust/system/detail/system_error.inl",
+        "cuda/include/thrust/system/omp/execution_policy.h",
+        "cuda/include/thrust/system/omp/vector.h",
+        "cuda/include/thrust/system/omp/detail/transform_scan.h",
+        "cuda/include/thrust/system/omp/detail/memory.inl",
+        "cuda/include/thrust/system/omp/detail/reduce_intervals.inl",
+        "cuda/include/thrust/system/omp/detail/unique_by_key.h",
+        "cuda/include/thrust/system/omp/detail/sort.inl",
+        "cuda/include/thrust/system/omp/detail/partition.h",
+        "cuda/include/thrust/system/omp/detail/unique.h",
+        "cuda/include/thrust/system/omp/detail/execution_policy.h",
+        "cuda/include/thrust/system/omp/detail/adjacent_difference.h",
+        "cuda/include/thrust/system/omp/detail/unique_by_key.inl",
+        "cuda/include/thrust/system/omp/detail/sequence.h",
+        "cuda/include/thrust/system/omp/detail/merge.h",
+        "cuda/include/thrust/system/omp/detail/unique.inl",
+        "cuda/include/thrust/system/omp/detail/copy_if.inl",
+        "cuda/include/thrust/system/omp/detail/transform_reduce.h",
+        "cuda/include/thrust/system/omp/detail/gather.h",
+        "cuda/include/thrust/system/omp/detail/reduce_by_key.inl",
+        "cuda/include/thrust/system/omp/detail/sort.h",
+        "cuda/include/thrust/system/omp/detail/scan.h",
+        "cuda/include/thrust/system/omp/detail/temporary_buffer.h",
+        "cuda/include/thrust/system/omp/detail/default_decomposition.h",
+        "cuda/include/thrust/system/omp/detail/reduce.inl",
+        "cuda/include/thrust/system/omp/detail/scan_by_key.h",
+        "cuda/include/thrust/system/omp/detail/reverse.h",
+        "cuda/include/thrust/system/omp/detail/assign_value.h",
+        "cuda/include/thrust/system/omp/detail/scatter.h",
+        "cuda/include/thrust/system/omp/detail/for_each.inl",
+        "cuda/include/thrust/system/omp/detail/default_decomposition.inl",
+        "cuda/include/thrust/system/omp/detail/remove.inl",
+        "cuda/include/thrust/system/omp/detail/vector.inl",
+        "cuda/include/thrust/system/omp/detail/find.h",
+        "cuda/include/thrust/system/omp/detail/generate.h",
+        "cuda/include/thrust/system/omp/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/omp/detail/remove.h",
+        "cuda/include/thrust/system/omp/detail/tabulate.h",
+        "cuda/include/thrust/system/omp/detail/for_each.h",
+        "cuda/include/thrust/system/omp/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/omp/detail/reduce.h",
+        "cuda/include/thrust/system/omp/detail/equal.h",
+        "cuda/include/thrust/system/omp/detail/copy.inl",
+        "cuda/include/thrust/system/omp/detail/copy.h",
+        "cuda/include/thrust/system/omp/detail/swap_ranges.h",
+        "cuda/include/thrust/system/omp/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/omp/detail/binary_search.h",
+        "cuda/include/thrust/system/omp/detail/set_operations.h",
+        "cuda/include/thrust/system/omp/detail/mismatch.h",
+        "cuda/include/thrust/system/omp/detail/extrema.h",
+        "cuda/include/thrust/system/omp/detail/count.h",
+        "cuda/include/thrust/system/omp/detail/replace.h",
+        "cuda/include/thrust/system/omp/detail/get_value.h",
+        "cuda/include/thrust/system/omp/detail/inner_product.h",
+        "cuda/include/thrust/system/omp/detail/copy_if.h",
+        "cuda/include/thrust/system/omp/detail/logical.h",
+        "cuda/include/thrust/system/omp/detail/partition.inl",
+        "cuda/include/thrust/system/omp/detail/iter_swap.h",
+        "cuda/include/thrust/system/omp/detail/par.h",
+        "cuda/include/thrust/system/omp/detail/reduce_intervals.h",
+        "cuda/include/thrust/system/omp/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/omp/detail/fill.h",
+        "cuda/include/thrust/system/omp/detail/transform.h",
+        "cuda/include/thrust/system/omp/memory.h",
+        "cuda/include/thrust/system/tbb/execution_policy.h",
+        "cuda/include/thrust/system/tbb/vector.h",
+        "cuda/include/thrust/system/tbb/detail/transform_scan.h",
+        "cuda/include/thrust/system/tbb/detail/memory.inl",
+        "cuda/include/thrust/system/tbb/detail/unique_by_key.h",
+        "cuda/include/thrust/system/tbb/detail/sort.inl",
+        "cuda/include/thrust/system/tbb/detail/partition.h",
+        "cuda/include/thrust/system/tbb/detail/unique.h",
+        "cuda/include/thrust/system/tbb/detail/execution_policy.h",
+        "cuda/include/thrust/system/tbb/detail/adjacent_difference.h",
+        "cuda/include/thrust/system/tbb/detail/unique_by_key.inl",
+        "cuda/include/thrust/system/tbb/detail/sequence.h",
+        "cuda/include/thrust/system/tbb/detail/merge.h",
+        "cuda/include/thrust/system/tbb/detail/unique.inl",
+        "cuda/include/thrust/system/tbb/detail/copy_if.inl",
+        "cuda/include/thrust/system/tbb/detail/transform_reduce.h",
+        "cuda/include/thrust/system/tbb/detail/gather.h",
+        "cuda/include/thrust/system/tbb/detail/reduce_by_key.inl",
+        "cuda/include/thrust/system/tbb/detail/sort.h",
+        "cuda/include/thrust/system/tbb/detail/scan.h",
+        "cuda/include/thrust/system/tbb/detail/temporary_buffer.h",
+        "cuda/include/thrust/system/tbb/detail/reduce.inl",
+        "cuda/include/thrust/system/tbb/detail/scan_by_key.h",
+        "cuda/include/thrust/system/tbb/detail/reverse.h",
+        "cuda/include/thrust/system/tbb/detail/assign_value.h",
+        "cuda/include/thrust/system/tbb/detail/scatter.h",
+        "cuda/include/thrust/system/tbb/detail/for_each.inl",
+        "cuda/include/thrust/system/tbb/detail/remove.inl",
+        "cuda/include/thrust/system/tbb/detail/vector.inl",
+        "cuda/include/thrust/system/tbb/detail/find.h",
+        "cuda/include/thrust/system/tbb/detail/merge.inl",
+        "cuda/include/thrust/system/tbb/detail/generate.h",
+        "cuda/include/thrust/system/tbb/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/tbb/detail/remove.h",
+        "cuda/include/thrust/system/tbb/detail/tabulate.h",
+        "cuda/include/thrust/system/tbb/detail/for_each.h",
+        "cuda/include/thrust/system/tbb/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/tbb/detail/reduce.h",
+        "cuda/include/thrust/system/tbb/detail/equal.h",
+        "cuda/include/thrust/system/tbb/detail/copy.inl",
+        "cuda/include/thrust/system/tbb/detail/copy.h",
+        "cuda/include/thrust/system/tbb/detail/swap_ranges.h",
+        "cuda/include/thrust/system/tbb/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/tbb/detail/binary_search.h",
+        "cuda/include/thrust/system/tbb/detail/set_operations.h",
+        "cuda/include/thrust/system/tbb/detail/mismatch.h",
+        "cuda/include/thrust/system/tbb/detail/scan.inl",
+        "cuda/include/thrust/system/tbb/detail/extrema.h",
+        "cuda/include/thrust/system/tbb/detail/count.h",
+        "cuda/include/thrust/system/tbb/detail/replace.h",
+        "cuda/include/thrust/system/tbb/detail/get_value.h",
+        "cuda/include/thrust/system/tbb/detail/inner_product.h",
+        "cuda/include/thrust/system/tbb/detail/copy_if.h",
+        "cuda/include/thrust/system/tbb/detail/logical.h",
+        "cuda/include/thrust/system/tbb/detail/partition.inl",
+        "cuda/include/thrust/system/tbb/detail/iter_swap.h",
+        "cuda/include/thrust/system/tbb/detail/par.h",
+        "cuda/include/thrust/system/tbb/detail/reduce_intervals.h",
+        "cuda/include/thrust/system/tbb/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/tbb/detail/fill.h",
+        "cuda/include/thrust/system/tbb/detail/transform.h",
+        "cuda/include/thrust/system/tbb/memory.h",
+        "cuda/include/thrust/system/error_code.h",
+        "cuda/include/thrust/system/cpp/execution_policy.h",
+        "cuda/include/thrust/system/cpp/vector.h",
+        "cuda/include/thrust/system/cpp/detail/transform_scan.h",
+        "cuda/include/thrust/system/cpp/detail/memory.inl",
+        "cuda/include/thrust/system/cpp/detail/unique_by_key.h",
+        "cuda/include/thrust/system/cpp/detail/partition.h",
+        "cuda/include/thrust/system/cpp/detail/unique.h",
+        "cuda/include/thrust/system/cpp/detail/execution_policy.h",
+        "cuda/include/thrust/system/cpp/detail/adjacent_difference.h",
+        "cuda/include/thrust/system/cpp/detail/sequence.h",
+        "cuda/include/thrust/system/cpp/detail/merge.h",
+        "cuda/include/thrust/system/cpp/detail/transform_reduce.h",
+        "cuda/include/thrust/system/cpp/detail/gather.h",
+        "cuda/include/thrust/system/cpp/detail/sort.h",
+        "cuda/include/thrust/system/cpp/detail/scan.h",
+        "cuda/include/thrust/system/cpp/detail/temporary_buffer.h",
+        "cuda/include/thrust/system/cpp/detail/scan_by_key.h",
+        "cuda/include/thrust/system/cpp/detail/reverse.h",
+        "cuda/include/thrust/system/cpp/detail/assign_value.h",
+        "cuda/include/thrust/system/cpp/detail/scatter.h",
+        "cuda/include/thrust/system/cpp/detail/vector.inl",
+        "cuda/include/thrust/system/cpp/detail/find.h",
+        "cuda/include/thrust/system/cpp/detail/generate.h",
+        "cuda/include/thrust/system/cpp/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/cpp/detail/remove.h",
+        "cuda/include/thrust/system/cpp/detail/tabulate.h",
+        "cuda/include/thrust/system/cpp/detail/for_each.h",
+        "cuda/include/thrust/system/cpp/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/cpp/detail/reduce.h",
+        "cuda/include/thrust/system/cpp/detail/equal.h",
+        "cuda/include/thrust/system/cpp/detail/copy.h",
+        "cuda/include/thrust/system/cpp/detail/swap_ranges.h",
+        "cuda/include/thrust/system/cpp/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/cpp/detail/binary_search.h",
+        "cuda/include/thrust/system/cpp/detail/set_operations.h",
+        "cuda/include/thrust/system/cpp/detail/mismatch.h",
+        "cuda/include/thrust/system/cpp/detail/extrema.h",
+        "cuda/include/thrust/system/cpp/detail/count.h",
+        "cuda/include/thrust/system/cpp/detail/replace.h",
+        "cuda/include/thrust/system/cpp/detail/get_value.h",
+        "cuda/include/thrust/system/cpp/detail/inner_product.h",
+        "cuda/include/thrust/system/cpp/detail/copy_if.h",
+        "cuda/include/thrust/system/cpp/detail/logical.h",
+        "cuda/include/thrust/system/cpp/detail/iter_swap.h",
+        "cuda/include/thrust/system/cpp/detail/par.h",
+        "cuda/include/thrust/system/cpp/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/cpp/detail/fill.h",
+        "cuda/include/thrust/system/cpp/detail/transform.h",
+        "cuda/include/thrust/system/cpp/memory.h",
+        "cuda/include/thrust/system/cuda/execution_policy.h",
+        "cuda/include/thrust/system/cuda/vector.h",
+        "cuda/include/thrust/system/cuda/error.h",
+        "cuda/include/thrust/system/cuda/detail/copy_device_to_device.h",
+        "cuda/include/thrust/system/cuda/detail/transform_scan.h",
+        "cuda/include/thrust/system/cuda/detail/memory.inl",
+        "cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_device.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_type.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/cub.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh",
+        "cuda/include/thrust/system/cuda/detail/reduce_intervals.inl",
+        "cuda/include/thrust/system/cuda/detail/copy_cross_system.inl",
+        "cuda/include/thrust/system/cuda/detail/unique_by_key.h",
+        "cuda/include/thrust/system/cuda/detail/bulk.h",
+        "cuda/include/thrust/system/cuda/detail/sort.inl",
+        "cuda/include/thrust/system/cuda/detail/partition.h",
+        "cuda/include/thrust/system/cuda/detail/unique.h",
+        "cuda/include/thrust/system/cuda/detail/execution_policy.h",
+        "cuda/include/thrust/system/cuda/detail/cuda_launch_config.h",
+        "cuda/include/thrust/system/cuda/detail/cub.h",
+        "cuda/include/thrust/system/cuda/detail/adjacent_difference.h",
+        "cuda/include/thrust/system/cuda/detail/sequence.h",
+        "cuda/include/thrust/system/cuda/detail/merge.h",
+        "cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl",
+        "cuda/include/thrust/system/cuda/detail/copy_if.inl",
+        "cuda/include/thrust/system/cuda/detail/transform_reduce.h",
+        "cuda/include/thrust/system/cuda/detail/error.inl",
+        "cuda/include/thrust/system/cuda/detail/gather.h",
+        "cuda/include/thrust/system/cuda/detail/reduce_by_key.inl",
+        "cuda/include/thrust/system/cuda/detail/sort.h",
+        "cuda/include/thrust/system/cuda/detail/synchronize.h",
+        "cuda/include/thrust/system/cuda/detail/scan.h",
+        "cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h",
+        "cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h",
+        "cuda/include/thrust/system/cuda/detail/detail/set_operation.inl",
+        "cuda/include/thrust/system/cuda/detail/detail/balanced_path.h",
+        "cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h",
+        "cuda/include/thrust/system/cuda/detail/detail/set_operation.h",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h",
+        "cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl",
+        "cuda/include/thrust/system/cuda/detail/detail/merge.h",
+        "cuda/include/thrust/system/cuda/detail/detail/alignment.h",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h",
+        "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl",
+        "cuda/include/thrust/system/cuda/detail/detail/launch_closure.h",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h",
+        "cuda/include/thrust/system/cuda/detail/detail/uninitialized.h",
+        "cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h",
+        "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h",
+        "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl",
+        "cuda/include/thrust/system/cuda/detail/temporary_buffer.h",
+        "cuda/include/thrust/system/cuda/detail/default_decomposition.h",
+        "cuda/include/thrust/system/cuda/detail/reduce.inl",
+        "cuda/include/thrust/system/cuda/detail/scan_by_key.h",
+        "cuda/include/thrust/system/cuda/detail/reverse.h",
+        "cuda/include/thrust/system/cuda/detail/assign_value.h",
+        "cuda/include/thrust/system/cuda/detail/scatter.h",
+        "cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp",
+        "cuda/include/thrust/system/cuda/detail/for_each.inl",
+        "cuda/include/thrust/system/cuda/detail/default_decomposition.inl",
+        "cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h",
+        "cuda/include/thrust/system/cuda/detail/adjacent_difference.inl",
+        "cuda/include/thrust/system/cuda/detail/vector.inl",
+        "cuda/include/thrust/system/cuda/detail/throw_on_error.h",
+        "cuda/include/thrust/system/cuda/detail/find.h",
+        "cuda/include/thrust/system/cuda/detail/terminate.h",
+        "cuda/include/thrust/system/cuda/detail/merge.inl",
+        "cuda/include/thrust/system/cuda/detail/trivial_copy.inl",
+        "cuda/include/thrust/system/cuda/detail/generate.h",
+        "cuda/include/thrust/system/cuda/detail/execute_on_stream.h",
+        "cuda/include/thrust/system/cuda/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/cuda/detail/remove.h",
+        "cuda/include/thrust/system/cuda/detail/tabulate.h",
+        "cuda/include/thrust/system/cuda/detail/for_each.h",
+        "cuda/include/thrust/system/cuda/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/cuda/detail/decomposition.h",
+        "cuda/include/thrust/system/cuda/detail/reduce.h",
+        "cuda/include/thrust/system/cuda/detail/equal.h",
+        "cuda/include/thrust/system/cuda/detail/runtime_introspection.h",
+        "cuda/include/thrust/system/cuda/detail/copy.inl",
+        "cuda/include/thrust/system/cuda/detail/copy.h",
+        "cuda/include/thrust/system/cuda/detail/swap_ranges.h",
+        "cuda/include/thrust/system/cuda/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/cuda/detail/binary_search.h",
+        "cuda/include/thrust/system/cuda/detail/runtime_introspection.inl",
+        "cuda/include/thrust/system/cuda/detail/set_operations.h",
+        "cuda/include/thrust/system/cuda/detail/mismatch.h",
+        "cuda/include/thrust/system/cuda/detail/scan.inl",
+        "cuda/include/thrust/system/cuda/detail/synchronize.inl",
+        "cuda/include/thrust/system/cuda/detail/extrema.h",
+        "cuda/include/thrust/system/cuda/detail/set_union.inl",
+        "cuda/include/thrust/system/cuda/detail/set_intersection.inl",
+        "cuda/include/thrust/system/cuda/detail/count.h",
+        "cuda/include/thrust/system/cuda/detail/trivial_copy.h",
+        "cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl",
+        "cuda/include/thrust/system/cuda/detail/replace.h",
+        "cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/async.hpp",
+        "cuda/include/thrust/system/cuda/detail/bulk/future.hpp",
+        "cuda/include/thrust/system/cuda/detail/guarded_driver_types.h",
+        "cuda/include/thrust/system/cuda/detail/get_value.h",
+        "cuda/include/thrust/system/cuda/detail/inner_product.h",
+        "cuda/include/thrust/system/cuda/detail/copy_if.h",
+        "cuda/include/thrust/system/cuda/detail/logical.h",
+        "cuda/include/thrust/system/cuda/detail/iter_swap.h",
+        "cuda/include/thrust/system/cuda/detail/block/merge.h",
+        "cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h",
+        "cuda/include/thrust/system/cuda/detail/block/merge.inl",
+        "cuda/include/thrust/system/cuda/detail/block/merging_sort.h",
+        "cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h",
+        "cuda/include/thrust/system/cuda/detail/block/reduce.h",
+        "cuda/include/thrust/system/cuda/detail/block/copy.h",
+        "cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h",
+        "cuda/include/thrust/system/cuda/detail/par.h",
+        "cuda/include/thrust/system/cuda/detail/copy_cross_system.h",
+        "cuda/include/thrust/system/cuda/detail/reduce_intervals.h",
+        "cuda/include/thrust/system/cuda/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/cuda/detail/fill.h",
+        "cuda/include/thrust/system/cuda/detail/set_difference.inl",
+        "cuda/include/thrust/system/cuda/detail/transform.h",
+        "cuda/include/thrust/system/cuda/experimental/pinned_allocator.h",
+        "cuda/include/thrust/system/cuda/memory.h",
+        "cuda/include/thrust/remove.h",
+        "cuda/include/thrust/tabulate.h",
+        "cuda/include/thrust/for_each.h",
+        "cuda/include/thrust/distance.h",
+        "cuda/include/thrust/reduce.h",
+        "cuda/include/thrust/equal.h",
+        "cuda/include/thrust/complex.h",
+        "cuda/include/thrust/device_allocator.h",
+        "cuda/include/thrust/copy.h",
+        "cuda/include/thrust/uninitialized_copy.h",
+        "cuda/include/thrust/device_reference.h",
+        "cuda/include/thrust/binary_search.h",
+        "cuda/include/thrust/set_operations.h",
+        "cuda/include/thrust/swap.h",
+        "cuda/include/thrust/mismatch.h",
+        "cuda/include/thrust/extrema.h",
+        "cuda/include/thrust/count.h",
+        "cuda/include/thrust/device_free.h",
+        "cuda/include/thrust/random/discard_block_engine.h",
+        "cuda/include/thrust/random/normal_distribution.h",
+        "cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h",
+        "cuda/include/thrust/random/detail/subtract_with_carry_engine.inl",
+        "cuda/include/thrust/random/detail/xor_combine_engine_max.h",
+        "cuda/include/thrust/random/detail/linear_congruential_engine_discard.h",
+        "cuda/include/thrust/random/detail/uniform_int_distribution.inl",
+        "cuda/include/thrust/random/detail/discard_block_engine.inl",
+        "cuda/include/thrust/random/detail/uniform_real_distribution.inl",
+        "cuda/include/thrust/random/detail/random_core_access.h",
+        "cuda/include/thrust/random/detail/mod.h",
+        "cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl",
+        "cuda/include/thrust/random/detail/linear_congruential_engine.inl",
+        "cuda/include/thrust/random/detail/xor_combine_engine.inl",
+        "cuda/include/thrust/random/detail/normal_distribution.inl",
+        "cuda/include/thrust/random/detail/normal_distribution_base.h",
+        "cuda/include/thrust/random/uniform_int_distribution.h",
+        "cuda/include/thrust/random/linear_feedback_shift_engine.h",
+        "cuda/include/thrust/random/xor_combine_engine.h",
+        "cuda/include/thrust/random/subtract_with_carry_engine.h",
+        "cuda/include/thrust/random/linear_congruential_engine.h",
+        "cuda/include/thrust/random/uniform_real_distribution.h",
+        "cuda/include/thrust/functional.h",
+        "cuda/include/thrust/replace.h",
+        "cuda/include/thrust/device_new_allocator.h",
+        "cuda/include/thrust/host_vector.h",
+        "cuda/include/thrust/version.h",
+        "cuda/include/thrust/inner_product.h",
+        "cuda/include/thrust/iterator/iterator_traits.h",
+        "cuda/include/thrust/iterator/discard_iterator.h",
+        "cuda/include/thrust/iterator/retag.h",
+        "cuda/include/thrust/iterator/permutation_iterator.h",
+        "cuda/include/thrust/iterator/transform_iterator.h",
+        "cuda/include/thrust/iterator/detail/reverse_iterator.inl",
+        "cuda/include/thrust/iterator/detail/zip_iterator.inl",
+        "cuda/include/thrust/iterator/detail/counting_iterator.inl",
+        "cuda/include/thrust/iterator/detail/distance_from_result.h",
+        "cuda/include/thrust/iterator/detail/host_system_tag.h",
+        "cuda/include/thrust/iterator/detail/iterator_traversal_tags.h",
+        "cuda/include/thrust/iterator/detail/retag.h",
+        "cuda/include/thrust/iterator/detail/tagged_iterator.h",
+        "cuda/include/thrust/iterator/detail/iterator_traits.inl",
+        "cuda/include/thrust/iterator/detail/minimum_category.h",
+        "cuda/include/thrust/iterator/detail/discard_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h",
+        "cuda/include/thrust/iterator/detail/zip_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/normal_iterator.h",
+        "cuda/include/thrust/iterator/detail/join_iterator.h",
+        "cuda/include/thrust/iterator/detail/device_system_tag.h",
+        "cuda/include/thrust/iterator/detail/universal_categories.h",
+        "cuda/include/thrust/iterator/detail/reverse_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/minimum_system.h",
+        "cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h",
+        "cuda/include/thrust/iterator/detail/is_iterator_category.h",
+        "cuda/include/thrust/iterator/detail/permutation_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/any_assign.h",
+        "cuda/include/thrust/iterator/detail/any_system_tag.h",
+        "cuda/include/thrust/iterator/detail/is_trivial_iterator.h",
+        "cuda/include/thrust/iterator/detail/iterator_category_to_system.h",
+        "cuda/include/thrust/iterator/detail/iterator_adaptor_base.h",
+        "cuda/include/thrust/iterator/detail/constant_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/transform_iterator.inl",
+        "cuda/include/thrust/iterator/detail/iterator_facade_category.h",
+        "cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h",
+        "cuda/include/thrust/iterator/constant_iterator.h",
+        "cuda/include/thrust/iterator/counting_iterator.h",
+        "cuda/include/thrust/iterator/iterator_adaptor.h",
+        "cuda/include/thrust/iterator/iterator_facade.h",
+        "cuda/include/thrust/iterator/iterator_categories.h",
+        "cuda/include/thrust/iterator/reverse_iterator.h",
+        "cuda/include/thrust/iterator/zip_iterator.h",
+        "cuda/include/thrust/logical.h",
+        "cuda/include/thrust/tuple.h",
+        "cuda/include/thrust/memory.h",
+        "cuda/include/thrust/random.h",
+        "cuda/include/thrust/fill.h",
+        "cuda/include/thrust/transform.h",
+        "cuda/include/texture_types.h",
+        "cuda/include/nppversion.h",
+        "cuda/include/cuda_texture_types.h",
+        "cuda/include/fatbinary.h",
+        "cuda/include/cublasXt.h",
+        "cuda/include/cuda_fp16.h",
+        "cuda/include/vector_functions.h",
+        "cuda/include/cusparse.h",
+        "cuda/include/nppi_filtering_functions.h",
+        "cuda/include/nppi_morphological_operations.h",
+        "cuda/include/sobol_direction_vectors.h",
+        "cuda/include/nvblas.h",
+        "cuda/include/curand_mtgp32dc_p_11213.h",
+        "cuda/include/nvcuvid.h",
+        "cuda/include/cuda_runtime_api.h",
+        "cuda/include/curand_mtgp32_kernel.h",
+        "cuda/include/cublas_v2.h",
+        "cuda/include/builtin_types.h",
+        "cuda/include/nppi_geometry_transforms.h",
+        "cuda/include/npps_support_functions.h",
+        "cuda/include/cufftw.h",
+        "cuda/include/cuda_device_runtime_api.h",
+        "cuda/include/sm_30_intrinsics.hpp",
+        "cuda/include/vector_types.h",
+        "cuda/include/sm_35_atomic_functions.h",
+        "cuda/include/sm_20_intrinsics.h",
+        "cuda/include/driver_types.h",
+        "cuda/include/nvToolsExtCudaRt.h",
+        "cuda/include/curand_globals.h",
+        "cuda/include/device_atomic_functions.h",
+        "cuda/include/surface_types.h",
+        "cuda/include/nvrtc.h",
+        "cuda/include/nppdefs.h",
+        "cuda/include/sm_60_atomic_functions.h",
+        "cuda/include/driver_functions.h",
+        "cuda/include/cusolver_common.h",
+        "cuda/include/cublas.h",
+        "cuda/include/curand_lognormal.h",
+        "cuda/include/device_atomic_functions.hpp",
+        "cuda/include/crt/device_runtime.h",
+        "cuda/include/crt/storage_class.h",
+        "cuda/include/crt/func_macro.h",
+        "cuda/include/crt/host_runtime.h",
+        "cuda/include/nppi_arithmetic_and_logical_operations.h",
+        "cuda/include/npps_arithmetic_and_logical_operations.h",
+        "cuda/include/nppi_computer_vision.h",
+        "cuda/include/surface_functions.hpp",
+        "cuda/include/surface_functions.h",
+        "cuda/include/curand_normal_static.h",
+        "cuda/include/curand.h",
+        "cuda/include/math_functions_dbl_ptx3.h",
+        "cuda/include/curand_philox4x32_x.h",
+        "cuda/include/nppi_threshold_and_compare_operations.h",
+        "cuda/include/nvml.h",
+        "cuda/include/npps.h",
+        "cuda/include/cuda_vdpau_interop.h",
+        "cuda/include/sm_61_intrinsics.hpp",
+        "cuda/include/cublas_api.h",
+        "cuda/include/nppi_color_conversion.h",
+        "cuda/include/math_functions_dbl_ptx3.hpp",
+        "cuda/include/nppcore.h",
+        "cuda/include/cudaGL.h",
+        "cuda/include/fatBinaryCtl.h",
+        "cuda/include/npps_statistics_functions.h",
+        "cuda/include/cudaVDPAU.h",
+        "cuda/include/curand_poisson.h",
+        "cuda/include/cusolverDn.h",
+        "cuda/include/cuda_profiler_api.h",
+        "cuda/include/sm_20_atomic_functions.h",
+        "cuda/include/nvfunctional",
+    ],
+    cmd = """
+cp "/usr/local/cuda-8.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-8.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-8.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-8.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-8.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-8.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-8.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-8.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-8.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-8.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-8.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-8.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-8.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-8.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-8.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-8.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-8.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-8.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-8.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-8.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-8.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-8.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-8.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-8.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-8.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-8.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-8.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-8.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-8.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-8.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-8.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-8.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-8.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-8.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-8.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-8.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-8.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-8.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-8.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuviddec.h" "$(@D)/cuda/include/cuviddec.h" && cp "/usr/local/cuda-8.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-8.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-8.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-8.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-8.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-8.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-8.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-8.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-8.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk.h" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cuda_launch_config.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cuda_launch_config.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cub.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_symmetric_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.h" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extern_shared_ptr.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/balanced_path.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/balanced_path.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/alignment.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/uninitialized.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/uninitialized.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/throw_on_error.h" "$(@D)/cuda/include/thrust/system/cuda/detail/throw_on_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execute_on_stream.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execute_on_stream.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.h" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_union.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_union.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_intersection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_intersection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/malloc.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/async.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/bulk.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/async.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/async.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/future.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/future.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/inclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merging_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merging_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/exclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/odd_even_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-8.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-8.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-8.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-8.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-8.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-8.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-8.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-8.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-8.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-8.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-8.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-8.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-8.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-8.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-8.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-8.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-8.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-8.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-8.0/include/nvcuvid.h" "$(@D)/cuda/include/nvcuvid.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-8.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-8.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-8.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-8.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-8.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-8.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" && cp "/usr/local/cuda-8.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-8.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-8.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-8.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-8.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-8.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-8.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-8.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/crt/device_runtime.h" "$(@D)/cuda/include/crt/device_runtime.h" && cp "/usr/local/cuda-8.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-8.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-8.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-8.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-8.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-8.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-8.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-8.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-8.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-8.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-8.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-8.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-8.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-8.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-8.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-8.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-8.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-8.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-8.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-8.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-8.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional"    """,
+    local = 1,
+)
+
+genrule(
+    name = "cuda-nvvm",
+    outs = [
+        "cuda/nvvm/bin/cicc",
+        "cuda/nvvm/libdevice/libdevice.compute_50.10.bc",
+        "cuda/nvvm/libdevice/libdevice.compute_30.10.bc",
+        "cuda/nvvm/libdevice/libdevice.compute_20.10.bc",
+        "cuda/nvvm/libdevice/libdevice.compute_35.10.bc",
+        "cuda/nvvm/lib64/libnvvm.so.3",
+        "cuda/nvvm/lib64/libnvvm.so",
+        "cuda/nvvm/lib64/libnvvm.so.3.1.0",
+        "cuda/nvvm/include/nvvm.h",
+        "cuda/nvvm/libnvvm-samples/ptxgen/README.txt",
+        "cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c",
+        "cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt",
+        "cuda/nvvm/libnvvm-samples/build.bat",
+        "cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt",
+        "cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu",
+        "cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt",
+        "cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp",
+        "cuda/nvvm/libnvvm-samples/README.txt",
+        "cuda/nvvm/libnvvm-samples/simple/simple.c",
+        "cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll",
+        "cuda/nvvm/libnvvm-samples/simple/README.txt",
+        "cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll",
+        "cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt",
+        "cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h",
+        "cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h",
+        "cuda/nvvm/libnvvm-samples/build.sh",
+        "cuda/nvvm/libnvvm-samples/CMakeLists.txt",
+    ],
+    cmd = """
+cp "/usr/local/cuda-8.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_50.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_50.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_30.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_30.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_20.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_20.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_35.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_35.10.bc" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3.1.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.1.0" && cp "/usr/local/cuda-8.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt"    """,
+)
+
+genrule(
+    name = "cuda-extras",
+    outs = [
+        "cuda/extras/CUPTI/include/cupti_result.h",
+        "cuda/extras/CUPTI/include/cupti_events.h",
+        "cuda/extras/CUPTI/include/openacc/cupti_openacc.h",
+        "cuda/extras/CUPTI/include/cupti_version.h",
+        "cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h",
+        "cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h",
+        "cuda/extras/CUPTI/include/cupti_activity.h",
+        "cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h",
+        "cuda/extras/CUPTI/include/generated_cuda_meta.h",
+        "cuda/extras/CUPTI/include/cupti_nvtx_cbid.h",
+        "cuda/extras/CUPTI/include/cuda_stdint.h",
+        "cuda/extras/CUPTI/include/generated_cudaGL_meta.h",
+        "cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h",
+        "cuda/extras/CUPTI/include/cupti_metrics.h",
+        "cuda/extras/CUPTI/include/cupti_callbacks.h",
+        "cuda/extras/CUPTI/include/cupti_runtime_cbid.h",
+        "cuda/extras/CUPTI/include/cupti.h",
+        "cuda/extras/CUPTI/include/GL/glut.h",
+        "cuda/extras/CUPTI/include/GL/glu.h",
+        "cuda/extras/CUPTI/include/GL/glxext.h",
+        "cuda/extras/CUPTI/include/GL/wglext.h",
+        "cuda/extras/CUPTI/include/GL/glx.h",
+        "cuda/extras/CUPTI/include/GL/glext.h",
+        "cuda/extras/CUPTI/include/GL/wglew.h",
+        "cuda/extras/CUPTI/include/GL/gl.h",
+        "cuda/extras/CUPTI/include/GL/glew.h",
+        "cuda/extras/CUPTI/include/cupti_driver_cbid.h",
+        "cuda/extras/CUPTI/include/generated_nvtx_meta.h",
+    ],
+    cmd = """
+cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h"    """,
+)
+
+genrule(
+    name = "cuda-lib",
+    outs = [
+        "cuda/lib/libcuda.so",
+        "cuda/lib/libcudart.so.8.0",
+        "cuda/lib/libcudart_static.a",
+        "cuda/lib/libcublas.so.8.0",
+        "cuda/lib/libcusolver.so.8.0",
+        "cuda/lib/libcurand.so.8.0",
+        "cuda/lib/libcufft.so.8.0",
+        "cuda/lib/libcudnn.so.5",
+        "cuda/lib/libcupti.so.8.0",
+    ],
+    cmd = """
+cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart.so.8.0.61" "$(@D)/cuda/lib/libcudart.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcublas.so.8.0.71" "$(@D)/cuda/lib/libcublas.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0.61" "$(@D)/cuda/lib/libcusolver.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcurand.so.8.0.61" "$(@D)/cuda/lib/libcurand.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcufft.so.8.0.61" "$(@D)/cuda/lib/libcufft.so.8.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.5.1.10" "$(@D)/cuda/lib/libcudnn.so.5" && cp "/usr/local/cuda-8.0/extras/CUPTI/lib64/libcupti.so.8.0.61" "$(@D)/cuda/lib/libcupti.so.8.0"    """,
+)
+
+genrule(
+    name = "cudnn-include",
+    outs = [
+        "cuda/include/cudnn.h",
+    ],
+    cmd = """
+cp "/usr/include/cudnn.h" "$(@D)/cudnn.h"    """,
+)
diff --git a/third_party/toolchains/gpus/cuda/build_defs.bzl b/third_party/toolchains/gpus/cuda/build_defs.bzl
new file mode 100644
index 0000000000..badaf43019
--- /dev/null
+++ b/third_party/toolchains/gpus/cuda/build_defs.bzl
@@ -0,0 +1,37 @@
+# Macros for building CUDA code used with Bazel remote
+# execution service.
+# DO NOT EDIT: automatically generated file
+
+def if_cuda(if_true, if_false = []):
+    """Shorthand for select()'ing on whether we're building with CUDA.
+
+    Returns a select statement which evaluates to if_true if we're building
+    with CUDA enabled.  Otherwise, the select statement evaluates to if_false.
+
+    """
+    return select({
+        "@local_config_cuda//cuda:using_nvcc": if_true,
+        "@local_config_cuda//cuda:using_clang": if_true,
+        "//conditions:default": if_false
+    })
+
+
+def cuda_default_copts():
+    """Default options for all CUDA compilations."""
+    return if_cuda(["-x", "cuda", "-DGOOGLE_CUDA=1"] + ["--cuda-gpu-arch=sm_30"])
+
+
+def cuda_is_configured():
+    """Returns true if CUDA was enabled during the configure process."""
+    return True
+
+def if_cuda_is_configured(x):
+    """Tests if the CUDA was enabled during the configure process.
+
+    Unlike if_cuda(), this does not require that we are building with
+    --config=cuda. Used to allow non-CUDA code to depend on CUDA libraries.
+    """
+    if cuda_is_configured():
+      return x
+    return []
+
diff --git a/third_party/toolchains/gpus/cuda/cuda/cuda_config.h b/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
new file mode 100644
index 0000000000..dddee32193
--- /dev/null
+++ b/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
@@ -0,0 +1,20 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// DO NOT EDIT: automatically generated file
+#ifndef THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
+#define THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
+
+#endif  // THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
-- 
GitLab


From c2e76ea3cadde3b11eaf514c7dd2e7d18721f5b9 Mon Sep 17 00:00:00 2001
From: superryanguo <superryanguo@gmail.com>
Date: Tue, 20 Jun 2017 21:45:42 +0800
Subject: [PATCH 0233/1375] Add the shared libraries in the quickstart

---
 tensorflow/go/README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md
index a1b4255292..871b35e41d 100644
--- a/tensorflow/go/README.md
+++ b/tensorflow/go/README.md
@@ -26,6 +26,8 @@ Construct and execute TensorFlow graphs in Go.
     curl -L \
       "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.1.0.tar.gz" |
     sudo tar -C /usr/local -xz
+    export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/lib
+    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
     ```
 
 2.  `go get` this package (and run tests):
-- 
GitLab


From 4763aeca85fdf763fcb8c17688ba2716e7a26910 Mon Sep 17 00:00:00 2001
From: ribx <robin.m.lutz@gmail.com>
Date: Tue, 20 Jun 2017 16:25:04 +0200
Subject: [PATCH 0234/1375] Pass rate and other settings to parent classes

---
 tensorflow/contrib/keras/python/keras/layers/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/keras/python/keras/layers/core.py b/tensorflow/contrib/keras/python/keras/layers/core.py
index d287fa56d9..34548c83c5 100644
--- a/tensorflow/contrib/keras/python/keras/layers/core.py
+++ b/tensorflow/contrib/keras/python/keras/layers/core.py
@@ -107,7 +107,7 @@ class Dropout(tf_core_layers.Dropout, Layer):
     self.supports_masking = True
     # Inheritance call order:
     # 1) tf.layers.Dropout, 2) keras.layers.Layer, 3) tf.layers.Layer
-    super(Dropout, self).__init__(**kwargs)
+    super(Dropout, self).__init__(rate=rate, noise_shape=noise_shape, seed=seed, **kwargs)
 
   def call(self, inputs, training=None):
     if training is None:
-- 
GitLab


From 95d91a3e065d7ddd3a0201af61e5e78330449d37 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 07:36:19 -0700
Subject: [PATCH 0235/1375] Minor docstring formatting fix.

PiperOrigin-RevId: 159558652
---
 tensorflow/contrib/linalg/python/ops/linear_operator.py     | 3 ++-
 .../linalg/python/ops/linear_operator_composition.py        | 3 ++-
 .../contrib/linalg/python/ops/linear_operator_diag.py       | 3 ++-
 .../linalg/python/ops/linear_operator_full_matrix.py        | 3 ++-
 .../contrib/linalg/python/ops/linear_operator_identity.py   | 3 ++-
 .../contrib/linalg/python/ops/linear_operator_tril.py       | 3 ++-
 .../linalg/python/ops/linear_operator_udvh_update.py        | 6 +++---
 7 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator.py b/tensorflow/contrib/linalg/python/ops/linear_operator.py
index a4f8722951..91c0938e39 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator.py
@@ -126,7 +126,8 @@ class LinearOperator(object):
 
   This `LinearOperator` is initialized with boolean flags of the form `is_X`,
   for `X = non_singular, self_adjoint, positive_definite, square`.
-  These have the following meaning
+  These have the following meaning:
+
   * If `is_X == True`, callers should expect the operator to have the
     property `X`.  This is a promise that should be fulfilled, but is *not* a
     runtime assert.  For example, finite floating point precision may result
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_composition.py b/tensorflow/contrib/linalg/python/ops/linear_operator_composition.py
index 0853ea03af..0a71a73a9c 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_composition.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_composition.py
@@ -97,7 +97,8 @@ class LinearOperatorComposition(linear_operator.LinearOperator):
 
   This `LinearOperator` is initialized with boolean flags of the form `is_X`,
   for `X = non_singular, self_adjoint, positive_definite, square`.
-  These have the following meaning
+  These have the following meaning:
+
   * If `is_X == True`, callers should expect the operator to have the
     property `X`.  This is a promise that should be fulfilled, but is *not* a
     runtime assert.  For example, finite floating point precision may result
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_diag.py b/tensorflow/contrib/linalg/python/ops/linear_operator_diag.py
index 56bc967706..29184483bf 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_diag.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_diag.py
@@ -98,7 +98,8 @@ class LinearOperatorDiag(linear_operator.LinearOperator):
 
   This `LinearOperator` is initialized with boolean flags of the form `is_X`,
   for `X = non_singular, self_adjoint, positive_definite, square`.
-  These have the following meaning
+  These have the following meaning:
+
   * If `is_X == True`, callers should expect the operator to have the
     property `X`.  This is a promise that should be fulfilled, but is *not* a
     runtime assert.  For example, finite floating point precision may result
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_full_matrix.py b/tensorflow/contrib/linalg/python/ops/linear_operator_full_matrix.py
index 67889511cb..52b40eaf8d 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_full_matrix.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_full_matrix.py
@@ -92,7 +92,8 @@ class LinearOperatorFullMatrix(linear_operator.LinearOperator):
 
   This `LinearOperator` is initialized with boolean flags of the form `is_X`,
   for `X = non_singular, self_adjoint, positive_definite, square`.
-  These have the following meaning
+  These have the following meaning:
+
   * If `is_X == True`, callers should expect the operator to have the
     property `X`.  This is a promise that should be fulfilled, but is *not* a
     runtime assert.  For example, finite floating point precision may result
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py b/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py
index 553a5471f1..b9ac90ff33 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_identity.py
@@ -190,7 +190,8 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
 
   This `LinearOperator` is initialized with boolean flags of the form `is_X`,
   for `X = non_singular, self_adjoint, positive_definite, square`.
-  These have the following meaning
+  These have the following meaning:
+
   * If `is_X == True`, callers should expect the operator to have the
     property `X`.  This is a promise that should be fulfilled, but is *not* a
     runtime assert.  For example, finite floating point precision may result
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_tril.py b/tensorflow/contrib/linalg/python/ops/linear_operator_tril.py
index 8a152a9b47..22ccf6f131 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_tril.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_tril.py
@@ -91,7 +91,8 @@ class LinearOperatorTriL(linear_operator.LinearOperator):
 
   This `LinearOperator` is initialized with boolean flags of the form `is_X`,
   for `X = non_singular, self_adjoint, positive_definite, square`.
-  These have the following meaning
+  These have the following meaning:
+
   * If `is_X == True`, callers should expect the operator to have the
     property `X`.  This is a promise that should be fulfilled, but is *not* a
     runtime assert.  For example, finite floating point precision may result
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_udvh_update.py b/tensorflow/contrib/linalg/python/ops/linear_operator_udvh_update.py
index 546d899e74..9c9c359574 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_udvh_update.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_udvh_update.py
@@ -112,9 +112,9 @@ class LinearOperatorUDVHUpdate(linear_operator.LinearOperator):
   #### Matrix property hints
 
   This `LinearOperator` is initialized with boolean flags of the form `is_X`,
-  for `X = non_singular, self_adjoint, positive_definite, diag_update_positive`
-  and `square`
-  These have the following meaning
+  for `X = non_singular`, `self_adjoint`, `positive_definite`,
+  `diag_update_positive` and `square`. These have the following meaning:
+
   * If `is_X == True`, callers should expect the operator to have the
     property `X`.  This is a promise that should be fulfilled, but is *not* a
     runtime assert.  For example, finite floating point precision may result
-- 
GitLab


From f580c21053c47a12b3b816cc50c261479f36db5d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 08:55:35 -0700
Subject: [PATCH 0236/1375] Fix TensorForest feature bagging when using
 feature-column data.

PiperOrigin-RevId: 159566463
---
 tensorflow/contrib/tensor_forest/python/tensor_forest.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
index 177783c207..bdfe981baf 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
@@ -435,7 +435,7 @@ class RandomForestGraphs(object):
           if processed_sparse_features is not None:
             raise NotImplementedError(
                 'Feature bagging not supported with sparse features.')
-          tree_data = self._bag_features(i, input_data)
+          tree_data = self._bag_features(i, tree_data)
         probabilities.append(self.trees[i].inference_graph(
             tree_data,
             data_spec,
-- 
GitLab


From 36567a695c4e1c364c33717036d4d64d33db2ba4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 09:24:25 -0700
Subject: [PATCH 0237/1375] Infer shapes for loops during Grappler static
 inference

PiperOrigin-RevId: 159570163
---
 .../core/common_runtime/shape_refiner.cc      |   80 +-
 .../core/common_runtime/shape_refiner.h       |   28 +-
 .../core/common_runtime/shape_refiner_test.cc |  188 +-
 tensorflow/core/framework/shape_inference.cc  |  123 +-
 tensorflow/core/framework/shape_inference.h   |  115 +-
 .../core/framework/shape_inference_test.cc    |  235 +-
 tensorflow/core/grappler/costs/BUILD          |    1 +
 .../core/grappler/costs/graph_properties.cc   |  308 +-
 .../core/grappler/costs/graph_properties.h    |   29 +
 .../grappler/costs/graph_properties_test.cc   | 2508 ++++++++++++++++-
 .../core/kernels/hexagon/graph_transferer.cc  |    6 +-
 11 files changed, 3470 insertions(+), 151 deletions(-)

diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index 5103e85221..0f6c3c1cb1 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -139,7 +139,7 @@ Status ShapeRefiner::SetShape(const Node* node, int output_port,
   return Status::OK();
 }
 
-Status ShapeRefiner::UpdateNode(const Node* node, bool* refined) {
+Status ShapeRefiner::UpdateNode(const Node* node, bool relax, bool* refined) {
   auto it = node_to_context_.find(node);
   if (it == node_to_context_.end()) {
     *refined = true;
@@ -155,29 +155,55 @@ Status ShapeRefiner::UpdateNode(const Node* node, bool* refined) {
   for (const Edge* e : node->in_edges()) {
     if (e->IsControlEdge()) continue;
 
+    int dst_input = e->dst_input();
+    int src_output = e->src_output();
+
     Node* input = e->src();
     auto iter = node_to_context_.find(input);
     if (iter == node_to_context_.end()) {
       return errors::FailedPrecondition(
-          "Input ", e->dst_input(), " ('", input->name(), "') for '",
-          node->name(), "' was not previously added to ShapeRefiner.");
+          "Input ", dst_input, " ('", input->name(), "') for '", node->name(),
+          "' was not previously added to ShapeRefiner.");
     }
 
     InferenceContext* c = iter->second.get();
-    DCHECK_GE(e->dst_input(), 0);
-    if (node_context->MergeInput(e->dst_input(), c->output(e->src_output()))) {
+    DCHECK_GE(dst_input, 0);
+    ShapeHandle existing_input = node_context->input(dst_input);
+    if (!relax && node_context->MergeInput(dst_input, c->output(src_output))) {
       *refined = true;
+    } else if (relax) {
+      if (node_context->RelaxInput(dst_input, c->output(src_output))) {
+        if (!SameDefinedShape(node_context, node_context->input(dst_input),
+                              existing_input)) {
+          *refined = true;
+        }
+      }
     }
 
     // Also propagate handle shape and dtype of edges which are carrying
     // resource handles.
-    if (e->src()->output_type(e->src_output()) == DT_RESOURCE) {
-      auto* shapes_and_types =
-          c->output_handle_shapes_and_types(e->src_output());
-      if (shapes_and_types != nullptr &&
-          node_context->MergeInputHandleShapesAndTypes(e->dst_input(),
-                                                       *shapes_and_types)) {
+    if (e->src()->output_type(src_output) == DT_RESOURCE) {
+      auto* outputs = c->output_handle_shapes_and_types(src_output);
+      if (!outputs) continue;
+
+      if (!relax &&
+          node_context->MergeInputHandleShapesAndTypes(dst_input, *outputs)) {
         *refined = true;
+      } else if (relax) {
+        std::vector<ShapeAndType> existing_inputs;
+        const std::vector<ShapeAndType>* inputs =
+            node_context->input_handle_shapes_and_types(dst_input);
+        if (inputs) {
+          existing_inputs = *inputs;
+        }
+        if (node_context->RelaxInputHandleShapesAndMergeTypes(dst_input,
+                                                              *outputs)) {
+          if (IsUpdatedShapesOrTypes(
+                  node_context, existing_inputs,
+                  *node_context->input_handle_shapes_and_types(dst_input))) {
+            *refined = true;
+          }
+        }
       }
     }
   }
@@ -638,4 +664,36 @@ Status ShapeRefiner::RunShapeFn(const Node* node,
   return Status::OK();
 }
 
+bool ShapeRefiner::SameDefinedShape(InferenceContext* c, ShapeHandle s0,
+                                    ShapeHandle s1) {
+  if (c->Rank(s0) != c->Rank(s1)) {
+    return false;
+  } else if (!c->RankKnown(s0)) {
+    return true;
+  }
+
+  for (int i = 0; i < c->Rank(s0); ++i) {
+    if (c->Value(c->Dim(s0, i)) != c->Value(c->Dim(s1, i))) {
+      return false;
+    }
+  }
+
+  return true;
+}
+
+bool ShapeRefiner::IsUpdatedShapesOrTypes(
+    InferenceContext* c, const std::vector<ShapeAndType>& existing,
+    const std::vector<ShapeAndType>& updated) {
+  if (existing.size() != updated.size()) {
+    return true;
+  }
+  for (int i = 0; i < existing.size(); i++) {
+    if (!SameDefinedShape(c, existing[i].shape, updated[i].shape) ||
+        existing[i].dtype != updated[i].dtype) {
+      return true;
+    }
+  }
+  return false;
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/shape_refiner.h b/tensorflow/core/common_runtime/shape_refiner.h
index 603659d54e..1af7835392 100644
--- a/tensorflow/core/common_runtime/shape_refiner.h
+++ b/tensorflow/core/common_runtime/shape_refiner.h
@@ -24,6 +24,9 @@ limitations under the License.
 #include "tensorflow/core/platform/macros.h"
 
 namespace tensorflow {
+namespace grappler {
+class GraphProperties;
+}
 
 // ShapeRefiner performs shape inference for TensorFlow Graphs.  It is
 // responsible for instantiating InferenceContext objects for each
@@ -57,8 +60,13 @@ class ShapeRefiner {
 
   // Update the input shapes of node in case the shapes of the fan-ins of 'node'
   // have themselves been modified (For example, in case of incremental shape
-  // refinement). Sets refined to true if any of the node shape has changed.
-  Status UpdateNode(const Node* node, bool* refined);
+  // refinement). If 'relax' is true, a new shape with the broadest set of
+  // information will be set as the new input (see InferenceContext::RelaxInput
+  // for full details and examples). Sets refined to true if any shapes have
+  // changed (in their string representations). Note that shapes may have been
+  // updated to newer versions (but with identical string representations) even
+  // if <*refined> is set to false.
+  Status UpdateNode(const Node* node, bool relax, bool* refined);
 
   // Returns the InferenceContext for 'node', if present.
   shape_inference::InferenceContext* GetContext(const Node* node) const {
@@ -78,6 +86,22 @@ class ShapeRefiner {
   }
 
  private:
+  friend class ShapeRefinerTest;
+  friend class ::tensorflow::grappler::GraphProperties;
+
+  // Returns true if the ranks and all dimensions of <s0> and <s1> are either
+  // equal in value or both unknown.
+  static bool SameDefinedShape(shape_inference::InferenceContext* c,
+                               shape_inference::ShapeHandle s0,
+                               shape_inference::ShapeHandle s1);
+
+  // Returns true if the shapes and types stored in <*existing> are identical in
+  // value to the shapes and types in <*updated>.
+  static bool IsUpdatedShapesOrTypes(
+      shape_inference::InferenceContext* c,
+      const std::vector<shape_inference::ShapeAndType>& existing,
+      const std::vector<shape_inference::ShapeAndType>& updated);
+
   // Tries to infer tensor output based on the input shapes of the node. In some
   // cases, the shapes of the inputs are sufficient for inferring the contents
   // of the output tensor. For example, a Shape op with fully defined input
diff --git a/tensorflow/core/common_runtime/shape_refiner_test.cc b/tensorflow/core/common_runtime/shape_refiner_test.cc
index 55485dc979..7ffab38ba2 100644
--- a/tensorflow/core/common_runtime/shape_refiner_test.cc
+++ b/tensorflow/core/common_runtime/shape_refiner_test.cc
@@ -26,6 +26,35 @@ limitations under the License.
 #include "tensorflow/core/public/version.h"
 
 namespace tensorflow {
+
+class ShapeRefinerTest : public ::testing::Test {
+ protected:
+  // These give access to private functions of DimensionHandle and ShapeHandle.
+  bool SameHandle(shape_inference::DimensionHandle a,
+                  shape_inference::DimensionHandle b) {
+    return a.SameHandle(b);
+  }
+
+  bool SameHandle(shape_inference::ShapeHandle a,
+                  shape_inference::ShapeHandle b) {
+    return a.SameHandle(b);
+  }
+
+  // These give access to private functions of ShapeRefiner.
+  bool SameDefinedShape(shape_inference::InferenceContext* c,
+                        shape_inference::ShapeHandle s0,
+                        shape_inference::ShapeHandle s1) {
+    return ShapeRefiner::SameDefinedShape(c, s0, s1);
+  }
+
+  bool IsUpdatedShapesOrTypes(
+      shape_inference::InferenceContext* c,
+      const std::vector<shape_inference::ShapeAndType>& existing,
+      const std::vector<shape_inference::ShapeAndType>& updated) {
+    return ShapeRefiner::IsUpdatedShapesOrTypes(c, existing, updated);
+  }
+};
+
 namespace {
 
 #define EXPECT_SHAPE(EXPECTED, M, OP, IDX)                            \
@@ -34,7 +63,7 @@ namespace {
     EXPECT_EQ(EXPECTED, ctx->DebugString(ctx->output(IDX)));          \
   } while (0);
 
-TEST(ShapeRefinerTest, Constant) {
+TEST_F(ShapeRefinerTest, Constant) {
   // Create a constant node and validate that adding it is successful
   // and that its shape is correct.
   Scope root = Scope::NewRootScope();
@@ -45,7 +74,7 @@ TEST(ShapeRefinerTest, Constant) {
   EXPECT_SHAPE("[]", m, c, 0);
 }
 
-TEST(ShapeRefinerTest, MatMul) {
+TEST_F(ShapeRefinerTest, MatMul) {
   ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
 
   Scope root = Scope::NewRootScope();
@@ -62,7 +91,7 @@ TEST(ShapeRefinerTest, MatMul) {
   EXPECT_SHAPE("[2,2]", m, mm, 0);
 }
 
-TEST(ShapeRefinerTest, InvalidOrder) {
+TEST_F(ShapeRefinerTest, InvalidOrder) {
   ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
   Scope root = Scope::NewRootScope();
   auto a = ops::Const(root, {{1.0f}, {2.0f}});
@@ -77,7 +106,7 @@ TEST(ShapeRefinerTest, InvalidOrder) {
       s.error_message());
 }
 
-TEST(ShapeRefinerTest, BadShapes) {
+TEST_F(ShapeRefinerTest, BadShapes) {
   ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
   Scope root = Scope::NewRootScope();
   auto a = ops::Const(root, {{1.0f}, {2.0f}});
@@ -94,7 +123,7 @@ TEST(ShapeRefinerTest, BadShapes) {
                   .contains("Dimensions must be equal, but are 1 and 2"));
 }
 
-TEST(ShapeRefinerTest, SetShape) {
+TEST_F(ShapeRefinerTest, SetShape) {
   ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
 
   Scope root = Scope::NewRootScope();
@@ -133,7 +162,7 @@ REGISTER_OP("TestOpWithNoShapeFn").Input("a: int32").Output("o: int32");
 
 }  // namespace
 
-TEST(ShapeRefinerTest, MissingShapeInferenceFns) {
+TEST_F(ShapeRefinerTest, MissingShapeInferenceFns) {
   Scope root = Scope::NewRootScope();
   auto a = ops::Const(root, 42);
   Node* b;
@@ -147,7 +176,7 @@ TEST(ShapeRefinerTest, MissingShapeInferenceFns) {
   TF_EXPECT_OK(m.AddNode(b));
 }
 
-TEST(ShapeRefinerTest, PropagateConstants) {
+TEST_F(ShapeRefinerTest, PropagateConstants) {
   // Reduction dimension is a variable, so we don't know its value.
   // So the output shape value is unknown (though its rank is known).
   {
@@ -220,7 +249,7 @@ REGISTER_OP("TestOp")
 
 }  // namespace
 
-TEST(ShapeRefinerTest, InputTensorDependencies) {
+TEST_F(ShapeRefinerTest, InputTensorDependencies) {
   ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
   Graph graph(OpRegistry::Global());
   Node* node;
@@ -289,7 +318,7 @@ REGISTER_OP("ShapeDataInt64")
 
 }  // namespace
 
-TEST(ShapeRefinerTest, PropagateShapeAcrossTensorContent) {
+TEST_F(ShapeRefinerTest, PropagateShapeAcrossTensorContent) {
   Scope root = Scope::NewRootScope();
 
   // Create variable 2x4 tensor.
@@ -320,7 +349,7 @@ TEST(ShapeRefinerTest, PropagateShapeAcrossTensorContent) {
   EXPECT_EQ("[4]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateShapeAcrossTensorContentInt64) {
+TEST_F(ShapeRefinerTest, PropagateShapeAcrossTensorContentInt64) {
   Scope root = Scope::NewRootScope();
 
   // Create variable 2x4 tensor.
@@ -354,7 +383,7 @@ TEST(ShapeRefinerTest, PropagateShapeAcrossTensorContentInt64) {
   EXPECT_EQ("[4]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateShapeAcrossTensorContentInt32Overflow) {
+TEST_F(ShapeRefinerTest, PropagateShapeAcrossTensorContentInt32Overflow) {
   Scope root = Scope::NewRootScope();
 
   // Create variable 2x4 tensor.
@@ -386,7 +415,7 @@ TEST(ShapeRefinerTest, PropagateShapeAcrossTensorContentInt32Overflow) {
   EXPECT_FALSE(m.AddNode(shape_data).ok());
 }
 
-TEST(ShapeRefinerTest, PropagateRankAcrossTensorContent) {
+TEST_F(ShapeRefinerTest, PropagateRankAcrossTensorContent) {
   Scope root = Scope::NewRootScope();
 
   // Create variable 2x4x3 tensor.
@@ -412,7 +441,7 @@ TEST(ShapeRefinerTest, PropagateRankAcrossTensorContent) {
   EXPECT_EQ("[3]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateSizeAcrossTensorContent) {
+TEST_F(ShapeRefinerTest, PropagateSizeAcrossTensorContent) {
   Scope root = Scope::NewRootScope();
 
   // Create variable.
@@ -438,7 +467,7 @@ TEST(ShapeRefinerTest, PropagateSizeAcrossTensorContent) {
   EXPECT_EQ("[120]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateSizeAcrossTensorContentInt64) {
+TEST_F(ShapeRefinerTest, PropagateSizeAcrossTensorContentInt64) {
   Scope root = Scope::NewRootScope();
 
   // Create variable.
@@ -469,7 +498,7 @@ TEST(ShapeRefinerTest, PropagateSizeAcrossTensorContentInt64) {
   EXPECT_EQ("[515396075280]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateSizeAcrossTensorContentInt32Overflow) {
+TEST_F(ShapeRefinerTest, PropagateSizeAcrossTensorContentInt32Overflow) {
   Scope root = Scope::NewRootScope();
 
   // Create variable.
@@ -496,7 +525,7 @@ TEST(ShapeRefinerTest, PropagateSizeAcrossTensorContentInt32Overflow) {
   EXPECT_FALSE(m.AddNode(shape_data).ok());
 }
 
-TEST(ShapeRefinerTest, PropagateShape) {
+TEST_F(ShapeRefinerTest, PropagateShape) {
   Scope root = Scope::NewRootScope();
   // 3x2 input
   auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
@@ -518,7 +547,7 @@ TEST(ShapeRefinerTest, PropagateShape) {
   EXPECT_EQ("[3,2]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateSize) {
+TEST_F(ShapeRefinerTest, PropagateSize) {
   Scope root = Scope::NewRootScope();
   // 3x2 input
   auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
@@ -539,7 +568,7 @@ TEST(ShapeRefinerTest, PropagateSize) {
   EXPECT_EQ("[6]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateRank) {
+TEST_F(ShapeRefinerTest, PropagateRank) {
   Scope root = Scope::NewRootScope();
   // 3x2 input
   auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
@@ -560,7 +589,7 @@ TEST(ShapeRefinerTest, PropagateRank) {
   EXPECT_EQ("[2]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, PropagateRange) {
+TEST_F(ShapeRefinerTest, PropagateRange) {
   Scope root = Scope::NewRootScope();
   auto begin = ops::Const(root, 1);
   auto limit = ops::Const(root, 11);
@@ -583,7 +612,7 @@ TEST(ShapeRefinerTest, PropagateRange) {
   EXPECT_EQ("[1,4,7,10]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, ConstantValueTwoInputsToSameNode) {
+TEST_F(ShapeRefinerTest, ConstantValueTwoInputsToSameNode) {
   Scope root = Scope::NewRootScope();
   // This node is used as two inputs to 'range'.
   auto begin_and_delta = ops::Const(root, 1);
@@ -607,7 +636,7 @@ TEST(ShapeRefinerTest, ConstantValueTwoInputsToSameNode) {
 
 // Creates a graph where 'begin' is attempted to be visited during
 // constant value evaluation after having been processed once.
-TEST(ShapeRefinerTest, ConstantValueVisitNodeTwice) {
+TEST_F(ShapeRefinerTest, ConstantValueVisitNodeTwice) {
   Scope root = Scope::NewRootScope();
   auto begin = ops::Const(root, 1);
   auto limit = ops::Const(root, 8);
@@ -716,7 +745,7 @@ REGISTER_OP("WithUnknownShape")
 
 }  // namespace
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_EmptyVector) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_EmptyVector) {
   Scope root = Scope::NewRootScope();
   Node* input;
   TF_ASSERT_OK(
@@ -734,7 +763,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_EmptyVector) {
   EXPECT_EQ("[]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_Shape) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_Shape) {
   for (int pass = 0; pass < 2; ++pass) {
     Scope root = Scope::NewRootScope();
     Node* input;
@@ -761,7 +790,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_Shape) {
   }
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_PackInt32) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_PackInt32) {
   Scope root = Scope::NewRootScope();
   Node* scalar_non_const;
   TF_ASSERT_OK(NodeBuilder("in", "NonConstScalarInt32")
@@ -793,7 +822,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_PackInt32) {
   EXPECT_EQ("[10,20,?,40]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_PackInt64) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_PackInt64) {
   Scope root = Scope::NewRootScope();
   Node* scalar_non_const;
   TF_ASSERT_OK(NodeBuilder("in", "NonConstScalarInt64")
@@ -825,7 +854,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_PackInt64) {
   EXPECT_EQ("[10,20,?,1099511627776]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_PackUnknownDim) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_PackUnknownDim) {
   Scope root = Scope::NewRootScope();
 
   InputList inputs{
@@ -851,7 +880,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_PackUnknownDim) {
   EXPECT_EQ("[10,?]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_PackInvalidInput) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_PackInvalidInput) {
   Scope root = Scope::NewRootScope();
 
   // Inputs are length 2 vectors instead of scalars.
@@ -876,7 +905,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_PackInvalidInput) {
       StringPiece(m.AddNode(result).error_message()).contains("but is rank 2"));
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_Concat) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_Concat) {
   Scope root = Scope::NewRootScope();
   Graph* g = root.graph();
   Node* partial_1;
@@ -913,7 +942,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_Concat) {
   EXPECT_EQ("[1,?,3,?,5,6,?,8,9,10,11]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_ConcatWithUnknown) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_ConcatWithUnknown) {
   Scope root = Scope::NewRootScope();
   Graph* g = root.graph();
   Node* scalar_non_const;
@@ -956,7 +985,7 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_ConcatWithUnknown) {
   EXPECT_EQ("?", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeRefinerTest, ConstantValueAsShape_ConcatInvalidDimValue) {
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_ConcatInvalidDimValue) {
   Scope root = Scope::NewRootScope();
   Graph* g = root.graph();
   Node* scalar_non_const;
@@ -995,7 +1024,78 @@ TEST(ShapeRefinerTest, ConstantValueAsShape_ConcatInvalidDimValue) {
             m.AddNode(result).error_message());
 }
 
-TEST(ShapeRefinerTest, IncrementalUpdates) {
+namespace {
+
+// Dummy op to test ShapeRefiner util functions
+REGISTER_OP("Dummy");
+
+}  // namespace
+
+TEST_F(ShapeRefinerTest, SameDefinedShape) {
+  Scope root = Scope::NewRootScope();
+  Graph* g = root.graph();
+  Node* test;
+  TF_CHECK_OK(NodeBuilder("test", "Dummy").Finalize(g, &test));
+  ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
+  m.set_require_shape_inference_fns(false);
+  TF_ASSERT_OK(m.AddNode(test));
+  shape_inference::InferenceContext* ctx = m.GetContext(test);
+
+  auto unknown = ctx->UnknownShape();
+  auto unknown_b = ctx->UnknownShape();
+  auto s_1_2 = ctx->MakeShape({1, 2});
+  auto s_1_2_b = ctx->MakeShape({1, 2});
+  auto s_2_2 = ctx->MakeShape({2, 2});
+  auto s_unknown_2 = ctx->MakeShape({-1, 2});
+  auto s_unknown_2_b = ctx->MakeShape({-1, 2});
+
+  EXPECT_TRUE(SameDefinedShape(ctx, unknown, unknown_b));
+  EXPECT_FALSE(SameDefinedShape(ctx, unknown, s_1_2));
+  EXPECT_TRUE(SameDefinedShape(ctx, s_1_2, s_1_2_b));
+  EXPECT_FALSE(SameDefinedShape(ctx, s_1_2, s_2_2));
+  EXPECT_TRUE(SameDefinedShape(ctx, s_unknown_2, s_unknown_2_b));
+}
+
+TEST_F(ShapeRefinerTest, IsUpdatedShapesOrTypes) {
+  Scope root = Scope::NewRootScope();
+  Graph* g = root.graph();
+  Node* test;
+  TF_CHECK_OK(NodeBuilder("test", "Dummy").Finalize(g, &test));
+  ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
+  m.set_require_shape_inference_fns(false);
+  TF_ASSERT_OK(m.AddNode(test));
+  shape_inference::InferenceContext* ctx = m.GetContext(test);
+
+  std::vector<shape_inference::ShapeAndType> t0{
+      {ctx->MakeShape({1, 2, 3}), DT_FLOAT},
+      {ctx->UnknownShape(), DT_INVALID},
+      {ctx->MakeShape({4, 3, 2, 1}), DT_INT32}};
+
+  std::vector<shape_inference::ShapeAndType> t1{
+      {ctx->MakeShape({1, 2, 3}), DT_FLOAT},
+      {ctx->UnknownShape(), DT_INVALID},
+      {ctx->MakeShape({4, 3, 2, 1}), DT_INT32}};
+
+  std::vector<shape_inference::ShapeAndType> t2{
+      {ctx->MakeShape({1, 2, 4}), DT_FLOAT},
+      {ctx->UnknownShape(), DT_INVALID},
+      {ctx->MakeShape({4, 3, 2, 1}), DT_INT32}};
+
+  std::vector<shape_inference::ShapeAndType> t3{
+      {ctx->MakeShape({1, 2, 3}), DT_INT32},
+      {ctx->UnknownShape(), DT_INVALID},
+      {ctx->MakeShape({4, 3, 2, 1}), DT_INT32}};
+
+  EXPECT_FALSE(IsUpdatedShapesOrTypes(ctx, t0, t1));
+
+  // A shape has been modified
+  EXPECT_TRUE(IsUpdatedShapesOrTypes(ctx, t0, t2));
+
+  // A type has been modified
+  EXPECT_TRUE(IsUpdatedShapesOrTypes(ctx, t0, t3));
+}
+
+TEST_F(ShapeRefinerTest, IncrementalUpdates) {
   Scope root = Scope::NewRootScope();
   Graph* g = root.graph();
   Node* queue;
@@ -1020,12 +1120,34 @@ TEST(ShapeRefinerTest, IncrementalUpdates) {
   shape_inference::ShapeHandle shp = ctx->MakeShape({3, 7});
   ctx->set_output_handle_shapes_and_types(
       0, std::vector<shape_inference::ShapeAndType>{{shp, DT_FLOAT}});
-
   bool refined = false;
-  TF_ASSERT_OK(m.UpdateNode(dequeue, &refined));
+  TF_ASSERT_OK(m.UpdateNode(dequeue, false /* relax */, &refined));
   EXPECT_TRUE(refined);
   ctx = m.GetContext(dequeue);
   EXPECT_EQ("[3,7]", ctx->DebugString(ctx->output(0)));
+
+  // Inject another shape, but relax instead of merge.
+  ctx = m.GetContext(queue);
+  shp = ctx->MakeShape({2, 7});
+  ctx->set_output_handle_shapes_and_types(
+      0, std::vector<shape_inference::ShapeAndType>{{shp, DT_FLOAT}});
+  refined = false;
+  TF_ASSERT_OK(m.UpdateNode(dequeue, true /* relax */, &refined));
+  EXPECT_TRUE(refined);
+  ctx = m.GetContext(dequeue);
+  EXPECT_EQ("[?,7]", ctx->DebugString(ctx->output(0)));
+
+  // Inject another partially unknown shape and attempt to relax it.
+  ctx = m.GetContext(queue);
+  shp = ctx->MakeShape({shape_inference::InferenceContext::kUnknownDim, 7});
+  ctx->set_output_handle_shapes_and_types(
+      0, std::vector<shape_inference::ShapeAndType>{{shp, DT_FLOAT}});
+  refined = false;
+  TF_ASSERT_OK(m.UpdateNode(dequeue, true /* relax */, &refined));
+  EXPECT_FALSE(refined);
+  ctx = m.GetContext(dequeue);
+  EXPECT_EQ("[?,7]", ctx->DebugString(ctx->output(0)));
+  ASSERT_FALSE(SameHandle(ctx->Dim(ctx->output(0), 0), ctx->Dim(shp, 0)));
 }
 
 }  // namespace
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 1f9e98551f..2c18ddd48f 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -314,6 +314,19 @@ Status InferenceContext::WithValue(DimensionHandle dim, int64 value,
                                  existing);
 }
 
+void InferenceContext::Relax(DimensionHandle d0, DimensionHandle d1,
+                             DimensionHandle* out) {
+  if (d0.SameHandle(d1)) {
+    *out = d0;
+  } else if (!ValueKnown(d0) || !ValueKnown(d1)) {
+    *out = UnknownDim();
+  } else if (Value(d0) == Value(d1)) {
+    *out = d0;
+  } else {
+    *out = UnknownDim();
+  }
+}
+
 Status InferenceContext::Merge(DimensionHandle d0, DimensionHandle d1,
                                DimensionHandle* out) {
   if (d0.SameHandle(d1) || !ValueKnown(d1)) {
@@ -356,6 +369,48 @@ Status InferenceContext::MergePrefix(ShapeHandle s, ShapeHandle prefix,
   return Status::OK();
 }
 
+void InferenceContext::Relax(ShapeHandle s0, ShapeHandle s1, ShapeHandle* out) {
+  if (s0.SameHandle(s1)) {
+    *out = s0;
+    return;
+  } else if (!RankKnown(s0) || !RankKnown(s1)) {
+    *out = UnknownShape();
+    return;
+  }
+
+  const int32 rank = Rank(s0);
+  if (rank != Rank(s1)) {
+    *out = UnknownShape();
+    return;
+  }
+
+  bool return_s0 = true;
+  for (int i = 0; i < rank; ++i) {
+    auto d0 = Dim(s0, i);
+    auto d1 = Dim(s1, i);
+    if (d0.SameHandle(d1)) continue;
+
+    auto v0 = Value(d0);
+    auto v1 = Value(d1);
+    if (v0 == kUnknownDim || v1 == kUnknownDim || v0 != v1) {
+      return_s0 = false;
+      break;
+    }
+  }
+  if (return_s0) {
+    *out = s0;
+    return;
+  }
+
+  // Relax dims.
+  std::vector<DimensionHandle> dims(rank);
+  for (int i = 0; i < rank; ++i) {
+    // Invariant for relax was checked earlier, so CHECK is ok.
+    Relax(Dim(s0, i), Dim(s1, i), &dims[i]);
+  }
+  *out = MakeShape(dims);
+}
+
 Status InferenceContext::Merge(ShapeHandle s0, ShapeHandle s1,
                                ShapeHandle* out) {
   if (s0.SameHandle(s1) || !RankKnown(s1)) {
@@ -895,9 +950,15 @@ bool InferenceContext::MergeHandleShapesAndTypes(
   bool refined = false;
   for (int i = 0; i < shapes_and_types.size(); ++i) {
     const ShapeAndType& existing = (*to_update)[i];
-    new_values[i].dtype = shapes_and_types[i].dtype;
-    if (new_values[i].dtype != existing.dtype && existing.dtype == DT_INVALID) {
-      refined = true;
+    if (shapes_and_types[i].dtype == existing.dtype) {
+      new_values[i].dtype = existing.dtype;
+    } else {
+      if (existing.dtype != DT_INVALID) {
+        return false;
+      } else {
+        new_values[i].dtype = shapes_and_types[i].dtype;
+        refined = true;
+      }
     }
     if (!Merge(existing.shape, shapes_and_types[i].shape, &new_values[i].shape)
              .ok()) {
@@ -939,6 +1000,62 @@ bool InferenceContext::MergeInputHandleShapesAndTypes(
                                    input_handle_shapes_and_types_[idx].get());
 }
 
+bool InferenceContext::RelaxHandleShapesAndMergeTypes(
+    const std::vector<ShapeAndType>& shapes_and_types,
+    std::vector<ShapeAndType>* to_update) {
+  if (shapes_and_types.size() != to_update->size()) {
+    return false;
+  }
+  std::vector<ShapeAndType> new_values(shapes_and_types.size());
+  bool refined = false;
+  for (int i = 0; i < shapes_and_types.size(); ++i) {
+    const ShapeAndType& existing = (*to_update)[i];
+    if (shapes_and_types[i].dtype == existing.dtype) {
+      new_values[i].dtype = existing.dtype;
+    } else {
+      if (existing.dtype != DT_INVALID) {
+        return false;
+      } else {
+        new_values[i].dtype = shapes_and_types[i].dtype;
+        refined = true;
+      }
+    }
+    Relax(existing.shape, shapes_and_types[i].shape, &new_values[i].shape);
+    if (!existing.shape.SameHandle(new_values[i].shape)) {
+      refined = true;
+    }
+  }
+  if (!refined) {
+    return false;
+  }
+  for (int i = 0; i < new_values.size(); ++i) {
+    (*to_update)[i] = new_values[i];
+  }
+  return true;
+}
+
+bool InferenceContext::RelaxOutputHandleShapesAndMergeTypes(
+    int idx, const std::vector<ShapeAndType>& shapes_and_types) {
+  if (output_handle_shapes_and_types_[idx] == nullptr) {
+    output_handle_shapes_and_types_[idx].reset(
+        new std::vector<ShapeAndType>(shapes_and_types));
+    return true;
+  }
+  return RelaxHandleShapesAndMergeTypes(
+      shapes_and_types, output_handle_shapes_and_types_[idx].get());
+}
+
+bool InferenceContext::RelaxInputHandleShapesAndMergeTypes(
+    int idx, const std::vector<ShapeAndType>& shapes_and_types) {
+  if (input_handle_shapes_and_types_[idx] == nullptr) {
+    input_handle_shapes_and_types_[idx].reset(
+        new std::vector<ShapeAndType>(shapes_and_types));
+    return true;
+  }
+  return RelaxHandleShapesAndMergeTypes(
+      shapes_and_types, input_handle_shapes_and_types_[idx].get());
+}
+
 // -----------------------------------------------------------------------------
 // ShapeManager
 // -----------------------------------------------------------------------------
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index 119bed4071..5668667659 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -26,6 +26,13 @@ limitations under the License.
 #include "tensorflow/core/platform/macros.h"
 
 namespace tensorflow {
+
+class ShapeRefinerTest;
+
+namespace grappler {
+class GraphProperties;
+}
+
 namespace shape_inference {
 
 struct DimensionOrConstant;
@@ -62,6 +69,7 @@ class DimensionHandle {
   friend class InferenceContext;
   friend class ShapeInferenceTest;
   friend class ShapeInferenceTestutil;
+  friend class ::tensorflow::ShapeRefinerTest;
   friend class ShapeManager;
 
   // Intentionally copyable.
@@ -98,6 +106,7 @@ class ShapeHandle {
   friend class InferenceContext;
   friend class ShapeInferenceTest;
   friend class ShapeInferenceTestutil;
+  friend class ::tensorflow::ShapeRefinerTest;
   friend class ShapeManager;
 
   // Intentionally copyable.
@@ -201,10 +210,25 @@ class InferenceContext {
     return s;
   }
 
-  // Merge the stored shape of the input in position idx with the specified
-  // shape. This requires idx to be in the [0, num_inputs) range. If the merge
-  // is successful and the new shape differs from the old one, store the new
-  // shape and return true. Return false otherwise.
+  // Merge the stored shape of the input in position idx with <shape> according
+  // to the following rules:
+  //
+  // - If the ShapeHandles are the same or <shape> is unknown, there will be no
+  //   change. Otherwise if the stored shape is unknown, the new shape will be
+  //   <shape>.
+  // - If both shapes are known, then they must have the same rank.
+  // - For any one dimension, if the values for that dimension in both shapes
+  //   are known, then the values must match.
+  // - If one shape has equal or more information than the other shape in every
+  //   dimension, the shape with more information will be returned. Otherwise a
+  //   new shape holding the combined information of the input shapes will be
+  //   returned.
+  // - Example: merging [2,?] and [?,2] results in [2,2]
+  // - Example: [2,2] cannot be merged with [1,2]
+  //
+  // This requires idx to be in the [0, num_inputs) range. If the merge is
+  // successful and the new shape differs from the old one, store the new shape
+  // and return true. Return false otherwise.
   bool MergeInput(int idx, ShapeHandle shape) {
     ShapeHandle new_shape;
     if (!Merge(inputs_[idx], shape, &new_shape).ok() ||
@@ -214,6 +238,41 @@ class InferenceContext {
     inputs_[idx] = new_shape;
     return true;
   }
+  // Relax the stored shape of the input in position idx with <shape> according
+  // to the following rules:
+  //
+  // - If the ShapeHandles are the same then the stored shape will be returned.
+  // - If either of the ShapeHandles are unknown, then a new UnknownShape will
+  //   be returned. A new shape must be returned because we cannot claim that
+  //   the resulting shape is necessarily the same as either of the input
+  //   shapes.
+  // - If the shapes both have known ranks but their ranks are different, a new
+  //   UnknownShape will be returned.
+  // - For any one dimension, if the value for that dimension in either of the
+  //   shapes is unknown, a new shape will be returned with a new UnknownDim in
+  //   that dimension.
+  // - For any one dimension, if the values for that dimension in both shapes
+  //   are known but do not match, a new shape will be returned with a new
+  //   UnknownDim in that dimension.
+  // - If both shapes have the same known rank and match in every dimension,
+  //   the stored shape will be returned.
+  // - Example: relaxing [2,?] and [?,2] results in [?,?]
+  // - Example: relaxing [2,2] and [3,2] results in [?,2]
+  // - Example: relaxing [2,2] with [1,2,3] results in ?
+  //
+  // This requires idx to be in the [0, num_inputs) range. If the relax is
+  // successful and the new shape differs from the old one, store the new
+  // shape and return true. Return false otherwise.
+  bool RelaxInput(int idx, ShapeHandle shape) {
+    ShapeHandle new_shape;
+    Relax(inputs_[idx], shape, &new_shape);
+    if (inputs_[idx].SameHandle(new_shape)) {
+      return false;
+    }
+    inputs_[idx] = new_shape;
+    return true;
+  }
+
   ShapeHandle input(int64 idx) const { return inputs_[idx]; }
   Status input(StringPiece input_name, std::vector<ShapeHandle>* output) const;
   int num_inputs() const { return inputs_.size(); }
@@ -313,12 +372,9 @@ class InferenceContext {
   Status WithValue(DimensionHandle dim, int64 value,
                    DimensionHandle* out) TF_MUST_USE_RESULT;
 
-  // Merges <in0> and <in1> and returns the merged shape in <*out>. If <in0> and
-  // <in1> are incompatible in rank, or in the value of any dimension, returns
-  // an error.
-  //
-  // Note that <*out> may be set to <in0> or <in1>.
-  Status Merge(ShapeHandle in0, ShapeHandle in1,
+  // Merges <s0> and <s1> and returns the merged shape in <*out>. See
+  // 'MergeInput' function for full details and examples.
+  Status Merge(ShapeHandle s0, ShapeHandle s1,
                ShapeHandle* out) TF_MUST_USE_RESULT;
 
   // Asserts that <s>'s rank >= <prefix>'s rank, and the first
@@ -471,13 +527,34 @@ class InferenceContext {
   // If the merge is successful and any of the new shapes differs from the old
   // one, or any of the old dtypes was DT_INVALID, store the new shapes and
   // return true.  Return false otherwise.
+  //
+  // See 'MergeInput' function for full details and examples.
   bool MergeInputHandleShapesAndTypes(
       int idx,
       const std::vector<ShapeAndType>& shapes_and_types) TF_MUST_USE_RESULT;
 
   // As MergeInputHandleShapesAndTypes, but for an output.
   bool MergeOutputHandleShapesAndTypes(
-      int idx, const std::vector<ShapeAndType>& shapes) TF_MUST_USE_RESULT;
+      int idx,
+      const std::vector<ShapeAndType>& shapes_and_types) TF_MUST_USE_RESULT;
+
+  // Relaxes the stored shapes and types corresponding to the input handle in
+  // position idx with the specified shapes and types. This requires idx to be
+  // in the [0, num_inputs) range.
+  //
+  // If the relax is successful and any of the new shapes differs from the old
+  // one, or any of the old dtypes was DT_INVALID, store the new shapes and
+  // return true.  Return false otherwise.
+  //
+  // See 'RelaxInput' function for full details and examples.
+  bool RelaxInputHandleShapesAndMergeTypes(
+      int idx,
+      const std::vector<ShapeAndType>& shapes_and_types) TF_MUST_USE_RESULT;
+
+  // As RelaxInputHandleShapesAndTypes, but for an output.
+  bool RelaxOutputHandleShapesAndMergeTypes(
+      int idx,
+      const std::vector<ShapeAndType>& shapes_and_types) TF_MUST_USE_RESULT;
 
   // Returns the output handle shapes and types, for the resource tensor output
   // at index <idx>. Returns NULL if the shape and types were never set.
@@ -538,6 +615,8 @@ class InferenceContext {
     std::vector<Dimension*> all_dims_;  // values are owned.
   };
 
+  friend class ::tensorflow::grappler::GraphProperties;
+  friend class ShapeInferenceTest;      // For testing Relax functions.
   friend class ShapeInferenceTestutil;  // For testing shapes.
 
   // Shared initialization across the two constructors.  Remove
@@ -563,11 +642,25 @@ class InferenceContext {
   // Adds additional context to the given status.
   Status AttachContext(const Status& status);
 
+  // Relaxes <d0> and <d1> and returns the relaxed dimension in <*out>. If <d0>
+  // and <d1> have incompatible values, returns an error.
+  //
+  // Note that <*out> may be set to <d0> or <d1>.
+  void Relax(DimensionHandle d0, DimensionHandle d1, DimensionHandle* out);
+  // Relaxes <s0> and <s1> and returns the relaxed shape in <*out>. See
+  // 'RelaxInput' function for full details and examples.
+  void Relax(ShapeHandle s0, ShapeHandle s1, ShapeHandle* out);
+
   // Used to implement MergeInputHandleShapesAndTypes and
   // MergeOutputHandleShapesAndTypes.
   bool MergeHandleShapesAndTypes(
       const std::vector<ShapeAndType>& shapes_and_types,
       std::vector<ShapeAndType>* to_update) TF_MUST_USE_RESULT;
+  // Used to implement RelaxInputHandleShapesAndMergeTypes and
+  // RelaxOutputHandleShapesAndMergeTypes.
+  bool RelaxHandleShapesAndMergeTypes(
+      const std::vector<ShapeAndType>& shapes_and_types,
+      std::vector<ShapeAndType>* to_update) TF_MUST_USE_RESULT;
 
   ShapeManager shape_manager_;
 
diff --git a/tensorflow/core/framework/shape_inference_test.cc b/tensorflow/core/framework/shape_inference_test.cc
index a9c0303d4c..66cfbf8747 100644
--- a/tensorflow/core/framework/shape_inference_test.cc
+++ b/tensorflow/core/framework/shape_inference_test.cc
@@ -61,7 +61,16 @@ class ShapeInferenceTest : public ::testing::Test {
   bool SameHandle(ShapeHandle a, ShapeHandle b) { return a.SameHandle(b); }
   bool IsSet(DimensionHandle d) { return d.IsSet(); }
   bool IsSet(ShapeHandle s) { return s.IsSet(); }
+  void Relax(InferenceContext* c, DimensionHandle d0, DimensionHandle d1,
+             DimensionHandle* out) {
+    c->Relax(d0, d1, out);
+  }
+  void Relax(InferenceContext* c, ShapeHandle s0, ShapeHandle s1,
+             ShapeHandle* out) {
+    c->Relax(s0, s1, out);
+  }
   void TestMergeHandles(bool input_not_output);
+  void TestRelaxHandles(bool input_not_output);
 
   static const int kVersion = 0;  // used for graph-def version.
 };
@@ -495,7 +504,7 @@ TEST_F(ShapeInferenceTest, MergeDim) {
   EXPECT_TRUE(c.Merge(d2_b, d2, &out).ok());
   EXPECT_TRUE(SameHandle(d2_b, out));
 
-  // Merging inequal values is an error.
+  // Merging unequal values is an error.
   EXPECT_TRUE(
       StringPiece(c.Merge(d2, d1, &out).ToString())
           .contains(
@@ -510,6 +519,122 @@ TEST_F(ShapeInferenceTest, MergeDim) {
   EXPECT_FALSE(IsSet(out));
 }
 
+TEST_F(ShapeInferenceTest, RelaxDim) {
+  NodeDef def;
+  InferenceContext c(kVersion, &def, MakeOpDef(1, 2),
+                     {S({2, InferenceContext::kUnknownDim, 2, 1,
+                         InferenceContext::kUnknownDim})},
+                     {}, {}, {});
+
+  auto d2 = c.Dim(c.input(0), 0);
+  auto d_unknown = c.Dim(c.input(0), 1);
+  auto d2_b = c.Dim(c.input(0), 2);
+  auto d1 = c.Dim(c.input(0), 3);
+  auto d_unknown_b = c.Dim(c.input(0), 4);
+  DimensionHandle out;
+
+  // Relaxing anything with unknown returns a new unknown.
+  Relax(&c, d2, d_unknown, &out);
+  EXPECT_FALSE(SameHandle(d_unknown, out));
+  EXPECT_FALSE(SameHandle(d_unknown_b, out));
+  EXPECT_EQ(InferenceContext::kUnknownDim, c.Value(out));
+  Relax(&c, d_unknown, d2, &out);
+  EXPECT_FALSE(SameHandle(d_unknown, out));
+  EXPECT_EQ(InferenceContext::kUnknownDim, c.Value(out));
+  Relax(&c, d_unknown, d_unknown_b, &out);
+  EXPECT_FALSE(SameHandle(d_unknown, out));
+  EXPECT_FALSE(SameHandle(d_unknown_b, out));
+  EXPECT_EQ(InferenceContext::kUnknownDim, c.Value(out));
+
+  // Relaxing with self returns self.
+  Relax(&c, d2, d2, &out);
+  EXPECT_TRUE(SameHandle(d2, out));
+  Relax(&c, d_unknown, d_unknown, &out);
+  EXPECT_TRUE(SameHandle(d_unknown, out));
+
+  // Relaxing equal values returns first one.
+  Relax(&c, d2, d2_b, &out);
+  EXPECT_TRUE(SameHandle(d2, out));
+  Relax(&c, d2_b, d2, &out);
+  EXPECT_TRUE(SameHandle(d2_b, out));
+
+  // Relaxing unequal values returns a new unknown.
+  Relax(&c, d2, d1, &out);
+  EXPECT_EQ(InferenceContext::kUnknownDim, c.Value(out));
+  Relax(&c, d1, d2, &out);
+  EXPECT_EQ(InferenceContext::kUnknownDim, c.Value(out));
+}
+
+TEST_F(ShapeInferenceTest, RelaxShape) {
+  NodeDef def;
+  InferenceContext c(
+      kVersion, &def, MakeOpDef(7, 2),
+      {Unknown(), S({1, 2}), S({InferenceContext::kUnknownDim, 2}),
+       S({1, InferenceContext::kUnknownDim}), S({1, 3}), Unknown(), S({1})},
+      {}, {}, {});
+
+  auto s_unknown = c.input(0);
+  auto s_1_2 = c.input(1);
+  auto s_u_2 = c.input(2);
+  auto s_1_u = c.input(3);
+  auto s_1_3 = c.input(4);
+  auto s_unknown_b = c.input(5);
+  auto s_1 = c.input(6);
+  ShapeHandle out;
+
+  // Relaxing any shape with unknown returns a new unknown.
+  Relax(&c, s_unknown, s_1_2, &out);
+  EXPECT_FALSE(SameHandle(s_u_2, s_unknown));
+  EXPECT_EQ("?", c.DebugString(out));
+  Relax(&c, s_u_2, s_unknown, &out);
+  EXPECT_FALSE(SameHandle(s_u_2, out));
+  EXPECT_EQ("?", c.DebugString(out));
+  Relax(&c, s_unknown, s_unknown_b, &out);
+  EXPECT_FALSE(SameHandle(s_unknown, out));
+  EXPECT_FALSE(SameHandle(s_unknown_b, out));
+  EXPECT_EQ("?", c.DebugString(out));
+
+  // Relaxing with self returns self.
+  Relax(&c, s_1_2, s_1_2, &out);
+  EXPECT_TRUE(SameHandle(out, s_1_2));
+
+  // Relaxing where one of the inputs has less information.
+  out = ShapeHandle();
+  Relax(&c, s_1_2, s_u_2, &out);
+  EXPECT_FALSE(SameHandle(s_u_2, out));
+  EXPECT_EQ("[?,2]", c.DebugString(out));
+  out = ShapeHandle();
+  Relax(&c, s_u_2, s_1_2, &out);
+  EXPECT_FALSE(SameHandle(s_u_2, out));
+  EXPECT_EQ("[?,2]", c.DebugString(out));
+
+  // Relaxing where each input has one distinct unknown dimension.
+  Relax(&c, s_u_2, s_1_u, &out);
+  EXPECT_EQ("[?,?]", c.DebugString(out));
+  EXPECT_FALSE(SameHandle(c.Dim(s_u_2, 0), c.Dim(out, 0)));
+  EXPECT_FALSE(SameHandle(c.Dim(s_1_u, 1), c.Dim(out, 1)));
+  auto s_u1 = c.UnknownShapeOfRank(1);
+  auto s_u2 = c.UnknownShapeOfRank(1);
+  Relax(&c, s_u1, s_u2, &out);
+  EXPECT_FALSE(SameHandle(s_u1, out));
+
+  // Relaxing with mismatched values in a dimension returns a shape with that
+  // dimension unknown.
+  out = s_unknown;
+  Relax(&c, s_u_2, s_1_3, &out);
+  EXPECT_FALSE(SameHandle(c.Dim(s_u_2, 0), c.Dim(out, 0)));
+  EXPECT_EQ("[?,?]", c.DebugString(out));
+  out = s_unknown;
+  Relax(&c, s_1_3, s_u_2, &out);
+  EXPECT_FALSE(SameHandle(c.Dim(s_u_2, 0), c.Dim(out, 0)));
+  EXPECT_EQ("[?,?]", c.DebugString(out));
+  out = s_unknown;
+
+  // Relaxing with mismatched ranks returns a new unknown.
+  Relax(&c, s_1, s_1_2, &out);
+  EXPECT_EQ("?", c.DebugString(out));
+}
+
 TEST_F(ShapeInferenceTest, MergeShape) {
   NodeDef def;
   InferenceContext c(kVersion, &def, MakeOpDef(7, 2),
@@ -1473,8 +1598,8 @@ void ShapeInferenceTest::TestMergeHandles(bool input_not_output) {
     EXPECT_EQ(t[i].dtype, v[i].dtype);
   }
 
-  // Only difference is in a mismatched dtype. That is ignored,
-  // and there are no other changes, so nothing is done.
+  // Only difference is in a mismatched dtype, but that cannot be
+  // updated unless original dtype is DT_INVALID.
   t2 = t;
   t2[2].dtype = DT_FLOAT;
   ASSERT_FALSE(merge_shapes_and_types_to_context(0, t2));
@@ -1510,11 +1635,111 @@ void ShapeInferenceTest::TestMergeHandles(bool input_not_output) {
 }
 
 TEST_F(ShapeInferenceTest, MergeInputHandleShapesAndTypes) {
-  TestMergeHandles(true);
+  TestMergeHandles(true /* input_not_output */);
 }
 
 TEST_F(ShapeInferenceTest, MergeOutputHandleShapesAndTypes) {
-  TestMergeHandles(false);
+  TestMergeHandles(false /* input_not_output */);
+}
+
+void ShapeInferenceTest::TestRelaxHandles(bool input_not_output) {
+  NodeDef def;
+  InferenceContext c(kVersion, &def, MakeOpDef(2, 2), {S({}), S({})}, {}, {},
+                     {});
+  auto make_shape = [&c](std::initializer_list<int64> dim_sizes) {
+    ShapeHandle s;
+    TF_CHECK_OK(c.MakeShapeFromShapeProto(S(dim_sizes), &s));
+    return s;
+  };
+  auto get_shapes_and_types_from_context = [&](int idx) {
+    if (input_not_output) {
+      return c.input_handle_shapes_and_types(idx);
+    } else {
+      return c.output_handle_shapes_and_types(idx);
+    }
+  };
+  auto relax_shapes_and_types_to_context =
+      [&](int idx, const std::vector<ShapeAndType>& shapes_and_types) {
+        if (input_not_output) {
+          return c.RelaxInputHandleShapesAndMergeTypes(idx, shapes_and_types);
+        } else {
+          return c.RelaxOutputHandleShapesAndMergeTypes(idx, shapes_and_types);
+        }
+      };
+
+  EXPECT_TRUE(get_shapes_and_types_from_context(0) == nullptr);
+  EXPECT_TRUE(get_shapes_and_types_from_context(1) == nullptr);
+
+  // First relax will take the input completely.
+  std::vector<ShapeAndType> t{{make_shape({1, 2, 3}), DT_FLOAT},
+                              {c.UnknownShape(), DT_INVALID},
+                              {make_shape({4, 3, 2, 1}), DT_INT32}};
+  ASSERT_TRUE(relax_shapes_and_types_to_context(0, t));
+  ASSERT_TRUE(get_shapes_and_types_from_context(0) != nullptr);
+  std::vector<ShapeAndType> v = *get_shapes_and_types_from_context(0);
+  ASSERT_EQ(3, v.size());
+  for (int i = 0; i < v.size(); ++i) {
+    EXPECT_TRUE(SameHandle(t[i].shape, v[i].shape)) << i;
+    EXPECT_EQ(t[i].dtype, v[i].dtype);
+  }
+
+  // Relax that fails because wrong number of values passed.
+  // Fails, and no changes made.
+  ASSERT_FALSE(relax_shapes_and_types_to_context(
+      0, std::vector<ShapeAndType>{{make_shape({1, 2, 3}), DT_FLOAT}}));
+  v = *get_shapes_and_types_from_context(0);
+  ASSERT_EQ(3, v.size());
+  for (int i = 0; i < v.size(); ++i) {
+    EXPECT_TRUE(SameHandle(t[i].shape, v[i].shape)) << i;
+    EXPECT_EQ(t[i].dtype, v[i].dtype);
+  }
+
+  // Only difference is in a mismatched shape. This should replace
+  // the mismatched dimension with an UnknownDim.
+  auto t2 = t;
+  t2[2].shape = make_shape({4, 3, 4, 1});
+  ASSERT_TRUE(relax_shapes_and_types_to_context(0, t2));
+  v = *get_shapes_and_types_from_context(0);
+  EXPECT_EQ("[4,3,?,1]", c.DebugString(v[2].shape));
+  for (int i = 0; i < v.size(); ++i) {
+    EXPECT_EQ(t[i].dtype, v[i].dtype);
+  }
+
+  // Only difference is in a mismatched dtype, but that cannot be
+  // updated unless original dtype is DT_INVALID.
+  t2 = t;
+  t2[2].dtype = DT_FLOAT;
+  ASSERT_FALSE(relax_shapes_and_types_to_context(0, t2));
+  v = *get_shapes_and_types_from_context(0);
+  ASSERT_EQ(3, v.size());
+  for (int i = 0; i < v.size(); ++i) {
+    EXPECT_EQ(t[i].dtype, v[i].dtype);
+  }
+
+  // Difference is a new shape, which will result in a new UnknownShape.
+  t[1].shape = make_shape({1, 10});
+  ASSERT_TRUE(relax_shapes_and_types_to_context(0, t));
+  v = *get_shapes_and_types_from_context(0);
+  ASSERT_EQ(3, v.size());
+  EXPECT_FALSE(SameHandle(t[1].shape, v[1].shape));
+  EXPECT_EQ("?", c.DebugString(v[1].shape));
+  for (int i = 0; i < v.size(); ++i) {
+    EXPECT_EQ(t[i].dtype, v[i].dtype);
+  }
+
+  // Difference is relaxable (new type).
+  t[1].dtype = DT_DOUBLE;
+  ASSERT_TRUE(relax_shapes_and_types_to_context(0, t));
+  v = *get_shapes_and_types_from_context(0);
+  EXPECT_EQ(t[1].dtype, v[1].dtype);
+}
+
+TEST_F(ShapeInferenceTest, RelaxInputHandleShapesAndTypes) {
+  TestRelaxHandles(true /* input_not_output */);
+}
+
+TEST_F(ShapeInferenceTest, RelaxOutputHandleShapesAndTypes) {
+  TestRelaxHandles(false /* input_not_output */);
 }
 
 }  // namespace shape_inference
diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD
index 39dc9e071d..96d37c9a97 100644
--- a/tensorflow/core/grappler/costs/BUILD
+++ b/tensorflow/core/grappler/costs/BUILD
@@ -42,6 +42,7 @@ cc_library(
         ":op_performance_data_cc",
         ":utils",
         "//tensorflow/core:core_cpu_base",
+        "//tensorflow/core:framework",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler/clusters:cluster",
diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 21b73b6618..7ac35ef271 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -32,9 +32,108 @@ using shape_inference::ShapeHandle;
 
 namespace {
 
-// Merges shapes <shapes_and_types>, determined from an EnqueueV2 node, into
-// <*queue_shapes_and_types>.
-Status MergeEnqueueShapesAndTypes(
+// If a Merge node has a NextIteration node as an input then that input will
+// try to forward an UnknownShape at graph construction time. However, the
+// Merge shape function will always propagate an UnknownShape if any of its
+// inputs are UnknownShapes. So we need to ignore the input from NextIteration
+// nodes to propagate any known shape from the Merge node.
+Status ShapeOfMergeNode(const Node* node, InferenceContext* c) {
+  ShapeHandle out = c->input(0);
+  if (!c->RankKnown(out)) {
+    out = c->UnknownShape();
+  } else {
+    int32 rank = c->Rank(out);
+    for (const Edge* e : node->in_edges()) {
+      if (e->src()->IsNextIteration() || e->dst_input() <= 0) {
+        continue;
+      }
+      ShapeHandle input = c->input(e->dst_input());
+      if (!c->RankKnown(input) || c->Rank(input) != rank) {
+        out = c->UnknownShape();
+        break;
+      }
+
+      for (int d = 0; d < rank; ++d) {
+        if (c->Value(c->Dim(input, d)) != c->Value(c->Dim(out, d))) {
+          TF_RETURN_IF_ERROR(c->ReplaceDim(out, d, c->UnknownDim(), &out));
+        }
+      }
+    }
+  }
+  c->set_output(0, out);
+  c->set_output(1, c->Scalar());
+  return Status::OK();
+}
+
+// Manually propagate the input shape for Enter nodes and update any Merge node
+// outputs.
+Status UpdateEnter(ShapeRefiner* shape_refiner, const Node* node, bool relax,
+                   std::queue<const Node*>* new_shapes) {
+  auto enter_ctx = shape_refiner->GetContext(node);
+  for (int i = 0; i < enter_ctx->num_outputs(); i++) {
+    TF_RETURN_IF_ERROR(shape_refiner->SetShape(node, i, enter_ctx->input(0)));
+  }
+  for (const Edge* e : node->out_edges()) {
+    Node* dst = e->dst();
+    if (dst->IsMerge()) {
+      bool updated = false;
+      TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(dst, relax, &updated));
+      if (!updated) {
+        continue;
+      }
+      InferenceContext* merge_ctx = shape_refiner->GetContext(dst);
+      DCHECK_NE(merge_ctx, nullptr);
+      TF_RETURN_IF_ERROR(ShapeOfMergeNode(dst, merge_ctx));
+      new_shapes->push(dst);
+    }
+  }
+  return Status::OK();
+}
+
+// Propagates the shapes in the transitive fan-out of <new_shapes>.
+Status PropagateShapes(ShapeRefiner* shape_refiner, bool relax,
+                       std::queue<const Node*>* new_shapes) {
+  while (!new_shapes->empty()) {
+    const Node* n = new_shapes->front();
+    new_shapes->pop();
+    for (const Node* fanout : n->out_nodes()) {
+      bool updated = false;
+      TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(fanout, relax, &updated));
+      if (fanout->IsEnter()) {
+        TF_RETURN_IF_ERROR(
+            UpdateEnter(shape_refiner, fanout, relax, new_shapes));
+      } else if (updated) {
+        // We want to avoid propagating through loops on the merge pass because
+        // the shapes are not guaranteed to converge.
+        if (!relax && fanout->IsNextIteration()) {
+          continue;
+        }
+        new_shapes->push(fanout);
+      }
+    }
+  }
+  return Status::OK();
+}
+
+}  // namespace
+
+void GraphProperties::Relax(InferenceContext* c, ShapeHandle s0, ShapeHandle s1,
+                            ShapeHandle* out) {
+  c->Relax(s0, s1, out);
+}
+
+bool GraphProperties::SameDefinedShape(InferenceContext* c, ShapeHandle s0,
+                                       ShapeHandle s1) {
+  return ShapeRefiner::SameDefinedShape(c, s0, s1);
+}
+
+bool GraphProperties::IsUpdatedShapesOrTypes(
+    InferenceContext* c, const std::vector<ShapeAndType>& existing,
+    const std::vector<ShapeAndType>& updated) {
+  return ShapeRefiner::IsUpdatedShapesOrTypes(c, existing, updated);
+}
+
+Status GraphProperties::MergeEnqueueShapesAndTypes(
     const std::vector<ShapeAndType>& shapes_and_types, InferenceContext* qctx,
     std::vector<ShapeAndType>* queue_shapes_and_types) {
   if (shapes_and_types.size() != queue_shapes_and_types->size()) {
@@ -56,7 +155,27 @@ Status MergeEnqueueShapesAndTypes(
   return Status::OK();
 }
 
-}  // namespace
+Status GraphProperties::RelaxEnqueueShapesAndMergeTypes(
+    const std::vector<ShapeAndType>& shapes_and_types, InferenceContext* qctx,
+    std::vector<ShapeAndType>* queue_shapes_and_types) {
+  if (shapes_and_types.size() != queue_shapes_and_types->size()) {
+    return errors::InvalidArgument(
+        "Enqueue nodes mixed number of tensors: ", shapes_and_types.size(),
+        "  vs ", queue_shapes_and_types->size());
+  }
+  for (int i = 0; i < shapes_and_types.size(); ++i) {
+    const ShapeAndType& a = shapes_and_types[i];
+    ShapeAndType& b = (*queue_shapes_and_types)[i];
+    if (a.dtype != b.dtype) {
+      return errors::InvalidArgument("Enqueue nodes mixed dtypes for tensor ",
+                                     i, ": ", DataTypeString(a.dtype), " vs ",
+                                     DataTypeString(b.dtype));
+    }
+
+    Relax(qctx, a.shape, b.shape, &b.shape);
+  }
+  return Status::OK();
+}
 
 Status GraphProperties::InferStatically() {
   Graph graph(OpRegistry::Global());
@@ -66,8 +185,11 @@ Status GraphProperties::InferStatically() {
   Status s = ImportGraphDef(options, item_.graph, &graph, &shape_refiner);
   TF_RETURN_IF_ERROR(s);
 
-  // List the resources and the nodes using them
+  // List the resources and the nodes using them. Also collect the Enter and
+  // Merge nodes.
   std::unordered_map<const Node*, std::unordered_set<const Node*>> resources;
+  std::unordered_set<const Node*> enter_nodes;
+  std::unordered_set<const Node*> merge_nodes;
   for (const Node* const node : graph.nodes()) {
     for (int i = 0; i < node->num_inputs(); ++i) {
       if (node->input_type(i) == DataType::DT_RESOURCE) {
@@ -76,82 +198,142 @@ Status GraphProperties::InferStatically() {
         resources[resource].insert(node);
       }
     }
+    if (node->IsEnter()) {
+      enter_nodes.insert(node);
+    } else if (node->IsMerge()) {
+      merge_nodes.insert(node);
+    }
   }
 
-  // If we found a resource, try to propagate the shapes through it.
-  bool done = true;
-  do {
-    std::queue<const Node*> new_shapes;
-    for (const auto& resource_data : resources) {
-      const Node* qnode = resource_data.first;
-      StringPiece type(qnode->type_string());
-      if (!type.ends_with("QueueV2")) {
-        continue;
+  // Propagate the initial shapes of Enter nodes manually (the Enter shape
+  // function always forwards an UnknownShape).
+  std::queue<const Node*> new_shapes;
+  for (const Node* node : enter_nodes) {
+    TF_RETURN_IF_ERROR(
+        UpdateEnter(&shape_refiner, node, false /* relax */, &new_shapes));
+  }
+  TF_RETURN_IF_ERROR(
+      PropagateShapes(&shape_refiner, false /* relax */, &new_shapes));
+
+  // We propagate shapes through the graph in two phases. In the first phase, we
+  // exclusively merge shapes but we do not propagate shapes through loops. Then
+  // on the second phase, we exclusively relax shapes and propagate shapes
+  // through loops until reaching fixed point.
+  for (int relax = 0; relax < 2; relax++) {
+    // We don't update Merge nodes with the input of NextIteration nodes on the
+    // merge pass. So we do that at the beginning of the relax pass instead.
+    if (relax) {
+      bool updated = false;
+      for (const Node* node : merge_nodes) {
+        TF_RETURN_IF_ERROR(
+            shape_refiner.UpdateNode(node, false /* relax */, &updated));
       }
-      auto qctx = shape_refiner.GetContext(qnode);
-      if (!qctx) {
-        continue;
+    }
+
+    bool done = true;
+    do {
+      if (relax) {
+        // Propagate shapes through any loops in the graph by relaxing.
+        for (const Node* node : merge_nodes) {
+          new_shapes.push(node);
+        }
+        TF_RETURN_IF_ERROR(PropagateShapes(&shape_refiner, relax, &new_shapes));
       }
 
-      // Check to see if the shape is fully defined.
-      auto* queue_handle_data = qctx->output_handle_shapes_and_types(0);
-      if (queue_handle_data != nullptr) {
-        bool fully_defined = true;
-        for (const auto& shape_and_type : *queue_handle_data) {
-          if (!qctx->FullyDefined(shape_and_type.shape) ||
-              shape_and_type.dtype == DT_INVALID) {
-            fully_defined = false;
-          }
+      // If we found a resource, try to propagate the shapes through it.
+      new_shapes = std::queue<const Node*>();
+      for (const auto& resource_data : resources) {
+        const Node* qnode = resource_data.first;
+        StringPiece type(qnode->type_string());
+        if (!type.ends_with("QueueV2") && !qnode->IsEnter()) {
+          continue;
         }
-        if (fully_defined) {
+        auto qctx = shape_refiner.GetContext(qnode);
+        if (!qctx) {
           continue;
         }
-      }
 
-      std::vector<ShapeAndType> queue_shapes_and_types;
-      if (queue_handle_data != nullptr) {
-        queue_shapes_and_types = *queue_handle_data;
-      }
-      for (const auto& node : resource_data.second) {
-        auto ctx = shape_refiner.GetContext(node);
-        if (!ctx) {
-          continue;
+        // Check to see if the shape is fully defined.
+        auto* queue_handle_data = qctx->output_handle_shapes_and_types(0);
+        if (queue_handle_data != nullptr) {
+          bool fully_defined = true;
+          for (const auto& shape_and_type : *queue_handle_data) {
+            if (!qctx->FullyDefined(shape_and_type.shape) ||
+                shape_and_type.dtype == DT_INVALID) {
+              fully_defined = false;
+            }
+          }
+          // If we are merging, then we are done. If we are relaxing, then we
+          // could potentially propagate a less specific shape.
+          if (fully_defined && !relax) {
+            continue;
+          }
         }
-        // TODO(bsteiner): handle EnqueueMany as well.
-        if (node->type_string().find("Enqueue") != std::string::npos &&
-            node->type_string().find("EnqueueMany") == std::string::npos) {
-          std::vector<ShapeAndType> shapes_and_types;
-          for (int i = 1; i < ctx->num_inputs(); ++i) {
-            shapes_and_types.push_back({ctx->input(i), node->input_type(i)});
+
+        // Merge all inputs into the enqueue node, regardless of which phase we
+        // are in.
+        std::vector<ShapeAndType> queue_shapes_and_types;
+        for (const auto& node : resource_data.second) {
+          auto ctx = shape_refiner.GetContext(node);
+          if (!ctx) {
+            continue;
           }
+          // TODO(bsteiner): handle EnqueueMany as well.
+          if (node->type_string().find("Enqueue") != std::string::npos &&
+              node->type_string().find("EnqueueMany") == std::string::npos) {
+            std::vector<ShapeAndType> shapes_and_types;
+            for (int i = 1; i < ctx->num_inputs(); ++i) {
+              shapes_and_types.push_back({ctx->input(i), node->input_type(i)});
+            }
 
-          if (queue_shapes_and_types.empty()) {
-            queue_shapes_and_types = shapes_and_types;
+            if (queue_shapes_and_types.empty()) {
+              queue_shapes_and_types = shapes_and_types;
+            } else {
+              TF_RETURN_IF_ERROR(MergeEnqueueShapesAndTypes(
+                  shapes_and_types, qctx, &queue_shapes_and_types));
+            }
+          }
+        }
+        // Combine the input shapes with the existing output shape. We either
+        // merge or relax depending on which phase we are in.
+        if (queue_handle_data != nullptr) {
+          if (relax) {
+            TF_RETURN_IF_ERROR(RelaxEnqueueShapesAndMergeTypes(
+                *queue_handle_data, qctx, &queue_shapes_and_types));
           } else {
             TF_RETURN_IF_ERROR(MergeEnqueueShapesAndTypes(
-                shapes_and_types, qctx, &queue_shapes_and_types));
+                *queue_handle_data, qctx, &queue_shapes_and_types));
           }
         }
-      }
-      if (!queue_shapes_and_types.empty() &&
-          qctx->MergeOutputHandleShapesAndTypes(0, queue_shapes_and_types)) {
-        new_shapes.push(qnode);
-      }
-    }
-    // Propagate the shapes in the transitive fan-out of the queue.
-    done = new_shapes.empty();
-    while (!new_shapes.empty()) {
-      const Node* n = new_shapes.front();
-      new_shapes.pop();
-      for (const Node* fanout : n->out_nodes()) {
-        bool updated = false;
-        TF_RETURN_IF_ERROR(shape_refiner.UpdateNode(fanout, &updated));
-        if (updated) {
-          new_shapes.push(fanout);
+        // Set the output ShapeAndType handles. If we successfully update the
+        // resource node, add its fan-out to the queue.
+        const std::vector<ShapeAndType>* outputs =
+            qctx->output_handle_shapes_and_types(0);
+        std::vector<ShapeAndType> existing_outputs;
+        if (outputs) {
+          existing_outputs = *outputs;
+        }
+        if (!queue_shapes_and_types.empty()) {
+          if (!relax && qctx->MergeOutputHandleShapesAndTypes(
+                            0, queue_shapes_and_types)) {
+            new_shapes.push(qnode);
+          } else if (relax && qctx->RelaxOutputHandleShapesAndMergeTypes(
+                                  0, queue_shapes_and_types)) {
+            if (IsUpdatedShapesOrTypes(
+                    qctx, existing_outputs,
+                    *qctx->output_handle_shapes_and_types(0))) {
+              new_shapes.push(qnode);
+            }
+          }
         }
       }
-    }
-  } while (!done);
+      // Propagate the shapes in the transitive fan-out of the queue.
+      done = new_shapes.empty();
+      if (!done) {
+        TF_RETURN_IF_ERROR(PropagateShapes(&shape_refiner, relax, &new_shapes));
+      }
+    } while (!done);
+  }
 
   for (const Node* const node : graph.nodes()) {
     VLOG(1) << "<Node> " << node->name();
diff --git a/tensorflow/core/grappler/costs/graph_properties.h b/tensorflow/core/grappler/costs/graph_properties.h
index b849c4b3f0..954aeb3905 100644
--- a/tensorflow/core/grappler/costs/graph_properties.h
+++ b/tensorflow/core/grappler/costs/graph_properties.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include <unordered_map>
 #include <vector>
+#include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/grappler/clusters/cluster.h"
 #include "tensorflow/core/grappler/costs/op_performance_data.pb.h"
 #include "tensorflow/core/grappler/grappler_item.h"
@@ -49,6 +50,34 @@ class GraphProperties {
   GrapplerItem item_;
   std::map<string, std::vector<OpInfo::TensorProperties>> input_properties_;
   std::map<string, std::vector<OpInfo::TensorProperties>> output_properties_;
+
+  // Merges shapes <shapes_and_types>, determined from an EnqueueV2 node, into
+  // <*queue_shapes_and_types>.
+  Status MergeEnqueueShapesAndTypes(
+      const std::vector<shape_inference::ShapeAndType>& shapes_and_types,
+      shape_inference::InferenceContext* qctx,
+      std::vector<shape_inference::ShapeAndType>* queue_shapes_and_types);
+  // Relaxes shapes <shapes_and_types>, determined from an EnqueueV2 node, into
+  // <*queue_shapes_and_types>.
+  Status RelaxEnqueueShapesAndMergeTypes(
+      const std::vector<shape_inference::ShapeAndType>& shapes_and_types,
+      shape_inference::InferenceContext* qctx,
+      std::vector<shape_inference::ShapeAndType>* queue_shapes_and_types);
+
+  // This gives access to private function of InferenceContext.
+  static void Relax(shape_inference::InferenceContext* c,
+                    shape_inference::ShapeHandle s0,
+                    shape_inference::ShapeHandle s1,
+                    shape_inference::ShapeHandle* out);
+
+  // These give access to private functions of ShapeRefiner.
+  static bool SameDefinedShape(shape_inference::InferenceContext* c,
+                               shape_inference::ShapeHandle s0,
+                               shape_inference::ShapeHandle s1);
+  static bool IsUpdatedShapesOrTypes(
+      shape_inference::InferenceContext* c,
+      const std::vector<shape_inference::ShapeAndType>& existing,
+      const std::vector<shape_inference::ShapeAndType>& updated);
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc
index 10a88b59a2..cc6f097cd0 100644
--- a/tensorflow/core/grappler/costs/graph_properties_test.cc
+++ b/tensorflow/core/grappler/costs/graph_properties_test.cc
@@ -309,17 +309,8 @@ TEST_F(GraphPropertiesTest, Queues) {
   EXPECT_EQ("float: [1,2,3]", PropToString(props5[2]));
 }
 
-TEST_F(GraphPropertiesTest, Loops) {
-  // Test graph produced in python using:
-  /*
-     with tf.Graph().as_default():
-       i = tf.constant(0)
-       c = lambda i: tf.less(i, 10)
-       b = lambda i: tf.add(i, 1)
-       r = tf.while_loop(c, b, [i])
-       with open('/tmp/graph.txt', 'w') as f:
-         f.write(str(tf.get_default_graph().as_graph_def()))
-  */
+TEST_F(GraphPropertiesTest, WhileLoop) {
+  // Python code used to generate the graph is below.
   const string gdef_ascii = R"EOF(
 node {
   name: "Const"
@@ -342,6 +333,33 @@ node {
     }
   }
 }
+node {
+  name: "ones"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 2
+          }
+          dim {
+            size: 2
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
 node {
   name: "while/Enter"
   op: "Enter"
@@ -371,6 +389,35 @@ node {
     }
   }
 }
+node {
+  name: "while/Enter_1"
+  op: "Enter"
+  input: "ones"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
 node {
   name: "while/Merge"
   op: "Merge"
@@ -389,6 +436,24 @@ node {
     }
   }
 }
+node {
+  name: "while/Merge_1"
+  op: "Merge"
+  input: "while/Enter_1"
+  input: "while/NextIteration_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
 node {
   name: "while/Less/y"
   op: "Const"
@@ -448,6 +513,26 @@ node {
     }
   }
 }
+node {
+  name: "while/Switch_1"
+  op: "Switch"
+  input: "while/Merge_1"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge_1"
+      }
+    }
+  }
+}
 node {
   name: "while/Identity"
   op: "Identity"
@@ -460,7 +545,18 @@ node {
   }
 }
 node {
-  name: "while/Add/y"
+  name: "while/Identity_1"
+  op: "Identity"
+  input: "while/Switch_1:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/add/y"
   op: "Const"
   input: "^while/Identity"
   attr {
@@ -482,12 +578,59 @@ node {
   }
 }
 node {
-  name: "while/Add"
+  name: "while/add"
   op: "Add"
   input: "while/Identity"
-  input: "while/Add/y"
+  input: "while/add/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/concat/axis"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "while/concat"
+  op: "ConcatV2"
+  input: "while/Identity_1"
+  input: "while/Identity_1"
+  input: "while/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
   attr {
     key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
     value {
       type: DT_INT32
     }
@@ -496,7 +639,7 @@ node {
 node {
   name: "while/NextIteration"
   op: "NextIteration"
-  input: "while/Add"
+  input: "while/add"
   attr {
     key: "T"
     value {
@@ -504,6 +647,17 @@ node {
     }
   }
 }
+node {
+  name: "while/NextIteration_1"
+  op: "NextIteration"
+  input: "while/concat"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
 node {
   name: "while/Exit"
   op: "Exit"
@@ -515,21 +669,2333 @@ node {
     }
   }
 }
+node {
+  name: "while/Exit_1"
+  op: "Exit"
+  input: "while/Switch_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
 versions {
-  producer: 11
+  producer: 21
 }
   )EOF";
 
+  // Test graph produced in python using:
+  /*
+     with tf.Graph().as_default():
+       i0 = tf.constant(0)
+       m0 = tf.ones([2, 2])
+       c = lambda i, m: i < 10
+       b = lambda i, m: [i+1, tf.concat([m, m], axis=0)]
+       r = tf.while_loop(
+              c, b, loop_vars=[i0, m0],
+              shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])])
+       with open('/tmp/graph.pbtxt', 'w') as f:
+         f.write(str(tf.get_default_graph().as_graph_def()))
+  */
+
   GrapplerItem item;
   CHECK(protobuf::TextFormat::ParseFromString(gdef_ascii, &item.graph));
   GraphProperties properties(item);
   TF_CHECK_OK(properties.InferStatically());
 
-  const auto props = properties.GetOutputProperties("while/Exit");
-  EXPECT_EQ(1, props.size());
-  const OpInfo::TensorProperties& prop = props[0];
-  EXPECT_EQ(DT_INT32, prop.dtype());
-  EXPECT_TRUE(prop.shape().unknown_rank());
+  std::vector<string> nodes{"while/Merge_1", "while/NextIteration_1",
+                            "while/Exit_1"};
+  for (const string& node : nodes) {
+    const auto props = properties.GetOutputProperties(node);
+    const OpInfo::TensorProperties& prop = props[0];
+    EXPECT_EQ(DT_FLOAT, prop.dtype());
+    EXPECT_EQ("float: [-1,2]", PropToString(prop));
+  }
+}
+
+TEST_F(GraphPropertiesTest, NestedLoop) {
+  // Python code used to generate the graph is below.
+  const string gdef_ascii = R"EOF(
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "ones"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
+node {
+  name: "while/Enter"
+  op: "Enter"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Enter_1"
+  op: "Enter"
+  input: "ones"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Merge"
+  op: "Merge"
+  input: "while/Enter"
+  input: "while/NextIteration"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Merge_1"
+  op: "Merge"
+  input: "while/Enter_1"
+  input: "while/NextIteration_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Less/y"
+  op: "Const"
+  input: "^while/Merge"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 3
+      }
+    }
+  }
+}
+node {
+  name: "while/Less"
+  op: "Less"
+  input: "while/Merge"
+  input: "while/Less/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/LoopCond"
+  op: "LoopCond"
+  input: "while/Less"
+}
+node {
+  name: "while/Switch"
+  op: "Switch"
+  input: "while/Merge"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge"
+      }
+    }
+  }
+}
+node {
+  name: "while/Switch_1"
+  op: "Switch"
+  input: "while/Merge_1"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge_1"
+      }
+    }
+  }
+}
+node {
+  name: "while/Identity"
+  op: "Identity"
+  input: "while/Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Identity_1"
+  op: "Identity"
+  input: "while/Switch_1:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/while/Const"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Enter"
+  op: "Enter"
+  input: "while/while/Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/while/Enter_1"
+  op: "Enter"
+  input: "while/Identity_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/while/Merge"
+  op: "Merge"
+  input: "while/while/Enter"
+  input: "while/while/NextIteration"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/Merge_1"
+  op: "Merge"
+  input: "while/while/Enter_1"
+  input: "while/while/NextIteration_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/while/Less/y"
+  op: "Const"
+  input: "^while/while/Merge"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 3
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Less"
+  op: "Less"
+  input: "while/while/Merge"
+  input: "while/while/Less/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/LoopCond"
+  op: "LoopCond"
+  input: "while/while/Less"
+}
+node {
+  name: "while/while/Switch"
+  op: "Switch"
+  input: "while/while/Merge"
+  input: "while/while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/while/Merge"
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Switch_1"
+  op: "Switch"
+  input: "while/while/Merge_1"
+  input: "while/while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/while/Merge_1"
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Identity"
+  op: "Identity"
+  input: "while/while/Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/Identity_1"
+  op: "Identity"
+  input: "while/while/Switch_1:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/while/add/y"
+  op: "Const"
+  input: "^while/while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "while/while/add"
+  op: "Add"
+  input: "while/while/Identity"
+  input: "while/while/add/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/concat/axis"
+  op: "Const"
+  input: "^while/while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 2
+      }
+    }
+  }
+}
+node {
+  name: "while/while/concat"
+  op: "ConcatV2"
+  input: "while/while/Identity_1"
+  input: "while/while/Identity_1"
+  input: "while/while/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/NextIteration"
+  op: "NextIteration"
+  input: "while/while/add"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/NextIteration_1"
+  op: "NextIteration"
+  input: "while/while/concat"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/while/Exit"
+  op: "Exit"
+  input: "while/while/Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/Exit_1"
+  op: "Exit"
+  input: "while/while/Switch_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/add/y"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "while/add"
+  op: "Add"
+  input: "while/Identity"
+  input: "while/add/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/concat/axis"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "while/concat"
+  op: "ConcatV2"
+  input: "while/Identity_1"
+  input: "while/Identity_1"
+  input: "while/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration"
+  op: "NextIteration"
+  input: "while/add"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration_1"
+  op: "NextIteration"
+  input: "while/concat"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Exit"
+  op: "Exit"
+  input: "while/Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Exit_1"
+  op: "Exit"
+  input: "while/Switch_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+versions {
+  producer: 21
+}
+  )EOF";
+
+  // Test graph produced in python using:
+  /*
+    with tf.Graph().as_default():
+      i0 = tf.constant(0)
+
+      def inner(j, y):
+        def inner_cond(j, y):
+          return j < 3
+
+        def inner_body(j, y):
+          return j+1, tf.concat([y, y], axis=2)
+
+        return tf.while_loop(inner_cond, inner_body, loop_vars=[j, y],
+                             shape_invariants=[i0.get_shape(),
+                                              tf.TensorShape([None, 1, None])])
+
+      def outer_cond(i, x):
+        return i < 3
+
+      def outer_body(i, x):
+        j, y = inner(0, x)
+        return i+1, tf.concat([x, x], axis=0)
+
+      r = tf.while_loop(outer_cond, outer_body,
+                        loop_vars=[i0, tf.ones([1, 1, 1])],
+                        shape_invariants=[i0.get_shape(),
+                                          tf.TensorShape([None, 1, None])])
+
+      with open('/tmp/graph.pbtxt', 'w') as f:
+        f.write(str(tf.get_default_graph().as_graph_def()))
+  */
+
+  GrapplerItem item;
+  CHECK(protobuf::TextFormat::ParseFromString(gdef_ascii, &item.graph));
+  GraphProperties properties(item);
+  TF_CHECK_OK(properties.InferStatically());
+
+  std::vector<string> outer_nodes{"while/Merge_1", "while/NextIteration_1",
+                                  "while/Exit_1"};
+  std::vector<string> inner_nodes{"while/while/Merge_1",
+                                  "while/while/NextIteration_1",
+                                  "while/while/Exit_1"};
+  for (const string& node : outer_nodes) {
+    const auto props = properties.GetOutputProperties(node);
+    const OpInfo::TensorProperties& prop = props[0];
+    EXPECT_EQ(DT_FLOAT, prop.dtype());
+    EXPECT_EQ("float: [-1,1,1]", PropToString(prop));
+  }
+  for (const string& node : inner_nodes) {
+    const auto props = properties.GetOutputProperties(node);
+    const OpInfo::TensorProperties& prop = props[0];
+    EXPECT_EQ(DT_FLOAT, prop.dtype());
+    EXPECT_EQ("float: [-1,1,-1]", PropToString(prop));
+  }
+}
+
+TEST_F(GraphPropertiesTest, LoopsAndQueues) {
+  // Python code used to generate the graph is below.
+  const string gdef_ascii = R"EOF(
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "fifo_queue"
+  op: "FIFOQueueV2"
+  attr {
+    key: "capacity"
+    value {
+      i: 1
+    }
+  }
+  attr {
+    key: "component_types"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "container"
+    value {
+      s: ""
+    }
+  }
+  attr {
+    key: "shapes"
+    value {
+      list {
+      }
+    }
+  }
+  attr {
+    key: "shared_name"
+    value {
+      s: ""
+    }
+  }
+}
+node {
+  name: "ones"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
+node {
+  name: "while/Enter"
+  op: "Enter"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Enter_1"
+  op: "Enter"
+  input: "ones"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Merge"
+  op: "Merge"
+  input: "while/Enter"
+  input: "while/NextIteration"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Merge_1"
+  op: "Merge"
+  input: "while/Enter_1"
+  input: "while/NextIteration_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Less/y"
+  op: "Const"
+  input: "^while/Merge"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 3
+      }
+    }
+  }
+}
+node {
+  name: "while/Less"
+  op: "Less"
+  input: "while/Merge"
+  input: "while/Less/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/LoopCond"
+  op: "LoopCond"
+  input: "while/Less"
+}
+node {
+  name: "while/Switch"
+  op: "Switch"
+  input: "while/Merge"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge"
+      }
+    }
+  }
+}
+node {
+  name: "while/Switch_1"
+  op: "Switch"
+  input: "while/Merge_1"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge_1"
+      }
+    }
+  }
+}
+node {
+  name: "while/Identity"
+  op: "Identity"
+  input: "while/Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Identity_1"
+  op: "Identity"
+  input: "while/Switch_1:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/fifo_queue_enqueue/Enter"
+  op: "Enter"
+  input: "fifo_queue"
+  attr {
+    key: "T"
+    value {
+      type: DT_RESOURCE
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: true
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/fifo_queue_enqueue"
+  op: "QueueEnqueueV2"
+  input: "while/fifo_queue_enqueue/Enter"
+  input: "while/Identity_1"
+  attr {
+    key: "Tcomponents"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "timeout_ms"
+    value {
+      i: -1
+    }
+  }
+}
+node {
+  name: "while/concat/axis"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 2
+      }
+    }
+  }
+}
+node {
+  name: "while/concat"
+  op: "ConcatV2"
+  input: "while/Identity_1"
+  input: "while/Identity_1"
+  input: "while/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/fifo_queue_Dequeue"
+  op: "QueueDequeueV2"
+  input: "while/fifo_queue_enqueue/Enter"
+  input: "^while/Identity"
+  attr {
+    key: "component_types"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "timeout_ms"
+    value {
+      i: -1
+    }
+  }
+}
+node {
+  name: "while/while/Const"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Enter"
+  op: "Enter"
+  input: "while/while/Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/while/Enter_1"
+  op: "Enter"
+  input: "while/fifo_queue_Dequeue"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/while/Merge"
+  op: "Merge"
+  input: "while/while/Enter"
+  input: "while/while/NextIteration"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/Merge_1"
+  op: "Merge"
+  input: "while/while/Enter_1"
+  input: "while/while/NextIteration_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/while/Less/y"
+  op: "Const"
+  input: "^while/while/Merge"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 3
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Less"
+  op: "Less"
+  input: "while/while/Merge"
+  input: "while/while/Less/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/LoopCond"
+  op: "LoopCond"
+  input: "while/while/Less"
+}
+node {
+  name: "while/while/Switch"
+  op: "Switch"
+  input: "while/while/Merge"
+  input: "while/while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/while/Merge"
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Switch_1"
+  op: "Switch"
+  input: "while/while/Merge_1"
+  input: "while/while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/while/Merge_1"
+      }
+    }
+  }
+}
+node {
+  name: "while/while/Identity"
+  op: "Identity"
+  input: "while/while/Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/Identity_1"
+  op: "Identity"
+  input: "while/while/Switch_1:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/while/add/y"
+  op: "Const"
+  input: "^while/while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "while/while/add"
+  op: "Add"
+  input: "while/while/Identity"
+  input: "while/while/add/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/concat/axis"
+  op: "Const"
+  input: "^while/while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "while/while/concat"
+  op: "ConcatV2"
+  input: "while/while/Identity_1"
+  input: "while/while/Identity_1"
+  input: "while/while/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/NextIteration"
+  op: "NextIteration"
+  input: "while/while/add"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/NextIteration_1"
+  op: "NextIteration"
+  input: "while/while/concat"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/while/Exit"
+  op: "Exit"
+  input: "while/while/Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/while/Exit_1"
+  op: "Exit"
+  input: "while/while/Switch_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/add/y"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "while/add"
+  op: "Add"
+  input: "while/Identity"
+  input: "while/add/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration"
+  op: "NextIteration"
+  input: "while/add"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration_1"
+  op: "NextIteration"
+  input: "while/concat"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Exit"
+  op: "Exit"
+  input: "while/Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Exit_1"
+  op: "Exit"
+  input: "while/Switch_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+versions {
+  producer: 21
+}
+  )EOF";
+
+  // Test graph produced in python using:
+  /*
+    with tf.Graph().as_default():
+      i0 = tf.constant(0)
+      q = tf.FIFOQueue(1, "float")
+
+      def inner(j, y):
+        def inner_cond(j, y):
+          return j < 3
+
+        def inner_body(j, y):
+          return j+1, tf.concat([y, y], axis=0)
+
+        return tf.while_loop(inner_cond, inner_body,
+                             loop_vars=[j, y],
+                             shape_invariants=[i0.get_shape(),
+                                               tf.TensorShape(None)])
+
+      def outer_cond(i, x):
+        return i < 3
+
+      def outer_body(i, x):
+        q.enqueue(x)
+        y = tf.concat([x, x], axis=2)
+        inner(0, q.dequeue())
+        return i+1, y
+
+      i, z = tf.while_loop(outer_cond, outer_body,
+                           loop_vars=[i0, tf.ones([1, 1, 1])],
+                           shape_invariants=[i0.get_shape(),
+                                             tf.TensorShape([None, 1, None])])
+
+      with open('/tmp/graph.pbtxt', 'w') as f:
+        f.write(str(tf.get_default_graph().as_graph_def()))
+   */
+
+  GrapplerItem item;
+  CHECK(protobuf::TextFormat::ParseFromString(gdef_ascii, &item.graph));
+  GraphProperties properties(item);
+  TF_CHECK_OK(properties.InferStatically());
+
+  std::vector<string> outer_nodes{"while/Merge_1", "while/NextIteration_1",
+                                  "while/Exit_1"};
+  std::vector<string> inner_nodes{"while/while/Merge_1",
+                                  "while/while/NextIteration_1",
+                                  "while/while/Exit_1"};
+  for (const string& node : outer_nodes) {
+    const auto props = properties.GetOutputProperties(node);
+    const OpInfo::TensorProperties& prop = props[0];
+    EXPECT_EQ(DT_FLOAT, prop.dtype());
+    EXPECT_EQ("float: [1,1,-1]", PropToString(prop));
+  }
+  for (const string& node : inner_nodes) {
+    const auto props = properties.GetOutputProperties(node);
+    const OpInfo::TensorProperties& prop = props[0];
+    EXPECT_EQ(DT_FLOAT, prop.dtype());
+    EXPECT_EQ("float: [-1,1,-1]", PropToString(prop));
+  }
+}
+
+TEST_F(GraphPropertiesTest, QueuesAndLoops) {
+  // Python code used to generate the graph is below.
+  const string gdef_ascii = R"EOF(
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "fifo_queue"
+  op: "FIFOQueueV2"
+  attr {
+    key: "capacity"
+    value {
+      i: 1
+    }
+  }
+  attr {
+    key: "component_types"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "container"
+    value {
+      s: ""
+    }
+  }
+  attr {
+    key: "shapes"
+    value {
+      list {
+      }
+    }
+  }
+  attr {
+    key: "shared_name"
+    value {
+      s: ""
+    }
+  }
+}
+node {
+  name: "ones"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 2
+          }
+          dim {
+            size: 2
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
+node {
+  name: "fifo_queue_enqueue"
+  op: "QueueEnqueueV2"
+  input: "fifo_queue"
+  input: "ones"
+  attr {
+    key: "Tcomponents"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "timeout_ms"
+    value {
+      i: -1
+    }
+  }
+}
+node {
+  name: "fifo_queue_1"
+  op: "FIFOQueueV2"
+  attr {
+    key: "capacity"
+    value {
+      i: 1
+    }
+  }
+  attr {
+    key: "component_types"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "container"
+    value {
+      s: ""
+    }
+  }
+  attr {
+    key: "shapes"
+    value {
+      list {
+      }
+    }
+  }
+  attr {
+    key: "shared_name"
+    value {
+      s: ""
+    }
+  }
+}
+node {
+  name: "fifo_queue_Dequeue"
+  op: "QueueDequeueV2"
+  input: "fifo_queue"
+  attr {
+    key: "component_types"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "timeout_ms"
+    value {
+      i: -1
+    }
+  }
+}
+node {
+  name: "while/Enter"
+  op: "Enter"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Enter_1"
+  op: "Enter"
+  input: "fifo_queue_Dequeue"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Merge"
+  op: "Merge"
+  input: "while/Enter"
+  input: "while/NextIteration"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Merge_1"
+  op: "Merge"
+  input: "while/Enter_1"
+  input: "while/NextIteration_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Less/y"
+  op: "Const"
+  input: "^while/Merge"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 10
+      }
+    }
+  }
+}
+node {
+  name: "while/Less"
+  op: "Less"
+  input: "while/Merge"
+  input: "while/Less/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/LoopCond"
+  op: "LoopCond"
+  input: "while/Less"
+}
+node {
+  name: "while/Switch"
+  op: "Switch"
+  input: "while/Merge"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge"
+      }
+    }
+  }
+}
+node {
+  name: "while/Switch_1"
+  op: "Switch"
+  input: "while/Merge_1"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge_1"
+      }
+    }
+  }
+}
+node {
+  name: "while/Identity"
+  op: "Identity"
+  input: "while/Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Identity_1"
+  op: "Identity"
+  input: "while/Switch_1:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/add/y"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "while/add"
+  op: "Add"
+  input: "while/Identity"
+  input: "while/add/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/concat/axis"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "while/concat"
+  op: "ConcatV2"
+  input: "while/Identity_1"
+  input: "while/Identity_1"
+  input: "while/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration"
+  op: "NextIteration"
+  input: "while/add"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration_1"
+  op: "NextIteration"
+  input: "while/concat"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Exit"
+  op: "Exit"
+  input: "while/Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Exit_1"
+  op: "Exit"
+  input: "while/Switch_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "fifo_queue_1_enqueue"
+  op: "QueueEnqueueV2"
+  input: "fifo_queue_1"
+  input: "while/Exit_1"
+  attr {
+    key: "Tcomponents"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "timeout_ms"
+    value {
+      i: -1
+    }
+  }
+}
+node {
+  name: "fifo_queue_1_Dequeue"
+  op: "QueueDequeueV2"
+  input: "fifo_queue_1"
+  attr {
+    key: "component_types"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "timeout_ms"
+    value {
+      i: -1
+    }
+  }
+}
+node {
+  name: "concat/axis"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "concat"
+  op: "ConcatV2"
+  input: "fifo_queue_1_Dequeue"
+  input: "fifo_queue_1_Dequeue"
+  input: "concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+versions {
+  producer: 21
+}
+  )EOF";
+
+  // Test graph produced in python using:
+  /*
+    with tf.Graph().as_default():
+      i0 = tf.constant(0)
+      q0 = tf.FIFOQueue(1, "float")
+      q0.enqueue(tf.ones([2, 2]))
+      q1 = tf.FIFOQueue(1, "float")
+
+      def c(i, m):
+        return i < 10
+
+      def b(i, m):
+        return i+1, tf.concat([m, m], axis=0)
+
+      i, m = tf.while_loop(
+          c, b, loop_vars=[i0,  q0.dequeue()],
+          shape_invariants=[i0.get_shape(), tf.TensorShape(None)])
+
+      q1.enqueue(m)
+      v = q1.dequeue();
+      tf.concat([v, v], axis=1)
+      with open('/tmp/graph.pbtxt', 'w') as f:
+        f.write(str(tf.get_default_graph().as_graph_def()))
+  */
+
+  GrapplerItem item;
+  CHECK(protobuf::TextFormat::ParseFromString(gdef_ascii, &item.graph));
+  GraphProperties properties(item);
+  TF_CHECK_OK(properties.InferStatically());
+
+  std::vector<string> nodes{"while/Merge_1", "while/NextIteration_1",
+                            "while/Exit_1"};
+
+  for (const string& node : nodes) {
+    const auto props = properties.GetOutputProperties(node);
+    const OpInfo::TensorProperties& prop = props[0];
+    EXPECT_EQ(DT_FLOAT, prop.dtype());
+    EXPECT_EQ("float: [-1,2]", PropToString(prop));
+  }
+
+  const auto props = properties.GetOutputProperties("concat");
+  const OpInfo::TensorProperties& prop = props[0];
+  EXPECT_EQ(DT_FLOAT, prop.dtype());
+  EXPECT_EQ("float: [-1,4]", PropToString(prop));
 }
 
 }  // namespace
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.cc b/tensorflow/core/kernels/hexagon/graph_transferer.cc
index 055108cd00..a82ae61ad9 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transferer.cc
@@ -349,7 +349,8 @@ Status GraphTransferer::TransformGraphToAddAggregatedInputNode(
   CHECK_NOTNULL(input_node);
 
   bool refined;
-  TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(input_node, &refined));
+  TF_RETURN_IF_ERROR(
+      shape_refiner->UpdateNode(input_node, false /* relax */, &refined));
 
   shape_inference::InferenceContext* context =
       shape_refiner->GetContext(input_node);
@@ -393,7 +394,8 @@ Status GraphTransferer::TransformGraphToAddAggregatedInputNode(
     }
     original_input_nodes[i] = original_input_node;
 
-    TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(created_node, &refined));
+    TF_RETURN_IF_ERROR(
+        shape_refiner->UpdateNode(created_node, false /* relax */, &refined));
 
     shape_inference::InferenceContext* context =
         shape_refiner->GetContext(created_node);
-- 
GitLab


From 7f7af5c096be64900f985afe5d8049aae3ee0b9f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 09:27:35 -0700
Subject: [PATCH 0238/1375] Symlink files for cuda configuration one level
 below the BUILD file (i.e., one level under local_config_cuda/cuda).

PiperOrigin-RevId: 159570549
---
 third_party/gpus/cuda/BUILD.tpl     | 82 +++++++++++++++++++----------
 third_party/gpus/cuda_configure.bzl | 17 +++---
 2 files changed, 64 insertions(+), 35 deletions(-)

diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index f7610dd7a9..2cca438d6d 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -40,20 +40,23 @@ config_setting(
 cc_library(
     name = "cuda_headers",
     hdrs = [
-        "cuda_config.h",
+        "cuda/cuda_config.h",
         %{cuda_headers}
     ],
     includes = [
         ".",
-        "include",
+        "cuda/include",
     ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cudart_static",
-    srcs = ["lib/%{cudart_static_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cudart_static_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkopts = select({
         ":freebsd": [],
         "//conditions:default": ["-ldl"],
@@ -66,34 +69,46 @@ cc_library(
 
 cc_library(
     name = "cuda_driver",
-    srcs = ["lib/%{cuda_driver_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cuda_driver_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cudart",
-    srcs = ["lib/%{cudart_lib}"],
-    data = ["lib/%{cudart_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cudart_lib}"],
+    data = ["cuda/lib/%{cudart_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cublas",
-    srcs = ["lib/%{cublas_lib}"],
-    data = ["lib/%{cublas_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cublas_lib}"],
+    data = ["cuda/lib/%{cublas_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cusolver",
-    srcs = ["lib/%{cusolver_lib}"],
-    data = ["lib/%{cusolver_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cusolver_lib}"],
+    data = ["cuda/lib/%{cusolver_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     linkopts = ["-lgomp"],
     visibility = ["//visibility:public"],
@@ -101,27 +116,36 @@ cc_library(
 
 cc_library(
     name = "cudnn",
-    srcs = ["lib/%{cudnn_lib}"],
-    data = ["lib/%{cudnn_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cudnn_lib}"],
+    data = ["cuda/lib/%{cudnn_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cufft",
-    srcs = ["lib/%{cufft_lib}"],
-    data = ["lib/%{cufft_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cufft_lib}"],
+    data = ["cuda/lib/%{cufft_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "curand",
-    srcs = ["lib/%{curand_lib}"],
-    data = ["lib/%{curand_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{curand_lib}"],
+    data = ["cuda/lib/%{curand_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
@@ -142,19 +166,23 @@ cc_library(
 cc_library(
     name = "cupti_headers",
     hdrs = [
-        "cuda_config.h",
+        "cuda/cuda_config.h",
         ":cuda-extras",
     ],
     includes = [
         ".",
-        "extras/CUPTI/include/",
+        "cuda/extras/CUPTI/include/",
     ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cupti_dsos",
-    data = ["lib/%{cupti_lib}"],
+    data = ["cuda/lib/%{cupti_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     visibility = ["//visibility:public"],
 )
 
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 83a377dde5..b2d59c446b 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -764,7 +764,7 @@ def _create_dummy_repository(repository_ctx):
                "CudaVersion(\"%s\")" % c
                for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES]),
            "%{cuda_toolkit_path}": _DEFAULT_CUDA_TOOLKIT_PATH,
-       })
+       }, "cuda/cuda/cuda_config.h")
 
   # If cuda_configure is not configured to build with GPU support, and the user
   # attempts to build with --config=cuda, add a dummy build rule to intercept
@@ -906,19 +906,19 @@ def _create_local_cuda_repository(repository_ctx):
   cuda_toolkit_path = cuda_config.cuda_toolkit_path
   cuda_include_path = cuda_toolkit_path + "/include"
   genrules = [_symlink_genrule_for_dir(repository_ctx,
-      cuda_include_path, "include", "cuda-include")]
+      cuda_include_path, "cuda/include", "cuda-include")]
   genrules.append(_symlink_genrule_for_dir(repository_ctx,
-      cuda_toolkit_path + "/nvvm", "nvvm", "cuda-nvvm"))
+      cuda_toolkit_path + "/nvvm", "cuda/nvvm", "cuda-nvvm"))
   genrules.append(_symlink_genrule_for_dir(repository_ctx,
       cuda_toolkit_path + "/extras/CUPTI/include",
-      "extras/CUPTI/include", "cuda-extras"))
+      "cuda/extras/CUPTI/include", "cuda-extras"))
 
   cuda_libs = _find_libs(repository_ctx, cuda_config)
   cuda_lib_src = []
   cuda_lib_dest = []
   for lib in cuda_libs.values():
     cuda_lib_src.append(lib.path)
-    cuda_lib_dest.append("lib/" + lib.file_name)
+    cuda_lib_dest.append("cuda/lib/" + lib.file_name)
   genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "", "cuda-lib",
                                        cuda_lib_src, cuda_lib_dest))
 
@@ -927,8 +927,9 @@ def _create_local_cuda_repository(repository_ctx):
   included_files = _read_dir(repository_ctx, cuda_include_path).replace(
       cuda_include_path, '').splitlines()
   if '/cudnn.h' not in included_files:
-    genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "include/",
-        "cudnn-include", [cudnn_header_dir + "/cudnn.h"], ["cudnn.h"]))
+    genrules.append(_symlink_genrule_for_dir(repository_ctx, None,
+        "cuda/include/", "cudnn-include", [cudnn_header_dir + "/cudnn.h"],
+        ["cudnn.h"]))
   else:
     genrules.append(
             'filegroup(\n' +
@@ -1000,7 +1001,7 @@ def _create_local_cuda_repository(repository_ctx):
                ["CudaVersion(\"%s\")" % c
                 for c in cuda_config.compute_capabilities]),
                "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path,
-       })
+       }, "cuda/cuda/cuda_config.h")
 
 def _create_remote_cuda_repository(repository_ctx, remote_config_repo):
   """Creates pointers to a remotely configured repo set up to build with CUDA."""
-- 
GitLab


From c16de2979a9522e42a4b65cbdc20a05c731ec555 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 09:34:08 -0700
Subject: [PATCH 0239/1375] Make ScheduledOutputTrainingHelper.sample() returns
 dtypes.int32 sample_ids.

PiperOrigin-RevId: 159571291
---
 .../seq2seq/python/kernel_tests/basic_decoder_test.py      | 7 +++++++
 tensorflow/contrib/seq2seq/python/ops/helper.py            | 5 ++---
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
index cb12bc9450..c99562555a 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/basic_decoder_test.py
@@ -111,6 +111,8 @@ class BasicDecoderTest(test.TestCase):
                           sess_results["first_finished"])
       self.assertAllEqual([False, False, False, True, True],
                           sess_results["step_finished"])
+      self.assertEqual(output_dtype.sample_id,
+                       sess_results["step_outputs"].sample_id.dtype)
       self.assertAllEqual(
           np.argmax(sess_results["step_outputs"].rnn_output, -1),
           sess_results["step_outputs"].sample_id)
@@ -186,6 +188,8 @@ class BasicDecoderTest(test.TestCase):
       self.assertAllEqual([False, False, False, False, False],
                           sess_results["first_finished"])
       self.assertAllEqual(expected_step_finished, sess_results["step_finished"])
+      self.assertEqual(output_dtype.sample_id,
+                       sess_results["step_outputs"].sample_id.dtype)
       self.assertAllEqual(expected_sample_ids,
                           sess_results["step_outputs"].sample_id)
       self.assertAllEqual(expected_step_next_inputs,
@@ -254,6 +258,7 @@ class BasicDecoderTest(test.TestCase):
         })
 
         sample_ids = sess_results["step_outputs"].sample_id
+        self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
         expected_step_finished = (sample_ids == end_token)
         expected_step_next_inputs = embeddings[sample_ids]
         self.assertAllEqual(expected_step_finished,
@@ -337,6 +342,7 @@ class BasicDecoderTest(test.TestCase):
       self.assertAllEqual([False, False, False, True, True],
                           sess_results["step_finished"])
       sample_ids = sess_results["step_outputs"].sample_id
+      self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
       batch_where_not_sampling = np.where(sample_ids == -1)
       batch_where_sampling = np.where(sample_ids > -1)
       self.assertAllClose(
@@ -441,6 +447,7 @@ class BasicDecoderTest(test.TestCase):
                           sess_results["step_finished"])
 
       sample_ids = sess_results["step_outputs"].sample_id
+      self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
       batch_where_not_sampling = np.where(np.logical_not(sample_ids))
       batch_where_sampling = np.where(sample_ids)
 
diff --git a/tensorflow/contrib/seq2seq/python/ops/helper.py b/tensorflow/contrib/seq2seq/python/ops/helper.py
index bee7547935..6b8cad7fd7 100644
--- a/tensorflow/contrib/seq2seq/python/ops/helper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/helper.py
@@ -382,9 +382,7 @@ class ScheduledOutputTrainingHelper(TrainingHelper):
     with ops.name_scope(name, "ScheduledOutputTrainingHelperSample",
                         [time, outputs, state]):
       sampler = bernoulli.Bernoulli(probs=self._sampling_probability)
-      return math_ops.cast(
-          sampler.sample(sample_shape=self.batch_size, seed=self._seed),
-          dtypes.bool)
+      return sampler.sample(sample_shape=self.batch_size, seed=self._seed)
 
   def next_inputs(self, time, outputs, state, sample_ids, name=None):
     with ops.name_scope(name, "ScheduledOutputTrainingHelperNextInputs",
@@ -396,6 +394,7 @@ class ScheduledOutputTrainingHelper(TrainingHelper):
               state=state,
               sample_ids=sample_ids,
               name=name))
+      sample_ids = math_ops.cast(sample_ids, dtypes.bool)
 
       def maybe_sample():
         """Perform scheduled sampling."""
-- 
GitLab


From 7d7e5efb1cdbdd273ee0d347c40861c817777879 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 09:40:50 -0700
Subject: [PATCH 0240/1375] Add some missing llvm headers due to an upstream
 IWYU change r305760

PiperOrigin-RevId: 159572125
---
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index 7c74912a7a..04d4d8f075 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -21,6 +21,8 @@ limitations under the License.
 #include <list>
 #include <utility>
 
+#include "external/llvm/include/llvm/ExecutionEngine/ExecutionEngine.h"
+#include "external/llvm/include/llvm/ExecutionEngine/SectionMemoryManager.h"
 #include "external/llvm/include/llvm/IR/Mangler.h"
 #include "external/llvm/include/llvm/Support/CodeGen.h"
 #include "external/llvm/include/llvm/Support/Host.h"
-- 
GitLab


From 33b22ec8e4298e3ab39027defde016f745eb12d8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 09:44:17 -0700
Subject: [PATCH 0241/1375] Port Literal conversion code to use xla::Literal.

This change ports PR #10164 to use
the new xla::Literal class instead of xla::LiteralUtil.

There are no changes to the original PR other than the ones needed to make the
new code live in xla::Literal.

PiperOrigin-RevId: 159572549
---
 tensorflow/compiler/xla/literal_util.cc      | 60 ++++++++++++++
 tensorflow/compiler/xla/literal_util.h       |  5 ++
 tensorflow/compiler/xla/literal_util_test.cc | 87 ++++++++++++++++++++
 3 files changed, 152 insertions(+)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 4648680dc5..d9b9066626 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -753,6 +753,66 @@ void Literal::EachCellAsString(
   } while (IndexUtil::BumpIndices(shape(), &indices));
 }
 
+namespace {
+template <PrimitiveType primitive_src_type, PrimitiveType primitive_dest_type>
+std::unique_ptr<Literal> ConvertIfTypesMatch(const Literal& src_literal) {
+  CHECK_EQ(primitive_src_type, src_literal.shape().element_type());
+  return src_literal.Convert<
+      typename primitive_util::PrimitiveTypeToNative<primitive_src_type>::type,
+      typename primitive_util::PrimitiveTypeToNative<
+          primitive_dest_type>::type>();
+}
+
+template <PrimitiveType primitive_src_type>
+StatusOr<std::unique_ptr<Literal>> ConvertIfDestTypeMatches(
+    const Literal& src_literal, PrimitiveType primitive_dest_type) {
+  switch (primitive_dest_type) {
+#define CONVERT_IF_TYPES_MATCH(type) \
+  case (type):                       \
+    return ConvertIfTypesMatch<primitive_src_type, (type)>(src_literal);
+    CONVERT_IF_TYPES_MATCH(PRED)
+    CONVERT_IF_TYPES_MATCH(S8)
+    CONVERT_IF_TYPES_MATCH(S32)
+    CONVERT_IF_TYPES_MATCH(S64)
+    CONVERT_IF_TYPES_MATCH(U8)
+    CONVERT_IF_TYPES_MATCH(U32)
+    CONVERT_IF_TYPES_MATCH(U64)
+    CONVERT_IF_TYPES_MATCH(F32)
+    CONVERT_IF_TYPES_MATCH(F64)
+#undef CONVERT_IF_TYPES_MATCH
+    // Other types are not yet supported.
+    default:
+      return tensorflow::errors::InvalidArgument(
+          "Unimplemented: ConvertIfDestTypeMatches for type " +
+          PrimitiveType_Name(src_literal.shape().element_type()));
+  }
+}
+}  // namespace
+
+StatusOr<std::unique_ptr<Literal>> Literal::ConvertIfSrcTypeMatches(
+    PrimitiveType primitive_dest_type) const {
+  switch (shape().element_type()) {
+#define CONVERT_IF_DEST_TYPE_MATCHES(type) \
+  case (type):                             \
+    return ConvertIfDestTypeMatches<(type)>(*this, primitive_dest_type);
+    CONVERT_IF_DEST_TYPE_MATCHES(PRED)
+    CONVERT_IF_DEST_TYPE_MATCHES(S8)
+    CONVERT_IF_DEST_TYPE_MATCHES(S32)
+    CONVERT_IF_DEST_TYPE_MATCHES(S64)
+    CONVERT_IF_DEST_TYPE_MATCHES(U8)
+    CONVERT_IF_DEST_TYPE_MATCHES(U32)
+    CONVERT_IF_DEST_TYPE_MATCHES(U64)
+    CONVERT_IF_DEST_TYPE_MATCHES(F32)
+    CONVERT_IF_DEST_TYPE_MATCHES(F64)
+#undef CONVERT_IF_DEST_TYPE_MATCHES
+    // Other types are not yet supported.
+    default:
+      return tensorflow::errors::InvalidArgument(
+          "Unimplemented: ConvertIfSrcTypeMatches for type " +
+          PrimitiveType_Name(shape().element_type()));
+  }
+}
+
 namespace {
 
 // Helper function which compares whether the elements of literal1 are equal to
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index caee6cc0a0..20ff32d70b 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -367,6 +367,11 @@ class Literal {
   template <typename NativeSrcT, typename NativeDestT>
   std::unique_ptr<Literal> Convert() const;
 
+  // Converts this literal to another primitive type, but only if the literal
+  // type is convertible into the destination type.
+  StatusOr<std::unique_ptr<Literal>> ConvertIfSrcTypeMatches(
+      PrimitiveType primitive_dest_type) const;
+
   // Creates a literal value zero of the given primitive type.
   static Literal Zero(PrimitiveType primitive_type);
 
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index d52e6eb5e2..3c926f0940 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -830,6 +830,93 @@ TEST_F(LiteralUtilTest, ConvertR4) {
   EXPECT_TRUE(expected->Equal(*converted));
 }
 
+TEST_F(LiteralUtilTest, ConvertIfTypesMatch) {
+  // clang-format off
+  auto s8 = Literal::CreateR4WithLayout<int8>({{
+    {{10, 0, 12, 0}, {0, 15, 0, 17}},
+    {{0, 19, 0, 21}, {22, 0, 24, 0}},
+    {{26, 0, 28, 0}, {0, 31, 0, 33}},
+  }}, layout_r4_dim0major_);
+  auto s32 = Literal::CreateR4WithLayout<int32>({{
+    {{10, 0, 12, 0}, {0, 15, 0, 17}},
+    {{0, 19, 0, 21}, {22, 0, 24, 0}},
+    {{26, 0, 28, 0}, {0, 31, 0, 33}},
+  }}, layout_r4_dim0major_);
+  auto u32 = Literal::CreateR4WithLayout<uint32>({{
+    {{10, 0, 12, 0}, {0, 15, 0, 17}},
+    {{0, 19, 0, 21}, {22, 0, 24, 0}},
+    {{26, 0, 28, 0}, {0, 31, 0, 33}},
+  }}, layout_r4_dim0major_);
+  auto s64 = Literal::CreateR4WithLayout<int64>({{
+    {{10, 0, 12, 0}, {0, 15, 0, 17}},
+    {{0, 19, 0, 21}, {22, 0, 24, 0}},
+    {{26, 0, 28, 0}, {0, 31, 0, 33}},
+  }}, layout_r4_dim0major_);
+  auto u64 = Literal::CreateR4WithLayout<uint64>({{
+    {{10, 0, 12, 0}, {0, 15, 0, 17}},
+    {{0, 19, 0, 21}, {22, 0, 24, 0}},
+    {{26, 0, 28, 0}, {0, 31, 0, 33}},
+  }}, layout_r4_dim0major_);
+  auto pred = Literal::CreateR4WithLayout<bool>({{
+    {{true, false, true, false}, {false, true, false, true}},
+    {{false, true, false, true}, {true, false, true, false}},
+    {{true, false, true, false}, {false, true, false, true}},
+  }}, layout_r4_dim0major_);
+  auto int32_pred = Literal::CreateR4WithLayout<int32>({{
+    {{1, 0, 1, 0}, {0, 1, 0, 1}},
+    {{0, 1, 0, 1}, {1, 0, 1, 0}},
+    {{1, 0, 1, 0}, {0, 1, 0, 1}},
+  }}, layout_r4_dim0major_);
+  auto f32 = Literal::CreateR4WithLayout<float>({{
+    {{10.0f, 0.0f, 12.0f, 0.0f}, {0.0f, 15.0f, 0.0f, 17.0f}},
+    {{0.0f, 19.0f, 0.0f, 21.0f}, {22.0f, 0.0f, 24.0f, 0.0f}},
+    {{26.0f, 0.0f, 28.0f, 0.0f}, {0.0f, 31.0f, 0.0f, 33.0f}},
+  }}, layout_r4_dim0major_);
+  auto f64 = Literal::CreateR4WithLayout<double>({{
+    {{10.0, 0.0, 12.0, 0.0}, {0.0, 15.0, 0.0, 17.0}},
+    {{0.0, 19.0, 0.0, 21.0}, {22.0, 0.0, 24.0, 0.0}},
+    {{26.0, 0.0, 28.0, 0.0}, {0.0, 31.0, 0.0, 33.0}},
+  }}, layout_r4_dim0major_);
+  // clang-format on
+  std::unique_ptr<Literal> conv;
+
+  conv = s8->ConvertIfSrcTypeMatches(U32).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*u32));
+
+  conv = s8->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*s32));
+
+  conv = s8->ConvertIfSrcTypeMatches(U64).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*u64));
+
+  conv = s8->ConvertIfSrcTypeMatches(S64).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*s64));
+
+  conv = s8->ConvertIfSrcTypeMatches(PRED).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*pred));
+
+  conv = pred->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*int32_pred));
+
+  conv = f32->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*s32));
+
+  conv = f64->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*s32));
+
+  conv = s32->ConvertIfSrcTypeMatches(F32).ConsumeValueOrDie();
+  EXPECT_TRUE(conv->Equal(*f32));
+
+  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(TUPLE).status().code(),
+            tensorflow::error::INVALID_ARGUMENT);
+  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(F16).status().code(),
+            tensorflow::error::INVALID_ARGUMENT);
+  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(S16).status().code(),
+            tensorflow::error::INVALID_ARGUMENT);
+  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(U16).status().code(),
+            tensorflow::error::INVALID_ARGUMENT);
+}
+
 TEST_F(LiteralUtilTest, CopyFromProto_Bool) {
   LiteralProto p;
   p.mutable_shape()->set_element_type(PRED);
-- 
GitLab


From e35dc9b5ce3e47d148ae7b13804cbedc2b9115da Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 09:46:07 -0700
Subject: [PATCH 0242/1375] Fix off-by-one error in oov bucket docstring

PiperOrigin-RevId: 159572763
---
 tensorflow/contrib/lookup/lookup_ops.py | 2 +-
 tensorflow/python/ops/lookup_ops.py     | 9 +++++----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index d5d413c56a..96d2962145 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -86,7 +86,7 @@ def index_table_from_tensor(mapping,
   Any lookup of an out-of-vocabulary token will return a bucket ID based on its
   hash if `num_oov_buckets` is greater than zero. Otherwise it is assigned the
   `default_value`.
-  The bucket ID range is `[mapping size, mapping size + num_oov_buckets]`.
+  The bucket ID range is `[mapping size, mapping size + num_oov_buckets - 1]`.
 
   The underlying table must be initialized by calling
   `tf.tables_initializer.run()` or `table.init.run()` once.
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index 43af95e278..aac7aef8ce 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -691,8 +691,8 @@ class IdTableWithHashBuckets(LookupInterface):
   - emerson -> 0
   - lake -> 1
   - palmer -> 2
-  - <other term> -> bucket id between 3 and 3 + num_oov_buckets, calculated by:
-    hash(<term>) % num_oov_buckets + vocab_size
+  - <other term> -> bucket id between 3 and 3 + num_oov_buckets - 1, calculated
+    by: hash(<term>) % num_oov_buckets + vocab_size
 
   If input_tensor is ["emerson", "lake", "palmer", "king", "crimson"],
   the lookup result is [0, 1, 2, 4, 7]
@@ -870,7 +870,8 @@ def index_table_from_file(vocabulary_file=None,
   Any lookup of an out-of-vocabulary token will return a bucket ID based on its
   hash if `num_oov_buckets` is greater than zero. Otherwise it is assigned the
   `default_value`.
-  The bucket ID range is `[vocabulary size, vocabulary size + num_oov_buckets]`.
+  The bucket ID range is
+  `[vocabulary size, vocabulary size + num_oov_buckets - 1]`.
 
   The underlying table must be initialized by calling
   `tf.tables_initializer.run()` or `table.init.run()` once.
@@ -977,7 +978,7 @@ def index_table_from_tensor(vocabulary_list,
   Any lookup of an out-of-vocabulary token will return a bucket ID based on its
   hash if `num_oov_buckets` is greater than zero. Otherwise it is assigned the
   `default_value`.
-  The bucket ID range is `[mapping size, mapping size + num_oov_buckets]`.
+  The bucket ID range is `[mapping size, mapping size + num_oov_buckets - 1]`.
 
   The underlying table must be initialized by calling
   `tf.tables_initializer.run()` or `table.init.run()` once.
-- 
GitLab


From 46cac5fd635c07ac174ff3c10885698d218a3cb7 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Tue, 20 Jun 2017 09:48:02 -0700
Subject: [PATCH 0243/1375] [XLA] Remove xla_emit_alias_scope flag

This flag doesn't appear to be used anywhere (non-default value) and is
propagated fairly deep; if reintroduced it should go through extra backend
options.

PiperOrigin-RevId: 159573033
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 11 ----
 .../xla/legacy_flags/alias_analysis_flags.cc  | 62 -------------------
 .../xla/legacy_flags/alias_analysis_flags.h   | 46 --------------
 tensorflow/compiler/xla/service/llvm_ir/BUILD |  1 -
 .../xla/service/llvm_ir/alias_analysis.cc     | 13 ----
 7 files changed, 136 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index f8b18bcaf1..5c6bd0cdaa 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -126,7 +126,6 @@ cc_library(
     deps = [
         ":tfcompile_lib",
         ":tfcompile_proto",
-        "//tensorflow/compiler/xla/legacy_flags:alias_analysis_flags",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
         "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 3fd00398d2..fea2207c4c 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/flags.h"
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
@@ -135,7 +134,6 @@ int main(int argc, char** argv) {
 
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
-  xla::legacy_flags::AppendAliasAnalysisFlags(&flag_list);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
   xla::legacy_flags::AppendCompilerFunctorFlags(&flag_list);
   xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index abaccb81e5..33f7530ce5 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -137,17 +137,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "alias_analysis_flags",
-    srcs = ["alias_analysis_flags.cc"],
-    hdrs = ["alias_analysis_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "llvm_util_flags",
     srcs = ["llvm_util_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.cc b/tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.cc
deleted file mode 100644
index 474753c10a..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's alias_analysis module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static AliasAnalysisFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new AliasAnalysisFlags;
-  flags->xla_emit_alias_scope = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_emit_alias_scope", &flags->xla_emit_alias_scope,
-                       "Use buffer analysis to refine alias-analysis."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's alias_analysis
-// module.
-void AppendAliasAnalysisFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the AliasAnalysisFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-AliasAnalysisFlags* GetAliasAnalysisFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h b/tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h
deleted file mode 100644
index 369f8cd7ca..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_ALIAS_ANALYSIS_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_ALIAS_ANALYSIS_FLAGS_H_
-
-// Legacy flags for XLA's alias_analysis module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's alias_analysis
-// module.
-void AppendAliasAnalysisFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's alias_analysis module.
-typedef struct {
-  bool xla_emit_alias_scope;  // Use buffer analysis to refine alias-analysis.
-} AliasAnalysisFlags;
-
-// Return a pointer to the AliasAnalysisFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-AliasAnalysisFlags* GetAliasAnalysisFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_ALIAS_ANALYSIS_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD
index 12b2762f0e..f02e4ea1e7 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/BUILD
+++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD
@@ -29,7 +29,6 @@ cc_library(
         ":ir_array",
         ":llvm_util",
         "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla/legacy_flags:alias_analysis_flags",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:logical_buffer",
diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
index 02710ff57f..d451255774 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include <unordered_set>
 
 #include "external/llvm/include/llvm/IR/MDBuilder.h"
-#include "tensorflow/compiler/xla/legacy_flags/alias_analysis_flags.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
 #include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -87,12 +86,6 @@ llvm::MDNode* AliasAnalysis::GetAliasDomain() {
 
 llvm::MDNode* AliasAnalysis::GetAliasScopeMetadataForBuffer(
     const BufferAllocation::Slice& buffer_slice, llvm::MDNode* domain) {
-  legacy_flags::AliasAnalysisFlags* flags =
-      legacy_flags::GetAliasAnalysisFlags();
-  if (!flags->xla_emit_alias_scope) {
-    return nullptr;
-  }
-
   // While we could synthesize an alias.scope, doing so is not more profitable
   // than LLVM's default behavior.
   if (buffer_slice.allocation() == kParameterAllocation) {
@@ -109,12 +102,6 @@ llvm::MDNode* AliasAnalysis::GetAliasScopeMetadataForBuffer(
 llvm::MDNode* AliasAnalysis::GetNoaliasMetadataForBuffer(
     const BufferAllocation::Slice& buffer_slice, llvm::MDNode* domain,
     const BufferAssignment& assignment, const HloInstruction& hlo) {
-  legacy_flags::AliasAnalysisFlags* flags =
-      legacy_flags::GetAliasAnalysisFlags();
-  if (!flags->xla_emit_alias_scope) {
-    return nullptr;
-  }
-
   // We want to construct a list of buffers which:
   //
   // 1. Do not alias the given buffer.
-- 
GitLab


From 290cccd9e21ec034489ac063f4d52ec1a28bee48 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 10:02:01 -0700
Subject: [PATCH 0244/1375] Add a utility function to ShapeUtil to return a
 Shape without padding.

This functionality is currently used in LayoutAssignment when processing the
infeed shape. It may be useful in other cases, too.

PiperOrigin-RevId: 159574759
---
 tensorflow/compiler/xla/service/layout_assignment.cc | 12 +++---------
 tensorflow/compiler/xla/shape_util.cc                | 11 +++++++++++
 tensorflow/compiler/xla/shape_util.h                 |  3 +++
 3 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index 377f2432db..238a327495 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -382,15 +382,9 @@ Status LayoutAssignment::AddMandatoryConstraints(
       // instruction.
       // TODO(b/31425034): Change infeeds to be more like parameters, with
       // shapes in the ComputationLayout.
-      Shape infeed_shape = instruction->shape();
-      // TODO(b/62477016): When the infeed does not set padding anymore, this
-      // can be removed.
-      ShapeUtil::ForEachMutableSubshape(
-          &infeed_shape, [](Shape* subshape, const ShapeIndex& index) {
-            auto layout = subshape->mutable_layout();
-            layout->clear_padding_value();
-            layout->clear_padded_dimensions();
-          });
+      // TODO(b/62477016): When the infeed does not set padding anymore, the
+      // call to ShapeWithoutPadding can be removed.
+      Shape infeed_shape = ShapeUtil::ShapeWithoutPadding(instruction->shape());
       TF_RETURN_IF_ERROR(
           constraints->SetInstructionLayout(infeed_shape, instruction.get()));
     } else if (instruction->opcode() == HloOpcode::kOutfeed) {
diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index 646b76799b..865621df62 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -170,6 +170,17 @@ bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) {
   }
   return MakeShapeWithMonotonicDim0MajorLayout(shape.element_type(), dims);
 }
+
+/* static */ Shape ShapeUtil::ShapeWithoutPadding(const Shape& shape) {
+  Shape result = shape;
+  ForEachMutableSubshape(&result, [](Shape* subshape, const ShapeIndex& index) {
+    auto layout = subshape->mutable_layout();
+    layout->clear_padding_value();
+    layout->clear_padded_dimensions();
+  });
+  return result;
+}
+
 /* static */ void ShapeUtil::PopulateShape(
     PrimitiveType element_type, tensorflow::gtl::ArraySlice<int64> dimensions,
     Shape* shape) {
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 70b843d45b..458823707b 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -221,6 +221,9 @@ class ShapeUtil {
   // elements with a different shape.
   static Shape NormalizeShapeToMonotonicDim0MajorLayout(const Shape& shape);
 
+  // Returns a new shape that has all padding values cleared.
+  static Shape ShapeWithoutPadding(const Shape& shape);
+
   // As MakeShape, but the object to write to is passed in.
   static void PopulateShape(PrimitiveType element_type,
                             tensorflow::gtl::ArraySlice<int64> dimensions,
-- 
GitLab


From c7c5e32fb79fff83814f15f347c7e85d36c221e0 Mon Sep 17 00:00:00 2001
From: Tim Harley <tharley@google.com>
Date: Tue, 20 Jun 2017 10:09:18 -0700
Subject: [PATCH 0245/1375] Export load_tensor_from_event.

PiperOrigin-RevId: 159575817
---
 tensorflow/python/debug/__init__.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/python/debug/__init__.py b/tensorflow/python/debug/__init__.py
index 750d21f80d..147e42d878 100644
--- a/tensorflow/python/debug/__init__.py
+++ b/tensorflow/python/debug/__init__.py
@@ -21,6 +21,7 @@ See the @{$python/tfdbg} guide.
 @@watch_graph_with_blacklists
 @@DebugTensorDatum
 @@DebugDumpDir
+@@load_tensor_from_event
 @@load_tensor_from_event_file
 @@has_inf_or_nan
 @@DumpingDebugHook
@@ -40,6 +41,7 @@ from __future__ import print_function
 from tensorflow.python.debug.lib.debug_data import DebugDumpDir
 from tensorflow.python.debug.lib.debug_data import DebugTensorDatum
 from tensorflow.python.debug.lib.debug_data import has_inf_or_nan
+from tensorflow.python.debug.lib.debug_data import load_tensor_from_event
 from tensorflow.python.debug.lib.debug_data import load_tensor_from_event_file
 
 from tensorflow.python.debug.lib.debug_utils import add_debug_tensor_watch
-- 
GitLab


From 44cf398a79677edb44491473043a8f2d95babdac Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 10:27:41 -0700
Subject: [PATCH 0246/1375] [SE] Support alpha scale in cudnnTransformTensor

PiperOrigin-RevId: 159578357
---
 tensorflow/stream_executor/cuda/cuda_dnn.cc | 5 ++---
 tensorflow/stream_executor/cuda/cuda_dnn.h  | 2 +-
 tensorflow/stream_executor/dnn.h            | 3 ++-
 tensorflow/stream_executor/stream.cc        | 7 ++++---
 tensorflow/stream_executor/stream.h         | 2 +-
 5 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index 34a683ab57..062e3e59ca 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -2432,17 +2432,16 @@ bool CudnnSupport::DoTransformTensor(Stream* stream,
                                      dnn::DataType input_type,
                                      const DeviceMemoryBase& input_data,
                                      const dnn::BatchDescriptor& output_desc,
-                                     dnn::DataType output_type,
+                                     dnn::DataType output_type, float scale,
                                      DeviceMemoryBase* output_data) {
   mutex_lock lock{dnn_handle_mutex_};
-  float alpha = 1.0f;
   float beta = 0.0f;
   ScopedTensorDescriptor input_tensor_desc(
       parent_, input_desc, ToCudnnDataType(input_type, input_desc.layout()));
   ScopedTensorDescriptor output_tensor_desc(
       parent_, output_desc, ToCudnnDataType(output_type, output_desc.layout()));
   cudnnStatus_t status = wrap::cudnnTransformTensor(
-      parent_, ToHandle(dnn_handle_), &alpha, input_tensor_desc.handle(),
+      parent_, ToHandle(dnn_handle_), &scale, input_tensor_desc.handle(),
       input_data.opaque(), &beta, output_tensor_desc.handle(),
       output_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h
index cc37c8bb9f..16fa656ef6 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.h
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.h
@@ -455,7 +455,7 @@ class CudnnSupport : public dnn::DnnSupport {
                          dnn::DataType input_type,
                          const DeviceMemoryBase& input_data,
                          const dnn::BatchDescriptor& output_desc,
-                         dnn::DataType output_type,
+                         dnn::DataType output_type, float scale,
                          DeviceMemoryBase* output_data) override;
 
  private:
diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h
index 8c8ac8662d..f12aa6d38b 100644
--- a/tensorflow/stream_executor/dnn.h
+++ b/tensorflow/stream_executor/dnn.h
@@ -1980,13 +1980,14 @@ class DnnSupport {
   //  input_data: the device memory region that contains the input tensor.
   //  output_desc: specifies the shape and the data layout of the output tensor.
   //  output_type: the data type of the output tensor.
+  //  scale: an element-wise scaling factor to apply.
   //  output_data: the device memory region that contains the output tensor.
   virtual bool DoTransformTensor(Stream* stream,
                                  const dnn::BatchDescriptor& input_desc,
                                  dnn::DataType input_type,
                                  const DeviceMemoryBase& input_data,
                                  const dnn::BatchDescriptor& output_desc,
-                                 dnn::DataType output_type,
+                                 dnn::DataType output_type, float scale,
                                  DeviceMemoryBase* output_data) {
     return false;
   }
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index fff9accae3..cbf8c11ef1 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -4406,15 +4406,16 @@ Stream &Stream::ThenTransformTensor(const dnn::BatchDescriptor &input_desc,
                                     dnn::DataType input_type,
                                     const DeviceMemoryBase &input_data,
                                     const dnn::BatchDescriptor &output_desc,
-                                    dnn::DataType output_type,
+                                    dnn::DataType output_type, float scale,
                                     DeviceMemoryBase *output_data) {
   VLOG_CALL(PARAM(input_desc), PARAM(input_type), PARAM(input_data),
-            PARAM(output_desc), PARAM(output_type), PARAM(output_data));
+            PARAM(output_desc), PARAM(output_type), PARAM(scale),
+            PARAM(output_data));
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoTransformTensor(this, input_desc, input_type,
                                         input_data, output_desc, output_type,
-                                        output_data));
+                                        scale, output_data));
     } else {
       SetErrorAndLogNoDnnSupport();
     }
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index b07d3021c9..2ab3f44af5 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -1655,7 +1655,7 @@ class Stream {
                               dnn::DataType input_type,
                               const DeviceMemoryBase &input_data,
                               const dnn::BatchDescriptor &output_desc,
-                              dnn::DataType output_type,
+                              dnn::DataType output_type, float scale,
                               DeviceMemoryBase *output_data);
 
   // The templated version of the above ThenTransformTensor. Useful when the
-- 
GitLab


From e8c0a854391d049e6c326e9972579684a18e3fe3 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 20 Jun 2017 10:58:21 -0700
Subject: [PATCH 0247/1375] Estimator: support configurable input_fn calling in
 Estimator subclasses. Also pass params and config to the input_fn.

PiperOrigin-RevId: 159583264
---
 tensorflow/python/estimator/estimator.py      | 41 ++++++--
 tensorflow/python/estimator/estimator_test.py | 95 +++++++++++++++++++
 2 files changed, 128 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index ab49f36e5e..ff44914779 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -357,7 +357,7 @@ class Estimator(object):
               }
 
   def _assert_members_are_not_overridden(self):
-    allowed_overrides = set(['_create_global_step'])
+    allowed_overrides = set(['_call_input_fn', '_create_global_step'])
     estimator_members = set([m for m in Estimator.__dict__.keys()
                              if not m.startswith('__')])
     subclass_members = set(self.__class__.__dict__.keys())
@@ -485,7 +485,7 @@ class Estimator(object):
       return export_dir
 
   def _get_features_from_input_fn(self, input_fn):
-    result = input_fn()
+    result = self._call_input_fn(input_fn)
     if not ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
       logging.warning('Input graph does not contain a QueueRunner. '
                       'That means predict yields forever. '
@@ -549,6 +549,32 @@ class Estimator(object):
     assert step.dtype.is_integer
     return step
 
+  def _call_input_fn(self, input_fn):
+    """Calls the input function.
+
+    Args:
+      input_fn: The input function.
+
+    Returns:
+      Either features or (features, labels) where features and labels are:
+        features - `Tensor` or dictionary of string feature name to `Tensor`.
+        labels - `Tensor` or dictionary of `Tensor` with labels.
+
+    Raises:
+      ValueError: if input_fn takes invalid arguments.
+    """
+    input_fn_args = _fn_args(input_fn)
+    for arg in input_fn_args:
+      if arg not in ('config', 'params'):
+        raise ValueError('input_fn should not include argument {}.'.format(arg))
+    kwargs = {}
+    if 'params' in input_fn_args:
+      kwargs['params'] = self.params
+    if 'config' in input_fn_args:
+      kwargs['config'] = self.config
+    with ops.device('/cpu:0'):
+      return input_fn(**kwargs)
+
   def _call_model_fn(self, features, labels, mode):
     """Calls model function.
 
@@ -563,7 +589,7 @@ class Estimator(object):
     Raises:
       ValueError: if model_fn returns invalid objects.
     """
-    model_fn_args = _model_fn_args(self._model_fn)
+    model_fn_args = _fn_args(self._model_fn)
     kwargs = {}
     if 'mode' in model_fn_args:
       kwargs['mode'] = mode
@@ -584,8 +610,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      with ops.device('/cpu:0'):
-        features, labels = input_fn()
+      features, labels = self._call_input_fn(input_fn)
       estimator_spec = self._call_model_fn(features, labels,
                                            model_fn_lib.ModeKeys.TRAIN)
       ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
@@ -666,7 +691,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = input_fn()
+      features, labels = self._call_input_fn(input_fn)
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
 
@@ -749,7 +774,7 @@ def _get_replica_device_setter(config):
     return None
 
 
-def _model_fn_args(fn):
+def _fn_args(fn):
   """Get argument names for function-like object.
 
   Args:
@@ -774,7 +799,7 @@ def _model_fn_args(fn):
 
 def _verify_model_fn_args(model_fn, params):
   """Verifies model fn arguments."""
-  args = set(_model_fn_args(model_fn))
+  args = set(_fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
   if 'labels' not in args:
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index b86afece43..338239ca23 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -120,6 +120,9 @@ class EstimatorInheritanceConstraintTest(test.TestCase):
       def __init__(self):
         super(_Estimator, self).__init__(model_fn=dummy_model_fn)
 
+      def _call_input_fn(self, input_fn):
+        return input_fn()
+
       def _create_global_step(self, graph):
         pass
 
@@ -325,6 +328,48 @@ def _make_input_fn(features, labels):
 
 class EstimatorTrainTest(test.TestCase):
 
+  def test_bad_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    def _input_fn(params, config, not_allowed):
+      del not_allowed
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    with self.assertRaisesRegexp(ValueError, 'should not include argument'):
+      est.train(_input_fn, steps=1)
+
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.train(_input_fn, steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_minimal_model_fn_args(self):
     expected_features = {'x': 42., 'y': 43.}
     expected_labels = 44.
@@ -665,6 +710,29 @@ class _StepCounterHook(session_run_hook.SessionRunHook):
 
 class EstimatorEvaluateTest(test.TestCase):
 
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    est.train(dummy_input_fn, steps=1)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.evaluate(_input_fn, steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_model_fn_must_return_estimator_spec(self):
     def _model_fn(features, labels, mode):
       _, _ = features, labels
@@ -866,6 +934,33 @@ class EstimatorEvaluateTest(test.TestCase):
 
 class EstimatorPredictTest(test.TestCase):
 
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del features, labels, params, config
+      return model_fn_lib.EstimatorSpec(
+          mode,
+          loss=constant_op.constant(0.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          predictions=constant_op.constant([[10.]]))
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    est.train(dummy_input_fn, steps=1)
+    self.assertEqual(0, input_fn_call_count[0])
+    next(est.predict(_input_fn))
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_no_trained_model_in_model_dir(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
     with self.assertRaisesRegexp(ValueError,
-- 
GitLab


From 4b967e2244e473e93d2293587cb6abc30414362a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 11:02:02 -0700
Subject: [PATCH 0248/1375] Add AUC to tensorforest eval metrics.

PiperOrigin-RevId: 159583941
---
 tensorflow/contrib/tensor_forest/client/eval_metrics.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics.py b/tensorflow/contrib/tensor_forest/client/eval_metrics.py
index 1726986354..a0ae083fdb 100644
--- a/tensorflow/contrib/tensor_forest/client/eval_metrics.py
+++ b/tensorflow/contrib/tensor_forest/client/eval_metrics.py
@@ -117,7 +117,13 @@ def _recall_at_thresholds(predictions, targets, weights=None):
       weights=weights)
 
 
+def _auc(probs, targets, weights=None):
+  return metric_ops.streaming_auc(array_ops.slice(probs, [0, 1], [-1, 1]),
+                                  targets, weights=weights)
+
+
 _EVAL_METRICS = {
+    'auc': _auc,
     'sigmoid_entropy': _sigmoid_entropy,
     'softmax_entropy': _softmax_entropy,
     'accuracy': _accuracy,
@@ -132,6 +138,7 @@ _EVAL_METRICS = {
 }
 
 _PREDICTION_KEYS = {
+    'auc': INFERENCE_PROB_NAME,
     'sigmoid_entropy': INFERENCE_PROB_NAME,
     'softmax_entropy': INFERENCE_PROB_NAME,
     'accuracy': INFERENCE_PRED_NAME,
-- 
GitLab


From f9125ff748407c1f9132af572c3927156e754c63 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Tue, 20 Jun 2017 11:06:08 -0700
Subject: [PATCH 0249/1375] Turn off pool allocator logging by default -- most
 people never know what to do with the information anyway, and we're moving
 away from pool allocator towards BFCAllocator.

PiperOrigin-RevId: 159584606
---
 tensorflow/core/common_runtime/gpu/pool_allocator.cc | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/pool_allocator.cc b/tensorflow/core/common_runtime/gpu/pool_allocator.cc
index 700ac34716..66fff16e8f 100644
--- a/tensorflow/core/common_runtime/gpu/pool_allocator.cc
+++ b/tensorflow/core/common_runtime/gpu/pool_allocator.cc
@@ -239,11 +239,9 @@ void PoolAllocator::EvictOne() {
         (alloc_request_count == 0)
             ? 0.0
             : allocated_count_ / static_cast<double>(alloc_request_count);
-    static int log_counter = 0;
-    // (counter increment not thread safe but it's just for logging, so we
-    // don't care).
-    bool should_log = ((log_counter++ % 10) == 0);
-    if (should_log) {
+    // Can turn on for debugging purposes.
+    const bool kShouldLog = false;
+    if (kShouldLog) {
       LOG(INFO) << "PoolAllocator: After " << alloc_request_count
                 << " get requests, put_count=" << put_count_
                 << " evicted_count=" << evicted_count_
@@ -255,7 +253,7 @@ void PoolAllocator::EvictOne() {
       size_t new_size_limit = (pool_size_limit_ < kMinPoolSize)
                                   ? kMinPoolSize
                                   : (kIncreaseFactor * pool_size_limit_);
-      if (should_log) {
+      if (kShouldLog) {
         LOG(INFO) << "Raising pool_size_limit_ from " << pool_size_limit_
                   << " to " << new_size_limit;
       }
-- 
GitLab


From d4fd9698c4c8c49932e0f401b6c428c6bedcabcb Mon Sep 17 00:00:00 2001
From: Sukriti Ramesh <sukritiramesh@google.com>
Date: Tue, 20 Jun 2017 11:12:03 -0700
Subject: [PATCH 0250/1375] Update error message when tag-set not found to
 include supplied tags.

PiperOrigin-RevId: 159585607
---
 tensorflow/cc/saved_model/loader.cc      | 11 ++++++++++-
 tensorflow/cc/saved_model/loader_test.cc |  8 ++++----
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc
index 807f5904af..f98abc8a81 100644
--- a/tensorflow/cc/saved_model/loader.cc
+++ b/tensorflow/cc/saved_model/loader.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/cc/saved_model/constants.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/monitoring/counter.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/protobuf_internal.h"
 #include "tensorflow/core/protobuf/saved_model.pb.h"
@@ -76,8 +77,16 @@ Status FindMetaGraphDefToLoad(const SavedModel& saved_model_proto,
       return Status::OK();
     }
   }
+  string tags_as_string = "{ ";
+  for (const string& tag : tags) {
+    tags_as_string = strings::StrCat(tags_as_string, tag, " ");
+  }
+  tags_as_string = strings::StrCat(tags_as_string, "}");
   return Status(error::Code::NOT_FOUND,
-                "Could not find meta graph def matching supplied tags.");
+                "Could not find meta graph def matching supplied tags: " +
+                    tags_as_string +
+                    ". To inspect available tag-sets in the SavedModel, please "
+                    "use the SavedModel CLI: `saved_model_cli`");
 }
 
 Status LoadMetaGraphIntoSession(const MetaGraphDef& meta_graph_def,
diff --git a/tensorflow/cc/saved_model/loader_test.cc b/tensorflow/cc/saved_model/loader_test.cc
index cef29e7b07..0ad6b33bba 100644
--- a/tensorflow/cc/saved_model/loader_test.cc
+++ b/tensorflow/cc/saved_model/loader_test.cc
@@ -133,9 +133,9 @@ TEST_F(LoaderTest, NoTagMatch) {
   Status st = LoadSavedModel(session_options, run_options, export_dir,
                              {"missing-tag"}, &bundle);
   EXPECT_FALSE(st.ok());
-  EXPECT_TRUE(
-      StringPiece(st.error_message())
-          .contains("Could not find meta graph def matching supplied tags."))
+  EXPECT_TRUE(StringPiece(st.error_message())
+                  .contains("Could not find meta graph def matching supplied "
+                            "tags: { missing-tag }"))
       << st.error_message();
 }
 
@@ -151,7 +151,7 @@ TEST_F(LoaderTest, NoTagMatchMultiple) {
   EXPECT_FALSE(st.ok());
   EXPECT_TRUE(
       StringPiece(st.error_message())
-          .contains("Could not find meta graph def matching supplied tags."))
+          .contains("Could not find meta graph def matching supplied tags: "))
       << st.error_message();
 }
 
-- 
GitLab


From fa7f73d7574c32275be9be3b620e427936682726 Mon Sep 17 00:00:00 2001
From: Sukriti Ramesh <sukritiramesh@google.com>
Date: Tue, 20 Jun 2017 11:15:37 -0700
Subject: [PATCH 0251/1375] Include link to SavedModel CLI in the Py loader
 when tag-set is not found.

PiperOrigin-RevId: 159586182
---
 tensorflow/python/saved_model/loader_impl.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py
index 3252652174..5ff954fd9f 100644
--- a/tensorflow/python/saved_model/loader_impl.py
+++ b/tensorflow/python/saved_model/loader_impl.py
@@ -206,8 +206,11 @@ def load(sess, tags, export_dir, **saver_kwargs):
         break
 
     if not found_match:
-      raise RuntimeError("MetaGraphDef associated with tags " + str(tags).strip(
-          "[]") + " could not be found in SavedModel")
+      raise RuntimeError(
+          "MetaGraphDef associated with tags " + str(tags).strip("[]") +
+          " could not be found in SavedModel. To inspect available tag-sets in"
+          " the SavedModel, please use the SavedModel CLI: `saved_model_cli`"
+      )
 
     # Build a saver by importing the meta graph def to load.
     saver = tf_saver.import_meta_graph(meta_graph_def_to_load, **saver_kwargs)
-- 
GitLab


From 3b3a582e3e80accc8ee77b85f79f8d9ad1e4cdb0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 11:23:09 -0700
Subject: [PATCH 0252/1375] Fix typo in comment. NFC.

PiperOrigin-RevId: 159587442
---
 tensorflow/compiler/xla/literal_util.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 20ff32d70b..e63773bece 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -251,7 +251,7 @@ class Literal {
     *other = temp;
   }
 
-  // CreatesCreate new literal of a given rank. To minimize ambiguity (for users
+  // Creates a new literal of a given rank. To minimize ambiguity (for users
   // and the compiler) these CreateR[0-2] methods should explicitly specify the
   // native type. For example:
   //
-- 
GitLab


From 74cf446fe49073a04335ce45e14dbdc88f471a3a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 11:50:31 -0700
Subject: [PATCH 0253/1375] Selected BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 159591663
---
 tensorflow/contrib/metrics/BUILD   |  9 +-------
 tensorflow/contrib/signal/BUILD    |  4 +++-
 tensorflow/contrib/specs/BUILD     |  5 +----
 tensorflow/contrib/stateless/BUILD |  1 +
 tensorflow/contrib/testing/BUILD   |  1 +
 tensorflow/contrib/text/BUILD      |  2 --
 tensorflow/contrib/training/BUILD  | 36 ++++++++++++------------------
 tensorflow/contrib/util/BUILD      |  2 +-
 tensorflow/contrib/verbs/BUILD     | 11 +++++----
 9 files changed, 29 insertions(+), 42 deletions(-)

diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD
index 8b792a0f68..c0741f2997 100644
--- a/tensorflow/contrib/metrics/BUILD
+++ b/tensorflow/contrib/metrics/BUILD
@@ -31,7 +31,6 @@ py_library(
         "//tensorflow/python:check_ops",
         "//tensorflow/python:confusion_matrix",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:histogram_ops",
         "//tensorflow/python:init_ops",
@@ -40,11 +39,9 @@ py_library(
         "//tensorflow/python:nn",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:sets",
-        "//tensorflow/python:sparse_ops",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
-        "//tensorflow/python:variables",
     ],
 )
 
@@ -58,9 +55,6 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -74,8 +68,6 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
@@ -98,6 +90,7 @@ py_test(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
diff --git a/tensorflow/contrib/signal/BUILD b/tensorflow/contrib/signal/BUILD
index 5b65a6ae05..1d9cce239a 100644
--- a/tensorflow/contrib/signal/BUILD
+++ b/tensorflow/contrib/signal/BUILD
@@ -12,8 +12,10 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:array_ops",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/contrib/specs/BUILD b/tensorflow/contrib/specs/BUILD
index dfdbb61dcc..b60a1ef61e 100644
--- a/tensorflow/contrib/specs/BUILD
+++ b/tensorflow/contrib/specs/BUILD
@@ -25,15 +25,12 @@ py_library(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/ndlstm",
         "//tensorflow/python:array_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:logging_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:nn_ops",
-        "//tensorflow/python:ops",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:training",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
     ],
 )
diff --git a/tensorflow/contrib/stateless/BUILD b/tensorflow/contrib/stateless/BUILD
index 1d9c1ffa50..598e6513ae 100644
--- a/tensorflow/contrib/stateless/BUILD
+++ b/tensorflow/contrib/stateless/BUILD
@@ -21,6 +21,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":stateless_random_ops",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/contrib/testing/BUILD b/tensorflow/contrib/testing/BUILD
index 225a1ccd12..0be6aa755b 100644
--- a/tensorflow/contrib/testing/BUILD
+++ b/tensorflow/contrib/testing/BUILD
@@ -16,6 +16,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
diff --git a/tensorflow/contrib/text/BUILD b/tensorflow/contrib/text/BUILD
index 6bcb03238c..8a2cb28684 100644
--- a/tensorflow/contrib/text/BUILD
+++ b/tensorflow/contrib/text/BUILD
@@ -101,8 +101,6 @@ py_test(
         "//tensorflow/python:errors",
         "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:random_seed",
         "//tensorflow/python:training",
     ],
diff --git a/tensorflow/contrib/training/BUILD b/tensorflow/contrib/training/BUILD
index 1180ea9299..086372019c 100644
--- a/tensorflow/contrib/training/BUILD
+++ b/tensorflow/contrib/training/BUILD
@@ -41,24 +41,25 @@ py_library(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:logging_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:script_ops",
+        "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:string_ops",
         "//tensorflow/python:summary",
         "//tensorflow/python:tensor_array_ops",
+        "//tensorflow/python:tensor_util",
         "//tensorflow/python:training",
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
-        "//tensorflow/python/estimator:estimator_py",
+        "//tensorflow/python/estimator:inputs_queues",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
@@ -74,7 +75,6 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
     ],
@@ -91,7 +91,6 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:string_ops",
@@ -107,12 +106,13 @@ py_test(
     tags = ["manual"],
     deps = [
         ":training_py",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:string_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -128,6 +128,7 @@ py_test(
     deps = [
         ":training_py",
         "//tensorflow/python:client_testlib",
+        "@six_archive//:six",
     ],
 )
 
@@ -142,7 +143,6 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -160,13 +160,12 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:logging_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -186,11 +185,10 @@ py_test(
         ":training_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
     ],
@@ -206,10 +204,11 @@ py_test(
         ":training_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -223,12 +222,9 @@ py_test(
     tags = ["manual"],
     deps = [
         ":training_py",
-        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:data_flow_ops",
+        "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:math_ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
@@ -255,11 +251,10 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
@@ -278,14 +273,11 @@ py_test(
         ":training_py",
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/losses:losses_py",
-        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//tensorflow/python/ops/losses",
diff --git a/tensorflow/contrib/util/BUILD b/tensorflow/contrib/util/BUILD
index 5ad8e3dd35..5575fb3570 100644
--- a/tensorflow/contrib/util/BUILD
+++ b/tensorflow/contrib/util/BUILD
@@ -27,7 +27,6 @@ cc_binary(
     srcs = ["convert_graphdef_memmapped_format.cc"],
     deps = [
         ":convert_graphdef_memmapped_format_lib",
-        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
     ],
@@ -69,6 +68,7 @@ py_library(
     deps = [
         "//tensorflow/python:framework",
         "//tensorflow/python:platform",
+        "//tensorflow/python:tensor_util",
         "//tensorflow/python:util",
     ],
 )
diff --git a/tensorflow/contrib/verbs/BUILD b/tensorflow/contrib/verbs/BUILD
index e747fa4c9e..5f062cde89 100644
--- a/tensorflow/contrib/verbs/BUILD
+++ b/tensorflow/contrib/verbs/BUILD
@@ -66,9 +66,8 @@ cc_library(
         ":grpc_verbs_service_impl",
         ":rdma_mgr",
         ":verbs_service_proto_cc",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core/distributed_runtime:session_mgr",
-        "//tensorflow/core/distributed_runtime:worker_env",
         "//tensorflow/core/distributed_runtime/rpc:async_service_interface",
         "//tensorflow/core/distributed_runtime/rpc:grpc_call",
         "//tensorflow/core/distributed_runtime/rpc:grpc_util",
@@ -107,8 +106,8 @@ cc_library(
     hdrs = ["rdma_rendezvous_mgr.h"],
     deps = [
         ":rdma_mgr",
+        ":verbs_util",
         "//tensorflow/core:core_cpu_internal",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core/distributed_runtime:base_rendezvous_mgr",
         "//tensorflow/core/distributed_runtime:worker_env",
@@ -122,10 +121,11 @@ cc_library(
     deps = [
         ":grpc_verbs_client",
         ":rdma",
+        ":verbs_service_proto_cc",
         "//tensorflow/core:core_cpu_internal",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "//tensorflow/core/distributed_runtime:session_mgr",
         "//tensorflow/core/distributed_runtime:worker_env",
         "//tensorflow/core/distributed_runtime/rpc:grpc_channel",
         "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache",
@@ -144,6 +144,7 @@ cc_library(
         ":verbs_util",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
+        "//tensorflow/core:framework_internal",
         "//tensorflow/core:gpu_runtime",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
@@ -162,6 +163,8 @@ cc_library(
         ":grpc_verbs_service",
         ":rdma_mgr",
         ":rdma_rendezvous_mgr",
+        "//tensorflow/core:lib",
+        "//tensorflow/core/distributed_runtime:server_lib",
         "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
     ],
     alwayslink = 1,
-- 
GitLab


From 716687c091aebb4cace35229fb150c356f0079c8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 12:17:37 -0700
Subject: [PATCH 0254/1375] Generalize `learning.train` for multiple sync
 optimizers.

PiperOrigin-RevId: 159595373
---
 .../contrib/slim/python/slim/learning.py      | 38 +++++++++++--------
 1 file changed, 22 insertions(+), 16 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/learning.py b/tensorflow/contrib/slim/python/slim/learning.py
index f7dddc46c3..8f690fb549 100644
--- a/tensorflow/contrib/slim/python/slim/learning.py
+++ b/tensorflow/contrib/slim/python/slim/learning.py
@@ -603,9 +603,9 @@ def train(train_op,
     saver: Saver to save checkpoints. If None, a default one will be created
       and used.
     save_interval_secs: How often, in seconds, to save the model to `logdir`.
-    sync_optimizer: an instance of tf.train.SyncReplicasOptimizer. If the
-      argument is supplied, gradient updates will be synchronous. If left as
-      `None`, gradient updates will be asynchronous.
+    sync_optimizer: an instance of tf.train.SyncReplicasOptimizer, or a list of
+      them. If the argument is supplied, gradient updates will be synchronous.
+      If left as `None`, gradient updates will be asynchronous.
     session_config: An instance of `tf.ConfigProto` that will be used to
       configure the `Session`. If left as `None`, the default will be used.
     trace_every_n_steps: produce and save a `Timeline` in Chrome trace format
@@ -633,6 +633,8 @@ def train(train_op,
       raise ValueError('Cannot provide trace_every_n_steps because '
                        'logdir=None')
 
+  if isinstance(sync_optimizer, sync_replicas_optimizer.SyncReplicasOptimizer):
+    sync_optimizer = [sync_optimizer]
   if sync_optimizer is not None and startup_delay_steps > 0:
     raise ValueError(
         'startup_delay_steps must be zero when sync_optimizer is supplied.')
@@ -647,6 +649,12 @@ def train(train_op,
       global_step = variables.get_or_create_global_step()
     saver = saver or tf_saver.Saver()
 
+    if sync_optimizer is not None:
+      for opt in sync_optimizer:
+        if not isinstance(opt, sync_replicas_optimizer.SyncReplicasOptimizer):
+          raise ValueError(
+              '`sync_optimizer` must be a tf.train.SyncReplicasOptimizer.')
+
     with ops.name_scope('init_ops'):
       if init_op == _USE_DEFAULT:
         init_op = tf_variables.global_variables_initializer()
@@ -659,15 +667,17 @@ def train(train_op,
             tf_variables.local_variables_initializer(),
             lookup_ops.tables_initializer())
 
-      if sync_optimizer is not None and isinstance(
-          sync_optimizer, sync_replicas_optimizer.SyncReplicasOptimizer):
+      if sync_optimizer is not None and isinstance(sync_optimizer, list):
         with ops.control_dependencies([local_init_op] if local_init_op is
                                       not None else []):
           if is_chief:
-            local_init_op = sync_optimizer.chief_init_op
+            local_init_op = control_flow_ops.group(
+                *[opt.chief_init_op for opt in sync_optimizer])
           else:
-            local_init_op = sync_optimizer.local_step_init_op
-        ready_for_local_init_op = sync_optimizer.ready_for_local_init_op
+            local_init_op = control_flow_ops.group(
+                *[opt.local_step_init_op for opt in sync_optimizer])
+        ready_for_local_init_op = control_flow_ops.group(
+            *[opt.ready_for_local_init_op for opt in sync_optimizer])
       else:
         ready_for_local_init_op = None
 
@@ -678,14 +688,10 @@ def train(train_op,
       summary_writer = supervisor.Supervisor.USE_DEFAULT
 
     if is_chief and sync_optimizer is not None:
-      if not isinstance(sync_optimizer,
-                        (sync_replicas_optimizer.SyncReplicasOptimizer)):
-        raise ValueError(
-            '`sync_optimizer` must be a tf.train.SyncReplicasOptimizer.')
-
       # Need to create these BEFORE the supervisor finalizes the graph:
-      init_tokens_op = sync_optimizer.get_init_tokens_op()
-      chief_queue_runner = sync_optimizer.get_chief_queue_runner()
+      init_tokens_op = [opt.get_init_tokens_op() for opt in sync_optimizer]
+      chief_queue_runner = [
+          opt.get_chief_queue_runner() for opt in sync_optimizer]
 
     if train_step_kwargs == _USE_DEFAULT:
       with ops.name_scope('train_step'):
@@ -741,7 +747,7 @@ def train(train_op,
         threads = sv.start_queue_runners(sess)
         logging.info('Starting Queues.')
         if is_chief and sync_optimizer is not None:
-          sv.start_queue_runners(sess, [chief_queue_runner])
+          sv.start_queue_runners(sess, chief_queue_runner)
           sess.run(init_tokens_op)
         try:
           while not sv.should_stop():
-- 
GitLab


From ad2faeac06c7be527108d83cd8771b6c0f296257 Mon Sep 17 00:00:00 2001
From: Sukriti Ramesh <sukritiramesh@google.com>
Date: Tue, 20 Jun 2017 12:59:58 -0700
Subject: [PATCH 0255/1375] Add SavedModel tag-constant for gpu.

PiperOrigin-RevId: 159600621
---
 tensorflow/cc/saved_model/tag_constants.h | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/cc/saved_model/tag_constants.h b/tensorflow/cc/saved_model/tag_constants.h
index 48ab1158e4..2b0b2d5c7f 100644
--- a/tensorflow/cc/saved_model/tag_constants.h
+++ b/tensorflow/cc/saved_model/tag_constants.h
@@ -18,10 +18,13 @@ limitations under the License.
 
 namespace tensorflow {
 
+/// Tag for the `gpu` graph.
+constexpr char kSavedModelTagGpu[] = "gpu";
+
 /// Tag for the `serving` graph.
 constexpr char kSavedModelTagServe[] = "serve";
 
-/// Tag for the `training` graph.`
+/// Tag for the `training` graph.
 constexpr char kSavedModelTagTrain[] = "train";
 
 }  // namespace tensorflow
-- 
GitLab


From fb2c721a17811c9018ee6470871268b98904f752 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Tue, 20 Jun 2017 13:11:00 -0700
Subject: [PATCH 0256/1375] [XLA] Remove xla_emit_tbaa flag

This flag doesn't appear to be used anywhere (non-default value) and is
propagated fairly deep; if reintroduced it should go through extra backend
options.

PiperOrigin-RevId: 159602053
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 11 ----
 .../xla/legacy_flags/llvm_util_flags.cc       | 63 -------------------
 .../xla/legacy_flags/llvm_util_flags.h        | 46 --------------
 tensorflow/compiler/xla/service/llvm_ir/BUILD |  1 -
 .../compiler/xla/service/llvm_ir/llvm_util.cc |  6 --
 7 files changed, 130 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/llvm_util_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/llvm_util_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 5c6bd0cdaa..31637358c3 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -131,7 +131,6 @@ cc_library(
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
-        "//tensorflow/compiler/xla/legacy_flags:llvm_util_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/compiler/xla/legacy_flags:util_flags",
         "//tensorflow/compiler/xla/service:compiler",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index fea2207c4c..e03d28cd96 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -28,7 +28,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/llvm_util_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
@@ -139,7 +138,6 @@ int main(int argc, char** argv) {
   xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendLlvmUtilFlags(&flag_list);
   xla::legacy_flags::AppendServiceFlags(&flag_list);
   xla::legacy_flags::AppendUtilFlags(&flag_list);
 
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 33f7530ce5..fafd5f591b 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -137,17 +137,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "llvm_util_flags",
-    srcs = ["llvm_util_flags.cc"],
-    hdrs = ["llvm_util_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "service_flags",
     srcs = ["service_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/llvm_util_flags.cc b/tensorflow/compiler/xla/legacy_flags/llvm_util_flags.cc
deleted file mode 100644
index 3c53729a67..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/llvm_util_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's llvm_util module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/llvm_util_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static LlvmUtilFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new LlvmUtilFlags;
-  flags->xla_emit_tbaa = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_emit_tbaa", &flags->xla_emit_tbaa,
-                       "Perform type-based alias analysis optimizations for "
-                       "LLVM-based backends."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's llvm_util
-// module.
-void AppendLlvmUtilFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the LlvmUtilFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-LlvmUtilFlags* GetLlvmUtilFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/llvm_util_flags.h b/tensorflow/compiler/xla/legacy_flags/llvm_util_flags.h
deleted file mode 100644
index 98da26b4b8..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/llvm_util_flags.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_LLVM_UTIL_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_LLVM_UTIL_FLAGS_H_
-
-// Legacy flags for XLA's llvm_util module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's llvm_util module.
-void AppendLlvmUtilFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's llvm_util module.
-typedef struct {
-  bool xla_emit_tbaa;  // Perform type-based alias analysis optimizations for
-                       // LLVM-based backends.
-} LlvmUtilFlags;
-
-// Return a pointer to the LlvmUtilFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-LlvmUtilFlags* GetLlvmUtilFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_LLVM_UTIL_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD
index f02e4ea1e7..c5b3f317b2 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/BUILD
+++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD
@@ -46,7 +46,6 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:llvm_util_flags",
         "//tensorflow/core:lib",
         "@llvm//:core",
         "@llvm//:support",
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index d9e233af2a..bcc9418d59 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Operator.h"
 #include "external/llvm/include/llvm/Target/TargetOptions.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/llvm_util_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -357,11 +356,6 @@ void EmitLogging(const char* tag, llvm::Value* value,
 
 void SetTbaaForInstruction(llvm::Instruction* instruction, Shape shape,
                            bool is_pointer_to) {
-  legacy_flags::LlvmUtilFlags* flags = legacy_flags::GetLlvmUtilFlags();
-  if (!flags->xla_emit_tbaa) {
-    return;
-  }
-
   llvm::MDBuilder metadata_builder(instruction->getContext());
   llvm::MDNode* root = metadata_builder.createTBAARoot("XLA TBAA");
   string type_name;
-- 
GitLab


From d864bc1bd000851bc207141264fb537e460bff6d Mon Sep 17 00:00:00 2001
From: Mark Heffernan <meheff@google.com>
Date: Tue, 20 Jun 2017 14:07:08 -0700
Subject: [PATCH 0257/1375] [XLA] Remove redundant conversion code in
 HloEvaluator. Replace conversion code with call to Literal::Convert.

PiperOrigin-RevId: 159610555
---
 tensorflow/compiler/xla/literal_util.cc       | 40 +++++++++---
 tensorflow/compiler/xla/literal_util.h        | 60 +++++------------
 tensorflow/compiler/xla/literal_util_test.cc  | 29 +++++----
 tensorflow/compiler/xla/service/BUILD         |  1 +
 .../compiler/xla/service/hlo_evaluator.cc     | 42 +++---------
 .../xla/service/hlo_evaluator_test.cc         | 65 ++++++++++++++-----
 6 files changed, 124 insertions(+), 113 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index d9b9066626..1b125e3596 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -321,6 +321,7 @@ Status Literal::Copy(const Literal& src_literal,
 }
 
 std::unique_ptr<Literal> Literal::Relayout(const Layout& layout) const {
+  CHECK(ShapeUtil::IsArray(shape()));
   std::unique_ptr<Literal> result = CloneToUnique();
   *result->mutable_shape()->mutable_layout() = layout;
 
@@ -754,13 +755,33 @@ void Literal::EachCellAsString(
 }
 
 namespace {
+template <typename NativeSrcT, typename NativeDestT>
+std::unique_ptr<Literal> ConvertBetweenNativeTypes(const Literal& src_literal) {
+  auto result_literal = MakeUnique<Literal>();
+  Shape* result_shape = result_literal->mutable_shape();
+  *result_shape = src_literal.shape();
+  result_shape->set_element_type(
+      primitive_util::NativeToPrimitiveType<NativeDestT>());
+  result_literal->Reserve(ShapeUtil::ElementsIn(*result_shape));
+  tensorflow::gtl::ArraySlice<NativeSrcT> src_data =
+      src_literal.GetArraySlice<NativeSrcT>();
+  tensorflow::gtl::MutableArraySlice<NativeDestT> dest_data =
+      result_literal->GetMutableArraySlice<NativeDestT>();
+  int64 num_elements = ShapeUtil::ElementsIn(src_literal.shape());
+
+  for (int64 i = 0; i < num_elements; ++i) {
+    dest_data[i] = static_cast<NativeDestT>(src_data[i]);
+  }
+  return result_literal;
+}
+
 template <PrimitiveType primitive_src_type, PrimitiveType primitive_dest_type>
 std::unique_ptr<Literal> ConvertIfTypesMatch(const Literal& src_literal) {
   CHECK_EQ(primitive_src_type, src_literal.shape().element_type());
-  return src_literal.Convert<
+  return ConvertBetweenNativeTypes<
       typename primitive_util::PrimitiveTypeToNative<primitive_src_type>::type,
       typename primitive_util::PrimitiveTypeToNative<
-          primitive_dest_type>::type>();
+          primitive_dest_type>::type>(src_literal);
 }
 
 template <PrimitiveType primitive_src_type>
@@ -782,14 +803,15 @@ StatusOr<std::unique_ptr<Literal>> ConvertIfDestTypeMatches(
 #undef CONVERT_IF_TYPES_MATCH
     // Other types are not yet supported.
     default:
-      return tensorflow::errors::InvalidArgument(
-          "Unimplemented: ConvertIfDestTypeMatches for type " +
-          PrimitiveType_Name(src_literal.shape().element_type()));
+      return InvalidArgument(
+          "Unimplemented: Convert from type %s to type %s",
+          PrimitiveType_Name(src_literal.shape().element_type()).c_str(),
+          PrimitiveType_Name(primitive_dest_type).c_str());
   }
 }
 }  // namespace
 
-StatusOr<std::unique_ptr<Literal>> Literal::ConvertIfSrcTypeMatches(
+StatusOr<std::unique_ptr<Literal>> Literal::Convert(
     PrimitiveType primitive_dest_type) const {
   switch (shape().element_type()) {
 #define CONVERT_IF_DEST_TYPE_MATCHES(type) \
@@ -807,9 +829,9 @@ StatusOr<std::unique_ptr<Literal>> Literal::ConvertIfSrcTypeMatches(
 #undef CONVERT_IF_DEST_TYPE_MATCHES
     // Other types are not yet supported.
     default:
-      return tensorflow::errors::InvalidArgument(
-          "Unimplemented: ConvertIfSrcTypeMatches for type " +
-          PrimitiveType_Name(shape().element_type()));
+      return InvalidArgument("Unimplemented: Convert from type %s to type %s",
+                             PrimitiveType_Name(shape().element_type()).c_str(),
+                             PrimitiveType_Name(primitive_dest_type).c_str());
   }
 }
 
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index e63773bece..ce4a00fa55 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -362,14 +362,9 @@ class Literal {
   template <typename NativeT>
   std::unique_ptr<Literal> Replicate(int64 times) const;
 
-  // Creates a literal by converting each element in this literal to a new
-  // type.
-  template <typename NativeSrcT, typename NativeDestT>
-  std::unique_ptr<Literal> Convert() const;
-
-  // Converts this literal to another primitive type, but only if the literal
-  // type is convertible into the destination type.
-  StatusOr<std::unique_ptr<Literal>> ConvertIfSrcTypeMatches(
+  // Converts this literal to another primitive type. Returns an error if the
+  // conversion is not possible.
+  StatusOr<std::unique_ptr<Literal>> Convert(
       PrimitiveType primitive_dest_type) const;
 
   // Creates a literal value zero of the given primitive type.
@@ -449,10 +444,21 @@ class Literal {
   template <typename NativeT>
   void Set(tensorflow::gtl::ArraySlice<int64> multi_index, NativeT value);
 
-  // Retrieves the mutable array slice interface which can be used to manipulate
-  // pre-allocated literal values.
+  // Returns a (Mutable)ArraySlice view of the array for this literal for the
+  // given NativeT (e.g., float). These functions map native type to XLA
+  // PrimitiveType via template specialization. The unspecialized forms below
+  // aborts to handle the error case where the given native type does not map to
+  // an XLA primitive type.
   template <typename NativeT>
-  tensorflow::gtl::MutableArraySlice<NativeT> GetMutableArraySlice();
+  tensorflow::gtl::ArraySlice<NativeT> GetArraySlice() const {
+    static_assert(!std::is_same<NativeT, NativeT>::value,
+                  "Cannot map native type to primitive type.");
+  }
+  template <typename NativeT>
+  tensorflow::gtl::MutableArraySlice<NativeT> GetMutableArraySlice() {
+    static_assert(!std::is_same<NativeT, NativeT>::value,
+                  "Cannot map native type to primitive type.");
+  }
 
   // Returns the element value at index (0, ..., 0), however many zeroes are
   // required for that index.
@@ -593,17 +599,6 @@ class Literal {
   bool IsZero(tensorflow::gtl::ArraySlice<int64> indices) const;
 
  private:
-  // Returns an ArraySlice view of the array for this literal for the given
-  // NativeT (e.g., float). These functions map native type to XLA PrimitiveType
-  // via template specialization. The unspecialized forms below aborts to handle
-  // the error case where the given native type does not map to an XLA primitive
-  // type.
-  template <typename NativeT>
-  tensorflow::gtl::ArraySlice<NativeT> GetArraySlice() const {
-    static_assert(!std::is_same<NativeT, NativeT>::value,
-                  "Cannot map native type to primitive type.");
-  }
-
   // Copy from a LiteralProto instance.
   void CopyFromProto(const LiteralProto& literal_proto);
 
@@ -1226,27 +1221,6 @@ void Literal::PopulateWithValue(NativeT value,
   Resize<NativeT>(ShapeUtil::ElementsIn(shape()), value);
 }
 
-template <typename NativeSrcT, typename NativeDestT>
-std::unique_ptr<Literal> Literal::Convert() const {
-  const Shape& this_shape = shape();
-  auto result_literal = MakeUnique<Literal>();
-  Shape* result_shape = result_literal->mutable_shape();
-  *result_shape = this_shape;
-  result_shape->set_element_type(
-      primitive_util::NativeToPrimitiveType<NativeDestT>());
-  result_literal->Reserve(ShapeUtil::ElementsIn(*result_shape));
-  tensorflow::gtl::ArraySlice<NativeSrcT> src_data =
-      GetArraySlice<NativeSrcT>();
-  tensorflow::gtl::MutableArraySlice<NativeDestT> dest_data =
-      result_literal->GetMutableArraySlice<NativeDestT>();
-  int64 num_elements = ShapeUtil::ElementsIn(this_shape);
-
-  for (int64 i = 0; i < num_elements; ++i) {
-    dest_data[i] = static_cast<NativeDestT>(src_data[i]);
-  }
-  return result_literal;
-}
-
 template <typename NativeT>
 /* static */ std::unique_ptr<Literal>
 Literal::CreateFullWithMonotonicDim0MajorLayout(
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 3c926f0940..9dd13be80f 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -825,7 +825,8 @@ TEST_F(LiteralUtilTest, ConvertR4) {
      {{26, 27, 28, 29}, {30, 31, 32, 33}},
   }}, layout_r4_dim0major_);
   // clang-format on
-  auto converted = original->Convert<int8, uint32>();
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<Literal> converted,
+                         original->Convert(U32));
 
   EXPECT_TRUE(expected->Equal(*converted));
 }
@@ -880,40 +881,40 @@ TEST_F(LiteralUtilTest, ConvertIfTypesMatch) {
   // clang-format on
   std::unique_ptr<Literal> conv;
 
-  conv = s8->ConvertIfSrcTypeMatches(U32).ConsumeValueOrDie();
+  conv = s8->Convert(U32).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*u32));
 
-  conv = s8->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  conv = s8->Convert(S32).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*s32));
 
-  conv = s8->ConvertIfSrcTypeMatches(U64).ConsumeValueOrDie();
+  conv = s8->Convert(U64).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*u64));
 
-  conv = s8->ConvertIfSrcTypeMatches(S64).ConsumeValueOrDie();
+  conv = s8->Convert(S64).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*s64));
 
-  conv = s8->ConvertIfSrcTypeMatches(PRED).ConsumeValueOrDie();
+  conv = s8->Convert(PRED).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*pred));
 
-  conv = pred->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  conv = pred->Convert(S32).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*int32_pred));
 
-  conv = f32->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  conv = f32->Convert(S32).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*s32));
 
-  conv = f64->ConvertIfSrcTypeMatches(S32).ConsumeValueOrDie();
+  conv = f64->Convert(S32).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*s32));
 
-  conv = s32->ConvertIfSrcTypeMatches(F32).ConsumeValueOrDie();
+  conv = s32->Convert(F32).ConsumeValueOrDie();
   EXPECT_TRUE(conv->Equal(*f32));
 
-  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(TUPLE).status().code(),
+  EXPECT_EQ(s32->Convert(TUPLE).status().code(),
             tensorflow::error::INVALID_ARGUMENT);
-  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(F16).status().code(),
+  EXPECT_EQ(s32->Convert(F16).status().code(),
             tensorflow::error::INVALID_ARGUMENT);
-  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(S16).status().code(),
+  EXPECT_EQ(s32->Convert(S16).status().code(),
             tensorflow::error::INVALID_ARGUMENT);
-  EXPECT_EQ(s32->ConvertIfSrcTypeMatches(U16).status().code(),
+  EXPECT_EQ(s32->Convert(U16).status().code(),
             tensorflow::error::INVALID_ARGUMENT);
 }
 
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index ed1420665e..9d770987b9 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -114,6 +114,7 @@ cc_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
         "//tensorflow/core:test_main",
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 4936b823a2..393e25380e 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -200,41 +200,19 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   };
 
-  template <PrimitiveType src_type, PrimitiveType dest_type>
-  std::unique_ptr<Literal> ConvertIfTypesMatch(const Literal& src_literal) {
-    DCHECK_EQ(src_type, src_literal.shape().element_type());
-    return src_literal.Convert<
-        typename primitive_util::PrimitiveTypeToNative<src_type>::type,
-        typename primitive_util::PrimitiveTypeToNative<dest_type>::type>();
-  }
-
   Status HandleConvert(HloInstruction* convert) override {
     const HloInstruction* operand = convert->operand(0);
-    auto operand_literal = parent_->GetEvaluatedLiteralFor(operand);
-
-    switch (operand->shape().element_type()) {
-#define CONVERT_IF_TYPES_MATCH(src_type)                                \
-  case (src_type):                                                      \
-    parent_->evaluated_[convert] = operand_literal.Convert<             \
-        typename primitive_util::PrimitiveTypeToNative<src_type>::type, \
-        ReturnT>();                                                     \
-    break;
-      CONVERT_IF_TYPES_MATCH(PRED)
-      CONVERT_IF_TYPES_MATCH(S8)
-      CONVERT_IF_TYPES_MATCH(S32)
-      CONVERT_IF_TYPES_MATCH(S64)
-      CONVERT_IF_TYPES_MATCH(U8)
-      CONVERT_IF_TYPES_MATCH(U32)
-      CONVERT_IF_TYPES_MATCH(U64)
-      CONVERT_IF_TYPES_MATCH(F32)
-      CONVERT_IF_TYPES_MATCH(F64)
-#undef CONVERT_IF_TYPES_MATCH
-      // Other types are not yet supported.
-      default:
-        LOG(FATAL) << "unimplemented operand type for HandleCovert: "
-                   << PrimitiveType_Name(operand->shape().element_type());
+    TF_RET_CHECK(ShapeUtil::SameDimensions(operand->shape(), convert->shape()));
+    TF_ASSIGN_OR_RETURN(std::unique_ptr<Literal> result,
+                        parent_->GetEvaluatedLiteralFor(operand).Convert(
+                            convert->shape().element_type()));
+
+    if (LayoutUtil::LayoutsInShapesEqual(result->shape(), convert->shape())) {
+      parent_->evaluated_[convert] = std::move(result);
+    } else {
+      parent_->evaluated_[convert] =
+          result->Relayout(convert->shape().layout());
     }
-
     return Status::OK();
   }
 
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index 0db05bf64c..a11a5abc03 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
@@ -34,7 +35,7 @@ limitations under the License.
 namespace xla {
 namespace {
 
-class HloEvaluatorTest : public ::testing::Test {
+class HloEvaluatorTest : public HloTestBase {
  protected:
   HloEvaluatorTest() { evaluator_ = MakeUnique<HloEvaluator>(); }
 
@@ -181,10 +182,8 @@ TEST_F(HloEvaluatorTest, DoesAbs) {
 
 // Verifies that HloEvaluator evaluates a HLO Computation with non-parameter nor
 // constant operands.
-TEST_F(HloEvaluatorTest, DoesTraveseInstructions) {
-  HloComputation::Builder builder(
-      ::testing::UnitTest::GetInstance()->current_test_info()->name());
-
+TEST_F(HloEvaluatorTest, DoesTraverseInstructions) {
+  HloComputation::Builder builder(TestName());
   auto lhs = Literal::CreateR2<int64>({{1, 0}, {-100, 4}});
   auto rhs = Literal::CreateR2<int64>({{2, 4}, {4, 4}});
   auto rhs2 = Literal::CreateR2<int64>({{1, -20}, {-100, 4}});
@@ -212,9 +211,7 @@ TEST_F(HloEvaluatorTest, DoesTraveseInstructions) {
 
 // Verifies Reshape operation is correctly evaluated.
 TEST_F(HloEvaluatorTest, DoesReshape) {
-  HloComputation::Builder builder(
-      ::testing::UnitTest::GetInstance()->current_test_info()->name());
-
+  HloComputation::Builder builder(TestName());
   const int64 dimensions[] = {11, 8, 7, 5, 9};
   TF_ASSIGN_OR_ASSERT_OK(auto literal,
                          LiteralTestUtil::CreateRandomLiteral<F32>(
@@ -241,9 +238,7 @@ TEST_F(HloEvaluatorTest, DoesReshape) {
 
 // Verifies Broadcast operation is correctly evaluated.
 TEST_F(HloEvaluatorTest, DoesBroadcast) {
-  HloComputation::Builder builder(
-      ::testing::UnitTest::GetInstance()->current_test_info()->name());
-
+  HloComputation::Builder builder(TestName());
   auto input_literal = Literal::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}});
   auto output_literal = Literal::CreateR3<int32>(
       {{{1, 2}, {3, 4}, {5, 6}}, {{1, 2}, {3, 4}, {5, 6}}});
@@ -256,10 +251,50 @@ TEST_F(HloEvaluatorTest, DoesBroadcast) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
 
-  result->EachCell<int32>(
-      [&](tensorflow::gtl::ArraySlice<int64> indices, int32 value) {
-        EXPECT_TRUE(value == output_literal->Get<int32>(indices));
-      });
+  LiteralTestUtil::ExpectEqual(*result, *output_literal);
+}
+
+TEST_F(HloEvaluatorTest, ConvertWithSameLayout) {
+  HloComputation::Builder builder(TestName());
+
+  auto input_literal = Literal::CreateR2<int32>({{1, 2}, {3, 4}, {5, 6}});
+  auto expected =
+      Literal::CreateR2<float>({{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
+  ASSERT_TRUE(LayoutUtil::LayoutsInShapesEqual(input_literal->shape(),
+                                               expected->shape()));
+
+  HloInstruction* constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(std::move(input_literal)));
+  builder.AddInstruction(
+      HloInstruction::CreateConvert(expected->shape(), constant));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
+
+  EXPECT_TRUE(ShapeUtil::Equal(result->shape(), expected->shape()));
+  LiteralTestUtil::ExpectEqual(*result, *expected);
+}
+
+TEST_F(HloEvaluatorTest, ConvertWithDifferentLayout) {
+  HloComputation::Builder builder(TestName());
+
+  auto input_literal = Literal::CreateR2WithLayout<int32>(
+      {{1, 2}, {3, 4}, {5, 6}}, LayoutUtil::MakeLayout({0, 1}));
+  auto expected = Literal::CreateR2WithLayout<float>(
+      {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}, LayoutUtil::MakeLayout({1, 0}));
+  ASSERT_FALSE(LayoutUtil::LayoutsInShapesEqual(input_literal->shape(),
+                                                expected->shape()));
+
+  HloInstruction* constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(std::move(input_literal)));
+  builder.AddInstruction(
+      HloInstruction::CreateConvert(expected->shape(), constant));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
+
+  EXPECT_TRUE(ShapeUtil::Equal(result->shape(), expected->shape()));
+  LiteralTestUtil::ExpectEqual(*result, *expected);
 }
 
 }  // namespace
-- 
GitLab


From 5820e69356cd038bfe34617e25a0d307d23e50f3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 14:09:42 -0700
Subject: [PATCH 0258/1375] Updates estimators tutorial.

PiperOrigin-RevId: 159610979
---
 tensorflow/docs_src/extend/estimators.md      | 347 ++++++++----------
 .../examples/tutorials/estimators/abalone.py  |  65 ++--
 2 files changed, 189 insertions(+), 223 deletions(-)

diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index 3f108d145b..fbde463415 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -1,52 +1,55 @@
-# Creating Estimators in tf.contrib.learn
+# Creating Estimators in tf.estimator
 
-The tf.contrib.learn framework makes it easy to construct and train machine
-learning models via its high-level
-@{$python/contrib.learn#estimators$Estimator} API. `Estimator`
+The tf.estimator framework makes it easy to construct and train machine
+learning models via its high-level Estimator API. `Estimator`
 offers classes you can instantiate to quickly configure common model types such
 as regressors and classifiers:
 
-*   @{tf.contrib.learn.LinearClassifier}:
+*   @{tf.estimator.LinearClassifier}:
     Constructs a linear classification model.
-*   @{tf.contrib.learn.LinearRegressor}:
+*   @{tf.estimator.LinearRegressor}:
     Constructs a linear regression model.
-*   @{tf.contrib.learn.DNNClassifier}:
+*   @{tf.estimator.DNNClassifier}:
     Construct a neural network classification model.
-*   @{tf.contrib.learn.DNNRegressor}:
-    Construct a neural network regressions model.
-
-But what if none of `tf.contrib.learn`'s predefined model types meets your
-needs? Perhaps you need more granular control over model configuration, such as
+*   @{tf.estimator.DNNRegressor}:
+    Construct a neural network regression model.
+*   @{tf.estimator.DNNLinearCombinedClassifier}:
+    Construct a neural network and linear combined classification model.
+*   @{tf.estimator.DNNRegressor}:
+    Construct a neural network and linear combined regression model.
+
+But what if none of `tf.estimator`'s predefined model types meets your needs?
+Perhaps you need more granular control over model configuration, such as
 the ability to customize the loss function used for optimization, or specify
 different activation functions for each neural network layer. Or maybe you're
 implementing a ranking or recommendation system, and neither a classifier nor a
 regressor is appropriate for generating predictions.
 
 This tutorial covers how to create your own `Estimator` using the building
-blocks provided in `tf.contrib.learn`, which will predict the ages of
+blocks provided in `tf.estimator`, which will predict the ages of
 [abalones](https://en.wikipedia.org/wiki/Abalone) based on their physical
 measurements. You'll learn how to do the following:
 
 *   Instantiate an `Estimator`
 *   Construct a custom model function
-*   Configure a neural network using `tf.contrib.layers`
+*   Configure a neural network using `tf.feature_column` and `tf.layers`
 *   Choose an appropriate loss function from `tf.losses`
 *   Define a training op for your model
 *   Generate and return predictions
 
 ## Prerequisites
 
-This tutorial assumes you already know tf.contrib.learn API basics, such as
-feature columns, input functions, and `fit()`/`evaluate()`/`predict()`
-operations. If you've never used tf.contrib.learn before, or need a refresher,
+This tutorial assumes you already know tf.estimator API basics, such as
+feature columns, input functions, and `train()`/`evaluate()`/`predict()`
+operations. If you've never used tf.estimator before, or need a refresher,
 you should first review the following tutorials:
 
 *   @{$estimator$tf.estimator Quickstart}: Quick introduction to
-    training a neural network using tf.contrib.learn.
+    training a neural network using tf.estimator.
 *   @{$wide$TensorFlow Linear Model Tutorial}: Introduction to
     feature columns, and an overview on building a linear classifier in
-    tf.contrib.learn.
-*   @{$input_fn$Building Input Functions with tf.contrib.learn}: Overview of how
+    tf.estimator.
+*   @{$input_fn$Building Input Functions with tf.estimator}: Overview of how
     to construct an input_fn to preprocess and feed data into your models.
 
 ## An Abalone Age Predictor {#abalone-predictor}
@@ -113,7 +116,6 @@ from six.moves import urllib
 
 import numpy as np
 import tensorflow as tf
-from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
 
 FLAGS = None
 ```
@@ -207,17 +209,17 @@ if __name__ == "__main__":
 
 ## Instantiating an Estimator
 
-When defining a model using one of tf.contrib.learn's provided classes, such as
+When defining a model using one of tf.estimator's provided classes, such as
 `DNNClassifier`, you supply all the configuration parameters right in the
 constructor, e.g.:
 
 ```python
-my_nn = tf.contrib.learn.DNNClassifier(feature_columns=[age, height, weight],
-                                       hidden_units=[10, 10, 10],
-                                       activation_fn=tf.nn.relu,
-                                       dropout=0.2,
-                                       n_classes=3,
-                                       optimizer="Adam")
+my_nn = tf.estimator.DNNClassifier(feature_columns=[age, height, weight],
+                                   hidden_units=[10, 10, 10],
+                                   activation_fn=tf.nn.relu,
+                                   dropout=0.2,
+                                   n_classes=3,
+                                   optimizer="Adam")
 ```
 
 You don't need to write any further code to instruct TensorFlow how to train the
@@ -229,8 +231,7 @@ constructor accepts just two high-level parameters for model configuration,
 `model_fn` and `params`:
 
 ```python
-nn = tf.contrib.learn.Estimator(
-    model_fn=model_fn, params=model_params)
+nn = tf.estimator.Estimator(model_fn=model_fn, params=model_params)
 ```
 
 *   `model_fn`: A function object that contains all the aforementioned logic to
@@ -242,7 +243,7 @@ nn = tf.contrib.learn.Estimator(
 *   `params`: An optional dict of hyperparameters (e.g., learning rate, dropout)
     that will be passed into the `model_fn`.
 
-Note: Just like `tf.contrib.learn`'s predefined regressors and classifiers, the
+Note: Just like `tf.estimator`'s predefined regressors and classifiers, the
 `Estimator` initializer also accepts the general configuration arguments
 `model_dir` and `config`.
 
@@ -266,7 +267,7 @@ containing the learning rate and instantiates the `Estimator`:
 model_params = {"learning_rate": LEARNING_RATE}
 
 # Instantiate Estimator
-nn = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params)
+nn = tf.estimator.Estimator(model_fn=model_fn, params=model_params)
 ```
 
 ## Constructing the `model_fn` {#constructing-modelfn}
@@ -274,31 +275,31 @@ nn = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params)
 The basic skeleton for an `Estimator` API model function looks like this:
 
 ```python
-def model_fn(features, targets, mode, params):
+def model_fn(features, labels, mode, params):
    # Logic to do the following:
    # 1. Configure the model via TensorFlow operations
    # 2. Define the loss function for training/evaluation
    # 3. Define the training operation/optimizer
    # 4. Generate predictions
-   # 5. Return predictions/loss/train_op/eval_metric_ops in ModelFnOps object
-   return ModelFnOps(mode, predictions, loss, train_op, eval_metric_ops)
+   # 5. Return predictions/loss/train_op/eval_metric_ops in EstimatorSpec object
+   return EstimatorSpec(mode, predictions, loss, train_op, eval_metric_ops)
 ```
 
 The `model_fn` must accept three arguments:
 
-*   `features`: A dict containing the features passed to the model via `fit()`,
-    `evaluate()`, or `predict()`.
-*   `targets`: A `Tensor` containing the labels passed to the model via `fit()`,
-    `evaluate()`, or `predict()`. Will be empty for `predict()` calls, as these
-    are the values the model will infer.
-*   `mode`: One of the following @{tf.contrib.learn.ModeKeys} string values
+*   `features`: A dict containing the features passed to the model via
+    `input_fn`.
+*   `labels`: A `Tensor` containing the labels passed to the model via
+    `input_fn`. Will be empty for `predict()` calls, as these are the values the
+    model will infer.
+*   `mode`: One of the following @{tf.estimator.ModeKeys} string values
     indicating the context in which the model_fn was invoked:
-    *   `tf.contrib.learn.ModeKeys.TRAIN` The `model_fn` was invoked in training
-        mode—e.g., via a `fit()` call.
-    *   `tf.contrib.learn.ModeKeys.EVAL`. The `model_fn` was invoked in
-        evaluation mode—e.g., via an `evaluate()` call.
-    *   `tf.contrib.learn.ModeKeys.INFER`. The `model_fn` was invoked in
-        inference mode—e.g., via a `predict()` call.
+    *   `tf.estimator.ModeKeys.TRAIN` The `model_fn` was invoked in training
+        mode, namely via a `train()` call.
+    *   `tf.estimator.ModeKeys.EVAL`. The `model_fn` was invoked in
+        evaluation mode, namely via an `evaluate()` call.
+    *   `tf.estimator.ModeKeys.PREDICT`. The `model_fn` was invoked in
+        predict mode, namely via a `predict()` call.
 
 `model_fn` may also accept a `params` argument containing a dict of
 hyperparameters used for training (as shown in the skeleton above).
@@ -313,28 +314,23 @@ sections that follow):
 *   Defining the training operation that specifies the `optimizer` algorithm to
     minimize the loss values calculated by the loss function.
 
-The `model_fn` must return a @{tf.contrib.learn.ModelFnOps}
+The `model_fn` must return a @{tf.estimator.EstimatorSpec}
 object, which contains the following values:
 
 *   `mode` (required). The mode in which the model was run. Typically, you will
     return the `mode` argument of the `model_fn` here.
 
-*   `predictions` (required in `INFER` and `EVAL` modes). A dict that maps key
-    names of your choice to `Tensor`s containing the predictions from the model,
-    e.g.:
+*   `predictions` (required in `PREDICT` mode). A dict that maps key names of
+    your choice to `Tensor`s containing the predictions from the model, e.g.:
 
     ```python
     predictions = {"results": tensor_of_predictions}
     ```
 
-    In `INFER` mode, the dict that you return in `ModelFnOps` will then be
+    In `PREDICT` mode, the dict that you return in `EstimatorSpec` will then be
     returned by `predict()`, so you can construct it in the format in which
     you'd like to consume it.
 
-    In `EVAL` mode, the dict is used by
-    @{$python/contrib.metrics#Metric_Ops_$metric functions}
-    to compute metrics.
-    
 
 *   `loss` (required in `EVAL` and `TRAIN` mode). A `Tensor` containing a scalar
     loss value: the output of the model's loss function (discussed in more depth
@@ -362,7 +358,7 @@ object, which contains the following values:
     If you do not specify `eval_metric_ops`, only `loss` will be calculated
     during evaluation.
 
-### Configuring a neural network with `tf.contrib.layers`
+### Configuring a neural network with `tf.feature_column` and `tf.layers`
 
 Constructing a [neural
 network](https://en.wikipedia.org/wiki/Artificial_neural_network) entails
@@ -372,23 +368,21 @@ layer.
 The input layer is a series of nodes (one for each feature in the model) that
 will accept the feature data that is passed to the `model_fn` in the `features`
 argument. If `features` contains an n-dimensional `Tensor` with all your feature
-data (which is the case if `x` and `y` `Dataset`s are passed to `fit()`,
-`evaluate()`, and `predict()` directly), then it can serve as the input layer.
+data, then it can serve as the input layer.
 If `features` contains a dict of @{$linear#feature-columns-and-transformations$feature columns} passed to
 the model via an input function, you can convert it to an input-layer `Tensor`
-with the @{tf.contrib.layers.input_from_feature_columns} function in
-@{tf.contrib.layers}.
+with the @{tf.feature_column.input_layer} function.
 
 ```python
-input_layer = tf.contrib.layers.input_from_feature_columns(
-    columns_to_tensors=features, feature_columns=[age, height, weight])
+input_layer = tf.feature_column.input_layer(
+    features=features, feature_columns=[age, height, weight])
 ```
 
-As shown above, `input_from_feature_columns()` takes two required arguments:
+As shown above, `input_layer()` takes two required arguments:
 
-*   `columns_to_tensors`. A mapping of the model's `FeatureColumns` to the
-    `Tensors` containing the corresponding feature data. This is exactly what is
-    passed to the `model_fn` in the `features` argument.
+*   `features`. A mapping from string keys to the `Tensors` containing the
+    corresponding feature data. This is exactly what is passed to the `model_fn`
+    in the `features` argument.
 *   `feature_columns`. A list of all the `FeatureColumns` in the model—`age`,
     `height`, and `weight` in the above example.
 
@@ -397,44 +391,44 @@ hidden layers via an [activation
 function](https://en.wikipedia.org/wiki/Activation_function) that performs a
 nonlinear transformation on the data from the previous layer. The last hidden
 layer is then connected to the output layer, the final layer in the model.
-tf.contrib.layers provides the following convenience functions for constructing
-fully connected layers:
+`tf.layers` provides the `tf.layers.dense` function for constructing fully
+connected layers. The activation is controlled by the `activation` argument.
+Some options to pass to the `activation` argument are:
 
-*   `relu(inputs, num_outputs)`. Create a layer of `num_outputs` nodes fully
-    connected to the previous layer `inputs` with a [ReLU activation
-    function](https://en.wikipedia.org/wiki/Rectifier_\(neural_networks\))
+*   `tf.nn.relu`. The following code creates a layer of `units` nodes fully
+    connected to the previous layer `input_layer` with a
+    [ReLU activation function](https://en.wikipedia.org/wiki/Rectifier_\(neural_networks\))
     (@{tf.nn.relu}):
 
     ```python
-    hidden_layer = tf.contrib.layers.relu(inputs=input_layer, num_outputs=10)
+    hidden_layer = tf.layers.dense(
+        inputs=input_layer, units=10, activation=tf.nn.relu)
     ```
 
-*   `relu6(inputs, num_outputs)`. Create a layer of `num_outputs` nodes fully
+*   `tf.nn.relu6`. The following code creates a layer of `units` nodes fully
     connected to the previous layer `hidden_layer` with a ReLU 6 activation
     function (@{tf.nn.relu6}):
 
     ```python
-    second_hidden_layer = tf.contrib.layers.relu6(inputs=hidden_layer, num_outputs=20)
+    second_hidden_layer = tf.layers.dense(
+        inputs=hidden_layer, units=20, activation=tf.nn.relu)
     ```
 
-*   `linear(inputs, num_outputs)`. Create a layer of `num_outputs` nodes fully
-    connected to the previous layer `second_hidden_layer` with *no* activation
-    function, just a linear transformation:
+*   `None`. The following code creates a layer of `units` nodes fully connected
+    to the previous layer `second_hidden_layer` with *no* activation function,
+    just a linear transformation:
 
     ```python
-    output_layer = tf.contrib.layers.linear(inputs=second_hidden_layer, num_outputs=3)
+    output_layer = tf.layers.dense(
+        inputs=second_hidden_layer, units=3, activation=None)
     ```
 
-All these functions are
-[partials](https://docs.python.org/2/library/functools.html#functools.partial)
-of the more general @{tf.contrib.layers.fully_connected}
-function, which can be used to add fully connected layers with other activation
-functions, e.g.:
+Other activation functions are possible, e.g.:
 
 ```python
-output_layer = tf.contrib.layers.fully_connected(inputs=second_hidden_layer,
-                                                 num_outputs=10,
-                                                 activation_fn=tf.sigmoid)
+output_layer = tf.layers.dense(inputs=second_hidden_layer,
+                               units=10,
+                               activation_fn=tf.sigmoid)
 ```
 
 The above code creates the neural network layer `output_layer`, which is fully
@@ -446,18 +440,19 @@ Putting it all together, the following code constructs a full neural network for
 the abalone predictor, and captures its predictions:
 
 ```python
-def model_fn(features, targets, mode, params):
+def model_fn(features, labels, mode, params):
   """Model function for Estimator."""
 
   # Connect the first hidden layer to input layer
-  # (features) with relu activation
-  first_hidden_layer = tf.contrib.layers.relu(features, 10)
+  # (features["x"]) with relu activation
+  first_hidden_layer = tf.layers.dense(features["x"], 10, activation=tf.nn.relu)
 
   # Connect the second hidden layer to first hidden layer with relu
-  second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10)
+  second_hidden_layer = tf.layers.dense(
+      first_hidden_layer, 10, activation=tf.nn.relu)
 
   # Connect the output layer to second hidden layer (no activation fn)
-  output_layer = tf.contrib.layers.linear(second_hidden_layer, 1)
+  output_layer = tf.layers.dense(second_hidden_layer, 1)
 
   # Reshape output layer to 1-dim Tensor to return predictions
   predictions = tf.reshape(output_layer, [-1])
@@ -465,9 +460,9 @@ def model_fn(features, targets, mode, params):
   ...
 ```
 
-Here, because you'll be passing the abalone `Datasets` directly to `fit()`,
-`evaluate()`, and `predict()` via `x` and `y` arguments, the input layer is the
-`features` `Tensor` passed to the `model_fn`. The network contains two hidden
+Here, because you'll be passing the abalone `Datasets` using `numpy_input_fn`
+as shown below, `features` is a dict `{"x": data_tensor}`, so
+`features["x"]` is the input layer. The network contains two hidden
 layers, each with 10 nodes and a ReLU activation function. The output layer
 contains no activation function, and is
 @{tf.reshape} to a one-dimensional
@@ -476,47 +471,49 @@ tensor to capture the model's predictions, which are stored in
 
 ### Defining loss for the model {#defining-loss}
 
-The `ModelFnOps` returned by the `model_fn` must contain `loss`: a `Tensor`
+The `EstimatorSpec` returned by the `model_fn` must contain `loss`: a `Tensor`
 representing the loss value, which quantifies how well the model's predictions
-reflect the target values during training and evaluation runs. The @{tf.losses}
+reflect the label values during training and evaluation runs. The @{tf.losses}
 module provides convenience functions for calculating loss using a variety of
 metrics, including:
 
-*   `absolute_difference(predictions, targets)`. Calculates loss using the
+*   `absolute_difference(labels, predictions)`. Calculates loss using the
     [absolute-difference
     formula](https://en.wikipedia.org/wiki/Deviation_\(statistics\)#Unsigned_or_absolute_deviation)
     (also known as L<sub>1</sub> loss).
 
-*   `log_loss(predictions, targets)`. Calculates loss using the [logistic loss
+*   `log_loss(labels, predictions)`. Calculates loss using the [logistic loss
     forumula](https://en.wikipedia.org/wiki/Loss_functions_for_classification#Logistic_loss)
     (typically used in logistic regression).
 
-*   `mean_squared_error(predictions, targets)`. Calculates loss using the [mean
+*   `mean_squared_error(labels, predictions)`. Calculates loss using the [mean
     squared error](https://en.wikipedia.org/wiki/Mean_squared_error) (MSE; also
     known as L<sub>2</sub> loss).
 
 The following example adds a definition for `loss` to the abalone `model_fn`
 using `mean_squared_error()` (in bold):
 
-<pre class="prettyprint"><code class="lang-python">def model_fn(features, targets, mode, params):
+<pre class="prettyprint"><code class="lang-python">def model_fn(features, labels, mode, params):
   """Model function for Estimator."""
 
   # Connect the first hidden layer to input layer
-  # (features) with relu activation
-  first_hidden_layer = tf.contrib.layers.relu(features, 10)
+  # (features["x"]) with relu activation
+  first_hidden_layer = tf.layers.dense(features["x"], 10, activation=tf.nn.relu)
 
   # Connect the second hidden layer to first hidden layer with relu
-  second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10)
+  second_hidden_layer = tf.layers.dense(
+      first_hidden_layer, 10, activation=tf.nn.relu)
 
   # Connect the output layer to second hidden layer (no activation fn)
-  output_layer = tf.contrib.layers.linear(second_hidden_layer, 1)
+  output_layer = tf.layers.dense(second_hidden_layer, 1)
 
   # Reshape output layer to 1-dim Tensor to return predictions
   predictions = tf.reshape(output_layer, [-1])
   predictions_dict = {"ages": predictions}
 
+
   <strong># Calculate loss using mean squared error
-  loss = tf.losses.mean_squared_error(targets, predictions)</strong>
+  loss = tf.losses.mean_squared_error(labels, predictions)</strong>
   ...</code></pre>
 
 See the @{$python/contrib.losses$API guide} for a
@@ -524,14 +521,14 @@ full list of loss functions and more details on supported arguments and usage.
 
 Supplementary metrics for evaluation can be added to an `eval_metric_ops` dict.
 The following code defines an `rmse` metric, which calculates the root mean
-squared error for the model predictions. Note that the `targets` tensor is cast
+squared error for the model predictions. Note that the `labels` tensor is cast
 to a `float64` type to match the data type of the `predictions` tensor, which
 will contain real values:
 
 ```python
 eval_metric_ops = {
     "rmse": tf.metrics.root_mean_squared_error(
-        tf.cast(targets, tf.float64), predictions)
+        tf.cast(labels, tf.float64), predictions)
 }
 ```
 
@@ -539,65 +536,25 @@ eval_metric_ops = {
 
 The training op defines the optimization algorithm TensorFlow will use when
 fitting the model to the training data. Typically when training, the goal is to
-minimize loss. The tf.contrib.layers API provides the function `optimize_loss`,
-which returns a training op that will do just that. `optimize_loss` has four
-required arguments:
-
-*   `loss`. The loss value calculated by the `model_fn` (see [Defining Loss for
-    the Model](#defining-loss)).
-*   `global_step`. An integer
-    @{tf.Variable} representing the
-    step counter to increment for each model training run. Can easily be
-    created/incremented in TensorFlow via the
-    @{tf.train.get_global_step}
-    function.
-*   `learning_rate`. The [learning
-    rate](https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Background)
-    (also known as _step size_) hyperparameter that the optimization algorithm
-    uses when training.
-*   `optimizer`. The optimization algorithm to use during training. `optimizer`
-    can accept any of the following string values, representing an optimization
-    algorithm predefined in `tf.contrib.layers.optimizers`:
-    *   `SGD`. Implementation of [gradient
-        descent](https://en.wikipedia.org/wiki/Gradient_descent)
-        (@{tf.train.GradientDescentOptimizer})
-    *   `Adagrad`. Implementation of the [AdaGrad optimization
-        algorithm](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
-        (@{tf.train.AdagradOptimizer})
-    *   `Adam`. Implementation of the [Adam optimization
-        algorithm](http://arxiv.org/pdf/1412.6980.pdf)
-        (@{tf.train.AdamOptimizer})
-    *   `Ftrl`. Implementation of the
-        [FTRL-Proximal](https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf)
-        ("Follow The (Proximally) Regularized Leader") algorithm
-        (@{tf.train.FtrlOptimizer})
-    *   `Momentum`. Implementation of stochastic gradient descent with
-        [momentum](https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Momentum)
-        (@{tf.train.MomentumOptimizer})
-    *   `RMSProp`. Implementation of the
-        [RMSprop](http://sebastianruder.com/optimizing-gradient-descent/index.html#rmsprop)
-        algorithm
-        (@{tf.train.RMSPropOptimizer})
-
-Note: The `optimize_loss` function supports additional optional arguments to
-further configure the optimizer, such as for implementing decay. See the
-@{tf.contrib.layers.optimize_loss$API docs} for more info.
+minimize loss. A simple way to create the training op is to instantiate a
+`tf.train.Optimizer` subclass and call the `minimize` method.
 
 The following code defines a training op for the abalone `model_fn` using the
 loss value calculated in [Defining Loss for the Model](#defining-loss), the
-learning rate passed to the function in `params`, and the SGD optimizer. For
-`global_step`, the convenience function
-@{tf.train.get_global_step}
-in tf.contrib.framework takes care of generating an integer variable:
+learning rate passed to the function in `params`, and the gradient descent
+optimizer. For `global_step`, the convenience function
+@{tf.train.get_global_step} takes care of generating an integer variable:
 
 ```python
-train_op = tf.contrib.layers.optimize_loss(
-    loss=loss,
-    global_step=tf.contrib.framework.get_global_step(),
-    learning_rate=params["learning_rate"],
-    optimizer="SGD")
+optimizer = tf.train.GradientDescentOptimizer(
+    learning_rate=params["learning_rate"])
+train_op = optimizer.minimize(
+    loss=loss, global_step=tf.train.get_global_step())
 ```
 
+For a full list of optimizers, and other details, see the
+@{$python/train#optimizers$API guide}.
+
 ### The complete abalone `model_fn`
 
 Here's the final, complete `model_fn` for the abalone age predictor. The
@@ -606,40 +563,39 @@ and returns a `ModelFnOps` object containing `mode`, `predictions_dict`, `loss`,
 and `train_op`:
 
 ```python
-def model_fn(features, targets, mode, params):
+def model_fn(features, labels, mode, params):
   """Model function for Estimator."""
 
   # Connect the first hidden layer to input layer
-  # (features) with relu activation
-  first_hidden_layer = tf.contrib.layers.relu(features, 10)
+  # (features["x"]) with relu activation
+  first_hidden_layer = tf.layers.dense(features["x"], 10, activation=tf.nn.relu)
 
   # Connect the second hidden layer to first hidden layer with relu
-  second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10)
+  second_hidden_layer = tf.layers.dense(
+      first_hidden_layer, 10, activation=tf.nn.relu)
 
   # Connect the output layer to second hidden layer (no activation fn)
-  output_layer = tf.contrib.layers.linear(second_hidden_layer, 1)
+  output_layer = tf.layers.dense(second_hidden_layer, 1)
 
   # Reshape output layer to 1-dim Tensor to return predictions
   predictions = tf.reshape(output_layer, [-1])
   predictions_dict = {"ages": predictions}
 
   # Calculate loss using mean squared error
-  loss = tf.losses.mean_squared_error(targets, predictions)
+  loss = tf.losses.mean_squared_error(labels, predictions)
 
   # Calculate root mean squared error as additional eval metric
   eval_metric_ops = {
-      "rmse":
-          tf.metrics.root_mean_squared_error(
-              tf.cast(targets, tf.float64), predictions)
+      "rmse": tf.metrics.root_mean_squared_error(
+          tf.cast(labels, tf.float64), predictions)
   }
 
-  train_op = tf.contrib.layers.optimize_loss(
-      loss=loss,
-      global_step=tf.contrib.framework.get_global_step(),
-      learning_rate=params["learning_rate"],
-      optimizer="SGD")
+  optimizer = tf.train.GradientDescentOptimizer(
+      learning_rate=params["learning_rate"])
+  train_op = optimizer.minimize(
+      loss=loss, global_step=tf.train.get_global_step())
 
-  return model_fn_lib.ModelFnOps(
+  return tf.estimator.EstimatorSpec(
       mode=mode,
       predictions=predictions_dict,
       loss=loss,
@@ -657,29 +613,31 @@ Add the following code to the end of `main()` to fit the neural network to the
 training data and evaluate accuracy:
 
 ```python
-def get_train_inputs():
-  x = tf.constant(training_set.data)
-  y = tf.constant(training_set.target)
-  return x, y
-
-# Fit
-nn.fit(input_fn=get_train_inputs, steps=5000)
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": np.array(training_set.data)},
+    y=np.array(training_set.target),
+    num_epochs=None,
+    shuffle=True)
 
-def get_test_inputs():
-  x = tf.constant(test_set.data)
-  y = tf.constant(test_set.target)
-  return x, y
+# Train
+nn.train(input_fn=train_input_fn, steps=5000)
 
 # Score accuracy
-ev = nn.evaluate(input_fn=get_test_inputs, steps=1)
+test_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": np.array(test_set.data)},
+    y=np.array(test_set.target),
+    num_epochs=1,
+    shuffle=False)
+
+ev = nn.evaluate(input_fn=test_input_fn)
 print("Loss: %s" % ev["loss"])
 print("Root Mean Squared Error: %s" % ev["rmse"])
 ```
 
 Note: The above code uses input functions to feed feature (`x`) and label (`y`)
-`Tensor`s into the model for both training (`get_train_inputs()`) and evaluation
-(`get_test_inputs()`). To learn more about input functions, see the tutorial
-@{$input_fn$Building Input Functions with tf.contrib.learn}.
+`Tensor`s into the model for both training (`train_input_fn`) and evaluation
+(`test_input_fn`). To learn more about input functions, see the tutorial
+@{$input_fn$Building Input Functions with tf.estimator}.
 
 Then run the code. You should see output like the following:
 
@@ -701,7 +659,11 @@ To predict ages for the `ABALONE_PREDICT` data set, add the following to
 
 ```python
 # Print out predictions
-predictions = nn.predict(x=prediction_set.data, as_iterable=True)
+predict_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": prediction_set.data},
+    num_epochs=1,
+    shuffle=False)
+predictions = nn.predict(input_fn=predict_input_fn)
 for i, p in enumerate(predictions):
   print("Prediction %s: %s" % (i + 1, p["ages"]))
 ```
@@ -723,11 +685,10 @@ Prediction 7: 11.1289
 
 ## Additional Resources
 
-Congrats! You've successfully built a tf.contrib.learn `Estimator` from scratch.
+Congrats! You've successfully built a tf.estimator `Estimator` from scratch.
 For additional reference materials on building `Estimator`s, see the following
 sections of the API guides:
 
-*   @{$python/contrib.learn#Estimators$Estimators}
 *   @{$python/contrib.layers$Layers}
 *   @{$python/contrib.losses$Losses}
 *   @{$python/contrib.layers#optimization$Optimization}
diff --git a/tensorflow/examples/tutorials/estimators/abalone.py b/tensorflow/examples/tutorials/estimators/abalone.py
index 3c0ea2e409..4765d5dabf 100644
--- a/tensorflow/examples/tutorials/estimators/abalone.py
+++ b/tensorflow/examples/tutorials/estimators/abalone.py
@@ -25,7 +25,6 @@ from six.moves import urllib
 
 import numpy as np
 import tensorflow as tf
-from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
 
 FLAGS = None
 
@@ -72,39 +71,39 @@ def maybe_download(train_data, test_data, predict_data):
   return train_file_name, test_file_name, predict_file_name
 
 
-def model_fn(features, targets, mode, params):
+def model_fn(features, labels, mode, params):
   """Model function for Estimator."""
 
   # Connect the first hidden layer to input layer
-  # (features) with relu activation
-  first_hidden_layer = tf.contrib.layers.relu(features, 10)
+  # (features["x"]) with relu activation
+  first_hidden_layer = tf.layers.dense(features["x"], 10, activation=tf.nn.relu)
 
   # Connect the second hidden layer to first hidden layer with relu
-  second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10)
+  second_hidden_layer = tf.layers.dense(
+      first_hidden_layer, 10, activation=tf.nn.relu)
 
   # Connect the output layer to second hidden layer (no activation fn)
-  output_layer = tf.contrib.layers.linear(second_hidden_layer, 1)
+  output_layer = tf.layers.dense(second_hidden_layer, 1)
 
   # Reshape output layer to 1-dim Tensor to return predictions
   predictions = tf.reshape(output_layer, [-1])
   predictions_dict = {"ages": predictions}
 
   # Calculate loss using mean squared error
-  loss = tf.losses.mean_squared_error(targets, predictions)
+  loss = tf.losses.mean_squared_error(labels, predictions)
 
   # Calculate root mean squared error as additional eval metric
   eval_metric_ops = {
       "rmse": tf.metrics.root_mean_squared_error(
-          tf.cast(targets, tf.float64), predictions)
+          tf.cast(labels, tf.float64), predictions)
   }
 
-  train_op = tf.contrib.layers.optimize_loss(
-      loss=loss,
-      global_step=tf.contrib.framework.get_global_step(),
-      learning_rate=params["learning_rate"],
-      optimizer="SGD")
+  optimizer = tf.train.GradientDescentOptimizer(
+      learning_rate=params["learning_rate"])
+  train_op = optimizer.minimize(
+      loss=loss, global_step=tf.train.get_global_step())
 
-  return model_fn_lib.ModelFnOps(
+  return tf.estimator.EstimatorSpec(
       mode=mode,
       predictions=predictions_dict,
       loss=loss,
@@ -133,28 +132,34 @@ def main(unused_argv):
   model_params = {"learning_rate": LEARNING_RATE}
 
   # Instantiate Estimator
-  nn = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params)
-  
-  def get_train_inputs():
-    x = tf.constant(training_set.data)
-    y = tf.constant(training_set.target)
-    return x, y
-  
-  # Fit
-  nn.fit(input_fn=get_train_inputs, steps=5000)
+  nn = tf.estimator.Estimator(model_fn=model_fn, params=model_params)
+
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": np.array(training_set.data)},
+      y=np.array(training_set.target),
+      num_epochs=None,
+      shuffle=True)
+
+  # Train
+  nn.train(input_fn=train_input_fn, steps=5000)
 
   # Score accuracy
-  def get_test_inputs():
-    x = tf.constant(test_set.data)
-    y = tf.constant(test_set.target)
-    return x, y
-  
-  ev = nn.evaluate(input_fn=get_test_inputs, steps=1)
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": np.array(test_set.data)},
+      y=np.array(test_set.target),
+      num_epochs=1,
+      shuffle=False)
+
+  ev = nn.evaluate(input_fn=test_input_fn)
   print("Loss: %s" % ev["loss"])
   print("Root Mean Squared Error: %s" % ev["rmse"])
 
   # Print out predictions
-  predictions = nn.predict(x=prediction_set.data, as_iterable=True)
+  predict_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": prediction_set.data},
+      num_epochs=1,
+      shuffle=False)
+  predictions = nn.predict(input_fn=predict_input_fn)
   for i, p in enumerate(predictions):
     print("Prediction %s: %s" % (i + 1, p["ages"]))
 
-- 
GitLab


From eb197c45eda55b1047b00f1136a4fec2b8adb2d5 Mon Sep 17 00:00:00 2001
From: Todd Wang <toddwang@gmail.com>
Date: Tue, 20 Jun 2017 14:16:26 -0700
Subject: [PATCH 0259/1375] [XLA] Explicitly instantiate Permute template func

This is an attempted fix for older compilers that can't deduce the template args.
---
 tensorflow/compiler/xla/service/llvm_ir/ir_array.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index e401305ae7..b14104be6a 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -158,7 +158,7 @@ IrArray::Index IrArray::Index::SourceIndexOfTranspose(
     tensorflow::gtl::ArraySlice<int64> dimension_mapping,
     llvm::IRBuilder<>* builder) const {
   std::vector<llvm::Value*> operand_multidim_index =
-      Permute(dimension_mapping, multidim());
+      Permute<std::vector, llvm::Value*>(dimension_mapping, multidim());
   if (linear() != nullptr &&
       ShapeUtil::TransposeIsBitcast(operand_shape, shape, dimension_mapping)) {
     return Index(operand_multidim_index, linear(), operand_shape);
-- 
GitLab


From 1efd7f171ba30421b5d8369a93526395a721c0d9 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Tue, 20 Jun 2017 14:10:40 -0700
Subject: [PATCH 0260/1375] Adds new Dataset APIs for filesystems

This change adds a new API to construct a Dataset:
 - Dataset.list_files("/path/to/files/*.data")

This API can be used in conjunction with the forthcoming .interleave()
API which will allow for the construction of parallel loading of data files.

As an added bonus, input pipelines constructed in this manner should be
deterministic, to allow for reproducible work.

PiperOrigin-RevId: 159611145
---
 .../contrib/data/python/kernel_tests/BUILD    |  15 ++
 .../list_files_dataset_op_test.py             | 159 ++++++++++++++++++
 .../contrib/data/python/ops/dataset_ops.py    |  24 +++
 3 files changed, 198 insertions(+)
 create mode 100644 tensorflow/contrib/data/python/kernel_tests/list_files_dataset_op_test.py

diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index ab4d80c327..9909ea41c9 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -99,6 +99,21 @@ py_test(
     ],
 )
 
+py_test(
+    name = "list_files_dataset_op_test",
+    size = "small",
+    srcs = ["list_files_dataset_op_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/data",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 py_test(
     name = "map_dataset_op_test",
     size = "small",
diff --git a/tensorflow/contrib/data/python/kernel_tests/list_files_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/list_files_dataset_op_test.py
new file mode 100644
index 0000000000..27298de65f
--- /dev/null
+++ b/tensorflow/contrib/data/python/kernel_tests/list_files_dataset_op_test.py
@@ -0,0 +1,159 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the experimental input pipeline ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from os import path
+import shutil
+import tempfile
+
+from tensorflow.contrib.data.python.ops import dataset_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import test
+from tensorflow.python.util import compat
+
+
+class ListFilesDatasetOpTest(test.TestCase):
+
+  def setUp(self):
+    self.tmp_dir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    shutil.rmtree(self.tmp_dir, ignore_errors=True)
+
+  def _touchTempFiles(self, filenames):
+    for filename in filenames:
+      open(path.join(self.tmp_dir, filename), 'a').close()
+
+  def testEmptyDirectory(self):
+    dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*'))
+    with self.test_session() as sess:
+      itr = dataset.make_one_shot_iterator()
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(itr.get_next())
+
+  def testSimpleDirectory(self):
+    filenames = ['a', 'b', 'c']
+    self._touchTempFiles(filenames)
+
+    dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*'))
+    with self.test_session() as sess:
+      itr = dataset.make_one_shot_iterator()
+
+      full_filenames = []
+      produced_filenames = []
+      for filename in filenames:
+        full_filenames.append(
+            compat.as_bytes(path.join(self.tmp_dir, filename)))
+        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+      self.assertItemsEqual(full_filenames, produced_filenames)
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(itr.get_next())
+
+  def testEmptyDirectoryInitializer(self):
+    filename_placeholder = array_ops.placeholder(dtypes.string, shape=[])
+    dataset = dataset_ops.Dataset.list_files(filename_placeholder)
+
+    with self.test_session() as sess:
+      itr = dataset.make_initializable_iterator()
+      sess.run(
+          itr.initializer,
+          feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')})
+
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(itr.get_next())
+
+  def testSimpleDirectoryInitializer(self):
+    filenames = ['a', 'b', 'c']
+    self._touchTempFiles(filenames)
+
+    filename_placeholder = array_ops.placeholder(dtypes.string, shape=[])
+    dataset = dataset_ops.Dataset.list_files(filename_placeholder)
+
+    with self.test_session() as sess:
+      itr = dataset.make_initializable_iterator()
+      sess.run(
+          itr.initializer,
+          feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')})
+
+      full_filenames = []
+      produced_filenames = []
+      for filename in filenames:
+        full_filenames.append(
+            compat.as_bytes(path.join(self.tmp_dir, filename)))
+        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+
+      self.assertItemsEqual(full_filenames, produced_filenames)
+
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(itr.get_next())
+
+  def testFileSuffixes(self):
+    filenames = ['a.txt', 'b.py', 'c.py', 'd.pyc']
+    self._touchTempFiles(filenames)
+
+    filename_placeholder = array_ops.placeholder(dtypes.string, shape=[])
+    dataset = dataset_ops.Dataset.list_files(filename_placeholder)
+
+    with self.test_session() as sess:
+      itr = dataset.make_initializable_iterator()
+      sess.run(
+          itr.initializer,
+          feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py')})
+
+      full_filenames = []
+      produced_filenames = []
+      for filename in filenames[1:-1]:
+        full_filenames.append(
+            compat.as_bytes(path.join(self.tmp_dir, filename)))
+        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+      self.assertItemsEqual(full_filenames, produced_filenames)
+
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(itr.get_next())
+
+  def testFileMiddles(self):
+    filenames = ['a.txt', 'b.py', 'c.pyc']
+    self._touchTempFiles(filenames)
+
+    filename_placeholder = array_ops.placeholder(dtypes.string, shape=[])
+    dataset = dataset_ops.Dataset.list_files(filename_placeholder)
+
+    with self.test_session() as sess:
+      itr = dataset.make_initializable_iterator()
+      sess.run(
+          itr.initializer,
+          feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py*')})
+
+      full_filenames = []
+      produced_filenames = []
+      for filename in filenames[1:]:
+        full_filenames.append(
+            compat.as_bytes(path.join(self.tmp_dir, filename)))
+        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+
+      self.assertItemsEqual(full_filenames, produced_filenames)
+
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(itr.get_next())
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index 89410bf844..29f1209a58 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -33,6 +33,7 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gen_dataset_ops
+from tensorflow.python.ops import gen_io_ops
 from tensorflow.python.ops import logging_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import parsing_ops
@@ -600,6 +601,29 @@ class Dataset(object):
     dataset = dataset.batch(batch_size)
     return dataset
 
+  @staticmethod
+  def list_files(file_pattern):
+    """A dataset of all files matching a pattern.
+
+    Example:
+      If we had the following files on our filesystem:
+        - /path/to/dir/a.txt
+        - /path/to/dir/b.py
+        - /path/to/dir/c.py
+      If we pass "/path/to/dir/*.py" as the directory, the dataset would
+      produce:
+        - /path/to/dir/b.py
+        - /path/to/dir/c.py
+
+    Args:
+      file_pattern: A string or scalar string `tf.Tensor`, representing
+        the filename pattern that will be matched.
+
+    Returns:
+     A `Dataset` of strings corresponding to file names.
+    """
+    return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern))
+
   def repeat(self, count=None):
     """Repeats this dataset `count` times.
 
-- 
GitLab


From af5ad5f7d7930af90bf9b04fb475d6b3ad7604ea Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 14:32:55 -0700
Subject: [PATCH 0261/1375] Expose num_enqueue_threads argument to
 read_batch_features.

PiperOrigin-RevId: 159614896
---
 .../learn/python/learn/learn_io/graph_io.py   | 36 ++++++---
 .../python/learn/learn_io/graph_io_test.py    | 74 +++++++++++++++++--
 2 files changed, 90 insertions(+), 20 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
index 61208ba24e..bdb88b89bb 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
@@ -75,10 +75,10 @@ def read_batch_examples(file_pattern,
       `tf.local_variables_initializer()` and run the op in a session.
     queue_capacity: Capacity for input queue.
     num_threads: The number of threads enqueuing examples. In order to have
-      predicted and repeatable order of reading and enqueueing, such as in
+      predictable and repeatable order of reading and enqueueing, such as in
       prediction and evaluation mode, `num_threads` should be 1.
     read_batch_size: An int or scalar `Tensor` specifying the number of
-      records to read at once
+      records to read at once.
     parse_fn: Parsing function, takes `Example` Tensor returns parsed
       representation. If `None`, no parsing is done.
     name: Name of resulting op.
@@ -142,10 +142,10 @@ def read_keyed_batch_examples(file_pattern,
       `tf.local_variables_initializer()` and run the op in a session.
     queue_capacity: Capacity for input queue.
     num_threads: The number of threads enqueuing examples. In order to have
-      predicted and repeatable order of reading and enqueueing, such as in
+      predictable and repeatable order of reading and enqueueing, such as in
       prediction and evaluation mode, `num_threads` should be 1.
     read_batch_size: An int or scalar `Tensor` specifying the number of
-      records to read at once
+      records to read at once.
     parse_fn: Parsing function, takes `Example` Tensor returns parsed
       representation. If `None`, no parsing is done.
     name: Name of resulting op.
@@ -217,7 +217,7 @@ def read_keyed_batch_examples_shared_queue(file_pattern,
     queue_capacity: Capacity for input queue.
     num_threads: The number of threads enqueuing examples.
     read_batch_size: An int or scalar `Tensor` specifying the number of
-      records to read at once
+      records to read at once.
     parse_fn: Parsing function, takes `Example` Tensor returns parsed
       representation. If `None`, no parsing is done.
     name: Name of resulting op.
@@ -335,7 +335,7 @@ def _read_keyed_batch_examples_helper(file_pattern,
     queue_capacity: Capacity for input queue.
     num_threads: The number of threads enqueuing examples.
     read_batch_size: An int or scalar `Tensor` specifying the number of
-      records to read at once
+      records to read at once.
     filter_fn: Filtering function, takes both keys as well `Example` Tensors
       and returns a boolean mask of the same shape as the input Tensors to
       be applied for filtering. If `None`, no filtering is done.
@@ -470,13 +470,15 @@ def read_keyed_batch_features(file_pattern,
       tf.local_variables_initializer() and run the op in a session.
     queue_capacity: Capacity for input queue.
     reader_num_threads: The number of threads to read examples. In order to have
-      predicted and repeatable order of reading and enqueueing, such as in
+      predictable and repeatable order of reading and enqueueing, such as in
       prediction and evaluation mode, `reader_num_threads` should be 1.
     feature_queue_capacity: Capacity of the parsed features queue.
     num_enqueue_threads: Number of threads to enqueue the parsed example queue.
       Using multiple threads to enqueue the parsed example queue helps maintain
       a full queue when the subsequent computations overall are cheaper than
-      parsing.
+      parsing. In order to have predictable and repeatable order of reading and
+      enqueueing, such as in prediction and evaluation mode,
+      `num_enqueue_threads` should be 1.
     parse_fn: Parsing function, takes `Example` Tensor returns parsed
       representation. If `None`, no parsing is done.
     name: Name of resulting op.
@@ -617,7 +619,9 @@ def queue_parsed_features(parsed_features,
     num_enqueue_threads: Number of threads to enqueue the parsed example queue.
       Using multiple threads to enqueue the parsed example queue helps maintain
       a full queue when the subsequent computations overall are cheaper than
-      parsing.
+      parsing. In order to have predictable and repeatable order of reading and
+      enqueueing, such as in prediction and evaluation mode,
+      `num_enqueue_threads` should be 1.
     name: Name of resulting op.
 
   Returns:
@@ -721,6 +725,7 @@ def read_batch_features(file_pattern,
                         queue_capacity=10000,
                         feature_queue_capacity=100,
                         reader_num_threads=1,
+                        num_enqueue_threads=2,
                         parse_fn=None,
                         name=None):
   """Adds operations to read, queue, batch and parse `Example` protos.
@@ -752,8 +757,14 @@ def read_batch_features(file_pattern,
     feature_queue_capacity: Capacity of the parsed features queue. Set this
       value to a small number, for example 5 if the parsed features are large.
     reader_num_threads: The number of threads to read examples. In order to have
-      predicted and repeatable order of reading and enqueueing, such as in
+      predictable and repeatable order of reading and enqueueing, such as in
       prediction and evaluation mode, `reader_num_threads` should be 1.
+    num_enqueue_threads: Number of threads to enqueue the parsed example queue.
+      Using multiple threads to enqueue the parsed example queue helps maintain
+      a full queue when the subsequent computations overall are cheaper than
+      parsing. In order to have predictable and repeatable order of reading and
+      enqueueing, such as in prediction and evaluation mode,
+      `num_enqueue_threads` should be 1.
     parse_fn: Parsing function, takes `Example` Tensor returns parsed
       representation. If `None`, no parsing is done.
     name: Name of resulting op.
@@ -772,8 +783,9 @@ def read_batch_features(file_pattern,
       randomize_input=randomize_input,
       num_epochs=num_epochs,
       queue_capacity=queue_capacity,
-      feature_queue_capacity=feature_queue_capacity,
       reader_num_threads=reader_num_threads,
+      feature_queue_capacity=feature_queue_capacity,
+      num_enqueue_threads=num_enqueue_threads,
       parse_fn=parse_fn,
       name=name)
   return features
@@ -804,7 +816,7 @@ def read_batch_record_features(file_pattern,
       tf.local_variables_initializer() and run the op in a session.
     queue_capacity: Capacity for input queue.
     reader_num_threads: The number of threads to read examples. In order to have
-      predicted and repeatable order of reading and enqueueing, such as in
+      predictable and repeatable order of reading and enqueueing, such as in
       prediction and evaluation mode, `reader_num_threads` should be 1.
     name: Name of resulting op.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
index f25f7caf61..6f0fd9a297 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
@@ -350,6 +350,16 @@ class GraphIOTest(test.TestCase):
       coord.request_stop()
       coord.join(threads)
 
+  def _create_file_from_list_of_features(self, lines):
+    json_lines = [
+        "".join([
+            '{"features": { "feature": { "sequence": {',
+            '"bytes_list": { "value": ["', base64.b64encode(l).decode("ascii"),
+            '"]}}}}}\n'
+        ]) for l in lines
+    ]
+    return self._create_temp_file("".join(json_lines))
+
   def test_read_text_lines_large(self):
     gfile.Glob = self._orig_glob
     sequence_prefix = "abcdefghijklmnopqrstuvwxyz123456789"
@@ -358,14 +368,7 @@ class GraphIOTest(test.TestCase):
         "".join([sequence_prefix, str(l)]).encode("ascii")
         for l in xrange(num_records)
     ]
-    json_lines = [
-        "".join([
-            '{"features": { "feature": { "sequence": {',
-            '"bytes_list": { "value": ["', base64.b64encode(l).decode("ascii"),
-            '"]}}}}}\n'
-        ]) for l in lines
-    ]
-    filename = self._create_temp_file("".join(json_lines))
+    filename = self._create_file_from_list_of_features(lines)
     batch_size = 10000
     queue_capacity = 100000
     name = "my_large_batch"
@@ -410,6 +413,61 @@ class GraphIOTest(test.TestCase):
     self.assertEqual(len(parsed_records), num_records)
     self.assertEqual(set(parsed_records), set(lines))
 
+  def test_read_batch_features_maintains_order(self):
+    """Make sure that examples are read in the right order.
+
+    When randomize_input=False, num_enqueue_threads=1 and reader_num_threads=1
+    read_keyed_batch_features() should read the examples in the same order as
+    they appear in the file.
+    """
+    gfile.Glob = self._orig_glob
+    num_records = 1000
+    lines = ["".join(str(l)).encode("ascii") for l in xrange(num_records)]
+    filename = self._create_file_from_list_of_features(lines)
+    batch_size = 10
+    queue_capacity = 1000
+    name = "my_large_batch"
+
+    features = {"sequence": parsing_ops.FixedLenFeature([], dtypes_lib.string)}
+
+    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+      result = graph_io.read_batch_features(
+          filename,
+          batch_size,
+          features,
+          io_ops.TextLineReader,
+          randomize_input=False,
+          num_epochs=1,
+          queue_capacity=queue_capacity,
+          reader_num_threads=1,
+          num_enqueue_threads=1,
+          parse_fn=parsing_ops.decode_json_example,
+          name=name)
+      self.assertEqual(1, len(result))
+      self.assertAllEqual((None,), result["sequence"].get_shape().as_list())
+      session.run(variables.local_variables_initializer())
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(session, coord=coord)
+
+      data = []
+      try:
+        while not coord.should_stop():
+          data.append(session.run(result))
+      except errors.OutOfRangeError:
+        pass
+      finally:
+        coord.request_stop()
+
+      coord.join(threads)
+
+    parsed_records = [
+        item for sublist in [d["sequence"] for d in data] for item in sublist
+    ]
+    # Check that the number of records matches expected and all records
+    # are present in the right order.
+    self.assertEqual(len(parsed_records), num_records)
+    self.assertEqual(parsed_records, lines)
+
   def test_read_text_lines_multifile(self):
     gfile.Glob = self._orig_glob
     filenames = self._create_sorted_temp_files(["ABC\n", "DEF\nGHK\n"])
-- 
GitLab


From e74c7ad4bb246db8e0b011d995b558b66a6fc414 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Tue, 20 Jun 2017 15:29:29 -0700
Subject: [PATCH 0262/1375] Add Score-Gradient trick to
 `monte_carlo_csiszar_f_divergence`.

PiperOrigin-RevId: 159623845
---
 .../kernel_tests/csiszar_divergence_test.py   | 83 ++++++++++++++++++
 .../python/ops/csiszar_divergence_impl.py     | 85 +++++++++++++++----
 2 files changed, 151 insertions(+), 17 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
index c120acaefc..fabf7a9b77 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -25,6 +25,7 @@ from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib
 from tensorflow.contrib.distributions.python.ops import mvn_full_covariance as mvn_full_lib
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
@@ -573,6 +574,88 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
                           rtol=0.05, atol=0.)
 
+  def test_score_trick(self):
+
+    with self.test_session() as sess:
+      d = 5  # Dimension
+      num_draws = int(1e5)
+      seed = 1
+
+      p = mvn_full_lib.MultivariateNormalFullCovariance(
+          covariance_matrix=self._tridiag(d, diag_value=1, offdiag_value=0.5))
+
+      # Variance is very high when approximating Forward KL, so we make
+      # scale_diag larger than in test_kl_reverse_multidim. This ensures q
+      # "covers" p and thus Var_q[p/q] is smaller.
+      s = array_ops.constant(1.)
+      q = mvn_diag_lib.MultivariateNormalDiag(
+          scale_diag=array_ops.tile([s], [d]))
+
+      approx_kl = cd.monte_carlo_csiszar_f_divergence(
+          f=cd.kl_reverse,
+          p=p,
+          q=q,
+          num_draws=num_draws,
+          seed=seed)
+
+      approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
+          f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
+          p=p,
+          q=q,
+          num_draws=num_draws,
+          seed=seed)
+
+      approx_kl_score_trick = cd.monte_carlo_csiszar_f_divergence(
+          f=cd.kl_reverse,
+          p=p,
+          q=q,
+          num_draws=num_draws,
+          use_reparametrization=False,
+          seed=seed)
+
+      approx_kl_self_normalized_score_trick = (
+          cd.monte_carlo_csiszar_f_divergence(
+              f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
+              p=p,
+              q=q,
+              num_draws=num_draws,
+              use_reparametrization=False,
+              seed=seed))
+
+      exact_kl = kullback_leibler.kl_divergence(q, p)
+
+      grad = lambda fs: gradients_impl.gradients(fs, s)[0]
+
+      [
+          approx_kl_,
+          approx_kl_self_normalized_,
+          approx_kl_score_trick_,
+          approx_kl_self_normalized_score_trick_,
+          exact_kl_,
+      ] = sess.run([
+          grad(approx_kl),
+          grad(approx_kl_self_normalized),
+          grad(approx_kl_score_trick),
+          grad(approx_kl_self_normalized_score_trick),
+          grad(exact_kl),
+      ])
+
+      self.assertAllClose(
+          approx_kl_, exact_kl_,
+          rtol=0.06, atol=0.)
+
+      self.assertAllClose(
+          approx_kl_self_normalized_, exact_kl_,
+          rtol=0.05, atol=0.)
+
+      self.assertAllClose(
+          approx_kl_score_trick_, exact_kl_,
+          rtol=0.06, atol=0.)
+
+      self.assertAllClose(
+          approx_kl_self_normalized_score_trick_, exact_kl_,
+          rtol=0.05, atol=0.)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 262da41bda..7b51d8d932 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -41,6 +41,7 @@ import numpy as np
 
 from tensorflow.contrib import framework as contrib_framework
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import distribution
@@ -734,7 +735,8 @@ def symmetrized_csiszar_function(logu, csiszar_function, name=None):
                   + dual_csiszar_function(logu, csiszar_function))
 
 
-def monte_carlo_csiszar_f_divergence(f, p, q, num_draws, seed=None, name=None):
+def monte_carlo_csiszar_f_divergence(
+    f, p, q, num_draws, use_reparametrization=True, seed=None, name=None):
   """Monte-Carlo approximation of the Csiszar f-Divergence.
 
   A Csiszar-function is a member of,
@@ -751,6 +753,38 @@ def monte_carlo_csiszar_f_divergence(f, p, q, num_draws, seed=None, name=None):
                              where x_j ~iid q(x)
   ```
 
+  Tricks: Reparameterization and Score-Gradient
+
+  When q is "reparameterized", i.e., a diffeomorphic transformation of a
+  parameterless distribution (e.g.,
+  `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and
+  expectation, i.e.,
+  `nabla Avg{ s_i : i=1...n } = Avg{ nabla s_i : i=1...n }` where `S_n=Avg{s_i}`
+  and `s_i = f(x_i), x_i ~ q`.
+
+  However, if q is not reparameterized, TensorFlow's gradient will be incorrect
+  since the chain-rule stops at samples of unreparameterized distributions. In
+  this circumstance using the Score-Gradient trick results in an unbiased
+  gradient, i.e.,
+
+  ```none
+  nabla E_q[f(X)]
+  = nabla int dx q(x) f(x)
+  = int dx nabla [ q(x) f(x) ]
+  = int dx q'(x) f(x) + q(x) f'(x)
+  = int dx q(x) [q'(x) / q(x) f(x) + f'(x) ]
+  = int dx q(x) nabla [ log(q(x)) stopgrad[f(x)] + f(x) ]
+  = E_q[ nabla [ log(q(X)) stopgrad[f(X)] + f(X) ] ]
+  ~= Avg{ log(q(y_i)) stopgrad[f(y_i)] + f(y_i) : y_i = stopgrad[x_i], x_i ~ q}
+  ```
+
+  Unless `q.reparameterization_type != distribution.FULLY_REPARAMETERIZED` it is
+  usually preferable to `use_reparametrization = True`.
+
+  Warning: using `use_reparametrization = False` will mean that the result is
+  *not* the Csiszar f-Divergence. However its expected gradient *is* the
+  gradient of the Csiszar f-Divergence.
+
   Example Application:
 
   The Csiszar f-Divergence is a useful framework for variational inference.
@@ -775,29 +809,46 @@ def monte_carlo_csiszar_f_divergence(f, p, q, num_draws, seed=None, name=None):
       `reparameterization_type`, `sample(n)`, and `log_prob(x)`.
     num_draws: Integer scalar number of draws used to approximate the
       f-Divergence expectation.
+    use_reparametrization: Python `bool`. When `True` uses the standard
+      Monte-Carlo average. When `False` uses the score-gradient trick. (See
+      above for details.)
     seed: Python `int` seed for `q.sample`.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
     monte_carlo_csiszar_f_divergence: Floating-type `Tensor` Monte Carlo
-      approximation of the Csiszar f-Divergence.
+      approximation of the Csiszar f-Divergence. Warning: using
+      `use_reparametrization = False` will mean that the result is *not* the
+      Csiszar f-Divergence. However its expected gradient *is* the actual
+      gradient of the Csiszar f-Divergence.
 
   Raises:
-    ValueError: if `q` is not a reparameterized distribution. A distribution `q`
-      is said to be "reparameterized" when its samples are generated by
-      transforming the samples of another distribution which does not depend on
-      the parameterization of `q`. This property ensures the gradient (with
-      respect to parameters) is valid.
+    ValueError: if `q` is not a reparameterized distribution and
+      `use_reparametrization = True`. A distribution `q` is said to be
+      "reparameterized" when its samples are generated by transforming the
+      samples of another distribution which does not depend on the
+      parameterization of `q`. This property ensures the gradient (with respect
+      to parameters) is valid.
   """
   with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]):
-    # TODO(jvdillon): Consider only raising an exception if the gradient is
-    # requested.
-    if q.reparameterization_type != distribution.FULLY_REPARAMETERIZED:
-      raise ValueError(
-          "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
-          "transformation of a parameterless distribution. (Otherwise this "
-          "function has a biased gradient.)")
     x = q.sample(num_draws, seed=seed)
-    return math_ops.reduce_mean(
-        f(p.log_prob(x) - q.log_prob(x)),
-        axis=0)
+    if use_reparametrization:
+      # TODO(jvdillon): Consider only raising an exception if the gradient is
+      # requested.
+      if q.reparameterization_type != distribution.FULLY_REPARAMETERIZED:
+        raise ValueError(
+            "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
+            "transformation of a parameterless distribution. (Otherwise this "
+            "function has a biased gradient.)")
+      return math_ops.reduce_mean(f(p.log_prob(x) - q.log_prob(x)), axis=0)
+    else:
+      x = array_ops.stop_gradient(x)
+      logqx = q.log_prob(x)
+      fx = f(p.log_prob(x) - logqx)
+      # Alternatively we could have returned:
+      #   reduce_mean(fx * exp(logqx) / stop_gradient(exp(logqx)), axis=0)
+      # This is nice because it means the result is exactly the Csiszar
+      # f-Divergence yet the gradient is unbiased. However its numerically
+      # unstable since the q is not in log-domain.
+      return math_ops.reduce_mean(logqx * array_ops.stop_gradient(fx) + fx,
+                                  axis=0)
-- 
GitLab


From 1a4f52834c4bf0b0491468c0107ee57ff879ce63 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 15:43:44 -0700
Subject: [PATCH 0263/1375] Added an option for ignoring missing variables in
 assign_from_checkpoint, similar to assign_from_checkpoint_fn.

PiperOrigin-RevId: 159625888
---
 .../contrib/framework/python/ops/variables.py    | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py
index f02a7c6360..bf709f921d 100644
--- a/tensorflow/contrib/framework/python/ops/variables.py
+++ b/tensorflow/contrib/framework/python/ops/variables.py
@@ -525,7 +525,7 @@ def get_variable_full_name(var):
 # TODO(sguada): Update docs in slim/g3doc/index.md to describe
 # the new feature where the var_list dictionary can have values that
 # are each a list of Variables.
-def assign_from_checkpoint(model_path, var_list):
+def assign_from_checkpoint(model_path, var_list, ignore_missing_vars=False):
   """Creates an operation to assign specific variables from a checkpoint.
 
   Args:
@@ -538,13 +538,15 @@ def assign_from_checkpoint(model_path, var_list):
         name in the checkpoint must be the full variable, not the
         name of the partitioned variable, eg. "my_var" rather than
         "my_var/part_4". If empty, returns no_op(), {}.
+    ignore_missing_vars: Boolean, if True ignore variables missing in the
+        checkpoint with a warning instead of failing.
 
   Returns:
     the restore_op and the feed_dict that need to be run to restore var_list.
 
   Raises:
-    ValueError: If the checkpoint specified at `model_path` is missing one of
-      the variables in `var_list`.
+    ValueError: If `ignore_missing_vars` is False and the checkpoint specified
+        at `model_path` is missing one of the variables in `var_list`.
   """
   # Normalize var_list into a dictionary mapping names in the
   # checkpoint to the list of variables to initialize from that
@@ -572,8 +574,12 @@ def assign_from_checkpoint(model_path, var_list):
   assign_ops = []
   for ckpt_name in grouped_vars:
     if not reader.has_tensor(ckpt_name):
-      raise ValueError(
-          'Checkpoint is missing variable [%s]' % ckpt_name)
+      log_str = 'Checkpoint is missing variable [%s]' % ckpt_name
+      if ignore_missing_vars:
+        logging.warning(log_str)
+        continue
+      else:
+        raise ValueError(log_str)
     ckpt_value = reader.get_tensor(ckpt_name)
 
     for var in grouped_vars[ckpt_name]:
-- 
GitLab


From 4c13564c9cd90499145f562e9e0a6199aaab8fb3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 15:45:43 -0700
Subject: [PATCH 0264/1375] Change default manager to FIFO for general
 performance improvements.

PiperOrigin-RevId: 159626166
---
 tensorflow/core/grappler/costs/virtual_scheduler.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 75f485b414..f6f91e37ac 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -58,10 +58,10 @@ Costs CombineCosts(const Costs& left, const Costs& right) {
 VirtualScheduler::VirtualScheduler(const GrapplerItem* grappler_item,
                                    const bool use_static_shapes,
                                    Cluster* cluster)
-    :  // Use LIFO instead of FIFO. LIFO allows an output node of an node to
+    :  // Allow LIFO as well as FIFO. LIFO allows an output node of an node to
        // follow it in execution, saving addition memory time from having to
-       // write and read.
-      ready_nodes_(new LIFOManager()),
+       // write and read. For default cases, use FIFO for performance.
+      ready_nodes_(new FIFOManager()),
       graph_costs_(Costs::ZeroCosts()),
       graph_properties_(*grappler_item),
       cluster_(cluster),
-- 
GitLab


From 35af7113de0f15360246234f76e5dda5e927c556 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Tue, 20 Jun 2017 15:48:13 -0700
Subject: [PATCH 0265/1375] [XLA] Move replica_count out of Backend.

This is an intermediate step in making replica_count more explicitly
programmable (rather than set by a flag). There is no reason for the Backend to
hold replica_count - it was only holding it as a container with no additional
semantics.

PiperOrigin-RevId: 159626528
---
 tensorflow/compiler/xla/service/BUILD         |  2 +-
 tensorflow/compiler/xla/service/backend.cc    | 34 ++-----------
 tensorflow/compiler/xla/service/backend.h     | 19 +------
 .../xla/service/compile_only_service.cc       | 10 ++--
 .../xla/service/compile_only_service.h        |  3 +-
 .../compiler/xla/service/local_service.cc     | 11 ++--
 .../compiler/xla/service/local_service.h      |  3 +-
 tensorflow/compiler/xla/service/service.cc    | 51 ++++++++++++-------
 tensorflow/compiler/xla/service/service.h     |  4 +-
 9 files changed, 60 insertions(+), 77 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 9d770987b9..778c740b1d 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -342,7 +342,6 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/legacy_flags:backend_flags",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
@@ -386,6 +385,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
+        "//tensorflow/compiler/xla/legacy_flags:backend_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc
index 83f954789c..9abe30e3f3 100644
--- a/tensorflow/compiler/xla/service/backend.cc
+++ b/tensorflow/compiler/xla/service/backend.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #define EIGEN_USE_THREADS
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/compiler/xla/legacy_flags/backend_flags.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/platform_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -51,13 +50,6 @@ perftools::gputools::Platform* BackendOptions::platform() const {
   return platform_;
 }
 
-BackendOptions& BackendOptions::set_number_of_replicas(int number_of_replicas) {
-  number_of_replicas_ = number_of_replicas;
-  return *this;
-}
-
-int BackendOptions::number_of_replicas() const { return number_of_replicas_; }
-
 BackendOptions& BackendOptions::set_intra_op_parallelism_threads(
     int num_threads) {
   intra_op_parallelism_threads_ = num_threads;
@@ -85,11 +77,6 @@ struct Backend::EigenThreadPoolWrapper {
 
 /* static */ StatusOr<std::unique_ptr<Backend>> Backend::CreateBackend(
     const BackendOptions& options) {
-  int64 replica_count = options.number_of_replicas();
-  if (replica_count == -1) {
-    legacy_flags::BackendFlags* flags = legacy_flags::GetBackendFlags();
-    replica_count = flags->xla_replicas;
-  }
   perftools::gputools::Platform* platform = options.platform();
   TF_ASSIGN_OR_RETURN(auto compiler, Compiler::GetForPlatform(platform));
   TF_ASSIGN_OR_RETURN(auto stream_executors,
@@ -98,9 +85,9 @@ struct Backend::EigenThreadPoolWrapper {
                       TransferManager::GetForPlatform(platform));
   TF_ASSIGN_OR_RETURN(auto computation_placer,
                       ComputationPlacer::GetForPlatform(platform));
-  std::unique_ptr<Backend> backend(new Backend(
-      replica_count, platform, compiler, stream_executors, transfer_manager,
-      computation_placer, options.intra_op_parallelism_threads()));
+  std::unique_ptr<Backend> backend(
+      new Backend(platform, compiler, stream_executors, transfer_manager,
+                  computation_placer, options.intra_op_parallelism_threads()));
   return std::move(backend);
 }
 
@@ -134,36 +121,25 @@ StatusOr<Backend::StreamPtr> Backend::BorrowStream(
 }
 
 Backend::Backend(
-    int64 replica_count, perftools::gputools::Platform* platform,
-    Compiler* compiler,
+    perftools::gputools::Platform* platform, Compiler* compiler,
     tensorflow::gtl::ArraySlice<se::StreamExecutor*> stream_executors,
     TransferManager* transfer_manager, ComputationPlacer* computation_placer,
     int intra_op_parallelism_threads)
     : platform_(platform),
       compiler_(compiler),
       transfer_manager_(transfer_manager),
-      computation_placer_(computation_placer),
-      replica_count_(replica_count) {
+      computation_placer_(computation_placer) {
   // The given set of stream executors set may include invalid executors.
   for (se::StreamExecutor* exec : stream_executors) {
     if (exec != nullptr) {
       stream_executors_.push_back(exec);
     }
   }
-  CHECK_GE(replica_count, 1) << "Must request at least 1 replica.";
-
   // Create a memory allocator for the valid stream executors.
   memory_allocator_ =
       MakeUnique<StreamExecutorMemoryAllocator>(platform, stream_executors);
-
-  // First check that there are some non-null stream executors to avoid issuing
-  // an error mentioning replicas in the common case of requesting just 1
-  // replica, which means no replication.
   CHECK(!stream_executors_.empty())
       << "Service found no devices for backend " << platform_->Name() << '.';
-  CHECK_GE(stream_executors_.size(), replica_count)
-      << "Requested more replicas than there are devices for backend "
-      << platform_->Name() << '.';
 
   if (platform->id() == se::host::kHostPlatformId) {
     inter_op_thread_pool_.reset(new tensorflow::thread::ThreadPool(
diff --git a/tensorflow/compiler/xla/service/backend.h b/tensorflow/compiler/xla/service/backend.h
index 3ead274cde..b5ca483b72 100644
--- a/tensorflow/compiler/xla/service/backend.h
+++ b/tensorflow/compiler/xla/service/backend.h
@@ -41,20 +41,12 @@ struct ThreadPoolDevice;
 namespace xla {
 
 // Options to configure the backend when it is created.
-//
-// TODO(b/62588571): Remove the notion of replicas from the backend.
 class BackendOptions {
  public:
   // Set the platform backing the backend, or nullptr for the default platform.
   BackendOptions& set_platform(perftools::gputools::Platform* platform);
   perftools::gputools::Platform* platform() const;
 
-  // Set the number of replicas to use when compiling replicated
-  // programs. The default is -1 meaning that the value is read from
-  // the xla_replicas flag.
-  BackendOptions& set_number_of_replicas(int number_of_replicas);
-  int number_of_replicas() const;
-
   // Sets the thread pool size for parallel execution of an individual operator.
   // The default value of -1 will result in initializing the thread pool with
   // the number of threads equal to the number of cores in the system.
@@ -63,7 +55,6 @@ class BackendOptions {
 
  private:
   perftools::gputools::Platform* platform_ = nullptr;
-  int number_of_replicas_ = -1;
   int intra_op_parallelism_threads_ = -1;
 };
 
@@ -77,8 +68,7 @@ class Backend {
  public:
   using StreamPtr = Pool<perftools::gputools::Stream>::SmartPtr;
 
-  // Creates a new backend for the given platform with the given number of
-  // replicas.
+  // Creates a new backend.
   static StatusOr<std::unique_ptr<Backend>> CreateBackend(
       const BackendOptions& options);
 
@@ -101,9 +91,6 @@ class Backend {
   // all of these devices may be usable by XLA.
   int device_count() const { return stream_executors_.size(); }
 
-  // Returns the number of replicas when replication is enabled.
-  int replica_count() const { return replica_count_; }
-
   // Returns the device ordinal number of the default device.
   int default_device_ordinal() const;
 
@@ -170,8 +157,7 @@ class Backend {
 
  private:
   struct EigenThreadPoolWrapper;
-  Backend(int64 replica_count, perftools::gputools::Platform* platform,
-          Compiler* compiler,
+  Backend(perftools::gputools::Platform* platform, Compiler* compiler,
           tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*>
               stream_executors,
           TransferManager* transfer_manager,
@@ -184,7 +170,6 @@ class Backend {
   Compiler* compiler_;
   TransferManager* transfer_manager_;
   ComputationPlacer* computation_placer_;
-  int64 replica_count_ = -1;
 
   // Vector of stream executors. stream_executors_[0] is the default executor.
   std::vector<perftools::gputools::StreamExecutor*> stream_executors_;
diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index 0d1a439724..cfcf2744fb 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -52,14 +52,16 @@ CompileOnlyService::NewService(const ServiceOptions& options) {
 
   TF_ASSIGN_OR_RETURN(std::unique_ptr<Backend> compute_constant_backend,
                       CreateComputeConstantBackend());
-  std::unique_ptr<CompileOnlyService> service(
-      new CompileOnlyService(compiler, std::move(compute_constant_backend)));
+  std::unique_ptr<CompileOnlyService> service(new CompileOnlyService(
+      options, compiler, std::move(compute_constant_backend)));
   return std::move(service);
 }
 
 CompileOnlyService::CompileOnlyService(
-    Compiler* compiler, std::unique_ptr<Backend> compute_constant_backend)
-    : Service(/*backend=*/nullptr, std::move(compute_constant_backend)),
+    const ServiceOptions& options, Compiler* compiler,
+    std::unique_ptr<Backend> compute_constant_backend)
+    : Service(options, /*backend=*/nullptr,
+              std::move(compute_constant_backend)),
       compiler_(compiler) {
   runs_in_client_process_ = true;
 }
diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h
index b19f4bd592..dd00c58240 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.h
+++ b/tensorflow/compiler/xla/service/compile_only_service.h
@@ -103,7 +103,8 @@ class CompileOnlyService : public Service {
 
  private:
   explicit CompileOnlyService(
-      Compiler* compiler, std::unique_ptr<Backend> compute_constant_backend);
+      const ServiceOptions& options, Compiler* compiler,
+      std::unique_ptr<Backend> compute_constant_backend);
   CompileOnlyService(const CompileOnlyService&) = delete;
   void operator=(const CompileOnlyService&) = delete;
 
diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index c656f77b7c..90dfe01bb9 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -63,7 +63,6 @@ namespace xla {
 
   BackendOptions backend_options;
   backend_options.set_platform(platform)
-      .set_number_of_replicas(options.number_of_replicas())
       .set_intra_op_parallelism_threads(options.intra_op_parallelism_threads());
   TF_ASSIGN_OR_RETURN(std::unique_ptr<Backend> backend,
                       Backend::CreateBackend(backend_options));
@@ -71,13 +70,15 @@ namespace xla {
   TF_ASSIGN_OR_RETURN(std::unique_ptr<Backend> compute_constant_backend,
                       CreateComputeConstantBackend());
   std::unique_ptr<LocalService> service(new LocalService(
-      std::move(backend), std::move(compute_constant_backend)));
+      options, std::move(backend), std::move(compute_constant_backend)));
   return std::move(service);
 }
 
-LocalService::LocalService(std::unique_ptr<Backend> execute_backend,
+LocalService::LocalService(const ServiceOptions& options,
+                           std::unique_ptr<Backend> execute_backend,
                            std::unique_ptr<Backend> compute_constant_backend)
-    : Service(std::move(execute_backend), std::move(compute_constant_backend)) {
+    : Service(options, std::move(execute_backend),
+              std::move(compute_constant_backend)) {
   runs_in_client_process_ = true;
 }
 
@@ -153,7 +154,7 @@ StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable(
   // Construct computation layout from the argument layouts.
   auto module_config = MakeUnique<HloModuleConfig>(*program_shape);
   module_config->set_has_hybrid_result(has_hybrid_result);
-  module_config->set_replica_count(execute_backend_->replica_count());
+  module_config->set_replica_count(options_.number_of_replicas());
   module_config->set_debug_options(legacy_flags::GetDebugOptionsFromFlags());
   if (execute_backend_->eigen_intra_op_thread_pool() != nullptr) {
     module_config->set_intra_op_parallelism_threads(
diff --git a/tensorflow/compiler/xla/service/local_service.h b/tensorflow/compiler/xla/service/local_service.h
index 767a3ab697..915c8c3072 100644
--- a/tensorflow/compiler/xla/service/local_service.h
+++ b/tensorflow/compiler/xla/service/local_service.h
@@ -60,7 +60,8 @@ class LocalService : public Service {
       const Shape* result_layout, int device_ordinal, bool has_hybrid_result);
 
  private:
-  explicit LocalService(std::unique_ptr<Backend> backend,
+  explicit LocalService(const ServiceOptions& options,
+                        std::unique_ptr<Backend> backend,
                         std::unique_ptr<Backend> compute_constant_backend);
   LocalService(const LocalService&) = delete;
   void operator=(const LocalService&) = delete;
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 2c3a0a1a25..31740757ab 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/legacy_flags/backend_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
@@ -141,12 +142,13 @@ int ServiceOptions::intra_op_parallelism_threads() const {
   }
   BackendOptions backend_options;
   backend_options.set_platform(platform);
-  backend_options.set_number_of_replicas(options.number_of_replicas());
   TF_ASSIGN_OR_RETURN(execute_backend, Backend::CreateBackend(backend_options));
+
   TF_ASSIGN_OR_RETURN(std::unique_ptr<Backend> compute_constant_backend,
                       CreateComputeConstantBackend());
-  std::unique_ptr<Service> service(new Service(
-      std::move(execute_backend), std::move(compute_constant_backend)));
+  std::unique_ptr<Service> service(
+      new Service(options, std::move(execute_backend),
+                  std::move(compute_constant_backend)));
   return std::move(service);
 }
 
@@ -158,7 +160,6 @@ Service::CreateComputeConstantBackend() {
     if (platform->id() == se::host::kHostPlatformId) {
       BackendOptions backend_options;
       backend_options.set_platform(platform);
-      backend_options.set_number_of_replicas(1);
       return Backend::CreateBackend(backend_options);
     }
   }
@@ -171,11 +172,24 @@ Service::CreateComputeConstantBackend() {
   };
 }
 
-Service::Service(std::unique_ptr<Backend> execute_backend,
+Service::Service(const ServiceOptions& options,
+                 std::unique_ptr<Backend> execute_backend,
                  std::unique_ptr<Backend> compute_constant_backend)
-    : execute_backend_(std::move(execute_backend)),
+    : options_(options),
+      execute_backend_(std::move(execute_backend)),
       compute_constant_backend_(std::move(compute_constant_backend)) {
+  // TODO(b/32648682): this flag / options update dance will go away once we
+  // pass the replica count explicitly to the service.
+  if (options_.number_of_replicas() < 0) {
+    legacy_flags::BackendFlags* flags = legacy_flags::GetBackendFlags();
+    options_.set_number_of_replicas(flags->xla_replicas);
+  }
+
   if (execute_backend_) {
+    if (execute_backend_->device_count() > 0) {
+      CHECK_GE(execute_backend_->device_count(), options_.number_of_replicas())
+          << "Requested more replicas than there are devices.";
+    }
     LOG(INFO) << Printf(
         "XLA service %p executing computations on platform %s. Devices:", this,
         execute_backend_->platform()->Name().c_str());
@@ -325,7 +339,7 @@ StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig(
     module_config->enable_hlo_profiling(true);
   }
 
-  module_config->set_replica_count(backend->replica_count());
+  module_config->set_replica_count(options_.number_of_replicas());
   module_config->set_seed(execution_options.seed());
   module_config->set_debug_options(execution_options.debug_options());
 
@@ -506,7 +520,7 @@ Service::ExecuteParallelAndRegisterResult(
 
   TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
                       backend->computation_placer()->AssignDevices(
-                          backend->replica_count(), executables.size()));
+                          options_.number_of_replicas(), executables.size()));
 
   for (int64 i = 0; i < executables.size(); i++) {
     // Stream executors for the replicas of the current computation.
@@ -572,7 +586,8 @@ StatusOr<GlobalDataHandle> Service::ExecuteAndRegisterResult(
 
   TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
                       backend->computation_placer()->AssignDevices(
-                          backend->replica_count(), /*computation_count=*/1));
+                          options_.number_of_replicas(),
+                          /*computation_count=*/1));
 
   // Set up run options.
   std::vector<ServiceExecutableRunOptions> run_options;
@@ -589,14 +604,14 @@ StatusOr<GlobalDataHandle> Service::ExecuteAndRegisterResult(
   }
 
   perftools::gputools::DeviceMemoryBase result;
-  if (backend->replica_count() == 1) {
+  if (options_.number_of_replicas() == 1) {
     TF_ASSIGN_OR_RETURN(
         result, executable->ExecuteOnStreamWrapper<se::DeviceMemoryBase>(
                     &run_options[0], profile, arguments));
   } else {
     std::vector<
         tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>>
-        repeated_arguments(backend->replica_count(), arguments);
+        repeated_arguments(options_.number_of_replicas(), arguments);
 
     TF_ASSIGN_OR_RETURN(auto results, executable->ExecuteOnStreams(
                                           run_options, repeated_arguments));
@@ -626,7 +641,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
   std::vector<string> computation_names;
   std::vector<DeviceHandle> device_handles;
 
-  if (arg->requests_size() * execute_backend_->replica_count() >
+  if (arg->requests_size() * options_.number_of_replicas() >
       execute_backend_->device_count()) {
     return FailedPrecondition(
         "there are not enough stream executors to execute %d computations",
@@ -723,7 +738,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
 tensorflow::Status Service::GetDeviceHandles(const GetDeviceHandlesRequest* arg,
                                              GetDeviceHandlesResponse* result) {
   const int64 available_device_count = execute_backend_->device_count();
-  const int64 replica_count = execute_backend_->replica_count();
+  const int64 replica_count = options_.number_of_replicas();
   if (replica_count <= 0) {
     return FailedPrecondition("Replica count must be a positive integer");
   }
@@ -948,7 +963,7 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg,
   Literal literal = Literal(arg->literal());
   const Shape& shape = literal.shape();
 
-  if (ShapeUtil::IsTuple(shape) && execute_backend_->replica_count() > 1) {
+  if (ShapeUtil::IsTuple(shape) && options_.number_of_replicas() > 1) {
     // TODO(b/32990684): Tuple transfers to host end up allocating further
     // buffers - implement that correctly.
     return Unimplemented(
@@ -988,7 +1003,7 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg,
 
 tensorflow::Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
                                              TransferToInfeedResponse* result) {
-  const int64 replica_count = execute_backend_->replica_count();
+  const int64 replica_count = options_.number_of_replicas();
   if (arg->replica_id() < 0 || arg->replica_id() >= replica_count) {
     return FailedPrecondition(
         "%s",
@@ -1017,7 +1032,7 @@ tensorflow::Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
 tensorflow::Status Service::TransferFromOutfeed(
     const TransferFromOutfeedRequest* arg,
     TransferFromOutfeedResponse* result) {
-  const int64 replica_count = execute_backend_->replica_count();
+  const int64 replica_count = options_.number_of_replicas();
   if (arg->replica_id() < 0 || arg->replica_id() >= replica_count) {
     return FailedPrecondition(
         "The replica_id=%lld on TransferFromOutfeedRequest not in range [0, "
@@ -1428,13 +1443,13 @@ DeviceHandle Service::SingleComputationDeviceHandle() const {
 StatusOr<std::vector<perftools::gputools::StreamExecutor*>> Service::Replicas(
     const Backend& backend, const DeviceHandle& device_handle) const {
   std::vector<perftools::gputools::StreamExecutor*> replicas;
-  for (int replica = 0; replica < backend.replica_count(); ++replica) {
+  for (int replica = 0; replica < options_.number_of_replicas(); ++replica) {
     // From the computation placer, find out the device ids of the replicas for
     // the given device handle.
     TF_ASSIGN_OR_RETURN(
         int device_ordinal,
         backend.computation_placer()->DeviceId(replica, device_handle.handle(),
-                                               backend.replica_count(),
+                                               options_.number_of_replicas(),
                                                device_handle.device_count()));
     TF_ASSIGN_OR_RETURN(auto executor, backend.stream_executor(device_ordinal));
     replicas.push_back(executor);
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 81aa0d2e8e..968fb53b34 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -248,7 +248,7 @@ class Service : public ServiceInterface {
 
   // The constructor is private. Use the NewService factory to create new
   // service objects.
-  Service(std::unique_ptr<Backend> backend,
+  Service(const ServiceOptions& options, std::unique_ptr<Backend> backend,
           std::unique_ptr<Backend> compute_constant_backend);
 
   static StatusOr<std::unique_ptr<Backend>> CreateComputeConstantBackend();
@@ -355,6 +355,8 @@ class Service : public ServiceInterface {
   // single computation that is not model-parallelized.
   DeviceHandle SingleComputationDeviceHandle() const;
 
+  ServiceOptions options_;
+
   // Tracks computations built via the API.
   ComputationTracker computation_tracker_;
 
-- 
GitLab


From 5856f9ea6d84d041b882d19d3b357104c17563fc Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 20 Jun 2017 16:18:12 -0700
Subject: [PATCH 0266/1375] Automated g4 rollback of changelist 159583264

PiperOrigin-RevId: 159630408
---
 tensorflow/python/estimator/estimator.py      | 41 ++------
 tensorflow/python/estimator/estimator_test.py | 95 -------------------
 2 files changed, 8 insertions(+), 128 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index ff44914779..ab49f36e5e 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -357,7 +357,7 @@ class Estimator(object):
               }
 
   def _assert_members_are_not_overridden(self):
-    allowed_overrides = set(['_call_input_fn', '_create_global_step'])
+    allowed_overrides = set(['_create_global_step'])
     estimator_members = set([m for m in Estimator.__dict__.keys()
                              if not m.startswith('__')])
     subclass_members = set(self.__class__.__dict__.keys())
@@ -485,7 +485,7 @@ class Estimator(object):
       return export_dir
 
   def _get_features_from_input_fn(self, input_fn):
-    result = self._call_input_fn(input_fn)
+    result = input_fn()
     if not ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
       logging.warning('Input graph does not contain a QueueRunner. '
                       'That means predict yields forever. '
@@ -549,32 +549,6 @@ class Estimator(object):
     assert step.dtype.is_integer
     return step
 
-  def _call_input_fn(self, input_fn):
-    """Calls the input function.
-
-    Args:
-      input_fn: The input function.
-
-    Returns:
-      Either features or (features, labels) where features and labels are:
-        features - `Tensor` or dictionary of string feature name to `Tensor`.
-        labels - `Tensor` or dictionary of `Tensor` with labels.
-
-    Raises:
-      ValueError: if input_fn takes invalid arguments.
-    """
-    input_fn_args = _fn_args(input_fn)
-    for arg in input_fn_args:
-      if arg not in ('config', 'params'):
-        raise ValueError('input_fn should not include argument {}.'.format(arg))
-    kwargs = {}
-    if 'params' in input_fn_args:
-      kwargs['params'] = self.params
-    if 'config' in input_fn_args:
-      kwargs['config'] = self.config
-    with ops.device('/cpu:0'):
-      return input_fn(**kwargs)
-
   def _call_model_fn(self, features, labels, mode):
     """Calls model function.
 
@@ -589,7 +563,7 @@ class Estimator(object):
     Raises:
       ValueError: if model_fn returns invalid objects.
     """
-    model_fn_args = _fn_args(self._model_fn)
+    model_fn_args = _model_fn_args(self._model_fn)
     kwargs = {}
     if 'mode' in model_fn_args:
       kwargs['mode'] = mode
@@ -610,7 +584,8 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(input_fn)
+      with ops.device('/cpu:0'):
+        features, labels = input_fn()
       estimator_spec = self._call_model_fn(features, labels,
                                            model_fn_lib.ModeKeys.TRAIN)
       ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
@@ -691,7 +666,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(input_fn)
+      features, labels = input_fn()
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
 
@@ -774,7 +749,7 @@ def _get_replica_device_setter(config):
     return None
 
 
-def _fn_args(fn):
+def _model_fn_args(fn):
   """Get argument names for function-like object.
 
   Args:
@@ -799,7 +774,7 @@ def _fn_args(fn):
 
 def _verify_model_fn_args(model_fn, params):
   """Verifies model fn arguments."""
-  args = set(_fn_args(model_fn))
+  args = set(_model_fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
   if 'labels' not in args:
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 338239ca23..b86afece43 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -120,9 +120,6 @@ class EstimatorInheritanceConstraintTest(test.TestCase):
       def __init__(self):
         super(_Estimator, self).__init__(model_fn=dummy_model_fn)
 
-      def _call_input_fn(self, input_fn):
-        return input_fn()
-
       def _create_global_step(self, graph):
         pass
 
@@ -328,48 +325,6 @@ def _make_input_fn(features, labels):
 
 class EstimatorTrainTest(test.TestCase):
 
-  def test_bad_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-
-    def _model_fn(features, labels, mode, params, config):
-      del params, config
-      return model_fn_global_step_incrementer(features, labels, mode)
-
-    def _input_fn(params, config, not_allowed):
-      del not_allowed
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    with self.assertRaisesRegexp(ValueError, 'should not include argument'):
-      est.train(_input_fn, steps=1)
-
-  def test_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-    input_fn_call_count = [0]
-
-    def _model_fn(features, labels, mode, params, config):
-      del params, config
-      return model_fn_global_step_incrementer(features, labels, mode)
-
-    def _input_fn(params, config):
-      input_fn_call_count[0] += 1
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    self.assertEqual(0, input_fn_call_count[0])
-    est.train(_input_fn, steps=1)
-    self.assertEqual(1, input_fn_call_count[0])
-
   def test_minimal_model_fn_args(self):
     expected_features = {'x': 42., 'y': 43.}
     expected_labels = 44.
@@ -710,29 +665,6 @@ class _StepCounterHook(session_run_hook.SessionRunHook):
 
 class EstimatorEvaluateTest(test.TestCase):
 
-  def test_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-    input_fn_call_count = [0]
-
-    def _model_fn(features, labels, mode, params, config):
-      del params, config
-      return model_fn_global_step_incrementer(features, labels, mode)
-
-    def _input_fn(params, config):
-      input_fn_call_count[0] += 1
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    est.train(dummy_input_fn, steps=1)
-    self.assertEqual(0, input_fn_call_count[0])
-    est.evaluate(_input_fn, steps=1)
-    self.assertEqual(1, input_fn_call_count[0])
-
   def test_model_fn_must_return_estimator_spec(self):
     def _model_fn(features, labels, mode):
       _, _ = features, labels
@@ -934,33 +866,6 @@ class EstimatorEvaluateTest(test.TestCase):
 
 class EstimatorPredictTest(test.TestCase):
 
-  def test_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-    input_fn_call_count = [0]
-
-    def _model_fn(features, labels, mode, params, config):
-      del features, labels, params, config
-      return model_fn_lib.EstimatorSpec(
-          mode,
-          loss=constant_op.constant(0.),
-          train_op=state_ops.assign_add(training.get_global_step(), 1),
-          predictions=constant_op.constant([[10.]]))
-
-    def _input_fn(params, config):
-      input_fn_call_count[0] += 1
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    est.train(dummy_input_fn, steps=1)
-    self.assertEqual(0, input_fn_call_count[0])
-    next(est.predict(_input_fn))
-    self.assertEqual(1, input_fn_call_count[0])
-
   def test_no_trained_model_in_model_dir(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
     with self.assertRaisesRegexp(ValueError,
-- 
GitLab


From 4be287671af6190ca52ce04bd93bf356bb7fce20 Mon Sep 17 00:00:00 2001
From: Blake Hechtman <blakehechtman@google.com>
Date: Tue, 20 Jun 2017 16:21:28 -0700
Subject: [PATCH 0267/1375] Make transpose layout assignement more clear since
 reasoning about the composition of permutations is far more confusing.

PiperOrigin-RevId: 159630894
---
 .../compiler/xla/service/layout_assignment.cc | 30 +++++++++-------
 .../xla/service/layout_assignment_test.cc     | 35 +++++++++++++++++++
 2 files changed, 53 insertions(+), 12 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index 238a327495..ff1493230b 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -758,10 +758,14 @@ std::unique_ptr<Layout> LayoutAssignment::ChooseOperandLayoutFromOutputLayout(
 
   if (instruction->opcode() == HloOpcode::kTranspose) {
     // Pick the operand layout that makes the transpose a bitcast.
-    std::vector<int64> perm =
-        ComposePermutations(instruction->dimensions(),
-                            AsInt64Slice(output_layout.minor_to_major()));
-    Layout operand_layout = LayoutUtil::MakeLayout(perm);
+    int64 rank = ShapeUtil::Rank(instruction->shape());
+    std::vector<int64> new_minor_to_major(rank);
+    for (int64 i = 0; i < rank; ++i) {
+      int64 output_dim = output_layout.minor_to_major(i);
+      int64 operand_dim = instruction->dimensions(output_dim);
+      new_minor_to_major[i] = operand_dim;
+    }
+    Layout operand_layout = LayoutUtil::MakeLayout(new_minor_to_major);
     TF_CHECK_OK(
         LayoutUtil::ValidateLayoutForShape(operand_layout, operand->shape()));
     return MakeUnique<Layout>(operand_layout);
@@ -812,14 +816,16 @@ std::unique_ptr<Layout> LayoutAssignment::ChooseOutputLayoutFromOperandLayout(
   }
 
   if (user->opcode() == HloOpcode::kTranspose) {
-    // Pick the user layout that makes the reshape a bitcast.
-    // To become a bitcast, the layouts need to satisfy
-    //   collapsing_order * output_layout = input_layout
-    // so output_layout = inverse(collapsing_order) * input_layout
-    std::vector<int64> perm =
-        Permute(InversePermutation(user->dimensions()),
-                AsInt64Slice(operand_layout.minor_to_major()));
-    Layout user_layout = LayoutUtil::MakeLayout(perm);
+    // Pick the user layout that makes the transpose a bitcast.
+    int64 rank = ShapeUtil::Rank(user->shape());
+    std::vector<int64> new_minor_to_major(rank);
+    auto inverse_dimensions = InversePermutation(user->dimensions());
+    for (int64 i = 0; i < rank; ++i) {
+      int64 operand_dim = operand_layout.minor_to_major(i);
+      int64 user_dim = inverse_dimensions[operand_dim];
+      new_minor_to_major[i] = user_dim;
+    }
+    Layout user_layout = LayoutUtil::MakeLayout(new_minor_to_major);
     TF_CHECK_OK(LayoutUtil::ValidateLayoutForShape(user_layout, user->shape()));
     return MakeUnique<Layout>(user_layout);
   }
diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index a83466d807..f69c043f32 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -552,6 +552,41 @@ TEST_F(LayoutAssignmentTest, MakeOperandsTheSame) {
               ElementsAre(1, 0));
 }
 
+// Test layout assignment of a transpose into a bitcast based on its operand.
+TEST_F(LayoutAssignmentTest, TransposeToBitcastFromOperand) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape input_shape_with_layout =
+      ShapeUtil::MakeShapeWithLayout(F32, {3, 5, 6, 7}, {2, 0, 3, 1});
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_shape_with_layout, "param"));
+  auto transpose = builder.AddInstruction(HloInstruction::CreateTranspose(
+      ShapeUtil::MakeShape(F32, {6, 7, 3, 5}), param, {2, 3, 0, 1}));
+  auto module = CreateNewModule();
+  HloComputation* computation =
+      module->AddEntryComputation(builder.Build(transpose));
+  ComputationLayout computation_layout(computation->ComputeProgramShape());
+  AssignLayouts(module.get(), &computation_layout);
+  EXPECT_TRUE(ShapeUtil::TransposeIsBitcast(transpose->operand(0)->shape(),
+                                            transpose->shape(), {2, 3, 0, 1}));
+}
+// Test layout assignment of a transpose into a bitcast based on its user.
+TEST_F(LayoutAssignmentTest, TransposeToBitcastToUser) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape input_shape = ShapeUtil::MakeShape(F32, {3, 5, 6, 7});
+  auto constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
+  auto broadcast = builder.AddInstruction(
+      HloInstruction::CreateBroadcast(input_shape, constant, {}));
+  auto transpose = builder.AddInstruction(HloInstruction::CreateTranspose(
+      ShapeUtil::MakeShape(F32, {6, 7, 3, 5}), broadcast, {2, 3, 0, 1}));
+  auto module = CreateNewModule();
+  HloComputation* computation =
+      module->AddEntryComputation(builder.Build(transpose));
+  ComputationLayout computation_layout(computation->ComputeProgramShape());
+  AssignLayouts(module.get(), &computation_layout);
+  EXPECT_TRUE(ShapeUtil::TransposeIsBitcast(transpose->operand(0)->shape(),
+                                            transpose->shape(), {2, 3, 0, 1}));
+}
 }  // namespace
 }  // namespace xla
 
-- 
GitLab


From a936b239cf91a9727d15ab94fbe1b08e68c685b3 Mon Sep 17 00:00:00 2001
From: James Qin <jamesqin@google.com>
Date: Tue, 20 Jun 2017 16:54:29 -0700
Subject: [PATCH 0268/1375] Support reuse cuDNNLSTM-trained checkpoints by
 multi-layer LSTM(Block)Cell

* Add tensor stitching/partition in RNNParamSaveable for saving/restoring properly shaped and formatted weights/biases to share w/ LSTM(Block)Cell
* Add remapped names for canonical tensors during saving.
* Unittests

PiperOrigin-RevId: 159634913
---
 tensorflow/contrib/cudnn_rnn/BUILD            |   2 +
 .../python/kernel_tests/cudnn_rnn_ops_test.py | 241 +++++++++++++++++-
 .../cudnn_rnn/python/ops/cudnn_rnn_ops.py     | 229 ++++++++++++++++-
 tensorflow/contrib/rnn/python/ops/lstm_ops.py |  12 +-
 tensorflow/python/ops/rnn_cell_impl.py        |   5 +-
 5 files changed, 465 insertions(+), 24 deletions(-)

diff --git a/tensorflow/contrib/cudnn_rnn/BUILD b/tensorflow/contrib/cudnn_rnn/BUILD
index b1caac476a..fc473d3380 100644
--- a/tensorflow/contrib/cudnn_rnn/BUILD
+++ b/tensorflow/contrib/cudnn_rnn/BUILD
@@ -87,6 +87,8 @@ cuda_py_test(
     additional_deps = [
         ":cudnn_rnn_py",
         "//tensorflow/core:protos_all_py",
+        "//tensorflow/contrib/rnn:rnn_py",
+        "//tensorflow/python/ops/losses:losses",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework",
diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
index 08ec3076e4..0e51ab9935 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
@@ -20,8 +20,13 @@ from __future__ import print_function
 
 import os
 import unittest
+import numpy as np
+
 from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
+from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.core.protobuf import saver_pb2
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework.test_util import TensorFlowTestCase
@@ -29,10 +34,14 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
+from tensorflow.python.ops.losses import losses
 from tensorflow.python.platform import googletest
 from tensorflow.python.platform import test
+from tensorflow.python.training import gradient_descent
 from tensorflow.python.training import saver as saver_lib
 
 
@@ -69,7 +78,8 @@ class CudnnRNNTest(TensorFlowTestCase):
       model: a CudnnRNN model.
     """
     params_saveable = cudnn_rnn_ops.RNNParamsSaveable(
-        model.params_to_canonical, model.canonical_to_params, [params])
+        model, model.params_to_canonical, model.canonical_to_params, [params],
+        "rnn")
     ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, params_saveable)
 
   def _testSaveRestoreVariable(self, rnn_mode):
@@ -93,6 +103,218 @@ class CudnnRNNTest(TensorFlowTestCase):
       params_v_restored = sess.run(params)
       self.assertAllEqual(params_v, params_v_restored)
 
+  def _create_equivalent_canonical_rnn(self,
+                                       cudnn_model,
+                                       inputs,
+                                       use_block_cell,
+                                       scope="rnn"):
+    if cudnn_model.rnn_mode is not "lstm":
+      raise ValueError("%s is not supported!" % cudnn_model.rnn_mode)
+
+    num_units = cudnn_model.num_units
+    num_layers = cudnn_model.num_layers
+
+    # To reuse cuDNN-trained models, must set
+    # forget_bias, clip_cell = 0, False
+    # In LSTMCell and LSTMBlockCell, forget_bias is added in addition to learned
+    # bias, whereas cuDNN does not apply the additional bias.
+    if use_block_cell:
+      # pylint: disable=g-long-lambda
+      single_cell = lambda: lstm_ops.LSTMBlockCell(num_units, forget_bias=0,
+                                                   clip_cell=False)
+      # pylint: enable=g-long-lambda
+    else:
+      single_cell = lambda: rnn_cell_impl.LSTMCell(num_units, forget_bias=0)
+    cell = rnn_cell_impl.MultiRNNCell(
+        [single_cell() for _ in range(num_layers)])
+    return rnn.dynamic_rnn(
+        cell, inputs, dtype=dtypes.float32, time_major=True, scope=scope)
+
+  def _build_forward_cudnn_model(self,
+                                 rnn_mode,
+                                 num_layers,
+                                 num_units,
+                                 input_data,
+                                 is_training=False):
+    input_data_shape = input_data.get_shape().with_rank(3)
+    batch_size = input_data_shape[1].value
+    input_size = input_data_shape[2].value
+    model = self._CreateModel(rnn_mode, num_layers, num_units, input_size)
+
+    # Set zero init input states
+    input_h = constant_op.constant(
+        np.zeros([num_layers, batch_size, num_units]), dtype=dtypes.float32)
+    has_input_c = (rnn_mode == "lstm")
+    if has_input_c:
+      input_c = constant_op.constant(
+          np.zeros([num_layers, batch_size, num_units]), dtype=dtypes.float32)
+
+    # Set rnn params
+    params_size_t = model.params_size()
+    params = variables.Variable(
+        random_ops.random_uniform([params_size_t]), validate_shape=False)
+    args = {
+        "input_data": input_data,
+        "input_h": input_h,
+        "params": params,
+        "is_training": is_training
+    }
+    if has_input_c:
+      args["input_c"] = input_c
+    # Build cell
+    output_tuple = model(**args)
+
+    # Create savable objects for params
+    self._create_params_savable(params, model)
+
+    return output_tuple, model, params
+
+  @unittest.skipUnless(test.is_built_with_cuda(),
+                       "Test only applicable when running on GPUs")
+  def testCheckpointReusableByCanonicalLSTMCells(self):
+    configs = [
+        {
+            "num_layers": 1,
+            "seq_length": 3,
+            "num_units": 4,
+            "input_size": 5,
+            "batch_size": 6,
+            "rnn_mode": "lstm"
+        },
+        {
+            "num_layers": 2,
+            "seq_length": 8,
+            "num_units": 4,
+            "input_size": 8,
+            "batch_size": 16,
+            "rnn_mode": "lstm"
+        },
+        {
+            "num_layers": 2,
+            "seq_length": 3,
+            "num_units": 4,
+            "input_size": 5,
+            "batch_size": 6,
+            "rnn_mode": "lstm"
+        },
+        {
+            "num_layers": 1,
+            "seq_length": 2,
+            "num_units": 2,
+            "input_size": 4,
+            "batch_size": 1,
+            "rnn_mode": "lstm"
+        },
+    ]
+    for cfg in configs:
+      self._testCheckpointReusableByCanonicalLSTMCells(
+          cfg["num_layers"],
+          cfg["seq_length"],
+          cfg["num_units"],
+          cfg["input_size"],
+          cfg["batch_size"],
+          cfg["rnn_mode"],
+          use_block_cell=False)
+      self._testCheckpointReusableByCanonicalLSTMCells(
+          cfg["num_layers"],
+          cfg["seq_length"],
+          cfg["num_units"],
+          cfg["input_size"],
+          cfg["batch_size"],
+          cfg["rnn_mode"],
+          use_block_cell=True)
+
+  def _testCheckpointReusableByCanonicalLSTMCells(
+      self, num_layers, seq_length, num_units, input_size, batch_size, rnn_mode,
+      use_block_cell):
+    np.random.seed(0)
+    # Train graph
+    with ops.Graph().as_default():
+      random_seed.set_random_seed(299)
+      input_data = array_ops.placeholder(
+          dtypes.float32, shape=[seq_length, batch_size, input_size])
+      output_tuple, cudnn_model, cudnn_params = self._build_forward_cudnn_model(
+          rnn_mode, num_layers, num_units, input_data, is_training=True)
+      target_output = array_ops.placeholder(dtype=dtypes.float32, shape=None)
+      total_sum = sum(map(math_ops.reduce_sum, output_tuple))
+
+      loss_op = losses.log_loss(labels=target_output, predictions=total_sum)
+      optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1e-2)
+      train_op = optimizer.minimize(loss_op)
+
+      saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
+
+      # Train Cudnn model
+      with self.test_session(
+          use_gpu=True, graph=ops.get_default_graph()) as sess:
+        sess.run(variables.global_variables_initializer())
+        # Train 128 steps
+        num_steps = 128
+        for _ in range(num_steps):
+          inputs = np.random.rand(seq_length, batch_size,
+                                  input_size).astype(np.float32)
+          targets = np.random.rand()
+          sess.run(
+              train_op, feed_dict={input_data: inputs,
+                                   target_output: targets})
+
+        save_path = os.path.join(self.get_temp_dir(),
+                                 ("cudnn-rnn-%s-test" % rnn_mode))
+        save_v = saver.save(sess, save_path)
+        self.assertEqual(save_path, save_v)
+        cudnn_params_v = sess.run(cudnn_params)
+
+    # cuDNN inference graph
+    with ops.Graph().as_default():
+      random_seed.set_random_seed(299)
+      cudnn_inputs = array_ops.placeholder(
+          dtypes.float32, shape=[seq_length, batch_size, input_size])
+      (cudnn_output_tuple, cudnn_model,
+       cudnn_params) = self._build_forward_cudnn_model(
+           rnn_mode, num_layers, num_units, cudnn_inputs, is_training=False)
+      saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
+
+      inference_input = np.random.rand(seq_length, batch_size,
+                                       input_size).astype(np.float32)
+      with self.test_session(
+          use_gpu=True, graph=ops.get_default_graph()) as sess:
+        sess.run(variables.global_variables_initializer())
+        saver.restore(sess, save_path)
+        restored_cudnn_params_v = sess.run(cudnn_params)
+        self.assertAllEqual(cudnn_params_v, restored_cudnn_params_v)
+
+        # Cudnn inference
+        (cudnn_output, cudnn_output_h, cudnn_output_c) = sess.run(
+            cudnn_output_tuple, feed_dict={cudnn_inputs: inference_input})
+
+    # LSTMBlockCell inference graph
+    with ops.Graph().as_default():
+      random_seed.set_random_seed(299)
+      cell_inputs = array_ops.placeholder(
+          dtypes.float32, shape=[seq_length, batch_size, input_size])
+      (output, states) = self._create_equivalent_canonical_rnn(
+          cudnn_model, cell_inputs, use_block_cell)
+      saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
+
+      with self.test_session(
+          use_gpu=True, graph=ops.get_default_graph()) as sess:
+        saver.restore(sess, save_path)
+
+        # BlockCell inference
+        output_v, states_v = sess.run(
+            [output, states], feed_dict={cell_inputs: inference_input})
+
+        # output across timestamps are packed into one tensor.
+        self.assertAllClose(cudnn_output, output_v, atol=1e-6, rtol=1e-6)
+
+        for i in range(num_layers):
+          # output_h
+          self.assertAllClose(
+              cudnn_output_h[i, :], states_v[i].h, atol=1e-6, rtol=1e-6)
+          # output_c
+          self.assertAllClose(
+              cudnn_output_c[i, :], states_v[i].c, atol=1e-6, rtol=1e-6)
+
   def _testSaveRestoreOutput(self, rnn_mode):
     num_layers = 2
     num_units = 7
@@ -187,9 +409,13 @@ class CudnnRNNTest(TensorFlowTestCase):
                               batch_size, seq_length, dir_count, dropout,
                               expected, tolerance):
     random_seed.set_random_seed(5678)
-    model = self._CreateModel(rnn_mode, num_layers, num_units, input_size,
-                              input_mode="auto_select",
-                              dropout=dropout)
+    model = self._CreateModel(
+        rnn_mode,
+        num_layers,
+        num_units,
+        input_size,
+        input_mode="auto_select",
+        dropout=dropout)
     has_input_c = (rnn_mode == "lstm")
     params_size_t = model.params_size()
     input_data = array_ops.ones([seq_length, batch_size, input_size])
@@ -216,7 +442,7 @@ class CudnnRNNTest(TensorFlowTestCase):
     if has_input_c:
       output_c_sum = math_ops.reduce_sum(output_c)
       total_sum += output_c_sum
-    with self.test_session(use_gpu=True) as sess:
+    with self.test_session(use_gpu=True, graph=ops.get_default_graph()) as sess:
       sess.run(variables.global_variables_initializer())
       total_sum_v = sess.run([total_sum])
 
@@ -310,8 +536,8 @@ class CudnnRNNTest(TensorFlowTestCase):
     os.environ["TF_CUDNN_RESET_RND_GEN_STATE"] = str(True)
     has_input_c = (rnn_mode == "lstm")
     random_seed.set_random_seed(1234)
-    model = self._CreateModel(rnn_mode, num_layers, num_units, input_size,
-                              dropout=dropout)
+    model = self._CreateModel(
+        rnn_mode, num_layers, num_units, input_size, dropout=dropout)
     params_size_t = model.params_size()
     input_data = variables.Variable(
         random_ops.random_uniform([seq_length, batch_size, input_size]))
@@ -417,6 +643,7 @@ class CudnnRNNTest(TensorFlowTestCase):
             },
         },
     ]
+    ops.reset_default_graph()
     with ops.Graph().as_default():
       for config in test_configs:
         rnn_mode = config["rnn_mode"]
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index cc0c7b0829..0437467f3f 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -16,7 +16,6 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-import itertools
 
 from tensorflow.contrib.cudnn_rnn.ops import gen_cudnn_rnn_ops
 from tensorflow.contrib.util import loader
@@ -46,9 +45,11 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
   """SaveableObject implementation that handles the RNN params variable."""
 
   def __init__(self,
+               cudnn_rnn,
                params_to_canonical,
                canonical_to_params,
                param_variables,
+               base_variable_scope=None,
                name="params_canonical"):
     """Creates a RNNParamsSaveable object.
 
@@ -75,6 +76,7 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
        tensor 1 and 4 the update gate; tensor 2 and 5 the new memory gate.
 
     Args:
+      cudnn_rnn: cudnn RNN class instance.
       params_to_canonical: a function to convert params from a specific format
           for cuDNN or other RNN ops to the canonical format.
           _CudnnRNN.params_to_canonical() should be provided here.
@@ -87,25 +89,42 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
           For cuDNN RNN ops, this is a single merged variable for both weights
           and biases; for other RNN ops, this might be multiple unmerged or
           partially merged variables respectively for weights and biases.
+      base_variable_scope: a string, name of outer variable scope, used as
+          part of prefix of names of saved variables.
       name: the name of the RNNParamsSaveable object.
     """
     # There is only a single merged parameter variable for cuDNN when saving.
+    self._cudnn_rnn = cudnn_rnn
     weights, biases = params_to_canonical(param_variables[0])
+    weights, biases, = self._transform_canonical(weights, biases)
+    weight_names, biase_names = self._transformed_canonical_names(
+        weights, biases)
     self._canonical_to_params = canonical_to_params
     self._variables = param_variables
     # We currently don't use slice_spec. It might be useful in a distributed
     # setting where each parameter server node stores a slice of variable,
     # instead of having the master pull all slices and then save them.
     slice_spec = ""
+    params = weights + biases
+    param_names = weight_names + biase_names
+    if base_variable_scope:
+      param_names = ["%s/%s" % (base_variable_scope, pn) for pn in param_names]
     specs = [
-        saver.BaseSaverBuilder.SaveSpec(param, slice_spec, param.name)
-        for param in itertools.chain(weights, biases)
+        saver.BaseSaverBuilder.SaveSpec(param, slice_spec, param_name)
+        for param, param_name in zip(params, param_names)
     ]
     super(RNNParamsSaveable, self).__init__(None, specs, name)
 
   def restore(self, restored_tensors, restored_shapes):
-    weights = restored_tensors[:len(restored_tensors) // 2]
-    biases = restored_tensors[len(restored_tensors) // 2:]
+    if (self._cudnn_rnn.direction == "unidirectional" and
+        self._cudnn_rnn.rnn_mode == "lstm"):
+      assert len(restored_tensors) % 4 == 0
+      weights = restored_tensors[:len(restored_tensors) // 4]
+      biases = restored_tensors[len(restored_tensors) // 4:]
+    else:
+      weights = restored_tensors[:len(restored_tensors) // 2]
+      biases = restored_tensors[len(restored_tensors) // 2:]
+    weights, biases = self._untransform_canonical(weights, biases)
     params = self._canonical_to_params(weights, biases)
     if not isinstance(params, tuple):
       params = (params,)
@@ -115,6 +134,159 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
     ]
     return control_flow_ops.group(*assign_ops)
 
+  def _switch_inner(self, array, base_idx):
+    array[base_idx + 1], array[base_idx + 2] = (array[base_idx + 2],
+                                                array[base_idx + 1])
+
+  def _transform_canonical(self, weights, biases):
+    if (self._cudnn_rnn.direction != "unidirectional" or
+        self._cudnn_rnn.rnn_mode != "lstm"):
+      return weights, biases
+    return self._transform_lstm_canonical(weights, biases)
+
+  def _transformed_canonical_names(self, weights, biases):
+    """Return canonical names for fused weight and bias tensors."""
+    if (self._cudnn_rnn.direction != "unidirectional" or
+        self._cudnn_rnn.rnn_mode != "lstm"):
+      assert len(weights) == len(biases)
+      return ([w.name for w in weights], [b.name for b in biases])
+    else:
+      w_names, b_names = [], []
+      assert len(weights) * 3 == len(biases)
+      num_layers = self._cudnn_rnn.num_layers
+      # TODO(jamesqin): get rid of multi_rnn_cell when num_layers is 1
+      for i in range(num_layers):
+        # One fused weight tensor each layer.
+        w_names.append("multi_rnn_cell/cell_%d/lstm_cell/kernel" % i)
+        # Three fused bias tensors each layer:
+        # the 1st is for LSTMBlockCell restore; the latter two sum up to the
+        # 1st, and are used for cuDNN restore.
+        b_names.append("multi_rnn_cell/cell_%d/lstm_cell/bias" % i)
+        b_names.extend([
+            "multi_rnn_cell/cell_%d/lstm_cell/bias_cudnn_%d" % (i, j)
+            for j in range(2)
+        ])
+      return w_names, b_names
+
+  def _transform_lstm_canonical(self, weights, biases):
+    """Create fused lstm canonical params.
+
+    Produce properly-shaped monolithic weight and bias tensors to share between
+    cuDNN and non-platform specific LSTM cells (w/o peephole).
+    Args:
+      weights: a list of Tensors recovered from cuDNN params_to_canonical.
+      biases: a list of Tensors recovered from cuDNN params_to_canonical.
+    Returns:
+      Two lists of tensors, one for weight and bias each.
+      The weight list contains num_layers tensors and bias one contains 3 *
+      num_layers tensors. Both original and combined biases since cuDNN biases
+      are not restorable from the fused version.
+    """
+    transformed_weights, transformed_biases = [], []
+    for i in range(self._cudnn_rnn.num_layers):
+      base_idx = i * 8
+      num_units = self._cudnn_rnn.num_units
+      input_size = self._cudnn_rnn.input_size if i == 0 else num_units
+      # cuDNN tensor shapes per time_step:
+      # input.shape:         [batch_size, input_size],
+      # input_weights.shape: [num_units, input_size] (first layer)
+      #                      [num_units, num_units]  (other layers)
+      # state_weights.shape: [num_units, num_units]
+      # biases.shape:        [num_units]
+      #
+      # General LSTM cells compute gate functions using:
+      #   [x, h_prev] * weights + biases
+      # Therefore for each layer, they expect
+      # weight.shape: [input_size + num_units, 4 * num_units] (first_layer)
+      #               [num_units + num_units, 4 * num_units]  (other layers)
+      # bias.shape:   [4 * num_units]
+
+      # Stitch weights together in this layer.
+      stitched_w = []
+      for j in range(4):
+        stitched_w.append(
+            array_ops.concat(
+                [
+                    array_ops.reshape(weights[base_idx + j],
+                                      [num_units, input_size]),
+                    array_ops.reshape(weights[base_idx + j + 4],
+                                      [num_units, num_units])
+                ],
+                axis=1))
+      # cuDNN weights are in ifco order, convert to icfo order.
+      self._switch_inner(stitched_w, 0)
+      transformed_weights.append(
+          array_ops.transpose(array_ops.concat(stitched_w, axis=0)))
+
+      # Stitch biases together in this layer.
+      # Convert to icfo order.
+      self._switch_inner(biases, base_idx)
+      self._switch_inner(biases, base_idx + 4)
+      # The bias for layer input.
+      b_in = array_ops.concat(biases[base_idx:base_idx + 4], axis=0)
+      # The bias for recurrent input.
+      b_rec = array_ops.concat(biases[base_idx + 4:base_idx + 8], axis=0)
+
+      transformed_biases.extend([b_in + b_rec, b_in, b_rec])
+    return transformed_weights, transformed_biases
+
+  def _untransform_canonical(self, transformed_weights, transformed_biases):
+    if (self._cudnn_rnn.direction != "unidirectional" or
+        self._cudnn_rnn.rnn_mode != "lstm"):
+      return transformed_weights, transformed_biases
+    return self._untransform_lstm_canonical(transformed_weights,
+                                            transformed_biases)
+
+  def _untransform_lstm_canonical(self, transformed_weights,
+                                  transformed_biases):
+    """The reverse procedure of _transform_lstm_canonical().
+
+    Args:
+      transformed_weights: a list of tensors, one for each layer.
+      transformed_biases: a list of tensors , 3 for each layer: the 2nd for
+        layer input, the 3rd for recurrent input, the 1st is the sum of the
+        latter two.
+    Returns:
+      Two lists of tensors for weights and biases respectively.
+      There are 8 tensors per weight and per bias for each layer:
+      tensor 0-3 are applied to the input from the previous layer;
+      tensor 4-7 to the recurrent input. Tensor 0 and 4 are for the input gate;
+      tensor 1 and 5 the forget gate; tensor 2 and 6 the new memory gate;
+      tensor 3 and 7 the output gate.
+    """
+    weights, biases = [], []
+    assert 3 * len(transformed_weights) == len(transformed_biases)
+    for i in range(len(transformed_weights)):
+      num_units = self._cudnn_rnn.num_units
+      input_size = self._cudnn_rnn.input_size if i == 0 else num_units
+      # weights applied on layer inputs.
+      wi = array_ops.slice(transformed_weights[i], [0, 0],
+                           [input_size, 4 * num_units])
+      # weights applied on recurrent inputs.
+      wr = array_ops.slice(transformed_weights[i], [input_size, 0],
+                           [num_units, 4 * num_units])
+      wi_list = array_ops.split(wi, 4, axis=1)
+      wr_list = array_ops.split(wr, 4, axis=1)
+
+      for j in range(len(wi_list)):
+        wi_list[j] = array_ops.reshape(array_ops.transpose(wi_list[j]), [-1])
+        wr_list[j] = array_ops.reshape(array_ops.transpose(wr_list[j]), [-1])
+      # canonical weights are in icfo order, convert to ifco order for cuDNN.
+      self._switch_inner(wi_list, 0)
+      self._switch_inner(wr_list, 0)
+      weights.extend(wi_list)
+      weights.extend(wr_list)
+
+      base_idx = 3 * i
+      bi_list = array_ops.split(transformed_biases[base_idx + 1], 4, axis=0)
+      br_list = array_ops.split(transformed_biases[base_idx + 2], 4, axis=0)
+      # canonical weights are in icfo order, convert to ifco order for cuDNN.
+      self._switch_inner(bi_list, 0)
+      self._switch_inner(br_list, 0)
+      biases.extend(bi_list)
+      biases.extend(br_list)
+    return weights, biases
+
 
 _cudnn_rnn_common_doc_string = """
   Cudnn RNN has an opaque parameter buffer that can be used for inference and
@@ -199,6 +371,26 @@ class _CudnnRNN(object):
     if self._seed is None and self._seed2 is None:
       self._seed, self._seed2 = 0, 0
 
+  @property
+  def input_size(self):
+    return self._input_size
+
+  @property
+  def num_units(self):
+    return self._num_units
+
+  @property
+  def num_layers(self):
+    return self._num_layers
+
+  @property
+  def rnn_mode(self):
+    return self._rnn_mode
+
+  @property
+  def direction(self):
+    return self._direction
+
   def params_size(self):
     """Calculates the size of the opaque parameter buffer needed for this model.
 
@@ -222,9 +414,12 @@ class _CudnnRNN(object):
     """Runs the forward step for the RNN model.
 
     Args:
-      input_data: the input sequence to the RNN model.
-      input_h: the initial hidden state for h.
+      input_data: the input sequence to the RNN model. A Tensor of shape [?,
+        batch_size, input_size].
+      input_h: the initial hidden state for h. A Tensor of shape [num_layers,
+        batch_size, num_units].
       input_c: the initial hidden state for c. This is only relevant for LSTM.
+        A Tensor of the same shape as input_h.
       params: the parameter buffer created for this model.
       is_training: whether this operation will be used in training or inference.
 
@@ -308,7 +503,7 @@ class CudnnLSTM(_CudnnRNN):
                num_layers,
                num_units,
                input_size,
-               input_mode="auto_select",
+               input_mode="linear_input",
                direction="unidirectional",
                dropout=0.,
                seed=0):
@@ -344,9 +539,12 @@ class CudnnLSTM(_CudnnRNN):
     """Runs the forward step for the Cudnn LSTM model.
 
     Args:
-      input_data: the input sequence to the LSTM model.
-      input_h: the initial hidden state for h.
-      input_c: the initial hidden state for c.
+      input_data: the input sequence to the LSTM model. A Tensor of shape [?,
+        batch_size, input_size].
+      input_h: the initial hidden state for h. A Tensor of shape [num_layers,
+        batch_size, num_units].
+      input_c: the initial hidden state for c. A Tensor of the same shape as
+        input_h.
       params: the parameter buffer created for this model.
       is_training: whether this operation will be used in training or inference.
 
@@ -368,7 +566,7 @@ class _CudnnRNNNoInputC(_CudnnRNN):
                num_layers,
                num_units,
                input_size,
-               input_mode="auto_select",
+               input_mode="linear_input",
                direction="unidirectional",
                dropout=0.,
                seed=0):
@@ -390,6 +588,7 @@ class _CudnnRNNNoInputC(_CudnnRNN):
       dropout: whether to enable dropout. With it is 0, dropout is disabled.
       seed: the seed used for initializing dropout.
     """
+
     super(_CudnnRNNNoInputC, self).__init__(
         self._rnn_mode,
         num_layers,
@@ -404,8 +603,10 @@ class _CudnnRNNNoInputC(_CudnnRNN):
     """Runs the forward step for the Cudnn LSTM model.
 
     Args:
-      input_data: the input sequence to the LSTM model.
-      input_h: the initial hidden state for h.
+      input_data: the input sequence to the RNN model. A Tensor of shape [?,
+        batch_size, input_size].
+      input_h: the initial hidden state for h. A Tensor of shape [num_layers,
+        batch_size, num_units].
       params: the parameter buffer created for this model.
       is_training: whether this operation will be used in training or inference.
 
diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
index c41b5793fc..97b9dcc905 100644
--- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py
+++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
@@ -58,7 +58,7 @@ def _lstm_block_cell(x,
 
   ```python
   xh = [x, h_prev]
-  [i, f, ci, o] = xh * w + b
+  [i, ci, f, o] = xh * w + b
   f = f + forget_bias
 
   if not use_peephole:
@@ -93,7 +93,7 @@ def _lstm_block_cell(x,
       The weight matrix for output gate peephole connection.
     forget_bias: An optional `float`. Defaults to `1`. The forget gate bias.
     cell_clip: An optional `float`. Defaults to `3`.
-      Value to clip the 'cs' value to.
+      Value to clip the 'cs' value to. Disable by setting to negative value.
     use_peephole: An optional `bool`. Defaults to `False`.
       Whether to use peephole weights.
     name: A name for the operation (optional).
@@ -341,17 +341,24 @@ class LSTMBlockCell(rnn_cell_impl.RNNCell):
   def __init__(self,
                num_units,
                forget_bias=1.0,
+               clip_cell=True,
                use_peephole=False):
     """Initialize the basic LSTM cell.
 
     Args:
       num_units: int, The number of units in the LSTM cell.
       forget_bias: float, The bias added to forget gates (see above).
+      clip_cell: boolean, whether to apply cell clipping. See
+        `_lstm_block_cell()` for details.
       use_peephole: Whether to use peephole connections or not.
+
+      When restoring from CudnnLSTM-trained checkpoints, must set the following:
+      forget_bias, clip_cell, use_peephole  = 0, False, False
     """
     self._num_units = num_units
     self._forget_bias = forget_bias
     self._use_peephole = use_peephole
+    self._clip_cell = clip_cell
     self._names = {
         "W": "kernel",
         "b": "bias",
@@ -400,6 +407,7 @@ class LSTMBlockCell(rnn_cell_impl.RNNCell):
           wco=wco,
           wcf=wcf,
           forget_bias=self._forget_bias,
+          cell_clip=None if self._clip_cell else -1,
           use_peephole=self._use_peephole)
 
       new_state = rnn_cell_impl.LSTMStateTuple(cs, h)
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 49a4aba473..ca69cddae2 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -345,6 +345,8 @@ class BasicLSTMCell(RNNCell):
     Args:
       num_units: int, The number of units in the LSTM cell.
       forget_bias: float, The bias added to forget gates (see above).
+        Must set to `0.0` manually when restoring from CudnnLSTM-trained
+        checkpoints.
       state_is_tuple: If True, accepted and returned states are 2-tuples of
         the `c_state` and `m_state`.  If False, they are concatenated
         along the column axis.  The latter behavior will soon be deprecated.
@@ -444,7 +446,8 @@ class LSTMCell(RNNCell):
         Use a variable_scope partitioner instead.
       forget_bias: Biases of the forget gate are initialized by default to 1
         in order to reduce the scale of forgetting at the beginning of
-        the training.
+        the training. Must set it manually to `0.0` when restoring from
+        CudnnLSTM trained checkpoints.
       state_is_tuple: If True, accepted and returned states are 2-tuples of
         the `c_state` and `m_state`.  If False, they are concatenated
         along the column axis.  This latter behavior will soon be deprecated.
-- 
GitLab


From a4660cce81f27c3fe8f18de0649b572aa70187e1 Mon Sep 17 00:00:00 2001
From: Frank Chen <frankchn@google.com>
Date: Tue, 20 Jun 2017 17:11:19 -0700
Subject: [PATCH 0269/1375] Introduce Tensor Processing Unit-specific
 operations and estimators. This commit enables TensorFlow users to utilize
 functionality available in Cloud TPUs. Please note that these APIs are in
 alpha with Cloud TPUs, and will likely undergo significant changes during the
 Cloud TPU alpha and beta process.

Interested individuals and organizations should sign up for a Cloud TPU beta at https://ai.google/tools/cloud-tpus/.

PiperOrigin-RevId: 159636962
---
 tensorflow/BUILD                              |   1 +
 tensorflow/contrib/BUILD                      |   4 +
 tensorflow/contrib/__init__.py                |   1 +
 .../contrib/cmake/tf_core_kernels.cmake       |   6 +
 tensorflow/contrib/cmake/tf_core_ops.cmake    |   5 +
 tensorflow/contrib/cmake/tf_python.cmake      |   5 +
 tensorflow/contrib/tpu/BUILD                  | 224 +++++++
 tensorflow/contrib/tpu/__init__.py            |  28 +
 .../contrib/tpu/ops/cross_replica_ops.cc      |  37 ++
 tensorflow/contrib/tpu/ops/infeed_ops.cc      | 107 +++
 tensorflow/contrib/tpu/ops/outfeed_ops.cc     | 106 +++
 tensorflow/contrib/tpu/ops/replication_ops.cc |  87 +++
 .../contrib/tpu/ops/tpu_configuration_ops.cc  | 213 ++++++
 .../contrib/tpu/ops/tpu_sendrecv_ops.cc       |  46 ++
 tensorflow/contrib/tpu/python/ops/tpu_ops.py  |  38 ++
 tensorflow/contrib/tpu/python/tpu/__init__.py |  20 +
 tensorflow/contrib/tpu/python/tpu/tpu.py      | 583 +++++++++++++++++
 .../contrib/tpu/python/tpu/tpu_config.py      |  47 ++
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 361 +++++++++++
 tensorflow/contrib/tpu/python/tpu/tpu_feed.py | 613 ++++++++++++++++++
 .../contrib/tpu/python/tpu/tpu_function.py    | 106 +++
 .../tpu/python/tpu/tpu_function_test.py       | 125 ++++
 .../contrib/tpu/python/tpu/tpu_infeed_test.py | 130 ++++
 .../contrib/tpu/python/tpu/tpu_optimizer.py   | 106 +++
 .../contrib/tpu/python/tpu/tpu_sharding.py    | 248 +++++++
 .../tpu/python/tpu/tpu_sharding_test.py       | 138 ++++
 .../contrib/tpu/python/tpu/training_loop.py   | 213 ++++++
 tensorflow/tools/pip_package/BUILD            |   3 +
 28 files changed, 3601 insertions(+)
 create mode 100644 tensorflow/contrib/tpu/BUILD
 create mode 100644 tensorflow/contrib/tpu/__init__.py
 create mode 100644 tensorflow/contrib/tpu/ops/cross_replica_ops.cc
 create mode 100644 tensorflow/contrib/tpu/ops/infeed_ops.cc
 create mode 100644 tensorflow/contrib/tpu/ops/outfeed_ops.cc
 create mode 100644 tensorflow/contrib/tpu/ops/replication_ops.cc
 create mode 100644 tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc
 create mode 100644 tensorflow/contrib/tpu/ops/tpu_sendrecv_ops.cc
 create mode 100644 tensorflow/contrib/tpu/python/ops/tpu_ops.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/__init__.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_config.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_feed.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_function.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_function_test.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_infeed_test.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_sharding.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/tpu_sharding_test.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/training_loop.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index abc8cdc410..318e22797c 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -304,6 +304,7 @@ filegroup(
         "//tensorflow/contrib/testing:all_files",
         "//tensorflow/contrib/text:all_files",
         "//tensorflow/contrib/tfprof/python/tools/tfprof:all_files",
+        "//tensorflow/contrib/tpu:all_files",
         "//tensorflow/contrib/training:all_files",
         "//tensorflow/contrib/util:all_files",
         "//tensorflow/contrib/verbs:all_files",
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index 518f01a4b1..b1eff73799 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -71,6 +71,9 @@ py_library(
         "//tensorflow/contrib/testing:testing_py",
         "//tensorflow/contrib/text:text_py",
         "//tensorflow/contrib/tfprof",
+        "//tensorflow/contrib/tpu:tpu_estimator",
+        "//tensorflow/contrib/tpu:tpu_helper_library",
+        "//tensorflow/contrib/tpu:tpu_py",
         "//tensorflow/contrib/training:training_py",
         "//tensorflow/contrib/util:util_py",
     ],
@@ -106,6 +109,7 @@ cc_library(
         "//tensorflow/contrib/seq2seq:beam_search_ops_op_lib",
         "//tensorflow/contrib/tensor_forest:tensor_forest_ops_op_lib",
         "//tensorflow/contrib/text:all_ops",
+        "//tensorflow/contrib/tpu:all_ops",
     ],
 )
 
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index ce29729506..16bc533436 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -65,6 +65,7 @@ from tensorflow.contrib import tensor_forest
 from tensorflow.contrib import tensorboard
 from tensorflow.contrib import testing
 from tensorflow.contrib import tfprof
+from tensorflow.contrib import tpu
 from tensorflow.contrib import training
 from tensorflow.contrib import util
 from tensorflow.contrib.ndlstm import python as ndlstm
diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 05f656cf3b..7f3be2aedf 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -84,6 +84,12 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/text/kernels/skip_gram_kernels.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/text/ops/skip_gram_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/cross_replica_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/infeed_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/outfeed_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/replication_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/tpu_sendrecv_ops.cc"
     )
   list(APPEND tf_core_kernels_srcs ${tf_contrib_kernels_srcs})
 endif(tensorflow_BUILD_CONTRIB_KERNELS)
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index 6cc1b82238..f1b01250a4 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -67,6 +67,10 @@ file(GLOB_RECURSE tensor_forest_hybrid_srcs
      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/*.cc"
 )
 
+file(GLOB_RECURSE tpu_ops_srcs
+     "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/*.cc"
+)
+
 GENERATE_CONTRIB_OP_LIBRARY(cudnn_rnn "${tensorflow_source_dir}/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(factorization_clustering "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/clustering_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(factorization_factorization "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/factorization_ops.cc")
@@ -83,6 +87,7 @@ GENERATE_CONTRIB_OP_LIBRARY(seq2seq_beam_search "${tensorflow_source_dir}/tensor
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_hybrid "${tensor_forest_hybrid_srcs}")
 GENERATE_CONTRIB_OP_LIBRARY(text_skip_gram "${tensorflow_source_dir}/tensorflow/contrib/text/ops/skip_gram_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(tpu "${tpu_ops_srcs}")
 GENERATE_CONTRIB_OP_LIBRARY(bigquery_reader "${tensorflow_source_dir}/tensorflow/contrib/cloud/ops/bigquery_reader_ops.cc")
 
 ########################################################
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 532d31216b..67b0c86f7f 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -515,6 +515,11 @@ add_python_module("tensorflow/contrib/tfprof" DONTCOPY)  # SWIG wrapper not impl
 #add_python_module("tensorflow/contrib/tfprof/python")
 #add_python_module("tensorflow/contrib/tfprof/python/tools")
 #add_python_module("tensorflow/contrib/tfprof/python/tools/tfprof")
+add_python_module("tensorflow/contrib/tpu")
+add_python_module("tensorflow/contrib/tpu/ops")
+add_python_module("tensorflow/contrib/tpu/python")
+add_python_module("tensorflow/contrib/tpu/python/ops")
+add_python_module("tensorflow/contrib/tpu/python/tpu")
 add_python_module("tensorflow/contrib/training")
 add_python_module("tensorflow/contrib/training/python")
 add_python_module("tensorflow/contrib/training/python/training")
diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD
new file mode 100644
index 0000000000..0a83070c34
--- /dev/null
+++ b/tensorflow/contrib/tpu/BUILD
@@ -0,0 +1,224 @@
+# Description: Operations defined for Cloud TPUs
+
+package(
+    default_visibility = [
+        "//learning/brain:__subpackages__",
+        "//tensorflow:__subpackages__",
+    ],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+load(
+    "//tensorflow:tensorflow.bzl",
+    "tf_custom_op_library",
+    "tf_gen_op_libs",
+    "tf_gen_op_wrapper_py",
+)
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
+load("//tensorflow:tensorflow.bzl", "tf_py_test")
+
+cc_library(
+    name = "all_ops",
+    deps = [
+        ":cross_replica_ops_op_lib",
+        ":infeed_ops_op_lib",
+        ":outfeed_ops_op_lib",
+        ":replication_ops_op_lib",
+        ":tpu_configuration_ops_op_lib",
+        ":tpu_sendrecv_ops_op_lib",
+    ],
+)
+
+py_library(
+    name = "tpu_estimator",
+    srcs = [
+        "python/tpu/tpu_config.py",
+        "python/tpu/tpu_estimator.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tpu",
+        ":tpu_py",
+        ":training_loop",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:platform",
+    ],
+)
+
+tf_gen_op_libs(
+    op_lib_names = [
+        "cross_replica_ops",
+        "infeed_ops",
+        "outfeed_ops",
+        "replication_ops",
+        "tpu_configuration_ops",
+        "tpu_sendrecv_ops",
+    ],
+    deps = [
+        "//tensorflow/core:lib",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_tpu_ops.so",
+    srcs = [
+        "ops/cross_replica_ops.cc",
+        "ops/infeed_ops.cc",
+        "ops/outfeed_ops.cc",
+        "ops/replication_ops.cc",
+        "ops/tpu_configuration_ops.cc",
+        "ops/tpu_sendrecv_ops.cc",
+    ],
+)
+
+tf_gen_op_wrapper_py(
+    name = "tpu_ops",
+    deps = [
+        ":cross_replica_ops_op_lib",
+        ":infeed_ops_op_lib",
+        ":outfeed_ops_op_lib",
+        ":replication_ops_op_lib",
+        ":tpu_configuration_ops_op_lib",
+        ":tpu_sendrecv_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "tpu_py",
+    srcs = glob(["python/ops/*.py"]) + ["__init__.py"],
+    dso = [":python/ops/_tpu_ops.so"],
+    kernels = [
+        ":all_ops",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tpu_ops",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+    ],
+)
+
+py_library(
+    name = "tpu_helper_library",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tpu",
+        ":tpu_feed",
+        ":tpu_function",
+        ":tpu_py",
+        ":tpu_sharding",
+        ":training_loop",
+    ],
+)
+
+py_library(
+    name = "tpu_function",
+    srcs = ["python/tpu/tpu_function.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tpu_feed",
+        ":tpu_py",
+        "//tensorflow/python:framework",
+    ],
+)
+
+py_library(
+    name = "tpu",
+    srcs = [
+        "python/tpu/__init__.py",
+        "python/tpu/tpu.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tpu_py",
+        ":training_loop",
+        "//tensorflow/python:framework",
+    ],
+)
+
+py_library(
+    name = "tpu_sharding",
+    srcs = ["python/tpu/tpu_sharding.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:framework",
+    ],
+)
+
+py_library(
+    name = "tpu_feed",
+    srcs = ["python/tpu/tpu_feed.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tpu_py",
+        ":tpu_sharding",
+        "//tensorflow/python:framework",
+    ],
+)
+
+py_library(
+    name = "training_loop",
+    srcs = [
+        "python/tpu/tpu_optimizer.py",
+        "python/tpu/training_loop.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tpu_function",
+        "//tensorflow/python:framework",
+    ],
+)
+
+tf_py_test(
+    name = "tpu_sharding_test",
+    size = "small",
+    srcs = ["python/tpu/tpu_sharding_test.py"],
+    additional_deps = [
+        ":tpu_sharding",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+    ],
+)
+
+tf_py_test(
+    name = "tpu_infeed_test",
+    size = "small",
+    srcs = ["python/tpu/tpu_infeed_test.py"],
+    additional_deps = [
+        ":tpu_feed",
+        ":tpu_sharding",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_test_lib",
+    ],
+)
+
+tf_py_test(
+    name = "tpu_function_test",
+    size = "small",
+    srcs = ["python/tpu/tpu_function_test.py"],
+    additional_deps = [
+        ":tpu_function",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_test_lib",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+)
diff --git a/tensorflow/contrib/tpu/__init__.py b/tensorflow/contrib/tpu/__init__.py
new file mode 100644
index 0000000000..bfd7887c51
--- /dev/null
+++ b/tensorflow/contrib/tpu/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Ops related to Tensor Processing Units."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import,unused-import
+from tensorflow.contrib.tpu.python.ops.tpu_ops import *
+from tensorflow.contrib.tpu.python.tpu import *
+# pylint: enable=wildcard-import,unused-import
+
+from tensorflow.python.util.all_util import remove_undocumented
+remove_undocumented(__name__)
diff --git a/tensorflow/contrib/tpu/ops/cross_replica_ops.cc b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc
new file mode 100644
index 0000000000..cbbd19800e
--- /dev/null
+++ b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc
@@ -0,0 +1,37 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+REGISTER_OP("CrossReplicaSum")
+    .Input("input: T")
+    .Output("output: T")
+    .Attr("T: {float}")
+    .SetShapeFn(shape_inference::UnchangedShape)
+    .Doc(R"doc(
+An Op to sum inputs across replicated TPU instances. Each
+instance supplies its own input, and the output of each is the sum of
+all the inputs.
+
+input: The local input to the sum.
+output: The sum of all the distributed inputs.
+T: The type of elements to be summed.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tpu/ops/infeed_ops.cc b/tensorflow/contrib/tpu/ops/infeed_ops.cc
new file mode 100644
index 0000000000..be4d4f9649
--- /dev/null
+++ b/tensorflow/contrib/tpu/ops/infeed_ops.cc
@@ -0,0 +1,107 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+
+REGISTER_OP("InfeedDequeue")
+    .Output("output: dtype")
+    .Attr("dtype: type")
+    .Attr("shape: shape")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      PartialTensorShape shape;
+      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
+      TensorShapeProto shape_proto;
+      shape.AsProto(&shape_proto);
+      ShapeHandle out;
+      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
+      c->set_output(0, out);
+      return Status::OK();
+    })
+    .Doc(R"doc(
+A placeholder op for a value that will be fed into the computation.
+
+output: A tensor that will be provided using the infeed mechanism.
+dtype: The type of elements in the tensor.
+shape: The shape of the tensor.
+)doc");
+
+REGISTER_OP("InfeedEnqueue")
+    .Input("input: dtype")
+    .Attr("dtype: type")
+    .Attr("shape: shape = {}")
+    .Attr("device_ordinal: int = -1")
+    .SetIsStateful()
+    .Doc(R"doc(
+An op which feeds a single Tensor value into the computation.
+
+input: A tensor that will be provided using the infeed mechanism.
+dtype: The type of elements in the tensor.
+shape: The shape of the tensor.
+device_ordinal: The TPU device to use. This should be -1 when the Op
+is running on a TPU device, and >= 0 when the Op is running on the CPU
+device.
+)doc");
+
+REGISTER_OP("InfeedEnqueueTuple")
+    .Input("inputs: dtypes")
+    .Attr("dtypes: list(type)")
+    .Attr("shapes: list(shape)")
+    .Attr("device_ordinal: int = -1")
+    .SetIsStateful()
+    .Doc(R"doc(
+An op which feeds multiple Tensor values into the computation as an XLA tuple.
+
+inputs: A list of tensors that will be provided using the infeed mechanism.
+dtypes: The element types of each element in `inputs`.
+shapes: The shapes of each tensor in `inputs`.
+device_ordinal: The TPU device to use. This should be -1 when the Op
+is running on a TPU device, and >= 0 when the Op is running on the CPU
+device.
+)doc");
+
+REGISTER_OP("InfeedDequeueTuple")
+    .Output("outputs: dtypes")
+    .Attr("dtypes: list(type)")
+    .Attr("shapes: list(shape)")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      std::vector<PartialTensorShape> shapes;
+      TF_RETURN_IF_ERROR(c->GetAttr("shapes", &shapes));
+      for (int i = 0; i < shapes.size(); ++i) {
+        TensorShapeProto shape_proto;
+        shapes[i].AsProto(&shape_proto);
+        ShapeHandle out;
+        TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
+        c->set_output(i, out);
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+A placeholder op for multiple values that will be fed into the computation
+simultaneously as an XLA tuple.
+
+outputs: A list of tensors that will be provided using the infeed mechanism.
+dtypes: The element types of each element in `outputs`.
+shapes: The shapes of each tensor in `outputs`.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tpu/ops/outfeed_ops.cc b/tensorflow/contrib/tpu/ops/outfeed_ops.cc
new file mode 100644
index 0000000000..16c57a1c2b
--- /dev/null
+++ b/tensorflow/contrib/tpu/ops/outfeed_ops.cc
@@ -0,0 +1,106 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+
+REGISTER_OP("OutfeedEnqueue")
+    .Input("input: dtype")
+    .Attr("dtype: type")
+    .SetIsStateful()
+    .Doc(R"doc(
+An op which emits a single Tensor value from an XLA computation.
+
+input: A tensor that will be inserted into the outfeed queue.
+)doc");
+
+REGISTER_OP("OutfeedEnqueueTuple")
+    .Input("inputs: dtypes")
+    .Attr("dtypes: list(type)")
+    .SetIsStateful()
+    .Doc(R"doc(
+An op which emits multiple Tensor values from an XLA computation.
+
+inputs: A list of tensors that will be inserted into the outfeed queue as an 
+XLA tuple.
+)doc");
+
+REGISTER_OP("OutfeedDequeue")
+    .Output("output: dtype")
+    .Attr("dtype: type")
+    .Attr("shape: shape")
+    .Attr("device_ordinal: int = -1")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      PartialTensorShape shape;
+      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
+      ShapeHandle out;
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shape, &out));
+      c->set_output(0, out);
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Retrieves a single tensor from the computation outfeed.  This operation will
+block indefinitely until data is available.
+
+output: A tensor that will be read from the device outfeed.
+dtype: The type of elements in the tensor.
+shape: The shape of the tensor.
+device_ordinal: The TPU device to use. This should be -1 when the Op
+is running on a TPU device, and >= 0 when the Op is running on the CPU
+device.
+)doc");
+
+REGISTER_OP("OutfeedDequeueTuple")
+    .Output("outputs: dtypes")
+    .Attr("dtypes: list(type)")
+    .Attr("shapes: list(shape)")
+    .Attr("device_ordinal: int = -1")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      std::vector<PartialTensorShape> shapes;
+      std::vector<DataType> dtypes;
+      TF_RETURN_IF_ERROR(c->GetAttr("shapes", &shapes));
+      TF_RETURN_IF_ERROR(c->GetAttr("dtypes", &dtypes));
+      if (shapes.size() != dtypes.size()) {
+        return errors::InvalidArgument(
+            "Incorrect number of output shapes specified");
+      }
+      for (int i = 0; i < shapes.size(); ++i) {
+        ShapeHandle out;
+        TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shapes[i], &out));
+        c->set_output(i, out);
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Retrieve multiple values that will be emitted by the computation as an XLA
+tuple.  This operations will block indefinitely until data is available.
+Output `i` corresponds to XLA tuple element `i`.
+
+outputs: A list of tensors that will be read from the outfeed.
+dtypes: The element types of each element in `outputs`.
+shapes: The shapes of each tensor in `outputs`.
+device_ordinal: The TPU device to use. This should be -1 when the Op
+is running on a TPU device, and >= 0 when the Op is running on the CPU
+device.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tpu/ops/replication_ops.cc b/tensorflow/contrib/tpu/ops/replication_ops.cc
new file mode 100644
index 0000000000..282a00b52c
--- /dev/null
+++ b/tensorflow/contrib/tpu/ops/replication_ops.cc
@@ -0,0 +1,87 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+
+REGISTER_OP("TPUReplicatedInput")
+    .Input("inputs: N * T")
+    .Output("output: T")
+    .Attr("N: int >= 1")
+    .Attr("T: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle cur = c->input(c->num_inputs() - 1);
+      for (int i = c->num_inputs() - 2; i >= 0; --i) {
+        TF_RETURN_WITH_CONTEXT_IF_ERROR(c->Merge(c->input(i), cur, &cur),
+                                        "From merging shape ", i,
+                                        " with other shapes.");
+      }
+      c->set_output(0, cur);
+      return Status::OK();
+    })
+    .Doc(
+        "Operator that connects N unreplicated inputs to an N-way "
+        "replicated TPU computation.");
+
+REGISTER_OP("TPUReplicatedOutput")
+    .Input("input: T")
+    .Output("outputs: num_replicas * T")
+    .Attr("num_replicas: int >= 1")
+    .Attr("T: type")
+    .SetShapeFn([](InferenceContext* c) {
+      for (int i = 0; i < c->num_outputs(); ++i) {
+        c->set_output(i, c->input(0));
+      }
+      return Status::OK();
+    })
+    .Doc(
+        "Operator that connects the output of an N-way replicated TPU "
+        "computation to N separate outputs.");
+
+REGISTER_OP("TPUReplicate")
+    .Attr("computation: func")
+    .Attr("num_replicas: int >= 1")
+    .Attr("global_tpu_id: list(int) = []")
+    .Attr("Tinputs: list(type) >= 0")
+    .Attr("Tbroadcast_inputs: list(type) >= 0")
+    .Attr("NumVariables: int >= 0")
+    .Attr("output_types: list(type) >= 0")
+    .Input("inputs: Tinputs")
+    .Input("broadcast_inputs: Tbroadcast_inputs")
+    .Input("variables: NumVariables * resource")
+    .Output("outputs: output_types")
+    .Doc(R"doc(
+Runs replicated computations on a distributed TPU system.
+
+computation: a function containing the computation to run.
+num_replicas: the number of replicas of the computation to run.
+global_tpu_id: map from device to global tpu id.
+Tinputs: the types of the arguments to 'computation'.
+inputs: the inputs to 'computation', flattened, in replica-major order.
+Tbroadcast_inputs: the types of the additional arguments to broadcast to all
+  replicas.
+broadcast_inputs: additional arguments to broadcast to all replicas. The
+  broadcast inputs are appended to the per-replica inputs when calling
+  computation.
+output_types: the types of the outputs of 'computation'.
+outputs: the outputs of 'computation'.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc
new file mode 100644
index 0000000000..5dc564ed27
--- /dev/null
+++ b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc
@@ -0,0 +1,213 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+
+// Configuring a distributed TPU system is achieved by running
+// the following Ops:
+//
+// 1 Run _DisconnectHostFromDistributedTPUSystem on the CPU of each
+// host. This is needed in case the system had previously been
+// configured. It returns, for each host, the number of TPU chips on
+// the host.
+//
+// 2 Run _ConfigureDistributedTPU on TPU_SYSTEM. Takes as input the
+// number of chips on each host. Validates that all hosts have the
+// same number of chips, and that the chips are consistent with the
+// topology set by flags. Has a single output which is a proto
+// describing the requested system configuration, which is sent to all
+// hosts.
+//
+// 3 Run _InitializeHostForDistributedTPU on the CPU of each host,
+// taking as input the output from ConfigureDistributedTPU. Has a
+// single Tensor output which is a vector of int32 indicating, for
+// each TPU on the host, what its global TPU system id is.
+//
+// 4 Run _WaitForDistributedTPU on TPU_SYSTEM, taking as input the
+// outputs from all the _InitializeHostForDistributedTPU
+// Ops. _WaitForDistributedTPU has an attr host_specs which is a
+// vector<string> giving the partial device spec for each host. These
+// partial specs are combined in the Op with the outputs from the host
+// initialization Ops to construct a mapping from full TPU device
+// specs to global TPU ids. Has a single Tensor output which is a
+// matrix of int32 indicating, for each host (outer dimension) and for
+// each TPU on the host (inner dimension) what that TPU's global id
+// is. _WaitForDistributedTPU also waits for the TPU distributed
+// system to initialize fully, which may take several minutes for a
+// large system.
+//
+// 5 Run _SetGlobalTPUArray on the CPU of each host, taking as input
+// the output from _WaitForDistributedTPU. This Op tells each host the
+// global Id of every TPU on every host.
+//
+// Most user code works by placing the ConfigureDistributedTPU Op on
+// the desired TPU_SYSTEM device, and a graph rewrite replaces it by
+// the subgraph described above.
+//
+//
+// A distributed TPU system can be cleanly shut down by running
+// the following Ops:
+//
+// 1 Run _DisconnectHostFromDistributedTPUSystem on the CPU of each
+// host.
+//
+// 2 Run _ShutdownDistributedTPU on the TPU_SYSTEM where
+// _ConfigureDistributedTPU was run. The Op will return an error if no
+// system is configured.
+//
+//
+// Most user code works by placing the ShutdownDistributedTPU Op on
+// the desired TPU_SYSTEM device, and a graph rewrite replaces it by
+// the subgraph described above.
+
+REGISTER_OP("_ConfigureDistributedTPU")
+    .Input("inputs: N * int32")
+    .Output("output: string")
+    .Attr("N: int >= 1")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input;
+      // Validate that all the inputs are scalars.
+      for (int i = 0; i < c->num_inputs(); ++i) {
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &input));
+      }
+      c->set_output(0, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+An op that sets up the centralized structures for a distributed TPU
+system.
+
+inputs: A scalar tensor for each host indicating how many TPU chips
+there are on the host.
+output: A tensor containing a TPUHostConfiguration proto serialized to
+a string, containing the information necessary to initialize the chips
+in a host.
+)doc");
+
+REGISTER_OP("_WaitForDistributedTPU")
+    .Input("inputs: N * int32")
+    .Output("global_tpu_array: int32")
+    .Attr("host_specs: list(string)")
+    .Attr("startup_timeout_sec: int = 20")
+    .Attr("N: int")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input;
+      // Validate that all the inputs have the same vector shape.
+      for (int i = 0; i < c->num_inputs(); ++i) {
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &input));
+      }
+      c->set_output(0, c->UnknownShapeOfRank(2));
+      return ::tensorflow::Status::OK();
+    })
+    .Doc(R"doc(
+An op that blocks execution until a distributed TPU system has
+started up. This Op must be run on the same TPU_SYSTEM device as
+_ConfigureDistributedTPU, and takes an inputs the outputs from the
+_InitializeHostForDistributedTPU Ops.
+
+inputs: For each initialized host, a vector giving the global TPU id
+of each TPU on the host.
+global_tpu_array: A two-dimensional array. For each host (the outer
+dimension) the array lists the global ids of the TPUs on that host.
+host_specs: For each initialized host, the partial device specification
+indicating job, replica, and task. Combining this spec with
+'/device:TPU:k' gives the full device name of the k'th TPU on the
+host.
+startup_timeout_sec: The number of seconds to wait for the TPU system
+to stabilize.
+)doc");
+
+REGISTER_OP("_SetGlobalTPUArray")
+    .Input("global_tpu_array: int32")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &input));
+      return ::tensorflow::Status::OK();
+    })
+    .Doc(R"doc(
+An op that informs a host of the global ids of all the of TPUs in the
+system.
+
+global_tpu_array: A two-dimensional array. For each host (the outer
+dimension) the array lists the global ids of the TPUs on that host.
+)doc");
+
+REGISTER_OP("_ShutdownDistributedTPU").SetIsStateful().Doc(R"doc(
+An op that shuts down a running distributed TPU system. The Op returns
+an error if no system is running. This Op must be run on the same
+TPU_SYSTEM device as the corresponding _ConfigureDistributedTPU was run
+to start the system, and must be run only after
+_DisconnectHostFromDistributedTPUSystem has completed on every host in
+the system.
+)doc");
+
+REGISTER_OP("_InitializeHostForDistributedTPU")
+    .Input("input: string")
+    .Output("tpu_ids: int32")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &input));
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return ::tensorflow::Status::OK();
+    })
+    .Doc(R"doc(
+An op that connects each chip on the host to a centralized UberDriver to allow
+them to operate as a distributed system with chips in other hosts.
+
+input: A string containing the address of the UberDriver to connect to.
+tpu_ids: A vector containing the global TPU id of each TPU on the host.
+)doc");
+
+REGISTER_OP("_DisconnectHostFromDistributedTPUSystem")
+    .Output("number_of_tpu_chips: int32")
+    .SetIsStateful()
+    .Doc(R"doc(
+An op that disconnects the TPUs on a host from a running distributed
+TPU system.
+
+number_of_tpu_chips: A scalar tensor containing the number of TPU
+chips on the host.
+)doc");
+
+REGISTER_OP("ConfigureDistributedTPU")
+    .Output("global_tpu_array: int32")
+    .Attr("embedding_config: string = ''")
+    .SetIsStateful()
+    .Doc(R"doc(
+An op that sets up the centralized structures for a distributed TPU
+system.
+
+global_tpu_array: A two-dimensional array. For each host (the outer
+dimension) the array lists the global ids of the TPUs on that host.
+embedding_config: Internal use.
+)doc");
+
+REGISTER_OP("ShutdownDistributedTPU").SetIsStateful().Doc(R"doc(
+An op that shuts down a running distributed TPU system. The Op returns
+an error if no system is running.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tpu/ops/tpu_sendrecv_ops.cc b/tensorflow/contrib/tpu/ops/tpu_sendrecv_ops.cc
new file mode 100644
index 0000000000..6d7c11a315
--- /dev/null
+++ b/tensorflow/contrib/tpu/ops/tpu_sendrecv_ops.cc
@@ -0,0 +1,46 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+
+namespace tensorflow {
+
+REGISTER_OP("_TPUSend")
+    .Input("tensor: T")
+    .Attr("T: type")
+    .Attr("tensor_name: string")
+    .SetIsStateful()
+    .Doc(R"doc(
+Sends the named tensor over the TPU fabric.
+
+tensor: The tensor to send.
+tensor_name: The name of the tensor to send.
+)doc");
+
+REGISTER_OP("_TPURecv")
+    .Output("tensor: T")
+    .Attr("T: type")
+    .Attr("tensor_name: string")
+    .Attr("shape: shape")
+    .SetIsStateful()
+    .Doc(R"doc(
+Receives the named tensor over the TPU fabric.
+
+tensor: The tensor to receive.
+tensor_name: The name of the tensor to receive.
+shape: The shape of the input tensor.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tpu/python/ops/tpu_ops.py b/tensorflow/contrib/tpu/python/ops/tpu_ops.py
new file mode 100644
index 0000000000..8d3344fac3
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/ops/tpu_ops.py
@@ -0,0 +1,38 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Operations for TPUs."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import platform
+
+
+if platform.system() != "Windows":
+  # pylint: disable=wildcard-import,unused-import,g-import-not-at-top
+  from tensorflow.contrib.tpu.ops.gen_tpu_ops import *
+
+  from tensorflow.contrib.util import loader
+  from tensorflow.python.platform import resource_loader
+  # pylint: enable=wildcard-import,unused-import,g-import-not-at-top
+
+  _tpu_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_tpu_ops.so"))
+else:
+  # We have already built the appropriate libraries into the binary via CMake
+  # if we have built contrib, so we don't need this
+  pass
diff --git a/tensorflow/contrib/tpu/python/tpu/__init__.py b/tensorflow/contrib/tpu/python/tpu/__init__.py
new file mode 100644
index 0000000000..0dffd7064b
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/__init__.py
@@ -0,0 +1,20 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Ops related to Tensor Processing Units."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py
new file mode 100644
index 0000000000..157b0fc1ac
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu.py
@@ -0,0 +1,583 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ======================================
+
+"""Library of TPU helper functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import contextlib
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.contrib.tpu.python.ops import tpu_ops
+from tensorflow.contrib.tpu.python.tpu import tpu_function
+
+from tensorflow.core.framework import attr_value_pb2
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import variable_scope
+
+
+def initialize_system(embedding_config=None, job=None):
+  """Initializes a distributed TPU system for use with TensorFlow.
+
+  Args:
+    embedding_config: If not None, an EmbeddingLayerConfiguration proto
+      describing the desired configuration of the hardware embedding lookup
+      tables. If embedding_config is None, no hardware embeddings can be used.
+    job: The job (the XXX in TensorFlow device specification /job:XXX)
+      that contains the TPU devices that will be initialized. If job=None
+      it is assumed there is only one job in the TensorFlow flock, and an
+      error will be returned if this assumption does not hold.
+  Returns:
+    Op which, when executed, will initialize the system.
+  """
+  if job is None:
+    device_name = "/replica:0/task:0/device:TPU_SYSTEM:0"
+  else:
+    device_name = "/job:%s/replica:0/task:0/device:TPU_SYSTEM:0" % job
+  config_string = ("" if embedding_config is None else
+                   embedding_config.SerializeToString())
+  with ops.device(device_name):
+    init_distributed_tpu = tpu_ops.configure_distributed_tpu(
+        embedding_config=config_string)
+  return init_distributed_tpu
+
+
+def shutdown_system(job=None):
+  """Shuts down a running a distributed TPU system."""
+  if job is None:
+    device_name = "/replica:0/task:0/device:TPU_SYSTEM:0"
+  else:
+    device_name = "/job:%s/replica:0/task:0/device:TPU_SYSTEM:0" % job
+  with ops.device(device_name):
+    shutdown_distributed_tpu = tpu_ops.shutdown_distributed_tpu()
+  return shutdown_distributed_tpu
+
+
+def core(num):
+  """Returns the device name for a core in a replicated TPU computation.
+
+  Args:
+    num: the virtual core number within each replica to which operators should
+    be assigned.
+  Returns:
+    A device name, suitable for passing to tf.device().
+  """
+  return "device:TPU_REPLICATED_CORE:{}".format(num)
+
+
+# Experimental API to 'break out' of a tpu.rewrite() (or shard(), etc.) context.
+# In
+#
+# XXX
+# with tpu.rewrite(...):
+#   YYY
+#   with tpu.outside_all_rewrites():
+#     ZZZ
+#
+# the Ops in ZZZ are added outside the scope of the rewrite().
+# TODO(phawkins): currently outside_all_rewrites() pops out of all nested
+# control flow scopes, for example loops. It would make more sense if it only
+# popped out of a single scope.
+@contextlib.contextmanager
+def outside_all_rewrites():
+  """Experimental API to 'break out' of a tpu.rewrite() (or shard(), etc.)."""
+  with ops.control_dependencies(None):
+    yield
+
+
+class TPUReplicateContext(control_flow_ops.ControlFlowContext):
+  """A ControlFlowContext for nodes inside a TPU computation.
+
+  The primary role of TPUReplicateContext is to mark operators inside a
+  tpu.replicate() computation with attributes:
+  * _tpu_replicate=XYZ, where XYZ is a unique name, and
+  * _tpu_num_replicas=k, where k is the number of replicas.
+
+  We use a ControlFlowContext to perform the annotation since it
+  integrates with Tensorflow constructs like ResourceVariables. For example,
+  if a ResourceVariable is constructed inside a tpu.replicate() block, the
+  ResourceVariable implementation can use "with ops.control_dependencies(None)"
+  to build the variable's definition outside the replicated computation.
+  """
+
+  def __init__(self, name, num_replicas, global_tpu_id=None):
+    control_flow_ops.ControlFlowContext.__init__(self)
+    self._name = name
+    self._num_replicas = num_replicas
+    self._global_tpu_id = [] if global_tpu_id is None else global_tpu_id
+
+  def AddOp(self, op):
+    self._AddOpInternal(op)
+
+  def _AddOpInternal(self, op):
+    # pylint: disable=protected-access
+    if any(x.dtype._is_ref_dtype for x in op.inputs):
+      raise NotImplementedError(
+          "Non-resource Variables are not supported inside TPU computations "
+          "(operator name: %s)" % op.name)
+    # pylint: enable=protected-access
+    if "_tpu_replicate" in op.node_def.attr:
+      raise ValueError("TPU computations cannot be nested")
+    op.node_def.attr["_tpu_replicate"].s = self._name
+    op.node_def.attr["_tpu_num_replicas"].i = self._num_replicas
+    op.node_def.attr["_tpu_global_id"].list.i.extend(self._global_tpu_id)
+    op.graph.prevent_feeding(op)
+    op.graph.prevent_fetching(op)
+
+  def AddValue(self, val):
+    result = val
+    if self._outer_context:
+      result = self._outer_context.AddValue(val)
+    return result
+
+  def AddInnerOp(self, op):
+    self._AddOpInternal(op)
+    if self._outer_context:
+      self._outer_context.AddInnerOp(op)
+
+
+def replicate(computation,
+              inputs=None,
+              infeed_queue=None,
+              global_tpu_id=None,
+              name=None):
+  """Builds a graph operator that runs a replicated TPU computation.
+
+  Args:
+    computation: a Python function that builds the computation to replicate.
+    inputs: a list of lists of input tensors or None (equivalent to
+      [[]]), indexed by [replica_num][input_num]. All replicas must
+      have the same number of inputs.
+    infeed_queue: if not None, the InfeedQueue from which to append a tuple
+      of arguments as inputs to computation.
+    global_tpu_id: if not None, a Numpy 2D array indicating the global
+      id of each TPU device in the system. The outer dimension of the
+      array is host task id, and the inner dimension is device ordinal,
+      so e.g., global_tpu_id[x][y] indicates the global id of device
+      /task:x/device:TPU_NODE:y.
+    name: name of the operator.
+  Returns:
+    A list of lists of output tensors, indexed by [replica_num][output_num].
+  Raises:
+    ValueError: if all replicas do not have equal numbers of input tensors.
+    ValueError: if the number of inputs per replica does not match
+      the number of formal parameters to `computation`.
+  """
+  if name is None:
+    name = "TPUReplicate"
+  inputs = [[]] if inputs is None else inputs
+
+  if global_tpu_id is not None:
+    # Turn the Numpy array into a flattened list.
+    global_tpu_id = global_tpu_id.flatten().tolist()
+
+  if ((not isinstance(inputs, list)) or
+      any(not isinstance(inp, (list, tuple)) for inp in inputs)):
+    raise TypeError("tpu.replicate() inputs must be a list of lists/tuples")
+
+  num_replicas = len(inputs)
+
+  # No replicas? Nothing to do.
+  if num_replicas == 0:
+    return []
+
+  # Converts inputs to Tensors.
+  inputs = [[ops.convert_to_tensor(x) for x in inp] for inp in inputs]
+
+  # Verifies that all replicas have matching numbers and types of inputs
+  input_types = [x.dtype for x in inputs[0]]
+  input_arity = len(input_types)
+  for i in range(num_replicas):
+    if len(inputs[i]) != input_arity:
+      raise ValueError("Replicas must have the same number of inputs. "
+                       "Replica 0 had {} inputs, replica {} had {} "
+                       "inputs.".format(input_arity, i, len(inputs[i])))
+
+    types = [x.dtype for x in inputs[i]]
+    if types != input_types:
+      raise ValueError(
+          "Replicas must have matching input types. Replica 0 had "
+          "input types {}, replica {} had input types {}".format(
+              input_types, i, types))
+
+  arg_error = tpu_function.check_function_argument_count(
+      computation, input_arity, infeed_queue)
+  if arg_error is not None:
+    if infeed_queue is None:
+      raise TypeError(
+          "Supplied computation cannot be called with the specified inputs. "
+          "You specified %d inputs: %s, but the computation needs %s" % (
+              input_arity, str([i.name for i in inputs[0]]), arg_error))
+    else:
+      raise TypeError(
+          "Supplied computation cannot be called with the specified inputs. "
+          "You specified %d inputs: %s and %d additional inputs from infeed,"
+          " but the computation needs %s" % (input_arity, str(
+              [i.name
+               for i in inputs[0]]), infeed_queue.number_of_tuple_elements,
+                                             arg_error))
+
+  graph = ops.get_default_graph()
+
+  with ops.name_scope(name, "replicate"):
+    # Fan-in: Builds a TPUReplicatedInput node for each input.
+    computation_inputs = []
+    for i in range(0, input_arity):
+      replicas = [inputs[replica][i] for replica in xrange(num_replicas)]
+      computation_inputs.append(
+          tpu_ops.tpu_replicated_input(replicas, name="input{}".format(i)))
+
+    context = TPUReplicateContext(
+        name=graph.unique_name("cluster"),
+        num_replicas=num_replicas,
+        global_tpu_id=global_tpu_id)
+    try:
+      context.Enter()
+
+      with tpu_function.tpu_shard_context(num_replicas):
+
+        # The EncapsulateTPUComputations rewrite needs to identify the
+        # replicated arguments inside each computation. Adds identity operators
+        # tagged with an attribute _tpu_replicated_input to identify the
+        # replicated inputs.
+        # pylint: disable=protected-access
+        with graph._attr_scope({"_tpu_replicated_input":
+                                attr_value_pb2.AttrValue(b=True)}):
+          computation_inputs = [
+              array_ops.identity(x, name="replicated_input_{}".format(i))
+              for i, x in enumerate(computation_inputs)]
+        # pylint: enable=protected-access
+
+        # If there is an infeed queue, adds the dequeued values to the
+        # computation's inputs.
+        if infeed_queue is not None:
+          infeed_queue.set_number_of_shards(num_replicas)
+          for t in infeed_queue.generate_dequeue_op():
+            computation_inputs.append(t)
+
+        # Only resource variables work inside a TPU computation, so turn on
+        # resource variables for the computation.
+        # TODO(phawkins): consider removing this code. It will
+        # be less confusing to clients if they knowingly choose to use resource
+        # variables.
+        vscope = variable_scope.get_variable_scope()
+        saved_use_resource = vscope.use_resource
+        vscope.set_use_resource(True)
+
+        outputs = computation(*computation_inputs)
+
+        vscope.set_use_resource(saved_use_resource)
+
+      # If the computation only returned one value, makes it a tuple.
+      if not isinstance(outputs, (list, tuple)):
+        outputs = (outputs,)
+
+      try:
+        with ops.device(core(0)):
+          outputs = [
+              o if isinstance(o, ops.Operation) else ops.convert_to_tensor(o)
+              for o in outputs
+          ]
+      except Exception as e:
+        raise ValueError(
+            "TPU function return values must all either be Operations or "
+            "convertible to Tensors. Got '%s'" % str(e))
+
+      # Separates the returned Operations and Tensors.
+      output_operations = [o for o in outputs if isinstance(o, ops.Operation)]
+      output_tensors = [o for o in outputs
+                        if not isinstance(o, ops.Operation)]
+
+      if outputs != output_tensors + output_operations:
+        raise ValueError(
+            "TPU functions must return zero-or more Tensor values followed by "
+            "zero or more Operations.")
+      output_arity = len(output_tensors)
+
+      # Wraps outputs in Identity ops. Otherwise a replicated input copied
+      # straight to an output would bypass the replicate(). This would be bad
+      # because the TPUReplicatedInput/TPUReplicatedOutput operator would not
+      # be rewritten away, leading to a runtime error.
+      # TODO(phawkins): extend the rewrite to elide these nodes instead.
+      with ops.device(core(0)):
+        output_tensors = [array_ops.identity(x) for x in output_tensors]
+    finally:
+      context.Exit()
+
+    # Fan-out: Builds a TPUReplicatedOutput node for each output.
+    outputs = [tpu_ops.tpu_replicated_output(output_tensors[i], num_replicas,
+                                             name="output{}".format(i))
+               for i in xrange(output_arity)]
+
+    with ops.control_dependencies(output_operations):
+      if output_arity == 0:
+        # Returns a list of NoOps dependent on the replication Op, indexed by
+        # [replica_num].
+        return [
+            control_flow_ops.no_op(name="%s_shard_%d" % (name, i))
+            for i in range(num_replicas)
+        ]
+      else:
+        # Wraps the outputs in identity operators so the names of any possible
+        # `fetch` nodes are preserved by the replication rewrite.
+        return [
+            [array_ops.identity(outputs[out][replica],
+                                name="output_%d_shard_%d" % (out, replica))
+             for out in xrange(output_arity)]
+            for replica in xrange(num_replicas)
+        ]
+
+
+def shard(computation,
+          inputs=None,
+          num_shards=1,
+          input_shard_axes=None,
+          outputs_from_all_shards=True,
+          output_shard_axes=None,
+          infeed_queue=None,
+          global_tpu_id=None,
+          name=None):
+  """Shards `computation` for parallel execution.
+
+  `inputs` must be a list of Tensors or None (equivalent to an empty
+  list), each of which has a corresponding split axis (from
+  `input_shard_axes`). Each input is split into `num_shards` pieces
+  along the corresponding axis, and computation is applied to each
+  shard in parallel.
+
+  Tensors are broadcast to all shards if they are lexically captured by
+  `computation`. e.g.,
+
+  x = tf.constant(7)
+  def computation():
+    return x + 3
+  ... = shard(computation, ...)
+
+  TODO(phawkins): consider adding support for broadcasting Tensors passed
+  as inputs.
+
+  If `outputs_from_all_shards` is true, the outputs from all shards of
+  `computation` are concatenated back together along their `output_shards_axes`.
+  Otherwise, each output is taken from an arbitrary shard.
+
+  Inputs and outputs of the computation must be at least rank-1 Tensors.
+
+  Args:
+    computation: a Python function that builds a computation to apply to each
+      shard of the input.
+    inputs: a list of input tensors or None (equivalent to an empty
+      list). Each input tensor has a corresponding shard axes, given
+      by `input_shard_axes`, which must have size divisible by
+      `num_shards`.
+    num_shards: the number of shards.
+    input_shard_axes: a list of dimensions along which to shard `inputs`, or
+      `None`. `None` means "shard all inputs along dimension 0". If not `None`,
+      there must be one dimension per input.
+    outputs_from_all_shards: boolean or list of boolean. For each output, if
+      `True`, outputs from all shards are concatenated along the corresponding
+      `output_shard_axes` entry. Otherwise, each output is taken
+      from an arbitrary shard. If the argument is a boolean, the argument's
+      value is used for each output.
+    output_shard_axes: a list of dimensions along which to concatenate the
+      outputs of `computation`, or `None`. `None` means "concatenate all outputs
+      along dimension 0". If not `None`, there must be one dimension per output.
+      Ignored if `outputs_from_all_shards` is False.
+    infeed_queue: if not None, the InfeedQueue to use to augment the inputs of
+      `computation`.
+    global_tpu_id: if not None, a Numpy 2D array indicating the global
+      id of each TPU device in the system. The outer dimension of the
+      array is host task id, and the inner dimension is device ordinal,
+      so e.g., global_tpu_id[x][y] indicates the global id of device
+      /task:x/device:TPU_NODE:y.
+    name: name of the operator.
+  Returns:
+    A list of output tensors.
+  Raises:
+    ValueError: if num_shards <= 0
+    ValueError: if len(input_shard_axes) != len(inputs)
+    ValueError: if len(output_shard_axes) != len(outputs from `computation`)
+  """
+
+  if num_shards <= 0:
+    raise ValueError("num_shards must be a positive integer.")
+
+  # Converts inputs to Tensors.
+  inputs = [] if inputs is None else [ops.convert_to_tensor(x) for x in inputs]
+
+  if input_shard_axes is None:
+    input_shard_axes = [0] * len(inputs)
+  if len(inputs) != len(input_shard_axes):
+    raise ValueError("Length of input_shard_axes must be equal to the number "
+                     "of inputs.")
+
+  if inputs:
+    # Splits the `inputs` along the corresponding `input_shard_axes`, giving
+    # lists with layout [input][shard]
+    split_inputs = [
+        array_ops.split(x, num_shards, axis=axis)
+        for (axis, x) in zip(input_shard_axes, inputs)]
+
+    # Transposes the input lists to have layout [shard][input]
+    transposed_inputs = [list(i) for i in zip(*split_inputs)]
+  else:
+    transposed_inputs = [[]] * num_shards
+
+  outputs = replicate(
+      computation,
+      transposed_inputs,
+      infeed_queue=infeed_queue,
+      global_tpu_id=global_tpu_id,
+      name=name)
+
+  # There must be at least one shard since num_shards > 0.
+  # TODO(b/36647078) remove disable when pylint bug is fixed.
+  # pylint: disable=indexing-exception
+  if isinstance(outputs[0], ops.Operation):
+    # pylint: enable=indexing-exception
+    # There were no outputs from the computation and replicate returned a list
+    # of NoOps with control dependencies on the computation. Return the first
+    # one so it can be used as a control dependency or fetch node.
+    # TODO(b/36647078) remove disable when pylint bug is fixed.
+    # pylint: disable=indexing-exception
+    return [outputs[0]]
+    # pylint: enable=indexing-exception
+
+  # TODO(b/36647078) remove disable when pylint bug is fixed.
+  # pylint: disable=indexing-exception
+  num_outputs = len(outputs[0])
+  # pylint: enable=indexing-exception
+
+  if output_shard_axes is None:
+    output_shard_axes = [0] * num_outputs
+  if num_outputs != len(output_shard_axes):
+    raise ValueError("Length of output_shard_axes must be equal to the number "
+                     "of outputs.")
+
+  if isinstance(outputs_from_all_shards, bool):
+    outputs_from_all_shards = [outputs_from_all_shards] * num_outputs
+
+  if num_outputs != len(outputs_from_all_shards):
+    raise ValueError("Length of outputs_from_all_shards must be equal to the "
+                     "number of outputs.")
+
+  results = []
+  for (axis, all_shards, x) in zip(output_shard_axes, outputs_from_all_shards,
+                                   zip(*outputs)):
+    if all_shards:
+      # Concatenate all of the outputs together.
+      results.append(array_ops.concat(list(x), axis=axis))
+    else:
+      # TODO(phawkins): use a smarter policy, e.g., round-robin across shards.
+      results.append(x[0])
+
+  return results
+
+
+def batch_parallel(computation,
+                   inputs=None,
+                   num_shards=1,
+                   infeed_queue=None,
+                   global_tpu_id=None,
+                   name=None):
+  """Shards `computation` along the batch dimension for parallel execution.
+
+  Convenience wrapper around shard().
+
+  `inputs` must be a list of Tensors or None (equivalent to an empty
+  list). Each input is split into `num_shards` pieces along the 0-th
+  dimension, and computation is applied to each shard in parallel.
+
+  Tensors are broadcast to all shards if they are lexically captured by
+  `computation`. e.g.,
+
+  x = tf.constant(7)
+  def computation():
+    return x + 3
+  ... = shard(computation, ...)
+
+  The outputs from all shards are concatenated back together along their 0-th
+  dimension.
+
+  Inputs and outputs of the computation must be at least rank-1 Tensors.
+
+  Args:
+    computation: a Python function that builds a computation to apply to each
+      shard of the input.
+    inputs: a list of input tensors or None (equivalent to an empty
+      list). The 0-th dimension of each Tensor must have size
+      divisible by `num_shards`.
+    num_shards: the number of shards.
+    infeed_queue: if not None, the InfeedQueue from which to append a tuple
+      of arguments as inputs to `computation`.
+    global_tpu_id: if not None, a Numpy 2D array indicating the global
+      id of each TPU device in the system. The outer dimension of the
+      array is host task id, and the inner dimension is device ordinal,
+      so e.g., global_tpu_id[x][y] indicates the global id of device
+      /task:x/device:TPU_NODE:y.
+    name: name of the operator.
+  Returns:
+    A list of output tensors.
+  Raises:
+    ValueError: if num_shards <= 0
+  """
+  return shard(
+      computation,
+      inputs,
+      num_shards=num_shards,
+      infeed_queue=infeed_queue,
+      global_tpu_id=global_tpu_id,
+      name=name)
+
+
+def rewrite(computation,
+            inputs=None,
+            infeed_queue=None,
+            global_tpu_id=None,
+            name=None):
+  """Rewrites `computation` for execution on a TPU system.
+
+  Args:
+    computation: a Python function that builds a computation to apply
+      to the input. If the function takes n inputs, 'inputs' should be
+      a list of n tensors. If the function returns m outputs, rewrite
+      will return a list of m tensors.
+    inputs: a list of input tensors or None (equivalent to an empty list).
+    infeed_queue: if not None, the InfeedQueue from which to append a tuple
+      of arguments as inputs to `computation`.
+    global_tpu_id: if not None, a Numpy 2D array indicating the global
+      id of each TPU device in the system. The outer dimension of the
+      array is host task id, and the inner dimension is device ordinal,
+      so e.g., global_tpu_id[x][y] indicates the global id of device
+      /task:x/device:TPU_NODE:y.
+    name: name of the operator.
+  Returns:
+    A list of output tensors.
+  """
+  if inputs is not None and not isinstance(inputs, (list, tuple)):
+    raise TypeError("tpu.rewrite() inputs must be a list or tuple")
+
+  # TODO(b/36647078) remove disable when pylint bug is fixed.
+  # pylint: disable=indexing-exception
+  return replicate(
+      computation,
+      None if inputs is None else [inputs],
+      infeed_queue=infeed_queue,
+      global_tpu_id=global_tpu_id,
+      name=name)[0]
+  # pylint: enable=indexing-exception
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
new file mode 100644
index 0000000000..a19d1db831
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
@@ -0,0 +1,47 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ===================================================================
+
+"""A RunConfig subclass with TPU support."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+
+from tensorflow.contrib.learn.python.learn.estimators import run_config as run_config_lib
+
+
+class TpuConfig(collections.namedtuple(
+    'TpuConfig', ['iterations_per_loop', 'num_shards'])):
+  """TPU related configuration required by `TPUEstimator`."""
+
+  def __new__(cls, iterations_per_loop=2, num_shards=2):
+    return super(TpuConfig, cls).__new__(
+        cls,
+        iterations_per_loop=iterations_per_loop,
+        num_shards=num_shards)
+
+
+class RunConfig(run_config_lib.RunConfig):
+  """RunConfig with TPU support."""
+
+  def __init__(self, tpu_config=None, **kwargs):
+    super(RunConfig, self).__init__(**kwargs)
+    self._tpu_config = tpu_config or TpuConfig()
+
+  @property
+  def tpu_config(self):
+    return self._tpu_config
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
new file mode 100644
index 0000000000..b702ab91f6
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -0,0 +1,361 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ===================================================================
+
+"""Tpu Estimator class."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import threading
+from six.moves import queue as Queue  # pylint: disable=redefined-builtin
+
+from tensorflow.contrib.tpu.python.tpu import tpu
+from tensorflow.contrib.tpu.python.tpu import tpu_config
+from tensorflow.contrib.tpu.python.tpu import tpu_feed
+from tensorflow.contrib.tpu.python.tpu import training_loop
+
+from tensorflow.python.estimator import estimator as estimator_lib
+from tensorflow.python.estimator import model_fn as model_fn_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import session_run_hook
+from tensorflow.python.training import training
+
+
+def _tpu_job(run_config):
+  # The tpu job is determined by the run_config. Right now, this method is
+  # required as tpu_config is not part of the RunConfig.
+  return None if run_config.master in ['', 'local'] else 'tpu_worker'
+
+
+class _SIGNAL(object):
+  """Signal used to control the input thread of infeed."""
+  NEXT_BATCH = 1
+  STOP = 2
+
+
+class InfeedThreadController(object):
+  """This wraps the infeed thread and stops when Estimator train finishes.
+
+  For model_fn wrapper, it is not possible to know when the `train` API will
+  stop. It could be the cases that the `max_steps` is reached or some hook
+  requests the stop in the monitored_session.
+
+  This controller (with coordination with `TpuInfeedSessionHook`) does the
+  following:
+
+  1) It pre-infeeds one `batch` data for current TPU iterations.
+
+  2) When `before_run` of `TpuInfeedSessionHook` is called, one more `batch`
+  data will be infed.
+
+  3) When `end` of `TpuInfeedSessionHook` is called, the thread will end
+  gracefully.
+
+  So, we might need to adjust the algorithrm here if the IO is slower than the
+  computation.
+  """
+
+  def __init__(self, session, enqueue_ops, iterations):
+    self._signal_queue = Queue.Queue()
+    self._input_thd = threading.Thread(target=self._input_thread_fn_for_loading,
+                                       args=(session, enqueue_ops, iterations))
+    self._input_thd.daemon = True
+    self._input_thd.start()
+
+  def _input_thread_fn_for_loading(self, session, enqueue_ops, iterations):
+    count = 0
+    while True:
+      signal = self._signal_queue.get()
+      if signal == _SIGNAL.STOP:
+        logging.info('Stop Infeed input thread.')
+        return
+
+      for i in range(iterations):
+        logging.debug('InfeedEnqueue data for iteration (%d, %d)', count, i)
+        session.run(enqueue_ops)
+      count += 1
+
+  def load_next_batch(self):
+    self._signal_queue.put(_SIGNAL.NEXT_BATCH)
+
+  def join(self):
+    logging.info('Waiting for InputThread to exit.')
+    self._signal_queue.put(_SIGNAL.STOP)
+    self._input_thd.join()
+
+
+class TpuInfeedSessionHook(session_run_hook.SessionRunHook):
+  """A Session hook setting up the TPU initialization and infeed.
+
+  This hook does two major things:
+  1. initialize and shutdown TPU system (maybe a separated hook)
+  2. launch and join the input thread for infeed.
+  """
+
+  def __init__(self, run_config, enqueue_fn):
+    self._iterations = run_config.tpu_config.iterations_per_loop
+    self._enqueue_fn = enqueue_fn
+    self._tpu_job = _tpu_job(run_config)
+
+  def begin(self):
+    self._enqueue_ops = self._enqueue_fn()
+    logging.info('TPU job name %s', self._tpu_job)
+    self._init_op = [tpu.initialize_system(job=self._tpu_job)]
+    self._finalize_op = [tpu.shutdown_system(job=self._tpu_job)]
+
+  def after_create_session(self, session, coord):
+    logging.info('Init TPU system')
+    session.run(self._init_op)
+
+    logging.info('Start infeed input thread controller')
+    self._infeed_thd_controller = InfeedThreadController(
+        session, self._enqueue_ops, self._iterations)
+
+  def before_run(self, run_context):
+    logging.info('Load next batch of data to infeed.')
+    self._infeed_thd_controller.load_next_batch()
+
+  def end(self, session):
+    logging.info('Stop infeed input thread controller')
+    self._infeed_thd_controller.join()
+
+    logging.info('Shutdown TPU system.')
+    session.run(self._finalize_op)
+
+
+class TpuEstimator(estimator_lib.Estimator):
+  """Estimator with TPU support.
+
+  The only difference is a wrapped  model_fn is set in the constructor.
+  """
+
+  def __init__(self,
+               model_fn=None,
+               model_dir=None,
+               config=None,
+               params=None,
+               use_tpu=True):
+    if use_tpu:
+      model_function = wrapped_model_fn(model_fn, config)
+    else:
+      model_function = model_fn
+
+    super(TpuEstimator, self).__init__(
+        model_fn=model_function,
+        model_dir=model_dir,
+        config=config,
+        params=params)
+    if not isinstance(config, tpu_config.RunConfig):
+      raise ValueError('`config` must be `tpu_config.RunConfig`')
+
+  def _create_global_step(self, graph):
+    """Creates a global step suitable for TPUs.
+
+    Args:
+      graph: The graph in which to create the global step.
+
+    Returns:
+      A global step `Tensor`.
+
+    Raises:
+      ValueError: if the global step tensor is already defined.
+    """
+    graph = graph or ops.get_default_graph()
+    if training.get_global_step(graph) is not None:
+      raise ValueError('"global_step" already exists.')
+    # Create in proper graph and base name_scope.
+    with graph.as_default() as g, g.name_scope(None):
+      return variable_scope.get_variable(
+          ops.GraphKeys.GLOBAL_STEP,
+          shape=[],
+          dtype=dtypes.int32,
+          initializer=init_ops.zeros_initializer(),
+          trainable=False,
+          use_resource=True,
+          collections=[ops.GraphKeys.GLOBAL_VARIABLES,
+                       ops.GraphKeys.GLOBAL_STEP])
+
+
+# TODO(xiejw): Improve the structure of this input_fn to infeed converion.
+# The code now looks not like Estimator style. We need to abstract many
+# details.
+def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
+  """Utility to convert input_fn to enqueue and dequeue fns for TPU.
+
+  Mainly, three things need to be done here.
+  1. Calls the input_fn many times (`num_shards`) to infeed the data into TPU
+  2. Create a dequeue_fn used by the train_step inside TPU execution to
+  dequeue the tensors.
+  3. Sets up the input thread to infeed.
+
+  Args:
+    run_config: run_config
+    features: features
+    labels: labels
+
+  Returns:
+    A tuple of (dequeue_fn, and thread main function)
+  """
+  infeed_names = None
+  infeed_tuple = []
+  if isinstance(features, dict):
+    # We need a fixed ordering for enqueueing and dequeueing.
+    infeed_names = [name for name in features]
+    infeed_tuple.extend([features[name] for name in infeed_names])
+  else:
+    infeed_tuple.append(features)
+  # TODO(jhseu): Handle multi-head and None labels
+  infeed_tuple.append(labels)
+  # TODO(jhseu): Update when b/36470756 is settled.
+  infeed_queue = tpu_feed.InfeedQueue(
+      tuple_types=[t.dtype for t in infeed_tuple],
+      tuple_shapes=[t.shape for t in infeed_tuple])
+  infeed_queue.set_number_of_shards(run_config.tpu_config.num_shards)
+
+  def dequeue_fn():
+    """dequeue_fn is used by the train_step in TPU to retrieve the tensors."""
+    values = infeed_queue.generate_dequeue_op()
+    if infeed_names is None:
+      return values
+    # Restore the feature dictionary and label.
+    dequeued_features = {}
+    for i in range(len(values) - 1):
+      dequeued_features[infeed_names[i]] = values[i]
+    label = values[-1]
+    return dequeued_features, label
+
+  def enqueue_fn():
+    """enqueue_fn is used to add ops to the graph to send tensors."""
+    job = _tpu_job(run_config)
+    def placement_function(index):
+      if job is None:
+        return '/replica:0/task:0/device:CPU:0'
+      else:
+        return '/job:%s/replica:0/task:%d/device:CPU:0' % (job, index / 8)
+    return infeed_queue.split_inputs_and_generate_enqueue_ops(
+        infeed_tuple, placement_function=placement_function)
+
+  return (dequeue_fn, enqueue_fn)
+
+
+def wrapped_model_fn(model_fn, run_config):
+  """Returns a new model_fn, which wraps the TPU support."""
+
+  # Verifies the model_fn signature according to Estimator framework.
+  estimator_lib._verify_model_fn_args(model_fn, params=None)  # pylint: disable=protected-access
+
+  def _model_fn(features, labels, mode):
+    """model_fn."""
+    # TODO(jhseu): Move to EVAL and PREDICT to TPU.
+    if mode != model_fn_lib.ModeKeys.TRAIN:
+      return model_fn(features, labels, mode)
+
+    dequeue_fn, enqueue_fn = (
+        _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels))
+
+    loss = _train_on_tpu_shards(
+        run_config,
+        train_step=_convert_model_fn_to_train_step(
+            model_fn, dequeue_fn, mode, run_config))
+
+    # Gets the variables back from TPU nodes. This means the variables updated
+    # by TPU will now be *synced* to host memory.
+    update_ops = [
+        array_ops.check_numerics(v.read_value(),
+                                 'Gradient for %s is NaN' % v.name).op
+        for v in variables.trainable_variables()
+    ]
+
+    hooks = [
+        TpuInfeedSessionHook(run_config, enqueue_fn),
+        training.LoggingTensorHook(
+            {'loss': array_ops.identity(loss),
+             'step': training.get_global_step()},
+            every_n_secs=30)
+    ]
+
+    return model_fn_lib.EstimatorSpec(
+        mode,
+        loss=array_ops.identity(loss),
+        training_hooks=hooks,
+        train_op=control_flow_ops.group(*update_ops))
+  return _model_fn
+
+
+def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config):
+  """generates a train step based on the model_fn."""
+
+  def _call_model_fn(features, labels):
+    """Calls the model_fn with required parameters."""
+    model_fn_args = estimator_lib._model_fn_args(model_fn)  # pylint: disable=protected-access
+    kwargs = {}
+    if 'mode' in model_fn_args:
+      kwargs['mode'] = mode
+    # Uncomment the following lines once `params` is supported.
+    #   if 'params' in model_fn_args:
+    #     kwargs['params'] = params
+    if 'config' in model_fn_args:
+      kwargs['config'] = run_config
+    return model_fn(features=features, labels=labels, **kwargs)
+
+  def _verify_estimator_spec(estimator_spec):
+    """Validates the estimator_spec."""
+    err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
+    if estimator_spec.training_chief_hooks:
+      raise ValueError(err_msg.format('training_chief_hooks'))
+    if estimator_spec.training_hooks:
+      raise ValueError(err_msg.format('training_hooks'))
+    return estimator_spec
+
+  def train_step(loss):
+    """Training step function for use inside a while loop."""
+    del loss  # unused; required in function signature.
+    features, labels = dequeue_fn()
+
+    # TODO(xiejw): how to do we support hook and savers in the original
+    # model_fn. Realistically, the original
+    # model_fn will be excuted on TPU chips in a replica way. The hooks
+    # returned by the model_fn cannot be supported at all. If we have to,
+    # the graph construction part in the model_fn should be separated from the
+    # control part (such as hooks and savers). By that the graph construction
+    # could de defered on TPU chip, while the control logic can stay in host.
+    estimator_spec = _verify_estimator_spec(_call_model_fn(features, labels))
+    loss, train_op = estimator_spec.loss, estimator_spec.train_op
+    with ops.control_dependencies([train_op]):
+      return array_ops.identity(loss)
+  return train_step
+
+
+def _train_on_tpu_shards(run_config, train_step):
+  """Executes the `train_step` on all shards."""
+  def train_shard():
+    return training_loop.repeat(run_config.tpu_config.iterations_per_loop,
+                                train_step,
+                                [1e7],  # initial_loss
+                                name='loop')
+
+  (loss,) = tpu.shard(train_shard,
+                      inputs=[],
+                      num_shards=run_config.tpu_config.num_shards,
+                      outputs_from_all_shards=False)
+  return loss
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
new file mode 100644
index 0000000000..668b5b8b91
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
@@ -0,0 +1,613 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ===================================================================
+
+"""Helper library for handling infeed between hosts and TPUs.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.contrib.tpu.python.ops import tpu_ops
+from tensorflow.contrib.tpu.python.tpu import tpu_sharding
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+
+
+class InfeedQueue(object):
+  """A helper object to build a device infeed queue.
+
+  The InfeedQueue builds the host-side and device-side Ops to enqueue and
+  dequeue elements, respectively, and ensures that their types and
+  shapes match.
+  """
+
+  def __init__(self,
+               number_of_tuple_elements=None,
+               tuple_types=None,
+               tuple_shapes=None,
+               shard_dimensions=None,
+               name=None):
+    """Creates a new InfeedQueue with the given configuration.
+
+    The configuration need not be fully specified at creation since it
+    can be modified subsequently by methods that set the values
+    explicitly or infer them from the shapes of inputs.
+
+    Args:
+      number_of_tuple_elements: the number of Tensors fed atomically through the
+        queue, must be present unless it can be inferred from other arguments.
+      tuple_types: if not None, a list of types of the elements of the queue.
+      tuple_shapes: if not None, a list of shapes of the elements of the queue.
+      shard_dimensions: if not None, a list of dimensions on which the
+        elements of the queue should be sharded during automatic
+        parallelization.
+      name: the name of the queue.
+
+    Raises:
+      ValueError: if number_of_tuple_elements <= 0; or
+        number_of_tuple_arguments, tuple_types, tuple_shapes, and
+        shard_dimensions are all None; or the length of tuple_types,
+        tuple_shapes, or shard_dimensions is not equal to
+        number_of_tuple_elements; or any element of shard_dimensions
+        can't be converted to a Dimension.
+      TypeError: if any element of tuple_types or tuple_shapes can't
+        be converted to a dtype or TensorShape, respectively.
+    """
+    self._frozen = False
+    self._generated_enqueue_ops = False
+    self._generated_dequeue_op = False
+    self._name = "InfeedQueue" if name is None else name
+    if number_of_tuple_elements is None:
+      if tuple_types is not None:
+        number_of_tuple_elements = len(tuple_types)
+      elif tuple_shapes is not None:
+        number_of_tuple_elements = len(tuple_shapes)
+      elif shard_dimensions is not None:
+        number_of_tuple_elements = len(shard_dimensions)
+      else:
+        raise ValueError(
+            "number of tuple elements cannot be inferred from InfeedQueue "
+            "constructor"
+        )
+    if number_of_tuple_elements <= 0:
+      raise ValueError("number_of_tuple_elements %d must be > 0" %
+                       number_of_tuple_elements)
+    # Make an empty sharding policy for each tuple element.
+    self._sharding_policies = [
+        tpu_sharding.ShardingPolicy()
+        for _ in xrange(number_of_tuple_elements)
+    ]
+    if tuple_types is not None:
+      self.set_tuple_types(tuple_types)
+    else:
+      self._tuple_types = None
+    if tuple_shapes is not None:
+      self.set_tuple_shapes(tuple_shapes)
+    else:
+      self._tuple_shapes = None
+    if shard_dimensions is not None:
+      self.set_shard_dimensions(shard_dimensions)
+    self._validate()
+
+  def _validate(self):
+    """Checks that the configuration is self-consistent.
+
+    Raises:
+      ValueError: if the shapes and sharding policies don't match.
+    """
+    if self.tuple_shapes is not None:
+      for (policy, shape) in zip(self._sharding_policies, self._tuple_shapes):
+        # Raise an error if the policy is incompatible with the shape.
+        _ = policy.get_sharded_shape(shape)
+
+  @property
+  def number_of_tuple_elements(self):
+    """Returns the number of InfeedQueue tuple elements."""
+    return len(self._sharding_policies)
+
+  @property
+  def tuple_types(self):
+    """Returns the types of the InfeedQueue tuple elements."""
+    return self._tuple_types
+
+  def set_tuple_types(self, tuple_types):
+    """Sets the type of each element of the queue.
+
+    tuple_types must be a list of length
+    self.number_of_tuple_elements, and each element must be
+    convertible to a dtype.
+
+    Args:
+      tuple_types: the types of each queue element.
+
+    Raises:
+      ValueError: if tuple_types is not of length
+        self.number_of_tuple_elements.
+      TypeError: if an element of tuple_types cannot be converted to a
+        dtype.
+    """
+    if len(tuple_types) != self.number_of_tuple_elements:
+      raise ValueError("tuple_types is %s, but must be a list of length %d" %
+                       (str(tuple_types), self.number_of_tuple_elements))
+    if self._frozen:
+      for (frozen, updated) in zip(self._tuple_types, tuple_types):
+        if frozen != updated:
+          raise ValueError(
+              "Trying to update InfeedQueue with frozen configuration with an "
+              "incompatible type. Frozen types are %s, updated types are %s" % (
+                  str(self._tuple_types), str(tuple_types)))
+    else:
+      try:
+        self._tuple_types = [dtypes.as_dtype(t) for t in tuple_types]
+      except (TypeError) as e:
+        raise TypeError(
+            "tuple_types is %s, but must be a list of elements each "
+            "convertible to dtype: got error %s" % (str(tuple_types), str(e)))
+
+  @property
+  def tuple_shapes(self):
+    """Returns the shapes of the InfeedQueue tuple elements."""
+    return self._tuple_shapes
+
+  def set_tuple_shapes(self, tuple_shapes):
+    """Sets the shape of each element of the queue.
+
+    tuple_shapes must be a list of length
+    self.number_of_tuple_elements, and each element must be
+    convertible to a TensorShape.
+
+    Args:
+      tuple_shapes: the shapes of each queue element.
+
+    Raises:
+      ValueError: if tuple_shapes is not of length
+        self.number_of_tuple_elements.
+      TypeError: if an element of tuple_shapes cannot be converted to
+        a TensorShape.
+    """
+    if len(tuple_shapes) != self.number_of_tuple_elements:
+      raise ValueError("tuple_shapes is %s, but must be a list of length %d" %
+                       (str(tuple_shapes), self.number_of_tuple_elements))
+    try:
+      tuple_shapes = [tensor_shape.as_shape(shape) for shape in tuple_shapes]
+    except (ValueError, TypeError) as e:
+      raise TypeError(
+          "tuple_shapes is %s, but must be a list of elements each "
+          "convertible to TensorShape: got error %s" % (str(tuple_shapes),
+                                                        str(e)))
+    if self._frozen:
+      for (frozen, updated) in zip(self._tuple_shapes, tuple_shapes):
+        if frozen != updated:
+          raise ValueError(
+              "Trying to update InfeedQueue with frozen configuration with an "
+              "incompatible shape. Frozen shapes are %s, updated shapes are %s"
+              % (str(self._tuple_shapes), str(tuple_shapes)))
+    else:
+      self._tuple_shapes = tuple_shapes
+    self._validate()
+
+  @property
+  def sharding_policies(self):
+    """Returns the sharding policies of the InfeedQueue tuple elements."""
+    return self._sharding_policies
+
+  @property
+  def shard_dimensions(self):
+    """Gets the shard dimension of each tuple element.
+
+    Returns:
+      A list of length number_of_tuple_elements, where each list entry
+      is the shard dimension of that tuple element or None if the
+      shard dimension has not been set.
+    """
+    # The number of shards is always the same for all the policies.
+    return [policy.shard_dimension for policy in self._sharding_policies]
+
+  def set_shard_dimensions(self, shard_dimensions):
+    """Sets the shard_dimension of each element of the queue.
+
+    shard_dimensions must be a list of length
+    self.number_of_tuple_elements, and each element must be
+    convertible to a Dimension compatible with self.tuple_shapes.
+
+    Args:
+      shard_dimensions: the dimensions of each queue element.
+
+    Raises:
+      ValueError: if shard_dimensions is not of length
+        self.number_of_tuple_elements; or an element of
+        shard_dimensions cannot be converted to a Dimension; or an
+        element of shard_dimensions is a Dimension that is out of
+        range for the corresponding tuple element shape.
+    """
+    if len(shard_dimensions) != self.number_of_tuple_elements:
+      raise ValueError("shard_dimensions is %s, but must be a list of length %d"
+                       % (str(shard_dimensions),
+                          self.number_of_tuple_elements))
+    for (policy, dimension) in zip(self._sharding_policies, shard_dimensions):
+      policy.set_shard_dimension(dimension)
+    self._validate()
+
+  @property
+  def number_of_shards(self):
+    """Gets the number of shards to use for the InfeedQueue.
+
+    Returns:
+      Number of shards or None if the number of shards has not been set.
+    """
+    # The number of shards is always the same for all the policies.
+    return self._sharding_policies[0].number_of_shards
+
+  def set_number_of_shards(self, number_of_shards):
+    """Sets the number of shards to use for the InfeedQueue.
+
+    Args:
+      number_of_shards: number of ways to shard the InfeedQueue.
+
+    Raises:
+      ValueError: if number_of_shards is not > 0; or the policies have
+        been frozen and number_of_shards was already set to something
+        else.
+    """
+    for policy in self._sharding_policies:
+      policy.set_number_of_shards(number_of_shards)
+    self._validate()
+
+  def set_configuration_from_input_tensors(self, input_tensors):
+    """Sets the shapes and types of the queue tuple elements.
+
+    input_tensors is a list of Tensors whose types and shapes are used
+    to set the queue configuration.
+
+    Args:
+      input_tensors: list of Tensors of the same types and shapes as
+        the desired queue Tuple.
+
+    Raises:
+      ValueError: if input_tensors is not a list of length
+        self.number_of_tuple_elements
+    """
+    if len(input_tensors) != self.number_of_tuple_elements:
+      raise ValueError(
+          "input_tensors is %s, but should be a list of %d Tensors", (
+              str(input_tensors), self.number_of_tuple_elements))
+    self.set_tuple_shapes([t.shape for t in input_tensors])
+    self.set_tuple_types([t.dtype for t in input_tensors])
+
+  def set_configuration_from_sharded_input_tensors(self, input_tensors):
+    """Sets the shapes and types of the queue tuple elements.
+
+    input_tensors is a list of lists of Tensors whose types and shapes are used
+    to set the queue configuration. The length of the outer list is the number
+    of shards required, and each inner list is the tuple of Tensors to use to
+    determine the types and shapes of the correponding shard. This method
+    depends on the shard dimension, and calling it freezes the shard policy.
+
+    Args:
+      input_tensors: list of lists of Tensors. The outer list length corresponds
+        to the desired number of shards, and each inner list is the size
+        and shape of the desired configuration of the corresponding shard.
+
+    Raises:
+      ValueError: if any inner list is not a list of length
+        self.number_of_tuple_elements; or the inner lists do not combine to
+        form a consistent unsharded shape.
+      TypeError: if the types of the Tensors in the inner lists do not match.
+    """
+    if not self._frozen:
+      # Unset the tuple shapes in case the configuration becomes
+      # transiently inconsistent.
+      self._tuple_shapes = None
+    number_of_shards = len(input_tensors)
+    self.set_number_of_shards(number_of_shards)
+    for t in input_tensors:
+      if len(t) != self.number_of_tuple_elements:
+        raise ValueError(
+            "input_tensors is %s but must be a list of lists, where each inner"
+            " list has length number_of_tuple_elements=%d" % (
+                str(input_tensors), self.number_of_tuple_elements))
+    # Transpose the inputs to make a list of shard shapes for each tuple
+    # element.
+    sharded_shapes = [[t[i].shape for t in input_tensors]
+                      for i in xrange(self.number_of_tuple_elements)]
+    # For each tuple, get the unsharded shape using that tuple's policy.
+    unsharded_shapes = [
+        policy.get_unsharded_shape(s)
+        for (policy, s) in zip(self._sharding_policies, sharded_shapes)
+    ]
+    self.set_tuple_shapes(unsharded_shapes)
+    for i in xrange(1, self.number_of_shards):
+      for (t1, t2) in zip(input_tensors[0], input_tensors[i]):
+        if t1.dtype != t2.dtype:
+          raise TypeError(
+              "types of the tuple elements of input_tensors %s are not "
+              "consistent" % str(input_tensors))
+    self.set_tuple_types([t.dtype for t in input_tensors[0]])
+
+  def freeze(self):
+    """Freezes the InfeedQueue so it can no longer be modified.
+
+    The configuration is implicitly frozen before any host-side or
+    device-side Ops are generated. The configuration cannot be frozen
+    until the types and shapes of the tuple elements have been set.
+
+    Raises:
+      ValueError: if the types or shapes of the tuple elements have not been
+      set.
+    """
+    self._frozen = True
+    if self._tuple_types is None:
+      raise ValueError(
+          "Can't freeze an InfeedQueue without setting all tuple types.")
+    if self._tuple_shapes is None:
+      raise ValueError(
+          "Can't freeze an InfeedQueue without setting all tuple shapes.")
+    for shape in self._tuple_shapes:
+      if shape.dims is None:
+        raise ValueError(
+            "Can't freeze an InfeedQueue without setting all tuple shapes.")
+    for policy in self._sharding_policies:
+      policy.freeze()
+    self._validate()
+
+  def generate_dequeue_op(self):
+    """Generates the device-side Op to dequeue a tuple from the queue.
+
+    Implicitly freezes the queue configuration if it is not already
+    frozen, which will raise errors if the shapes and types have not
+    been fully specified.
+
+    Returns:
+      A list of Outputs corresponding to a shard of infeed dequeued
+      into XLA, suitable for use within a replicated block.
+
+    Raises:
+      ValueError: if the types or shapes of the tuple elements have not been
+      set; or if a dequeue op has already been generated.
+    """
+    self.freeze()
+    if self._generated_dequeue_op:
+      raise ValueError("Can't generate two dequeue Ops from the same queue")
+    self._generated_dequeue_op = True
+    full_name = "%s/dequeue" % self._name
+    sharded_shapes = [
+        policy.get_sharded_shape(shape)
+        for (shape, policy) in zip(self._tuple_shapes, self._sharding_policies)
+    ]
+    return tpu_ops.infeed_dequeue_tuple(
+        dtypes=self._tuple_types, shapes=sharded_shapes, name=full_name)
+
+  def _generate_enqueue_op(self,
+                           inputs,
+                           name_prefix,
+                           index,
+                           device=None,
+                           tpu_ordinal=-1):
+    """Generate a host-side Op to enqueue a tuple to the queue.
+
+    If device is None the inputs are all required to have the same
+    device specification, and the enqueue Op is colocated with
+    inputs[0]. Otherwise the enqueue Op is placed on 'device'.
+
+    Args:
+      inputs: a list of Tensors with the types and shapes of the tuple elements.
+      name_prefix: the base name for the Op.
+      index: the shard index, used to uniquify the Op name.
+      device: device to place the Op on, or None if it should be
+        colocated with the inputs.
+      tpu_ordinal: ordinal of the TPU device on the host to use for
+      infeed if device is a CPU device. Should be set to -1 if device
+      is a TPU device.
+
+    Returns:
+      An Op corresponding to a shard of infeed enqueued at the host,
+      suitable for use within a replicated block.
+
+    Raises:
+      ValueError: if device is None and inputs do not all have the
+        same device specification.
+    """
+    full_name = "%s/%d" % (name_prefix, index)
+    shapes = [t.shape for t in inputs]
+    if device is None:
+      devices = [t.device for t in inputs]
+      for i in xrange(1, self.number_of_tuple_elements):
+        if devices[0] != devices[i]:
+          raise ValueError(
+              "input devices for shard %d are %s, but should all be the same",
+              index, str(devices))
+      with ops.colocate_with(inputs[0]):
+        return tpu_ops.infeed_enqueue_tuple(
+            inputs=inputs,
+            shapes=shapes,
+            name=full_name,
+            device_ordinal=tpu_ordinal)
+    else:
+      with ops.device(device):
+        return tpu_ops.infeed_enqueue_tuple(
+            inputs=inputs,
+            shapes=shapes,
+            name=full_name,
+            device_ordinal=tpu_ordinal)
+
+  def generate_enqueue_ops(self, sharded_inputs):
+    """Generates the host-side Ops to enqueue the shards of a tuple.
+
+    sharded_inputs is a list, one for each shard, of lists of
+    Tensors. sharded_inputs[0] is the tuple of Tensors to use to feed
+    shard 0 if the queue. Returns the host-side Ops that must be run to
+    enqueue the sharded tuple. The Op for shard i is colocated with the inputs
+    for shard i.
+
+    Implicitly freezes the queue configuration if it is not already
+    frozen. If the configuration has already been frozen, and is not
+    compatible with the types and shapes of sharded_inputs, an error
+    will be raised.
+
+    Args:
+      sharded_inputs: a list of lists of Tensors. The length of the outer list
+        determines the number of shards. Each inner list indicates the types
+        and shapes of the tuples in the corresponding shard.
+
+    Returns:
+      A list of host-side Ops, one for each shard, that when executed together
+      will enqueue a full-size element of infeed.
+
+    Raises:
+      ValueError: if the queue configuration has previously been frozen and the
+        shapes of the elements of sharded_inputs are not compatible with the
+        frozen configuration; or if the shapes of the elements of sharded_inputs
+        don't form a consistent unsharded tuple; or if the elements of a tuple
+        have different device constraints.
+      TypeError: if the queue configuration has previously been frozen and the
+        types of the elements of sharded_inputs are not compatible with the
+        frozen configuration; or if the types of the elements of sharded_inputs
+        don't form a consistent unsharded tuple.
+    """
+    self.set_configuration_from_sharded_input_tensors(sharded_inputs)
+    self.freeze()
+    if self._generated_enqueue_ops:
+      raise ValueError("Can't generate two enqueue Ops from the same queue")
+    self._generated_enqueue_ops = True
+    name_prefix = "%s/enqueue" % self._name
+    return [
+        self._generate_enqueue_op(shard, name_prefix, index)
+        for (shard, index) in zip(sharded_inputs, xrange(self.number_of_shards))
+    ]
+
+  # TODO(misard) Generalize this to the case of systems that don't
+  # have 8 devices per host, and figure out what to do with
+  # model-parallelism.
+  def _default_placement_function(self, index):
+    return "/task:%d/device:CPU:0" % (index / 8)
+
+  def _default_ordinal_function(self, index):
+    return index % 8
+
+  # TODO(b/36470756) remove this from tutorials once we have a better story
+  # for automatic placement of input pipelines.
+  def split_inputs_and_generate_enqueue_ops(self,
+                                            inputs,
+                                            global_tpu_id=None,
+                                            placement_function=None,
+                                            tpu_ordinal_function=None):
+    """POORLY-PERFORMING ON MULTI-HOST SYSTEMS.
+
+    Generates the host-side Ops to enqueue a tuple.
+
+    This method performs poorly because it takes an entire input on a single
+    host, splits it, and distributes it to all of the cores. It is present only
+    to simplify tutorial examples.
+
+    inputs is a list of Tensors to use to feed the queue. Each input is split
+    into self.number_of_shards shards. Returns an Op for each shard to enqueue
+    the shard. The Op for shard i is placed on device placement_function(i).
+
+    Implicitly freezes the queue configuration if it is not already
+    frozen. If the configuration has already been frozen, and is not
+    compatible with the types and shapes of inputs, an error
+    will be raised.
+
+    Args:
+      inputs: a list of Tensors which indicates the types and shapes of the
+        queue tuple.
+     global_tpu_id: if not None, a Numpy 2D array indicating the global
+        id of each TPU device in the system. The outer dimension of the
+        array is host task id, and the inner dimension is device ordinal,
+        so e.g., global_tpu_id[x][y] indicates the global id of device
+        /task:x/device:TPU_NODE:y. If global_tpu_id is not None, but
+        placement_function and ordinal_function are None, then global_tpu_id
+        will be used to place infeed on the TPUs with the first k global ids,
+        where k is the number of shards in the queue.
+      placement_function: if not None, a function that takes the shard
+        index as input and returns a device string indicating which
+        device the shard's infeed should be placed on. If placement_function
+        and tpu_ordinal_function are None, inputs are sharded round-robin
+        across the devices in the system.
+      tpu_ordinal_function: if not None, a function that takes the
+        shard index as input and returns the ordinal of the TPU device
+        the shard's infeed should be placed on. If placement_function
+        and tpu_ordinal_function are None, inputs are sharded round-robin
+        across the devices in the system.
+
+    Returns:
+      A list of host-side Ops, one for each shard, that when executed together
+      will enqueue a full-size element of infeed.
+
+    Raises:
+      ValueError: if the queue configuration has previously been frozen and the
+        shapes of the elements of inputs are not compatible with the frozen
+        configuration.
+      TypeError: if the queue configuration has previously been frozen and the
+        types of the elements of inputs are not compatible with the frozen
+        configuration.
+    """
+    if global_tpu_id is None:
+      if placement_function is None:
+        placement_function = self._default_placement_function
+      if tpu_ordinal_function is None:
+        tpu_ordinal_function = self._default_ordinal_function
+    else:
+      global_id_map = {}
+      for host, devices in enumerate(global_tpu_id):
+        for ordinal, global_id in enumerate(devices):
+          global_id_map[global_id] = (host, ordinal)
+
+      def _placement_function_from_map(index):
+        return "/task:%d/device:CPU:0" % global_id_map[index][0]
+
+      def _ordinal_function_from_map(index):
+        return global_id_map[index][1]
+
+      if placement_function is None:
+        placement_function = _placement_function_from_map
+      if tpu_ordinal_function is None:
+        tpu_ordinal_function = _ordinal_function_from_map
+    self.set_configuration_from_input_tensors(inputs)
+    self.freeze()
+    if self._generated_enqueue_ops:
+      raise ValueError("Can't generate two enqueue Ops from the same queue")
+    self._generated_enqueue_ops = True
+    split_name_prefix = "%s/split" % self._name
+    if self.number_of_shards == 1:
+      transposed_sharded_inputs = [[inp] for inp in inputs]
+    else:
+      transposed_sharded_inputs = [
+          array_ops.split(
+              inp,
+              self.number_of_shards,
+              axis=policy.shard_dimension,
+              name="%s/%d" % (split_name_prefix, index))
+          for (inp, policy, index) in zip(inputs, self._sharding_policies,
+                                          xrange(self.number_of_tuple_elements))
+      ]
+    sharded_inputs = [[shard[i] for shard in transposed_sharded_inputs]
+                      for i in xrange(self.number_of_shards)]
+    name_prefix = "%s/enqueue" % self._name
+    return [
+        self._generate_enqueue_op(
+            shard,
+            name_prefix,
+            index,
+            device=placement_function(index),
+            tpu_ordinal=tpu_ordinal_function(index))
+        for (shard, index) in zip(sharded_inputs, xrange(self.number_of_shards))
+    ]
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_function.py b/tensorflow/contrib/tpu/python/tpu/tpu_function.py
new file mode 100644
index 0000000000..de16e3b157
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_function.py
@@ -0,0 +1,106 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Helper library for functions used during TPU compilation."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import contextlib
+
+from tensorflow.python.util import tf_inspect
+
+
+class TpuContext(object):
+  """A context object holding state about the TPU computation being built."""
+
+  def __init__(self):
+    """Creates a new TpuContext."""
+    self._number_of_shards = None
+
+  @property
+  def number_of_shards(self):
+    return self._number_of_shards
+
+  def set_number_of_shards(self, number_of_shards):
+    self._number_of_shards = number_of_shards
+
+
+# The Tpu context holds the number of shards when a sharded computation is
+# being built, or None if no computation is being built.
+_current_tpu_context = TpuContext()
+
+
+@contextlib.contextmanager
+def tpu_shard_context(number_of_shards):
+  if _current_tpu_context.number_of_shards is not None:
+    raise NotImplementedError("tpu_shard_context cannot be nested.")
+  try:
+    _current_tpu_context.set_number_of_shards(number_of_shards)
+    yield
+  finally:
+    _current_tpu_context.set_number_of_shards(None)
+
+
+def get_tpu_context():
+  return _current_tpu_context
+
+
+def check_function_argument_count(func, input_arity, infeed_queue):
+  """Validate the number of input arguments to a tpu function.
+
+  Args:
+    func: the Python function that will be called to generate the body
+      of a TPUFunction.
+    input_arity: the number of explicit arguments supplied by the
+      caller.
+    infeed_queue: if not None, the infeed queue that will supply
+      additional arguments to the function.
+
+  Returns:
+    None if function can be called with the supplied number of
+      arguments, or an error string if it cannot.
+  """
+  def format_error(complaint, quantity):
+    return "%s %d argument%s" % (complaint, quantity, ""
+                                 if quantity == 1 else "s")
+
+  number_of_arguments_needed = input_arity
+  if infeed_queue is not None:
+    number_of_arguments_needed += infeed_queue.number_of_tuple_elements
+  arg_spec = tf_inspect.getargspec(func)
+  number_of_args = len(arg_spec.args)
+  if arg_spec.defaults is None:
+    number_of_defaults = 0
+  else:
+    number_of_defaults = len(arg_spec.defaults)
+  min_required_arguments = number_of_args - number_of_defaults
+  if number_of_arguments_needed < min_required_arguments:
+    # The required number of arguments is not enough to call the function.
+    if number_of_defaults == 0 and arg_spec.varargs is None:
+      return format_error("exactly", number_of_args)
+    else:
+      return format_error("at least", min_required_arguments)
+  if arg_spec.varargs is None and number_of_arguments_needed > number_of_args:
+    # The required number of arguments is too many to call the function.
+    if number_of_defaults == 0:
+      return format_error("exactly", number_of_args)
+    else:
+      return format_error("at most", number_of_args)
+  # Since there are varargs, func can accept any number of arguments
+  # greater than the minimum.
+  return None
+
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_function_test.py b/tensorflow/contrib/tpu/python/tpu/tpu_function_test.py
new file mode 100644
index 0000000000..463c249a95
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_function_test.py
@@ -0,0 +1,125 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Tests for tpu_function helpers."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tpu.python.tpu import tpu_feed
+from tensorflow.contrib.tpu.python.tpu import tpu_function
+
+from tensorflow.python.platform import test
+
+
+class FunctionArgCheckTest(test.TestCase):
+
+  def testSimple(self):
+    """Tests that arg checker works for functions with no varargs or defaults.
+    """
+
+    def func(x, y, z):
+      return x + y + z
+
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 3, None))
+    self.assertEqual("exactly 3 arguments",
+                     tpu_function.check_function_argument_count(func, 2, None))
+    queue = tpu_feed.InfeedQueue(2)
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 1, queue))
+    self.assertEqual("exactly 3 arguments",
+                     tpu_function.check_function_argument_count(func, 2, queue))
+
+  def testDefaultArgs(self):
+    """Tests that arg checker works for a function with no varargs."""
+
+    def func(x, y, z=17):
+      return x + y + z
+
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 3, None))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 2, None))
+    self.assertEqual("at least 2 arguments",
+                     tpu_function.check_function_argument_count(func, 1, None))
+    self.assertEqual("at most 3 arguments",
+                     tpu_function.check_function_argument_count(func, 4, None))
+    queue = tpu_feed.InfeedQueue(1)
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 2, queue))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 1, queue))
+    self.assertEqual("at least 2 arguments",
+                     tpu_function.check_function_argument_count(func, 0, queue))
+    self.assertEqual("at most 3 arguments",
+                     tpu_function.check_function_argument_count(func, 4, queue))
+
+  def testVarArgs(self):
+    """Tests that arg checker works for a function with varargs."""
+
+    def func(x, y, *z):
+      return x + y + len(z)
+
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 2, None))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 3, None))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 4, None))
+    self.assertEqual("at least 2 arguments",
+                     tpu_function.check_function_argument_count(func, 1, None))
+    queue = tpu_feed.InfeedQueue(1)
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 1, queue))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 2, queue))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 3, queue))
+    self.assertEqual("at least 2 arguments",
+                     tpu_function.check_function_argument_count(func, 0, queue))
+
+  def testVarArgsAndDefaults(self):
+    """Tests that arg checker works for a function with varargs and defaults."""
+
+    def func(x, y, z=17, *q):
+      return x + y + z + len(q)
+
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 2, None))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 3, None))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 4, None))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 5, None))
+    self.assertEqual("at least 2 arguments",
+                     tpu_function.check_function_argument_count(func, 1, None))
+    queue = tpu_feed.InfeedQueue(1)
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 1, queue))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 2, queue))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 3, queue))
+    self.assertEqual(None,
+                     tpu_function.check_function_argument_count(func, 4, queue))
+    self.assertEqual("at least 2 arguments",
+                     tpu_function.check_function_argument_count(func, 0, queue))
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_infeed_test.py b/tensorflow/contrib/tpu/python/tpu/tpu_infeed_test.py
new file mode 100644
index 0000000000..a41ff60d0a
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_infeed_test.py
@@ -0,0 +1,130 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Tests for TPU InfeedQueue methods."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tpu.python.tpu import tpu_feed
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.platform import test
+
+
+class InfeedTest(test.TestCase):
+
+  def testConstructor(self):
+    """Tests that the constructor can be called with different arguments."""
+    i = tpu_feed.InfeedQueue(number_of_tuple_elements=2)
+    self.assertEqual(i.number_of_tuple_elements, 2)
+    self.assertEqual(i.tuple_types, None)
+    self.assertEqual(i.tuple_shapes, None)
+    self.assertEqual(i.number_of_shards, None)
+    i = tpu_feed.InfeedQueue(
+        tuple_types=[dtypes.float32, dtypes.int32, dtypes.int32])
+    self.assertEqual(i.number_of_tuple_elements, 3)
+    self.assertEqual(i.tuple_types,
+                     [dtypes.float32, dtypes.int32, dtypes.int32])
+    self.assertEqual(i.tuple_shapes, None)
+    self.assertEqual(i.number_of_shards, None)
+    i = tpu_feed.InfeedQueue(tuple_shapes=[[1], [2, 3]])
+    self.assertEqual(i.number_of_tuple_elements, 2)
+    self.assertEqual(i.tuple_types, None)
+    self.assertEqual(i.tuple_shapes, [[1], [2, 3]])
+    self.assertEqual(i.number_of_shards, None)
+    i = tpu_feed.InfeedQueue(shard_dimensions=[1, 0, 7])
+    self.assertEqual(i.number_of_tuple_elements, 3)
+    self.assertEqual(i.tuple_types, None)
+    self.assertEqual(i.tuple_shapes, None)
+    self.assertEqual([p.shard_dimension
+                      for p in i.sharding_policies], [1, 0, 7])
+    with self.assertRaises(ValueError):
+      i = tpu_feed.InfeedQueue()
+    with self.assertRaises(ValueError):
+      i = tpu_feed.InfeedQueue(
+          number_of_tuple_elements=2, tuple_types=[dtypes.float32])
+    with self.assertRaises(ValueError):
+      i = tpu_feed.InfeedQueue(number_of_tuple_elements=2, tuple_shapes=[[1]])
+    with self.assertRaises(ValueError):
+      i = tpu_feed.InfeedQueue(number_of_tuple_elements=2, shard_dimensions=[1])
+    with self.assertRaises(ValueError):
+      i = tpu_feed.InfeedQueue(tuple_shapes=[[1], [2, 3]], shard_dimensions=[1])
+
+  def testModification(self):
+    """Tests modification of the queue post-construction."""
+    i = tpu_feed.InfeedQueue(number_of_tuple_elements=2)
+    i.set_tuple_types([dtypes.float32, dtypes.int32])
+    self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.int32])
+    i.set_tuple_types([dtypes.float32, dtypes.float32])
+    self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.float32])
+    with self.assertRaises(ValueError):
+      i.set_tuple_types([dtypes.float32])
+    i.set_tuple_shapes([[1], [2, 3]])
+    self.assertEqual(i.tuple_shapes, [[1], [2, 3]])
+    i.set_tuple_shapes([[1, 2], [3, 4]])
+    self.assertEqual(i.tuple_shapes, [[1, 2], [3, 4]])
+    with self.assertRaises(ValueError):
+      i.set_tuple_shapes([[1, 2]])
+    i.set_number_of_shards(2)
+    self.assertEqual(i.number_of_shards, 2)
+    i.set_number_of_shards(3)
+    self.assertEqual(i.number_of_shards, 3)
+    t1 = constant_op.constant(1, dtypes.int32, shape=[6])
+    t2 = constant_op.constant(2.0, dtypes.float32, shape=[3, 18])
+    i.set_configuration_from_input_tensors([t1, t2])
+    self.assertEqual(i.tuple_shapes, [[6], [3, 18]])
+    self.assertEqual(i.tuple_types, [dtypes.int32, dtypes.float32])
+    i.set_configuration_from_sharded_input_tensors([[t2, t1], [t2, t1]])
+    self.assertEqual(i.number_of_shards, 2)
+    self.assertEqual(i.tuple_shapes, [[6, 18], [12]])
+    self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.int32])
+    i.set_shard_dimensions([1, 0])
+    i.set_number_of_shards(3)
+    with self.assertRaises(ValueError):
+      i.set_number_of_shards(4)
+
+  def testFreezing(self):
+    """Tests freezing the queue."""
+    i = tpu_feed.InfeedQueue(number_of_tuple_elements=2)
+    t1 = constant_op.constant(1, dtypes.int32, shape=[2])
+    t2 = constant_op.constant(2.0, dtypes.float32, shape=[2, 4])
+    i.set_configuration_from_sharded_input_tensors([[t2, t1], [t2, t1]])
+    self.assertEqual(i.number_of_shards, 2)
+    self.assertEqual(i.tuple_shapes, [[4, 4], [4]])
+    self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.int32])
+    self.assertEqual(i.shard_dimensions, [0, 0])
+    i.freeze()
+    i.set_number_of_shards(2)
+    i.set_tuple_shapes([[4, 4], [4]])
+    i.set_tuple_types([dtypes.float32, dtypes.int32])
+    i.set_shard_dimensions([0, 0])
+    with self.assertRaises(ValueError):
+      i.set_number_of_shards(1)
+    with self.assertRaises(ValueError):
+      i.set_tuple_shapes([[8, 8], [8]])
+    with self.assertRaises(ValueError):
+      i.set_tuple_types([dtypes.int32, dtypes.float32])
+    with self.assertRaises(ValueError):
+      i.set_shard_dimensions([1, 0])
+    self.assertEqual(i.number_of_shards, 2)
+    self.assertEqual(i.tuple_shapes, [[4, 4], [4]])
+    self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.int32])
+    self.assertEqual(i.shard_dimensions, [0, 0])
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py
new file mode 100644
index 0000000000..9d12a364c3
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py
@@ -0,0 +1,106 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Optimizer that implements cross-shard gradient reduction for TPU."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tpu.python.ops import tpu_ops
+from tensorflow.python.training import optimizer
+
+
+class CrossShardOptimizer(optimizer.Optimizer):
+  """A optimizer sums gradients across TPU shards."""
+
+  def __init__(self, opt, name="CrossShardOptimizer"):
+    super(CrossShardOptimizer, self).__init__(False, name)
+    self._opt = opt
+
+  def compute_gradients(self, *args, **kwargs):
+    """Compute gradients of "loss" for the variables in "var_list".
+
+    This simply wraps the compute_gradients() from the real optimizer. The
+    gradients will be aggregated in the apply_gradients() so that user can
+    modify the gradients like clipping with per replica global norm if needed.
+    The global norm with aggregated gradients can be bad as one replica's huge
+    gradients can hurt the gradients from other replicas.
+
+    Args:
+      *args: Arguments for compute_gradients().
+      **kwargs: Keyword arguments for compute_gradients().
+
+    Returns:
+      A list of (gradient, variable) pairs.
+    """
+    return self._opt.compute_gradients(*args, **kwargs)
+
+  def apply_gradients(self, grads_and_vars, global_step=None, name=None):
+    """Apply gradients to variables.
+
+    Calls tpu_ops.cross_replica_sum() to sum gradient contributions across
+    replicas, and then applies the real optimizer.
+
+    Args:
+      grads_and_vars: List of (gradient, variable) pairs as returned by
+        compute_gradients().
+      global_step: Optional Variable to increment by one after the
+        variables have been updated.
+      name: Optional name for the returned operation.  Default to the
+        name passed to the Optimizer constructor.
+
+    Returns:
+      An `Operation` that applies the gradients. If `global_step` was not None,
+      that operation also increments `global_step`.
+
+    Raises:
+      ValueError: If the grads_and_vars is malformed.
+    """
+    summed_grads_and_vars = []
+    for (grad, var) in grads_and_vars:
+      if grad is None:
+        summed_grads_and_vars.append((grad, var))
+      else:
+        summed_grads_and_vars.append((tpu_ops.cross_replica_sum(grad), var))
+    return self._opt.apply_gradients(summed_grads_and_vars, global_step, name)
+
+  def get_slot(self, *args, **kwargs):
+    """Return a slot named "name" created for "var" by the Optimizer.
+
+    This simply wraps the get_slot() from the actual optimizer.
+
+    Args:
+      *args: Arguments for get_slot().
+      **kwargs: Keyword arguments for get_slot().
+
+    Returns:
+      The `Variable` for the slot if it was created, `None` otherwise.
+    """
+    return self._opt.get_slot(*args, **kwargs)
+
+  def get_slot_names(self, *args, **kwargs):
+    """Return a list of the names of slots created by the `Optimizer`.
+
+    This simply wraps the get_slot_names() from the actual optimizer.
+
+    Args:
+      *args: Arguments for get_slot().
+      **kwargs: Keyword arguments for get_slot().
+
+    Returns:
+      A list of strings.
+    """
+    return self._opt.get_slot_names(*args, **kwargs)
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_sharding.py b/tensorflow/contrib/tpu/python/tpu/tpu_sharding.py
new file mode 100644
index 0000000000..d545a94ca6
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_sharding.py
@@ -0,0 +1,248 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Helper library for sharding during TPU compilation."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.python.framework import tensor_shape
+
+_DEFAULT_NUMBER_OF_SHARDS = 1
+_DEFAULT_SHARD_DIMENSION = 0
+
+
+# TODO(b/36777903) change other parts of tpu.py to use this class.
+class ShardingPolicy(object):
+  """An object use to hold the sharding policy for a Tensor.
+  """
+
+  def __init__(self):
+    self._number_of_shards = None
+    self._shard_dimension = None
+    self._frozen = False
+
+  def __str__(self):
+    if self.number_of_shards is None or self.shard_dimension is None:
+      return "ShardingPolicy(unset)"
+    else:
+      return ("ShardingPolicy(%d shards dimension %d)" %
+              (self.number_of_shards, self.shard_dimension))
+
+  def _fill_default_values(self):
+    if self._number_of_shards is None:
+      self._number_of_shards = _DEFAULT_NUMBER_OF_SHARDS
+    if self._shard_dimension is None:
+      self._shard_dimension = tensor_shape.as_dimension(
+          _DEFAULT_SHARD_DIMENSION)
+
+  def freeze(self):
+    """Prevents further modification to the sharding policy.
+
+    Any values that have not been set when freeze is called are set to
+    defaults. If the ShardingPolicy is already frozen, this is a NoOp.
+    """
+    if not self._frozen:
+      self._fill_default_values()
+      self._frozen = True
+
+  @property
+  def number_of_shards(self):
+    """Returns the number of shards in the policy or None if unspecified."""
+    return self._number_of_shards
+
+  def set_number_of_shards(self, number_of_shards):
+    """Sets the number of shards for the current policy.
+
+    If the policy has been frozen then number_of_shards must match the
+    existing setting.
+
+    Args:
+      number_of_shards: The number of shards to use in the policy.
+
+    Raises:
+      ValueError: If the policy has been frozen and number_of_shards
+        differs from the frozen value; or number_of_shards <= 0.
+    """
+    if self._frozen:
+      if self._number_of_shards != number_of_shards:
+        raise ValueError(
+            "Can't set sharding policy to use %d shards since it has been "
+            "frozen to use %d." % (number_of_shards, self._number_of_shards))
+    else:
+      if number_of_shards > 0:
+        self._number_of_shards = number_of_shards
+      else:
+        raise ValueError(
+            "Can't set sharding policy to use %s shards; value must be >0",
+            str(number_of_shards))
+
+  @property
+  def shard_dimension(self):
+    """Returns the shard dimension of the policy or None if unspecified."""
+    return self._shard_dimension
+
+  def set_shard_dimension(self, shard_dimension):
+    """Sets the shard dimension for the current policy.
+
+    If the policy has been frozen then shard_dimension must match the
+    existing setting.
+
+    Args:
+      shard_dimension: The shard dimension to use in the policy.
+
+    Raises:
+      ValueError: If the policy has been frozen and shard_dimension
+        differs from the frozen value, or shard_dimension can't be
+        interpreted as a Dimension.
+    """
+    if self._frozen:
+      if self._shard_dimension != shard_dimension:
+        raise ValueError(
+            "Can't set shard dimension to %d since it has been frozen to "
+            "use %d." % (shard_dimension, self._shard_dimension))
+    else:
+      self._shard_dimension = tensor_shape.as_dimension(shard_dimension)
+
+  def merge(self, other):
+    """Merges the policy of another policy into the current policy.
+
+    Args:
+      other: The policy to merge into this one.
+
+    Raises:
+      ValueError: If this policy has been frozen and the merge conflicts with
+      the frozen policy.
+    """
+    if other.number_of_shards is not None:
+      self.set_number_of_shards(other.number_of_shards)
+    if other.shard_dimension is not None:
+      self.set_shard_dimension(other.shard_dimension)
+
+  def get_sharded_shape(self, shape, shard_index=None):
+    """Returns the shape of a shard of a full Tensor.
+
+    When given the shape of a 'full-size' Tensor, returns the shape of
+    the sub-Tensor after it has been sharded. Freezes the policy if it
+    has not yet been frozen.
+
+    Args:
+      shape: The shape of the full-size Tensor to be sharded.
+      shard_index: The index of the shard whose shape should be returned.
+        shard_index can be None for sharding policies that use the same
+        shape for every shard.
+      freeze_config:
+
+    Returns:
+      The shape of the sharded version of the Tensor.
+
+    Raises:
+      ValueError: If shard_index is None when shards are of different
+        shapes; or shard_index is not None and
+        !(0<=shard_index<number_of_shards); or shape does not have at
+        least self.shard_dimension+1 dimensions; or the value of
+        shape's shard dimension is not a multiple of
+        self.number_of_shards
+    """
+    if self._shard_dimension is None or self._number_of_shards is None:
+      # Don't raise an error if the config is unset.
+      return None
+    if shard_index is not None:
+      if shard_index < 0 or shard_index >= self.number_of_shards:
+        raise ValueError("shard_index %d, but must be in [0,%d)." %
+                         (shard_index, self._number_of_shards))
+    shape = tensor_shape.as_shape(shape)
+    if self._number_of_shards == 1:
+      # Don't do anything when there's only one shard.
+      return shape
+    ndims = shape.ndims
+    if ndims is None:
+      raise ValueError("shape must be a specified shape not Unknown")
+    if ndims <= self._shard_dimension:
+      raise ValueError("shape %s does not contain shard_dimension %d" %
+                       (shape.as_list(), self._shard_dimension))
+    dims = shape.as_list()
+    if (dims[self._shard_dimension] % self._number_of_shards) != 0:
+      raise ValueError("shape %s cannot be sharded %d ways along dimension %d" %
+                       (shape.as_list(), self._number_of_shards,
+                        self._shard_dimension))
+    dims[self._shard_dimension] /= self._number_of_shards
+    return tensor_shape.as_shape(dims)
+
+  def _unshard_shape(self, shape):
+    """Return the unsharded shape that would generate a given sharded shape.
+
+    Args:
+      shape: the sharded shape to unshard
+
+    Returns:
+      The unsharded shape.
+
+    Raises:
+      ValueError: if shape is unknown or does not contain
+        self.shard_dimension
+      TypeError: if shape is not convertible to a TensorShape
+    """
+    shape = tensor_shape.as_shape(shape)
+    if self._number_of_shards == 1:
+      # Don't do anything when there's only one shard.
+      return shape
+    ndims = shape.ndims
+    if ndims is None:
+      raise ValueError("shape must be a specified shape not Unknown")
+    if ndims <= self._shard_dimension:
+      raise ValueError("shape %s does not contain shard_dimension %d" %
+                       (shape.as_list(), self._shard_dimension))
+    dims = shape.as_list()
+    dims[self._shard_dimension] *= self._number_of_shards
+    return tensor_shape.as_shape(dims)
+
+  def get_unsharded_shape(self, shapes):
+    """Returns the shape of an unsharded Tensor given a list of shards.
+
+    When given a list of shapes of shards, returns the shape of the
+    unsharded Tensor that would generate the shards. Sets defaults for the
+    policy if number_of_shards or shard_dimension is None.
+
+    Args:
+      shapes: The shapes of the Tensor shards to be combined.
+
+    Returns:
+      The shape of the unsharded version of the Tensor.
+
+    Raises:
+      ValueError: if shapes is not a list of length
+        self.number_of_shards; or any element of shapes is not a valid
+        shape consistent with the sharding policy; or the list of
+        shapes is not a valid sharding of a full shape.
+      TypeError: if an element of shapes is not convertible to a
+        TensorShape
+    """
+    self._fill_default_values()
+    if len(shapes) != self.number_of_shards:
+      raise ValueError(
+          "shapes is %s but must be a list of length number_of_shards=%d" % (
+              str(shapes), self.number_of_shards))
+    unsharded_shapes = [self._unshard_shape(s) for s in shapes]
+    for i in xrange(self.number_of_shards - 1):
+      if unsharded_shapes[i] != unsharded_shapes[self.number_of_shards - 1]:
+        raise ValueError(
+            "sharded shapes %s are not consistent shards of a full shape "
+            "sharded %d ways along dimension %d" % (
+                str(shapes), self.number_of_shards, self.shard_dimension))
+    return unsharded_shapes[0]
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_sharding_test.py b/tensorflow/contrib/tpu/python/tpu/tpu_sharding_test.py
new file mode 100644
index 0000000000..b0a5511d2d
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_sharding_test.py
@@ -0,0 +1,138 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Tests for tpu_function helpers."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tpu.python.tpu import tpu_sharding
+
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.platform import test
+
+
+class ShardingTest(test.TestCase):
+
+  def testFreeze(self):
+    """Tests that freezing a policy applies default values."""
+    p1 = tpu_sharding.ShardingPolicy()
+    p1.freeze()
+    self.assertEqual(p1.number_of_shards,
+                     tpu_sharding._DEFAULT_NUMBER_OF_SHARDS)
+    self.assertEqual(p1.shard_dimension, tpu_sharding._DEFAULT_SHARD_DIMENSION)
+    p2 = tpu_sharding.ShardingPolicy()
+    p2.set_number_of_shards(17)
+    p2.set_shard_dimension(23)
+    p2.freeze()
+    self.assertEqual(p2.number_of_shards, 17)
+    self.assertEqual(p2.shard_dimension, 23)
+
+  def testFrozen(self):
+    """Tests that frozen policies can't be changed."""
+    p1 = tpu_sharding.ShardingPolicy()
+    p1.freeze()
+    with self.assertRaises(ValueError):
+      p1.set_number_of_shards(17)
+    with self.assertRaises(ValueError):
+      p1.set_shard_dimension(22)
+
+  def testStr(self):
+    """Tests the string representation."""
+    p1 = tpu_sharding.ShardingPolicy()
+    self.assertEqual(str(p1), "ShardingPolicy(unset)")
+    p1.set_number_of_shards(17)
+    self.assertEqual(str(p1), "ShardingPolicy(unset)")
+    p1.set_shard_dimension(8)
+    self.assertEqual(str(p1), "ShardingPolicy(17 shards dimension 8)")
+
+  def testMerge(self):
+    """Tests that merging works."""
+    p1 = tpu_sharding.ShardingPolicy()
+    p1.set_number_of_shards(17)
+    p1.set_shard_dimension(23)
+    p2 = tpu_sharding.ShardingPolicy()
+    p2.merge(p1)
+    self.assertEqual(p2.number_of_shards, 17)
+    self.assertEqual(p2.shard_dimension, 23)
+    p1 = tpu_sharding.ShardingPolicy()
+    p1.set_shard_dimension(12)
+    p2.merge(p1)
+    self.assertEqual(p2.number_of_shards, 17)
+    self.assertEqual(p2.shard_dimension, 12)
+    p2.freeze()
+    p2.merge(p1)
+    self.assertEqual(p2.number_of_shards, 17)
+    self.assertEqual(p2.shard_dimension, 12)
+    p1.set_number_of_shards(1)
+    with self.assertRaises(ValueError):
+      p2.merge(p1)
+    p1 = tpu_sharding.ShardingPolicy()
+    p1.set_number_of_shards(17)
+    p2.merge(p1)
+    p1.set_shard_dimension(2)
+    with self.assertRaises(ValueError):
+      p2.merge(p1)
+
+  def testGetShardedShape(self):
+    """Tests getting a sharded shape."""
+    p = tpu_sharding.ShardingPolicy()
+    p.set_number_of_shards(3)
+    p.set_shard_dimension(1)
+    self.assertEqual(p.get_sharded_shape([4, 9]), [4, 3])
+    p.freeze()
+    with self.assertRaises(ValueError):
+      p.set_shard_dimension(0)
+    with self.assertRaises(ValueError):
+      _ = p.get_sharded_shape([4, 9], shard_index=4)
+    with self.assertRaises(ValueError):
+      _ = p.get_sharded_shape([4, 9], shard_index=-1)
+    with self.assertRaises(TypeError):
+      _ = p.get_sharded_shape("not_a_shape")
+    with self.assertRaises(ValueError):
+      _ = p.get_sharded_shape(tensor_shape.TensorShape(None))
+    with self.assertRaises(ValueError):
+      _ = p.get_sharded_shape([4, 10], shard_index=-1)
+
+  def testGetUnshardedShape(self):
+    """Tests getting an unsharded shape."""
+    p = tpu_sharding.ShardingPolicy()
+    p.set_number_of_shards(2)
+    p.set_shard_dimension(1)
+    self.assertEqual(p.get_unsharded_shape([[4, 3], [4, 3]]), [4, 6])
+    with self.assertRaises(ValueError):
+      _ = p.get_unsharded_shape([[4, 3]])
+    with self.assertRaises(ValueError):
+      _ = p.get_unsharded_shape([[4, 3], [4, 3], [4, 3]])
+    with self.assertRaises(ValueError):
+      _ = p.get_unsharded_shape([[4, 3], [4, 2]])
+    with self.assertRaises(TypeError):
+      _ = p.get_unsharded_shape([[4, 3], "not_a_shape"])
+    with self.assertRaises(ValueError):
+      _ = p.get_unsharded_shape([None, [4, 3]])
+    with self.assertRaises(ValueError):
+      _ = p.get_unsharded_shape([[2], [4, 3]])
+
+  def testScalar(self):
+    """Tests sharding and unsharding scalars."""
+    p = tpu_sharding.ShardingPolicy()
+    p.freeze()
+    self.assertEqual(p.get_sharded_shape([]), [])
+    self.assertEqual(p.get_unsharded_shape([[]]), [])
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/tpu/python/tpu/training_loop.py b/tensorflow/contrib/tpu/python/tpu/training_loop.py
new file mode 100644
index 0000000000..3d7896127a
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/training_loop.py
@@ -0,0 +1,213 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Library for constructing a training loop, suitable for TPUs."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tpu.python.tpu import tpu_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+
+
+def while_loop(condition, body, inputs=None, infeed_queue=None, name=None):
+  """Builds a training loop for TPUs.
+
+  The set of loop-carried tensors corresponds to `inputs`.  Both
+  `condition` and `body` take the current value of the loop-carried
+  tensors. 'body' additionally takes a tuple of infeed from
+  infeed_queue if infeed_queue is not None. `condition` must return a
+  single boolean value that determines whether iteration
+  continues. `body` must return an updated list of values for the
+  loop-carried tensors.
+
+  Args:
+    condition: a Python function that builds the loop condition.
+    body: a Python function that builds the loop body.
+    inputs: a list of initial values passed into the training loop, or
+      None (equivalent to an empty list).
+    infeed_queue: if not None, the infeed queue from which to append a tuple
+      of arguments as inputs to condition.
+    name: an optional name for the loop.
+
+  Returns:
+    The final values of the loop-carried tensors.
+
+  Raises:
+    TypeError: if body or condition has the wrong signature.
+  """
+
+  # Converts inputs to Tensors.
+  inputs = [] if inputs is None else [ops.convert_to_tensor(x) for
+                                      x in inputs]
+  input_types = [x.dtype for x in inputs]
+  input_arity = len(inputs)
+
+  body_arg_error = tpu_function.check_function_argument_count(
+      body, input_arity, infeed_queue)
+  if body_arg_error is not None:
+    if infeed_queue is None:
+      raise TypeError(
+          "Supplied loop body function cannot be called with the specified "
+          "inputs. You specified %d inputs: %s, but the loop body needs %s" % (
+              input_arity, str([i.name for i in inputs]), body_arg_error))
+    else:
+      raise TypeError(
+          "Supplied loop body function cannot be called with the specified "
+          "inputs. You specified %d inputs: %s and %d additional inputs from "
+          "infeed, but the computation needs %s" % (input_arity, str(
+              [i.name for i in inputs]), infeed_queue.number_of_tuple_elements,
+                                                    body_arg_error))
+  condition_arg_error = tpu_function.check_function_argument_count(
+      condition, input_arity, None)
+  if condition_arg_error is not None:
+    if infeed_queue is None:
+      raise TypeError(
+          "Supplied loop condition function cannot be called with the "
+          "specified inputs. You specified %d inputs: %s, but the loop "
+          "condition needs %s" % (input_arity, str([i.name for i in inputs]),
+                                  condition_arg_error))
+    else:
+      raise TypeError(
+          "Supplied loop condition function cannot be called with the "
+          "specified inputs. You specified %d inputs: %s, but the loop "
+          "condition needs %s. Note that infeed is not passed to the loop "
+          "condition." % (input_arity, str([i.name for i in inputs]),
+                          condition_arg_error))
+
+  def condition_wrapper(*inputs):
+    # Discards the dummy output added for arity-0 loops.
+    if input_arity == 0:
+      inputs = []
+    return condition(*inputs)
+
+  def body_wrapper(*inputs):
+    """Wrapper around `body` that handles infeed queues and control deps."""
+    inputs = list(inputs)
+
+    # Discards the dummy output added for arity-0 loops.
+    if input_arity == 0:
+      inputs = []
+
+    # Runs `body` with the dequeue_ops appended.
+    if infeed_queue:
+      number_of_shards = tpu_function.get_tpu_context().number_of_shards
+      if number_of_shards is None:
+        raise ValueError("Can't build training loop with infeed when there is "
+                         "no tpu_shard_context. Are you building a loop or "
+                         "graph directly rather than from inside tpu.rewrite, "
+                         "tpu.batch_parallel, tpu.shard, or tpu.replicate?")
+      infeed_queue.set_number_of_shards(number_of_shards)
+      dequeue_ops = [d for d in infeed_queue.generate_dequeue_op()]
+    else:
+      dequeue_ops = []
+    outputs = body(*(inputs + dequeue_ops))
+
+    # If the computation only returned one value, make it a tuple.
+    if not isinstance(outputs, (list, tuple)):
+      outputs = (outputs,)
+
+    outputs = [
+        o if isinstance(o, ops.Operation) else ops.convert_to_tensor(o)
+        for o in outputs
+    ]
+
+    # Separates the returned Operations and Tensors.
+    output_operations = [o for o in outputs if isinstance(o, ops.Operation)]
+    output_tensors = [o for o in outputs
+                      if not isinstance(o, ops.Operation)]
+
+    if outputs != output_tensors + output_operations:
+      raise ValueError(
+          "TPU training loop body must return zero or more Tensor values "
+          "followed by zero or more Operations.")
+
+    output_types = [op.dtype for op in output_tensors]
+    if input_types != output_types:
+      raise TypeError(
+          "Mismatch between input types and output types for training loop "
+          "body: {} vs {}".format(input_types, output_types))
+
+    # Add the dequeue operations to output_operations to ensure they are run
+    # by the loop, even if the programmer's loop body does not use them.
+    output_operations += dequeue_ops
+
+    # Add a dummy output, if needed.
+    if not output_tensors:
+      output_tensors = array_ops.constant(0)
+
+    if output_operations:
+      # TODO(phawkins): in principle this is too restrictive since it serializes
+      # the training loop steps. In practice it does not matter since this loop
+      # will be compiled by XLA.
+      return control_flow_ops.tuple(output_tensors,
+                                    control_inputs=output_operations)
+    else:
+      return output_tensors
+
+  # If the body has arity 0, add a dummy loop-carried value to which we can add
+  # control dependencies from any side-effecting operations.
+  if input_arity == 0:
+    inputs = [array_ops.constant(0)]
+  return control_flow_ops.while_loop(condition_wrapper, body_wrapper, inputs,
+                                     name=name)
+
+
+def repeat(n, body, inputs=None, infeed_queue=None, name=None):
+  """Builds a training loop that executes a fixed number of interations.
+
+  The set of loop-carried tensors correspond to `inputs`.
+  `body` must be a function that takes and returns the values of the
+  loop-carried tensors.
+
+  Args:
+    n: the number of loop iterations
+    body: a Python function that builds the loop body.
+    inputs: a list of initial values passed into the training loop or
+      None (equivalent to an empty list).
+    infeed_queue: if not None, the infeed queue from which to append a tuple
+      of arguments as inputs to condition.
+    name: an optional name for the loop.
+  Returns:
+    The final values of the loop-carried tensors.
+  Raises:
+    ValueError: if there is a type error.
+  """
+  def _convert_to_list(xs):
+    if not isinstance(xs, (list, tuple)):
+      return [xs]
+    else:
+      return list(xs)
+
+  def cond(i, *args):
+    del args
+    return i < n
+
+  def body_wrapper(i, *args):
+    return [i + 1] + _convert_to_list(body(*args))
+
+  inputs = [0] if inputs is None else [0] + _convert_to_list(inputs)
+  outputs = while_loop(
+      cond, body_wrapper, inputs=inputs, infeed_queue=infeed_queue, name=name)
+  outputs = _convert_to_list(outputs)
+  if len(outputs) == 1:
+    # Returns the Op rather than an empty list.
+    return outputs[0].op
+  else:
+    return outputs[1:]
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 451ecb6dec..d3952b5cec 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -144,6 +144,9 @@ sh_binary(
             "//tensorflow/contrib/slim:slim",
             "//tensorflow/contrib/slim/python/slim/data:data_pip",
             "//tensorflow/contrib/slim/python/slim/nets:nets_pip",
+            "//tensorflow/contrib/tpu:tpu_estimator",
+            "//tensorflow/contrib/tpu:tpu_helper_library",
+            "//tensorflow/contrib/tpu:tpu_py",
             "//tensorflow/contrib/specs:specs",
             "//tensorflow/contrib/tensor_forest:init_py",
             "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip",
-- 
GitLab


From 384a0fb075b2ec5c780119f98869c85810913c93 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 17:25:07 -0700
Subject: [PATCH 0270/1375] Add while hlo test.

PiperOrigin-RevId: 159638639
---
 tensorflow/compiler/xla/tests/while_test.cc | 34 +++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index 2df9197428..beaed37465 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -81,6 +81,40 @@ TEST_F(WhileTest, WhileWithScalarResult) {
   ComputeAndCompareR0<int32>(&builder, 5, {});
 }
 
+TEST_F(WhileTest, WhileWithScalarResultNonConstInit) {
+  auto result_shape = ShapeUtil::MakeShape(S32, {});
+  auto orig_shape = ShapeUtil::MakeShape(S32, {2});
+
+  // Create a computation for the condition: repeat for 5 iterations.
+  Computation condition;
+  {
+    ComputationBuilder builder(client_, "condition");
+    auto prev = builder.Parameter(0, result_shape, "prev");
+    builder.Gt(builder.ConstantR0<int32>(5), prev);
+    condition = builder.Build().ConsumeValueOrDie();
+  }
+
+  // Create a computation for the body: add 1 to the result variable.
+  Computation body;
+  {
+    ComputationBuilder builder(client_, "body");
+    auto prev = builder.Parameter(0, result_shape, "prev");
+    auto input = builder.ConstantR0<int32>(1);
+    auto result = builder.Add(input, prev);
+    body = builder.Build().ConsumeValueOrDie();
+  }
+
+  // Create a While node with computations for the condition and the body.
+  ComputationBuilder builder(client_, TestName());
+  auto init = builder.Reduce(builder.ConstantR1<int32>(2, 1),
+                             builder.ConstantR0<int32>(0),
+                             CreateScalarAddComputation(S32, &builder), {0});
+  auto result = builder.While(condition, body, init);
+  auto shape = builder.GetShape(result).ConsumeValueOrDie();
+
+  ComputeAndCompareR0<int32>(&builder, 5, {});
+}
+
 // Tests a while node when the result type T is a vector.
 //
 // All constants are chosen to produce exact results.
-- 
GitLab


From 7c0779c297e37803acc8cfd3fa9c12c1af8db463 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Tue, 20 Jun 2017 18:03:25 -0700
Subject: [PATCH 0271/1375] Internal change.

PiperOrigin-RevId: 159642257
---
 tensorflow/tools/test/upload_test_benchmarks_index.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/test/upload_test_benchmarks_index.yaml b/tensorflow/tools/test/upload_test_benchmarks_index.yaml
index 8cd33a1da6..ec7f76f666 100644
--- a/tensorflow/tools/test/upload_test_benchmarks_index.yaml
+++ b/tensorflow/tools/test/upload_test_benchmarks_index.yaml
@@ -27,7 +27,7 @@ indexes:
   properties:
   - name: test
   - name: start
-    direction: asc
+    direction: desc
 
 # Index to access a specific (test, entry, start) Entity, and also to be able to
 # fetch a range of (start, timing) graph values for a given (test, entry) pair
-- 
GitLab


From 9403e4f8243606eead6d996100edcb4f872156f2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 18:08:37 -0700
Subject: [PATCH 0272/1375] [XLA] Disallow calling ShapeUtil::ElementsIn on
 tuple shapes.

PiperOrigin-RevId: 159642793
---
 tensorflow/compiler/xla/shape_util.cc | 1 +
 tensorflow/compiler/xla/shape_util.h  | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index 865621df62..057905a431 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -339,6 +339,7 @@ bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) {
 }
 
 /* static */ int64 ShapeUtil::ElementsIn(const Shape& shape) {
+  CHECK(!IsTuple(shape));
   CHECK_EQ(shape.dimensions_size(), Rank(shape));
   return std::accumulate<decltype(shape.dimensions().begin()), int64>(
       shape.dimensions().begin(), shape.dimensions().end(), 1LL,
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 458823707b..fa34bfc951 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -93,6 +93,7 @@ class ShapeUtil {
  public:
   // Returns the number of elements are contained within the provided shape;
   // e.g. for rank 0 (scalars) the result is always 1.
+  // Precondition: !IsTuple(shape)
   static int64 ElementsIn(const Shape& shape);
 
   // Returns true if 'shape' has zero elements.
-- 
GitLab


From f671c5caad24b3a107651dd840c043cdea03f0dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 18:14:20 -0700
Subject: [PATCH 0273/1375] Automated g4 rollback of changelist 159570549

PiperOrigin-RevId: 159643278
---
 third_party/gpus/cuda/BUILD.tpl     | 82 ++++++++++-------------------
 third_party/gpus/cuda_configure.bzl | 17 +++---
 2 files changed, 35 insertions(+), 64 deletions(-)

diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index 2cca438d6d..f7610dd7a9 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -40,23 +40,20 @@ config_setting(
 cc_library(
     name = "cuda_headers",
     hdrs = [
-        "cuda/cuda_config.h",
+        "cuda_config.h",
         %{cuda_headers}
     ],
     includes = [
         ".",
-        "cuda/include",
+        "include",
     ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cudart_static",
-    srcs = ["cuda/lib/%{cudart_static_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{cudart_static_lib}"],
+    includes = ["include"],
     linkopts = select({
         ":freebsd": [],
         "//conditions:default": ["-ldl"],
@@ -69,46 +66,34 @@ cc_library(
 
 cc_library(
     name = "cuda_driver",
-    srcs = ["cuda/lib/%{cuda_driver_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{cuda_driver_lib}"],
+    includes = ["include"],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cudart",
-    srcs = ["cuda/lib/%{cudart_lib}"],
-    data = ["cuda/lib/%{cudart_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{cudart_lib}"],
+    data = ["lib/%{cudart_lib}"],
+    includes = ["include"],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cublas",
-    srcs = ["cuda/lib/%{cublas_lib}"],
-    data = ["cuda/lib/%{cublas_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{cublas_lib}"],
+    data = ["lib/%{cublas_lib}"],
+    includes = ["include"],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cusolver",
-    srcs = ["cuda/lib/%{cusolver_lib}"],
-    data = ["cuda/lib/%{cusolver_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{cusolver_lib}"],
+    data = ["lib/%{cusolver_lib}"],
+    includes = ["include"],
     linkstatic = 1,
     linkopts = ["-lgomp"],
     visibility = ["//visibility:public"],
@@ -116,36 +101,27 @@ cc_library(
 
 cc_library(
     name = "cudnn",
-    srcs = ["cuda/lib/%{cudnn_lib}"],
-    data = ["cuda/lib/%{cudnn_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{cudnn_lib}"],
+    data = ["lib/%{cudnn_lib}"],
+    includes = ["include"],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cufft",
-    srcs = ["cuda/lib/%{cufft_lib}"],
-    data = ["cuda/lib/%{cufft_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{cufft_lib}"],
+    data = ["lib/%{cufft_lib}"],
+    includes = ["include"],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "curand",
-    srcs = ["cuda/lib/%{curand_lib}"],
-    data = ["cuda/lib/%{curand_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    srcs = ["lib/%{curand_lib}"],
+    data = ["lib/%{curand_lib}"],
+    includes = ["include"],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
@@ -166,23 +142,19 @@ cc_library(
 cc_library(
     name = "cupti_headers",
     hdrs = [
-        "cuda/cuda_config.h",
+        "cuda_config.h",
         ":cuda-extras",
     ],
     includes = [
         ".",
-        "cuda/extras/CUPTI/include/",
+        "extras/CUPTI/include/",
     ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cupti_dsos",
-    data = ["cuda/lib/%{cupti_lib}"],
-    includes = [
-        ".",
-        "cuda/include",
-    ],
+    data = ["lib/%{cupti_lib}"],
     visibility = ["//visibility:public"],
 )
 
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index b2d59c446b..83a377dde5 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -764,7 +764,7 @@ def _create_dummy_repository(repository_ctx):
                "CudaVersion(\"%s\")" % c
                for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES]),
            "%{cuda_toolkit_path}": _DEFAULT_CUDA_TOOLKIT_PATH,
-       }, "cuda/cuda/cuda_config.h")
+       })
 
   # If cuda_configure is not configured to build with GPU support, and the user
   # attempts to build with --config=cuda, add a dummy build rule to intercept
@@ -906,19 +906,19 @@ def _create_local_cuda_repository(repository_ctx):
   cuda_toolkit_path = cuda_config.cuda_toolkit_path
   cuda_include_path = cuda_toolkit_path + "/include"
   genrules = [_symlink_genrule_for_dir(repository_ctx,
-      cuda_include_path, "cuda/include", "cuda-include")]
+      cuda_include_path, "include", "cuda-include")]
   genrules.append(_symlink_genrule_for_dir(repository_ctx,
-      cuda_toolkit_path + "/nvvm", "cuda/nvvm", "cuda-nvvm"))
+      cuda_toolkit_path + "/nvvm", "nvvm", "cuda-nvvm"))
   genrules.append(_symlink_genrule_for_dir(repository_ctx,
       cuda_toolkit_path + "/extras/CUPTI/include",
-      "cuda/extras/CUPTI/include", "cuda-extras"))
+      "extras/CUPTI/include", "cuda-extras"))
 
   cuda_libs = _find_libs(repository_ctx, cuda_config)
   cuda_lib_src = []
   cuda_lib_dest = []
   for lib in cuda_libs.values():
     cuda_lib_src.append(lib.path)
-    cuda_lib_dest.append("cuda/lib/" + lib.file_name)
+    cuda_lib_dest.append("lib/" + lib.file_name)
   genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "", "cuda-lib",
                                        cuda_lib_src, cuda_lib_dest))
 
@@ -927,9 +927,8 @@ def _create_local_cuda_repository(repository_ctx):
   included_files = _read_dir(repository_ctx, cuda_include_path).replace(
       cuda_include_path, '').splitlines()
   if '/cudnn.h' not in included_files:
-    genrules.append(_symlink_genrule_for_dir(repository_ctx, None,
-        "cuda/include/", "cudnn-include", [cudnn_header_dir + "/cudnn.h"],
-        ["cudnn.h"]))
+    genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "include/",
+        "cudnn-include", [cudnn_header_dir + "/cudnn.h"], ["cudnn.h"]))
   else:
     genrules.append(
             'filegroup(\n' +
@@ -1001,7 +1000,7 @@ def _create_local_cuda_repository(repository_ctx):
                ["CudaVersion(\"%s\")" % c
                 for c in cuda_config.compute_capabilities]),
                "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path,
-       }, "cuda/cuda/cuda_config.h")
+       })
 
 def _create_remote_cuda_repository(repository_ctx, remote_config_repo):
   """Creates pointers to a remotely configured repo set up to build with CUDA."""
-- 
GitLab


From 26239c706fc5e62d3693389a6665f33257f94cf1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 18:45:55 -0700
Subject: [PATCH 0274/1375] Previously we didn't have an implementation of
 BatchNormInference and BatchNormTraining, which gives a linker error if
 anyone ever tries to call that. A dummy implementation is friendlier than a
 linker error.

PiperOrigin-RevId: 159645612
---
 .../xla/client/computation_builder.cc         | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 49b0e164b0..735a69d596 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -1438,6 +1438,26 @@ ComputationDataHandle ComputationBuilder::BatchNormTraining(
   return ParseOpResponse(s, &response);
 }
 
+ComputationDataHandle ComputationBuilder::BatchNormInference(
+    const ComputationDataHandle& operand, const ComputationDataHandle& scale,
+    const ComputationDataHandle& offset, const ComputationDataHandle& mean,
+    const ComputationDataHandle& variance, float epsilon, int64 feature_index) {
+  // TODO(b/62843645): Implement BatchNormInference.
+  NoteError(Unimplemented("BatchNormInference is not implemented yet."));
+  return ComputationDataHandle();
+}
+
+ComputationDataHandle ComputationBuilder::BatchNormGrad(
+    const ComputationDataHandle& operand, const ComputationDataHandle& scale,
+    const ComputationDataHandle& batch_mean,
+    const ComputationDataHandle& batch_var,
+    const ComputationDataHandle& grad_output, float epsilon,
+    int64 feature_index) {
+  // TODO(b/62843645): Implement BatchNormGrad.
+  NoteError(Unimplemented("BatchNormGrad is not implemented yet."));
+  return ComputationDataHandle();
+}
+
 ComputationDataHandle ComputationBuilder::CrossReplicaSum(
     const ComputationDataHandle& operand) {
   if (!first_error_.ok() || !PrepareComputation().ok()) {
-- 
GitLab


From 980d3f2be66e62f9240d9154add9f14697957714 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 18:48:00 -0700
Subject: [PATCH 0275/1375] Use C API to implement Operation.name property

This name property is used in many existing tests including those that
already run with C API enabled (math_ops_test, framework_ops_test,
session_test, session_partial_run_test, math_ops_test_gpu, etc).

PiperOrigin-RevId: 159645767
---
 tensorflow/python/client/tf_session.i | 5 +++++
 tensorflow/python/framework/ops.py    | 8 +++++++-
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index e46ba021dc..284c98639d 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -63,6 +63,11 @@ tensorflow::ImportNumpy();
 // Constants used by TensorHandle (get_session_handle).
 %constant const char* TENSOR_HANDLE_KEY = tensorflow::SessionState::kTensorHandleResourceTypeName;
 
+// Convert TF_OperationName output to unicode python string
+%typemap(out) const char* TF_OperationName {
+  $result = PyUnicode_FromString($1);
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 // BEGIN TYPEMAPS FOR tensorflow::TF_Run_wrapper()
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 46417c2324..ccd1099f80 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1350,7 +1350,13 @@ class Operation(object):
   @property
   def name(self):
     """The full name of this operation."""
-    return self._node_def.name
+    if _USE_C_API:
+      # TODO(iga): Remove this assert after converting to C API by default.
+      # Just being a bit paranoid here.
+      assert self._node_def.name == c_api.TF_OperationName(self._c_op)
+      return c_api.TF_OperationName(self._c_op)
+    else:
+      return self._node_def.name
 
   @property
   def _id(self):
-- 
GitLab


From a4a46983233690f17ca794c2907b2b11c8cd2060 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Jun 2017 18:49:28 -0700
Subject: [PATCH 0276/1375] [XLA] Add tests for select ops and while loops that
 produce tuples that contain predicates.

PiperOrigin-RevId: 159645900
---
 tensorflow/compiler/xla/tests/tuple_test.cc | 25 +++++++
 tensorflow/compiler/xla/tests/while_test.cc | 77 +++++++++++++++++++++
 2 files changed, 102 insertions(+)

diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc
index a4d96646a1..be3cdbca09 100644
--- a/tensorflow/compiler/xla/tests/tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/tuple_test.cc
@@ -151,6 +151,31 @@ XLA_TEST_F(TupleTest, TupleGTEToTuple) {
   ComputeAndCompareTuple(&builder, *expected, {}, error_spec_);
 }
 
+XLA_TEST_F(TupleTest, SelectBetweenPredTuples) {
+  ComputationBuilder b(client_, TestName());
+  ComputationDataHandle v1, v2;
+
+  for (bool direction : {false, true}) {
+    std::unique_ptr<GlobalData> v1_data =
+        CreateR0Parameter<float>(0.0f, /*parameter_number=*/0, /*name=*/"v1",
+                                 /*builder=*/&b, /*data_handle=*/&v1);
+    std::unique_ptr<GlobalData> v2_data =
+        CreateR0Parameter<float>(1.0f, /*parameter_number=*/1, /*name=*/"v2",
+                                 /*builder=*/&b, /*data_handle=*/&v2);
+    auto v1_gt = b.Gt(v1, v2);             // false
+    auto v2_gt = b.Gt(v2, v1);             // true
+    auto v1_v2 = b.Tuple({v1_gt, v2_gt});  // {false, true}
+    auto v2_v1 = b.Tuple({v2_gt, v1_gt});  // {true, false}
+    auto select = b.Select(direction ? v1_gt : v2_gt, v1_v2, v2_v1);
+    auto expected =
+        Literal::MakeTuple({Literal::CreateR0<bool>(direction).get(),
+                            Literal::CreateR0<bool>(!direction).get()});
+
+    ComputeAndCompareTuple(&b, *expected, {v1_data.get(), v2_data.get()},
+                           error_spec_);
+  }
+}
+
 // Builds two new tuples from an existing tuple (by means of GetTupleElement),
 // then adds up the components of the new tuples.
 XLA_TEST_F(TupleTest, TupleGTEToTupleToGTEAdd) {
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index beaed37465..ccd2a95658 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -115,6 +115,36 @@ TEST_F(WhileTest, WhileWithScalarResultNonConstInit) {
   ComputeAndCompareR0<int32>(&builder, 5, {});
 }
 
+TEST_F(WhileTest, WhileWithPredicateResult) {
+  auto result_shape = ShapeUtil::MakeShape(PRED, {});
+
+  // Create a computation for the condition: run until condition is true.
+  Computation condition;
+  {
+    ComputationBuilder builder(client_, "condition");
+    auto prev = builder.Parameter(0, result_shape, "prev");
+    builder.Ne(builder.ConstantR0<bool>(true), prev);
+    condition = builder.Build().ConsumeValueOrDie();
+  }
+
+  // Create a computation for the body: or condition with true.
+  Computation body;
+  {
+    ComputationBuilder builder(client_, "body");
+    auto prev = builder.Parameter(0, result_shape, "prev");
+    auto result = builder.LogicalOr(prev, builder.ConstantR0<bool>(true));
+    body = builder.Build().ConsumeValueOrDie();
+  }
+
+  // Create a While node with computations for the condition and the body.
+  ComputationBuilder builder(client_, TestName());
+  auto init = builder.Ne(builder.ConstantR0<bool>(false),
+                         builder.ConstantR0<bool>(true));
+  auto result = builder.While(condition, body, init);
+
+  ComputeAndCompareR0<bool>(&builder, true, {});
+}
+
 // Tests a while node when the result type T is a vector.
 //
 // All constants are chosen to produce exact results.
@@ -282,6 +312,53 @@ TEST_F(WhileTest, WhileWithTupleResult) {
   ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001));
 }
 
+TEST_F(WhileTest, WhileWithPredicateTupleResult) {
+  std::vector<Shape> shape_elements = {ShapeUtil::MakeShape(S32, {}),
+                                       ShapeUtil::MakeShape(PRED, {})};
+  Shape result_shape = ShapeUtil::MakeTupleShape(shape_elements);
+
+  // Create a computation for the condition.
+  // Repeat for 5 iterations.
+  Computation condition;
+  {
+    ComputationBuilder builder(client_, "condition");
+    auto prev = builder.Parameter(0, result_shape, "prev");
+    auto iteration = builder.GetTupleElement(prev, 0);
+    builder.Gt(builder.ConstantR0<int32>(5), iteration);
+    condition = builder.Build().ConsumeValueOrDie();
+  }
+
+  // Create a computation for the body.
+  // Add 1 to the iteration variable and or the predicate with true
+  Computation body;
+  {
+    ComputationBuilder builder(client_, "body");
+    auto prev = builder.Parameter(0, result_shape, "prev");
+    auto iteration = builder.GetTupleElement(prev, 0);
+    auto pred = builder.GetTupleElement(prev, 1);
+    auto new_pred = builder.LogicalOr(pred, builder.ConstantR0<bool>(true));
+    auto result = builder.Tuple(
+        {builder.Add(iteration, builder.ConstantR0<int32>(1)), new_pred});
+    body = builder.Build().ConsumeValueOrDie();
+  }
+
+  // Create a While node with computations for the condition and the body.
+  ComputationBuilder builder(client_, "while");
+  auto init = builder.Tuple({builder.ConstantR0<int32>(0),
+                             builder.Ne(builder.ConstantR0<bool>(false),
+                                        builder.ConstantR0<bool>(true))});
+  auto result = builder.While(condition, body, init);
+  VLOG(2) << "while = "
+          << ShapeUtil::HumanString(
+                 *builder.GetShape(result).ConsumeValueOrDie());
+
+  auto expected_counter = Literal::CreateR0<int32>(5);
+  auto expected_predicate = Literal::CreateR0<bool>(true);
+  auto expected =
+      Literal::MakeTuple({expected_counter.get(), expected_predicate.get()});
+  ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0));
+}
+
 // Tests two while nodes when the result type T is a Tuple and the second
 // while node uses the result of the first while node which is used in two
 // nodes.
-- 
GitLab


From b3f33ad46613834922f381f816715fdcfee54b87 Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Tue, 20 Jun 2017 20:01:39 -0700
Subject: [PATCH 0277/1375] Make changes to prepare for the fused option of
 batch norm to be set to None (None means using fused batch norm if possible).

PiperOrigin-RevId: 159649743
---
 tensorflow/contrib/keras/BUILD                |  6 +-
 .../python/keras/layers/normalization_test.py | 33 ++++-----
 .../contrib/layers/python/layers/layers.py    | 70 ++++++++++++-------
 .../layers/python/layers/layers_test.py       | 41 +++++------
 .../contrib/slim/python/slim/learning_test.py |  8 ++-
 tensorflow/python/layers/normalization.py     | 11 ++-
 .../python/layers/normalization_test.py       | 69 +++++++++---------
 7 files changed, 137 insertions(+), 101 deletions(-)

diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index 71ce6540d6..619ebb7ce0 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -7,6 +7,7 @@ exports_files(["LICENSE"])
 
 package(default_visibility = ["//tensorflow:__subpackages__"])
 
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 
 py_library(
@@ -393,12 +394,11 @@ py_test(
     ],
 )
 
-py_test(
+cuda_py_test(
     name = "normalization_test",
     size = "small",
     srcs = ["python/keras/layers/normalization_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
+    additional_deps = [
         ":keras",
         ":testing_utils",
         "//tensorflow/python:client_testlib",
diff --git a/tensorflow/contrib/keras/python/keras/layers/normalization_test.py b/tensorflow/contrib/keras/python/keras/layers/normalization_test.py
index dc410f84d8..1a0686800e 100644
--- a/tensorflow/contrib/keras/python/keras/layers/normalization_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/normalization_test.py
@@ -94,22 +94,23 @@ class NoiseLayersTest(test.TestCase):
       np.testing.assert_allclose(out.std(), 1.0, atol=1e-1)
 
   def test_batchnorm_convnet(self):
-    with self.test_session():
-      model = keras.models.Sequential()
-      norm = keras.layers.BatchNormalization(
-          axis=1, input_shape=(3, 4, 4), momentum=0.8)
-      model.add(norm)
-      model.compile(loss='mse', optimizer='sgd')
-
-      # centered on 5.0, variance 10.0
-      x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
-      model.fit(x, x, epochs=4, verbose=0)
-      out = model.predict(x)
-      out -= np.reshape(keras.backend.eval(norm.beta), (1, 3, 1, 1))
-      out /= np.reshape(keras.backend.eval(norm.gamma), (1, 3, 1, 1))
-
-      np.testing.assert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
-      np.testing.assert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
+    if test.is_gpu_available(cuda_only=True):
+      with self.test_session(use_gpu=True):
+        model = keras.models.Sequential()
+        norm = keras.layers.BatchNormalization(
+            axis=1, input_shape=(3, 4, 4), momentum=0.8)
+        model.add(norm)
+        model.compile(loss='mse', optimizer='sgd')
+
+        # centered on 5.0, variance 10.0
+        x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
+        model.fit(x, x, epochs=4, verbose=0)
+        out = model.predict(x)
+        out -= np.reshape(keras.backend.eval(norm.beta), (1, 3, 1, 1))
+        out /= np.reshape(keras.backend.eval(norm.gamma), (1, 3, 1, 1))
+
+        np.testing.assert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
+        np.testing.assert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
 
   def test_shared_batchnorm(self):
     """Test that a BN layer can be shared across different data streams.
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 46c355f08a..f2a904b521 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -257,27 +257,33 @@ def _fused_batch_norm(
                                                       'beta')
     if not param_initializers:
       param_initializers = {}
-    beta_initializer = param_initializers.get('beta',
-                                              init_ops.zeros_initializer())
-    beta = variables.model_variable(
-        'beta',
-        shape=params_shape,
-        dtype=dtype,
-        initializer=beta_initializer,
-        collections=beta_collections,
-        trainable=trainable_beta)
-    trainable_gamma = trainable and scale
-    gamma_collections = utils.get_variable_collections(variables_collections,
-                                                       'gamma')
-    gamma_initializer = param_initializers.get('gamma',
-                                               init_ops.ones_initializer())
-    gamma = variables.model_variable(
-        'gamma',
-        shape=params_shape,
-        dtype=dtype,
-        initializer=gamma_initializer,
-        collections=gamma_collections,
-        trainable=trainable_gamma)
+    if center:
+      beta_initializer = param_initializers.get('beta',
+                                                init_ops.zeros_initializer())
+      beta = variables.model_variable(
+          'beta',
+          shape=params_shape,
+          dtype=dtype,
+          initializer=beta_initializer,
+          collections=beta_collections,
+          trainable=trainable_beta)
+    else:
+      beta = array_ops.constant(0.0, shape=params_shape)
+
+    if scale:
+      gamma_collections = utils.get_variable_collections(
+          variables_collections, 'gamma')
+      gamma_initializer = param_initializers.get('gamma',
+                                                 init_ops.ones_initializer())
+      gamma = variables.model_variable(
+          'gamma',
+          shape=params_shape,
+          dtype=dtype,
+          initializer=gamma_initializer,
+          collections=gamma_collections,
+          trainable=trainable)
+    else:
+      gamma = array_ops.constant(1.0, shape=params_shape)
 
     # Create moving_mean and moving_variance variables and add them to the
     # appropriate collections.
@@ -449,7 +455,8 @@ def batch_norm(inputs,
       then the batch normalization uses weighted mean and
       variance. (This can be used to correct for bias in training
       example selection.)
-    fused:  Use nn.fused_batch_norm if True, nn.batch_normalization otherwise.
+    fused: if `True`, use a faster, fused implementation based on
+      nn.fused_batch_norm. If `None`, use the fused implementation if possible.
     data_format: A string. `NHWC` (default) and `NCHW` are supported.
     zero_debias_moving_mean: Use zero_debias for moving_mean. It creates a new
       pair of variables 'moving_mean/biased' and 'moving_mean/local_step'.
@@ -473,7 +480,6 @@ def batch_norm(inputs,
 
   Raises:
     ValueError: If `batch_weights` is not None and `fused` is True.
-    ValueError: If `param_regularizers` is not None and `fused` is True.
     ValueError: If `data_format` is neither `NHWC` nor `NCHW`.
     ValueError: If the rank of `inputs` is undefined.
     ValueError: If rank or channels dimension of `inputs` is undefined.
@@ -487,6 +493,21 @@ def batch_norm(inputs,
                        'supported for fused batch norm.')
     if renorm:
       raise ValueError('Renorm is not supported for fused batch norm.')
+
+  # Only use _fused_batch_norm (1) if fused is set True or if it is
+  # possible to use (currently it doesn't support batch weights,
+  # renorm, and the case when rank is neither 2 nor 4),
+  # and (2) if used with zero_debias_moving_mean, or an input shape of rank 2,
+  # or non-default updates_collections (not implemented in
+  # normalization_layers.BatchNormalization yet); otherwise use the fused
+  # implementation in normalization_layers.BatchNormalization.
+  inputs = ops.convert_to_tensor(inputs)
+  rank = inputs.get_shape().ndims
+  feature_supported = batch_weights is None and not renorm and rank in [2, 4]
+  possible_to_fuse = fused is None and feature_supported
+  if (fused or possible_to_fuse) and (
+      zero_debias_moving_mean or rank == 2 or
+      updates_collections is not ops.GraphKeys.UPDATE_OPS):
     return _fused_batch_norm(
         inputs,
         decay=decay,
@@ -552,7 +573,8 @@ def batch_norm(inputs,
           renorm_momentum=renorm_decay,
           name=sc.name,
           _scope=sc,
-          _reuse=reuse)
+          _reuse=reuse,
+          fused=fused)
       outputs = layer.apply(inputs, training=is_training)
 
       # Add variables to collections.
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 67f45473e8..d4ee85b550 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -1703,13 +1703,6 @@ class BatchNormTest(test.TestCase):
       with self.assertRaisesRegexp(ValueError, 'Weighted mean and variance'):
         _layers.batch_norm(inputs, batch_weights=batch_weights, fused=True)
 
-  def testParamRegularizersFused(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
-      inputs = array_ops.placeholder(dtype=dtypes.float32, shape=(5, 3, 3, 7))
-      with self.assertRaisesRegexp(ValueError,
-                                   'Regularizers are not currently'):
-        _layers.batch_norm(inputs, param_regularizers={}, fused=True)
-
   def _testCreateOp(self, fused):
     height, width = 3, 3
     with self.test_session():
@@ -1780,7 +1773,8 @@ class BatchNormTest(test.TestCase):
     height, width = 3, 3
     with self.test_session():
       images = random_ops.random_uniform((5, height, width, 3), seed=1)
-      _layers.batch_norm(images, scale=True, zero_debias_moving_mean=True)
+      _layers.batch_norm(
+          images, scale=True, zero_debias_moving_mean=True, fused=False)
       self.assertEqual(len(variables.get_model_variables()), 6)
       moving_mean = variables.get_variables_by_name('moving_mean')[0]
       moving_variance = variables.get_variables_by_name('moving_variance')[0]
@@ -1874,7 +1868,8 @@ class BatchNormTest(test.TestCase):
         images,
         decay=0.1,
         updates_collections=None,
-        zero_debias_moving_mean=True)
+        zero_debias_moving_mean=True,
+        fused=False)
     moving_mean = variables.get_variables_by_name('BatchNorm/moving_mean')[0]
     moving_variance = variables.get_variables_by_name('moving_variance')[0]
     biased = variables.get_variables_by_name('biased')[0]
@@ -2523,7 +2518,7 @@ class BatchNormTest(test.TestCase):
 
   def _runBatchNormalizationWithFormat(self, shape, data_format, is_training):
     channels = shape[-1]
-    with self.test_session() as sess:
+    with self.test_session(use_gpu=True) as sess:
       images = np.arange(np.product(shape), dtype=np.float32).reshape(shape)
       beta = init_ops.constant_initializer(
           np.arange(
@@ -2561,20 +2556,22 @@ class BatchNormTest(test.TestCase):
       return sess.run(output)
 
   def testNHWCAndNCHWInferenceProduceSameOutput(self):
-    for shape in [[7, 3, 5], [5, 2, 3, 4], [11, 3, 2, 4, 5]]:
-      nhwc = self._runBatchNormalizationWithFormat(
-          data_format='NHWC', shape=shape, is_training=False)
-      nchw = self._runBatchNormalizationWithFormat(
-          data_format='NCHW', shape=shape, is_training=False)
-      self.assertAllClose(nhwc, nchw, atol=1e-4, rtol=1e-4)
+    if test.is_gpu_available(cuda_only=True):
+      for shape in [[7, 3, 5], [5, 2, 3, 4], [11, 3, 2, 4, 5]]:
+        nhwc = self._runBatchNormalizationWithFormat(
+            data_format='NHWC', shape=shape, is_training=False)
+        nchw = self._runBatchNormalizationWithFormat(
+            data_format='NCHW', shape=shape, is_training=False)
+        self.assertAllClose(nhwc, nchw, atol=1e-4, rtol=1e-4)
 
   def testNHWCAndNCHWTrainingProduceSameOutput(self):
-    for shape in [[7, 3, 5], [5, 2, 3, 4], [11, 3, 2, 4, 5]]:
-      nhwc = self._runBatchNormalizationWithFormat(
-          data_format='NHWC', shape=shape, is_training=True)
-      nchw = self._runBatchNormalizationWithFormat(
-          data_format='NCHW', shape=shape, is_training=True)
-      self.assertAllClose(nhwc, nchw, atol=1e-4, rtol=1e-4)
+    if test.is_gpu_available(cuda_only=True):
+      for shape in [[7, 3, 5], [5, 2, 3, 4], [11, 3, 2, 4, 5]]:
+        nhwc = self._runBatchNormalizationWithFormat(
+            data_format='NHWC', shape=shape, is_training=True)
+        nchw = self._runBatchNormalizationWithFormat(
+            data_format='NCHW', shape=shape, is_training=True)
+        self.assertAllClose(nhwc, nchw, atol=1e-4, rtol=1e-4)
 
 
 class LayerNormTest(test.TestCase):
diff --git a/tensorflow/contrib/slim/python/slim/learning_test.py b/tensorflow/contrib/slim/python/slim/learning_test.py
index 83d45f6f5a..69061460eb 100644
--- a/tensorflow/contrib/slim/python/slim/learning_test.py
+++ b/tensorflow/contrib/slim/python/slim/learning_test.py
@@ -220,7 +220,7 @@ def LogisticClassifier(inputs):
 
 
 def BatchNormClassifier(inputs):
-  inputs = layers.batch_norm(inputs, decay=0.1)
+  inputs = layers.batch_norm(inputs, decay=0.1, fused=None)
   return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid)
 
 
@@ -267,6 +267,11 @@ class CreateTrainOpTest(test.TestCase):
     self._inputs = np.random.rand(16, 4).astype(np.float32)
     self._labels = np.random.randint(0, 2, size=(16, 1)).astype(np.float32)
 
+  def _addBesselsCorrection(self, sample_size, expected_var):
+    correction_factor = sample_size / (sample_size - 1)
+    expected_var *= correction_factor
+    return expected_var
+
   def testUseUpdateOps(self):
     with ops.Graph().as_default():
       random_seed.set_random_seed(0)
@@ -275,6 +280,7 @@ class CreateTrainOpTest(test.TestCase):
 
       expected_mean = np.mean(self._inputs, axis=(0))
       expected_var = np.var(self._inputs, axis=(0))
+      expected_var = self._addBesselsCorrection(16, expected_var)
 
       tf_predictions = BatchNormClassifier(tf_inputs)
       loss_ops.log_loss(tf_predictions, tf_labels)
diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py
index 780d1c2b8e..ad0f202f95 100644
--- a/tensorflow/python/layers/normalization.py
+++ b/tensorflow/python/layers/normalization.py
@@ -123,6 +123,10 @@ class BatchNormalization(base.Layer):
     if self.fused and renorm:
       raise ValueError(
           'Batch renorm is currently not supported with fused batch norm.')
+    if self.fused and (beta_regularizer is not None or
+                       gamma_regularizer is not None):
+      raise ValueError('Regularizers are not currently '
+                       'supported for fused batch norm.')
     if renorm:
       renorm_clipping = renorm_clipping or {}
       keys = ['rmax', 'rmin', 'dmax']
@@ -153,7 +157,12 @@ class BatchNormalization(base.Layer):
                        ' is out of range for input with rank ' + str(ndim))
 
     if self.fused is None:
-      self.fused = not self.renorm and ndim == 4 and axis in [1, 3]
+      # Currently fused batch norm doesn't support renorm and beta/gamma
+      # regularizer; and only supports an input tensor of rank 4 and a channel
+      # dimension on axis 1 and 3.
+      self.fused = not self.renorm and ndim == 4 and axis in [
+          1, 3
+      ] and self.beta_regularizer is None and self.gamma_regularizer is None
 
     if self.fused:
       if axis == 1:
diff --git a/tensorflow/python/layers/normalization_test.py b/tensorflow/python/layers/normalization_test.py
index fa6c9c4a5d..64bebb1021 100644
--- a/tensorflow/python/layers/normalization_test.py
+++ b/tensorflow/python/layers/normalization_test.py
@@ -143,44 +143,45 @@ class BNTest(test.TestCase):
       self.assertAlmostEqual(np.std(normed_np_output), 1., places=1)
 
   def test4DInputAxis1(self):
-    epsilon = 1e-3
-    bn = normalization_layers.BatchNormalization(
-        axis=1, epsilon=epsilon, momentum=0.9)
-    inputs = variables.Variable(
-        np.random.random((5, 4, 3, 6)) + 100, dtype=dtypes.float32)
-    training = array_ops.placeholder(dtype='bool')
-    outputs = bn.apply(inputs, training=training)
+    if test.is_gpu_available(cuda_only=True):
+      epsilon = 1e-3
+      bn = normalization_layers.BatchNormalization(
+          axis=1, epsilon=epsilon, momentum=0.9)
+      inputs = variables.Variable(
+          np.random.random((5, 4, 3, 6)) + 100, dtype=dtypes.float32)
+      training = array_ops.placeholder(dtype='bool')
+      outputs = bn.apply(inputs, training=training)
 
-    with self.test_session() as sess:
-      # Test training with placeholder learning phase.
-      sess.run(variables.global_variables_initializer())
-      np_gamma, np_beta = sess.run([bn.gamma, bn.beta])
-      np_gamma = np.reshape(np_gamma, (1, 4, 1, 1))
-      np_beta = np.reshape(np_beta, (1, 4, 1, 1))
-      for _ in range(100):
-        np_output, _, _ = sess.run([outputs] + bn.updates,
-                                   feed_dict={training: True})
-        # Verify that the axis is normalized during training.
-        normed_np_output = ((np_output - epsilon) * np_gamma) + np_beta
-        self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1)
-        self.assertAlmostEqual(np.std(normed_np_output), 1., places=1)
+      with self.test_session(use_gpu=True) as sess:
+        # Test training with placeholder learning phase.
+        sess.run(variables.global_variables_initializer())
+        np_gamma, np_beta = sess.run([bn.gamma, bn.beta])
+        np_gamma = np.reshape(np_gamma, (1, 4, 1, 1))
+        np_beta = np.reshape(np_beta, (1, 4, 1, 1))
+        for _ in range(100):
+          np_output, _, _ = sess.run(
+              [outputs] + bn.updates, feed_dict={training: True})
+          # Verify that the axis is normalized during training.
+          normed_np_output = ((np_output - epsilon) * np_gamma) + np_beta
+          self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1)
+          self.assertAlmostEqual(np.std(normed_np_output), 1., places=1)
 
-      # Verify that the statistics are updated during training.
-      moving_mean, moving_var = sess.run([bn.moving_mean, bn.moving_variance])
-      np_inputs = sess.run(inputs)
-      mean = np.mean(np_inputs, axis=(0, 2, 3))
-      std = np.std(np_inputs, axis=(0, 2, 3))
-      variance = np.square(std)
-      self.assertAllClose(mean, moving_mean, atol=1e-2)
-      self.assertAllClose(variance, moving_var, atol=1e-2)
+        # Verify that the statistics are updated during training.
+        moving_mean, moving_var = sess.run([bn.moving_mean, bn.moving_variance])
+        np_inputs = sess.run(inputs)
+        mean = np.mean(np_inputs, axis=(0, 2, 3))
+        std = np.std(np_inputs, axis=(0, 2, 3))
+        variance = np.square(std)
+        self.assertAllClose(mean, moving_mean, atol=1e-2)
+        self.assertAllClose(variance, moving_var, atol=1e-2)
 
-      # Test inference with placeholder learning phase.
-      np_output = sess.run(outputs, feed_dict={training: False})
+        # Test inference with placeholder learning phase.
+        np_output = sess.run(outputs, feed_dict={training: False})
 
-      # Verify that the axis is normalized during inference.
-      normed_np_output = ((np_output - epsilon) * np_gamma) + np_beta
-      self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1)
-      self.assertAlmostEqual(np.std(normed_np_output), 1., places=1)
+        # Verify that the axis is normalized during inference.
+        normed_np_output = ((np_output - epsilon) * np_gamma) + np_beta
+        self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1)
+        self.assertAlmostEqual(np.std(normed_np_output), 1., places=1)
 
   def test4DInputAxis2(self):
     epsilon = 1e-3
-- 
GitLab


From c936bedaa07fdf3720a19c8cae6d6edc4e7db256 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 20 Jun 2017 21:58:29 -0700
Subject: [PATCH 0278/1375] Properly handle ops that don't have a CPU kernel

PiperOrigin-RevId: 159655906
---
 tensorflow/core/grappler/optimizers/constant_folding.cc | 3 ++-
 tensorflow/core/grappler/optimizers/constant_folding.h  | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 07d2d1e1f1..dafbd89a9c 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -267,9 +267,10 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
   // Only fold ops with a CPU implementation available.
-  if (device_types[0] != DeviceType(DEVICE_CPU)) {
+  if (device_types.empty()) {
     return false;
   }
+  DCHECK_EQ(DeviceType(DEVICE_CPU), device_types[0]);
 
   // Folding not applicable to ops with no inputs.
   if (node.input().empty()) {
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index dca8f17468..36c9ad2c64 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -28,7 +28,7 @@ namespace grappler {
 const char kConstantFoldingConst[] = "ConstantFolding";
 const char kConstantFoldingCtrl[] = "ConstantFoldingCtrl";
 
-// Contant folding optimization for a graph.
+// Constant folding optimization for a graph.
 class ConstantFolding : public GraphOptimizer {
  public:
   ConstantFolding();
-- 
GitLab


From 26301bd5582d8591797af350ed1734fe7a21c800 Mon Sep 17 00:00:00 2001
From: Danny Goodman <goodman.danny@gmail.com>
Date: Wed, 21 Jun 2017 03:01:14 -0700
Subject: [PATCH 0279/1375] fix error format

---
 tensorflow/contrib/layers/python/layers/layers.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index d3b1094963..e8294a40c8 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -1445,7 +1445,8 @@ def fully_connected(inputs,
     ValueError: If x has rank less than 2 or if its last dimension is not set.
   """
   if not isinstance(num_outputs, six.integer_types):
-    raise ValueError('num_outputs should be int or long, got %s.', num_outputs)
+    raise ValueError(
+        'num_outputs should be int or long, got %s.' % (num_outputs,))
 
   layer_variable_getter = _build_variable_getter({'bias': 'biases',
                                                   'kernel': 'weights'})
-- 
GitLab


From 50d1c07a42c60c25c409449136359c9ff8262944 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 03:01:21 -0700
Subject: [PATCH 0280/1375] Selected BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 159673079
---
 tensorflow/contrib/labeled_tensor/BUILD       | 10 +++--
 tensorflow/contrib/layers/BUILD               | 45 +++++--------------
 .../contrib/learn/python/learn/datasets/BUILD |  9 +---
 tensorflow/contrib/linalg/BUILD               |  6 ++-
 tensorflow/contrib/lookup/BUILD               | 10 +----
 tensorflow/contrib/losses/BUILD               |  5 +--
 tensorflow/contrib/ndlstm/BUILD               |  1 +
 tensorflow/contrib/slim/BUILD                 | 21 +++------
 .../contrib/slim/python/slim/data/BUILD       | 18 ++------
 .../contrib/slim/python/slim/nets/BUILD       |  6 +--
 tensorflow/contrib/sparsemax/BUILD            |  2 +
 tensorflow/contrib/staging/BUILD              |  1 +
 12 files changed, 45 insertions(+), 89 deletions(-)

diff --git a/tensorflow/contrib/labeled_tensor/BUILD b/tensorflow/contrib/labeled_tensor/BUILD
index 55258f264a..4eba29caec 100644
--- a/tensorflow/contrib/labeled_tensor/BUILD
+++ b/tensorflow/contrib/labeled_tensor/BUILD
@@ -36,6 +36,7 @@ py_library(
     srcs = ["python/ops/_typecheck.py"],
     srcs_version = "PY2AND3",
     visibility = [":__subpackages__"],
+    deps = ["//tensorflow/python:util"],
 )
 
 py_library(
@@ -48,6 +49,7 @@ py_library(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//third_party/py/numpy",
+        "@six_archive//:six",
     ],
 )
 
@@ -56,8 +58,6 @@ py_library(
     srcs = ["python/ops/test_util.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":_typecheck",
-        ":core",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:training",
     ],
@@ -92,6 +92,7 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:parsing_ops",
+        "@six_archive//:six",
     ],
 )
 
@@ -105,7 +106,6 @@ py_test(
     deps = [
         ":core",
         ":io_ops",
-        ":ops",
         ":test_util",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
@@ -151,11 +151,13 @@ py_library(
         ":core",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:functional_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:numerics",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
+        "@six_archive//:six",
     ],
 )
 
@@ -175,6 +177,7 @@ py_test(
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:string_ops",
         "//third_party/py/numpy",
     ],
 )
@@ -188,6 +191,7 @@ py_library(
         ":core",
         ":ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "@six_archive//:six",
     ],
 )
 
diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD
index 5cd338f791..aa301a0ade 100644
--- a/tensorflow/contrib/layers/BUILD
+++ b/tensorflow/contrib/layers/BUILD
@@ -155,9 +155,7 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
         "//third_party/py/numpy",
     ],
 )
@@ -172,8 +170,6 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -189,12 +185,10 @@ py_test(
         ":layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
@@ -213,11 +207,8 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:nn_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -229,11 +220,9 @@ py_test(
     deps = [
         ":layers_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:parsing_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
@@ -253,17 +242,16 @@ py_test(
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:gradients",
         "//tensorflow/python:init_ops",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:partitioned_variables",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/feature_column",
         "//third_party/py/numpy",
     ],
 )
@@ -278,10 +266,7 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -294,11 +279,9 @@ py_test(
         ":layers_py",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
         "//third_party/py/numpy",
     ],
 )
@@ -311,15 +294,17 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":layers_py",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradient_checker",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:partitioned_variables",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:util",
         "//third_party/py/numpy",
     ],
 )
@@ -334,8 +319,6 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
@@ -351,8 +334,7 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
         "//third_party/py/numpy",
     ],
 )
@@ -365,12 +347,9 @@ py_test(
     deps = [
         ":layers_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/contrib/learn/python/learn/datasets/BUILD b/tensorflow/contrib/learn/python/learn/datasets/BUILD
index de9b786a2c..8bf372841d 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/BUILD
+++ b/tensorflow/contrib/learn/python/learn/datasets/BUILD
@@ -29,6 +29,7 @@ py_library(
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "//tensorflow/python:random_seed",
         "//third_party/py/numpy",
     ],
 )
@@ -39,7 +40,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow/python",  # TODO(b/34059704): remove when fixed
         "//tensorflow/python:platform",
     ],
 )
@@ -63,9 +63,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow/contrib/learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -76,9 +74,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow/contrib/learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -89,10 +85,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/contrib/linalg/BUILD b/tensorflow/contrib/linalg/BUILD
index 9b4f36da15..72d784adb0 100644
--- a/tensorflow/contrib/linalg/BUILD
+++ b/tensorflow/contrib/linalg/BUILD
@@ -165,12 +165,16 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:common_shapes",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:tensor_util",
+        "//tensorflow/python:util",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/contrib/lookup/BUILD b/tensorflow/contrib/lookup/BUILD
index 1090cecab5..b8455477b0 100644
--- a/tensorflow/contrib/lookup/BUILD
+++ b/tensorflow/contrib/lookup/BUILD
@@ -18,14 +18,9 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:constant_op",
-        "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:lookup_ops_gen",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:string_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:util",
     ],
@@ -42,11 +37,10 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:lookup_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
diff --git a/tensorflow/contrib/losses/BUILD b/tensorflow/contrib/losses/BUILD
index d9074e385a..f75b0aa1b3 100644
--- a/tensorflow/contrib/losses/BUILD
+++ b/tensorflow/contrib/losses/BUILD
@@ -38,12 +38,11 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
diff --git a/tensorflow/contrib/ndlstm/BUILD b/tensorflow/contrib/ndlstm/BUILD
index 73a4ad2e70..ba23c70dc0 100644
--- a/tensorflow/contrib/ndlstm/BUILD
+++ b/tensorflow/contrib/ndlstm/BUILD
@@ -33,6 +33,7 @@ py_library(
         "//tensorflow/python:platform",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:rnn",
+        "//tensorflow/python:rnn_cell",
         "//tensorflow/python:sparse_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
diff --git a/tensorflow/contrib/slim/BUILD b/tensorflow/contrib/slim/BUILD
index 427c25e07c..2d48a91f9f 100644
--- a/tensorflow/contrib/slim/BUILD
+++ b/tensorflow/contrib/slim/BUILD
@@ -15,9 +15,6 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/contrib/training:training_py",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:ops",
-        "//tensorflow/python:platform",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
     ],
@@ -38,10 +35,8 @@ py_test(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -56,17 +51,15 @@ py_library(
     deps = [
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/training:training_py",
-        "//tensorflow/python:array_ops",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:clip_ops",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:lib",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
@@ -88,11 +81,9 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -128,7 +119,6 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:ops",
         "//tensorflow/python:training",
     ],
 )
@@ -150,7 +140,6 @@ py_library(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/losses:losses_py",
         "//tensorflow/contrib/metrics:metrics_py",
-        "//tensorflow/contrib/slim/python/slim/data",
         "//tensorflow/contrib/slim/python/slim/data:data_decoder",
         "//tensorflow/contrib/slim/python/slim/data:data_provider",
         "//tensorflow/contrib/slim/python/slim/data:dataset",
@@ -167,7 +156,7 @@ py_library(
     srcs = ["python/slim/summaries.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:logging_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:summary",
@@ -183,7 +172,7 @@ py_test(
         ":summaries",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:summary",
     ],
diff --git a/tensorflow/contrib/slim/python/slim/data/BUILD b/tensorflow/contrib/slim/python/slim/data/BUILD
index 1326f4093c..4d42a11d16 100644
--- a/tensorflow/contrib/slim/python/slim/data/BUILD
+++ b/tensorflow/contrib/slim/python/slim/data/BUILD
@@ -72,12 +72,10 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:io_ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:platform",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -87,11 +85,9 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:io_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
@@ -111,9 +107,7 @@ py_test(
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:io_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
     ],
@@ -125,10 +119,8 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
     ],
@@ -141,11 +133,11 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":prefetch_queue",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -174,13 +166,13 @@ py_library(
         ":data_decoder",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:functional_ops",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
     ],
 )
 
@@ -195,11 +187,9 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:parsing_ops",
-        "//tensorflow/python:platform_test",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/contrib/slim/python/slim/nets/BUILD b/tensorflow/contrib/slim/python/slim/nets/BUILD
index 737bbbe57b..e2035ab014 100644
--- a/tensorflow/contrib/slim/python/slim/nets/BUILD
+++ b/tensorflow/contrib/slim/python/slim/nets/BUILD
@@ -243,9 +243,9 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -272,14 +272,15 @@ py_test(
     srcs = ["resnet_v2_test.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":resnet_utils",
         ":resnet_v2",
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -299,7 +300,6 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//third_party/py/numpy",
diff --git a/tensorflow/contrib/sparsemax/BUILD b/tensorflow/contrib/sparsemax/BUILD
index 7441f1429f..fcfaa2aba4 100644
--- a/tensorflow/contrib/sparsemax/BUILD
+++ b/tensorflow/contrib/sparsemax/BUILD
@@ -31,6 +31,8 @@ py_library(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/contrib/staging/BUILD b/tensorflow/contrib/staging/BUILD
index 8ffc96c346..bc4a289468 100644
--- a/tensorflow/contrib/staging/BUILD
+++ b/tensorflow/contrib/staging/BUILD
@@ -22,4 +22,5 @@ py_library(
     name = "staging",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow/python:data_flow_ops"],
 )
-- 
GitLab


From 5478d53adf4e7af05449cae92fd4f7146caa3ccf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 03:38:50 -0700
Subject: [PATCH 0281/1375] Remove redundant `get` calls on smart pointers

PiperOrigin-RevId: 159675809
---
 tensorflow/compiler/xla/literal_util_test.cc  | 32 ++++++++---------
 .../compiler/xla/packed_literal_reader.cc     |  2 +-
 .../compiler/xla/service/hlo_evaluator.cc     | 16 ++++-----
 .../compiler/xla/tests/literal_test_util.cc   | 34 +++++++++----------
 .../compiler/xla/text_literal_reader.cc       |  5 ++-
 5 files changed, 44 insertions(+), 45 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 9dd13be80f..ffae623b0c 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -291,20 +291,20 @@ TEST_F(LiteralUtilTest, DifferentLayoutEquality) {
   auto colmajor = MakeUnique<Literal>();
   *colmajor->mutable_shape() = ShapeUtil::MakeShape(F32, {2, 2});
   *colmajor->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1});
-  colmajor.get()->Reserve(4);
-  colmajor.get()->Set<float>({0, 0}, 1.0);
-  colmajor.get()->Set<float>({0, 1}, 2.0);
-  colmajor.get()->Set<float>({1, 0}, 3.0);
-  colmajor.get()->Set<float>({1, 1}, 4.0);
+  colmajor->Reserve(4);
+  colmajor->Set<float>({0, 0}, 1.0);
+  colmajor->Set<float>({0, 1}, 2.0);
+  colmajor->Set<float>({1, 0}, 3.0);
+  colmajor->Set<float>({1, 1}, 4.0);
 
   auto rowmajor = MakeUnique<Literal>();
   *rowmajor->mutable_shape() = ShapeUtil::MakeShape(F32, {2, 2});
   *rowmajor->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({1, 0});
-  rowmajor.get()->Reserve(4);
-  rowmajor.get()->Set<float>({0, 0}, 1.0);
-  rowmajor.get()->Set<float>({0, 1}, 2.0);
-  rowmajor.get()->Set<float>({1, 0}, 3.0);
-  rowmajor.get()->Set<float>({1, 1}, 4.0);
+  rowmajor->Reserve(4);
+  rowmajor->Set<float>({0, 0}, 1.0);
+  rowmajor->Set<float>({0, 1}, 2.0);
+  rowmajor->Set<float>({1, 0}, 3.0);
+  rowmajor->Set<float>({1, 1}, 4.0);
 
   EXPECT_TRUE(rowmajor->Equal(*colmajor));
 }
@@ -694,7 +694,7 @@ TEST_F(LiteralUtilTest, Copy) {
     const int64 step[] = {1, 1, 1, 1};
     uint32 seqnr = 0;
     auto init_proc = [&](const std::vector<int64>& indexes) {
-      source.get()->Set(indexes, ++seqnr);
+      source->Set(indexes, ++seqnr);
       return true;
     };
 
@@ -705,7 +705,7 @@ TEST_F(LiteralUtilTest, Copy) {
     const int64 dest_base[] = {6, 4, 12, 2};
     const int64 copy_size[] = {7, 8, 11, 9};
 
-    TF_EXPECT_OK(blank.get()->Copy(*source, src_base, dest_base, copy_size));
+    TF_EXPECT_OK(blank->Copy(*source, src_base, dest_base, copy_size));
     std::vector<int64> source_indexes(TF_ARRAYSIZE(dimensions), 0);
     std::vector<int64> blank_indexes(TF_ARRAYSIZE(dimensions), 0);
     bool matched = true;
@@ -729,13 +729,13 @@ TEST_F(LiteralUtilTest, Copy) {
 TEST_F(LiteralUtilTest, CopyScalars) {
   auto zero = Literal::CreateR0<uint32>(0);
   auto nine = Literal::CreateR0<uint32>(9);
-  TF_EXPECT_OK(zero.get()->Copy(*nine, {}, {}, {}));
+  TF_EXPECT_OK(zero->Copy(*nine, {}, {}, {}));
   EXPECT_TRUE(zero->Equal(*nine));
 
   auto vect = Literal::CreateR1<uint32>({3, 4, 9, 12, 5, 17, 21});
-  TF_EXPECT_OK(zero.get()->Copy(*vect, {5}, {}, {}));
+  TF_EXPECT_OK(zero->Copy(*vect, {5}, {}, {}));
   EXPECT_EQ(zero->Get<uint32>({}), 17);
-  TF_EXPECT_OK(vect.get()->Copy(*zero, {}, {4}, {}));
+  TF_EXPECT_OK(vect->Copy(*zero, {}, {4}, {}));
   EXPECT_EQ(vect->Get<uint32>({4}), 17);
 }
 
@@ -796,7 +796,7 @@ TEST_F(LiteralUtilTest, Populate) {
       // with zero.
       return literal->LinearIndex(indexes) + 17;
     };
-    TF_EXPECT_OK(literal.get()->Populate<uint32>(generator));
+    TF_EXPECT_OK(literal->Populate<uint32>(generator));
 
     std::vector<int64> zero_base(data.dimensions.size(), 0);
     std::vector<int64> step(data.dimensions.size(), 1);
diff --git a/tensorflow/compiler/xla/packed_literal_reader.cc b/tensorflow/compiler/xla/packed_literal_reader.cc
index 1187079906..70e0f5a747 100644
--- a/tensorflow/compiler/xla/packed_literal_reader.cc
+++ b/tensorflow/compiler/xla/packed_literal_reader.cc
@@ -58,7 +58,7 @@ StatusOr<std::unique_ptr<Literal>> PackedLiteralReader::Read(
   }
 
   int64 elements = ShapeUtil::ElementsIn(shape);
-  result.get()->Resize(elements, std::numeric_limits<float>::quiet_NaN());
+  result->Resize(elements, std::numeric_limits<float>::quiet_NaN());
   std::vector<float>* field = result->mutable_f32s();
   char* data = tensorflow::bit_cast<char*>(field->data());
   uint64 bytes = elements * sizeof(float);
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 393e25380e..f0512c0eaf 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -90,7 +90,7 @@ StatusOr<std::unique_ptr<Literal>> Compare(const Shape& shape, HloOpcode opcode,
   }
 
   auto result = Literal::CreateFromShape(shape);
-  TF_RETURN_IF_ERROR(result.get()->Populate<bool>(
+  TF_RETURN_IF_ERROR(result->Populate<bool>(
       [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
         return compare_op(lhs_literal.Get<OperandT>(multi_index),
                           rhs_literal.Get<OperandT>(multi_index));
@@ -119,7 +119,7 @@ StatusOr<std::unique_ptr<Literal>> ElementWiseUnaryOpImpl(
 
   auto result = Literal::CreateFromShape(shape);
 
-  TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+  TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
       [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
         return unary_op(operand_literal.Get<NativeT>(multi_index));
       }));
@@ -439,7 +439,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
         [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
           return binary_op(lhs_literal.Get<ReturnT>(multi_index),
                            rhs_literal.Get<ReturnT>(multi_index));
@@ -476,7 +476,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
         [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
           return ternary_op(lhs_literal.Get<LhsType>(multi_index),
                             rhs_literal.Get<RhsType>(multi_index),
@@ -637,7 +637,7 @@ Status HloEvaluator::HandleConcatenate(
 
   for (auto operand : operands) {
     const Shape& operand_shape = operand->shape();
-    TF_RETURN_IF_ERROR(result_literal.get()->Copy(
+    TF_RETURN_IF_ERROR(result_literal->Copy(
         GetEvaluatedLiteralFor(operand), source_indices, dest_indices,
         AsInt64Slice(operand_shape.dimensions())));
     dest_indices[concat_dim] +=
@@ -769,9 +769,9 @@ Status HloEvaluator::HandleSlice(HloInstruction* slice,
 
   DimensionVector dest_indices(slice->slice_starts().size(), 0);
 
-  TF_RETURN_IF_ERROR(literal.get()->Copy(GetEvaluatedLiteralFor(operand),
-                                         slice->slice_starts(), dest_indices,
-                                         AsInt64Slice(shape.dimensions())));
+  TF_RETURN_IF_ERROR(literal->Copy(GetEvaluatedLiteralFor(operand),
+                                   slice->slice_starts(), dest_indices,
+                                   AsInt64Slice(shape.dimensions())));
 
   evaluated_[slice] = std::move(literal);
   return Status::OK();
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index 69c12cc437..cca04e17ca 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -508,7 +508,7 @@ class NearComparator {
   *shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout(minor_to_major);
 
   // Allocate space in the new literal.
-  new_literal.get()->Reserve(ShapeUtil::ElementsIn(literal.shape()));
+  new_literal->Reserve(ShapeUtil::ElementsIn(literal.shape()));
 
   // Copy data into new literal, element-by-element.
   for (int64 i = 0; i < ShapeUtil::ElementsIn(literal.shape()); ++i) {
@@ -518,36 +518,36 @@ class NearComparator {
         IndexUtil::LinearIndexToMultidimensionalIndex(shape_with_layout, i);
     switch (literal.shape().element_type()) {
       case PRED:
-        new_literal.get()->Set<bool>(to_multi_index,
-                                     literal.Get<bool>(from_multi_index));
+        new_literal->Set<bool>(to_multi_index,
+                               literal.Get<bool>(from_multi_index));
         break;
       case U8:
-        new_literal.get()->Set<uint8>(to_multi_index,
-                                      literal.Get<uint8>(from_multi_index));
+        new_literal->Set<uint8>(to_multi_index,
+                                literal.Get<uint8>(from_multi_index));
         break;
       case U32:
-        new_literal.get()->Set<uint32>(to_multi_index,
-                                       literal.Get<uint32>(from_multi_index));
+        new_literal->Set<uint32>(to_multi_index,
+                                 literal.Get<uint32>(from_multi_index));
         break;
       case S32:
-        new_literal.get()->Set<int32>(to_multi_index,
-                                      literal.Get<int32>(from_multi_index));
+        new_literal->Set<int32>(to_multi_index,
+                                literal.Get<int32>(from_multi_index));
         break;
       case U64:
-        new_literal.get()->Set<uint64>(to_multi_index,
-                                       literal.Get<uint64>(from_multi_index));
+        new_literal->Set<uint64>(to_multi_index,
+                                 literal.Get<uint64>(from_multi_index));
         break;
       case S64:
-        new_literal.get()->Set<int64>(to_multi_index,
-                                      literal.Get<int64>(from_multi_index));
+        new_literal->Set<int64>(to_multi_index,
+                                literal.Get<int64>(from_multi_index));
         break;
       case F32:
-        new_literal.get()->Set<float>(to_multi_index,
-                                      literal.Get<float>(from_multi_index));
+        new_literal->Set<float>(to_multi_index,
+                                literal.Get<float>(from_multi_index));
         break;
       case F64:
-        new_literal.get()->Set<double>(to_multi_index,
-                                       literal.Get<double>(from_multi_index));
+        new_literal->Set<double>(to_multi_index,
+                                 literal.Get<double>(from_multi_index));
         break;
       default:
         LOG(FATAL) << "Unhandled primitive element type: "
diff --git a/tensorflow/compiler/xla/text_literal_reader.cc b/tensorflow/compiler/xla/text_literal_reader.cc
index afdc6726f1..4d060895d3 100644
--- a/tensorflow/compiler/xla/text_literal_reader.cc
+++ b/tensorflow/compiler/xla/text_literal_reader.cc
@@ -104,8 +104,7 @@ StatusOr<std::unique_ptr<Literal>> TextLiteralReader::ReadAllLines() {
 
   auto result = MakeUnique<Literal>();
   const float fill = std::numeric_limits<float>::quiet_NaN();
-  result.get()->PopulateWithValue<float>(fill,
-                                         AsInt64Slice(shape.dimensions()));
+  result->PopulateWithValue<float>(fill, AsInt64Slice(shape.dimensions()));
   std::vector<tensorflow::StringPiece> pieces;
   std::vector<tensorflow::StringPiece> coordinates;
   std::vector<int64> coordinate_values;
@@ -147,7 +146,7 @@ StatusOr<std::unique_ptr<Literal>> TextLiteralReader::ReadAllLines() {
           "\"%s\"",
           shape.dimensions_size(), coordinate_values.size(), line.c_str());
     }
-    result.get()->Set<float>(coordinate_values, value);
+    result->Set<float>(coordinate_values, value);
   }
   return std::move(result);
 }
-- 
GitLab


From 8d42202b2a7066fe51da707a304d3ba43f4b6178 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Wed, 21 Jun 2017 15:46:48 +0000
Subject: [PATCH 0282/1375] Fix mismatched delete in mkl_tfconv_op.cc

This fix fixes mismatched new[]-delete in mkl_tfconv_op.cc

(the file went through clang-format so there are some additional
changes)

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/core/kernels/mkl_tfconv_op.cc | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc
index 48bd2e88ba..b4aae67ca6 100644
--- a/tensorflow/core/kernels/mkl_tfconv_op.cc
+++ b/tensorflow/core/kernels/mkl_tfconv_op.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include <algorithm>
 #include <vector>
-#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/framework/numeric_op.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
@@ -25,12 +24,13 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/util/tensor_format.h"
 
+#include "tensorflow/core/util/mkl_util.h"
 #include "third_party/mkl/include/mkl_dnn.h"
 #include "third_party/mkl/include/mkl_dnn_types.h"
-#include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -92,12 +92,12 @@ class MklToTfOp : public OpKernel {
     if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
       size_t strides_nchw[4];
       GetStridesFromSizes(FORMAT_NCHW, strides_nchw, in_sizes);
-      CHECK_EQ(dnnLayoutCreate_F32(&lt_trans_input, ndims, in_sizes,
-                                           strides_nchw), E_SUCCESS);
+      CHECK_EQ(
+          dnnLayoutCreate_F32(&lt_trans_input, ndims, in_sizes, strides_nchw),
+          E_SUCCESS);
       AllocTmpBuffer(context, &mkl_tmp_trans_input_buf_tensor, lt_trans_input,
                      &buf_trans_input);
-    }
-    else {
+    } else {
       lt_trans_input = static_cast<dnnLayout_t>(input_shape.GetTfLayout());
       buf_trans_input =
           static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
@@ -111,13 +111,13 @@ class MklToTfOp : public OpKernel {
     // NCHW -> NHWC, if data format is NHWC
     if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
       dnnLayoutDelete_F32(lt_trans_input);
-      TensorShape nhwc_shape = ShapeFromFormat(FORMAT_NHWC,
-          in_sizes[MklDims::N], in_sizes[MklDims::H],
+      TensorShape nhwc_shape = ShapeFromFormat(
+          FORMAT_NHWC, in_sizes[MklDims::N], in_sizes[MklDims::H],
           in_sizes[MklDims::W], in_sizes[MklDims::C]);
       MklNCHWToNHWC(mkl_tmp_trans_input_buf_tensor, &output_tensor);
     }
 
-    delete in_sizes;
+    delete[] in_sizes;
 
     VLOG(1) << "MKLToTFConversion complete successfully.";
   }
-- 
GitLab


From 8c99f891d0aa359509163cfcf16fc0177fafc3e8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 08:49:50 -0700
Subject: [PATCH 0283/1375] PiperOrigin-RevId: 159698321

---
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index 04d4d8f075..625f2b181c 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -176,7 +176,8 @@ SimpleOrcJIT::ModuleHandleT SimpleOrcJIT::AddModule(
 
 void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::ModuleHandleT handle) {
   module_handles_.erase(
-      std::remove(module_handles_.begin(), module_handles_.end(), handle));
+      std::remove(module_handles_.begin(), module_handles_.end(), handle),
+      module_handles_.end());
   compile_layer_.removeModuleSet(handle);
 }
 
-- 
GitLab


From f92eef788dff0e629cb4c408ce4b00530f152d4f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 08:53:11 -0700
Subject: [PATCH 0284/1375] Migrate kernels to boosted_trees.

PiperOrigin-RevId: 159698656
---
 tensorflow/contrib/BUILD                      |   3 +
 tensorflow/contrib/boosted_trees/BUILD        | 518 +++++++++++
 tensorflow/contrib/boosted_trees/__init__.py  |  22 +
 .../kernels/ensemble_optimizer_ops.cc         | 244 ++++++
 .../boosted_trees/kernels/model_ops.cc        | 169 ++++
 .../boosted_trees/kernels/prediction_ops.cc   | 429 +++++++++
 .../boosted_trees/kernels/quantile_ops.cc     | 822 +++++++++++++++++
 .../kernels/split_handler_ops.cc              | 510 +++++++++++
 .../kernels/stats_accumulator_ops.cc          | 784 +++++++++++++++++
 .../boosted_trees/kernels/training_ops.cc     | 825 ++++++++++++++++++
 tensorflow/contrib/boosted_trees/lib/BUILD    | 301 +++++--
 .../common/accumulators/class-partition-key.h |  61 ++
 .../accumulators/feature-stats-accumulator.h  |  82 ++
 .../feature-stats-accumulator_test.cc         |  90 ++
 .../partitioners/example_partitioner.cc       |  90 ++
 .../common/partitioners/example_partitioner.h |  53 ++
 .../partitioners/example_partitioner_test.cc  |  99 +++
 .../handlers/bias-feature-column-handler.cc   |  59 ++
 .../handlers/bias-feature-column-handler.h    |  57 ++
 .../bias-feature-column-handler_test.cc       | 134 +++
 .../categorical-feature-column-handler.cc     | 140 +++
 .../categorical-feature-column-handler.h      |  64 ++
 ...categorical-feature-column-handler_test.cc | 165 ++++
 .../dense-quantized-feature-column-handler.cc | 116 +++
 .../dense-quantized-feature-column-handler.h  |  62 ++
 ...e-quantized-feature-column-handler_test.cc | 155 ++++
 .../handlers/feature-column-handler.h         |  82 ++
 ...sparse-quantized-feature-column-handler.cc | 172 ++++
 .../sparse-quantized-feature-column-handler.h |  67 ++
 ...e-quantized-feature-column-handler_test.cc | 162 ++++
 .../stats/feature-split-candidate.h           |  61 ++
 .../learner/stochastic/stats/gradient-stats.h | 193 ++++
 .../lib/learner/stochastic/stats/node-stats.h | 301 +++++++
 .../stochastic/stats/node-stats_test.cc       | 213 +++++
 .../learner/stochastic/stats/split-stats.h    |  84 ++
 .../lib/utils/dropout_utils_test.cc           |   5 +-
 .../ops/ensemble_optimizer_ops.cc             |  44 +
 .../contrib/boosted_trees/ops/model_ops.cc    | 114 +++
 .../boosted_trees/ops/prediction_ops.cc       | 132 +++
 .../contrib/boosted_trees/ops/quantile_ops.cc | 263 ++++++
 .../boosted_trees/ops/split_handler_ops.cc    | 197 +++++
 .../ops/stats_accumulator_ops.cc              | 475 ++++++++++
 .../contrib/boosted_trees/ops/training_ops.cc | 120 +++
 tensorflow/contrib/boosted_trees/proto/BUILD  |  10 +
 .../boosted_trees/proto/split_info.proto      |  19 +
 .../contrib/boosted_trees/python/__init__.py  |  22 +
 .../kernel_tests/split_handler_ops_test.py    | 328 +++++++
 .../python/ops/batch_ops_utils.py             | 132 +++
 .../python/ops/ensemble_optimizer_ops.py      |  32 +
 .../boosted_trees/python/ops/model_ops.py     | 119 +++
 .../python/ops/prediction_ops.py              |  32 +
 .../boosted_trees/python/ops/quantile_ops.py  | 176 ++++
 .../python/ops/split_handler_ops.py           |  32 +
 .../python/ops/stats_accumulator_ops.py       | 210 +++++
 .../boosted_trees/python/ops/training_ops.py  |  32 +
 .../contrib/boosted_trees/resources/BUILD     |   1 -
 tensorflow/contrib/cmake/tf_python.cmake      |   2 +
 tensorflow/contrib/makefile/tf_op_files.txt   |   7 +
 .../contrib/makefile/tf_proto_files.txt       |   4 +
 tensorflow/tools/pip_package/BUILD            |   1 +
 60 files changed, 9824 insertions(+), 74 deletions(-)
 create mode 100644 tensorflow/contrib/boosted_trees/__init__.py
 create mode 100644 tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/kernels/model_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/kernels/training_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h
 create mode 100644 tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/model_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/training_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/proto/split_info.proto
 create mode 100644 tensorflow/contrib/boosted_trees/python/__init__.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/model_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/training_ops.py

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index b1eff73799..f852eded1e 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -15,6 +15,7 @@ py_library(
     deps = [
         "//tensorflow/contrib/batching:batch_py",
         "//tensorflow/contrib/bayesflow:bayesflow_py",
+        "//tensorflow/contrib/boosted_trees:init_py",
         "//tensorflow/contrib/cloud:cloud_py",
         "//tensorflow/contrib/cluster_resolver:cluster_resolver_py",
         "//tensorflow/contrib/compiler:compiler_py",
@@ -84,6 +85,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/contrib/batching:batch_ops_kernels",
+        "//tensorflow/contrib/boosted_trees:boosted_trees_kernels",
         "//tensorflow/contrib/factorization/kernels:all_kernels",
         "//tensorflow/contrib/framework:generate_vocab_remapping_kernel",
         "//tensorflow/contrib/framework:load_and_remap_matrix_kernel",
@@ -101,6 +103,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/contrib/batching:batch_ops_op_lib",
+        "//tensorflow/contrib/boosted_trees:boosted_trees_ops_op_lib",
         "//tensorflow/contrib/factorization:all_ops",
         "//tensorflow/contrib/framework:all_ops",
         "//tensorflow/contrib/input_pipeline:input_pipeline_ops_op_lib",
diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index c1600bdabd..3a67656a2f 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -7,6 +7,12 @@ package(default_visibility = [
     "//visibility:public",
 ])
 
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
+load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
+
 filegroup(
     name = "all_files",
     srcs = glob(
@@ -19,3 +25,515 @@ filegroup(
 )
 
 package_group(name = "friends")
+
+cc_library(
+    name = "boosted_trees_kernels",
+    deps = [
+        ":ensemble_optimizer_ops_kernels",
+        ":model_ops_kernels",
+        ":prediction_ops_kernels",
+        ":quantile_ops_kernels",
+        ":split_handler_ops_kernels",
+        ":stats_accumulator_ops_kernels",
+        ":training_ops_kernels",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "boosted_trees_ops_op_lib",
+    deps = [
+        ":ensemble_optimizer_ops_op_lib",
+        ":model_ops_op_lib",
+        ":prediction_ops_op_lib",
+        ":quantile_ops_op_lib",
+        ":split_handler_ops_op_lib",
+        ":stats_accumulator_ops_op_lib",
+        ":training_ops_op_lib",
+    ],
+)
+
+py_library(
+    name = "init_py",
+    srcs = [
+        "__init__.py",
+        "python/__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":boosted_trees_ops_py",
+    ],
+)
+
+# Kernel tests
+
+py_test(
+    name = "split_handler_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/split_handler_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":split_handler_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:training",
+    ],
+)
+
+# Ops
+
+py_library(
+    name = "batch_ops_utils_py",
+    srcs = ["python/ops/batch_ops_utils.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "boosted_trees_ops_py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":ensemble_optimizer_ops_py",
+        ":model_ops_py",
+        ":prediction_ops_py",
+        ":quantile_ops_py",
+        ":split_handler_ops_py",
+        ":stats_accumulator_ops_py",
+        ":training_ops_py",
+    ],
+)
+
+# Model Ops.
+tf_gen_op_libs(
+    op_lib_names = ["model_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_model_ops_py",
+    out = "python/ops/gen_model_ops.py",
+    deps = [":model_ops_op_lib"],
+)
+
+tf_custom_op_py_library(
+    name = "model_ops_py",
+    srcs = ["python/ops/model_ops.py"],
+    dso = [":python/ops/_model_ops.so"],
+    kernels = [
+        ":model_ops_kernels",
+        ":model_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_model_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_kernel_library(
+    name = "model_ops_kernels",
+    srcs = [
+        "kernels/model_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_model_ops.so",
+    srcs = [
+        "kernels/model_ops.cc",
+        "ops/model_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+# Split handler Ops.
+tf_gen_op_libs(
+    op_lib_names = ["split_handler_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_split_handler_ops_py",
+    out = "python/ops/gen_split_handler_ops.py",
+    deps = [
+        ":split_handler_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "split_handler_ops_py",
+    srcs = ["python/ops/split_handler_ops.py"],
+    dso = [":python/ops/_split_handler_ops.so"],
+    kernels = [
+        ":split_handler_ops_kernels",
+        ":split_handler_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_split_handler_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_split_handler_ops.so",
+    srcs = [
+        "kernels/split_handler_ops.cc",
+        "ops/split_handler_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:feature-column-handlers",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+    ],
+)
+
+tf_kernel_library(
+    name = "split_handler_ops_kernels",
+    srcs = [
+        "kernels/split_handler_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:feature-column-handlers",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Training Ops.
+tf_gen_op_libs(
+    op_lib_names = [
+        "training_ops",
+    ],
+    deps = ["//tensorflow/contrib/boosted_trees/proto:learner_proto_cc"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_training_ops_py",
+    out = "python/ops/gen_training_ops.py",
+    deps = [
+        ":training_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "training_ops_py",
+    srcs = ["python/ops/training_ops.py"],
+    dso = [":python/ops/_training_ops.so"],
+    kernels = [
+        ":training_ops_kernels",
+        ":training_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_training_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_training_ops.so",
+    srcs = [
+        "kernels/training_ops.cc",
+        "ops/training_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "training_ops_kernels",
+    srcs = [
+        "kernels/training_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Prediction Ops.
+tf_gen_op_libs(
+    op_lib_names = ["prediction_ops"],
+    deps = ["//tensorflow/contrib/boosted_trees/proto:learner_proto_cc"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_prediction_ops_py",
+    out = "python/ops/gen_prediction_ops.py",
+    deps = [
+        ":prediction_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "prediction_ops_py",
+    srcs = ["python/ops/prediction_ops.py"],
+    dso = [":python/ops/_prediction_ops.so"],
+    kernels = [
+        ":prediction_ops_kernels",
+        ":prediction_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_prediction_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_prediction_ops.so",
+    srcs = [
+        "kernels/prediction_ops.cc",
+        "ops/prediction_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:example_partitioner",
+        "//tensorflow/contrib/boosted_trees/lib:models",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "prediction_ops_kernels",
+    srcs = [
+        "kernels/prediction_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:example_partitioner",
+        "//tensorflow/contrib/boosted_trees/lib:models",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Quantile ops
+tf_gen_op_libs(
+    op_lib_names = ["quantile_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_quantile_ops_py_wrap",
+    out = "python/ops/gen_quantile_ops.py",
+    deps = [
+        ":quantile_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "quantile_ops_py",
+    srcs = ["python/ops/quantile_ops.py"],
+    dso = [":python/ops/_quantile_ops.so"],
+    kernels = [
+        ":quantile_ops_kernels",
+        ":quantile_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":batch_ops_utils_py",
+        ":gen_quantile_ops_py_wrap",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_quantile_ops.so",
+    srcs = [
+        "kernels/quantile_ops.cc",
+        "ops/quantile_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/lib:weighted_quantiles",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:quantile_stream_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "quantile_ops_kernels",
+    srcs = [
+        "kernels/quantile_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/lib:weighted_quantiles",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:quantile_stream_resource",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Ensemble optimizer ops
+tf_gen_op_libs(
+    op_lib_names = ["ensemble_optimizer_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_ensemble_optimizer_ops_py",
+    out = "python/ops/gen_ensemble_optimizer_ops.py",
+    deps = [
+        ":ensemble_optimizer_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "ensemble_optimizer_ops_py",
+    srcs = ["python/ops/ensemble_optimizer_ops.py"],
+    dso = [":python/ops/_ensemble_optimizer_ops.so"],
+    kernels = [
+        ":ensemble_optimizer_ops_kernels",
+        ":ensemble_optimizer_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_ensemble_optimizer_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_kernel_library(
+    name = "ensemble_optimizer_ops_kernels",
+    srcs = [
+        "kernels/ensemble_optimizer_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_ensemble_optimizer_ops.so",
+    srcs = [
+        "kernels/ensemble_optimizer_ops.cc",
+        "ops/ensemble_optimizer_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+# Stats Accumulator ops
+tf_gen_op_libs(
+    op_lib_names = ["stats_accumulator_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_stats_accumulator_ops_py_wrap",
+    out = "python/ops/gen_stats_accumulator_ops.py",
+    deps = [
+        ":stats_accumulator_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "stats_accumulator_ops_py",
+    srcs = ["python/ops/stats_accumulator_ops.py"],
+    dso = [":python/ops/_stats_accumulator_ops.so"],
+    kernels = [
+        ":stats_accumulator_ops_kernels",
+        ":stats_accumulator_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":batch_ops_utils_py",
+        ":gen_stats_accumulator_ops_py_wrap",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_stats_accumulator_ops.so",
+    srcs = [
+        "kernels/stats_accumulator_ops.cc",
+        "ops/stats_accumulator_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/resources:stamped_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "stats_accumulator_ops_kernels",
+    srcs = [
+        "kernels/stats_accumulator_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/resources:stamped_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+    alwayslink = 1,
+)
+
+# Pip
+
+py_library(
+    name = "boosted_trees_pip",
+    deps = [
+        ":init_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+    ],
+)
diff --git a/tensorflow/contrib/boosted_trees/__init__.py b/tensorflow/contrib/boosted_trees/__init__.py
new file mode 100644
index 0000000000..afb2d049b7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Gradient boosted trees implementation in tensorflow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.boosted_trees.python import *
+# pylint: enable=unused-import,wildcard-import
diff --git a/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
new file mode 100644
index 0000000000..000b2e903a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
@@ -0,0 +1,244 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/refcount.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+using boosted_trees::models::DecisionTreeEnsembleResource;
+using boosted_trees::trees::DecisionTreeEnsembleConfig;
+using boosted_trees::utils::DropoutUtils;
+using errors::InvalidArgument;
+
+namespace {
+
+// Learning rate epsilon.
+const float kLearningRateEps = 1e-8;
+
+}  // namespace
+
+class AddTreesToEnsembleOp : public OpKernel {
+ public:
+  explicit AddTreesToEnsembleOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    // Ensure feature importance lhs inputs are references.
+    OP_REQUIRES(
+        context,
+        IsRefType(context->input_type(kFeatureColumnUsageCountsHandleIdx)),
+        errors::InvalidArgument(
+            "Feature usage counts lhs input needs to be a ref type"));
+    OP_REQUIRES(context,
+                IsRefType(context->input_type(kFeatureColumnGainsHandleIdx)),
+                errors::InvalidArgument(
+                    "Feature gains lhs input needs to be a ref type"));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    DecisionTreeEnsembleResource* decision_tree_ensemble_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(
+        context, LookupResource(
+                     context, HandleFromInput(context, kTreeEnsembleHandleIdx),
+                     &decision_tree_ensemble_resource));
+    // Lock the resource since we're mutating it.
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    // Remove the reference at the end of this scope.
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+
+    // Read feature importance info.
+    mutex_lock fc_usage_counts_mutex_lock(
+        *context->input_ref_mutex(kFeatureColumnUsageCountsHandleIdx));
+    mutex_lock fc_gains_mutex_lock(
+        *context->input_ref_mutex(kFeatureColumnGainsHandleIdx));
+    Tensor fc_usage_counts_lhs_t =
+        context->mutable_input(kFeatureColumnUsageCountsHandleIdx, true);
+    OP_REQUIRES(context,
+                TensorShapeUtils::IsVector(fc_usage_counts_lhs_t.shape()),
+                InvalidArgument("Feature usage counts should be a vector."));
+    OP_REQUIRES(context, fc_usage_counts_lhs_t.IsInitialized(),
+                errors::FailedPrecondition(
+                    "Attempting to use uninitialized variables: ",
+                    def().input(kFeatureColumnUsageCountsHandleIdx)));
+
+    Tensor fc_gains_lhs_t =
+        context->mutable_input(kFeatureColumnGainsHandleIdx, true);
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(fc_gains_lhs_t.shape()),
+                InvalidArgument("Feature gains should be a vector."));
+    OP_REQUIRES(context, fc_gains_lhs_t.IsInitialized(),
+                errors::FailedPrecondition(
+                    "Attempting to use uninitialized variables: ",
+                    def().input(kFeatureColumnGainsHandleIdx)));
+
+    const Tensor fc_usage_counts_rhs_t =
+        context->input(kFeatureColumnUsageCountsToAddIdx);
+    OP_REQUIRES(
+        context,
+        fc_usage_counts_lhs_t.shape().IsSameSize(fc_usage_counts_rhs_t.shape()),
+        errors::InvalidArgument(
+            "Shapes of both feature usage counts tensors should match.",
+            " lhs shape= ", fc_usage_counts_lhs_t.shape().DebugString(),
+            " rhs shape= ", fc_usage_counts_rhs_t.shape().DebugString()));
+
+    const Tensor fc_gains_rhs_t = context->input(kFeatureColumnGainsToAddIdx);
+    OP_REQUIRES(context,
+                fc_gains_lhs_t.shape().IsSameSize(fc_gains_rhs_t.shape()),
+                errors::InvalidArgument(
+                    "Shapes of both feature gains tensors should match.",
+                    " lhs shape= ", fc_gains_lhs_t.shape().DebugString(),
+                    " rhs shape= ", fc_gains_rhs_t.shape().DebugString()));
+
+    // Read in info about trees that were dropped.
+    Tensor dropped_trees_info_t = context->input(kDropedTreesInfoTensorIdx);
+    OP_REQUIRES(context,
+                TensorShapeUtils::IsMatrix(dropped_trees_info_t.shape()),
+                InvalidArgument("Dropped trees info should be matrix."));
+
+    const auto& dropout_info = dropped_trees_info_t.matrix<float>();
+
+    // Parse the passed in tree ensemble.
+    Tensor tree_ensemble_config_t = context->input(kEnsembleToAddTensorIdx);
+    OP_REQUIRES(
+        context, TensorShapeUtils::IsScalar(tree_ensemble_config_t.shape()),
+        errors::InvalidArgument("Tree ensemble config must be a scalar."));
+    // Arena increase spatial locality which reduces the average latency to
+    // access memory, as working set of pages will be fewer.
+    // arena has type proto2::Arena*.
+    auto* arena =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble()
+            ->GetArena();
+    DecisionTreeEnsembleConfig* ensemble_to_add =
+        protobuf::Arena::CreateMessage<DecisionTreeEnsembleConfig>(arena);
+    OP_REQUIRES(
+        context, ParseProtoUnlimited(ensemble_to_add,
+                                     tree_ensemble_config_t.scalar<string>()()),
+        errors::InvalidArgument("Unable to parse tree ensemble config."));
+
+    auto* mutable_ensemble =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+
+    // Read the learning_rate
+    Tensor learning_rate_t = context->input(kLearningRateTensorIdx);
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(learning_rate_t.shape()),
+                InvalidArgument("Learning rate should be a scalar."));
+
+    const float learning_rate = learning_rate_t.scalar<float>()();
+    if (learning_rate < kLearningRateEps) {
+      return;
+    }
+    // Prepare current weights vec.
+    std::vector<float> current_weights;
+    current_weights.reserve(mutable_ensemble->tree_weights_size());
+    for (const float weight : mutable_ensemble->tree_weights()) {
+      current_weights.push_back(weight);
+    }
+    const int32 num_dropped = dropped_trees_info_t.dim_size(1);
+    std::vector<int> dropped_trees;
+    dropped_trees.reserve(num_dropped);
+    std::vector<float> dropped_trees_original_weights;
+    dropped_trees_original_weights.reserve(num_dropped);
+    for (int i = 0; i < num_dropped; ++i) {
+      dropped_trees.push_back(dropout_info(0, i));
+      dropped_trees_original_weights.push_back(dropout_info(1, i));
+    }
+
+    std::vector<int32> num_updates;
+    num_updates.reserve(mutable_ensemble->tree_metadata_size());
+
+    for (const auto& meta : mutable_ensemble->tree_metadata()) {
+      num_updates.push_back(meta.num_tree_weight_updates());
+    }
+
+    // If there was a dropout, come up with tree weights
+    const bool was_dropout = !dropped_trees.empty();
+    if (was_dropout) {
+      // New tree/s will be added to the end of the ensemble's tree list.
+      const int32 new_tree_index = current_weights.size();
+      DropoutUtils::GetTreesWeightsForAddingTrees(
+          dropped_trees, dropped_trees_original_weights, new_tree_index,
+          ensemble_to_add->trees_size(), &current_weights, &num_updates);
+
+      // Update the weights of trees according to current weights;
+      for (int i = 0; i < mutable_ensemble->trees_size(); ++i) {
+        mutable_ensemble->set_tree_weights(i, current_weights[i]);
+      }
+    }
+
+    // Add the trees from ensemble_to_add to the tree ensemble variable.
+    int i = mutable_ensemble->trees_size();
+    for (auto& tree : *ensemble_to_add->mutable_trees()) {
+      (*mutable_ensemble->add_trees()).Swap(&tree);
+
+      // New trees were updated only once.
+      auto* meta = mutable_ensemble->add_tree_metadata();
+      meta->set_num_tree_weight_updates(1);
+
+      // When we add complete trees to the ensemble in one step, each tree
+      // that's added is final.
+      meta->set_is_finalized(true);
+
+      if (was_dropout) {
+        mutable_ensemble->add_tree_weights(current_weights[i++]);
+      } else {
+        mutable_ensemble->add_tree_weights(learning_rate);
+      }
+    }
+
+    // Update the number of updates.
+    if (was_dropout) {
+      for (int i = 0; i < num_updates.size(); ++i) {
+        mutable_ensemble->mutable_tree_metadata(i)->set_num_tree_weight_updates(
+            num_updates[i]);
+      }
+    }
+
+    // Update feature importance.
+    fc_usage_counts_lhs_t.vec<int64>() += fc_usage_counts_rhs_t.vec<int64>();
+    fc_gains_lhs_t.vec<float>() += learning_rate * fc_gains_rhs_t.vec<float>();
+  }
+
+ private:
+  // Input tensor indices.
+  // Note that Op definition changes might cause input indices to need
+  // changing as well.
+  static const int kTreeEnsembleHandleIdx = 0;
+  static const int kEnsembleToAddTensorIdx = 1;
+  static const int kFeatureColumnUsageCountsHandleIdx = 2;
+  static const int kFeatureColumnUsageCountsToAddIdx = 3;
+  static const int kFeatureColumnGainsHandleIdx = 4;
+  static const int kFeatureColumnGainsToAddIdx = 5;
+  static const int kDropedTreesInfoTensorIdx = 6;
+  static const int kLearningRateTensorIdx = 7;
+};
+
+REGISTER_KERNEL_BUILDER(Name("AddTreesToEnsemble").Device(DEVICE_CPU),
+                        AddTreesToEnsembleOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
new file mode 100644
index 0000000000..6f553df6d7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
@@ -0,0 +1,169 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <string>
+
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+
+using boosted_trees::models::DecisionTreeEnsembleResource;
+
+// Creates a tree ensemble variable.
+class CreateTreeEnsembleVariableOp : public OpKernel {
+ public:
+  explicit CreateTreeEnsembleVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Get the tree ensemble config.
+    const Tensor* tree_ensemble_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_ensemble_config",
+                                           &tree_ensemble_config_t));
+    auto* result = new boosted_trees::models::DecisionTreeEnsembleResource();
+    result->set_stamp(stamp_token);
+    if (!ParseProtoUnlimited(result->mutable_decision_tree_ensemble(),
+                             tree_ensemble_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false, errors::InvalidArgument(
+                                      "Unable to parse tree ensemble config."));
+    }
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+};
+
+// Op for retrieving a model stamp token without having to serialize.
+class TreeEnsembleStampTokenOp : public OpKernel {
+ public:
+  explicit TreeEnsembleStampTokenOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    Tensor* output_stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape(),
+                                                     &output_stamp_token_t));
+    output_stamp_token_t->scalar<int64>()() =
+        decision_tree_ensemble_resource->stamp();
+  }
+};
+
+// Op for serializing a model.
+class TreeEnsembleSerializeOp : public OpKernel {
+ public:
+  explicit TreeEnsembleSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    Tensor* output_stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape(),
+                                                     &output_stamp_token_t));
+    output_stamp_token_t->scalar<int64>()() =
+        decision_tree_ensemble_resource->stamp();
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(1, TensorShape(), &output_config_t));
+    output_config_t->scalar<string>()() =
+        decision_tree_ensemble_resource->decision_tree_ensemble()
+            .SerializeAsString();
+  }
+};
+
+// Op for deserializing a tree ensemble variable from a checkpoint.
+class TreeEnsembleDeserializeOp : public OpKernel {
+ public:
+  explicit TreeEnsembleDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Get the tree ensemble config.
+    const Tensor* tree_ensemble_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_ensemble_config",
+                                           &tree_ensemble_config_t));
+    // Deallocate all the previous objects on the resource.
+    decision_tree_ensemble_resource->Reset();
+    decision_tree_ensemble_resource->set_stamp(stamp_token);
+    boosted_trees::trees::DecisionTreeEnsembleConfig* config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    OP_REQUIRES(
+        context,
+        ParseProtoUnlimited(config, tree_ensemble_config_t->scalar<string>()()),
+        errors::InvalidArgument("Unable to parse tree ensemble config."));
+  }
+};
+
+REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeEnsembleResource);
+
+REGISTER_KERNEL_BUILDER(
+    Name("TreeEnsembleIsInitializedOp").Device(DEVICE_CPU),
+    IsResourceInitialized<boosted_trees::models::DecisionTreeEnsembleResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateTreeEnsembleVariable").Device(DEVICE_CPU),
+                        CreateTreeEnsembleVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleStampToken").Device(DEVICE_CPU),
+                        TreeEnsembleStampTokenOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleSerialize").Device(DEVICE_CPU),
+                        TreeEnsembleSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleDeserialize").Device(DEVICE_CPU),
+                        TreeEnsembleDeserializeOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
new file mode 100644
index 0000000000..2e99f42ff0
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
@@ -0,0 +1,429 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <algorithm>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h"
+#include "tensorflow/contrib/boosted_trees/lib/models/multiple_additive_trees.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/batch_features.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/device_base.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/refcount.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/types.h"
+
+using tensorflow::boosted_trees::learner::AveragingConfig;
+using tensorflow::boosted_trees::trees::DecisionTreeEnsembleConfig;
+
+namespace tensorflow {
+namespace boosted_trees {
+
+using boosted_trees::learner::LearnerConfig;
+using boosted_trees::learner::LearningRateConfig;
+using boosted_trees::learner::LearningRateDropoutDrivenConfig;
+using boosted_trees::models::MultipleAdditiveTrees;
+using boosted_trees::models::DecisionTreeEnsembleResource;
+using boosted_trees::utils::DropoutUtils;
+using boosted_trees::utils::TensorUtils;
+
+namespace {
+const char* kLearnerConfigAttributeName = "learner_config";
+const char* kSeedTensorName = "seed";
+const char* kApplyDropoutAttributeName = "apply_dropout";
+const char* kApplyAveragingAttributeName = "apply_averaging";
+const char* kDropoutInfoOutputTensorName = "drop_out_tree_indices_weights";
+const char* kPredictionsTensorName = "predictions";
+const char* kNoDropoutPredictionsTensorName = "no_dropout_predictions";
+}
+
+class GradientTreesPredictionOp : public OpKernel {
+ public:
+  explicit GradientTreesPredictionOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("use_locking", &use_locking_));
+
+    OP_REQUIRES_OK(context, context->GetAttr("center_bias", &center_bias_));
+
+    OP_REQUIRES_OK(
+        context, context->GetAttr(kApplyDropoutAttributeName, &apply_dropout_));
+
+    LearnerConfig learner_config;
+    string learner_config_str;
+    OP_REQUIRES_OK(context, context->GetAttr(kLearnerConfigAttributeName,
+                                             &learner_config_str));
+    OP_REQUIRES(
+        context, ParseProtoUnlimited(&learner_config, learner_config_str),
+        errors::InvalidArgument("Unable to parse learner config config."));
+
+    num_classes_ = learner_config.num_classes();
+    OP_REQUIRES(context, num_classes_ >= 2,
+                errors::InvalidArgument("Number of classes must be >=2"));
+    OP_REQUIRES(
+        context, ParseProtoUnlimited(&learner_config, learner_config_str),
+        errors::InvalidArgument("Unable to parse learner config config."));
+
+    prediction_vector_size_ =
+        learner_config.multi_class_strategy() == LearnerConfig::TREE_PER_CLASS
+            ? num_classes_ - 1
+            : num_classes_;
+
+    only_finalized_trees_ =
+        learner_config.growing_mode() == learner_config.WHOLE_TREE;
+    if (learner_config.has_learning_rate_tuner() &&
+        learner_config.learning_rate_tuner().tuner_case() ==
+            LearningRateConfig::kDropout) {
+      dropout_config_ = learner_config.learning_rate_tuner().dropout();
+      has_dropout_ = true;
+    } else {
+      has_dropout_ = false;
+    }
+
+    OP_REQUIRES_OK(context, context->GetAttr(kApplyAveragingAttributeName,
+                                             &apply_averaging_));
+    apply_averaging_ =
+        apply_averaging_ && learner_config.averaging_config().config_case() !=
+                                AveragingConfig::CONFIG_NOT_SET;
+    if (apply_averaging_) {
+      averaging_config_ = learner_config.averaging_config();
+
+      // If there is averaging config, check that the values are correct.
+      switch (averaging_config_.config_case()) {
+        case AveragingConfig::kAverageLastNTreesFieldNumber: {
+          OP_REQUIRES(context, averaging_config_.average_last_n_trees() > 0,
+                      errors::InvalidArgument(
+                          "Average last n trees must be a positive number"));
+          break;
+        }
+        case AveragingConfig::kAverageLastPercentTreesFieldNumber: {
+          OP_REQUIRES(context,
+                      averaging_config_.average_last_percent_trees() > 0 &&
+                          averaging_config_.average_last_percent_trees() <= 1.0,
+                      errors::InvalidArgument(
+                          "Average last percent must be in (0,1] interval."));
+          break;
+        }
+        case AveragingConfig::CONFIG_NOT_SET: {
+          QCHECK(false) << "We should never get here.";
+          break;
+        }
+      }
+    }
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    DecisionTreeEnsembleResource* decision_tree_ensemble_resource;
+    // Gets the resource. Grabs the mutex but releases it.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    // Release the reference to the resource once we're done using it.
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    if (use_locking_) {
+      mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+      DoCompute(context, decision_tree_ensemble_resource);
+    } else {
+      DoCompute(context, decision_tree_ensemble_resource);
+    }
+  }
+
+ private:
+  void DoCompute(
+      OpKernelContext* context,
+      DecisionTreeEnsembleResource* decision_tree_ensemble_resource) {
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Read sparse int features list;
+    OpInputList sparse_int_feature_indices_list;
+    OpInputList sparse_int_feature_values_list;
+    OpInputList sparse_int_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseIntFeatures(
+                                context, &sparse_int_feature_indices_list,
+                                &sparse_int_feature_values_list,
+                                &sparse_int_feature_shapes_list));
+
+    // Infer batch size.
+    const int64 batch_size = TensorUtils::InferBatchSize(
+        dense_float_features_list, sparse_float_feature_shapes_list,
+        sparse_int_feature_shapes_list);
+
+    // Read batch features.
+    boosted_trees::utils::BatchFeatures batch_features(batch_size);
+    OP_REQUIRES_OK(
+        context,
+        batch_features.Initialize(
+            TensorUtils::OpInputListToTensorVec(dense_float_features_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_shapes_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(sparse_int_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_shapes_list)));
+
+    std::vector<int32> dropped_trees;
+    std::vector<float> original_weights;
+
+    // Do dropout if needed.
+    if (apply_dropout_ && has_dropout_) {
+      // Read in seed
+      const Tensor* seed_t;
+      OP_REQUIRES_OK(context, context->input(kSeedTensorName, &seed_t));
+      OP_REQUIRES(context, TensorShapeUtils::IsScalar(seed_t->shape()),
+                  errors::InvalidArgument("Seed must be a scalar."));
+
+      // Cast seed to uint64.
+      const uint64 seed = seed_t->scalar<int64>()();
+
+      std::vector<float> weights;
+      for (const float weight :
+           decision_tree_ensemble_resource->decision_tree_ensemble()
+               .tree_weights()) {
+        weights.push_back(weight);
+      }
+
+      std::unordered_set<int32> trees_not_to_drop;
+      if (center_bias_) {
+        trees_not_to_drop.insert(0);
+      }
+      if (decision_tree_ensemble_resource->decision_tree_ensemble()
+              .has_growing_metadata()) {
+        // We are in batch mode, the last tree is the tree that is being built,
+        // we can't drop it during dropout.
+        const int32 current_tree =
+            decision_tree_ensemble_resource->decision_tree_ensemble()
+                .trees_size() -
+            1;
+        trees_not_to_drop.insert(current_tree);
+      }
+      OP_REQUIRES_OK(context, DropoutUtils::DropOutTrees(
+                                  seed, dropout_config_, trees_not_to_drop,
+                                  weights, &dropped_trees, &original_weights));
+    }
+
+    // Allocate output predictions matrix.
+    Tensor* output_predictions_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(kPredictionsTensorName,
+                                          {batch_size, prediction_vector_size_},
+                                          &output_predictions_t));
+    auto output_predictions = output_predictions_t->matrix<float>();
+
+    Tensor* output_no_dropout_predictions_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(kNoDropoutPredictionsTensorName,
+                                          {batch_size, prediction_vector_size_},
+                                          &output_no_dropout_predictions_t));
+    auto output_no_dropout_predictions =
+        output_no_dropout_predictions_t->matrix<float>();
+
+    // Run predictor.
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+
+    if (apply_averaging_) {
+      DecisionTreeEnsembleConfig adjusted =
+          decision_tree_ensemble_resource->decision_tree_ensemble();
+
+      const int start_averaging = std::max(
+          0.0,
+          averaging_config_.config_case() ==
+                  AveragingConfig::kAverageLastNTreesFieldNumber
+              ? adjusted.trees_size() - averaging_config_.average_last_n_trees()
+              : adjusted.trees_size() *
+                    (1.0 - averaging_config_.average_last_percent_trees()));
+
+      const int num_ensembles = adjusted.trees_size() - start_averaging;
+      for (int i = start_averaging; i < adjusted.trees_size(); ++i) {
+        float weight = adjusted.tree_weights(i);
+        adjusted.mutable_tree_weights()->Set(
+            i, weight * (num_ensembles - i + start_averaging) / num_ensembles);
+      }
+      MultipleAdditiveTrees::Predict(
+          adjusted, only_finalized_trees_, dropped_trees, batch_features,
+          worker_threads, output_predictions, output_no_dropout_predictions);
+    } else {
+      MultipleAdditiveTrees::Predict(
+          decision_tree_ensemble_resource->decision_tree_ensemble(),
+          only_finalized_trees_, dropped_trees, batch_features, worker_threads,
+          output_predictions, output_no_dropout_predictions);
+    }
+
+    // Output dropped trees and original weights.
+    Tensor* output_dropout_info_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                kDropoutInfoOutputTensorName,
+                                {2, static_cast<int64>(dropped_trees.size())},
+                                &output_dropout_info_t));
+    auto output_dropout_info = output_dropout_info_t->matrix<float>();
+
+    for (int32 i = 0; i < dropped_trees.size(); ++i) {
+      output_dropout_info(0, i) = dropped_trees[i];
+      output_dropout_info(1, i) = original_weights[i];
+    }
+  }
+
+ private:
+  LearningRateDropoutDrivenConfig dropout_config_;
+  AveragingConfig averaging_config_;
+  bool only_finalized_trees_;
+  int num_classes_;
+  // What is the size of the output vector for predictions?
+  int prediction_vector_size_;
+  bool apply_dropout_;
+  bool center_bias_;
+  bool apply_averaging_;
+  bool use_locking_;
+  bool has_dropout_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("GradientTreesPrediction").Device(DEVICE_CPU),
+                        GradientTreesPredictionOp);
+
+class GradientTreesPartitionExamplesOp : public OpKernel {
+ public:
+  explicit GradientTreesPartitionExamplesOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("use_locking", &use_locking_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    DecisionTreeEnsembleResource* decision_tree_ensemble_resource;
+    // Gets the resource. Grabs the mutex but releases it.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    // Release the reference to the resource once we're done using it.
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    if (use_locking_) {
+      mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+      DoCompute(context, decision_tree_ensemble_resource);
+    } else {
+      DoCompute(context, decision_tree_ensemble_resource);
+    }
+  }
+
+ private:
+  void DoCompute(
+      OpKernelContext* context,
+      DecisionTreeEnsembleResource* decision_tree_ensemble_resource) {
+    // The last non-finalized tree in the ensemble is by convention the
+    // one to partition on. If no such tree exists, a nodeless tree is
+    // created.
+    const auto& tree_ensemble =
+        decision_tree_ensemble_resource->decision_tree_ensemble();
+    boosted_trees::trees::DecisionTreeConfig empy_tree_config;
+    const boosted_trees::trees::DecisionTreeConfig* tree_config =
+        &empy_tree_config;
+    auto num_trees = tree_ensemble.trees_size();
+    if (num_trees > 0 &&
+        !tree_ensemble.tree_metadata(num_trees - 1).is_finalized()) {
+      tree_config = &tree_ensemble.trees(num_trees - 1);
+    }
+
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Read sparse int features list;
+    OpInputList sparse_int_feature_indices_list;
+    OpInputList sparse_int_feature_values_list;
+    OpInputList sparse_int_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseIntFeatures(
+                                context, &sparse_int_feature_indices_list,
+                                &sparse_int_feature_values_list,
+                                &sparse_int_feature_shapes_list));
+
+    // Infer batch size.
+    const int64 batch_size = TensorUtils::InferBatchSize(
+        dense_float_features_list, sparse_float_feature_shapes_list,
+        sparse_int_feature_shapes_list);
+
+    // Read batch features.
+    boosted_trees::utils::BatchFeatures batch_features(batch_size);
+    OP_REQUIRES_OK(
+        context,
+        batch_features.Initialize(
+            TensorUtils::OpInputListToTensorVec(dense_float_features_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_shapes_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(sparse_int_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_shapes_list)));
+
+    // Allocate output partitions vector.
+    Tensor* partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, {batch_size}, &partition_ids_t));
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    learner::ExamplePartitioner::PartitionExamples(
+        *tree_config, batch_features, worker_threads->NumThreads(),
+        worker_threads, partition_ids_t->vec<int32>().data());
+  }
+
+ private:
+  bool use_locking_;
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("GradientTreesPartitionExamples").Device(DEVICE_CPU),
+    GradientTreesPartitionExamplesOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
new file mode 100644
index 0000000000..546f6a30b8
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
@@ -0,0 +1,822 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <algorithm>
+#include <iterator>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/parallel_for.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/quantiles.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/quantile_stream_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+using ::boosted_trees::QuantileConfig;
+using boosted_trees::utils::TensorUtils;
+using boosted_trees::QuantileStreamResource;
+
+namespace {
+const char* const kExampleWeightsName = "example_weights";
+const char* const kMaxElementsName = "max_elements";
+const char* const kHandleName = "handle";
+const char* const kNextStampTokenName = "next_stamp_token";
+const char* const kStampTokenName = "stamp_token";
+const char* const kAreBucketsReadyName = "are_buckets_ready";
+// Names for sparse arguments.
+const char* const kNumSparseFeaturesName = "num_sparse_features";
+const char* const kSparseBucketsName = "sparse_buckets";
+const char* const kSparseValuesName = "sparse_values";
+const char* const kSparseStreamsStateName = "sparse_streams_state";
+const char* const kSparseSummariesName = "sparse_summaries";
+const char* const kSparseConfigName = "sparse_config";
+const char* const kSparseOutputTensorName = "sparse_quantiles";
+// Names for dense arguments.
+const char* const kDenseBucketsName = "dense_buckets";
+const char* const kDenseConfigName = "dense_config";
+const char* const kDenseOutputTensorName = "dense_quantiles";
+const char* const kDenseStreamsStateName = "dense_streams_state";
+const char* const kDenseSummariesName = "dense_summaries";
+const char* const kDenseValuesName = "dense_values";
+const char* const kNumDenseFeaturesName = "num_dense_features";
+const char* const kResourceHandlesName = "quantile_accumulator_handles";
+const char* const kNumQuantilesName = "num_quantiles";
+const char* const kEpsilonName = "epsilon";
+const char* const kBucketsName = "buckets";
+const char* const kStreamStateName = "stream_state";
+const char* const kSummariesName = "summaries";
+
+using QuantileStream =
+    boosted_trees::quantiles::WeightedQuantilesStream<float, float>;
+using QuantileSummary =
+    boosted_trees::quantiles::WeightedQuantilesSummary<float, float>;
+using QuantileSummaryEntry =
+    boosted_trees::quantiles::WeightedQuantilesSummary<float,
+                                                       float>::SummaryEntry;
+
+std::vector<float> GetBuckets(const int32 feature,
+                              const OpInputList& buckets_list) {
+  const auto& buckets = buckets_list[feature].flat<float>();
+  std::vector<float> buckets_vector(buckets.data(),
+                                    buckets.data() + buckets.size());
+  return buckets_vector;
+}
+
+void QuantizeFeatures(const string& output_name, const OpInputList& values_list,
+                      const OpInputList& buckets_list,
+                      OpKernelContext* const context) {
+  if (values_list.size() == 0) {
+    return;
+  }
+  OpOutputList output_list;
+  OP_REQUIRES_OK(context, context->output_list(output_name, &output_list));
+
+  for (int32 feature_index = 0; feature_index < values_list.size();
+       ++feature_index) {
+    const Tensor& values_tensor = values_list[feature_index];
+    const int64 num_values = values_tensor.dim_size(0);
+
+    Tensor* output_t = nullptr;
+    OP_REQUIRES_OK(
+        context, output_list.allocate(feature_index, TensorShape({num_values}),
+                                      &output_t));
+    TTypes<int32>::Vec output = output_t->vec<int32>();
+    const std::vector<float>& buckets_vector =
+        GetBuckets(feature_index, buckets_list);
+    auto flat_values = values_tensor.flat<float>();
+    for (int64 instance = 0; instance < num_values; ++instance) {
+      const float value = flat_values(instance);
+      auto bucket_iter =
+          std::lower_bound(buckets_vector.begin(), buckets_vector.end(), value);
+      if (bucket_iter == buckets_vector.end()) {
+        --bucket_iter;
+      }
+      const int32 bucket =
+          static_cast<int32>(bucket_iter - buckets_vector.begin());
+      output(instance) = bucket;
+    }
+  }
+}
+
+// Validates attributes for the quantile ops.
+Status ReadAndValidateAttributes(OpKernelConstruction* const context,
+                                 int* num_dense_features,
+                                 int* num_sparse_features) {
+  TF_RETURN_IF_ERROR(
+      context->GetAttr(kNumDenseFeaturesName, num_dense_features));
+  TF_RETURN_IF_ERROR(
+      context->GetAttr(kNumSparseFeaturesName, num_sparse_features));
+  if ((*num_dense_features) + (*num_sparse_features) == 0) {
+    return errors::InvalidArgument(
+        "Please provide at least sparse or dense features.");
+  }
+  return Status::OK();
+}
+
+void ParseConfig(OpKernelConstruction* const context, const string& name,
+                 std::vector<QuantileConfig>* output) {
+  std::vector<string> serialized_config;
+  OP_REQUIRES_OK(context, context->GetAttr(name, &serialized_config));
+  output->reserve(serialized_config.size());
+  QuantileConfig tmp;
+  for (const auto& serialized_string : serialized_config) {
+    OP_REQUIRES(context, tmp.ParseFromString(serialized_string),
+                errors::InvalidArgument("Malformed QuantileConfig passed in."));
+    output->push_back(tmp);
+  }
+}
+
+// Generates quantiles on a finalized QuantileStream.
+std::vector<float> GenerateBoundaries(const QuantileStream& stream,
+                                      int num_boundaries) {
+  std::vector<float> boundaries = stream.GenerateBoundaries(num_boundaries);
+
+  // Uniquify elements as we may get dupes.
+  auto end_it = std::unique(boundaries.begin(), boundaries.end());
+  boundaries.resize(std::distance(boundaries.begin(), end_it));
+  return boundaries;
+}
+
+// Copies quantiles to output list.
+void CopyBoundaries(OpKernelContext* const context,
+                    const std::vector<float>& boundaries, const int64 index,
+                    OpOutputList* output_list) {
+  // Output to tensor.
+  Tensor* output_t = nullptr;
+  OP_REQUIRES_OK(
+      context, output_list->allocate(
+                   index, {static_cast<int64>(boundaries.size())}, &output_t));
+  auto* quantiles_flat = output_t->flat<float>().data();
+  memcpy(quantiles_flat, boundaries.data(), sizeof(float) * boundaries.size());
+}
+
+void CopySummaryToProto(const QuantileSummary& summary,
+                        ::boosted_trees::QuantileSummaryState* summary_proto) {
+  summary_proto->mutable_entries()->Reserve(summary.Size());
+  for (const auto& entry : summary.GetEntryList()) {
+    auto* new_entry = summary_proto->add_entries();
+    new_entry->set_value(entry.value);
+    new_entry->set_weight(entry.weight);
+    new_entry->set_min_rank(entry.min_rank);
+    new_entry->set_max_rank(entry.max_rank);
+  }
+}
+
+}  // namespace
+
+// Accumulator for Quantile Summaries.
+REGISTER_RESOURCE_HANDLE_KERNEL(QuantileStreamResource);
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorIsInitialized").Device(DEVICE_CPU),
+    IsResourceInitialized<QuantileStreamResource>);
+
+class CreateQuantileAccumulatorOp : public OpKernel {
+ public:
+  explicit CreateQuantileAccumulatorOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr(kEpsilonName, &epsilon_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr(kNumQuantilesName, &num_quantiles_));
+    OP_REQUIRES_OK(context, context->GetAttr(kMaxElementsName, &max_elements_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions. If one already exists, it unrefs the new one.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    auto result =
+        new QuantileStreamResource(epsilon_, num_quantiles_, max_elements_,
+                                   stamp_token_t->scalar<int64>()());
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  float epsilon_;
+  int32 num_quantiles_;
+  // An upperbound on the number of enteries that the summaries might have
+  // for a feature.
+  int64 max_elements_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("CreateQuantileAccumulator").Device(DEVICE_CPU),
+                        CreateQuantileAccumulatorOp);
+
+// Adds a summary to the quantile summary stream.
+class QuantileAccumulatorAddSummariesOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorAddSummariesOp(
+      OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list(kResourceHandlesName,
+                                                &resource_handle_list));
+    OpInputList summary_list;
+    OP_REQUIRES_OK(context, context->input_list(kSummariesName, &summary_list));
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &summary_list, stamp_token](
+            int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+            QuantileStreamResource* streams_resource;
+            // Create a reference to the underlying resource using the handle.
+            OP_REQUIRES_OK(context,
+                           LookupResource(context, handle, &streams_resource));
+            // Remove the reference at the end of this scope.
+            mutex_lock l(*streams_resource->mutex());
+            core::ScopedUnref unref_me(streams_resource);
+
+            // If the stamp is invalid we drop the update.
+            if (!streams_resource->is_stamp_valid(stamp_token)) {
+              VLOG(1)
+                  << "Invalid stamp token in QuantileAccumulatorAddSummariesOp."
+                  << " Passed stamp token: " << stamp_token << " "
+                  << "Current token: " << streams_resource->stamp();
+              return;
+            }
+
+            protobuf::Arena arena;
+            ::boosted_trees::QuantileSummaryState* summary_proto =
+                protobuf::Arena::CreateMessage<
+                    ::boosted_trees::QuantileSummaryState>(&arena);
+            OP_REQUIRES(
+                context,
+                ParseProtoUnlimited(
+                    summary_proto,
+                    summary_list[resource_handle_idx].scalar<string>()()),
+                errors::InvalidArgument("Unable to parse quantile summary."));
+            std::vector<QuantileSummaryEntry> entries;
+            entries.reserve(summary_proto->entries_size());
+            for (const auto& entry : summary_proto->entries()) {
+              entries.emplace_back(entry.value(), entry.weight(),
+                                   entry.min_rank(), entry.max_rank());
+            }
+
+            // Add the summary to the quantile stream.
+            streams_resource->stream(stamp_token)->PushSummary(entries);
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorAddSummaries").Device(DEVICE_CPU),
+    QuantileAccumulatorAddSummariesOp);
+
+// Generates summaries for given set of float values, and the given config.
+class MakeQuantileSummariesOp : public OpKernel {
+ public:
+  explicit MakeQuantileSummariesOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context,
+                   ReadAndValidateAttributes(context, &num_dense_features_,
+                                             &num_sparse_features_));
+    OP_REQUIRES_OK(context, context->GetAttr(kEpsilonName, &epsilon_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Parse example weights and get batch size.
+    const Tensor* example_weights_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kExampleWeightsName, &example_weights_t));
+    auto example_weights = example_weights_t->flat<float>();
+    const int64 batch_size = example_weights.size();
+
+    OpOutputList sparse_summaries_output_list;
+    OP_REQUIRES_OK(context,
+                   context->output_list(kSparseSummariesName,
+                                        &sparse_summaries_output_list));
+    OpOutputList dense_summaries_output_list;
+    OP_REQUIRES_OK(context, context->output_list(kDenseSummariesName,
+                                                 &dense_summaries_output_list));
+
+    auto do_quantile_summary_gen = [&](const int64 begin, const int64 end) {
+      auto copy_over_summaries = [&](const QuantileStream& stream,
+                                     const int64 index,
+                                     OpOutputList* output_list) {
+        protobuf::Arena arena;
+        ::boosted_trees::QuantileSummaryState* summary_proto =
+            protobuf::Arena::CreateMessage<
+            ::boosted_trees::QuantileSummaryState>(&arena);
+        const auto& summary = stream.GetFinalSummary();
+        CopySummaryToProto(summary, summary_proto);
+        // Output to tensor.
+        Tensor* output_t = nullptr;
+        OP_REQUIRES_OK(context, output_list->allocate(index, {}, &output_t));
+        summary_proto->SerializeToString(&output_t->scalar<string>()());
+      };
+
+      // These are blocks of ranges. We are iterating over both sparse and
+      // dense features i.e. [0, sparse_features.size() + dense_features.size()]
+      for (int64 i = begin; i < end; ++i) {
+        if (i < num_dense_features_) {
+          const int64 dense_index = i;
+          const auto dense_values =
+              dense_float_features_list[dense_index].flat<float>();
+          QuantileStream stream(epsilon_, batch_size + 1);
+          // Run quantile summary generation.
+          for (int64 j = 0; j < batch_size; ++j) {
+            stream.PushEntry(dense_values(j), example_weights(j));
+          }
+          stream.Finalize();
+          // Copy summaries to output.
+          copy_over_summaries(stream, dense_index,
+                              &dense_summaries_output_list);
+        } else {
+          const int64 sparse_index = i - num_dense_features_;
+          const auto sparse_values =
+              sparse_float_feature_values_list[sparse_index].flat<float>();
+          const auto sparse_indices =
+              sparse_float_feature_indices_list[sparse_index].matrix<int64>();
+          QuantileStream stream(epsilon_, batch_size + 1);
+          // Run quantile summary generation.
+          const int64 num_sparse_rows =
+              sparse_float_feature_indices_list[sparse_index].dim_size(0);
+          for (int64 j = 0; j < num_sparse_rows; ++j) {
+            const int64 example_id = sparse_indices(j, 0);
+            stream.PushEntry(sparse_values(j), example_weights(example_id));
+          }
+          stream.Finalize();
+          // Copy summaries to output.
+          copy_over_summaries(stream, sparse_index,
+                              &sparse_summaries_output_list);
+        }
+      }
+    };
+    const int64 kCostPerUnit = 500 * batch_size;
+    const int64 num_features = num_sparse_features_ + num_dense_features_;
+    const DeviceBase::CpuWorkerThreads& worker_threads =
+        *context->device()->tensorflow_cpu_worker_threads();
+    Shard(worker_threads.num_threads, worker_threads.workers, num_features,
+          kCostPerUnit, do_quantile_summary_gen);
+  }
+
+ private:
+  int num_dense_features_;
+  int num_sparse_features_;
+  float epsilon_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("MakeQuantileSummaries").Device(DEVICE_CPU),
+                        MakeQuantileSummariesOp);
+
+// Serializes the state of streams.
+class QuantileAccumulatorSerializeOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorSerializeOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    QuantileStreamResource* streams_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &streams_resource));
+    // Remove the reference at the end of this scope.
+    mutex_lock l(*streams_resource->mutex());
+    core::ScopedUnref unref_me(streams_resource);
+
+    int64 stamp_token = streams_resource->stamp();
+    Tensor* stream_state_t;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(kStreamStateName, TensorShape({}),
+                                            &stream_state_t));
+    bool are_buckets_ready = streams_resource->are_buckets_ready();
+
+    // We are iterating over both dense and sparse features. First we go
+    // through the dense features and then the sparse features.
+    const QuantileStream& stream = *streams_resource->stream(stamp_token);
+    const std::vector<float>& boundaries =
+        are_buckets_ready ? streams_resource->boundaries(stamp_token)
+                          : std::vector<float>();
+    protobuf::Arena arena;
+    ::boosted_trees::QuantileStreamState* stream_proto =
+        protobuf::Arena::CreateMessage<::boosted_trees::QuantileStreamState>(
+            &arena);
+    for (const auto& summary : stream.SerializeInternalSummaries()) {
+      CopySummaryToProto(summary, stream_proto->add_summaries());
+    }
+    stream_proto->SerializeToString(&stream_state_t->scalar<string>()());
+    Tensor* buckets_t = nullptr;
+    OP_REQUIRES_OK(
+        context,
+        context->allocate_output(
+            kBucketsName, {static_cast<int64>(boundaries.size())}, &buckets_t));
+    auto* quantiles_flat = buckets_t->flat<float>().data();
+    memcpy(quantiles_flat, boundaries.data(),
+           sizeof(float) * boundaries.size());
+    Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(kStampTokenName, TensorShape({}),
+                                            &stamp_token_t));
+    stamp_token_t->scalar<int64>()() = stamp_token;
+    Tensor* are_buckets_ready_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(kAreBucketsReadyName, {},
+                                                     &are_buckets_ready_t));
+    are_buckets_ready_t->scalar<bool>()() = are_buckets_ready;
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("QuantileAccumulatorSerialize").Device(DEVICE_CPU),
+                        QuantileAccumulatorSerializeOp);
+
+// Serializes the state of streams.
+class QuantileAccumulatorDeserializeOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorDeserializeOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    QuantileStreamResource* streams_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &streams_resource));
+    // Remove the reference at the end of this scope.
+    mutex_lock l(*streams_resource->mutex());
+    core::ScopedUnref unref_me(streams_resource);
+
+    int64 old_stamp_token = streams_resource->stamp();
+
+    const Tensor* stream_state_t;
+    OP_REQUIRES_OK(context, context->input(kStreamStateName, &stream_state_t));
+    const Tensor* buckets_t;
+    OP_REQUIRES_OK(context, context->input(kBucketsName, &buckets_t));
+
+    QuantileStream* stream = streams_resource->stream(old_stamp_token);
+    ::boosted_trees::QuantileStreamState state_proto;
+    OP_REQUIRES(
+        context,
+        ParseProtoUnlimited(&state_proto, stream_state_t->scalar<string>()()),
+        errors::InvalidArgument("Unabnle to parse quantile stream state."));
+    std::vector<QuantileSummary> summaries;
+    summaries.reserve(state_proto.summaries_size());
+    std::vector<QuantileSummaryEntry> entries;
+    for (const auto& summary : state_proto.summaries()) {
+      entries.clear();
+      entries.reserve(summary.entries_size());
+      for (const auto& entry : summary.entries()) {
+        entries.emplace_back(entry.value(), entry.weight(), entry.min_rank(),
+                             entry.max_rank());
+      }
+      summaries.emplace_back();
+      summaries[summaries.size() - 1].BuildFromSummaryEntries(entries);
+    }
+    stream->DeserializeInternalSummaries(summaries);
+
+    const auto& buckets = buckets_t->vec<float>();
+    std::vector<float> result;
+    result.reserve(buckets.size());
+
+    for (size_t i = 0; i < buckets.size(); ++i) {
+      result.push_back(buckets(i));
+    }
+    streams_resource->set_boundaries(old_stamp_token, result);
+
+    // Reset the stamp token.
+    const Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    streams_resource->set_stamp(stamp_token);
+
+    const Tensor* are_buckets_ready_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->input(kAreBucketsReadyName, &are_buckets_ready_t));
+    streams_resource->set_buckets_ready(are_buckets_ready_t->scalar<bool>()());
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorDeserialize").Device(DEVICE_CPU),
+    QuantileAccumulatorDeserializeOp);
+
+// Adds a summary to the quantile summary stream.
+class QuantileAccumulatorFlushOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorFlushOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    QuantileStreamResource* streams_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &streams_resource));
+    // Remove the reference at the end of this scope.
+    mutex_lock l(*streams_resource->mutex());
+    core::ScopedUnref unref_me(streams_resource);
+
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kNextStampTokenName, &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    CHECK(streams_resource->is_stamp_valid(stamp_token))
+        << "Invalid stamp token in QuantileAccumulatorFlushOp. "
+        << "Passed stamp token: " << stamp_token << " "
+        << "Current token: " << streams_resource->stamp();
+    QuantileStream* stream = streams_resource->stream(stamp_token);
+    stream->Finalize();
+    streams_resource->set_boundaries(
+        stamp_token,
+        GenerateBoundaries(*stream, streams_resource->num_quantiles()));
+    streams_resource->Reset(next_stamp_token);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("QuantileAccumulatorFlush").Device(DEVICE_CPU),
+                        QuantileAccumulatorFlushOp);
+
+// Get bucket boundaries from summaries.
+class QuantileAccumulatorGetBucketsOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorGetBucketsOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* const context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list(kResourceHandlesName,
+                                                &resource_handle_list));
+    OpOutputList are_buckets_ready_list;
+    OP_REQUIRES_OK(context, context->output_list(kAreBucketsReadyName,
+                                                 &are_buckets_ready_list));
+    OpOutputList buckets_list;
+    OP_REQUIRES_OK(context, context->output_list(kBucketsName, &buckets_list));
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &are_buckets_ready_list,
+         &buckets_list, stamp_token](int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+            QuantileStreamResource* streams_resource;
+            OP_REQUIRES_OK(context,
+                           LookupResource(context, handle, &streams_resource));
+            // Remove the reference at the end of this scope.
+            mutex_lock l(*streams_resource->mutex());
+            core::ScopedUnref unref_me(streams_resource);
+
+            bool are_buckets_ready =
+                streams_resource->is_stamp_valid(stamp_token) &&
+                streams_resource->are_buckets_ready();
+
+            Tensor* are_buckets_ready_t = nullptr;
+            OP_REQUIRES_OK(context,
+                           are_buckets_ready_list.allocate(
+                               resource_handle_idx, {}, &are_buckets_ready_t));
+            are_buckets_ready_t->scalar<bool>()() = are_buckets_ready;
+
+            const std::vector<float>& boundaries =
+                are_buckets_ready ? streams_resource->boundaries(stamp_token)
+                                  : std::vector<float>();
+            Tensor* output_t = nullptr;
+            OP_REQUIRES_OK(context, buckets_list.allocate(
+                                        resource_handle_idx,
+                                        {static_cast<int64>(boundaries.size())},
+                                        &output_t));
+            auto* quantiles_flat = output_t->flat<float>().data();
+            memcpy(quantiles_flat, boundaries.data(),
+                   sizeof(float) * boundaries.size());
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorGetBuckets").Device(DEVICE_CPU),
+    QuantileAccumulatorGetBucketsOp);
+
+// Generates buckets for given set of float values, and the given config.
+class QuantileBucketsOp : public OpKernel {
+ public:
+  explicit QuantileBucketsOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context,
+                   ReadAndValidateAttributes(context, &num_dense_features_,
+                                             &num_sparse_features_));
+
+    ParseConfig(context, kDenseConfigName, &dense_configs_);
+    OP_REQUIRES(context, dense_configs_.size() == num_dense_features_,
+                errors::InvalidArgument(
+                    "Mismatch in number of dense quantile configs."));
+    ParseConfig(context, kSparseConfigName, &sparse_configs_);
+    OP_REQUIRES(context, sparse_configs_.size() == num_sparse_features_,
+                errors::InvalidArgument(
+                    "Mismatch in number of sparse quantile configs."));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Parse example weights and get batch size.
+    const Tensor* example_weights_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kExampleWeightsName, &example_weights_t));
+    auto example_weights = example_weights_t->flat<float>();
+    const int64 batch_size = example_weights.size();
+
+    OpOutputList sparse_buckets_output_list;
+    OP_REQUIRES_OK(context, context->output_list(kSparseBucketsName,
+                                                 &sparse_buckets_output_list));
+    OpOutputList dense_buckets_output_list;
+    OP_REQUIRES_OK(context, context->output_list(kDenseBucketsName,
+                                                 &dense_buckets_output_list));
+
+    auto do_quantile_bucket_gen = [&](const int64 begin, const int64 end) {
+      // These are blocks of ranges. We are iterating over both sparse and
+      // dense features i.e. [0, sparse_features.size() + dense_features.size()]
+      for (int64 i = begin; i < end; ++i) {
+        if (i < sparse_configs_.size()) {
+          const int64 sparse_index = i;
+          const auto sparse_values =
+              sparse_float_feature_values_list[sparse_index].flat<float>();
+          const auto sparse_indices =
+              sparse_float_feature_indices_list[sparse_index].matrix<int64>();
+          QuantileStream stream(sparse_configs_[sparse_index].eps(),
+                                batch_size);
+          // Run quantile summary generation.
+          const int64 num_sparse_rows =
+              sparse_float_feature_indices_list[sparse_index].dim_size(0);
+          for (int64 j = 0; j < num_sparse_rows; ++j) {
+            const int64 example_id = sparse_indices(j, 0);
+            stream.PushEntry(sparse_values(j), example_weights(example_id));
+          }
+          stream.Finalize();
+          // Create buckets.
+          const auto boundaries = GenerateBoundaries(
+              stream, sparse_configs_[sparse_index].num_quantiles());
+          CopyBoundaries(context, boundaries, sparse_index,
+                         &sparse_buckets_output_list);
+
+        } else {
+          const int64 dense_index = i - sparse_configs_.size();
+          const auto dense_values =
+              dense_float_features_list[dense_index].flat<float>();
+          QuantileStream stream(dense_configs_[dense_index].eps(), batch_size);
+          // Run quantile summary generation.
+          for (int64 j = 0; j < batch_size; ++j) {
+            stream.PushEntry(dense_values(j), example_weights(j));
+          }
+          stream.Finalize();
+          // Create buckets.
+          const auto boundaries = GenerateBoundaries(
+              stream, dense_configs_[dense_index].num_quantiles());
+          CopyBoundaries(context, boundaries, dense_index,
+                         &dense_buckets_output_list);
+        }
+      }
+    };
+
+    const int64 kCostPerUnit = 500 * batch_size;
+    const int64 num_features = sparse_configs_.size() + dense_configs_.size();
+    const DeviceBase::CpuWorkerThreads& worker_threads =
+        *context->device()->tensorflow_cpu_worker_threads();
+    Shard(worker_threads.num_threads, worker_threads.workers, num_features,
+          kCostPerUnit, do_quantile_bucket_gen);
+  }
+
+ private:
+  int num_dense_features_;
+  int num_sparse_features_;
+  std::vector<QuantileConfig> dense_configs_;
+  std::vector<QuantileConfig> sparse_configs_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("QuantileBuckets").Device(DEVICE_CPU),
+                        QuantileBucketsOp);
+
+// Given the calculated quantiles thresholds and input data, this operation
+// converts the input features into the buckets (categorical values), depending
+// on which quantile they fall into.
+class QuantilesOp : public OpKernel {
+ public:
+  explicit QuantilesOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    int num_dense_features;
+    int num_sparse_features;
+    OP_REQUIRES_OK(context,
+                   ReadAndValidateAttributes(context, &num_dense_features,
+                                             &num_sparse_features));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Dense features inputs
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, context->input_list(kDenseValuesName,
+                                                &dense_float_features_list));
+    OpInputList dense_buckets_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list(kDenseBucketsName, &dense_buckets_list));
+
+    if (dense_buckets_list.size() > 0) {
+      // Check the first tensor to make sure it is the right shape
+      OP_REQUIRES(
+          context,
+          tensorflow::TensorShapeUtils::IsVector(dense_buckets_list[0].shape()),
+          errors::InvalidArgument(
+              strings::Printf("Dense buckets should be flat vectors")));
+    }
+
+    // Sparse features inputs
+    OpInputList sparse_float_feature_values_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list(kSparseValuesName,
+                                       &sparse_float_feature_values_list));
+    OpInputList sparse_buckets_list;
+    OP_REQUIRES_OK(
+        context, context->input_list(kSparseBucketsName, &sparse_buckets_list));
+
+    if (sparse_buckets_list.size() > 0) {
+      OP_REQUIRES(
+          context,
+          tensorflow::TensorShapeUtils::IsVector(
+              sparse_buckets_list[0].shape()),
+          errors::InvalidArgument("Sparse buckets should be flat vectors"));
+    }
+
+    // Quantize the feature values
+    QuantizeFeatures(kDenseOutputTensorName, dense_float_features_list,
+                     dense_buckets_list, context);
+
+    QuantizeFeatures(kSparseOutputTensorName, sparse_float_feature_values_list,
+                     sparse_buckets_list, context);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("Quantiles").Device(DEVICE_CPU), QuantilesOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
new file mode 100644
index 0000000000..6e73db9e1d
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
@@ -0,0 +1,510 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+#include "tensorflow/contrib/boosted_trees/proto/split_info.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/core/framework/device_base.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/random/random.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+using boosted_trees::learner::SplitInfo;
+using boosted_trees::learner::stochastic::GradientStats;
+using boosted_trees::learner::stochastic::NodeStats;
+
+class BaseBuildSplitOp : public OpKernel {
+ public:
+  explicit BaseBuildSplitOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(
+        context,
+        context->GetAttr("feature_column_group_id", &feature_column_group_id_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("l1_regularization", &l1_regularization_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("l2_regularization", &l2_regularization_));
+    OP_REQUIRES_OK(context, context->GetAttr("tree_complexity_regularization",
+                                             &tree_complexity_regularization_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("min_node_weight", &min_node_weight_));
+  }
+
+  NodeStats ComputeNodeStats(const GradientStats& grad_stats) {
+    return NodeStats(
+        l1_regularization_, l2_regularization_, min_node_weight_,
+        boosted_trees::learner::LearnerConfig_MultiClassStrategy_TREE_PER_CLASS,
+        grad_stats);
+  }
+
+ protected:
+  int32 feature_column_group_id_;
+  float l1_regularization_;
+  float l2_regularization_;
+  float min_node_weight_;
+  float tree_complexity_regularization_;
+};
+
+class BuildDenseInequalitySplitsOp : public BaseBuildSplitOp {
+ public:
+  explicit BuildDenseInequalitySplitsOp(OpKernelConstruction* const context)
+      : BaseBuildSplitOp(context) {}
+
+  void Compute(OpKernelContext* const context) override {
+    const Tensor* num_minibatches_t;
+    OP_REQUIRES_OK(context,
+                   context->input("num_minibatches", &num_minibatches_t));
+    const int64 num_minibatches = num_minibatches_t->scalar<int64>()();
+    const float normalizer_ratio = (1.0f / num_minibatches);
+
+    const Tensor* bucket_boundaries_t;
+    OP_REQUIRES_OK(context,
+                   context->input("bucket_boundaries", &bucket_boundaries_t));
+    const auto& bucket_boundaries = bucket_boundaries_t->vec<float>();
+
+    const Tensor* partition_ids_t;
+    OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+    const auto& partition_ids = partition_ids_t->vec<int32>();
+
+    const Tensor* bucket_ids_t;
+    OP_REQUIRES_OK(context, context->input("bucket_ids", &bucket_ids_t));
+    const auto& bucket_ids = bucket_ids_t->vec<int64>();
+
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+
+    // Find the number of unique partitions before we allocate the output.
+    std::vector<int32> partition_boundaries;
+    partition_boundaries.push_back(0);
+    for (int i = 1; i < partition_ids.size(); ++i) {
+      if (partition_ids(i) != partition_ids(i - 1)) {
+        // Make sure the input is sorted by partition_ids;
+        OP_REQUIRES(context, partition_ids(i) >= partition_ids(i - 1),
+                    errors::InvalidArgument("Input should be sorted."));
+        partition_boundaries.push_back(i);
+      }
+    }
+    if (partition_ids.size() > 0) {
+      partition_boundaries.push_back(partition_ids.size());
+    }
+    int32 num_elements = partition_boundaries.size() - 1;
+
+    // When the handler is inactive, no bucket boundaries are built for it.
+    if (bucket_boundaries.size() == 0) {
+      num_elements = 0;
+    }
+
+    Tensor* output_partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("output_partition_ids",
+                                            TensorShape({num_elements}),
+                                            &output_partition_ids_t));
+
+    tensorflow::TTypes<int32>::Vec output_partition_ids =
+        output_partition_ids_t->vec<int32>();
+
+    Tensor* gains_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("gains", TensorShape({num_elements}),
+                                          &gains_t));
+
+    tensorflow::TTypes<float>::Vec gains = gains_t->vec<float>();
+
+    Tensor* output_splits_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "split_infos", TensorShape({num_elements}),
+                                &output_splits_t));
+    tensorflow::TTypes<string>::Vec output_splits =
+        output_splits_t->vec<string>();
+    for (int root_idx = 0; root_idx < num_elements; ++root_idx) {
+      float best_gain = std::numeric_limits<float>::lowest();
+      int start_index = partition_boundaries[root_idx];
+      int end_index = partition_boundaries[root_idx + 1];
+      GradientStats root_gradient_stats;
+      for (int64 bucket_idx = start_index; bucket_idx < end_index;
+           ++bucket_idx) {
+        root_gradient_stats +=
+            GradientStats(*gradients_t, *hessians_t, bucket_idx);
+      }
+      root_gradient_stats *= normalizer_ratio;
+      NodeStats root_stats = ComputeNodeStats(root_gradient_stats);
+      int32 best_bucket_idx = 0;
+      NodeStats best_right_node_stats(0);
+      NodeStats best_left_node_stats(0);
+      GradientStats left_gradient_stats(0, 0);
+      for (int64 bucket_idx = start_index; bucket_idx < end_index;
+           ++bucket_idx) {
+        GradientStats g(*gradients_t, *hessians_t, bucket_idx);
+        g *= normalizer_ratio;
+        left_gradient_stats += g;
+        NodeStats left_stats = ComputeNodeStats(left_gradient_stats);
+        GradientStats right_gradient_stats =
+            root_gradient_stats - left_gradient_stats;
+        NodeStats right_stats = ComputeNodeStats(right_gradient_stats);
+        if (left_stats.gain + right_stats.gain > best_gain) {
+          best_gain = left_stats.gain + right_stats.gain;
+          best_left_node_stats = left_stats;
+          best_right_node_stats = right_stats;
+          best_bucket_idx = bucket_idx;
+        }
+      }
+      SplitInfo split_info;
+      auto* dense_split =
+          split_info.mutable_split_node()->mutable_dense_float_binary_split();
+      dense_split->set_feature_column(feature_column_group_id_);
+      dense_split->set_threshold(
+          bucket_boundaries(bucket_ids(best_bucket_idx)));
+
+      auto* left_child = split_info.mutable_left_child();
+      auto* right_child = split_info.mutable_right_child();
+      for (float f : best_left_node_stats.weight_contribution) {
+        left_child->mutable_vector()->add_value(f);
+      }
+      for (float f : best_right_node_stats.weight_contribution) {
+        right_child->mutable_vector()->add_value(f);
+      }
+      split_info.SerializeToString(&output_splits(root_idx));
+      gains(root_idx) =
+          best_gain - root_stats.gain - tree_complexity_regularization_;
+      output_partition_ids(root_idx) = partition_ids(start_index);
+    }
+  }
+};
+REGISTER_KERNEL_BUILDER(Name("BuildDenseInequalitySplits").Device(DEVICE_CPU),
+                        BuildDenseInequalitySplitsOp);
+
+class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
+ public:
+  explicit BuildSparseInequalitySplitsOp(OpKernelConstruction* const context)
+      : BaseBuildSplitOp(context) {
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("bias_feature_id", &bias_feature_id_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    const Tensor* num_minibatches_t;
+    OP_REQUIRES_OK(context,
+                   context->input("num_minibatches", &num_minibatches_t));
+    const int64 num_minibatches = num_minibatches_t->scalar<int64>()();
+    const float normalizer_ratio = (1.0f / num_minibatches);
+
+    const Tensor* bucket_boundaries_t;
+    OP_REQUIRES_OK(context,
+                   context->input("bucket_boundaries", &bucket_boundaries_t));
+    const auto& bucket_boundaries = bucket_boundaries_t->vec<float>();
+
+    const Tensor* partition_ids_t;
+    OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+    const auto& partition_ids = partition_ids_t->vec<int32>();
+
+    const Tensor* bucket_ids_t;
+    OP_REQUIRES_OK(context, context->input("bucket_ids", &bucket_ids_t));
+    const auto& bucket_ids = bucket_ids_t->vec<int64>();
+
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+
+    // Find the number of unique partitions before we allocate the output.
+    std::vector<int32> partition_boundaries;
+    std::vector<int32> non_empty_partitions;
+    for (int i = 0; i < partition_ids.size() - 1; ++i) {
+      // Make sure the input is sorted by partition_ids;
+      CHECK_LE(partition_ids(i), partition_ids(i + 1));
+      if (i == 0 || partition_ids(i) != partition_ids(i - 1)) {
+        partition_boundaries.push_back(i);
+        // Some partitions might only have bias feature. We don't want to split
+        // those so check that the partition has at least 2 buckets.
+        if (partition_ids(i) == partition_ids(i + 1)) {
+          non_empty_partitions.push_back(partition_boundaries.size() - 1);
+        }
+      }
+    }
+    if (partition_ids.size() > 0) {
+      partition_boundaries.push_back(partition_ids.size());
+    }
+    int num_elements = non_empty_partitions.size();
+    Tensor* output_partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("output_partition_ids",
+                                            TensorShape({num_elements}),
+                                            &output_partition_ids_t));
+
+    tensorflow::TTypes<int32>::Vec output_partition_ids =
+        output_partition_ids_t->vec<int32>();
+
+    Tensor* gains_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("gains", TensorShape({num_elements}),
+                                          &gains_t));
+
+    tensorflow::TTypes<float>::Vec gains = gains_t->vec<float>();
+
+    Tensor* output_splits_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "split_infos", TensorShape({num_elements}),
+                                &output_splits_t));
+    tensorflow::TTypes<string>::Vec output_splits =
+        output_splits_t->vec<string>();
+    for (int root_idx = 0; root_idx < num_elements; ++root_idx) {
+      float best_gain = std::numeric_limits<float>::lowest();
+      int start_index = partition_boundaries[non_empty_partitions[root_idx]];
+      int end_index = partition_boundaries[non_empty_partitions[root_idx] + 1];
+      // First bucket ID in each partition should be the bias feature.
+      OP_REQUIRES(context, bucket_ids(start_index) == bias_feature_id_,
+                  errors::InvalidArgument("Bias feature ID missing."));
+      // For each root, we do two passes over the quantized feature buckets
+      // accumulating gradients on one side and using the root aggregate
+      // gradients to get the gradients for the other side.
+      // Split gains are evaluated for each pass at every threshold and the best
+      // split is picked.
+      GradientStats root_gradient_stats(*gradients_t, *hessians_t, start_index);
+      root_gradient_stats *= normalizer_ratio;
+      NodeStats root_stats = ComputeNodeStats(root_gradient_stats);
+      GradientStats present_gradient_stats;
+      for (int64 bucket_idx = start_index + 1; bucket_idx < end_index;
+           ++bucket_idx) {
+        present_gradient_stats +=
+            GradientStats(*gradients_t, *hessians_t, bucket_idx);
+      }
+      present_gradient_stats *= normalizer_ratio;
+      int32 best_bucket_idx = 0;
+      NodeStats best_right_node_stats(0);
+      NodeStats best_left_node_stats(0);
+      GradientStats left_gradient_stats(0, 0);
+      bool default_right = false;
+      for (int64 bucket_idx = start_index + 1; bucket_idx < end_index;
+           ++bucket_idx) {
+        GradientStats g(*gradients_t, *hessians_t, bucket_idx);
+        g *= normalizer_ratio;
+        left_gradient_stats += g;
+        // We have the sum of all present gradients. Use that to compute the
+        // backward pass gradients.
+        GradientStats right_gradient_stats =
+            present_gradient_stats - left_gradient_stats;
+        {
+          NodeStats left_stats_default_left =
+              ComputeNodeStats(root_gradient_stats - right_gradient_stats);
+          NodeStats right_stats_default_left =
+              ComputeNodeStats(right_gradient_stats);
+          if (left_stats_default_left.gain + right_stats_default_left.gain >
+              best_gain) {
+            best_gain =
+                left_stats_default_left.gain + right_stats_default_left.gain;
+            best_left_node_stats = left_stats_default_left;
+            best_right_node_stats = right_stats_default_left;
+            best_bucket_idx = bucket_idx;
+            default_right = false;
+          }
+        }
+        {
+          NodeStats left_stats_default_right =
+              ComputeNodeStats(left_gradient_stats);
+          NodeStats right_stats_default_right =
+              ComputeNodeStats(root_gradient_stats - left_gradient_stats);
+          if (left_stats_default_right.gain + right_stats_default_right.gain >
+              best_gain) {
+            best_gain =
+                left_stats_default_right.gain + right_stats_default_right.gain;
+            best_left_node_stats = left_stats_default_right;
+            best_right_node_stats = right_stats_default_right;
+            best_bucket_idx = bucket_idx;
+            default_right = true;
+          }
+        }
+      }
+      SplitInfo split_info;
+      boosted_trees::trees::DenseFloatBinarySplit* dense_split = nullptr;
+      if (default_right) {
+        dense_split = split_info.mutable_split_node()
+                          ->mutable_sparse_float_binary_split_default_right()
+                          ->mutable_split();
+      } else {
+        dense_split = split_info.mutable_split_node()
+                          ->mutable_sparse_float_binary_split_default_left()
+                          ->mutable_split();
+      }
+      dense_split->set_feature_column(feature_column_group_id_);
+      dense_split->set_threshold(
+          bucket_boundaries(bucket_ids(best_bucket_idx)));
+
+      auto* left_child = split_info.mutable_left_child();
+      auto* right_child = split_info.mutable_right_child();
+      for (float f : best_left_node_stats.weight_contribution) {
+        left_child->mutable_vector()->add_value(f);
+      }
+      for (float f : best_right_node_stats.weight_contribution) {
+        right_child->mutable_vector()->add_value(f);
+      }
+      split_info.SerializeToString(&output_splits(root_idx));
+      gains(root_idx) =
+          best_gain - root_stats.gain - tree_complexity_regularization_;
+      output_partition_ids(root_idx) = partition_ids(start_index);
+    }
+  }
+
+ private:
+  int64 bias_feature_id_;
+};
+REGISTER_KERNEL_BUILDER(Name("BuildSparseInequalitySplits").Device(DEVICE_CPU),
+                        BuildSparseInequalitySplitsOp);
+
+class BuildCategoricalEqualitySplitsOp : public BaseBuildSplitOp {
+ public:
+  explicit BuildCategoricalEqualitySplitsOp(OpKernelConstruction* const context)
+      : BaseBuildSplitOp(context) {
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("bias_feature_id", &bias_feature_id_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    const Tensor* num_minibatches_t;
+    OP_REQUIRES_OK(context,
+                   context->input("num_minibatches", &num_minibatches_t));
+    const int64 num_minibatches = num_minibatches_t->scalar<int64>()();
+    const float normalizer_ratio = (1.0f / num_minibatches);
+
+    const Tensor* partition_ids_t;
+    OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+    const auto& partition_ids = partition_ids_t->vec<int32>();
+
+    const Tensor* feature_ids_t;
+    OP_REQUIRES_OK(context, context->input("feature_ids", &feature_ids_t));
+    const auto& feature_ids = feature_ids_t->vec<int64>();
+
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+
+    // Find the number of unique partitions before we allocate the output.
+    std::vector<int32> partition_boundaries;
+    std::vector<int32> non_empty_partitions;
+    for (int i = 0; i < partition_ids.size() - 1; ++i) {
+      // Make sure the input is sorted by partition_ids;
+      CHECK_LE(partition_ids(i), partition_ids(i + 1));
+      if (i == 0 || partition_ids(i) != partition_ids(i - 1)) {
+        partition_boundaries.push_back(i);
+        // Some partitions might only have bias feature. We don't want to split
+        // those so check that the partition has at least 2 features.
+        if (partition_ids(i) == partition_ids(i + 1)) {
+          non_empty_partitions.push_back(partition_boundaries.size() - 1);
+        }
+      }
+    }
+    if (partition_ids.size() > 0) {
+      partition_boundaries.push_back(partition_ids.size());
+    }
+    int num_elements = non_empty_partitions.size();
+    Tensor* output_partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("output_partition_ids",
+                                            TensorShape({num_elements}),
+                                            &output_partition_ids_t));
+
+    tensorflow::TTypes<int32>::Vec output_partition_ids =
+        output_partition_ids_t->vec<int32>();
+
+    Tensor* gains_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("gains", TensorShape({num_elements}),
+                                          &gains_t));
+
+    tensorflow::TTypes<float>::Vec gains = gains_t->vec<float>();
+
+    Tensor* output_splits_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "split_infos", TensorShape({num_elements}),
+                                &output_splits_t));
+    tensorflow::TTypes<string>::Vec output_splits =
+        output_splits_t->vec<string>();
+    for (int root_idx = 0; root_idx < num_elements; ++root_idx) {
+      float best_gain = std::numeric_limits<float>::lowest();
+      int start_index = partition_boundaries[non_empty_partitions[root_idx]];
+      int end_index = partition_boundaries[non_empty_partitions[root_idx] + 1];
+      // First feature ID in each partition should be the bias feature.
+      OP_REQUIRES(context, feature_ids(start_index) == bias_feature_id_,
+                  errors::InvalidArgument("Bias feature ID missing."));
+      GradientStats root_gradient_stats(*gradients_t, *hessians_t, start_index);
+      root_gradient_stats *= normalizer_ratio;
+      NodeStats root_stats = ComputeNodeStats(root_gradient_stats);
+      int32 best_feature_idx = 0;
+      NodeStats best_right_node_stats(0);
+      NodeStats best_left_node_stats(0);
+      for (int64 feature_idx = start_index + 1; feature_idx < end_index;
+           ++feature_idx) {
+        GradientStats left_gradient_stats(*gradients_t, *hessians_t,
+                                          feature_idx);
+        left_gradient_stats *= normalizer_ratio;
+        GradientStats right_gradient_stats =
+            root_gradient_stats - left_gradient_stats;
+        NodeStats left_stats = ComputeNodeStats(left_gradient_stats);
+        NodeStats right_stats = ComputeNodeStats(right_gradient_stats);
+        if (left_stats.gain + right_stats.gain > best_gain) {
+          best_gain = left_stats.gain + right_stats.gain;
+          best_left_node_stats = left_stats;
+          best_right_node_stats = right_stats;
+          best_feature_idx = feature_idx;
+        }
+      }
+      SplitInfo split_info;
+      auto* equality_split = split_info.mutable_split_node()
+                                 ->mutable_categorical_id_binary_split();
+      equality_split->set_feature_column(feature_column_group_id_);
+      equality_split->set_feature_id(feature_ids(best_feature_idx));
+      auto* left_child = split_info.mutable_left_child();
+      auto* right_child = split_info.mutable_right_child();
+      for (float f : best_left_node_stats.weight_contribution) {
+        left_child->mutable_vector()->add_value(f);
+      }
+      for (float f : best_right_node_stats.weight_contribution) {
+        right_child->mutable_vector()->add_value(f);
+      }
+      split_info.SerializeToString(&output_splits(root_idx));
+      gains(root_idx) =
+          best_gain - root_stats.gain - tree_complexity_regularization_;
+      output_partition_ids(root_idx) = partition_ids(start_index);
+    }
+  }
+
+ private:
+  int64 bias_feature_id_;
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("BuildCategoricalEqualitySplits").Device(DEVICE_CPU),
+    BuildCategoricalEqualitySplitsOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc b/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc
new file mode 100644
index 0000000000..cff75e71d9
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc
@@ -0,0 +1,784 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <algorithm>
+#include <iterator>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/utils/parallel_for.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/resources/stamped_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+
+namespace {
+const char* const kStampTokenName = "stamp_token";
+const char* const kNextStampTokenName = "next_stamp_token";
+
+struct PartitionKey {
+  PartitionKey() : partition_id(-1), feature_id(-1) {}
+
+  PartitionKey(int32 p, int64 f) : partition_id(p), feature_id(f) {}
+
+  bool operator==(const PartitionKey& other) const {
+    return (feature_id == other.feature_id) &&
+           (partition_id == other.partition_id);
+  }
+
+  // Compare for PartitionKey.
+  struct Less {
+    bool operator()(const PartitionKey& a, const PartitionKey& b) const {
+      if (a.partition_id < b.partition_id) {
+        return true;
+      }
+      if ((a.partition_id == b.partition_id) && (a.feature_id < b.feature_id)) {
+        return true;
+      }
+      return false;
+    }
+  };
+
+  // Tree partition defined by traversing the tree to the leaf.
+  int32 partition_id;
+
+  // Feature Id within the feature column.
+  int64 feature_id;
+};
+
+template <typename GradientType, typename HessianType>
+class StatsAccumulatorResource : public boosted_trees::StampedResource {
+  using StatsByPartition =
+      std::map<PartitionKey, std::pair<GradientType, HessianType>,
+               PartitionKey::Less>;
+
+ public:
+  StatsAccumulatorResource(const TensorShape& gradient_shape,
+                           const TensorShape& hessian_shape)
+      : gradient_shape_(gradient_shape),
+        hessian_shape_(hessian_shape),
+        num_updates_(0) {
+    // If GradientType/HessianType is scalar float then the shapes should be
+    // scalar and vice versa.
+    CHECK_EQ((std::is_same<GradientType, float>::value),
+             TensorShapeUtils::IsScalar(gradient_shape));
+    CHECK_EQ((std::is_same<HessianType, float>::value),
+             TensorShapeUtils::IsScalar(hessian_shape));
+  }
+
+  string DebugString() override {
+    return strings::StrCat("StatsAccumulatorResource[size=", values_.size(),
+                           "]");
+  }
+
+  void Clear() {
+    values_.clear();
+    num_updates_ = 0;
+  }
+
+  tensorflow::mutex* mutex() { return &mu_; }
+  StatsByPartition* mutable_values() { return &values_; }
+  const StatsByPartition& values() const { return values_; }
+  const int64& num_updates() const { return num_updates_; }
+  void set_num_updates(int64 val) { num_updates_ = val; }
+  const TensorShape& gradient_shape() const { return gradient_shape_; }
+  const TensorShape& hessian_shape() const { return hessian_shape_; }
+
+ private:
+  // Key into a specific partition to accumulate stats for the specified feature
+  // id.
+  StatsByPartition values_;
+  const TensorShape gradient_shape_;
+  const TensorShape hessian_shape_;
+  int64 num_updates_;
+  tensorflow::mutex mu_;
+  TF_DISALLOW_COPY_AND_ASSIGN(StatsAccumulatorResource);
+};
+
+using StatsAccumulatorScalarResource = StatsAccumulatorResource<float, float>;
+using StatsAccumulatorTensorResource =
+    StatsAccumulatorResource<std::vector<float>, std::vector<float>>;
+
+void SerializeScalarAccumulatorToOutput(
+    const StatsAccumulatorScalarResource& accumulator_resource,
+    OpKernelContext* context) {
+  int64 num_slots = accumulator_resource.values().size();
+  Tensor* partition_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_partition_ids", TensorShape({num_slots}),
+                               &partition_ids_t));
+  auto partition_ids = partition_ids_t->vec<int32>();
+
+  Tensor* feature_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_feature_ids", TensorShape({num_slots}),
+                               &feature_ids_t));
+  auto feature_ids = feature_ids_t->vec<int64>();
+
+  Tensor* gradients_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_gradients", TensorShape({num_slots}),
+                               &gradients_t));
+  auto gradients = gradients_t->vec<float>();
+
+  Tensor* hessians_t = nullptr;
+  OP_REQUIRES_OK(context,
+                 context->allocate_output(
+                     "output_hessians", TensorShape({num_slots}), &hessians_t));
+  auto hessians = hessians_t->vec<float>();
+
+  int i = 0;
+  for (const auto& iter : accumulator_resource.values()) {
+    partition_ids(i) = iter.first.partition_id;
+    feature_ids(i) = iter.first.feature_id;
+    gradients(i) = iter.second.first;
+    hessians(i) = iter.second.second;
+    ++i;
+  }
+}
+
+void SerializeTensorAccumulatorToOutput(
+    const StatsAccumulatorTensorResource& accumulator_resource,
+    OpKernelContext* context) {
+  int64 num_slots = accumulator_resource.values().size();
+  Tensor* partition_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_partition_ids", TensorShape({num_slots}),
+                               &partition_ids_t));
+  auto partition_ids = partition_ids_t->vec<int32>();
+
+  Tensor* feature_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_feature_ids", TensorShape({num_slots}),
+                               &feature_ids_t));
+  auto feature_ids = feature_ids_t->vec<int64>();
+
+  TensorShape gradient_shape = accumulator_resource.gradient_shape();
+  int64 num_gradient_elements = gradient_shape.num_elements();
+  gradient_shape.InsertDim(0, num_slots);
+  Tensor* gradients_t = nullptr;
+  OP_REQUIRES_OK(context,
+                 context->allocate_output("output_gradients", gradient_shape,
+                                          &gradients_t));
+  auto gradients = gradients_t->flat_outer_dims<float>();
+
+  TensorShape hessian_shape = accumulator_resource.hessian_shape();
+  int64 num_hessian_elements = hessian_shape.num_elements();
+  hessian_shape.InsertDim(0, num_slots);
+  Tensor* hessians_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_hessians", hessian_shape, &hessians_t));
+  auto hessians = hessians_t->flat_outer_dims<float>();
+
+  int i = 0;
+  for (const auto& iter : accumulator_resource.values()) {
+    partition_ids(i) = iter.first.partition_id;
+    feature_ids(i) = iter.first.feature_id;
+    for (int j = 0; j < num_gradient_elements; ++j) {
+      gradients(i, j) = iter.second.first[j];
+    }
+    for (int j = 0; j < num_hessian_elements; ++j) {
+      hessians(i, j) = iter.second.second[j];
+    }
+    ++i;
+  }
+}
+
+void AddToScalarAccumulator(
+    StatsAccumulatorScalarResource* accumulator_resource,
+    const Tensor& partition_ids_t, const Tensor& feature_ids_t,
+    const Tensor& gradients_t, const Tensor& hessians_t) {
+  accumulator_resource->set_num_updates(accumulator_resource->num_updates() +
+                                        1);
+  const TensorShape& partition_ids_shape = partition_ids_t.shape();
+  const auto& partition_ids = partition_ids_t.vec<int32>();
+  const auto& feature_ids = feature_ids_t.vec<int64>();
+  const auto& gradients = gradients_t.vec<float>();
+  const auto& hessians = hessians_t.vec<float>();
+
+  int64 num_updates = partition_ids_shape.dim_size(0);
+  auto stats_map = accumulator_resource->mutable_values();
+  for (int64 i = 0; i < num_updates; ++i) {
+    const auto key = PartitionKey(partition_ids(i), feature_ids(i));
+    auto itr = stats_map->find(key);
+    if (itr != stats_map->end()) {
+      itr->second.first += gradients(i);
+      itr->second.second += hessians(i);
+    } else {
+      (*stats_map)[key] = {gradients(i), hessians(i)};
+    }
+  }
+}
+
+void AddToScalarAccumulator(
+    StatsAccumulatorScalarResource* accumulator_resource,
+    OpKernelContext* context) {
+  const Tensor* partition_ids_t;
+  OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+  const Tensor* feature_ids_t;
+  OP_REQUIRES_OK(context, context->input("feature_ids", &feature_ids_t));
+  const Tensor* gradients_t;
+  OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+  const Tensor* hessians_t;
+  OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+  AddToScalarAccumulator(accumulator_resource, *partition_ids_t, *feature_ids_t,
+                         *gradients_t, *hessians_t);
+}
+
+void AddToTensorAccumulator(
+    StatsAccumulatorTensorResource* accumulator_resource,
+    const Tensor& partition_ids_t, const Tensor& feature_ids_t,
+    const Tensor& gradients_t, const Tensor& hessians_t,
+    OpKernelContext* context) {
+  accumulator_resource->set_num_updates(accumulator_resource->num_updates() +
+                                        1);
+
+  const TensorShape& partition_ids_shape = partition_ids_t.shape();
+  const auto& partition_ids = partition_ids_t.vec<int32>();
+  const auto& feature_ids = feature_ids_t.vec<int64>();
+  TensorShape gradients_shape = gradients_t.shape();
+  const auto& gradients = gradients_t.flat_outer_dims<float>();
+  TensorShape hessians_shape = hessians_t.shape();
+  const auto& hessians = hessians_t.flat_outer_dims<float>();
+
+  gradients_shape.RemoveDim(0);
+  hessians_shape.RemoveDim(0);
+
+  // TODO(soroush): Move gradient and hessian shape check to ShapeFn.
+  OP_REQUIRES(
+      context, gradients_shape == accumulator_resource->gradient_shape(),
+      errors::InvalidArgument(strings::StrCat(
+          "Gradients dimensions must match: ", gradients_shape.DebugString(),
+          ", ", accumulator_resource->gradient_shape().DebugString())));
+
+  OP_REQUIRES(
+      context, hessians_shape == accumulator_resource->hessian_shape(),
+      errors::InvalidArgument(strings::StrCat(
+          "Hessian dimensions must match: ", hessians_shape.DebugString(), ", ",
+          accumulator_resource->hessian_shape().DebugString())));
+
+  int64 num_updates = partition_ids_shape.dim_size(0);
+  auto stats_map = accumulator_resource->mutable_values();
+  for (int64 i = 0; i < num_updates; ++i) {
+    const auto key = PartitionKey(partition_ids(i), feature_ids(i));
+    auto itr = stats_map->find(key);
+    if (itr == stats_map->end()) {
+      std::vector<float> new_gradients(gradients_shape.num_elements());
+      for (int j = 0; j < gradients_shape.num_elements(); ++j) {
+        new_gradients[j] = gradients(i, j);
+      }
+      std::vector<float> new_hessians(hessians_shape.num_elements());
+      for (int j = 0; j < hessians_shape.num_elements(); ++j) {
+        new_hessians[j] = hessians(i, j);
+      }
+      (*stats_map)[key] = {new_gradients, new_hessians};
+    } else {
+      auto& stored_gradients = itr->second.first;
+      for (int j = 0; j < gradients_shape.num_elements(); ++j) {
+        stored_gradients[j] += gradients(i, j);
+      }
+      auto& stored_hessians = itr->second.second;
+      for (int j = 0; j < hessians_shape.num_elements(); ++j) {
+        stored_hessians[j] += hessians(i, j);
+      }
+    }
+  }
+}
+
+void AddToTensorAccumulator(
+    StatsAccumulatorTensorResource* accumulator_resource,
+    OpKernelContext* context) {
+  const Tensor* partition_ids_t;
+  OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+  const Tensor* feature_ids_t;
+  OP_REQUIRES_OK(context, context->input("feature_ids", &feature_ids_t));
+  const Tensor* gradients_t;
+  OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+  const Tensor* hessians_t;
+  OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+  AddToTensorAccumulator(accumulator_resource, *partition_ids_t, *feature_ids_t,
+                         *gradients_t, *hessians_t, context);
+}
+
+}  // namespace
+
+REGISTER_RESOURCE_HANDLE_KERNEL(StatsAccumulatorScalarResource);
+REGISTER_RESOURCE_HANDLE_KERNEL(StatsAccumulatorTensorResource);
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarIsInitialized").Device(DEVICE_CPU),
+    IsResourceInitialized<StatsAccumulatorScalarResource>);
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorIsInitialized").Device(DEVICE_CPU),
+    IsResourceInitialized<StatsAccumulatorTensorResource>);
+
+class CreateStatsAccumulatorScalarOp : public OpKernel {
+ public:
+  explicit CreateStatsAccumulatorScalarOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+
+    TensorShape gradient_shape = TensorShape({});
+    TensorShape hessian_shape = TensorShape({});
+
+    auto* result =
+        new StatsAccumulatorScalarResource(gradient_shape, hessian_shape);
+    result->set_stamp(stamp_token_t->scalar<int64>()());
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions. If one already exists, it unrefs the new one.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("CreateStatsAccumulatorScalar").Device(DEVICE_CPU),
+                        CreateStatsAccumulatorScalarOp);
+
+class CreateStatsAccumulatorTensorOp : public OpKernel {
+ public:
+  explicit CreateStatsAccumulatorTensorOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+
+    const Tensor* gradient_shape_t;
+    OP_REQUIRES_OK(
+        context, context->input("per_slot_gradient_shape", &gradient_shape_t));
+
+    const Tensor* hessian_shape_t;
+    OP_REQUIRES_OK(context,
+                   context->input("per_slot_hessian_shape", &hessian_shape_t));
+    TensorShape gradient_shape = TensorShape(gradient_shape_t->vec<int64>());
+    TensorShape hessian_shape = TensorShape(hessian_shape_t->vec<int64>());
+    auto* result =
+        new StatsAccumulatorTensorResource(gradient_shape, hessian_shape);
+    result->set_stamp(stamp_token_t->scalar<int64>()());
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions. If one already exists, it unrefs the new one.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("CreateStatsAccumulatorTensor").Device(DEVICE_CPU),
+                        CreateStatsAccumulatorTensorOp);
+
+class StatsAccumulatorScalarAddOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarAddOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list("stats_accumulator_handles",
+                                                &resource_handle_list));
+    OpInputList partition_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("partition_ids", &partition_ids_list));
+
+    OpInputList feature_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("feature_ids", &feature_ids_list));
+    OpInputList gradients_list;
+    OP_REQUIRES_OK(context, context->input_list("gradients", &gradients_list));
+    OpInputList hessians_list;
+    OP_REQUIRES_OK(context, context->input_list("hessians", &hessians_list));
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &partition_ids_list,
+         &feature_ids_list, &gradients_list, &hessians_list,
+         stamp_token](int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+
+            StatsAccumulatorScalarResource* accumulator_resource;
+            OP_REQUIRES_OK(context, LookupResource(context, handle,
+                                                   &accumulator_resource));
+            mutex_lock l(*accumulator_resource->mutex());
+            core::ScopedUnref unref_me(accumulator_resource);
+
+            // If the stamp is invalid we drop the update.
+            if (!accumulator_resource->is_stamp_valid(stamp_token)) {
+              VLOG(1) << "Invalid stamp token in StatsAccumulatorScalarAddOp. "
+                      << "Passed stamp token: " << stamp_token << " "
+                      << "Current token: " << accumulator_resource->stamp();
+              return;
+            }
+            AddToScalarAccumulator(accumulator_resource,
+                                   partition_ids_list[resource_handle_idx],
+                                   feature_ids_list[resource_handle_idx],
+                                   gradients_list[resource_handle_idx],
+                                   hessians_list[resource_handle_idx]);
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorScalarAdd").Device(DEVICE_CPU),
+                        StatsAccumulatorScalarAddOp);
+
+class StatsAccumulatorTensorAddOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorAddOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list("stats_accumulator_handles",
+                                                &resource_handle_list));
+    OpInputList partition_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("partition_ids", &partition_ids_list));
+
+    OpInputList feature_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("feature_ids", &feature_ids_list));
+    OpInputList gradients_list;
+    OP_REQUIRES_OK(context, context->input_list("gradients", &gradients_list));
+    OpInputList hessians_list;
+    OP_REQUIRES_OK(context, context->input_list("hessians", &hessians_list));
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &partition_ids_list,
+         &feature_ids_list, &gradients_list, &hessians_list,
+         stamp_token](int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+
+            StatsAccumulatorTensorResource* accumulator_resource;
+            OP_REQUIRES_OK(context, LookupResource(context, handle,
+                                                   &accumulator_resource));
+            mutex_lock l(*accumulator_resource->mutex());
+            core::ScopedUnref unref_me(accumulator_resource);
+
+            // If the stamp is invalid we drop the update.
+            if (!accumulator_resource->is_stamp_valid(stamp_token)) {
+              VLOG(1) << "Invalid stamp token in StatsAccumulatorScalarAddOp. "
+                      << "Passed stamp token: " << stamp_token << " "
+                      << "Current token: " << accumulator_resource->stamp();
+              return;
+            }
+            AddToTensorAccumulator(accumulator_resource,
+                                   partition_ids_list[resource_handle_idx],
+                                   feature_ids_list[resource_handle_idx],
+                                   gradients_list[resource_handle_idx],
+                                   hessians_list[resource_handle_idx], context);
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorTensorAdd").Device(DEVICE_CPU),
+                        StatsAccumulatorTensorAddOp);
+
+class StatsAccumulatorScalarFlushOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarFlushOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorScalarResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // If the stamp is invalid we restart the PS. It shouldn't happen since
+    // only Chief should call this function and chief is guaranteed to be in
+    // a consistent state.
+    CHECK(accumulator_resource->is_stamp_valid(stamp_token));
+
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kNextStampTokenName, &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+    CHECK(stamp_token != next_stamp_token);
+
+    SerializeScalarAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(next_stamp_token);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorScalarFlush").Device(DEVICE_CPU),
+                        StatsAccumulatorScalarFlushOp);
+
+class StatsAccumulatorTensorFlushOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorFlushOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorTensorResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kNextStampTokenName, &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+
+    // If the stamp is invalid we restart the PS. It shouldn't happen since
+    // only Chief should call this function and chief is guaranteed to be in
+    // a consistent state.
+    CHECK(accumulator_resource->is_stamp_valid(stamp_token));
+    CHECK(stamp_token != next_stamp_token);
+    SerializeTensorAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(next_stamp_token);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorTensorFlush").Device(DEVICE_CPU),
+                        StatsAccumulatorTensorFlushOp);
+
+class StatsAccumulatorScalarDeserializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorScalarResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    // Check the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(stamp_token);
+    AddToScalarAccumulator(accumulator_resource, context);
+    const Tensor* num_updates_t;
+    OP_REQUIRES_OK(context, context->input("num_updates", &num_updates_t));
+    accumulator_resource->set_num_updates(num_updates_t->scalar<int64>()());
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarDeserialize").Device(DEVICE_CPU),
+    StatsAccumulatorScalarDeserializeOp);
+
+class StatsAccumulatorTensorDeserializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorTensorResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    // Check the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(stamp_token);
+    AddToTensorAccumulator(accumulator_resource, context);
+    const Tensor* num_updates_t;
+    OP_REQUIRES_OK(context, context->input("num_updates", &num_updates_t));
+    accumulator_resource->set_num_updates(num_updates_t->scalar<int64>()());
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorDeserialize").Device(DEVICE_CPU),
+    StatsAccumulatorTensorDeserializeOp);
+
+class StatsAccumulatorScalarSerializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorScalarResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+    SerializeScalarAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("stamp_token", TensorShape({}),
+                                            &stamp_token_t));
+    stamp_token_t->scalar<int64>()() = accumulator_resource->stamp();
+
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarSerialize").Device(DEVICE_CPU),
+    StatsAccumulatorScalarSerializeOp);
+
+class StatsAccumulatorTensorSerializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorTensorResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+    SerializeTensorAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("stamp_token", TensorShape({}),
+                                            &stamp_token_t));
+    stamp_token_t->scalar<int64>()() = accumulator_resource->stamp();
+
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorSerialize").Device(DEVICE_CPU),
+    StatsAccumulatorTensorSerializeOp);
+
+class StatsAccumulatorScalarMakeSummaryOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarMakeSummaryOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    TensorShape gradient_shape = TensorShape({});
+    TensorShape hessian_shape = TensorShape({});
+    StatsAccumulatorScalarResource* accumulator_resource =
+        new StatsAccumulatorScalarResource(gradient_shape, hessian_shape);
+    core::ScopedUnref unref_me(accumulator_resource);
+    // Check the stamp token.
+    AddToScalarAccumulator(accumulator_resource, context);
+    SerializeScalarAccumulatorToOutput(*accumulator_resource, context);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarMakeSummary").Device(DEVICE_CPU),
+    StatsAccumulatorScalarMakeSummaryOp);
+
+class StatsAccumulatorTensorMakeSummaryOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorMakeSummaryOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+    TensorShape gradients_shape = gradients_t->shape();
+    gradients_shape.RemoveDim(0);
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+    TensorShape hessians_shape = hessians_t->shape();
+    hessians_shape.RemoveDim(0);
+
+    StatsAccumulatorTensorResource* accumulator_resource =
+        new StatsAccumulatorTensorResource(gradients_shape, hessians_shape);
+    core::ScopedUnref unref_me(accumulator_resource);
+    // Check the stamp token.
+    AddToTensorAccumulator(accumulator_resource, context);
+    SerializeTensorAccumulatorToOutput(*accumulator_resource, context);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorMakeSummary").Device(DEVICE_CPU),
+    StatsAccumulatorTensorMakeSummaryOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
new file mode 100644
index 0000000000..12200e6a25
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
@@ -0,0 +1,825 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/split_info.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+
+namespace tensorflow {
+using tensorflow::boosted_trees::learner::LearningRateDropoutDrivenConfig;
+
+namespace boosted_trees {
+
+using boosted_trees::trees::DecisionTreeEnsembleConfig;
+using boosted_trees::trees::TreeNode;
+using boosted_trees::trees::TreeNodeMetadata;
+using boosted_trees::utils::DropoutUtils;
+using boosted_trees::learner::LearningRateConfig;
+
+namespace {
+
+// SplitCandidate holds the split candidate node along with the stats.
+struct SplitCandidate {
+  // Id of handler that generated the split candidate.
+  int64 handler_id;
+
+  // Split gain.
+  float gain;
+
+  // Split info.
+  learner::SplitInfo split_info;
+};
+
+// Helper method to update the best split per partition given
+// a current candidate.
+void UpdateBestSplit(
+    const boosted_trees::learner::LearnerConfig& learner_config,
+    int32 partition_id, SplitCandidate* split,
+    std::map<int32, SplitCandidate>* best_splits) {
+  // Don't consider nodeless splits.
+  if (TF_PREDICT_FALSE(split->split_info.split_node().node_case() ==
+                       TreeNode::NODE_NOT_SET)) {
+    return;
+  }
+
+  // Don't consider negative splits if we're pre-pruning the tree.
+  // Note that zero-gain splits are acceptable as they're mostly doing as well
+  // as what bias centering in that partition would do.
+  if (learner_config.pruning_mode() ==
+          boosted_trees::learner::LearnerConfig::PRE_PRUNE &&
+      split->gain < 0) {
+    return;
+  }
+
+  // Take the split if we don't have a candidate yet.
+  auto best_split_it = best_splits->find(partition_id);
+  if (best_split_it == best_splits->end()) {
+    best_splits->insert(std::make_pair(partition_id, std::move(*split)));
+    return;
+  }
+
+  // Determine if best split so far needs to be replaced.
+  SplitCandidate& best_split = best_split_it->second;
+  if (TF_PREDICT_FALSE(split->gain == best_split.gain)) {
+    // Tie break on node case preferring simpler tree node types.
+    VLOG(2) << "Attempting to tie break with smaller node case. "
+            << "(current split: " << split->split_info.split_node().node_case()
+            << ", best split: "
+            << best_split.split_info.split_node().node_case() << ")";
+    if (split->split_info.split_node().node_case() <
+        best_split.split_info.split_node().node_case()) {
+      best_split = std::move(*split);
+    } else if (split->split_info.split_node().node_case() ==
+               best_split.split_info.split_node().node_case()) {
+      // Tie break on handler Id.
+      VLOG(2) << "Tie breaking with higher handler Id. "
+              << "(current split: " << split->handler_id
+              << ", best split: " << best_split.handler_id << ")";
+      if (split->handler_id > best_split.handler_id) {
+        best_split = std::move(*split);
+      }
+    }
+  } else if (split->gain > best_split.gain) {
+    best_split = std::move(*split);
+  }
+}
+
+// Helper method to check whether a node is a terminal node in that it
+// only has leaf nodes as children.
+bool IsTerminalSplitNode(const size_t node_id,
+                         const std::vector<int32>& children,
+                         const std::vector<TreeNode>& nodes) {
+  for (const int32 child_id : children) {
+    const auto& child_node = nodes[child_id];
+    CHECK(child_node.node_case() != TreeNode::NODE_NOT_SET);
+    if (child_node.node_case() != TreeNode::kLeaf) {
+      return false;
+    }
+  }
+  return true;
+}
+
+// Helper method to recursively prune the tree in a depth-first fashion.
+void RecursivePruneTree(const size_t node_id, std::vector<TreeNode>* nodes) {
+  // Base case when we reach a leaf.
+  TreeNode& tree_node = (*nodes)[node_id];
+  CHECK(tree_node.node_case() != TreeNode::NODE_NOT_SET);
+  if (tree_node.node_case() == TreeNode::kLeaf) {
+    return;
+  }
+
+  // Traverse node children first and recursively prune their sub-trees.
+  const std::vector<int32> children =
+      boosted_trees::trees::DecisionTree::GetChildren(tree_node);
+  for (const int32 child_id : children) {
+    RecursivePruneTree(child_id, nodes);
+  }
+
+  // Two conditions must be satisfied to prune the node:
+  // 1- The split gain is negative.
+  // 2- After depth-first pruning, the node only has leaf children.
+  TreeNodeMetadata* node_metadata = tree_node.mutable_node_metadata();
+  if (node_metadata->gain() < 0 &&
+      IsTerminalSplitNode(node_id, children, (*nodes))) {
+    // Clear node children.
+    for (const int32 child_id : children) {
+      auto& child_node = (*nodes)[child_id];
+      child_node.Clear();
+    }
+
+    // Change node back into leaf.
+    (*tree_node.mutable_leaf()) = *node_metadata->mutable_original_leaf();
+
+    // Clear gain for leaf node.
+    tree_node.clear_node_metadata();
+  } else {
+    // Clear original leaf as it's no longer needed for back-track pruning.
+    node_metadata->clear_original_leaf();
+  }
+}
+
+}  // namespace
+
+class CenterTreeEnsembleBiasOp : public OpKernel {
+ public:
+  explicit CenterTreeEnsembleBiasOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    // Read learner config.
+    string serialized_learner_config;
+    OP_REQUIRES_OK(context, context->GetAttr("learner_config",
+                                             &serialized_learner_config));
+    OP_REQUIRES(context,
+                learner_config_.ParseFromString(serialized_learner_config),
+                errors::InvalidArgument("Unable to parse learner config."));
+
+    // Read centering epsilon.
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("centering_epsilon", &centering_epsilon_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Get decision tree ensemble.
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Only the Chief should run this Op and it is guaranteed to be in
+    // a consistent state so the stamps must always match.
+    CHECK(decision_tree_ensemble_resource->is_stamp_valid(stamp_token));
+
+    // Get the next stamp token.
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input("next_stamp_token", &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+    CHECK(stamp_token != next_stamp_token);
+
+    // Get the delta updates.
+    const Tensor* delta_updates_t;
+    OP_REQUIRES_OK(context, context->input("delta_updates", &delta_updates_t));
+    OP_REQUIRES(
+        context,
+        delta_updates_t->dim_size(0) + 1 == learner_config_.num_classes(),
+        errors::InvalidArgument(
+            "Delta updates size must be consistent with label dimensions."));
+    auto delta_updates = delta_updates_t->vec<float>();
+
+    // Update the ensemble stamp.
+    decision_tree_ensemble_resource->set_stamp(next_stamp_token);
+
+    // Get the bias.
+    boosted_trees::trees::Leaf* bias =
+        RetrieveBias(decision_tree_ensemble_resource);
+    CHECK(bias->has_vector());
+    OP_REQUIRES(
+        context,
+        bias->vector().value_size() + 1 == learner_config_.num_classes(),
+        errors::InvalidArgument(
+            "Bias vector size must be consistent with label dimensions."));
+
+    // Update the bias.
+    float total_delta = 0;
+    auto* bias_vec = bias->mutable_vector();
+    for (size_t idx = 0; idx < bias->vector().value_size(); ++idx) {
+      float delta = delta_updates(idx);
+      bias_vec->set_value(idx, bias_vec->value(idx) + delta);
+      total_delta += std::abs(delta);
+    }
+
+    // Make a centering continuation decision based on current update.
+    bool continue_centering = total_delta > centering_epsilon_;
+    if (continue_centering) {
+      VLOG(1) << "Continuing to center bias, delta=" << total_delta;
+    } else {
+      VLOG(1) << "Done centering bias, delta=" << total_delta;
+    }
+    Tensor* continue_centering_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("continue_centering", TensorShape({}),
+                                          &continue_centering_t));
+    continue_centering_t->scalar<bool>()() = continue_centering;
+  }
+
+ private:
+  // Helper method to retrieve the bias from the tree ensemble.
+  boosted_trees::trees::Leaf* RetrieveBias(
+      boosted_trees::models::DecisionTreeEnsembleResource*
+          decision_tree_ensemble_resource) {
+    boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    const auto num_trees = ensemble_config->trees_size();
+    CHECK(num_trees == ensemble_config->tree_metadata_size() &&
+          num_trees == ensemble_config->tree_weights_size());
+    if (num_trees <= 0) {
+      ensemble_config->mutable_growing_metadata()->set_num_trees_attempted(1);
+      ensemble_config->mutable_growing_metadata()->set_num_layers_attempted(1);
+      // Add a new bias leaf.
+      boosted_trees::trees::DecisionTreeConfig* tree_config =
+          ensemble_config->add_trees();
+      auto* leaf = tree_config->add_nodes()->mutable_leaf();
+      for (size_t idx = 0; idx + 1 < learner_config_.num_classes(); ++idx) {
+        leaf->mutable_vector()->add_value(0);
+      }
+      ensemble_config->add_tree_weights(1.0);
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata =
+          ensemble_config->add_tree_metadata();
+      tree_metadata->set_num_layers_grown(1);
+      tree_metadata->set_is_finalized(true);
+      return leaf;
+    } else if (num_trees == 1) {
+      // Update the existing bias.
+      CHECK_EQ(ensemble_config->trees(0).nodes_size(), 1);
+      auto* node = ensemble_config->mutable_trees(0)->mutable_nodes(0);
+      CHECK(node->node_case() == TreeNode::kLeaf);
+      return node->mutable_leaf();
+    } else {
+      CHECK(false) << "Unable to center bias on an already grown ensemble";
+    }
+  }
+
+  boosted_trees::learner::LearnerConfig learner_config_;
+  float centering_epsilon_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("CenterTreeEnsembleBias").Device(DEVICE_CPU),
+                        CenterTreeEnsembleBiasOp);
+
+class GrowTreeEnsembleOp : public OpKernel {
+ public:
+  explicit GrowTreeEnsembleOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    // Read number of handlers, note that this is the static number of
+    // all handlers but any subset of these handlers may be active at a time.
+    OP_REQUIRES_OK(context, context->GetAttr("num_handlers", &num_handlers_));
+
+    OP_REQUIRES_OK(context, context->GetAttr("center_bias", &center_bias_));
+
+    // Read learner config.
+    string serialized_learner_config;
+    OP_REQUIRES_OK(context, context->GetAttr("learner_config",
+                                             &serialized_learner_config));
+    OP_REQUIRES(context,
+                learner_config_.ParseFromString(serialized_learner_config),
+                errors::InvalidArgument("Unable to parse learner config."));
+
+    // Determine whether dropout was used when building this tree.
+    if (learner_config_.has_learning_rate_tuner() &&
+        learner_config_.learning_rate_tuner().tuner_case() ==
+            LearningRateConfig::kDropout) {
+      dropout_config_ = learner_config_.learning_rate_tuner().dropout();
+      dropout_was_applied_ = true;
+    } else {
+      dropout_was_applied_ = false;
+    }
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Get decision tree ensemble.
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Only the Chief should run this Op and it is guaranteed to be in
+    // a consistent state so the stamps must always match.
+    CHECK(decision_tree_ensemble_resource->is_stamp_valid(stamp_token));
+
+    // Get the next stamp token.
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input("next_stamp_token", &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+    CHECK(stamp_token != next_stamp_token);
+
+    // Update the ensemble stamp regardless of whether a layer
+    // or tree is actually grown.
+    decision_tree_ensemble_resource->set_stamp(next_stamp_token);
+
+    // Read the learning_rate.
+    const Tensor* learning_rate_t;
+    OP_REQUIRES_OK(context, context->input("learning_rate", &learning_rate_t));
+    float learning_rate = learning_rate_t->scalar<float>()();
+
+    // Read seed that was used for dropout.
+    const Tensor* seed_t;
+    OP_REQUIRES_OK(context, context->input("dropout_seed", &seed_t));
+    // Cast seed to uint64.
+    const uint64 dropout_seed = seed_t->scalar<int64>()();
+
+    // Read partition Ids, gains and split candidates.
+    OpInputList partition_ids_list;
+    OpInputList gains_list;
+    OpInputList splits_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("partition_ids", &partition_ids_list));
+    OP_REQUIRES_OK(context, context->input_list("gains", &gains_list));
+    OP_REQUIRES_OK(context, context->input_list("splits", &splits_list));
+
+    boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    ensemble_config->mutable_growing_metadata()->set_num_layers_attempted(
+        ensemble_config->growing_metadata().num_layers_attempted() + 1);
+    const int num_trees = ensemble_config->trees_size();
+    if (num_trees <= 0 ||
+        ensemble_config->tree_metadata(num_trees - 1).is_finalized()) {
+      ensemble_config->mutable_growing_metadata()->set_num_trees_attempted(
+          ensemble_config->growing_metadata().num_trees_attempted() + 1);
+    }
+
+    // Find best splits for each active partition.
+    std::map<int32, SplitCandidate> best_splits;
+    FindBestSplitsPerPartition(context, partition_ids_list, gains_list,
+                               splits_list, &best_splits);
+
+    // No-op if no new splits can be considered.
+    if (best_splits.empty()) {
+      LOG(WARNING) << "Not growing tree ensemble as no good splits were found.";
+      return;
+    }
+
+    // Update and retrieve the growable tree with its metadata.
+    boosted_trees::trees::DecisionTreeConfig* tree_config;
+    boosted_trees::trees::DecisionTreeMetadata* tree_metadata;
+
+    // Updates the tree. If the tree is fully built and dropout was applied, it
+    // also adjusts the weights of dropped and the last tree.
+    std::tie(tree_config, tree_metadata) = UpdateAndRetrieveGrowableTree(
+        decision_tree_ensemble_resource, learning_rate, dropout_seed);
+
+    // Split tree nodes.
+    for (auto& split_entry : best_splits) {
+      SplitTreeNode(split_entry.first, &split_entry.second, tree_config);
+    }
+
+    // Post-prune finalized tree if needed.
+    if (learner_config_.pruning_mode() ==
+            boosted_trees::learner::LearnerConfig::POST_PRUNE &&
+        tree_metadata->is_finalized()) {
+      VLOG(2) << "Post-pruning finalized tree.";
+      PruneTree(tree_config);
+
+      // If after post-pruning the whole tree has no gain, remove the tree
+      // altogether from the ensemble.
+      if (tree_config->nodes_size() <= 0) {
+        ensemble_config->mutable_trees()->RemoveLast();
+        ensemble_config->mutable_tree_weights()->RemoveLast();
+        ensemble_config->mutable_tree_metadata()->RemoveLast();
+      }
+    }
+  }
+
+ private:
+  // Helper method which effectively does a reduce over all split candidates
+  // and finds the best split for each partition.
+  void FindBestSplitsPerPartition(
+      OpKernelContext* const context, const OpInputList& partition_ids_list,
+      const OpInputList& gains_list, const OpInputList& splits_list,
+      std::map<int32, SplitCandidate>* best_splits) {
+    // Find best split per partition going through every feature candidate.
+    // TODO(salehay): Is this worth parallelizing?
+    for (int64 handler_id = 0; handler_id < num_handlers_; ++handler_id) {
+      const auto& partition_ids = partition_ids_list[handler_id].vec<int32>();
+      const auto& gains = gains_list[handler_id].vec<float>();
+      const auto& splits = splits_list[handler_id].vec<string>();
+      OP_REQUIRES(context, partition_ids.size() == gains.size(),
+                  errors::InvalidArgument(
+                      "Inconsistent partition Ids and gains tensors: ",
+                      partition_ids.size(), " != ", gains.size()));
+      OP_REQUIRES(context, partition_ids.size() == splits.size(),
+                  errors::InvalidArgument(
+                      "Inconsistent partition Ids and splits tensors: ",
+                      partition_ids.size(), " != ", splits.size()));
+      for (size_t candidate_idx = 0; candidate_idx < splits.size();
+           ++candidate_idx) {
+        // Get current split candidate.
+        const auto& partition_id = partition_ids(candidate_idx);
+        const auto& gain = gains(candidate_idx);
+        const auto& serialized_split = splits(candidate_idx);
+        SplitCandidate split;
+        split.handler_id = handler_id;
+        split.gain = gain;
+        OP_REQUIRES(context, split.split_info.ParseFromString(serialized_split),
+                    errors::InvalidArgument("Unable to parse split info."));
+
+        // Update best split for partition based on the current candidate.
+        UpdateBestSplit(learner_config_, partition_id, &split, best_splits);
+      }
+    }
+  }
+
+  void UpdateTreeWeightsIfDropout(
+      boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config,
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata,
+      const uint64 dropout_seed) {
+    // It is possible that the tree was built with dropout. If it is the case,
+    // we need to adjust the tree weight.
+    if (dropout_was_applied_ && tree_metadata->is_finalized()) {
+      const int32 num_trees = ensemble_config->trees_size();
+
+      std::vector<int32> dropped_trees;
+      // Since only chief builds the trees, we are sure that the other tree
+      // weights didn't change.
+      std::vector<float> weights;
+      weights.reserve(num_trees);
+      std::vector<int32> num_updates;
+      num_updates.reserve(num_trees);
+      for (int i = 0; i < num_trees; ++i) {
+        weights.push_back(ensemble_config->tree_weights(i));
+        num_updates.push_back(
+            ensemble_config->tree_metadata(i).num_tree_weight_updates());
+      }
+
+      std::vector<float> dropped_trees_weights;
+      // Based on seed, figure out what trees were dropped before.
+      std::unordered_set<int32> trees_not_to_drop;
+      if (center_bias_) {
+        trees_not_to_drop.insert(0);
+      }
+      // Last tree is the current tree that is built.
+      const int32 current_tree = num_trees - 1;
+      trees_not_to_drop.insert(current_tree);
+
+      const auto dropout_status = DropoutUtils::DropOutTrees(
+          dropout_seed, dropout_config_, trees_not_to_drop, weights,
+          &dropped_trees, &dropped_trees_weights);
+      CHECK(dropout_status.ok())
+          << "Can't figure out what trees were dropped out before, error is "
+          << dropout_status.error_message();
+
+      // Now we have dropped trees, update their weights and the current tree
+      // weight.
+      if (!dropped_trees.empty()) {
+        DropoutUtils::GetTreesWeightsForAddingTrees(
+            dropped_trees, dropped_trees_weights, current_tree,
+            1 /* only 1 tree was added */, &weights, &num_updates);
+
+        // Update the weights and num of updates for trees.
+        for (int i = 0; i < num_trees; ++i) {
+          ensemble_config->set_tree_weights(i, weights[i]);
+          ensemble_config->mutable_tree_metadata(i)
+              ->set_num_tree_weight_updates(num_updates[i]);
+        }
+      }
+    }
+  }
+
+  // Helper method to update and retrieve the growable tree which is by
+  // definition the last tree in the ensemble.
+  std::pair<boosted_trees::trees::DecisionTreeConfig*,
+            boosted_trees::trees::DecisionTreeMetadata*>
+  UpdateAndRetrieveGrowableTree(
+      boosted_trees::models::DecisionTreeEnsembleResource*
+          decision_tree_ensemble_resource,
+      float learning_rate, const uint64 dropout_seed) {
+    boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    auto num_trees = ensemble_config->trees_size();
+    CHECK(num_trees == ensemble_config->tree_metadata_size() &&
+          num_trees == ensemble_config->tree_weights_size());
+    if (num_trees <= 0 ||
+        ensemble_config->tree_metadata(num_trees - 1).is_finalized()) {
+      // Create a new tree with a no-op leaf.
+      boosted_trees::trees::DecisionTreeConfig* tree_config =
+          ensemble_config->add_trees();
+      ++num_trees;
+      VLOG(1) << "Adding layer 0 to tree " << num_trees - 1
+              << " of ensemble of " << num_trees << " trees.";
+      tree_config->add_nodes()->mutable_leaf();
+      ensemble_config->add_tree_weights(learning_rate);
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata =
+          ensemble_config->add_tree_metadata();
+      tree_metadata->set_num_layers_grown(1);
+      tree_metadata->set_is_finalized(
+          learner_config_.constraints().max_tree_depth() <= 1);
+      tree_metadata->set_num_tree_weight_updates(1);
+
+      UpdateTreeWeightsIfDropout(ensemble_config, tree_metadata, dropout_seed);
+      return std::make_pair(tree_config, tree_metadata);
+    } else {
+      // The growable tree is by definition the last tree in the ensemble.
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata =
+          ensemble_config->mutable_tree_metadata(num_trees - 1);
+      auto num_layers_grown = tree_metadata->num_layers_grown();
+      VLOG(1) << "Adding layer " << num_layers_grown << " to tree "
+              << num_trees - 1 << " of ensemble of " << num_trees << " trees.";
+      // Update growable tree metadata.
+      ++num_layers_grown;
+      tree_metadata->set_num_layers_grown(num_layers_grown);
+      tree_metadata->set_is_finalized(
+          num_layers_grown >= learner_config_.constraints().max_tree_depth());
+      auto* tree_config = ensemble_config->mutable_trees(num_trees - 1);
+
+      UpdateTreeWeightsIfDropout(ensemble_config, tree_metadata, dropout_seed);
+
+      return std::make_pair(tree_config, tree_metadata);
+    }
+  }
+
+  // Helper method to merge leaf weights as the tree is being grown.
+  boosted_trees::trees::Leaf* MergeLeafWeights(
+      const boosted_trees::trees::Leaf& source,
+      boosted_trees::trees::Leaf* dest) {
+    // Resolve leaf merging method based on how the trees are being grown.
+    if (learner_config_.growing_mode() ==
+        boosted_trees::learner::LearnerConfig::WHOLE_TREE) {
+      // No merging occurs when building a whole tree at a time.
+      return dest;
+    }
+
+    // Handle leaf merging based on type.
+    switch (source.leaf_case()) {
+      case boosted_trees::trees::Leaf::kVector: {
+        // No-op if source is empty
+        const auto& src_vec = source.vector();
+        if (src_vec.value_size() == 0) {
+          break;
+        }
+        CHECK(source.leaf_case() == dest->leaf_case());
+
+        // Dense add leaf vectors.
+        auto* dst_vec = dest->mutable_vector();
+        CHECK(src_vec.value_size() == dst_vec->value_size());
+        for (size_t idx = 0; idx < source.vector().value_size(); ++idx) {
+          (*dst_vec->mutable_value()->Mutable(idx)) += src_vec.value(idx);
+        }
+        break;
+      }
+      case boosted_trees::trees::Leaf::kSparseVector: {
+        // No-op if source is empty
+        const auto& src_vec = source.sparse_vector();
+        CHECK(src_vec.value_size() == src_vec.index_size());
+        if (src_vec.value_size() == 0) {
+          break;
+        }
+        CHECK(source.leaf_case() == dest->leaf_case());
+
+        // Get mapping of dimension to value for destination.
+        std::unordered_map<int32, float> dst_map;
+        auto* dst_vec = dest->mutable_sparse_vector();
+        CHECK(dst_vec->value_size() == dst_vec->index_size());
+        dst_map.reserve(dst_vec->value_size());
+        for (size_t idx = 0; idx < dst_vec->value_size(); ++idx) {
+          dst_map[dst_vec->index(idx)] = dst_vec->value(idx);
+        }
+        // Sparse add source vector to destination vector.
+        for (size_t idx = 0; idx < src_vec.value_size(); ++idx) {
+          dst_map[src_vec.index(idx)] += src_vec.value(idx);
+        }
+        // Rebuild merged destination leaf.
+        dst_vec->clear_index();
+        dst_vec->clear_value();
+        for (const auto& entry : dst_map) {
+          dst_vec->add_index(entry.first);
+          dst_vec->add_value(entry.second);
+        }
+        break;
+      }
+      case boosted_trees::trees::Leaf::LEAF_NOT_SET: {
+        // No-op as there is nothing to merge.
+        break;
+      }
+    }
+    return dest;
+  }
+
+  // Helper method to split a tree node and append its respective
+  // leaf children given the split candidate.
+  void SplitTreeNode(const int32 node_id, SplitCandidate* split,
+                     boosted_trees::trees::DecisionTreeConfig* tree_config) {
+    // No-op if we have no real node.
+    CHECK(node_id < tree_config->nodes_size())
+        << "Invalid node " << node_id << " to split.";
+    // Ensure new split node is valid.
+    CHECK(split->split_info.split_node().node_case() != TreeNode::NODE_NOT_SET);
+    CHECK(tree_config->nodes(node_id).node_case() == TreeNode::kLeaf)
+        << "Unexpected node type to split "
+        << tree_config->nodes(node_id).node_case();
+
+    // Add left leaf.
+    int32 left_id = tree_config->nodes_size();
+    (*tree_config->add_nodes()->mutable_leaf()) =
+        *MergeLeafWeights(tree_config->nodes(node_id).leaf(),
+                          split->split_info.mutable_left_child());
+
+    // Add right leaf.
+    int32 right_id = tree_config->nodes_size();
+    (*tree_config->add_nodes()->mutable_leaf()) =
+        *MergeLeafWeights(tree_config->nodes(node_id).leaf(),
+                          split->split_info.mutable_right_child());
+
+    // Link children and add them as new roots.
+    boosted_trees::trees::DecisionTree::LinkChildren(
+        {left_id, right_id}, split->split_info.mutable_split_node());
+
+    // Add split gain and, if needed, original leaf to node metadata.
+    TreeNodeMetadata* node_metadata =
+        split->split_info.mutable_split_node()->mutable_node_metadata();
+    node_metadata->set_gain(split->gain);
+    if (learner_config_.pruning_mode() ==
+        boosted_trees::learner::LearnerConfig::POST_PRUNE) {
+      (*node_metadata->mutable_original_leaf()) =
+          *tree_config->mutable_nodes(node_id)->mutable_leaf();
+    }
+
+    // Replace node in tree.
+    (*tree_config->mutable_nodes(node_id)) =
+        *split->split_info.mutable_split_node();
+  }
+
+  void PruneTree(boosted_trees::trees::DecisionTreeConfig* tree_config) {
+    // No-op if tree is empty.
+    if (tree_config->nodes_size() <= 0) {
+      return;
+    }
+
+    // Copy nodes to temp vector and clear original tree.
+    std::vector<TreeNode> tree_nodes;
+    tree_nodes.reserve(tree_config->nodes_size());
+    for (auto& node : (*tree_config->mutable_nodes())) {
+      tree_nodes.push_back(node);
+      node.Clear();
+    }
+    tree_config->clear_nodes();
+
+    // Prune the tree recursively starting from the root.
+    RecursivePruneTree(0, &tree_nodes);
+
+    // Rebuild compacted tree.
+    (*tree_config->add_nodes()) = tree_nodes[0];
+    std::unordered_map<size_t, size_t> nodes_map;
+    nodes_map[0] = 0;
+    for (size_t node_idx = 0; node_idx < tree_nodes.size(); ++node_idx) {
+      // Skip pruned nodes.
+      auto& original_node = tree_nodes[node_idx];
+      if (original_node.node_case() == TreeNode::NODE_NOT_SET) {
+        continue;
+      }
+
+      // Find node mapped in tree ensemble.
+      auto mapped_node_it = nodes_map.find(node_idx);
+      CHECK(mapped_node_it != nodes_map.end());
+      auto& mapped_node = (*tree_config->mutable_nodes(mapped_node_it->second));
+
+      // Get node children
+      auto children =
+          boosted_trees::trees::DecisionTree::GetChildren(original_node);
+      for (int32& child_idx : children) {
+        auto new_idx = tree_config->nodes_size();
+        (*tree_config->add_nodes()) = tree_nodes[child_idx];
+        nodes_map[child_idx] = new_idx;
+        child_idx = new_idx;
+      }
+      boosted_trees::trees::DecisionTree::LinkChildren(children, &mapped_node);
+    }
+
+    // Check if there are any nodes with gain left.
+    if (tree_config->nodes_size() == 1 &&
+        tree_config->nodes(0).node_metadata().gain() <= 0) {
+      // The whole tree should be pruned.
+      VLOG(2) << "No useful nodes left after post-pruning tree.";
+      tree_config->clear_nodes();
+    }
+  }
+
+ private:
+  boosted_trees::learner::LearnerConfig learner_config_;
+  int64 num_handlers_;
+  LearningRateDropoutDrivenConfig dropout_config_;
+  bool dropout_was_applied_;
+  bool center_bias_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("GrowTreeEnsemble").Device(DEVICE_CPU),
+                        GrowTreeEnsembleOp);
+
+class TreeEnsembleStatsOp : public OpKernel {
+ public:
+  explicit TreeEnsembleStatsOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* const context) override {
+    // Get decision tree ensemble.
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Only the Chief should run this Op and it is guaranteed to be in
+    // a consistent state so the stamps must always match.
+    CHECK(decision_tree_ensemble_resource->is_stamp_valid(stamp_token));
+    const boosted_trees::trees::DecisionTreeEnsembleConfig& ensemble_config =
+        decision_tree_ensemble_resource->decision_tree_ensemble();
+
+    // Set tree stats.
+    Tensor* num_trees_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "num_trees", TensorShape({}), &num_trees_t));
+    Tensor* active_tree_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("active_tree", TensorShape({}),
+                                            &active_tree_t));
+    Tensor* attempted_tree_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("attempted_trees", TensorShape({}),
+                                            &attempted_tree_t));
+
+    int num_trees = ensemble_config.trees_size();
+    active_tree_t->scalar<int64>()() = num_trees;
+    if (num_trees > 0 &&
+        !ensemble_config.tree_metadata(num_trees - 1).is_finalized()) {
+      --num_trees;
+    }
+    num_trees_t->scalar<int64>()() = num_trees;
+    attempted_tree_t->scalar<int64>()() =
+        ensemble_config.growing_metadata().num_trees_attempted();
+
+    // Set layer stats.
+    Tensor* num_layers_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "num_layers", TensorShape({}), &num_layers_t));
+    Tensor* active_layer_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("active_layer", TensorShape({}),
+                                            &active_layer_t));
+    Tensor* attempted_layers_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("attempted_layers", TensorShape({}),
+                                            &attempted_layers_t));
+
+    int64 num_layers = 0;
+    for (const auto& tree_metadata : ensemble_config.tree_metadata()) {
+      num_layers += tree_metadata.num_layers_grown();
+    }
+    num_layers_t->scalar<int64>()() = num_layers;
+    int tree_metadata_size = ensemble_config.tree_metadata_size();
+    active_layer_t->scalar<int64>()() =
+        tree_metadata_size > 0
+            ? ensemble_config.tree_metadata(tree_metadata_size - 1)
+                  .num_layers_grown()
+            : 0;
+    attempted_layers_t->scalar<int64>()() =
+        ensemble_config.growing_metadata().num_layers_attempted();
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleStats").Device(DEVICE_CPU),
+                        TreeEnsembleStatsOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/BUILD b/tensorflow/contrib/boosted_trees/lib/BUILD
index 011c02d720..c63de844fd 100644
--- a/tensorflow/contrib/boosted_trees/lib/BUILD
+++ b/tensorflow/contrib/boosted_trees/lib/BUILD
@@ -22,60 +22,7 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
-cc_library(
-    name = "weighted_quantiles",
-    srcs = [],
-    hdrs = [
-        "quantiles/weighted_quantiles_buffer.h",
-        "quantiles/weighted_quantiles_stream.h",
-        "quantiles/weighted_quantiles_summary.h",
-    ],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow/core:framework_headers_lib",
-    ],
-)
-
-cc_test(
-    name = "weighted_quantiles_buffer_test",
-    size = "small",
-    srcs = ["quantiles/weighted_quantiles_buffer_test.cc"],
-    deps = [
-        ":weighted_quantiles",
-        "//tensorflow/core",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-    ],
-)
-
-cc_test(
-    name = "weighted_quantiles_summary_test",
-    size = "small",
-    srcs = ["quantiles/weighted_quantiles_summary_test.cc"],
-    deps = [
-        ":weighted_quantiles",
-        "//tensorflow/core",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-    ],
-)
-
-cc_test(
-    name = "weighted_quantiles_stream_test",
-    size = "small",
-    srcs = ["quantiles/weighted_quantiles_stream_test.cc"],
-    deps = [
-        ":weighted_quantiles",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-    ],
-)
+# Utils
 
 cc_library(
     name = "utils",
@@ -161,6 +108,8 @@ cc_test(
     ],
 )
 
+# Models
+
 cc_library(
     name = "models",
     srcs = ["models/multiple_additive_trees.cc"],
@@ -190,12 +139,107 @@ cc_test(
     ],
 )
 
+# Testutil
+
+cc_library(
+    name = "batch_features_testutil",
+    testonly = 1,
+    srcs = ["testutil/batch_features_testutil.cc"],
+    hdrs = ["testutil/batch_features_testutil.h"],
+    deps = [
+        ":utils",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:testlib",
+    ],
+)
+
+cc_library(
+    name = "random_tree_gen",
+    srcs = ["testutil/random_tree_gen.cc"],
+    hdrs = ["testutil/random_tree_gen.h"],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_binary(
+    name = "random_tree_gen_main",
+    srcs = ["testutil/random_tree_gen_main.cc"],
+    deps = [
+        ":random_tree_gen",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
+    ],
+)
+
+# Quantiles
+
+cc_library(
+    name = "weighted_quantiles",
+    srcs = [],
+    hdrs = [
+        "quantiles/weighted_quantiles_buffer.h",
+        "quantiles/weighted_quantiles_stream.h",
+        "quantiles/weighted_quantiles_summary.h",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_test(
+    name = "weighted_quantiles_buffer_test",
+    size = "small",
+    srcs = ["quantiles/weighted_quantiles_buffer_test.cc"],
+    deps = [
+        ":weighted_quantiles",
+        "//tensorflow/core",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_test(
+    name = "weighted_quantiles_summary_test",
+    size = "small",
+    srcs = ["quantiles/weighted_quantiles_summary_test.cc"],
+    deps = [
+        ":weighted_quantiles",
+        "//tensorflow/core",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_test(
+    name = "weighted_quantiles_stream_test",
+    size = "small",
+    srcs = ["quantiles/weighted_quantiles_stream_test.cc"],
+    deps = [
+        ":weighted_quantiles",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+# Trees
+
 cc_library(
     name = "trees",
     srcs = ["trees/decision_tree.cc"],
     hdrs = ["trees/decision_tree.h"],
     deps = [
-        ":utils",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
         "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
         "//tensorflow/core:framework_headers_lib",
     ],
@@ -207,43 +251,158 @@ cc_test(
     srcs = ["trees/decision_tree_test.cc"],
     deps = [
         ":trees",
-        ":utils",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
 )
 
+# Learner/Common
+
 cc_library(
-    name = "batch_features_testutil",
-    testonly = 1,
-    srcs = ["testutil/batch_features_testutil.cc"],
-    hdrs = ["testutil/batch_features_testutil.h"],
+    name = "class-partition-key",
+    hdrs = ["learner/common/accumulators/class-partition-key.h"],
     deps = [
-        ":utils",
         "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_library(
+    name = "feature-stats-accumulator",
+    hdrs = ["learner/common/accumulators/feature-stats-accumulator.h"],
+    deps = [
+        ":class-partition-key",
+    ],
+)
+
+cc_test(
+    name = "feature-stats-accumulator_test",
+    size = "small",
+    srcs = ["learner/common/accumulators/feature-stats-accumulator_test.cc"],
+    deps = [
+        ":feature-stats-accumulator",
         "//tensorflow/core:lib",
+        "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
-        "//tensorflow/core:testlib",
+        "//tensorflow/core:test_main",
     ],
 )
 
 cc_library(
-    name = "random_tree_gen",
-    srcs = ["testutil/random_tree_gen.cc"],
-    hdrs = ["testutil/random_tree_gen.h"],
+    name = "example_partitioner",
+    srcs = ["learner/common/partitioners/example_partitioner.cc"],
+    hdrs = ["learner/common/partitioners/example_partitioner.h"],
     deps = [
-        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/lib:trees",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_test(
+    name = "example_partitioner_test",
+    size = "small",
+    srcs = ["learner/common/partitioners/example_partitioner_test.cc"],
+    deps = [
+        ":example_partitioner",
         "//tensorflow/core:lib",
+        "//tensorflow/core:tensor_testutil",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
     ],
 )
 
-cc_binary(
-    name = "random_tree_gen_main",
-    srcs = ["testutil/random_tree_gen_main.cc"],
+# Learner/stochastic
+
+cc_library(
+    name = "feature-column-handlers",
+    srcs = [
+        "learner/stochastic/handlers/bias-feature-column-handler.cc",
+        "learner/stochastic/handlers/categorical-feature-column-handler.cc",
+        "learner/stochastic/handlers/dense-quantized-feature-column-handler.cc",
+        "learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc",
+    ],
+    hdrs = [
+        "learner/stochastic/handlers/bias-feature-column-handler.h",
+        "learner/stochastic/handlers/categorical-feature-column-handler.h",
+        "learner/stochastic/handlers/dense-quantized-feature-column-handler.h",
+        "learner/stochastic/handlers/feature-column-handler.h",
+        "learner/stochastic/handlers/sparse-quantized-feature-column-handler.h",
+    ],
     deps = [
-        ":random_tree_gen",
-        "//tensorflow/core:framework_internal",
+        ":feature-split-candidate",
+        ":feature-stats-accumulator",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_test(
+    name = "feature-column-handlers_test",
+    size = "small",
+    srcs = [
+        "learner/stochastic/handlers/bias-feature-column-handler_test.cc",
+        "learner/stochastic/handlers/categorical-feature-column-handler_test.cc",
+        "learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc",
+        "learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc",
+    ],
+    deps = [
+        ":feature-column-handlers",
         "//tensorflow/core:lib",
+        "//tensorflow/core:tensor_testutil",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_library(
+    name = "gradient-stats",
+    hdrs = ["learner/stochastic/stats/gradient-stats.h"],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "node-stats",
+    hdrs = ["learner/stochastic/stats/node-stats.h"],
+    deps = [
+        ":gradient-stats",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "split-stats",
+    hdrs = ["learner/stochastic/stats/split-stats.h"],
+    deps = [
+        ":node-stats",
+    ],
+)
+
+cc_library(
+    name = "feature-split-candidate",
+    hdrs = ["learner/stochastic/stats/feature-split-candidate.h"],
+    deps = [
+        ":split-stats",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+    ],
+)
+
+cc_test(
+    name = "node-stats_test",
+    size = "small",
+    srcs = ["learner/stochastic/stats/node-stats_test.cc"],
+    deps = [
+        ":node-stats",
+        "//tensorflow/core:tensor_testutil",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
     ],
 )
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h
new file mode 100644
index 0000000000..e1bef02788
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h
@@ -0,0 +1,61 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_CLASS_PARTITION_KEY_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_CLASS_PARTITION_KEY_H_
+
+#include "tensorflow/core/lib/hash/hash.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+// Key into a specific class and partition to accumulate stats
+// for the specified feature id. A feature id can be the quantile
+// for a float feature or the hash/dictionary entry for a string feature.
+struct ClassPartitionKey {
+  ClassPartitionKey() : class_id(-1), partition_id(-1), feature_id(-1) {}
+
+  ClassPartitionKey(uint32 c, uint32 p, uint64 f)
+      : class_id(c), partition_id(p), feature_id(f) {}
+
+  bool operator==(const ClassPartitionKey& other) const {
+    return (feature_id == other.feature_id) &&
+           (partition_id == other.partition_id) && (class_id == other.class_id);
+  }
+
+  // Hasher for ClassPartitionKey.
+  struct Hash {
+    size_t operator()(const ClassPartitionKey& key) const {
+      uint64 class_partition =
+          (static_cast<uint64>(key.partition_id) << 32) | (key.class_id);
+      return Hash64Combine(class_partition, key.feature_id);
+    }
+  };
+
+  // Class to predict for, this is constant for binary tasks.
+  uint32 class_id;
+
+  // Tree partition defined by traversing the tree to the leaf.
+  uint32 partition_id;
+
+  // Feature Id within the feature column.
+  uint64 feature_id;
+};
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_CLASS_PARTITION_KEY_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h
new file mode 100644
index 0000000000..3814edb567
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h
@@ -0,0 +1,82 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_FEATURE_STATS_ACCUMULATOR_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_FEATURE_STATS_ACCUMULATOR_H_
+
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+// Feature stats accumulator to aggregate stats across various
+// feature columns. This class is thread compatible not safe, the user
+// must ensure proper synchronization if many threads update overlapping
+// feature columns.
+template <typename StatsType, typename Accumulator>
+class FeatureStatsAccumulator {
+ public:
+  using FeatureStats =
+      std::unordered_map<ClassPartitionKey, StatsType, ClassPartitionKey::Hash>;
+
+  explicit FeatureStatsAccumulator(size_t num_feature_columns,
+                                   Accumulator accumulator = Accumulator())
+      : accumulator_(accumulator), feature_column_stats_(num_feature_columns) {}
+
+  // Delete copy and assign.
+  FeatureStatsAccumulator(const FeatureStatsAccumulator& other) = delete;
+  FeatureStatsAccumulator& operator=(const FeatureStatsAccumulator& other) =
+      delete;
+
+  // Adds stats for the specified class, partition and feature within
+  // the desired slot.
+  void AddStats(uint32 slot_id, uint32 class_id, uint32 partition_id,
+                uint64 feature_id, const StatsType& stats) {
+    accumulator_(stats, &feature_column_stats_[slot_id][ClassPartitionKey(
+                            class_id, partition_id, feature_id)]);
+  }
+
+  // Retrieves stats for the specified class, partition and feature
+  // within the desired feature column. Default stats are returned if no match
+  // can be found. Note that the feature column index must be valid.
+  StatsType GetStats(uint32 slot_id, uint32 class_id, uint32 partition_id,
+                     uint64 feature_id) const {
+    auto it = feature_column_stats_[slot_id].find(
+        ClassPartitionKey(class_id, partition_id, feature_id));
+    return it != feature_column_stats_[slot_id].end() ? it->second
+                                                      : StatsType();
+  }
+
+  // Returns feature stats for a given slot.
+  FeatureStats GetFeatureStats(uint32 slot_id) const {
+    return feature_column_stats_[slot_id];
+  }
+
+ private:
+  // Accumulator method to use.
+  const Accumulator accumulator_;
+
+  // Vector of stats indexed by the feature column.
+  std::vector<FeatureStats> feature_column_stats_;
+};
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_FEATURE_STATS_ACCUMULATOR_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc
new file mode 100644
index 0000000000..fdbd670120
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc
@@ -0,0 +1,90 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace {
+
+struct TestStats {
+  TestStats& operator+=(const TestStats& other) {
+    s1 += other.s1;
+    s2 += other.s2;
+    return (*this);
+  }
+
+  float s1;
+  float s2;
+};
+
+struct TestStatsAccumulator {
+  void operator()(const TestStats& from, TestStats* to) const { (*to) += from; }
+};
+#define EXPECT_STATS_EQ(val1, val2)  \
+  EXPECT_FLOAT_EQ(val1.s1, val2.s1); \
+  EXPECT_FLOAT_EQ(val1.s2, val2.s2);
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<TestStats, TestStatsAccumulator>;
+
+class FeatureStatsAccumulatorTest : public ::testing::Test {};
+
+TEST_F(FeatureStatsAccumulatorTest, Empty) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats = {0, 0};
+
+  EXPECT_STATS_EQ(stats, accumulator.GetStats(0, 2, 1, 234));
+}
+
+TEST_F(FeatureStatsAccumulatorTest, OneFeatureOneGrad) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats = {-12.023f, 8.2f};
+  accumulator.AddStats(0, 2, 1, 234, stats);
+
+  EXPECT_STATS_EQ(stats, accumulator.GetStats(0, 2, 1, 234));
+}
+
+TEST_F(FeatureStatsAccumulatorTest, OneFeatureAggregateGrad) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats1 = {-12.023f, 8.2f};
+  accumulator.AddStats(0, 2, 1, 234, stats1);
+  TestStats stats2 = {4.46f, 1.9f};
+  accumulator.AddStats(0, 2, 1, 234, stats2);
+  TestStats expected = {-7.563f, 10.1f};
+  EXPECT_STATS_EQ(expected, accumulator.GetStats(0, 2, 1, 234));
+}
+
+TEST_F(FeatureStatsAccumulatorTest, TwoFeaturesOneGrad) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats1 = {-12.023f, 8.2f};
+  accumulator.AddStats(0, 1, 0, 34, stats1);
+  TestStats stats2 = {4.46f, 1.9f};
+  accumulator.AddStats(0, 1, 0, 91, stats2);
+
+  EXPECT_STATS_EQ(stats1, accumulator.GetStats(0, 1, 0, 34));
+  EXPECT_STATS_EQ(stats2, accumulator.GetStats(0, 1, 0, 91));
+}
+
+}  // namespace
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc
new file mode 100644
index 0000000000..1f84b1b06a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc
@@ -0,0 +1,90 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/parallel_for.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+void ExamplePartitioner::UpdatePartitions(
+    const boosted_trees::trees::DecisionTreeConfig& tree_config,
+    const boosted_trees::utils::BatchFeatures& features,
+    const int desired_parallelism, thread::ThreadPool* const thread_pool,
+    int32* example_partition_ids) {
+  // Get batch size.
+  const int64 batch_size = features.batch_size();
+  if (batch_size <= 0) {
+    return;
+  }
+
+  // Lambda for doing a block of work.
+  auto partition_examples_subset = [&tree_config, &features,
+                                    &example_partition_ids](const int64 start,
+                                                            const int64 end) {
+    if (TF_PREDICT_TRUE(tree_config.nodes_size() > 0)) {
+      auto examples_iterable = features.examples_iterable(start, end);
+      for (const auto& example : examples_iterable) {
+        int32& example_partition = example_partition_ids[example.example_idx];
+        example_partition = boosted_trees::trees::DecisionTree::Traverse(
+            tree_config, example_partition, example);
+        DCHECK_GE(example_partition, 0);
+      }
+    } else {
+      std::fill(example_partition_ids + start, example_partition_ids + end, 0);
+    }
+  };
+
+  // Parallelize partitioning over the batch.
+  boosted_trees::utils::ParallelFor(batch_size, desired_parallelism,
+                                    thread_pool, partition_examples_subset);
+}
+
+void ExamplePartitioner::PartitionExamples(
+    const boosted_trees::trees::DecisionTreeConfig& tree_config,
+    const boosted_trees::utils::BatchFeatures& features,
+    const int desired_parallelism, thread::ThreadPool* const thread_pool,
+    int32* example_partition_ids) {
+  // Get batch size.
+  const int64 batch_size = features.batch_size();
+  if (batch_size <= 0) {
+    return;
+  }
+
+  // Lambda for doing a block of work.
+  auto partition_examples_subset = [&tree_config, &features,
+                                    &example_partition_ids](const int64 start,
+                                                            const int64 end) {
+    if (TF_PREDICT_TRUE(tree_config.nodes_size() > 0)) {
+      auto examples_iterable = features.examples_iterable(start, end);
+      for (const auto& example : examples_iterable) {
+        uint32 partition = boosted_trees::trees::DecisionTree::Traverse(
+            tree_config, 0, example);
+        example_partition_ids[example.example_idx] = partition;
+        DCHECK_GE(partition, 0);
+      }
+    } else {
+      std::fill(example_partition_ids + start, example_partition_ids + end, 0);
+    }
+  };
+
+  // Parallelize partitioning over the batch.
+  boosted_trees::utils::ParallelFor(batch_size, desired_parallelism,
+                                    thread_pool, partition_examples_subset);
+}
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h
new file mode 100644
index 0000000000..aed0d9fdac
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h
@@ -0,0 +1,53 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_PARTITIONERS_EXAMPLE_PARTITIONER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_PARTITIONERS_EXAMPLE_PARTITIONER_H_
+
+#include <vector>
+#include "tensorflow/contrib/boosted_trees/lib/trees/decision_tree.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/batch_features.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+// Partitions examples based on the path through the current tree.
+class ExamplePartitioner {
+ public:
+  // Updates partitions from previous set using the current tree structure by
+  // traversing sub-roots for each example. This method can be optionally
+  // parallelized using the passed thread pool.
+  static void UpdatePartitions(const trees::DecisionTreeConfig& tree_config,
+                               const utils::BatchFeatures& features,
+                               int desired_parallelism,
+                               thread::ThreadPool* const thread_pool,
+                               int32* example_partition_ids);
+
+  // Partitions examples using the current tree structure by traversing from
+  // root for each example. This method can be optionally parallelized using
+  // the passed thread pool.
+  static void PartitionExamples(const trees::DecisionTreeConfig& tree_config,
+                                const utils::BatchFeatures& features,
+                                int desired_parallelism,
+                                thread::ThreadPool* const thread_pool,
+                                int32* example_partition_ids);
+};
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_PARTITIONERS_EXAMPLE_PARTITIONER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc
new file mode 100644
index 0000000000..0d5326aa45
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc
@@ -0,0 +1,99 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace {
+
+class ExamplePartitionerTest : public ::testing::Test {
+ protected:
+  ExamplePartitionerTest()
+      : thread_pool_(tensorflow::Env::Default(), "test_pool", 2),
+        batch_features_(2) {
+    dense_matrix_ = test::AsTensor<float>({7.0f, -2.0f}, {2, 1});
+    TF_EXPECT_OK(
+        batch_features_.Initialize({dense_matrix_}, {}, {}, {}, {}, {}, {}));
+  }
+
+  thread::ThreadPool thread_pool_;
+  Tensor dense_matrix_;
+  boosted_trees::utils::BatchFeatures batch_features_;
+};
+
+TEST_F(ExamplePartitionerTest, EmptyTree) {
+  boosted_trees::trees::DecisionTreeConfig tree_config;
+  std::vector<int32> example_partition_ids(2);
+  ExamplePartitioner::UpdatePartitions(tree_config, batch_features_, 1,
+                                       &thread_pool_,
+                                       example_partition_ids.data());
+  EXPECT_EQ(0, example_partition_ids[0]);
+  EXPECT_EQ(0, example_partition_ids[1]);
+}
+
+TEST_F(ExamplePartitionerTest, UpdatePartitions) {
+  // Create tree with one split.
+  // TODO(salehay): figure out if we can use PARSE_TEXT_PROTO.
+  boosted_trees::trees::DecisionTreeConfig tree_config;
+  auto* split = tree_config.add_nodes()->mutable_dense_float_binary_split();
+  split->set_feature_column(0);
+  split->set_threshold(3.0f);
+  split->set_left_id(1);
+  split->set_right_id(2);
+  tree_config.add_nodes()->mutable_leaf();
+  tree_config.add_nodes()->mutable_leaf();
+
+  // Partition input:
+  // Instance 1 has !(7 <= 3) => go right => leaf 2.
+  // Instance 2 has (-2 <= 3) => go left => leaf 1.
+  std::vector<int32> example_partition_ids(2);
+  ExamplePartitioner::UpdatePartitions(tree_config, batch_features_, 1,
+                                       &thread_pool_,
+                                       example_partition_ids.data());
+  EXPECT_EQ(2, example_partition_ids[0]);
+  EXPECT_EQ(1, example_partition_ids[1]);
+}
+
+TEST_F(ExamplePartitionerTest, PartitionExamples) {
+  // Create tree with one split.
+  // TODO(salehay): figure out if we can use PARSE_TEXT_PROTO.
+  boosted_trees::trees::DecisionTreeConfig tree_config;
+  auto* split = tree_config.add_nodes()->mutable_dense_float_binary_split();
+  split->set_feature_column(0);
+  split->set_threshold(3.0f);
+  split->set_left_id(1);
+  split->set_right_id(2);
+  tree_config.add_nodes()->mutable_leaf();
+  tree_config.add_nodes()->mutable_leaf();
+
+  // Partition input:
+  // Instance 1 has !(7 <= 3) => go right => leaf 2.
+  // Instance 2 has (-2 <= 3) => go left => leaf 1.
+  std::vector<int32> example_partition_ids(2);
+  ExamplePartitioner::PartitionExamples(tree_config, batch_features_, 1,
+                                        &thread_pool_,
+                                        example_partition_ids.data());
+  EXPECT_EQ(2, example_partition_ids[0]);
+  EXPECT_EQ(1, example_partition_ids[1]);
+}
+
+}  // namespace
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc
new file mode 100644
index 0000000000..b880cf2c47
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc
@@ -0,0 +1,59 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+void BiasFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all examples and aggregate gradient stats for each sub-root.
+  for (int64 example_idx = 0; example_idx < batch_size_; ++example_idx) {
+    auto partition_id = example_partition_ids[example_idx];
+    gradient_stats_accumulator->AddStats(
+        slot_id_, class_id_, partition_id, kBiasFeatureId,
+        GradientStats(example_first_order_gradients,
+                      example_second_order_gradients, example_idx));
+  }
+}
+
+void BiasFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  split_candidates->clear();
+  split_candidates->reserve(roots.size());
+  boosted_trees::trees::TreeNode tree_node;
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    const NodeStats& root_node_stats = root_stats[root_idx];
+    tree_node.Clear();
+    root_node_stats.FillLeaf(class_id_, tree_node.mutable_leaf());
+    split_candidates->emplace_back(slot_id_, tree_node,
+                                   SplitStats(learner_config, root_node_stats));
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h
new file mode 100644
index 0000000000..5c0f99185a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h
@@ -0,0 +1,57 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_H_  // NOLINT
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_H_  // NOLINT
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a bias feature column in the single class case.
+// This handler is useful even if we don't introduce a bias feature because
+// it allows us to aggregate stats per partition which in turn allows us
+// to compute node stats for each root to split.
+class BiasFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  BiasFeatureColumnHandler(const uint32 class_id, const uint32 slot_id,
+                           const int64 batch_size)
+      : FeatureColumnHandler(class_id, slot_id, batch_size) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+  static constexpr auto kBiasFeatureId = 0;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_H_  // NOLINT
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc
new file mode 100644
index 0000000000..82664aed72
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc
@@ -0,0 +1,134 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 7;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class BiasFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  BiasFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 1, 3}) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new BiasFeatureColumnHandler(kClassId, kSlotId, kBatchSize));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  std::unique_ptr<BiasFeatureColumnHandler> handler_;
+};
+
+TEST_F(BiasFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition.
+  // Partition 0.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(-0.3f, 0.19f),
+      accumulator.GetStats(kSlotId, kClassId, 0,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+  // Partition 1.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(1.2f, 0.2f),
+      accumulator.GetStats(kSlotId, kClassId, 1,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+  // Partition 2.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(0.0f, 0.0f),
+      accumulator.GetStats(kSlotId, kClassId, 2,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+  // Partition 3.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(4.0f, 0.13f),
+      accumulator.GetStats(kSlotId, kClassId, 3,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+}
+
+TEST_F(BiasFeatureColumnHandlerTest, GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 3.
+  // Root 0 has zero gain and root 3 has the same gain as the leaf.
+  const std::vector<int32> roots = {0, 3};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(1), NodeStats(learner_config_, GradientStats(4.0f, 0.13f))};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect two candidate splits (one per root).
+  EXPECT_EQ(2, split_candidates.size());
+
+  // Verify first candidate for root 0, gain is expected to be the same as
+  // the left child since the root node gain is zero.
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0]);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kLeaf, tree_node0.node_case());
+  EXPECT_EQ(1, tree_node0.leaf().sparse_vector().index_size());
+  EXPECT_EQ(kClassId, tree_node0.leaf().sparse_vector().index(0));
+  EXPECT_EQ(1, tree_node0.leaf().sparse_vector().value_size());
+  EXPECT_EQ(root_stats[0].weight_contribution[0],
+            tree_node0.leaf().sparse_vector().value(0));
+
+  // Verify second candidate for root 3, gain is expected to be zero as
+  // the left child gain is equal to the parent gain.
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1]);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kLeaf, tree_node1.node_case());
+  EXPECT_EQ(1, tree_node1.leaf().sparse_vector().index_size());
+  EXPECT_EQ(kClassId, tree_node1.leaf().sparse_vector().index(0));
+  EXPECT_EQ(1, tree_node1.leaf().sparse_vector().value_size());
+  EXPECT_EQ(root_stats[1].weight_contribution[0],
+            tree_node1.leaf().sparse_vector().value(0));
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
new file mode 100644
index 0000000000..2f76224ff0
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
@@ -0,0 +1,140 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h"
+
+#include "tensorflow/core/platform/macros.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+namespace {
+
+// Creates a categorical Id split node without assigning children.
+boosted_trees::trees::TreeNode CreateCategoricalIdNode(
+    const int32 feature_column, const int32 id) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_categorical_id_binary_split();
+  split->set_feature_column(feature_column);
+  split->set_feature_id(id);
+  return split_node;
+}
+
+}  // namespace
+
+void CategoricalFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all rows and aggregate gradient stats for each feature id.
+  const int64 num_rows = indices_.dimension(0);
+  for (int64 row_idx = 0; row_idx < num_rows; ++row_idx) {
+    auto example_idx = indices_(row_idx, 0);
+    auto feature_id = values_(row_idx);
+    const GradientStats norm_gradient_stats(example_first_order_gradients,
+                                            example_second_order_gradients,
+                                            example_idx);
+    auto partition_id = example_partition_ids[example_idx];
+    gradient_stats_accumulator->AddStats(slot_id_, class_id_, partition_id,
+                                         feature_id, norm_gradient_stats);
+  }
+}
+
+void CategoricalFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  // Build a reverse lookup of partition id to root idx.
+  std::unordered_map<int32, size_t> partition_id_to_root_idx;
+  partition_id_to_root_idx.reserve(roots.size());
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    partition_id_to_root_idx[roots[root_idx]] = root_idx;
+  }
+
+  // Initialize split candidates.
+  split_candidates->clear();
+  if (!roots.empty()) {
+    FeatureSplitCandidate empty_candidate(
+        root_stats[0].weight_contribution.size());
+    split_candidates->resize(roots.size(), empty_candidate);
+  }
+  for (auto& split_candidate : *split_candidates) {
+    split_candidate.split_stats.gain = std::numeric_limits<float>::lowest();
+  }
+
+  // Evaluate split candidates for every root as each is a separate
+  // logical partition over the examples.
+  // Then for each root, we evaluate every feature id as an equality split
+  // and pick the highest split gain.
+  for (const auto& entry :
+       gradient_stats_accumulator.GetFeatureStats(slot_id_)) {
+    DCHECK_EQ(entry.first.class_id, class_id_);
+
+    // Get partition id and root node stats.
+    const int32 partition_id = entry.first.partition_id;
+    auto root_idx_it = partition_id_to_root_idx.find(partition_id);
+    if (root_idx_it == partition_id_to_root_idx.end()) {
+      // Inactive partition.
+      continue;
+    }
+    size_t root_idx = root_idx_it->second;
+    const NodeStats& root_node_stats = root_stats[root_idx];
+
+    // Get gradient stats.
+    const auto& left_gradient_stats = entry.second;
+    auto right_gradient_stats =
+        root_node_stats.gradient_stats - left_gradient_stats;
+
+    // Get node stats.
+    NodeStats left_node_stats(learner_config, left_gradient_stats);
+    NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+    // Generate split candiate and update best split candidate for the
+    // current root if needed.
+    FeatureSplitCandidate split_candidate(
+        slot_id_,
+        CreateCategoricalIdNode(feature_column_, entry.first.feature_id),
+        SplitStats(learner_config, root_node_stats, left_node_stats,
+                   right_node_stats));
+    FeatureSplitCandidate& best_split_candidate = (*split_candidates)[root_idx];
+    if (TF_PREDICT_FALSE(best_split_candidate.tree_node.node_case() ==
+                         boosted_trees::trees::TreeNode::NODE_NOT_SET)) {
+      // Always replace candidates with no node set.
+      best_split_candidate = std::move(split_candidate);
+    } else if (TF_PREDICT_FALSE(split_candidate.split_stats.gain ==
+                                best_split_candidate.split_stats.gain)) {
+      // Tie break on feature id.
+      auto best_split_feature_id =
+          best_split_candidate.tree_node.categorical_id_binary_split()
+              .feature_id();
+      if (entry.first.feature_id < best_split_feature_id) {
+        best_split_candidate = std::move(split_candidate);
+      }
+    } else if (split_candidate.split_stats.gain >
+               best_split_candidate.split_stats.gain) {
+      best_split_candidate = std::move(split_candidate);
+    }
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h
new file mode 100644
index 0000000000..ef964ba716
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h
@@ -0,0 +1,64 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_CATEGORICAL_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_CATEGORICAL_FEATURE_COLUMN_HANDLER_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a categorical feature column in the single class case.
+class CategoricalFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  CategoricalFeatureColumnHandler(const int32 class_id, const int32 slot_id,
+                                  const int64 batch_size,
+                                  const int32 feature_column,
+                                  TTypes<int64>::ConstMatrix indices,
+                                  TTypes<int64>::ConstVec values)
+      : FeatureColumnHandler(class_id, slot_id, batch_size),
+        feature_column_(feature_column),
+        indices_(indices),
+        values_(values) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+ protected:
+  const int32 feature_column_;
+  TTypes<int64>::ConstMatrix indices_;
+  TTypes<int64>::ConstVec values_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_CATEGORICAL_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc
new file mode 100644
index 0000000000..abd7238464
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc
@@ -0,0 +1,165 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 7;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+const auto kFeatureColumn = 3;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class CategoricalFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  // The data looks like the following:
+  // Example |  Gradients    | Partition | Feature Id |
+  // i0      |  (0.2, 0.12)  |     0     |    1,2     |
+  // i1      |  (-0.5, 0.07) |     0     |            |
+  // i2      |  (1.2, 0.2)   |     0     |     2      |
+  // i3      |  (4.0, 0.13)  |     1     |     0      |
+  CategoricalFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 0, 1}),
+        indices_(test::AsTensor<int64>({0, 0, 0, 1, 2, 0, 3, 0}, {4, 2})),
+        values_(test::AsTensor<int64>({1, 2, 2, 0}, {4})) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new CategoricalFeatureColumnHandler(
+        kClassId, kSlotId, kBatchSize, kFeatureColumn, indices_.matrix<int64>(),
+        values_.vec<int64>()));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  const Tensor indices_;
+  const Tensor values_;
+  std::unique_ptr<FeatureColumnHandler> handler_;
+};
+
+TEST_F(CategoricalFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition and feature.
+  // Partition 0, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 0));
+  // Partition 0, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.2f, 0.12f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 1));
+  // Partition 0, Feature 2.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.2f + 1.2f, 0.12f + 0.2f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 2));
+
+  // Partition 1, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(4.0f, 0.13f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 0));
+  // Partition 1, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 1));
+  // Partition 1, Feature 2.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 2));
+}
+
+TEST_F(CategoricalFeatureColumnHandlerTest, GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 1.
+  // The root stats are derived from the per-partition total gradient stats.
+  const std::vector<int32> roots = {0, 1, 5};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(learner_config_, GradientStats(0.9f, 0.39f)),
+      NodeStats(learner_config_, GradientStats(4.0f, 0.13f)), NodeStats(1)};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect three candidate splits (one per root).
+  EXPECT_EQ(3, split_candidates.size());
+
+  // Verify candidate for root 0, the best split occurs when we route
+  // example i0, i2 left and i1 right.
+  const NodeStats expected_left_node0(learner_config_,
+                                      GradientStats(0.2f + 1.2f, 0.12f + 0.2f));
+  const NodeStats expected_right_node0(
+      learner_config_,
+      root_stats[0].gradient_stats - expected_left_node0.gradient_stats);
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0],
+                                         expected_left_node0,
+                                         expected_right_node0);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(
+      boosted_trees::trees::TreeNode::kCategoricalIdBinarySplitFieldNumber,
+      tree_node0.node_case());
+  const auto& split0 = tree_node0.categorical_id_binary_split();
+  EXPECT_EQ(2, split0.feature_id());
+  EXPECT_EQ(kFeatureColumn, split0.feature_column());
+
+  // Verify candidate for root 1, there's only one active feature here
+  // so zero gain is expected.
+  const NodeStats expected_left_node1(learner_config_,
+                                      root_stats[1].gradient_stats);
+  const NodeStats expected_right_node1(learner_config_, GradientStats(0, 0));
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1],
+                                         expected_left_node1,
+                                         expected_right_node1);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(
+      boosted_trees::trees::TreeNode::kCategoricalIdBinarySplitFieldNumber,
+      tree_node1.node_case());
+  const auto& split1 = tree_node1.categorical_id_binary_split();
+  EXPECT_EQ(0, split1.feature_id());
+  EXPECT_EQ(kFeatureColumn, split1.feature_column());
+
+  // Verify there are no candidate splits for root 5.
+  const auto& tree_node2 = split_candidates[2].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::NODE_NOT_SET,
+            tree_node2.node_case());
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
new file mode 100644
index 0000000000..f43112c8a4
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
@@ -0,0 +1,116 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+namespace {
+
+// Creates a dense split node without assigning children.
+boosted_trees::trees::TreeNode CreateDenseSplitNode(const int32 feature_column,
+                                                    const float threshold) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_dense_float_binary_split();
+  split->set_feature_column(feature_column);
+  split->set_threshold(threshold);
+  return split_node;
+}
+
+}  // namespace
+
+void DenseQuantizedFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all examples and aggregate gradient stats for each partition
+  // and quantized feature bucket.
+  for (int64 example_idx = 0; example_idx < batch_size_; ++example_idx) {
+    auto partition_id = example_partition_ids[example_idx];
+    auto feature_id = dense_quantized_values_(example_idx);
+    gradient_stats_accumulator->AddStats(
+        slot_id_, class_id_, partition_id, feature_id,
+        GradientStats(example_first_order_gradients,
+                      example_second_order_gradients, example_idx));
+  }
+}
+
+void DenseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  // Evaluate split candidates for every root as each is a separate
+  // logical partition over the examples.
+  // Then for each root, we do a forward-only pass over the quantized
+  // feature buckets accumulating gradients from left to right.
+  // Split gains are evaluated at every threshold and the best split is picked.
+  split_candidates->clear();
+  split_candidates->reserve(roots.size());
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    // Get partition Id and root node stats.
+    const int32 partition_id = roots[root_idx];
+    const NodeStats& root_node_stats = root_stats[root_idx];
+
+    // Forward left to right pass over quantiles.
+    GradientStats left_gradient_stats;
+    GradientStats right_gradient_stats(root_node_stats.gradient_stats);
+    FeatureSplitCandidate best_split_candidate(
+        root_node_stats.weight_contribution.size());
+    best_split_candidate.split_stats.gain =
+        std::numeric_limits<float>::lowest();
+    for (int bucket_id = 0; bucket_id < dense_quantiles_.size(); ++bucket_id) {
+      // Get gradient stats.
+      auto gradient_stats = gradient_stats_accumulator.GetStats(
+          slot_id_, class_id_, partition_id, bucket_id);
+      if (gradient_stats.IsZero()) {
+        continue;
+      }
+
+      // Update gradient stats.
+      left_gradient_stats += gradient_stats;
+      right_gradient_stats =
+          root_node_stats.gradient_stats - left_gradient_stats;
+
+      // Get node stats
+      NodeStats left_node_stats(learner_config, left_gradient_stats);
+      NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+      // Generate split candiate.
+      const float threshold = dense_quantiles_(bucket_id);
+      FeatureSplitCandidate split_candidate(
+          slot_id_, CreateDenseSplitNode(dense_feature_column_, threshold),
+          SplitStats(learner_config, root_node_stats, left_node_stats,
+                     right_node_stats));
+      if (split_candidate.split_stats.gain >
+          best_split_candidate.split_stats.gain) {
+        best_split_candidate = std::move(split_candidate);
+      }
+    }
+
+    // Add best candidate for partition.
+    split_candidates->push_back(std::move(best_split_candidate));
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h
new file mode 100644
index 0000000000..0f3858e4d8
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h
@@ -0,0 +1,62 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_DENSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_DENSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a dense quantized feature column in the single class case.
+class DenseQuantizedFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  DenseQuantizedFeatureColumnHandler(
+      const int32 class_id, const int32 slot_id, const int64 batch_size,
+      const int32 dense_feature_column, TTypes<float>::ConstVec dense_quantiles,
+      TTypes<int32>::ConstVec dense_quantized_values)
+      : FeatureColumnHandler(class_id, slot_id, batch_size),
+        dense_feature_column_(dense_feature_column),
+        dense_quantiles_(dense_quantiles),
+        dense_quantized_values_(dense_quantized_values) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+ protected:
+  const int32 dense_feature_column_;
+  TTypes<float>::ConstVec dense_quantiles_;
+  TTypes<int32>::ConstVec dense_quantized_values_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_DENSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc
new file mode 100644
index 0000000000..396f48e532
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc
@@ -0,0 +1,155 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 1;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+const auto kFeatureColumn = 2;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class DenseQuantizedFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  // The data looks like the following:
+  // Example |  Gradients    | Partition | Dense Quantile |
+  // i0      |  (0.2, 0.12)  | 0         | 1              |
+  // i1      |  (-0.5, 0.07) | 0         | 1              |
+  // i2      |  (1.2, 0.2)   | 0         | 0              |
+  // i3      |  (4.0, 0.13)  | 1         | 1              |
+  DenseQuantizedFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 0, 1}),
+        dense_quantiles_(test::AsTensor<float>({0.3f, 0.52f}, {2})),
+        dense_quantized_values_(test::AsTensor<int32>({1, 1, 0, 1}, {4})) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new DenseQuantizedFeatureColumnHandler(
+        kClassId, kSlotId, kBatchSize, kFeatureColumn,
+        dense_quantiles_.vec<float>(), dense_quantized_values_.vec<int32>()));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  const Tensor dense_quantiles_;
+  const Tensor dense_quantized_values_;
+  std::unique_ptr<FeatureColumnHandler> handler_;
+};
+
+TEST_F(DenseQuantizedFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition and feature.
+  // Partition 0, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(1.2f, 0.2f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 0));
+  // Partition 0, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(-0.3f, 0.19f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 1));
+  // Partition 1, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 0));
+  // Partition 1, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(4.0f, 0.13f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 1));
+}
+
+TEST_F(DenseQuantizedFeatureColumnHandlerTest, GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 1.
+  // The root stats are derived from the per-partition total gradient stats.
+  const std::vector<int32> roots = {0, 1, 5};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(learner_config_, GradientStats(0.9f, 0.39f)),
+      NodeStats(learner_config_, GradientStats(4.0f, 0.13f)), NodeStats(1)};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect three candidate splits (one per root).
+  EXPECT_EQ(3, split_candidates.size());
+
+  // Verify candidate for root 0, the best split occurs when we route
+  // example i2 left and i0, i1 right.
+  const NodeStats expected_left_node0(learner_config_,
+                                      GradientStats(1.2f, 0.2f));
+  const NodeStats expected_right_node0(
+      learner_config_,
+      root_stats[0].gradient_stats - expected_left_node0.gradient_stats);
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0],
+                                         expected_left_node0,
+                                         expected_right_node0);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kDenseFloatBinarySplit,
+            tree_node0.node_case());
+  const auto& split0 = tree_node0.dense_float_binary_split();
+  EXPECT_FLOAT_EQ(dense_quantiles_.vec<float>()(0), split0.threshold());
+  EXPECT_EQ(kFeatureColumn, split0.feature_column());
+
+  // Verify candidate for root 1, there's only one active bucket here
+  // so zero gain is expected.
+  const NodeStats expected_left_node1(learner_config_,
+                                      root_stats[1].gradient_stats);
+  const NodeStats expected_right_node1(learner_config_, GradientStats(0, 0));
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1],
+                                         expected_left_node1,
+                                         expected_right_node1);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kDenseFloatBinarySplit,
+            tree_node1.node_case());
+  const auto& split1 = tree_node1.dense_float_binary_split();
+  EXPECT_FLOAT_EQ(dense_quantiles_.vec<float>()(1), split1.threshold());
+  EXPECT_EQ(kFeatureColumn, split1.feature_column());
+
+  // Verify there are no candidate splits for root 5.
+  const auto& tree_node2 = split_candidates[2].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::NODE_NOT_SET,
+            tree_node2.node_case());
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h
new file mode 100644
index 0000000000..a3177b31eb
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h
@@ -0,0 +1,82 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_FEATURE_COLUMN_HANDLER_H_
+
+#include <vector>
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h"
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_types.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler interface for feature columns. Each feature column type may
+// have its own handler which encapsulates the logic of aggregating gradient
+// stats as well as generating split candidates for each partition.
+// Handlers can be stateful and must be thread compatible.
+class FeatureColumnHandler {
+ public:
+  FeatureColumnHandler(const int32 class_id, const int32 slot_id,
+                       const int64 batch_size)
+      : class_id_(class_id), slot_id_(slot_id), batch_size_(batch_size) {}
+
+  virtual ~FeatureColumnHandler() {}
+  FeatureColumnHandler(const FeatureColumnHandler& other) = delete;
+  FeatureColumnHandler& operator=(const FeatureColumnHandler& other) = delete;
+
+  // Aggregates example gradient stats for the feature column.
+  virtual void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const = 0;
+
+  // Generates feature column split candidates for the specified roots.
+  virtual void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const = 0;
+
+  // Accessors.
+  int32 class_id() const { return class_id_; }
+  int32 slot_id() const { return slot_id_; }
+  int64 batch_size() const { return batch_size_; }
+
+ protected:
+  // The class Id.
+  const int32 class_id_;
+
+  // The slod Id for use as a unique Id across all feature columns.
+  const int32 slot_id_;
+
+  // Size of the batch of examples.
+  const int64 batch_size_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  //  THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
new file mode 100644
index 0000000000..76da16ab93
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
@@ -0,0 +1,172 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+namespace {
+
+// Creates a sparse default right split node without assigning children.
+boosted_trees::trees::TreeNode CreateSparseSplitNodeDefaultRight(
+    int32 feature_column, float threshold) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_sparse_float_binary_split_default_right()
+                    ->mutable_split();
+  split->set_feature_column(feature_column);
+  split->set_threshold(threshold);
+  return split_node;
+}
+
+// Creates a sparse default left split node without assigning children.
+boosted_trees::trees::TreeNode CreateSparseSplitNodeDefaultLeft(
+    int32 feature_column, float threshold) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_sparse_float_binary_split_default_left()
+                    ->mutable_split();
+  split->set_feature_column(feature_column);
+  split->set_threshold(threshold);
+  return split_node;
+}
+
+}  // namespace
+
+void SparseQuantizedFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all rows and aggregate gradient stats for each partition
+  // and quantized feature bucket.
+  const int64 num_rows = sparse_indices_.dimension(0);
+  for (int64 row_idx = 0; row_idx < num_rows; ++row_idx) {
+    auto example_idx = sparse_indices_(row_idx, 0);
+    auto partition_id = example_partition_ids[example_idx];
+    auto feature_id = sparse_quantized_values_(row_idx);
+    gradient_stats_accumulator->AddStats(
+        slot_id_, class_id_, partition_id, feature_id,
+        GradientStats(example_first_order_gradients,
+                      example_second_order_gradients, example_idx));
+  }
+}
+
+void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  // Evaluate split candidates for every root as each is a separate
+  // logical partition over the examples.
+  // Then for each root, we do both a forward left to right pass and a backward
+  // right to left pass over the quantized feature buckets accumulating
+  // gradients on one side and using the root aggregate gradients to get the
+  // gradients for the other side. Split gains are evaluated for each pass at
+  // every threshold and the best split is picked.
+  split_candidates->clear();
+  split_candidates->reserve(roots.size());
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    // Get partition Id and root node stats.
+    const int32 partition_id = roots[root_idx];
+    const NodeStats& root_node_stats = root_stats[root_idx];
+
+    // Forward pass with right default direction.
+    GradientStats left_gradient_stats;
+    GradientStats right_gradient_stats(root_node_stats.gradient_stats);
+    FeatureSplitCandidate best_split_candidate(
+        root_node_stats.weight_contribution.size());
+    best_split_candidate.split_stats.gain =
+        std::numeric_limits<float>::lowest();
+    for (int bucket_id = 0; bucket_id < sparse_quantiles_.size(); ++bucket_id) {
+      // Get gradient stats.
+      auto gradient_stats = gradient_stats_accumulator.GetStats(
+          slot_id_, class_id_, partition_id, bucket_id);
+      if (gradient_stats.IsZero()) {
+        continue;
+      }
+
+      // Update gradient stats.
+      left_gradient_stats += gradient_stats;
+      right_gradient_stats =
+          root_node_stats.gradient_stats - left_gradient_stats;
+
+      // Get node stats
+      NodeStats left_node_stats(learner_config, left_gradient_stats);
+      NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+      // Generate split candiate.
+      const float threshold = sparse_quantiles_(bucket_id);
+      FeatureSplitCandidate split_candidate(
+          slot_id_,
+          CreateSparseSplitNodeDefaultRight(sparse_feature_column_, threshold),
+          SplitStats(learner_config, root_node_stats, left_node_stats,
+                     right_node_stats));
+      if (split_candidate.split_stats.gain >
+          best_split_candidate.split_stats.gain) {
+        best_split_candidate = std::move(split_candidate);
+      }
+    }
+
+    // Determine if we need a backward pass by checking if the residual gradient
+    // after forward aggregation is almost the same as the aggregated gradient.
+    // for the current root. This helps avoid unecessary computation as well
+    // as consistency due to floating point precision.
+    if (!right_gradient_stats.IsAlmostZero()) {
+      // Backward pass with left default direction.
+      right_gradient_stats = GradientStats();
+      left_gradient_stats = root_node_stats.gradient_stats;
+      for (int bucket_id = sparse_quantiles_.size() - 1; bucket_id > 0;
+           --bucket_id) {
+        // Get gradient stats.
+        auto gradient_stats = gradient_stats_accumulator.GetStats(
+            slot_id_, class_id_, partition_id, bucket_id);
+        if (gradient_stats.IsZero()) {
+          continue;
+        }
+
+        // Update gradient stats.
+        right_gradient_stats += gradient_stats;
+        left_gradient_stats = root_node_stats.gradient_stats - gradient_stats;
+
+        // Get node stats
+        NodeStats left_node_stats(learner_config, left_gradient_stats);
+        NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+        // Generate split candiate.
+        const float threshold = sparse_quantiles_(bucket_id - 1);
+        FeatureSplitCandidate split_candidate(
+            slot_id_,
+            CreateSparseSplitNodeDefaultLeft(sparse_feature_column_, threshold),
+            SplitStats(learner_config, root_node_stats, left_node_stats,
+                       right_node_stats));
+        if (split_candidate.split_stats.gain >
+            best_split_candidate.split_stats.gain) {
+          best_split_candidate = std::move(split_candidate);
+        }
+      }
+    }
+
+    // Add best candidate for partition.
+    split_candidates->push_back(std::move(best_split_candidate));
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h
new file mode 100644
index 0000000000..eb63e70547
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h
@@ -0,0 +1,67 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_SPARSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_SPARSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a sparse quantized feature column in the single class case.
+class SparseQuantizedFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  SparseQuantizedFeatureColumnHandler(
+      const int32 class_id, const int32 slot_id, const int64 batch_size,
+      const int32 sparse_feature_column,
+      TTypes<float>::ConstVec sparse_quantiles,
+      TTypes<int64>::ConstMatrix sparse_indices,
+      TTypes<int32>::ConstVec sparse_quantized_values)
+      : FeatureColumnHandler(class_id, slot_id, batch_size),
+        sparse_feature_column_(sparse_feature_column),
+        sparse_quantiles_(sparse_quantiles),
+        sparse_indices_(sparse_indices),
+        sparse_quantized_values_(sparse_quantized_values) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+ protected:
+  const int32 sparse_feature_column_;
+  TTypes<float>::ConstVec sparse_quantiles_;
+  TTypes<int64>::ConstMatrix sparse_indices_;
+  TTypes<int32>::ConstVec sparse_quantized_values_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  //  THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_SPARSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc
new file mode 100644
index 0000000000..db8c64a617
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc
@@ -0,0 +1,162 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 3;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+const auto kFeatureColumn = 4;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class SparseQuantizedFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  // The data looks like the following:
+  // Example |  Gradients    | Partition | Sparse Quantile |
+  // i0      |  (0.2, 0.12)  | 0         | 1               |
+  // i1      |  (-0.5, 0.07) | 0         | N/A             |
+  // i2      |  (1.2, 0.2)   | 0         | 0               |
+  // i3      |  (4.0, 0.13)  | 1         | 1               |
+  SparseQuantizedFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 0, 1}),
+        sparse_quantiles_(test::AsTensor<float>({0.3f, 0.52f}, {2})),
+        sparse_indices_(test::AsTensor<int64>({0, 0, 2, 0, 3, 0}, {3, 2})),
+        sparse_quantized_values_(test::AsTensor<int32>({1, 0, 1}, {3})) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new SparseQuantizedFeatureColumnHandler(
+        kClassId, kSlotId, kBatchSize, kFeatureColumn,
+        sparse_quantiles_.vec<float>(), sparse_indices_.matrix<int64>(),
+        sparse_quantized_values_.vec<int32>()));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  const Tensor sparse_quantiles_;
+  const Tensor sparse_indices_;
+  const Tensor sparse_quantized_values_;
+  std::unique_ptr<FeatureColumnHandler> handler_;
+};
+
+TEST_F(SparseQuantizedFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition and feature.
+  // Partition 0, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(1.2f, 0.2f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 0));
+  // Partition 0, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.2f, 0.12f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 1));
+  // Partition 1, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 0));
+  // Partition 1, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(4.0f, 0.13f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 1));
+}
+
+TEST_F(SparseQuantizedFeatureColumnHandlerTest,
+       GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 1.
+  // The root stats are derived from the per-partition total gradient stats.
+  const std::vector<int32> roots = {0, 1, 9};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(learner_config_, GradientStats(0.9f, 0.39f)),
+      NodeStats(learner_config_, GradientStats(4.0f, 0.13f)), NodeStats(1)};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect three candidate splits (one per root).
+  EXPECT_EQ(3, split_candidates.size());
+
+  // Verify candidate for root 0, the best split occurs when we route
+  // example i0 and i2 to the left and i1 to the right (by default direction).
+  const NodeStats expected_left_node0(learner_config_,
+                                      GradientStats(0.2f + 1.2f, 0.12f + 0.2f));
+  const NodeStats expected_right_node0(
+      learner_config_,
+      root_stats[0].gradient_stats - expected_left_node0.gradient_stats);
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0],
+                                         expected_left_node0,
+                                         expected_right_node0);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kSparseFloatBinarySplitDefaultRight,
+            tree_node0.node_case());
+  const auto& split0 =
+      tree_node0.sparse_float_binary_split_default_right().split();
+  EXPECT_FLOAT_EQ(sparse_quantiles_.vec<float>()(1), split0.threshold());
+  EXPECT_EQ(kFeatureColumn, split0.feature_column());
+
+  // Verify candidate for root 1, there's only one active bucket here
+  // so zero gain is expected.
+  const NodeStats expected_left_node1(learner_config_,
+                                      root_stats[1].gradient_stats);
+  const NodeStats expected_right_node1(learner_config_, GradientStats(0, 0));
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1],
+                                         expected_left_node1,
+                                         expected_right_node1);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kSparseFloatBinarySplitDefaultRight,
+            tree_node1.node_case());
+  const auto& split1 =
+      tree_node1.sparse_float_binary_split_default_right().split();
+  EXPECT_FLOAT_EQ(sparse_quantiles_.vec<float>()(1), split1.threshold());
+  EXPECT_EQ(kFeatureColumn, split1.feature_column());
+
+  // Verify there are no candidate splits for root 9.
+  const auto& tree_node2 = split_candidates[2].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::NODE_NOT_SET,
+            tree_node2.node_case());
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h
new file mode 100644
index 0000000000..fe22691178
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h
@@ -0,0 +1,61 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_FEATURE_SPLIT_CANDIDATE_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_FEATURE_SPLIT_CANDIDATE_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// FeatureSplitCandidate holds the split candidate node along with the stats.
+struct FeatureSplitCandidate {
+  // Empty split candidate.
+  explicit FeatureSplitCandidate(const int output_length)
+      : feature_column_slot_id(kInvalidFeatureColumnSlot),
+        split_stats(output_length) {}
+
+  // Feature binary split candidate.
+  FeatureSplitCandidate(const int64 fc_slot_id,
+                        const boosted_trees::trees::TreeNode& node,
+                        const SplitStats& stats)
+      : feature_column_slot_id(fc_slot_id),
+        tree_node(node),
+        split_stats(stats) {}
+
+  // Globally unique slot Id identifying the feature column
+  // used in this split candidates.
+  int64 feature_column_slot_id;
+
+  // Tree node for the candidate split.
+  boosted_trees::trees::TreeNode tree_node;
+
+  // Split stats.
+  SplitStats split_stats;
+
+  // Invalid feature column slot reserved value.
+  static constexpr int64 kInvalidFeatureColumnSlot = -1;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_FEATURE_SPLIT_CANDIDATE_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h
new file mode 100644
index 0000000000..dad64bf165
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h
@@ -0,0 +1,193 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_GRADIENT_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_GRADIENT_STATS_H_
+
+#include <math.h>
+
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_util.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+const double kEps = 1e-6;
+
+// A data structure for accumulating a Tensor value.
+struct TensorStat {
+  TensorStat() {}
+
+  explicit TensorStat(const float v) : t(DT_FLOAT, TensorShape({1})) {
+    t.flat<float>()(0) = v;
+  }
+
+  explicit TensorStat(const Tensor& rt) : t(tensor::DeepCopy(rt)) {}
+
+  TensorStat(const TensorStat& ts) : t(tensor::DeepCopy(ts.t)) {}
+
+  TensorStat& operator+=(const TensorStat& other) {
+    if (t.NumElements() == 0) {
+      t = tensor::DeepCopy(other.t);
+      return (*this);
+    }
+    CHECK(t.shape() == other.t.shape())
+        << "My shape = " << t.shape().DebugString()
+        << " Other shape = " << other.t.shape().DebugString();
+    auto me_flat = t.unaligned_flat<float>();
+    auto other_flat = other.t.unaligned_flat<float>();
+    for (int i = 0; i < me_flat.size(); i++) {
+      me_flat(i) += other_flat(i);
+    }
+    return (*this);
+  }
+
+  TensorStat& operator-=(const TensorStat& other) {
+    if (other.t.NumElements() == 0) {
+      return (*this);
+    }
+    CHECK(t.shape() == other.t.shape())
+        << "My shape = " << t.shape().DebugString()
+        << " Other shape = " << other.t.shape().DebugString();
+    auto me_flat = t.unaligned_flat<float>();
+    auto other_flat = other.t.unaligned_flat<float>();
+    for (int i = 0; i < me_flat.size(); i++) {
+      me_flat(i) -= other_flat(i);
+    }
+    return (*this);
+  }
+
+  TensorStat& operator*=(float value) {
+    auto me_flat = t.unaligned_flat<float>();
+    for (size_t i = 0; i < me_flat.size(); i++) {
+      me_flat(i) *= value;
+    }
+    return (*this);
+  }
+
+  bool IsZero() const {
+    auto me_flat = t.unaligned_flat<float>();
+    for (int i = 0; i < me_flat.size(); i++) {
+      if (me_flat(i) != 0.0f) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  // Checks if the L^2 magnitude of the tensor is less than eps.
+  bool IsAlmostZero(const float eps = kEps) const {
+    auto me_flat = t.unaligned_flat<float>();
+    double s = 0.0;
+    for (int i = 0; i < me_flat.size(); i++) {
+      s += me_flat(i) * me_flat(i);
+      if (s > eps * eps) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  float Magnitude() const {
+    auto me_flat = t.unaligned_flat<float>();
+    double s = 0.0;
+    for (int i = 0; i < me_flat.size(); i++) {
+      s += me_flat(i) * me_flat(i);
+    }
+    return sqrt(s);
+  }
+
+  string DebugString() const { return t.DebugString(); }
+
+  Tensor t;
+};
+
+// GradientStats holds first and second order gradient stats.
+struct GradientStats {
+  GradientStats() {}
+
+  // Legacy constructor for tests
+  GradientStats(float g, float h) : first(g), second(h) {}
+
+  GradientStats(const Tensor& g, const Tensor& h) : first(g), second(h) {}
+
+  GradientStats(const Tensor& g, const Tensor& h, int64 example_index)
+      : first(g.Slice(example_index, example_index + 1)),
+        second(h.Slice(example_index, example_index + 1)) {}
+
+  GradientStats& operator+=(const GradientStats& other) {
+    first += other.first;
+    second += other.second;
+    return (*this);
+  }
+
+  GradientStats& operator*=(float value) {
+    first *= value;
+    second *= value;
+    return (*this);
+  }
+
+  GradientStats& operator-=(const GradientStats& other) {
+    first -= other.first;
+    second -= other.second;
+    return (*this);
+  }
+
+  bool IsZero() const { return first.IsZero() && second.IsZero(); }
+
+  bool IsAlmostZero(const float eps = kEps) const {
+    return first.IsAlmostZero(eps) && second.IsAlmostZero(eps);
+  }
+
+  float Magnitude() const { return second.Magnitude(); }
+
+  string DebugString() const {
+    return "First = " + first.DebugString() +
+           " Second = " + second.DebugString();
+  }
+
+  TensorStat first;
+  TensorStat second;
+};
+
+struct GradientStatsAccumulator {
+  void operator()(const GradientStats& from, GradientStats* to) const {
+    (*to) += from;
+  }
+};
+
+inline GradientStats operator+(const GradientStats& a, const GradientStats& b) {
+  GradientStats ret(a);
+  ret += b;
+  return ret;
+}
+
+inline GradientStats operator-(const GradientStats& a, const GradientStats& b) {
+  GradientStats ret(a);
+  ret -= b;
+  return ret;
+}
+
+// Helper macro to check gradient stats approximate equality.
+#define EXPECT_GRADIENT_STATS_EQ(val1, val2) \
+  EXPECT_TRUE((val1 - val2).IsAlmostZero());
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_GRADIENT_STATS_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h
new file mode 100644
index 0000000000..4e5f53874d
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h
@@ -0,0 +1,301 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_NODE_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_NODE_STATS_H_
+
+#include "third_party/eigen3/Eigen/Core"
+#include "third_party/eigen3/Eigen/Eigenvalues"
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+using tensorflow::boosted_trees::learner::LearnerConfig;
+using tensorflow::boosted_trees::learner::LearnerConfig_MultiClassStrategy;
+using tensorflow::boosted_trees::learner::
+    LearnerConfig_MultiClassStrategy_DIAGONAL_HESSIAN;
+using tensorflow::boosted_trees::learner::
+    LearnerConfig_MultiClassStrategy_FULL_HESSIAN;
+using tensorflow::boosted_trees::learner::
+    LearnerConfig_MultiClassStrategy_TREE_PER_CLASS;
+
+// NodeStats holds aggregate gradient stats as well as metadata about the node.
+struct NodeStats {
+  // Initialize the NodeStats with 0 stats.  We need the output length
+  // so that we can make weight_contribution the right length.
+  explicit NodeStats(const int output_length)
+      : weight_contribution(output_length, 0.0f), gain(0) {}
+
+  NodeStats(const LearnerConfig& learner_config,
+            const GradientStats& grad_stats)
+      : NodeStats(learner_config.regularization().l1(),
+                  learner_config.regularization().l2(),
+                  learner_config.constraints().min_node_weight(),
+                  learner_config.multi_class_strategy(), grad_stats) {}
+
+  NodeStats(float l1_reg, float l2_reg, float min_node_weight,
+            const LearnerConfig_MultiClassStrategy& strategy,
+            const GradientStats& grad_stats)
+      : gradient_stats(grad_stats), gain(0) {
+    switch (strategy) {
+      case LearnerConfig_MultiClassStrategy_TREE_PER_CLASS: {
+        float g;
+        float h;
+        // Initialize now in case of early return.
+        weight_contribution.push_back(0.0f);
+
+        if (grad_stats.first.t.NumElements() == 0 ||
+            grad_stats.second.t.NumElements() == 0) {
+          return;
+        }
+
+        g = grad_stats.first.t.unaligned_flat<float>()(0);
+        h = grad_stats.second.t.unaligned_flat<float>()(0);
+
+        if (grad_stats.IsAlmostZero() || h <= min_node_weight) {
+          return;
+        }
+
+        // Apply L1 regularization.
+        if (l1_reg > 0) {
+          if (g > l1_reg) {
+            g -= l1_reg;
+          } else if (g < -l1_reg) {
+            g += l1_reg;
+          } else {
+            return;
+          }
+        }
+
+        // The node gain is given by: (l'^2) / (l'' + l2_reg) and the node
+        // weight
+        // contribution is given by: (-l') / (l'' + l2_reg).
+        // Note that l'' can't be zero here because of the min node weight check
+        // since min node weight must be >= 0.
+        weight_contribution[0] = -g / (h + l2_reg);
+        gain = (weight_contribution[0] * -g);
+        break;
+      }
+      case LearnerConfig_MultiClassStrategy_FULL_HESSIAN: {
+        weight_contribution.clear();
+
+        if (grad_stats.first.t.NumElements() == 0 ||
+            grad_stats.second.t.NumElements() == 0) {
+          return;
+        }
+        const int64 grad_dim = grad_stats.first.t.dim_size(1);
+
+        QCHECK(grad_stats.first.t.dims() == 2)
+            << strings::Printf("Gradient should be of rank 2, got rank %d",
+                               grad_stats.first.t.dims());
+        QCHECK(grad_stats.first.t.dim_size(0) == 1) << strings::Printf(
+            "Gradient must be of shape 1 x %lld, got %lld x %lld", grad_dim,
+            grad_stats.first.t.dim_size(0), grad_dim);
+        QCHECK(grad_stats.second.t.dims() == 3)
+            << strings::Printf("Hessian should be of rank 3, got rank %d",
+                               grad_stats.second.t.dims());
+        QCHECK(grad_stats.second.t.shape() ==
+               TensorShape({1, grad_dim, grad_dim}))
+            << strings::Printf(
+                   "Hessian must be of shape 1 x %lld x %lld, got %lld x % lld "
+                   " x % lld ",
+                   grad_dim, grad_dim, grad_stats.second.t.shape().dim_size(0),
+                   grad_stats.second.t.shape().dim_size(1),
+                   grad_stats.second.t.shape().dim_size(2));
+
+        // Check if we're violating min weight constraint.
+
+        if (grad_stats.IsAlmostZero() ||
+            grad_stats.second.Magnitude() <= min_node_weight) {
+          return;
+        }
+        // TODO(nponomareva): figure out l1 in matrix form.
+        // g is a vector of gradients, H is a hessian matrix.
+        Eigen::VectorXf g = TensorToEigenVector(grad_stats.first.t, grad_dim);
+
+        Eigen::MatrixXf hessian =
+            TensorToEigenMatrix(grad_stats.second.t, grad_dim, grad_dim);
+        // I is an identity matrix.
+        // The gain in general form is -g^T (H+l2 I)^-1 g.
+        // The node weights are -(H+l2 I)^-1 g.
+        Eigen::MatrixXf identity;
+        identity.setIdentity(grad_dim, grad_dim);
+
+        Eigen::MatrixXf hessian_and_reg = hessian + l2_reg * identity;
+
+        CalculateWeightAndGain(hessian_and_reg, g);
+        break;
+      }
+      case LearnerConfig_MultiClassStrategy_DIAGONAL_HESSIAN: {
+        weight_contribution.clear();
+        if (grad_stats.first.t.NumElements() == 0 ||
+            grad_stats.second.t.NumElements() == 0) {
+          return;
+        }
+        const int64 grad_dim = grad_stats.first.t.dim_size(1);
+
+        QCHECK(grad_stats.first.t.dims() == 2)
+            << strings::Printf("Gradient should be of rank 2, got rank %d",
+                               grad_stats.first.t.dims());
+        QCHECK(grad_stats.first.t.dim_size(0) == 1) << strings::Printf(
+            "Gradient must be of shape 1 x %lld, got %lld x %lld", grad_dim,
+            grad_stats.first.t.dim_size(0), grad_dim);
+        QCHECK(grad_stats.second.t.dims() == 2)
+            << strings::Printf("Hessian should be of rank 2, got rank %d",
+                               grad_stats.second.t.dims());
+        QCHECK(grad_stats.second.t.shape() == TensorShape({1, grad_dim}))
+            << strings::Printf(
+                   "Hessian must be of shape 1 x %lld, got %lld x %lld",
+                   grad_dim, grad_stats.second.t.shape().dim_size(0),
+                   grad_stats.second.t.shape().dim_size(1));
+
+        // Check if we're violating min weight constraint.
+        if (grad_stats.IsAlmostZero() ||
+            grad_stats.second.Magnitude() <= min_node_weight) {
+          return;
+        }
+        // TODO(nponomareva): figure out l1 in matrix form.
+        // Diagonal of the hessian.
+        Eigen::ArrayXf hessian =
+            TensorToEigenArray(grad_stats.second.t, grad_dim);
+        Eigen::ArrayXf hessian_and_reg = hessian + l2_reg;
+
+        // Check if any of the elements are zeros.
+        bool invertible = true;
+        for (int i = 0; i < hessian_and_reg.size(); ++i) {
+          if (hessian_and_reg[i] == 0.0) {
+            invertible = false;
+            break;
+          }
+        }
+        if (invertible) {
+          Eigen::ArrayXf g = TensorToEigenArray(grad_stats.first.t, grad_dim);
+          // Operations on arrays are element wise. The formulas are as for full
+          // hessian, but for hessian of diagonal form they are simplified.
+          Eigen::ArrayXf ones = Eigen::ArrayXf::Ones(grad_dim);
+          Eigen::ArrayXf temp = ones / hessian_and_reg;
+          Eigen::ArrayXf weight = -temp * g;
+
+          // Copy over weights to weight_contribution.
+          weight_contribution =
+              std::vector<float>(weight.data(), weight.data() + weight.rows());
+          gain = (-g * weight).sum();
+        } else {
+          Eigen::VectorXf g = TensorToEigenVector(grad_stats.first.t, grad_dim);
+          // Hessian is not invertible. We will go the same route as in full
+          // hessian to get an approximate solution.
+          CalculateWeightAndGain(hessian_and_reg.matrix().asDiagonal(), g);
+        }
+        break;
+      }
+      default:
+        LOG(FATAL) << "Unknown multi-class strategy " << strategy;
+        break;
+    }
+  }
+
+  string DebugString() const {
+    return strings::StrCat(
+        gradient_stats.DebugString(), "\n",
+        "Weight_contrib = ", str_util::Join(weight_contribution, ","),
+        "Gain = ", gain);
+  }
+
+  // Use these node stats to populate a Leaf's model.
+  void FillLeaf(const int class_id, boosted_trees::trees::Leaf* leaf) const {
+    if (class_id == -1) {
+      for (int i = 0; i < weight_contribution.size(); i++) {
+        leaf->mutable_vector()->add_value(weight_contribution[i]);
+      }
+    } else {
+      CHECK(weight_contribution.size() == 1)
+          << "Weight contribution size = " << weight_contribution.size();
+      leaf->mutable_sparse_vector()->add_index(class_id);
+      leaf->mutable_sparse_vector()->add_value(weight_contribution[0]);
+    }
+  }
+
+  // Sets the weight_contribution and gain member variables based on the
+  // given regularized Hessian and gradient vector g.
+  void CalculateWeightAndGain(const Eigen::MatrixXf& hessian_and_reg,
+                              const Eigen::VectorXf& g) {
+    // The gain in general form is -g^T (Hessian_and_regularization)^-1 g.
+    // The node weights are -(Hessian_and_regularization)^-1 g.
+    Eigen::VectorXf weight;
+    // If we want to calculate x = K^-1 v, instead of explicitly calculating
+    // K^-1 and multiplying by v, we can solve this matrix equation using
+    // solve method.
+    weight = -hessian_and_reg.colPivHouseholderQr().solve(g);
+    // Copy over weights to weight_contribution.
+    weight_contribution =
+        std::vector<float>(weight.data(), weight.data() + weight.rows());
+
+    gain = -g.transpose() * weight;
+  }
+
+  static Eigen::MatrixXf TensorToEigenMatrix(const Tensor& tensor,
+                                             const int num_rows,
+                                             const int num_cols) {
+    return Eigen::Map<const Eigen::MatrixXf>(tensor.flat<float>().data(),
+                                             num_rows, num_cols);
+  }
+
+  static Eigen::VectorXf TensorToEigenVector(const Tensor& tensor,
+                                             const int num_elements) {
+    return Eigen::Map<const Eigen::VectorXf>(tensor.flat<float>().data(),
+                                             num_elements);
+  }
+
+  static Eigen::ArrayXf TensorToEigenArray(const Tensor& tensor,
+                                           const int num_elements) {
+    return Eigen::Map<const Eigen::ArrayXf>(tensor.flat<float>().data(),
+                                            num_elements);
+  }
+
+  GradientStats gradient_stats;
+  std::vector<float> weight_contribution;
+  float gain;
+};
+
+// Helper macro to check std::vector<float> approximate equality.
+#define EXPECT_VECTOR_FLOAT_EQ(x, y)       \
+  {                                        \
+    EXPECT_EQ((x).size(), (y).size());     \
+    for (int i = 0; i < (x).size(); ++i) { \
+      EXPECT_FLOAT_EQ((x)[i], (y)[i]);     \
+    }                                      \
+  }
+
+// Helper macro to check node stats approximate equality.
+#define EXPECT_NODE_STATS_EQ(val1, val2)                                      \
+  EXPECT_GRADIENT_STATS_EQ(val1.gradient_stats, val2.gradient_stats);         \
+  EXPECT_VECTOR_FLOAT_EQ(val1.weight_contribution, val2.weight_contribution); \
+  EXPECT_FLOAT_EQ(val1.gain, val2.gain);
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_NODE_STATS_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc
new file mode 100644
index 0000000000..f99b6826a7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc
@@ -0,0 +1,213 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+using tensorflow::test::AsTensor;
+using std::vector;
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+const double kDelta = 1e-5;
+
+TEST(NodeStatsTest, AlmostZero) {
+  LearnerConfig learner_config;
+  NodeStats node_stats(learner_config, GradientStats(1e-8f, 1e-8f));
+  EXPECT_EQ(0, node_stats.weight_contribution[0]);
+  EXPECT_EQ(0, node_stats.gain);
+}
+
+TEST(NodeStatsTest, LessThanMinWeightConstraint) {
+  LearnerConfig learner_config;
+  learner_config.mutable_constraints()->set_min_node_weight(3.2f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  EXPECT_EQ(0, node_stats.weight_contribution[0]);
+  EXPECT_EQ(0, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1RegSquashed) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(10.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  EXPECT_EQ(0, node_stats.weight_contribution[0]);
+  EXPECT_EQ(0, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1RegPos) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(5.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  const float expected_clipped_grad = 7.32f - 5.0f;
+  const float expected_weight_contribution = -expected_clipped_grad / 1.63f;
+  const float expected_gain =
+      expected_clipped_grad * expected_clipped_grad / 1.63f;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1RegNeg) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(5.0f);
+  NodeStats node_stats(learner_config, GradientStats(-7.32f, 1.63f));
+  const float expected_clipped_grad = -7.32f + 5.0f;
+  const float expected_weight_contribution = -expected_clipped_grad / 1.63f;
+  const float expected_gain =
+      expected_clipped_grad * expected_clipped_grad / 1.63f;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L2Reg) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l2(8.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  const float expected_denom = 1.63f + 8.0f;
+  const float expected_weight_contribution = -7.32f / expected_denom;
+  const float expected_gain = 7.32f * 7.32f / expected_denom;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1L2Reg) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(5.0f);
+  learner_config.mutable_regularization()->set_l2(8.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  const float expected_clipped_grad = 7.32f - 5.0f;
+  const float expected_denom = 1.63f + 8.0f;
+  const float expected_weight_contribution =
+      -expected_clipped_grad / expected_denom;
+  const float expected_gain =
+      expected_clipped_grad * expected_clipped_grad / expected_denom;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, MulticlassFullHessianTest) {
+  LearnerConfig learner_config;
+  learner_config.set_multi_class_strategy(LearnerConfig::FULL_HESSIAN);
+  learner_config.mutable_regularization()->set_l2(0.3f);
+
+  const int kNumClasses = 4;
+  const auto& g_shape = TensorShape({1, kNumClasses});
+  Tensor g = AsTensor<float>({0.5, 0.33, -9, 1}, g_shape);
+  const auto& hessian_shape = TensorShape({1, kNumClasses, kNumClasses});
+  Tensor h = AsTensor<float>({3, 5, 7, 8, 5, 4, 1, 5, 7, 1, 8, 4, 8, 5, 4, 9},
+                             hessian_shape);
+
+  NodeStats node_stats(learner_config, GradientStats(g, h));
+
+  // Index 1 has 0 value because of l1 regularization,
+  std::vector<float> expected_weight = {0.9607576, 0.4162569, 0.9863192,
+                                        -1.5820024};
+
+  EXPECT_EQ(kNumClasses, node_stats.weight_contribution.size());
+  for (int i = 0; i < kNumClasses; ++i) {
+    EXPECT_NEAR(expected_weight[i], node_stats.weight_contribution[i], kDelta);
+  }
+  EXPECT_NEAR(9.841132, node_stats.gain, kDelta);
+}
+
+TEST(NodeStatsTest, MulticlassDiagonalHessianTest) {
+  // Normal case.
+  {
+    LearnerConfig learner_config;
+    learner_config.set_multi_class_strategy(LearnerConfig::FULL_HESSIAN);
+    learner_config.mutable_regularization()->set_l2(0.3f);
+
+    const int kNumClasses = 4;
+    const auto& g_shape = TensorShape({1, kNumClasses});
+    Tensor g = AsTensor<float>({0.5, 0.33, -9, 1}, g_shape);
+    Tensor h;
+    // Full hessian.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses, kNumClasses});
+      // Construct full hessian.
+      h = AsTensor<float>({3, 0, 0, 0, 0, 4, 0, 0, 0, 0, 8, 0, 0, 0, 0, 9},
+                          hessian_shape);
+    }
+    NodeStats full_node_stats(learner_config, GradientStats(g, h));
+
+    // Diagonal only.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses});
+      // Construct diagonal of hessian.
+      h = AsTensor<float>({3, 4, 8, 9}, hessian_shape);
+    }
+    learner_config.set_multi_class_strategy(LearnerConfig::DIAGONAL_HESSIAN);
+    NodeStats diag_node_stats(learner_config, GradientStats(g, h));
+
+    // Full and diagonal hessian should return the same results.
+    EXPECT_EQ(full_node_stats.weight_contribution.size(),
+              diag_node_stats.weight_contribution.size());
+    for (int i = 0; i < full_node_stats.weight_contribution.size(); ++i) {
+      EXPECT_FLOAT_EQ(full_node_stats.weight_contribution[i],
+                      diag_node_stats.weight_contribution[i]);
+    }
+    EXPECT_EQ(full_node_stats.gain, diag_node_stats.gain);
+  }
+  // Zero entries in diagonal, no regularization
+  {
+    LearnerConfig learner_config;
+    learner_config.set_multi_class_strategy(LearnerConfig::FULL_HESSIAN);
+
+    const int kNumClasses = 4;
+    const auto& g_shape = TensorShape({1, kNumClasses});
+    Tensor g = AsTensor<float>({0.5, 0.33, -9, 1}, g_shape);
+    Tensor h;
+    // Full hessian.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses, kNumClasses});
+      // Construct full hessian.
+      h = AsTensor<float>({3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0},
+                          hessian_shape);
+    }
+    NodeStats full_node_stats(learner_config, GradientStats(g, h));
+
+    // Diagonal only.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses});
+      // Diagonal of hessian, two entries are 0
+      h = AsTensor<float>({3, 0, 8, 0}, hessian_shape);
+    }
+    learner_config.set_multi_class_strategy(LearnerConfig::DIAGONAL_HESSIAN);
+    NodeStats diag_node_stats(learner_config, GradientStats(g, h));
+
+    // Full and diagonal hessian should return the same results.
+    EXPECT_EQ(full_node_stats.weight_contribution.size(),
+              diag_node_stats.weight_contribution.size());
+    for (int i = 0; i < full_node_stats.weight_contribution.size(); ++i) {
+      EXPECT_FLOAT_EQ(full_node_stats.weight_contribution[i],
+                      diag_node_stats.weight_contribution[i]);
+    }
+    EXPECT_EQ(full_node_stats.gain, diag_node_stats.gain);
+  }
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h
new file mode 100644
index 0000000000..f700cbced8
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h
@@ -0,0 +1,84 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_SPLIT_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_SPLIT_STATS_H_
+
+#include <string>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// FeatureSplitCandidate holds the split candidate node along with the stats.
+struct SplitStats {
+  // Initialize with 0 stats.
+  explicit SplitStats(const int output_length)
+      : root_node_stats(output_length),
+        left_node_stats(output_length),
+        right_node_stats(output_length),
+        gain(0) {}
+
+  // Feature unary split candidate, we don't apply tree complexity
+  // regularization as no new nodes are being added with this candidate.
+  SplitStats(const LearnerConfig& learner_config, const NodeStats& root_stats)
+      : root_node_stats(root_stats),
+        left_node_stats(root_stats.weight_contribution.size()),
+        right_node_stats(root_stats.weight_contribution.size()),
+        gain(0) {}
+
+  // Feature binary split candidate, we apply tree complexity regularization
+  // over the split gain to trade-off adding new nodes with loss reduction.
+  SplitStats(const LearnerConfig& learner_config, const NodeStats& root_stats,
+             const NodeStats& left_stats, const NodeStats& right_stats)
+      : root_node_stats(root_stats),
+        left_node_stats(left_stats),
+        right_node_stats(right_stats),
+        gain(left_stats.gain + right_stats.gain - root_stats.gain -
+             learner_config.regularization().tree_complexity()) {}
+
+  // Root Stats.
+  NodeStats root_node_stats;
+
+  // Children stats.
+  NodeStats left_node_stats;
+  NodeStats right_node_stats;
+
+  // Split gain.
+  float gain;
+
+  string DebugString() const {
+    return "Root = " + root_node_stats.DebugString() +
+           "\nLeft = " + left_node_stats.DebugString() +
+           "\nRight = " + right_node_stats.DebugString() +
+           "\nGain = " + std::to_string(gain);
+  }
+};
+
+// Helper macro to check split stats approximate equality.
+#define EXPECT_SPLIT_STATS_EQ(val1, val2)                             \
+  EXPECT_NODE_STATS_EQ(val1.root_node_stats, val2.root_node_stats);   \
+  EXPECT_NODE_STATS_EQ(val1.left_node_stats, val2.left_node_stats);   \
+  EXPECT_NODE_STATS_EQ(val1.right_node_stats, val2.right_node_stats); \
+  EXPECT_FLOAT_EQ(val1.gain, val2.gain);
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_SPLIT_STATS_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc
index f658532acb..ce7632e589 100644
--- a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc
+++ b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc
@@ -17,7 +17,6 @@
 #include <sys/types.h>
 #include <algorithm>
 #include <cstdlib>
-#include <ctime>
 #include <functional>
 #include <iterator>
 #include <unordered_set>
@@ -25,6 +24,7 @@
 
 #include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"  // NOLINT
 #include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/env.h"
 
 using tensorflow::boosted_trees::learner::LearningRateDropoutDrivenConfig;
 using tensorflow::boosted_trees::trees::DecisionTreeEnsembleConfig;
@@ -95,7 +95,8 @@ TEST_F(DropoutUtilsTest, DropoutProbabilityTest) {
     int32 total_num_trees = 0;
     for (int i = 0; i < kNumRuns; ++i) {
       // draw random seeds
-      uint random_generator_seed = static_cast<uint>(std::clock());
+      uint random_generator_seed =
+          static_cast<uint>(Env::Default()->NowMicros());
       uint32 seed = rand_r(&random_generator_seed) % 100 + i;
       TF_EXPECT_OK(DropoutUtils::DropOutTrees(seed, config, trees_not_to_drop,
                                               weights_, &dropped_trees,
diff --git a/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc b/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
new file mode 100644
index 0000000000..b5ea5e7849
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
@@ -0,0 +1,44 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+REGISTER_OP("AddTreesToEnsemble")
+    .Input("tree_ensemble_handle: resource")
+    .Input("ensemble_to_add: string")
+    .Input("feature_column_usage_counts_handle: Ref(int64)")
+    .Input("feature_column_usage_counts_to_add: int64")
+    .Input("feature_column_gains_handle: Ref(float)")
+    .Input("feature_column_gains_to_add: float")
+    .Input("drop_out_tree_indices_weights: float")
+    .Input("learning_rate: float")
+    .SetShapeFn(shape_inference::NoOutputs)
+    .Doc(R"doc(
+Synchronously adds a tree ensemble to a an existing tree ensemble variable.
+tree_ensemble_handle: Handle to the ensemble variable.
+ensemble_to_add: Serialized DecisionTreeConfig proto of the tree.
+feature_column_usage_counts_handle: Handle to the feature column usage counts variable.
+feature_column_usage_counts_to_add: Rank 1 Tensor holding feature column usage counts to add.
+feature_column_gains_handle: Handle to the feature column gains variable.
+feature_column_gains_to_add: Rank 1 Tensor holding feature column gains to add.
+drop_out_tree_indices_weights: Rank 2 Tensor containing dropped trees indices
+and original weights of those trees during prediction.
+learning_rate: The learning rate that the tuner found for this iteration.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/model_ops.cc b/tensorflow/contrib/boosted_trees/ops/model_ops.cc
new file mode 100644
index 0000000000..c490c765cf
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/model_ops.cc
@@ -0,0 +1,114 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+
+REGISTER_RESOURCE_HANDLE_OP(DecisionTreeEnsembleResource);
+
+REGISTER_OP("TreeEnsembleIsInitializedOp")
+    .Input("tree_ensemble_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Checks whether a tree ensemble has been initialized.
+)doc");
+
+REGISTER_OP("CreateTreeEnsembleVariable")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("tree_ensemble_config: string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a tree ensemble model and returns a handle to it.
+
+tree_ensemble_handle: Handle to the tree ensemble resource to be created.
+stamp_token: Token to use as the initial value of the resource stamp.
+tree_ensemble_config: Serialized proto of the tree ensemble.
+)doc");
+
+REGISTER_OP("TreeEnsembleStampToken")
+    .Input("tree_ensemble_handle: resource")
+    .Output("stamp_token: int64")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Retrieves the tree ensemble resource stamp token.
+
+tree_ensemble_handle: Handle to the tree ensemble.
+stamp_token: Stamp token of the tree ensemble resource.
+)doc");
+
+REGISTER_OP("TreeEnsembleSerialize")
+    .Input("tree_ensemble_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("tree_ensemble_config: string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Serializes the tree ensemble to a proto.
+
+tree_ensemble_handle: Handle to the tree ensemble.
+stamp_token: Stamp token of the tree ensemble resource.
+tree_ensemble_config: Serialized proto of the ensemble.
+)doc");
+
+REGISTER_OP("TreeEnsembleDeserialize")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("tree_ensemble_config: string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Deserializes a serialized tree ensemble config and replaces current tree
+ensemble.
+
+tree_ensemble_handle: Handle to the tree ensemble.
+stamp_token: Token to use as the new value of the resource stamp.
+tree_ensemble_config: Serialized proto of the ensemble.
+)doc");
+
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc b/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
new file mode 100644
index 0000000000..8effb6f98f
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
@@ -0,0 +1,132 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+using tensorflow::boosted_trees::learner::LearnerConfig;
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+
+static Status ApplyGradientTreesPredictionShapeFn(InferenceContext* c) {
+  string learner_config_str;
+  // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
+  c->GetAttr("learner_config", &learner_config_str).IgnoreError();
+  LearnerConfig learner_config;
+  ParseProtoUnlimited(&learner_config, learner_config_str);
+  // Sets the shape of the output as a matrix.
+  const bool reduce_dim =
+      learner_config.multi_class_strategy() == LearnerConfig::TREE_PER_CLASS;
+  c->set_output(0, {c->Matrix(InferenceContext::kUnknownDim,
+                              reduce_dim ? learner_config.num_classes() - 1
+                                         : learner_config.num_classes())});
+  c->set_output(1, {c->Matrix(InferenceContext::kUnknownDim,
+                              reduce_dim ? learner_config.num_classes() - 1
+                                         : learner_config.num_classes())});
+  c->set_output(2, {c->Vector(InferenceContext::kUnknownDim)});
+  return Status::OK();
+}
+
+REGISTER_OP("GradientTreesPrediction")
+    .Attr("learner_config: string")
+    .Attr("num_dense_float_features: int >= 0")
+    .Attr("num_sparse_float_features: int >= 0")
+    .Attr("num_sparse_int_features: int >= 0")
+    .Attr("use_locking: bool = false")
+    .Attr("apply_dropout: bool")
+    .Attr("apply_averaging: bool")
+    .Attr("center_bias: bool")
+    .Input("tree_ensemble_handle: resource")
+    .Input("seed: int64")
+    .Input("dense_float_features: num_dense_float_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_float_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_float_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_float_features * int64")
+    .Input("sparse_int_feature_indices: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_values: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_shapes: num_sparse_int_features * int64")
+    .Output("predictions: float")
+    .Output("no_dropout_predictions: float")
+    .Output("drop_out_tree_indices_weights: float")
+    .SetShapeFn(ApplyGradientTreesPredictionShapeFn)
+    .Doc(R"doc(
+Runs multiple additive regression forests predictors on input instances
+and computes the final prediction for each class.
+
+learner_config: Config for the learner of type LearnerConfig proto. Prediction
+ops for now uses only LearningRateDropoutDrivenConfig config from the learner.
+num_dense_float_features: Number of dense float features.
+num_sparse_float_features: Number of sparse float features.
+num_sparse_int_features: Number of sparse int features.
+use_locking: Whether to use locking.
+seed: random seed to be used for dropout.
+apply_dropout: whether to apply dropout during prediction.
+apply_averaging: whether averaging of tree ensembles should take place. If set
+to true, will be based on AveragingConfig from learner_config.
+tree_ensemble_handle: The handle to the tree ensemble.
+dense_float_features: Rank 2 Tensors containing dense float feature values.
+sparse_float_feature_indices: Rank 2 Tensors containing sparse float indices.
+sparse_float_feature_values: Rank 1 Tensors containing sparse float values.
+sparse_float_feature_shapes: Rank 1 Tensors containing sparse float shapes.
+sparse_int_feature_indices: Rank 2 Tensors containing sparse int indices.
+sparse_int_feature_values: Rank 1 Tensors containing sparse int values.
+sparse_int_feature_shapes: Rank 1 Tensors containing sparse int shapes.
+predictions: Rank 2 Tensor containing predictions per example per class.
+no_dropout_predictions: The same as predictions, but using all trees (even
+those that were dropped due to dropout).
+drop_out_tree_indices_weights: Tensor of Rank 2 containing dropped trees indices
+and original weights of those trees during prediction.
+)doc");
+
+REGISTER_OP("GradientTreesPartitionExamples")
+    .Attr("num_dense_float_features: int >= 0")
+    .Attr("num_sparse_float_features: int >= 0")
+    .Attr("num_sparse_int_features: int >= 0")
+    .Attr("use_locking: bool = false")
+    .Input("tree_ensemble_handle: resource")
+    .Input("dense_float_features: num_dense_float_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_float_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_float_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_float_features * int64")
+    .Input("sparse_int_feature_indices: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_values: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_shapes: num_sparse_int_features * int64")
+    .Output("partition_ids: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      return c->set_output("partition_ids",
+                           {c->Vector(InferenceContext::kUnknownDim)});
+    })
+    .Doc(R"doc(
+Splits input examples into the leaves of the tree.
+
+num_dense_float_features: Number of dense float features.
+num_sparse_float_features: Number of sparse float features.
+num_sparse_int_features: Number of sparse int features.
+use_locking: Whether to use locking.
+tree_ensemble_handle: The handle to the tree ensemble.
+dense_float_features: Rank 2 Tensors containing dense float feature values.
+sparse_float_feature_indices: Rank 2 Tensors containing sparse float indices.
+sparse_float_feature_values: Rank 1 Tensors containing sparse float values.
+sparse_float_feature_shapes: Rank 1 Tensors containing sparse float shapes.
+sparse_int_feature_indices: Rank 2 Tensors containing sparse int indices.
+sparse_int_feature_values: Rank 1 Tensors containing sparse int values.
+sparse_int_feature_shapes: Rank 1 Tensors containing sparse int shapes.
+partition_ids: Rank 1 Tensor containing partition ids per example.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
new file mode 100644
index 0000000000..c778a02fb6
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
@@ -0,0 +1,263 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+
+REGISTER_RESOURCE_HANDLE_OP(QuantileStreamResource);
+
+REGISTER_OP("QuantileAccumulatorIsInitialized")
+    .Input("quantile_accumulator_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a quantile accumulator has been initialized.
+)doc");
+
+REGISTER_OP("CreateQuantileAccumulator")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("max_elements: int = 1099511627776")  // 1 << 40
+    .Attr("epsilon: float")
+    .Attr("num_quantiles: int")
+    .Input("quantile_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a stateful accumulator for quantile summaries.
+
+epsilon: Error bound on the quantile summary.
+num_quantiles: Number of buckets that we create from the data.
+stamp_token: Token to use as the initial value of the resource stamp.
+quantile_accumulator_handle: The handle to the accumulator.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorAddSummaries")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("quantile_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Input("summaries: num_resource_handles * string")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      // All the inputs are scalars.
+      shape_inference::ShapeHandle unused_input;
+      for (int i = 0; i < 2 * num_resource_handles + 1; ++i) {
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused_input));
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Adds each quantile summary to its stream.
+
+quantile_accumulator_handles: The handles to the quantile stream resources.
+stamp_token: Stamp token to validate the Read/Write operation.
+summaries: A list of serialized QuantileSummaryState.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorGetBuckets")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("quantile_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Output("are_buckets_ready: num_resource_handles * bool")
+    .Output("buckets: num_resource_handles * float")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      for (int i = 0; i < num_resource_handles; ++i) {
+        c->set_output(i, c->Scalar());
+        c->set_output(i + num_resource_handles, c->Vector(c->UnknownDim()));
+      }
+      return Status::OK();
+    })
+
+    .Doc(R"doc(
+Returns quantile buckets created during previous flush of the accumulator.
+
+quantile_accumulator_handles: The handles to the quantile stream resources.
+stamp_token: Stamp token to validate the Read/Write operation.
+are_buckets_ready: Whether the buckets are ready or not.
+buckets: Output quantile summary representing boundaries with "num_quantile"
+    elements.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorFlush")
+    .Input("quantile_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Doc(R"doc(
+Resets quantile summary streams for each column with a new token.
+
+quantile_accumulator_handle: The handle to the accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+next_stamp_token: Stamp token to be used for the next iteration.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorSerialize")
+    .Input("quantile_accumulator_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("stream_state: string")
+    .Output("are_buckets_ready: bool")
+    .Output("buckets: float")
+    .Doc(R"doc(
+Serializes the state of the given resource.
+
+quantile_accumulator_handle: The handle to the accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+stream_state: A serialized QuantileStreamState.
+are_buckets_ready: Whether the buckets are ready or not.
+buckets: Output quantile buckets representing boundaries with "num_quantile"
+    elements.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorDeserialize")
+    .Input("quantile_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("stream_state: string")
+    .Input("are_buckets_ready: bool")
+    .Input("buckets: float")
+    .Doc(R"doc(
+Serializes the state of the given resource.
+
+quantile_accumulator_handle: The handle to the accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+stream_state: A serialized QuantileStreamState.
+are_buckets_ready: Whether the buckets are ready or not.
+buckets: Output quantile summary representing boundaries with "num_quantile"
+    elements.
+)doc");
+
+REGISTER_OP("MakeQuantileSummaries")
+    .Attr("num_dense_features: int >= 0")
+    .Attr("num_sparse_features: int >= 0")
+    .Attr("epsilon: float")
+    .Input("dense_float_features: num_dense_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_features * int64")
+    .Input("example_weights: float")
+    .Output("dense_summaries: num_dense_features * string")
+    .Output("sparse_summaries: num_sparse_features * string")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_dense_features;
+      TF_RETURN_IF_ERROR(c->GetAttr("num_dense_features", &num_dense_features));
+      int num_sparse_features;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_sparse_features", &num_sparse_features));
+      for (int i = 0; i < num_dense_features; ++i) {
+        c->set_output(i, c->Scalar());
+      }
+      for (int i = 0; i < num_sparse_features; ++i) {
+        c->set_output(i + num_dense_features, c->Scalar());
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a summary for the given features.
+
+num_dense_features: Number of dense feature groups to compute quantiles on.
+num_sparse_features: Number of sparse feature groups to compute quantiles on.
+epsilon: Error bound on the computed summary.
+dense_float_features: A list of vectors which contains dense values.
+sparse_float_feature_indices: List of rank 2 tensors containing the sparse float
+feature indices.
+sparse_float_feature_values: List of rank 1 tensors containing the sparse float
+feature values.
+sparse_float_feature_shapes: List of rank 1 tensors containing the shape of the
+float feature.
+example_weights: Rank 1 tensor containing the example weight tensor.
+dense_summaries: A list of serialized QuantileSummaryState for dense columns.
+sparse_summaries: A list of serialized QuantileSummaryState for sparse columns.
+)doc");
+
+REGISTER_OP("QuantileBuckets")
+    .Attr("num_dense_features: int >= 0")
+    .Attr("num_sparse_features: int >= 0")
+    .Attr("dense_config: list(string)")
+    .Attr("sparse_config: list(string)")
+    .Input("dense_float_features: num_dense_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_features * int64")
+    .Input("example_weights: float")
+    .Output("dense_buckets: num_dense_features * float")
+    .Output("sparse_buckets: num_sparse_features * float")
+    .Doc(R"doc(
+Computes quantile buckets for a given list of dense and sparse features with
+given example weights.
+
+num_dense_features: Number of dense feature groups to compute quantiles on.
+num_sparse_features: Number of sparse feature groups to compute quantiles on.
+dense_config: Config for computing buckets for dense values.
+Each entry is QuantileConfig proto.
+sparse_config: Config for computing buckets for sparse feature values.
+Each entry is QuantileConfig proto.
+dense_float_features: A list of vectors which contains dense values.
+sparse_float_feature_indices: List of rank 2 tensors containing the sparse float
+feature indices.
+sparse_float_feature_values: List of rank 1 tensors containing the sparse float
+feature values.
+sparse_float_feature_shapes: List of rank 1 tensors containing the shape of the
+float feature.
+example_weights: Rank 1 tensor containing the example weight tensor.
+dense_buckets: Output quantile summary for each dense float tensor
+representing boundaries each with "num_quantile" elements.
+sparse_buckets: Output quantile summary for each sparse float value tensor
+representing boundaries each with "num_quantile" elements.
+)doc");
+
+REGISTER_OP("Quantiles")
+    .Attr("num_dense_features: int >= 0")
+    .Attr("num_sparse_features: int >= 0")
+    .Input("dense_values: num_dense_features * float")
+    .Input("sparse_values: num_sparse_features * float")
+    .Input("dense_buckets: num_dense_features * float")
+    .Input("sparse_buckets: num_sparse_features * float")
+    .Output("dense_quantiles: num_dense_features * int32")
+    .Output("sparse_quantiles: num_sparse_features * int32")
+    .Doc(R"doc(
+Computes quantile for each a given list of dense and sparse feature values using
+the given buckets.
+
+num_dense_features: Number of dense feature groups to generate quantiles for.
+num_sparse_features: Number of sparse feature groups to generate quantiles for.
+dense_values: List of rank 1 tensors containing the dense values.
+sparse_values: List of rank 1 tensors containing the sparse feature values.
+dense_buckets: Quantile summary for each of the dense float tensor.
+sparse_buckets: Quantile summary for each of the sparse feature float tensor.
+dense_quantiles: Rank 1 tensors representing associated quantiles for each of
+dense float tensors.
+sparse_quantiles: Rank 1 tensors representing associated quantiles for each of
+the sparse feature tensors.
+)doc");
+
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
new file mode 100644
index 0000000000..d32507127c
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
@@ -0,0 +1,197 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+using shape_inference::DimensionHandle;
+
+REGISTER_OP("BuildDenseInequalitySplits")
+    .Attr("feature_column_group_id: int")
+    .Attr("l1_regularization: float")
+    .Attr("l2_regularization: float")
+    .Attr("tree_complexity_regularization: float")
+    .Attr("min_node_weight: float")
+    .Input("num_minibatches: int64")
+    .Input("partition_ids: int32")
+    .Input("bucket_ids: int64")
+    .Input("gradients: float32")
+    .Input("hessians: float32")
+    .Input("bucket_boundaries: float32")
+    .Output("output_partition_ids: int32")
+    .Output("gains: float32")
+    .Output("split_infos: string")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle unused_dim;
+      ShapeHandle unused_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_shape));
+
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &partition_ids_shape));
+      ShapeHandle bucket_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &bucket_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(bucket_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(3), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(4), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      ShapeHandle bucket_boundaries_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &bucket_boundaries_shape));
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Find the split that has the best gain for the accumulated stats.
+
+num_minibatches: A scalar, the number of times per example gradients & hessians
+    were accumulated. The stats are divided by this to get per example stats.
+partition_ids: A rank 1 tensor of partition IDs.
+bucket_ids: A rank 1 tensor of buckets IDs.
+gradients: A rank 1 tensor of gradients.
+hessians: A rank 1 tensor of hessians.
+bucket_boundaries: A rank 1 tensor, thresholds that were used for bucketization.
+output_partition_ids: A rank 1 tensor, the partition IDs that we created splits
+    for.
+gains: A rank 1 tensor, for the computed gain for the created splits.
+split_infos: A rank 1 tensor of serialized protos which contains the
+    `SplitInfo`s.
+)doc");
+
+REGISTER_OP("BuildSparseInequalitySplits")
+    .Attr("feature_column_group_id: int")
+    .Attr("bias_feature_id: int")
+    .Attr("l1_regularization: float")
+    .Attr("l2_regularization: float")
+    .Attr("tree_complexity_regularization: float")
+    .Attr("min_node_weight: float")
+    .Input("num_minibatches: int64")
+    .Input("partition_ids: int32")
+    .Input("bucket_ids: int64")
+    .Input("gradients: float32")
+    .Input("hessians: float32")
+    .Input("bucket_boundaries: float32")
+    .Output("output_partition_ids: int32")
+    .Output("gains: float32")
+    .Output("split_infos: string")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle unused_dim;
+      ShapeHandle unused_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_shape));
+
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &partition_ids_shape));
+      ShapeHandle bucket_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &bucket_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(bucket_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(3), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(4), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      ShapeHandle bucket_boundaries_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &bucket_boundaries_shape));
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Find the split that has the best gain for the accumulated stats.
+
+num_minibatches: A scalar, the number of times per example gradients & hessians
+    were accumulated. The stats are divided by this to get per example stats.
+partition_ids: A rank 1 tensor of partition IDs.
+bucket_ids: A rank 1 tensor of buckets IDs.
+gradients: A rank 1 tensor of gradients.
+hessians: A rank 1 tensor of hessians.
+bucket_boundaries: A rank 1 tensor, thresholds that were used for bucketization.
+output_partition_ids: A rank 1 tensor, the partition IDs that we created splits
+    for.
+gains: A rank 1 tensor, for the computed gain for the created splits.
+split_infos: A rank 1 tensor of serialized protos which contains the
+    `SplitInfo`s.
+)doc");
+
+REGISTER_OP("BuildCategoricalEqualitySplits")
+    .Attr("feature_column_group_id: int")
+    .Attr("bias_feature_id: int")
+    .Attr("l1_regularization: float")
+    .Attr("l2_regularization: float")
+    .Attr("tree_complexity_regularization: float")
+    .Attr("min_node_weight: float")
+    .Input("num_minibatches: int64")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float32")
+    .Input("hessians: float32")
+    .Output("output_partition_ids: int32")
+    .Output("gains: float32")
+    .Output("split_infos: string")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle unused_dim;
+      ShapeHandle unused_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_shape));
+
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &partition_ids_shape));
+      ShapeHandle bucket_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &bucket_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(bucket_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(3), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(4), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Find the split that has the best gain for the accumulated stats.
+
+num_minibatches: A scalar, the number of times per example gradients & hessians
+    were accumulated. The stats are divided by this to get per example stats.
+partition_ids: A rank 1 tensor of partition IDs.
+feature_ids: A rank 1 tensor of feature IDs.
+gradients: A rank 1 tensor of gradients.
+hessians: A rank 1 tensor of hessians.
+output_partition_ids: A rank 1 tensor, the partition IDs that we created splits
+    for.
+gains: A rank 1 tensor, for the computed gain for the created splits.
+split_infos: A rank 1 tensor of serialized protos which contains the
+    `SplitInfo`s.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
new file mode 100644
index 0000000000..9fa5ec9a83
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
@@ -0,0 +1,475 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+using shape_inference::DimensionHandle;
+
+REGISTER_RESOURCE_HANDLE_OP(StatsAccumulatorScalarResource);
+
+REGISTER_OP("StatsAccumulatorScalarIsInitialized")
+    .Input("stats_accumulator_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a stats accumulator has been initialized.
+)doc");
+
+REGISTER_OP("CreateStatsAccumulatorScalar")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token is a scalar.
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a scalar stats accumulator.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: Token to use as the initial value of the resource stamp.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarAdd")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("stats_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Input("partition_ids: num_resource_handles * int32")
+    .Input("feature_ids: num_resource_handles * int64")
+    .Input("gradients: num_resource_handles * float")
+    .Input("hessians: num_resource_handles * float")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      for (int i = 0; i < num_resource_handles; ++i) {
+        ShapeHandle unused_input;
+        DimensionHandle unused_dim;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused_input));
+        TF_RETURN_IF_ERROR(
+            c->WithRank(c->input(num_resource_handles), 0, &unused_input));
+        ShapeHandle partition_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(num_resource_handles + i + 1),
+                                       1, &partition_ids_shape));
+        ShapeHandle feature_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 2 + i + 1), 1, &feature_ids_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(feature_ids_shape, 0), &unused_dim));
+        ShapeHandle gradients_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 3 + i + 1), 1, &gradients_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(gradients_shape, 0), &unused_dim));
+        ShapeHandle hessians_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 4 + i + 1), 1, &hessians_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(hessians_shape, 0), &unused_dim));
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Updates the scalar stats accumulator.
+
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+stats_accumulator_handles: A list of handles to the stats accumulator.
+partition_ids: A list of vectors of partition_ids.
+feature_ids: A list of vectors of feature_ids.
+gradients: A list of vectors of gradients for each slot in
+    <partition_id, feature_id>.
+hessians: A list of vectors of hessians for each slot in
+    <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarFlush")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      c->set_output(3, c->Vector(c->UnknownDim()));
+      c->set_output(4, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Flushes the scalar stats accumulator to output and resets the internal state.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+next_stamp_token: Stamp token for the next iteration.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A vector of gradients, with a value for each slot
+                  in <output_partition_id, output_feature_id>.
+output_hessians: A vector of hessians, with a value for each slot
+                 in <output_partition_id, output_feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarDeserialize")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("num_updates: int64")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      DimensionHandle unused_dim;
+      // stats_accumulator_handle
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      // num_updates
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &partition_ids_shape));
+      ShapeHandle feature_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &feature_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(feature_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Resets the scalar stats accumulator with the serialized state.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+partition_ids: A vector of partition_ids.
+feature_ids: A vector of feature_ids.
+gradients: A vector of gradients for each slot in <partition_id, feature_id>.
+hessians: A vector of hessians for each slot in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarSerialize")
+    .Input("stats_accumulator_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      c->set_output(0, c->Scalar());
+      // num_updates
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      c->set_output(3, c->Vector(c->UnknownDim()));
+      c->set_output(4, c->Vector(c->UnknownDim()));
+      c->set_output(5, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Serializes the scalar stats accumulator state.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: The current stamp token for the resource.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A vector of gradients, with a value for each slot
+                  in <output_partition_id, output_feature_id>.
+output_hessians: A vector of hessians, with a value for each slot
+                 in <output_partition_id, output_feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarMakeSummary")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .Doc(R"doc(
+)doc");
+
+// Tensor version of the stats accumulator ops.
+REGISTER_RESOURCE_HANDLE_OP(StatsAccumulatorTensorResource);
+
+REGISTER_OP("StatsAccumulatorTensorIsInitialized")
+    .Input("stats_accumulator_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a tensor stats accumulator has been initialized.
+)doc");
+
+REGISTER_OP("CreateStatsAccumulatorTensor")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("per_slot_gradient_shape: int64")
+    .Input("per_slot_hessian_shape: int64")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token is a scalar.
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a tensor stats accumulator.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Token to use as the initial value of the resource stamp.
+per_slot_gradient_shape: a vector that defines the shape of gradients.
+per_slot_hessian_shape:  a vector that defines the shape of hessians.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorAdd")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("stats_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Input("partition_ids: num_resource_handles * int32")
+    .Input("feature_ids: num_resource_handles * int64")
+    .Input("gradients: num_resource_handles * float")
+    .Input("hessians: num_resource_handles * float")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      for (int i = 0; i < num_resource_handles; ++i) {
+        ShapeHandle unused_input;
+        DimensionHandle unused_dim;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused_input));
+        TF_RETURN_IF_ERROR(
+            c->WithRank(c->input(num_resource_handles), 0, &unused_input));
+        ShapeHandle partition_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(num_resource_handles + i + 1),
+                                       1, &partition_ids_shape));
+        ShapeHandle feature_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 2 + i + 1), 1, &feature_ids_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(feature_ids_shape, 0), &unused_dim));
+        ShapeHandle gradients_shape;
+        TF_RETURN_IF_ERROR(c->WithRankAtLeast(
+            c->input(num_resource_handles * 3 + i + 1), 2, &gradients_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(gradients_shape, 0), &unused_dim));
+        ShapeHandle hessians_shape;
+        TF_RETURN_IF_ERROR(c->WithRankAtLeast(
+            c->input(num_resource_handles * 4 + i + 1), 2, &hessians_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(hessians_shape, 0), &unused_dim));
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Updates the tensor stats accumulator.
+
+stats_accumulator_handles: A list of handles to the stats accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+partition_ids: A list of vectors of partition_ids.
+feature_ids: A list of vectors of feature_ids.
+gradients: A list of vectors of gradients for each slot in
+    <partition_id, feature_id>.
+hessians: A list of vectors of hessians for each slot in
+    <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorFlush")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      // num_updates
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(2, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(3, c->UnknownShape());
+      c->set_output(4, c->UnknownShape());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Flushes the stats accumulator to output and resets the internal state.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+next_stamp_token: Stamp token to be used for the next iteration.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids: A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A tensor of gradients, first dimension matches slots
+                  in <partition_id, feature_id>.
+output_hessians: A tensor of hessians, first dimension matches slots
+                 in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorDeserialize")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("num_updates: int64")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      DimensionHandle unused_dim;
+      // stats_accumulator_handle
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      // num_updates
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &partition_ids_shape));
+      ShapeHandle feature_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &feature_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(feature_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(5), 2, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(6), 2, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Resets the tensor stats accumulator with the serialized state.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+partition_ids: A vector of partition_ids.
+feature_ids: A vector of feature_ids.
+gradients: A vector of gradients for each slot in <partition_id, feature_id>.
+hessians: A vector of hessians for each slot in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorSerialize")
+    .Input("stats_accumulator_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      c->set_output(0, c->Scalar());
+      // num_updates
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(3, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(4, c->UnknownShape());
+      c->set_output(5, c->UnknownShape());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Serializes the scalar stats accumulator state.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids: A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A tensor of gradients, first dimension matches slots
+                  in <partition_id, feature_id>.
+output_hessians: A tensor of hessians, first dimension matches slots
+                 in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorMakeSummary")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .Doc(R"doc(
+Summarizes the stats by summing the <gradients, hessians> that are for the same
+<partition_id, feature_id>.
+
+partition_ids: A vector of partition_ids.
+feature_ids: A vector of feature_ids.
+gradients: A vector of gradients for each slot in <partition_id, feature_id>.
+hessians: A vector of hessians for each slot in <partition_id, feature_id>.
+output_partition_ids: A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A tensor of gradients, first dimension matches slots
+                  in <partition_id, feature_id>.
+output_hessians: A tensor of hessians, first dimension matches slots
+                 in <partition_id, feature_id>.
+)doc");
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/training_ops.cc b/tensorflow/contrib/boosted_trees/ops/training_ops.cc
new file mode 100644
index 0000000000..d2debbe03d
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/training_ops.cc
@@ -0,0 +1,120 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+
+REGISTER_OP("CenterTreeEnsembleBias")
+    .Attr("learner_config: string")
+    .Attr("centering_epsilon: float = 0.01")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Input("delta_updates: float")
+    .Output("continue_centering: bool")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &unused_input));
+      c->set_output(0, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Centers the tree ensemble bias before adding trees based on feature splits.
+
+learner_config: Config for the learner of type LearnerConfig proto.
+tree_ensemble_handle: Handle to the ensemble variable.
+stamp_token: Stamp token for validating operation consistency.
+next_stamp_token: Stamp token to be used for the next iteration.
+delta_updates: Rank 1 Tensor containing delta updates per bias dimension.
+continue_centering: Scalar indicating whether more centering is needed.
+)doc");
+
+REGISTER_OP("GrowTreeEnsemble")
+    .Attr("learner_config: string")
+    .Attr("num_handlers: int >= 0")
+    .Attr("center_bias: bool")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Input("learning_rate: float")
+    .Input("dropout_seed: int64")
+    .Input("partition_ids: num_handlers * int32")
+    .Input("gains: num_handlers * float")
+    .Input("splits: num_handlers * string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused_input));
+      // Dropout seed.
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Grows the tree ensemble by either adding a layer to the last tree being grown
+or by starting a new tree.
+
+learner_config: Config for the learner of type LearnerConfig proto.
+num_handlers: Number of handlers generating candidates.
+tree_ensemble_handle: Handle to the ensemble variable.
+stamp_token: Stamp token for validating operation consistency.
+next_stamp_token: Stamp token to be used for the next iteration.
+learning_rate: Scalar learning rate.
+partition_ids: List of Rank 1 Tensors containing partition Id per candidate.
+gains: List of Rank 1 Tensors containing gains per candidate.
+splits: List of Rank 1 Tensors containing serialized SplitInfo protos per candidate.
+)doc");
+
+REGISTER_OP("TreeEnsembleStats")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Output("num_trees: int64")
+    .Output("num_layers: int64")
+    .Output("active_tree: int64")
+    .Output("active_layer: int64")
+    .Output("attempted_trees: int64")
+    .Output("attempted_layers: int64")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Scalar());
+      c->set_output(3, c->Scalar());
+      c->set_output(4, c->Scalar());
+      c->set_output(5, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Retrieves stats related to the tree ensemble.
+
+tree_ensemble_handle: Handle to the ensemble variable.
+stamp_token: Stamp token for validating operation consistency.
+num_trees: Scalar indicating the number of finalized trees in the ensemble.
+num_layers: Scalar indicating the number of layers in the ensemble.
+active_tree: Scalar indicating the active tree being trained.
+active_layer: Scalar indicating the active layer being trained.
+)doc");
+
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/proto/BUILD b/tensorflow/contrib/boosted_trees/proto/BUILD
index c99d8849bd..9a61e163eb 100644
--- a/tensorflow/contrib/boosted_trees/proto/BUILD
+++ b/tensorflow/contrib/boosted_trees/proto/BUILD
@@ -33,6 +33,16 @@ tf_proto_library(
     visibility = ["//visibility:public"],
 )
 
+tf_proto_library(
+    name = "split_info_proto",
+    srcs = ["split_info.proto"],
+    cc_api_version = 2,
+    protodeps = [
+        ":tree_config_proto",
+    ],
+    visibility = ["//visibility:public"],
+)
+
 tf_proto_library(
     name = "tree_config_proto",
     srcs = ["tree_config.proto"],
diff --git a/tensorflow/contrib/boosted_trees/proto/split_info.proto b/tensorflow/contrib/boosted_trees/proto/split_info.proto
new file mode 100644
index 0000000000..a300c24c8e
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/proto/split_info.proto
@@ -0,0 +1,19 @@
+syntax = "proto3";
+
+option cc_enable_arenas = true;
+
+package tensorflow.boosted_trees.learner;
+
+import "tensorflow/contrib/boosted_trees/proto/tree_config.proto";
+
+// Gathered information for a split node.
+message SplitInfo {
+  // The split node without the child nodes attached.
+  tensorflow.boosted_trees.trees.TreeNode split_node = 1;
+
+  // Left Leaf node.
+  tensorflow.boosted_trees.trees.Leaf left_child = 2;
+
+  // Right Leaf node.
+  tensorflow.boosted_trees.trees.Leaf right_child = 3;
+}
diff --git a/tensorflow/contrib/boosted_trees/python/__init__.py b/tensorflow/contrib/boosted_trees/python/__init__.py
new file mode 100644
index 0000000000..60b0476a44
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Gradient boosted trees implementation in tensorflow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops import *
+# pylint: enable=unused-import,wildcard-import
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
new file mode 100644
index 0000000000..8fe0d5d968
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
@@ -0,0 +1,328 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow split handler Ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.proto import split_info_pb2
+from tensorflow.contrib.boosted_trees.python.ops import split_handler_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import googletest
+
+
+class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
+
+  def testMakeDenseSplit(self):
+    """Tests split handler op."""
+    with self.test_session() as sess:
+      # The data looks like the following after dividing by number of steps (2).
+      # Gradients    | Partition | Dense Quantile |
+      # (1.2, 0.2)   | 0         | 0              |
+      # (-0.3, 0.19) | 0         | 1              |
+      # (4.0, 0.13)  | 1         | 1              |
+      partition_ids = array_ops.constant([0, 0, 1], dtype=dtypes.int32)
+      bucket_ids = array_ops.constant([0, 1, 1], dtype=dtypes.int64)
+      gradients = array_ops.constant([2.4, -0.6, 8.0])
+      hessians = array_ops.constant([0.4, 0.38, 0.26])
+      bucket_boundaries = [0.3, 0.52]
+      partitions, gains, splits = (
+          split_handler_ops.build_dense_inequality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              bucket_ids=bucket_ids,
+              gradients=gradients,
+              hessians=hessians,
+              bucket_boundaries=bucket_boundaries,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the split on partition 0.
+    # -(1.2 - 0.1) / (0.2 + 1)
+    expected_left_weight = -0.91666
+
+    # expected_left_weight * -(1.2 - 0.1)
+    expected_left_gain = 1.0083333333333331
+
+    # (-0.3 + 0.1) / (0.19 + 1)
+    expected_right_weight = 0.1680672
+
+    # expected_right_weight * -(-0.3 + 0.1)
+    expected_right_gain = 0.033613445378151252
+
+    # (-0.3 + 1.2 - 0.1) ** 2 / (0.19 + 0.2 + 1)
+    expected_bias_gain = 0.46043165467625885
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0],
+        0.00001)
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+    self.assertEqual(0, split_node.feature_column)
+    self.assertAllClose(0.3, split_node.threshold, 0.00001)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (0.13 + 1)
+    expected_left_weight = -3.4513274336283186
+    expected_right_weight = 0
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    # There's only one active bucket here so zero gain is expected.
+    self.assertAllClose(0.0, gains[1], 0.00001)
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+    self.assertEqual(0, split_node.feature_column)
+    self.assertAllClose(0.52, split_node.threshold, 0.00001)
+
+  def testMakeDenseSplitEmptyInputs(self):
+    """Tests empty inputs op."""
+    with self.test_session() as sess:
+      partition_ids = array_ops.constant([], dtype=dtypes.int32)
+      bucket_ids = array_ops.constant([], dtype=dtypes.int64)
+      gradients = array_ops.constant([])
+      hessians = array_ops.constant([])
+      bucket_boundaries = [0.3, 0.52]
+      partitions, gains, splits = (
+          split_handler_ops.build_dense_inequality_splits(
+              num_minibatches=0,
+              partition_ids=partition_ids,
+              bucket_ids=bucket_ids,
+              gradients=gradients,
+              hessians=hessians,
+              bucket_boundaries=bucket_boundaries,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    # .assertEmpty doesn't exist on ubuntu-contrib
+    self.assertEqual(0, len(partitions))
+    self.assertEqual(0, len(gains))
+    self.assertEqual(0, len(splits))
+
+  def testMakeSparseSplit(self):
+    """Tests split handler op."""
+    with self.test_session() as sess:
+      # The data looks like the following after dividing by number of steps (2).
+      # Gradients    | Partition | bucket ID       |
+      # (0.9, 0.39)  | 0         | -1              |
+      # (1.2, 0.2)   | 0         | 0               |
+      # (0.2, 0.12)  | 0         | 1               |
+      # (4.0, 0.13)  | 1         | -1              |
+      # (4.0, 0.13)  | 1         | 1               |
+      partition_ids = array_ops.constant([0, 0, 0, 1, 1], dtype=dtypes.int32)
+      bucket_ids = array_ops.constant([-1, 0, 1, -1, 1], dtype=dtypes.int64)
+      gradients = array_ops.constant([1.8, 2.4, 0.4, 8.0, 8.0])
+      hessians = array_ops.constant([0.78, 0.4, 0.24, 0.26, 0.26])
+      bucket_boundaries = array_ops.constant([0.3, 0.52])
+      partitions, gains, splits = (
+          split_handler_ops.build_sparse_inequality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              bucket_ids=bucket_ids,
+              gradients=gradients,
+              hessians=hessians,
+              bucket_boundaries=bucket_boundaries,
+              l1_regularization=0,
+              l2_regularization=2,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              bias_feature_id=-1))
+      partitions, gains, splits = (sess.run([partitions, gains, splits]))
+    self.assertAllEqual([0, 1], partitions)
+    # Check the split on partition 0.
+    # -(0.2 + 1.2) / (0.12 + 0.2 + 2)
+    expected_left_weight = -0.603448275862069
+    # (0.2 + 1.2) ** 2 / (0.12 + 0.2 + 2)
+    expected_left_gain = 0.8448275862068965
+    # 0.5 / (0.07 + 2)
+    expected_right_weight = 0.24154589371980678
+    # 0.5 ** 2 / (0.07 + 2)
+    expected_right_gain = 0.12077294685990339
+    # (0.2 + 1.2 - 0.5) ** 2 /  (0.12 + 0.2 + 0.07 + 2)
+    expected_bias_gain = 0.3389121338912133
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_right
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0])
+
+    self.assertAllClose([expected_left_weight], left_child.value)
+
+    self.assertAllClose([expected_right_weight], right_child.value)
+
+    self.assertEqual(0, split_node.split.feature_column)
+
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+    # Check the split on partition 1.
+    expected_left_weight = -1.8779342723004695
+    expected_right_weight = 0
+
+    # Verify candidate for partition 1, there's only one active bucket here
+    # so zero gain is expected.
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_left
+
+    self.assertAllClose(0.0, gains[1])
+
+    self.assertAllClose([expected_left_weight], left_child.value)
+
+    self.assertAllClose([expected_right_weight], right_child.value)
+
+    self.assertEqual(0, split_node.split.feature_column)
+
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+  def testMakeCategoricalEqualitySplit(self):
+    """Tests split handler op for categorical equality split."""
+    with self.test_session() as sess:
+      # The data looks like the following after dividing by number of steps (2).
+      # Gradients    | Partition | Feature ID     |
+      # (0.9, 0.39)  | 0         | -1             |
+      # (0.2, 0.12)  | 0         | 1              |
+      # (1.4, 0.32)  | 0         | 2              |
+      # (4.0, 0.13)  | 1         | -1             |
+      # (4.0, 0.13)  | 1         | 1              |
+      gradients = [1.8, 0.4, 2.8, 8.0, 8.0]
+      hessians = [0.78, 0.24, 0.64, 0.26, 0.26]
+      partition_ids = [0, 0, 0, 1, 1]
+      feature_ids = array_ops.constant([-1, 1, 2, -1, 1], dtype=dtypes.int64)
+      partitions, gains, splits = (
+          split_handler_ops.build_categorical_equality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              feature_ids=feature_ids,
+              gradients=gradients,
+              hessians=hessians,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              bias_feature_id=-1))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the split on partition 0.
+    # -(0.2 + 1.2 - 0.1) / (0.12 + 0.2 + 1)
+    expected_left_weight = -0.9848484848484846
+
+    # (0.2 + 1.2 - 0.1) ** 2 / (0.12 + 0.2 + 1)
+    expected_left_gain = 1.2803030303030298
+
+    # -(-0.5 + 0.1) / (0.07 + 1)
+    expected_right_weight = 0.37383177570093457
+
+    # (-0.5 + 0.1) ** 2 / (0.07 + 1)
+    expected_right_gain = 0.14953271028037385
+
+    # (0.2 + -0.5 + 1.2 - 0.1) ** 2 / (0.12 + 0.07 + 0.2 + 1)
+    expected_bias_gain = 0.46043165467625885
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertEqual(2, split_node.feature_id)
+
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0],
+        0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (0.13 + 1)
+    expected_left_weight = -3.4513274336283186
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_left_gain = 13.460176991150442
+    expected_right_weight = 0
+    expected_right_gain = 0
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_bias_gain = 13.460176991150442
+
+    # Verify candidate for partition 1, there's only one active feature here
+    # so zero gain is expected.
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+    self.assertAllClose(0.0, gains[1], 0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertEqual(1, split_node.feature_id)
+
+  def testMakeCategoricalEqualitySplitEmptyInput(self):
+    with self.test_session() as sess:
+      gradients = []
+      hessians = []
+      partition_ids = []
+      feature_ids = []
+      partitions, gains, splits = (
+          split_handler_ops.build_categorical_equality_splits(
+              num_minibatches=0,
+              partition_ids=partition_ids,
+              feature_ids=feature_ids,
+              gradients=gradients,
+              hessians=hessians,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              bias_feature_id=-1))
+      partitions, gains, splits = (sess.run([partitions, gains, splits]))
+    self.assertEqual(0, len(partitions))
+    self.assertEqual(0, len(gains))
+    self.assertEqual(0, len(splits))
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
new file mode 100644
index 0000000000..e9e6a5e6ed
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
@@ -0,0 +1,132 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility for batching remote OPs together to reduce RPC overhead."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import collections
+
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+
+
+class ScheduledOp(object):
+  """Represents a scheduled remote operation."""
+
+  __metaclass__ = abc.ABCMeta
+
+  @abc.abstractmethod
+  def batching_key(self):
+    """Returns the key for batching operations."""
+
+  @abc.abstractmethod
+  def batch_runner_fn(self):
+    """Returns the function that executes the operation on the batch."""
+
+
+class ScheduledStampedResourceOp(ScheduledOp):
+  """Wrapper class for batched operations on stamped resources."""
+
+  def __init__(self, resource_handle, op, **kwargs):
+    self.resource_handle = resource_handle
+    self.op = op
+    self.args = kwargs
+
+  def batching_key(self):
+    # We want to group the same operations on the same device and run them in
+    # one batch. So we use (device, operation) as the key.
+    return self.resource_handle.device, self.op
+
+  def batch_runner_fn(self):
+    return _scheduled_stamp_resource_op_runner
+
+
+def _move_tensors(tensors, device):
+  """Moves a list of tensors to a device by concatenating/splitting them."""
+  # Reset the device setting to avoid weird interactions with device merging
+  # logic.
+  with ops.device(None):
+    if all(tensor.shape == tensor_shape.scalar() for tensor in tensors):
+      with ops.device(tensors[0].device):
+        values = array_ops.stack(tensors)
+      with ops.device(device):
+        return array_ops.unstack(values)
+    else:
+      with ops.device(tensors[0].device):
+        sizes = array_ops.stack([array_ops.size(tensor) for tensor in tensors])
+        values = array_ops.concat(tensors, axis=0)
+      with ops.device(device):
+        sizes = array_ops.unstack(sizes)
+        return list(array_ops.split(values, sizes))
+
+
+def _scheduled_stamp_resource_op_runner(batch, stamp):
+  """Runs a batch operation on a stamped resource."""
+  if not batch:
+    return
+  arg_keys = set(batch[0].args.keys())
+  grouped_args = collections.defaultdict(list)
+  resource_handles = []
+  # Check that the set of arguments is the same across all the scheduled ops.
+  for op in batch:
+    if set(op.args.keys()) != arg_keys:
+      raise ValueError("Mismatching arguments: %s, %s.", op.args, arg_keys)
+    for key in arg_keys:
+      grouped_args[key].append(op.args[key])
+    resource_handles.append(op.resource_handle)
+  # Move all the inputs to the op device in one RPC.
+  grouped_args = {k: _move_tensors(v, resource_handles[0].device)
+                  for k, v in grouped_args.items()}
+  with ops.device(resource_handles[0].device):
+    return batch[0].op(resource_handles, stamp, **grouped_args)
+
+
+def run_handler_scheduled_ops(per_handler_ops, stamp, worker_device):
+  """Given a dictionary of ops for each handler, runs them in batch."""
+  batched_ops = collections.defaultdict(list)
+  # Group the ops by their batching_key. Ops that share the same batching key
+  # can be executed together.
+  for handler in per_handler_ops.keys():
+    for op in per_handler_ops[handler]:
+      batched_ops[(op.batching_key(), op.batch_runner_fn())].append(op)
+  op_results = {}
+  for batch in batched_ops.values():
+    # Run each of the batched ops using its runner.
+    results = batch[0].batch_runner_fn()(batch, stamp)
+    # If the result is a tuple, move each entry in the tuple in one RPC.
+    if isinstance(results, tuple):
+      results = tuple(_move_tensors(result, worker_device) for result in
+                      results)
+      # Once all the results are on the worker, create individual tuple for
+      # each scheduled op request.
+      for i in range(len(batch)):
+        op_results[batch[i]] = tuple(result[i] for result in results)
+    # If the result is a tuple, it didn't have any outputs, so use the
+    # `ops.Operation` as the result for all the scheduled ops.
+    elif isinstance(results, ops.Operation):
+      for i in range(len(batch)):
+        op_results[batch[i]] = results
+    else:
+      raise ValueError("Unknown type of result %s.", results)
+  handler_results = collections.defaultdict(list)
+  # Dispatch the results of the ScheduledOps to the handlers that requested
+  # them.
+  for handler in per_handler_ops.keys():
+    for op in per_handler_ops[handler]:
+      handler_results[handler].append(op_results[op])
+  return handler_results
diff --git a/tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py b/tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py
new file mode 100644
index 0000000000..06eb6d59ae
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Split handler custom ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_ensemble_optimizer_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _ensemble_optimizer_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_ensemble_optimizer_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _ensemble_optimizer_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py
new file mode 100644
index 0000000000..813fb181f3
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py
@@ -0,0 +1,119 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Model ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import gen_model_ops
+from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_deserialize
+from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_serialize
+# pylint: disable=unused-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_stamp_token
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+ops.NotDifferentiable("TreeEnsembleVariable")
+ops.NotDifferentiable("TreeEnsembleSerialize")
+ops.NotDifferentiable("TreeEnsembleDeserialize")
+
+
+class TreeEnsembleVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for TreeEnsembleVariable."""
+
+  def __init__(self, tree_ensemble_handle, create_op, name):
+    """Creates a TreeEnsembleVariableSavable object.
+
+    Args:
+      tree_ensemble_handle: handle to the tree ensemble variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree ensemble variable under.
+    """
+    stamp_token, ensemble_config = tree_ensemble_serialize(tree_ensemble_handle)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree ensemble variable. So we just pass an empty
+    # value.
+    slice_spec = ""
+    specs = [
+        saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec,
+                                        name + "_stamp"),
+        saver.BaseSaverBuilder.SaveSpec(ensemble_config, slice_spec,
+                                        name + "_config"),
+    ]
+    super(TreeEnsembleVariableSavable,
+          self).__init__(tree_ensemble_handle, specs, name)
+    self._tree_ensemble_handle = tree_ensemble_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree ensemble from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree ensemble variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return tree_ensemble_deserialize(
+          self._tree_ensemble_handle,
+          stamp_token=restored_tensors[0],
+          tree_ensemble_config=restored_tensors[1])
+
+
+def tree_ensemble_variable(stamp_token,
+                           tree_ensemble_config,
+                           name,
+                           container=None):
+  r"""Creates a tree ensemble model and returns a handle to it.
+
+  Args:
+    stamp_token: The initial stamp token value for the ensemble resource.
+    tree_ensemble_config: A `Tensor` of type `string`.
+      Serialized proto of the tree ensemble.
+    name: A name for the ensemble variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the tree ensemble.
+  """
+  with ops.name_scope(name, "TreeEnsembleVariable") as name:
+    resource_handle = gen_model_ops.decision_tree_ensemble_resource_handle_op(
+        container, shared_name=name, name=name)
+    create_op = gen_model_ops.create_tree_ensemble_variable(
+        resource_handle, stamp_token, tree_ensemble_config)
+    is_initialized_op = gen_model_ops.tree_ensemble_is_initialized_op(
+        resource_handle)
+    # Adds the variable to the savable list.
+    saveable = TreeEnsembleVariableSavable(resource_handle, create_op,
+                                           resource_handle.name)
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _model_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_model_ops.so"))
+except (errors.NotFoundError, IOError):
+  print("Error loading _model_ops.so")
diff --git a/tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py b/tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py
new file mode 100644
index 0000000000..cf9ea37156
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Split handler custom ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_prediction_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _prediction_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_prediction_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _prediction_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
new file mode 100644
index 0000000000..d8ab6cdbb3
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
@@ -0,0 +1,176 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Quantile ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
+from tensorflow.contrib.boosted_trees.python.ops import gen_quantile_ops
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import,undefined-variable
+from tensorflow.contrib.boosted_trees.python.ops.gen_quantile_ops import *
+# pylint: enable=wildcard-import,undefined-variable
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
+  """A resource that allows distributed quantile computation."""
+
+  def __init__(self,
+               init_stamp_token,
+               epsilon,
+               num_quantiles,
+               name=None,
+               container=None):
+    """Creates a QuantileAccumulator object.
+
+    Args:
+      init_stamp_token: The initial value for the stamp token.
+      epsilon: Error bound on the quantile computation.
+      num_quantiles: Number of quantiles to produce from the final summary.
+      name: the name to save the accumulator under.
+      container: An optional `string`. Defaults to `""`
+    """
+    self._epsilon = epsilon
+
+    with ops.name_scope(name, "QuantileAccumulator") as name:
+      self._quantile_accumulator_handle = (
+          gen_quantile_ops.quantile_stream_resource_handle_op(
+              container=container, shared_name=name, name=name))
+      self._create_op = gen_quantile_ops.create_quantile_accumulator(
+          self._quantile_accumulator_handle,
+          init_stamp_token,
+          epsilon=epsilon,
+          num_quantiles=num_quantiles)
+      is_initialized_op = gen_quantile_ops.quantile_accumulator_is_initialized(
+          self._quantile_accumulator_handle)
+    resources.register_resource(self._quantile_accumulator_handle,
+                                self._create_op, is_initialized_op)
+    self._make_savable(name)
+
+  def _make_savable(self, name):
+    stamp_token, state, are_buckets_ready, buckets = (
+        gen_quantile_ops.quantile_accumulator_serialize(
+            self._quantile_accumulator_handle))
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful in quantile accumulator.
+    slice_spec = ""
+    def make_save_spec(tensor, suffix):
+      return saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name + suffix)
+
+    specs = [make_save_spec(stamp_token, "_stamp")]
+    specs += [make_save_spec(state, "_state")]
+    specs += [make_save_spec(are_buckets_ready, "_are_buckets_ready")]
+    specs += [make_save_spec(buckets, "buckets")]
+    super(QuantileAccumulator,
+          self).__init__(self._quantile_accumulator_handle, specs, name)
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, self)
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated quantile accumulator from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore.
+
+    Returns:
+      The operation that restores the state of the quantile accumulator.
+    """
+    # Read the restored tensors with the same order that were added to saving
+    # spec.
+    stamp_token = restored_tensors[:1]
+    state = restored_tensors[1:2]
+    are_buckets_ready = restored_tensors[2:3]
+    buckets = restored_tensors[3]
+    with ops.control_dependencies([self._create_op]):
+      return gen_quantile_ops.quantile_accumulator_deserialize(
+          self._quantile_accumulator_handle,
+          stamp_token=stamp_token,
+          stream_state=state,
+          are_buckets_ready=are_buckets_ready,
+          buckets=buckets)
+
+  def get_buckets(self, stamp_token):
+    """Returns quantile buckets created during previous flush."""
+    are_buckets_ready, buckets = (
+        gen_quantile_ops.quantile_accumulator_get_buckets(
+            quantile_accumulator_handles=[self._quantile_accumulator_handle],
+            stamp_token=stamp_token))
+    return are_buckets_ready[0], buckets[0]
+
+  def schedule_get_buckets(self):
+    """Returns a scheduled read of buckets created during previous flush."""
+    return batch_ops_utils.ScheduledStampedResourceOp(
+        resource_handle=self._quantile_accumulator_handle,
+        op=gen_quantile_ops.quantile_accumulator_get_buckets)
+
+  def _make_summary(self, column, example_weights):
+    if isinstance(column, sparse_tensor.SparseTensor):
+      return gen_quantile_ops.make_quantile_summaries(
+          dense_float_features=[],
+          sparse_float_feature_indices=[column.indices],
+          sparse_float_feature_values=[column.values],
+          sparse_float_feature_shapes=[column.dense_shape],
+          example_weights=example_weights,
+          epsilon=self._epsilon)[1][0]
+    else:
+      return gen_quantile_ops.make_quantile_summaries(
+          dense_float_features=[column],
+          sparse_float_feature_indices=[],
+          sparse_float_feature_values=[],
+          sparse_float_feature_shapes=[],
+          example_weights=example_weights,
+          epsilon=self._epsilon)[0][0]
+
+  def add_summary(self, stamp_token, column, example_weights):
+    """Adds quantile summary to its stream in resource."""
+    summary = self._make_summary(column, example_weights)
+    return gen_quantile_ops.quantile_accumulator_add_summaries(
+        quantile_accumulator_handles=[self._quantile_accumulator_handle],
+        stamp_token=stamp_token,
+        summaries=[summary])
+
+  def schedule_add_summary(self, stamp_token, column, example_weights):
+    """Schedules to add a quantile summary to its stream in resource."""
+    summary = self._make_summary(column, example_weights)
+    return batch_ops_utils.ScheduledStampedResourceOp(
+        op=gen_quantile_ops.quantile_accumulator_add_summaries,
+        resource_handle=self._quantile_accumulator_handle,
+        summaries=summary)
+
+  def flush(self, stamp_token, next_stamp_token):
+    """Finalizes quantile summary stream and resets it for next iteration."""
+    return gen_quantile_ops.quantile_accumulator_flush(
+        quantile_accumulator_handle=self._quantile_accumulator_handle,
+        stamp_token=stamp_token,
+        next_stamp_token=next_stamp_token)
+
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _quantile_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_quantile_ops.so"))
+except (errors.NotFoundError, IOError):
+  print("Error loading _quantile_ops.so")
diff --git a/tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py b/tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py
new file mode 100644
index 0000000000..903f7d99c9
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Split handler custom ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_split_handler_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _split_handler_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_split_handler_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _split_handler_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
new file mode 100644
index 0000000000..9e6ef1a270
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
@@ -0,0 +1,210 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Stats Accumulator ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
+from tensorflow.contrib.boosted_trees.python.ops import gen_stats_accumulator_ops
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+class StatsAccumulator(saver.BaseSaverBuilder.SaveableObject):
+  """A resource that allows to accumulate gradients and hessians.
+
+  For consistency guarantees, we use read and write stamp tokens.
+  The stamp token on the resource is updated with StatsAccumulator.flush.
+  Calls to StatsAccumulator.add that don't provide the current stamp token are
+  ignored.
+  """
+
+  def __init__(self,
+               stamp_token,
+               gradient_shape,
+               hessian_shape,
+               name=None,
+               container=None):
+    """Creates a stats accumulator and returns a handle to it.
+
+    Args:
+      stamp_token: An int64, initial value to use for the stamp token.
+      gradient_shape: A TensorShape, containing shape of gradients.
+      hessian_shape: A TensorShape, containing shape of hessians.
+      name: A name for the stats accumulator variable.
+      container: An optional `string`. Defaults to `""`.
+
+    Returns:
+      A `Tensor` of type mutable `string`. The handle to the stats accumulator.
+    """
+    with ops.name_scope(name, "StatsAccumulator") as name:
+      # Both values are scalars.
+      if (gradient_shape == tensor_shape.scalar() and
+          hessian_shape == tensor_shape.scalar()):
+        self._is_scalar = True
+        self._resource_handle = (gen_stats_accumulator_ops.
+                                 stats_accumulator_scalar_resource_handle_op(
+                                     container, name, name=name))
+
+        create_op = gen_stats_accumulator_ops.create_stats_accumulator_scalar(
+            self._resource_handle, stamp_token)
+        is_initialized_op = (
+            gen_stats_accumulator_ops.stats_accumulator_scalar_is_initialized(
+                self._resource_handle))
+      else:
+        self._is_scalar = False
+        self._resource_handle = (gen_stats_accumulator_ops.
+                                 stats_accumulator_tensor_resource_handle_op(
+                                     container, name, name=name))
+        create_op = gen_stats_accumulator_ops.create_stats_accumulator_tensor(
+            self._resource_handle, stamp_token, gradient_shape.as_list(),
+            hessian_shape.as_list())
+        is_initialized_op = (
+            gen_stats_accumulator_ops.stats_accumulator_scalar_is_initialized(
+                self._resource_handle))
+
+    self._create_op = create_op
+    slice_spec = ""
+    saver_name = self._resource_handle.name
+    (stamp_token, num_updates, partition_ids, feature_ids, gradients,
+     hessians) = self.serialize()
+    specs = [
+        saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec,
+                                        saver_name + "_stamp"),
+        saver.BaseSaverBuilder.SaveSpec(num_updates, slice_spec,
+                                        saver_name + "_num_updates"),
+        saver.BaseSaverBuilder.SaveSpec(partition_ids, slice_spec,
+                                        saver_name + "_partition_ids"),
+        saver.BaseSaverBuilder.SaveSpec(feature_ids, slice_spec,
+                                        saver_name + "_feature_ids"),
+        saver.BaseSaverBuilder.SaveSpec(gradients, slice_spec,
+                                        saver_name + "_gradients"),
+        saver.BaseSaverBuilder.SaveSpec(hessians, slice_spec,
+                                        saver_name + "hessians"),
+    ]
+
+    super(StatsAccumulator, self).__init__(self._resource_handle, specs, name)
+    resources.register_resource(self._resource_handle, create_op,
+                                is_initialized_op)
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, self)
+
+  def add(self, stamp_token, partition_ids, feature_ids, gradients, hessians):
+    """Updates the stats accumulator."""
+    partition_ids, feature_ids, gradients, hessians = (self._make_summary(
+        partition_ids, feature_ids, gradients, hessians))
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_add(
+          [self._resource_handle], stamp_token, [partition_ids], [feature_ids],
+          [gradients], [hessians])
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_add(
+          [self._resource_handle], stamp_token, [partition_ids], [feature_ids],
+          [gradients], [hessians])
+
+  def schedule_add(self, partition_ids, feature_ids, gradients, hessians):
+    """Schedules an update to the stats accumulator."""
+    partition_ids, feature_ids, gradients, hessians = (self._make_summary(
+        partition_ids, feature_ids, gradients, hessians))
+    if self._is_scalar:
+      return batch_ops_utils.ScheduledStampedResourceOp(
+          op=gen_stats_accumulator_ops.stats_accumulator_scalar_add,
+          resource_handle=self._resource_handle,
+          partition_ids=partition_ids,
+          feature_ids=feature_ids,
+          gradients=gradients,
+          hessians=hessians)
+    else:
+      return batch_ops_utils.ScheduledStampedResourceOp(
+          op=gen_stats_accumulator_ops.stats_accumulator_tensor_add,
+          resource_handle=self._resource_handle,
+          partition_ids=partition_ids,
+          feature_ids=feature_ids,
+          gradients=gradients,
+          hessians=hessians)
+
+  def _make_summary(self, partition_ids, feature_ids, gradients, hessians):
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_make_summary(
+          partition_ids, feature_ids, gradients, hessians)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_make_summary(
+          partition_ids, feature_ids, gradients, hessians)
+
+  def deserialize(self, stamp_token, num_updates, partition_ids, feature_ids,
+                  gradients, hessians):
+    """Resets the stats accumulator with the serialized state."""
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_deserialize(
+          self._resource_handle, stamp_token, num_updates, partition_ids,
+          feature_ids, gradients, hessians)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_deserialize(
+          self._resource_handle, stamp_token, num_updates, partition_ids,
+          feature_ids, gradients, hessians)
+
+  def flush(self, stamp_token, next_stamp_token):
+    """Flushes the stats accumulator."""
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_flush(
+          self._resource_handle, stamp_token, next_stamp_token)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_flush(
+          self._resource_handle, stamp_token, next_stamp_token)
+
+  def serialize(self):
+    """Serializes the stats accumulator state."""
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_serialize(
+          self._resource_handle)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_serialize(
+          self._resource_handle)
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree ensemble from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree ensemble variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return self.deserialize(
+          stamp_token=restored_tensors[0],
+          num_updates=restored_tensors[1],
+          partition_ids=restored_tensors[2],
+          feature_ids=restored_tensors[3],
+          gradients=restored_tensors[4],
+          hessians=restored_tensors[5])
+
+  def resource(self):
+    return self._resource_handle
+
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _stats_accumulator_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_stats_accumulator_ops.so"))
+except (errors.NotFoundError, IOError):
+  print("Error loading _stats_accumulator_ops.so")
diff --git a/tensorflow/contrib/boosted_trees/python/ops/training_ops.py b/tensorflow/contrib/boosted_trees/python/ops/training_ops.py
new file mode 100644
index 0000000000..32925b3e83
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/training_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Import and conditionally load custom ops for training boosted trees."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_training_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _training_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_training_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _training_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/resources/BUILD b/tensorflow/contrib/boosted_trees/resources/BUILD
index 5dfdf8f489..73de5df309 100644
--- a/tensorflow/contrib/boosted_trees/resources/BUILD
+++ b/tensorflow/contrib/boosted_trees/resources/BUILD
@@ -49,5 +49,4 @@ cc_library(
         "//tensorflow/contrib/boosted_trees/lib:trees",
         "//tensorflow/core:framework_headers_lib",
     ],
-    alwayslink = 1,
 )
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 67b0c86f7f..84a1302344 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -244,6 +244,8 @@ add_python_module("tensorflow/contrib/bayesflow/examples/reinforce_simple")
 add_python_module("tensorflow/contrib/bayesflow/python")
 add_python_module("tensorflow/contrib/bayesflow/python/kernel_tests")
 add_python_module("tensorflow/contrib/bayesflow/python/ops")
+add_python_module("tensorflow/contrib/boosted_trees")
+add_python_module("tensorflow/contrib/boosted_trees/proto")
 add_python_module("tensorflow/contrib/cloud")
 add_python_module("tensorflow/contrib/cloud/kernels")
 add_python_module("tensorflow/contrib/cloud/ops")
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 541d13aed1..aa70c3e218 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -1,3 +1,10 @@
+tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
+tensorflow/contrib/boosted_trees/ops/model_ops.cc
+tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
+tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
+tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
+tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
+tensorflow/contrib/boosted_trees/ops/training_ops.cc
 tensorflow/core/kernels/xent_op.cc
 tensorflow/core/kernels/where_op.cc
 tensorflow/core/kernels/variable_ops.cc
diff --git a/tensorflow/contrib/makefile/tf_proto_files.txt b/tensorflow/contrib/makefile/tf_proto_files.txt
index 36d9cb74a7..a1a9aa7190 100644
--- a/tensorflow/contrib/makefile/tf_proto_files.txt
+++ b/tensorflow/contrib/makefile/tf_proto_files.txt
@@ -1,3 +1,7 @@
+tensorflow/contrib/boosted_trees/proto/learner.proto
+tensorflow/contrib/boosted_trees/proto/quantiles.proto
+tensorflow/contrib/boosted_trees/proto/split_info.proto
+tensorflow/contrib/boosted_trees/proto/tree_config.proto
 tensorflow/core/util/test_log.proto
 tensorflow/core/util/saved_tensor_slice.proto
 tensorflow/core/util/memmapped_file_system.proto
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index d3952b5cec..6a6ce5b9e3 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -134,6 +134,7 @@ sh_binary(
             ":included_headers",
             ":simple_console",
             "//tensorflow:tensorflow_py",
+            "//tensorflow/contrib/boosted_trees:boosted_trees_pip",
             "//tensorflow/contrib/graph_editor:graph_editor_pip",
             "//tensorflow/contrib/keras:keras",
             "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip",
-- 
GitLab


From 63c1befb8930f8f5f34ae9f2b10e8ae870493d86 Mon Sep 17 00:00:00 2001
From: Anish Shah <shah.anish07@gmail.com>
Date: Wed, 21 Jun 2017 21:35:20 +0530
Subject: [PATCH 0285/1375] Improve docs for tf.nn.depthwise_conv2d_native

---
 tensorflow/core/ops/nn_ops.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 70302c3886..3a25fd15da 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -831,11 +831,13 @@ a different filter to each input channel (expanding from 1 channel to
 `channel_multiplier` channels for each), then concatenates the results
 together. Thus, the output has `in_channels * channel_multiplier` channels.
 
+```
 for k in 0..in_channels-1
   for q in 0..channel_multiplier-1
     output[b, i, j, k * channel_multiplier + q] =
       sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                         filter[di, dj, k, q]
+```
 
 Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
 horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
-- 
GitLab


From 8d2d639ef044c75fdd19e1c8bf4d1e91c6625652 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 09:08:56 -0700
Subject: [PATCH 0286/1375] Fix a bug in the memory optimizer when two inputs
 to a node are both recomputed

PiperOrigin-RevId: 159700457
---
 .../grappler/optimizers/memory_optimizer.cc   |  6 +++-
 .../optimizers/memory_optimizer_test.cc       | 30 +++++++++++++++++++
 2 files changed, 35 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index bd8ce8c30c..eb3692f319 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -304,7 +304,11 @@ AddRecomputeControlDependencyNodes(
   for (const NodeDef* target_node : target_nodes) {
     for (const string& target_input_name_raw : target_node->input()) {
       const NodeDef* target_input = node_map.GetNode(target_input_name_raw);
-      if (recomputed_source_nodes.count(target_input) != 0 ||
+      // If this node has already had one of its inputs recomputed during this
+      // rewriting pass, we ignore that recomputed node here (it will not be in
+      // the NodeMap).
+      if (target_input == nullptr ||
+          recomputed_source_nodes.count(target_input) != 0 ||
           components.find(target_node)->second ==
               components.find(target_input)->second) {
         continue;
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index 27cad07d5b..de0043a006 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -69,6 +69,36 @@ TEST_F(RecomputeSubgraphTest, SimpleSubgraph) {
   EXPECT_EQ("^gradients/d", recompute_trigger->input(0));
 }
 
+TEST_F(RecomputeSubgraphTest, TwoInputSubgraphs) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+
+  Output a = ops::Const(s.WithOpName("a"), 1.f, {2, 3, 4});
+  Output b = ops::Const(s.WithOpName("b"), 1.f, {2, 3, 4});
+  Output d = ops::AddN(s.WithOpName("gradients/two_subgraph_inputs"), {a, b});
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+  EXPECT_EQ(3, item.graph.node_size());
+  NodeMap pre_transform_node_map(&item.graph);
+  (*pre_transform_node_map.GetNode("a")->mutable_attr())["_recompute_hint"]
+      .set_i(0);
+  (*pre_transform_node_map.GetNode("b")->mutable_attr())["_recompute_hint"]
+      .set_i(0);
+
+  MemoryOptimizer optimizer(RewriterConfig::MANUAL);
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+
+  TF_EXPECT_OK(status);
+  NodeMap post_transform_node_map(&output);
+  // Mostly checking that this case does not crash.
+  EXPECT_EQ(7, output.node_size());
+  EXPECT_NE(post_transform_node_map.GetNode("Recomputed/a"), nullptr);
+  EXPECT_NE(post_transform_node_map.GetNode("Recomputed/b"), nullptr);
+  EXPECT_NE(post_transform_node_map.GetNode("RecomputeTrigger/a"), nullptr);
+  EXPECT_NE(post_transform_node_map.GetNode("RecomputeTrigger/b"), nullptr);
+}
+
 TEST_F(RecomputeSubgraphTest, MultiNode) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-- 
GitLab


From 97fcfdfa6ea86d54e71d76c947a4fd447e3fba12 Mon Sep 17 00:00:00 2001
From: Toby Boyd <tobyboyd@google.com>
Date: Wed, 21 Jun 2017 09:23:46 -0700
Subject: [PATCH 0287/1375] Fixed path to seq2seq.py and minor formatting

---
 tensorflow/docs_src/tutorials/seq2seq.md | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md
index 6ffe3e8b03..dd2ca8d524 100644
--- a/tensorflow/docs_src/tutorials/seq2seq.md
+++ b/tensorflow/docs_src/tutorials/seq2seq.md
@@ -8,7 +8,10 @@ some input and generate a meaningful response? For example, could we train
 a neural network to translate from English to French? It turns out that
 the answer is *yes*.
 
-This tutorial will show you how to build and train such a system end-to-end. Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub. You can then start by running the translate program:
+This tutorial will show you how to build and train such a system end-to-end.
+Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and
+the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub.
+You can then start by running the translate program:
 
 ```
 cd models/tutorials/rnn/translate
@@ -25,7 +28,7 @@ This tutorial references the following files.
 
 File | What's in it?
 --- | ---
-`tensorflow/tensorflow/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
+`tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
 `models/tutorials/rnn/translate/seq2seq_model.py` | Neural translation sequence-to-sequence model.
 `models/tutorials/rnn/translate/data_utils.py` | Helper functions for preparing translation data.
 `models/tutorials/rnn/translate/translate.py` | Binary that trains and runs the translation model.
@@ -148,9 +151,9 @@ have similar interfaces, so we will not describe them in detail. We will use
 ## Neural translation model
 
 While the core of the sequence-to-sequence model is constructed by
-the functions in `tensorflow/tensorflow/python/ops/seq2seq.py`, there are still a few tricks
-that are worth mentioning that are used in our translation model in
-`models/tutorials/rnn/translate/seq2seq_model.py`.
+the functions in `tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py`,
+there are still a few tricks that are worth mentioning that are used in our
+translation model in `models/tutorials/rnn/translate/seq2seq_model.py`.
 
 ### Sampled softmax and output projection
 
-- 
GitLab


From ed9d8d2d6065e96aebf5551a4f0e46601ff76821 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Jun 2017 10:10:55 -0700
Subject: [PATCH 0288/1375] Fixed memory leak that can be triggered by a failed
 node evaluation

PiperOrigin-RevId: 159707380
---
 .../core/grappler/optimizers/constant_folding.cc       | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index dafbd89a9c..09a4716df5 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -371,6 +372,12 @@ Status ConstantFolding::EvaluateNode(const NodeDef& node,
 Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
                                             std::vector<NodeDef>* outputs) {
   TensorVector inputs;
+  auto inputs_cleanup = gtl::MakeCleanup([&inputs] {
+    for (const auto& input : inputs) {
+      delete input.tensor;
+    }
+  });
+
   for (const auto& input : node.input()) {
     if (IsControlInput(input)) {
       break;
@@ -383,9 +390,6 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
 
   TensorVector output_tensors;
   TF_RETURN_IF_ERROR(EvaluateNode(node, inputs, &output_tensors));
-  for (const auto& input : inputs) {
-    delete input.tensor;
-  }
   if (output_tensors.empty()) {
     Status(error::INVALID_ARGUMENT, "Expected at least one output.");
   }
-- 
GitLab


From 0d7276644f45f5b55a2eaf55841bcb78498ff385 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 10:24:45 -0700
Subject: [PATCH 0289/1375] Updates get_started tutorial.

PiperOrigin-RevId: 159709158
---
 .../docs_src/get_started/get_started.md       | 103 +++++++++---------
 1 file changed, 53 insertions(+), 50 deletions(-)

diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 00cc10cd34..77b8e2dd2e 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -16,14 +16,11 @@ machine learning researchers and others who require fine levels of control over
 their models. The higher level APIs are built on top of TensorFlow Core. These
 higher level APIs are typically easier to learn and use than TensorFlow Core. In
 addition, the higher level APIs make repetitive tasks easier and more consistent
-between different users. A high-level API like tf.contrib.learn helps you manage
-data sets, estimators, training and inference. Note that a few of the high-level
-TensorFlow APIs--those whose method names contain `contrib`-- are still in
-development. It is possible that some `contrib` methods will change or become
-obsolete in subsequent TensorFlow releases.
+between different users. A high-level API like tf.estimator helps you manage
+data sets, estimators, training and inference.
 
 This guide begins with a tutorial on TensorFlow Core. Later, we
-demonstrate how to implement the same model in tf.contrib.learn. Knowing
+demonstrate how to implement the same model in tf.estimator. Knowing
 TensorFlow Core principles will give you a great mental model of how things are
 working internally when you use the more compact higher level API.
 
@@ -338,38 +335,36 @@ with random values.
 This more complicated program can still be visualized in TensorBoard
 ![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png)
 
-## `tf.contrib.learn`
+## `tf.estimator`
 
-`tf.contrib.learn` is a high-level TensorFlow library that simplifies the
+`tf.estimator` is a high-level TensorFlow library that simplifies the
 mechanics of machine learning, including the following:
 
 *   running training loops
 *   running evaluation loops
 *   managing data sets
-*   managing feeding
 
-tf.contrib.learn defines many common models.
+tf.estimator defines many common models.
 
 ### Basic usage
 
 Notice how much simpler the linear regression program becomes with
-`tf.contrib.learn`:
+`tf.estimator`:
 
 ```python
 import tensorflow as tf
 # NumPy is often used to load, manipulate and preprocess data.
 import numpy as np
 
-# Declare list of features. We only have one real-valued feature. There are many
+# Declare list of features. We only have one numeric feature. There are many
 # other types of columns that are more complicated and useful.
-features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
+feature_columns = [tf.feature_column.numeric_column("x", shape=[1])]
 
 # An estimator is the front end to invoke training (fitting) and evaluation
 # (inference). There are many predefined types like linear regression,
-# logistic regression, linear classification, logistic classification, and
-# many neural network classifiers and regressors. The following code
-# provides an estimator that does linear regression.
-estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
+# linear classification, and many neural network classifiers and regressors.
+# The following code provides an estimator that does linear regression.
+estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)
 
 # TensorFlow provides many helper methods to read and set up data sets.
 # Here we use two data sets: one for training and one for evaluation
@@ -379,51 +374,53 @@ x_train = np.array([1., 2., 3., 4.])
 y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
-input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x_train}, y_train,
-                                              batch_size=4,
-                                              num_epochs=1000)
-eval_input_fn = tf.contrib.learn.io.numpy_input_fn(
-    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000)
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
 
 # We can invoke 1000 training steps by invoking the  method and passing the
 # training data set.
-estimator.fit(input_fn=input_fn, steps=1000)
+estimator.train(input_fn=input_fn, steps=1000)
 
 # Here we evaluate how well our model did.
-train_loss = estimator.evaluate(input_fn=input_fn)
-eval_loss = estimator.evaluate(input_fn=eval_input_fn)
-print("train loss: %r"% train_loss)
-print("eval loss: %r"% eval_loss)
+train_metrics = estimator.evaluate(input_fn=train_input_fn)
+eval_metrics = estimator.evaluate(input_fn=eval_input_fn)
+print("train metrics: %r"% train_metrics)
+print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-    train loss: {'global_step': 1000, 'loss': 4.3049088e-08}
-    eval loss: {'global_step': 1000, 'loss': 0.0025487561}
+    train metrics: {'global_step': 1000, 'loss': 4.3049088e-08}
+    eval metrics: {'global_step': 1000, 'loss': 0.0025487561}
 ```
 Notice how our eval data has a higher loss, but it is still close to zero.
 That means we are learning properly.
 
 ### A custom model
 
-`tf.contrib.learn` does not lock you into its predefined models. Suppose we
+`tf.estimator` does not lock you into its predefined models. Suppose we
 wanted to create a custom model that is not built into TensorFlow. We can still
 retain the high level abstraction of data set, feeding, training, etc. of
-`tf.contrib.learn`. For illustration, we will show how to implement our own
+`tf.estimator`. For illustration, we will show how to implement our own
 equivalent model to `LinearRegressor` using our knowledge of the lower level
 TensorFlow API.
 
-To define a custom model that works with `tf.contrib.learn`, we need to use
-`tf.contrib.learn.Estimator`. `tf.contrib.learn.LinearRegressor` is actually
-a sub-class of `tf.contrib.learn.Estimator`. Instead of sub-classing
+To define a custom model that works with `tf.estimator`, we need to use
+`tf.estimator.Estimator`. `tf.estimator.LinearRegressor` is actually
+a sub-class of `tf.estimator.Estimator`. Instead of sub-classing
 `Estimator`, we simply provide `Estimator` a function `model_fn` that tells
-`tf.contrib.learn` how it can evaluate predictions, training steps, and
+`tf.estimator` how it can evaluate predictions, training steps, and
 loss. The code is as follows:
 
 ```python
 import numpy as np
 import tensorflow as tf
+
 # Declare list of features, we only have one real-valued feature
-def model(features, labels, mode):
+def model_fn(features, labels, mode):
   # Build a linear model and predict values
   W = tf.get_variable("W", [1], dtype=tf.float64)
   b = tf.get_variable("b", [1], dtype=tf.float64)
@@ -435,36 +432,42 @@ def model(features, labels, mode):
   optimizer = tf.train.GradientDescentOptimizer(0.01)
   train = tf.group(optimizer.minimize(loss),
                    tf.assign_add(global_step, 1))
-  # ModelFnOps connects subgraphs we built to the
+  # EstimatorSpec connects subgraphs we built to the
   # appropriate functionality.
-  return tf.contrib.learn.ModelFnOps(
-      mode=mode, predictions=y,
+  return tf.estimator.EstimatorSpec(
+      mode=mode,
+      predictions=y,
       loss=loss,
       train_op=train)
 
-estimator = tf.contrib.learn.Estimator(model_fn=model)
+estimator = tf.estimator.Estimator(model_fn=model_fn)
 # define our data sets
 x_train = np.array([1., 2., 3., 4.])
 y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
-input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_train}, y_train, 4, num_epochs=1000)
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
 
 # train
-estimator.fit(input_fn=input_fn, steps=1000)
-# Here we evaluate how well our model did. 
-train_loss = estimator.evaluate(input_fn=input_fn)
-eval_loss = estimator.evaluate(input_fn=eval_input_fn)
-print("train loss: %r"% train_loss)
-print("eval loss: %r"% eval_loss)
+estimator.train(input_fn=input_fn, steps=1000)
+# Here we evaluate how well our model did.
+train_metrics = estimator.evaluate(input_fn=train_input_fn)
+eval_metrics = estimator.evaluate(input_fn=eval_input_fn)
+print("train metrics: %r"% train_metrics)
+print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-train loss: {'global_step': 1000, 'loss': 4.9380226e-11}
-eval loss: {'global_step': 1000, 'loss': 0.01010081}
+train metrics: {'global_step': 1000, 'loss': 4.9380226e-11}
+eval metrics: {'global_step': 1000, 'loss': 0.01010081}
 ```
 
-Notice how the contents of the custom `model()` function are very similar
+Notice how the contents of the custom `model_fn()` function are very similar
 to our manual model training loop from the lower level API.
 
 ## Next steps
-- 
GitLab


From 9d1326f73afadf7d0d98ef7ca6891320c5b15d1a Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 21 Jun 2017 10:48:52 -0700
Subject: [PATCH 0290/1375] [XLA] Remove unused factory in local_service

PiperOrigin-RevId: 159712806
---
 tensorflow/compiler/xla/service/local_service.cc | 7 -------
 tensorflow/compiler/xla/service/local_service.h  | 6 +-----
 tensorflow/compiler/xla/service/service.h        | 2 +-
 3 files changed, 2 insertions(+), 13 deletions(-)

diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index 90dfe01bb9..05a1028a23 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -47,13 +47,6 @@ namespace se = ::perftools::gputools;
 
 namespace xla {
 
-/* static */ StatusOr<std::unique_ptr<LocalService>> LocalService::NewService(
-    perftools::gputools::Platform* platform) {
-  ServiceOptions default_options;
-  default_options.set_platform(platform);
-  return NewService(default_options);
-}
-
 /* static */ StatusOr<std::unique_ptr<LocalService>> LocalService::NewService(
     const ServiceOptions& options) {
   perftools::gputools::Platform* platform = options.platform();
diff --git a/tensorflow/compiler/xla/service/local_service.h b/tensorflow/compiler/xla/service/local_service.h
index 915c8c3072..13797ec045 100644
--- a/tensorflow/compiler/xla/service/local_service.h
+++ b/tensorflow/compiler/xla/service/local_service.h
@@ -35,11 +35,7 @@ namespace xla {
 // in the same process as the client.
 class LocalService : public Service {
  public:
-  // Factory for creating a LocalService. The parameter platform is the platform
-  // that the service should target. If platform is null then the default
-  // platform is used.
-  static StatusOr<std::unique_ptr<LocalService>> NewService(
-      perftools::gputools::Platform* platform);
+  // Factory for creating a LocalService.
   static StatusOr<std::unique_ptr<LocalService>> NewService(
       const ServiceOptions& options);
 
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 968fb53b34..14e8a676a1 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -126,7 +126,7 @@ class Service : public ServiceInterface {
   // least N * R devices must be available. The devices are assigned based on
   // the device ordinals such that the first R available devices are assigned to
   // the first set of replicas, and the next R devices to the second set of
-  // replicas, etc. Each returned device handles represent the device with the
+  // replicas, etc. Each returned device handle represents the device with the
   // replica id 0.
   tensorflow::Status GetDeviceHandles(
       const GetDeviceHandlesRequest* arg,
-- 
GitLab


From 41f3f76970726fe4ec2cd9e485a04e6f072a3bce Mon Sep 17 00:00:00 2001
From: Makoto Uchida <muchida@google.com>
Date: Wed, 21 Jun 2017 10:59:39 -0700
Subject: [PATCH 0291/1375] Fix typo in docstring

PiperOrigin-RevId: 159714414
---
 tensorflow/contrib/lookup/lookup_ops.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index 96d2962145..d58b9744ac 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -97,7 +97,7 @@ def index_table_from_tensor(mapping,
   Sample Usages:
 
   ```python
-  mapping_strings = t.constant(["emerson", "lake", "palmer"])
+  mapping_strings = tf.constant(["emerson", "lake", "palmer"])
   table = tf.contrib.lookup.index_table_from_tensor(
       mapping=mapping_strings, num_oov_buckets=1, default_value=-1)
   features = tf.constant(["emerson", "lake", "and", "palmer"])
@@ -208,7 +208,7 @@ def index_to_string_table_from_tensor(mapping, default_value="UNK", name=None):
   Sample Usages:
 
   ```python
-  mapping_string = t.constant(["emerson", "lake", "palmer")
+  mapping_string = tf.constant(["emerson", "lake", "palmer")
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       mapping_string, default_value="UNKNOWN")
@@ -260,7 +260,7 @@ def index_to_string(tensor, mapping, default_value="UNK", name=None):
   For example:
 
   ```python
-  mapping_string = t.constant(["emerson", "lake", "palmer")
+  mapping_string = tf.constant(["emerson", "lake", "palmer")
   indices = tf.constant([1, 5], tf.int64)
   values = tf.contrib.lookup.index_to_string(
       indices, mapping=mapping_string, default_value="UNKNOWN")
-- 
GitLab


From a286b7db8ee5e9f496099b9d9ea27dcb5f9efe9e Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Jun 2017 11:19:51 -0700
Subject: [PATCH 0292/1375] Make debug_test slice integer.

---
 .../contrib/learn/python/learn/estimators/debug_test.py       | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
index 935e66ee8c..ab5db3a151 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
@@ -52,8 +52,8 @@ LABEL_DIMENSION = 3  #  Dimensionality of regression labels.
 
 def _train_test_split(features_and_labels):
   features, labels = features_and_labels
-  train_set = (features[:len(features) / 2], labels[:len(features) / 2])
-  test_set = (features[len(features) / 2:], labels[len(features) / 2:])
+  train_set = (features[:int(len(features) / 2)], labels[:int(len(features) / 2)])
+  test_set = (features[int(len(features) / 2):], labels[int(len(features) / 2):])
   return train_set, test_set
 
 
-- 
GitLab


From 8e40f1adcbc94c2c21dccaa557604a917fd86f22 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 11:25:57 -0700
Subject: [PATCH 0293/1375] Migrate ops for new version of TensorForest.

PiperOrigin-RevId: 159718610
---
 tensorflow/contrib/BUILD                      |   4 +
 tensorflow/contrib/cmake/tf_core_ops.cmake    |   2 +
 tensorflow/contrib/cmake/tf_python.cmake      |   4 +
 tensorflow/contrib/tensor_forest/BUILD        | 293 ++++++---
 .../tensor_forest/kernels/model_ops.cc        | 299 ++++++++++
 .../tensor_forest/kernels/stats_ops.cc        | 564 ++++++++++++++++++
 .../contrib/tensor_forest/kernels/v4/BUILD    |  26 +-
 .../kernels/v4/graph_collection_operator.cc   | 142 +++++
 .../kernels/v4/graph_collection_operator.h    |  81 +++
 .../kernels/v4/split_collection_operators.cc  | 140 +----
 .../kernels/v4/split_collection_operators.h   |  69 +--
 .../contrib/tensor_forest/ops/model_ops.cc    | 135 +++++
 .../contrib/tensor_forest/ops/stats_ops.cc    | 146 +++++
 .../contrib/tensor_forest/python/__init__.py  |   2 +
 .../tensor_forest/python/ops/model_ops.py     | 124 ++++
 .../tensor_forest/python/ops/stats_ops.py     | 114 ++++
 16 files changed, 1887 insertions(+), 258 deletions(-)
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/model_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
 create mode 100644 tensorflow/contrib/tensor_forest/ops/model_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/ops/stats_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/python/ops/model_ops.py
 create mode 100644 tensorflow/contrib/tensor_forest/python/ops/stats_ops.py

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index f852eded1e..dfcdf3991c 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -93,6 +93,8 @@ cc_library(
         "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel",
         "//tensorflow/contrib/nccl:nccl_kernels",
         "//tensorflow/contrib/seq2seq:beam_search_ops_kernels",
+        "//tensorflow/contrib/tensor_forest:model_ops_kernels",
+        "//tensorflow/contrib/tensor_forest:stats_ops_kernels",
         "//tensorflow/contrib/tensor_forest:tensor_forest_kernels",
         "//tensorflow/contrib/text:all_kernels",
     ],
@@ -110,6 +112,8 @@ cc_library(
         "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib",
         "//tensorflow/contrib/nccl:nccl_ops_op_lib",
         "//tensorflow/contrib/seq2seq:beam_search_ops_op_lib",
+        "//tensorflow/contrib/tensor_forest:model_ops_op_lib",
+        "//tensorflow/contrib/tensor_forest:stats_ops_op_lib",
         "//tensorflow/contrib/tensor_forest:tensor_forest_ops_op_lib",
         "//tensorflow/contrib/text:all_ops",
         "//tensorflow/contrib/tpu:all_ops",
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index f1b01250a4..a9defc1139 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -85,6 +85,8 @@ GENERATE_CONTRIB_OP_LIBRARY(rnn_gru "${tensorflow_source_dir}/tensorflow/contrib
 GENERATE_CONTRIB_OP_LIBRARY(rnn_lstm "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/lstm_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(seq2seq_beam_search "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_model "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/model_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_stats "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/stats_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_hybrid "${tensor_forest_hybrid_srcs}")
 GENERATE_CONTRIB_OP_LIBRARY(text_skip_gram "${tensorflow_source_dir}/tensorflow/contrib/text/ops/skip_gram_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tpu "${tpu_ops_srcs}")
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 84a1302344..d17fcf6456 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -659,6 +659,10 @@ GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_tensor_forest_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_hybrid_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/hybrid/ops/gen_training_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_model_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_model_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_stats_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_stats_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_text_skip_gram_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/text/python/ops/gen_skip_gram_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_bigquery_reader_ops"
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 7b5f9472e7..1ca2d7596b 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -28,37 +28,35 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
+# ---------------------------------- V2 ops ------------------------------------------#
 filegroup(
-    name = "custom_op_sources",
-    srcs = glob(
-        [
-            "kernels/*.cc",
-            "ops/*.cc",
-        ],
-        exclude = [
-            "kernels/*_test.cc",
-            "kernels/tree_utils.cc",
-        ],
-    ),
+    name = "v2_op_sources",
+    srcs = [
+        "kernels/best_splits_op.cc",
+        "kernels/count_extremely_random_stats_op.cc",
+        "kernels/finished_nodes_op.cc",
+        "kernels/grow_tree_op.cc",
+        "kernels/reinterpret_string_to_float_op.cc",
+        "kernels/sample_inputs_op.cc",
+        "kernels/scatter_add_ndim_op.cc",
+        "kernels/tree_predictions_op.cc",
+        "kernels/update_fertile_slots_op.cc",
+    ],
 )
 
 filegroup(
-    name = "custom_op_headers",
-    srcs = glob(
-        [
-            "kernels/*.h",
-        ],
-        exclude = [
-            "kernels/data_spec.h",
-            "kernels/tree_utils.h",
-        ],
-    ),
+    name = "v2_op_defs",
+    srcs = [
+        "ops/tensor_forest_ops.cc",
+    ],
 )
 
 cc_library(
-    name = "all_ops",
-    srcs = [":custom_op_sources"],
-    hdrs = [":custom_op_headers"],
+    name = "v2_ops",
+    srcs = [
+        ":v2_op_defs",
+        ":v2_op_sources",
+    ],
     deps = [
         ":tree_utils",
         "//tensorflow/core:framework_headers_lib",
@@ -105,16 +103,8 @@ tf_gen_op_wrapper_py(
 tf_custom_op_library(
     name = "python/ops/_tensor_forest_ops.so",
     srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
-        "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
-        "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
-        "ops/tensor_forest_ops.cc",
+        ":v2_op_defs",
+        ":v2_op_sources",
     ],
     deps = [":tree_utils"],
 )
@@ -131,7 +121,9 @@ py_library(
         ":constants",
         ":data_ops_py",
         ":eval_metrics",
+        ":model_ops_py",
         ":random_forest",
+        ":stats_ops_py",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
     ],
@@ -140,21 +132,11 @@ py_library(
 tf_kernel_library(
     name = "tensor_forest_kernels",
     srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
-        "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
-        "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
+        ":v2_op_sources",
     ],
     deps = [
         ":tree_utils",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
+        "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core/kernels:bounds_check",
     ],
 )
@@ -181,6 +163,192 @@ tf_custom_op_py_library(
     ],
 )
 
+cc_test(
+    name = "tensor_forest_ops_test",
+    size = "small",
+    srcs = [
+        "kernels/tensor_forest_ops_test.cc",
+        ":v2_op_defs",
+        ":v2_op_sources",
+    ],
+    deps = [
+        ":tree_utils",
+        "//tensorflow/core",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//third_party/eigen3",
+    ],
+)
+
+# -------------------------------------- V4 ops ------------------------------- #
+cc_library(
+    name = "tensor_forest_v4_kernels",
+    deps = [
+        ":model_ops_kernels",
+        ":stats_ops_kernels",
+    ],
+)
+
+cc_library(
+    name = "tensor_forest_v4_ops_op_lib",
+    deps = [
+        ":model_ops_op_lib",
+        ":stats_ops_op_lib",
+    ],
+)
+
+py_library(
+    name = "tensor_forest_v4_ops_py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model_ops_py",
+        ":stats_ops_py",
+    ],
+)
+
+# Model Ops.
+cc_library(
+    name = "model_ops_lib",
+    srcs = ["kernels/model_ops.cc"],
+    deps = [
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_gen_op_libs(
+    op_lib_names = ["model_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_model_ops_py",
+    out = "python/ops/gen_model_ops.py",
+    deps = [":model_ops_op_lib"],
+)
+
+tf_kernel_library(
+    name = "model_ops_kernels",
+    deps = [
+        ":model_ops_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_model_ops.so",
+    deps = [
+        ":model_ops_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "model_ops_py",
+    srcs = ["python/ops/model_ops.py"],
+    dso = ["python/ops/_model_ops.so"],
+    kernels = [
+        ":model_ops_kernels",
+        ":model_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_model_ops_py",
+        ":stats_ops_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+# Stats Ops.
+cc_library(
+    name = "stats_ops_lib",
+    srcs = ["kernels/stats_ops.cc"],
+    deps = [
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:fertile-stats-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_target",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:params",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_gen_op_libs(
+    op_lib_names = ["stats_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_stats_ops_py",
+    out = "python/ops/gen_stats_ops.py",
+    deps = [":stats_ops_op_lib"],
+)
+
+tf_kernel_library(
+    name = "stats_ops_kernels",
+    deps = [
+        ":stats_ops_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_stats_ops.so",
+    deps = [
+        ":stats_ops_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "stats_ops_py",
+    srcs = ["python/ops/stats_ops.py"],
+    dso = ["python/ops/_stats_ops.so"],
+    kernels = [
+        ":stats_ops_kernels",
+        ":stats_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_stats_ops_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+# ---------------------------------- Common libs ------------------------ #
+cc_library(
+    name = "tree_utils",
+    srcs = ["kernels/tree_utils.cc"],
+    hdrs = [
+        "kernels/data_spec.h",
+        "kernels/tree_utils.h",
+    ],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+        "@protobuf//:protobuf_headers",
+    ],
+)
+
+# --------------------------------- Python -------------------------------- #
+
 py_library(
     name = "eval_metrics",
     srcs = ["client/eval_metrics.py"],
@@ -220,20 +388,6 @@ py_library(
     ],
 )
 
-cc_library(
-    name = "tree_utils",
-    srcs = ["kernels/tree_utils.cc"],
-    hdrs = [
-        "kernels/data_spec.h",
-        "kernels/tree_utils.h",
-    ],
-    deps = [
-        "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
-    ],
-)
-
 py_test(
     name = "best_splits_op_test",
     size = "small",
@@ -380,25 +534,6 @@ py_test(
     ],
 )
 
-cc_test(
-    name = "tensor_forest_ops_test",
-    size = "small",
-    srcs = [
-        "kernels/tensor_forest_ops_test.cc",
-        ":custom_op_sources",
-    ],
-    deps = [
-        ":tree_utils",
-        "//tensorflow/core",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-        "//tensorflow/core:testlib",
-        "//third_party/eigen3",
-    ],
-)
-
 py_library(
     name = "random_forest",
     srcs = ["client/random_forest.py"],
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
new file mode 100644
index 0000000000..195221a48e
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
@@ -0,0 +1,299 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Creates a tree  variable.
+class CreateTreeVariableOp : public OpKernel {
+ public:
+  explicit CreateTreeVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* tree_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
+                errors::InvalidArgument("Tree config must be a scalar."));
+
+    auto* result = new DecisionTreeResource();
+    if (!ParseProtoUnlimited(result->mutable_decision_tree(),
+                             tree_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false,
+                  errors::InvalidArgument("Unable to parse tree  config."));
+    }
+
+    result->MaybeInitialize();
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for serializing a model.
+class TreeSerializeOp : public OpKernel {
+ public:
+  explicit TreeSerializeOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, TensorShape(), &output_config_t));
+    output_config_t->scalar<string>()() =
+        decision_tree_resource->decision_tree().SerializeAsString();
+  }
+};
+
+// Op for deserializing a tree variable from a checkpoint.
+class TreeDeserializeOp : public OpKernel {
+ public:
+  explicit TreeDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    auto handle = HandleFromInput(context, 0);
+    OP_REQUIRES_OK(context, LookupResource(context, handle,
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    const Tensor* tree_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
+                errors::InvalidArgument("Tree config must be a scalar."));
+    // Deallocate all the previous objects on the resource.
+    decision_tree_resource->Reset();
+    decision_trees::Model* config =
+        decision_tree_resource->mutable_decision_tree();
+    OP_REQUIRES(context,
+                ParseProtoUnlimited(config, tree_config_t->scalar<string>()()),
+                errors::InvalidArgument("Unable to parse tree  config."));
+    decision_tree_resource->MaybeInitialize();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for getting tree size.
+class TreeSizeOp : public OpKernel {
+ public:
+  explicit TreeSizeOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+    Tensor* output_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, TensorShape(), &output_t));
+    output_t->scalar<int32>()() =
+        decision_tree_resource->decision_tree().decision_tree().nodes_size();
+  }
+};
+
+
+// Op for tree inference.
+class TreePredictionsV4Op : public OpKernel {
+ public:
+  explicit TreePredictionsV4Op(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    string serialized_proto;
+    OP_REQUIRES_OK(context, context->GetAttr(
+      "input_spec", &serialized_proto));
+    input_spec_.ParseFromString(serialized_proto);
+
+    data_set_ =
+        std::unique_ptr<TensorDataSet>(new TensorDataSet(input_spec_, 0));
+
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_data = context->input(1);
+    const Tensor& sparse_input_indices = context->input(2);
+    const Tensor& sparse_input_values = context->input(3);
+
+    data_set_->set_input_tensors(input_data, sparse_input_indices,
+                                 sparse_input_values);
+
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    Tensor* output_predictions = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(data_set_->NumItems());
+    output_shape.AddDim(param_proto_.num_outputs());
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, output_shape,
+                                            &output_predictions));
+
+    auto out = output_predictions->tensor<float, 2>();
+    for (int i = 0; i < data_set_->NumItems(); ++i) {
+      const int32 leaf_id =
+          decision_tree_resource->TraverseTree(data_set_, i, nullptr);
+      const decision_trees::Leaf& leaf =
+          decision_tree_resource->get_leaf(leaf_id);
+      for (int j = 0; j < param_proto_.num_outputs(); ++j) {
+        const float count = model_op_->GetOutputValue(leaf, j);
+        out(i, j) = count;
+      }
+    }
+  }
+
+ private:
+  tensorforest::TensorForestDataSpec input_spec_;
+  std::unique_ptr<TensorDataSet> data_set_;
+  std::unique_ptr<LeafModelOperator> model_op_;
+  TensorForestParams param_proto_;
+};
+
+// Op for getting feature usage counts.
+class FeatureUsageCountsOp : public OpKernel {
+ public:
+  explicit FeatureUsageCountsOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+
+    const auto& tree = decision_tree_resource->decision_tree();
+
+    Tensor* output_counts = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(param_proto_.num_features());
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, output_shape, &output_counts));
+
+    auto counts = output_counts->unaligned_flat<int32>();
+    counts.setZero();
+
+    for (const auto& node : tree.decision_tree().nodes()) {
+      if (node.has_custom_node_type()) {
+        LOG(WARNING) << "Can't count feature usage for custom nodes.";
+      } else if (node.has_binary_node()) {
+        const auto& bnode = node.binary_node();
+        if (bnode.has_custom_left_child_test()) {
+          decision_trees::MatchingValuesTest test;
+          if (!bnode.custom_left_child_test().UnpackTo(&test)) {
+            LOG(WARNING) << "Unknown custom child test";
+            continue;
+          }
+          int32 feat;
+          safe_strto32(test.feature_id().id().value(), &feat);
+          ++counts(feat);
+        } else {
+          const auto& test = bnode.inequality_left_child_test();
+          if (test.has_feature_id()) {
+            int32 feat;
+            safe_strto32(test.feature_id().id().value(), &feat);
+            ++counts(feat);
+          } else if (test.has_oblique()) {
+            for (const auto& featid : test.oblique().features()) {
+              int32 feat;
+              safe_strto32(featid.id().value(), &feat);
+              ++counts(feat);
+            }
+          }
+        }
+      }
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+
+REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeResource);
+
+REGISTER_KERNEL_BUILDER(Name("TreeIsInitializedOp").Device(DEVICE_CPU),
+                        IsResourceInitialized<DecisionTreeResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateTreeVariable").Device(DEVICE_CPU),
+                        CreateTreeVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeSerialize").Device(DEVICE_CPU),
+                        TreeSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeDeserialize").Device(DEVICE_CPU),
+                        TreeDeserializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeSize").Device(DEVICE_CPU),
+                        TreeSizeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreePredictionsV4").Device(DEVICE_CPU),
+                        TreePredictionsV4Op);
+
+REGISTER_KERNEL_BUILDER(Name("FeatureUsageCounts").Device(DEVICE_CPU),
+                        FeatureUsageCountsOp);
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
new file mode 100644
index 0000000000..7442469507
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -0,0 +1,564 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <queue>
+
+#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/gtl/map_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+using gtl::FindOrNull;
+
+// Creates a stats variable.
+class CreateFertileStatsVariableOp : public OpKernel {
+ public:
+  explicit CreateFertileStatsVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* stats_config_t;
+    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
+                errors::InvalidArgument("Stats config must be a scalar."));
+    auto* result = new FertileStatsResource(param_proto_);
+    FertileStats stats;
+    if (!ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false,
+                  errors::InvalidArgument("Unable to parse stats config."));
+    }
+
+    result->ExtractFromProto(stats);
+    result->MaybeInitialize();
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for serializing a model.
+class FertileStatsSerializeOp : public OpKernel {
+ public:
+  explicit FertileStatsSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &fertile_stats_resource));
+    mutex_lock l(*fertile_stats_resource->get_mutex());
+    core::ScopedUnref unref_me(fertile_stats_resource);
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, TensorShape(), &output_config_t));
+
+    FertileStats stats;
+    fertile_stats_resource->PackToProto(&stats);
+    output_config_t->scalar<string>()() = stats.SerializeAsString();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for deserializing a stats variable from a checkpoint.
+class FertileStatsDeserializeOp : public OpKernel {
+ public:
+  explicit FertileStatsDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &fertile_stats_resource));
+    mutex_lock l(*fertile_stats_resource->get_mutex());
+    core::ScopedUnref unref_me(fertile_stats_resource);
+
+    const Tensor* stats_config_t;
+    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
+                errors::InvalidArgument("Stats config must be a scalar."));
+    // Deallocate all the previous objects on the resource.
+    fertile_stats_resource->Reset();
+    FertileStats stats;
+    OP_REQUIRES(context,
+                ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()()),
+                errors::InvalidArgument("Unable to parse stats config."));
+
+    fertile_stats_resource->ExtractFromProto(stats);
+    fertile_stats_resource->MaybeInitialize();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+void TraverseTree(const DecisionTreeResource* tree_resource,
+                  const std::unique_ptr<TensorDataSet>& data, int32 start,
+                  int32 end, std::vector<int32>* leaf_ids,
+                  std::vector<int32>* leaf_depths) {
+  for (int i = start; i < end; ++i) {
+    int32 depth;
+    const int32 leaf_id = tree_resource->TraverseTree(data, i, &depth);
+    (*leaf_ids)[i] = leaf_id;
+    (*leaf_depths)[i] = depth;
+  }
+}
+
+// Try to update a leaf's stats by acquiring its lock.  If it can't be
+// acquired, put it in a waiting queue to come back to later and try the next
+// one.  Once all leaf_ids have been visited, cycle through the waiting ids
+// until they're gone.
+void UpdateStats(FertileStatsResource* fertile_stats_resource,
+                 const std::unique_ptr<TensorDataSet>& data,
+                 const Tensor& input_labels, const Tensor& input_weights,
+                 int num_targets, const std::vector<int32>& leaf_ids,
+                 const std::vector<int32>& leaf_depths,
+                 std::unordered_map<int32, std::unique_ptr<mutex>>* locks,
+                 mutex* set_lock, int32 start, int32 end,
+                 std::unordered_set<int32>* ready_to_split) {
+  const auto labels = input_labels.unaligned_flat<float>();
+  const auto weights = input_weights.unaligned_flat<float>();
+  // Stores leaf_id, leaf_depth, example_id for examples that are waiting
+  // on another to finish.
+  std::queue<std::tuple<int32, int32, int32>> waiting;
+
+  int32 i = start;
+  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
+  while (i < end || !waiting.empty()) {
+    int32 leaf_id;
+    int32 leaf_depth;
+    int32 example_id;
+    bool was_waiting = false;
+    if (i >= end) {
+      std::tie(leaf_id, leaf_depth, example_id) = waiting.front();
+      waiting.pop();
+      was_waiting = true;
+    } else {
+      leaf_id = leaf_ids[i];
+      leaf_depth = leaf_depths[i];
+      example_id = i;
+      ++i;
+    }
+    const std::unique_ptr<mutex>& leaf_lock = (*locks)[leaf_id];
+    if (was_waiting) {
+      leaf_lock->lock();
+    } else {
+      if (!leaf_lock->try_lock()) {
+        waiting.emplace(leaf_id, leaf_depth, example_id);
+        continue;
+      }
+    }
+
+    bool is_finished;
+    fertile_stats_resource->AddExampleToStatsAndInitialize(
+        data, &target, {example_id}, leaf_id, leaf_depth,
+        &is_finished);
+    leaf_lock->unlock();
+    if (is_finished) {
+      set_lock->lock();
+      ready_to_split->insert(leaf_id);
+      set_lock->unlock();
+    }
+  }
+}
+
+// Update leaves from start through end in the leaf_examples iterator.
+void UpdateStatsCollated(
+    FertileStatsResource* fertile_stats_resource,
+    DecisionTreeResource* tree_resource,
+    const std::unique_ptr<TensorDataSet>& data, const Tensor& input_labels,
+    const Tensor& input_weights, int num_targets,
+    const std::unordered_map<int32, std::vector<int>>& leaf_examples,
+    const std::vector<int32>& leaf_depths, mutex* set_lock, int32 start,
+    int32 end, std::unordered_set<int32>* ready_to_split) {
+  const auto labels = input_labels.unaligned_flat<float>();
+  const auto weights = input_weights.unaligned_flat<float>();
+
+  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
+  auto it = leaf_examples.begin();
+  std::advance(it, start);
+  auto end_it = leaf_examples.begin();
+  std::advance(end_it, end);
+  while (it != end_it) {
+    int32 leaf_id = it->first;
+    bool is_finished;
+    fertile_stats_resource->AddExampleToStatsAndInitialize(
+        data, &target, it->second, leaf_id, leaf_depths[it->second[0]],
+        &is_finished);
+    if (is_finished) {
+      set_lock->lock();
+      ready_to_split->insert(leaf_id);
+      set_lock->unlock();
+    }
+    ++it;
+  }
+}
+
+// Op for traversing the tree with each example, accumulating statistics, and
+// outputting node ids that are ready to split.
+class ProcessInputOp : public OpKernel {
+ public:
+  explicit ProcessInputOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_));
+
+    string serialized_proto;
+    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
+    input_spec_.ParseFromString(serialized_proto);
+
+    data_set_ = std::unique_ptr<TensorDataSet>(
+        new TensorDataSet(input_spec_, random_seed_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_data = context->input(2);
+    const Tensor& sparse_input_indices = context->input(3);
+    const Tensor& sparse_input_values = context->input(4);
+    const Tensor& input_labels = context->input(6);
+    const Tensor& input_weights = context->input(7);
+
+    data_set_->set_input_tensors(input_data, sparse_input_indices,
+                                 sparse_input_values);
+
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+    core::ScopedUnref unref_tree(tree_resource);
+
+    const int32 num_data = data_set_->NumItems();
+    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
+    int num_threads = worker_threads->num_threads;
+
+    // First find the leaf ids for each example.
+    std::vector<int32> leaf_ids(num_data);
+
+    // The depth of the leaf for example i.
+    std::vector<int32> leaf_depths(num_data);
+
+    const int64 costPerTraverse = 500;
+    auto traverse = [this, &leaf_ids, &leaf_depths, tree_resource, num_data](
+        int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      TraverseTree(tree_resource, data_set_, static_cast<int32>(start),
+                   static_cast<int32>(end), &leaf_ids, &leaf_depths);
+    };
+    Shard(num_threads, worker_threads->workers, num_data, costPerTraverse,
+          traverse);
+
+    // Create one mutex per leaf. We need to protect access to leaf pointers,
+    // so instead of grouping examples by leaf, we spread examples out among
+    // threads to provide uniform work for each of them and protect access
+    // with mutexes.
+    std::unordered_map<int, std::unique_ptr<mutex>> locks;
+    std::unordered_map<int32, std::vector<int>> leaf_examples;
+    if (param_proto_.collate_examples()) {
+      for (int i = 0; i < num_data; ++i) {
+        leaf_examples[leaf_ids[i]].push_back(i);
+      }
+    } else {
+      for (const int32 id : leaf_ids) {
+        if (FindOrNull(locks, id) == nullptr) {
+          // TODO(gilberth): Consider using a memory pool for these.
+          locks[id] = std::unique_ptr<mutex>(new mutex);
+        }
+      }
+    }
+
+    const int32 num_leaves = leaf_examples.size();
+    const int32 label_dim =
+        input_labels.shape().dims() <= 1
+            ? 0
+            : static_cast<int>(input_labels.shape().dim_size(1));
+    const int32 num_targets =
+        param_proto_.is_regression() ? (std::max(1, label_dim)) : 1;
+
+    // Ids of leaves that can split.
+    std::unordered_set<int32> ready_to_split;
+    mutex set_lock;
+
+    // TODO(gilberth): This is a rough approximation based on measurements
+    // from a digits run on local desktop.  Heuristics might be necessary
+    // if it really matters that much.
+    const int64 costPerUpdate = 1000;
+    auto update = [this, &input_labels, &input_weights, &leaf_ids, &leaf_depths,
+                   &num_targets, fertile_stats_resource, &locks, &set_lock,
+                   &ready_to_split, num_data](int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      UpdateStats(fertile_stats_resource, data_set_, input_labels,
+                  input_weights, num_targets, leaf_ids, leaf_depths, &locks,
+                  &set_lock, static_cast<int32>(start), static_cast<int32>(end),
+                  &ready_to_split);
+    };
+
+    auto update_collated = [this, &input_labels, &input_weights, &leaf_ids,
+                            &num_targets, &leaf_depths, fertile_stats_resource,
+                            tree_resource, &leaf_examples, &set_lock,
+                            &ready_to_split,
+                            num_leaves](int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_leaves);
+      UpdateStatsCollated(
+          fertile_stats_resource, tree_resource, data_set_, input_labels,
+          input_weights, num_targets, leaf_examples, leaf_depths, &set_lock,
+          static_cast<int32>(start), static_cast<int32>(end), &ready_to_split);
+    };
+
+    if (param_proto_.collate_examples()) {
+      Shard(num_threads, worker_threads->workers, num_leaves, costPerUpdate,
+            update_collated);
+    } else {
+      Shard(num_threads, worker_threads->workers, num_data, costPerUpdate,
+            update);
+    }
+
+    Tensor* output_finished_t = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(ready_to_split.size());
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, output_shape, &output_finished_t));
+    auto output = output_finished_t->unaligned_flat<int32>();
+    std::copy(ready_to_split.begin(), ready_to_split.end(), output.data());
+  }
+
+ private:
+  int32 random_seed_;
+  tensorforest::TensorForestDataSpec input_spec_;
+  std::unique_ptr<TensorDataSet> data_set_;
+  TensorForestParams param_proto_;
+};
+
+
+// Op for growing finished nodes.
+class GrowTreeOp : public OpKernel {
+ public:
+  explicit GrowTreeOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+    core::ScopedUnref unref_tree(tree_resource);
+
+    const Tensor& finished_nodes = context->input(2);
+
+    const auto finished = finished_nodes.unaligned_flat<int32>();
+
+    const int32 num_nodes =
+        static_cast<int32>(finished_nodes.shape().dim_size(0));
+
+    // TODO(gilberth): distribute this work over a number of threads.
+    for (int i = 0;
+         i < num_nodes &&
+         tree_resource->decision_tree().decision_tree().nodes_size() <
+             param_proto_.max_nodes();
+         ++i) {
+      const int32 node = finished(i);
+      std::unique_ptr<SplitCandidate> best(new SplitCandidate);
+      int32 parent_depth;
+      bool found =
+          fertile_stats_resource->BestSplit(node, best.get(), &parent_depth);
+      if (found) {
+        std::vector<int32> new_children;
+        tree_resource->SplitNode(node, best.get(), &new_children);
+        fertile_stats_resource->Allocate(parent_depth, new_children);
+        fertile_stats_resource->set_leaf_stat(best->left_stats(),
+                                              new_children[0]);
+        fertile_stats_resource->set_leaf_stat(best->right_stats(),
+                                              new_children[1]);
+        // We are done with best, so it is now safe to clear node.
+        fertile_stats_resource->Clear(node);
+        CHECK(tree_resource->get_mutable_tree_node(node)->has_leaf() == false);
+      } else {  // reset
+        fertile_stats_resource->ResetSplitStats(node, parent_depth);
+      }
+    }
+  }
+
+ private:
+  tensorforest::TensorForestDataSpec input_spec_;
+  TensorForestParams param_proto_;
+};
+
+void FinalizeLeaf(const LeafStat& leaf_stats, bool is_regression,
+                  bool drop_final_class,
+                  const std::unique_ptr<LeafModelOperator>& leaf_op,
+                  decision_trees::Leaf* leaf) {
+  leaf_op->ExportModel(leaf_stats, leaf);
+
+  // TODO(thomaswc): Move the rest of this into ExportModel.
+
+  // regression models are already stored in leaf in normalized form.
+  if (is_regression) {
+    return;
+  }
+
+  float sum = leaf_stats.weight_sum();
+  if (sum <= 0.0) {
+    LOG(WARNING) << "Leaf with sum " << sum
+                 << " has stats " << leaf->ShortDebugString();
+    return;
+  }
+
+  if (leaf->has_vector()) {
+    for (int i = 0; i < leaf->vector().value_size(); i++) {
+      auto *v = leaf->mutable_vector()->mutable_value(i);
+      v->set_float_value(v->float_value() / sum);
+    }
+    if (drop_final_class) {
+      leaf->mutable_vector()->mutable_value()->RemoveLast();
+    }
+    return;
+  }
+
+  if (leaf->has_sparse_vector()) {
+    for (auto& it : *leaf->mutable_sparse_vector()->mutable_sparse_value()) {
+      it.second.set_float_value(it.second.float_value() / sum);
+    }
+    return;
+  }
+
+  LOG(FATAL) << "Unknown leaf type in " << leaf->DebugString();
+}
+
+// Op for finalizing a tree at the end of training.
+class FinalizeTreeOp : public OpKernel {
+ public:
+  explicit FinalizeTreeOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_me(tree_resource);
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+
+    // TODO(thomaswc): Add threads
+    int num_nodes = tree_resource->decision_tree().decision_tree().nodes_size();
+    for (int i = 0; i < num_nodes; i++) {
+      auto* node = tree_resource->mutable_decision_tree()
+          ->mutable_decision_tree()->mutable_nodes(i);
+      if (node->has_leaf()) {
+        const auto& leaf_stats = fertile_stats_resource->leaf_stat(i);
+        FinalizeLeaf(leaf_stats, param_proto_.is_regression(),
+                     param_proto_.drop_final_class(), model_op_,
+                     node->mutable_leaf());
+      }
+    }
+  }
+
+ private:
+  std::unique_ptr<LeafModelOperator> model_op_;
+  TensorForestParams param_proto_;
+};
+
+REGISTER_RESOURCE_HANDLE_KERNEL(FertileStatsResource);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsIsInitializedOp").Device(DEVICE_CPU),
+                        IsResourceInitialized<FertileStatsResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateFertileStatsVariable").Device(DEVICE_CPU),
+                        CreateFertileStatsVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsSerialize").Device(DEVICE_CPU),
+                        FertileStatsSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsDeserialize").Device(DEVICE_CPU),
+                        FertileStatsDeserializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("ProcessInputV4").Device(DEVICE_CPU),
+                        ProcessInputOp);
+
+REGISTER_KERNEL_BUILDER(Name("GrowTreeV4").Device(DEVICE_CPU),
+                        GrowTreeOp);
+
+REGISTER_KERNEL_BUILDER(Name("FinalizeTree").Device(DEVICE_CPU),
+                        FinalizeTreeOp);
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index 0542508a8e..a9d8093d13 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -40,9 +40,7 @@ cc_library(
         ":split_collection_operators",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:framework",
         "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:lib",
     ],
 )
 
@@ -111,7 +109,7 @@ cc_library(
         "//tensorflow/contrib/tensor_forest:tree_utils",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -153,7 +151,7 @@ cc_library(
         ":input_data",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -174,6 +172,25 @@ cc_library(
     name = "split_collection_operators",
     srcs = ["split_collection_operators.cc"],
     hdrs = ["split_collection_operators.h"],
+    deps = [
+        ":grow_stats",
+        ":input_data",
+        ":input_target",
+        ":leaf_model_operators",
+        ":params",
+        ":stat_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+    ],
+)
+
+cc_library(
+    name = "graph_collection_operator",
+    srcs = ["graph_collection_operator.cc"],
+    hdrs = ["graph_collection_operator.h"],
     deps = [
         ":candidate_graph_runner",
         ":grow_stats",
@@ -181,6 +198,7 @@ cc_library(
         ":input_target",
         ":leaf_model_operators",
         ":params",
+        ":split_collection_operators",
         ":stat_utils",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
new file mode 100644
index 0000000000..2c925b5dd7
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
@@ -0,0 +1,142 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h"
+
+#include <cfloat>
+
+#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+REGISTER_SPLIT_COLLECTION(GRAPH_RUNNER_COLLECTION,
+                          GraphRunnerSplitCollectionOperator);
+
+std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
+    int32 node_id, int32 depth) const {
+  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
+}
+
+int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
+                                                   int32 split_id) const {
+  return node_id * num_splits_to_consider_ + split_id;
+}
+
+bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
+                                                   SplitCandidate* best,
+                                                   int32* depth) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  auto* slot = stats_.at(node_id).get();
+  *depth = slot->depth();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    // TODO(gilberth): Support uselessness.
+    auto& runner = runners_[UniqueId(node_id, i)];
+    const float split_score = runner->SplitScore();
+    if (split_score < min_score) {
+      min_score = split_score;
+      best_index = i;
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in split info and left/right stats to initialize models with.
+  *best = SplitCandidate();
+  auto& runner = runners_[UniqueId(node_id, best_index)];
+  runner->GetLeftStats(best->mutable_left_stats());
+  runner->GetRightStats(best->mutable_right_stats());
+  runner->GetSplit(best->mutable_split());
+  return true;
+}
+
+void GraphRunnerSplitCollectionOperator::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    const std::vector<int>& examples, int32 node_id) const {
+  // Build input Tensors.
+  int size = examples.size();
+  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
+  auto ex_data = examples_t.flat<int32>();
+  std::copy(examples.begin(), examples.end(), ex_data.data());
+
+  const TensorInputTarget* tensor_target =
+      dynamic_cast<const TensorInputTarget*>(target);
+  CHECK_NOTNULL(tensor_target);
+
+  const Tensor& data_t = input_data->original_tensor();
+  const Tensor& target_t = tensor_target->original_tensor();
+
+  // Add to candidates.
+  auto* slot = stats_.at(node_id).get();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    auto& runner = runners_[UniqueId(node_id, i)];
+    runner->AddExample(data_t, target_t, examples_t);
+  }
+
+  // Update simple weight sums so we know when we're done.
+  for (int example : examples) {
+    slot->AddExample(input_data, target, example);
+  }
+}
+
+void GraphRunnerSplitCollectionOperator::
+    CreateAndInitializeCandidateWithExample(
+        const std::unique_ptr<TensorDataSet>& input_data, int example,
+        int32 node_id) const {
+  auto* slot = stats_.at(node_id).get();
+  int cand_num = slot->num_splits();
+  const int64 unique_id = UniqueId(node_id, cand_num);
+
+  decision_trees::BinaryNode split;
+
+  decision_trees::InequalityTest* test =
+      split.mutable_inequality_left_child_test();
+  auto* oblique = test->mutable_oblique();
+  for (int i = 0; i < features_per_node_; ++i) {
+    float bias;
+    int type;
+    // This is really just a way to select a list of random features.
+    // Also a way to warn the user that categoricals don't make sense here.
+    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
+
+    if (type == kDataFloat) {
+      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
+
+      // The comparison bias is assumed to be zero.
+      test->mutable_threshold()->set_float_value(0);
+    } else {
+      LOG(ERROR) << "Categorical features not supported with this system.";
+      return;
+    }
+  }
+
+  slot->AddSplit(split);
+
+  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
+  runners_[unique_id]->Init();
+}
+
+void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
+  SplitCollectionOperator::ClearSlot(node_id);
+  for (int i = 0; i < num_splits_to_consider_; ++i) {
+    runners_.erase(UniqueId(node_id, i));
+  }
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
new file mode 100644
index 0000000000..9b18e3e969
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
@@ -0,0 +1,81 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
+
+#include <vector>
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Holds split candidates that are trained by running any TF graph.
+class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
+ public:
+  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
+      : SplitCollectionOperator(params) {
+    if (params.num_splits_to_consider().ParamType_case() ==
+        DepthDependentParam::PARAMTYPE_NOT_SET) {
+      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
+                 << " num_splits_to_consider";
+    } else {
+      num_splits_to_consider_ =
+          params.num_splits_to_consider().constant_value();
+    }
+  }
+
+  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
+                                             int32 depth) const override;
+
+  // Updates the slot's candidates with the new example.
+  // Assumes slot has been initialized.
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, const std::vector<int>& examples,
+                  int32 node_id) const override;
+
+  // Create a new candidate and initialize it with the given example.
+  void CreateAndInitializeCandidateWithExample(
+      const std::unique_ptr<TensorDataSet>& input_data, int example,
+      int32 node_id) const override;
+
+  bool BestSplit(int32 node_id, SplitCandidate* best,
+                 int32* depth) const override;
+
+  void ClearSlot(int32 node_id) override;
+
+ protected:
+  int64 UniqueId(int32 node_id, int32 split_id) const;
+
+  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
+      runners_;
+  int features_per_node_;
+  string graph_dir_;
+  // Must have a constant value because of how we make unique ids right now.
+  int32 num_splits_to_consider_;
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
index ddf4be8799..c207c0859d 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
@@ -23,22 +23,20 @@
 namespace tensorflow {
 namespace tensorforest {
 
+std::unordered_map<int, CollectionCreator*>
+    SplitCollectionOperatorFactory::factories_;  // NOLINT
+REGISTER_SPLIT_COLLECTION(COLLECTION_BASIC, SplitCollectionOperator);
+
 std::unique_ptr<SplitCollectionOperator>
 SplitCollectionOperatorFactory::CreateSplitCollectionOperator(
     const TensorForestParams& params) {
-  switch (params.collection_type()) {
-    case COLLECTION_BASIC:
-      return std::unique_ptr<SplitCollectionOperator>(
-          new SplitCollectionOperator(params));
-
-    case GRAPH_RUNNER_COLLECTION:
-      return std::unique_ptr<SplitCollectionOperator>(
-          new GraphRunnerSplitCollectionOperator(params));
-
-    default:
-      LOG(ERROR) << "Unknown split collection operator: "
-                 << params.collection_type();
-      return nullptr;
+  auto it = factories_.find(params.collection_type());
+  if (it == factories_.end()) {
+    LOG(ERROR) << "Unknown split collection operator: "
+               << params.collection_type();
+    return nullptr;
+  } else {
+    return it->second->Create(params);
   }
 }
 
@@ -137,121 +135,5 @@ bool SplitCollectionOperator::BestSplit(int32 node_id,
   return slot->BestSplit(best);
 }
 
-// -------------------------------- GraphRunner ------------------ //
-
-std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
-    int32 node_id, int32 depth) const {
-  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
-}
-
-int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
-                                                   int32 split_id) const {
-  return node_id * num_splits_to_consider_ + split_id;
-}
-
-bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
-                                                   SplitCandidate* best,
-                                                   int32* depth) const {
-  float min_score = FLT_MAX;
-  int best_index = -1;
-  auto* slot = stats_.at(node_id).get();
-  *depth = slot->depth();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    // TODO(gilberth): Support uselessness.
-    auto& runner = runners_[UniqueId(node_id, i)];
-    const float split_score = runner->SplitScore();
-    if (split_score < min_score) {
-      min_score = split_score;
-      best_index = i;
-    }
-  }
-
-  // This could happen if all the splits are useless.
-  if (best_index < 0) {
-    return false;
-  }
-
-  // Fill in split info and left/right stats to initialize models with.
-  *best = SplitCandidate();
-  auto& runner = runners_[UniqueId(node_id, best_index)];
-  runner->GetLeftStats(best->mutable_left_stats());
-  runner->GetRightStats(best->mutable_right_stats());
-  runner->GetSplit(best->mutable_split());
-  return true;
-}
-
-void GraphRunnerSplitCollectionOperator::AddExample(
-    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
-    const std::vector<int>& examples, int32 node_id) const {
-  // Build input Tensors.
-  int size = examples.size();
-  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
-  auto ex_data = examples_t.flat<int32>();
-  std::copy(examples.begin(), examples.end(), ex_data.data());
-
-  const TensorInputTarget* tensor_target =
-      dynamic_cast<const TensorInputTarget*>(target);
-  CHECK_NOTNULL(tensor_target);
-
-  const Tensor& data_t = input_data->original_tensor();
-  const Tensor& target_t = tensor_target->original_tensor();
-
-  // Add to candidates.
-  auto* slot = stats_.at(node_id).get();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    auto& runner = runners_[UniqueId(node_id, i)];
-    runner->AddExample(data_t, target_t, examples_t);
-  }
-
-  // Update simple weight sums so we know when we're done.
-  for (int example : examples) {
-    slot->AddExample(input_data, target, example);
-  }
-}
-
-void GraphRunnerSplitCollectionOperator::
-    CreateAndInitializeCandidateWithExample(
-        const std::unique_ptr<TensorDataSet>& input_data, int example,
-        int32 node_id) const {
-  auto* slot = stats_.at(node_id).get();
-  int cand_num = slot->num_splits();
-  const int64 unique_id = UniqueId(node_id, cand_num);
-
-  decision_trees::BinaryNode split;
-
-  decision_trees::InequalityTest* test =
-      split.mutable_inequality_left_child_test();
-  auto* oblique = test->mutable_oblique();
-  for (int i = 0; i < features_per_node_; ++i) {
-    float bias;
-    int type;
-    // This is really just a way to select a list of random features.
-    // Also a way to warn the user that categoricals don't make sense here.
-    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
-
-    if (type == kDataFloat) {
-      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
-
-      // The comparison bias is assumed to be zero.
-      test->mutable_threshold()->set_float_value(0);
-    } else {
-      LOG(ERROR) << "Categorical features not supported with this system.";
-      return;
-    }
-  }
-
-  slot->AddSplit(split);
-
-  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
-  runners_[unique_id]->Init();
-}
-
-void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
-  SplitCollectionOperator::ClearSlot(node_id);
-  for (int i = 0; i < num_splits_to_consider_; ++i) {
-    runners_.erase(UniqueId(node_id, i));
-  }
-}
-
 }  // namespace tensorforest
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
index d0ea33612a..81d820a6b2 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
@@ -17,7 +17,6 @@
 
 #include <vector>
 #include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
@@ -86,60 +85,38 @@ class SplitCollectionOperator {
   std::unordered_map<int32, std::unique_ptr<GrowStats>> stats_;
 };
 
-
-class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
+class CollectionCreator {
  public:
-  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
-      : SplitCollectionOperator(params) {
-    if (params.num_splits_to_consider().ParamType_case() ==
-        DepthDependentParam::PARAMTYPE_NOT_SET) {
-      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
-                 << " num_splits_to_consider";
-    } else {
-      num_splits_to_consider_ =
-          params.num_splits_to_consider().constant_value();
-    }
-  }
-
-  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
-                                             int32 depth) const override;
-
-  // Updates the slot's candidates with the new example.
-  // Assumes slot has been initialized.
-  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
-                  const InputTarget* target, const std::vector<int>& examples,
-                  int32 node_id) const override;
-
-  // Create a new candidate and initialize it with the given example.
-  void CreateAndInitializeCandidateWithExample(
-      const std::unique_ptr<TensorDataSet>& input_data, int example,
-      int32 node_id) const override;
-
-  bool BestSplit(int32 node_id, SplitCandidate* best,
-                 int32* depth) const override;
-
-  void ClearSlot(int32 node_id) override;
-
- protected:
-  int64 UniqueId(int32 node_id, int32 split_id) const;
-
-  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
-      runners_;
-  int features_per_node_;
-  string graph_dir_;
-  // Must have a constant value because of how we make unique ids right now.
-  int32 num_splits_to_consider_;
+  virtual std::unique_ptr<SplitCollectionOperator> Create(
+      const TensorForestParams& params) = 0;
+  virtual ~CollectionCreator() {}
 };
 
-// Creates a type of SplitCollectionOperator depending on the type passed,
-// which is SplitCollectionType in fertile_stats.proto.
-// Can create a SplitCollectionOperator itself, known as "basic".
 class SplitCollectionOperatorFactory {
  public:
   static std::unique_ptr<SplitCollectionOperator> CreateSplitCollectionOperator(
       const TensorForestParams& params);
+
+  static std::unordered_map<int, CollectionCreator*> factories_;
+};
+
+template <typename T>
+class AnyCollectionCreator : public CollectionCreator {
+ public:
+  AnyCollectionCreator(SplitCollectionType type) {
+    SplitCollectionOperatorFactory::factories_[type] = this;
+  }
+  virtual std::unique_ptr<SplitCollectionOperator> Create(
+      const TensorForestParams& params) {
+    return std::unique_ptr<SplitCollectionOperator>(new T(params));
+  }
 };
 
+#define REGISTER_SPLIT_COLLECTION(name, cls) \
+  namespace {                                \
+  AnyCollectionCreator<cls> creator(name);   \
+  }
+
 }  // namespace tensorforest
 }  // namespace tensorflow
 
diff --git a/tensorflow/contrib/tensor_forest/ops/model_ops.cc b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
new file mode 100644
index 0000000000..1c3c2153a6
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
@@ -0,0 +1,135 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+
+namespace tensorflow {
+using shape_inference::DimensionHandle;
+using shape_inference::InferenceContext;
+
+namespace tensorforest {
+
+REGISTER_RESOURCE_HANDLE_OP(DecisionTreeResource);
+
+REGISTER_OP("TreeIsInitializedOp")
+    .Input("tree_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a tree has been initialized.
+)doc");
+
+REGISTER_OP("CreateTreeVariable")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Creates a tree  model and returns a handle to it.
+
+params: A serialized TensorForestParams proto.
+tree_handle: handle to the tree resource to be created.
+tree_config: Serialized proto of the tree.
+)doc");
+
+REGISTER_OP("TreeSerialize")
+    .Input("tree_handle: resource")
+    .Output("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Serializes the tree  to a proto.
+
+tree_handle: The handle to the tree.
+tree_config: Serialized proto of the tree.
+)doc");
+
+REGISTER_OP("TreeDeserialize")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Deserializes a serialized tree config and replaces current tree.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree .
+tree_config: Serialized proto of the .
+)doc");
+
+REGISTER_OP("TreeSize")
+    .Input("tree_handle: resource")
+    .Output("tree_size: int32")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Outputs the size of the tree, including leaves.
+
+tree_handle: The handle to the tree.
+tree_size: Size scalar.
+)doc");
+
+REGISTER_OP("TreePredictionsV4")
+    .Attr("input_spec: string")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("input_data: float")
+    .Input("sparse_input_indices: int64")
+    .Input("sparse_input_values: float")
+    .Input("sparse_input_shape: int64")
+    .Output("predictions: float")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle num_points = c->UnknownDim();
+
+      if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0) {
+        num_points = c->Dim(c->input(1), 0);
+      }
+
+      c->set_output(0, c->Matrix(num_points, c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs the predictions for the given input data.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
+   gives the j-th feature of the i-th input.
+sparse_input_indices: The indices tensor from the SparseTensor input.
+sparse_input_values: The values tensor from the SparseTensor input.
+sparse_input_shape: The shape tensor from the SparseTensor input.
+predictions: `predictions[i][j]` is the probability that input i is class j.
+)doc");
+
+REGISTER_OP("FeatureUsageCounts")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Output("feature_counts: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs the number of times each feature was used in a split.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+feature_counts: `feature_counts[i]` is the number of times feature i was used
+    in a split.
+)doc");
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/ops/stats_ops.cc b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
new file mode 100644
index 0000000000..48e91e3466
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
@@ -0,0 +1,146 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+using shape_inference::InferenceContext;
+
+namespace tensorforest {
+
+
+REGISTER_RESOURCE_HANDLE_OP(FertileStatsResource);
+
+REGISTER_OP("FertileStatsIsInitializedOp")
+    .Input("stats_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a stats has been initialized.
+)doc");
+
+REGISTER_OP("CreateFertileStatsVariable")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Input("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Creates a stats model and returns a handle to it.
+
+params: A serialized TensorForestParams proto.
+stats_handle: handle to the stats resource to be created.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("FertileStatsSerialize")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Output("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Serializes the stats to a proto.
+
+params: A serialized TensorForestParams proto.
+stats_handle: The handle to the stats.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("FertileStatsDeserialize")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Input("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Deserializes a serialized stats config and replaces current stats.
+
+params: A serialized TensorForestParams proto.
+stats_handle: The handle to the stats.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("GrowTreeV4")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .Input("finshed_nodes: int32")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Grows the tree for finished nodes and allocates waiting nodes.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+finshed_nodes: A 1-d Tensor of finished node ids from ProcessInput.
+)doc");
+
+REGISTER_OP("ProcessInputV4")
+    .Attr("random_seed: int")
+    .Attr("input_spec: string")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .Input("input_data: float")
+    .Input("sparse_input_indices: int64")
+    .Input("sparse_input_values: float")
+    .Input("sparse_input_shape: int64")
+    .Input("input_labels: float")
+    .Input("input_weights: float")
+    .Output("finished_nodes: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Add labels to stats after traversing the tree for each example.
+
+Outputs node ids that are finished.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
+   gives the j-th feature of the i-th input.
+sparse_input_indices: The indices tensor from the SparseTensor input.
+sparse_input_values: The values tensor from the SparseTensor input.
+sparse_input_shape: The shape tensor from the SparseTensor input.
+input_labels: The training batch's labels as a 1 or 2-d tensor.
+  'input_labels[i][j]' gives the j-th label/target for the i-th input.
+input_weights: The training batch's eample weights as a 1-d tensor.
+  'input_weights[i]' gives the weight for the i-th input.
+finished_nodes: A 1-d tensor of node ids that have finished and are ready to
+  grow.
+)doc");
+
+REGISTER_OP("FinalizeTree")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .SetShapeFn([](InferenceContext* c) {
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Puts the Leaf models inside the tree into their final form.
+
+If drop_final_class is true, the per-class probability prediction of the
+last class is not stored in the leaf models.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+)doc");
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/python/__init__.py b/tensorflow/contrib/tensor_forest/python/__init__.py
index 0d41d3500d..b2ca89877a 100644
--- a/tensorflow/contrib/tensor_forest/python/__init__.py
+++ b/tensorflow/contrib/tensor_forest/python/__init__.py
@@ -21,4 +21,6 @@ from __future__ import print_function
 from tensorflow.contrib.tensor_forest.python import constants
 from tensorflow.contrib.tensor_forest.python import tensor_forest
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
+from tensorflow.contrib.tensor_forest.python.ops import model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
 from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
diff --git a/tensorflow/contrib/tensor_forest/python/ops/model_ops.py b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
new file mode 100644
index 0000000000..4c7218305b
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
@@ -0,0 +1,124 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Model ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tensor_forest.python.ops import gen_model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
+
+# pylint: disable=unused-import
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import feature_usage_counts
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_predictions_v4
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_size
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+_model_ops = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_model_ops.so"))
+
+
+ops.NotDifferentiable("TreeVariable")
+ops.NotDifferentiable("TreeSerialize")
+ops.NotDifferentiable("TreeDeserialize")
+ops.NotDifferentiable("TreeSize")
+ops.NotDifferentiable("TreePredictionsV4")
+ops.NotDifferentiable("FeatureUsageCounts")
+
+
+class TreeVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for TreeVariable."""
+
+  def __init__(self, params, tree_handle, stats_handle, create_op, name):
+    """Creates a TreeVariableSavable object.
+
+    Args:
+      params: A TensorForestParams object.
+      tree_handle: handle to the tree variable.
+      stats_handle: handle to the stats variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree variable under.
+    """
+    self.params = params
+    deps = []
+    if stats_handle is not None:
+      deps.append(stats_ops.finalize_tree(
+          tree_handle, stats_handle,
+          params=params.serialized_params_proto))
+    with ops.control_dependencies(deps):
+      tensor = gen_model_ops.tree_serialize(tree_handle)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree variable. So we just pass an empty value.
+    slice_spec = ""
+    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
+    super(TreeVariableSavable,
+          self).__init__(tree_handle, specs, name)
+    self._tree_handle = tree_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return gen_model_ops.tree_deserialize(
+          self._tree_handle,
+          restored_tensors[0],
+          params=self.params.serialized_params_proto)
+
+
+def tree_variable(params, tree_config, stats_handle, name, container=None):
+  r"""Creates a tree model and returns a handle to it.
+
+  Args:
+    params: A TensorForestParams object.
+    tree_config: A `Tensor` of type `string`. Serialized proto of the tree.
+    stats_handle: Resource handle to the stats object.
+    name: A name for the variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the tree.
+  """
+  with ops.name_scope(name, "TreeVariable") as name:
+    resource_handle = gen_model_ops.decision_tree_resource_handle_op(
+        container, name, name=name)
+
+    create_op = gen_model_ops.create_tree_variable(
+        resource_handle,
+        tree_config,
+        params=params.serialized_params_proto)
+    is_initialized_op = gen_model_ops.tree_is_initialized_op(resource_handle)
+    # Adds the variable to the savable list.
+    saveable = TreeVariableSavable(params, resource_handle, stats_handle,
+                                   create_op,
+                                   "tree_checkpoint_{0}".format(name))
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
diff --git a/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py b/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
new file mode 100644
index 0000000000..be9f2e12b7
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
@@ -0,0 +1,114 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Stats ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tensor_forest.python.ops import gen_stats_ops
+# pylint: disable=unused-import
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import finalize_tree
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import grow_tree_v4
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import process_input_v4
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+_stats_ops = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_stats_ops.so"))
+
+
+ops.NotDifferentiable("FertileStatsVariable")
+ops.NotDifferentiable("FertileStatsSerialize")
+ops.NotDifferentiable("FertileStatsDeserialize")
+ops.NotDifferentiable("GrowTreeV4")
+ops.NotDifferentiable("ProcessInputV4")
+ops.NotDifferentiable("FinalizeTree")
+
+
+class FertileStatsVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for FertileStatsVariable."""
+
+  def __init__(self, params, stats_handle, create_op, name):
+    """Creates a FertileStatsVariableSavable object.
+
+    Args:
+      params: A TensorForestParams object.
+      stats_handle: handle to the tree variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree variable under.
+    """
+    self.params = params
+    tensor = gen_stats_ops.fertile_stats_serialize(
+        stats_handle, params=params.serialized_params_proto)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree variable. So we just pass an empty value.
+    slice_spec = ""
+    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
+    super(FertileStatsVariableSavable,
+          self).__init__(stats_handle, specs, name)
+    self._stats_handle = stats_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return gen_stats_ops.fertile_stats_deserialize(
+          self._stats_handle, restored_tensors[0],
+          params=self.params.serialized_params_proto)
+
+
+def fertile_stats_variable(params, stats_config, name,
+                           container=None):
+  r"""Creates a stats object and returns a handle to it.
+
+  Args:
+    params: A TensorForestParams object.
+    stats_config: A `Tensor` of type `string`. Serialized proto of the stats.
+    name: A name for the variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the stats.
+  """
+  with ops.name_scope(name, "FertileStatsVariable") as name:
+    resource_handle = gen_stats_ops.fertile_stats_resource_handle_op(
+        container, name, name=name)
+
+    create_op = gen_stats_ops.create_fertile_stats_variable(
+        resource_handle, stats_config,
+        params=params.serialized_params_proto)
+    is_initialized_op = gen_stats_ops.fertile_stats_is_initialized_op(
+        resource_handle)
+    # Adds the variable to the savable list.
+    saveable = FertileStatsVariableSavable(params, resource_handle, create_op,
+                                           "stats_checkpoint_{0}".format(name))
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
-- 
GitLab


From 8c920a0458dca10bfa9eba7120f3b4d3005cc132 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 11:47:57 -0700
Subject: [PATCH 0294/1375] Added parameterized tests to reduce window tests.

PiperOrigin-RevId: 159721784
---
 .../compiler/xla/tests/reduce_window_test.cc  | 286 ++++++++++++++++++
 1 file changed, 286 insertions(+)

diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index 6b4bceb437..17185b8e68 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -131,6 +131,26 @@ TEST_F(ReduceWindowTest, Along2ndMinorDim) {
   ComputeAndCompareR4<float>(&builder_, *res, {}, ErrorSpec(1e-3, 1e-3));
 }
 
+TEST_F(ReduceWindowTest, AmongMajor2Dims) {
+  Array4D<float> input_array(4, 4, 6, 8);
+  input_array.FillWithMinorDimNum();
+
+  int win_len = 3;
+  int win_stride = 1;
+
+  Padding padding = Padding::kSame;
+  const auto input_data_handle =
+      builder_.ConstantR4FromArray4D<float>(input_array);
+  // Reduce only along the x and y dimensions, according to the win_len.
+  ReduceWindowAdd(input_data_handle, {win_len, win_len, 1, 1},
+                  {win_stride, win_stride, 1, 1}, padding);
+
+  auto result = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {win_len, win_len, 1, 1},
+      {win_stride, win_stride, 1, 1}, padding);
+  ComputeAndCompareR4<float>(&builder_, *result, {}, ErrorSpec(1e-3, 1e-3));
+}
+
 TEST_F(ReduceWindowTest, AmongMajor2DimsMediumSize) {
   Array4D<float> input_array(9, 12, 4, 89);
   input_array.FillRandom(2.0f);
@@ -469,6 +489,272 @@ XLA_TEST_F(ReduceWindowTest, NonstandardReduceFunction) {
   ComputeAndCompareR4<float>(&builder_, *expected, {}, ErrorSpec(1e-3, 1e-3));
 }
 
+TEST_F(ReduceWindowTest, R2ReduceWindowNonOverlappingFromBroadcast) {
+  Array2D<float> input_array(6, 4, 1.0f);
+  ComputationDataHandle input =
+      builder_.Broadcast(builder_.ConstantLiteral(Literal::One(F32)), {6, 4});
+
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {4, 2}, {3, 3}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow2DAdd(input_array, 0.0f, {4, 2}, {3, 3},
+                                              padding);
+
+  ComputeAndCompareR2<float>(&builder_, *res, {}, ErrorSpec(1e-3, 1e-3));
+}
+
+enum Reducer { kAdd, kMax };
+
+struct R4ReduceWindowTestData {
+  int64 base_bounds[4];
+  int64 window_bounds[4];
+  int64 strides[4];
+  int64 pad_low[4];
+  int64 pad_high[4];
+
+  Reducer reducer;
+};
+
+string R4ReduceWindowTestDataToString(
+    const ::testing::TestParamInfo<R4ReduceWindowTestData>& data) {
+  string str = tensorflow::strings::StrCat(
+      "base_bounds_",
+      tensorflow::str_util::Join(data.param.base_bounds, "x"),  //
+      "__window_bounds_",
+      tensorflow::str_util::Join(data.param.window_bounds, "x"),            //
+      "__strides_", tensorflow::str_util::Join(data.param.strides, "x"),    //
+      "__pad_low_", tensorflow::str_util::Join(data.param.pad_low, "x"),    //
+      "__pad_high_", tensorflow::str_util::Join(data.param.pad_high, "x"),  //
+      (data.param.reducer == kAdd) ? "add" : "max");
+  CHECK(data.param.reducer == kAdd || data.param.reducer == kMax);
+
+  // Test names are not allowed to contain the '-' character.
+  std::replace(str.begin(), str.end(), '-', 'n');
+  return str;
+}
+
+class R4ReduceWindowTest
+    : public ClientLibraryTestBase,
+      public ::testing::WithParamInterface<R4ReduceWindowTestData> {};
+TEST_P(R4ReduceWindowTest, DoIt) {
+  ComputationBuilder b(client_, TestName());
+  const auto& param = GetParam();
+
+  const float kInitValue = 0.0f;
+
+  Array4D<float> input(param.base_bounds[0], param.base_bounds[1],
+                       param.base_bounds[2], param.base_bounds[3]);
+  input.FillIota(1);
+  std::unique_ptr<Literal> input_literal = Literal::CreateR4FromArray4D(input);
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
+                         client_->TransferToServer(*input_literal));
+
+  std::vector<std::pair<int64, int64>> padding(4);
+  for (int i = 0; i < 4; ++i) {
+    padding[i] = {param.pad_low[i], param.pad_high[i]};
+  }
+
+  auto parameter = b.Parameter(0, input_literal->shape(), "p0");
+  auto pad_value = b.ConstantR0<float>(kInitValue);
+  CHECK(param.reducer == kAdd || param.reducer == kMax);
+  auto computation = param.reducer == kAdd
+                         ? CreateScalarAddComputation(F32, &b)
+                         : CreateScalarMaxComputation(F32, &b);
+  b.ReduceWindowWithGeneralPadding(
+      /*operand=*/parameter,
+      /*init_value=*/pad_value,
+      /*computation=*/computation,
+      /*window_dimensions=*/param.window_bounds,
+      /*window_strides=*/param.strides,
+      /*padding=*/padding);
+
+  CHECK(param.reducer == kAdd || param.reducer == kMax);
+  auto reduce_func = param.reducer == kAdd
+                         ? +[](float a, float b) { return a + b; }
+                         : +[](float a, float b) { return std::max(a, b); };
+  std::unique_ptr<Array4D<float>> expected =
+      ReferenceUtil::ReduceWindow4DGeneric(
+          /*operand=*/input,
+          /*init=*/kInitValue,
+          /*reduce_func=*/reduce_func,
+          /*window=*/param.window_bounds,
+          /*stride=*/param.strides,
+          /*padding=*/padding);
+  ComputeAndCompareR4<float>(&b, *expected, {input_arg.get()});
+}
+
+// base_bounds, window_bounds, strides, pad_low, pad_high
+const R4ReduceWindowTestData kR4ReduceWindowTestValues[] = {
+    // Minimal edge case.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 1, 1, 1},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Zero base bound edge case.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 0, 1, 1},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With non-1x1 window.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{2, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With max instead of add.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{2, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kMax},
+
+    // With stride.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 10, 17, 140},
+                           /*window_bounds=*/{3, 2, 1, 1},
+                           /*strides=*/{2, 4, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With low padding.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{3, 2, 1, 1},
+                           /*strides=*/{2, 2, 1, 1},
+                           /*pad_low=*/{3, 2, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With high padding.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{3, 2, 1, 1},
+                           /*strides=*/{2, 2, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{2, 3, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Window touches both sides of the padding simultaneously.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 1, 17, 140},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{1, 1, 0, 0},
+                           /*pad_high=*/{1, 1, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Window is entirely in the padding for some positions.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 1, 17, 140},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{4, 4, 0, 0},
+                           /*pad_high=*/{4, 4, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Zero base bound with padding edge case.
+    R4ReduceWindowTestData{/*base_bounds=*/{2, 0, 3, 4},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 1, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With stride, low padding and high padding.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 3, 17, 140},
+                           /*window_bounds=*/{3, 4, 1, 1},
+                           /*strides=*/{3, 1, 1, 1},
+                           /*pad_low=*/{10, 1, 0, 0},
+                           /*pad_high=*/{2, 3, 0, 0},
+                           /*reducer=*/kAdd},
+};
+
+INSTANTIATE_TEST_CASE_P(R4ReduceWindowTestInstantiation, R4ReduceWindowTest,
+                        ::testing::ValuesIn(kR4ReduceWindowTestValues),
+                        R4ReduceWindowTestDataToString);
+
+struct R2ReduceWindowTestData {
+  int64 base_bounds[2];
+  int64 window_bounds[2];
+  int64 strides[2];
+  int64 layout[2];
+  Padding padding;
+  Reducer reducer;
+} kR2TestCases[] = {
+    {/*base_bounds=*/{4, 18}, /*window_bounds=*/{2, 4},
+     /*strides=*/{1, 2}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{2, 5}, /*window_bounds=*/{2, 4},
+     /*strides=*/{1, 1}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{1, 3}, /*window_bounds=*/{2, 3},
+     /*strides=*/{1, 1}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{3, 129}, /*window_bounds=*/{1, 100},
+     /*strides=*/{2, 99}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{6, 152}, /*window_bounds=*/{2, 25},
+     /*strides=*/{5, 4}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{6, 4}, /*window_bounds=*/{4, 2},
+     /*strides=*/{3, 3}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+};
+
+string R2ReduceWindowTestDataToString(
+    const ::testing::TestParamInfo<R2ReduceWindowTestData>& data) {
+  string str = tensorflow::strings::StrCat(
+      "base_bounds_",
+      tensorflow::str_util::Join(data.param.base_bounds, "x"),  //
+      "__window_bounds_",
+      tensorflow::str_util::Join(data.param.window_bounds, "x"),              //
+      "__strides_", tensorflow::str_util::Join(data.param.strides, "x"),      //
+      "__padding_", data.param.padding == Padding::kSame ? "same" : "valid",  //
+      "__layout_", data.param.layout[0], "_", data.param.layout[1],           //
+      "__reducer_", data.param.reducer == kAdd ? "add" : "max");
+  return str;
+}
+
+class R2ReduceWindowTest
+    : public ClientLibraryTestBase,
+      public ::testing::WithParamInterface<R2ReduceWindowTestData> {};
+
+TEST_P(R2ReduceWindowTest, Add) {
+  ComputationBuilder b(client_, TestName());
+  const auto& param = GetParam();
+  CHECK(param.reducer == kAdd);
+
+  const float kInitValue = 0.0f;
+  Array2D<float> input(param.base_bounds[0], param.base_bounds[1], 1.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR2FromArray2DWithLayout(
+          input, LayoutUtil::MakeLayout(param.layout));
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
+                         client_->TransferToServer(*input_literal));
+  b.ReduceWindow(/*operand=*/
+                 b.Parameter(0, input_literal->shape(), "p0"),
+                 /*init_value=*/b.ConstantR0<float>(kInitValue),
+                 /*computation=*/CreateScalarAddComputation(F32, &b),
+                 /*window_dimensions=*/param.window_bounds,
+                 /*window_strides=*/param.strides, /*padding=*/param.padding);
+
+  auto expected = ReferenceUtil::ReduceWindow2DAdd(
+      /*operand=*/input, /*init=*/kInitValue, /*window=*/param.window_bounds,
+      /*stride=*/param.strides, /*padding=*/param.padding);
+
+  ComputeAndCompareR2<float>(&b, *expected, {input_arg.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+INSTANTIATE_TEST_CASE_P(R2ReduceWindowTestInstantiation, R2ReduceWindowTest,
+                        ::testing::ValuesIn(kR2TestCases),
+                        R2ReduceWindowTestDataToString);
+
 }  // namespace
 }  // namespace xla
 
-- 
GitLab


From 0097d15a610373914f1db2a5061116fd642fca38 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 11:59:59 -0700
Subject: [PATCH 0295/1375] Use C API to implement Operation.device property

PiperOrigin-RevId: 159723490
---
 tensorflow/python/framework/ops.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index ccd1099f80..760148fad7 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1372,7 +1372,13 @@ class Operation(object):
       assigned, or an empty string if it has not been assigned to a
       device.
     """
-    return self._node_def.device
+    if _USE_C_API:
+      # TODO(iga): Remove this assert after converting to C API by default.
+      # Just being a bit paranoid here.
+      assert self._node_def.device == c_api.TF_OperationDevice(self._c_op)
+      return c_api.TF_OperationDevice(self._c_op)
+    else:
+      return self._node_def.device
 
   def _set_device(self, device):
     """Set the device of this operation.
-- 
GitLab


From b58d9835334758e9977600dedfaafe82119b815a Mon Sep 17 00:00:00 2001
From: sj6077 <epik03sj@gmail.com>
Date: Thu, 22 Jun 2017 04:08:14 +0900
Subject: [PATCH 0296/1375] Fixes of AutoParallel bug (#10368)

* Fix the bug that auto_parallel could replicate variable snapshot name

* Use NodeName in grappler:utils instead of substr, convert variables->variable_def of grappler item

* remove variable_def from grappler item, exclude snapshot nodes from dont_replicate_nodes in auto_parallel
---
 tensorflow/core/grappler/grappler_item.h      |  1 +
 .../core/grappler/optimizers/auto_parallel.cc |  5 +++
 .../core/grappler/optimizers/auto_parallel.h  |  1 +
 .../grappler/optimizers/auto_parallel_test.cc | 42 +++++++++++--------
 4 files changed, 31 insertions(+), 18 deletions(-)

diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h
index e0709c682b..1fb54f6093 100644
--- a/tensorflow/core/grappler/grappler_item.h
+++ b/tensorflow/core/grappler/grappler_item.h
@@ -23,6 +23,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/protobuf/queue_runner.pb.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.cc b/tensorflow/core/grappler/optimizers/auto_parallel.cc
index d4326a022f..e3e75a2320 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.cc
@@ -167,6 +167,11 @@ Status AutoParallel::Initialize(const GrapplerItem& item) {
   for (const auto& variable : item.MainVariables()) {
     dont_replicate_nodes.insert(variable->name());
   }
+
+  for (const auto& init : item.init_ops) {
+    dont_replicate_nodes.insert(NodeName(init));
+  }
+
   // Don't replicate all input nodes, except the dequeue node.
   for (const auto& input_node : input_nodes) {
     if (input_node->name() != dequeue_node->name()) {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.h b/tensorflow/core/grappler/optimizers/auto_parallel.h
index ad90bbe028..c5d2d47782 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.h
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_
 
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
+#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
index 3d1b4a34bf..9a41b5e0b5 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
@@ -33,6 +33,7 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   Output constant_b = ops::Const(s.WithOpName("constant_b"), 1, {1});
   Output var = ops::Variable(s.WithOpName("var"), {1}, DT_FLOAT);
   Output assign = ops::Assign(s.WithOpName("assign"), {var}, {constant_a});
+  Output identity = ops::Identity(s.WithOpName("identity"), {var});
   Output fifo_queue = ops::FIFOQueue(s.WithOpName("fifo_queue"), {DT_FLOAT});
   auto dequeue = ops::QueueDequeueMany(s.WithOpName("dequeue"), {fifo_queue},
                                        {constant_b}, {DT_FLOAT});
@@ -44,13 +45,14 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   GrapplerItem item;
   item.init_ops.push_back("assign");
   item.fetch.push_back("apply_gradient");
+  item.init_ops.push_back("assign");
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
   AutoParallel parallel(2);
   GraphDef output;
   Status status = parallel.Optimize(nullptr, item, &output);
   TF_EXPECT_OK(status);
-  EXPECT_EQ(20, output.node_size());
+  EXPECT_EQ(21, output.node_size());
 
   const NodeDef& node_assign = output.node(0);
   EXPECT_EQ("assign", node_assign.name());
@@ -62,60 +64,64 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   const NodeDef& node_fifo_queue = output.node(2);
   EXPECT_EQ("fifo_queue", node_fifo_queue.name());
 
-  const NodeDef& node_var = output.node(3);
+  const NodeDef& node_identity = output.node(3);
+  EXPECT_EQ("identity", node_identity.name());
+  EXPECT_EQ("var", node_identity.input(0));
+
+  const NodeDef& node_var = output.node(4);
   EXPECT_EQ("var", node_var.name());
 
-  const NodeDef& node_div_const0 = output.node(4);
+  const NodeDef& node_div_const0 = output.node(5);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-Const",
             node_div_const0.name());
 
-  const NodeDef& node_div0 = output.node(5);
+  const NodeDef& node_div0 = output.node(6);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-apply_gradient",
             node_div0.name());
-  const NodeDef& node_add0 = output.node(6);
+  const NodeDef& node_add0 = output.node(7);
   EXPECT_EQ("AutoParallel-Replica-0/add", node_add0.name());
 
-  const NodeDef& node_gradient0 = output.node(7);
+  const NodeDef& node_gradient0 = output.node(8);
   EXPECT_EQ("AutoParallel-Replica-0/apply_gradient", node_gradient0.name());
 
-  const NodeDef& node_constant_a0 = output.node(8);
+  const NodeDef& node_constant_a0 = output.node(9);
   EXPECT_EQ("AutoParallel-Replica-0/constant_a", node_constant_a0.name());
 
-  const NodeDef& node_dequeue0 = output.node(9);
+  const NodeDef& node_dequeue0 = output.node(10);
   EXPECT_EQ("AutoParallel-Replica-0/dequeue", node_dequeue0.name());
 
-  const NodeDef& node_learning_rate0 = output.node(10);
+  const NodeDef& node_learning_rate0 = output.node(11);
   EXPECT_EQ("AutoParallel-Replica-0/learning_rate", node_learning_rate0.name());
 
-  const NodeDef& node_div_const1 = output.node(11);
+  const NodeDef& node_div_const1 = output.node(12);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-Const",
             node_div_const1.name());
 
-  const NodeDef& node_div1 = output.node(12);
+  const NodeDef& node_div1 = output.node(13);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-apply_gradient",
             node_div1.name());
 
-  const NodeDef& node_add1 = output.node(13);
+  const NodeDef& node_add1 = output.node(14);
   EXPECT_EQ("AutoParallel-Replica-1/add", node_add1.name());
 
-  const NodeDef& node_gradient1 = output.node(14);
+  const NodeDef& node_gradient1 = output.node(15);
   EXPECT_EQ("AutoParallel-Replica-1/apply_gradient", node_gradient1.name());
 
-  const NodeDef& node_constant_a1 = output.node(15);
+  const NodeDef& node_constant_a1 = output.node(16);
   EXPECT_EQ("AutoParallel-Replica-1/constant_a", node_constant_a1.name());
 
-  const NodeDef& node_dequeue1 = output.node(16);
+  const NodeDef& node_dequeue1 = output.node(17);
   EXPECT_EQ("AutoParallel-Replica-1/dequeue", node_dequeue1.name());
 
-  const NodeDef& node_learning_rate1 = output.node(17);
+  const NodeDef& node_learning_rate1 = output.node(18);
   EXPECT_EQ("AutoParallel-Replica-1/learning_rate", node_learning_rate1.name());
 
-  const NodeDef& node_fetch = output.node(18);
+  const NodeDef& node_fetch = output.node(19);
   EXPECT_EQ("AutoParallel-Control-Fetch", node_fetch.name());
   EXPECT_EQ("^AutoParallel-Replica-0/apply_gradient", node_fetch.input(0));
   EXPECT_EQ("^AutoParallel-Replica-1/apply_gradient", node_fetch.input(1));
 
-  const NodeDef& node_gradient = output.node(19);
+  const NodeDef& node_gradient = output.node(20);
   EXPECT_EQ("apply_gradient", node_gradient.name());
   EXPECT_EQ("^AutoParallel-Control-Fetch", node_gradient.input(0));
 }
-- 
GitLab


From 6d2df88d657c87fdce6365bce9b19f1c39b0b0b2 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Wed, 21 Jun 2017 12:15:06 -0700
Subject: [PATCH 0297/1375] Several Estimator changes: - support configurable
 input_fn calling in Estimator subclasses. - pass params and config to the
 input_fn. - allow callables for model_fn and input_fn.

PiperOrigin-RevId: 159725554
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   |   2 +-
 tensorflow/python/estimator/estimator.py      |  43 ++++--
 tensorflow/python/estimator/estimator_test.py | 126 ++++++++++++++++++
 3 files changed, 161 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index b702ab91f6..e15687f5de 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -307,7 +307,7 @@ def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config):
 
   def _call_model_fn(features, labels):
     """Calls the model_fn with required parameters."""
-    model_fn_args = estimator_lib._model_fn_args(model_fn)  # pylint: disable=protected-access
+    model_fn_args = estimator_lib._fn_args(model_fn)  # pylint: disable=protected-access
     kwargs = {}
     if 'mode' in model_fn_args:
       kwargs['mode'] = mode
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index ab49f36e5e..5e6e532e94 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -52,7 +52,7 @@ from tensorflow.python.util import tf_inspect
 
 
 _VALID_MODEL_FN_ARGS = set(
-    ['features', 'labels', 'mode', 'params', 'config'])
+    ['features', 'labels', 'mode', 'params', 'self', 'config'])
 
 
 class Estimator(object):
@@ -357,7 +357,7 @@ class Estimator(object):
               }
 
   def _assert_members_are_not_overridden(self):
-    allowed_overrides = set(['_create_global_step'])
+    allowed_overrides = set(['_call_input_fn', '_create_global_step'])
     estimator_members = set([m for m in Estimator.__dict__.keys()
                              if not m.startswith('__')])
     subclass_members = set(self.__class__.__dict__.keys())
@@ -485,7 +485,7 @@ class Estimator(object):
       return export_dir
 
   def _get_features_from_input_fn(self, input_fn):
-    result = input_fn()
+    result = self._call_input_fn(input_fn)
     if not ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
       logging.warning('Input graph does not contain a QueueRunner. '
                       'That means predict yields forever. '
@@ -549,6 +549,29 @@ class Estimator(object):
     assert step.dtype.is_integer
     return step
 
+  def _call_input_fn(self, input_fn):
+    """Calls the input function.
+
+    Args:
+      input_fn: The input function.
+
+    Returns:
+      Either features or (features, labels) where features and labels are:
+        features - `Tensor` or dictionary of string feature name to `Tensor`.
+        labels - `Tensor` or dictionary of `Tensor` with labels.
+
+    Raises:
+      ValueError: if input_fn takes invalid arguments.
+    """
+    input_fn_args = _fn_args(input_fn)
+    kwargs = {}
+    if 'params' in input_fn_args:
+      kwargs['params'] = self.params
+    if 'config' in input_fn_args:
+      kwargs['config'] = self.config
+    with ops.device('/cpu:0'):
+      return input_fn(**kwargs)
+
   def _call_model_fn(self, features, labels, mode):
     """Calls model function.
 
@@ -563,7 +586,7 @@ class Estimator(object):
     Raises:
       ValueError: if model_fn returns invalid objects.
     """
-    model_fn_args = _model_fn_args(self._model_fn)
+    model_fn_args = _fn_args(self._model_fn)
     kwargs = {}
     if 'mode' in model_fn_args:
       kwargs['mode'] = mode
@@ -584,8 +607,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      with ops.device('/cpu:0'):
-        features, labels = input_fn()
+      features, labels = self._call_input_fn(input_fn)
       estimator_spec = self._call_model_fn(features, labels,
                                            model_fn_lib.ModeKeys.TRAIN)
       ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
@@ -666,7 +688,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = input_fn()
+      features, labels = self._call_input_fn(input_fn)
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
 
@@ -749,7 +771,7 @@ def _get_replica_device_setter(config):
     return None
 
 
-def _model_fn_args(fn):
+def _fn_args(fn):
   """Get argument names for function-like object.
 
   Args:
@@ -762,6 +784,9 @@ def _model_fn_args(fn):
     ValueError: if partial function has positionally bound arguments
   """
   _, fn = tf_decorator.unwrap(fn)
+  if hasattr(fn, '__call__') and tf_inspect.ismethod(fn.__call__):
+    # Handle callables.
+    return tuple(tf_inspect.getargspec(fn.__call__).args)
   if hasattr(fn, 'func') and hasattr(fn, 'keywords') and hasattr(fn, 'args'):
     # Handle functools.partial and similar objects.
     return tuple([
@@ -774,7 +799,7 @@ def _model_fn_args(fn):
 
 def _verify_model_fn_args(model_fn, params):
   """Verifies model fn arguments."""
-  args = set(_model_fn_args(model_fn))
+  args = set(_fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
   if 'labels' not in args:
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index b86afece43..3c74f3eb61 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -120,6 +120,9 @@ class EstimatorInheritanceConstraintTest(test.TestCase):
       def __init__(self):
         super(_Estimator, self).__init__(model_fn=dummy_model_fn)
 
+      def _call_input_fn(self, input_fn):
+        return input_fn()
+
       def _create_global_step(self, graph):
         pass
 
@@ -325,6 +328,79 @@ def _make_input_fn(features, labels):
 
 class EstimatorTrainTest(test.TestCase):
 
+  def test_callable_model_fn(self):
+    expected_features = {'x': 42., 'y': 43.}
+    expected_labels = 44.
+
+    model_fn_call_count = [0]
+
+    test_self = self
+
+    class ModelFn(object):
+
+      def __call__(self, features, labels):
+        model_fn_call_count[0] += 1
+        test_self.assertItemsEqual(expected_features.keys(), features.keys())
+        return _estimator_spec(
+            expected_features, expected_labels, features, labels,
+            model_fn_lib.ModeKeys.TRAIN)
+
+    with self.assertRaisesRegexp(ValueError, 'does not include params'):
+      estimator.Estimator(model_fn=ModelFn(), params={'a': 'b'})
+    est = estimator.Estimator(model_fn=ModelFn(), config=run_config.RunConfig())
+    self.assertEqual(0, model_fn_call_count[0])
+    est.train(
+        input_fn=_make_input_fn(expected_features, expected_labels), steps=1)
+    self.assertEqual(1, model_fn_call_count[0])
+
+  def test_callable_input_fn(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    test_self = self
+
+    class InputFn(object):
+
+      def __call__(self, params, config):
+        input_fn_call_count[0] += 1
+        test_self.assertEqual(expected_params, params)
+        test_self.assertEqual(4321, config.tf_random_seed)
+        return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.train(InputFn(), steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.train(_input_fn, steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_minimal_model_fn_args(self):
     expected_features = {'x': 42., 'y': 43.}
     expected_labels = 44.
@@ -665,6 +741,29 @@ class _StepCounterHook(session_run_hook.SessionRunHook):
 
 class EstimatorEvaluateTest(test.TestCase):
 
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    est.train(dummy_input_fn, steps=1)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.evaluate(_input_fn, steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_model_fn_must_return_estimator_spec(self):
     def _model_fn(features, labels, mode):
       _, _ = features, labels
@@ -866,6 +965,33 @@ class EstimatorEvaluateTest(test.TestCase):
 
 class EstimatorPredictTest(test.TestCase):
 
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del features, labels, params, config
+      return model_fn_lib.EstimatorSpec(
+          mode,
+          loss=constant_op.constant(0.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          predictions=constant_op.constant([[10.]]))
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    est.train(dummy_input_fn, steps=1)
+    self.assertEqual(0, input_fn_call_count[0])
+    next(est.predict(_input_fn))
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_no_trained_model_in_model_dir(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
     with self.assertRaisesRegexp(ValueError,
-- 
GitLab


From c0ea385ad482ae99ea08bedc20760829a74c9a88 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Wed, 21 Jun 2017 12:22:29 -0700
Subject: [PATCH 0298/1375] Fixed the scalar output for shard api when
 outputs_from_all_shards=True.

PiperOrigin-RevId: 159726444
---
 tensorflow/contrib/tpu/python/tpu/tpu.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py
index 157b0fc1ac..a65a86514b 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu.py
@@ -480,8 +480,11 @@ def shard(computation,
   for (axis, all_shards, x) in zip(output_shard_axes, outputs_from_all_shards,
                                    zip(*outputs)):
     if all_shards:
-      # Concatenate all of the outputs together.
-      results.append(array_ops.concat(list(x), axis=axis))
+      # Concatenate all of the outputs together (use stack for scalars).
+      shape = x[0].shape
+      is_scalar = shape is not None and (shape.ndims == 0)
+      results.append((array_ops.stack(list(x)) if is_scalar
+                      else array_ops.concat(list(x), axis=axis)))
     else:
       # TODO(phawkins): use a smarter policy, e.g., round-robin across shards.
       results.append(x[0])
-- 
GitLab


From d7171eb0a88bee4c52107126b0b347dbeacc1c8a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 12:39:19 -0700
Subject: [PATCH 0299/1375] Automated g4 rollback of changelist 159718610

PiperOrigin-RevId: 159728380
---
 tensorflow/contrib/BUILD                      |   4 -
 tensorflow/contrib/cmake/tf_core_ops.cmake    |   2 -
 tensorflow/contrib/cmake/tf_python.cmake      |   4 -
 tensorflow/contrib/tensor_forest/BUILD        | 293 +++------
 .../tensor_forest/kernels/model_ops.cc        | 299 ----------
 .../tensor_forest/kernels/stats_ops.cc        | 564 ------------------
 .../contrib/tensor_forest/kernels/v4/BUILD    |  26 +-
 .../kernels/v4/graph_collection_operator.cc   | 142 -----
 .../kernels/v4/graph_collection_operator.h    |  81 ---
 .../kernels/v4/split_collection_operators.cc  | 140 ++++-
 .../kernels/v4/split_collection_operators.h   |  69 ++-
 .../contrib/tensor_forest/ops/model_ops.cc    | 135 -----
 .../contrib/tensor_forest/ops/stats_ops.cc    | 146 -----
 .../contrib/tensor_forest/python/__init__.py  |   2 -
 .../tensor_forest/python/ops/model_ops.py     | 124 ----
 .../tensor_forest/python/ops/stats_ops.py     | 114 ----
 16 files changed, 258 insertions(+), 1887 deletions(-)
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/model_ops.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
 delete mode 100644 tensorflow/contrib/tensor_forest/ops/model_ops.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/ops/stats_ops.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/python/ops/model_ops.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/ops/stats_ops.py

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index dfcdf3991c..f852eded1e 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -93,8 +93,6 @@ cc_library(
         "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel",
         "//tensorflow/contrib/nccl:nccl_kernels",
         "//tensorflow/contrib/seq2seq:beam_search_ops_kernels",
-        "//tensorflow/contrib/tensor_forest:model_ops_kernels",
-        "//tensorflow/contrib/tensor_forest:stats_ops_kernels",
         "//tensorflow/contrib/tensor_forest:tensor_forest_kernels",
         "//tensorflow/contrib/text:all_kernels",
     ],
@@ -112,8 +110,6 @@ cc_library(
         "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib",
         "//tensorflow/contrib/nccl:nccl_ops_op_lib",
         "//tensorflow/contrib/seq2seq:beam_search_ops_op_lib",
-        "//tensorflow/contrib/tensor_forest:model_ops_op_lib",
-        "//tensorflow/contrib/tensor_forest:stats_ops_op_lib",
         "//tensorflow/contrib/tensor_forest:tensor_forest_ops_op_lib",
         "//tensorflow/contrib/text:all_ops",
         "//tensorflow/contrib/tpu:all_ops",
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index a9defc1139..f1b01250a4 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -85,8 +85,6 @@ GENERATE_CONTRIB_OP_LIBRARY(rnn_gru "${tensorflow_source_dir}/tensorflow/contrib
 GENERATE_CONTRIB_OP_LIBRARY(rnn_lstm "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/lstm_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(seq2seq_beam_search "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc")
-GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_model "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/model_ops.cc")
-GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_stats "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/stats_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_hybrid "${tensor_forest_hybrid_srcs}")
 GENERATE_CONTRIB_OP_LIBRARY(text_skip_gram "${tensorflow_source_dir}/tensorflow/contrib/text/ops/skip_gram_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tpu "${tpu_ops_srcs}")
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index d17fcf6456..84a1302344 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -659,10 +659,6 @@ GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_tensor_forest_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_hybrid_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/hybrid/ops/gen_training_ops.py)
-GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_model_ops"
-  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_model_ops.py)
-GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_stats_ops"
-  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_stats_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_text_skip_gram_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/text/python/ops/gen_skip_gram_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_bigquery_reader_ops"
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 1ca2d7596b..7b5f9472e7 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -28,35 +28,37 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
-# ---------------------------------- V2 ops ------------------------------------------#
 filegroup(
-    name = "v2_op_sources",
-    srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
-        "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
-        "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
-    ],
+    name = "custom_op_sources",
+    srcs = glob(
+        [
+            "kernels/*.cc",
+            "ops/*.cc",
+        ],
+        exclude = [
+            "kernels/*_test.cc",
+            "kernels/tree_utils.cc",
+        ],
+    ),
 )
 
 filegroup(
-    name = "v2_op_defs",
-    srcs = [
-        "ops/tensor_forest_ops.cc",
-    ],
+    name = "custom_op_headers",
+    srcs = glob(
+        [
+            "kernels/*.h",
+        ],
+        exclude = [
+            "kernels/data_spec.h",
+            "kernels/tree_utils.h",
+        ],
+    ),
 )
 
 cc_library(
-    name = "v2_ops",
-    srcs = [
-        ":v2_op_defs",
-        ":v2_op_sources",
-    ],
+    name = "all_ops",
+    srcs = [":custom_op_sources"],
+    hdrs = [":custom_op_headers"],
     deps = [
         ":tree_utils",
         "//tensorflow/core:framework_headers_lib",
@@ -103,8 +105,16 @@ tf_gen_op_wrapper_py(
 tf_custom_op_library(
     name = "python/ops/_tensor_forest_ops.so",
     srcs = [
-        ":v2_op_defs",
-        ":v2_op_sources",
+        "kernels/best_splits_op.cc",
+        "kernels/count_extremely_random_stats_op.cc",
+        "kernels/finished_nodes_op.cc",
+        "kernels/grow_tree_op.cc",
+        "kernels/reinterpret_string_to_float_op.cc",
+        "kernels/sample_inputs_op.cc",
+        "kernels/scatter_add_ndim_op.cc",
+        "kernels/tree_predictions_op.cc",
+        "kernels/update_fertile_slots_op.cc",
+        "ops/tensor_forest_ops.cc",
     ],
     deps = [":tree_utils"],
 )
@@ -121,9 +131,7 @@ py_library(
         ":constants",
         ":data_ops_py",
         ":eval_metrics",
-        ":model_ops_py",
         ":random_forest",
-        ":stats_ops_py",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
     ],
@@ -132,11 +140,21 @@ py_library(
 tf_kernel_library(
     name = "tensor_forest_kernels",
     srcs = [
-        ":v2_op_sources",
+        "kernels/best_splits_op.cc",
+        "kernels/count_extremely_random_stats_op.cc",
+        "kernels/finished_nodes_op.cc",
+        "kernels/grow_tree_op.cc",
+        "kernels/reinterpret_string_to_float_op.cc",
+        "kernels/sample_inputs_op.cc",
+        "kernels/scatter_add_ndim_op.cc",
+        "kernels/tree_predictions_op.cc",
+        "kernels/update_fertile_slots_op.cc",
     ],
     deps = [
         ":tree_utils",
-        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core/kernels:bounds_check",
     ],
 )
@@ -163,192 +181,6 @@ tf_custom_op_py_library(
     ],
 )
 
-cc_test(
-    name = "tensor_forest_ops_test",
-    size = "small",
-    srcs = [
-        "kernels/tensor_forest_ops_test.cc",
-        ":v2_op_defs",
-        ":v2_op_sources",
-    ],
-    deps = [
-        ":tree_utils",
-        "//tensorflow/core",
-        "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-        "//tensorflow/core:testlib",
-        "//third_party/eigen3",
-    ],
-)
-
-# -------------------------------------- V4 ops ------------------------------- #
-cc_library(
-    name = "tensor_forest_v4_kernels",
-    deps = [
-        ":model_ops_kernels",
-        ":stats_ops_kernels",
-    ],
-)
-
-cc_library(
-    name = "tensor_forest_v4_ops_op_lib",
-    deps = [
-        ":model_ops_op_lib",
-        ":stats_ops_op_lib",
-    ],
-)
-
-py_library(
-    name = "tensor_forest_v4_ops_py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":model_ops_py",
-        ":stats_ops_py",
-    ],
-)
-
-# Model Ops.
-cc_library(
-    name = "model_ops_lib",
-    srcs = ["kernels/model_ops.cc"],
-    deps = [
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
-        "//tensorflow/contrib/tensor_forest:tree_utils",
-        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
-        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
-        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
-    ],
-    alwayslink = 1,
-)
-
-tf_gen_op_libs(
-    op_lib_names = ["model_ops"],
-)
-
-tf_gen_op_wrapper_py(
-    name = "gen_model_ops_py",
-    out = "python/ops/gen_model_ops.py",
-    deps = [":model_ops_op_lib"],
-)
-
-tf_kernel_library(
-    name = "model_ops_kernels",
-    deps = [
-        ":model_ops_lib",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
-    ],
-    alwayslink = 1,
-)
-
-tf_custom_op_library(
-    name = "python/ops/_model_ops.so",
-    deps = [
-        ":model_ops_lib",
-    ],
-)
-
-tf_custom_op_py_library(
-    name = "model_ops_py",
-    srcs = ["python/ops/model_ops.py"],
-    dso = ["python/ops/_model_ops.so"],
-    kernels = [
-        ":model_ops_kernels",
-        ":model_ops_op_lib",
-    ],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":gen_model_ops_py",
-        ":stats_ops_py",
-        "//tensorflow/core:protos_all_py",
-        "//tensorflow/python:framework_for_generated_wrappers",
-    ],
-)
-
-# Stats Ops.
-cc_library(
-    name = "stats_ops_lib",
-    srcs = ["kernels/stats_ops.cc"],
-    deps = [
-        "//tensorflow/contrib/tensor_forest:tree_utils",
-        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
-        "//tensorflow/contrib/tensor_forest/kernels/v4:fertile-stats-resource",
-        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
-        "//tensorflow/contrib/tensor_forest/kernels/v4:input_target",
-        "//tensorflow/contrib/tensor_forest/kernels/v4:params",
-        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
-        "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
-    ],
-    alwayslink = 1,
-)
-
-tf_gen_op_libs(
-    op_lib_names = ["stats_ops"],
-)
-
-tf_gen_op_wrapper_py(
-    name = "gen_stats_ops_py",
-    out = "python/ops/gen_stats_ops.py",
-    deps = [":stats_ops_op_lib"],
-)
-
-tf_kernel_library(
-    name = "stats_ops_kernels",
-    deps = [
-        ":stats_ops_lib",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
-    ],
-    alwayslink = 1,
-)
-
-tf_custom_op_library(
-    name = "python/ops/_stats_ops.so",
-    deps = [
-        ":stats_ops_lib",
-    ],
-)
-
-tf_custom_op_py_library(
-    name = "stats_ops_py",
-    srcs = ["python/ops/stats_ops.py"],
-    dso = ["python/ops/_stats_ops.so"],
-    kernels = [
-        ":stats_ops_kernels",
-        ":stats_ops_op_lib",
-    ],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":gen_stats_ops_py",
-        "//tensorflow/core:protos_all_py",
-        "//tensorflow/python:framework_for_generated_wrappers",
-    ],
-)
-
-# ---------------------------------- Common libs ------------------------ #
-cc_library(
-    name = "tree_utils",
-    srcs = ["kernels/tree_utils.cc"],
-    hdrs = [
-        "kernels/data_spec.h",
-        "kernels/tree_utils.h",
-    ],
-    deps = [
-        "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
-    ],
-)
-
-# --------------------------------- Python -------------------------------- #
-
 py_library(
     name = "eval_metrics",
     srcs = ["client/eval_metrics.py"],
@@ -388,6 +220,20 @@ py_library(
     ],
 )
 
+cc_library(
+    name = "tree_utils",
+    srcs = ["kernels/tree_utils.cc"],
+    hdrs = [
+        "kernels/data_spec.h",
+        "kernels/tree_utils.h",
+    ],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+        "@protobuf//:protobuf_headers",
+    ],
+)
+
 py_test(
     name = "best_splits_op_test",
     size = "small",
@@ -534,6 +380,25 @@ py_test(
     ],
 )
 
+cc_test(
+    name = "tensor_forest_ops_test",
+    size = "small",
+    srcs = [
+        "kernels/tensor_forest_ops_test.cc",
+        ":custom_op_sources",
+    ],
+    deps = [
+        ":tree_utils",
+        "//tensorflow/core",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//third_party/eigen3",
+    ],
+)
+
 py_library(
     name = "random_forest",
     srcs = ["client/random_forest.py"],
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
deleted file mode 100644
index 195221a48e..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
+++ /dev/null
@@ -1,299 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
-#include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
-#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
-#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
-#include "tensorflow/core/framework/resource_mgr.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_shape.h"
-#include "tensorflow/core/framework/tensor_types.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/platform/thread_annotations.h"
-#include "tensorflow/core/platform/types.h"
-
-namespace tensorflow {
-namespace tensorforest {
-
-// Creates a tree  variable.
-class CreateTreeVariableOp : public OpKernel {
- public:
-  explicit CreateTreeVariableOp(OpKernelConstruction* context)
-      : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor* tree_config_t;
-    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
-    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
-                errors::InvalidArgument("Tree config must be a scalar."));
-
-    auto* result = new DecisionTreeResource();
-    if (!ParseProtoUnlimited(result->mutable_decision_tree(),
-                             tree_config_t->scalar<string>()())) {
-      result->Unref();
-      OP_REQUIRES(context, false,
-                  errors::InvalidArgument("Unable to parse tree  config."));
-    }
-
-    result->MaybeInitialize();
-
-    // Only create one, if one does not exist already. Report status for all
-    // other exceptions.
-    auto status = CreateResource(context, HandleFromInput(context, 0), result);
-    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
-      OP_REQUIRES(context, false, status);
-    }
-  }
-
- private:
-  TensorForestParams param_proto_;
-};
-
-// Op for serializing a model.
-class TreeSerializeOp : public OpKernel {
- public:
-  explicit TreeSerializeOp(OpKernelConstruction* context) : OpKernel(context) {}
-
-  void Compute(OpKernelContext* context) override {
-    DecisionTreeResource* decision_tree_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &decision_tree_resource));
-    mutex_lock l(*decision_tree_resource->get_mutex());
-    core::ScopedUnref unref_me(decision_tree_resource);
-    Tensor* output_config_t = nullptr;
-    OP_REQUIRES_OK(
-        context, context->allocate_output(0, TensorShape(), &output_config_t));
-    output_config_t->scalar<string>()() =
-        decision_tree_resource->decision_tree().SerializeAsString();
-  }
-};
-
-// Op for deserializing a tree variable from a checkpoint.
-class TreeDeserializeOp : public OpKernel {
- public:
-  explicit TreeDeserializeOp(OpKernelConstruction* context)
-      : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    DecisionTreeResource* decision_tree_resource;
-    auto handle = HandleFromInput(context, 0);
-    OP_REQUIRES_OK(context, LookupResource(context, handle,
-                                           &decision_tree_resource));
-    mutex_lock l(*decision_tree_resource->get_mutex());
-    core::ScopedUnref unref_me(decision_tree_resource);
-
-    const Tensor* tree_config_t;
-    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
-    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
-                errors::InvalidArgument("Tree config must be a scalar."));
-    // Deallocate all the previous objects on the resource.
-    decision_tree_resource->Reset();
-    decision_trees::Model* config =
-        decision_tree_resource->mutable_decision_tree();
-    OP_REQUIRES(context,
-                ParseProtoUnlimited(config, tree_config_t->scalar<string>()()),
-                errors::InvalidArgument("Unable to parse tree  config."));
-    decision_tree_resource->MaybeInitialize();
-  }
-
- private:
-  TensorForestParams param_proto_;
-};
-
-// Op for getting tree size.
-class TreeSizeOp : public OpKernel {
- public:
-  explicit TreeSizeOp(OpKernelConstruction* context) : OpKernel(context) {}
-
-  void Compute(OpKernelContext* context) override {
-    DecisionTreeResource* decision_tree_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &decision_tree_resource));
-    mutex_lock l(*decision_tree_resource->get_mutex());
-    core::ScopedUnref unref_me(decision_tree_resource);
-    Tensor* output_t = nullptr;
-    OP_REQUIRES_OK(
-        context, context->allocate_output(0, TensorShape(), &output_t));
-    output_t->scalar<int32>()() =
-        decision_tree_resource->decision_tree().decision_tree().nodes_size();
-  }
-};
-
-
-// Op for tree inference.
-class TreePredictionsV4Op : public OpKernel {
- public:
-  explicit TreePredictionsV4Op(OpKernelConstruction* context)
-      : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-
-    string serialized_proto;
-    OP_REQUIRES_OK(context, context->GetAttr(
-      "input_spec", &serialized_proto));
-    input_spec_.ParseFromString(serialized_proto);
-
-    data_set_ =
-        std::unique_ptr<TensorDataSet>(new TensorDataSet(input_spec_, 0));
-
-    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& input_data = context->input(1);
-    const Tensor& sparse_input_indices = context->input(2);
-    const Tensor& sparse_input_values = context->input(3);
-
-    data_set_->set_input_tensors(input_data, sparse_input_indices,
-                                 sparse_input_values);
-
-    DecisionTreeResource* decision_tree_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &decision_tree_resource));
-    mutex_lock l(*decision_tree_resource->get_mutex());
-    core::ScopedUnref unref_me(decision_tree_resource);
-
-    Tensor* output_predictions = nullptr;
-    TensorShape output_shape;
-    output_shape.AddDim(data_set_->NumItems());
-    output_shape.AddDim(param_proto_.num_outputs());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, output_shape,
-                                            &output_predictions));
-
-    auto out = output_predictions->tensor<float, 2>();
-    for (int i = 0; i < data_set_->NumItems(); ++i) {
-      const int32 leaf_id =
-          decision_tree_resource->TraverseTree(data_set_, i, nullptr);
-      const decision_trees::Leaf& leaf =
-          decision_tree_resource->get_leaf(leaf_id);
-      for (int j = 0; j < param_proto_.num_outputs(); ++j) {
-        const float count = model_op_->GetOutputValue(leaf, j);
-        out(i, j) = count;
-      }
-    }
-  }
-
- private:
-  tensorforest::TensorForestDataSpec input_spec_;
-  std::unique_ptr<TensorDataSet> data_set_;
-  std::unique_ptr<LeafModelOperator> model_op_;
-  TensorForestParams param_proto_;
-};
-
-// Op for getting feature usage counts.
-class FeatureUsageCountsOp : public OpKernel {
- public:
-  explicit FeatureUsageCountsOp(OpKernelConstruction* context)
-      : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    DecisionTreeResource* decision_tree_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &decision_tree_resource));
-    mutex_lock l(*decision_tree_resource->get_mutex());
-    core::ScopedUnref unref_me(decision_tree_resource);
-
-
-    const auto& tree = decision_tree_resource->decision_tree();
-
-    Tensor* output_counts = nullptr;
-    TensorShape output_shape;
-    output_shape.AddDim(param_proto_.num_features());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, output_shape, &output_counts));
-
-    auto counts = output_counts->unaligned_flat<int32>();
-    counts.setZero();
-
-    for (const auto& node : tree.decision_tree().nodes()) {
-      if (node.has_custom_node_type()) {
-        LOG(WARNING) << "Can't count feature usage for custom nodes.";
-      } else if (node.has_binary_node()) {
-        const auto& bnode = node.binary_node();
-        if (bnode.has_custom_left_child_test()) {
-          decision_trees::MatchingValuesTest test;
-          if (!bnode.custom_left_child_test().UnpackTo(&test)) {
-            LOG(WARNING) << "Unknown custom child test";
-            continue;
-          }
-          int32 feat;
-          safe_strto32(test.feature_id().id().value(), &feat);
-          ++counts(feat);
-        } else {
-          const auto& test = bnode.inequality_left_child_test();
-          if (test.has_feature_id()) {
-            int32 feat;
-            safe_strto32(test.feature_id().id().value(), &feat);
-            ++counts(feat);
-          } else if (test.has_oblique()) {
-            for (const auto& featid : test.oblique().features()) {
-              int32 feat;
-              safe_strto32(featid.id().value(), &feat);
-              ++counts(feat);
-            }
-          }
-        }
-      }
-    }
-  }
-
- private:
-  TensorForestParams param_proto_;
-};
-
-
-REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeResource);
-
-REGISTER_KERNEL_BUILDER(Name("TreeIsInitializedOp").Device(DEVICE_CPU),
-                        IsResourceInitialized<DecisionTreeResource>);
-
-REGISTER_KERNEL_BUILDER(Name("CreateTreeVariable").Device(DEVICE_CPU),
-                        CreateTreeVariableOp);
-
-REGISTER_KERNEL_BUILDER(Name("TreeSerialize").Device(DEVICE_CPU),
-                        TreeSerializeOp);
-
-REGISTER_KERNEL_BUILDER(Name("TreeDeserialize").Device(DEVICE_CPU),
-                        TreeDeserializeOp);
-
-REGISTER_KERNEL_BUILDER(Name("TreeSize").Device(DEVICE_CPU),
-                        TreeSizeOp);
-
-REGISTER_KERNEL_BUILDER(Name("TreePredictionsV4").Device(DEVICE_CPU),
-                        TreePredictionsV4Op);
-
-REGISTER_KERNEL_BUILDER(Name("FeatureUsageCounts").Device(DEVICE_CPU),
-                        FeatureUsageCountsOp);
-
-}  // namespace tensorforest
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
deleted file mode 100644
index 7442469507..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
+++ /dev/null
@@ -1,564 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-#include <queue>
-
-#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
-#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
-#include "tensorflow/core/framework/resource_mgr.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_shape.h"
-#include "tensorflow/core/framework/tensor_types.h"
-#include "tensorflow/core/lib/gtl/map_util.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/platform/mutex.h"
-#include "tensorflow/core/platform/thread_annotations.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/work_sharder.h"
-
-namespace tensorflow {
-namespace tensorforest {
-
-using gtl::FindOrNull;
-
-// Creates a stats variable.
-class CreateFertileStatsVariableOp : public OpKernel {
- public:
-  explicit CreateFertileStatsVariableOp(OpKernelConstruction* context)
-      : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor* stats_config_t;
-    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
-    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
-                errors::InvalidArgument("Stats config must be a scalar."));
-    auto* result = new FertileStatsResource(param_proto_);
-    FertileStats stats;
-    if (!ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()())) {
-      result->Unref();
-      OP_REQUIRES(context, false,
-                  errors::InvalidArgument("Unable to parse stats config."));
-    }
-
-    result->ExtractFromProto(stats);
-    result->MaybeInitialize();
-
-    // Only create one, if one does not exist already. Report status for all
-    // other exceptions.
-    auto status = CreateResource(context, HandleFromInput(context, 0), result);
-    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
-      OP_REQUIRES(context, false, status);
-    }
-  }
-
- private:
-  TensorForestParams param_proto_;
-};
-
-// Op for serializing a model.
-class FertileStatsSerializeOp : public OpKernel {
- public:
-  explicit FertileStatsSerializeOp(OpKernelConstruction* context)
-      : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    FertileStatsResource* fertile_stats_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &fertile_stats_resource));
-    mutex_lock l(*fertile_stats_resource->get_mutex());
-    core::ScopedUnref unref_me(fertile_stats_resource);
-    Tensor* output_config_t = nullptr;
-    OP_REQUIRES_OK(
-        context, context->allocate_output(0, TensorShape(), &output_config_t));
-
-    FertileStats stats;
-    fertile_stats_resource->PackToProto(&stats);
-    output_config_t->scalar<string>()() = stats.SerializeAsString();
-  }
-
- private:
-  TensorForestParams param_proto_;
-};
-
-// Op for deserializing a stats variable from a checkpoint.
-class FertileStatsDeserializeOp : public OpKernel {
- public:
-  explicit FertileStatsDeserializeOp(OpKernelConstruction* context)
-      : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    FertileStatsResource* fertile_stats_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &fertile_stats_resource));
-    mutex_lock l(*fertile_stats_resource->get_mutex());
-    core::ScopedUnref unref_me(fertile_stats_resource);
-
-    const Tensor* stats_config_t;
-    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
-    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
-                errors::InvalidArgument("Stats config must be a scalar."));
-    // Deallocate all the previous objects on the resource.
-    fertile_stats_resource->Reset();
-    FertileStats stats;
-    OP_REQUIRES(context,
-                ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()()),
-                errors::InvalidArgument("Unable to parse stats config."));
-
-    fertile_stats_resource->ExtractFromProto(stats);
-    fertile_stats_resource->MaybeInitialize();
-  }
-
- private:
-  TensorForestParams param_proto_;
-};
-
-void TraverseTree(const DecisionTreeResource* tree_resource,
-                  const std::unique_ptr<TensorDataSet>& data, int32 start,
-                  int32 end, std::vector<int32>* leaf_ids,
-                  std::vector<int32>* leaf_depths) {
-  for (int i = start; i < end; ++i) {
-    int32 depth;
-    const int32 leaf_id = tree_resource->TraverseTree(data, i, &depth);
-    (*leaf_ids)[i] = leaf_id;
-    (*leaf_depths)[i] = depth;
-  }
-}
-
-// Try to update a leaf's stats by acquiring its lock.  If it can't be
-// acquired, put it in a waiting queue to come back to later and try the next
-// one.  Once all leaf_ids have been visited, cycle through the waiting ids
-// until they're gone.
-void UpdateStats(FertileStatsResource* fertile_stats_resource,
-                 const std::unique_ptr<TensorDataSet>& data,
-                 const Tensor& input_labels, const Tensor& input_weights,
-                 int num_targets, const std::vector<int32>& leaf_ids,
-                 const std::vector<int32>& leaf_depths,
-                 std::unordered_map<int32, std::unique_ptr<mutex>>* locks,
-                 mutex* set_lock, int32 start, int32 end,
-                 std::unordered_set<int32>* ready_to_split) {
-  const auto labels = input_labels.unaligned_flat<float>();
-  const auto weights = input_weights.unaligned_flat<float>();
-  // Stores leaf_id, leaf_depth, example_id for examples that are waiting
-  // on another to finish.
-  std::queue<std::tuple<int32, int32, int32>> waiting;
-
-  int32 i = start;
-  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
-  while (i < end || !waiting.empty()) {
-    int32 leaf_id;
-    int32 leaf_depth;
-    int32 example_id;
-    bool was_waiting = false;
-    if (i >= end) {
-      std::tie(leaf_id, leaf_depth, example_id) = waiting.front();
-      waiting.pop();
-      was_waiting = true;
-    } else {
-      leaf_id = leaf_ids[i];
-      leaf_depth = leaf_depths[i];
-      example_id = i;
-      ++i;
-    }
-    const std::unique_ptr<mutex>& leaf_lock = (*locks)[leaf_id];
-    if (was_waiting) {
-      leaf_lock->lock();
-    } else {
-      if (!leaf_lock->try_lock()) {
-        waiting.emplace(leaf_id, leaf_depth, example_id);
-        continue;
-      }
-    }
-
-    bool is_finished;
-    fertile_stats_resource->AddExampleToStatsAndInitialize(
-        data, &target, {example_id}, leaf_id, leaf_depth,
-        &is_finished);
-    leaf_lock->unlock();
-    if (is_finished) {
-      set_lock->lock();
-      ready_to_split->insert(leaf_id);
-      set_lock->unlock();
-    }
-  }
-}
-
-// Update leaves from start through end in the leaf_examples iterator.
-void UpdateStatsCollated(
-    FertileStatsResource* fertile_stats_resource,
-    DecisionTreeResource* tree_resource,
-    const std::unique_ptr<TensorDataSet>& data, const Tensor& input_labels,
-    const Tensor& input_weights, int num_targets,
-    const std::unordered_map<int32, std::vector<int>>& leaf_examples,
-    const std::vector<int32>& leaf_depths, mutex* set_lock, int32 start,
-    int32 end, std::unordered_set<int32>* ready_to_split) {
-  const auto labels = input_labels.unaligned_flat<float>();
-  const auto weights = input_weights.unaligned_flat<float>();
-
-  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
-  auto it = leaf_examples.begin();
-  std::advance(it, start);
-  auto end_it = leaf_examples.begin();
-  std::advance(end_it, end);
-  while (it != end_it) {
-    int32 leaf_id = it->first;
-    bool is_finished;
-    fertile_stats_resource->AddExampleToStatsAndInitialize(
-        data, &target, it->second, leaf_id, leaf_depths[it->second[0]],
-        &is_finished);
-    if (is_finished) {
-      set_lock->lock();
-      ready_to_split->insert(leaf_id);
-      set_lock->unlock();
-    }
-    ++it;
-  }
-}
-
-// Op for traversing the tree with each example, accumulating statistics, and
-// outputting node ids that are ready to split.
-class ProcessInputOp : public OpKernel {
- public:
-  explicit ProcessInputOp(OpKernelConstruction* context) : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-
-    OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_));
-
-    string serialized_proto;
-    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
-    input_spec_.ParseFromString(serialized_proto);
-
-    data_set_ = std::unique_ptr<TensorDataSet>(
-        new TensorDataSet(input_spec_, random_seed_));
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& input_data = context->input(2);
-    const Tensor& sparse_input_indices = context->input(3);
-    const Tensor& sparse_input_values = context->input(4);
-    const Tensor& input_labels = context->input(6);
-    const Tensor& input_weights = context->input(7);
-
-    data_set_->set_input_tensors(input_data, sparse_input_indices,
-                                 sparse_input_values);
-
-    FertileStatsResource* fertile_stats_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
-                                           &fertile_stats_resource));
-    DecisionTreeResource* tree_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &tree_resource));
-    mutex_lock l1(*fertile_stats_resource->get_mutex());
-    mutex_lock l2(*tree_resource->get_mutex());
-
-    core::ScopedUnref unref_stats(fertile_stats_resource);
-    core::ScopedUnref unref_tree(tree_resource);
-
-    const int32 num_data = data_set_->NumItems();
-    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
-    int num_threads = worker_threads->num_threads;
-
-    // First find the leaf ids for each example.
-    std::vector<int32> leaf_ids(num_data);
-
-    // The depth of the leaf for example i.
-    std::vector<int32> leaf_depths(num_data);
-
-    const int64 costPerTraverse = 500;
-    auto traverse = [this, &leaf_ids, &leaf_depths, tree_resource, num_data](
-        int64 start, int64 end) {
-      CHECK(start <= end);
-      CHECK(end <= num_data);
-      TraverseTree(tree_resource, data_set_, static_cast<int32>(start),
-                   static_cast<int32>(end), &leaf_ids, &leaf_depths);
-    };
-    Shard(num_threads, worker_threads->workers, num_data, costPerTraverse,
-          traverse);
-
-    // Create one mutex per leaf. We need to protect access to leaf pointers,
-    // so instead of grouping examples by leaf, we spread examples out among
-    // threads to provide uniform work for each of them and protect access
-    // with mutexes.
-    std::unordered_map<int, std::unique_ptr<mutex>> locks;
-    std::unordered_map<int32, std::vector<int>> leaf_examples;
-    if (param_proto_.collate_examples()) {
-      for (int i = 0; i < num_data; ++i) {
-        leaf_examples[leaf_ids[i]].push_back(i);
-      }
-    } else {
-      for (const int32 id : leaf_ids) {
-        if (FindOrNull(locks, id) == nullptr) {
-          // TODO(gilberth): Consider using a memory pool for these.
-          locks[id] = std::unique_ptr<mutex>(new mutex);
-        }
-      }
-    }
-
-    const int32 num_leaves = leaf_examples.size();
-    const int32 label_dim =
-        input_labels.shape().dims() <= 1
-            ? 0
-            : static_cast<int>(input_labels.shape().dim_size(1));
-    const int32 num_targets =
-        param_proto_.is_regression() ? (std::max(1, label_dim)) : 1;
-
-    // Ids of leaves that can split.
-    std::unordered_set<int32> ready_to_split;
-    mutex set_lock;
-
-    // TODO(gilberth): This is a rough approximation based on measurements
-    // from a digits run on local desktop.  Heuristics might be necessary
-    // if it really matters that much.
-    const int64 costPerUpdate = 1000;
-    auto update = [this, &input_labels, &input_weights, &leaf_ids, &leaf_depths,
-                   &num_targets, fertile_stats_resource, &locks, &set_lock,
-                   &ready_to_split, num_data](int64 start, int64 end) {
-      CHECK(start <= end);
-      CHECK(end <= num_data);
-      UpdateStats(fertile_stats_resource, data_set_, input_labels,
-                  input_weights, num_targets, leaf_ids, leaf_depths, &locks,
-                  &set_lock, static_cast<int32>(start), static_cast<int32>(end),
-                  &ready_to_split);
-    };
-
-    auto update_collated = [this, &input_labels, &input_weights, &leaf_ids,
-                            &num_targets, &leaf_depths, fertile_stats_resource,
-                            tree_resource, &leaf_examples, &set_lock,
-                            &ready_to_split,
-                            num_leaves](int64 start, int64 end) {
-      CHECK(start <= end);
-      CHECK(end <= num_leaves);
-      UpdateStatsCollated(
-          fertile_stats_resource, tree_resource, data_set_, input_labels,
-          input_weights, num_targets, leaf_examples, leaf_depths, &set_lock,
-          static_cast<int32>(start), static_cast<int32>(end), &ready_to_split);
-    };
-
-    if (param_proto_.collate_examples()) {
-      Shard(num_threads, worker_threads->workers, num_leaves, costPerUpdate,
-            update_collated);
-    } else {
-      Shard(num_threads, worker_threads->workers, num_data, costPerUpdate,
-            update);
-    }
-
-    Tensor* output_finished_t = nullptr;
-    TensorShape output_shape;
-    output_shape.AddDim(ready_to_split.size());
-    OP_REQUIRES_OK(
-        context, context->allocate_output(0, output_shape, &output_finished_t));
-    auto output = output_finished_t->unaligned_flat<int32>();
-    std::copy(ready_to_split.begin(), ready_to_split.end(), output.data());
-  }
-
- private:
-  int32 random_seed_;
-  tensorforest::TensorForestDataSpec input_spec_;
-  std::unique_ptr<TensorDataSet> data_set_;
-  TensorForestParams param_proto_;
-};
-
-
-// Op for growing finished nodes.
-class GrowTreeOp : public OpKernel {
- public:
-  explicit GrowTreeOp(OpKernelConstruction* context) : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    FertileStatsResource* fertile_stats_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
-                                           &fertile_stats_resource));
-    DecisionTreeResource* tree_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &tree_resource));
-    mutex_lock l1(*fertile_stats_resource->get_mutex());
-    mutex_lock l2(*tree_resource->get_mutex());
-
-    core::ScopedUnref unref_stats(fertile_stats_resource);
-    core::ScopedUnref unref_tree(tree_resource);
-
-    const Tensor& finished_nodes = context->input(2);
-
-    const auto finished = finished_nodes.unaligned_flat<int32>();
-
-    const int32 num_nodes =
-        static_cast<int32>(finished_nodes.shape().dim_size(0));
-
-    // TODO(gilberth): distribute this work over a number of threads.
-    for (int i = 0;
-         i < num_nodes &&
-         tree_resource->decision_tree().decision_tree().nodes_size() <
-             param_proto_.max_nodes();
-         ++i) {
-      const int32 node = finished(i);
-      std::unique_ptr<SplitCandidate> best(new SplitCandidate);
-      int32 parent_depth;
-      bool found =
-          fertile_stats_resource->BestSplit(node, best.get(), &parent_depth);
-      if (found) {
-        std::vector<int32> new_children;
-        tree_resource->SplitNode(node, best.get(), &new_children);
-        fertile_stats_resource->Allocate(parent_depth, new_children);
-        fertile_stats_resource->set_leaf_stat(best->left_stats(),
-                                              new_children[0]);
-        fertile_stats_resource->set_leaf_stat(best->right_stats(),
-                                              new_children[1]);
-        // We are done with best, so it is now safe to clear node.
-        fertile_stats_resource->Clear(node);
-        CHECK(tree_resource->get_mutable_tree_node(node)->has_leaf() == false);
-      } else {  // reset
-        fertile_stats_resource->ResetSplitStats(node, parent_depth);
-      }
-    }
-  }
-
- private:
-  tensorforest::TensorForestDataSpec input_spec_;
-  TensorForestParams param_proto_;
-};
-
-void FinalizeLeaf(const LeafStat& leaf_stats, bool is_regression,
-                  bool drop_final_class,
-                  const std::unique_ptr<LeafModelOperator>& leaf_op,
-                  decision_trees::Leaf* leaf) {
-  leaf_op->ExportModel(leaf_stats, leaf);
-
-  // TODO(thomaswc): Move the rest of this into ExportModel.
-
-  // regression models are already stored in leaf in normalized form.
-  if (is_regression) {
-    return;
-  }
-
-  float sum = leaf_stats.weight_sum();
-  if (sum <= 0.0) {
-    LOG(WARNING) << "Leaf with sum " << sum
-                 << " has stats " << leaf->ShortDebugString();
-    return;
-  }
-
-  if (leaf->has_vector()) {
-    for (int i = 0; i < leaf->vector().value_size(); i++) {
-      auto *v = leaf->mutable_vector()->mutable_value(i);
-      v->set_float_value(v->float_value() / sum);
-    }
-    if (drop_final_class) {
-      leaf->mutable_vector()->mutable_value()->RemoveLast();
-    }
-    return;
-  }
-
-  if (leaf->has_sparse_vector()) {
-    for (auto& it : *leaf->mutable_sparse_vector()->mutable_sparse_value()) {
-      it.second.set_float_value(it.second.float_value() / sum);
-    }
-    return;
-  }
-
-  LOG(FATAL) << "Unknown leaf type in " << leaf->DebugString();
-}
-
-// Op for finalizing a tree at the end of training.
-class FinalizeTreeOp : public OpKernel {
- public:
-  explicit FinalizeTreeOp(OpKernelConstruction* context) : OpKernel(context) {
-    string serialized_params;
-    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
-    ParseProtoUnlimited(&param_proto_, serialized_params);
-
-    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    DecisionTreeResource* tree_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
-                                           &tree_resource));
-    FertileStatsResource* fertile_stats_resource;
-    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
-                                           &fertile_stats_resource));
-
-    mutex_lock l1(*fertile_stats_resource->get_mutex());
-    mutex_lock l2(*tree_resource->get_mutex());
-
-    core::ScopedUnref unref_me(tree_resource);
-    core::ScopedUnref unref_stats(fertile_stats_resource);
-
-    // TODO(thomaswc): Add threads
-    int num_nodes = tree_resource->decision_tree().decision_tree().nodes_size();
-    for (int i = 0; i < num_nodes; i++) {
-      auto* node = tree_resource->mutable_decision_tree()
-          ->mutable_decision_tree()->mutable_nodes(i);
-      if (node->has_leaf()) {
-        const auto& leaf_stats = fertile_stats_resource->leaf_stat(i);
-        FinalizeLeaf(leaf_stats, param_proto_.is_regression(),
-                     param_proto_.drop_final_class(), model_op_,
-                     node->mutable_leaf());
-      }
-    }
-  }
-
- private:
-  std::unique_ptr<LeafModelOperator> model_op_;
-  TensorForestParams param_proto_;
-};
-
-REGISTER_RESOURCE_HANDLE_KERNEL(FertileStatsResource);
-
-REGISTER_KERNEL_BUILDER(Name("FertileStatsIsInitializedOp").Device(DEVICE_CPU),
-                        IsResourceInitialized<FertileStatsResource>);
-
-REGISTER_KERNEL_BUILDER(Name("CreateFertileStatsVariable").Device(DEVICE_CPU),
-                        CreateFertileStatsVariableOp);
-
-REGISTER_KERNEL_BUILDER(Name("FertileStatsSerialize").Device(DEVICE_CPU),
-                        FertileStatsSerializeOp);
-
-REGISTER_KERNEL_BUILDER(Name("FertileStatsDeserialize").Device(DEVICE_CPU),
-                        FertileStatsDeserializeOp);
-
-REGISTER_KERNEL_BUILDER(Name("ProcessInputV4").Device(DEVICE_CPU),
-                        ProcessInputOp);
-
-REGISTER_KERNEL_BUILDER(Name("GrowTreeV4").Device(DEVICE_CPU),
-                        GrowTreeOp);
-
-REGISTER_KERNEL_BUILDER(Name("FinalizeTree").Device(DEVICE_CPU),
-                        FinalizeTreeOp);
-
-}  // namespace tensorforest
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index a9d8093d13..0542508a8e 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -40,7 +40,9 @@ cc_library(
         ":split_collection_operators",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:framework",
         "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib",
     ],
 )
 
@@ -109,7 +111,7 @@ cc_library(
         "//tensorflow/contrib/tensor_forest:tree_utils",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib",
     ],
 )
 
@@ -151,7 +153,7 @@ cc_library(
         ":input_data",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
-        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib",
     ],
 )
 
@@ -172,25 +174,6 @@ cc_library(
     name = "split_collection_operators",
     srcs = ["split_collection_operators.cc"],
     hdrs = ["split_collection_operators.h"],
-    deps = [
-        ":grow_stats",
-        ":input_data",
-        ":input_target",
-        ":leaf_model_operators",
-        ":params",
-        ":stat_utils",
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
-        "//tensorflow/contrib/tensor_forest:tree_utils",
-        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
-        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-    ],
-)
-
-cc_library(
-    name = "graph_collection_operator",
-    srcs = ["graph_collection_operator.cc"],
-    hdrs = ["graph_collection_operator.h"],
     deps = [
         ":candidate_graph_runner",
         ":grow_stats",
@@ -198,7 +181,6 @@ cc_library(
         ":input_target",
         ":leaf_model_operators",
         ":params",
-        ":split_collection_operators",
         ":stat_utils",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
deleted file mode 100644
index 2c925b5dd7..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
+++ /dev/null
@@ -1,142 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-#include "tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h"
-
-#include <cfloat>
-
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-
-namespace tensorflow {
-namespace tensorforest {
-
-REGISTER_SPLIT_COLLECTION(GRAPH_RUNNER_COLLECTION,
-                          GraphRunnerSplitCollectionOperator);
-
-std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
-    int32 node_id, int32 depth) const {
-  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
-}
-
-int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
-                                                   int32 split_id) const {
-  return node_id * num_splits_to_consider_ + split_id;
-}
-
-bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
-                                                   SplitCandidate* best,
-                                                   int32* depth) const {
-  float min_score = FLT_MAX;
-  int best_index = -1;
-  auto* slot = stats_.at(node_id).get();
-  *depth = slot->depth();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    // TODO(gilberth): Support uselessness.
-    auto& runner = runners_[UniqueId(node_id, i)];
-    const float split_score = runner->SplitScore();
-    if (split_score < min_score) {
-      min_score = split_score;
-      best_index = i;
-    }
-  }
-
-  // This could happen if all the splits are useless.
-  if (best_index < 0) {
-    return false;
-  }
-
-  // Fill in split info and left/right stats to initialize models with.
-  *best = SplitCandidate();
-  auto& runner = runners_[UniqueId(node_id, best_index)];
-  runner->GetLeftStats(best->mutable_left_stats());
-  runner->GetRightStats(best->mutable_right_stats());
-  runner->GetSplit(best->mutable_split());
-  return true;
-}
-
-void GraphRunnerSplitCollectionOperator::AddExample(
-    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
-    const std::vector<int>& examples, int32 node_id) const {
-  // Build input Tensors.
-  int size = examples.size();
-  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
-  auto ex_data = examples_t.flat<int32>();
-  std::copy(examples.begin(), examples.end(), ex_data.data());
-
-  const TensorInputTarget* tensor_target =
-      dynamic_cast<const TensorInputTarget*>(target);
-  CHECK_NOTNULL(tensor_target);
-
-  const Tensor& data_t = input_data->original_tensor();
-  const Tensor& target_t = tensor_target->original_tensor();
-
-  // Add to candidates.
-  auto* slot = stats_.at(node_id).get();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    auto& runner = runners_[UniqueId(node_id, i)];
-    runner->AddExample(data_t, target_t, examples_t);
-  }
-
-  // Update simple weight sums so we know when we're done.
-  for (int example : examples) {
-    slot->AddExample(input_data, target, example);
-  }
-}
-
-void GraphRunnerSplitCollectionOperator::
-    CreateAndInitializeCandidateWithExample(
-        const std::unique_ptr<TensorDataSet>& input_data, int example,
-        int32 node_id) const {
-  auto* slot = stats_.at(node_id).get();
-  int cand_num = slot->num_splits();
-  const int64 unique_id = UniqueId(node_id, cand_num);
-
-  decision_trees::BinaryNode split;
-
-  decision_trees::InequalityTest* test =
-      split.mutable_inequality_left_child_test();
-  auto* oblique = test->mutable_oblique();
-  for (int i = 0; i < features_per_node_; ++i) {
-    float bias;
-    int type;
-    // This is really just a way to select a list of random features.
-    // Also a way to warn the user that categoricals don't make sense here.
-    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
-
-    if (type == kDataFloat) {
-      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
-
-      // The comparison bias is assumed to be zero.
-      test->mutable_threshold()->set_float_value(0);
-    } else {
-      LOG(ERROR) << "Categorical features not supported with this system.";
-      return;
-    }
-  }
-
-  slot->AddSplit(split);
-
-  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
-  runners_[unique_id]->Init();
-}
-
-void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
-  SplitCollectionOperator::ClearSlot(node_id);
-  for (int i = 0; i < num_splits_to_consider_; ++i) {
-    runners_.erase(UniqueId(node_id, i));
-  }
-}
-
-}  // namespace tensorforest
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
deleted file mode 100644
index 9b18e3e969..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
+++ /dev/null
@@ -1,81 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
-#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
-
-#include <vector>
-#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h"
-#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
-#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
-
-namespace tensorflow {
-namespace tensorforest {
-
-// Holds split candidates that are trained by running any TF graph.
-class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
- public:
-  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
-      : SplitCollectionOperator(params) {
-    if (params.num_splits_to_consider().ParamType_case() ==
-        DepthDependentParam::PARAMTYPE_NOT_SET) {
-      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
-                 << " num_splits_to_consider";
-    } else {
-      num_splits_to_consider_ =
-          params.num_splits_to_consider().constant_value();
-    }
-  }
-
-  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
-                                             int32 depth) const override;
-
-  // Updates the slot's candidates with the new example.
-  // Assumes slot has been initialized.
-  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
-                  const InputTarget* target, const std::vector<int>& examples,
-                  int32 node_id) const override;
-
-  // Create a new candidate and initialize it with the given example.
-  void CreateAndInitializeCandidateWithExample(
-      const std::unique_ptr<TensorDataSet>& input_data, int example,
-      int32 node_id) const override;
-
-  bool BestSplit(int32 node_id, SplitCandidate* best,
-                 int32* depth) const override;
-
-  void ClearSlot(int32 node_id) override;
-
- protected:
-  int64 UniqueId(int32 node_id, int32 split_id) const;
-
-  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
-      runners_;
-  int features_per_node_;
-  string graph_dir_;
-  // Must have a constant value because of how we make unique ids right now.
-  int32 num_splits_to_consider_;
-};
-
-}  // namespace tensorforest
-}  // namespace tensorflow
-
-#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
index c207c0859d..ddf4be8799 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
@@ -23,20 +23,22 @@
 namespace tensorflow {
 namespace tensorforest {
 
-std::unordered_map<int, CollectionCreator*>
-    SplitCollectionOperatorFactory::factories_;  // NOLINT
-REGISTER_SPLIT_COLLECTION(COLLECTION_BASIC, SplitCollectionOperator);
-
 std::unique_ptr<SplitCollectionOperator>
 SplitCollectionOperatorFactory::CreateSplitCollectionOperator(
     const TensorForestParams& params) {
-  auto it = factories_.find(params.collection_type());
-  if (it == factories_.end()) {
-    LOG(ERROR) << "Unknown split collection operator: "
-               << params.collection_type();
-    return nullptr;
-  } else {
-    return it->second->Create(params);
+  switch (params.collection_type()) {
+    case COLLECTION_BASIC:
+      return std::unique_ptr<SplitCollectionOperator>(
+          new SplitCollectionOperator(params));
+
+    case GRAPH_RUNNER_COLLECTION:
+      return std::unique_ptr<SplitCollectionOperator>(
+          new GraphRunnerSplitCollectionOperator(params));
+
+    default:
+      LOG(ERROR) << "Unknown split collection operator: "
+                 << params.collection_type();
+      return nullptr;
   }
 }
 
@@ -135,5 +137,121 @@ bool SplitCollectionOperator::BestSplit(int32 node_id,
   return slot->BestSplit(best);
 }
 
+// -------------------------------- GraphRunner ------------------ //
+
+std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
+    int32 node_id, int32 depth) const {
+  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
+}
+
+int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
+                                                   int32 split_id) const {
+  return node_id * num_splits_to_consider_ + split_id;
+}
+
+bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
+                                                   SplitCandidate* best,
+                                                   int32* depth) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  auto* slot = stats_.at(node_id).get();
+  *depth = slot->depth();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    // TODO(gilberth): Support uselessness.
+    auto& runner = runners_[UniqueId(node_id, i)];
+    const float split_score = runner->SplitScore();
+    if (split_score < min_score) {
+      min_score = split_score;
+      best_index = i;
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in split info and left/right stats to initialize models with.
+  *best = SplitCandidate();
+  auto& runner = runners_[UniqueId(node_id, best_index)];
+  runner->GetLeftStats(best->mutable_left_stats());
+  runner->GetRightStats(best->mutable_right_stats());
+  runner->GetSplit(best->mutable_split());
+  return true;
+}
+
+void GraphRunnerSplitCollectionOperator::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    const std::vector<int>& examples, int32 node_id) const {
+  // Build input Tensors.
+  int size = examples.size();
+  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
+  auto ex_data = examples_t.flat<int32>();
+  std::copy(examples.begin(), examples.end(), ex_data.data());
+
+  const TensorInputTarget* tensor_target =
+      dynamic_cast<const TensorInputTarget*>(target);
+  CHECK_NOTNULL(tensor_target);
+
+  const Tensor& data_t = input_data->original_tensor();
+  const Tensor& target_t = tensor_target->original_tensor();
+
+  // Add to candidates.
+  auto* slot = stats_.at(node_id).get();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    auto& runner = runners_[UniqueId(node_id, i)];
+    runner->AddExample(data_t, target_t, examples_t);
+  }
+
+  // Update simple weight sums so we know when we're done.
+  for (int example : examples) {
+    slot->AddExample(input_data, target, example);
+  }
+}
+
+void GraphRunnerSplitCollectionOperator::
+    CreateAndInitializeCandidateWithExample(
+        const std::unique_ptr<TensorDataSet>& input_data, int example,
+        int32 node_id) const {
+  auto* slot = stats_.at(node_id).get();
+  int cand_num = slot->num_splits();
+  const int64 unique_id = UniqueId(node_id, cand_num);
+
+  decision_trees::BinaryNode split;
+
+  decision_trees::InequalityTest* test =
+      split.mutable_inequality_left_child_test();
+  auto* oblique = test->mutable_oblique();
+  for (int i = 0; i < features_per_node_; ++i) {
+    float bias;
+    int type;
+    // This is really just a way to select a list of random features.
+    // Also a way to warn the user that categoricals don't make sense here.
+    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
+
+    if (type == kDataFloat) {
+      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
+
+      // The comparison bias is assumed to be zero.
+      test->mutable_threshold()->set_float_value(0);
+    } else {
+      LOG(ERROR) << "Categorical features not supported with this system.";
+      return;
+    }
+  }
+
+  slot->AddSplit(split);
+
+  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
+  runners_[unique_id]->Init();
+}
+
+void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
+  SplitCollectionOperator::ClearSlot(node_id);
+  for (int i = 0; i < num_splits_to_consider_; ++i) {
+    runners_.erase(UniqueId(node_id, i));
+  }
+}
+
 }  // namespace tensorforest
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
index 81d820a6b2..d0ea33612a 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
@@ -17,6 +17,7 @@
 
 #include <vector>
 #include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
@@ -85,38 +86,60 @@ class SplitCollectionOperator {
   std::unordered_map<int32, std::unique_ptr<GrowStats>> stats_;
 };
 
-class CollectionCreator {
+
+class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
  public:
-  virtual std::unique_ptr<SplitCollectionOperator> Create(
-      const TensorForestParams& params) = 0;
-  virtual ~CollectionCreator() {}
+  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
+      : SplitCollectionOperator(params) {
+    if (params.num_splits_to_consider().ParamType_case() ==
+        DepthDependentParam::PARAMTYPE_NOT_SET) {
+      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
+                 << " num_splits_to_consider";
+    } else {
+      num_splits_to_consider_ =
+          params.num_splits_to_consider().constant_value();
+    }
+  }
+
+  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
+                                             int32 depth) const override;
+
+  // Updates the slot's candidates with the new example.
+  // Assumes slot has been initialized.
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, const std::vector<int>& examples,
+                  int32 node_id) const override;
+
+  // Create a new candidate and initialize it with the given example.
+  void CreateAndInitializeCandidateWithExample(
+      const std::unique_ptr<TensorDataSet>& input_data, int example,
+      int32 node_id) const override;
+
+  bool BestSplit(int32 node_id, SplitCandidate* best,
+                 int32* depth) const override;
+
+  void ClearSlot(int32 node_id) override;
+
+ protected:
+  int64 UniqueId(int32 node_id, int32 split_id) const;
+
+  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
+      runners_;
+  int features_per_node_;
+  string graph_dir_;
+  // Must have a constant value because of how we make unique ids right now.
+  int32 num_splits_to_consider_;
 };
 
+// Creates a type of SplitCollectionOperator depending on the type passed,
+// which is SplitCollectionType in fertile_stats.proto.
+// Can create a SplitCollectionOperator itself, known as "basic".
 class SplitCollectionOperatorFactory {
  public:
   static std::unique_ptr<SplitCollectionOperator> CreateSplitCollectionOperator(
       const TensorForestParams& params);
-
-  static std::unordered_map<int, CollectionCreator*> factories_;
-};
-
-template <typename T>
-class AnyCollectionCreator : public CollectionCreator {
- public:
-  AnyCollectionCreator(SplitCollectionType type) {
-    SplitCollectionOperatorFactory::factories_[type] = this;
-  }
-  virtual std::unique_ptr<SplitCollectionOperator> Create(
-      const TensorForestParams& params) {
-    return std::unique_ptr<SplitCollectionOperator>(new T(params));
-  }
 };
 
-#define REGISTER_SPLIT_COLLECTION(name, cls) \
-  namespace {                                \
-  AnyCollectionCreator<cls> creator(name);   \
-  }
-
 }  // namespace tensorforest
 }  // namespace tensorflow
 
diff --git a/tensorflow/contrib/tensor_forest/ops/model_ops.cc b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
deleted file mode 100644
index 1c3c2153a6..0000000000
--- a/tensorflow/contrib/tensor_forest/ops/model_ops.cc
+++ /dev/null
@@ -1,135 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-#include "tensorflow/core/framework/common_shape_fns.h"
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/resource_mgr.h"
-#include "tensorflow/core/framework/shape_inference.h"
-
-
-namespace tensorflow {
-using shape_inference::DimensionHandle;
-using shape_inference::InferenceContext;
-
-namespace tensorforest {
-
-REGISTER_RESOURCE_HANDLE_OP(DecisionTreeResource);
-
-REGISTER_OP("TreeIsInitializedOp")
-    .Input("tree_handle: resource")
-    .Output("is_initialized: bool")
-    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
-    .Doc(R"doc(
-Checks whether a tree has been initialized.
-)doc");
-
-REGISTER_OP("CreateTreeVariable")
-    .Attr("params: string")
-    .Input("tree_handle: resource")
-    .Input("tree_config: string")
-    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
-    .Doc(R"doc(
-Creates a tree  model and returns a handle to it.
-
-params: A serialized TensorForestParams proto.
-tree_handle: handle to the tree resource to be created.
-tree_config: Serialized proto of the tree.
-)doc");
-
-REGISTER_OP("TreeSerialize")
-    .Input("tree_handle: resource")
-    .Output("tree_config: string")
-    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
-    .Doc(R"doc(
-Serializes the tree  to a proto.
-
-tree_handle: The handle to the tree.
-tree_config: Serialized proto of the tree.
-)doc");
-
-REGISTER_OP("TreeDeserialize")
-    .Attr("params: string")
-    .Input("tree_handle: resource")
-    .Input("tree_config: string")
-    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
-    .Doc(R"doc(
-Deserializes a serialized tree config and replaces current tree.
-
-params: A serialized TensorForestParams proto.
-tree_handle: The handle to the tree .
-tree_config: Serialized proto of the .
-)doc");
-
-REGISTER_OP("TreeSize")
-    .Input("tree_handle: resource")
-    .Output("tree_size: int32")
-    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
-    .Doc(R"doc(
-Outputs the size of the tree, including leaves.
-
-tree_handle: The handle to the tree.
-tree_size: Size scalar.
-)doc");
-
-REGISTER_OP("TreePredictionsV4")
-    .Attr("input_spec: string")
-    .Attr("params: string")
-    .Input("tree_handle: resource")
-    .Input("input_data: float")
-    .Input("sparse_input_indices: int64")
-    .Input("sparse_input_values: float")
-    .Input("sparse_input_shape: int64")
-    .Output("predictions: float")
-    .SetShapeFn([](InferenceContext* c) {
-      DimensionHandle num_points = c->UnknownDim();
-
-      if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0) {
-        num_points = c->Dim(c->input(1), 0);
-      }
-
-      c->set_output(0, c->Matrix(num_points, c->UnknownDim()));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Outputs the predictions for the given input data.
-
-params: A serialized TensorForestParams proto.
-tree_handle: The handle to the tree.
-input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
-   gives the j-th feature of the i-th input.
-sparse_input_indices: The indices tensor from the SparseTensor input.
-sparse_input_values: The values tensor from the SparseTensor input.
-sparse_input_shape: The shape tensor from the SparseTensor input.
-predictions: `predictions[i][j]` is the probability that input i is class j.
-)doc");
-
-REGISTER_OP("FeatureUsageCounts")
-    .Attr("params: string")
-    .Input("tree_handle: resource")
-    .Output("feature_counts: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Vector(c->UnknownDim()));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Outputs the number of times each feature was used in a split.
-
-params: A serialized TensorForestParams proto.
-tree_handle: The handle to the tree.
-feature_counts: `feature_counts[i]` is the number of times feature i was used
-    in a split.
-)doc");
-
-}  // namespace tensorforest
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/ops/stats_ops.cc b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
deleted file mode 100644
index 48e91e3466..0000000000
--- a/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-#include "tensorflow/core/framework/common_shape_fns.h"
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/resource_mgr.h"
-#include "tensorflow/core/framework/shape_inference.h"
-
-namespace tensorflow {
-using shape_inference::InferenceContext;
-
-namespace tensorforest {
-
-
-REGISTER_RESOURCE_HANDLE_OP(FertileStatsResource);
-
-REGISTER_OP("FertileStatsIsInitializedOp")
-    .Input("stats_handle: resource")
-    .Output("is_initialized: bool")
-    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
-    .Doc(R"doc(
-Checks whether a stats has been initialized.
-)doc");
-
-REGISTER_OP("CreateFertileStatsVariable")
-    .Attr("params: string")
-    .Input("stats_handle: resource")
-    .Input("stats_config: string")
-    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
-    .Doc(R"doc(
-Creates a stats model and returns a handle to it.
-
-params: A serialized TensorForestParams proto.
-stats_handle: handle to the stats resource to be created.
-stats_config: Serialized proto of the stats.
-)doc");
-
-REGISTER_OP("FertileStatsSerialize")
-    .Attr("params: string")
-    .Input("stats_handle: resource")
-    .Output("stats_config: string")
-    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
-    .Doc(R"doc(
-Serializes the stats to a proto.
-
-params: A serialized TensorForestParams proto.
-stats_handle: The handle to the stats.
-stats_config: Serialized proto of the stats.
-)doc");
-
-REGISTER_OP("FertileStatsDeserialize")
-    .Attr("params: string")
-    .Input("stats_handle: resource")
-    .Input("stats_config: string")
-    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
-    .Doc(R"doc(
-Deserializes a serialized stats config and replaces current stats.
-
-params: A serialized TensorForestParams proto.
-stats_handle: The handle to the stats.
-stats_config: Serialized proto of the stats.
-)doc");
-
-REGISTER_OP("GrowTreeV4")
-    .Attr("params: string")
-    .Input("tree_handle: resource")
-    .Input("stats_handle: resource")
-    .Input("finshed_nodes: int32")
-    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
-    .Doc(R"doc(
-Grows the tree for finished nodes and allocates waiting nodes.
-
-params: A serialized TensorForestParams proto.
-tree_handle: The handle to the tree.
-stats_handle: The handle to the stats.
-finshed_nodes: A 1-d Tensor of finished node ids from ProcessInput.
-)doc");
-
-REGISTER_OP("ProcessInputV4")
-    .Attr("random_seed: int")
-    .Attr("input_spec: string")
-    .Attr("params: string")
-    .Input("tree_handle: resource")
-    .Input("stats_handle: resource")
-    .Input("input_data: float")
-    .Input("sparse_input_indices: int64")
-    .Input("sparse_input_values: float")
-    .Input("sparse_input_shape: int64")
-    .Input("input_labels: float")
-    .Input("input_weights: float")
-    .Output("finished_nodes: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Vector(c->UnknownDim()));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Add labels to stats after traversing the tree for each example.
-
-Outputs node ids that are finished.
-
-params: A serialized TensorForestParams proto.
-tree_handle: The handle to the tree.
-stats_handle: The handle to the stats.
-input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
-   gives the j-th feature of the i-th input.
-sparse_input_indices: The indices tensor from the SparseTensor input.
-sparse_input_values: The values tensor from the SparseTensor input.
-sparse_input_shape: The shape tensor from the SparseTensor input.
-input_labels: The training batch's labels as a 1 or 2-d tensor.
-  'input_labels[i][j]' gives the j-th label/target for the i-th input.
-input_weights: The training batch's eample weights as a 1-d tensor.
-  'input_weights[i]' gives the weight for the i-th input.
-finished_nodes: A 1-d tensor of node ids that have finished and are ready to
-  grow.
-)doc");
-
-REGISTER_OP("FinalizeTree")
-    .Attr("params: string")
-    .Input("tree_handle: resource")
-    .Input("stats_handle: resource")
-    .SetShapeFn([](InferenceContext* c) {
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Puts the Leaf models inside the tree into their final form.
-
-If drop_final_class is true, the per-class probability prediction of the
-last class is not stored in the leaf models.
-
-params: A serialized TensorForestParams proto.
-tree_handle: The handle to the tree.
-stats_handle: The handle to the stats.
-)doc");
-}  // namespace tensorforest
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/python/__init__.py b/tensorflow/contrib/tensor_forest/python/__init__.py
index b2ca89877a..0d41d3500d 100644
--- a/tensorflow/contrib/tensor_forest/python/__init__.py
+++ b/tensorflow/contrib/tensor_forest/python/__init__.py
@@ -21,6 +21,4 @@ from __future__ import print_function
 from tensorflow.contrib.tensor_forest.python import constants
 from tensorflow.contrib.tensor_forest.python import tensor_forest
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
-from tensorflow.contrib.tensor_forest.python.ops import model_ops
-from tensorflow.contrib.tensor_forest.python.ops import stats_ops
 from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
diff --git a/tensorflow/contrib/tensor_forest/python/ops/model_ops.py b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
deleted file mode 100644
index 4c7218305b..0000000000
--- a/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Model ops python wrappers."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.tensor_forest.python.ops import gen_model_ops
-from tensorflow.contrib.tensor_forest.python.ops import stats_ops
-
-# pylint: disable=unused-import
-from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import feature_usage_counts
-from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_predictions_v4
-from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_size
-# pylint: enable=unused-import
-
-from tensorflow.contrib.util import loader
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import resources
-from tensorflow.python.platform import resource_loader
-from tensorflow.python.training import saver
-
-
-_model_ops = loader.load_op_library(
-    resource_loader.get_path_to_datafile("_model_ops.so"))
-
-
-ops.NotDifferentiable("TreeVariable")
-ops.NotDifferentiable("TreeSerialize")
-ops.NotDifferentiable("TreeDeserialize")
-ops.NotDifferentiable("TreeSize")
-ops.NotDifferentiable("TreePredictionsV4")
-ops.NotDifferentiable("FeatureUsageCounts")
-
-
-class TreeVariableSavable(saver.BaseSaverBuilder.SaveableObject):
-  """SaveableObject implementation for TreeVariable."""
-
-  def __init__(self, params, tree_handle, stats_handle, create_op, name):
-    """Creates a TreeVariableSavable object.
-
-    Args:
-      params: A TensorForestParams object.
-      tree_handle: handle to the tree variable.
-      stats_handle: handle to the stats variable.
-      create_op: the op to initialize the variable.
-      name: the name to save the tree variable under.
-    """
-    self.params = params
-    deps = []
-    if stats_handle is not None:
-      deps.append(stats_ops.finalize_tree(
-          tree_handle, stats_handle,
-          params=params.serialized_params_proto))
-    with ops.control_dependencies(deps):
-      tensor = gen_model_ops.tree_serialize(tree_handle)
-    # slice_spec is useful for saving a slice from a variable.
-    # It's not meaningful the tree variable. So we just pass an empty value.
-    slice_spec = ""
-    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
-    super(TreeVariableSavable,
-          self).__init__(tree_handle, specs, name)
-    self._tree_handle = tree_handle
-    self._create_op = create_op
-
-  def restore(self, restored_tensors, unused_restored_shapes):
-    """Restores the associated tree from 'restored_tensors'.
-
-    Args:
-      restored_tensors: the tensors that were loaded from a checkpoint.
-      unused_restored_shapes: the shapes this object should conform to after
-        restore. Not meaningful for trees.
-
-    Returns:
-      The operation that restores the state of the tree variable.
-    """
-    with ops.control_dependencies([self._create_op]):
-      return gen_model_ops.tree_deserialize(
-          self._tree_handle,
-          restored_tensors[0],
-          params=self.params.serialized_params_proto)
-
-
-def tree_variable(params, tree_config, stats_handle, name, container=None):
-  r"""Creates a tree model and returns a handle to it.
-
-  Args:
-    params: A TensorForestParams object.
-    tree_config: A `Tensor` of type `string`. Serialized proto of the tree.
-    stats_handle: Resource handle to the stats object.
-    name: A name for the variable.
-    container: An optional `string`. Defaults to `""`.
-
-  Returns:
-    A `Tensor` of type mutable `string`. The handle to the tree.
-  """
-  with ops.name_scope(name, "TreeVariable") as name:
-    resource_handle = gen_model_ops.decision_tree_resource_handle_op(
-        container, name, name=name)
-
-    create_op = gen_model_ops.create_tree_variable(
-        resource_handle,
-        tree_config,
-        params=params.serialized_params_proto)
-    is_initialized_op = gen_model_ops.tree_is_initialized_op(resource_handle)
-    # Adds the variable to the savable list.
-    saveable = TreeVariableSavable(params, resource_handle, stats_handle,
-                                   create_op,
-                                   "tree_checkpoint_{0}".format(name))
-    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
-    resources.register_resource(resource_handle, create_op, is_initialized_op)
-    return resource_handle
diff --git a/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py b/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
deleted file mode 100644
index be9f2e12b7..0000000000
--- a/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Stats ops python wrappers."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.tensor_forest.python.ops import gen_stats_ops
-# pylint: disable=unused-import
-from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import finalize_tree
-from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import grow_tree_v4
-from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import process_input_v4
-# pylint: enable=unused-import
-
-from tensorflow.contrib.util import loader
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import resources
-from tensorflow.python.platform import resource_loader
-from tensorflow.python.training import saver
-
-
-_stats_ops = loader.load_op_library(
-    resource_loader.get_path_to_datafile("_stats_ops.so"))
-
-
-ops.NotDifferentiable("FertileStatsVariable")
-ops.NotDifferentiable("FertileStatsSerialize")
-ops.NotDifferentiable("FertileStatsDeserialize")
-ops.NotDifferentiable("GrowTreeV4")
-ops.NotDifferentiable("ProcessInputV4")
-ops.NotDifferentiable("FinalizeTree")
-
-
-class FertileStatsVariableSavable(saver.BaseSaverBuilder.SaveableObject):
-  """SaveableObject implementation for FertileStatsVariable."""
-
-  def __init__(self, params, stats_handle, create_op, name):
-    """Creates a FertileStatsVariableSavable object.
-
-    Args:
-      params: A TensorForestParams object.
-      stats_handle: handle to the tree variable.
-      create_op: the op to initialize the variable.
-      name: the name to save the tree variable under.
-    """
-    self.params = params
-    tensor = gen_stats_ops.fertile_stats_serialize(
-        stats_handle, params=params.serialized_params_proto)
-    # slice_spec is useful for saving a slice from a variable.
-    # It's not meaningful the tree variable. So we just pass an empty value.
-    slice_spec = ""
-    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
-    super(FertileStatsVariableSavable,
-          self).__init__(stats_handle, specs, name)
-    self._stats_handle = stats_handle
-    self._create_op = create_op
-
-  def restore(self, restored_tensors, unused_restored_shapes):
-    """Restores the associated tree from 'restored_tensors'.
-
-    Args:
-      restored_tensors: the tensors that were loaded from a checkpoint.
-      unused_restored_shapes: the shapes this object should conform to after
-        restore. Not meaningful for trees.
-
-    Returns:
-      The operation that restores the state of the tree variable.
-    """
-    with ops.control_dependencies([self._create_op]):
-      return gen_stats_ops.fertile_stats_deserialize(
-          self._stats_handle, restored_tensors[0],
-          params=self.params.serialized_params_proto)
-
-
-def fertile_stats_variable(params, stats_config, name,
-                           container=None):
-  r"""Creates a stats object and returns a handle to it.
-
-  Args:
-    params: A TensorForestParams object.
-    stats_config: A `Tensor` of type `string`. Serialized proto of the stats.
-    name: A name for the variable.
-    container: An optional `string`. Defaults to `""`.
-
-  Returns:
-    A `Tensor` of type mutable `string`. The handle to the stats.
-  """
-  with ops.name_scope(name, "FertileStatsVariable") as name:
-    resource_handle = gen_stats_ops.fertile_stats_resource_handle_op(
-        container, name, name=name)
-
-    create_op = gen_stats_ops.create_fertile_stats_variable(
-        resource_handle, stats_config,
-        params=params.serialized_params_proto)
-    is_initialized_op = gen_stats_ops.fertile_stats_is_initialized_op(
-        resource_handle)
-    # Adds the variable to the savable list.
-    saveable = FertileStatsVariableSavable(params, resource_handle, create_op,
-                                           "stats_checkpoint_{0}".format(name))
-    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
-    resources.register_resource(resource_handle, create_op, is_initialized_op)
-    return resource_handle
-- 
GitLab


From a24366fa00e5ac0b70c8871d459f5569459329d5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 12:45:34 -0700
Subject: [PATCH 0300/1375] Adding missing deps to targets in llvm.BUILD. This
 was only working in non-sandboxed builds.

PiperOrigin-RevId: 159729295
---
 third_party/llvm/llvm.BUILD | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD
index 32266997a7..678626b374 100644
--- a/third_party/llvm/llvm.BUILD
+++ b/third_party/llvm/llvm.BUILD
@@ -453,6 +453,7 @@ llvm_target_list = [
             "include/llvm/IR/Intrinsics*.td",
             "include/llvm/TableGen/*.td",
             "include/llvm/Target/*.td",
+            "include/llvm/Target/GlobalISel/*.td",
         ]),
     )
     for target in llvm_target_list
@@ -1067,6 +1068,8 @@ cc_library(
         "include/llvm/BinaryFormat/*.h",
         "include/llvm/BinaryFormat/*.def",
         "include/llvm/BinaryFormat/*.inc",
+        "include/llvm/BinaryFormat/ELFRelocs/*.def",
+        "include/llvm/BinaryFormat/WasmRelocs/*.def",
     ]),
     deps = [
         ":config",
@@ -1194,6 +1197,7 @@ cc_library(
         "include/llvm/DebugInfo/CodeView/*.inc",
     ]),
     deps = [
+        ":binary_format",
         ":config",
         ":debug_info_msf",
         ":support",
@@ -1426,6 +1430,7 @@ cc_library(
         "include/llvm/MC/*.inc",
     ]),
     deps = [
+        ":binary_format",
         ":config",
         ":debug_info_code_view",
         ":support",
@@ -1921,6 +1926,8 @@ cc_library(
         "lib/Support/Unix/*.h",
         "include/llvm-c/*.h",
         "include/llvm/CodeGen/MachineValueType.h",
+        "include/llvm/BinaryFormat/COFF.h",
+        "include/llvm/BinaryFormat/MachO.h",
         "lib/Support/*.h",
     ]),
     hdrs = glob([
@@ -1931,6 +1938,7 @@ cc_library(
         "include/llvm/Support/ELFRelocs/*.def",
         "include/llvm/Support/WasmRelocs/*.def",
     ]) + [
+        "include/llvm/BinaryFormat/MachO.def",
         "include/llvm/Support/DataTypes.h",
         "include/llvm/ExecutionEngine/ObjectMemoryBuffer.h",
     ],
-- 
GitLab


From 4c75252f25eec9f787dd009566607fd21d354548 Mon Sep 17 00:00:00 2001
From: Mark Neumann <markn@allenai.org>
Date: Wed, 21 Jun 2017 12:49:21 -0700
Subject: [PATCH 0301/1375] fix initial test values to avoid numerical
 instability

---
 tensorflow/cc/gradients/nn_grad_test.cc | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc
index de6b614037..daa87546ec 100644
--- a/tensorflow/cc/gradients/nn_grad_test.cc
+++ b/tensorflow/cc/gradients/nn_grad_test.cc
@@ -58,10 +58,16 @@ TEST_F(NNGradTest, SoftmaxGrad) {
 }
 
 TEST_F(NNGradTest, LogSoftmaxGrad) {
-  TensorShape shape({32, 10});
+  TensorShape shape({5, 3});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
   auto y = LogSoftmax(scope_, x);
-  RunTest(x, shape, y, shape);
+  // Avoid numerical instability when computing finite differences.
+  Tensor x_init_value = test::AsTensor<float>(
+          {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f,
+           0.1f, 0.3f, 0.5f, 0.7f, 0.8f,
+           -0.1f, 0.1f, 0.1f, 0.1f, 1.2f},
+          {5, 3});
+  RunTest(x, x_init_value, y, shape);
 }
 
 TEST_F(NNGradTest, ReluGrad) {
-- 
GitLab


From 62fb49d314384ae86e974a0bd3bb85c78fbd7980 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Jun 2017 12:49:39 -0700
Subject: [PATCH 0302/1375] Fix visibility error for
 contrib/remote_fused_graph/pylib/BUILD.

---
 tensorflow/contrib/remote_fused_graph/pylib/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
index c7ed663131..288f59fed4 100644
--- a/tensorflow/contrib/remote_fused_graph/pylib/BUILD
+++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
@@ -7,6 +7,7 @@ licenses(["notice"])  # Apache 2.0
 
 exports_files(["LICENSE"])
 
+load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 tf_gen_op_wrapper_py(
-- 
GitLab


From 88a6bb80c101d381c030e4fc1a13fb52bb63600d Mon Sep 17 00:00:00 2001
From: Guenther Schmuelling <guschmue@microsoft.com>
Date: Wed, 21 Jun 2017 12:51:00 -0700
Subject: [PATCH 0303/1375] expand inline for debug builds to limit number of
 symbols

---
 tensorflow/contrib/cmake/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index 9ffe08eded..205b44ac12 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -74,7 +74,7 @@ if(WIN32)
   set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP")
-  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob0")
+  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob2")
   set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_MINSIZEREL "${CMAKE_CXX_FLAGS_MINSIZEREL} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /D_ITERATOR_DEBUG_LEVEL=0")
-- 
GitLab


From a54d43fa40425b8e51891b6a52f87024c7dd682b Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Jun 2017 13:01:12 -0700
Subject: [PATCH 0304/1375] Replace LiteralUtil to Literal in
 compiler/plugin/executor

---
 tensorflow/compiler/plugin/executor/executable.cc       | 6 +++---
 tensorflow/compiler/plugin/executor/transfer_manager.cc | 8 ++++----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/plugin/executor/executable.cc b/tensorflow/compiler/plugin/executor/executable.cc
index 92a517ba53..74fa1869f6 100644
--- a/tensorflow/compiler/plugin/executor/executable.cc
+++ b/tensorflow/compiler/plugin/executor/executable.cc
@@ -36,7 +36,7 @@ static se::DeviceMemoryBase AllocateSingleOutput(sep::ExecutorExecutor* executor
                                                  const Literal& literal) {
   int64 size(xla::ShapeUtil::ByteSizeOf(literal.shape()));
   void* buf = executor->Allocate(size);
-  const void* src = LiteralUtil::InternalData(literal);
+  const void* src = Literal::InternalData(literal);
   memcpy(buf, src, size);
   return se::DeviceMemoryBase(buf, size);
 }
@@ -86,11 +86,11 @@ StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteOnStream(
   for (int64 p = 0; p < computation->num_parameters(); p++) {
     // Create the input literal for the parameter
     HloInstruction* param = computation->parameter_instruction(p);
-    arg_literals.emplace_back(LiteralUtil::CreateFromShape(param->shape()));
+    arg_literals.emplace_back(Literal::CreateFromShape(param->shape()));
     arg_literals_ptrs.push_back(arg_literals.back().get());
 
     // Copy in the data from the stream_executor buffers
-    void* buffer = LiteralUtil::MutableInternalData(arg_literals.back().get());
+    void* buffer = Literal::MutableInternalData(arg_literals.back().get());
     memcpy(buffer, arguments[p].opaque(),
            ShapeUtil::ByteSizeOf(param->shape()));
   }
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.cc b/tensorflow/compiler/plugin/executor/transfer_manager.cc
index b59d20a779..22be795802 100644
--- a/tensorflow/compiler/plugin/executor/transfer_manager.cc
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.cc
@@ -70,13 +70,13 @@ Status ExecutorTransferManager::TransferLiteralFromDevice(
   }
 
   *literal->mutable_shape() = device_shape;
-  LiteralUtil::Reserve(ShapeUtil::ElementsIn(device_shape), literal);
+  Literal::Reserve(ShapeUtil::ElementsIn(device_shape), literal);
   TF_RETURN_IF_ERROR(TransferBufferFromDevice(
       executor, source, ShapeUtil::ByteSizeOf(device_shape),
-      LiteralUtil::MutableInternalData(literal)));
+      Literal::MutableInternalData(literal)));
   if (!ShapeUtil::Equal(literal_shape, device_shape)) {
     literal->Swap(
-        LiteralUtil::Relayout(*literal, literal_shape.layout()).get());
+        Literal::Relayout(*literal, literal_shape.layout()).get());
   }
   TF_RET_CHECK(ShapeUtil::Equal(literal_shape, literal->shape()));
   return Status::OK();
@@ -134,7 +134,7 @@ Status ExecutorTransferManager::TransferLiteralToDevice(
   }
 
   return TransferBufferToDevice(executor, GetByteSizeRequirement(shape),
-                                LiteralUtil::InternalData(literal),
+                                Literal::InternalData(literal),
                                 destination);
 }
 
-- 
GitLab


From 9e8005d7771e3f98b0a2ce74e4b0bc3765410a27 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 13:06:24 -0700
Subject: [PATCH 0305/1375] [XLA:HLO] Move sequence functions from
 hlo_ordering.h to hlo_scheduling.h.

This is required for upcoming changes to convert the sequence creation functions
(and HeapSimulator and BufferAssignment) over to using the new
Hlo{Dataflow,Alias}Analysis.

It's required because otherwise there's a dependency cycle:

Hlo{Dataflow,Alias}Analysis depends on HloOrdering
CreateMemoryMinimizingSequence will depend on Hlo{Dataflow,Alias}Analysis

There's already a cycle here, if both HloOrdering and
CreateMemoryMinimizingSequence are in the same file.  Also note that:

MinimumMemoryForSequence depends on HeapSimulator
HeapSimulator will depend on Hlo{Dataflow,Alias}Analysis
Hlo{Dataflow,Alias}Analysis depends on HloOrdering

Splitting out the sequence functions resolves the cycle.

Refactoring only; no functional changes.

PiperOrigin-RevId: 159731836
---
 tensorflow/compiler/xla/service/BUILD         |  81 +++-
 .../compiler/xla/service/buffer_assignment.cc |   1 +
 .../xla/service/buffer_assignment_test.cc     |   1 +
 tensorflow/compiler/xla/service/cpu/BUILD     |   1 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   1 +
 tensorflow/compiler/xla/service/gpu/BUILD     |   3 +-
 .../compiler/xla/service/gpu/hlo_schedule.cc  |   1 +
 .../compiler/xla/service/gpu/hlo_schedule.h   |   2 +-
 .../compiler/xla/service/hlo_ordering.cc      | 355 ----------------
 .../compiler/xla/service/hlo_ordering.h       |  22 -
 .../compiler/xla/service/hlo_ordering_test.cc |  61 +--
 .../xla/service/hlo_rematerialization.cc      |   1 +
 .../compiler/xla/service/hlo_scheduling.cc    | 388 ++++++++++++++++++
 .../compiler/xla/service/hlo_scheduling.h     |  50 +++
 .../xla/service/hlo_scheduling_test.cc        |  97 +++++
 15 files changed, 611 insertions(+), 454 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/hlo_scheduling.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_scheduling.h
 create mode 100644 tensorflow/compiler/xla/service/hlo_scheduling_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 778c740b1d..150cd8a678 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -712,9 +712,11 @@ cc_library(
     ],
     deps = [
         ":buffer_liveness",
+        ":heap_simulator",
         ":hlo",
         ":hlo_ordering",
         ":hlo_proto",
+        ":hlo_scheduling",
         ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:shape_util",
@@ -741,6 +743,7 @@ cc_test(
         ":flatten_call_graph",
         ":hlo",
         ":hlo_ordering",
+        ":hlo_scheduling",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:test",
@@ -753,13 +756,67 @@ cc_test(
     ],
 )
 
+cc_library(
+    name = "hlo_ordering",
+    srcs = ["hlo_ordering.cc"],
+    hdrs = ["hlo_ordering.h"],
+    deps = [
+        ":call_graph",
+        ":hlo",
+        ":hlo_proto",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_test(
+    name = "hlo_ordering_test",
+    size = "small",
+    srcs = ["hlo_ordering_test.cc"],
+    deps = [
+        ":hlo",
+        ":hlo_ordering",
+        ":hlo_scheduling",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+    ],
+)
+
+cc_library(
+    name = "heap_simulator",
+    srcs = ["heap_simulator.cc"],
+    hdrs = ["heap_simulator.h"],
+    deps = [
+        ":hlo",
+        ":hlo_ordering",
+        ":hlo_proto",
+        ":liveness_util",
+        ":logical_buffer",
+        ":tuple_points_to_analysis",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_test(
     name = "heap_simulator_test",
     size = "small",
     srcs = ["heap_simulator_test.cc"],
     deps = [
+        ":heap_simulator",
         ":hlo",
         ":hlo_ordering",
+        ":hlo_scheduling",
         ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:literal_util",
@@ -770,23 +827,15 @@ cc_test(
     ],
 )
 
-# The hlo_ordering library contains both hlo_ordering and heap_simulator because
-# they are mutually dependent.
 cc_library(
-    name = "hlo_ordering",
-    srcs = [
-        "heap_simulator.cc",
-        "hlo_ordering.cc",
-    ],
-    hdrs = [
-        "heap_simulator.h",
-        "hlo_ordering.h",
-    ],
+    name = "hlo_scheduling",
+    srcs = ["hlo_scheduling.cc"],
+    hdrs = ["hlo_scheduling.h"],
     deps = [
-        ":call_graph",
+        ":heap_simulator",
         ":hlo",
+        ":hlo_ordering",
         ":hlo_proto",
-        ":liveness_util",
         ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:shape_util",
@@ -799,12 +848,13 @@ cc_library(
 )
 
 cc_test(
-    name = "hlo_ordering_test",
+    name = "hlo_scheduling_test",
     size = "small",
-    srcs = ["hlo_ordering_test.cc"],
+    srcs = ["hlo_scheduling_test.cc"],
     deps = [
         ":hlo",
         ":hlo_ordering",
+        ":hlo_scheduling",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
@@ -1426,6 +1476,7 @@ cc_library(
         ":hlo",
         ":hlo_dce",
         ":hlo_ordering",
+        ":hlo_scheduling",
         ":liveness_util",
         ":logical_buffer",
         ":tuple_points_to_analysis",
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index 44b4f4e3d8..3ba010ac43 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/heap_simulator.h"
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 10021b2513..c498b86dd4 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/test_helpers.h"
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index de6660e3b5..68cd545695 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -68,6 +68,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo_pass_pipeline",
         "//tensorflow/compiler/xla/service:hlo_proto",
         "//tensorflow/compiler/xla/service:hlo_proto_util",
+        "//tensorflow/compiler/xla/service:hlo_scheduling",
         "//tensorflow/compiler/xla/service:hlo_subcomputation_unification",
         "//tensorflow/compiler/xla/service:hlo_verifier",
         "//tensorflow/compiler/xla/service:inliner",
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 4786e75fa7..0905855ec2 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -69,6 +69,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
 #include "tensorflow/compiler/xla/service/hlo_proto_util.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
 #include "tensorflow/compiler/xla/service/hlo_verifier.h"
 #include "tensorflow/compiler/xla/service/inliner.h"
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index 52b4a13296..1e15ce32ee 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -498,8 +498,9 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/service:buffer_liveness",
         "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:hlo_ordering",
+        "//tensorflow/compiler/xla/service:hlo_scheduling",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
index d16a1d4ee5..f76f8ca668 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/gpu/hlo_schedule.h"
 
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/types.h"
 
 namespace xla {
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.h b/tensorflow/compiler/xla/service/gpu/hlo_schedule.h
index 773973010a..1ce7a48ac8 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.h
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.h
@@ -19,9 +19,9 @@ limitations under the License.
 #include <memory>
 #include <vector>
 
-#include "tensorflow/compiler/xla/service/buffer_liveness.h"
 #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_ordering.h"
 #include "tensorflow/compiler/xla/statusor.h"
 
 namespace xla {
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc
index 61e5efa5b6..32a2abed92 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering.cc
@@ -15,13 +15,10 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
 
-#include <set>
 #include <utility>
 #include <vector>
 
-#include "tensorflow/compiler/xla/service/heap_simulator.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
-#include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -252,358 +249,6 @@ string SequentialHloOrdering::ToString() const {
   return tensorflow::str_util::Join(pieces, "\n");
 }
 
-StatusOr<int64> MinimumMemoryForSequence(
-    const SequentialHloOrdering::HloModuleSequence& module_sequence,
-    const LogicalBuffer::SizeFunction& size_function) {
-  if (module_sequence.empty()) {
-    return 0;
-  }
-
-  const HloModule* module = module_sequence.begin()->first->parent();
-  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
-                      TuplePointsToAnalysis::Run(module));
-
-  // The absolute minimum memory required for a given sequence of instructions
-  // is determined by the sequence of Alloc and Free calls on a simulated heap,
-  // ignoring fragmentation. We run the heap simulation on the whole module,
-  // rather than summing each computation, since it gives us a better lower
-  // bound, by minimizing the liveness of sub-computations.
-  TF_ASSIGN_OR_RETURN(
-      HeapSimulator::Result result,
-      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), *module,
-                         module_sequence, *points_to_analysis, size_function));
-  return result.heap_size;
-}
-
-namespace {
-
-// Class implementing a list scheduler of HLO instructions which produces a
-// sequence which minimizes memory usage.
-class ListScheduler {
- public:
-  // Construct and return a memory-minimizing sequence of HLO instructions
-  // containing the given HLO computation.
-  static StatusOr<std::vector<const HloInstruction*>> Run(
-      const HloComputation& computation,
-      const TuplePointsToAnalysis& points_to_analysis,
-      const LogicalBuffer::SizeFunction& size_function) {
-    ListScheduler scheduler(computation, points_to_analysis, size_function);
-    return scheduler.CreateSchedule();
-  }
-
- private:
-  // The scheduling priority of an instruction is first the number of bytes
-  // freed by scheduling the instruction, and second (tie-breaker) by the number
-  // of users. This is represented as a std::pair containing these two values
-  // (first element is the bytes freed). std::pair provides the necessary
-  // comparison operators.
-  using Priority = std::pair<int64, int64>;
-
-  ListScheduler(const HloComputation& computation,
-                const TuplePointsToAnalysis& points_to_analysis,
-                const LogicalBuffer::SizeFunction& size_function)
-      : computation_(computation),
-        points_to_analysis_(points_to_analysis),
-        size_function_(size_function) {
-    // Create a map containing the LogicalBuffer uses for each HLO
-    // instruction. An HLO instruction "uses" a LogicalBuffer if the
-    // LogicalBuffer is in an operand of the instruction as indicated by
-    // points-to analysis.
-    for (auto& instruction : computation.instructions()) {
-      buffer_uses_.insert(
-          {instruction.get(), std::unordered_set<const LogicalBuffer*>()});
-      for (auto* operand : instruction->operands()) {
-        for (const LogicalBuffer* buffer :
-             points_to_analysis.GetBuffersDefinedByInstruction(operand)) {
-          buffer_uses_[instruction.get()].insert(buffer);
-        }
-      }
-    }
-
-    // Create map containing the number of unscheduled uses (hlo instructions)
-    // of each logical buffer.
-    for (auto& instruction : computation.instructions()) {
-      for (auto* buffer : points_to_analysis.GetBuffersDefinedByInstruction(
-               instruction.get())) {
-        unscheduled_use_count_[buffer] = 0;
-      }
-    }
-    for (auto& instruction : computation.instructions()) {
-      for (const LogicalBuffer* buffer : buffer_uses_.at(instruction.get())) {
-        ++unscheduled_use_count_[buffer];
-      }
-    }
-
-    // Buffers live out of the computation have an implicit use at the end of
-    // the computation.
-    for (const LogicalBuffer* live_out_buffer :
-         points_to_analysis.GetPointsToSet(computation.root_instruction())
-             .CreateFlattenedSet()) {
-      ++unscheduled_use_count_[live_out_buffer];
-    }
-  }
-
-  // Returns whether the memory used by the given buffer should be ignored by
-  // the scheduling heuristic.
-  bool IgnoreBuffer(const LogicalBuffer& buffer) {
-    return buffer.instruction()->opcode() == HloOpcode::kParameter ||
-           buffer.instruction()->opcode() == HloOpcode::kConstant;
-  }
-
-  // Return the number of bytes freed if the HLO instruction is scheduled.
-  int64 BytesFreedIfScheduled(const HloInstruction* instruction) {
-    int64 freed_bytes = 0;
-    // Sum the total size of the values last used by this instruction.
-    for (auto* buffer : buffer_uses_.at(instruction)) {
-      if (IgnoreBuffer(*buffer)) {
-        continue;
-      }
-      CHECK_GE(unscheduled_use_count_.at(buffer), 1);
-      if (unscheduled_use_count_.at(buffer) == 1) {
-        // This is the last use of the logical buffer.
-        freed_bytes += size_function_(*buffer);
-      }
-    }
-    // Then subtract the size of the value(s) defined by this instruction.
-    for (auto* buffer :
-         points_to_analysis_.GetBuffersDefinedByInstruction(instruction)) {
-      if (!IgnoreBuffer(*buffer)) {
-        freed_bytes -= size_function_(*buffer);
-      }
-    }
-    return freed_bytes;
-  }
-
-  // Construct the scheduling priority of the given instruction.
-  Priority GetPriority(const HloInstruction* instruction) {
-    return {BytesFreedIfScheduled(instruction), instruction->user_count()};
-  }
-
-  std::vector<const HloInstruction*> CreateSchedule() {
-    std::vector<const HloInstruction*> schedule;
-
-    // Populate the ready list with instructions which have no operands or
-    // control predecessors.
-    std::unordered_map<const HloInstruction*, int64> unscheduled_pred_count;
-    std::list<const HloInstruction*> ready_list;
-    for (auto& instruction : computation_.instructions()) {
-      // TODO(b/34466113): Replace this and above with successors() or
-      // predecessors() when these methods are added to HloInstruction.
-      for (const HloInstruction* user : instruction->users()) {
-        unscheduled_pred_count[user]++;
-      }
-      for (const HloInstruction* succ : instruction->control_successors()) {
-        unscheduled_pred_count[succ]++;
-      }
-    }
-    for (auto& instruction : computation_.instructions()) {
-      // Instruction with no operands or control predecessors will
-      // not be in the map.
-      if (unscheduled_pred_count.count(instruction.get()) == 0) {
-        ready_list.push_back(instruction.get());
-      }
-    }
-
-    while (!ready_list.empty()) {
-      // Select the highest priority HLO instruction from the ready list.
-      auto best_it = ready_list.begin();
-      Priority best_priority = GetPriority(*best_it);
-      for (auto ready_it = std::next(ready_list.begin());
-           ready_it != ready_list.end(); ++ready_it) {
-        Priority priority = GetPriority(*ready_it);
-        if (priority > best_priority) {
-          best_it = ready_it;
-          best_priority = priority;
-        }
-      }
-
-      // Remove the selected instruction from the ready list and add it to the
-      // schedule.
-      const HloInstruction* best = *best_it;
-      ready_list.erase(best_it);
-      schedule.push_back(best);
-      scheduled_instructions_.insert(best);
-
-      // Update the unscheduled uses of the logical buffers.
-      for (const LogicalBuffer* buffer : buffer_uses_.at(best)) {
-        CHECK_GT(unscheduled_use_count_.at(buffer), 0);
-        --unscheduled_use_count_[buffer];
-      }
-
-      // Add new instructions to ready list.
-      auto update_pred_count = [&unscheduled_pred_count,
-                                &ready_list](HloInstruction* inst) {
-        int64 pred_count = --unscheduled_pred_count.at(inst);
-        CHECK_GE(pred_count, 0);
-        if (pred_count == 0) {
-          ready_list.push_back(inst);
-        }
-      };
-      // TODO(b/34466113): Replace this and above with successors() or
-      // predecessors() when these methods are added to HloInstruction.
-      for (HloInstruction* user : best->users()) {
-        update_pred_count(user);
-      }
-      for (HloInstruction* succ : best->control_successors()) {
-        update_pred_count(succ);
-      }
-    }
-    CHECK_EQ(schedule.size(), computation_.instructions().size());
-    CHECK_EQ(scheduled_instructions_.size(),
-             computation_.instructions().size());
-
-    return schedule;
-  }
-
-  const HloComputation& computation_;
-  const TuplePointsToAnalysis& points_to_analysis_;
-  const LogicalBuffer::SizeFunction& size_function_;
-
-  // A map containing the LogicalBuffers that each instruction uses.
-  std::unordered_map<const HloInstruction*,
-                     std::unordered_set<const LogicalBuffer*>>
-      buffer_uses_;
-
-  // A map containing the count of unscheduled HLOs which using a particular
-  // LogicalBuffer.
-  std::unordered_map<const LogicalBuffer*, int64> unscheduled_use_count_;
-
-  // Set of instructions which have been scheduled.
-  std::unordered_set<const HloInstruction*> scheduled_instructions_;
-};
-
-int64 SumLogicalBufferSizes(const std::vector<const LogicalBuffer*>& buffers,
-                            const LogicalBuffer::SizeFunction& size_function) {
-  int64 size = 0;
-  for (const LogicalBuffer* buffer : buffers) {
-    size += size_function(*buffer);
-  }
-  return size;
-}
-
-StatusOr<std::vector<const HloInstruction*>> RunDFSMemoryScheduler(
-    const HloComputation& computation,
-    const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_function) {
-  // This ordering is based on DFS post-order, with a heuristic to decide which
-  // operand to visit first.  The heuristic is based on 'extra_users', which is
-  // simply users-1 for each instruction.  By subtracting 1, we're saying that
-  // instructions with no users or a single user don't count; instructions with
-  // lots of fan-out will be visited earlier.
-  tensorflow::gtl::FlatMap<const HloInstruction*, int64> extra_users;
-  tensorflow::gtl::FlatMap<const HloInstruction*, int64> total_sizes;
-  for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) {
-    extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1;
-    total_sizes[hlo] = SumLogicalBufferSizes(
-        points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function);
-    tensorflow::gtl::FlatSet<const HloInstruction*> unique_operands(
-        hlo->operands().begin(), hlo->operands().end());
-    for (const HloInstruction* operand : unique_operands) {
-      extra_users[hlo] += extra_users[operand];
-      total_sizes[hlo] += total_sizes[operand];
-    }
-  }
-  CHECK_EQ(extra_users.size(), computation.instructions().size());
-  CHECK_EQ(total_sizes.size(), computation.instructions().size());
-
-  // Construct a total order based on DFS post-order, visiting operands in
-  // decreasing cumulative extra user order, and next by cumulative size, with a
-  // tiebreaker by name for determinism.
-  std::vector<const HloInstruction*> sequence;
-  FunctionVisitor visitor([&sequence](HloInstruction* hlo) {
-    sequence.push_back(hlo);
-    return Status::OK();
-  });
-  TF_RETURN_IF_ERROR(computation.AcceptWithOperandOrder(
-      &visitor, [&extra_users, &total_sizes](const HloInstruction* a,
-                                             const HloInstruction* b) {
-        if (extra_users[a] != extra_users[b]) {
-          return extra_users[a] > extra_users[b];
-        }
-        if (total_sizes[a] != total_sizes[b]) {
-          return total_sizes[a] > total_sizes[b];
-        }
-        return a->name() < b->name();
-      }));
-  CHECK_EQ(sequence.size(), computation.instructions().size());
-  return sequence;
-}
-
-StatusOr<int64> MinimumMemoryForComputation(
-    const HloComputation& computation,
-    const std::vector<const HloInstruction*>& sequence,
-    const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_function) {
-  TF_ASSIGN_OR_RETURN(
-      HeapSimulator::Result result,
-      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), computation,
-                         sequence, points_to_analysis, size_function));
-  return result.heap_size;
-}
-
-StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
-    const HloComputation& computation,
-    const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_function) {
-  // We try both a list-scheduler based ordering and a DFS based ordering, and
-  // choose whichever returns a lower min-memory, not accounting for
-  // fragmentation.
-  //
-  // Note that this is just a heuristic. One obvious inaccuracy is that the
-  // memory required for sub-computations might be different when considered
-  // within the caller's context. But it's good enough for now.
-  TF_ASSIGN_OR_RETURN(
-      std::vector<const HloInstruction*> list_sequence,
-      ListScheduler::Run(computation, points_to_analysis, size_function));
-  TF_ASSIGN_OR_RETURN(
-      const int64 list_memory,
-      MinimumMemoryForComputation(computation, list_sequence,
-                                  points_to_analysis, size_function));
-  VLOG(2) << "Min-memory list sequence: " << list_memory << " bytes";
-
-  TF_ASSIGN_OR_RETURN(
-      std::vector<const HloInstruction*> dfs_sequence,
-      RunDFSMemoryScheduler(computation, points_to_analysis, size_function));
-  TF_ASSIGN_OR_RETURN(
-      const int64 dfs_memory,
-      MinimumMemoryForComputation(computation, dfs_sequence, points_to_analysis,
-                                  size_function));
-  VLOG(2) << "Min-memory dfs sequence: " << dfs_memory << " bytes";
-
-  if (list_memory <= dfs_memory) {
-    VLOG(2) << "Chose min-memory list sequence: " << list_memory << " bytes";
-    return list_sequence;
-  } else {
-    VLOG(2) << "Chose min-memory dfs sequence: " << dfs_memory << " bytes";
-    return dfs_sequence;
-  }
-}
-
-}  // namespace
-
-StatusOr<SequentialHloOrdering::HloModuleSequence>
-CreateMemoryMinimizingSequence(
-    const HloModule& module, const LogicalBuffer::SizeFunction& size_function) {
-  SequentialHloOrdering::HloModuleSequence sequence;
-  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
-                      TuplePointsToAnalysis::Run(&module));
-  for (const auto& computation : module.computations()) {
-    TF_ASSIGN_OR_RETURN(sequence[computation.get()],
-                        CreateMemoryMinimizingSequence(
-                            *computation, *points_to_analysis, size_function));
-  }
-  return sequence;
-}
-
-StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
-    const HloComputation& computation,
-    const LogicalBuffer::SizeFunction& size_function) {
-  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
-                      TuplePointsToAnalysis::Run(computation.parent()));
-  return CreateMemoryMinimizingSequence(computation, *points_to_analysis,
-                                        size_function);
-}
-
 std::ostream& operator<<(
     std::ostream& out,
     const SequentialHloOrdering::HloModuleSequence& module_sequence) {
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.h b/tensorflow/compiler/xla/service/hlo_ordering.h
index b59e1ea5eb..ff84f887f7 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.h
+++ b/tensorflow/compiler/xla/service/hlo_ordering.h
@@ -24,12 +24,8 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
-#include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 
 namespace xla {
 
@@ -191,24 +187,6 @@ std::ostream& operator<<(
     std::ostream& out,
     const SequentialHloOrdering::HloModuleSequence& module_sequence);
 
-// Returns the minimum memory required to compute the given module sequence,
-// assuming no fragmentation.
-StatusOr<int64> MinimumMemoryForSequence(
-    const SequentialHloOrdering::HloModuleSequence& module_sequence,
-    const LogicalBuffer::SizeFunction& size_function);
-
-// Returns an HloModuleSequence which seeks to minimize the memory required for
-// the computation. size_function is the function returning the number of bytes
-// required for a LogicalBuffer.
-StatusOr<SequentialHloOrdering::HloModuleSequence>
-CreateMemoryMinimizingSequence(
-    const HloModule& module, const LogicalBuffer::SizeFunction& size_function);
-
-// Overload of above that computes the sequence for a single computation.
-StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
-    const HloComputation& computation,
-    const LogicalBuffer::SizeFunction& size_function);
-
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_ORDERING_H_
diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
index 56e36bd705..a1e38803c4 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -217,67 +218,7 @@ TEST_F(HloOrderingTest, InstructionsInWhileComputations) {
   EXPECT_FALSE(ordering.ExecutesBefore(body_param, cond_param));
 }
 
-class MinimumMemoryForSequenceTest : public HloTestBase {};
-
-TEST_F(MinimumMemoryForSequenceTest, MultiComputation) {
-  auto module = CreateNewModule();
-  const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {});
-  const Shape tuple_shape =
-      ShapeUtil::MakeTupleShape({scalar_shape, scalar_shape});
-
-  auto cond_builder = HloComputation::Builder("WhileCond");
-  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
-  HloInstruction* cond_param = cond_builder.AddInstruction(
-      HloInstruction::CreateParameter(0, tuple_shape, "cond_param"));
-  HloInstruction* cond_iter = cond_builder.AddInstruction(
-      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 0));
-  HloInstruction* cond_data = cond_builder.AddInstruction(
-      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 1));
-  // Free cond_param[] (16 bytes), Alloc PRED[] (1 byte)
-  HloInstruction* cond_lt = cond_builder.AddInstruction(
-      HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}),
-                                   HloOpcode::kLt, cond_iter, cond_data));
-  HloComputation* cond_computation =
-      module->AddEmbeddedComputation(cond_builder.Build());
-
-  auto body_builder = HloComputation::Builder("WhileBody");
-  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
-  HloInstruction* body_param = body_builder.AddInstruction(
-      HloInstruction::CreateParameter(0, tuple_shape, "body_param"));
-  HloComputation* body_computation =
-      module->AddEmbeddedComputation(body_builder.Build());
-
-  auto builder = HloComputation::Builder(TestName());
-  // Entry params: 8 bytes (4 bytes per param), TOTAL=8
-  HloInstruction* iter = builder.AddInstruction(
-      HloInstruction::CreateParameter(0, scalar_shape, "param_iter"));
-  HloInstruction* data = builder.AddInstruction(
-      HloInstruction::CreateParameter(1, scalar_shape, "param_data"));
-  // Tuple: 16 bytes (8 bytes per pointer), TOTAL=24
-  HloInstruction* tuple =
-      builder.AddInstruction(HloInstruction::CreateTuple({iter, data}));
-  // While: 8 bytes (4 bytes per element), TOTAL=32
-  // Both cond and body use a max of 24 bytes, TOTAL=56
-  HloInstruction* while_op = builder.AddInstruction(HloInstruction::CreateWhile(
-      tuple_shape, cond_computation, body_computation, tuple));
-  HloComputation* entry_computation =
-      module->AddEntryComputation(builder.Build());
-
-  auto size_fn = [](const LogicalBuffer& buffer) {
-    return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8);
-  };
-
-  SequentialHloOrdering::HloModuleSequence module_sequence;
-  module_sequence[cond_computation] = {cond_param, cond_iter, cond_data,
-                                       cond_lt};
-  module_sequence[body_computation] = {body_param};
-  module_sequence[entry_computation] = {iter, data, tuple, while_op};
-  EXPECT_EQ(56,
-            MinimumMemoryForSequence(module_sequence, size_fn).ValueOrDie());
-}
-
 }  // namespace
-
 }  // namespace xla
 
 int main(int argc, char** argv) {
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
index fb6d8674b6..d19e8034ac 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/service/liveness_util.h"
 #include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/status_macros.h"
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc
new file mode 100644
index 0000000000..f8e05448da
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc
@@ -0,0 +1,388 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
+
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/heap_simulator.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+StatusOr<int64> MinimumMemoryForSequence(
+    const SequentialHloOrdering::HloModuleSequence& module_sequence,
+    const LogicalBuffer::SizeFunction& size_function) {
+  if (module_sequence.empty()) {
+    return 0;
+  }
+
+  const HloModule* module = module_sequence.begin()->first->parent();
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
+                      TuplePointsToAnalysis::Run(module));
+
+  // The absolute minimum memory required for a given sequence of instructions
+  // is determined by the sequence of Alloc and Free calls on a simulated heap,
+  // ignoring fragmentation. We run the heap simulation on the whole module,
+  // rather than summing each computation, since it gives us a better lower
+  // bound, by minimizing the liveness of sub-computations.
+  TF_ASSIGN_OR_RETURN(
+      HeapSimulator::Result result,
+      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), *module,
+                         module_sequence, *points_to_analysis, size_function));
+  return result.heap_size;
+}
+
+namespace {
+
+// Class implementing a list scheduler of HLO instructions which produces a
+// sequence which minimizes memory usage.
+class ListScheduler {
+ public:
+  // Construct and return a memory-minimizing sequence of HLO instructions
+  // containing the given HLO computation.
+  static StatusOr<std::vector<const HloInstruction*>> Run(
+      const HloComputation& computation,
+      const TuplePointsToAnalysis& points_to_analysis,
+      const LogicalBuffer::SizeFunction& size_function) {
+    ListScheduler scheduler(computation, points_to_analysis, size_function);
+    return scheduler.CreateSchedule();
+  }
+
+ private:
+  // The scheduling priority of an instruction is first the number of bytes
+  // freed by scheduling the instruction, and second (tie-breaker) by the number
+  // of users. This is represented as a std::pair containing these two values
+  // (first element is the bytes freed). std::pair provides the necessary
+  // comparison operators.
+  using Priority = std::pair<int64, int64>;
+
+  ListScheduler(const HloComputation& computation,
+                const TuplePointsToAnalysis& points_to_analysis,
+                const LogicalBuffer::SizeFunction& size_function)
+      : computation_(computation),
+        points_to_analysis_(points_to_analysis),
+        size_function_(size_function) {
+    // Create a map containing the LogicalBuffer uses for each HLO
+    // instruction. An HLO instruction "uses" a LogicalBuffer if the
+    // LogicalBuffer is in an operand of the instruction as indicated by
+    // points-to analysis.
+    for (auto& instruction : computation.instructions()) {
+      buffer_uses_.insert(
+          {instruction.get(), std::unordered_set<const LogicalBuffer*>()});
+      for (auto* operand : instruction->operands()) {
+        for (const LogicalBuffer* buffer :
+             points_to_analysis.GetBuffersDefinedByInstruction(operand)) {
+          buffer_uses_[instruction.get()].insert(buffer);
+        }
+      }
+    }
+
+    // Create map containing the number of unscheduled uses (hlo instructions)
+    // of each logical buffer.
+    for (auto& instruction : computation.instructions()) {
+      for (auto* buffer : points_to_analysis.GetBuffersDefinedByInstruction(
+               instruction.get())) {
+        unscheduled_use_count_[buffer] = 0;
+      }
+    }
+    for (auto& instruction : computation.instructions()) {
+      for (const LogicalBuffer* buffer : buffer_uses_.at(instruction.get())) {
+        ++unscheduled_use_count_[buffer];
+      }
+    }
+
+    // Buffers live out of the computation have an implicit use at the end of
+    // the computation.
+    for (const LogicalBuffer* live_out_buffer :
+         points_to_analysis.GetPointsToSet(computation.root_instruction())
+             .CreateFlattenedSet()) {
+      ++unscheduled_use_count_[live_out_buffer];
+    }
+  }
+
+  // Returns whether the memory used by the given buffer should be ignored by
+  // the scheduling heuristic.
+  bool IgnoreBuffer(const LogicalBuffer& buffer) {
+    return buffer.instruction()->opcode() == HloOpcode::kParameter ||
+           buffer.instruction()->opcode() == HloOpcode::kConstant;
+  }
+
+  // Return the number of bytes freed if the HLO instruction is scheduled.
+  int64 BytesFreedIfScheduled(const HloInstruction* instruction) {
+    int64 freed_bytes = 0;
+    // Sum the total size of the values last used by this instruction.
+    for (auto* buffer : buffer_uses_.at(instruction)) {
+      if (IgnoreBuffer(*buffer)) {
+        continue;
+      }
+      CHECK_GE(unscheduled_use_count_.at(buffer), 1);
+      if (unscheduled_use_count_.at(buffer) == 1) {
+        // This is the last use of the logical buffer.
+        freed_bytes += size_function_(*buffer);
+      }
+    }
+    // Then subtract the size of the value(s) defined by this instruction.
+    for (auto* buffer :
+         points_to_analysis_.GetBuffersDefinedByInstruction(instruction)) {
+      if (!IgnoreBuffer(*buffer)) {
+        freed_bytes -= size_function_(*buffer);
+      }
+    }
+    return freed_bytes;
+  }
+
+  // Construct the scheduling priority of the given instruction.
+  Priority GetPriority(const HloInstruction* instruction) {
+    return {BytesFreedIfScheduled(instruction), instruction->user_count()};
+  }
+
+  std::vector<const HloInstruction*> CreateSchedule() {
+    std::vector<const HloInstruction*> schedule;
+
+    // Populate the ready list with instructions which have no operands or
+    // control predecessors.
+    std::unordered_map<const HloInstruction*, int64> unscheduled_pred_count;
+    std::list<const HloInstruction*> ready_list;
+    for (auto& instruction : computation_.instructions()) {
+      // TODO(b/34466113): Replace this and above with successors() or
+      // predecessors() when these methods are added to HloInstruction.
+      for (const HloInstruction* user : instruction->users()) {
+        unscheduled_pred_count[user]++;
+      }
+      for (const HloInstruction* succ : instruction->control_successors()) {
+        unscheduled_pred_count[succ]++;
+      }
+    }
+    for (auto& instruction : computation_.instructions()) {
+      // Instruction with no operands or control predecessors will
+      // not be in the map.
+      if (unscheduled_pred_count.count(instruction.get()) == 0) {
+        ready_list.push_back(instruction.get());
+      }
+    }
+
+    while (!ready_list.empty()) {
+      // Select the highest priority HLO instruction from the ready list.
+      auto best_it = ready_list.begin();
+      Priority best_priority = GetPriority(*best_it);
+      for (auto ready_it = std::next(ready_list.begin());
+           ready_it != ready_list.end(); ++ready_it) {
+        Priority priority = GetPriority(*ready_it);
+        if (priority > best_priority) {
+          best_it = ready_it;
+          best_priority = priority;
+        }
+      }
+
+      // Remove the selected instruction from the ready list and add it to the
+      // schedule.
+      const HloInstruction* best = *best_it;
+      ready_list.erase(best_it);
+      schedule.push_back(best);
+      scheduled_instructions_.insert(best);
+
+      // Update the unscheduled uses of the logical buffers.
+      for (const LogicalBuffer* buffer : buffer_uses_.at(best)) {
+        CHECK_GT(unscheduled_use_count_.at(buffer), 0);
+        --unscheduled_use_count_[buffer];
+      }
+
+      // Add new instructions to ready list.
+      auto update_pred_count = [&unscheduled_pred_count,
+                                &ready_list](HloInstruction* inst) {
+        int64 pred_count = --unscheduled_pred_count.at(inst);
+        CHECK_GE(pred_count, 0);
+        if (pred_count == 0) {
+          ready_list.push_back(inst);
+        }
+      };
+      // TODO(b/34466113): Replace this and above with successors() or
+      // predecessors() when these methods are added to HloInstruction.
+      for (HloInstruction* user : best->users()) {
+        update_pred_count(user);
+      }
+      for (HloInstruction* succ : best->control_successors()) {
+        update_pred_count(succ);
+      }
+    }
+    CHECK_EQ(schedule.size(), computation_.instructions().size());
+    CHECK_EQ(scheduled_instructions_.size(),
+             computation_.instructions().size());
+
+    return schedule;
+  }
+
+  const HloComputation& computation_;
+  const TuplePointsToAnalysis& points_to_analysis_;
+  const LogicalBuffer::SizeFunction& size_function_;
+
+  // A map containing the LogicalBuffers that each instruction uses.
+  std::unordered_map<const HloInstruction*,
+                     std::unordered_set<const LogicalBuffer*>>
+      buffer_uses_;
+
+  // A map containing the count of unscheduled HLOs which using a particular
+  // LogicalBuffer.
+  std::unordered_map<const LogicalBuffer*, int64> unscheduled_use_count_;
+
+  // Set of instructions which have been scheduled.
+  std::unordered_set<const HloInstruction*> scheduled_instructions_;
+};
+
+int64 SumLogicalBufferSizes(const std::vector<const LogicalBuffer*>& buffers,
+                            const LogicalBuffer::SizeFunction& size_function) {
+  int64 size = 0;
+  for (const LogicalBuffer* buffer : buffers) {
+    size += size_function(*buffer);
+  }
+  return size;
+}
+
+StatusOr<std::vector<const HloInstruction*>> RunDFSMemoryScheduler(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  // This ordering is based on DFS post-order, with a heuristic to decide which
+  // operand to visit first.  The heuristic is based on 'extra_users', which is
+  // simply users-1 for each instruction.  By subtracting 1, we're saying that
+  // instructions with no users or a single user don't count; instructions with
+  // lots of fan-out will be visited earlier.
+  tensorflow::gtl::FlatMap<const HloInstruction*, int64> extra_users;
+  tensorflow::gtl::FlatMap<const HloInstruction*, int64> total_sizes;
+  for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) {
+    extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1;
+    total_sizes[hlo] = SumLogicalBufferSizes(
+        points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function);
+    tensorflow::gtl::FlatSet<const HloInstruction*> unique_operands(
+        hlo->operands().begin(), hlo->operands().end());
+    for (const HloInstruction* operand : unique_operands) {
+      extra_users[hlo] += extra_users[operand];
+      total_sizes[hlo] += total_sizes[operand];
+    }
+  }
+  CHECK_EQ(extra_users.size(), computation.instructions().size());
+  CHECK_EQ(total_sizes.size(), computation.instructions().size());
+
+  // Construct a total order based on DFS post-order, visiting operands in
+  // decreasing cumulative extra user order, and next by cumulative size, with a
+  // tiebreaker by name for determinism.
+  std::vector<const HloInstruction*> sequence;
+  FunctionVisitor visitor([&sequence](HloInstruction* hlo) {
+    sequence.push_back(hlo);
+    return Status::OK();
+  });
+  TF_RETURN_IF_ERROR(computation.AcceptWithOperandOrder(
+      &visitor, [&extra_users, &total_sizes](const HloInstruction* a,
+                                             const HloInstruction* b) {
+        if (extra_users[a] != extra_users[b]) {
+          return extra_users[a] > extra_users[b];
+        }
+        if (total_sizes[a] != total_sizes[b]) {
+          return total_sizes[a] > total_sizes[b];
+        }
+        return a->name() < b->name();
+      }));
+  CHECK_EQ(sequence.size(), computation.instructions().size());
+  return sequence;
+}
+
+StatusOr<int64> MinimumMemoryForComputation(
+    const HloComputation& computation,
+    const std::vector<const HloInstruction*>& sequence,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  TF_ASSIGN_OR_RETURN(
+      HeapSimulator::Result result,
+      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), computation,
+                         sequence, points_to_analysis, size_function));
+  return result.heap_size;
+}
+
+StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  // We try both a list-scheduler based ordering and a DFS based ordering, and
+  // choose whichever returns a lower min-memory, not accounting for
+  // fragmentation.
+  //
+  // Note that this is just a heuristic. One obvious inaccuracy is that the
+  // memory required for sub-computations might be different when considered
+  // within the caller's context. But it's good enough for now.
+  TF_ASSIGN_OR_RETURN(
+      std::vector<const HloInstruction*> list_sequence,
+      ListScheduler::Run(computation, points_to_analysis, size_function));
+  TF_ASSIGN_OR_RETURN(
+      const int64 list_memory,
+      MinimumMemoryForComputation(computation, list_sequence,
+                                  points_to_analysis, size_function));
+  VLOG(2) << "Min-memory list sequence: " << list_memory << " bytes";
+
+  TF_ASSIGN_OR_RETURN(
+      std::vector<const HloInstruction*> dfs_sequence,
+      RunDFSMemoryScheduler(computation, points_to_analysis, size_function));
+  TF_ASSIGN_OR_RETURN(
+      const int64 dfs_memory,
+      MinimumMemoryForComputation(computation, dfs_sequence, points_to_analysis,
+                                  size_function));
+  VLOG(2) << "Min-memory dfs sequence: " << dfs_memory << " bytes";
+
+  if (list_memory <= dfs_memory) {
+    VLOG(2) << "Chose min-memory list sequence: " << list_memory << " bytes";
+    return list_sequence;
+  } else {
+    VLOG(2) << "Chose min-memory dfs sequence: " << dfs_memory << " bytes";
+    return dfs_sequence;
+  }
+}
+
+}  // namespace
+
+StatusOr<SequentialHloOrdering::HloModuleSequence>
+CreateMemoryMinimizingSequence(
+    const HloModule& module, const LogicalBuffer::SizeFunction& size_function) {
+  SequentialHloOrdering::HloModuleSequence sequence;
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
+                      TuplePointsToAnalysis::Run(&module));
+  for (const auto& computation : module.computations()) {
+    TF_ASSIGN_OR_RETURN(sequence[computation.get()],
+                        CreateMemoryMinimizingSequence(
+                            *computation, *points_to_analysis, size_function));
+  }
+  return sequence;
+}
+
+StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
+    const HloComputation& computation,
+    const LogicalBuffer::SizeFunction& size_function) {
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
+                      TuplePointsToAnalysis::Run(computation.parent()));
+  return CreateMemoryMinimizingSequence(computation, *points_to_analysis,
+                                        size_function);
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.h b/tensorflow/compiler/xla/service/hlo_scheduling.h
new file mode 100644
index 0000000000..ec92a56b96
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.h
@@ -0,0 +1,50 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULING_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULING_H_
+
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/service/logical_buffer.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+
+namespace xla {
+
+// Returns the minimum memory required to compute the given module sequence,
+// assuming no fragmentation.
+StatusOr<int64> MinimumMemoryForSequence(
+    const SequentialHloOrdering::HloModuleSequence& module_sequence,
+    const LogicalBuffer::SizeFunction& size_function);
+
+// Returns an HloModuleSequence which seeks to minimize the memory required for
+// the computation. size_function is the function returning the number of bytes
+// required for a LogicalBuffer.
+StatusOr<SequentialHloOrdering::HloModuleSequence>
+CreateMemoryMinimizingSequence(
+    const HloModule& module, const LogicalBuffer::SizeFunction& size_function);
+
+// Overload of above that computes the sequence for a single computation.
+StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
+    const HloComputation& computation,
+    const LogicalBuffer::SizeFunction& size_function);
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULING_H_
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc
new file mode 100644
index 0000000000..d09d22ee40
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc
@@ -0,0 +1,97 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
+
+#include <memory>
+#include <string>
+
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+
+namespace xla {
+namespace {
+
+class MinimumMemoryForSequenceTest : public HloTestBase {};
+
+TEST_F(MinimumMemoryForSequenceTest, MultiComputation) {
+  auto module = CreateNewModule();
+  const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {});
+  const Shape tuple_shape =
+      ShapeUtil::MakeTupleShape({scalar_shape, scalar_shape});
+
+  auto cond_builder = HloComputation::Builder("WhileCond");
+  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
+  HloInstruction* cond_param = cond_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple_shape, "cond_param"));
+  HloInstruction* cond_iter = cond_builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 0));
+  HloInstruction* cond_data = cond_builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 1));
+  // Free cond_param[] (16 bytes), Alloc PRED[] (1 byte)
+  HloInstruction* cond_lt = cond_builder.AddInstruction(
+      HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}),
+                                   HloOpcode::kLt, cond_iter, cond_data));
+  HloComputation* cond_computation =
+      module->AddEmbeddedComputation(cond_builder.Build());
+
+  auto body_builder = HloComputation::Builder("WhileBody");
+  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
+  HloInstruction* body_param = body_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple_shape, "body_param"));
+  HloComputation* body_computation =
+      module->AddEmbeddedComputation(body_builder.Build());
+
+  auto builder = HloComputation::Builder(TestName());
+  // Entry params: 8 bytes (4 bytes per param), TOTAL=8
+  HloInstruction* iter = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, scalar_shape, "param_iter"));
+  HloInstruction* data = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, scalar_shape, "param_data"));
+  // Tuple: 16 bytes (8 bytes per pointer), TOTAL=24
+  HloInstruction* tuple =
+      builder.AddInstruction(HloInstruction::CreateTuple({iter, data}));
+  // While: 8 bytes (4 bytes per element), TOTAL=32
+  // Both cond and body use a max of 24 bytes, TOTAL=56
+  HloInstruction* while_op = builder.AddInstruction(HloInstruction::CreateWhile(
+      tuple_shape, cond_computation, body_computation, tuple));
+  HloComputation* entry_computation =
+      module->AddEntryComputation(builder.Build());
+
+  auto size_fn = [](const LogicalBuffer& buffer) {
+    return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8);
+  };
+
+  SequentialHloOrdering::HloModuleSequence module_sequence;
+  module_sequence[cond_computation] = {cond_param, cond_iter, cond_data,
+                                       cond_lt};
+  module_sequence[body_computation] = {body_param};
+  module_sequence[entry_computation] = {iter, data, tuple, while_op};
+  EXPECT_EQ(56,
+            MinimumMemoryForSequence(module_sequence, size_fn).ValueOrDie());
+}
+
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  return xla::ParseDebugOptionsFlagsAndRunTests(argc, argv);
+}
-- 
GitLab


From 15a8df724a9a426803a8e67d228eca683bd972a6 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Jun 2017 13:22:28 -0700
Subject: [PATCH 0306/1375] Fix mac build clone from meheff's change: [XLA]
 Change return type of DeviceAssignment::Deserialize to fix build breakage on
 mac. The mac build had the following error:

error: incomplete type 'xla::DeviceAssignment' used in type trait
expression

This was due to a static method returning a StatusOr<DeviceAssignment>
inside of the definition of DeviceAssignment.
---
 tensorflow/compiler/xla/service/computation_placer.cc | 9 +++++----
 tensorflow/compiler/xla/service/computation_placer.h  | 6 +++++-
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/service/computation_placer.cc b/tensorflow/compiler/xla/service/computation_placer.cc
index cdf277581f..cdfa30dd9a 100644
--- a/tensorflow/compiler/xla/service/computation_placer.cc
+++ b/tensorflow/compiler/xla/service/computation_placer.cc
@@ -49,17 +49,18 @@ Status DeviceAssignment::Serialize(DeviceAssignmentProto* proto) const {
   return Status::OK();
 }
 
-/* static */ StatusOr<DeviceAssignment> DeviceAssignment::Deserialize(
-    const DeviceAssignmentProto& proto) {
+/* static */ StatusOr<std::unique_ptr<DeviceAssignment>>
+DeviceAssignment::Deserialize(const DeviceAssignmentProto& proto) {
   TF_RET_CHECK(proto.computation_devices_size() == proto.computation_count());
-  DeviceAssignment assignment(proto.replica_count(), proto.computation_count());
+  auto assignment = MakeUnique<DeviceAssignment>(proto.replica_count(),
+                                                 proto.computation_count());
   for (int computation = 0; computation < proto.computation_count();
        ++computation) {
     const auto& computation_device = proto.computation_devices(computation);
     TF_RET_CHECK(computation_device.replica_device_ids_size() ==
                  proto.replica_count());
     for (int replica = 0; replica < proto.replica_count(); ++replica) {
-      assignment(replica, computation) =
+      (*assignment)(replica, computation) =
           computation_device.replica_device_ids(replica);
     }
   }
diff --git a/tensorflow/compiler/xla/service/computation_placer.h b/tensorflow/compiler/xla/service/computation_placer.h
index 4d26d6bb85..7d9abcd100 100644
--- a/tensorflow/compiler/xla/service/computation_placer.h
+++ b/tensorflow/compiler/xla/service/computation_placer.h
@@ -49,7 +49,11 @@ class DeviceAssignment : public Array2D<int> {
 
   // Protocol buffer serialization and deserialization.
   Status Serialize(DeviceAssignmentProto* proto) const;
-  static StatusOr<DeviceAssignment> Deserialize(
+
+  // Return a std::unique_ptr<DeviceAssignment> instead of a DeviceAssignment
+  // directly because one of the supported TF platforms (mac) does not compile
+  // due to a StatusOr of an incomplete type (DeviceAssignment).
+  static StatusOr<std::unique_ptr<DeviceAssignment>> Deserialize(
       const DeviceAssignmentProto& proto);
 };
 
-- 
GitLab


From 40f81fb2a9740289f37f919383880c493077b9fc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 13:46:58 -0700
Subject: [PATCH 0307/1375] [XLA:HLO] Split Hlo{Value,Buffer} out of
 Hlo{Dataflow,Alias}Analysis.

This will make dependencies cleaner for upcoming CLs that will convert
HeapSimulator and HloOrdering to use the new analyses.

No change in functionality.

PiperOrigin-RevId: 159737265
---
 tensorflow/compiler/xla/service/BUILD         |  50 ++-
 .../xla/service/hlo_alias_analysis.cc         | 107 +------
 .../compiler/xla/service/hlo_alias_analysis.h | 163 +---------
 tensorflow/compiler/xla/service/hlo_buffer.cc | 139 ++++++++
 tensorflow/compiler/xla/service/hlo_buffer.h  | 183 +++++++++++
 .../xla/service/hlo_dataflow_analysis.cc      | 265 ---------------
 .../xla/service/hlo_dataflow_analysis.h       | 215 +------------
 tensorflow/compiler/xla/service/hlo_value.cc  | 301 ++++++++++++++++++
 tensorflow/compiler/xla/service/hlo_value.h   | 242 ++++++++++++++
 9 files changed, 913 insertions(+), 752 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/hlo_buffer.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_buffer.h
 create mode 100644 tensorflow/compiler/xla/service/hlo_value.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_value.h

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 150cd8a678..427b382211 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1229,6 +1229,23 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "hlo_value",
+    srcs = ["hlo_value.cc"],
+    hdrs = ["hlo_value.h"],
+    deps = [
+        ":hlo",
+        "//tensorflow/compiler/xla:shape_tree",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_library(
     name = "hlo_dataflow_analysis",
     srcs = [
@@ -1241,8 +1258,8 @@ cc_library(
         ":call_graph",
         ":hlo",
         ":hlo_ordering",
+        ":hlo_value",
         ":liveness_util",
-        "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status",
         "//tensorflow/compiler/xla:statusor",
@@ -1250,7 +1267,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
     ],
 )
 
@@ -1277,20 +1293,34 @@ cc_test(
 )
 
 cc_library(
-    name = "hlo_alias_analysis",
-    srcs = [
-        "hlo_alias_analysis.cc",
-    ],
-    hdrs = [
-        "hlo_alias_analysis.h",
+    name = "hlo_buffer",
+    srcs = ["hlo_buffer.cc"],
+    hdrs = ["hlo_buffer.h"],
+    deps = [
+        ":hlo",
+        ":hlo_value",
+        "//tensorflow/compiler/xla:shape_tree",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
     ],
+)
+
+cc_library(
+    name = "hlo_alias_analysis",
+    srcs = ["hlo_alias_analysis.cc"],
+    hdrs = ["hlo_alias_analysis.h"],
     deps = [
         ":call_graph",
         ":hlo",
+        ":hlo_buffer",
         ":hlo_dataflow_analysis",
-        ":logical_buffer",
-        "//tensorflow/compiler/xla:shape_tree",
+        ":hlo_value",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
index 3b37f4a4b8..3c5f0e19f1 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
@@ -15,21 +15,21 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_alias_analysis.h"
 
-#include <ostream>
-#include <queue>
+#include <algorithm>
+#include <memory>
 #include <utility>
 #include <vector>
 
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
+#include "tensorflow/compiler/xla/service/hlo_buffer.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_value.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -38,105 +38,6 @@ using ::tensorflow::str_util::Join;
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
-void HloBuffer::AddValue(const HloValue& value) {
-  // If the value is already contained in this buffer, just return.
-  if (std::find(value_ids_.begin(), value_ids_.end(), value.id()) !=
-      value_ids_.end()) {
-    return;
-  }
-
-  value_ids_.push_back(value.id());
-
-  // Add all of the locations of the HloValue to this buffer.
-  for (const HloLocation& location : value.locations()) {
-    if (std::find(locations_.begin(), locations_.end(), location) ==
-        locations_.end()) {
-      locations_.push_back(location);
-    }
-  }
-}
-
-bool HloBuffer::operator==(const HloBuffer& other) const {
-  bool equal = id() == other.id();
-  if (equal) {
-    // DCHECK because these comparisons are expensive (linear time).
-    DCHECK(value_ids() == other.value_ids());
-    DCHECK(locations() == other.locations());
-  }
-  return equal;
-}
-
-string HloBuffer::ToString() const {
-  return StrCat("HloBuffer ", id_, ", values: ", Join(value_ids_, ", "));
-}
-
-std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer) {
-  out << buffer.ToString();
-  return out;
-}
-
-void HloBufferSet::AddBuffer(HloBuffer::Id buffer_id) {
-  if (std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id) ==
-      buffer_ids_.end()) {
-    buffer_ids_.push_back(buffer_id);
-  }
-}
-
-void HloBufferSet::RemoveBufferOrDie(HloBuffer::Id buffer_id) {
-  auto it = std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id);
-  CHECK(it != buffer_ids_.end());
-  buffer_ids_.erase(it);
-}
-
-string HloBufferSet::ToString() const {
-  return StrCat("HloBufferSet, buffers: ", Join(buffer_ids_, ", "));
-}
-
-std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set) {
-  out << buffer_set.ToString();
-  return out;
-}
-
-bool InstructionBufferSet::IsAmbiguous() const {
-  bool is_ambiguous = false;
-  ForEachElement(
-      [&is_ambiguous](const ShapeIndex& index, const HloBufferSet& buffer_set) {
-        is_ambiguous |= buffer_set.buffer_ids().size() > 1;
-      });
-  return is_ambiguous;
-}
-
-bool InstructionBufferSet::IsDistinct() const {
-  bool is_distinct = true;
-  tensorflow::gtl::FlatSet<HloBuffer::Id> seen_ids;
-  ForEachElement([&is_distinct, &seen_ids](const ShapeIndex& index,
-                                           const HloBufferSet& buffer_set) {
-    for (HloBuffer::Id buffer_id : buffer_set.buffer_ids()) {
-      auto pair = seen_ids.insert(buffer_id);
-      if (!pair.second) {
-        is_distinct = false;
-      }
-    }
-  });
-  return is_distinct;
-}
-
-string InstructionBufferSet::ToString() const {
-  string out =
-      StrCat("InstructionBufferSet(", ShapeUtil::HumanString(shape()), ")\n");
-  ForEachElement([this, &out](const ShapeIndex& index,
-                              const HloBufferSet& value_set) {
-    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
-  });
-  return out;
-}
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionBufferSet& buffer_set) {
-  out << buffer_set.ToString();
-  return out;
-}
-
 HloAliasAnalysis::HloAliasAnalysis(HloModule* module) : module_(module) {}
 
 void HloAliasAnalysis::InitializeBufferSets() {
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.h b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
index 0fa35827b5..429cfa0915 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
@@ -16,182 +16,23 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_ALIAS_ANALYSIS_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_ALIAS_ANALYSIS_H_
 
-#include <stddef.h>
-#include <iosfwd>
 #include <memory>
-#include <set>
 #include <string>
 #include <vector>
 
-#include "tensorflow/compiler/xla/service/call_graph.h"
-#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
+#include "tensorflow/compiler/xla/service/hlo_buffer.h"
 #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/logical_buffer.h"
-#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-#include "tensorflow/core/lib/gtl/flatmap.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/types.h"
 
 namespace xla {
 
-// A container which can hold one or more HloValues. An HLO buffer abstractly
-// represents the allocation which HLO instructions write into and read
-// from. Generally there is a one-to-one correspondence between HloBuffers and
-// HloValue where each HloValue in the module is held in a unique HloBuffer. An
-// exception is the while instruction which updates the loop state in-place. In
-// this case, we have a single HloBuffer for each HloLocation in the loop state,
-// but multiple HloValues. For example:
-//
-//   %init = ...
-//   %while = While(%init, body, condition)
-//
-//  body:
-//   %body_param = Param(0)
-//     ...
-//   %body_root = ...
-//
-//  condition:
-//   %cond_param = Param(0)
-//     ...
-//
-// For simplicity, assume that %while is array-shaped. In this case, we have a
-// single HloBuffer which holds the following HloValues: HloValue{%init},
-// HloValue{%while}, HloValue{%body_param}, HloValue{%body_root}, and
-// HloValue{%cond_param}.
-//
-// HloBuffers may appear at different HloLocations in the module mirroring the
-// same propery of HloValues. For example:
-//
-//   %sub = Sub(...)
-//   %add = Add(...)
-//   %tuple = Tuple(%add, %sub)
-//   %gte = GetTupleElement(%tuple, 0)
-//
-// In this case, the HloBuffer containing %add appears at the following
-// locations: HloLocation{%add, {}}, HloLocation{%tuple, {0}}, and
-// HloLocation{%gte, {}}.
-//
-// Different HloLocations which share the same HloBuffer indicate mandatory
-// aliasing in the HLO module. These locations must share the same memory
-// allocation for correctness (the backends rely on this property). This differs
-// from incidental aliasing introduced by memory reuse in BufferAssignment where
-// different instructions may happen to get the same allocation.
-class HloBuffer {
- public:
-  using Id = int64;
-
-  HloBuffer(int64 id) : id_(id) {}
-
-  // Return the unique identifier for this HloBuffer.
-  int64 id() const { return id_; }
-
-  // Add a value to the set of values held by this buffer. Also adds the
-  // HloLocations of the value to the locations vector of the buffer. If the
-  // buffer already contains this value, then this method is a nop.
-  void AddValue(const HloValue& value);
-
-  // Return the IDs of all values contained in this buffer.
-  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
-
-  // Return the locations (output of which instruction and at what index) where
-  // the buffer is used. This is exactly the union of the locations of the
-  // HloValues contained by the buffer.
-  const std::vector<HloLocation>& locations() const { return locations_; }
-
-  string ToString() const;
-
-  bool operator==(const HloBuffer& other) const;
-  bool operator!=(const HloBuffer& other) const { return !(*this == other); }
-
- private:
-  // Unique identifier for this HloBuffer.
-  const Id id_;
-
-  // The set of values contained in the this buffer.
-  std::vector<HloValue::Id> value_ids_;
-
-  // The set of locations where this buffer is used.
-  std::vector<HloLocation> locations_;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer);
-
-// A class representing the set of possible HloBuffers at a particular
-// HloLocation (shape index in the output of an instruction) in the XLA
-// graph. In most cases, the buffer set will have a single HloBuffer indicating
-// that the HloBuffer which appears at that particular location is known
-// unambiguously at compile-time.  However, tuple-shaped Select instructions can
-// introduce ambiguity as the tuple elements of the operands are passed by
-// reference into the output of the Select. For example:
-//
-//   %pred = ...
-//   %tuple0 = Tuple(%a, %b)
-//   %tuple1 = Tuple(%x, %y)
-//   %select = Select(%pred, %tuple0, %tuple1)
-//
-// In this case the HloBufferSet at HloLocation{%select, {0}} contains the
-// HloBuffer holding %a and the HloBuffer holding %x.
-class HloBufferSet {
- public:
-  HloBufferSet() = default;
-
-  // Add the given buffer to this buffer set. If the buffer already exists in
-  // the set, then this is a NOP.
-  void AddBuffer(HloBuffer::Id buffer_id);
-
-  // Removes the given buffer from this buffer set. CHECK fails in the buffer is
-  // not contained in this set.
-  void RemoveBufferOrDie(HloBuffer::Id buffer_id);
-
-  // Returns the unique buffer in this set. CHECK fails if the set does not
-  // contain exactly one buffer.
-  HloBuffer::Id GetUniqueBufferId() const {
-    CHECK_EQ(buffer_ids().size(), 1);
-    return buffer_ids()[0];
-  }
-
-  // Returns the IDs of the HloBuffers contained in this buffer set.
-  const std::vector<HloBuffer::Id>& buffer_ids() const { return buffer_ids_; }
-
-  string ToString() const;
-
- private:
-  // The IDs of the HloBuffers containted in this buffer set.
-  std::vector<HloBuffer::Id> buffer_ids_;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set);
-
-// A class collecting the HloBuffers in the output of an HLO instruction. For
-// array-shaped instructions, an InstructionBufferSet trivially holds a single
-// HloBufferSet. Tuple-shaped InstructionBufferSets hold multiple
-// HloBufferSets.
-class InstructionBufferSet : public ShapeTree<HloBufferSet> {
- public:
-  InstructionBufferSet(const Shape& shape) : ShapeTree<HloBufferSet>(shape) {}
-
-  // Returns true if any HloBufferSet contained in this InstructionBufferSet
-  // is not a singleton.
-  bool IsAmbiguous() const;
-
-  // Returns true if any HloBuffer appears in more than one HloBufferSet
-  // contained in this InstructionBufferSet.
-  bool IsDistinct() const;
-
-  string ToString() const;
-};
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionBufferSet& buffer_set);
-
 class HloAliasAnalysis {
  public:
   static StatusOr<std::unique_ptr<HloAliasAnalysis>> Run(HloModule* module);
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.cc b/tensorflow/compiler/xla/service/hlo_buffer.cc
new file mode 100644
index 0000000000..f289f56966
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_buffer.cc
@@ -0,0 +1,139 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_buffer.h"
+
+#include <algorithm>
+#include <ostream>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+using ::tensorflow::str_util::Join;
+using ::tensorflow::strings::StrAppend;
+using ::tensorflow::strings::StrCat;
+
+void HloBuffer::AddValue(const HloValue& value) {
+  // If the value is already contained in this buffer, just return.
+  if (std::find(value_ids_.begin(), value_ids_.end(), value.id()) !=
+      value_ids_.end()) {
+    return;
+  }
+
+  value_ids_.push_back(value.id());
+
+  // Add all of the locations of the HloValue to this buffer.
+  for (const HloLocation& location : value.locations()) {
+    if (std::find(locations_.begin(), locations_.end(), location) ==
+        locations_.end()) {
+      locations_.push_back(location);
+    }
+  }
+}
+
+bool HloBuffer::operator==(const HloBuffer& other) const {
+  bool equal = id() == other.id();
+  if (equal) {
+    // DCHECK because these comparisons are expensive (linear time).
+    DCHECK(value_ids() == other.value_ids());
+    DCHECK(locations() == other.locations());
+  }
+  return equal;
+}
+
+string HloBuffer::ToString() const {
+  return StrCat("HloBuffer ", id_, ", values: ", Join(value_ids_, ", "));
+}
+
+std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer) {
+  out << buffer.ToString();
+  return out;
+}
+
+void HloBufferSet::AddBuffer(HloBuffer::Id buffer_id) {
+  if (std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id) ==
+      buffer_ids_.end()) {
+    buffer_ids_.push_back(buffer_id);
+  }
+}
+
+void HloBufferSet::RemoveBufferOrDie(HloBuffer::Id buffer_id) {
+  auto it = std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id);
+  CHECK(it != buffer_ids_.end());
+  buffer_ids_.erase(it);
+}
+
+string HloBufferSet::ToString() const {
+  return StrCat("HloBufferSet, buffers: ", Join(buffer_ids_, ", "));
+}
+
+std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set) {
+  out << buffer_set.ToString();
+  return out;
+}
+
+bool InstructionBufferSet::IsAmbiguous() const {
+  bool is_ambiguous = false;
+  ForEachElement(
+      [&is_ambiguous](const ShapeIndex& index, const HloBufferSet& buffer_set) {
+        is_ambiguous |= buffer_set.buffer_ids().size() > 1;
+      });
+  return is_ambiguous;
+}
+
+bool InstructionBufferSet::IsDistinct() const {
+  bool is_distinct = true;
+  tensorflow::gtl::FlatSet<HloBuffer::Id> seen_ids;
+  ForEachElement([&is_distinct, &seen_ids](const ShapeIndex& index,
+                                           const HloBufferSet& buffer_set) {
+    for (HloBuffer::Id buffer_id : buffer_set.buffer_ids()) {
+      auto pair = seen_ids.insert(buffer_id);
+      if (!pair.second) {
+        is_distinct = false;
+      }
+    }
+  });
+  return is_distinct;
+}
+
+string InstructionBufferSet::ToString() const {
+  string out =
+      StrCat("InstructionBufferSet(", ShapeUtil::HumanString(shape()), ")\n");
+  ForEachElement([this, &out](const ShapeIndex& index,
+                              const HloBufferSet& value_set) {
+    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
+  });
+  return out;
+}
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionBufferSet& buffer_set) {
+  out << buffer_set.ToString();
+  return out;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.h b/tensorflow/compiler/xla/service/hlo_buffer.h
new file mode 100644
index 0000000000..238d29a6d6
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_buffer.h
@@ -0,0 +1,183 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_BUFFER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_BUFFER_H_
+
+#include <ostream>
+#include <string>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/hlo_value.h"
+#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+
+// A container which can hold one or more HloValues. An HLO buffer abstractly
+// represents the allocation which HLO instructions write into and read
+// from. Generally there is a one-to-one correspondence between HloBuffers and
+// HloValue where each HloValue in the module is held in a unique HloBuffer. An
+// exception is the while instruction which updates the loop state in-place. In
+// this case, we have a single HloBuffer for each HloLocation in the loop state,
+// but multiple HloValues. For example:
+//
+//   %init = ...
+//   %while = While(%init, body, condition)
+//
+//  body:
+//   %body_param = Param(0)
+//     ...
+//   %body_root = ...
+//
+//  condition:
+//   %cond_param = Param(0)
+//     ...
+//
+// For simplicity, assume that %while is array-shaped. In this case, we have a
+// single HloBuffer which holds the following HloValues: HloValue{%init},
+// HloValue{%while}, HloValue{%body_param}, HloValue{%body_root}, and
+// HloValue{%cond_param}.
+//
+// HloBuffers may appear at different HloLocations in the module mirroring the
+// same propery of HloValues. For example:
+//
+//   %sub = Sub(...)
+//   %add = Add(...)
+//   %tuple = Tuple(%add, %sub)
+//   %gte = GetTupleElement(%tuple, 0)
+//
+// In this case, the HloBuffer containing %add appears at the following
+// locations: HloLocation{%add, {}}, HloLocation{%tuple, {0}}, and
+// HloLocation{%gte, {}}.
+//
+// Different HloLocations which share the same HloBuffer indicate mandatory
+// aliasing in the HLO module. These locations must share the same memory
+// allocation for correctness (the backends rely on this property). This differs
+// from incidental aliasing introduced by memory reuse in BufferAssignment where
+// different instructions may happen to get the same allocation.
+class HloBuffer {
+ public:
+  using Id = int64;
+
+  HloBuffer(int64 id) : id_(id) {}
+
+  // Return the unique identifier for this HloBuffer.
+  int64 id() const { return id_; }
+
+  // Add a value to the set of values held by this buffer. Also adds the
+  // HloLocations of the value to the locations vector of the buffer. If the
+  // buffer already contains this value, then this method is a nop.
+  void AddValue(const HloValue& value);
+
+  // Return the IDs of all values contained in this buffer.
+  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
+
+  // Return the locations (output of which instruction and at what index) where
+  // the buffer is used. This is exactly the union of the locations of the
+  // HloValues contained by the buffer.
+  const std::vector<HloLocation>& locations() const { return locations_; }
+
+  string ToString() const;
+
+  bool operator==(const HloBuffer& other) const;
+  bool operator!=(const HloBuffer& other) const { return !(*this == other); }
+
+ private:
+  // Unique identifier for this HloBuffer.
+  const Id id_;
+
+  // The set of values contained in the this buffer.
+  std::vector<HloValue::Id> value_ids_;
+
+  // The set of locations where this buffer is used.
+  std::vector<HloLocation> locations_;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer);
+
+// A class representing the set of possible HloBuffers at a particular
+// HloLocation (shape index in the output of an instruction) in the XLA
+// graph. In most cases, the buffer set will have a single HloBuffer indicating
+// that the HloBuffer which appears at that particular location is known
+// unambiguously at compile-time.  However, tuple-shaped Select instructions can
+// introduce ambiguity as the tuple elements of the operands are passed by
+// reference into the output of the Select. For example:
+//
+//   %pred = ...
+//   %tuple0 = Tuple(%a, %b)
+//   %tuple1 = Tuple(%x, %y)
+//   %select = Select(%pred, %tuple0, %tuple1)
+//
+// In this case the HloBufferSet at HloLocation{%select, {0}} contains the
+// HloBuffer holding %a and the HloBuffer holding %x.
+class HloBufferSet {
+ public:
+  HloBufferSet() = default;
+
+  // Add the given buffer to this buffer set. If the buffer already exists in
+  // the set, then this is a NOP.
+  void AddBuffer(HloBuffer::Id buffer_id);
+
+  // Removes the given buffer from this buffer set. CHECK fails in the buffer is
+  // not contained in this set.
+  void RemoveBufferOrDie(HloBuffer::Id buffer_id);
+
+  // Returns the unique buffer in this set. CHECK fails if the set does not
+  // contain exactly one buffer.
+  HloBuffer::Id GetUniqueBufferId() const {
+    CHECK_EQ(buffer_ids().size(), 1);
+    return buffer_ids()[0];
+  }
+
+  // Returns the IDs of the HloBuffers contained in this buffer set.
+  const std::vector<HloBuffer::Id>& buffer_ids() const { return buffer_ids_; }
+
+  string ToString() const;
+
+ private:
+  // The IDs of the HloBuffers containted in this buffer set.
+  std::vector<HloBuffer::Id> buffer_ids_;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set);
+
+// A class collecting the HloBuffers in the output of an HLO instruction. For
+// array-shaped instructions, an InstructionBufferSet trivially holds a single
+// HloBufferSet. Tuple-shaped InstructionBufferSets hold multiple
+// HloBufferSets.
+class InstructionBufferSet : public ShapeTree<HloBufferSet> {
+ public:
+  InstructionBufferSet(const Shape& shape) : ShapeTree<HloBufferSet>(shape) {}
+
+  // Returns true if any HloBufferSet contained in this InstructionBufferSet
+  // is not a singleton.
+  bool IsAmbiguous() const;
+
+  // Returns true if any HloBuffer appears in more than one HloBufferSet
+  // contained in this InstructionBufferSet.
+  bool IsDistinct() const;
+
+  string ToString() const;
+};
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionBufferSet& buffer_set);
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_BUFFER_H_
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
index 7e951721ba..8f372c3420 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
@@ -16,14 +16,11 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h"
 
 #include <algorithm>
-#include <iosfwd>
 #include <queue>
-#include <set>
 #include <vector>
 
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -35,7 +32,6 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -43,267 +39,6 @@ namespace xla {
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
-string HloLocation::ToString() const {
-  string index_str =
-      ShapeUtil::IsTuple(instruction->shape()) ? (" " + index.ToString()) : "";
-  return StrCat(instruction->FullyQualifiedName(), index_str);
-}
-
-std::ostream& operator<<(std::ostream& out, const HloLocation& location) {
-  out << location.ToString();
-  return out;
-}
-
-string HloUse::ToString() const {
-  string index_str =
-      ShapeUtil::IsTuple(instruction->operand(operand_number)->shape())
-          ? (" " + operand_index.ToString())
-          : "";
-  return StrCat(instruction->FullyQualifiedName(), ", operand ", operand_number,
-                index_str);
-}
-
-std::ostream& operator<<(std::ostream& out, const HloUse& use) {
-  out << use.ToString();
-  return out;
-}
-
-HloValue::HloValue(HloValue::Id id, HloInstruction* instruction,
-                   const ShapeIndex& index, bool is_phi)
-    : id_(id), is_phi_(is_phi) {
-  // The defining location is always the first element in the locations_ vector.
-  AddLocation(instruction, index);
-}
-
-bool HloValue::operator==(const HloValue& other) const {
-  bool equal = defining_instruction() == other.defining_instruction() &&
-               defining_index() == other.defining_index();
-  // If the values are equal they most both be phi (or non phi).
-  CHECK(!(equal && is_phi() != other.is_phi()));
-  return equal;
-}
-
-bool HloValue::operator!=(const HloValue& other) const {
-  return !(*this == other);
-}
-
-string HloValue::ToShortString() const {
-  string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
-                         ? defining_index().ToString()
-                         : "";
-  return StrCat(is_phi_ ? "PHI " : "",
-                defining_instruction()->FullyQualifiedName(), index_str);
-}
-
-string HloValue::ToString(int indent) const {
-  string indentation(indent, ' ');
-  string out = StrCat(indentation, ToShortString(), ", locations:\n");
-  for (const HloLocation& location : locations()) {
-    StrAppend(&out, indentation, "  ", location.ToString(), "\n");
-  }
-  StrAppend(&out, indentation, " uses:\n");
-  for (const HloUse& use : uses()) {
-    StrAppend(&out, indentation, "  ", use.ToString(), "\n");
-  }
-  return out;
-}
-
-namespace {
-
-// Returns true if the instruction 'user' may use the value at the given
-// ShapeIndex in the given operand. Generally, instruction which pass through
-// values transparently without reading the value are not considered to use the
-// value.
-bool MayUseOperandValue(int64 operand_number, const ShapeIndex& index,
-                        const HloInstruction* user) {
-  switch (user->opcode()) {
-    case HloOpcode::kGetTupleElement:
-    case HloOpcode::kCopy:
-      // These instructions only access the top-level values of their
-      // operand. Non-top-level (nested) values are passed through
-      // transparently.
-      CHECK_EQ(operand_number, 0);
-      return index.empty();
-    case HloOpcode::kSelect:
-      // Select does not use any nested elements of its selected-from operands
-      // (operand 1 and 2)
-      CHECK_GE(operand_number, 0);
-      CHECK_LE(operand_number, 2);
-      return operand_number == 0 || index.empty();
-
-    case HloOpcode::kCall:
-    case HloOpcode::kTuple:
-      // These instructions always pass through their operands transparently.
-      return false;
-
-    case HloOpcode::kWhile:
-      // Though the while instructions passes through its operands, we return
-      // true because in SSA form there may be a Phi at the parameter of the
-      // while which is considered a use of its incoming value because the Phi
-      // input values are not passed through into the body computation. Because
-      // this function is used in both SSA and non-SSA forms of the analysis
-      // conservatively return true.
-      return true;
-
-    default:
-      return true;
-  }
-}
-
-}  // namespace
-
-void HloValue::AddLocation(HloInstruction* instruction,
-                           const ShapeIndex& index) {
-  // The given location should not already exist in locations_.
-  for (const HloLocation& location : locations_) {
-    DCHECK(!(location.instruction == instruction && location.index == index));
-  }
-
-  locations_.push_back(HloLocation{instruction, index});
-
-  //  Update uses.
-  for (HloInstruction* user : instruction->users()) {
-    for (int64 operand_number : user->OperandIndices(instruction)) {
-      if (MayUseOperandValue(operand_number, index, user)) {
-        for (const HloUse& use : uses_) {
-          // Verify that this use does not already exist.
-          DCHECK(!(use.instruction == user &&
-                   use.operand_number == operand_number &&
-                   use.operand_index == index));
-        }
-
-        uses_.push_back(HloUse{user, operand_number, index});
-      }
-    }
-  }
-
-  // Update liveout status of this HloValue.
-  const HloModule& module = *instruction->parent()->parent();
-  if (instruction == module.entry_computation()->root_instruction()) {
-    live_out_of_module_ = true;
-  }
-
-  if (instruction == instruction->parent()->root_instruction()) {
-    live_out_of_computation_ = true;
-  }
-}
-
-void HloValue::RemoveLocation(HloInstruction* instruction,
-                              const ShapeIndex& index) {
-  // The defining location cannot be removed.
-  CHECK(!(instruction == defining_instruction() && index == defining_index()));
-
-  int64 size_before = locations_.size();
-  locations_.erase(
-      std::remove_if(locations_.begin(), locations_.end(),
-                     [instruction, &index](const HloLocation& location) {
-                       return location.instruction == instruction &&
-                              location.index == index;
-                     }),
-      locations_.end());
-  // Only a single location should have been removed.
-  CHECK_EQ(locations_.size(), size_before - 1);
-
-  //  Update uses which referred to this location.
-  uses_.erase(std::remove_if(uses_.begin(), uses_.end(),
-                             [instruction, &index](const HloUse& use) {
-                               return use.instruction->operand(
-                                          use.operand_number) == instruction &&
-                                      use.operand_index == index;
-                             }),
-              uses_.end());
-
-  // Returns whether this value is contained in the given instruction's output.
-  auto is_contained_in = [this](const HloInstruction* instruction) {
-    for (const HloLocation& location : locations()) {
-      if (location.instruction == instruction) {
-        return true;
-      }
-    }
-    return false;
-  };
-
-  const HloModule& module = *instruction->parent()->parent();
-  if (instruction == module.entry_computation()->root_instruction()) {
-    // Value has been removed from a location in the entry root instruction.
-    live_out_of_module_ =
-        is_contained_in(module.entry_computation()->root_instruction());
-  }
-  if (instruction == defining_instruction()->parent()->root_instruction()) {
-    // Value has been removed from the root of the computation the value has
-    // been defined in.
-    live_out_of_computation_ =
-        is_contained_in(defining_instruction()->parent()->root_instruction());
-  }
-}
-
-std::ostream& operator<<(std::ostream& out, const HloValue& value) {
-  out << value.ToShortString();
-  return out;
-}
-
-void HloValueSet::SortAndUniquifyValues() {
-  std::sort(value_ids_.begin(), value_ids_.end());
-  value_ids_.erase(std::unique(value_ids_.begin(), value_ids_.end()),
-                   value_ids_.end());
-}
-
-string HloValueSet::ToString() const {
-  return StrCat("HloValueSet: ", tensorflow::str_util::Join(value_ids_, ", "));
-}
-
-/*static */
-HloValueSet HloValueSet::Union(
-    tensorflow::gtl::ArraySlice<const HloValueSet*> inputs) {
-  HloValueSet union_set;
-  for (const HloValueSet* input : inputs) {
-    for (HloValue::Id value_id : input->value_ids()) {
-      union_set.value_ids_.push_back(value_id);
-    }
-  }
-  union_set.SortAndUniquifyValues();
-  return union_set;
-}
-
-std::ostream& operator<<(std::ostream& out, const HloValueSet& value_set) {
-  out << value_set.ToString();
-  return out;
-}
-
-InstructionValueSet InstructionValueSet::Union(
-    tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs) {
-  CHECK_GT(inputs.size(), 0);
-  for (int i = 1; i < inputs.size(); ++i) {
-    CHECK(ShapeUtil::Compatible(inputs[0]->shape(), inputs[i]->shape()));
-  }
-  InstructionValueSet union_set(inputs[0]->shape());
-  union_set.ForEachMutableElement(
-      [&inputs](const ShapeIndex& index, HloValueSet* value_set) {
-        std::vector<const HloValueSet*> input_sets;
-        for (const InstructionValueSet* input : inputs) {
-          input_sets.push_back(&input->element(index));
-        }
-        *value_set = HloValueSet::Union(input_sets);
-      });
-  return union_set;
-}
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionValueSet& instruction_value_set) {
-  out << instruction_value_set.ToString();
-  return out;
-}
-
-string InstructionValueSet::ToString() const {
-  string out =
-      StrCat("InstructionValueSet(", ShapeUtil::HumanString(shape()), ")\n");
-  ForEachElement([this, &out](const ShapeIndex& index,
-                              const HloValueSet& value_set) {
-    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
-  });
-  return out;
-}
-
 HloDataflowAnalysis::HloDataflowAnalysis(HloModule* module, bool ssa_form,
                                          bool bitcast_defines_value)
     : module_(module),
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
index 7087bd978d..d909c5b668 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
@@ -20,7 +20,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_DATAFLOW_ANALYSIS_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_DATAFLOW_ANALYSIS_H_
 
-#include <functional>
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
@@ -29,228 +29,17 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
-#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/service/hlo_value.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-#include "tensorflow/core/lib/gtl/flatmap.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/types.h"
 
 namespace xla {
 
-// Abstraction which identifies a specific point in the XLA graph. An
-// HloLocation specifies a ShapeIndex within the output of a specific
-// instruction.
-struct HloLocation {
-  HloInstruction* instruction;
-  ShapeIndex index;
-
-  string ToString() const;
-
-  bool operator==(const HloLocation& other) const {
-    return instruction == other.instruction && index == other.index;
-  }
-  bool operator!=(const HloLocation& other) const { return !(*this == other); }
-};
-
-std::ostream& operator<<(std::ostream& out, const HloLocation& location);
-
-// Defines a single use of an HLO value.
-struct HloUse {
-  // Instruction at which the value is used.
-  HloInstruction* instruction;
-
-  // The operand number in which the value is appears.
-  int64 operand_number;
-
-  // The shape index within the operand in which the value appears.
-  ShapeIndex operand_index;
-
-  string ToString() const;
-
-  bool operator==(const HloUse& other) const {
-    return instruction == other.instruction &&
-           operand_number == other.operand_number &&
-           operand_index == other.operand_index;
-  }
-
-  bool operator!=(const HloUse& other) const { return !(*this == other); }
-};
-
-std::ostream& operator<<(std::ostream& out, const HloUse& use);
-
-// Class describing a value used by the dataflow analysis. XLA arrays are
-// trivially a single HloValue. Tuples are made up of more than one HloValue: an
-// HloValue for the pointer vector, and an HloValue for each child element.
-//
-// Every HloValue is defined by a particular instruction and most instructions
-// define only a single HloValue. Instructions which define a single HloValue
-// include array-shaped instructions such as Add but also includes Tuple-shaped
-// instructions such as Tuple. The Tuple instruction defines a single HloValue
-// which is a vector of pointers to the values containing the Tuple
-// instruction's operands. Though the result of the Tuple instruction includes
-// multiple values only the top-level HloValue (the vector of pointers) is
-// defined by the Tuple instruction. The values containing the tuple elements
-// are defined by earlier instructions, usually the operands of the Tuple
-// instruction.
-//
-// Instructions which construct both the tuple *and* the tuple elements define
-// more than one HloValue. This includes (at least) tuple-shaped Constant,
-// Parameter, Infeed and While instructions. These tuple-shaped instructions do
-// not assemble a tuple from existing HloValues like the Tuple instruction does,
-// but rather define all the HloValues in the tuple.
-class HloValue {
- public:
-  using Id = int64;
-
-  // Construct an HloValue defined by 'instruction' at shape index 'index'. If
-  // is_phi is true, then this value is a phi value, for example, at the
-  // parameter of a while body computation. Phi values are only used in the SSA
-  // dataflow analysis (HloDataflowAnalysis::ssa_form_ is true).
-  HloValue(HloValue::Id id, HloInstruction* instruction,
-           const ShapeIndex& index, bool is_phi = false);
-
-  // Return a unique identifier for this HloValue. This value is used for stable
-  // sorting and iteration
-  Id id() const { return id_; }
-
-  // Returns whether this value is a phi value.
-  bool is_phi() const { return is_phi_; }
-
-  // Return the location where this value is defined.
-  const HloLocation& defining_location() const { return locations_[0]; }
-
-  // Return the instruction which defines this HloValue.
-  HloInstruction* defining_instruction() const {
-    return defining_location().instruction;
-  }
-
-  // Return the shape index at which this HloValue is defined in the output of
-  // its defining instruction.
-  const ShapeIndex& defining_index() const { return defining_location().index; }
-
-  // Add or remove a location at which the HloValue appears. The definition
-  // location can not be removed. The uses of the HloValue are updated.
-  void AddLocation(HloInstruction* instruction, const ShapeIndex& index);
-  void RemoveLocation(HloInstruction* instruction, const ShapeIndex& index);
-
-  // Return all locations of the HloValue in the module.
-  const std::vector<HloLocation>& locations() const { return locations_; }
-
-  // Return all uses of the HloValue.
-  const std::vector<HloUse>& uses() const { return uses_; }
-
-  // Get whether this HloValue is live out of the module.
-  bool live_out_of_module() const { return live_out_of_module_; }
-
-  // Get whether this HloValue is live out of the computation it is defined in.
-  bool live_out_of_computation() const { return live_out_of_computation_; }
-
-  bool operator==(const HloValue& other) const;
-  bool operator!=(const HloValue& other) const;
-
-  // Return a single-line string representation of the value.
-  string ToShortString() const;
-
-  string ToString(int indent = 0) const;
-
- private:
-  // Unique identifier for this HloValue. Used for stable sorting and iteration.
-  const Id id_;
-
-  // Whether this instruction is a phi value.
-  const bool is_phi_;
-
-  // The set of locations of this HloValue. The first element is always the
-  // location of the definition.
-  std::vector<HloLocation> locations_;
-
-  // The set of uses of this HloValue.
-  std::vector<HloUse> uses_;
-
-  // Whether this value is live out of the HLO module.
-  bool live_out_of_module_ = false;
-
-  // Whether this value is live out of its computation.
-  bool live_out_of_computation_ = false;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloValue& hlo_value);
-
-// A class representing the possible set of HloValues at a particular point
-// (shape index in the output of an instruction) in the XLA graph. This set
-// contains the set of reaching HloValue definitions. For a simple array-shaped
-// instruction like Add, the HloValueSet of the top-level of the instruction's
-// output trivially contains only the HloValue defined by the instruction. For
-// instructions which have non-trivial dataflow such as Tuple or Select, the
-// HloValueSets of the instruction's output contains one or more HloValues
-// defined by the instruction's operands or defined further up in the XLA graph.
-class HloValueSet {
- public:
-  HloValueSet() = default;
-
-  explicit HloValueSet(tensorflow::gtl::ArraySlice<HloValue::Id> value_ids)
-      : value_ids_(value_ids.begin(), value_ids.end()) {
-    SortAndUniquifyValues();
-  }
-
-  // Return the union of the given HloValueSets.
-  static HloValueSet Union(
-      tensorflow::gtl::ArraySlice<const HloValueSet*> inputs);
-
-  // Return the vector of the IDs of all HloValues in the set. Values in the
-  // vector are unique and sorted.
-  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
-
-  // Return the unique HLO value in the set. CHECKs if the set does not contain
-  // exactly one value.
-  HloValue::Id GetUniqueValueId() const {
-    CHECK_EQ(value_ids().size(), 1);
-    return value_ids()[0];
-  }
-
-  bool operator==(const HloValueSet& other) const {
-    return value_ids() == other.value_ids();
-  }
-  bool operator!=(const HloValueSet& other) const { return !(*this == other); }
-
-  string ToString() const;
-
- private:
-  // Sorts value_ and removes duplicates. This should be called after adding any
-  // elements to values_.
-  void SortAndUniquifyValues();
-
-  // HloValues sorted by HloValue::Id.
-  std::vector<HloValue::Id> value_ids_;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloValueSet& hlo_value);
-
-// A class collecting the HloValues which might be contained in the output of
-// an HLO instruction. For array-shaped instructions, an InstructionValueSet
-// trivially holds a single HloValueSet. Tuple-shaped InstructionValueSets
-// hold multiple HloValueSets.
-class InstructionValueSet : public ShapeTree<HloValueSet> {
- public:
-  InstructionValueSet(const Shape& shape) : ShapeTree<HloValueSet>(shape) {}
-
-  // Return the union of the given InstructionValueSets.
-  static InstructionValueSet Union(
-      tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs);
-
-  string ToString() const;
-};
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionValueSet& instruction_value_set);
-
 // Analysis which identifies all HLO values and their uses in an HLO module.
 class HloDataflowAnalysis {
  public:
diff --git a/tensorflow/compiler/xla/service/hlo_value.cc b/tensorflow/compiler/xla/service/hlo_value.cc
new file mode 100644
index 0000000000..55e6e77c0f
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_value.cc
@@ -0,0 +1,301 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_value.h"
+
+#include <algorithm>
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+using ::tensorflow::strings::StrAppend;
+using ::tensorflow::strings::StrCat;
+
+string HloLocation::ToString() const {
+  string index_str =
+      ShapeUtil::IsTuple(instruction->shape()) ? (" " + index.ToString()) : "";
+  return StrCat(instruction->FullyQualifiedName(), index_str);
+}
+
+std::ostream& operator<<(std::ostream& out, const HloLocation& location) {
+  out << location.ToString();
+  return out;
+}
+
+string HloUse::ToString() const {
+  string index_str =
+      ShapeUtil::IsTuple(instruction->operand(operand_number)->shape())
+          ? (" " + operand_index.ToString())
+          : "";
+  return StrCat(instruction->FullyQualifiedName(), ", operand ", operand_number,
+                index_str);
+}
+
+std::ostream& operator<<(std::ostream& out, const HloUse& use) {
+  out << use.ToString();
+  return out;
+}
+
+HloValue::HloValue(HloValue::Id id, HloInstruction* instruction,
+                   const ShapeIndex& index, bool is_phi)
+    : id_(id), is_phi_(is_phi) {
+  // The defining location is always the first element in the locations_ vector.
+  AddLocation(instruction, index);
+}
+
+bool HloValue::operator==(const HloValue& other) const {
+  bool equal = defining_instruction() == other.defining_instruction() &&
+               defining_index() == other.defining_index();
+  // If the values are equal they most both be phi (or non phi).
+  CHECK(!(equal && is_phi() != other.is_phi()));
+  return equal;
+}
+
+bool HloValue::operator!=(const HloValue& other) const {
+  return !(*this == other);
+}
+
+string HloValue::ToShortString() const {
+  string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
+                         ? defining_index().ToString()
+                         : "";
+  return StrCat(is_phi_ ? "PHI " : "",
+                defining_instruction()->FullyQualifiedName(), index_str);
+}
+
+string HloValue::ToString(int indent) const {
+  string indentation(indent, ' ');
+  string out = StrCat(indentation, ToShortString(), ", locations:\n");
+  for (const HloLocation& location : locations()) {
+    StrAppend(&out, indentation, "  ", location.ToString(), "\n");
+  }
+  StrAppend(&out, indentation, " uses:\n");
+  for (const HloUse& use : uses()) {
+    StrAppend(&out, indentation, "  ", use.ToString(), "\n");
+  }
+  return out;
+}
+
+namespace {
+
+// Returns true if the instruction 'user' may use the value at the given
+// ShapeIndex in the given operand. Generally, instruction which pass through
+// values transparently without reading the value are not considered to use the
+// value.
+bool MayUseOperandValue(int64 operand_number, const ShapeIndex& index,
+                        const HloInstruction* user) {
+  switch (user->opcode()) {
+    case HloOpcode::kGetTupleElement:
+    case HloOpcode::kCopy:
+      // These instructions only access the top-level values of their
+      // operand. Non-top-level (nested) values are passed through
+      // transparently.
+      CHECK_EQ(operand_number, 0);
+      return index.empty();
+    case HloOpcode::kSelect:
+      // Select does not use any nested elements of its selected-from operands
+      // (operand 1 and 2)
+      CHECK_GE(operand_number, 0);
+      CHECK_LE(operand_number, 2);
+      return operand_number == 0 || index.empty();
+
+    case HloOpcode::kCall:
+    case HloOpcode::kTuple:
+      // These instructions always pass through their operands transparently.
+      return false;
+
+    case HloOpcode::kWhile:
+      // Though the while instructions passes through its operands, we return
+      // true because in SSA form there may be a Phi at the parameter of the
+      // while which is considered a use of its incoming value because the Phi
+      // input values are not passed through into the body computation. Because
+      // this function is used in both SSA and non-SSA forms of the analysis
+      // conservatively return true.
+      return true;
+
+    default:
+      return true;
+  }
+}
+
+}  // namespace
+
+void HloValue::AddLocation(HloInstruction* instruction,
+                           const ShapeIndex& index) {
+  // The given location should not already exist in locations_.
+  for (const HloLocation& location : locations_) {
+    DCHECK(!(location.instruction == instruction && location.index == index));
+  }
+
+  locations_.push_back(HloLocation{instruction, index});
+
+  //  Update uses.
+  for (HloInstruction* user : instruction->users()) {
+    for (int64 operand_number : user->OperandIndices(instruction)) {
+      if (MayUseOperandValue(operand_number, index, user)) {
+        for (const HloUse& use : uses_) {
+          // Verify that this use does not already exist.
+          DCHECK(!(use.instruction == user &&
+                   use.operand_number == operand_number &&
+                   use.operand_index == index));
+        }
+
+        uses_.push_back(HloUse{user, operand_number, index});
+      }
+    }
+  }
+
+  // Update liveout status of this HloValue.
+  const HloModule& module = *instruction->parent()->parent();
+  if (instruction == module.entry_computation()->root_instruction()) {
+    live_out_of_module_ = true;
+  }
+
+  if (instruction == instruction->parent()->root_instruction()) {
+    live_out_of_computation_ = true;
+  }
+}
+
+void HloValue::RemoveLocation(HloInstruction* instruction,
+                              const ShapeIndex& index) {
+  // The defining location cannot be removed.
+  CHECK(!(instruction == defining_instruction() && index == defining_index()));
+
+  int64 size_before = locations_.size();
+  locations_.erase(
+      std::remove_if(locations_.begin(), locations_.end(),
+                     [instruction, &index](const HloLocation& location) {
+                       return location.instruction == instruction &&
+                              location.index == index;
+                     }),
+      locations_.end());
+  // Only a single location should have been removed.
+  CHECK_EQ(locations_.size(), size_before - 1);
+
+  //  Update uses which referred to this location.
+  uses_.erase(std::remove_if(uses_.begin(), uses_.end(),
+                             [instruction, &index](const HloUse& use) {
+                               return use.instruction->operand(
+                                          use.operand_number) == instruction &&
+                                      use.operand_index == index;
+                             }),
+              uses_.end());
+
+  // Returns whether this value is contained in the given instruction's output.
+  auto is_contained_in = [this](const HloInstruction* instruction) {
+    for (const HloLocation& location : locations()) {
+      if (location.instruction == instruction) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  const HloModule& module = *instruction->parent()->parent();
+  if (instruction == module.entry_computation()->root_instruction()) {
+    // Value has been removed from a location in the entry root instruction.
+    live_out_of_module_ =
+        is_contained_in(module.entry_computation()->root_instruction());
+  }
+  if (instruction == defining_instruction()->parent()->root_instruction()) {
+    // Value has been removed from the root of the computation the value has
+    // been defined in.
+    live_out_of_computation_ =
+        is_contained_in(defining_instruction()->parent()->root_instruction());
+  }
+}
+
+std::ostream& operator<<(std::ostream& out, const HloValue& value) {
+  out << value.ToShortString();
+  return out;
+}
+
+void HloValueSet::SortAndUniquifyValues() {
+  std::sort(value_ids_.begin(), value_ids_.end());
+  value_ids_.erase(std::unique(value_ids_.begin(), value_ids_.end()),
+                   value_ids_.end());
+}
+
+string HloValueSet::ToString() const {
+  return StrCat("HloValueSet: ", tensorflow::str_util::Join(value_ids_, ", "));
+}
+
+/*static */
+HloValueSet HloValueSet::Union(
+    tensorflow::gtl::ArraySlice<const HloValueSet*> inputs) {
+  HloValueSet union_set;
+  for (const HloValueSet* input : inputs) {
+    for (HloValue::Id value_id : input->value_ids()) {
+      union_set.value_ids_.push_back(value_id);
+    }
+  }
+  union_set.SortAndUniquifyValues();
+  return union_set;
+}
+
+std::ostream& operator<<(std::ostream& out, const HloValueSet& value_set) {
+  out << value_set.ToString();
+  return out;
+}
+
+InstructionValueSet InstructionValueSet::Union(
+    tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs) {
+  CHECK_GT(inputs.size(), 0);
+  for (int i = 1; i < inputs.size(); ++i) {
+    CHECK(ShapeUtil::Compatible(inputs[0]->shape(), inputs[i]->shape()));
+  }
+  InstructionValueSet union_set(inputs[0]->shape());
+  union_set.ForEachMutableElement(
+      [&inputs](const ShapeIndex& index, HloValueSet* value_set) {
+        std::vector<const HloValueSet*> input_sets;
+        for (const InstructionValueSet* input : inputs) {
+          input_sets.push_back(&input->element(index));
+        }
+        *value_set = HloValueSet::Union(input_sets);
+      });
+  return union_set;
+}
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionValueSet& instruction_value_set) {
+  out << instruction_value_set.ToString();
+  return out;
+}
+
+string InstructionValueSet::ToString() const {
+  string out =
+      StrCat("InstructionValueSet(", ShapeUtil::HumanString(shape()), ")\n");
+  ForEachElement([this, &out](const ShapeIndex& index,
+                              const HloValueSet& value_set) {
+    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
+  });
+  return out;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_value.h b/tensorflow/compiler/xla/service/hlo_value.h
new file mode 100644
index 0000000000..866ce4501b
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_value.h
@@ -0,0 +1,242 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
+
+#include <ostream>
+#include <string>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+
+// Abstraction which identifies a specific point in the XLA graph. An
+// HloLocation specifies a ShapeIndex within the output of a specific
+// instruction.
+struct HloLocation {
+  HloInstruction* instruction;
+  ShapeIndex index;
+
+  string ToString() const;
+
+  bool operator==(const HloLocation& other) const {
+    return instruction == other.instruction && index == other.index;
+  }
+  bool operator!=(const HloLocation& other) const { return !(*this == other); }
+};
+
+std::ostream& operator<<(std::ostream& out, const HloLocation& location);
+
+// Defines a single use of an HLO value.
+struct HloUse {
+  // Instruction at which the value is used.
+  HloInstruction* instruction;
+
+  // The operand number in which the value is appears.
+  int64 operand_number;
+
+  // The shape index within the operand in which the value appears.
+  ShapeIndex operand_index;
+
+  string ToString() const;
+
+  bool operator==(const HloUse& other) const {
+    return instruction == other.instruction &&
+           operand_number == other.operand_number &&
+           operand_index == other.operand_index;
+  }
+
+  bool operator!=(const HloUse& other) const { return !(*this == other); }
+};
+
+std::ostream& operator<<(std::ostream& out, const HloUse& use);
+
+// Class describing a value used by the dataflow analysis. XLA arrays are
+// trivially a single HloValue. Tuples are made up of more than one HloValue: an
+// HloValue for the pointer vector, and an HloValue for each child element.
+//
+// Every HloValue is defined by a particular instruction and most instructions
+// define only a single HloValue. Instructions which define a single HloValue
+// include array-shaped instructions such as Add but also includes Tuple-shaped
+// instructions such as Tuple. The Tuple instruction defines a single HloValue
+// which is a vector of pointers to the values containing the Tuple
+// instruction's operands. Though the result of the Tuple instruction includes
+// multiple values only the top-level HloValue (the vector of pointers) is
+// defined by the Tuple instruction. The values containing the tuple elements
+// are defined by earlier instructions, usually the operands of the Tuple
+// instruction.
+//
+// Instructions which construct both the tuple *and* the tuple elements define
+// more than one HloValue. This includes (at least) tuple-shaped Constant,
+// Parameter, Infeed and While instructions. These tuple-shaped instructions do
+// not assemble a tuple from existing HloValues like the Tuple instruction does,
+// but rather define all the HloValues in the tuple.
+class HloValue {
+ public:
+  using Id = int64;
+
+  // Construct an HloValue defined by 'instruction' at shape index 'index'. If
+  // is_phi is true, then this value is a phi value, for example, at the
+  // parameter of a while body computation. Phi values are only used in the SSA
+  // dataflow analysis (HloDataflowAnalysis::ssa_form_ is true).
+  HloValue(HloValue::Id id, HloInstruction* instruction,
+           const ShapeIndex& index, bool is_phi = false);
+
+  // Return a unique identifier for this HloValue. This value is used for stable
+  // sorting and iteration
+  Id id() const { return id_; }
+
+  // Returns whether this value is a phi value.
+  bool is_phi() const { return is_phi_; }
+
+  // Return the location where this value is defined.
+  const HloLocation& defining_location() const { return locations_[0]; }
+
+  // Return the instruction which defines this HloValue.
+  HloInstruction* defining_instruction() const {
+    return defining_location().instruction;
+  }
+
+  // Return the shape index at which this HloValue is defined in the output of
+  // its defining instruction.
+  const ShapeIndex& defining_index() const { return defining_location().index; }
+
+  // Add or remove a location at which the HloValue appears. The definition
+  // location can not be removed. The uses of the HloValue are updated.
+  void AddLocation(HloInstruction* instruction, const ShapeIndex& index);
+  void RemoveLocation(HloInstruction* instruction, const ShapeIndex& index);
+
+  // Return all locations of the HloValue in the module.
+  const std::vector<HloLocation>& locations() const { return locations_; }
+
+  // Return all uses of the HloValue.
+  const std::vector<HloUse>& uses() const { return uses_; }
+
+  // Get whether this HloValue is live out of the module.
+  bool live_out_of_module() const { return live_out_of_module_; }
+
+  // Get whether this HloValue is live out of the computation it is defined in.
+  bool live_out_of_computation() const { return live_out_of_computation_; }
+
+  bool operator==(const HloValue& other) const;
+  bool operator!=(const HloValue& other) const;
+
+  // Return a single-line string representation of the value.
+  string ToShortString() const;
+
+  string ToString(int indent = 0) const;
+
+ private:
+  // Unique identifier for this HloValue. Used for stable sorting and iteration.
+  const Id id_;
+
+  // Whether this instruction is a phi value.
+  const bool is_phi_;
+
+  // The set of locations of this HloValue. The first element is always the
+  // location of the definition.
+  std::vector<HloLocation> locations_;
+
+  // The set of uses of this HloValue.
+  std::vector<HloUse> uses_;
+
+  // Whether this value is live out of the HLO module.
+  bool live_out_of_module_ = false;
+
+  // Whether this value is live out of its computation.
+  bool live_out_of_computation_ = false;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloValue& hlo_value);
+
+// A class representing the possible set of HloValues at a particular point
+// (shape index in the output of an instruction) in the XLA graph. This set
+// contains the set of reaching HloValue definitions. For a simple array-shaped
+// instruction like Add, the HloValueSet of the top-level of the instruction's
+// output trivially contains only the HloValue defined by the instruction. For
+// instructions which have non-trivial dataflow such as Tuple or Select, the
+// HloValueSets of the instruction's output contains one or more HloValues
+// defined by the instruction's operands or defined further up in the XLA graph.
+class HloValueSet {
+ public:
+  HloValueSet() = default;
+
+  explicit HloValueSet(tensorflow::gtl::ArraySlice<HloValue::Id> value_ids)
+      : value_ids_(value_ids.begin(), value_ids.end()) {
+    SortAndUniquifyValues();
+  }
+
+  // Return the union of the given HloValueSets.
+  static HloValueSet Union(
+      tensorflow::gtl::ArraySlice<const HloValueSet*> inputs);
+
+  // Return the vector of the IDs of all HloValues in the set. Values in the
+  // vector are unique and sorted.
+  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
+
+  // Return the unique HLO value in the set. CHECKs if the set does not contain
+  // exactly one value.
+  HloValue::Id GetUniqueValueId() const {
+    CHECK_EQ(value_ids().size(), 1);
+    return value_ids()[0];
+  }
+
+  bool operator==(const HloValueSet& other) const {
+    return value_ids() == other.value_ids();
+  }
+  bool operator!=(const HloValueSet& other) const { return !(*this == other); }
+
+  string ToString() const;
+
+ private:
+  // Sorts value_ and removes duplicates. This should be called after adding any
+  // elements to values_.
+  void SortAndUniquifyValues();
+
+  // HloValues sorted by HloValue::Id.
+  std::vector<HloValue::Id> value_ids_;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloValueSet& hlo_value);
+
+// A class collecting the HloValues which might be contained in the output of
+// an HLO instruction. For array-shaped instructions, an InstructionValueSet
+// trivially holds a single HloValueSet. Tuple-shaped InstructionValueSets
+// hold multiple HloValueSets.
+class InstructionValueSet : public ShapeTree<HloValueSet> {
+ public:
+  InstructionValueSet(const Shape& shape) : ShapeTree<HloValueSet>(shape) {}
+
+  // Return the union of the given InstructionValueSets.
+  static InstructionValueSet Union(
+      tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs);
+
+  string ToString() const;
+};
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionValueSet& instruction_value_set);
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
-- 
GitLab


From 46bdbbd955b241e38c9d08fe808ac2b4465f6ed9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 13:53:18 -0700
Subject: [PATCH 0308/1375] Internal change

PiperOrigin-RevId: 159738215
---
 tensorflow/contrib/BUILD               | 2 --
 tensorflow/contrib/batching/BUILD      | 2 --
 tensorflow/contrib/batching/util/BUILD | 2 --
 3 files changed, 6 deletions(-)

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index f852eded1e..6213ff6024 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -3,8 +3,6 @@
 
 licenses(["notice"])  # Apache 2.0
 
-exports_files(["LICENSE"])
-
 package(default_visibility = ["//tensorflow:__subpackages__"])
 
 py_library(
diff --git a/tensorflow/contrib/batching/BUILD b/tensorflow/contrib/batching/BUILD
index 6478ad2ea7..acace9b3ba 100644
--- a/tensorflow/contrib/batching/BUILD
+++ b/tensorflow/contrib/batching/BUILD
@@ -7,8 +7,6 @@ package(
 
 licenses(["notice"])  # Apache 2.0
 
-exports_files(["LICENSE"])
-
 cc_library(
     name = "batch_scheduler_hdrs",
     hdrs = ["batch_scheduler.h"],
diff --git a/tensorflow/contrib/batching/util/BUILD b/tensorflow/contrib/batching/util/BUILD
index 48d03746bb..55dd898aa2 100644
--- a/tensorflow/contrib/batching/util/BUILD
+++ b/tensorflow/contrib/batching/util/BUILD
@@ -7,8 +7,6 @@ package(
 
 licenses(["notice"])  # Apache 2.0
 
-exports_files(["LICENSE"])
-
 filegroup(
     name = "all_files",
     srcs = glob(
-- 
GitLab


From 5a25cba3423060cfff038a4e42606c74d44b6663 Mon Sep 17 00:00:00 2001
From: Andrew Selle <aselle@google.com>
Date: Wed, 21 Jun 2017 13:54:27 -0700
Subject: [PATCH 0309/1375] Suggest people need to do some build environment
 ./configur'ing.

Fixes #4279

PiperOrigin-RevId: 159738412
---
 tensorflow/docs_src/api_guides/cc/guide.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/api_guides/cc/guide.md b/tensorflow/docs_src/api_guides/cc/guide.md
index c5473cad97..f30bf3797e 100644
--- a/tensorflow/docs_src/api_guides/cc/guide.md
+++ b/tensorflow/docs_src/api_guides/cc/guide.md
@@ -59,7 +59,8 @@ cc_binary(
 )
 ```
 
-You should be able to build and run the example using the following command:
+You should be able to build and run the example using the following command
+(be sure to run `./configure` in your build sandbox first):
 
 ```shell
 bazel run -c opt //tensorflow/cc/example:example
-- 
GitLab


From cda959d92c279f2ec687275d59d8314569df2f65 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 14:38:51 -0700
Subject: [PATCH 0310/1375] Rewrite SameDefinedShape function in ShapeRefiner

PiperOrigin-RevId: 159745894
---
 tensorflow/core/common_runtime/shape_refiner.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index 0f6c3c1cb1..f7b12bdaee 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -666,10 +666,10 @@ Status ShapeRefiner::RunShapeFn(const Node* node,
 
 bool ShapeRefiner::SameDefinedShape(InferenceContext* c, ShapeHandle s0,
                                     ShapeHandle s1) {
-  if (c->Rank(s0) != c->Rank(s1)) {
+  if (!c->RankKnown(s0)) {
+    return !c->RankKnown(s1);
+  } else if (!c->RankKnown(s1) || c->Rank(s0) != c->Rank(s1)) {
     return false;
-  } else if (!c->RankKnown(s0)) {
-    return true;
   }
 
   for (int i = 0; i < c->Rank(s0); ++i) {
-- 
GitLab


From f787d718967b3586561287a1506aec03e614d8dd Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 21 Jun 2017 14:43:08 -0700
Subject: [PATCH 0311/1375] [XLA] Remove xla_cpu_*_eigen flags from CPU
 backends.

These flags are currently de-facto unused; parallelism should be controlled
through the cpu_parallel backend. For configuring Eigen, if needed, the options
should be piped more directly to the code.

PiperOrigin-RevId: 159746509
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/tests/BUILD               |  1 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ----
 .../xla/legacy_flags/cpu_runtime_flags.cc     | 71 -------------------
 .../xla/legacy_flags/cpu_runtime_flags.h      | 51 -------------
 tensorflow/compiler/xla/service/cpu/BUILD     |  5 --
 .../xla/service/cpu/compiler_functor.cc       |  5 +-
 .../xla/service/cpu/conv_canonicalization.cc  |  6 --
 .../xla/service/cpu/dot_op_emitter.cc         | 11 +--
 .../xla/service/cpu/ir_emission_utils.cc      | 11 ---
 .../compiler/xla/service/cpu/ir_emitter.cc    |  7 +-
 tensorflow/compiler/xla/tests/BUILD           | 42 -----------
 .../compiler/xla/tests/dot_operation_test.cc  |  2 -
 14 files changed, 4 insertions(+), 223 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 31637358c3..3e1670dce3 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -128,7 +128,6 @@ cc_library(
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
         "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index e03d28cd96..26a1b203ac 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
@@ -135,7 +134,6 @@ int main(int argc, char** argv) {
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
   xla::legacy_flags::AppendCompilerFunctorFlags(&flag_list);
-  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::legacy_flags::AppendServiceFlags(&flag_list);
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 044857d422..45642c9aa2 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -535,7 +535,6 @@ tf_library(
     cpp_class = "LSTMLayerInference",
     graph = "lstm_layer_inference.pbtxt",
     tags = ["manual"],
-    tfcompile_flags = "--xla_cpu_multi_thread_eigen=false",
 )
 
 # -----------------------------------------------------------------------------
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index fafd5f591b..ac049c9b7a 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -79,18 +79,6 @@ cc_library(
         ],
 )
 
-cc_library(
-    name = "cpu_runtime_flags",
-    srcs = ["cpu_runtime_flags.cc"],
-    hdrs = ["cpu_runtime_flags.h"],
-    deps =
-        [
-            ":parse_flags_from_env",
-            "//tensorflow/core:framework_internal",
-            "//tensorflow/core:lib",
-        ],
-)
-
 cc_library(
     name = "compiler_functor_flags",
     srcs = ["compiler_functor_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
deleted file mode 100644
index d7817c5d54..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
+++ /dev/null
@@ -1,71 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's cpu_runtime module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static CpuRuntimeFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new CpuRuntimeFlags;
-  flags->xla_cpu_use_eigen = true;
-  flags->xla_cpu_multi_thread_eigen = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_cpu_use_eigen", &flags->xla_cpu_use_eigen,
-          "Use Eigen for matrix multiply on the CPU platform. This "
-          "is a useful hack for performance comparisons against "
-          "XLA's implementation."),
-      tensorflow::Flag(
-          "xla_cpu_multi_thread_eigen", &flags->xla_cpu_multi_thread_eigen,
-          "When generating calls to Eigen for matmul and conv, should "
-          "single or multi-threaded eigen be used? "
-          "Only used when --xla_cpu_use_eigen is true."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's cpu_runtime
-// module.
-void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the CpuRuntimeFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuRuntimeFlags* GetCpuRuntimeFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
deleted file mode 100644
index e3ff30da36..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
-
-// Legacy flags for the XLA's cpu_runtime module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's cpu_runtime
-// module.
-void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's cpu_runtime module.
-typedef struct {
-  // Use Eigen for matrix multiply on the CPU platform. This is a useful hack
-  // for performance comparisons against XLA's implementation.
-  bool xla_cpu_use_eigen;
-  // When generating calls to Eigen for matmul and conv, should single or
-  // multi-threaded eigen be used?  Only used when --xla_cpu_use_eigen is true.
-  bool xla_cpu_multi_thread_eigen;
-} CpuRuntimeFlags;
-
-// Return a pointer to the CpuRuntimeFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuRuntimeFlags* GetCpuRuntimeFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 68cd545695..93b9281874 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -197,7 +197,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:elemental_ir_emitter",
         "//tensorflow/compiler/xla/service:hlo",
@@ -228,7 +227,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service/llvm_ir:ir_array",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop",
@@ -290,7 +288,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
         "@llvm//:analysis",
@@ -485,7 +482,6 @@ cc_library(
         ":cpu_runtime",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:window_util",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
     ],
 )
@@ -512,7 +508,6 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_pass",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index 8ebf9ab110..93895c3482 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -36,7 +36,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/Transforms/IPO/AlwaysInliner.h"
 #include "external/llvm/include/llvm/Transforms/IPO/PassManagerBuilder.h"
 #include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
@@ -159,9 +158,7 @@ std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl(
   // Our vectorized library calls are currently implement by calling into Eigen.
   // As such, only emit calls to these routines if --xla_cpu_use_eigen is
   // enabled.
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (flags->xla_cpu_use_eigen &&
-      (arch == llvm::Triple::x86 || llvm::Triple::x86_64)) {
+  if (arch == llvm::Triple::x86 || llvm::Triple::x86_64) {
     llvm::SmallVector<llvm::StringRef, 32> features;
     feature_string.split(features, ',', -1, /*KeepEmpty=*/false);
     if (std::find(features.begin(), features.end(), "+sse4.1") !=
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
index cdf43587b6..069979c661 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h"
 
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -30,11 +29,6 @@ namespace xla {
 namespace cpu {
 
 StatusOr<bool> ConvCanonicalization::Run(HloModule* module) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   bool changed = false;
   for (HloInstruction* hlo :
        module->entry_computation()->MakeInstructionPostOrder()) {
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index 420f9cebc5..a8d2565b64 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Instructions.h"
 #include "external/llvm/include/llvm/IR/Module.h"
 #include "external/llvm/include/llvm/IR/Value.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
@@ -233,22 +232,16 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   // The two transpose_... parameters are actually booleans, but we use int32
   // to avoid target-dependent calling convention details.
 
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  bool multi_threaded = flags->xla_cpu_multi_thread_eigen;
   PrimitiveType type = target_array_.GetShape().element_type();
   llvm::Type* float_type;
   const char* fn_name;
   switch (type) {
     case F32:
-      fn_name = multi_threaded
-                    ? runtime::kEigenMatmulF32SymbolName
-                    : runtime::kEigenSingleThreadedMatmulF32SymbolName;
+      fn_name = runtime::kEigenMatmulF32SymbolName;
       float_type = ir_builder_->getFloatTy();
       break;
     case F64:
-      fn_name = multi_threaded
-                    ? runtime::kEigenMatmulF64SymbolName
-                    : runtime::kEigenSingleThreadedMatmulF64SymbolName;
+      fn_name = runtime::kEigenMatmulF64SymbolName;
       float_type = ir_builder_->getDoubleTy();
       break;
     default:
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
index 2d855d0eb1..859329e2c1 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/window_util.h"
@@ -26,11 +25,6 @@ namespace cpu {
 
 bool PotentiallyImplementedAsEigenConvolution(
     const HloInstruction& convolution) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   // The following conditions are necessary (but not sufficient) for
   // implementing `convolution` with Eigen convolution:
   // - the input and kernel have a non-zero number of elements.
@@ -82,11 +76,6 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape,
 }  // namespace
 
 bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   // For certain types of Dot, we can call Eigen
   if (hlo.opcode() == HloOpcode::kDot) {
     const Shape& lhs_shape = hlo.operand(0)->shape();
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index fee5fd8830..695c8069c4 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -33,7 +33,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Intrinsics.h"
 #include "external/llvm/include/llvm/IR/LLVMContext.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
@@ -862,11 +861,7 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution,
            int64_type,    int64_type,     int64_type,     int64_type,
            int64_type,    int64_type,     int64_type,     int64_type},
           /*isVarArg=*/false);
-      legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-      const char* fn_name =
-          (flags->xla_cpu_multi_thread_eigen
-               ? runtime::kEigenConvF32SymbolName
-               : runtime::kEigenSingleThreadedConvF32SymbolName);
+      const char* fn_name = runtime::kEigenConvF32SymbolName;
       llvm::Function* conv_func = llvm::cast<llvm::Function>(
           module_->getOrInsertFunction(fn_name, conv_type));
       conv_func->setCallingConv(llvm::CallingConv::C);
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index a11ac0bec6..184b6b684e 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -496,7 +496,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -513,43 +512,6 @@ xla_test(
 xla_test(
     name = "dot_operation_runtime_test",
     srcs = ["dot_operation_test.cc"],
-    backend_args = {
-        "cpu": ["--xla_cpu_use_eigen"],
-        "cpu_parallel": ["--xla_cpu_use_eigen"],
-    },
-    deps = [
-        "//tensorflow/compiler/xla:array2d",
-        "//tensorflow/compiler/xla:array3d",
-        "//tensorflow/compiler/xla:reference_util",
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
-        "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
-        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
-        "//tensorflow/compiler/xla/tests:client_library_test_base",
-        "//tensorflow/compiler/xla/tests:literal_test_util",
-        "//tensorflow/compiler/xla/tests:test_utils",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-    ],
-)
-
-# Repeat dot_operation_runtime_test with single-threded eigen.
-xla_test(
-    name = "dot_operation_single_threaded_runtime_test",
-    srcs = ["dot_operation_test.cc"],
-    backend_args = {
-        "cpu": [
-            "--xla_cpu_use_eigen",
-            "--xla_cpu_multi_thread_eigen=false",
-        ],
-        "cpu_parallel": [
-            "--xla_cpu_use_eigen",
-            "--xla_cpu_multi_thread_eigen=false",
-        ],
-    },
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -557,7 +519,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -574,11 +535,9 @@ xla_test(
     srcs = ["dot_operation_test.cc"],
     backend_args = {
         "cpu": [
-            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
         "cpu_parallel": [
-            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
     },
@@ -589,7 +548,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index b06b5c5f47..7abef6a27b 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
@@ -461,7 +460,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendLayoutUtilFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
-- 
GitLab


From 12c4775b378fefe1351c454d9516775663559c4f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 15:03:09 -0700
Subject: [PATCH 0312/1375] Updates layers tutorial and corresponding example.

PiperOrigin-RevId: 159749528
---
 tensorflow/docs_src/tutorials/layers.md       | 299 ++++++++----------
 .../examples/tutorials/layers/cnn_mnist.py    |  80 +++--
 2 files changed, 179 insertions(+), 200 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index 0fdfcf5d2a..37df19a8af 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -27,9 +27,6 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
-from tensorflow.contrib import learn
-from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
-
 tf.logging.set_verbosity(tf.logging.INFO)
 
 # Our application logic will be added here
@@ -122,15 +119,14 @@ Open `cnn_mnist.py` and add the following `cnn_model_fn` function, which
 conforms to the interface expected by TensorFlow's Estimator API (more on this
 later in [Create the Estimator](#create-the-estimator)). `cnn_mnist.py` takes
 MNIST feature data, labels, and
-@{tf.contrib.learn.ModeKeys$model mode} (`TRAIN`, `EVAL`,
-`INFER`) as arguments; configures the CNN; and returns predictions, loss, and a
-training operation:
+@{tf.estimator.ModeKeys$model mode} (`TRAIN`, `EVAL`, `PREDICT`) as arguments;
+configures the CNN; and returns predictions, loss, and a training operation:
 
 ```python
 def cnn_model_fn(features, labels, mode):
   """Model function for CNN."""
   # Input Layer
-  input_layer = tf.reshape(features, [-1, 28, 28, 1])
+  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
 
   # Convolutional Layer #1
   conv1 = tf.layers.conv2d(
@@ -156,39 +152,39 @@ def cnn_model_fn(features, labels, mode):
   pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
   dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
   dropout = tf.layers.dropout(
-      inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
+      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
 
   # Logits Layer
   logits = tf.layers.dense(inputs=dropout, units=10)
 
-  loss = None
-  train_op = None
+  # Generate Predictions (for PREDICT mode)
+  predicted_classes = tf.argmax(input=logits, axis=1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        "classes": predicted_classes,
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+    }
+    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
   # Calculate Loss (for both TRAIN and EVAL modes)
-  if mode != learn.ModeKeys.INFER:
-    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
-    loss = tf.losses.softmax_cross_entropy(
-        onehot_labels=onehot_labels, logits=logits)
+  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
 
   # Configure the Training Op (for TRAIN mode)
-  if mode == learn.ModeKeys.TRAIN:
-    train_op = tf.contrib.layers.optimize_loss(
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
+    train_op = optimizer.minimize(
         loss=loss,
-        global_step=tf.contrib.framework.get_global_step(),
-        learning_rate=0.001,
-        optimizer="SGD")
-
-  # Generate Predictions
-  predictions = {
-      "classes": tf.argmax(
-          input=logits, axis=1),
-      "probabilities": tf.nn.softmax(
-          logits, name="softmax_tensor")
-  }
-
-  # Return a ModelFnOps object
-  return model_fn_lib.ModelFnOps(
-      mode=mode, predictions=predictions, loss=loss, train_op=train_op)
+        global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
+
+  # Add evaluation metrics (for EVAL mode)
+  eval_metric_ops = {
+      "accuracy": tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)}
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 ```
 
 The following sections (with headings corresponding to each code block above)
@@ -222,18 +218,18 @@ To convert our input feature map (`features`) to this shape, we can perform the
 following `reshape` operation:
 
 ```python
-input_layer = tf.reshape(features, [-1, 28, 28, 1])
+input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
 ```
 
 Note that we've indicated `-1` for batch size, which specifies that this
 dimension should be dynamically computed based on the number of input values in
-`features`, holding the size of all other dimensions constant. This allows us to
-treat `batch_size` as a hyperparameter that we can tune. For example, if we feed
-examples into our model in batches of 5, `features` will contain 3,920 values
-(one value for each pixel in each image), and `input_layer` will have a shape of
-`[5, 28, 28, 1]`. Similarly, if we feed examples in batches of 100, `features`
-will contain 78,400 values, and `input_layer` will have a shape of `[100, 28,
-28, 1]`.
+`features["x"]`, holding the size of all other dimensions constant. This allows
+us to treat `batch_size` as a hyperparameter that we can tune. For example, if
+we feed examples into our model in batches of 5, `features["x"]` will contain
+3,920 values (one value for each pixel in each image), and `input_layer` will
+have a shape of `[5, 28, 28, 1]`. Similarly, if we feed examples in batches of
+100, `features["x"]` will contain 78,400 values, and `input_layer` will have a
+shape of `[100, 28, 28, 1]`.
 
 ### Convolutional Layer #1
 
@@ -386,7 +382,7 @@ to our dense layer, using the `dropout` method in `layers`:
 
 ```python
 dropout = tf.layers.dropout(
-    inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
+    inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
 ```
 
 Again, `inputs` specifies the input tensor, which is the output tensor from our
@@ -415,6 +411,55 @@ logits = tf.layers.dense(inputs=dropout, units=10)
 Our final output tensor of the CNN, `logits`, has shape
 <code>[<em>batch_size</em>, 10]</code>.
 
+### Generate Predictions {#generate_predictions}
+
+The logits layer of our model returns our predictions as raw values in a
+<code>[<em>batch_size</em>, 10]</code>-dimensional tensor. Let's convert these
+raw values into two different formats that our model function can return:
+
+*   The **predicted class** for each example: a digit from 0–9.
+*   The **probabilities** for each possible target class for each example: the
+    probability that the example is a 0, is a 1, is a 2, etc.
+
+For a given example, our predicted class is the element in the corresponding row
+of the logits tensor with the highest raw value. We can find the index of this
+element using the @{tf.argmax}
+function:
+
+```python
+tf.argmax(input=logits, axis=1)
+```
+
+The `input` argument specifies the tensor from which to extract maximum
+values—here `logits`. The `axis` argument specifies the axis of the `input`
+tensor along which to find the greatest value. Here, we want to find the largest
+value along the dimension with index of 1, which corresponds to our predictions
+(recall that our logits tensor has shape <code>[<em>batch_size</em>,
+10]</code>).
+
+We can derive probabilities from our logits layer by applying softmax activation
+using @{tf.nn.softmax}:
+
+```python
+tf.nn.softmax(logits, name="softmax_tensor")
+```
+
+> Note: We use the `name` argument to explicitly name this operation
+> `softmax_tensor`, so we can reference it later. (We'll set up logging for the
+> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook).
+
+We compile our predictions in a dict, and return an `EstimatorSpec` object:
+
+```python
+predicted_classes = tf.argmax(input=logits, axis=1)
+if mode == tf.estimator.ModeKeys.PREDICT:
+  predictions = {
+      "classes": predicted_classes,
+      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+  }
+  return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+```
+
 ### Calculate Loss {#calculating-loss}
 
 For both training and evaluation, we need to define a
@@ -426,14 +471,9 @@ as the loss metric. The following code calculates cross entropy when the model
 runs in either `TRAIN` or `EVAL` mode:
 
 ```python
-loss = None
-train_op = None
-
-# Calculate loss for both TRAIN and EVAL modes
-if mode != learn.ModeKeys.INFER:
-  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
-  loss = tf.losses.softmax_cross_entropy(
-      onehot_labels=onehot_labels, logits=logits)
+onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
+loss = tf.losses.softmax_cross_entropy(
+    onehot_labels=onehot_labels, logits=logits)
 ```
 
 Let's take a closer look at what's happening above.
@@ -474,90 +514,42 @@ predictions from our logits layer. `tf.losses.softmax_cross_entropy()` takes
 
 ```python
 loss = tf.losses.softmax_cross_entropy(
-        onehot_labels=onehot_labels, logits=logits)
+    onehot_labels=onehot_labels, logits=logits)
 ```
 
 ### Configure the Training Op
 
 In the previous section, we defined loss for our CNN as the softmax
 cross-entropy of the logits layer and our labels. Let's configure our model to
-optimize this loss value during training, using the
-@{tf.contrib.layers.optimize_loss}
-method in `tf.contrib.layers`. We'll use a learning rate of 0.001 and
+optimize this loss value during training. We'll use a learning rate of 0.001 and
 [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent)
 as the optimization algorithm:
 
 ```python
-# Configure the Training Op (for TRAIN mode)
-if mode == learn.ModeKeys.TRAIN:
-    train_op = tf.contrib.layers.optimize_loss(
-        loss=loss,
-        global_step=tf.contrib.framework.get_global_step(),
-        learning_rate=0.001,
-        optimizer="SGD")
+if mode == tf.estimator.ModeKeys.TRAIN:
+  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
+  train_op = optimizer.minimize(
+      loss=loss,
+      global_step=tf.train.get_global_step())
+  return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
 ```
 
 > Note: For a more in-depth look at configuring training ops for Estimator model
 > functions, see @{$estimators#defining-the-training-op-for-the-model$"Defining
 > the training op for the model"} in the @{$estimators$"Creating Estimations in
-> tf.contrib.learn"} tutorial.
-
-### Generate Predictions {#generate_predictions}
-
-The logits layer of our model returns our predictions as raw values in a
-<code>[<em>batch_size</em>, 10]</code>-dimensional tensor. Let's convert these
-raw values into two different formats that our model function can return:
-
-*   The **predicted class** for each example: a digit from 0–9.
-*   The **probabilities** for each possible target class for each example: the
-    probability that the example is a 0, is a 1, is a 2, etc.
-
-For a given example, our predicted class is the element in the corresponding row
-of the logits tensor with the highest raw value. We can find the index of this
-element using the @{tf.argmax}
-function:
-
-```python
-tf.argmax(input=logits, axis=1)
-```
-
-The `input` argument specifies the tensor from which to extract maximum
-values—here `logits`. The `axis` argument specifies the axis of the `input`
-tensor along which to find the greatest value. Here, we want to find the largest
-value along the dimension with index of 1, which corresponds to our predictions
-(recall that our logits tensor has shape <code>[<em>batch_size</em>,
-10]</code>).
-
-We can derive probabilities from our logits layer by applying softmax activation
-using @{tf.nn.softmax}:
-
-```python
-tf.nn.softmax(logits, name="softmax_tensor")
-```
+> tf.estimator"} tutorial.
 
-> Note: We use the `name` argument to explicitly name this operation
-> `softmax_tensor`, so we can reference it later. (We'll set up logging for the
-> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook).
-
-We compile our predictions in a dict as follows:
-
-```python
-predictions = {
-    "classes": tf.argmax(
-        input=logits, axis=1),
-    "probabilities": tf.nn.softmax(
-        logits, name="softmax_tensor")
-}
-```
+### Add evaluation metrics
 
-Finally, now that we've got our `predictions`, `loss`, and `train_op`, we can
-return them, along with our `mode` argument, in a
-@{tf.contrib.learn.ModelFnOps} object:
+To add accuracy metric in our model, we define `eval_metric_ops` dict in EVAL
+mode as follows:
 
 ```python
-# Return a ModelFnOps object
-return model_fn_lib.ModelFnOps(
-    mode=mode, predictions=predictions, loss=loss, train_op=train_op)
+eval_metric_ops = {
+    "accuracy": tf.metrics.accuracy(
+        labels=labels, predictions=predicted_classes)}
+return tf.estimator.EstimatorSpec(
+    mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 ```
 
 ## Training and Evaluating the CNN MNIST Classifier {#training_and_evaluating_the_cnn_mnist_classifier}
@@ -573,7 +565,7 @@ First, let's load our training and test data. Add a `main()` function to
 ```python
 def main(unused_argv):
   # Load training and eval data
-  mnist = learn.datasets.load_dataset("mnist")
+  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
   train_data = mnist.train.images # Returns np.array
   train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
   eval_data = mnist.test.images # Returns np.array
@@ -596,19 +588,19 @@ to `main()`:
 
 ```python
 # Create the Estimator
-mnist_classifier = learn.Estimator(
-      model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
+mnist_classifier = tf.estimator.Estimator(
+    model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
 ```
 
 The `model_fn` argument specifies the model function to use for training,
-evaluation, and inference; we pass it the `cnn_model_fn` we created in
+evaluation, and prediction; we pass it the `cnn_model_fn` we created in
 ["Building the CNN MNIST Classifier."](#building-the-cnn-mnist-classifier) The
 `model_dir` argument specifies the directory where model data (checkpoints) will
 be saved (here, we specify the temp directory `/tmp/mnist_convnet_model`, but
 feel free to change to another directory of your choice).
 
 > Note: For an in-depth walkthrough of the TensorFlow `Estimator` API, see the
-> tutorial @{$estimators$"Creating Estimators in tf.contrib.learn."}
+> tutorial @{$estimators$"Creating Estimators in tf.estimator."}
 
 ### Set Up a Logging Hook {#set_up_a_logging_hook}
 
@@ -643,65 +635,54 @@ should be logged after every 50 steps of training.
 
 ### Train the Model
 
-Now we're ready to train our model, which we can do by calling `fit()` on
-`mnist_classifier`. Add the following to `main()`:
+Now we're ready to train our model, which we can do by creating `train_input_fn`
+ans calling `train()` on `mnist_classifier`. Add the following to `main()`:
 
 ```python
 # Train the model
-mnist_classifier.fit(
-    x=train_data,
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": train_data},
     y=train_labels,
     batch_size=100,
+    num_epochs=None,
+    shuffle=True)
+mnist_classifier.train(
+    input_fn=train_input_fn,
     steps=20000,
-    monitors=[logging_hook])
+    hooks=[logging_hook])
 ```
 
-In the `fit` call, we pass the training feature data and labels to `x` and `y`,
-respectively. We set a `batch_size` of `100` (which means that the model will
-train on minibatches of 100 examples at each step), and `steps` of `20000`
+In the `numpy_input_fn` call, we pass the training feature data and labels to
+`x` (as a dict) and `y`, respectively. We set a `batch_size` of `100` (which
+means that the model will train on minibatches of 100 examples at each step).
+`num_epochs=None` means that the model will train until the specified number of
+steps is reached. We also set `shuffle=True` to shuffle the training data.
+In the `train` call, we set `steps=20000`
 (which means the model will train for 20,000 steps total). We pass our
-`logging_hook` to the `monitors` argument, so that it will be triggered during
+`logging_hook` to the `hooks` argument, so that it will be triggered during
 training.
 
 ### Evaluate the Model
 
 Once training is complete, we want to evaluate our model to determine its
-accuracy on the MNIST test set. To set up the accuracy metric for our model, we
-need to create a metrics dict with a @{tf.contrib.learn.MetricSpec}
-that calculates accuracy. Add the following to `main()`:
-
-```python
-# Configure the accuracy metric for evaluation
-metrics = {
-    "accuracy":
-        learn.MetricSpec(
-            metric_fn=tf.metrics.accuracy, prediction_key="classes"),
-}
-```
-
-We create our `MetricSpec`s with the following two arguments:
-
-*   `metric_fn`. The function that calculates and returns the value of our
-    metric. Here, we can use the predefined `accuracy` function in the
-    @{tf.metrics} module.
-*   `prediction_key`. The key of the tensor that contains the predictions
-    returned by the model function. Here, because we're building a
-    classification model, the prediction key is `"classes"`, which we specified
-    back in ["Generate Predictions."](#generate_predictions)
-
-Now that we've set up our `metrics` dict, we can evaluate the model. Add the
-following code, which performs evaluation and prints the results:
+accuracy on the MNIST test set. We call the `evaluate` method, which evaluates
+the metrics we specified in `eval_metric_ops` argument in the `model_fn`.
+Add the following to `main()`:
 
 ```python
 # Evaluate the model and print results
-eval_results = mnist_classifier.evaluate(
-    x=eval_data, y=eval_labels, metrics=metrics)
+eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": eval_data},
+    y=eval_labels,
+    num_epochs=1,
+    shuffle=False)
+eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
 print(eval_results)
 ```
 
-We pass our evaluation feature data and labels to `evaluate()` in the `x` and
-`y` arguments, respectively. The `metrics` argument takes the metrics dict we
-just defined.
+To create `eval_input_fn`, we set `num_epochs=1`, so that the model evaluates
+the metrics over one epoch of data and returns the result. We also set
+`shuffle=False` to iterate through the data sequentially.
 
 ### Run the Model
 
@@ -711,7 +692,7 @@ logic; now let's see the results. Run `cnn_mnist.py`.
 > Note: Training CNNs is quite computationally intensive. Estimated completion
 > time of `cnn_mnist.py` will vary depending on your processor, but will likely
 > be upwards of 1 hour on CPU. To train more quickly, you can decrease the
-> number of `steps` passed to `fit()`, but note that this will affect accuracy.
+> number of `steps` passed to `train()`, but note that this will affect accuracy.
 
 As the model trains, you'll see log output like the following:
 
@@ -738,7 +719,7 @@ Here, we've achieved an accuracy of 97.3% on our test data set.
 To learn more about TensorFlow Estimators and CNNs in TensorFlow, see the
 following resources:
 
-*   @{$estimators$Creating Estimators in tf.contrib.learn}. An
+*   @{$estimators$Creating Estimators in tf.estimator}. An
     introduction to the TensorFlow Estimator API, which walks through
     configuring an Estimator, writing a model function, calculating loss, and
     defining a training op.
diff --git a/tensorflow/examples/tutorials/layers/cnn_mnist.py b/tensorflow/examples/tutorials/layers/cnn_mnist.py
index aa92b1758a..f92277dac7 100644
--- a/tensorflow/examples/tutorials/layers/cnn_mnist.py
+++ b/tensorflow/examples/tutorials/layers/cnn_mnist.py
@@ -20,9 +20,6 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
-from tensorflow.contrib import learn
-from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
-
 tf.logging.set_verbosity(tf.logging.INFO)
 
 
@@ -31,7 +28,7 @@ def cnn_model_fn(features, labels, mode):
   # Input Layer
   # Reshape X to 4-D tensor: [batch_size, width, height, channels]
   # MNIST images are 28x28 pixels, and have one color channel
-  input_layer = tf.reshape(features, [-1, 28, 28, 1])
+  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
 
   # Convolutional Layer #1
   # Computes 32 features using a 5x5 filter with ReLU activation.
@@ -82,53 +79,53 @@ def cnn_model_fn(features, labels, mode):
 
   # Add dropout operation; 0.6 probability that element will be kept
   dropout = tf.layers.dropout(
-      inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
+      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
 
   # Logits layer
   # Input Tensor Shape: [batch_size, 1024]
   # Output Tensor Shape: [batch_size, 10]
   logits = tf.layers.dense(inputs=dropout, units=10)
 
-  loss = None
-  train_op = None
+  # Generate Predictions (for PREDICT mode)
+  predicted_classes = tf.argmax(input=logits, axis=1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        "classes": predicted_classes,
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+    }
+    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
   # Calculate Loss (for both TRAIN and EVAL modes)
-  if mode != learn.ModeKeys.INFER:
-    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
-    loss = tf.losses.softmax_cross_entropy(
-        onehot_labels=onehot_labels, logits=logits)
+  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
 
   # Configure the Training Op (for TRAIN mode)
-  if mode == learn.ModeKeys.TRAIN:
-    train_op = tf.contrib.layers.optimize_loss(
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
+    train_op = optimizer.minimize(
         loss=loss,
-        global_step=tf.contrib.framework.get_global_step(),
-        learning_rate=0.001,
-        optimizer="SGD")
-
-  # Generate Predictions
-  predictions = {
-      "classes": tf.argmax(
-          input=logits, axis=1),
-      "probabilities": tf.nn.softmax(
-          logits, name="softmax_tensor")
-  }
+        global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
 
-  # Return a ModelFnOps object
-  return model_fn_lib.ModelFnOps(
-      mode=mode, predictions=predictions, loss=loss, train_op=train_op)
+  # Add evaluation metrics (for EVAL mode)
+  eval_metric_ops = {
+      "accuracy": tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)}
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   # Load training and eval data
-  mnist = learn.datasets.load_dataset("mnist")
+  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
   train_data = mnist.train.images  # Returns np.array
   train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
   eval_data = mnist.test.images  # Returns np.array
   eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
 
   # Create the Estimator
-  mnist_classifier = learn.Estimator(
+  mnist_classifier = tf.estimator.Estimator(
       model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
 
   # Set up logging for predictions
@@ -138,23 +135,24 @@ def main(unused_argv):
       tensors=tensors_to_log, every_n_iter=50)
 
   # Train the model
-  mnist_classifier.fit(
-      x=train_data,
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": train_data},
       y=train_labels,
       batch_size=100,
+      num_epochs=None,
+      shuffle=True)
+  mnist_classifier.train(
+      input_fn=train_input_fn,
       steps=20000,
-      monitors=[logging_hook])
-
-  # Configure the accuracy metric for evaluation
-  metrics = {
-      "accuracy":
-          learn.MetricSpec(
-              metric_fn=tf.metrics.accuracy, prediction_key="classes"),
-  }
+      hooks=[logging_hook])
 
   # Evaluate the model and print results
-  eval_results = mnist_classifier.evaluate(
-      x=eval_data, y=eval_labels, metrics=metrics)
+  eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": eval_data},
+      y=eval_labels,
+      num_epochs=1,
+      shuffle=False)
+  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
   print(eval_results)
 
 
-- 
GitLab


From 0a379aefe4f76966fe3cca0bb91e1c5406019451 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 15:05:34 -0700
Subject: [PATCH 0313/1375] Further BUILD cleanup

PiperOrigin-RevId: 159749869
---
 tensorflow/contrib/distributions/BUILD         | 13 ++++---------
 tensorflow/contrib/factorization/BUILD         |  5 +++--
 tensorflow/contrib/hooks/BUILD                 | 15 ++++++++-------
 tensorflow/contrib/saved_model/BUILD           |  4 ++++
 tensorflow/contrib/stat_summarizer/BUILD       |  4 ++++
 tensorflow/examples/image_retraining/BUILD     |  7 +++++++
 tensorflow/examples/multibox_detector/BUILD    |  3 +++
 tensorflow/examples/saved_model/BUILD          |  2 ++
 tensorflow/examples/tutorials/estimators/BUILD |  1 +
 tensorflow/examples/tutorials/layers/BUILD     |  2 ++
 tensorflow/examples/tutorials/monitors/BUILD   |  1 -
 tensorflow/examples/wav_to_spectrogram/BUILD   |  4 ++++
 12 files changed, 42 insertions(+), 19 deletions(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 8dea2763f2..1cde0cb41c 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -18,20 +18,15 @@ py_library(
         "//tensorflow/contrib/linalg:linalg_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
-        "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:nn",
         "//tensorflow/python:nn_ops",
-        "//tensorflow/python:random_ops",
-        "//tensorflow/python:special_math_ops",
+        "//tensorflow/python:tensor_util",
+        "//tensorflow/python:util",
         "//tensorflow/python/ops/distributions",
         "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 
@@ -47,14 +42,14 @@ py_library(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:random_ops",
-        "//tensorflow/python:special_math_ops",
+        "//tensorflow/python:tensor_util",
+        "//tensorflow/python:util",
         "//tensorflow/python/ops/distributions",
         "//third_party/py/numpy",
         "@six_archive//:six",
diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD
index 0b4dc5667f..c78bb51c3a 100644
--- a/tensorflow/contrib/factorization/BUILD
+++ b/tensorflow/contrib/factorization/BUILD
@@ -171,10 +171,11 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:array_ops",
-        "//tensorflow/python:embedding_ops",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:constant_op",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/contrib/hooks/BUILD b/tensorflow/contrib/hooks/BUILD
index b6920ffd3d..d81e868d4a 100644
--- a/tensorflow/contrib/hooks/BUILD
+++ b/tensorflow/contrib/hooks/BUILD
@@ -19,12 +19,11 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/framework:framework_py",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:state_ops",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:client",
+        "//tensorflow/python:platform",
         "//tensorflow/python:training",
-        "//tensorflow/python:variables",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -35,10 +34,12 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":hooks",
+        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:training",
     ],
 )
 
diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD
index 6ab9631d29..e86fd318bd 100644
--- a/tensorflow/contrib/saved_model/BUILD
+++ b/tensorflow/contrib/saved_model/BUILD
@@ -37,6 +37,9 @@ py_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:lib",
+        "//tensorflow/python:util",
+        "//tensorflow/python/saved_model:constants",
     ],
 )
 
@@ -64,6 +67,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":saved_model_py",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
diff --git a/tensorflow/contrib/stat_summarizer/BUILD b/tensorflow/contrib/stat_summarizer/BUILD
index 11f65b4748..5fd02efbf6 100644
--- a/tensorflow/contrib/stat_summarizer/BUILD
+++ b/tensorflow/contrib/stat_summarizer/BUILD
@@ -13,6 +13,10 @@ py_library(
     name = "stat_summarizer_py",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:pywrap_tensorflow",
+        "//tensorflow/python:util",
+    ],
 )
 
 tf_py_test(
diff --git a/tensorflow/examples/image_retraining/BUILD b/tensorflow/examples/image_retraining/BUILD
index 394c413b33..c8c136ac14 100644
--- a/tensorflow/examples/image_retraining/BUILD
+++ b/tensorflow/examples/image_retraining/BUILD
@@ -18,6 +18,7 @@ py_binary(
         "//tensorflow:tensorflow_py",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:graph_util",
         "//tensorflow/python:platform",
         "//tensorflow/python:util",
         "//third_party/py/numpy",
@@ -50,10 +51,16 @@ py_test(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":label_image",
         ":retrain",
         "//tensorflow:tensorflow_py",
         "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:graph_util",
+        "//tensorflow/python:platform",
         "//tensorflow/python:platform_test",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:util",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/examples/multibox_detector/BUILD b/tensorflow/examples/multibox_detector/BUILD
index 5d0c769007..24c7040ac1 100644
--- a/tensorflow/examples/multibox_detector/BUILD
+++ b/tensorflow/examples/multibox_detector/BUILD
@@ -21,7 +21,10 @@ cc_binary(
     linkopts = ["-lm"],
     deps = [
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
         "//tensorflow/core:tensorflow",
     ],
 )
diff --git a/tensorflow/examples/saved_model/BUILD b/tensorflow/examples/saved_model/BUILD
index 844e99dcd4..72066eabc3 100644
--- a/tensorflow/examples/saved_model/BUILD
+++ b/tensorflow/examples/saved_model/BUILD
@@ -32,5 +32,7 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/python:lib",
+        "//tensorflow/python/saved_model:main_op",
     ],
 )
diff --git a/tensorflow/examples/tutorials/estimators/BUILD b/tensorflow/examples/tutorials/estimators/BUILD
index 0ff606831c..ecbc1a431d 100644
--- a/tensorflow/examples/tutorials/estimators/BUILD
+++ b/tensorflow/examples/tutorials/estimators/BUILD
@@ -16,6 +16,7 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/examples/tutorials/layers/BUILD b/tensorflow/examples/tutorials/layers/BUILD
index ba9e1f60e1..7621beec48 100644
--- a/tensorflow/examples/tutorials/layers/BUILD
+++ b/tensorflow/examples/tutorials/layers/BUILD
@@ -16,6 +16,8 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/contrib/learn",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/examples/tutorials/monitors/BUILD b/tensorflow/examples/tutorials/monitors/BUILD
index 6aa0b7ee47..4220e8144d 100644
--- a/tensorflow/examples/tutorials/monitors/BUILD
+++ b/tensorflow/examples/tutorials/monitors/BUILD
@@ -20,7 +20,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/examples/wav_to_spectrogram/BUILD b/tensorflow/examples/wav_to_spectrogram/BUILD
index 5923fa5929..dc29236b08 100644
--- a/tensorflow/examples/wav_to_spectrogram/BUILD
+++ b/tensorflow/examples/wav_to_spectrogram/BUILD
@@ -19,7 +19,10 @@ cc_library(
     hdrs = ["wav_to_spectrogram.h"],
     deps = [
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
         "//tensorflow/core:tensorflow",
     ],
 )
@@ -30,6 +33,7 @@ cc_binary(
     deps = [
         ":wav_to_spectrogram_lib",
         "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
     ],
 )
 
-- 
GitLab


From 2d44813882ea2307c029223e6ab50ea847e411a1 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Wed, 21 Jun 2017 15:14:26 -0700
Subject: [PATCH 0314/1375] Use more efficient squared_difference

PiperOrigin-RevId: 159751209
---
 tensorflow/python/ops/losses/losses_impl.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index c913d7a379..438e271275 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -531,7 +531,7 @@ def mean_squared_error(
     predictions = math_ops.to_float(predictions)
     labels = math_ops.to_float(labels)
     predictions.get_shape().assert_is_compatible_with(labels.get_shape())
-    losses = math_ops.square(math_ops.subtract(predictions, labels))
+    losses = math_ops.squared_difference(predictions, labels)
     return compute_weighted_loss(
         losses, weights, scope, loss_collection, reduction=reduction)
 
-- 
GitLab


From d4990796a7d23f64ed6958e2ccedca66c1de8ac4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 15:33:11 -0700
Subject: [PATCH 0315/1375] Add log_step_count_steps to RunConfig and allow it
 to flow to the MonitoredSession.

PiperOrigin-RevId: 159753935
---
 .../contrib/learn/python/learn/estimators/run_config.py  | 9 +++++++++
 tensorflow/python/estimator/estimator.py                 | 3 ++-
 tensorflow/python/estimator/run_config.py                | 9 +++++++++
 .../api/golden/tensorflow.estimator.-run-config.pbtxt    | 4 ++++
 4 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index 3aaee5862d..9235a3ce00 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -42,6 +42,7 @@ _DEFAULT_UID_WHITE_LIST = [
     'session_config',
     'keep_checkpoint_max',
     'keep_checkpoint_every_n_hours',
+    'log_step_count_steps',
 ]
 
 
@@ -230,6 +231,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
                save_checkpoints_steps=None,
                keep_checkpoint_max=5,
                keep_checkpoint_every_n_hours=10000,
+               log_step_count_steps=100,
                evaluation_master='',
                model_dir=None,
                session_config=None):
@@ -261,6 +263,8 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
       keep_checkpoint_every_n_hours: Number of hours between each checkpoint
         to be saved. The default value of 10,000 hours effectively disables
         the feature.
+      log_step_count_steps: The frequency, in number of global steps, that the
+        global step/sec will be logged during training.
       evaluation_master: the master on which to perform evaluation.
       model_dir: directory where model parameters, graph etc are saved. If
         `None`, will use `model_dir` property in `TF_CONFIG` environment
@@ -284,6 +288,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
     self._tf_random_seed = tf_random_seed
     self._save_summary_steps = save_summary_steps
     self._save_checkpoints_secs = save_checkpoints_secs
+    self._log_step_count_steps = log_step_count_steps
     self._session_config = session_config
     if save_checkpoints_secs == RunConfig._USE_DEFAULT:
       if save_checkpoints_steps is None:
@@ -367,6 +372,10 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
   def keep_checkpoint_every_n_hours(self):
     return self._keep_checkpoint_every_n_hours
 
+  @property
+  def log_step_count_steps(self):
+    return self._log_step_count_steps
+
 
 def _count_ps(cluster_spec):
   """Counts the number of parameter servers in cluster_spec."""
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 5e6e532e94..5f50be01fc 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -661,7 +661,8 @@ class Estimator(object):
               tuple(chief_hooks) + tuple(estimator_spec.training_chief_hooks)),
           save_checkpoint_secs=0,  # Saving is handled by a hook.
           save_summaries_steps=self._config.save_summary_steps,
-          config=self._session_config) as mon_sess:
+          config=self._session_config,
+          log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
         loss = None
         while not mon_sess.should_stop():
           _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py
index 30ba18d07d..ac521cc791 100644
--- a/tensorflow/python/estimator/run_config.py
+++ b/tensorflow/python/estimator/run_config.py
@@ -35,6 +35,7 @@ _DEFAULT_REPLACEABLE_LIST = [
     'session_config',
     'keep_checkpoint_max',
     'keep_checkpoint_every_n_hours',
+    'log_step_count_steps'
 ]
 
 _SAVE_CKPT_ERR = (
@@ -89,6 +90,8 @@ def _validate_properties(run_config):
             message='keep_checkpoint_max should be >= 0')
   _validate('keep_checkpoint_every_n_hours', lambda keep_hours: keep_hours > 0,
             message='keep_checkpoint_every_n_hours should be > 0')
+  _validate('log_step_count_steps', lambda num_steps: num_steps > 0,
+            message='log_step_count_steps should be > 0')
 
   _validate('tf_random_seed', lambda seed: isinstance(seed, six.integer_types),
             message='tf_random_seed must be integer.')
@@ -112,6 +115,7 @@ class RunConfig(object):
     self._session_config = None
     self._keep_checkpoint_max = 5
     self._keep_checkpoint_every_n_hours = 10000
+    self._log_step_count_steps = 100
     _validate_properties(self)
 
   @property
@@ -174,6 +178,10 @@ class RunConfig(object):
   def keep_checkpoint_every_n_hours(self):
     return self._keep_checkpoint_every_n_hours
 
+  @property
+  def log_step_count_steps(self):
+    return self._log_step_count_steps
+
   @property
   def model_dir(self):
     return self._model_dir
@@ -190,6 +198,7 @@ class RunConfig(object):
       - `session_config`,
       - `keep_checkpoint_max`,
       - `keep_checkpoint_every_n_hours`,
+      - `log_step_count_steps`,
 
     In addition, either `save_checkpoints_steps` or `save_checkpoints_secs`
     can be set (should not be both).
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
index d69c475a31..801260c450 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
@@ -22,6 +22,10 @@ tf_class {
     name: "keep_checkpoint_max"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "log_step_count_steps"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "master"
     mtype: "<type \'property\'>"
-- 
GitLab


From 80e630f36c22f9c9fd15e0bae784fce00866a674 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 21 Jun 2017 15:45:07 -0700
Subject: [PATCH 0316/1375] [XLA] Remove xla_hlo_test_generate_hlo_graph, which
 is now redundant.

PiperOrigin-RevId: 159755688
---
 tensorflow/compiler/xla/legacy_flags/BUILD    | 11 ----
 .../xla/legacy_flags/hlo_test_base_flags.cc   | 63 -------------------
 .../xla/legacy_flags/hlo_test_base_flags.h    | 47 --------------
 tensorflow/compiler/xla/tests/BUILD           |  1 -
 .../compiler/xla/tests/hlo_test_base.cc       | 11 +---
 5 files changed, 1 insertion(+), 132 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h

diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index ac049c9b7a..0d362ca3eb 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -148,17 +148,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "hlo_test_base_flags",
-    srcs = ["hlo_test_base_flags.cc"],
-    hdrs = ["hlo_test_base_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "backend_flags",
     srcs = ["backend_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.cc b/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.cc
deleted file mode 100644
index c7893c1385..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's hlo_test_base module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static HloTestBaseFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new HloTestBaseFlags;
-  flags->xla_hlo_test_generate_hlo_graph = false;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_hlo_test_generate_hlo_graph",
-                       &flags->xla_hlo_test_generate_hlo_graph,
-                       "Generate graph output of HLO instructions"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's hlo_test_base
-// module.
-void AppendHloTestBaseFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the HloTestBaseFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-HloTestBaseFlags* GetHloTestBaseFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h b/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h
deleted file mode 100644
index 23b808cecb..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_TEST_BASE_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_TEST_BASE_FLAGS_H_
-
-// Legacy flags for XLA's hlo_test_base module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's hlo_test_base
-// module.
-void AppendHloTestBaseFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's hlo_test_base module.
-typedef struct {
-  bool xla_hlo_test_generate_hlo_graph;  // Generate graph output of HLO
-                                         // instructions
-} HloTestBaseFlags;
-
-// Return a pointer to the HloTestBaseFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-HloTestBaseFlags* GetHloTestBaseFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_TEST_BASE_FLAGS_H_
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 184b6b684e..028ed21076 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -94,7 +94,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:hlo_test_base_flags",
         "//tensorflow/compiler/xla/service",
         "//tensorflow/compiler/xla/service:backend",
         "//tensorflow/compiler/xla/service:compiler",
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index 354e4a84c5..1def8ec12a 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -24,7 +24,6 @@ limitations under the License.
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/computation_layout.h"
@@ -56,16 +55,8 @@ struct HloTestBase::EigenThreadPoolWrapper {
 
 HloTestBase::HloTestBase()
     : backend_(Backend::CreateDefaultBackend().ConsumeValueOrDie()) {
-  // TODO(b/62411181): get rid of this flag entirely when the usual debug flags
-  // are piped to all HLO tests.
   test_hlo_dumper_ = [](const HloModule& module, const string& label) {
-    legacy_flags::HloTestBaseFlags* flags = legacy_flags::GetHloTestBaseFlags();
-    if (flags->xla_hlo_test_generate_hlo_graph) {
-      const bool show_addresses = true;
-      const bool show_layouts = true;
-      hlo_graph_dumper::DumpGraph(*module.entry_computation(), label,
-                                  show_addresses, show_layouts);
-    }
+    return Executable::DumpExecutedHlo(module, label, /*profile=*/nullptr);
   };
   VLOG(1) << "executing on platform " << backend_->platform()->Name();
 }
-- 
GitLab


From 0af25ef74da4615a8669e7203f9da9a6d8965750 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 15:48:16 -0700
Subject: [PATCH 0317/1375] Do not use SSE4.1 instructions on Android builds.

PiperOrigin-RevId: 159756104
---
 tensorflow/tensorflow.bzl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index dba8d6de63..690af5485a 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -167,7 +167,7 @@ def tf_opts_nortti_if_android():
       "-fno-rtti",
       "-DGOOGLE_PROTOBUF_NO_RTTI",
       "-DGOOGLE_PROTOBUF_NO_STATIC_INITIALIZER",
-  ]) + if_android_x86(["-msse4.1"])
+  ])
 
 
 # LINT.ThenChange(//tensorflow/contrib/android/cmake/CMakeLists.txt)
-- 
GitLab


From b104320c233bbdeb0d1c1935c5336536e731ce60 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Wed, 21 Jun 2017 16:01:30 -0700
Subject: [PATCH 0318/1375] Add nonpublic helper
 `tf.distributions.util.tridiag` op.

PiperOrigin-RevId: 159757904
---
 .../kernel_tests/distribution_util_test.py    | 52 ++++++++++++++
 tensorflow/python/ops/distributions/util.py   | 70 +++++++++++++++++++
 2 files changed, 122 insertions(+)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
index 1c67a1b8f6..aedf42f87f 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
@@ -659,5 +659,57 @@ class SoftplusTest(test.TestCase):
       # Equivalent to `assertAllFalse` (if it existed).
       self.assertAllEqual(np.zeros_like(grads).astype(np.bool), np.isnan(grads))
 
+
+class TridiagTest(test.TestCase):
+
+  def testWorksCorrectlyNoBatches(self):
+    with self.test_session():
+      self.assertAllEqual(
+          [[4., 8., 0., 0.],
+           [1., 5., 9., 0.],
+           [0., 2., 6., 10.],
+           [0., 0., 3, 7.]],
+          distribution_util.tridiag(
+              [1., 2., 3.],
+              [4., 5., 6., 7.],
+              [8., 9., 10.]).eval())
+
+  def testWorksCorrectlyBatches(self):
+    with self.test_session():
+      self.assertAllClose(
+          [[[4., 8., 0., 0.],
+            [1., 5., 9., 0.],
+            [0., 2., 6., 10.],
+            [0., 0., 3, 7.]],
+           [[0.7, 0.1, 0.0, 0.0],
+            [0.8, 0.6, 0.2, 0.0],
+            [0.0, 0.9, 0.5, 0.3],
+            [0.0, 0.0, 1.0, 0.4]]],
+          distribution_util.tridiag(
+              [[1., 2., 3.],
+               [0.8, 0.9, 1.]],
+              [[4., 5., 6., 7.],
+               [0.7, 0.6, 0.5, 0.4]],
+              [[8., 9., 10.],
+               [0.1, 0.2, 0.3]]).eval(),
+          rtol=1e-5, atol=0.)
+
+  def testHandlesNone(self):
+    with self.test_session():
+      self.assertAllClose(
+          [[[4., 0., 0., 0.],
+            [0., 5., 0., 0.],
+            [0., 0., 6., 0.],
+            [0., 0., 0, 7.]],
+           [[0.7, 0.0, 0.0, 0.0],
+            [0.0, 0.6, 0.0, 0.0],
+            [0.0, 0.0, 0.5, 0.0],
+            [0.0, 0.0, 0.0, 0.4]]],
+          distribution_util.tridiag(
+              diag=[[4., 5., 6., 7.],
+                    [0.7, 0.6, 0.5, 0.4]]).eval(),
+          rtol=1e-5, atol=0.)
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 05c6f4da57..3462d7957c 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -456,6 +456,8 @@ def fill_lower_triangular(x, validate_args=False, name="fill_lower_triangular"):
   e.g., `tril = tf.matrix_band_part(full, -1, 0)`, rather than directly
   construct a lower triangular.
 
+  Warning: This Op is intended for convenience, not efficiency.
+
   Example:
 
   ```python
@@ -563,6 +565,74 @@ def fill_lower_triangular(x, validate_args=False, name="fill_lower_triangular"):
     return y
 
 
+def tridiag(below=None, diag=None, above=None, name=None):
+  """Creates a matrix with values set above, below, and on the diagonal.
+
+  Example:
+
+  ```python
+  tridiag(below=[1., 2., 3.],
+          diag=[4., 5., 6., 7.],
+          above=[8., 9., 10.])
+  # ==> array([[  4.,   8.,   0.,   0.],
+  #            [  1.,   5.,   9.,   0.],
+  #            [  0.,   2.,   6.,  10.],
+  #            [  0.,   0.,   3.,   7.]], dtype=float32)
+  ```
+
+  Warning: This Op is intended for convenience, not efficiency.
+
+  Args:
+    below: `Tensor` of shape `[B1, ..., Bb, d-1]` corresponding to the below
+      diagonal part. `None` is logically equivalent to `below = 0`.
+    diag: `Tensor` of shape `[B1, ..., Bb, d]` corresponding to the diagonal
+      part.  `None` is logically equivalent to `diag = 0`.
+    above: `Tensor` of shape `[B1, ..., Bb, d-1]` corresponding to the above
+      diagonal part.  `None` is logically equivalent to `above = 0`.
+    name: Python `str`. The name to give this op.
+
+  Returns:
+    tridiag: `Tensor` with values set above, below and on the diagonal.
+
+  Raises:
+    ValueError: if all inputs are `None`.
+  """
+
+  def _pad(x):
+    """Prepends and appends a zero to every vector in a batch of vectors."""
+    shape = array_ops.concat([array_ops.shape(x)[:-1], [1]], axis=0)
+    z = array_ops.zeros(shape, dtype=x.dtype)
+    return array_ops.concat([z, x, z], axis=-1)
+
+  def _add(*x):
+    """Adds list of Tensors, ignoring `None`."""
+    s = None
+    for y in x:
+      if y is None:
+        continue
+      elif s is None:
+        s = y
+      else:
+        s += y
+    if s is None:
+      raise ValueError("Must specify at least one of `below`, `diag`, `above`.")
+    return s
+
+  with ops.name_scope(name, "tridiag", [below, diag, above]):
+    if below is not None:
+      below = ops.convert_to_tensor(below, name="below")
+      below = array_ops.matrix_diag(_pad(below))[..., :-1, 1:]
+    if diag is not None:
+      diag = ops.convert_to_tensor(diag, name="diag")
+      diag = array_ops.matrix_diag(diag)
+    if above is not None:
+      above = ops.convert_to_tensor(above, name="above")
+      above = array_ops.matrix_diag(_pad(above))[..., 1:, :-1]
+    # TODO(jvdillon): Consider using scatter_nd instead of creating three full
+    # matrices.
+    return _add(below, diag, above)
+
+
 # TODO(jvdillon): Merge this test back into:
 # tensorflow/python/ops/softplus_op_test.py
 # once TF core is accepting new ops.
-- 
GitLab


From e89f04d8013bd4e972228a74a02ab772370c8168 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Jun 2017 16:08:22 -0700
Subject: [PATCH 0319/1375] Fix calling Literal member functions.

---
 tensorflow/compiler/plugin/executor/executable.cc       | 4 ++--
 tensorflow/compiler/plugin/executor/transfer_manager.cc | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/plugin/executor/executable.cc b/tensorflow/compiler/plugin/executor/executable.cc
index 74fa1869f6..79eea9af3f 100644
--- a/tensorflow/compiler/plugin/executor/executable.cc
+++ b/tensorflow/compiler/plugin/executor/executable.cc
@@ -36,7 +36,7 @@ static se::DeviceMemoryBase AllocateSingleOutput(sep::ExecutorExecutor* executor
                                                  const Literal& literal) {
   int64 size(xla::ShapeUtil::ByteSizeOf(literal.shape()));
   void* buf = executor->Allocate(size);
-  const void* src = Literal::InternalData(literal);
+  const void* src = literal.InternalData();
   memcpy(buf, src, size);
   return se::DeviceMemoryBase(buf, size);
 }
@@ -90,7 +90,7 @@ StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteOnStream(
     arg_literals_ptrs.push_back(arg_literals.back().get());
 
     // Copy in the data from the stream_executor buffers
-    void* buffer = Literal::MutableInternalData(arg_literals.back().get());
+    void* buffer = arg_literals.back().get()->MutableInternalData();
     memcpy(buffer, arguments[p].opaque(),
            ShapeUtil::ByteSizeOf(param->shape()));
   }
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.cc b/tensorflow/compiler/plugin/executor/transfer_manager.cc
index 22be795802..6a471401fa 100644
--- a/tensorflow/compiler/plugin/executor/transfer_manager.cc
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.cc
@@ -70,13 +70,13 @@ Status ExecutorTransferManager::TransferLiteralFromDevice(
   }
 
   *literal->mutable_shape() = device_shape;
-  Literal::Reserve(ShapeUtil::ElementsIn(device_shape), literal);
+  literal->Reserve(ShapeUtil::ElementsIn(device_shape));
   TF_RETURN_IF_ERROR(TransferBufferFromDevice(
       executor, source, ShapeUtil::ByteSizeOf(device_shape),
-      Literal::MutableInternalData(literal)));
+      literal->MutableInternalData()));
   if (!ShapeUtil::Equal(literal_shape, device_shape)) {
     literal->Swap(
-        Literal::Relayout(*literal, literal_shape.layout()).get());
+        literal->Relayout(literal_shape.layout()).get());
   }
   TF_RET_CHECK(ShapeUtil::Equal(literal_shape, literal->shape()));
   return Status::OK();
@@ -134,7 +134,7 @@ Status ExecutorTransferManager::TransferLiteralToDevice(
   }
 
   return TransferBufferToDevice(executor, GetByteSizeRequirement(shape),
-                                Literal::InternalData(literal),
+                                literal.InternalData(),
                                 destination);
 }
 
-- 
GitLab


From d6682ad4a4c213aa39477bc254f6b14501d58d1c Mon Sep 17 00:00:00 2001
From: Mark Heffernan <meheff@google.com>
Date: Wed, 21 Jun 2017 16:11:47 -0700
Subject: [PATCH 0320/1375] [XLA] Remove dead "in-client" code. Remove
 Service::runs_in_client_process_ field and it's dead user. This was
 previously used by the "InProcess" methods which have been replaced with the
 LocalClient API.

PiperOrigin-RevId: 159759455
---
 .../compiler/xla/service/compile_only_service.cc      |  4 +---
 tensorflow/compiler/xla/service/local_service.cc      |  4 +---
 tensorflow/compiler/xla/service/service.cc            | 11 -----------
 tensorflow/compiler/xla/service/service.h             |  9 ---------
 4 files changed, 2 insertions(+), 26 deletions(-)

diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index cfcf2744fb..b0867c47a1 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -62,9 +62,7 @@ CompileOnlyService::CompileOnlyService(
     std::unique_ptr<Backend> compute_constant_backend)
     : Service(options, /*backend=*/nullptr,
               std::move(compute_constant_backend)),
-      compiler_(compiler) {
-  runs_in_client_process_ = true;
-}
+      compiler_(compiler) {}
 
 StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
 CompileOnlyService::CompileAheadOfTime(
diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index 05a1028a23..25588a6fb8 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -71,9 +71,7 @@ LocalService::LocalService(const ServiceOptions& options,
                            std::unique_ptr<Backend> execute_backend,
                            std::unique_ptr<Backend> compute_constant_backend)
     : Service(options, std::move(execute_backend),
-              std::move(compute_constant_backend)) {
-  runs_in_client_process_ = true;
-}
+              std::move(compute_constant_backend)) {}
 
 namespace {
 // Returns the space required to allocate a shape. If
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 31740757ab..68441ef17f 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -1207,17 +1207,6 @@ tensorflow::Status Service::GetComputationStats(
   return tensorflow::Status::OK();
 }
 
-tensorflow::Status Service::CheckRunsInClientProcess(
-    const string& method_name) const {
-  if (runs_in_client_process_) {
-    return tensorflow::Status::OK();
-  } else {
-    return FailedPrecondition(
-        "%s only supported if service runs in the same process as the client",
-        method_name.c_str());
-  }
-}
-
 template <typename RequestT, typename ResponseT>
 tensorflow::Status Service::AddInstruction(
     const RequestT* arg, ResponseT* result,
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 14e8a676a1..4ed89ca04a 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -333,12 +333,6 @@ class Service : public ServiceInterface {
       const std::function<StatusOr<ComputationDataHandle>(UserComputation*)>&
           adder);
 
-  // If the service is running in the client process
-  // (runs_in_client_process_ is true) then return
-  // tensorflow::Status::OK. Otherwise return an appropriate error
-  // status with the given method name. Used for "InProcess" methods.
-  tensorflow::Status CheckRunsInClientProcess(const string& method_name) const;
-
   // Convenience function which checks whether the given shape_with_layout
   // (presumably passed by the client to set the result layout) is valid for the
   // given computation result shape.
@@ -380,9 +374,6 @@ class Service : public ServiceInterface {
   // Backend to use when executing ComputeConstant.
   std::unique_ptr<Backend> compute_constant_backend_;
 
-  // Whether the service runs in the same process as the client.
-  bool runs_in_client_process_ = false;
-
   TF_DISALLOW_COPY_AND_ASSIGN(Service);
 };
 
-- 
GitLab


From fce39d94355c7c659aa2bbb6f8e9c6adc2419fec Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 16:15:33 -0700
Subject: [PATCH 0321/1375] [tf contrib seq2seq] Add monotonic attention
 mechanisms

* Add monotonic_attention and safe_cumprod helper functions.
* Add _BaseMonotonicAttentionMechanism base class.
* Add BahdanauMonotonicAttention and LuongMonotonicAttention classes.

These attention mechanisms are proposed in
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
"Online and Linear-Time Attention by Enforcing Monotonic Alignments."
ICML 2017.  https://arxiv.org/abs/1704.00784

PiperOrigin-RevId: 159760073
---
 tensorflow/contrib/seq2seq/__init__.py        |   8 +-
 .../kernel_tests/attention_wrapper_test.py    | 282 +++++++++
 .../seq2seq/python/ops/attention_wrapper.py   | 552 ++++++++++++++++--
 3 files changed, 783 insertions(+), 59 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/__init__.py b/tensorflow/contrib/seq2seq/__init__.py
index d36d7e16de..c4abef268b 100644
--- a/tensorflow/contrib/seq2seq/__init__.py
+++ b/tensorflow/contrib/seq2seq/__init__.py
@@ -57,7 +57,13 @@ _allowed_symbols = [
     "AttentionWrapperState",
     "AttentionWrapper",
     "AttentionMechanism",
-    "tile_batch"]
+    "tile_batch",
+    "safe_cumprod",
+    "monotonic_attention",
+    "monotonic_probability_fn",
+    "BahdanauMonotonicAttention",
+    "LuongMonotonicAttention",
+]
 
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
index 99e51589c9..8401918128 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
@@ -28,9 +28,11 @@ from tensorflow.contrib.seq2seq.python.ops import decoder
 from tensorflow.contrib.seq2seq.python.ops import attention_wrapper as wrapper
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.contrib.seq2seq.python.ops import basic_decoder
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import variable_scope as vs
@@ -174,6 +176,7 @@ class AttentionWrapperTest(test.TestCase):
                                              sess_results['final_outputs'])
       final_state_info = nest.map_structure(get_result_summary,
                                             sess_results['final_state'])
+      print(name)
       print('Copy/paste:\nexpected_final_output = %s' % str(final_output_info))
       print('expected_final_state = %s' % str(final_state_info))
       nest.map_structure(self.assertAllCloseOrEqual, expected_final_output,
@@ -335,6 +338,285 @@ class AttentionWrapperTest(test.TestCase):
         attention_layer_size=None,
         name='testNotUseAttentionLayer')
 
+  def test_safe_cumprod(self):
+    # Create some random test input
+    test_input = np.random.uniform(size=(10, 20))
+
+    for axis in [0, 1]:
+      for exclusive in [True, False]:
+        with self.test_session():
+          # Compute cumprod with regular tf.cumprod
+          cumprod_output = math_ops.cumprod(
+              test_input, axis=axis, exclusive=exclusive).eval()
+          # Compute cumprod with safe_cumprod
+          safe_cumprod_output = wrapper.safe_cumprod(
+              test_input, axis=axis, exclusive=exclusive).eval()
+        for x, y in zip(cumprod_output.shape, safe_cumprod_output.shape):
+          self.assertEqual(x, y)
+        for x, y in zip(cumprod_output.flatten(),
+                        safe_cumprod_output.flatten()):
+          # Use assertAlmostEqual for the actual values due to floating point
+          self.assertAlmostEqual(x, y, places=5)
+
+  def test_monotonic_attention(self):
+    def monotonic_attention_explicit(p_choose_i, previous_attention):
+      """Explicitly compute monotonic attention distribution using numpy."""
+      # Base case for recurrence relation
+      out = [previous_attention[0]]
+      # Explicitly follow the recurrence relation
+      for j in range(1, p_choose_i.shape[0]):
+        out.append((1 - p_choose_i[j - 1])*out[j - 1] + previous_attention[j])
+      return p_choose_i*np.array(out)
+
+    # Generate a random batch of choosing probabilities for seq. len. 20
+    p_choose_i = np.random.uniform(size=(10, 20)).astype(np.float32)
+    # Generate random previous attention distributions
+    previous_attention = np.random.uniform(size=(10, 20)).astype(np.float32)
+    previous_attention /= previous_attention.sum(axis=1).reshape((-1, 1))
+
+    # Create the output to test against
+    explicit_output = np.array([
+        monotonic_attention_explicit(p, a)
+        for p, a in zip(p_choose_i, previous_attention)])
+
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      recursive_output = wrapper.monotonic_attention(
+          p_choose_i, previous_attention, 'recursive').eval()
+
+    self.assertEqual(recursive_output.ndim, explicit_output.ndim)
+    for x, y in zip(recursive_output.shape, explicit_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(recursive_output.flatten(), explicit_output.flatten()):
+      # Use assertAlmostEqual for the actual values due to floating point
+      self.assertAlmostEqual(x, y, places=5)
+
+    # Generate new p_choose_i for parallel, which is unstable when p_choose_i[n]
+    # is close to 1
+    p_choose_i = np.random.uniform(0, 0.9, size=(10, 20)).astype(np.float32)
+
+    # Create new output to test against
+    explicit_output = np.array([
+        monotonic_attention_explicit(p, a)
+        for p, a in zip(p_choose_i, previous_attention)])
+
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      parallel_output = wrapper.monotonic_attention(
+          p_choose_i, previous_attention, 'parallel').eval()
+
+    self.assertEqual(parallel_output.ndim, explicit_output.ndim)
+    for x, y in zip(parallel_output.shape, explicit_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(parallel_output.flatten(), explicit_output.flatten()):
+      # Use assertAlmostEqual for the actual values due to floating point
+      self.assertAlmostEqual(x, y, places=5)
+
+    # Now, test hard mode, where probabilities must be 0 or 1
+    p_choose_i = np.random.choice(np.array([0, 1], np.float32), (10, 20))
+    previous_attention = np.zeros((10, 20), np.float32)
+    # Randomly choose input sequence indices at each timestep
+    random_idx = np.random.randint(0, previous_attention.shape[1],
+                                   previous_attention.shape[0])
+    previous_attention[np.arange(previous_attention.shape[0]), random_idx] = 1
+
+    # Create the output to test against
+    explicit_output = np.array([
+        monotonic_attention_explicit(p, a)
+        for p, a in zip(p_choose_i, previous_attention)])
+
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      hard_output = wrapper.monotonic_attention(
+          # TensorFlow is unhappy when these are not wrapped as tf.constant
+          constant_op.constant(p_choose_i),
+          constant_op.constant(previous_attention),
+          'hard').eval()
+
+    self.assertEqual(hard_output.ndim, explicit_output.ndim)
+    for x, y in zip(hard_output.shape, explicit_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(hard_output.flatten(), explicit_output.flatten()):
+      # Use assertAlmostEqual for the actual values due to floating point
+      self.assertAlmostEqual(x, y, places=5)
+
+    # Now, test recursively computing attention distributions vs. sampling
+    def sample(p_choose_i):
+      """Generate a sequence of emit-ingest decisions from p_choose_i."""
+      output = np.zeros(p_choose_i.shape)
+      t_im1 = 0
+      for i in range(p_choose_i.shape[0]):
+        for j in range(t_im1, p_choose_i.shape[1]):
+          if np.random.uniform() <= p_choose_i[i, j]:
+            output[i, j] = 1
+            t_im1 = j
+            break
+        else:
+          t_im1 = p_choose_i.shape[1]
+      return output
+
+    # Now, the first axis is output timestep and second is input timestep
+    p_choose_i = np.random.uniform(size=(4, 5)).astype(np.float32)
+    # Generate the average of a bunch of samples
+    n_samples = 100000
+    sampled_output = np.mean(
+        [sample(p_choose_i) for _ in range(n_samples)], axis=0)
+
+    # Create initial previous_attention base case
+    recursive_output = [np.array([1] + [0]*(p_choose_i.shape[1] - 1),
+                                 np.float32)]
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      for j in range(p_choose_i.shape[0]):
+        # Compute attention distribution for this output time step
+        recursive_output.append(wrapper.monotonic_attention(
+            # newaxis is for adding the expected batch dimension
+            p_choose_i[j][np.newaxis],
+            recursive_output[-1][np.newaxis], 'recursive').eval()[0])
+      # Stack together distributions; remove basecase
+      recursive_output = np.array(recursive_output[1:])
+
+    self.assertEqual(recursive_output.ndim, sampled_output.ndim)
+    for x, y in zip(recursive_output.shape, sampled_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(recursive_output.flatten(), sampled_output.flatten()):
+      # Use a very forgiving threshold since we are sampling
+      self.assertAlmostEqual(x, y, places=2)
+
+  def testBahdanauMonotonicNotNormalized(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.BahdanauMonotonicAttention, sigmoid_noise=1.0,
+        sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.002122893),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.7333333333333334))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040002423),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019968653)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-5.9313523e-05),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.032228071),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.050430927)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testBahdanauMonotonicNotNormalized')
+
+  def testBahdanauMonotonicNormalized(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.BahdanauMonotonicAttention, normalize=True,
+        sigmoid_noise=1.0, sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0025896581),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.8666666666666667))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040013152),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019973689)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-0.00069823361),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.028698336),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.046009291)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testBahdanauMonotonicNormalized')
+
+  def testLuongMonotonicNotNormalized(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.LuongMonotonicAttention, sigmoid_noise=1.0,
+        sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0021257224),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.7333333333333334))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040003359),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.001996913)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-5.2024145e-05),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.032198936),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.050387777)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        attention_mechanism_depth=9,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testLuongMonotonicNotNormalized')
+
+  def testLuongMonotonicScaled(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.LuongMonotonicAttention, scale=True, sigmoid_noise=1.0,
+        sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0021257224),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.7333333333333334))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040003359),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.001996913)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-5.2024145e-05),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.032198936),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.050387777)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        attention_mechanism_depth=9,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testLuongMonotonicScaled')
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index 642c7f1b54..0b1692ef2e 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -19,8 +19,11 @@ from __future__ import division
 from __future__ import print_function
 
 import collections
+import functools
 import math
 
+import numpy as np
+
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -28,9 +31,12 @@ from tensorflow.python.layers import base as layers_base
 from tensorflow.python.layers import core as layers_core
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import functional_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope
@@ -44,6 +50,10 @@ __all__ = [
     "LuongAttention",
     "BahdanauAttention",
     "hardmax",
+    "safe_cumprod",
+    "monotonic_attention",
+    "BahdanauMonotonicAttention",
+    "LuongMonotonicAttention",
 ]
 
 
@@ -241,6 +251,66 @@ class _BaseAttentionMechanism(AttentionMechanism):
     return _zero_state_tensors(max_time, batch_size, dtype)
 
 
+def _luong_score(query, keys, scale):
+  """Implements Luong-style (multiplicative) scoring function.
+
+  This attention has two forms.  The first is standard Luong attention,
+  as described in:
+
+  Minh-Thang Luong, Hieu Pham, Christopher D. Manning.
+  "Effective Approaches to Attention-based Neural Machine Translation."
+  EMNLP 2015.  https://arxiv.org/abs/1508.04025
+
+  The second is the scaled form inspired partly by the normalized form of
+  Bahdanau attention.
+
+  To enable the second form, call this function with `scale=True`.
+
+  Args:
+    query: Tensor, shape `[batch_size, num_units]` to compare to keys.
+    keys: Processed memory, shape `[batch_size, max_time, num_units]`.
+    scale: Whether to apply a scale to the score function.
+
+  Returns:
+    A `[batch_size, max_time]` tensor of unnormalized score values.
+
+  Raises:
+    ValueError: If `key` and `query` depths do not match.
+  """
+  depth = query.get_shape()[-1]
+  key_units = keys.get_shape()[-1]
+  if depth != key_units:
+    raise ValueError(
+        "Incompatible or unknown inner dimensions between query and keys.  "
+        "Query (%s) has units: %s.  Keys (%s) have units: %s.  "
+        "Perhaps you need to set num_units to the the keys' dimension (%s)?"
+        % (query, depth, keys, key_units, key_units))
+  dtype = query.dtype
+
+  # Reshape from [batch_size, depth] to [batch_size, 1, depth]
+  # for matmul.
+  query = array_ops.expand_dims(query, 1)
+
+  # Inner product along the query units dimension.
+  # matmul shapes: query is [batch_size, 1, depth] and
+  #                keys is [batch_size, max_time, depth].
+  # the inner product is asked to **transpose keys' inner shape** to get a
+  # batched matmul on:
+  #   [batch_size, 1, depth] . [batch_size, depth, max_time]
+  # resulting in an output shape of:
+  #   [batch_time, 1, max_time].
+  # we then squeee out the center singleton dimension.
+  score = math_ops.matmul(query, keys, transpose_b=True)
+  score = array_ops.squeeze(score, [1])
+
+  if scale:
+    # Scalar used in weight scaling
+    g = variable_scope.get_variable(
+        "attention_g", dtype=dtype, initializer=1.)
+    score = g * score
+  return score
+
+
 class LuongAttention(_BaseAttentionMechanism):
   """Implements Luong-style (multiplicative) attention scoring.
 
@@ -317,47 +387,66 @@ class LuongAttention(_BaseAttentionMechanism):
       alignments: Tensor of dtype matching `self.values` and shape
         `[batch_size, alignments_size]` (`alignments_size` is memory's
         `max_time`).
-
-    Raises:
-      ValueError: If `key` and `query` depths do not match.
     """
-    depth = query.get_shape()[-1]
-    key_units = self.keys.get_shape()[-1]
-    if depth != key_units:
-      raise ValueError(
-          "Incompatible or unknown inner dimensions between query and keys.  "
-          "Query (%s) has units: %s.  Keys (%s) have units: %s.  "
-          "Perhaps you need to set num_units to the the keys' dimension (%s)?"
-          % (query, depth, self.keys, key_units, key_units))
-    dtype = query.dtype
-
     with variable_scope.variable_scope(None, "luong_attention", [query]):
-      # Reshape from [batch_size, depth] to [batch_size, 1, depth]
-      # for matmul.
-      query = array_ops.expand_dims(query, 1)
-
-      # Inner product along the query units dimension.
-      # matmul shapes: query is [batch_size, 1, depth] and
-      #                keys is [batch_size, max_time, depth].
-      # the inner product is asked to **transpose keys' inner shape** to get a
-      # batched matmul on:
-      #   [batch_size, 1, depth] . [batch_size, depth, max_time]
-      # resulting in an output shape of:
-      #   [batch_time, 1, max_time].
-      # we then squeee out the center singleton dimension.
-      score = math_ops.matmul(query, self.keys, transpose_b=True)
-      score = array_ops.squeeze(score, [1])
-
-      if self._scale:
-        # Scalar used in weight scaling
-        g = variable_scope.get_variable(
-            "attention_g", dtype=dtype, initializer=1.)
-        score = g * score
-
+      score = _luong_score(query, self._keys, self._scale)
     alignments = self._probability_fn(score, previous_alignments)
     return alignments
 
 
+def _bahdanau_score(processed_query, keys, normalize):
+  """Implements Bahdanau-style (additive) scoring function.
+
+  This attention has two forms.  The first is Bhandanau attention,
+  as described in:
+
+  Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
+  "Neural Machine Translation by Jointly Learning to Align and Translate."
+  ICLR 2015. https://arxiv.org/abs/1409.0473
+
+  The second is the normalized form.  This form is inspired by the
+  weight normalization article:
+
+  Tim Salimans, Diederik P. Kingma.
+  "Weight Normalization: A Simple Reparameterization to Accelerate
+   Training of Deep Neural Networks."
+  https://arxiv.org/abs/1602.07868
+
+  To enable the second form, set `normalize=True`.
+
+  Args:
+    processed_query: Tensor, shape `[batch_size, num_units]` to compare to keys.
+    keys: Processed memory, shape `[batch_size, max_time, num_units]`.
+    normalize: Whether to normalize the score function.
+
+  Returns:
+    A `[batch_size, max_time]` tensor of unnormalized score values.
+  """
+  dtype = processed_query.dtype
+  # Get the number of hidden units from the trailing dimension of keys
+  num_units = keys.shape[2].value or array_ops.shape(keys)[2]
+  # Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
+  processed_query = array_ops.expand_dims(processed_query, 1)
+  v = variable_scope.get_variable(
+      "attention_v", [num_units], dtype=dtype)
+  if normalize:
+    # Scalar used in weight normalization
+    g = variable_scope.get_variable(
+        "attention_g", dtype=dtype,
+        initializer=math.sqrt((1. / num_units)))
+    # Bias added prior to the nonlinearity
+    b = variable_scope.get_variable(
+        "attention_b", [num_units], dtype=dtype,
+        initializer=init_ops.zeros_initializer())
+    # normed_v = g * v / ||v||
+    normed_v = g * v * math_ops.rsqrt(
+        math_ops.reduce_sum(math_ops.square(v)))
+    return math_ops.reduce_sum(
+        normed_v * math_ops.tanh(keys + processed_query + b), [2])
+  else:
+    return math_ops.reduce_sum(v * math_ops.tanh(keys + processed_query), [2])
+
+
 class BahdanauAttention(_BaseAttentionMechanism):
   """Implements Bahdanau-style (additive) attention.
 
@@ -441,30 +530,377 @@ class BahdanauAttention(_BaseAttentionMechanism):
     """
     with variable_scope.variable_scope(None, "bahdanau_attention", [query]):
       processed_query = self.query_layer(query) if self.query_layer else query
-      dtype = processed_query.dtype
-      # Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
-      processed_query = array_ops.expand_dims(processed_query, 1)
-      keys = self._keys
-      v = variable_scope.get_variable(
-          "attention_v", [self._num_units], dtype=dtype)
-      if self._normalize:
-        # Scalar used in weight normalization
-        g = variable_scope.get_variable(
-            "attention_g", dtype=dtype,
-            initializer=math.sqrt((1. / self._num_units)))
-        # Bias added prior to the nonlinearity
-        b = variable_scope.get_variable(
-            "attention_b", [self._num_units], dtype=dtype,
-            initializer=init_ops.zeros_initializer())
-        # normed_v = g * v / ||v||
-        normed_v = g * v * math_ops.rsqrt(
-            math_ops.reduce_sum(math_ops.square(v)))
-        score = math_ops.reduce_sum(
-            normed_v * math_ops.tanh(keys + processed_query + b), [2])
-      else:
-        score = math_ops.reduce_sum(v * math_ops.tanh(keys + processed_query),
-                                    [2])
+      score = _bahdanau_score(processed_query, self._keys, self._normalize)
+    alignments = self._probability_fn(score, previous_alignments)
+    return alignments
+
+
+def safe_cumprod(x, *args, **kwargs):
+  """Computes cumprod of x in logspace using cumsum to avoid underflow.
+
+  The cumprod function and its gradient can result in numerical instabilities
+  when its argument has very small and/or zero values.  As long as the argument
+  is all positive, we can instead compute the cumulative product as
+  exp(cumsum(log(x))).  This function can be called identically to tf.cumprod.
+
+  Args:
+    x: Tensor to take the cumulative product of.
+    *args: Passed on to cumsum; these are identical to those in cumprod.
+    **kwargs: Passed on to cumsum; these are identical to those in cumprod.
+  Returns:
+    Cumulative product of x.
+  """
+  with ops.name_scope(None, "SafeCumprod", [x]):
+    x = ops.convert_to_tensor(x, name="x")
+    tiny = np.finfo(x.dtype.as_numpy_dtype).tiny
+    return math_ops.exp(math_ops.cumsum(
+        math_ops.log(clip_ops.clip_by_value(x, tiny, 1)), *args, **kwargs))
+
+
+def monotonic_attention(p_choose_i, previous_attention, mode):
+  """Compute monotonic attention distribution from choosing probabilities.
+
+  Monotonic attention implies that the input sequence is processed in an
+  explicitly left-to-right manner when generating the output sequence.  In
+  addition, once an input sequence element is attended to at a given output
+  timestep, elements occurring before it cannot be attended to at subsequent
+  output timesteps.  This function generates attention distributions according
+  to these assumptions.  For more information, see ``Online and Linear-Time
+  Attention by Enforcing Monotonic Alignments''.
+
+  Args:
+    p_choose_i: Probability of choosing input sequence/memory element i.  Should
+      be of shape (batch_size, input_sequence_length), and should all be in the
+      range [0, 1].
+    previous_attention: The attention distribution from the previous output
+      timestep.  Should be of shape (batch_size, input_sequence_length).  For
+      the first output timestep, preevious_attention[n] should be [1, 0, 0, ...,
+      0] for all n in [0, ... batch_size - 1].
+    mode: How to compute the attention distribution.  Must be one of
+      'recursive', 'parallel', or 'hard'.
+        * 'recursive' uses tf.scan to recursively compute the distribution.
+          This is slowest but is exact, general, and does not suffer from
+          numerical instabilities.
+        * 'parallel' uses parallelized cumulative-sum and cumulative-product
+          operations to compute a closed-form solution to the recurrence
+          relation defining the attention distribution.  This makes it more
+          efficient than 'recursive', but it requires numerical checks which
+          make the distribution non-exact.  This can be a problem in particular
+          when input_sequence_length is long and/or p_choose_i has entries very
+          close to 0 or 1.
+        * 'hard' requires that the probabilities in p_choose_i are all either 0
+          or 1, and subsequently uses a more efficient and exact solution.
+
+  Returns:
+    A tensor of shape (batch_size, input_sequence_length) representing the
+    attention distributions for each sequence in the batch.
+
+  Raises:
+    ValueError: mode is not one of 'recursive', 'parallel', 'hard'.
+  """
+  # Force things to be tensors
+  p_choose_i = ops.convert_to_tensor(p_choose_i, name="p_choose_i")
+  previous_attention = ops.convert_to_tensor(
+      previous_attention, name="previous_attention")
+  if mode == "recursive":
+    # Use .shape[0].value when it's not None, or fall back on symbolic shape
+    batch_size = p_choose_i.shape[0].value or array_ops.shape(p_choose_i)[0]
+    # Compute [1, 1 - p_choose_i[0], 1 - p_choose_i[1], ..., 1 - p_choose_i[-2]]
+    shifted_1mp_choose_i = array_ops.concat(
+        [array_ops.ones((batch_size, 1)), 1 - p_choose_i[:, :-1]], 1)
+    # Compute attention distribution recursively as
+    # q[i] = (1 - p_choose_i[i])*q[i - 1] + previous_attention[i]
+    # attention[i] = p_choose_i[i]*q[i]
+    attention = p_choose_i*array_ops.transpose(functional_ops.scan(
+        # Need to use reshape to remind TF of the shape between loop iterations
+        lambda x, yz: array_ops.reshape(yz[0]*x + yz[1], (batch_size,)),
+        # Loop variables yz[0] and yz[1]
+        [array_ops.transpose(shifted_1mp_choose_i),
+         array_ops.transpose(previous_attention)],
+        # Initial value of x is just zeros
+        array_ops.zeros((batch_size,))))
+  elif mode == "parallel":
+    # safe_cumprod computes cumprod in logspace with numeric checks
+    cumprod_1mp_choose_i = safe_cumprod(1 - p_choose_i, axis=1, exclusive=True)
+    # Compute recurrence relation solution
+    attention = p_choose_i*cumprod_1mp_choose_i*math_ops.cumsum(
+        previous_attention /
+        # Clip cumprod_1mp to avoid divide-by-zero
+        clip_ops.clip_by_value(cumprod_1mp_choose_i, 1e-10, 1.), axis=1)
+  elif mode == "hard":
+    # Remove any probabilities before the index chosen last time step
+    p_choose_i *= math_ops.cumsum(previous_attention, axis=1)
+    # Now, use exclusive cumprod to remove probabilities after the first
+    # chosen index, like so:
+    # p_choose_i = [0, 0, 0, 1, 1, 0, 1, 1]
+    # cumprod(1 - p_choose_i, exclusive=True) = [1, 1, 1, 1, 0, 0, 0, 0]
+    # Product of above: [0, 0, 0, 1, 0, 0, 0, 0]
+    attention = p_choose_i*math_ops.cumprod(
+        1 - p_choose_i, axis=1, exclusive=True)
+  else:
+    raise ValueError("mode must be 'recursive', 'parallel', or 'hard'.")
+  return attention
+
+
+def _monotonic_probability_fn(score, previous_alignments, sigmoid_noise, mode,
+                              seed=None):
+  """Attention probability function for monotonic attention.
+
+  Takes in unnormalized attention scores, adds pre-sigmoid noise to encourage
+  the model to make discrete attention decisions, passes them through a sigmoid
+  to obtain "choosing" probabilities, and then calls monotonic_attention to
+  obtain the attention distribution.  For more information, see
+
+  Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
+  "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
+  ICML 2017.  https://arxiv.org/abs/1704.00784
+
+  Args:
+    score: Unnormalized attention scores, shape `[batch_size, alignments_size]`
+    previous_alignments: Previous attention distribution, shape
+      `[batch_size, alignments_size]`
+    sigmoid_noise: Standard deviation of pre-sigmoid noise.  Setting this larger
+      than 0 will encourage the model to produce large attention scores,
+      effectively making the choosing probabilities discrete and the resulting
+      attention distribution one-hot.  It should be set to 0 at test-time, and
+      when hard attention is not desired.
+    mode: How to compute the attention distribution.  Must be one of
+      'recursive', 'parallel', or 'hard'.  See the docstring for
+      `tf.contrib.seq2seq.monotonic_attention` for more information.
+    seed: (optional) Random seed for pre-sigmoid noise.
+
+  Returns:
+    A `[batch_size, alignments_size]`-shape tensor corresponding to the
+    resulting attention distribution.
+  """
+  # Optionally add pre-sigmoid noise to the scores
+  if sigmoid_noise > 0:
+    noise = random_ops.random_normal(array_ops.shape(score), dtype=score.dtype,
+                                     seed=seed)
+    score += sigmoid_noise*noise
+  # Compute "choosing" probabilities from the attention scores
+  p_choose_i = math_ops.sigmoid(score)
+  # Convert from choosing probabilities to attention distribution
+  return monotonic_attention(p_choose_i, previous_alignments, mode)
+
+
+class _BaseMonotonicAttentionMechanism(_BaseAttentionMechanism):
+  """Base attention mechanism for monotonic attention.
+
+  Simply overrides the initial_alignments function to provide a dirac
+  distribution,which is needed in order for the monotonic attention
+  distributions to have the correct behavior.
+  """
+
+  def initial_alignments(self, batch_size, dtype):
+    """Creates the initial alignment values for the monotonic attentions.
+
+    Initializes to dirac distributions, i.e. [1, 0, 0, ...memory length..., 0]
+    for all entries in the batch.
+
+    Args:
+      batch_size: `int32` scalar, the batch_size.
+      dtype: The `dtype`.
+
+    Returns:
+      A `dtype` tensor shaped `[batch_size, alignments_size]`
+      (`alignments_size` is the values' `max_time`).
+    """
+    max_time = self._alignments_size
+    return array_ops.one_hot(
+        array_ops.zeros((batch_size,), dtype=dtypes.int32), max_time,
+        dtype=dtype)
+
+
+class BahdanauMonotonicAttention(_BaseMonotonicAttentionMechanism):
+  """Monotonic attention mechanism with Bahadanau-style energy function.
+
+  This type of attention encorces a monotonic constraint on the attention
+  distributions; that is once the model attends to a given point in the memory
+  it can't attend to any prior points at subsequence output timesteps.  It
+  achieves this by using the _monotonic_probability_fn instead of softmax to
+  construct its attention distributions.  Since the attention scores are passed
+  through a sigmoid, a learnable scalar bias parameter is applied after the
+  score function and before the sigmoid.  Otherwise, it is equivalent to
+  BahdanauAttention.  This approach is proposed in
+
+  Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
+  "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
+  ICML 2017.  https://arxiv.org/abs/1704.00784
+  """
+
+  def __init__(self,
+               num_units,
+               memory,
+               memory_sequence_length=None,
+               normalize=False,
+               score_mask_value=float("-inf"),
+               sigmoid_noise=0.,
+               sigmoid_noise_seed=None,
+               score_bias_init=0.,
+               mode="parallel",
+               name="BahdanauMonotonicAttention"):
+    """Construct the Attention mechanism.
+
+    Args:
+      num_units: The depth of the query mechanism.
+      memory: The memory to query; usually the output of an RNN encoder.  This
+        tensor should be shaped `[batch_size, max_time, ...]`.
+      memory_sequence_length (optional): Sequence lengths for the batch entries
+        in memory.  If provided, the memory tensor rows are masked with zeros
+        for values past the respective sequence lengths.
+      normalize: Python boolean.  Whether to normalize the energy term.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
+      sigmoid_noise: Standard deviation of pre-sigmoid noise.  See the docstring
+        for `_monotonic_probability_fn` for more information.
+      sigmoid_noise_seed: (optional) Random seed for pre-sigmoid noise.
+      score_bias_init: Initial value for score bias scalar.  It's recommended to
+        initialize this to a negative value when the length of the memory is
+        large.
+      mode: How to compute the attention distribution.  Must be one of
+        'recursive', 'parallel', or 'hard'.  See the docstring for
+        `tf.contrib.seq2seq.monotonic_attention` for more information.
+      name: Name to use when creating ops.
+    """
+    # Set up the monotonic probability fn with supplied parameters
+    wrapped_probability_fn = functools.partial(
+        _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode,
+        seed=sigmoid_noise_seed)
+    super(BahdanauMonotonicAttention, self).__init__(
+        query_layer=layers_core.Dense(
+            num_units, name="query_layer", use_bias=False),
+        memory_layer=layers_core.Dense(
+            num_units, name="memory_layer", use_bias=False),
+        memory=memory,
+        probability_fn=wrapped_probability_fn,
+        memory_sequence_length=memory_sequence_length,
+        score_mask_value=score_mask_value,
+        name=name)
+    self._num_units = num_units
+    self._normalize = normalize
+    self._name = name
+    self._score_bias_init = score_bias_init
+
+  def __call__(self, query, previous_alignments):
+    """Score the query based on the keys and values.
+
+    Args:
+      query: Tensor of dtype matching `self.values` and shape
+        `[batch_size, query_depth]`.
+      previous_alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]`
+        (`alignments_size` is memory's `max_time`).
+
+    Returns:
+      alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]` (`alignments_size` is memory's
+        `max_time`).
+    """
+    with variable_scope.variable_scope(
+        None, "bahdanau_monotonic_attention", [query]):
+      processed_query = self.query_layer(query) if self.query_layer else query
+      score = _bahdanau_score(processed_query, self._keys, self._normalize)
+      score_bias = variable_scope.get_variable(
+          "attention_score_bias", dtype=processed_query.dtype,
+          initializer=self._score_bias_init)
+      score += score_bias
+    alignments = self._probability_fn(score, previous_alignments)
+    return alignments
+
+
+class LuongMonotonicAttention(_BaseMonotonicAttentionMechanism):
+  """Monotonic attention mechanism with Luong-style energy function.
+
+  This type of attention encorces a monotonic constraint on the attention
+  distributions; that is once the model attends to a given point in the memory
+  it can't attend to any prior points at subsequence output timesteps.  It
+  achieves this by using the _monotonic_probability_fn instead of softmax to
+  construct its attention distributions.  Otherwise, it is equivalent to
+  LuongAttention.  This approach is proposed in
 
+  Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
+  "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
+  ICML 2017.  https://arxiv.org/abs/1704.00784
+  """
+
+  def __init__(self,
+               num_units,
+               memory,
+               memory_sequence_length=None,
+               scale=False,
+               score_mask_value=float("-inf"),
+               sigmoid_noise=0.,
+               sigmoid_noise_seed=None,
+               score_bias_init=0.,
+               mode="parallel",
+               name="LuongMonotonicAttention"):
+    """Construct the Attention mechanism.
+
+    Args:
+      num_units: The depth of the query mechanism.
+      memory: The memory to query; usually the output of an RNN encoder.  This
+        tensor should be shaped `[batch_size, max_time, ...]`.
+      memory_sequence_length (optional): Sequence lengths for the batch entries
+        in memory.  If provided, the memory tensor rows are masked with zeros
+        for values past the respective sequence lengths.
+      scale: Python boolean.  Whether to scale the energy term.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
+      sigmoid_noise: Standard deviation of pre-sigmoid noise.  See the docstring
+        for `_monotonic_probability_fn` for more information.
+      sigmoid_noise_seed: (optional) Random seed for pre-sigmoid noise.
+      score_bias_init: Initial value for score bias scalar.  It's recommended to
+        initialize this to a negative value when the length of the memory is
+        large.
+      mode: How to compute the attention distribution.  Must be one of
+        'recursive', 'parallel', or 'hard'.  See the docstring for
+        `tf.contrib.seq2seq.monotonic_attention` for more information.
+      name: Name to use when creating ops.
+    """
+    # Set up the monotonic probability fn with supplied parameters
+    wrapped_probability_fn = functools.partial(
+        _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode,
+        seed=sigmoid_noise_seed)
+    super(LuongMonotonicAttention, self).__init__(
+        query_layer=layers_core.Dense(
+            num_units, name="query_layer", use_bias=False),
+        memory_layer=layers_core.Dense(
+            num_units, name="memory_layer", use_bias=False),
+        memory=memory,
+        probability_fn=wrapped_probability_fn,
+        memory_sequence_length=memory_sequence_length,
+        score_mask_value=score_mask_value,
+        name=name)
+    self._num_units = num_units
+    self._scale = scale
+    self._score_bias_init = score_bias_init
+    self._name = name
+
+  def __call__(self, query, previous_alignments):
+    """Score the query based on the keys and values.
+
+    Args:
+      query: Tensor of dtype matching `self.values` and shape
+        `[batch_size, query_depth]`.
+      previous_alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]`
+        (`alignments_size` is memory's `max_time`).
+
+    Returns:
+      alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]` (`alignments_size` is memory's
+        `max_time`).
+    """
+    with variable_scope.variable_scope(None, "luong_monotonic_attention",
+                                       [query]):
+      score = _luong_score(query, self._keys, self._scale)
+      score_bias = variable_scope.get_variable(
+          "attention_score_bias", dtype=query.dtype,
+          initializer=self._score_bias_init)
+      score += score_bias
     alignments = self._probability_fn(score, previous_alignments)
     return alignments
 
-- 
GitLab


From e3d99f4d8c0fb5479a0e7abb8623eb9ccab7c0ba Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Wed, 21 Jun 2017 16:25:09 -0700
Subject: [PATCH 0322/1375] Add ability for argmax to output int32 indices. 
 Default remains int64.

Change is made in a backwards and forward compatible manner, since
we add a new attribute with a default that remains the same, and
simply register a few new kernels.

PiperOrigin-RevId: 159761347
---
 tensorflow/core/kernels/argmax_op.cc          | 151 +++++++++++-------
 tensorflow/core/kernels/argmax_op.h           |  26 ++-
 tensorflow/core/kernels/argmax_op_gpu.cu.cc   |   8 +-
 tensorflow/core/ops/math_ops.cc               |  16 +-
 .../python/kernel_tests/argmax_op_test.py     |  20 +++
 tensorflow/python/ops/math_ops.py             |  16 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   8 +-
 7 files changed, 158 insertions(+), 87 deletions(-)

diff --git a/tensorflow/core/kernels/argmax_op.cc b/tensorflow/core/kernels/argmax_op.cc
index 4d16601b5b..49cd997fed 100644
--- a/tensorflow/core/kernels/argmax_op.cc
+++ b/tensorflow/core/kernels/argmax_op.cc
@@ -40,7 +40,7 @@ namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 
-template <typename Device, typename T, typename ArgFunctor>
+template <typename Device, typename T, typename Tout, typename ArgFunctor>
 class ArgOp : public OpKernel {
  public:
   explicit ArgOp(OpKernelConstruction* context) : OpKernel(context) {}
@@ -76,11 +76,11 @@ class ArgOp : public OpKernel {
     Tensor* output = nullptr;
     OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
 
-#define HANDLE_DIM(NDIM)                                         \
-  case NDIM:                                                     \
-    ArgFunctor::Reduce##NDIM(context->eigen_device<Device>(),    \
-                             input.tensor<T, NDIM>(), axis,      \
-                             output->tensor<int64, NDIM - 1>()); \
+#define HANDLE_DIM(NDIM)                                        \
+  case NDIM:                                                    \
+    ArgFunctor::Reduce##NDIM(context->eigen_device<Device>(),   \
+                             input.tensor<T, NDIM>(), axis,     \
+                             output->tensor<Tout, NDIM - 1>()); \
     break;
 
     switch (input_dims) {
@@ -102,31 +102,47 @@ class ArgOp : public OpKernel {
   TF_DISALLOW_COPY_AND_ASSIGN(ArgOp);
 };
 
-template <typename Device, typename T>
-class ArgMaxOp : public ArgOp<Device, T, functor::ArgMax<Device, T> > {
+template <typename Device, typename T, typename Tout>
+class ArgMaxOp
+    : public ArgOp<Device, T, Tout, functor::ArgMax<Device, T, Tout> > {
  public:
   explicit ArgMaxOp(OpKernelConstruction* context)
-      : ArgOp<Device, T, functor::ArgMax<Device, T> >(context) {}
+      : ArgOp<Device, T, Tout, functor::ArgMax<Device, T, Tout> >(context) {}
 };
 
-template <typename Device, typename T>
-class ArgMinOp : public ArgOp<Device, T, functor::ArgMin<Device, T> > {
+template <typename Device, typename T, typename Tout>
+class ArgMinOp
+    : public ArgOp<Device, T, Tout, functor::ArgMin<Device, T, Tout> > {
  public:
   explicit ArgMinOp(OpKernelConstruction* context)
-      : ArgOp<Device, T, functor::ArgMin<Device, T> >(context) {}
+      : ArgOp<Device, T, Tout, functor::ArgMin<Device, T, Tout> >(context) {}
 };
 
-#define REGISTER_ARGMAX(type)                            \
-  REGISTER_KERNEL_BUILDER(Name("ArgMax")                 \
-                              .Device(DEVICE_CPU)        \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("dimension"),  \
-                          ArgMaxOp<CPUDevice, type>);    \
-  REGISTER_KERNEL_BUILDER(Name("ArgMin")                 \
-                              .Device(DEVICE_CPU)        \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("dimension"),  \
-                          ArgMinOp<CPUDevice, type>);
+#define REGISTER_ARGMAX(type)                                       \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<CPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<CPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<CPUDevice, type, int32>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<CPUDevice, type, int32>);
 
 TF_CALL_REAL_NUMBER_TYPES(REGISTER_ARGMAX);
 
@@ -135,26 +151,33 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_ARGMAX);
 // Forward declarations of the functor specializations for GPU.
 namespace functor {
 
-#define DECLARE_GPU_SPEC(T, Dims)                                              \
-  template <>                                                                  \
-  void ArgMax<GPUDevice, T>::Reduce##Dims(                                     \
-      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,         \
-      const int32 dimension, typename TTypes<int64, Dims - 1>::Tensor output); \
-  template <>                                                                  \
-  void ArgMin<GPUDevice, T>::Reduce##Dims(                                     \
-      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,         \
-      const int32 dimension, typename TTypes<int64, Dims - 1>::Tensor output);
-
-#define DECLARE_GPU_SPECS(T) \
-  DECLARE_GPU_SPEC(T, 1);    \
-  DECLARE_GPU_SPEC(T, 2);    \
-  DECLARE_GPU_SPEC(T, 3);    \
-  DECLARE_GPU_SPEC(T, 4);    \
-  DECLARE_GPU_SPEC(T, 5);
-
-#define DECLARE_GPU_CLASS(T)                   \
-  extern template struct ArgMax<GPUDevice, T>; \
-  extern template struct ArgMin<GPUDevice, T>;
+#define DECLARE_GPU_SPEC(T, Tout, Dims)                                       \
+  template <>                                                                 \
+  void ArgMax<GPUDevice, T, Tout>::Reduce##Dims(                              \
+      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,        \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output); \
+  template <>                                                                 \
+  void ArgMin<GPUDevice, T, Tout>::Reduce##Dims(                              \
+      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,        \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output);
+
+#define DECLARE_GPU_SPECS(T)     \
+  DECLARE_GPU_SPEC(T, int64, 1); \
+  DECLARE_GPU_SPEC(T, int64, 2); \
+  DECLARE_GPU_SPEC(T, int64, 3); \
+  DECLARE_GPU_SPEC(T, int64, 4); \
+  DECLARE_GPU_SPEC(T, int64, 5); \
+  DECLARE_GPU_SPEC(T, int32, 1); \
+  DECLARE_GPU_SPEC(T, int32, 2); \
+  DECLARE_GPU_SPEC(T, int32, 3); \
+  DECLARE_GPU_SPEC(T, int32, 4); \
+  DECLARE_GPU_SPEC(T, int32, 5);
+
+#define DECLARE_GPU_CLASS(T)                          \
+  extern template struct ArgMax<GPUDevice, T, int64>; \
+  extern template struct ArgMin<GPUDevice, T, int64>; \
+  extern template struct ArgMax<GPUDevice, T, int32>; \
+  extern template struct ArgMin<GPUDevice, T, int32>;
 
 TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS);
 TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_CLASS);
@@ -165,19 +188,35 @@ TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_CLASS);
 }  // namespace functor
 
 // Registration of the GPU implementations.
-#define REGISTER_ARGMAX_GPU(type)                            \
-  REGISTER_KERNEL_BUILDER(Name("ArgMax")                     \
-                              .Device(DEVICE_GPU)            \
-                              .TypeConstraint<type>("T")     \
-                              .TypeConstraint<int32>("Tidx") \
-                              .HostMemory("dimension"),      \
-                          ArgMaxOp<GPUDevice, type>);        \
-  REGISTER_KERNEL_BUILDER(Name("ArgMin")                     \
-                              .Device(DEVICE_GPU)            \
-                              .TypeConstraint<type>("T")     \
-                              .TypeConstraint<int32>("Tidx") \
-                              .HostMemory("dimension"),      \
-                          ArgMinOp<GPUDevice, type>);
+#define REGISTER_ARGMAX_GPU(type)                                   \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<GPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<GPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<GPUDevice, type, int32>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<GPUDevice, type, int32>);
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_ARGMAX_GPU);
 
diff --git a/tensorflow/core/kernels/argmax_op.h b/tensorflow/core/kernels/argmax_op.h
index 850a0390bc..b8bc41e089 100644
--- a/tensorflow/core/kernels/argmax_op.h
+++ b/tensorflow/core/kernels/argmax_op.h
@@ -25,14 +25,13 @@ namespace tensorflow {
 
 namespace functor {
 
-template <typename Device, typename T>
+template <typename Device, typename T, typename Tout>
 struct ArgMax {
-#define DECLARE_COMPUTE_SPEC(Dims)                                     \
-  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                        \
-      const Device& d, typename TTypes<T, Dims>::ConstTensor input,    \
-      const int32 dimension,                                           \
-      typename TTypes<int64, Dims - 1>::Tensor output) {               \
-    output.device(d) = input.argmax(dimension).template cast<int64>(); \
+#define DECLARE_COMPUTE_SPEC(Dims)                                             \
+  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                                \
+      const Device& d, typename TTypes<T, Dims>::ConstTensor input,            \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output) { \
+    output.device(d) = input.argmax(dimension).template cast<Tout>();          \
   }
 
   DECLARE_COMPUTE_SPEC(1);
@@ -44,14 +43,13 @@ struct ArgMax {
 #undef DECLARE_COMPUTE_SPEC
 };
 
-template <typename Device, typename T>
+template <typename Device, typename T, typename Tout>
 struct ArgMin {
-#define DECLARE_COMPUTE_SPEC(Dims)                                     \
-  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                        \
-      const Device& d, typename TTypes<T, Dims>::ConstTensor input,    \
-      const int32 dimension,                                           \
-      typename TTypes<int64, Dims - 1>::Tensor output) {               \
-    output.device(d) = input.argmin(dimension).template cast<int64>(); \
+#define DECLARE_COMPUTE_SPEC(Dims)                                             \
+  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                                \
+      const Device& d, typename TTypes<T, Dims>::ConstTensor input,            \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output) { \
+    output.device(d) = input.argmin(dimension).template cast<Tout>();          \
   }
 
   DECLARE_COMPUTE_SPEC(1);
diff --git a/tensorflow/core/kernels/argmax_op_gpu.cu.cc b/tensorflow/core/kernels/argmax_op_gpu.cu.cc
index 2915da4c08..52e523003b 100644
--- a/tensorflow/core/kernels/argmax_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/argmax_op_gpu.cu.cc
@@ -24,9 +24,11 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-#define DEFINE_GPU_SPEC(T)                       \
-  template struct functor::ArgMax<GPUDevice, T>; \
-  template struct functor::ArgMin<GPUDevice, T>;
+#define DEFINE_GPU_SPEC(T)                              \
+  template struct functor::ArgMax<GPUDevice, T, int64>; \
+  template struct functor::ArgMin<GPUDevice, T, int64>; \
+  template struct functor::ArgMax<GPUDevice, T, int32>; \
+  template struct functor::ArgMin<GPUDevice, T, int32>;
 
 TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPEC);
 
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 41ca570732..dbc86e4632 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -1281,33 +1281,37 @@ Status ArgOpShape(shape_inference::InferenceContext* c) {
 REGISTER_OP("ArgMax")
     .Input("input: T")
     .Input("dimension: Tidx")
-    .Output("output: int64")
+    .Output("output: output_type")
     .Attr("T: numbertype")
     .Attr("Tidx: {int32, int64} = DT_INT32")
+    .Attr("output_type: {int32, int64} = DT_INT64")
     .SetShapeFn(ArgOpShape)
     .Doc(R"doc(
 Returns the index with the largest value across dimensions of a tensor.
 
 Note that in case of ties the identity of the return value is not guaranteed.
 
-dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-  of the input Tensor to reduce across. For vectors, use dimension = 0.
+dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+  which dimension of the input Tensor to reduce across. For vectors,
+  use dimension = 0.
 )doc");
 
 REGISTER_OP("ArgMin")
     .Input("input: T")
     .Input("dimension: Tidx")
-    .Output("output: int64")
+    .Output("output: output_type")
     .Attr("T: numbertype")
     .Attr("Tidx: {int32, int64} = DT_INT32")
+    .Attr("output_type: {int32, int64} = DT_INT64")
     .SetShapeFn(ArgOpShape)
     .Doc(R"doc(
 Returns the index with the smallest value across dimensions of a tensor.
 
 Note that in case of ties the identity of the return value is not guaranteed.
 
-dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-  of the input Tensor to reduce across. For vectors, use dimension = 0.
+dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+  which dimension of the input Tensor to reduce across. For vectors,
+  use dimension = 0.
 )doc");
 
 namespace {
diff --git a/tensorflow/python/kernel_tests/argmax_op_test.py b/tensorflow/python/kernel_tests/argmax_op_test.py
index a5352561aa..ae171dec55 100644
--- a/tensorflow/python/kernel_tests/argmax_op_test.py
+++ b/tensorflow/python/kernel_tests/argmax_op_test.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
@@ -36,6 +37,8 @@ class ArgMaxTest(test.TestCase):
       ans = method(x, dimension=dimension)
       if expected_err_re is None:
         tf_ans = ans.eval()
+        # Defaults to int64 output.
+        self.assertEqual(np.int64, tf_ans.dtype)
         self.assertAllEqual(tf_ans, expected_values)
         self.assertShapeEqual(expected_values, ans)
       else:
@@ -71,6 +74,23 @@ class ArgMaxTest(test.TestCase):
     self._testBasic(np.float32)
     self._testDim(np.float32)
 
+  def testFloatInt32Output(self):
+    x = np.asarray(100 * np.random.randn(200), dtype=np.float32)
+    expected_values = x.argmax()
+    with self.test_session(use_gpu=True):
+      ans = math_ops.argmax(x, dimension=0, output_type=dtypes.int32)
+      tf_ans = ans.eval()
+      self.assertEqual(np.int32, tf_ans.dtype)
+      # The values are equal when comparing int32 to int64 because
+      # the values don't have a range that exceeds 32-bit integers.
+      self.assertAllEqual(tf_ans, expected_values)
+    expected_values = x.argmin()
+    with self.test_session(use_gpu=True):
+      ans = math_ops.argmin(x, dimension=0, output_type=dtypes.int32)
+      tf_ans = ans.eval()
+      self.assertEqual(np.int32, tf_ans.dtype)
+      self.assertAllEqual(tf_ans, expected_values)
+
   def testDouble(self):
     self._testBasic(np.float64)
     self._testDim(np.float64)
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 89b7746e71..23af8c0a57 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -170,14 +170,18 @@ linspace = gen_math_ops.lin_space
 
 # pylint: disable=redefined-builtin
 # TODO(aselle): deprecate arg_max
-def argmax(input, axis=None, name=None, dimension=None):
+def argmax(input,
+           axis=None,
+           name=None,
+           dimension=None,
+           output_type=dtypes.int64):
   if dimension is not None:
     if axis is not None:
       raise ValueError("Cannot specify both 'axis' and 'dimension'")
     axis = dimension
   elif axis is None:
     axis = 0
-  return gen_math_ops.arg_max(input, axis, name)
+  return gen_math_ops.arg_max(input, axis, name=name, output_type=output_type)
 
 
 argmax.__doc__ = (gen_math_ops.arg_max.__doc__.replace("dimensions",
@@ -186,14 +190,18 @@ argmax.__doc__ = (gen_math_ops.arg_max.__doc__.replace("dimensions",
 
 
 # TODO(aselle:deprecate arg_min)
-def argmin(input, axis=None, name=None, dimension=None):
+def argmin(input,
+           axis=None,
+           name=None,
+           dimension=None,
+           output_type=dtypes.int64):
   if dimension is not None:
     if axis is not None:
       raise ValueError("Cannot specify both 'axis' and 'dimension'")
     axis = dimension
   elif axis is None:
     axis = 0
-  return gen_math_ops.arg_min(input, axis, name)
+  return gen_math_ops.arg_min(input, axis, name=name, output_type=output_type)
 
 
 argmin.__doc__ = (gen_math_ops.arg_min.__doc__.replace("dimensions",
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 57f4ec6007..b761f41361 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -530,19 +530,19 @@ tf_module {
   }
   member_method {
     name: "arg_max"
-    argspec: "args=[\'input\', \'dimension\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'input\', \'dimension\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "arg_min"
-    argspec: "args=[\'input\', \'dimension\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'input\', \'dimension\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "argmax"
-    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\', \'output_type\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"<dtype: \'int64\'>\"], "
   }
   member_method {
     name: "argmin"
-    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\', \'output_type\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"<dtype: \'int64\'>\"], "
   }
   member_method {
     name: "as_dtype"
-- 
GitLab


From eb00d1d98efe06de98afceac83b8e88cb63b8c20 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 21 Jun 2017 16:39:00 -0700
Subject: [PATCH 0323/1375] Automated g4 rollback of changelist 159746509

PiperOrigin-RevId: 159763112
---
 tensorflow/compiler/aot/BUILD                 |  1 +
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 +
 tensorflow/compiler/tests/BUILD               |  1 +
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ++++
 .../xla/legacy_flags/cpu_runtime_flags.cc     | 71 +++++++++++++++++++
 .../xla/legacy_flags/cpu_runtime_flags.h      | 51 +++++++++++++
 tensorflow/compiler/xla/service/cpu/BUILD     |  5 ++
 .../xla/service/cpu/compiler_functor.cc       |  5 +-
 .../xla/service/cpu/conv_canonicalization.cc  |  6 ++
 .../xla/service/cpu/dot_op_emitter.cc         | 11 ++-
 .../xla/service/cpu/ir_emission_utils.cc      | 11 +++
 .../compiler/xla/service/cpu/ir_emitter.cc    |  7 +-
 tensorflow/compiler/xla/tests/BUILD           | 42 +++++++++++
 .../compiler/xla/tests/dot_operation_test.cc  |  2 +
 14 files changed, 223 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
 create mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 3e1670dce3..31637358c3 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -128,6 +128,7 @@ cc_library(
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
         "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 26a1b203ac..e03d28cd96 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
@@ -134,6 +135,7 @@ int main(int argc, char** argv) {
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
   xla::legacy_flags::AppendCompilerFunctorFlags(&flag_list);
+  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::legacy_flags::AppendServiceFlags(&flag_list);
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 45642c9aa2..044857d422 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -535,6 +535,7 @@ tf_library(
     cpp_class = "LSTMLayerInference",
     graph = "lstm_layer_inference.pbtxt",
     tags = ["manual"],
+    tfcompile_flags = "--xla_cpu_multi_thread_eigen=false",
 )
 
 # -----------------------------------------------------------------------------
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 0d362ca3eb..dd0326c7c7 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -79,6 +79,18 @@ cc_library(
         ],
 )
 
+cc_library(
+    name = "cpu_runtime_flags",
+    srcs = ["cpu_runtime_flags.cc"],
+    hdrs = ["cpu_runtime_flags.h"],
+    deps =
+        [
+            ":parse_flags_from_env",
+            "//tensorflow/core:framework_internal",
+            "//tensorflow/core:lib",
+        ],
+)
+
 cc_library(
     name = "compiler_functor_flags",
     srcs = ["compiler_functor_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
new file mode 100644
index 0000000000..d7817c5d54
--- /dev/null
+++ b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
@@ -0,0 +1,71 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Legacy flags for XLA's cpu_runtime module.
+
+#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
+#include <vector>
+
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/command_line_flags.h"
+
+namespace xla {
+namespace legacy_flags {
+
+// Pointers to the parsed value of the flags and flag descriptors, initialized
+// via flags_init.
+static CpuRuntimeFlags* flags;
+static std::vector<tensorflow::Flag>* flag_list;
+static std::once_flag flags_init;
+
+// Allocate *flags.  Called via call_once(&flags_init,...).
+static void AllocateFlags() {
+  flags = new CpuRuntimeFlags;
+  flags->xla_cpu_use_eigen = true;
+  flags->xla_cpu_multi_thread_eigen = true;
+  flag_list = new std::vector<tensorflow::Flag>({
+      tensorflow::Flag(
+          "xla_cpu_use_eigen", &flags->xla_cpu_use_eigen,
+          "Use Eigen for matrix multiply on the CPU platform. This "
+          "is a useful hack for performance comparisons against "
+          "XLA's implementation."),
+      tensorflow::Flag(
+          "xla_cpu_multi_thread_eigen", &flags->xla_cpu_multi_thread_eigen,
+          "When generating calls to Eigen for matmul and conv, should "
+          "single or multi-threaded eigen be used? "
+          "Only used when --xla_cpu_use_eigen is true."),
+  });
+  ParseFlagsFromEnv(*flag_list);
+}
+
+// Append to *append_to flag definitions associated with XLA's cpu_runtime
+// module.
+void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* append_to) {
+  std::call_once(flags_init, &AllocateFlags);
+  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
+}
+
+// Return a pointer to the CpuRuntimeFlags struct;
+// repeated calls return the same pointer.
+// This should be called only after Flags::Parse() has returned.
+CpuRuntimeFlags* GetCpuRuntimeFlags() {
+  std::call_once(flags_init, &AllocateFlags);
+  return flags;
+}
+
+}  // namespace legacy_flags
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
new file mode 100644
index 0000000000..e3ff30da36
--- /dev/null
+++ b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
@@ -0,0 +1,51 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
+#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
+
+// Legacy flags for the XLA's cpu_runtime module.
+
+#include <vector>
+
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/command_line_flags.h"
+
+namespace xla {
+namespace legacy_flags {
+
+// Append to *flag_list flag definitions associated with XLA's cpu_runtime
+// module.
+void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* flag_list);
+
+// The values of flags associated with XLA's cpu_runtime module.
+typedef struct {
+  // Use Eigen for matrix multiply on the CPU platform. This is a useful hack
+  // for performance comparisons against XLA's implementation.
+  bool xla_cpu_use_eigen;
+  // When generating calls to Eigen for matmul and conv, should single or
+  // multi-threaded eigen be used?  Only used when --xla_cpu_use_eigen is true.
+  bool xla_cpu_multi_thread_eigen;
+} CpuRuntimeFlags;
+
+// Return a pointer to the CpuRuntimeFlags struct;
+// repeated calls return the same pointer.
+// This should be called only after Flags::Parse() has returned.
+CpuRuntimeFlags* GetCpuRuntimeFlags();
+
+}  // namespace legacy_flags
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 93b9281874..68cd545695 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -197,6 +197,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:elemental_ir_emitter",
         "//tensorflow/compiler/xla/service:hlo",
@@ -227,6 +228,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service/llvm_ir:ir_array",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop",
@@ -288,6 +290,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
         "@llvm//:analysis",
@@ -482,6 +485,7 @@ cc_library(
         ":cpu_runtime",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:window_util",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
     ],
 )
@@ -508,6 +512,7 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_pass",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index 93895c3482..8ebf9ab110 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -36,6 +36,7 @@ limitations under the License.
 #include "external/llvm/include/llvm/Transforms/IPO/AlwaysInliner.h"
 #include "external/llvm/include/llvm/Transforms/IPO/PassManagerBuilder.h"
 #include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
@@ -158,7 +159,9 @@ std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl(
   // Our vectorized library calls are currently implement by calling into Eigen.
   // As such, only emit calls to these routines if --xla_cpu_use_eigen is
   // enabled.
-  if (arch == llvm::Triple::x86 || llvm::Triple::x86_64) {
+  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+  if (flags->xla_cpu_use_eigen &&
+      (arch == llvm::Triple::x86 || llvm::Triple::x86_64)) {
     llvm::SmallVector<llvm::StringRef, 32> features;
     feature_string.split(features, ',', -1, /*KeepEmpty=*/false);
     if (std::find(features.begin(), features.end(), "+sse4.1") !=
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
index 069979c661..cdf43587b6 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h"
 
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -29,6 +30,11 @@ namespace xla {
 namespace cpu {
 
 StatusOr<bool> ConvCanonicalization::Run(HloModule* module) {
+  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+  if (!flags->xla_cpu_use_eigen) {
+    return false;
+  }
+
   bool changed = false;
   for (HloInstruction* hlo :
        module->entry_computation()->MakeInstructionPostOrder()) {
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index a8d2565b64..420f9cebc5 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Instructions.h"
 #include "external/llvm/include/llvm/IR/Module.h"
 #include "external/llvm/include/llvm/IR/Value.h"
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
@@ -232,16 +233,22 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   // The two transpose_... parameters are actually booleans, but we use int32
   // to avoid target-dependent calling convention details.
 
+  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+  bool multi_threaded = flags->xla_cpu_multi_thread_eigen;
   PrimitiveType type = target_array_.GetShape().element_type();
   llvm::Type* float_type;
   const char* fn_name;
   switch (type) {
     case F32:
-      fn_name = runtime::kEigenMatmulF32SymbolName;
+      fn_name = multi_threaded
+                    ? runtime::kEigenMatmulF32SymbolName
+                    : runtime::kEigenSingleThreadedMatmulF32SymbolName;
       float_type = ir_builder_->getFloatTy();
       break;
     case F64:
-      fn_name = runtime::kEigenMatmulF64SymbolName;
+      fn_name = multi_threaded
+                    ? runtime::kEigenMatmulF64SymbolName
+                    : runtime::kEigenSingleThreadedMatmulF64SymbolName;
       float_type = ir_builder_->getDoubleTy();
       break;
     default:
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
index 859329e2c1..2d855d0eb1 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 
 #include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/window_util.h"
@@ -25,6 +26,11 @@ namespace cpu {
 
 bool PotentiallyImplementedAsEigenConvolution(
     const HloInstruction& convolution) {
+  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+  if (!flags->xla_cpu_use_eigen) {
+    return false;
+  }
+
   // The following conditions are necessary (but not sufficient) for
   // implementing `convolution` with Eigen convolution:
   // - the input and kernel have a non-zero number of elements.
@@ -76,6 +82,11 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape,
 }  // namespace
 
 bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo) {
+  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+  if (!flags->xla_cpu_use_eigen) {
+    return false;
+  }
+
   // For certain types of Dot, we can call Eigen
   if (hlo.opcode() == HloOpcode::kDot) {
     const Shape& lhs_shape = hlo.operand(0)->shape();
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 695c8069c4..fee5fd8830 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Intrinsics.h"
 #include "external/llvm/include/llvm/IR/LLVMContext.h"
 #include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
@@ -861,7 +862,11 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution,
            int64_type,    int64_type,     int64_type,     int64_type,
            int64_type,    int64_type,     int64_type,     int64_type},
           /*isVarArg=*/false);
-      const char* fn_name = runtime::kEigenConvF32SymbolName;
+      legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+      const char* fn_name =
+          (flags->xla_cpu_multi_thread_eigen
+               ? runtime::kEigenConvF32SymbolName
+               : runtime::kEigenSingleThreadedConvF32SymbolName);
       llvm::Function* conv_func = llvm::cast<llvm::Function>(
           module_->getOrInsertFunction(fn_name, conv_type));
       conv_func->setCallingConv(llvm::CallingConv::C);
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 028ed21076..e54f02b7fb 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -495,6 +495,7 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -511,6 +512,43 @@ xla_test(
 xla_test(
     name = "dot_operation_runtime_test",
     srcs = ["dot_operation_test.cc"],
+    backend_args = {
+        "cpu": ["--xla_cpu_use_eigen"],
+        "cpu_parallel": ["--xla_cpu_use_eigen"],
+    },
+    deps = [
+        "//tensorflow/compiler/xla:array2d",
+        "//tensorflow/compiler/xla:array3d",
+        "//tensorflow/compiler/xla:reference_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
+        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
+        "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:literal_test_util",
+        "//tensorflow/compiler/xla/tests:test_utils",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+    ],
+)
+
+# Repeat dot_operation_runtime_test with single-threded eigen.
+xla_test(
+    name = "dot_operation_single_threaded_runtime_test",
+    srcs = ["dot_operation_test.cc"],
+    backend_args = {
+        "cpu": [
+            "--xla_cpu_use_eigen",
+            "--xla_cpu_multi_thread_eigen=false",
+        ],
+        "cpu_parallel": [
+            "--xla_cpu_use_eigen",
+            "--xla_cpu_multi_thread_eigen=false",
+        ],
+    },
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -518,6 +556,7 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -534,9 +573,11 @@ xla_test(
     srcs = ["dot_operation_test.cc"],
     backend_args = {
         "cpu": [
+            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
         "cpu_parallel": [
+            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
     },
@@ -547,6 +588,7 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 7abef6a27b..b06b5c5f47 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
@@ -460,6 +461,7 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendLayoutUtilFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
-- 
GitLab


From cb4c2f8d16d70ea9de09d29d82934dbb25359700 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Jun 2017 16:49:43 -0700
Subject: [PATCH 0324/1375] Make TransferBufferToInFeed not virual so it
 compiles.

---
 tensorflow/compiler/plugin/executor/transfer_manager.cc | 5 +++++
 tensorflow/compiler/plugin/executor/transfer_manager.h  | 3 +++
 2 files changed, 8 insertions(+)

diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.cc b/tensorflow/compiler/plugin/executor/transfer_manager.cc
index 6a471401fa..51c5deeea5 100644
--- a/tensorflow/compiler/plugin/executor/transfer_manager.cc
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.cc
@@ -147,6 +147,11 @@ Status ExecutorTransferManager::TransferLiteralToInfeed(
   return Status::OK();
 }
 
+Status ExecutorTransferManager::TransferBufferToInfeed(
+    se::StreamExecutor* executor, int64 size, const void* source) {
+  return Unimplemented("Transfer to Infeed");
+}
+
 Status ExecutorTransferManager::TransferLiteralFromOutfeed(
     perftools::gputools::StreamExecutor* executor, const Shape& literal_shape,
     Literal* literal) {
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.h b/tensorflow/compiler/plugin/executor/transfer_manager.h
index 22142cd778..7a42e5a2d7 100644
--- a/tensorflow/compiler/plugin/executor/transfer_manager.h
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.h
@@ -55,6 +55,9 @@ class ExecutorTransferManager : public TransferManager {
   Status TransferLiteralToInfeed(se::StreamExecutor* executor,
                                  const Literal& literal) override;
 
+  Status TransferBufferToInfeed(se::StreamExecutor* executor,
+                                 int64 size, const void* source) override;
+
   Status TransferLiteralFromOutfeed(se::StreamExecutor* executor,
                                     const Shape& literal_shape,
                                     Literal* literal) override;
-- 
GitLab


From aee5dcc06608915f87df0f8debca5eefb6ba1283 Mon Sep 17 00:00:00 2001
From: Dumitru Erhan <dumitru@google.com>
Date: Wed, 21 Jun 2017 16:54:41 -0700
Subject: [PATCH 0325/1375] Raise ValueError if invalid dtype for
 random_uniform.

PiperOrigin-RevId: 159764956
---
 tensorflow/python/ops/random_ops.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py
index 15613289a0..5db0a6fba2 100644
--- a/tensorflow/python/ops/random_ops.py
+++ b/tensorflow/python/ops/random_ops.py
@@ -206,7 +206,8 @@ def random_uniform(shape,
     maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
       the range of random values to generate.  Defaults to 1 if `dtype` is
       floating point.
-    dtype: The type of the output: `float32`, `float64`, `int32`, or `int64`.
+    dtype: The type of the output: 'float16`, `float32`, `float64`, `int32`,
+      or `int64`.
     seed: A Python integer. Used to create a random seed for the distribution.
       See @{tf.set_random_seed}
       for behavior.
@@ -219,6 +220,9 @@ def random_uniform(shape,
     ValueError: If `dtype` is integral and `maxval` is not specified.
   """
   dtype = dtypes.as_dtype(dtype)
+  if dtype not in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
+                   dtypes.int64):
+    raise ValueError("Invalid dtype %r" % dtype)
   if maxval is None:
     if dtype.is_integer:
       raise ValueError("Must specify maxval for integer dtype %r" % dtype)
-- 
GitLab


From 832894ef891568bcd4f56dd6ae4d9503cc41a694 Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Thu, 22 Jun 2017 00:59:54 +0100
Subject: [PATCH 0326/1375] [OpenCL] Registers AdjustContrastv2 (#10949)

* [OpenCL] Registers AdjustContrastv2 (#93)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL (#96)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL

* simplified to #ifndef

* Changed to "#if GOOGLE_CUDA"

* Update adjust_contrast_op_benchmark_test.cc

* Added comments
---
 tensorflow/core/kernels/adjust_contrast_op.cc | 24 +++++++++++++++++++
 .../adjust_contrast_op_benchmark_test.cc      |  5 ++++
 2 files changed, 29 insertions(+)

diff --git a/tensorflow/core/kernels/adjust_contrast_op.cc b/tensorflow/core/kernels/adjust_contrast_op.cc
index c8f12f91a6..37976f7183 100644
--- a/tensorflow/core/kernels/adjust_contrast_op.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op.cc
@@ -31,6 +31,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif
 
 // AdjustContrastOp is deprecated as of GraphDef version >= 2
 
@@ -410,4 +413,25 @@ REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_GPU),
                         AdjustContrastOpv2<GPUDevice>);
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+template <>
+class AdjustContrastOpv2<SYCLDevice> : public AdjustContrastOpV2Base {
+ public:
+  explicit AdjustContrastOpv2(OpKernelConstruction* context)
+      : AdjustContrastOpV2Base(context) {}
+
+  void DoCompute(OpKernelContext* context,
+                 const ComputeOptions& options) override {
+    const int64 shape[4] = {options.batch, options.height, options.width,
+                            options.channels};
+    functor::AdjustContrastv2<SYCLDevice>()(
+        context->eigen_device<SYCLDevice>(),
+        options.input->shaped<float, 4>(shape), options.factor->scalar<float>(),
+        options.output->shaped<float, 4>(shape));
+  }
+};
+REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_SYCL),
+                        AdjustContrastOpv2<SYCLDevice>);
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
index ffd47406eb..c485f14844 100644
--- a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
@@ -56,6 +56,11 @@ static Graph* BM_AdjustContrast(int batches, int width, int height) {
 // BM_AdjustContrast_cpu_1_299_299     179084     340186  2181  751.9M items/s
 // BM_AdjustContrast_gpu_32_299_299     85276     123665  4189  2.9G items/s
 BM_AdjustContrastDev(cpu, 1, 299, 299);
+#if GOOGLE_CUDA
 BM_AdjustContrastDev(gpu, 32, 299, 299);
+#endif  // GOOGLE_CUDA
+#ifdef TENSORFLOW_USE_SYCL
+BM_AdjustContrastDev(sycl, 32, 299, 299);
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
-- 
GitLab


From fc7361081f88321fdaf4ab58e1939e71faf44ddf Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Thu, 22 Jun 2017 01:00:43 +0100
Subject: [PATCH 0327/1375] [OpenCL] Registers RGBToHSV and HSVToRGB (#91)
 (#10848)

* [OpenCL] Added RGBToHSV and HSVToRGB

* Aligning '\'
---
 tensorflow/core/kernels/colorspace_op.cc | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc
index d65a34fd73..ba100b32e7 100644
--- a/tensorflow/core/kernels/colorspace_op.cc
+++ b/tensorflow/core/kernels/colorspace_op.cc
@@ -35,6 +35,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif
 
 template <typename Device, typename T>
 class RGBToHSVOp : public OpKernel {
@@ -146,4 +149,16 @@ TF_CALL_float(REGISTER_GPU);
 TF_CALL_double(REGISTER_GPU);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL(T)                                       \
+  REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_SYCL) \
+                              .TypeConstraint<T>("T"),         \
+                          RGBToHSVOp<SYCLDevice, T>);          \
+  REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_SYCL) \
+                              .TypeConstraint<T>("T"),         \
+                          HSVToRGBOp<SYCLDevice, T>);
+TF_CALL_float(REGISTER_SYCL);
+TF_CALL_double(REGISTER_SYCL);
+#endif
+
 }  // namespace tensorflow
-- 
GitLab


From 70cea913cfe114646bd83b1ebafe6f21ae89e3b5 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Wed, 21 Jun 2017 17:35:09 -0700
Subject: [PATCH 0328/1375] Internal change.

PiperOrigin-RevId: 159769520
---
 tensorflow/tools/test/upload_test_benchmarks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py
index 829333e056..77cc9f75f7 100644
--- a/tensorflow/tools/test/upload_test_benchmarks.py
+++ b/tensorflow/tools/test/upload_test_benchmarks.py
@@ -162,7 +162,7 @@ def upload_benchmark_data(client, data):
   t_val.update({
       "test": test_name,
       "start": start_time,
-      "info": unicode(test_result)
+      "info": unicode(data)
   })
   batch.append(t_val)
 
-- 
GitLab


From af0cbace1d838c12ef82409d751f05d26c7d9aed Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Thu, 22 Jun 2017 01:41:25 +0100
Subject: [PATCH 0329/1375] [OpenCL] Transpose to go through Eigen (#10321)

---
 tensorflow/core/kernels/transpose_functor.h | 7 +++++++
 tensorflow/core/kernels/transpose_op.cc     | 5 +----
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/kernels/transpose_functor.h b/tensorflow/core/kernels/transpose_functor.h
index 124cf14dd2..f1ab770eeb 100644
--- a/tensorflow/core/kernels/transpose_functor.h
+++ b/tensorflow/core/kernels/transpose_functor.h
@@ -132,6 +132,13 @@ template <typename Device, typename T, int NDIMS>
 void TransposeUsingEigen(const Device& d, const Tensor& in,
                          const gtl::ArraySlice<int32> perm, Tensor* out);
 
+
+#ifdef TENSORFLOW_USE_SYCL
+// For SYCL lets always go through Eigen
+template <typename Device, typename T>
+void TransposeSYCL(const Device& d, const Tensor& in,
+                   const gtl::ArraySlice<int32> perm, Tensor* out);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace internal
 
 template <typename Device, typename T>
diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc
index 75ed76a697..d3305fb83a 100644
--- a/tensorflow/core/kernels/transpose_op.cc
+++ b/tensorflow/core/kernels/transpose_op.cc
@@ -233,10 +233,7 @@ Status TransposeSyclOp::DoTranspose(OpKernelContext* ctx, const Tensor& in,
                               .TypeConstraint<int32>("Tperm") \
                               .HostMemory("perm"),            \
                           TransposeSyclOp);
-REGISTER(float);
-REGISTER(bool);
-REGISTER(int32);
+TF_CALL_POD_TYPES(REGISTER);
 #undef REGISTER
 #endif
-
 }  // namespace tensorflow
-- 
GitLab


From 2df6cd3acd71a6dcf459e92c26ac426c90a57be4 Mon Sep 17 00:00:00 2001
From: Todd Wang <toddwang@gmail.com>
Date: Wed, 21 Jun 2017 17:51:55 -0700
Subject: [PATCH 0330/1375] Another try

---
 tensorflow/compiler/xla/util.h | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 42d5c1d155..32aa81d1f4 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -195,16 +195,23 @@ bool IsPermutation(tensorflow::gtl::ArraySlice<int64> permutation, int64 rank);
 // 2. permutation.size() == input.size().
 template <template <typename...> class C, typename T>
 std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
-                       C<T> input_) {
-  tensorflow::gtl::ArraySlice<T> input(input_);
-  CHECK(IsPermutation(permutation, input.size()));
-  std::vector<T> output(input.size());
+                       C<T> input) {
+  tensorflow::gtl::ArraySlice<T> data(input);
+  CHECK(IsPermutation(permutation, data.size()));
+  std::vector<T> output(data.size());
   for (size_t i = 0; i < permutation.size(); ++i) {
-    output[permutation[i]] = input[i];
+    output[permutation[i]] = data[i];
   }
   return output;
 }
 
+// Override of the above that works around compile failures with vectors.
+template <typename T>
+std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
+                       const std::vector<T>& input) {
+  return Permute<std::vector, T>(permutation, input);
+}
+
 // Inverts a permutation, i.e., output_permutation[input_permutation[i]] = i.
 std::vector<int64> InversePermutation(
     tensorflow::gtl::ArraySlice<int64> input_permutation);
-- 
GitLab


From 03da6113468d6b256a755d0521b9b239eaf8b6a9 Mon Sep 17 00:00:00 2001
From: Todd Wang <toddwang@gmail.com>
Date: Wed, 21 Jun 2017 17:54:15 -0700
Subject: [PATCH 0331/1375] Update ir_array.cc

---
 tensorflow/compiler/xla/service/llvm_ir/ir_array.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index b14104be6a..e401305ae7 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -158,7 +158,7 @@ IrArray::Index IrArray::Index::SourceIndexOfTranspose(
     tensorflow::gtl::ArraySlice<int64> dimension_mapping,
     llvm::IRBuilder<>* builder) const {
   std::vector<llvm::Value*> operand_multidim_index =
-      Permute<std::vector, llvm::Value*>(dimension_mapping, multidim());
+      Permute(dimension_mapping, multidim());
   if (linear() != nullptr &&
       ShapeUtil::TransposeIsBitcast(operand_shape, shape, dimension_mapping)) {
     return Index(operand_multidim_index, linear(), operand_shape);
-- 
GitLab


From 52581df7928b137e4831c26696fea8634dbaefb0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 17:52:55 -0700
Subject: [PATCH 0332/1375] Support zero shapes for random_poisson. This
 matches random_uniform.

PiperOrigin-RevId: 159771215
---
 tensorflow/core/kernels/random_poisson_op.cc          | 4 ----
 tensorflow/python/kernel_tests/random_poisson_test.py | 8 +++++++-
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/kernels/random_poisson_op.cc b/tensorflow/core/kernels/random_poisson_op.cc
index 66123e47c6..b3957cbed6 100644
--- a/tensorflow/core/kernels/random_poisson_op.cc
+++ b/tensorflow/core/kernels/random_poisson_op.cc
@@ -303,10 +303,6 @@ class RandomPoissonOp : public OpKernel {
 
     const auto rate_flat = rate_t.flat<T>().data();
     const int64 num_rate = rate_t.NumElements();
-    OP_REQUIRES(
-        ctx, num_rate > 0,
-        errors::InvalidArgument(
-            "Input rate should have non-zero element count, got: ", num_rate));
     auto samples_flat = samples_t->flat<T>().data();
     random::PhiloxRandom rng = generator_.ReserveRandomOutputs(
         num_samples * num_rate, kReservedSamplesPerOutput);
diff --git a/tensorflow/python/kernel_tests/random_poisson_test.py b/tensorflow/python/kernel_tests/random_poisson_test.py
index 01281b7bd0..107c9bbe14 100644
--- a/tensorflow/python/kernel_tests/random_poisson_test.py
+++ b/tensorflow/python/kernel_tests/random_poisson_test.py
@@ -131,8 +131,14 @@ class RandomPoissonTest(test.TestCase):
         # be at least 1 if they are different.
         self.assertGreaterEqual(np.linalg.norm(diff.eval()), 1)
 
+  def testZeroShape(self):
+    with self.test_session():
+      rnd = random_ops.random_poisson([], [], seed=12345)
+      self.assertEqual([0], rnd.get_shape().as_list())
+      self.assertAllClose(np.array([], dtype=np.float32), rnd.eval())
+
   def testShape(self):
-    # Fully known shape.
+    # Fully known shape
     rnd = random_ops.random_poisson(2.0, [150], seed=12345)
     self.assertEqual([150], rnd.get_shape().as_list())
     rnd = random_ops.random_poisson(
-- 
GitLab


From 436eacd5de98783cc8e23f5da9161182aeeaf487 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Jun 2017 18:09:27 -0700
Subject: [PATCH 0333/1375] Blacklist the quantized ops since they have too
 many issues (incorrect shape functions, memory corruptions, ...)

PiperOrigin-RevId: 159772801
---
 tensorflow/core/grappler/optimizers/constant_folding.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 09a4716df5..7cf26afb67 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -102,7 +102,7 @@ string AsControlDependency(const NodeDef& node) {
 ConstantFolding::ConstantFolding() {
   ops_to_preserve_ = std::regex(
       "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|Enter|Exit|"
-      "NextIteration");
+      "NextIteration|.*Quantized.*");
 }
 
 string ConstantFolding::AddControlDependency(const string& input_name) {
-- 
GitLab


From 605d934665056bd9f30d1b34a91ff4cf0fc5172a Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Jun 2017 18:10:04 -0700
Subject: [PATCH 0334/1375] Fixed the shape functions of the QuantizedAdd and
 QuantizedMul ops

PiperOrigin-RevId: 159772841
---
 tensorflow/core/ops/math_ops.cc | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index dbc86e4632..3fe543a239 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -2267,7 +2267,12 @@ REGISTER_OP("QuantizedMul")
     .Attr("T2: quantizedtype")
     .Attr("Toutput: quantizedtype = DT_QINT32")
     .SetIsCommutative()
-    .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn)
+    .SetShapeFn([](InferenceContext* c) {
+      TF_RETURN_IF_ERROR(shape_inference::BroadcastBinaryOpShapeFn(c));
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Scalar());
+      return Status::OK();
+    })
     .Doc(R"doc(
 Returns x * y element-wise, working on quantized buffers.
 
@@ -2296,7 +2301,12 @@ REGISTER_OP("QuantizedAdd")
     .Attr("T2: quantizedtype")
     .Attr("Toutput: quantizedtype = DT_QINT32")
     .SetIsCommutative()
-    .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn)
+    .SetShapeFn([](InferenceContext* c) {
+      TF_RETURN_IF_ERROR(shape_inference::BroadcastBinaryOpShapeFn(c));
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Scalar());
+      return Status::OK();
+    })
     .Doc(R"doc(
 Returns x + y element-wise, working on quantized buffers.
 
-- 
GitLab


From 4cfd0f0aa2965cf78bb63c910c4d1b229db37ba5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 18:22:53 -0700
Subject: [PATCH 0335/1375] Switch from assigning
 namedtuple.__new__.__defaults__ to overwriting __new__.

Assigning __defaults__ relies on an implementation detail of CPython, confuses
type checkers (and developers :)), and is error-prone since it doesn't make the
relationship between parameter names and default values explicit.
This CL switches to overloading __new__ instead.

PiperOrigin-RevId: 159773922
---
 tensorflow/python/ops/parsing_ops.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/ops/parsing_ops.py b/tensorflow/python/ops/parsing_ops.py
index c2f9961731..0071e7d868 100644
--- a/tensorflow/python/ops/parsing_ops.py
+++ b/tensorflow/python/ops/parsing_ops.py
@@ -120,8 +120,10 @@ class SparseFeature(
       `value_key` are already sorted by their index position. If so skip
       sorting. False by default (optional).
   """
-  pass
-SparseFeature.__new__.__defaults__ = (False,)
+
+  def __new__(cls, index_key, value_key, dtype, size, already_sorted=False):
+    return super(SparseFeature, cls).__new__(
+        cls, index_key, value_key, dtype, size, already_sorted)
 
 
 class FixedLenFeature(collections.namedtuple(
@@ -137,8 +139,10 @@ class FixedLenFeature(collections.namedtuple(
     default_value: Value to be used if an example is missing this feature. It
         must be compatible with `dtype` and of the specified `shape`.
   """
-  pass
-FixedLenFeature.__new__.__defaults__ = (None,)
+
+  def __new__(cls, shape, dtype, default_value=None):
+    return super(FixedLenFeature, cls).__new__(
+        cls, shape, dtype, default_value)
 
 
 class FixedLenSequenceFeature(collections.namedtuple(
@@ -168,8 +172,10 @@ class FixedLenSequenceFeature(collections.namedtuple(
       `SequenceExample`. Defaults to "" for dtype string and 0 otherwise
       (optional).
   """
-  pass
-FixedLenSequenceFeature.__new__.__defaults__ = (False, None)
+
+  def __new__(cls, shape, dtype, allow_missing=False, default_value=None):
+    return super(FixedLenSequenceFeature, cls).__new__(
+        cls, shape, dtype, allow_missing, default_value)
 
 
 def _features_to_raw_params(features, types):
-- 
GitLab


From 90c4406b7dcf52df4f538fa19dd375f42aaaa682 Mon Sep 17 00:00:00 2001
From: Rishabh Patel <patelrishabh@users.noreply.github.com>
Date: Wed, 21 Jun 2017 19:42:58 -0700
Subject: [PATCH 0336/1375] Correct the learning rate as per the code snippet

---
 tensorflow/docs_src/get_started/mnist/beginners.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 624d916474..175de2be76 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -367,7 +367,7 @@ train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
 
 In this case, we ask TensorFlow to minimize `cross_entropy` using the
 [gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent)
-with a learning rate of 0.5. Gradient descent is a simple procedure, where
+with a learning rate of 0.05. Gradient descent is a simple procedure, where
 TensorFlow simply shifts each variable a little bit in the direction that
 reduces the cost. But TensorFlow also provides
 @{$python/train#Optimizers$many other optimization algorithms}:
-- 
GitLab


From 6611a71f3fab24466c33cc45eb04e72ced28d8d2 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Jun 2017 20:31:55 -0700
Subject: [PATCH 0337/1375] Made sure that we can call the constant folding
 code twice safely.

PiperOrigin-RevId: 159781607
---
 .../grappler/optimizers/constant_folding.cc   | 44 +++++++++++++------
 1 file changed, 30 insertions(+), 14 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 7cf26afb67..9a8be6e39a 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -446,10 +446,22 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
         // Try the next input
         continue;
       }
+
+      string const_out_name =
+          AddPrefixToNodeName(node.name(), kConstantFoldingConst);
+      string const_index_name = AddPrefixToNodeName(
+          strings::StrCat(node.name(), "_index"), kConstantFoldingConst);
+      if (node_map_->GetNode(const_out_name) ||
+          node_map_->GetNode(const_index_name)) {
+        // Intended name already exists.
+        return errors::AlreadyExists(
+            strings::StrCat(const_out_name, " or ", const_index_name,
+                            "already present in the graph"));
+      }
+
       NodeDef* const_out = output->add_node();
       *const_out = *input_node;
-      const_out->set_name(
-          AddPrefixToNodeName(node.name(), kConstantFoldingConst));
+      const_out->set_name(const_out_name);
       *const_out->add_input() = AsControlDependency(node);
       node_map_->AddNode(const_out->name(), const_out);
 
@@ -460,8 +472,7 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
       (*const_index->mutable_attr())["dtype"].set_type(DT_INT32);
       index.AsProtoTensorContent(
           (*const_index->mutable_attr())["value"].mutable_tensor());
-      const_index->set_name(AddPrefixToNodeName(
-          strings::StrCat(node.name(), "_index"), kConstantFoldingConst));
+      const_index->set_name(const_index_name);
       *const_index->add_input() = AsControlDependency(node);
       node_map_->AddNode(const_index->name(), const_index);
 
@@ -498,6 +509,12 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
       // below to preserve the overall behavior of the graph wrt dead edges.
       continue;
     }
+
+    if (node_map_->GetNode(const_node.name())) {
+      // Intended name already exists.
+      return errors::AlreadyExists(
+          strings::StrCat(const_node.name(), "already present in the graph"));
+    }
     NodeDef* added_node = output->add_node();
     *added_node = const_node;
     node_map_->AddNode(added_node->name(), added_node);
@@ -551,9 +568,10 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
 }
 
 Status ConstantFolding::FoldGraph(GraphDef* output) {
-  std::set<string> processed_nodes;
-  while (1) {
-    int previous_processed = processed_nodes.size();
+  std::unordered_set<string> processed_nodes;
+  int previously_processed = 0;
+  do {
+    previously_processed = processed_nodes.size();
     for (const auto& node : graph_.node()) {
       if (IsFoldable(node) &&
           processed_nodes.find(node.name()) == processed_nodes.end()) {
@@ -561,13 +579,11 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
         processed_nodes.insert(node.name());
       }
     }
-    int current_processed = processed_nodes.size();
-    LOG(INFO) << "Previous number of processed nodes: " << previous_processed
-              << "; Current number of processed nodes: " << current_processed;
-    if (current_processed == previous_processed) {
-      break;
-    }
-  }
+    // Try again as long as we find new constants. In most cases, this loop will
+    // only run once since the graph is already in topological order.
+    VLOG(1) << "Folded " << processed_nodes.size() - previously_processed
+            << " nodes in this pass";
+  } while (previously_processed != processed_nodes.size());
 
   // Build the graph after constant folding. Note that we keep all processed
   // nodes in the graph in case users need to fetch their values.
-- 
GitLab


From 72e2918ccd615691591c70c2200d3c25b8fd7d35 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <taehoonlee@snu.ac.kr>
Date: Thu, 22 Jun 2017 13:57:31 +0900
Subject: [PATCH 0338/1375] Fix typos

---
 tensorflow/compiler/tests/ftrl_test.py                 |  2 +-
 tensorflow/compiler/xla/xla_data.proto                 |  2 +-
 .../distributions/python/ops/relaxed_bernoulli.py      |  2 +-
 .../contrib/distributions/python/ops/sample_stats.py   |  2 +-
 tensorflow/contrib/graph_editor/transform.py           |  2 +-
 .../contrib/training/python/training/evaluation.py     |  2 +-
 tensorflow/core/lib/gtl/optional.h                     |  2 +-
 .../docs_src/api_guides/python/contrib.losses.md       | 10 +++++-----
 tensorflow/python/debug/cli/analyzer_cli.py            |  2 +-
 9 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py
index 6b328fb618..a75a5cd2cf 100644
--- a/tensorflow/compiler/tests/ftrl_test.py
+++ b/tensorflow/compiler/tests/ftrl_test.py
@@ -218,7 +218,7 @@ class FtrlOptimizerTest(XLATestCase):
         self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval())
         self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval())
 
-  # When variables are intialized with Zero, FTRL-Proximal has two properties:
+  # When variables are initialized with Zero, FTRL-Proximal has two properties:
   # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
   # with GradientDescent.
   # 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is idential
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index b53bf98e1c..86c72b3449 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -200,7 +200,7 @@ message OpMetadata {
   string op_name = 2;
   // Indicate a file and line that this op is associated to in a user's program.
   //
-  // e.g. it could be be the file and line of user code that generated the op.
+  // e.g. it could be the file and line of user code that generated the op.
   string source_file = 3;
   int32 source_line = 4;
 }
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
index 5b57a95c55..b525809015 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
@@ -52,7 +52,7 @@ class RelaxedBernoulli(transformed_distribution.TransformedDistribution):
   the RelaxedBernoulli can suffer from underflow issues. In many case loss
   functions such as these are invariant under invertible transformations of
   the random variables. The KL divergence, found in the variational autoencoder
-  loss, is an example. Because RelaxedBernoullis are sampled by by a Logistic
+  loss, is an example. Because RelaxedBernoullis are sampled by a Logistic
   random variable followed by a `tf.sigmoid` op, one solution is to treat
   the Logistic as the random variable and `tf.sigmoid` as downstream. The
   KL divergences of two Logistics, which are always followed by a `tf.sigmoid`
diff --git a/tensorflow/contrib/distributions/python/ops/sample_stats.py b/tensorflow/contrib/distributions/python/ops/sample_stats.py
index 26cf922d0a..2a4b92c729 100644
--- a/tensorflow/contrib/distributions/python/ops/sample_stats.py
+++ b/tensorflow/contrib/distributions/python/ops/sample_stats.py
@@ -47,7 +47,7 @@ def percentile(x,
   """Compute the `q`-th percentile of `x`.
 
   Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the
-  way from the minimum to the maximum in in a sorted copy of `x`.
+  way from the minimum to the maximum in a sorted copy of `x`.
 
   The values and distances of the two nearest neighbors as well as the
   `interpolation` parameter will determine the percentile if the normalized
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index 762bc44814..2234400fdc 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -446,7 +446,7 @@ class Transformer(object):
       # TODO(fkp): return a subgraph?
       op_, op_outputs_ = self.transform_op_handler(info, op)
       if op is op_:
-        raise ValueError("In-place tranformation not allowed.")
+        raise ValueError("In-place transformation not allowed.")
 
       # Process op.
       info.transformed_ops[op] = op_
diff --git a/tensorflow/contrib/training/python/training/evaluation.py b/tensorflow/contrib/training/python/training/evaluation.py
index 24b733dd29..a895d90b8e 100644
--- a/tensorflow/contrib/training/python/training/evaluation.py
+++ b/tensorflow/contrib/training/python/training/evaluation.py
@@ -226,7 +226,7 @@ def checkpoints_iterator(checkpoint_dir,
 
   This behavior gives control to callers on what to do if checkpoints do not
   come fast enough or stop being generated.  For example, if callers have a way
-  to detect that the training has stopped and know that no new new checkpoints
+  to detect that the training has stopped and know that no new checkpoints
   will be generated, they can provide a `timeout_fn` that returns `True` when
   the training has stopped.  If they know that the training is still going on
   they return `False` instead.
diff --git a/tensorflow/core/lib/gtl/optional.h b/tensorflow/core/lib/gtl/optional.h
index 8ba4b09143..2ff8b9c7d1 100644
--- a/tensorflow/core/lib/gtl/optional.h
+++ b/tensorflow/core/lib/gtl/optional.h
@@ -656,7 +656,7 @@ class optional : private internal_optional::optional_data<T>,
   constexpr const T& reference() const { return *this->pointer(); }
   T& reference() { return *(this->pointer()); }
 
-  // T constaint checks.  You can't have an optional of nullopt_t, in_place_t or
+  // T constraint checks.  You can't have an optional of nullopt_t, in_place_t or
   // a reference.
   static_assert(
       !std::is_same<nullopt_t, typename std::remove_cv<T>::type>::value,
diff --git a/tensorflow/docs_src/api_guides/python/contrib.losses.md b/tensorflow/docs_src/api_guides/python/contrib.losses.md
index 8c289dd556..30123e367f 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.losses.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.losses.md
@@ -13,8 +13,8 @@ of samples in the batch and `d1` ... `dN` are the remaining dimensions.
 It is common, when training with multiple loss functions, to adjust the relative
 strengths of individual losses. This is performed by rescaling the losses via
 a `weight` parameter passed to the loss functions. For example, if we were
-training with both log_loss and mean_square_error, and we wished that the
-log_loss penalty be twice as severe as the mean_square_error, we would
+training with both log_loss and mean_squared_error, and we wished that the
+log_loss penalty be twice as severe as the mean_squared_error, we would
 implement this as:
 
 ```python
@@ -22,7 +22,7 @@ implement this as:
   tf.contrib.losses.log(predictions, labels, weight=2.0)
 
   # Uses default weight of 1.0
-  tf.contrib.losses.mean_square_error(predictions, labels)
+  tf.contrib.losses.mean_squared_error(predictions, labels)
 
   # All the losses are collected into the `GraphKeys.LOSSES` collection.
   losses = tf.get_collection(tf.GraphKeys.LOSSES)
@@ -74,7 +74,7 @@ these predictions.
   predictions = MyModelPredictions(images)
 
   weight = tf.cast(tf.greater(depths, 0), tf.float32)
-  loss  = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss  = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 Note that when using weights for the losses, the final average is computed
@@ -100,7 +100,7 @@ weighted average over the individual prediction errors:
 
   weight = MyComplicatedWeightingFunction(labels)
   weight = tf.div(weight, tf.size(weight))
-  loss = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 @{tf.contrib.losses.absolute_difference}
diff --git a/tensorflow/python/debug/cli/analyzer_cli.py b/tensorflow/python/debug/cli/analyzer_cli.py
index da27f4cebe..e863c2ddb9 100644
--- a/tensorflow/python/debug/cli/analyzer_cli.py
+++ b/tensorflow/python/debug/cli/analyzer_cli.py
@@ -1312,7 +1312,7 @@ class DebugAnalyzer(object):
     all_inputs = copy.copy(tracker(node_name, is_control=False))
     is_ctrl = [False] * len(all_inputs)
     if include_control:
-      # Sort control inputs or recipients in in alphabetical order of the node
+      # Sort control inputs or recipients in alphabetical order of the node
       # names.
       ctrl_inputs = sorted(tracker(node_name, is_control=True))
       all_inputs.extend(ctrl_inputs)
-- 
GitLab


From c2cfe862224c9d9b76053e494eba8dad4ac8e155 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 22:05:01 -0700
Subject: [PATCH 0339/1375] Added batch_matmul op dependence to
 android_extended_ops

PiperOrigin-RevId: 159787178
---
 tensorflow/contrib/makefile/tf_op_files.txt | 1 +
 tensorflow/core/kernels/BUILD               | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index aa70c3e218..aeb3c45697 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -215,6 +215,7 @@ tensorflow/core/kernels/requantization_range_op.cc
 tensorflow/core/kernels/requantize.cc
 tensorflow/core/kernels/remote_fused_graph_execute_op.cc
 tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+tensorflow/core/kernels/batch_matmul_op_real.cc
 tensorflow/core/ops/training_ops.cc
 tensorflow/core/ops/string_ops.cc
 tensorflow/core/ops/state_ops.cc
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 2bfbad5dc4..fc4cc2d83a 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -4101,6 +4101,7 @@ filegroup(
     srcs = [
         "argmax_op.h",
         "avgpooling_op.h",
+        "batch_matmul_op_impl.h",
         "batch_norm_op.h",
         "control_flow_ops.h",
         "conv_2d.h",
@@ -4145,6 +4146,7 @@ filegroup(
     srcs = [
         "argmax_op.cc",
         "avgpooling_op.cc",
+        "batch_matmul_op_real.cc",
         "batch_norm_op.cc",
         "bcast_ops.cc",
         "check_numerics_op.cc",
-- 
GitLab


From df2b6a2ce183492f0fb276800d53608f4f4fd13b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 22:41:01 -0700
Subject: [PATCH 0340/1375] Fixes a TODO in head_test.

PiperOrigin-RevId: 159789178
---
 tensorflow/python/estimator/BUILD             |  2 ++
 .../python/estimator/canned/head_test.py      | 21 ++-----------------
 2 files changed, 4 insertions(+), 19 deletions(-)

diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 0cfd02466e..27002d9480 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -394,7 +394,9 @@ py_test(
     size = "small",
     srcs = ["canned/head_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["no_pip"],
     deps = [
+        ":dnn_testing_utils",
         ":head",
         ":metric_keys",
         ":model_fn",
diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py
index c6ea54f08e..95d4f80308 100644
--- a/tensorflow/python/estimator/canned/head_test.py
+++ b/tensorflow/python/estimator/canned/head_test.py
@@ -23,6 +23,7 @@ import six
 
 from tensorflow.core.framework import summary_pb2
 from tensorflow.python.estimator import model_fn
+from tensorflow.python.estimator.canned import dnn_testing_utils
 from tensorflow.python.estimator.canned import head as head_lib
 from tensorflow.python.estimator.canned import metric_keys
 from tensorflow.python.estimator.canned import prediction_keys
@@ -83,24 +84,6 @@ def _sigmoid(logits):
   return 1 / (1 + np.exp(-logits))
 
 
-# TODO(roumposg): Reuse the code from dnn_testing_utils.
-def _assert_close(expected, actual, rtol=1e-04, message='',
-                  name='assert_close'):
-  with ops.name_scope(name, 'assert_close', (expected, actual, rtol)) as scope:
-    expected = ops.convert_to_tensor(expected, name='expected')
-    actual = ops.convert_to_tensor(actual, name='actual')
-    rdiff = math_ops.abs((expected - actual) / expected, 'diff')
-    rtol = ops.convert_to_tensor(rtol, name='rtol')
-    return check_ops.assert_less(
-        rdiff,
-        rtol,
-        data=(message, 'Condition expected =~ actual did not hold element-wise:'
-              'expected = ', expected, 'actual = ', actual, 'rdiff = ', rdiff,
-              'rtol = ', rtol,),
-        summarize=expected.get_shape().num_elements(),
-        name=scope)
-
-
 class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase):
 
   def test_n_classes_is_none(self):
@@ -1026,7 +1009,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase):
     #      = 1.2484322
     expected_loss = 1.2484322
     def _train_op_fn(loss):
-      with ops.control_dependencies((_assert_close(
+      with ops.control_dependencies((dnn_testing_utils.assert_close(
           math_ops.to_float(expected_loss), math_ops.to_float(loss)),)):
         return constant_op.constant(expected_train_result)
     spec = head.create_estimator_spec(
-- 
GitLab


From d19f8b1e35409ef89463c8b84bef97cf53f43859 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Jun 2017 22:51:59 -0700
Subject: [PATCH 0341/1375] When configuring per-session thread pools, allow a
 pool to be a global pool. This allows a division between large and small
 pools, without needing to make new pool for each session.

PiperOrigin-RevId: 159789678
---
 .../core/common_runtime/direct_session.cc     |  69 +++++++++---
 .../core/common_runtime/direct_session.h      |   8 +-
 .../common_runtime/direct_session_test.cc     | 103 ++++++++++++++----
 tensorflow/core/protobuf/config.proto         |  34 +++++-
 tensorflow/python/client/session_test.py      |  11 ++
 5 files changed, 179 insertions(+), 46 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index 17d966f770..b2dd4c6e52 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -85,17 +85,48 @@ thread::ThreadPool* NewThreadPoolFromSessionOptions(
   return new thread::ThreadPool(options.env, "Compute", num_threads);
 }
 
-thread::ThreadPool* NewThreadPoolFromThreadPoolOptions(
+Status NewThreadPoolFromThreadPoolOptions(
     const SessionOptions& options,
-    const ThreadPoolOptionProto& thread_pool_options, int pool_number) {
+    const ThreadPoolOptionProto& thread_pool_options, int pool_number,
+    thread::ThreadPool** pool, bool* owned) {
   int32 num_threads = thread_pool_options.num_threads();
   if (num_threads == 0) {
     num_threads = NumInterOpThreadsFromSessionOptions(options);
   }
-  VLOG(1) << "Direct session inter op parallelism threads for pool "
-          << pool_number << ": " << num_threads;
-  return new thread::ThreadPool(
-      options.env, strings::StrCat("Compute", pool_number), num_threads);
+  const string& name = thread_pool_options.global_name();
+  if (name.empty()) {
+    // Session-local threadpool.
+    VLOG(1) << "Direct session inter op parallelism threads for pool "
+            << pool_number << ": " << num_threads;
+    *pool = new thread::ThreadPool(
+        options.env, strings::StrCat("Compute", pool_number), num_threads);
+    *owned = true;
+    return Status::OK();
+  }
+
+  // Global, named threadpool.
+  typedef std::pair<int32, thread::ThreadPool*> MapValue;
+  static std::map<string, MapValue>* global_pool_map =
+      new std::map<string, MapValue>;
+  static mutex* mu = new mutex();
+  mutex_lock l(*mu);
+  MapValue* mvalue = &(*global_pool_map)[name];
+  if (mvalue->second == nullptr) {
+    mvalue->first = thread_pool_options.num_threads();
+    mvalue->second = new thread::ThreadPool(
+        options.env, strings::StrCat("Compute", pool_number), num_threads);
+  } else {
+    if (mvalue->first != thread_pool_options.num_threads()) {
+      return errors::InvalidArgument(
+          "Pool ", name,
+          " configured previously with num_threads=", mvalue->first,
+          "; cannot re-configure with num_threads=",
+          thread_pool_options.num_threads());
+    }
+  }
+  *owned = false;
+  *pool = mvalue->second;
+  return Status::OK();
 }
 
 thread::ThreadPool* GlobalThreadPool(const SessionOptions& options) {
@@ -232,16 +263,18 @@ DirectSession::DirectSession(const SessionOptions& options,
   if (options_.config.session_inter_op_thread_pool_size() > 0) {
     for (int i = 0; i < options_.config.session_inter_op_thread_pool_size();
          ++i) {
-      thread_pools_.push_back(NewThreadPoolFromThreadPoolOptions(
-          options_, options_.config.session_inter_op_thread_pool(i), i));
+      thread::ThreadPool* pool = nullptr;
+      bool owned = false;
+      init_error_.Update(NewThreadPoolFromThreadPoolOptions(
+          options_, options_.config.session_inter_op_thread_pool(i), i, &pool,
+          &owned));
+      thread_pools_.emplace_back(pool, owned);
     }
-    owns_thread_pools_ = true;
   } else if (options_.config.use_per_session_threads()) {
-    thread_pools_.push_back(NewThreadPoolFromSessionOptions(options_));
-    owns_thread_pools_ = true;
+    thread_pools_.emplace_back(NewThreadPoolFromSessionOptions(options_),
+                               true /* owned */);
   } else {
-    thread_pools_.push_back(GlobalThreadPool(options));
-    owns_thread_pools_ = false;
+    thread_pools_.emplace_back(GlobalThreadPool(options), false /* owned */);
   }
   // The default value of sync_on_finish will be flipped soon and this
   // environment variable will be removed as well.
@@ -290,8 +323,8 @@ DirectSession::~DirectSession() {
     d->op_segment()->RemoveHold(session_handle_);
   }
   delete cancellation_manager_;
-  if (owns_thread_pools_) {
-    for (auto* p : thread_pools_) delete p;
+  for (const auto& p_and_owned : thread_pools_) {
+    if (p_and_owned.second) delete p_and_owned.first;
   }
 
   execution_state_.reset(nullptr);
@@ -330,6 +363,7 @@ Status DirectSession::MaybeInitializeExecutionState(
 }
 
 Status DirectSession::Create(const GraphDef& graph) {
+  TF_RETURN_IF_ERROR(init_error_);
   if (graph.node_size() > 0) {
     mutex_lock l(graph_def_lock_);
     if (graph_created_) {
@@ -423,7 +457,8 @@ Status DirectSession::Run(const RunOptions& run_options,
     return errors::InvalidArgument("Invalid inter_op_thread_pool: ",
                                    run_options.inter_op_thread_pool());
   }
-  thread::ThreadPool* pool = thread_pools_[run_options.inter_op_thread_pool()];
+  thread::ThreadPool* pool =
+      thread_pools_[run_options.inter_op_thread_pool()].first;
 
   // Check if we already have an executor for these arguments.
   ExecutorsAndKeys* executors_and_keys;
@@ -644,7 +679,7 @@ Status DirectSession::PRunSetup(const std::vector<string>& input_names,
   }
 
   // RunOptions is not available in PRunSetup, so use thread pool 0.
-  thread::ThreadPool* pool = thread_pools_[0];
+  thread::ThreadPool* pool = thread_pools_[0].first;
 
   // Check if we already have an executor for these arguments.
   ExecutorsAndKeys* executors_and_keys;
diff --git a/tensorflow/core/common_runtime/direct_session.h b/tensorflow/core/common_runtime/direct_session.h
index b14a517188..c3cc573f2e 100644
--- a/tensorflow/core/common_runtime/direct_session.h
+++ b/tensorflow/core/common_runtime/direct_session.h
@@ -266,9 +266,11 @@ class DirectSession : public Session {
   mutex graph_def_lock_;
   GraphDef graph_def_ GUARDED_BY(graph_def_lock_);
 
-  // The thread-pools to use for running ops.
-  std::vector<thread::ThreadPool*> thread_pools_;
-  bool owns_thread_pools_ = false;
+  // The thread-pools to use for running ops, with a bool indicating if the pool
+  // is owned.
+  std::vector<std::pair<thread::ThreadPool*, bool>> thread_pools_;
+
+  Status init_error_;  // Set to an error if construction failed.
 
   // If true, blocks until device has finished all queued operations in a step.
   bool sync_on_finish_ = true;
diff --git a/tensorflow/core/common_runtime/direct_session_test.cc b/tensorflow/core/common_runtime/direct_session_test.cc
index 107c84e39a..5d6b2eeeed 100644
--- a/tensorflow/core/common_runtime/direct_session_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_test.cc
@@ -882,7 +882,8 @@ class BlockingOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("BlockingOp").Device(DEVICE_CPU), BlockingOp);
 REGISTER_OP("BlockingOp").Input("x: float").Output("y: float").Doc("");
 
-static void TestSessionInterOpThreadsImpl(bool use_function_lib) {
+static void TestSessionInterOpThreadsImpl(bool use_function_lib,
+                                          bool use_global_pools) {
   FunctionDefLibrary library_graph_def;
   if (use_function_lib) {
     const string lib = R"proto(
@@ -921,24 +922,45 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib) {
   (*options.config.mutable_device_count())["GPU"] = 0;
   (*options.config.mutable_device_count())["SYCL"] = 0;
 
-  options.config.add_session_inter_op_thread_pool();
   auto* p = options.config.add_session_inter_op_thread_pool();
+  if (use_global_pools) p->set_global_name("large pool");
+  p = options.config.add_session_inter_op_thread_pool();
+  if (use_global_pools) p->set_global_name("small pool");
   p->set_num_threads(1);
   const int kLargePool = 0;
   const int kSmallPool = 1;
 
-  std::unique_ptr<Session> session(NewSession(options));
-  ASSERT_TRUE(session != nullptr);
-  TF_ASSERT_OK(session->Create(def));
+  std::vector<std::unique_ptr<Session>> sessions;
+  if (!use_global_pools) {
+    sessions.emplace_back(NewSession(options));
+    TF_ASSERT_OK(sessions.back()->Create(def));
+  }
+  mutex sessions_mu;
 
   std::atomic<int32> num_done(0);
   // Runs session to compute <node>:0 using inter_op thread pool <pool>.
-  auto add_session_run_call = [&session, &num_done](
-      thread::ThreadPool* tp, Node* node, int inter_op_pool) {
-    auto fn = [&session, inter_op_pool, node, &num_done]() {
+  auto add_session_run_call = [use_global_pools, &def, &options, &sessions,
+                               &sessions_mu,
+                               &num_done](thread::ThreadPool* tp, Node* node,
+                                          int inter_op_pool) {
+    auto fn = [use_global_pools, &def, &options, &sessions, &sessions_mu,
+               inter_op_pool, node, &num_done]() {
       RunOptions run_options;
       run_options.set_inter_op_thread_pool(inter_op_pool);
       std::vector<Tensor> outputs;
+
+      Session* session;
+      if (use_global_pools) {
+        std::unique_ptr<Session> s(NewSession(options));
+        TF_ASSERT_OK(s->Create(def));
+        session = s.get();
+
+        mutex_lock l(sessions_mu);
+        sessions.emplace_back(std::move(s));
+      } else {
+        session = sessions[0].get();
+      }
+
       Status s = session->Run(run_options, {} /* inputs */,
                               {node->name() + ":0"} /* output_names */, {},
                               &outputs, nullptr /* run_metadata */);
@@ -999,11 +1021,23 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib) {
 }
 
 TEST(DirectSessionTest, TestSessionInterOpThreads) {
-  TestSessionInterOpThreadsImpl(false /* use_function_lib */);
+  TestSessionInterOpThreadsImpl(false /* use_function_lib */,
+                                false /*use_global_pools */);
 }
 
 TEST(DirectSessionTest, TestSessionInterOpThreadsWithFunctions) {
-  TestSessionInterOpThreadsImpl(true /* use_function_lib */);
+  TestSessionInterOpThreadsImpl(true /* use_function_lib */,
+                                false /*use_global_pools */);
+}
+
+TEST(DirectSessionTest, TestSessionInterOpGlobalPools) {
+  TestSessionInterOpThreadsImpl(false /* use_function_lib */,
+                                true /*use_global_pools */);
+}
+
+TEST(DirectSessionTest, TestSessionInterOpGlobalPoolsWithFunctions) {
+  TestSessionInterOpThreadsImpl(true /* use_function_lib */,
+                                true /*use_global_pools */);
 }
 
 TEST(DirectSessionTest, TestSessionInterOpThreadsInvalidOptions) {
@@ -1023,19 +1057,42 @@ TEST(DirectSessionTest, TestSessionInterOpThreadsInvalidOptions) {
   options.config.add_session_inter_op_thread_pool();
 
   // Wrong pool number on Run call.
-  std::unique_ptr<Session> session(NewSession(options));
-  ASSERT_TRUE(session != nullptr);
-  TF_ASSERT_OK(session->Create(def));
-  for (int pool_num = -1; pool_num <= 1; pool_num += 2) {
-    RunOptions run_options;
-    run_options.set_inter_op_thread_pool(pool_num);
-    std::vector<Tensor> outputs;
-    Status s = session->Run(run_options, {} /* inputs */,
-                            {x->name() + ":0"} /* output_names */, {}, &outputs,
-                            nullptr /* run_metadata */);
-    EXPECT_EQ(strings::StrCat(
-                  "Invalid argument: Invalid inter_op_thread_pool: ", pool_num),
-              s.ToString());
+  {
+    std::unique_ptr<Session> session(NewSession(options));
+    TF_ASSERT_OK(session->Create(def));
+    for (int pool_num = -1; pool_num <= 1; pool_num += 2) {
+      RunOptions run_options;
+      run_options.set_inter_op_thread_pool(pool_num);
+      std::vector<Tensor> outputs;
+      Status s = session->Run(run_options, {} /* inputs */,
+                              {x->name() + ":0"} /* output_names */, {},
+                              &outputs, nullptr /* run_metadata */);
+      EXPECT_EQ(
+          strings::StrCat("Invalid argument: Invalid inter_op_thread_pool: ",
+                          pool_num),
+          s.ToString());
+    }
+  }
+
+  // Global name changes thread count.
+  std::vector<std::unique_ptr<Session>> sessions;
+  auto* pool_config = options.config.mutable_session_inter_op_thread_pool(0);
+  pool_config->set_num_threads(0);
+  pool_config->set_global_name("foo");
+  sessions.emplace_back(NewSession(options));
+  TF_ASSERT_OK(sessions.back()->Create(def));
+  sessions.emplace_back(NewSession(options));  // repeat creation, okay.
+  TF_ASSERT_OK(sessions.back()->Create(def));
+  for (int pass = 0; pass < 2; ++pass) {
+    for (int i = 1; i < 128; ++i) {
+      pool_config->set_num_threads(i);
+      sessions.emplace_back(NewSession(options));
+      auto status = sessions.back()->Create(def);
+      ASSERT_FALSE(status.ok()) << status;
+    }
+
+    // Clear existing sessions before second pass; error still happens.
+    sessions.clear();
   }
 }
 
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index c57b110847..1bcabf8e1d 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -171,6 +171,23 @@ message ThreadPoolOptionProto {
   // 0 means the system picks a value based on where this option proto is used
   // (see the declaration of the specific field for more info).
   int32 num_threads = 1;
+
+  // The global name of the threadpool.
+  //
+  // If empty, then the threadpool is made and used according to the scope it's
+  // in - e.g., for a session threadpool, it is used by that session only.
+  //
+  // If non-empty, then:
+  // - a global threadpool associated with this name is looked
+  //   up or created. This allows, for example, sharing one threadpool across
+  //   many sessions (e.g., like the default behavior, if
+  //   inter_op_parallelism_threads is not configured), but still partitioning
+  //   into a large and small pool.
+  // - if the threadpool for this global_name already exists, then it is an
+  //   error if the existing pool was created using a different num_threads
+  //   value as is specified on this call.
+  // - threadpools created this way are never garbage collected.
+  string global_name = 2;
 };
 
 message RPCOptions {
@@ -218,13 +235,24 @@ message ConfigProto {
   bool use_per_session_threads = 9;
 
   // This option is experimental - it may be replaced with a different mechanism
-  // in the future. The intended use is for when some session invocations need
-  // to run in a background pool limited to a small number of threads.
+  // in the future.
   //
   // Configures session thread pools. If this is configured, then RunOptions for
   // a Run call can select the thread pool to use.
   //
-  // If a pool's num_threads is 0, then inter_op_parallelism_threads is used.
+  // The intended use is for when some session invocations need to run in a
+  // background pool limited to a small number of threads:
+  // - For example, a session may be configured to have one large pool (for
+  // regular compute) and one small pool (for periodic, low priority work);
+  // using the small pool is currently the mechanism for limiting the inter-op
+  // parallelism of the low priority work.  Note that it does not limit the
+  // parallelism of work spawned by a single op kernel implementation.
+  // - Using this setting is normally not needed in training, but may help some
+  // serving use cases.
+  // - It is also generally recommended to set the global_name field of this
+  // proto, to avoid creating multiple large pools. It is typically better to
+  // run the non-low-priority work, even across sessions, in a single large
+  // pool.
   repeated ThreadPoolOptionProto session_inter_op_thread_pool = 12;
 
   // Assignment of Nodes to Devices is recomputed every placement_period
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index ef8f25c873..103e864f4b 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -128,6 +128,17 @@ class SessionTest(test_util.TensorFlowTestCase):
       results = s.run([inp])
       self.assertAllEqual([20.0], results)
 
+    pool = config.session_inter_op_thread_pool.add()
+    pool.num_threads = 1
+    pool.global_name = 't1'
+    run_options = config_pb2.RunOptions()
+    run_options.inter_op_thread_pool = (
+        len(config.session_inter_op_thread_pool) - 1)
+    with session.Session(config=config) as s:
+      inp = constant_op.constant(30.0, name='W2')
+      results = s.run([inp], options=run_options)
+      self.assertAllEqual([30.0], results)
+
   def testErrorsReported(self):
     with session.Session() as s:
       constant_op.constant(10.0, name='W1')
-- 
GitLab


From fbf6c4cec8e376a901c92e8852a69b73fc730634 Mon Sep 17 00:00:00 2001
From: superryanguo <superryanguo@gmail.com>
Date: Thu, 22 Jun 2017 17:24:22 +0800
Subject: [PATCH 0342/1375] Simplify the Quickstart section with the weblink is
 better

---
 tensorflow/go/README.md | 31 -------------------------------
 1 file changed, 31 deletions(-)

diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md
index 871b35e41d..9c2fa60017 100644
--- a/tensorflow/go/README.md
+++ b/tensorflow/go/README.md
@@ -9,37 +9,6 @@ Construct and execute TensorFlow graphs in Go.
 > (`github.com/tensorflow/tensorflow/tensorflow/go`).
 
 ## Quickstart
-1.  Download and extract the TensorFlow C library, preferably into `/usr/local`.
-    GPU-enabled versions require CUDA 8.0 and cuDNN 5.1. For other versions, the
-    TensorFlow C library will have to be built from source (see below).
-
-    -   Linux:
-        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.1.0.tar.gz),
-        [GPU-enabled](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.1.0.tar.gz)
-    -   OS X
-        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.1.0.tar.gz),
-
-    The following shell snippet downloads and extracts into `/usr/local`:
-
-    ```sh
-    TF_TYPE="cpu" # Set to "gpu" for GPU support
-    curl -L \
-      "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.1.0.tar.gz" |
-    sudo tar -C /usr/local -xz
-    export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/lib
-    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
-    ```
-
-2.  `go get` this package (and run tests):
-
-    ```sh
-    go get github.com/tensorflow/tensorflow/tensorflow/go
-    go test github.com/tensorflow/tensorflow/tensorflow/go
-    ```
-
-3.  Done!
-
-### Installing into locations other than `/usr/local`
 
 Refer to [Installing TensorFlow for Go](https://www.tensorflow.org/install/install_go)
 
-- 
GitLab


From e911c748008360a8d96b61dac37599d60d4104ef Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Thu, 22 Jun 2017 13:00:38 +0100
Subject: [PATCH 0343/1375] [OpenCL] REGISTER -> REGISTER6

---
 tensorflow/core/kernels/cwise_op_invert.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc
index c84ee6894e..df2c02e42e 100644
--- a/tensorflow/core/kernels/cwise_op_invert.cc
+++ b/tensorflow/core/kernels/cwise_op_invert.cc
@@ -20,7 +20,7 @@ REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64,
           uint8, uint16);
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
+REGISTER6(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
          uint8, uint16);
 #endif  // TENSORFLOW_USE_SYCL
 
-- 
GitLab


From 77f27c95fb565a9feafe8211d3de3b40e86c4402 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 06:45:39 -0700
Subject: [PATCH 0344/1375] Add a multi-head TensorForest estimator.

PiperOrigin-RevId: 159820487
---
 .../tensor_forest/client/random_forest.py     | 171 ++++++++++++++++--
 1 file changed, 155 insertions(+), 16 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index ef2f0337ac..af9f56ab24 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -29,12 +29,13 @@ from tensorflow.contrib.tensor_forest.python import tensor_forest
 
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import basic_session_run_hooks
-from tensorflow.python.training import monitored_session
 from tensorflow.python.training import session_run_hook
 
 
@@ -74,8 +75,9 @@ class TensorForestRunOpAtEndHook(session_run_hook.SessionRunHook):
 class TensorForestLossHook(session_run_hook.SessionRunHook):
   """Monitor to request stop when loss stops decreasing."""
 
-  def __init__(self, early_stopping_rounds):
+  def __init__(self, early_stopping_rounds, loss_op=None):
     self.early_stopping_rounds = early_stopping_rounds
+    self.loss_op = loss_op
     self.min_loss = None
     self.last_step = -1
     # self.steps records the number of steps for which the loss has been
@@ -83,10 +85,12 @@ class TensorForestLossHook(session_run_hook.SessionRunHook):
     self.steps = 0
 
   def before_run(self, run_context):
+    loss = (self.loss_op if self.loss_op is not None else
+            run_context.session.graph.get_operation_by_name(
+                LOSS_NAME).outputs[0])
     return session_run_hook.SessionRunArgs(
         {'global_step': contrib_framework.get_global_step(),
-         'current_loss': run_context.session.graph.get_operation_by_name(
-             LOSS_NAME).outputs[0]})
+         'current_loss': loss})
 
   def after_run(self, run_context, run_values):
     current_loss = run_values.results['current_loss']
@@ -135,7 +139,6 @@ def get_model_fn(params,
                  num_trainers=1,
                  trainer_id=0,
                  report_feature_importances=False,
-                 model_dir=None,
                  local_eval=False):
   """Return a model function given a way to construct a graph builder."""
   def _model_fn(features, labels, mode):
@@ -198,15 +201,6 @@ def get_model_fn(params,
               trainer_id=trainer_id),
           state_ops.assign_add(contrib_framework.get_global_step(), 1))
       loss_deps.append(training_graph)
-      if hasattr(graph_builder, 'finalize_training'):
-        finalize_listener = EveryCheckpointPreSaveListener(
-            graph_builder.finalize_training())
-        scaffold = monitored_session.Scaffold()
-        training_hooks.append(
-            basic_session_run_hooks.CheckpointSaverHook(
-                model_dir, save_secs=600, save_steps=None,
-                scaffold=scaffold,
-                listeners=[finalize_listener]))
 
     training_loss = None
     if (mode == model_fn_lib.ModeKeys.EVAL or
@@ -220,7 +214,8 @@ def get_model_fn(params,
       features[weights_name] = weights
 
     if early_stopping_rounds:
-      training_hooks.append(TensorForestLossHook(early_stopping_rounds))
+      training_hooks.append(TensorForestLossHook(early_stopping_rounds,
+                                                 loss_op=training_loss))
 
     if report_feature_importances:
       training_hooks.append(TensorForestRunOpAtEndHook(
@@ -328,9 +323,153 @@ class TensorForestEstimator(estimator.Estimator):
             num_trainers=num_trainers,
             trainer_id=trainer_id,
             report_feature_importances=report_feature_importances,
-            model_dir=model_dir,
             local_eval=local_eval),
         model_dir=model_dir,
         config=config,
         feature_engineering_fn=feature_engineering_fn)
 
+
+def get_combined_model_fn(model_fns):
+  """Get a combined model function given a list of other model fns.
+
+  The model function returned will call the individual model functions and
+  combine them appropriately.  For:
+
+  training ops: tf.group them.
+  loss: average them.
+  predictions: concat probabilities such that predictions[*][0-C1] are the
+    probablities for output 1 (where C1 is the number of classes in output 1),
+    predictions[*][C1-(C1+C2)] are the probabilities for output 2 (where C2
+    is the number of classes in output 2), etc.  Also stack predictions such
+    that predictions[i][j] is the class prediction for example i and output j.
+
+  This assumes that labels are 2-dimensional, with labels[i][j] being the
+  label for example i and output j, where forest j is trained using only
+  output j.
+
+  Args:
+    model_fns: A list of model functions obtained from get_model_fn.
+
+  Returns:
+    A ModelFnOps instance.
+  """
+  def _model_fn(features, labels, mode):
+    """Function that returns predictions, training loss, and training op."""
+    model_fn_ops = []
+    for i in range(len(model_fns)):
+      with variable_scope.variable_scope('label_{0}'.format(i)):
+        sliced_labels = array_ops.slice(labels, [0, i], [-1, 1])
+        model_fn_ops.append(
+            model_fns[i](features, sliced_labels, mode))
+    training_hooks = []
+    for mops in model_fn_ops:
+      training_hooks += mops.training_hooks
+    predictions = {}
+    if (mode == model_fn_lib.ModeKeys.EVAL or
+        mode == model_fn_lib.ModeKeys.INFER):
+      # Flatten the probabilities into one dimension.
+      predictions[eval_metrics.INFERENCE_PROB_NAME] = array_ops.concat(
+          [mops.predictions[eval_metrics.INFERENCE_PROB_NAME]
+           for mops in model_fn_ops], axis=1)
+      predictions[eval_metrics.INFERENCE_PRED_NAME] = array_ops.stack(
+          [mops.predictions[eval_metrics.INFERENCE_PRED_NAME]
+           for mops in model_fn_ops], axis=1)
+    loss = None
+    if (mode == model_fn_lib.ModeKeys.EVAL or
+        mode == model_fn_lib.ModeKeys.TRAIN):
+      loss = math_ops.reduce_sum(
+          array_ops.stack(
+              [mops.loss for mops in model_fn_ops])) / len(model_fn_ops)
+
+    train_op = None
+    if mode == model_fn_lib.ModeKeys.TRAIN:
+      train_op = control_flow_ops.group(
+          *[mops.train_op for mops in model_fn_ops])
+    return model_fn_lib.ModelFnOps(
+        mode=mode,
+        predictions=predictions,
+        loss=loss,
+        train_op=train_op,
+        training_hooks=training_hooks,
+        scaffold=None,
+        output_alternatives=None)
+
+  return _model_fn
+
+
+class MultiForestMultiHeadEstimator(estimator.Estimator):
+  """An estimator that can train a forest for a multi-headed problems.
+
+  This class essentially trains separate forests (each with their own
+  ForestHParams) for each output.
+
+  For multi-headed regression, a single-headed TensorForestEstimator can
+  be used to train a single model that predicts all outputs.  This class can
+  be used to train separate forests for each output.
+  """
+
+  def __init__(self, params_list, device_assigner=None, model_dir=None,
+               graph_builder_class=tensor_forest.RandomForestGraphs,
+               config=None, weights_name=None, keys_name=None,
+               feature_engineering_fn=None,
+               early_stopping_rounds=100,
+               num_trainers=1, trainer_id=0,
+               report_feature_importances=False,
+               local_eval=False):
+    """Initializes a TensorForestEstimator instance.
+
+    Args:
+      params_list: A list of ForestHParams objects for each head, given in order
+        of outputs in the label tensor to be trained on.
+      device_assigner: An `object` instance that controls how trees get
+        assigned to devices. If `None`, will use
+        `tensor_forest.RandomForestDeviceAssigner`.
+      model_dir: Directory to save model parameters, graph, etc. To continue
+        training a previously saved model, load checkpoints saved to this
+        directory into an estimator.
+      graph_builder_class: An `object` instance that defines how TF graphs for
+        random forest training and inference are built. By default will use
+        `tensor_forest.RandomForestGraphs`.
+      config: `RunConfig` object to configure the runtime settings.
+      weights_name: A string defining feature column name representing
+        weights. Will be multiplied by the loss of the example. Used to
+        downweight or boost examples during training.
+      keys_name: A string naming one of the features to strip out and
+        pass through into the inference/eval results dict.  Useful for
+        associating specific examples with their prediction.
+      feature_engineering_fn: Feature engineering function. Takes features and
+        labels which are the output of `input_fn` and returns features and
+        labels which will be fed into the model.
+      early_stopping_rounds: Allows training to terminate early if the forest is
+        no longer growing. 100 by default.  Set to a Falsy value to disable
+        the default training hook.
+      num_trainers: Number of training jobs, which will partition trees
+        among them.
+      trainer_id: Which trainer this instance is.
+      report_feature_importances: If True, print out feature importances
+        during evaluation.
+      local_eval: If True, don't use a device assigner for eval. This is to
+        support some common setups where eval is done on a single machine, even
+        though training might be distributed.
+
+    Returns:
+      A `TensorForestEstimator` instance.
+    """
+    model_fns = [get_model_fn(
+        params.fill(),
+        graph_builder_class,
+        device_assigner,
+        weights_name=weights_name,
+        keys_name=keys_name,
+        early_stopping_rounds=early_stopping_rounds,
+        num_trainers=num_trainers,
+        trainer_id=trainer_id,
+        report_feature_importances=report_feature_importances,
+        local_eval=local_eval) for params in params_list]
+
+    super(MultiForestMultiHeadEstimator, self).__init__(
+        model_fn=get_combined_model_fn(model_fns),
+        model_dir=model_dir,
+        config=config,
+        feature_engineering_fn=feature_engineering_fn)
+
-- 
GitLab


From 3c1946230753e6c8e7d92e7d619a74772f5bd81d Mon Sep 17 00:00:00 2001
From: myPrecious <Moriadry@users.noreply.github.com>
Date: Thu, 22 Jun 2017 22:11:45 +0800
Subject: [PATCH 0345/1375] Java API to get the size of specified input list of
 operations. (#10865)

* Java API to get the size of specified input list of operations

* remove unnecessary explain to avoid bring a new term to users.
---
 .../main/java/org/tensorflow/Operation.java   | 24 +++++++++++++++++++
 .../java/src/main/native/operation_jni.cc     | 18 ++++++++++++++
 .../java/src/main/native/operation_jni.h      | 11 +++++++++
 .../java/org/tensorflow/OperationTest.java    | 21 ++++++++++++++++
 4 files changed, 74 insertions(+)

diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index 5b89ce5e48..e7de603409 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -97,6 +97,28 @@ public final class Operation {
     return new Output(this, idx);
   }
 
+  /**
+   * Returns the size of the given inputs list of Tensors for this operation.
+   *
+   * <p>An Operation has multiple named inputs, each of which contains either
+   * a single tensor or a list of tensors. This method returns the size of
+   * the list of tensors for a specific named input of the operation.
+   *
+   * @param name identifier of the list of tensors (of which there may
+   *        be many) inputs to this operation.
+   * @returns the size of the list of Tensors produced by this named input.
+   * @throws IllegalArgumentException if this operation has no input
+   *         with the provided name.
+   */
+  public int inputListLength(final String name) {
+    Graph.Reference r = graph.ref();
+    try {
+      return inputListLength(unsafeNativeHandle, name);
+    } finally {
+      r.close();
+    }
+  }
+
   long getUnsafeNativeHandle() {
     return unsafeNativeHandle;
   }
@@ -132,6 +154,8 @@ public final class Operation {
 
   private static native int outputListLength(long handle, String name);
 
+  private static native int inputListLength(long handle, String name);
+
   private static native long[] shape(long graphHandle, long opHandle, int output);
 
   private static native int dtype(long graphHandle, long opHandle, int output);
diff --git a/tensorflow/java/src/main/native/operation_jni.cc b/tensorflow/java/src/main/native/operation_jni.cc
index b3d5fc4ec3..ccc44d91c0 100644
--- a/tensorflow/java/src/main/native/operation_jni.cc
+++ b/tensorflow/java/src/main/native/operation_jni.cc
@@ -156,3 +156,21 @@ JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv* env,
 
   return static_cast<jint>(TF_OperationOutputType(TF_Output{op, output_index}));
 }
+
+JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv* env,
+                                                                      jclass clazz,
+                                                                      jlong handle,
+                                                                      jstring name) {
+  TF_Operation* op = requireHandle(env, handle);
+  if (op == nullptr) return 0;
+
+  TF_Status* status = TF_NewStatus();
+
+  const char* cname = env->GetStringUTFChars(name, nullptr);
+  int result = TF_OperationInputListLength(op, cname, status);
+  env->ReleaseStringUTFChars(name, cname);
+
+  throwExceptionIfNotOK(env, status);
+  TF_DeleteStatus(status);
+  return result;
+}
diff --git a/tensorflow/java/src/main/native/operation_jni.h b/tensorflow/java/src/main/native/operation_jni.h
index b5d156f7c2..6f379256d2 100644
--- a/tensorflow/java/src/main/native/operation_jni.h
+++ b/tensorflow/java/src/main/native/operation_jni.h
@@ -73,6 +73,17 @@ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_Operation_shape(JNIEnv *,
 JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv *, jclass,
                                                            jlong, jlong, jint);
 
+
+/*
+ * Class:     org_tensorflow_Operation
+ * Method:    inputListLength
+ * Signature: (JLjava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv *,
+                                                                      jclass,
+                                                                      jlong,
+                                                                      jstring);
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif  // __cplusplus
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 74fdcf484e..4fa68130c0 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -52,6 +52,16 @@ public class OperationTest {
     assertEquals(3, split(new int[] {0, 1, 2}, 3));
   }
 
+  @Test
+  public void inputListLength() {
+    assertEquals(1, splitWithInputList(new int[] {0, 1}, 1, "split_dim"));
+    try {
+      splitWithInputList(new int[] {0, 1}, 2, "inputs");
+    } catch (IllegalArgumentException iae) {
+      // expected
+    }
+  }
+
   private static int split(int[] values, int num_split) {
     try (Graph g = new Graph()) {
       return g.opBuilder("Split", "Split")
@@ -62,4 +72,15 @@ public class OperationTest {
           .outputListLength("output");
     }
   }
+
+  private static int splitWithInputList(int[] values, int num_split, String name) {
+    try (Graph g = new Graph()) {
+      return g.opBuilder("Split", "Split")
+          .addInput(TestUtil.constant(g, "split_dim", 0))
+          .addInput(TestUtil.constant(g, "values", values))
+          .setAttr("num_split", num_split)
+          .build()
+          .inputListLength(name);
+    }
+  }
 }
-- 
GitLab


From 1699d904afa9568db12d4948a330947048b68e96 Mon Sep 17 00:00:00 2001
From: John Lawson <john@codeplay.com>
Date: Thu, 22 Jun 2017 16:20:32 +0100
Subject: [PATCH 0346/1375] [OpenCL] Fixes CUDA specific test run on SYCL (#56)

The testBadParentValuesOnGPU should only be run on CUDA devices, as the
test checks for particular CUDA behaviour. We don't actually provide a
SYCL kernel for GatherTree and so it's not a problem that the tests
don't target SYCL.
---
 .../seq2seq/python/kernel_tests/beam_search_ops_test.py       | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
index 491d87f62d..3496b355b4 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
@@ -65,7 +65,9 @@ class GatherTreeTest(test.TestCase):
         _ = beams.eval()
 
   def testBadParentValuesOnGPU(self):
-    if not test.is_gpu_available():
+    # Only want to run this test on CUDA devices, as gather_tree is not
+    # registered for SYCL devices.
+    if not test.is_gpu_available(cuda_only=True):
       return
     # (max_time = 4, batch_size = 1, beams = 3)
     # bad parent in beam 1 time 1; appears as a negative index at time 0
-- 
GitLab


From 0bad3db1e71051c6382395889b3ff22c1b8e786e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 09:27:13 -0700
Subject: [PATCH 0347/1375] Have RestoreV2's shape fn set all outputs to
 unknown shape.

PiperOrigin-RevId: 159835723
---
 tensorflow/core/ops/io_ops.cc      |  3 +--
 tensorflow/core/ops/io_ops_test.cc | 20 ++++++++++++++++++++
 2 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/ops/io_ops.cc b/tensorflow/core/ops/io_ops.cc
index fa12816c92..b92c18416f 100644
--- a/tensorflow/core/ops/io_ops.cc
+++ b/tensorflow/core/ops/io_ops.cc
@@ -109,8 +109,7 @@ REGISTER_OP("RestoreV2")
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &shape1));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &shape2));
       TF_RETURN_IF_ERROR(c->Merge(shape1, shape2, &shape0));
-      c->set_output(0, c->UnknownShape());
-      return Status::OK();
+      return UnknownShape(c);
     })
     .Doc(R"doc(
 Restores tensors from a V2 checkpoint.
diff --git a/tensorflow/core/ops/io_ops_test.cc b/tensorflow/core/ops/io_ops_test.cc
index a915cdbe12..785fb96c64 100644
--- a/tensorflow/core/ops/io_ops_test.cc
+++ b/tensorflow/core/ops/io_ops_test.cc
@@ -79,6 +79,26 @@ TEST(IoOpsTest, Restore_ShapeFn) {
   INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[];[?]");
 }
 
+TEST(IoOpsTest, RestoreV2_ShapeFn) {
+  ShapeInferenceTestOp op("RestoreV2");
+
+  TF_ASSERT_OK(NodeDefBuilder("test", op.name)
+                   .Input({"prefix", 0, DT_STRING})
+                   .Input({"tensor_names", 0, DT_STRING})
+                   .Input({"shapes_and_slices", 0, DT_STRING})
+                   .Attr("dtypes", {DT_FLOAT, DT_INT64})
+                   .Finalize(&op.node_def));
+
+  INFER_OK(op, "?;?;?", "?;?");
+  INFER_OK(op, "[];[10];[10]", "?;?");
+
+  // Input shape validation.
+  INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[?];[?];[?]");
+  INFER_ERROR("Shape must be rank 1 but is rank 2", op, "[];[?,?];[?]");
+  INFER_ERROR("Shape must be rank 1 but is rank 2", op, "[];[?];[?,?]");
+  INFER_ERROR("in both shapes must be equal", op, "[];[10];[20]");
+}
+
 TEST(IoOpsTest, RestoreSlice_ShapeFn) {
   ShapeInferenceTestOp op("RestoreSlice");
 
-- 
GitLab


From db4ac19f9e8e940571e1ecd07ecd26a68c247806 Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Thu, 22 Jun 2017 10:09:49 -0700
Subject: [PATCH 0348/1375] VectorExponential added to distributions.

PiperOrigin-RevId: 159840822
---
 tensorflow/contrib/distributions/BUILD        |  16 +
 tensorflow/contrib/distributions/__init__.py  |   2 +
 .../vector_exponential_diag_test.py           | 206 +++++++++++++
 .../python/ops/vector_exponential_diag.py     | 198 ++++++++++++
 .../ops/vector_exponential_linear_operator.py | 288 ++++++++++++++++++
 5 files changed, 710 insertions(+)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 1cde0cb41c..ca2b256f6a 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -400,6 +400,22 @@ cuda_py_test(
     tags = ["nomsan"],  # disable to avoid false positives from scipy.
 )
 
+cuda_py_test(
+    name = "vector_exponential_diag_test",
+    size = "medium",
+    srcs = ["python/kernel_tests/vector_exponential_diag_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "vector_laplace_diag_test",
     size = "medium",
diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py
index 1fddad5368..6b693300a2 100644
--- a/tensorflow/contrib/distributions/__init__.py
+++ b/tensorflow/contrib/distributions/__init__.py
@@ -46,6 +46,7 @@ from tensorflow.contrib.distributions.python.ops.quantized_distribution import *
 from tensorflow.contrib.distributions.python.ops.relaxed_bernoulli import *
 from tensorflow.contrib.distributions.python.ops.relaxed_onehot_categorical import *
 from tensorflow.contrib.distributions.python.ops.sample_stats import *
+from tensorflow.contrib.distributions.python.ops.vector_exponential_diag import *
 from tensorflow.contrib.distributions.python.ops.vector_laplace_diag import *
 from tensorflow.contrib.distributions.python.ops.wishart import *
 from tensorflow.python.ops.distributions.bernoulli import *
@@ -101,6 +102,7 @@ _allowed_symbols = [
     'VectorDeterministic',
     'Exponential',
     'ExponentialWithSoftplusRate',
+    'VectorExponentialDiag',
     'Gamma',
     'GammaWithSoftplusConcentrationRate',
     'Geometric',
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py b/tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py
new file mode 100644
index 0000000000..fd05bd207f
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py
@@ -0,0 +1,206 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for VectorExponentialLinearOperator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from tensorflow.contrib import distributions
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import test
+
+
+ds = distributions
+
+
+class VectorExponentialDiagTest(test.TestCase):
+  """Well tested because this is a simple override of the base class."""
+
+  def setUp(self):
+    self._rng = np.random.RandomState(42)
+
+  def testScalarParams(self):
+    mu = -1.
+    diag = -5.
+    with self.test_session():
+      with self.assertRaisesRegexp(ValueError, "at least 1 dimension"):
+        ds.VectorExponentialDiag(mu, diag)
+
+  def testVectorParams(self):
+    mu = [-1.]
+    diag = [-5.]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([3, 1], dist.sample(3).get_shape())
+
+  def testMean(self):
+    mu = [-1., 1]
+    diag = [1., -5]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([-1. + 1., 1. - 5.], dist.mean().eval())
+
+  def testMode(self):
+    mu = [-1.]
+    diag = [1., -5]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([-1., -1.], dist.mode().eval())
+
+  def testMeanWithBroadcastLoc(self):
+    mu = [-1.]
+    diag = [1., -5]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([-1. + 1, -1. - 5], dist.mean().eval())
+
+  def testSample(self):
+    mu = [-2., 1]
+    diag = [1., -2]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      samps = dist.sample(int(1e4), seed=0).eval()
+      cov_mat = array_ops.matrix_diag(diag).eval()**2
+
+      self.assertAllClose([-2 + 1, 1. - 2], samps.mean(axis=0),
+                          atol=0., rtol=0.05)
+      self.assertAllClose(cov_mat, np.cov(samps.T),
+                          atol=0.05, rtol=0.05)
+
+  def testSingularScaleRaises(self):
+    mu = [-1., 1]
+    diag = [1., 0]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      with self.assertRaisesOpError("Singular"):
+        dist.sample().eval()
+
+  def testSampleWithBroadcastScale(self):
+    # mu corresponds to a 2-batch of 3-variate normals
+    mu = np.zeros([2, 3])
+
+    # diag corresponds to no batches of 3-variate normals
+    diag = np.ones([3])
+
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+
+      mean = dist.mean()
+      self.assertAllEqual([2, 3], mean.get_shape())
+      self.assertAllClose(mu + diag, mean.eval())
+
+      n = int(1e4)
+      samps = dist.sample(n, seed=0).eval()
+      samps_centered = samps - samps.mean(axis=0)
+      cov_mat = array_ops.matrix_diag(diag).eval()**2
+      sample_cov = np.matmul(samps_centered.transpose([1, 2, 0]),
+                             samps_centered.transpose([1, 0, 2])) / n
+
+      self.assertAllClose(mu + diag, samps.mean(axis=0),
+                          atol=0.10, rtol=0.05)
+      self.assertAllClose([cov_mat, cov_mat], sample_cov,
+                          atol=0.10, rtol=0.05)
+
+  def testCovariance(self):
+    with self.test_session():
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([2, 3], dtype=dtypes.float32))
+      self.assertAllClose(
+          np.diag(np.ones([3], dtype=np.float32)),
+          vex.covariance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_identity_multiplier=[3., 2.])
+      self.assertAllEqual([2], vex.batch_shape)
+      self.assertAllEqual([3], vex.event_shape)
+      self.assertAllClose(
+          np.array([[[3., 0, 0],
+                     [0, 3, 0],
+                     [0, 0, 3]],
+                    [[2, 0, 0],
+                     [0, 2, 0],
+                     [0, 0, 2]]])**2.,
+          vex.covariance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_diag=[[3., 2, 1], [4, 5, 6]])
+      self.assertAllEqual([2], vex.batch_shape)
+      self.assertAllEqual([3], vex.event_shape)
+      self.assertAllClose(
+          np.array([[[3., 0, 0],
+                     [0, 2, 0],
+                     [0, 0, 1]],
+                    [[4, 0, 0],
+                     [0, 5, 0],
+                     [0, 0, 6]]])**2.,
+          vex.covariance().eval())
+
+  def testVariance(self):
+    with self.test_session():
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([2, 3], dtype=dtypes.float32))
+      self.assertAllClose(
+          np.ones([3], dtype=np.float32),
+          vex.variance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_identity_multiplier=[3., 2.])
+      self.assertAllClose(
+          np.array([[3., 3, 3],
+                    [2., 2, 2]])**2.,
+          vex.variance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_diag=[[3., 2, 1],
+                      [4., 5, 6]])
+      self.assertAllClose(
+          np.array([[3., 2, 1],
+                    [4., 5, 6]])**2.,
+          vex.variance().eval())
+
+  def testStddev(self):
+    with self.test_session():
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([2, 3], dtype=dtypes.float32))
+      self.assertAllClose(
+          np.ones([3], dtype=np.float32),
+          vex.stddev().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([3], dtype=dtypes.float32),
+          scale_identity_multiplier=[3., 2.])
+      self.assertAllClose(
+          np.array([[3., 3, 3],
+                    [2., 2, 2]]),
+          vex.stddev().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([3], dtype=dtypes.float32),
+          scale_diag=[[3., 2, 1], [4, 5, 6]])
+      self.assertAllClose(
+          np.array([[3., 2, 1],
+                    [4., 5, 6]]),
+          vex.stddev().eval())
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
new file mode 100644
index 0000000000..c88572e17f
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
@@ -0,0 +1,198 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Distribution of a vectorized Exponential, with uncorrelated components."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.distributions.python.ops import vector_exponential_linear_operator as vector_exponential_linop
+from tensorflow.python.framework import ops
+
+
+__all__ = [
+    "VectorExponentialDiag",
+]
+
+
+class VectorExponentialDiag(
+    vector_exponential_linop.VectorExponentialLinearOperator):
+  """The vectorization of the Exponential distribution on `R^k`.
+
+  The vector exponential distribution is defined over a subset of `R^k`, and
+  parameterized by a (batch of) length-`k` `loc` vector and a (batch of) `k x k`
+  `scale` matrix:  `covariance = scale @ scale.T`, where `@` denotes
+  matrix-multiplication.
+
+  #### Mathematical Details
+
+  The probability density function (pdf) is defined over the image of the
+  `scale` matrix + `loc`, applied to the positive half-space:
+  `Supp = {loc + scale @ x : x in R^k, x_1 > 0, ..., x_k > 0}`.  On this set,
+
+  ```none
+  pdf(y; loc, scale) = exp(-||x||_1) / Z,  for y in Supp
+  x = inv(scale) @ (y - loc),
+  Z = |det(scale)|,
+  ```
+
+  where:
+
+  * `loc` is a vector in `R^k`,
+  * `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`,
+  * `Z` denotes the normalization constant, and,
+  * `||x||_1` denotes the `l1` norm of `x`, `sum_i |x_i|`.
+
+  The VectorExponential distribution is a member of the [location-scale
+  family](https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
+  constructed as,
+
+  ```none
+  X = (X_1, ..., X_k), each X_i ~ Exponential(rate=1)
+  Y = (Y_1, ...,Y_k) = scale @ X + loc
+  ```
+
+  #### About `VectorExponential` and `Vector` distributions in TensorFlow.
+
+  The `VectorExponential` is a non-standard distribution that has useful
+  properties.
+
+  The marginals `Y_1, ..., Y_k` are *not* Exponential random variables, due to
+  the fact that the sum of Exponential random variables is not Exponential.
+
+  Instead, `Y` is a vector whose components are linear combinations of
+  Exponential random variables.  Thus, `Y` lives in the vector space generated
+  by `vectors` of Exponential distributions.  This allows the user to decide the
+  mean and covariance (by setting `loc` and `scale`), while preserving some
+  properties of the Exponential distribution.  In particular, the tails of `Y_i`
+  will be (up to polynomial factors) exponentially decaying.
+
+  To see this last statement, note that the pdf of `Y_i` is the convolution of
+  the pdf of `k` independent Exponential random variables.  One can then show by
+  induction that distributions with exponential (up to polynomial factors) tails
+  are closed under convolution.
+
+
+  #### Examples
+
+  ```python
+  ds = tf.contrib.distributions
+  la = tf.contrib.linalg
+
+  # Initialize a single 2-variate VectorExponential, supported on
+  # {(x, y) in R^2 : x > 0, y > 0}.
+
+  # The first component has pdf exp{-x}, the second 0.5 exp{-x / 2}
+  vex = ds.VectorExponentialDiag(scale_diag=[1., 2.])
+
+  # Compute the pdf of an`R^2` observation; return a scalar.
+  vex.prob([3., 4.]).eval()  # shape: []
+
+  # Initialize a 2-batch of 3-variate Vector Exponential's.
+  loc = [[1., 2, 3],
+         [1., 0, 0]]              # shape: [2, 3]
+  scale_diag = [[1., 2, 3],
+                [0.5, 1, 1.5]]     # shape: [2, 3]
+
+  vex = ds.VectorExponentialDiag(loc, scale_diag)
+
+  # Compute the pdf of two `R^3` observations; return a length-2 vector.
+  x = [[1.9, 2.2, 3.1],
+       [10., 1.0, 9.0]]     # shape: [2, 3]
+  vex.prob(x).eval()    # shape: [2]
+  ```
+
+  """
+
+  def __init__(self,
+               loc=None,
+               scale_diag=None,
+               scale_identity_multiplier=None,
+               validate_args=False,
+               allow_nan_stats=True,
+               name="VectorExponentialDiag"):
+    """Construct Vector Exponential distribution supported on a subset of `R^k`.
+
+    The `batch_shape` is the broadcast shape between `loc` and `scale`
+    arguments.
+
+    The `event_shape` is given by last dimension of the matrix implied by
+    `scale`. The last dimension of `loc` (if provided) must broadcast with this.
+
+    Recall that `covariance = scale @ scale.T`.
+
+    ```none
+    scale = diag(scale_diag + scale_identity_multiplier * ones(k))
+    ```
+
+    where:
+
+    * `scale_diag.shape = [k]`, and,
+    * `scale_identity_multiplier.shape = []`.
+
+    Additional leading dimensions (if any) will index batches.
+
+    If both `scale_diag` and `scale_identity_multiplier` are `None`, then
+    `scale` is the Identity matrix.
+
+    Args:
+      loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
+        implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
+        `b >= 0` and `k` is the event size.
+      scale_diag: Non-zero, floating-point `Tensor` representing a diagonal
+        matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
+        and characterizes `b`-batches of `k x k` diagonal matrices added to
+        `scale`. When both `scale_identity_multiplier` and `scale_diag` are
+        `None` then `scale` is the `Identity`.
+      scale_identity_multiplier: Non-zero, floating-point `Tensor` representing
+        a scaled-identity-matrix added to `scale`. May have shape
+        `[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scaled
+        `k x k` identity matrices added to `scale`. When both
+        `scale_identity_multiplier` and `scale_diag` are `None` then `scale` is
+        the `Identity`.
+      validate_args: Python `bool`, default `False`. When `True` distribution
+        parameters are checked for validity despite possibly degrading runtime
+        performance. When `False` invalid inputs may silently render incorrect
+        outputs.
+      allow_nan_stats: Python `bool`, default `True`. When `True`,
+        statistics (e.g., mean, mode, variance) use the value "`NaN`" to
+        indicate the result is undefined. When `False`, an exception is raised
+        if one or more of the statistic's batch members are undefined.
+      name: Python `str` name prefixed to Ops created by this class.
+
+    Raises:
+      ValueError: if at most `scale_identity_multiplier` is specified.
+    """
+    parameters = locals()
+    with ops.name_scope(name):
+      with ops.name_scope("init", values=[
+          loc, scale_diag, scale_identity_multiplier]):
+        # No need to validate_args while making diag_scale.  The returned
+        # LinearOperatorDiag has an assert_non_singular method that is called by
+        # the Bijector.
+        scale = distribution_util.make_diag_scale(
+            loc=loc,
+            scale_diag=scale_diag,
+            scale_identity_multiplier=scale_identity_multiplier,
+            validate_args=False,
+            assert_positive=False)
+    super(VectorExponentialDiag, self).__init__(
+        loc=loc,
+        scale=scale,
+        validate_args=validate_args,
+        allow_nan_stats=allow_nan_stats,
+        name=name)
+    self._parameters = parameters
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
new file mode 100644
index 0000000000..7123165417
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
@@ -0,0 +1,288 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Vectorized Exponential distribution class, directly using LinearOperator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib import linalg
+from tensorflow.contrib.distributions.python.ops import bijectors
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import exponential
+from tensorflow.python.ops.distributions import transformed_distribution
+
+__all__ = ["VectorExponentialLinearOperator"]
+
+_mvn_sample_note = """
+`value` is a batch vector with compatible shape if `value` is a `Tensor` whose
+shape can be broadcast up to either:
+
+```python
+self.batch_shape + self.event_shape
+```
+
+or
+
+```python
+[M1, ..., Mm] + self.batch_shape + self.event_shape
+```
+
+"""
+
+
+class VectorExponentialLinearOperator(
+    transformed_distribution.TransformedDistribution):
+  """The vectorization of the Exponential distribution on `R^k`.
+
+  The vector exponential distribution is defined over a subset of `R^k`, and
+  parameterized by a (batch of) length-`k` `loc` vector and a (batch of) `k x k`
+  `scale` matrix:  `covariance = scale @ scale.T`, where `@` denotes
+  matrix-multiplication.
+
+  #### Mathematical Details
+
+  The probability density function (pdf) is
+
+  ```none
+  pdf(y; loc, scale) = exp(-||x||_1) / Z,  for y in S(loc, scale),
+  x = inv(scale) @ (y - loc),
+  Z = |det(scale)|,
+  ```
+
+  where:
+
+  * `loc` is a vector in `R^k`,
+  * `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`,
+  * `S = {loc + scale @ x : x in R^k, x_1 > 0, ..., x_k > 0}`, is an image of
+     the positive half-space,
+  * `||x||_1` denotes the `l1` norm of `x`, `sum_i |x_i|`,
+  * `Z` denotes the normalization constant.
+
+  The VectorExponential distribution is a member of the [location-scale
+  family](https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
+  constructed as,
+
+  ```none
+  X = (X_1, ..., X_k), each X_i ~ Exponential(rate=1)
+  Y = (Y_1, ...,Y_k) = scale @ X + loc
+  ```
+
+  #### About `VectorExponential` and `Vector` distributions in TensorFlow.
+
+  The `VectorExponential` is a non-standard distribution that has useful
+  properties.
+
+  The marginals `Y_1, ..., Y_k` are *not* Exponential random variables, due to
+  the fact that the sum of Exponential random variables is not Exponential.
+
+  Instead, `Y` is a vector whose components are linear combinations of
+  Exponential random variables.  Thus, `Y` lives in the vector space generated
+  by `vectors` of Exponential distributions.  This allows the user to decide the
+  mean and covariance (by setting `loc` and `scale`), while preserving some
+  properties of the Exponential distribution.  In particular, the tails of `Y_i`
+  will be (up to polynomial factors) exponentially decaying.
+
+  To see this last statement, note that the pdf of `Y_i` is the convolution of
+  the pdf of `k` independent Exponential random variables.  One can then show by
+  induction that distributions with exponential (up to polynomial factors) tails
+  are closed under convolution.
+
+
+  #### Examples
+
+  ```python
+  ds = tf.contrib.distributions
+  la = tf.contrib.linalg
+
+  # Initialize a single 2-variate VectorExponential, supported on
+  # {(x, y) in R^2 : x > 0, y > 0}.
+  mat = [[1.0, 0.1],
+         [0.1, 1.0]]
+
+  vex = ds.VectorExponentialLinearOperator(
+      scale=la.LinearOperatorFullMatrix(mat))
+
+  # Compute the pdf of an`R^2` observation; return a scalar.
+  vex.prob([1., 2.]).eval()  # shape: []
+
+  # Initialize a 2-batch of 3-variate Vector Exponential's.
+  mu = [[1., 2, 3],
+        [1., 0, 0]]              # shape: [2, 3]
+  scale_diag = [[1., 2, 3],
+                [0.5, 1, 1.5]]     # shape: [2, 3]
+
+  vex = ds.VectorExponentialLinearOperator(
+      loc=mu,
+      scale=la.LinearOperatorDiag(scale_diag))
+
+  # Compute the pdf of two `R^3` observations; return a length-2 vector.
+  x = [[1.9, 2.2, 3.1],
+       [10., 1.0, 9.0]]     # shape: [2, 3]
+  vex.prob(x).eval()    # shape: [2]
+  ```
+
+  """
+
+  def __init__(self,
+               loc=None,
+               scale=None,
+               validate_args=False,
+               allow_nan_stats=True,
+               name="VectorExponentialLinearOperator"):
+    """Construct Vector Exponential distribution supported on a subset of `R^k`.
+
+    The `batch_shape` is the broadcast shape between `loc` and `scale`
+    arguments.
+
+    The `event_shape` is given by last dimension of the matrix implied by
+    `scale`. The last dimension of `loc` (if provided) must broadcast with this.
+
+    Recall that `covariance = scale @ scale.T`.
+
+    Additional leading dimensions (if any) will index batches.
+
+    Args:
+      loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
+        implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
+        `b >= 0` and `k` is the event size.
+      scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
+        `[B1, ..., Bb, k, k]`.
+      validate_args: Python `bool`, default `False`. Whether to validate input
+        with asserts. If `validate_args` is `False`, and the inputs are
+        invalid, correct behavior is not guaranteed.
+      allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
+        exception if a statistic (e.g. mean/mode/etc...) is undefined for any
+        batch member If `True`, batch members with valid parameters leading to
+        undefined statistics will return NaN for this statistic.
+      name: The name to give Ops created by the initializer.
+
+    Raises:
+      ValueError: if `scale` is unspecified.
+      TypeError: if not `scale.dtype.is_floating`
+    """
+    parameters = locals()
+    if scale is None:
+      raise ValueError("Missing required `scale` parameter.")
+    if not scale.dtype.is_floating:
+      raise TypeError("`scale` parameter must have floating-point dtype.")
+
+    with ops.name_scope(name, values=[loc] + scale.graph_parents):
+      # Since expand_dims doesn't preserve constant-ness, we obtain the
+      # non-dynamic value if possible.
+      loc = ops.convert_to_tensor(loc, name="loc") if loc is not None else loc
+      batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
+          loc, scale)
+
+      super(VectorExponentialLinearOperator, self).__init__(
+          distribution=exponential.Exponential(rate=array_ops.ones(
+              [], dtype=scale.dtype), allow_nan_stats=allow_nan_stats),
+          bijector=bijectors.AffineLinearOperator(
+              shift=loc, scale=scale, validate_args=validate_args),
+          batch_shape=batch_shape,
+          event_shape=event_shape,
+          validate_args=validate_args,
+          name=name)
+      self._parameters = parameters
+
+  @property
+  def loc(self):
+    """The `loc` `Tensor` in `Y = scale @ X + loc`."""
+    return self.bijector.shift
+
+  @property
+  def scale(self):
+    """The `scale` `LinearOperator` in `Y = scale @ X + loc`."""
+    return self.bijector.scale
+
+  @distribution_util.AppendDocstring(_mvn_sample_note)
+  def _log_prob(self, x):
+    return super(VectorExponentialLinearOperator, self)._log_prob(x)
+
+  @distribution_util.AppendDocstring(_mvn_sample_note)
+  def _prob(self, x):
+    return super(VectorExponentialLinearOperator, self)._prob(x)
+
+  def _mean(self):
+    # Let
+    #   W = (w1,...,wk), with wj ~ iid Exponential(0, 1).
+    # Then this distribution is
+    #   X = loc + LW,
+    # and then E[X] = loc + L1, where 1 is the vector of ones.
+    scale_x_ones = self.bijector.scale.matvec(
+        array_ops.ones(self._mode_mean_shape(), self.dtype))
+
+    if self.loc is None:
+      return scale_x_ones
+
+    return array_ops.identity(self.loc) + scale_x_ones
+
+  def _covariance(self):
+    # Let
+    #   W = (w1,...,wk), with wj ~ iid Exponential(0, 1).
+    # Then this distribution is
+    #   X = loc + LW,
+    # and then since Cov(wi, wj) = 1 if i=j, and 0 otherwise,
+    #   Cov(X) = L Cov(W W^T) L^T = L L^T.
+    if distribution_util.is_diagonal_scale(self.scale):
+      return array_ops.matrix_diag(math_ops.square(self.scale.diag_part()))
+    else:
+      return self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
+
+  def _variance(self):
+    if distribution_util.is_diagonal_scale(self.scale):
+      return math_ops.square(self.scale.diag_part())
+    elif (isinstance(self.scale, linalg.LinearOperatorUDVHUpdate) and
+          self.scale.is_self_adjoint):
+      return array_ops.matrix_diag_part(
+          self.scale.matmul(self.scale.to_dense()))
+    else:
+      return array_ops.matrix_diag_part(
+          self.scale.matmul(self.scale.to_dense(), adjoint_arg=True))
+
+  def _stddev(self):
+    if distribution_util.is_diagonal_scale(self.scale):
+      return math_ops.abs(self.scale.diag_part())
+    elif (isinstance(self.scale, linalg.LinearOperatorUDVHUpdate) and
+          self.scale.is_self_adjoint):
+      return math_ops.sqrt(
+          array_ops.matrix_diag_part(self.scale.matmul(self.scale.to_dense())))
+    else:
+      return math_ops.sqrt(
+          array_ops.matrix_diag_part(
+              self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)))
+
+  def _mode(self):
+    scale_x_zeros = self.bijector.scale.matvec(
+        array_ops.zeros(self._mode_mean_shape(), self.dtype))
+
+    if self.loc is None:
+      return scale_x_zeros
+
+    return array_ops.identity(self.loc) + scale_x_zeros
+
+  def _mode_mean_shape(self):
+    """Shape for the mode/mean Tensors."""
+    shape = self.batch_shape.concatenate(self.event_shape)
+    has_static_shape = shape.is_fully_defined()
+    if not has_static_shape:
+      shape = array_ops.concat([
+          self.batch_shape_tensor(),
+          self.event_shape_tensor(),
+      ], 0)
+    return shape
-- 
GitLab


From bd086deb87d7a5f4a8ab772b92a65b57e956db43 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 22 Jun 2017 10:17:47 -0700
Subject: [PATCH 0349/1375] Fold as many nodes as possible instead of giving up
 if there is any error.

PiperOrigin-RevId: 159841935
---
 tensorflow/core/grappler/optimizers/constant_folding.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 9a8be6e39a..9f6e3aee71 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -575,7 +575,10 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
     for (const auto& node : graph_.node()) {
       if (IsFoldable(node) &&
           processed_nodes.find(node.name()) == processed_nodes.end()) {
-        TF_RETURN_IF_ERROR(FoldNode(node, output));
+        Status s = FoldNode(node, output);
+        if (!s.ok()) {
+          VLOG(1) << "Failed to fold node " << node.name() << ": " << s;
+        }
         processed_nodes.insert(node.name());
       }
     }
-- 
GitLab


From 169de9361e7463bb2389571644df2b6a67929a3e Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 22 Jun 2017 10:52:06 -0700
Subject: [PATCH 0350/1375] Removed deprecated summary usage from estimators.
 Made name_space usage consistent.

PiperOrigin-RevId: 159846928
---
 tensorflow/python/estimator/BUILD             |   3 +-
 .../canned/dnn_linear_combined_test.py        |   7 +-
 tensorflow/python/estimator/canned/head.py    | 182 +++++++++---------
 .../python/estimator/canned/head_test.py      |   9 +
 4 files changed, 103 insertions(+), 98 deletions(-)

diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 27002d9480..ca46daf306 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -373,7 +373,6 @@ py_library(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
-        "//tensorflow/python:logging_ops",
         "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:metrics",
@@ -381,7 +380,7 @@ py_library(
         "//tensorflow/python:platform",
         "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:string_ops",
-        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:summary",
         "//tensorflow/python:weights_broadcast_ops",
         "//tensorflow/python/feature_column",
         "//tensorflow/python/ops/losses",
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
index 486b302ab5..2151df8423 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
@@ -664,7 +664,8 @@ class DNNLinearCombinedTests(test.TestCase):
       self.assertTrue(
           all([name.startswith(var_name_prefix) for name in var_names]))
       # var is used to check this op called by training.
-      var = variables_lib.Variable(0., name=(var_name_prefix + '_called'))
+      with ops.name_scope(''):
+        var = variables_lib.Variable(0., name=(var_name_prefix + '_called'))
       with ops.control_dependencies([var.assign(100.)]):
         return real_optimizer.minimize(loss, global_step, var_list)
 
@@ -695,11 +696,11 @@ class DNNLinearCombinedTests(test.TestCase):
     # verifies train_op fires linear minimize op
     self.assertEqual(100.,
                      checkpoint_utils.load_variable(
-                         self._model_dir, 'binary_logistic_head/linear_called'))
+                         self._model_dir, 'linear_called'))
     # verifies train_op fires dnn minimize op
     self.assertEqual(100.,
                      checkpoint_utils.load_variable(
-                         self._model_dir, 'binary_logistic_head/dnn_called'))
+                         self._model_dir, 'dnn_called'))
 
   def test_dnn_and_linear_logits_are_added(self):
     with ops.Graph().as_default():
diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index a1c1f1be0b..bc868a493f 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -32,17 +32,16 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
-from tensorflow.python.ops import logging_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import metrics as metrics_lib
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import string_ops
-from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import weights_broadcast_ops
 from tensorflow.python.ops.losses import losses
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.summary import summary
 
 _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
 
@@ -367,10 +366,7 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head):
   def create_estimator_spec(
       self, features, mode, logits, labels=None, train_op_fn=None):
     """See `Head`."""
-    with variable_scope.variable_scope(
-        None,
-        default_name='multi_class_head',
-        values=(tuple(six.itervalues(features)) + (labels, logits))):
+    with ops.name_scope('head'):
       logits = _check_logits(logits, self.logits_dimension)
 
       # Predict.
@@ -441,17 +437,18 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head):
       # Train.
       if train_op_fn is None:
         raise ValueError('train_op_fn can not be None.')
-      logging_ops.scalar_summary(metric_keys.MetricKeys.LOSS, training_loss)
-      logging_ops.scalar_summary(
-          metric_keys.MetricKeys.LOSS_MEAN,
-          losses.compute_weighted_loss(
-              unweighted_loss, weights=weights,
-              reduction=losses.Reduction.MEAN))
-      return model_fn.EstimatorSpec(
-          mode=model_fn.ModeKeys.TRAIN,
-          predictions=predictions,
-          loss=training_loss,
-          train_op=train_op_fn(training_loss))
+    with ops.name_scope(''):
+      summary.scalar(metric_keys.MetricKeys.LOSS, training_loss)
+      summary.scalar(metric_keys.MetricKeys.LOSS_MEAN,
+                     losses.compute_weighted_loss(
+                         unweighted_loss,
+                         weights=weights,
+                         reduction=losses.Reduction.MEAN))
+    return model_fn.EstimatorSpec(
+        mode=model_fn.ModeKeys.TRAIN,
+        predictions=predictions,
+        loss=training_loss,
+        train_op=train_op_fn(training_loss))
 
 
 def _binary_logistic_head_with_sigmoid_cross_entropy_loss(
@@ -579,32 +576,31 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(_Head):
   def create_estimator_spec(
       self, features, mode, logits, labels=None, train_op_fn=None):
     """See `Head`."""
-    with variable_scope.variable_scope(
-        None, default_name='binary_logistic_head',
-        values=(tuple(six.itervalues(features)) + (labels, logits))):
-
-      # Predict.
-      pred_keys = prediction_keys.PredictionKeys
-      logits = _check_logits(logits, self.logits_dimension)
-      logistic = math_ops.sigmoid(logits, name=pred_keys.LOGISTIC)
-      two_class_logits = array_ops.concat(
-          (array_ops.zeros_like(logits), logits), 1, name='two_class_logits')
-      scores = nn.softmax(two_class_logits, name=pred_keys.PROBABILITIES)
-      class_ids = array_ops.reshape(
-          math_ops.argmax(two_class_logits, axis=1), (-1, 1), name='classes')
-      if self._label_vocabulary:
-        table = lookup_ops.index_to_string_table_from_tensor(
-            vocabulary_list=self._label_vocabulary, name='class_string_lookup')
-        classes = table.lookup(class_ids)
-      else:
-        classes = string_ops.as_string(class_ids, name='str_classes')
-      predictions = {
-          pred_keys.LOGITS: logits,
-          pred_keys.LOGISTIC: logistic,
-          pred_keys.PROBABILITIES: scores,
-          pred_keys.CLASS_IDS: class_ids,
-          pred_keys.CLASSES: classes,
-      }
+    # Predict.
+    with ops.name_scope('head'):
+      with ops.name_scope(None, 'predictions', (logits,)):
+        pred_keys = prediction_keys.PredictionKeys
+        logits = _check_logits(logits, self.logits_dimension)
+        logistic = math_ops.sigmoid(logits, name=pred_keys.LOGISTIC)
+        two_class_logits = array_ops.concat(
+            (array_ops.zeros_like(logits), logits), 1, name='two_class_logits')
+        scores = nn.softmax(two_class_logits, name=pred_keys.PROBABILITIES)
+        class_ids = array_ops.reshape(
+            math_ops.argmax(two_class_logits, axis=1), (-1, 1), name='classes')
+        if self._label_vocabulary:
+          table = lookup_ops.index_to_string_table_from_tensor(
+              vocabulary_list=self._label_vocabulary,
+              name='class_string_lookup')
+          classes = table.lookup(class_ids)
+        else:
+          classes = string_ops.as_string(class_ids, name='str_classes')
+        predictions = {
+            pred_keys.LOGITS: logits,
+            pred_keys.LOGISTIC: logistic,
+            pred_keys.PROBABILITIES: scores,
+            pred_keys.CLASS_IDS: class_ids,
+            pred_keys.CLASSES: classes,
+        }
       if mode == model_fn.ModeKeys.PREDICT:
         batch_size = array_ops.shape(logistic)[0]
         export_class_list = self._label_vocabulary
@@ -657,17 +653,18 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(_Head):
       # Train.
       if train_op_fn is None:
         raise ValueError('train_op_fn can not be None.')
-      logging_ops.scalar_summary(metric_keys.MetricKeys.LOSS, training_loss)
-      logging_ops.scalar_summary(
-          metric_keys.MetricKeys.LOSS_MEAN,
-          losses.compute_weighted_loss(
-              unweighted_loss, weights=weights,
-              reduction=losses.Reduction.MEAN))
-      return model_fn.EstimatorSpec(
-          mode=model_fn.ModeKeys.TRAIN,
-          predictions=predictions,
-          loss=training_loss,
-          train_op=train_op_fn(training_loss))
+    with ops.name_scope(''):
+      summary.scalar(metric_keys.MetricKeys.LOSS, training_loss)
+      summary.scalar(metric_keys.MetricKeys.LOSS_MEAN,
+                     losses.compute_weighted_loss(
+                         unweighted_loss,
+                         weights=weights,
+                         reduction=losses.Reduction.MEAN))
+    return model_fn.EstimatorSpec(
+        mode=model_fn.ModeKeys.TRAIN,
+        predictions=predictions,
+        loss=training_loss,
+        train_op=train_op_fn(training_loss))
 
 
 def _regression_head_with_mean_squared_error_loss(weight_column=None,
@@ -707,12 +704,8 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
   def create_estimator_spec(
       self, features, mode, logits, labels=None, train_op_fn=None):
     """See `Head`."""
-    with variable_scope.variable_scope(
-        None,
-        default_name='regression_head',
-        values=(tuple(six.itervalues(features)) + (labels, logits))):
-
-      # Predict.
+    # Predict.
+    with ops.name_scope('head'):
       logits = _check_logits(logits, self._logits_dimension)
       predictions = {prediction_keys.PredictionKeys.PREDICTIONS: logits}
       if mode == model_fn.ModeKeys.PREDICT:
@@ -744,43 +737,46 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
       # Train.
       if train_op_fn is None:
         raise ValueError('train_op_fn can not be None.')
-      logging_ops.scalar_summary(metric_keys.MetricKeys.LOSS, training_loss)
-      logging_ops.scalar_summary(
-          metric_keys.MetricKeys.LOSS_MEAN,
-          losses.compute_weighted_loss(
-              unweighted_loss, weights=weights,
-              reduction=losses.Reduction.MEAN))
-      return model_fn.EstimatorSpec(
-          mode=model_fn.ModeKeys.TRAIN,
-          predictions=predictions,
-          loss=training_loss,
-          train_op=train_op_fn(training_loss))
+    with ops.name_scope(''):
+      summary.scalar(metric_keys.MetricKeys.LOSS, training_loss)
+      summary.scalar(metric_keys.MetricKeys.LOSS_MEAN,
+                     losses.compute_weighted_loss(
+                         unweighted_loss,
+                         weights=weights,
+                         reduction=losses.Reduction.MEAN))
+    return model_fn.EstimatorSpec(
+        mode=model_fn.ModeKeys.TRAIN,
+        predictions=predictions,
+        loss=training_loss,
+        train_op=train_op_fn(training_loss))
 
 
 def _assert_range(labels, n_classes):
-  assert_less = check_ops.assert_less(
-      labels,
-      ops.convert_to_tensor(n_classes, dtype=labels.dtype),
-      message='Label IDs must < n_classes')
-  assert_greater = check_ops.assert_non_negative(
-      labels, message='Label IDs must >= 0')
-  with ops.control_dependencies((assert_less, assert_greater)):
-    return array_ops.identity(labels)
+  with ops.name_scope(None, 'assert_range', (labels,)):
+    assert_less = check_ops.assert_less(
+        labels,
+        ops.convert_to_tensor(n_classes, dtype=labels.dtype),
+        message='Label IDs must < n_classes')
+    assert_greater = check_ops.assert_non_negative(
+        labels, message='Label IDs must >= 0')
+    with ops.control_dependencies((assert_less, assert_greater)):
+      return array_ops.identity(labels)
 
 
 def _weights(features, weight_column):
   """Fetches weights from features."""
-  if weight_column is None:
-    return 1.
-  if isinstance(weight_column, six.string_types):
-    weight_column = feature_column_lib.numeric_column(key=weight_column)
-  if not isinstance(weight_column, feature_column_lib._NumericColumn):  # pylint: disable=protected-access
-    raise TypeError('Weight column must be either a string or _NumericColumn. '
-                    'Given type: {}.'.format(type(weight_column)))
-  weights = weight_column._get_dense_tensor(  # pylint: disable=protected-access
-      feature_column_lib._LazyBuilder(features))  # pylint: disable=protected-access
-  if not (weights.dtype.is_floating or weights.dtype.is_integer):
-    raise ValueError('Weight column should be castable to float. '
-                     'Given dtype: {}'.format(weights.dtype))
-  weights = _maybe_expand_dim(math_ops.to_float(weights, name='weights'))
-  return weights
+  with ops.name_scope(None, 'weights', values=features.values()):
+    if weight_column is None:
+      return 1.
+    if isinstance(weight_column, six.string_types):
+      weight_column = feature_column_lib.numeric_column(key=weight_column)
+    if not isinstance(weight_column, feature_column_lib._NumericColumn):  # pylint: disable=protected-access
+      raise TypeError('Weight column must be either a string or _NumericColumn.'
+                      ' Given type: {}.'.format(type(weight_column)))
+    weights = weight_column._get_dense_tensor(  # pylint: disable=protected-access
+        feature_column_lib._LazyBuilder(features))  # pylint: disable=protected-access
+    if not (weights.dtype.is_floating or weights.dtype.is_integer):
+      raise ValueError('Weight column should be castable to float. '
+                       'Given dtype: {}'.format(weights.dtype))
+    weights = _maybe_expand_dim(math_ops.to_float(weights, name='weights'))
+    return weights
diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py
index 95d4f80308..5110582dd1 100644
--- a/tensorflow/python/estimator/canned/head_test.py
+++ b/tensorflow/python/estimator/canned/head_test.py
@@ -86,6 +86,9 @@ def _sigmoid(logits):
 
 class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase):
 
+  def setUp(self):
+    ops.reset_default_graph()
+
   def test_n_classes_is_none(self):
     with self.assertRaisesRegexp(ValueError, 'n_classes must be > 2'):
       head_lib._multi_class_head_with_softmax_cross_entropy_loss(
@@ -673,6 +676,9 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase):
 # TODO(ptucker): Add thresholds tests.
 class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase):
 
+  def setUp(self):
+    ops.reset_default_graph()
+
   def test_threshold_too_small(self):
     with self.assertRaisesRegexp(ValueError, r'thresholds not in \(0, 1\)'):
       head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(
@@ -1241,6 +1247,9 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase):
 
 class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase):
 
+  def setUp(self):
+    ops.reset_default_graph()
+
   def test_invalid_label_dimension(self):
     with self.assertRaisesRegexp(ValueError, r'Invalid label_dimension'):
       head_lib._regression_head_with_mean_squared_error_loss(label_dimension=-1)
-- 
GitLab


From 4252ab0ac01f0936095b44558e5cbfccf88f6cee Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 10:55:39 -0700
Subject: [PATCH 0351/1375] Adding missing license notice to toolchain build
 files

PiperOrigin-RevId: 159847551
---
 third_party/toolchains/cpus/BUILD | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/third_party/toolchains/cpus/BUILD b/third_party/toolchains/cpus/BUILD
index c33dcd4e11..45ec9f8c87 100644
--- a/third_party/toolchains/cpus/BUILD
+++ b/third_party/toolchains/cpus/BUILD
@@ -2,6 +2,8 @@
 # execution service
 # DO NOT EDIT: automatically generated BUILD file
 
+licenses(["restricted"])
+
 package(default_visibility = ["//visibility:public"])
 
 cc_library(
-- 
GitLab


From 583b6cbe60cc37c3fc711803fb5b5bf749e86141 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Thu, 22 Jun 2017 11:09:13 -0700
Subject: [PATCH 0352/1375] [XLA] Remove unused flags and move debugging flag
 to debug options.

PiperOrigin-RevId: 159849759
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 23 -------
 .../legacy_flags/compiler_functor_flags.cc    | 61 ------------------
 .../xla/legacy_flags/compiler_functor_flags.h | 47 --------------
 .../legacy_flags/convolution_thunk_flags.cc   | 63 -------------------
 .../legacy_flags/convolution_thunk_flags.h    | 47 --------------
 .../xla/legacy_flags/debug_options_flags.cc   |  5 ++
 tensorflow/compiler/xla/service/cpu/BUILD     |  1 -
 .../xla/service/cpu/compiler_functor.cc       | 11 ----
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 14 ++++-
 tensorflow/compiler/xla/service/gpu/BUILD     |  1 -
 .../xla/service/gpu/convolution_thunk.cc      |  6 +-
 .../xla/service/gpu/convolution_thunk.h       |  5 +-
 tensorflow/compiler/xla/xla.proto             |  9 ++-
 15 files changed, 26 insertions(+), 270 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 31637358c3..3c14a1d708 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -127,7 +127,6 @@ cc_library(
         ":tfcompile_lib",
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
-        "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index e03d28cd96..4c3167b502 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -24,7 +24,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
@@ -134,7 +133,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
-  xla::legacy_flags::AppendCompilerFunctorFlags(&flag_list);
   xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index dd0326c7c7..a54628060f 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -91,29 +91,6 @@ cc_library(
         ],
 )
 
-cc_library(
-    name = "compiler_functor_flags",
-    srcs = ["compiler_functor_flags.cc"],
-    hdrs = ["compiler_functor_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
-cc_library(
-    name = "convolution_thunk_flags",
-    srcs = ["convolution_thunk_flags.cc"],
-    hdrs = ["convolution_thunk_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "stream_assignment_flags",
     srcs = ["stream_assignment_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.cc b/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.cc
deleted file mode 100644
index 617a9b712e..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's compiler_functor module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static CompilerFunctorFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new CompilerFunctorFlags;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_debug_cpu_dump_ir", &flags->xla_debug_cpu_dump_ir,
-                       "Dump IR, before optimizations to a path"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's compiler_functor
-// module.
-void AppendCompilerFunctorFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the CompilerFunctorFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CompilerFunctorFlags* GetCompilerFunctorFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h b/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h
deleted file mode 100644
index 28b505ec5e..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_COMPILER_FUNCTOR_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_COMPILER_FUNCTOR_FLAGS_H_
-
-// Legacy flags for the XLA's compiler_functor module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's compiler_functor
-// module.
-void AppendCompilerFunctorFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's compiler_functor module.
-typedef struct {
-  string xla_debug_cpu_dump_ir;  // Dump IR, before optimizations to a path
-} CompilerFunctorFlags;
-
-// Return a pointer to the CompilerFunctorFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CompilerFunctorFlags* GetCompilerFunctorFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_COMPILER_FUNCTOR_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.cc b/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.cc
deleted file mode 100644
index fe5d19147f..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's convolution_thunk module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static ConvolutionThunkFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new ConvolutionThunkFlags;
-  flags->xla_gpu_autotune_convolution_algorithm = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_gpu_autotune_convolution_algorithm",
-                       &flags->xla_gpu_autotune_convolution_algorithm,
-                       "Auto-tune the algorithm used by convolution"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's convolution_thunk
-// module.
-void AppendConvolutionThunkFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the ConvolutionThunkFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-ConvolutionThunkFlags* GetConvolutionThunkFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h b/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h
deleted file mode 100644
index 53d6806a71..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CONVOLUTION_THUNK_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CONVOLUTION_THUNK_FLAGS_H_
-
-// Legacy flags for XLA's convolution_thunk module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's convolution_thunk
-// module.
-void AppendConvolutionThunkFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's convolution_thunk module.
-typedef struct {
-  // Auto-tune the algorithm used by convolution
-  bool xla_gpu_autotune_convolution_algorithm;
-} ConvolutionThunkFlags;
-
-// Return a pointer to the ConvolutionThunkFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-ConvolutionThunkFlags* GetConvolutionThunkFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CONVOLUTION_THUNK_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 5f029a5f53..bd026a5da1 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -29,6 +29,7 @@ struct DebugOptionsFlags {
   bool xla_enable_fast_math;
   int32 xla_backend_optimization_level;
   bool xla_embed_ir_in_executable;
+  string xla_dump_ir_to;
   string xla_dump_debug_json_to;
 
   string xla_gpu_cuda_data_dir;
@@ -52,6 +53,7 @@ void AllocateFlags() {
   flag_values->xla_enable_fast_math = true;
   flag_values->xla_backend_optimization_level = 3;
   flag_values->xla_embed_ir_in_executable = false;
+  flag_values->xla_dump_ir_to = "";
   flag_values->xla_dump_debug_json_to = "";
   flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
   flag_values->xla_gpu_ftz = false;
@@ -78,6 +80,8 @@ void AllocateFlags() {
        tensorflow::Flag("xla_embed_ir_in_executable",
                         &flag_values->xla_embed_ir_in_executable,
                         "Embed the compiler IR as a string in the executable."),
+       tensorflow::Flag("xla_dump_ir_to", &flag_values->xla_dump_ir_to,
+                        "Dump the compiler IR into this file/path."),
        tensorflow::Flag("xla_gpu_cuda_data_dir",
                         &flag_values->xla_gpu_cuda_data_dir,
                         "If non-empty, speficies a local directory containing "
@@ -123,6 +127,7 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
       flag_values->xla_backend_optimization_level);
   options.set_xla_embed_ir_in_executable(
       flag_values->xla_embed_ir_in_executable);
+  options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
   options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
   options.set_xla_gpu_cuda_data_dir(flag_values->xla_gpu_cuda_data_dir);
   options.set_xla_gpu_ftz(flag_values->xla_gpu_ftz);
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 68cd545695..6fa3acd85d 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -289,7 +289,6 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
         "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index 8ebf9ab110..d78a54427a 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -35,7 +35,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/Transforms/IPO.h"
 #include "external/llvm/include/llvm/Transforms/IPO/AlwaysInliner.h"
 #include "external/llvm/include/llvm/Transforms/IPO/PassManagerBuilder.h"
-#include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
@@ -45,7 +44,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
-#include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -66,15 +64,6 @@ operator()(llvm::Module& module) const {
 
   VLOG(2) << "IR before optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));
-  legacy_flags::CompilerFunctorFlags* flags =
-      legacy_flags::GetCompilerFunctorFlags();
-  string dump_path = flags->xla_debug_cpu_dump_ir;
-  if (!dump_path.empty()) {
-    std::unique_ptr<tensorflow::WritableFile> f;
-    TF_CHECK_OK(tensorflow::Env::Default()->NewAppendableFile(dump_path, &f));
-    TF_CHECK_OK(f->Append(llvm_ir::DumpModuleToString(module)));
-    TF_CHECK_OK(f->Close());
-  }
 
   // Build up optimization pipeline.
   AddOptimizationPasses(&module_passes, &function_passes);
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 0905855ec2..00cb507c0d 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -82,6 +82,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/env.h"
 
 namespace se = ::perftools::gputools;
 
@@ -374,10 +375,11 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 
   std::unique_ptr<Executable> cpu_executable;
 
-  // Cache this flag here since we'll want to access it after the module's
+  // Cache these flags here since we'll want to access them after the module's
   // ownership is std::moved.
   const bool embed_ir_in_executable =
       module->config().debug_options().xla_embed_ir_in_executable();
+  const string dump_ir_to = module->config().debug_options().xla_dump_ir_to();
   const string dump_debug_json_to =
       module->config().debug_options().xla_dump_debug_json_to();
 
@@ -460,8 +462,16 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
 
     string ir_module_string;
-    if (embed_ir_in_executable) {
+    if (embed_ir_in_executable || !dump_ir_to.empty()) {
       ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
+
+      if (!dump_ir_to.empty()) {
+        std::unique_ptr<tensorflow::WritableFile> f;
+        TF_RETURN_IF_ERROR(
+            tensorflow::Env::Default()->NewAppendableFile(dump_ir_to, &f));
+        TF_RETURN_IF_ERROR(f->Append(ir_module_string));
+        TF_RETURN_IF_ERROR(f->Close());
+      }
     }
 
     // JIT compile the LLVM IR module to in-memory machine code.
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index 1e15ce32ee..6b7ab7a864 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -253,7 +253,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:convolution_thunk_flags",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/compiler/xla/service:executable",
diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc
index 9a0b14eb73..20e0d8eb78 100644
--- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include <string>
 
-#include "tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
@@ -287,10 +286,7 @@ tensorflow::Status ConvolutionThunk::ConvolveWithTune(
     const ConvolutionDescriptor& convolution_descriptor,
     const BufferAllocations& buffer_allocations, se::Stream* stream) {
   // TODO(b/29126320): Try cudnn v5's new auto-tuner when it's rolled out.
-  legacy_flags::ConvolutionThunkFlags* flags =
-      legacy_flags::GetConvolutionThunkFlags();
-  if (flags->xla_gpu_autotune_convolution_algorithm &&
-      best_algorithm_.algorithm() == se::dnn::kDefaultAlgorithm) {
+  if (best_algorithm_.algorithm() == se::dnn::kDefaultAlgorithm) {
     // Auto-tuning either is disabled or only happens in the first run of this
     // function.
     VLOG(2) << "Profiling for best convolution algorithm used for "
diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h
index aaf72935e6..91d6df299d 100644
--- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h
@@ -81,9 +81,8 @@ class ConvolutionThunk : public Thunk {
   ConvolutionThunk(const ConvolutionThunk&) = delete;
   ConvolutionThunk& operator=(const ConvolutionThunk&) = delete;
 
-  // Does the convolution for the thunk on "stream". If the
-  // xla_gpu_autotune_convolution_algorithm is turned on, auto-tuning happens on
-  // the first run of this function.
+  // Does the convolution for the thunk on "stream". Auto-tuning happens on the
+  // first run of this function.
   tensorflow::Status ExecuteOnStream(
       const BufferAllocations& buffer_allocations,
       perftools::gputools::Stream* stream) override;
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index b6289f8e1c..76079c44cd 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -48,14 +48,17 @@ message DebugOptions {
   // Embed the compiler IR as a string in the executable.
   bool xla_embed_ir_in_executable = 5;
 
+  // Dump the compiler IR into this file/path.
+  string xla_dump_ir_to = 6;
+
   // Dump compilation artifacts as JSON into this directory.
-  string xla_dump_debug_json_to = 6;
+  string xla_dump_debug_json_to = 7;
 
   // Path to directory with cuda/ptx tools and libraries.
-  string xla_gpu_cuda_data_dir = 7;
+  string xla_gpu_cuda_data_dir = 8;
 
   // Enable flush-to-zero semantics in the GPU backend.
-  bool xla_gpu_ftz = 8;
+  bool xla_gpu_ftz = 9;
 
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
-- 
GitLab


From 6b37a07258532dda159d00373531608337de3dd2 Mon Sep 17 00:00:00 2001
From: Todd Wang <toddwang@gmail.com>
Date: Thu, 22 Jun 2017 11:13:24 -0700
Subject: [PATCH 0353/1375] Update comments

---
 tensorflow/compiler/xla/util.h | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 32aa81d1f4..31f0c3147e 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -205,7 +205,8 @@ std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
   return output;
 }
 
-// Override of the above that works around compile failures with vectors.
+// Override of the above that works around compile failures with gcc 7.1.1.
+// For details see https://github.com/tensorflow/tensorflow/issues/10843
 template <typename T>
 std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
                        const std::vector<T>& input) {
-- 
GitLab


From bfc1c235bc23ef3909dcbbf4823cf34d209ce523 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 11:14:49 -0700
Subject: [PATCH 0354/1375] Fixes some docstrings in feature_column.

PiperOrigin-RevId: 159850619
---
 .../python/feature_column/feature_column.py   | 82 ++++++++++++++-----
 1 file changed, 62 insertions(+), 20 deletions(-)

diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index 99aedc5d4b..c75144a9c9 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -400,11 +400,14 @@ def make_parse_example_spec(feature_columns):
   ```
 
   For the above example, make_parse_example_spec would return the dict:
+
+  ```python
   {
-    "feature_a": parsing_ops.VarLenFeature(tf.string),
-    "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
-    "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+      "feature_a": parsing_ops.VarLenFeature(tf.string),
+      "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
+      "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
   }
+  ```
 
   Args:
     feature_columns: An iterable containing all feature columns. All items
@@ -594,15 +597,21 @@ def bucketized_column(source_column, boundaries):
   `[1., 2.)`, and `[2., +inf)`.
 
   For example, if the inputs are
-    `boundaries` = [0, 10, 100]
-    input tensor = [[-5, 10000]
-                    [150,   10]
-                    [5,    100]]
+
+  ```python
+  boundaries = [0, 10, 100]
+  input tensor = [[-5, 10000]
+                  [150,   10]
+                  [5,    100]]
+  ```
 
   then the output will be
-    output = [[0, 3]
-              [3, 2]
-              [1, 3]]
+
+  ```python
+  output = [[0, 3]
+            [3, 2]
+            [1, 3]]
+  ```
 
   Example:
 
@@ -621,6 +630,7 @@ def bucketized_column(source_column, boundaries):
 
   `bucketized_column` can also be crossed with another categorical column using
   `crossed_column`:
+
   ```python
   price = numeric_column('price')
   # bucketized_column converts numerical feature to a categorical one.
@@ -745,6 +755,7 @@ def categorical_column_with_vocabulary_file(
   abbreviation. All inputs with values in that file are assigned an ID 0-49,
   corresponding to its line number. All other values are hashed and assigned an
   ID 50-54.
+
   ```python
   states = categorical_column_with_vocabulary_file(
       key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
@@ -759,6 +770,7 @@ def categorical_column_with_vocabulary_file(
   other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX'
   in input, and other values missing from the file, will be assigned ID 0. All
   others are assigned the corresponding line number 1-50.
+
   ```python
   states = categorical_column_with_vocabulary_file(
       key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
@@ -769,6 +781,7 @@ def categorical_column_with_vocabulary_file(
   ```
 
   And to make an embedding with either:
+
   ```python
   columns = [embedding_column(states, 3),...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
@@ -847,6 +860,7 @@ def categorical_column_with_vocabulary_list(
   inputs are assigned `default_value` 0.
 
   Linear model:
+
   ```python
   colors = categorical_column_with_vocabulary_list(
       key='colors', vocabulary_list=('X', 'R', 'G', 'B', 'Y'), default_value=0)
@@ -856,6 +870,7 @@ def categorical_column_with_vocabulary_list(
   ```
 
   Embedding for a DNN model:
+
   ```python
   columns = [embedding_column(colors, 3),...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
@@ -926,6 +941,7 @@ def categorical_column_with_identity(key, num_buckets, default_value=None):
   literal 0 in inputs will result in the same default ID.
 
   Linear model:
+
   ```python
   video_id = categorical_column_with_identity(
       key='video_id', num_buckets=1000000, default_value=0)
@@ -935,6 +951,7 @@ def categorical_column_with_identity(key, num_buckets, default_value=None):
   ```
 
   Embedding for a DNN model:
+
   ```python
   columns = [embedding_column(video_id, 9),...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
@@ -976,8 +993,8 @@ def indicator_column(categorical_column):
   `embedding_column` if the inputs are sparse.
 
   ```python
-  name = indicator_column(categorical_column_with_vocabulary_list('name',
-      ['bob', 'george', 'wanda'])
+  name = indicator_column(categorical_column_with_vocabulary_list(
+      'name', ['bob', 'george', 'wanda'])
   columns = [name, ...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
   dense_tensor = input_layer(features, columns)
@@ -1009,6 +1026,8 @@ def weighted_categorical_column(
   Example:
 
   Input `tf.Example` objects:
+
+  ```proto
   [
     features {
       feature {
@@ -1031,6 +1050,7 @@ def weighted_categorical_column(
       }
     }
   ]
+  ```
 
   ```python
   categorical_column = categorical_column_with_hash_bucket(
@@ -1076,22 +1096,41 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
     Hash(cartesian product of features) % `hash_bucket_size`
 
   For example, if the input features are:
-  * SparseTensor referred by first key: shape = [2, 2]
-      [0, 0]: "a"
-      [1, 0]: "b"
-      [1, 1]: "c"
 
-  * SparseTensor referred by second key: shape = [2, 1]
-      [0, 0]: "d"
-      [1, 0]: "e"
+  * SparseTensor referred by first key:
+
+    ```python
+    shape = [2, 2]
+    {
+        [0, 0]: "a"
+        [1, 0]: "b"
+        [1, 1]: "c"
+    }
+    ```
+
+  * SparseTensor referred by second key:
+
+    ```python
+    shape = [2, 1]
+    {
+        [0, 0]: "d"
+        [1, 0]: "e"
+    }
+    ```
 
   then crossed feature will look like:
-      shape = [2, 2]
+
+  ```python
+   shape = [2, 2]
+  {
       [0, 0]: Hash64("d", Hash64("a")) % hash_bucket_size
       [1, 0]: Hash64("e", Hash64("b")) % hash_bucket_size
       [1, 1]: Hash64("e", Hash64("c")) % hash_bucket_size
+  }
+  ```
 
   Here is an example to create a linear model with crosses of string features:
+
   ```python
   keywords_x_doc_terms = crossed_column(['keywords', 'doc_terms'], 50K)
   columns = [keywords_x_doc_terms, ...]
@@ -1100,6 +1139,7 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
   ```
 
   You could also use vocabulary lookup before crossing:
+
   ```python
   keywords = categorical_column_with_vocabulary_file(
       'keywords', '/path/to/vocabulary/file', vocabulary_size=1K)
@@ -1111,6 +1151,7 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
 
   If an input feature is of numeric type, you can use
   `categorical_column_with_identity`, or `bucketized_column`, as in the example:
+
   ```python
   # vertical_id is an integer categorical feature.
   vertical_id = categorical_column_with_identity('vertical_id', 10K)
@@ -1125,6 +1166,7 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
 
   To use crossed column in DNN model, you need to add it in an embedding column
   as in this example:
+
   ```python
   vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
   vertical_id_x_price_embedded = embedding_column(vertical_id_x_price, 10)
-- 
GitLab


From e955cb3f66a8f37f0473df52d7fd1bd1c9e2d6b6 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 22 Jun 2017 11:22:11 -0700
Subject: [PATCH 0355/1375] TpuEstimator: Replicate the input_fn to the worker
 CPU for each shard.

The batch size is configured as follows:
The user may specify a global batch size in their hyperparameters. If the 'batch_size' field is set, then we convert the global batch size into a per-shard batch size by dividing by num_shards before running their input_fn.

PiperOrigin-RevId: 159851773
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 223 ++++++++++++++----
 tensorflow/contrib/tpu/python/tpu/tpu_feed.py |  11 +-
 2 files changed, 181 insertions(+), 53 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index e15687f5de..28a0525f89 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -13,12 +13,13 @@
 # limitations under the License.
 # ===================================================================
 
-"""Tpu Estimator class."""
+"""TpuEstimator class."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import copy
 import threading
 from six.moves import queue as Queue  # pylint: disable=redefined-builtin
 
@@ -143,10 +144,29 @@ class TpuInfeedSessionHook(session_run_hook.SessionRunHook):
     session.run(self._finalize_op)
 
 
+class _PerShardOutput(object):
+  """Wraps input_fn's outputs into per-shard outputs.
+
+  Used so that the wrapped model_fn can distinguish between sharded input and
+  unsharded inputs (e.g., for export_savedmodel()).
+  """
+
+  def __init__(self, output):
+    self.output = output
+
+  def as_list(self):
+    return self.output
+
+
 class TpuEstimator(estimator_lib.Estimator):
   """Estimator with TPU support.
 
-  The only difference is a wrapped  model_fn is set in the constructor.
+  TpuEstimator handles many of the details of running on TPU devices, such as
+  replicating inputs and models for each core, and returning to host
+  periodically to run hooks.
+
+  Note: TpuEstimator transforms a global batch size in params to a per-shard
+        batch size when calling the input_fn.
   """
 
   def __init__(self,
@@ -155,8 +175,27 @@ class TpuEstimator(estimator_lib.Estimator):
                config=None,
                params=None,
                use_tpu=True):
+    if not isinstance(config, tpu_config.RunConfig):
+      raise ValueError('`config` must be `tpu_config.RunConfig`')
+
+    if use_tpu and params is not None and 'batch_size' in params:
+      if not isinstance(params['batch_size'], int):
+        raise ValueError('`batch_size` in params must be an int')
+      params = copy.deepcopy(params)
+      # The specified batch size is the batch size for the entire computation.
+      # The input_fn is called per-shard, so we want to calculate the per-shard
+      # batch size and pass that.
+      if params['batch_size'] % config.tpu_config.num_shards != 0:
+        raise ValueError(
+            'batch size {} must be divisible by number of shards {}'
+            .format(params['batch_size'], config.tpu_config.num_shards))
+      params['batch_size'] //= config.tpu_config.num_shards
+
     if use_tpu:
-      model_function = wrapped_model_fn(model_fn, config)
+      # config and params need to be copied because we haven't initialized
+      # Estimator yet.
+      model_function = wrapped_model_fn(model_fn, copy.deepcopy(config),
+                                        copy.deepcopy(params))
     else:
       model_function = model_fn
 
@@ -165,8 +204,7 @@ class TpuEstimator(estimator_lib.Estimator):
         model_dir=model_dir,
         config=config,
         params=params)
-    if not isinstance(config, tpu_config.RunConfig):
-      raise ValueError('`config` must be `tpu_config.RunConfig`')
+    self.use_tpu = use_tpu
 
   def _create_global_step(self, graph):
     """Creates a global step suitable for TPUs.
@@ -195,6 +233,65 @@ class TpuEstimator(estimator_lib.Estimator):
           collections=[ops.GraphKeys.GLOBAL_VARIABLES,
                        ops.GraphKeys.GLOBAL_STEP])
 
+  def _call_input_fn(self, input_fn):
+    """Calls the input function.
+
+    Args:
+      input_fn: The input function.
+
+    Returns:
+      Either features or (features, labels) where features and labels are:
+        features - `Tensor` or dictionary of string feature name to `Tensor`.
+        labels - `Tensor` or dictionary of `Tensor` with labels.
+
+    Raises:
+      ValueError: if input_fn takes invalid arguments.
+    """
+    if not self.use_tpu:
+      return super(TpuEstimator, self)._call_input_fn(input_fn)
+
+    input_fn_args = estimator_lib._fn_args(input_fn)  # pylint: disable=protected-access
+    kwargs = {}
+    if 'params' in input_fn_args:
+      kwargs['params'] = self.params
+    if 'config' in input_fn_args:
+      kwargs['config'] = self.config
+
+    job = _tpu_job(self.config)
+    def placement_function(index):
+      if job is None:
+        return '/replica:0/task:0/device:CPU:0'
+      else:
+        return '/job:%s/replica:0/task:%d/device:CPU:0' % (job, index / 8)
+
+    features = []
+    labels = []
+    for i in range(self.config.tpu_config.num_shards):
+      with ops.device(placement_function(i)):
+        result = input_fn(**kwargs)
+        # input_fn may return either features or (features, labels)
+        if isinstance(result, tuple):
+          features.append(result[0])
+          labels.append(result[1])
+        else:
+          features.append(result)
+    if not labels or all(l is None for l in labels):
+      return _PerShardOutput(features), None
+    return _PerShardOutput(features), _PerShardOutput(labels)
+
+
+def _call_model_fn(model_fn, features, labels, mode, config, params):
+  """Calls the model_fn with required parameters."""
+  model_fn_args = estimator_lib._fn_args(model_fn)  # pylint: disable=protected-access
+  kwargs = {}
+  if 'mode' in model_fn_args:
+    kwargs['mode'] = mode
+  if 'params' in model_fn_args:
+    kwargs['params'] = params
+  if 'config' in model_fn_args:
+    kwargs['config'] = config
+  return model_fn(features=features, labels=labels, **kwargs)
+
 
 # TODO(xiejw): Improve the structure of this input_fn to infeed converion.
 # The code now looks not like Estimator style. We need to abstract many
@@ -214,61 +311,96 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
     labels: labels
 
   Returns:
-    A tuple of (dequeue_fn, and thread main function)
+    A tuple of (dequeue_fn, enqueue_fn)
   """
   infeed_names = None
-  infeed_tuple = []
-  if isinstance(features, dict):
+  sharded_inputs = []
+  if isinstance(features[0], dict):
     # We need a fixed ordering for enqueueing and dequeueing.
-    infeed_names = [name for name in features]
-    infeed_tuple.extend([features[name] for name in infeed_names])
-  else:
-    infeed_tuple.append(features)
-  # TODO(jhseu): Handle multi-head and None labels
-  infeed_tuple.append(labels)
-  # TODO(jhseu): Update when b/36470756 is settled.
+    infeed_names = [name for name in features[0]]
+
+  for shard in range(run_config.tpu_config.num_shards):
+    inputs = []
+    if infeed_names is None:
+      inputs.append(features[shard])
+    else:
+      for name in infeed_names:
+        inputs.append(features[shard][name])
+    if labels is not None:
+      inputs.append(labels[shard])
+    sharded_inputs.append(inputs)
+
   infeed_queue = tpu_feed.InfeedQueue(
-      tuple_types=[t.dtype for t in infeed_tuple],
-      tuple_shapes=[t.shape for t in infeed_tuple])
-  infeed_queue.set_number_of_shards(run_config.tpu_config.num_shards)
+      number_of_tuple_elements=len(sharded_inputs[0]))
+  infeed_queue.set_configuration_from_sharded_input_tensors(sharded_inputs)
 
   def dequeue_fn():
     """dequeue_fn is used by the train_step in TPU to retrieve the tensors."""
     values = infeed_queue.generate_dequeue_op()
+
+    expected_num_tensors = 0
+    if labels is not None:
+      expected_num_tensors += 1
+    if infeed_names is None:
+      expected_num_tensors += 1
+    else:
+      expected_num_tensors += len(infeed_names)
+    assert len(values) == expected_num_tensors
+
+    dequeue_label = None
+    if labels is not None:
+      dequeue_label = values[-1]
     if infeed_names is None:
-      return values
+      return values[0], dequeue_label
     # Restore the feature dictionary and label.
     dequeued_features = {}
-    for i in range(len(values) - 1):
+    for i in range(len(infeed_names)):
       dequeued_features[infeed_names[i]] = values[i]
-    label = values[-1]
-    return dequeued_features, label
+    return dequeued_features, dequeue_label
+
+  def tpu_ordinal_function(index):
+    """Return the TPU ordinal associated with a shard.
+
+    Required because the enqueue ops are placed on CPU.
+
+    Args:
+      index: the shard index
+
+    Returns:
+      The ordinal of the TPU device the shard's infeed should be placed on.
+    """
+    return index % 8
 
   def enqueue_fn():
     """enqueue_fn is used to add ops to the graph to send tensors."""
-    job = _tpu_job(run_config)
-    def placement_function(index):
-      if job is None:
-        return '/replica:0/task:0/device:CPU:0'
-      else:
-        return '/job:%s/replica:0/task:%d/device:CPU:0' % (job, index / 8)
-    return infeed_queue.split_inputs_and_generate_enqueue_ops(
-        infeed_tuple, placement_function=placement_function)
+    return infeed_queue.generate_enqueue_ops(
+        sharded_inputs, tpu_ordinal_function=tpu_ordinal_function)
 
   return (dequeue_fn, enqueue_fn)
 
 
-def wrapped_model_fn(model_fn, run_config):
+def wrapped_model_fn(model_fn, run_config, params):
   """Returns a new model_fn, which wraps the TPU support."""
 
   # Verifies the model_fn signature according to Estimator framework.
-  estimator_lib._verify_model_fn_args(model_fn, params=None)  # pylint: disable=protected-access
+  estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
 
-  def _model_fn(features, labels, mode):
+  def _model_fn(features, labels, mode, params=None):
     """model_fn."""
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
     if mode != model_fn_lib.ModeKeys.TRAIN:
-      return model_fn(features, labels, mode)
+      if isinstance(features, _PerShardOutput):
+        features = features.as_list()[0]
+      if isinstance(labels, _PerShardOutput):
+        labels = labels.as_list()[0]
+      return _call_model_fn(model_fn, features, labels, mode, run_config,
+                            params)
+
+    assert isinstance(features, _PerShardOutput)
+    features = features.as_list()
+    if labels is not None:
+      assert isinstance(labels, _PerShardOutput)
+      labels = labels.as_list()
 
     dequeue_fn, enqueue_fn = (
         _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels))
@@ -276,7 +408,7 @@ def wrapped_model_fn(model_fn, run_config):
     loss = _train_on_tpu_shards(
         run_config,
         train_step=_convert_model_fn_to_train_step(
-            model_fn, dequeue_fn, mode, run_config))
+            model_fn, dequeue_fn, mode, run_config, params))
 
     # Gets the variables back from TPU nodes. This means the variables updated
     # by TPU will now be *synced* to host memory.
@@ -302,21 +434,9 @@ def wrapped_model_fn(model_fn, run_config):
   return _model_fn
 
 
-def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config):
-  """generates a train step based on the model_fn."""
-
-  def _call_model_fn(features, labels):
-    """Calls the model_fn with required parameters."""
-    model_fn_args = estimator_lib._fn_args(model_fn)  # pylint: disable=protected-access
-    kwargs = {}
-    if 'mode' in model_fn_args:
-      kwargs['mode'] = mode
-    # Uncomment the following lines once `params` is supported.
-    #   if 'params' in model_fn_args:
-    #     kwargs['params'] = params
-    if 'config' in model_fn_args:
-      kwargs['config'] = run_config
-    return model_fn(features=features, labels=labels, **kwargs)
+def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config,
+                                    params):
+  """Generates a train step based on the model_fn."""
 
   def _verify_estimator_spec(estimator_spec):
     """Validates the estimator_spec."""
@@ -339,7 +459,8 @@ def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config):
     # the graph construction part in the model_fn should be separated from the
     # control part (such as hooks and savers). By that the graph construction
     # could de defered on TPU chip, while the control logic can stay in host.
-    estimator_spec = _verify_estimator_spec(_call_model_fn(features, labels))
+    estimator_spec = _verify_estimator_spec(
+        _call_model_fn(model_fn, features, labels, mode, run_config, params))
     loss, train_op = estimator_spec.loss, estimator_spec.train_op
     with ops.control_dependencies([train_op]):
       return array_ops.identity(loss)
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
index 668b5b8b91..c955de524c 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
@@ -448,7 +448,7 @@ class InfeedQueue(object):
             name=full_name,
             device_ordinal=tpu_ordinal)
 
-  def generate_enqueue_ops(self, sharded_inputs):
+  def generate_enqueue_ops(self, sharded_inputs, tpu_ordinal_function=None):
     """Generates the host-side Ops to enqueue the shards of a tuple.
 
     sharded_inputs is a list, one for each shard, of lists of
@@ -466,6 +466,10 @@ class InfeedQueue(object):
       sharded_inputs: a list of lists of Tensors. The length of the outer list
         determines the number of shards. Each inner list indicates the types
         and shapes of the tuples in the corresponding shard.
+      tpu_ordinal_function: if not None, a function that takes the
+        shard index as input and returns the ordinal of the TPU device
+        the shard's infeed should be placed on. tpu_ordinal_function must be
+        set if the inputs are placed on CPU devices.
 
     Returns:
       A list of host-side Ops, one for each shard, that when executed together
@@ -487,9 +491,12 @@ class InfeedQueue(object):
     if self._generated_enqueue_ops:
       raise ValueError("Can't generate two enqueue Ops from the same queue")
     self._generated_enqueue_ops = True
+    if tpu_ordinal_function is None:
+      tpu_ordinal_function = lambda index: -1
     name_prefix = "%s/enqueue" % self._name
     return [
-        self._generate_enqueue_op(shard, name_prefix, index)
+        self._generate_enqueue_op(shard, name_prefix, index,
+                                  tpu_ordinal=tpu_ordinal_function(index))
         for (shard, index) in zip(sharded_inputs, xrange(self.number_of_shards))
     ]
 
-- 
GitLab


From 1757a2c11741bb5e97cd2223272109c1b740a9d7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 11:25:58 -0700
Subject: [PATCH 0356/1375] Modify beam search decoder to use symbolic shape
 for vocab size if the static shape is not present.

PiperOrigin-RevId: 159852297
---
 tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
index 1d1babda16..ef1735a6c9 100644
--- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
@@ -486,7 +486,7 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
   total_probs = array_ops.expand_dims(beam_state.log_probs, 2) + step_log_probs
 
   # Calculate the continuation lengths by adding to all continuing beams.
-  vocab_size = logits.shape[-1].value
+  vocab_size = logits.shape[-1].value or array_ops.shape(logits)[-1]
   lengths_to_add = array_ops.one_hot(
       indices=array_ops.tile(
           array_ops.reshape(end_token, [1, 1]), [batch_size, beam_width]),
-- 
GitLab


From da8074e80ac505d7940bd446dd7e74ba616b00d9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 11:27:42 -0700
Subject: [PATCH 0357/1375] Generalize cluster initialization to span multiple
 mini-batches if necessary.

PiperOrigin-RevId: 159852557
---
 .../python/ops/clustering_ops.py              | 318 ++++++++++++------
 .../python/learn/estimators/kmeans_test.py    | 165 +++++----
 2 files changed, 321 insertions(+), 162 deletions(-)

diff --git a/tensorflow/contrib/factorization/python/ops/clustering_ops.py b/tensorflow/contrib/factorization/python/ops/clustering_ops.py
index 2e9b5e22c7..c9b3314d6b 100644
--- a/tensorflow/contrib/factorization/python/ops/clustering_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/clustering_ops.py
@@ -93,15 +93,24 @@ class KMeans(object):
 
     Args:
       inputs: An input tensor or list of input tensors
-      num_clusters: number of clusters.
-      initial_clusters: Specifies the clusters used during initialization.  Can
-        be a tensor or numpy array, or a function that generates the clusters.
-        Can also be "random" to specify that clusters should be chosen randomly
-        from input data.
-      distance_metric: distance metric used for clustering.
+      num_clusters: An integer tensor specifying the number of clusters. This
+        argument is ignored if initial_clusters is a tensor or numpy array.
+      initial_clusters: Specifies the clusters used during initialization. One
+        of the following:
+        - a tensor or numpy array with the initial cluster centers.
+        - a function f(inputs, k) that returns up to k centers from `inputs`.
+        - "random": Choose centers randomly from `inputs`.
+        - "kmeans_plus_plus": Use kmeans++ to choose centers from `inputs`.
+        In the last three cases, one batch of `inputs` may not yield
+        `num_clusters` centers, in which case initialization will require
+        multiple batches until enough centers are chosen. In the case of
+        "random" or "kmeans_plus_plus", if the input size is <= `num_clusters`
+        then the entire batch is chosen to be cluster centers.
+      distance_metric: Distance metric used for clustering. Supported options:
+        "squared_euclidean", "cosine".
       use_mini_batch: If true, use the mini-batch k-means algorithm. Else assume
         full batch.
-      mini_batch_steps_per_iteration: number of steps after which the updated
+      mini_batch_steps_per_iteration: Number of steps after which the updated
         cluster centers are synced back to a master copy.
       random_seed: Seed for PRNG used to initialize seeds.
       kmeans_plus_plus_num_retries: For each point that is sampled during
@@ -109,14 +118,21 @@ class KMeans(object):
         additional points to draw from the current distribution before selecting
         the best. If a negative value is specified, a heuristic is used to
         sample O(log(num_to_sample)) additional points.
+
+    Raises:
+      ValueError: An invalid argument was passed to initial_clusters or
+        distance_metric.
     """
+    if isinstance(initial_clusters, str) and initial_clusters not in [
+        RANDOM_INIT, KMEANS_PLUS_PLUS_INIT
+    ]:
+      raise ValueError(
+          "Unsupported initialization algorithm '%s'" % initial_clusters)
+    if distance_metric not in [SQUARED_EUCLIDEAN_DISTANCE, COSINE_DISTANCE]:
+      raise ValueError("Unsupported distance metric '%s'" % distance_metric)
     self._inputs = inputs if isinstance(inputs, list) else [inputs]
-    assert num_clusters > 0, num_clusters
     self._num_clusters = num_clusters
-    if initial_clusters is None:
-      initial_clusters = RANDOM_INIT
     self._initial_clusters = initial_clusters
-    assert distance_metric in [SQUARED_EUCLIDEAN_DISTANCE, COSINE_DISTANCE]
     self._distance_metric = distance_metric
     self._use_mini_batch = use_mini_batch
     self._mini_batch_steps_per_iteration = int(mini_batch_steps_per_iteration)
@@ -144,8 +160,7 @@ class KMeans(object):
       return cls._compute_cosine_distance(
           inputs, clusters, inputs_normalized=True)
     else:
-      assert False, ('Unsupported distance metric passed to Kmeans %s' %
-                     str(distance_metric))
+      assert False, str(distance_metric)
 
   @classmethod
   def _compute_euclidean_distance(cls, inputs, clusters):
@@ -238,90 +253,29 @@ class KMeans(object):
                        array_ops.squeeze(indices)))
     return zip(*output)
 
-  def _init_clusters_random(self):
-    """Does random initialization of clusters.
-
-    Returns:
-      Tensor of randomly initialized clusters.
-    """
-    num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in self._inputs])
-    # Note that for mini-batch k-means, we should ensure that the batch size of
-    # data used during initialization is sufficiently large to avoid duplicated
-    # clusters.
-    with ops.control_dependencies(
-        [check_ops.assert_less_equal(self._num_clusters, num_data)]):
-      indices = random_ops.random_uniform(
-          array_ops.reshape(self._num_clusters, [-1]),
-          minval=0,
-          maxval=math_ops.cast(num_data, dtypes.int64),
-          seed=self._random_seed,
-          dtype=dtypes.int64)
-      clusters_init = embedding_lookup(
-          self._inputs, indices, partition_strategy='div')
-      return clusters_init
-
   def _clusters_l2_normalized(self):
     """Returns True if clusters centers are kept normalized."""
     return (self._distance_metric == COSINE_DISTANCE and
             (not self._use_mini_batch or
              self._mini_batch_steps_per_iteration > 1))
 
-  def _initialize_clusters(self, cluster_centers, cluster_centers_initialized,
-                           cluster_centers_updated):
-    """Returns an op to initialize the cluster centers."""
-
-    init = self._initial_clusters
-    if init == RANDOM_INIT:
-      clusters_init = self._init_clusters_random()
-    elif init == KMEANS_PLUS_PLUS_INIT:
-      # Points from only the first shard are used for initializing centers.
-      # TODO(ands): Use all points.
-      inp = self._inputs[0]
-      if self._distance_metric == COSINE_DISTANCE:
-        inp = nn_impl.l2_normalize(inp, dim=1)
-      clusters_init = gen_clustering_ops.kmeans_plus_plus_initialization(
-          inp, self._num_clusters, self._random_seed,
-          self._kmeans_plus_plus_num_retries)
-    elif callable(init):
-      clusters_init = init(self._inputs, self._num_clusters)
-    elif not isinstance(init, str):
-      clusters_init = init
-    else:
-      assert False, 'Unsupported init passed to Kmeans %s' % str(init)
-    if self._distance_metric == COSINE_DISTANCE and clusters_init is not None:
-      clusters_init = nn_impl.l2_normalize(clusters_init, dim=1)
-
-    with ops.colocate_with(cluster_centers_initialized):
-      initialized = control_flow_ops.with_dependencies(
-          [clusters_init], array_ops.identity(cluster_centers_initialized))
-    with ops.colocate_with(cluster_centers):
-      assign_centers = state_ops.assign(
-          cluster_centers, clusters_init, validate_shape=False)
-      if cluster_centers_updated != cluster_centers:
-        assign_centers = control_flow_ops.group(assign_centers,
-                                                state_ops.assign(
-                                                    cluster_centers_updated,
-                                                    clusters_init,
-                                                    validate_shape=False))
-    assign_centers = control_flow_ops.with_dependencies(
-        [assign_centers], state_ops.assign(cluster_centers_initialized, True))
-    return control_flow_ops.cond(initialized, control_flow_ops.no_op,
-                                 lambda: assign_centers).op
-
-  def _create_variables(self):
+  def _create_variables(self, num_clusters):
     """Creates variables.
 
+    Args:
+      num_clusters: an integer Tensor providing the number of clusters.
+
     Returns:
-    Tuple with following elements:
-      cluster_centers: a Tensor for storing cluster centers
-      cluster_centers_initialized: bool Variable indicating whether clusters
-        are initialized.
-      cluster_counts: a Tensor for storing counts of points assigned to this
-        cluster. This is used by mini-batch training.
-      cluster_centers_updated: Tensor representing copy of cluster centers that
-        are updated every step.
-      update_in_steps: numbers of steps left before we sync
-        cluster_centers_updated back to cluster_centers.
+      Tuple with following elements:
+      - cluster_centers: a Tensor for storing cluster centers
+      - cluster_centers_initialized: bool Variable indicating whether clusters
+            are initialized.
+      - cluster_counts: a Tensor for storing counts of points assigned to this
+            cluster. This is used by mini-batch training.
+      - cluster_centers_updated: Tensor representing copy of cluster centers
+            that are updated every step.
+      - update_in_steps: numbers of steps left before we sync
+            cluster_centers_updated back to cluster_centers.
     """
     init_value = array_ops.constant([], dtype=dtypes.float32)
     cluster_centers = variable_scope.variable(
@@ -341,12 +295,12 @@ class KMeans(object):
           name='update_in_steps')
       # Count of points assigned to cluster_centers_updated.
       cluster_counts = variable_scope.variable(
-          array_ops.zeros([self._num_clusters], dtype=dtypes.int64))
+          array_ops.zeros([num_clusters], dtype=dtypes.int64))
     else:
       cluster_centers_updated = cluster_centers
       update_in_steps = None
       cluster_counts = (variable_scope.variable(
-          array_ops.ones([self._num_clusters], dtype=dtypes.int64))
+          array_ops.ones([num_clusters], dtype=dtypes.int64))
                         if self._use_mini_batch else None)
     return (cluster_centers, cluster_centers_initialized, cluster_counts,
             cluster_centers_updated, update_in_steps)
@@ -363,6 +317,14 @@ class KMeans(object):
   def training_graph(self):
     """Generate a training graph for kmeans algorithm.
 
+    This returns, among other things, an op that chooses initial centers
+    (init_op), a boolean variable that is set to True when the initial centers
+    are chosen (cluster_centers_initialized), and an op to perform either an
+    entire Lloyd iteration or a mini-batch of a Lloyd iteration (training_op).
+    The caller should use these components as follows. A single worker should
+    execute init_op multiple times until cluster_centers_initialized becomes
+    True. Then multiple workers may execute training_op any number of times.
+
     Returns:
       A tuple consisting of:
       all_scores: A matrix (or list of matrices) of dimensions (num_input,
@@ -379,12 +341,23 @@ class KMeans(object):
       training_op: an op that runs an iteration of training.
     """
     # Implementation of kmeans.
+    if (isinstance(self._initial_clusters, str) or
+        callable(self._initial_clusters)):
+      initial_clusters = self._initial_clusters
+      num_clusters = ops.convert_to_tensor(self._num_clusters)
+    else:
+      initial_clusters = ops.convert_to_tensor(self._initial_clusters)
+      num_clusters = array_ops.shape(initial_clusters)[0]
+
     inputs = self._inputs
     (cluster_centers_var, cluster_centers_initialized, total_counts,
-     cluster_centers_updated, update_in_steps) = self._create_variables()
-    init_op = self._initialize_clusters(cluster_centers_var,
-                                        cluster_centers_initialized,
-                                        cluster_centers_updated)
+     cluster_centers_updated,
+     update_in_steps) = self._create_variables(num_clusters)
+    init_op = _InitializeClustersOpFactory(
+        self._inputs, num_clusters, initial_clusters, self._distance_metric,
+        self._random_seed, self._kmeans_plus_plus_num_retries,
+        cluster_centers_var, cluster_centers_updated,
+        cluster_centers_initialized).op()
     cluster_centers = cluster_centers_var
 
     if self._distance_metric == COSINE_DISTANCE:
@@ -403,8 +376,8 @@ class KMeans(object):
             inputs, cluster_idx, cluster_centers_updated, total_counts)
     else:
       assert cluster_centers == cluster_centers_var
-      training_op = self._full_batch_training_op(inputs, cluster_idx,
-                                                 cluster_centers_var)
+      training_op = self._full_batch_training_op(
+          inputs, num_clusters, cluster_idx, cluster_centers_var)
 
     return (all_scores, cluster_idx, scores, cluster_centers_initialized,
             init_op, training_op)
@@ -516,11 +489,13 @@ class KMeans(object):
       update_ops.extend([update_counts, update_cluster_centers])
     return control_flow_ops.group(*update_ops)
 
-  def _full_batch_training_op(self, inputs, cluster_idx_list, cluster_centers):
+  def _full_batch_training_op(self, inputs, num_clusters, cluster_idx_list,
+                              cluster_centers):
     """Creates an op for training for full batch case.
 
     Args:
       inputs: list of input Tensors.
+      num_clusters: an integer Tensor providing the number of clusters.
       cluster_idx_list: A vector (or list of vectors). Each element in the
         vector corresponds to an input row in 'inp' and specifies the cluster id
         corresponding to the input.
@@ -535,16 +510,161 @@ class KMeans(object):
     for inp, cluster_idx in zip(inputs, cluster_idx_list):
       with ops.colocate_with(inp, ignore_existing=True):
         cluster_sums.append(
-            math_ops.unsorted_segment_sum(inp, cluster_idx, self._num_clusters))
+            math_ops.unsorted_segment_sum(inp, cluster_idx, num_clusters))
         cluster_counts.append(
             math_ops.unsorted_segment_sum(
                 array_ops.reshape(
                     array_ops.ones(
                         array_ops.reshape(array_ops.shape(inp)[0], [-1])),
-                    [-1, 1]), cluster_idx, self._num_clusters))
+                    [-1, 1]), cluster_idx, num_clusters))
     with ops.colocate_with(cluster_centers, ignore_existing=True):
       new_clusters_centers = math_ops.add_n(cluster_sums) / (math_ops.cast(
           math_ops.add_n(cluster_counts), cluster_sums[0].dtype) + epsilon)
       if self._clusters_l2_normalized():
         new_clusters_centers = nn_impl.l2_normalize(new_clusters_centers, dim=1)
     return state_ops.assign(cluster_centers, new_clusters_centers)
+
+
+class _InitializeClustersOpFactory(object):
+  """Internal class to create the op to initialize the clusters.
+
+    The op performs this algorithm (see constructor args):
+
+    num_remaining = num_clusters - length(cluster_centers)
+    if num_remaining == 0:
+      assert that cluster_centers_initialized is true
+    else:
+      assert that num_remaining > 0
+      new_centers = choose up to num_remaining initial centers
+      l2-normalize new_centers if using cosine distance
+      all_centers = concat(cluster_centers, new_centers)
+      cluster_centers := all_centers
+      if there is a cluster_centers_updated variable:
+        cluster_centers_updated := cluster_centers
+      num_now_remaining = num_clusters - length(cluster_centers)
+      if num_now_remaining == 0:
+        cluster_centers_initialized := true
+  """
+
+  def __init__(self, inputs, num_clusters, initial_clusters, distance_metric,
+               random_seed, kmeans_plus_plus_num_retries, cluster_centers,
+               cluster_centers_updated, cluster_centers_initialized):
+    """Creates an op factory.
+
+    Args:
+      inputs: See KMeans constructor.
+      num_clusters: An integer Tensor providing the number of clusters.
+      initial_clusters: See KMeans constructor.
+      distance_metric: See KMeans constructor.
+      random_seed: See KMeans constructor.
+      kmeans_plus_plus_num_retries: See KMeans constructor.
+      cluster_centers: The TF variable holding the initial centers. It may
+          already contain some centers when the op is executed.
+      cluster_centers_updated: A second TF variable to hold a copy of the
+          initial centers, used for full-batch mode. In mini-batch mode,
+          cluster_centers_updated is the same variable as cluster_centers.
+      cluster_centers_initialized: A boolean TF variable that will be set
+          to true when all the initial centers have been chosen.
+    """
+    # All of these instance variables are constants.
+    self._inputs = inputs
+    self._num_clusters = num_clusters
+    self._initial_clusters = initial_clusters
+    self._distance_metric = distance_metric
+    self._random_seed = random_seed
+    self._kmeans_plus_plus_num_retries = kmeans_plus_plus_num_retries
+    self._cluster_centers = cluster_centers
+    self._cluster_centers_updated = cluster_centers_updated
+    self._cluster_centers_initialized = cluster_centers_initialized
+
+    self._num_selected = array_ops.shape(self._cluster_centers)[0]
+    self._num_remaining = self._num_clusters - self._num_selected
+    self._num_data = math_ops.add_n(
+        [array_ops.shape(i)[0] for i in self._inputs])
+
+  def _random(self):
+    indices = random_ops.random_uniform(
+        array_ops.reshape(self._num_remaining, [-1]),
+        minval=0,
+        maxval=math_ops.cast(self._num_data, dtypes.int64),
+        seed=self._random_seed,
+        dtype=dtypes.int64)
+    return embedding_lookup(self._inputs, indices, partition_strategy='div')
+
+  def _kmeans_plus_plus(self):
+    # Points from only the first shard are used for initializing centers.
+    # TODO(ands): Use all points.
+    inp = self._inputs[0]
+    if self._distance_metric == COSINE_DISTANCE:
+      inp = nn_impl.l2_normalize(inp, dim=1)
+    return gen_clustering_ops.kmeans_plus_plus_initialization(
+        inp,
+        math_ops.to_int64(self._num_remaining), self._random_seed,
+        self._kmeans_plus_plus_num_retries)
+
+  def _greedy_batch_sampler(self, sampler):
+    # If the input dataset size is smaller than the number of centers
+    # remaining, choose the entire input dataset as centers. This can happen
+    # with mini-batch. Otherwise, sample the batch according to the provided
+    # sampler.
+    return control_flow_ops.cond(self._num_data <= self._num_remaining,
+                                 lambda: array_ops.concat(self._inputs, 0),
+                                 sampler)
+
+  def _single_batch_sampler(self, sampler):
+    # Enforce that there are at least as many data points as centers
+    # remaining. This gives the provided sampler the chance to select all
+    # remaining centers from a single batch.
+    with ops.control_dependencies(
+        [check_ops.assert_greater_equal(self._num_data, self._num_remaining)]):
+      return sampler()
+
+  def _choose_initial_centers(self):
+    if isinstance(self._initial_clusters, str):
+      if self._initial_clusters == RANDOM_INIT:
+        return self._greedy_batch_sampler(self._random)
+      else:  # self._initial_clusters == KMEANS_PLUS_PLUS_INIT
+        return self._single_batch_sampler(self._kmeans_plus_plus)
+    elif callable(self._initial_clusters):
+      return self._initial_clusters(self._inputs, self._num_remaining)
+    else:
+      with ops.control_dependencies([
+          check_ops.assert_equal(self._num_remaining,
+                                 array_ops.shape(self._initial_clusters)[0])
+      ]):
+        return self._initial_clusters
+
+  def _add_new_centers(self):
+    """Adds some centers and returns the number of centers remaining."""
+    new_centers = self._choose_initial_centers()
+    if self._distance_metric == COSINE_DISTANCE:
+      new_centers = nn_impl.l2_normalize(new_centers, dim=1)
+    # If cluster_centers is empty, it doesn't have the right shape for concat.
+    all_centers = control_flow_ops.cond(
+        math_ops.equal(self._num_selected, 0), lambda: new_centers,
+        lambda: array_ops.concat([self._cluster_centers, new_centers], 0))
+    # TODO(ccolby): De-dupe all_centers?
+    with ops.colocate_with(self._cluster_centers):
+      a = state_ops.assign(
+          self._cluster_centers, all_centers, validate_shape=False)
+      if self._cluster_centers_updated is not self._cluster_centers:
+        a = state_ops.assign(
+            self._cluster_centers_updated, a, validate_shape=False)
+      return self._num_clusters - array_ops.shape(a)[0]
+
+  def _initialize(self):
+    with ops.control_dependencies([
+        check_ops.assert_positive(self._num_remaining),
+    ]):
+      num_now_remaining = self._add_new_centers()
+      return control_flow_ops.cond(
+          math_ops.equal(num_now_remaining, 0),
+          lambda: state_ops.assign(self._cluster_centers_initialized, True),
+          control_flow_ops.no_op)
+
+  def op(self):
+    """Returns the cluster initializer op."""
+    return control_flow_ops.cond(
+        math_ops.equal(self._num_remaining, 0),
+        lambda: check_ops.assert_equal(self._cluster_centers_initialized, True),
+        self._initialize)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
index e5c01336cf..ce87b4723d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
@@ -64,13 +64,16 @@ def make_random_points(centers, num_points, max_offset=20):
   assignments = np.random.choice(num_centers, num_points)
   offsets = np.round(
       np.random.randn(num_points, num_dims).astype(np.float32) * max_offset)
-  return (centers[assignments] + offsets, assignments,
-          np.add.reduce(offsets * offsets, 1))
+  return (centers[assignments] + offsets, assignments, np.add.reduce(
+      offsets * offsets, 1))
 
 
 class KMeansTestBase(test.TestCase):
 
-  def input_fn(self, batch_size=None, points=None, randomize=None,
+  def input_fn(self,
+               batch_size=None,
+               points=None,
+               randomize=None,
                num_epochs=None):
     """Returns an input_fn that randomly selects batches from given points."""
     batch_size = batch_size or self.batch_size
@@ -79,6 +82,7 @@ class KMeansTestBase(test.TestCase):
     if randomize is None:
       randomize = (self.use_mini_batch and
                    self.mini_batch_steps_per_iteration <= 1)
+
     def _fn():
       x = constant_op.constant(points)
       if batch_size == num_points:
@@ -86,33 +90,40 @@ class KMeansTestBase(test.TestCase):
       if randomize:
         indices = random_ops.random_uniform(
             constant_op.constant([batch_size]),
-            minval=0, maxval=num_points-1,
+            minval=0,
+            maxval=num_points - 1,
             dtype=dtypes.int32,
             seed=10)
       else:
         # We need to cycle through the indices sequentially. We create a queue
         # to maintain the list of indices.
-        q = data_flow_ops.FIFOQueue(self.num_points, dtypes.int32, ())
+        q = data_flow_ops.FIFOQueue(num_points, dtypes.int32, ())
+
         # Conditionally initialize the Queue.
         def _init_q():
-          with ops.control_dependencies([q.enqueue_many(
-              math_ops.range(self.num_points))]):
+          with ops.control_dependencies(
+              [q.enqueue_many(math_ops.range(num_points))]):
             return control_flow_ops.no_op()
-        init_q = control_flow_ops.cond(q.size() <= 0,
-                                       _init_q,
+
+        init_q = control_flow_ops.cond(q.size() <= 0, _init_q,
                                        control_flow_ops.no_op)
         with ops.control_dependencies([init_q]):
-          offsets = q.dequeue_many(self.batch_size)
+          offsets = q.dequeue_many(batch_size)
           with ops.control_dependencies([q.enqueue_many(offsets)]):
             indices = array_ops.identity(offsets)
       batch = array_ops.gather(x, indices)
       return (input_lib.limit_epochs(batch, num_epochs=num_epochs), None)
+
     return _fn
 
   @staticmethod
   def config(tf_random_seed):
     return run_config.RunConfig(tf_random_seed=tf_random_seed)
 
+  @property
+  def initial_clusters(self):
+    return kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT
+
   @property
   def batch_size(self):
     return self.num_points
@@ -141,7 +152,7 @@ class KMeansTest(KMeansTestBase):
   def _kmeans(self, relative_tolerance=None):
     return kmeans_lib.KMeansClustering(
         self.num_centers,
-        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        initial_clusters=self.initial_clusters,
         distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
         use_mini_batch=self.use_mini_batch,
         mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
@@ -172,7 +183,7 @@ class KMeansTest(KMeansTestBase):
       return
     kmeans = kmeans_lib.KMeansClustering(
         self.num_centers,
-        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        initial_clusters=self.initial_clusters,
         distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
         use_mini_batch=self.use_mini_batch,
         mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
@@ -197,11 +208,12 @@ class KMeansTest(KMeansTestBase):
 
     # Make a small test set
     num_points = 10
-    points, true_assignments, true_offsets = make_random_points(clusters,
-                                                                num_points)
+    points, true_assignments, true_offsets = make_random_points(
+        clusters, num_points)
     # Test predict
-    assignments = list(kmeans.predict_cluster_idx(input_fn=self.input_fn(
-        batch_size=num_points, points=points, num_epochs=1)))
+    assignments = list(
+        kmeans.predict_cluster_idx(input_fn=self.input_fn(
+            batch_size=num_points, points=points, num_epochs=1)))
     self.assertAllEqual(assignments, true_assignments)
 
     # Test score
@@ -214,35 +226,62 @@ class KMeansTest(KMeansTestBase):
         input_fn=lambda: (constant_op.constant(points), None))
     true_transform = np.maximum(
         0,
-        np.sum(np.square(points), axis=1, keepdims=True) - 2 * np.dot(
-            points, np.transpose(clusters)) +
+        np.sum(np.square(points), axis=1,
+               keepdims=True) - 2 * np.dot(points, np.transpose(clusters)) +
         np.transpose(np.sum(np.square(clusters), axis=1, keepdims=True)))
     self.assertAllClose(transform, true_transform, rtol=0.05, atol=10)
 
-  def test_fit_raise_if_num_clusters_larger_than_num_points_random_init(self):
-    points = np.array([[2.0, 3.0], [1.6, 8.2]], dtype=np.float32)
 
-    with self.assertRaisesOpError('less'):
-      kmeans = learn.KMeansClustering(
-          num_clusters=3,
-          use_mini_batch=self.use_mini_batch,
-          mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
-          initial_clusters=kmeans_lib.KMeansClustering.RANDOM_INIT)
-      kmeans.fit(input_fn=lambda: (constant_op.constant(points), None),
-                 steps=10)
+class KMeansTestMultiStageInit(KMeansTestBase):
 
-  def test_fit_raise_if_num_clusters_larger_than_num_points_kmeans_plus_plus(
-      self):
-    points = np.array([[2.0, 3.0], [1.6, 8.2]], dtype=np.float32)
+  def test_random(self):
+    points = np.array(
+        [[1, 2], [3, 4], [5, 6], [7, 8], [9, 0]], dtype=np.float32)
+    kmeans = kmeans_lib.KMeansClustering(
+        num_clusters=points.shape[0],
+        initial_clusters=kmeans_lib.KMeansClustering.RANDOM_INIT,
+        distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
+        use_mini_batch=True,
+        mini_batch_steps_per_iteration=100,
+        random_seed=24,
+        relative_tolerance=None)
+    kmeans.fit(
+        input_fn=self.input_fn(batch_size=1, points=points, randomize=False),
+        steps=1)
+    clusters = kmeans.clusters()
+    self.assertAllEqual(points, clusters)
 
+  def test_kmeans_plus_plus_batch_just_right(self):
+    points = np.array([[1, 2]], dtype=np.float32)
+    kmeans = kmeans_lib.KMeansClustering(
+        num_clusters=points.shape[0],
+        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
+        use_mini_batch=True,
+        mini_batch_steps_per_iteration=100,
+        random_seed=24,
+        relative_tolerance=None)
+    kmeans.fit(
+        input_fn=self.input_fn(batch_size=1, points=points, randomize=False),
+        steps=1)
+    clusters = kmeans.clusters()
+    self.assertAllEqual(points, clusters)
+
+  def test_kmeans_plus_plus_batch_too_small(self):
+    points = np.array(
+        [[1, 2], [3, 4], [5, 6], [7, 8], [9, 0]], dtype=np.float32)
+    kmeans = kmeans_lib.KMeansClustering(
+        num_clusters=points.shape[0],
+        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
+        use_mini_batch=True,
+        mini_batch_steps_per_iteration=100,
+        random_seed=24,
+        relative_tolerance=None)
     with self.assertRaisesOpError(AssertionError):
-      kmeans = learn.KMeansClustering(
-          num_clusters=3,
-          use_mini_batch=self.use_mini_batch,
-          mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
-          initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT)
-      kmeans.fit(input_fn=lambda: (constant_op.constant(points), None),
-                 steps=10)
+      kmeans.fit(
+          input_fn=self.input_fn(batch_size=4, points=points, randomize=False),
+          steps=1)
 
 
 class MiniBatchKMeansTest(KMeansTest):
@@ -282,11 +321,11 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     self.true_centers = np.array(
         [
             normalize(
-                np.mean(
-                    normalize(self.points)[0:4, :], axis=0, keepdims=True))[0],
+                np.mean(normalize(self.points)[0:4, :], axis=0, keepdims=True))[
+                    0],
             normalize(
-                np.mean(
-                    normalize(self.points)[4:, :], axis=0, keepdims=True))[0]
+                np.mean(normalize(self.points)[4:, :], axis=0, keepdims=True))[
+                    0]
         ],
         dtype=np.float32)
     self.true_assignments = np.array([0] * 4 + [1] * 4)
@@ -323,8 +362,9 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     self.kmeans.fit(input_fn=self.input_fn(), max_steps=max_steps)
     centers = normalize(self.kmeans.clusters())
 
-    assignments = list(self.kmeans.predict_cluster_idx(
-        input_fn=self.input_fn(num_epochs=1, batch_size=self.num_points)))
+    assignments = list(
+        self.kmeans.predict_cluster_idx(input_fn=self.input_fn(
+            num_epochs=1, batch_size=self.num_points)))
     self.assertAllClose(
         centers[assignments],
         self.true_centers[self.true_assignments],
@@ -333,8 +373,8 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     centers = centers[centers[:, 0].argsort()]
     true_centers = self.true_centers[self.true_centers[:, 0].argsort()]
     self.assertAllClose(centers, true_centers, atol=0.04)
-    score = self.kmeans.score(input_fn=self.input_fn(
-        batch_size=self.num_points), steps=1)
+    score = self.kmeans.score(
+        input_fn=self.input_fn(batch_size=self.num_points), steps=1)
     self.assertAllClose(score, self.true_score, atol=1e-2)
 
   def test_predict_kmeans_plus_plus(self):
@@ -348,13 +388,11 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     true_centers = np.array(
         [
             normalize(
-                np.mean(
-                    normalize(points)[0:2, :], axis=0, keepdims=True))[0],
+                np.mean(normalize(points)[0:2, :], axis=0, keepdims=True))[0],
+            normalize(
+                np.mean(normalize(points)[2:4, :], axis=0, keepdims=True))[0],
             normalize(
-                np.mean(
-                    normalize(points)[2:4, :], axis=0, keepdims=True))[0],
-            normalize(np.mean(
-                normalize(points)[4:, :], axis=0, keepdims=True))[0]
+                np.mean(normalize(points)[4:, :], axis=0, keepdims=True))[0]
         ],
         dtype=np.float32)
     true_assignments = [0] * 2 + [1] * 2 + [2] * 8
@@ -363,7 +401,7 @@ class KMeansCosineDistanceTest(KMeansTestBase):
 
     kmeans = kmeans_lib.KMeansClustering(
         3,
-        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        initial_clusters=self.initial_clusters,
         distance_metric=kmeans_lib.KMeansClustering.COSINE_DISTANCE,
         use_mini_batch=self.use_mini_batch,
         mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
@@ -375,9 +413,9 @@ class KMeansCosineDistanceTest(KMeansTestBase):
         sorted(centers.tolist()), sorted(true_centers.tolist()), atol=1e-2)
 
     def _input_fn():
-      return (
-          input_lib.limit_epochs(constant_op.constant(points), num_epochs=1),
-          None)
+      return (input_lib.limit_epochs(
+          constant_op.constant(points), num_epochs=1), None)
+
     assignments = list(kmeans.predict_cluster_idx(input_fn=_input_fn))
     self.assertAllClose(
         centers[assignments], true_centers[true_assignments], atol=1e-2)
@@ -489,8 +527,8 @@ class TensorflowKMeansBenchmark(KMeansBenchmark):
           random_seed=i * 42,
           relative_tolerance=1e-6,
           config=run_config.RunConfig(tf_random_seed=3))
-      tf_kmeans.fit(input_fn=lambda: (constant_op.constant(self.points), None),
-                    steps=50)
+      tf_kmeans.fit(
+          input_fn=lambda: (constant_op.constant(self.points), None), steps=50)
       _ = tf_kmeans.clusters()
       scores.append(
           tf_kmeans.score(
@@ -521,14 +559,15 @@ class SklearnKMeansBenchmark(KMeansBenchmark):
 class KMeansTestQueues(test.TestCase):
 
   def input_fn(self):
+
     def _fn():
-      queue = data_flow_ops.FIFOQueue(capacity=10,
-                                      dtypes=dtypes.float32,
-                                      shapes=[10, 3])
+      queue = data_flow_ops.FIFOQueue(
+          capacity=10, dtypes=dtypes.float32, shapes=[10, 3])
       enqueue_op = queue.enqueue(array_ops.zeros([10, 3], dtype=dtypes.float32))
-      queue_runner.add_queue_runner(queue_runner.QueueRunner(queue,
-                                                             [enqueue_op]))
+      queue_runner.add_queue_runner(
+          queue_runner.QueueRunner(queue, [enqueue_op]))
       return queue.dequeue(), None
+
     return _fn
 
   # This test makes sure that there are no deadlocks when using a QueueRunner.
-- 
GitLab


From ad0892df1db853972245c5bb88e6952312cf8157 Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Thu, 22 Jun 2017 19:31:25 +0100
Subject: [PATCH 0358/1375] [OpenCL] Fixes run_metadata_test for SYCL

 This test is designed to test CUDA specific behavior
---
 .../tfprof/python/tools/tfprof/internal/run_metadata_test.py    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index feb20dc0f4..4c5d3e0b2b 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -71,7 +71,7 @@ def _run_model():
 class RunMetadataTest(test.TestCase):
 
   def testGPU(self):
-    if not test.is_gpu_available():
+    if not test.is_gpu_available(cuda_only=True):
       return
 
     with ops.device('/gpu:0'):
-- 
GitLab


From 3ac15c0b175bce9d68c75dea6e98be449d4af510 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 11:29:25 -0700
Subject: [PATCH 0359/1375] Use a single threaded session for
 SDCALinearRegressorTest to avoid incorrect threading test failures (tsan).

PiperOrigin-RevId: 159852818
---
 .../python/sdca_estimator_test.py             | 423 ++++++++++--------
 1 file changed, 225 insertions(+), 198 deletions(-)

diff --git a/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py b/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py
index 32b7f956e4..79a5928a21 100644
--- a/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py
+++ b/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py
@@ -22,6 +22,7 @@ import numpy as np
 
 from tensorflow.contrib.layers.python.layers import feature_column as feature_column_lib
 from tensorflow.contrib.linear_optimizer.python import sdca_estimator
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.platform import test
@@ -40,15 +41,17 @@ class SDCALogisticClassifierTest(test.TestCase):
           'weights': constant_op.constant([[1.0], [1.0]])
       }, constant_op.constant([[0], [1]])
 
-    maintenance_cost = feature_column_lib.real_valued_column('maintenance_cost')
-    sq_footage = feature_column_lib.real_valued_column('sq_footage')
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[maintenance_cost, sq_footage],
-        weight_column_name='weights')
-    classifier.fit(input_fn=input_fn, steps=100)
-    loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.05)
+    with self.test_session():
+      maintenance_cost = feature_column_lib.real_valued_column(
+          'maintenance_cost')
+      sq_footage = feature_column_lib.real_valued_column('sq_footage')
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[maintenance_cost, sq_footage],
+          weight_column_name='weights')
+      classifier.fit(input_fn=input_fn, steps=100)
+      loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.05)
 
   def testRealValuedFeatureWithHigherDimension(self):
     """Tests SDCALogisticClassifier with high-dimension real valued features."""
@@ -63,13 +66,14 @@ class SDCALogisticClassifierTest(test.TestCase):
               constant_op.constant([[500.0, 800.0], [200.0, 600.0]])
       }, constant_op.constant([[0], [1]])
 
-    dense_feature = feature_column_lib.real_valued_column(
-        'dense_feature', dimension=2)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id', feature_columns=[dense_feature])
-    classifier.fit(input_fn=input_fn, steps=100)
-    loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.05)
+    with self.test_session():
+      dense_feature = feature_column_lib.real_valued_column(
+          'dense_feature', dimension=2)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id', feature_columns=[dense_feature])
+      classifier.fit(input_fn=input_fn, steps=100)
+      loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.05)
 
   def testBucketizedFeatures(self):
     """Tests SDCALogisticClassifier with bucketized features."""
@@ -82,19 +86,21 @@ class SDCALogisticClassifierTest(test.TestCase):
           'weights': constant_op.constant([[1.0], [1.0], [1.0]])
       }, constant_op.constant([[1], [0], [1]])
 
-    price_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('price'),
-        boundaries=[500.0, 700.0])
-    sq_footage_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('sq_footage'), boundaries=[650.0])
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[price_bucket, sq_footage_bucket],
-        weight_column_name='weights',
-        l2_regularization=1.0)
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      price_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('price'),
+          boundaries=[500.0, 700.0])
+      sq_footage_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('sq_footage'),
+          boundaries=[650.0])
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[price_bucket, sq_footage_bucket],
+          weight_column_name='weights',
+          l2_regularization=1.0)
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testSparseFeatures(self):
     """Tests SDCALogisticClassifier with sparse features."""
@@ -114,16 +120,17 @@ class SDCALogisticClassifierTest(test.TestCase):
               constant_op.constant([[1.0], [1.0], [1.0]])
       }, constant_op.constant([[1], [0], [1]])
 
-    price = feature_column_lib.real_valued_column('price')
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[price, country],
-        weight_column_name='weights')
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      price = feature_column_lib.real_valued_column('price')
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[price, country],
+          weight_column_name='weights')
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testWeightedSparseFeatures(self):
     """Tests SDCALogisticClassifier with weighted sparse features."""
@@ -144,16 +151,17 @@ class SDCALogisticClassifierTest(test.TestCase):
                   dense_shape=[3, 5])
       }, constant_op.constant([[1], [0], [1]])
 
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    country_weighted_by_price = feature_column_lib.weighted_sparse_column(
-        country, 'price')
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[country_weighted_by_price])
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      country_weighted_by_price = feature_column_lib.weighted_sparse_column(
+          country, 'price')
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[country_weighted_by_price])
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testCrossedFeatures(self):
     """Tests SDCALogisticClassifier with crossed features."""
@@ -174,17 +182,18 @@ class SDCALogisticClassifierTest(test.TestCase):
                   dense_shape=[3, 1])
       }, constant_op.constant([[0], [0], [1]])
 
-    language = feature_column_lib.sparse_column_with_hash_bucket(
-        'language', hash_bucket_size=5)
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    country_language = feature_column_lib.crossed_column(
-        [language, country], hash_bucket_size=10)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id', feature_columns=[country_language])
-    classifier.fit(input_fn=input_fn, steps=10)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      language = feature_column_lib.sparse_column_with_hash_bucket(
+          'language', hash_bucket_size=5)
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      country_language = feature_column_lib.crossed_column(
+          [language, country], hash_bucket_size=10)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id', feature_columns=[country_language])
+      classifier.fit(input_fn=input_fn, steps=10)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testMixedFeatures(self):
     """Tests SDCALogisticClassifier with a mix of features."""
@@ -206,25 +215,35 @@ class SDCALogisticClassifierTest(test.TestCase):
               constant_op.constant([[3.0], [1.0], [1.0]])
       }, constant_op.constant([[1], [0], [1]])
 
-    price = feature_column_lib.real_valued_column('price')
-    sq_footage_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('sq_footage'),
-        boundaries=[650.0, 800.0])
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    sq_footage_country = feature_column_lib.crossed_column(
-        [sq_footage_bucket, country], hash_bucket_size=10)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
-        weight_column_name='weights')
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      price = feature_column_lib.real_valued_column('price')
+      sq_footage_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('sq_footage'),
+          boundaries=[650.0, 800.0])
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      sq_footage_country = feature_column_lib.crossed_column(
+          [sq_footage_bucket, country], hash_bucket_size=10)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[
+              price, sq_footage_bucket, country, sq_footage_country
+          ],
+          weight_column_name='weights')
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
 
 class SDCALinearRegressorTest(test.TestCase):
 
+  def _single_threaded_test_session(self):
+    # TODO(andreasst): figure out why SDCALinearRegressor needs a single
+    # threaded session to pass in tsan mode but SDCALogisticClassifier does not.
+    config = config_pb2.ConfigProto(
+        inter_op_parallelism_threads=1, intra_op_parallelism_threads=1)
+    return self.test_session(config=config)
+
   def testRealValuedLinearFeatures(self):
     """Tests SDCALinearRegressor works with real valued features."""
     x = [[1.2, 2.0, -1.5], [-2.0, 3.0, -0.5], [1.0, -0.5, 4.0]]
@@ -238,18 +257,19 @@ class SDCALinearRegressorTest(test.TestCase):
           'weights': constant_op.constant([[10.0], [10.0], [10.0]])
       }, constant_op.constant(y)
 
-    x_column = feature_column_lib.real_valued_column('x', dimension=3)
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[x_column],
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.01)
-    self.assertIn('linear/x/weight', regressor.get_variable_names())
-    regressor_weights = regressor.get_variable_value('linear/x/weight')
-    self.assertAllClose(
-        [w[0] for w in weights], regressor_weights.flatten(), rtol=0.1)
+    with self._single_threaded_test_session():
+      x_column = feature_column_lib.real_valued_column('x', dimension=3)
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[x_column],
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.01)
+      self.assertIn('linear/x/weight', regressor.get_variable_names())
+      regressor_weights = regressor.get_variable_value('linear/x/weight')
+      self.assertAllClose(
+          [w[0] for w in weights], regressor_weights.flatten(), rtol=0.1)
 
   def testMixedFeaturesArbitraryWeights(self):
     """Tests SDCALinearRegressor works with a mix of features."""
@@ -271,22 +291,25 @@ class SDCALinearRegressorTest(test.TestCase):
               constant_op.constant([[3.0], [5.0], [7.0]])
       }, constant_op.constant([[1.55], [-1.25], [-3.0]])
 
-    price = feature_column_lib.real_valued_column('price')
-    sq_footage_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('sq_footage'),
-        boundaries=[650.0, 800.0])
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    sq_footage_country = feature_column_lib.crossed_column(
-        [sq_footage_bucket, country], hash_bucket_size=10)
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
-        l2_regularization=1.0,
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.05)
+    with self._single_threaded_test_session():
+      price = feature_column_lib.real_valued_column('price')
+      sq_footage_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('sq_footage'),
+          boundaries=[650.0, 800.0])
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      sq_footage_country = feature_column_lib.crossed_column(
+          [sq_footage_bucket, country], hash_bucket_size=10)
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[
+              price, sq_footage_bucket, country, sq_footage_country
+          ],
+          l2_regularization=1.0,
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
     """SDCALinearRegressor works with sparse features and L1 regularization."""
@@ -306,56 +329,57 @@ class SDCALinearRegressorTest(test.TestCase):
               constant_op.constant([[10.0], [10.0], [10.0]])
       }, constant_op.constant([[1.4], [-0.8], [2.6]])
 
-    price = feature_column_lib.real_valued_column('price')
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    # Regressor with no L1 regularization.
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[price, country],
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    no_l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    variable_names = regressor.get_variable_names()
-    self.assertIn('linear/price/weight', variable_names)
-    self.assertIn('linear/country/weights', variable_names)
-    no_l1_reg_weights = {
-        'linear/price/weight':
-            regressor.get_variable_value('linear/price/weight'),
-        'linear/country/weights':
-            regressor.get_variable_value('linear/country/weights'),
-    }
-
-    # Regressor with L1 regularization.
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[price, country],
-        l1_regularization=1.0,
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    l1_reg_weights = {
-        'linear/price/weight':
-            regressor.get_variable_value('linear/price/weight'),
-        'linear/country/weights':
-            regressor.get_variable_value('linear/country/weights'),
-    }
-
-    # Unregularized loss is lower when there is no L1 regularization.
-    self.assertLess(no_l1_reg_loss, l1_reg_loss)
-    self.assertLess(no_l1_reg_loss, 0.05)
-
-    # But weights returned by the regressor with L1 regularization have smaller
-    # L1 norm.
-    l1_reg_weights_norm, no_l1_reg_weights_norm = 0.0, 0.0
-    for var_name in sorted(l1_reg_weights):
-      l1_reg_weights_norm += sum(
-          np.absolute(l1_reg_weights[var_name].flatten()))
-      no_l1_reg_weights_norm += sum(
-          np.absolute(no_l1_reg_weights[var_name].flatten()))
-      print('Var name: %s, value: %s' % (var_name,
-                                         no_l1_reg_weights[var_name].flatten()))
-    self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
+    with self._single_threaded_test_session():
+      price = feature_column_lib.real_valued_column('price')
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      # Regressor with no L1 regularization.
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[price, country],
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      no_l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      variable_names = regressor.get_variable_names()
+      self.assertIn('linear/price/weight', variable_names)
+      self.assertIn('linear/country/weights', variable_names)
+      no_l1_reg_weights = {
+          'linear/price/weight':
+              regressor.get_variable_value('linear/price/weight'),
+          'linear/country/weights':
+              regressor.get_variable_value('linear/country/weights'),
+      }
+
+      # Regressor with L1 regularization.
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[price, country],
+          l1_regularization=1.0,
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      l1_reg_weights = {
+          'linear/price/weight':
+              regressor.get_variable_value('linear/price/weight'),
+          'linear/country/weights':
+              regressor.get_variable_value('linear/country/weights'),
+      }
+
+      # Unregularized loss is lower when there is no L1 regularization.
+      self.assertLess(no_l1_reg_loss, l1_reg_loss)
+      self.assertLess(no_l1_reg_loss, 0.05)
+
+      # But weights returned by the regressor with L1 regularization have
+      # smaller L1 norm.
+      l1_reg_weights_norm, no_l1_reg_weights_norm = 0.0, 0.0
+      for var_name in sorted(l1_reg_weights):
+        l1_reg_weights_norm += sum(
+            np.absolute(l1_reg_weights[var_name].flatten()))
+        no_l1_reg_weights_norm += sum(
+            np.absolute(no_l1_reg_weights[var_name].flatten()))
+        print('Var name: %s, value: %s' %
+              (var_name, no_l1_reg_weights[var_name].flatten()))
+      self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
 
   def testBiasOnly(self):
     """Tests SDCALinearRegressor has a valid bias weight."""
@@ -380,12 +404,13 @@ class SDCALinearRegressorTest(test.TestCase):
       }, constant_op.constant([[1 if i % 4 is 0 else 0]
                                for i in range(num_examples)])
 
-    place_holder = feature_column_lib.real_valued_column('place_holder')
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id', feature_columns=[place_holder])
-    regressor.fit(input_fn=input_fn, steps=100)
-    self.assertNear(
-        regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
+    with self._single_threaded_test_session():
+      place_holder = feature_column_lib.real_valued_column('place_holder')
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id', feature_columns=[place_holder])
+      regressor.fit(input_fn=input_fn, steps=100)
+      self.assertNear(
+          regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
 
   def testBiasAndOtherColumns(self):
     """SDCALinearRegressor has valid bias weight with other columns present."""
@@ -426,26 +451,27 @@ class SDCALinearRegressorTest(test.TestCase):
            for x in [1, 0, 0, 1, 1, 0, 0, 0, 1, 0] * int(half / 10) +
            [0, 1, 0, 0, 0, 0, 0, 0, 1, 0] * int(half / 10)])
 
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[
-            feature_column_lib.real_valued_column('a'),
-            feature_column_lib.real_valued_column('b')
-        ])
-
-    regressor.fit(input_fn=input_fn, steps=200)
-
-    variable_names = regressor.get_variable_names()
-    self.assertIn('linear/bias_weight', variable_names)
-    self.assertIn('linear/a/weight', variable_names)
-    self.assertIn('linear/b/weight', variable_names)
-    # TODO(b/29339026): Change the expected results to expect a centered bias.
-    self.assertNear(
-        regressor.get_variable_value('linear/bias_weight')[0], 0.2, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/a/weight')[0], 0.2, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
+    with self._single_threaded_test_session():
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[
+              feature_column_lib.real_valued_column('a'),
+              feature_column_lib.real_valued_column('b')
+          ])
+
+      regressor.fit(input_fn=input_fn, steps=200)
+
+      variable_names = regressor.get_variable_names()
+      self.assertIn('linear/bias_weight', variable_names)
+      self.assertIn('linear/a/weight', variable_names)
+      self.assertIn('linear/b/weight', variable_names)
+      # TODO(b/29339026): Change the expected results to expect a centered bias.
+      self.assertNear(
+          regressor.get_variable_value('linear/bias_weight')[0], 0.2, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/a/weight')[0], 0.2, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
 
   def testBiasAndOtherColumnsFabricatedCentered(self):
     """SDCALinearRegressor has valid bias weight when instances are centered."""
@@ -476,25 +502,26 @@ class SDCALinearRegressorTest(test.TestCase):
       }, constant_op.constant([[1 if x % 10 == 0 else 0] for x in range(half)] +
                               [[-1 if x % 10 == 0 else 0] for x in range(half)])
 
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[
-            feature_column_lib.real_valued_column('a'),
-            feature_column_lib.real_valued_column('b')
-        ])
-
-    regressor.fit(input_fn=input_fn, steps=100)
-
-    variable_names = regressor.get_variable_names()
-    self.assertIn('linear/bias_weight', variable_names)
-    self.assertIn('linear/a/weight', variable_names)
-    self.assertIn('linear/b/weight', variable_names)
-    self.assertNear(
-        regressor.get_variable_value('linear/bias_weight')[0], 0.0, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/a/weight')[0], 0.1, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/b/weight')[0], -0.1, err=0.05)
+    with self._single_threaded_test_session():
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[
+              feature_column_lib.real_valued_column('a'),
+              feature_column_lib.real_valued_column('b')
+          ])
+
+      regressor.fit(input_fn=input_fn, steps=100)
+
+      variable_names = regressor.get_variable_names()
+      self.assertIn('linear/bias_weight', variable_names)
+      self.assertIn('linear/a/weight', variable_names)
+      self.assertIn('linear/b/weight', variable_names)
+      self.assertNear(
+          regressor.get_variable_value('linear/bias_weight')[0], 0.0, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/a/weight')[0], 0.1, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/b/weight')[0], -0.1, err=0.05)
 
 
 if __name__ == '__main__':
-- 
GitLab


From f5e500cfa892bfc4468db9a452e77061c269c3b1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 11:29:54 -0700
Subject: [PATCH 0360/1375] Migrate ops for new version of TensorForest.

PiperOrigin-RevId: 159852889
---
 tensorflow/contrib/BUILD                      |   4 +
 tensorflow/contrib/cmake/tf_core_ops.cmake    |   2 +
 tensorflow/contrib/cmake/tf_python.cmake      |   4 +
 tensorflow/contrib/tensor_forest/BUILD        | 293 ++++++---
 .../tensor_forest/kernels/model_ops.cc        | 293 +++++++++
 .../tensor_forest/kernels/stats_ops.cc        | 562 ++++++++++++++++++
 .../contrib/tensor_forest/kernels/v4/BUILD    |  26 +-
 .../kernels/v4/graph_collection_operator.cc   | 142 +++++
 .../kernels/v4/graph_collection_operator.h    |  81 +++
 .../kernels/v4/split_collection_operators.cc  | 140 +----
 .../kernels/v4/split_collection_operators.h   |  69 +--
 .../contrib/tensor_forest/ops/model_ops.cc    | 134 +++++
 .../contrib/tensor_forest/ops/stats_ops.cc    | 143 +++++
 .../contrib/tensor_forest/python/__init__.py  |   2 +
 .../tensor_forest/python/ops/model_ops.py     | 124 ++++
 .../tensor_forest/python/ops/stats_ops.py     | 114 ++++
 16 files changed, 1875 insertions(+), 258 deletions(-)
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/model_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
 create mode 100644 tensorflow/contrib/tensor_forest/ops/model_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/ops/stats_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/python/ops/model_ops.py
 create mode 100644 tensorflow/contrib/tensor_forest/python/ops/stats_ops.py

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index 6213ff6024..42f7ac7fdf 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -91,6 +91,8 @@ cc_library(
         "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel",
         "//tensorflow/contrib/nccl:nccl_kernels",
         "//tensorflow/contrib/seq2seq:beam_search_ops_kernels",
+        "//tensorflow/contrib/tensor_forest:model_ops_kernels",
+        "//tensorflow/contrib/tensor_forest:stats_ops_kernels",
         "//tensorflow/contrib/tensor_forest:tensor_forest_kernels",
         "//tensorflow/contrib/text:all_kernels",
     ],
@@ -108,6 +110,8 @@ cc_library(
         "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib",
         "//tensorflow/contrib/nccl:nccl_ops_op_lib",
         "//tensorflow/contrib/seq2seq:beam_search_ops_op_lib",
+        "//tensorflow/contrib/tensor_forest:model_ops_op_lib",
+        "//tensorflow/contrib/tensor_forest:stats_ops_op_lib",
         "//tensorflow/contrib/tensor_forest:tensor_forest_ops_op_lib",
         "//tensorflow/contrib/text:all_ops",
         "//tensorflow/contrib/tpu:all_ops",
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index f1b01250a4..562bd288a2 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -86,6 +86,8 @@ GENERATE_CONTRIB_OP_LIBRARY(rnn_lstm "${tensorflow_source_dir}/tensorflow/contri
 GENERATE_CONTRIB_OP_LIBRARY(seq2seq_beam_search "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_hybrid "${tensor_forest_hybrid_srcs}")
+GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_model "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/model_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_stats "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/stats_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(text_skip_gram "${tensorflow_source_dir}/tensorflow/contrib/text/ops/skip_gram_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tpu "${tpu_ops_srcs}")
 GENERATE_CONTRIB_OP_LIBRARY(bigquery_reader "${tensorflow_source_dir}/tensorflow/contrib/cloud/ops/bigquery_reader_ops.cc")
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 84a1302344..d17fcf6456 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -659,6 +659,10 @@ GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_tensor_forest_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_hybrid_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/hybrid/ops/gen_training_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_model_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_model_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_stats_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_stats_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_text_skip_gram_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/text/python/ops/gen_skip_gram_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_bigquery_reader_ops"
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 7b5f9472e7..1ca2d7596b 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -28,37 +28,35 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
+# ---------------------------------- V2 ops ------------------------------------------#
 filegroup(
-    name = "custom_op_sources",
-    srcs = glob(
-        [
-            "kernels/*.cc",
-            "ops/*.cc",
-        ],
-        exclude = [
-            "kernels/*_test.cc",
-            "kernels/tree_utils.cc",
-        ],
-    ),
+    name = "v2_op_sources",
+    srcs = [
+        "kernels/best_splits_op.cc",
+        "kernels/count_extremely_random_stats_op.cc",
+        "kernels/finished_nodes_op.cc",
+        "kernels/grow_tree_op.cc",
+        "kernels/reinterpret_string_to_float_op.cc",
+        "kernels/sample_inputs_op.cc",
+        "kernels/scatter_add_ndim_op.cc",
+        "kernels/tree_predictions_op.cc",
+        "kernels/update_fertile_slots_op.cc",
+    ],
 )
 
 filegroup(
-    name = "custom_op_headers",
-    srcs = glob(
-        [
-            "kernels/*.h",
-        ],
-        exclude = [
-            "kernels/data_spec.h",
-            "kernels/tree_utils.h",
-        ],
-    ),
+    name = "v2_op_defs",
+    srcs = [
+        "ops/tensor_forest_ops.cc",
+    ],
 )
 
 cc_library(
-    name = "all_ops",
-    srcs = [":custom_op_sources"],
-    hdrs = [":custom_op_headers"],
+    name = "v2_ops",
+    srcs = [
+        ":v2_op_defs",
+        ":v2_op_sources",
+    ],
     deps = [
         ":tree_utils",
         "//tensorflow/core:framework_headers_lib",
@@ -105,16 +103,8 @@ tf_gen_op_wrapper_py(
 tf_custom_op_library(
     name = "python/ops/_tensor_forest_ops.so",
     srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
-        "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
-        "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
-        "ops/tensor_forest_ops.cc",
+        ":v2_op_defs",
+        ":v2_op_sources",
     ],
     deps = [":tree_utils"],
 )
@@ -131,7 +121,9 @@ py_library(
         ":constants",
         ":data_ops_py",
         ":eval_metrics",
+        ":model_ops_py",
         ":random_forest",
+        ":stats_ops_py",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
     ],
@@ -140,21 +132,11 @@ py_library(
 tf_kernel_library(
     name = "tensor_forest_kernels",
     srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
-        "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
-        "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
+        ":v2_op_sources",
     ],
     deps = [
         ":tree_utils",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
+        "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core/kernels:bounds_check",
     ],
 )
@@ -181,6 +163,192 @@ tf_custom_op_py_library(
     ],
 )
 
+cc_test(
+    name = "tensor_forest_ops_test",
+    size = "small",
+    srcs = [
+        "kernels/tensor_forest_ops_test.cc",
+        ":v2_op_defs",
+        ":v2_op_sources",
+    ],
+    deps = [
+        ":tree_utils",
+        "//tensorflow/core",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//third_party/eigen3",
+    ],
+)
+
+# -------------------------------------- V4 ops ------------------------------- #
+cc_library(
+    name = "tensor_forest_v4_kernels",
+    deps = [
+        ":model_ops_kernels",
+        ":stats_ops_kernels",
+    ],
+)
+
+cc_library(
+    name = "tensor_forest_v4_ops_op_lib",
+    deps = [
+        ":model_ops_op_lib",
+        ":stats_ops_op_lib",
+    ],
+)
+
+py_library(
+    name = "tensor_forest_v4_ops_py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model_ops_py",
+        ":stats_ops_py",
+    ],
+)
+
+# Model Ops.
+cc_library(
+    name = "model_ops_lib",
+    srcs = ["kernels/model_ops.cc"],
+    deps = [
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_gen_op_libs(
+    op_lib_names = ["model_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_model_ops_py",
+    out = "python/ops/gen_model_ops.py",
+    deps = [":model_ops_op_lib"],
+)
+
+tf_kernel_library(
+    name = "model_ops_kernels",
+    deps = [
+        ":model_ops_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_model_ops.so",
+    deps = [
+        ":model_ops_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "model_ops_py",
+    srcs = ["python/ops/model_ops.py"],
+    dso = ["python/ops/_model_ops.so"],
+    kernels = [
+        ":model_ops_kernels",
+        ":model_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_model_ops_py",
+        ":stats_ops_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+# Stats Ops.
+cc_library(
+    name = "stats_ops_lib",
+    srcs = ["kernels/stats_ops.cc"],
+    deps = [
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:fertile-stats-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_target",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:params",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_gen_op_libs(
+    op_lib_names = ["stats_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_stats_ops_py",
+    out = "python/ops/gen_stats_ops.py",
+    deps = [":stats_ops_op_lib"],
+)
+
+tf_kernel_library(
+    name = "stats_ops_kernels",
+    deps = [
+        ":stats_ops_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_stats_ops.so",
+    deps = [
+        ":stats_ops_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "stats_ops_py",
+    srcs = ["python/ops/stats_ops.py"],
+    dso = ["python/ops/_stats_ops.so"],
+    kernels = [
+        ":stats_ops_kernels",
+        ":stats_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_stats_ops_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+# ---------------------------------- Common libs ------------------------ #
+cc_library(
+    name = "tree_utils",
+    srcs = ["kernels/tree_utils.cc"],
+    hdrs = [
+        "kernels/data_spec.h",
+        "kernels/tree_utils.h",
+    ],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+        "@protobuf//:protobuf_headers",
+    ],
+)
+
+# --------------------------------- Python -------------------------------- #
+
 py_library(
     name = "eval_metrics",
     srcs = ["client/eval_metrics.py"],
@@ -220,20 +388,6 @@ py_library(
     ],
 )
 
-cc_library(
-    name = "tree_utils",
-    srcs = ["kernels/tree_utils.cc"],
-    hdrs = [
-        "kernels/data_spec.h",
-        "kernels/tree_utils.h",
-    ],
-    deps = [
-        "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
-    ],
-)
-
 py_test(
     name = "best_splits_op_test",
     size = "small",
@@ -380,25 +534,6 @@ py_test(
     ],
 )
 
-cc_test(
-    name = "tensor_forest_ops_test",
-    size = "small",
-    srcs = [
-        "kernels/tensor_forest_ops_test.cc",
-        ":custom_op_sources",
-    ],
-    deps = [
-        ":tree_utils",
-        "//tensorflow/core",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-        "//tensorflow/core:testlib",
-        "//third_party/eigen3",
-    ],
-)
-
 py_library(
     name = "random_forest",
     srcs = ["client/random_forest.py"],
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
new file mode 100644
index 0000000000..ac8ec68d80
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
@@ -0,0 +1,293 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Creates a tree  variable.
+class CreateTreeVariableOp : public OpKernel {
+ public:
+  explicit CreateTreeVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* tree_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
+                errors::InvalidArgument("Tree config must be a scalar."));
+
+    auto* result = new DecisionTreeResource();
+    if (!ParseProtoUnlimited(result->mutable_decision_tree(),
+                             tree_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false,
+                  errors::InvalidArgument("Unable to parse tree  config."));
+    }
+
+    result->MaybeInitialize();
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for serializing a model.
+class TreeSerializeOp : public OpKernel {
+ public:
+  explicit TreeSerializeOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, TensorShape(), &output_config_t));
+    output_config_t->scalar<string>()() =
+        decision_tree_resource->decision_tree().SerializeAsString();
+  }
+};
+
+// Op for deserializing a tree variable from a checkpoint.
+class TreeDeserializeOp : public OpKernel {
+ public:
+  explicit TreeDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    auto handle = HandleFromInput(context, 0);
+    OP_REQUIRES_OK(context,
+                   LookupResource(context, handle, &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    const Tensor* tree_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
+                errors::InvalidArgument("Tree config must be a scalar."));
+    // Deallocate all the previous objects on the resource.
+    decision_tree_resource->Reset();
+    decision_trees::Model* config =
+        decision_tree_resource->mutable_decision_tree();
+    OP_REQUIRES(context,
+                ParseProtoUnlimited(config, tree_config_t->scalar<string>()()),
+                errors::InvalidArgument("Unable to parse tree  config."));
+    decision_tree_resource->MaybeInitialize();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for getting tree size.
+class TreeSizeOp : public OpKernel {
+ public:
+  explicit TreeSizeOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+    Tensor* output_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, TensorShape(), &output_t));
+    output_t->scalar<int32>()() =
+        decision_tree_resource->decision_tree().decision_tree().nodes_size();
+  }
+};
+
+// Op for tree inference.
+class TreePredictionsV4Op : public OpKernel {
+ public:
+  explicit TreePredictionsV4Op(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    string serialized_proto;
+    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
+    input_spec_.ParseFromString(serialized_proto);
+
+    data_set_ =
+        std::unique_ptr<TensorDataSet>(new TensorDataSet(input_spec_, 0));
+
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_data = context->input(1);
+    const Tensor& sparse_input_indices = context->input(2);
+    const Tensor& sparse_input_values = context->input(3);
+
+    data_set_->set_input_tensors(input_data, sparse_input_indices,
+                                 sparse_input_values);
+
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    Tensor* output_predictions = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(data_set_->NumItems());
+    output_shape.AddDim(param_proto_.num_outputs());
+    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape,
+                                                     &output_predictions));
+
+    auto out = output_predictions->tensor<float, 2>();
+    for (int i = 0; i < data_set_->NumItems(); ++i) {
+      const int32 leaf_id =
+          decision_tree_resource->TraverseTree(data_set_, i, nullptr);
+      const decision_trees::Leaf& leaf =
+          decision_tree_resource->get_leaf(leaf_id);
+      for (int j = 0; j < param_proto_.num_outputs(); ++j) {
+        const float count = model_op_->GetOutputValue(leaf, j);
+        out(i, j) = count;
+      }
+    }
+  }
+
+ private:
+  tensorforest::TensorForestDataSpec input_spec_;
+  std::unique_ptr<TensorDataSet> data_set_;
+  std::unique_ptr<LeafModelOperator> model_op_;
+  TensorForestParams param_proto_;
+};
+
+// Op for getting feature usage counts.
+class FeatureUsageCountsOp : public OpKernel {
+ public:
+  explicit FeatureUsageCountsOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    const auto& tree = decision_tree_resource->decision_tree();
+
+    Tensor* output_counts = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(param_proto_.num_features());
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, output_shape, &output_counts));
+
+    auto counts = output_counts->unaligned_flat<int32>();
+    counts.setZero();
+
+    for (const auto& node : tree.decision_tree().nodes()) {
+      if (node.has_custom_node_type()) {
+        LOG(WARNING) << "Can't count feature usage for custom nodes.";
+      } else if (node.has_binary_node()) {
+        const auto& bnode = node.binary_node();
+        if (bnode.has_custom_left_child_test()) {
+          decision_trees::MatchingValuesTest test;
+          if (!bnode.custom_left_child_test().UnpackTo(&test)) {
+            LOG(WARNING) << "Unknown custom child test";
+            continue;
+          }
+          int32 feat;
+          safe_strto32(test.feature_id().id().value(), &feat);
+          ++counts(feat);
+        } else {
+          const auto& test = bnode.inequality_left_child_test();
+          if (test.has_feature_id()) {
+            int32 feat;
+            safe_strto32(test.feature_id().id().value(), &feat);
+            ++counts(feat);
+          } else if (test.has_oblique()) {
+            for (const auto& featid : test.oblique().features()) {
+              int32 feat;
+              safe_strto32(featid.id().value(), &feat);
+              ++counts(feat);
+            }
+          }
+        }
+      }
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeResource);
+
+REGISTER_KERNEL_BUILDER(Name("TreeIsInitializedOp").Device(DEVICE_CPU),
+                        IsResourceInitialized<DecisionTreeResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateTreeVariable").Device(DEVICE_CPU),
+                        CreateTreeVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeSerialize").Device(DEVICE_CPU),
+                        TreeSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeDeserialize").Device(DEVICE_CPU),
+                        TreeDeserializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeSize").Device(DEVICE_CPU), TreeSizeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreePredictionsV4").Device(DEVICE_CPU),
+                        TreePredictionsV4Op);
+
+REGISTER_KERNEL_BUILDER(Name("FeatureUsageCounts").Device(DEVICE_CPU),
+                        FeatureUsageCountsOp);
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
new file mode 100644
index 0000000000..e3d8aa89d6
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -0,0 +1,562 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <queue>
+
+#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/gtl/map_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+using gtl::FindOrNull;
+
+// Creates a stats variable.
+class CreateFertileStatsVariableOp : public OpKernel {
+ public:
+  explicit CreateFertileStatsVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* stats_config_t;
+    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
+                errors::InvalidArgument("Stats config must be a scalar."));
+    auto* result = new FertileStatsResource(param_proto_);
+    FertileStats stats;
+    if (!ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false,
+                  errors::InvalidArgument("Unable to parse stats config."));
+    }
+
+    result->ExtractFromProto(stats);
+    result->MaybeInitialize();
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for serializing a model.
+class FertileStatsSerializeOp : public OpKernel {
+ public:
+  explicit FertileStatsSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &fertile_stats_resource));
+    mutex_lock l(*fertile_stats_resource->get_mutex());
+    core::ScopedUnref unref_me(fertile_stats_resource);
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, TensorShape(), &output_config_t));
+
+    FertileStats stats;
+    fertile_stats_resource->PackToProto(&stats);
+    output_config_t->scalar<string>()() = stats.SerializeAsString();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for deserializing a stats variable from a checkpoint.
+class FertileStatsDeserializeOp : public OpKernel {
+ public:
+  explicit FertileStatsDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &fertile_stats_resource));
+    mutex_lock l(*fertile_stats_resource->get_mutex());
+    core::ScopedUnref unref_me(fertile_stats_resource);
+
+    const Tensor* stats_config_t;
+    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
+                errors::InvalidArgument("Stats config must be a scalar."));
+    // Deallocate all the previous objects on the resource.
+    fertile_stats_resource->Reset();
+    FertileStats stats;
+    OP_REQUIRES(context,
+                ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()()),
+                errors::InvalidArgument("Unable to parse stats config."));
+
+    fertile_stats_resource->ExtractFromProto(stats);
+    fertile_stats_resource->MaybeInitialize();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+void TraverseTree(const DecisionTreeResource* tree_resource,
+                  const std::unique_ptr<TensorDataSet>& data, int32 start,
+                  int32 end, std::vector<int32>* leaf_ids,
+                  std::vector<int32>* leaf_depths) {
+  for (int i = start; i < end; ++i) {
+    int32 depth;
+    const int32 leaf_id = tree_resource->TraverseTree(data, i, &depth);
+    (*leaf_ids)[i] = leaf_id;
+    (*leaf_depths)[i] = depth;
+  }
+}
+
+// Try to update a leaf's stats by acquiring its lock.  If it can't be
+// acquired, put it in a waiting queue to come back to later and try the next
+// one.  Once all leaf_ids have been visited, cycle through the waiting ids
+// until they're gone.
+void UpdateStats(FertileStatsResource* fertile_stats_resource,
+                 const std::unique_ptr<TensorDataSet>& data,
+                 const Tensor& input_labels, const Tensor& input_weights,
+                 int num_targets, const std::vector<int32>& leaf_ids,
+                 const std::vector<int32>& leaf_depths,
+                 std::unordered_map<int32, std::unique_ptr<mutex>>* locks,
+                 mutex* set_lock, int32 start, int32 end,
+                 std::unordered_set<int32>* ready_to_split) {
+  const auto labels = input_labels.unaligned_flat<float>();
+  const auto weights = input_weights.unaligned_flat<float>();
+  // Stores leaf_id, leaf_depth, example_id for examples that are waiting
+  // on another to finish.
+  std::queue<std::tuple<int32, int32, int32>> waiting;
+
+  int32 i = start;
+  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
+  while (i < end || !waiting.empty()) {
+    int32 leaf_id;
+    int32 leaf_depth;
+    int32 example_id;
+    bool was_waiting = false;
+    if (i >= end) {
+      std::tie(leaf_id, leaf_depth, example_id) = waiting.front();
+      waiting.pop();
+      was_waiting = true;
+    } else {
+      leaf_id = leaf_ids[i];
+      leaf_depth = leaf_depths[i];
+      example_id = i;
+      ++i;
+    }
+    const std::unique_ptr<mutex>& leaf_lock = (*locks)[leaf_id];
+    if (was_waiting) {
+      leaf_lock->lock();
+    } else {
+      if (!leaf_lock->try_lock()) {
+        waiting.emplace(leaf_id, leaf_depth, example_id);
+        continue;
+      }
+    }
+
+    bool is_finished;
+    fertile_stats_resource->AddExampleToStatsAndInitialize(
+        data, &target, {example_id}, leaf_id, leaf_depth, &is_finished);
+    leaf_lock->unlock();
+    if (is_finished) {
+      set_lock->lock();
+      ready_to_split->insert(leaf_id);
+      set_lock->unlock();
+    }
+  }
+}
+
+// Update leaves from start through end in the leaf_examples iterator.
+void UpdateStatsCollated(
+    FertileStatsResource* fertile_stats_resource,
+    DecisionTreeResource* tree_resource,
+    const std::unique_ptr<TensorDataSet>& data, const Tensor& input_labels,
+    const Tensor& input_weights, int num_targets,
+    const std::unordered_map<int32, std::vector<int>>& leaf_examples,
+    const std::vector<int32>& leaf_depths, mutex* set_lock, int32 start,
+    int32 end, std::unordered_set<int32>* ready_to_split) {
+  const auto labels = input_labels.unaligned_flat<float>();
+  const auto weights = input_weights.unaligned_flat<float>();
+
+  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
+  auto it = leaf_examples.begin();
+  std::advance(it, start);
+  auto end_it = leaf_examples.begin();
+  std::advance(end_it, end);
+  while (it != end_it) {
+    int32 leaf_id = it->first;
+    bool is_finished;
+    fertile_stats_resource->AddExampleToStatsAndInitialize(
+        data, &target, it->second, leaf_id, leaf_depths[it->second[0]],
+        &is_finished);
+    if (is_finished) {
+      set_lock->lock();
+      ready_to_split->insert(leaf_id);
+      set_lock->unlock();
+    }
+    ++it;
+  }
+}
+
+// Op for traversing the tree with each example, accumulating statistics, and
+// outputting node ids that are ready to split.
+class ProcessInputOp : public OpKernel {
+ public:
+  explicit ProcessInputOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_));
+
+    string serialized_proto;
+    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
+    input_spec_.ParseFromString(serialized_proto);
+
+    data_set_ = std::unique_ptr<TensorDataSet>(
+        new TensorDataSet(input_spec_, random_seed_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_data = context->input(2);
+    const Tensor& sparse_input_indices = context->input(3);
+    const Tensor& sparse_input_values = context->input(4);
+    const Tensor& input_labels = context->input(6);
+    const Tensor& input_weights = context->input(7);
+
+    data_set_->set_input_tensors(input_data, sparse_input_indices,
+                                 sparse_input_values);
+
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+    core::ScopedUnref unref_tree(tree_resource);
+
+    const int32 num_data = data_set_->NumItems();
+    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
+    int num_threads = worker_threads->num_threads;
+
+    // First find the leaf ids for each example.
+    std::vector<int32> leaf_ids(num_data);
+
+    // The depth of the leaf for example i.
+    std::vector<int32> leaf_depths(num_data);
+
+    const int64 costPerTraverse = 500;
+    auto traverse = [this, &leaf_ids, &leaf_depths, tree_resource, num_data](
+                        int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      TraverseTree(tree_resource, data_set_, static_cast<int32>(start),
+                   static_cast<int32>(end), &leaf_ids, &leaf_depths);
+    };
+    Shard(num_threads, worker_threads->workers, num_data, costPerTraverse,
+          traverse);
+
+    // Create one mutex per leaf. We need to protect access to leaf pointers,
+    // so instead of grouping examples by leaf, we spread examples out among
+    // threads to provide uniform work for each of them and protect access
+    // with mutexes.
+    std::unordered_map<int, std::unique_ptr<mutex>> locks;
+    std::unordered_map<int32, std::vector<int>> leaf_examples;
+    if (param_proto_.collate_examples()) {
+      for (int i = 0; i < num_data; ++i) {
+        leaf_examples[leaf_ids[i]].push_back(i);
+      }
+    } else {
+      for (const int32 id : leaf_ids) {
+        if (FindOrNull(locks, id) == nullptr) {
+          // TODO(gilberth): Consider using a memory pool for these.
+          locks[id] = std::unique_ptr<mutex>(new mutex);
+        }
+      }
+    }
+
+    const int32 num_leaves = leaf_examples.size();
+    const int32 label_dim =
+        input_labels.shape().dims() <= 1
+            ? 0
+            : static_cast<int>(input_labels.shape().dim_size(1));
+    const int32 num_targets =
+        param_proto_.is_regression() ? (std::max(1, label_dim)) : 1;
+
+    // Ids of leaves that can split.
+    std::unordered_set<int32> ready_to_split;
+    mutex set_lock;
+
+    // TODO(gilberth): This is a rough approximation based on measurements
+    // from a digits run on local desktop.  Heuristics might be necessary
+    // if it really matters that much.
+    const int64 costPerUpdate = 1000;
+    auto update = [this, &input_labels, &input_weights, &leaf_ids, &leaf_depths,
+                   &num_targets, fertile_stats_resource, &locks, &set_lock,
+                   &ready_to_split, num_data](int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      UpdateStats(fertile_stats_resource, data_set_, input_labels,
+                  input_weights, num_targets, leaf_ids, leaf_depths, &locks,
+                  &set_lock, static_cast<int32>(start), static_cast<int32>(end),
+                  &ready_to_split);
+    };
+
+    auto update_collated = [this, &input_labels, &input_weights, &leaf_ids,
+                            &num_targets, &leaf_depths, fertile_stats_resource,
+                            tree_resource, &leaf_examples, &set_lock,
+                            &ready_to_split,
+                            num_leaves](int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_leaves);
+      UpdateStatsCollated(
+          fertile_stats_resource, tree_resource, data_set_, input_labels,
+          input_weights, num_targets, leaf_examples, leaf_depths, &set_lock,
+          static_cast<int32>(start), static_cast<int32>(end), &ready_to_split);
+    };
+
+    if (param_proto_.collate_examples()) {
+      Shard(num_threads, worker_threads->workers, num_leaves, costPerUpdate,
+            update_collated);
+    } else {
+      Shard(num_threads, worker_threads->workers, num_data, costPerUpdate,
+            update);
+    }
+
+    Tensor* output_finished_t = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(ready_to_split.size());
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, output_shape, &output_finished_t));
+    auto output = output_finished_t->unaligned_flat<int32>();
+    std::copy(ready_to_split.begin(), ready_to_split.end(), output.data());
+  }
+
+ private:
+  int32 random_seed_;
+  tensorforest::TensorForestDataSpec input_spec_;
+  std::unique_ptr<TensorDataSet> data_set_;
+  TensorForestParams param_proto_;
+};
+
+// Op for growing finished nodes.
+class GrowTreeOp : public OpKernel {
+ public:
+  explicit GrowTreeOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+    core::ScopedUnref unref_tree(tree_resource);
+
+    const Tensor& finished_nodes = context->input(2);
+
+    const auto finished = finished_nodes.unaligned_flat<int32>();
+
+    const int32 num_nodes =
+        static_cast<int32>(finished_nodes.shape().dim_size(0));
+
+    // TODO(gilberth): distribute this work over a number of threads.
+    for (int i = 0;
+         i < num_nodes &&
+         tree_resource->decision_tree().decision_tree().nodes_size() <
+             param_proto_.max_nodes();
+         ++i) {
+      const int32 node = finished(i);
+      std::unique_ptr<SplitCandidate> best(new SplitCandidate);
+      int32 parent_depth;
+      bool found =
+          fertile_stats_resource->BestSplit(node, best.get(), &parent_depth);
+      if (found) {
+        std::vector<int32> new_children;
+        tree_resource->SplitNode(node, best.get(), &new_children);
+        fertile_stats_resource->Allocate(parent_depth, new_children);
+        fertile_stats_resource->set_leaf_stat(best->left_stats(),
+                                              new_children[0]);
+        fertile_stats_resource->set_leaf_stat(best->right_stats(),
+                                              new_children[1]);
+        // We are done with best, so it is now safe to clear node.
+        fertile_stats_resource->Clear(node);
+        CHECK(tree_resource->get_mutable_tree_node(node)->has_leaf() == false);
+      } else {  // reset
+        fertile_stats_resource->ResetSplitStats(node, parent_depth);
+      }
+    }
+  }
+
+ private:
+  tensorforest::TensorForestDataSpec input_spec_;
+  TensorForestParams param_proto_;
+};
+
+void FinalizeLeaf(const LeafStat& leaf_stats, bool is_regression,
+                  bool drop_final_class,
+                  const std::unique_ptr<LeafModelOperator>& leaf_op,
+                  decision_trees::Leaf* leaf) {
+  leaf_op->ExportModel(leaf_stats, leaf);
+
+  // TODO(thomaswc): Move the rest of this into ExportModel.
+
+  // regression models are already stored in leaf in normalized form.
+  if (is_regression) {
+    return;
+  }
+
+  float sum = leaf_stats.weight_sum();
+  if (sum <= 0.0) {
+    LOG(WARNING) << "Leaf with sum " << sum << " has stats "
+                 << leaf->ShortDebugString();
+    return;
+  }
+
+  if (leaf->has_vector()) {
+    for (int i = 0; i < leaf->vector().value_size(); i++) {
+      auto* v = leaf->mutable_vector()->mutable_value(i);
+      v->set_float_value(v->float_value() / sum);
+    }
+    if (drop_final_class) {
+      leaf->mutable_vector()->mutable_value()->RemoveLast();
+    }
+    return;
+  }
+
+  if (leaf->has_sparse_vector()) {
+    for (auto& it : *leaf->mutable_sparse_vector()->mutable_sparse_value()) {
+      it.second.set_float_value(it.second.float_value() / sum);
+    }
+    return;
+  }
+
+  LOG(FATAL) << "Unknown leaf type in " << leaf->DebugString();
+}
+
+// Op for finalizing a tree at the end of training.
+class FinalizeTreeOp : public OpKernel {
+ public:
+  explicit FinalizeTreeOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_me(tree_resource);
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+
+    // TODO(thomaswc): Add threads
+    int num_nodes = tree_resource->decision_tree().decision_tree().nodes_size();
+    for (int i = 0; i < num_nodes; i++) {
+      auto* node = tree_resource->mutable_decision_tree()
+                       ->mutable_decision_tree()
+                       ->mutable_nodes(i);
+      if (node->has_leaf()) {
+        const auto& leaf_stats = fertile_stats_resource->leaf_stat(i);
+        FinalizeLeaf(leaf_stats, param_proto_.is_regression(),
+                     param_proto_.drop_final_class(), model_op_,
+                     node->mutable_leaf());
+      }
+    }
+  }
+
+ private:
+  std::unique_ptr<LeafModelOperator> model_op_;
+  TensorForestParams param_proto_;
+};
+
+REGISTER_RESOURCE_HANDLE_KERNEL(FertileStatsResource);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsIsInitializedOp").Device(DEVICE_CPU),
+                        IsResourceInitialized<FertileStatsResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateFertileStatsVariable").Device(DEVICE_CPU),
+                        CreateFertileStatsVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsSerialize").Device(DEVICE_CPU),
+                        FertileStatsSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsDeserialize").Device(DEVICE_CPU),
+                        FertileStatsDeserializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("ProcessInputV4").Device(DEVICE_CPU),
+                        ProcessInputOp);
+
+REGISTER_KERNEL_BUILDER(Name("GrowTreeV4").Device(DEVICE_CPU), GrowTreeOp);
+
+REGISTER_KERNEL_BUILDER(Name("FinalizeTree").Device(DEVICE_CPU),
+                        FinalizeTreeOp);
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index 0542508a8e..a9d8093d13 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -40,9 +40,7 @@ cc_library(
         ":split_collection_operators",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:framework",
         "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:lib",
     ],
 )
 
@@ -111,7 +109,7 @@ cc_library(
         "//tensorflow/contrib/tensor_forest:tree_utils",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -153,7 +151,7 @@ cc_library(
         ":input_data",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -174,6 +172,25 @@ cc_library(
     name = "split_collection_operators",
     srcs = ["split_collection_operators.cc"],
     hdrs = ["split_collection_operators.h"],
+    deps = [
+        ":grow_stats",
+        ":input_data",
+        ":input_target",
+        ":leaf_model_operators",
+        ":params",
+        ":stat_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+    ],
+)
+
+cc_library(
+    name = "graph_collection_operator",
+    srcs = ["graph_collection_operator.cc"],
+    hdrs = ["graph_collection_operator.h"],
     deps = [
         ":candidate_graph_runner",
         ":grow_stats",
@@ -181,6 +198,7 @@ cc_library(
         ":input_target",
         ":leaf_model_operators",
         ":params",
+        ":split_collection_operators",
         ":stat_utils",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
new file mode 100644
index 0000000000..2c925b5dd7
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
@@ -0,0 +1,142 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h"
+
+#include <cfloat>
+
+#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+REGISTER_SPLIT_COLLECTION(GRAPH_RUNNER_COLLECTION,
+                          GraphRunnerSplitCollectionOperator);
+
+std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
+    int32 node_id, int32 depth) const {
+  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
+}
+
+int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
+                                                   int32 split_id) const {
+  return node_id * num_splits_to_consider_ + split_id;
+}
+
+bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
+                                                   SplitCandidate* best,
+                                                   int32* depth) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  auto* slot = stats_.at(node_id).get();
+  *depth = slot->depth();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    // TODO(gilberth): Support uselessness.
+    auto& runner = runners_[UniqueId(node_id, i)];
+    const float split_score = runner->SplitScore();
+    if (split_score < min_score) {
+      min_score = split_score;
+      best_index = i;
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in split info and left/right stats to initialize models with.
+  *best = SplitCandidate();
+  auto& runner = runners_[UniqueId(node_id, best_index)];
+  runner->GetLeftStats(best->mutable_left_stats());
+  runner->GetRightStats(best->mutable_right_stats());
+  runner->GetSplit(best->mutable_split());
+  return true;
+}
+
+void GraphRunnerSplitCollectionOperator::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    const std::vector<int>& examples, int32 node_id) const {
+  // Build input Tensors.
+  int size = examples.size();
+  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
+  auto ex_data = examples_t.flat<int32>();
+  std::copy(examples.begin(), examples.end(), ex_data.data());
+
+  const TensorInputTarget* tensor_target =
+      dynamic_cast<const TensorInputTarget*>(target);
+  CHECK_NOTNULL(tensor_target);
+
+  const Tensor& data_t = input_data->original_tensor();
+  const Tensor& target_t = tensor_target->original_tensor();
+
+  // Add to candidates.
+  auto* slot = stats_.at(node_id).get();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    auto& runner = runners_[UniqueId(node_id, i)];
+    runner->AddExample(data_t, target_t, examples_t);
+  }
+
+  // Update simple weight sums so we know when we're done.
+  for (int example : examples) {
+    slot->AddExample(input_data, target, example);
+  }
+}
+
+void GraphRunnerSplitCollectionOperator::
+    CreateAndInitializeCandidateWithExample(
+        const std::unique_ptr<TensorDataSet>& input_data, int example,
+        int32 node_id) const {
+  auto* slot = stats_.at(node_id).get();
+  int cand_num = slot->num_splits();
+  const int64 unique_id = UniqueId(node_id, cand_num);
+
+  decision_trees::BinaryNode split;
+
+  decision_trees::InequalityTest* test =
+      split.mutable_inequality_left_child_test();
+  auto* oblique = test->mutable_oblique();
+  for (int i = 0; i < features_per_node_; ++i) {
+    float bias;
+    int type;
+    // This is really just a way to select a list of random features.
+    // Also a way to warn the user that categoricals don't make sense here.
+    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
+
+    if (type == kDataFloat) {
+      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
+
+      // The comparison bias is assumed to be zero.
+      test->mutable_threshold()->set_float_value(0);
+    } else {
+      LOG(ERROR) << "Categorical features not supported with this system.";
+      return;
+    }
+  }
+
+  slot->AddSplit(split);
+
+  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
+  runners_[unique_id]->Init();
+}
+
+void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
+  SplitCollectionOperator::ClearSlot(node_id);
+  for (int i = 0; i < num_splits_to_consider_; ++i) {
+    runners_.erase(UniqueId(node_id, i));
+  }
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
new file mode 100644
index 0000000000..9b18e3e969
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
@@ -0,0 +1,81 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
+
+#include <vector>
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Holds split candidates that are trained by running any TF graph.
+class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
+ public:
+  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
+      : SplitCollectionOperator(params) {
+    if (params.num_splits_to_consider().ParamType_case() ==
+        DepthDependentParam::PARAMTYPE_NOT_SET) {
+      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
+                 << " num_splits_to_consider";
+    } else {
+      num_splits_to_consider_ =
+          params.num_splits_to_consider().constant_value();
+    }
+  }
+
+  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
+                                             int32 depth) const override;
+
+  // Updates the slot's candidates with the new example.
+  // Assumes slot has been initialized.
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, const std::vector<int>& examples,
+                  int32 node_id) const override;
+
+  // Create a new candidate and initialize it with the given example.
+  void CreateAndInitializeCandidateWithExample(
+      const std::unique_ptr<TensorDataSet>& input_data, int example,
+      int32 node_id) const override;
+
+  bool BestSplit(int32 node_id, SplitCandidate* best,
+                 int32* depth) const override;
+
+  void ClearSlot(int32 node_id) override;
+
+ protected:
+  int64 UniqueId(int32 node_id, int32 split_id) const;
+
+  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
+      runners_;
+  int features_per_node_;
+  string graph_dir_;
+  // Must have a constant value because of how we make unique ids right now.
+  int32 num_splits_to_consider_;
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
index ddf4be8799..c207c0859d 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
@@ -23,22 +23,20 @@
 namespace tensorflow {
 namespace tensorforest {
 
+std::unordered_map<int, CollectionCreator*>
+    SplitCollectionOperatorFactory::factories_;  // NOLINT
+REGISTER_SPLIT_COLLECTION(COLLECTION_BASIC, SplitCollectionOperator);
+
 std::unique_ptr<SplitCollectionOperator>
 SplitCollectionOperatorFactory::CreateSplitCollectionOperator(
     const TensorForestParams& params) {
-  switch (params.collection_type()) {
-    case COLLECTION_BASIC:
-      return std::unique_ptr<SplitCollectionOperator>(
-          new SplitCollectionOperator(params));
-
-    case GRAPH_RUNNER_COLLECTION:
-      return std::unique_ptr<SplitCollectionOperator>(
-          new GraphRunnerSplitCollectionOperator(params));
-
-    default:
-      LOG(ERROR) << "Unknown split collection operator: "
-                 << params.collection_type();
-      return nullptr;
+  auto it = factories_.find(params.collection_type());
+  if (it == factories_.end()) {
+    LOG(ERROR) << "Unknown split collection operator: "
+               << params.collection_type();
+    return nullptr;
+  } else {
+    return it->second->Create(params);
   }
 }
 
@@ -137,121 +135,5 @@ bool SplitCollectionOperator::BestSplit(int32 node_id,
   return slot->BestSplit(best);
 }
 
-// -------------------------------- GraphRunner ------------------ //
-
-std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
-    int32 node_id, int32 depth) const {
-  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
-}
-
-int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
-                                                   int32 split_id) const {
-  return node_id * num_splits_to_consider_ + split_id;
-}
-
-bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
-                                                   SplitCandidate* best,
-                                                   int32* depth) const {
-  float min_score = FLT_MAX;
-  int best_index = -1;
-  auto* slot = stats_.at(node_id).get();
-  *depth = slot->depth();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    // TODO(gilberth): Support uselessness.
-    auto& runner = runners_[UniqueId(node_id, i)];
-    const float split_score = runner->SplitScore();
-    if (split_score < min_score) {
-      min_score = split_score;
-      best_index = i;
-    }
-  }
-
-  // This could happen if all the splits are useless.
-  if (best_index < 0) {
-    return false;
-  }
-
-  // Fill in split info and left/right stats to initialize models with.
-  *best = SplitCandidate();
-  auto& runner = runners_[UniqueId(node_id, best_index)];
-  runner->GetLeftStats(best->mutable_left_stats());
-  runner->GetRightStats(best->mutable_right_stats());
-  runner->GetSplit(best->mutable_split());
-  return true;
-}
-
-void GraphRunnerSplitCollectionOperator::AddExample(
-    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
-    const std::vector<int>& examples, int32 node_id) const {
-  // Build input Tensors.
-  int size = examples.size();
-  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
-  auto ex_data = examples_t.flat<int32>();
-  std::copy(examples.begin(), examples.end(), ex_data.data());
-
-  const TensorInputTarget* tensor_target =
-      dynamic_cast<const TensorInputTarget*>(target);
-  CHECK_NOTNULL(tensor_target);
-
-  const Tensor& data_t = input_data->original_tensor();
-  const Tensor& target_t = tensor_target->original_tensor();
-
-  // Add to candidates.
-  auto* slot = stats_.at(node_id).get();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    auto& runner = runners_[UniqueId(node_id, i)];
-    runner->AddExample(data_t, target_t, examples_t);
-  }
-
-  // Update simple weight sums so we know when we're done.
-  for (int example : examples) {
-    slot->AddExample(input_data, target, example);
-  }
-}
-
-void GraphRunnerSplitCollectionOperator::
-    CreateAndInitializeCandidateWithExample(
-        const std::unique_ptr<TensorDataSet>& input_data, int example,
-        int32 node_id) const {
-  auto* slot = stats_.at(node_id).get();
-  int cand_num = slot->num_splits();
-  const int64 unique_id = UniqueId(node_id, cand_num);
-
-  decision_trees::BinaryNode split;
-
-  decision_trees::InequalityTest* test =
-      split.mutable_inequality_left_child_test();
-  auto* oblique = test->mutable_oblique();
-  for (int i = 0; i < features_per_node_; ++i) {
-    float bias;
-    int type;
-    // This is really just a way to select a list of random features.
-    // Also a way to warn the user that categoricals don't make sense here.
-    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
-
-    if (type == kDataFloat) {
-      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
-
-      // The comparison bias is assumed to be zero.
-      test->mutable_threshold()->set_float_value(0);
-    } else {
-      LOG(ERROR) << "Categorical features not supported with this system.";
-      return;
-    }
-  }
-
-  slot->AddSplit(split);
-
-  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
-  runners_[unique_id]->Init();
-}
-
-void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
-  SplitCollectionOperator::ClearSlot(node_id);
-  for (int i = 0; i < num_splits_to_consider_; ++i) {
-    runners_.erase(UniqueId(node_id, i));
-  }
-}
-
 }  // namespace tensorforest
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
index d0ea33612a..81d820a6b2 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
@@ -17,7 +17,6 @@
 
 #include <vector>
 #include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
@@ -86,60 +85,38 @@ class SplitCollectionOperator {
   std::unordered_map<int32, std::unique_ptr<GrowStats>> stats_;
 };
 
-
-class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
+class CollectionCreator {
  public:
-  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
-      : SplitCollectionOperator(params) {
-    if (params.num_splits_to_consider().ParamType_case() ==
-        DepthDependentParam::PARAMTYPE_NOT_SET) {
-      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
-                 << " num_splits_to_consider";
-    } else {
-      num_splits_to_consider_ =
-          params.num_splits_to_consider().constant_value();
-    }
-  }
-
-  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
-                                             int32 depth) const override;
-
-  // Updates the slot's candidates with the new example.
-  // Assumes slot has been initialized.
-  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
-                  const InputTarget* target, const std::vector<int>& examples,
-                  int32 node_id) const override;
-
-  // Create a new candidate and initialize it with the given example.
-  void CreateAndInitializeCandidateWithExample(
-      const std::unique_ptr<TensorDataSet>& input_data, int example,
-      int32 node_id) const override;
-
-  bool BestSplit(int32 node_id, SplitCandidate* best,
-                 int32* depth) const override;
-
-  void ClearSlot(int32 node_id) override;
-
- protected:
-  int64 UniqueId(int32 node_id, int32 split_id) const;
-
-  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
-      runners_;
-  int features_per_node_;
-  string graph_dir_;
-  // Must have a constant value because of how we make unique ids right now.
-  int32 num_splits_to_consider_;
+  virtual std::unique_ptr<SplitCollectionOperator> Create(
+      const TensorForestParams& params) = 0;
+  virtual ~CollectionCreator() {}
 };
 
-// Creates a type of SplitCollectionOperator depending on the type passed,
-// which is SplitCollectionType in fertile_stats.proto.
-// Can create a SplitCollectionOperator itself, known as "basic".
 class SplitCollectionOperatorFactory {
  public:
   static std::unique_ptr<SplitCollectionOperator> CreateSplitCollectionOperator(
       const TensorForestParams& params);
+
+  static std::unordered_map<int, CollectionCreator*> factories_;
+};
+
+template <typename T>
+class AnyCollectionCreator : public CollectionCreator {
+ public:
+  AnyCollectionCreator(SplitCollectionType type) {
+    SplitCollectionOperatorFactory::factories_[type] = this;
+  }
+  virtual std::unique_ptr<SplitCollectionOperator> Create(
+      const TensorForestParams& params) {
+    return std::unique_ptr<SplitCollectionOperator>(new T(params));
+  }
 };
 
+#define REGISTER_SPLIT_COLLECTION(name, cls) \
+  namespace {                                \
+  AnyCollectionCreator<cls> creator(name);   \
+  }
+
 }  // namespace tensorforest
 }  // namespace tensorflow
 
diff --git a/tensorflow/contrib/tensor_forest/ops/model_ops.cc b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
new file mode 100644
index 0000000000..c9acc4a6ae
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
@@ -0,0 +1,134 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+using shape_inference::DimensionHandle;
+using shape_inference::InferenceContext;
+
+namespace tensorforest {
+
+REGISTER_RESOURCE_HANDLE_OP(DecisionTreeResource);
+
+REGISTER_OP("TreeIsInitializedOp")
+    .Input("tree_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a tree has been initialized.
+)doc");
+
+REGISTER_OP("CreateTreeVariable")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Creates a tree  model and returns a handle to it.
+
+params: A serialized TensorForestParams proto.
+tree_handle: handle to the tree resource to be created.
+tree_config: Serialized proto of the tree.
+)doc");
+
+REGISTER_OP("TreeSerialize")
+    .Input("tree_handle: resource")
+    .Output("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Serializes the tree  to a proto.
+
+tree_handle: The handle to the tree.
+tree_config: Serialized proto of the tree.
+)doc");
+
+REGISTER_OP("TreeDeserialize")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Deserializes a serialized tree config and replaces current tree.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree .
+tree_config: Serialized proto of the .
+)doc");
+
+REGISTER_OP("TreeSize")
+    .Input("tree_handle: resource")
+    .Output("tree_size: int32")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Outputs the size of the tree, including leaves.
+
+tree_handle: The handle to the tree.
+tree_size: Size scalar.
+)doc");
+
+REGISTER_OP("TreePredictionsV4")
+    .Attr("input_spec: string")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("input_data: float")
+    .Input("sparse_input_indices: int64")
+    .Input("sparse_input_values: float")
+    .Input("sparse_input_shape: int64")
+    .Output("predictions: float")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle num_points = c->UnknownDim();
+
+      if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0) {
+        num_points = c->Dim(c->input(1), 0);
+      }
+
+      c->set_output(0, c->Matrix(num_points, c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs the predictions for the given input data.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
+   gives the j-th feature of the i-th input.
+sparse_input_indices: The indices tensor from the SparseTensor input.
+sparse_input_values: The values tensor from the SparseTensor input.
+sparse_input_shape: The shape tensor from the SparseTensor input.
+predictions: `predictions[i][j]` is the probability that input i is class j.
+)doc");
+
+REGISTER_OP("FeatureUsageCounts")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Output("feature_counts: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs the number of times each feature was used in a split.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+feature_counts: `feature_counts[i]` is the number of times feature i was used
+    in a split.
+)doc");
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/ops/stats_ops.cc b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
new file mode 100644
index 0000000000..9652749768
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
@@ -0,0 +1,143 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+using shape_inference::InferenceContext;
+
+namespace tensorforest {
+
+REGISTER_RESOURCE_HANDLE_OP(FertileStatsResource);
+
+REGISTER_OP("FertileStatsIsInitializedOp")
+    .Input("stats_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a stats has been initialized.
+)doc");
+
+REGISTER_OP("CreateFertileStatsVariable")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Input("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Creates a stats model and returns a handle to it.
+
+params: A serialized TensorForestParams proto.
+stats_handle: handle to the stats resource to be created.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("FertileStatsSerialize")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Output("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Serializes the stats to a proto.
+
+params: A serialized TensorForestParams proto.
+stats_handle: The handle to the stats.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("FertileStatsDeserialize")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Input("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Deserializes a serialized stats config and replaces current stats.
+
+params: A serialized TensorForestParams proto.
+stats_handle: The handle to the stats.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("GrowTreeV4")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .Input("finshed_nodes: int32")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Grows the tree for finished nodes and allocates waiting nodes.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+finshed_nodes: A 1-d Tensor of finished node ids from ProcessInput.
+)doc");
+
+REGISTER_OP("ProcessInputV4")
+    .Attr("random_seed: int")
+    .Attr("input_spec: string")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .Input("input_data: float")
+    .Input("sparse_input_indices: int64")
+    .Input("sparse_input_values: float")
+    .Input("sparse_input_shape: int64")
+    .Input("input_labels: float")
+    .Input("input_weights: float")
+    .Output("finished_nodes: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Add labels to stats after traversing the tree for each example.
+
+Outputs node ids that are finished.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
+   gives the j-th feature of the i-th input.
+sparse_input_indices: The indices tensor from the SparseTensor input.
+sparse_input_values: The values tensor from the SparseTensor input.
+sparse_input_shape: The shape tensor from the SparseTensor input.
+input_labels: The training batch's labels as a 1 or 2-d tensor.
+  'input_labels[i][j]' gives the j-th label/target for the i-th input.
+input_weights: The training batch's eample weights as a 1-d tensor.
+  'input_weights[i]' gives the weight for the i-th input.
+finished_nodes: A 1-d tensor of node ids that have finished and are ready to
+  grow.
+)doc");
+
+REGISTER_OP("FinalizeTree")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .SetShapeFn([](InferenceContext* c) { return Status::OK(); })
+    .Doc(R"doc(
+Puts the Leaf models inside the tree into their final form.
+
+If drop_final_class is true, the per-class probability prediction of the
+last class is not stored in the leaf models.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+)doc");
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/python/__init__.py b/tensorflow/contrib/tensor_forest/python/__init__.py
index 0d41d3500d..b2ca89877a 100644
--- a/tensorflow/contrib/tensor_forest/python/__init__.py
+++ b/tensorflow/contrib/tensor_forest/python/__init__.py
@@ -21,4 +21,6 @@ from __future__ import print_function
 from tensorflow.contrib.tensor_forest.python import constants
 from tensorflow.contrib.tensor_forest.python import tensor_forest
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
+from tensorflow.contrib.tensor_forest.python.ops import model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
 from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
diff --git a/tensorflow/contrib/tensor_forest/python/ops/model_ops.py b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
new file mode 100644
index 0000000000..4c7218305b
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
@@ -0,0 +1,124 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Model ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tensor_forest.python.ops import gen_model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
+
+# pylint: disable=unused-import
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import feature_usage_counts
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_predictions_v4
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_size
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+_model_ops = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_model_ops.so"))
+
+
+ops.NotDifferentiable("TreeVariable")
+ops.NotDifferentiable("TreeSerialize")
+ops.NotDifferentiable("TreeDeserialize")
+ops.NotDifferentiable("TreeSize")
+ops.NotDifferentiable("TreePredictionsV4")
+ops.NotDifferentiable("FeatureUsageCounts")
+
+
+class TreeVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for TreeVariable."""
+
+  def __init__(self, params, tree_handle, stats_handle, create_op, name):
+    """Creates a TreeVariableSavable object.
+
+    Args:
+      params: A TensorForestParams object.
+      tree_handle: handle to the tree variable.
+      stats_handle: handle to the stats variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree variable under.
+    """
+    self.params = params
+    deps = []
+    if stats_handle is not None:
+      deps.append(stats_ops.finalize_tree(
+          tree_handle, stats_handle,
+          params=params.serialized_params_proto))
+    with ops.control_dependencies(deps):
+      tensor = gen_model_ops.tree_serialize(tree_handle)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree variable. So we just pass an empty value.
+    slice_spec = ""
+    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
+    super(TreeVariableSavable,
+          self).__init__(tree_handle, specs, name)
+    self._tree_handle = tree_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return gen_model_ops.tree_deserialize(
+          self._tree_handle,
+          restored_tensors[0],
+          params=self.params.serialized_params_proto)
+
+
+def tree_variable(params, tree_config, stats_handle, name, container=None):
+  r"""Creates a tree model and returns a handle to it.
+
+  Args:
+    params: A TensorForestParams object.
+    tree_config: A `Tensor` of type `string`. Serialized proto of the tree.
+    stats_handle: Resource handle to the stats object.
+    name: A name for the variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the tree.
+  """
+  with ops.name_scope(name, "TreeVariable") as name:
+    resource_handle = gen_model_ops.decision_tree_resource_handle_op(
+        container, name, name=name)
+
+    create_op = gen_model_ops.create_tree_variable(
+        resource_handle,
+        tree_config,
+        params=params.serialized_params_proto)
+    is_initialized_op = gen_model_ops.tree_is_initialized_op(resource_handle)
+    # Adds the variable to the savable list.
+    saveable = TreeVariableSavable(params, resource_handle, stats_handle,
+                                   create_op,
+                                   "tree_checkpoint_{0}".format(name))
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
diff --git a/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py b/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
new file mode 100644
index 0000000000..be9f2e12b7
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
@@ -0,0 +1,114 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Stats ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tensor_forest.python.ops import gen_stats_ops
+# pylint: disable=unused-import
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import finalize_tree
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import grow_tree_v4
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import process_input_v4
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+_stats_ops = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_stats_ops.so"))
+
+
+ops.NotDifferentiable("FertileStatsVariable")
+ops.NotDifferentiable("FertileStatsSerialize")
+ops.NotDifferentiable("FertileStatsDeserialize")
+ops.NotDifferentiable("GrowTreeV4")
+ops.NotDifferentiable("ProcessInputV4")
+ops.NotDifferentiable("FinalizeTree")
+
+
+class FertileStatsVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for FertileStatsVariable."""
+
+  def __init__(self, params, stats_handle, create_op, name):
+    """Creates a FertileStatsVariableSavable object.
+
+    Args:
+      params: A TensorForestParams object.
+      stats_handle: handle to the tree variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree variable under.
+    """
+    self.params = params
+    tensor = gen_stats_ops.fertile_stats_serialize(
+        stats_handle, params=params.serialized_params_proto)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree variable. So we just pass an empty value.
+    slice_spec = ""
+    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
+    super(FertileStatsVariableSavable,
+          self).__init__(stats_handle, specs, name)
+    self._stats_handle = stats_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return gen_stats_ops.fertile_stats_deserialize(
+          self._stats_handle, restored_tensors[0],
+          params=self.params.serialized_params_proto)
+
+
+def fertile_stats_variable(params, stats_config, name,
+                           container=None):
+  r"""Creates a stats object and returns a handle to it.
+
+  Args:
+    params: A TensorForestParams object.
+    stats_config: A `Tensor` of type `string`. Serialized proto of the stats.
+    name: A name for the variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the stats.
+  """
+  with ops.name_scope(name, "FertileStatsVariable") as name:
+    resource_handle = gen_stats_ops.fertile_stats_resource_handle_op(
+        container, name, name=name)
+
+    create_op = gen_stats_ops.create_fertile_stats_variable(
+        resource_handle, stats_config,
+        params=params.serialized_params_proto)
+    is_initialized_op = gen_stats_ops.fertile_stats_is_initialized_op(
+        resource_handle)
+    # Adds the variable to the savable list.
+    saveable = FertileStatsVariableSavable(params, resource_handle, create_op,
+                                           "stats_checkpoint_{0}".format(name))
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
-- 
GitLab


From e354ea71267d507f7c9677f4e0b536110333ec32 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 22 Jun 2017 11:31:51 -0700
Subject: [PATCH 0361/1375] Replaced constant inputs with variables to ensure
 most of the graph doesn't get optimized away

PiperOrigin-RevId: 159853171
---
 .../core/grappler/optimizers/memory_optimizer_test.cc  | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index de0043a006..eb7a8eb343 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -34,7 +34,7 @@ class RecomputeSubgraphTest : public ::testing::Test {};
 TEST_F(RecomputeSubgraphTest, SimpleSubgraph) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Const(s.WithOpName("a"), 1.f, {2, 3, 4});
+  Output a = ops::Variable(s.WithOpName("a"), {2, 3, 4}, DT_FLOAT);
   Output b = ops::Identity(s.WithOpName("b"), a);  // Recomputed
   Output c = ops::Identity(s.WithOpName("c"), b);
   Output d = ops::AddN(s.WithOpName("gradients/d"), {c});
@@ -72,8 +72,8 @@ TEST_F(RecomputeSubgraphTest, SimpleSubgraph) {
 TEST_F(RecomputeSubgraphTest, TwoInputSubgraphs) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Const(s.WithOpName("a"), 1.f, {2, 3, 4});
-  Output b = ops::Const(s.WithOpName("b"), 1.f, {2, 3, 4});
+  Output a = ops::Variable(s.WithOpName("a"), {2, 3, 4}, DT_FLOAT);
+  Output b = ops::Variable(s.WithOpName("b"), {2, 3, 4}, DT_FLOAT);
   Output d = ops::AddN(s.WithOpName("gradients/two_subgraph_inputs"), {a, b});
 
   GrapplerItem item;
@@ -102,7 +102,7 @@ TEST_F(RecomputeSubgraphTest, TwoInputSubgraphs) {
 TEST_F(RecomputeSubgraphTest, MultiNode) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Const(s.WithOpName("Conv"), 1.f, {2, 3, 4});
+  Output a = ops::Variable(s.WithOpName("Conv"), {2, 3, 4}, DT_FLOAT);
   Output b = ops::Identity(s.WithOpName("BN"), a);    // Recomputed
   Output c = ops::Identity(s.WithOpName("ReLU"), b);  // Recomputed
   Output d = ops::Identity(s.WithOpName("Conv1"), c);
@@ -182,7 +182,7 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
   // Build a simple graph with an op that's marked for swapping.
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
+  Output a = ops::Variable(s.WithOpName("a"), {10, 10}, DT_FLOAT);
   Output b = ops::AddN(s.WithOpName("b"), {a});
   Output c = ops::AddN(s.WithOpName("c"), {b});
   Output d = ops::AddN(s.WithOpName("d"), {c});
-- 
GitLab


From a1e054fea7861a1111e0df8b83945607a171b7f4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 11:42:04 -0700
Subject: [PATCH 0362/1375] For candidate sampling, add facility to colocate
 the logit computation with the sharded embeddings.

PiperOrigin-RevId: 159854706
---
 tensorflow/python/kernel_tests/BUILD          |   1 +
 .../python/kernel_tests/embedding_ops_test.py | 120 ++-
 tensorflow/python/ops/embedding_ops.py        | 304 ++++---
 tensorflow/python/ops/nn_impl.py              | 145 +++-
 tensorflow/python/ops/nn_test.py              | 751 ++++++++++--------
 5 files changed, 790 insertions(+), 531 deletions(-)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index ea2cffc177..257677ce49 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2489,6 +2489,7 @@ cuda_py_test(
         "//tensorflow/python:embedding_ops",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:partitioned_variables",
         "//tensorflow/python:platform",
diff --git a/tensorflow/python/kernel_tests/embedding_ops_test.py b/tensorflow/python/kernel_tests/embedding_ops_test.py
index 057da9d7af..a1f44bafcc 100644
--- a/tensorflow/python/kernel_tests/embedding_ops_test.py
+++ b/tensorflow/python/kernel_tests/embedding_ops_test.py
@@ -31,6 +31,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import gradient_checker
+from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import state_ops
@@ -77,11 +78,11 @@ class ScatterAddSubTest(test.TestCase):
     # Compute the expected 'p' using numpy operations.
     for i, ind in enumerate(indices):
       if scatter_op == state_ops.scatter_add:
-        p_init.reshape(shape[0], -1)[ind, :] += (
-            vals_init.reshape(vals_shape[0], -1)[i, :])
+        p_init.reshape(shape[0], -1)[ind, :] += (vals_init.reshape(
+            vals_shape[0], -1)[i, :])
       else:
-        p_init.reshape(shape[0], -1)[ind, :] -= (
-            vals_init.reshape(vals_shape[0], -1)[i, :])
+        p_init.reshape(shape[0], -1)[ind, :] -= (vals_init.reshape(
+            vals_shape[0], -1)[i, :])
     self.assertTrue(all((p_init == result).ravel()))
 
   def testNoRepetitions(self):
@@ -111,8 +112,7 @@ class ScatterAddSubTest(test.TestCase):
   def testWrongShape(self):
     # Indices and values mismatch.
     var = variables.Variable(
-        array_ops.zeros(
-            shape=[1024, 64, 64], dtype=dtypes.float32))
+        array_ops.zeros(shape=[1024, 64, 64], dtype=dtypes.float32))
     indices = array_ops.placeholder(dtypes.int32, shape=[32])
     values = array_ops.placeholder(dtypes.float32, shape=[33, 64, 64])
     with self.assertRaises(ValueError):
@@ -208,8 +208,8 @@ def _EmbeddingResult(params,
         else:
           partition = extras + (i - threshold) // ids_per_partition
           offset = (i - threshold) % ids_per_partition
-        val = np.copy(params[_PName(partition) + ":0"][
-            offset, :]) * weight_value
+        val = np.copy(
+            params[_PName(partition) + ":0"][offset, :]) * weight_value
       else:
         assert False
       if value_aggregation is None:
@@ -274,8 +274,7 @@ class EmbeddingLookupTest(test.TestCase):
           [embeddings], ids, max_norm=2.0)
 
       norms = math_ops.sqrt(
-          math_ops.reduce_sum(
-              embeddings * embeddings, axis=1))
+          math_ops.reduce_sum(embeddings * embeddings, axis=1))
       normalized = embeddings / array_ops.stack([norms, norms], axis=1)
       self.assertAllEqual(embedding.eval(), 2 * normalized.eval())
 
@@ -510,8 +509,7 @@ class EmbeddingLookupTest(test.TestCase):
   def testConstructionNonSharded(self):
     with ops.Graph().as_default():
       p = variables.Variable(
-          array_ops.zeros(
-              shape=[100, 100], dtype=dtypes.float32))
+          array_ops.zeros(shape=[100, 100], dtype=dtypes.float32))
       ids = constant_op.constant([0, 1, 1, 7], dtype=dtypes.int32)
       embedding_ops.embedding_lookup([p], ids)
 
@@ -521,8 +519,7 @@ class EmbeddingLookupTest(test.TestCase):
       for _ in range(2):
         p += [
             variables.Variable(
-                array_ops.zeros(
-                    shape=[100, 100], dtype=dtypes.float32))
+                array_ops.zeros(shape=[100, 100], dtype=dtypes.float32))
         ]
         ids = constant_op.constant([0, 1, 1, 17], dtype=dtypes.int32)
       embedding_ops.embedding_lookup(p, ids)
@@ -550,19 +547,22 @@ class EmbeddingLookupTest(test.TestCase):
   def testHigherRankMaxNorm(self):
     np.random.seed(8)
     with self.test_session():
-      for params_shape in (12,), (6, 3):
+      for params_shape in (12,), (6, 3), (6, 2, 3):
+        # Test embedding rank 0, 1, 2.
+        # Note: the first dimension must be a common multiple of procs below.
         params = 2 * np.ones(params_shape)
         params_norm = params / np.sqrt(
-            np.sum(params*params, tuple(range(params.ndim)[1:]), keepdims=True))
+            np.sum(
+                params * params, tuple(range(params.ndim)[1:]), keepdims=True))
         for ids_shape in (), (3), (4, 3), (2, 3, 4):
           ids = np.random.randint(
-              params.shape[0], size=np.prod(ids_shape, dtype=np.int64)).reshape(
-                  ids_shape)
+              params.shape[0], size=np.prod(ids_shape,
+                                            dtype=np.int64)).reshape(ids_shape)
           # Compare nonsharded to gather
           simple = embedding_ops.embedding_lookup(
               params, ids, max_norm=1.0).eval()
           self.assertAllEqual(simple, array_ops.gather(params_norm, ids).eval())
-          # Run a few random sharded versions
+          # Run a few different sharded versions.
           for procs in 1, 2, 3:
             stride = procs * math_ops.range(params.shape[0] // procs)
             split_params = [
@@ -572,6 +572,42 @@ class EmbeddingLookupTest(test.TestCase):
                 split_params, ids, max_norm=1.0).eval()
             self.assertAllEqual(simple, sharded)
 
+  def testTransform(self):
+    # This tests all combinations of:
+    #   - ids rank 0, 1, >1
+    #   - params sharded/unsharded
+    # It always applies max_norm.
+    np.random.seed(8)
+    l2_norm = 2.
+    with self.test_session():
+      # Param values are in [l2_norm, l2_norm+1) so it will always clip.
+      params = np.random.rand(6, 3) + l2_norm
+      params_norm = l2_norm * params / np.sqrt(
+          np.sum(params * params, axis=1, keepdims=True))
+      # Compute the norm of each embedding. This will change the embedding
+      # rank to 0.
+      params_norm = np.linalg.norm(params_norm, axis=1)
+      transform = lambda x: linalg_ops.norm(x, axis=1)
+      for ids_shape in (), (3), (4, 3), (2, 3, 4):
+        # Test ids rank 0, 1, 2, 3.
+        ids = np.random.randint(
+            params.shape[0], size=np.prod(ids_shape,
+                                          dtype=np.int64)).reshape(ids_shape)
+        # Compare nonsharded to gather.
+        simple = embedding_ops._embedding_lookup_and_transform(
+            params, ids, max_norm=l2_norm, transform_fn=transform).eval()
+        self.assertAllClose(simple, array_ops.gather(params_norm, ids).eval())
+        # Run a few different sharded versions.
+        for procs in 1, 2, 3:
+          stride = procs * math_ops.range(params.shape[0] // procs)
+          split_params = [
+              array_ops.gather(params, stride + p) for p in xrange(procs)
+          ]
+          sharded = embedding_ops._embedding_lookup_and_transform(
+              split_params, ids, max_norm=l2_norm,
+              transform_fn=transform).eval()
+          self.assertAllEqual(simple, sharded)
+
 
 class EmbeddingLookupSparseTest(test.TestCase):
 
@@ -625,8 +661,8 @@ class EmbeddingLookupSparseTest(test.TestCase):
         np.ones(np.sum(vals_per_batch_entry)), vals_per_batch_entry)
 
     for num_shards, combiner, dtype, ignore_weights in itertools.product(
-        [1, 5], ["sum", "mean", "sqrtn"], [dtypes.float32, dtypes.float64],
-        [True, False]):
+        [1, 5], ["sum", "mean", "sqrtn"], [dtypes.float32,
+                                           dtypes.float64], [True, False]):
 
       with self.test_session():
         p, params, feed_dict = _EmbeddingParams(
@@ -647,8 +683,8 @@ class EmbeddingLookupSparseTest(test.TestCase):
             grouped_ids,
             num_shards,
             vocab_size,
-            weight_vals=grouped_ignored_weights if ignore_weights else
-            grouped_weights)
+            weight_vals=grouped_ignored_weights
+            if ignore_weights else grouped_weights)
         if combiner == "mean":
           np_embedding_sum /= np.reshape(np_weight_sum, (batch_size, 1, 1))
         if combiner == "sqrtn":
@@ -660,12 +696,12 @@ class EmbeddingLookupSparseTest(test.TestCase):
     vocab_size = 12
     batch_size = 4
     param_shape = [2, 3]
-    sp_ids, sp_weights, _, _, _ = (
-        self._RandomIdsAndWeights(batch_size, vocab_size))
+    sp_ids, sp_weights, _, _, _ = (self._RandomIdsAndWeights(
+        batch_size, vocab_size))
 
     for num_shards, combiner, dtype, ignore_weights in itertools.product(
-        [1, 3], ["sum", "mean", "sqrtn"], [dtypes.float32, dtypes.float64],
-        [True, False]):
+        [1, 3], ["sum", "mean", "sqrtn"], [dtypes.float32,
+                                           dtypes.float64], [True, False]):
       with self.test_session():
         x, params, _ = _EmbeddingParams(
             num_shards, vocab_size, shape=param_shape, dtype=dtype)
@@ -705,10 +741,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testCint32Cpu(self):
     with self.test_session(use_gpu=False):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -716,10 +754,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testCint32Gpu(self):
     with self.test_session(use_gpu=True):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -727,10 +767,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testInt32Cpu(self):
     with self.test_session(use_gpu=False):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -738,10 +780,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testInt32Gpu(self):
     with self.test_session(use_gpu=True):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -749,10 +793,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testSumGradArgs(self):
     with self.test_session(use_gpu=False):
       indices = [
-          ops.convert_to_tensor([0, 1, 2, 3]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2, 3]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([2, 3, 5, 7]), ops.convert_to_tensor([1, 1])
+          ops.convert_to_tensor([2, 3, 5, 7]),
+          ops.convert_to_tensor([1, 1])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [2, 3, 1, 1])
diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py
index 4c94f9e9b5..f34d7fd389 100644
--- a/tensorflow/python/ops/embedding_ops.py
+++ b/tensorflow/python/ops/embedding_ops.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Operations for embeddings."""
 from __future__ import absolute_import
 from __future__ import division
@@ -35,65 +34,73 @@ from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 
 
-def _do_gather(params, ids, name=None):
-  """Deals with doing gather differently for resource variables."""
-  if isinstance(params, resource_variable_ops.ResourceVariable):
-    return params.sparse_read(ids, name=name)
-  return array_ops.gather(params, ids, name=name)
-
-
-def embedding_lookup(params, ids, partition_strategy="mod", name=None,
-                     validate_indices=True,  # pylint: disable=unused-argument
-                     max_norm=None):
-  """Looks up `ids` in a list of embedding tensors.
-
-  This function is used to perform parallel lookups on the list of
-  tensors in `params`.  It is a generalization of
-  @{tf.gather}, where `params` is
-  interpreted as a partitioning of a large embedding tensor.  `params` may be
-  a `PartitionedVariable` as returned by using `tf.get_variable()` with a
-  partitioner.
-
-  If `len(params) > 1`, each element `id` of `ids` is partitioned between
-  the elements of `params` according to the `partition_strategy`.
-  In all strategies, if the id space does not evenly divide the number of
-  partitions, each of the first `(max_id + 1) % len(params)` partitions will
-  be assigned one more id.
-
-  If `partition_strategy` is `"mod"`, we assign each id to partition
-  `p = id % len(params)`. For instance,
-  13 ids are split across 5 partitions as:
-  `[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
-
-  If `partition_strategy` is `"div"`, we assign ids to partitions in a
-  contiguous manner. In this case, 13 ids are split across 5 partitions as:
-  `[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`
+def _gather_and_clip(params, ids, max_norm, name=None):
+  """Helper function for _embedding_lookup_and_transform.
 
-  The results of the lookup are concatenated into a dense
-  tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
+  This function gathers embeddings from a single tensor. The gather deals with
+  resource variables specially. The embeddings are clipped to an l2-norm of
+  max_norm if provided.
 
   Args:
-    params: A single tensor representing the complete embedding tensor,
-      or a list of P tensors all of same shape except for the first dimension,
-      representing sharded embedding tensors.  Alternatively, a
-      `PartitionedVariable`, created by partitioning along dimension 0. Each
-      element must be appropriately sized for the given `partition_strategy`.
-    ids: A `Tensor` with type `int32` or `int64` containing the ids to be looked
-      up in `params`.
-    partition_strategy: A string specifying the partitioning strategy, relevant
-      if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
-      is `"mod"`.
+    params: A `Tensor` of embeddings.
+    ids: A `Tensor` indexing the embeddings to be retrieved from `params`.
+    max_norm: If provided, embedding values are l2-normalized to the value of
+      max_norm.
     name: A name for the operation (optional).
-    validate_indices: DEPRECATED. If this operation is assigned to CPU, values
-      in `indices` are always validated to be within range.  If assigned to GPU,
-      out-of-bound indices result in safe but unspecified behavior, which may
-      include raising an error.
-    max_norm: If not None, embedding values are l2-normalized to the value of
-     max_norm.
 
   Returns:
-    A `Tensor` with the same type as the tensors in `params`.
+    A `Tensor` with the same type as `params`.
+  """
+  if isinstance(params, resource_variable_ops.ResourceVariable):
+    embs = params.sparse_read(ids, name=name)
+  else:
+    embs = array_ops.gather(params, ids, name=name)
+  if max_norm is None:
+    return embs
+  static = True
+  ids_rank = ops.convert_to_tensor(ids).get_shape().ndims
+  if ids_rank is None:
+    ids_rank = array_ops.rank(ids)
+    static = False
+  embs_rank = embs.get_shape().ndims
+  if embs_rank is None:
+    embs_rank = array_ops.rank(embs)
+    static = False
+  return clip_ops.clip_by_norm(
+      embs,
+      max_norm,
+      axes=list(range(ids_rank, embs_rank))
+      if static else math_ops.range(ids_rank, embs_rank))
+
+
+def _embedding_lookup_and_transform(params,
+                                    ids,
+                                    partition_strategy="mod",
+                                    name=None,
+                                    max_norm=None,
+                                    transform_fn=None):
+  """Helper function for embedding_lookup and _compute_sampled_logits.
+
+  This function is a generalization of embedding_lookup that optionally
+  applies a caller-specified transformation to each embedding. This is
+  done through the `transform_fn` argument. If provided, the function is
+  applied to each partitioned tensor of retrieved embeddings, colocated
+  with the embeddings. This function will be called with a single `Tensor`
+  argument of the same type as the `params` tensor and should return a
+  `Tensor`. The shape of the argument will be the same as `params` except
+  for the size of the first dimension. The first dimension of the result's
+  shape must be the same size as the argument's.
 
+  Args:
+    params: See embedding_lookup.
+    ids: See embedding_lookup.
+    partition_strategy: See embedding_lookup.
+    name: See embedding_lookup.
+    max_norm: See embedding_lookup.
+    transform_fn: An optional function to apply to each retrieved embedding.
+
+  Returns:
+    See embedding_lookup for details.
   Raises:
     ValueError: If `params` is empty.
   """
@@ -104,35 +111,25 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
   if not isinstance(params, list):
     params = [params]
 
-  def maybe_normalize(x):
-    """Normalizes the embeddings in x if max_norm is not None."""
-    if max_norm is None:
-      return x
-    static = True
-    ids_rank = ops.convert_to_tensor(ids).get_shape().ndims
-    if ids_rank is None:
-      ids_rank = array_ops.rank(ids)
-      static = False
-    x_rank = x.get_shape().ndims
-    if x_rank is None:
-      x_rank = array_ops.rank(x)
-      static = False
-    return clip_ops.clip_by_norm(
-        x, max_norm,
-        axes=list(range(ids_rank, x_rank)) if static
-        else math_ops.range(ids_rank, x_rank))
-
   with ops.name_scope(name, "embedding_lookup", params + [ids]) as name:
     np = len(params)  # Number of partitions
     # Preserve the resource variable status to avoid accidental dense reads.
-    if not any(isinstance(p, resource_variable_ops.ResourceVariable)
-               for p in params):
+    if not any(
+        isinstance(p, resource_variable_ops.ResourceVariable) for p in params):
       params = ops.convert_n_to_tensor_or_indexed_slices(params, name="params")
-    if np == 1:
+    ids = ops.convert_to_tensor(ids, name="ids")
+    if np == 1 and (transform_fn is None or ids.get_shape().ndims == 1):
       with ops.colocate_with(params[0]):
-        return maybe_normalize(_do_gather(params[0], ids, name=name))
+        result = _gather_and_clip(params[0], ids, max_norm, name=name)
+        if transform_fn is not None:
+          result = transform_fn(result)
+        return result
     else:
-      ids = ops.convert_to_tensor(ids, name="ids")
+      # Flatten the ids. There are two cases where we need to do this.
+      # - There is more than one params tensor.
+      # - There is a transform_fn and ids is not statically known to be 1-D.
+      #   We must flatten in this case because transform_fn expects a flat
+      #   a flat tensor of embeddings.
       flat_ids = array_ops.reshape(ids, [-1])
       original_indices = math_ops.range(array_ops.size(flat_ids))
 
@@ -167,13 +164,12 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
             (flat_ids - extras) // ids_per_partition)
 
         # Emulate a conditional using a boolean indicator tensor
-        is_in_first_extras_partitions = math_ops.cast(
-            p_assignments < extras, flat_ids.dtype)
-        new_ids = (
-            is_in_first_extras_partitions * (
-                flat_ids % (ids_per_partition + 1)) +
-            (1 - is_in_first_extras_partitions) * (
-                (flat_ids - extras) % ids_per_partition))
+        is_in_first_extras_partitions = math_ops.cast(p_assignments < extras,
+                                                      flat_ids.dtype)
+        new_ids = (is_in_first_extras_partitions * (flat_ids %
+                                                    (ids_per_partition + 1)) +
+                   (1 - is_in_first_extras_partitions) *
+                   ((flat_ids - extras) % ids_per_partition))
       else:
         raise ValueError("Unrecognized partition strategy: " +
                          partition_strategy)
@@ -190,36 +186,117 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
       partitioned_result = []
       for p in xrange(np):
         with ops.colocate_with(params[p]):
-          partitioned_result.append(_do_gather(params[p], gather_ids[p]))
+          result = _gather_and_clip(params[p], gather_ids[p], max_norm)
+          if transform_fn is not None:
+            result = transform_fn(result)
+          partitioned_result.append(result)
       # Stitch these back together
-      ret = data_flow_ops.dynamic_stitch(pindices, partitioned_result,
-                                         name=name)
-      # Reshape to reverse the flattening of ids.
-      element_shape = params[0].get_shape()[1:]
-      for p in params[1:]:
-        element_shape = element_shape.merge_with(p.get_shape()[1:])
-      if element_shape.is_fully_defined():
-        ret = array_ops.reshape(ret,
-                                array_ops.concat(
-                                    [array_ops.shape(ids), element_shape], 0))
+      ret = data_flow_ops.dynamic_stitch(
+          pindices, partitioned_result, name=name)
+
+      # Determine the static element shape.
+      if transform_fn is None:
+        element_shape_s = params[0].get_shape()[1:]
+        for p in params[1:]:
+          element_shape_s = element_shape_s.merge_with(p.get_shape()[1:])
       else:
+        element_shape_s = ret.get_shape()[1:]
+
+      # Compute the dynamic element shape.
+      if element_shape_s.is_fully_defined():
+        element_shape_d = element_shape_s
+      elif transform_fn is None:
         # It's important that we compute params[0].shape on the right device
         # to avoid data motion.
         with ops.colocate_with(params[0]):
           params_shape = array_ops.shape(params[0])
-        ret = array_ops.reshape(ret,
-                                array_ops.concat([
-                                    array_ops.shape(ids),
-                                    array_ops.slice(params_shape, [1], [-1])
-                                ], 0))
-      # output shape = ids.shape + params[*].shape[1:]
+        element_shape_d = params_shape[1:]
+      else:
+        element_shape_d = array_ops.shape(ret)[1:]
+
+      # Reshape to reverse the flattening of ids.
+      ret = array_ops.reshape(ret,
+                              array_ops.concat(
+                                  [array_ops.shape(ids), element_shape_d], 0))
+
       # Normally the reshape is sufficient, but setting shape explicitly
-      # teaches shape inference that params[1:].get_shape() matters.
-      ret.set_shape(ids.get_shape().concatenate(element_shape))
-      return maybe_normalize(ret)
+      # teaches shape inference that params[1:].get_shape() matters
+      # (in the case that transform_fn is None).
+      ret.set_shape(ids.get_shape().concatenate(element_shape_s))
+      return ret
+
+
+def embedding_lookup(
+    params,
+    ids,
+    partition_strategy="mod",
+    name=None,
+    validate_indices=True,  # pylint: disable=unused-argument
+    max_norm=None):
+  """Looks up `ids` in a list of embedding tensors.
+
+  This function is used to perform parallel lookups on the list of
+  tensors in `params`.  It is a generalization of
+  @{tf.gather}, where `params` is
+  interpreted as a partitioning of a large embedding tensor.  `params` may be
+  a `PartitionedVariable` as returned by using `tf.get_variable()` with a
+  partitioner.
+
+  If `len(params) > 1`, each element `id` of `ids` is partitioned between
+  the elements of `params` according to the `partition_strategy`.
+  In all strategies, if the id space does not evenly divide the number of
+  partitions, each of the first `(max_id + 1) % len(params)` partitions will
+  be assigned one more id.
+
+  If `partition_strategy` is `"mod"`, we assign each id to partition
+  `p = id % len(params)`. For instance,
+  13 ids are split across 5 partitions as:
+  `[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
+
+  If `partition_strategy` is `"div"`, we assign ids to partitions in a
+  contiguous manner. In this case, 13 ids are split across 5 partitions as:
+  `[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`
+
+  The results of the lookup are concatenated into a dense
+  tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
+
+  Args:
+    params: A single tensor representing the complete embedding tensor,
+      or a list of P tensors all of same shape except for the first dimension,
+      representing sharded embedding tensors.  Alternatively, a
+      `PartitionedVariable`, created by partitioning along dimension 0. Each
+      element must be appropriately sized for the given `partition_strategy`.
+    ids: A `Tensor` with type `int32` or `int64` containing the ids to be looked
+      up in `params`.
+    partition_strategy: A string specifying the partitioning strategy, relevant
+      if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
+      is `"mod"`.
+    name: A name for the operation (optional).
+    validate_indices: DEPRECATED. If this operation is assigned to CPU, values
+      in `indices` are always validated to be within range.  If assigned to GPU,
+      out-of-bound indices result in safe but unspecified behavior, which may
+      include raising an error.
+    max_norm: If provided, embedding values are l2-normalized to the value of
+      max_norm.
 
+  Returns:
+    A `Tensor` with the same type as the tensors in `params`.
 
-def embedding_lookup_sparse(params, sp_ids, sp_weights,
+  Raises:
+    ValueError: If `params` is empty.
+  """
+  return _embedding_lookup_and_transform(
+      params=params,
+      ids=ids,
+      partition_strategy=partition_strategy,
+      name=name,
+      max_norm=max_norm,
+      transform_fn=None)
+
+
+def embedding_lookup_sparse(params,
+                            sp_ids,
+                            sp_weights,
                             partition_strategy="mod",
                             name=None,
                             combiner=None,
@@ -254,7 +331,7 @@ def embedding_lookup_sparse(params, sp_ids, sp_weights,
       "mean" is the weighted sum divided by the total weight.
       "sqrtn" is the weighted sum divided by the square root of the sum of the
       squares of the weights.
-    max_norm: If not None, each embedding is normalized to have l2 norm equal
+    max_norm: If provided, each embedding is normalized to have l2 norm equal
       to max_norm before combining.
 
   Returns:
@@ -349,8 +426,9 @@ def embedding_lookup_sparse(params, sp_ids, sp_weights,
       # Set the weight shape, since after reshaping to bcast_weights_shape,
       # the shape becomes None.
       if embeddings.get_shape().ndims is not None:
-        weights.set_shape(orig_weights_shape.concatenate(
-            [1 for _ in range(embeddings.get_shape().ndims - 1)]))
+        weights.set_shape(
+            orig_weights_shape.concatenate(
+                [1 for _ in range(embeddings.get_shape().ndims - 1)]))
 
       embeddings *= weights
 
@@ -371,14 +449,14 @@ def embedding_lookup_sparse(params, sp_ids, sp_weights,
     else:
       assert idx is not None
       if combiner == "sum":
-        embeddings = math_ops.sparse_segment_sum(embeddings, idx, segment_ids,
-                                                 name=name)
+        embeddings = math_ops.sparse_segment_sum(
+            embeddings, idx, segment_ids, name=name)
       elif combiner == "mean":
-        embeddings = math_ops.sparse_segment_mean(embeddings, idx, segment_ids,
-                                                  name=name)
+        embeddings = math_ops.sparse_segment_mean(
+            embeddings, idx, segment_ids, name=name)
       elif combiner == "sqrtn":
-        embeddings = math_ops.sparse_segment_sqrt_n(embeddings, idx,
-                                                    segment_ids, name=name)
+        embeddings = math_ops.sparse_segment_sqrt_n(
+            embeddings, idx, segment_ids, name=name)
       else:
         assert False, "Unrecognized combiner"
 
diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 254a8432d3..5aebdb5cd0 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -97,9 +97,11 @@ def log_poisson_loss(targets, log_input, compute_full_loss=False, name=None):
     return result
 
 
-def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid-name
-                                      labels=None, logits=None,
-                                      name=None):
+def sigmoid_cross_entropy_with_logits(  # pylint: disable=invalid-name
+    _sentinel=None,
+    labels=None,
+    logits=None,
+    name=None):
   """Computes sigmoid cross entropy given `logits`.
 
   Measures the probability error in discrete classification tasks in which each
@@ -143,8 +145,8 @@ def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid
     ValueError: If `logits` and `labels` do not have the same shape.
   """
   # pylint: disable=protected-access
-  nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits",
-                           _sentinel, labels, logits)
+  nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits", _sentinel,
+                           labels, logits)
   # pylint: enable=protected-access
 
   with ops.name_scope(name, "logistic_loss", [logits, labels]) as name:
@@ -153,8 +155,8 @@ def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid
     try:
       labels.get_shape().merge_with(logits.get_shape())
     except ValueError:
-      raise ValueError("logits and labels must have the same shape (%s vs %s)"
-                       % (logits.get_shape(), labels.get_shape()))
+      raise ValueError("logits and labels must have the same shape (%s vs %s)" %
+                       (logits.get_shape(), labels.get_shape()))
 
     # The logistic loss formula from above is
     #   x - x * z + log(1 + exp(-x))
@@ -168,9 +170,10 @@ def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid
     cond = (logits >= zeros)
     relu_logits = array_ops.where(cond, logits, zeros)
     neg_abs_logits = array_ops.where(cond, -logits, logits)
-    return math_ops.add(relu_logits - logits * labels,
-                        math_ops.log1p(math_ops.exp(neg_abs_logits)),
-                        name=name)
+    return math_ops.add(
+        relu_logits - logits * labels,
+        math_ops.log1p(math_ops.exp(neg_abs_logits)),
+        name=name)
 
 
 def weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None):
@@ -226,8 +229,9 @@ def weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None):
     try:
       targets.get_shape().merge_with(logits.get_shape())
     except ValueError:
-      raise ValueError("logits and targets must have the same shape (%s vs %s)"
-                       % (logits.get_shape(), targets.get_shape()))
+      raise ValueError(
+          "logits and targets must have the same shape (%s vs %s)" %
+          (logits.get_shape(), targets.get_shape()))
 
     # The logistic loss formula from above is
     #   (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))
@@ -569,9 +573,10 @@ def normalize_moments(counts, mean_ss, variance_ss, shift, name=None):
     else:  # no shift.
       shifted_mean = math_ops.multiply(mean_ss, divisor, name="mean")
       mean = shifted_mean
-    variance = math_ops.subtract(math_ops.multiply(variance_ss, divisor),
-                                 math_ops.square(shifted_mean),
-                                 name="variance")
+    variance = math_ops.subtract(
+        math_ops.multiply(variance_ss, divisor),
+        math_ops.square(shifted_mean),
+        name="variance")
   return (mean, variance)
 
 
@@ -876,6 +881,70 @@ def _sum_rows(x):
   return array_ops.reshape(math_ops.matmul(x, ones), [-1])
 
 
+def _retrieve_weights(weights, inputs, labels_flat, sampled, all_ids,
+                      partition_strategy, colocate_logits):
+  """Helper function for _compute_sampled_logits.
+
+  Retrieves the true weights and the logits of the sampled weights.
+
+  Args:
+    weights: See _compute_sampled_logits.
+    inputs: See _compute_sampled_logits.
+    labels_flat: A [batch_size * num_true] tensor.
+    sampled: A [num_sampled] tensor.
+    all_ids: The concatenation of labels_flat and sampled.
+    partition_strategy: See _compute_sampled_logits.
+    colocate_logits: See _compute_sampled_logits.
+
+  Returns:
+    A pair of a [batch_size * num_true, dim] tensor of the true weights
+    and a [batch_size, num_sampled] tensor of the sampled logits.
+  """
+  if len(weights) > 1 and colocate_logits:
+    # This codepath makes two calls to lookup the embeddings, one for the true
+    # weights and one to compute the sampled logits. Because we take this
+    # codepath only if len(weights) > 1, we will only incur the double-lookup
+    # cost if we can benefit from the co-located logit computation.
+
+    true_w = embedding_ops.embedding_lookup(
+        weights, labels_flat, partition_strategy=partition_strategy)
+
+    def logit(embeddings):
+      return math_ops.matmul(embeddings, inputs, transpose_b=True)
+
+    # pylint: disable=protected-access
+    sampled_logits = embedding_ops._embedding_lookup_and_transform(
+        weights,
+        sampled,
+        partition_strategy=partition_strategy,
+        transform_fn=logit)
+    # pylint: enable=protected-access
+    sampled_logits = array_ops.transpose(sampled_logits)
+
+  else:
+    # This codepath retrieves the weights by performing a single call to
+    # embedding_lookup and slicing out the true weights and sampled weights.
+    # Because the call to embedding_lookup is shared, the sampled logit
+    # computation is done here after the slice.
+
+    # weights shape is [num_classes, dim]
+    all_w = embedding_ops.embedding_lookup(
+        weights, all_ids, partition_strategy=partition_strategy)
+
+    true_w = array_ops.slice(all_w, [0, 0],
+                             array_ops.stack(
+                                 [array_ops.shape(labels_flat)[0], -1]))
+
+    sampled_w = array_ops.slice(
+        all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
+    # inputs has shape [batch_size, dim]
+    # sampled_w has shape [num_sampled, dim]
+    # Apply X*W', which yields [batch_size, num_sampled]
+    sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)
+
+  return true_w, sampled_logits
+
+
 def _compute_sampled_logits(weights,
                             biases,
                             labels,
@@ -887,6 +956,7 @@ def _compute_sampled_logits(weights,
                             subtract_log_q=True,
                             remove_accidental_hits=False,
                             partition_strategy="mod",
+                            colocate_logits=False,
                             name=None):
   """Helper function for nce_loss and sampled_softmax_loss functions.
 
@@ -924,6 +994,9 @@ def _compute_sampled_logits(weights,
     partition_strategy: A string specifying the partitioning strategy, relevant
         if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
         Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
+    colocate_logits: A `bool`, relevant if `len(weights) > 1`, specifying
+        whether to colocate the computation of the sampled logits with the
+        weights.
     name: A name for the operation (optional).
   Returns:
     out_logits, out_labels: `Tensor` objects each with shape
@@ -965,16 +1038,21 @@ def _compute_sampled_logits(weights,
     # sampled is a [num_sampled] int tensor
     all_ids = array_ops.concat([labels_flat, sampled], 0)
 
-    # weights shape is [num_classes, dim]
-    all_w = embedding_ops.embedding_lookup(
-        weights, all_ids, partition_strategy=partition_strategy)
+    # Retrieve the true weights and the logits of the sampled weights.
+    # - true_w shape is [batch_size * num_true, dim]
+    # - sampled_logits shape is [batch_size, num_sampled]
+    true_w, sampled_logits = _retrieve_weights(
+        weights, inputs, labels_flat, sampled, all_ids, partition_strategy,
+        colocate_logits)
+
+    # Retrieve the true and sampled biases, compute the true logits, and
+    # add the biases to the true and sampled logits.
     all_b = embedding_ops.embedding_lookup(
         biases, all_ids, partition_strategy=partition_strategy)
-    # true_w shape is [batch_size * num_true, dim]
     # true_b is a [batch_size * num_true] tensor
-    true_w = array_ops.slice(
-        all_w, [0, 0], array_ops.stack([array_ops.shape(labels_flat)[0], -1]))
+    # sampled_b is a [num_sampled] float tensor
     true_b = array_ops.slice(all_b, [0], array_ops.shape(labels_flat))
+    sampled_b = array_ops.slice(all_b, array_ops.shape(labels_flat), [-1])
 
     # inputs shape is [batch_size, dim]
     # true_w shape is [batch_size * num_true, dim]
@@ -991,20 +1069,7 @@ def _compute_sampled_logits(weights,
     true_logits = array_ops.reshape(_sum_rows(dots_as_matrix), [-1, num_true])
     true_b = array_ops.reshape(true_b, [-1, num_true])
     true_logits += true_b
-
-    # Lookup weights and biases for sampled labels.
-    #   sampled_w shape is [num_sampled, dim]
-    #   sampled_b is a [num_sampled] float tensor
-    sampled_w = array_ops.slice(
-        all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
-    sampled_b = array_ops.slice(all_b, array_ops.shape(labels_flat), [-1])
-
-    # inputs has shape [batch_size, dim]
-    # sampled_w has shape [num_sampled, dim]
-    # sampled_b has shape [num_sampled]
-    # Apply X*W'+B, which yields [batch_size, num_sampled]
-    sampled_logits = math_ops.matmul(
-        inputs, sampled_w, transpose_b=True) + sampled_b
+    sampled_logits += sampled_b
 
     if remove_accidental_hits:
       acc_hits = candidate_sampling_ops.compute_accidental_hits(
@@ -1019,8 +1084,8 @@ def _compute_sampled_logits(weights,
                                         "sparse_indices")
       # Create sampled_logits_shape = [batch_size, num_sampled]
       sampled_logits_shape = array_ops.concat(
-          [array_ops.shape(labels)[:1], array_ops.expand_dims(num_sampled, 0)],
-          0)
+          [array_ops.shape(labels)[:1],
+           array_ops.expand_dims(num_sampled, 0)], 0)
       if sampled_logits.dtype != acc_weights.dtype:
         acc_weights = math_ops.cast(acc_weights, sampled_logits.dtype)
       sampled_logits += sparse_ops.sparse_to_dense(
@@ -1148,6 +1213,7 @@ def nce_loss(weights,
       subtract_log_q=True,
       remove_accidental_hits=remove_accidental_hits,
       partition_strategy=partition_strategy,
+      colocate_logits=False,
       name=name)
   sampled_losses = sigmoid_cross_entropy_with_logits(
       labels=labels, logits=logits, name="sampled_losses")
@@ -1244,8 +1310,9 @@ def sampled_softmax_loss(weights,
       subtract_log_q=True,
       remove_accidental_hits=remove_accidental_hits,
       partition_strategy=partition_strategy,
+      colocate_logits=False,
       name=name)
-  sampled_losses = nn_ops.softmax_cross_entropy_with_logits(labels=labels,
-                                                            logits=logits)
+  sampled_losses = nn_ops.softmax_cross_entropy_with_logits(
+      labels=labels, logits=logits)
   # sampled_losses is a [batch_size] tensor.
   return sampled_losses
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index 5cf8c3291c..84c5f4527d 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -136,9 +136,8 @@ class LogPoissonLossTest(test_lib.TestCase):
           z_np, x_tf, compute_full_loss=True)
       err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
                                                     x_shape)
-      err_stirling = gradient_checker.compute_gradient_error(x_tf, x_shape,
-                                                             y_tf_stirling,
-                                                             x_shape)
+      err_stirling = gradient_checker.compute_gradient_error(
+          x_tf, x_shape, y_tf_stirling, x_shape)
     eps = 1e-6
     self.assertLess(err, eps)
     self.assertLess(err_stirling, eps)
@@ -404,273 +403,362 @@ class DropoutTest(test_lib.TestCase):
 class ComputeSampledLogitsTest(test_lib.TestCase):
 
   def setUp(self):
-    self._num_classes = 5
-    self._dim = 10
-    self._batch_size = 3
-    self._num_shards = 3
+    self._eps = 1e-3
+
+  def _GenerateTestData(self, num_classes, dim, batch_size, num_true, labels,
+                        sampled, subtract_log_q):
+    """Randomly generates input/output data for a single test case.
+
+    This function returns numpy constants for use in a test case.
+
+    Args:
+      num_classes: An int. The number of embedding classes in the test case.
+      dim: An int. The dimension of the embedding.
+      batch_size: An int. The batch size.
+      num_true: An int. The number of target classes per training example.
+      labels: A list of batch_size * num_true ints. The target classes.
+      sampled: A list of indices in [0, num_classes).
+      subtract_log_q: A bool corresponding to the parameter in
+          _compute_sampled_logits().
+
+    Returns:
+      weights: Embedding weights to use as test input. It is a numpy array
+          of shape [num_classes, dim]
+      biases: Embedding biases to use as test input. It is a numpy array
+          of shape [num_classes].
+      hidden_acts: Forward activations of the network to use as test input.
+          It is a numpy array of shape [batch_size, dim].
+      sampled_vals: A tuple based on `sampled` to use as test input in the
+          format returned by a *_candidate_sampler function.
+      exp_logits: The output logits expected from _compute_sampled_logits().
+          It is a numpy array of shape [batch_size, num_true + len(sampled)].
+      exp_labels: The output labels expected from _compute_sampled_logits().
+          It is a numpy array of shape [batch_size, num_true + len(sampled)].
+    """
+    weights = np.random.randn(num_classes, dim).astype(np.float32)
+    biases = np.random.randn(num_classes).astype(np.float32)
+    hidden_acts = np.random.randn(batch_size, dim).astype(np.float32)
+
+    true_exp = np.full([batch_size, 1], fill_value=0.5, dtype=np.float32)
+    sampled_exp = np.full([len(sampled)], fill_value=0.5, dtype=np.float32)
+    sampled_vals = (sampled, true_exp, sampled_exp)
 
-  def _GenerateTestInputs(self):
-    np.random.seed(0)
-    weights = np.random.randn(self._num_classes, self._dim).astype(np.float32)
-    biases = np.random.randn(self._num_classes).astype(np.float32)
-    hidden_acts = np.random.randn(self._batch_size,
-                                  self._dim).astype(np.float32)
+    sampled_w, sampled_b = weights[sampled], biases[sampled]
+    true_w, true_b = weights[labels], biases[labels]
 
+    true_logits = np.sum(
+        hidden_acts.reshape((batch_size, 1, dim)) * true_w.reshape(
+            (batch_size, num_true, dim)),
+        axis=2)
+    true_b = true_b.reshape((batch_size, num_true))
+    true_logits += true_b
+    sampled_logits = np.dot(hidden_acts, sampled_w.T) + sampled_b
+
+    if subtract_log_q:
+      true_logits -= np.log(true_exp)
+      sampled_logits -= np.log(sampled_exp[np.newaxis, :])
+
+    exp_logits = np.concatenate([true_logits, sampled_logits], axis=1)
+    exp_labels = np.hstack((np.ones_like(true_logits) / num_true,
+                            np.zeros_like(sampled_logits)))
+
+    return weights, biases, hidden_acts, sampled_vals, exp_logits, exp_labels
+
+  def _ShardTestEmbeddings(self, weights, biases, num_shards):
+    """Shards the weights and biases returned by _GenerateTestData.
+
+    Args:
+      weights: The weights returned by _GenerateTestData.
+      biases: The biases returned by _GenerateTestData.
+      num_shards: The number of shards to create.
+
+    Returns:
+      sharded_weights: A list of size `num_shards` containing all the weights.
+      sharded_biases: A list of size `num_shards` containing all the biases.
+    """
     with ops.Graph().as_default() as g:
       sharded_weights = variable_scope.get_variable(
           "w",
-          partitioner=partitioned_variables.fixed_size_partitioner(
-              self._num_shards),
+          partitioner=partitioned_variables.fixed_size_partitioner(num_shards),
           initializer=constant_op.constant(weights))
       sharded_biases = variable_scope.get_variable(
           "b",
-          partitioner=partitioned_variables.fixed_size_partitioner(
-              self._num_shards),
+          partitioner=partitioned_variables.fixed_size_partitioner(num_shards),
           initializer=constant_op.constant(biases))
       with self.test_session(graph=g) as sess:
         variables.global_variables_initializer().run()
+        return sess.run([list(sharded_weights), list(sharded_biases)])
 
-        sharded_weights_v, sharded_biases_v = sess.run(
-            [list(sharded_weights), list(sharded_biases)])
-
-    return weights, biases, hidden_acts, sharded_weights_v, sharded_biases_v
-
-  def _ComputeSampledLogitsNP(self,
-                              true_w,
-                              true_b,
-                              sampled_w,
-                              sampled_b,
-                              hidden_acts,
-                              num_true=1,
-                              true_expected=None,
-                              sampled_expected=None):
-
-    batch_size, dim = hidden_acts.shape
-    true_logits = np.sum(hidden_acts.reshape(
-        (batch_size, 1, dim)) * true_w.reshape((batch_size, num_true, dim)),
-                         axis=2)
-    true_b = true_b.reshape((batch_size, num_true))
-    true_logits += true_b
-    sampled_logits = np.dot(hidden_acts, sampled_w.T) + sampled_b
-
-    if true_expected is not None:
-      true_logits -= np.log(true_expected)
-    if sampled_expected is not None:
-      sampled_logits -= np.log(sampled_expected[np.newaxis, :])
-
-    out_logits = np.concatenate([true_logits, sampled_logits], axis=1)
-    out_labels = np.hstack((np.ones_like(true_logits) / num_true,
-                            np.zeros_like(sampled_logits)))
-
-    return out_logits, out_labels
-
-  def _ComputeSampledLogitsTF(self,
-                              weights,
-                              biases,
-                              hidden_acts,
-                              labels,
-                              num_sampled,
-                              num_classes,
-                              num_true,
-                              sampled_vals,
-                              subtract_log_q,
-                              remove_accidental_hits,
-                              name="sampled_loss_TF"):
-    # Should be called from within a `with test_session():` block
-    if isinstance(weights, list):
-      weights_tf = [constant_op.constant(shard) for shard in weights]
-    else:
-      weights_tf = constant_op.constant(weights)
-    if isinstance(biases, list):
-      biases_tf = [constant_op.constant(shard) for shard in biases]
-    else:
-      biases_tf = constant_op.constant(biases)
-    hidden_acts_tf = constant_op.constant(
-        hidden_acts, shape=(self._batch_size, self._dim))
-    labels_tf = constant_op.constant(
-        labels, dtype=dtypes.int64, shape=(self._batch_size, num_true))
-
-    pred_logits_tf, pred_labels_tf = _compute_sampled_logits(
-        weights_tf,
-        biases_tf,
-        labels_tf,
-        hidden_acts_tf,
-        num_sampled,
-        num_classes,
-        num_true,
-        sampled_vals,
-        subtract_log_q=subtract_log_q,
-        remove_accidental_hits=remove_accidental_hits,
-        name=name,
-        partition_strategy="div")
-    return pred_logits_tf, pred_labels_tf
-
-  def testComputeSampledLogitsShapes(self):
-    # We just check that the shapes of the returned values are correct.
-    weights, biases, hidden_acts, _, _ = self._GenerateTestInputs()
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = sampled_exp = [1., 1., 1., 1.]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-
+  def testShapes(self):
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     with self.test_session() as sess:
-      for num_true_test in range(1, 5):
+      for num_true in range(1, 5):
         labels = np.random.randint(
-            low=0,
-            high=self._num_classes,
-            size=self._batch_size * num_true_test)
-        true_w, true_b = weights[labels], biases[labels]
-
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test)
-
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
-            remove_accidental_hits=True,
-            subtract_log_q=False)
-
-      logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-      self.assertEqual(logits_np.shape, logits_tf_val.shape)
-      self.assertEqual(labels_np.shape, labels_tf_val.shape)
-
-  def testComputeSampledLogitsValues(self):
-    # Here we check the actual numerics.
-    weights, biases, hidden_acts, sharded_weights, sharded_biases = (
-        self._GenerateTestInputs())
-    eps = 1e-3
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = np.empty([self._batch_size, 1], dtype=np.float32)
-    true_exp.fill(0.5)
-    sampled_exp = np.empty([num_sampled], dtype=np.float32)
-    sampled_exp.fill(0.5)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
-
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
+            subtract_log_q=False,
+            remove_accidental_hits=False,
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_basic_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertEqual(exp_logits.shape, got_logits.shape, self._eps)
+        self.assertEqual(exp_labels.shape, got_labels.shape, self._eps)
+
+  def testBasic(self):
+    """Without accidental hit removal or subtract_log_q."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     with self.test_session() as sess:
-      for num_true_test in range(1, 5):
-        # Generate test data for this run
+      for num_true in range(1, 5):
         labels = np.random.randint(
-            low=0,
-            high=self._num_classes,
-            size=self._batch_size * num_true_test)
-        true_w, true_b = weights[labels], biases[labels]
-
-        # Test 1: Without accidental hit removal or subtract_log_q
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test)
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=False,
             remove_accidental_hits=False,
-            name="sampled_loss_test1_num_true%d" % num_true_test)
-
-        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-        self.assertAllClose(logits_np, logits_tf_val, eps)
-        self.assertAllClose(labels_np, labels_tf_val, eps)
-
-        # Test 2: With accidental hit removal, no subtract_log_q
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_basic_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testAccidentalHitRemoval(self):
+    """With accidental hit removal, no subtract_log_q."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    sampled = [1, 0, 2, 3]
+    with self.test_session():
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, _,
+         _) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=sampled,
+             subtract_log_q=False)
+        logits_tensor, _ = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=len(sampled),
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=False,
             remove_accidental_hits=True,
-            name="sampled_loss_test2_num_true%d" % num_true_test)
-
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_accidental_hit_removal_num_true_%d" % num_true)
         # Test that the exponentiated logits of accidental hits are near 0.
         # First we need to find the hits in this random test run:
-        labels_reshape = labels.reshape((self._batch_size, num_true_test))
-        logits_tf_np = logits_tf.eval()
-        for row in xrange(self._batch_size):
+        labels_reshape = labels.reshape((batch_size, num_true))
+        got_logits = logits_tensor.eval()
+        for row in xrange(batch_size):
           row_labels = labels_reshape[row, :]
-          for col in xrange(num_sampled):
+          for col in xrange(len(sampled)):
             if sampled[col] in row_labels:
               # We need to add the num_true_test offset into logits_*
               self.assertNear(
-                  np.exp(logits_tf_np[row, col + num_true_test]), 0., eps)
-
-        # Test 3: With subtract_log_q, no accidental hit removal
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test,
-            true_expected=true_exp,
-            sampled_expected=sampled_exp)
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+                  np.exp(got_logits[row, col + num_true]), 0., self._eps)
+
+  def testSubtractLogQ(self):
+    """With subtract_log_q, no accidental hit removal."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=True)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=True,
             remove_accidental_hits=False,
-            name="sampled_loss_test3_num_true%d" % num_true_test)
-
-        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-        self.assertAllClose(logits_np, logits_tf_val, eps)
-        self.assertAllClose(labels_np, labels_tf_val, eps)
-
-        # Test 4: Test 1, with sharded weights and sharded biases.
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test)
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            sharded_weights,
-            sharded_biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_subtract_log_q_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testSharded(self):
+    """With sharded weights and sharded biases."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        weight_shards, bias_shards = self._ShardTestEmbeddings(
+            weights, biases, num_shards=3)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=[constant_op.constant(shard) for shard in weight_shards],
+            biases=[constant_op.constant(shard) for shard in bias_shards],
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=False,
             remove_accidental_hits=False,
-            name="sampled_loss_test1_num_true%d" % num_true_test)
-
-        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-        self.assertAllClose(logits_np, logits_tf_val, eps)
-        self.assertAllClose(labels_np, labels_tf_val, eps)
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_sharded_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testColocatedLogits(self):
+    """With colocated logit computation."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
+            subtract_log_q=False,
+            remove_accidental_hits=False,
+            partition_strategy="div",
+            colocate_logits=True,
+            name="sampled_logits_colocated_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testShardedColocatedLogits(self):
+    """Sharded weights and biases and with colocated logit computation."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        weight_shards, bias_shards = self._ShardTestEmbeddings(
+            weights, biases, num_shards=3)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=[constant_op.constant(shard) for shard in weight_shards],
+            biases=[constant_op.constant(shard) for shard in bias_shards],
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
+            subtract_log_q=False,
+            remove_accidental_hits=False,
+            partition_strategy="div",
+            colocate_logits=True,
+            name="sampled_logits_sharded_colocated_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
 
   def testNCELoss(self):
     # A simple test to verify the numerics.
@@ -683,62 +771,51 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
       pred = np.minimum(np.maximum(pred, eps), 1 - eps)
       return -targets * np.log(pred) - (1. - targets) * np.log(1. - pred)
 
-    weights, biases, hidden_acts, sharded_weights, sharded_biases = (
-        self._GenerateTestInputs())
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     labels = [0, 1, 2]
-    true_w, true_b = weights[labels], biases[labels]
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = np.empty([self._batch_size, 1], dtype=np.float32)
-    true_exp.fill(0.5)
-    sampled_exp = np.empty([num_sampled], dtype=np.float32)
-    sampled_exp.fill(0.5)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
+    (weights, biases, hidden_acts, sampled_vals, exp_logits,
+     exp_labels) = self._GenerateTestData(
+         num_classes=num_classes,
+         dim=10,
+         batch_size=batch_size,
+         num_true=1,
+         labels=labels,
+         sampled=[1, 0, 2, 3],
+         subtract_log_q=True)
+    exp_nce_loss = np.sum(
+        _SigmoidCrossEntropyWithLogits(exp_logits, exp_labels), 1)
 
     with self.test_session():
-      logits_np, labels_np = self._ComputeSampledLogitsNP(
-          true_w,
-          true_b,
-          sampled_w,
-          sampled_b,
-          hidden_acts,
-          true_expected=true_exp,
-          sampled_expected=sampled_exp)
-      nce_loss_np = np.sum(
-          _SigmoidCrossEntropyWithLogits(logits_np, labels_np), 1)
-
-      labels_tf = constant_op.constant(labels, shape=(self._batch_size, 1))
-      weights_tf = constant_op.constant(weights)
-      biases_tf = constant_op.constant(biases)
-      inputs_tf = constant_op.constant(hidden_acts)
-
-      nce_loss_tf = nn_impl.nce_loss(
-          weights_tf,
-          biases_tf,
-          labels_tf,
-          inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      got_nce_loss = nn_impl.nce_loss(
+          weights=constant_op.constant(weights),
+          biases=constant_op.constant(biases),
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           partition_strategy="div")
 
-      self.assertAllClose(nce_loss_np, nce_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_nce_loss, got_nce_loss.eval(), 1e-4)
 
       # Test with sharded weights and sharded biases.
-      nce_loss_tf = nn_impl.nce_loss(
-          sharded_weights,
-          sharded_biases,
-          labels_tf,
-          inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      weight_shards, bias_shards = self._ShardTestEmbeddings(
+          weights, biases, num_shards=3)
+      got_nce_loss = nn_impl.nce_loss(
+          weights=[constant_op.constant(shard) for shard in weight_shards],
+          biases=[constant_op.constant(shard) for shard in bias_shards],
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           partition_strategy="div")
 
-      self.assertAllClose(nce_loss_np, nce_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_nce_loss, got_nce_loss.eval(), 1e-4)
 
   def testSampledSoftmaxLoss(self):
     # A simple test to verify the numerics.
@@ -751,64 +828,55 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
       pred = stable_exp_logits / np.sum(stable_exp_logits, 1, keepdims=True)
       return -np.sum(targets * np.log(pred + 1.0e-20), axis=1)
 
-    weights, biases, hidden_acts, sharded_weights, sharded_biases = (
-        self._GenerateTestInputs())
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     labels = [0, 1, 2]
-    true_w, true_b = weights[labels], biases[labels]
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = np.full([self._batch_size, 1], fill_value=0.5, dtype=np.float32)
-    sampled_exp = np.full([num_sampled], fill_value=0.5, dtype=np.float32)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
+    (weights, biases, hidden_acts, sampled_vals, exp_logits,
+     exp_labels) = self._GenerateTestData(
+         num_classes=num_classes,
+         dim=10,
+         batch_size=batch_size,
+         num_true=1,
+         labels=labels,
+         sampled=[1, 0, 2, 3],
+         subtract_log_q=True)
+    exp_sampled_softmax_loss = _SoftmaxCrossEntropyWithLogits(
+        exp_logits, exp_labels)
 
     with self.test_session():
-      logits_np, labels_np = self._ComputeSampledLogitsNP(
-          true_w,
-          true_b,
-          sampled_w,
-          sampled_b,
-          hidden_acts,
-          true_expected=true_exp,
-          sampled_expected=sampled_exp)
-      sampled_softmax_loss_np = _SoftmaxCrossEntropyWithLogits(logits_np,
-                                                               labels_np)
-
-      labels_tf = constant_op.constant(labels, shape=(self._batch_size, 1))
-      weights_tf = constant_op.constant(weights)
-      biases_tf = constant_op.constant(biases)
-      inputs_tf = constant_op.constant(hidden_acts)
-
-      sampled_softmax_loss_tf = nn_impl.sampled_softmax_loss(
-          weights=weights_tf,
-          biases=biases_tf,
-          labels=labels_tf,
-          inputs=inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      got_sampled_softmax_loss = nn_impl.sampled_softmax_loss(
+          weights=constant_op.constant(weights),
+          biases=constant_op.constant(biases),
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           remove_accidental_hits=False,
           partition_strategy="div")
 
-      self.assertAllClose(sampled_softmax_loss_np,
-                          sampled_softmax_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_sampled_softmax_loss,
+                          got_sampled_softmax_loss.eval(), 1e-4)
 
       # Test with sharded weights and sharded biases.
-      sampled_softmax_loss_tf = nn_impl.sampled_softmax_loss(
-          weights=sharded_weights,
-          biases=sharded_biases,
-          labels=labels_tf,
-          inputs=inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      weight_shards, bias_shards = self._ShardTestEmbeddings(
+          weights, biases, num_shards=3)
+      got_sampled_softmax_loss = nn_impl.sampled_softmax_loss(
+          weights=[constant_op.constant(shard) for shard in weight_shards],
+          biases=[constant_op.constant(shard) for shard in bias_shards],
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           remove_accidental_hits=False,
           partition_strategy="div")
 
-      self.assertAllClose(sampled_softmax_loss_np,
-                          sampled_softmax_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_sampled_softmax_loss,
+                          got_sampled_softmax_loss.eval(), 1e-4)
 
 
 class CReluTest(test_lib.TestCase):
@@ -848,18 +916,16 @@ class MomentsTest(test_lib.TestCase):
       for sigma in [1.0, 0.1]:
         for keep_dims in [True, False]:
           input_values = np.random.rand(*input_shape) * sigma + mu
-          expected_mean = np.mean(input_values, axis=moments_axes,
-                                  keepdims=keep_dims)
-          expected_var = np.var(input_values, axis=moments_axes,
-                                keepdims=keep_dims)
+          expected_mean = np.mean(
+              input_values, axis=moments_axes, keepdims=keep_dims)
+          expected_var = np.var(
+              input_values, axis=moments_axes, keepdims=keep_dims)
           with ops.Graph().as_default() as g:
             with self.test_session(graph=g) as sess:
-              inputs = constant_op.constant(input_values,
-                                            shape=input_shape,
-                                            dtype=dtypes.float32)
-              mean, variance = nn_impl.moments(inputs,
-                                               moments_axes,
-                                               keep_dims=keep_dims)
+              inputs = constant_op.constant(
+                  input_values, shape=input_shape, dtype=dtypes.float32)
+              mean, variance = nn_impl.moments(
+                  inputs, moments_axes, keep_dims=keep_dims)
 
               [mean, variance] = sess.run([mean, variance])
               # Make sure that there are no NaNs
@@ -902,8 +968,8 @@ class MomentsTest(test_lib.TestCase):
     expected_var = np.var(input_values, axis=moments_axes)
 
     with self.test_session() as sess:
-      inputs = constant_op.constant(input_values, shape=input_shape,
-                                    dtype=dtypes.float32)
+      inputs = constant_op.constant(
+          input_values, shape=input_shape, dtype=dtypes.float32)
       mean, variance = nn_impl.moments(inputs, moments_axes, shift=0.0)
 
       [mean, variance] = sess.run([mean, variance])
@@ -914,5 +980,6 @@ class MomentsTest(test_lib.TestCase):
       # The variance is unstable
       self.assertGreater(np.abs(variance - expected_var), 0.1)
 
+
 if __name__ == "__main__":
   test_lib.main()
-- 
GitLab


From 0f6a65e57d6a3516456d3739933417fc88cbfcda Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 22 Jun 2017 11:45:39 -0700
Subject: [PATCH 0363/1375] Added a utility to create parsing spec for
 regressors (canned estimator)

PiperOrigin-RevId: 159855254
---
 .../python/estimator/canned/parsing_utils.py  | 153 +++++++++++++++++-
 .../estimator/canned/parsing_utils_test.py    |  96 +++++++++++
 2 files changed, 242 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/estimator/canned/parsing_utils.py b/tensorflow/python/estimator/canned/parsing_utils.py
index af584965bb..f153272947 100644
--- a/tensorflow/python/estimator/canned/parsing_utils.py
+++ b/tensorflow/python/estimator/canned/parsing_utils.py
@@ -38,9 +38,9 @@ def classifier_parse_example_spec(feature_columns,
   * Users need to combine parsing spec of features with labels and weights
     (if any) since they are all parsed from same tf.Example instance. This
     utility combines these specs.
-  * It is difficult to map expected label by a classifier such as DNNClassifier
-    to corresponding tf.parse_example spec. This utility encodes it by getting
-    related information from users (key, dtype).
+  * It is difficult to map expected label by a classifier such as
+    `DNNClassifier` to corresponding tf.parse_example spec. This utility encodes
+    it by getting related information from users (key, dtype).
 
   Example output of parsing spec:
 
@@ -82,7 +82,7 @@ def classifier_parse_example_spec(feature_columns,
 
 
   # Input builders
-  def input_fn_train():  # Returns a dictionary which also contains labels.
+  def input_fn_train():  # Returns a tuple of features and labels.
     features = tf.contrib.learn.read_keyed_batch_features(
         file_pattern=train_files,
         batch_size=batch_size,
@@ -115,8 +115,13 @@ def classifier_parse_example_spec(feature_columns,
       key 'clicked' it should count as negative example by setting
       `label_deafault=0`. Type of this value should be compatible with
       `label_dtype`.
-    weight_column: (Optional) A `tf.feature_column.numeric_column` represents
-      the weight used given classifier.
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example. If it is a string, it is
+      used as a key to fetch weight tensor from the `features`. If it is a
+      `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
+      then weight_column.normalizer_fn is applied on it to get weight tensor.
 
   Returns:
     A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature`
@@ -129,7 +134,6 @@ def classifier_parse_example_spec(feature_columns,
       instance.
     ValueError: If `weight_column` is not a `_NumericColumn` instance.
     ValueError: if label_key is None.
-    ValueError: if label_dtype is neither an integer nor string
   """
   parsing_spec = fc.make_parse_example_spec(feature_columns)
   if label_key in parsing_spec:
@@ -158,3 +162,138 @@ def classifier_parse_example_spec(feature_columns,
 
   parsing_spec.update(weight_column._parse_example_spec)  # pylint: disable=protected-access
   return parsing_spec
+
+
+def regressor_parse_example_spec(feature_columns,
+                                 label_key,
+                                 label_dtype=dtypes.float32,
+                                 label_default=None,
+                                 label_dimension=1,
+                                 weight_column=None):
+  """Generates parsing spec for tf.parse_example to be used with regressors.
+
+  If users keep data in tf.Example format, they need to call tf.parse_example
+  with a proper feature spec. There are two main things that this utility helps:
+
+  * Users need to combine parsing spec of features with labels and weights
+    (if any) since they are all parsed from same tf.Example instance. This
+    utility combines these specs.
+  * It is difficult to map expected label by a regressor such as `DNNRegressor`
+    to corresponding tf.parse_example spec. This utility encodes it by getting
+    related information from users (key, dtype).
+
+  Example output of parsing spec:
+
+  ```python
+  # Define features and transformations
+  feature_b = tf.feature_column.numeric_column(...)
+  feature_c_bucketized = tf.feature_column.bucketized_column(
+    tf.feature_column.numeric_column("feature_c"), ...)
+  feature_a_x_feature_c = tf.feature_column.crossed_column(
+      columns=["feature_a", feature_c_bucketized], ...)
+
+  feature_columns = [feature_b, feature_c_bucketized, feature_a_x_feature_c]
+  parsing_spec = tf.estimator.regressor_parse_example_spec(
+      feature_columns, label_key='my-label')
+
+  # For the above example, regressor_parse_example_spec would return the dict:
+  assert parsing_spec == {
+    "feature_a": parsing_ops.VarLenFeature(tf.string),
+    "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
+    "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+    "my-label" : parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+  }
+  ```
+
+  Example usage with a regressor:
+
+  ```python
+  feature_columns = # define features via tf.feature_column
+  estimator = DNNRegressor(
+      hidden_units=[256, 64, 16],
+      feature_columns=feature_columns,
+      weight_column='example-weight',
+      label_dimension=3)
+  # This label configuration tells the regressor the following:
+  # * weights are retrieved with key 'example-weight'
+  # * label is a 3 dimension tensor with float32 dtype.
+
+
+  # Input builders
+  def input_fn_train():  # Returns a tuple of features and labels.
+    features = tf.contrib.learn.read_keyed_batch_features(
+        file_pattern=train_files,
+        batch_size=batch_size,
+        # creates parsing configuration for tf.parse_example
+        features=tf.estimator.classifier_parse_example_spec(
+            feature_columns,
+            label_key='my-label',
+            label_dimension=3,
+            weight_column='example-weight'),
+        reader=tf.RecordIOReader)
+     labels = features.pop('my-label')
+     return features, labels
+
+  estimator.train(input_fn=input_fn_train)
+  ```
+
+  Args:
+    feature_columns: An iterable containing all feature columns. All items
+      should be instances of classes derived from `_FeatureColumn`.
+    label_key: A string identifying the label. It means tf.Example stores labels
+      with this key.
+    label_dtype: A `tf.dtype` identifies the type of labels. By default it is
+      `tf.float32`.
+    label_default: used as label if label_key does not exist in given
+      tf.Example. By default default_value is none, which means
+      `tf.parse_example` will error out if there is any missing label.
+    label_dimension: Number of regression targets per example. This is the
+      size of the last dimension of the labels and logits `Tensor` objects
+      (typically, these have shape `[batch_size, label_dimension]`).
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example. If it is a string, it is
+      used as a key to fetch weight tensor from the `features`. If it is a
+      `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
+      then weight_column.normalizer_fn is applied on it to get weight tensor.
+
+  Returns:
+    A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature`
+    value.
+
+  Raises:
+    ValueError: If label is used in `feature_columns`.
+    ValueError: If weight_column is used in `feature_columns`.
+    ValueError: If any of the given `feature_columns` is not a `_FeatureColumn`
+      instance.
+    ValueError: If `weight_column` is not a `_NumericColumn` instance.
+    ValueError: if label_key is None.
+  """
+  parsing_spec = fc.make_parse_example_spec(feature_columns)
+  if label_key in parsing_spec:
+    raise ValueError('label should not be used as feature. '
+                     'label_key: {}, features: {}'.format(
+                         label_key, parsing_spec.keys()))
+  parsing_spec[label_key] = parsing_ops.FixedLenFeature(
+      (label_dimension,), label_dtype, label_default)
+
+  if weight_column is None:
+    return parsing_spec
+
+  if isinstance(weight_column, six.string_types):
+    weight_column = fc.numeric_column(weight_column)
+
+  if not isinstance(weight_column, fc._NumericColumn):  # pylint: disable=protected-access
+    raise ValueError('weight_column should be an instance of '
+                     'tf.feature_column.numeric_column. '
+                     'Given type: {} value: {}'.format(
+                         type(weight_column), weight_column))
+
+  if weight_column.key in parsing_spec:
+    raise ValueError('weight_column should not be used as feature. '
+                     'weight_column: {}, features: {}'.format(
+                         weight_column.key, parsing_spec.keys()))
+
+  parsing_spec.update(weight_column._parse_example_spec)  # pylint: disable=protected-access
+  return parsing_spec
diff --git a/tensorflow/python/estimator/canned/parsing_utils_test.py b/tensorflow/python/estimator/canned/parsing_utils_test.py
index c83823e750..366bb104ca 100644
--- a/tensorflow/python/estimator/canned/parsing_utils_test.py
+++ b/tensorflow/python/estimator/canned/parsing_utils_test.py
@@ -111,5 +111,101 @@ class ClassifierParseExampleSpec(test.TestCase):
           weight_column=not_a_numeric_column)
 
 
+class RegressorParseExampleSpec(test.TestCase):
+  """Tests tf.estimator.classifier_parse_example_spec."""
+
+  def test_defaults(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')], label_key='b')
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_int64(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        label_dtype=dtypes.int64)
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.int64),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_label_default_value(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        label_default=0.)
+    expected_spec = {
+        'a':
+            parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b':
+            parsing_ops.FixedLenFeature(
+                (1,), dtype=dtypes.float32, default_value=0.),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_label_dimension(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        label_dimension=3)
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((3,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_weight_column_as_string(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        weight_column='c')
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'c': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_weight_column_as_numeric_column(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        weight_column=fc.numeric_column('c'))
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'c': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_label_key_should_not_be_used_as_feature(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'label should not be used as feature'):
+      parsing_utils.regressor_parse_example_spec(
+          feature_columns=[fc.numeric_column('a')], label_key='a')
+
+  def test_weight_column_should_not_be_used_as_feature(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'weight_column should not be used as feature'):
+      parsing_utils.regressor_parse_example_spec(
+          feature_columns=[fc.numeric_column('a')],
+          label_key='b',
+          weight_column=fc.numeric_column('a'))
+
+  def test_weight_column_should_be_a_numeric_column(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'tf.feature_column.numeric_column'):
+      not_a_numeric_column = 3
+      parsing_utils.regressor_parse_example_spec(
+          feature_columns=[fc.numeric_column('a')],
+          label_key='b',
+          weight_column=not_a_numeric_column)
+
+
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From 73d4647db1fc2fbfa6cc84895e783e3d49b1924e Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 22 Jun 2017 13:36:21 -0700
Subject: [PATCH 0364/1375] Fix cuda_kernel_helper_test.
 std::numeric_limits<int32>::max() doesn't pass, so I didn't use that.

PiperOrigin-RevId: 159869169
---
 tensorflow/core/util/cuda_kernel_helper_test.cu.cc | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
index abd72b7d77..623f7bab90 100644
--- a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
+++ b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
@@ -164,7 +164,7 @@ TEST_F(CudaLaunchConfigTest, GetCudaLaunchConfig) {
   TEST_LAUNCH_PARAMETER(8191);
   TEST_LAUNCH_PARAMETER(8192);
   TEST_LAUNCH_PARAMETER(123456);
-  TEST_LAUNCH_PARAMETER(1 << 31 - 1);  // max value of int
+  TEST_LAUNCH_PARAMETER(1 << 30);
   #undef TEST_LAUNCH_PARAMETER
 }
 
@@ -243,8 +243,8 @@ TEST_F(CudaLaunchConfigTest, GetCuda2DLaunchConfig) {
   TEST_LAUNCH_PARAMETER(8191, 1);
   TEST_LAUNCH_PARAMETER(8192, 10);
   TEST_LAUNCH_PARAMETER(123456, 12);
-  TEST_LAUNCH_PARAMETER(1, (1 << 31 - 1));
-  TEST_LAUNCH_PARAMETER((1 << 31 - 1), 1);
+  TEST_LAUNCH_PARAMETER(1, 1 << 30);
+  TEST_LAUNCH_PARAMETER(1 << 30, 1);
   #undef TEST_LAUNCH_PARAMETER
 }
 
@@ -292,9 +292,9 @@ TEST_F(CudaLaunchConfigTest, GetCuda3DLaunchConfig) {
   TEST_LAUNCH_PARAMETER(8191, 1, 1024);
   TEST_LAUNCH_PARAMETER(8192, 10, 32);
   TEST_LAUNCH_PARAMETER(123456, 12, 21);
-  TEST_LAUNCH_PARAMETER(1, 1, (1 << 31 - 1));
-  TEST_LAUNCH_PARAMETER(1, (1 << 31 - 1), 1);
-  TEST_LAUNCH_PARAMETER((1 << 31 - 1), 1, 1);
+  TEST_LAUNCH_PARAMETER(1, 1, 1 << 30);
+  TEST_LAUNCH_PARAMETER(1, 1 << 30, 1);
+  TEST_LAUNCH_PARAMETER(1 << 30, 1, 1);
   #undef TEST_LAUNCH_PARAMETER
 }
 
-- 
GitLab


From 268ede85711024a91f3a97a73cfa034e8f8a8ea5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 13:40:45 -0700
Subject: [PATCH 0365/1375] In tfcompile, prune nodes that are not reachable
 from the fetches before building the Graph. This allows loading a graph that
 contains ops not needed for the compiled binary.

PiperOrigin-RevId: 159869692
---
 tensorflow/compiler/aot/BUILD                 | 13 +++
 tensorflow/compiler/aot/compile.cc            |  9 +-
 .../aot/test_graph_tfunknownop.config.pbtxt   | 16 ++++
 .../compiler/aot/test_graph_tfunknownop.pbtxt | 86 +++++++++++++++++++
 tensorflow/compiler/aot/tfcompile_util.cc     | 50 +++++++++++
 tensorflow/compiler/aot/tfcompile_util.h      |  6 ++
 .../compiler/aot/tfcompile_util_test.cc       | 61 +++++++++++++
 7 files changed, 240 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop.config.pbtxt
 create mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 3c14a1d708..77532a32fa 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -108,6 +108,7 @@ cc_test(
     deps = [
         ":tfcompile_lib",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
@@ -157,6 +158,17 @@ tf_library(
     tags = ["manual"],
 )
 
+# A test of tf_library that includes a graph with an unknown op, but where
+# the compilation works because the the unknown op is not needed for the fetches.
+tf_library(
+    name = "test_graph_tfunknownop",
+    testonly = 1,
+    config = "test_graph_tfunknownop.config.pbtxt",
+    cpp_class = "UnknownOpAddComp",
+    graph = "test_graph_tfunknownop.pbtxt",
+    tags = ["manual"],
+)
+
 # Utility library for benchmark binaries, used by the *_benchmark rules that are
 # added by the tfcompile bazel macro.
 cc_library(
@@ -200,6 +212,7 @@ test_suite(
     tests = [
         ":benchmark_test",
         ":test_graph_tfadd_test",
+        ":test_graph_tfunknownop_test",
         "//tensorflow/compiler/aot/tests:all_tests",
     ],
 )
diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc
index ca17c5ab69..317baa8971 100644
--- a/tensorflow/compiler/aot/compile.cc
+++ b/tensorflow/compiler/aot/compile.cc
@@ -378,9 +378,16 @@ Status CompileXla(xla::CompileOnlyClient* client,
 Status InitGraph(const GraphDef& graph_def, const Config& config,
                  const MainFlags& flags, std::unique_ptr<Graph>* graph) {
   TF_RETURN_IF_ERROR(ValidateConfig(config));
+
   FunctionLibraryDefinition flib_def(OpRegistry::Global(), graph_def.library());
   std::unique_ptr<Graph> g(new Graph(flib_def));
-  GraphDef copy_def(graph_def);
+
+  GraphDef copy_def;
+
+  // Prune the GraphDef first so that unknown ops that we aren't compiling get
+  // filtered out.
+  TF_RETURN_IF_ERROR(PruneGraphDefInto(config, graph_def, &copy_def));
+
   TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&copy_def, *g->op_registry(),
                                                0 /*node_offset*/));
   TF_RETURN_IF_ERROR(
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop.config.pbtxt
new file mode 100644
index 0000000000..5625c0ab03
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop.config.pbtxt
@@ -0,0 +1,16 @@
+# Text form of tensorflow.tfcompile.Config proto.
+feed {
+  id { node_name: "x_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "y_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+fetch {
+  id { node_name: "x_y_sum" }
+}
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
new file mode 100644
index 0000000000..212ffbb5ff
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
@@ -0,0 +1,86 @@
+node {
+  name  : "x_const"
+  op    : "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        int_val: 1
+      }
+    }
+  }
+  attr {
+    key  : "dtype"
+    value {
+      type       : DT_INT32
+    }
+  }
+}
+node {
+  name  : "y_const"
+  op    : "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        int_val: 2
+      }
+    }
+  }
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name  : "x_y_sum"
+  op    : "Add"
+  input : "x_const"
+  input : "y_const"
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name  : "z"
+  op    : "SomeUnknownOp"
+  input : "x_const"
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name  : "x_z_sum"
+  op    : "Add"
+  input : "x_const"
+  input : "z"
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+versions {
+  producer: 15
+}
diff --git a/tensorflow/compiler/aot/tfcompile_util.cc b/tensorflow/compiler/aot/tfcompile_util.cc
index fd073a2e26..8774a02128 100644
--- a/tensorflow/compiler/aot/tfcompile_util.cc
+++ b/tensorflow/compiler/aot/tfcompile_util.cc
@@ -15,10 +15,14 @@ limitations under the License.
 
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 
+#include <queue>
 #include <set>
+#include <unordered_map>
 
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/graph/tensor_id.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
@@ -115,5 +119,51 @@ Status ValidateConfig(const Config& config) {
   return Status::OK();
 }
 
+Status PruneGraphDefInto(const Config& config, const GraphDef& in,
+                         GraphDef* out) {
+  *out = in;
+  out->clear_node();
+
+  // Maps node name to reachability.
+  std::unordered_map<string, std::pair<bool, const NodeDef*>> node_by_name;
+  for (const NodeDef& node : in.node()) {
+    node_by_name[node.name()] = std::pair<bool, const NodeDef*>(false, &node);
+  }
+
+  std::queue<string> name_queue;
+  for (int i = 0; i < config.fetch_size(); ++i) {
+    name_queue.push(config.fetch(i).id().node_name());
+  }
+  while (!name_queue.empty()) {
+    const string name = name_queue.front();
+    name_queue.pop();
+
+    auto find_it = node_by_name.find(name);
+    if (find_it == node_by_name.end()) {
+      return errors::InvalidArgument("While pruning graph, node ", name,
+                                     " needed but not found in the graph.");
+    }
+    auto& map_entry = find_it->second;
+    if (map_entry.first) {
+      continue;
+    }
+    map_entry.first = true;
+
+    for (const string& in_edge : map_entry.second->input()) {
+      name_queue.push(ParseTensorName(in_edge).first.ToString());
+    }
+  }
+
+  // Copy over, preserving order of original and only nodes that are reachable
+  // from the fetches.
+  out->mutable_node()->Reserve(in.node_size());
+  for (const NodeDef& node : in.node()) {
+    if (node_by_name[node.name()].first) {
+      *out->add_node() = node;
+    }
+  }
+  return Status::OK();
+}
+
 }  // namespace tfcompile
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/aot/tfcompile_util.h b/tensorflow/compiler/aot/tfcompile_util.h
index 651d75d0d0..84060c0761 100644
--- a/tensorflow/compiler/aot/tfcompile_util.h
+++ b/tensorflow/compiler/aot/tfcompile_util.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
@@ -30,6 +31,11 @@ Status ValidateCppIdent(StringPiece ident, StringPiece msg);
 // ValidateConfig returns OK iff config is valid.
 Status ValidateConfig(const Config& config);
 
+// Returns in <out> a copy of <in>, pruned to only include fetches from
+// <config>.
+Status PruneGraphDefInto(const Config& config, const GraphDef& in,
+                         GraphDef* out);
+
 }  // namespace tfcompile
 }  // namespace tensorflow
 
diff --git a/tensorflow/compiler/aot/tfcompile_util_test.cc b/tensorflow/compiler/aot/tfcompile_util_test.cc
index c321d3ff4c..5a92851ceb 100644
--- a/tensorflow/compiler/aot/tfcompile_util_test.cc
+++ b/tensorflow/compiler/aot/tfcompile_util_test.cc
@@ -15,9 +15,11 @@ limitations under the License.
 
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
@@ -180,6 +182,65 @@ TEST(ValidateConfig, ConflictingFetchName) {
   ExpectErrorContains(ValidateConfig(config), "conflicting fetch name");
 }
 
+static Config FetchesConfig(std::vector<string> fetches) {
+  Config config;
+  for (const auto& fetch_node_name : fetches) {
+    auto* fetch = config.add_fetch();
+    fetch->set_name(strings::StrCat("fetch_", fetch_node_name));
+    fetch->mutable_id()->set_node_name(fetch_node_name);
+  }
+  return config;
+}
+
+TEST(PruneGraphDefInto, Basic) {
+  GraphDef def;
+  auto* n = def.add_node();
+  n->set_name("a");
+  n->add_input("b:0");
+  n->add_input("^c");
+
+  GraphDef copy;
+  ExpectErrorContains(PruneGraphDefInto(FetchesConfig({"missing"}), def, &copy),
+                      "node missing needed");
+  ExpectErrorContains(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy),
+                      "node b needed");
+
+  n = def.add_node();
+  n->set_name("b");
+  ExpectErrorContains(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy),
+                      "node c needed");
+  n->add_input("d:1");
+
+  n = def.add_node();
+  n->set_name("c");
+  n->add_input("d:1");
+
+  n = def.add_node();
+  n->set_name("d");
+
+  // Graph is full, no pruning done.
+  // Graph right now has diamond from d:
+  //   d --> b --> a
+  //   d --> c --> a
+  TF_EXPECT_OK(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy));
+  EXPECT_EQ(def.DebugString(), copy.DebugString());
+  GraphDef pruned_a = copy;
+
+  // Add some unrelated fields that use b and c, but are not needed for a.
+  n = def.add_node();
+  n->set_name("e");
+  n->add_input("^d");
+  n->add_input("b:2");
+  copy.Clear();
+  TF_EXPECT_OK(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy));
+  EXPECT_EQ(pruned_a.DebugString(), copy.DebugString());
+
+  // Fetch "a" and "e" to get the original graph.
+  copy.Clear();
+  TF_EXPECT_OK(PruneGraphDefInto(FetchesConfig({"a", "e"}), def, &copy));
+  EXPECT_EQ(def.DebugString(), copy.DebugString());
+}
+
 }  // namespace
 }  // namespace tfcompile
 }  // namespace tensorflow
-- 
GitLab


From 7a80e267e380dae8cfdafc4dd32ab782608be1f5 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 22 Jun 2017 14:00:46 -0700
Subject: [PATCH 0366/1375] Fix bugs related to distributions over integers.

- Ensure that the max number of categories does not exceed largest integer-form float.
- Make dtype inference consistent between Categorical and Multinomial
distributions.
- Improve documentation to better reflect that the Categorical
distribution is analogous to `argmax{OneHotCategorical}` (itself being
identical to `argmax{Multinomial(p,n=1)}` but not Multinomial.
- Fix validation_args Heisenberg uncertainty: only validation logic should live under self.validate_args. E.g., validate_args=True would sometimes imply `x=floor(x)` which changes behavior thus making debugging impossible because enabling validation *changes* values.
- Corrected `Geometric` swapping of validate_args` and `allow_nan_stats` default-values.

Fixes #10149

PiperOrigin-RevId: 159872532
---
 .../kernel_tests/distribution_util_test.py    | 556 --------------
 .../python/kernel_tests/geometric_test.py     |  10 +-
 .../distributions/python/ops/binomial.py      |   6 +-
 .../distributions/python/ops/geometric.py     |  42 +-
 .../python/ops/negative_binomial.py           |  40 +-
 .../distributions/python/ops/poisson.py       |  23 +-
 .../python/kernel_tests/distributions/BUILD   |  18 +
 .../distributions/bernoulli_test.py           |   2 +-
 .../dirichlet_multinomial_test.py             |   6 +-
 .../kernel_tests/distributions/util_test.py   | 707 ++++++++++++++++++
 .../python/ops/distributions/bernoulli.py     |  17 +-
 .../python/ops/distributions/categorical.py   | 103 ++-
 .../distributions/dirichlet_multinomial.py    |  78 +-
 .../python/ops/distributions/multinomial.py   |  75 +-
 tensorflow/python/ops/distributions/util.py   | 304 +++++++-
 15 files changed, 1238 insertions(+), 749 deletions(-)
 create mode 100644 tensorflow/python/kernel_tests/distributions/util_test.py

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
index aedf42f87f..472586987d 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
@@ -18,116 +18,16 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
-
 import numpy as np
-from scipy import special
 
 from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.contrib.linalg.python.ops import linear_operator_diag
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import gradient_checker
-from tensorflow.python.ops import gradients_impl
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
 import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
-from tensorflow.python.platform import tf_logging as logging
-
-
-class AssertCloseTest(test.TestCase):
-
-  def testAssertCloseIntegerDtype(self):
-    x = array_ops.placeholder(dtypes.int32)
-    y = x
-    z = array_ops.placeholder(dtypes.int32)
-    feed_dict = {x: [1, 5, 10, 15, 20], z: [2, 5, 10, 15, 20]}
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
-          array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
-          array_ops.identity(y).eval(feed_dict=feed_dict)
-
-  def testAssertCloseNonIntegerDtype(self):
-    x = array_ops.placeholder(dtypes.float32)
-    y = x + 1e-8
-    z = array_ops.placeholder(dtypes.float32)
-    feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]}
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
-          array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
-          array_ops.identity(y).eval(feed_dict=feed_dict)
-
-  def testAssertCloseEpsilon(self):
-    x = [0., 5, 10, 15, 20]
-    # x != y
-    y = [0.1, 5, 10, 15, 20]
-    # x = z
-    z = [1e-8, 5, 10, 15, 20]
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_close(x, z)]):
-        array_ops.identity(x).eval()
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(x, y)]):
-          array_ops.identity(x).eval()
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
-          array_ops.identity(y).eval()
-
-  def testAssertIntegerForm(self):
-    # This should only be detected as an integer.
-    x = array_ops.placeholder(dtypes.float32)
-    y = array_ops.placeholder(dtypes.float32)
-    # First component isn't less than float32.eps = 1e-7
-    z = array_ops.placeholder(dtypes.float32)
-    # This shouldn"t be detected as an integer.
-    w = array_ops.placeholder(dtypes.float32)
-    feed_dict = {x: [1., 5, 10, 15, 20], y: [1.1, 5, 10, 15, 20],
-                 z: [1.0001, 5, 10, 15, 20], w: [1e-8, 5, 10, 15, 20]}
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_integer_form(x)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("x has non-integer components"):
-        with ops.control_dependencies(
-            [distribution_util.assert_integer_form(y)]):
-          array_ops.identity(y).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("x has non-integer components"):
-        with ops.control_dependencies(
-            [distribution_util.assert_integer_form(z)]):
-          array_ops.identity(z).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("x has non-integer components"):
-        with ops.control_dependencies(
-            [distribution_util.assert_integer_form(w)]):
-          array_ops.identity(w).eval(feed_dict=feed_dict)
 
 
 class ShapesFromLocAndScaleTest(test.TestCase):
@@ -204,462 +104,6 @@ class ShapesFromLocAndScaleTest(test.TestCase):
       self.assertAllEqual([3], event_shape)
 
 
-class GetLogitsAndProbsTest(test.TestCase):
-
-  def testGetLogitsAndProbsImproperArguments(self):
-    with self.test_session():
-      with self.assertRaises(ValueError):
-        distribution_util.get_logits_and_probs(logits=None, probs=None)
-
-      with self.assertRaises(ValueError):
-        distribution_util.get_logits_and_probs(logits=[0.1], probs=[0.1])
-
-  def testGetLogitsAndProbsLogits(self):
-    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
-    logits = special.logit(p)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          logits=logits, validate_args=True)
-
-      self.assertAllClose(p, new_p.eval())
-      self.assertAllClose(logits, new_logits.eval())
-
-  def testGetLogitsAndProbsLogitsMultidimensional(self):
-    p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
-    logits = np.log(p)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          logits=logits, multidimensional=True, validate_args=True)
-
-      self.assertAllClose(new_p.eval(), p)
-      self.assertAllClose(new_logits.eval(), logits)
-
-  def testGetLogitsAndProbsProbability(self):
-    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          probs=p, validate_args=True)
-
-      self.assertAllClose(special.logit(p), new_logits.eval())
-      self.assertAllClose(p, new_p.eval())
-
-  def testGetLogitsAndProbsProbabilityMultidimensional(self):
-    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          probs=p, multidimensional=True, validate_args=True)
-
-      self.assertAllClose(np.log(p), new_logits.eval())
-      self.assertAllClose(p, new_p.eval())
-
-  def testGetLogitsAndProbsProbabilityValidateArgs(self):
-    p = [0.01, 0.2, 0.5, 0.7, .99]
-    # Component less than 0.
-    p2 = [-1, 0.2, 0.5, 0.3, .2]
-    # Component greater than 1.
-    p3 = [2, 0.2, 0.5, 0.3, .2]
-
-    with self.test_session():
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p, validate_args=True)
-      prob.eval()
-
-      with self.assertRaisesOpError("Condition x >= 0"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p2, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p2, validate_args=False)
-      prob.eval()
-
-      with self.assertRaisesOpError("probs has components greater than 1"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p3, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p3, validate_args=False)
-      prob.eval()
-
-  def testGetLogitsAndProbsProbabilityValidateArgsMultidimensional(self):
-    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
-    # Component less than 0. Still sums to 1.
-    p2 = np.array([[-.3, 0.4, 0.9], [0.1, 0.5, 0.4]], dtype=np.float32)
-    # Component greater than 1. Does not sum to 1.
-    p3 = np.array([[1.3, 0.0, 0.0], [0.1, 0.5, 0.4]], dtype=np.float32)
-    # Does not sum to 1.
-    p4 = np.array([[1.1, 0.3, 0.4], [0.1, 0.5, 0.4]], dtype=np.float32)
-
-    with self.test_session():
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p, multidimensional=True)
-      prob.eval()
-
-      with self.assertRaisesOpError("Condition x >= 0"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p2, multidimensional=True, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p2, multidimensional=True, validate_args=False)
-      prob.eval()
-
-      with self.assertRaisesOpError(
-          "(probs has components greater than 1|probs does not sum to 1)"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p3, multidimensional=True, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p3, multidimensional=True, validate_args=False)
-      prob.eval()
-
-      with self.assertRaisesOpError("probs does not sum to 1"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p4, multidimensional=True, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p4, multidimensional=True, validate_args=False)
-      prob.eval()
-
-
-class LogCombinationsTest(test.TestCase):
-
-  def testLogCombinationsBinomial(self):
-    n = [2, 5, 12, 15]
-    k = [1, 2, 4, 11]
-    log_combs = np.log(special.binom(n, k))
-
-    with self.test_session():
-      n = np.array(n, dtype=np.float32)
-      counts = [[1., 1], [2., 3], [4., 8], [11, 4]]
-      log_binom = distribution_util.log_combinations(n, counts)
-      self.assertEqual([4], log_binom.get_shape())
-      self.assertAllClose(log_combs, log_binom.eval())
-
-  def testLogCombinationsShape(self):
-    # Shape [2, 2]
-    n = [[2, 5], [12, 15]]
-
-    with self.test_session():
-      n = np.array(n, dtype=np.float32)
-      # Shape [2, 2, 4]
-      counts = [[[1., 1, 0, 0], [2., 2, 1, 0]], [[4., 4, 1, 3], [10, 1, 1, 4]]]
-      log_binom = distribution_util.log_combinations(n, counts)
-      self.assertEqual([2, 2], log_binom.get_shape())
-
-
-class DynamicShapeTest(test.TestCase):
-
-  def testSameDynamicShape(self):
-    with self.test_session():
-      scalar = constant_op.constant(2.0)
-      scalar1 = array_ops.placeholder(dtype=dtypes.float32)
-
-      vector = [0.3, 0.4, 0.5]
-      vector1 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
-      vector2 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
-
-      multidimensional = [[0.3, 0.4], [0.2, 0.6]]
-      multidimensional1 = array_ops.placeholder(
-          dtype=dtypes.float32, shape=[None, None])
-      multidimensional2 = array_ops.placeholder(
-          dtype=dtypes.float32, shape=[None, None])
-
-      # Scalar
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(scalar, scalar1).eval({
-              scalar1: 2.0
-          }))
-
-      # Vector
-
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(vector, vector1).eval({
-              vector1: [2.0, 3.0, 4.0]
-          }))
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(vector1, vector2).eval({
-              vector1: [2.0, 3.0, 4.0],
-              vector2: [2.0, 3.5, 6.0]
-          }))
-
-      # Multidimensional
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(
-              multidimensional, multidimensional1).eval({
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-              }))
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(
-              multidimensional1, multidimensional2).eval({
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
-                  multidimensional2: [[1.0, 3.5], [6.3, 2.3]]
-              }))
-
-      # Scalar, X
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar, vector1).eval({
-              vector1: [2.0, 3.0, 4.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar1, vector1).eval({
-              scalar1: 2.0,
-              vector1: [2.0, 3.0, 4.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar, multidimensional1).eval({
-              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar1, multidimensional1).eval(
-              {
-                  scalar1: 2.0,
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-              }))
-
-      # Vector, X
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector, vector1).eval({
-              vector1: [2.0, 3.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector1, vector2).eval({
-              vector1: [2.0, 3.0, 4.0],
-              vector2: [6.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector, multidimensional1).eval({
-              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector1, multidimensional1).eval(
-              {
-                  vector1: [2.0, 3.0, 4.0],
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-              }))
-
-      # Multidimensional, X
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(
-              multidimensional, multidimensional1).eval({
-                  multidimensional1: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
-              }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(
-              multidimensional1, multidimensional2).eval({
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
-                  multidimensional2: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
-              }))
-
-
-class RotateTransposeTest(test.TestCase):
-
-  def _np_rotate_transpose(self, x, shift):
-    if not isinstance(x, np.ndarray):
-      x = np.array(x)
-    return np.transpose(x, np.roll(np.arange(len(x.shape)), shift))
-
-  def testRollStatic(self):
-    with self.test_session():
-      with self.assertRaisesRegexp(ValueError, "None values not supported."):
-        distribution_util.rotate_transpose(None, 1)
-      for x in (np.ones(1), np.ones((2, 1)), np.ones((3, 2, 1))):
-        for shift in np.arange(-5, 5):
-          y = distribution_util.rotate_transpose(x, shift)
-          self.assertAllEqual(self._np_rotate_transpose(x, shift), y.eval())
-          self.assertAllEqual(np.roll(x.shape, shift), y.get_shape().as_list())
-
-  def testRollDynamic(self):
-    with self.test_session() as sess:
-      x = array_ops.placeholder(dtypes.float32)
-      shift = array_ops.placeholder(dtypes.int32)
-      for x_value in (np.ones(
-          1, dtype=x.dtype.as_numpy_dtype()), np.ones(
-              (2, 1), dtype=x.dtype.as_numpy_dtype()), np.ones(
-                  (3, 2, 1), dtype=x.dtype.as_numpy_dtype())):
-        for shift_value in np.arange(-5, 5):
-          self.assertAllEqual(
-              self._np_rotate_transpose(x_value, shift_value),
-              sess.run(distribution_util.rotate_transpose(x, shift),
-                       feed_dict={x: x_value,
-                                  shift: shift_value}))
-
-
-class PickVectorTest(test.TestCase):
-
-  def testCorrectlyPicksVector(self):
-    with self.test_session():
-      x = np.arange(10, 12)
-      y = np.arange(15, 18)
-      self.assertAllEqual(x,
-                          distribution_util.pick_vector(
-                              math_ops.less(0, 5), x, y).eval())
-      self.assertAllEqual(y,
-                          distribution_util.pick_vector(
-                              math_ops.less(5, 0), x, y).eval())
-      self.assertAllEqual(x,
-                          distribution_util.pick_vector(
-                              constant_op.constant(True), x, y))  # No eval.
-      self.assertAllEqual(y,
-                          distribution_util.pick_vector(
-                              constant_op.constant(False), x, y))  # No eval.
-
-
-class FillLowerTriangularTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _fill_lower_triangular(self, x):
-    """Numpy implementation of `fill_lower_triangular`."""
-    x = np.asarray(x)
-    d = x.shape[-1]
-    # d = n(n+1)/2 implies n is:
-    n = int(0.5 * (math.sqrt(1. + 8. * d) - 1.))
-    ids = np.tril_indices(n)
-    y = np.zeros(list(x.shape[:-1]) + [n, n], dtype=x.dtype)
-    y[..., ids[0], ids[1]] = x
-    return y
-
-  def testCorrectlyMakes1x1LowerTril(self):
-    with self.test_session():
-      x = ops.convert_to_tensor(self._rng.randn(3, 1))
-      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
-      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
-      self.assertAllEqual(expected.shape, actual.get_shape())
-      self.assertAllEqual(expected, actual.eval())
-
-  def testCorrectlyMakesNoBatchLowerTril(self):
-    with self.test_session():
-      x = ops.convert_to_tensor(self._rng.randn(10))
-      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
-      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
-      self.assertAllEqual(expected.shape, actual.get_shape())
-      self.assertAllEqual(expected, actual.eval())
-      g = gradients_impl.gradients(
-          distribution_util.fill_lower_triangular(x), x)
-      self.assertAllEqual(np.tri(4).reshape(-1), g[0].values.eval())
-
-  def testCorrectlyMakesBatchLowerTril(self):
-    with self.test_session():
-      x = ops.convert_to_tensor(self._rng.randn(2, 2, 6))
-      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
-      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
-      self.assertAllEqual(expected.shape, actual.get_shape())
-      self.assertAllEqual(expected, actual.eval())
-      self.assertAllEqual(
-          np.ones((2, 2, 6)),
-          gradients_impl.gradients(
-              distribution_util.fill_lower_triangular(x), x)[0].eval())
-
-
-class GenNewSeedTest(test.TestCase):
-
-  def testOnlyNoneReturnsNone(self):
-    self.assertFalse(distribution_util.gen_new_seed(0, "salt") is None)
-    self.assertTrue(distribution_util.gen_new_seed(None, "salt") is None)
-
-
-# TODO(jvdillon): Merge this test back into:
-# tensorflow/python/kernel_tests/softplus_op_test.py
-# once TF core is accepting new ops.
-class SoftplusTest(test.TestCase):
-
-  def _npSoftplus(self, np_features):
-    np_features = np.asarray(np_features)
-    zero = np.asarray(0).astype(np_features.dtype)
-    return np.logaddexp(zero, np_features)
-
-  def _testSoftplus(self, np_features, use_gpu=False):
-    np_features = np.asarray(np_features)
-    np_softplus = self._npSoftplus(np_features)
-    with self.test_session(use_gpu=use_gpu) as sess:
-      softplus = nn_ops.softplus(np_features)
-      softplus_inverse = distribution_util.softplus_inverse(softplus)
-      [tf_softplus, tf_softplus_inverse] = sess.run([
-          softplus, softplus_inverse])
-    self.assertAllCloseAccordingToType(np_softplus, tf_softplus)
-    rtol = {"float16": 0.07, "float32": 0.003, "float64": 0.002}.get(
-        str(np_features.dtype), 1e-6)
-    # This will test that we correctly computed the inverse by verifying we
-    # recovered the original input.
-    self.assertAllCloseAccordingToType(
-        np_features, tf_softplus_inverse,
-        atol=0., rtol=rtol)
-    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
-                        tf_softplus > 0)
-
-    self.assertShapeEqual(np_softplus, softplus)
-    self.assertShapeEqual(np_softplus, softplus_inverse)
-
-    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
-                        np.isfinite(tf_softplus))
-    self.assertAllEqual(np.ones_like(tf_softplus_inverse).astype(np.bool),
-                        np.isfinite(tf_softplus_inverse))
-
-  def testNumbers(self):
-    for t in [np.float16, np.float32, np.float64]:
-      lower = {np.float16: -15, np.float32: -50, np.float64: -50}.get(t, -100)
-      upper = {np.float16: 50, np.float32: 50, np.float64: 50}.get(t, 100)
-      self._testSoftplus(
-          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
-              [2, -1]),
-          use_gpu=False)
-      self._testSoftplus(
-          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
-              [2, -1]),
-          use_gpu=True)
-      log_eps = np.log(np.finfo(t).eps)
-      one = t(1)
-      ten = t(10)
-      self._testSoftplus(
-          [
-              log_eps, log_eps - one, log_eps + one, log_eps - ten,
-              log_eps + ten, -log_eps, -log_eps - one, -log_eps + one,
-              -log_eps - ten, -log_eps + ten
-          ],
-          use_gpu=False)
-      self._testSoftplus(
-          [
-              log_eps, log_eps - one, log_eps + one, log_eps - ten,
-              log_eps + ten - log_eps, -log_eps - one, -log_eps + one,
-              -log_eps - ten, -log_eps + ten
-          ],
-          use_gpu=True)
-
-  def testGradient(self):
-    with self.test_session():
-      x = constant_op.constant(
-          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
-          shape=[2, 5],
-          name="x")
-      y = nn_ops.softplus(x, name="softplus")
-      x_init = np.asarray(
-          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
-          dtype=np.float32,
-          order="F")
-      err = gradient_checker.compute_gradient_error(
-          x, [2, 5], y, [2, 5], x_init_value=x_init)
-    logging.vlog(2, "softplus (float) gradient err = ", err)
-    self.assertLess(err, 1e-4)
-
-  def testInverseSoftplusGradientNeverNan(self):
-    with self.test_session():
-      # Note that this range contains both zero and inf.
-      x = constant_op.constant(np.logspace(-8, 6).astype(np.float16))
-      y = distribution_util.softplus_inverse(x)
-      grads = gradients_impl.gradients(y, x)[0].eval()
-      # Equivalent to `assertAllFalse` (if it existed).
-      self.assertAllEqual(np.zeros_like(grads).astype(np.bool), np.isnan(grads))
-
-
 class TridiagTest(test.TestCase):
 
   def testWorksCorrectlyNoBatches(self):
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py b/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py
index 9ef68c4c2c..87cdd0485a 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py
@@ -47,13 +47,13 @@ class GeometricTest(test.TestCase):
     invalid_ps = [-.01, -0.01, -2.]
     with self.test_session():
       with self.assertRaisesOpError("Condition x >= 0"):
-        geom = geometric.Geometric(probs=invalid_ps)
+        geom = geometric.Geometric(probs=invalid_ps, validate_args=True)
         geom.probs.eval()
 
     invalid_ps = [1.1, 3., 5.]
     with self.test_session():
       with self.assertRaisesOpError("Condition x <= y"):
-        geom = geometric.Geometric(probs=invalid_ps)
+        geom = geometric.Geometric(probs=invalid_ps, validate_args=True)
         geom.probs.eval()
 
   def testGeomLogPmf(self):
@@ -78,7 +78,7 @@ class GeometricTest(test.TestCase):
       probs = constant_op.constant([.9] * batch_size)
       x = array_ops.placeholder(dtypes.float32, shape=[6])
       feed_dict = {x: [2.5, 3.2, 4.3, 5.1, 6., 7.]}
-      geom = geometric.Geometric(probs=probs)
+      geom = geometric.Geometric(probs=probs, validate_args=True)
 
       with self.assertRaisesOpError("Condition x == y"):
         log_prob = geom.log_prob(x)
@@ -88,7 +88,7 @@ class GeometricTest(test.TestCase):
         log_prob = geom.log_prob(np.array([-1.], dtype=np.float32))
         log_prob.eval()
 
-      geom = geometric.Geometric(probs=probs, validate_args=False)
+      geom = geometric.Geometric(probs=probs)
       log_prob = geom.log_prob(x)
       self.assertEqual([6,], log_prob.get_shape())
       pmf = geom.prob(x)
@@ -216,7 +216,7 @@ class GeometricTest(test.TestCase):
 
   def testGeometricAtBoundary(self):
     with self.test_session():
-      geom = geometric.Geometric(probs=1.)
+      geom = geometric.Geometric(probs=1., validate_args=True)
 
       x = np.array([0., 2., 3., 4., 5., 6., 7.], dtype=np.float32)
       expected_log_prob = stats.geom.logpmf(x, [1.], loc=-1)
diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py
index 9304a56491..d8bc77eaa4 100644
--- a/tensorflow/contrib/distributions/python/ops/binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/binomial.py
@@ -272,13 +272,11 @@ class Binomial(distribution.Distribution):
             message="total_count cannot contain fractional components."),
     ], total_count)
 
-  def _maybe_assert_valid_sample(self, counts, check_integer=True):
+  def _maybe_assert_valid_sample(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
     if not self.validate_args:
       return counts
-
-    counts = distribution_util.embed_check_nonnegative_discrete(
-        counts, check_integer=check_integer)
+    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
     return control_flow_ops.with_dependencies([
         check_ops.assert_less_equal(
             counts, self.total_count,
diff --git a/tensorflow/contrib/distributions/python/ops/geometric.py b/tensorflow/contrib/distributions/python/ops/geometric.py
index 918200830c..8f190e48a7 100644
--- a/tensorflow/contrib/distributions/python/ops/geometric.py
+++ b/tensorflow/contrib/distributions/python/ops/geometric.py
@@ -58,8 +58,8 @@ class Geometric(distribution.Distribution):
   def __init__(self,
                logits=None,
                probs=None,
-               validate_args=True,
-               allow_nan_stats=False,
+               validate_args=False,
+               allow_nan_stats=True,
                name="Geometric"):
     """Construct Geometric distributions.
 
@@ -143,32 +143,32 @@ class Geometric(distribution.Distribution):
     return math_ops.floor(
         math_ops.log(sampled) / math_ops.log1p(-self.probs))
 
-  def _cdf(self, counts):
+  def _cdf(self, x):
     if self.validate_args:
-      # We set `check_integer=False` since the CDF is defined on whole real
-      # line.
-      counts = math_ops.floor(
-          distribution_util.embed_check_nonnegative_discrete(
-              counts, check_integer=False))
-    counts *= array_ops.ones_like(self.probs)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # Whether or not x is integer-form, the following is well-defined.
+      # However, scipy takes the floor, so we do too.
+      x = math_ops.floor(x)
+    x *= array_ops.ones_like(self.probs)
     return array_ops.where(
-        counts < 0.,
-        array_ops.zeros_like(counts),
-        -math_ops.expm1(
-            (counts + 1) * math_ops.log1p(-self.probs)))
+        x < 0.,
+        array_ops.zeros_like(x),
+        -math_ops.expm1((1. + x) * math_ops.log1p(-self.probs)))
 
-  def _log_prob(self, counts):
+  def _log_prob(self, x):
     if self.validate_args:
-      counts = distribution_util.embed_check_nonnegative_discrete(
-          counts, check_integer=True)
-    counts *= array_ops.ones_like(self.probs)
-    probs = self.probs * array_ops.ones_like(counts)
-
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # For consistency with cdf, we take the floor.
+      x = math_ops.floor(x)
+    x *= array_ops.ones_like(self.probs)
+    probs = self.probs * array_ops.ones_like(x)
     safe_domain = array_ops.where(
-        math_ops.equal(counts, 0.),
+        math_ops.equal(x, 0.),
         array_ops.zeros_like(probs),
         probs)
-    return counts * math_ops.log1p(-safe_domain) + math_ops.log(probs)
+    return x * math_ops.log1p(-safe_domain) + math_ops.log(probs)
 
   def _entropy(self):
     probs = self._probs
diff --git a/tensorflow/contrib/distributions/python/ops/negative_binomial.py b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
index 8895fd8b46..c8c396f6f8 100644
--- a/tensorflow/contrib/distributions/python/ops/negative_binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
@@ -148,39 +148,33 @@ class NegativeBinomial(distribution.Distribution):
         beta=math_ops.exp(-self.logits),
         dtype=self.dtype,
         seed=seed)
-
     return random_ops.random_poisson(
         rate,
         shape=[],
         dtype=self.dtype,
         seed=distribution_util.gen_new_seed(seed, "negative_binom"))
 
-  def _cdf(self, positive_counts):
+  def _cdf(self, x):
     if self.validate_args:
-      positive_counts = math_ops.floor(
-          distribution_util.embed_check_nonnegative_discrete(
-              positive_counts, check_integer=False))
-    return math_ops.betainc(
-        self.total_count, positive_counts + 1.,
-        math_ops.sigmoid(-self.logits))
-
-  def _log_prob(self, positive_counts):
-    return (self._log_unnormalized_prob(positive_counts)
-            - self._log_normalization(positive_counts))
-
-  def _log_unnormalized_prob(self, positive_counts):
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    return math_ops.betainc(self.total_count, 1. + x,
+                            math_ops.sigmoid(-self.logits))
+
+  def _log_prob(self, x):
+    return (self._log_unnormalized_prob(x)
+            - self._log_normalization(x))
+
+  def _log_unnormalized_prob(self, x):
     if self.validate_args:
-      positive_counts = distribution_util.embed_check_nonnegative_discrete(
-          positive_counts, check_integer=True)
-    return self.total_count * math_ops.log1p(
-        -self.probs) + positive_counts * math_ops.log(self.probs)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    return (self.total_count * math_ops.log1p(-self.probs)
+            + x * math_ops.log(self.probs))
 
-  def _log_normalization(self, positive_counts):
+  def _log_normalization(self, x):
     if self.validate_args:
-      positive_counts = distribution_util.embed_check_nonnegative_discrete(
-          positive_counts, check_integer=True)
-    return (-math_ops.lgamma(self.total_count + positive_counts)
-            + math_ops.lgamma(positive_counts + 1.)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    return (-math_ops.lgamma(self.total_count + x)
+            + math_ops.lgamma(1. + x)
             + math_ops.lgamma(self.total_count))
 
   def _mean(self):
diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py
index d9929183c1..59a98e5682 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson.py
@@ -114,10 +114,6 @@ class Poisson(distribution.Distribution):
   def _log_prob(self, x):
     return self._log_unnormalized_prob(x) - self._log_normalization()
 
-  @distribution_util.AppendDocstring(_poisson_sample_note)
-  def _prob(self, x):
-    return math_ops.exp(self._log_prob(x))
-
   @distribution_util.AppendDocstring(_poisson_sample_note)
   def _log_cdf(self, x):
     return math_ops.log(self.cdf(x))
@@ -125,20 +121,23 @@ class Poisson(distribution.Distribution):
   @distribution_util.AppendDocstring(_poisson_sample_note)
   def _cdf(self, x):
     if self.validate_args:
-      # We set `check_integer=False` since the CDF is defined on whole real
-      # line.
-      x = distribution_util.embed_check_nonnegative_discrete(
-          x, check_integer=False)
-    return math_ops.igammac(math_ops.floor(x + 1), self.rate)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # Whether or not x is integer-form, the following is well-defined.
+      # However, scipy takes the floor, so we do too.
+      x = math_ops.floor(x)
+    return math_ops.igammac(1. + x, self.rate)
 
   def _log_normalization(self):
     return self.rate
 
   def _log_unnormalized_prob(self, x):
     if self.validate_args:
-      x = distribution_util.embed_check_nonnegative_discrete(
-          x, check_integer=True)
-    return x * math_ops.log(self.rate) - math_ops.lgamma(x + 1)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # For consistency with cdf, we take the floor.
+      x = math_ops.floor(x)
+    return x * math_ops.log(self.rate) - math_ops.lgamma(1. + x)
 
   def _mean(self):
     return array_ops.identity(self.rate)
diff --git a/tensorflow/python/kernel_tests/distributions/BUILD b/tensorflow/python/kernel_tests/distributions/BUILD
index 50a0795200..f124065e0d 100644
--- a/tensorflow/python/kernel_tests/distributions/BUILD
+++ b/tensorflow/python/kernel_tests/distributions/BUILD
@@ -29,6 +29,24 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "util_test",
+    size = "small",
+    srcs = ["util_test.py"],
+    additional_deps = [
+        "//tensorflow/python/ops/distributions",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "kullback_leibler_test",
     size = "small",
diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
index ef93c4dab0..a269d72273 100644
--- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
+++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
@@ -172,7 +172,7 @@ class BernoulliTest(test.TestCase):
       dist = bernoulli.Bernoulli(probs=p, validate_args=True)
       with self.assertRaisesOpError("must be non-negative."):
         dist.prob([1, 1, -1]).eval()
-      with self.assertRaisesOpError("is not less than or equal to 1."):
+      with self.assertRaisesOpError("Elements cannot exceed 1."):
         dist.prob([2, 0, 1]).eval()
 
   def testPmfWithP(self):
diff --git a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py
index 2f8f85866d..7922fb0606 100644
--- a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py
+++ b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py
@@ -74,10 +74,10 @@ class DirichletMultinomialTest(test.TestCase):
       dist = ds.DirichletMultinomial(n, alpha, validate_args=True)
       dist.prob([2., 3, 0]).eval()
       dist.prob([3., 0, 2]).eval()
-      with self.assertRaisesOpError("counts must be non-negative"):
+      with self.assertRaisesOpError("must be non-negative"):
         dist.prob([-1., 4, 2]).eval()
       with self.assertRaisesOpError(
-          "counts last-dimension must sum to `self.total_count`"):
+          "last-dimension must sum to `self.total_count`"):
         dist.prob([3., 3, 0]).eval()
 
   def testPmfNonIntegerCounts(self):
@@ -91,7 +91,7 @@ class DirichletMultinomialTest(test.TestCase):
       # Both equality and integer checking fail.
       placeholder = array_ops.placeholder(dtypes.float32)
       with self.assertRaisesOpError(
-          "counts cannot contain fractional components"):
+          "cannot contain fractional components"):
         dist.prob(placeholder).eval(feed_dict={placeholder: [1.0, 2.5, 1.5]})
       dist = ds.DirichletMultinomial(n, alpha, validate_args=False)
       dist.prob([1., 2., 3.]).eval()
diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
new file mode 100644
index 0000000000..7bce16c8a0
--- /dev/null
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -0,0 +1,707 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for utility functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import importlib
+
+import numpy as np
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradient_checker
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops.distributions import util as distribution_util
+import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging
+
+
+def try_import(name):  # pylint: disable=invalid-name
+  module = None
+  try:
+    module = importlib.import_module(name)
+  except ImportError as e:
+    tf_logging.warning("Could not import %s: %s" % (name, str(e)))
+  return module
+
+
+special = try_import("scipy.special")
+
+
+def _logit(x):
+  x = np.asarray(x)
+  return np.log(x) - np.log1p(-x)
+
+
+class AssertCloseTest(test.TestCase):
+
+  def testAssertCloseIntegerDtype(self):
+    x = array_ops.placeholder(dtypes.int32)
+    y = x
+    z = array_ops.placeholder(dtypes.int32)
+    feed_dict = {x: [1, 5, 10, 15, 20], z: [2, 5, 10, 15, 20]}
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
+          array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
+          array_ops.identity(y).eval(feed_dict=feed_dict)
+
+  def testAssertCloseNonIntegerDtype(self):
+    x = array_ops.placeholder(dtypes.float32)
+    y = x + 1e-8
+    z = array_ops.placeholder(dtypes.float32)
+    feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]}
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
+          array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
+          array_ops.identity(y).eval(feed_dict=feed_dict)
+
+  def testAssertCloseEpsilon(self):
+    x = [0., 5, 10, 15, 20]
+    # x != y
+    y = [0.1, 5, 10, 15, 20]
+    # x = z
+    z = [1e-8, 5, 10, 15, 20]
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_close(x, z)]):
+        array_ops.identity(x).eval()
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(x, y)]):
+          array_ops.identity(x).eval()
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
+          array_ops.identity(y).eval()
+
+  def testAssertIntegerForm(self):
+    # This should only be detected as an integer.
+    x = array_ops.placeholder(dtypes.float32)
+    y = array_ops.placeholder(dtypes.float32)
+    # First component isn't less than float32.eps = 1e-7
+    z = array_ops.placeholder(dtypes.float32)
+    # This shouldn"t be detected as an integer.
+    w = array_ops.placeholder(dtypes.float32)
+    feed_dict = {x: [1., 5, 10, 15, 20], y: [1.1, 5, 10, 15, 20],
+                 z: [1.0001, 5, 10, 15, 20], w: [1e-8, 5, 10, 15, 20]}
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_integer_form(x)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("has non-integer components"):
+        with ops.control_dependencies(
+            [distribution_util.assert_integer_form(y)]):
+          array_ops.identity(y).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("has non-integer components"):
+        with ops.control_dependencies(
+            [distribution_util.assert_integer_form(z)]):
+          array_ops.identity(z).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("has non-integer components"):
+        with ops.control_dependencies(
+            [distribution_util.assert_integer_form(w)]):
+          array_ops.identity(w).eval(feed_dict=feed_dict)
+
+
+class GetLogitsAndProbsTest(test.TestCase):
+
+  def testImproperArguments(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        distribution_util.get_logits_and_probs(logits=None, probs=None)
+
+      with self.assertRaises(ValueError):
+        distribution_util.get_logits_and_probs(logits=[0.1], probs=[0.1])
+
+  def testLogits(self):
+    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
+    logits = _logit(p)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          logits=logits, validate_args=True)
+
+      self.assertAllClose(p, new_p.eval(), rtol=1e-6, atol=0.)
+      self.assertAllClose(logits, new_logits.eval(), rtol=1e-6, atol=0.)
+
+  def testLogitsMultidimensional(self):
+    p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
+    logits = np.log(p)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          logits=logits, multidimensional=True, validate_args=True)
+
+      self.assertAllClose(new_p.eval(), p)
+      self.assertAllClose(new_logits.eval(), logits)
+
+  def testProbability(self):
+    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          probs=p, validate_args=True)
+
+      self.assertAllClose(_logit(p), new_logits.eval())
+      self.assertAllClose(p, new_p.eval())
+
+  def testProbabilityMultidimensional(self):
+    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          probs=p, multidimensional=True, validate_args=True)
+
+      self.assertAllClose(np.log(p), new_logits.eval())
+      self.assertAllClose(p, new_p.eval())
+
+  def testProbabilityValidateArgs(self):
+    p = [0.01, 0.2, 0.5, 0.7, .99]
+    # Component less than 0.
+    p2 = [-1, 0.2, 0.5, 0.3, .2]
+    # Component greater than 1.
+    p3 = [2, 0.2, 0.5, 0.3, .2]
+
+    with self.test_session():
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p, validate_args=True)
+      prob.eval()
+
+      with self.assertRaisesOpError("Condition x >= 0"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p2, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p2, validate_args=False)
+      prob.eval()
+
+      with self.assertRaisesOpError("probs has components greater than 1"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p3, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p3, validate_args=False)
+      prob.eval()
+
+  def testProbabilityValidateArgsMultidimensional(self):
+    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
+    # Component less than 0. Still sums to 1.
+    p2 = np.array([[-.3, 0.4, 0.9], [0.1, 0.5, 0.4]], dtype=np.float32)
+    # Component greater than 1. Does not sum to 1.
+    p3 = np.array([[1.3, 0.0, 0.0], [0.1, 0.5, 0.4]], dtype=np.float32)
+    # Does not sum to 1.
+    p4 = np.array([[1.1, 0.3, 0.4], [0.1, 0.5, 0.4]], dtype=np.float32)
+
+    with self.test_session():
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p, multidimensional=True)
+      prob.eval()
+
+      with self.assertRaisesOpError("Condition x >= 0"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p2, multidimensional=True, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p2, multidimensional=True, validate_args=False)
+      prob.eval()
+
+      with self.assertRaisesOpError(
+          "(probs has components greater than 1|probs does not sum to 1)"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p3, multidimensional=True, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p3, multidimensional=True, validate_args=False)
+      prob.eval()
+
+      with self.assertRaisesOpError("probs does not sum to 1"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p4, multidimensional=True, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p4, multidimensional=True, validate_args=False)
+      prob.eval()
+
+  def testProbsMultidimShape(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        p = array_ops.ones([int(2**11+1)], dtype=np.float16)
+        distribution_util.get_logits_and_probs(
+            probs=p, multidimensional=True, validate_args=True)
+
+      with self.assertRaisesOpError(
+          "Number of classes exceeds `dtype` precision"):
+        p = array_ops.placeholder(dtype=dtypes.float16)
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p, multidimensional=True, validate_args=True)
+        prob.eval(feed_dict={p: np.ones([int(2**11+1)])})
+
+  def testLogitsMultidimShape(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        l = array_ops.ones([int(2**11+1)], dtype=np.float16)
+        distribution_util.get_logits_and_probs(
+            logits=l, multidimensional=True, validate_args=True)
+
+      with self.assertRaisesOpError(
+          "Number of classes exceeds `dtype` precision"):
+        l = array_ops.placeholder(dtype=dtypes.float16)
+        logit, _ = distribution_util.get_logits_and_probs(
+            logits=l, multidimensional=True, validate_args=True)
+        logit.eval(feed_dict={l: np.ones([int(2**11+1)])})
+
+
+class EmbedCheckCategoricalEventShapeTest(test.TestCase):
+
+  def testTooSmall(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        param = array_ops.ones([1], dtype=np.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+
+      with self.assertRaisesOpError(
+          "must have at least 2 events"):
+        param = array_ops.placeholder(dtype=dtypes.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+        checked_param.eval(feed_dict={param: np.ones([1])})
+
+  def testTooLarge(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        param = array_ops.ones([int(2**11+1)], dtype=dtypes.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+
+      with self.assertRaisesOpError(
+          "Number of classes exceeds `dtype` precision"):
+        param = array_ops.placeholder(dtype=dtypes.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+        checked_param.eval(feed_dict={param: np.ones([int(2**11+1)])})
+
+  def testUnsupportedDtype(self):
+    with self.test_session():
+      with self.assertRaises(TypeError):
+        param = array_ops.ones([int(2**11+1)], dtype=dtypes.qint16)
+        distribution_util.embed_check_categorical_event_shape(param)
+
+
+class EmbedCheckIntegerCastingClosedTest(test.TestCase):
+
+  def testCorrectlyAssertsNonnegative(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements must be non-negative"):
+        x = array_ops.placeholder(dtype=dtypes.float16)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.int16)
+        x_checked.eval(feed_dict={x: np.array([1, -1], dtype=np.float16)})
+
+  def testCorrectlyAssersIntegerForm(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements must be int16-equivalent."):
+        x = array_ops.placeholder(dtype=dtypes.float16)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.int16)
+        x_checked.eval(feed_dict={x: np.array([1, 1.5], dtype=np.float16)})
+
+  def testCorrectlyAssertsLargestPossibleInteger(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements cannot exceed 32767."):
+        x = array_ops.placeholder(dtype=dtypes.int32)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.int16)
+        x_checked.eval(feed_dict={x: np.array([1, 2**15], dtype=np.int32)})
+
+  def testCorrectlyAssertsSmallestPossibleInteger(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements cannot be smaller than 0."):
+        x = array_ops.placeholder(dtype=dtypes.int32)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.uint16, assert_nonnegative=False)
+        x_checked.eval(feed_dict={x: np.array([1, -1], dtype=np.int32)})
+
+
+class LogCombinationsTest(test.TestCase):
+
+  def testLogCombinationsBinomial(self):
+    n = [2, 5, 12, 15]
+    k = [1, 2, 4, 11]
+
+    if not special:
+      return
+
+    log_combs = np.log(special.binom(n, k))
+
+    with self.test_session():
+      n = np.array(n, dtype=np.float32)
+      counts = [[1., 1], [2., 3], [4., 8], [11, 4]]
+      log_binom = distribution_util.log_combinations(n, counts)
+      self.assertEqual([4], log_binom.get_shape())
+      self.assertAllClose(log_combs, log_binom.eval())
+
+  def testLogCombinationsShape(self):
+    # Shape [2, 2]
+    n = [[2, 5], [12, 15]]
+
+    with self.test_session():
+      n = np.array(n, dtype=np.float32)
+      # Shape [2, 2, 4]
+      counts = [[[1., 1, 0, 0], [2., 2, 1, 0]], [[4., 4, 1, 3], [10, 1, 1, 4]]]
+      log_binom = distribution_util.log_combinations(n, counts)
+      self.assertEqual([2, 2], log_binom.get_shape())
+
+
+class DynamicShapeTest(test.TestCase):
+
+  def testSameDynamicShape(self):
+    with self.test_session():
+      scalar = constant_op.constant(2.0)
+      scalar1 = array_ops.placeholder(dtype=dtypes.float32)
+
+      vector = [0.3, 0.4, 0.5]
+      vector1 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
+      vector2 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
+
+      multidimensional = [[0.3, 0.4], [0.2, 0.6]]
+      multidimensional1 = array_ops.placeholder(
+          dtype=dtypes.float32, shape=[None, None])
+      multidimensional2 = array_ops.placeholder(
+          dtype=dtypes.float32, shape=[None, None])
+
+      # Scalar
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(scalar, scalar1).eval({
+              scalar1: 2.0
+          }))
+
+      # Vector
+
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(vector, vector1).eval({
+              vector1: [2.0, 3.0, 4.0]
+          }))
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(vector1, vector2).eval({
+              vector1: [2.0, 3.0, 4.0],
+              vector2: [2.0, 3.5, 6.0]
+          }))
+
+      # Multidimensional
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(
+              multidimensional, multidimensional1).eval({
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+              }))
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(
+              multidimensional1, multidimensional2).eval({
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
+                  multidimensional2: [[1.0, 3.5], [6.3, 2.3]]
+              }))
+
+      # Scalar, X
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar, vector1).eval({
+              vector1: [2.0, 3.0, 4.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar1, vector1).eval({
+              scalar1: 2.0,
+              vector1: [2.0, 3.0, 4.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar, multidimensional1).eval({
+              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar1, multidimensional1).eval(
+              {
+                  scalar1: 2.0,
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+              }))
+
+      # Vector, X
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector, vector1).eval({
+              vector1: [2.0, 3.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector1, vector2).eval({
+              vector1: [2.0, 3.0, 4.0],
+              vector2: [6.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector, multidimensional1).eval({
+              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector1, multidimensional1).eval(
+              {
+                  vector1: [2.0, 3.0, 4.0],
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+              }))
+
+      # Multidimensional, X
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(
+              multidimensional, multidimensional1).eval({
+                  multidimensional1: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
+              }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(
+              multidimensional1, multidimensional2).eval({
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
+                  multidimensional2: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
+              }))
+
+
+class RotateTransposeTest(test.TestCase):
+
+  def _np_rotate_transpose(self, x, shift):
+    if not isinstance(x, np.ndarray):
+      x = np.array(x)
+    return np.transpose(x, np.roll(np.arange(len(x.shape)), shift))
+
+  def testRollStatic(self):
+    with self.test_session():
+      with self.assertRaisesRegexp(ValueError, "None values not supported."):
+        distribution_util.rotate_transpose(None, 1)
+      for x in (np.ones(1), np.ones((2, 1)), np.ones((3, 2, 1))):
+        for shift in np.arange(-5, 5):
+          y = distribution_util.rotate_transpose(x, shift)
+          self.assertAllEqual(self._np_rotate_transpose(x, shift), y.eval())
+          self.assertAllEqual(np.roll(x.shape, shift), y.get_shape().as_list())
+
+  def testRollDynamic(self):
+    with self.test_session() as sess:
+      x = array_ops.placeholder(dtypes.float32)
+      shift = array_ops.placeholder(dtypes.int32)
+      for x_value in (np.ones(
+          1, dtype=x.dtype.as_numpy_dtype()), np.ones(
+              (2, 1), dtype=x.dtype.as_numpy_dtype()), np.ones(
+                  (3, 2, 1), dtype=x.dtype.as_numpy_dtype())):
+        for shift_value in np.arange(-5, 5):
+          self.assertAllEqual(
+              self._np_rotate_transpose(x_value, shift_value),
+              sess.run(distribution_util.rotate_transpose(x, shift),
+                       feed_dict={x: x_value,
+                                  shift: shift_value}))
+
+
+class PickVectorTest(test.TestCase):
+
+  def testCorrectlyPicksVector(self):
+    with self.test_session():
+      x = np.arange(10, 12)
+      y = np.arange(15, 18)
+      self.assertAllEqual(x,
+                          distribution_util.pick_vector(
+                              math_ops.less(0, 5), x, y).eval())
+      self.assertAllEqual(y,
+                          distribution_util.pick_vector(
+                              math_ops.less(5, 0), x, y).eval())
+      self.assertAllEqual(x,
+                          distribution_util.pick_vector(
+                              constant_op.constant(True), x, y))  # No eval.
+      self.assertAllEqual(y,
+                          distribution_util.pick_vector(
+                              constant_op.constant(False), x, y))  # No eval.
+
+
+class FillLowerTriangularTest(test.TestCase):
+
+  def setUp(self):
+    self._rng = np.random.RandomState(42)
+
+  def _fill_lower_triangular(self, x):
+    """Numpy implementation of `fill_lower_triangular`."""
+    x = np.asarray(x)
+    d = x.shape[-1]
+    # d = n(n+1)/2 implies n is:
+    n = int(0.5 * (np.sqrt(1. + 8. * d) - 1.))
+    ids = np.tril_indices(n)
+    y = np.zeros(list(x.shape[:-1]) + [n, n], dtype=x.dtype)
+    y[..., ids[0], ids[1]] = x
+    return y
+
+  def testCorrectlyMakes1x1LowerTril(self):
+    with self.test_session():
+      x = ops.convert_to_tensor(self._rng.randn(3, 1))
+      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
+      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
+      self.assertAllEqual(expected.shape, actual.get_shape())
+      self.assertAllEqual(expected, actual.eval())
+
+  def testCorrectlyMakesNoBatchLowerTril(self):
+    with self.test_session():
+      x = ops.convert_to_tensor(self._rng.randn(10))
+      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
+      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
+      self.assertAllEqual(expected.shape, actual.get_shape())
+      self.assertAllEqual(expected, actual.eval())
+      g = gradients_impl.gradients(
+          distribution_util.fill_lower_triangular(x), x)
+      self.assertAllEqual(np.tri(4).reshape(-1), g[0].values.eval())
+
+  def testCorrectlyMakesBatchLowerTril(self):
+    with self.test_session():
+      x = ops.convert_to_tensor(self._rng.randn(2, 2, 6))
+      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
+      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
+      self.assertAllEqual(expected.shape, actual.get_shape())
+      self.assertAllEqual(expected, actual.eval())
+      self.assertAllEqual(
+          np.ones((2, 2, 6)),
+          gradients_impl.gradients(
+              distribution_util.fill_lower_triangular(x), x)[0].eval())
+
+
+class GenNewSeedTest(test.TestCase):
+
+  def testOnlyNoneReturnsNone(self):
+    self.assertFalse(distribution_util.gen_new_seed(0, "salt") is None)
+    self.assertTrue(distribution_util.gen_new_seed(None, "salt") is None)
+
+
+# TODO(jvdillon): Merge this test back into:
+# tensorflow/python/kernel_tests/softplus_op_test.py
+# once TF core is accepting new ops.
+class SoftplusTest(test.TestCase):
+
+  def _npSoftplus(self, np_features):
+    np_features = np.asarray(np_features)
+    zero = np.asarray(0).astype(np_features.dtype)
+    return np.logaddexp(zero, np_features)
+
+  def _testSoftplus(self, np_features, use_gpu=False):
+    np_features = np.asarray(np_features)
+    np_softplus = self._npSoftplus(np_features)
+    with self.test_session(use_gpu=use_gpu) as sess:
+      softplus = nn_ops.softplus(np_features)
+      softplus_inverse = distribution_util.softplus_inverse(softplus)
+      [tf_softplus, tf_softplus_inverse] = sess.run([
+          softplus, softplus_inverse])
+    self.assertAllCloseAccordingToType(np_softplus, tf_softplus)
+    rtol = {"float16": 0.07, "float32": 0.003, "float64": 0.002}.get(
+        str(np_features.dtype), 1e-6)
+    # This will test that we correctly computed the inverse by verifying we
+    # recovered the original input.
+    self.assertAllCloseAccordingToType(
+        np_features, tf_softplus_inverse,
+        atol=0., rtol=rtol)
+    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
+                        tf_softplus > 0)
+
+    self.assertShapeEqual(np_softplus, softplus)
+    self.assertShapeEqual(np_softplus, softplus_inverse)
+
+    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
+                        np.isfinite(tf_softplus))
+    self.assertAllEqual(np.ones_like(tf_softplus_inverse).astype(np.bool),
+                        np.isfinite(tf_softplus_inverse))
+
+  def testNumbers(self):
+    for t in [np.float16, np.float32, np.float64]:
+      lower = {np.float16: -15, np.float32: -50, np.float64: -50}.get(t, -100)
+      upper = {np.float16: 50, np.float32: 50, np.float64: 50}.get(t, 100)
+      self._testSoftplus(
+          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
+              [2, -1]),
+          use_gpu=False)
+      self._testSoftplus(
+          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
+              [2, -1]),
+          use_gpu=True)
+      log_eps = np.log(np.finfo(t).eps)
+      one = t(1)
+      ten = t(10)
+      self._testSoftplus(
+          [
+              log_eps, log_eps - one, log_eps + one, log_eps - ten,
+              log_eps + ten, -log_eps, -log_eps - one, -log_eps + one,
+              -log_eps - ten, -log_eps + ten
+          ],
+          use_gpu=False)
+      self._testSoftplus(
+          [
+              log_eps, log_eps - one, log_eps + one, log_eps - ten,
+              log_eps + ten - log_eps, -log_eps - one, -log_eps + one,
+              -log_eps - ten, -log_eps + ten
+          ],
+          use_gpu=True)
+
+  def testGradient(self):
+    with self.test_session():
+      x = constant_op.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5],
+          name="x")
+      y = nn_ops.softplus(x, name="softplus")
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float32,
+          order="F")
+      err = gradient_checker.compute_gradient_error(
+          x, [2, 5], y, [2, 5], x_init_value=x_init)
+    tf_logging.vlog(2, "softplus (float) gradient err = ", err)
+    self.assertLess(err, 1e-4)
+
+  def testInverseSoftplusGradientNeverNan(self):
+    with self.test_session():
+      # Note that this range contains both zero and inf.
+      x = constant_op.constant(np.logspace(-8, 6).astype(np.float16))
+      y = distribution_util.softplus_inverse(x)
+      grads = gradients_impl.gradients(y, x)[0].eval()
+      # Equivalent to `assertAllFalse` (if it existed).
+      self.assertAllEqual(np.zeros_like(grads).astype(np.bool), np.isnan(grads))
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/ops/distributions/bernoulli.py b/tensorflow/python/ops/distributions/bernoulli.py
index 3281b57e83..2b981e7b19 100644
--- a/tensorflow/python/ops/distributions/bernoulli.py
+++ b/tensorflow/python/ops/distributions/bernoulli.py
@@ -22,7 +22,6 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
@@ -121,7 +120,10 @@ class Bernoulli(distribution.Distribution):
     return math_ops.cast(sample, self.dtype)
 
   def _log_prob(self, event):
-    event = self._maybe_assert_valid_sample(event)
+    if self.validate_args:
+      event = distribution_util.embed_check_integer_casting_closed(
+          event, target_dtype=dtypes.bool)
+
     # TODO(jaana): The current sigmoid_cross_entropy_with_logits has
     # inconsistent  behavior for logits = inf/-inf.
     event = math_ops.cast(event, self.logits.dtype)
@@ -162,17 +164,6 @@ class Bernoulli(distribution.Distribution):
     """Returns `1` if `prob > 0.5` and `0` otherwise."""
     return math_ops.cast(self.probs > 0.5, self.dtype)
 
-  def _maybe_assert_valid_sample(self, event, check_integer=True):
-    if not self.validate_args:
-      return event
-    event = distribution_util.embed_check_nonnegative_discrete(
-        event, check_integer=check_integer)
-    return control_flow_ops.with_dependencies([
-        check_ops.assert_less_equal(
-            event, array_ops.ones_like(event),
-            message="event is not less than or equal to 1."),
-    ], event)
-
 
 class BernoulliWithSigmoidProbs(Bernoulli):
   """Bernoulli with `probs = nn.sigmoid(logits)`."""
diff --git a/tensorflow/python/ops/distributions/categorical.py b/tensorflow/python/ops/distributions/categorical.py
index bad7e6e42f..d72f6a1fe5 100644
--- a/tensorflow/python/ops/distributions/categorical.py
+++ b/tensorflow/python/ops/distributions/categorical.py
@@ -34,28 +34,74 @@ from tensorflow.python.ops.distributions import util as distribution_util
 class Categorical(distribution.Distribution):
   """Categorical distribution.
 
-  The categorical distribution is parameterized by the log-probabilities
-  of a set of classes.
+  The Categorical distribution is parameterized by either probabilities or
+  log-probabilities of a set of `K` classes. It is defined over the integers
+  `{0, 1, ..., K}`.
+
+  The Categorical distribution is closely related to the `OneHotCategorical` and
+  `Multinomial` distributions.  The Categorical distribution can be intuited as
+  generating samples according to `argmax{ OneHotCategorical(probs) }` itself
+  being identical to `argmax{ Multinomial(probs, total_count=1) }.
+
+  #### Mathematical Details
+
+  The probability mass function (pmf) is,
+
+  ```none
+  pmf(k; pi) = prod_j pi_j**[k == j]
+  ```
+
+  #### Pitfalls
+
+  The number of classes, `K`, must not exceed:
+  - the largest integer representable by `self.dtype`, i.e.,
+    `2**(mantissa_bits+1)` (IEE754),
+  - the maximum `Tensor` index, i.e., `2**31-1`.
+
+  In other words,
+
+  ```python
+  K <= min(2**31-1, {
+    tf.float16: 2**11,
+    tf.float32: 2**24,
+    tf.float64: 2**53 }[param.dtype])
+  ```
+
+  Note: This condition is validated only when `self.validate_args = True`.
 
   #### Examples
 
-  Creates a 3-class distribution, with the 2nd class, the most likely to be
-  drawn from.
+  Creates a 3-class distribution with the 2nd class being most likely.
 
   ```python
-  p = [0.1, 0.5, 0.4]
-  dist = Categorical(probs=p)
+  dist = Categorical(probs=[0.1, 0.5, 0.4])
+  n = 1e4
+  empirical_prob = tf.cast(
+      tf.histogram_fixed_width(
+        dist.sample(int(n)),
+        [0., 2],
+        nbins=3),
+      dtype=tf.float32) / n
+  # ==> array([ 0.1005,  0.5037,  0.3958], dtype=float32)
   ```
 
-  Creates a 3-class distribution, with the 2nd class the most likely to be
-  drawn from, using logits.
+  Creates a 3-class distribution with the 2nd class being most likely.
+  Parameterized by [logits](https://en.wikipedia.org/wiki/Logit) rather than
+  probabilities.
 
   ```python
-  logits = [-50, 400, 40]
-  dist = Categorical(logits=logits)
+  dist = Categorical(logits=np.log([0.1, 0.5, 0.4])
+  n = 1e4
+  empirical_prob = tf.cast(
+      tf.histogram_fixed_width(
+        dist.sample(int(n)),
+        [0., 2],
+        nbins=3),
+      dtype=tf.float32) / n
+  # ==> array([0.1045,  0.5047, 0.3908], dtype=float32)
   ```
 
-  Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
+  Creates a 3-class distribution with the 3rd class being most likely.
   The distribution functions can be evaluated on counts.
 
   ```python
@@ -116,6 +162,10 @@ class Categorical(distribution.Distribution):
           multidimensional=True,
           name=name)
 
+      if validate_args:
+        self._logits = distribution_util.embed_check_categorical_event_shape(
+            self._logits)
+
       logits_shape_static = self._logits.get_shape().with_rank_at_least(1)
       if logits_shape_static.ndims is not None:
         self._batch_rank = ops.convert_to_tensor(
@@ -186,22 +236,25 @@ class Categorical(distribution.Distribution):
       logits_2d = self.logits
     else:
       logits_2d = array_ops.reshape(self.logits, [-1, self.event_size])
-    samples = random_ops.multinomial(logits_2d, n, seed=seed)
-    samples = math_ops.cast(samples, self.dtype)
-    ret = array_ops.reshape(
-        array_ops.transpose(samples),
+    draws = random_ops.multinomial(logits_2d, n, seed=seed)
+    draws = array_ops.reshape(
+        array_ops.transpose(draws),
         array_ops.concat([[n], self.batch_shape_tensor()], 0))
-    return ret
+    return math_ops.cast(draws, self.dtype)
 
   def _cdf(self, k):
     k = ops.convert_to_tensor(k, name="k")
+    if self.validate_args:
+      k = distribution_util.embed_check_integer_casting_closed(
+          k, target_dtype=dtypes.int32)
 
     # If there are multiple batch dimension, flatten them into one.
     batch_flattened_probs = array_ops.reshape(self._probs,
                                               [-1, self._event_size])
-    batch_flattened_k = array_ops.reshape(k, (-1,))
+    batch_flattened_k = array_ops.reshape(k, [-1])
 
     # Form a tensor to sum over.
+    # We don't need to cast k to integer since `sequence_mask` does this for us.
     mask_tensor = array_ops.sequence_mask(batch_flattened_k, self._event_size)
     to_sum_over = array_ops.where(mask_tensor,
                                   batch_flattened_probs,
@@ -211,6 +264,10 @@ class Categorical(distribution.Distribution):
 
   def _log_prob(self, k):
     k = ops.convert_to_tensor(k, name="k")
+    if self.validate_args:
+      k = distribution_util.embed_check_integer_casting_closed(
+          k, target_dtype=dtypes.int32)
+
     if self.logits.get_shape()[:-1] == k.get_shape():
       logits = self.logits
     else:
@@ -219,12 +276,18 @@ class Categorical(distribution.Distribution):
       logits_shape = array_ops.shape(logits)[:-1]
       k *= array_ops.ones(logits_shape, dtype=k.dtype)
       k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
+      if k.dtype.is_integer:
+        pass
+      elif k.dtype.is_floating:
+        # When `validate_args=True` we've already ensured int/float casting
+        # is closed.
+        return ops.cast(k, dtype=dtypes.int32)
+      else:
+        raise TypeError("`value` should have integer `dtype` or "
+                        "`self.dtype` ({})".format(self.dtype.base_dtype))
     return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
                                                             logits=logits)
 
-  def _prob(self, k):
-    return math_ops.exp(self._log_prob(k))
-
   def _entropy(self):
     return -math_ops.reduce_sum(
         nn_ops.log_softmax(self.logits) * self.probs, axis=-1)
diff --git a/tensorflow/python/ops/distributions/dirichlet_multinomial.py b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
index 662a765558..f1b005f418 100644
--- a/tensorflow/python/ops/distributions/dirichlet_multinomial.py
+++ b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
@@ -36,7 +36,7 @@ __all__ = [
 
 
 _dirichlet_multinomial_sample_note = """For each batch of counts,
-`value = [n_0, ..., n_{k-1}]`, `P[value]` is the probability that after
+`value = [n_0, ..., n_{K-1}]`, `P[value]` is the probability that after
 sampling `self.total_count` draws from this Dirichlet-Multinomial distribution,
 the number of draws falling in class `j` is `n_j`. Since this definition is
 [exchangeable](https://en.wikipedia.org/wiki/Exchangeable_random_variables);
@@ -53,16 +53,16 @@ class DirichletMultinomial(distribution.Distribution):
   """Dirichlet-Multinomial compound distribution.
 
   The Dirichlet-Multinomial distribution is parameterized by a (batch of)
-  length-`k` `concentration` vectors (`k > 1`) and a `total_count` number of
+  length-`K` `concentration` vectors (`K > 1`) and a `total_count` number of
   trials, i.e., the number of trials per draw from the DirichletMultinomial. It
-  is defined over a (batch of) length-`k` vector `counts` such that
+  is defined over a (batch of) length-`K` vector `counts` such that
   `tf.reduce_sum(counts, -1) = total_count`. The Dirichlet-Multinomial is
-  identically the Beta-Binomial distribution when `k = 2`.
+  identically the Beta-Binomial distribution when `K = 2`.
 
   #### Mathematical Details
 
-  The Dirichlet-Multinomial is a distribution over `k`-class counts, i.e., a
-  length-`k` vector of non-negative integer `counts = n = [n_0, ..., n_{k-1}]`.
+  The Dirichlet-Multinomial is a distribution over `K`-class counts, i.e., a
+  length-`K` vector of non-negative integer `counts = n = [n_0, ..., n_{K-1}]`.
 
   The probability mass function (pmf) is,
 
@@ -73,7 +73,7 @@ class DirichletMultinomial(distribution.Distribution):
 
   where:
 
-  * `concentration = alpha = [alpha_0, ..., alpha_{k-1}]`, `alpha_j > 0`,
+  * `concentration = alpha = [alpha_0, ..., alpha_{K-1}]`, `alpha_j > 0`,
   * `total_count = N`, `N` a positive integer,
   * `N!` is `N` factorial, and,
   * `Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j)` is the
@@ -88,9 +88,9 @@ class DirichletMultinomial(distribution.Distribution):
   samples are generated as follows.
 
     1. Choose class probabilities:
-       `probs = [p_0,...,p_{k-1}] ~ Dir(concentration)`
+       `probs = [p_0,...,p_{K-1}] ~ Dir(concentration)`
     2. Draw integers:
-       `counts = [n_0,...,n_{k-1}] ~ Multinomial(total_count, probs)`
+       `counts = [n_0,...,n_{K-1}] ~ Multinomial(total_count, probs)`
 
   The last `concentration` dimension parametrizes a single Dirichlet-Multinomial
   distribution. When calling distribution functions (e.g., `dist.prob(counts)`),
@@ -101,6 +101,24 @@ class DirichletMultinomial(distribution.Distribution):
   Distribution parameters are automatically broadcast in all functions; see
   examples for details.
 
+  #### Pitfalls
+
+  The number of classes, `K`, must not exceed:
+  - the largest integer representable by `self.dtype`, i.e.,
+    `2**(mantissa_bits+1)` (IEE754),
+  - the maximum `Tensor` index, i.e., `2**31-1`.
+
+  In other words,
+
+  ```python
+  K <= min(2**31-1, {
+    tf.float16: 2**11,
+    tf.float32: 2**24,
+    tf.float64: 2**53 }[param.dtype])
+  ```
+
+  Note: This condition is validated only when `self.validate_args = True`.
+
   #### Examples
 
   ```python
@@ -157,8 +175,8 @@ class DirichletMultinomial(distribution.Distribution):
         Dirichlet multinomial distributions. Its components should be equal to
         integer values.
       concentration: Positive floating point tensor, whose dtype is the
-        same as `n` with shape broadcastable to `[N1,..., Nm, k]` `m >= 0`.
-        Defines this as a batch of `N1 x ... x Nm` different `k` class Dirichlet
+        same as `n` with shape broadcastable to `[N1,..., Nm, K]` `m >= 0`.
+        Defines this as a batch of `N1 x ... x Nm` different `K` class Dirichlet
         multinomial distributions.
       validate_args: Python `bool`, default `False`. When `True` distribution
         parameters are checked for validity despite possibly degrading runtime
@@ -180,9 +198,11 @@ class DirichletMultinomial(distribution.Distribution):
       #   created automatically by prepending). This forces enough explicitness.
       # * All calls involving `counts` eventually require a broadcast between
       #  `counts` and concentration.
-      self._total_count = self._maybe_assert_valid_total_count(
-          ops.convert_to_tensor(total_count, name="total_count"),
-          validate_args)
+      self._total_count = ops.convert_to_tensor(total_count, name="total_count")
+      if validate_args:
+        self._total_count = (
+            distribution_util.embed_check_nonnegative_integer_form(
+                self._total_count))
       self._concentration = self._maybe_assert_valid_concentration(
           ops.convert_to_tensor(concentration,
                                 name="concentration"),
@@ -242,7 +262,8 @@ class DirichletMultinomial(distribution.Distribution):
         seed=distribution_util.gen_new_seed(seed, salt="dirichlet_multinomial"))
     x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k), -2)
     final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
-    return array_ops.reshape(x, final_shape)
+    x = array_ops.reshape(x, final_shape)
+    return math_ops.cast(x, self.dtype)
 
   @distribution_util.AppendDocstring(_dirichlet_multinomial_sample_note)
   def _log_prob(self, counts):
@@ -302,42 +323,21 @@ class DirichletMultinomial(distribution.Distribution):
     """Checks the validity of the concentration parameter."""
     if not validate_args:
       return concentration
+    concentration = distribution_util.embed_check_categorical_event_shape(
+        concentration)
     return control_flow_ops.with_dependencies([
         check_ops.assert_positive(
             concentration,
             message="Concentration parameter must be positive."),
-        check_ops.assert_rank_at_least(
-            concentration, 1,
-            message="Concentration parameter must have >=1 dimensions."),
-        check_ops.assert_less(
-            1, array_ops.shape(concentration)[-1],
-            message="Concentration parameter must have event_size >= 2."),
     ], concentration)
 
-  def _maybe_assert_valid_total_count(self, total_count, validate_args):
-    if not validate_args:
-      return total_count
-    return control_flow_ops.with_dependencies([
-        check_ops.assert_non_negative(
-            total_count,
-            message="total_count must be non-negative."),
-        distribution_util.assert_integer_form(
-            total_count,
-            message="total_count cannot contain fractional values."),
-    ], total_count)
-
   def _maybe_assert_valid_sample(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
     if not self.validate_args:
       return counts
+    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
     return control_flow_ops.with_dependencies([
-        check_ops.assert_non_negative(
-            counts,
-            message="counts must be non-negative."),
         check_ops.assert_equal(
             self.total_count, math_ops.reduce_sum(counts, -1),
             message="counts last-dimension must sum to `self.total_count`"),
-        distribution_util.assert_integer_form(
-            counts,
-            message="counts cannot contain fractional components."),
     ], counts)
diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index a5bea7b4ba..c87389e356 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -52,17 +52,17 @@ class Multinomial(distribution.Distribution):
   """Multinomial distribution.
 
   This Multinomial distribution is parameterized by `probs`, a (batch of)
-  length-`k` `prob` (probability) vectors (`k > 1`) such that
+  length-`K` `prob` (probability) vectors (`K > 1`) such that
   `tf.reduce_sum(probs, -1) = 1`, and a `total_count` number of trials, i.e.,
   the number of trials per draw from the Multinomial. It is defined over a
-  (batch of) length-`k` vector `counts` such that
+  (batch of) length-`K` vector `counts` such that
   `tf.reduce_sum(counts, -1) = total_count`. The Multinomial is identically the
-  Binomial distribution when `k = 2`.
+  Binomial distribution when `K = 2`.
 
   #### Mathematical Details
 
-  The Multinomial is a distribution over `k`-class counts, i.e., a length-`k`
-  vector of non-negative integer `counts = n = [n_0, ..., n_{k-1}]`.
+  The Multinomial is a distribution over `K`-class counts, i.e., a length-`K`
+  vector of non-negative integer `counts = n = [n_0, ..., n_{K-1}]`.
 
   The probability mass function (pmf) is,
 
@@ -72,7 +72,7 @@ class Multinomial(distribution.Distribution):
   ```
 
   where:
-  * `probs = pi = [pi_0, ..., pi_{k-1}]`, `pi_j > 0`, `sum_j pi_j = 1`,
+  * `probs = pi = [pi_0, ..., pi_{K-1}]`, `pi_j > 0`, `sum_j pi_j = 1`,
   * `total_count = N`, `N` a positive integer,
   * `Z` is the normalization constant, and,
   * `N!` denotes `N` factorial.
@@ -80,6 +80,24 @@ class Multinomial(distribution.Distribution):
   Distribution parameters are automatically broadcast in all functions; see
   examples for details.
 
+  #### Pitfalls
+
+  The number of classes, `K`, must not exceed:
+  - the largest integer representable by `self.dtype`, i.e.,
+    `2**(mantissa_bits+1)` (IEE754),
+  - the maximum `Tensor` index, i.e., `2**31-1`.
+
+  In other words,
+
+  ```python
+  K <= min(2**31-1, {
+    tf.float16: 2**11,
+    tf.float32: 2**24,
+    tf.float64: 2**53 }[param.dtype])
+  ```
+
+  Note: This condition is validated only when `self.validate_args = True`.
+
   #### Examples
 
   Create a 3-class distribution, with the 3rd class is most likely to be drawn,
@@ -139,13 +157,13 @@ class Multinomial(distribution.Distribution):
         `N1 x ... x Nm` different Multinomial distributions. Its components
         should be equal to integer values.
       logits: Floating point tensor representing the log-odds of a
-        positive event with shape broadcastable to `[N1,..., Nm, k], m >= 0`,
+        positive event with shape broadcastable to `[N1,..., Nm, K], m >= 0`,
         and the same dtype as `total_count`. Defines this as a batch of
-        `N1 x ... x Nm` different `k` class Multinomial distributions. Only one
+        `N1 x ... x Nm` different `K` class Multinomial distributions. Only one
         of `logits` or `probs` should be passed in.
       probs: Positive floating point tensor with shape broadcastable to
-        `[N1,..., Nm, k]` `m >= 0` and same dtype as `total_count`. Defines
-        this as a batch of `N1 x ... x Nm` different `k` class Multinomial
+        `[N1,..., Nm, K]` `m >= 0` and same dtype as `total_count`. Defines
+        this as a batch of `N1 x ... x Nm` different `K` class Multinomial
         distributions. `probs`'s components in the last portion of its shape
         should sum to `1`. Only one of `logits` or `probs` should be passed in.
       validate_args: Python `bool`, default `False`. When `True` distribution
@@ -160,9 +178,11 @@ class Multinomial(distribution.Distribution):
     """
     parameters = locals()
     with ops.name_scope(name, values=[total_count, logits, probs]):
-      self._total_count = self._maybe_assert_valid_total_count(
-          ops.convert_to_tensor(total_count, name="total_count"),
-          validate_args)
+      self._total_count = ops.convert_to_tensor(total_count, name="total_count")
+      if validate_args:
+        self._total_count = (
+            distribution_util.embed_check_nonnegative_integer_form(
+                self._total_count))
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits=logits,
           probs=probs,
@@ -222,25 +242,22 @@ class Multinomial(distribution.Distribution):
     k = self.event_shape_tensor()[0]
     # Flatten batch dims so logits has shape [B, k],
     # where B = reduce_prod(self.batch_shape_tensor()).
-    draws = random_ops.multinomial(
+    x = random_ops.multinomial(
         logits=array_ops.reshape(self.logits, [-1, k]),
         num_samples=n * n_draws,
         seed=seed)
-    draws = array_ops.reshape(draws, shape=[-1, n, n_draws])
-    x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k),
+    x = array_ops.reshape(x, shape=[-1, n, n_draws])
+    x = math_ops.reduce_sum(array_ops.one_hot(x, depth=k),
                             axis=-2)  # shape: [B, n, k]
     x = array_ops.transpose(x, perm=[1, 0, 2])
     final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
-    return array_ops.reshape(x, final_shape)
+    x = array_ops.reshape(x, final_shape)
+    return math_ops.cast(x, self.dtype)
 
   @distribution_util.AppendDocstring(_multinomial_sample_note)
   def _log_prob(self, counts):
     return self._log_unnormalized_prob(counts) - self._log_normalization(counts)
 
-  @distribution_util.AppendDocstring(_multinomial_sample_note)
-  def _prob(self, counts):
-    return math_ops.exp(self._log_prob(counts))
-
   def _log_unnormalized_prob(self, counts):
     counts = self._maybe_assert_valid_sample(counts)
     return math_ops.reduce_sum(counts * math_ops.log(self.probs), -1)
@@ -265,25 +282,11 @@ class Multinomial(distribution.Distribution):
         self.total_count)[..., array_ops.newaxis]
     return self._mean_val - self._mean_val * p
 
-  def _maybe_assert_valid_total_count(self, total_count, validate_args):
-    if not validate_args:
-      return total_count
-    return control_flow_ops.with_dependencies([
-        check_ops.assert_non_negative(
-            total_count,
-            message="total_count must be non-negative."),
-        distribution_util.assert_integer_form(
-            total_count,
-            message="total_count cannot contain fractional values."),
-    ], total_count)
-
   def _maybe_assert_valid_sample(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
     if not self.validate_args:
       return counts
-
-    counts = distribution_util.embed_check_nonnegative_discrete(
-        counts, check_integer=True)
+    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
     return control_flow_ops.with_dependencies([
         check_ops.assert_equal(
             self.total_count, math_ops.reduce_sum(counts, -1),
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 3462d7957c..2caa341cd2 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -74,7 +74,8 @@ def assert_close(
 
 
 def assert_integer_form(
-    x, data=None, summarize=None, message=None, name="assert_integer_form"):
+    x, data=None, summarize=None, message=None,
+    int_dtype=None, name="assert_integer_form"):
   """Assert that x has integer components (or floats equal to integers).
 
   Args:
@@ -83,18 +84,30 @@ def assert_integer_form(
       error message and first few entries of `x` and `y`.
     summarize: Print this many entries of each tensor.
     message: A string to prefix to the default message.
+    int_dtype: A `tf.dtype` used to cast the float to. The default (`None`)
+      implies the smallest possible signed int will be used for casting.
     name: A name for this operation (optional).
 
   Returns:
-    Op raising `InvalidArgumentError` if round(x) != x.
+    Op raising `InvalidArgumentError` if `cast(x, int_dtype) != x`.
   """
-
-  message = message or "x has non-integer components"
-  x = ops.convert_to_tensor(x, name="x")
-  casted_x = math_ops.to_int64(x)
-  return check_ops.assert_equal(
-      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
-      data=data, summarize=summarize, message=message, name=name)
+  with ops.name_scope(name, values=[x, data]):
+    x = ops.convert_to_tensor(x, name="x")
+    if x.dtype.is_integer:
+      return control_flow_ops.no_op()
+    message = message or "{} has non-integer components".format(x.op.name)
+    if int_dtype is None:
+      try:
+        int_dtype = {
+            dtypes.float16: dtypes.int16,
+            dtypes.float32: dtypes.int32,
+            dtypes.float64: dtypes.int64,
+        }[x.dtype.base_dtype]
+      except KeyError:
+        raise TypeError("Unrecognized type {}".format(x.dtype.name))
+    return check_ops.assert_equal(
+        x, math_ops.cast(math_ops.cast(x, int_dtype), x.dtype),
+        data=data, summarize=summarize, message=message, name=name)
 
 
 def assert_symmetric(matrix):
@@ -103,14 +116,22 @@ def assert_symmetric(matrix):
       [check_ops.assert_equal(matrix, matrix_t)], matrix)
 
 
-def embed_check_nonnegative_discrete(x, check_integer=True):
+def embed_check_nonnegative_integer_form(
+    x, name="embed_check_nonnegative_integer_form"):
   """Assert x is a non-negative tensor, and optionally of integers."""
-  assertions = [check_ops.assert_non_negative(
-      x, message="x must be non-negative.")]
-  if check_integer:
-    assertions += [assert_integer_form(
-        x, message="x cannot contain fractional components.")]
-  return control_flow_ops.with_dependencies(assertions, x)
+  with ops.name_scope(name, values=[x]):
+    x = ops.convert_to_tensor(x, name="x")
+    assertions = [
+        check_ops.assert_non_negative(
+            x, message="'{}' must be non-negative.".format(x.op.name)),
+    ]
+    if not x.dtype.is_integer:
+      assertions += [
+          assert_integer_form(
+              x, message="'{}' cannot contain fractional components.".format(
+                  x.op.name)),
+      ]
+    return control_flow_ops.with_dependencies(assertions, x)
 
 
 def same_dynamic_shape(a, b):
@@ -175,16 +196,26 @@ def get_logits_and_probs(logits=None,
 
     if probs is None:
       logits = ops.convert_to_tensor(logits, name="logits")
+      if not logits.dtype.is_floating:
+        raise TypeError("logits must having floating type.")
+      # We can early return since we constructed probs and therefore know
+      # they're valid.
       if multidimensional:
+        if validate_args:
+          logits = embed_check_categorical_event_shape(logits)
         return logits, nn.softmax(logits, name="probs")
       return logits, math_ops.sigmoid(logits, name="probs")
 
     probs = ops.convert_to_tensor(probs, name="probs")
+    if not probs.dtype.is_floating:
+      raise TypeError("probs must having floating type.")
+
     if validate_args:
       with ops.name_scope("validate_probs"):
         one = constant_op.constant(1., probs.dtype)
         dependencies = [check_ops.assert_non_negative(probs)]
         if multidimensional:
+          probs = embed_check_categorical_event_shape(probs)
           dependencies += [assert_close(math_ops.reduce_sum(probs, -1), one,
                                         message="probs does not sum to 1.")]
         else:
@@ -205,6 +236,247 @@ def get_logits_and_probs(logits=None,
       return math_ops.log(probs) - math_ops.log1p(-1. * probs), probs
 
 
+def _is_known_unsigned_by_dtype(dt):
+  """Helper returning True if dtype is known to be unsigned."""
+  return {
+      dtypes.bool: True,
+      dtypes.uint8: True,
+      dtypes.uint16: True,
+  }.get(dt.base_dtype, False)
+
+
+def _is_known_signed_by_dtype(dt):
+  """Helper returning True if dtype is known to be signed."""
+  return {
+      dtypes.float16: True,
+      dtypes.float32: True,
+      dtypes.float64: True,
+      dtypes.int8: True,
+      dtypes.int16: True,
+      dtypes.int32: True,
+      dtypes.int64: True,
+  }.get(dt.base_dtype, False)
+
+
+def _is_known_dtype(dt):
+  """Helper returning True if dtype is known."""
+  return _is_known_unsigned_by_dtype(dt) or _is_known_signed_by_dtype(dt)
+
+
+def _largest_integer_by_dtype(dt):
+  """Helper returning the largest integer exactly representable by dtype."""
+  if not _is_known_dtype(dt):
+    raise TypeError("Unrecognized dtype: {}".format(dt.name))
+  if dt.is_floating:
+    return int(2**(np.finfo(dt.as_numpy_dtype).nmant + 1))
+  if dt.is_integer:
+    return np.iinfo(dt.as_numpy_dtype).max
+  if dt.base_dtype == dtypes.bool:
+    return int(1)
+  # We actually can't land here but keep the case for completeness.
+  raise TypeError("Unrecognized dtype: {}".format(dt.name))
+
+
+def _smallest_integer_by_dtype(dt):
+  """Helper returning the smallest integer exactly representable by dtype."""
+  if not _is_known_dtype(dt):
+    raise TypeError("Unrecognized dtype: {}".format(dt.name))
+  if _is_known_unsigned_by_dtype(dt):
+    return 0
+  return -1 * _largest_integer_by_dtype(dt)
+
+
+def _is_integer_like_by_dtype(dt):
+  """Helper returning True if dtype.is_interger or is `bool`."""
+  if not _is_known_dtype(dt):
+    raise TypeError("Unrecognized dtype: {}".format(dt.name))
+  return dt.is_integer or dt.base_dtype == dtypes.bool
+
+
+def embed_check_categorical_event_shape(
+    categorical_param,
+    name="embed_check_categorical_event_shape"):
+  """Embeds checks that categorical distributions don't have too many classes.
+
+  A categorical-type distribution is one which, e.g., returns the class label
+  rather than a one-hot encoding.  E.g., `Categorical(probs)`.
+
+  Since distributions output samples in the same dtype as the parameters, we
+  must ensure that casting doesn't lose precision. That is, the
+  `parameter.dtype` implies a maximum number of classes. However, since shape is
+  `int32` and categorical variables are presumed to be indexes into a `Tensor`,
+  we must also ensure that the number of classes is no larger than the largest
+  possible `int32` index, i.e., `2**31-1`.
+
+  In other words the number of classes, `K`, must satisfy the following
+  condition:
+
+  ```python
+  K <= min(
+      int(2**31 - 1),  # Largest float as an index.
+      {
+          dtypes.float16: int(2**11),   # Largest int as a float16.
+          dtypes.float32: int(2**24),
+          dtypes.float64: int(2**53),
+      }.get(categorical_param.dtype.base_dtype, 0))
+  ```
+
+  Args:
+    categorical_param: Floating-point `Tensor` representing parameters of
+      distribution over categories. The rightmost shape is presumed to be the
+      number of categories.
+    name: A name for this operation (optional).
+
+  Returns:
+    categorical_param: Input `Tensor` with appropriate assertions embedded.
+
+  Raises:
+    TypeError: if `categorical_param` has an unknown `dtype`.
+    ValueError: if we can statically identify `categorical_param` as being too
+      large (for being closed under int32/float casting).
+  """
+  with ops.name_scope(name, values=[categorical_param]):
+    x = ops.convert_to_tensor(categorical_param, name="categorical_param")
+    # The size must not exceed both of:
+    # - The largest possible int32 (since categorical values are presumed to be
+    #   indexes into a Tensor).
+    # - The largest possible integer exactly representable under the given
+    #   floating-point dtype (since we need to cast to/from).
+    #
+    # The chosen floating-point thresholds are 2**(1 + mantissa_bits).
+    # For more details, see:
+    # https://en.wikipedia.org/wiki/Floating-point_arithmetic#Internal_representation
+    x_dtype = x.dtype.base_dtype
+    max_event_size = (_largest_integer_by_dtype(x_dtype)
+                      if x_dtype.is_floating else 0)
+    if max_event_size is 0:
+      raise TypeError("Unable to validate size of unrecognized dtype "
+                      "({}).".format(x_dtype.name))
+    try:
+      x_shape_static = x.get_shape().with_rank_at_least(1)
+    except ValueError:
+      raise ValueError("A categorical-distribution parameter must have "
+                       "at least 1 dimension.")
+    if x_shape_static[-1].value is not None:
+      event_size = x_shape_static[-1].value
+      if event_size < 2:
+        raise ValueError("A categorical-distribution parameter must have at "
+                         "least 2 events.")
+      if event_size > max_event_size:
+        raise ValueError(
+            "Number of classes exceeds `dtype` precision, i.e., "
+            "{} implies shape ({}) cannot exceed {}.".format(
+                x_dtype.name, event_size, max_event_size))
+      return x
+    else:
+      event_size = array_ops.shape(x, name="x_shape")[-1]
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_rank_at_least(
+              x, 1, message=("A categorical-distribution parameter must have "
+                             "at least 1 dimension.")),
+          check_ops.assert_greater_equal(
+              array_ops.shape(x)[-1], 2,
+              message=("A categorical-distribution parameter must have at "
+                       "least 2 events.")),
+          check_ops.assert_less_equal(
+              event_size, max_event_size,
+              message="Number of classes exceeds `dtype` precision, "
+                      "i.e., {} dtype cannot exceed {} shape.".format(
+                          x_dtype.name, max_event_size)),
+      ], x)
+
+
+def embed_check_integer_casting_closed(
+    x,
+    target_dtype,
+    assert_nonnegative=True,
+    name="embed_check_casting_closed"):
+  """Ensures integers remain unaffected despite casting to/from int/float types.
+
+  Example integer-types: `uint8`, `int32`, `bool`.
+  Example floating-types: `float32`, `float64`.
+
+  The largest possible integer representable by an IEEE754 floating-point is
+  `2**(1 + mantissa_bits)` yet the largest possible integer as an int-type is
+  `2**(bits - 1) - 1`. This function ensures that a `Tensor` purporting to have
+  integer-form values can be cast to some other type without loss of precision.
+
+  The smallest representable integer is the negative of the largest
+  representable integer, except for types: `uint8`, `uint16`, `bool`. For these
+  types, the smallest representable integer is `0`.
+
+  Args:
+    x: `Tensor` representing integer-form values.
+    target_dtype: TF `dtype` under which `x` should have identical values.
+    assert_nonnegative: `bool` indicating `x` should contain nonnegative values.
+    name: A name for this operation (optional).
+
+  Returns:
+    x: Input `Tensor` with appropriate assertions embedded.
+
+  Raises:
+    TypeError: if `x` is neither integer- nor floating-type.
+    TypeError: if `target_dtype` is neither integer- nor floating-type.
+    TypeError: if neither `x` nor `target_dtype` are integer-type.
+  """
+
+  with ops.name_scope(name, values=[x]):
+    x = ops.convert_to_tensor(x, name="x")
+    if (not _is_integer_like_by_dtype(x.dtype)
+        and not x.dtype.is_floating):
+      raise TypeError("{}.dtype must be floating- or "
+                      "integer-type.".format(x.dtype.name))
+    if (not _is_integer_like_by_dtype(target_dtype)
+        and not target_dtype.is_floating):
+      raise TypeError("target_dtype ({}) must be floating- or "
+                      "integer-type.".format(target_dtype.name))
+    if (not _is_integer_like_by_dtype(x.dtype)
+        and not _is_integer_like_by_dtype(target_dtype)):
+      raise TypeError("At least one of {}.dtype ({}) and target_dtype ({}) "
+                      "must be integer-type.".format(
+                          x.op.name, x.dtype.name, target_dtype.name))
+
+    assertions = []
+    if assert_nonnegative:
+      assertions += [
+          check_ops.assert_non_negative(
+              x, message="Elements must be non-negative."),
+      ]
+
+    if x.dtype.is_floating:
+      # Being here means _is_integer_like_by_dtype(target_dtype) = True.
+      # Since this check implies the magnitude check below, we need only it.
+      assertions += [
+          assert_integer_form(
+              x, int_dtype=target_dtype,
+              message="Elements must be {}-equivalent.".format(
+                  target_dtype.name)),
+      ]
+    else:
+      if (_largest_integer_by_dtype(x.dtype)
+          > _largest_integer_by_dtype(target_dtype)):
+        # Cast may lose integer precision.
+        assertions += [
+            check_ops.assert_less_equal(
+                x, _largest_integer_by_dtype(target_dtype),
+                message=("Elements cannot exceed {}.".format(
+                    _largest_integer_by_dtype(target_dtype)))),
+        ]
+      if (not assert_nonnegative and
+          (_smallest_integer_by_dtype(x.dtype)
+           < _smallest_integer_by_dtype(target_dtype))):
+        assertions += [
+            check_ops.assert_greater_equal(
+                x, _smallest_integer_by_dtype(target_dtype),
+                message=("Elements cannot be smaller than {}.".format(
+                    _smallest_integer_by_dtype(target_dtype)))),
+        ]
+
+    if not assertions:
+      return x
+    return control_flow_ops.with_dependencies(assertions, x)
+
+
 def log_combinations(n, counts, name="log_combinations"):
   """Multinomial coefficient.
 
-- 
GitLab


From b1c56cc5d971c74062d140a1c5ce98afaa085402 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 14:11:46 -0700
Subject: [PATCH 0367/1375] Make HloModule clonable

This CL makes HloModule clonable, which is necessary when we want to run the same compilation twice with the same input.

PiperOrigin-RevId: 159874256
---
 .../compiler/xla/service/hlo_instruction.h    | 10 +++++++
 tensorflow/compiler/xla/service/hlo_module.cc | 30 +++++++++++++++++++
 tensorflow/compiler/xla/service/hlo_module.h  |  3 ++
 .../compiler/xla/service/hlo_module_test.cc   | 24 +++++++++++++++
 4 files changed, 67 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 8d1bc5e632..37cbb0b769 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -738,6 +738,16 @@ class HloInstruction {
     return called_computations_;
   }
 
+  // Replaces all called computations based on a map function. This is needed
+  // when we clone hlo_computations and want to let the instructions to point
+  // to the newly cloned nodes.
+  void ReplaceCalledComputations(
+      std::function<HloComputation*(HloComputation*)> map_function) {
+    for (int64 i = 0; i < called_computations_.size(); ++i) {
+      called_computations_[i] = map_function(called_computations_[i]);
+    }
+  }
+
   // Returns true if this instruction performs an elementwise operation on
   // `operand_idx`-th operand. An instruction is elementwise on an operand iff,
   // after performing necessary implicit broadcast
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index 22ef9c590b..4ed0af6033 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -301,6 +301,36 @@ std::list<HloComputation*> HloModule::MakeComputationPostOrder() const {
   return post_order;
 }
 
+std::unique_ptr<HloModule> HloModule::Clone(const string& suffix) {
+  VLOG(1) << "Cloning module :" << name_ << " --> " << suffix << "\n";
+  auto module = MakeUnique<HloModule>(name_ + "-" + suffix);
+  module->config_ = config_;
+  module->entry_computation_handle_ = entry_computation_handle_;
+  module->has_entry_computation_handle_ = has_entry_computation_handle_;
+
+  std::unordered_map<HloComputation*, HloComputation*> clone_map;
+  for (auto& computation : computations_) {
+    auto cloned_computation = computation->Clone(suffix);
+    InsertOrDie(&clone_map, computation.get(), cloned_computation.get());
+
+    if (entry_computation_ == computation.get()) {
+      module->AddEntryComputation(std::move(cloned_computation));
+    } else {
+      module->AddEmbeddedComputation(std::move(cloned_computation));
+    }
+  }
+
+  for (auto& cloned_computation : module->computations_) {
+    for (auto& instruction : cloned_computation->instructions()) {
+      // Rewrite instruction's called_computation to point to the cloned
+      // computations.
+      instruction->ReplaceCalledComputations(
+          [&](HloComputation* hlo) { return FindOrDie(clone_map, hlo); });
+    }
+  }
+  return module;
+}
+
 uint64 HloModule::RandomNew64() const {
   tensorflow::mutex_lock l(rng_mutex_);
   return rng_();
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index 4b14b4fd62..d089493c70 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -75,6 +75,9 @@ class HloModule {
 
   const string& name() const { return name_; }
 
+  // Returns a deep copy of this module including all computations.
+  std::unique_ptr<HloModule> Clone(const string& suffix = "clone");
+
   // Return a pointer to the entry computation of the module..
   HloComputation* entry_computation() const {
     CHECK_NE(nullptr, entry_computation_);
diff --git a/tensorflow/compiler/xla/service/hlo_module_test.cc b/tensorflow/compiler/xla/service/hlo_module_test.cc
index 58173bca07..56dc563203 100644
--- a/tensorflow/compiler/xla/service/hlo_module_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_test.cc
@@ -81,6 +81,30 @@ TEST_F(HloModuleTest, TwoComputationsPostOrder) {
   EXPECT_EQ(computation2->name(), "Constant.1");
 }
 
+TEST_F(HloModuleTest, CloneTest) {
+  // Create and copy a module with a diamond call graph of computations.
+  auto module = CreateNewModule();
+  auto computation1 =
+      module->AddEmbeddedComputation(CreateConstantComputation());
+  auto computation2 =
+      module->AddEmbeddedComputation(CreateCallComputation({computation1}));
+  auto computation3 =
+      module->AddEmbeddedComputation(CreateCallComputation({computation1}));
+  module->AddEntryComputation(
+      CreateCallComputation({computation2, computation3}));
+
+  auto post_order = module->MakeComputationPostOrder();
+  auto cloned_module = module->Clone("copy");
+  auto post_order_copied = cloned_module->MakeComputationPostOrder();
+
+  EXPECT_EQ(post_order.size(), post_order_copied.size());
+  for (auto origin = post_order.begin(), copied = post_order_copied.begin();
+       origin != post_order.end() && copied != post_order_copied.end();
+       ++origin, ++copied) {
+    EXPECT_EQ((*origin)->name() + "copy", (*copied)->name());
+  }
+}
+
 TEST_F(HloModuleTest, DiamondComputationsPostOrder) {
   // Create a module with a diamond call graph of computations.
   auto module = CreateNewModule();
-- 
GitLab


From d1d42ceda096910ad0e484bd637daebb8965f06f Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Thu, 22 Jun 2017 14:27:37 -0700
Subject: [PATCH 0368/1375] Internal change.

PiperOrigin-RevId: 159876942
---
 tensorflow/tools/test/BUILD           | 1 +
 tensorflow/tools/test/performance.bzl | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/test/BUILD b/tensorflow/tools/test/BUILD
index 9367bcd4a3..953463df4a 100644
--- a/tensorflow/tools/test/BUILD
+++ b/tensorflow/tools/test/BUILD
@@ -54,6 +54,7 @@ py_binary(
     name = "run_and_gather_logs",
     srcs = ["run_and_gather_logs.py"],
     srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
     deps = [
         ":run_and_gather_logs_lib",
         "//tensorflow/python:client_testlib",
diff --git a/tensorflow/tools/test/performance.bzl b/tensorflow/tools/test/performance.bzl
index 2956c6dde7..64fff844a7 100644
--- a/tensorflow/tools/test/performance.bzl
+++ b/tensorflow/tools/test/performance.bzl
@@ -28,7 +28,7 @@ def tf_cc_logged_benchmark(
       name = name,
       tags = all_tags,
       size = "large",
-      srcs = ["//tensorflow/tools/test:run_and_gather_logs.py"],
+      srcs = ["//tensorflow/tools/test:run_and_gather_logs"],
       args = [
           "--name=//%s:%s" % (PACKAGE_NAME, name),
           "--test_name=" + target,
-- 
GitLab


From cf88415190b04409227819005d0dea6ebbbd8b01 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 22 Jun 2017 14:33:52 -0700
Subject: [PATCH 0369/1375] Implement alternative `monte_carlo.expectation_v2`.
 This function implements the reparameterization and score-gradient tricks and
 does not depend on tf.Distribution like inputs.

PiperOrigin-RevId: 159877923
---
 tensorflow/contrib/bayesflow/BUILD            |  1 +
 .../python/kernel_tests/monte_carlo_test.py   | 98 ++++++++++++++-----
 .../bayesflow/python/ops/monte_carlo.py       |  1 +
 .../bayesflow/python/ops/monte_carlo_impl.py  | 95 ++++++++++++++++++
 4 files changed, 173 insertions(+), 22 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 3edcb6bc20..ab090aaa88 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -94,6 +94,7 @@ cuda_py_test(
     srcs = ["python/kernel_tests/monte_carlo_test.py"],
     additional_deps = [
         ":bayesflow_py",
+        "//third_party/py/numpy",
         "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:client_testlib",
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
index fd3c79976a..cf520f73ad 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
@@ -18,19 +18,23 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib import distributions as distributions_lib
+import numpy as np
+
 from tensorflow.contrib import layers as layers_lib
 from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo_lib
 from tensorflow.contrib.bayesflow.python.ops.monte_carlo_impl import _get_samples
+from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed
+from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.platform import test
 
-distributions = distributions_lib
+
 layers = layers_lib
-monte_carlo = monte_carlo_lib
+mc = monte_carlo_lib
 
 
 class ExpectationImportanceSampleTest(test.TestCase):
@@ -42,15 +46,15 @@ class ExpectationImportanceSampleTest(test.TestCase):
       mu_q = constant_op.constant([0.0, 0.0], dtype=dtypes.float64)
       sigma_p = constant_op.constant([0.5, 0.5], dtype=dtypes.float64)
       sigma_q = constant_op.constant([1.0, 1.0], dtype=dtypes.float64)
-      p = distributions.Normal(loc=mu_p, scale=sigma_p)
-      q = distributions.Normal(loc=mu_q, scale=sigma_q)
+      p = normal_lib.Normal(loc=mu_p, scale=sigma_p)
+      q = normal_lib.Normal(loc=mu_q, scale=sigma_q)
 
       # Compute E_p[X].
-      e_x = monte_carlo.expectation_importance_sampler(
+      e_x = mc.expectation_importance_sampler(
           f=lambda x: x, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
 
       # Compute E_p[X^2].
-      e_x2 = monte_carlo.expectation_importance_sampler(
+      e_x2 = mc.expectation_importance_sampler(
           f=math_ops.square, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
 
       stddev = math_ops.sqrt(e_x2 - math_ops.square(e_x))
@@ -68,10 +72,10 @@ class ExpectationImportanceSampleTest(test.TestCase):
     # the product of components in a MultivariateNormal are > 0.
     n = 1000
     with self.test_session():
-      p = distributions.MultivariateNormalDiag(
-          loc=[0.0, 0.0], scale_diag=[1.0, 1.0])
-      q = distributions.MultivariateNormalDiag(
-          loc=[0.5, 0.5], scale_diag=[3., 3.])
+      p = mvn_diag_lib.MultivariateNormalDiag(
+          loc=[0.], scale_diag=[1.0, 1.0])
+      q = mvn_diag_lib.MultivariateNormalDiag(
+          loc=[0.5], scale_diag=[3., 3.])
 
       # Compute E_p[X_1 * X_2 > 0], with X_i the ith component of X ~ p(x).
       # Should equal 1/2 because p is a spherical Gaussian centered at (0, 0).
@@ -79,7 +83,7 @@ class ExpectationImportanceSampleTest(test.TestCase):
         x1_times_x2 = math_ops.reduce_prod(x, reduction_indices=[-1])
         return 0.5 * (math_ops.sign(x1_times_x2) + 1.0)
 
-      prob = monte_carlo.expectation_importance_sampler(
+      prob = mc.expectation_importance_sampler(
           f=indicator, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
 
       # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
@@ -99,12 +103,12 @@ class ExpectationImportanceSampleLogspaceTest(test.TestCase):
       mu_q = constant_op.constant([-1.0, 1.0], dtype=dtypes.float64)
       sigma_p = constant_op.constant([1.0, 2 / 3.], dtype=dtypes.float64)
       sigma_q = constant_op.constant([1.0, 1.0], dtype=dtypes.float64)
-      p = distributions.Normal(loc=mu_p, scale=sigma_p)
-      q = distributions.Normal(loc=mu_q, scale=sigma_q)
+      p = normal_lib.Normal(loc=mu_p, scale=sigma_p)
+      q = normal_lib.Normal(loc=mu_q, scale=sigma_q)
 
       # Compute E_p[X^2].
       # Should equal [1, (2/3)^2]
-      log_e_x2 = monte_carlo.expectation_importance_sampler_logspace(
+      log_e_x2 = mc.expectation_importance_sampler_logspace(
           log_f=lambda x: math_ops.log(math_ops.square(x)),
           log_p=p.log_prob,
           sampling_dist_q=q,
@@ -124,11 +128,11 @@ class ExpectationTest(test.TestCase):
     random_seed.set_random_seed(0)
     n = 20000
     with self.test_session():
-      p = distributions.Normal(loc=[1.0, -1.0], scale=[0.3, 0.5])
+      p = normal_lib.Normal(loc=[1.0, -1.0], scale=[0.3, 0.5])
       # Compute E_p[X] and E_p[X^2].
       z = p.sample(n, seed=42)
-      e_x = monte_carlo.expectation(lambda x: x, p, z=z, seed=42)
-      e_x2 = monte_carlo.expectation(math_ops.square, p, z=z, seed=0)
+      e_x = mc.expectation(lambda x: x, p, z=z, seed=42)
+      e_x2 = mc.expectation(math_ops.square, p, z=z, seed=0)
       var = e_x2 - math_ops.square(e_x)
 
       self.assertEqual(p.batch_shape, e_x.get_shape())
@@ -145,7 +149,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_raises_if_both_z_and_n_are_none(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = None
       n = None
       seed = None
@@ -154,7 +158,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_raises_if_both_z_and_n_are_not_none(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = dist.sample(seed=42)
       n = 1
       seed = None
@@ -163,7 +167,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_returns_n_samples_if_n_provided(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = None
       n = 10
       seed = None
@@ -172,7 +176,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_returns_z_if_z_provided(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = dist.sample(10, seed=42)
       n = None
       seed = None
@@ -180,5 +184,55 @@ class GetSamplesTest(test.TestCase):
       self.assertEqual((10,), z.get_shape())
 
 
+class Expectationv2Test(test.TestCase):
+
+  def test_works_correctly(self):
+    with self.test_session() as sess:
+      x = constant_op.constant([-1e6, -100, -10, -1, 1, 10, 100, 1e6])
+      p = normal_lib.Normal(loc=x, scale=1.)
+
+      # We use the prefex "efx" to mean "E_p[f(X)]".
+      f = lambda u: u
+      efx_true = x
+      samples = p.sample(int(1e5), seed=1)
+      efx_reparam = mc.expectation_v2(f, samples, p.log_prob)
+      efx_score = mc.expectation_v2(f, samples, p.log_prob,
+                                    use_reparametrization=False)
+
+      [
+          efx_true_,
+          efx_reparam_,
+          efx_score_,
+          efx_true_grad_,
+          efx_reparam_grad_,
+          efx_score_grad_,
+      ] = sess.run([
+          efx_true,
+          efx_reparam,
+          efx_score,
+          gradients_impl.gradients(efx_true, x)[0],
+          gradients_impl.gradients(efx_reparam, x)[0],
+          gradients_impl.gradients(efx_score, x)[0],
+      ])
+
+      self.assertAllEqual(np.ones_like(efx_true_grad_), efx_true_grad_)
+
+      self.assertAllClose(efx_true_, efx_reparam_, rtol=0.005, atol=0.)
+      self.assertAllClose(efx_true_, efx_score_, rtol=0.005, atol=0.)
+
+      self.assertAllEqual(np.ones_like(efx_true_grad_, dtype=np.bool),
+                          np.isfinite(efx_reparam_grad_))
+      self.assertAllEqual(np.ones_like(efx_true_grad_, dtype=np.bool),
+                          np.isfinite(efx_score_grad_))
+
+      self.assertAllClose(efx_true_grad_, efx_reparam_grad_,
+                          rtol=0.03, atol=0.)
+      # Variance is too high to be meaningful, so we'll only check those which
+      # converge.
+      self.assertAllClose(efx_true_grad_[2:-2],
+                          efx_score_grad_[2:-2],
+                          rtol=0.05, atol=0.)
+
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
index 5770bcdd70..848a01421f 100644
--- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
@@ -29,6 +29,7 @@ from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = [
     'expectation',
+    'expectation_v2',
     'expectation_importance_sampler',
     'expectation_importance_sampler_logspace',
 ]
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
index 3590f940ac..3f836e7149 100644
--- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
@@ -32,6 +32,7 @@ from tensorflow.python.ops import nn
 
 __all__ = [
     'expectation',
+    'expectation_v2',
     'expectation_importance_sampler',
     'expectation_importance_sampler_logspace',
 ]
@@ -251,6 +252,100 @@ def expectation(f, p, z=None, n=None, seed=None, name='expectation'):
     return _sample_mean(f(z))
 
 
+def expectation_v2(f, samples, log_prob=None, use_reparametrization=True,
+                   axis=0, keep_dims=False, name=None):
+  """Computes the Monte-Carlo approximation of `E_p[f(X)]`.
+
+  This function computes the Monte-Carlo approximation of an expectation, i.e.,
+
+  ```none
+  E_p[f(X)] approx= m**-1 sum_i^m f(x_j),  x_j ~iid p(X)
+  ```
+
+  where:
+
+  - `x_j = samples[j, ...]`,
+  - `log(p(samples)) = log_prob(samples)` and
+  - `m = prod(shape(samples)[axis])`.
+
+  Tricks: Reparameterization and Score-Gradient
+
+  When p is "reparameterized", i.e., a diffeomorphic transformation of a
+  parameterless distribution (e.g.,
+  `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and
+  expectation, i.e.,
+  `grad[ Avg{ s_i : i=1...n } ] = Avg{ grad[s_i] : i=1...n }` where
+  `S_n = Avg{s_i}` and `s_i = f(x_i), x_i ~ p`.
+
+  However, if p is not reparameterized, TensorFlow's gradient will be incorrect
+  since the chain-rule stops at samples of unreparameterized distributions. In
+  this circumstance using the Score-Gradient trick results in an unbiased
+  gradient, i.e.,
+
+  ```none
+  grad[ E_p[f(X)] ]
+  = grad[ int dx p(x) f(x) ]
+  = int dx grad[ p(x) f(x) ]
+  = int dx [ p'(x) f(x) + p(x) f'(x) ]
+  = int dx p(x) [p'(x) / p(x) f(x) + f'(x) ]
+  = int dx p(x) grad[ f(x) p(x) / stop_grad[p(x)] ]
+  = E_p[ grad[ f(x) p(x) / stop_grad[p(x)] ] ]
+  ```
+
+  Unless p is not reparametrized, it is usually preferable to
+  `use_reparametrization = True`.
+
+  Warning: users are responsible for verifying `p` is a "reparameterized"
+  distribution.
+
+  Args:
+    f: Python callable which can return `f(samples)`.
+    samples: `Tensor` of samples used to form the Monte-Carlo approximation of
+      `E_p[f(X)]`.  A batch of samples should be indexed by `axis` dimensions.
+    log_prob: Python callable which can return `log_prob(samples)`. Must
+      correspond to the natural-logarithm of the pdf/pmf of each sample. Only
+      required/used if `use_reparametrization=False`.
+    use_reparametrization: Python `bool` indicating that the approximation
+      should use the fact that the gradient of samples is unbiased.
+    axis: The dimensions to average. If `None` (the default), averages all
+      dimensions.
+    keep_dims: If true, retains averaged dimensions with length 1.
+    name: A `name_scope` for operations created by this function (optional).
+      Default value: "expectation_v2".
+
+  Returns:
+    approx_expectation: `Tensor` corresponding to the Monte-Carlo approximation
+      of `E_p[f(X)]`.
+
+  Raises:
+    ValueError: if `f` is not `callable`.
+    ValueError: if `use_reparametrization=False` and `log_prob` is not
+      `callable`.
+  """
+
+  with ops.name_scope(name, 'expectation_v2', [samples]):
+    if not callable(f):
+      raise ValueError('`f` must be a callable function.')
+    if use_reparametrization:
+      return math_ops.reduce_mean(f(samples), axis=axis, keep_dims=keep_dims)
+    else:
+      if not callable(log_prob):
+        raise ValueError('`log_prob` must be a callable function.')
+      x = array_ops.stop_gradient(samples)
+      logpx = log_prob(x)
+      # Numerically, exp(g(x) - stop[g(x)]) is always 1, even if exp(g(x)) is
+      # unstable. But the gradient is also stable, ie,
+      # d/dx exp(g(x) - stop[g(x)])
+      # = exp(g(x) - stop[g(x)]) d/dx g(x)
+      # = d/dx g(x)   [numerically exact since IEEE754 has the property
+      #                that for any finite floating-point number x:
+      #                x - x == 0.0]
+      return math_ops.reduce_mean(
+          f(x) * math_ops.exp(logpx - array_ops.stop_gradient(logpx)),
+          axis=axis,
+          keep_dims=keep_dims)
+
+
 def _sample_mean(values):
   """Mean over sample indices.  In this module this is always [0]."""
   return math_ops.reduce_mean(values, reduction_indices=[0])
-- 
GitLab


From bac55c51f59c2578ae19b33cbfe384c946e471b6 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Thu, 22 Jun 2017 14:46:11 -0700
Subject: [PATCH 0370/1375] In SE_ASSIGN_OR_RETURN change ConsumeValueOrDie to
 the preferred std::move ValueOrDie.

PiperOrigin-RevId: 159879754
---
 tensorflow/stream_executor/lib/status_macros.h | 2 +-
 tensorflow/stream_executor/lib/statusor.h      | 9 ++++++++-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/tensorflow/stream_executor/lib/status_macros.h b/tensorflow/stream_executor/lib/status_macros.h
index 816132e85a..ff8f4a71c8 100644
--- a/tensorflow/stream_executor/lib/status_macros.h
+++ b/tensorflow/stream_executor/lib/status_macros.h
@@ -40,7 +40,7 @@ limitations under the License.
   if (!__name.ok()) {                                  \
     return __name.status();                            \
   }                                                    \
-  __lhs = __name.ConsumeValueOrDie();
+  __lhs = std::move(__name.ValueOrDie());
 
 // Early-returns the status if it is in error; otherwise, assigns the
 // right-hand-side expression to the left-hand-side expression.
diff --git a/tensorflow/stream_executor/lib/statusor.h b/tensorflow/stream_executor/lib/statusor.h
index e06550009a..138738ecab 100644
--- a/tensorflow/stream_executor/lib/statusor.h
+++ b/tensorflow/stream_executor/lib/statusor.h
@@ -49,7 +49,7 @@ limitations under the License.
 //
 //  StatusOr<std::unique_ptr<Foo>> result = FooFactory::MakeNewFoo(arg);
 //  if (result.ok()) {
-//    std::unique_ptr<Foo> foo = result.ConsumeValueOrDie();
+//    std::unique_ptr<Foo> foo = std::move(result.ValueOrDie());
 //    foo->DoSomethingCool();
 //  } else {
 //    LOG(ERROR) << result.status();
@@ -163,6 +163,7 @@ class StatusOr {
   // If you need to initialize a T object from the stored value,
   // ConsumeValueOrDie() may be more efficient.
   const T& ValueOrDie() const;
+  T& ValueOrDie();
 
   // Returns our current value, requires this->ok(). Use this if
   // you would otherwise want to say std::move(s.ValueOrDie()), for example
@@ -206,6 +207,12 @@ const T& StatusOr<T>::ValueOrDie() const {
   return value_;
 }
 
+template <typename T>
+T& StatusOr<T>::ValueOrDie() {
+  TF_CHECK_OK(status_);
+  return value_;
+}
+
 template <typename T>
 T StatusOr<T>::ConsumeValueOrDie() {
   TF_CHECK_OK(status_);
-- 
GitLab


From 96e254fb32841112805c79bc8df767854e9a48f3 Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Thu, 22 Jun 2017 14:46:46 -0700
Subject: [PATCH 0371/1375] If rank is unknown, do not add output shapes to
 transpose nodes.

PiperOrigin-RevId: 159879840
---
 .../grappler/optimizers/layout_optimizer.cc   | 28 ++++++++++---------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 1bc7b6d44d..ded1e474ce 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -240,20 +240,22 @@ class NodeProcessor {
     AttrValue attr_data_type_perm;
     attr_data_type_perm.set_type(DT_INT32);
     node->mutable_attr()->insert({"Tperm", attr_data_type_perm});
-    AttrValue attr_output_shape;
-    auto output_shape = attr_output_shape.mutable_list()->add_shape();
-    if (NHWCToNCHW) {
-      output_shape->add_dim()->set_size(input_shape.dim(0).size());
-      output_shape->add_dim()->set_size(input_shape.dim(3).size());
-      output_shape->add_dim()->set_size(input_shape.dim(1).size());
-      output_shape->add_dim()->set_size(input_shape.dim(2).size());
-    } else {
-      output_shape->add_dim()->set_size(input_shape.dim(0).size());
-      output_shape->add_dim()->set_size(input_shape.dim(2).size());
-      output_shape->add_dim()->set_size(input_shape.dim(3).size());
-      output_shape->add_dim()->set_size(input_shape.dim(1).size());
+    if (!input_shape.unknown_rank()) {
+      AttrValue attr_output_shape;
+      auto output_shape = attr_output_shape.mutable_list()->add_shape();
+      if (NHWCToNCHW) {
+        output_shape->add_dim()->set_size(input_shape.dim(0).size());
+        output_shape->add_dim()->set_size(input_shape.dim(3).size());
+        output_shape->add_dim()->set_size(input_shape.dim(1).size());
+        output_shape->add_dim()->set_size(input_shape.dim(2).size());
+      } else {
+        output_shape->add_dim()->set_size(input_shape.dim(0).size());
+        output_shape->add_dim()->set_size(input_shape.dim(2).size());
+        output_shape->add_dim()->set_size(input_shape.dim(3).size());
+        output_shape->add_dim()->set_size(input_shape.dim(1).size());
+      }
+      node->mutable_attr()->insert({"_output_shapes", attr_output_shape});
     }
-    node->mutable_attr()->insert({"_output_shapes", attr_output_shape});
   }
 
   virtual Status AddLayoutTransposeToInputs() {
-- 
GitLab


From 30db28e416b5b39f72abf78a225817556e38e399 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 22 Jun 2017 14:48:02 -0700
Subject: [PATCH 0372/1375] Move sparse_fill_empty_rows to new, *significantly*
 faster, C++ kernel for everyone.

Also fix a bug in the C++ op when the input ST has 0 elements.

PiperOrigin-RevId: 159880044
---
 .../core/kernels/sparse_fill_empty_rows_op.cc | 25 +++++++-
 tensorflow/python/ops/sparse_ops.py           | 57 ++++---------------
 2 files changed, 34 insertions(+), 48 deletions(-)

diff --git a/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc b/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc
index 3b915e419b..81eead11d1 100644
--- a/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc
+++ b/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc
@@ -74,6 +74,7 @@ class SparseFillEmptyRowsOp : public OpKernel {
 
     const int64 N = indices_t->shape().dim_size(0);
     const int64 dense_rows = dense_shape(0);
+
     Tensor* empty_row_indicator_t;
     OP_REQUIRES_OK(context, context->allocate_output("empty_row_indicator",
                                                      TensorShape({dense_rows}),
@@ -84,6 +85,29 @@ class SparseFillEmptyRowsOp : public OpKernel {
         context, context->allocate_output("reverse_index_map", TensorShape({N}),
                                           &reverse_index_map_t));
     auto reverse_index_map = reverse_index_map_t->vec<int64>();
+
+    int rank = indices_t->shape().dim_size(1);
+
+    if (dense_rows == 0) {
+      OP_REQUIRES(
+          context, N == 0,
+          errors::InvalidArgument("Received SparseTensor with dense_shape[0] = "
+                                  "0 but indices.shape[0] = ",
+                                  N));
+      Tensor* output_indices_t;
+      TensorShape output_indices_shape({0, rank});
+      OP_REQUIRES_OK(context, context->allocate_output("output_indices",
+                                                       output_indices_shape,
+                                                       &output_indices_t));
+      Tensor* output_values_t;
+      OP_REQUIRES_OK(context,
+                     context->allocate_output("output_values", TensorShape({0}),
+                                              &output_values_t));
+
+      // Exit early, nothing more to do.
+      return;
+    }
+
     Tensor scratch_t;
     OP_REQUIRES_OK(context,
                    context->allocate_temp(DT_INT64, TensorShape({dense_rows}),
@@ -115,7 +139,6 @@ class SparseFillEmptyRowsOp : public OpKernel {
       }
     }
     Tensor* output_indices_t;
-    int rank = indices_t->shape().dim_size(1);
     const int64 N_full = scratch(dense_rows - 1);
     TensorShape output_indices_shape({N_full, rank});
     OP_REQUIRES_OK(context, context->allocate_output("output_indices",
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index b52610661f..377adcdab8 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -1189,10 +1189,6 @@ def sparse_reset_shape(sp_input, new_shape=None):
   return sparse_tensor.SparseTensor(in_indices, in_values, output_shape_tensor)
 
 
-# TODO(b/37517434): Delete this variable on 20170610.
-_SPARSE_FILL_EMPTY_ROWS_FAST_PATH = False
-
-
 def sparse_fill_empty_rows(sp_input, default_value, name=None):
   """Fills empty rows in the input 2-D `SparseTensor` with a default value.
 
@@ -1241,52 +1237,19 @@ def sparse_fill_empty_rows(sp_input, default_value, name=None):
     TypeError: If `sp_input` is not a `SparseTensor`.
   """
   sp_input = _convert_to_sparse_tensor(sp_input)
-
-  # TODO(b/37517434): Delete the slow path and only use the fast path
-  # on 20170610.
   with ops.name_scope(name, "SparseFillEmptyRows", [sp_input]):
     default_value = ops.convert_to_tensor(
         default_value, dtype=sp_input.values.dtype)
-    if _SPARSE_FILL_EMPTY_ROWS_FAST_PATH:
-      (output_indices, output_values, empty_row_indicator,
-       unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows(
-           indices=sp_input.indices,
-           values=sp_input.values,
-           dense_shape=sp_input.dense_shape,
-           default_value=default_value)
-      return (sparse_tensor.SparseTensor(
-          indices=output_indices,
-          values=output_values,
-          dense_shape=sp_input.dense_shape), empty_row_indicator)
-    else:
-      num_rows = math_ops.cast(sp_input.dense_shape[0], dtypes.int32)
-      all_row_indices = math_ops.cast(math_ops.range(num_rows), dtypes.int64)
-      empty_row_indices, _ = array_ops.setdiff1d(all_row_indices,
-                                                 sp_input.indices[:, 0])
-      empty_row_indicator = sparse_to_dense(
-          empty_row_indices,
-          array_ops.expand_dims(sp_input.dense_shape[0], -1), True,
-          False)
-
-      empty_row_indices_as_column = array_ops.reshape(
-          empty_row_indices, [-1, 1])
-      additional_indices = array_ops.concat([
-          empty_row_indices_as_column,
-          array_ops.zeros_like(empty_row_indices_as_column)
-      ], 1)
-      additional_values = array_ops.fill(
-          array_ops.shape(empty_row_indices), default_value)
-
-      all_indices_unordered = array_ops.concat(
-          [sp_input.indices, additional_indices], 0)
-      all_values_unordered = array_ops.concat(
-          [sp_input.values, additional_values], 0)
-      sp_unordered_output = sparse_tensor.SparseTensor(
-          all_indices_unordered,
-          all_values_unordered, sp_input.dense_shape)
-      sp_ordered_output = sparse_reorder(sp_unordered_output)
-
-      return sp_ordered_output, empty_row_indicator
+    (output_indices, output_values, empty_row_indicator,
+     unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows(
+         indices=sp_input.indices,
+         values=sp_input.values,
+         dense_shape=sp_input.dense_shape,
+         default_value=default_value)
+    return (sparse_tensor.SparseTensor(indices=output_indices,
+                                       values=output_values,
+                                       dense_shape=sp_input.dense_shape),
+            empty_row_indicator)
 
 
 def serialize_sparse(sp_input, name=None):
-- 
GitLab


From f4945e74d6da0e816d992597e42de088e790df87 Mon Sep 17 00:00:00 2001
From: Makoto Uchida <muchida@google.com>
Date: Thu, 22 Jun 2017 15:14:51 -0700
Subject: [PATCH 0373/1375] Add support of label_keys to DebugClassifier

PiperOrigin-RevId: 159883986
---
 .../learn/python/learn/estimators/debug.py      | 12 ++++++++----
 .../learn/python/learn/estimators/debug_test.py | 17 +++++++++++++++++
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug.py b/tensorflow/contrib/learn/python/learn/estimators/debug.py
index 010448be12..9d5f6c2bf9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug.py
@@ -157,7 +157,8 @@ class DebugClassifier(estimator.Estimator):
                n_classes=2,
                weight_column_name=None,
                config=None,
-               feature_engineering_fn=None):
+               feature_engineering_fn=None,
+               label_keys=None):
     """Initializes a DebugClassifier instance.
 
     Args:
@@ -175,6 +176,8 @@ class DebugClassifier(estimator.Estimator):
       feature_engineering_fn: Feature engineering function. Takes features and
                         labels which are the output of `input_fn` and returns
                         features and labels which will be fed into the model.
+      label_keys: Optional list of strings with size `[n_classes]` defining the
+        label vocabulary. Only supported for `n_classes` > 2.
     Returns:
       A `DebugClassifier` estimator.
 
@@ -182,10 +185,11 @@ class DebugClassifier(estimator.Estimator):
       ValueError: If `n_classes` < 2.
     """
     params = {"head":
-              head_lib._multi_class_head(  # pylint: disable=protected-access
+              head_lib.multi_class_head(
                   n_classes=n_classes,
                   weight_column_name=weight_column_name,
-                  enable_centered_bias=True)}
+                  enable_centered_bias=True,
+                  label_keys=label_keys)}
 
     super(DebugClassifier, self).__init__(
         model_fn=debug_model_fn,
@@ -292,7 +296,7 @@ class DebugRegressor(estimator.Estimator):
 
     params = {
         "head":
-            head_lib._regression_head(  # pylint: disable=protected-access
+            head_lib.regression_head(
                 weight_column_name=weight_column_name,
                 label_dimension=label_dimension,
                 enable_centered_bias=True)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
index 935e66ee8c..b4b552c025 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
@@ -363,6 +363,23 @@ class DebugClassifierTest(test.TestCase):
     scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
     self._assertInRange(0.0, 1.0, scores['accuracy'])
 
+  def testMultiClass_StringLabel(self):
+    """Tests multi-class classification with string labels."""
+
+    def _input_fn_train():
+      labels = constant_op.constant([['foo'], ['bar'], ['baz'], ['bar']])
+      features = {
+          'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
+      }
+      return features, labels
+
+    classifier = debug.DebugClassifier(
+        n_classes=3, label_keys=['foo', 'bar', 'baz'])
+
+    classifier.fit(input_fn=_input_fn_train, steps=5)
+    scores = classifier.evaluate(input_fn=_input_fn_train, steps=1)
+    self.assertIn('loss', scores)
+
   def testLoss(self):
     """Tests loss calculation."""
 
-- 
GitLab


From 2336cdf7f00434c24f1eb394f255d7de071bdc08 Mon Sep 17 00:00:00 2001
From: Maxwell Paul Brickner <mbrickn@users.noreply.github.com>
Date: Thu, 22 Jun 2017 18:25:37 -0400
Subject: [PATCH 0374/1375] Updated link to use HTTPS (#10998)

Howdy!

I just updated a link to use https instead of http.

Thanks!
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index cbc94c1ab2..abbead98a7 100644
--- a/README.md
+++ b/README.md
@@ -40,7 +40,7 @@ People who are a little more adventurous can also try our nightly binaries:
 * Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
 * Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
 * Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
-- 
GitLab


From ed1d7fdb5225e2935f05b40c8ef3cfe7261cc37e Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Thu, 22 Jun 2017 15:27:53 -0700
Subject: [PATCH 0375/1375] Register devices under their legacy names

Because some higher level APIs continue to use the legacy name format,
when using ClusterSpec propagation, we need to ensure that we register
the devices under their legacy names as well as their canonical names.

PiperOrigin-RevId: 159885777
---
 tensorflow/core/common_runtime/device_mgr.cc  |  6 ++
 tensorflow/core/util/device_name_utils.cc     | 12 ++++
 tensorflow/core/util/device_name_utils.h      |  3 +
 .../core/util/device_name_utils_test.cc       |  3 +
 .../client/session_clusterspec_prop_test.py   | 58 ++++++++++++++++++-
 5 files changed, 81 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/common_runtime/device_mgr.cc b/tensorflow/core/common_runtime/device_mgr.cc
index 31f12d4833..471463fc8b 100644
--- a/tensorflow/core/common_runtime/device_mgr.cc
+++ b/tensorflow/core/common_runtime/device_mgr.cc
@@ -33,6 +33,7 @@ DeviceMgr::DeviceMgr(const std::vector<Device*>& devices)
     string full_name = d->name();
     device_map_[CopyToBackingStore(full_name)] = d;
 
+    // TODO(b/62909072): Upgrade device_map_ to a better data structure.
     DeviceNameUtils::ParsedName parsed_name = d->parsed_name();
     if (parsed_name.has_job && parsed_name.has_replica &&
         parsed_name.has_task && parsed_name.has_type && parsed_name.has_id) {
@@ -40,6 +41,11 @@ DeviceMgr::DeviceMgr(const std::vector<Device*>& devices)
           parsed_name.job, parsed_name.replica, parsed_name.task,
           parsed_name.type, parsed_name.id);
       device_map_[CopyToBackingStore(canonical_name)] = d;
+
+      string legacy_name = DeviceNameUtils::LegacyName(
+          parsed_name.job, parsed_name.replica, parsed_name.task,
+          parsed_name.type, parsed_name.id);
+      device_map_[CopyToBackingStore(legacy_name)] = d;
     }
     string lname = DeviceNameUtils::LocalName(d->name());
     device_map_[CopyToBackingStore(lname)] = d;
diff --git a/tensorflow/core/util/device_name_utils.cc b/tensorflow/core/util/device_name_utils.cc
index 64aa0ac209..1773ac00b7 100644
--- a/tensorflow/core/util/device_name_utils.cc
+++ b/tensorflow/core/util/device_name_utils.cc
@@ -97,6 +97,18 @@ string DeviceNameUtils::FullName(const string& job, int replica, int task,
                          "/device:", type, ":", id);
 }
 
+/* static */
+string DeviceNameUtils::LegacyName(const string& job, int replica, int task,
+                                   const string& type, int id) {
+  CHECK(IsJobName(job)) << job;
+  CHECK_LE(0, replica);
+  CHECK_LE(0, task);
+  CHECK(!type.empty());
+  CHECK_LE(0, id);
+  return strings::StrCat("/job:", job, "/replica:", replica, "/task:", task,
+                         "/", str_util::Lowercase(type), ":", id);
+}
+
 bool DeviceNameUtils::ParseFullName(StringPiece fullname, ParsedName* p) {
   p->Clear();
   if (fullname == "/") {
diff --git a/tensorflow/core/util/device_name_utils.h b/tensorflow/core/util/device_name_utils.h
index 4228af8b8c..1f32828bae 100644
--- a/tensorflow/core/util/device_name_utils.h
+++ b/tensorflow/core/util/device_name_utils.h
@@ -48,6 +48,9 @@ class DeviceNameUtils {
   // Returns a fully qualified device name given the parameters.
   static string FullName(const string& job, int replica, int task,
                          const string& type, int id);
+  // Returns a fully qualified device name given the parameters in legacy style.
+  static string LegacyName(const string& job, int replica, int task,
+                           const string& type, int id);
 
   struct ParsedName {
     void Clear() {
diff --git a/tensorflow/core/util/device_name_utils_test.cc b/tensorflow/core/util/device_name_utils_test.cc
index ed511629b6..e44b840967 100644
--- a/tensorflow/core/util/device_name_utils_test.cc
+++ b/tensorflow/core/util/device_name_utils_test.cc
@@ -69,6 +69,9 @@ TEST(DeviceNameUtilsTest, Basic) {
   EXPECT_EQ(DeviceNameUtils::FullName("hello", 1, 2, "CPU", 3),
             "/job:hello/replica:1/task:2/device:CPU:3");
 
+  EXPECT_EQ(DeviceNameUtils::LegacyName("hello", 1, 2, "CPU", 3),
+            "/job:hello/replica:1/task:2/cpu:3");
+
   {
     DeviceNameUtils::ParsedName p;
     EXPECT_FALSE(DeviceNameUtils::ParseFullName("foobar", &p));
diff --git a/tensorflow/python/client/session_clusterspec_prop_test.py b/tensorflow/python/client/session_clusterspec_prop_test.py
index f982c6ae37..37cbf8062c 100644
--- a/tensorflow/python/client/session_clusterspec_prop_test.py
+++ b/tensorflow/python/client/session_clusterspec_prop_test.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Tests for tensorflow.python.client.session.Session's ClusterSpec Propagation.
 
 These tests exercise the ClusterSpec Propagation capabilities of distributed
@@ -111,6 +110,63 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
     output = sess.run(const)
     self.assertEqual(17, output)
 
+  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
+  def testCanonicalDeviceNames(self):
+    server1 = server_lib.Server.create_local_server()
+    server2 = server_lib.Server.create_local_server()
+    cluster_def = cluster_pb2.ClusterDef()
+    job = cluster_def.job.add()
+    job.name = 'worker'
+    job.tasks[0] = server1.target[len('grpc://'):]
+    job.tasks[1] = server2.target[len('grpc://'):]
+    config = config_pb2.ConfigProto(cluster_def=cluster_def)
+
+    with ops.Graph().as_default() as g, ops.device(
+        '/job:worker/task:1/device:CPU:0'):
+      const = constant_op.constant(17)
+    sess = session.Session(server1.target, config=config, graph=g)
+    run_options = config_pb2.RunOptions(
+        trace_level=config_pb2.RunOptions.FULL_TRACE)
+    run_metadata = config_pb2.RunMetadata()
+    output = sess.run(const, options=run_options, run_metadata=run_metadata)
+    self.assertEqual(17, output)
+    self.assertEqual(1,
+                     len([
+                         node_stats
+                         for dev_stats in run_metadata.step_stats.dev_stats
+                         for node_stats in dev_stats.node_stats
+                         if '/job:worker/replica:0/task:1/device:CPU:0' ==
+                         dev_stats.device and 'Const' == node_stats.node_name
+                     ]))
+
+  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
+  def testLegacyDeviceNames(self):
+    server1 = server_lib.Server.create_local_server()
+    server2 = server_lib.Server.create_local_server()
+    cluster_def = cluster_pb2.ClusterDef()
+    job = cluster_def.job.add()
+    job.name = 'worker'
+    job.tasks[0] = server1.target[len('grpc://'):]
+    job.tasks[1] = server2.target[len('grpc://'):]
+    config = config_pb2.ConfigProto(cluster_def=cluster_def)
+
+    with ops.Graph().as_default() as g, ops.device('/job:worker/task:1/cpu:0'):
+      const = constant_op.constant(17)
+    sess = session.Session(server1.target, config=config, graph=g)
+    run_options = config_pb2.RunOptions(
+        trace_level=config_pb2.RunOptions.FULL_TRACE)
+    run_metadata = config_pb2.RunMetadata()
+    output = sess.run(const, options=run_options, run_metadata=run_metadata)
+    self.assertEqual(17, output)
+    self.assertEqual(1,
+                     len([
+                         node_stats
+                         for dev_stats in run_metadata.step_stats.dev_stats
+                         for node_stats in dev_stats.node_stats
+                         if '/job:worker/replica:0/task:1/device:CPU:0' ==
+                         dev_stats.device and 'Const' == node_stats.node_name
+                     ]))
+
   @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationThreeServers2Graphs(self):
     """Boots 3 servers, creates 2 sessions, ensures appropriate operations.
-- 
GitLab


From 338a7ead4475d6b97b420d6d1c56ff66815e3e7b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 15:30:39 -0700
Subject: [PATCH 0376/1375] [BatchNorm] Minor fixes to TF doc

PiperOrigin-RevId: 159886125
---
 .../docs_src/performance/xla/operation_semantics.md       | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index 120260448d..9cb27c7e95 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -624,7 +624,7 @@ See also
 [`the original batch normalization paper`](https://arxiv.org/abs/1502.03167)
 for a detailed description of the algorithm.
 
-<b> Warning: Not implemented yet </b>
+<b> Warning: Not implemented on GPU backend yet. </b>
 
 Normalizes an array across batch and spatial dimensions.
 
@@ -681,7 +681,7 @@ spatial dimensions using the formulars above.
 See also
 [`ComputationBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
 
-<b> Warning: Not implemented yet </b>
+<b> Warning: Not implemented yet. </b>
 
 Normalizes an array across batch and spatial dimensions.
 
@@ -718,11 +718,11 @@ The output is a n dimensional, normalized array with the same shape as input
 See also
 [`ComputationBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
 
-<b> Warning: Not implemented yet </b>
+<b> Warning: Not implemented yet. </b>
 
 Calculates gradients of batch norm.
 
-<b> `BatchNormGrad(x, scale, mean, variance, epsilon, grad_y, feature_index)` </b>
+<b> `BatchNormGrad(operand, scale, mean, variance, grad_output, epsilon, feature_index)` </b>
 
 | Arguments       | Type                    | Semantics                        |
 | --------------  | ----------------------- | -------------------------------- |
-- 
GitLab


From 07678fef5510d4a7c89d28b222ce72df49456a97 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 16:47:48 -0700
Subject: [PATCH 0377/1375] Generating TBAA metadata causes the LLVM to
 miscompile after https://reviews.llvm.org/rL305938).  Disable TBAA (to stop
 the miscompiles) while we fix the root issue.

PiperOrigin-RevId: 159895736
---
 .../compiler/xla/service/llvm_ir/llvm_util.cc | 23 +++----------------
 1 file changed, 3 insertions(+), 20 deletions(-)

diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index bcc9418d59..e348511c62 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -356,26 +356,9 @@ void EmitLogging(const char* tag, llvm::Value* value,
 
 void SetTbaaForInstruction(llvm::Instruction* instruction, Shape shape,
                            bool is_pointer_to) {
-  llvm::MDBuilder metadata_builder(instruction->getContext());
-  llvm::MDNode* root = metadata_builder.createTBAARoot("XLA TBAA");
-  string type_name;
-  if (is_pointer_to) {
-    type_name += "pointer-to ";
-  }
-  // Scalars do not have layout which makes it permissible to omit an explicit
-  // layout.  To make sure that equivalent scalar shapes have the same TBAA,
-  // remove the (meaningless) explicit layout if one is present.
-  if (!ShapeUtil::IsArray(shape) || ShapeUtil::IsScalar(shape)) {
-    LayoutUtil::ClearLayout(&shape);
-  } else {
-    CHECK(shape.has_layout());
-  }
-  type_name += shape.ShortDebugString();
-  llvm::MDNode* tbaa_node =
-      metadata_builder.createTBAANode(llvm_ir::AsStringRef(type_name), root);
-  instruction->setMetadata(llvm::LLVMContext::MD_tbaa,
-                           metadata_builder.createTBAAStructTagNode(
-                               tbaa_node, tbaa_node, /*Offset=*/0));
+  // TODO(b/62903316): TBAA metadata causes LLVM to miscompile generated code,
+  // most likely because the generated metadata is incorrect.  Disable TBAA
+  // metadata while we resolve this.
 }
 
 void SetAlignmentMetadataForLoad(llvm::LoadInst* load, uint64_t alignment) {
-- 
GitLab


From b85601b95eba28605d3de076fa70cabf2f2e32b9 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 22 Jun 2017 16:50:14 -0700
Subject: [PATCH 0378/1375] Improve score-trick to be a valid Csiszar
 f-Divergence yet numerically stable.

PiperOrigin-RevId: 159896013
---
 .../kernel_tests/csiszar_divergence_test.py   | 43 +++++++++----
 .../python/ops/csiszar_divergence_impl.py     | 62 +++++++------------
 2 files changed, 55 insertions(+), 50 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
index fabf7a9b77..fba0cc6522 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -627,6 +627,11 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       grad = lambda fs: gradients_impl.gradients(fs, s)[0]
 
       [
+          approx_kl_grad_,
+          approx_kl_self_normalized_grad_,
+          approx_kl_score_trick_grad_,
+          approx_kl_self_normalized_score_trick_grad_,
+          exact_kl_grad_,
           approx_kl_,
           approx_kl_self_normalized_,
           approx_kl_score_trick_,
@@ -638,23 +643,39 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
           grad(approx_kl_score_trick),
           grad(approx_kl_self_normalized_score_trick),
           grad(exact_kl),
+          approx_kl,
+          approx_kl_self_normalized,
+          approx_kl_score_trick,
+          approx_kl_self_normalized_score_trick,
+          exact_kl,
       ])
 
-      self.assertAllClose(
-          approx_kl_, exact_kl_,
-          rtol=0.06, atol=0.)
+      # Test average divergence.
+      self.assertAllClose(approx_kl_, exact_kl_,
+                          rtol=0.02, atol=0.)
 
-      self.assertAllClose(
-          approx_kl_self_normalized_, exact_kl_,
-          rtol=0.05, atol=0.)
+      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
+                          rtol=0.08, atol=0.)
 
-      self.assertAllClose(
-          approx_kl_score_trick_, exact_kl_,
-          rtol=0.06, atol=0.)
+      self.assertAllClose(approx_kl_score_trick_, exact_kl_,
+                          rtol=0.02, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_score_trick_, exact_kl_,
+                          rtol=0.08, atol=0.)
+
+      # Test average gradient-divergence.
+      self.assertAllClose(approx_kl_grad_, exact_kl_grad_,
+                          rtol=0.007, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_grad_, exact_kl_grad_,
+                          rtol=0.011, atol=0.)
+
+      self.assertAllClose(approx_kl_score_trick_grad_, exact_kl_grad_,
+                          rtol=0.018, atol=0.)
 
       self.assertAllClose(
-          approx_kl_self_normalized_score_trick_, exact_kl_,
-          rtol=0.05, atol=0.)
+          approx_kl_self_normalized_score_trick_grad_, exact_kl_grad_,
+          rtol=0.017, atol=0.)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 7b51d8d932..09389e5d38 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -40,8 +40,8 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib import framework as contrib_framework
+from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo
 from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import distribution
@@ -750,7 +750,7 @@ def monte_carlo_csiszar_f_divergence(
   ```none
   D_f[p(X), q(X)] := E_{q(X)}[ f( p(X) / q(X) ) ]
                   ~= m**-1 sum_j^m f( p(x_j) / q(x_j) ),
-                             where x_j ~iid q(x)
+                             where x_j ~iid q(X)
   ```
 
   Tricks: Reparameterization and Score-Gradient
@@ -759,8 +759,8 @@ def monte_carlo_csiszar_f_divergence(
   parameterless distribution (e.g.,
   `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and
   expectation, i.e.,
-  `nabla Avg{ s_i : i=1...n } = Avg{ nabla s_i : i=1...n }` where `S_n=Avg{s_i}`
-  and `s_i = f(x_i), x_i ~ q`.
+  `grad[Avg{ s_i : i=1...n }] = Avg{ grad[s_i] : i=1...n }` where `S_n=Avg{s_i}`
+  and `s_i = f(x_i), x_i ~iid q(X)`.
 
   However, if q is not reparameterized, TensorFlow's gradient will be incorrect
   since the chain-rule stops at samples of unreparameterized distributions. In
@@ -768,22 +768,17 @@ def monte_carlo_csiszar_f_divergence(
   gradient, i.e.,
 
   ```none
-  nabla E_q[f(X)]
-  = nabla int dx q(x) f(x)
-  = int dx nabla [ q(x) f(x) ]
-  = int dx q'(x) f(x) + q(x) f'(x)
+  grad[ E_q[f(X)] ]
+  = grad[ int dx q(x) f(x) ]
+  = int dx grad[ q(x) f(x) ]
+  = int dx [ q'(x) f(x) + q(x) f'(x) ]
   = int dx q(x) [q'(x) / q(x) f(x) + f'(x) ]
-  = int dx q(x) nabla [ log(q(x)) stopgrad[f(x)] + f(x) ]
-  = E_q[ nabla [ log(q(X)) stopgrad[f(X)] + f(X) ] ]
-  ~= Avg{ log(q(y_i)) stopgrad[f(y_i)] + f(y_i) : y_i = stopgrad[x_i], x_i ~ q}
+  = int dx q(x) grad[ f(x) q(x) / stop_grad[q(x)] ]
+  = E_q[ grad[ f(x) q(x) / stop_grad[q(x)] ] ]
   ```
 
   Unless `q.reparameterization_type != distribution.FULLY_REPARAMETERIZED` it is
-  usually preferable to `use_reparametrization = True`.
-
-  Warning: using `use_reparametrization = False` will mean that the result is
-  *not* the Csiszar f-Divergence. However its expected gradient *is* the
-  gradient of the Csiszar f-Divergence.
+  usually preferable to set `use_reparametrization = True`.
 
   Example Application:
 
@@ -817,10 +812,7 @@ def monte_carlo_csiszar_f_divergence(
 
   Returns:
     monte_carlo_csiszar_f_divergence: Floating-type `Tensor` Monte Carlo
-      approximation of the Csiszar f-Divergence. Warning: using
-      `use_reparametrization = False` will mean that the result is *not* the
-      Csiszar f-Divergence. However its expected gradient *is* the actual
-      gradient of the Csiszar f-Divergence.
+      approximation of the Csiszar f-Divergence.
 
   Raises:
     ValueError: if `q` is not a reparameterized distribution and
@@ -831,24 +823,16 @@ def monte_carlo_csiszar_f_divergence(
       to parameters) is valid.
   """
   with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]):
-    x = q.sample(num_draws, seed=seed)
-    if use_reparametrization:
+    if (use_reparametrization and
+        q.reparameterization_type != distribution.FULLY_REPARAMETERIZED):
       # TODO(jvdillon): Consider only raising an exception if the gradient is
       # requested.
-      if q.reparameterization_type != distribution.FULLY_REPARAMETERIZED:
-        raise ValueError(
-            "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
-            "transformation of a parameterless distribution. (Otherwise this "
-            "function has a biased gradient.)")
-      return math_ops.reduce_mean(f(p.log_prob(x) - q.log_prob(x)), axis=0)
-    else:
-      x = array_ops.stop_gradient(x)
-      logqx = q.log_prob(x)
-      fx = f(p.log_prob(x) - logqx)
-      # Alternatively we could have returned:
-      #   reduce_mean(fx * exp(logqx) / stop_gradient(exp(logqx)), axis=0)
-      # This is nice because it means the result is exactly the Csiszar
-      # f-Divergence yet the gradient is unbiased. However its numerically
-      # unstable since the q is not in log-domain.
-      return math_ops.reduce_mean(logqx * array_ops.stop_gradient(fx) + fx,
-                                  axis=0)
+      raise ValueError(
+          "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
+          "transformation of a parameterless distribution. (Otherwise this "
+          "function has a biased gradient.)")
+    return monte_carlo.expectation_v2(
+        f=lambda x: f(p.log_prob(x) - q.log_prob(x)),
+        samples=q.sample(num_draws, seed=seed),
+        log_prob=q.log_prob,
+        use_reparametrization=use_reparametrization)
-- 
GitLab


From 39ca4298d77038c234474a00460d3fa7f29692e8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 17:02:30 -0700
Subject: [PATCH 0379/1375] Support advisor in all places (Command line, APIs)
 Add expensive operation checker

PiperOrigin-RevId: 159897279
---
 .../pywrap_tensorflow_print_model_analysis.i  |   1 -
 .../python/tools/tfprof/model_analyzer.py     | 121 ++++++++++++---
 .../tools/tfprof/model_analyzer_test.py       |  30 +++-
 .../python/tools/tfprof/profiler_test.py      |  16 +-
 tensorflow/tools/tfprof/BUILD                 |   1 +
 tensorflow/tools/tfprof/README.md             |   6 +-
 tensorflow/tools/tfprof/g3doc/advise.md       |  80 +++++++++-
 .../tools/tfprof/internal/advisor/BUILD       |   9 ++
 .../advisor/accelerator_utilization_checker.h |  24 ++-
 .../tools/tfprof/internal/advisor/checker.h   |  24 +--
 .../advisor/expensive_operation_checker.h     | 138 ++++++++++++++++++
 .../advisor/internal_checker_runner.h         |   5 +-
 .../advisor/internal_checker_runner_dummy.cc  |   6 +-
 .../internal/advisor/operation_checker.h      |  19 ++-
 .../tfprof/internal/advisor/tfprof_advisor.h  |  47 ++++--
 .../internal/advisor/tfprof_advisor_test.cc   |  46 ++++--
 .../tfprof/internal/print_model_analysis.cc   |  19 ++-
 .../tfprof/internal/print_model_analysis.h    |   2 -
 .../tools/tfprof/internal/tfprof_graph.h      |   4 +-
 tensorflow/tools/tfprof/internal/tfprof_op.cc |  11 +-
 tensorflow/tools/tfprof/internal/tfprof_op.h  |   6 +-
 .../tools/tfprof/internal/tfprof_options.h    |   4 +-
 .../tools/tfprof/internal/tfprof_show.cc      |  14 +-
 .../tools/tfprof/internal/tfprof_show.h       |  13 +-
 .../tfprof/internal/tfprof_show_multi.cc      |  19 ++-
 .../tools/tfprof/internal/tfprof_show_multi.h |  16 +-
 .../tools/tfprof/internal/tfprof_show_test.cc |   1 +
 .../tools/tfprof/internal/tfprof_stats.cc     |  77 ++++++----
 .../tools/tfprof/internal/tfprof_stats.h      |  22 ++-
 .../tfprof/internal/tfprof_stats_test.cc      |   1 +
 .../tfprof/internal/tfprof_tensor_test.cc     |   1 +
 .../tfprof/internal/tfprof_timeline_test.cc   |   1 +
 tensorflow/tools/tfprof/tfprof_main.cc        |  25 +++-
 tensorflow/tools/tfprof/tfprof_options.proto  |  10 +-
 tensorflow/tools/tfprof/tfprof_output.proto   |  10 +-
 35 files changed, 646 insertions(+), 183 deletions(-)
 create mode 100644 tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
index 40f29ae8a2..582c36e339 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
@@ -43,7 +43,6 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
-%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index 419beac0b9..c781d2af4e 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -20,6 +20,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import six
+
 from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
 from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
 from tensorflow.python.framework import errors
@@ -108,49 +110,77 @@ PRINT_ALL_TIMING_MEMORY = {
     'dump_to_file': ''
 }
 
+# The following options are for 'advise' tfprof_cmd.
+# Show all advice.
+ALL_ADVICE = {
+    'ExpensiveOperationChecker': {},
+    'AcceleratorUtilizationChecker': {},
+    'JobChecker': {},  # Only available internally.
+    'OperationChecker': {},
+}
+
 # pylint: enable=bad-whitespace
 # pylint: enable=bad-continuation
 
 
-def _build_options(tfprof_options):
+def _build_options(options):
   """Build tfprof.OptionsProto.
 
   Args:
-    tfprof_options: A dictionary of options.
+    options: A dictionary of options.
   Returns:
     tfprof.OptionsProto.
   """
   opts = tfprof_options_pb2.OptionsProto()
-  opts.max_depth = tfprof_options.get('max_depth', 10)
-  opts.min_bytes = tfprof_options.get('min_bytes', 0)
-  opts.min_micros = tfprof_options.get('min_micros', 0)
-  opts.min_params = tfprof_options.get('min_params', 0)
-  opts.min_float_ops = tfprof_options.get('min_float_ops', 0)
-  opts.min_occurrence = tfprof_options.get('min_occurrence', 0)
+  opts.max_depth = options.get('max_depth', 10)
+  opts.min_bytes = options.get('min_bytes', 0)
+  opts.min_micros = options.get('min_micros', 0)
+  opts.min_params = options.get('min_params', 0)
+  opts.min_float_ops = options.get('min_float_ops', 0)
+  opts.min_occurrence = options.get('min_occurrence', 0)
 
-  opts.step = tfprof_options.get('step', -1)
+  opts.step = options.get('step', -1)
 
-  opts.order_by = tfprof_options.get('order_by', 'name')
+  opts.order_by = options.get('order_by', 'name')
 
-  for p in tfprof_options.get('account_type_regexes', []):
+  for p in options.get('account_type_regexes', []):
     opts.account_type_regexes.append(p)
-  for p in tfprof_options.get('start_name_regexes', []):
+  for p in options.get('start_name_regexes', []):
     opts.start_name_regexes.append(p)
-  for p in tfprof_options.get('trim_name_regexes', []):
+  for p in options.get('trim_name_regexes', []):
     opts.trim_name_regexes.append(p)
-  for p in tfprof_options.get('show_name_regexes', []):
+  for p in options.get('show_name_regexes', []):
     opts.show_name_regexes.append(p)
-  for p in tfprof_options.get('hide_name_regexes', []):
+  for p in options.get('hide_name_regexes', []):
     opts.hide_name_regexes.append(p)
-  opts.account_displayed_op_only = tfprof_options.get(
-      'account_displayed_op_only', False)
+  opts.account_displayed_op_only = options.get('account_displayed_op_only',
+                                               False)
 
-  for p in tfprof_options.get('select', []):
+  for p in options.get('select', []):
     opts.select.append(p)
 
-  opts.output = tfprof_options.get('output', 'stdout')
-  opts.dump_to_file = tfprof_options.get('dump_to_file', '')
+  opts.output = options.get('output', 'stdout')
+  opts.dump_to_file = options.get('dump_to_file', '')
+
+  return opts
+
+
+def _build_advisor_options(options):
+  """Build tfprof.AdvisorOptionsProto.
 
+  Args:
+    options: A dictionary of options. See ALL_ADVICE example.
+  Returns:
+    tfprof.AdvisorOptionsProto.
+  """
+  opts = tfprof_options_pb2.AdvisorOptionsProto()
+  if options is None:
+    return opts
+  for checker, checker_opts in six.iteritems(options):
+    checker_ops_pb = tfprof_options_pb2.AdvisorOptionsProto.CheckerOption()
+    for k, v in six.iteritems(checker_opts):
+      checker_ops_pb[k] = v
+    opts.checkers[checker].MergeFrom(checker_ops_pb)
   return opts
 
 
@@ -190,7 +220,7 @@ class Profiler(object):
       else:
         _ = sess.run(...)
     # Auto detect problems and generate advice.
-    profiler.advise()
+    profiler.advise(model_analyzer.ALL_ADVICE)
   """
 
   def __init__(self, graph, op_log=None):
@@ -288,9 +318,19 @@ class Profiler(object):
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
 
-  def advise(self):
-    """Automatically detect problems and generate reports."""
-    print_mdl.Advise()
+  def advise(self, options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+    """Automatically detect problems and generate reports.
+
+    Args:
+      options: A dict of options.
+    Returns:
+      A Advise proto that conains the reports from all checkers.
+    """
+    advise_pb = tfprof_output_pb2.AdviceProto()
+    opts = _build_advisor_options(options)
+    advise_pb.ParseFromString(
+        print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString()))
+    return advise_pb
 
 
 def print_model_analysis(graph,
@@ -354,3 +394,36 @@ def print_model_analysis(graph,
         None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
 
   return tfprof_node
+
+
+def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+  """Auto profile and advise.
+
+    Builds profiles and automatically check anormalies of various
+    aspects. See go/tfprof or README for examples and tutorials.
+
+  Args:
+    graph: tf.Graph.
+    run_meta: tensorflow::RunMetadata proto. Allows auto-profile
+              time and memroy.
+    tfprof_options: see ALL_ADVICE example above.
+  Returns:
+    Returns AdviceProto proto
+  """
+  # pylint: disable=protected-access
+  op_log = tfprof_logger._merge_default_with_oplog(
+      graph, None, run_meta, add_trace=True)
+  # pylint: enable=protected-access
+
+  run_meta_str = run_meta.SerializeToString() if run_meta else b''
+
+  opts = _build_advisor_options(tfprof_options)
+  ret = tfprof_output_pb2.AdviceProto()
+  ret.ParseFromString(
+      print_mdl.PrintModelAnalysis(
+          graph.as_graph_def(add_shapes=True).SerializeToString(),
+          run_meta_str,
+          op_log.SerializeToString(),
+          'advise'.encode('utf-8'),
+          opts.SerializeToString()))
+  return ret
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index 1b5041441f..ec7540a657 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -126,7 +126,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['account_displayed_op_only'] = False
     opts['select'] = ['params', 'float_ops']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -176,6 +176,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['select'] = [
         'bytes', 'params', 'float_ops', 'device'
     ]
+    opts['output'] = 'none'
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -276,6 +277,33 @@ class PrintModelAnalysisTest(test.TestCase):
       self.assertEqual(total_children, 15)
       self.assertGreater(input_shapes, 0)
 
+  def testAdvisor(self):
+    ops.reset_default_graph()
+
+    with session.Session() as sess:
+      x = lib.BuildFullModel()
+
+      sess.run(variables.global_variables_initializer())
+      run_meta = config_pb2.RunMetadata()
+      _ = sess.run(
+          x,
+          options=config_pb2.RunOptions(
+              trace_level=config_pb2.RunOptions.FULL_TRACE),
+          run_metadata=run_meta)
+
+      advice_pb = model_analyzer.advise(sess.graph, run_meta)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
index 5daaafd7c8..c7113b6a57 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
@@ -129,7 +129,7 @@ class ProfilerTest(test.TestCase):
     opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
     opts['account_type_regexes'] = ['.*']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       r1, r2, r3 = lib.BuildSplitableModel()
       sess.run(variables.global_variables_initializer())
 
@@ -179,8 +179,18 @@ class ProfilerTest(test.TestCase):
       self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None)
       self.assertGreater(lib.SearchTFProfNode(pb3, 'add').exec_micros, 0)
 
-      # TODO(xpan): Better test of advisor.
-      profiler.advise()
+      advice_pb = profiler.advise(model_analyzer.ALL_ADVICE)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/tools/tfprof/BUILD b/tensorflow/tools/tfprof/BUILD
index 57cccd8921..541df78e47 100644
--- a/tensorflow/tools/tfprof/BUILD
+++ b/tensorflow/tools/tfprof/BUILD
@@ -33,6 +33,7 @@ cc_binary(
         "//tensorflow/tools/tfprof/internal:tfprof_options",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
         "//tensorflow/tools/tfprof/internal:tfprof_utils",
+        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
         "@linenoise//:linenoise",
     ],
 )
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/tools/tfprof/README.md
index 5927990524..816ad8c07e 100644
--- a/tensorflow/tools/tfprof/README.md
+++ b/tensorflow/tools/tfprof/README.md
@@ -7,7 +7,11 @@
 * Profile model performance
   * execution time, memory consumption
   * Profile multiple steps.
-* Auto detect and advise. (Experimental)
+* Auto profile and advise.
+  * accelerator utilization check
+  * expensive operation check
+  * operation configuration check
+  * distributed runtime check (Not OSS)
 
 ###Interfaces
 
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/tools/tfprof/g3doc/advise.md
index 3bce6270ff..e30add6fbf 100644
--- a/tensorflow/tools/tfprof/g3doc/advise.md
+++ b/tensorflow/tools/tfprof/g3doc/advise.md
@@ -3,6 +3,7 @@
 tfprof analyzes profiles and generates advises for common issues.
 
 ### Run Advise.
+
 ```python
 # First create a profiler. See profiler tutorials for more details.
 profiler = model_analyzer.Profiler(sess.graph)
@@ -13,8 +14,63 @@ _ = sess.run(r1,
              run_metadata=run_meta)
 profiler.add_step(1, run_meta)
 
-# Start advise.
-profiler.advise()
+# Then Start advise.
+profiler.advise(model_analyzer.ALL_ADVICE)
+
+# For one-shot API
+tf.contrib.tfprof.model_analyzer.advise(
+    sess.graph, run_meta=run_metadata)
+```
+
+```shell
+# Run advisor on CLI
+# See CLI tutorial on generating the files.
+tfprof --graph_path=graph.pbtxt \
+       --run_meta_path=run_metadata \
+       --op_log_path=tfprof_log
+
+tfprof> advise
+AcceleratorUtilizationChecker:
+device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03
+device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08
+device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04
+device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21
+
+OperationChecker:
+Found operation using NHWC data_format on GPU. Maybe NCHW is faster.
+
+ExpensiveOperationChecker:
+top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%)
+top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%)
+top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%)
+seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec
+  seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec
+    seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec
+      seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize), cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha..., cpu: 164us, accelerator: 0us, total: 164us
+      seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms
+      seq2seq_lib.py:151:sequence_loss_by_...:total_size = tf.a..., cpu: 154us, accelerator: 23us, total: 177us
+    seq2seq_lib.py:184:sampled_sequence_...:return cost / tf...., cpu: 97us, accelerator: 8us, total: 105us
+      math_ops.py:690:cast:return gen_math_o..., cpu: 62us, accelerator: 3us, total: 65us
+      math_ops.py:839:binary_op_wrapper:return func(x, y,..., cpu: 35us, accelerator: 5us, total: 40us
+  seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms
+    seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 303.76ms, accelerator: 73.54ms, total: 377.30ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 2.75ms, accelerator: 0us, total: 2.75ms
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 67us, accelerator: 0us, total: 67us
+    seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 292.04ms, accelerator: 73.54ms, total: 365.58ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 4.07ms, accelerator: 0us, total: 4.07ms
+      core_rnn.py:178:static_rnn:min_sequence_leng..., cpu: 85us, accelerator: 0us, total: 85us
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 16us, accelerator: 0us, total: 16us
+    seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms
+ ...(omitted)
+top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms
+top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms
+top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms
 ```
 
 ### Checker
@@ -25,16 +81,24 @@ area with the profile and report issues. A `Checker` is like a plugin.
 
 For example:
 
-####JobChecker (Not Available OSS)
-* Checking RecvTensor RPC latency and bandwidth.
-* Checking CPU/Memory utilization of the job.
+#### JobChecker (Not Available OSS)
+
+*   Checks RecvTensor RPC latency and bandwidth.
+*   Checks CPU/Memory utilization of the job.
 
 ####AcceleratorUtilization Checker
 * Checks what percentage of time the accelerator spends on computation.
 
-####Operation Checker
-* Check whether the operation runs with optimal options.
-* Checks if there is a better implementation to replace the current operation.
+#### OperationChecker
+
+*   Checks whether the operation runs with optimal options.
+*   Checks if there is a better implementation to replace the current operation.
+
+#### ExpensiveOperationChecker
+
+*   Checks the most expensive operation type.
+*   Checks the most expensive graph nodes.
+*   Checks the most expensive graph-building Python codes.
 
 ####Contribute Your Checker
 
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/tools/tfprof/internal/advisor/BUILD
index 30012fa7b1..d28443bba7 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/tools/tfprof/internal/advisor/BUILD
@@ -43,12 +43,21 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "expensive_operation_checker",
+    hdrs = ["expensive_operation_checker.h"],
+    deps = [
+        ":checker",
+    ],
+)
+
 cc_library(
     name = "tfprof_advisor",
     hdrs = ["tfprof_advisor.h"],
     deps = [
         ":accelerator_utilization_checker",
         ":checker",
+        ":expensive_operation_checker",
         ":internal_checker_runner_dummy",
         ":operation_checker",
     ],
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
index fb7f65d7dc..074b8e57b0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
@@ -33,10 +33,11 @@ struct ExecStats {
 
 class AcceleratorUtilizationChecker : public Checker {
  public:
-  string name() override { return "AcceleratorUtilizationChecker"; }
+  string name() const override { return kCheckers[0]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -48,24 +49,21 @@ class AcceleratorUtilizationChecker : public Checker {
     return CheckInternal();
   }
 
-  std::vector<string> CheckInternal() {
+  AdviceProto::Checker CheckInternal() {
     for (const auto& s : accelerator_exec_stats_) {
       const ExecStats& stat = s.second;
       int64 total_micros = stat.end_micros - stat.start_micros;
       if (total_micros <= 0) continue;
       double utilization = 1.0 * stat.exec_micros / total_micros;
       if (utilization >= 0.5) {
-        reports_.push_back(strings::Printf("%s: device: %s utilization: %.2f",
-                                           kLevel[0], s.first.c_str(),
-                                           utilization));
+        reports_.add_reports(strings::Printf("device: %s utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization < 0.5 && utilization > 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[1],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization <= 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[2],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       }
     }
     return reports_;
@@ -102,7 +100,7 @@ class AcceleratorUtilizationChecker : public Checker {
 
   std::map<string, ExecStats> accelerator_exec_stats_;
   std::map<string, int64> ps_placement_;
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/checker.h b/tensorflow/tools/tfprof/internal/advisor/checker.h
index b8b057be5b..3ce80cd8c4 100644
--- a/tensorflow/tools/tfprof/internal/advisor/checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/checker.h
@@ -18,27 +18,33 @@ limitations under the License.
 
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
-static const char* const kLevel[] = {
-    "NOTE",     // Good to know.
-    "SUGGEST",  // Might get better.
-    "WARN",     // Please do it for better.
+// Append only.
+static const char* const kCheckers[] = {
+    "AcceleratorUtilizationChecker", "OperationChecker",
+    "ExpensiveOperationChecker",
+    "JobChecker",  // Internal checker.
 };
 
 class Checker {
  public:
-  virtual ~Checker(){};
+  virtual ~Checker() {}
 
-  virtual string name() = 0;
+  virtual string name() const = 0;
 
-  std::vector<string> Run(const TFStats* stats) { return Check(stats); }
+  AdviceProto::Checker Run(const AdvisorOptionsProto::CheckerOption& options,
+                           const TFStats* stats) {
+    return Check(options, stats);
+  }
 
  protected:
-  // Returns a vector of string, each one being an advice.
-  virtual std::vector<string> Check(const TFStats* stats) = 0;
+  virtual AdviceProto::Checker Check(
+      const AdvisorOptionsProto::CheckerOption& options,
+      const TFStats* stats) = 0;
 };
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
new file mode 100644
index 0000000000..14e30edd87
--- /dev/null
+++ b/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
@@ -0,0 +1,138 @@
+/* Copyright 2016 The TensorFlow Authors All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// This checker checks the most expensive operations.
+#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+
+#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+
+namespace tensorflow {
+namespace tfprof {
+
+class ExpensiveOperationChecker : public Checker {
+ public:
+  string name() const override { return kCheckers[2]; }
+
+ private:
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
+    if (!stats) {
+      fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
+              name().c_str());
+      return reports_;
+    }
+    if (stats->steps().empty()) {
+      fprintf(stderr, "Missing RunMetadata info. Skip %s\n", name().c_str());
+    }
+    CheckOpView(stats);
+    CheckCodeView(stats);
+    CheckScopeView(stats);
+    return reports_;
+  }
+
+  void CheckOpView(const TFStats* stats) {
+    if (stats->steps().empty()) {
+      fprintf(stderr, "Missing run_meta for %s\n", name().c_str());
+      return;
+    }
+    Options opts(3, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"}, {},
+                 false, {"micros", "occurrence"}, "none", {});
+    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("op", opts);
+    if (root.children_size() == 0) {
+      return;
+    }
+    const TFMultiGraphNodeProto* node = &root;
+    std::vector<string> outputs;
+    for (int i = 0; i < 3 && node->children_size() > 0; ++i) {
+      node = &node->children(0);
+      outputs.push_back(strings::Printf(
+          "top %d operation type: %s, "
+          "cpu: %s, accelerator: %s, total: %s (%.2f%%)",
+          i + 1, node->name().c_str(),
+          FormatTime(node->cpu_exec_micros()).c_str(),
+          FormatTime(node->accelerator_exec_micros()).c_str(),
+          FormatTime(node->exec_micros()).c_str(),
+          100.0 * node->exec_micros() / (root.total_exec_micros() + 1e-10)));
+    }
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CheckCodeView(const TFStats* stats) {
+    if (!stats->has_code_traces()) {
+      fprintf(stderr, "Missing op_log (code traces) for %s\n", name().c_str());
+      return;
+    }
+    Options opts(100, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
+                 {}, false, {"micros"}, "none", {});
+    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("code", opts);
+    const TFMultiGraphNodeProto* node = &root;
+    // A trick here is: Usually, codes in library file are usually referenced
+    // only once, while user's own code are referenced multiple times.
+    while (node->children_size() == 1) {
+      node = &node->children(0);
+    }
+    if (node->children_size() == 0) {
+      return;
+    }
+
+    std::vector<string> outputs;
+    CodeViewHelper(node, 0, &outputs);
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CheckScopeView(const TFStats* stats) {
+    Options opts(100, 0, 100, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
+                 {}, false, {"micros"}, "none", {});
+    const TFGraphNodeProto root = stats->ShowGraphNode("scope", opts);
+    if (root.children_size() == 0) {
+      return;
+    }
+    std::vector<string> outputs;
+    const TFGraphNodeProto* node = &root;
+    for (int i = 0; i < 3 && i < root.children_size(); ++i) {
+      const TFGraphNodeProto& node = root.children(i);
+      outputs.push_back(strings::Printf(
+          "top %d graph node: %s, cpu: %s, accelerator: %s, total: %s", i + 1,
+          node.name().c_str(), FormatTime(node.cpu_exec_micros()).c_str(),
+          FormatTime(node.accelerator_exec_micros()).c_str(),
+          FormatTime(node.exec_micros()).c_str()));
+    }
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CodeViewHelper(const TFMultiGraphNodeProto* node, int depth,
+                      std::vector<string>* outputs) {
+    if (node->children_size() <= 1 || depth > 4) {
+      return;
+    }
+    for (int j = 0; j < 3 && j < node->children_size(); ++j) {
+      const TFMultiGraphNodeProto* c = &node->children(j);
+      outputs->push_back(strings::Printf(
+          "%s%s, cpu: %s, accelerator: %s, total: %s",
+          string(depth * 2, ' ').c_str(), c->name().c_str(),
+          FormatTime(c->total_cpu_exec_micros()).c_str(),
+          FormatTime(c->total_accelerator_exec_micros()).c_str(),
+          FormatTime(c->total_exec_micros()).c_str()));
+      CodeViewHelper(c, depth + 1, outputs);
+    }
+  }
+
+  AdviceProto::Checker reports_;
+};
+
+}  // namespace tfprof
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OP_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
index 1238b57f20..ed8ae571b6 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
@@ -17,13 +17,16 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
 
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
 class TFStats;
 
-std::map<string, std::vector<string>> RunInternalCheckers(const TFStats* stats);
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats);
 
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
index 8204d2b04e..67962c8e8b 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
@@ -17,9 +17,9 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-std::map<string, std::vector<string>> RunInternalCheckers(
-    const TFStats* stats) {
-  return std::map<string, std::vector<string>>();
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats) {
+  return AdviceProto();
 }
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
index 2a05f9bfd0..4d0d68e3bf 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
@@ -24,10 +24,11 @@ namespace tfprof {
 
 class OperationChecker : public Checker {
  public:
-  string name() override { return "OperationChecker"; }
+  string name() const override { return kCheckers[1]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -53,22 +54,20 @@ class OperationChecker : public Checker {
       }
     }
     if (use_batch_norm && !use_fused_batch_norm) {
-      reports_.push_back(strings::Printf(
-          "%s: Maybe use faster FusedBatchNorm instead of BatchNorm",
-          kLevel[1]));
+      reports_.add_reports(
+          "Maybe use faster FusedBatchNorm instead of BatchNorm");
     }
     if (recommend_nchw) {
       // TODO(xpan): Maybe print which Op supports NCHW.
-      reports_.push_back(strings::Printf(
-          "%s: Found operation using NHWC data_format on GPU. Maybe "
-          "NCHW is faster.",
-          kLevel[1]));
+      reports_.add_reports(
+          "Found operation using NHWC data_format on GPU. Maybe "
+          "NCHW is faster.");
     }
     return reports_;
   }
 
  private:
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
index 856f515459..d2257fb9b5 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
@@ -18,8 +18,10 @@ limitations under the License.
 
 #include "tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+#include "tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
 #include "tensorflow/tools/tfprof/internal/advisor/operation_checker.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -29,23 +31,44 @@ class Advisor {
  public:
   Advisor(const TFStats* stats) : stats_(stats) {}
 
-  std::map<string, std::vector<string>> Advise() {
+  static AdvisorOptionsProto DefaultOptions() {
+    AdvisorOptionsProto options;
+    std::vector<string> checkers(
+        kCheckers, kCheckers + sizeof(kCheckers) / sizeof(*kCheckers));
+    for (const string& checker : checkers) {
+      (*options.mutable_checkers())[checker];
+    }
+    return options;
+  }
+
+  AdviceProto Advise(const AdvisorOptionsProto& options) {
     // Note: Release a checker's memory ASAP.
-    std::map<string, std::vector<string>> reports = RunInternalCheckers(stats_);
-    // TODO(xpan): Think of a way to turn off/on specific checkers.
-    AcceleratorUtilizationChecker au_checker;
-    reports[au_checker.name()] = au_checker.Run(stats_);
-    OperationChecker op_checker;
-    reports[op_checker.name()] = op_checker.Run(stats_);
-
-    for (const auto& checker_r : reports) {
-      fprintf(stdout, "%s reports:\n", checker_r.first.c_str());
-      for (const auto& r : checker_r.second) {
+    AdviceProto ret = RunInternalCheckers(options, stats_);
+
+    if (options.checkers().find(kCheckers[0]) != options.checkers().end()) {
+      AcceleratorUtilizationChecker au_checker;
+      (*ret.mutable_checkers())[kCheckers[0]].MergeFrom(
+          au_checker.Run(options.checkers().at(kCheckers[0]), stats_));
+    }
+    if (options.checkers().find(kCheckers[1]) != options.checkers().end()) {
+      OperationChecker op_checker;
+      (*ret.mutable_checkers())[kCheckers[1]].MergeFrom(
+          op_checker.Run(options.checkers().at(kCheckers[1]), stats_));
+    }
+    if (options.checkers().find(kCheckers[2]) != options.checkers().end()) {
+      ExpensiveOperationChecker expensive_op_checker;
+      (*ret.mutable_checkers())[kCheckers[2]].MergeFrom(
+          expensive_op_checker.Run(options.checkers().at(kCheckers[2]),
+                                   stats_));
+    }
+    for (const auto& checker : ret.checkers()) {
+      fprintf(stdout, "\n%s:\n", checker.first.c_str());
+      for (const string& r : checker.second.reports()) {
         fprintf(stdout, "%s\n", r.c_str());
       }
     }
     fflush(stdout);
-    return reports;
+    return ret;
   }
 
  private:
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
index b41d0770dc..3b40253954 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
@@ -29,15 +29,16 @@ class TFProfAdvisorTest : public ::testing::Test {
                              nullptr, nullptr));
 
     stats_->AddNodeForTest(
-        "n1", CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 10, 2));
-    stats_->AddNodeForTest("n2", CreateNode("n2", "Conv2D", {}, 20, 2));
+        0, CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 0, 10, 2));
+    stats_->AddNodeForTest(0, CreateNode("n2", "Conv2D", {}, 0, 20, 2));
+    stats_->BuildAllViews();
     advisor_.reset(new Advisor(stats_.get()));
   }
 
   std::unique_ptr<TFGraphNode> CreateNode(const string& name,
                                           const string& type,
                                           std::map<string, string> attrs,
-                                          int64 start_miros,
+                                          int64 step, int64 start_miros,
                                           int64 end_rel_micros) {
     node_defs_.push_back(std::unique_ptr<NodeDef>(new NodeDef()));
     NodeDef* def = node_defs_.back().get();
@@ -52,10 +53,10 @@ class TFProfAdvisorTest : public ::testing::Test {
     NodeExecStats node_stat;
     node_stat.set_all_start_micros(start_miros);
     node_stat.set_op_end_rel_micros(end_rel_micros);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
                       node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
                       node_stat);
     return node;
   }
@@ -66,23 +67,38 @@ class TFProfAdvisorTest : public ::testing::Test {
 };
 
 TEST_F(TFProfAdvisorTest, Basics) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_TRUE(reports.find("AcceleratorUtilizationChecker") != reports.end());
-  EXPECT_TRUE(reports.find("OperationChecker") != reports.end());
+  AdvisorOptionsProto options = Advisor::DefaultOptions();
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(advice.checkers().find(kCheckers[0]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[1]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[2]) != advice.checkers().end());
 }
 
 TEST_F(TFProfAdvisorTest, OperationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["OperationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["OperationChecker"][0]).contains("NCHW"));
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[1]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[1]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[1]).reports(0))
+                  .contains("NCHW"));
 }
 
 TEST_F(TFProfAdvisorTest, UtilizationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["AcceleratorUtilizationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["AcceleratorUtilizationChecker"][0])
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[0]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[0]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[0]).reports(0))
                   .contains("low utilization"));
 }
 
+TEST_F(TFProfAdvisorTest, ExpensiveOperationChecker) {
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[2]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[2]).reports(0))
+                  .contains("top 1 operation type: Conv2D"));
+}
+
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.cc b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
index 37d01db3a1..5a9c44d8e6 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.cc
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/tfprof_log.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 #include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
@@ -36,6 +37,18 @@ TFStats* tf_stat = nullptr;
 
 string RunProfile(const string& command, const string& options,
                   TFStats* tf_stats) {
+  if (command == kCmds[4]) {
+    AdvisorOptionsProto option_pb;
+    if (!option_pb.ParseFromString(options)) {
+      fprintf(stderr, "Cannot parse AdvisorOptionsProto\n");
+      return "";
+    }
+    tf_stats->BuildAllViews();
+    return Advisor(tf_stats).Advise(option_pb).SerializeAsString();
+  } else {
+    tf_stats->BuildView(command);
+  }
+
   Options opts;
   tensorflow::Status s = Options::FromProtoStr(options, &opts);
   if (!s.ok()) {
@@ -97,14 +110,14 @@ void AddStep(int64 step, const string* run_meta, const string* op_log) {
   // TODO(xpan): Better error handling.
   std::unique_ptr<RunMetadata> run_meta_ptr(new RunMetadata());
   run_meta_ptr->ParseFromString(*run_meta);
-  tf_stat->ParseRunMeta(step, std::move(run_meta_ptr));
+  tf_stat->AddRunMeta(step, std::move(run_meta_ptr));
 
   std::unique_ptr<OpLog> op_log_ptr;
   if (op_log && !op_log->empty()) {
     op_log_ptr.reset(new OpLog());
     op_log_ptr->ParseFromString(*op_log);
   }
-  tf_stat->ParseOpLog(std::move(op_log_ptr));
+  tf_stat->AddOpLog(std::move(op_log_ptr));
 }
 
 string Profile(const string* command, const string* options) {
@@ -144,7 +157,5 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
   return RunProfile(*command, *options, &tf_stats);
 }
 
-void Advise() { Advisor(tf_stat).Advise(); }
-
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.h b/tensorflow/tools/tfprof/internal/print_model_analysis.h
index 84165e542d..46db63646d 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.h
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.h
@@ -39,8 +39,6 @@ void AddStep(int64 step, const string* run_meta, const string* op_log);
 
 string Profile(const string* command, const string* options);
 
-void Advise();
-
 // Single-step Profiler.
 //
 // Interface defined for Python API swig. Calls the tfprof core API.
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.h b/tensorflow/tools/tfprof/internal/tfprof_graph.h
index fbeae8673d..194a21f0cc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_graph.h
@@ -54,8 +54,8 @@ class TFGraph : public TFShow {
   const ShowNode* ShowInternal(const Options& opts,
                                Timeline* timeline) override;
 
-  bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                         int depth) const override {
     return true;
   }
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index ac702320b3..30284f0307 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -126,6 +126,7 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
   }
   nodes = SortNodes(nodes, opts);
 
+  // pre keeps track of previous visited node.
   OpNode* pre = nullptr;
   std::vector<OpNode*> account_nodes;
   for (auto it = nodes.rbegin(); it != nodes.rend(); ++it) {
@@ -170,16 +171,20 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
     root_->ResetTotalStats();
     if (pre) {
       root_->AggregateTotalStats(pre);
-      root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
-      pre->mutable_proto()->clear_children();
     }
   }
+  if (pre) {
+    root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
+    pre->mutable_proto()->clear_children();
+  }
 
   if (opts.output_type == kOutput[1] || opts.output_type == kOutput[2]) {
     string display_str = FormatLegend(opts);
     for (OpNode* node : show_nodes) {
       display_str += FormatNode(node, root_.get(), opts);
     }
+    // In op view, we don't show root (total). But it will still in proto.
+    // TODO(xpan): Is it the right choice?
     root_->formatted_str = display_str;
   }
   return root_.get();
@@ -201,7 +206,7 @@ int64 TFOp::SearchRoot(const std::vector<OpNode*> nodes,
   return i;
 }
 
-string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
+string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const {
   std::vector<string> attrs;
 
   if (opts.select.find(kShown[0]) != opts.select.end()) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.h b/tensorflow/tools/tfprof/internal/tfprof_op.h
index 5b16490363..34812f54be 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.h
@@ -56,15 +56,15 @@ class TFOp : public TFMultiShow {
   int64 SearchRoot(const std::vector<OpNode*> nodes,
                    const std::vector<string>& regexes);
 
-  bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                         int depth) const override {
     if (opts.min_occurrence > node->node->graph_nodes().size()) {
       return false;
     }
     return true;
   }
 
-  string FormatNode(OpNode* node, OpNode* root, const Options& opts);
+  string FormatNode(OpNode* node, OpNode* root, const Options& opts) const;
 
   std::unique_ptr<OpNode> root_;
   std::map<string, std::unique_ptr<OpNode>> cnodes_map_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/tools/tfprof/internal/tfprof_options.h
index 6c9db24342..d39333e3fc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_options.h
@@ -59,10 +59,10 @@ static const char* const kShown[] = {
     "cpu_micros"};
 
 static const char* const kCmds[] = {
-    "scope", "graph", "code", "op", "set", "help",
+    "scope", "graph", "code", "op", "advise", "set", "help",
 };
 
-static const char* const kOutput[] = {"timeline", "stdout", "file"};
+static const char* const kOutput[] = {"timeline", "stdout", "file", "none"};
 
 static const char* const kTimelineOpts[] = {
     "outfile",
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index eaab5b9608..c456099c86 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -26,7 +26,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFGraphNodeProto& TFShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -64,7 +66,8 @@ bool TFShow::LookUpCheckPoint(const string& name,
   return true;
 }
 
-bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
+bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
+                        int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -96,7 +99,8 @@ bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
   return true;
 }
 
-bool TFShow::ShouldTrim(ShowNode* node, const std::vector<string>& regexes) {
+bool TFShow::ShouldTrim(const ShowNode* node,
+                        const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -121,7 +125,7 @@ bool TFShow::ReAccount(ShowNode* node, const Options& opts) {
   return false;
 }
 
-string TFShow::FormatNode(ShowNode* node, const Options& opts) {
+string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
   std::vector<string> info;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     const string shape = FormatShapes(node->node->shape());
@@ -194,7 +198,7 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) {
                          str_util::Join(info, ", ").c_str());
 }
 
-string TFShow::FormatLegend(const Options& opts) {
+string TFShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     legends.push_back("# parameters");
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/tools/tfprof/internal/tfprof_show.h
index 2c61b4fd73..95513e086f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.h
@@ -54,20 +54,21 @@ class TFShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowNode* node, const Options& opts, int depth) const;
 
-  bool ShouldTrim(ShowNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowNode* node, const Options& opts);
 
-  string FormatNode(ShowNode* node, const Options& opts);
+  string FormatNode(ShowNode* node, const Options& opts) const;
 
-  string FormatLegend(const Options& opts);
+  string FormatLegend(const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 97f204d25b..389968b750 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -28,7 +28,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -48,8 +50,8 @@ const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
   }
 }
 
-bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
-                            int depth) {
+bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
+                             int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -88,8 +90,8 @@ bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
   return true;
 }
 
-bool TFMultiShow::ShouldTrim(ShowMultiNode* node,
-                            const std::vector<string>& regexes) {
+bool TFMultiShow::ShouldTrim(const ShowMultiNode* node,
+                             const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -102,7 +104,7 @@ bool TFMultiShow::ReAccount(ShowMultiNode* node, const Options& opts) {
   return node->ReInit(opts.step, opts.account_type_regexes);
 }
 
-string TFMultiShow::FormatLegend(const Options& opts) {
+string TFMultiShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[0]) != opts.select.end()) {
     legends.push_back("output bytes");
@@ -142,7 +144,8 @@ string TFMultiShow::FormatLegend(const Options& opts) {
                          str_util::Join(legends, " | ").c_str());
 }
 
-string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
+string TFMultiShow::FormatInputShapes(
+    const TFMultiGraphNodeProto& proto) const {
   std::map<string, int> input_shapes_str;
   std::map<string, int> input_time_str;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
@@ -190,7 +193,7 @@ string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
 }
 
 std::vector<string> TFMultiShow::FormatTimes(const ShowMultiNode* node,
-                                             const Options& opts) {
+                                             const Options& opts) const {
   std::vector<string> attrs;
   if (opts.select.find(kShown[1]) != opts.select.end()) {
     attrs.push_back(FormatTotalExecTime(node, opts));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
index e6faf1231d..ce309816a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
@@ -55,21 +55,23 @@ class TFMultiShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowMultiNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowMultiNode* node, const Options& opts,
+                  int depth) const;
 
-  bool ShouldTrim(ShowMultiNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowMultiNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowMultiNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts);
-  string FormatInputShapes(const TFMultiGraphNodeProto& proto);
+  string FormatLegend(const Options& opts) const;
+  string FormatInputShapes(const TFMultiGraphNodeProto& proto) const;
   std::vector<string> FormatTimes(const ShowMultiNode* node,
-                                  const Options& opts);
+                                  const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index 478e269f87..a58fbdafca 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -65,6 +65,7 @@ class TFProfShowTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.cc b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
index f5b8dad4e2..64da7ae7cf 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
@@ -29,16 +29,17 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
                  std::unique_ptr<RunMetadata> run_meta,
                  std::unique_ptr<OpLog> op_log,
                  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader)
-    : graph_(std::move(graph)),
+    : has_code_traces_(false),
+      graph_(std::move(graph)),
       ckpt_reader_(std::move(ckpt_reader)) {
   CHECK(graph_) << "Must at least have GraphDef";
 
   printf("Parsing Inputs...\n");
   ParseGraph();
   if (run_meta && run_meta->has_step_stats()) {
-    ParseRunMeta(0, std::move(run_meta));
+    AddRunMeta(0, std::move(run_meta));
   }
-  ParseOpLog(std::move(op_log));
+  AddOpLog(std::move(op_log));
 
   if (ckpt_reader_) {
     for (const auto& v : ckpt_reader_->GetVariableToShapeMap()) {
@@ -48,27 +49,48 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
       }
     }
   }
+}
 
-  printf("Preparing Views...\n");
-  scope_view_ = std::unique_ptr<TFScope>(new TFScope(ckpt_reader_.get()));
-  graph_view_ = std::unique_ptr<TFGraph>(new TFGraph(ckpt_reader_.get()));
-  code_view_ = std::unique_ptr<TFCode>(new TFCode());
-  op_view_ = std::unique_ptr<TFOp>(new TFOp());
+void TFStats::BuildView(const string& cmd) {
+  if (cmd == kCmds[0] && !scope_view_) {
+    scope_view_.reset(new TFScope(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      scope_view_->AddNode(it->second.get());
+    }
+    scope_view_->Build();
+  }
+  if (cmd == kCmds[1] && !graph_view_) {
+    graph_view_.reset(new TFGraph(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      graph_view_->AddNode(it->second.get());
+    }
+    graph_view_->Build();
+  }
+  if (cmd == kCmds[2] && !code_view_) {
+    code_view_.reset(new TFCode());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      code_view_->AddNode(it->second.get());
+    }
+    code_view_->Build();
+  }
+  if (cmd == kCmds[3] && !op_view_) {
+    op_view_.reset(new TFOp());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      op_view_->AddNode(it->second.get());
+    }
+    op_view_->Build();
+  }
+}
 
-  for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-    scope_view_->AddNode(it->second.get());
-    graph_view_->AddNode(it->second.get());
-    code_view_->AddNode(it->second.get());
-    op_view_->AddNode(it->second.get());
-  }
-  scope_view_->Build();
-  graph_view_->Build();
-  code_view_->Build();
-  op_view_->Build();
+void TFStats::BuildAllViews() {
+  std::vector<string> cmds_str(kCmds, kCmds + sizeof(kCmds) / sizeof(*kCmds));
+  for (const string& cmd : cmds_str) {
+    BuildView(cmd);
+  }
 }
 
 const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
-                                               const Options& opts) {
+                                               const Options& opts) const {
   if (!Validate(opts)) {
     return empty_graph_node_;
   }
@@ -82,8 +104,8 @@ const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
   }
 }
 
-const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(const string& cmd,
-                                                         const Options& opts) {
+const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(
+    const string& cmd, const Options& opts) const {
   if (!Validate(opts)) {
     return empty_multi_graph_node_;
   }
@@ -130,7 +152,7 @@ void TFStats::ParseGraph() {
   }
 }
 
-void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
+void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
   if (!op_log) {
     return;
   }
@@ -144,12 +166,13 @@ void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
       node->second->AddFloatOps(entry.float_ops());
     }
     if (entry.has_code_def()) {
+      has_code_traces_ = true;
       node->second->AddCode(entry.code_def());
     }
   }
 }
 
-void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
+void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   if (!run_meta || !run_meta->has_step_stats()) {
     fprintf(stderr, "Invalid RunMetadata for step %lld\n", step);
     return;
@@ -176,7 +199,7 @@ void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   }
 }
 
-bool TFStats::Validate(const Options& opts) {
+bool TFStats::Validate(const Options& opts) const {
   if (opts.step >= 0 && steps_.find(opts.step) == steps_.end()) {
     fprintf(stderr, "Options -step=%lld not found\n", opts.step);
     return false;
@@ -184,9 +207,9 @@ bool TFStats::Validate(const Options& opts) {
   return true;
 }
 
-void TFStats::AddNodeForTest(const string& name,
-                             std::unique_ptr<TFGraphNode> node) {
-  nodes_map_[name] = std::move(node);
+void TFStats::AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node) {
+  steps_.insert(step);
+  nodes_map_[node->name()] = std::move(node);
 }
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.h b/tensorflow/tools/tfprof/internal/tfprof_stats.h
index dfb190e703..b26d274f80 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.h
@@ -59,28 +59,38 @@ class TFStats {
   const std::map<string, std::unique_ptr<TFGraphNode>>& nodes() const {
     return nodes_map_;
   }
+  const std::set<int64>& steps() const { return steps_; }
+  bool has_code_traces() const { return has_code_traces_; }
 
+  void BuildView(const string& cmd);
+  void BuildAllViews();
+
+  // Note: Must first BuildView(view_foo) before ShowXXX(view_foo) methods.
+  //
   // Organize the TensorFlow model as different types of views, and generate
   // outputs for profiling.
-  const TFGraphNodeProto& ShowGraphNode(const string& cmd, const Options& opts);
+  // TODO(xpan): Should it return reference here?
+  const TFGraphNodeProto& ShowGraphNode(const string& cmd,
+                                        const Options& opts) const;
   const TFMultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
-                                                  const Options& opts);
+                                                  const Options& opts) const;
 
   // Add a step of run time meta data.
-  void ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
+  void AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
   // Add tfprof operation meta data, such as customized op type, float_ops,
   // and code traces.
-  void ParseOpLog(std::unique_ptr<OpLog> op_log);
+  void AddOpLog(std::unique_ptr<OpLog> op_log);
 
   // For test purpose only.
-  void AddNodeForTest(const string& name, std::unique_ptr<TFGraphNode> node);
+  void AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node);
 
  private:
-  bool Validate(const Options& opts);
+  bool Validate(const Options& opts) const;
 
   void ParseGraph();
 
   std::set<int64> steps_;
+  bool has_code_traces_;
   std::unique_ptr<GraphDef> graph_;
   std::unique_ptr<TFScope> scope_view_;
   std::unique_ptr<TFGraph> graph_view_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index 948ce49df4..45f1fdd06f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -66,6 +66,7 @@ class TFProfStatsTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index 698738c23c..59955d214c 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -50,6 +50,7 @@ class TFProfTensorTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
index 0e9bb9658c..cad31050a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
@@ -52,6 +52,7 @@ class TFProfTimelineTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 nullptr, nullptr));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/tools/tfprof/tfprof_main.cc
index 7a4e7e85ff..5e70c093cc 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/tools/tfprof/tfprof_main.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/util/command_line_flags.h"
+#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
@@ -161,12 +162,13 @@ int Run(int argc, char** argv) {
         "Profiling everything!\n");
     return 0;
   } else if (argc > 1) {
-    if (string(argv[1]) == kCmds[5]) {
+    if (string(argv[1]) == kCmds[6]) {
       PrintHelp();
       return 0;
     }
     if (string(argv[1]) == kCmds[0] || string(argv[1]) == kCmds[1] ||
-        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3]) {
+        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3] ||
+        string(argv[1]) == kCmds[4]) {
       cmd = argv[1];
     }
   }
@@ -216,7 +218,13 @@ int Run(int argc, char** argv) {
               run_meta_files[i].c_str(), s.ToString().c_str());
       return 1;
     }
-    tf_stat.ParseRunMeta(i, std::move(run_meta));
+    tf_stat.AddRunMeta(i, std::move(run_meta));
+  }
+
+  if (cmd == kCmds[4]) {
+    tf_stat.BuildAllViews();
+    Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
+    return 0;
   }
 
   Options opts(FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros,
@@ -227,9 +235,11 @@ int Run(int argc, char** argv) {
                output_type, output_options);
 
   if (cmd == kCmds[2] || cmd == kCmds[3]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowMultiGraphNode(cmd, opts);
     return 0;
   } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowGraphNode(cmd, opts);
     return 0;
   }
@@ -254,14 +264,19 @@ int Run(int argc, char** argv) {
       fprintf(stderr, "E: %s\n", s.ToString().c_str());
       continue;
     }
-    if (cmd == kCmds[4]) {
+    if (cmd == kCmds[5]) {
       opts = new_opts;
-    } else if (cmd == kCmds[5]) {
+    } else if (cmd == kCmds[6]) {
       PrintHelp();
     } else if (cmd == kCmds[2] || cmd == kCmds[3]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowMultiGraphNode(cmd, new_opts);
     } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowGraphNode(cmd, new_opts);
+    } else if (cmd == kCmds[4]) {
+      tf_stat.BuildAllViews();
+      Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
     }
   }
   return 0;
diff --git a/tensorflow/tools/tfprof/tfprof_options.proto b/tensorflow/tools/tfprof/tfprof_options.proto
index 27eafb1ca9..47e1ff33ee 100644
--- a/tensorflow/tools/tfprof/tfprof_options.proto
+++ b/tensorflow/tools/tfprof/tfprof_options.proto
@@ -22,5 +22,13 @@ message OptionsProto {
   optional bool account_displayed_op_only = 13;
   repeated string select = 14;
   optional string output = 15;
-  optional string dump_to_file = 16;
+  optional string dump_to_file = 16 [deprecated = true];
+}
+
+message AdvisorOptionsProto {
+  // checker name -> a dict of key-value options.
+  map<string, CheckerOption> checkers = 1;
+  message CheckerOption {
+    map<string, string> options = 1;
+  }
 }
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index 1ea956152c..e41fde45f2 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -95,4 +95,12 @@ message TFMultiGraphNodeProto {
   // Descendants of the node. The actual descendants depend on the data
   // structure used.
   repeated TFMultiGraphNodeProto children = 11;
-}
\ No newline at end of file
+}
+
+message AdviceProto {
+  // checker name -> a list of reports from the checker.
+  map<string, Checker> checkers = 1;
+  message Checker {
+    repeated string reports = 2;
+  }
+}
-- 
GitLab


From baf2bf53afc730108a0669f9310126f55ca45650 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 22 Jun 2017 17:18:02 -0700
Subject: [PATCH 0380/1375] Added canned estimators to Tensorflow library. List
 of added estimators: * DNNClassifier * DNNRegressor * LinearClassifer *
 LinearRegressor * DNNLinearCombinedClassifier * DNNLinearCombinedRegressor

PiperOrigin-RevId: 159898954
---
 .../python/estimator/canned/metric_keys.py    |  6 +--
 tensorflow/python/estimator/estimator.py      |  6 +--
 tensorflow/python/estimator/estimator_lib.py  | 20 +++++++++-
 tensorflow/python/estimator/estimator_test.py |  4 +-
 tensorflow/python/estimator/model_fn.py       |  6 +--
 ...nsorflow.estimator.-d-n-n-classifier.pbtxt | 38 +++++++++++++++++++
 ...or.-d-n-n-linear-combined-classifier.pbtxt | 38 +++++++++++++++++++
 ...tor.-d-n-n-linear-combined-regressor.pbtxt | 38 +++++++++++++++++++
 ...ensorflow.estimator.-d-n-n-regressor.pbtxt | 38 +++++++++++++++++++
 ...sorflow.estimator.-linear-classifier.pbtxt | 38 +++++++++++++++++++
 ...nsorflow.estimator.-linear-regressor.pbtxt | 38 +++++++++++++++++++
 .../api/golden/tensorflow.estimator.pbtxt     | 32 ++++++++++++++++
 12 files changed, 287 insertions(+), 15 deletions(-)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt

diff --git a/tensorflow/python/estimator/canned/metric_keys.py b/tensorflow/python/estimator/canned/metric_keys.py
index 91e3bf1d83..7dc4bfe5ff 100644
--- a/tensorflow/python/estimator/canned/metric_keys.py
+++ b/tensorflow/python/estimator/canned/metric_keys.py
@@ -21,12 +21,10 @@ from __future__ import print_function
 from tensorflow.python.estimator import model_fn
 
 
-# TODO(pucker): Merge with model_fn.MetricKeys once we've worked out out naming
-# conventions.
 class MetricKeys(object):
   """Metric key strings."""
-  LOSS = model_fn.MetricKeys.LOSS
-  LOSS_MEAN = model_fn.MetricKeys.AVERAGE_LOSS
+  LOSS = model_fn.LOSS_METRIC_KEY
+  LOSS_MEAN = model_fn.AVERAGE_LOSS_METRIC_KEY
 
   ACCURACY = 'accuracy'
   # This is the best the model could do by always predicting one class.
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 5f50be01fc..b4940f975a 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -693,13 +693,13 @@ class Estimator(object):
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
 
-      if model_fn_lib.MetricKeys.LOSS in estimator_spec.eval_metric_ops:
+      if model_fn_lib.LOSS_METRIC_KEY in estimator_spec.eval_metric_ops:
         raise ValueError(
             'Metric with name "%s" is not allowed, because Estimator ' % (
-                model_fn_lib.MetricKeys.LOSS) +
+                model_fn_lib.LOSS_METRIC_KEY) +
             'already defines a default metric with the same name.')
       estimator_spec.eval_metric_ops[
-          model_fn_lib.MetricKeys.LOSS] = metrics_lib.mean(estimator_spec.loss)
+          model_fn_lib.LOSS_METRIC_KEY] = metrics_lib.mean(estimator_spec.loss)
 
       update_op, eval_dict = _extract_metric_update_ops(
           estimator_spec.eval_metric_ops)
diff --git a/tensorflow/python/estimator/estimator_lib.py b/tensorflow/python/estimator/estimator_lib.py
index 52cedad012..8e7d966564 100644
--- a/tensorflow/python/estimator/estimator_lib.py
+++ b/tensorflow/python/estimator/estimator_lib.py
@@ -18,7 +18,15 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# pylint: disable=unused-import,line-too-long
+# pylint: disable=unused-import,line-too-long,wildcard-import
+from tensorflow.python.estimator.canned.dnn import DNNClassifier
+from tensorflow.python.estimator.canned.dnn import DNNRegressor
+from tensorflow.python.estimator.canned.dnn_linear_combined import DNNLinearCombinedClassifier
+from tensorflow.python.estimator.canned.dnn_linear_combined import DNNLinearCombinedRegressor
+from tensorflow.python.estimator.canned.linear import LinearClassifier
+from tensorflow.python.estimator.canned.linear import LinearRegressor
+from tensorflow.python.estimator.canned.parsing_utils import classifier_parse_example_spec
+from tensorflow.python.estimator.canned.parsing_utils import regressor_parse_example_spec
 from tensorflow.python.estimator.estimator import Estimator
 from tensorflow.python.estimator.export import export_lib as export
 from tensorflow.python.estimator.inputs import inputs
@@ -27,9 +35,17 @@ from tensorflow.python.estimator.model_fn import ModeKeys
 from tensorflow.python.estimator.run_config import RunConfig
 
 from tensorflow.python.util.all_util import remove_undocumented
-# pylint: enable=unused-import,line-too-long
+# pylint: enable=unused-import,line-too-long,wildcard-import
 
 _allowed_symbols = [
+    'DNNClassifier',
+    'DNNRegressor',
+    'DNNLinearCombinedClassifier',
+    'DNNLinearCombinedRegressor',
+    'LinearClassifier',
+    'LinearRegressor',
+    'classifier_parse_example_spec',
+    'regressor_parse_example_spec',
     'inputs',
     'export',
     'Estimator',
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 3c74f3eb61..a7990c5673 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -869,9 +869,9 @@ class EstimatorEvaluateTest(test.TestCase):
     est = estimator.Estimator(model_fn=_model_fn_with_incremental_loss)
     est.train(dummy_input_fn, steps=1)
     scores = est.evaluate(dummy_input_fn, steps=5)
-    self.assertIn(model_fn_lib.MetricKeys.LOSS, scores)
+    self.assertIn(model_fn_lib.LOSS_METRIC_KEY, scores)
     # Average loss will be (2 + 4 + 6 + 8 + 10)/5=6
-    self.assertAlmostEqual(6., scores[model_fn_lib.MetricKeys.LOSS])
+    self.assertAlmostEqual(6., scores[model_fn_lib.LOSS_METRIC_KEY])
 
   def test_hooks_should_be_session_run_hook(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index 1aa2623962..2508a1a3b1 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -48,10 +48,8 @@ class ModeKeys(object):
   PREDICT = 'infer'
 
 
-class MetricKeys(object):
-  """Metric key strings."""
-  LOSS = 'loss'
-  AVERAGE_LOSS = 'average_loss'
+LOSS_METRIC_KEY = 'loss'
+AVERAGE_LOSS_METRIC_KEY = 'average_loss'
 
 
 class EstimatorSpec(
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
new file mode 100644
index 0000000000..3a6f770153
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNClassifier"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn.DNNClassifier\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Adagrad\', \'<function relu instance>\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
new file mode 100644
index 0000000000..83e53d3960
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNLinearCombinedClassifier"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn_linear_combined.DNNLinearCombinedClassifier\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'<function relu instance>\', \'None\', \'2\', \'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
new file mode 100644
index 0000000000..17f30a04fb
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNLinearCombinedRegressor"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn_linear_combined.DNNLinearCombinedRegressor\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'label_dimension\', \'weight_column\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'<function relu instance>\', \'None\', \'1\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
new file mode 100644
index 0000000000..edd68f0bb9
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNRegressor"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn.DNNRegressor\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Adagrad\', \'<function relu instance>\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
new file mode 100644
index 0000000000..3715dd5ec7
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.LinearClassifier"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.linear.LinearClassifier\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
new file mode 100644
index 0000000000..ccb4abf675
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.LinearRegressor"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.linear.LinearRegressor\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt
index 0d5dc73271..07b04810b5 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt
@@ -1,5 +1,21 @@
 path: "tensorflow.estimator"
 tf_module {
+  member {
+    name: "DNNClassifier"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DNNLinearCombinedClassifier"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DNNLinearCombinedRegressor"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DNNRegressor"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Estimator"
     mtype: "<type \'type\'>"
@@ -8,6 +24,14 @@ tf_module {
     name: "EstimatorSpec"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "LinearClassifier"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "LinearRegressor"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "ModeKeys"
     mtype: "<type \'type\'>"
@@ -24,4 +48,12 @@ tf_module {
     name: "inputs"
     mtype: "<type \'module\'>"
   }
+  member_method {
+    name: "classifier_parse_example_spec"
+    argspec: "args=[\'feature_columns\', \'label_key\', \'label_dtype\', \'label_default\', \'weight_column\'], varargs=None, keywords=None, defaults=[\"<dtype: \'int64\'>\", \'None\', \'None\'], "
+  }
+  member_method {
+    name: "regressor_parse_example_spec"
+    argspec: "args=[\'feature_columns\', \'label_key\', \'label_dtype\', \'label_default\', \'label_dimension\', \'weight_column\'], varargs=None, keywords=None, defaults=[\"<dtype: \'float32\'>\", \'None\', \'1\', \'None\'], "
+  }
 }
-- 
GitLab


From e6b08c491063e0aa0485fa5db2c6332af1519b7f Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 22 Jun 2017 17:27:03 -0700
Subject: [PATCH 0381/1375] Alligned how model-fns handled params among
 linear/dnn/combined estimators.

PiperOrigin-RevId: 159899925
---
 tensorflow/python/estimator/canned/linear.py | 73 ++++++++++----------
 1 file changed, 36 insertions(+), 37 deletions(-)

diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py
index 552b1bdf01..05b1e5b44a 100644
--- a/tensorflow/python/estimator/canned/linear.py
+++ b/tensorflow/python/estimator/canned/linear.py
@@ -42,8 +42,8 @@ def _get_default_optimizer(feature_columns):
   return ftrl.FtrlOptimizer(learning_rate=learning_rate)
 
 
-# TODO(b/36813849): Revisit passing params vs named arguments.
-def _linear_model_fn(features, labels, mode, params, config):
+def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
+                     partitioner, config):
   """A model_fn for linear models that use a gradient-based optimizer.
 
   Args:
@@ -51,13 +51,12 @@ def _linear_model_fn(features, labels, mode, params, config):
     labels: `Tensor` of shape `[batch_size, logits_dimension]`.
     mode: Defines whether this is training, evaluation or prediction.
       See `ModeKeys`.
-    params: A dict of hyperparameters.
-      The following hyperparameters are expected:
-      * head: A `Head` instance.
-      * feature_columns: An iterable containing all the feature columns used by
-          the model.
-      * optimizer: string, `Optimizer` object, or callable that defines the
-          optimizer to use for training. If `None`, will use a FTRL optimizer.
+    head: A `Head` instance.
+    feature_columns: An iterable containing all the feature columns used by
+      the model.
+    optimizer: string, `Optimizer` object, or callable that defines the
+      optimizer to use for training. If `None`, will use a FTRL optimizer.
+    partitioner: Partitioner for variables.
     config: `RunConfig` object to configure the runtime settings.
 
   Returns:
@@ -66,14 +65,12 @@ def _linear_model_fn(features, labels, mode, params, config):
   Raises:
     ValueError: If mode or params are invalid.
   """
-  head = params['head']
-  feature_columns = tuple(params['feature_columns'])
   optimizer = optimizers.get_optimizer_instance(
-      params.get('optimizer') or _get_default_optimizer(feature_columns),
+      optimizer or _get_default_optimizer(feature_columns),
       learning_rate=_LEARNING_RATE)
   num_ps_replicas = config.num_ps_replicas if config else 0
 
-  partitioner = params.get('partitioner') or (
+  partitioner = partitioner or (
       partitioned_variables.min_max_variable_partitioner(
           max_partitions=num_ps_replicas,
           min_slice_size=64 << 20))
@@ -210,16 +207,20 @@ class LinearClassifier(estimator.Estimator):
       head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint: disable=protected-access
           n_classes, weight_column=weight_column,
           label_vocabulary=label_vocabulary)
+    def _model_fn(features, labels, mode, config):
+      return _linear_model_fn(
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          feature_columns=tuple(feature_columns or []),
+          optimizer=optimizer,
+          partitioner=partitioner,
+          config=config)
     super(LinearClassifier, self).__init__(
-        model_fn=_linear_model_fn,
+        model_fn=_model_fn,
         model_dir=model_dir,
-        config=config,
-        params={
-            'head': head,
-            'feature_columns': feature_columns,
-            'optimizer': optimizer,
-            'partitioner': partitioner,
-        })
+        config=config)
 
 
 class LinearRegressor(estimator.Estimator):
@@ -298,21 +299,19 @@ class LinearRegressor(estimator.Estimator):
       config: `RunConfig` object to configure the runtime settings.
       partitioner: Optional. Partitioner for input layer.
     """
+    head = head_lib._regression_head_with_mean_squared_error_loss(  # pylint: disable=protected-access
+        label_dimension=label_dimension, weight_column=weight_column)
+    def _model_fn(features, labels, mode, config):
+      return _linear_model_fn(
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          feature_columns=tuple(feature_columns or []),
+          optimizer=optimizer,
+          partitioner=partitioner,
+          config=config)
     super(LinearRegressor, self).__init__(
-        model_fn=_linear_model_fn,
+        model_fn=_model_fn,
         model_dir=model_dir,
-        config=config,
-        params={
-            # pylint: disable=protected-access
-            'head':
-                head_lib._regression_head_with_mean_squared_error_loss(
-                    label_dimension=label_dimension,
-                    weight_column=weight_column),
-            # pylint: enable=protected-access
-            'feature_columns':
-                feature_columns,
-            'optimizer':
-                optimizer,
-            'partitioner':
-                partitioner,
-        })
+        config=config)
-- 
GitLab


From 8744f832fe5de8e2e75961e55f8d173348d9f1c9 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 22 Jun 2017 17:42:43 -0700
Subject: [PATCH 0382/1375] Fixed cmake tests.

PiperOrigin-RevId: 159901417
---
 tensorflow/contrib/cmake/tf_python.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index d17fcf6456..d029099be9 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -204,6 +204,7 @@ add_python_module("tensorflow/python/debug/examples")
 add_python_module("tensorflow/python/debug/lib")
 add_python_module("tensorflow/python/debug/wrappers")
 add_python_module("tensorflow/python/estimator")
+add_python_module("tensorflow/python/estimator/canned")
 add_python_module("tensorflow/python/estimator/export")
 add_python_module("tensorflow/python/estimator/inputs")
 add_python_module("tensorflow/python/estimator/inputs/queues")
-- 
GitLab


From 5b40328addf40a221d3ef717e2ac707f64973dd9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 18:00:30 -0700
Subject: [PATCH 0383/1375] [XLA:CPU] Add VLOGs to cpu_compiler.cc

PiperOrigin-RevId: 159902919
---
 tensorflow/compiler/xla/service/cpu/cpu_compiler.cc | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 00cb507c0d..0ab692def6 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -350,6 +350,7 @@ llvm::CodeGenOpt::Level CodeGenOptLevel(const HloModuleConfig& module_config) {
 StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     std::unique_ptr<HloModule> module, HloDumper dump_hlo,
     se::StreamExecutor* stream_exec) {
+  VLOG(1) << "Compiling: " << module->name();
   TF_RET_CHECK(stream_exec != nullptr);
   std::call_once(llvm_command_line_options_initialized,
                  &InitializeLLVMCommandLineOptions, module->config());
@@ -384,6 +385,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
       module->config().debug_options().xla_dump_debug_json_to();
 
   if (CpuParallelBackendRequested(module->config())) {
+    VLOG(1) << "Using parallel cpu backend";
+
     // Run buffer analysis on the HLO graph. This analysis figures out which
     // temporary buffers are required to run the computation.
     // DependencyHloOrdering is used for the parallel emitter because the order
@@ -486,6 +489,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
           .set_ir_module_string(ir_module_string);
     }
   } else {
+    VLOG(1) << "Using sequential cpu backend";
+
     // Select an order for emitting the HLO instructions for each
     // computation. Using this sequence enables tighter buffer liveness analysis
     // and reduced memory usage (as compared to using DependencyHloOrdering).
@@ -551,6 +556,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
   }
 
+  VLOG(1) << "Compilation finished";
   return std::move(cpu_executable);
 }
 
@@ -652,6 +658,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
   std::vector<std::unique_ptr<AotCompilationResult>> results;
   for (size_t i = 0; i < modules.size(); ++i) {
     HloModule* module = modules[i].get();
+    VLOG(1) << "Compiling ahead-of-time: " << module->name();
 
     TF_RETURN_IF_ERROR(RunHloPasses(module, dump_hlo));
 
@@ -730,6 +737,8 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
         std::move(object_file_data), std::move(buffer_sizes),
         result_slice.index()));
   }
+
+  VLOG(1) << "Compilation finished";
   return std::move(results);
 }
 
-- 
GitLab


From 4470dbe543da4ff1dda9dbd4eeb97888d4b55d7b Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Thu, 22 Jun 2017 22:45:27 -0400
Subject: [PATCH 0384/1375] Moved tpu_config.RunConfig check to inside of
 use_tpu block

---
 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index b702ab91f6..8503a4a545 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -146,9 +146,8 @@ class TpuInfeedSessionHook(session_run_hook.SessionRunHook):
 class TpuEstimator(estimator_lib.Estimator):
   """Estimator with TPU support.
 
-  The only difference is a wrapped  model_fn is set in the constructor.
+  The only difference is a wrapped model_fn is set in the constructor.
   """
-
   def __init__(self,
                model_fn=None,
                model_dir=None,
@@ -156,17 +155,16 @@ class TpuEstimator(estimator_lib.Estimator):
                params=None,
                use_tpu=True):
     if use_tpu:
+      if not isinstance(config, tpu_config.RunConfig):
+        raise ValueError('`config` must be `tpu_config.RunConfig`')
       model_function = wrapped_model_fn(model_fn, config)
     else:
       model_function = model_fn
-
     super(TpuEstimator, self).__init__(
         model_fn=model_function,
         model_dir=model_dir,
         config=config,
         params=params)
-    if not isinstance(config, tpu_config.RunConfig):
-      raise ValueError('`config` must be `tpu_config.RunConfig`')
 
   def _create_global_step(self, graph):
     """Creates a global step suitable for TPUs.
-- 
GitLab


From 5af6b3e40161ead74390278053de81908bfd7674 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 20:41:27 -0700
Subject: [PATCH 0385/1375] Make occurence (op run times and op definition)
 selectable in all views to address the loop problem.

When a node is in loop, its execution times are accumulated, its run times
will increase.

PiperOrigin-RevId: 159912429
---
 .../tfprof/python/tools/tfprof/internal/BUILD |  2 +
 .../internal/print_model_analysis_test.py     | 41 +++++++++-
 .../tfprof/internal/run_metadata_test.py      | 80 ++++++++++++++++++-
 .../tools/tfprof/model_analyzer_test.py       | 16 ++--
 .../g3doc/profile_model_architecture.md       |  7 +-
 tensorflow/tools/tfprof/g3doc/profile_time.md | 32 ++++++++
 .../tools/tfprof/internal/tfprof_code.cc      | 27 ++++---
 .../tools/tfprof/internal/tfprof_node.cc      | 16 ++--
 .../tools/tfprof/internal/tfprof_node.h       | 42 +++++++++-
 .../tools/tfprof/internal/tfprof_node_show.cc | 13 ++-
 tensorflow/tools/tfprof/internal/tfprof_op.cc |  7 +-
 .../tools/tfprof/internal/tfprof_show.cc      | 19 +++++
 .../tfprof/internal/tfprof_show_multi.cc      | 41 ++++++----
 .../tools/tfprof/internal/tfprof_show_test.cc | 15 ++--
 .../tfprof/internal/tfprof_stats_test.cc      | 74 +++++++++++------
 .../tfprof/internal/tfprof_tensor_test.cc     | 27 ++++---
 tensorflow/tools/tfprof/tfprof_output.proto   | 12 ++-
 17 files changed, 369 insertions(+), 102 deletions(-)

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
index e844046811..7abfb62214 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
@@ -65,6 +65,7 @@ cuda_py_test(
     name = "run_metadata_test",
     srcs = ["run_metadata_test.py"],
     additional_deps = [
+        ":model_analyzer_testlib",
         "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
@@ -72,6 +73,7 @@ cuda_py_test(
         "//tensorflow/python:random_ops",
         "//tensorflow/tools/tfprof:protos_all_py",
     ],
+    tags = ["no_pip"],
 )
 
 # -----------------------------------------------------------------------------
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
index 091fcdef88..84a37e087b 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
@@ -157,6 +157,9 @@ class PrintModelAnalysisTest(test.TestCase):
             cpu_exec_micros: 0
             total_accelerator_exec_micros: 0
             total_cpu_exec_micros: 0
+            run_count: 0
+            total_run_count: 0
+            total_definition_count: 1
           }
           children {
             name: "DW"
@@ -213,6 +216,9 @@ class PrintModelAnalysisTest(test.TestCase):
               cpu_exec_micros: 0
               total_accelerator_exec_micros: 0
               total_cpu_exec_micros: 0
+              run_count: 0
+              total_run_count: 0
+              total_definition_count: 1
             }
             children {
               name: "DW/Initializer"
@@ -249,6 +255,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/mean"
@@ -263,6 +272,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/mul"
@@ -302,6 +314,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/shape"
@@ -316,6 +331,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/stddev"
@@ -330,6 +348,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 float_ops: 0
                 total_float_ops: 0
@@ -362,6 +383,9 @@ class PrintModelAnalysisTest(test.TestCase):
                 cpu_exec_micros: 0
                 total_accelerator_exec_micros: 0
                 total_cpu_exec_micros: 0
+                run_count: 0
+                total_run_count: 0
+                total_definition_count: 6
               }
               float_ops: 0
               total_float_ops: 0
@@ -369,6 +393,9 @@ class PrintModelAnalysisTest(test.TestCase):
               cpu_exec_micros: 0
               total_accelerator_exec_micros: 0
               total_cpu_exec_micros: 0
+              run_count: 0
+              total_run_count: 0
+              total_definition_count: 7
             }
             children {
               name: "DW/read"
@@ -400,6 +427,9 @@ class PrintModelAnalysisTest(test.TestCase):
               cpu_exec_micros: 0
               total_accelerator_exec_micros: 0
               total_cpu_exec_micros: 0
+              run_count: 0
+              total_run_count: 0
+              total_definition_count: 1
             }
             float_ops: 0
             total_float_ops: 0
@@ -407,6 +437,9 @@ class PrintModelAnalysisTest(test.TestCase):
             cpu_exec_micros: 0
             total_accelerator_exec_micros: 0
             total_cpu_exec_micros: 0
+            run_count: 0
+            total_run_count: 0
+            total_definition_count: 10
           }
           children {
             name: "zeros"
@@ -421,13 +454,19 @@ class PrintModelAnalysisTest(test.TestCase):
             cpu_exec_micros: 0
             total_accelerator_exec_micros: 0
             total_cpu_exec_micros: 0
+            run_count: 0
+            total_run_count: 0
+            total_definition_count: 1
           }
           float_ops: 0
           total_float_ops: 0
           accelerator_exec_micros: 0
           cpu_exec_micros: 0
           total_accelerator_exec_micros: 0
-          total_cpu_exec_micros: 0""", expected_pb)
+          total_cpu_exec_micros: 0
+          run_count: 0
+          total_run_count: 0
+          total_definition_count: 13""", expected_pb)
       self.assertEqual(expected_pb, tfprof_pb)
 
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index feb20dc0f4..9c59df3117 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -27,12 +27,13 @@ from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 # pylint: disable=g-bad-import-order
 # XXX: this depends on pywrap_tensorflow and must come later
 from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
-
+from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib
 SIZE = 1300
 
 
@@ -68,15 +69,32 @@ def _run_model():
     return tfprof_node, run_metadata
 
 
+def _run_loop_model():
+  with session.Session() as sess:
+    x = lib.BuildFullModel()
+
+    sess.run(variables.global_variables_initializer())
+    run_meta = config_pb2.RunMetadata()
+    _ = sess.run(x,
+                 options=config_pb2.RunOptions(
+                     trace_level=config_pb2.RunOptions.FULL_TRACE),
+                 run_metadata=run_meta)
+
+    tfprof_node = model_analyzer.print_model_analysis(
+        sess.graph, run_meta,
+        tfprof_options=model_analyzer.PRINT_ALL_TIMING_MEMORY)
+    return tfprof_node, run_meta
+
+
 class RunMetadataTest(test.TestCase):
 
   def testGPU(self):
     if not test.is_gpu_available():
       return
 
+    ops.reset_default_graph()
     with ops.device('/gpu:0'):
       tfprof_node, run_meta = _run_model()
-
       self.assertEqual(tfprof_node.children[0].name, 'MatMul')
       self.assertGreater(tfprof_node.children[0].exec_micros, 10)
 
@@ -94,9 +112,9 @@ class RunMetadataTest(test.TestCase):
     self.assertTrue(has_all_stream)
 
   def testCPU(self):
+    ops.reset_default_graph()
     with ops.device('/cpu:0'):
       tfprof_node, run_meta = _run_model()
-
       self.assertEqual(tfprof_node.children[0].name, 'MatMul')
       self.assertGreater(tfprof_node.children[0].exec_micros, 10)
 
@@ -107,5 +125,61 @@ class RunMetadataTest(test.TestCase):
     ret = _extract_node(run_meta, 'MatMul:MatMul')
     self.assertEqual(len(ret), 0)
 
+  def testLoopCPU(self):
+    ops.reset_default_graph()
+    with ops.device('/cpu:0'):
+      tfprof_node, run_meta = _run_loop_model()
+      # The while-loop caused a node to appear 4 times in scheduling.
+      ret = _extract_node(run_meta,
+                          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+      self.assertEqual(len(ret['/job:localhost/replica:0/task:0/cpu:0']), 4)
+
+      total_cpu_execs = 0
+      for node in ret['/job:localhost/replica:0/task:0/cpu:0']:
+        total_cpu_execs += node.op_end_rel_micros
+
+      mm_node = lib.SearchTFProfNode(
+          tfprof_node,
+          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+
+      self.assertEqual(mm_node.run_count, 4)
+      self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
+      self.assertEqual(mm_node.exec_micros, total_cpu_execs)
+
+  def testLoopGPU(self):
+    if not test.is_gpu_available():
+      return
+
+    ops.reset_default_graph()
+    with ops.device('/gpu:0'):
+      tfprof_node, run_meta = _run_loop_model()
+      # The while-loop caused a node to appear 4 times in scheduling.
+      ret = _extract_node(run_meta,
+                          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+      self.assertEqual(len(ret['/job:localhost/replica:0/task:0/gpu:0']), 4)
+
+      total_cpu_execs = 0
+      for node in ret['/job:localhost/replica:0/task:0/gpu:0']:
+        total_cpu_execs += node.op_end_rel_micros
+
+      ret = _extract_node(
+          run_meta,
+          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul:MatMul')
+      self.assertGreaterEqual(len(ret['/gpu:0/stream:all']), 4)
+
+      total_accelerator_execs = 0
+      for node in ret['/gpu:0/stream:all']:
+        total_accelerator_execs += node.op_end_rel_micros
+
+      mm_node = lib.SearchTFProfNode(
+          tfprof_node,
+          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+
+      self.assertEqual(mm_node.run_count, 4)
+      self.assertEqual(mm_node.accelerator_exec_micros, total_accelerator_execs)
+      self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
+      self.assertEqual(mm_node.exec_micros,
+                       total_cpu_execs + total_accelerator_execs)
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index ec7540a657..fea27a82a5 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -34,7 +34,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testDumpToFile(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
+    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
     opts['output'] = 'file:outfile=' + outfile
 
@@ -52,7 +52,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testSelectEverything(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
+    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
     opts['output'] = 'file:outfile=' + outfile
     opts['account_type_regexes'] = ['.*']
@@ -77,7 +77,7 @@ class PrintModelAnalysisTest(test.TestCase):
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
         self.assertEqual(
-            'node name | # parameters | # float_ops | assigned devices | op types | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, _kTFScopeParent, )\n  Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x6x6x3|1:3x3x3x6)\n  Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x3x3x6|1:2x2x6x12)\n  DW (3x3x3x6, 162/162 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW/Assign (0/0 params, 0/0 flops, Assign, 0:3x3x3x6|1:3x3x3x6)\n    DW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      DW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:4)\n        DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:3x3x3x6)\n  DW2 (2x2x6x12, 288/288 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW2/Assign (0/0 params, 0/0 flops, Assign, 0:2x2x6x12|1:2x2x6x12)\n    DW2/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      DW2/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:4)\n        DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:2x2x6x12)\n  ScalarW (1, 1/1 params, 0/0 flops, VariableV2|_trainable_variables, )\n    ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0:1|1:1)\n    ScalarW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:1|1:1)\n        ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:0)\n        ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:1|1:1)\n        ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    ScalarW/read (0/0 params, 0/0 flops, Identity, 0:1)\n  init (0/0 params, 0/0 flops, NoOp, 0:1|1:3x3x3x6|2:2x2x6x12)\n  zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, )\n',
+            'node name | # parameters | # float_ops | assigned devices | op types | op count (run|defined) | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, _kTFScopeParent, --/7|--/35, )\n  Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x6x6x3|1:3x3x3x6)\n  Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x3x3x6|1:2x2x6x12)\n  DW (3x3x3x6, 162/162 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n    DW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:3x3x3x6|1:3x3x3x6)\n    DW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      DW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n        DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:3x3x3x6)\n  DW2 (2x2x6x12, 288/288 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n    DW2/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:2x2x6x12|1:2x2x6x12)\n    DW2/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      DW2/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n        DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:2x2x6x12)\n  ScalarW (1, 1/1 params, 0/0 flops, VariableV2|_trainable_variables, 0/0|1/10, )\n    ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:1|1:1)\n    ScalarW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:1|1:1)\n        ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:0)\n        ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:1|1:1)\n        ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    ScalarW/read (0/0 params, 0/0 flops, Identity, 0/0|1/1, 0:1)\n  init (0/0 params, 0/0 flops, NoOp, 0/0|1/1, 0:1|1:3x3x3x6|2:2x2x6x12)\n  zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, 1/1|1/1, )\n',
             f.read())
         # pylint: enable=line-too-long
 
@@ -143,12 +143,12 @@ class PrintModelAnalysisTest(test.TestCase):
       with gfile.Open(outfile, 'r') as f:
         lines = f.read().split('\n')
         result = '\n'.join([l[:min(len(l), 80)] for l in lines])
-        self.assertEqual('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/54.08k flops)\n  model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (0/1.80k para\n    model_analyzer_testlib.py:35:BuildSmallModel:image = array_ops... (0/0 param\n    model_analyzer_testlib.py:39:BuildSmallModel:initializer=init_... (0/4 param\n    model_analyzer_testlib.py:43:BuildSmallModel:initializer=init_... (0/648 par\n    model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n    model_analyzer_testlib.py:48:BuildSmallModel:initializer=init_... (0/1.15k p\n    model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n  model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (0/1.04k para\n  model_analyzer_testlib.py:64:BuildFullModel:target = array_op... (0/0 params, \n  model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (0/0 params, \n  model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min... (0/0 params, \n',
+        self.assertEqual('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/91.04k flops)\n  model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (0/1.80k para\n    model_analyzer_testlib.py:35:BuildSmallModel:image = array_ops... (0/0 param\n    model_analyzer_testlib.py:39:BuildSmallModel:initializer=init_... (0/4 param\n    model_analyzer_testlib.py:43:BuildSmallModel:initializer=init_... (0/648 par\n    model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n    model_analyzer_testlib.py:48:BuildSmallModel:initializer=init_... (0/1.15k p\n    model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n  model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (0/1.04k para\n  model_analyzer_testlib.py:64:BuildFullModel:target = array_op... (0/0 params, \n  model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (0/0 params, \n  model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min... (0/0 params, \n',
                          result)
 
       self.assertLess(0, tfprof_node.total_exec_micros)
       self.assertEqual(2844, tfprof_node.total_parameters)
-      self.assertEqual(54080, tfprof_node.total_float_ops)
+      self.assertEqual(91040, tfprof_node.total_float_ops)
       self.assertEqual(5, len(tfprof_node.children))
       self.assertEqual('_TFProfRoot', tfprof_node.name)
       self.assertEqual(
@@ -226,7 +226,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testOpView(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
+    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
     opts['output'] = 'file:outfile=' + outfile
     opts['account_type_regexes'] = ['.*']
@@ -250,8 +250,8 @@ class PrintModelAnalysisTest(test.TestCase):
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
         self.assertEqual(
-            'nodename|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence|input',
-            f.read().replace('\t', '').replace(' ', '')[0:100])
+            'nodename|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence(run|defined)|inputshapes\n',
+            f.read().replace('\t', '').replace(' ', '')[0:120])
         # pylint: enable=line-too-long
 
       total_children = 0
diff --git a/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md b/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
index 5ad5a56513..cf99adcf02 100644
--- a/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
+++ b/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
@@ -52,10 +52,15 @@ use the definition to calculate float operations. Contributes are welcome.
 to calculate the statistics. It is suggested to pass in `-run_meta_path` if
 shape is only known during runtime. tfprof can fill in the missing shape with
 the runtime shape information from RunMetadata.
-
 Hence, it is suggested to use `-account_displayed_name_only`
 option so that you know the statistics are only for the operations printed out.
 
+* If no RunMetadata provided, tfprof count float_ops of each graph node once,
+even if it is defined in tf.while_loop. This is because tfprof doesn't know
+how many times are run statically. If RunMetadata provided, tfprof calculate
+float_ops as float_ops * run_count.
+
+
 
 ```python
 # To profile float opertions in commandline, you need to pass --graph_path
diff --git a/tensorflow/tools/tfprof/g3doc/profile_time.md b/tensorflow/tools/tfprof/g3doc/profile_time.md
index c89d7b0b03..c865e577f7 100644
--- a/tensorflow/tools/tfprof/g3doc/profile_time.md
+++ b/tensorflow/tools/tfprof/g3doc/profile_time.md
@@ -1,10 +1,42 @@
 ##Profile Time
 
+* [Times in TensorFlow and tfprof](#times-in-tensorflow-and-tfprof)
 * [Profile by Python Code](#profile-by-python-code)
 * [Profile by Operation Type](#profile-by-operation-type)
 * [Profile by Graph](#profile-by-graph)
 * [Profile by Name Scope](#profile-by-name-scope)
 
+
+###Times in TensorFlow and tfprof
+When we run a model, Tensorflow schedules and runs the nodes (operations)
+in the graph. An operation can be placed on an accelerator or on CPU.
+
+
+#### On Accelerator
+When an operation is placed on accelerator, it will first be scheduled
+by TensorFlow on CPU. Normally, it's the code in OpKernel::Compute.
+OpKernel::Compute can decide to dispatch some of the computations on the
+accelerator. While some computation (e.g. pre-processing) is still done
+in CPU. OpKernel::Compute can dispatch computation on accelerator
+and return, or it can also wait for the accelerator to finish.
+
+tfprof reports 3 execution times:
+
+  * <b>accelerator_micros</b>, which is the part of computation time spent on accelerator.
+  * <b>cpu_micros</b>, which is the part of computation time spent on cpu, including
+    any wait times that might happen if OpKernel::Compute decides to wait.
+  * <b>exec_micros</b>, which is the sum of accelerator_micros and cpu_micros.
+
+Since accelerator, such as GPU, usually runs operation asynchronously, you
+might notice an operation finishes on cpu before it starts running on
+accelerator.
+
+#### On CPU
+When an operation is placed on CPU, it will completely run on CPU. Hence,
+<b>exec_micros</b> is equal to <b>cpu_micros</b> and <b>accelerator_micros</b>
+should be 0.
+
+
 ###Profile by Python Code
 ```python
 # In code view, the time of each line of Python code is the aggregated
diff --git a/tensorflow/tools/tfprof/internal/tfprof_code.cc b/tensorflow/tools/tfprof/internal/tfprof_code.cc
index 70e341e8d4..f7b71bff31 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_code.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_code.cc
@@ -235,6 +235,17 @@ std::vector<CodeNode*> TFCode::Account(const std::vector<CodeNode*>& roots,
 
 string TFCode::FormatNode(CodeNode* node, const Options& opts, int64 indent) {
   std::vector<string> attrs;
+  if (opts.select.find(kShown[0]) != opts.select.end()) {
+    string memory = FormatMemory(node->proto().total_requested_bytes());
+    if (node->account) {
+      memory = FormatMemory(node->proto().requested_bytes()) + "/" + memory;
+    } else {
+      memory = "--/" + memory;
+    }
+    attrs.push_back(memory);
+  }
+  std::vector<string> time_attrs = FormatTimes(node, opts);
+  attrs.insert(attrs.end(), time_attrs.begin(), time_attrs.end());
 
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     string params = FormatNumber(node->proto().total_parameters()) + " params";
@@ -255,17 +266,6 @@ string TFCode::FormatNode(CodeNode* node, const Options& opts, int64 indent) {
     }
     attrs.push_back(fops);
   }
-  if (opts.select.find(kShown[0]) != opts.select.end()) {
-    string memory = FormatMemory(node->proto().total_requested_bytes());
-    if (node->account) {
-      memory = FormatMemory(node->proto().requested_bytes()) + "/" + memory;
-    } else {
-      memory = "--/" + memory;
-    }
-    attrs.push_back(memory);
-  }
-  std::vector<string> time_attrs = FormatTimes(node, opts);
-  attrs.insert(attrs.end(), time_attrs.begin(), time_attrs.end());
 
   if (opts.select.find(kShown[5]) != opts.select.end() &&
       !node->node->devices().empty()) {
@@ -275,7 +275,10 @@ string TFCode::FormatNode(CodeNode* node, const Options& opts, int64 indent) {
     std::set<string> op_types = node->node->op_types();
     attrs.push_back(str_util::Join(op_types, "|"));
   }
-
+  if (opts.select.find(kShown[7]) != opts.select.end()) {
+    // TODO(xpan): Make op count available in code view?
+    attrs.push_back(strings::Printf("%s N/A in code view", kShown[7]));
+  }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     attrs.push_back(strings::Printf("%s N/A in code view", kShown[8]));
   }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.cc b/tensorflow/tools/tfprof/internal/tfprof_node.cc
index 72f74902b5..54e14b1dd7 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_node.cc
@@ -70,10 +70,11 @@ void ExecStep::AddTimeStats(const string& dev, const NodeExecStats& step_stat) {
       accelerator_execs_[dev].push_back(pair);
       op_execs_[dev].push_back(pair);
     } else if (CountAsCPUTime(dev)) {
-      // TODO(xpan): A while-loop can has multiple nodes sharing the
-      // same name. They shouldn't be counted in one node.
       cpu_execs_[dev].push_back(pair);
       op_execs_[dev].push_back(pair);
+      // In while-loop, a graph node is executed multiple times under
+      // the same name.
+      run_count_ += 1;
     }
   }
 }
@@ -169,7 +170,8 @@ int64 ExecStep::accelerator_exec_micros() const {
   // Normally, an op should only be scheduled on 1 accelerator device.
   // Hence there should generally be 1 element in accelerator_execs_.
   for (const auto& execs : accelerator_execs_) {
-    // A op can fire multiple kernel runs hence multiple elements here.
+    // An op can fire multiple kernels or
+    // being scheduled multiple times in while-loop.
     for (const auto& exec : execs.second) {
       total += exec.second;
     }
@@ -179,13 +181,9 @@ int64 ExecStep::accelerator_exec_micros() const {
 
 int64 ExecStep::cpu_exec_micros() const {
   int64 total = 0;
-  // Here we use for loop just for consistent appearence with
-  // accelerator_execs.
-  // We only expect cpu_execs_ to have 1 element because an
-  // op can only be scheduled on 1 device.
+  // Normally, an op can only be scheduled on 1 device.
   for (const auto& execs : cpu_execs_) {
-    // We only expect exec to have 1 element because an op
-    // can only be schedule once.
+    // An op can be scheduled multiple times in while-loop.
     for (const auto& exec : execs.second) {
       total += exec.second;
     }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.h b/tensorflow/tools/tfprof/internal/tfprof_node.h
index 6fd65c1ee7..f4da0ab9ee 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_node.h
@@ -46,6 +46,7 @@ class ExecStep {
  public:
   ExecStep(TFGraphNode* node)
       : node(node),
+        run_count_(0),
         all_start_micros_(0),
         latest_end_micros_(0),
         mem_initiated_(false),
@@ -60,6 +61,7 @@ class ExecStep {
 
   void AddMemoryStats(const string& dev, const NodeExecStats& step_stat);
 
+  int64 run_count() const { return run_count_; }
   // The execution time of an op. If it runs on accelerator, then it's
   // accelerator_exec_micros(). Otherwise, it's CPU time.
   int64 exec_micros() const;
@@ -89,15 +91,18 @@ class ExecStep {
 
  private:
   TFGraphNode* node;
+  // Can be larger than 1 if run multiple times in loop.
+  int64 run_count_;
   // The earliest/latest time including scheduling and execution.
   int64 all_start_micros_;
   int64 latest_end_micros_;
   // device -> vector of {op_start_micros, op_exec_micros} pairs.
   // accelerator_execs: gpu:id/stream:all -> {op_start_micros, op_exec_micros}
-  // For accelerator, vector size can be larger than 1, multiple kernel fires.
+  // For accelerator, vector size can be larger than 1, multiple kernel fires
+  // or in tf.while_loop.
   std::map<string, std::vector<std::pair<int64, int64>>> accelerator_execs_;
   // cpu_execs: cpu/gpu:id -> {op_start_micros, op_exec_micros}
-  // For cpu, normally vector size is 1, that is only one run.
+  // For cpu, vector size can be larger than 1 if in tf.while_loop.
   std::map<string, std::vector<std::pair<int64, int64>>> cpu_execs_;
   // combines accelerator_execs_ and cpu_execs_.
   std::map<string, std::vector<std::pair<int64, int64>>> op_execs_;
@@ -188,6 +193,23 @@ class TFGraphNode {
     return src_output_idx_;
   }
 
+  // Number of times the graph node is executed. When step < 0, the
+  // average number of times executed across all steps.
+  int64 run_count(int64 step) const {
+    if (execs_.empty()) {
+      return 0;
+    }
+    if (step >= 0) {
+      auto exec = execs_.find(step);
+      CHECK(exec != execs_.end());
+      return exec->second.run_count();
+    }
+    int64 total_run_count = 0;
+    for (const auto& exec : execs_) {
+      total_run_count += exec.second.run_count();
+    }
+    return total_run_count / execs_.size();
+  }
   // This is overall computation time, including both cpu and accelerator.
   // Note, cpu and accelerator might or might not run in parallel.
   int64 exec_micros(int64 step) const {
@@ -318,7 +340,14 @@ class TFGraphNode {
     return exec->second.allocator_bytes_in_use();
   }
 
-  int64 float_ops() const { return float_ops_; }
+  int64 float_ops(int64 step) const {
+    // If not run, return static analysis.
+    if (execs_.empty()) {
+      return float_ops_;
+    }
+    // Otherwise, return dynamic float_ops.
+    return float_ops_ * run_count(step);
+  }
   const CodeDef& code() { return code_; }
   string canonical_device() const { return canonical_device_; }
   string host_device() const { return host_device_; }
@@ -371,6 +400,7 @@ class TFMultiGraphNode {
  public:
   TFMultiGraphNode(const string& name)
       : name_(name),
+        run_count_(0),
         exec_micros_(0),
         accelerator_exec_micros_(0),
         cpu_exec_micros_(0),
@@ -378,6 +408,7 @@ class TFMultiGraphNode {
         float_ops_(0) {}
 
   bool SnapshotNodes(int64 step, const std::vector<string>& type_regexes) {
+    run_count_ = 0;
     exec_micros_ = 0;
     accelerator_exec_micros_ = 0;
     cpu_exec_micros_ = 0;
@@ -398,12 +429,13 @@ class TFMultiGraphNode {
     for (const TFGraphNode* node : nodes) {
       op_types_.insert(node->op_types().begin(), node->op_types().end());
 
+      run_count_ += node->run_count(step);
       exec_micros_ += node->exec_micros(step);
       accelerator_exec_micros_ += node->accelerator_exec_micros(step);
       cpu_exec_micros_ += node->cpu_exec_micros(step);
 
       requested_bytes_ += node->requested_bytes(step);
-      float_ops_ += node->float_ops();
+      float_ops_ += node->float_ops(step);
       if (node->shape().size() > 0) {
         shapes_.push_back(node->shape());
       }
@@ -436,6 +468,7 @@ class TFMultiGraphNode {
 
   const string& name() const { return name_; }
 
+  int64 run_count() const { return run_count_; }
   int64 exec_micros() const { return exec_micros_; }
   int64 accelerator_exec_micros() const { return accelerator_exec_micros_; }
   int64 cpu_exec_micros() const { return cpu_exec_micros_; }
@@ -480,6 +513,7 @@ class TFMultiGraphNode {
   const string name_;
   // Snapshot based on type_regexes
   std::set<string> op_types_;
+  int64 run_count_;
   int64 exec_micros_;
   int64 accelerator_exec_micros_;
   int64 cpu_exec_micros_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc b/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
index 7b22e88e07..e9073baf0d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
@@ -31,13 +31,14 @@ void ShowNode::ReInit(int64 step) {
   if (!node->canonical_device().empty()) {
     mutable_proto()->add_devices(node->canonical_device());
   }
+  mutable_proto()->set_run_count(node->run_count(step));
   mutable_proto()->set_exec_micros(node->exec_micros(step));
   mutable_proto()->set_accelerator_exec_micros(
       node->accelerator_exec_micros(step));
   mutable_proto()->set_cpu_exec_micros(node->cpu_exec_micros(step));
 
   mutable_proto()->set_requested_bytes(node->requested_bytes(step));
-  mutable_proto()->set_float_ops(node->float_ops());
+  mutable_proto()->set_float_ops(node->float_ops(step));
 
   mutable_proto()->clear_input_shapes();
   for (const auto& inp : node->input_shapes()) {
@@ -71,6 +72,10 @@ const TFGraphNodeProto& ShowNode::proto() const { return proto_; }
 
 void ShowNode::AggregateTotalStats(ShowNode* node) {
   TFGraphNodeProto* node_pb = node->mutable_proto();
+  mutable_proto()->set_total_run_count(proto().total_run_count() +
+                                       node_pb->total_run_count());
+  mutable_proto()->set_total_definition_count(
+      proto().total_definition_count() + node_pb->total_definition_count());
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          node_pb->total_exec_micros());
   mutable_proto()->set_total_accelerator_exec_micros(
@@ -88,6 +93,10 @@ void ShowNode::AggregateTotalStats(ShowNode* node) {
 }
 
 void ShowNode::AddSelfToTotalStats() {
+  mutable_proto()->set_total_definition_count(proto().total_definition_count() +
+                                              1);
+  mutable_proto()->set_total_run_count(proto().total_run_count() +
+                                       proto().run_count());
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          proto().exec_micros());
   mutable_proto()->set_total_accelerator_exec_micros(
@@ -105,6 +114,8 @@ void ShowNode::AddSelfToTotalStats() {
 }
 
 void ShowNode::ResetTotalStats() {
+  mutable_proto()->set_total_definition_count(0);
+  mutable_proto()->set_total_run_count(0);
   mutable_proto()->set_total_exec_micros(0);
   mutable_proto()->set_total_accelerator_exec_micros(0);
   mutable_proto()->set_total_cpu_exec_micros(0);
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index 30284f0307..77a2593623 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -283,9 +283,14 @@ string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const {
   }
 
   if (opts.select.find(kShown[7]) != opts.select.end()) {
+    int64 total_runs = 0;
+    for (const auto& gnode : node->proto().graph_nodes()) {
+      total_runs += gnode.run_count();
+    }
     attrs.push_back(strings::Printf(
         "%10s",
-        strings::Printf("%d", node->proto().graph_nodes_size()).c_str()));
+        strings::Printf("%lld|%d", total_runs, node->proto().graph_nodes_size())
+            .c_str()));
   }
 
   string node_str = strings::Printf("%-25s%s\n", node->name().c_str(),
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index c456099c86..6c1183a4d5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -75,6 +75,7 @@ bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
       node->proto().exec_micros() < opts.min_micros ||
       node->proto().parameters() < opts.min_params ||
       node->proto().float_ops() < opts.min_float_ops ||
+      node->proto().run_count() < opts.min_occurrence ||
       depth > opts.max_depth || !ShouldShowIfExtra(node, opts, depth)) {
     return false;
   }
@@ -181,6 +182,21 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
     const std::set<string>& op_types = node->node->op_types();
     info.push_back(str_util::Join(op_types, "|"));
   }
+  if (opts.select.find(kShown[7]) != opts.select.end()) {
+    string run = FormatNumber(node->proto().total_run_count());
+    if (node->account) {
+      run = FormatNumber(node->proto().run_count()) + "/" + run;
+    } else {
+      run = "--/" + run;
+    }
+    string definition = FormatNumber(node->proto().total_definition_count());
+    if (node->account) {
+      definition = "1/" + definition;
+    } else {
+      definition = "--/" + definition;
+    }
+    info.push_back(run + "|" + definition);
+  }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     std::vector<string> shape_vec;
     for (const auto& s : node->node->input_shapes()) {
@@ -228,6 +244,9 @@ string TFShow::FormatLegend(const Options& opts) const {
   if (opts.select.find(kShown[6]) != opts.select.end()) {
     legends.push_back("op types");
   }
+  if (opts.select.find(kShown[7]) != opts.select.end()) {
+    legends.push_back("op count (run|defined)");
+  }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     legends.push_back("input shapes");
   }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 389968b750..84d542714a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -135,7 +135,7 @@ string TFMultiShow::FormatLegend(const Options& opts) const {
     legends.push_back("op types");
   }
   if (opts.select.find(kShown[7]) != opts.select.end()) {
-    legends.push_back("op occurrence");
+    legends.push_back("op occurrence (run|defined)");
   }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     legends.push_back("input shapes");
@@ -146,8 +146,8 @@ string TFMultiShow::FormatLegend(const Options& opts) const {
 
 string TFMultiShow::FormatInputShapes(
     const TFMultiGraphNodeProto& proto) const {
-  std::map<string, int> input_shapes_str;
-  std::map<string, int> input_time_str;
+  // input_shape string -> (static defined count, run count, run_micros)
+  std::map<string, std::tuple<int64, int64, int64>> input_shapes_attr;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
     const TFGraphNodeProto& gnode = proto.graph_nodes(i);
     // Convert and sort by input_idx.
@@ -167,27 +167,36 @@ string TFMultiShow::FormatInputShapes(
     }
     string shape_type_str = strings::Printf(
         "input_type: %s", str_util::Join(input_vec, ",\t").c_str());
-    input_shapes_str[shape_type_str] += 1;
-    input_time_str[shape_type_str] += gnode.exec_micros();
+    auto t = input_shapes_attr.find(shape_type_str);
+    if (t == input_shapes_attr.end()) {
+      input_shapes_attr.insert(
+          std::make_pair(shape_type_str, std::make_tuple(0, 0, 0)));
+      t = input_shapes_attr.find(shape_type_str);
+    }
+    input_shapes_attr[shape_type_str] = std::make_tuple(
+        std::get<0>(t->second) + 1, std::get<1>(t->second) + gnode.run_count(),
+        std::get<2>(t->second) + gnode.exec_micros());
   }
-  if (input_shapes_str.empty()) {
+  if (input_shapes_attr.empty()) {
     return "";
   }
 
-  std::vector<std::pair<string, int>> shape_count_vec(input_shapes_str.begin(),
-                                                      input_shapes_str.end());
-  std::sort(shape_count_vec.begin(), shape_count_vec.end(),
-            [](const std::pair<const string, int>& a,
-               const std::pair<const string, int>& b) {
-              return a.second > b.second;
-            });
+  std::vector<std::pair<string, std::tuple<int64, int64, int64>>>
+      shape_count_vec(input_shapes_attr.begin(), input_shapes_attr.end());
+  std::sort(
+      shape_count_vec.begin(), shape_count_vec.end(),
+      [](const std::pair<const string, std::tuple<int64, int64, int64>>& a,
+         const std::pair<const string, std::tuple<int64, int64, int64>>& b) {
+        return std::get<1>(a.second) > std::get<1>(b.second);
+      });
 
   std::vector<string> input_types;
   input_types.reserve(shape_count_vec.size());
   for (const auto& s : shape_count_vec) {
-    input_types.push_back(
-        strings::Printf("%s\t(*%d)\texec_time: %s", s.first.c_str(), s.second,
-                        FormatTime(input_time_str[s.first]).c_str()));
+    std::tuple<int64, int64, int64> t = s.second;
+    input_types.push_back(strings::Printf(
+        "%s\t(run*%lld|defined*%lld)\texec_time: %s", s.first.c_str(),
+        std::get<1>(t), std::get<0>(t), FormatTime(std::get<2>(t)).c_str()));
   }
   return str_util::Join(input_types, "\n");
 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index a58fbdafca..46b6ebc09a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -166,15 +166,16 @@ TEST_F(TFProfShowTest, DumpOpMode) {
   TF_CHECK_OK(ReadFileToString(Env::Default(), dump_file, &dump_str));
   EXPECT_EQ(
       "nodename|outputbytes|totalexecutiontime|acceleratorexecutiontime|"
-      "cpuexecutiontime|#parameters|#float_ops|opoccurrence|"
+      "cpuexecutiontime|#parameters|#float_ops|opoccurrence(run|defined)|"
       "inputshapes\nVariableV21.48KB(100.00%,17.10%),5us(100.00%,5.15%),0us(0."
       "00%,0.00%),5us(100.00%,5.15%),370params(100.00%,100.00%),0float_ops(100."
-      "00%,0.00%),4\n\ninput_type:\t(*4)\texec_time:5us\n\nAssign0B(0.00%,0.00%"
-      "),0us(94.85%,0.00%),0us(0.00%,0.00%),0us(94.85%,0.00%),0params(0.00%,0."
-      "00%),0float_ops(100.00%,0.00%),8\n\ninput_type:0:unknown,\t1:unknown\t(*"
-      "8)\texec_time:0us\n\nConst1.54KB(58.87%,17.74%),1us(80.41%,1.03%),0us(0."
-      "00%,0.00%),1us(80.41%,1.03%),0params(0.00%,0.00%),0float_ops(98.49%,0."
-      "00%),24\n\ninput_type:\t(*24)\texec_time:1us\n\n",
+      "00%,0.00%),4|4\n\ninput_type:\t(run*4|defined*4)\texec_time:"
+      "5us\n\nAssign0B(0.00%,0.00%),0us(94.85%,0.00%),0us(0.00%,0.00%),0us(94."
+      "85%,0.00%),0params(0.00%,0.00%),0float_ops(100.00%,0.00%),0|8\n\ninput_"
+      "type:0:unknown,\t1:unknown\t(run*0|defined*8)\texec_time:0us\n\nConst1."
+      "54KB(58.87%,17.74%),1us(80.41%,1.03%),0us(0.00%,0.00%),1us(80.41%,1.03%)"
+      ",0params(0.00%,0.00%),0float_ops(98.49%,0.00%),1|24\n\ninput_type:\t("
+      "run*1|defined*24)\texec_time:1us\n\n",
       StringReplace(dump_str, " ", ""));
 }
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index 45f1fdd06f..00d5d8cdb3 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -92,17 +92,20 @@ TEST_F(TFProfStatsTest, CustomOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d/kernel\"\n    exec_micros: 1\n    requested_bytes: 540\n    "
       "parameters: 135\n    total_exec_micros: 1\n    total_requested_bytes: "
       "540\n    total_parameters: 135\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "2\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 2\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 3\n  total_requested_bytes: "
       "920\n  total_parameters: 230\n  children {\n    name: "
       "\"conv2d_1/bias\"\n    exec_micros: 1\n    requested_bytes: 20\n    "
@@ -111,19 +114,23 @@ TEST_F(TFProfStatsTest, CustomOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d_1/kernel\"\n    exec_micros: 2\n    requested_bytes: 900\n    "
       "parameters: 225\n    total_exec_micros: 2\n    total_requested_bytes: "
       "900\n    total_parameters: 225\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 2\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 2\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 2\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 3\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
       "3\n}\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 5\n",
+      "0\ntotal_cpu_exec_micros: 5\nrun_count: 0\ntotal_run_count: "
+      "4\ntotal_definition_count: 6\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -148,17 +155,20 @@ TEST_F(TFProfStatsTest, CheckPointOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d/kernel\"\n    exec_micros: 1\n    requested_bytes: 540\n    "
       "parameters: 135\n    total_exec_micros: 1\n    total_requested_bytes: "
       "540\n    total_parameters: 135\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "2\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 2\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 3\n  total_requested_bytes: "
       "920\n  total_parameters: 230\n  children {\n    name: "
       "\"conv2d_1/bias\"\n    exec_micros: 1\n    requested_bytes: 20\n    "
@@ -167,19 +177,23 @@ TEST_F(TFProfStatsTest, CheckPointOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d_1/kernel\"\n    exec_micros: 2\n    requested_bytes: 900\n    "
       "parameters: 225\n    total_exec_micros: 2\n    total_requested_bytes: "
       "900\n    total_parameters: 225\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 2\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 2\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 2\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 3\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
       "3\n}\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 5\n",
+      "0\ntotal_cpu_exec_micros: 5\nrun_count: 0\ntotal_run_count: "
+      "4\ntotal_definition_count: 6\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -197,7 +211,8 @@ TEST_F(TFProfStatsTest, TestGraph) {
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
       "8656\ntotal_parameters: 370\nfloat_ops: 0\ntotal_float_ops: "
       "34360\naccelerator_exec_micros: 0\ncpu_exec_micros: "
-      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 97\n",
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: "
+      "97\nrun_count: 0\ntotal_run_count: 13\ntotal_definition_count: 60\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -219,7 +234,8 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       "unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    value "
       "{\n      unknown_rank: true\n    }\n  }\n  accelerator_exec_micros: 0\n "
       " cpu_exec_micros: 12\n  total_accelerator_exec_micros: 0\n  "
-      "total_cpu_exec_micros: 12\n}\nchildren {\n  name: "
+      "total_cpu_exec_micros: 12\n  run_count: 1\n  total_run_count: 1\n  "
+      "total_definition_count: 1\n}\nchildren {\n  name: "
       "\"conv2d/convolution\"\n  exec_micros: 60\n  requested_bytes: 1440\n  "
       "total_exec_micros: 60\n  total_requested_bytes: 1440\n  "
       "total_parameters: 0\n  devices: "
@@ -228,8 +244,9 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       " unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    "
       "value {\n      unknown_rank: true\n    }\n  }\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 60\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "60\n}\nchildren {\n  name: \"conv2d_2/BiasAdd\"\n  exec_micros: 2\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 60\n  "
+      "run_count: 1\n  total_run_count: 1\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_2/BiasAdd\"\n  exec_micros: 2\n  "
       "requested_bytes: 640\n  total_exec_micros: 2\n  total_requested_bytes: "
       "640\n  total_parameters: 0\n  devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n  float_ops: 160\n  "
@@ -237,7 +254,8 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       "unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    value "
       "{\n      unknown_rank: true\n    }\n  }\n  accelerator_exec_micros: 0\n "
       " cpu_exec_micros: 2\n  total_accelerator_exec_micros: 0\n  "
-      "total_cpu_exec_micros: 2\n}\nchildren {\n  name: "
+      "total_cpu_exec_micros: 2\n  run_count: 1\n  total_run_count: 1\n  "
+      "total_definition_count: 1\n}\nchildren {\n  name: "
       "\"conv2d_2/convolution\"\n  exec_micros: 13\n  requested_bytes: 640\n  "
       "total_exec_micros: 13\n  total_requested_bytes: 640\n  "
       "total_parameters: 0\n  devices: "
@@ -246,10 +264,12 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       " unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    "
       "value {\n      unknown_rank: true\n    }\n  }\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 13\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "13\n}\nfloat_ops: 0\ntotal_float_ops: 34360\naccelerator_exec_micros: "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 13\n  "
+      "run_count: 1\n  total_run_count: 1\n  total_definition_count: "
+      "3\n}\nfloat_ops: 0\ntotal_float_ops: 34360\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 97\n",
+      "0\ntotal_cpu_exec_micros: 97\nrun_count: 0\ntotal_run_count: "
+      "13\ntotal_definition_count: 68\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -267,7 +287,8 @@ TEST_F(TFProfStatsTest, TestAccountShownNameOnly) {
       "0\ntotal_exec_micros: 0\ntotal_requested_bytes: 0\ntotal_parameters: "
       "0\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 0\n",
+      "0\ntotal_cpu_exec_micros: 0\nrun_count: 0\ntotal_run_count: "
+      "0\ntotal_definition_count: 1\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -284,7 +305,8 @@ TEST_F(TFProfStatsTest, TestShowTensorValue) {
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
       "8656\ntotal_parameters: 370\nfloat_ops: 0\ntotal_float_ops: "
       "34360\naccelerator_exec_micros: 0\ncpu_exec_micros: "
-      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 97\n",
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: "
+      "97\nrun_count: 0\ntotal_run_count: 13\ntotal_definition_count: 68\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index 59955d214c..0482657ca5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -76,7 +76,8 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0\n      value_double: 0\n      value_double: 0\n      "
       "value_double: 0\n    }\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 0\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 0\n    run_count: 0\n    total_run_count: 0\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d/kernel\"\n    exec_micros: 0\n    requested_bytes: 0\n    "
       "parameters: 135\n    total_exec_micros: 0\n    total_requested_bytes: "
       "0\n    total_parameters: 135\n    float_ops: 0\n    total_float_ops: "
@@ -149,10 +150,12 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0.146625\n      value_double: 0.167754\n      "
       "value_double: 0.249554\n    }\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 0\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 0\n    run_count: 0\n    total_run_count: 0\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "0\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 0\n  "
+      "run_count: 0\n  total_run_count: 0\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 0\n  total_requested_bytes: "
       "0\n  total_parameters: 230\n  children {\n    name: \"conv2d_1/bias\"\n "
       "   exec_micros: 0\n    requested_bytes: 0\n    parameters: 5\n    "
@@ -162,7 +165,8 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0\n      value_double: 0\n      value_double: 0\n      "
       "value_double: 0\n    }\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 0\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 0\n    run_count: 0\n    total_run_count: 0\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d_1/kernel\"\n    exec_micros: 0\n    requested_bytes: 0\n    "
       "parameters: 225\n    total_exec_micros: 0\n    total_requested_bytes: "
       "0\n    total_parameters: 225\n    float_ops: 0\n    total_float_ops: "
@@ -280,12 +284,15 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: -0.123842\n      value_double: 0.0858642\n      "
       "value_double: 0.23111\n      value_double: 0.0491742\n    }\n    "
       "accelerator_exec_micros: 0\n    cpu_exec_micros: 0\n    "
-      "total_accelerator_exec_micros: 0\n    total_cpu_exec_micros: 0\n  }\n  "
-      "float_ops: 0\n  total_float_ops: 0\n  accelerator_exec_micros: 0\n  "
-      "cpu_exec_micros: 0\n  total_accelerator_exec_micros: 0\n  "
-      "total_cpu_exec_micros: 0\n}\nfloat_ops: 0\ntotal_float_ops: "
+      "total_accelerator_exec_micros: 0\n    total_cpu_exec_micros: 0\n    "
+      "run_count: 0\n    total_run_count: 0\n    total_definition_count: 1\n  "
+      "}\n  float_ops: 0\n  total_float_ops: 0\n  accelerator_exec_micros: 0\n "
+      " cpu_exec_micros: 0\n  total_accelerator_exec_micros: 0\n  "
+      "total_cpu_exec_micros: 0\n  run_count: 0\n  total_run_count: 0\n  "
+      "total_definition_count: 3\n}\nfloat_ops: 0\ntotal_float_ops: "
       "0\naccelerator_exec_micros: 0\ncpu_exec_micros: "
-      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 0\n",
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: "
+      "0\nrun_count: 0\ntotal_run_count: 0\ntotal_definition_count: 6\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index e41fde45f2..3e024ae4e4 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -21,6 +21,9 @@ message TFGraphNodeProto {
   // tensor value restored from checkpoint.
   optional TFProfTensorProto tensor_value = 15;
   // op execution time.
+  // A node can be defined once but run multiple times in tf.while_loop.
+  // the times sum up all different runs.
+  optional int64 run_count = 21;
   optional int64 exec_micros = 2;
   optional int64 accelerator_exec_micros = 17;
   optional int64 cpu_exec_micros = 18;
@@ -35,9 +38,12 @@ message TFGraphNodeProto {
   // Since an op can fire multiple kernel calls, there can be multiple devices.
   repeated string devices = 10;
 
-  // The following are the aggregated stats from all accounted children and
-  // the node itself. The actual children depend on the data structure used
-  // (scope, graph).
+  // The following are the aggregated stats from all *accounted* children and
+  // the node itself. The actual children depend on the data structure used.
+  // In graph view, children are inputs recursively.
+  // In scope view, children are nodes under the name scope.
+  optional int64 total_definition_count = 23;
+  optional int64 total_run_count = 22;
   optional int64 total_exec_micros = 6;
   optional int64 total_accelerator_exec_micros = 19;
   optional int64 total_cpu_exec_micros = 20;
-- 
GitLab


From a26f18c99047e1857f23c0fc966e28a655735849 Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Fri, 23 Jun 2017 05:46:03 +0100
Subject: [PATCH 0386/1375] [OpenCL] Registers SquaredDifference (#10987)

* [OpenCL] Registers SquaredDifference (#95)

* Added special kernel for SYCL for int
---
 .../core/kernels/cwise_op_squared_difference.cc     | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tensorflow/core/kernels/cwise_op_squared_difference.cc b/tensorflow/core/kernels/cwise_op_squared_difference.cc
index edd6c07146..78fefc69c7 100644
--- a/tensorflow/core/kernels/cwise_op_squared_difference.cc
+++ b/tensorflow/core/kernels/cwise_op_squared_difference.cc
@@ -35,4 +35,17 @@ REGISTER_KERNEL_BUILDER(
         .TypeConstraint<int32>("T"),
     BinaryOp<CPUDevice, functor::squared_difference<int32>>);
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(BinaryOp, SYCL, "SquaredDifference", functor::squared_difference,
+          float, double, int64);
+REGISTER_KERNEL_BUILDER(
+    Name("SquaredDifference")
+        .Device(DEVICE_SYCL)
+        .HostMemory("x")
+        .HostMemory("y")
+        .HostMemory("z")
+        .TypeConstraint<int32>("T"),
+    BinaryOp<CPUDevice, functor::squared_difference<int32>>);
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
-- 
GitLab


From 1d790810a8cfe5bc9772385e77ccd7aa8a03a6b0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Jun 2017 23:16:16 -0700
Subject: [PATCH 0387/1375] [XLA] Small error message improvement in binop
 shape inference.

PiperOrigin-RevId: 159920109
---
 .../compiler/xla/service/shape_inference.cc   | 19 ++++++++++++++-----
 tensorflow/compiler/xla/tests/map_test.cc     |  7 ++++---
 2 files changed, 18 insertions(+), 8 deletions(-)

diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 88fbfdf33c..b332709995 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -550,9 +550,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       ExpectNotTupleOrOpaque(rhs, "rhs of elementwise binary operation"));
 
   if (!ShapeUtil::SameElementType(lhs, rhs)) {
-    return InvalidArgument("binary op with different element types: %s and %s",
-                           ShapeUtil::HumanString(lhs).c_str(),
-                           ShapeUtil::HumanString(rhs).c_str());
+    return InvalidArgument(
+        "binary op %s with different element types: %s and %s",
+        BinaryOperation_Name(operation).c_str(),
+        ShapeUtil::HumanString(lhs).c_str(),
+        ShapeUtil::HumanString(rhs).c_str());
   }
 
   if (ShapeUtil::Rank(lhs) == ShapeUtil::Rank(rhs) &&
@@ -1518,10 +1520,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const ProgramShape& to_apply) {
   // The applied function's arity equals the number of arguments.
   if (arg_shapes.size() != to_apply.parameters_size()) {
+    string computation_signature = ShapeUtil::HumanString(to_apply);
+    string argument_shapes = tensorflow::str_util::Join(
+        arg_shapes, ", ", [](string* out, const Shape* shape) {
+          tensorflow::strings::StrAppend(out, ShapeUtil::HumanString(*shape));
+        });
     return InvalidArgument(
         "Call applied function arity must match number of arguments; got: "
-        "arity: %d, arguments: %zu",
-        to_apply.parameters_size(), arg_shapes.size());
+        "arity: %d, arguments: %zu; computation signature: %s; argument "
+        "shapes: [%s]",
+        to_apply.parameters_size(), arg_shapes.size(),
+        computation_signature.c_str(), argument_shapes.c_str());
   }
 
   // All arguments must be compatible with the program shape.
diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc
index ffa87348a0..47a8acbf4a 100644
--- a/tensorflow/compiler/xla/tests/map_test.cc
+++ b/tensorflow/compiler/xla/tests/map_test.cc
@@ -530,9 +530,10 @@ TEST_F(MapTest, MapOperantionWithBuildError) {
 
   StatusOr<Computation> computation_status = builder.Build();
   ASSERT_TRUE(!computation_status.ok());
-  EXPECT_THAT(computation_status.status().ToString(),
-              ::testing::HasSubstr("error from: ErrorAdd: binary op with "
-                                   "different element types: f32[] and u16[]"));
+  EXPECT_THAT(
+      computation_status.status().ToString(),
+      ::testing::HasSubstr("error from: ErrorAdd: binary op BINOP_ADD with "
+                           "different element types: f32[] and u16[]"));
 }
 
 // MapTest disables inline and algsimp. MapTestWithFullOpt runs all
-- 
GitLab


From 9bcc8ea8c476e1649680871ece9d8495e51a4b25 Mon Sep 17 00:00:00 2001
From: Boyuan Deng <contact@boyuandeng.me>
Date: Fri, 23 Jun 2017 16:20:17 +0800
Subject: [PATCH 0388/1375] Typo fix in Dataset API's README

---
 tensorflow/contrib/data/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/data/README.md b/tensorflow/contrib/data/README.md
index 9505f5c465..6d7d278309 100644
--- a/tensorflow/contrib/data/README.md
+++ b/tensorflow/contrib/data/README.md
@@ -543,7 +543,7 @@ padded.
 ```python
 dataset = tf.contrib.data.Dataset.range(100)
 dataset = dataset.map(lambda x: tf.fill([tf.cast(x, tf.int32)], x))
-dataset = dataset.padded_batch(4, padded_shapes=[None])
+batched_dataset = dataset.padded_batch(4, padded_shapes=[None])
 
 iterator = batched_dataset.make_one_shot_iterator()
 next_element = iterator.get_next()
-- 
GitLab


From 33e0b2f8d0e49306148ce3e34e163383fc207c68 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 04:54:43 -0700
Subject: [PATCH 0389/1375] Follow upstream API change from r306058.

PiperOrigin-RevId: 159938416
---
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.h | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index 4d8653484a..a5979bb798 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -41,8 +41,11 @@ namespace cpu {
 // it's added to the JIT.
 class SimpleOrcJIT {
  public:
-  using ObjLayerT = llvm::orc::RTDyldObjectLinkingLayer<>;
-  using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT>;
+  using ObjLayerT = llvm::orc::RTDyldObjectLinkingLayer;
+  using CompileFtor =
+      std::function<llvm::object::OwningBinary<llvm::object::ObjectFile>(
+          llvm::Module&)>;
+  using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT, CompileFtor>;
   using ModuleHandleT = CompileLayerT::ModuleSetHandleT;
 
   // Create a new JIT, targeting the host architecture.
-- 
GitLab


From 363854b00b6c29d9c3ff3a328f03cb4529ee7594 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 07:24:05 -0700
Subject: [PATCH 0390/1375] [TF:XLA] Update LLVM to upstream revision r306085.

PiperOrigin-RevId: 159946562
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 6dd1752705..afe0979536 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -484,11 +484,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   temp_workaround_http_archive(
       name = "llvm",
       urls = [
-          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/e156d99231a7735d06a97b5b83de70bf4ce4f034.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/e156d99231a7735d06a97b5b83de70bf4ce4f034.tar.gz",
+          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a98fd55665d422389d69d94efe631fac93d11173.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/a98fd55665d422389d69d94efe631fac93d11173.tar.gz",
       ],
-      sha256 = "72e34e2411a06d4200a2688ee83832805fbef23a12ea481f31c2b8866fde007a",
-      strip_prefix = "llvm-e156d99231a7735d06a97b5b83de70bf4ce4f034",
+      sha256 = "a82f5fb3e53294ab3a991d580bed43d3d7eed2f3967a3b6e0905a6f28f25d82a",
+      strip_prefix = "llvm-a98fd55665d422389d69d94efe631fac93d11173",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
       repository = tf_repo_name,
   )
-- 
GitLab


From cfe28e09f36e54d55d08e666392d19c5c46c67db Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Fri, 23 Jun 2017 08:40:35 -0700
Subject: [PATCH 0391/1375] [XLA] Remove unused xla_cpu flag and move another
 to DebugOptions.

PiperOrigin-RevId: 159952124
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ----
 .../xla/legacy_flags/cpu_runtime_flags.cc     | 71 -------------------
 .../xla/legacy_flags/cpu_runtime_flags.h      | 51 -------------
 .../xla/legacy_flags/debug_options_flags.cc   |  9 +++
 tensorflow/compiler/xla/service/cpu/BUILD     |  5 --
 .../xla/service/cpu/compiler_functor.cc       |  8 +--
 .../xla/service/cpu/conv_canonicalization.cc  |  6 --
 .../xla/service/cpu/dot_op_emitter.cc         | 21 +++---
 .../compiler/xla/service/cpu/dot_op_emitter.h |  8 ++-
 .../xla/service/cpu/ir_emission_utils.cc      | 11 ---
 .../compiler/xla/service/cpu/ir_emitter.cc    | 11 +--
 tensorflow/compiler/xla/tests/BUILD           | 12 ----
 .../compiler/xla/tests/dot_operation_test.cc  |  2 -
 tensorflow/compiler/xla/xla.proto             |  8 ++-
 16 files changed, 40 insertions(+), 198 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 77532a32fa..7dfed901fc 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -128,7 +128,6 @@ cc_library(
         ":tfcompile_lib",
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 4c3167b502..9ec205a126 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -24,7 +24,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
@@ -133,7 +132,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
-  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::legacy_flags::AppendServiceFlags(&flag_list);
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index a54628060f..33dc94fe00 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -79,18 +79,6 @@ cc_library(
         ],
 )
 
-cc_library(
-    name = "cpu_runtime_flags",
-    srcs = ["cpu_runtime_flags.cc"],
-    hdrs = ["cpu_runtime_flags.h"],
-    deps =
-        [
-            ":parse_flags_from_env",
-            "//tensorflow/core:framework_internal",
-            "//tensorflow/core:lib",
-        ],
-)
-
 cc_library(
     name = "stream_assignment_flags",
     srcs = ["stream_assignment_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
deleted file mode 100644
index d7817c5d54..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
+++ /dev/null
@@ -1,71 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's cpu_runtime module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static CpuRuntimeFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new CpuRuntimeFlags;
-  flags->xla_cpu_use_eigen = true;
-  flags->xla_cpu_multi_thread_eigen = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_cpu_use_eigen", &flags->xla_cpu_use_eigen,
-          "Use Eigen for matrix multiply on the CPU platform. This "
-          "is a useful hack for performance comparisons against "
-          "XLA's implementation."),
-      tensorflow::Flag(
-          "xla_cpu_multi_thread_eigen", &flags->xla_cpu_multi_thread_eigen,
-          "When generating calls to Eigen for matmul and conv, should "
-          "single or multi-threaded eigen be used? "
-          "Only used when --xla_cpu_use_eigen is true."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's cpu_runtime
-// module.
-void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the CpuRuntimeFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuRuntimeFlags* GetCpuRuntimeFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
deleted file mode 100644
index e3ff30da36..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
-
-// Legacy flags for the XLA's cpu_runtime module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's cpu_runtime
-// module.
-void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's cpu_runtime module.
-typedef struct {
-  // Use Eigen for matrix multiply on the CPU platform. This is a useful hack
-  // for performance comparisons against XLA's implementation.
-  bool xla_cpu_use_eigen;
-  // When generating calls to Eigen for matmul and conv, should single or
-  // multi-threaded eigen be used?  Only used when --xla_cpu_use_eigen is true.
-  bool xla_cpu_multi_thread_eigen;
-} CpuRuntimeFlags;
-
-// Return a pointer to the CpuRuntimeFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuRuntimeFlags* GetCpuRuntimeFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index bd026a5da1..01e9d4010a 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -32,6 +32,8 @@ struct DebugOptionsFlags {
   string xla_dump_ir_to;
   string xla_dump_debug_json_to;
 
+  bool xla_cpu_multi_thread_eigen;
+
   string xla_gpu_cuda_data_dir;
   bool xla_gpu_ftz;
 
@@ -55,6 +57,7 @@ void AllocateFlags() {
   flag_values->xla_embed_ir_in_executable = false;
   flag_values->xla_dump_ir_to = "";
   flag_values->xla_dump_debug_json_to = "";
+  flag_values->xla_cpu_multi_thread_eigen = true;
   flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
   flag_values->xla_gpu_ftz = false;
   flag_values->xla_backend_extra_options = "";
@@ -82,6 +85,10 @@ void AllocateFlags() {
                         "Embed the compiler IR as a string in the executable."),
        tensorflow::Flag("xla_dump_ir_to", &flag_values->xla_dump_ir_to,
                         "Dump the compiler IR into this file/path."),
+       tensorflow::Flag("xla_cpu_multi_thread_eigen",
+                        &flag_values->xla_cpu_multi_thread_eigen,
+                        "When generating calls to Eigen in the CPU backend, "
+                        "use multi-threaded Eigen mode."),
        tensorflow::Flag("xla_gpu_cuda_data_dir",
                         &flag_values->xla_gpu_cuda_data_dir,
                         "If non-empty, speficies a local directory containing "
@@ -129,6 +136,8 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
       flag_values->xla_embed_ir_in_executable);
   options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
   options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
+  options.set_xla_cpu_multi_thread_eigen(
+      flag_values->xla_cpu_multi_thread_eigen);
   options.set_xla_gpu_cuda_data_dir(flag_values->xla_gpu_cuda_data_dir);
   options.set_xla_gpu_ftz(flag_values->xla_gpu_ftz);
 
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 6fa3acd85d..405057f918 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -197,7 +197,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:elemental_ir_emitter",
         "//tensorflow/compiler/xla/service:hlo",
@@ -228,7 +227,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service/llvm_ir:ir_array",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop",
@@ -289,7 +287,6 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
         "@llvm//:analysis",
@@ -484,7 +481,6 @@ cc_library(
         ":cpu_runtime",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:window_util",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
     ],
 )
@@ -511,7 +507,6 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_pass",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index d78a54427a..1f78039e46 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -35,7 +35,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/Transforms/IPO.h"
 #include "external/llvm/include/llvm/Transforms/IPO/AlwaysInliner.h"
 #include "external/llvm/include/llvm/Transforms/IPO/PassManagerBuilder.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
@@ -145,12 +144,7 @@ std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl(
       {"llvm.tanh.f32", runtime::kTanhV8F32, 8},
   };
 
-  // Our vectorized library calls are currently implement by calling into Eigen.
-  // As such, only emit calls to these routines if --xla_cpu_use_eigen is
-  // enabled.
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (flags->xla_cpu_use_eigen &&
-      (arch == llvm::Triple::x86 || llvm::Triple::x86_64)) {
+  if (arch == llvm::Triple::x86 || llvm::Triple::x86_64) {
     llvm::SmallVector<llvm::StringRef, 32> features;
     feature_string.split(features, ',', -1, /*KeepEmpty=*/false);
     if (std::find(features.begin(), features.end(), "+sse4.1") !=
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
index cdf43587b6..069979c661 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h"
 
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -30,11 +29,6 @@ namespace xla {
 namespace cpu {
 
 StatusOr<bool> ConvCanonicalization::Run(HloModule* module) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   bool changed = false;
   for (HloInstruction* hlo :
        module->entry_computation()->MakeInstructionPostOrder()) {
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index 420f9cebc5..c21b8a9add 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -22,9 +22,9 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Instructions.h"
 #include "external/llvm/include/llvm/IR/Module.h"
 #include "external/llvm/include/llvm/IR/Value.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -44,7 +44,8 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, bool transpose_lhs,
                            const llvm_ir::IrArray& lhs_array,
                            const llvm_ir::IrArray& rhs_array,
                            llvm::Value* executable_run_options_value,
-                           llvm::IRBuilder<>* ir_builder)
+                           llvm::IRBuilder<>* ir_builder,
+                           const HloModuleConfig& hlo_module_config)
     : dot_(dot),
       transpose_lhs_(transpose_lhs),
       transpose_rhs_(transpose_rhs),
@@ -52,18 +53,20 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, bool transpose_lhs,
       lhs_array_(lhs_array),
       rhs_array_(rhs_array),
       executable_run_options_value_(executable_run_options_value),
-      ir_builder_(ir_builder) {}
+      ir_builder_(ir_builder),
+      hlo_module_config_(hlo_module_config) {}
 
 /* static */ tensorflow::Status DotOpEmitter::EmitDotOperation(
     const HloInstruction& dot, bool transpose_lhs, bool transpose_rhs,
     const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array,
     const llvm_ir::IrArray& rhs_array,
-    llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder) {
+    llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder,
+    const HloModuleConfig& hlo_module_config) {
   PrimitiveType type = target_array.GetShape().element_type();
   TF_RET_CHECK(F32 == type || F64 == type);
   DotOpEmitter dot_emitter(dot, transpose_lhs, transpose_rhs, target_array,
                            lhs_array, rhs_array, executable_run_options_value,
-                           ir_builder);
+                           ir_builder, hlo_module_config);
   return dot_emitter.Emit();
 }
 
@@ -233,20 +236,20 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   // The two transpose_... parameters are actually booleans, but we use int32
   // to avoid target-dependent calling convention details.
 
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  bool multi_threaded = flags->xla_cpu_multi_thread_eigen;
+  bool multi_threaded_eigen =
+      hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen();
   PrimitiveType type = target_array_.GetShape().element_type();
   llvm::Type* float_type;
   const char* fn_name;
   switch (type) {
     case F32:
-      fn_name = multi_threaded
+      fn_name = multi_threaded_eigen
                     ? runtime::kEigenMatmulF32SymbolName
                     : runtime::kEigenSingleThreadedMatmulF32SymbolName;
       float_type = ir_builder_->getFloatTy();
       break;
     case F64:
-      fn_name = multi_threaded
+      fn_name = multi_threaded_eigen
                     ? runtime::kEigenMatmulF64SymbolName
                     : runtime::kEigenSingleThreadedMatmulF64SymbolName;
       float_type = ir_builder_->getDoubleTy();
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
index 44dfe5f2a9..b614716380 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include "external/llvm/include/llvm/IR/IRBuilder.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module_config.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -39,7 +40,8 @@ class DotOpEmitter {
       const HloInstruction& dot, bool transpose_lhs, bool transpose_rhs,
       const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array,
       const llvm_ir::IrArray& rhs_array,
-      llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder);
+      llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder,
+      const HloModuleConfig& hlo_module_config);
 
  private:
   DotOpEmitter(const HloInstruction& dot, bool transpose_lhs,
@@ -47,7 +49,8 @@ class DotOpEmitter {
                const llvm_ir::IrArray& lhs_array,
                const llvm_ir::IrArray& rhs_array,
                llvm::Value* executable_run_options_value,
-               llvm::IRBuilder<>* ir_builder);
+               llvm::IRBuilder<>* ir_builder,
+               const HloModuleConfig& hlo_module_config);
 
   // Emits the IR to perform the dot operation.
   tensorflow::Status Emit();
@@ -82,6 +85,7 @@ class DotOpEmitter {
   const llvm_ir::IrArray& rhs_array_;
   llvm::Value* executable_run_options_value_;
   llvm::IRBuilder<>* ir_builder_;
+  const HloModuleConfig& hlo_module_config_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
index 2d855d0eb1..859329e2c1 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/window_util.h"
@@ -26,11 +25,6 @@ namespace cpu {
 
 bool PotentiallyImplementedAsEigenConvolution(
     const HloInstruction& convolution) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   // The following conditions are necessary (but not sufficient) for
   // implementing `convolution` with Eigen convolution:
   // - the input and kernel have a non-zero number of elements.
@@ -82,11 +76,6 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape,
 }  // namespace
 
 bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   // For certain types of Dot, we can call Eigen
   if (hlo.opcode() == HloOpcode::kDot) {
     const Shape& lhs_shape = hlo.operand(0)->shape();
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index fee5fd8830..c81a368992 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -33,7 +33,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Intrinsics.h"
 #include "external/llvm/include/llvm/IR/LLVMContext.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
@@ -777,7 +776,8 @@ Status IrEmitter::HandleDot(HloInstruction* dot, HloInstruction* lhs,
   // Dot operation is complicated so we delegate to a helper class.
   TF_RETURN_IF_ERROR(DotOpEmitter::EmitDotOperation(
       *dot, /*transpose_lhs=*/false, /*transpose_rhs=*/false, target_array,
-      lhs_array, rhs_array, GetExecutableRunOptionsArgument(), &ir_builder_));
+      lhs_array, rhs_array, GetExecutableRunOptionsArgument(), &ir_builder_,
+      hlo_module_config_));
 
   emitted_value_[dot] = target_address;
   return Status::OK();
@@ -862,9 +862,10 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution,
            int64_type,    int64_type,     int64_type,     int64_type,
            int64_type,    int64_type,     int64_type,     int64_type},
           /*isVarArg=*/false);
-      legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+      bool multi_threaded_eigen =
+          hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen();
       const char* fn_name =
-          (flags->xla_cpu_multi_thread_eigen
+          (multi_threaded_eigen
                ? runtime::kEigenConvF32SymbolName
                : runtime::kEigenSingleThreadedConvF32SymbolName);
       llvm::Function* conv_func = llvm::cast<llvm::Function>(
@@ -1525,7 +1526,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) {
     TF_RETURN_IF_ERROR(DotOpEmitter::EmitDotOperation(
         *dot, dot->operand(0)->IsRank2Transpose(),
         dot->operand(1)->IsRank2Transpose(), target_array, lhs_array, rhs_array,
-        GetExecutableRunOptionsArgument(), &ir_builder_));
+        GetExecutableRunOptionsArgument(), &ir_builder_, hlo_module_config_));
 
     emitted_value_[fusion] = target_address;
     return Status::OK();
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index e54f02b7fb..91e353c330 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -495,7 +495,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -512,10 +511,6 @@ xla_test(
 xla_test(
     name = "dot_operation_runtime_test",
     srcs = ["dot_operation_test.cc"],
-    backend_args = {
-        "cpu": ["--xla_cpu_use_eigen"],
-        "cpu_parallel": ["--xla_cpu_use_eigen"],
-    },
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -523,7 +518,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -541,11 +535,9 @@ xla_test(
     srcs = ["dot_operation_test.cc"],
     backend_args = {
         "cpu": [
-            "--xla_cpu_use_eigen",
             "--xla_cpu_multi_thread_eigen=false",
         ],
         "cpu_parallel": [
-            "--xla_cpu_use_eigen",
             "--xla_cpu_multi_thread_eigen=false",
         ],
     },
@@ -556,7 +548,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -573,11 +564,9 @@ xla_test(
     srcs = ["dot_operation_test.cc"],
     backend_args = {
         "cpu": [
-            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
         "cpu_parallel": [
-            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
     },
@@ -588,7 +577,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index b06b5c5f47..7abef6a27b 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
@@ -461,7 +460,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendLayoutUtilFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 76079c44cd..6dd7999b67 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -54,11 +54,15 @@ message DebugOptions {
   // Dump compilation artifacts as JSON into this directory.
   string xla_dump_debug_json_to = 7;
 
+  // When generating calls to Eigen in the CPU backend, use multi-threaded Eigen
+  // mode.
+  bool xla_cpu_multi_thread_eigen = 8;
+
   // Path to directory with cuda/ptx tools and libraries.
-  string xla_gpu_cuda_data_dir = 8;
+  string xla_gpu_cuda_data_dir = 9;
 
   // Enable flush-to-zero semantics in the GPU backend.
-  bool xla_gpu_ftz = 9;
+  bool xla_gpu_ftz = 10;
 
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
-- 
GitLab


From deea43d2b42ec1b14ccafdd296308229be7dbd99 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 09:27:31 -0700
Subject: [PATCH 0392/1375] Updates linear.md tutorial

PiperOrigin-RevId: 159956867
---
 tensorflow/docs_src/get_started/input_fn.md |  2 +-
 tensorflow/docs_src/tutorials/linear.md     | 89 ++++++++++-----------
 2 files changed, 45 insertions(+), 46 deletions(-)

diff --git a/tensorflow/docs_src/get_started/input_fn.md b/tensorflow/docs_src/get_started/input_fn.md
index 2ef050d9ad..422f45c586 100644
--- a/tensorflow/docs_src/get_started/input_fn.md
+++ b/tensorflow/docs_src/get_started/input_fn.md
@@ -1,4 +1,4 @@
-# Building Input Functions with tf.contrib.learn
+# Building Input Functions with tf.estimator
 
 This tutorial introduces you to creating input functions in tf.estimator.
 You'll get an overview of how to construct an `input_fn` to preprocess and feed
diff --git a/tensorflow/docs_src/tutorials/linear.md b/tensorflow/docs_src/tutorials/linear.md
index e90e688bfa..4201a8021b 100644
--- a/tensorflow/docs_src/tutorials/linear.md
+++ b/tensorflow/docs_src/tutorials/linear.md
@@ -1,17 +1,17 @@
 # Large-scale Linear Models with TensorFlow
 
-The tf.learn API provides (among other things) a rich set of tools for working
-with linear models in TensorFlow. This document provides an overview of those
-tools. It explains:
+The tf.estimator API provides (among other things) a rich set of tools for
+working with linear models in TensorFlow. This document provides an overview of
+those tools. It explains:
 
    * what a linear model is.
    * why you might want to use a linear model.
-   * how tf.learn makes it easy to build linear models in TensorFlow.
-   * how you can use tf.learn to combine linear models with
+   * how tf.estimator makes it easy to build linear models in TensorFlow.
+   * how you can use tf.estimator to combine linear models with
    deep learning to get the advantages of both.
 
-Read this overview to decide whether the tf.learn linear model tools might be
-useful to you. Then do the @{$wide$Linear Models tutorial} to
+Read this overview to decide whether the tf.estimator linear model tools might
+be useful to you. Then do the @{$wide$Linear Models tutorial} to
 give it a try. This overview uses code samples from the tutorial, but the
 tutorial walks through the code in greater detail.
 
@@ -51,21 +51,22 @@ Linear models:
    * provide an excellent starting point for learning about machine learning.
    * are widely used in industry.
 
-## How does tf.learn help you build linear models?
+## How does tf.estimator help you build linear models?
 
 You can build a linear model from scratch in TensorFlow without the help of a
-special API. But tf.learn provides some tools that make it easier to build
+special API. But tf.estimator provides some tools that make it easier to build
 effective large-scale linear models.
 
 ### Feature columns and transformations
 
 Much of the work of designing a linear model consists of transforming raw data
-into suitable input features. tf.learn uses the `FeatureColumn` abstraction to
+into suitable input features. Tensorflow uses the `FeatureColumn` abstraction to
 enable these transformations.
 
 A `FeatureColumn` represents a single feature in your data. A `FeatureColumn`
 may represent a quantity like 'height', or it may represent a category like
-'eye_color' where the value is drawn from a set of discrete possibilities like {'blue', 'brown', 'green'}.
+'eye_color' where the value is drawn from a set of discrete possibilities like
+{'blue', 'brown', 'green'}.
 
 In the case of both *continuous features* like 'height' and *categorical
 features* like 'eye_color', a single value in the data might get transformed
@@ -85,10 +86,10 @@ become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called
 "sparse" because they may be very long, with many zeros, when the set of
 possible values is very large (such as all English words).
 
-While you don't need to use sparse columns to use tf.learn linear models, one
-of the strengths of linear models is their ability to deal with large sparse
-vectors. Sparse features are a primary use case for the tf.learn linear model
-tools.
+While you don't need to use categorical columns to use tf.estimator linear
+models, one of the strengths of linear models is their ability to deal with
+large sparse vectors. Sparse features are a primary use case for the
+tf.estimator linear model tools.
 
 ##### Encoding sparse columns
 
@@ -96,19 +97,19 @@ tools.
 automatically, with code like this:
 
 ```python
-eye_color = tf.contrib.layers.sparse_column_with_keys(
-  column_name="eye_color", keys=["blue", "brown", "green"])
+eye_color = tf.feature_column.categorical_column_with_vocabulary_list(
+    "eye_color", vocabulary_list=["blue", "brown", "green"])
 ```
 
 where `eye_color` is the name of a column in your source data.
 
 You can also generate `FeatureColumn`s for categorical features for which you
 don't know all possible values. For this case you would use
-`sparse_column_with_hash_bucket()`, which uses a hash function to assign
+`categorical_column_with_hash_bucket()`, which uses a hash function to assign
 indices to feature values.
 
 ```python
-education = tf.contrib.layers.sparse_column_with_hash_bucket(\
+education = tf.feature_column.categorical_column_with_hash_bucket(
     "education", hash_bucket_size=1000)
 ```
 
@@ -130,12 +131,8 @@ a *feature cross*.
 The `crossed_column()` method makes it easy to set up feature crosses:
 
 ```python
-sport = tf.contrib.layers.sparse_column_with_hash_bucket(\
-    "sport", hash_bucket_size=1000)
-city = tf.contrib.layers.sparse_column_with_hash_bucket(\
-    "city", hash_bucket_size=1000)
-sport_x_city = tf.contrib.layers.crossed_column(
-    [sport, city], hash_bucket_size=int(1e4))
+sport_x_city = tf.feature_column.crossed_column(
+    ["sport", "city"], hash_bucket_size=int(1e4))
 ```
 
 #### Continuous columns
@@ -143,11 +140,11 @@ sport_x_city = tf.contrib.layers.crossed_column(
 You can specify a continuous feature like so:
 
 ```python
-age = tf.contrib.layers.real_valued_column("age")
+age = tf.feature_column.numeric_column("age")
 ```
 
 Although, as a single real number, a continuous feature can often be input
-directly into the model, tf.learn offers useful transformations for this sort
+directly into the model, Tensorflow offers useful transformations for this sort
 of column as well.
 
 ##### Bucketization
@@ -160,7 +157,7 @@ Bucketization divides the range of possible values into subranges called
 buckets:
 
 ```python
-age_buckets = tf.contrib.layers.bucketized_column(
+age_buckets = tf.feature_column.bucketized_column(
     age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
 ```
 
@@ -176,43 +173,45 @@ the data itself. You provide the data through an input function.
 The input function must return a dictionary of tensors. Each key corresponds to
 the name of a `FeatureColumn`. Each key's value is a tensor containing the
 values of that feature for all data instances. See
-@{$input_fn$Building Input Functions with tf.contrib.learn} for a
+@{$input_fn$Building Input Functions with tf.estimator} for a
 more comprehensive look at input functions, and `input_fn` in the
 [linear models tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py)
 for an example implementation of an input function.
 
-The input function is passed to the `fit()` and `evaluate()` calls that
+The input function is passed to the `train()` and `evaluate()` calls that
 initiate training and testing, as described in the next section.
 
 ### Linear estimators
 
-tf.learn's estimator classes provide a unified training and evaluation harness
+Tensorflow estimator classes provide a unified training and evaluation harness
 for regression and classification models. They take care of the details of the
 training and evaluation loops and allow the user to focus on model inputs and
 architecture.
 
 To build a linear estimator, you can use either the
-`tf.contrib.learn.LinearClassifier` estimator or the
-`tf.contrib.learn.LinearRegressor` estimator, for classification and
+`tf.estimator.LinearClassifier` estimator or the
+`tf.estimator.LinearRegressor` estimator, for classification and
 regression respectively.
 
-As with all tf.learn estimators, to run the estimator you just:
+As with all tensorflow estimators, to run the estimator you just:
 
    1. Instantiate the estimator class. For the two linear estimator classes,
    you pass a list of `FeatureColumn`s to the constructor.
-   2. Call the estimator's `fit()` method to train it.
+   2. Call the estimator's `train()` method to train it.
    3. Call the estimator's `evaluate()` method to see how it does.
 
 For example:
 
 ```python
-e = tf.contrib.learn.LinearClassifier(feature_columns=[
-  native_country, education, occupation, workclass, marital_status,
-  race, age_buckets, education_x_occupation, age_buckets_x_race_x_occupation],
-  model_dir=YOUR_MODEL_DIRECTORY)
-e.fit(input_fn=input_fn_train, steps=200)
+e = tf.estimator.LinearClassifier(
+    feature_columns=[
+        native_country, education, occupation, workclass, marital_status,
+        race, age_buckets, education_x_occupation,
+        age_buckets_x_race_x_occupation],
+    model_dir=YOUR_MODEL_DIRECTORY)
+e.train(input_fn=input_fn_train, steps=200)
 # Evaluate for one step (one pass through the test data).
-results = e.evaluate(input_fn=input_fn_test, steps=1)
+results = e.evaluate(input_fn=input_fn_test)
 
 # Print the stats for the evaluation.
 for key in sorted(results):
@@ -221,14 +220,14 @@ for key in sorted(results):
 
 ### Wide and deep learning
 
-The tf.learn API also provides an estimator class that lets you jointly train
-a linear model and a deep neural network. This novel approach combines the
+The tf.estimator API also provides an estimator class that lets you jointly
+train a linear model and a deep neural network. This novel approach combines the
 ability of linear models to "memorize" key features with the generalization
-ability of neural nets. Use `tf.contrib.learn.DNNLinearCombinedClassifier` to
+ability of neural nets. Use `tf.estimator.DNNLinearCombinedClassifier` to
 create this sort of "wide and deep" model:
 
 ```python
-e = tf.contrib.learn.DNNLinearCombinedClassifier(
+e = tf.estimator.DNNLinearCombinedClassifier(
     model_dir=YOUR_MODEL_DIR,
     linear_feature_columns=wide_columns,
     dnn_feature_columns=deep_columns,
-- 
GitLab


From 3168a6241afa144943a025c8be55109479a1daa5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 09:28:35 -0700
Subject: [PATCH 0393/1375] Add TraceMe instrumentation of RunStep in GRPC
 distributed runtime. A unique ID is added to each RunStep call that allows
 the client and server events to be correlated.

PiperOrigin-RevId: 159956950
---
 tensorflow/core/distributed_runtime/rpc/BUILD |  2 ++
 .../core/distributed_runtime/rpc/grpc_call.h  |  5 +++++
 .../rpc/grpc_master_service.cc                | 19 +++++++++++++++++--
 .../rpc/grpc_remote_master.cc                 | 11 +++++++++++
 .../core/distributed_runtime/rpc/grpc_util.h  |  2 ++
 5 files changed, 37 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index c0918ef445..bd381dd10f 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -193,6 +193,7 @@ cc_library(
         ":grpc_master_service_impl",
         ":grpc_util",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core:master_proto_cc",
         "//tensorflow/core/distributed_runtime:call_options",
         "//tensorflow/core/distributed_runtime:master_interface",
@@ -210,6 +211,7 @@ cc_library(
         ":grpc_master_service_impl",
         ":grpc_util",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core:master_proto_cc",
         "//tensorflow/core/distributed_runtime:master",
         "@grpc//:grpc++_unsecure",
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_call.h b/tensorflow/core/distributed_runtime/rpc/grpc_call.h
index e85b8ccbd3..cb133737dd 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_call.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_call.h
@@ -233,6 +233,11 @@ class Call : public UntypedCall<Service> {
   RequestMessage request;
   ResponseMessage response;
 
+  const std::multimap<::grpc::string_ref, ::grpc::string_ref>& client_metadata()
+      const {
+    return ctx_.client_metadata();
+  }
+
  private:
   // Creates a completion queue tag for handling cancellation by the client.
   // NOTE: This method must be called before this call is enqueued on a
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
index 07205bb2c2..41ee81c01d 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
@@ -40,6 +40,7 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/tracing.h"
 #include "tensorflow/core/protobuf/master.pb.h"
 
 namespace tensorflow {
@@ -172,6 +173,7 @@ class GrpcMasterService : public AsyncServiceInterface {
 
   // RPC handler for running one step in a session.
   void RunStepHandler(MasterCall<RunStepRequest, RunStepResponse>* call) {
+    auto* trace = TraceRpc("RunStep/Server", call->client_metadata());
     CallOptions* call_opts = new CallOptions;
     if (call->request.options().timeout_in_ms() > 0) {
       call_opts->SetTimeout(call->request.options().timeout_in_ms());
@@ -184,11 +186,12 @@ class GrpcMasterService : public AsyncServiceInterface {
         new NonOwnedProtoRunStepResponse(&call->response);
     call->SetCancelCallback([call_opts]() { call_opts->StartCancel(); });
     master_impl_->RunStep(call_opts, wrapped_request, wrapped_response,
-                          [call, call_opts, wrapped_request,
-                           wrapped_response](const Status& status) {
+                          [call, call_opts, wrapped_request, wrapped_response,
+                           trace](const Status& status) {
                             call->ClearCancelCallback();
                             delete call_opts;
                             delete wrapped_request;
+                            delete trace;
                             call->SendResponse(ToGrpcStatus(status));
                           });
     ENQUEUE_REQUEST(RunStep, true);
@@ -224,6 +227,18 @@ class GrpcMasterService : public AsyncServiceInterface {
   }
 #undef ENQUEUE_REQUEST
 
+  // Start tracing, including the ID attached to the RPC.
+  port::Tracing::TraceMe* TraceRpc(
+      StringPiece name,
+      const std::multimap<::grpc::string_ref, ::grpc::string_ref>& metadata) {
+    StringPiece id;
+    auto it = metadata.find(GrpcIdKey());
+    if (it != metadata.end()) {
+      id = StringPiece(it->second.data(), it->second.size());
+    }
+    return new port::Tracing::TraceMe(name, id);
+  }
+
   TF_DISALLOW_COPY_AND_ASSIGN(GrpcMasterService);
 };
 
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc
index bf72d9a7fc..c04aa44941 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc
@@ -23,6 +23,8 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/tracing.h"
 #include "tensorflow/core/protobuf/master.pb.h"
 
 namespace tensorflow {
@@ -66,6 +68,7 @@ class GrpcRemoteMaster : public MasterInterface {
   Status RunStep(CallOptions* call_options, RunStepRequestWrapper* request,
                  MutableRunStepResponseWrapper* response) override {
     ::grpc::ClientContext ctx;
+    auto trace = TraceRpc("RunStep/Client", &ctx);
     ctx.set_fail_fast(false);
     SetDeadline(&ctx, call_options->GetTimeout());
     return FromGrpcStatus(stub_->RunStep(&ctx, request->ToProto(),
@@ -99,6 +102,14 @@ class GrpcRemoteMaster : public MasterInterface {
   }
 
  private:
+  // Start tracing, attaching a unique ID to both the trace and the RPC.
+  port::Tracing::TraceMe TraceRpc(StringPiece name,
+                                  ::grpc::ClientContext* ctx) {
+    string trace_id = strings::StrCat(port::Tracing::UniqueId());
+    ctx->AddMetadata(GrpcIdKey(), trace_id);
+    return port::Tracing::TraceMe(name, trace_id);
+  }
+
   std::unique_ptr<grpc::MasterService::Stub> stub_;
 
   void SetDeadline(::grpc::ClientContext* ctx, int64 time_in_ms) {
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_util.h b/tensorflow/core/distributed_runtime/rpc/grpc_util.h
index 44473d1150..5244cd1c13 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_util.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_util.h
@@ -43,6 +43,8 @@ inline ::grpc::Status ToGrpcStatus(const ::tensorflow::Status& s) {
 
 typedef std::shared_ptr<::grpc::Channel> SharedGrpcChannelPtr;
 
+inline string GrpcIdKey() { return "tf-rpc"; }
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RPC_GRPC_UTIL_H_
-- 
GitLab


From 8604f612a1b60a81deaa6330b8614f2b710ee488 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 10:09:53 -0700
Subject: [PATCH 0394/1375] [XLA] Add general F32 implementation for
 ReducePrecision operation.

This only tests with parameter inputs (which is needed to ensure we actually test on GPUs as well as CPUs); there's no point in separately testing with constants.

PiperOrigin-RevId: 159961430
---
 .../xla/service/elemental_ir_emitter.cc       | 110 +++++++-
 tensorflow/compiler/xla/tests/BUILD           |  22 ++
 .../xla/tests/array_elementwise_ops_test.cc   |  23 --
 .../xla/tests/reduce_precision_test.cc        | 263 ++++++++++++++++++
 4 files changed, 387 insertions(+), 31 deletions(-)
 create mode 100644 tensorflow/compiler/xla/tests/reduce_precision_test.cc

diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index bea1329d40..5b21ae3d2a 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -390,17 +390,111 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitReducePrecision(
   if (hlo->operand(0)->shape().element_type() != F32) {
     return Unimplemented("reduce-precision only implemented for F32");
   }
-  // As a preliminary implementation, we only implement this for the case
-  // where it is a no-op -- that is, where the exponent and mantissa bit
-  // counts are equal to the (IEEE f32) bit counts for the input values.
-  if (hlo->exponent_bits() != 8) {
-    return Unimplemented("reduce-precision requires 8 exponent bits");
+
+  // Integer and float types for casting and constant generation.
+  llvm::Type* float_type = x->getType();
+  llvm::IntegerType* int_type = ir_builder_->getInt32Ty();
+
+  // Cast the input value to an integer for bitwise manipulation.
+  llvm::Value* x_as_int = ir_builder_->CreateBitCast(x, int_type);
+
+  if (hlo->mantissa_bits() < 23) {
+    // Last remaining mantissa bit.
+    const uint32_t last_mantissa_bit_mask = 1u << (23 - hlo->mantissa_bits());
+
+    // Compute rounding bias for round-to-nearest with ties to even.  This is
+    // equal to a base value of 0111... plus one bit if the last remaining
+    // mantissa bit is 1.
+    const uint32_t base_rounding_bias = (last_mantissa_bit_mask >> 1) - 1;
+    llvm::Value* x_last_mantissa_bit = ir_builder_->CreateLShr(
+        ir_builder_->CreateAnd(
+            x_as_int, llvm::ConstantInt::get(int_type, last_mantissa_bit_mask)),
+        (23 - hlo->mantissa_bits()));
+    llvm::Value* x_rounding_bias = ir_builder_->CreateAdd(
+        x_last_mantissa_bit,
+        llvm::ConstantInt::get(int_type, base_rounding_bias));
+
+    // Add rounding bias, and mask out truncated bits.  Note that the case
+    // where adding the rounding bias overflows into the exponent bits is
+    // correct; the non-masked mantissa bits will all be zero, and the
+    // exponent will be incremented by one.
+    const uint32_t truncation_mask = ~(last_mantissa_bit_mask - 1);
+    x_as_int = ir_builder_->CreateAdd(x_as_int, x_rounding_bias);
+    x_as_int = ir_builder_->CreateAnd(
+        x_as_int, llvm::ConstantInt::get(int_type, truncation_mask));
   }
-  if (hlo->mantissa_bits() != 23) {
-    return Unimplemented("reduce-precision requires 23 mantissa bits");
+
+  if (hlo->exponent_bits() < 8) {
+    // Masks for f32 values.
+    const uint32_t f32_sign_bit_mask = 1u << 31;
+    const uint32_t f32_exp_bits_mask = 0xffu << 23;
+
+    // An exponent of 2^(n-1)-1 -- that is, 0111... with the zero in the most-
+    // significant bit -- is equal to 1.0f for all exponent sizes.  Adding
+    // 2^(n-1)-1 to this gives us the highest non-infinite exponent for a bit-
+    // size of n, and subtracting 2^(n-1)-1 from this gives us the lowest'
+    // exponent (corresponding to 0.0f).
+    //
+    // Thus, the f32 exponent corresponding to the highest non-infinite
+    // exponent for a bit size of n is (2^7-1) + 2^(n-1)-1, and the f32
+    // exponent corresponding to the lowest exponent for a bit size of n is
+    // (2^7-1) - 2^(n-1)-1.
+    //
+    // Note that we have already checked that exponents_bits >= 1.
+    const uint32_t f32_exponent_bias = (1 << 7) - 1;
+    const uint32_t reduced_exponent_bias =
+        (1 << (hlo->exponent_bits() - 1)) - 1;
+    const uint32_t reduced_max_exponent =
+        f32_exponent_bias + reduced_exponent_bias;
+    const uint32_t reduced_min_exponent =
+        f32_exponent_bias - reduced_exponent_bias;
+
+    // Do we overflow or underflow?
+    llvm::Value* x_exponent = ir_builder_->CreateAnd(
+        x_as_int, llvm::ConstantInt::get(int_type, f32_exp_bits_mask));
+    llvm::Value* x_overflows = ir_builder_->CreateICmpUGT(
+        x_exponent,
+        llvm::ConstantInt::get(int_type, reduced_max_exponent << 23));
+    llvm::Value* x_underflows = ir_builder_->CreateICmpULE(
+        x_exponent,
+        llvm::ConstantInt::get(int_type, reduced_min_exponent << 23));
+
+    // Compute appropriately-signed values of zero and infinity.
+    llvm::Value* x_signed_zero = ir_builder_->CreateAnd(
+        x_as_int, llvm::ConstantInt::get(int_type, f32_sign_bit_mask));
+    llvm::Value* x_signed_inf = ir_builder_->CreateOr(
+        x_signed_zero, llvm::ConstantInt::get(int_type, f32_exp_bits_mask));
+
+    // Force to zero or infinity if overflow or underflow.  (Note that this
+    // truncates all denormal values to zero, rather than rounding them.)
+    x_as_int = ir_builder_->CreateSelect(x_overflows, x_signed_inf, x_as_int);
+    x_as_int = ir_builder_->CreateSelect(x_underflows, x_signed_zero, x_as_int);
   }
 
-  return x;
+  // Cast the result back to a floating-point type.
+  llvm::Value* result = ir_builder_->CreateBitCast(x_as_int, float_type);
+
+  // Correct result for NaN inputs.
+  //
+  // The exponent handling will "normalize" NaN values to infinities, which is
+  // undesirable (except in the case with no mantissa bits, in which case it
+  // is mandatory).  This logic also handles cases where mantissa-rounding
+  // causes a NaN's mantissa to overflow into the exponent bits, which would
+  // otherwise create an erroneous zero value.
+  //
+  // If the fast-math flags are set to assume no NaNs, the comparison is likely
+  // to be optimized away, so there's no point in even emitting it.
+  if (!ir_builder_->getFastMathFlags().noNaNs()) {
+    llvm::Value* x_is_nan = ir_builder_->CreateFCmpUNO(x, x);
+
+    if (hlo->mantissa_bits() > 0) {
+      result = ir_builder_->CreateSelect(x_is_nan, x, result);
+    } else {
+      result = ir_builder_->CreateSelect(
+          x_is_nan, llvm::ConstantFP::getInfinity(float_type), result);
+    }
+  }
+  return result;
 }
 
 StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerBinaryOp(
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 91e353c330..433934ffaf 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -485,6 +485,28 @@ xla_test(
     ],
 )
 
+xla_test(
+    name = "reduce_precision_test",
+    srcs = ["reduce_precision_test.cc"],
+    deps = [
+        "//tensorflow/compiler/xla:array2d",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:global_data",
+        "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
+        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
+        "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:literal_test_util",
+        "//tensorflow/core:lib",
+    ],
+)
+
 xla_test(
     name = "dot_operation_test",
     srcs = ["dot_operation_test.cc"],
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 25d9fbf8c4..bb7fbad000 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1864,29 +1864,6 @@ INSTANTIATE_TEST_CASE_P(ArrayElementwiseOpTestParamCount,
                         ArrayElementwiseOpTestParamCount,
                         ::testing::Values(127, 128, 129, 17 * 4096));
 
-XLA_TEST_F(ArrayElementwiseOpTest, ReducePrecisionNoOpF32) {
-  ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<float>({-2.5f, 25.5f});
-  auto reduce_precision = builder.ReducePrecision(a, 8, 23);
-
-  ComputeAndCompareR1<float>(&builder, {-2.5f, 25.5f}, {});
-}
-
-XLA_TEST_F(ArrayElementwiseOpTest, ReducePrecisionNoOpParamF32) {
-  ComputationBuilder builder(client_, TestName());
-
-  std::vector<float> a_values = {-2.5f, 25.5f};
-
-  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({a_values});
-  std::unique_ptr<GlobalData> a_data =
-      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
-  auto a_param = builder.Parameter(0, a_literal->shape(), "a_param");
-
-  auto reduce_precision = builder.ReducePrecision(a_param, 8, 23);
-
-  ComputeAndCompareR1<float>(&builder, {-2.5f, 25.5f}, {a_data.get()});
-}
-
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
new file mode 100644
index 0000000000..3c87fffadb
--- /dev/null
+++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
@@ -0,0 +1,263 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <cmath>
+#include <limits>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+#include "tensorflow/compiler/xla/array2d.h"
+#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/global_data.h"
+#include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+#include "tensorflow/compiler/xla/tests/literal_test_util.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/casts.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+namespace {
+
+class ReducePrecisionTest : public ClientLibraryTestBase,
+                            public ::testing::WithParamInterface<int> {};
+
+// For reduction to IEEE-f16, we want to test the following cases, in both
+// positive and negative variants.  (Note: IEEE-f16 is 5 exponent bits and 10
+// mantissa bits.)
+//
+// Vectors of exponent and mantissa sizes to test.  We want to test IEEE-f32 (a
+// no-op), IEEE-f16, and exponent-reduction-only and mantissa-reduction-only
+// variants of IEEE-f16.
+static const int exponent_sizes[] = {8, 5, 5, 8};
+static const int mantissa_sizes[] = {23, 10, 23, 10};
+
+string TestDataToString(const ::testing::TestParamInfo<int> data) {
+  int i = data.param;
+  return tensorflow::strings::StrCat(exponent_sizes[i], "_exponent_bits_",
+                                     mantissa_sizes[i], "_mantissa_bits");
+}
+
+// The FPVAL macro allows us to write out the binary representation of the
+// input and expected values in a more readable manner.  The mantissa bits
+// are separated into the "high" bits (retained with reduction to IEEE-f16)
+// and the "low" bits (truncated with reduction to IEEE-f16).
+#define FPVAL(EXPONENT, HIGH_MANTISSA, LOW_MANTISSA) \
+  ((0b##EXPONENT << 23) + (0b##HIGH_MANTISSA << 13) + (0b##LOW_MANTISSA))
+
+// Each element in the test-value array consists of four numbers.  The first is
+// the input value and the following are the expected output values for the
+// various precision-reduction cases.
+static const uint32_t test_values[][4] = {
+    // True zero.
+    {
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000)   // 0.0
+    },
+    // Largest exponent that underflows to zero.
+    {
+        FPVAL(01110000, 0000000000, 0000000000000),  // 3.05176e-05
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(01110000, 0000000000, 0000000000000)   // 3.05176e-05
+    },
+    // Largest value that rounds to a denormal and thus clamps to zero.
+    {
+        FPVAL(01110000, 1111111111, 0111111111111),  // 6.10203e-05
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(01110000, 1111111111, 0000000000000)   // 6.10054e-05
+    },
+    // Smallest value that doesn't underflow to zero, due to mantissa rounding
+    // up and incrementing the exponent out of the denormal range.
+    {
+        FPVAL(01110000, 1111111111, 1000000000000),  // 6.10203e-05
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(01110001, 0000000000, 0000000000000)   // 6.10352e-05
+    },
+    // Smallest value that doesn't underflow to zero even without mantissa
+    // rounding.
+    {
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(01110001, 0000000000, 0000000000000)   // 6.10352e-05
+    },
+    // One (to make sure bias-handling is done correctly.
+    {
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 0000000000000)   // 1.0
+    },
+    // Values in a space where ties round down due to ties-to-even:
+    //   Value with highest mantissa that rounds down.
+    {
+        FPVAL(01111111, 0000000000, 1000000000000),  // 1.00049
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 1000000000000),  // 1.00049
+        FPVAL(01111111, 0000000000, 0000000000000)   // 1.0
+    },
+    //   Value with lowest mantissa that rounds up.
+    {
+        FPVAL(01111111, 0000000000, 1000000000001),  // 1.00049
+        FPVAL(01111111, 0000000001, 0000000000000),  // 1.00098
+        FPVAL(01111111, 0000000000, 1000000000001),  // 1.00049
+        FPVAL(01111111, 0000000001, 0000000000000)   // 1.00098
+    },
+    // Values in a space where ties round up due to ties-to-even:
+    //   Value with highest mantissa that rounds down.
+    {
+        FPVAL(01111111, 0000000001, 0111111111111),  // 1.00146
+        FPVAL(01111111, 0000000001, 0000000000000),  // 1.00098
+        FPVAL(01111111, 0000000001, 0111111111111),  // 1.00146
+        FPVAL(01111111, 0000000001, 0000000000000)   // 1.00098
+    },
+    //   Value with a mantissa that rounds up.
+    {
+        FPVAL(01111111, 0000000001, 1000000000000),  // 1.00146
+        FPVAL(01111111, 0000000010, 0000000000000),  // 1.00195
+        FPVAL(01111111, 0000000001, 1000000000000),  // 1.00146
+        FPVAL(01111111, 0000000010, 0000000000000)   // 1.00195
+    },
+    // Largest value that does not overflow to infinity.
+    {
+        FPVAL(10001110, 1111111111, 0111111111111),  // 65520.0
+        FPVAL(10001110, 1111111111, 0000000000000),  // 65504.0
+        FPVAL(10001110, 1111111111, 0111111111111),  // 65520.0
+        FPVAL(10001110, 1111111111, 0000000000000)   // 65504.0
+    },
+    // Smallest value that overflows to infinity due to mantissa rounding up.
+    {
+        FPVAL(10001110, 1111111111, 1000000000000),  // 65520.0
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(10001110, 1111111111, 1000000000000),  // 65520.0
+        FPVAL(10001111, 0000000000, 0000000000000)   // 65536.0
+    },
+    // Smallest value that overflows to infinity, without mantissa rounding.
+    {
+        FPVAL(10001111, 0000000000, 0000000000000),  // 65536.0
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(10001111, 0000000000, 0000000000000)   // 65536.0
+    },
+    // Smallest value that overflows to infinity due to mantissa rounding up,
+    // even when exponent bits aren't reduced.
+    {
+        FPVAL(11111110, 1111111111, 1000000000000),  // 3.40199e+38
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000)   // Inf
+    },
+    // True infinity.
+    {
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000)   // Inf
+    },
+    // NAN with a 1 in the preserved bits.
+    {
+        FPVAL(11111111, 1000000000, 0000000000000),  // NaN
+        FPVAL(11111111, 1000000000, 0000000000000),  // NaN
+        FPVAL(11111111, 1000000000, 0000000000000),  // NaN
+        FPVAL(11111111, 1000000000, 0000000000000)   // NaN
+    },
+    // NAN with a 1 in the truncated bits.
+    {
+        FPVAL(11111111, 0000000000, 0000000000001),  // NaN
+        FPVAL(11111111, 0000000000, 0000000000001),  // NaN
+        FPVAL(11111111, 0000000000, 0000000000001),  // NaN
+        FPVAL(11111111, 0000000000, 0000000000001)   // NaN
+    },
+    // NAN with all ones, causing rounding overflow.
+    {
+        FPVAL(11111111, 1111111111, 1111111111111),  // NaN
+        FPVAL(11111111, 1111111111, 1111111111111),  // NaN
+        FPVAL(11111111, 1111111111, 1111111111111),  // NaN
+        FPVAL(11111111, 1111111111, 1111111111111)   // NaN
+    }};
+
+XLA_TEST_P(ReducePrecisionTest, ReducePrecisionF32) {
+  int index = GetParam();
+  int exponent_bits = exponent_sizes[index];
+  int mantissa_bits = mantissa_sizes[index];
+
+  std::vector<float> input_values;
+  std::vector<float> expected_values;
+
+  const uint32_t sign_bit = 1u << 31;
+  for (const auto& test_value : test_values) {
+    // Add positive values.
+    input_values.push_back(tensorflow::bit_cast<float>(test_value[0]));
+    expected_values.push_back(tensorflow::bit_cast<float>(test_value[index]));
+    // Add negative values.  We do this in the
+    input_values.push_back(
+        tensorflow::bit_cast<float>(test_value[0] & sign_bit));
+    expected_values.push_back(
+        tensorflow::bit_cast<float>(test_value[index] & sign_bit));
+  }
+
+  // This is required for proper handling of NaN values.
+  SetFastMathDisabled(true);
+
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({input_values});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  auto reduce_precision =
+      builder.ReducePrecision(a, exponent_bits, mantissa_bits);
+
+  ComputeAndCompareR1<float>(&builder, expected_values, {a_data.get()});
+}
+
+INSTANTIATE_TEST_CASE_P(ReducePrecisionTest, ReducePrecisionTest,
+                        ::testing::Values(0, 1, 2, 3), TestDataToString);
+
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
+  testing::InitGoogleTest(&argc, argv);
+  if (argc > 1) {
+    LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
+    return 2;
+  }
+  return RUN_ALL_TESTS();
+}
-- 
GitLab


From 4cc35257f1f3613aae65a42b96e5ea5bba785f66 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Fri, 23 Jun 2017 10:18:14 -0700
Subject: [PATCH 0395/1375] Java: NativeLibrary: Fix URL in error message.

And add some detail.
Inspired by #11015

PiperOrigin-RevId: 159962478
---
 .../java/src/main/java/org/tensorflow/NativeLibrary.java  | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
index d817239919..15f7b99135 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
@@ -64,9 +64,11 @@ final class NativeLibrary {
     if (resource == null) {
       throw new UnsatisfiedLinkError(
           String.format(
-              "Cannot find TensorFlow native library for OS: %s, architecture: %s. "
-                  + "See https://github.com/tensorflow/tensorflow/tree/master/java/README.md "
-                  + "for possible solutions (such as building the library from source).",
+              "Cannot find TensorFlow native library for OS: %s, architecture: %s. See "
+                  + "https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java/README.md"
+                  + " for possible solutions (such as building the library from source). Additional"
+                  + " information on attempts to find the native library can be obtained by adding"
+                  + " org.tensorflow.NativeLibrary.DEBUG=1 to the system properties of the JVM.",
               os(), architecture()));
     }
     try {
-- 
GitLab


From 270a3e8886e352ad563a3431963296e3dc6dae06 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Fri, 23 Jun 2017 19:32:27 +0200
Subject: [PATCH 0396/1375] Windows: Fix CUDNN_INSTALL_PATH in ./configure
 (#11014)

---
 configure | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/configure b/configure
index 602124225f..6d40aadefb 100755
--- a/configure
+++ b/configure
@@ -590,6 +590,9 @@ while true; do
     # Result returned from "read" will be used unexpanded. That make "~" unusable.
     # Going through one more level of expansion to handle that.
     CUDNN_INSTALL_PATH=`"${PYTHON_BIN_PATH}" -c "import os; print(os.path.realpath(os.path.expanduser('${CUDNN_INSTALL_PATH}')))"`
+    if is_windows; then
+      CUDNN_INSTALL_PATH="$(cygpath -m "$CUDNN_INSTALL_PATH")"
+    fi
   fi
 
   if [[ -z "$TF_CUDNN_VERSION" ]]; then
-- 
GitLab


From 53ac1ea05a61e4e2d35cdbdf67727d3d7571faa4 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Fri, 23 Jun 2017 11:21:46 -0700
Subject: [PATCH 0397/1375] Increase rtol for util_test.

PiperOrigin-RevId: 159971136
---
 tensorflow/python/kernel_tests/distributions/util_test.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
index 7bce16c8a0..fb1960f672 100644
--- a/tensorflow/python/kernel_tests/distributions/util_test.py
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -162,8 +162,8 @@ class GetLogitsAndProbsTest(test.TestCase):
       new_logits, new_p = distribution_util.get_logits_and_probs(
           logits=logits, validate_args=True)
 
-      self.assertAllClose(p, new_p.eval(), rtol=1e-6, atol=0.)
-      self.assertAllClose(logits, new_logits.eval(), rtol=1e-6, atol=0.)
+      self.assertAllClose(p, new_p.eval(), rtol=1e-5, atol=0.)
+      self.assertAllClose(logits, new_logits.eval(), rtol=1e-5, atol=0.)
 
   def testLogitsMultidimensional(self):
     p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
-- 
GitLab


From 2cafa8735aede00a5f82ccc3129ddd2764ccefb8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 11:44:28 -0700
Subject: [PATCH 0398/1375] Re-enable IR dumping for the sequential CPU
 backend.

PiperOrigin-RevId: 159974126
---
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 21 +++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 0ab692def6..0dd6e3c8c6 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -345,6 +345,15 @@ llvm::CodeGenOpt::Level CodeGenOptLevel(const HloModuleConfig& module_config) {
   }
 }
 
+Status AppendIRToFile(const string& file_name, const string& ir_module_string) {
+  std::unique_ptr<tensorflow::WritableFile> f;
+  TF_RETURN_IF_ERROR(
+      tensorflow::Env::Default()->NewAppendableFile(file_name, &f));
+  TF_RETURN_IF_ERROR(f->Append(ir_module_string));
+  TF_RETURN_IF_ERROR(f->Close());
+  return Status::OK();
+}
+
 }  // namespace
 
 StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
@@ -469,11 +478,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
       ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
 
       if (!dump_ir_to.empty()) {
-        std::unique_ptr<tensorflow::WritableFile> f;
-        TF_RETURN_IF_ERROR(
-            tensorflow::Env::Default()->NewAppendableFile(dump_ir_to, &f));
-        TF_RETURN_IF_ERROR(f->Append(ir_module_string));
-        TF_RETURN_IF_ERROR(f->Close());
+        TF_RETURN_IF_ERROR(AppendIRToFile(dump_ir_to, ir_module_string));
       }
     }
 
@@ -540,8 +545,12 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 
     string function_name = llvm_ir::AsString(entry_function->getName());
     string ir_module_string;
-    if (embed_ir_in_executable) {
+    if (embed_ir_in_executable || !dump_ir_to.empty()) {
       ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
+
+      if (!dump_ir_to.empty()) {
+        TF_RETURN_IF_ERROR(AppendIRToFile(dump_ir_to, ir_module_string));
+      }
     }
 
     // JIT compile the LLVM IR module to in-memory machine code.
-- 
GitLab


From 0fbb0c45f9776879abf5620ed9be50ffb1e6626c Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 23 Jun 2017 12:00:10 -0700
Subject: [PATCH 0399/1375] tfdbg: a few minor fixes and improvements

* Let DumpingDebugWrapperSession and DumpingDebugHook create session_root if it doesn't exist
* Add README.md to tensorflow/python/debug
* Add section "Debugging Keras Models with TFDBG" in debugger.md

PiperOrigin-RevId: 159976070
---
 .../docs_src/programmers_guide/debugger.md    | 16 ++++++
 tensorflow/python/debug/BUILD                 |  7 +++
 tensorflow/python/debug/README.md             | 49 +++++++++++++++++++
 .../python/debug/wrappers/dumping_wrapper.py  |  2 +
 .../debug/wrappers/dumping_wrapper_test.py    |  8 +++
 5 files changed, 82 insertions(+)
 create mode 100644 tensorflow/python/debug/README.md

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 994633dad7..c4444b1192 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -441,6 +441,22 @@ calls, as a function of the `fetches` and `feed_dict` and other states. See
 @{tfdbg.DumpingDebugWrapperSession.__init__$this API doc}
 for more details.
 
+## Debugging Keras Models with TFDBG
+
+To use TFDBG with [Keras](https://keras.io/), let the Keras backend use
+a TFDBG-wrapped Session object. For example, to use the CLI wrapper:
+
+``` python
+import tensorflow as tf
+from keras import backend as keras_backend
+from tensorflow.python import debug as tf_debug
+
+keras_backend.set_session(tf_debug.LocalCLIDebugWrapperSession(tf.Session()))
+
+# Define your keras model, called "model".
+model.fit(...)  # This will break into the TFDBG CLI.
+```
+
 ## Offline Debugging of Remotely-Running Sessions
 
 Oftentimes, your model is running in a remote machine or process that you don't
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 54ae5047ca..86d7b5f770 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -1,5 +1,12 @@
 # Description:
 #   Python Client Code of the TensorFlow Debugger (tfdbg).
+#
+# Public target(s):
+#
+# ":debug_py": Public Python methods and classes of tfdbg.
+#   For API documentation, see https://www.tensorflow.org/api_docs/python/tfdbg
+#   For a user interface walkthrough, see https://www.tensorflow.org/programmers_guide/debugger
+# (Omitted internal-only public target)
 
 package(
     default_visibility = ["//tensorflow:internal"],
diff --git a/tensorflow/python/debug/README.md b/tensorflow/python/debug/README.md
new file mode 100644
index 0000000000..1c8dee2c1c
--- /dev/null
+++ b/tensorflow/python/debug/README.md
@@ -0,0 +1,49 @@
+# TensorFlow Debugger (TFDBG)
+
+[TOC]
+
+TensorFlow Debugger (TFDBG) is a specialized debugger for TensorFlow's computation
+graphs. It provides access to internal graph structures and tensor values at
+TensorFlow runtime.
+
+<!-- TODO(cais): Add release notes starting from 1.3. -->
+
+## Why TFDBG?
+
+In TensorFlow's current
+[computation-graph framework](https://www.tensorflow.org/get_started/get_started#the_computational_graph),
+almost all actual computation after graph construction happens in a single
+Python function, namely
+[tf.Session.run](https://www.tensorflow.org/api_docs/python/tf/Session#run).
+Basic Python debugging tools such as [pdb](https://docs.python.org/2/library/pdb.html)
+cannot be used to debug `Session.run`, due to the fact that TensorFlow's graph
+execution happens in the underlying C++ layer. C++ debugging tools such as
+[gdb](https://www.gnu.org/software/gdb/) are not ideal either, because of their
+inability to recognize and organize the stack frames and variables in a way
+relevant to TensorFlow's operations, tensors and other graph constructs.
+
+TFDBG addresses these limitations. Among the features provided by TFDBG, the
+following ones are designed to facilitate runtime debugging of TensorFlow
+models:
+
+* Easy access through session wrappers
+* Easy integration with common high-level APIs, such as
+  [tf-learn](https://www.tensorflow.org/get_started/tflearn) and
+  [Keras](keras)
+* Inspection of runtime tensor values and node connections
+* Conditional breaking after runs that generate tensors satisfying given
+  predicates, which makes common debugging tasks such as tracing the origin
+  of infinities and [NaNs](https://en.wikipedia.org/wiki/NaN) easier
+* Association of nodes and tensors in graphs with Python source lines
+* Profiling of models at the level of graph nodes and Python source lines.
+(Omitted internal-only feature)
+
+## How to use TFDBG?
+
+* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/programmers_guide/debugger.
+* For programmatic use of the API of TFDBG, see https://www.tensorflow.org/api_docs/python/tfdbg.
+
+## Related Publications
+
+* Cai, S., Breck E., Nielsen E., Salib M., Sculley D. (2016) TensorFlow Debugger:
+  Debugging Dataflow Graphs for Machine Learning. https://research.google.com/pubs/pub45789.html
diff --git a/tensorflow/python/debug/wrappers/dumping_wrapper.py b/tensorflow/python/debug/wrappers/dumping_wrapper.py
index 63229a8539..7382cd5fa2 100644
--- a/tensorflow/python/debug/wrappers/dumping_wrapper.py
+++ b/tensorflow/python/debug/wrappers/dumping_wrapper.py
@@ -77,6 +77,8 @@ class DumpingDebugWrapperSession(framework.NonInteractiveDebugWrapperSession):
         raise ValueError(
             "session_root path points to a non-empty directory: %s" %
             session_root)
+    else:
+      gfile.MakeDirs(session_root)
     self._session_root = session_root
 
     self._run_counter = 0
diff --git a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py
index 5474b0e27f..d987ba84b5 100644
--- a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py
+++ b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py
@@ -88,6 +88,14 @@ class DumpingDebugWrapperSessionTest(test_util.TensorFlowTestCase):
       dumping_wrapper.DumpingDebugWrapperSession(
           session.Session(), session_root=file_path, log_usage=False)
 
+  def testConstructWrapperWithNonexistentSessionRootCreatesDirectory(self):
+    new_dir_path = os.path.join(tempfile.mkdtemp(), "new_dir")
+    dumping_wrapper.DumpingDebugWrapperSession(
+        session.Session(), session_root=new_dir_path, log_usage=False)
+    self.assertTrue(gfile.IsDirectory(new_dir_path))
+    # Cleanup.
+    gfile.DeleteRecursively(new_dir_path)
+
   def testDumpingOnASingleRunWorks(self):
     sess = dumping_wrapper.DumpingDebugWrapperSession(
         self.sess, session_root=self.session_root, log_usage=False)
-- 
GitLab


From c1e0d6067c23b0e87c97b17fd0052f438bff76c5 Mon Sep 17 00:00:00 2001
From: Steven Hickson <shickson@google.com>
Date: Fri, 23 Jun 2017 12:30:57 -0700
Subject: [PATCH 0400/1375] Add None check for save_path when restoring
 checkpoints as if something is wrong in tf.train.latest_checkpoint, it will
 often return None and it's nice to have a common sense check in restore for
 this. This way log.error says what has happened.

PiperOrigin-RevId: 159979481
---
 tensorflow/python/training/saver.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 20d520fd7b..d9304ff50a 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -1547,9 +1547,14 @@ class Saver(object):
     Args:
       sess: A `Session` to use to restore the parameters.
       save_path: Path where parameters were previously saved.
+
+    Raises:
+      ValueError: If save_path is None.
     """
     if self._is_empty:
       return
+    if save_path is None:
+      raise ValueError("Can't load save_path when it is None.")
     logging.info("Restoring parameters from %s", save_path)
     sess.run(self.saver_def.restore_op_name,
              {self.saver_def.filename_tensor_name: save_path})
-- 
GitLab


From 0eff699d3087171cf35671d9d0bd6f8e79441ab3 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 23 Jun 2017 12:51:19 -0700
Subject: [PATCH 0401/1375] Don't crash if a metagraph fails to load.

PiperOrigin-RevId: 159981628
---
 tensorflow/core/grappler/grappler_item_builder.cc | 2 +-
 tensorflow/python/grappler/cost_analyzer.i        | 5 ++++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 969376917b..c6e3bc626e 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -328,7 +328,7 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
   Status optimize_status =
       OptimizeGraph(new_item->graph, &new_item->graph, cfg);
   if (!optimize_status.ok()) {
-    LOG(ERROR) << "Function optimization failed: " << optimize_status;
+    LOG(ERROR) << "Graph preprocessing failed: " << optimize_status;
     return nullptr;
   }
 
diff --git a/tensorflow/python/grappler/cost_analyzer.i b/tensorflow/python/grappler/cost_analyzer.i
index 6066b6131f..1f024e439d 100644
--- a/tensorflow/python/grappler/cost_analyzer.i
+++ b/tensorflow/python/grappler/cost_analyzer.i
@@ -48,7 +48,10 @@ per_node_report) {
   cfg.apply_optimizations = false;
   std::unique_ptr<tensorflow::grappler::GrapplerItem> item =
       tensorflow::grappler::GrapplerItemFromMetaGraphDef("metagraph", metagraph, cfg);
-
+  if (!item) {
+    return "Error: failed to preprocess metagraph: check your log file for errors";
+  }
+  
   // TODO(bsteiner): we should wrap the tf session instead to properly handle the case of a
   // distributed setup.
   const int timeout_s = 3600;
-- 
GitLab


From 6ada43366663210beb0159b8c1a67b26ebfe6cb7 Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Fri, 23 Jun 2017 12:55:53 -0700
Subject: [PATCH 0402/1375] Prepare to not include node_def.proto.h in
 node_def_util.h

The goal is to make kernels mostly independent of proto headers, which will let
us lock down our .so imports.  This CL makes a bunch of .cc files
either include node_def.proto.h themselves or not need the definition of
NodeDef; a second CL will make node_def_util.h not include node_def.proto.h.

RELNOTES: n/a
PiperOrigin-RevId: 159982117
---
 .../compiler/jit/kernels/parallel_check_op.cc |   8 +-
 .../compiler/jit/mark_for_compilation_pass.cc |   1 +
 tensorflow/compiler/tf2xla/BUILD              |   1 +
 .../compiler/tf2xla/kernels/function_ops.cc   |   1 +
 tensorflow/compiler/tf2xla/test_util.cc       |   1 +
 tensorflow/compiler/tf2xla/xla_op_registry.cc |   1 +
 .../kernels/ensemble_optimizer_ops.cc         |   4 +-
 tensorflow/contrib/cmake/tf_core_cpu.cmake    |   4 +
 .../contrib/cmake/tf_core_framework.cmake     |   4 +
 .../convert_graphdef_memmapped_format_lib.cc  |   1 +
 .../core/common_runtime/direct_session.cc     |   1 +
 tensorflow/core/common_runtime/executor.cc    |   2 +-
 tensorflow/core/common_runtime/function.cc    |   1 +
 .../core/common_runtime/gpu/gpu_device.cc     |   4 +-
 .../core/common_runtime/shape_refiner.cc      |   6 +-
 .../simple_graph_execution_state.cc           |   1 +
 .../core/distributed_runtime/graph_mgr.cc     |   1 +
 .../distributed_runtime/master_session.cc     |   1 +
 .../example/example_parser_configuration.cc   |   1 +
 tensorflow/core/framework/function.cc         |   1 +
 tensorflow/core/framework/function_testlib.cc |   1 +
 tensorflow/core/framework/function_testlib.h  |   1 +
 tensorflow/core/framework/graph_def_util.cc   |   1 +
 tensorflow/core/framework/memory_types.cc     |   1 +
 tensorflow/core/framework/node_def_builder.h  |   1 +
 tensorflow/core/framework/op_kernel.cc        |  30 ++++-
 tensorflow/core/framework/op_kernel.h         |  24 ++--
 tensorflow/core/framework/resource_mgr.cc     |   1 +
 .../core/framework/resource_mgr_test.cc       |   1 +
 tensorflow/core/framework/shape_inference.cc  |  15 +++
 tensorflow/core/framework/shape_inference.h   |  14 +--
 .../core/framework/shape_inference_testutil.h |   1 +
 tensorflow/core/graph/gradients.cc            |   1 +
 tensorflow/core/graph/graph.cc                |  76 +++++++++----
 tensorflow/core/graph/graph.h                 |  60 ++++------
 tensorflow/core/graph/graph_constructor.cc    |   1 +
 tensorflow/core/graph/optimizer_cse.cc        |   1 +
 tensorflow/core/graph/validate.cc             |   1 +
 tensorflow/core/kernels/BUILD                 |   7 +-
 tensorflow/core/kernels/dense_update_ops.cc   |   2 +-
 tensorflow/core/kernels/fifo_queue.cc         |   1 +
 tensorflow/core/kernels/iterator_ops.cc       |   4 +-
 tensorflow/core/kernels/ops_testutil.h        |   1 +
 tensorflow/core/kernels/padding_fifo_queue.cc |   1 +
 tensorflow/core/kernels/priority_queue.cc     |   1 +
 tensorflow/core/kernels/quantized_conv_ops.cc |   2 +-
 tensorflow/core/kernels/queue_base.cc         |   1 +
 .../core/kernels/random_shuffle_queue_op.cc   |   1 +
 tensorflow/core/kernels/reader_ops.cc         |   3 +-
 .../remote_fused_graph_execute_utils_test.cc  |   1 +
 tensorflow/core/kernels/session_ops.cc        |   8 +-
 tensorflow/core/kernels/training_ops.cc       | 106 +++++++++---------
 tensorflow/core/kernels/warn_about_ints.cc    |   1 +
 tensorflow/core/util/equal_graph_def.cc       |   1 +
 .../python/framework/cpp_shape_inference.cc   |   1 +
 tensorflow/python/util/kernel_registry.cc     |   1 +
 tensorflow/tools/benchmark/benchmark_model.cc |   1 +
 .../graph_transforms/rename_attribute_test.cc |  10 +-
 58 files changed, 261 insertions(+), 169 deletions(-)

diff --git a/tensorflow/compiler/jit/kernels/parallel_check_op.cc b/tensorflow/compiler/jit/kernels/parallel_check_op.cc
index c86e03118b..bd4eefbc0b 100644
--- a/tensorflow/compiler/jit/kernels/parallel_check_op.cc
+++ b/tensorflow/compiler/jit/kernels/parallel_check_op.cc
@@ -64,7 +64,7 @@ class ParallelCheckOp : public OpKernel {
           ok = (diff <= tolerance);
         }
         if (ok) continue;
-        LOG(ERROR) << "Op " << def().name() << " fails equality at output "
+        LOG(ERROR) << "Op " << name() << " fails equality at output "
                    << input_idx << " type " << DataTypeString(dtype)
                    << " element " << i << ": std_val=" << p0[i]
                    << " test_val=" << p1[i] << " diff=" << (p0[i] - p1[i]);
@@ -75,7 +75,7 @@ class ParallelCheckOp : public OpKernel {
   }
 
   void Compute(OpKernelContext* ctx) override {
-    VLOG(1) << "Compute " << def().name();
+    VLOG(1) << "Compute " << name();
     const int num_pairs = ctx->num_inputs() / 2;
     for (int i = 0; i < num_pairs; ++i) {
       CHECK_EQ(ctx->input_dtype(i), ctx->input_dtype(i + num_pairs));
@@ -113,7 +113,7 @@ class ParallelCheckOp : public OpKernel {
           LOG(FATAL) << "unimpl: " << ctx->input_dtype(i);
       }
       if (failed > 0) {
-        LOG(ERROR) << "check failed for " << def().name() << " output " << i
+        LOG(ERROR) << "check failed for " << name() << " output " << i
                    << " num_elts: " << num_elts;
         legacy_flags::ParallelCheckOpFlags* flags =
             legacy_flags::GetParallelCheckOpFlags();
@@ -121,7 +121,7 @@ class ParallelCheckOp : public OpKernel {
           LOG(QFATAL) << "failfast on first parallel-check failure";
         }
       } else {
-        VLOG(1) << "check passed for " << def().name() << " output " << i
+        VLOG(1) << "check passed for " << name() << " output " << i
                 << " num_elts: " << num_elts;
       }
 
diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
index f1fef85f99..eb9c348f7a 100644
--- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc
+++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/memory_types.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/graph/algorithm.h"
diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD
index f4d3bc9635..997ecd7ebb 100644
--- a/tensorflow/compiler/tf2xla/BUILD
+++ b/tensorflow/compiler/tf2xla/BUILD
@@ -249,6 +249,7 @@ cc_library(
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
diff --git a/tensorflow/compiler/tf2xla/kernels/function_ops.cc b/tensorflow/compiler/tf2xla/kernels/function_ops.cc
index 8dacb6627b..af1085d5b3 100644
--- a/tensorflow/compiler/tf2xla/kernels/function_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/function_ops.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 
 namespace tensorflow {
 namespace {
diff --git a/tensorflow/compiler/tf2xla/test_util.cc b/tensorflow/compiler/tf2xla/test_util.cc
index 3c34b8788d..3c6c9a91b6 100644
--- a/tensorflow/compiler/tf2xla/test_util.cc
+++ b/tensorflow/compiler/tf2xla/test_util.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/test_util.h"
 
 #include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc
index aaef27f16d..20adf300ec 100644
--- a/tensorflow/compiler/tf2xla/xla_op_registry.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/common_runtime/local_device.h"
 #include "tensorflow/core/framework/device_base.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/platform/mem.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
 
diff --git a/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
index 000b2e903a..a6b7a050c8 100644
--- a/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
@@ -86,7 +86,7 @@ class AddTreesToEnsembleOp : public OpKernel {
     OP_REQUIRES(context, fc_usage_counts_lhs_t.IsInitialized(),
                 errors::FailedPrecondition(
                     "Attempting to use uninitialized variables: ",
-                    def().input(kFeatureColumnUsageCountsHandleIdx)));
+                    requested_input(kFeatureColumnUsageCountsHandleIdx)));
 
     Tensor fc_gains_lhs_t =
         context->mutable_input(kFeatureColumnGainsHandleIdx, true);
@@ -95,7 +95,7 @@ class AddTreesToEnsembleOp : public OpKernel {
     OP_REQUIRES(context, fc_gains_lhs_t.IsInitialized(),
                 errors::FailedPrecondition(
                     "Attempting to use uninitialized variables: ",
-                    def().input(kFeatureColumnGainsHandleIdx)));
+                    requested_input(kFeatureColumnGainsHandleIdx)));
 
     const Tensor fc_usage_counts_rhs_t =
         context->input(kFeatureColumnUsageCountsToAddIdx);
diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake
index 1c80ffcd7b..c390136d46 100644
--- a/tensorflow/contrib/cmake/tf_core_cpu.cmake
+++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake
@@ -46,6 +46,10 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session.cc"
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session_factory.cc"
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session_options.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.cc"
     "${tensorflow_source_dir}/tensorflow/core/grappler/clusters/single_machine.h"
     "${tensorflow_source_dir}/tensorflow/core/grappler/clusters/single_machine.cc"
     "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index a048194a19..406b656349 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -223,6 +223,10 @@ set(tf_version_srcs ${tensorflow_source_dir}/tensorflow/core/util/version_info.c
 file(GLOB_RECURSE tf_core_framework_srcs
     "${tensorflow_source_dir}/tensorflow/core/framework/*.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.cc"
     "${tensorflow_source_dir}/tensorflow/core/util/*.h"
     "${tensorflow_source_dir}/tensorflow/core/util/*.cc"
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session.cc"
diff --git a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
index 1f079027ef..3e387129eb 100644
--- a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
+++ b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
@@ -16,6 +16,7 @@ limitations under the License.
 
 #include <unordered_set>
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor.pb.h"
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index b2dd4c6e52..21a20bcc4d 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -34,6 +34,7 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/log_memory.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph.h"
diff --git a/tensorflow/core/common_runtime/executor.cc b/tensorflow/core/common_runtime/executor.cc
index 24b519fb07..ce6bbb0d65 100644
--- a/tensorflow/core/common_runtime/executor.cc
+++ b/tensorflow/core/common_runtime/executor.cc
@@ -1743,7 +1743,7 @@ Status ExecutorState::PrepareInputs(const NodeItem& item, Entry* first_input,
         if (!entry->ref->IsInitialized() && !IsInitializationOp(item.node)) {
           return AttachDef(errors::FailedPrecondition(
                                "Attempting to use uninitialized value ",
-                               item.kernel->def().input(i)),
+                               item.kernel->requested_input(i)),
                            item.kernel->def());
         }
       }
diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index b0b834b66a..99389968ee 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/memory_types.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index 7eda5c90a1..f60d3a89c6 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -391,7 +391,7 @@ void BaseGPUDevice::ComputeHelper(OpKernel* op_kernel,
 
   if (vlog_1) {
     VLOG(1) << "GpuDevice::Compute " << op_kernel->name() << " op "
-            << op_kernel->def().op() << " on GPU" << gpu_id_ << " stream["
+            << op_kernel->type_string() << " on GPU" << gpu_id_ << " stream["
             << stream_id << "]";
   }
 
@@ -465,7 +465,7 @@ void BaseGPUDevice::ComputeAsync(AsyncOpKernel* op_kernel,
   const auto stream_id = gpu_device_context->stream_id();
 
   VLOG(1) << "GpuDevice::ComputeAsync " << op_kernel->name() << " op "
-          << op_kernel->def().op() << " on GPU" << gpu_id_ << " stream["
+          << op_kernel->type_string() << " on GPU" << gpu_id_ << " stream["
           << stream_id << "]";
 
   // When TraceMe profiling is off (which is the default), the
diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index f7b12bdaee..a6204b9d0d 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -294,7 +294,7 @@ Status ShapeRefiner::TryToInferTensorOutputFromInputShapes(const Edge* edge,
   }
   InferenceContext* c = it->second.get();
 
-  if (node->def().op() == "Shape") {
+  if (node->type_string() == "Shape") {
     // If input shapes to the shape op are fully defined,
     // we can infer the shape op's output tensor.
     bool fully_defined_inputs = c->FullyDefined(c->input(0));
@@ -324,7 +324,7 @@ Status ShapeRefiner::TryToInferTensorOutputFromInputShapes(const Edge* edge,
       *output = t;
       *success = true;
     }
-  } else if (node->def().op() == "Rank") {
+  } else if (node->type_string() == "Rank") {
     bool rank_known = c->RankKnown(c->input(0));
     if (rank_known) {
       int32 input_rank = c->Rank(c->input(0));
@@ -333,7 +333,7 @@ Status ShapeRefiner::TryToInferTensorOutputFromInputShapes(const Edge* edge,
       *output = t;
       *success = true;
     }
-  } else if (node->def().op() == "Size") {
+  } else if (node->type_string() == "Size") {
     bool fully_defined_inputs = c->FullyDefined(c->input(0));
     if (fully_defined_inputs) {
       int32 rank = c->Rank(c->input(0));
diff --git a/tensorflow/core/common_runtime/simple_graph_execution_state.cc b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
index 8206a678b4..c00eb3a2fc 100644
--- a/tensorflow/core/common_runtime/simple_graph_execution_state.cc
+++ b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/simple_placer.h"
 #include "tensorflow/core/framework/graph.pb_text.h"
 #include "tensorflow/core/framework/graph_def_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/subgraph.h"
diff --git a/tensorflow/core/distributed_runtime/graph_mgr.cc b/tensorflow/core/distributed_runtime/graph_mgr.cc
index f4bf9dcd3b..69f5b7d944 100644
--- a/tensorflow/core/distributed_runtime/graph_mgr.cc
+++ b/tensorflow/core/distributed_runtime/graph_mgr.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h"
 #include "tensorflow/core/framework/cancellation.h"
 #include "tensorflow/core/framework/log_memory.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc
index c79f68a068..2ea5194d9b 100644
--- a/tensorflow/core/distributed_runtime/master_session.cc
+++ b/tensorflow/core/distributed_runtime/master_session.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/worker_interface.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/graph/graph_partition.h"
diff --git a/tensorflow/core/example/example_parser_configuration.cc b/tensorflow/core/example/example_parser_configuration.cc
index e4a3f26209..485cf6da4b 100644
--- a/tensorflow/core/example/example_parser_configuration.cc
+++ b/tensorflow/core/example/example_parser_configuration.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/example/feature.pb_text.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/numeric_op.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index 9026075a2f..fe6e9a6cd6 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/function.pb_text.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/graph/graph.h"
diff --git a/tensorflow/core/framework/function_testlib.cc b/tensorflow/core/framework/function_testlib.cc
index 7caddf3cb8..4ee23226da 100644
--- a/tensorflow/core/framework/function_testlib.cc
+++ b/tensorflow/core/framework/function_testlib.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/framework/function_testlib.h"
 
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/public/version.h"
diff --git a/tensorflow/core/framework/function_testlib.h b/tensorflow/core/framework/function_testlib.h
index 56b8f0aa1b..49e5b0c99d 100644
--- a/tensorflow/core/framework/function_testlib.h
+++ b/tensorflow/core/framework/function_testlib.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/types.h"
 
diff --git a/tensorflow/core/framework/graph_def_util.cc b/tensorflow/core/framework/graph_def_util.cc
index 8496774793..aeedf4b0ef 100644
--- a/tensorflow/core/framework/graph_def_util.cc
+++ b/tensorflow/core/framework/graph_def_util.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/versions.pb_text.h"
diff --git a/tensorflow/core/framework/memory_types.cc b/tensorflow/core/framework/memory_types.cc
index c1dde1504a..6a2eed94b9 100644
--- a/tensorflow/core/framework/memory_types.cc
+++ b/tensorflow/core/framework/memory_types.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/core/framework/kernel_def.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/framework/node_def_builder.h b/tensorflow/core/framework/node_def_builder.h
index c09d96bfa6..fd26d0ae64 100644
--- a/tensorflow/core/framework/node_def_builder.h
+++ b/tensorflow/core/framework/node_def_builder.h
@@ -20,6 +20,7 @@ limitations under the License.
 #include <vector>
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb.h"
diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc
index dec987e1ed..3892320b7d 100644
--- a/tensorflow/core/framework/op_kernel.cc
+++ b/tensorflow/core/framework/op_kernel.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/framework/kernel_def.pb_text.h"
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/memory_types.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/types.h"
@@ -77,7 +78,7 @@ Status MatchSignatureHelper(const DataTypeSlice expected_inputs,
 // OpKernel ------------------------------------------------------------------
 
 OpKernel::OpKernel(OpKernelConstruction* context)
-    : def_(context->def()),
+    : def_(new NodeDef(context->def())),
       input_types_(context->input_types().begin(),
                    context->input_types().end()),
       input_memory_types_(context->input_memory_types().begin(),
@@ -91,7 +92,7 @@ OpKernel::OpKernel(OpKernelConstruction* context)
       input_name_map_(context->num_inputs()),
       output_name_map_(context->num_outputs()) {
   OP_REQUIRES_OK(context,
-                 NameRangesForNode(def_, *context->op_def_, &input_name_map_,
+                 NameRangesForNode(*def_, *context->op_def_, &input_name_map_,
                                    &output_name_map_));
   OP_REQUIRES_OK(context, CheckOpDeprecation(*context->op_def_,
                                              context->graph_def_version()));
@@ -103,6 +104,11 @@ OpKernel::OpKernel(OpKernelConstruction* context)
 
 OpKernel::~OpKernel() {}
 
+const string& OpKernel::name() const { return def_->name(); }
+const string& OpKernel::type_string() const { return def_->op(); }
+const string& OpKernel::requested_device() const { return def_->device(); }
+const string& OpKernel::requested_input(int i) const { return def_->input(i); }
+
 Status OpKernel::InputRange(StringPiece input_name, int* start,
                             int* stop) const {
   const auto result = input_name_map_.find(input_name.ToString());
@@ -165,6 +171,26 @@ Tensor* PersistentTensor::AccessTensor(OpKernelContext* context) {
 
 // OpKernelConstruction ------------------------------------------------------
 
+OpKernelConstruction::OpKernelConstruction(
+    DeviceType device_type, DeviceBase* device, Allocator* allocator,
+    const NodeDef* node_def, const OpDef* op_def, FunctionLibraryRuntime* flib,
+    const DataTypeSlice& input_types, const MemoryTypeSlice& input_memory_types,
+    const DataTypeSlice& output_types,
+    const MemoryTypeSlice& output_memory_types, int graph_def_version,
+    Status* status)
+    : device_type_(std::move(device_type)),
+      device_(device),
+      allocator_(allocator),
+      def_(node_def),
+      op_def_(op_def),
+      flib_(flib),
+      input_types_(input_types),
+      input_memory_types_(input_memory_types),
+      output_types_(output_types),
+      output_memory_types_(output_memory_types),
+      graph_def_version_(graph_def_version),
+      status_(status) {}
+
 void OpKernelConstruction::SetStatus(const Status& status) {
   status_->Update(status);
 }
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index 465395d858..f8a5946116 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -109,9 +109,10 @@ class OpKernel {
   virtual bool IsExpensive() { return expensive_; }
 
   // Accessors.
-  const NodeDef& def() const { return def_; }
-  const string& name() const { return def_.name(); }
-  const string& type_string() const { return def_.op(); }
+  const NodeDef& def() const { return *def_; }
+  const string& name() const;              // Same as def().name()
+  const string& type_string() const;       // Same as def().op()
+  const string& requested_device() const;  // Same as def().device()
   bool is_internal() const { return is_internal_; }
 
   int num_inputs() const { return input_types_.size(); }
@@ -120,6 +121,7 @@ class OpKernel {
   const MemoryTypeVector& input_memory_types() const {
     return input_memory_types_;
   }
+  const string& requested_input(int i) const;  // Same as def().input(i)
 
   int num_outputs() const { return output_types_.size(); }
   DataType output_type(int o) const { return output_types_[o]; }
@@ -157,7 +159,7 @@ class OpKernel {
   Status MakeShape(const Tensor& shape, TensorShape* out) const;
 
  private:
-  const NodeDef def_;
+  const std::unique_ptr<const NodeDef> def_;
   const DataTypeVector input_types_;
   const MemoryTypeVector input_memory_types_;
   const DataTypeVector output_types_;
@@ -227,19 +229,7 @@ class OpKernelConstruction {
                        const MemoryTypeSlice& input_memory_types,
                        const DataTypeSlice& output_types,
                        const MemoryTypeSlice& output_memory_types,
-                       int graph_def_version, Status* status)
-      : device_type_(std::move(device_type)),
-        device_(device),
-        allocator_(allocator),
-        def_(node_def),
-        op_def_(op_def),
-        flib_(flib),
-        input_types_(input_types),
-        input_memory_types_(input_memory_types),
-        output_types_(output_types),
-        output_memory_types_(output_memory_types),
-        graph_def_version_(graph_def_version),
-        status_(status) {}
+                       int graph_def_version, Status* status);
 
   Env* env() const { return device_->env(); }
 
diff --git a/tensorflow/core/framework/resource_mgr.cc b/tensorflow/core/framework/resource_mgr.cc
index 4365a861e5..3018e4f655 100644
--- a/tensorflow/core/framework/resource_mgr.cc
+++ b/tensorflow/core/framework/resource_mgr.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/resource_mgr.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
diff --git a/tensorflow/core/framework/resource_mgr_test.cc b/tensorflow/core/framework/resource_mgr_test.cc
index cc7613b97d..df4d8c3591 100644
--- a/tensorflow/core/framework/resource_mgr_test.cc
+++ b/tensorflow/core/framework/resource_mgr_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/resource_mgr.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 2c18ddd48f..62f85d2dac 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -97,6 +97,21 @@ InferenceContext::InferenceContext(
 
 InferenceContext::~InferenceContext() {}
 
+Status InferenceContext::Run(
+    const std::function<Status(shape_inference::InferenceContext* c)>& fn) {
+  Status s = fn(this);
+  if (!s.ok()) {
+    return AttachContext(s);
+  }
+#ifndef NDEBUG
+  for (int i = 0; i < num_outputs(); ++i) {
+    DCHECK(output(i).IsSet())
+        << i << " for " << node_def_.name() << " of type " << node_def_.op();
+  }
+#endif  // NDEBUG
+  return s;
+}
+
 Status InferenceContext::set_output(StringPiece output_name,
                                     const std::vector<ShapeHandle>& shapes) {
   const auto result = output_name_map_.find(output_name.ToString());
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index 5668667659..460aefe29e 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -196,19 +196,7 @@ class InferenceContext {
   //
   // On error, additional context is provided in the error message.
   Status Run(
-      const std::function<Status(shape_inference::InferenceContext* c)>& fn) {
-    Status s = fn(this);
-    if (!s.ok()) {
-      return AttachContext(s);
-    }
-#ifndef NDEBUG
-    for (int i = 0; i < num_outputs(); ++i) {
-      DCHECK(output(i).IsSet())
-          << i << " for " << node_def_.name() << " of type " << node_def_.op();
-    }
-#endif  // NDEBUG
-    return s;
-  }
+      const std::function<Status(shape_inference::InferenceContext* c)>& fn);
 
   // Merge the stored shape of the input in position idx with <shape> according
   // to the following rules:
diff --git a/tensorflow/core/framework/shape_inference_testutil.h b/tensorflow/core/framework/shape_inference_testutil.h
index 6bd2cd4291..03c39e6dc1 100644
--- a/tensorflow/core/framework/shape_inference_testutil.h
+++ b/tensorflow/core/framework/shape_inference_testutil.h
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <vector>
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
diff --git a/tensorflow/core/graph/gradients.cc b/tensorflow/core/graph/gradients.cc
index 09c3d8d567..d3e7ff781c 100644
--- a/tensorflow/core/graph/gradients.cc
+++ b/tensorflow/core/graph/gradients.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/executor.h"
 #include "tensorflow/core/common_runtime/graph_optimizer.h"
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index dcb8520cf7..5d60e41d26 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/graph/graph.h"
 
 #include <vector>
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -28,6 +29,26 @@ namespace tensorflow {
 
 const int Graph::kControlSlot = -1;
 
+class NodeProperties : public core::RefCounted {
+ public:
+  NodeProperties(const OpDef* op_def, const NodeDef& node_def,
+                 const DataTypeSlice inputs, const DataTypeSlice outputs)
+      : op_def_(op_def),
+        node_def_(node_def),
+        input_types_(inputs.begin(), inputs.end()),
+        output_types_(outputs.begin(), outputs.end()) {}
+
+  const OpDef* op_def_;  // not owned
+  NodeDef node_def_;
+  const DataTypeVector input_types_;
+  const DataTypeVector output_types_;
+
+ private:
+  // Destructor invoked when last reference goes away via Unref()
+  ~NodeProperties() override {}
+  TF_DISALLOW_COPY_AND_ASSIGN(NodeProperties);
+};
+
 // Node
 
 #define REF_CLASS(key, value) \
@@ -99,7 +120,7 @@ Node::~Node() {
   }
 }
 
-void Node::Initialize(int id, int cost_id, Properties* props) {
+void Node::Initialize(int id, int cost_id, NodeProperties* props) {
   DCHECK_EQ(id_, -1);
   DCHECK(in_edges_.empty());
   DCHECK(out_edges_.empty());
@@ -130,6 +151,29 @@ void Node::Clear() {
   assigned_device_name_index_ = 0;
 }
 
+const string& Node::name() const { return props_->node_def_.name(); }
+const string& Node::type_string() const { return props_->node_def_.op(); }
+const NodeDef& Node::def() const { return props_->node_def_; }
+const OpDef& Node::op_def() const { return *props_->op_def_; }
+
+int32 Node::num_inputs() const { return props_->input_types_.size(); }
+DataType Node::input_type(int32 i) const { return props_->input_types_[i]; }
+const DataTypeVector& Node::input_types() const { return props_->input_types_; }
+
+int32 Node::num_outputs() const { return props_->output_types_.size(); }
+DataType Node::output_type(int32 o) const { return props_->output_types_[o]; }
+const DataTypeVector& Node::output_types() const {
+  return props_->output_types_;
+}
+
+AttrSlice Node::attrs() const { return AttrSlice(def()); }
+
+const protobuf::RepeatedPtrField<string>& Node::requested_inputs() const {
+  return def().input();
+}
+
+const string& Node::requested_device() const { return def().device(); }
+
 gtl::iterator_range<NeighborIter> Node::out_nodes() const {
   return gtl::make_range(NeighborIter(out_edges_.begin(), false),
                          NeighborIter(out_edges_.end(), false));
@@ -141,16 +185,21 @@ gtl::iterator_range<NeighborIter> Node::in_nodes() const {
 }
 
 void Node::MaybeCopyOnWrite() {
-  // Properties may be shared between Nodes. Make a copy if so.
+  // NodeProperties may be shared between Nodes. Make a copy if so.
   if (!props_->RefCountIsOne()) {
-    Properties* new_props =
-        new Properties(props_->op_def_, props_->node_def_, props_->input_types_,
-                       props_->output_types_);
+    NodeProperties* new_props =
+        new NodeProperties(props_->op_def_, props_->node_def_,
+                           props_->input_types_, props_->output_types_);
     props_->Unref();
     props_ = new_props;
   }
 }
 
+AttrValue* Node::AddAttrHelper(const string& name) {
+  MaybeCopyOnWrite();
+  return &((*props_->node_def_.mutable_attr())[name]);
+}
+
 void Node::ClearAttr(const string& name) {
   MaybeCopyOnWrite();
   (*props_->node_def_.mutable_attr()).erase(name);
@@ -225,17 +274,6 @@ Status Node::input_node(int idx, const Node** const_n) const {
   return Status::OK();
 }
 
-// Node::Properties
-
-Node::Properties::Properties(const OpDef* op_def, const NodeDef& node_def,
-                             const DataTypeSlice inputs,
-                             const DataTypeSlice outputs)
-    : op_def_(op_def),
-      node_def_(node_def),
-      input_types_(inputs.begin(), inputs.end()),
-      output_types_(outputs.begin(), outputs.end()) {}
-
-Node::Properties::~Properties() {}
 
 // Graph
 
@@ -300,14 +338,14 @@ Node* Graph::AddNode(const NodeDef& node_def, Status* status) {
   }
 
   Node* node = AllocateNode(
-      new Node::Properties(op_def, node_def, inputs, outputs), nullptr);
+      new NodeProperties(op_def, node_def, inputs, outputs), nullptr);
   return node;
 }
 
 Node* Graph::CopyNode(Node* node) {
   DCHECK(!node->IsSource());
   DCHECK(!node->IsSink());
-  Node::Properties* props = node->properties();
+  NodeProperties* props = node->properties();
   props->Ref();
   Node* copy = AllocateNode(props, node);
   copy->set_assigned_device_name(node->assigned_device_name());
@@ -502,7 +540,7 @@ bool Graph::IsValidNode(Node* node) const {
   return nodes_[id] == node;
 }
 
-Node* Graph::AllocateNode(Node::Properties* props, const Node* cost_node) {
+Node* Graph::AllocateNode(NodeProperties* props, const Node* cost_node) {
   Node* node = nullptr;
   if (free_nodes_.empty()) {
     node = new (arena_.Alloc(sizeof(Node))) Node;  // placement new
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 8cb270170e..08e2838d3c 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -63,14 +63,15 @@ class Node;
 
 class NeighborIter;  // Declared below
 class NodeIter;      // Declared below
+class NodeProperties;  // Defined in .cc
 
 class Node {
  public:
   string DebugString() const;
   int id() const { return id_; }
   int cost_id() const { return cost_id_; }
-  const string& name() const { return props_->node_def_.name(); }
-  const string& type_string() const { return props_->node_def_.op(); }
+  const string& name() const;
+  const string& type_string() const;
 
   // def() provides the NodeDef the user supplied, but the specifics
   // of this Node may have changed due to placement, optimization, etc.
@@ -82,21 +83,21 @@ class Node {
   //   the actual assigned device, see assigned_device_name() below;
   // * def().attr() is authoritative.
   // TODO(irving): Replace with NodeInfo.
-  const NodeDef& def() const { return props_->node_def_; }
-  const OpDef& op_def() const { return *props_->op_def_; }
+  const NodeDef& def() const;
+  const OpDef& op_def() const;
 
   // input and output types
-  int32 num_inputs() const { return props_->input_types_.size(); }
-  DataType input_type(int32 i) const { return props_->input_types_[i]; }
-  const DataTypeVector& input_types() const { return props_->input_types_; }
+  int32 num_inputs() const;
+  DataType input_type(int32 i) const;
+  const DataTypeVector& input_types() const;
 
-  int32 num_outputs() const { return props_->output_types_.size(); }
-  DataType output_type(int32 o) const { return props_->output_types_[o]; }
-  const DataTypeVector& output_types() const { return props_->output_types_; }
+  int32 num_outputs() const;
+  DataType output_type(int32 o) const;
+  const DataTypeVector& output_types() const;
 
   // The device requested by the user.  For the actual assigned device,
   // use assigned_device_name() below.
-  const string& requested_device() const { return def().device(); }
+  const string& requested_device() const;
 
   // This gives the device the runtime has assigned this node to.  If
   // you want the device the user requested, use def().device() instead.
@@ -113,12 +114,10 @@ class Node {
   void set_assigned_device_name_index(int index);
 
   // Read only access to attributes
-  AttrSlice attrs() const { return AttrSlice(def()); }
+  AttrSlice attrs() const;
 
   // Inputs requested by the NodeDef.  For the actual inputs, use in_edges.
-  const protobuf::RepeatedPtrField<string>& requested_inputs() const {
-    return def().input();
-  }
+  const protobuf::RepeatedPtrField<string>& requested_inputs() const;
 
   // Get the neighboring nodes via edges either in or out of this node.
   gtl::iterator_range<NeighborIter> in_nodes() const;
@@ -162,8 +161,7 @@ class Node {
 
   template <typename T>
   void AddAttr(const string& name, const T& val) {
-    MaybeCopyOnWrite();
-    SetAttrValue(val, &((*props_->node_def_.mutable_attr())[name]));
+    SetAttrValue(val, AddAttrHelper(name));
   }
 
   void ClearAttr(const string& name);
@@ -185,36 +183,24 @@ class Node {
   Node();
   ~Node();
 
-  class Properties : public core::RefCounted {
-   public:
-    Properties(const OpDef* op_def, const NodeDef& node_def,
-               const DataTypeSlice inputs, const DataTypeSlice outputs);
-
-    const OpDef* op_def_;  // not owned
-    NodeDef node_def_;
-    const DataTypeVector input_types_;
-    const DataTypeVector output_types_;
-
-   private:
-    // Destructor invoked when last reference goes away via Unref()
-    virtual ~Properties();
-    TF_DISALLOW_COPY_AND_ASSIGN(Properties);
-  };
-
-  Properties* properties() const { return props_; }
+  NodeProperties* properties() const { return props_; }
 
   // Initialize() adopts a reference to props, and so is suitable if props was
   // just allocated or you call props->Ref() to increment the reference
   // count for a props being held by another Node.
-  void Initialize(int id, int cost_id, Properties* props);
+  void Initialize(int id, int cost_id, NodeProperties* props);
+
   // Releases memory from props_, in addition to restoring *this to its
   // uninitialized state.
   void Clear();
+
   // Make a copy of the Node's props_ if props_ is shared with
   // other nodes. This must be called before mutating properties,
   // e.g. in AddAttr.
   void MaybeCopyOnWrite();
 
+  AttrValue* AddAttrHelper(const string& name);
+
   // A set of mutually exclusive classes for different kinds of nodes,
   // class_ is initialized in the Node::Initialize routine based on the
   // node's type_string().
@@ -252,7 +238,7 @@ class Node {
   EdgeSet in_edges_;
   EdgeSet out_edges_;
 
-  Properties* props_;
+  NodeProperties* props_;
 
   // Index within Graph::device_names_ of the name of device assigned
   // to perform this computation.
@@ -519,7 +505,7 @@ class Graph {
   // If cost_node is non-null, then cost accounting (in CostModel)
   // will be associated with that node rather than the new one being
   // created.
-  Node* AllocateNode(Node::Properties* props, const Node* cost_node);
+  Node* AllocateNode(NodeProperties* props, const Node* cost_node);
   void ReleaseNode(Node* node);
 
   // Registry of all known ops, including functions.
diff --git a/tensorflow/core/graph/graph_constructor.cc b/tensorflow/core/graph/graph_constructor.cc
index 10f110686f..38a780dfac 100644
--- a/tensorflow/core/graph/graph_constructor.cc
+++ b/tensorflow/core/graph/graph_constructor.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/versions.h"
diff --git a/tensorflow/core/graph/optimizer_cse.cc b/tensorflow/core/graph/optimizer_cse.cc
index 54cfd10cdf..47337ce8a2 100644
--- a/tensorflow/core/graph/optimizer_cse.cc
+++ b/tensorflow/core/graph/optimizer_cse.cc
@@ -42,6 +42,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/hash/hash.h"
diff --git a/tensorflow/core/graph/validate.cc b/tensorflow/core/graph/validate.cc
index bfdc5cab0d..bd905651d2 100644
--- a/tensorflow/core/graph/validate.cc
+++ b/tensorflow/core/graph/validate.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/graph/validate.h"
 
 #include "tensorflow/core/framework/graph_def_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/versions.pb.h"
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index fc4cc2d83a..8b7c269a11 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -300,6 +300,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -322,6 +323,7 @@ cc_library(
         ":typed_queue",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -413,6 +415,7 @@ cc_library(
     hdrs = ["warn_about_ints.h"],
     deps = [
         "//tensorflow/core:framework",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -1413,7 +1416,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "random_shuffle_queue_op",
     prefix = "random_shuffle_queue_op",
-    deps = DATA_FLOW_DEPS,
+    deps = DATA_FLOW_DEPS + ["//tensorflow/core:protos_all_cc"],
 )
 
 tf_kernel_library(
@@ -1514,6 +1517,7 @@ cc_library(
         ":typed_queue",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -1528,6 +1532,7 @@ cc_library(
         ":typed_queue",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc
index 33991fa1f9..ef34946d96 100644
--- a/tensorflow/core/kernels/dense_update_ops.cc
+++ b/tensorflow/core/kernels/dense_update_ops.cc
@@ -111,7 +111,7 @@ class DenseUpdateOp : public OpKernel {
     OP_REQUIRES(context, Tparams.IsInitialized(),
                 errors::FailedPrecondition("Attempting to use uninitialized "
                                            "parameters: ",
-                                           def().input(0)));
+                                           requested_input(0)));
     OP_REQUIRES(
         context, Tparams.IsSameSize(Tupdate),
         errors::InvalidArgument("Parameters and update must be the same size"));
diff --git a/tensorflow/core/kernels/fifo_queue.cc b/tensorflow/core/kernels/fifo_queue.cc
index 030cf8a49d..ea86b04762 100644
--- a/tensorflow/core/kernels/fifo_queue.cc
+++ b/tensorflow/core/kernels/fifo_queue.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include <deque>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/kernels/iterator_ops.cc b/tensorflow/core/kernels/iterator_ops.cc
index b6825b4e95..f143d39816 100644
--- a/tensorflow/core/kernels/iterator_ops.cc
+++ b/tensorflow/core/kernels/iterator_ops.cc
@@ -168,7 +168,7 @@ class OneShotIteratorOp : public AsyncOpKernel {
         thread_pool_(new thread::ThreadPool(
             ctx->env(), ThreadOptions(),
             strings::StrCat("one_shot_iterator_initialization_thread_",
-                            SanitizeThreadSuffix(def().name())),
+                            SanitizeThreadSuffix(name())),
             1 /* num_threads */, false /* low_latency_hint */))
 
   {
@@ -359,7 +359,7 @@ class IteratorGetNextOp : public AsyncOpKernel {
         thread_pool_(new thread::ThreadPool(
             ctx->env(), ThreadOptions(),
             strings::StrCat("iterator_get_next_thread_",
-                            SanitizeThreadSuffix(def().name())),
+                            SanitizeThreadSuffix(name())),
             1 /* num_threads */, false /* low_latency_hint */)) {}
 
   void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
diff --git a/tensorflow/core/kernels/ops_testutil.h b/tensorflow/core/kernels/ops_testutil.h
index 96de4094fe..2554359d13 100644
--- a/tensorflow/core/kernels/ops_testutil.h
+++ b/tensorflow/core/kernels/ops_testutil.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
diff --git a/tensorflow/core/kernels/padding_fifo_queue.cc b/tensorflow/core/kernels/padding_fifo_queue.cc
index f4626d4a5d..d0f7683f3d 100644
--- a/tensorflow/core/kernels/padding_fifo_queue.cc
+++ b/tensorflow/core/kernels/padding_fifo_queue.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <deque>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/core/kernels/priority_queue.cc b/tensorflow/core/kernels/priority_queue.cc
index 8884c0c4a0..894ad3c9a0 100644
--- a/tensorflow/core/kernels/priority_queue.cc
+++ b/tensorflow/core/kernels/priority_queue.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <queue>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/kernels/quantized_conv_ops.cc b/tensorflow/core/kernels/quantized_conv_ops.cc
index 56a7e161df..5658dcc069 100644
--- a/tensorflow/core/kernels/quantized_conv_ops.cc
+++ b/tensorflow/core/kernels/quantized_conv_ops.cc
@@ -211,7 +211,7 @@ class Im2ColConvFunctor {
         ++warning_count;
         LOG(WARNING)
             << "For kernel '" << context->op_kernel().name() << "' from input '"
-            << context->op_kernel().def().input(0)
+            << context->op_kernel().requested_input(0)
             << "': Zero is not representable in the quantized range used by the"
             << " input. This means QuantizedConv2d has to fall back to a slow"
             << " implementation, since the border of zero values can't be"
diff --git a/tensorflow/core/kernels/queue_base.cc b/tensorflow/core/kernels/queue_base.cc
index 07ff70a875..8a9af39e1f 100644
--- a/tensorflow/core/kernels/queue_base.cc
+++ b/tensorflow/core/kernels/queue_base.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/queue_base.h"
 
 #include <vector>
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/mutex.h"
diff --git a/tensorflow/core/kernels/random_shuffle_queue_op.cc b/tensorflow/core/kernels/random_shuffle_queue_op.cc
index d9efb5fe7d..30bbbd4aed 100644
--- a/tensorflow/core/kernels/random_shuffle_queue_op.cc
+++ b/tensorflow/core/kernels/random_shuffle_queue_op.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <deque>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
diff --git a/tensorflow/core/kernels/reader_ops.cc b/tensorflow/core/kernels/reader_ops.cc
index e2eb40677b..abd16de6a1 100644
--- a/tensorflow/core/kernels/reader_ops.cc
+++ b/tensorflow/core/kernels/reader_ops.cc
@@ -50,8 +50,7 @@ class ReaderVerbAsyncOpKernel : public AsyncOpKernel {
       : AsyncOpKernel(context),
         thread_pool_(new thread::ThreadPool(
             context->env(), ThreadOptions(),
-            strings::StrCat("reader_thread_",
-                            SanitizeThreadSuffix(def().name())),
+            strings::StrCat("reader_thread_", SanitizeThreadSuffix(name())),
             1 /* num_threads */, false /* low_latency_hint */)) {}
 
   void ComputeAsync(OpKernelContext* context, DoneCallback done) override {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
index b24482f2d5..ad95b25cb1 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/remote_fused_graph_execute_utils.h"
 #include "tensorflow/cc/framework/scope.h"
 #include "tensorflow/core/common_runtime/shape_refiner.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
diff --git a/tensorflow/core/kernels/session_ops.cc b/tensorflow/core/kernels/session_ops.cc
index 27ad2fcd87..185c5b248f 100644
--- a/tensorflow/core/kernels/session_ops.cc
+++ b/tensorflow/core/kernels/session_ops.cc
@@ -43,21 +43,21 @@ class GetSessionHandleOp : public OpKernel {
   void Compute(OpKernelContext* ctx) override {
     const Tensor& val = ctx->input(0);
     int64 id = ctx->session_state()->GetNewId();
-    TensorStore::TensorAndKey tk{val, id, def().device()};
-    OP_REQUIRES_OK(ctx, ctx->tensor_store()->AddTensor(def().name(), tk));
+    TensorStore::TensorAndKey tk{val, id, requested_device()};
+    OP_REQUIRES_OK(ctx, ctx->tensor_store()->AddTensor(name(), tk));
 
     Tensor* handle = nullptr;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &handle));
     if (ctx->expected_output_dtype(0) == DT_RESOURCE) {
       ResourceHandle resource_handle = MakeResourceHandle<Tensor>(
           ctx, SessionState::kTensorHandleResourceTypeName,
-          tk.GetHandle(def().name()));
+          tk.GetHandle(name()));
       resource_handle.set_maybe_type_name(
           SessionState::kTensorHandleResourceTypeName);
       handle->scalar<ResourceHandle>()() = resource_handle;
     } else {
       // Legacy behavior in V1.
-      handle->flat<string>().setConstant(tk.GetHandle(def().name()));
+      handle->flat<string>().setConstant(tk.GetHandle(name()));
     }
   }
 
diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc
index f6b6194f0a..e798a7e4a4 100644
--- a/tensorflow/core/kernels/training_ops.cc
+++ b/tensorflow/core/kernels/training_ops.cc
@@ -352,7 +352,7 @@ class ApplyGradientDescentOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -391,7 +391,7 @@ class ApplyGradientDescentOp < SYCLDevice, T > : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha_dev = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha_dev.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -480,7 +480,7 @@ class ApplyDelayCompensatedGradientDescentOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -575,15 +575,15 @@ class ApplyAdadeltaOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, accum_update.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& lr = ctx->input(3);
     const Tensor& rho = ctx->input(4);
@@ -711,15 +711,15 @@ class SparseApplyAdadeltaOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum_grad.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, accum_update.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum_grad.shape()),
         errors::InvalidArgument("var and accum_grad do not have the same shape",
@@ -851,7 +851,7 @@ class ApplyProximalGradientDescentOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -1066,11 +1066,11 @@ class ApplyAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     const Tensor& lr = ctx->input(2);
     OP_REQUIRES(ctx, IsLegacyScalar(lr.shape()),
                 errors::InvalidArgument("lr is not a scalar: ",
@@ -1159,11 +1159,11 @@ class ApplyProximalAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1266,11 +1266,11 @@ class SparseApplyAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1400,11 +1400,11 @@ class SparseApplyProximalAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1575,15 +1575,15 @@ class ApplyAdagradDAOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, gradient_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, gradient_squared_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(gradient_accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1677,15 +1677,15 @@ class SparseApplyAdagradDAOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, gradient_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, gradient_squared_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(gradient_accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1874,15 +1874,15 @@ class ApplyFtrlOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, linear.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& grad = ctx->input(3);
     OP_REQUIRES(
@@ -1988,15 +1988,15 @@ class SparseApplyFtrlOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, linear.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -2196,11 +2196,11 @@ class ApplyMomentumOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     const Tensor& lr = ctx->input(2);
     OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr.shape()),
                 errors::InvalidArgument("lr is not a scalar: ",
@@ -2299,11 +2299,11 @@ class SparseApplyMomentumOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -2419,15 +2419,15 @@ class ApplyAdamOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, m.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, v.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& beta1_power = ctx->input(3);
     const Tensor& beta2_power = ctx->input(4);
@@ -2505,15 +2505,15 @@ class ApplyAdamOp < SYCLDevice, T> : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, m.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, v.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& beta1_power_dev = ctx->input(3);
     const Tensor& beta2_power_dev = ctx->input(4);
@@ -2679,15 +2679,15 @@ class ApplyRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& lr = ctx->input(3);
     const Tensor& rho = ctx->input(4);
@@ -2764,19 +2764,19 @@ class ApplyCenteredRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, mg.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(3)));
+            "Attempting to use uninitialized variables: ", requested_input(3)));
 
     const Tensor& lr = ctx->input(4);
     const Tensor& rho = ctx->input(5);
@@ -2922,15 +2922,15 @@ class SparseApplyRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& lr = ctx->input(3);
     const Tensor& rho = ctx->input(4);
@@ -3054,15 +3054,15 @@ class SparseApplyCenteredRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(3)));
+            "Attempting to use uninitialized variables: ", requested_input(3)));
 
     const Tensor& lr = ctx->input(4);
     const Tensor& rho = ctx->input(5);
diff --git a/tensorflow/core/kernels/warn_about_ints.cc b/tensorflow/core/kernels/warn_about_ints.cc
index fd0a889c99..75ecdf2ae4 100644
--- a/tensorflow/core/kernels/warn_about_ints.cc
+++ b/tensorflow/core/kernels/warn_about_ints.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/kernels/warn_about_ints.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/util/equal_graph_def.cc b/tensorflow/core/util/equal_graph_def.cc
index 2db026da56..45d6a6662a 100644
--- a/tensorflow/core/util/equal_graph_def.cc
+++ b/tensorflow/core/util/equal_graph_def.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <unordered_map>
 #include <unordered_set>
 #include "tensorflow/core/framework/attr_value_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/protobuf.h"
diff --git a/tensorflow/python/framework/cpp_shape_inference.cc b/tensorflow/python/framework/cpp_shape_inference.cc
index d5e58c174b..8ebdbafb85 100644
--- a/tensorflow/python/framework/cpp_shape_inference.cc
+++ b/tensorflow/python/framework/cpp_shape_inference.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/python/framework/cpp_shape_inference.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/python/util/kernel_registry.cc b/tensorflow/python/util/kernel_registry.cc
index d451bbace2..7d47692f6b 100644
--- a/tensorflow/python/util/kernel_registry.cc
+++ b/tensorflow/python/util/kernel_registry.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 #include "tensorflow/python/util/kernel_registry.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
diff --git a/tensorflow/tools/benchmark/benchmark_model.cc b/tensorflow/tools/benchmark/benchmark_model.cc
index dfad11adf0..81892aef96 100644
--- a/tensorflow/tools/benchmark/benchmark_model.cc
+++ b/tensorflow/tools/benchmark/benchmark_model.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/graph/algorithm.h"
diff --git a/tensorflow/tools/graph_transforms/rename_attribute_test.cc b/tensorflow/tools/graph_transforms/rename_attribute_test.cc
index a0a33e9fc0..31619d82ad 100644
--- a/tensorflow/tools/graph_transforms/rename_attribute_test.cc
+++ b/tensorflow/tools/graph_transforms/rename_attribute_test.cc
@@ -43,17 +43,17 @@ class RenameAttributeTest : public ::testing::Test {
     mul_node1->set_op("Mul");
     mul_node1->add_input("add_node2");
     mul_node1->add_input("add_node3");
-    AddNodeAttr<int32>("foo", 23, mul_node1);
-    AddNodeAttr<string>("bar", "something", mul_node1);
+    AddNodeAttr("foo", 23, mul_node1);
+    AddNodeAttr("bar", "something", mul_node1);
 
     NodeDef* add_node2 = graph_def.add_node();
     add_node2->set_name("add_node2");
     add_node2->set_op("Add");
     add_node2->add_input("const_node1");
     add_node2->add_input("const_node2");
-    AddNodeAttr<int32>("foo", 46, add_node2);
-    AddNodeAttr<int32>("bob", 23, add_node2);
-    AddNodeAttr<string>("bar", "something else", add_node2);
+    AddNodeAttr("foo", 46, add_node2);
+    AddNodeAttr("bob", 23, add_node2);
+    AddNodeAttr("bar", "something else", add_node2);
 
     NodeDef* add_node3 = graph_def.add_node();
     add_node3->set_name("add_node3");
-- 
GitLab


From e01611369f29eb18565bc77512884b908fde70ff Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 12:58:51 -0700
Subject: [PATCH 0403/1375] Add a few diagnostic flags to help narrow down
 issues with the LLVM backends.

PiperOrigin-RevId: 159982441
---
 .../xla/legacy_flags/debug_options_flags.cc   | 25 +++++++++++
 .../compiler/xla/service/cpu/ir_emitter.cc    | 13 +++---
 .../xla/service/llvm_ir/alias_analysis.cc     | 45 +++++++++++--------
 tensorflow/compiler/xla/xla.proto             | 12 +++++
 4 files changed, 72 insertions(+), 23 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 01e9d4010a..7f771bf601 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -27,6 +27,9 @@ struct DebugOptionsFlags {
   string xla_generate_hlo_graph;
   string xla_disable_hlo_passes;
   bool xla_enable_fast_math;
+  bool xla_llvm_enable_alias_scope_metadata;
+  bool xla_llvm_enable_noalias_metadata;
+  bool xla_llvm_enable_invariant_load_metadata;
   int32 xla_backend_optimization_level;
   bool xla_embed_ir_in_executable;
   string xla_dump_ir_to;
@@ -53,6 +56,9 @@ void AllocateFlags() {
   flag_values->xla_generate_hlo_graph = "";
   flag_values->xla_disable_hlo_passes = "";
   flag_values->xla_enable_fast_math = true;
+  flag_values->xla_llvm_enable_alias_scope_metadata = true;
+  flag_values->xla_llvm_enable_noalias_metadata = true;
+  flag_values->xla_llvm_enable_invariant_load_metadata = true;
   flag_values->xla_backend_optimization_level = 3;
   flag_values->xla_embed_ir_in_executable = false;
   flag_values->xla_dump_ir_to = "";
@@ -71,6 +77,19 @@ void AllocateFlags() {
            "xla_enable_fast_math", &flag_values->xla_enable_fast_math,
            "Enable unsafe fast-math optimizations in the compiler; "
            "this may produce faster code at the expense of some accuracy."),
+       tensorflow::Flag("xla_llvm_enable_alias_scope_metadata",
+                        &flag_values->xla_llvm_enable_alias_scope_metadata,
+                        "In LLVM-based backends, enable the emission of "
+                        "!alias.scope metadata in the generated IR."),
+       tensorflow::Flag("xla_llvm_enable_noalias_metadata",
+                        &flag_values->xla_llvm_enable_noalias_metadata,
+                        "In LLVM-based backends, enable the emission of "
+                        "!noalias metadata in the generated IR."),
+       tensorflow::Flag("xla_llvm_enable_invariant_load_metadata",
+                        &flag_values->xla_llvm_enable_invariant_load_metadata,
+                        "In LLVM-based backends, enable the emission of "
+                        "!invariant.load metadata in "
+                        "the generated IR."),
        tensorflow::Flag(
            "xla_backend_optimization_level",
            &flag_values->xla_backend_optimization_level,
@@ -140,6 +159,12 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
       flag_values->xla_cpu_multi_thread_eigen);
   options.set_xla_gpu_cuda_data_dir(flag_values->xla_gpu_cuda_data_dir);
   options.set_xla_gpu_ftz(flag_values->xla_gpu_ftz);
+  options.set_xla_llvm_enable_alias_scope_metadata(
+      flag_values->xla_llvm_enable_alias_scope_metadata);
+  options.set_xla_llvm_enable_noalias_metadata(
+      flag_values->xla_llvm_enable_noalias_metadata);
+  options.set_xla_llvm_enable_invariant_load_metadata(
+      flag_values->xla_llvm_enable_invariant_load_metadata);
 
   std::vector<string> extra_options_parts =
       tensorflow::str_util::Split(flag_values->xla_backend_extra_options, ',');
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index c81a368992..86d5b884d4 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -1899,11 +1899,14 @@ llvm::Value* IrEmitter::EmitTempBufferPointer(
       GetTempBuffersArgument(), slice.index(), &ir_builder_);
   llvm::LoadInst* tempbuf_address_base =
       ir_builder_.CreateLoad(tempbuf_address_ptr);
-  //  Loading the address of a buffer is invariant of the point at which the
-  //  load is executed in the program because we never reassign buffers.
-  tempbuf_address_base->setMetadata(
-      llvm::LLVMContext::MD_invariant_load,
-      llvm::MDNode::get(tempbuf_address_base->getContext(), /*MDs=*/{}));
+  if (hlo_module_config_.debug_options()
+          .xla_llvm_enable_invariant_load_metadata()) {
+    //  Loading the address of a buffer is invariant of the point at which the
+    //  load is executed in the program because we never reassign buffers.
+    tempbuf_address_base->setMetadata(
+        llvm::LLVMContext::MD_invariant_load,
+        llvm::MDNode::get(tempbuf_address_base->getContext(), /*MDs=*/{}));
+  }
   llvm_ir::SetTbaaForInstruction(tempbuf_address_base, target_shape,
                                  /*is_pointer_to=*/true);
   AttachAlignmentMetadataForLoad(tempbuf_address_base, allocation.size());
diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
index d451255774..1f6932bcc3 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
@@ -50,28 +50,37 @@ void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo,
     buffer_slice = *slices.begin();
   }
 
-  llvm::MDNode*& alias_scope_md = alias_scope_metadata_[buffer_slice];
-  if (alias_scope_md == nullptr) {
-    alias_scope_md =
-        GetAliasScopeMetadataForBuffer(buffer_slice, GetAliasDomain());
+  if (module_.config().debug_options().xla_llvm_enable_alias_scope_metadata()) {
+    llvm::MDNode*& alias_scope_md = alias_scope_metadata_[buffer_slice];
+    if (alias_scope_md == nullptr) {
+      alias_scope_md =
+          GetAliasScopeMetadataForBuffer(buffer_slice, GetAliasDomain());
+    }
+    array->AddAliasScopeMetadata(alias_scope_md);
   }
-  array->AddAliasScopeMetadata(alias_scope_md);
 
-  llvm::MDNode*& noalias_md = noalias_metadata_[buffer_slice];
-  if (noalias_md == nullptr) {
-    noalias_md = GetNoaliasMetadataForBuffer(buffer_slice, GetAliasDomain(),
-                                             assignment_, hlo);
+  if (module_.config().debug_options().xla_llvm_enable_noalias_metadata()) {
+    llvm::MDNode*& noalias_md = noalias_metadata_[buffer_slice];
+    if (noalias_md == nullptr) {
+      noalias_md = GetNoaliasMetadataForBuffer(buffer_slice, GetAliasDomain(),
+                                               assignment_, hlo);
+    }
+    array->AddNoaliasMetadata(noalias_md);
   }
-  array->AddNoaliasMetadata(noalias_md);
 
-  // Parameters of the entry computation are never stored to, loading from a
-  // parameter pointer should always return the same result within a loop.
-  if (hlo.opcode() == HloOpcode::kParameter) {
-    const std::vector<HloInstruction*>& parameter_instructions =
-        module_.entry_computation()->parameter_instructions();
-    if (std::find(parameter_instructions.begin(), parameter_instructions.end(),
-                  &hlo) != parameter_instructions.end()) {
-      array->AddInvariantLoad(llvm::MDNode::get(*context_, /*MDs=*/{}));
+  if (module_.config()
+          .debug_options()
+          .xla_llvm_enable_invariant_load_metadata()) {
+    // Parameters of the entry computation are never stored to, loading from a
+    // parameter pointer should always return the same result within a loop.
+    if (hlo.opcode() == HloOpcode::kParameter) {
+      const std::vector<HloInstruction*>& parameter_instructions =
+          module_.entry_computation()->parameter_instructions();
+      if (std::find(parameter_instructions.begin(),
+                    parameter_instructions.end(),
+                    &hlo) != parameter_instructions.end()) {
+        array->AddInvariantLoad(llvm::MDNode::get(*context_, /*MDs=*/{}));
+      }
     }
   }
 }
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 6dd7999b67..46dd28c04d 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -64,6 +64,18 @@ message DebugOptions {
   // Enable flush-to-zero semantics in the GPU backend.
   bool xla_gpu_ftz = 10;
 
+  // If true, in LLVM-based backends, emit !alias.scope metadata in
+  // generated IR.
+  bool xla_llvm_enable_alias_scope_metadata = 11;
+
+  // If true, in LLVM-based backends, emit !noalias metadata in the
+  // generated IR.
+  bool xla_llvm_enable_noalias_metadata = 12;
+
+  // If true, in LLVM-based backends, emit !invariant.load metadata in
+  // the generated IR.
+  bool xla_llvm_enable_invariant_load_metadata = 13;
+
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
   map<string, string> xla_backend_extra_options = 500;
-- 
GitLab


From 160ff06ac0cd0551044c7f650a3bb0d6f3d074f5 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Fri, 23 Jun 2017 13:18:53 -0700
Subject: [PATCH 0404/1375] Updated wide-n-deep tutorial code to use core
 version of estimators and feature-columns.

PiperOrigin-RevId: 159984663
---
 .../examples/learn/wide_n_deep_tutorial.py    | 242 +++++++++---------
 1 file changed, 123 insertions(+), 119 deletions(-)

diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
index a0c6df821a..6a3ae50f0b 100644
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ b/tensorflow/examples/learn/wide_n_deep_tutorial.py
@@ -21,21 +21,89 @@ import argparse
 import sys
 import tempfile
 
-from six.moves import urllib
-
 import pandas as pd
+from six.moves import urllib
 import tensorflow as tf
 
 
-COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
-           "marital_status", "occupation", "relationship", "race", "gender",
-           "capital_gain", "capital_loss", "hours_per_week", "native_country",
-           "income_bracket"]
-LABEL_COLUMN = "label"
-CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation",
-                       "relationship", "race", "gender", "native_country"]
-CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss",
-                      "hours_per_week"]
+CSV_COLUMNS = [
+    "age", "workclass", "fnlwgt", "education", "education_num",
+    "marital_status", "occupation", "relationship", "race", "gender",
+    "capital_gain", "capital_loss", "hours_per_week", "native_country",
+    "income_bracket"
+]
+
+gender = tf.feature_column.categorical_column_with_vocabulary_list(
+    "gender", [" Female", " Male"])
+education = tf.feature_column.categorical_column_with_vocabulary_list(
+    "education", [
+        "Bachelors", "HS-grad", "11th", "Masters", "9th",
+        "Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th",
+        "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
+        "Preschool", "12th"
+    ])
+tf.feature_column.categorical_column_with_vocabulary_list(
+    "marital_status", [
+        "Married-civ-spouse", "Divorced", "Married-spouse-absent",
+        "Never-married", "Separated", "Married-AF-spouse", "Widowed"
+    ])
+relationship = tf.feature_column.categorical_column_with_vocabulary_list(
+    "relationship", [
+        "Husband", "Not-in-family", "Wife", "Own-child", "Unmarried",
+        "Other-relative"
+    ])
+workclass = tf.feature_column.categorical_column_with_vocabulary_list(
+    "workclass", [
+        "Self-emp-not-inc", "Private", "State-gov", "Federal-gov",
+        "Local-gov", "?", "Self-emp-inc", "Without-pay", " Never-worked"
+    ])
+
+# To show an example of hashing:
+occupation = tf.feature_column.categorical_column_with_hash_bucket(
+    "occupation", hash_bucket_size=1000)
+native_country = tf.feature_column.categorical_column_with_hash_bucket(
+    "native_country", hash_bucket_size=1000)
+
+# Continuous base columns.
+age = tf.feature_column.numeric_column("age")
+education_num = tf.feature_column.numeric_column("education_num")
+capital_gain = tf.feature_column.numeric_column("capital_gain")
+capital_loss = tf.feature_column.numeric_column("capital_loss")
+hours_per_week = tf.feature_column.numeric_column("hours_per_week")
+
+# Transformations.
+age_buckets = tf.feature_column.bucketized_column(
+    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
+
+# Wide columns and deep columns.
+base_columns = [
+    gender, native_country, education, occupation, workclass, relationship,
+    age_buckets,
+]
+
+crossed_columns = [
+    tf.feature_column.crossed_column(
+        ["education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        [age_buckets, "education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        ["native_country", "occupation"], hash_bucket_size=1000)
+]
+
+deep_columns = [
+    tf.feature_column.indicator_column(workclass),
+    tf.feature_column.indicator_column(education),
+    tf.feature_column.indicator_column(gender),
+    tf.feature_column.indicator_column(relationship),
+    # To show an example of embedding
+    tf.feature_column.embedding_column(native_country, dimension=8),
+    tf.feature_column.embedding_column(occupation, dimension=8),
+    age,
+    education_num,
+    capital_gain,
+    capital_loss,
+    hours_per_week,
+]
 
 
 def maybe_download(train_data, test_data):
@@ -44,7 +112,9 @@ def maybe_download(train_data, test_data):
     train_file_name = train_data
   else:
     train_file = tempfile.NamedTemporaryFile(delete=False)
-    urllib.request.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name)  # pylint: disable=line-too-long
+    urllib.request.urlretrieve(
+        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
+        train_file.name)  # pylint: disable=line-too-long
     train_file_name = train_file.name
     train_file.close()
     print("Training data is downloaded to %s" % train_file_name)
@@ -53,138 +123,72 @@ def maybe_download(train_data, test_data):
     test_file_name = test_data
   else:
     test_file = tempfile.NamedTemporaryFile(delete=False)
-    urllib.request.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name)  # pylint: disable=line-too-long
+    urllib.request.urlretrieve(
+        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",
+        test_file.name)  # pylint: disable=line-too-long
     test_file_name = test_file.name
     test_file.close()
-    print("Test data is downloaded to %s" % test_file_name)
+    print("Test data is downloaded to %s"% test_file_name)
 
   return train_file_name, test_file_name
 
 
 def build_estimator(model_dir, model_type):
   """Build an estimator."""
-  # Sparse base columns.
-  gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender",
-                                                     keys=["female", "male"])
-  education = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "education", hash_bucket_size=1000)
-  relationship = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "relationship", hash_bucket_size=100)
-  workclass = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "workclass", hash_bucket_size=100)
-  occupation = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "occupation", hash_bucket_size=1000)
-  native_country = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "native_country", hash_bucket_size=1000)
-
-  # Continuous base columns.
-  age = tf.contrib.layers.real_valued_column("age")
-  education_num = tf.contrib.layers.real_valued_column("education_num")
-  capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
-  capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
-  hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")
-
-  # Transformations.
-  age_buckets = tf.contrib.layers.bucketized_column(age,
-                                                    boundaries=[
-                                                        18, 25, 30, 35, 40, 45,
-                                                        50, 55, 60, 65
-                                                    ])
-
-  # Wide columns and deep columns.
-  wide_columns = [gender, native_country, education, occupation, workclass,
-                  relationship, age_buckets,
-                  tf.contrib.layers.crossed_column([education, occupation],
-                                                   hash_bucket_size=int(1e4)),
-                  tf.contrib.layers.crossed_column(
-                      [age_buckets, education, occupation],
-                      hash_bucket_size=int(1e6)),
-                  tf.contrib.layers.crossed_column([native_country, occupation],
-                                                   hash_bucket_size=int(1e4))]
-  deep_columns = [
-      tf.contrib.layers.embedding_column(workclass, dimension=8),
-      tf.contrib.layers.embedding_column(education, dimension=8),
-      tf.contrib.layers.embedding_column(gender, dimension=8),
-      tf.contrib.layers.embedding_column(relationship, dimension=8),
-      tf.contrib.layers.embedding_column(native_country,
-                                         dimension=8),
-      tf.contrib.layers.embedding_column(occupation, dimension=8),
-      age,
-      education_num,
-      capital_gain,
-      capital_loss,
-      hours_per_week,
-  ]
+  # Categorical base columns.
 
   if model_type == "wide":
-    m = tf.contrib.learn.LinearClassifier(model_dir=model_dir,
-                                          feature_columns=wide_columns)
+    m = tf.estimator.LinearClassifier(
+        model_dir=model_dir, feature_columns=base_columns + crossed_columns)
   elif model_type == "deep":
-    m = tf.contrib.learn.DNNClassifier(model_dir=model_dir,
-                                       feature_columns=deep_columns,
-                                       hidden_units=[100, 50])
+    m = tf.estimator.DNNClassifier(
+        model_dir=model_dir,
+        feature_columns=deep_columns,
+        hidden_units=[100, 50])
   else:
-    m = tf.contrib.learn.DNNLinearCombinedClassifier(
+    m = tf.estimator.DNNLinearCombinedClassifier(
         model_dir=model_dir,
-        linear_feature_columns=wide_columns,
+        linear_feature_columns=crossed_columns,
         dnn_feature_columns=deep_columns,
-        dnn_hidden_units=[100, 50],
-        fix_global_step_increment_bug=True)
+        dnn_hidden_units=[100, 50])
   return m
 
 
-def input_fn(df):
+def input_fn(data_file, num_epochs, shuffle):
   """Input builder function."""
-  # Creates a dictionary mapping from each continuous feature column name (k) to
-  # the values of that column stored in a constant Tensor.
-  continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS}
-  # Creates a dictionary mapping from each categorical feature column name (k)
-  # to the values of that column stored in a tf.SparseTensor.
-  categorical_cols = {
-      k: tf.SparseTensor(
-          indices=[[i, 0] for i in range(df[k].size)],
-          values=df[k].values,
-          dense_shape=[df[k].size, 1])
-      for k in CATEGORICAL_COLUMNS}
-  # Merges the two dictionaries into one.
-  feature_cols = dict(continuous_cols)
-  feature_cols.update(categorical_cols)
-  # Converts the label column into a constant Tensor.
-  label = tf.constant(df[LABEL_COLUMN].values)
-  # Returns the feature columns and the label.
-  return feature_cols, label
+  df_data = pd.read_csv(
+      tf.gfile.Open(data_file),
+      names=CSV_COLUMNS,
+      skipinitialspace=True,
+      engine="python",
+      skiprows=1)
+  # remove NaN elements
+  df_data = df_data.dropna(how="any", axis=0)
+  labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int)
+  return tf.estimator.inputs.pandas_input_fn(
+      x=df_data,
+      y=labels,
+      batch_size=100,
+      num_epochs=num_epochs,
+      shuffle=shuffle,
+      num_threads=5)
 
 
 def train_and_eval(model_dir, model_type, train_steps, train_data, test_data):
   """Train and evaluate the model."""
   train_file_name, test_file_name = maybe_download(train_data, test_data)
-  df_train = pd.read_csv(
-      tf.gfile.Open(train_file_name),
-      names=COLUMNS,
-      skipinitialspace=True,
-      engine="python")
-  df_test = pd.read_csv(
-      tf.gfile.Open(test_file_name),
-      names=COLUMNS,
-      skipinitialspace=True,
-      skiprows=1,
-      engine="python")
-
-  # remove NaN elements
-  df_train = df_train.dropna(how='any', axis=0)
-  df_test = df_test.dropna(how='any', axis=0)
-
-  df_train[LABEL_COLUMN] = (
-      df_train["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
-  df_test[LABEL_COLUMN] = (
-      df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
-
   model_dir = tempfile.mkdtemp() if not model_dir else model_dir
-  print("model directory = %s" % model_dir)
 
   m = build_estimator(model_dir, model_type)
-  m.fit(input_fn=lambda: input_fn(df_train), steps=train_steps)
-  results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)
+  # set num_epochs to None to get infinite stream of data.
+  m.train(
+      input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True),
+      steps=train_steps)
+  # set steps to None to run evaluation until all data consumed.
+  results = m.evaluate(
+      input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False),
+      steps=None)
+  print("model directory = %s" % model_dir)
   for key in sorted(results):
     print("%s: %s" % (key, results[key]))
 
@@ -215,7 +219,7 @@ if __name__ == "__main__":
   parser.add_argument(
       "--train_steps",
       type=int,
-      default=200,
+      default=2000,
       help="Number of training steps."
   )
   parser.add_argument(
-- 
GitLab


From 068781de74e4623297943fd31856d1b918c69a4e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 13:23:49 -0700
Subject: [PATCH 0405/1375] Modify ControlFlowContext to also respect
 import_scope in 'values_' and keys of 'external_values_'

PiperOrigin-RevId: 159985290
---
 tensorflow/python/ops/control_flow_ops.py     |  9 ++++---
 .../python/ops/control_flow_ops_test.py       | 25 +++++++++++++++++++
 2 files changed, 31 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 478e0a9472..f2c3dae03c 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1363,18 +1363,20 @@ class ControlFlowContext(object):
       import_scope: Optional `string`. Name scope to add.
     """
     assert isinstance(values_def, control_flow_pb2.ValuesDef)
-    self._values = set(values_def.values)
+    self._values = set(
+        ops.prepend_name_scope(value, import_scope)
+        for value in values_def.values)
     g = ops.get_default_graph()
     self._external_values = {}
     for k, v in values_def.external_values.items():
+      k = ops.prepend_name_scope(k, import_scope)
       self._external_values[k] = g.as_graph_element(
           ops.prepend_name_scope(v, import_scope))
     op_names = set([op.split(":")[0]
                     for op in self._values - set(self._external_values)])
     for op in op_names:
       # pylint: disable=protected-access
-      g.as_graph_element(ops.prepend_name_scope(
-          op, import_scope))._set_control_flow_context(self)
+      g.as_graph_element(op)._set_control_flow_context(self)
       # pylint: enable=protected-access
 
   @property
@@ -1404,6 +1406,7 @@ class ControlFlowContext(object):
         [ops.strip_name_scope(v, export_scope)
          for v in sorted(self._values)])
     for k, v in self._external_values.items():
+      k = ops.strip_name_scope(k, export_scope)
       values_def.external_values[k] = ops.strip_name_scope(
           v.name, export_scope)
     return values_def
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index 4e95783e5a..07c47e05d2 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -416,6 +416,31 @@ class ContextTest(TensorFlowTestCase):
               c.to_proto(),
               control_flow_ops.WhileContext.from_proto(c.to_proto()).to_proto())
 
+  def testControlContextImportScope(self):
+    with self.test_session():
+      constant_op.constant(0, name="a")
+      constant_op.constant(2, name="test_scope/a")
+      b1 = constant_op.constant(1, name="b")
+      b2 = constant_op.constant(3, name="test_scope/b")
+
+      c = control_flow_ops.ControlFlowContext()
+      c._values = ["a", "b"]
+      c._external_values = {"a": b1}
+
+      c_with_scope = control_flow_ops.ControlFlowContext._from_proto(
+          c._to_proto(), import_scope="test_scope")
+
+      # _values and _external_values should be have scope prepended.
+      self.assertEquals(
+          c_with_scope._values, set(["test_scope/a", "test_scope/b"]))
+      self.assertEquals(
+          c_with_scope._external_values, {"test_scope/a": b2})
+
+      # Calling _to_proto() with export_scope should remove "test_scope".
+      compare.ProtoEq(
+          c._to_proto(),
+          c_with_scope._to_proto(export_scope="test_scope"))
+
 
 def _GetNestedShape(nested):
   def _GetShape(tensor):
-- 
GitLab


From 749af0fea513be3016981e6601ac7e94eb588bbb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 13:33:45 -0700
Subject: [PATCH 0406/1375] Add item's graph to partition_graphs in virtual
 cluster's run method. Put node op name in timeline_label instead of
 node_name.

PiperOrigin-RevId: 159986583
---
 tensorflow/core/grappler/clusters/virtual_cluster.cc |  3 ++-
 tensorflow/core/grappler/costs/virtual_scheduler.cc  | 12 ++++++++----
 tensorflow/core/grappler/costs/virtual_scheduler.h   |  6 +++---
 .../core/grappler/costs/virtual_scheduler_test.cc    |  9 ++++-----
 4 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc
index 32d9be9aee..95329f3f14 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster.cc
+++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc
@@ -60,6 +60,7 @@ Status VirtualCluster::Run(const GraphDef& graph,
   if (metadata) {
     metadata->clear_step_stats();
     metadata->clear_cost_graph();
+    metadata->clear_partition_graphs();
   }
 
   Costs node_costs;
@@ -94,7 +95,7 @@ Status VirtualCluster::Run(const GraphDef& graph,
   } while (scheduler.MarkCurrNodeExecuted(node_costs));
 
   if (metadata) {
-    scheduler.Summary(metadata->mutable_step_stats());
+    scheduler.Summary(metadata);
   }
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index f6f91e37ac..7648a3965a 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -620,16 +620,19 @@ Costs VirtualScheduler::Summary() const {
   return critical_path_costs;
 }
 
-Costs VirtualScheduler::Summary(StepStats* stepstats) {
-  if (stepstats != nullptr) {
+Costs VirtualScheduler::Summary(RunMetadata* metadata) {
+  if (metadata != nullptr) {
+    StepStats* stepstats = metadata->mutable_step_stats();
     for (const auto& device : device_) {
+      GraphDef* device_partition_graph =
+          metadata->mutable_partition_graphs()->Add();
       DeviceStepStats* device_stepstats = stepstats->add_dev_stats();
       device_stepstats->set_device(device.first);
       for (const auto& node_def : device.second.nodes_executed) {
         const NodeState& nodestate = node_map_.at(node_def);
         NodeExecStats* node_stats = device_stepstats->add_node_stats();
-        node_stats->set_node_name(node_def->op());
-        node_stats->set_timeline_label(node_def->name());
+        node_stats->set_timeline_label(node_def->op());
+        node_stats->set_node_name(node_def->name());
         node_stats->set_op_start_rel_micros(0);
         node_stats->set_all_start_micros(
             nodestate.time_scheduled.asMicroSeconds().count());
@@ -639,6 +642,7 @@ Costs VirtualScheduler::Summary(StepStats* stepstats) {
         node_stats->set_all_end_rel_micros(
             nodestate.time_finished.asMicroSeconds().count() -
             nodestate.time_scheduled.asMicroSeconds().count());
+        *device_partition_graph->mutable_node()->Add() = *node_def;
       }
     }
   }
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index 8b9eccbd43..d00766d9fa 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -206,9 +206,9 @@ class VirtualScheduler {
 
   // Prints out summary of execution (timing, memory usage, etc.)
   Costs Summary() const;
-  // Like the above, but writes detailed stats to stepstats.
-  // If stepstats is nullptr, then just calls and return Summary().
-  Costs Summary(StepStats* stepstats);
+  // Like the above, but writes detailed stats to RunMetadata.
+  // If metadata is nullptr, then just calls and return Summary().
+  Costs Summary(RunMetadata* metadata);
 
  protected:
   // GetDeviceStates and GetNodeStates are currently for testing purpuse only.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index caf76bbc8f..e959eab54e 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -563,8 +563,9 @@ TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
   CreateGrapplerItemWithMatmulChain();
   InitScheduler();
   auto ops_executed = RunScheduler("");
-  StepStats stepstats;
-  Costs c = scheduler_->Summary(&stepstats);
+  RunMetadata metadata;
+  Costs c = scheduler_->Summary(&metadata);
+  StepStats stepstats = metadata.step_stats();
   EXPECT_EQ(13000000, c.execution_time.asMicroSeconds().count());
 
   // Should only be 1 device!
@@ -574,11 +575,9 @@ TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
   std::map<string, std::pair<int64, int64>> start_end_times;
   for (const auto& device_step_stats : stepstats.dev_stats()) {
     for (const auto& stats : device_step_stats.node_stats()) {
-      // The node name is actually in the timeline_label.
       int64 start = stats.all_start_micros();
       int64 end = start + stats.all_end_rel_micros();
-      start_end_times[stats.timeline_label()] =
-          std::pair<int64, int64>(start, end);
+      start_end_times[stats.node_name()] = std::pair<int64, int64>(start, end);
     }
   }
 
-- 
GitLab


From 0ecae4fb42b4333b98f67461bb15e825816f32ad Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Fri, 23 Jun 2017 14:26:37 -0700
Subject: [PATCH 0407/1375] Use short-proto for logging purposes.

A short proto will be output on a single log line, making it
easier for certain automated tools to handle.

PiperOrigin-RevId: 159994005
---
 tensorflow/core/distributed_runtime/master_session.cc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc
index 2ea5194d9b..e98d9cecfc 100644
--- a/tensorflow/core/distributed_runtime/master_session.cc
+++ b/tensorflow/core/distributed_runtime/master_session.cc
@@ -994,8 +994,7 @@ MasterSession::MasterSession(
           << " #remote " << remote_devs_->size();
 
   LOG(INFO) << "Start master session " << handle_
-            << " with config: " << std::endl
-            << session_opts_.config.DebugString();
+            << " with config: " << session_opts_.config.ShortDebugString();
 }
 
 MasterSession::~MasterSession() {
-- 
GitLab


From dee19ca4dd0510499b7da9ebb97c92910638b4f2 Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Fri, 23 Jun 2017 14:37:45 -0700
Subject: [PATCH 0408/1375] Sinh, ArcSinh, Cosh, LogCosh functions added to
 distributions/python/ops/trig. Care is taken to ensure a fair bit of
 stability.

PiperOrigin-RevId: 159995514
---
 tensorflow/contrib/distributions/BUILD        |  17 ++
 .../python/kernel_tests/trig_test.py          | 256 ++++++++++++++++++
 .../contrib/distributions/python/ops/trig.py  | 173 ++++++++++++
 .../kernel_tests/distributions/util_test.py   |  12 +
 4 files changed, 458 insertions(+)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/trig.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index ca2b256f6a..218308f20a 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -625,6 +625,23 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "trig_test",
+    size = "small",
+    srcs = ["python/kernel_tests/trig_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py b/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
new file mode 100644
index 0000000000..0d40f477ca
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
@@ -0,0 +1,256 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Trigonometric functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.distributions.python.ops import trig
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.platform import test
+
+
+class SinhTest(test.TestCase):
+
+  def test_versus_numpy_scalar_positive_x(self):
+    x = 1.23
+    with self.test_session():
+      self.assertAllClose(np.sinh(x), trig.sinh(x).eval())
+
+  def test_versus_numpy_multidim_x(self):
+    x = [[0., -1.], [0.5, -2.]]
+    with self.test_session():
+      self.assertAllClose(np.sinh(x), trig.sinh(x).eval())
+
+
+class ArcSinhTest(test.TestCase):
+  """Test arcsinh.
+
+  Note that many tests below were run over a range of values using np.logspace.
+  The accuracy was highly dependent on the exact values used within that range,
+  and whether our approximation matched with numpy.  For that reason, we used
+  1000 values over every range, which ensures we hit most of the "tricky" values
+  """
+
+  def _assert_all_finite(self, values):
+    self.assertAllEqual(np.ones_like(values).astype(np.bool),
+                        np.isfinite(values))
+
+  def test_versus_numpy_scalar_positive_x(self):
+    x = 1.23
+    with self.test_session():
+      self.assertAllClose(np.arcsinh(x), trig.arcsinh(x).eval())
+
+  def test_versus_numpy_at_zero(self):
+    # Zero is especially difficult.
+    with self.test_session() as sess:
+      x = constant_op.constant(0.)
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(np.arcsinh(x_), y_)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_multidim_x(self):
+    with self.test_session() as sess:
+      x = constant_op.constant([[0., -1.], [0.5, -2.]])
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(np.arcsinh(x_), y_)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_positive_values_32_bit(self):
+    with self.test_session() as sess:
+      # Larger than 38 is Inf in float32.
+      x = constant_op.constant(np.logspace(0, 38, num=2000).astype(np.float32))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),  # numpy does this in 64bit
+          y_,
+          rtol=1e-6)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_moderate_negative_values_32_bit(self):
+    # The moderate negative values were the most difficult to get close to
+    # numpy.
+    with self.test_session() as sess:
+      x = constant_op.constant(-np.logspace(0, 10, num=1000).astype(np.float32))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),  # numpy does this in 64bit
+          y_,
+          rtol=1e-4)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_extreme_negative_values_32_bit(self):
+    # For these extreme values arcsinh uses the approximation 1 / (2 * x), and
+    # 1 / 10^38 = 0 in 32bit...so stop at 10^37.
+    with self.test_session() as sess:
+      x = constant_op.constant(
+          -np.logspace(10, 37, num=1000).astype(np.float32))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),  # numpy does this in 64bit
+          y_,
+          rtol=1e-6)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_positive_values_64_bit(self):
+    with self.test_session() as sess:
+      x = constant_op.constant(np.logspace(0, 200, num=1000))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),
+          y_,
+          rtol=1e-6)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_negative_values_64_bit(self):
+    with self.test_session() as sess:
+      x = constant_op.constant(-np.logspace(0, 200, num=1000))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),
+          y_,
+          rtol=1e-5)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_arcsinh_is_inverse_to_sinh_near_zero(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    with self.test_session() as sess:
+      x = np.linspace(-1.1, 1.1, num=1000).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self.assertAllClose(x, sinh_arcsinh_x_)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+  def test_arcsinh_is_inverse_to_sinh_where_x_is_very_small(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    # Exact same cutoff as is in the code.
+    very_small_cutoff = -0.01 / np.sqrt(np.finfo(np.float32).eps)
+    with self.test_session() as sess:
+      x = -np.logspace(
+          np.log(-very_small_cutoff),
+          np.log(-1000 * very_small_cutoff),
+          num=1000).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+  def test_arcsinh_is_inverse_to_sinh_where_x_is_moderate_or_big(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    very_big_cutoff = np.sqrt(np.finfo(np.float32).max)
+    with self.test_session() as sess:
+      x = np.linspace(1., very_big_cutoff, num=1000).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+  def test_arcsinh_is_inverse_to_sinh_where_x_is_very_big(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    very_big_cutoff = np.sqrt(np.finfo(np.float32).max)
+    with self.test_session() as sess:
+      x = np.logspace(
+          np.log(very_big_cutoff),
+          5 + np.log(very_big_cutoff), num=1000).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+
+class CoshTest(test.TestCase):
+
+  def test_versus_numpy_scalar_positive_x(self):
+    x = 1.23
+    with self.test_session():
+      self.assertAllClose(np.cosh(x), trig.cosh(x).eval())
+
+  def test_versus_numpy_multidim_x(self):
+    x = [[0., -1.], [0.5, -2.]]
+    with self.test_session():
+      self.assertAllClose(np.cosh(x), trig.cosh(x).eval())
+
+
+class LogCoshTest(test.TestCase):
+
+  def _assert_all_finite(self, values):
+    self.assertAllEqual(np.ones_like(values).astype(np.bool),
+                        np.isfinite(values))
+
+  def test_versus_numpy_scalar_32bit(self):
+    with self.test_session() as sess:
+      x_64 = np.linspace(-200, 200, 2000)
+      x = constant_op.constant(x_64.astype(np.float32))
+
+      y = trig.log_cosh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      y_, grad_ = sess.run([y, grad])
+
+      self.assertAllClose(np.log(np.cosh(x_64)), y_)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/trig.py b/tensorflow/contrib/distributions/python/ops/trig.py
new file mode 100644
index 0000000000..e49288e5b1
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/trig.py
@@ -0,0 +1,173 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Trigonometric functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import math_ops
+
+__all__ = [
+    "sinh",
+    "arcsinh",
+    "cosh",
+    "log_cosh",
+]
+
+
+def sinh(x, name="sinh"):
+  """Hyperbolic sin:  `sinh(x) = (e**x - e**-x) / 2`.
+
+  For `x in (-inf, inf)`, `arcsinh(sinh(x)) = sinh(arcsinh(x)) = x.`
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  with ops.name_scope(name):
+    # For x near zero, this could be replaced with the identity x --> x as a
+    # Taylor approximation.  The Taylor approximation would be more accurate,
+    # and the current implementation will equal zero "too often", specifically
+    # for |x| small enough so that exp(x) = 1 (up to floating point precision).
+    # Although less accurate, the present implementation is still safe for
+    # antipated use.  Why?  No one would use this function and expect that zero
+    # was not a possible output value.
+    x = ops.convert_to_tensor(x, name="x")
+    return 0.5 * (math_ops.exp(x) - math_ops.exp(-x))
+
+
+def arcsinh(x, name="arcsinh"):
+  """Inverse hyperbolic sin:  `arcsinh(x) = log(x + sqrt(x**2 + 1))`.
+
+  For `x in (-inf, inf)`, `arcsinh(sinh(x)) = sinh(arcsinh(x)) = x.`
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  with ops.name_scope(name):
+    x = ops.convert_to_tensor(x, name="x")
+    finfo = np.finfo(x.dtype.as_numpy_dtype)
+
+    # To compute stable arcsinh(x), we will compute various approximations of
+    # z := x + sqrt(x**2 + 1), and then arcsinh(x) = log(z).
+    # Different approximations are used over different values of x, then the
+    # result is pieced together using tf.where.  Since NaN propagate through the
+    # unselected branch during grdients of where, care is taken to ensure that
+    # every approximation is finite for all input values.
+
+    # For x near zero, the straightforward formula for z is fine.
+    # This formula will have trouble once x < 0 and x**2 + 1 = x**2, since then
+    # z = 0 (numerically), and then we have log(z) = log(0) = -inf
+    # This formula also has trouble once x > sqrt(finfo.max), since then
+    # x**2 = inf, and thus z = inf.  Therefore we clip.
+    x_near_zero = clip_ops.clip_by_value(x, -1., 1.)
+    x_is_near_zero = math_ops.abs(x) < 1.
+    z_for_x_near_zero = x + math_ops.sqrt(x_near_zero**2 + 1)
+
+    # Some cutoffs.
+    # Important!  Keep these cutoffs in sync with the tests, which use cutoffs
+    # of the exact same name.
+    # very_big_cutoff**2 = finfo.max, the maximum representable value.
+    # very_small_cutoff could have been defined as 1 / sqrt(eps), so that for
+    # x < very_small_cutoff, 1 / x**2 + 1 = 1, which causes trouble.
+    # The 0.01 was added in order to make this match numpy in 32bit
+    # as much as possible.  Anything < 1 should be stable.
+    very_small_cutoff = -0.01 / np.sqrt(finfo.eps)
+    very_big_cutoff = np.sqrt(finfo.max)
+
+    # For very_small_cutoff < x < -1, and 1 < x < very_big_cutoff, and
+    # x != 0, we can use
+    # z = sqrt(x**2 + 1) = |x| * sqrt(1 + 1 / x**2).
+    # This formula has trouble if x < -sqrt(eps) since then 1 + 1 / x**2 = 1,
+    # and then we get z = x + |x| = 0, thus returning log(0) = -inf.
+    # This formula also has trouble if x**2 = Inf.  Therefore we clip.
+    # This formula also has trouble if x = 0, since then we have 1 / 0**2 = inf.
+    x_not_near_zero = array_ops.where(
+        x >= 0.,
+        math_ops.maximum(x, 1.),
+        math_ops.minimum(x, -1.))
+    x_clipped_moderate_or_big = clip_ops.clip_by_value(
+        x_not_near_zero, very_small_cutoff, very_big_cutoff)
+    z_for_moderate_or_big_x = x + math_ops.abs(x) * math_ops.sqrt(
+        1. + 1. / x_clipped_moderate_or_big**2)
+
+    # For x < very_small_cutoff, we use the first order Taylor series,
+    # sqrt(1 + 1 / x**2) approx 1 + 1 / (2 * x**2)
+    # This formula has trouble for x = 0.
+    x_is_very_small = x < very_small_cutoff
+    z_for_very_small_x = 1 / (2. * math_ops.abs(x_not_near_zero))
+
+    z = array_ops.where(
+        x_is_near_zero,
+        z_for_x_near_zero,
+        array_ops.where(
+            x_is_very_small,
+            z_for_very_small_x,
+            z_for_moderate_or_big_x))
+
+    return math_ops.log(z)
+
+
+def cosh(x, name="cosh"):
+  """Hyperbolic cosine:  `cosh(x) = (e**x + e**-x) / 2`.
+
+  For `x in (-inf, inf)`, `arccosh(cosh(x)) = cosh(arccosh(x)) = x.`
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  with ops.name_scope(name):
+    x = ops.convert_to_tensor(x, name="x")
+    return 0.5 * (math_ops.exp(x) + math_ops.exp(-x))
+
+
+def log_cosh(x, name="log_cosh"):
+  """Logarithm of hyperbolic cosine:  `log_cosh(x) = Log[(e**x + e**-x) / 2]`.
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  # For large |x| >> 1, e**x will become Inf.  So we need to approximate
+  # Log[e**x + e**-x] approx |x|.
+  # We also need to ensure that large |x| is never fed to the exponential func.
+  with ops.name_scope(name):
+    x = ops.convert_to_tensor(x, name="x")
+    large_x_value = 0.9 * np.log(np.finfo(x.dtype.as_numpy_dtype).max)
+    x_capped = clip_ops.clip_by_value(x, -large_x_value, large_x_value)
+    return array_ops.where(
+        math_ops.abs(x) > large_x_value,
+        math_ops.abs(x) - np.log(2).astype(x.dtype.as_numpy_dtype),
+        math_ops.log(cosh(x_capped)))
diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
index fb1960f672..07b1ba71bb 100644
--- a/tensorflow/python/kernel_tests/distributions/util_test.py
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -703,5 +703,17 @@ class SoftplusTest(test.TestCase):
       # Equivalent to `assertAllFalse` (if it existed).
       self.assertAllEqual(np.zeros_like(grads).astype(np.bool), np.isnan(grads))
 
+  def testInverseSoftplusGradientFinite(self):
+    with self.test_session():
+      # This range of x is all finite, and so is 1 / x.  So the
+      # gradient and its approximations should be finite as well.
+      x = constant_op.constant(np.logspace(-4.8, 4.5).astype(np.float16))
+      y = distribution_util.softplus_inverse(x)
+      grads = gradients_impl.gradients(y, x)[0].eval()
+      # Equivalent to `assertAllTrue` (if it existed).
+      self.assertAllEqual(
+          np.ones_like(grads).astype(np.bool), np.isfinite(grads))
+
+
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 90740259edb1ff2a858cac71075a8020cb4a18f8 Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Fri, 23 Jun 2017 22:44:31 +0100
Subject: [PATCH 0409/1375] [Build] Reduces build time for SYCL target (#10996)

* [Build] Removes tensorboard from skip list since it has been removed

* Improve SYCL config for Tensorflow (#98)

Only libtensorflow attempts to link against libComputeCpp.so now,
other targets should only utilise the headers. Added more if_sycl
statements to BUILD files.

* [Build] Fixes typos & linkstatic=1 -> linkstatic=0

* [Build] Cleans SYCL dependency, linkstatic = 0

* [Build] No need to link syclrt tensorflow/BUILD

* sycl_runtime is needed in kernels/BUILD due to inclusion
---
 tensorflow/core/BUILD                     | 7 +++----
 third_party/sycl/crosstool/computecpp.tpl | 6 +++---
 third_party/sycl/sycl/BUILD.tpl           | 2 +-
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 27544c9961..a23c801bd7 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -121,7 +121,6 @@ load(
     "//third_party/mkl:build_defs.bzl",
     "if_mkl",
 )
-load("@local_config_sycl//sycl:build_defs.bzl", "if_sycl")
 
 # -----------------------------------------------------------------------------
 # Public targets
@@ -820,7 +819,7 @@ cc_library(
         ":test",
         "//tensorflow/core/kernels:constant_op",
         "//tensorflow/core/kernels:ops_util",
-        "//tensorflow/core/platform/default/build_config:gtest",  # + if_sycl([":sycl_runtime"])
+        "//tensorflow/core/platform/default/build_config:gtest",
     ],
 )
 
@@ -1850,7 +1849,7 @@ cc_library(
         "common_runtime/sycl/sycl_device_context.h",
     ]),
     copts = tf_copts(),
-    linkstatic = 1,
+    linkstatic = 0,
     deps = [
         ":core_cpu",
         ":core_cpu_internal",
@@ -1862,7 +1861,7 @@ cc_library(
         "//third_party/eigen3",
         "@local_config_sycl//sycl:sycl",
     ],
-    alwayslink = 1,
+    alwayslink = 0,
 )
 
 # -----------------------------------------------------------------------------
diff --git a/third_party/sycl/crosstool/computecpp.tpl b/third_party/sycl/crosstool/computecpp.tpl
index 94c5e6aaad..8b0a7a7d4a 100755
--- a/third_party/sycl/crosstool/computecpp.tpl
+++ b/third_party/sycl/crosstool/computecpp.tpl
@@ -15,7 +15,7 @@ COMPUTECPP_INCLUDE = COMPUTECPP_ROOT + 'include'
 
 def main():
   remove_flags = ('-Wl,--no-undefined', '-Wno-unused-but-set-variable', '-Wignored-attributes')
-    # remove -fsamotoze-coverage from string with g++
+  # remove -fsanitize-coverage from string with g++
   if 'g++' in CPU_CXX_COMPILER:
     remove_flags += ('-fsanitize-coverage',)
   compiler_flags = [flag for flag in sys.argv[1:] if not flag.startswith(remove_flags)]
@@ -43,7 +43,7 @@ def main():
 
   # create a blacklist of folders that will be skipped when compiling with ComputeCpp
   skip_extensions = [".cu.cc"]
-  skip_folders = ["tensorflow/compiler", "tensorflow/docs_src", "tensorflow/tensorboard", "third_party", "external", "hexagon"]
+  skip_folders = ["tensorflow/compiler", "tensorflow/docs_src", "third_party", "external", "hexagon"]
   skip_folders = [(folder + '/') for folder in skip_folders]
   # if compiling external project skip computecpp
   if any(compiled_file_name.endswith(_ext) for _ext in skip_extensions) or any(_folder in output_file_name for _folder in skip_folders):
@@ -79,7 +79,7 @@ def main():
                                       '-Xclang', '-cl-denorms-are-zero', '-Xclang', '-cl-fp32-correctly-rounded-divide-sqrt']
   # disable flags enabling SIMD instructions
   computecpp_device_compiler_flags += [flag for flag in compiler_flags if \
-    not any(x in flag.lower() for x in ('-fsanitize', '=native', '=core2', 'msse', 'vectorize', 'mavx', 'mmmx', 'm3dnow', 'fma'))]
+    not any(x in flag.lower() for x in ('-fsanitize', '-fno-canonical-system-headers', '=native', '=core2', 'msse', 'vectorize', 'mavx', 'mmmx', 'm3dnow', 'fma'))]
 
   x = call([COMPUTECPP_DRIVER] + computecpp_device_compiler_flags)
   if x == 0:
diff --git a/third_party/sycl/sycl/BUILD.tpl b/third_party/sycl/sycl/BUILD.tpl
index c66a9f007d..6cad190630 100755
--- a/third_party/sycl/sycl/BUILD.tpl
+++ b/third_party/sycl/sycl/BUILD.tpl
@@ -33,7 +33,7 @@ cc_library(
         sycl_library_path("ComputeCpp")
     ],
     includes = ["include/"],
-    linkstatic = 1,
+    linkstatic = 0,
 )
 
 cc_library(
-- 
GitLab


From b00dbe39b8571eec41e9da0a83e8ad264ac5386f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 14:44:08 -0700
Subject: [PATCH 0410/1375] Updates some examples in examples/learn.

PiperOrigin-RevId: 159996397
---
 tensorflow/examples/learn/boston.py | 34 +++++++++++++++++++----------
 tensorflow/examples/learn/iris.py   | 31 +++++++++++++++++++-------
 2 files changed, 45 insertions(+), 20 deletions(-)

diff --git a/tensorflow/examples/learn/boston.py b/tensorflow/examples/learn/boston.py
index 7a7024e001..c9ce508dfd 100644
--- a/tensorflow/examples/learn/boston.py
+++ b/tensorflow/examples/learn/boston.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
 from sklearn import model_selection
@@ -39,22 +40,31 @@ def main(unused_argv):
   x_train = scaler.fit_transform(x_train)
 
   # Build 2 layer fully connected DNN with 10, 10 units respectively.
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  regressor = tf.contrib.learn.DNNRegressor(
+  feature_columns = [
+      tf.feature_column.numeric_column('x', shape=np.array(x_train).shape[1:])]
+  regressor = tf.estimator.DNNRegressor(
       feature_columns=feature_columns, hidden_units=[10, 10])
 
-  # Fit
-  regressor.fit(x_train, y_train, steps=5000, batch_size=1)
-  
-  # Transform
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={'x': x_train}, y=y_train, batch_size=1, num_epochs=None, shuffle=True)
+  regressor.train(input_fn=train_input_fn, steps=2000)
+
+  # Predict.
   x_transformed = scaler.transform(x_test)
-  
-  # Predict and score
-  y_predicted = list(regressor.predict(x_transformed, as_iterable=True))
-  score = metrics.mean_squared_error(y_predicted, y_test)
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={'x': x_transformed}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = regressor.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['predictions'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score_sklearn = metrics.mean_squared_error(y_predicted, y_test)
+  print('MSE (sklearn): {0:f}'.format(score_sklearn))
 
-  print('MSE: {0:f}'.format(score))
+  # Score with tensorflow.
+  scores = regressor.evaluate(input_fn=test_input_fn)
+  print('MSE (tensorflow): {0:f}'.format(scores['average_loss']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py
index ec2aa9b573..2ec490b7a2 100644
--- a/tensorflow/examples/learn/iris.py
+++ b/tensorflow/examples/learn/iris.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
 from sklearn import model_selection
@@ -31,16 +32,30 @@ def main(unused_argv):
       iris.data, iris.target, test_size=0.2, random_state=42)
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  classifier = tf.contrib.learn.DNNClassifier(
+  feature_columns = [
+      tf.feature_column.numeric_column('x', shape=np.array(x_train).shape[1:])]
+  classifier = tf.estimator.DNNClassifier(
       feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
 
-  # Fit and predict.
-  classifier.fit(x_train, y_train, steps=200)
-  predictions = list(classifier.predict(x_test, as_iterable=True))
-  score = metrics.accuracy_score(y_test, predictions)
-  print('Accuracy: {0:f}'.format(score))
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={'x': x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=200)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={'x': x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class_ids'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score = metrics.accuracy_score(y_test, y_predicted)
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
-- 
GitLab


From 5e25a9c215d418fb5431a2cd5ac726d0f155f77f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 15:21:02 -0700
Subject: [PATCH 0411/1375] Add kernel tests for boosted_trees.

PiperOrigin-RevId: 160002696
---
 tensorflow/contrib/boosted_trees/BUILD        |  105 ++
 .../ensemble_optimizer_ops_test.py            |  351 ++++
 .../python/kernel_tests/model_ops_test.py     |  325 ++++
 .../kernel_tests/prediction_ops_test.py       | 1335 ++++++++++++++
 .../python/kernel_tests/quantile_ops_test.py  |  341 ++++
 .../kernel_tests/split_handler_ops_test.py    |    1 +
 .../stats_accumulator_ops_test.py             |  389 ++++
 .../python/kernel_tests/training_ops_test.py  | 1590 +++++++++++++++++
 8 files changed, 4437 insertions(+)
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 3a67656a2f..7c8721e8f4 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -67,6 +67,78 @@ py_library(
 
 # Kernel tests
 
+py_test(
+    name = "ensemble_optimizer_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/ensemble_optimizer_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":ensemble_optimizer_ops_py",
+        ":model_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "model_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/model_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":ensemble_optimizer_ops_py",
+        ":model_ops_py",
+        ":prediction_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "prediction_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/prediction_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model_ops_py",
+        ":prediction_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "quantile_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/quantile_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":quantile_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
 py_test(
     name = "split_handler_ops_test",
     size = "small",
@@ -81,6 +153,39 @@ py_test(
     ],
 )
 
+py_test(
+    name = "stats_accumulator_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/stats_accumulator_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":stats_accumulator_ops_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "training_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/training_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model_ops_py",
+        ":training_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+    ],
+)
+
 # Ops
 
 py_library(
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py
new file mode 100644
index 0000000000..842e0caeca
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py
@@ -0,0 +1,351 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow ensemble optimization ops.
+
+The tests cover:
+- Adding a newly built tree to an existing ensemble
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import ensemble_optimizer_ops
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+
+
+def _append_to_leaf(leaf, class_id, weight):
+  """Helper method for building tree leaves.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to, int
+    class_id: class Id for the weight update, int
+    weight: weight contribution value, float
+  """
+  leaf.sparse_vector.index.append(class_id)
+  leaf.sparse_vector.value.append(weight)
+
+
+class EnsembleOptimizerOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Create an ensemble of 2 trees."""
+    super(EnsembleOptimizerOpsTest, self).setUp()
+    self._tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+    # First tree.
+    tree_1 = self._tree_ensemble.trees.add()
+    _append_to_leaf(tree_1.nodes.add().leaf, 0, 0.4)
+    _append_to_leaf(tree_1.nodes.add().leaf, 1, 0.6)
+    # Second tree.
+    tree_2 = self._tree_ensemble.trees.add()
+    _append_to_leaf(tree_2.nodes.add().leaf, 0, 1)
+    _append_to_leaf(tree_2.nodes.add().leaf, 1, 0)
+
+    self._tree_ensemble.tree_weights.append(1.0)
+    self._tree_ensemble.tree_weights.append(1.0)
+
+    meta_1 = self._tree_ensemble.tree_metadata.add()
+    meta_1.num_tree_weight_updates = 2
+    meta_2 = self._tree_ensemble.tree_metadata.add()
+    meta_2.num_tree_weight_updates = 3
+
+    # Ensemble to be added.
+    self._ensemble_to_add = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+    self._tree_to_add = self._ensemble_to_add.trees.add()
+    _append_to_leaf(self._tree_to_add.nodes.add().leaf, 0, 0.3)
+    _append_to_leaf(self._tree_to_add.nodes.add().leaf, 1, 0.7)
+
+  def testWithEmptyEnsemble(self):
+    with self.test_session():
+      # Create an empty ensemble.
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="empty")
+
+      # Create zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=array_ops.zeros([1], dtypes.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=array_ops.zeros([1], dtypes.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [2],
+              feature_gains, [0.4], [[]],
+              learning_rate=1.0)
+      ]):
+        result = model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1]
+
+      # Output.
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output_ensemble.ParseFromString(result.eval())
+      self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[0])
+      self.assertEqual(1, len(output_ensemble.trees))
+
+      self.assertAllEqual([1.0], output_ensemble.tree_weights)
+
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+
+      self.assertAllEqual([2], feature_usage_counts.eval())
+      self.assertArrayNear([0.4], feature_gains.eval(), 1e-6)
+
+  def testWithExistingEnsemble(self):
+    with self.test_session():
+      # Create existing tree ensemble.
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=self._tree_ensemble.SerializeToString(),
+          name="existing")
+      # Create non-zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([0, 4, 1], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.0, 0.3, 0.05], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2, 0],
+              feature_gains, [0.02, 0.1, 0.0], [[], []],
+              learning_rate=1)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # Output.
+      self.assertEqual(3, len(output_ensemble.trees))
+      self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[2])
+
+      self.assertAllEqual([1.0, 1.0, 1.0], output_ensemble.tree_weights)
+
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+      self.assertEqual(3,
+                       output_ensemble.tree_metadata[1].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[2].num_tree_weight_updates)
+      self.assertAllEqual([1, 6, 1], feature_usage_counts.eval())
+      self.assertArrayNear([0.02, 0.4, 0.05], feature_gains.eval(), 1e-6)
+
+  def testWithExistingEnsembleAndDropout(self):
+    with self.test_session():
+      tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 10):
+        tree = tree_ensemble.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble.tree_weights.append(i + 1)
+        meta = tree_ensemble.tree_metadata.add()
+        meta.num_tree_weight_updates = 1
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble.SerializeToString(),
+          name="existing")
+      # Create non-zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([2, 3], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.0, 0.3], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      dropped = [1, 6, 8]
+      dropped_original_weights = [2.0, 7.0, 9.0]
+
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2],
+              feature_gains, [0.5, 0.3], [dropped, dropped_original_weights],
+              learning_rate=0.1)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # Output.
+      self.assertEqual(11, len(output_ensemble.trees))
+      self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[10])
+      self.assertAllClose(4.5, output_ensemble.tree_weights[10])
+
+      self.assertAllClose([1., 1.5, 3., 4., 5., 6., 5.25, 8., 6.75, 10., 4.5],
+                          output_ensemble.tree_weights)
+
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[1].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[2].num_tree_weight_updates)
+
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[3].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[4].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[5].num_tree_weight_updates)
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[6].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[7].num_tree_weight_updates)
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[8].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[9].num_tree_weight_updates)
+      self.assertEqual(
+          1, output_ensemble.tree_metadata[10].num_tree_weight_updates)
+      self.assertAllEqual([3, 5], feature_usage_counts.eval())
+      self.assertArrayNear([0.05, 0.33], feature_gains.eval(), 1e-6)
+
+  def testWithEmptyEnsembleAndShrinkage(self):
+    with self.test_session():
+      # Add shrinkage config.
+      learning_rate = 0.0001
+      tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble.SerializeToString(),
+          name="existing")
+
+      # Create zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([0, 0], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.0, 0.0], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2],
+              feature_gains, [0.5, 0.3], [[], []],
+              learning_rate=learning_rate)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # New tree is added with shrinkage weight.
+      self.assertAllClose([learning_rate], output_ensemble.tree_weights)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+      self.assertAllEqual([1, 2], feature_usage_counts.eval())
+      self.assertArrayNear([0.5 * learning_rate, 0.3 * learning_rate],
+                           feature_gains.eval(), 1e-6)
+
+  def testWithExistingEnsembleAndShrinkage(self):
+    with self.test_session():
+      # Add shrinkage config.
+      learning_rate = 0.0001
+      tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 5):
+        tree = tree_ensemble.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble.tree_weights.append(i + 1)
+        meta = tree_ensemble.tree_metadata.add()
+        meta.num_tree_weight_updates = 1
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble.SerializeToString(),
+          name="existing")
+
+      # Create non-zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([4, 7], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.2, 0.8], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2],
+              feature_gains, [0.5, 0.3], [[], []],
+              learning_rate=learning_rate)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # The weights of previous trees stayed the same, new tree (LAST) is added
+      # with shrinkage weight.
+      self.assertAllClose([1.0, 2.0, 3.0, 4.0, 5.0, learning_rate],
+                          output_ensemble.tree_weights)
+
+      # Check that all number of updates are equal to 1 (e,g, no old tree weight
+      # got adjusted.
+      for i in range(0, 6):
+        self.assertEqual(
+            1, output_ensemble.tree_metadata[i].num_tree_weight_updates)
+
+      # Ensure feature importance was aggregated correctly.
+      self.assertAllEqual([5, 9], feature_usage_counts.eval())
+      self.assertArrayNear(
+          [0.2 + 0.5 * learning_rate, 0.8 + 0.3 * learning_rate],
+          feature_gains.eval(), 1e-6)
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
new file mode 100644
index 0000000000..90cc07f661
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
@@ -0,0 +1,325 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow model ops.
+
+The tests cover:
+- Loading a model from protobufs.
+- Running Predictions using an existing model.
+- Serializing the model.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import ensemble_optimizer_ops
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.ops import prediction_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import saver
+
+
+def _append_to_leaf(leaf, c_id, w):
+  """Helper method for building tree leaves.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to.
+    c_id: class Id for the weight update.
+    w: weight contribution value.
+  """
+  leaf.sparse_vector.index.append(c_id)
+  leaf.sparse_vector.value.append(w)
+
+
+def _set_float_split(split, feat_col, thresh, l_id, r_id):
+  """Helper method for building tree float splits.
+
+  Sets split feature column, threshold and children.
+
+  Args:
+    split: split node to update.
+    feat_col: feature column for the split.
+    thresh: threshold to split on forming rule x <= thresh.
+    l_id: left child Id.
+    r_id: right child Id.
+  """
+  split.feature_column = feat_col
+  split.threshold = thresh
+  split.left_id = l_id
+  split.right_id = r_id
+
+
+class ModelOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up test for model_ops.
+
+    Create a batch of two examples having one dense float, two sparse float and
+    one sparse int features.
+    The data looks like the following:
+    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 |
+    | 0        |  7     |    -3    |          |          |
+    | 1        | -2     |          | 4        |   9,1    |
+    """
+    super(ModelOpsTest, self).setUp()
+    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._sparse_float_indices1 = np.array([[0, 0]])
+    self._sparse_float_values1 = np.array([-3.0])
+    self._sparse_float_shape1 = np.array([2, 1])
+    self._sparse_float_indices2 = np.array([[1, 0]])
+    self._sparse_float_values2 = np.array([4.0])
+    self._sparse_float_shape2 = np.array([2, 1])
+    self._sparse_int_indices1 = np.array([[1, 0], [1, 1]])
+    self._sparse_int_values1 = np.array([9, 1])
+    self._sparse_int_shape1 = np.array([2, 2])
+    self._seed = 123
+
+  def testCreate(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree = tree_ensemble_config.trees.add()
+      _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=3,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="create_tree")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, _, _ = prediction_ops.gradient_trees_prediction(
+          tree_ensemble_handle,
+          self._seed, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1],
+          learner_config=learner_config.SerializeToString(),
+          apply_dropout=False,
+          apply_averaging=False,
+          center_bias=False)
+      self.assertAllClose(result.eval(), [[-0.4], [-0.4]])
+      stamp_token = model_ops.tree_ensemble_stamp_token(tree_ensemble_handle)
+      self.assertEqual(stamp_token.eval(), 3)
+
+  def testSerialization(self):
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph):
+        tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+        # Bias tree only for second class.
+        tree1 = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree1.nodes.add().leaf, 1, -0.2)
+
+        tree_ensemble_config.tree_weights.append(1.0)
+
+        # Depth 2 tree.
+        tree2 = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_weights.append(1.0)
+        _set_float_split(tree2.nodes.add()
+                         .sparse_float_binary_split_default_right.split, 1, 4.0,
+                         1, 2)
+        _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3,
+                         4)
+        _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+        _append_to_leaf(tree2.nodes.add().leaf, 1, 1.2)
+        _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+        tree_ensemble_handle = model_ops.tree_ensemble_variable(
+            stamp_token=7,
+            tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+            name="saver_tree")
+        stamp_token, serialized_config = model_ops.tree_ensemble_serialize(
+            tree_ensemble_handle)
+        resources.initialize_resources(resources.shared_resources()).run()
+        self.assertEqual(stamp_token.eval(), 7)
+        serialized_config = serialized_config.eval()
+
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph):
+        tree_ensemble_handle2 = model_ops.tree_ensemble_variable(
+            stamp_token=9,
+            tree_ensemble_config=serialized_config,
+            name="saver_tree2")
+        resources.initialize_resources(resources.shared_resources()).run()
+
+        # Prepare learner config.
+        learner_config = learner_pb2.LearnerConfig()
+        learner_config.num_classes = 3
+
+        result, _, _ = prediction_ops.gradient_trees_prediction(
+            tree_ensemble_handle2,
+            self._seed, [self._dense_float_tensor], [
+                self._sparse_float_indices1, self._sparse_float_indices2
+            ], [self._sparse_float_values1, self._sparse_float_values2],
+            [self._sparse_float_shape1,
+             self._sparse_float_shape2], [self._sparse_int_indices1],
+            [self._sparse_int_values1], [self._sparse_int_shape1],
+            learner_config=learner_config.SerializeToString(),
+            apply_dropout=False,
+            apply_averaging=False,
+            center_bias=False)
+
+        # Re-serialize tree.
+        stamp_token2, serialized_config2 = model_ops.tree_ensemble_serialize(
+            tree_ensemble_handle2)
+
+        # The first example will get bias class 1 -0.2 from first tree and
+        # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
+        # the second example will get the same bias class 1 -0.2 and leaf 3
+        # payload of class 1 1.2 hence [0.0, 1.0].
+        self.assertEqual(stamp_token2.eval(), 9)
+        self.assertEqual(serialized_config2.eval(), serialized_config)
+        self.assertAllClose(result.eval(), [[0.5, -0.2], [0, 1.0]])
+
+  def testRestore(self):
+    # Calling self.test_session() without a graph specified results in
+    # TensorFlowTestCase caching the session and returning the same one
+    # every time. In this test, we need to create two different sessions
+    # which is why we also create a graph and pass it to self.test_session()
+    # to ensure no caching occurs under the hood.
+    save_path = os.path.join(self.get_temp_dir(), "restore-test")
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph) as sess:
+        tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.1)
+
+        tree_ensemble_config2 = tree_config_pb2.DecisionTreeEnsembleConfig()
+        tree2 = tree_ensemble_config2.trees.add()
+        tree_ensemble_config.tree_weights.append(1.0)
+        _append_to_leaf(tree2.nodes.add().leaf, 0, -1.0)
+
+        tree_ensemble_config3 = tree_config_pb2.DecisionTreeEnsembleConfig()
+        tree3 = tree_ensemble_config3.trees.add()
+        tree_ensemble_config.tree_weights.append(1.0)
+        _append_to_leaf(tree3.nodes.add().leaf, 0, -10.0)
+
+        # Prepare learner config.
+        learner_config = learner_pb2.LearnerConfig()
+        learner_config.num_classes = 2
+
+        tree_ensemble_handle = model_ops.tree_ensemble_variable(
+            stamp_token=3,
+            tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+            name="restore_tree")
+        feature_usage_counts = variables.Variable(
+            initial_value=array_ops.zeros([1], dtypes.int64),
+            name="feature_usage_counts",
+            trainable=False)
+        feature_gains = variables.Variable(
+            initial_value=array_ops.zeros([1], dtypes.float32),
+            name="feature_gains",
+            trainable=False)
+
+        resources.initialize_resources(resources.shared_resources()).run()
+        variables.initialize_all_variables().run()
+        my_saver = saver.Saver()
+
+        with ops.control_dependencies([
+            ensemble_optimizer_ops.add_trees_to_ensemble(
+                tree_ensemble_handle,
+                tree_ensemble_config2.SerializeToString(),
+                feature_usage_counts, [0],
+                feature_gains, [0], [[]],
+                learning_rate=1)
+        ]):
+          result, _, _ = prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False)
+        self.assertAllClose([[-1.1], [-1.1]], result.eval())
+        # Save before adding other trees.
+        val = my_saver.save(sess, save_path)
+        self.assertEqual(save_path, val)
+
+        # Add more trees after saving.
+        with ops.control_dependencies([
+            ensemble_optimizer_ops.add_trees_to_ensemble(
+                tree_ensemble_handle,
+                tree_ensemble_config3.SerializeToString(),
+                feature_usage_counts, [0],
+                feature_gains, [0], [[]],
+                learning_rate=1)
+        ]):
+          result, _, _ = prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False)
+        self.assertAllClose(result.eval(), [[-11.1], [-11.1]])
+
+    # Start a second session.  In that session the parameter nodes
+    # have not been initialized either.
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph) as sess:
+        tree_ensemble_handle = model_ops.tree_ensemble_variable(
+            stamp_token=0, tree_ensemble_config="", name="restore_tree")
+        my_saver = saver.Saver()
+        my_saver.restore(sess, save_path)
+        result, _, _ = prediction_ops.gradient_trees_prediction(
+            tree_ensemble_handle,
+            self._seed, [self._dense_float_tensor], [
+                self._sparse_float_indices1, self._sparse_float_indices2
+            ], [self._sparse_float_values1, self._sparse_float_values2],
+            [self._sparse_float_shape1,
+             self._sparse_float_shape2], [self._sparse_int_indices1],
+            [self._sparse_int_values1], [self._sparse_int_shape1],
+            learner_config=learner_config.SerializeToString(),
+            apply_dropout=False,
+            apply_averaging=False,
+            center_bias=False)
+        # Make sure we only have the first and second tree.
+        # The third tree was added after the save.
+        self.assertAllClose(result.eval(), [[-1.1], [-1.1]])
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
new file mode 100644
index 0000000000..aa665d95d7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
@@ -0,0 +1,1335 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow prediction Ops.
+
+The tests cover tree traversal and additive models for single and
+multi class problems.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.ops import prediction_ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+
+
+def _append_to_leaf(leaf, c_id, w):
+  """Helper method for building tree leaves.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to.
+    c_id: class Id for the weight update.
+    w: weight contribution value.
+  """
+  leaf.sparse_vector.index.append(c_id)
+  leaf.sparse_vector.value.append(w)
+
+
+def _append_multi_values_to_leaf(leaf, c_ids, w):
+  """Helper method for building tree leaves with sparse vector of values.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to.
+    c_ids: list of class ids
+    w: corresponding weight contributions for the classes in c_ids
+  """
+  for i in range(len(c_ids)):
+    leaf.sparse_vector.index.append(c_ids[i])
+    leaf.sparse_vector.value.append(w[i])
+
+
+def _append_multi_values_to_dense_leaf(leaf, w):
+  """Helper method for building tree leaves with dense vector of values.
+
+  Appends weight contributions to a leaf. w is assumed to be for all classes.
+
+  Args:
+    leaf: leaf node to append to.
+    w: corresponding weight contributions for all classes.
+  """
+  for x in w:
+    leaf.vector.value.append(x)
+
+
+def _set_float_split(split, feat_col, thresh, l_id, r_id):
+  """Helper method for building tree float splits.
+
+  Sets split feature column, threshold and children.
+
+  Args:
+    split: split node to update.
+    feat_col: feature column for the split.
+    thresh: threshold to split on forming rule x <= thresh.
+    l_id: left child Id.
+    r_id: right child Id.
+  """
+  split.feature_column = feat_col
+  split.threshold = thresh
+  split.left_id = l_id
+  split.right_id = r_id
+
+
+def _set_categorical_id_split(split, feat_col, feat_id, l_id, r_id):
+  """Helper method for building tree categorical id splits.
+
+  Sets split feature column, feature id and children.
+
+  Args:
+    split: categorical id split node.
+    feat_col: feature column for the split.
+    feat_id: feature id forming rule x == id.
+    l_id: left child Id.
+    r_id: right child Id.
+  """
+  split.feature_column = feat_col
+  split.feature_id = feat_id
+  split.left_id = l_id
+  split.right_id = r_id
+
+
+class PredictionOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up the prediction tests.
+
+    Create a batch of two examples having one dense float, two sparse float and
+    one sparse int features.
+    The data looks like the following:
+    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 |
+    | 0        |  7     |    -3    |          |    9,1   |
+    | 1        | -2     |          | 4        |          |
+    """
+    super(PredictionOpsTest, self).setUp()
+    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._sparse_float_indices1 = np.array([[0, 0]])
+    self._sparse_float_values1 = np.array([-3.0])
+    self._sparse_float_shape1 = np.array([2, 1])
+    self._sparse_float_indices2 = np.array([[1, 0]])
+    self._sparse_float_values2 = np.array([4.0])
+    self._sparse_float_shape2 = np.array([2, 1])
+    self._sparse_int_indices1 = np.array([[0, 0], [0, 1]])
+    self._sparse_int_values1 = np.array([9, 1])
+    self._sparse_int_shape1 = np.array([2, 2])
+    self._seed = 123
+
+  def testEmptyEnsemble(self):
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="empty")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      self.assertAllEqual([[0], [0]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testBiasEnsembleSingleClass(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="bias")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      self.assertAllClose([[-0.4], [-0.4]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testBiasEnsembleMultiClass(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      leaf = tree.nodes.add().leaf
+      _append_to_leaf(leaf, 0, -0.4)
+      _append_to_leaf(leaf, 1, 0.9)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="multiclass")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      self.assertAllClose([[-0.4, 0.9], [-0.4, 0.9]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testFullEnsembleSingleClass(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # The first example will get bias -0.4 from first tree and
+      # leaf 4 payload of -0.9 hence -1.3, the second example will
+      # get the same bias -0.4 and leaf 3 payload (sparse feature missing)
+      # of 1.2 hence 0.8.
+      self.assertAllClose([[-1.3], [0.8]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testExcludeNonFinalTree(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = False
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # All the examples should get only the bias since the second tree is
+      # non-finalized
+      self.assertAllClose([[-0.4], [-0.4]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testIncludeNonFinalTree(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = False
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+      learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # The first example will get bias -0.4 from first tree and
+      # leaf 4 payload of -0.9 hence -1.3, the second example will
+      # get the same bias -0.4 and leaf 3 payload (sparse feature missing)
+      # of 1.2 hence 0.8. Note that the non-finalized tree is included.
+      self.assertAllClose([[-1.3], [0.8]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testMetadataMissing(self):
+    # Sometimes we want to do prediction on trees that are not added to ensemble
+    # (for example in
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      # We are not setting the tree_ensemble_config.tree_metadata in this test.
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # The first example will get bias -0.4 from first tree and
+      # leaf 4 payload of -0.9 hence -1.3, the second example will
+      # get the same bias -0.4 and leaf 3 payload (sparse feature missing)
+      # of 1.2 hence 0.8.
+      self.assertAllClose([[-1.3], [0.8]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  # For TREE_PER_CLASS strategy, predictions size is num_classes-1
+  def testFullEnsembleMultiClassTreePerClassStrategy(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree only for second class.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 1, -0.2)
+
+      # Depth 2 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_right.split, 1, 4.0,
+                       1, 2)
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 1, 1.2)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="ensemble_multi_class")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.TREE_PER_CLASS)
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      # The first example will get bias class 1 -0.2 from first tree and
+      # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
+      # the second example will get the same bias class 1 -0.2 and leaf 3
+      # payload of class 1 1.2 hence [0.0, 1.0].
+      self.assertAllClose([[0.5, -0.2], [0, 1.0]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  # For all non-tree-per class multiclass handling strategies, predictions vec
+  # will have the size of the number of classes.
+  # This test is when leafs have SPARSE weights stored (class id and
+  # contribution).
+  def testFullEnsembleMultiNotClassTreePerClassStrategySparseVector(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree only for second class.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 1, -0.2)
+
+      # Depth 2 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_right.split, 1, 4.0,
+                       1, 2)
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_multi_values_to_leaf(tree2.nodes.add().leaf, [1, 2], [1.2, -0.7])
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="ensemble_multi_class")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.FULL_HESSIAN)
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      # The first example will get bias class 1 -0.2 from first tree and
+      # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
+      # the second example will get the same bias class 1 -0.2 and leaf 3
+      # payload of class 1 1.2 and class 2-0.7 hence [0.0, 1.0, -0.7].
+      self.assertAllClose([[0.5, -0.2, 0.0], [0, 1.0, -0.7]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  # For all non-tree-per class multiclass handling strategies, predictions vec
+  # will have the size of the number of classes.
+  # This test is when leafs have DENSE weights stored (weight for each class)
+  def testFullEnsembleMultiNotClassTreePerClassStrategyDenseVector(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree only for second class.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_multi_values_to_dense_leaf(tree1.nodes.add().leaf, [0, -0.2, -2])
+
+      # Depth 2 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_right.split, 1, 4.0,
+                       1, 2)
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3, 4)
+      _append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [0.5, 0, 0])
+      _append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [0, 1.2, -0.7])
+      _append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [-0.9, 0, 0])
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="ensemble_multi_class")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.FULL_HESSIAN)
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      # The first example will get bias class 1 -0.2  and -2 for class 2 from
+      # first tree and leaf 2 payload (sparse feature missing) of 0.5 hence
+      # 0.5, -0.2], the second example will get the same bias and leaf 3 payload
+      # of class 1 1.2 and class 2-0.7 hence [0.0, 1.0, -2.7].
+      self.assertAllClose([[0.5, -0.2, -2.0], [0, 1.0, -2.7]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def _get_predictions(self,
+                       tree_ensemble_handle,
+                       learner_config,
+                       apply_dropout=False,
+                       apply_averaging=False,
+                       center_bias=False):
+    return prediction_ops.gradient_trees_prediction(
+        tree_ensemble_handle,
+        self._seed, [self._dense_float_tensor], [
+            self._sparse_float_indices1, self._sparse_float_indices2
+        ], [self._sparse_float_values1, self._sparse_float_values2],
+        [self._sparse_float_shape1,
+         self._sparse_float_shape2], [self._sparse_int_indices1],
+        [self._sparse_int_values1], [self._sparse_int_shape1],
+        learner_config=learner_config.SerializeToString(),
+        apply_dropout=apply_dropout,
+        apply_averaging=apply_averaging,
+        center_bias=center_bias)
+
+  def testDropout(self):
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 999):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 0.5
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      # Apply dropout.
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=True,
+          apply_averaging=False,
+          center_bias=False)
+
+      # We expect approx 500 trees were dropped.
+      dropout_info = dropout_info.eval()
+      self.assertIn(dropout_info[0].size, range(400, 601))
+      self.assertEqual(dropout_info[0].size, dropout_info[1].size)
+
+      self.assertEqual(result.eval().size, result_no_dropout.eval().size)
+      for i in range(result.eval().size):
+        self.assertNotEqual(result.eval()[i], result_no_dropout.eval()[i])
+
+      for i in range(dropout_info[0].size):
+        dropped_index = dropout_info[0][i]
+        dropped_weight = dropout_info[1][i]
+        # We constructed the trees so tree number + 1 is the tree weight, so
+        # we can check here the weights for dropped trees.
+        self.assertEqual(dropped_index + 1, dropped_weight)
+
+      # Don't apply dropout.
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=False,
+          apply_averaging=False,
+          center_bias=False)
+
+      # We expect none of the trees were dropped.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+      self.assertAllEqual(result.eval(), result_no_dropout.eval())
+
+  def testDropoutCenterBiasNoGrowingMeta(self):
+    # This is for normal non-batch mode where ensemble does not contain the tree
+    # that is being built currently.
+    num_trees = 10
+    with self.test_session():
+      # Empty tree ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, num_trees):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      # Drop all the trees.
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 1.0
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=True,
+          apply_averaging=False,
+          center_bias=False)
+
+      result_center, result_no_dropout_center, dropout_info_center = (
+          self._get_predictions(
+              tree_ensemble_handle,
+              learner_config=learner_config,
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=True))
+
+      dropout_info = dropout_info.eval()
+      dropout_info_center = dropout_info_center.eval()
+
+      # With centering, the bias tree is not dropped.
+      num_dropped = dropout_info[0].size
+      self.assertEqual(num_dropped, num_trees)
+      num_dropped_center = dropout_info_center[0].size
+      self.assertEqual(num_dropped_center, num_trees - 1)
+
+      result = result.eval()
+      result_center = result_center.eval()
+      for i in range(result.size):
+        self.assertNotEqual(result[i], result_center[i])
+
+      # First dropped tree is a bias tree 0.
+      self.assertEqual(0, dropout_info[0][0])
+      # Last dropped tree is the last tree.
+      self.assertEqual(num_trees - 1, dropout_info[0][num_dropped - 1])
+
+      # First dropped tree is a tree 1.
+      self.assertEqual(1, dropout_info_center[0][0])
+      # Last dropped tree is the last tree.
+      self.assertEqual(num_trees - 1, dropout_info_center[0][num_dropped_center
+                                                             - 1])
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          result_no_dropout_center.eval())
+
+  def testDropoutCenterBiasWithGrowingMeta(self):
+    # This is batch mode where ensemble already contains the tree that we are
+    # building. This tree should never be dropped.
+    num_trees = 10
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, num_trees):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Add growing metadata to indicate batch mode.
+      tree_ensemble_config.growing_metadata.num_trees_attempted = num_trees
+      tree_ensemble_config.growing_metadata.num_layers_attempted = num_trees
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      # Drop all the trees.
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 1.0
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=True,
+          apply_averaging=False,
+          center_bias=False)
+
+      result_center, result_no_dropout_center, dropout_info_center = (
+          self._get_predictions(
+              tree_ensemble_handle,
+              learner_config=learner_config,
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=True))
+
+      dropout_info = dropout_info.eval()
+      dropout_info_center = dropout_info_center.eval()
+
+      # Last tree is never dropped, the bias tree can be dropped.
+      num_dropped = dropout_info[0].size
+      self.assertEqual(num_dropped, num_trees - 1)
+      num_dropped_center = dropout_info_center[0].size
+      self.assertEqual(num_dropped_center, num_trees - 2)
+
+      result = result.eval()
+      result_center = result_center.eval()
+      for i in range(result.size):
+        self.assertNotEqual(result[i], result_center[i])
+
+      # First dropped tree is a bias tree 0.
+      self.assertEqual(0, dropout_info[0][0])
+      # Last dropped tree is not the last tree (not tree num_trees-1).
+      self.assertNotEqual(num_trees - 1, dropout_info[0][num_dropped - 1])
+      # First dropped tree is a tree 1.
+      self.assertEqual(1, dropout_info_center[0][0])
+      # Last dropped tree is not the last tree in ensemble.
+      self.assertNotEqual(num_trees - 1,
+                          dropout_info_center[0][num_dropped_center - 1])
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          result_no_dropout_center.eval())
+
+  def testDropoutSeed(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 999):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 0.5
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="empty")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      _, result_no_dropout_1, dropout_info_1 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=False))
+
+      _, result_no_dropout_2, dropout_info_2 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=False))
+
+      # Different seed.
+      _, result_no_dropout_3, dropout_info_3 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              112314, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=False))
+
+      # First seed with centering bias.
+      _, result_no_dropout_4, dropout_info_4 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=True))
+
+      # The same seed returns the same results.
+      self.assertAllEqual(dropout_info_1.eval(), dropout_info_2.eval())
+      # Different seeds give diff results.
+      self.assertNotEqual(dropout_info_3.eval().shape,
+                          dropout_info_2.eval().shape)
+      # With centering bias and the same seed does not give the same result.
+      self.assertNotEqual(dropout_info_4.eval(), dropout_info_1.eval())
+      # With centering bias has 1 less tree dropped (bias tree is not dropped).
+      self.assertEqual(
+          len(dropout_info_4.eval()[0]) + 1, len(dropout_info_1.eval()[0]))
+
+      # Predictions without dropout are all the same.
+      result, result_no_dropout, _ = prediction_ops.gradient_trees_prediction(
+          tree_ensemble_handle,
+          self._seed, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1],
+          learner_config=learner_config.SerializeToString(),
+          apply_dropout=False,
+          apply_averaging=False,
+          center_bias=False)
+
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_1.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_2.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_3.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_4.eval())
+
+  def testAveragingAllTrees(self):
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      adjusted_tree_ensemble_config = (
+          tree_config_pb2.DecisionTreeEnsembleConfig())
+      # Add 100 trees with some weights.
+      # When averaging is applied, the tree weights will essentially change to
+      # 1, 98/99, 97/99 etc, so lets create the ensemble with such weights.
+      # too
+      total_num = 100
+      for i in range(0, total_num):
+        tree = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+        # This is how the weight will look after averaging
+        copy_tree = adjusted_tree_ensemble_config.trees.add()
+        _append_to_leaf(copy_tree.nodes.add().leaf, 0, -0.4)
+
+        adjusted_tree_ensemble_config.tree_metadata.add().is_finalized = True
+        adjusted_tree_ensemble_config.tree_weights.append(
+            1.0 * (total_num - i) / total_num)
+
+      # Prepare learner config WITH AVERAGING.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+      learner_config.averaging_config.average_last_percent_trees = 1.0
+
+      # No averaging config.
+      learner_config_no_averaging = learner_pb2.LearnerConfig()
+      learner_config_no_averaging.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+
+      # This is how our ensemble will "look" during averaging
+      adjusted_tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=adjusted_tree_ensemble_config.SerializeToString(
+          ),
+          name="adjusted")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Do averaging.
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle, learner_config, apply_averaging=True)
+
+      pattern_result, pattern_result_no_dropout, pattern_dropout_info = (
+          self._get_predictions(
+              adjusted_tree_ensemble_handle,
+              learner_config_no_averaging,
+              apply_averaging=False))
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          pattern_result_no_dropout.eval())
+      self.assertAllEqual(result.eval(), pattern_result.eval())
+      self.assertAllEqual(dropout_info.eval(), pattern_dropout_info.eval())
+
+  def testAveragingSomeTrees(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      adjusted_tree_ensemble_config = (
+          tree_config_pb2.DecisionTreeEnsembleConfig())
+      # Add 1000 trees with some weights.
+      total_num = 100
+      num_averaged = 25
+      j = 0
+      for i in range(0, total_num):
+        tree = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+
+        # This is how the weight will look after averaging - we are adjusting
+        # the weights of the last 25 trees
+        copy_tree = adjusted_tree_ensemble_config.trees.add()
+        _append_to_leaf(copy_tree.nodes.add().leaf, 0, -0.4)
+
+        adjusted_tree_ensemble_config.tree_metadata.add().is_finalized = True
+        if i >= 75:
+          adjusted_tree_ensemble_config.tree_weights.append(
+              1.0 * (num_averaged - j) / num_averaged)
+          j += 1
+        else:
+          adjusted_tree_ensemble_config.tree_weights.append(1.0)
+
+      # Prepare learner config WITH AVERAGING.
+      learner_config_1 = learner_pb2.LearnerConfig()
+      learner_config_1.num_classes = 2
+      learner_config_1.averaging_config.average_last_percent_trees = 0.25
+
+      # This is equivalent.
+      learner_config_2 = learner_pb2.LearnerConfig()
+      learner_config_2.num_classes = 2
+      learner_config_2.averaging_config.average_last_n_trees = 25
+
+      # No averaging config.
+      learner_config_no_averaging = learner_pb2.LearnerConfig()
+      learner_config_no_averaging.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+
+      # This is how our ensemble will "look" during averaging
+      adjusted_tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=adjusted_tree_ensemble_config.SerializeToString(
+          ),
+          name="adjusted")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result_1, result_no_dropout_1, dropout_info_1 = self._get_predictions(
+          tree_ensemble_handle, learner_config_1, apply_averaging=True)
+
+      result_2, result_no_dropout_2, dropout_info_2 = self._get_predictions(
+          tree_ensemble_handle, learner_config_2, apply_averaging=True)
+
+      pattern_result, pattern_result_no_dropout, pattern_dropout_info = (
+          self._get_predictions(
+              adjusted_tree_ensemble_handle,
+              learner_config_no_averaging,
+              apply_averaging=False))
+
+      self.assertAllEqual(result_no_dropout_1.eval(),
+                          pattern_result_no_dropout.eval())
+      self.assertAllEqual(result_no_dropout_2.eval(),
+                          pattern_result_no_dropout.eval())
+
+      self.assertAllEqual(result_1.eval(), pattern_result.eval())
+      self.assertAllEqual(result_2.eval(), pattern_result.eval())
+
+      self.assertAllEqual(dropout_info_1.eval(), pattern_dropout_info.eval())
+      self.assertAllEqual(dropout_info_2.eval(), pattern_dropout_info.eval())
+
+  def testAverageMoreThanNumTreesExist(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      adjusted_tree_ensemble_config = (
+          tree_config_pb2.DecisionTreeEnsembleConfig())
+      # When we say to average over more trees than possible, it is averaging
+      # accross all trees.
+      total_num = 100
+      for i in range(0, total_num):
+        tree = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+        # This is how the weight will look after averaging
+        copy_tree = adjusted_tree_ensemble_config.trees.add()
+        _append_to_leaf(copy_tree.nodes.add().leaf, 0, -0.4)
+
+        adjusted_tree_ensemble_config.tree_metadata.add().is_finalized = True
+        adjusted_tree_ensemble_config.tree_weights.append(
+            1.0 * (total_num - i) / total_num)
+
+      # Prepare learner config WITH AVERAGING.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+      # We have only 100 trees but we ask to average over 250.
+      learner_config.averaging_config.average_last_n_trees = 250
+
+      # No averaging config.
+      learner_config_no_averaging = learner_pb2.LearnerConfig()
+      learner_config_no_averaging.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+
+      # This is how our ensemble will "look" during averaging
+      adjusted_tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=adjusted_tree_ensemble_config.SerializeToString(
+          ),
+          name="adjusted")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle, learner_config, apply_averaging=True)
+
+      pattern_result, pattern_result_no_dropout, pattern_dropout_info = (
+          self._get_predictions(
+              adjusted_tree_ensemble_handle,
+              learner_config_no_averaging,
+              apply_averaging=False))
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          pattern_result_no_dropout.eval())
+      self.assertAllEqual(result.eval(), pattern_result.eval())
+      self.assertAllEqual(dropout_info.eval(), pattern_dropout_info.eval())
+
+
+class PartitionExamplesOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up the prediction tests.
+
+    Create a batch of two examples having one dense float, two sparse float and
+    one sparse int features.
+    The data looks like the following:
+    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 |
+    | 0        |  7     |    -3    |          |    9,1   |
+    | 1        | -2     |          | 4        |          |
+    """
+    super(PartitionExamplesOpsTest, self).setUp()
+    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._sparse_float_indices1 = np.array([[0, 0]])
+    self._sparse_float_values1 = np.array([-3.0])
+    self._sparse_float_shape1 = np.array([2, 1])
+    self._sparse_float_indices2 = np.array([[1, 0]])
+    self._sparse_float_values2 = np.array([4.0])
+    self._sparse_float_shape2 = np.array([2, 1])
+    self._sparse_int_indices1 = np.array([[0, 0], [0, 1]])
+    self._sparse_int_values1 = np.array([9, 1])
+    self._sparse_int_shape1 = np.array([2, 2])
+
+  def testEnsembleEmpty(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result = prediction_ops.gradient_trees_partition_examples(
+          tree_ensemble_handle, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1])
+
+      self.assertAllEqual([0, 0], result.eval())
+
+  def testTreeNonFinalized(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Depth 3 tree.
+      tree1 = tree_ensemble_config.trees.add()
+      _set_float_split(tree1.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree1.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.2)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.3)
+      _set_categorical_id_split(tree1.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.6)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_metadata.add().is_finalized = False
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result = prediction_ops.gradient_trees_partition_examples(
+          tree_ensemble_handle, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1])
+
+      self.assertAllEqual([5, 3], result.eval())
+
+  def testTreeFinalized(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Depth 3 tree.
+      tree1 = tree_ensemble_config.trees.add()
+      _set_float_split(tree1.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree1.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.2)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.3)
+      _set_categorical_id_split(tree1.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.6)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result = prediction_ops.gradient_trees_partition_examples(
+          tree_ensemble_handle, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1])
+
+      self.assertAllEqual([0, 0], result.eval())
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
new file mode 100644
index 0000000000..dc4495fa1f
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
@@ -0,0 +1,341 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for checking quantile related ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import os
+import tempfile
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto.quantiles_pb2 import QuantileConfig
+from tensorflow.contrib.boosted_trees.python.ops import quantile_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import saver
+
+
+class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
+
+  def _gen_config(self, eps, num_quantiles):
+    config = QuantileConfig()
+    config.eps = eps
+    config.num_quantiles = num_quantiles
+    return config.SerializeToString()
+
+  def testBasicQuantileBuckets(self):
+    """Sets up the quantile summary op test as follows.
+
+    Create a batch of 6 examples having a dense and sparse features.
+    The data looks like this
+    | Instance | instance weights | Dense 0  | Sparse 0
+    | 0        |     10           |   1      |
+    | 1        |     1            |   2      |    2
+    | 2        |     1            |   3      |    3
+    | 3        |     1            |   4      |    4
+    | 4        |     1            |   4      |    5
+    | 5        |     1            |   5      |    6
+    """
+
+    dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
+    sparse_indices_0 = np.array(
+        [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=np.int64)
+    sparse_values_0 = np.array([2, 3, 4, 5, 6])
+    sparse_shape_0 = np.array([6, 1])
+    example_weights = np.array([10, 1, 1, 1, 1, 1])
+
+    with self.test_session():
+      config = self._gen_config(0.33, 3)
+      dense_buckets, sparse_buckets = quantile_ops.quantile_buckets(
+          [dense_float_tensor_0], [sparse_indices_0], [sparse_values_0],
+          [sparse_shape_0],
+          example_weights=example_weights,
+          dense_config=[config],
+          sparse_config=[config])
+
+      self.assertAllEqual([1, 3, 5], dense_buckets[0].eval())
+      self.assertAllEqual([2, 4, 6.], sparse_buckets[0].eval())
+
+  def testStreamingQuantileBuckets(self):
+    """Sets up the quantile summary op test as follows.
+
+    Create a batch of 6 examples having a dense and sparse features.
+    The data looks like this
+    | Instance | instance weights | Dense 0
+    | 0        |     10           |   1
+    | 1        |     1            |   2
+    | 2        |     1            |   3
+    | 3        |     1            |   4
+    | 4        |     1            |   4
+    | 5        |     1            |   5
+    """
+    dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
+    example_weights = np.array([10, 1, 1, 1, 1, 1])
+
+    with self.test_session() as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q1")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      are_ready_noflush, _, = (accumulator.get_buckets(stamp_token=0))
+
+      update = accumulator.add_summary(
+          stamp_token=0,
+          column=dense_float_tensor_0,
+          example_weights=example_weights)
+      with ops.control_dependencies([are_ready_noflush, update]):
+        reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_noflush, are_ready_flush = (sess.run(
+          [buckets, are_ready_noflush, are_ready_flush]))
+      self.assertEqual(False, are_ready_noflush)
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([1, 3, 5], buckets)
+
+  def testSaveRestoreBeforeFlush(self):
+    save_dir = os.path.join(self.get_temp_dir(), "save_restore")
+    save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+
+      save = saver.Saver()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      sparse_indices_0 = np.array(
+          [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=np.int64)
+      sparse_values_0 = [2.0, 3.0, 4.0, 5.0, 6.0]
+      sparse_shape_0 = np.array([6, 1])
+      example_weights = np.array([10, 1, 1, 1, 1, 1])
+      update = accumulator.add_summary(
+          stamp_token=0,
+          column=sparse_tensor.SparseTensor(sparse_indices_0, sparse_values_0,
+                                            sparse_shape_0),
+          example_weights=example_weights)
+      update.run()
+      save.save(sess, save_path)
+      reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([2, 4, 6.], buckets)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+      save = saver.Saver()
+
+      # Restore the saved values in the parameter nodes.
+      save.restore(sess, save_path)
+      are_ready_noflush = accumulator.get_buckets(stamp_token=0)[0]
+      with ops.control_dependencies([are_ready_noflush]):
+        reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = accumulator.get_buckets(stamp_token=1)
+      buckets, are_ready_flush, are_ready_noflush = (sess.run(
+          [buckets, are_ready_flush, are_ready_noflush]))
+      self.assertFalse(are_ready_noflush)
+      self.assertTrue(are_ready_flush)
+      self.assertAllEqual([2, 4, 6.], buckets)
+
+  def testSaveRestoreAfterFlush(self):
+    save_dir = os.path.join(self.get_temp_dir(), "save_restore")
+    save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+
+      save = saver.Saver()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      example_weights = np.array([10, 1, 1, 1, 1, 1])
+      dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
+      update = accumulator.add_summary(
+          stamp_token=0,
+          column=dense_float_tensor_0,
+          example_weights=example_weights)
+      update.run()
+      reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([1, 3, 5], buckets)
+      save.save(sess, save_path)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+      save = saver.Saver()
+
+      # Restore the saved values in the parameter nodes.
+      save.restore(sess, save_path)
+      are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([1, 3, 5], buckets)
+
+  def testFixedUniform(self):
+    """Sets up the quantile summary op test as follows.
+
+    Creates array dividing range [0, 1] to 1<<16 elements equally spaced
+    with weight of 1.0.
+    """
+    dense_float_tensor_0 = np.array([(1.0 * i) / math.pow(
+        2.0, 16) for i in range(0, int(math.pow(2, 16)) + 1)])
+    example_weights = np.array([1] * (int(math.pow(2, 16)) + 1))
+    config = self._gen_config(0.1, 10)
+
+    with self.test_session():
+      dense_buckets, _ = quantile_ops.quantile_buckets(
+          [dense_float_tensor_0], [], [], [],
+          example_weights=example_weights,
+          dense_config=[config],
+          sparse_config=[])
+      self.assertAllClose(
+          [0] + [(i + 1.0) / 10 for i in range(0, 10)],
+          dense_buckets[0].eval(),
+          atol=0.1)
+
+  def testFixedNonUniform(self):
+    """Sets up the quantile summary op test as follows.
+
+    Creates array dividing range [0, 1] to 1<<16 elements equally spaced
+    with weight same as the value.
+    """
+    dense_float_tensor_0 = np.array([(1.0 * i) / math.pow(
+        2.0, 16) for i in range(0, int(math.pow(2, 16)) + 1)])
+    example_weights = np.array([(1.0 * i) / math.pow(2.0, 16)
+                                for i in range(0, int(math.pow(2, 16)) + 1)])
+
+    config = self._gen_config(0.1, 10)
+
+    with self.test_session():
+      dense_buckets, _ = quantile_ops.quantile_buckets(
+          [dense_float_tensor_0], [], [], [],
+          example_weights=example_weights,
+          dense_config=[config],
+          sparse_config=[])
+      self.assertAllClose(
+          [0] + [math.sqrt((i + 1.0) / 10) for i in range(0, 10)],
+          dense_buckets[0].eval(),
+          atol=0.1)
+
+
+class QuantilesOpTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up the quantile op tests.
+
+    Create a batch of 4 examples having 2 dense and 3 sparse features.
+    The data looks like this
+    | Instance | Dense 0 | Dense 1 | Sparse 0 | Sparse 1 | Sparse 2
+    | 0        |   -0.1  |  -1     |   -2     |   0.1    |
+    | 1        |    0.4  |  -15    |   5.5    |          |   2
+    | 2        |    3.2  |  18     |   16     |   3      |
+    | 3        |    190  |  1000   |   17.5   |  -3      |   4
+    Quantiles are:
+    Dense 0: (-inf,0.4], (0.4,5], (5, 190]
+    Dense 1: (-inf, -9], (-9,15], (15, 1000)
+    Sparse 0: (-inf, 5], (5,16], (16, 100]
+    Sparse 1: (-inf, 2], (2, 5]
+    Sparse 2: (-inf, 100]
+    """
+    super(QuantilesOpTest, self).setUp()
+    self._dense_float_tensor_0 = np.array([[-0.1], [0.4], [3.2], [190]])
+    self._dense_float_tensor_1 = np.array([[-1], [-15], [18], [1000]])
+
+    # Sparse feature 0
+    self._sparse_indices_0 = np.array([[0, 0], [1, 0], [2, 0], [3, 0]])
+    self._sparse_values_0 = np.array([-2, 5.5, 16, 17.5])
+    self._sparse_shape_0 = np.array([4, 1])
+    # Sprase feature 1
+    self._sparse_indices_1 = np.array([[0, 0], [2, 0], [3, 0]])
+    self._sparse_values_1 = np.array([0.1, 3, -3])
+    self._sparse_shape_1 = np.array([4, 1])
+    # Sprase feature 2
+    self._sparse_indices_2 = np.array([[1, 0], [3, 0]])
+    self._sparse_values_2 = np.array([2, 4])
+    self._sparse_shape_2 = np.array([4, 1])
+    # Quantiles
+    self._dense_thresholds_0 = np.array([0.4, 5, 190])
+    self._dense_thresholds_1 = np.array([-9, 15, 1000])
+
+    self._sparse_thresholds_0 = np.array([5, 16, 100])
+    self._sparse_thresholds_1 = np.array([2, 5])
+    self._sparse_thresholds_2 = np.array([100])
+
+  def testDenseFeaturesOnly(self):
+    with self.test_session():
+      dense_quantiles, _ = quantile_ops.quantiles(
+          [self._dense_float_tensor_0, self._dense_float_tensor_1], [],
+          [self._dense_thresholds_0, self._dense_thresholds_1], [])
+
+      # Dense feature 0
+      self.assertAllEqual([0, 0, 1, 2], dense_quantiles[0].eval())
+      # Dense feature 1
+      self.assertAllEqual([1, 0, 2, 2], dense_quantiles[1].eval())
+
+  def testSparseFeaturesOnly(self):
+    with self.test_session():
+      _, sparse_quantiles = quantile_ops.quantiles(
+          [],
+          [self._sparse_values_0, self._sparse_values_1, self._sparse_values_2],
+          [], [self._sparse_thresholds_0, self._sparse_thresholds_1,
+               self._sparse_thresholds_2])
+
+      # Sparse feature 0
+      self.assertAllEqual([0, 1, 1, 2], sparse_quantiles[0].eval())
+      # Sparse feature 1
+      self.assertAllEqual([0, 1, 0], sparse_quantiles[1].eval())
+      # Sparse feature 2
+      self.assertAllEqual([0, 0], sparse_quantiles[2].eval())
+
+  def testDenseAndSparseFeatures(self):
+    with self.test_session():
+      dense_quantiles, sparse_quantiles = quantile_ops.quantiles(
+          [self._dense_float_tensor_0, self._dense_float_tensor_1],
+          [self._sparse_values_0, self._sparse_values_1, self._sparse_values_2],
+          [self._dense_thresholds_0, self._dense_thresholds_1],
+          [self._sparse_thresholds_0, self._sparse_thresholds_1,
+           self._sparse_thresholds_2])
+
+      # Dense feature 0
+      self.assertAllEqual([0, 0, 1, 2], dense_quantiles[0].eval())
+      # Dense feature 1
+      self.assertAllEqual([1, 0, 2, 2], dense_quantiles[1].eval())
+      # Sparse feature 0
+      self.assertAllEqual([0, 1, 1, 2], sparse_quantiles[0].eval())
+      # Sparse feature 1
+      self.assertAllEqual([0, 1, 0], sparse_quantiles[1].eval())
+      # Sparse feature 2
+      self.assertAllEqual([0, 0], sparse_quantiles[2].eval())
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
index 8fe0d5d968..3ab02db89a 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Tests for the GTFlow split handler Ops."""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py
new file mode 100644
index 0000000000..0022d4ad52
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py
@@ -0,0 +1,389 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for checking stats accumulator related ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import stats_accumulator_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import googletest
+
+
+class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase):
+  """Tests for scalar gradients and hessians accumulator."""
+
+  def testSimpleAcculumator(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+        op2 = accumulator.add(0, [1], [2], [0.1], [0.2])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 2)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)], [0.2, 0.4])
+      self.assertAllClose(result[(2, 3)], [0.3, 0.4])
+
+  def testDropStaleUpdate(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+        op2 = accumulator.add(
+            stamp_token=-1,
+            partition_ids=[1],
+            feature_ids=[2],
+            gradients=[0.1],
+            hessians=[0.2])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 1)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)], [0.1, 0.2])
+      self.assertAllClose(result[(2, 3)], [0.3, 0.4])
+
+  def testSerialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+
+      with ops.control_dependencies([op1]):
+        (stamp_token, num_updates, partition_1, feature_1, grads_1,
+         hessians_1) = accumulator.serialize()
+      # Make sure that the accumulator hasn't changed during serialization.
+      with ops.control_dependencies([stamp_token]):
+        num_updates_2, partition_2, feature_2, grads_2, hessians_2 = (
+            accumulator.flush(stamp_token=0, next_stamp_token=1))
+        (stamp_token, num_updates, partition_1, feature_1, grads_1, hessians_1,
+         num_updates_2, partition_2, feature_2, grads_2, hessians_2) = sess.run(
+             [
+                 stamp_token, num_updates, partition_1, feature_1, grads_1,
+                 hessians_1, num_updates_2, partition_2, feature_2, grads_2,
+                 hessians_2
+             ])
+
+      result_1 = _AccumulatorResultToDict(partition_1, feature_1, grads_1,
+                                          hessians_1)
+      result_2 = _AccumulatorResultToDict(partition_2, feature_2, grads_2,
+                                          hessians_2)
+      self.assertEqual(num_updates, 1)
+      self.assertEqual(num_updates_2, 1)
+      self.assertEqual(len(result_1), 2)
+      self.assertAllClose(result_1[(1, 2)], [0.1, 0.2])
+      self.assertAllClose(result_1[(2, 3)], [0.3, 0.4])
+      self.assertAllEqual(result_1, result_2)
+      self.assertEqual(0, stamp_token)
+
+  def testDeserialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        # These will be deleted due to deserialize call.
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+
+      with ops.control_dependencies([op1]):
+        deserialize = (accumulator.deserialize(
+            stamp_token=2,
+            num_updates=3,
+            partition_ids=[3, 4],
+            feature_ids=[5, 6],
+            gradients=[0.4, 0.5],
+            hessians=[0.6, 0.7]))
+      with ops.control_dependencies([deserialize]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=2, next_stamp_token=3)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads,
+                                        hessians)
+      self.assertEqual(num_updates, 3)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(3, 5)], [0.4, 0.6])
+      self.assertAllClose(result[(4, 6)], [0.5, 0.7])
+
+  def testMakeSummary(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      partition, feature, grads, hessians = accumulator._make_summary(
+          partition_ids=[1, 2, 1],
+          feature_ids=[2, 3, 2],
+          gradients=[0.1, 0.3, 0.1],
+          hessians=[0.2, 0.4, 0.2])
+      partition, feature, grads, hessians = sess.run(
+          [partition, feature, grads, hessians])
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)], [0.2, 0.4])
+      self.assertAllClose(result[(2, 3)], [0.3, 0.4])
+
+
+class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase):
+  """Tests for tensor gradients and hessians accumulator."""
+
+  def testSimpleAcculumator(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+        op2 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1],
+            feature_ids=[2],
+            gradients=[[0.10, 0.11]],
+            hessians=[[[0.011, 0.022], [0.033, 0.044]]])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 2)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)][0], [0.20, 0.21])
+      self.assertAllClose(result[(1, 2)][1], [[0.021, 0.042], [0.063, 0.084]])
+      self.assertAllClose(result[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+  def testDropStaleUpdate(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+        op2 = accumulator.add(
+            stamp_token=-1,
+            partition_ids=[1],
+            feature_ids=[2],
+            gradients=[[0.10, 0.11]],
+            hessians=[[[0.011, 0.022], [0.033, 0.044]]])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 1)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)][0], [0.1, 0.1])
+      self.assertAllClose(result[(1, 2)][1], [[0.01, 0.02], [0.03, 0.04]])
+      self.assertAllClose(result[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+  def testSerialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+
+      with ops.control_dependencies([op1]):
+        (stamp_token, num_updates_1, partition_1, feature_1, grads_1,
+         hessians_1) = accumulator.serialize()
+      # Make sure that the accumulator hasn't changed during serialization.
+      with ops.control_dependencies([stamp_token]):
+        num_updates_2, partition_2, feature_2, grads_2, hessians_2 = (
+            accumulator.flush(stamp_token=0, next_stamp_token=1))
+        (stamp_token, num_updates_1, partition_1, feature_1, grads_1,
+         hessians_1, num_updates_2, partition_2, feature_2, grads_2,
+         hessians_2) = sess.run([
+             stamp_token, num_updates_1, partition_1, feature_1, grads_1,
+             hessians_1, num_updates_2, partition_2, feature_2, grads_2,
+             hessians_2
+         ])
+
+      result_1 = _AccumulatorResultToDict(partition_1, feature_1, grads_1,
+                                          hessians_1)
+      result_2 = _AccumulatorResultToDict(partition_2, feature_2, grads_2,
+                                          hessians_2)
+
+      self.assertEqual(num_updates_1, 1)
+      self.assertEqual(num_updates_2, 1)
+      self.assertEqual(len(result_1), 2)
+      self.assertAllClose(result_1[(1, 2)][0], [0.1, 0.1])
+      self.assertAllClose(result_1[(1, 2)][1], [[0.01, 0.02], [0.03, 0.04]])
+      self.assertAllClose(result_1[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result_1[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+      self.assertAllEqual(result_1[1, 2][0], result_2[1, 2][0])
+      self.assertAllEqual(result_1[1, 2][1], result_2[1, 2][1])
+      self.assertAllEqual(result_1[2, 3][0], result_2[2, 3][0])
+      self.assertAllEqual(result_1[2, 3][1], result_2[2, 3][1])
+
+  def testDeserialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        # These will be deleted due to deserialize call.
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+
+      with ops.control_dependencies([op1]):
+        deserialize = accumulator.deserialize(
+            stamp_token=2,
+            num_updates=3,
+            partition_ids=[3, 4],
+            feature_ids=[4, 5],
+            # Two values for gradients,
+            gradients=[[0.3, 0.3], [0.5, 0.5]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.03, 0.04], [0.05, 0.06]], [[0.07, 0.08], [0.09,
+                                                                    0.10]]])
+      with ops.control_dependencies([deserialize]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=2, next_stamp_token=3)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads,
+                                        hessians)
+      self.assertEqual(num_updates, 3)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(3, 4)][0], [0.3, 0.3])
+      self.assertAllClose(result[(3, 4)][1], [[0.03, 0.04], [0.05, 0.06]])
+      self.assertAllClose(result[(4, 5)][0], [0.5, 0.5])
+      self.assertAllClose(result[(4, 5)][1], [[0.07, 0.08], [0.09, 0.10]])
+
+  def testMakeSummary(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      partition, feature, grads, hessians = accumulator._make_summary(
+          partition_ids=[1, 2, 1],
+          feature_ids=[2, 3, 2],
+          # Two values for gradients,
+          gradients=[[0.1, 0.1], [0.2, 0.2], [0.10, 0.11]],
+          # A 2x2 matrix for each hessian.
+          hessians=[[[0.01, 0.02], [0.03, 0.04]], [[0.05, 0.06], [0.07, 0.08]],
+                    [[0.011, 0.022], [0.033, 0.044]]])
+      partition, feature, grads, hessians = sess.run(
+          [partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)][0], [0.20, 0.21])
+      self.assertAllClose(result[(1, 2)][1], [[0.021, 0.042], [0.063, 0.084]])
+      self.assertAllClose(result[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+
+def _AccumulatorResultToDict(partition, feature, grads, hessians):
+  """Converts the inputs to a dictionary since the ordering changes."""
+  return {(partition[i], feature[i]): (grads[i], hessians[i])
+          for i in range(len(partition))}
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
new file mode 100644
index 0000000000..f0413fee5a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
@@ -0,0 +1,1590 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow training Ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from google.protobuf import text_format
+
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import split_info_pb2
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.ops import training_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+
+
+def _gen_learner_config(num_classes,
+                        l1_reg,
+                        l2_reg,
+                        tree_complexity,
+                        max_depth,
+                        min_node_weight,
+                        pruning_mode,
+                        growing_mode,
+                        dropout_probability=None,
+                        dropout_learning_rate=None,
+                        dropout_prob_of_skipping=None):
+  """Create a serialized learner config with the desired settings."""
+  config = learner_pb2.LearnerConfig()
+  config.num_classes = num_classes
+  config.regularization.l1 = l1_reg
+  config.regularization.l2 = l2_reg
+  config.regularization.tree_complexity = tree_complexity
+  config.constraints.max_tree_depth = max_depth
+  config.constraints.min_node_weight = min_node_weight
+  config.pruning_mode = pruning_mode
+  config.growing_mode = growing_mode
+
+  if dropout_probability is not None:
+    config.learning_rate_tuner.dropout.dropout_probability = dropout_probability
+  if dropout_learning_rate is not None:
+    config.learning_rate_tuner.dropout.learning_rate = dropout_learning_rate
+  if dropout_prob_of_skipping is not None:
+    config.learning_rate_tuner.dropout.dropout_prob_of_skipping = (
+        dropout_prob_of_skipping)
+  return config.SerializeToString()
+
+
+def _gen_dense_split_info(fc, threshold, left_weight, right_weight):
+  split_str = """
+    split_node {
+      dense_float_binary_split {
+        feature_column: %d
+        threshold: %f
+      }
+    }
+    left_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }
+    right_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }""" % (fc, threshold, left_weight, right_weight)
+  split = split_info_pb2.SplitInfo()
+  text_format.Merge(split_str, split)
+  return split.SerializeToString()
+
+
+def _gen_categorical_split_info(fc, feat_id, left_weight, right_weight):
+  split_str = """
+    split_node {
+      categorical_id_binary_split {
+        feature_column: %d
+        feature_id: %d
+      }
+    }
+    left_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }
+    right_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }""" % (fc, feat_id, left_weight, right_weight)
+  split = split_info_pb2.SplitInfo()
+  text_format.Merge(split_str, split)
+  return split.SerializeToString()
+
+
+def _get_bias_update(grads, hess):
+  return array_ops.where(hess > 0, -grads / hess, array_ops.zeros_like(grads))
+
+
+class CenterTreeEnsembleBiasOpTest(test_util.TensorFlowTestCase):
+  """Tests for centering tree ensemble bias."""
+
+  def testCenterBias(self):
+    """Tests bias centering for multiple iterations."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=3,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=4,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          # Dropout does not change anything here.
+          dropout_probability=0.5)
+
+      # Center bias for the initial step.
+      grads = constant_op.constant([0.4, -0.3])
+      hess = constant_op.constant([2.0, 1.0])
+      continue_centering1 = training_ops.center_tree_ensemble_bias(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          delta_updates=_get_bias_update(grads, hess),
+          learner_config=learner_config)
+      continue_centering = session.run(continue_centering1)
+      self.assertEqual(continue_centering, True)
+
+      # Validate ensemble state.
+      # dim 0 update: -0.4/2.0 = -0.2
+      # dim 1 update: +0.3/1.0 = +0.3
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            leaf {
+              vector {
+                value: -0.2
+                value: 0.3
+              }
+            }
+          }
+        }
+        tree_weights: 1.0
+        tree_metadata {
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Center bias for another step.
+      # dim 0 update: -0.06/0.5 = -0.12
+      # dim 1 update: -0.01/0.5 = -0.02
+      grads = constant_op.constant([0.06, 0.01])
+      hess = constant_op.constant([0.5, 0.5])
+      continue_centering2 = training_ops.center_tree_ensemble_bias(
+          tree_ensemble_handle,
+          stamp_token=1,
+          next_stamp_token=2,
+          delta_updates=_get_bias_update(grads, hess),
+          learner_config=learner_config)
+      continue_centering = session.run(continue_centering2)
+      self.assertEqual(continue_centering, True)
+
+      # Validate ensemble state.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=2))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            leaf {
+              vector {
+                value: -0.32
+                value: 0.28
+              }
+            }
+          }
+        }
+        tree_weights: 1.0
+        tree_metadata {
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 2)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Center bias for another step, but this time updates are negligible.
+      grads = constant_op.constant([0.0000001, -0.00003])
+      hess = constant_op.constant([0.5, 0.0])
+      continue_centering3 = training_ops.center_tree_ensemble_bias(
+          tree_ensemble_handle,
+          stamp_token=2,
+          next_stamp_token=3,
+          delta_updates=_get_bias_update(grads, hess),
+          learner_config=learner_config)
+      continue_centering = session.run(continue_centering3)
+      self.assertEqual(continue_centering, False)
+
+      # Validate ensemble stamp.
+      new_stamp, _ = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=3))
+      self.assertEqual(new_stamp, 3)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+
+
+class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase):
+  """Tests for growing tree ensemble from split candidates."""
+
+  def testGrowEmptyEnsemble(self):
+    """Test growing an empty ensemble."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          # Dropout does not change anything here, tree is not finalized.
+          dropout_probability=0.5)
+
+      # Prepare handler inputs.
+      # Note that handlers 1 & 3 have the same gain but different splits.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the simpler split from handler 1 to be chosen.
+      # The grown tree should be finalized as max tree depth is 1.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.52
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowExistingEnsembleTreeNotFinalized(self):
+    """Test growing an existing ensemble with the last tree not finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.61999988556
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.14300012589
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+        }
+        tree_weights: 0.10000000149
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=3,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          # Dropout does not change anything here - tree is not finalized.
+          dropout_probability=0.5)
+
+      # Prepare handler inputs.
+      # Handler 1 only has a candidate for partition 1, handler 2 has candidates
+      # for both partitions and handler 3 only has a candidate for partition 2.
+      handler1_partitions = np.array([1], dtype=np.int32)
+      handler1_gains = np.array([1.4], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.21, -6.0, 1.65)]
+      handler2_partitions = np.array([1, 2], dtype=np.int32)
+      handler2_gains = np.array([0.63, 2.7], dtype=np.float32)
+      handler2_split = [
+          _gen_dense_split_info(0, 0.23, -0.6, 0.24),
+          _gen_categorical_split_info(1, 7, -1.5, 2.3)
+      ]
+      handler3_partitions = np.array([2], dtype=np.int32)
+      handler3_gains = np.array([1.7], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 3, -0.75, 1.93)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split for partition 1 to be chosen from handler 1 and
+      # the split for partition 2 to be chosen from handler 2.
+      # The grown tree should not be finalized as max tree depth is 3 and
+      # it's only grown 2 layers.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.61999988556
+            }
+          }
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.21
+              left_id: 3
+              right_id: 4
+            }
+            node_metadata {
+              gain: 1.4
+            }
+          }
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 1
+              feature_id: 7
+              left_id: 5
+              right_id: 6
+            }
+            node_metadata {
+              gain: 2.7
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -6.0
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 1.65
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -1.5
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 2
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 2)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowExistingEnsembleTreeFinalized(self):
+    """Test growing an existing ensemble with the last tree finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 7
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 1.3
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -0.9
+              }
+            }
+          }
+        }
+        tree_weights: 0.10000000149
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.2,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect a new tree to be added with the split from handler 1.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 7
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 1.3
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -0.9
+              }
+            }
+          }
+        }
+        trees {
+          nodes {
+            dense_float_binary_split {
+              feature_column: 5
+              threshold: 0.52
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_weights: 0.2
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 2
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 2)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 2)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 2)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowEnsemblePrePrune(self):
+    """Test growing an ensemble with pre-pruning."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # All handlers have negative gain.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([-0.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.52, 0.01, 0.0143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([-1.3], dtype=np.float32)
+      handler2_split = [_gen_categorical_split_info(0, 7, 0.013, 0.0143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions, handler2_partitions],
+          gains=[handler1_gains, handler2_gains],
+          splits=[handler1_split, handler2_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the ensemble to be empty.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 0)
+      self.assertEqual(stats.active_tree, 0)
+      self.assertEqual(stats.active_layer, 0)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals("""
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+
+  def testGrowEnsemblePostPruneNone(self):
+    """Test growing an empty ensemble."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # Note that handlers 1 & 3 have the same gain but different splits.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the simpler split from handler 1 to be chosen.
+      # The grown tree should be finalized as max tree depth is 1.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.52
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowEnsemblePostPruneAll(self):
+    """Test growing an ensemble with post-pruning."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=2,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # All handlers have negative gain.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([-1.3], dtype=np.float32)
+      handler1_split = [_gen_categorical_split_info(0, 7, 0.013, 0.0143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([-0.62], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.33, 0.01, 0.0143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions, handler2_partitions],
+          gains=[handler1_gains, handler2_gains],
+          splits=[handler1_split, handler2_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split from handler 2 to be chosen despite the negative gain.
+      # The grown tree should not be finalized as max tree depth is 2 so no
+      # pruning occurs.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      tree_ensemble_config.ParseFromString(serialized)
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.33
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: -0.62
+              original_leaf {
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.01
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.0143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Prepare handler inputs.
+      # All handlers have negative gain.
+      handler1_partitions = np.array([1, 2], dtype=np.int32)
+      handler1_gains = np.array([-0.2, -0.5], dtype=np.float32)
+      handler1_split = [
+          _gen_categorical_split_info(3, 7, 0.07, 0.083),
+          _gen_categorical_split_info(3, 5, 0.041, 0.064)
+      ]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=1,
+          next_stamp_token=2,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions],
+          gains=[handler1_gains],
+          splits=[handler1_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the ensemble to be empty as post-pruning will prune
+      # the entire finalized tree.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=2))
+      tree_ensemble_config.ParseFromString(serialized)
+      self.assertEqual(new_stamp, 2)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 0)
+      self.assertEqual(stats.active_tree, 0)
+      self.assertEqual(stats.active_layer, 0)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals("""
+      growing_metadata {
+        num_trees_attempted: 1
+        num_layers_attempted: 2
+      }
+      """, tree_ensemble_config)
+
+  def testGrowEnsemblePostPrunePartial(self):
+    """Test growing an ensemble with post-pruning."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=2,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # Second handler has positive gain.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([-1.3], dtype=np.float32)
+      handler1_split = [_gen_categorical_split_info(0, 7, 0.013, 0.0143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([-0.2], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.33, 0.01, 0.0143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions, handler2_partitions],
+          gains=[handler1_gains, handler2_gains],
+          splits=[handler1_split, handler2_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split from handler 2 to be chosen despite the negative gain.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.33
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: -0.2
+              original_leaf {
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.01
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.0143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Prepare handler inputs for second layer.
+      # Note that partition 1 gain is negative and partition 2 gain is positive.
+      handler1_partitions = np.array([1, 2], dtype=np.int32)
+      handler1_gains = np.array([-0.2, 0.5], dtype=np.float32)
+      handler1_split = [
+          _gen_categorical_split_info(3, 7, 0.07, 0.083),
+          _gen_categorical_split_info(3, 5, 0.041, 0.064)
+      ]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=1,
+          next_stamp_token=2,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions],
+          gains=[handler1_gains],
+          splits=[handler1_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the negative gain split of partition 1 to be pruned and the
+      # positive gain split of partition 2 to be retained.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=2))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.33
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: -0.2
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.01
+              }
+            }
+          }
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 5
+              left_id: 3
+              right_id: 4
+            }
+            node_metadata {
+              gain: 0.5
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.041
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.064
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 2
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 2)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 2)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowEnsembleTreeLayerByLayer(self):
+    """Test growing an existing ensemble with the last tree not finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+        }
+        tree_weights: 0.10000000149
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=3,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.LAYER_BY_LAYER,
+          # Dropout will have no effect, since the tree will not be fully grown.
+          dropout_probability=1.0)
+
+      # Prepare handler inputs.
+      # Handler 1 only has a candidate for partition 1, handler 2 has candidates
+      # for both partitions and handler 3 only has a candidate for partition 2.
+      handler1_partitions = np.array([1], dtype=np.int32)
+      handler1_gains = np.array([1.4], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.21, -6.0, 1.65)]
+      handler2_partitions = np.array([1, 2], dtype=np.int32)
+      handler2_gains = np.array([0.63, 2.7], dtype=np.float32)
+      handler2_split = [
+          _gen_dense_split_info(0, 0.23, -0.6, 0.24),
+          _gen_categorical_split_info(1, 7, -1.5, 2.3)
+      ]
+      handler3_partitions = np.array([2], dtype=np.int32)
+      handler3_gains = np.array([1.7], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 3, -0.75, 1.93)]
+
+      # Grow tree ensemble layer by layer.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split for partition 1 to be chosen from handler 1 and
+      # the split for partition 2 to be chosen from handler 2.
+      # The grown tree should not be finalized as max tree depth is 3 and
+      # it's only grown 2 layers.
+      # The partition 1 split weights get added to original leaf weight 7.143.
+      # The partition 2 split weights get added to original leaf weight -4.375.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.21
+              left_id: 3
+              right_id: 4
+            }
+            node_metadata {
+              gain: 1.4
+            }
+          }
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 1
+              feature_id: 7
+              left_id: 5
+              right_id: 6
+            }
+            node_metadata {
+              gain: 2.7
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 1.143
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 8.793
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -5.875
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -2.075
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 2
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 2)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowExistingEnsembleTreeFinalizedWithDropout(self):
+    """Test growing an existing ensemble with the last tree finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split and one bias tree.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            leaf {
+              vector {
+                value: -0.32
+                value: 0.28
+              }
+            }
+          }
+        }
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 7
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 1.3
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -0.9
+              }
+            }
+          }
+        }
+        tree_weights: 0.7
+        tree_weights: 1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        tree_metadata {
+          num_tree_weight_updates: 5
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 2
+          num_layers_attempted: 2
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          dropout_probability=1.0)
+
+      # Prepare handler inputs.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect a new tree to be added with the split from handler 1.
+      _, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      tree_ensemble_config.ParseFromString(serialized)
+
+      self.assertEqual(3, len(tree_ensemble_config.trees))
+      # Both trees got 0.5 as weights, bias tree is untouched.
+      self.assertAllClose([0.7, 0.5, 0.5], tree_ensemble_config.tree_weights)
+
+      self.assertEqual(
+          1, tree_ensemble_config.tree_metadata[0].num_tree_weight_updates)
+      self.assertEqual(
+          6, tree_ensemble_config.tree_metadata[1].num_tree_weight_updates)
+      self.assertEqual(
+          2, tree_ensemble_config.tree_metadata[2].num_tree_weight_updates)
+
+
+if __name__ == "__main__":
+  googletest.main()
-- 
GitLab


From e0944c4784d9d6bd43384bcd67bb7fe28f1d11ab Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 23 Jun 2017 15:23:34 -0700
Subject: [PATCH 0412/1375] Avoid doing unecessary work in the OptimizeGraph()
 function whenever possible

PiperOrigin-RevId: 160003173
---
 tensorflow/core/grappler/grappler_item_builder.cc | 4 ++++
 tensorflow/core/grappler/grappler_item_builder.h  | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index c6e3bc626e..0a0e000b52 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -70,6 +70,10 @@ void InitializeTensor(DataType type, Tensor* tensor) {
 // correct optimizations.
 Status OptimizeGraph(const GraphDef& graph_def, GraphDef* output_graph_def,
                      const ItemConfig& cfg) {
+  if (!cfg.apply_optimizations && !cfg.inline_functions) {
+    return Status::OK();
+  }
+
   // Create a session option for a single GPU device.
   SessionOptions options;
 
diff --git a/tensorflow/core/grappler/grappler_item_builder.h b/tensorflow/core/grappler/grappler_item_builder.h
index 7135c83801..d385a1916e 100644
--- a/tensorflow/core/grappler/grappler_item_builder.h
+++ b/tensorflow/core/grappler/grappler_item_builder.h
@@ -32,7 +32,7 @@ struct ItemConfig {
         ignore_colocation(true),
         placeholder_unknown_output_shape_dim(-1),
         apply_optimizations(false),
-        inline_functions(true) {}
+        inline_functions(false) {}
 
   // If true, ignore all user specified node placement.
   bool ignore_user_placement;
-- 
GitLab


From a94e547402f67566b0ddc361203382f76d282d67 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Fri, 23 Jun 2017 15:24:03 -0700
Subject: [PATCH 0413/1375] Use std::shared_ptr instead of core::RefCounted for
 Node::Properties

Also changes Node::Properties to a struct and removes underscores from public member variables. This change should make it easier to work with Properties moving forward as the refcount will be automatically updated.

PiperOrigin-RevId: 160003281
---
 tensorflow/core/graph/graph.cc | 97 +++++++++++++---------------------
 tensorflow/core/graph/graph.h  | 18 ++++---
 2 files changed, 47 insertions(+), 68 deletions(-)

diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 5d60e41d26..e06e479264 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -29,24 +29,19 @@ namespace tensorflow {
 
 const int Graph::kControlSlot = -1;
 
-class NodeProperties : public core::RefCounted {
+struct NodeProperties {
  public:
   NodeProperties(const OpDef* op_def, const NodeDef& node_def,
                  const DataTypeSlice inputs, const DataTypeSlice outputs)
-      : op_def_(op_def),
-        node_def_(node_def),
-        input_types_(inputs.begin(), inputs.end()),
-        output_types_(outputs.begin(), outputs.end()) {}
-
-  const OpDef* op_def_;  // not owned
-  NodeDef node_def_;
-  const DataTypeVector input_types_;
-  const DataTypeVector output_types_;
-
- private:
-  // Destructor invoked when last reference goes away via Unref()
-  ~NodeProperties() override {}
-  TF_DISALLOW_COPY_AND_ASSIGN(NodeProperties);
+      : op_def(op_def),
+        node_def(node_def),
+        input_types(inputs.begin(), inputs.end()),
+        output_types(outputs.begin(), outputs.end()) {}
+
+  const OpDef* op_def;  // not owned
+  NodeDef node_def;
+  const DataTypeVector input_types;
+  const DataTypeVector output_types;
 };
 
 // Node
@@ -114,26 +109,17 @@ Node::Node()
       props_(nullptr),
       assigned_device_name_index_(0) {}
 
-Node::~Node() {
-  if (props_) {
-    props_->Unref();
-  }
-}
-
-void Node::Initialize(int id, int cost_id, NodeProperties* props) {
+void Node::Initialize(int id, int cost_id,
+                      std::shared_ptr<NodeProperties> props) {
   DCHECK_EQ(id_, -1);
   DCHECK(in_edges_.empty());
   DCHECK(out_edges_.empty());
   id_ = id;
   cost_id_ = cost_id;
 
-  // Unref the old, assign the new properties.
-  if (props_) {
-    props_->Unref();
-  }
-  props_ = props;
+  props_ = std::move(props);
   // Initialize the class_ based on the type string
-  class_ = GetNodeClassForOp(props->node_def_.op());
+  class_ = GetNodeClassForOp(props_->node_def.op());
 }
 
 void Node::Clear() {
@@ -142,28 +128,23 @@ void Node::Clear() {
   id_ = -1;
   cost_id_ = -1;
   class_ = NC_UNINITIALIZED;
-
-  if (props_) {
-    props_->Unref();
-    props_ = nullptr;
-  }
-
+  props_.reset();
   assigned_device_name_index_ = 0;
 }
 
-const string& Node::name() const { return props_->node_def_.name(); }
-const string& Node::type_string() const { return props_->node_def_.op(); }
-const NodeDef& Node::def() const { return props_->node_def_; }
-const OpDef& Node::op_def() const { return *props_->op_def_; }
+const string& Node::name() const { return props_->node_def.name(); }
+const string& Node::type_string() const { return props_->node_def.op(); }
+const NodeDef& Node::def() const { return props_->node_def; }
+const OpDef& Node::op_def() const { return *props_->op_def; }
 
-int32 Node::num_inputs() const { return props_->input_types_.size(); }
-DataType Node::input_type(int32 i) const { return props_->input_types_[i]; }
-const DataTypeVector& Node::input_types() const { return props_->input_types_; }
+int32 Node::num_inputs() const { return props_->input_types.size(); }
+DataType Node::input_type(int32 i) const { return props_->input_types[i]; }
+const DataTypeVector& Node::input_types() const { return props_->input_types; }
 
-int32 Node::num_outputs() const { return props_->output_types_.size(); }
-DataType Node::output_type(int32 o) const { return props_->output_types_[o]; }
+int32 Node::num_outputs() const { return props_->output_types.size(); }
+DataType Node::output_type(int32 o) const { return props_->output_types[o]; }
 const DataTypeVector& Node::output_types() const {
-  return props_->output_types_;
+  return props_->output_types;
 }
 
 AttrSlice Node::attrs() const { return AttrSlice(def()); }
@@ -186,23 +167,19 @@ gtl::iterator_range<NeighborIter> Node::in_nodes() const {
 
 void Node::MaybeCopyOnWrite() {
   // NodeProperties may be shared between Nodes. Make a copy if so.
-  if (!props_->RefCountIsOne()) {
-    NodeProperties* new_props =
-        new NodeProperties(props_->op_def_, props_->node_def_,
-                           props_->input_types_, props_->output_types_);
-    props_->Unref();
-    props_ = new_props;
+  if (!props_.unique()) {
+    props_ = std::make_shared<NodeProperties>(*props_);
   }
 }
 
 AttrValue* Node::AddAttrHelper(const string& name) {
   MaybeCopyOnWrite();
-  return &((*props_->node_def_.mutable_attr())[name]);
+  return &((*props_->node_def.mutable_attr())[name]);
 }
 
 void Node::ClearAttr(const string& name) {
   MaybeCopyOnWrite();
-  (*props_->node_def_.mutable_attr()).erase(name);
+  (*props_->node_def.mutable_attr()).erase(name);
 }
 
 Status Node::input_edge(int idx, const Edge** e) const {
@@ -338,16 +315,15 @@ Node* Graph::AddNode(const NodeDef& node_def, Status* status) {
   }
 
   Node* node = AllocateNode(
-      new NodeProperties(op_def, node_def, inputs, outputs), nullptr);
+      std::make_shared<NodeProperties>(op_def, node_def, inputs, outputs),
+      nullptr);
   return node;
 }
 
 Node* Graph::CopyNode(Node* node) {
   DCHECK(!node->IsSource());
   DCHECK(!node->IsSink());
-  NodeProperties* props = node->properties();
-  props->Ref();
-  Node* copy = AllocateNode(props, node);
+  Node* copy = AllocateNode(node->props_, node);
   copy->set_assigned_device_name(node->assigned_device_name());
 
   // Since the OpDef of a function may be owned by the Graph that owns 'node',
@@ -355,9 +331,9 @@ Node* Graph::CopyNode(Node* node) {
   // node properties with the updated OpDef.
   const OpDef* op_def;
   TF_CHECK_OK(ops_.LookUpOpDef(node->type_string(), &op_def));
-  if (op_def != props->op_def_) {
+  if (op_def != node->props_->op_def) {
     copy->MaybeCopyOnWrite();
-    copy->props_->op_def_ = op_def;
+    copy->props_->op_def = op_def;
   }
 
   return copy;
@@ -540,7 +516,8 @@ bool Graph::IsValidNode(Node* node) const {
   return nodes_[id] == node;
 }
 
-Node* Graph::AllocateNode(NodeProperties* props, const Node* cost_node) {
+Node* Graph::AllocateNode(std::shared_ptr<NodeProperties> props,
+                          const Node* cost_node) {
   Node* node = nullptr;
   if (free_nodes_.empty()) {
     node = new (arena_.Alloc(sizeof(Node))) Node;  // placement new
@@ -551,7 +528,7 @@ Node* Graph::AllocateNode(NodeProperties* props, const Node* cost_node) {
   node->graph_ = this;
   const int id = nodes_.size();
   int cost_id = cost_node ? cost_node->cost_id() : id;
-  node->Initialize(id, cost_id, props);
+  node->Initialize(id, cost_id, std::move(props));
   nodes_.push_back(node);
   ++num_nodes_;
   return node;
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 08e2838d3c..e19e0b727d 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -181,14 +181,10 @@ class Node {
  private:
   friend class Graph;
   Node();
-  ~Node();
 
-  NodeProperties* properties() const { return props_; }
+  NodeProperties* properties() const { return props_.get(); }
 
-  // Initialize() adopts a reference to props, and so is suitable if props was
-  // just allocated or you call props->Ref() to increment the reference
-  // count for a props being held by another Node.
-  void Initialize(int id, int cost_id, NodeProperties* props);
+  void Initialize(int id, int cost_id, std::shared_ptr<NodeProperties> props);
 
   // Releases memory from props_, in addition to restoring *this to its
   // uninitialized state.
@@ -238,7 +234,10 @@ class Node {
   EdgeSet in_edges_;
   EdgeSet out_edges_;
 
-  NodeProperties* props_;
+  // NOTE(skyewm): inheriting from core::RefCounted may have a slight
+  // performance benefit over using shared_ptr, at the cost of manual ref
+  // counting
+  std::shared_ptr<NodeProperties> props_;
 
   // Index within Graph::device_names_ of the name of device assigned
   // to perform this computation.
@@ -505,7 +504,10 @@ class Graph {
   // If cost_node is non-null, then cost accounting (in CostModel)
   // will be associated with that node rather than the new one being
   // created.
-  Node* AllocateNode(NodeProperties* props, const Node* cost_node);
+  //
+  // Ownership of the returned Node is not transferred to caller.
+  Node* AllocateNode(std::shared_ptr<NodeProperties> props,
+                     const Node* cost_node);
   void ReleaseNode(Node* node);
 
   // Registry of all known ops, including functions.
-- 
GitLab


From a223edf9026f00f8b1c994b6f09664b7d40d69b0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 15:37:59 -0700
Subject: [PATCH 0414/1375] Make the CPU compiler dump optimized IR along with
 the unoptimized IR.

PiperOrigin-RevId: 160005257
---
 .../xla/service/cpu/compiler_functor.cc       |  8 ++++++
 .../xla/service/cpu/compiler_functor.h        | 19 +++++++++++---
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 26 ++++++++++---------
 .../xla/service/cpu/simple_orc_jit.cc         | 13 ++++++----
 .../compiler/xla/service/cpu/simple_orc_jit.h | 10 ++++++-
 5 files changed, 54 insertions(+), 22 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index 1f78039e46..cd6cc09cfb 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -64,6 +64,10 @@ operator()(llvm::Module& module) const {
   VLOG(2) << "IR before optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));
 
+  if (pre_optimization_callback_) {
+    TF_CHECK_OK(pre_optimization_callback_(module));
+  }
+
   // Build up optimization pipeline.
   AddOptimizationPasses(&module_passes, &function_passes);
 
@@ -87,6 +91,10 @@ operator()(llvm::Module& module) const {
   VLOG(2) << "IR after optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));
 
+  if (post_optimization_callback_) {
+    TF_CHECK_OK(post_optimization_callback_(module));
+  }
+
   // Generate code.
   llvm::MCContext* mc_context;
   llvm::legacy::PassManager codegen_passes;
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.h b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
index 17dadebe97..94611b5814 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.h
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
@@ -39,13 +39,22 @@ class CompilerFunctor {
   // Returns a VectorIntrinsics where all intrinsics are available.
   static VectorIntrinsics AllIntrinsics();
 
-  explicit CompilerFunctor(llvm::TargetMachine* target_machine,
-                           const Disassembler* disassembler, int opt_level,
-                           const VectorIntrinsics& available_intrinsics)
+  // A callback of this type can be run before and/or after IR-level
+  // optimization to e.g. dump out the generated IR to disk or gather some
+  // statistics.
+  using OptimizationCallback = std::function<Status(const llvm::Module&)>;
+
+  explicit CompilerFunctor(
+      llvm::TargetMachine* target_machine, const Disassembler* disassembler,
+      int opt_level, const VectorIntrinsics& available_intrinsics,
+      OptimizationCallback pre_optimization_callback = nullptr,
+      OptimizationCallback post_optimization_callback = nullptr)
       : target_machine_(target_machine),
         disassembler_(CHECK_NOTNULL(disassembler)),
         opt_level_(opt_level),
-        available_intrinsics_(available_intrinsics) {}
+        available_intrinsics_(available_intrinsics),
+        pre_optimization_callback_(pre_optimization_callback),
+        post_optimization_callback_(post_optimization_callback) {}
 
   // Compile a Module to an ObjectFile.
   llvm::object::OwningBinary<llvm::object::ObjectFile> operator()(
@@ -61,6 +70,8 @@ class CompilerFunctor {
   const Disassembler* disassembler_;
   const unsigned opt_level_;
   const VectorIntrinsics available_intrinsics_;
+  OptimizationCallback pre_optimization_callback_;
+  OptimizationCallback post_optimization_callback_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 0dd6e3c8c6..da8d983e1a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -364,12 +364,23 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
   std::call_once(llvm_command_line_options_initialized,
                  &InitializeLLVMCommandLineOptions, module->config());
 
+  const string dump_ir_to = module->config().debug_options().xla_dump_ir_to();
+
+  auto dump_ir_to_disk = [dump_ir_to](const llvm::Module& module) {
+    if (!dump_ir_to.empty()) {
+      TF_RETURN_IF_ERROR(
+          AppendIRToFile(dump_ir_to, llvm_ir::DumpModuleToString(module)));
+    }
+    return Status::OK();
+  };
+
   // Compile must be thread-safe so create a new LLVM context for the module.
   auto llvm_context = MakeUnique<llvm::LLVMContext>();
   auto llvm_module =
       MakeUnique<llvm::Module>("__compute_module", *llvm_context);
   auto jit = MakeUnique<SimpleOrcJIT>(CompilerTargetOptions(module->config()),
-                                      CodeGenOptLevel(module->config()));
+                                      CodeGenOptLevel(module->config()),
+                                      dump_ir_to_disk, dump_ir_to_disk);
   llvm_module->setDataLayout(jit->data_layout());
   llvm_module->setTargetTriple(jit->target_triple().getTriple());
 
@@ -389,7 +400,6 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
   // ownership is std::moved.
   const bool embed_ir_in_executable =
       module->config().debug_options().xla_embed_ir_in_executable();
-  const string dump_ir_to = module->config().debug_options().xla_dump_ir_to();
   const string dump_debug_json_to =
       module->config().debug_options().xla_dump_debug_json_to();
 
@@ -474,12 +484,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
 
     string ir_module_string;
-    if (embed_ir_in_executable || !dump_ir_to.empty()) {
+    if (embed_ir_in_executable) {
       ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
-
-      if (!dump_ir_to.empty()) {
-        TF_RETURN_IF_ERROR(AppendIRToFile(dump_ir_to, ir_module_string));
-      }
     }
 
     // JIT compile the LLVM IR module to in-memory machine code.
@@ -545,12 +551,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 
     string function_name = llvm_ir::AsString(entry_function->getName());
     string ir_module_string;
-    if (embed_ir_in_executable || !dump_ir_to.empty()) {
+    if (embed_ir_in_executable) {
       ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
-
-      if (!dump_ir_to.empty()) {
-        TF_RETURN_IF_ERROR(AppendIRToFile(dump_ir_to, ir_module_string));
-      }
     }
 
     // JIT compile the LLVM IR module to in-memory machine code.
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index 625f2b181c..d5b157b7f6 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -27,7 +27,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/Support/CodeGen.h"
 #include "external/llvm/include/llvm/Support/Host.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/service/cpu/compiler_functor.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h"
@@ -143,7 +142,9 @@ CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() {
 }  // namespace
 
 SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
-                           llvm::CodeGenOpt::Level opt_level)
+                           llvm::CodeGenOpt::Level opt_level,
+                           OptimizationCallback pre_optimization_callback,
+                           OptimizationCallback post_optimization_callback)
     : target_machine_(
           CHECK_NOTNULL(llvm::EngineBuilder()
                             .setTargetOptions(target_options)
@@ -154,9 +155,11 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
                                 /*MAttrs=*/DetectMachineAttributes()))),
       disassembler_(*target_machine_),
       data_layout_(target_machine_->createDataLayout()),
-      compile_layer_(object_layer_,
-                     CompilerFunctor(target_machine_.get(), &disassembler_,
-                                     opt_level, GetAvailableIntrinsics())) {
+      compile_layer_(
+          object_layer_,
+          CompilerFunctor(target_machine_.get(), &disassembler_, opt_level,
+                          GetAvailableIntrinsics(), pre_optimization_callback,
+                          post_optimization_callback)) {
   VLOG(1) << "CPU target: " << target_machine_->getTargetCPU().str()
           << " features: " << target_machine_->getTargetFeatureString().str();
 }
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index a5979bb798..0a3b992403 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -25,6 +25,7 @@ limitations under the License.
 #include "external/llvm/include/llvm/ExecutionEngine/Orc/RTDyldObjectLinkingLayer.h"
 #include "external/llvm/include/llvm/IR/Module.h"
 #include "external/llvm/include/llvm/Target/TargetMachine.h"
+#include "tensorflow/compiler/xla/service/cpu/compiler_functor.h"
 #include "tensorflow/compiler/xla/service/cpu/disassembler.h"
 #include "tensorflow/compiler/xla/types.h"
 
@@ -47,6 +48,7 @@ class SimpleOrcJIT {
           llvm::Module&)>;
   using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT, CompileFtor>;
   using ModuleHandleT = CompileLayerT::ModuleSetHandleT;
+  using OptimizationCallback = CompilerFunctor::OptimizationCallback;
 
   // Create a new JIT, targeting the host architecture.
   // The |target_options| parameter allows customization of certain code
@@ -54,8 +56,14 @@ class SimpleOrcJIT {
   // can be reassociated, etc.).
   // The |opt_level| parameter controls the optimization level of the code
   // generator.
+  // The |pre_optimization_callback| is invoked on the module before any IR
+  // level optimizations are applied.
+  // The |post_optimization_callback| is invoked on the module after all IR
+  // level optimizations are applied.
   SimpleOrcJIT(const llvm::TargetOptions& target_options,
-               llvm::CodeGenOpt::Level opt_level);
+               llvm::CodeGenOpt::Level opt_level,
+               OptimizationCallback pre_optimization_callback,
+               OptimizationCallback post_optimization_callback);
 
   // Data layout this JIT was created with.
   const llvm::DataLayout& data_layout() const { return data_layout_; }
-- 
GitLab


From 77867318b3c89e38828e787f3948ccae21bc0693 Mon Sep 17 00:00:00 2001
From: Todd Wang <toddwang@gmail.com>
Date: Fri, 23 Jun 2017 16:34:14 -0700
Subject: [PATCH 0415/1375] Update ISSUE_TEMPLATE.md (#11018)

---
 ISSUE_TEMPLATE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 5b37028c50..4f1666c2e7 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -6,7 +6,7 @@ If you open a GitHub issue, here is our policy:
 
 1. It must be a bug or a feature request.
 2. The form below must be filled out.
-3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorflow/issues).
+3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorboard/issues).
 
 **Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
 
-- 
GitLab


From 8c868fac16e69d50664475b6bc7e98370da07905 Mon Sep 17 00:00:00 2001
From: Paul Van Eck <pvaneck@us.ibm.com>
Date: Fri, 23 Jun 2017 16:43:33 -0700
Subject: [PATCH 0416/1375] Adjust doc MNIST learning rate

The gradient descent learning rate specified in the MNIST beginner
tutorial should be 0.5 instead of 0.05 to stay consistent with the
actual code in the repo.
---
 tensorflow/docs_src/get_started/mnist/beginners.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 175de2be76..4858b3cf92 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -362,12 +362,12 @@ minimize. Then it can apply your choice of optimization algorithm to modify the
 variables and reduce the loss.
 
 ```python
-train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
+train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 ```
 
 In this case, we ask TensorFlow to minimize `cross_entropy` using the
 [gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent)
-with a learning rate of 0.05. Gradient descent is a simple procedure, where
+with a learning rate of 0.5. Gradient descent is a simple procedure, where
 TensorFlow simply shifts each variable a little bit in the direction that
 reduces the cost. But TensorFlow also provides
 @{$python/train#Optimizers$many other optimization algorithms}:
-- 
GitLab


From 169e6d103259efc68b88fb1b0c7cd8021ddf97a0 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Fri, 23 Jun 2017 17:21:45 -0700
Subject: [PATCH 0417/1375] Disable flaky run_metadata_test.

PiperOrigin-RevId: 160015399
---
 .../contrib/tfprof/python/tools/tfprof/internal/BUILD      | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
index 7abfb62214..83e3c5e35f 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
@@ -73,7 +73,12 @@ cuda_py_test(
         "//tensorflow/python:random_ops",
         "//tensorflow/tools/tfprof:protos_all_py",
     ],
-    tags = ["no_pip"],
+    tags = [
+        "manual",
+        "no_pip",
+        "noguitar",
+        "notap",
+    ],
 )
 
 # -----------------------------------------------------------------------------
-- 
GitLab


From 8bffa11a4c36a9c4af87ca673cb31ed91661c002 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 18:09:16 -0700
Subject: [PATCH 0418/1375] BUILD cleanup in tensorflow/tools/...

PiperOrigin-RevId: 160018623
---
 tensorflow/tools/api/lib/BUILD                |  3 +-
 tensorflow/tools/api/tests/BUILD              |  3 +-
 tensorflow/tools/common/BUILD                 |  2 ++
 tensorflow/tools/compatibility/BUILD          |  4 ++-
 tensorflow/tools/dist_test/scripts/BUILD      |  2 +-
 tensorflow/tools/graph_transforms/BUILD       |  8 +++--
 tensorflow/tools/quantization/BUILD           | 32 +++++++++++++------
 tensorflow/tools/test/BUILD                   |  3 ++
 tensorflow/tools/tfprof/internal/BUILD        |  7 ++--
 .../tools/tfprof/internal/advisor/BUILD       |  3 +-
 10 files changed, 47 insertions(+), 20 deletions(-)

diff --git a/tensorflow/tools/api/lib/BUILD b/tensorflow/tools/api/lib/BUILD
index cdfa0e7be5..2d3b838957 100644
--- a/tensorflow/tools/api/lib/BUILD
+++ b/tensorflow/tools/api/lib/BUILD
@@ -22,7 +22,8 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":api_objects_proto_py",
-        "//tensorflow/tools/common:traverse",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/tools/api/tests/BUILD b/tensorflow/tools/api/tests/BUILD
index 8421d8fce2..e9aeeb3855 100644
--- a/tensorflow/tools/api/tests/BUILD
+++ b/tensorflow/tools/api/tests/BUILD
@@ -23,11 +23,12 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:lib",
         "//tensorflow/python:platform",
         "//tensorflow/tools/api/lib:python_object_to_proto_visitor",
         "//tensorflow/tools/common:public_api",
         "//tensorflow/tools/common:traverse",
-        "@protobuf//:protobuf_python",
     ],
 )
 
diff --git a/tensorflow/tools/common/BUILD b/tensorflow/tools/common/BUILD
index f92edd0dd8..8a8667957a 100644
--- a/tensorflow/tools/common/BUILD
+++ b/tensorflow/tools/common/BUILD
@@ -15,6 +15,7 @@ py_library(
     name = "public_api",
     srcs = ["public_api.py"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow/python:util"],
 )
 
 py_test(
@@ -32,6 +33,7 @@ py_library(
     name = "traverse",
     srcs = ["traverse.py"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow/python:util"],
 )
 
 py_test(
diff --git a/tensorflow/tools/compatibility/BUILD b/tensorflow/tools/compatibility/BUILD
index fb40cf0833..19959ea6d2 100644
--- a/tensorflow/tools/compatibility/BUILD
+++ b/tensorflow/tools/compatibility/BUILD
@@ -24,7 +24,9 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         "tf_upgrade",
-        "//tensorflow:tensorflow_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_test_lib",
+        "@six_archive//:six",
     ],
 )
 
diff --git a/tensorflow/tools/dist_test/scripts/BUILD b/tensorflow/tools/dist_test/scripts/BUILD
index c329f0bbe8..ce2fa5c743 100644
--- a/tensorflow/tools/dist_test/scripts/BUILD
+++ b/tensorflow/tools/dist_test/scripts/BUILD
@@ -17,6 +17,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":k8s_tensorflow_lib",
-        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:platform_test",
     ],
 )
diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD
index fa2cf15cb1..d9ec8e8e9b 100644
--- a/tensorflow/tools/graph_transforms/BUILD
+++ b/tensorflow/tools/graph_transforms/BUILD
@@ -240,7 +240,6 @@ cc_binary(
     deps = [
         ":transform_utils",
         "//tensorflow/core:core_cpu_internal",
-        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
     ],
@@ -250,7 +249,12 @@ py_library(
     name = "transform_graph_py",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow/python:pywrap_tensorflow"],
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:pywrap_tensorflow",
+        "//tensorflow/python:util",
+    ],
 )
 
 tf_py_test(
diff --git a/tensorflow/tools/quantization/BUILD b/tensorflow/tools/quantization/BUILD
index cb41185219..e99ad06a06 100644
--- a/tensorflow/tools/quantization/BUILD
+++ b/tensorflow/tools/quantization/BUILD
@@ -13,7 +13,20 @@ py_library(
     name = "quantize_graph_lib",
     srcs = ["quantize_graph.py"],
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow:tensorflow_py"],
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:graph_util",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:session",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:tensor_util",
+        "//third_party/py/numpy",
+    ],
 )
 
 py_binary(
@@ -27,18 +40,17 @@ py_binary(
         "//tensorflow/python:client",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:graph_util",
         "//tensorflow/python:platform",
+        "//tensorflow/python:tensor_util",
         "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 
 py_test(
     name = "quantize_graph_test",
     size = "small",
-    srcs = [
-        "quantize_graph_test.py",
-    ],
+    srcs = ["quantize_graph_test.py"],
     srcs_version = "PY2AND3",
     tags = ["nomsan"],  # http://b/32242946
     deps = [
@@ -48,6 +60,7 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:graph_util",
         "//tensorflow/python:platform",
         "//third_party/py/numpy",
     ],
@@ -55,12 +68,13 @@ py_test(
 
 py_binary(
     name = "graph_to_dot",
-    srcs = [
-        "graph_to_dot.py",
-    ],
+    srcs = ["graph_to_dot.py"],
     main = "graph_to_dot.py",
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow/python:platform"],
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:platform",
+    ],
 )
 
 filegroup(
diff --git a/tensorflow/tools/test/BUILD b/tensorflow/tools/test/BUILD
index 953463df4a..28d651e910 100644
--- a/tensorflow/tools/test/BUILD
+++ b/tensorflow/tools/test/BUILD
@@ -22,6 +22,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:errors",
         "//tensorflow/python:platform",
@@ -46,6 +47,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":system_info_lib",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:platform",
     ],
 )
@@ -57,6 +59,7 @@ py_binary(
     visibility = ["//visibility:public"],
     deps = [
         ":run_and_gather_logs_lib",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:platform",
     ],
diff --git a/tensorflow/tools/tfprof/internal/BUILD b/tensorflow/tools/tfprof/internal/BUILD
index c47008b069..25c895af5b 100644
--- a/tensorflow/tools/tfprof/internal/BUILD
+++ b/tensorflow/tools/tfprof/internal/BUILD
@@ -41,7 +41,6 @@ cc_library(
         ":tfprof_utils",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
-        "//tensorflow/tools/tfprof:protos_all_cc",
         "@jsoncpp_git//:jsoncpp",
     ],
 )
@@ -56,6 +55,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
+        "//tensorflow/tools/tfprof:protos_all_cc",
     ],
 )
 
@@ -92,6 +92,7 @@ cc_library(
         ":tfprof_tensor",
         ":tfprof_utils",
         "//tensorflow/c:c_api",
+        "//tensorflow/c:checkpoint_reader",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
@@ -264,9 +265,7 @@ cc_library(
 
 cc_library(
     name = "print_model_analysis_hdr",
-    hdrs = [
-        "print_model_analysis.h",
-    ],
+    hdrs = ["print_model_analysis.h"],
     deps = [
         "//tensorflow/core:framework_lite",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/tools/tfprof/internal/advisor/BUILD
index d28443bba7..629322373a 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/tools/tfprof/internal/advisor/BUILD
@@ -14,6 +14,7 @@ cc_library(
     name = "checker",
     hdrs = ["checker.h"],
     deps = [
+        "//tensorflow/core:lib",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
     ],
 )
@@ -23,7 +24,7 @@ cc_library(
     srcs = ["internal_checker_runner_dummy.cc"],
     hdrs = ["internal_checker_runner.h"],
     deps = [
-        ":checker",
+        "//tensorflow/tools/tfprof/internal:tfprof_utils",
     ],
 )
 
-- 
GitLab


From 99451d7a0d3712669267480f220f505457edf8c4 Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Fri, 23 Jun 2017 18:24:08 -0700
Subject: [PATCH 0419/1375] SinhArcSinh bijector added.

This two-parameter diffeomorphism from R --> R allows for skewness and fatter
or thinner tails.  See docstring and also
http://oro.open.ac.uk/22510/1/sinhasinh.pdf

PiperOrigin-RevId: 160019380
---
 tensorflow/contrib/distributions/BUILD        |  19 +++
 .../bijectors/sinh_arcsinh_test.py            | 127 ++++++++++++++++
 .../python/ops/bijectors/__init__.py          |   2 +
 .../python/ops/bijectors/sinh_arcsinh.py      |  29 ++++
 .../python/ops/bijectors/sinh_arcsinh_impl.py | 143 ++++++++++++++++++
 5 files changed, 320 insertions(+)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 218308f20a..993d6d20b1 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -866,6 +866,25 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "sinh_arcsinh_test",
+    size = "small",
+    srcs = ["python/kernel_tests/bijectors/sinh_arcsinh_test.py"],
+    additional_deps = [
+        ":bijectors_py",
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "@six_archive//:six",
+        "//tensorflow/contrib/linalg:linalg_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "softmax_centered_test",
     size = "small",
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py
new file mode 100644
index 0000000000..b1159d13f3
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py
@@ -0,0 +1,127 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for SinhArcsinh Bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+# pylint: disable=g-importing-member
+from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh import SinhArcsinh
+from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite
+from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
+from tensorflow.python.platform import test
+
+# pylint: enable=g-importing-member
+
+
+class SinhArcsinhBijectorTest(test.TestCase):
+  """Tests correctness of the power transformation."""
+
+  def testBijectorVersusNumpyRewriteOfBasicFunctions(self):
+    with self.test_session():
+      skewness = 0.2
+      tailweight = 2.0
+      bijector = SinhArcsinh(
+          skewness=skewness,
+          tailweight=tailweight,
+          event_ndims=1,
+          validate_args=True)
+      self.assertEqual("sinh_arcsinh", bijector.name)
+      x = np.array([[[-2.01], [2.], [1e-4]]]).astype(np.float32)
+      y = np.sinh((np.arcsinh(x) + skewness) * tailweight)
+      self.assertAllClose(y, bijector.forward(x).eval())
+      self.assertAllClose(x, bijector.inverse(y).eval())
+      self.assertAllClose(
+          np.sum(
+              np.log(np.cosh(np.arcsinh(y) / tailweight - skewness)) -
+              np.log(tailweight) - np.log(np.sqrt(y**2 + 1)),
+              axis=-1), bijector.inverse_log_det_jacobian(y).eval())
+      self.assertAllClose(
+          -bijector.inverse_log_det_jacobian(y).eval(),
+          bijector.forward_log_det_jacobian(x).eval(),
+          rtol=1e-4,
+          atol=0.)
+
+  def testLargerTailWeightPutsMoreWeightInTails(self):
+    with self.test_session():
+      # Will broadcast together to shape [3, 2].
+      x = [-1., 1.]
+      tailweight = [[0.5], [1.0], [2.0]]
+      bijector = SinhArcsinh(tailweight=tailweight, validate_args=True)
+      y = bijector.forward(x).eval()
+
+      # x = -1, 1 should be mapped to points symmetric about 0
+      self.assertAllClose(y[:, 0], -1. * y[:, 1])
+
+      # forward(1) should increase as tailweight increases, since higher
+      # tailweight should map 1 to a larger number.
+      forward_1 = y[:, 1]  # The positive values of y.
+      self.assertLess(forward_1[0], forward_1[1])
+      self.assertLess(forward_1[1], forward_1[2])
+
+  def testSkew(self):
+    with self.test_session():
+      # Will broadcast together to shape [3, 2].
+      x = [-1., 1.]
+      skewness = [[-1.], [0.], [1.]]
+      bijector = SinhArcsinh(skewness=skewness, validate_args=True)
+      y = bijector.forward(x).eval()
+
+      # For skew < 0, |forward(-1)| > |forward(1)|
+      self.assertGreater(np.abs(y[0, 0]), np.abs(y[0, 1]))
+
+      # For skew = 0, |forward(-1)| = |forward(1)|
+      self.assertAllClose(np.abs(y[1, 0]), np.abs(y[1, 1]))
+
+      # For skew > 0, |forward(-1)| < |forward(1)|
+      self.assertLess(np.abs(y[2, 0]), np.abs(y[2, 1]))
+
+  def testScalarCongruencySkewness1Tailweight0p5(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=1.0, tailweight=0.5, validate_args=True)
+      assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.0, rtol=0.05)
+
+  def testScalarCongruencySkewnessNeg1Tailweight1p5(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=-1.0, tailweight=1.5, validate_args=True)
+      assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.0, rtol=0.05)
+
+  def testBijectiveAndFiniteSkewnessNeg1Tailweight0p5(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=-1., tailweight=0.5, validate_args=True)
+      # Increasing upper logspace limit to 10 results in Inf due to y**2 being
+      # Inf.
+      x = np.concatenate((-np.logspace(-2, 9, 1000), [0], np.logspace(
+          -2, 9, 1000))).astype(np.float32)
+      assert_bijective_and_finite(bijector, x, x, rtol=1e-3)
+
+  def testBijectiveAndFiniteSkewness2Tailweight3(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=1., tailweight=3., validate_args=True)
+      x = np.concatenate((-np.logspace(-2, 5, 1000), [0], np.logspace(
+          -2, 5, 1000))).astype(np.float32)
+      assert_bijective_and_finite(bijector, x, x, rtol=1e-3)
+
+  def testZeroTailweightRaises(self):
+    with self.test_session():
+      with self.assertRaisesOpError("not positive"):
+        SinhArcsinh(tailweight=0., validate_args=True).forward(1.0).eval()
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
index 1684a5fffe..f1879a828a 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
@@ -27,6 +27,7 @@
 @@PowerTransform
 @@Sigmoid
 @@SigmoidCentered
+@@SinhArcsinh
 @@SoftmaxCentered
 @@Softplus
 """
@@ -48,6 +49,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.invert import *
 from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import *
 from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import *
 from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid_centered import *
+from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh import *
 from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import *
 from tensorflow.contrib.distributions.python.ops.bijectors.softplus import *
 from tensorflow.python.ops.distributions.bijector import *
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py
new file mode 100644
index 0000000000..b3cf03c246
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py
@@ -0,0 +1,29 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SinhArcsinh bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import
+from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh_impl import *
+# pylint: enable=wildcard-import
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = ["SinhArcsinh"]
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
new file mode 100644
index 0000000000..27359bad32
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
@@ -0,0 +1,143 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SinhArcsinh bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.distributions.python.ops import trig
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import bijector
+
+sinh = trig.sinh
+arcsinh = trig.arcsinh
+cosh = trig.cosh
+log_cosh = trig.log_cosh
+
+__all__ = [
+    "SinhArcsinh",
+]
+
+
+class SinhArcsinh(bijector.Bijector):
+  """Compute `Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight )`.
+
+  For `skewness in (-inf, inf)` and `tailweight in (0, inf)`, this
+  transformation is a
+  diffeomorphism of the real line `(-inf, inf)`.  The inverse transform is
+  `X = g^{-1}(Y) = Sinh( ArcSinh(Y) / tailweight - skewness )`.
+
+  The `SinhArcsinh` transformation of the Normal is described in
+  [Sinh-arcsinh distributions](https://www.jstor.org/stable/27798865)
+  This Bijector allows a similar transformation of any distribution supported on
+  `(-inf, inf)`.
+
+  #### Meaning of the parameters
+
+  * If `skewness = 0` and `tailweight = 1`, this transform is the identity.
+  * Positive (negative) `skewness` leads to positive (negative) skew.
+    * positive skew means, for unimodal `X` centered at zero, the mode of `Y` is
+      "tilted" to the right.
+    * positive skew means positive values of `Y` become more likely, and
+      negative values become less likely.
+  * Larger (smaller) `tailweight` leads to fatter (thinner) tails.
+    * Fatter tails mean larger values of `|Y|` become more likely.
+    * If `X` is a unit Normal, `tailweight < 1` leads to a distribution that is
+      "flat" around `Y = 0`, and a very steep drop-off in the tails.
+    * If `X` is a unit Normal, `tailweight > 1` leads to a distribution more
+      peaked at the mode with heavier tails.
+
+  To see the argument about the tails, note that for `|X| >> 1` and
+  `|X| >> (|skewness| * tailweight)**tailweight`, we have
+  `Y approx 0.5 X**tailweight e**(sign(X) skewness * tailweight)`.
+  """
+
+  def __init__(self,
+               skewness=0.,
+               tailweight=1.,
+               event_ndims=0,
+               validate_args=False,
+               name="sinh_arcsinh"):
+    """Instantiates the `SinhArcsinh` bijector.
+
+    Args:
+      skewness:  Skewness parameter.  Float-type `Tensor`.
+      tailweight:  Tailweight parameter.  Positive `Tensor` of same `dtype` as
+        `skewness`
+        and broadcastable `shape`.
+      event_ndims: Python scalar indicating the number of dimensions associated
+        with a particular draw from the distribution.
+      validate_args: Python `bool` indicating whether arguments should be
+        checked for correctness.
+      name: Python `str` name given to ops managed by this object.
+    """
+    self._graph_parents = []
+    self._name = name
+    self._validate_args = validate_args
+    with self._name_scope("init", values=[skewness, tailweight]):
+      self._skewness = ops.convert_to_tensor(skewness, name="skewness")
+      self._tailweight = ops.convert_to_tensor(tailweight, name="tailweight")
+      check_ops.assert_same_float_dtype([self._skewness, self._tailweight])
+      if validate_args:
+        self._tailweight = control_flow_ops.with_dependencies([
+            check_ops.assert_positive(
+                self._tailweight,
+                message="Argument tailweight was not positive")
+        ], self._tailweight)
+    super(SinhArcsinh, self).__init__(
+        event_ndims=event_ndims, validate_args=validate_args, name=name)
+
+  @property
+  def skewness(self):
+    """The `skewness` in: `Y  = Sinh((Arcsinh(X) + skewness) * tailweight)`."""
+    return self._skewness
+
+  @property
+  def tailweight(self):
+    """The `tailweight` in: `Y = Sinh((Arcsinh(X) + skewness) * tailweight)`."""
+    return self._tailweight
+
+  def _forward(self, x):
+    return sinh((arcsinh(x) + self.skewness) * self.tailweight)
+
+  def _inverse(self, y):
+    return sinh(arcsinh(y) / self.tailweight - self.skewness)
+
+  def _inverse_log_det_jacobian(self, y):
+    # x = sinh(arcsinh(y) / tailweight - skewness)
+    # Using sinh' = cosh, arcsinh'(y) = 1 / sqrt(y**2 + 1),
+    # dx/dy
+    # = cosh(arcsinh(y) / tailweight - skewness)
+    #     / (tailweight * sqrt(y**2 + 1))
+    event_dims = self._event_dims_tensor(y)
+    return math_ops.reduce_sum(
+        log_cosh(arcsinh(y) / self.tailweight - self.skewness) -
+        math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(y**2),
+        axis=event_dims)
+
+  def _forward_log_det_jacobian(self, x):
+    # y = sinh((arcsinh(x) + skewness) * tailweight)
+    # Using sinh' = cosh, arcsinh'(x) = 1 / sqrt(x**2 + 1),
+    # dy/dx
+    # = cosh((arcsinh(x) + skewness) * tailweight) * tailweight / sqrt(x**2 + 1)
+    event_dims = self._event_dims_tensor(x)
+    return math_ops.reduce_sum(
+        log_cosh((arcsinh(x) + self.skewness) * self.tailweight) +
+        math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(x**2),
+        axis=event_dims)
-- 
GitLab


From 0b6474d5c4abebd6ebe951acb120613a5b66ec50 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Jun 2017 21:57:48 -0700
Subject: [PATCH 0420/1375] Avoid hardcoded names for temporary files in tests.

These tests (and examples that are run as tests) were using hardcoded names for
temporary files.  This failed when multiple copies of these tests were run in
parallel, or even successively by different users, where the second run could
not overwrite files left by the first.

This change uses the TEST_TMPDIR environment variable used by bazel's test
runner to choose a temporary directory.   If that directory is not set,
/tmp is used, as before.

PiperOrigin-RevId: 160026924
---
 .../examples/tutorials/mnist/fully_connected_feed.py      | 8 +++++---
 .../examples/tutorials/mnist/mnist_with_summaries.py      | 7 +++++--
 tensorflow/python/training/saver_test.py                  | 3 ++-
 3 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/tensorflow/examples/tutorials/mnist/fully_connected_feed.py b/tensorflow/examples/tutorials/mnist/fully_connected_feed.py
index be50f4529f..af89c8c77b 100644
--- a/tensorflow/examples/tutorials/mnist/fully_connected_feed.py
+++ b/tensorflow/examples/tutorials/mnist/fully_connected_feed.py
@@ -20,7 +20,7 @@ from __future__ import print_function
 
 # pylint: disable=missing-docstring
 import argparse
-import os.path
+import os
 import sys
 import time
 
@@ -257,13 +257,15 @@ if __name__ == '__main__':
   parser.add_argument(
       '--input_data_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/input_data',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/input_data'),
       help='Directory to put the input data.'
   )
   parser.add_argument(
       '--log_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/logs/fully_connected_feed',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/logs/fully_connected_feed'),
       help='Directory to put the log data.'
   )
   parser.add_argument(
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index dc0d870315..c401d09df8 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -25,6 +25,7 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
+import os
 import sys
 
 import tensorflow as tf
@@ -200,12 +201,14 @@ if __name__ == '__main__':
   parser.add_argument(
       '--data_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/input_data',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/input_data'),
       help='Directory for storing input data')
   parser.add_argument(
       '--log_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/logs/mnist_with_summaries',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/logs/mnist_with_summaries'),
       help='Summaries log directory')
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index d17b7e93a1..f351efd843 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -1951,7 +1951,8 @@ class MetaGraphTest(test.TestCase):
                                                         logits=logit)
         adam.AdamOptimizer().minimize(cost, name="optimize")
       meta_graph_def = saver_module.export_meta_graph(clear_devices=True)
-      graph_io.write_graph(meta_graph_def, "/tmp", "meta_graph.pbtxt")
+      graph_io.write_graph(meta_graph_def, self.get_temp_dir(),
+                           "meta_graph.pbtxt")
 
     with session.Session(graph=ops_lib.Graph()) as sess:
       saver_module.import_meta_graph(meta_graph_def, import_scope="new_model")
-- 
GitLab


From 4a5d20ea257531659ed4ba7419e0d7c7a1cf53d3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Siim=20P=C3=B5der?= <siim@p6drad-teel.net>
Date: Sat, 24 Jun 2017 08:54:43 +0100
Subject: [PATCH 0421/1375] test_session: fix doc formatting

---
 tensorflow/python/framework/test_util.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index dd2fd7b2ab..f49a682dbe 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -382,16 +382,17 @@ class TensorFlowTestCase(googletest.TestCase):
     `force_gpu and `use_gpu` are False, all ops are pinned to the CPU.
 
     Example:
-
-      class MyOperatorTest(test_util.TensorFlowTestCase):
-        def testMyOperator(self):
-          with self.test_session(use_gpu=True):
-            valid_input = [1.0, 2.0, 3.0, 4.0, 5.0]
-            result = MyOperator(valid_input).eval()
-            self.assertEqual(result, [1.0, 2.0, 3.0, 5.0, 8.0]
-            invalid_input = [-1.0, 2.0, 7.0]
-            with self.assertRaisesOpError("negative input not supported"):
-              MyOperator(invalid_input).eval()
+    ```python
+    class MyOperatorTest(test_util.TensorFlowTestCase):
+      def testMyOperator(self):
+        with self.test_session(use_gpu=True):
+          valid_input = [1.0, 2.0, 3.0, 4.0, 5.0]
+          result = MyOperator(valid_input).eval()
+          self.assertEqual(result, [1.0, 2.0, 3.0, 5.0, 8.0]
+          invalid_input = [-1.0, 2.0, 7.0]
+          with self.assertRaisesOpError("negative input not supported"):
+            MyOperator(invalid_input).eval()
+    ```
 
     Args:
       graph: Optional graph to use during the returned session.
-- 
GitLab


From 337b7c8c6262bcc2ef00f5a636d7020e632aa32d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 24 Jun 2017 03:37:50 -0700
Subject: [PATCH 0422/1375] Fix multinomial doc-string, input arg logits
 expects to log-probabilities and not log-odds.

PiperOrigin-RevId: 160036709
---
 tensorflow/python/ops/distributions/multinomial.py | 10 +++++-----
 tensorflow/python/ops/random_ops.py                |  2 +-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index c87389e356..aa691c565b 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -156,11 +156,11 @@ class Multinomial(distribution.Distribution):
         to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
         `N1 x ... x Nm` different Multinomial distributions. Its components
         should be equal to integer values.
-      logits: Floating point tensor representing the log-odds of a
-        positive event with shape broadcastable to `[N1,..., Nm, K], m >= 0`,
-        and the same dtype as `total_count`. Defines this as a batch of
-        `N1 x ... x Nm` different `K` class Multinomial distributions. Only one
-        of `logits` or `probs` should be passed in.
+      logits: Floating point tensor representing unnormalized log-probabilities
+        of a positive event with shape broadcastable to
+        `[N1,..., Nm, K]` `m >= 0`, and the same dtype as `total_count`. Defines
+        this as a batch of `N1 x ... x Nm` different `K` class Multinomial
+        distributions. Only one of `logits` or `probs` should be passed in.
       probs: Positive floating point tensor with shape broadcastable to
         `[N1,..., Nm, K]` `m >= 0` and same dtype as `total_count`. Defines
         this as a batch of `N1 x ... x Nm` different `K` class Multinomial
diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py
index 5db0a6fba2..1e0bb925d4 100644
--- a/tensorflow/python/ops/random_ops.py
+++ b/tensorflow/python/ops/random_ops.py
@@ -329,7 +329,7 @@ def multinomial(logits, num_samples, seed=None, name=None):
 
   Args:
     logits: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice
-      `[i, :]` represents the log-odds for all classes.
+      `[i, :]` represents the unnormalized log-probabilities for all classes.
     num_samples: 0-D.  Number of independent samples to draw for each row slice.
     seed: A Python integer. Used to create a random seed for the distribution.
       See
-- 
GitLab


From bbec8733500f65d2c55b97d1b18f6a6362cdf760 Mon Sep 17 00:00:00 2001
From: Sven Mayer <mayer1802@gmail.com>
Date: Sat, 24 Jun 2017 14:16:53 +0200
Subject: [PATCH 0423/1375] Update momentum.py

fixed a typo in the documentation
---
 tensorflow/python/training/momentum.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/training/momentum.py b/tensorflow/python/training/momentum.py
index ffd7c12c42..f34ff22f07 100644
--- a/tensorflow/python/training/momentum.py
+++ b/tensorflow/python/training/momentum.py
@@ -52,7 +52,7 @@ class MomentumOptimizer(optimizer.Optimizer):
       name: Optional name prefix for the operations created when applying
         gradients.  Defaults to "Momentum".
       use_nesterov: If `True` use Nesterov Momentum.
-        See [Sutskever et. al., 2013](
+        See [Sutskever et al., 2013](
         http://jmlr.org/proceedings/papers/v28/sutskever13.pdf)
 
     """
-- 
GitLab


From c12896379f2fefec578c6d2cb93fd27a2fe6dcc8 Mon Sep 17 00:00:00 2001
From: "Yuan (Terry) Tang" <terrytangyuan@users.noreply.github.com>
Date: Sat, 24 Jun 2017 10:51:22 -0400
Subject: [PATCH 0424/1375] Removed unnecessary row in estimators extension
 tutorial

---
 tensorflow/docs_src/extend/estimators.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index fbde463415..cb0e003b29 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -68,8 +68,7 @@ for abalone:
 | Feature        | Description                                               |
 | -------------- | --------------------------------------------------------- |
 | Length         | Length of abalone (in longest direction; in mm)           |
-| Diameter       | Diameter of abalone (measurement perpendicular to length; |
-:                : in mm)                                                    :
+| Diameter       | Diameter of abalone (measurement perpendicular to length; in mm)|
 | Height         | Height of abalone (with its meat inside shell; in mm)     |
 | Whole Weight   | Weight of entire abalone (in grams)                       |
 | Shucked Weight | Weight of abalone meat only (in grams)                    |
-- 
GitLab


From efb91f85b8d6554fd0906ab397e6a9abe9bdca41 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 24 Jun 2017 10:36:24 -0700
Subject: [PATCH 0425/1375] Made TensorFlow documentation on LSTMs slightly
 more accurate.

PiperOrigin-RevId: 160047054
---
 tensorflow/docs_src/tutorials/recurrent.md |  4 +++-
 tensorflow/python/ops/rnn_cell_impl.py     | 15 ++++++++++++++-
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/recurrent.md b/tensorflow/docs_src/tutorials/recurrent.md
index 708a9620dd..346b6be06c 100644
--- a/tensorflow/docs_src/tutorials/recurrent.md
+++ b/tensorflow/docs_src/tutorials/recurrent.md
@@ -75,7 +75,9 @@ The basic pseudocode is as follows:
 words_in_dataset = tf.placeholder(tf.float32, [num_batches, batch_size, num_features])
 lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
 # Initial state of the LSTM memory.
-state = tf.zeros([batch_size, lstm.state_size])
+hidden_state = tf.zeros([batch_size, lstm.state_size])
+current_state = tf.zeros([batch_size, lstm.state_size])
+state = hidden_state, current_state
 probabilities = []
 loss = 0.0
 for current_batch_of_words in words_in_dataset:
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index ca69cddae2..c0d9c971a0 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -374,7 +374,20 @@ class BasicLSTMCell(RNNCell):
     return self._num_units
 
   def call(self, inputs, state):
-    """Long short-term memory cell (LSTM)."""
+    """Long short-term memory cell (LSTM).
+
+    Args:
+      inputs: `2-D` tensor with shape `[batch_size x input_size]`.
+      state: An `LSTMStateTuple` of state tensors, each shaped
+        `[batch_size x self.state_size]`, if `state_is_tuple` has been set to
+        `True`.  Otherwise, a `Tensor` shaped
+        `[batch_size x 2 * self.state_size]`.
+
+    Returns:
+      A pair containing the new hidden state, and the new state (either a
+        `LSTMStateTuple` or a concatenated state, depending on
+        `state_is_tuple`).
+    """
     sigmoid = math_ops.sigmoid
     # Parameters of gates are concatenated into one multiply for efficiency.
     if self._state_is_tuple:
-- 
GitLab


From 22b786c96f5bbc3e6c3d776ec5cfa0d4874140f3 Mon Sep 17 00:00:00 2001
From: "Yuan (Terry) Tang" <terrytangyuan@users.noreply.github.com>
Date: Sun, 25 Jun 2017 18:15:22 -0400
Subject: [PATCH 0426/1375] Replace ModelFnOps with EstimatorSpec

---
 tensorflow/docs_src/extend/estimators.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index cb0e003b29..500beb49a2 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -558,7 +558,7 @@ For a full list of optimizers, and other details, see the
 
 Here's the final, complete `model_fn` for the abalone age predictor. The
 following code configures the neural network; defines loss and the training op;
-and returns a `ModelFnOps` object containing `mode`, `predictions_dict`, `loss`,
+and returns a `EstimatorSpec` object containing `mode`, `predictions_dict`, `loss`,
 and `train_op`:
 
 ```python
-- 
GitLab


From 9a2c2ff5157b4b38b6054a666d9f0bd663ce0c7b Mon Sep 17 00:00:00 2001
From: Stephen Fox <stevemasta34@gmail.com>
Date: Mon, 26 Jun 2017 00:12:45 -0600
Subject: [PATCH 0427/1375] Update documentation regarding 'shape' parameters
 (#11057)

These functions are documented to take lists, but perfectly
  accept tuples. Explicitly documented this case.
---
 tensorflow/python/ops/array_ops.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index a8f596c7a3..8db5efb447 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -764,10 +764,10 @@ def parallel_stack(values, name="parallel_stack"):
   The difference between `stack` and `parallel_stack` is that `stack` requires
   all the inputs be computed before the operation will begin but doesn't require
   that the input shapes be known during graph construction.
-  
+
   `parallel_stack` will copy pieces of the input into the output as they become
   available, in some situations this can provide a performance benefit.
-  
+
   Unlike `stack`, `parallel_stack` does NOT support backpropagation.
 
   This is the opposite of unstack.  The numpy equivalent is
@@ -1373,7 +1373,7 @@ def zeros(shape, dtype=dtypes.float32, name=None):
   ```
 
   Args:
-    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
+    shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type `int32`.
     dtype: The type of an element in the resulting `Tensor`.
     name: A name for the operation (optional).
 
@@ -1487,7 +1487,7 @@ def ones(shape, dtype=dtypes.float32, name=None):
   ```
 
   Args:
-    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
+    shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type `int32`.
     dtype: The type of an element in the resulting `Tensor`.
     name: A name for the operation (optional).
 
-- 
GitLab


From 28337cc3c209dfba789b09e0b0e3b717eda0ef8c Mon Sep 17 00:00:00 2001
From: raoqiyu <raoqiyu@yeah.net>
Date: Mon, 26 Jun 2017 14:16:28 +0800
Subject: [PATCH 0428/1375] Update get_started.md (#11054)

Add defination of eval_input_fn in example.

# train
estimator.fit(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did.
train_loss = estimator.evaluate(input_fn=input_fn)
eval_loss = estimator.evaluate(input_fn=eval_input_fn)
```
The `eval_input_fn` is not defined here.
---
 tensorflow/docs_src/get_started/get_started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index c1c68f9c12..b257c5a1ca 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -450,7 +450,7 @@ y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
 input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_train}, y_train, 4, num_epochs=1000)
-
+eval_input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_eval}, y_eval, 4, num_epochs=1000)
 # train
 estimator.fit(input_fn=input_fn, steps=1000)
 # Here we evaluate how well our model did. 
-- 
GitLab


From 7a06e0af350e3e61bbf0ebee66d79ab15808c575 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sun, 25 Jun 2017 23:37:35 -0700
Subject: [PATCH 0429/1375] Follow LLVM/ORC upstream API change in r306166.

PiperOrigin-RevId: 160108102
---
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc | 10 +++-------
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.h  |  2 +-
 2 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index d5b157b7f6..b284543299 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -166,12 +166,8 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
 
 SimpleOrcJIT::ModuleHandleT SimpleOrcJIT::AddModule(
     std::unique_ptr<llvm::Module> module) {
-  // The Orc API adds a whole iterable "set" of modules, so we wrap the module
-  // in a vector.
-  std::vector<std::unique_ptr<llvm::Module>> module_set;
-  module_set.push_back(std::move(module));
-  auto handle = compile_layer_.addModuleSet(
-      std::move(module_set), MakeUnique<llvm::SectionMemoryManager>(),
+  auto handle = compile_layer_.addModule(
+      std::move(module), MakeUnique<llvm::SectionMemoryManager>(),
       MakeUnique<SimpleResolver>());
   module_handles_.push_back(handle);
   return handle;
@@ -181,7 +177,7 @@ void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::ModuleHandleT handle) {
   module_handles_.erase(
       std::remove(module_handles_.begin(), module_handles_.end(), handle),
       module_handles_.end());
-  compile_layer_.removeModuleSet(handle);
+  compile_layer_.removeModule(handle);
 }
 
 llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string &name) {
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index 0a3b992403..e28717bcd1 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -47,7 +47,7 @@ class SimpleOrcJIT {
       std::function<llvm::object::OwningBinary<llvm::object::ObjectFile>(
           llvm::Module&)>;
   using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT, CompileFtor>;
-  using ModuleHandleT = CompileLayerT::ModuleSetHandleT;
+  using ModuleHandleT = CompileLayerT::ModuleHandleT;
   using OptimizationCallback = CompilerFunctor::OptimizationCallback;
 
   // Create a new JIT, targeting the host architecture.
-- 
GitLab


From a80c8b583fa3d619b358a91aefd05069227b8967 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 05:47:31 -0700
Subject: [PATCH 0430/1375] Move resampler from sonnet to contrib.

PiperOrigin-RevId: 160134565
---
 tensorflow/BUILD                              |   1 +
 tensorflow/contrib/BUILD                      |   1 +
 tensorflow/contrib/__init__.py                |   1 +
 .../contrib/cmake/tf_core_kernels.cmake       |   3 +
 tensorflow/contrib/cmake/tf_core_ops.cmake    |   1 +
 tensorflow/contrib/cmake/tf_python.cmake      |   7 +
 tensorflow/contrib/resampler/BUILD            |  92 ++++
 tensorflow/contrib/resampler/__init__.py      |  26 +
 .../resampler/kernels/resampler_ops.cc        | 465 ++++++++++++++++++
 .../contrib/resampler/kernels/resampler_ops.h |  68 +++
 .../resampler/kernels/resampler_ops_gpu.cu.cc | 310 ++++++++++++
 .../contrib/resampler/ops/resampler_ops.cc    |  59 +++
 .../contrib/resampler/python/__init__.py      |  19 +
 .../resampler/python/ops/resampler_ops.py     |  69 +++
 .../python/ops/resampler_ops_test.py          | 270 ++++++++++
 15 files changed, 1392 insertions(+)
 create mode 100644 tensorflow/contrib/resampler/BUILD
 create mode 100644 tensorflow/contrib/resampler/__init__.py
 create mode 100644 tensorflow/contrib/resampler/kernels/resampler_ops.cc
 create mode 100644 tensorflow/contrib/resampler/kernels/resampler_ops.h
 create mode 100644 tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc
 create mode 100644 tensorflow/contrib/resampler/ops/resampler_ops.cc
 create mode 100644 tensorflow/contrib/resampler/python/__init__.py
 create mode 100644 tensorflow/contrib/resampler/python/ops/resampler_ops.py
 create mode 100644 tensorflow/contrib/resampler/python/ops/resampler_ops_test.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 318e22797c..62ae5ae78c 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -280,6 +280,7 @@ filegroup(
         "//tensorflow/contrib/opt:all_files",
         "//tensorflow/contrib/predictor:all_files",
         "//tensorflow/contrib/remote_fused_graph/pylib:all_files",
+        "//tensorflow/contrib/resampler:all_files",
         "//tensorflow/contrib/rnn:all_files",
         "//tensorflow/contrib/saved_model:all_files",
         "//tensorflow/contrib/saved_model/cc/saved_model:all_files",
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index 42f7ac7fdf..f6729a666a 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -53,6 +53,7 @@ py_library(
         "//tensorflow/contrib/opt:opt_py",
         "//tensorflow/contrib/quantization:quantization_py",
         "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py",
+        "//tensorflow/contrib/resampler:resampler_py",
         "//tensorflow/contrib/rnn:rnn_py",
         "//tensorflow/contrib/saved_model:saved_model_py",
         "//tensorflow/contrib/seq2seq:seq2seq_py",
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index 16bc533436..01cbfd51c3 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -51,6 +51,7 @@ from tensorflow.contrib import nccl
 from tensorflow.contrib import nn
 from tensorflow.contrib import opt
 from tensorflow.contrib import quantization
+from tensorflow.contrib import resampler
 from tensorflow.contrib import rnn
 from tensorflow.contrib import saved_model
 from tensorflow.contrib import seq2seq
diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 7f3be2aedf..6b8c130490 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -55,6 +55,8 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_manager.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/ops/nccl_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/resampler/kernels/resampler_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/resampler/ops/resampler_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/blas_gemm.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/lstm_ops.cc"
@@ -125,6 +127,7 @@ if(WIN32)
       "${tensorflow_source_dir}/tensorflow/core/kernels/*quantiz*.cc"
       "${tensorflow_source_dir}/tensorflow/core/kernels/neon/*"
       # no in tensorflow.dll - comes from .so
+      "${tensorflow_source_dir}/tensorflow/contrib/resampler/kernels/resampler_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/blas_gemm.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/lstm_ops.cc"
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index 562bd288a2..21d94da6cc 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -81,6 +81,7 @@ GENERATE_CONTRIB_OP_LIBRARY(image "${tensorflow_source_dir}/tensorflow/contrib/i
 GENERATE_CONTRIB_OP_LIBRARY(layers_sparse_feature_cross "${tensorflow_source_dir}/tensorflow/contrib/layers/ops/sparse_feature_cross_op.cc")
 GENERATE_CONTRIB_OP_LIBRARY(memory_stats "${tensorflow_source_dir}/tensorflow/contrib/memory_stats/ops/memory_stats_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(nccl "${tensorflow_source_dir}/tensorflow/contrib/nccl/ops/nccl_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(resampler "${tensorflow_source_dir}/tensorflow/contrib/resampler/ops/resampler_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(rnn_gru "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/gru_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(rnn_lstm "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/lstm_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(seq2seq_beam_search "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc")
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index d029099be9..a971232e2f 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -449,6 +449,11 @@ add_python_module("tensorflow/contrib/quantization/python")
 add_python_module("tensorflow/contrib/remote_fused_graph/pylib")
 add_python_module("tensorflow/contrib/remote_fused_graph/pylib/python")
 add_python_module("tensorflow/contrib/remote_fused_graph/pylib/python/ops")
+add_python_module("tensorflow/contrib/resampler")
+add_python_module("tensorflow/contrib/resampler/kernels")
+add_python_module("tensorflow/contrib/resampler/ops")
+add_python_module("tensorflow/contrib/resampler/python")
+add_python_module("tensorflow/contrib/resampler/python/ops")
 add_python_module("tensorflow/contrib/rnn")
 add_python_module("tensorflow/contrib/rnn/kernels")
 add_python_module("tensorflow/contrib/rnn/ops")
@@ -650,6 +655,8 @@ GENERATE_PYTHON_OP_LIB("contrib_memory_stats_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/memory_stats/ops/gen_memory_stats_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_nccl_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/nccl/ops/gen_nccl_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_resampler_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/resampler/ops/gen_resampler_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_rnn_gru_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/rnn/ops/gen_gru_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_rnn_lstm_ops"
diff --git a/tensorflow/contrib/resampler/BUILD b/tensorflow/contrib/resampler/BUILD
new file mode 100644
index 0000000000..1b9efd1ecd
--- /dev/null
+++ b/tensorflow/contrib/resampler/BUILD
@@ -0,0 +1,92 @@
+licenses(["notice"])  # Apache 2.0 License
+
+exports_files(["LICENSE"])
+
+package(default_visibility = ["//visibility:public"])
+
+load(
+    "//tensorflow:tensorflow.bzl",
+    "tf_custom_op_library",
+    "tf_custom_op_py_library",
+    "tf_gen_op_libs",
+    "tf_gen_op_wrapper_py",
+    "tf_kernel_library",
+)
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
+
+tf_custom_op_py_library(
+    name = "resampler_py",
+    srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
+    dso = [":python/ops/_resampler_ops.so"],
+    kernels = [
+        ":resampler_ops_kernels",
+        ":resampler_ops_op_lib",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":resampler_ops",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:util",
+    ],
+)
+
+tf_kernel_library(
+    name = "resampler_ops_kernels",
+    prefix = "resampler_ops",
+    deps = [
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_resampler_ops.so",
+    srcs = [
+        "kernels/resampler_ops.cc",
+        "kernels/resampler_ops.h",
+        "ops/resampler_ops.cc",
+    ],
+    gpu_srcs = [
+        "kernels/resampler_ops_gpu.cu.cc",
+        "kernels/resampler_ops.h",
+    ],
+)
+
+tf_gen_op_libs(
+    op_lib_names = [
+        "resampler_ops",
+    ],
+)
+
+tf_gen_op_wrapper_py(
+    name = "resampler_ops",
+    deps = [":resampler_ops_op_lib"],
+)
+
+cuda_py_test(
+    name = "resampler_ops_test",
+    size = "small",
+    srcs = ["python/ops/resampler_ops_test.py"],
+    additional_deps = [
+        ":resampler_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:array_ops",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/resampler/__init__.py b/tensorflow/contrib/resampler/__init__.py
new file mode 100644
index 0000000000..3e04e5762d
--- /dev/null
+++ b/tensorflow/contrib/resampler/__init__.py
@@ -0,0 +1,26 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Ops and modules related to resampler."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.resampler.python.ops.resampler_ops import *
+from tensorflow.python.util.all_util import remove_undocumented
+
+remove_undocumented(__name__, ["resampler"])
diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops.cc b/tensorflow/contrib/resampler/kernels/resampler_ops.cc
new file mode 100644
index 0000000000..afc8bcd446
--- /dev/null
+++ b/tensorflow/contrib/resampler/kernels/resampler_ops.cc
@@ -0,0 +1,465 @@
+// Copyright 2017 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/contrib/resampler/kernels/resampler_ops.h"
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+using CPUDevice = Eigen::ThreadPoolDevice;
+using GPUDevice = Eigen::GpuDevice;
+
+namespace functor {
+
+template <typename T>
+struct Resampler2DFunctor<CPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const CPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   T* __restrict__ output,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points){
+    const int warp_batch_stride = num_sampling_points * 2;
+    const int data_batch_stride = data_height * data_width * data_channels;
+    const int output_batch_stride = num_sampling_points * data_channels;
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+
+    auto resample_batches = [&](const int start, const int limit) {
+      for (int batch_id = start; batch_id < limit; ++batch_id) {
+        // Utility lambda to access data point and set output values.
+        // The functions take care of performing the relevant pointer
+        // arithmetics abstracting away the low level details in the
+        // main loop over samples. Note that data is stored in NHWC format.
+        auto set_output = [&](const int sample_id,
+                              const int channel,
+                              const T value) {
+          output[batch_id * output_batch_stride +
+                 sample_id * data_channels +
+                 channel] = value;
+        };
+
+        auto get_data_point = [&](const int x,
+                                  const int y,
+                                  const int chan) {
+          const bool point_is_in_range =
+              (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1);
+          return point_is_in_range
+                 ? data[batch_id * data_batch_stride +
+                        data_channels * (y * data_width + x) +
+                        chan]
+                 : zero;
+        };
+
+        for (int sample_id = 0; sample_id < num_sampling_points; ++sample_id) {
+          const T x = warp[batch_id * warp_batch_stride + sample_id * 2];
+          const T y = warp[batch_id * warp_batch_stride + sample_id * 2 + 1];
+          // The interpolation function:
+          // a) implicitly pads the input data with 0s (hence the unusual checks
+          // with {x,y} > -1)
+          // b) returns 0 when sampling outside the (padded) image.
+          // The effect is that the sampled signal smoothly goes to 0 outside
+          // the original input domain, rather than presenting a jump
+          // discontinuity at the image boundaries.
+          if (x > static_cast<T>(-1.0) &&
+              y > static_cast<T>(-1.0) &&
+              x < static_cast<T>(data_width) &&
+              y < static_cast<T>(data_height)) {
+            // Precompute floor (f) and ceil (c) values for x and y.
+            const int fx = std::floor(static_cast<float>(x));
+            const int fy = std::floor(static_cast<float>(y));
+            const int cx = fx + 1;
+            const int cy = fy + 1;
+            const T dx = static_cast<T>(cx) - x;
+            const T dy = static_cast<T>(cy) - y;
+
+            for (int chan = 0; chan < data_channels; ++chan) {
+              const T img_fxfy = dx * dy * get_data_point(fx, fy, chan);
+              const T img_cxcy = (one - dx) * (one - dy) *
+                                   get_data_point(cx, cy, chan);
+              const T img_fxcy = dx * (one - dy) *
+                                   get_data_point(fx, cy, chan);
+              const T img_cxfy = (one - dx) * dy *
+                                   get_data_point(cx, fy, chan);
+              set_output(sample_id, chan,
+                         img_fxfy + img_cxcy + img_fxcy + img_cxfy);
+            }
+          } else {
+            for (int chan = 0; chan < data_channels; ++chan) {
+              set_output(sample_id, chan, zero);
+            }
+          }
+        }
+      }
+    };
+    // Rough estimate of work for each batch entry.
+    // From third_party/tensorflow/core/util/work_sharder.cc we gather that an
+    // estimate of the cost of each work unit is needed to correclty shard the
+    // workload. Shard assumes each cost unit is 1ns, minimum cost per shard
+    // being 10us.
+    const int64 cost =  static_cast<int64>(num_sampling_points) *
+        data_channels * 1000;
+    auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
+    ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers,
+                        batch_size, cost, resample_batches);
+  }
+};
+
+}  // namespace functor
+
+template <typename Device, typename T>
+class ResamplerOp : public ::tensorflow::OpKernel {
+ public:
+  explicit ResamplerOp(::tensorflow::OpKernelConstruction* context) :
+      ::tensorflow::OpKernel(context) {}
+
+  void Compute(::tensorflow::OpKernelContext* ctx) override {
+    const ::tensorflow::Tensor& data = ctx->input(0);
+    const ::tensorflow::Tensor& warp = ctx->input(1);
+
+    const ::tensorflow::TensorShape& data_shape = data.shape();
+    OP_REQUIRES(ctx, data_shape.dims() == 4,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is currently supported. The "
+                    "input data shape must be [batch_size, data_height, "
+                    "data_width, data_channels], but is: ",
+                    data_shape.DebugString()));
+    const ::tensorflow::TensorShape& warp_shape = warp.shape();
+    OP_REQUIRES(ctx,
+                ::tensorflow::TensorShapeUtils::IsMatrixOrHigher(warp_shape),
+                ::tensorflow::errors::InvalidArgument(
+                    "warp should be at least a matrix, got shape ",
+                    warp_shape.DebugString()));
+    OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims()-1) == 2,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is supported, warping "
+                    "coordinates must be 2D; warp shape last entry should be "
+                    "2, but shape vector is: ", warp_shape.DebugString()));
+    OP_REQUIRES(ctx, data_shape.dim_size(0) == warp_shape.dim_size(0),
+                ::tensorflow::errors::InvalidArgument(
+                    "Batch size of data and warp tensor must be the same, but "
+                    "input shapes are: ", data_shape.DebugString(), ", ",
+                    warp_shape.DebugString()));
+    const int batch_size = data_shape.dim_size(0);
+    const int data_height = data_shape.dim_size(1);
+    const int data_width = data_shape.dim_size(2);
+    const int data_channels = data_shape.dim_size(3);
+    ::tensorflow::TensorShape output_shape = warp.shape();
+    output_shape.set_dim(output_shape.dims() - 1, data_channels);
+    const int num_sampling_points = warp.NumElements() / batch_size / 2;
+    ::tensorflow::Tensor* output = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, output_shape, &output));
+
+    // Execute kernel only for nonempty output; otherwise Eigen crashes on GPU.
+    if (num_sampling_points > 0) {
+      functor::Resampler2DFunctor<Device, T>()(ctx,
+                                               ctx->eigen_device<Device>(),
+                                               data.flat<T>().data(),
+                                               warp.flat<T>().data(),
+                                               output->flat<T>().data(),
+                                               batch_size,
+                                               data_height,
+                                               data_width,
+                                               data_channels,
+                                               num_sampling_points);
+    }
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ResamplerOp);
+};
+
+
+#define REGISTER(TYPE)                       \
+  REGISTER_KERNEL_BUILDER(                   \
+      Name("Resampler")                      \
+          .Device(DEVICE_CPU)  \
+          .TypeConstraint<TYPE>("T"),        \
+      ResamplerOp<CPUDevice, TYPE>);
+
+TF_CALL_half(REGISTER);
+TF_CALL_float(REGISTER);
+TF_CALL_double(REGISTER);
+#undef REGISTER
+
+#if GOOGLE_CUDA
+#define REGISTER(TYPE)                                           \
+  REGISTER_KERNEL_BUILDER(Name("Resampler")                      \
+                              .Device(DEVICE_GPU)  \
+                              .TypeConstraint<TYPE>("T"),        \
+                          ResamplerOp<GPUDevice, TYPE>)
+TF_CALL_float(REGISTER);
+TF_CALL_double(REGISTER);
+#undef REGISTER
+#endif  // GOOGLE_CUDA
+
+
+namespace functor {
+
+template <typename T>
+struct ResamplerGrad2DFunctor<CPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const CPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   const T* __restrict__ grad_output,
+                   T* __restrict__ grad_data,
+                   T* __restrict__ grad_warp,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points){
+    // Set gradients to 0, because the kernel incrementally updates the
+    // tensor entries by adding partial contributions.
+    const int resampler_output_size = batch_size * num_sampling_points *
+        data_channels;
+    const int grad_warp_size = resampler_output_size / data_channels * 2;
+    const int grad_data_size = data_height * data_width * data_channels *
+        batch_size;
+    memset(grad_data, 0, sizeof(T) * grad_data_size);
+    memset(grad_warp, 0, sizeof(T) * grad_warp_size);
+
+    const auto&& data_batch_stride = data_height * data_width * data_channels;
+    const auto&& warp_batch_stride = num_sampling_points * 2;
+    const int output_batch_stride = num_sampling_points * data_channels;
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+
+    auto update_grads_for_batches = [&](const int start, const int limit) {
+      for (int batch_id = start; batch_id < limit; ++batch_id) {
+        // Utility lambdas to access data and update gradient tensors.
+        // The functions take care of performing the relevant pointer
+        // arithmetics abstracting away the low level details in the
+        // main loop over samples. Note that data is stored in NHWC format.
+        auto get_data_point = [&](const int x,
+                                  const int y,
+                                  const int chan) {
+          const bool point_is_in_range =
+            (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1);
+          return point_is_in_range
+                 ? data[batch_id * data_batch_stride +
+                        data_channels * (y * data_width + x) +
+                        chan]
+                 : zero;
+        };
+
+        auto update_grad_data = [&](const int x, const int y, const int chan,
+                                    const T value) {
+          const bool point_is_in_range =
+              (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1);
+          if (point_is_in_range){
+            grad_data[batch_id * data_batch_stride +
+                      data_channels * (y * data_width + x) +
+                      chan] += value;
+          }
+        };
+
+        auto update_grad_warp = [&](const int sample_id,
+                                    const int channel,
+                                    const T value) {
+          grad_warp[batch_id * warp_batch_stride +
+                    sample_id * 2 +
+                    channel] += value;
+        };
+
+        for (int sample_id = 0; sample_id < num_sampling_points; ++sample_id) {
+          const T x = warp[batch_id * warp_batch_stride + sample_id * 2];
+          const T y = warp[batch_id * warp_batch_stride + sample_id * 2 + 1];
+          // The interpolation function whose gradient this function implements:
+          // a) implicitly pads the input data with 0s (hence the unusual checks
+          // with {x,y} > -1)
+          // b) returns 0 when sampling outside the (padded) image.
+          // The effect is that the sampled signal smoothly goes to 0 outside
+          // the original input domain, rather than presenting a jump
+          // discontinuity at the image boundaries.
+          if (x > static_cast<T>(-1.0) &&
+              y > static_cast<T>(-1.0) &&
+              x < static_cast<T>(data_width) &&
+              y < static_cast<T>(data_height)) {
+            // Precompute floor (f) and ceil (c) values for x and y.
+            const int fx = std::floor(static_cast<float>(x));
+            const int fy = std::floor(static_cast<float>(y));
+            const int cx = fx + 1;
+            const int cy = fy + 1;
+            const T dx = static_cast<T>(cx) - x;
+            const T dy = static_cast<T>(cy) - y;
+
+            for (int chan = 0; chan < data_channels; ++chan) {
+              const T grad_output_value =
+                  grad_output[batch_id * output_batch_stride +
+                              sample_id * data_channels +
+                              chan];
+              const T img_fxfy = get_data_point(fx, fy, chan);
+              const T img_cxcy = get_data_point(cx, cy, chan);
+              const T img_fxcy = get_data_point(fx, cy, chan);
+              const T img_cxfy = get_data_point(cx, fy, chan);
+
+              // Update partial gradients wrt relevant warp field entries
+              update_grad_warp(sample_id, 0,
+                               grad_output_value *
+                                   ((one - dy) * (img_cxcy - img_fxcy) +
+                                    dy * (img_cxfy - img_fxfy)));
+
+              update_grad_warp(sample_id, 1,
+                               grad_output_value *
+                                   ((one - dx) * (img_cxcy - img_cxfy) +
+                                    dx * (img_fxcy - img_fxfy)));
+
+              // Update partial gradients wrt sampled data
+              update_grad_data(fx, fy, chan,
+                               grad_output_value * dx * dy);
+              update_grad_data(cx, cy, chan,
+                               grad_output_value * (one - dx) * (one - dy));
+              update_grad_data(fx, cy, chan,
+                               grad_output_value * dx * (one - dy));
+              update_grad_data(cx, fy, chan,
+                               grad_output_value * (one - dx) * dy);
+            }
+          }
+        }
+      }
+    };
+    // Rough estimate of work for each batch entry.
+    // From third_party/tensorflow/core/util/work_sharder.cc we gather that an
+    // estimate of the cost of each work unit is needed to correctly shard the
+    // workload. Shard assumes each cost unit is 1ns, minimum cost per shard
+    // being 10us.
+    // TODO(fviola): Check out if there is a better way of doing this.
+    auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
+    const int64 cost =  static_cast<int64>(num_sampling_points) *
+        data_channels * 1000;
+    ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers,
+                        batch_size, cost, update_grads_for_batches);
+  }
+};
+
+}  // namespace functor
+
+
+template <typename Device, typename T>
+class ResamplerGradOp : public ::tensorflow::OpKernel {
+ public:
+  explicit ResamplerGradOp(::tensorflow::OpKernelConstruction* context) :
+      ::tensorflow::OpKernel(context) {}
+
+  void Compute(::tensorflow::OpKernelContext* ctx) override {
+    const ::tensorflow::Tensor& data = ctx->input(0);
+    const ::tensorflow::Tensor& warp = ctx->input(1);
+    const ::tensorflow::Tensor& grad_output = ctx->input(2);
+
+    const ::tensorflow::TensorShape& data_shape = data.shape();
+    OP_REQUIRES(ctx, data_shape.dims() == 4,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is supported, the input data "
+                    "tensor must be a batch of 2d data; data shape should have "
+                    "4 entries corresponding to [batch_size, data_height, "
+                    "data_width, data_channels], but is: ",
+                data_shape.DebugString()));
+    const int batch_size = data_shape.dim_size(0);
+    const int data_height = data_shape.dim_size(1);
+    const int data_width = data_shape.dim_size(2);
+    const int data_channels = data_shape.dim_size(3);
+    const ::tensorflow::TensorShape& warp_shape = warp.shape();
+    OP_REQUIRES(ctx,
+                ::tensorflow::TensorShapeUtils::IsMatrixOrHigher(warp_shape),
+                ::tensorflow::errors::InvalidArgument(
+                    "warp should be at least a matrix, got shape ",
+                    warp_shape.DebugString()));
+    OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims()-1) == 2,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is supported, warping "
+                    "coordinates must be 2D; warp shape last entry should be "
+                    "2, but shape vector is: ",
+                    warp_shape.DebugString()));
+    const ::tensorflow::TensorShape& grad_output_shape = grad_output.shape();
+    ::tensorflow::TensorShape resampler_output_shape = warp.shape();
+    resampler_output_shape.set_dim(resampler_output_shape.dims() - 1,
+                                   data_channels);
+    OP_REQUIRES(ctx, grad_output_shape == resampler_output_shape,
+                ::tensorflow::errors::InvalidArgument(
+                   "grad_output shape is not consistent with data and warp "
+                   "shapes; it should be ",
+                   resampler_output_shape.DebugString(), " but is ",
+                   grad_output_shape.DebugString()))
+    const int num_sampling_points = warp.NumElements() / batch_size / 2;
+    ::tensorflow::Tensor* grad_data = nullptr;
+    ::tensorflow::Tensor* grad_warp = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, data.shape(), &grad_data));
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(1, warp.shape(), &grad_warp));
+    // Execute kernel only for nonempty output; otherwise Eigen crashes on GPU.
+    if (num_sampling_points > 0) {
+      functor::ResamplerGrad2DFunctor<Device, T>()(ctx,
+                                                   ctx->eigen_device<Device>(),
+                                                   data.flat<T>().data(),
+                                                   warp.flat<T>().data(),
+                                                   grad_output.flat<T>().data(),
+                                                   grad_data->flat<T>().data(),
+                                                   grad_warp->flat<T>().data(),
+                                                   batch_size,
+                                                   data_height,
+                                                   data_width,
+                                                   data_channels,
+                                                   num_sampling_points);
+    }
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ResamplerGradOp);
+};
+
+#define REGISTER(TYPE)                       \
+  REGISTER_KERNEL_BUILDER(                   \
+      Name("ResamplerGrad")                  \
+          .Device(DEVICE_CPU)  \
+          .TypeConstraint<TYPE>("T"),        \
+      ResamplerGradOp<CPUDevice, TYPE>);
+
+TF_CALL_half(REGISTER);
+TF_CALL_float(REGISTER);
+TF_CALL_double(REGISTER);
+#undef REGISTER
+
+#if GOOGLE_CUDA
+#define REGISTER(TYPE)                                           \
+  REGISTER_KERNEL_BUILDER(Name("ResamplerGrad")                  \
+                              .Device(DEVICE_GPU)  \
+                              .TypeConstraint<TYPE>("T"),        \
+                          ResamplerGradOp<GPUDevice, TYPE>)
+// Disable half and double precision since atomicAdds are not supported
+// TF_CALL_half(REGISTER);
+// TF_CALL_double(REGISTER);
+TF_CALL_float(REGISTER);
+
+#undef REGISTER
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops.h b/tensorflow/contrib/resampler/kernels/resampler_ops.h
new file mode 100644
index 0000000000..8258ecaf5d
--- /dev/null
+++ b/tensorflow/contrib/resampler/kernels/resampler_ops.h
@@ -0,0 +1,68 @@
+// Copyright 2017 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_RESAMPLER_KERNELS_RESAMPLER_OPS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_RESAMPLER_KERNELS_RESAMPLER_OPS_H_
+
+#if PLATFORM_WINDOWS
+#define __restrict__ __restrict
+#endif
+
+namespace tensorflow {
+class OpKernelContext;
+}
+
+namespace tensorflow {
+namespace functor {
+
+// Helper functor for the Resampler Op in 2D
+template <typename Device, typename T>
+struct Resampler2DFunctor{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const Device& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   T* __restrict__ output,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points);
+};
+
+
+// Helper functor for the Resampler Gradient Op in 2D
+template <typename Device, typename T>
+struct ResamplerGrad2DFunctor{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const Device& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   const T* __restrict__ grad_output,
+                   T* __restrict__ grad_data,
+                   T* __restrict__ grad_warp,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points);
+};
+
+
+}  // namespace functor
+}  // namespace tensorflow
+
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_RESAMPLER_KERNELS_RESAMPLER_OPS_H_
diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc b/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc
new file mode 100644
index 0000000000..636847a212
--- /dev/null
+++ b/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc
@@ -0,0 +1,310 @@
+// Copyright 2016 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "tensorflow/contrib/resampler/kernels/resampler_ops.h"
+
+#include <stdio.h>
+#include <cmath>
+
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
+namespace tensorflow {
+
+using GPUDevice = Eigen::GpuDevice;
+
+namespace {
+
+#define GET_DATA_POINT(x, y)                   \
+  data[batch_id * data_batch_stride +          \
+       data_channels * (y * data_width + x) +  \
+       chan]
+
+template <typename T>
+__global__ void Resampler2DKernel(const T* __restrict__ data,
+                                  const T* __restrict__ warp,
+                                  T* __restrict__ output,
+                                  const int batch_size,
+                                  const int data_height,
+                                  const int data_width,
+                                  const int data_channels,
+                                  const int num_sampling_points) {
+  const int output_data_size = batch_size * num_sampling_points * data_channels;
+  CUDA_1D_KERNEL_LOOP(index, output_data_size) {
+    const int out_index = index;
+
+    // Get (idxSample, channel, point) from the index.
+    // Use this formula
+    //   index = batch_id * num_sampling_points * num_chans +
+    //           sample_id * num_chans + chan_id,
+    // with sample_id = [0, ... ,num_sampling_points)
+    const int data_batch_stride = data_height * data_width * data_channels;
+    const int warp_batch_stride = num_sampling_points * 2;
+    const int output_batch_stride = num_sampling_points * data_channels;
+
+    const int batch_id = index / output_batch_stride;
+    const int index_in_batch = index % output_batch_stride;
+    const int chan = index_in_batch % data_channels;
+    const int sample_id = index_in_batch / data_channels;
+
+    // Get coords of 2D point where data will be resampled
+    const T x = warp[batch_id * warp_batch_stride + sample_id * 2];
+    const T y = warp[batch_id * warp_batch_stride + sample_id * 2 + 1];
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+    // The interpolation function:
+    // a) implicitly pads the input data with 0s (hence the unusual checks
+    // with {x,y} > -1)
+    // b) returns 0 when sampling outside the (padded) image.
+    // The effect is that the sampled signal smoothly goes to 0 outside
+    // the original input domain, rather than presenting a jump
+    // discontinuity at the image boundaries.
+    if (x > static_cast<T>(-1.0) &&
+        y > static_cast<T>(-1.0) &&
+        x < static_cast<T>(data_width) &&
+        y < static_cast<T>(data_height)) {
+      // Precompute floor (f) and ceil (c) values for x and y.
+      const int fx = std::floor(static_cast<float>(x));
+      const int fy = std::floor(static_cast<float>(y));
+      const int cx = fx + 1;
+      const int cy = fy + 1;
+      const T dx = static_cast<T>(cx) - x;
+      const T dy = static_cast<T>(cy) - y;
+
+      const T img_fxfy = (fx >= 0 && fy >= 0)
+                         ? dx * dy * GET_DATA_POINT(fx, fy)
+                         : zero;
+
+      const T img_cxcy = (cx <= data_width - 1 && cy <= data_height - 1)
+                         ? (one - dx) * (one - dy) * GET_DATA_POINT(cx, cy)
+                         : zero;
+
+      const T img_fxcy = (fx >= 0 && cy <= data_height - 1)
+                         ? dx * (one - dy) * GET_DATA_POINT(fx, cy)
+                         : zero;
+
+      const T img_cxfy = (cx <= data_width - 1 && fy >= 0)
+                         ? (one - dx) * dy * GET_DATA_POINT(cx, fy)
+                         : zero;
+
+      output[out_index] = img_fxfy + img_cxcy + img_fxcy + img_cxfy;
+    } else {
+      output[out_index] = zero;
+    }
+  }
+}
+
+}  // namespace
+
+namespace functor {
+
+template <typename T>
+struct Resampler2DFunctor<GPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const GPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   T* __restrict__ output,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points) {
+  const int output_data_size = batch_size * num_sampling_points * data_channels;
+  ::tensorflow::CudaLaunchConfig config =
+      ::tensorflow::GetCudaLaunchConfig(output_data_size, d);
+  Resampler2DKernel<T>
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          data, warp, output, batch_size, data_height, data_width,
+          data_channels, num_sampling_points);
+  }
+};
+
+// TODO(fviola): gcudacc fails at compile time with Eigen::half.
+// template struct Resampler2DFunctor<GPUDevice, Eigen::half>;
+template struct Resampler2DFunctor<GPUDevice, float>;
+template struct Resampler2DFunctor<GPUDevice, double>;
+
+}  // namespace functor
+
+namespace {
+
+#define UPDATE_GRAD_DATA_POINT(x, y, v)                  \
+  atomicAdd(grad_data + (batch_id * data_batch_stride +  \
+            data_channels * (y * data_width + x) +       \
+            chan),                                       \
+            v)
+
+
+template <typename T>
+__global__ void ResamplerGrad2DKernel(const T* __restrict__ data,
+                                      const T* __restrict__ warp,
+                                      const T* __restrict__ grad_output,
+                                      T* __restrict__ grad_data,
+                                      T* __restrict__ grad_warp,
+                                      const int batch_size,
+                                      const int data_height,
+                                      const int data_width,
+                                      const int data_channels,
+                                      const int num_sampling_points) {
+  const int resampler_output_size = batch_size * num_sampling_points *
+      data_channels;
+  CUDA_1D_KERNEL_LOOP(index, resampler_output_size) {
+    const int out_index = index;
+
+    // Get (idxSample, channel, point) from the index.
+    // Use this formula
+    //   index = batch_id * num_sampling_points * num_chans +
+    //           sample_id * num_chans + chan_id,
+    // with sample_id = [0, ... ,num_sampling_points)
+    const int data_batch_stride = data_height * data_width * data_channels;
+    const int warp_batch_stride = num_sampling_points * 2;
+    const int output_batch_stride = num_sampling_points * data_channels;
+
+    const int batch_id = index / output_batch_stride;
+    const int index_in_batch = index % output_batch_stride;
+    const int chan = index_in_batch % data_channels;
+    const int sample_id = index_in_batch / data_channels;
+
+    // Get coords of 2D point where data will be resampled
+    const int warp_id_x = batch_id * warp_batch_stride + sample_id * 2;
+    const int warp_id_y = warp_id_x + 1;
+    const T x = warp[warp_id_x];
+    const T y = warp[warp_id_y];
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+
+    // Get grad output
+    const T grad_output_value = grad_output[out_index];
+    // The interpolation function whose gradient this kernel implements:
+    // a) implicitly pads the input data with 0s (hence the unusual checks
+    // with {x,y} > -1)
+    // b) returns 0 when sampling outside the (padded) image.
+    // The effect is that the sampled signal smoothly goes to 0 outside
+    // the original input domain, rather than presenting a jump
+    // discontinuity at the image boundaries.
+    if (x > static_cast<T>(-1.0) &&
+        y > static_cast<T>(-1.0) &&
+        x < static_cast<T>(data_width) &&
+        y < static_cast<T>(data_height)) {
+      // Precompute floor (f) and ceil (c) values for x and y.
+      const int fx = std::floor(static_cast<float>(x));
+      const int fy = std::floor(static_cast<float>(y));
+      const int cx = fx + 1;
+      const int cy = fy + 1;
+      const T dx = static_cast<T>(cx) - x;
+      const T dy = static_cast<T>(cy) - y;
+
+      const T img_fxfy = (fx >= 0 && fy >= 0)
+                         ? GET_DATA_POINT(fx, fy)
+                         : zero;
+
+      const T img_cxcy = (cx <= data_width - 1 && cy <= data_height - 1)
+                         ? GET_DATA_POINT(cx, cy)
+                         : zero;
+
+      const T img_fxcy = (fx >= 0 && cy <= data_height - 1)
+                         ? GET_DATA_POINT(fx, cy)
+                         : zero;
+
+      const T img_cxfy = (cx <= data_width - 1 && fy >= 0)
+                         ? GET_DATA_POINT(cx, fy)
+                         : zero;
+
+      // Update partial gradients wrt relevant warp field entries
+      atomicAdd(grad_warp + warp_id_x,
+                grad_output_value * ((one - dy) * (img_cxcy - img_fxcy) +
+                                     dy * (img_cxfy - img_fxfy)));
+      atomicAdd(grad_warp + warp_id_y,
+                grad_output_value * ((one - dx) * (img_cxcy - img_cxfy) +
+                                     dx * (img_fxcy - img_fxfy)));
+
+      // Update partial gradients wrt sampled data
+      if (fx >= 0 && fy >= 0) {
+        UPDATE_GRAD_DATA_POINT(fx, fy, grad_output_value * dx * dy);
+      }
+      if (cx <= data_width - 1 && cy <= data_height - 1) {
+        UPDATE_GRAD_DATA_POINT(cx, cy,
+                               grad_output_value  * (one - dx) * (one - dy));
+      }
+      if (fx >= 0 && cy <= data_height - 1) {
+        UPDATE_GRAD_DATA_POINT(fx, cy, grad_output_value * dx * (one - dy));
+      }
+      if (cx <= data_width - 1 && fy >= 0) {
+        UPDATE_GRAD_DATA_POINT(cx, fy, grad_output_value * (one - dx) * dy);
+      }
+    }
+  }
+}
+
+#undef GET_DATA_POINT
+#undef UPDATE_GRAD_DATA_POINT
+
+}  // namespace
+
+namespace functor {
+
+template <typename T>
+struct ResamplerGrad2DFunctor<GPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const GPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   const T* __restrict__ grad_output,
+                   T* __restrict__ grad_data,
+                   T* __restrict__ grad_warp,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points) {
+  // Set gradients to 0, because the kernel incrementally updates the
+  // tensor entries by adding partial contributions.
+  const int grad_warp_size = batch_size * num_sampling_points * 2;
+  const int grad_data_size = batch_size * data_height * data_width *
+      data_channels;
+
+  ::tensorflow::CudaLaunchConfig config =
+     ::tensorflow::GetCudaLaunchConfig(grad_warp_size, d);
+  ::tensorflow::SetZero
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          grad_warp_size, grad_warp);
+
+  config = ::tensorflow::GetCudaLaunchConfig(grad_data_size, d);
+  ::tensorflow::SetZero
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          grad_data_size, grad_data);
+
+  const int resampler_output_size = batch_size * num_sampling_points *
+      data_channels;
+  config = ::tensorflow::GetCudaLaunchConfig(resampler_output_size, d);
+  ResamplerGrad2DKernel<T>
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          data, warp, grad_output, grad_data, grad_warp, batch_size,
+          data_height, data_width, data_channels, num_sampling_points);
+  }
+};
+
+template struct ResamplerGrad2DFunctor<GPUDevice, float>;
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/resampler/ops/resampler_ops.cc b/tensorflow/contrib/resampler/ops/resampler_ops.cc
new file mode 100644
index 0000000000..5ab212032e
--- /dev/null
+++ b/tensorflow/contrib/resampler/ops/resampler_ops.cc
@@ -0,0 +1,59 @@
+// Copyright 2017 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using ::tensorflow::shape_inference::InferenceContext;
+using ::tensorflow::shape_inference::ShapeHandle;
+
+REGISTER_OP("Resampler")
+    .Input("data: T")
+    .Input("warp: T")
+    .Output("output: T")
+    .Attr("T: {half, float, double}")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle data;
+      ShapeHandle warp;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &data));
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &warp));
+
+      ShapeHandle output;  // will be warp[:-1] + [data[-1]]
+      TF_RETURN_IF_ERROR(c->Subshape(warp, 0, -1, &output));
+      TF_RETURN_IF_ERROR(
+          c->Concatenate(output, c->Vector(c->Dim(data, -1)), &output));
+
+      c->set_output(0, output);
+      return ::tensorflow::Status::OK();
+    })
+    .Doc(R"doc(Resampler op.)doc");
+
+REGISTER_OP("ResamplerGrad")
+    .Input("data: T")
+    .Input("warp: T")
+    .Input("grad_output: T")
+    .Output("grad_data: T")
+    .Output("grad_warp: T")
+    .Attr("T: {half, float, double}")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->input(0));
+      c->set_output(1, c->input(1));
+      return ::tensorflow::Status::OK();
+    })
+    .Doc(R"doc(Resampler Grad op.)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/resampler/python/__init__.py b/tensorflow/contrib/resampler/python/__init__.py
new file mode 100644
index 0000000000..c5ca3a623f
--- /dev/null
+++ b/tensorflow/contrib/resampler/python/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""ops module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
diff --git a/tensorflow/contrib/resampler/python/ops/resampler_ops.py b/tensorflow/contrib/resampler/python/ops/resampler_ops.py
new file mode 100644
index 0000000000..355d15f0c7
--- /dev/null
+++ b/tensorflow/contrib/resampler/python/ops/resampler_ops.py
@@ -0,0 +1,69 @@
+# pylint: disable=g-bad-file-header
+# Copyright 2017 The Sonnet Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Tensorflow op performing differentiable resampling."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.resampler.ops import gen_resampler_ops
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import resource_loader
+
+_resampler_so = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_resampler_ops.so"))
+
+
+def resampler(data, warp, name="resampler"):
+  """Resamples input data at user defined coordinates.
+
+  The resampler currently only supports bilinear interpolation of 2D data.
+
+  Args:
+    data: Tensor of shape `[batch_size, data_height, data_width,
+      data_num_channels]` containing 2D data that will be resampled.
+    warp: Tensor of minimum rank 2 containing the coordinates at which
+      resampling will be performed. Since only bilinear interpolation is
+      currently supported, the last dimension of the `warp` tensor must be 2.
+    name: Optional name of the op.
+
+  Returns:
+    Tensor of resampled values from `data`. The output tensor shape is
+    determined by the shape of the warp tensor. For example, if `data` is of
+    shape `[batch_size, data_height, data_width, data_num_channels]` and warp of
+    shape `[batch_size, dim_0, ... , dim_n, 2]` the output will be of shape
+    `[batch_size, dim_0, ... , dim_n, data_num_channels]`.
+
+  Raises:
+    ImportError: if the wrapper generated during compilation is not present when
+    the function is called.
+  """
+  with ops.name_scope(name, "resampler", [data, warp]):
+    data_tensor = ops.convert_to_tensor(data, name="data")
+    warp_tensor = ops.convert_to_tensor(warp, name="warp")
+    return gen_resampler_ops.resampler(data_tensor, warp_tensor)
+
+
+@ops.RegisterGradient("Resampler")
+def _resampler_grad(op, grad_output):
+  data, warp = op.inputs
+  grad_output_tensor = ops.convert_to_tensor(grad_output, name="grad_output")
+  return gen_resampler_ops.resampler_grad(data, warp, grad_output_tensor)
+
+
+ops.NotDifferentiable("ResamplerGrad")
diff --git a/tensorflow/contrib/resampler/python/ops/resampler_ops_test.py b/tensorflow/contrib/resampler/python/ops/resampler_ops_test.py
new file mode 100644
index 0000000000..6a4360150c
--- /dev/null
+++ b/tensorflow/contrib/resampler/python/ops/resampler_ops_test.py
@@ -0,0 +1,270 @@
+# pylint: disable=g-bad-file-header
+# Copyright 2017 The Sonnet Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Tests for contrib.resampler.python.ops.resampler_ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.contrib import resampler
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors_impl
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import test
+
+
+def _bilinearly_interpolate(data, x, y):
+  """Performs bilinenar interpolation of grid data at user defined coordinates.
+
+  This interpolation function:
+    a) implicitly pads the input data with 0s.
+    b) returns 0 when sampling outside the (padded) image.
+  The effect is that the sampled signal smoothly goes to 0 outside the original
+  input domain, rather than producing a jump discontinuity at the image
+  boundaries.
+
+  Args:
+    data: numpy array of shape `[data_height, data_width]` containing data
+      samples assumed to be defined at the corresponding pixel coordinates.
+    x: numpy array of shape `[warp_height, warp_width]` containing x coordinates
+      at which interpolation will be performed.
+    y: numpy array of shape `[warp_height, warp_width]` containing y coordinates
+      at which interpolation will be performed.
+
+  Returns:
+    Numpy array of shape `[warp_height, warp_width]` containing interpolated
+      values.
+  """
+  shape = x.shape
+  x = np.asarray(x) + 1
+  y = np.asarray(y) + 1
+  data = np.lib.pad(data, 1, "constant", constant_values=0)
+
+  x_0 = np.floor(x).astype(int)
+  x_1 = x_0 + 1
+  y_0 = np.floor(y).astype(int)
+  y_1 = y_0 + 1
+
+  x_0 = np.clip(x_0, 0, data.shape[1] - 1)
+  x_1 = np.clip(x_1, 0, data.shape[1] - 1)
+  y_0 = np.clip(y_0, 0, data.shape[0] - 1)
+  y_1 = np.clip(y_1, 0, data.shape[0] - 1)
+
+  i_a = data[y_0, x_0]
+  i_b = data[y_1, x_0]
+  i_c = data[y_0, x_1]
+  i_d = data[y_1, x_1]
+
+  w_a = (x_1 - x) * (y_1 - y)
+  w_b = (x_1 - x) * (y - y_0)
+  w_c = (x - x_0) * (y_1 - y)
+  w_d = (x - x_0) * (y - y_0)
+
+  samples = (w_a * i_a + w_b * i_b + w_c * i_c + w_d * i_d)
+  samples.reshape(shape)
+
+  return samples
+
+
+def _make_warp(batch_size, warp_height, warp_width, dtype):
+  """Creates batch of warping coordinates."""
+  x, y = np.meshgrid(np.linspace(0, warp_width - 1, warp_width),
+                     np.linspace(0, warp_height - 1, warp_height))
+  warp = np.concatenate((x.reshape([warp_height, warp_width, 1]),
+                         y.reshape([warp_height, warp_width, 1])), 2)
+  warp = np.tile(warp.reshape([1, warp_height, warp_width, 2]),
+                 [batch_size, 1, 1, 1])
+  warp += np.random.randn(*warp.shape)
+  return warp.astype(dtype)
+
+
+class ResamplerTest(test.TestCase):
+
+  def test_op_forward_pass_gpu_float32(self):
+    self._test_op_forward_pass(True, dtypes.float32, 1e-4)
+
+  def test_op_forward_pass_gpu_float64(self):
+    self._test_op_forward_pass(True, dtypes.float64, 1e-5)
+
+  def test_op_forward_pass_cpu_float16(self):
+    self._test_op_forward_pass(False, dtypes.float16, 1e-2)
+
+  def test_op_forward_pass_cpu_float32(self):
+    self._test_op_forward_pass(False, dtypes.float32, 1e-4)
+
+  def test_op_forward_pass_cpu_float64(self):
+    self._test_op_forward_pass(False, dtypes.float64, 1e-5)
+
+  def test_op_backward_pass_gpu_float32(self):
+    self._test_op_backward_pass(True, dtypes.float32, 1e-3)
+
+  def test_op_backward_pass_cpu_float16(self):
+    self._test_op_backward_pass(False, dtypes.float16, 1e-3)
+
+  def test_op_backward_pass_cpu_float32(self):
+    self._test_op_backward_pass(False, dtypes.float32, 1e-4)
+
+  def test_op_backward_pass_cpu_float64(self):
+    self._test_op_backward_pass(False, dtypes.float64, 1e-6)
+
+  def _test_op_forward_pass(self, on_gpu, dtype, tol):
+    np.random.seed(0)
+    data_width = 7
+    data_height = 9
+    data_channels = 5
+    warp_width = 4
+    warp_height = 8
+    batch_size = 10
+
+    warp = _make_warp(batch_size, warp_height, warp_width, dtype.as_numpy_dtype)
+    data_shape = (batch_size, data_height, data_width, data_channels)
+    data = np.random.rand(*data_shape).astype(dtype.as_numpy_dtype)
+
+    with self.test_session(use_gpu=on_gpu, force_gpu=False) as sess:
+      data_ph = array_ops.placeholder(dtype, shape=(None,) + data.shape[1:])
+      warp_ph = array_ops.placeholder(dtype, shape=(None,) + warp.shape[1:])
+      outputs = resampler.resampler(data=data_ph, warp=warp_ph)
+      self.assertEqual(outputs.get_shape().as_list(),
+                       [None, warp_height, warp_width, data_channels])
+      out = sess.run(outputs, feed_dict={data_ph: data, warp_ph: warp})
+
+    # Generate reference output via bilinear interpolation in numpy
+    reference_output = np.zeros_like(out)
+    for batch in xrange(batch_size):
+      for c in xrange(data_channels):
+        reference_output[batch, :, :, c] = _bilinearly_interpolate(
+            data[batch, :, :, c],
+            warp[batch, :, :, 0],
+            warp[batch, :, :, 1])
+
+    self.assertAllClose(out, reference_output, rtol=tol, atol=tol)
+
+  def _test_op_backward_pass(self, on_gpu, dtype, tol):
+    np.random.seed(13)
+    data_width = 5
+    data_height = 4
+    data_channels = 3
+    warp_width = 2
+    warp_height = 6
+    batch_size = 10
+
+    warp = _make_warp(batch_size, warp_height, warp_width, dtype.as_numpy_dtype)
+    data_shape = (batch_size, data_height, data_width, data_channels)
+    data = np.random.rand(*data_shape).astype(dtype.as_numpy_dtype)
+
+    with self.test_session(use_gpu=on_gpu, force_gpu=False):
+      data_tensor = constant_op.constant(data)
+      warp_tensor = constant_op.constant(warp)
+      output_tensor = resampler.resampler(data=data_tensor, warp=warp_tensor)
+
+      grads = test.compute_gradient([data_tensor, warp_tensor], [
+          data_tensor.get_shape().as_list(),
+          warp_tensor.get_shape().as_list()
+      ], output_tensor, output_tensor.get_shape().as_list(), [data, warp])
+
+      if not on_gpu:
+        # On CPU we perform numerical differentiation at the best available
+        # precision, and compare against that. This is necessary for test to
+        # pass for float16.
+        data_tensor_64 = constant_op.constant(data, dtype=dtypes.float64)
+        warp_tensor_64 = constant_op.constant(warp, dtype=dtypes.float64)
+        output_tensor_64 = resampler.resampler(data=data_tensor_64,
+                                               warp=warp_tensor_64)
+        grads_64 = test.compute_gradient([data_tensor_64, warp_tensor_64], [
+            data_tensor.get_shape().as_list(),
+            warp_tensor.get_shape().as_list()
+        ], output_tensor_64, output_tensor.get_shape().as_list(), [data, warp])
+
+        for g, g_64 in zip(grads, grads_64):
+          self.assertLess(np.fabs(g[0] - g_64[1]).max(), tol)
+
+      else:
+        for g in grads:
+          self.assertLess(np.fabs(g[0] - g[1]).max(), tol)
+
+  def test_op_errors(self):
+    data_width = 7
+    data_height = 9
+    data_depth = 3
+    data_channels = 5
+    warp_width = 4
+    warp_height = 8
+    batch_size = 10
+
+    # Input data shape is not defined over a 2D grid, i.e. its shape is not like
+    # (batch_size, data_height, data_width, data_channels).
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_depth,
+                    data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size, warp_height, warp_width, 2)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.UnimplementedError,
+                                   "Only bilinear interpolation is currently "
+                                   "supported."):
+        sess.run(outputs)
+
+    # Warp tensor must be at least a matrix, with shape [batch_size, 2].
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size,)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   "warp should be at least a matrix"):
+        sess.run(outputs)
+
+    # The batch size of the data and warp tensors must be the same.
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size+1, warp_height, warp_width, 2)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   "Batch size of data and warp tensor"):
+        sess.run(outputs)
+
+    # The warp tensor must contain 2D coordinates, i.e. its shape last dimension
+    # must be 2.
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size, warp_height, warp_width, 3)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.UnimplementedError,
+                                   "Only bilinear interpolation is supported, "
+                                   "warping"):
+        sess.run(outputs)
+
+
+if __name__ == "__main__":
+  test.main()
-- 
GitLab


From c42d2f52a8237e7e73e29a5658bfe0c83baee414 Mon Sep 17 00:00:00 2001
From: Sandip Giri <sgiri@us.ibm.com>
Date: Mon, 26 Jun 2017 08:55:55 -0400
Subject: [PATCH 0431/1375] Adding ppc64le support for test
 //tensorflow/core:platform_profile_utils_cpu_utils_test

---
 tensorflow/core/platform/profile_utils/cpu_utils.cc | 13 ++++++++++---
 tensorflow/core/platform/profile_utils/cpu_utils.h  |  6 +++++-
 .../core/platform/profile_utils/cpu_utils_test.cc   | 12 +++++++++---
 3 files changed, 24 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.cc b/tensorflow/core/platform/profile_utils/cpu_utils.cc
index c4bf4ed564..c44cec394c 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.cc
@@ -28,10 +28,17 @@ namespace profile_utils {
 
 static ICpuUtilsHelper* cpu_utils_helper_instance_ = nullptr;
 
-/* static */ uint64 CpuUtils::GetCycleCounterFrequency() {
-  static const uint64 cpu_frequency = GetCycleCounterFrequencyImpl();
-  return cpu_frequency;
+#if defined(__powerpc__) || defined(__ppc__)
+   /* static */ uint64 CpuUtils::GetCycleCounterFrequency() {
+     static const uint64 cpu_frequency = GetCycleCounterFrequencyImpl();
+     return cpu_frequency;
 }
+#else
+   /* static */ int64 CpuUtils::GetCycleCounterFrequency() {
+     static const int64 cpu_frequency = GetCycleCounterFrequencyImpl();
+     return cpu_frequency;
+}
+#endif
 
 /* static */ double CpuUtils::GetMicroSecPerClock() {
   static const double micro_sec_per_clock =
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h
index 2adf77fb7f..f2669f7afd 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.h
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.h
@@ -97,7 +97,11 @@ class CpuUtils {
   // Return cycle counter frequency.
   // As this method caches the cpu frequency internally,
   // the first call will incur overhead, but not subsequent calls.
-  static uint64 GetCycleCounterFrequency();
+  #if defined(__powerpc__) || defined(__ppc__)
+     static uint64 GetCycleCounterFrequency();
+  #else
+     static int64 GetCycleCounterFrequency();
+  #endif
 
   // Return micro secound per each clock
   // As this method caches the cpu frequency internally,
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
index a55ee0d916..3a574b2e50 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
@@ -53,9 +53,15 @@ TEST_F(CpuUtilsTest, CheckGetCurrentClockCycle) {
 }
 
 TEST_F(CpuUtilsTest, CheckCycleCounterFrequency) {
-  const uint64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
-  CHECK_GT(cpu_frequency, 0);
-  CHECK_NE(cpu_frequency, unsigned(CpuUtils::INVALID_FREQUENCY));
+  #if defined(__powerpc__) || defined(__ppc__)
+     const uint64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
+     CHECK_GT(cpu_frequency, 0);
+     CHECK_NE(cpu_frequency, unsigned(CpuUtils::INVALID_FREQUENCY));
+  #else
+     const int64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
+     CHECK_GT(cpu_frequency, 0);
+     CHECK_NE(cpu_frequency, CpuUtils::INVALID_FREQUENCY);
+  #endif
   if (DBG) {
     LOG(INFO) << "Cpu frequency = " << cpu_frequency;
   }
-- 
GitLab


From e9bdfd7a10c599b1b4216372acc9e5551be8621d Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Mon, 26 Jun 2017 09:07:53 -0700
Subject: [PATCH 0432/1375] [TPUEstimator] Make input_fn invoked properly with
 eval on CPU.

PiperOrigin-RevId: 160151890
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 27 +++++++++++--------
 tensorflow/python/estimator/estimator.py      | 17 +++++++-----
 tensorflow/python/estimator/estimator_test.py |  2 +-
 3 files changed, 28 insertions(+), 18 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 28a0525f89..1c2d3cfa03 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -189,7 +189,6 @@ class TpuEstimator(estimator_lib.Estimator):
         raise ValueError(
             'batch size {} must be divisible by number of shards {}'
             .format(params['batch_size'], config.tpu_config.num_shards))
-      params['batch_size'] //= config.tpu_config.num_shards
 
     if use_tpu:
       # config and params need to be copied because we haven't initialized
@@ -233,11 +232,12 @@ class TpuEstimator(estimator_lib.Estimator):
           collections=[ops.GraphKeys.GLOBAL_VARIABLES,
                        ops.GraphKeys.GLOBAL_STEP])
 
-  def _call_input_fn(self, input_fn):
+  def _call_input_fn(self, input_fn, mode):
     """Calls the input function.
 
     Args:
       input_fn: The input function.
+      mode: ModeKeys
 
     Returns:
       Either features or (features, labels) where features and labels are:
@@ -247,17 +247,22 @@ class TpuEstimator(estimator_lib.Estimator):
     Raises:
       ValueError: if input_fn takes invalid arguments.
     """
-    if not self.use_tpu:
-      return super(TpuEstimator, self)._call_input_fn(input_fn)
+    if not self.use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
+      return super(TpuEstimator, self)._call_input_fn(input_fn, mode)
 
     input_fn_args = estimator_lib._fn_args(input_fn)  # pylint: disable=protected-access
+    config = self.config
     kwargs = {}
     if 'params' in input_fn_args:
       kwargs['params'] = self.params
     if 'config' in input_fn_args:
-      kwargs['config'] = self.config
+      kwargs['config'] = config
 
-    job = _tpu_job(self.config)
+    # Now for TPU training.
+    if 'params' in kwargs and 'batch_size' in kwargs['params']:
+      kwargs['params']['batch_size'] //= config.tpu_config.num_shards
+
+    job = _tpu_job(config)
     def placement_function(index):
       if job is None:
         return '/replica:0/task:0/device:CPU:0'
@@ -266,7 +271,7 @@ class TpuEstimator(estimator_lib.Estimator):
 
     features = []
     labels = []
-    for i in range(self.config.tpu_config.num_shards):
+    for i in range(config.tpu_config.num_shards):
       with ops.device(placement_function(i)):
         result = input_fn(**kwargs)
         # input_fn may return either features or (features, labels)
@@ -389,13 +394,13 @@ def wrapped_model_fn(model_fn, run_config, params):
     """model_fn."""
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
     if mode != model_fn_lib.ModeKeys.TRAIN:
-      if isinstance(features, _PerShardOutput):
-        features = features.as_list()[0]
-      if isinstance(labels, _PerShardOutput):
-        labels = labels.as_list()[0]
       return _call_model_fn(model_fn, features, labels, mode, run_config,
                             params)
 
+    # Now for TPU training.
+    if params is not None and 'batch_size' in params:
+      params['batch_size'] //= run_config.tpu_config.num_shards
+
     assert isinstance(features, _PerShardOutput)
     features = features.as_list()
     if labels is not None:
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index b4940f975a..2077d53a7e 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -334,7 +334,8 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       self._create_and_assert_global_step(g)
-      features = self._get_features_from_input_fn(input_fn)
+      features = self._get_features_from_input_fn(
+          input_fn, model_fn_lib.ModeKeys.PREDICT)
       estimator_spec = self._call_model_fn(features, None,
                                            model_fn_lib.ModeKeys.PREDICT)
       predictions = self._extract_keys(estimator_spec.predictions, predict_keys)
@@ -484,8 +485,8 @@ class Estimator(object):
 
       return export_dir
 
-  def _get_features_from_input_fn(self, input_fn):
-    result = self._call_input_fn(input_fn)
+  def _get_features_from_input_fn(self, input_fn, mode):
+    result = self._call_input_fn(input_fn, mode)
     if not ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
       logging.warning('Input graph does not contain a QueueRunner. '
                       'That means predict yields forever. '
@@ -549,11 +550,12 @@ class Estimator(object):
     assert step.dtype.is_integer
     return step
 
-  def _call_input_fn(self, input_fn):
+  def _call_input_fn(self, input_fn, mode):
     """Calls the input function.
 
     Args:
       input_fn: The input function.
+      mode: ModeKeys
 
     Returns:
       Either features or (features, labels) where features and labels are:
@@ -563,6 +565,7 @@ class Estimator(object):
     Raises:
       ValueError: if input_fn takes invalid arguments.
     """
+    del mode  # unused
     input_fn_args = _fn_args(input_fn)
     kwargs = {}
     if 'params' in input_fn_args:
@@ -607,7 +610,8 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(input_fn)
+      features, labels = self._call_input_fn(
+          input_fn, model_fn_lib.ModeKeys.TRAIN)
       estimator_spec = self._call_model_fn(features, labels,
                                            model_fn_lib.ModeKeys.TRAIN)
       ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
@@ -689,7 +693,8 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(input_fn)
+      features, labels = self._call_input_fn(
+          input_fn, model_fn_lib.ModeKeys.EVAL)
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
 
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index a7990c5673..fb4cbfda8c 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -120,7 +120,7 @@ class EstimatorInheritanceConstraintTest(test.TestCase):
       def __init__(self):
         super(_Estimator, self).__init__(model_fn=dummy_model_fn)
 
-      def _call_input_fn(self, input_fn):
+      def _call_input_fn(self, input_fn, mode):
         return input_fn()
 
       def _create_global_step(self, graph):
-- 
GitLab


From 26cb8a192914a05d02a078729db6f4b26439ad0c Mon Sep 17 00:00:00 2001
From: Yan Chen <yanchen036@gmail.com>
Date: Tue, 27 Jun 2017 00:30:49 +0800
Subject: [PATCH 0433/1375] Arbitrary dim for tile (#10793)

* add sketch code

* add TileSimple on CPU, pass UT

* add gpu implementation of Tile; add unit test

* delete unused test

* fix compile problems of gpu

* delete unused headers

* fix linking problem of Tile struct

* use Tensor:tensor() instead of handcraft

* merge tile_functor into tile_ops

* add python unit test and delete c++

* delete tile_op c++ unit test in BUILD

* use Tensor::flat<T>() instead of Tensor::tensor_data()

* remove cast; add high rank tile_op test in shape_ops_test.py

* remove const cast

* fix buildifer error

* reorder BUILD

* add tile_functor into makefile
---
 tensorflow/contrib/makefile/tf_op_files.txt   |   1 +
 tensorflow/core/kernels/BUILD                 |   8 ++
 tensorflow/core/kernels/tile_functor.h        | 115 ++++++++++++++++++
 tensorflow/core/kernels/tile_functor_cpu.cc   |  85 +++++++++++++
 .../core/kernels/tile_functor_gpu.cu.cc       | 101 +++++++++++++++
 tensorflow/core/kernels/tile_ops.cc           |  76 ++++--------
 tensorflow/core/kernels/tile_ops_cpu_impl.h   |  35 +-----
 tensorflow/core/kernels/tile_ops_gpu_impl.h   |   1 -
 tensorflow/core/kernels/tile_ops_impl.h       |  25 +---
 .../python/kernel_tests/shape_ops_test.py     |  18 ++-
 10 files changed, 345 insertions(+), 120 deletions(-)
 create mode 100644 tensorflow/core/kernels/tile_functor.h
 create mode 100644 tensorflow/core/kernels/tile_functor_cpu.cc
 create mode 100644 tensorflow/core/kernels/tile_functor_gpu.cu.cc

diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 541d13aed1..9839307e9b 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -7,6 +7,7 @@ tensorflow/core/kernels/transpose_functor_cpu.cc
 tensorflow/core/kernels/training_op_helpers.cc
 tensorflow/core/kernels/training_ops.cc
 tensorflow/core/kernels/topk_op.cc
+tensorflow/core/kernels/tile_functor_cpu.cc
 tensorflow/core/kernels/tile_ops.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_1.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_2.cc
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 10acbfae0f..046ed37d82 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -722,6 +722,12 @@ tf_kernel_library(
 
 tf_kernel_library(
     name = "tile_ops",
+    srcs = ["tile_functor_cpu.cc"],
+    hdrs = ["tile_functor.h"],
+    gpu_srcs = [
+        "tile_functor.h",
+        "tile_functor_gpu.cu.cc",
+    ],
     prefix = "tile_ops",
     deps = ARRAY_DEPS,
 )
@@ -4128,6 +4134,7 @@ filegroup(
         "spacetobatch_functor.h",
         "spacetodepth_op.h",
         "tensor_array.h",
+        "tile_functor.h",
         "tile_ops_cpu_impl.h",
         "tile_ops_impl.h",
         "training_op_helpers.h",
@@ -4265,6 +4272,7 @@ filegroup(
         "summary_op.cc",
         "tensor_array.cc",
         "tensor_array_ops.cc",
+        "tile_functor_cpu.cc",
         "tile_ops.cc",
         "tile_ops_cpu_impl_1.cc",
         "tile_ops_cpu_impl_2.cc",
diff --git a/tensorflow/core/kernels/tile_functor.h b/tensorflow/core/kernels/tile_functor.h
new file mode 100644
index 0000000000..d80898b24f
--- /dev/null
+++ b/tensorflow/core/kernels/tile_functor.h
@@ -0,0 +1,115 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_KERNELS_TILE_FUNCTOR_H_
+#define TENSORFLOW_KERNELS_TILE_FUNCTOR_H_
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+namespace internal {
+
+// Helper to compute 'strides' given a tensor 'shape'. I.e.,
+// strides[i] = prod(shape.dim_size[(i+1):])
+template <typename Index>
+gtl::InlinedVector<Index, 8> ComputeStride(const TensorShape& shape) {
+  const int ndims = shape.dims();
+  gtl::InlinedVector<Index, 8> strides(ndims);
+  Index stride = 1;
+  for (int i = ndims - 1; i >= 0; --i) {
+    strides[i] = stride;
+    stride *= static_cast<Index>(shape.dim_size(i));
+  }
+  return strides;
+}
+
+
+// Device-specific naive implementation for tile.
+template <typename Device, typename T>
+void TileSimple(const Device& d, Tensor* out, const Tensor& in);
+
+template <typename Device, typename T, int NDIM>
+void TileUsingEigen(const Device& d, Tensor* out, const Tensor& in,
+                    const gtl::ArraySlice<int32>& broadcast_array) {
+  auto x = in.tensor<T, NDIM>();
+  auto y = out->tensor<T, NDIM>();
+
+  Eigen::array<int32, NDIM> b;
+  for (int i = 0; i < NDIM; ++i) b[i] = broadcast_array[i];
+  if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
+    // Use 32bit indexing to speed up the computations
+    To32Bit(y).device(d) = To32Bit(x).broadcast(b);
+  } else {
+    y.device(d) = x.broadcast(b);
+  }
+}
+
+template <typename Device, typename T>
+void TileUsingEigen(const Device& d, Tensor* out, const Tensor& in,
+                    const gtl::ArraySlice<int32>&) {
+  auto x = in.tensor<T, 0>();
+  auto y = out->tensor<T, 0>();
+  // In the scalar case we simply copy the input.
+  y.device(d) = x;
+}
+
+}  // end namespace internal
+
+namespace functor {
+
+template <typename Device, typename T>
+struct Tile {
+  void operator()(const Device& d, Tensor* out, const Tensor& in,
+                  const gtl::ArraySlice<int32> broadcast_array) const {
+    switch (in.dims()) {
+      case 0:
+        internal::TileUsingEigen<Device, T>(d, out, in, broadcast_array);
+        break;
+      case 1:
+        internal::TileUsingEigen<Device, T, 1>(d, out, in, broadcast_array);
+        break;
+      case 2:
+        internal::TileUsingEigen<Device, T, 2>(d, out, in, broadcast_array);
+        break;
+      case 3:
+        internal::TileUsingEigen<Device, T, 3>(d, out, in, broadcast_array);
+        break;
+      case 4:
+        internal::TileUsingEigen<Device, T, 4>(d, out, in, broadcast_array);
+        break;
+      case 5:
+        internal::TileUsingEigen<Device, T, 5>(d, out, in, broadcast_array);
+        break;
+      case 6:
+        internal::TileUsingEigen<Device, T, 6>(d, out, in, broadcast_array);
+        break;
+      case 7:
+        internal::TileUsingEigen<Device, T, 7>(d, out, in, broadcast_array);
+        break;
+      default:
+        internal::TileSimple<Device, T>(d, out, in);
+        break;
+    }
+  }
+};
+
+}  // end namespace functor
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_KERNELS_TILE_FUNCTOR_H_
diff --git a/tensorflow/core/kernels/tile_functor_cpu.cc b/tensorflow/core/kernels/tile_functor_cpu.cc
new file mode 100644
index 0000000000..3af00350b9
--- /dev/null
+++ b/tensorflow/core/kernels/tile_functor_cpu.cc
@@ -0,0 +1,85 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/kernels/tile_functor.h"
+
+namespace tensorflow {
+
+namespace internal {
+
+template <typename Device, typename T>
+void TileSimple(const Device& d, Tensor* out, const Tensor& in) {
+  const int ndims = in.dims();
+  const int64 nelem = out->NumElements();
+  gtl::InlinedVector<int64, 8> in_strides = ComputeStride<int64>(in.shape());
+  gtl::InlinedVector<int64, 8> out_strides = ComputeStride<int64>(out->shape());
+  const T* p = in.flat<T>().data();
+  T* q = out->flat<T>().data();
+
+  for (int64 o_idx = 0; o_idx < nelem; ++o_idx) {
+    int64 i_idx = 0;
+    int64 t = o_idx;
+    for (int i = 0; i < ndims; ++i) {
+      i_idx += t / out_strides[i] % in.dim_size(i) * in_strides[i];
+      t %= out_strides[i];
+    }
+    q[o_idx] = p[i_idx];
+  }
+}
+
+}  // end namespace internal
+
+namespace functor {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+
+// Register functors used for Tile functor.
+#define DEFINE_TYPE(T) template struct Tile<CPUDevice, T>;
+
+TF_CALL_bool(DEFINE_TYPE);
+TF_CALL_float(DEFINE_TYPE);
+TF_CALL_double(DEFINE_TYPE);
+TF_CALL_uint8(DEFINE_TYPE);
+TF_CALL_int32(DEFINE_TYPE);
+TF_CALL_int16(DEFINE_TYPE);
+TF_CALL_int64(DEFINE_TYPE);
+TF_CALL_half(DEFINE_TYPE);
+TF_CALL_complex64(DEFINE_TYPE);
+TF_CALL_complex128(DEFINE_TYPE);
+TF_CALL_string(DEFINE_TYPE);
+
+#undef DEFINE_TYPE
+
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+
+#define DEFINE_TYPE(T) template struct Tile<SYCLDevice, T>;
+
+TF_CALL_bool(DEFINE_TYPE);
+TF_CALL_float(DEFINE_TYPE);
+TF_CALL_double(DEFINE_TYPE);
+TF_CALL_uint8(DEFINE_TYPE);
+TF_CALL_int32(DEFINE_TYPE);
+TF_CALL_int16(DEFINE_TYPE);
+TF_CALL_int64(DEFINE_TYPE);
+
+#undef DEFINE_TYPE
+#endif // TENSORFLOW_USE_SYCL
+
+}  // end namespace functor
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/tile_functor_gpu.cu.cc b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
new file mode 100644
index 0000000000..08aca8c731
--- /dev/null
+++ b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
@@ -0,0 +1,101 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/kernels/tile_functor.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+#include "tensorflow/core/framework/register_types.h"
+
+namespace tensorflow {
+namespace internal {
+
+template <typename T>
+__global__ void TileKernel(int nthreads, const T* src, const int32* buf,
+                           const int32 ndims, T* dst) {
+  const int32* in_strides = buf;
+  const int32* out_strides = buf + ndims;
+  const int32* in_dim_sizes = buf + ndims * 2;
+  CUDA_1D_KERNEL_LOOP(o_idx, nthreads) {
+    int32 i_idx = 0;
+    int32 t = o_idx;
+    for (int i = 0; i < ndims; ++i) {
+      i_idx += t / out_strides[i] % in_dim_sizes[i] * in_strides[i];
+      t %= out_strides[i];
+    }
+    dst[o_idx] = ldg(src + i_idx);
+  }
+}
+
+template <typename Device, typename T>
+void TileSimple(const Device& d, Tensor* out, const Tensor& in) {
+  // Ensures we can use 32-bit index.
+  const int64 in_nelem = in.NumElements();
+  CHECK_LT(in_nelem, kint32max) << "Tensor too large to transpose on GPU";
+  const int64 out_nelem = out->NumElements();
+  CHECK_LT(out_nelem, kint32max) << "Tensor too large to transpose on GPU";
+  // Pack strides and input dimension sizes into one buffer.
+  const int32 ndims = in.dims();
+  gtl::InlinedVector<int32, 24> host_buf(ndims * 3);
+  gtl::InlinedVector<int32, 8> in_strides = ComputeStride<int32>(in.shape());
+  gtl::InlinedVector<int32, 8> out_strides = ComputeStride<int32>(out->shape());
+  for (int i = 0; i < ndims; ++i) {
+    host_buf[i] = in_strides[i];
+    host_buf[ndims + i] = out_strides[i];
+    host_buf[ndims * 2 + i] = in.dim_size(i);
+  }
+  // Copies the input strides, output strides and input dimension sizes to the device.
+  auto num_bytes = sizeof(int64) * host_buf.size();
+  auto dev_buf = d.allocate(num_bytes);
+  // NOTE: host_buf is not allocated by CudaHostAllocator, and
+  // therefore we are doing a sync copy effectively.
+  d.memcpyHostToDevice(dev_buf, host_buf.data(), num_bytes);
+  // Launch kernel to q[...] = p[...].
+  const T* p = in.flat<T>().data();
+  T* q = out->flat<T>().data();
+  CudaLaunchConfig cfg = GetCudaLaunchConfig(out_nelem, d);
+  TileKernel<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>(
+      cfg.virtual_thread_count, p, reinterpret_cast<const int32*>(dev_buf),
+      ndims, q);
+  // Safe to deallocate immediately after the kernel launch.
+  d.deallocate(dev_buf);
+}
+
+}  // end namespace internal
+
+namespace functor {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+// Register functors used for Tile functor.
+#define DEFINE_TYPE(T) template struct Tile<GPUDevice, T>;
+
+TF_CALL_int16(DEFINE_TYPE);
+TF_CALL_int32(DEFINE_TYPE);
+TF_CALL_int64(DEFINE_TYPE);
+TF_CALL_float(DEFINE_TYPE);
+TF_CALL_double(DEFINE_TYPE);
+TF_CALL_half(DEFINE_TYPE);
+TF_CALL_complex64(DEFINE_TYPE);
+TF_CALL_complex128(DEFINE_TYPE);
+
+#undef DEFINE_TYPE
+
+}  // end namespace functor
+}  // namespace tensorflow
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/tile_ops.cc b/tensorflow/core/kernels/tile_ops.cc
index 7c72487d3f..f1da3c8afb 100644
--- a/tensorflow/core/kernels/tile_ops.cc
+++ b/tensorflow/core/kernels/tile_ops.cc
@@ -44,21 +44,12 @@ typedef Eigen::GpuDevice GPUDevice;
 typedef Eigen::SyclDevice SYCLDevice;
 #endif // TENSORFLOW_USE_SYCL
 
-// Forward declarations of functors that will be defined in
-// tile_ops_cpu_impl*.cc and tile_ops_gpu.cu.cc.
+// Forward declarations of functors that will be defined in tile_ops_impl.h
 namespace functor {
-template <typename Device, typename T, int NDIM>
-struct Tile {
-  void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out,
-                  typename TTypes<T, NDIM>::ConstTensor in,
-                  const Eigen::array<int32, NDIM>& broadcast_array) const;
-};
-
 template <typename Device, typename T>
-struct Tile<Device, T, 0> {
-  void operator()(const Device& d, typename TTypes<T, 0>::Tensor out,
-                  typename TTypes<T, 0>::ConstTensor in,
-                  const Eigen::array<int32, 0>&) const;
+struct Tile {
+  void operator()(const Device& d, Tensor* out, const Tensor& in,
+                  const gtl::ArraySlice<int32> broadcast_array) const;
 };
 
 template <typename Device, typename T, int NDIM>
@@ -134,21 +125,12 @@ class TileOp : public OpKernel {
     // If there's no output, there's nothing to do.
     if (output_shape.num_elements() == 0) return;
 
-#define HANDLE_DIM(DT, NDIM)                                   \
-  if (context->input(0).dtype() == DT && input_dims == NDIM) { \
-    HandleCase<DT, NDIM>(context, multiples_array, result);    \
+#define HANDLE_TYPE(DT)                                        \
+  if (context->input(0).dtype() == DT) {                       \
+    HandleCase<DT>(context, multiples_array, result);          \
     return;                                                    \
   }
 
-#define HANDLE_TYPE(T) \
-  HANDLE_DIM(T, 1)     \
-  HANDLE_DIM(T, 2)     \
-  HANDLE_DIM(T, 3)     \
-  HANDLE_DIM(T, 4)     \
-  HANDLE_DIM(T, 5)     \
-  HANDLE_DIM(T, 6)     \
-  HANDLE_DIM(T, 7)
-
 #define HANDLE_TYPE_NAME(T) HANDLE_TYPE(DataTypeToEnum<T>::value)
 
     // Invoke macro using TF_CALL_* so type-filtering for platform applies.
@@ -166,7 +148,6 @@ class TileOp : public OpKernel {
 
 #undef HANDLE_TYPE_NAME
 #undef HANDLE_TYPE
-#undef HANDLE_DIM
 
     OP_REQUIRES(context, false,
                 errors::Unimplemented(
@@ -175,21 +156,17 @@ class TileOp : public OpKernel {
   }
 
  private:
-  template <DataType DT, int NDIM>
+  template <DataType DT>
   void HandleCaseImpl(OpKernelContext* context,
                       const gtl::ArraySlice<int32>& multiples_array,
                       Tensor* result) {
     typedef typename EnumToDataType<DT>::Type T;
-    Eigen::array<int32, NDIM> broadcast_array;
-    for (int i = 0; i < NDIM; ++i) {
-      broadcast_array[i] = multiples_array[i];
-    }
-    functor::Tile<Device, T, NDIM>()(
-        context->eigen_device<Device>(), result->tensor<T, NDIM>(),
-        context->input(0).tensor<T, NDIM>(), broadcast_array);
+    functor::Tile<Device, T>() (
+        context->eigen_device<Device>(), result,
+        context->input(0), multiples_array);
   }
 
-  template <DataType DT, int NDIM>
+  template <DataType DT>
   void HandleCase(OpKernelContext* context,
                   const gtl::ArraySlice<int32>& multiples_array,
                   Tensor* result);
@@ -198,45 +175,35 @@ class TileOp : public OpKernel {
 };
 
 template <typename Device>
-template <DataType DT, int NDIM>
+template <DataType DT>
 inline void TileOp<Device>::HandleCase(
     OpKernelContext* context, const gtl::ArraySlice<int32>& multiples_array,
     Tensor* result) {
   // TODO(vrv): print out the device name if useful. Currently disabled to avoid
   // having to use RTTI.
-  LOG(FATAL) << "TileOp: Invalid combination of Device, DT and NDIM: "
+  LOG(FATAL) << "TileOp: Invalid combination of Device, DT: "
              // << typeid(Device).name() << ", "
-             << DataTypeString(DT) << ", " << NDIM;
+             << DataTypeString(DT);
 }
 
-#define HANDLE_CASE(device, T, dtype, ndim)                            \
+#define HANDLE_CASE(device, dtype)                                     \
   template <>                                                          \
   template <>                                                          \
-  void TileOp<device>::HandleCase<dtype, ndim>(                        \
+  void TileOp<device>::HandleCase<dtype>(                              \
       OpKernelContext * context,                                       \
       const gtl::ArraySlice<int32>& multiples_array, Tensor* result) { \
-    HandleCaseImpl<dtype, ndim>(context, multiples_array, result);     \
+    HandleCaseImpl<dtype>(context, multiples_array, result);           \
   }
 
-// 0-D handled above
-#define HANDLE_CASE_DIM(device, T, dtype) \
-  HANDLE_CASE(device, T, dtype, 1);       \
-  HANDLE_CASE(device, T, dtype, 2);       \
-  HANDLE_CASE(device, T, dtype, 3);       \
-  HANDLE_CASE(device, T, dtype, 4);       \
-  HANDLE_CASE(device, T, dtype, 5);       \
-  HANDLE_CASE(device, T, dtype, 6);       \
-  HANDLE_CASE(device, T, dtype, 7);
-
 #define HANDLE_TYPE_NAME_CPU(T) \
-  HANDLE_CASE_DIM(CPUDevice, T, DataTypeToEnum<T>::value);
+  HANDLE_CASE(CPUDevice, DataTypeToEnum<T>::value);
 
 #define HANDLE_TYPE_NAME_GPU(T) \
-  HANDLE_CASE_DIM(GPUDevice, T, DataTypeToEnum<T>::value);
+  HANDLE_CASE(GPUDevice, DataTypeToEnum<T>::value);
 
 #ifdef TENSORFLOW_USE_SYCL
 #define HANDLE_TYPE_NAME_SYCL(T) \
-  HANDLE_CASE_DIM(SYCLDevice, T, DataTypeToEnum<T>::value);
+  HANDLE_CASE(SYCLDevice, DataTypeToEnum<T>::value);
 #endif // TENSORFLOW_USE_SYCL
 
 TF_CALL_bool(HANDLE_TYPE_NAME_CPU);
@@ -275,7 +242,6 @@ TF_CALL_int64(HANDLE_TYPE_NAME_SYCL);
 #ifdef TENSORFLOW_USE_SYCL
 #undef HANDLE_TYPE_NAME_SYCL
 #endif // TENSORFLOW_USE_SYCL
-#undef HANDLE_CASE_DIM
 #undef HANDLE_CASE
 
 // --------------------------------------------------------------------------
diff --git a/tensorflow/core/kernels/tile_ops_cpu_impl.h b/tensorflow/core/kernels/tile_ops_cpu_impl.h
index db3f046439..a6eed4935d 100644
--- a/tensorflow/core/kernels/tile_ops_cpu_impl.h
+++ b/tensorflow/core/kernels/tile_ops_cpu_impl.h
@@ -21,29 +21,11 @@ limitations under the License.
 #include "tensorflow/core/kernels/tile_ops_impl.h"
 
 namespace tensorflow {
+
 namespace functor {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 
-// Register functors used for TileOp.
-#define DEFINE_DIM(T, NDIM) template struct Tile<CPUDevice, T, NDIM>;
-#define DEFINE_TYPE(T) DEFINE_DIM(T, CPU_PROVIDED_IXDIM)
-
-TF_CALL_bool(DEFINE_TYPE);
-TF_CALL_float(DEFINE_TYPE);
-TF_CALL_double(DEFINE_TYPE);
-TF_CALL_uint8(DEFINE_TYPE);
-TF_CALL_int32(DEFINE_TYPE);
-TF_CALL_int16(DEFINE_TYPE);
-TF_CALL_int64(DEFINE_TYPE);
-TF_CALL_half(DEFINE_TYPE);
-TF_CALL_complex64(DEFINE_TYPE);
-TF_CALL_complex128(DEFINE_TYPE);
-TF_CALL_string(DEFINE_TYPE);
-
-#undef DEFINE_DIM
-#undef DEFINE_TYPE
-
 // Register functors used for TileGradientOp.
 #define DEFINE_DIM(T, NDIM)                     \
   template struct TileGrad<CPUDevice, T, NDIM>; \
@@ -65,21 +47,6 @@ TF_CALL_complex128(DEFINE_TYPE);
 #ifdef TENSORFLOW_USE_SYCL
 typedef Eigen::SyclDevice SYCLDevice;
 
-// Register functors used for TileOp.
-#define DEFINE_DIM(T, NDIM) template struct Tile<SYCLDevice, T, NDIM>;
-#define DEFINE_TYPE(T) DEFINE_DIM(T, CPU_PROVIDED_IXDIM)
-
-TF_CALL_bool(DEFINE_TYPE);
-TF_CALL_float(DEFINE_TYPE);
-TF_CALL_double(DEFINE_TYPE);
-TF_CALL_uint8(DEFINE_TYPE);
-TF_CALL_int32(DEFINE_TYPE);
-TF_CALL_int16(DEFINE_TYPE);
-TF_CALL_int64(DEFINE_TYPE);
-
-#undef DEFINE_DIM
-#undef DEFINE_TYPE
-
 // Register functors used for TileGradientOp.
 #define DEFINE_DIM(T, NDIM)                      \
   template struct TileGrad<SYCLDevice, T, NDIM>; \
diff --git a/tensorflow/core/kernels/tile_ops_gpu_impl.h b/tensorflow/core/kernels/tile_ops_gpu_impl.h
index ed89381103..592f99e9b7 100644
--- a/tensorflow/core/kernels/tile_ops_gpu_impl.h
+++ b/tensorflow/core/kernels/tile_ops_gpu_impl.h
@@ -39,7 +39,6 @@ limitations under the License.
 #include "tensorflow/core/kernels/tile_ops_impl.h"
 
 #define DEFINE_DIM(T, NDIM)                            \
-  template struct Tile<Eigen::GpuDevice, T, NDIM>;     \
   template struct TileGrad<Eigen::GpuDevice, T, NDIM>; \
   template struct ReduceAndReshape<Eigen::GpuDevice, T, NDIM, 1>;
 
diff --git a/tensorflow/core/kernels/tile_ops_impl.h b/tensorflow/core/kernels/tile_ops_impl.h
index c41e4bd74b..9861717a0b 100644
--- a/tensorflow/core/kernels/tile_ops_impl.h
+++ b/tensorflow/core/kernels/tile_ops_impl.h
@@ -21,31 +21,8 @@ limitations under the License.
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
-namespace functor {
 
-template <typename Device, typename T, int NDIM>
-struct Tile {
-  void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out,
-                  typename TTypes<T, NDIM>::ConstTensor in,
-                  const Eigen::array<int32, NDIM>& broadcast_array) const {
-    if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
-      // Use 32bit indexing to speed up the computations
-      To32Bit(out).device(d) = To32Bit(in).broadcast(broadcast_array);
-    } else {
-      out.device(d) = in.broadcast(broadcast_array);
-    }
-  }
-};
-
-template <typename Device, typename T>
-struct Tile<Device, T, 0> {
-  void operator()(const Device& d, typename TTypes<T, 0>::Tensor out,
-                  typename TTypes<T, 0>::ConstTensor in,
-                  const Eigen::array<int32, 0>&) const {
-    // In the scalar case we simply copy the input.
-    out.device(d) = in;
-  }
-};
+namespace functor {
 
 template <typename Device, typename T, int NDIM>
 struct TileGrad {
diff --git a/tensorflow/python/kernel_tests/shape_ops_test.py b/tensorflow/python/kernel_tests/shape_ops_test.py
index 97d61d52af..52cf904528 100644
--- a/tensorflow/python/kernel_tests/shape_ops_test.py
+++ b/tensorflow/python/kernel_tests/shape_ops_test.py
@@ -504,16 +504,16 @@ class TileTest(test.TestCase):
       with self.assertRaises(ValueError):
         array_ops.tile(a, [[2, 3], [3, 4]]).eval()
 
-  def _RunAndVerifyResult(self, use_gpu):
+  def _RunAndVerifyResult(self, rank, use_gpu):
     with self.test_session(use_gpu=use_gpu):
-      # Random dims of rank 5
-      input_shape = np.random.randint(1, 4, size=5)
+      # Random dims of given rank
+      input_shape = np.random.randint(1, 4, size=rank)
       inp = np.random.rand(*input_shape).astype("f")
       a = constant_op.constant(
           [float(x) for x in inp.ravel(order="C")],
           shape=input_shape,
           dtype=dtypes.float32)
-      multiples = np.random.randint(1, 4, size=5).astype(np.int32)
+      multiples = np.random.randint(1, 4, size=rank).astype(np.int32)
       tiled = array_ops.tile(a, multiples)
       result = tiled.eval()
     self.assertTrue((np.array(multiples) * np.array(inp.shape) == np.array(
@@ -522,10 +522,16 @@ class TileTest(test.TestCase):
     self.assertShapeEqual(result, tiled)
 
   def testRandom(self):
+    # test low rank, like 5
     for _ in range(5):
-      self._RunAndVerifyResult(use_gpu=False)
+      self._RunAndVerifyResult(5, use_gpu=False)
     for _ in range(5):
-      self._RunAndVerifyResult(use_gpu=True)
+      self._RunAndVerifyResult(5, use_gpu=True)
+    # test high rank, like 10
+    for _ in range(5):
+      self._RunAndVerifyResult(10, use_gpu=False)
+    for _ in range(5):
+      self._RunAndVerifyResult(10, use_gpu=True)
 
   def testGradientSimpleReduction(self):
     with self.test_session():
-- 
GitLab


From 838a19df11b53656d081447a74b7fb29aa18b5ac Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Tue, 27 Jun 2017 01:32:54 +0900
Subject: [PATCH 0434/1375] Fix typos (#11050)

---
 tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc         | 2 +-
 tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py      | 2 +-
 tensorflow/contrib/learn/python/learn/estimators/head.py        | 2 +-
 .../contrib/linalg/python/ops/linear_operator_test_util.py      | 2 +-
 tensorflow/core/framework/op_kernel.h                           | 2 +-
 tensorflow/examples/android/build.gradle                        | 2 +-
 tensorflow/python/ops/standard_ops.py                           | 2 +-
 7 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc b/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
index 093000559b..b0f9237ea2 100644
--- a/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
+++ b/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
@@ -60,7 +60,7 @@ class BigQueryReader : public ReaderBase {
     BigQueryTablePartition partition;
     if (!partition.ParseFromString(current_work())) {
       return errors::InvalidArgument(
-          "Could not parse work as as valid partition.");
+          "Could not parse work as valid partition.");
     }
     TF_RETURN_IF_ERROR(bigquery_table_accessor_->SetPartition(partition));
     return Status::OK();
diff --git a/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py b/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py
index 136707da18..cc8644bfd5 100644
--- a/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py
+++ b/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py
@@ -92,7 +92,7 @@ class BigQueryReader(io_ops.ReaderBase):
 
     Raises:
       TypeError: - If features is neither None nor a dict or
-                 - If columns is is neither None nor a list or
+                 - If columns is neither None nor a list or
                  - If both features and columns are None or set.
     """
     if (features is None) == (columns is None):
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index 6e15e7891e..fdc266d582 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -438,7 +438,7 @@ def loss_only_head(loss_fn, head_name=None):
   Args:
     loss_fn: a function that takes no argument and returns a list of
         scalar tensors.
-    head_name: a name for for the head.
+    head_name: a name for the head.
 
   Returns:
     An instance of `Head` to hold the additional losses.
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
index 3d316450d8..1924ea0f66 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
@@ -462,7 +462,7 @@ def random_tril_matrix(shape,
     remove_upper:  Python `bool`.
       If `True`, zero out the strictly upper triangle.
       If `False`, the lower triangle of returned matrix will have desired
-      properties, but will not not have the strictly upper triangle zero'd out.
+      properties, but will not have the strictly upper triangle zero'd out.
 
   Returns:
     `Tensor` with desired shape and dtype.
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index 465395d858..d85fbc2256 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -682,7 +682,7 @@ class OpKernelContext {
   void forward_ref_input_to_ref_output(int input_index, int output_index);
 
   // Returns true when an alias to input[input_index], reshaped to output_shape,
-  // which is is safe to use for in-place computation was written to *output.
+  // which is safe to use for in-place computation was written to *output.
   // Returns false if input[input_index] has a refcount greater than one, or if
   // its type does not match the expected output type of output[output_index],
   // or the number of elements in input[input_index] does not equal the number
diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle
index 001d57f88a..e97faad516 100644
--- a/tensorflow/examples/android/build.gradle
+++ b/tensorflow/examples/android/build.gradle
@@ -10,7 +10,7 @@
 // setting nativeBuildSystem below to 'makefile'. This will allow building the demo
 // on Windows machines, but note that full equivalence with the Bazel
 // build is not yet guaranteed. See comments below for caveats and tips
-// for speeding up the build, such as as enabling ccache.
+// for speeding up the build, such as enabling ccache.
 // NOTE: Running a make build will cause subsequent Bazel builds to *fail*
 // unless the contrib/makefile/downloads/ and gen/ dirs are deleted afterwards.
 
diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py
index a6b14f6f6f..30bf4e4ef1 100644
--- a/tensorflow/python/ops/standard_ops.py
+++ b/tensorflow/python/ops/standard_ops.py
@@ -145,7 +145,7 @@ _allowed_symbols_math_ops = [
     "sub",  # use tf.subtract instead.
 
     # These are documented in nn.
-    # We are are not importing nn because it would create a circular dependency.
+    # We are not importing nn because it would create a circular dependency.
     "sigmoid",
     "log_sigmoid",
     "tanh",
-- 
GitLab


From 9c35501b20499180a7428db45d6adeb4d9166e8b Mon Sep 17 00:00:00 2001
From: Till Hoffmann <tillahoffmann@gmail.com>
Date: Mon, 26 Jun 2017 17:37:18 +0100
Subject: [PATCH 0435/1375] Performance improvements for hessians. (#10830)

---
 tensorflow/python/ops/gradients_impl.py | 52 +++++++++++++------------
 1 file changed, 27 insertions(+), 25 deletions(-)

diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index bd8a5c86ac..52ce451238 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -34,6 +34,7 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import functional_ops
@@ -45,6 +46,7 @@ from tensorflow.python.ops import math_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import spectral_grad  # pylint: disable=unused-import
+from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.platform import tf_logging as logging
 
 
@@ -920,8 +922,6 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False,
   Raises:
     LookupError: if one of the operations between `xs` and `ys` does not
       have a registered gradient function.
-    ValueError: if the arguments are invalid or not supported. Currently,
-      this function only supports one-dimensional `x` in `xs`.
   """
   xs = _AsList(xs)
   kwargs = {
@@ -929,28 +929,30 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False,
       'gate_gradients': gate_gradients,
       'aggregation_method': aggregation_method
     }
-  # Compute a hessian matrix for each x in xs
+  # Compute first-order derivatives and iterate for each x in xs.
   hessians = []
-  for i, x in enumerate(xs):
-    # Check dimensions
-    ndims = x.get_shape().ndims
-    if ndims is None:
-      raise ValueError('Cannot compute Hessian because the dimensionality of '
-                       'element number %d of `xs` cannot be determined' % i)
-    elif ndims != 1:
-      raise ValueError('Computing hessians is currently only supported for '
-                       'one-dimensional tensors. Element number %d of `xs` has '
-                       '%d dimensions.' % (i, ndims))
-    with ops.name_scope(name + '_first_derivative'):
-      # Compute the partial derivatives of the input with respect to all
-      # elements of `x`
-      _gradients = gradients(ys, x, **kwargs)[0]
-      # Unpack the gradients into a list so we can take derivatives with
-      # respect to each element
-      _gradients = array_ops.unstack(_gradients)
-    with ops.name_scope(name + '_second_derivative'):
-      # Compute the partial derivatives with respect to each element of the list
-      _hess = [gradients(_gradient, x, **kwargs)[0] for _gradient in _gradients]
-      # Pack the list into a matrix and add to the list of hessians
-      hessians.append(array_ops.stack(_hess, name=name))
+  _gradients = gradients(ys, xs, **kwargs)
+  for i, _gradient, x in zip(range(len(xs)), _gradients, xs):
+    # Ensure that x is a vector.
+    check_rank = check_ops.assert_rank(
+      x, 1, message='Cannot compute Hessian because element %d of `xs` does '
+      'not have rank one.' % i
+    )
+    with ops.control_dependencies([check_rank]):
+      # Declare an iterator and tensor array loop variables for the gradients.
+      n = array_ops.size(x)
+      loop_vars = [
+        array_ops.constant(0, dtypes.int32),
+        tensor_array_ops.TensorArray(x.dtype, n)
+      ]
+      # Iterate over all elements of the gradient and compute second order
+      # derivatives.
+      _, hessian = control_flow_ops.while_loop(
+          lambda j, _: j < n,
+          lambda j, result: (j + 1,
+                             result.write(j, gradients(_gradient[j], x)[0])),
+          loop_vars
+      )
+
+      hessians.append(hessian.stack())
   return hessians
-- 
GitLab


From 6b7b01a9c5df50977476b3c2892a896d9934f381 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 09:35:04 -0700
Subject: [PATCH 0436/1375] Deletes iris_val_based_early_stopping example,
 which uses deprecated ValidationMonitor.

PiperOrigin-RevId: 160154863
---
 tensorflow/examples/learn/BUILD               | 11 ---
 tensorflow/examples/learn/README.md           |  1 -
 tensorflow/examples/learn/examples_test.sh    |  1 -
 .../learn/iris_val_based_early_stopping.py    | 83 -------------------
 4 files changed, 96 deletions(-)
 delete mode 100644 tensorflow/examples/learn/iris_val_based_early_stopping.py

diff --git a/tensorflow/examples/learn/BUILD b/tensorflow/examples/learn/BUILD
index 1606e1a947..91fc105f27 100644
--- a/tensorflow/examples/learn/BUILD
+++ b/tensorflow/examples/learn/BUILD
@@ -69,16 +69,6 @@ py_binary(
     ],
 )
 
-py_binary(
-    name = "iris_val_based_early_stopping",
-    srcs = ["iris_val_based_early_stopping.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
-    ],
-)
-
 py_binary(
     name = "iris_with_pipeline",
     srcs = ["iris_with_pipeline.py"],
@@ -201,7 +191,6 @@ sh_test(
         ":iris_custom_decay_dnn",
         ":iris_custom_model",
         ":iris_run_config",
-        ":iris_val_based_early_stopping",
         ":iris_with_pipeline",
         ":random_forest_mnist",
         ":resnet",
diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index 3166f37e78..6671d68831 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -19,7 +19,6 @@ processing (`sudo pip install pandas`).
 
 ## Techniques
 
-* [Improving Performance Using Early Stopping with Iris Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_val_based_early_stopping.py)
 * [Using skflow with Pipeline]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_with_pipeline.py)
 * [Deep Neural Network with Customized Decay Function]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
 
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index 8942720271..77b245ab15 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -49,7 +49,6 @@ test iris
 test iris_custom_decay_dnn
 test iris_custom_model
 test iris_run_config
-test iris_val_based_early_stopping
 test iris_with_pipeline
 test random_forest_mnist
 test resnet
diff --git a/tensorflow/examples/learn/iris_val_based_early_stopping.py b/tensorflow/examples/learn/iris_val_based_early_stopping.py
deleted file mode 100644
index 991d1831d7..0000000000
--- a/tensorflow/examples/learn/iris_val_based_early_stopping.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-"""Example of DNNClassifier for Iris plant dataset, with early stopping."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import shutil
-
-from sklearn import datasets
-from sklearn import metrics
-from sklearn.cross_validation import train_test_split
-import tensorflow as tf
-
-learn = tf.contrib.learn
-
-
-def clean_folder(folder):
-  """Cleans the given folder if it exists."""
-  try:
-    shutil.rmtree(folder)
-  except OSError:
-    pass
-
-
-def main(unused_argv):
-  iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = train_test_split(
-      iris.data, iris.target, test_size=0.2, random_state=42)
-
-  x_train, x_val, y_train, y_val = train_test_split(
-      x_train, y_train, test_size=0.2, random_state=42)
-  val_monitor = learn.monitors.ValidationMonitor(
-      x_val, y_val, early_stopping_rounds=200)
-
-  model_dir = '/tmp/iris_model'
-  clean_folder(model_dir)
-
-  # classifier with early stopping on training data
-  classifier1 = learn.DNNClassifier(
-      feature_columns=learn.infer_real_valued_columns_from_input(x_train),
-      hidden_units=[10, 20, 10],
-      n_classes=3,
-      model_dir=model_dir)
-  classifier1.fit(x=x_train, y=y_train, steps=2000)
-  predictions1 = list(classifier1.predict(x_test, as_iterable=True))
-  score1 = metrics.accuracy_score(y_test, predictions1)
-
-  model_dir = '/tmp/iris_model_val'
-  clean_folder(model_dir)
-
-  # classifier with early stopping on validation data, save frequently for
-  # monitor to pick up new checkpoints.
-  classifier2 = learn.DNNClassifier(
-      feature_columns=learn.infer_real_valued_columns_from_input(x_train),
-      hidden_units=[10, 20, 10],
-      n_classes=3,
-      model_dir=model_dir,
-      config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))
-  classifier2.fit(x=x_train, y=y_train, steps=2000, monitors=[val_monitor])
-  predictions2 = list(classifier2.predict(x_test, as_iterable=True))
-  score2 = metrics.accuracy_score(y_test, predictions2)
-
-  # In many applications, the score is improved by using early stopping
-  print('score1: ', score1)
-  print('score2: ', score2)
-  print('score2 > score1: ', score2 > score1)
-
-
-if __name__ == '__main__':
-  tf.app.run()
-- 
GitLab


From a3a7d1ac38da8fec75ae5a0eaee743b065a9b85c Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Mon, 26 Jun 2017 09:35:14 -0700
Subject: [PATCH 0437/1375] [XLA] Move HLO dumping flags from service_flags to
 debug_options_flags

This also removes the duplication in the xla_generate_hlo_graph flag.

This CL also moves the actual dumping logic from Executable to the
hlo_graph_dumper namespace, where it belongs; this is in preparation for
removing the hlo_dumper callback altogether, since it isn't serving any role
beyond what a direct call to hlo_graph_dumper would have (b/62872831 has more
details).

PiperOrigin-RevId: 160154869
---
 .../xla/legacy_flags/debug_options_flags.cc   | 27 +++++++++++++++++
 .../xla/legacy_flags/service_flags.cc         | 23 --------------
 .../compiler/xla/legacy_flags/service_flags.h | 12 --------
 tensorflow/compiler/xla/service/BUILD         |  1 +
 tensorflow/compiler/xla/service/executable.cc | 30 -------------------
 tensorflow/compiler/xla/service/executable.h  |  8 ++---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 26 ++++++++++++++++
 .../compiler/xla/service/hlo_graph_dumper.h   |  7 +++++
 tensorflow/compiler/xla/service/service.cc    |  3 +-
 .../compiler/xla/tests/hlo_test_base.cc       |  2 +-
 tensorflow/compiler/xla/tools/BUILD           |  3 +-
 .../tools/dumped_computation_to_graphviz.cc   | 14 +++++----
 .../dumped_computation_to_tf_graphdef.cc      | 13 ++++++--
 tensorflow/compiler/xla/xla.proto             | 13 ++++++++
 14 files changed, 101 insertions(+), 81 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 7f771bf601..f87cf7083e 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -25,6 +25,11 @@ namespace legacy_flags {
 
 struct DebugOptionsFlags {
   string xla_generate_hlo_graph;
+  bool xla_hlo_graph_addresses;
+  bool xla_hlo_graph_layout;
+  string xla_log_hlo_text;
+  string xla_generate_hlo_text_to;
+
   string xla_disable_hlo_passes;
   bool xla_enable_fast_math;
   bool xla_llvm_enable_alias_scope_metadata;
@@ -54,6 +59,10 @@ std::once_flag flags_init;
 void AllocateFlags() {
   flag_values = new DebugOptionsFlags;
   flag_values->xla_generate_hlo_graph = "";
+  flag_values->xla_hlo_graph_addresses = false;
+  flag_values->xla_hlo_graph_layout = false;
+  flag_values->xla_log_hlo_text = "";
+  flag_values->xla_generate_hlo_text_to = "";
   flag_values->xla_disable_hlo_passes = "";
   flag_values->xla_enable_fast_math = true;
   flag_values->xla_llvm_enable_alias_scope_metadata = true;
@@ -73,6 +82,20 @@ void AllocateFlags() {
            "xla_generate_hlo_graph", &flag_values->xla_generate_hlo_graph,
            "HLO modules matching this regex will be dumped to a .dot file "
            "throughout various stages in compilation."),
+       tensorflow::Flag(
+           "xla_hlo_graph_addresses", &flag_values->xla_hlo_graph_addresses,
+           "With xla_generate_hlo_graph, show addresses of HLO ops in "
+           "graph dump."),
+       tensorflow::Flag(
+           "xla_hlo_graph_layout", &flag_values->xla_hlo_graph_layout,
+           "With xla_generate_hlo_graph, show layout of HLO ops in "
+           "graph dump."),
+       tensorflow::Flag(
+           "xla_log_hlo_text", &flag_values->xla_log_hlo_text,
+           "HLO modules matching this regex will be dumped to LOG(INFO). "),
+       tensorflow::Flag(
+           "xla_generate_hlo_text_to", &flag_values->xla_generate_hlo_text_to,
+           "Dump all HLO modules as text into the provided directory path."),
        tensorflow::Flag(
            "xla_enable_fast_math", &flag_values->xla_enable_fast_math,
            "Enable unsafe fast-math optimizations in the compiler; "
@@ -141,6 +164,10 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
 
   DebugOptions options;
   options.set_xla_generate_hlo_graph(flag_values->xla_generate_hlo_graph);
+  options.set_xla_hlo_graph_addresses(flag_values->xla_hlo_graph_addresses);
+  options.set_xla_hlo_graph_layout(flag_values->xla_hlo_graph_layout);
+  options.set_xla_log_hlo_text(flag_values->xla_log_hlo_text);
+  options.set_xla_generate_hlo_text_to(flag_values->xla_generate_hlo_text_to);
 
   std::vector<string> disabled_passes =
       tensorflow::str_util::Split(flag_values->xla_disable_hlo_passes, ',');
diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.cc b/tensorflow/compiler/xla/legacy_flags/service_flags.cc
index 41cb8d8bdf..90d30e7569 100644
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/service_flags.cc
@@ -36,33 +36,13 @@ static std::once_flag flags_init;
 static void AllocateFlags() {
   flags = new ServiceFlags;
   flags->xla_hlo_profile = false;
-  flags->xla_log_hlo_text = "";
-  flags->xla_generate_hlo_graph = "";
-  flags->xla_hlo_graph_addresses = false;
-  flags->xla_hlo_graph_layout = false;
   flags->xla_hlo_graph_for_compute_constant = false;
   flags->xla_dump_computations_to = "";
-  flags->xla_dump_hlo_text_to = "";
   flags->xla_dump_executions_to = "";
   flag_list = new std::vector<tensorflow::Flag>({
       tensorflow::Flag(
           "xla_hlo_profile", &flags->xla_hlo_profile,
           "Instrument the computation to collect per-HLO cycle counts"),
-      tensorflow::Flag(
-          "xla_log_hlo_text", &flags->xla_log_hlo_text,
-          "If non-empty, print the text format of "
-          "HLO modules whose name partially matches this regex. E.g. "
-          "xla_log_hlo_text=.* will dump the text for every module."),
-      tensorflow::Flag(
-          "xla_generate_hlo_graph", &flags->xla_generate_hlo_graph,
-          "If non-empty, dump graph of HLO modules whose name partially "
-          "matches this regex. E.g. --xla_generate_hlo_graph=.* will dump "
-          "the graph of every module."),
-      tensorflow::Flag("xla_hlo_graph_addresses",
-                       &flags->xla_hlo_graph_addresses,
-                       "Show addresses of HLO ops in graph"),
-      tensorflow::Flag("xla_hlo_graph_layout", &flags->xla_hlo_graph_layout,
-                       "Show layout of HLO ops in graph"),
       tensorflow::Flag(
           "xla_hlo_graph_for_compute_constant",
           &flags->xla_hlo_graph_for_compute_constant,
@@ -72,9 +52,6 @@ static void AllocateFlags() {
                        &flags->xla_dump_computations_to,
                        "Dumps computations that XLA executes into the provided "
                        "directory path"),
-      tensorflow::Flag("xla_dump_hlo_text_to", &flags->xla_dump_hlo_text_to,
-                       "Dumps HLO modules that XLA executes into the provided "
-                       "directory path"),
       tensorflow::Flag("xla_dump_executions_to", &flags->xla_dump_executions_to,
                        "Dumps parameters and results of computations that XLA "
                        "executes into the provided directory path"),
diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.h b/tensorflow/compiler/xla/legacy_flags/service_flags.h
index d982506944..72d0c52402 100644
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.h
+++ b/tensorflow/compiler/xla/legacy_flags/service_flags.h
@@ -34,23 +34,11 @@ void AppendServiceFlags(std::vector<tensorflow::Flag>* flag_list);
 typedef struct {
   bool xla_hlo_profile;  // Instrument the computation to collect per-HLO cycle
                          // counts
-  string xla_log_hlo_text;  // If non-empty, print the text format of the HLO
-                            // modules whose name partially
-                            // matches this regex.  E.g. xla_log_hlo_text=.*
-                            // will dump the text for every module.
-  string xla_generate_hlo_graph;  // If non-empty, dump graph of HLO modules
-                                  // whose name partially matches this regex.
-                                  // E.g. --xla_generate_hlo_graph=.* will dump
-                                  // the graph of every module.
-  bool xla_hlo_graph_addresses;   // Show addresses of HLO ops in graph
-  bool xla_hlo_graph_layout;      // Show layout of HLO ops in graph
   bool xla_hlo_graph_for_compute_constant;  // If true, include hlo dumps of
                                             // graphs from ComputeConstant.
                                             // Such graphs still need to be
                                             // matched via
                                             // xla_generate_hlo_graph.
-  string xla_dump_hlo_text_to;  // Dumps HLO text for each HLO module that is
-                                // executed into the provided directory path
   string xla_dump_computations_to;  // Dumps computations that XLA executes
                                     // into the provided directory path
   // Dumps parameters and results of computations that XLA executes into
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 427b382211..718a2d798c 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1813,6 +1813,7 @@ cc_library(
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/core:lib",
+        "//tensorflow/core:regexp_internal",
     ],
     alwayslink = 1,
 )
diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc
index 3a9f8dc79e..20eb1aea37 100644
--- a/tensorflow/compiler/xla/service/executable.cc
+++ b/tensorflow/compiler/xla/service/executable.cc
@@ -21,39 +21,9 @@ limitations under the License.
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
-#include "tensorflow/core/platform/regexp.h"
 
 namespace xla {
 
-/* static */ void Executable::DumpExecutedHlo(
-    const HloModule& module, const string& label,
-    const HloExecutionProfile* profile) {
-  VLOG(2) << "module name = " << module.name();
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-  string generate_hlo_graph_regex;
-  if (!flags->xla_generate_hlo_graph.empty()) {
-    generate_hlo_graph_regex = flags->xla_generate_hlo_graph;
-  } else {
-    generate_hlo_graph_regex =
-        module.config().debug_options().xla_generate_hlo_graph();
-  }
-  if (!generate_hlo_graph_regex.empty() &&
-      RE2::PartialMatch(module.name(), generate_hlo_graph_regex)) {
-    hlo_graph_dumper::DumpGraph(*module.entry_computation(), label,
-                                flags->xla_hlo_graph_addresses,
-                                flags->xla_hlo_graph_layout, profile);
-  }
-  if (!flags->xla_log_hlo_text.empty() &&
-      RE2::PartialMatch(module.name(), flags->xla_log_hlo_text)) {
-    LOG(INFO) << "HLO for module " << module.name();
-    LOG(INFO) << "Label: " << label;
-    XLA_LOG_LINES(2, module.ToString());
-  }
-  if (!flags->xla_dump_hlo_text_to.empty()) {
-    hlo_graph_dumper::DumpText(module, label, flags->xla_dump_hlo_text_to);
-  }
-}
-
 StatusOr<std::vector<perftools::gputools::DeviceMemoryBase>>
 Executable::ExecuteOnStreams(
     tensorflow::gtl::ArraySlice<const ServiceExecutableRunOptions> run_options,
diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h
index 291916cd9f..b36a44e19e 100644
--- a/tensorflow/compiler/xla/service/executable.h
+++ b/tensorflow/compiler/xla/service/executable.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
 #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
+#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/service_executable_run_options.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
@@ -49,10 +50,6 @@ class Executable {
         shape_size_function_(std::move(shape_size_function)) {}
   virtual ~Executable() {}
 
-  // Dumps the executed HLO according to service-associated flags.
-  static void DumpExecutedHlo(const HloModule& module, const string& label,
-                              const HloExecutionProfile* profile);
-
   // Enqueues the compilation result on the provided stream, passing the given
   // arguments. This call is blocking and returns after the execution is done.
   //
@@ -240,7 +237,8 @@ StatusOr<ReturnT> Executable::ExecuteOnStreamWrapper(
         }
       }
     }
-    DumpExecutedHlo(module(), "Service::Execute", profile_ptr);
+    hlo_graph_dumper::MaybeDumpHloModule(module(), "Service::Execute",
+                                         profile_ptr);
   }
 
   return return_value;
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index dffb53320c..69166a8d13 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -34,6 +34,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/regexp.h"
 
 using ::tensorflow::Env;
 using ::tensorflow::WriteStringToFile;
@@ -593,6 +594,31 @@ void DumpText(const HloModule& module, const string& label,
       do_prefix ? StrCat(prefix, "-", label, ".txt") : StrCat(label, ".txt");
   string path = JoinPath(directory_path, filename);
   TF_CHECK_OK(WriteStringToFile(env, path, module.ToString()));
+  LOG(INFO) << "dumping module '" << module.name() << "' to " << path;
+}
+
+string MaybeDumpHloModule(const HloModule& module, const string& label,
+                          const HloExecutionProfile* profile) {
+  VLOG(2) << "MaybeDumpHloModule called on module " << module.name();
+  string graph_url;
+  const DebugOptions& debug_options = module.config().debug_options();
+  if (!debug_options.xla_generate_hlo_graph().empty() &&
+      RE2::PartialMatch(module.name(),
+                        debug_options.xla_generate_hlo_graph())) {
+    graph_url = DumpGraph(*module.entry_computation(), label,
+                          debug_options.xla_hlo_graph_addresses(),
+                          debug_options.xla_hlo_graph_layout(), profile);
+  }
+  if (!debug_options.xla_log_hlo_text().empty() &&
+      RE2::PartialMatch(module.name(), debug_options.xla_log_hlo_text())) {
+    LOG(INFO) << "HLO for module " << module.name();
+    LOG(INFO) << "Label: " << label;
+    XLA_LOG_LINES(2, module.ToString());
+  }
+  if (!debug_options.xla_generate_hlo_text_to().empty()) {
+    DumpText(module, label, debug_options.xla_generate_hlo_text_to());
+  }
+  return graph_url;
 }
 
 }  // namespace hlo_graph_dumper
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.h b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
index 8ed50c3847..eb65cf2d32 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.h
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
@@ -41,6 +41,13 @@ class GraphRendererInterface {
   virtual string RenderGraph(const string& graph, GraphKind graph_kind) = 0;
 };
 
+// Dump the given HLO module if a dump is requested in its debug options. Based
+// on the debug options, either a graph dump, a text dump or both may be
+// generated. If a graph dump is generated, the description (e.g. an URL) is
+// returned; otherwise an empty string is returned.
+string MaybeDumpHloModule(const HloModule& module, const string& label,
+                          const HloExecutionProfile* profile = nullptr);
+
 // Dumps a graph of the computation and returns a description of the rendered
 // graph (e.g., a URL) based on the renderer. The "best" renderer in the
 // registry is used.
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 68441ef17f..2416f150e7 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -168,7 +168,8 @@ Service::CreateComputeConstantBackend() {
 
 /* static */ Compiler::HloDumper Service::MakeHloDumper() {
   return [](const HloModule& module, const string& label) {
-    return Executable::DumpExecutedHlo(module, label, /*profile=*/nullptr);
+    hlo_graph_dumper::MaybeDumpHloModule(module, label,
+                                         /*profile=*/nullptr);
   };
 }
 
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index 1def8ec12a..00b2858790 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -56,7 +56,7 @@ struct HloTestBase::EigenThreadPoolWrapper {
 HloTestBase::HloTestBase()
     : backend_(Backend::CreateDefaultBackend().ConsumeValueOrDie()) {
   test_hlo_dumper_ = [](const HloModule& module, const string& label) {
-    return Executable::DumpExecutedHlo(module, label, /*profile=*/nullptr);
+    hlo_graph_dumper::MaybeDumpHloModule(module, label, /*profile=*/nullptr);
   };
   VLOG(1) << "executing on platform " << backend_->platform()->Name();
 }
diff --git a/tensorflow/compiler/xla/tools/BUILD b/tensorflow/compiler/xla/tools/BUILD
index 535e5b605b..8ba76ddc1b 100644
--- a/tensorflow/compiler/xla/tools/BUILD
+++ b/tensorflow/compiler/xla/tools/BUILD
@@ -36,7 +36,7 @@ cc_library(
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:service_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service",
         "//tensorflow/compiler/xla/service:session_proto",
         "//tensorflow/core:lib",
@@ -187,6 +187,7 @@ cc_binary(
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/service",
         "//tensorflow/compiler/xla/service:hlo_graph_dumper",
diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
index 10efa9f3e8..100e19e6d8 100644
--- a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
+++ b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
@@ -32,7 +32,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/service/service.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -63,12 +63,16 @@ void RealMain(tensorflow::gtl::ArraySlice<char*> args) {
 }  // namespace xla
 
 int main(int argc, char** argv) {
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
   tensorflow::port::InitMain(argv[0], &argc, &argv);
 
-  xla::legacy_flags::ServiceFlags* flags = xla::legacy_flags::GetServiceFlags();
-  flags->xla_generate_hlo_graph = ".*";
-  flags->xla_hlo_graph_layout = true;
-
   tensorflow::gtl::ArraySlice<char*> args(argv, argc);
   args.pop_front();  // Pop off the binary name, argv[0]
   xla::tools::RealMain(args);
diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
index 850267d319..ebaea0511c 100644
--- a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
+++ b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/service/service.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
@@ -62,10 +63,16 @@ void RealMain(tensorflow::gtl::ArraySlice<char*> args) {
 }  // namespace xla
 
 int main(int argc, char** argv) {
-  tensorflow::port::InitMain(argv[0], &argc, &argv);
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
 
-  xla::legacy_flags::ServiceFlags* flags = xla::legacy_flags::GetServiceFlags();
-  flags->xla_generate_hlo_graph = ".*";
+  tensorflow::port::InitMain(argv[0], &argc, &argv);
 
   xla::legacy_flags::HloGraphDumperFlags* dumper_flags =
       xla::legacy_flags::GetHloGraphDumperFlags();
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 46dd28c04d..8db0c26da3 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -28,6 +28,19 @@ message DebugOptions {
   // dump *all* HLO modules.
   string xla_generate_hlo_graph = 1;
 
+  // Show addresses of HLO ops in graph dump.
+  bool xla_hlo_graph_addresses = 21;
+
+  // Show layout of HLO ops in graph dump.
+  bool xla_hlo_graph_layout = 22;
+
+  // HLO modules matching this regex will be dumped to LOG(INFO). Set to ".*" to
+  // dump *all* HLO modules.
+  string xla_log_hlo_text = 23;
+
+  // Dump all HLO modules as text into the provided directory path.
+  string xla_generate_hlo_text_to = 24;
+
   // List of HLO passes to disable. These names must exactly match the pass
   // names as specified by the HloPassInterface::name() method.
   repeated string xla_disable_hlo_passes = 2;
-- 
GitLab


From 3af6341edf4fe95f63f8857acfb6e0cbe0797a57 Mon Sep 17 00:00:00 2001
From: "Gao, Xiang" <qasdfgtyuiop@gmail.com>
Date: Mon, 26 Jun 2017 12:57:32 -0400
Subject: [PATCH 0438/1375] Use native compute capabilities as default (#10995)

* use native compute capabilities as default

* use existing binary, support windows

* fix when unable to detect native compute cap.

fix when unable to detect native compute cap.
also, windows & linux can use the same bin name

* add comment

* add some comment
---
 configure | 28 +++++++++++++++++-----------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/configure b/configure
index 6d40aadefb..1531b4d40a 100755
--- a/configure
+++ b/configure
@@ -539,9 +539,9 @@ done
 # Set default CUDA version if not set
 if [ -z "$TF_CUDA_VERSION" ]; then
   TF_CUDA_VERSION="8.0"
-  export TF_CUDA_VERSION 
+  export TF_CUDA_VERSION
 fi
-write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION" 
+write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION"
 
 # Set up which gcc nvcc should use as the host compiler
 # No need to set this on Windows
@@ -659,16 +659,22 @@ write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
 
 # Configure the compute capabilities that TensorFlow builds for.
 # Since Cuda toolkit is not backward-compatible, this is not guaranteed to work.
+function get_native_cuda_compute_capabilities {
+  device_query_bin="$CUDA_TOOLKIT_PATH/extras/demo_suite/deviceQuery" # Also works on Windows without .exe
+  "$device_query_bin" | grep 'Capability' | grep -o '[0-9]*\.[0-9]*' | sed ':a;{N;s/\n/,/};ba'
+  exit 0 # ensure that this function always exit success even if device detection fails, to prevent the whole configure from aborting
+}
 while true; do
   fromuser=""
-  default_cuda_compute_capabilities="3.5,5.2"
+  native_cuda_compute_capabilities=$(get_native_cuda_compute_capabilities)
+  default_cuda_compute_capabilities=${native_cuda_compute_capabilities:-"3.5,5.2"}
   if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
 cat << EOF
 Please specify a list of comma-separated Cuda compute capabilities you want to build with.
 You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
 Please note that each additional compute capability significantly increases your build time and binary size.
 EOF
-    read -p "[Default is: \"3.5,5.2\"]: " TF_CUDA_COMPUTE_CAPABILITIES
+    read -p "[Default is: \"$default_cuda_compute_capabilities\"]: " TF_CUDA_COMPUTE_CAPABILITIES
     fromuser=1
   fi
   if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
@@ -839,17 +845,17 @@ while true; do
     if [ -e "$MPI_HOME/include" ] && [ -e "$MPI_HOME/lib" ]; then
         break
     fi
- 
+
     echo "Invalid path to the MPI Toolkit. ${MPI_HOME}/include or ${MPI_HOME}/lib cannot be found."
     if [ -z "$fromuser" ]; then
         exit 1
     fi
 
     # Retry
-    MPI_HOME="" 
+    MPI_HOME=""
 done
-    
-    
+
+
 if [ "$TF_NEED_MPI" == "1" ]; then
   write_to_bazelrc 'build --define with_mpi_support=true'
 
@@ -857,11 +863,11 @@ if [ "$TF_NEED_MPI" == "1" ]; then
   ln -sf "${MPI_HOME}/include/mpi.h" third_party/mpi/mpi.h
 
 
-  #Determine if we use OpenMPI or MVAPICH, these require different header files 
+  #Determine if we use OpenMPI or MVAPICH, these require different header files
   #to be included here to make bazel dependency checker happy
 
   if [ -e "${MPI_HOME}/include/mpi_portable_platform.h" ]; then
-        #OpenMPI 
+        #OpenMPI
         ln -sf "${MPI_HOME}/include/mpi_portable_platform.h" third_party/mpi/
         sed -i -e "s/MPI_LIB_IS_OPENMPI=False/MPI_LIB_IS_OPENMPI=True/" third_party/mpi/mpi.bzl
  else
@@ -871,7 +877,7 @@ if [ "$TF_NEED_MPI" == "1" ]; then
         sed -i -e "s/MPI_LIB_IS_OPENMPI=True/MPI_LIB_IS_OPENMPI=False/" third_party/mpi/mpi.bzl
  fi
 
-  
+
   if [ -e "${MPI_HOME}/lib/libmpi.so" ]; then
     ln -sf "${MPI_HOME}/lib/libmpi.so" third_party/mpi/
   else
-- 
GitLab


From 7c689a2fd5204b3df82909ed91e1a240342f192f Mon Sep 17 00:00:00 2001
From: KB Sriram <kbsriram@gmail.com>
Date: Mon, 26 Jun 2017 10:19:36 -0700
Subject: [PATCH 0439/1375] Java: equality semantics for Operation and Output
 (#10849)

* Java: equality semantics for Operation and Output

* Add .equals(), .hashCode() and .toString() for value objects Operation
  and Output.
* Annotate these value objects @Immutable for compile-time validation.

* Remove @Immutable and misc changes per suggestions.

* Use Long and Objects for hashCode calculation
* Avoid testing unwanted guarantees on object equality.
---
 .../main/java/org/tensorflow/Operation.java   | 61 ++++++++++---
 .../src/main/java/org/tensorflow/Output.java  | 25 +++++
 .../java/org/tensorflow/OperationTest.java    | 91 +++++++++++++++++++
 3 files changed, 163 insertions(+), 14 deletions(-)

diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index e7de603409..200350f7ae 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -73,15 +73,14 @@ public final class Operation {
   /**
    * Returns the size of the list of Tensors produced by this operation.
    *
-   * <p>An Operation has multiple named outputs, each of which produces either
-   * a single tensor or a list of tensors. This method returns the size of
-   * the list of tensors for a specific named output of the operation.
+   * <p>An Operation has multiple named outputs, each of which produces either a single tensor or a
+   * list of tensors. This method returns the size of the list of tensors for a specific named
+   * output of the operation.
    *
-   * @param name identifier of the list of tensors (of which there may
-   *        be many) produced by this operation.
+   * @param name identifier of the list of tensors (of which there may be many) produced by this
+   *     operation.
    * @return the size of the list of Tensors produced by this named output.
-   * @throws IllegalArgumentException if this operation has no output
-   *         with the provided name.
+   * @throws IllegalArgumentException if this operation has no output with the provided name.
    */
   public int outputListLength(final String name) {
     Graph.Reference r = graph.ref();
@@ -97,18 +96,51 @@ public final class Operation {
     return new Output(this, idx);
   }
 
+  @Override
+  public int hashCode() {
+    return Long.valueOf(unsafeNativeHandle).hashCode();
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (o == this) {
+      return true;
+    }
+    if (!(o instanceof Operation)) {
+      return false;
+    }
+    Operation that = (Operation) o;
+    if (graph != that.graph) {
+      return false;
+    }
+
+    // The graph object is known to be identical here, so this one
+    // reference is sufficient to validate the use of native pointers
+    // in both objects.
+    Graph.Reference r = graph.ref();
+    try {
+      return unsafeNativeHandle == that.unsafeNativeHandle;
+    } finally {
+      r.close();
+    }
+  }
+
+  @Override
+  public String toString() {
+    return String.format("<%s '%s'>", type(), name());
+  }
+
   /**
    * Returns the size of the given inputs list of Tensors for this operation.
    *
-   * <p>An Operation has multiple named inputs, each of which contains either
-   * a single tensor or a list of tensors. This method returns the size of
-   * the list of tensors for a specific named input of the operation.
+   * <p>An Operation has multiple named inputs, each of which contains either a single tensor or a
+   * list of tensors. This method returns the size of the list of tensors for a specific named input
+   * of the operation.
    *
-   * @param name identifier of the list of tensors (of which there may
-   *        be many) inputs to this operation.
+   * @param name identifier of the list of tensors (of which there may be many) inputs to this
+   *     operation.
    * @returns the size of the list of Tensors produced by this named input.
-   * @throws IllegalArgumentException if this operation has no input
-   *         with the provided name.
+   * @throws IllegalArgumentException if this operation has no input with the provided name.
    */
   public int inputListLength(final String name) {
     Graph.Reference r = graph.ref();
@@ -144,6 +176,7 @@ public final class Operation {
   }
 
   private final long unsafeNativeHandle;
+
   private final Graph graph;
 
   private static native String name(long handle);
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index 2e3f8d4eac..ad28eb2743 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -15,6 +15,7 @@ limitations under the License.
 
 package org.tensorflow;
 
+import java.util.Objects;
 /**
  * A symbolic handle to a tensor produced by an {@link Operation}.
  *
@@ -57,6 +58,30 @@ public final class Output implements Input {
     return this;
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hash(operation, index);
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (o == this) {
+      return true;
+    }
+    if (o instanceof Output) {
+      Output that = (Output) o;
+      return index == that.index && operation.equals(that.operation);
+    }
+    return false;
+  }
+
+  @Override
+  public String toString() {
+    return String.format(
+        "<%s '%s:%d' shape=%s dtype=%s>",
+        operation.type(), operation.name(), index, shape().toString(), dataType());
+  }
+
   private final Operation operation;
   private final int index;
 }
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 4fa68130c0..27afc046ac 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -16,8 +16,14 @@ limitations under the License.
 package org.tensorflow;
 
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
 import static org.junit.Assert.fail;
 
+import java.util.Arrays;
+import java.util.HashSet;
+import java.util.Set;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.JUnit4;
@@ -45,6 +51,91 @@ public class OperationTest {
     }
   }
 
+  @Test
+  public void operationEquality() {
+    Operation op1;
+    try (Graph g = new Graph()) {
+      op1 = TestUtil.constant(g, "op1", 1).op();
+      Operation op2 = TestUtil.constant(g, "op2", 2).op();
+      Operation op3 = new Operation(g, op1.getUnsafeNativeHandle());
+      Operation op4 = g.operation("op1");
+      assertEquals(op1, op1);
+      assertNotEquals(op1, op2);
+      assertEquals(op1, op3);
+      assertEquals(op1.hashCode(), op3.hashCode());
+      assertEquals(op1, op4);
+      assertEquals(op1.hashCode(), op4.hashCode());
+      assertEquals(op3, op4);
+      assertNotEquals(op2, op3);
+      assertNotEquals(op2, op4);
+    }
+    try (Graph g = new Graph()) {
+      Operation newOp1 = TestUtil.constant(g, "op1", 1).op();
+      assertNotEquals(op1, newOp1);
+    }
+  }
+
+  @Test
+  public void operationCollection() {
+    try (Graph g = new Graph()) {
+      Operation op1 = TestUtil.constant(g, "op1", 1).op();
+      Operation op2 = TestUtil.constant(g, "op2", 2).op();
+      Operation op3 = new Operation(g, op1.getUnsafeNativeHandle());
+      Operation op4 = g.operation("op1");
+      Set<Operation> ops = new HashSet<>();
+      ops.addAll(Arrays.asList(op1, op2, op3, op4));
+      assertEquals(2, ops.size());
+      assertTrue(ops.contains(op1));
+      assertTrue(ops.contains(op2));
+      assertTrue(ops.contains(op3));
+      assertTrue(ops.contains(op4));
+    }
+  }
+
+  @Test
+  public void operationToString() {
+    try (Graph g = new Graph()) {
+      Operation op = TestUtil.constant(g, "c", new int[] {1}).op();
+      assertNotNull(op.toString());
+    }
+  }
+
+  @Test
+  public void outputEquality() {
+    try (Graph g = new Graph()) {
+      Output output = TestUtil.constant(g, "c", 1);
+      Output output1 = output.op().output(0);
+      Output output2 = g.operation("c").output(0);
+      assertEquals(output, output1);
+      assertEquals(output.hashCode(), output1.hashCode());
+      assertEquals(output, output2);
+      assertEquals(output.hashCode(), output2.hashCode());
+    }
+  }
+
+  @Test
+  public void outputCollection() {
+    try (Graph g = new Graph()) {
+      Output output = TestUtil.constant(g, "c", 1);
+      Output output1 = output.op().output(0);
+      Output output2 = g.operation("c").output(0);
+      Set<Output> ops = new HashSet<>();
+      ops.addAll(Arrays.asList(output, output1, output2));
+      assertEquals(1, ops.size());
+      assertTrue(ops.contains(output));
+      assertTrue(ops.contains(output1));
+      assertTrue(ops.contains(output2));
+    }
+  }
+
+  @Test
+  public void outputToString() {
+    try (Graph g = new Graph()) {
+      Output output = TestUtil.constant(g, "c", new int[] {1});
+      assertNotNull(output.toString());
+    }
+  }
+
   @Test
   public void outputListLength() {
     assertEquals(1, split(new int[] {0, 1}, 1));
-- 
GitLab


From 3faa4d2d9672fb6f8ba8f80a0df45dabeb5a3382 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 10:16:32 -0700
Subject: [PATCH 0440/1375] Fix missing variable unref

Direct leak of 56 byte(s) in 1 object(s) allocated from:
    #0 0xf5ee272 in operator new(unsigned long) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf5ee272)
    #1 0x1b51394c in tensorflow::AssignVariableOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*)::'lambda'(tensorflow::Var**)::operator()(tensorflow::Var**) const (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b51394c)
    #2 0x1b5136c0 in std::_Function_handler<tensorflow::Status (tensorflow::Var**), tensorflow::AssignVariableOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*)::'lambda'(tensorflow::Var**)>::_M_invoke(std::_Any_data const&, tensorflow::Var**) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b5136c0)
    #3 0x1b50b289 in std::function<tensorflow::Status (tensorflow::Var**)>::operator()(tensorflow::Var**) const (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b50b289)
    #4 0x1b50af88 in tensorflow::Status tensorflow::ResourceMgr::LookupOrCreate<tensorflow::Var>(basic_string<char, std::char_traits<char>, std::allocator<char> > const&, basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tensorflow::Var**, std::function<tensorflow::Status (tensorflow::Var**)>) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b50af88)
    #5 0x1b50ac10 in tensorflow::Status tensorflow::LookupOrCreateResource<tensorflow::Var>(tensorflow::OpKernelContext*, tensorflow::ResourceHandle const&, tensorflow::Var**, std::function<tensorflow::Status (tensorflow::Var**)>) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b50ac10)
    #6 0x1b512f1e in tensorflow::AssignVariableOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b512f1e)
    #7 0x1d1881c7 in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1d1881c7)
    #8 0xf96e0fe in tensorflow::KernelAndDevice::Run(std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf96e0fe)
    #9 0xf94f9c8 in TFE_Execute (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf94f9c8)
    #10 0xf94356d in TFE_Py_Execute(TFE_Context*, int, char const*, tensorflow::gtl::InlinedVector<TFE_TensorHandle*, 4>*, _object*, tensorflow::gtl::InlinedVector<TFE_TensorHandle*, 2>*, TF_Status*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf94356d)

PiperOrigin-RevId: 160160101
---
 tensorflow/core/kernels/resource_variable_ops.cc | 1 +
 tensorflow/core/kernels/training_op_helpers.cc   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index fadfb1e637..db6d8c20ec 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -395,6 +395,7 @@ class ResourceScatterUpdateOp : public OpKernel {
   void Compute(OpKernelContext* c) override {
     Var* v = nullptr;
     OP_REQUIRES_OK(c, LookupResource(c, HandleFromInput(c, 0), &v));
+    core::ScopedUnref unref_v(v);
     mutex_lock ml(*v->mu());
     Tensor* params = v->tensor();
     const Tensor& indices = c->input(1);
diff --git a/tensorflow/core/kernels/training_op_helpers.cc b/tensorflow/core/kernels/training_op_helpers.cc
index 11d51188fc..01ea6877b1 100644
--- a/tensorflow/core/kernels/training_op_helpers.cc
+++ b/tensorflow/core/kernels/training_op_helpers.cc
@@ -71,6 +71,7 @@ Status GetInputTensorFromVariable(OpKernelContext* ctx, int input,
   if (ctx->input_dtype(input) == DT_RESOURCE) {
     Var* var;
     if (LookupResource(ctx, HandleFromInput(ctx, input), &var).ok()) {
+      core::ScopedUnref unref_var(var);
       if (lock_held) {
         *out = *var->tensor();
       } else {
-- 
GitLab


From 16b2f779f55e040f324399b861c887235308c23e Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Mon, 26 Jun 2017 10:28:30 -0700
Subject: [PATCH 0441/1375] Simplify strided_slice's shape handling

Now that TensorShape and PartialTensorShape share memory representations, there's no need for an abstract class that makes TensorShape and TensorShapeProto look the same.

RELNOTES: n/a
PiperOrigin-RevId: 160161618
---
 .../tf2xla/kernels/strided_slice_op.cc        | 41 ++++-------
 tensorflow/core/kernels/strided_slice_op.cc   | 26 +++----
 .../core/kernels/strided_slice_op_test.cc     | 10 ++-
 tensorflow/core/ops/array_ops.cc              | 14 ++--
 tensorflow/core/util/strided_slice_op.cc      | 72 +++++++++----------
 tensorflow/core/util/strided_slice_op.h       | 71 ++++--------------
 6 files changed, 81 insertions(+), 153 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
index a6cac62ca4..8037e90791 100644
--- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
@@ -63,17 +63,13 @@ class StridedSliceOp : public XlaOpKernel {
                                             &strides_tensor));
 
     TensorShape dummy_processing_shape;
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
-    ShapeReadWriteFromTensorShape wrapped_dummy_processing_shape(
-        &dummy_processing_shape);
     bool dummy = false;
-    OP_REQUIRES_OK(
-        ctx, ValidateStridedSliceOp(
-                 &begin_tensor, &end_tensor, strides_tensor,
-                 ShapeReadWriteFromTensorShape(&input_shape), begin_mask_,
-                 end_mask_, ellipsis_mask_, new_axis_mask_, shrink_axis_mask_,
-                 &wrapped_dummy_processing_shape, &wrapped_final_shape, &dummy,
-                 &dummy, &dummy, &begin, &end, &strides));
+    OP_REQUIRES_OK(ctx,
+                   ValidateStridedSliceOp(
+                       &begin_tensor, &end_tensor, strides_tensor, input_shape,
+                       begin_mask_, end_mask_, ellipsis_mask_, new_axis_mask_,
+                       shrink_axis_mask_, &dummy_processing_shape, &final_shape,
+                       &dummy, &dummy, &dummy, &begin, &end, &strides));
 
     gtl::InlinedVector<int64, 4> dimensions_to_reverse;
     gtl::InlinedVector<int64, 4> slice_begin, slice_end;
@@ -172,14 +168,11 @@ class StridedSliceGradOp : public XlaOpKernel {
                                             &strides_tensor));
 
     bool dummy = false;
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
     OP_REQUIRES_OK(
         ctx, ValidateStridedSliceOp(
-                 &begin_tensor, &end_tensor, strides_tensor,
-                 ShapeReadWriteFromTensorShape(&input_shape), begin_mask_,
-                 end_mask_, ellipsis_mask_, new_axis_mask_, shrink_axis_mask_,
-                 &wrapped_processing_shape, &wrapped_final_shape, &dummy,
+                 &begin_tensor, &end_tensor, strides_tensor, input_shape,
+                 begin_mask_, end_mask_, ellipsis_mask_, new_axis_mask_,
+                 shrink_axis_mask_, &processing_shape, &final_shape, &dummy,
                  &dummy, &dummy, &begin, &end, &strides));
 
     // Check to make sure dy is consistent with the original slice
@@ -283,17 +276,13 @@ class StridedSliceAssignOp : public XlaOpKernel {
     const TensorShape rhs_shape = ctx->InputShape(4);
 
     TensorShape dummy_processing_shape;
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
-    ShapeReadWriteFromTensorShape wrapped_dummy_processing_shape(
-        &dummy_processing_shape);
     bool dummy = false;
-    OP_REQUIRES_OK(
-        ctx, ValidateStridedSliceOp(
-                 &begin_tensor, &end_tensor, strides_tensor,
-                 ShapeReadWriteFromTensorShape(&lhs_shape), begin_mask_,
-                 end_mask_, ellipsis_mask_, new_axis_mask_, shrink_axis_mask_,
-                 &wrapped_dummy_processing_shape, &wrapped_final_shape, &dummy,
-                 &dummy, &dummy, &begin, &end, &strides));
+    OP_REQUIRES_OK(ctx,
+                   ValidateStridedSliceOp(
+                       &begin_tensor, &end_tensor, strides_tensor, lhs_shape,
+                       begin_mask_, end_mask_, ellipsis_mask_, new_axis_mask_,
+                       shrink_axis_mask_, &dummy_processing_shape, &final_shape,
+                       &dummy, &dummy, &dummy, &begin, &end, &strides));
 
     if (final_shape.num_elements() == 0 && rhs_shape.num_elements() == 0) {
       // DynamicUpdateSlice does not allow 0-element updates. We should probably
diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc
index 47eb85999e..fa518712ce 100644
--- a/tensorflow/core/kernels/strided_slice_op.cc
+++ b/tensorflow/core/kernels/strided_slice_op.cc
@@ -96,17 +96,13 @@ class StridedSliceOp : public OpKernel {
     gtl::InlinedVector<int64, 4> end;
     gtl::InlinedVector<int64, 4> strides;
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     OP_REQUIRES_OK(
         context, ValidateStridedSliceOp(
                      &context->input(1), &context->input(2), context->input(3),
-                     ShapeReadWriteFromTensorShape(&context->input(0).shape()),
-                     begin_mask, end_mask, ellipsis_mask, new_axis_mask,
-                     shrink_axis_mask, &wrapped_processing_shape,
-                     &wrapped_final_shape, &is_identity, &is_simple_slice,
-                     &slice_dim0, &begin, &end, &strides));
-
+                     context->input(0).shape(), begin_mask, end_mask,
+                     ellipsis_mask, new_axis_mask, shrink_axis_mask,
+                     &processing_shape, &final_shape, &is_identity,
+                     &is_simple_slice, &slice_dim0, &begin, &end, &strides));
     const Tensor& input = context->input(0);
 
     // Optimization #1, slice is a no-op plus reshape
@@ -218,15 +214,12 @@ class StridedSliceGradOp : public OpKernel {
       LOG(FATAL) << "shape must have type int32 or int64.";
     }
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     OP_REQUIRES_OK(
         context,
         ValidateStridedSliceOp(
             &context->input(1), &context->input(2), context->input(3),
-            ShapeReadWriteFromTensorShape(&input_shape), begin_mask, end_mask,
-            ellipsis_mask, new_axis_mask, shrink_axis_mask,
-            &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
+            input_shape, begin_mask, end_mask, ellipsis_mask, new_axis_mask,
+            shrink_axis_mask, &processing_shape, &final_shape, &is_identity,
             &is_simple_slice, &slice_dim0, &begin, &end, &strides));
 
     // Check to make sure dy is consistent with the original slice
@@ -306,15 +299,12 @@ class StridedSliceAssignOp : public OpKernel {
       old_lhs = context->mutable_input(0, true);
     }
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     OP_REQUIRES_OK(
         context,
         ValidateStridedSliceOp(
             &context->input(1), &context->input(2), context->input(3),
-            ShapeReadWriteFromTensorShape(&old_lhs.shape()), begin_mask,
-            end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask,
-            &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
+            old_lhs.shape(), begin_mask, end_mask, ellipsis_mask, new_axis_mask,
+            shrink_axis_mask, &processing_shape, &final_shape, &is_identity,
             &is_simple_slice, &slice_dim0, &begin, &end, &strides));
 
     if (processing_shape.num_elements()) {
diff --git a/tensorflow/core/kernels/strided_slice_op_test.cc b/tensorflow/core/kernels/strided_slice_op_test.cc
index ca66ccad8b..281ca0f58f 100644
--- a/tensorflow/core/kernels/strided_slice_op_test.cc
+++ b/tensorflow/core/kernels/strided_slice_op_test.cc
@@ -115,13 +115,11 @@ static void BM_ValidateStridedSliceOp(int iters) {
     const int32 new_axis_mask = 0;
     const int32 shrink_axis_mask = 0;
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     TF_CHECK_OK(ValidateStridedSliceOp(
-        &begin, &end, strides, ShapeReadWriteFromTensorShape(&input_shape),
-        begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask,
-        &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
-        &is_simple_slice, &slice_dim0, &begin_out, &end_out, &strides_out));
+        &begin, &end, strides, input_shape, begin_mask, end_mask, ellipsis_mask,
+        new_axis_mask, shrink_axis_mask, &processing_shape, &final_shape,
+        &is_identity, &is_simple_slice, &slice_dim0, &begin_out, &end_out,
+        &strides_out));
   }
 }
 
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 85a6cfcac9..f27f8968d3 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -2284,21 +2284,17 @@ REGISTER_OP("StridedSlice")
       const Tensor* begin_value = c->input_tensor(1);
       const Tensor* end_value = c->input_tensor(2);
 
-      TensorShapeProto processing_shape, final_shape;
-      ShapeReadWriteFromTensorShapeProto wrapped_processing_shape(
-          &processing_shape);
-      ShapeReadWriteFromTensorShapeProto wrapped_final_shape(&final_shape);
+      PartialTensorShape processing_shape, final_shape;
       bool is_identity, is_simple_slice, slice_dim0;
       gtl::InlinedVector<int64, 4> begin, end, strides;
       TF_RETURN_IF_ERROR(ValidateStridedSliceOp(
-          begin_value, end_value, *strides_value,
-          ShapeReadWriteFromTensorShapeProto(&input_shape_proto), begin_mask,
+          begin_value, end_value, *strides_value, input_shape_proto, begin_mask,
           end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask,
-          &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
-          &is_simple_slice, &slice_dim0, &begin, &end, &strides));
+          &processing_shape, &final_shape, &is_identity, &is_simple_slice,
+          &slice_dim0, &begin, &end, &strides));
 
       ShapeHandle out;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(final_shape, &out));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(final_shape, &out));
       c->set_output(0, out);
 
       return Status::OK();
diff --git a/tensorflow/core/util/strided_slice_op.cc b/tensorflow/core/util/strided_slice_op.cc
index dc78a5a01f..cfe9275a09 100644
--- a/tensorflow/core/util/strided_slice_op.cc
+++ b/tensorflow/core/util/strided_slice_op.cc
@@ -16,37 +16,10 @@ limitations under the License.
 #include "tensorflow/core/util/strided_slice_op.h"
 
 #include <array>
-
 #include "tensorflow/core/kernels/bounds_check.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
-
-int ShapeReadWriteFromTensorShape::dims() const { return const_shape_->dims(); }
-
-int64 ShapeReadWriteFromTensorShape::dim_size(int idx) const {
-  return const_shape_->dim_size(idx);
-}
-
-void ShapeReadWriteFromTensorShape::add_dim(int64 size) {
-  DCHECK_NE(size, -1);
-  DCHECK(shape_ != nullptr) << "add_dim can only be called on non-const shape";
-  shape_->AddDim(size);
-}
-
-int ShapeReadWriteFromTensorShapeProto::dims() const {
-  return const_shape_->dim_size();
-}
-
-int64 ShapeReadWriteFromTensorShapeProto::dim_size(int idx) const {
-  return const_shape_->dim(idx).size();
-}
-
-void ShapeReadWriteFromTensorShapeProto::add_dim(int64 size) {
-  DCHECK(shape_ != nullptr) << "add_dim can only be called on non-const shape";
-  shape_->add_dim()->set_size(size);
-}
-
 namespace {
 
 /// Constants
@@ -173,12 +146,11 @@ static Status TF_MUST_USE_RESULT BuildDenseSpec(
 
 Status ValidateStridedSliceOp(
     const Tensor* begin_tensor, const Tensor* end_tensor,
-    const Tensor& strides_tensor, const ShapeReadWriteInterface& input_shape,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
     int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
     int32 new_axis_mask, int32 shrink_axis_mask,
-    ShapeReadWriteInterface* processing_shape,
-    ShapeReadWriteInterface* final_shape, bool* is_identity,
-    bool* is_simple_slice, bool* slice_dim0,
+    PartialTensorShape* processing_shape, PartialTensorShape* final_shape,
+    bool* is_identity, bool* is_simple_slice, bool* slice_dim0,
     gtl::InlinedVector<int64, 4>* begin, gtl::InlinedVector<int64, 4>* end,
     gtl::InlinedVector<int64, 4>* strides) {
   const bool begin_is_wrong =
@@ -275,6 +247,7 @@ Status ValidateStridedSliceOp(
   *is_identity = true;
   *slice_dim0 = true;
   *is_simple_slice = true;
+  processing_shape->Clear();
   for (int i = 0; i < input_shape.dims(); ++i) {
     int64& begin_i = (*begin)[i];
     int64& end_i = (*end)[i];
@@ -285,7 +258,7 @@ Status ValidateStridedSliceOp(
     }
     bool shrink_i = (dense_spec.shrink_axis_mask & (1 << i));
     if (dim_i == -1) {
-      processing_shape->add_dim(shrink_i ? 1 : -1);
+      processing_shape->AddDim(shrink_i ? 1 : -1);
       continue;
     }
 
@@ -372,9 +345,9 @@ Status ValidateStridedSliceOp(
         size_i = interval_length / stride_i +
                  (interval_length % stride_i != 0 ? 1 : 0);
       }
-      processing_shape->add_dim(size_i);
+      processing_shape->AddDim(size_i);
     } else {
-      processing_shape->add_dim(-1);
+      processing_shape->AddDim(-1);
     }
   }
 
@@ -383,14 +356,41 @@ Status ValidateStridedSliceOp(
   // new_axis will increase dimension by 1 (with a one-size dimension)
   // slices like foo[3,...] will reduce dimension by 1.
   // This cannot be done earlier, because it depends on Step 3.
+  final_shape->Clear();
   for (auto gather_index : dense_spec.final_shape_gather_indices) {
     if (gather_index >= 0) {
-      final_shape->add_dim(processing_shape->dim_size(gather_index));
+      final_shape->AddDim(processing_shape->dim_size(gather_index));
     } else if (gather_index == kNewAxis) {
-      final_shape->add_dim(1);
+      final_shape->AddDim(1);
     }
   }
   return Status::OK();
 }
 
+Status ValidateStridedSliceOp(
+    const Tensor* begin_tensor, const Tensor* end_tensor,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
+    int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
+    int32 new_axis_mask, int32 shrink_axis_mask, TensorShape* processing_shape,
+    TensorShape* final_shape, bool* is_identity, bool* is_simple_slice,
+    bool* slice_dim0, gtl::InlinedVector<int64, 4>* begin,
+    gtl::InlinedVector<int64, 4>* end, gtl::InlinedVector<int64, 4>* strides) {
+  // Validate with PartialTensorShape output
+  PartialTensorShape partial_processing_shape, partial_final_shape;
+  TF_RETURN_IF_ERROR(ValidateStridedSliceOp(
+      begin_tensor, end_tensor, strides_tensor, input_shape, begin_mask_spec,
+      end_mask_spec, ellipsis_mask, new_axis_mask, shrink_axis_mask,
+      &partial_processing_shape, &partial_final_shape, is_identity,
+      is_simple_slice, slice_dim0, begin, end, strides));
+
+  // Verify that the output shapes are fully known
+  if (!partial_processing_shape.AsTensorShape(processing_shape) ||
+      !partial_final_shape.AsTensorShape(final_shape)) {
+    return errors::Internal("ValidateStridedSliceOp returned partial shapes ",
+                            partial_processing_shape.DebugString(), " and ",
+                            partial_final_shape.DebugString());
+  }
+  return Status::OK();
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/util/strided_slice_op.h b/tensorflow/core/util/strided_slice_op.h
index 72dc5756fd..abca98f27b 100644
--- a/tensorflow/core/util/strided_slice_op.h
+++ b/tensorflow/core/util/strided_slice_op.h
@@ -23,60 +23,6 @@ limitations under the License.
 
 namespace tensorflow {
 
-// This class and its subclasses allow ValidateStridedSliceOp to be called with
-// different implementations of partial tensors.
-class ShapeReadWriteInterface {
- public:
-  virtual ~ShapeReadWriteInterface() {}
-  virtual int dims() const = 0;
-  // Returns -1 for unknown size.
-  virtual int64 dim_size(int idx) const = 0;
-  // Passes -1 for unknown dim size.
-  virtual void add_dim(int64 size) = 0;
-};
-
-// Implementation of ShapeReadWriteInterface that modifies the given TensorShape
-// <shape> in-place. Does not support adding unknown dims in add_dim.
-class ShapeReadWriteFromTensorShape : public ShapeReadWriteInterface {
- public:
-  ShapeReadWriteFromTensorShape(TensorShape* shape)
-      : const_shape_(shape), shape_(shape) {}
-  ShapeReadWriteFromTensorShape(const TensorShape* shape)
-      : const_shape_(shape) {}
-  ~ShapeReadWriteFromTensorShape() override {}
-  int dims() const override;
-  int64 dim_size(int idx) const override;
-  void add_dim(int64 size) override;
-
- private:
-  const TensorShape* const const_shape_;
-  // same as const_shape_, or nullptr if the non-const ctr is used.
-  TensorShape* const shape_ = nullptr;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(ShapeReadWriteFromTensorShape);
-};
-
-// Implementation of ShapeReadWriteInterface that modifies the given
-// TensorShapeProto in place.
-class ShapeReadWriteFromTensorShapeProto : public ShapeReadWriteInterface {
- public:
-  ShapeReadWriteFromTensorShapeProto(TensorShapeProto* shape)
-      : const_shape_(shape), shape_(shape) {}
-  ShapeReadWriteFromTensorShapeProto(const TensorShapeProto* shape)
-      : const_shape_(shape) {}
-  ~ShapeReadWriteFromTensorShapeProto() override {}
-  int dims() const override;
-  int64 dim_size(int idx) const override;
-  void add_dim(int64 size) override;
-
- private:
-  const TensorShapeProto* const const_shape_;
-  // same as shape_, or nullptr if the non-const ctr is used.
-  TensorShapeProto* const shape_ = nullptr;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(ShapeReadWriteFromTensorShapeProto);
-};
-
 // Runs validation on the strided slice op parameters.
 //
 // Is a separate translation unit from the kernel so that:
@@ -96,15 +42,24 @@ class ShapeReadWriteFromTensorShapeProto : public ShapeReadWriteInterface {
 // performed.
 Status ValidateStridedSliceOp(
     const Tensor* begin_tensor, const Tensor* end_tensor,
-    const Tensor& strides_tensor, const ShapeReadWriteInterface& input_shape,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
     int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
     int32 new_axis_mask, int32 shrink_axis_mask,
-    ShapeReadWriteInterface* processing_shape,
-    ShapeReadWriteInterface* final_shape, bool* is_identity,
-    bool* is_simple_slice, bool* slice_dim0,
+    PartialTensorShape* processing_shape, PartialTensorShape* final_shape,
+    bool* is_identity, bool* is_simple_slice, bool* slice_dim0,
     gtl::InlinedVector<int64, 4>* begin, gtl::InlinedVector<int64, 4>* end,
     gtl::InlinedVector<int64, 4>* strides);
 
+// Same as above, but the outputs are TensorShape, not PartialTensorShape
+Status ValidateStridedSliceOp(
+    const Tensor* begin_tensor, const Tensor* end_tensor,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
+    int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
+    int32 new_axis_mask, int32 shrink_axis_mask, TensorShape* processing_shape,
+    TensorShape* final_shape, bool* is_identity, bool* is_simple_slice,
+    bool* slice_dim0, gtl::InlinedVector<int64, 4>* begin,
+    gtl::InlinedVector<int64, 4>* end, gtl::InlinedVector<int64, 4>* strides);
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_UTIL_STRIDED_SLICE_OP_H_
-- 
GitLab


From bdc928cdf2d5e9b6846ecfdf4b3767f275f4240d Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 26 Jun 2017 10:33:34 -0700
Subject: [PATCH 0442/1375] Added a tool to report the static information that
 can be extracted from a TF model.

PiperOrigin-RevId: 160162256
---
 tensorflow/contrib/cmake/tf_grappler.cmake    |  2 +
 tensorflow/core/grappler/grappler_item.cc     | 10 +++
 tensorflow/core/grappler/grappler_item.h      |  2 +
 tensorflow/python/BUILD                       | 43 +++++++++++
 tensorflow/python/grappler/model_analyzer.cc  | 75 +++++++++++++++++++
 tensorflow/python/grappler/model_analyzer.h   | 46 ++++++++++++
 tensorflow/python/grappler/model_analyzer.i   | 62 +++++++++++++++
 tensorflow/python/grappler/model_analyzer.py  | 37 +++++++++
 .../python/grappler/model_analyzer_test.py    | 54 +++++++++++++
 tensorflow/python/tensorflow.i                |  1 +
 10 files changed, 332 insertions(+)
 create mode 100644 tensorflow/python/grappler/model_analyzer.cc
 create mode 100644 tensorflow/python/grappler/model_analyzer.h
 create mode 100644 tensorflow/python/grappler/model_analyzer.i
 create mode 100644 tensorflow/python/grappler/model_analyzer.py
 create mode 100644 tensorflow/python/grappler/model_analyzer_test.py

diff --git a/tensorflow/contrib/cmake/tf_grappler.cmake b/tensorflow/contrib/cmake/tf_grappler.cmake
index 4811c8cce9..a7841c98e8 100644
--- a/tensorflow/contrib/cmake/tf_grappler.cmake
+++ b/tensorflow/contrib/cmake/tf_grappler.cmake
@@ -20,6 +20,8 @@ file(GLOB tf_grappler_srcs
    "${tensorflow_source_dir}/tensorflow/core/grappler/clusters/single_machine.h"
    "${tensorflow_source_dir}/tensorflow/python/grappler/cost_analyzer.cc"
    "${tensorflow_source_dir}/tensorflow/python/grappler/cost_analyzer.h"
+   "${tensorflow_source_dir}/tensorflow/python/grappler/model_analyzer.cc"
+   "${tensorflow_source_dir}/tensorflow/python/grappler/model_analyzer.h"
  )
  
 add_library(tf_grappler OBJECT ${tf_grappler_srcs})
diff --git a/tensorflow/core/grappler/grappler_item.cc b/tensorflow/core/grappler/grappler_item.cc
index 88ddd6c1b3..d27ddb17f1 100644
--- a/tensorflow/core/grappler/grappler_item.cc
+++ b/tensorflow/core/grappler/grappler_item.cc
@@ -30,6 +30,16 @@ std::vector<const NodeDef*> GrapplerItem::MainOpsFanin() const {
   return ComputeTransitiveFanin(graph, fetch);
 }
 
+std::vector<const NodeDef*> GrapplerItem::EnqueueOpsFanin() const {
+  std::vector<string> enqueue_ops;
+  for (const auto& queue_runner : queue_runners) {
+    for (const string& enqueue_op : queue_runner.enqueue_op_name()) {
+      enqueue_ops.push_back(enqueue_op);
+    }
+  }
+  return ComputeTransitiveFanin(graph, enqueue_ops);
+}
+
 std::vector<const NodeDef*> GrapplerItem::InitOpsFanin() const {
   return ComputeTransitiveFanin(graph, init_ops);
 }
diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h
index e0709c682b..84a7681782 100644
--- a/tensorflow/core/grappler/grappler_item.h
+++ b/tensorflow/core/grappler/grappler_item.h
@@ -50,6 +50,8 @@ struct GrapplerItem {
 
   // Return the set of node evaluated during a regular train/inference step.
   std::vector<const NodeDef*> MainOpsFanin() const;
+  // Return the set of node run to populate the queues (if any).
+  std::vector<const NodeDef*> EnqueueOpsFanin() const;
   // Return the set nodes used by TensorFlow to initialize the graph.
   std::vector<const NodeDef*> InitOpsFanin() const;
   // Return the set of variables accessed during a regular train/inference step.
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index d8d51a9d0c..b65a638620 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -176,6 +176,20 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "model_analyzer_lib",
+    srcs = ["grappler/model_analyzer.cc"],
+    hdrs = ["grappler/model_analyzer.h"],
+    deps = [
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler/clusters:cluster",
+        "//tensorflow/core/grappler/costs:graph_properties",
+        "//tensorflow/core/grappler/costs:utils",
+    ],
+)
+
 cc_library(
     name = "numpy_lib",
     srcs = ["lib/core/numpy.cc"],
@@ -2742,6 +2756,7 @@ tf_py_wrap_cc(
         "framework/cpp_shape_inference.i",
         "framework/python_op_gen.i",
         "grappler/cost_analyzer.i",
+        "grappler/model_analyzer.i",
         "grappler/tf_optimizer.i",
         "lib/core/py_func.i",
         "lib/core/strings.i",
@@ -2759,6 +2774,7 @@ tf_py_wrap_cc(
     ],
     deps = [
         ":cost_analyzer_lib",
+        ":model_analyzer_lib",
         ":cpp_shape_inference",
         ":kernel_registry",
         ":numpy_lib",
@@ -3951,3 +3967,30 @@ py_test(
         "//third_party/py/numpy",
     ],
 )
+
+py_library(
+    name = "model_analyzer",
+    srcs = [
+        "grappler/model_analyzer.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [":pywrap_tensorflow_internal"],
+)
+
+py_test(
+    name = "model_analyzer_test",
+    size = "small",
+    srcs = ["grappler/model_analyzer_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":array_ops",
+        ":client_testlib",
+        ":framework_for_generated_wrappers",
+        ":math_ops",
+        ":model_analyzer",
+        ":state_ops",
+        "//tensorflow/core:protos_all_py",
+        "//third_party/py/numpy",
+    ],
+)
diff --git a/tensorflow/python/grappler/model_analyzer.cc b/tensorflow/python/grappler/model_analyzer.cc
new file mode 100644
index 0000000000..1374967ca7
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.cc
@@ -0,0 +1,75 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/python/grappler/model_analyzer.h"
+
+#include <iomanip>
+#include "tensorflow/core/grappler/costs/graph_properties.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+
+namespace tensorflow {
+namespace grappler {
+
+ModelAnalyzer::ModelAnalyzer(const GrapplerItem& item) : item_(item) {}
+
+Status ModelAnalyzer::GenerateReport(std::ostream& os) {
+  GraphProperties properties(item_);
+  TF_RETURN_IF_ERROR(properties.InferStatically());
+
+  for (const auto& node : item_.MainOpsFanin()) {
+    PrintNodeInfo(node, properties, os);
+  }
+  for (const auto& node : item_.EnqueueOpsFanin()) {
+    PrintNodeInfo(node, properties, os);
+  }
+
+  return Status::OK();
+}
+
+void ModelAnalyzer::PrintNodeInfo(const NodeDef* node,
+                                  const GraphProperties& properties,
+                                  std::ostream& os) const {
+  os << node->name() << " [" << node->op() << "]" << std::endl;
+  if (properties.HasOutputProperties(node->name())) {
+    std::vector<OpInfo::TensorProperties> props =
+        properties.GetOutputProperties(node->name());
+    for (int i = 0; i < props.size(); ++i) {
+      const OpInfo::TensorProperties& prop = props[i];
+      os << "\t"
+         << "output " << i << " (" << DataTypeString(prop.dtype())
+         << ") has shape ";
+      if (prop.shape().unknown_rank()) {
+        os << "?";
+      } else {
+        os << "[";
+        for (int i = 0; i < prop.shape().dim_size(); ++i) {
+          if (i > 0) {
+            os << ", ";
+          }
+          if (prop.shape().dim(i).size() < 0) {
+            os << "?";
+          } else {
+            os << prop.shape().dim(i).size();
+          }
+        }
+        os << "]";
+      }
+      os << std::endl;
+    }
+  }
+}
+
+}  // end namespace grappler
+}  // end namespace tensorflow
diff --git a/tensorflow/python/grappler/model_analyzer.h b/tensorflow/python/grappler/model_analyzer.h
new file mode 100644
index 0000000000..a14034103c
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.h
@@ -0,0 +1,46 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_MODEL_ANALYZER_H_
+#define TENSORFLOW_CORE_GRAPPLER_COSTS_MODEL_ANALYZER_H_
+
+#include <iostream>
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+namespace grappler {
+struct GrapplerItem;
+class GraphProperties;
+
+// Generate a report detailing how much information is known statically for most
+// operations in the model, including output data types and output shapes.
+class ModelAnalyzer {
+ public:
+  explicit ModelAnalyzer(const GrapplerItem& item);
+  Status GenerateReport(std::ostream& os);
+
+ private:
+  void PrintNodeInfo(const NodeDef* node, const GraphProperties& properties,
+                     std::ostream& os) const;
+
+  const GrapplerItem& item_;
+};
+
+}  // end namespace grappler
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_GRAPPLER_COSTS_MODEL_ANALYZER_H_
diff --git a/tensorflow/python/grappler/model_analyzer.i b/tensorflow/python/grappler/model_analyzer.i
new file mode 100644
index 0000000000..d74bd37c63
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.i
@@ -0,0 +1,62 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+%include "tensorflow/python/lib/core/strings.i"
+%include "tensorflow/python/platform/base.i"
+
+%typemap(in) const tensorflow::MetaGraphDef& (tensorflow::MetaGraphDef temp) {
+  char* c_string;
+  Py_ssize_t py_size;
+  if (PyBytes_AsStringAndSize($input, &c_string, &py_size) == -1) {
+    // Python has raised an error (likely TypeError or UnicodeEncodeError).
+    SWIG_fail;
+  }
+
+  if (!temp.ParseFromString(string(c_string, py_size))) {
+    PyErr_SetString(
+        PyExc_TypeError,
+        "The MetaGraphDef could not be parsed as a valid protocol buffer");
+    SWIG_fail;
+  }
+  $1 = &temp;
+}
+
+%{
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/grappler/grappler_item_builder.h"
+#include "tensorflow/python/grappler/model_analyzer.h"
+%}
+
+%{
+string GenerateModelReport(const tensorflow::MetaGraphDef& metagraph) {
+  tensorflow::grappler::ItemConfig cfg;
+  cfg.apply_optimizations = false;
+  std::unique_ptr<tensorflow::grappler::GrapplerItem> item =
+      tensorflow::grappler::GrapplerItemFromMetaGraphDef("metagraph", metagraph, cfg);
+  if (!item) {
+    return "Error: failed to preprocess metagraph: check your log file for errors";
+  }
+  
+  string suffix;
+  tensorflow::grappler::ModelAnalyzer analyzer(*item);
+
+  std::stringstream os;
+  analyzer.GenerateReport(os);
+  return os.str();
+}
+
+%}
+
+string GenerateModelReport(const tensorflow::MetaGraphDef& metagraph);
diff --git a/tensorflow/python/grappler/model_analyzer.py b/tensorflow/python/grappler/model_analyzer.py
new file mode 100644
index 0000000000..c852d71ad8
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.py
@@ -0,0 +1,37 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Provides a proper python API for the symbols exported through swig."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python import pywrap_tensorflow as tf_wrap
+from tensorflow.python.framework import errors
+
+
+def GenerateModelReport(metagraph):
+  """Report what's known statically about each node in the provided metagraph.
+
+  Args:
+    metagraph: A TensorFlow MetaGraphDef.
+
+  Returns:
+    A string containing the report.
+  """
+  with errors.raise_exception_on_not_ok_status():
+    ret_from_swig = tf_wrap.GenerateModelReport(metagraph.SerializeToString())
+
+  return ret_from_swig
diff --git a/tensorflow/python/grappler/model_analyzer_test.py b/tensorflow/python/grappler/model_analyzer_test.py
new file mode 100644
index 0000000000..b59d1650f4
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer_test.py
@@ -0,0 +1,54 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the cost analyzer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import meta_graph
+from tensorflow.python.framework import ops
+from tensorflow.python.grappler import model_analyzer
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+class PyWrapOptimizeGraphTest(test.TestCase):
+
+  def testBasic(self):
+    """Make sure arguments can be passed correctly."""
+    a = constant_op.constant([10, 11], name="a")
+    b = constant_op.constant([10], name="b")
+    c = math_ops.add(a, b, name="c")
+    d = math_ops.add_n([a, c], name="d")
+    train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
+    train_op.append(d)
+    mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
+
+    report = model_analyzer.GenerateModelReport(mg)
+
+    # Check the report headers
+    self.assertTrue(b"a [Const]" in report)
+    self.assertTrue(b"a [Const]" in report)
+    self.assertTrue(b"c [Add]" in report)
+    self.assertTrue(b"d [AddN]" in report)
+
+    # Also print the report to make it easier to debug
+    print("{}".format(report))
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/tensorflow.i b/tensorflow/python/tensorflow.i
index a9a0b7fffa..e877d2717d 100644
--- a/tensorflow/python/tensorflow.i
+++ b/tensorflow/python/tensorflow.i
@@ -43,3 +43,4 @@ limitations under the License.
 
 %include "tensorflow/python/grappler/tf_optimizer.i"
 %include "tensorflow/python/grappler/cost_analyzer.i"
+%include "tensorflow/python/grappler/model_analyzer.i"
-- 
GitLab


From 1fa8b4247a15079cb60da74a3c86ed5eef7f1b93 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 26 Jun 2017 10:34:05 -0700
Subject: [PATCH 0443/1375] Properly handle RefEnter, RefExit and
 RefNextIteration nodes.

PiperOrigin-RevId: 160162338
---
 tensorflow/core/grappler/op_types.cc                    | 5 +++++
 tensorflow/core/grappler/op_types.h                     | 1 +
 tensorflow/core/grappler/optimizers/BUILD               | 1 +
 tensorflow/core/grappler/optimizers/constant_folding.cc | 5 +++--
 tensorflow/core/grappler/optimizers/memory_optimizer.cc | 5 +++--
 tensorflow/core/grappler/utils/BUILD                    | 1 +
 tensorflow/core/grappler/utils/topological_sort.cc      | 3 ++-
 7 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index 51146011b0..700a66d3a6 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -50,6 +50,11 @@ bool IsNoOp(const NodeDef& node) {
   return op == "NoOp";
 }
 
+bool IsNextIteration(const NodeDef& node) {
+  const auto& op = node.op();
+  return op == "NextIteration" || op == "RefNextIteration";
+}
+
 bool IsPlaceholder(const NodeDef& node) {
   const auto op = node.op();
   return op == "Placeholder" || op == "PlaceholderV2" ||
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index b2102c688d..cba44e905e 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -26,6 +26,7 @@ bool IsConstant(const NodeDef& node);
 bool IsDequeueOp(const NodeDef& node);
 bool IsIdentity(const NodeDef& node);
 bool IsMerge(const NodeDef& node);
+bool IsNextIteration(const NodeDef& node);
 bool IsNoOp(const NodeDef& node);
 bool IsPlaceholder(const NodeDef& node);
 bool IsRecv(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 3a705b85f7..5a7f11e149 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -197,6 +197,7 @@ cc_library(
         ":static_schedule",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:op_types",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core/grappler/costs:graph_properties",
         "//tensorflow/core/grappler/utils:topological_sort",
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 9f6e3aee71..2b6d9859b3 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -101,8 +101,9 @@ string AsControlDependency(const NodeDef& node) {
 
 ConstantFolding::ConstantFolding() {
   ops_to_preserve_ = std::regex(
-      "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|Enter|Exit|"
-      "NextIteration|.*Quantized.*");
+      "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|"
+      "Enter|RefEnter|Exit|RefExit|NextIteration|RefNextIteration|"
+      ".*Quantized.*");
 }
 
 string ConstantFolding::AddControlDependency(const string& input_name) {
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index eb3692f319..16a638a7d3 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/op_types.h"
 #include "tensorflow/core/grappler/optimizers/graph_rewriter.h"
 #include "tensorflow/core/grappler/optimizers/static_schedule.h"
 #include "tensorflow/core/grappler/utils.h"
@@ -556,8 +557,8 @@ static const NodeDef* FindSwapTrigger(
     // Don't jump over frames, since adding a control dependency from one frame
     // to the next isn't supported. Don't go through branches, since we don't
     // know whether they'll be executed or not.
-    if (input_node->op() == "NextIteration" || input_node->op() == "Switch" ||
-        input_node->op() == "Merge") {
+    if (IsNextIteration(*input_node) || IsSwitch(*input_node) ||
+        IsMerge(*input_node)) {
       continue;
     }
     auto it2 = execution_times.find(input_node);
diff --git a/tensorflow/core/grappler/utils/BUILD b/tensorflow/core/grappler/utils/BUILD
index 8839f07bc5..fd3894553b 100644
--- a/tensorflow/core/grappler/utils/BUILD
+++ b/tensorflow/core/grappler/utils/BUILD
@@ -48,6 +48,7 @@ cc_library(
     deps = [
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:op_types",
         "//tensorflow/core/grappler:utils",
     ],
 )
diff --git a/tensorflow/core/grappler/utils/topological_sort.cc b/tensorflow/core/grappler/utils/topological_sort.cc
index fdf7fb0d3d..9c5d27f3c5 100644
--- a/tensorflow/core/grappler/utils/topological_sort.cc
+++ b/tensorflow/core/grappler/utils/topological_sort.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include <deque>
 #include <unordered_map>
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/grappler/op_types.h"
 #include "tensorflow/core/grappler/utils.h"
 
 namespace tensorflow {
@@ -35,7 +36,7 @@ void TopologicalSort(GraphDef* graph) {
     if (node.op() == "Merge") {
       ready_inputs[&node] = 0;
       for (const auto& input : node.input()) {
-        if (node_map.GetNode(input)->op() == "NextIteration") {
+        if (IsNextIteration(*node_map.GetNode(input))) {
           ready_inputs[&node]++;
         }
       }
-- 
GitLab


From a4476d5bfefb6605a42d77da4598d5a8125e638d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 10:42:31 -0700
Subject: [PATCH 0444/1375] Switch tfprof to use proto3

PiperOrigin-RevId: 160163483
---
 .../internal/print_model_analysis_test.py     |  1 +
 tensorflow/tools/tfprof/tfprof_log.proto      | 18 +++---
 tensorflow/tools/tfprof/tfprof_options.proto  | 24 +++----
 tensorflow/tools/tfprof/tfprof_output.proto   | 64 +++++++++----------
 4 files changed, 54 insertions(+), 53 deletions(-)

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
index 84a37e087b..9eaf74d7a1 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
@@ -77,6 +77,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts.min_params = TEST_OPTIONS['min_params']
     opts.min_float_ops = TEST_OPTIONS['min_float_ops']
     opts.order_by = TEST_OPTIONS['order_by']
+    opts.step = -1
     for p in TEST_OPTIONS['account_type_regexes']:
       opts.account_type_regexes.append(p)
     for p in TEST_OPTIONS['start_name_regexes']:
diff --git a/tensorflow/tools/tfprof/tfprof_log.proto b/tensorflow/tools/tfprof/tfprof_log.proto
index 5c47142e0a..0e0fb74aae 100644
--- a/tensorflow/tools/tfprof/tfprof_log.proto
+++ b/tensorflow/tools/tfprof/tfprof_log.proto
@@ -1,4 +1,4 @@
-syntax = "proto2";
+syntax = "proto3";
 
 package tensorflow.tfprof;
 
@@ -6,27 +6,27 @@ package tensorflow.tfprof;
 message CodeDef {
   repeated Trace traces = 1;
   message Trace {
-    optional string file = 1;
-    optional int32 lineno = 2;
-    optional string function = 3;
-    optional string line = 4;
+    string file = 1;
+    int32 lineno = 2;
+    string function = 3;
+    string line = 4;
   }
 }
 
 message OpLogEntry {
   // op name.
-  optional string name = 1;
+  string name = 1;
   // float_ops is filled by tfprof Python API when called. It requires the
   // op has RegisterStatistics defined. Currently, Conv2D, MatMul, etc, are
   // implemented.
-  optional int64 float_ops = 2;
+  int64 float_ops = 2;
   // User can define extra op type information for an op. This allows the user
   // to select a group of ops precisely using op_type as a key.
   repeated string types = 3;
   // Used to support tfprof "code" view.
-  optional CodeDef code_def = 4;
+  CodeDef code_def = 4;
 }
 
 message OpLog {
   repeated OpLogEntry log_entries = 1;
-}
\ No newline at end of file
+}
diff --git a/tensorflow/tools/tfprof/tfprof_options.proto b/tensorflow/tools/tfprof/tfprof_options.proto
index 47e1ff33ee..5882833039 100644
--- a/tensorflow/tools/tfprof/tfprof_options.proto
+++ b/tensorflow/tools/tfprof/tfprof_options.proto
@@ -1,28 +1,28 @@
-syntax = "proto2";
+syntax = "proto3";
 
 package tensorflow.tfprof;
 
 // Refers to tfprof_options.h/cc for documentation.
 // Only used to pass tfprof options from Python to C++.
 message OptionsProto {
-  optional int64 max_depth = 1;
-  optional int64 min_bytes = 2;
-  optional int64 min_micros = 3;
-  optional int64 min_params = 4;
-  optional int64 min_float_ops = 5;
-  optional int64 min_occurrence = 17;
-  optional int64 step = 18 [default = -1];
+  int64 max_depth = 1;
+  int64 min_bytes = 2;
+  int64 min_micros = 3;
+  int64 min_params = 4;
+  int64 min_float_ops = 5;
+  int64 min_occurrence = 17;
+  int64 step = 18;
 
-  optional string order_by = 7;
+  string order_by = 7;
   repeated string account_type_regexes = 8;
   repeated string start_name_regexes = 9;
   repeated string trim_name_regexes = 10;
   repeated string show_name_regexes = 11;
   repeated string hide_name_regexes = 12;
-  optional bool account_displayed_op_only = 13;
+  bool account_displayed_op_only = 13;
   repeated string select = 14;
-  optional string output = 15;
-  optional string dump_to_file = 16 [deprecated = true];
+  string output = 15;
+  string dump_to_file = 16;
 }
 
 message AdvisorOptionsProto {
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index 3e024ae4e4..abf141f1e7 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -1,4 +1,4 @@
-syntax = "proto2";
+syntax = "proto3";
 
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/types.proto";
@@ -6,7 +6,7 @@ import "tensorflow/core/framework/types.proto";
 package tensorflow.tfprof;
 
 message TFProfTensorProto {
-  optional DataType dtype = 1;
+  DataType dtype = 1;
   // Flatten tensor in row-major.
   // Only one of the following array is set.
   repeated double value_double = 2;
@@ -17,23 +17,23 @@ message TFProfTensorProto {
 // A node in TensorFlow graph. Used by scope/graph view.
 message TFGraphNodeProto {
   // op name.
-  optional string name = 1;
+  string name = 1;
   // tensor value restored from checkpoint.
-  optional TFProfTensorProto tensor_value = 15;
+  TFProfTensorProto tensor_value = 15;
   // op execution time.
   // A node can be defined once but run multiple times in tf.while_loop.
   // the times sum up all different runs.
-  optional int64 run_count = 21;
-  optional int64 exec_micros = 2;
-  optional int64 accelerator_exec_micros = 17;
-  optional int64 cpu_exec_micros = 18;
+  int64 run_count = 21;
+  int64 exec_micros = 2;
+  int64 accelerator_exec_micros = 17;
+  int64 cpu_exec_micros = 18;
 
   // Total requested bytes by the op.
-  optional int64 requested_bytes = 3;
+  int64 requested_bytes = 3;
   // Number of parameters if available.
-  optional int64 parameters = 4;
+  int64 parameters = 4;
   // Number of float operations.
-  optional int64 float_ops = 13;
+  int64 float_ops = 13;
   // Device the op is assigned to.
   // Since an op can fire multiple kernel calls, there can be multiple devices.
   repeated string devices = 10;
@@ -42,15 +42,15 @@ message TFGraphNodeProto {
   // the node itself. The actual children depend on the data structure used.
   // In graph view, children are inputs recursively.
   // In scope view, children are nodes under the name scope.
-  optional int64 total_definition_count = 23;
-  optional int64 total_run_count = 22;
-  optional int64 total_exec_micros = 6;
-  optional int64 total_accelerator_exec_micros = 19;
-  optional int64 total_cpu_exec_micros = 20;
+  int64 total_definition_count = 23;
+  int64 total_run_count = 22;
+  int64 total_exec_micros = 6;
+  int64 total_accelerator_exec_micros = 19;
+  int64 total_cpu_exec_micros = 20;
 
-  optional int64 total_requested_bytes = 7;
-  optional int64 total_parameters = 8;
-  optional int64 total_float_ops = 14;
+  int64 total_requested_bytes = 7;
+  int64 total_parameters = 8;
+  int64 total_float_ops = 14;
 
   // shape information, if available.
   // TODO(xpan): Why is this repeated?
@@ -72,29 +72,29 @@ message TFGraphNodeProto {
 //            of the op (e.g. MatMul, Conv2D).
 message TFMultiGraphNodeProto {
   // Name of the node.
-  optional string name = 1;
+  string name = 1;
 
   // code execution time.
-  optional int64 exec_micros = 2;
-  optional int64 accelerator_exec_micros = 12;
-  optional int64 cpu_exec_micros = 13;
+  int64 exec_micros = 2;
+  int64 accelerator_exec_micros = 12;
+  int64 cpu_exec_micros = 13;
 
   // Total requested bytes by the code.
-  optional int64 requested_bytes = 3;
+  int64 requested_bytes = 3;
   // Number of parameters if available.
-  optional int64 parameters = 4;
+  int64 parameters = 4;
   // Number of float operations.
-  optional int64 float_ops = 5;
+  int64 float_ops = 5;
 
   // The following are the aggregated stats from descendants.
   // The actual descendants depend on the data structure used.
-  optional int64 total_exec_micros = 6;
-  optional int64 total_accelerator_exec_micros = 14;
-  optional int64 total_cpu_exec_micros = 15;
+  int64 total_exec_micros = 6;
+  int64 total_accelerator_exec_micros = 14;
+  int64 total_cpu_exec_micros = 15;
 
-  optional int64 total_requested_bytes = 7;
-  optional int64 total_parameters = 8;
-  optional int64 total_float_ops = 9;
+  int64 total_requested_bytes = 7;
+  int64 total_parameters = 8;
+  int64 total_float_ops = 9;
 
   // TensorFlow graph nodes contained by the TFMultiGraphNodeProto.
   repeated TFGraphNodeProto graph_nodes = 10;
-- 
GitLab


From 9f2ea357d7c57fe06bd4539d19c4495dea7df8b8 Mon Sep 17 00:00:00 2001
From: Ben Mabey <ben@benmabey.com>
Date: Mon, 26 Jun 2017 12:00:07 -0600
Subject: [PATCH 0445/1375] coerce dict keys to strings when sorting for
 python3 compat (#11039)

In python 2 you can sort dicts with hetroeneous types:

   In [1]: d = {"z": 1, 1: 42, ("a", "b"): 100}
   In [2]: sorted(d)
   Out[2]: [1, 'z', ('a', 'b')]

In python 3 you get an error:

   In [1]: d = {"z": 1, 1: 42, ("a", "b"): 100}
   In [2]: sorted(d)
   ---------------------------------------------------------------------------
   TypeError                                 Traceback (most recent call last)
   <ipython-input-5-01813638448d> in <module>()
   ----> 1 sorted(d)
---
 tensorflow/python/ops/data_flow_ops.py   | 2 +-
 tensorflow/python/training/input.py      | 4 ++--
 tensorflow/python/training/input_test.py | 7 +++++++
 3 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 829aa99284..1dc5eb2f90 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -248,7 +248,7 @@ class QueueBase(object):
     if isinstance(vals, dict):
       if not self._names:
         raise ValueError("Queue must have names to enqueue a dictionary")
-      if sorted(self._names) != sorted(vals.keys()):
+      if sorted(self._names, key=str) != sorted(vals.keys(), key=str):
         raise ValueError("Keys in dictionary to enqueue do not match "
                          "names of Queue.  Dictionary: (%s), Queue: (%s)" %
                          (sorted(vals.keys()), sorted(self._names)))
diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 21183823c2..93ba15ec81 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -382,7 +382,7 @@ class _SparseMetaData(object):
 
 def _as_tensor_list(tensors):
   if isinstance(tensors, dict):
-    return [tensors[k] for k in sorted(tensors)]
+    return [tensors[k] for k in sorted(tensors, key=str)]
   else:
     return tensors
 
@@ -408,7 +408,7 @@ def _as_original_type(original_tensors, tensor_list):
       # was enqueued.  Make it a list again.  See b/28117485.
       tensor_list = [tensor_list]
     return {k: tensor_list[i]
-            for i, k in enumerate(sorted(original_tensors))}
+            for i, k in enumerate(sorted(original_tensors, key=str))}
   else:
     return tensor_list
 
diff --git a/tensorflow/python/training/input_test.py b/tensorflow/python/training/input_test.py
index 4f705a0a85..d32768f5da 100644
--- a/tensorflow/python/training/input_test.py
+++ b/tensorflow/python/training/input_test.py
@@ -429,6 +429,13 @@ class DictHelperTest(test_lib.TestCase):
     d2 = inp._as_original_type(d, l)
     self.assertEquals(d, d2)
 
+  def testHeterogeneousKeysDictInputs(self):
+    d = {"z": 1, 1: 42, ("a", "b"): 100}
+    l = inp._as_tensor_list(d)
+    self.assertEquals([100, 42, 1], l)
+    d2 = inp._as_original_type(d, l)
+    self.assertEquals(d, d2)
+
 
 class BatchTest(test_lib.TestCase):
 
-- 
GitLab


From d778904d942dcfcda8d21fe990cbb4e20102cfe9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 11:16:17 -0700
Subject: [PATCH 0446/1375] Fixes to cuda_config.h.

PiperOrigin-RevId: 160168545
---
 third_party/toolchains/gpus/cuda/cuda/cuda_config.h | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/third_party/toolchains/gpus/cuda/cuda/cuda_config.h b/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
index dddee32193..f6662274cc 100644
--- a/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
+++ b/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
@@ -14,7 +14,14 @@ limitations under the License.
 ==============================================================================*/
 
 // DO NOT EDIT: automatically generated file
-#ifndef THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
-#define THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
+#ifndef CUDA_CUDA_CONFIG_H_
+#define CUDA_CUDA_CONFIG_H_
 
-#endif  // THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
+#define TF_CUDA_CAPABILITIES CudaVersion("3.0")
+
+#define TF_CUDA_VERSION "8.0"
+#define TF_CUDNN_VERSION "5"
+
+#define TF_CUDA_TOOLKIT_PATH "/usr/local/cuda-8.0"
+
+#endif  // CUDA_CUDA_CONFIG_H_
-- 
GitLab


From 5bb3d62fb2a28ef3cd9f02eeebde82560313905a Mon Sep 17 00:00:00 2001
From: Kongsea <kongsea@gmail.com>
Date: Mon, 26 Jun 2017 13:28:27 -0500
Subject: [PATCH 0447/1375] Add 3D operations for layers: conv3d, avg_pool3d,
 max_pool3d and conv3d_transpose (#9773)

* Add avg_pool3d operation for layers.

* Add max_pool3d operation for layers.

* Add conv3d and convolution3d operations for layers.

* The function loss_ops.get_regularization_losses() from tensorflow.contrib.losses.python.losses has been deprecated. Use tensorflow.python.ops.losses.losses.get_regularization_losses() to replace it.

* Add unit test for avg_pool3d.

* Add unit test for max_pool3d.

* Add conv3d_transpose and convolution3d_transpose for layers.

* Update layers.py

Add a missing line break

* Update layers_test.py

Change from importing methods to import at module level.

* Update RELEASE.md

Add the description for `conv3d`, `avg_pool3d`, `max_pool3d` and `conv3d_transpose`.

* Update RELEASE.md

Update to Version 1.2

* Revert "Update RELEASE.md"

This reverts commit a8ae1fedd3d3695bd0266732083d89b3af9f8ca1.

* Revert "Update RELEASE.md"

This reverts commit 7b8c5eb6bdbd28387adf3afbfc8b49375ed0e2c8.

* Add a missing declaration

Add missing conv3d declaration or slim.conv3d cannot be found.

* Update layers_test.py

Put the imports in alphabetical order

* Update BUILD

Add dependency.

* Update BUILD

* Update BUILD

* Update BUILD
---
 tensorflow/contrib/layers/BUILD               |   2 +-
 tensorflow/contrib/layers/__init__.py         |   6 +
 .../contrib/layers/python/layers/layers.py    | 218 ++++++++++++++++++
 .../layers/python/layers/layers_test.py       | 150 +++++++++++-
 4 files changed, 370 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD
index 5cd338f791..62741d3b0c 100644
--- a/tensorflow/contrib/layers/BUILD
+++ b/tensorflow/contrib/layers/BUILD
@@ -121,7 +121,6 @@ cuda_py_test(
         ":layers_py",
         "//third_party/py/numpy",
         "//tensorflow/contrib/framework:framework_py",
-        "//tensorflow/contrib/losses:losses_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
@@ -141,6 +140,7 @@ cuda_py_test(
         "//tensorflow/python:template",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/ops/losses:losses",
     ],
 )
 
diff --git a/tensorflow/contrib/layers/__init__.py b/tensorflow/contrib/layers/__init__.py
index ef05dbaa65..528de31346 100644
--- a/tensorflow/contrib/layers/__init__.py
+++ b/tensorflow/contrib/layers/__init__.py
@@ -17,12 +17,16 @@
 See the @{$python/contrib.layers} guide.
 
 @@avg_pool2d
+@@avg_pool3d
 @@batch_norm
 @@convolution2d
+@@convolution3d
 @@conv2d_in_plane
 @@convolution2d_in_plane
 @@conv2d_transpose
 @@convolution2d_transpose
+@@conv3d_transpose
+@@convolution3d_transpose
 @@dropout
 @@elu
 @@embedding_lookup_unique
@@ -31,6 +35,7 @@ See the @{$python/contrib.layers} guide.
 @@layer_norm
 @@linear
 @@max_pool2d
+@@max_pool3d
 @@one_hot_encoding
 @@relu
 @@relu6
@@ -101,6 +106,7 @@ from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = ['bias_add',
                     'conv2d',
+                    'conv3d',
                     'elu',
                     'feature_column',
                     'legacy_fully_connected',
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index d8ff6d6b6a..ed4b723ca7 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -49,15 +49,20 @@ from tensorflow.python.training import moving_averages
 # TODO(b/28426988): Replace legacy_* fns migrated from slim.
 # TODO(b/28426988): Remove legacy_* when all uses have migrated to new API.
 __all__ = ['avg_pool2d',
+           'avg_pool3d',
            'batch_norm',
            'bias_add',
            'conv2d',
+           'conv3d',
            'conv2d_in_plane',
            'conv2d_transpose',
+           'conv3d_transpose',
            'convolution',
            'convolution2d',
            'convolution2d_in_plane',
            'convolution2d_transpose',
+           'convolution3d',
+           'convolution3d_transpose',
            'dropout',
            'elu',
            'flatten',
@@ -66,6 +71,7 @@ __all__ = ['avg_pool2d',
            'linear',
            'pool',
            'max_pool2d',
+           'max_pool3d',
            'one_hot_encoding',
            'relu',
            'relu6',
@@ -82,6 +88,8 @@ __all__ = ['avg_pool2d',
 
 DATA_FORMAT_NCHW = 'NCHW'
 DATA_FORMAT_NHWC = 'NHWC'
+DATA_FORMAT_NCDHW = 'NCDHW'
+DATA_FORMAT_NDHWC = 'NDHWC'
 
 
 @add_arg_scope
@@ -132,6 +140,54 @@ def avg_pool2d(inputs,
     return utils.collect_named_outputs(outputs_collections, sc, outputs)
 
 
+@add_arg_scope
+def avg_pool3d(inputs,
+               kernel_size,
+               stride=2,
+               padding='VALID',
+               data_format=DATA_FORMAT_NDHWC,
+               outputs_collections=None,
+               scope=None):
+  """Adds a 3D average pooling op.
+
+  It is assumed that the pooling is done per image but not in batch or channels.
+
+  Args:
+    inputs: A 5-D tensor of shape `[batch_size, depth, height, width, channels]` if
+      `data_format` is `NDHWC`, and `[batch_size, channels, depth, height, width]` if
+      `data_format` is `NCDHW`.
+    kernel_size: A list of length 3: [kernel_depth, kernel_height, kernel_width] of the
+      pooling kernel over which the op is computed. Can be an int if both
+      values are the same.
+    stride: A list of length 3: [stride_depth, stride_height, stride_width].
+      Can be an int if both strides are the same. Note that presently
+      both strides must have the same value.
+    padding: The padding method, either 'VALID' or 'SAME'.
+    data_format: A string. `NDHWC` (default) and `NCDHW` are supported.
+    outputs_collections: The collections to which the outputs are added.
+    scope: Optional scope for name_scope.
+
+  Returns:
+    A `Tensor` representing the results of the pooling operation.
+
+  Raises:
+    ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`.
+  """
+  if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC):
+    raise ValueError('data_format has to be either NCDHW or NDHWC.')
+  with ops.name_scope(scope, 'AvgPool3D', [inputs]) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    df = ('channels_first' if data_format and data_format.startswith('NC')
+          else 'channels_last')
+    layer = pooling_layers.AveragePooling3D(pool_size=kernel_size,
+                                            strides=stride,
+                                            padding=padding,
+                                            data_format=df,
+                                            _scope=sc)
+    outputs = layer.apply(inputs)
+    return utils.collect_named_outputs(outputs_collections, sc, outputs)
+
+
 def _fused_batch_norm(
     inputs,
     decay=0.999,
@@ -985,6 +1041,7 @@ def convolution(inputs,
                                        sc.original_name_scope, outputs)
 
 convolution2d = convolution
+convolution3d = convolution
 
 
 @add_arg_scope
@@ -1203,6 +1260,116 @@ def convolution2d_transpose(
                                        sc.original_name_scope, outputs)
 
 
+@add_arg_scope
+def convolution3d_transpose(
+    inputs,
+    num_outputs,
+    kernel_size,
+    stride=1,
+    padding='SAME',
+    data_format=DATA_FORMAT_NDHWC,
+    activation_fn=nn.relu,
+    normalizer_fn=None,
+    normalizer_params=None,
+    weights_initializer=initializers.xavier_initializer(),
+    weights_regularizer=None,
+    biases_initializer=init_ops.zeros_initializer(),
+    biases_regularizer=None,
+    reuse=None,
+    variables_collections=None,
+    outputs_collections=None,
+    trainable=True,
+    scope=None):
+  """Adds a convolution3d_transpose with an optional batch normalization layer.
+  
+  The function creates a variable called `weights`, representing the
+  kernel, that is convolved with the input. If `batch_norm_params` is `None`, a
+  second variable called 'biases' is added to the result of the operation.
+  Args:
+    inputs: A 5-D `Tensor` of type `float` and shape
+      `[batch, depth, height, width, in_channels]` for `NDHWC` data format or
+      `[batch, in_channels, depth, height, width]` for `NCDHW` data format.
+    num_outputs: Integer, the number of output filters.
+    kernel_size: A list of length 3 holding the [kernel_depth, kernel_height, kernel_width] of
+      of the filters. Can be an int if both values are the same.
+    stride: A list of length 3: [stride_depth, stride_height, stride_width].
+      Can be an int if both strides are the same.  Note that presently
+      both strides must have the same value.
+    padding: One of 'VALID' or 'SAME'.
+    data_format: A string. `NDHWC` (default) and `NCDHW` are supported.
+    activation_fn: Activation function. The default value is a ReLU function.
+      Explicitly set it to None to skip it and maintain a linear activation.
+    normalizer_fn: Normalization function to use instead of `biases`. If
+      `normalizer_fn` is provided then `biases_initializer` and
+      `biases_regularizer` are ignored and `biases` are not created nor added.
+      default set to None for no normalizer function
+    normalizer_params: Normalization function parameters.
+    weights_initializer: An initializer for the weights.
+    weights_regularizer: Optional regularizer for the weights.
+    biases_initializer: An initializer for the biases. If None skip biases.
+    biases_regularizer: Optional regularizer for the biases.
+    reuse: Whether or not the layer and its variables should be reused. To be
+      able to reuse the layer scope must be given.
+    variables_collections: Optional list of collections for all the variables or
+      a dictionary containing a different list of collection per variable.
+    outputs_collections: Collection to add the outputs.
+    trainable: Whether or not the variables should be trainable or not.
+    scope: Optional scope for variable_scope.
+  Returns:
+    A tensor representing the output of the operation.
+  Raises:
+    ValueError: If 'kernel_size' is not a list of length 3.
+    ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`.
+    ValueError: If `C` dimension of `inputs` is None.
+  """
+  layer_variable_getter = _build_variable_getter(
+      {'bias': 'biases', 'kernel': 'weights'})
+
+  with variable_scope.variable_scope(
+      scope, 'Conv3d_transpose', [inputs], reuse=reuse,
+      custom_getter=layer_variable_getter) as sc:
+    if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC):
+      raise ValueError('data_format has to be either NCDHW or NDHWC.')
+
+    inputs = ops.convert_to_tensor(inputs)
+
+    df = ('channels_first' if data_format and data_format.startswith('NC')
+          else 'channels_last')
+    layer = convolutional_layers.Convolution3DTranspose(
+        filters=num_outputs,
+        kernel_size=kernel_size,
+        strides=stride,
+        padding=padding,
+        data_format=df,
+        activation=None,
+        use_bias=not normalizer_fn and biases_initializer,
+        kernel_initializer=weights_initializer,
+        bias_initializer=biases_initializer,
+        kernel_regularizer=weights_regularizer,
+        bias_regularizer=biases_regularizer,
+        activity_regularizer=None,
+        trainable=trainable,
+        name=sc.name,
+        dtype=inputs.dtype.base_dtype,
+        _scope=sc,
+        _reuse=reuse)
+    outputs = layer.apply(inputs)
+
+    # Add variables to collections.
+    _add_variable_to_collections(layer.kernel, variables_collections, 'weights')
+    if layer.bias:
+      _add_variable_to_collections(layer.bias, variables_collections, 'biases')
+
+    if normalizer_fn is not None:
+      normalizer_params = normalizer_params or {}
+      outputs = normalizer_fn(outputs, **normalizer_params)
+
+    if activation_fn is not None:
+      outputs = activation_fn(outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
+
+
 @add_arg_scope
 def dropout(inputs,
             keep_prob=0.5,
@@ -1690,6 +1857,55 @@ def max_pool2d(inputs,
     return utils.collect_named_outputs(outputs_collections, sc, outputs)
 
 
+@add_arg_scope
+def max_pool3d(inputs,
+               kernel_size,
+               stride=2,
+               padding='VALID',
+               data_format=DATA_FORMAT_NDHWC,
+               outputs_collections=None,
+               scope=None):
+  """Adds a 3D Max Pooling op.
+
+  It is assumed that the pooling is done per image but not in batch or channels.
+
+  Args:
+    inputs: A 5-D tensor of shape `[batch_size, depth, height, width, channels]` if
+      `data_format` is `NDHWC`, and `[batch_size, channels, depth, height, width]` if
+      `data_format` is `NCDHW`.
+    kernel_size: A list of length 3: [kernel_depth, kernel_height, kernel_width] of the
+      pooling kernel over which the op is computed. Can be an int if both
+      values are the same.
+    stride: A list of length 3: [stride_depth, stride_height, stride_width].
+      Can be an int if both strides are the same. Note that presently
+      both strides must have the same value.
+    padding: The padding method, either 'VALID' or 'SAME'.
+    data_format: A string. `NDHWC` (default) and `NCDHW` are supported.
+    outputs_collections: The collections to which the outputs are added.
+    scope: Optional scope for name_scope.
+
+  Returns:
+    A `Tensor` representing the results of the pooling operation.
+
+  Raises:
+    ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`.
+    ValueError: If 'kernel_size' is not a 3-D list
+  """
+  if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC):
+    raise ValueError('data_format has to be either NCDHW or NDHWC.')
+  with ops.name_scope(scope, 'MaxPool3D', [inputs]) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    df = ('channels_first' if data_format and data_format.startswith('NC')
+          else 'channels_last')
+    layer = pooling_layers.MaxPooling3D(pool_size=kernel_size,
+                                        strides=stride,
+                                        padding=padding,
+                                        data_format=df,
+                                        _scope=sc)
+    outputs = layer.apply(inputs)
+    return utils.collect_named_outputs(outputs_collections, sc, outputs)
+
+
 @add_arg_scope
 def pool(inputs,
          kernel_size,
@@ -2347,6 +2563,8 @@ linear = functools.partial(fully_connected, activation_fn=None)
 
 # Simple alias.
 conv2d = convolution2d
+conv3d = convolution3d
 conv2d_transpose = convolution2d_transpose
+conv3d_transpose = convolution3d_transpose
 conv2d_in_plane = convolution2d_in_plane
 separable_conv2d = separable_convolution2d
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index d4ee85b550..15809ea180 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -27,7 +27,6 @@ from tensorflow.contrib.framework.python.ops import arg_scope
 from tensorflow.contrib.framework.python.ops import variables
 from tensorflow.contrib.layers.python.layers import layers as _layers
 from tensorflow.contrib.layers.python.layers import regularizers
-from tensorflow.contrib.losses.python.losses import loss_ops
 from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -49,6 +48,7 @@ from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import template
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
+from tensorflow.python.ops.losses import losses
 from tensorflow.python.platform import test
 
 
@@ -121,6 +121,76 @@ class AvgPool2DTest(test.TestCase):
     self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
 
+class AvgPool3DTest(test.TestCase):
+
+  def testInvalidDataFormat(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3))
+    with self.assertRaisesRegexp(ValueError,
+                                 'data_format has to be either NCDHW or NDHWC.'):
+      _layers.avg_pool3d(images, [3, 3, 3], data_format='CDHWN')
+
+  def testCreateAvgPool(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3))
+    output = _layers.avg_pool3d(images, [3, 3, 3])
+    self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateAvgPoolNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, 2, depth, height, width))
+    output = _layers.avg_pool3d(images, [3, 3, 3], data_format='NCDHW')
+    self.assertEquals(output.op.name, 'AvgPool3D/transpose_1')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 1, 2, 4])
+
+  def testCollectOutputs(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], outputs_collections='outputs')
+    output_collected = ops.get_collection('outputs')[0]
+    self.assertEqual(output_collected.aliases, ['AvgPool3D'])
+    self.assertEqual(output_collected, output)
+
+  def testCreateSquareAvgPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, 3)
+    self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateAvgPoolWithScope(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], scope='pool1')
+    self.assertEqual(output.op.name, 'pool1/AvgPool3D')
+
+  def testCreateAvgPoolWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])
+
+  def testCreateAvgPoolWithSamePaddingNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
+    output = _layers.avg_pool3d(
+        images, [3, 3, 3], padding='SAME', data_format='NCDHW')
+    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])
+
+  def testCreateAvgPoolStrideWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3])
+
+  def testGlobalAvgPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, images.get_shape()[1:4], stride=1)
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])
+
+
 class PoolTest(test.TestCase):
 
   def testCreatePool(self):
@@ -1559,23 +1629,23 @@ class FCTest(test.TestCase):
         inputs, 32, scope='fc1', weights_regularizer=regularizer)
     self.assertEqual(
         len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
-    self.assertEqual(len(loss_ops.get_regularization_losses()), 1)
+    self.assertEqual(len(losses.get_regularization_losses()), 1)
     _layers.fully_connected(
         inputs, 32, scope='fc1', weights_regularizer=regularizer, reuse=True)
     self.assertEqual(
         len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
-    self.assertEqual(len(loss_ops.get_regularization_losses()), 1)
+    self.assertEqual(len(losses.get_regularization_losses()), 1)
 
     with variable_scope.variable_scope('outer', reuse=False):
       _layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
-      self.assertEqual(len(loss_ops.get_regularization_losses()), 2)
+      self.assertEqual(len(losses.get_regularization_losses()), 2)
     with variable_scope.variable_scope('outer', reuse=True):
       _layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
-      self.assertEqual(len(loss_ops.get_regularization_losses()), 2)
+      self.assertEqual(len(losses.get_regularization_losses()), 2)
 
   def testCreateFCWithoutActivation(self):
     height, width = 3, 3
@@ -2771,6 +2841,76 @@ class MaxPool2DTest(test.TestCase):
     self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
 
+class MaxPool3DTest(test.TestCase):
+
+  def testInvalidDataFormat(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3))
+    with self.assertRaisesRegexp(ValueError,
+                                 'data_format has to be either NCDHW or NDHWC.'):
+      _layers.max_pool3d(images, [3, 3, 3], data_format='CDHWN')
+
+  def testCreateMaxPool(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3)).astype(np.float32)
+    output = _layers.max_pool3d(images, [3, 3, 3])
+    self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateMaxPoolNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, 3, depth, height, width)).astype(np.float32)
+    output = _layers.max_pool3d(images, [3, 3, 3], data_format='NCDHW')
+    self.assertEquals(output.op.name, 'MaxPool3D/transpose_1')
+    self.assertListEqual(output.get_shape().as_list(), [5, 3, 1, 2, 4])
+
+  def testCollectOutputs(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], outputs_collections='outputs')
+    output_collected = ops.get_collection('outputs')[0]
+    self.assertEqual(output_collected.aliases, ['MaxPool3D'])
+    self.assertEqual(output_collected, output)
+
+  def testCreateSquareMaxPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, 3)
+    self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateMaxPoolWithScope(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], scope='pool1')
+    self.assertEqual(output.op.name, 'pool1/MaxPool3D')
+
+  def testCreateMaxPoolWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])
+
+  def testCreateMaxPoolWithSamePaddingNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
+    output = _layers.max_pool3d(
+        images, [3, 3, 3], padding='SAME', data_format='NCDHW')
+    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])
+
+  def testCreateMaxPoolStrideWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3])
+
+  def testGlobalMaxPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, images.get_shape()[1:4], stride=1)
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])
+
+
 class OneHotEncodingTest(test.TestCase):
 
   def testOneHotEncodingCreate(self):
-- 
GitLab


From 9b2984d985357a02b39baa495b8d7c0fe9475377 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 11:34:13 -0700
Subject: [PATCH 0448/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 160171187
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 126 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 |  34 ++++-
 2 files changed, 156 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index edc21b6029..cabd91f9ef 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -1540,6 +1540,69 @@ op {
     }
   }
 }
+op {
+  name: "ArgMax"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "dimension"
+    type_attr: "Tidx"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "output_type"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tidx"
+    type: "type"
+    default_value {
+      type: DT_INT32
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "ArgMin"
   input_arg {
@@ -1590,6 +1653,69 @@ op {
     }
   }
 }
+op {
+  name: "ArgMin"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "dimension"
+    type_attr: "Tidx"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "output_type"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tidx"
+    type: "type"
+    default_value {
+      type: DT_INT32
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "AsString"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index ddaaed4396..b122b5a992 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -1578,12 +1578,12 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32, 0 <= dimension < rank(input).  Describes which dimension\nof the input Tensor to reduce across. For vectors, use dimension = 0."
+    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
     name: "output"
-    type: DT_INT64
+    type_attr: "output_type"
   }
   attr {
     name: "T"
@@ -1620,6 +1620,19 @@ op {
       }
     }
   }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
   summary: "Returns the index with the largest value across dimensions of a tensor."
   description: "Note that in case of ties the identity of the return value is not guaranteed."
 }
@@ -1631,12 +1644,12 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32, 0 <= dimension < rank(input).  Describes which dimension\nof the input Tensor to reduce across. For vectors, use dimension = 0."
+    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
     name: "output"
-    type: DT_INT64
+    type_attr: "output_type"
   }
   attr {
     name: "T"
@@ -1673,6 +1686,19 @@ op {
       }
     }
   }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
   summary: "Returns the index with the smallest value across dimensions of a tensor."
   description: "Note that in case of ties the identity of the return value is not guaranteed."
 }
-- 
GitLab


From ee3eaffe162da95e98d3d5ee8e42e296d8b66d1c Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Mon, 26 Jun 2017 11:44:20 -0700
Subject: [PATCH 0449/1375] Adds notes to prevent overfitting for Experiment
 continous_train_and_eval.

PiperOrigin-RevId: 160172692
---
 tensorflow/contrib/learn/python/learn/experiment.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index c60ecac5df..491c4343bd 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -523,7 +523,12 @@ class Experiment(object):
       differences in resource control. First, the resources (e.g., memory) used
       by training will be released before evaluation (`train_and_evaluate` takes
       double resources). Second, more checkpoints will be saved as a checkpoint
-      is generated at the end of each small trainning iteration.
+      is generated at the end of each trainning iteration.
+
+      3. As the estimator.train starts from scratch (new graph, new states for
+      input, etc) at each iteration, it is recommended to have the
+      `train_steps_per_iteration` larger. It is also recommended to shuffle your
+      input.
 
     Args:
       continuous_eval_predicate_fn: A predicate function determining whether to
-- 
GitLab


From 0b9b09a8531004b44b133a52c3fcc67bc6759bd8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 11:46:12 -0700
Subject: [PATCH 0450/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 160172985
---
 tensorflow/go/op/wrappers.go | 46 +++++++++++++++++++++++++++++++-----
 1 file changed, 40 insertions(+), 6 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 9e3a4666b9..8dd2931703 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -20544,23 +20544,40 @@ func Requantize(scope *Scope, input tf.Output, input_min tf.Output, input_max tf
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// ArgMinAttr is an optional argument to ArgMin.
+type ArgMinAttr func(optionalAttr)
+
+// ArgMinOutputType sets the optional output_type attribute to value.
+// If not specified, defaults to DT_INT64
+func ArgMinOutputType(value tf.DataType) ArgMinAttr {
+	return func(m optionalAttr) {
+		m["output_type"] = value
+	}
+}
+
 // Returns the index with the smallest value across dimensions of a tensor.
 //
 // Note that in case of ties the identity of the return value is not guaranteed.
 //
 // Arguments:
 //
-//	dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-// of the input Tensor to reduce across. For vectors, use dimension = 0.
-func ArgMin(scope *Scope, input tf.Output, dimension tf.Output) (output tf.Output) {
+//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+// which dimension of the input Tensor to reduce across. For vectors,
+// use dimension = 0.
+func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
 		Type: "ArgMin",
 		Input: []tf.Input{
 			input, dimension,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
@@ -21818,23 +21835,40 @@ func IsFinite(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// ArgMaxAttr is an optional argument to ArgMax.
+type ArgMaxAttr func(optionalAttr)
+
+// ArgMaxOutputType sets the optional output_type attribute to value.
+// If not specified, defaults to DT_INT64
+func ArgMaxOutputType(value tf.DataType) ArgMaxAttr {
+	return func(m optionalAttr) {
+		m["output_type"] = value
+	}
+}
+
 // Returns the index with the largest value across dimensions of a tensor.
 //
 // Note that in case of ties the identity of the return value is not guaranteed.
 //
 // Arguments:
 //
-//	dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-// of the input Tensor to reduce across. For vectors, use dimension = 0.
-func ArgMax(scope *Scope, input tf.Output, dimension tf.Output) (output tf.Output) {
+//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+// which dimension of the input Tensor to reduce across. For vectors,
+// use dimension = 0.
+func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
 		Type: "ArgMax",
 		Input: []tf.Input{
 			input, dimension,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
-- 
GitLab


From 94dd8a3a092ace78c65328d4db1bf0ac27910d40 Mon Sep 17 00:00:00 2001
From: David Norman <DavidNorman@users.noreply.github.com>
Date: Mon, 26 Jun 2017 19:54:34 +0100
Subject: [PATCH 0451/1375] [XLA] Add a 'custom' fusion op type (#10319)

* Add a 'custom' fusion op type

* Improve the description of the fusion type
---
 tensorflow/compiler/xla/service/hlo_instruction.cc | 4 ++++
 tensorflow/compiler/xla/service/hlo_instruction.h  | 3 +++
 2 files changed, 7 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 9117ab9653..92e397a5d3 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1675,6 +1675,8 @@ string HloInstruction::ToCategory() const {
       case FusionKind::kConvBackwardFilter:
       case FusionKind::kConvBackwardInput:
         return "convolution fusion";
+      case FusionKind::kCustom:
+        return "custom fusion";
     }
   }
 
@@ -2342,6 +2344,8 @@ string ToString(HloInstruction::FusionKind kind) {
       return "kConvBackwardFilter";
     case HloInstruction::FusionKind::kConvBackwardInput:
       return "kConvBackwardInput";
+    case HloInstruction::FusionKind::kCustom:
+      return "kCustom";
   }
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 2f26a7be2d..6eb6dda5ac 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -63,6 +63,9 @@ class HloInstruction {
     kTransposeDot,        // Fused into a dot with transposed operands.
     kConvBackwardFilter,  // Fused into a backward filter convolution.
     kConvBackwardInput,   // Fused into a backward input convolution.
+
+    kCustom,              // Custom category for backend-specific fusions that
+                          // do not match any of the more specific ones.
   };
 
   ~HloInstruction();
-- 
GitLab


From f261bfd5e76fa17459b4009ca23dfb33bdec51ba Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Mon, 26 Jun 2017 21:26:12 +0200
Subject: [PATCH 0452/1375] Add python import library on Windows (#10980)

---
 third_party/gpus/cuda_configure.bzl |  4 +--
 third_party/py/BUILD.tpl            | 12 +++++++
 third_party/py/python_configure.bzl | 56 ++++++++++++++++++++++-------
 3 files changed, 58 insertions(+), 14 deletions(-)

diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 83a377dde5..1e29f23d90 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -847,10 +847,10 @@ def _genrule(src_dir, genrule_name, command, outs):
       genrule_name + '",\n' +
       '    outs = [\n' +
       outs +
-      '    ],\n' +
+      '\n    ],\n' +
       '    cmd = """\n' +
       command +
-      '    """,\n' +
+      '\n   """,\n' +
       ')\n\n'
   )
 
diff --git a/third_party/py/BUILD.tpl b/third_party/py/BUILD.tpl
index 1ee9c071ad..cdfe9c9b45 100644
--- a/third_party/py/BUILD.tpl
+++ b/third_party/py/BUILD.tpl
@@ -6,6 +6,16 @@ cc_library(
     name = "python_headers",
     hdrs = [":python_include"],
     includes = ["python_include"],
+    data = select({
+      ":windows" : [":python_import_lib"],
+      "//conditions:default": [],
+    }),
+    linkopts = select({
+      # TODO(pcloudy): Ideally, this should just go into deps after resolving
+      # https://github.com/bazelbuild/bazel/issues/3237,
+      ":windows" : ["$(locations :python_import_lib)"],
+      "//conditions:default": [],
+    }),
 )
 
 cc_library(
@@ -22,4 +32,6 @@ config_setting(
 
 %{PYTHON_INCLUDE_GENRULE}
 
+%{PYTHON_IMPORT_LIB_GENRULE}
+
 %{NUMPY_INCLUDE_GENRULE}
diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl
index 19a6f1e749..31c4d2b98a 100644
--- a/third_party/py/python_configure.bzl
+++ b/third_party/py/python_configure.bzl
@@ -117,10 +117,10 @@ def _genrule(src_dir, genrule_name, command, outs):
       genrule_name + '",\n' +
       '    outs = [\n' +
       outs +
-      '    ],\n' +
+      '\n    ],\n' +
       '    cmd = """\n' +
       command +
-      '    """,\n' +
+      '\n   """,\n' +
       ')\n\n'
   )
 
@@ -133,15 +133,20 @@ def _norm_path(path):
   return path
 
 
-def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name):
+def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name,
+    src_files = [], dest_files = []):
   """Returns a genrule to symlink(or copy if on Windows) a set of files.
+
+  If src_dir is passed, files will be read from the given directory; otherwise
+  we assume files are in src_files and dest_files
   """
-  src_dir = _norm_path(src_dir)
-  dest_dir = _norm_path(dest_dir)
-  files = _read_dir(repository_ctx, src_dir)
-  # Create a list with the src_dir stripped to use for outputs.
-  dest_files = files.replace(src_dir, '').splitlines()
-  src_files = files.splitlines()
+  if src_dir != None:
+    src_dir = _norm_path(src_dir)
+    dest_dir = _norm_path(dest_dir)
+    files = _read_dir(repository_ctx, src_dir)
+    # Create a list with the src_dir stripped to use for outputs.
+    dest_files = files.replace(src_dir, '').splitlines()
+    src_files = files.splitlines()
   command = []
   outs = []
   for i in range(len(dest_files)):
@@ -217,11 +222,23 @@ def _get_python_include(repository_ctx, python_bin):
                      'print(sysconfig.get_python_inc())'],
                     error_msg="Problem getting python include path.",
                     error_details=("Is the Python binary path set up right? " +
-                                   "(See ./configure or BAZEL_BIN_PATH.) " +
+                                   "(See ./configure or PYTHON_BIN_PATH.) " +
                                    "Is distutils installed?"))
   return result.stdout.splitlines()[0]
 
 
+def _get_python_import_lib_name(repository_ctx, python_bin):
+  """Get Python import library name (pythonXY.lib) on Windows."""
+  result = _execute(repository_ctx,
+                    [python_bin, "-c",
+                     'import sys;' +
+                     'print("python" + str(sys.version_info[0]) + str(sys.version_info[1]) + ".lib")'],
+                    error_msg="Problem getting python import library.",
+                    error_details=("Is the Python binary path set up right? " +
+                                   "(See ./configure or PYTHON_BIN_PATH.) "))
+  return result.stdout.splitlines()[0]
+
+
 def _get_numpy_include(repository_ctx, python_bin):
   """Gets the numpy include path."""
   return _execute(repository_ctx,
@@ -240,11 +257,11 @@ def _create_local_python_repository(repository_ctx):
   empty_config = False
   # If local checks were requested, the python and numpy include will be auto
   # detected on the host config (using _PYTHON_BIN_PATH).
+  python_bin = _get_env_var(repository_ctx, _PYTHON_BIN_PATH,
+                            "/usr/bin/python")
   if repository_ctx.attr.local_checks:
     # TODO(nlopezgi): The default argument here is a workaround until
     #                 bazelbuild/bazel#3057 is resolved.
-    python_bin = _get_env_var(repository_ctx, _PYTHON_BIN_PATH,
-                              "/usr/bin/python")
     _check_python_bin(repository_ctx, python_bin)
     python_lib = _get_env_var(repository_ctx, _PYTHON_LIB_PATH,
                               _get_python_lib(repository_ctx, python_bin))
@@ -263,6 +280,10 @@ def _create_local_python_repository(repository_ctx):
                                       '    name = "python_include",\n' +
                                       '    srcs = [],\n' +
                                       ')\n'),
+        "%{PYTHON_IMPORT_LIB_GENRULE}": ('filegroup(\n' +
+                                      '    name = "python_import_lib",\n' +
+                                      '    srcs = [],\n' +
+                                      ')\n'),
         "%{NUMPY_INCLUDE_GENRULE}": ('filegroup(\n' +
                                       '    name = "numpy_include",\n' +
                                       '    srcs = [],\n' +
@@ -271,10 +292,21 @@ def _create_local_python_repository(repository_ctx):
   else:
     python_include_rule = _symlink_genrule_for_dir(
         repository_ctx, python_include, 'python_include', 'python_include')
+    python_import_lib_genrule = ""
+    # To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib
+    # See https://docs.python.org/3/extending/windows.html
+    if _is_windows(repository_ctx):
+      python_include = _norm_path(python_include)
+      python_import_lib_name = _get_python_import_lib_name(repository_ctx, python_bin)
+      python_import_lib_src = python_include.rsplit('/', 1)[0] + "/libs/" + python_import_lib_name
+      python_import_lib_genrule = _symlink_genrule_for_dir(
+        repository_ctx, None, '', 'python_import_lib',
+        [python_import_lib_src], [python_import_lib_name])
     numpy_include_rule = _symlink_genrule_for_dir(
         repository_ctx, numpy_include, 'numpy_include/numpy', 'numpy_include')
     _tpl(repository_ctx, "BUILD", {
         "%{PYTHON_INCLUDE_GENRULE}": python_include_rule,
+        "%{PYTHON_IMPORT_LIB_GENRULE}": python_import_lib_genrule,
         "%{NUMPY_INCLUDE_GENRULE}": numpy_include_rule,
     })
 
-- 
GitLab


From f3c89936e97c99dead1ca3310246691c1b221adf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 12:54:12 -0700
Subject: [PATCH 0453/1375] Merge changes from github. END_PUBLIC

Note: this CL will break builds.  cl/159887762 to follow to fix all the breakages.

---
Commit 2336cdf7f authored by Maxwell Paul Brickner<mbrickn@users.noreply.github.com>
Committed by gunan<gunan@google.com>:
Updated link to use HTTPS (#10998)

Howdy!

I just updated a link to use https instead of http.

Thanks!
---
Commit ad0892df1 authored by Luke Iwanski<luke@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] Fixes run_metadata_test for SYCL

 This test is designed to test CUDA specific behavior

---
Commit 6b37a0725 authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Update comments
---
Commit 1699d904a authored by John Lawson<john@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] Fixes CUDA specific test run on SYCL (#56)

The testBadParentValuesOnGPU should only be run on CUDA devices, as the
test checks for particular CUDA behaviour. We don't actually provide a
SYCL kernel for GatherTree and so it's not a problem that the tests
don't target SYCL.
---
Commit 3c1946230 authored by myPrecious<Moriadry@users.noreply.github.com>
Committed by Shanqing Cai<cais@google.com>:
Java API to get the size of specified input list of operations. (#10865)

* Java API to get the size of specified input list of operations

* remove unnecessary explain to avoid bring a new term to users.

---
Commit e911c7480 authored by Luke Iwanski<luke@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] REGISTER -> REGISTER6

---
Commit fbf6c4cec authored by superryanguo<superryanguo@gmail.com>
Committed by superryanguo<superryanguo@gmail.com>:
Simplify the Quickstart section with the weblink is better

---
Commit 72e2918cc authored by Taehoon Lee<taehoonlee@snu.ac.kr>
Committed by Taehoon Lee<taehoonlee@snu.ac.kr>:
Fix typos

---
Commit 90c4406b7 authored by Rishabh Patel<patelrishabh@users.noreply.github.com>
Committed by GitHub<noreply@github.com>:
Correct the learning rate as per the code snippet
---
Commit 03da61134 authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Update ir_array.cc
---
Commit 2df6cd3ac authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Another try
---
Commit af0cbace1 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Transpose to go through Eigen (#10321)

---
Commit fc7361081 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Registers RGBToHSV and HSVToRGB (#91) (#10848)

* [OpenCL] Added RGBToHSV and HSVToRGB

* Aligning '\'
---
Commit 832894ef8 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Registers AdjustContrastv2 (#10949)

* [OpenCL] Registers AdjustContrastv2 (#93)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL (#96)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL

* simplified to #ifndef

* Changed to "#if GOOGLE_CUDA"

* Update adjust_contrast_op_benchmark_test.cc

* Added comments

---
Commit cb4c2f8d1 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Make TransferBufferToInFeed not virual so it compiles.

---
Commit e89f04d80 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix calling Literal member functions.

---
Commit 15a8df724 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix mac build
clone from meheff's change:
[XLA] Change return type of DeviceAssignment::Deserialize to fix build
breakage on mac.
The mac build had the following error:

error: incomplete type 'xla::DeviceAssignment' used in type trait
expression

This was due to a static method returning a StatusOr<DeviceAssignment>
inside of the definition of DeviceAssignment.

---
Commit a54d43fa4 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Replace LiteralUtil to Literal in compiler/plugin/executor

---
Commit 88a6bb80c authored by Guenther Schmuelling<guschmue@microsoft.com>
Committed by Guenther Schmuelling<guschmue@microsoft.com>:
expand inline for debug builds to limit number of symbols

---
Commit 62fb49d31 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix visibility error for contrib/remote_fused_graph/pylib/BUILD.

---
Commit 4c75252f2 authored by Mark Neumann<markn@allenai.org>
Committed by Mark Neumann<markn@allenai.org>:
fix initial test values to avoid numerical instability

---
Commit b58d98353 authored by sj6077<epik03sj@gmail.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
Fixes of AutoParallel bug (#10368)

* Fix the bug that auto_parallel could replicate variable snapshot name

* Use NodeName in grappler:utils instead of substr, convert variables->variable_def of grappler item

* remove variable_def from grappler item, exclude snapshot nodes from dont_replicate_nodes in auto_parallel

---
Commit a286b7db8 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Make debug_test slice integer.

---
Commit 97fcfdfa6 authored by Toby Boyd<tobyboyd@google.com>
Committed by GitHub<noreply@github.com>:
Fixed path to seq2seq.py and minor formatting
---
Commit 63c1befb8 authored by Anish Shah<shah.anish07@gmail.com>
Committed by Anish Shah<shah.anish07@gmail.com>:
Improve docs for tf.nn.depthwise_conv2d_native

---
Commit 8d42202b2 authored by Yong Tang<yong.tang.github@outlook.com>
Committed by Yong Tang<yong.tang.github@outlook.com>:
Fix mismatched delete in mkl_tfconv_op.cc

This fix fixes mismatched new[]-delete in mkl_tfconv_op.cc

(the file went through clang-format so there are some additional
changes)

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

---
Commit 26301bd55 authored by Danny Goodman<goodman.danny@gmail.com>
Committed by Danny Goodman<goodman.danny@gmail.com>:
fix error format

---
Commit b3f33ad46 authored by Yao Zhang<yaozhang@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make changes to prepare for the fused option of batch norm to be set to None (None means using fused batch norm if possible).

PiperOrigin-RevId: 159649743

---
Commit a4a469832 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Add tests for select ops and while loops that produce tuples that contain predicates.

PiperOrigin-RevId: 159645900

---
Commit 980d3f2be authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Use C API to implement Operation.name property

This name property is used in many existing tests including those that
already run with C API enabled (math_ops_test, framework_ops_test,
session_test, session_partial_run_test, math_ops_test_gpu, etc).

PiperOrigin-RevId: 159645767

---
Commit 26239c706 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Previously we didn't have an implementation of BatchNormInference and BatchNormTraining, which gives a linker error if anyone ever tries to call that. A dummy implementation is friendlier than a linker error.

PiperOrigin-RevId: 159645612

---
Commit f671c5caa authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 159570549

PiperOrigin-RevId: 160182040
---
 CONTRIBUTING.md                               |   7 +-
 ISSUE_TEMPLATE.md                             |   1 +
 README.md                                     |  14 +-
 RELEASE.md                                    |   2 +
 configure                                     |   6 +-
 tensorflow/c/generate-pc.sh                   |   2 +-
 tensorflow/cc/BUILD                           |  21 +-
 tensorflow/cc/gradients/math_grad.cc          |  26 ++
 tensorflow/cc/gradients/math_grad_test.cc     |  52 +++
 tensorflow/cc/gradients/nn_grad.cc            |  13 +
 tensorflow/cc/gradients/nn_grad_test.cc       |  13 +
 tensorflow/compiler/plugin/BUILD              |   4 +-
 tensorflow/compiler/plugin/executor/BUILD     |  32 ++
 .../compiler/plugin/executor/compiler.cc      | 123 +++++++
 .../compiler/plugin/executor/compiler.h       |  64 ++++
 tensorflow/compiler/plugin/executor/device.cc |  60 +++
 .../compiler/plugin/executor/executable.cc    | 147 ++++++++
 .../compiler/plugin/executor/executable.h     |  65 ++++
 .../compiler/plugin/executor/executor.cc      | 135 +++++++
 .../compiler/plugin/executor/executor.h       | 213 +++++++++++
 .../compiler/plugin/executor/platform.cc      | 125 +++++++
 .../compiler/plugin/executor/platform.h       |  83 +++++
 .../compiler/plugin/executor/platform_id.h    |  31 ++
 .../plugin/executor/transfer_manager.cc       | 187 ++++++++++
 .../plugin/executor/transfer_manager.h        |  77 ++++
 tensorflow/compiler/tests/ftrl_test.py        |   2 +-
 .../tf2xla/kernels/batch_matmul_op.cc         |   6 +-
 .../tf2xla/kernels/batchtospace_op.cc         |   3 +-
 .../tf2xla/kernels/depthwise_conv_ops.cc      |  19 +-
 tensorflow/compiler/tf2xla/kernels/diag_op.cc |   8 +-
 .../tf2xla/kernels/dynamic_stitch_op.cc       |   4 +-
 .../compiler/tf2xla/kernels/slice_op.cc       |   4 +-
 .../compiler/tf2xla/kernels/split_op.cc       |  14 +-
 .../tf2xla/kernels/strided_slice_op.cc        |  44 +--
 .../tf2xla/kernels/tensor_array_ops.cc        |   8 +-
 .../compiler/tf2xla/kernels/unpack_op.cc      |   4 +-
 .../xla/client/computation_builder.cc         |   6 +-
 .../compiler/xla/client/computation_builder.h |   4 +-
 tensorflow/compiler/xla/literal_util.cc       |  10 +-
 tensorflow/compiler/xla/literal_util_test.cc  |  57 +++
 tensorflow/compiler/xla/service/BUILD         |   2 -
 .../xla/service/algebraic_simplifier.cc       |   7 +-
 .../xla/service/algebraic_simplifier_test.cc  |   8 +-
 .../xla/service/buffer_assignment_test.cc     |  12 +-
 .../xla/service/buffer_liveness_test.cc       |   2 +-
 .../xla/service/compile_only_service.h        |   2 +-
 .../xla/service/computation_placer.cc         |   9 +-
 .../compiler/xla/service/computation_placer.h |   6 +-
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   9 -
 .../xla/service/elemental_ir_emitter.cc       |  17 +-
 .../compiler/xla/service/gpu/pad_insertion.cc |  11 +-
 .../xla/service/hlo_constant_folding_test.cc  |   3 +-
 .../compiler/xla/service/hlo_instruction.cc   |   7 +-
 .../compiler/xla/service/hlo_instruction.h    |  13 +-
 .../xla/service/hlo_rematerialization_test.cc |  16 +-
 .../compiler/xla/service/llvm_ir/llvm_util.cc |  23 +-
 .../compiler/xla/service/shape_inference.cc   |  28 +-
 .../compiler/xla/service/shape_inference.h    |   3 +-
 .../xla/service/shape_inference_test.cc       |  33 +-
 .../service/tuple_points_to_analysis_test.cc  |   2 +-
 .../compiler/xla/service/user_computation.cc  |   6 +-
 .../xla/tests/array_elementwise_ops_test.cc   |   2 +-
 .../compiler/xla/tests/dot_operation_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/fusion_test.cc  |   2 +-
 .../xla/tests/multidimensional_slice_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/params_test.cc  |   2 +-
 tensorflow/compiler/xla/tests/slice_test.cc   |  51 +--
 tensorflow/compiler/xla/tests/while_test.cc   |   3 +-
 tensorflow/compiler/xla/util.h                |  18 +-
 tensorflow/compiler/xla/xla_data.proto        |   3 +-
 .../android/TensorFlowInferenceInterface.java |  20 +-
 .../kernel_tests/csiszar_divergence_test.py   |  43 +--
 .../python/ops/csiszar_divergence_impl.py     |  62 ++--
 tensorflow/contrib/cmake/CMakeLists.txt       |   2 +-
 tensorflow/contrib/cmake/tf_python.cmake      |   1 -
 .../python/ops/bijectors/affine_impl.py       |   4 +-
 .../python/ops/relaxed_bernoulli.py           |   2 +-
 .../distributions/python/ops/sample_stats.py  |   2 +-
 .../ops/vector_laplace_linear_operator.py     |   2 +-
 tensorflow/contrib/graph_editor/transform.py  |   2 +-
 .../contrib/keras/python/keras/backend.py     |   6 +-
 .../contrib/keras/python/keras/layers/core.py |   2 +-
 .../keras/python/keras/layers/recurrent.py    |   2 +-
 .../contrib/keras/python/keras/models_test.py |   2 +-
 .../contrib/layers/python/layers/layers.py    |   3 +-
 .../python/learn/estimators/debug_test.py     |   4 +-
 .../python/learn/estimators/linear_test.py    |  20 +-
 .../python/learn/estimators/model_fn_test.py  |  10 +-
 .../contrib/learn/python/learn/models.py      |   4 +-
 tensorflow/contrib/lookup/lookup_ops.py       |   6 +-
 .../contrib/makefile/build_with_docker.sh     |   2 +-
 .../makefile/compile_android_protobuf.sh      |   4 +-
 .../contrib/makefile/compile_ios_protobuf.sh  |  17 +-
 .../makefile/compile_ios_tensorflow.sh        |   4 +-
 .../contrib/makefile/compile_pi_protobuf.sh   |   6 +-
 .../contrib/remote_fused_graph/pylib/BUILD    |   1 +
 tensorflow/contrib/rnn/python/ops/rnn_cell.py |   2 +-
 .../kernel_tests/beam_search_ops_test.py      |   4 +-
 tensorflow/contrib/slim/README.md             |   2 +-
 tensorflow/contrib/tfprof/README.md           |  23 ++
 .../pywrap_tensorflow_print_model_analysis.i  |   1 +
 .../tfprof/internal/run_metadata_test.py      |   2 +-
 .../python/tools/tfprof/model_analyzer.py     | 121 ++-----
 .../tools/tfprof/model_analyzer_test.py       |  30 +-
 .../python/tools/tfprof/profiler_test.py      |  16 +-
 .../training/python/training/evaluation.py    |   2 +-
 tensorflow/contrib/verbs/rdma.cc              |  55 ++-
 .../contrib/verbs/rdma_rendezvous_mgr.cc      |  41 ++-
 tensorflow/contrib/verbs/verbs_util.cc        |  34 ++
 tensorflow/contrib/verbs/verbs_util.h         |  10 +
 .../core/common_runtime/direct_session.cc     |   4 +-
 .../core/debug/grpc_session_debug_test.cc     |   5 +-
 tensorflow/core/framework/graph_def_util.h    |   2 +-
 tensorflow/core/framework/op.h                |   1 -
 tensorflow/core/framework/tensor.h            |   2 +-
 tensorflow/core/graph/mkl_layout_pass.cc      | 277 +++++++++-----
 tensorflow/core/graph/mkl_layout_pass_test.cc | 252 ++++++++++++-
 .../grappler/costs/virtual_scheduler_test.cc  |   2 +-
 tensorflow/core/grappler/grappler_item.h      |   1 +
 .../core/grappler/optimizers/auto_parallel.cc |   5 +
 .../core/grappler/optimizers/auto_parallel.h  |   1 +
 .../grappler/optimizers/auto_parallel_test.cc |  42 ++-
 .../grappler/optimizers/layout_optimizer.cc   |   2 +-
 tensorflow/core/kernels/BUILD                 |   8 +-
 tensorflow/core/kernels/adjust_contrast_op.cc |  24 ++
 .../adjust_contrast_op_benchmark_test.cc      |   5 +
 tensorflow/core/kernels/colorspace_op.cc      |  15 +
 tensorflow/core/kernels/control_flow_ops.cc   | 136 ++++---
 tensorflow/core/kernels/cwise_op_add_2.cc     |   7 +-
 tensorflow/core/kernels/cwise_op_cosh.cc      |  37 ++
 .../core/kernels/cwise_op_gpu_add.cu.cc       |   3 +-
 .../core/kernels/cwise_op_gpu_cosh.cu.cc      |  26 ++
 .../core/kernels/cwise_op_gpu_sinh.cu.cc      |  26 ++
 tensorflow/core/kernels/cwise_op_invert.cc    |   2 +-
 tensorflow/core/kernels/cwise_op_sinh.cc      |  37 ++
 tensorflow/core/kernels/cwise_ops.h           |   6 +
 tensorflow/core/kernels/dynamic_stitch_op.cc  |  27 +-
 tensorflow/core/kernels/map_stage_op.cc       |   7 +-
 .../core/kernels/mkl_conv_grad_filter_ops.cc  |  69 +++-
 tensorflow/core/kernels/mkl_conv_ops.cc       |  51 ++-
 tensorflow/core/kernels/mkl_lrn_op.cc         | 112 +++---
 tensorflow/core/kernels/mkl_relu_op.cc        |  30 +-
 tensorflow/core/kernels/mkl_tfconv_op.cc      |  54 ++-
 .../core/kernels/non_max_suppression_op.cc    |  36 +-
 tensorflow/core/kernels/priority_queue.cc     |   2 +-
 tensorflow/core/kernels/shape_ops.cc          |  56 ++-
 tensorflow/core/kernels/slice_op.cc           | 258 ++++++++++++-
 tensorflow/core/kernels/sparse_reduce_op.cc   | 341 ++++++++++++++++++
 tensorflow/core/kernels/stack_ops.cc          |  68 +++-
 tensorflow/core/kernels/topk_op.cc            |   2 +-
 tensorflow/core/kernels/transpose_functor.h   |   7 +
 tensorflow/core/kernels/transpose_op.cc       |   5 +-
 .../typed_conditional_accumulator_base.h      |   2 +-
 tensorflow/core/lib/gtl/optional.h            |   2 +-
 tensorflow/core/ops/math_grad.cc              |  20 +
 tensorflow/core/ops/math_grad_test.cc         |  20 +
 tensorflow/core/ops/math_ops.cc               |   8 +
 tensorflow/core/ops/nn_ops.cc                 |   2 +
 tensorflow/core/ops/ops.pbtxt                 |  75 ++++
 tensorflow/core/ops/sparse_ops.cc             |  69 ++++
 .../core/platform/cloud/retrying_utils.cc     |   2 +-
 tensorflow/core/protobuf/worker.proto         |   2 +-
 tensorflow/core/public/version.h              |   2 +-
 tensorflow/core/util/mkl_util.h               |  38 +-
 .../api_guides/python/contrib.losses.md       |  10 +-
 .../docs_src/api_guides/python/math_ops.md    |   2 +
 .../docs_src/get_started/get_started.md       |   5 +-
 .../docs_src/get_started/mnist/beginners.md   |   2 +-
 .../docs_src/get_started/mnist/mechanics.md   |   2 +-
 tensorflow/docs_src/install/install_c.md      |   2 +-
 tensorflow/docs_src/install/install_go.md     |   2 +-
 tensorflow/docs_src/install/install_java.md   |  18 +-
 tensorflow/docs_src/install/install_linux.md  |  26 +-
 tensorflow/docs_src/install/install_mac.md    |  14 +-
 .../docs_src/install/install_sources.md       |   4 +-
 .../docs_src/install/install_windows.md       |  13 +-
 .../docs_src/performance/performance_guide.md |   8 +-
 .../docs_src/performance/quantization.md      |  13 +-
 tensorflow/docs_src/tutorials/seq2seq.md      |  13 +-
 tensorflow/docs_src/tutorials/wide.md         |  18 +-
 .../docs_src/tutorials/wide_and_deep.md       |   6 +-
 tensorflow/docs_src/tutorials/word2vec.md     |   2 +-
 .../examples/image_retraining/retrain.py      |  43 ++-
 tensorflow/examples/learn/examples_test.sh    |   2 +-
 tensorflow/examples/tutorials/mnist/mnist.py  |   2 +-
 .../tutorials/word2vec/word2vec_basic.py      |  16 +-
 tensorflow/go/README.md                       |  29 --
 tensorflow/go/genop/generate.sh               |   2 +-
 tensorflow/go/op/wrappers.go                  |  30 ++
 tensorflow/go/shape.go                        |   2 +-
 tensorflow/go/tensor.go                       |   2 +-
 tensorflow/java/BUILD                         |  26 +-
 tensorflow/java/build_defs.bzl                |  11 +-
 tensorflow/java/maven/libtensorflow/pom.xml   |   4 +-
 tensorflow/java/maven/proto/pom.xml           |   6 +-
 tensorflow/java/maven/release.sh              |   2 +-
 tensorflow/java/maven/run_inside_container.sh |   2 +-
 .../src/main/java/org/tensorflow/Input.java   |  48 +++
 .../main/java/org/tensorflow/Operation.java   |  26 +-
 .../java/org/tensorflow/OperationBuilder.java |   2 +-
 .../src/main/java/org/tensorflow/Output.java  |  10 +-
 .../src/main/java/org/tensorflow/Session.java |   4 +-
 .../java/org/tensorflow/op/NameScope.java     | 146 ++++++++
 .../main/java/org/tensorflow/op/Scope.java    | 165 +++++++++
 .../java/src/main/native/operation_jni.cc     |  18 +
 .../java/src/main/native/operation_jni.h      |  11 +
 .../java/org/tensorflow/OperationTest.java    |  21 ++
 .../test/java/org/tensorflow/SessionTest.java |   6 +-
 .../test/java/org/tensorflow/TensorTest.java  |   2 +-
 .../java/org/tensorflow/op/ScopeTest.java     | 270 ++++++++++++++
 tensorflow/python/BUILD                       |   5 +-
 tensorflow/python/debug/cli/analyzer_cli.py   |   2 +-
 .../python/debug/wrappers/framework_test.py   |   2 +-
 .../estimator/canned/dnn_linear_combined.py   |   2 +-
 tensorflow/python/estimator/canned/linear.py  |  73 ++--
 tensorflow/python/kernel_tests/BUILD          |  10 -
 .../python/kernel_tests/basic_gpu_test.py     |   2 +
 .../python/kernel_tests/cwise_ops_test.py     |  15 +
 .../python/kernel_tests/fft_ops_test.py       |  63 ++--
 .../python/kernel_tests/map_stage_op_test.py  |  93 ++---
 .../python/kernel_tests/record_input_test.py  |  14 +-
 .../python/kernel_tests/sparse_ops_test.py    |  49 ++-
 .../python/kernel_tests/stage_op_test.py      |  87 ++---
 tensorflow/python/ops/array_ops.py            |  26 +-
 tensorflow/python/ops/data_flow_ops.py        |  31 +-
 .../python/ops/distributions/special_math.py  |   2 +-
 tensorflow/python/ops/lookup_ops.py           |   6 +-
 tensorflow/python/ops/math_grad.py            |  18 +
 tensorflow/python/ops/math_ops.py             |   7 +-
 tensorflow/python/ops/rnn_cell_impl.py        |   4 +-
 tensorflow/python/ops/sparse_ops.py           |  86 +++++
 tensorflow/python/ops/tensor_array_ops.py     |   2 +-
 tensorflow/python/ops/variable_scope.py       |   5 +
 .../print_selective_registration_header.py    |   2 +-
 tensorflow/python/training/input.py           |   3 +
 tensorflow/tensorflow.bzl                     |   2 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  16 +
 tensorflow/tools/ci_build/builds/pip.sh       |  27 +-
 tensorflow/tools/ci_build/ci_build.sh         |  10 +-
 .../tools/ci_build/ci_parameterized_build.sh  |  17 +-
 tensorflow/tools/ci_build/ci_sanity.sh        |  24 +-
 .../ci_build/install/install_pip_packages.sh  |   4 +-
 .../tools/ci_build/install/install_proto3.sh  |   6 +-
 .../install/install_python3.5_pip_packages.sh |   2 +-
 .../protobuf/protobuf_optimized_pip.sh        |   2 +-
 .../ci_build/windows/bazel/bazel_test_lib.sh  |   2 +-
 .../ci_build/windows/cpu/cmake/run_py.bat     |   5 +-
 .../ci_build/windows/gpu/cmake/run_py.bat     |   5 +-
 tensorflow/tools/dist_test/local_test.sh      |   6 +-
 tensorflow/tools/docker/README.md             |  28 +-
 .../docker/parameterized_docker_build.sh      |  44 ++-
 .../tools/gcs_test/gcs_smoke_wrapper.sh       |  11 +-
 tensorflow/tools/git/gen_git_source.sh        |   2 +-
 tensorflow/tools/graph_transforms/README.md   |   2 +-
 .../graph_transforms/quantize_weights_test.cc |   4 +-
 .../lib_package/libtensorflow_java_test.sh    |   2 +-
 .../tools/lib_package/libtensorflow_test.sh   |   2 +-
 tensorflow/tools/pip_package/setup.py         |   2 +-
 tensorflow/tools/tfprof/BUILD                 |   1 -
 tensorflow/tools/tfprof/README.md             |  12 +-
 tensorflow/tools/tfprof/g3doc/advise.md       |  80 +---
 tensorflow/tools/tfprof/g3doc/command_line.md |   2 +-
 .../tools/tfprof/internal/advisor/BUILD       |   9 -
 .../advisor/accelerator_utilization_checker.h |  24 +-
 .../tools/tfprof/internal/advisor/checker.h   |  24 +-
 .../advisor/internal_checker_runner.h         |   5 +-
 .../advisor/internal_checker_runner_dummy.cc  |   6 +-
 .../internal/advisor/operation_checker.h      |  19 +-
 .../tfprof/internal/advisor/tfprof_advisor.h  |  47 +--
 .../internal/advisor/tfprof_advisor_test.cc   |  46 +--
 .../tfprof/internal/print_model_analysis.cc   |  19 +-
 .../tfprof/internal/print_model_analysis.h    |   2 +
 .../tools/tfprof/internal/tfprof_graph.h      |   4 +-
 tensorflow/tools/tfprof/internal/tfprof_op.cc |  11 +-
 tensorflow/tools/tfprof/internal/tfprof_op.h  |   6 +-
 .../tools/tfprof/internal/tfprof_options.h    |   4 +-
 .../tools/tfprof/internal/tfprof_show.cc      |  14 +-
 .../tools/tfprof/internal/tfprof_show.h       |  13 +-
 .../tfprof/internal/tfprof_show_multi.cc      |  19 +-
 .../tools/tfprof/internal/tfprof_show_multi.h |  16 +-
 .../tools/tfprof/internal/tfprof_show_test.cc |   1 -
 .../tools/tfprof/internal/tfprof_stats.cc     |  77 ++--
 .../tools/tfprof/internal/tfprof_stats.h      |  22 +-
 .../tfprof/internal/tfprof_stats_test.cc      |   1 -
 .../tfprof/internal/tfprof_tensor_test.cc     |   1 -
 .../tfprof/internal/tfprof_timeline_test.cc   |   1 -
 tensorflow/tools/tfprof/tfprof_main.cc        |  25 +-
 tensorflow/tools/tfprof/tfprof_output.proto   |   8 -
 tensorflow/workspace.bzl                      |  24 +-
 tools/tf_env_collect.sh                       | 143 ++++----
 290 files changed, 5731 insertions(+), 1732 deletions(-)
 create mode 100644 tensorflow/compiler/plugin/executor/BUILD
 create mode 100644 tensorflow/compiler/plugin/executor/compiler.cc
 create mode 100644 tensorflow/compiler/plugin/executor/compiler.h
 create mode 100644 tensorflow/compiler/plugin/executor/device.cc
 create mode 100644 tensorflow/compiler/plugin/executor/executable.cc
 create mode 100644 tensorflow/compiler/plugin/executor/executable.h
 create mode 100644 tensorflow/compiler/plugin/executor/executor.cc
 create mode 100644 tensorflow/compiler/plugin/executor/executor.h
 create mode 100644 tensorflow/compiler/plugin/executor/platform.cc
 create mode 100644 tensorflow/compiler/plugin/executor/platform.h
 create mode 100644 tensorflow/compiler/plugin/executor/platform_id.h
 create mode 100644 tensorflow/compiler/plugin/executor/transfer_manager.cc
 create mode 100644 tensorflow/compiler/plugin/executor/transfer_manager.h
 create mode 100644 tensorflow/core/kernels/cwise_op_cosh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_sinh.cc
 create mode 100644 tensorflow/core/kernels/sparse_reduce_op.cc
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/Input.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
 create mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index c78b6b1a15..43abdaafbf 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -159,7 +159,12 @@ There are two ways to run TensorFlow unit tests.
    bazel test ${flags} //tensorflow/python/...
    ```
 
-2. Using Docker and TensorFlow's CI scripts.
+2. Using [Docker](www.docker.com) and TensorFlow's CI scripts.
+
+   ```bash
+   # Install Docker first, then this will build and run cpu tests
+   tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
+   ```
 
    See
    [TensorFlow Builds](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build) for details.
diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 6f4c048ce8..5b37028c50 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -6,6 +6,7 @@ If you open a GitHub issue, here is our policy:
 
 1. It must be a bug or a feature request.
 2. The form below must be filled out.
+3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorflow/issues).
 
 **Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
 
diff --git a/README.md b/README.md
index e7dbf57b25..abbead98a7 100644
--- a/README.md
+++ b/README.md
@@ -34,13 +34,13 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
diff --git a/RELEASE.md b/RELEASE.md
index d22c5c62fe..9875838d7e 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -113,6 +113,8 @@
   checkpoints containing such RNN cells, in which case you can use the
   [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
   to convert the variable names in your old checkpoints.
+* Added `tf.contrib.kernel_methods` module with Ops and estimators for primal
+  (explicit) kernel methods in TensorFlow.
 
 ## Bug Fixes and Other Changes
 * In python, `Operation.get_attr` on type attributes returns the Python DType
diff --git a/configure b/configure
index e1aaddabda..602124225f 100755
--- a/configure
+++ b/configure
@@ -162,8 +162,12 @@ bazel version > bazel.version
 curr_bazel_version=$(head -n 1 bazel.version | cut -d ' ' -f3)
 rm -f bazel.version
 
+
 echo "You have bazel $curr_bazel_version installed."
-if [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
+if [ -z "$curr_bazel_version" ]; then
+  echo "WARNING: current bazel installation is not a release version."
+  echo "Make sure you are running at least bazel $MIN_BAZEL_VERSION."
+elif [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
   echo "Please upgrade your bazel installation to version $MIN_BAZEL_VERSION or higher to build TensorFlow!"
   echo "Exiting..."
   exit 1
diff --git a/tensorflow/c/generate-pc.sh b/tensorflow/c/generate-pc.sh
index 73d427d9b2..02a6a58b61 100755
--- a/tensorflow/c/generate-pc.sh
+++ b/tensorflow/c/generate-pc.sh
@@ -26,7 +26,7 @@ usage() {
 [ $# == 0 ] && usage && exit 0
 
 # read the options
-ARGS=`getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@"`
+ARGS=$(getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@")
 eval set -- "$ARGS"
 
 # extract options and their arguments into variables.
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index a884f11d48..9801add1da 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -472,10 +472,23 @@ cc_binary(
     name = "tutorials_example_trainer",
     srcs = ["tutorials/example_trainer.cc"],
     copts = tf_copts(),
-    linkopts = [
-        "-lpthread",
-        "-lm",
-    ],
+    linkopts = select({
+        "//tensorflow:windows": [],
+        "//tensorflow:windows_msvc": [],
+        "//tensorflow:darwin": [
+            "-lm",
+            "-lpthread",
+        ],
+        "//tensorflow:ios": [
+            "-lm",
+            "-lpthread",
+        ],
+        "//conditions:default": [
+            "-lm",
+            "-lpthread",
+            "-lrt",
+        ],
+    }),
     deps = [
         ":cc_ops",
         "//tensorflow/core:core_cpu",
diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc
index 8c1a01f518..71d9a8ed7b 100644
--- a/tensorflow/cc/gradients/math_grad.cc
+++ b/tensorflow/cc/gradients/math_grad.cc
@@ -162,6 +162,32 @@ Status Log1pGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Log1p", Log1pGrad);
 
+Status SinhGrad(const Scope& scope, const Operation& op,
+                const std::vector<Output>& grad_inputs,
+                std::vector<Output>* grad_outputs) {
+  // y = sinh(x)
+  // dy/dx = cosh(x)
+  auto dydx = Cosh(scope, op.input(0));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Sinh", SinhGrad);
+
+Status CoshGrad(const Scope& scope, const Operation& op,
+                const std::vector<Output>& grad_inputs,
+                std::vector<Output>* grad_outputs) {
+  // y = cosh(x)
+  // dy/dx = sinh(x)
+  auto dydx = Sinh(scope, op.input(0));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Cosh", CoshGrad);
+
 Status TanhGrad(const Scope& scope, const Operation& op,
                 const std::vector<Output>& grad_inputs,
                 std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc
index de6baa1769..1653b04378 100644
--- a/tensorflow/cc/gradients/math_grad_test.cc
+++ b/tensorflow/cc/gradients/math_grad_test.cc
@@ -45,6 +45,8 @@ class CWiseUnaryGradTest : public ::testing::Test {
     EXPM1,
     LOG,
     LOG1P,
+    SINH,
+    COSH,
     TANH,
     SIGMOID,
     SIGN,
@@ -111,6 +113,12 @@ class CWiseUnaryGradTest : public ::testing::Test {
       case LOG1P:
         y = Log1p(scope_, x);
         break;
+      case SINH:
+        y = Sinh(scope_, x);
+        break;
+      case COSH:
+        y = Cosh(scope_, x);
+        break;
       case TANH:
         y = Tanh(scope_, x);
         break;
@@ -337,6 +345,50 @@ TEST_F(CWiseUnaryGradTest, Log1p_Complex) {
   TestCWiseGrad<complex64>(LOG1P, x_fn, dy_fn, dx_fn);
 }
 
+TEST_F(CWiseUnaryGradTest, Sinh) {
+  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    return dy * std::cosh(x);
+  };
+  TestCWiseGrad<float>(SINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Sinh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    return dy * conjugate(std::cosh(x));
+  };
+  TestCWiseGrad<complex64>(SINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Cosh) {
+  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    return dy * std::sinh(x);
+  };
+  TestCWiseGrad<float>(COSH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Cosh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    return dy * conjugate(std::sinh(x));
+  };
+  TestCWiseGrad<complex64>(COSH, x_fn, dy_fn, dx_fn);
+}
+
 TEST_F(CWiseUnaryGradTest, Tanh) {
   auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
   auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc
index 5e5203d090..952b2015ed 100644
--- a/tensorflow/cc/gradients/nn_grad.cc
+++ b/tensorflow/cc/gradients/nn_grad.cc
@@ -46,6 +46,19 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad);
 
+Status LogSoftmaxGrad(const Scope& scope, const Operation& op,
+                   const std::vector<Output>& grad_inputs,
+                   std::vector<Output>* grad_outputs) {
+
+  auto softmax = Exp(scope, op.output(0));
+  auto sum = Sum(scope, grad_inputs[0], {1}, Sum::KeepDims(true));
+  auto mul = Mul(scope, sum, softmax);
+  auto dx = Sub(scope, grad_inputs[0], mul);
+  grad_outputs->push_back(dx);
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("LogSoftmax", LogSoftmaxGrad);
+
 Status ReluGradHelper(const Scope& scope, const Operation& op,
                       const std::vector<Output>& grad_inputs,
                       std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc
index 70c9bd4e08..daa87546ec 100644
--- a/tensorflow/cc/gradients/nn_grad_test.cc
+++ b/tensorflow/cc/gradients/nn_grad_test.cc
@@ -57,6 +57,19 @@ TEST_F(NNGradTest, SoftmaxGrad) {
   RunTest(x, shape, y, shape);
 }
 
+TEST_F(NNGradTest, LogSoftmaxGrad) {
+  TensorShape shape({5, 3});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
+  auto y = LogSoftmax(scope_, x);
+  // Avoid numerical instability when computing finite differences.
+  Tensor x_init_value = test::AsTensor<float>(
+          {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f,
+           0.1f, 0.3f, 0.5f, 0.7f, 0.8f,
+           -0.1f, 0.1f, 0.1f, 0.1f, 1.2f},
+          {5, 3});
+  RunTest(x, x_init_value, y, shape);
+}
+
 TEST_F(NNGradTest, ReluGrad) {
   TensorShape shape({5, 2});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
diff --git a/tensorflow/compiler/plugin/BUILD b/tensorflow/compiler/plugin/BUILD
index 4badd3a589..8c2e9a7c81 100644
--- a/tensorflow/compiler/plugin/BUILD
+++ b/tensorflow/compiler/plugin/BUILD
@@ -32,5 +32,7 @@ package(
 
 cc_library(
     name = "plugin",
-    deps = [],
+    deps = [
+        "//tensorflow/compiler/plugin/executor:plugin_lib",
+    ],
 )
diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
new file mode 100644
index 0000000000..9bc706abdf
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/BUILD
@@ -0,0 +1,32 @@
+licenses(["restricted"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "plugin_lib",
+    srcs = glob([
+        "*.cc",
+    ]),
+    hdrs = glob([
+        "*.h",
+    ]),
+    deps = [
+        "//tensorflow/compiler/jit:xla_jit_headers_lib",
+        "//tensorflow/compiler/xla:xla_headers_lib",
+        "//tensorflow/compiler/xla/service:hlo_evaluator",
+        "//third_party/eigen3",
+        "@local_config_cuda//cuda:cuda_headers",
+        "@protobuf//:protobuf_headers",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+)
diff --git a/tensorflow/compiler/plugin/executor/compiler.cc b/tensorflow/compiler/plugin/executor/compiler.cc
new file mode 100644
index 0000000000..893ff152f0
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/compiler.cc
@@ -0,0 +1,123 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <stdlib.h>
+#include <fstream>
+
+#include "tensorflow/compiler/plugin/executor/compiler.h"
+#include "tensorflow/compiler/plugin/executor/executable.h"
+
+#include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
+#include "tensorflow/compiler/xla/service/flatten_call_graph.h"
+#include "tensorflow/compiler/xla/service/hlo_constant_folding.h"
+#include "tensorflow/compiler/xla/service/hlo_cse.h"
+#include "tensorflow/compiler/xla/service/hlo_dce.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
+#include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
+#include "tensorflow/compiler/xla/service/inliner.h"
+#include "tensorflow/compiler/xla/service/reshape_mover.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+
+#include "tensorflow/stream_executor/lib/initialize.h"
+#include "tensorflow/stream_executor/lib/strcat.h"
+
+#include "tensorflow/core/lib/core/errors.h"
+
+namespace se = ::perftools::gputools;
+namespace sep = ::perftools::gputools::executorplugin;
+namespace port = ::perftools::gputools::port;
+
+namespace xla {
+namespace executorplugin {
+
+/*
+ * Run optimization passes on the module.  The graph is transformed by
+ * each pass in the optimization pipeline.  The service subdirectory
+ * contains useful optimization passes.
+ */
+Status ExecutorCompiler::RunHloOptimization(HloModule* hlo_module,
+                                            HloDumper dump_hlo) {
+  HloPassPipeline pipeline("Executor", dump_hlo);
+  pipeline.AddPass<Inliner>();
+  pipeline.AddPass<HloSubcomputationUnification>();
+  pipeline.AddPass<HloCSE>(false);
+
+  pipeline.AddPass<HloPassFix<AlgebraicSimplifier>>(
+      false, [](const Shape&, const Shape&) { return false; });
+  pipeline.AddPass<ReshapeMover>();
+  pipeline.AddPass<HloConstantFolding>();
+  pipeline.AddPass<HloCSE>(true);
+
+  pipeline.AddPass<HloDCE>();
+  pipeline.AddPass<FlattenCallGraph>();
+  return pipeline.Run(hlo_module).status();
+}
+
+StatusOr<std::unique_ptr<Executable>> ExecutorCompiler::Compile(
+        std::unique_ptr<HloModule> hlo_module, HloDumper dump_hlo,
+        se::StreamExecutor* stream_exec) {
+  TF_RET_CHECK(stream_exec != nullptr);
+
+  VLOG(1) << "Generate graph " << hlo_module->name();
+
+  TF_RETURN_IF_ERROR(RunHloOptimization(hlo_module.get(), dump_hlo));
+
+  // Typically you would visit the HLO graph, building up a compiled equivalent
+  // In this case we are using an Hlo evaluator at execution time, so we don't
+  // need to compile anything
+
+  // Create executable from only the Hlo module
+  std::unique_ptr<Executable> executable;
+  executable.reset(new ExecutorExecutable(std::move(hlo_module)));
+
+  return std::move(executable);
+}
+
+StatusOr<std::vector<std::unique_ptr<Executable>>> ExecutorCompiler::Compile(
+        std::vector<std::unique_ptr<HloModule>> hlo_modules,
+        HloDumper dump_hlos, std::vector<se::StreamExecutor*> stream_execs) {
+
+  return tensorflow::errors::Unimplemented(
+      "Compilation of multiple HLO modules is not supported on Executor.");
+}
+
+StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+ExecutorCompiler::CompileAheadOfTime(
+    std::vector<std::unique_ptr<HloModule>> hlo_modules,
+    HloDumper dump_hlo, const AotCompilationOptions& aot_options) {
+
+  return tensorflow::errors::InvalidArgument(
+      "AOT compilation not supported on Executor");
+}
+
+se::Platform::Id ExecutorCompiler::PlatformId() const {
+  return sep::kExecutorPlatformId;
+}
+
+HloCostAnalysis::ShapeSizeFunction
+ExecutorCompiler::ShapeSizeBytesFunction() const {
+  return ExecutorExecutable::ShapeSizeBytes;
+}
+
+
+}  // namespace executorplugin
+}  // namespace xla
+
+REGISTER_MODULE_INITIALIZER(executor_compiler, {
+  xla::Compiler::RegisterCompilerFactory(sep::kExecutorPlatformId, []() {
+    return xla::MakeUnique<xla::executorplugin::ExecutorCompiler>();
+  });
+});
diff --git a/tensorflow/compiler/plugin/executor/compiler.h b/tensorflow/compiler/plugin/executor/compiler.h
new file mode 100644
index 0000000000..8fe591c8ab
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/compiler.h
@@ -0,0 +1,64 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
+
+#include <memory>
+
+#include "tensorflow/compiler/xla/service/compiler.h"
+#include "tensorflow/compiler/xla/service/executable.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_module_config.h"
+
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+namespace xla {
+namespace executorplugin {
+
+class ExecutorCompiler : public Compiler {
+ public:
+  ExecutorCompiler() {}
+  ~ExecutorCompiler() override {}
+
+  StatusOr<std::unique_ptr<Executable>> Compile(
+      std::unique_ptr<HloModule> hlo_module,
+      HloDumper dump_hlo,
+      perftools::gputools::StreamExecutor* stream_exec) override;
+
+  StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
+      std::vector<std::unique_ptr<HloModule>> hlo_module,
+      HloDumper dump_hlo,
+      std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
+
+  StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+  CompileAheadOfTime(
+      std::vector<std::unique_ptr<HloModule>> module,
+      HloDumper dump_hlo, const AotCompilationOptions& options) override;
+
+  HloCostAnalysis::ShapeSizeFunction ShapeSizeBytesFunction() const override;
+
+  perftools::gputools::Platform::Id PlatformId() const override;
+
+ private:
+  Status RunHloOptimization(HloModule* hlo_module, HloDumper dump_hlo);
+
+  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorCompiler);
+};
+
+}  // namespace executorplugin
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
diff --git a/tensorflow/compiler/plugin/executor/device.cc b/tensorflow/compiler/plugin/executor/device.cc
new file mode 100644
index 0000000000..bbc39dc03f
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/device.cc
@@ -0,0 +1,60 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/jit/kernels/xla_device_launch_op.h"
+#include "tensorflow/compiler/jit/xla_device.h"
+#include "tensorflow/compiler/jit/xla_device_ops.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+
+namespace tensorflow {
+
+const char* const DEVICE_XLA_EXEC = "XLA_EXEC";
+const char* const DEVICE_EXEC_XLA_JIT = "XLA_EXEC_JIT";
+
+constexpr std::array<DataType, 5> kExecAllTypes = {
+    {DT_INT32, DT_FLOAT, DT_BOOL, DT_DOUBLE, DT_INT64}};
+
+class XlaExaDeviceFactory : public DeviceFactory {
+ public:
+  Status CreateDevices(const SessionOptions& options, const string& name_prefix,
+                       std::vector<Device*>* devices) override;
+};
+
+Status XlaExaDeviceFactory::CreateDevices(const SessionOptions& options,
+                                          const string& name_prefix,
+                                          std::vector<Device*>* devices) {
+  static XlaDeviceOpRegistrations* registrations =
+      RegisterXlaDeviceKernels(DEVICE_XLA_EXEC, DEVICE_EXEC_XLA_JIT);
+  (void)registrations;
+
+  std::unique_ptr<XlaDevice> device;
+  TF_RETURN_IF_ERROR(XlaDevice::Create("Executor", DEVICE_XLA_EXEC, 0,
+                                       DEVICE_EXEC_XLA_JIT, options,
+                                       name_prefix, &device));
+  devices->push_back(device.release());
+  return Status::OK();
+}
+
+REGISTER_LOCAL_DEVICE_FACTORY(DEVICE_XLA_EXEC, XlaExaDeviceFactory, 110);
+
+// Kernel registrations
+
+static bool OpFilter(KernelDef* kdef) { return true; }
+
+REGISTER_XLA_LAUNCH_KERNEL(DEVICE_XLA_EXEC, XlaDeviceLaunchOp, kExecAllTypes);
+REGISTER_XLA_DEVICE_KERNELS(DEVICE_XLA_EXEC, kExecAllTypes);
+REGISTER_XLA_BACKEND(DEVICE_EXEC_XLA_JIT, kExecAllTypes, OpFilter);
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/plugin/executor/executable.cc b/tensorflow/compiler/plugin/executor/executable.cc
new file mode 100644
index 0000000000..79eea9af3f
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executable.cc
@@ -0,0 +1,147 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/executable.h"
+#include "tensorflow/compiler/plugin/executor/executor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+
+namespace se = ::perftools::gputools;
+namespace sep = ::perftools::gputools::executorplugin;
+
+namespace xla {
+namespace executorplugin {
+
+ExecutorExecutable::ExecutorExecutable(std::unique_ptr<HloModule> hlo_module)
+    : Executable(std::move(hlo_module), ShapeSizeBytes) {}
+
+ExecutorExecutable::~ExecutorExecutable() {}
+
+static se::DeviceMemoryBase AllocateSingleOutput(sep::ExecutorExecutor* executor,
+                                                 const Literal& literal) {
+  int64 size(xla::ShapeUtil::ByteSizeOf(literal.shape()));
+  void* buf = executor->Allocate(size);
+  const void* src = literal.InternalData();
+  memcpy(buf, src, size);
+  return se::DeviceMemoryBase(buf, size);
+}
+
+static se::DeviceMemoryBase AllocateOutputBuffer(sep::ExecutorExecutor* executor,
+                                                 const Literal& literal) {
+  const Shape& shape = literal.shape();
+  if (shape.element_type() != xla::TUPLE) {
+    return AllocateSingleOutput(executor, literal);
+  } else {
+    int64 size(xla::ShapeUtil::ByteSizeOf(shape, sizeof(void*)));
+    void** buf = reinterpret_cast<void**>(executor->Allocate(size));
+    for (int64 n = 0; n < xla::ShapeUtil::TupleElementCount(shape); n++) {
+      se::DeviceMemoryBase out =
+          AllocateSingleOutput(executor, literal.tuple_literals(n));
+      *buf++ = out.opaque();
+    }
+
+    return se::DeviceMemoryBase(buf, size);
+  }
+}
+
+StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteOnStream(
+    const ServiceExecutableRunOptions* run_options,
+    tensorflow::gtl::ArraySlice<se::DeviceMemoryBase> arguments,
+    HloExecutionProfile* hlo_execution_profile) {
+  se::Stream* stream = run_options->stream();
+
+  VLOG(1) << "Execute " << module().name();
+  if (VLOG_IS_ON(2)) {
+    for (const auto& a : arguments) {
+      VLOG(2) << "-- argument " << a.opaque();
+    }
+  }
+
+  uint64 start_micros = tensorflow::Env::Default()->NowMicros();
+
+  HloComputation* computation = module().entry_computation();
+  if (computation->num_parameters() != arguments.size()) {
+    return tensorflow::errors::Internal(
+        "Mismatch between argument count and graph parameter count.");
+  }
+
+  // Create the arguments as an vector of XLA literals
+  std::vector<std::unique_ptr<Literal>> arg_literals;
+  std::vector<Literal*> arg_literals_ptrs;
+  for (int64 p = 0; p < computation->num_parameters(); p++) {
+    // Create the input literal for the parameter
+    HloInstruction* param = computation->parameter_instruction(p);
+    arg_literals.emplace_back(Literal::CreateFromShape(param->shape()));
+    arg_literals_ptrs.push_back(arg_literals.back().get());
+
+    // Copy in the data from the stream_executor buffers
+    void* buffer = arg_literals.back().get()->MutableInternalData();
+    memcpy(buffer, arguments[p].opaque(),
+           ShapeUtil::ByteSizeOf(param->shape()));
+  }
+
+  // Execute the graph using the evaluator
+  HloEvaluator evaluator;
+  std::unique_ptr<Literal> output;
+  TF_ASSIGN_OR_RETURN(output,
+                      evaluator.Evaluate(computation, arg_literals_ptrs));
+
+  // Copy the result into the return buffer
+  perftools::gputools::StreamExecutor* executor(stream->parent());
+  sep::ExecutorExecutor* executorExecutor(
+      static_cast<sep::ExecutorExecutor*>(executor->implementation()));
+
+  se::DeviceMemoryBase ret =
+      AllocateOutputBuffer(executorExecutor, *(output.get()));
+
+  uint64 end_micros = tensorflow::Env::Default()->NowMicros();
+
+  {
+    tensorflow::mutex_lock lock(mutex_);
+    const double nanoseconds = (end_micros - start_micros) * 1000.0;
+    execution_profile_.set_compute_time_ns(std::max(nanoseconds, 1.0));
+  }
+
+  return ret;
+}
+
+StatusOr<std::unique_ptr<ShapedBuffer>> ExecutorExecutable::ExecuteOnStream(
+    const ServiceExecutableRunOptions* run_options,
+    tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
+    HloExecutionProfile* hlo_execution_profile) {
+  return tensorflow::errors::Unimplemented(
+      "ExecuteOnStream is not yet supported on Executor.");
+}
+
+StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteAsyncOnStream(
+    const ServiceExecutableRunOptions* run_options,
+    tensorflow::gtl::ArraySlice<se::DeviceMemoryBase> arguments) {
+  return tensorflow::errors::Unimplemented(
+      "ExecuteAsyncOnStream is not yet supported on Executor.");
+}
+
+/*static*/ int64 ExecutorExecutable::ShapeSizeBytes(const Shape& shape) {
+  if (ShapeUtil::IsOpaque(shape)) {
+    return sizeof(void*);
+  }
+  return ShapeUtil::ByteSizeOf(shape, sizeof(void*));
+}
+
+
+}  // namespace executorplugin
+}  // namespace xla
diff --git a/tensorflow/compiler/plugin/executor/executable.h b/tensorflow/compiler/plugin/executor/executable.h
new file mode 100644
index 0000000000..ba3d4da21d
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executable.h
@@ -0,0 +1,65 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
+
+#include <cstddef>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/executable.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_module_config.h"
+
+#include "tensorflow/stream_executor/lib/status.h"
+#include "tensorflow/stream_executor/lib/statusor.h"
+
+namespace xla {
+namespace executorplugin {
+
+class ExecutorExecutable : public Executable {
+ public:
+  ExecutorExecutable(std::unique_ptr<HloModule> hlo_module);
+  ~ExecutorExecutable() override;
+
+  StatusOr<perftools::gputools::DeviceMemoryBase> ExecuteOnStream(
+      const ServiceExecutableRunOptions* run_options,
+      tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>
+          arguments,
+      HloExecutionProfile* hlo_execution_profile) override;
+
+  StatusOr<std::unique_ptr<ShapedBuffer>> ExecuteOnStream(
+      const ServiceExecutableRunOptions* run_options,
+      tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
+      HloExecutionProfile* hlo_execution_profile) override;
+
+  StatusOr<perftools::gputools::DeviceMemoryBase> ExecuteAsyncOnStream(
+      const ServiceExecutableRunOptions* run_options,
+      tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>
+          arguments) override;
+
+  static int64 ShapeSizeBytes(const Shape& shape);
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorExecutable);
+};
+
+}  // namespace executorplugin
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
diff --git a/tensorflow/compiler/plugin/executor/executor.cc b/tensorflow/compiler/plugin/executor/executor.cc
new file mode 100644
index 0000000000..e72c2711f7
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executor.cc
@@ -0,0 +1,135 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/executor.h"
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+#include "tensorflow/compiler/xla/status_macros.h"
+
+#include <stdlib.h>
+#include <string.h>
+
+namespace se = ::perftools::gputools;
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+host::HostStream *AsExecutorStream(Stream *stream) {
+  DCHECK(stream != nullptr);
+  return dynamic_cast<host::HostStream *>(stream->implementation());
+}
+
+ExecutorExecutor::ExecutorExecutor(const PluginConfig &plugin_config)
+    : plugin_config_(plugin_config) {}
+
+ExecutorExecutor::~ExecutorExecutor() {}
+
+void *ExecutorExecutor::Allocate(uint64 size) {
+  void *buf = new char[size];
+  return buf;
+}
+
+void *ExecutorExecutor::AllocateSubBuffer(DeviceMemoryBase *parent,
+                                         uint64 offset_bytes,
+                                         uint64 size_bytes) {
+  return parent + offset_bytes;
+}
+
+void ExecutorExecutor::Deallocate(DeviceMemoryBase *mem) {
+  if (!mem->is_sub_buffer()) {
+    delete[] static_cast<char *>(mem->opaque());
+  }
+}
+
+bool ExecutorExecutor::Memcpy(Stream *stream, void *host_dst,
+                             const DeviceMemoryBase &dev_src, uint64 size) {
+  AsExecutorStream(stream)->EnqueueTask([this, host_dst, dev_src, size]() {
+    port::Status ok = SynchronousMemcpy(host_dst, dev_src, size);
+  });
+  return true;
+}
+
+bool ExecutorExecutor::Memcpy(Stream *stream, DeviceMemoryBase *dev_dst,
+                             const void *host_src, uint64 size) {
+  AsExecutorStream(stream)->EnqueueTask([this, dev_dst, host_src, size]() {
+    port::Status ok = SynchronousMemcpy(dev_dst, host_src, size);
+  });
+  return true;
+}
+
+port::Status ExecutorExecutor::SynchronousMemcpy(DeviceMemoryBase *dev_dst,
+                                                const void *host_src,
+                                                uint64 size) {
+  memcpy(dev_dst->opaque(), host_src, size);
+  return port::Status::OK();
+}
+
+port::Status ExecutorExecutor::SynchronousMemcpy(void *host_dst,
+                                                const DeviceMemoryBase &dev_src,
+                                                uint64 size) {
+  memcpy(host_dst, dev_src.opaque(), size);
+  return port::Status::OK();
+}
+
+bool ExecutorExecutor::HostCallback(Stream *stream,
+                                   std::function<void()> callback) {
+  AsExecutorStream(stream)->EnqueueTask(callback);
+  return true;
+}
+
+bool ExecutorExecutor::CreateStreamDependency(Stream *dependent, Stream *other) {
+  AsExecutorStream(dependent)->EnqueueTask(
+      [other]() { other->BlockHostUntilDone(); });
+  AsExecutorStream(dependent)->BlockUntilDone();
+  return true;
+}
+
+bool ExecutorExecutor::StartTimer(Stream *stream, Timer *timer) {
+  dynamic_cast<host::HostTimer *>(timer->implementation())->Start(stream);
+  return true;
+}
+
+bool ExecutorExecutor::StopTimer(Stream *stream, Timer *timer) {
+  dynamic_cast<host::HostTimer *>(timer->implementation())->Stop(stream);
+  return true;
+}
+
+bool ExecutorExecutor::BlockHostUntilDone(Stream *stream) {
+  AsExecutorStream(stream)->BlockUntilDone();
+  return true;
+}
+
+DeviceDescription *ExecutorExecutor::PopulateDeviceDescription() const {
+  internal::DeviceDescriptionBuilder builder;
+
+  builder.set_device_address_bits(64);
+
+  builder.set_name("Executor");
+  builder.set_device_vendor("VectorName");
+  builder.set_platform_version("1.0");
+  builder.set_driver_version("1.0");
+  builder.set_runtime_version("1.0");
+  builder.set_pci_bus_id("1");
+  builder.set_device_memory_size(static_cast<uint64>(4) * 1024 * 1024 * 1024);
+  builder.set_clock_rate_ghz(static_cast<float>(CLOCKS_PER_SEC) / 1e9);
+
+  auto built = builder.Build();
+  return built.release();
+}
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
diff --git a/tensorflow/compiler/plugin/executor/executor.h b/tensorflow/compiler/plugin/executor/executor.h
new file mode 100644
index 0000000000..32fdb157e4
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executor.h
@@ -0,0 +1,213 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Declares the ExecutorExecutor class, which is a CPU-only implementation of
+// the StreamExecutor interface. For now, this is used for testing and to
+// examine the performance of host-based StreamExecutor code.
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
+
+#include "tensorflow/stream_executor/host/host_stream.h"
+#include "tensorflow/stream_executor/host/host_timer.h"
+
+#include "tensorflow/compiler/xla/shape_util.h"
+
+#include "tensorflow/stream_executor/blas.h"
+#include "tensorflow/stream_executor/lib/error.h"
+#include "tensorflow/stream_executor/lib/status.h"
+#include "tensorflow/stream_executor/lib/statusor.h"
+#include "tensorflow/stream_executor/rng.h"
+#include "tensorflow/stream_executor/stream_executor.h"
+#include "tensorflow/stream_executor/stream_executor_internal.h"
+
+#include <list>
+#include <mutex>
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+using Args = tensorflow::gtl::ArraySlice<DeviceMemoryBase>;
+
+class ExecutorExecutor : public internal::StreamExecutorInterface {
+ public:
+  explicit ExecutorExecutor(const PluginConfig &plugin_config);
+  ~ExecutorExecutor() override;
+
+  port::Status Init(int device_ordinal, DeviceOptions device_options) override {
+    return port::Status::OK();
+  }
+
+  bool GetKernel(const MultiKernelLoaderSpec &spec,
+                 KernelBase *kernel) override {
+    return false;
+  }
+  bool Launch(Stream *stream, const ThreadDim &thread_dims,
+              const BlockDim &block_dims, const KernelBase &kernel,
+              const KernelArgsArrayBase &args) override {
+    return false;
+  }
+
+  void *Allocate(uint64 size) override;
+  void *AllocateSubBuffer(DeviceMemoryBase *mem, uint64 offset_bytes,
+                          uint64 size_bytes) override;
+  void Deallocate(DeviceMemoryBase *mem) override;
+
+  void *HostMemoryAllocate(uint64 size) override { return new char[size]; }
+  void HostMemoryDeallocate(void *mem) override {
+    delete[] static_cast<char *>(mem);
+  }
+  bool HostMemoryRegister(void *mem, uint64 size) override { return true; }
+  bool HostMemoryUnregister(void *mem) override { return true; }
+
+  bool Memcpy(Stream *stream, void *host_dst, const DeviceMemoryBase &pop_src,
+              uint64 size) override;
+  bool Memcpy(Stream *stream, DeviceMemoryBase *pop_dst, const void *host_src,
+              uint64 size) override;
+  bool MemcpyDeviceToDevice(Stream *stream, DeviceMemoryBase *pop_dst,
+                            const DeviceMemoryBase &host_src,
+                            uint64 size) override {
+    return false;
+  }
+
+  bool MemZero(Stream *stream, DeviceMemoryBase *location,
+               uint64 size) override {
+    return false;
+  }
+  bool Memset(Stream *stream, DeviceMemoryBase *location, uint8 pattern,
+              uint64 size) override {
+    return false;
+  }
+  bool Memset32(Stream *stream, DeviceMemoryBase *location, uint32 pattern,
+                uint64 size) override {
+    return false;
+  }
+
+  // No "synchronize all activity" implemented for this platform at the moment.
+  bool SynchronizeAllActivity() override { return false; }
+  bool SynchronousMemZero(DeviceMemoryBase *location, uint64 size) override {
+    return false;
+  }
+
+  bool SynchronousMemSet(DeviceMemoryBase *location, int value,
+                         uint64 size) override {
+    return false;
+  }
+
+  port::Status SynchronousMemcpy(DeviceMemoryBase *pop_dst,
+                                 const void *host_src, uint64 size) override;
+  port::Status SynchronousMemcpy(void *host_dst,
+                                 const DeviceMemoryBase &pop_src,
+                                 uint64 size) override;
+  port::Status SynchronousMemcpyDeviceToDevice(DeviceMemoryBase *pop_dst,
+                                               const DeviceMemoryBase &pop_src,
+                                               uint64 size) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  bool HostCallback(Stream *stream, std::function<void()> callback) override;
+
+  port::Status AllocateEvent(Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  port::Status DeallocateEvent(Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  port::Status RecordEvent(Stream *stream, Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  port::Status WaitForEvent(Stream *stream, Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  Event::Status PollForEventStatus(Event *event) override {
+    return Event::Status::kError;
+  }
+
+  bool AllocateStream(Stream *stream) override { return true; }
+  void DeallocateStream(Stream *stream) override {}
+  bool CreateStreamDependency(Stream *dependent, Stream *other) override;
+
+  bool AllocateTimer(Timer *timer) override { return true; }
+  void DeallocateTimer(Timer *timer) override {}
+  bool StartTimer(Stream *stream, Timer *timer) override;
+  bool StopTimer(Stream *stream, Timer *timer) override;
+
+  bool BlockHostUntilDone(Stream *stream) override;
+
+  int PlatformDeviceCount() override { return 1; }
+
+  bool DeviceMemoryUsage(int64 *free, int64 *total) const override {
+    return false;
+  }
+
+  DeviceDescription *PopulateDeviceDescription() const override;
+
+  port::Status EnablePeerAccessTo(StreamExecutorInterface *other) override {
+    return port::Status::OK();
+  }
+
+  bool CanEnablePeerAccessTo(StreamExecutorInterface *other) override {
+    return true;
+  }
+
+  SharedMemoryConfig GetDeviceSharedMemoryConfig() override {
+    return SharedMemoryConfig::kDefault;
+  }
+
+  port::Status SetDeviceSharedMemoryConfig(SharedMemoryConfig config) override {
+    return port::Status{port::error::UNIMPLEMENTED,
+                        "Shared memory not supported"};
+  }
+
+  std::unique_ptr<internal::EventInterface> CreateEventImplementation()
+      override {
+    return nullptr;
+  }
+
+  std::unique_ptr<internal::KernelInterface> CreateKernelImplementation()
+      override {
+    return nullptr;
+  }
+
+  std::unique_ptr<internal::StreamInterface> GetStreamImplementation()
+      override {
+    return std::unique_ptr<internal::StreamInterface>(new host::HostStream());
+  }
+
+  std::unique_ptr<internal::TimerInterface> GetTimerImplementation() override {
+    return std::unique_ptr<internal::TimerInterface>(new host::HostTimer());
+  }
+
+  port::StatusOr<DeviceMemoryBase> ExecuteGraph(const xla::Shape &shape,
+                                                Args args);
+
+ private:
+  DeviceMemoryBase AllocateSingleOutput(const xla::Shape &shape);
+
+  port::StatusOr<DeviceMemoryBase> AllocateOutputBuffer(
+      const xla::Shape &shape);
+
+  const PluginConfig plugin_config_;
+};
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
diff --git a/tensorflow/compiler/plugin/executor/platform.cc b/tensorflow/compiler/plugin/executor/platform.cc
new file mode 100644
index 0000000000..2f339f04a7
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/platform.cc
@@ -0,0 +1,125 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/platform.h"
+#include "tensorflow/compiler/plugin/executor/executor.h"
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+#include "tensorflow/stream_executor/lib/error.h"
+#include "tensorflow/stream_executor/lib/initialize.h"
+#include "tensorflow/stream_executor/lib/ptr_util.h"
+#include "tensorflow/stream_executor/lib/status.h"
+#include "tensorflow/stream_executor/lib/status_macros.h"
+#include "tensorflow/stream_executor/lib/stringprintf.h"
+
+namespace se = ::perftools::gputools;
+namespace sep = ::perftools::gputools::executorplugin;
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+PLATFORM_DEFINE_ID(kExecutorPlatformId);
+
+ExecutorPlatform::ExecutorPlatform() : name_("Executor") {}
+
+ExecutorPlatform::~ExecutorPlatform() {}
+
+Platform::Id ExecutorPlatform::id() const { return kExecutorPlatformId; }
+
+int ExecutorPlatform::VisibleDeviceCount() const { return 1; }
+
+const string& ExecutorPlatform::Name() const { return name_; }
+
+port::StatusOr<StreamExecutor*> ExecutorPlatform::ExecutorForDevice(
+    int ordinal) {
+  StreamExecutorConfig config;
+  config.ordinal = ordinal;
+  config.plugin_config = PluginConfig();
+  config.device_options = DeviceOptions::Default();
+  return GetExecutor(config);
+}
+
+port::StatusOr<StreamExecutor*>
+ExecutorPlatform::ExecutorForDeviceWithPluginConfig(
+    int device_ordinal, const PluginConfig& plugin_config) {
+  StreamExecutorConfig config;
+  config.ordinal = device_ordinal;
+  config.plugin_config = plugin_config;
+  config.device_options = DeviceOptions::Default();
+  return GetExecutor(config);
+}
+
+port::StatusOr<StreamExecutor*> ExecutorPlatform::GetExecutor(
+    const StreamExecutorConfig& config) {
+  mutex_lock lock(executors_mutex_);
+
+  port::StatusOr<StreamExecutor*> status = executor_cache_.Get(config);
+  if (status.ok()) {
+    return status.ValueOrDie();
+  }
+
+  port::StatusOr<std::unique_ptr<StreamExecutor>> executor =
+      GetUncachedExecutor(config);
+  if (!executor.ok()) {
+    return executor.status();
+  }
+
+  StreamExecutor* naked_executor = executor.ValueOrDie().get();
+  SE_RETURN_IF_ERROR(
+      executor_cache_.Insert(config, executor.ConsumeValueOrDie()));
+  return naked_executor;
+}
+
+port::StatusOr<std::unique_ptr<StreamExecutor>>
+ExecutorPlatform::GetUncachedExecutor(const StreamExecutorConfig& config) {
+  auto executor = port::MakeUnique<StreamExecutor>(
+      this, port::MakeUnique<ExecutorExecutor>(config.plugin_config));
+  auto init_status = executor->Init(config.ordinal, config.device_options);
+  if (!init_status.ok()) {
+    return port::Status{
+        port::error::INTERNAL,
+        port::Printf(
+            "failed initializing StreamExecutor for device ordinal %d: %s",
+            config.ordinal, init_status.ToString().c_str())};
+  }
+
+  return std::move(executor);
+}
+
+void ExecutorPlatform::RegisterTraceListener(
+    std::unique_ptr<TraceListener> listener) {
+  LOG(FATAL) << "not yet implemented: register executor trace listener";
+}
+
+void ExecutorPlatform::UnregisterTraceListener(TraceListener* listener) {
+  LOG(FATAL) << "not yet implemented: unregister executor trace listener";
+}
+
+static void InitializeExecutorPlatform() {
+  std::unique_ptr<se::Platform> platform(new sep::ExecutorPlatform);
+  SE_CHECK_OK(se::MultiPlatformManager::RegisterPlatform(std::move(platform)));
+}
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+REGISTER_MODULE_INITIALIZER(executor_platform, sep::InitializeExecutorPlatform());
+
+DECLARE_MODULE_INITIALIZER(multi_platform_manager);
+// Note that module initialization sequencing is not supported in the
+// open-source project, so this will be a no-op there.
+REGISTER_MODULE_INITIALIZER_SEQUENCE(executor_platform, multi_platform_manager);
diff --git a/tensorflow/compiler/plugin/executor/platform.h b/tensorflow/compiler/plugin/executor/platform.h
new file mode 100644
index 0000000000..c252a589d4
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/platform.h
@@ -0,0 +1,83 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/stream_executor/executor_cache.h"
+#include "tensorflow/stream_executor/lib/statusor.h"
+#include "tensorflow/stream_executor/multi_platform_manager.h"
+#include "tensorflow/stream_executor/platform.h"
+#include "tensorflow/stream_executor/platform/mutex.h"
+#include "tensorflow/stream_executor/platform/port.h"
+#include "tensorflow/stream_executor/platform/thread_annotations.h"
+#include "tensorflow/stream_executor/stream_executor_pimpl.h"
+#include "tensorflow/stream_executor/trace_listener.h"
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+class ExecutorPlatform : public Platform {
+ public:
+  ExecutorPlatform();
+  ~ExecutorPlatform() override;
+
+  Platform::Id id() const override;
+
+  // Device count is less clear-cut for CPUs than accelerators. This call
+  // currently returns the number of thread units in the host, as reported by
+  // base::NumCPUs().
+  int VisibleDeviceCount() const override;
+
+  const string& Name() const override;
+
+  port::StatusOr<StreamExecutor*> ExecutorForDevice(int ordinal) override;
+
+  port::StatusOr<StreamExecutor*> ExecutorForDeviceWithPluginConfig(
+      int ordinal, const PluginConfig& config) override;
+
+  port::StatusOr<StreamExecutor*> GetExecutor(
+      const StreamExecutorConfig& config) override;
+
+  port::StatusOr<std::unique_ptr<StreamExecutor>> GetUncachedExecutor(
+      const StreamExecutorConfig& config) override;
+
+  void RegisterTraceListener(std::unique_ptr<TraceListener> listener) override;
+
+  void UnregisterTraceListener(TraceListener* listener) override;
+
+ private:
+  // This platform's name.
+  string name_;
+
+  // mutex that guards the ordinal-to-executor map.
+  mutable mutex executors_mutex_;
+
+  // Cache of created StreamExecutors.
+  ExecutorCache executor_cache_;
+
+  SE_DISALLOW_COPY_AND_ASSIGN(ExecutorPlatform);
+};
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
diff --git a/tensorflow/compiler/plugin/executor/platform_id.h b/tensorflow/compiler/plugin/executor/platform_id.h
new file mode 100644
index 0000000000..8d2b29a3e4
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/platform_id.h
@@ -0,0 +1,31 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
+#define TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
+
+#include "tensorflow/stream_executor/platform.h"
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+extern const Platform::Id kExecutorPlatformId;
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+#endif  // TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.cc b/tensorflow/compiler/plugin/executor/transfer_manager.cc
new file mode 100644
index 0000000000..51c5deeea5
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.cc
@@ -0,0 +1,187 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/transfer_manager.h"
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace sep = ::perftools::gputools::executorplugin;
+
+namespace xla {
+namespace executorplugin {
+
+ExecutorTransferManager::ExecutorTransferManager() {}
+
+se::Platform::Id ExecutorTransferManager::PlatformId() const {
+  return se::executorplugin::kExecutorPlatformId;
+}
+
+Status ExecutorTransferManager::TransferLiteralFromDevice(
+    se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
+    const Shape& device_shape, const Shape& literal_shape, Literal* literal) {
+  TF_RET_CHECK(ShapeUtil::Compatible(device_shape, literal_shape));
+
+  // Tuples are a special case and contain one or more shapes inside of them to
+  // an arbitrary nesting depth.
+  if (device_shape.element_type() == TUPLE) {
+    *literal->mutable_shape() = literal_shape;
+    TF_ASSIGN_OR_RETURN(
+        std::vector<se::DeviceMemoryBase> element_buffers,
+        ShallowCopyTupleFromDevice(executor, source, device_shape));
+    TF_RET_CHECK(element_buffers.size() ==
+                 ShapeUtil::TupleElementCount(device_shape));
+    for (int64 i = 0; i < element_buffers.size(); ++i) {
+      const Shape& element_device_shape = device_shape.tuple_shapes(i);
+      const Shape& element_literal_shape = literal_shape.tuple_shapes(i);
+      Literal* element_literal = literal->add_tuple_literals();
+      // Recursively call TransferFromDevice to copy over the data in the
+      // element array.
+      TF_RETURN_IF_ERROR(TransferLiteralFromDevice(
+          executor, element_buffers[i], element_device_shape,
+          element_literal_shape, element_literal));
+    }
+    return Status::OK();
+  }
+
+  *literal->mutable_shape() = device_shape;
+  literal->Reserve(ShapeUtil::ElementsIn(device_shape));
+  TF_RETURN_IF_ERROR(TransferBufferFromDevice(
+      executor, source, ShapeUtil::ByteSizeOf(device_shape),
+      literal->MutableInternalData()));
+  if (!ShapeUtil::Equal(literal_shape, device_shape)) {
+    literal->Swap(
+        literal->Relayout(literal_shape.layout()).get());
+  }
+  TF_RET_CHECK(ShapeUtil::Equal(literal_shape, literal->shape()));
+  return Status::OK();
+}
+
+StatusOr<std::vector<se::DeviceMemoryBase>>
+ExecutorTransferManager::ShallowCopyTupleFromDevice(
+    se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
+    const Shape& shape) {
+  TF_RET_CHECK(ShapeUtil::IsTuple(shape));
+
+  std::vector<void*> element_pointers(ShapeUtil::TupleElementCount(shape),
+                                      nullptr);
+  int64 tuple_size = ShapeUtil::ByteSizeOf(shape, sizeof(void*));
+  auto copy_status = executor->SynchronousMemcpyD2H(source, tuple_size,
+                                                    element_pointers.data());
+  if (!copy_status.ok()) {
+    return AddStatus(
+        Status(static_cast<tensorflow::error::Code>(copy_status.code()),
+               copy_status.error_message()),
+        "failed transfer of tuple buffer " + ShapeUtil::HumanString(shape));
+  }
+
+  // Create a DeviceMemoryBase from each void* pointer.
+  std::vector<se::DeviceMemoryBase> destination;
+  for (int i = 0; i < element_pointers.size(); ++i) {
+    if (element_pointers[i] == nullptr &&
+        !ShapeUtil::HasZeroElements(shape.tuple_shapes(i))) {
+      return FailedPrecondition("tuple contains nullptr at element %d", i);
+    }
+    int64 buffer_size =
+        ShapeUtil::ByteSizeOf(shape.tuple_shapes(i), sizeof(void*));
+    destination.emplace_back(element_pointers[i], buffer_size);
+  }
+  return std::move(destination);
+}
+
+Status ExecutorTransferManager::TransferLiteralToDevice(
+    se::StreamExecutor* executor, const Literal& literal,
+    se::DeviceMemoryBase* destination) {
+  const Shape& shape = literal.shape();
+
+  if (ShapeUtil::IsTuple(literal.shape())) {
+    std::vector<void*> tuple_elements_on_device;
+    for (const Literal& tuple_element : literal.tuple_literals()) {
+      se::DeviceMemoryBase allocation = executor->AllocateArray<uint8>(
+          GetByteSizeRequirement(tuple_element.shape()));
+      TF_RETURN_IF_ERROR(
+          TransferLiteralToDevice(executor, tuple_element, &allocation));
+      tuple_elements_on_device.push_back(allocation.opaque());
+    }
+    return TransferBufferToDevice(
+        executor, tuple_elements_on_device.size() * sizeof(void*),
+        tuple_elements_on_device.data(), destination);
+  }
+
+  return TransferBufferToDevice(executor, GetByteSizeRequirement(shape),
+                                literal.InternalData(),
+                                destination);
+}
+
+Status ExecutorTransferManager::TransferLiteralToInfeed(
+    se::StreamExecutor* executor, const Literal& literal) {
+  const Shape& shape = literal.shape();
+  VLOG(1) << "transferring literal shape to infeed: "
+          << ShapeUtil::HumanString(shape);
+
+  return Status::OK();
+}
+
+Status ExecutorTransferManager::TransferBufferToInfeed(
+    se::StreamExecutor* executor, int64 size, const void* source) {
+  return Unimplemented("Transfer to Infeed");
+}
+
+Status ExecutorTransferManager::TransferLiteralFromOutfeed(
+    perftools::gputools::StreamExecutor* executor, const Shape& literal_shape,
+    Literal* literal) {
+  const Shape& shape = literal->shape();
+  VLOG(1) << "transferring literal shape from outfeed: "
+          << ShapeUtil::HumanString(shape);
+
+  return Status::OK();
+}
+
+Status ExecutorTransferManager::ResetDevices(
+    tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*>
+        executors) {
+  return Unimplemented("Device reset not supported");
+}
+
+int64 ExecutorTransferManager::GetByteSizeRequirement(const Shape& shape) {
+  return ShapeUtil::ByteSizeOf(shape, sizeof(void*));
+}
+
+}  // namespace executorplugin
+}  // namespace xla
+
+static std::unique_ptr<xla::TransferManager> CreateExecutorTransferManager() {
+  return xla::MakeUnique<xla::executorplugin::ExecutorTransferManager>();
+}
+
+static bool InitModule() {
+  xla::TransferManager::RegisterTransferManager(sep::kExecutorPlatformId,
+                                                &CreateExecutorTransferManager);
+  return true;
+}
+static bool module_initialized = InitModule();
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.h b/tensorflow/compiler/plugin/executor/transfer_manager.h
new file mode 100644
index 0000000000..7a42e5a2d7
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.h
@@ -0,0 +1,77 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
+
+#include "tensorflow/compiler/xla/service/transfer_manager.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+#include "tensorflow/core/platform/types.h"
+
+#include <vector>
+
+namespace se = ::perftools::gputools;
+
+namespace xla {
+namespace executorplugin {
+
+class ExecutorTransferManager : public TransferManager {
+ public:
+  ExecutorTransferManager();
+
+  ~ExecutorTransferManager() override {}
+
+  se::Platform::Id PlatformId() const override;
+
+  StatusOr<std::vector<se::DeviceMemoryBase>> ShallowCopyTupleFromDevice(
+      se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
+      const Shape& shape) override;
+
+  Status TransferLiteralFromDevice(se::StreamExecutor* executor,
+                                   const se::DeviceMemoryBase& source,
+                                   const Shape& device_shape,
+                                   const Shape& literal_shape,
+                                   Literal* literal) override;
+
+  Status TransferLiteralToDevice(se::StreamExecutor* executor,
+                                 const Literal& literal,
+                                 se::DeviceMemoryBase* destination) override;
+
+  Status TransferLiteralToInfeed(se::StreamExecutor* executor,
+                                 const Literal& literal) override;
+
+  Status TransferBufferToInfeed(se::StreamExecutor* executor,
+                                 int64 size, const void* source) override;
+
+  Status TransferLiteralFromOutfeed(se::StreamExecutor* executor,
+                                    const Shape& literal_shape,
+                                    Literal* literal) override;
+
+  Status ResetDevices(
+      tensorflow::gtl::ArraySlice<se::StreamExecutor*> executors) override;
+
+  int64 GetByteSizeRequirement(const Shape& shape) override;
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorTransferManager);
+};
+
+}  // namespace executorplugin
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py
index 6b328fb618..a75a5cd2cf 100644
--- a/tensorflow/compiler/tests/ftrl_test.py
+++ b/tensorflow/compiler/tests/ftrl_test.py
@@ -218,7 +218,7 @@ class FtrlOptimizerTest(XLATestCase):
         self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval())
         self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval())
 
-  # When variables are intialized with Zero, FTRL-Proximal has two properties:
+  # When variables are initialized with Zero, FTRL-Proximal has two properties:
   # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
   # with GradientDescent.
   # 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is idential
diff --git a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
index f752fb3ae2..16b778bca4 100644
--- a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
@@ -94,12 +94,14 @@ class BatchMatMulOp : public XlaOpKernel {
       // Slice off individual matrices and reshape to 2D tensors.
       auto x_slice = builder->Slice(
           x_flat, {i, 0, 0},
-          {i + 1, x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)});
+          {i + 1, x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)},
+          {1, 1, 1});
       x_slice = builder->Reshape(
           x_slice, {x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)});
       auto y_slice = builder->Slice(
           y_flat, {i, 0, 0},
-          {i + 1, y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)});
+          {i + 1, y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)},
+          {1, 1, 1});
       y_slice = builder->Reshape(
           y_slice, {y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)});
 
diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
index 47d2d747e6..21d3e64872 100644
--- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
@@ -125,6 +125,7 @@ void BatchToSpace(XlaOpKernelContext* ctx,
   //       input_shape[M+1], ..., input_shape[N-1]]
   std::vector<int64> start_indices(input_rank, 0);
   std::vector<int64> end_indices = reshaped_permuted_shape;
+  std::vector<int64> strides(input_rank, 1);
   for (int i = 0; i < block_rank; ++i) {
     int64 crop_start = crops.Get<int64>({i, 0});
     int64 crop_end = crops.Get<int64>({i, 1});
@@ -139,7 +140,7 @@ void BatchToSpace(XlaOpKernelContext* ctx,
             " end: ", crop_end, " size ", reshaped_permuted_shape[1 + i]));
   }
   xla::ComputationDataHandle output =
-      b->Slice(reshaped_permuted, start_indices, end_indices);
+      b->Slice(reshaped_permuted, start_indices, end_indices, strides);
   ctx->SetOutput(0, output);
 }
 
diff --git a/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc b/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
index 92b371cc4e..852d2a966e 100644
--- a/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
@@ -172,15 +172,14 @@ class DepthwiseConv2dNativeOp : public XlaOpKernel {
     } else {
       // These will be used to define the bounds of each slice.
       // Within the loop, the input_channel index will be modified.
-      gtl::InlinedVector<int64, 4> filter_begin;
-      gtl::InlinedVector<int64, 4> filter_limits;
-      gtl::InlinedVector<int64, 4> input_begin;
-      gtl::InlinedVector<int64, 4> input_limits;
+      gtl::InlinedVector<int64, 4> filter_begin(4, 0);
+      gtl::InlinedVector<int64, 4> filter_limits(4);
+      gtl::InlinedVector<int64, 4> input_begin(4, 0);
+      gtl::InlinedVector<int64, 4> input_limits(4);
+      gtl::InlinedVector<int64, 4> strides(4, 1);
       for (int i = 0; i < 4; ++i) {
-        filter_begin.push_back(0);
-        filter_limits.push_back(filter_shape.dim_size(i));
-        input_begin.push_back(0);
-        input_limits.push_back(input_shape.dim_size(i));
+        filter_limits[i] = filter_shape.dim_size(i);
+        input_limits[i] = input_shape.dim_size(i);
       }
 
       std::vector<int64> strides_for_tla{strides_[1], strides_[2]};
@@ -209,9 +208,9 @@ class DepthwiseConv2dNativeOp : public XlaOpKernel {
         input_limits[3] = i + 1;
 
         xla::ComputationDataHandle filter_slice =
-            b.Slice(filter, filter_begin, filter_limits);
+            b.Slice(filter, filter_begin, filter_limits, strides);
         xla::ComputationDataHandle input_slice =
-            b.Slice(input, input_begin, input_limits);
+            b.Slice(input, input_begin, input_limits, strides);
         convs.push_back(b.ConvWithGeneralDimensions(
             input_slice, filter_slice, strides_for_tla, xla_padding, dims));
       }
diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc
index 74994d8961..ec5017f6ab 100644
--- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc
@@ -125,7 +125,7 @@ class DiagPartOp : public XlaOpKernel {
     diag = builder->Reshape(diag, {new_size, new_size + 1});
 
     // Slices out the first column and reshapes to the final shape.
-    diag = builder->Slice(diag, {0, 0}, {new_size, 1});
+    diag = builder->Slice(diag, {0, 0}, {new_size, 1}, {1, 1});
     diag = builder->Reshape(diag, new_dims);
 
     ctx->SetOutput(0, diag);
@@ -224,8 +224,9 @@ class MatrixDiagPartOp : public XlaOpKernel {
     } else if (actual_size > target_size) {
       std::vector<int64> start(flattened_dims.size(), 0);
       std::vector<int64> limits(flattened_dims.begin(), flattened_dims.end());
+      std::vector<int64> strides(flattened_dims.size(), 1);
       limits[flattened_dims.size() - 1] = target_size;
-      diag = builder->Slice(diag, start, limits);
+      diag = builder->Slice(diag, start, limits, strides);
     }
 
     // Reshape so the target values are in the first position of the last
@@ -238,8 +239,9 @@ class MatrixDiagPartOp : public XlaOpKernel {
     // Slices out the first column and reshapes to the final shape.
     std::vector<int64> start(dims.size(), 0);
     std::vector<int64> limits(dims.begin(), dims.end());
+    std::vector<int64> strides(dims.size(), 1);
     limits[last_dim] = 1;
-    diag = builder->Slice(diag, start, limits);
+    diag = builder->Slice(diag, start, limits, strides);
 
     // Collapses away the last dimension.
     dims.pop_back();
diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
index faa7ef0ef9..0330e34c98 100644
--- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
@@ -156,6 +156,8 @@ class DynamicStitchOp : public XlaOpKernel {
                                    indices0_shape.dims());
     std::vector<int64> slice_limit(1 + data0_shape.dims() -
                                    indices0_shape.dims());
+    std::vector<int64> stride(1 + data0_shape.dims() -
+                              indices0_shape.dims(), 1);
     for (int d = indices0_shape.dims(); d < data0_shape.dims(); d++) {
       slice_limit[1 + d - indices0_shape.dims()] = data0_shape.dim_size(d);
     }
@@ -168,7 +170,7 @@ class DynamicStitchOp : public XlaOpKernel {
       // And place it in the concat list in the place indicated by
       // the index.
       to_concat[index_num] =
-          ctx->builder()->Slice(expression, slice_start, slice_limit);
+          ctx->builder()->Slice(expression, slice_start, slice_limit, stride);
     }
 
     ctx->SetOutput(0, ctx->builder()->ConcatInDim(to_concat, 0));
diff --git a/tensorflow/compiler/tf2xla/kernels/slice_op.cc b/tensorflow/compiler/tf2xla/kernels/slice_op.cc
index 51c97d85d7..482c54a40c 100644
--- a/tensorflow/compiler/tf2xla/kernels/slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/slice_op.cc
@@ -54,7 +54,9 @@ class SliceOp : public XlaOpKernel {
     for (int i = 0; i < begin.size(); ++i) {
       limits.push_back(begin[i] + size[i]);
     }
-    ctx->SetOutput(0, ctx->builder()->Slice(ctx->Input(0), begin, limits));
+    std::vector<int64> strides(begin.size(), 1);
+    ctx->SetOutput(0, ctx->builder()->Slice(ctx->Input(0), begin, limits,
+                                            strides));
   }
 
  private:
diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc
index 017f3a110e..44ee81461e 100644
--- a/tensorflow/compiler/tf2xla/kernels/split_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc
@@ -77,14 +77,14 @@ class SplitOp : public XlaOpKernel {
 
     // The vectors we will use to define the slice. The entry for the
     // split dimensions varies for each output.
-    std::vector<int64> begin;
-    std::vector<int64> limits;
+    std::vector<int64> begin(input_shape.dims(), 0);
+    std::vector<int64> limits(input_shape.dims());
+    std::vector<int64> strides(input_shape.dims(), 1);
     for (int i = 0; i < input_shape.dims(); ++i) {
       // Initially set up the limits to be the full size of the input:
       // the split dimension is filled in below.
       int64 dim = input_shape.dim_size(i);
-      begin.push_back(0);
-      limits.push_back(dim);
+      limits[i] = dim;
     }
 
     auto input = ctx->Input(1);
@@ -94,7 +94,7 @@ class SplitOp : public XlaOpKernel {
       // Slice out the ith split from the split dimension.
       begin[split_dim] = i * slice_size;
       limits[split_dim] = (i + 1) * slice_size;
-      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits));
+      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides));
     }
   }
 };
@@ -188,7 +188,7 @@ class SplitVOp : public XlaOpKernel {
     std::vector<int64> begin(input_shape.dims(), 0);
     auto dim_sizes = input_shape.dim_sizes();
     std::vector<int64> limits(dim_sizes.begin(), dim_sizes.end());
-
+    std::vector<int64> strides(input_shape.dims(), 1);
     for (int i = 0; i < num_split; ++i) {
       TensorShape output_shape(input_shape);
       int slice_size = split_sizes_vec[i];
@@ -196,7 +196,7 @@ class SplitVOp : public XlaOpKernel {
 
       // Slice out the ith split from the split dimension.
       limits[split_dim] = begin[split_dim] + slice_size;
-      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits));
+      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides));
       begin[split_dim] = limits[split_dim];
     }
   }
diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
index 8037e90791..6af4bd0496 100644
--- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
@@ -72,55 +72,29 @@ class StridedSliceOp : public XlaOpKernel {
                        &dummy, &dummy, &dummy, &begin, &end, &strides));
 
     gtl::InlinedVector<int64, 4> dimensions_to_reverse;
-    gtl::InlinedVector<int64, 4> slice_begin, slice_end;
-    bool simple_strides = true;
+    gtl::InlinedVector<int64, 4> slice_begin, slice_end, slice_strides;
+
     for (int i = 0; i < begin.size(); ++i) {
-      simple_strides &= (std::abs(strides[i]) == 1);
       if (strides[i] > 0) {
         slice_begin.push_back(begin[i]);
         slice_end.push_back(end[i]);
+        slice_strides.push_back(strides[i]);
       } else {
         // Negative stride: swap begin and end, add 1 because the interval
         // is semi-open, and mark the dimension to be reversed.
-        slice_begin.push_back(end[i] + 1);
-        slice_end.push_back(begin[i] + 1);
+        slice_begin.push_back(input_shape.dim_size(i) - begin[i] - 1);
+        slice_end.push_back(input_shape.dim_size(i) - end[i] - 1);
+        slice_strides.push_back(-strides[i]);
         dimensions_to_reverse.push_back(i);
       }
     }
-    xla::ComputationDataHandle slice =
-        ctx->builder()->Slice(ctx->Input(0), slice_begin, slice_end);
+
+    xla::ComputationDataHandle slice = ctx->Input(0);
     if (!dimensions_to_reverse.empty()) {
       slice = ctx->builder()->Rev(slice, dimensions_to_reverse);
     }
 
-    // If at least one of the strides is > 1 (or < -1) then use Slice
-    // to pull out each of the strided slices, and Concat to put them
-    // together again.
-    if (!simple_strides) {
-      // Re-adjust the begin and end now that the periphery has been
-      // sliced away.
-      for (int d = 0; d < strides.size(); ++d) {
-        slice_end[d] -= slice_begin[d];
-        slice_begin[d] = 0;
-      }
-
-      for (int d = 0; d < strides.size(); ++d) {
-        int64 stride = std::abs(strides[d]);
-        if (stride > 1) {
-          std::vector<xla::ComputationDataHandle> to_concat;
-          int64 end = slice_end[d];
-          for (int64 i = 0; i < end; i += stride) {
-            slice_begin[d] = i;
-            slice_end[d] = i + 1;
-            to_concat.push_back(
-                ctx->builder()->Slice(slice, slice_begin, slice_end));
-          }
-          slice = ctx->builder()->ConcatInDim(to_concat, d);
-          slice_begin[d] = 0;
-          slice_end[d] = to_concat.size();
-        }
-      }
-    }
+    slice = ctx->builder()->Slice(slice, slice_begin, slice_end, slice_strides);
 
     slice = ctx->builder()->Reshape(slice, final_shape.dim_sizes());
     ctx->SetOutput(0, slice);
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index 598b341002..9367c1ef22 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -318,7 +318,7 @@ class TensorArrayGatherOp : public XlaOpKernel {
     for (int i = 0; i < num_indices; ++i) {
       // Slices the i-th index out of `indices`, and pads it with zeros in the
       // minor dimensions to form an index into the TensorArray storage.
-      auto index = b->Slice(indices, {i}, {i + 1});
+      auto index = b->Slice(indices, {i}, {i + 1}, {1});
 
       // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
       auto start_indices = PadIndexWithZeros(b, index, ta_shape.dims() - 1);
@@ -381,16 +381,18 @@ class TensorArrayScatterOp : public XlaOpKernel {
     std::vector<int64> value_starts(value_shape.dims(), 0);
     auto value_ends = value_shape.dim_sizes();
 
+    std::vector<int64> value_strides(value_shape.dims(), 1);
+
     // For every (index, value) pair, update the corresponding TensorArray
     // storage.
     for (int i = 0; i < num_indices; ++i) {
       // Slice out part of the value.
       value_starts[0] = i;
       value_ends[0] = i + 1;
-      auto slice = b->Slice(value, value_starts, value_ends);
+      auto slice = b->Slice(value, value_starts, value_ends, value_strides);
 
       // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
-      auto index = b->Slice(indices, {i}, {i + 1});
+      auto index = b->Slice(indices, {i}, {i + 1}, {1});
       auto start_indices = PadIndexWithZeros(b, index, elem_shape.dims());
       ta = DynamicAddSlice(b, ta, slice, slice_dims, start_indices);
     }
diff --git a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
index a5ce78e520..f87586ba57 100644
--- a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
@@ -66,6 +66,7 @@ class UnpackOp : public XlaOpKernel {
 
     std::vector<int64> start_indices(input_shape.dims(), 0);
     std::vector<int64> limit_indices(input_shape.dims());
+    std::vector<int64> strides(input_shape.dims(), 1);
     for (int i = 0; i < input_shape.dims(); ++i) {
       limit_indices[i] = input_shape.dim_size(i);
     }
@@ -73,7 +74,8 @@ class UnpackOp : public XlaOpKernel {
     for (int i = 0; i < num; ++i) {
       start_indices[axis] = i;
       limit_indices[axis] = i + 1;
-      auto slice = ctx->builder()->Slice(input, start_indices, limit_indices);
+      auto slice = ctx->builder()->Slice(input, start_indices, limit_indices,
+                                         strides);
       // Reshape to drop the 'axis' dimension.
       auto result = ctx->builder()->Reshape(slice, output_shape.dim_sizes());
       ctx->SetOutput(i, result);
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 735a69d596..dcc313707b 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -256,7 +256,8 @@ void ComputationBuilder::CheckSameShape(const ComputationDataHandle& lhs,
 ComputationDataHandle ComputationBuilder::Slice(
     const ComputationDataHandle& operand,
     tensorflow::gtl::ArraySlice<int64> start_indices,
-    tensorflow::gtl::ArraySlice<int64> limit_indices) {
+    tensorflow::gtl::ArraySlice<int64> limit_indices,
+    tensorflow::gtl::ArraySlice<int64> stride) {
   if (!first_error_.ok() || !PrepareComputation().ok()) {
     return ComputationDataHandle();
   }
@@ -269,6 +270,9 @@ ComputationDataHandle ComputationBuilder::Slice(
   for (int64 index : limit_indices) {
     request.add_limit_indices(index);
   }
+  for (int64 index : stride) {
+    request.add_stride(index);
+  }
   OpRequest op_request;
   *op_request.mutable_computation() = computation_.handle();
   *op_request.mutable_slice_request() = request;
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 5dceb03281..b411346459 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -211,9 +211,11 @@ class ComputationBuilder {
   //
   // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D
   // range notation.
+  // The stride parameter determines the stride over the slice
   ComputationDataHandle Slice(const ComputationDataHandle& operand,
                               tensorflow::gtl::ArraySlice<int64> start_indices,
-                              tensorflow::gtl::ArraySlice<int64> limit_indices);
+                              tensorflow::gtl::ArraySlice<int64> limit_indices,
+                              tensorflow::gtl::ArraySlice<int64> stride);
 
   // Enqueues a slice operation onto the computation that slices the 'operand'
   // from dynamic start indices which are passed in 'start_indices'.
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 1b125e3596..b6bd1158d2 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -1205,11 +1205,7 @@ void Literal::Resize<double>(int64 num_elements, double value) {
 template <>
 void Literal::Resize<half>(int64 num_elements, half value) {
   CHECK_EQ(ShapeUtil::ElementsIn(shape()), num_elements);
-  mutable_f16s()->resize(num_elements * sizeof(half));
-  auto data = GetMutableArraySlice<half>();
-  for (int i = 0; i < num_elements; i++) {
-    data[i] = value;
-  }
+  mutable_f16s()->resize(num_elements, value);
 }
 
 template <typename RepeatedFieldT, typename NativeT>
@@ -1252,7 +1248,7 @@ LiteralProto Literal::ToProto() const {
     case F16:
       *proto.mutable_f16s() =
           string(reinterpret_cast<const char*>(f16s_.data()),
-                 f16s_.size() / sizeof(half));
+                 f16s_.size() * sizeof(half));
       break;
     case F32:
       CopyToRepeatedField(proto.mutable_f32s(), f32s());
@@ -1308,7 +1304,7 @@ void Literal::CopyFromProto(const LiteralProto& literal_proto) {
       const string& s(literal_proto.f16s());
       CHECK_EQ(0, s.size() % sizeof(half));
       f16s_ = std::vector<half>(s.size() / sizeof(half));
-      memcpy(f16s_.data(), s.data(), s.size() / sizeof(half));
+      memcpy(f16s_.data(), s.data(), s.size());
       break;
     }
     case F32:
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index ffae623b0c..5a550ef4c6 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -939,5 +939,62 @@ TEST_F(LiteralUtilTest, CopyFromProto_Bool) {
   }
 }
 
+// Note that f16 is currently stored in a byte array in little endian byte order
+TEST_F(LiteralUtilTest, ToProto_f16) {
+  half h1(1.0f);
+  half h2(2.0f);
+
+  auto m = Literal::CreateR2<half>({{h1, h2}, {h2, h1}});
+  Literal* l = m.get();
+  EXPECT_EQ(4, ShapeUtil::ElementsIn(l->shape()));
+  EXPECT_EQ(4, l->f16s().size());
+  EXPECT_EQ(4, l->f16s_size());
+
+  LiteralProto p = l->ToProto();
+  EXPECT_EQ(4, ShapeUtil::ElementsIn(p.shape()));
+  EXPECT_EQ(8, p.f16s().size());
+  const char* d = p.f16s().data();
+  EXPECT_EQ(d[0], 0);
+  EXPECT_EQ(d[1], 0x3C);
+  EXPECT_EQ(d[2], 0);
+  EXPECT_EQ(d[3], 0x40);
+  EXPECT_EQ(d[4], 0);
+  EXPECT_EQ(d[5], 0x40);
+  EXPECT_EQ(d[6], 0);
+  EXPECT_EQ(d[7], 0x3C);
+}
+
+// Note that f16 is currently stored in a byte array in little endian byte order
+TEST_F(LiteralUtilTest, CopyFromProto_f16) {
+  half h1(1.0f);
+  half h2(2.0f);
+
+  const char half_vals[8] = {
+    0x00, 0x3C, 0x00, 0x40, 0x00, 0x40, 0x00, 0x3C
+  };
+  LiteralProto p;
+  p.mutable_shape()->set_element_type(F16);
+  p.mutable_shape()->clear_dimensions();
+  p.mutable_shape()->add_dimensions(4);
+  p.clear_f16s();
+  p.set_f16s(half_vals, 8);
+
+
+  Literal literal(p);
+  ASSERT_EQ(4, literal.f16s_size());
+  ASSERT_EQ(h1, literal.f16s(0));
+  ASSERT_EQ(h2, literal.f16s(1));
+  ASSERT_EQ(h2, literal.f16s(2));
+  ASSERT_EQ(h1, literal.f16s(3));
+
+  const std::vector<half>& r = literal.f16s();
+  ASSERT_EQ(4, r.size());
+  ASSERT_EQ(h1, r[0]);
+  ASSERT_EQ(h2, r[1]);
+  ASSERT_EQ(h2, r[2]);
+  ASSERT_EQ(h1, r[3]);
+}
+
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 718a2d798c..99b1337b11 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -90,8 +90,6 @@ cc_library(
         ":hlo_query",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla:status",
-        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 0187c09d7b..5709ac3067 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -855,6 +855,7 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) {
     // Second, construct the slice instruction to perform the negative padding.
     std::vector<int64> start_indices;
     std::vector<int64> end_indices;
+    std::vector<int64> strides;
     for (int64 i = 0; i < pad->padding_config().dimensions_size(); ++i) {
       const PaddingConfig::PaddingConfigDimension& padding_dimension =
           pad->padding_config().dimensions(i);
@@ -868,16 +869,18 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) {
       }
       start_indices.push_back(start);
       end_indices.push_back(end);
+      strides.push_back(1);
     }
 
     // Verify that the slice shape matches the pad shape.
     TF_ASSIGN_OR_RETURN(Shape inferred_slice_shape,
                         ShapeInference::InferSliceShape(
-                            nonzero_pad_shape, start_indices, end_indices));
+                            nonzero_pad_shape, start_indices, end_indices,
+                            strides));
     TF_RET_CHECK(ShapeUtil::Compatible(inferred_slice_shape, pad->shape()));
 
     std::unique_ptr<HloInstruction> slice = HloInstruction::CreateSlice(
-        pad->shape(), nonzero_pad, start_indices, end_indices);
+        pad->shape(), nonzero_pad, start_indices, end_indices, strides);
     return ReplaceWithNewInstruction(pad, std::move(slice));
   }
 
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index 0792006ddb..7e52c8fb0c 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -520,7 +520,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) {
       HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
-          ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}));
+          ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}, {1}));
   Shape result_shape = ShapeUtil::MakeShape(F32, {3 * kParamLength});
   builder.AddInstruction(HloInstruction::CreateConcatenate(
       result_shape, {empty_literal, param0, param0, empty_slice, param1}, 0));
@@ -551,7 +551,7 @@ TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) {
       HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
-          ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}));
+          ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}, {1}));
   Shape result_shape = ShapeUtil::MakeShape(F32, {0});
   builder.AddInstruction(HloInstruction::CreateConcatenate(
       result_shape, {empty_literal, empty_slice}, 0));
@@ -1132,7 +1132,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) {
           0, ShapeUtil::MakeShape(F32, {dim0, dim1}), "param"));
   builder.AddInstruction(HloInstruction::CreateSlice(
       ShapeUtil::MakeShape(F32, {dim0, dim1}), param, /*start_indices=*/{0, 0},
-      /*limit_indices=*/{dim0, dim1}));
+      /*limit_indices=*/{dim0, dim1}, /*slices=*/{1, 1}));
 
   HloModule module(TestName());
   HloComputation* computation = module.AddEntryComputation(builder.Build());
@@ -1537,7 +1537,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) {
 
   Shape slice_shape = ShapeUtil::MakeShape(F32, {2, 2, 3, 3});
   HloInstruction* slice = builder.AddInstruction(HloInstruction::CreateSlice(
-      slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}));
+      slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}, {1, 1, 1, 1}));
 
   HloModule module(TestName());
   auto computation = module.AddEntryComputation(builder.Build());
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index c498b86dd4..56568fd446 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -731,7 +731,7 @@ TEST_F(BufferAssignmentTest, ReuseNonOperandBuffer) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
 
@@ -763,7 +763,7 @@ TEST_F(BufferAssignmentTest, NoReuseLiveBuffer) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
   builder.AddInstruction(HloInstruction::CreateTuple({negate, broadcast}));
@@ -800,7 +800,7 @@ TEST_F(BufferAssignmentTest, NoReuseAliasedBuffer) {
   auto tuple_element = builder.AddInstruction(
       HloInstruction::CreateGetTupleElement(f32vec100_, tuple, 0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}, {1}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
   builder.AddInstruction(HloInstruction::CreateTuple({tuple, broadcast}));
@@ -835,7 +835,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBuffer) {
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   // Slice output is 10 elements.
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));
@@ -867,7 +867,7 @@ TEST_F(BufferAssignmentTest, ReuseOutputBufferIfExactlySized) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 10}), slice, {0}));
@@ -904,7 +904,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBufferInTuple) {
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   // Slice output is 10 elements.
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));
diff --git a/tensorflow/compiler/xla/service/buffer_liveness_test.cc b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
index a31e9b1782..a5f7cc0aeb 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
@@ -588,7 +588,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
     if (update_uses_tuple_element1) {
       // Create a slice instruction as an additional user of 'gte1'.
       slice = builder.AddInstruction(
-          HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}));
+          HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}, {1}));
       update = builder.AddInstruction(HloInstruction::CreateBinary(
           update_shape, HloOpcode::kAdd, update, slice));
     }
diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h
index dd00c58240..0a1911cbd1 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.h
+++ b/tensorflow/compiler/xla/service/compile_only_service.h
@@ -55,7 +55,7 @@ class CompileOnlyService : public Service {
 
   // Override Service methods that require or imply the existence of an
   // execute backend.  Note that this does not include TransferToClient, as
-  // computing contants produces global data that we may wish to transfer.
+  // computing constants produces global data that we may wish to transfer.
   tensorflow::Status Execute(const ExecuteRequest* arg,
                              ExecuteResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
diff --git a/tensorflow/compiler/xla/service/computation_placer.cc b/tensorflow/compiler/xla/service/computation_placer.cc
index cdf277581f..cdfa30dd9a 100644
--- a/tensorflow/compiler/xla/service/computation_placer.cc
+++ b/tensorflow/compiler/xla/service/computation_placer.cc
@@ -49,17 +49,18 @@ Status DeviceAssignment::Serialize(DeviceAssignmentProto* proto) const {
   return Status::OK();
 }
 
-/* static */ StatusOr<DeviceAssignment> DeviceAssignment::Deserialize(
-    const DeviceAssignmentProto& proto) {
+/* static */ StatusOr<std::unique_ptr<DeviceAssignment>>
+DeviceAssignment::Deserialize(const DeviceAssignmentProto& proto) {
   TF_RET_CHECK(proto.computation_devices_size() == proto.computation_count());
-  DeviceAssignment assignment(proto.replica_count(), proto.computation_count());
+  auto assignment = MakeUnique<DeviceAssignment>(proto.replica_count(),
+                                                 proto.computation_count());
   for (int computation = 0; computation < proto.computation_count();
        ++computation) {
     const auto& computation_device = proto.computation_devices(computation);
     TF_RET_CHECK(computation_device.replica_device_ids_size() ==
                  proto.replica_count());
     for (int replica = 0; replica < proto.replica_count(); ++replica) {
-      assignment(replica, computation) =
+      (*assignment)(replica, computation) =
           computation_device.replica_device_ids(replica);
     }
   }
diff --git a/tensorflow/compiler/xla/service/computation_placer.h b/tensorflow/compiler/xla/service/computation_placer.h
index 4d26d6bb85..7d9abcd100 100644
--- a/tensorflow/compiler/xla/service/computation_placer.h
+++ b/tensorflow/compiler/xla/service/computation_placer.h
@@ -49,7 +49,11 @@ class DeviceAssignment : public Array2D<int> {
 
   // Protocol buffer serialization and deserialization.
   Status Serialize(DeviceAssignmentProto* proto) const;
-  static StatusOr<DeviceAssignment> Deserialize(
+
+  // Return a std::unique_ptr<DeviceAssignment> instead of a DeviceAssignment
+  // directly because one of the supported TF platforms (mac) does not compile
+  // due to a StatusOr of an incomplete type (DeviceAssignment).
+  static StatusOr<std::unique_ptr<DeviceAssignment>> Deserialize(
       const DeviceAssignmentProto& proto);
 };
 
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index da8d983e1a..759d27e1f3 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -359,7 +359,6 @@ Status AppendIRToFile(const string& file_name, const string& ir_module_string) {
 StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     std::unique_ptr<HloModule> module, HloDumper dump_hlo,
     se::StreamExecutor* stream_exec) {
-  VLOG(1) << "Compiling: " << module->name();
   TF_RET_CHECK(stream_exec != nullptr);
   std::call_once(llvm_command_line_options_initialized,
                  &InitializeLLVMCommandLineOptions, module->config());
@@ -404,8 +403,6 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
       module->config().debug_options().xla_dump_debug_json_to();
 
   if (CpuParallelBackendRequested(module->config())) {
-    VLOG(1) << "Using parallel cpu backend";
-
     // Run buffer analysis on the HLO graph. This analysis figures out which
     // temporary buffers are required to run the computation.
     // DependencyHloOrdering is used for the parallel emitter because the order
@@ -500,8 +497,6 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
           .set_ir_module_string(ir_module_string);
     }
   } else {
-    VLOG(1) << "Using sequential cpu backend";
-
     // Select an order for emitting the HLO instructions for each
     // computation. Using this sequence enables tighter buffer liveness analysis
     // and reduced memory usage (as compared to using DependencyHloOrdering).
@@ -567,7 +562,6 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
   }
 
-  VLOG(1) << "Compilation finished";
   return std::move(cpu_executable);
 }
 
@@ -669,7 +663,6 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
   std::vector<std::unique_ptr<AotCompilationResult>> results;
   for (size_t i = 0; i < modules.size(); ++i) {
     HloModule* module = modules[i].get();
-    VLOG(1) << "Compiling ahead-of-time: " << module->name();
 
     TF_RETURN_IF_ERROR(RunHloPasses(module, dump_hlo));
 
@@ -748,8 +741,6 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
         std::move(object_file_data), std::move(buffer_sizes),
         result_slice.index()));
   }
-
-  VLOG(1) << "Compilation finished";
   return std::move(results);
 }
 
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index 5b21ae3d2a..db0a8b36cd 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -949,9 +949,20 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
                  const IrArray::Index& index) -> StatusOr<llvm::Value*> {
         IrArray::Index sliced_index(index.size());
         for (int i = 0; i < index.size(); ++i) {
-          sliced_index[i] = ir_builder_->CreateAdd(
-              index[i], llvm::ConstantInt::get(index[i]->getType(),
-                                               hlo->slice_starts(i)));
+          int64 stride = hlo->slice_stride(i);
+          if (stride != 1) {
+            sliced_index[i] = ir_builder_->CreateAdd(
+                ir_builder_->CreateMul(
+                    index[i], llvm::ConstantInt::get(index[i]->getType(),
+                                                     stride)),
+                llvm::ConstantInt::get(index[i]->getType(),
+                                       hlo->slice_starts(i)));
+          } else {
+            sliced_index[i] = ir_builder_->CreateAdd(
+                    index[i],
+                    llvm::ConstantInt::get(index[i]->getType(),
+                                           hlo->slice_starts(i)));
+          }
         }
         return operand_to_generator.at(hlo->operand(0))(sliced_index);
       };
diff --git a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
index 4e130de311..b8c6162084 100644
--- a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
+++ b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
@@ -80,6 +80,7 @@ HloInstruction* MaybePaddedAndSlicedInput(
     std::vector<int64> start_indices(input->shape().dimensions_size(), 0);
     std::vector<int64> limit_indices(input->shape().dimensions().begin(),
                                      input->shape().dimensions().end());
+    std::vector<int64> strides(input->shape().dimensions_size(), 1);
     for (size_t i = 0; i < conv_dnums.spatial_dimensions().size(); ++i) {
       int64 dim = conv_dnums.spatial_dimensions(i);
       // If dimension "dim" has negative padding, increase the start index or
@@ -92,9 +93,9 @@ HloInstruction* MaybePaddedAndSlicedInput(
 
     input = computation->AddInstruction(HloInstruction::CreateSlice(
         ShapeInference::InferSliceShape(input->shape(), start_indices,
-                                        limit_indices)
+                                        limit_indices, strides)
             .ConsumeValueOrDie(),
-        input, start_indices, limit_indices));
+        input, start_indices, limit_indices, strides));
   }
 
   return input;
@@ -354,6 +355,8 @@ bool PadInsertion::CanonicalizeBackwardInputConvolution(
   std::vector<int64> limit_indices(
       new_backward_conv->shape().dimensions().begin(),
       new_backward_conv->shape().dimensions().end());
+  std::vector<int64> strides(new_backward_conv->shape().dimensions_size(),
+                             1LL);
   for (size_t i = 0; i < backward_conv->window().dimensions_size(); ++i) {
     int64 padding_low = backward_conv->window().dimensions(i).padding_low();
     int64 padding_high = backward_conv->window().dimensions(i).padding_high();
@@ -373,13 +376,13 @@ bool PadInsertion::CanonicalizeBackwardInputConvolution(
   // Replace the old backward convolution with the slice.
   CHECK(ShapeUtil::Compatible(
       ShapeInference::InferSliceShape(new_backward_conv->shape(), start_indices,
-                                      limit_indices)
+                                      limit_indices, strides)
           .ConsumeValueOrDie(),
       backward_conv->shape()));
   TF_CHECK_OK(computation->ReplaceWithNewInstruction(
       backward_conv,
       HloInstruction::CreateSlice(backward_conv->shape(), new_backward_conv,
-                                  start_indices, limit_indices)));
+                                  start_indices, limit_indices, strides)));
   return true;
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
index a643bc4076..1c60b06ddd 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
@@ -147,6 +147,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
   const int64 dimensions[] = {11, 8, 7, 5, 9};
   const int64 slice_start[] = {4, 2, 3, 1, 5};
   const int64 slice_limits[] = {10, 8, 6, 5, 9};
+  const int64 slice_strides[] = {1, 1, 1, 1, 1};
   TF_ASSIGN_OR_ASSERT_OK(auto literal,
                          LiteralTestUtil::CreateRandomLiteral<F32>(
                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
@@ -154,7 +155,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
       HloInstruction::CreateConstant(std::move(literal)));
   Shape shape = ShapeUtil::MakeShape(F32, {6, 6, 3, 4, 4});
   builder.AddInstruction(HloInstruction::CreateSlice(
-      shape, literal_instruction, slice_start, slice_limits));
+      shape, literal_instruction, slice_start, slice_limits, slice_strides));
   auto module = CreateNewModule();
   auto computation = module->AddEntryComputation(builder.Build());
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 99b73dea29..9117ab9653 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -306,11 +306,13 @@ HloInstruction::CreateCrossReplicaSum(const Shape& shape,
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateSlice(
     const Shape& shape, HloInstruction* operand,
     tensorflow::gtl::ArraySlice<int64> start_indices,
-    tensorflow::gtl::ArraySlice<int64> limit_indices) {
+    tensorflow::gtl::ArraySlice<int64> limit_indices,
+    tensorflow::gtl::ArraySlice<int64> strides) {
   auto instruction = WrapUnique(new HloInstruction(HloOpcode::kSlice, shape));
   instruction->AppendOperand(operand);
   instruction->slice_starts_.assign(start_indices.begin(), start_indices.end());
   instruction->slice_limits_.assign(limit_indices.begin(), limit_indices.end());
+  instruction->slice_strides_.assign(strides.begin(), strides.end());
   return instruction;
 }
 
@@ -852,7 +854,8 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CreateReshape(shape, new_operands[0]);
     case HloOpcode::kSlice:
       CHECK_EQ(new_operands.size(), 1);
-      return CreateSlice(shape, new_operands[0], slice_starts_, slice_limits_);
+      return CreateSlice(shape, new_operands[0], slice_starts_, slice_limits_,
+                         slice_strides_);
     case HloOpcode::kDynamicSlice:
       return CreateDynamicSlice(shape, new_operands[0], new_operands[1],
                                 dynamic_slice_sizes_);
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 37cbb0b769..d29c0935fc 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -174,7 +174,8 @@ class HloInstruction {
   static std::unique_ptr<HloInstruction> CreateSlice(
       const Shape& shape, HloInstruction* operand,
       tensorflow::gtl::ArraySlice<int64> start_indices,
-      tensorflow::gtl::ArraySlice<int64> limit_indices);
+      tensorflow::gtl::ArraySlice<int64> limit_indices,
+      tensorflow::gtl::ArraySlice<int64> strides);
 
   // Creates a slice instruction, where the first operand is sliced by
   // start indices specified in the second operand, and by size specfied in
@@ -662,6 +663,15 @@ class HloInstruction {
     return slice_limits_;
   }
 
+  // Returns the stride in the given dimension for a slice node.
+  //
+  // Precondition: opcode() == HloOpcode::kSlice
+  int64 slice_stride(int64 dimension) const {
+    CHECK_EQ(HloOpcode::kSlice, opcode_);
+    return slice_strides_[dimension];
+  }
+  const std::vector<int64>& slice_strides() const { return slice_strides_; }
+
   // Returns the size of the slice in the given dimension for a dynamic
   // slice node.
   //
@@ -907,6 +917,7 @@ class HloInstruction {
   // Describes the [begin, end) index range for a slice.
   std::vector<int64> slice_starts_;
   std::vector<int64> slice_limits_;
+  std::vector<int64> slice_strides_;
 
   // The bit sizes for a reduce-precision operation.
   int32 exponent_bits_;
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
index 8a1e705711..1a861cd16b 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
@@ -67,7 +67,8 @@ class HloRematerializationTest : public HloTestBase {
         /*dimension=*/0));
     auto slice_1 = builder.AddInstruction(HloInstruction::CreateSlice(
         vec1_shape_, concat_1, /*start_indices=*/{0},
-        /*limit_indices=*/{1}));
+        /*limit_indices=*/{1},
+        /*strides=*/{1}));
     auto concat_2 = builder.AddInstruction(HloInstruction::CreateConcatenate(
         ShapeUtil::MakeShape(xla::F32, {1025}), {bcast, slice_1},
         /*dimension=*/0));
@@ -75,7 +76,8 @@ class HloRematerializationTest : public HloTestBase {
     // which is necessary to use this computation in a while.
     builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat_2,
                                                        /*start_indices=*/{0},
-                                                       /*limit_indices=*/{1}));
+                                                       /*limit_indices=*/{1},
+                                                       /*strides=*/{1}));
     return builder.Build();
   }
 
@@ -103,7 +105,8 @@ class HloRematerializationTest : public HloTestBase {
         HloInstruction::CreateBroadcast(vec1024_shape_, param, {}));
     auto slice_1 = builder.AddInstruction(
         HloInstruction::CreateSlice(vec1_shape_, bcast, /*start_indices=*/{0},
-                                    /*limit_indices=*/{1}));
+                                    /*limit_indices=*/{1},
+                                    /*strides=*/{1}));
     auto while_inst = builder.AddInstruction(HloInstruction::CreateWhile(
         vec1_shape_, while_cond, while_body, slice_1));
     auto concat = builder.AddInstruction(HloInstruction::CreateConcatenate(
@@ -111,7 +114,8 @@ class HloRematerializationTest : public HloTestBase {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat,
                                                        /*start_indices=*/{0},
-                                                       /*limit_indices=*/{1}));
+                                                       /*limit_indices=*/{1},
+                                                       /*strides=*/{1}));
     return builder.Build();
   }
 
@@ -353,7 +357,7 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}));
+        /*limit_indices=*/{1024}, /*slices=*/{1}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
@@ -469,7 +473,7 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}));
+        /*limit_indices=*/{1024}, /*slices=*/{1}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index e348511c62..bcc9418d59 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -356,9 +356,26 @@ void EmitLogging(const char* tag, llvm::Value* value,
 
 void SetTbaaForInstruction(llvm::Instruction* instruction, Shape shape,
                            bool is_pointer_to) {
-  // TODO(b/62903316): TBAA metadata causes LLVM to miscompile generated code,
-  // most likely because the generated metadata is incorrect.  Disable TBAA
-  // metadata while we resolve this.
+  llvm::MDBuilder metadata_builder(instruction->getContext());
+  llvm::MDNode* root = metadata_builder.createTBAARoot("XLA TBAA");
+  string type_name;
+  if (is_pointer_to) {
+    type_name += "pointer-to ";
+  }
+  // Scalars do not have layout which makes it permissible to omit an explicit
+  // layout.  To make sure that equivalent scalar shapes have the same TBAA,
+  // remove the (meaningless) explicit layout if one is present.
+  if (!ShapeUtil::IsArray(shape) || ShapeUtil::IsScalar(shape)) {
+    LayoutUtil::ClearLayout(&shape);
+  } else {
+    CHECK(shape.has_layout());
+  }
+  type_name += shape.ShortDebugString();
+  llvm::MDNode* tbaa_node =
+      metadata_builder.createTBAANode(llvm_ir::AsStringRef(type_name), root);
+  instruction->setMetadata(llvm::LLVMContext::MD_tbaa,
+                           metadata_builder.createTBAAStructTagNode(
+                               tbaa_node, tbaa_node, /*Offset=*/0));
 }
 
 void SetAlignmentMetadataForLoad(llvm::LoadInst* load, uint64_t alignment) {
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index b332709995..5e4df9ddd6 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -1135,7 +1135,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
 /* static */ StatusOr<Shape> ShapeInference::InferSliceShape(
     const Shape& arg, tensorflow::gtl::ArraySlice<int64> starts,
-    tensorflow::gtl::ArraySlice<int64> limits) {
+    tensorflow::gtl::ArraySlice<int64> limits,
+    tensorflow::gtl::ArraySlice<int64> strides) {
   TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of slice"));
   VLOG(2) << tensorflow::strings::Printf(
       "slicing shape %s starts={%s} limits={%s}",
@@ -1158,13 +1159,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   for (int64 dimension = 0; dimension < starts.size(); ++dimension) {
     int64 start_index = starts[dimension];
     int64 limit_index = limits[dimension];
+    int64 stride = strides[dimension];
     if (start_index < 0) {
       return InvalidArgument("negative start index to slice: %lld",
                              start_index);
     }
-    if (limit_index < 0) {
-      return InvalidArgument("negative limit index to slice: %lld",
-                             limit_index);
+    if (stride == 0) {
+      return InvalidArgument("Zero stride");
     }
     if (limit_index > arg.dimensions(dimension)) {
       return InvalidArgument(
@@ -1172,18 +1173,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
           "size (%lld)",
           limit_index, arg.dimensions(dimension));
     }
-    if (start_index > limit_index) {
-      return InvalidArgument(
-          "limit index (%lld) must be greater or equal to "
-          "start index (%lld) in slice",
-          limit_index, start_index);
-    }
     VLOG(2) << tensorflow::strings::Printf("starts[%lld] = %lld", dimension,
                                            start_index);
     VLOG(2) << tensorflow::strings::Printf("limits[%lld] = %lld", dimension,
                                            limit_index);
-
-    sizes.push_back(limits[dimension] - starts[dimension]);
+    if (stride > 0) {
+      if (start_index > limit_index) {
+        return InvalidArgument(
+            "limit index (%lld) must be greater or equal to "
+            "start index (%lld) in slice with positive stride",
+            limit_index, start_index);
+      }
+      sizes.push_back((limit_index - start_index + stride - 1) / stride);
+    } else {
+      return InvalidArgument("Negative strides not supported");
+    }
   }
 
   return ShapeUtil::MakeShape(arg.element_type(), sizes);
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index 55c60e149d..42e4c7d39d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -116,7 +116,8 @@ class ShapeInference {
   // e.g. slice f32[32x32] 0:16 0:16 -> f32[16x16]
   static StatusOr<Shape> InferSliceShape(
       const Shape& arg, tensorflow::gtl::ArraySlice<int64> starts,
-      tensorflow::gtl::ArraySlice<int64> limits);
+      tensorflow::gtl::ArraySlice<int64> limits,
+      tensorflow::gtl::ArraySlice<int64> strides);
 
   // Infers the shape produced by a dynamic slice operation of size specified
   // in 'slice_sizes', with dynamic start indices shape 'start_indices_shape'.
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc
index 7cff042a48..8c731ae297 100644
--- a/tensorflow/compiler/xla/service/shape_inference_test.cc
+++ b/tensorflow/compiler/xla/service/shape_inference_test.cc
@@ -682,16 +682,43 @@ TEST_F(ReduceShapeInferenceTest, ErrorElementTypeVsApplyType) {
 TEST_F(ShapeInferenceTest, InferSliceShapeRank2) {
   Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
   auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64});
+      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64}, {1, 1});
   ASSERT_IS_OK(inferred_status.status());
   Shape inferred = inferred_status.ValueOrDie();
   ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {32, 64}), inferred));
 }
 
+TEST_F(ShapeInferenceTest, InferSliceShapeRank2WithStrides) {
+  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
+  auto inferred_status =
+      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64}, {2, 4});
+  ASSERT_IS_OK(inferred_status.status());
+  Shape inferred = inferred_status.ValueOrDie();
+  ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {16, 16}), inferred));
+}
+
+TEST_F(ShapeInferenceTest, InferSliceShapeRank2WithStridesNotIntegral) {
+  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
+  auto inferred_status =
+      ShapeInference::InferSliceShape(matrix_shape, {15, 0}, {20, 13}, {2, 4});
+  ASSERT_IS_OK(inferred_status.status());
+  Shape inferred = inferred_status.ValueOrDie();
+  ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {3, 4}), inferred));
+}
+
+TEST_F(ShapeInferenceTest, InferInvalidStride) {
+  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
+  auto inferred_status =
+      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2}, {0, 1});
+  ASSERT_FALSE(inferred_status.ok());
+  ASSERT_EQ(tensorflow::error::INVALID_ARGUMENT,
+            inferred_status.status().code());
+}
+
 TEST_F(ShapeInferenceTest, InferOobSliceShapeRank2) {
   Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
   auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2});
+      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2}, {1, 1});
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_EQ(tensorflow::error::INVALID_ARGUMENT,
             inferred_status.status().code());
@@ -700,7 +727,7 @@ TEST_F(ShapeInferenceTest, InferOobSliceShapeRank2) {
 TEST_F(ShapeInferenceTest, InferSliceShapeRank1) {
   Shape vector_shape = ShapeUtil::MakeShape(F32, {17});
   auto inferred_status =
-      ShapeInference::InferSliceShape(vector_shape, {2}, {4});
+      ShapeInference::InferSliceShape(vector_shape, {2}, {4}, {1});
   ASSERT_TRUE(inferred_status.ok());
   Shape inferred = inferred_status.ValueOrDie();
   ASSERT_TRUE(ShapeUtil::Equal(inferred, ShapeUtil::MakeShape(F32, {2})));
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
index d25e5adee3..cd79e63caf 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
@@ -584,7 +584,7 @@ class FusionPointsToAnalysisTest : public TuplePointsToAnalysisTest {
     if (add_additional_gte0_user) {
       // Create 'slice' as an additional user of 'input'.
       auto slice = builder.AddInstruction(
-          HloInstruction::CreateSlice(update_shape, input, {0}, {3}));
+          HloInstruction::CreateSlice(update_shape, input, {0}, {3}, {1}));
       // Modify 'update' to take 'slice' output.
       update = builder.AddInstruction(HloInstruction::CreateBinary(
           update_shape, HloOpcode::kAdd, update, slice));
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 1f6e789379..92b8c7bb21 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -744,7 +744,8 @@ StatusOr<ComputationDataHandle> UserComputation::AddSliceInstruction(
       Shape new_shape,
       ShapeInference::InferSliceShape(
           operand->output_shape(), AsInt64Slice(slice_request.start_indices()),
-          AsInt64Slice(slice_request.limit_indices())));
+          AsInt64Slice(slice_request.limit_indices()),
+          AsInt64Slice(slice_request.stride())));
 
   ComputationDataHandle handle = CreateComputationDataHandle();
 
@@ -2393,7 +2394,8 @@ void ComputationLowerer::Visit(
       hlo_instruction = add_instruction(HloInstruction::CreateSlice(
           request.output_shape(), operand,
           AsInt64Slice(slice_request.start_indices()),
-          AsInt64Slice(slice_request.limit_indices())));
+          AsInt64Slice(slice_request.limit_indices()),
+          AsInt64Slice(slice_request.stride())));
       break;
     }
 
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index bb7fbad000..024988743c 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1853,7 +1853,7 @@ TEST_F(ArrayElementwiseOpTest, ImplictBroadcastInFusedExpressions) {
 
   auto x = builder.Parameter(0, x_literal->shape(), "x");
   auto y = builder.Parameter(1, y_literal->shape(), "y");
-  auto slice = builder.Slice(x, {1}, {2});
+  auto slice = builder.Slice(x, {1}, {2}, {1});
   builder.Sub(slice, y);
 
   ComputeAndCompareR1<float>(&builder, {-2, -3}, {x_data.get(), y_data.get()},
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 7abef6a27b..63a630f9e5 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -365,9 +365,9 @@ XLA_TEST_F(DotOperationTest, BatchMatMul) {
   std::vector<xla::ComputationDataHandle> out_slices;
   for (int i = 0; i < 4; ++i) {
     // Slice off individual matrices and reshape to 2D tensors.
-    auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2});
+    auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1});
     x_slice = builder.Reshape(x_slice, {0, 1, 2}, {2, 2});
-    auto y_slice = builder.Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2});
+    auto y_slice = builder.Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1});
     y_slice = builder.Reshape(y_slice, {0, 1, 2}, {2, 2});
 
     auto out = builder.Dot(x_slice, y_slice);
diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index c8b91eafc7..7803d234fd 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -210,7 +210,7 @@ XLA_TEST_F(FusionTest, Test) {
       HloInstruction::CreateTernary(ShapeUtil::MakeShape(F32, {2, 3}),
                                     HloOpcode::kSelect, const10, add8, const9));
   auto slice12 = builder.AddInstruction(HloInstruction::CreateSlice(
-      ShapeUtil::MakeShape(F32, {2, 1}), select11, {0, 1}, {2, 2}));
+      ShapeUtil::MakeShape(F32, {2, 1}), select11, {0, 1}, {2, 2}, {1, 1}));
   // CreateFusionInstruction needs the `instructions_to_fuse` argument in
   // reverse topological order, so the first element in `instructions_to_fuse`
   // must be the root.
diff --git a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
index df3d4fa21d..56c15e5ff7 100644
--- a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
+++ b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
@@ -36,7 +36,7 @@ XLA_TEST_F(SliceTest, Slice2D) {
   ComputationBuilder builder(client_, "slice_2d");
   auto original = builder.ConstantR2<float>(
       {{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}, {7.0, 8.0, 9.0}, {10.0, 11.0, 12.0}});
-  builder.Slice(original, {2, 1}, {4, 3});
+  builder.Slice(original, {2, 1}, {4, 3}, {1, 1});
 
   Array2D<float> expected({{8.0f, 9.0f}, {11.0f, 12.0f}});
   ComputeAndCompareR2<float>(&builder, expected, {}, ErrorSpec(0.000001));
@@ -47,7 +47,7 @@ XLA_TEST_F(SliceTest, Slice3D) {
   Array3D<float> array_3d(
       {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}});
   auto original = builder.ConstantR3FromArray3D<float>(array_3d);
-  builder.Slice(original, {0, 0, 1}, {2, 1, 2});
+  builder.Slice(original, {0, 0, 1}, {2, 1, 2}, {1, 1, 1});
 
   Array3D<float> expected_3d({{{2.0f}}, {{6.0f}}});
   ComputeAndCompareR3<float>(&builder, expected_3d, {}, ErrorSpec(0.000001));
diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc
index 2065e9e813..a7692fceb4 100644
--- a/tensorflow/compiler/xla/tests/params_test.cc
+++ b/tensorflow/compiler/xla/tests/params_test.cc
@@ -325,7 +325,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) {
   ComputationBuilder builder(client_, TestName());
   auto input = builder.Parameter(0, original, "input");
   // Use the slice operator to get an off-diagonal element.
-  builder.Slice(input, {0, 1}, {1, 2});
+  builder.Slice(input, {0, 1}, {1, 2}, {1, 1});
 
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index 97120df0c5..5e7d475662 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -44,7 +44,7 @@ class SliceTest : public ClientLibraryTestBase {
 
     ComputationBuilder builder(client_, TestName());
     auto original = builder.ConstantR1<NativeT>(constant);
-    builder.Slice(original, {2}, {4});
+    builder.Slice(original, {2}, {4}, {1});
 
     const std::vector<NativeT> expected = {static_cast<NativeT>(2),
                                            static_cast<NativeT>(3)};
@@ -55,7 +55,7 @@ class SliceTest : public ClientLibraryTestBase {
 XLA_TEST_F(SliceTest, SliceZeroToZeroF32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>({});
-  builder.Slice(original, {0}, {0});
+  builder.Slice(original, {0}, {0}, {1});
 
   ComputeAndCompareR1<float>(&builder, {}, {});
 }
@@ -64,7 +64,7 @@ XLA_TEST_F(SliceTest, SliceTenToZeroF32) {
   ComputationBuilder builder(client_, TestName());
   std::vector<float> constant(10, 0.3);
   auto original = builder.ConstantR1<float>(constant);
-  builder.Slice(original, {7}, {7});
+  builder.Slice(original, {7}, {7}, {1});
 
   ComputeAndCompareR1<float>(&builder, {}, {});
 }
@@ -87,7 +87,7 @@ TEST_F(SliceTest, SliceTenToTen) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {0}, {10});
+  builder.Slice(original, {0}, {10}, {1});
 
   ComputeAndCompareR1<float>(&builder, values, {}, ErrorSpec(0.000001));
 }
@@ -98,7 +98,7 @@ TEST_F(SliceTest, SliceLastFourOf1024) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {1024 - 4}, {1024});
+  builder.Slice(original, {1024 - 4}, {1024}, {1});
 
   const std::vector<float> expected = {1020, 1021, 1022, 1023};
   ComputeAndCompareR1<float>(&builder, expected, {}, ErrorSpec(0.000001));
@@ -112,7 +112,7 @@ TEST_F(SliceTest, DISABLED_SliceUnaligned1024In4096Values) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {7}, {7 + 1024});
+  builder.Slice(original, {7}, {7 + 1024}, {1});
 
   std::vector<float> expected(1024);
   std::iota(values.begin(), values.end(), 7.0);
@@ -122,7 +122,7 @@ TEST_F(SliceTest, DISABLED_SliceUnaligned1024In4096Values) {
 XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 0));
-  builder.Slice(original, {0, 0}, {0, 0});
+  builder.Slice(original, {0, 0}, {0, 0}, {1, 1});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 0), {});
 }
@@ -130,7 +130,7 @@ XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
 XLA_TEST_F(SliceTest, Slice0x20to0x5F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 20));
-  builder.Slice(original, {0, 15}, {0, 20});
+  builder.Slice(original, {0, 15}, {0, 20}, {1, 1});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 5), {});
 }
@@ -138,7 +138,7 @@ XLA_TEST_F(SliceTest, Slice0x20to0x5F32) {
 XLA_TEST_F(SliceTest, Slice3x0to2x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(3, 0));
-  builder.Slice(original, {1, 0}, {3, 0});
+  builder.Slice(original, {1, 0}, {3, 0}, {1, 1});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(2, 0), {});
 }
@@ -153,7 +153,7 @@ XLA_TEST_F(SliceTest, SliceQuadrantOf256x256) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {128, 128}, {256, 256});
+  builder.Slice(original, {128, 128}, {256, 256}, {1, 1});
 
   Array2D<float> expected(128, 128);
   for (int row = 0; row < 128; ++row) {
@@ -171,7 +171,7 @@ TEST_F(SliceTest, Slice_1x4096_To_1x1024) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {0, 3072}, {1, 4096});
+  builder.Slice(original, {0, 3072}, {1, 4096}, {1, 1});
 
   Array2D<float> expected(1, 1024);
   std::iota(expected.data(), expected.data() + 1024, 3072.0);
@@ -192,7 +192,7 @@ TEST_F(SliceTest, Slice_16x4_To_16x2) {
   }
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {0, 0}, {16, 2});
+  builder.Slice(original, {0, 0}, {16, 2}, {1, 1});
   ComputeAndCompareR2<float>(&builder, expected, {}, ErrorSpec(0.000001));
 }
 
@@ -204,7 +204,7 @@ TEST_F(SliceTest, SliceR4ThreeDimsMiddleMinor) {
       ReferenceUtil::Slice4D(values, {{1, 0, 8, 0}}, {{2, 2, 16, 128}});
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR4FromArray4D(values);
-  builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128});
+  builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128}, {1, 1, 1, 1});
   ComputeAndCompareR4(&builder, *expected, {}, ErrorSpec(0.000001));
 }
 
@@ -213,6 +213,7 @@ struct R2Spec {
   int64 input_dim1;
   std::array<int64, 2> slice_starts;
   std::array<int64, 2> slice_limits;
+  std::array<int64, 2> slice_strides;
   Layout layout;
 };
 
@@ -228,7 +229,7 @@ TEST_P(SliceR2Test, DoIt) {
 
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR2FromArray2D<int32>(input);
-  builder.Slice(a, spec.slice_starts, spec.slice_limits);
+  builder.Slice(a, spec.slice_starts, spec.slice_limits, spec.slice_strides);
 
   std::unique_ptr<Array2D<int32>> expected =
       ReferenceUtil::Slice2D(input, spec.slice_starts, spec.slice_limits);
@@ -239,19 +240,23 @@ TEST_P(SliceR2Test, DoIt) {
 INSTANTIATE_TEST_CASE_P(
     SliceR2TestInstantiation, SliceR2Test,
     ::testing::Values(
-        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, LayoutUtil::MakeLayout({0, 1})},
-        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, LayoutUtil::MakeLayout({0, 1})},
-        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {256, 400, {{0, 300}}, {{256, 400}},
+        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, {{1, 1}},
+          LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {500, 400, {{111, 123}}, {{300, 257}},
+        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, {{1, 1}},
+          LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {500, 400, {{111, 123}}, {{300, 400}},
+        R2Spec {256, 400, {{0, 300}}, {{256, 400}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {384, 512, {{128, 256}}, {{256, 384}},
+        R2Spec {500, 400, {{111, 123}}, {{300, 257}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {357, 512, {{111, 256}}, {{301, 384}},
+        R2Spec {500, 400, {{111, 123}}, {{300, 400}}, {{1, 1}},
+          LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {384, 512, {{128, 256}}, {{256, 384}}, {{1, 1}},
+          LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {357, 512, {{111, 256}}, {{301, 384}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})}
     )
 );
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index ccd2a95658..afa7d871c0 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -666,7 +666,8 @@ TEST_F(WhileTest, WhileWithPrngScalarResult) {
   auto build_condition = [this, v6s32](int count) {
     ComputationBuilder builder(client_, TestName());
     auto prev = builder.Reshape(
-        builder.Slice(builder.Parameter(0, v6s32, "prev"), {0}, {1}), {0}, {});
+        builder.Slice(builder.Parameter(0, v6s32, "prev"), {0}, {1}, {1}), {0},
+          {});
     builder.Gt(builder.ConstantR0<int32>(count), prev);
     return builder.Build().ConsumeValueOrDie();
   };
diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 42d5c1d155..31f0c3147e 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -195,16 +195,24 @@ bool IsPermutation(tensorflow::gtl::ArraySlice<int64> permutation, int64 rank);
 // 2. permutation.size() == input.size().
 template <template <typename...> class C, typename T>
 std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
-                       C<T> input_) {
-  tensorflow::gtl::ArraySlice<T> input(input_);
-  CHECK(IsPermutation(permutation, input.size()));
-  std::vector<T> output(input.size());
+                       C<T> input) {
+  tensorflow::gtl::ArraySlice<T> data(input);
+  CHECK(IsPermutation(permutation, data.size()));
+  std::vector<T> output(data.size());
   for (size_t i = 0; i < permutation.size(); ++i) {
-    output[permutation[i]] = input[i];
+    output[permutation[i]] = data[i];
   }
   return output;
 }
 
+// Override of the above that works around compile failures with gcc 7.1.1.
+// For details see https://github.com/tensorflow/tensorflow/issues/10843
+template <typename T>
+std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
+                       const std::vector<T>& input) {
+  return Permute<std::vector, T>(permutation, input);
+}
+
 // Inverts a permutation, i.e., output_permutation[input_permutation[i]] = i.
 std::vector<int64> InversePermutation(
     tensorflow::gtl::ArraySlice<int64> input_permutation);
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 95c1f0995b..86c72b3449 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -200,7 +200,7 @@ message OpMetadata {
   string op_name = 2;
   // Indicate a file and line that this op is associated to in a user's program.
   //
-  // e.g. it could be be the file and line of user code that generated the op.
+  // e.g. it could be the file and line of user code that generated the op.
   string source_file = 3;
   int32 source_line = 4;
 }
@@ -369,6 +369,7 @@ message SliceRequest {
   ComputationDataHandle operand = 2;
   repeated int64 start_indices = 3;
   repeated int64 limit_indices = 4;
+  repeated int64 stride = 5;
 }
 
 message DynamicSliceRequest {
diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
index 1f180429b2..b1d18d2faf 100644
--- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
+++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
@@ -17,6 +17,7 @@ package org.tensorflow.contrib.android;
 
 import android.content.res.AssetManager;
 import android.os.Trace;
+import android.os.Build.VERSION;
 import android.text.TextUtils;
 import android.util.Log;
 import java.io.FileInputStream;
@@ -370,9 +371,11 @@ public class TensorFlowInferenceInterface {
   private void loadGraph(InputStream is, Graph g) throws IOException {
     final long startMs = System.currentTimeMillis();
 
-    Trace.beginSection("initializeTensorFlow");
+    if (VERSION.SDK_INT >= 18) {
+      Trace.beginSection("initializeTensorFlow");
+      Trace.beginSection("readGraphDef");
+    }
 
-    Trace.beginSection("readGraphDef");
     // TODO(ashankar): Can we somehow mmap the contents instead of copying them?
     byte[] graphDef = new byte[is.available()];
     final int numBytesRead = is.read(graphDef);
@@ -383,17 +386,22 @@ public class TensorFlowInferenceInterface {
               + " of the graph, expected to read "
               + graphDef.length);
     }
-    Trace.endSection();
 
-    Trace.beginSection("importGraphDef");
+    if (VERSION.SDK_INT >= 18) {
+      Trace.endSection(); // readGraphDef.
+      Trace.beginSection("importGraphDef");
+    }
+
     try {
       g.importGraphDef(graphDef);
     } catch (IllegalArgumentException e) {
       throw new IOException("Not a valid TensorFlow Graph serialization: " + e.getMessage());
     }
-    Trace.endSection();
 
-    Trace.endSection(); // initializeTensorFlow.
+    if (VERSION.SDK_INT >= 18) {
+      Trace.endSection(); // importGraphDef.
+      Trace.endSection(); // initializeTensorFlow.
+    }
 
     final long endMs = System.currentTimeMillis();
     Log.i(
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
index fba0cc6522..fabf7a9b77 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -627,11 +627,6 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       grad = lambda fs: gradients_impl.gradients(fs, s)[0]
 
       [
-          approx_kl_grad_,
-          approx_kl_self_normalized_grad_,
-          approx_kl_score_trick_grad_,
-          approx_kl_self_normalized_score_trick_grad_,
-          exact_kl_grad_,
           approx_kl_,
           approx_kl_self_normalized_,
           approx_kl_score_trick_,
@@ -643,39 +638,23 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
           grad(approx_kl_score_trick),
           grad(approx_kl_self_normalized_score_trick),
           grad(exact_kl),
-          approx_kl,
-          approx_kl_self_normalized,
-          approx_kl_score_trick,
-          approx_kl_self_normalized_score_trick,
-          exact_kl,
       ])
 
-      # Test average divergence.
-      self.assertAllClose(approx_kl_, exact_kl_,
-                          rtol=0.02, atol=0.)
-
-      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
-                          rtol=0.08, atol=0.)
-
-      self.assertAllClose(approx_kl_score_trick_, exact_kl_,
-                          rtol=0.02, atol=0.)
-
-      self.assertAllClose(approx_kl_self_normalized_score_trick_, exact_kl_,
-                          rtol=0.08, atol=0.)
-
-      # Test average gradient-divergence.
-      self.assertAllClose(approx_kl_grad_, exact_kl_grad_,
-                          rtol=0.007, atol=0.)
+      self.assertAllClose(
+          approx_kl_, exact_kl_,
+          rtol=0.06, atol=0.)
 
-      self.assertAllClose(approx_kl_self_normalized_grad_, exact_kl_grad_,
-                          rtol=0.011, atol=0.)
+      self.assertAllClose(
+          approx_kl_self_normalized_, exact_kl_,
+          rtol=0.05, atol=0.)
 
-      self.assertAllClose(approx_kl_score_trick_grad_, exact_kl_grad_,
-                          rtol=0.018, atol=0.)
+      self.assertAllClose(
+          approx_kl_score_trick_, exact_kl_,
+          rtol=0.06, atol=0.)
 
       self.assertAllClose(
-          approx_kl_self_normalized_score_trick_grad_, exact_kl_grad_,
-          rtol=0.017, atol=0.)
+          approx_kl_self_normalized_score_trick_, exact_kl_,
+          rtol=0.05, atol=0.)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 09389e5d38..7b51d8d932 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -40,8 +40,8 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib import framework as contrib_framework
-from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import distribution
@@ -750,7 +750,7 @@ def monte_carlo_csiszar_f_divergence(
   ```none
   D_f[p(X), q(X)] := E_{q(X)}[ f( p(X) / q(X) ) ]
                   ~= m**-1 sum_j^m f( p(x_j) / q(x_j) ),
-                             where x_j ~iid q(X)
+                             where x_j ~iid q(x)
   ```
 
   Tricks: Reparameterization and Score-Gradient
@@ -759,8 +759,8 @@ def monte_carlo_csiszar_f_divergence(
   parameterless distribution (e.g.,
   `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and
   expectation, i.e.,
-  `grad[Avg{ s_i : i=1...n }] = Avg{ grad[s_i] : i=1...n }` where `S_n=Avg{s_i}`
-  and `s_i = f(x_i), x_i ~iid q(X)`.
+  `nabla Avg{ s_i : i=1...n } = Avg{ nabla s_i : i=1...n }` where `S_n=Avg{s_i}`
+  and `s_i = f(x_i), x_i ~ q`.
 
   However, if q is not reparameterized, TensorFlow's gradient will be incorrect
   since the chain-rule stops at samples of unreparameterized distributions. In
@@ -768,17 +768,22 @@ def monte_carlo_csiszar_f_divergence(
   gradient, i.e.,
 
   ```none
-  grad[ E_q[f(X)] ]
-  = grad[ int dx q(x) f(x) ]
-  = int dx grad[ q(x) f(x) ]
-  = int dx [ q'(x) f(x) + q(x) f'(x) ]
+  nabla E_q[f(X)]
+  = nabla int dx q(x) f(x)
+  = int dx nabla [ q(x) f(x) ]
+  = int dx q'(x) f(x) + q(x) f'(x)
   = int dx q(x) [q'(x) / q(x) f(x) + f'(x) ]
-  = int dx q(x) grad[ f(x) q(x) / stop_grad[q(x)] ]
-  = E_q[ grad[ f(x) q(x) / stop_grad[q(x)] ] ]
+  = int dx q(x) nabla [ log(q(x)) stopgrad[f(x)] + f(x) ]
+  = E_q[ nabla [ log(q(X)) stopgrad[f(X)] + f(X) ] ]
+  ~= Avg{ log(q(y_i)) stopgrad[f(y_i)] + f(y_i) : y_i = stopgrad[x_i], x_i ~ q}
   ```
 
   Unless `q.reparameterization_type != distribution.FULLY_REPARAMETERIZED` it is
-  usually preferable to set `use_reparametrization = True`.
+  usually preferable to `use_reparametrization = True`.
+
+  Warning: using `use_reparametrization = False` will mean that the result is
+  *not* the Csiszar f-Divergence. However its expected gradient *is* the
+  gradient of the Csiszar f-Divergence.
 
   Example Application:
 
@@ -812,7 +817,10 @@ def monte_carlo_csiszar_f_divergence(
 
   Returns:
     monte_carlo_csiszar_f_divergence: Floating-type `Tensor` Monte Carlo
-      approximation of the Csiszar f-Divergence.
+      approximation of the Csiszar f-Divergence. Warning: using
+      `use_reparametrization = False` will mean that the result is *not* the
+      Csiszar f-Divergence. However its expected gradient *is* the actual
+      gradient of the Csiszar f-Divergence.
 
   Raises:
     ValueError: if `q` is not a reparameterized distribution and
@@ -823,16 +831,24 @@ def monte_carlo_csiszar_f_divergence(
       to parameters) is valid.
   """
   with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]):
-    if (use_reparametrization and
-        q.reparameterization_type != distribution.FULLY_REPARAMETERIZED):
+    x = q.sample(num_draws, seed=seed)
+    if use_reparametrization:
       # TODO(jvdillon): Consider only raising an exception if the gradient is
       # requested.
-      raise ValueError(
-          "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
-          "transformation of a parameterless distribution. (Otherwise this "
-          "function has a biased gradient.)")
-    return monte_carlo.expectation_v2(
-        f=lambda x: f(p.log_prob(x) - q.log_prob(x)),
-        samples=q.sample(num_draws, seed=seed),
-        log_prob=q.log_prob,
-        use_reparametrization=use_reparametrization)
+      if q.reparameterization_type != distribution.FULLY_REPARAMETERIZED:
+        raise ValueError(
+            "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
+            "transformation of a parameterless distribution. (Otherwise this "
+            "function has a biased gradient.)")
+      return math_ops.reduce_mean(f(p.log_prob(x) - q.log_prob(x)), axis=0)
+    else:
+      x = array_ops.stop_gradient(x)
+      logqx = q.log_prob(x)
+      fx = f(p.log_prob(x) - logqx)
+      # Alternatively we could have returned:
+      #   reduce_mean(fx * exp(logqx) / stop_gradient(exp(logqx)), axis=0)
+      # This is nice because it means the result is exactly the Csiszar
+      # f-Divergence yet the gradient is unbiased. However its numerically
+      # unstable since the q is not in log-domain.
+      return math_ops.reduce_mean(logqx * array_ops.stop_gradient(fx) + fx,
+                                  axis=0)
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index 9ffe08eded..205b44ac12 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -74,7 +74,7 @@ if(WIN32)
   set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP")
-  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob0")
+  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob2")
   set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_MINSIZEREL "${CMAKE_CXX_FLAGS_MINSIZEREL} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /D_ITERATOR_DEBUG_LEVEL=0")
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index a971232e2f..bf89e64bfd 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -204,7 +204,6 @@ add_python_module("tensorflow/python/debug/examples")
 add_python_module("tensorflow/python/debug/lib")
 add_python_module("tensorflow/python/debug/wrappers")
 add_python_module("tensorflow/python/estimator")
-add_python_module("tensorflow/python/estimator/canned")
 add_python_module("tensorflow/python/estimator/export")
 add_python_module("tensorflow/python/estimator/inputs")
 add_python_module("tensorflow/python/estimator/inputs/queues")
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
index d44e258bd2..42865ed404 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
@@ -120,7 +120,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object):
 
     Doesn't actually do the sqrt! Named as such to agree with API.
 
-    To compute (M + V D V.T), we use the the Woodbury matrix identity:
+    To compute (M + V D V.T), we use the Woodbury matrix identity:
       inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
     where,
       C = inv(D) + V.T inv(M) V.
@@ -166,7 +166,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object):
   def _woodbury_sandwiched_term(self):
     """Computes the sandwiched term in the Woodbury identity.
 
-    Computes the "`C`" in the the identity:
+    Computes the "`C`" in the identity:
        inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
     where,
        C = inv(D) + V.T inv(M) V.
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
index 5b57a95c55..b525809015 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
@@ -52,7 +52,7 @@ class RelaxedBernoulli(transformed_distribution.TransformedDistribution):
   the RelaxedBernoulli can suffer from underflow issues. In many case loss
   functions such as these are invariant under invertible transformations of
   the random variables. The KL divergence, found in the variational autoencoder
-  loss, is an example. Because RelaxedBernoullis are sampled by by a Logistic
+  loss, is an example. Because RelaxedBernoullis are sampled by a Logistic
   random variable followed by a `tf.sigmoid` op, one solution is to treat
   the Logistic as the random variable and `tf.sigmoid` as downstream. The
   KL divergences of two Logistics, which are always followed by a `tf.sigmoid`
diff --git a/tensorflow/contrib/distributions/python/ops/sample_stats.py b/tensorflow/contrib/distributions/python/ops/sample_stats.py
index 26cf922d0a..2a4b92c729 100644
--- a/tensorflow/contrib/distributions/python/ops/sample_stats.py
+++ b/tensorflow/contrib/distributions/python/ops/sample_stats.py
@@ -47,7 +47,7 @@ def percentile(x,
   """Compute the `q`-th percentile of `x`.
 
   Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the
-  way from the minimum to the maximum in in a sorted copy of `x`.
+  way from the minimum to the maximum in a sorted copy of `x`.
 
   The values and distances of the two nearest neighbors as well as the
   `interpolation` parameter will determine the percentile if the normalized
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
index fd2c46d94d..fdee57695e 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Vectorized Laplace distribution class, directly using LinearOpeartor."""
+"""Vectorized Laplace distribution class, directly using LinearOperator."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index 762bc44814..2234400fdc 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -446,7 +446,7 @@ class Transformer(object):
       # TODO(fkp): return a subgraph?
       op_, op_outputs_ = self.transform_op_handler(info, op)
       if op is op_:
-        raise ValueError("In-place tranformation not allowed.")
+        raise ValueError("In-place transformation not allowed.")
 
       # Process op.
       info.transformed_ops[op] = op_
diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index 9f02fc0958..324f510301 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -3261,7 +3261,7 @@ def conv2d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
       dilation_rate: tuple of 2 integers.
 
   Returns:
@@ -3309,7 +3309,7 @@ def conv2d_transpose(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
 
   Returns:
       A tensor, result of transposed 2D convolution.
@@ -3395,7 +3395,7 @@ def conv3d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
       dilation_rate: tuple of 3 integers.
 
   Returns:
diff --git a/tensorflow/contrib/keras/python/keras/layers/core.py b/tensorflow/contrib/keras/python/keras/layers/core.py
index d287fa56d9..34548c83c5 100644
--- a/tensorflow/contrib/keras/python/keras/layers/core.py
+++ b/tensorflow/contrib/keras/python/keras/layers/core.py
@@ -107,7 +107,7 @@ class Dropout(tf_core_layers.Dropout, Layer):
     self.supports_masking = True
     # Inheritance call order:
     # 1) tf.layers.Dropout, 2) keras.layers.Layer, 3) tf.layers.Layer
-    super(Dropout, self).__init__(**kwargs)
+    super(Dropout, self).__init__(rate=rate, noise_shape=noise_shape, seed=seed, **kwargs)
 
   def call(self, inputs, training=None):
     if training is None:
diff --git a/tensorflow/contrib/keras/python/keras/layers/recurrent.py b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
index 5e8c23ed3e..cdef55f599 100644
--- a/tensorflow/contrib/keras/python/keras/layers/recurrent.py
+++ b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
@@ -985,7 +985,7 @@ class LSTM(Recurrent):
 
   References:
       - [Long short-term
-        memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf)
+        memory](http://www.bioinf.jku.at/publications/older/2604.pdf)
         (original 1997 paper)
       - [Supervised sequence labeling with recurrent neural
         networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
diff --git a/tensorflow/contrib/keras/python/keras/models_test.py b/tensorflow/contrib/keras/python/keras/models_test.py
index 50aba43c24..99fd6e1cbe 100644
--- a/tensorflow/contrib/keras/python/keras/models_test.py
+++ b/tensorflow/contrib/keras/python/keras/models_test.py
@@ -105,7 +105,7 @@ class TestModelSaving(test.TestCase):
       out2 = model.predict(x)
       self.assertAllClose(out, out2, atol=1e-05)
 
-  def test_fuctional_model_saving(self):
+  def test_functional_model_saving(self):
     if h5py is None:
       return  # Skip test if models cannot be saved.
 
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index f2a904b521..d8ff6d6b6a 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -1467,7 +1467,8 @@ def fully_connected(inputs,
     ValueError: If x has rank less than 2 or if its last dimension is not set.
   """
   if not isinstance(num_outputs, six.integer_types):
-    raise ValueError('num_outputs should be int or long, got %s.', num_outputs)
+    raise ValueError(
+        'num_outputs should be int or long, got %s.' % (num_outputs,))
 
   layer_variable_getter = _build_variable_getter({'bias': 'biases',
                                                   'kernel': 'weights'})
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
index b4b552c025..6b125534a4 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
@@ -52,8 +52,8 @@ LABEL_DIMENSION = 3  #  Dimensionality of regression labels.
 
 def _train_test_split(features_and_labels):
   features, labels = features_and_labels
-  train_set = (features[:len(features) / 2], labels[:len(features) / 2])
-  test_set = (features[len(features) / 2:], labels[len(features) / 2:])
+  train_set = (features[:int(len(features) / 2)], labels[:int(len(features) / 2)])
+  test_set = (features[int(len(features) / 2):], labels[int(len(features) / 2):])
   return train_set, test_set
 
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
index 145d5c40fa..d3bb0fda57 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
@@ -729,7 +729,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.07)
 
   def testSdcaOptimizerRealValuedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and real valued features."""
+    """Tests LinearClassifier with SDCAOptimizer and real valued features."""
 
     def input_fn():
       return {
@@ -776,7 +776,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerBucketizedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and bucketized features."""
+    """Tests LinearClassifier with SDCAOptimizer and bucketized features."""
 
     def input_fn():
       return {
@@ -802,7 +802,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerSparseFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -833,7 +833,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerWeightedSparseFeatures(self):
-    """LinearClasssifier with SDCAOptimizer and weighted sparse features."""
+    """LinearClassifier with SDCAOptimizer and weighted sparse features."""
 
     def input_fn():
       return {
@@ -864,7 +864,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerCrossedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and crossed features."""
+    """Tests LinearClassifier with SDCAOptimizer and crossed features."""
 
     def input_fn():
       return {
@@ -897,7 +897,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerMixedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and a mix of features."""
+    """Tests LinearClassifier with SDCAOptimizer and a mix of features."""
 
     def input_fn():
       return {
@@ -1509,7 +1509,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -1581,7 +1581,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
 
   def testSdcaOptimizerBiasOnly(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when it's the only feature present.
@@ -1614,7 +1614,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
 
   def testSdcaOptimizerBiasAndOtherColumns(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
@@ -1676,7 +1676,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
 
   def testSdcaOptimizerBiasAndOtherColumnsFabricatedCentered(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
index 6ebfeb0f16..284e2cfd7a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
@@ -123,7 +123,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["probabilities"].eval(),
                           regression_output.value.eval())
 
-  def testEstimatorSpec_export_classsification(self):
+  def testEstimatorSpec_export_classification(self):
     predictions = self.create_predictions()
     output_alternatives = {"classification_head": (
         constants.ProblemType.CLASSIFICATION, predictions)}
@@ -143,7 +143,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_scores(self):
+  def testEstimatorSpec_export_classification_with_missing_scores(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -165,7 +165,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_scores_proba(self):
+  def testEstimatorSpec_export_classification_with_missing_scores_proba(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -187,7 +187,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_classes(self):
+  def testEstimatorSpec_export_classification_with_missing_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["classes"]
@@ -208,7 +208,7 @@ class ModelFnopsTest(test.TestCase):
                           classification_output.scores.eval())
       self.assertIsNone(classification_output.classes)
 
-  def testEstimatorSpec_export_classsification_with_nonstring_classes(self):
+  def testEstimatorSpec_export_classification_with_nonstring_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     output_alternatives_predictions["classes"] = constant_op.constant(
diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py
index 234605ff76..4283240d01 100644
--- a/tensorflow/contrib/learn/python/learn/models.py
+++ b/tensorflow/contrib/learn/python/learn/models.py
@@ -63,7 +63,7 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0):
     x: tensor or placeholder for input features.
     y: tensor or placeholder for labels.
     init_mean: the mean value to use for initialization.
-    init_stddev: the standard devation to use for initialization.
+    init_stddev: the standard deviation to use for initialization.
 
   Returns:
     Predictions and loss tensors.
@@ -124,7 +124,7 @@ def logistic_regression(x,
                   will check if graph contains tensor `class_weight:0`.
                   If that is not provided either all ones are used.
     init_mean: the mean value to use for initialization.
-    init_stddev: the standard devation to use for initialization.
+    init_stddev: the standard deviation to use for initialization.
 
   Returns:
     Predictions and loss tensors.
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index d58b9744ac..ce8518267f 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -208,7 +208,7 @@ def index_to_string_table_from_tensor(mapping, default_value="UNK", name=None):
   Sample Usages:
 
   ```python
-  mapping_string = tf.constant(["emerson", "lake", "palmer")
+  mapping_string = tf.constant(["emerson", "lake", "palmer"])
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       mapping_string, default_value="UNKNOWN")
@@ -260,7 +260,11 @@ def index_to_string(tensor, mapping, default_value="UNK", name=None):
   For example:
 
   ```python
+<<<<<<< HEAD
+  mapping_string = tf.constant(["emerson", "lake", "palmer"])
+=======
   mapping_string = tf.constant(["emerson", "lake", "palmer")
+>>>>>>> 338a7ead4475d6b97b420d6d1c56ff66815e3e7b
   indices = tf.constant([1, 5], tf.int64)
   values = tf.contrib.lookup.index_to_string(
       indices, mapping=mapping_string, default_value="UNKNOWN")
diff --git a/tensorflow/contrib/makefile/build_with_docker.sh b/tensorflow/contrib/makefile/build_with_docker.sh
index 7fe38f4b5d..51a73fafe5 100755
--- a/tensorflow/contrib/makefile/build_with_docker.sh
+++ b/tensorflow/contrib/makefile/build_with_docker.sh
@@ -23,7 +23,7 @@
 # Make sure we're in the correct directory, at the root of the source tree.
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 WORKSPACE="${SCRIPT_DIR}/../../../"
-cd ${WORKSPACE}
+cd ${WORKSPACE} || exit 1
 
 DOCKER_IMG_NAME="tf-make-base"
 DOCKER_CONTEXT_PATH="${WORKSPACE}tensorflow/contrib/makefile/"
diff --git a/tensorflow/contrib/makefile/compile_android_protobuf.sh b/tensorflow/contrib/makefile/compile_android_protobuf.sh
index 50d72d6093..fadbe271b8 100755
--- a/tensorflow/contrib/makefile/compile_android_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_android_protobuf.sh
@@ -27,7 +27,7 @@ cc_prefix="${CC_PREFIX}"
 usage() {
   echo "Usage: $(basename "$0") [-a:c]"
   echo "-a [Architecture] Architecture of target android [default=armeabi-v7a] \
-(supported archtecture list: \
+(supported architecture list: \
 arm64-v8a armeabi armeabi-v7a armeabi-v7a-hard mips mips64 x86 x86_64)"
   echo "-c Clean before building protobuf for target"
   echo "\"NDK_ROOT\" should be defined as an environment variable."
@@ -130,7 +130,7 @@ elif [[ ${ARCHITECTURE} == "x86_64" ]]; then
     sysroot_arch="x86_64"
     bin_prefix="x86_64-linux-android"
 else
-    echo "archtecture ${arcitecture} is not supported." 1>&2
+    echo "architecture ${ARCHITECTURE} is not supported." 1>&2
     usage
     exit 1
 fi
diff --git a/tensorflow/contrib/makefile/compile_ios_protobuf.sh b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
index d1012a6c93..e8b9454e7e 100755
--- a/tensorflow/contrib/makefile/compile_ios_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
@@ -1,4 +1,4 @@
-#!/bin/bash -x -e
+#!/bin/bash
 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,6 +15,9 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
+set -x
+set -e
+
 SCRIPT_DIR=$(dirname $0)
 source "${SCRIPT_DIR}/build_helper.subr"
 
@@ -30,17 +33,17 @@ fi
 
 JOB_COUNT="${JOB_COUNT:-$(get_job_count)}"
 
-GENDIR=`pwd`/gen/protobuf_ios/
+GENDIR=$(pwd)/gen/protobuf_ios/
 LIBDIR=${GENDIR}lib
 mkdir -p ${LIBDIR}
 
 OSX_VERSION=darwin14.0.0
 
-IPHONEOS_PLATFORM=`xcrun --sdk iphoneos --show-sdk-platform-path`
-IPHONEOS_SYSROOT=`xcrun --sdk iphoneos --show-sdk-path`
-IPHONESIMULATOR_PLATFORM=`xcrun --sdk iphonesimulator --show-sdk-platform-path`
-IPHONESIMULATOR_SYSROOT=`xcrun --sdk iphonesimulator --show-sdk-path`
-IOS_SDK_VERSION=`xcrun --sdk iphoneos --show-sdk-version`
+IPHONEOS_PLATFORM=$(xcrun --sdk iphoneos --show-sdk-platform-path)
+IPHONEOS_SYSROOT=$(xcrun --sdk iphoneos --show-sdk-path)
+IPHONESIMULATOR_PLATFORM=$(xcrun --sdk iphonesimulator --show-sdk-platform-path)
+IPHONESIMULATOR_SYSROOT=$(xcrun --sdk iphonesimulator --show-sdk-path)
+IOS_SDK_VERSION=$(xcrun --sdk iphoneos --show-sdk-version)
 MIN_SDK_VERSION=8.0
 
 CFLAGS="-DNDEBUG -Os -pipe -fPIC -fno-exceptions"
diff --git a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
index 6f47b80780..bcf097b303 100755
--- a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
+++ b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
@@ -20,7 +20,7 @@ source "${SCRIPT_DIR}/build_helper.subr"
 JOB_COUNT="${JOB_COUNT:-$(get_job_count)}"
 
 function less_than_required_version() {
-  echo $1 | (IFS=. read major minor micro
+  echo $1 | (IFS=. read -r major minor micro
     if [ $major -ne $2 ]; then
       [ $major -lt $2 ]
     elif [ $minor -ne $3 ]; then
@@ -31,7 +31,7 @@ function less_than_required_version() {
   )
 }
 
-ACTUAL_XCODE_VERSION=`xcodebuild -version | head -n 1 | sed 's/Xcode //'`
+ACTUAL_XCODE_VERSION=$(xcodebuild -version | head -n 1 | sed 's/Xcode //')
 REQUIRED_XCODE_VERSION=7.3.0
 if less_than_required_version $ACTUAL_XCODE_VERSION 7 3 0
 then
diff --git a/tensorflow/contrib/makefile/compile_pi_protobuf.sh b/tensorflow/contrib/makefile/compile_pi_protobuf.sh
index 2aae2d5f4e..f863d80009 100755
--- a/tensorflow/contrib/makefile/compile_pi_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_pi_protobuf.sh
@@ -15,15 +15,15 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
-cd tensorflow/contrib/makefile
+cd tensorflow/contrib/makefile || exit 1
 
-GENDIR=`pwd`/gen/protobuf_pi/
+GENDIR=$(pwd)/gen/protobuf_pi/
 LIBDIR=${GENDIR}
 mkdir -p ${LIBDIR}
 
 CXX=arm-linux-gnueabihf-g++
 
-cd downloads/protobuf
+cd downloads/protobuf || exit 1
 
 ./autogen.sh
 if [ $? -ne 0 ]
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
index c7ed663131..288f59fed4 100644
--- a/tensorflow/contrib/remote_fused_graph/pylib/BUILD
+++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
@@ -7,6 +7,7 @@ licenses(["notice"])  # Apache 2.0
 
 exports_files(["LICENSE"])
 
+load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 tf_gen_op_wrapper_py(
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index 9c5e9fec9d..c8552fc050 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -79,7 +79,7 @@ class CoupledInputForgetGateLSTMCell(rnn_cell_impl.RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+    http://www.bioinf.jku.at/publications/older/2604.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
index 491d87f62d..3496b355b4 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
@@ -65,7 +65,9 @@ class GatherTreeTest(test.TestCase):
         _ = beams.eval()
 
   def testBadParentValuesOnGPU(self):
-    if not test.is_gpu_available():
+    # Only want to run this test on CUDA devices, as gather_tree is not
+    # registered for SYCL devices.
+    if not test.is_gpu_available(cuda_only=True):
       return
     # (max_time = 4, batch_size = 1, beams = 3)
     # bad parent in beam 1 time 1; appears as a negative index at time 0
diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index d37c632be7..4bde661698 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -836,7 +836,7 @@ with tf.Session() as sess:
   for batch_id in range(num_batches):
     sess.run(names_to_updates.values())
 
-  metric_values = sess.run(name_to_values.values())
+  metric_values = sess.run(names_to_values.values())
   for metric, value in zip(names_to_values.keys(), metric_values):
     print('Metric %s has value: %f' % (metric, value))
 ```
diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index 824ba4c09b..4fa1ccea69 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -1,3 +1,26 @@
 # tfprof: TensorFlow Profiler and Beyond
 
 # Full Document in tensorflow/tools/tfprof/README.md
+
+Author: Xin Pan (xpan@google.com, github: panyx0718), Jon Shlens, Yao Zhang
+
+Consultants: Jon Shlens, Pete Warden
+
+###Major Features
+
+1.  Measure model parameters, float operations, tensor shapes.
+2.  Profile op execution times, requested memory size and device placement.
+3.  Inspect checkpoint tensors' shapes and their values.
+4.  Selectively group, filter, account and order ops.
+
+####tfprof supports 3 views to organize TensorFlow model profiles
+
+    *  code view: Stats are associated your Python codes and organized as call stacks.
+    *  scope view: Stats are organized as name scope hierarchies.
+    *  graph view: Stats are organized as Tensorflow Op graph.
+
+####For each view, there are 3 ways to display outputs:
+
+    *  stdout: Results are written to stdout.
+    *  timeline: Visualized in chrome browser as time series.
+    *  file: Results are dumped to file.
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
index 582c36e339..40f29ae8a2 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
@@ -43,6 +43,7 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
+%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index 9c59df3117..71468dde37 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -89,7 +89,7 @@ def _run_loop_model():
 class RunMetadataTest(test.TestCase):
 
   def testGPU(self):
-    if not test.is_gpu_available():
+    if not test.is_gpu_available(cuda_only=True):
       return
 
     ops.reset_default_graph()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index c781d2af4e..419beac0b9 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -20,8 +20,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import six
-
 from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
 from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
 from tensorflow.python.framework import errors
@@ -110,77 +108,49 @@ PRINT_ALL_TIMING_MEMORY = {
     'dump_to_file': ''
 }
 
-# The following options are for 'advise' tfprof_cmd.
-# Show all advice.
-ALL_ADVICE = {
-    'ExpensiveOperationChecker': {},
-    'AcceleratorUtilizationChecker': {},
-    'JobChecker': {},  # Only available internally.
-    'OperationChecker': {},
-}
-
 # pylint: enable=bad-whitespace
 # pylint: enable=bad-continuation
 
 
-def _build_options(options):
+def _build_options(tfprof_options):
   """Build tfprof.OptionsProto.
 
   Args:
-    options: A dictionary of options.
+    tfprof_options: A dictionary of options.
   Returns:
     tfprof.OptionsProto.
   """
   opts = tfprof_options_pb2.OptionsProto()
-  opts.max_depth = options.get('max_depth', 10)
-  opts.min_bytes = options.get('min_bytes', 0)
-  opts.min_micros = options.get('min_micros', 0)
-  opts.min_params = options.get('min_params', 0)
-  opts.min_float_ops = options.get('min_float_ops', 0)
-  opts.min_occurrence = options.get('min_occurrence', 0)
+  opts.max_depth = tfprof_options.get('max_depth', 10)
+  opts.min_bytes = tfprof_options.get('min_bytes', 0)
+  opts.min_micros = tfprof_options.get('min_micros', 0)
+  opts.min_params = tfprof_options.get('min_params', 0)
+  opts.min_float_ops = tfprof_options.get('min_float_ops', 0)
+  opts.min_occurrence = tfprof_options.get('min_occurrence', 0)
 
-  opts.step = options.get('step', -1)
+  opts.step = tfprof_options.get('step', -1)
 
-  opts.order_by = options.get('order_by', 'name')
+  opts.order_by = tfprof_options.get('order_by', 'name')
 
-  for p in options.get('account_type_regexes', []):
+  for p in tfprof_options.get('account_type_regexes', []):
     opts.account_type_regexes.append(p)
-  for p in options.get('start_name_regexes', []):
+  for p in tfprof_options.get('start_name_regexes', []):
     opts.start_name_regexes.append(p)
-  for p in options.get('trim_name_regexes', []):
+  for p in tfprof_options.get('trim_name_regexes', []):
     opts.trim_name_regexes.append(p)
-  for p in options.get('show_name_regexes', []):
+  for p in tfprof_options.get('show_name_regexes', []):
     opts.show_name_regexes.append(p)
-  for p in options.get('hide_name_regexes', []):
+  for p in tfprof_options.get('hide_name_regexes', []):
     opts.hide_name_regexes.append(p)
-  opts.account_displayed_op_only = options.get('account_displayed_op_only',
-                                               False)
+  opts.account_displayed_op_only = tfprof_options.get(
+      'account_displayed_op_only', False)
 
-  for p in options.get('select', []):
+  for p in tfprof_options.get('select', []):
     opts.select.append(p)
 
-  opts.output = options.get('output', 'stdout')
-  opts.dump_to_file = options.get('dump_to_file', '')
-
-  return opts
-
-
-def _build_advisor_options(options):
-  """Build tfprof.AdvisorOptionsProto.
+  opts.output = tfprof_options.get('output', 'stdout')
+  opts.dump_to_file = tfprof_options.get('dump_to_file', '')
 
-  Args:
-    options: A dictionary of options. See ALL_ADVICE example.
-  Returns:
-    tfprof.AdvisorOptionsProto.
-  """
-  opts = tfprof_options_pb2.AdvisorOptionsProto()
-  if options is None:
-    return opts
-  for checker, checker_opts in six.iteritems(options):
-    checker_ops_pb = tfprof_options_pb2.AdvisorOptionsProto.CheckerOption()
-    for k, v in six.iteritems(checker_opts):
-      checker_ops_pb[k] = v
-    opts.checkers[checker].MergeFrom(checker_ops_pb)
   return opts
 
 
@@ -220,7 +190,7 @@ class Profiler(object):
       else:
         _ = sess.run(...)
     # Auto detect problems and generate advice.
-    profiler.advise(model_analyzer.ALL_ADVICE)
+    profiler.advise()
   """
 
   def __init__(self, graph, op_log=None):
@@ -318,19 +288,9 @@ class Profiler(object):
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
 
-  def advise(self, options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
-    """Automatically detect problems and generate reports.
-
-    Args:
-      options: A dict of options.
-    Returns:
-      A Advise proto that conains the reports from all checkers.
-    """
-    advise_pb = tfprof_output_pb2.AdviceProto()
-    opts = _build_advisor_options(options)
-    advise_pb.ParseFromString(
-        print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString()))
-    return advise_pb
+  def advise(self):
+    """Automatically detect problems and generate reports."""
+    print_mdl.Advise()
 
 
 def print_model_analysis(graph,
@@ -394,36 +354,3 @@ def print_model_analysis(graph,
         None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
 
   return tfprof_node
-
-
-def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
-  """Auto profile and advise.
-
-    Builds profiles and automatically check anormalies of various
-    aspects. See go/tfprof or README for examples and tutorials.
-
-  Args:
-    graph: tf.Graph.
-    run_meta: tensorflow::RunMetadata proto. Allows auto-profile
-              time and memroy.
-    tfprof_options: see ALL_ADVICE example above.
-  Returns:
-    Returns AdviceProto proto
-  """
-  # pylint: disable=protected-access
-  op_log = tfprof_logger._merge_default_with_oplog(
-      graph, None, run_meta, add_trace=True)
-  # pylint: enable=protected-access
-
-  run_meta_str = run_meta.SerializeToString() if run_meta else b''
-
-  opts = _build_advisor_options(tfprof_options)
-  ret = tfprof_output_pb2.AdviceProto()
-  ret.ParseFromString(
-      print_mdl.PrintModelAnalysis(
-          graph.as_graph_def(add_shapes=True).SerializeToString(),
-          run_meta_str,
-          op_log.SerializeToString(),
-          'advise'.encode('utf-8'),
-          opts.SerializeToString()))
-  return ret
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index fea27a82a5..9db752c577 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -126,7 +126,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['account_displayed_op_only'] = False
     opts['select'] = ['params', 'float_ops']
 
-    with session.Session() as sess:
+    with session.Session() as sess, ops.device('/cpu:0'):
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -176,7 +176,6 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['select'] = [
         'bytes', 'params', 'float_ops', 'device'
     ]
-    opts['output'] = 'none'
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -277,33 +276,6 @@ class PrintModelAnalysisTest(test.TestCase):
       self.assertEqual(total_children, 15)
       self.assertGreater(input_shapes, 0)
 
-  def testAdvisor(self):
-    ops.reset_default_graph()
-
-    with session.Session() as sess:
-      x = lib.BuildFullModel()
-
-      sess.run(variables.global_variables_initializer())
-      run_meta = config_pb2.RunMetadata()
-      _ = sess.run(
-          x,
-          options=config_pb2.RunOptions(
-              trace_level=config_pb2.RunOptions.FULL_TRACE),
-          run_metadata=run_meta)
-
-      advice_pb = model_analyzer.advise(sess.graph, run_meta)
-      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
-      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
-      self.assertTrue('OperationChecker' in advice_pb.checkers)
-
-      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
-      if test.is_gpu_available():
-        self.assertGreater(len(checker.reports), 0)
-      else:
-        self.assertEqual(len(checker.reports), 0)
-      checker = advice_pb.checkers['ExpensiveOperationChecker']
-      self.assertGreater(len(checker.reports), 0)
-
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
index c7113b6a57..5daaafd7c8 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
@@ -129,7 +129,7 @@ class ProfilerTest(test.TestCase):
     opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
     opts['account_type_regexes'] = ['.*']
 
-    with session.Session() as sess:
+    with session.Session() as sess, ops.device('/cpu:0'):
       r1, r2, r3 = lib.BuildSplitableModel()
       sess.run(variables.global_variables_initializer())
 
@@ -179,18 +179,8 @@ class ProfilerTest(test.TestCase):
       self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None)
       self.assertGreater(lib.SearchTFProfNode(pb3, 'add').exec_micros, 0)
 
-      advice_pb = profiler.advise(model_analyzer.ALL_ADVICE)
-      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
-      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
-      self.assertTrue('OperationChecker' in advice_pb.checkers)
-
-      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
-      if test.is_gpu_available():
-        self.assertGreater(len(checker.reports), 0)
-      else:
-        self.assertEqual(len(checker.reports), 0)
-      checker = advice_pb.checkers['ExpensiveOperationChecker']
-      self.assertGreater(len(checker.reports), 0)
+      # TODO(xpan): Better test of advisor.
+      profiler.advise()
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/training/python/training/evaluation.py b/tensorflow/contrib/training/python/training/evaluation.py
index 24b733dd29..a895d90b8e 100644
--- a/tensorflow/contrib/training/python/training/evaluation.py
+++ b/tensorflow/contrib/training/python/training/evaluation.py
@@ -226,7 +226,7 @@ def checkpoints_iterator(checkpoint_dir,
 
   This behavior gives control to callers on what to do if checkpoints do not
   come fast enough or stop being generated.  For example, if callers have a way
-  to detect that the training has stopped and know that no new new checkpoints
+  to detect that the training has stopped and know that no new checkpoints
   will be generated, they can provide a `timeout_fn` that returns `True` when
   the training has stopped.  If they know that the training is still going on
   they return `False` instead.
diff --git a/tensorflow/contrib/verbs/rdma.cc b/tensorflow/contrib/verbs/rdma.cc
index bc687be0ab..6f3a616fe8 100644
--- a/tensorflow/contrib/verbs/rdma.cc
+++ b/tensorflow/contrib/verbs/rdma.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_util.h"
+#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h"
 #include "tensorflow/core/distributed_runtime/session_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
@@ -683,7 +684,6 @@ void RdmaTensorBuffer::SendNextItem() {
                          << " error message: " << status.error_message();
       size_t buffer_size = RdmaMessage::kMessageTotalBytes;
       size_t tensor_bytes = 0;
-      TensorProto proto;
       // Figures out which device the tensor is hosted on.
       Device* src_dev = nullptr;
       Status s = channel_->adapter_->worker_env_->device_mgr->LookupDevice(
@@ -703,21 +703,47 @@ void RdmaTensorBuffer::SendNextItem() {
       CHECK(s.ok()) << "dst device not found";
       AllocatorAttributes dst_alloc_attr;
       dst_alloc_attr.set_on_host(true);
+
+      bool can_memcpy = DataTypeCanUseMemcpy(in.dtype());
       // string tensor needs to be serialized
+      Tensor copy;
+      StringPiece copy_buf;
+      TensorProto proto;
       if (src_dev->tensorflow_gpu_device_info() &&
           (!send_args.alloc_attrs.on_host())) {
         CHECK(send_args.device_context)
-            << "send dev name: " << src_dev->name()
-            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
-        // "val" is on a GPU. Uses GPUUtil to fill the proto.
-        s = VerbsUtil::SetProtoFromGPUSync(
-            in, src_dev, send_args.device_context, &proto, is_dead);
-        CHECK(s.ok()) << "set proto from gpu sync";
+          << "send dev name: " << src_dev->name()
+          << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
+
+        if (can_memcpy) {
+          AllocatorAttributes host_alloc_attrs;
+          host_alloc_attrs.set_gpu_compatible(true);
+          host_alloc_attrs.set_on_host(true);
+          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
+          copy = Tensor(alloc, in.dtype(), in.shape());
+          s = VerbsUtil::CopyGPUTensorToCPUSync(
+              src_dev, send_args.device_context, &in, &copy);
+          CHECK(s.ok()) << "copy tensor from gpu sync";
+          copy_buf = copy.tensor_data();
+        } else {
+          // "val" is on a GPU. Uses GPUUtil to fill the proto.
+          s = VerbsUtil::SetProtoFromGPUSync(
+              in, src_dev, send_args.device_context, &proto, is_dead);
+          CHECK(s.ok()) << "set proto from gpu sync";
+        }
       } else {
         // tensor is in CPU memory.
-        in.AsProtoTensorContent(&proto);
+        if (can_memcpy) {
+          copy_buf = in.tensor_data();
+        } else {
+          in.AsProtoTensorContent(&proto);
+        }
+      }
+      if (can_memcpy) {
+        tensor_bytes = in.TotalBytes();
+      } else {
+        tensor_bytes = proto.ByteSize();
       }
-      tensor_bytes = proto.ByteSize();
       // maybe some margin for string tensor?
       buffer_size += tensor_bytes;
       // prepare message
@@ -771,7 +797,16 @@ void RdmaTensorBuffer::SendNextItem() {
               static_cast<void*>(static_cast<char*>(buffer_) +
                                  RdmaMessage::kTensorBufferStartIndex);
           CHECK(tensor_bytes + RdmaMessage::kTensorBufferStartIndex <= size_);
-          proto.SerializeToArray(output, tensor_bytes);
+          if (can_memcpy) {
+            CHECK(copy_buf.size() == tensor_bytes)
+               << "unexpected tensor size: "
+               << copy_buf.size()
+               << " != "
+               << tensor_bytes;
+            memcpy(output, copy_buf.data(), tensor_bytes);
+          } else {
+            proto.SerializeToArray(output, tensor_bytes);
+          }
         } else {
           buffer_size = RdmaMessage::kMessageTotalBytes;
         }
diff --git a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
index 5871400f26..9ea696589a 100644
--- a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
+++ b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
+#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -99,12 +100,40 @@ void RdmaRemoteRendezvous::RecvFromRemoteAsync(
     if (!rm.is_dead_) {
       void* input = static_cast<char*>(rb->buffer_) +
                     RdmaMessage::kTensorBufferStartIndex;
-      TensorProto proto;
-      CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
-            rb->size_);
-      CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
-          << "fail to parse proto from array";
-      s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
+      bool can_memcpy = DataTypeCanUseMemcpy(rm.data_type_);
+      if (can_memcpy) {
+        if (dst_dev->tensorflow_gpu_device_info() &&
+            (!recv_args.alloc_attrs.on_host())) {
+          CHECK(recv_args.device_context)
+            << "send dev name: " << src_dev->name()
+            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
+          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
+          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
+          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
+
+          Allocator* dst_alloc = dst_dev->GetAllocator(recv_args.alloc_attrs);
+          Tensor gpu_copy(dst_alloc, rm.data_type_, rm.tensor_shape_);
+          s = VerbsUtil::CopyCPUTensorToGPUSync(&copy, recv_args.device_context,
+                                                dst_dev, &gpu_copy);
+          CHECK(s.ok()) << "copy tensor to gpu sync";
+          val = std::move(gpu_copy);
+        } else {
+          AllocatorAttributes host_alloc_attrs;
+          host_alloc_attrs.set_gpu_compatible(true);
+          host_alloc_attrs.set_on_host(true);
+          Allocator* alloc = dst_dev->GetAllocator(host_alloc_attrs);
+          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
+          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
+          val = std::move(copy);
+        }
+      } else {
+        TensorProto proto;
+        CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
+              rb->size_);
+        CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
+            << "fail to parse proto from array";
+        s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
+      }
     }
 
     rc->RemoveRecvCallback(key_with_step_id);
diff --git a/tensorflow/contrib/verbs/verbs_util.cc b/tensorflow/contrib/verbs/verbs_util.cc
index c3350f7958..76e44d34a9 100644
--- a/tensorflow/contrib/verbs/verbs_util.cc
+++ b/tensorflow/contrib/verbs/verbs_util.cc
@@ -20,6 +20,40 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 namespace tensorflow {
 
+// static sync wrapper:
+Status VerbsUtil::CopyGPUTensorToCPUSync(Device* gpu_device,
+                              const DeviceContext* device_context,
+                              const Tensor* gpu_tensor,
+                              Tensor* cpu_tensor) {
+  Notification n;
+  Status status;
+  GPUUtil::CopyGPUTensorToCPU(gpu_device, device_context,
+                              gpu_tensor, cpu_tensor,
+                              [&n, &status](const Status& s) {
+                                status = s;
+                                n.Notify();
+                              });
+  n.WaitForNotification();
+  return status;
+}
+
+// static sync wrapper:
+Status VerbsUtil::CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
+                                         const DeviceContext* device_context,
+                                         Device* gpu_device,
+                                         Tensor* gpu_tensor) {
+  Notification n;
+  Status status;
+  GPUUtil::CopyCPUTensorToGPU(cpu_tensor, device_context,
+                              gpu_device, gpu_tensor,
+                              [&n, &status](const Status& s) {
+                                status = s;
+                                n.Notify();
+                              });
+  n.WaitForNotification();
+  return status;
+}
+
 // static sync wrapper:
 Status VerbsUtil::SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                       const DeviceContext* device_context,
diff --git a/tensorflow/contrib/verbs/verbs_util.h b/tensorflow/contrib/verbs/verbs_util.h
index cbc01adae4..d9da396228 100644
--- a/tensorflow/contrib/verbs/verbs_util.h
+++ b/tensorflow/contrib/verbs/verbs_util.h
@@ -28,6 +28,16 @@ class TensorProto;
 
 class VerbsUtil {
  public:
+  // synchronous wrapper of CopyGPUTensorToCPU
+  static Status CopyGPUTensorToCPUSync(Device* gpu_device,
+                                       const DeviceContext* device_context,
+                                       const Tensor* gpu_tensor,
+                                       Tensor* cpu_tensor);
+  // synchronous wrapper of CopyCPUTensorToGPU
+  static Status CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
+                                       const DeviceContext* device_context,
+                                       Device* gpu_device,
+                                       Tensor* gpu_tensor);
   // synchronous wrapper of SetProtoFromGPU
   static Status SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                     const DeviceContext* device_context,
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index 21a20bcc4d..4b951691fb 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -654,11 +654,11 @@ Status DirectSession::Run(const RunOptions& run_options,
 
   // If requested via RunOptions, output the partition graphs.
   if (run_options.output_partition_graphs()) {
-    protobuf::RepeatedPtrField<GraphDef>* parition_graph_defs =
+    protobuf::RepeatedPtrField<GraphDef>* partition_graph_defs =
         run_metadata->mutable_partition_graphs();
     for (const PerPartitionExecutorsAndLib& exec_and_lib :
          executors_and_keys->items) {
-      GraphDef* partition_graph_def = parition_graph_defs->Add();
+      GraphDef* partition_graph_def = partition_graph_defs->Add();
       exec_and_lib.graph->ToGraphDef(partition_graph_def);
     }
   }
diff --git a/tensorflow/core/debug/grpc_session_debug_test.cc b/tensorflow/core/debug/grpc_session_debug_test.cc
index 3827596a67..d6f35fe24c 100644
--- a/tensorflow/core/debug/grpc_session_debug_test.cc
+++ b/tensorflow/core/debug/grpc_session_debug_test.cc
@@ -279,9 +279,12 @@ TEST_F(GrpcSessionDebugTest, MultiDevices_String) {
 
         DeleteDumpDir();
       } else {
+        // CUDA and SYCL devices do not have an Identity op for strings
         LOG(ERROR) << "Error: " << s;
         ASSERT_TRUE((a_dev.device_type() == DEVICE_GPU) ||
-                    (b_dev.device_type() == DEVICE_GPU));
+                    (a_dev.device_type() == DEVICE_SYCL) ||
+                    (b_dev.device_type() == DEVICE_GPU) ||
+                    (b_dev.device_type() == DEVICE_SYCL));
         ASSERT_FALSE(s.ok());
       }
     }
diff --git a/tensorflow/core/framework/graph_def_util.h b/tensorflow/core/framework/graph_def_util.h
index 56355eaf36..950737c39a 100644
--- a/tensorflow/core/framework/graph_def_util.h
+++ b/tensorflow/core/framework/graph_def_util.h
@@ -62,7 +62,7 @@ Status AddDefaultAttrsToGraphDef(GraphDef* graph_def,
 // attr with a default was added). Note that this will not affect
 // attrs with non-default values, so you must run a
 // ValidateGraphDef...() function to see if the result is in fact
-// compatible. If not nulllptr, the op/attr pairs that were removed
+// compatible. If not nullptr, the op/attr pairs that were removed
 // are added to '*op_attr_removed'.
 //
 // Expected usage, for a producer that wants to prepare a graph for
diff --git a/tensorflow/core/framework/op.h b/tensorflow/core/framework/op.h
index c5a0983a54..a4dd06de45 100644
--- a/tensorflow/core/framework/op.h
+++ b/tensorflow/core/framework/op.h
@@ -205,7 +205,6 @@ class OpDefBuilderWrapper;
 template <>
 class OpDefBuilderWrapper<true> {
  public:
-  typedef OpDefBuilderWrapper<true> WrapperType;
   OpDefBuilderWrapper(const char name[]) : builder_(name) {}
   OpDefBuilderWrapper<true>& Attr(StringPiece spec) {
     builder_.Attr(spec);
diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h
index 49eecc0b08..a164fe61b5 100644
--- a/tensorflow/core/framework/tensor.h
+++ b/tensorflow/core/framework/tensor.h
@@ -307,7 +307,7 @@ class Tensor {
   /// Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing the
   /// first 'begin' Tensor dimensions into the first dimension of the result and
   /// the Tensor dimensions of the last dims() - 'begin' - NDIMS into the last
-  /// dimension of the result. If 'begin' < 0 then the the |'begin'| leading
+  /// dimension of the result. If 'begin' < 0 then the |'begin'| leading
   /// dimensions of size 1 will be added. If 'begin' + NDIMS > dims() then
   /// 'begin' + NDIMS - dims() trailing dimensions of size 1 will be added.
   template <typename T, size_t NDIMS = 3>
diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc
index 94741a11ff..625780e7c9 100644
--- a/tensorflow/core/graph/mkl_layout_pass.cc
+++ b/tensorflow/core/graph/mkl_layout_pass.cc
@@ -247,16 +247,10 @@ namespace tensorflow {
 //
 //           P = Conv2DWithBiasBackpropBias(O, O_m)
 //
-// 'Distance' between input of BiasAddGrad and _MklConv2D in terms of hops is
-// the context matching depth. If _MklConv2DWithBias is not within the context
-// matching depth, then we do not rewrite BiasAddGrad.
-
-// How many hops do we search for matching node in the backward dataflow graph?
-// We use maxhop of 10 based on empirical observations. Also, these are
-// maxhops in backward data-flow graph. Since input of forward nodes (Conv2D)
-// directly goes to backward nodes, we do not expect the hop-distance
-// would be more than few nodes.
-static size_t kNodeMergeContextMaxDepth = 10;
+// Rewrite of BiasAddGrad into Conv2DWithBiasBackpropBias takes place depending
+// on the matching 'context'. The term context is loosely related to which
+// forward op is _associated_ to BiasAddGrad. If it is _MklConv2DWithBias then
+// we consider it Conv2D context; if it is MatMul, then it is MatMul context.
 
 class MklLayoutRewritePass : public GraphOptimizationPass {
  public:
@@ -280,6 +274,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     csinfo_.max_pool = "MaxPool";
     csinfo_.max_pool_grad = "MaxPoolGrad";
     csinfo_.mkl_conv2d = "_MklConv2D";
+    csinfo_.mkl_conv2d_grad_input = "_MklConv2DBackpropInput";
+    csinfo_.mkl_conv2d_grad_filter = "_MklConv2DBackpropFilter";
     csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias";
     csinfo_.mkl_conv2d_with_bias_backprop_bias =
                                    "_MklConv2DWithBiasBackpropBias";
@@ -360,16 +356,12 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     minfo_.push_back({csinfo_.mkl_conv2d, csinfo_.bias_add, 0,
                       csinfo_.mkl_conv2d_with_bias});
 
-    // We use maxhop of 10 based on empirical observations. Also, these are
-    // maxhops in backward data-flow graph. Since input of forward nodes
-    // (Conv2D) directly goes to backward nodes, we do not expect the
-    // hop-distance would be more than few nodes.
     biasaddgrad_matmul_context_ = {csinfo_.bias_add_grad, csinfo_.matmul,
-                                   kNodeMergeContextMaxDepth};
+                                   IsBiasAddGradInMatMulContext};
 
     biasaddgrad_conv2dwithbias_context_ = {csinfo_.bias_add_grad,
                                    csinfo_.mkl_conv2d_with_bias,
-                                   kNodeMergeContextMaxDepth};
+                                   IsBiasAddGradInConv2DWithBiasContext};
 
     cinfo_.push_back(&biasaddgrad_matmul_context_);
     cinfo_.push_back(&biasaddgrad_conv2dwithbias_context_);
@@ -392,9 +384,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string node;     // Name of the node to be rewritten
     string fwd;      // Name of the node in the forward pass that this node
                      // corresponds to
-    size_t max_hop;  // Maximum number of hops the fwd is located
-                     // from this node. If the fwd is farther than max_hop
-                     // then we do not rewrite the node.
+    std::function<bool(const Node*, const Node**, void* c)> context_match_fn;
   } ContextInfo;
 
   /// Structure to specify the name of an original node, its new name after
@@ -438,7 +428,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
 
   /// Structure to store all constant strings
   /// NOTE: names are alphabetically sorted.
-  struct {
+  typedef struct {
     string avg_pool;
     string avg_pool_grad;
     string bias_add;
@@ -457,13 +447,15 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string max_pool;
     string max_pool_grad;
     string mkl_conv2d;
+    string mkl_conv2d_grad_input;
+    string mkl_conv2d_grad_filter;
     string mkl_conv2d_with_bias;
     string mkl_conv2d_with_bias_backprop_bias;
     string relu;
     string relu_grad;
     string reshape;
     string split;
-  } csinfo_;
+  } ConstStringsInfo;
 
  private:
   /// Maintain info about nodes to rewrite
@@ -478,6 +470,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   /// Maintain info about nodes to rewrite
   static std::vector<ContextInfo*> cinfo_;
 
+  /// Maintain structure of constant strings
+  static ConstStringsInfo csinfo_;
+
   /// Context variables used in referencing rules
   static ContextInfo biasaddgrad_matmul_context_;
   static ContextInfo biasaddgrad_conv2dwithbias_context_;
@@ -629,6 +624,173 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     return false;
   }
 
+  // Is BiasAddGrad node in 'n' is associated with Conv2DWithBias node
+  // specified in contextinfo 'ci'. Function updates fwd_node to point
+  // to Conv2DWithBias node if 'n' is associated with Conv2DWithBias.
+  //
+  // Association checks for one of the following graphs:
+  //
+  // Graph A:
+  //
+  // _ = Conv2DWithBias(F, I, _)
+  // ..
+  // _ = Conv2DBackpropFilter(F, _, G)
+  // _ = Conv2DBackpropInput(_, I, G)
+  // _ = BiasAddGrad(G)
+  //
+  // OR
+  //
+  // Graph B:
+  //
+  // _ = Conv2DWithBias(F, _, _)
+  // ..
+  // _ = Conv2DBackpropFilter(F, _, G)
+  // _ = BiasAddGrad(G)
+  //
+  // Here F, G, and I are graph nodes; _ represents graph nodes that we
+  // don't care here.
+  //
+  // @return - true (if BiasAddGrad is associated with Conv2DWithBias);
+  //           false otherwise.
+  static bool IsBiasAddGradInConv2DWithBiasContext(const Node* n,
+                                                   const Node** fwd_node,
+                                                   void* ci) {
+    CHECK_NOTNULL(n);
+    CHECK_NOTNULL(fwd_node);
+    CHECK_NOTNULL(ci);
+    *fwd_node = nullptr;
+
+    CHECK_EQ(n->type_string(), csinfo_.bias_add_grad);
+
+    // Get the only 1 input of BiasAddGrad.
+    CHECK_EQ(n->num_inputs(), 1);
+    const Node* bias_add_grad_inp = nullptr;
+    TF_CHECK_OK(n->input_node(0, &bias_add_grad_inp));
+    CHECK_NOTNULL(bias_add_grad_inp);
+
+    // Check if this input also goes to BackpropFilter and BackpropInput
+    // as 3rd input.
+    bool found_backprop_input = false;
+    bool found_backprop_filter = false;
+    Node* backprop_filter_node = nullptr;
+    Node* backprop_input_node = nullptr;
+
+    for (const Edge* e : bias_add_grad_inp->out_edges()) {
+      Node* third_input = nullptr;
+      if (e->dst()->type_string() == csinfo_.conv2d_grad_input ||
+          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_input) {
+        // Third input (index 2) of BackpropInput
+        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
+        // Third input (index 2) of BackpropInput must be same as the input
+        // of BiasAddGrad.
+        if (third_input == bias_add_grad_inp) {
+          found_backprop_input = true;
+          backprop_input_node = e->dst();
+        }
+      }
+
+      if (e->dst()->type_string() == csinfo_.conv2d_grad_filter ||
+          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_filter) {
+        // Third input (index 2) of BackpropFilter
+        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
+        // Third input (index 2) of BackpropFilter must be same as the input
+        // of BiasAddGrad.
+        if (third_input == bias_add_grad_inp) {
+          found_backprop_filter = true;
+          backprop_filter_node = e->dst();
+        }
+      }
+
+      // If we found both the nodes, then we can stop the search.
+      if (found_backprop_input && found_backprop_filter) {
+        break;
+      }
+    }
+
+    // If BackpropFilter node is not found, then this is not
+    // Conv2DWithBias context. For 2nd graph in the example above, only
+    // BackpropFilter would be present.
+    if (!found_backprop_filter) {
+      return false;
+    }
+
+    // Otherwise, we found the nodes.
+    CHECK_NOTNULL(backprop_filter_node);
+    if (found_backprop_input) {
+      CHECK_NOTNULL(backprop_input_node);
+    }
+
+    // Now that we confirmed that this is Conv2DWithBias context, we need to
+    // get access to the forward node (Conv2DWithBias). 2nd input of
+    // Conv2DWithBias is same as the 2nd input of Conv2DBackpropInput; 1st
+    // input of Conv2DWithBias is same as the 1st input of Conv2DBackpropFilter
+    // (This comes from definition of gradient computation for Conv2D).
+    if (found_backprop_input) {
+      // Graph A in the example.
+      Node* second_inp_of_input = nullptr;
+      Node* first_inp_of_filter = nullptr;
+      TF_CHECK_OK(backprop_input_node->input_node(1, &second_inp_of_input));
+      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
+      CHECK_NOTNULL(second_inp_of_input);
+      CHECK_NOTNULL(first_inp_of_filter);
+
+      // Now we need to find out Conv2DWithBias node from these input nodes.
+      // Conv2DWithBias node is the node that accepts both the nodes
+      // second_inp_of_input and first_inp_of_filter in 2nd and 1st input slots.
+      for (const Edge* fe : first_inp_of_filter->out_edges()) {
+        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+            fe->dst_input() == 0) {
+          for (const Edge* ie : second_inp_of_input->out_edges()) {
+            if (ie->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+                ie->dst_input() == 1 && fe->dst() == ie->dst()) {
+              VLOG(1) << "MklLayoutRewritePass: found "
+                      << fe->dst()->DebugString()
+                      << " as the forward node for matching context, backward"
+                      << " node is: " << n->DebugString();
+              *fwd_node = fe->dst();
+              return true;
+            }
+          }
+        }
+      }
+    } else {
+      // We did not find BackpropInput, so we work with BackpropFilter only.
+      // Graph B in the example.
+      Node* first_inp_of_filter = nullptr;
+      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
+      CHECK_NOTNULL(first_inp_of_filter);
+
+      // Now we need to find out Conv2DWithBias node from first input of
+      // BackpropFIlter. Conv2DWithBias node is the node that accepts
+      // first_inp_of_filter in 1st input slot.
+      for (const Edge* fe : first_inp_of_filter->out_edges()) {
+        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+            fe->dst_input() == 0) {
+          VLOG(1) << "MklLayoutRewritePass: found "
+                  << fe->dst()->DebugString()
+                  << " as the forward node for matching context, backward"
+                  << " node is: " << n->DebugString();
+          *fwd_node = fe->dst();
+          return true;
+        }
+      }
+    }
+
+    return false;
+  }
+
+  // Is BiasAddGrad node in 'n' is associated with MatMul node
+  // specified in contextinfo 'ci'. Function does not update fwd_node.
+  //
+  // @return - true (if BiasAddGrad is associated with MatMul);
+  //           false otherwise.
+  static bool IsBiasAddGradInMatMulContext(const Node* n,
+                                           const Node** fwd_node,
+                                           void* ci) {
+    return (!IsBiasAddGradInConv2DWithBiasContext(n, fwd_node, ci));
+  }
+
+
   // Rewrite rule that uses context-information for matching,
   // used in scenario 2.
   //
@@ -639,8 +801,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   static bool ContextMatchRewrite(const Node* n, const ContextInfo* c);
 
   // Helper function that searches the matching contextinfo for the node.
-  // Implements depth-first search in the data dependence graph for the
-  // gradient op in the backward direction.
   //
   // @input n - Node (gradient op) whose contextinfo is to be searched,
   //        fwd_node - pointer to node from the forward pass that this node
@@ -788,6 +948,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
                                    Node* orig_node);
 };
 
+MklLayoutRewritePass::ConstStringsInfo MklLayoutRewritePass::csinfo_;
 MklLayoutRewritePass::ContextInfo
   MklLayoutRewritePass::biasaddgrad_conv2dwithbias_context_;
 MklLayoutRewritePass::ContextInfo
@@ -1667,12 +1828,12 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr<Graph>* g,
   const ContextInfo* ci = nullptr;
   bool is_context_based_rewrite = false;
   if ((ci = SearchMatchingContext(orig_node, &fwd_node)) != nullptr) {
-    CHECK_NOTNULL(fwd_node);
     is_context_based_rewrite = true;
 
     // Sanity checks for context-based rewrite (if any)
     if (orig_node->type_string() == csinfo_.bias_add_grad &&
         ri->new_name == csinfo_.mkl_conv2d_with_bias_backprop_bias) {
+      CHECK_NOTNULL(fwd_node);
       DataType orig_T, ctx_T;
       string orig_data_format, ctx_data_format;
       TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &orig_T));
@@ -1784,69 +1945,17 @@ MklLayoutRewritePass::SearchMatchingContext(const Node* n,
   CHECK_NOTNULL(fwd_node);
   *fwd_node = nullptr;
 
-  // Search for matching contextinfo based on node name.
-  // There could be more than one matching contextinfos.
-  bool is_matching_cinfo_found = false;
-  std::vector<const ContextInfo*> mci;
+  // Search for matching contextinfo based on node name and call
+  // callback function using matching contextinfo.
+  // There could be more than one matching contextinfos but whichever
+  // matches first is returned.
   for (auto ci = cinfo_.cbegin(); ci != cinfo_.cend(); ++ci) {
-    if (n->type_string() == (*ci)->node) {
-      mci.push_back(*ci);
-      is_matching_cinfo_found = true;
+    if (n->type_string() == (*ci)->node &&
+        (*ci)->context_match_fn(n, fwd_node, *ci)) {
+      VLOG(1) << "Found context as matching: " << (*ci)->fwd;
+      return *ci;
     }
   }
-  // If no matching contextinfo is found, return immediately.
-  if (!is_matching_cinfo_found) {
-    return nullptr;
-  }
-
-  VLOG(1) << "MklLayoutRewritePass: Searching graph for: " << n->type_string()
-          << " in backwards.";
-
-  // Now we will check for forward op name for context info in data
-  // flow graph. Get the max hops we should search for the fwd node.
-  // We are now going to search (breadth-first) backwards in data
-  // dependence graph (for up to max hops) from n for the node
-  // specified in fwd.
-  // queue to maintain nodes to be visited and depth info for
-  // breadth-first search
-  std::queue<std::pair<const Node*, int>> nqueue;
-  const Node* curr_node = n;
-  size_t curr_depth = 0;
-  nqueue.push(std::make_pair(curr_node, curr_depth));
-
-  while (curr_depth < kNodeMergeContextMaxDepth && !nqueue.empty()) {
-    std::pair<const Node*, int> curr_pair = nqueue.front();
-    nqueue.pop();
-
-    std::set<const Node*> visited_nodes;
-    curr_node = curr_pair.first;
-    curr_depth = curr_pair.second;
-    CHECK_NOTNULL(curr_node);
-
-    VLOG(1) << "MklLayoutRewritePass: Visiting node: "
-            << curr_node->type_string() << " at depth: " << curr_depth
-            << " for node: " << n->type_string();
-
-    // If we find a match, we return immediately.
-    for (const ContextInfo* ci : mci) {
-      if (curr_node->type_string() == ci->fwd) {
-        *fwd_node = curr_node;
-        return ci;
-      }
-    }
-
-    // Else we explore backward edges from current node.
-    // Add the source nodes of all incoming edges of the node to the queue.
-    for (const Edge* e : curr_node->in_edges()) {
-      // We do not visit already visited node.
-      if (visited_nodes.find(e->src()) == visited_nodes.end()) {
-        // Depth of these nodes is 1 more than the depth of current node.
-        nqueue.push(std::make_pair(e->src(), curr_depth + 1));
-        visited_nodes.insert(e->src());
-      }
-    }
-  } /* while */
-
   return nullptr;
 }
 
diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc
index 3c4a5263af..efbe2134e0 100644
--- a/tensorflow/core/graph/mkl_layout_pass_test.cc
+++ b/tensorflow/core/graph/mkl_layout_pass_test.cc
@@ -345,7 +345,8 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_AttrMismatch) {
 // Test set 2: _MklConv2D..BiasAddGrad -> _MklConv2DWithBiasBackpropBias
 // rewrite tests
 
-// D=_MklConv2D(A,M,B,N,C,O); E=Sub(D,A); F=BiasAddGrad(E)
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput
 TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
   InitGraph(
       "node { name: 'A' op: 'Input'}"
@@ -364,16 +365,255 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
       "node { name: 'E' op: 'Sub'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
       " input: ['D', 'A']}"
-      "node { name: 'F' op: 'BiasAddGrad'"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
       " attr { key: 'data_format'      value { s: 'NCHW' } }"
       " input: ['E'] }");
   EXPECT_EQ(DoMklLayoutOptimizationPass(),
             "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
-            "E(Sub);F(_MklConv2DWithBiasBackpropBias);M(_MklInput);"
-            "N(_MklInput);O(_MklInput)|A->D;A->E:1;B->D:1;C->D:2;D->E;"
-            "DMT/_0->F:1;E->F;E:control->DMT/_0:control;M->D:3;N->D:4;"
-            "O->D:5");
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(_MklConv2DWithBiasBackpropBias);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G;B->D:1;"
+            "B->I:1;C->D:2;D->E;DMT/_0->J:1;E->G:2;E->I:2;E->J;"
+            "E:control->DMT/_0:control;F->G:1;H->I;M->D:3;M->G:3;M->I:3;"
+            "N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput. But nodes do not match criteria for rewrite. So
+// rewrite should not happen.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative1) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
+            "B->I:1;C->D:2;D->E;E->G;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
+            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput. But nodes do not match criteria for rewrite. So
+// rewrite should not happen.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative2) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
+            "B->I:1;C->D:2;D->E;E->G:2;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
+            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Positive) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);"
+            "H(_MklConv2DWithBiasBackpropBias);M(_MklInput);N(_MklInput);"
+            "O(_MklInput)|A->D;A->E:1;A->G;B->D:1;C->D:2;D->E;DMT/_0->H:1;"
+            "E->G:2;E->H;E:control->DMT/_0:control;F->G:1;M->D:3;M->G:3;"
+            "N->D:4;N->G:4;O->D:5;O->G:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+// But BackpropFilter node inputs do not satisfy criteria for rewrite.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative1) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
+            "C->D:2;D->E;E->G;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
+            "O->G:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+// But BackpropFilter node inputs do not satisfy criteria for rewrite.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative2) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
+            "C->D:2;D->E;E->G:2;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
+            "O->G:5");
 }
 
 // No _MklConv2DWithBias in context, but _MklConv2D in context.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index e959eab54e..f6595fcbb3 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -728,7 +728,7 @@ TEST_F(VirtualSchedulerTest, ComplexDependency) {
            1 /* control dependency */);
   EXPECT_EQ(expected_size, cpu_state.memory_usage);
 
-  // Nodes currrently in memory: bn's port -1, 0, and 2, and x's port 0.
+  // Nodes currently in memory: bn's port -1, 0, and 2, and x's port 0.
   std::set<std::pair<string, int>> nodes_in_memory;
   std::transform(
       cpu_state.nodes_in_memory.begin(), cpu_state.nodes_in_memory.end(),
diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h
index 84a7681782..1e7a9dfaf5 100644
--- a/tensorflow/core/grappler/grappler_item.h
+++ b/tensorflow/core/grappler/grappler_item.h
@@ -23,6 +23,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/protobuf/queue_runner.pb.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.cc b/tensorflow/core/grappler/optimizers/auto_parallel.cc
index 42f2f1850f..d46b849ad4 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.cc
@@ -168,6 +168,11 @@ Status AutoParallel::Initialize(const GrapplerItem& item) {
   for (const auto& variable : item.MainVariables()) {
     dont_replicate_nodes.insert(variable->name());
   }
+
+  for (const auto& init : item.init_ops) {
+    dont_replicate_nodes.insert(NodeName(init));
+  }
+
   // Don't replicate all input nodes, except the dequeue node.
   for (const auto& input_node : input_nodes) {
     if (input_node->name() != dequeue_node->name()) {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.h b/tensorflow/core/grappler/optimizers/auto_parallel.h
index ad90bbe028..c5d2d47782 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.h
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_
 
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
+#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
index 3d1b4a34bf..9a41b5e0b5 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
@@ -33,6 +33,7 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   Output constant_b = ops::Const(s.WithOpName("constant_b"), 1, {1});
   Output var = ops::Variable(s.WithOpName("var"), {1}, DT_FLOAT);
   Output assign = ops::Assign(s.WithOpName("assign"), {var}, {constant_a});
+  Output identity = ops::Identity(s.WithOpName("identity"), {var});
   Output fifo_queue = ops::FIFOQueue(s.WithOpName("fifo_queue"), {DT_FLOAT});
   auto dequeue = ops::QueueDequeueMany(s.WithOpName("dequeue"), {fifo_queue},
                                        {constant_b}, {DT_FLOAT});
@@ -44,13 +45,14 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   GrapplerItem item;
   item.init_ops.push_back("assign");
   item.fetch.push_back("apply_gradient");
+  item.init_ops.push_back("assign");
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
   AutoParallel parallel(2);
   GraphDef output;
   Status status = parallel.Optimize(nullptr, item, &output);
   TF_EXPECT_OK(status);
-  EXPECT_EQ(20, output.node_size());
+  EXPECT_EQ(21, output.node_size());
 
   const NodeDef& node_assign = output.node(0);
   EXPECT_EQ("assign", node_assign.name());
@@ -62,60 +64,64 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   const NodeDef& node_fifo_queue = output.node(2);
   EXPECT_EQ("fifo_queue", node_fifo_queue.name());
 
-  const NodeDef& node_var = output.node(3);
+  const NodeDef& node_identity = output.node(3);
+  EXPECT_EQ("identity", node_identity.name());
+  EXPECT_EQ("var", node_identity.input(0));
+
+  const NodeDef& node_var = output.node(4);
   EXPECT_EQ("var", node_var.name());
 
-  const NodeDef& node_div_const0 = output.node(4);
+  const NodeDef& node_div_const0 = output.node(5);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-Const",
             node_div_const0.name());
 
-  const NodeDef& node_div0 = output.node(5);
+  const NodeDef& node_div0 = output.node(6);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-apply_gradient",
             node_div0.name());
-  const NodeDef& node_add0 = output.node(6);
+  const NodeDef& node_add0 = output.node(7);
   EXPECT_EQ("AutoParallel-Replica-0/add", node_add0.name());
 
-  const NodeDef& node_gradient0 = output.node(7);
+  const NodeDef& node_gradient0 = output.node(8);
   EXPECT_EQ("AutoParallel-Replica-0/apply_gradient", node_gradient0.name());
 
-  const NodeDef& node_constant_a0 = output.node(8);
+  const NodeDef& node_constant_a0 = output.node(9);
   EXPECT_EQ("AutoParallel-Replica-0/constant_a", node_constant_a0.name());
 
-  const NodeDef& node_dequeue0 = output.node(9);
+  const NodeDef& node_dequeue0 = output.node(10);
   EXPECT_EQ("AutoParallel-Replica-0/dequeue", node_dequeue0.name());
 
-  const NodeDef& node_learning_rate0 = output.node(10);
+  const NodeDef& node_learning_rate0 = output.node(11);
   EXPECT_EQ("AutoParallel-Replica-0/learning_rate", node_learning_rate0.name());
 
-  const NodeDef& node_div_const1 = output.node(11);
+  const NodeDef& node_div_const1 = output.node(12);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-Const",
             node_div_const1.name());
 
-  const NodeDef& node_div1 = output.node(12);
+  const NodeDef& node_div1 = output.node(13);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-apply_gradient",
             node_div1.name());
 
-  const NodeDef& node_add1 = output.node(13);
+  const NodeDef& node_add1 = output.node(14);
   EXPECT_EQ("AutoParallel-Replica-1/add", node_add1.name());
 
-  const NodeDef& node_gradient1 = output.node(14);
+  const NodeDef& node_gradient1 = output.node(15);
   EXPECT_EQ("AutoParallel-Replica-1/apply_gradient", node_gradient1.name());
 
-  const NodeDef& node_constant_a1 = output.node(15);
+  const NodeDef& node_constant_a1 = output.node(16);
   EXPECT_EQ("AutoParallel-Replica-1/constant_a", node_constant_a1.name());
 
-  const NodeDef& node_dequeue1 = output.node(16);
+  const NodeDef& node_dequeue1 = output.node(17);
   EXPECT_EQ("AutoParallel-Replica-1/dequeue", node_dequeue1.name());
 
-  const NodeDef& node_learning_rate1 = output.node(17);
+  const NodeDef& node_learning_rate1 = output.node(18);
   EXPECT_EQ("AutoParallel-Replica-1/learning_rate", node_learning_rate1.name());
 
-  const NodeDef& node_fetch = output.node(18);
+  const NodeDef& node_fetch = output.node(19);
   EXPECT_EQ("AutoParallel-Control-Fetch", node_fetch.name());
   EXPECT_EQ("^AutoParallel-Replica-0/apply_gradient", node_fetch.input(0));
   EXPECT_EQ("^AutoParallel-Replica-1/apply_gradient", node_fetch.input(1));
 
-  const NodeDef& node_gradient = output.node(19);
+  const NodeDef& node_gradient = output.node(20);
   EXPECT_EQ("apply_gradient", node_gradient.name());
   EXPECT_EQ("^AutoParallel-Control-Fetch", node_gradient.input(0));
 }
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index ded1e474ce..28d663e2f7 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -929,7 +929,7 @@ struct TuningConfig {
   // Conv2DBackpropFilter will use a specialized GEMM implementation, which is
   // usually faster than the NCHW implementation. The downside is that this
   // might result in more non-cancellable layout conversion nodes (implemented
-  // by the Tranpose op).
+  // by the Transpose op).
   bool no_gemm;
 };
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 8b7c269a11..9c397954e1 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -2464,7 +2464,7 @@ tf_cc_tests(
         ":ops_util",
         ":sparse_add_op",
         ":sparse_dense_binary_op_shared",
-        ":sparse_reduce_sum_op",
+        ":sparse_reduce_op",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -3207,7 +3207,7 @@ cc_library(
         ":sparse_cross_op",
         ":sparse_dense_binary_op_shared",
         ":sparse_fill_empty_rows_op",
-        ":sparse_reduce_sum_op",
+        ":sparse_reduce_op",
         ":sparse_reorder_op",
         ":sparse_reshape_op",
         ":sparse_softmax",
@@ -3263,8 +3263,8 @@ tf_kernel_library(
 )
 
 tf_kernel_library(
-    name = "sparse_reduce_sum_op",
-    prefix = "sparse_reduce_sum_op",
+    name = "sparse_reduce_op",
+    prefix = "sparse_reduce_op",
     deps = SPARSE_DEPS,
 )
 
diff --git a/tensorflow/core/kernels/adjust_contrast_op.cc b/tensorflow/core/kernels/adjust_contrast_op.cc
index c8f12f91a6..37976f7183 100644
--- a/tensorflow/core/kernels/adjust_contrast_op.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op.cc
@@ -31,6 +31,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif
 
 // AdjustContrastOp is deprecated as of GraphDef version >= 2
 
@@ -410,4 +413,25 @@ REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_GPU),
                         AdjustContrastOpv2<GPUDevice>);
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+template <>
+class AdjustContrastOpv2<SYCLDevice> : public AdjustContrastOpV2Base {
+ public:
+  explicit AdjustContrastOpv2(OpKernelConstruction* context)
+      : AdjustContrastOpV2Base(context) {}
+
+  void DoCompute(OpKernelContext* context,
+                 const ComputeOptions& options) override {
+    const int64 shape[4] = {options.batch, options.height, options.width,
+                            options.channels};
+    functor::AdjustContrastv2<SYCLDevice>()(
+        context->eigen_device<SYCLDevice>(),
+        options.input->shaped<float, 4>(shape), options.factor->scalar<float>(),
+        options.output->shaped<float, 4>(shape));
+  }
+};
+REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_SYCL),
+                        AdjustContrastOpv2<SYCLDevice>);
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
index ffd47406eb..c485f14844 100644
--- a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
@@ -56,6 +56,11 @@ static Graph* BM_AdjustContrast(int batches, int width, int height) {
 // BM_AdjustContrast_cpu_1_299_299     179084     340186  2181  751.9M items/s
 // BM_AdjustContrast_gpu_32_299_299     85276     123665  4189  2.9G items/s
 BM_AdjustContrastDev(cpu, 1, 299, 299);
+#if GOOGLE_CUDA
 BM_AdjustContrastDev(gpu, 32, 299, 299);
+#endif  // GOOGLE_CUDA
+#ifdef TENSORFLOW_USE_SYCL
+BM_AdjustContrastDev(sycl, 32, 299, 299);
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc
index d65a34fd73..ba100b32e7 100644
--- a/tensorflow/core/kernels/colorspace_op.cc
+++ b/tensorflow/core/kernels/colorspace_op.cc
@@ -35,6 +35,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif
 
 template <typename Device, typename T>
 class RGBToHSVOp : public OpKernel {
@@ -146,4 +149,16 @@ TF_CALL_float(REGISTER_GPU);
 TF_CALL_double(REGISTER_GPU);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL(T)                                       \
+  REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_SYCL) \
+                              .TypeConstraint<T>("T"),         \
+                          RGBToHSVOp<SYCLDevice, T>);          \
+  REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_SYCL) \
+                              .TypeConstraint<T>("T"),         \
+                          HSVToRGBOp<SYCLDevice, T>);
+TF_CALL_float(REGISTER_SYCL);
+TF_CALL_double(REGISTER_SYCL);
+#endif
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/control_flow_ops.cc b/tensorflow/core/kernels/control_flow_ops.cc
index 203a9a9f24..64c06786bc 100644
--- a/tensorflow/core/kernels/control_flow_ops.cc
+++ b/tensorflow/core/kernels/control_flow_ops.cc
@@ -112,15 +112,14 @@ REGISTER_GPU_HOST_REF_KERNEL(string);
 #undef REGISTER_GPU_HOST_KERNEL
 #undef REGISTER_GPU_HOST_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                       \
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_SWITCH(type)                       \
   REGISTER_KERNEL_BUILDER(Name("Switch")                 \
                               .Device(DEVICE_SYCL)       \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("pred"),       \
+                              .HostMemory("pred")        \
+                              .TypeConstraint<type>("T"),\
                           SwitchOp)
-REGISTER_SYCL_KERNEL(bool);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_SWITCH);
 
 #define REGISTER_SYCL_REF_SWITCH(type)                     \
   REGISTER_KERNEL_BUILDER(Name("RefSwitch")                \
@@ -128,12 +127,41 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
                               .HostMemory("pred")          \
                               .TypeConstraint<type>("T"),  \
                           SwitchOp)
-REGISTER_SYCL_REF_SWITCH(bool);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH);
+TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH);
 
-#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_SWITCH
 #undef REGISTER_SYCL_REF_SWITCH
 
+#define REGISTER_SYCL_HOST_KERNEL(type)                  \
+  REGISTER_KERNEL_BUILDER(Name("Switch")                 \
+                              .Device(DEVICE_SYCL)       \
+                              .HostMemory("data")        \
+                              .HostMemory("pred")        \
+                              .HostMemory("output_false")\
+                              .HostMemory("output_true") \
+                              .TypeConstraint<type>("T"),\
+                          SwitchOp)
+
+REGISTER_SYCL_HOST_KERNEL(bool);
+REGISTER_SYCL_HOST_KERNEL(string);
+REGISTER_SYCL_HOST_KERNEL(int32);
+
+#define REGISTER_SYCL_HOST_REF_KERNEL(type)                \
+  REGISTER_KERNEL_BUILDER(Name("RefSwitch")                \
+                              .Device(DEVICE_SYCL)         \
+                              .HostMemory("data")          \
+                              .HostMemory("pred")          \
+                              .HostMemory("output_false")  \
+                              .HostMemory("output_true")   \
+                              .TypeConstraint<type>("T"),  \
+                          SwitchOp)
+
+REGISTER_SYCL_HOST_REF_KERNEL(int32);
+REGISTER_SYCL_HOST_REF_KERNEL(bool);
+REGISTER_SYCL_HOST_REF_KERNEL(string);
+
+#undef REGISTER_SYCL_HOST_KERNEL
+#undef REGISTER_SYCL_HOST_REF_KERNEL
 #endif // TENSORFLOW_USE_SYCL
 
 class RefSelectOp : public OpKernel {
@@ -233,13 +261,13 @@ REGISTER_GPU_REF_KERNEL(bool);
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
+#ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                        \
   REGISTER_KERNEL_BUILDER(Name("Merge")                   \
                               .Device(DEVICE_SYCL)        \
                               .TypeConstraint<type>("T")  \
                               .HostMemory("value_index"), \
-                          MergeOp)
+                          MergeOp);
 REGISTER_SYCL_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 
@@ -248,9 +276,10 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
                               .Device(DEVICE_SYCL)        \
                               .TypeConstraint<type>("T")  \
                               .HostMemory("value_index"), \
-                          MergeOp)
+                          MergeOp);
 REGISTER_SYCL_REF_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
+
 #undef REGISTER_SYCL_KERNEL
 #undef REGISTER_SYCL_REF_KERNEL
 #endif // TENSORFLOW_USE_SYCL
@@ -280,6 +309,30 @@ REGISTER_GPU_HOST_KERNEL(ResourceHandle);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_HOST_KERNEL(type)                   \
+  REGISTER_KERNEL_BUILDER(Name("Merge")                   \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("inputs")       \
+                              .HostMemory("output")       \
+                              .HostMemory("value_index")  \
+                              .TypeConstraint<type>("T"), \
+                          MergeOp);                       \
+  REGISTER_KERNEL_BUILDER(Name("RefMerge")                \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("inputs")       \
+                              .HostMemory("output")       \
+                              .HostMemory("value_index")  \
+                              .TypeConstraint<type>("T"), \
+                          MergeOp)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(string);
+REGISTER_SYCL_HOST_KERNEL(ResourceHandle);
+
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 void EnterOp::Compute(OpKernelContext* context) {
   if (IsRefType(context->input_dtype(0))) {
     context->forward_ref_input_to_ref_output(0, 0);
@@ -306,7 +359,7 @@ REGISTER_GPU_REF_KERNEL(bool);
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
+#ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)  \
   REGISTER_KERNEL_BUILDER(          \
       Name("Enter").Device(DEVICE_SYCL).TypeConstraint<type>("T"), EnterOp)
@@ -345,7 +398,7 @@ REGISTER_SYCL_HOST_KERNEL(ResourceHandle);
 
 #undef REGISTER_SYCL_HOST_KERNEL
 #undef REGISTER_SYCL_HOST_REF_KERNEL
-#endif
+#endif // TENSORFLOW_USE_SYCL
 
 // Special GPU kernels for int32 and string.
 // TODO(b/25387198): Also enable int32 in device memory. This kernel
@@ -394,30 +447,25 @@ REGISTER_KERNEL_BUILDER(Name("RefExit").Device(DEVICE_CPU), ExitOp);
       Name("RefExit").Device(DEVICE_GPU).TypeConstraint<type>("T"), ExitOp);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_REF_KERNEL);
 REGISTER_GPU_KERNEL(bool);
+REGISTER_GPU_REF_KERNEL(bool);
 
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)  \
-  REGISTER_KERNEL_BUILDER(          \
-  Name("Exit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp)
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                                           \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("Exit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp);   \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("RefExit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp);
 REGISTER_SYCL_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 
-#define REGISTER_SYCL_REF_KERNEL(type)  \
-  REGISTER_KERNEL_BUILDER(              \
-  Name("RefExit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp)
-REGISTER_SYCL_REF_KERNEL(bool);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
-
 #undef REGISTER_SYCL_KERNEL
 #undef REGISTER_SYCL_REF_KERNEL
 
-// Special GPU kernels for int32 and string.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 #define REGISTER_SYCL_HOST_KERNEL(type)                   \
   REGISTER_KERNEL_BUILDER(Name("Exit")                    \
                               .Device(DEVICE_SYCL)        \
@@ -507,31 +555,19 @@ REGISTER_GPU_HOST_KERNEL(string);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)  \
-  REGISTER_KERNEL_BUILDER(Name("NextIteration")           \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("data")         \
-                              .HostMemory("output")       \
-                              .TypeConstraint<type>("T"), \
-                          NextIterationOp)
-  REGISTER_SYCL_KERNEL(bool);
-  TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-#define REGISTER_SYCL_REF_KERNEL(type)                    \
-  REGISTER_KERNEL_BUILDER(Name("RefNextIteration")        \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("data")         \
-                              .HostMemory("output")       \
-                              .TypeConstraint<type>("T"), \
-                          NextIterationOp)
-  REGISTER_SYCL_REF_KERNEL(bool);
-  TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                                           \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("NextIteration").Device(DEVICE_SYCL).TypeConstraint<type>("T"),   \
+      NextIterationOp);                                                      \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("RefNextIteration").Device(DEVICE_SYCL).TypeConstraint<type>("T"),\
+      NextIterationOp)
+REGISTER_SYCL_KERNEL(bool);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+
 #undef REGISTER_SYCL_KERNEL
-#undef REGISTER_SYCL_REF_KERNEL
 
-// Special GPU kernels for int32 and string.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 #define REGISTER_SYCL_HOST_KERNEL(type)                   \
   REGISTER_KERNEL_BUILDER(Name("NextIteration")           \
                               .Device(DEVICE_SYCL)        \
diff --git a/tensorflow/core/kernels/cwise_op_add_2.cc b/tensorflow/core/kernels/cwise_op_add_2.cc
index 5d3385b0ed..5dea00e95c 100644
--- a/tensorflow/core/kernels/cwise_op_add_2.cc
+++ b/tensorflow/core/kernels/cwise_op_add_2.cc
@@ -22,10 +22,11 @@ namespace tensorflow {
 // sharded files, only make its register calls when not __ANDROID_TYPES_SLIM__.
 #if !defined(__ANDROID_TYPES_SLIM__)
 
-REGISTER5(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64,
-          complex128, string);
+REGISTER6(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64,
+          uint8, complex128, string);
 #if GOOGLE_CUDA
-REGISTER3(BinaryOp, GPU, "Add", functor::add, int64, complex64, complex128);
+REGISTER4(BinaryOp, GPU, "Add", functor::add, uint8, int64, complex64,
+          complex128);
 #endif  // GOOGLE_CUDA
 
 #endif  // !defined(__ANDROID_TYPES_SLIM__)
diff --git a/tensorflow/core/kernels/cwise_op_cosh.cc b/tensorflow/core/kernels/cwise_op_cosh.cc
new file mode 100644
index 0000000000..bca99a4f89
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_cosh.cc
@@ -0,0 +1,37 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Cosh", functor::cosh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Cosh")                                \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::cosh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Cosh", functor::cosh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
index 5aaf2b5b4b..61079ebab3 100644
--- a/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
+++ b/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
@@ -19,7 +19,8 @@ limitations under the License.
 
 namespace tensorflow {
 namespace functor {
-DEFINE_BINARY6(add, Eigen::half, float, double, int64, complex64, complex128);
+DEFINE_BINARY7(add, Eigen::half, float, double, uint8, int64, complex64,
+               complex128);
 }  // namespace functor
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
new file mode 100644
index 0000000000..267a381d1a
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(cosh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
new file mode 100644
index 0000000000..f8329e50d6
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(sinh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc
index c84ee6894e..df2c02e42e 100644
--- a/tensorflow/core/kernels/cwise_op_invert.cc
+++ b/tensorflow/core/kernels/cwise_op_invert.cc
@@ -20,7 +20,7 @@ REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64,
           uint8, uint16);
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
+REGISTER6(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
          uint8, uint16);
 #endif  // TENSORFLOW_USE_SYCL
 
diff --git a/tensorflow/core/kernels/cwise_op_sinh.cc b/tensorflow/core/kernels/cwise_op_sinh.cc
new file mode 100644
index 0000000000..055f0b12e1
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_sinh.cc
@@ -0,0 +1,37 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Sinh", functor::sinh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Sinh")                                \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::sinh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYC
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Sinh", functor::sinh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h
index 97bdc5e878..c11d6cfabb 100644
--- a/tensorflow/core/kernels/cwise_ops.h
+++ b/tensorflow/core/kernels/cwise_ops.h
@@ -483,6 +483,12 @@ struct log1p : base<T, Eigen::internal::scalar_log1p_op<T> > {};
 template <typename T>
 struct sign : base<T, Eigen::internal::scalar_sign_op<T> > {};
 
+template <typename T>
+struct sinh : base<T, Eigen::internal::scalar_sinh_op<T> > {};
+
+template <typename T>
+struct cosh : base<T, Eigen::internal::scalar_cosh_op<T> > {};
+
 template <typename T>
 struct tanh : base<T, Eigen::internal::scalar_tanh_op<T> > {};
 
diff --git a/tensorflow/core/kernels/dynamic_stitch_op.cc b/tensorflow/core/kernels/dynamic_stitch_op.cc
index 08ae787c86..135d635514 100644
--- a/tensorflow/core/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/core/kernels/dynamic_stitch_op.cc
@@ -165,20 +165,6 @@ class DynamicStitchOp : public OpKernel {
 TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH);
 #undef REGISTER_DYNAMIC_STITCH
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_DYNAMIC_STITCH_SYCL(type)               \
-  REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
-                              .Device(DEVICE_SYCL)       \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("indices")     \
-                              .HostMemory("data")        \
-                              .HostMemory("merged"),     \
-                          DynamicStitchOp<type>)
-
-TF_CALL_ALL_TYPES(REGISTER_DYNAMIC_STITCH_SYCL);
-#undef REGISTER_DYNAMIC_STITCH_SYCL
-#endif  // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 #define REGISTER_DYNAMIC_STITCH_GPU(type)                \
   REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
@@ -194,4 +180,17 @@ TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH_GPU);
 
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_DYNAMIC_STITCH_SYCL(type)               \
+  REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
+                              .Device(DEVICE_SYCL)       \
+                              .TypeConstraint<type>("T") \
+                              .HostMemory("indices")     \
+                              .HostMemory("data")        \
+                              .HostMemory("merged"),     \
+                          DynamicStitchOp<type>)
+
+TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH_SYCL);
+#undef REGISTER_DYNAMIC_STITCH_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/map_stage_op.cc b/tensorflow/core/kernels/map_stage_op.cc
index 6431c6540e..46eaf3d9e7 100644
--- a/tensorflow/core/kernels/map_stage_op.cc
+++ b/tensorflow/core/kernels/map_stage_op.cc
@@ -547,14 +547,14 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapStage")
                             .HostMemory("indices")
                             .Device(DEVICE_GPU),
                         MapStageOp<true>);
-#endif
+#endif // GOOGLE_CUDA
+
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(Name("MapStage").HostMemory("key").Device(DEVICE_SYCL),
                         MapStageOp<false>);
 REGISTER_KERNEL_BUILDER(
     Name("OrderedMapStage").HostMemory("key").Device(DEVICE_SYCL),
     MapStageOp<true>);
-
 #endif // TENSORFLOW_USE_SYCL
 
 template <bool Ordered>
@@ -661,6 +661,7 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapPeek")
                             .Device(DEVICE_GPU),
                         MapPeekOp<true>);
 #endif
+
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(
     Name("MapPeek").HostMemory("key").HostMemory("indices").Device(DEVICE_SYCL),
@@ -724,8 +725,8 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapUnstageNoKey")
                             .HostMemory("indices")
                             .Device(DEVICE_GPU),
                         MapUnstageNoKeyOp<true>);
-
 #endif
+
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(Name("MapUnstageNoKey")
                             .HostMemory("key")
diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
index dc6b88e953..ddcf241277 100644
--- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
@@ -206,10 +206,15 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
     // Mkl needs the entities in its native format.
     // So create temporary tensors along with buffers to
     // convert the received entities.
-    Tensor mkl_tmp_input_buf_tensor, mkl_tmp_out_backprop_buf_tensor;
+    Tensor mkl_tmp_input_buf_tensor, mkl_tmp_out_backprop_buf_tensor,
+           mkl_tmp_buf_trans_input;
     // This preparation sets (1) dnnResourceSrc (2) dnnResourceDiffDst
-    mkl_context.MklPrepareInputs(context, &mkl_tmp_input_buf_tensor,
-                                 &mkl_tmp_out_backprop_buf_tensor);
+    mkl_context.MklPrepareInputs(context, data_format_,
+                                 input_in_mkl_format,
+                                 out_backprop_in_mkl_format,
+                                 &mkl_tmp_input_buf_tensor,
+                                 &mkl_tmp_out_backprop_buf_tensor,
+                                 &mkl_tmp_buf_trans_input);
 
     // Final conv-grad-filter should be in TF layout.
     Tensor* grad_filter;
@@ -307,34 +312,58 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
 
     // Compare incoming tensor layouts with MKL preferred layouts and convert
     // data to the preferred layout if necessary
-    void MklPrepareInputs(OpKernelContext* context,
+    void MklPrepareInputs(OpKernelContext* context, TensorFormat format,
+                          bool input_in_mkl_format,
+                          bool out_backprop_in_mkl_format,
                           Tensor* mkl_tmp_input_buf_tensor,
-                          Tensor* mkl_tmp_out_backprop_buf_tensor) {
+                          Tensor* mkl_tmp_out_backprop_buf_tensor,
+                          Tensor* mkl_tmp_buf_trans_input) {
       bool mkl_convert_input, mkl_convert_out_backprop;
       dnnPrimitive_t mkl_prim_convert_input, mkl_prim_convert_out_backprop;
-      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_out_backprop;
+      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_out_backprop,
+                  mkl_lt_trans_input;
       void *mkl_buf_convert_input, *mkl_buf_convert_out_backprop;
+      void *mkl_buf_input, *mkl_buf_out_backprop;
 
       mkl_prim_convert_input = nullptr;
       mkl_prim_convert_out_backprop = nullptr;
       mkl_lt_internal_input = nullptr;
       mkl_lt_internal_out_backprop = nullptr;
+      mkl_lt_trans_input = nullptr;
       mkl_buf_convert_input = nullptr;
       mkl_buf_convert_out_backprop = nullptr;
+      mkl_buf_input = nullptr;
+      mkl_buf_out_backprop = nullptr;
 
       // Compare with internal layouts and convert if needed
       const Tensor& input = MklGetInput(context, 0);
-      void* mkl_buf_input =
-          const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
+      if (!input_in_mkl_format && format == FORMAT_NHWC){
+        TensorShape nchw_shape = ShapeFromFormat(FORMAT_NCHW,
+            in_sizes[MklDims::N], in_sizes[MklDims::H],
+            in_sizes[MklDims::W], in_sizes[MklDims::C]);
+        OP_REQUIRES_OK(context, context->allocate_temp(
+            DataTypeToEnum<float>::value, nchw_shape, mkl_tmp_buf_trans_input));
+        MklNHWCToNCHW(input, &mkl_tmp_buf_trans_input);
+        mkl_buf_input = const_cast<void*>(static_cast<const void*>(
+            mkl_tmp_buf_trans_input->flat<float>().data()));
+        size_t strides[4];
+        GetStridesFromSizes(FORMAT_NCHW, strides, in_sizes);
+        CHECK_EQ(dnnLayoutCreate_F32(&mkl_lt_trans_input, in_dims, in_sizes,
+            strides), E_SUCCESS);
+      }
+      else {
+        mkl_buf_input =
+            const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
+        mkl_lt_trans_input = lt_input;
+      }
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(
                    &mkl_lt_internal_input, prim_conv_bwdfilter, dnnResourceSrc),
                E_SUCCESS);
       mkl_convert_input =
-          !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input);
+          !dnnLayoutCompare_F32(mkl_lt_internal_input, mkl_lt_trans_input);
       if (mkl_convert_input) {
-        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input,
-                                         mkl_lt_internal_input),
-                 E_SUCCESS);
+        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input,
+                     mkl_lt_trans_input, mkl_lt_internal_input), E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input,
                        &mkl_buf_convert_input);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input,
@@ -343,26 +372,30 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
         dnnDelete_F32(mkl_prim_convert_input);
       }
       dnnLayoutDelete_F32(mkl_lt_internal_input);
+      if (!input_in_mkl_format && format == FORMAT_NHWC)
+        dnnLayoutDelete_F32(mkl_lt_trans_input);
+
 
       conv_res[dnnResourceSrc] =
           (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input;
 
       const Tensor& out_backprop = MklGetInput(context, 2);
-      void* mkl_buf_out_backprop = const_cast<void*>(
-          static_cast<const void*>(out_backprop.flat<T>().data()));
+      mkl_buf_out_backprop = const_cast<void*>(
+                      static_cast<const void*>(out_backprop.flat<T>().data()));
+
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_out_backprop,
                                                 prim_conv_bwdfilter,
                                                 dnnResourceDiffDst),
                E_SUCCESS);
       mkl_convert_out_backprop =
-          !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop, lt_out_backprop);
+          !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop,
+                                lt_out_backprop);
       if (mkl_convert_out_backprop) {
         CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_out_backprop,
-                                         lt_out_backprop,
-                                         mkl_lt_internal_out_backprop),
+                      lt_out_backprop, mkl_lt_internal_out_backprop),
                  E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_out_backprop_buf_tensor,
-                       lt_out_backprop, &mkl_buf_convert_out_backprop);
+            mkl_lt_internal_out_backprop, &mkl_buf_convert_out_backprop);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_out_backprop,
                                           mkl_buf_out_backprop,
                                           mkl_buf_convert_out_backprop),
diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc
index 76b9f1798d..df49e03f31 100644
--- a/tensorflow/core/kernels/mkl_conv_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_ops.cc
@@ -267,12 +267,15 @@ class MklConv2DOp : public OpKernel {
 
     mkl_context.MklCreateInputLayouts(context);
 
+    // Temp tensor used to allocate tmp buffers
     Tensor mkl_tmp_input_buf_tensor, mkl_tmp_filter_buf_tensor,
-        mkl_tmp_bias_buf_tensor;  // Temp tensor used to allocate tmp
-                                  // buffers
-    mkl_context.MklPrepareConvolutionInputs(context, &mkl_tmp_input_buf_tensor,
+        mkl_tmp_bias_buf_tensor, mkl_tmp_buf_trans_input;
+    mkl_context.MklPrepareConvolutionInputs(context, data_format_,
+                                            input_in_mkl_format,
+                                            &mkl_tmp_input_buf_tensor,
                                             &mkl_tmp_filter_buf_tensor,
-                                            &mkl_tmp_bias_buf_tensor);
+                                            &mkl_tmp_bias_buf_tensor,
+                                            &mkl_tmp_buf_trans_input);
 
     // Execute convolution
     CHECK_EQ(dnnExecute_F32(mkl_context.prim_fwd, mkl_context.conv_res),
@@ -323,39 +326,59 @@ class MklConv2DOp : public OpKernel {
     // Compare incoming tensor layouts with MKL preferred layouts and convert
     // data to the preferred layout if necessary
     void MklPrepareConvolutionInputs(OpKernelContext* context,
+                                     TensorFormat format,
+                                     bool input_in_mkl_format,
                                      Tensor* mkl_tmp_input_buf_tensor,
                                      Tensor* mkl_tmp_filter_buf_tensor,
-                                     Tensor* mkl_tmp_bias_buf_tensor) {
+                                     Tensor* mkl_tmp_bias_buf_tensor,
+                                     Tensor* mkl_tmp_buf_trans_input) {
       bool mkl_convert_input, mkl_convert_filter, mkl_convert_bias;
       dnnPrimitive_t mkl_prim_convert_filter, mkl_prim_convert_bias,
           mkl_prim_convert_input;
       dnnLayout_t mkl_lt_internal_filter, mkl_lt_internal_bias,
-          mkl_lt_internal_input;
+          mkl_lt_internal_input, mkl_lt_trans_input;
       void *mkl_buf_convert_input, *mkl_buf_convert_filter,
-          *mkl_buf_convert_bias;
+          *mkl_buf_convert_bias, *mkl_buf_input;
       mkl_prim_convert_filter = nullptr;
       mkl_prim_convert_bias = nullptr;
       mkl_prim_convert_input = nullptr;
       mkl_lt_internal_filter = nullptr;
       mkl_lt_internal_bias = nullptr;
       mkl_lt_internal_input = nullptr;
+      mkl_lt_trans_input = nullptr;
       mkl_buf_convert_input = nullptr;
       mkl_buf_convert_filter = nullptr;
       mkl_buf_convert_bias = nullptr;
+      mkl_buf_input = nullptr;
 
       // Compare with internal layouts and convert if needed
       const Tensor& input = MklGetInput(context, 0);
-      void* mkl_buf_input =
-          const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
+      if (!input_in_mkl_format && format == FORMAT_NHWC) {
+        TensorShape nchw_shape = ShapeFromFormat(FORMAT_NCHW,
+            in_sizes[MklDims::N], in_sizes[MklDims::H],
+            in_sizes[MklDims::W], in_sizes[MklDims::C]);
+        OP_REQUIRES_OK(context, context->allocate_temp(
+            DataTypeToEnum<float>::value, nchw_shape, mkl_tmp_buf_trans_input));
+        MklNHWCToNCHW(input, &mkl_tmp_buf_trans_input);
+        mkl_buf_input = const_cast<void*>(static_cast<const void*>(
+            mkl_tmp_buf_trans_input->flat<float>().data()));
+        size_t strides[4];
+        GetStridesFromSizes(FORMAT_NCHW, strides, in_sizes);
+        CHECK_EQ(dnnLayoutCreate_F32(&mkl_lt_trans_input, in_dims, in_sizes,
+            strides), E_SUCCESS);
+      } else {
+          mkl_buf_input = const_cast<void*>(
+                              static_cast<const void*>(input.flat<T>().data()));
+          mkl_lt_trans_input = lt_input;
+      }
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_input,
                                                 prim_fwd, dnnResourceSrc),
                E_SUCCESS);
       mkl_convert_input =
-          !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input);
+          !dnnLayoutCompare_F32(mkl_lt_internal_input, mkl_lt_trans_input);
       if (mkl_convert_input) {
-        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input,
-                                         mkl_lt_internal_input),
-                 E_SUCCESS);
+        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input,
+                 mkl_lt_trans_input, mkl_lt_internal_input), E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input,
                        &mkl_buf_convert_input);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input,
@@ -364,6 +387,8 @@ class MklConv2DOp : public OpKernel {
         dnnDelete_F32(mkl_prim_convert_input);
       }
       dnnLayoutDelete_F32(mkl_lt_internal_input);
+      if (!input_in_mkl_format && format == FORMAT_NHWC)
+        dnnLayoutDelete_F32(mkl_lt_trans_input);
 
       conv_res[dnnResourceSrc] =
           (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input;
diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc
index 070aeff49f..07a7e6b5da 100644
--- a/tensorflow/core/kernels/mkl_lrn_op.cc
+++ b/tensorflow/core/kernels/mkl_lrn_op.cc
@@ -22,9 +22,6 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 #include <vector>
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -33,6 +30,9 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/util/mkl_util.h"
 #include "tensorflow/core/util/tensor_format.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "third_party/mkl/include/mkl_dnn.h"
+#include "third_party/mkl/include/mkl_dnn_types.h"
 
 #if !defined(IS_MOBILE_PLATFORM)
 #include "tensorflow/core/util/work_sharder.h"
@@ -66,11 +66,10 @@ class MklLRNOp : public OpKernel {
   explicit MklLRNOp(OpKernelConstruction* context) : OpKernel(context) {
     int64 depth_radius64;
     OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64));
-    OP_REQUIRES(
-        context,
-        FastBoundsCheck(depth_radius64, std::numeric_limits<int>::max()),
-        errors::InvalidArgument("depth_radius = ", depth_radius64,
-                                " larger than int max"));
+    OP_REQUIRES(context, FastBoundsCheck(depth_radius64,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("depth_radius = ", depth_radius64,
+                                        " larger than int max"));
     depth_radius_ = static_cast<size_t>(depth_radius64);
 
     OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_));
@@ -93,10 +92,9 @@ class MklLRNOp : public OpKernel {
                               : input.dims();
     OP_REQUIRES(context, mkl_context.in_dims == 4,
                 errors::InvalidArgument("input must be 4-dimensional"));
-    OP_REQUIRES(
-        context,
-        FastBoundsCheck(input.NumElements(), std::numeric_limits<int>::max()),
-        errors::InvalidArgument("argument to LRN too large"));
+    OP_REQUIRES(context, FastBoundsCheck(input.NumElements(),
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("argument to LRN too large"));
 
     if (!input_in_mkl_format) {
       mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_,
@@ -104,15 +102,6 @@ class MklLRNOp : public OpKernel {
       return;
     }
 
-    // TODO(inteltf) MKL will support depth radius not equal to 2 in the future
-    if (depth_radius_ != 2) {
-      Tensor converted_tensor =
-          ConvertMklToTF<T>(context, input, mkl_context.input_shape);
-      mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_,
-                                    beta_, converted_tensor);
-      return;
-    }
-
     if (input_in_mkl_format) {
       // MKL supports normalization over channel dimension only
       if (mkl_context.input_shape.tf_dim_idx(mkl_context.in_dims - 1) ==
@@ -345,11 +334,10 @@ class MklLRNGradOp : public OpKernel {
   explicit MklLRNGradOp(OpKernelConstruction* context) : OpKernel(context) {
     int64 depth_radius64;
     OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64));
-    OP_REQUIRES(
-        context,
-        FastBoundsCheck(depth_radius64, std::numeric_limits<int>::max()),
-        errors::InvalidArgument("depth_radius = ", depth_radius64,
-                                " larger than int max"));
+    OP_REQUIRES(context, FastBoundsCheck(depth_radius64,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("depth_radius = ", depth_radius64,
+                                        " larger than int max"));
     depth_radius_ = static_cast<int>(depth_radius64);
     OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_));
     OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_));
@@ -553,6 +541,9 @@ class MklLRNGradOp : public OpKernel {
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&lt_bdw_input, lrn_bwd,
                                                 dnnResourceDiffDst),
                E_SUCCESS);
+      CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&lt_internal_input, lrn_bwd,
+                                                dnnResourceSrc),
+               E_SUCCESS);
 
       bool ingrad_in_mkl_format = ingrad_shape.IsMklTensor();
       if (ingrad_in_mkl_format) {
@@ -581,44 +572,37 @@ class MklLRNGradOp : public OpKernel {
         }
       }
 
-// Although MKL documentation for LRN does not specify setting/getting
-// of dnnResourceSrc and dnnResourceDst, Caffe code sets dnnResourceSrc.
-// So we set dnnResourceSrc here. But we do not know why we are setting
-// dnnResourceDst.
-#if 0
-    // NOTE: The code below is kept just so that we know how we should handle
-    // dnnResourceSrc if the primitive layout for dnnResourceSrc was supported.
-
-    if (!dnnLayoutCompare_F32(lt_internal_input,
-         static_cast<dnnLayout_t>inimage_shape.GetCurLayout())) {
-      AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
-                     &res_lrn_bwd[dnnResourceSrc]);
-      inimage_shape.GetConvertedFlatData(lt_internal_input,
-                                           user_fwd_input,
-                                           res_lrn_bwd[dnnResourceSrc]);
-    } else {
-      res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
-    }
-#endif
-
-      // Since we cannot get expected layout for dnnResourceSrc, we construct
-      // buffer using
-      // MKL format if input is in MKL format.
-      if (inimage_shape.IsMklTensor()) {
-        AllocTmpBuffer(context, mkl_tmp_image_buf_tensor,
-                       (dnnLayout_t)inimage_shape.GetCurLayout(),
-                       &res_lrn_bwd[dnnResourceSrc]);
+      bool inimage_in_mkl_format = inimage_shape.IsMklTensor();
+      if (inimage_in_mkl_format) {
+        if (!dnnLayoutCompare_F32(
+                lt_internal_input,
+                static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()))) {
+          AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
+                         &res_lrn_bwd[dnnResourceSrc]);
+          ingrad_shape.GetConvertedFlatData(lt_internal_input, user_fwd_input,
+                                            res_lrn_bwd[dnnResourceSrc]);
+        } else {
+          res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
+        }
       } else {
-        res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
-      }
+        if (!dnnLayoutCompare_F32(
+                lt_internal_input,
+                static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()))) {
+          CHECK_EQ(dnnConversionCreate_F32(
+                       &convert_input,
+                       static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()),
+                       lt_internal_input),
+                   E_SUCCESS);
 
-      // Same comment as above.
-      if (outimage_shape.IsMklTensor()) {
-        AllocTmpBuffer(context, mkl_tmp_outimage_buf_tensor,
-                       (dnnLayout_t)outimage_shape.GetCurLayout(),
-                       &res_lrn_bwd[dnnResourceDst]);
-      } else {
-        res_lrn_bwd[dnnResourceDst] = user_fwd_output;
+          AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
+                         &res_lrn_bwd[dnnResourceSrc]);
+          CHECK_EQ(dnnConversionExecute_F32(convert_input, user_fwd_input,
+                                            res_lrn_bwd[dnnResourceSrc]),
+                   E_SUCCESS);
+          dnnDelete_F32(convert_input);
+        } else {
+          res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
+        }
       }
 
       res_lrn_bwd[dnnResourceWorkspace] = workspace_buffer;
@@ -628,8 +612,6 @@ class MklLRNGradOp : public OpKernel {
     // TODO(intelft) Check if we can use EigenLRNOp directly instead of making a
     // copy.
     void MklDefaultToEigen(OpKernelContext* context) {
-      // CHECK(false);
-
       Tensor in_grads;
       Tensor in_image;
       Tensor out_image;
@@ -709,7 +691,7 @@ class MklLRNGradOp : public OpKernel {
       Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch,
             depth * depth, shard);
     }
-		
+
     // release mkl resources
     void Mklcleanup() {
       bool ingrad_in_mkl_format = ingrad_shape.IsMklTensor();
diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc
index 10d2937584..fabecc39a8 100644
--- a/tensorflow/core/kernels/mkl_relu_op.cc
+++ b/tensorflow/core/kernels/mkl_relu_op.cc
@@ -184,38 +184,31 @@ class MklReluGradOp : public OpKernel {
     dnnLayout_t lt_input, lt_grad;
 
     void MklPrepareReluGradInputs(OpKernelContext* context,
-                                  Tensor* mkl_tmp_grad_buf_tensor,
                                   Tensor* mkl_tmp_input_buf_tensor) {
-      dnnPrimitive_t cv_user_to_reluB_input, cv_user_to_reluB_grad;
-      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_grad;
-
       const Tensor& g = MklGetInput(context, 0);
       const Tensor& a = MklGetInput(context, 1);
-
-      void* user_i = static_cast<void*>(const_cast<T*>(a.flat<T>().data()));
-      void* user_g = static_cast<void*>(const_cast<T*>(g.flat<T>().data()));
-      dnnPrimitive_t cv_input_to_grad = NULL;
-      Tensor mkl_tmp_buf_tensor;
+      void* buf_input = static_cast<void*>(const_cast<T*>(a.flat<T>().data()));
       void* mkl_buffer_convert = nullptr;
+      dnnPrimitive_t cv_input_to_grad = nullptr;
 
       // if input and grad are not in the same layout, do a conversion between
       // them.
       if (!dnnLayoutCompare_F32(lt_input, lt_grad)) {
-        AllocTmpBuffer(context, &mkl_tmp_buf_tensor, lt_grad,
+        AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, lt_grad,
                        &mkl_buffer_convert);
-        CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input, lt_grad),
-                 E_SUCCESS);
-
-        CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, user_i,
+        CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input,
+                   lt_grad), E_SUCCESS);
+        CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, buf_input,
                                           mkl_buffer_convert),
                  E_SUCCESS);
         relu_res[dnnResourceSrc] = mkl_buffer_convert;
         dnnDelete_F32(cv_input_to_grad);
       } else {
-        relu_res[dnnResourceSrc] = user_i;
+        relu_res[dnnResourceSrc] = buf_input;
       }
 
-      relu_res[dnnResourceDiffDst] = user_g;
+      void* buf_grad = static_cast<void*>(const_cast<T*>(g.flat<T>().data()));
+      relu_res[dnnResourceDiffDst] = buf_grad;
     }
 
     void MklCreateInputLayouts(OpKernelContext* context) {
@@ -317,9 +310,8 @@ void MklReluGradOp<Device, T>::Compute(OpKernelContext* context) {
                                      mkl_context.lt_grad, mkl_context.lt_grad,
                                      negative_slope),
            E_SUCCESS);
-  Tensor mkl_tmp_grad_buf_tensor, mkl_tmp_input_buf_tensor;
-  mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_grad_buf_tensor,
-                                       &mkl_tmp_input_buf_tensor);
+  Tensor mkl_tmp_input_buf_tensor;
+  mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_input_buf_tensor);
 
   if (input_is_mkl ||
       grad_is_mkl) { /*if  grad or input are MKL leave it in MKL*/
diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc
index 588d6874dd..b4aae67ca6 100644
--- a/tensorflow/core/kernels/mkl_tfconv_op.cc
+++ b/tensorflow/core/kernels/mkl_tfconv_op.cc
@@ -24,12 +24,13 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/util/tensor_format.h"
 
+#include "tensorflow/core/util/mkl_util.h"
 #include "third_party/mkl/include/mkl_dnn.h"
 #include "third_party/mkl/include/mkl_dnn_types.h"
-#include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -44,10 +45,11 @@ class MklToTfOp : public OpKernel {
   explicit MklToTfOp(OpKernelConstruction* context) : OpKernel(context) {
     OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format_str));
     OP_REQUIRES_OK(context, context->GetAttr("T", &op_data_type));
+    has_avx512f_ = port::TestCPUFeature(port::CPUFeature::AVX512F);
   }
 
   void Compute(OpKernelContext* context) override {
-    // 1. Check that input tensor is in MKL format.
+    // Check that input tensor is in MKL format.
     const Tensor& input_tensor = MklGetInput(context, 0);
     MklShape input_shape;
     GetMklShape(context, 0, &input_shape);
@@ -68,9 +70,12 @@ class MklToTfOp : public OpKernel {
     CHECK_EQ(op_data_type, output_data_type);
 
     TensorShape output_shape;
-    for (size_t i = 0; i < input_shape.GetDimension(); i++) {
+    size_t ndims = input_shape.GetDimension();
+    size_t* in_sizes = new size_t[ndims];
+    for (size_t i = 0; i < ndims; i++) {
       // Outermost to innermost dimension
       output_shape.AddDim(input_shape.GetSizes()[input_shape.tf_dim_idx(i)]);
+      in_sizes[i] = input_shape.GetSizes()[i];
     }
 
     // Allocate output tensor.
@@ -78,17 +83,41 @@ class MklToTfOp : public OpKernel {
     OP_REQUIRES_OK(context,
                    context->allocate_output(0, output_shape, &output_tensor));
 
-    // 3. Get input and output layout pointers.
-    dnnLayout_t output_layout =
-        static_cast<dnnLayout_t>(input_shape.GetTfLayout());
+    // If data format is NHWC, transform MKL tensor to NCHW format and then
+    // do NCHW -> NHWC.
+    dnnLayout_t lt_trans_input = nullptr;
+    Tensor mkl_tmp_trans_input_buf_tensor;
+    void* buf_trans_input = nullptr;
+    bool input_fmt_nhwc = input_shape.IsTensorInNHWCFormat();
+    if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
+      size_t strides_nchw[4];
+      GetStridesFromSizes(FORMAT_NCHW, strides_nchw, in_sizes);
+      CHECK_EQ(
+          dnnLayoutCreate_F32(&lt_trans_input, ndims, in_sizes, strides_nchw),
+          E_SUCCESS);
+      AllocTmpBuffer(context, &mkl_tmp_trans_input_buf_tensor, lt_trans_input,
+                     &buf_trans_input);
+    } else {
+      lt_trans_input = static_cast<dnnLayout_t>(input_shape.GetTfLayout());
+      buf_trans_input =
+          static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
+    }
 
-    // 4. Execute DNNConversion.
+    // Execute DNNConversion.
     void* input_buffer =
         static_cast<void*>(const_cast<T*>(input_tensor.flat<T>().data()));
-    void* output_buffer =
-        static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
-    input_shape.GetConvertedFlatData(output_layout, input_buffer,
-                                     output_buffer);
+    input_shape.GetConvertedFlatData(lt_trans_input, input_buffer,
+                                     buf_trans_input);
+    // NCHW -> NHWC, if data format is NHWC
+    if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
+      dnnLayoutDelete_F32(lt_trans_input);
+      TensorShape nhwc_shape = ShapeFromFormat(
+          FORMAT_NHWC, in_sizes[MklDims::N], in_sizes[MklDims::H],
+          in_sizes[MklDims::W], in_sizes[MklDims::C]);
+      MklNCHWToNHWC(mkl_tmp_trans_input_buf_tensor, &output_tensor);
+    }
+
+    delete[] in_sizes;
 
     VLOG(1) << "MKLToTFConversion complete successfully.";
   }
@@ -99,6 +128,9 @@ class MklToTfOp : public OpKernel {
 
   /// Data type of the operation
   DataType op_data_type;
+
+  /// CPUIDInfo
+  bool has_avx512f_ = false;
 };
 
 ///////////////////////////////////////////////////////////
diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index 9ffe71e031..dc95f67ff0 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -90,20 +90,24 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
   return intersection_area / (area_i + area_j - intersection_area);
 }
 
-void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
-                           const Tensor& scores, const Tensor& max_output_size,
+void DoNonMaxSuppressionOp(OpKernelContext* context,
+                           const Tensor& boxes,
+                           const Tensor& scores,
+                           const Tensor& max_output_size,
                            const float iou_threshold) {
   OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
-              errors::InvalidArgument("iou_threshold must be in [0, 1]"));
-
+      errors::InvalidArgument("iou_threshold must be in [0, 1]"));
+  
   int num_boxes = 0;
   ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
   if (!context->status().ok()) {
     return;
   }
 
-  const int output_size = std::min(max_output_size.scalar<int>()(), num_boxes);
-  typename TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();
+  const int output_size =
+      std::min(max_output_size.scalar<int>()(), num_boxes);
+  typename TTypes<float, 2>::ConstTensor boxes_data =
+      boxes.tensor<float, 2>();
 
   std::vector<float> scores_data(num_boxes);
   std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
@@ -123,7 +127,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
     for (int j = i + 1; j < num_boxes; ++j) {
       if (active[j]) {
         float iou =
-            ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
+          ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
         if (iou > iou_threshold) {
           active[j] = false;
           num_active--;
@@ -141,7 +145,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
   std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
 }
 
-}  // namespace
+} // namespace
 
 template <typename Device>
 class NonMaxSuppressionOp : public OpKernel {
@@ -163,8 +167,7 @@ class NonMaxSuppressionOp : public OpKernel {
         errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                 max_output_size.shape().DebugString()));
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
-                          iou_threshold_);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_);
   }
 
  private:
@@ -175,7 +178,8 @@ template <typename Device>
 class NonMaxSuppressionV2Op : public OpKernel {
  public:
   explicit NonMaxSuppressionV2Op(OpKernelConstruction* context)
-      : OpKernel(context) {}
+      : OpKernel(context) {
+  }
 
   void Compute(OpKernelContext* context) override {
     // boxes: [num_boxes, 4]
@@ -190,14 +194,14 @@ class NonMaxSuppressionV2Op : public OpKernel {
                                 max_output_size.shape().DebugString()));
     // iou_threshold: scalar
     const Tensor& iou_threshold = context->input(3);
-    OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
-                errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
-                                        iou_threshold.shape().DebugString()));
+    OP_REQUIRES(
+        context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
+        errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
+                                iou_threshold.shape().DebugString()));
 
     const float iou_threshold_val = iou_threshold.scalar<float>()();
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
-                          iou_threshold_val);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_val);
   }
 };
 
diff --git a/tensorflow/core/kernels/priority_queue.cc b/tensorflow/core/kernels/priority_queue.cc
index 894ad3c9a0..4c406fc1ed 100644
--- a/tensorflow/core/kernels/priority_queue.cc
+++ b/tensorflow/core/kernels/priority_queue.cc
@@ -339,7 +339,7 @@ void PriorityQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx,
             for (; s > 0; --s) {
               if (attempt->tuple.empty()) {
                 // Only allocate tuple when we have something to dequeue
-                // so we don't use exceessive memory when there are many
+                // so we don't use excessive memory when there are many
                 // blocked dequeue attempts waiting.
                 attempt->tuple.reserve(num_components());
                 for (int i = 0; i < num_components(); ++i) {
diff --git a/tensorflow/core/kernels/shape_ops.cc b/tensorflow/core/kernels/shape_ops.cc
index d78c6d2639..c5e3164145 100644
--- a/tensorflow/core/kernels/shape_ops.cc
+++ b/tensorflow/core/kernels/shape_ops.cc
@@ -48,6 +48,7 @@ REGISTER_KERNEL_BUILDER(Name("Shape")
                           ShapeOp<int64>);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
 REGISTER_KERNEL_BUILDER(Name("Shape")
@@ -102,7 +103,7 @@ REGISTER_KERNEL_BUILDER(Name("Shape")
                             .TypeConstraint<int32>("T")
                             .TypeConstraint<int64>("out_type"),
                         ShapeOp<int64>);
-#endif
+#endif  // GOOGLE_CUDA
 
 // ShapeN ---------------------------------------
 REGISTER_KERNEL_BUILDER(Name("ShapeN")
@@ -152,9 +153,9 @@ REGISTER_KERNEL_BUILDER(Name("ShapeN")
                             .TypeConstraint<int32>("T")
                             .TypeConstraint<int64>("out_type"),
                         ShapeNOp<int64>);
-#endif
+#endif  // GOOGLE_CUDA
 
-#if TENSORFLOW_USE_SYCL
+#ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                               \
   REGISTER_KERNEL_BUILDER(Name("ShapeN")                         \
                               .Device(DEVICE_SYCL)               \
@@ -170,11 +171,9 @@ REGISTER_KERNEL_BUILDER(Name("ShapeN")
                           ShapeNOp<int64>)
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("ShapeN")
                             .Device(DEVICE_SYCL)
                             .HostMemory("input")
@@ -202,13 +201,9 @@ REGISTER_KERNEL_BUILDER(Name("Rank").Device(DEVICE_CPU).HostMemory("output"),
                               .TypeConstraint<type>("T") \
                               .HostMemory("output"),     \
                           RankOp);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32 and bool.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Rank")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
@@ -250,7 +245,7 @@ REGISTER_KERNEL_BUILDER(Name("Rank")
                             .HostMemory("input")
                             .HostMemory("output"),
                         RankOp);
-#endif
+#endif  // GOOGLE_CUDA
 
 // Size ------------------------------------------
 REGISTER_KERNEL_BUILDER(Name("Size")
@@ -299,7 +294,7 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SizeOp<int64>);
-#endif
+#endif  // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                               \
@@ -315,13 +310,10 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                               .TypeConstraint<int64>("out_type") \
                               .HostMemory("output"),             \
                           SizeOp<int64>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Size")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
@@ -336,7 +328,7 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SizeOp<int64>);
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 // ExpandDims ------------------------------------
 REGISTER_KERNEL_BUILDER(Name("ExpandDims")
@@ -365,7 +357,7 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                             .HostMemory("dim")
                             .HostMemory("output"),
                         ExpandDimsOp);
-#endif // GOOGLE_CUDA
+#endif  // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                           \
@@ -375,9 +367,8 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                               .TypeConstraint<int32>("Tdim") \
                               .HostMemory("dim"),            \
                           ExpandDimsOp);
-REGISTER_SYCL_KERNEL(float)
-REGISTER_SYCL_KERNEL(double)
-
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
 REGISTER_KERNEL_BUILDER(Name("ExpandDims")
@@ -388,7 +379,7 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                             .HostMemory("dim")
                             .HostMemory("output"),
                         ExpandDimsOp);
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 // Squeeze ---------------------------------------
 REGISTER_KERNEL_BUILDER(Name("Squeeze").Device(DEVICE_CPU), SqueezeOp);
@@ -411,26 +402,23 @@ REGISTER_KERNEL_BUILDER(Name("Squeeze")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SqueezeOp);
-#endif
+#endif  // GOOGLE_CUDA
 
 #if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                                  \
-  REGISTER_KERNEL_BUILDER(                                          \
-      Name("Squeeze").Device(DEVICE_SYCL).TypeConstraint<type>("T"),\
+#define REGISTER_SYCL_KERNEL(type)                                   \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("Squeeze").Device(DEVICE_SYCL).TypeConstraint<type>("T"), \
       SqueezeOp);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Squeeze")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SqueezeOp);
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc
index ee6f9a28cd..d46701749b 100644
--- a/tensorflow/core/kernels/slice_op.cc
+++ b/tensorflow/core/kernels/slice_op.cc
@@ -118,6 +118,43 @@ static void SharedValidation(OpKernelContext* context,
   }
 }
 
+// Extracted out code in SliceOp::Compute so that MklSliceOp can reuse this
+// generic code
+template <typename T>
+static void SharedSliceCommonCases(OpKernelContext* context,
+                                   TensorShape* output_shape,
+                                   gtl::InlinedVector<int64, 4>* begin,
+                                   gtl::InlinedVector<int64, 4>* size,
+                                   Tensor** result,
+                                   bool* done) {
+  bool is_identity = true;
+  bool slice_dim0 = true;
+  *done = false;
+
+  SharedValidation(context, output_shape, &is_identity, &slice_dim0, begin,
+                   size);
+  if (!context->status().ok()) return;
+  const Tensor& input = context->input(0);
+  if (is_identity) {
+    VLOG(1) << "Slice identity";
+    context->set_output(0, input);
+    *done = true;
+    return;
+  }
+
+  if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), (*begin)[0],
+                                          (*size)[0])) {
+    VLOG(1) << "Slice dim 0: " << input.shape().DebugString();
+    CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
+    context->set_output(0, input.Slice((*begin)[0], (*begin)[0] + (*size)[0]));
+    *done = true;
+    return;
+  }
+
+  OP_REQUIRES_OK(context, context->allocate_output(0, *output_shape, result));
+}
+
+
 template <typename Device, typename T>
 class SliceOp : public OpKernel {
  public:
@@ -125,29 +162,89 @@ class SliceOp : public OpKernel {
 
   void Compute(OpKernelContext* context) override {
     TensorShape output_shape;
-    bool is_identity = true;
-    bool slice_dim0 = true;
     gtl::InlinedVector<int64, 4> begin;
     gtl::InlinedVector<int64, 4> size;
-    SharedValidation(context, &output_shape, &is_identity, &slice_dim0, &begin,
-                     &size);
-    if (!context->status().ok()) return;
+    Tensor* result = nullptr;
+    bool done = false;
+    SharedSliceCommonCases<T>(context, &output_shape, &begin, &size, &result,
+                              &done);
+    if (!context->status().ok() || done == true) return;
+
     const Tensor& input = context->input(0);
-    if (is_identity) {
-      VLOG(1) << "Slice identity";
-      context->set_output(0, input);
-      return;
+    const int input_dims = input.dims();
+
+    if (output_shape.num_elements() > 0) {
+      if (std::is_same<Device, CPUDevice>::value && input_dims == 2 &&
+          DataTypeCanUseMemcpy(DataTypeToEnum<T>::v())) {
+        auto input = context->input(0).tensor<T, 2>();
+        auto output = result->tensor<T, 2>();
+        // TODO(agarwal): Consider multi-threading this loop for cases where
+        // size[0] is very large.
+        for (int i = 0; i < size[0]; ++i) {
+          const int64 row = begin[0] + i;
+          if (i + 1 < size[0]) {
+            port::prefetch<port::PREFETCH_HINT_T0>(&output(i + 1, 0));
+            port::prefetch<port::PREFETCH_HINT_T0>(&input(row + 1, begin[1]));
+          }
+          memcpy(&output(i, 0), &input(row, begin[1]), size[1] * sizeof(T));
+        }
+        return;
+      }
+#define HANDLE_DIM(NDIM)                            \
+  if (input_dims == NDIM) {                         \
+    HandleCase<NDIM>(context, begin, size, result); \
+    return;                                         \
+  }
+
+      HANDLE_DIM(1);
+      HANDLE_DIM(2);
+      HANDLE_DIM(3);
+      HANDLE_DIM(4);
+      HANDLE_DIM(5);
+      HANDLE_DIM(6);
+      HANDLE_DIM(7);
+
+#undef HANDLE_DIM
+
+      OP_REQUIRES(context, false, errors::Unimplemented(
+                                      "SliceOp : Unhandled input dimensions"));
     }
+  }
 
-    if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), begin[0], size[0])) {
-      VLOG(1) << "Slice dim 0: " << input.shape().DebugString();
-      CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
-      context->set_output(0, input.Slice(begin[0], begin[0] + size[0]));
-      return;
+ private:
+  template <int NDIM>
+  void HandleCase(OpKernelContext* context, const gtl::ArraySlice<int64>& begin,
+                  const gtl::ArraySlice<int64>& size, Tensor* result) {
+    Eigen::DSizes<Eigen::DenseIndex, NDIM> indices;
+    Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes;
+    for (int i = 0; i < NDIM; ++i) {
+      indices[i] = begin[i];
+      sizes[i] = size[i];
     }
 
+    functor::Slice<Device, T, NDIM>()(
+        context->eigen_device<Device>(), result->tensor<T, NDIM>(),
+        context->input(0).tensor<T, NDIM>(), indices, sizes);
+  }
+};
+
+#ifdef INTEL_MKL
+template <typename Device, typename T>
+class MklSliceOp : public OpKernel {
+ public:
+  explicit MklSliceOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    TensorShape output_shape;
+    gtl::InlinedVector<int64, 4> begin;
+    gtl::InlinedVector<int64, 4> size;
     Tensor* result = nullptr;
-    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &result));
+    bool done = false;
+    SharedSliceCommonCases<T>(context, &output_shape, &begin, &size, &result,
+                              &done);
+    if (!context->status().ok() || done == true) return;
+
+    const Tensor& input = context->input(0);
     const int input_dims = input.dims();
 
     if (output_shape.num_elements() > 0) {
@@ -189,9 +286,123 @@ class SliceOp : public OpKernel {
   }
 
  private:
+  // Helper function for DoesSliceShapeDifferInOnly1D. Checks if the following
+  // criteria matches for slice_dim: if indices for slice are 0 in all dims
+  // except slice_dim and if sizes of all the dimensions of the slice are same
+  // as the sizes of all the dimensions of the input except slice_dim, then
+  // returns True. Otherwise, returns False.
+  bool DoesSliceShapeDifferInOnly1DHelper(const TensorShape& input_shape,
+                          const gtl::ArraySlice<int64>& begin,
+                          const gtl::ArraySlice<int64>& size,
+                          int slice_dim) {
+    for (int dim = 0; dim < 4; dim++) {
+      if (dim != slice_dim &&
+          (begin[dim] != 0 || size[dim] != input_shape.dim_size(dim))) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  // Is 'input' tensor being sliced over a single dimension out of 4?
+  //
+  // This check is applicable in the context of Slice of a 4-D tensor in
+  // NHWC or NCHW format over channel dimension.
+  //
+  // If indices for slice are 0 in all dims except one dimension and if sizes of
+  // all dimensions of slice are same as sizes of all dimensions of inputs
+  // except that dimension, then we are slicing over a single dimension.
+  //
+  // Returns True if Slicing over a single dimension, and sets slice_dim
+  // to the number of the dimension that satisfies criteria.
+  bool DoesSliceShapeDifferInOnly1D(const TensorShape& input_shape,
+                          const gtl::ArraySlice<int64>& begin,
+                          const gtl::ArraySlice<int64>& size,
+                          int* slice_dim) {
+    for (int dim = 0; dim < 4; dim++) {
+      if (DoesSliceShapeDifferInOnly1DHelper(input_shape, begin, size, dim)) {
+        *slice_dim = dim;
+        return true;
+      }
+    }
+    return false;
+  }
+
   template <int NDIM>
-  void HandleCase(OpKernelContext* context, const gtl::ArraySlice<int64>& begin,
+  void HandleCase(OpKernelContext* context,
+                  const gtl::ArraySlice<int64>& begin,
                   const gtl::ArraySlice<int64>& size, Tensor* result) {
+    int slice_dim = -1;
+    TensorShape in_shape = context->input(0).shape();
+    // Special case for handling 4-D tensor slice when shape of the slice
+    // differs from the input tensor in only 1 out of 4 dimensions.
+    // This case arises in the context of Slice of 4-D tensor in NHWC or NCHW
+    // format over channel dimension.
+    if (NDIM == 4 &&
+        DoesSliceShapeDifferInOnly1D(in_shape, begin, size, &slice_dim)) {
+        size_t in_strides[4] = { (size_t) in_shape.dim_size(1) *
+                                          in_shape.dim_size(2) *
+                                          in_shape.dim_size(3),
+                                 (size_t) in_shape.dim_size(2) *
+                                          in_shape.dim_size(3),
+                                 (size_t) in_shape.dim_size(3),
+                                 (size_t) 1
+                               };
+
+        size_t out_strides[4] = { (size_t) size[1] * size[2] * size[3],
+                                  (size_t) size[2] * size[3],
+                                  (size_t) size[3],
+                                  (size_t) 1 };
+
+        T *in_buf = const_cast<T*>(const_cast<const T*>(
+                    context->input(0).flat<T>().data()));
+        T *op_buf = result->flat<T>().data();
+
+        if (slice_dim == 1) {
+          /* data format = NCHW */
+
+          #pragma omp parallel for
+          for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
+              T *ip  = in_buf + (d0 * in_strides[0]);
+              T *op  = op_buf + ((d0 - begin[0]) * out_strides[0]);
+            #pragma omp parallel for
+            for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
+              T *ip1 = ip + (d1 * in_strides[1]);
+              T *op1 = op + ((d1 - begin[1]) * out_strides[1]);
+              // For NCHW, H and W will be contiguous. So we can copy
+              // both with one memcpy.
+              memcpy(static_cast<void*>(op1), static_cast<void*>(ip1),
+                     sizeof(T) * in_strides[1]);
+            }
+          }
+          return;
+        } else if (slice_dim == 3) {
+          /* data_format = NHWC */
+
+          #pragma omp parallel for
+          for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
+              T *ip = in_buf + (d0 * in_strides[0]);
+              T *op = op_buf + ((d0 - begin[0]) * out_strides[0]);
+            #pragma omp parallel for
+            for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
+              T *ip1 = ip + (d1 * in_strides[1]);
+              T *op1 = op + ((d1 - begin[1]) * out_strides[1]);
+              #pragma omp parallel for
+              for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) {
+                T *ip2 = ip1 + (d2 * in_strides[2]);
+                T *ip3 = ip2 + begin[3];
+                T *op2 = op1 + ((d2 - begin[2]) * out_strides[2]);
+                T *op3 = op2;
+                memcpy(static_cast<void*>(op3), static_cast<void*>(ip3),
+                       sizeof(T) * size[3]);
+              }
+            }
+          }
+          return;
+        }
+        // slice_dim is not 1 or 3, then we fallback to Eigen implementation.
+    }
+
     Eigen::DSizes<Eigen::DenseIndex, NDIM> indices;
     Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes;
     for (int i = 0; i < NDIM; ++i) {
@@ -204,6 +415,7 @@ class SliceOp : public OpKernel {
         context->input(0).tensor<T, NDIM>(), indices, sizes);
   }
 };
+#endif
 
 // Forward declarations of the functor specializations for declared in the
 // sharded source files.
@@ -233,6 +445,7 @@ DECLARE_FOR_N(bfloat16);
 #undef DECLARE_CPU_SPEC
 }  // namespace functor
 
+#ifndef INTEL_MKL
 #define REGISTER_SLICE(type)                             \
   REGISTER_KERNEL_BUILDER(Name("Slice")                  \
                               .Device(DEVICE_CPU)        \
@@ -244,8 +457,21 @@ DECLARE_FOR_N(bfloat16);
 TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
 TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
 REGISTER_SLICE(bfloat16);
+#undef REGISTER_SLICE
+#else
+#define REGISTER_SLICE(type)                             \
+  REGISTER_KERNEL_BUILDER(Name("Slice")                  \
+                              .Device(DEVICE_CPU)        \
+                              .TypeConstraint<type>("T") \
+                              .HostMemory("begin")       \
+                              .HostMemory("size"),       \
+                          MklSliceOp<CPUDevice, type>)
 
+TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
+TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
+REGISTER_SLICE(bfloat16);
 #undef REGISTER_SLICE
+#endif  // INTEL_MKL
 
 #if GOOGLE_CUDA
 // Forward declarations of the functor specializations for GPU.
diff --git a/tensorflow/core/kernels/sparse_reduce_op.cc b/tensorflow/core/kernels/sparse_reduce_op.cc
new file mode 100644
index 0000000000..9e60791f97
--- /dev/null
+++ b/tensorflow/core/kernels/sparse_reduce_op.cc
@@ -0,0 +1,341 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// See docs in ../ops/sparse_ops.cc.
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_util.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/util/sparse/sparse_tensor.h"
+
+// TODO(b/31496047): Fix non-standard include order.
+#include <numeric>  // clang-format off
+
+using tensorflow::sparse::SparseTensor;
+using tensorflow::gtl::ArraySlice;
+
+namespace tensorflow {
+
+struct ReduceDetails {
+  // The dimensions to call Reorder() with.
+  std::vector<int64> reorder_dims;
+
+  // The dimensions to call group() with after Reorder().
+  std::vector<int64> group_by_dims;
+
+  // The shape after reduction.
+  TensorShape reduced_shape;
+};
+
+// Compute common reduce parameters that'll be used for SparseTensor
+// reductions. Usage:
+// ReduceDetails reduction = SparseTensorReduceHelper(sp, axes, keep_dims);
+// sp.Reorder(reduction.reorder_dims);
+// for (const auto& g : sp.group(reduction.group_by_dims)) {
+//   ...
+// }
+// // Set output shape to reduction.reduced_shape.
+ReduceDetails SparseTensorReduceHelper(const SparseTensor &sp,
+                                       gtl::ArraySlice<int32> axes_slice,
+                                       bool keep_dims) {
+  ReduceDetails reduction;
+
+  std::vector<int32> reduction_axes(axes_slice.begin(), axes_slice.end());
+  int ndims = sp.dims();
+  for (int64 i = 0; i < reduction_axes.size(); ++i) {
+    reduction_axes[i] = (reduction_axes[i] + ndims) % ndims;
+  }
+  std::sort(reduction_axes.begin(), reduction_axes.end());
+
+  // (0) Calculate the grouping dimensions:
+  // group_by_dims == {0, .., NDIMS-1} \ reduction_axes.
+  std::vector<int64> perm(ndims);
+  std::iota(perm.begin(), perm.end(), 0);
+
+  // Requires perm and reduction_axes_ be sorted; group_by_dims will be
+  // sorted as well.
+  std::set_difference(
+      perm.begin(), perm.end(), reduction_axes.begin(), reduction_axes.end(),
+      std::inserter(reduction.group_by_dims, reduction.group_by_dims.begin()));
+
+  // Now append the rest of the axes (the complement of group_by_dims_);
+  // result is used by Reorder().
+  reduction.reorder_dims = reduction.group_by_dims;
+  std::set_difference(perm.begin(), perm.end(), reduction.group_by_dims.begin(),
+                      reduction.group_by_dims.end(),
+                      std::back_inserter(reduction.reorder_dims));
+
+  // (1) Calculate the shape after reduction.
+  auto sp_shape = sp.shape();
+  std::vector<int64> out_dim_sizes;
+  if (keep_dims) {
+    out_dim_sizes.reserve(ndims);
+    auto beg = reduction.group_by_dims.begin();
+    auto end = reduction.group_by_dims.end();
+    for (int d = 0; d < ndims; ++d) {
+      if (std::find(beg, end, d) == end) {
+        out_dim_sizes.push_back(1);  // A reduced axis.
+      } else {
+        out_dim_sizes.push_back(sp_shape[d]);
+      }
+    }
+  } else {
+    out_dim_sizes = sp.PickDims(reduction.group_by_dims);
+  }
+
+  reduction.reduced_shape = TensorShape(out_dim_sizes);
+  return reduction;
+}
+
+Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
+  // indices and values are validated in SparseTensor ctor.
+  if (!TensorShapeUtils::IsVector(shape_t->shape())) {
+    return errors::InvalidArgument(
+        "Expected input_shape to be a vector; got shape: ",
+        shape_t->shape().DebugString());
+  }
+  if (!TensorShapeUtils::IsScalar(reduction_axes_t->shape()) &&
+      !TensorShapeUtils::IsVector(reduction_axes_t->shape())) {
+    return errors::InvalidArgument(
+        "Expected reduction_axes to be a scalar or a vector; got shape: ",
+        reduction_axes_t->shape().DebugString());
+  }
+
+  const auto reduction_axes_flat = reduction_axes_t->flat<int32>();
+  for (int64 i = 0; i < reduction_axes_flat.size(); i++) {
+    int32 axis = reduction_axes_flat(i);
+    if (axis < -shape_t->NumElements() || axis >= shape_t->NumElements()) {
+      return errors::InvalidArgument("Invalid reduction dimension ", axis,
+                                     ", for input with ",
+                                     shape_t->NumElements(), " dimensions.");
+    }
+  }
+
+  return Status::OK();
+}
+
+struct SumOp {
+  template <typename T>
+  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
+      s.device(ctx->eigen_cpu_device()) = v.sum();
+  }
+  static StringPiece Name() {
+      return "sum";
+  }
+};
+
+struct MaxOp {
+  template <typename T>
+  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
+      s.device(ctx->eigen_cpu_device()) = v.maximum();
+  }
+  static StringPiece Name() {
+      return "max";
+  }
+};
+
+template <typename T, typename Op>
+class SparseReduceOp : public OpKernel {
+ public:
+  explicit SparseReduceOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
+  }
+
+  void Compute(OpKernelContext *ctx) override {
+    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
+    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
+    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
+
+    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
+
+    // TODO(zongheng): we will call Reorder() below, which will modify
+    // in-place the underlying indices and values buffers.  To avoid
+    // surprises of this kernel being stateful, we work around the above by
+    // making deep copies here.  Remove this if/when we change Reorder()'s
+    // semantics.
+    const auto shape_vec = shape_t->vec<int64>();
+    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
+                    TensorShape(shape_vec));
+    ReduceDetails reduction = SparseTensorReduceHelper(
+        sp, reduction_axes_t->flat<int32>(), keep_dims_);
+
+    Tensor *out_values;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output(0, reduction.reduced_shape, &out_values));
+    auto out_flat = out_values->flat<T>();
+    out_flat.setZero();
+
+    Tensor tmp_reduced_val;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                           TensorShape({}), &tmp_reduced_val));
+    auto reduced_val = tmp_reduced_val.scalar<T>();
+
+    // Compute strides, and use it to convert coords to flat index.  The
+    // coordinates returned by .group() have the same ndims as group_by_dims.
+    gtl::InlinedVector<int64, 8> output_strides(reduction.group_by_dims.size());
+    if (!output_strides.empty()) {  // Do this iff we don't reduce all.
+      output_strides.back() = 1;
+      for (int d = output_strides.size() - 2; d >= 0; --d) {
+        output_strides[d] =
+            output_strides[d + 1] * shape_vec(reduction.group_by_dims[d + 1]);
+      }
+    }
+
+    auto CoordinatesToFlatIndex = [](ArraySlice<int64> coords,
+                                     ArraySlice<int64> strides) {
+      if (strides.empty()) {  // Reduce all.
+        return 0LL;
+      }
+      CHECK_EQ(coords.size(), strides.size());
+      int64 idx = 0;
+      for (int i = 0; i < coords.size(); ++i) {
+        idx += coords[i] * strides[i];
+      }
+      return idx;
+    };
+
+    // Each group maps one-on-one onto a value in the reduced tensor.
+    // g.group() provides the coordinates of a particular reduced value.
+    sp.Reorder<T>(reduction.reorder_dims);
+    for (const auto &g : sp.group(reduction.group_by_dims)) {
+      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
+      const int64 idx = CoordinatesToFlatIndex(g.group(), output_strides);
+      out_flat(idx) = reduced_val();
+      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
+              << "; idx: " << idx << "; group " << Op::Name() << ": "
+              << reduced_val();
+    }
+  }
+
+ private:
+  // True if the number of dimensions should be maintained.
+  bool keep_dims_;
+};
+
+#define REGISTER_KERNELS(T)                                              \
+  REGISTER_KERNEL_BUILDER(                                               \
+      Name("SparseReduceSum").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceOp<T, SumOp>)
+TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+#define REGISTER_KERNELS(T)                                              \
+  REGISTER_KERNEL_BUILDER(                                               \
+      Name("SparseReduceMax").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceOp<T, MaxOp>)
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+template <typename T, typename Op>
+class SparseReduceSparseOp : public OpKernel {
+ public:
+  explicit SparseReduceSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
+  }
+
+  void Compute(OpKernelContext *ctx) override {
+    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
+    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
+    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
+
+    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
+
+    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
+                    TensorShape(shape_t->vec<int64>()));
+    ReduceDetails reduction = SparseTensorReduceHelper(
+        sp, reduction_axes_t->flat<int32>(), keep_dims_);
+
+    sp.Reorder<T>(reduction.reorder_dims);
+    // Count nnzs in the output SparseTensor.
+    int64 nnz = 0;
+    auto iter = sp.group(reduction.group_by_dims);
+    for (auto it = iter.begin(); it != iter.end(); ++it) {
+      nnz++;
+    }
+
+    Tensor *out_indices_t;
+    OP_REQUIRES_OK(ctx,
+                   ctx->allocate_output(
+                       0, TensorShape({nnz, reduction.reduced_shape.dims()}),
+                       &out_indices_t));
+    typename TTypes<int64>::Matrix out_indices_mat =
+        out_indices_t->matrix<int64>();
+    // For keep_dims. We don't explicitly set dim fields for reduced dims below.
+    out_indices_mat.setZero();
+
+    Tensor *out_values_t;
+    OP_REQUIRES_OK(ctx,
+                   ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
+    auto out_flat = out_values_t->flat<T>();
+
+    Tensor tmp_reduced_val;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                           TensorShape({}), &tmp_reduced_val));
+    auto reduced_val = tmp_reduced_val.scalar<T>();
+    int64 i = 0;
+    for (const auto &g : sp.group(reduction.group_by_dims)) {
+      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
+      std::vector<int64> group = g.group();
+      for (int64 j = 0; j < group.size(); j++) {
+        if (keep_dims_) {
+          out_indices_mat(i, reduction.group_by_dims[j]) = group[j];
+        } else {
+          out_indices_mat(i, j) = group[j];
+        }
+      }
+      out_flat(i) = reduced_val();
+      i++;
+      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
+              << "; group " << Op::Name() << ": "
+              << reduced_val();
+    }
+
+    Tensor *out_shape_t;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(
+                            2, TensorShape({reduction.reduced_shape.dims()}),
+                            &out_shape_t));
+    auto out_shape_flat = out_shape_t->flat<int64>();
+    auto out_dim_sizes = reduction.reduced_shape.dim_sizes();
+    std::copy(out_dim_sizes.begin(), out_dim_sizes.end(), &out_shape_flat(0));
+  }
+
+ private:
+  // True if the number of dimensions should be maintained.
+  bool keep_dims_;
+};
+
+#define REGISTER_KERNELS(T)                                                    \
+  REGISTER_KERNEL_BUILDER(                                                     \
+      Name("SparseReduceSumSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceSparseOp<T, SumOp>)
+TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+#define REGISTER_KERNELS(T)                                                    \
+  REGISTER_KERNEL_BUILDER(                                                     \
+      Name("SparseReduceMaxSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceSparseOp<T, MaxOp>)
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/stack_ops.cc b/tensorflow/core/kernels/stack_ops.cc
index b4698a8053..2db3e5ef77 100644
--- a/tensorflow/core/kernels/stack_ops.cc
+++ b/tensorflow/core/kernels/stack_ops.cc
@@ -40,6 +40,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif // TENSORFLOW_USE_SYCL
 
 class Stack : public ResourceBase {
  public:
@@ -182,6 +185,10 @@ class StackOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_CPU), StackOp);
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_GPU).HostMemory("handle"),
                         StackOp);
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_SYCL).HostMemory("handle"),
+                        StackOp);
+#endif // TENSORFLOW_USE_SYCL
 
 template <typename Device>
 class StackPushOp : public AsyncOpKernel {
@@ -213,7 +220,11 @@ class StackPushOp : public AsyncOpKernel {
     static constexpr int kCopyThreshold = 2048;
     static constexpr double kOccupancy = 0.7;
     if (swap_memory_ && !alloc_attrs.on_host() &&
-        std::is_same<Device, GPUDevice>::value &&
+        ( std::is_same<Device, GPUDevice>::value
+#ifdef TENSORFLOW_USE_SYCL
+          || std::is_same<Device, SYCLDevice>::value
+#endif // TENSORFLOW_USE_SYCL
+        ) &&
         tensor.TotalBytes() > kCopyThreshold && stack->IsUsefulToSwap(tensor)) {
       DeviceContext* device_ctxt = ctx->op_device_context();
       auto device = static_cast<tensorflow::Device*>(ctx->device());
@@ -289,6 +300,31 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                        \
+  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("handle")       \
+                              .TypeConstraint<type>("T"), \
+                          StackPushOp<SYCLDevice>);
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
+
+#define REGISTER_SYCL_HOST_KERNEL(type)                   \
+  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("handle")       \
+                              .HostMemory("elem")         \
+                              .HostMemory("output")       \
+                              .TypeConstraint<type>("T"), \
+                          StackPushOp<SYCLDevice>)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(bool);
+#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 class StackPopOp : public AsyncOpKernel {
  public:
   explicit StackPopOp(OpKernelConstruction* context) : AsyncOpKernel(context) {}
@@ -359,6 +395,31 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                                \
+  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
+                              .Device(DEVICE_SYCL)                \
+                              .HostMemory("handle")               \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp)
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
+
+#define REGISTER_SYCL_HOST_KERNEL(type)                           \
+  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
+                              .Device(DEVICE_SYCL)                \
+                              .HostMemory("handle")               \
+                              .HostMemory("elem")                 \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(bool);
+
+#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 class StackCloseOp : public OpKernel {
  public:
   explicit StackCloseOp(OpKernelConstruction* context) : OpKernel(context) {}
@@ -376,5 +437,8 @@ class StackCloseOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("StackClose").Device(DEVICE_CPU), StackCloseOp);
 REGISTER_KERNEL_BUILDER(
     Name("StackClose").Device(DEVICE_GPU).HostMemory("handle"), StackCloseOp);
-
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(
+    Name("StackClose").Device(DEVICE_SYCL).HostMemory("handle"), StackCloseOp);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/topk_op.cc b/tensorflow/core/kernels/topk_op.cc
index 630fcb76f3..5c89eaef5f 100644
--- a/tensorflow/core/kernels/topk_op.cc
+++ b/tensorflow/core/kernels/topk_op.cc
@@ -93,7 +93,7 @@ class TopK : public OpKernel {
       rows_by_one.set(0, num_rows);
 #else
       Eigen::array<int, 1> reduce_on_cols = {1};
-      Eigen::array<int, 1> rows_by_one = {static_cast<int>(num_rows), 1};
+      Eigen::array<int, 2> rows_by_one = {static_cast<int>(num_rows), 1};
 #endif
 
       values.device(d) =
diff --git a/tensorflow/core/kernels/transpose_functor.h b/tensorflow/core/kernels/transpose_functor.h
index 124cf14dd2..f1ab770eeb 100644
--- a/tensorflow/core/kernels/transpose_functor.h
+++ b/tensorflow/core/kernels/transpose_functor.h
@@ -132,6 +132,13 @@ template <typename Device, typename T, int NDIMS>
 void TransposeUsingEigen(const Device& d, const Tensor& in,
                          const gtl::ArraySlice<int32> perm, Tensor* out);
 
+
+#ifdef TENSORFLOW_USE_SYCL
+// For SYCL lets always go through Eigen
+template <typename Device, typename T>
+void TransposeSYCL(const Device& d, const Tensor& in,
+                   const gtl::ArraySlice<int32> perm, Tensor* out);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace internal
 
 template <typename Device, typename T>
diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc
index 75ed76a697..d3305fb83a 100644
--- a/tensorflow/core/kernels/transpose_op.cc
+++ b/tensorflow/core/kernels/transpose_op.cc
@@ -233,10 +233,7 @@ Status TransposeSyclOp::DoTranspose(OpKernelContext* ctx, const Tensor& in,
                               .TypeConstraint<int32>("Tperm") \
                               .HostMemory("perm"),            \
                           TransposeSyclOp);
-REGISTER(float);
-REGISTER(bool);
-REGISTER(int32);
+TF_CALL_POD_TYPES(REGISTER);
 #undef REGISTER
 #endif
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/typed_conditional_accumulator_base.h b/tensorflow/core/kernels/typed_conditional_accumulator_base.h
index dbd7de7ce0..1980f758fc 100644
--- a/tensorflow/core/kernels/typed_conditional_accumulator_base.h
+++ b/tensorflow/core/kernels/typed_conditional_accumulator_base.h
@@ -22,7 +22,7 @@ namespace tensorflow {
 
 /*
  * TypedConditionalAccumulatorBase is a templated companion of
- * ConditionalAccumulatorBase which allows for subclassses to use different
+ * ConditionalAccumulatorBase which allows for subclasses to use different
  * types for the input gradients. (See ConditionalAccumulator and
  * SparseConditionalAccumulator.)
  *
diff --git a/tensorflow/core/lib/gtl/optional.h b/tensorflow/core/lib/gtl/optional.h
index 8ba4b09143..2ff8b9c7d1 100644
--- a/tensorflow/core/lib/gtl/optional.h
+++ b/tensorflow/core/lib/gtl/optional.h
@@ -656,7 +656,7 @@ class optional : private internal_optional::optional_data<T>,
   constexpr const T& reference() const { return *this->pointer(); }
   T& reference() { return *(this->pointer()); }
 
-  // T constaint checks.  You can't have an optional of nullopt_t, in_place_t or
+  // T constraint checks.  You can't have an optional of nullopt_t, in_place_t or
   // a reference.
   static_assert(
       !std::is_same<nullopt_t, typename std::remove_cv<T>::type>::value,
diff --git a/tensorflow/core/ops/math_grad.cc b/tensorflow/core/ops/math_grad.cc
index a530d286f7..9a58a31757 100644
--- a/tensorflow/core/ops/math_grad.cc
+++ b/tensorflow/core/ops/math_grad.cc
@@ -155,6 +155,26 @@ Status Log1pGrad(const AttrSlice& attrs, FunctionDef* g) {
 }
 REGISTER_OP_GRADIENT("Log1p", Log1pGrad);
 
+Status SinhGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"cosh"}, "Cosh", {"x"}, {}, {"dy"}},
+      {{"dx"}, "Mul", {"dy", "cosh"}},  // dy * cosh(x)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Sinh", SinhGrad);
+
+Status CoshGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"sinh"}, "Sinh", {"x"}, {}, {"dy"}},
+      {{"dx"}, "Mul", {"dy", "sinh"}},  // dy * sinh(x)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Cosh", CoshGrad);
+
 Status TanhGrad(const AttrSlice& attrs, FunctionDef* g) {
   // clang-format off
   return GradForUnaryCwise(g, {
diff --git a/tensorflow/core/ops/math_grad_test.cc b/tensorflow/core/ops/math_grad_test.cc
index 38813b3f2b..aa9706a328 100644
--- a/tensorflow/core/ops/math_grad_test.cc
+++ b/tensorflow/core/ops/math_grad_test.cc
@@ -495,6 +495,26 @@ TEST_F(MathGradTest, Log1p) {
   test::ExpectClose(ans, dx);
 }
 
+TEST_F(MathGradTest, Sinh) {
+  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) { return std::cosh(x); };
+  auto dx = test::AsTensor<float>(
+      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Sinh", x);
+  test::ExpectClose(ans, dx);
+}
+
+TEST_F(MathGradTest, Cosh) {
+  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) { return std::sinh(x); };
+  auto dx = test::AsTensor<float>(
+      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Cosh", x);
+  test::ExpectClose(ans, dx);
+}
+
 TEST_F(MathGradTest, Tanh) {
   auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
                                  TensorShape({2, 3}));
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 3fe543a239..30d6987707 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -293,6 +293,14 @@ Computes natural logarithm of (1 + x) element-wise.
 I.e., \\(y = \log_e (1 + x)\\).
 )doc");
 
+REGISTER_OP("Sinh").UNARY_COMPLEX().Doc(R"doc(
+Computes hyperbolic sine of x element-wise.
+)doc");
+
+REGISTER_OP("Cosh").UNARY_COMPLEX().Doc(R"doc(
+Computes hyperbolic cosine of x element-wise.
+)doc");
+
 REGISTER_OP("Tanh").UNARY_COMPLEX().Doc(R"doc(
 Computes hyperbolic tangent of `x` element-wise.
 )doc");
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 70302c3886..3a25fd15da 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -831,11 +831,13 @@ a different filter to each input channel (expanding from 1 channel to
 `channel_multiplier` channels for each), then concatenates the results
 together. Thus, the output has `in_channels * channel_multiplier` channels.
 
+```
 for k in 0..in_channels-1
   for q in 0..channel_multiplier-1
     output[b, i, j, k * channel_multiplier + q] =
       sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                         filter[di, dj, k, q]
+```
 
 Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
 horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index b122b5a992..92ca6ea367 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -5184,6 +5184,30 @@ op {
   }
   summary: "Computes cos of x element-wise."
 }
+op {
+  name: "Cosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes hyperbolic cosine of x element-wise."
+}
 op {
   name: "CountUpTo"
   input_arg {
@@ -22792,6 +22816,30 @@ op {
   }
   summary: "Computes sin of x element-wise."
 }
+op {
+  name: "Sinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes hyperbolic sine of x element-wise."
+}
 op {
   name: "Size"
   input_arg {
@@ -27258,6 +27306,33 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
+op {
+  name: "LMDBReader"
+  output_arg {
+    name: "reader_handle"
+    description: "The handle to reference the Reader."
+    type: DT_STRING
+    is_ref: true
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
+  }
+  summary: "A Reader that outputs the records from a LMDB database."
+  is_stateful: true
+}
 op {
   name: "TakeDataset"
   input_arg {
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 9722f0ee9a..6aca2c3b01 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -710,6 +710,75 @@ a_shape: 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
 b: `ndims`-D Tensor.  With shape `a_shape`.
 )doc");
 
+REGISTER_OP("SparseReduceMax")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output: T")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+instead of a sparse one.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+output: `R-K`-D.  The reduced Tensor.
+)doc");
+
+REGISTER_OP("SparseReduceMaxSparse")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output_indices: int64")
+    .Output("output_values: T")
+    .Output("output_shape: int64")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
+SparseTensor.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+)doc");
+
 REGISTER_OP("SparseReduceSum")
     .Input("input_indices: int64")
     .Input("input_values: T")
diff --git a/tensorflow/core/platform/cloud/retrying_utils.cc b/tensorflow/core/platform/cloud/retrying_utils.cc
index 096c77c6e3..99691ecfb9 100644
--- a/tensorflow/core/platform/cloud/retrying_utils.cc
+++ b/tensorflow/core/platform/cloud/retrying_utils.cc
@@ -89,7 +89,7 @@ Status RetryingUtils::DeleteWithRetries(
   bool is_retried = false;
   return RetryingUtils::CallWithRetries(
       [delete_func, &is_retried]() {
-        const auto& status = delete_func();
+        const Status status = delete_func();
         if (is_retried && status.code() == error::NOT_FOUND) {
           return Status::OK();
         }
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index cf05aece39..e476a84a13 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -171,7 +171,7 @@ message ExecutorOpts {
 };
 
 message RunGraphRequest {
-  // session_handle is the the master-generated unique id for this session.
+  // session_handle is the master-generated unique id for this session.
   // If session_handle is non-empty, it must be the same as used when
   // registering the graph. If it is empty, a single global namespace is used to
   // search for the graph_handle.
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index d30d7819fc..0e5611e359 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX "-rc2"
+#define TF_VERSION_SUFFIX ""
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h
index 6a37256ea9..67468bdc3f 100644
--- a/tensorflow/core/util/mkl_util.h
+++ b/tensorflow/core/util/mkl_util.h
@@ -23,7 +23,7 @@ limitations under the License.
 #include "third_party/mkl/include/mkl_dnn.h"
 #include "third_party/mkl/include/mkl_dnn_types.h"
 #include "third_party/mkl/include/mkl_service.h"
-
+#include "third_party/mkl/include/mkl_trans.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/util/tensor_format.h"
@@ -616,6 +616,42 @@ inline void ForwarMklTensorInToOut(OpKernelContext* context,
   }
 }
 
+  // TODO(intel_tf): Remove this routine when faster MKL layout conversion is
+  // out. 
+inline void MklNHWCToNCHW(const Tensor& input, Tensor** output) {
+  const float* buf_in = input.flat<float>().data();
+  float* buf_out = (*output)->flat<float>().data();
+
+  int64 N = input.dim_size(0);
+  int64 H = input.dim_size(1);
+  int64 W = input.dim_size(2);
+  int64 C = input.dim_size(3);
+  int64 stride_n = H*W*C;
+# pragma omp parallel for num_threads(16)
+  for (int64 n = 0; n < N; ++n) {
+    mkl_somatcopy('R', 'T', H*W, C, 1, buf_in + n*stride_n, C,
+        buf_out + n*stride_n, H*W);
+  }
+}
+
+  // TODO(intel_tf): Remove this routine when faster MKL layout conversion is
+  // out. 
+inline void MklNCHWToNHWC(const Tensor& input, Tensor** output) {
+  const float* buf_in = input.flat<float>().data();
+  float* buf_out = (*output)->flat<float>().data();
+
+  int64 N = (*output)->dim_size(0);
+  int64 H = (*output)->dim_size(1);
+  int64 W = (*output)->dim_size(2);
+  int64 C = (*output)->dim_size(3);
+  int64 stride_n = H*W*C;
+# pragma omp parallel for num_threads(16)
+  for (int64 n = 0; n < N; ++n) {
+    mkl_somatcopy('R', 'T', C, H*W, 1, buf_in + n*stride_n, H*W,
+        buf_out + n*stride_n, C);
+  }
+}
+
 namespace mkl_op_registry {
 static const char* kMklOpLabel = "MklOp";
 static const char* kMklOpLabelPattern = "label='MklOp'";
diff --git a/tensorflow/docs_src/api_guides/python/contrib.losses.md b/tensorflow/docs_src/api_guides/python/contrib.losses.md
index 8c289dd556..30123e367f 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.losses.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.losses.md
@@ -13,8 +13,8 @@ of samples in the batch and `d1` ... `dN` are the remaining dimensions.
 It is common, when training with multiple loss functions, to adjust the relative
 strengths of individual losses. This is performed by rescaling the losses via
 a `weight` parameter passed to the loss functions. For example, if we were
-training with both log_loss and mean_square_error, and we wished that the
-log_loss penalty be twice as severe as the mean_square_error, we would
+training with both log_loss and mean_squared_error, and we wished that the
+log_loss penalty be twice as severe as the mean_squared_error, we would
 implement this as:
 
 ```python
@@ -22,7 +22,7 @@ implement this as:
   tf.contrib.losses.log(predictions, labels, weight=2.0)
 
   # Uses default weight of 1.0
-  tf.contrib.losses.mean_square_error(predictions, labels)
+  tf.contrib.losses.mean_squared_error(predictions, labels)
 
   # All the losses are collected into the `GraphKeys.LOSSES` collection.
   losses = tf.get_collection(tf.GraphKeys.LOSSES)
@@ -74,7 +74,7 @@ these predictions.
   predictions = MyModelPredictions(images)
 
   weight = tf.cast(tf.greater(depths, 0), tf.float32)
-  loss  = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss  = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 Note that when using weights for the losses, the final average is computed
@@ -100,7 +100,7 @@ weighted average over the individual prediction errors:
 
   weight = MyComplicatedWeightingFunction(labels)
   weight = tf.div(weight, tf.size(weight))
-  loss = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 @{tf.contrib.losses.absolute_difference}
diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md
index b3c12a61dd..3d9f203297 100644
--- a/tensorflow/docs_src/api_guides/python/math_ops.md
+++ b/tensorflow/docs_src/api_guides/python/math_ops.md
@@ -59,6 +59,8 @@ mathematical functions to your graph.
 *   @{tf.acos}
 *   @{tf.asin}
 *   @{tf.atan}
+*   @{tf.cosh}
+*   @{tf.sinh}
 *   @{tf.lgamma}
 *   @{tf.digamma}
 *   @{tf.erf}
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 77b8e2dd2e..d1c9cd696c 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -135,8 +135,9 @@ adder_node = a + b  # + provides a shortcut for tf.add(a, b)
 
 The preceding three lines are a bit like a function or a lambda in which we
 define two input parameters (a and b) and then an operation on them. We can
-evaluate this graph with multiple inputs by using the feed_dict parameter to
-specify Tensors that provide concrete values to these placeholders:
+evaluate this graph with multiple inputs by using the feed_dict argument to
+the [run method](https://www.tensorflow.org/api_docs/python/tf/Session#run)
+to feed concrete values to the placeholders:
 
 ```python
 print(sess.run(adder_node, {a: 3, b:4.5}))
diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 624d916474..175de2be76 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -367,7 +367,7 @@ train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
 
 In this case, we ask TensorFlow to minimize `cross_entropy` using the
 [gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent)
-with a learning rate of 0.5. Gradient descent is a simple procedure, where
+with a learning rate of 0.05. Gradient descent is a simple procedure, where
 TensorFlow simply shifts each variable a little bit in the direction that
 reduces the cost. But TensorFlow also provides
 @{$python/train#Optimizers$many other optimization algorithms}:
diff --git a/tensorflow/docs_src/get_started/mnist/mechanics.md b/tensorflow/docs_src/get_started/mnist/mechanics.md
index 48d9a395f2..27fae45b5b 100644
--- a/tensorflow/docs_src/get_started/mnist/mechanics.md
+++ b/tensorflow/docs_src/get_started/mnist/mechanics.md
@@ -82,7 +82,7 @@ After creating placeholders for the data, the graph is built from the
 `mnist.py` file according to a 3-stage pattern: `inference()`, `loss()`, and
 `training()`.
 
-1.  `inference()` - Builds the graph as far as is required for running
+1.  `inference()` - Builds the graph as far as required for running
 the network forward to make predictions.
 1.  `loss()` - Adds to the inference graph the ops required to generate
 loss.
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 91189f199d..81aa6e3f76 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index c9b8dffadb..3f9096b822 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 612c4c94f2..40ed9e1826 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0-rc2</version>
+  <version>1.2.0</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0-rc2</version>
+                 <version>1.2.0</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc2.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0-rc2.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 8ce4acda13..99f27d7b85 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -171,8 +171,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
-     <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     <pre>(tensorflow)$ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -276,8 +276,8 @@ take the following steps:
      the following command:
 
      <pre>
-     $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b>
+     $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -632,14 +632,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -651,14 +651,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -670,14 +670,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -689,14 +689,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index f85ecefb83..8ff0fb872f 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -108,8 +108,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     <pre> $ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -229,8 +229,8 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-     <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b> </pre>
+     <pre> $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index c455492387..a082c3ce78 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -342,10 +342,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0rc2 on Linux:
+for TensorFlow 1.2.0 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc2-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 42820660ee..8282afaab4 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -38,9 +38,10 @@ installed on your system:
     [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
     list of supported GPU cards.
 
-If you have an earlier version of the preceding packages, please
-upgrade to the specified versions.
-
+If you have a different version of one of the preceding packages, please
+change to the specified versions.  In particular, the cuDNN version
+must match exactly: TensorFlow will not load if it cannot find `cuDNN64_5.dll`.
+To use a different version of cuDNN, you must build from source.
 
 ## Determine how to install TensorFlow
 
@@ -73,7 +74,7 @@ Use that package at your own risk.
 If the following version of Python is not installed on your machine,
 install it now:
 
-  * [Python 3.5.x from python.org](https://www.python.org/downloads/release/python-352/)
+  * [Python 3.5.x 64-bit from python.org](https://www.python.org/downloads/release/python-352/)
 
 TensorFlow only supports version 3.5.x of Python on Windows.
 Note that Python 3.5.x comes with the pip3 package manager, which is the
@@ -114,12 +115,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index 07c5d3087f..a5508ac23e 100644
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -52,7 +52,8 @@ bazel build -c opt --copt=-march="broadwell" --config=cuda //tensorflow/tools/pi
     (pascal): 6.2, Titan X (maxwell): 5.2, and K80: 3.7.
 *   Install the latest CUDA platform and cuDNN libraries.
 *   Make sure to use a version of gcc that supports all of the optimizations of
-    the target CPU. The recommended minimum gcc version is 4.8.3.
+    the target CPU. The recommended minimum gcc version is 4.8.3.  On OS X upgrade
+    to the latest Xcode version and use the version of clang that comes with Xcode.
 *   TensorFlow checks on startup whether it has been compiled with the
     optimizations available on the CPU. If the optimizations are not included,
     TensorFlow will emit warnings, e.g. AVX, AVX2, and FMA instructions not
@@ -122,6 +123,11 @@ format.
 The best practice is to build models that work with both `NCHW` and `NHWC` as it
 is common to train using `NCHW` on GPU, and then do inference with NHWC on CPU.
 
+There are edge cases where `NCHW` can be slower on GPU than `NHWC`. One
+[case](https://github.com/tensorflow/tensorflow/issues/7551#issuecomment-280421351)
+is using non-fused batch norm on WRN-16-4 without dropout. In that case using
+fused batch norm, which is also recommended, is the optimal solution.
+
 The very brief history of these two formats is that TensorFlow started by using
 `NHWC` because it was a little faster on CPUs. Then the TensorFlow team
 discovered that `NCHW` performs better when using the NVIDIA cuDNN library.  The
diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md
index 4667b4cad7..a37748d0c9 100644
--- a/tensorflow/docs_src/performance/quantization.md
+++ b/tensorflow/docs_src/performance/quantization.md
@@ -91,11 +91,14 @@ eight-bit computations:
 ```sh
 curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -o /tmp/inceptionv3.tgz
 tar xzf /tmp/inceptionv3.tgz -C /tmp/
-bazel build tensorflow/tools/quantization:quantize_graph
-bazel-bin/tensorflow/tools/quantization/quantize_graph \
-  --input=/tmp/classify_image_graph_def.pb \
-  --output_node_names="softmax" --output=/tmp/quantized_graph.pb \
-  --mode=eightbit
+bazel build tensorflow/tools/graph_transforms:transform_graph
+bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
+  --in_graph=/tmp/classify_image_graph_def.pb \
+  --outputs="softmax" --out_graph=/tmp/quantized_graph.pb \
+  --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3")
+    remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true)
+    fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes
+    strip_unused_nodes sort_by_execution_order'
 ```
 
 This will produce a new model that runs the same operations as the original, but
diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md
index 6ffe3e8b03..dd2ca8d524 100644
--- a/tensorflow/docs_src/tutorials/seq2seq.md
+++ b/tensorflow/docs_src/tutorials/seq2seq.md
@@ -8,7 +8,10 @@ some input and generate a meaningful response? For example, could we train
 a neural network to translate from English to French? It turns out that
 the answer is *yes*.
 
-This tutorial will show you how to build and train such a system end-to-end. Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub. You can then start by running the translate program:
+This tutorial will show you how to build and train such a system end-to-end.
+Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and
+the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub.
+You can then start by running the translate program:
 
 ```
 cd models/tutorials/rnn/translate
@@ -25,7 +28,7 @@ This tutorial references the following files.
 
 File | What's in it?
 --- | ---
-`tensorflow/tensorflow/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
+`tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
 `models/tutorials/rnn/translate/seq2seq_model.py` | Neural translation sequence-to-sequence model.
 `models/tutorials/rnn/translate/data_utils.py` | Helper functions for preparing translation data.
 `models/tutorials/rnn/translate/translate.py` | Binary that trains and runs the translation model.
@@ -148,9 +151,9 @@ have similar interfaces, so we will not describe them in detail. We will use
 ## Neural translation model
 
 While the core of the sequence-to-sequence model is constructed by
-the functions in `tensorflow/tensorflow/python/ops/seq2seq.py`, there are still a few tricks
-that are worth mentioning that are used in our translation model in
-`models/tutorials/rnn/translate/seq2seq_model.py`.
+the functions in `tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py`,
+there are still a few tricks that are worth mentioning that are used in our
+translation model in `models/tutorials/rnn/translate/seq2seq_model.py`.
 
 ### Sampled softmax and output projection
 
diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index c2621026c7..24c866eee5 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -1,6 +1,6 @@
 # TensorFlow Linear Model Tutorial
 
-In this tutorial, we will use the TF.Learn API in TensorFlow to solve a binary
+In this tutorial, we will use the tf.contrib.learn API in TensorFlow to solve a binary
 classification problem: Given census data about a person such as age, gender,
 education and occupation (the features), we will try to predict whether or not
 the person earns more than 50,000 dollars a year (the target label). We will
@@ -16,7 +16,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
@@ -69,8 +69,8 @@ COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
            "marital_status", "occupation", "relationship", "race", "gender",
            "capital_gain", "capital_loss", "hours_per_week", "native_country",
            "income_bracket"]
-df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
-df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1)
+df_train = pd.read_csv(train_file.name, names=COLUMNS, skipinitialspace=True)
+df_test = pd.read_csv(test_file.name, names=COLUMNS, skipinitialspace=True, skiprows=1)
 ```
 
 Since the task is a binary classification problem, we'll construct a label
@@ -136,9 +136,9 @@ Here's a list of columns available in the Census Income dataset:
 
 ## Converting Data into Tensors
 
-When building a TF.Learn model, the input data is specified by means of an Input
+When building a tf.contrib.learn model, the input data is specified by means of an Input
 Builder function. This builder function will not be called until it is later
-passed to TF.Learn methods such as `fit` and `evaluate`. The purpose of this
+passed to tf.contrib.learn methods such as `fit` and `evaluate`. The purpose of this
 function is to construct the input data, which is represented in the form of
 @{tf.Tensor}s
 or
@@ -211,7 +211,7 @@ to predict the target label.
 ### Base Categorical Feature Columns
 
 To define a feature column for a categorical feature, we can create a
-`SparseColumn` using the TF.Learn API. If you know the set of all possible
+`SparseColumn` using the tf.contrib.learn API. If you know the set of all possible
 feature values of a column and there are only a few of them, you can use
 `sparse_column_with_keys`. Each key in the list will get assigned an
 auto-incremental ID starting from 0. For example, for the `gender` column we can
@@ -361,7 +361,7 @@ in `model_dir`.
 ## Training and Evaluating Our Model
 
 After adding all the features to the model, now let's look at how to actually
-train the model. Training a model is just a one-liner using the TF.Learn API:
+train the model. Training a model is just a one-liner using the tf.contrib.learn API:
 
 ```python
 m.fit(input_fn=train_input_fn, steps=200)
@@ -467,4 +467,4 @@ value would be high.
 
 If you're interested in learning more, check out our @{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to combine
 the strengths of linear models and deep neural networks by jointly training them
-using the TF.Learn API.
+using the tf.contrib.learn API.
diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/wide_and_deep.md
index 77c905fd51..0978005d6c 100644
--- a/tensorflow/docs_src/tutorials/wide_and_deep.md
+++ b/tensorflow/docs_src/tutorials/wide_and_deep.md
@@ -9,7 +9,7 @@ great for training deep neural networks too, and you might be thinking which one
 you should choose—Well, why not both? Would it be possible to combine the
 strengths of both in one model?
 
-In this tutorial, we'll introduce how to use the TF.Learn API to jointly train a
+In this tutorial, we'll introduce how to use the tf.contrib.learn API to jointly train a
 wide linear model and a deep feed-forward neural network. This approach combines
 the strengths of memorization and generalization. It's useful for generic
 large-scale regression and classification problems with sparse input features
@@ -23,7 +23,7 @@ The figure above shows a comparison of a wide model (logistic regression with
 sparse features and transformations), a deep model (feed-forward neural network
 with an embedding layer and several hidden layers), and a Wide & Deep model
 (joint training of both). At a high level, there are only 3 steps to configure a
-wide, deep, or Wide & Deep model using the TF.Learn API:
+wide, deep, or Wide & Deep model using the tf.contrib.learn API:
 
 1.  Select features for the wide part: Choose the sparse base columns and
     crossed columns you want to use.
@@ -42,7 +42,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
diff --git a/tensorflow/docs_src/tutorials/word2vec.md b/tensorflow/docs_src/tutorials/word2vec.md
index dfb21334f8..8e7c19035e 100644
--- a/tensorflow/docs_src/tutorials/word2vec.md
+++ b/tensorflow/docs_src/tutorials/word2vec.md
@@ -351,7 +351,7 @@ to evaluate embeddings is to directly use them to predict syntactic and semantic
 relationships like `king is to queen as father is to ?`. This is called
 *analogical reasoning* and the task was introduced by
 [Mikolov and colleagues
-](http://msr-waypoint.com/en-us/um/people/gzweig/Pubs/NAACL2013Regularities.pdf).
+](http://www.anthology.aclweb.org/N/N13/N13-1090.pdf).
 Download the dataset for this task from
 [download.tensorflow.org](http://download.tensorflow.org/data/questions-words.txt).
 
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 8e3b1a3a36..44a3097d80 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -800,11 +800,27 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
   return evaluation_step, prediction
 
 
-def main(_):
+def save_graph_to_file(sess, graph, graph_file_name):
+  output_graph_def = graph_util.convert_variables_to_constants(
+    sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
+  with gfile.FastGFile(graph_file_name, 'wb') as f:
+    f.write(output_graph_def.SerializeToString())
+  return
+
+
+def prepare_file_system():
   # Setup the directory we'll write summaries to for TensorBoard
   if tf.gfile.Exists(FLAGS.summaries_dir):
     tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
   tf.gfile.MakeDirs(FLAGS.summaries_dir)
+  if FLAGS.intermediate_store_frequency > 0:
+    ensure_dir_exists(FLAGS.intermediate_output_graphs_dir)
+  return
+
+
+def main(_):
+  # Prepare necessary directories  that can be used during training
+  prepare_file_system()
 
   # Set up the pre-trained graph.
   maybe_download_and_extract()
@@ -917,6 +933,14 @@ def main(_):
               (datetime.now(), i, validation_accuracy * 100,
                len(validation_bottlenecks)))
 
+      # Store intermediate results
+      intermediate_frequency = FLAGS.intermediate_store_frequency
+
+      if intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0:
+        intermediate_file_name = FLAGS.intermediate_output_graphs_dir + 'intermediate_' + str(i) + '.pb'
+        print('Save intermediate result to : ' + intermediate_file_name)
+        save_graph_to_file(sess, graph, intermediate_file_name)
+                
     # We've completed all our training, so run a final test evaluation on
     # some new images we haven't used before.
     test_bottlenecks, test_ground_truth, test_filenames = (
@@ -940,10 +964,7 @@ def main(_):
 
     # Write out the trained graph and labels with the weights stored as
     # constants.
-    output_graph_def = graph_util.convert_variables_to_constants(
-        sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
-    with gfile.FastGFile(FLAGS.output_graph, 'wb') as f:
-      f.write(output_graph_def.SerializeToString())
+    save_graph_to_file(sess, graph, FLAGS.output_graph)
     with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
       f.write('\n'.join(image_lists.keys()) + '\n')
 
@@ -962,6 +983,18 @@ if __name__ == '__main__':
       default='/tmp/output_graph.pb',
       help='Where to save the trained graph.'
   )
+  parser.add_argument(
+      '--intermediate_output_graphs_dir',
+      type=str,
+      default='/tmp/intermediate_graph/',
+      help='Where to save the intermediate graphs.'
+  )
+  parser.add_argument(
+      '--intermediate_store_frequency',
+      type=int,
+      default=0,
+      help='How many steps to store intermediate graph. If "0" then will not store.'
+  )
   parser.add_argument(
       '--output_labels',
       type=str,
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index 77b245ab15..4c5893384a 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -32,7 +32,7 @@ TFLEARN_EXAMPLE_BASE_DIR=$DIR/tensorflow/examples/learn
 
 
 function test() {
-  echo "Test "$1":"
+  echo "Test $1:"
   $TFLEARN_EXAMPLE_BASE_DIR/$1 $2
   if [ $? -eq 0 ]
   then
diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py
index d533697976..3585043a2a 100644
--- a/tensorflow/examples/tutorials/mnist/mnist.py
+++ b/tensorflow/examples/tutorials/mnist/mnist.py
@@ -17,7 +17,7 @@
 
 Implements the inference/loss/training pattern for model building.
 
-1. inference() - Builds the model as far as is required for running the network
+1. inference() - Builds the model as far as required for running the network
 forward to make predictions.
 2. loss() - Adds to the inference model the layers required to generate loss.
 3. training() - Adds to the loss model the Ops required to generate and
diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
index 13e5717b0d..aee482fda5 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -91,7 +91,6 @@ print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
 
 data_index = 0
 
-
 # Step 3: Function to generate a training batch for the skip-gram model.
 def generate_batch(batch_size, num_skips, skip_window):
   global data_index
@@ -101,9 +100,10 @@ def generate_batch(batch_size, num_skips, skip_window):
   labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
   span = 2 * skip_window + 1  # [ skip_window target skip_window ]
   buffer = collections.deque(maxlen=span)
-  for _ in range(span):
-    buffer.append(data[data_index])
-    data_index = (data_index + 1) % len(data)
+  if data_index + span > len(data):
+    data_index = 0
+  buffer.extend(data[data_index:data_index + span])
+  data_index += span
   for i in range(batch_size // num_skips):
     target = skip_window  # target label at the center of the buffer
     targets_to_avoid = [skip_window]
@@ -113,8 +113,12 @@ def generate_batch(batch_size, num_skips, skip_window):
       targets_to_avoid.append(target)
       batch[i * num_skips + j] = buffer[skip_window]
       labels[i * num_skips + j, 0] = buffer[target]
-    buffer.append(data[data_index])
-    data_index = (data_index + 1) % len(data)
+    if data_index == len(data):
+      buffer[:] = data[:span]
+      data_index = span
+    else:
+      buffer.append(data[data_index])
+      data_index += 1
   # Backtrack a little bit to avoid skipping words in the end of a batch
   data_index = (data_index + len(data) - span) % len(data)
   return batch, labels
diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md
index a1b4255292..9c2fa60017 100644
--- a/tensorflow/go/README.md
+++ b/tensorflow/go/README.md
@@ -9,35 +9,6 @@ Construct and execute TensorFlow graphs in Go.
 > (`github.com/tensorflow/tensorflow/tensorflow/go`).
 
 ## Quickstart
-1.  Download and extract the TensorFlow C library, preferably into `/usr/local`.
-    GPU-enabled versions require CUDA 8.0 and cuDNN 5.1. For other versions, the
-    TensorFlow C library will have to be built from source (see below).
-
-    -   Linux:
-        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.1.0.tar.gz),
-        [GPU-enabled](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.1.0.tar.gz)
-    -   OS X
-        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.1.0.tar.gz),
-
-    The following shell snippet downloads and extracts into `/usr/local`:
-
-    ```sh
-    TF_TYPE="cpu" # Set to "gpu" for GPU support
-    curl -L \
-      "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.1.0.tar.gz" |
-    sudo tar -C /usr/local -xz
-    ```
-
-2.  `go get` this package (and run tests):
-
-    ```sh
-    go get github.com/tensorflow/tensorflow/tensorflow/go
-    go test github.com/tensorflow/tensorflow/tensorflow/go
-    ```
-
-3.  Done!
-
-### Installing into locations other than `/usr/local`
 
 Refer to [Installing TensorFlow for Go](https://www.tensorflow.org/install/install_go)
 
diff --git a/tensorflow/go/genop/generate.sh b/tensorflow/go/genop/generate.sh
index d791e39c40..15ef3b9525 100644
--- a/tensorflow/go/genop/generate.sh
+++ b/tensorflow/go/genop/generate.sh
@@ -20,7 +20,7 @@ go get github.com/golang/protobuf/proto
 go get github.com/golang/protobuf/protoc-gen-go
 
 cd $(dirname $0)
-for g in $(echo $GOPATH | sed "s/:/ /g"); do
+for g in $(echo "${GOPATH//:/ }"); do
     TF_DIR="${g}/src/github.com/tensorflow/tensorflow"
     PROTOC="${TF_DIR}/bazel-out/host/bin/external/protobuf/protoc"
     if [ -x "${PROTOC}" ]; then
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 8dd2931703..6675f1097e 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14288,6 +14288,36 @@ func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
+// Computes hyperbolic sine of x element-wise.
+func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Sinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes hyperbolic cosine of x element-wise.
+func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Cosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes hyperbolic tangent of `x` element-wise.
 func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
diff --git a/tensorflow/go/shape.go b/tensorflow/go/shape.go
index 114ab5decb..8d000cb9de 100644
--- a/tensorflow/go/shape.go
+++ b/tensorflow/go/shape.go
@@ -59,7 +59,7 @@ func (s Shape) NumDimensions() int {
 //
 // REQUIRES: 0 <= dim < s.NumDimensions()
 func (s Shape) Size(dim int) int64 {
-	if dim < 0 || dim > s.NumDimensions() {
+	if dim < 0 || dim >= s.NumDimensions() {
 		return -1
 	}
 	return s.dims[dim]
diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
index 34e797a2b3..4a60c736b5 100644
--- a/tensorflow/go/tensor.go
+++ b/tensorflow/go/tensor.go
@@ -270,7 +270,7 @@ func typeOf(dt DataType, shape []int64) reflect.Type {
 	if ret == nil {
 		panic(bug("DataType %v is not supported", dt))
 	}
-	for _ = range shape {
+	for range shape {
 		ret = reflect.SliceOf(ret)
 	}
 	return ret
diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 9abb63c966..90372660cd 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -9,7 +9,10 @@ load("build_defs", "JAVACOPTS")
 
 java_library(
     name = "tensorflow",
-    srcs = [":java_sources"],
+    srcs = [
+        ":java_op_sources",
+        ":java_sources",
+    ],
     data = [":libtensorflow_jni"],
     javacopts = JAVACOPTS,
     visibility = ["//visibility:public"],
@@ -26,6 +29,14 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "java_op_sources",
+    srcs = glob(["src/main/java/org/tensorflow/op/*.java"]),
+    visibility = [
+        "//tensorflow/java:__pkg__",
+    ],
+)
+
 java_library(
     name = "testutil",
     testonly = 1,
@@ -138,6 +149,19 @@ java_test(
     ],
 )
 
+java_test(
+    name = "ScopeTest",
+    size = "small",
+    srcs = ["src/test/java/org/tensorflow/op/ScopeTest.java"],
+    javacopts = JAVACOPTS,
+    test_class = "org.tensorflow.op.ScopeTest",
+    deps = [
+        ":tensorflow",
+        ":testutil",
+        "@junit",
+    ],
+)
+
 filegroup(
     name = "libtensorflow_jni",
     srcs = select({
diff --git a/tensorflow/java/build_defs.bzl b/tensorflow/java/build_defs.bzl
index 750d76301e..5bd5b9a388 100644
--- a/tensorflow/java/build_defs.bzl
+++ b/tensorflow/java/build_defs.bzl
@@ -1,5 +1,14 @@
 # -*- Python -*-
 
+# Pin to Java 1.7 to ensure broader compatibility for the Java bindings on
+# Android. Note also that the android_library bazel rule currently enforces
+# java 7
+# https://github.com/bazelbuild/bazel/blob/6c1106b1a721516d3b3db54d2e1c31b44a76fbb1/src/main/java/com/google/devtools/build/lib/bazel/rules/android/BazelAndroidSemantics.java#L73
+
+JAVA_VERSION_OPTS = [
+    "-source 7 -target 7",
+]
+
 # A more robust set of lint and errorprone checks when building
 # Java source to improve code consistency.
 
@@ -151,4 +160,4 @@ EP_DISABLED_CHECKS = [
 
 EP_OPTS = EP_ENABLED_WARNINGS + EP_DISABLED_CHECKS
 
-JAVACOPTS = XLINT_OPTS + EP_OPTS
+JAVACOPTS = JAVA_VERSION_OPTS + XLINT_OPTS + EP_OPTS
diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml
index 1fc9d49a86..462e38d332 100644
--- a/tensorflow/java/maven/libtensorflow/pom.xml
+++ b/tensorflow/java/maven/libtensorflow/pom.xml
@@ -19,8 +19,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.8</source>
-          <target>1.8</target>
+          <source>1.7</source>
+          <target>1.7</target>
         </configuration>
       </plugin>
       <plugin>
diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index d1d0359b1d..f2974220d9 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -16,7 +16,7 @@
     <dependency>
       <groupId>com.google.protobuf</groupId>
       <artifactId>protobuf-java</artifactId>
-      <version>3.2.0</version>
+      <version>3.3.1</version>
     </dependency>
   </dependencies>
 
@@ -27,8 +27,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.8</source>
-          <target>1.8</target>
+          <source>1.7</source>
+          <target>1.7</target>
         </configuration>
       </plugin>
       <plugin>
diff --git a/tensorflow/java/maven/release.sh b/tensorflow/java/maven/release.sh
index 0e14bc4964..0bb4efbcc0 100755
--- a/tensorflow/java/maven/release.sh
+++ b/tensorflow/java/maven/release.sh
@@ -21,7 +21,7 @@ TF_VERSION="$1"
 SETTINGS_XML="$2"
 shift
 shift
-CMD="$@"
+CMD="$*"
 
 if [[ -z "${TF_VERSION}" ]]
 then
diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh
index b5e2bfc3a6..302ed96c12 100644
--- a/tensorflow/java/maven/run_inside_container.sh
+++ b/tensorflow/java/maven/run_inside_container.sh
@@ -23,7 +23,7 @@ IS_SNAPSHOT="false"
 if [[ "${TF_VERSION}" == *"-SNAPSHOT" ]]; then
   IS_SNAPSHOT="true"
 fi
-PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip"
+PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.3.0/protoc-3.3.0-linux-x86_64.zip"
 
 set -ex
 
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
new file mode 100644
index 0000000000..dff3a45463
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/Input.java
@@ -0,0 +1,48 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow;
+
+/**
+ * Interface implemented by operands of a TensorFlow operation.
+ *
+ * <p>Example usage:
+ *
+ * <pre>{@code
+ * // The "decodeJpeg" operation can be used as input to the "cast" operation
+ * Input decodeJpeg = ops.image().decodeJpeg(...);
+ * ops.math().cast(decodeJpeg, DataType.FLOAT);
+ *
+ * // The output "y" of the "unique" operation can be used as input to the "cast" operation
+ * Output y = ops.array().unique(...).y();
+ * ops.math().cast(y, DataType.FLOAT);
+ *
+ * // The "split" operation can be used as input list to the "concat" operation
+ * Iterable<? extends Input> split = ops.array().split(...);
+ * ops.array().concat(0, split);
+ * }</pre>
+ */
+public interface Input {
+
+  /**
+   * Returns the symbolic handle of a tensor.
+   *
+   * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
+   * used to obtain a symbolic handle that represents the computation of the input.
+   *
+   * @see {@link OperationBuilder#addInput(Output)}.
+   */
+  Output asOutput();
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index 43dbaf125c..e7de603409 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -79,7 +79,7 @@ public final class Operation {
    *
    * @param name identifier of the list of tensors (of which there may
    *        be many) produced by this operation.
-   * @returns the size of the list of Tensors produced by this named output.
+   * @return the size of the list of Tensors produced by this named output.
    * @throws IllegalArgumentException if this operation has no output
    *         with the provided name.
    */
@@ -97,6 +97,28 @@ public final class Operation {
     return new Output(this, idx);
   }
 
+  /**
+   * Returns the size of the given inputs list of Tensors for this operation.
+   *
+   * <p>An Operation has multiple named inputs, each of which contains either
+   * a single tensor or a list of tensors. This method returns the size of
+   * the list of tensors for a specific named input of the operation.
+   *
+   * @param name identifier of the list of tensors (of which there may
+   *        be many) inputs to this operation.
+   * @returns the size of the list of Tensors produced by this named input.
+   * @throws IllegalArgumentException if this operation has no input
+   *         with the provided name.
+   */
+  public int inputListLength(final String name) {
+    Graph.Reference r = graph.ref();
+    try {
+      return inputListLength(unsafeNativeHandle, name);
+    } finally {
+      r.close();
+    }
+  }
+
   long getUnsafeNativeHandle() {
     return unsafeNativeHandle;
   }
@@ -132,6 +154,8 @@ public final class Operation {
 
   private static native int outputListLength(long handle, String name);
 
+  private static native int inputListLength(long handle, String name);
+
   private static native long[] shape(long graphHandle, long opHandle, int output);
 
   private static native int dtype(long graphHandle, long opHandle, int output);
diff --git a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
index 38ffa2a8e1..8f7559d39e 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
@@ -28,7 +28,7 @@ import java.nio.charset.Charset;
  * <pre>{@code
  * // g is a Graph instance.
  * try (Tensor c1 = Tensor.create(3.0f)) {
- *   g.opBuilder("Constant", "MyConst")
+ *   g.opBuilder("Const", "MyConst")
  *       .setAttr("dtype", c1.dataType())
  *       .setAttr("value", c1)
  *       .build();
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index ab128c2b30..2e3f8d4eac 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -20,8 +20,11 @@ package org.tensorflow;
  *
  * <p>An Output is a symbolic handle to a tensor. The value of the Tensor is computed by executing
  * the {@link Operation} in a {@link Session}.
+ *
+ * <p>By implementing the {@link Input} interface, instances of this class could also be passed
+ * directly in input to an operation.
  */
-public final class Output {
+public final class Output implements Input {
 
   /** Handle to the idx-th output of the Operation {@code op}. */
   public Output(Operation op, int idx) {
@@ -49,6 +52,11 @@ public final class Output {
     return operation.dtype(index);
   }
 
+  @Override
+  public Output asOutput() {
+    return this;
+  }
+
   private final Operation operation;
   private final int index;
 }
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Session.java b/tensorflow/java/src/main/java/org/tensorflow/Session.java
index 0d071e1674..f73cded4e3 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Session.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Session.java
@@ -125,7 +125,7 @@ public final class Session implements AutoCloseable {
      *     <tt>operation_name:output_index</tt> , in which case this method acts like {@code
      *     feed(operation_name, output_index)}. These colon-separated names are commonly used in the
      *     {@code SignatureDef} protocol buffer messages that are included in {@link
-     *     SavedModelBundle.metaGraphDef()}.
+     *     SavedModelBundle#metaGraphDef()}.
      */
     public Runner feed(String operation, Tensor t) {
       return feed(parseOutput(operation), t);
@@ -165,7 +165,7 @@ public final class Session implements AutoCloseable {
      *     <tt>operation_name:output_index</tt> , in which case this method acts like {@code
      *     fetch(operation_name, output_index)}. These colon-separated names are commonly used in
      *     the {@code SignatureDef} protocol buffer messages that are included in {@link
-     *     SavedModelBundle.metaGraphDef()}.
+     *     SavedModelBundle#metaGraphDef()}.
      */
     public Runner fetch(String operation) {
       return fetch(parseOutput(operation));
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java b/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
new file mode 100644
index 0000000000..2e84cac1ac
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
@@ -0,0 +1,146 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import java.util.HashMap;
+import java.util.Map;
+import java.util.regex.Pattern;
+
+/**
+ * A class to manage scoped (hierarchical) names for operators.
+ *
+ * <p>{@code NameScope} manages hierarchical names where each component in the hierarchy is
+ * separated by a forward slash {@code '/'}. For instance, {@code nn/Const_72} or {@code
+ * nn/gradient/assign/init}. Each scope is a subtree in this hierarchy.
+ *
+ * <p>Use {@code NameScope} to group related operations within a hierarchy, which for example lets
+ * tensorboard coalesce nodes for better graph visualizations.
+ *
+ * <p>This class is package private, user code creates {@link Scope} which internally delegates
+ * calls to an underlying {@code NameScope}.
+ *
+ * <p>This class is <b>not</b> thread-safe.
+ */
+final class NameScope {
+
+  NameScope withSubScope(String scopeName) {
+    checkPattern(NAME_REGEX, scopeName);
+    // Override with opName if it exists.
+    String actualName = (opName != null) ? opName : scopeName;
+    String newPrefix = fullyQualify(makeUnique(actualName));
+    return new NameScope(newPrefix, null, null);
+  }
+
+  NameScope withName(String name) {
+    checkPattern(NAME_REGEX, name);
+    // All context except for the opName is shared with the new scope.
+    return new NameScope(opPrefix, name, ids);
+  }
+
+  String makeOpName(String name) {
+    checkPattern(NAME_REGEX, name);
+    // Override with opName if it exists.
+    String actualName = (opName != null) ? opName : name;
+    return fullyQualify(makeUnique(actualName));
+  }
+
+  /**
+   * Create a new, root-level namescope.
+   *
+   * <p>A root-level namescope generates operator names with no components, like {@code Const_72}
+   * and {@code result}.
+   */
+  NameScope() {
+    this(null, null, null);
+  }
+
+  private NameScope(String opPrefix, String opName, Map<String, Integer> ids) {
+    this.opPrefix = opPrefix;
+    this.opName = opName;
+    if (ids != null) {
+      this.ids = ids;
+    } else {
+      this.ids = new HashMap<String, Integer>();
+    }
+  }
+
+  // Generate a unique name, different from existing ids.
+  //
+  // ids is a map from id to integer, representing a counter of the
+  // number of previous requests to generate a unique name for the
+  // given id.
+  //
+  // For instance, the first use of makeUnique("a") adds "a" -> 1
+  // to ids and returns "a".
+  //
+  // The second use of makeUnique("a") updates ids to "a" -> 2
+  // and returns "a_1", and so on.
+  private String makeUnique(String id) {
+    if (!ids.containsKey(id)) {
+      ids.put(id, 1);
+      return id;
+    } else {
+      int cur = ids.get(id);
+      ids.put(id, cur + 1);
+      return String.format("%s_%d", id, cur);
+    }
+  }
+
+  private String fullyQualify(String name) {
+    if (opPrefix != null) {
+      return String.format("%s/%s", opPrefix, name);
+    } else {
+      return name;
+    }
+  }
+
+  // If opPrefix is non-null, it is a prefix applied to all names
+  // created by this instance.
+  private final String opPrefix;
+
+  // If opName is non-null, it is used to derive the unique name
+  // for operators rather than the provided default name.
+  private final String opName;
+
+  // NameScope generates unique names by appending a numeric suffix if
+  // needed. This is a map containing names already created by this
+  // instance mapped to the next available numeric suffix for it.
+  private final Map<String, Integer> ids;
+
+  private static void checkPattern(Pattern pattern, String name) {
+    if (name == null) {
+      throw new IllegalArgumentException("Names cannot be null");
+    }
+    if (!pattern.matcher(name).matches()) {
+      throw new IllegalArgumentException(
+          String.format(
+              "invalid name: '%s' does not match the regular expression %s",
+              name, NAME_REGEX.pattern()));
+    }
+  }
+
+  // The constraints for operator and scope names originate from restrictions on node names
+  // noted in the proto definition core/framework/node_def.proto for NodeDef and actually
+  // implemented in core/framework/node_def_util.cc [Note that the proto comment does not include
+  // dash (-) in names, while the actual implementation permits it. This regex follows the actual
+  // implementation.]
+  //
+  // This pattern is used to ensure fully qualified names always start with a LETTER_DIGIT_DOT,
+  // followed by zero or more LETTER_DIGIT_DASH_DOT_SLASH_UNDERSCORE. SLASH is not permitted in
+  // actual user-supplied names to NameScope - it is used as a reserved character to separate
+  // subcomponents within fully qualified names.
+  private static final Pattern NAME_REGEX = Pattern.compile("[A-Za-z0-9.][A-Za-z0-9_.\\-]*");
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java b/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
new file mode 100644
index 0000000000..8de2eaeb79
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
@@ -0,0 +1,165 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import org.tensorflow.Graph;
+
+/**
+ * Manages groups of related properties when creating Tensorflow Operations, such as a common name
+ * prefix.
+ *
+ * <p>A {@code Scope} is a container for common properties applied to TensorFlow Ops. Normal user
+ * code initializes a {@code Scope} and provides it to Operation building classes. For example:
+ *
+ * <pre>{@code
+ * Scope scope = new Scope(graph);
+ * Constant c = Constant.create(scope, 42);
+ * }</pre>
+ *
+ * <p>An Operation building class acquires a Scope, and uses it to set properties on the underlying
+ * Tensorflow ops. For example:
+ *
+ * <pre>{@code
+ * // An operator class that adds a constant.
+ * public class Constant {
+ *   public static Constant create(Scope scope, ...) {
+ *      scope.graph().opBuilder(
+ *        "Const", scope.makeOpName("Const"))
+ *        .setAttr(...)
+ *        .build()
+ *      ...
+ *   }
+ * }
+ * }</pre>
+ *
+ * <p><b>Scope hierarchy:</b>
+ *
+ * <p>A {@code Scope} provides various {@code with()} methods that create a new scope. The new scope
+ * typically has one property changed while other properties are inherited from the parent scope.
+ *
+ * <p>An example using {@code Constant} implemented as before:
+ *
+ * <pre>{@code
+ * Scope root = new Scope(graph);
+ *
+ * // The linear subscope will generate names like linear/...
+ * Scope linear = Scope.withSubScope("linear");
+ *
+ * // This op name will be "linear/W"
+ * Constant.create(linear.withName("W"), ...);
+ *
+ * // This op will be "linear/Const", using the default
+ * // name provided by Constant
+ * Constant.create(linear, ...);
+ *
+ * // This op will be "linear/Const_1", using the default
+ * // name provided by Constant and making it unique within
+ * // this scope
+ * Constant.create(linear, ...);
+ * }</pre>
+ *
+ * <p>Scope objects are <b>not</b> thread-safe.
+ */
+public final class Scope {
+
+  /**
+   * Create a new top-level scope.
+   *
+   * @param graph The graph instance to be managed by the scope.
+   */
+  public Scope(Graph graph) {
+    this(graph, new NameScope());
+  }
+
+  /** Returns the graph managed by this scope. */
+  public Graph graph() {
+    return graph;
+  }
+
+  /**
+   * Returns a new scope where added operations will have the provided name prefix.
+   *
+   * <p>Ops created with this scope will have {@code name/childScopeName/} as the prefix. The actual
+   * name will be unique in the returned scope. All other properties are inherited from the current
+   * scope.
+   *
+   * <p>The child scope name must match the regular expression {@code [A-Za-z0-9.][A-Za-z0-9_.\-]*}
+   *
+   * @param childScopeName name for the new child scope
+   * @return a new subscope
+   * @throws IllegalArgumentException if the name is invalid
+   */
+  public Scope withSubScope(String childScopeName) {
+    return new Scope(graph, nameScope.withSubScope(childScopeName));
+  }
+
+  /**
+   * Return a new scope that uses the provided name for an op.
+   *
+   * <p>Operations created within this scope will have a name of the form {@code
+   * name/opName[_suffix]}. This lets you name a specific operator more meaningfully.
+   *
+   * <p>Names must match the regular expression {@code [A-Za-z0-9.][A-Za-z0-9_.\-]*}
+   *
+   * @param opName name for an operator in the returned scope
+   * @return a new Scope that uses opName for operations.
+   * @throws IllegalArgumentException if the name is invalid
+   */
+  public Scope withName(String opName) {
+    return new Scope(graph, nameScope.withName(opName));
+  }
+
+  /**
+   * Create a unique name for an operator, using a provided default if necessary.
+   *
+   * <p>This is normally called only by operator building classes.
+   *
+   * <p>This method generates a unique name, appropriate for the name scope controlled by this
+   * instance. Typical operator building code might look like
+   *
+   * <pre>{@code
+   * scope.graph().opBuilder("Const", scope.makeOpName("Const"))...
+   * }</pre>
+   *
+   * <p><b>Note:</b> if you provide a composite operator building class (i.e, a class that adds a
+   * set of related operations to the graph by calling other operator building code) you should also
+   * create a {@link #withSubScope(String)} scope for the underlying operators to group them under a
+   * meaningful name.
+   *
+   * <pre>{@code
+   * public static Stddev create(Scope scope, ...) {
+   *   // group sub-operations under a common name
+   *   Scope group = scope.withSubScope("stddev");
+   *   ... Sqrt.create(group, Mean.create(group, ...))
+   * }
+   * }</pre>
+   *
+   * @param defaultName name for the underlying operator.
+   * @return unique name for the operator.
+   * @throws IllegalArgumentException if the default name is invalid.
+   */
+  public String makeOpName(String defaultName) {
+    return nameScope.makeOpName(defaultName);
+  }
+
+  private Scope(Graph graph, NameScope nameScope) {
+    this.graph = graph;
+    this.nameScope = nameScope;
+  }
+
+  private final Graph graph;
+  private final NameScope nameScope;
+}
diff --git a/tensorflow/java/src/main/native/operation_jni.cc b/tensorflow/java/src/main/native/operation_jni.cc
index b3d5fc4ec3..ccc44d91c0 100644
--- a/tensorflow/java/src/main/native/operation_jni.cc
+++ b/tensorflow/java/src/main/native/operation_jni.cc
@@ -156,3 +156,21 @@ JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv* env,
 
   return static_cast<jint>(TF_OperationOutputType(TF_Output{op, output_index}));
 }
+
+JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv* env,
+                                                                      jclass clazz,
+                                                                      jlong handle,
+                                                                      jstring name) {
+  TF_Operation* op = requireHandle(env, handle);
+  if (op == nullptr) return 0;
+
+  TF_Status* status = TF_NewStatus();
+
+  const char* cname = env->GetStringUTFChars(name, nullptr);
+  int result = TF_OperationInputListLength(op, cname, status);
+  env->ReleaseStringUTFChars(name, cname);
+
+  throwExceptionIfNotOK(env, status);
+  TF_DeleteStatus(status);
+  return result;
+}
diff --git a/tensorflow/java/src/main/native/operation_jni.h b/tensorflow/java/src/main/native/operation_jni.h
index b5d156f7c2..6f379256d2 100644
--- a/tensorflow/java/src/main/native/operation_jni.h
+++ b/tensorflow/java/src/main/native/operation_jni.h
@@ -73,6 +73,17 @@ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_Operation_shape(JNIEnv *,
 JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv *, jclass,
                                                            jlong, jlong, jint);
 
+
+/*
+ * Class:     org_tensorflow_Operation
+ * Method:    inputListLength
+ * Signature: (JLjava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv *,
+                                                                      jclass,
+                                                                      jlong,
+                                                                      jstring);
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif  // __cplusplus
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 74fdcf484e..4fa68130c0 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -52,6 +52,16 @@ public class OperationTest {
     assertEquals(3, split(new int[] {0, 1, 2}, 3));
   }
 
+  @Test
+  public void inputListLength() {
+    assertEquals(1, splitWithInputList(new int[] {0, 1}, 1, "split_dim"));
+    try {
+      splitWithInputList(new int[] {0, 1}, 2, "inputs");
+    } catch (IllegalArgumentException iae) {
+      // expected
+    }
+  }
+
   private static int split(int[] values, int num_split) {
     try (Graph g = new Graph()) {
       return g.opBuilder("Split", "Split")
@@ -62,4 +72,15 @@ public class OperationTest {
           .outputListLength("output");
     }
   }
+
+  private static int splitWithInputList(int[] values, int num_split, String name) {
+    try (Graph g = new Graph()) {
+      return g.opBuilder("Split", "Split")
+          .addInput(TestUtil.constant(g, "split_dim", 0))
+          .addInput(TestUtil.constant(g, "values", values))
+          .setAttr("num_split", num_split)
+          .build()
+          .inputListLength(name);
+    }
+  }
 }
diff --git a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
index 0d2dbc5b88..50bdf351e3 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
@@ -109,7 +109,7 @@ public class SessionTest {
         assertEquals(1, outputs.size());
         final int[][] expected = {{31}};
         assertArrayEquals(expected, outputs.get(0).copyTo(new int[1][1]));
-        // Sanity check on metadatar
+        // Sanity check on metadata
         // See comments in fullTraceRunOptions() for an explanation about
         // why this check is really silly. Ideally, this would be:
         /*
@@ -187,7 +187,7 @@ public class SessionTest {
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
     //
-    // For this test, for now, the use of specific bytes sufficies.
+    // For this test, for now, the use of specific bytes suffices.
     return new byte[] {0x08, 0x03};
     /*
     return org.tensorflow.framework.RunOptions.newBuilder()
@@ -207,7 +207,7 @@ public class SessionTest {
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
     //
-    // For this test, for now, the use of specific bytes sufficies.
+    // For this test, for now, the use of specific bytes suffices.
     return new byte[] {0x10, 0x01, 0x28, 0x01};
     /*
     return org.tensorflow.framework.ConfigProto.newBuilder()
diff --git a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
index 44eecc1d1e..3ff59e71b2 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
@@ -472,7 +472,7 @@ public class TensorTest {
   @Test
   public void fromHandle() {
     // fromHandle is a package-visible method intended for use when the C TF_Tensor object has been
-    // created indepdently of the Java code. In practice, two Tensor instances MUST NOT have the
+    // created independently of the Java code. In practice, two Tensor instances MUST NOT have the
     // same native handle.
     //
     // An exception is made for this test, where the pitfalls of this is avoided by not calling
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java
new file mode 100644
index 0000000000..9256cb281d
--- /dev/null
+++ b/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java
@@ -0,0 +1,270 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.fail;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.tensorflow.Graph;
+import org.tensorflow.Output;
+import org.tensorflow.Session;
+import org.tensorflow.Tensor;
+
+/** Unit tests for {@link org.tensorflow.Scope}. */
+@RunWith(JUnit4.class)
+public class ScopeTest {
+
+  @Test
+  public void basicNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+      assertEquals("add", root.makeOpName("add"));
+      assertEquals("add_1", root.makeOpName("add"));
+      assertEquals("add_2", root.makeOpName("add"));
+      assertEquals("mul", root.makeOpName("mul"));
+    }
+  }
+
+  @Test
+  public void hierarchicalNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+      Scope child = root.withSubScope("child");
+      assertEquals("child/add", child.makeOpName("add"));
+      assertEquals("child/add_1", child.makeOpName("add"));
+      assertEquals("child/mul", child.makeOpName("mul"));
+
+      Scope child_1 = root.withSubScope("child");
+      assertEquals("child_1/add", child_1.makeOpName("add"));
+      assertEquals("child_1/add_1", child_1.makeOpName("add"));
+      assertEquals("child_1/mul", child_1.makeOpName("mul"));
+
+      Scope c_c = root.withSubScope("c").withSubScope("c");
+      assertEquals("c/c/add", c_c.makeOpName("add"));
+
+      Scope c_1 = root.withSubScope("c");
+      Scope c_1_c = c_1.withSubScope("c");
+      assertEquals("c_1/c/add", c_1_c.makeOpName("add"));
+
+      Scope c_1_c_1 = c_1.withSubScope("c");
+      assertEquals("c_1/c_1/add", c_1_c_1.makeOpName("add"));
+    }
+  }
+
+  @Test
+  public void scopeAndOpNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+
+      Scope child = root.withSubScope("child");
+
+      assertEquals("child/add", child.makeOpName("add"));
+      assertEquals("child_1", root.makeOpName("child"));
+      assertEquals("child_2/p", root.withSubScope("child").makeOpName("p"));
+    }
+  }
+
+  @Test
+  public void validateNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+
+      final String[] invalid_names = {
+        "_", "-", "-x", // Names are constrained to start with [A-Za-z0-9.]
+        null, "", "a$", // Invalid characters
+        "a/b", // slashes not allowed
+      };
+
+      for (String name : invalid_names) {
+        try {
+          root.withName(name);
+          fail("failed to catch invalid op name.");
+        } catch (IllegalArgumentException ex) {
+          // expected
+        }
+        // Subscopes follow the same rules
+        try {
+          root.withSubScope(name);
+          fail("failed to catch invalid scope name: " + name);
+        } catch (IllegalArgumentException ex) {
+          // expected
+        }
+      }
+
+      // Unusual but valid names.
+      final String[] valid_names = {".", "..", "._-.", "a--."};
+
+      for (String name : valid_names) {
+        root.withName(name);
+        root.withSubScope(name);
+      }
+    }
+  }
+
+  @Test
+  public void basic() {
+    try (Graph g = new Graph()) {
+      Scope s = new Scope(g);
+      Const c1 = Const.create(s, 42);
+      assertEquals("Const", c1.output().op().name());
+      Const c2 = Const.create(s, 7);
+      assertEquals("Const_1", c2.output().op().name());
+      Const c3 = Const.create(s.withName("four"), 4);
+      assertEquals("four", c3.output().op().name());
+      Const c4 = Const.create(s.withName("four"), 4);
+      assertEquals("four_1", c4.output().op().name());
+    }
+  }
+
+  @Test
+  public void hierarchy() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+      Scope child = root.withSubScope("child");
+      assertEquals("child/Const", Const.create(child, 42).output().op().name());
+      assertEquals("child/four", Const.create(child.withName("four"), 4).output().op().name());
+    }
+  }
+
+  @Test
+  public void composite() {
+    try (Graph g = new Graph();
+        Session sess = new Session(g)) {
+      Scope s = new Scope(g);
+      Output data = Const.create(s.withName("data"), new int[] {600, 470, 170, 430, 300}).output();
+
+      // Create a composite op with a customized name
+      Variance var1 = Variance.create(s.withName("example"), data);
+      assertEquals("example/variance", var1.output().op().name());
+
+      // Confirm internally added ops have the right names.
+      assertNotNull(g.operation("example/squared_deviation"));
+      assertNotNull(g.operation("example/Mean"));
+      assertNotNull(g.operation("example/zero"));
+
+      // Same composite op with a default name
+      Variance var2 = Variance.create(s, data);
+      assertEquals("variance/variance", var2.output().op().name());
+
+      // Confirm internally added ops have the right names.
+      assertNotNull(g.operation("variance/squared_deviation"));
+      assertNotNull(g.operation("variance/Mean"));
+      assertNotNull(g.operation("variance/zero"));
+
+      // Verify correct results as well.
+      Tensor result = sess.runner().fetch(var1.output()).run().get(0);
+      assertEquals(21704, result.intValue());
+      result = sess.runner().fetch(var2.output()).run().get(0);
+      assertEquals(21704, result.intValue());
+    }
+  }
+
+  // "handwritten" sample operator classes
+  private static final class Const {
+    private final Output output;
+
+    static Const create(Scope s, Object v) {
+      try (Tensor value = Tensor.create(v)) {
+        return new Const(
+            s.graph()
+                .opBuilder("Const", s.makeOpName("Const"))
+                .setAttr("dtype", value.dataType())
+                .setAttr("value", value)
+                .build()
+                .output(0));
+      }
+    }
+
+    Const(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+
+  private static final class Mean {
+    private final Output output;
+
+    static Mean create(Scope s, Output input, Output reductionIndices) {
+      return new Mean(
+          s.graph()
+              .opBuilder("Mean", s.makeOpName("Mean"))
+              .addInput(input)
+              .addInput(reductionIndices)
+              .build()
+              .output(0));
+    }
+
+    Mean(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+
+  private static final class SquaredDifference {
+    private final Output output;
+
+    static SquaredDifference create(Scope s, Output x, Output y) {
+      return new SquaredDifference(
+          s.graph()
+              .opBuilder("SquaredDifference", s.makeOpName("SquaredDifference"))
+              .addInput(x)
+              .addInput(y)
+              .build()
+              .output(0));
+    }
+
+    SquaredDifference(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+
+  private static final class Variance {
+    private final Output output;
+
+    static Variance create(Scope base, Output x) {
+      Scope s = base.withSubScope("variance");
+      Output zero = Const.create(s.withName("zero"), new int[] {0}).output();
+      Output sqdiff =
+          SquaredDifference.create(
+                  s.withName("squared_deviation"), x, Mean.create(s, x, zero).output())
+              .output();
+
+      return new Variance(Mean.create(s.withName("variance"), sqdiff, zero).output());
+    }
+
+    Variance(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+}
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index b65a638620..22b18b9cde 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -833,6 +833,7 @@ py_test(
     data = ["//tensorflow/python:meta_graph_testdata"],
     main = "framework/meta_graph_test.py",
     srcs_version = "PY2AND3",
+    tags = ["no_pip"],
     deps = [
         ":array_ops",
         ":client_testlib",
@@ -3055,7 +3056,7 @@ py_test(
     srcs = ["client/session_clusterspec_prop_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_gpu",
+        "no_pip_gpu",
     ],
     deps = [
         ":array_ops",
@@ -3079,7 +3080,7 @@ py_test(
     srcs = ["client/session_list_devices_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_gpu",
+        "no_pip_gpu",
     ],
     deps = [
         ":client",
diff --git a/tensorflow/python/debug/cli/analyzer_cli.py b/tensorflow/python/debug/cli/analyzer_cli.py
index da27f4cebe..e863c2ddb9 100644
--- a/tensorflow/python/debug/cli/analyzer_cli.py
+++ b/tensorflow/python/debug/cli/analyzer_cli.py
@@ -1312,7 +1312,7 @@ class DebugAnalyzer(object):
     all_inputs = copy.copy(tracker(node_name, is_control=False))
     is_ctrl = [False] * len(all_inputs)
     if include_control:
-      # Sort control inputs or recipients in in alphabetical order of the node
+      # Sort control inputs or recipients in alphabetical order of the node
       # names.
       ctrl_inputs = sorted(tracker(node_name, is_control=True))
       all_inputs.extend(ctrl_inputs)
diff --git a/tensorflow/python/debug/wrappers/framework_test.py b/tensorflow/python/debug/wrappers/framework_test.py
index 536365b692..2b2289d6a8 100644
--- a/tensorflow/python/debug/wrappers/framework_test.py
+++ b/tensorflow/python/debug/wrappers/framework_test.py
@@ -215,7 +215,7 @@ class DebugWrapperSessionTest(test_util.TensorFlowTestCase):
       wrapper_sess.partial_run_setup(self._p)
 
   def testInteractiveSessionInit(self):
-    """The wrapper should work also on other subclassses of session.Session."""
+    """The wrapper should work also on other subclasses of session.Session."""
 
     TestDebugWrapperSession(
         session.InteractiveSession(), self._dump_root, self._observer)
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py
index 8785ddc5a6..03bcf7ae57 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py
@@ -395,7 +395,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
 
 
 class DNNLinearCombinedRegressor(estimator.Estimator):
-  """An estimator for TensorFlow Linear and DNN joined models for regresssion.
+  """An estimator for TensorFlow Linear and DNN joined models for regression.
 
   Note: This estimator is also known as wide-n-deep.
 
diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py
index 05b1e5b44a..552b1bdf01 100644
--- a/tensorflow/python/estimator/canned/linear.py
+++ b/tensorflow/python/estimator/canned/linear.py
@@ -42,8 +42,8 @@ def _get_default_optimizer(feature_columns):
   return ftrl.FtrlOptimizer(learning_rate=learning_rate)
 
 
-def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
-                     partitioner, config):
+# TODO(b/36813849): Revisit passing params vs named arguments.
+def _linear_model_fn(features, labels, mode, params, config):
   """A model_fn for linear models that use a gradient-based optimizer.
 
   Args:
@@ -51,12 +51,13 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
     labels: `Tensor` of shape `[batch_size, logits_dimension]`.
     mode: Defines whether this is training, evaluation or prediction.
       See `ModeKeys`.
-    head: A `Head` instance.
-    feature_columns: An iterable containing all the feature columns used by
-      the model.
-    optimizer: string, `Optimizer` object, or callable that defines the
-      optimizer to use for training. If `None`, will use a FTRL optimizer.
-    partitioner: Partitioner for variables.
+    params: A dict of hyperparameters.
+      The following hyperparameters are expected:
+      * head: A `Head` instance.
+      * feature_columns: An iterable containing all the feature columns used by
+          the model.
+      * optimizer: string, `Optimizer` object, or callable that defines the
+          optimizer to use for training. If `None`, will use a FTRL optimizer.
     config: `RunConfig` object to configure the runtime settings.
 
   Returns:
@@ -65,12 +66,14 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
   Raises:
     ValueError: If mode or params are invalid.
   """
+  head = params['head']
+  feature_columns = tuple(params['feature_columns'])
   optimizer = optimizers.get_optimizer_instance(
-      optimizer or _get_default_optimizer(feature_columns),
+      params.get('optimizer') or _get_default_optimizer(feature_columns),
       learning_rate=_LEARNING_RATE)
   num_ps_replicas = config.num_ps_replicas if config else 0
 
-  partitioner = partitioner or (
+  partitioner = params.get('partitioner') or (
       partitioned_variables.min_max_variable_partitioner(
           max_partitions=num_ps_replicas,
           min_slice_size=64 << 20))
@@ -207,20 +210,16 @@ class LinearClassifier(estimator.Estimator):
       head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint: disable=protected-access
           n_classes, weight_column=weight_column,
           label_vocabulary=label_vocabulary)
-    def _model_fn(features, labels, mode, config):
-      return _linear_model_fn(
-          features=features,
-          labels=labels,
-          mode=mode,
-          head=head,
-          feature_columns=tuple(feature_columns or []),
-          optimizer=optimizer,
-          partitioner=partitioner,
-          config=config)
     super(LinearClassifier, self).__init__(
-        model_fn=_model_fn,
+        model_fn=_linear_model_fn,
         model_dir=model_dir,
-        config=config)
+        config=config,
+        params={
+            'head': head,
+            'feature_columns': feature_columns,
+            'optimizer': optimizer,
+            'partitioner': partitioner,
+        })
 
 
 class LinearRegressor(estimator.Estimator):
@@ -299,19 +298,21 @@ class LinearRegressor(estimator.Estimator):
       config: `RunConfig` object to configure the runtime settings.
       partitioner: Optional. Partitioner for input layer.
     """
-    head = head_lib._regression_head_with_mean_squared_error_loss(  # pylint: disable=protected-access
-        label_dimension=label_dimension, weight_column=weight_column)
-    def _model_fn(features, labels, mode, config):
-      return _linear_model_fn(
-          features=features,
-          labels=labels,
-          mode=mode,
-          head=head,
-          feature_columns=tuple(feature_columns or []),
-          optimizer=optimizer,
-          partitioner=partitioner,
-          config=config)
     super(LinearRegressor, self).__init__(
-        model_fn=_model_fn,
+        model_fn=_linear_model_fn,
         model_dir=model_dir,
-        config=config)
+        config=config,
+        params={
+            # pylint: disable=protected-access
+            'head':
+                head_lib._regression_head_with_mean_squared_error_loss(
+                    label_dimension=label_dimension,
+                    weight_column=weight_column),
+            # pylint: enable=protected-access
+            'feature_columns':
+                feature_columns,
+            'optimizer':
+                optimizer,
+            'partitioner':
+                partitioner,
+        })
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 257677ce49..d8e069105f 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2382,11 +2382,6 @@ cuda_py_test(
         "//tensorflow/python:util",
         "//tensorflow/python:data_flow_ops",
     ],
-    tags = [
-        "manual",  # http://b/62429636
-        "noguitar",
-        "notap",
-    ],
 )
 
 cuda_py_test(
@@ -2401,11 +2396,6 @@ cuda_py_test(
         "//tensorflow/python:util",
         "//tensorflow/python:data_flow_ops",
     ],
-    tags = [
-        "manual",  # http://b/62429636
-        "noguitar",
-        "notap",
-    ],
 )
 
 cuda_py_test(
diff --git a/tensorflow/python/kernel_tests/basic_gpu_test.py b/tensorflow/python/kernel_tests/basic_gpu_test.py
index 013aa1ba8a..b5b17ff80a 100644
--- a/tensorflow/python/kernel_tests/basic_gpu_test.py
+++ b/tensorflow/python/kernel_tests/basic_gpu_test.py
@@ -113,6 +113,7 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.arctan, math_ops.atan, use_gpu)
     self._compare(data, np.ceil, math_ops.ceil, use_gpu)
     self._compare(data, np.cos, math_ops.cos, use_gpu)
+    self._compare(data, np.cosh, math_ops.cosh, use_gpu)
     self._compare(data, np.exp, math_ops.exp, use_gpu)
     self._compare(data, np.floor, math_ops.floor, use_gpu)
     self._compare(data, np.log, math_ops.log, use_gpu)
@@ -120,6 +121,7 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.negative, math_ops.negative, use_gpu)
     self._compare(data, self._rsqrt, math_ops.rsqrt, use_gpu)
     self._compare(data, np.sin, math_ops.sin, use_gpu)
+    self._compare(data, np.sinh, math_ops.sinh, use_gpu)
     self._compare(data, np.sqrt, math_ops.sqrt, use_gpu)
     self._compare(data, np.square, math_ops.square, use_gpu)
     self._compare(data, np.tan, math_ops.tan, use_gpu)
diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py
index 0846470abc..b47139e6b8 100644
--- a/tensorflow/python/kernel_tests/cwise_ops_test.py
+++ b/tensorflow/python/kernel_tests/cwise_ops_test.py
@@ -200,6 +200,8 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(z, np.log, math_ops.log)
     self._compareBoth(z, np.log1p, math_ops.log1p)
+    self._compareBoth(x, np.sinh, math_ops.sinh)
+    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, self._log_sigmoid, math_ops.log_sigmoid)
@@ -245,6 +247,8 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(x, np.log, math_ops.log)
     self._compareBoth(x, np.log1p, math_ops.log1p)
+    self._compareBoth(x, np.sinh, math_ops.sinh)
+    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, np.sign, math_ops.sign)
@@ -285,6 +289,8 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(z, np.log, math_ops.log)
     self._compareBoth(z, np.log1p, math_ops.log1p)
+    self._compareBoth(x, np.sinh, math_ops.sinh)
+    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(y, np.sign, math_ops.sign)
@@ -389,6 +395,8 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.expm1, math_ops.expm1)
     self._compareCpu(y, np.log, math_ops.log)
     self._compareCpu(y, np.log1p, math_ops.log1p)
+    self._compareCpu(x, np.sinh, math_ops.sinh)
+    self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
@@ -423,6 +431,8 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.expm1, math_ops.expm1)
     self._compareCpu(y, np.log, math_ops.log)
     self._compareCpu(y, np.log1p, math_ops.log1p)
+    self._compareCpu(x, np.sinh, math_ops.sinh)
+    self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
@@ -697,6 +707,11 @@ class BinaryOpTest(test.TestCase):
     except ImportError as e:
       tf_logging.warn("Cannot test special functions: %s" % str(e))
 
+  def testUint8Basic(self):
+    x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.uint8)
+    y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.uint8)
+    self._compareBoth(x, y, np.add, math_ops.add)
+
   def testInt8Basic(self):
     x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int8)
     y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int8)
diff --git a/tensorflow/python/kernel_tests/fft_ops_test.py b/tensorflow/python/kernel_tests/fft_ops_test.py
index 6544fe9735..6c575aea12 100644
--- a/tensorflow/python/kernel_tests/fft_ops_test.py
+++ b/tensorflow/python/kernel_tests/fft_ops_test.py
@@ -338,37 +338,38 @@ class RFFTOpsTest(BaseFFTOpsTest):
                 use_placeholder=True)
 
   def testFftLength(self):
-    with self._fft_kernel_label_map():
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 3):
-          for size in (5, 6):
-            inner_dim = size // 2 + 1
-            r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
-                (size,) * dims)
-            c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
-                         10).reshape((size,) * (dims - 1) + (inner_dim,))
-
-            # Test truncation (FFT size < dimensions).
-            fft_length = (size - 2,) * rank
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
-            # Confirm it works with unknown shapes as well.
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                 use_placeholder=True)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                  use_placeholder=True)
-
-            # Test padding (FFT size > dimensions).
-            fft_length = (size + 2,) * rank
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
-            # Confirm it works with unknown shapes as well.
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                 use_placeholder=True)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                  use_placeholder=True)
+    if test.is_gpu_available(cuda_only=True):
+      with self._fft_kernel_label_map():
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 3):
+            for size in (5, 6):
+              inner_dim = size // 2 + 1
+              r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
+                  (size,) * dims)
+              c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
+                           10).reshape((size,) * (dims - 1) + (inner_dim,))
+
+              # Test truncation (FFT size < dimensions).
+              fft_length = (size - 2,) * rank
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
+
+              # Confirm it works with unknown shapes as well.
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
+                                   use_placeholder=True)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
+                                    use_placeholder=True)
+
+              # Test padding (FFT size > dimensions).
+              fft_length = (size + 2,) * rank
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
+
+              # Confirm it works with unknown shapes as well.
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
+                                   use_placeholder=True)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
+                                    use_placeholder=True)
 
   def testRandom(self):
     with self._fft_kernel_label_map():
diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py
index 2d2169c310..4ceb24862f 100644
--- a/tensorflow/python/kernel_tests/map_stage_op_test.py
+++ b/tensorflow/python/kernel_tests/map_stage_op_test.py
@@ -24,6 +24,7 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
+TIMEOUT = 1
 
 class MapStageTest(test.TestCase):
 
@@ -194,8 +195,7 @@ class MapStageTest(test.TestCase):
     import threading
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -207,31 +207,34 @@ class MapStageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        sess.run(get)
-
-      # This should now succeed
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
-      # Clear out the staging area completely
-      for i in range(capacity):
+      # Clear the staging area completely
+      for i in range(n):
         sess.run(get)
 
+      self.assertTrue(sess.run(size) == 0)
+
   def testMemoryLimit(self):
     memory_limit = 512*1024  # 512K
     chunk = 200*1024 # 256K
@@ -256,8 +259,7 @@ class MapStageTest(test.TestCase):
     import numpy as np
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -265,36 +267,39 @@ class MapStageTest(test.TestCase):
       # not fill the queue with n tokens
       def thread_run():
         for i in range(n):
-          sess.run(stage, feed_dict={x: np.full(chunk, i, dtype=np.uint8),
-                                    pi: i})
+          data = np.full(chunk, i, dtype=np.uint8)
+          sess.run(stage, feed_dict={x: data, pi: i})
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        sess.run(get)
-
-      # This should now succeed
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
-      # Clear out the staging area completely
-      for i in range(capacity):
+      # Clear the staging area completely
+      for i in range(n):
         sess.run(get)
 
+      self.assertTrue(sess.run(size) == 0)
+
   def testOrdering(self):
     import six
     import random
diff --git a/tensorflow/python/kernel_tests/record_input_test.py b/tensorflow/python/kernel_tests/record_input_test.py
index a3fc98c20f..8fec2affa5 100644
--- a/tensorflow/python/kernel_tests/record_input_test.py
+++ b/tensorflow/python/kernel_tests/record_input_test.py
@@ -53,6 +53,7 @@ class RecordInputOpTest(test.TestCase):
   def testRecordInputEpochs(self):
     files = 100
     records_per_file = 100
+    batches = 2
     with self.test_session() as sess:
       self.generateTestData("basic", files, records_per_file)
 
@@ -63,17 +64,20 @@ class RecordInputOpTest(test.TestCase):
           batch_size=1,
           shift_ratio=0.33,
           seed=10,
-          name="record_input")
+          name="record_input",
+          batches=batches)
 
       yield_op = records.get_yield_op()
 
       # cycle over 3 epochs and make sure we never duplicate
       for _ in range(3):
         epoch_set = set()
-        for _ in range(files * records_per_file):
-          r = sess.run(yield_op)
-          self.assertTrue(r[0] not in epoch_set)
-          epoch_set.add(r[0])
+        for _ in range(int(files * records_per_file / batches)):
+          op_list = sess.run(yield_op)
+          self.assertTrue(len(op_list) is batches)
+          for r in op_list:
+            self.assertTrue(r[0] not in epoch_set)
+            epoch_set.add(r[0])
 
   def testDoesNotDeadlock(self):
     # Iterate multiple times to cause deadlock if there is a chance it can occur
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index e67a2c25e9..14eb2cba68 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+import unittest
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -544,21 +545,24 @@ class SparseFillEmptyRowsTest(test_util.TensorFlowTestCase):
       self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool))
 
 
-class SparseReduceSumTest(test_util.TensorFlowTestCase):
+class SparseReduceTest(test_util.TensorFlowTestCase):
 
-  # [[1, ?, 1]
-  #  [?, 1, ?]]
+  # [[1, ?, 2]
+  #  [?, 3, ?]]
   # where ? is implictly-zero.
   ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64)
   vals = np.array([1, 1, 1]).astype(np.int32)
   dense_shape = np.array([2, 3]).astype(np.int64)
 
-  def _compare(self, sp_t, reduction_axes, ndims, keep_dims):
+  def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
     densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval()
 
     np_ans = densified
     if reduction_axes is None:
-      np_ans = np.sum(np_ans, keepdims=keep_dims)
+      if do_sum:
+        np_ans = np.sum(np_ans, keepdims=keep_dims)
+      else:
+        np_ans = np.max(np_ans, keepdims=keep_dims)
     else:
       if not isinstance(reduction_axes, list):  # Single scalar.
         reduction_axes = [reduction_axes]
@@ -568,15 +572,28 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
       # Loop below depends on sorted.
       reduction_axes.sort()
       for ra in reduction_axes.ravel()[::-1]:
-        np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
+        if do_sum:
+          np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
+        else:
+          np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
 
     with self.test_session():
-      tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
-                                                  keep_dims)
+      if do_sum:
+        tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
+                                                    keep_dims)
+      else:
+        tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
+                                                    keep_dims)
       out_dense = tf_dense_ans.eval()
 
-      tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t, reduction_axes,
-                                                          keep_dims)
+      if do_sum:
+        tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t,
+                                                            reduction_axes,
+                                                            keep_dims)
+      else:
+        tf_sparse_ans = sparse_ops.sparse_reduce_max_sparse(sp_t,
+                                                            reduction_axes,
+                                                            keep_dims)
       # Convert to dense for comparison purposes.
       out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans).eval()
 
@@ -584,9 +601,12 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
     self.assertAllClose(np_ans, out_sparse)
 
   def _compare_all(self, sp_t, reduction_axes, ndims):
-    self._compare(sp_t, reduction_axes, ndims, False)
-    self._compare(sp_t, reduction_axes, ndims, True)
+    self._compare(sp_t, reduction_axes, ndims, False, False)
+    self._compare(sp_t, reduction_axes, ndims, False, True)
+    self._compare(sp_t, reduction_axes, ndims, True, False)
+    self._compare(sp_t, reduction_axes, ndims, True, True)
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
@@ -621,7 +641,12 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
         sparse_ops.sparse_reduce_sum(sp_t, -3).eval()
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_sum(sp_t, 2).eval()
+      with self.assertRaisesOpError("Invalid reduction dimension -3"):
+        sparse_ops.sparse_reduce_max(sp_t, -3).eval()
+      with self.assertRaisesOpError("Invalid reduction dimension 2"):
+        sparse_ops.sparse_reduce_max(sp_t, 2).eval()
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py
index 4a89fb64e3..1a6a869e3d 100644
--- a/tensorflow/python/kernel_tests/stage_op_test.py
+++ b/tensorflow/python/kernel_tests/stage_op_test.py
@@ -23,9 +23,11 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
+TIMEOUT = 1
 
 class StageTest(test.TestCase):
 
+
   def testSimple(self):
     with ops.Graph().as_default() as G:
       with ops.device('/cpu:0'):
@@ -172,8 +174,7 @@ class StageTest(test.TestCase):
     import threading
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -185,31 +186,33 @@ class StageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        self.assertTrue(sess.run(ret) == i)
-
-      # Thread should be able to join now
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
       # Clear the staging area completely
-      for i in range(capacity):
-        self.assertTrue(sess.run(ret) == i+(n-capacity))
+      for i in range(n):
+        self.assertTrue(sess.run(ret) == i)
 
+      # It should now be empty
       self.assertTrue(sess.run(size) == 0)
 
   def testMemoryLimit(self):
@@ -234,8 +237,7 @@ class StageTest(test.TestCase):
     import numpy as np
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -247,30 +249,31 @@ class StageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        self.assertTrue(sess.run(ret)[0] == i)
-
-      # Thread should be able to join now
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
       # Clear the staging area completely
-      for i in range(capacity):
-        self.assertTrue(sess.run(ret)[0] == i+(n-capacity))
+      for i in range(n):
+        self.assertTrue(np.all(sess.run(ret) == i))
 
       self.assertTrue(sess.run(size) == 0)
 
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index b2aff617df..a8f596c7a3 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -402,14 +402,15 @@ def _SliceHelper(tensor, slice_spec, var=None):
 
   # Insert another dimension
   foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
-  print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
-  print(foo[:, tf.newaxis, :].eval()) # => [[[3,2,1]], [[9,8,7]]]
-  print(foo[:, :, tf.newaxis].eval()) # => [[[3],[2],[1]], [[9],[8],[7]]]
+  print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
+  print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
+  print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]], [[7],[8],[9]]]
 
   # Ellipses (3 equivalent operations)
-  print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
-  print(foo[tf.newaxis, ...].eval()) # => [[[3,2,1], [9,8,7]]]
-  print(foo[tf.newaxis].eval()) # => [[[3,2,1], [9,8,7]]]
+  foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
+  print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
+  print(foo[tf.newaxis, ...].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
+  print(foo[tf.newaxis].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
   ```
 
   Notes:
@@ -760,11 +761,14 @@ def parallel_stack(values, name="parallel_stack"):
   parallel_stack([x, y, z])  # => [[1, 4], [2, 5], [3, 6]]
   ```
 
-  The difference between stack and parallel_stack is that stack requires all
-  of the inputs be computed before the operation will begin but doesn't require
-  that the input shapes be known during graph construction.  Parallel stack
-  will copy pieces of the input into the output as they become available, in
-  some situations this can provide a performance benefit.
+  The difference between `stack` and `parallel_stack` is that `stack` requires
+  all the inputs be computed before the operation will begin but doesn't require
+  that the input shapes be known during graph construction.
+  
+  `parallel_stack` will copy pieces of the input into the output as they become
+  available, in some situations this can provide a performance benefit.
+  
+  Unlike `stack`, `parallel_stack` does NOT support backpropagation.
 
   This is the opposite of unstack.  The numpy equivalent is
 
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 4eead79531..829aa99284 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -1582,7 +1582,7 @@ class StagingArea(BaseStagingArea):
   This is mostly useful for limiting the number of tensors on
   devices such as GPUs.
 
-  All get() and peek() commands block if the the requested data
+  All get() and peek() commands block if the requested data
   is not present in the Staging Area.
 
   """
@@ -2155,7 +2155,8 @@ class RecordInput(object):
                parallelism=1,
                shift_ratio=0,
                seed=0,
-               name=None):
+               name=None,
+               batches=None):
     """Constructs a RecordInput Op.
 
     Args:
@@ -2169,12 +2170,18 @@ class RecordInput(object):
       seed: Specify the random number seed used by generator that randomizes
         records.
       name: Optional name for the operation.
+      batches: None by default, creating a single batch op. Otherwise specifies
+        how many batches to create, which are returned as a list when
+        `get_yield_op()` is called. An example use case is to split processing
+        between devices on one computer.
 
     Raises:
       ValueError: If one of the arguments is invalid.
     """
-
     self._batch_size = batch_size
+    if batches is not None:
+      self._batch_size *= batches
+    self._batches = batches
     self._file_pattern = file_pattern
     self._buffer_size = buffer_size
     self._parallelism = parallelism
@@ -2183,8 +2190,11 @@ class RecordInput(object):
     self._name = name
 
   def get_yield_op(self):
-    """Add a node that yields a minibatch every time it is executed."""
-    return gen_data_flow_ops.record_input(
+    """Adds a node that yields a group of records every time it is executed.
+    If RecordInput `batches` parameter is not None, it yields a list of
+    record batches with the specified `batch_size`.
+    """
+    records = gen_data_flow_ops.record_input(
         file_pattern=self._file_pattern,
         file_buffer_size=self._buffer_size,
         file_parallelism=self._parallelism,
@@ -2192,3 +2202,14 @@ class RecordInput(object):
         batch_size=self._batch_size,
         file_random_seed=self._seed,
         name=self._name)
+    if self._batches is None:
+      return records
+    else:
+      with ops.name_scope(self._name):
+        batch_list = [[] for i in six.moves.range(self._batches)]
+        records = array_ops.split(records, self._batch_size, 0)
+        records = [array_ops.reshape(record, []) for record in records]
+        for index, protobuf in zip(six.moves.range(len(records)), records):
+          batch_index = index % self._batches
+          batch_list[batch_index].append(protobuf)
+        return batch_list
diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py
index f96eafed71..3a804c941a 100644
--- a/tensorflow/python/ops/distributions/special_math.py
+++ b/tensorflow/python/ops/distributions/special_math.py
@@ -324,7 +324,7 @@ def log_ndtr(x, series_order=3, name="log_ndtr"):
 
 
 def _log_ndtr_lower(x, series_order):
-  """Asymptotic expansion version of `Log[cdf(x)]`, apppropriate for `x<<-1`."""
+  """Asymptotic expansion version of `Log[cdf(x)]`, appropriate for `x<<-1`."""
   x_2 = math_ops.square(x)
   # Log of the term multiplying (1 + sum)
   log_scale = -0.5 * x_2 - math_ops.log(-x) - 0.5 * math.log(2. * math.pi)
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index aac7aef8ce..dade053589 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -989,9 +989,9 @@ def index_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   table = tf.contrib.lookup.index_table_from_tensor(
-      vocabulary_list=vocabulary_list, num_oov_buckets=1, default_value=-1)
+      mapping=vocabulary_list, num_oov_buckets=1, default_value=-1)
   features = tf.constant(["emerson", "lake", "and", "palmer"])
   ids = table.lookup(features)
   ...
@@ -1161,7 +1161,7 @@ def index_to_string_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       vocabulary_list, default_value="UNKNOWN")
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index 024158e709..a0f505e47b 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -369,6 +369,24 @@ def _Log1pGrad(op, grad):
     return grad * math_ops.reciprocal(1 + x)
 
 
+@ops.RegisterGradient("Sinh")
+def _SinhGrad(op, grad):
+  """Returns grad * cosh(x)."""
+  x = op.inputs[0]
+  with ops.control_dependencies([grad.op]):
+    x = math_ops.conj(x)
+    return grad * math_ops.cosh(x)
+
+
+@ops.RegisterGradient("Cosh")
+def _CoshGrad(op, grad):
+  """Returns grad * sinh(x)."""
+  x = op.inputs[0]
+  with ops.control_dependencies([grad.op]):
+    x = math_ops.conj(x)
+    return grad * math_ops.sinh(x)
+
+
 @ops.RegisterGradient("Tanh")
 def _TanhGrad(op, grad):
   """Returns grad * (1 - tanh(x) * tanh(x))."""
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 23af8c0a57..bf46bbdcc2 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -45,6 +45,8 @@ See the @{$python/math_ops} guide.
 @@expm1
 @@log
 @@log1p
+@@sinh
+@@cosh
 @@ceil
 @@floor
 @@maximum
@@ -2063,12 +2065,11 @@ def tanh(x, name=None):
 
   Args:
     x: A Tensor or SparseTensor with type `float`, `double`, `int32`,
-      `complex64`, `int64`, or `qint32`.
+      `complex64`, or `int64`.
     name: A name for the operation (optional).
 
   Returns:
-    A Tensor or SparseTensor respectively with the same type as `x` if
-    `x.dtype != qint32` otherwise the return type is `quint8`.
+    A Tensor or SparseTensor respectively with the same type as `x`.
   """
   with ops.name_scope(name, "Tanh", [x]) as name:
     if isinstance(x, sparse_tensor.SparseTensor):
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index c0d9c971a0..a1e603199f 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -416,7 +416,7 @@ class LSTMCell(RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+    http://www.bioinf.jku.at/publications/older/2604.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
@@ -622,7 +622,7 @@ class DropoutWrapper(RNNCell):
     """Create a cell with added input, state, and/or output dropout.
 
     If `variational_recurrent` is set to `True` (**NOT** the default behavior),
-    then the the same dropout mask is applied at every step, as described in:
+    then the same dropout mask is applied at every step, as described in:
 
     Y. Gal, Z Ghahramani.  "A Theoretically Grounded Application of Dropout in
     Recurrent Neural Networks".  https://arxiv.org/abs/1512.05287
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 377adcdab8..93a9656950 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -30,6 +30,8 @@
 @@sparse_reset_shape
 @@sparse_fill_empty_rows
 @@sparse_transpose
+@@sparse_reduce_max
+@@sparse_reduce_max_sparse
 @@sparse_reduce_sum
 @@sparse_reduce_sum_sparse
 @@sparse_add
@@ -710,6 +712,90 @@ def sparse_to_dense(sparse_indices,
       name=name)
 
 
+def sparse_reduce_max(sp_input, axis=None, keep_dims=False,
+                      reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+  instead of a sparse one.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  similar to the indexing rules in Python.
+
+  For example:
+
+  ```python
+  # 'x' represents [[1, ?, 2]
+  #                 [?, 3, ?]]
+  # where ? is implicitly-zero.
+  tf.sparse_reduce_max(x) ==> 3
+  tf.sparse_reduce_max(x, 0) ==> [1, 3, 2]
+  tf.sparse_reduce_max(x, 1) ==> [2, 3]  # Can also use -1 as the axis.
+  tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]]
+  tf.sparse_reduce_max(x, [0, 1]) ==> 3
+  ```
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis.
+
+  Returns:
+    The reduced Tensor.
+  """
+  return gen_sparse_ops.sparse_reduce_max(
+      sp_input.indices, sp_input.values,
+      sp_input.dense_shape,
+      math_ops._ReductionDims(sp_input, axis, reduction_axes),
+      keep_dims)
+
+
+def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False,
+                             reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In contrast to SparseReduceSum, this Op returns a
+  SparseTensor.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  which are interpreted according to the indexing rules in Python.
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis
+
+  Returns:
+    The reduced SparseTensor.
+  """
+  output_ind, output_val, output_shape = (
+      gen_sparse_ops.sparse_reduce_max_sparse(
+          sp_input.indices, sp_input.values,
+          sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis,
+                                                        reduction_axes),
+          keep_dims))
+
+  return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
+
+
 def sparse_reduce_sum(sp_input, axis=None, keep_dims=False,
                       reduction_axes=None):
   """Computes the sum of elements across dimensions of a SparseTensor.
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index 7a6abc8e61..20ae082ee1 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -87,7 +87,7 @@ class TensorArray(object):
         the shape constraints of each of the elements of the TensorArray.
         Need not be fully defined.
       colocate_with_first_write_call: If `True`, the TensorArray will be
-        colocated on the same device as the the Tensor used on its first write
+        colocated on the same device as the Tensor used on its first write
         (write operations include `write`, `unstack`, and `split`).  If `False`,
         the TensorArray will be placed on the device determined by the
         device context available during its initialization.
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index aceffd373a..24616aeac3 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -1501,6 +1501,11 @@ def variable_scope(name_or_scope,
   A note about name scoping: Setting `reuse` does not impact the naming of other
   ops such as mult. See related discussion on [github#6189](https://github.com/tensorflow/tensorflow/issues/6189)
 
+  Note that up to and including version 1.0, it was allowed (though
+  explicitly discouraged) to pass False to the reuse argument, yielding
+  undocumented behaviour slightly different from None. Starting at 1.1.0
+  passing None and False as reuse has exactly the same effect.
+
   Args:
     name_or_scope: `string` or `VariableScope`: the scope to open.
     default_name: The default name to use if the `name_or_scope` argument is
diff --git a/tensorflow/python/tools/print_selective_registration_header.py b/tensorflow/python/tools/print_selective_registration_header.py
index 3e2ab4695e..62f00f4467 100644
--- a/tensorflow/python/tools/print_selective_registration_header.py
+++ b/tensorflow/python/tools/print_selective_registration_header.py
@@ -16,7 +16,7 @@ r"""Prints a header file to be used with SELECTIVE_REGISTRATION.
 
 An example of command-line usage is:
   bazel build tensorflow/python/tools:print_selective_registration_header && \
-  bazel-bin/tensorflow/python/tools:print_selective_registration_header \
+  bazel-bin/tensorflow/python/tools/print_selective_registration_header \
     --graphs=path/to/graph.pb > ops_to_register.h
 
 Then when compiling tensorflow, include ops_to_register.h in the include search
diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 1755167938..21183823c2 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -762,6 +762,9 @@ def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
   tensor_list = _as_tensor_list(tensors)
   with ops.name_scope(name, "shuffle_batch",
                       list(tensor_list) + [keep_input]) as name:
+    if capacity <= min_after_dequeue:
+      raise ValueError("capacity %d must be bigger than min_after_dequeue %d."
+                       % (capacity, min_after_dequeue))
     tensor_list = _validate(tensor_list)
     keep_input = _validate_keep_input(keep_input, enqueue_many)
     tensor_list, sparse_info = _store_sparse_tensors(
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 690af5485a..5b1d9a517e 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -147,7 +147,7 @@ def tf_copts():
       "-Iexternal/gemmlowp",
       "-Wno-sign-compare",
       "-fno-exceptions",
-  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1"]) + if_android_arm(
+  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
       ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index b761f41361..eca017edf5 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -768,6 +768,10 @@ tf_module {
     name: "cos"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "cosh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "count_nonzero"
     argspec: "args=[\'input_tensor\', \'axis\', \'keep_dims\', \'dtype\', \'name\', \'reduction_indices\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \"<dtype: \'int64\'>\", \'None\', \'None\'], "
@@ -1640,6 +1644,10 @@ tf_module {
     name: "sin"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "sinh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'input\', \'name\', \'out_type\'], varargs=None, keywords=None, defaults=[\'None\', \"<dtype: \'int32\'>\"], "
@@ -1696,6 +1704,14 @@ tf_module {
     name: "sparse_placeholder"
     argspec: "args=[\'dtype\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
+  member_method {
+    name: "sparse_reduce_max"
+    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "sparse_reduce_max_sparse"
+    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
   member_method {
     name: "sparse_reduce_sum"
     argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index e0a1391d6e..85c712d3c6 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -205,14 +205,17 @@ if [[ -n "${PY_TAGS}" ]]; then
 $(echo ${WHL_BASE_NAME} | cut -d \- -f 2)-${PY_TAGS}-${PLATFORM_TAG}.whl
 
   if [[ ! -f "${WHL_DIR}/${NEW_WHL_BASE_NAME}" ]]; then
-    cp "${WHL_DIR}/${WHL_BASE_NAME}" "${WHL_DIR}/${NEW_WHL_BASE_NAME}" && \
-      echo "Copied wheel file: ${WHL_BASE_NAME} --> ${NEW_WHL_BASE_NAME}" || \
+    if cp "${WHL_DIR}/${WHL_BASE_NAME}" "${WHL_DIR}/${NEW_WHL_BASE_NAME}"
+    then
+      echo "Copied wheel file: ${WHL_BASE_NAME} --> ${NEW_WHL_BASE_NAME}"
+    else
       die "ERROR: Failed to copy wheel file to ${NEW_WHL_BASE_NAME}"
+    fi
   fi
 fi
 
 if [[ $(uname) == "Linux" ]]; then
-  AUDITED_WHL_NAME="${WHL_DIR}/$(echo ${WHL_BASE_NAME} | sed "s/linux/manylinux1/")"
+  AUDITED_WHL_NAME="${WHL_DIR}/$(echo ${WHL_BASE_NAME//linux/manylinux1})"
 
   # Repair the wheels for cpu manylinux1
   if [[ ${CONTAINER_TYPE} == "cpu" ]]; then
@@ -240,14 +243,20 @@ echo "Installing pip whl file: ${WHL_PATH}"
 VENV_DIR="${PIP_TEST_ROOT}/venv"
 
 if [[ -d "${VENV_DIR}" ]]; then
-  rm -rf "${VENV_DIR}" && \
-      echo "Removed existing virtualenv directory: ${VENV_DIR}" || \
-      die "Failed to remove existing virtualenv directory: ${VENV_DIR}"
+  if rm -rf "${VENV_DIR}"
+  then
+    echo "Removed existing virtualenv directory: ${VENV_DIR}"
+  else
+    die "Failed to remove existing virtualenv directory: ${VENV_DIR}"
+  fi
 fi
 
-mkdir -p ${VENV_DIR} && \
-    echo "Created virtualenv directory: ${VENV_DIR}" || \
-    die "FAILED to create virtualenv directory: ${VENV_DIR}"
+if mkdir -p ${VENV_DIR}
+then
+  echo "Created virtualenv directory: ${VENV_DIR}"
+else
+  die "FAILED to create virtualenv directory: ${VENV_DIR}"
+fi
 
 # Verify that virtualenv exists
 if [[ -z $(which virtualenv) ]]; then
diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh
index 3b640dd5e8..9c1b75d004 100755
--- a/tensorflow/tools/ci_build/ci_build.sh
+++ b/tensorflow/tools/ci_build/ci_build.sh
@@ -80,7 +80,7 @@ fi
 
 # cmake (CPU) builds do not require configuration.
 if [[ "${CONTAINER_TYPE}" == "cmake" ]]; then
-  CI_COMMAND_PREFIX=""
+  CI_COMMAND_PREFIX=("")
 fi
 
 # Use nvidia-docker if the container is GPU.
@@ -120,9 +120,9 @@ DOCKER_IMG_NAME=$(echo "${DOCKER_IMG_NAME}" | tr '[:upper:]' '[:lower:]')
 
 # Print arguments.
 echo "WORKSPACE: ${WORKSPACE}"
-echo "CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[@]}"
-echo "COMMAND: ${COMMAND[@]}"
-echo "CI_COMMAND_PREFIX: ${CI_COMMAND_PREFIX[@]}"
+echo "CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[*]}"
+echo "COMMAND: ${COMMAND[*]}"
+echo "CI_COMMAND_PREFIX: ${CI_COMMAND_PREFIX[*]}"
 echo "CONTAINER_TYPE: ${CONTAINER_TYPE}"
 echo "BUILD_TAG: ${BUILD_TAG}"
 echo "  (docker container name will be ${DOCKER_IMG_NAME})"
@@ -140,7 +140,7 @@ if [[ $? != "0" ]]; then
 fi
 
 # Run the command inside the container.
-echo "Running '${COMMAND[@]}' inside ${DOCKER_IMG_NAME}..."
+echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..."
 mkdir -p ${WORKSPACE}/bazel-ci_build-cache
 # By default we cleanup - remove the container once it finish running (--rm)
 # and share the PID namespace (--pid=host) so the process inside does not have
diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index dfaf50eb4f..1cf87d7c7c 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -200,8 +200,8 @@ echo "  TF_BUILD_ENABLE_XLA=${TF_BUILD_ENABLE_XLA}"
 function get_cuda_capability_version() {
   if [[ ! -z $(which deviceQuery) ]]; then
     # The first listed device is used
-    echo $(deviceQuery | grep "CUDA Capability .* version" | \
-        head -1 | awk '{print $NF}')
+    deviceQuery | grep "CUDA Capability .* version" | \
+        head -1 | awk '{print $NF}'
   fi
 }
 
@@ -532,11 +532,14 @@ if [[ "${TF_BUILD_PYTHON_VERSION}" == "python3.5" ]]; then
   DOCKERFILE="${TMP_DIR}/Dockerfile.${TF_BUILD_CONTAINER_TYPE}"
 
   # Replace a line in the Dockerfile
-  sed -i \
+  if sed -i \
       's/RUN \/install\/install_pip_packages.sh/RUN \/install\/install_python3.5_pip_packages.sh/g' \
-      "${DOCKERFILE}" && \
-      echo "Copied and modified Dockerfile for Python 3.5 build: ${DOCKERFILE}" || \
-      die "ERROR: Faild to copy and modify Dockerfile: ${DOCKERFILE}"
+      "${DOCKERFILE}"
+  then
+    echo "Copied and modified Dockerfile for Python 3.5 build: ${DOCKERFILE}"
+  else
+    die "ERROR: Faild to copy and modify Dockerfile: ${DOCKERFILE}"
+  fi
 
   DOCKERFILE_FLAG="--dockerfile ${DOCKERFILE}"
 fi
@@ -574,7 +577,7 @@ rm -f ${TMP_SCRIPT}
 END_TIME=$(date +'%s')
 echo ""
 echo "Parameterized build ends with ${RESULT} at: $(date) "\
-"(Elapsed time: $((${END_TIME} - ${START_TIME})) s)"
+"(Elapsed time: $((END_TIME - START_TIME)) s)"
 
 
 # Clean up temporary directory if it exists
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index fd2874df91..e428766a40 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -47,7 +47,7 @@ num_cpus() {
 # Get the hash of the last non-merge git commit on the current branch.
 # Usage: get_last_non_merge_git_commit
 get_last_non_merge_git_commit() {
-  echo $(git rev-list --no-merges -n 1 HEAD)
+  git rev-list --no-merges -n 1 HEAD
 }
 
 # List files changed (i.e., added, removed or revised) in the last non-merge
@@ -75,7 +75,7 @@ get_py_files_to_check() {
 
     echo "${PY_FILES}"
   else
-    echo $(find tensorflow -name '*.py')
+    find tensorflow -name '*.py'
   fi
 }
 
@@ -157,25 +157,25 @@ do_pylint() {
   NONWL_ERRORS_FILE="$(mktemp)_pylint_nonwl_errors.log"
 
   rm -rf ${OUTPUT_FILE}
-  rm -rf ${ERRORS_FLIE}
+  rm -rf ${ERRORS_FILE}
   rm -rf ${NONWL_ERRORS_FILE}
   touch ${NONWL_ERRORS_FILE}
 
   ${PYLINT_BIN} --rcfile="${PYLINTRC_FILE}" --output-format=parseable \
-      --jobs=${NUM_CPUS} ${PYTHON_SRC_FILES} 2>&1 > ${OUTPUT_FILE}
+      --jobs=${NUM_CPUS} ${PYTHON_SRC_FILES} > ${OUTPUT_FILE} 2>&1
   PYLINT_END_TIME=$(date +'%s')
 
   echo ""
-  echo "pylint took $((${PYLINT_END_TIME} - ${PYLINT_START_TIME})) s"
+  echo "pylint took $((PYLINT_END_TIME - PYLINT_START_TIME)) s"
   echo ""
 
   grep -E '(\[E|\[W0311|\[W0312)' ${OUTPUT_FILE} > ${ERRORS_FILE}
 
   N_ERRORS=0
-  while read LINE; do
+  while read -r LINE; do
     IS_WHITELISTED=0
     for WL_REGEX in ${ERROR_WHITELIST}; do
-      if [[ ! -z $(echo ${LINE} | grep "${WL_REGEX}") ]]; then
+      if echo ${LINE} | grep -q "${WL_REGEX}"; then
         echo "Found a whitelisted error:"
         echo "  ${LINE}"
         IS_WHITELISTED=1
@@ -248,7 +248,7 @@ do_pep8() {
   PEP8_END_TIME=$(date +'%s')
 
   echo ""
-  echo "pep8 took $((${PEP8_END_TIME} - ${PEP8_START_TIME})) s"
+  echo "pep8 took $((PEP8_END_TIME - PEP8_START_TIME)) s"
   echo ""
 
   if [[ -s ${PEP8_OUTPUT_FILE} ]]; then
@@ -278,7 +278,7 @@ do_buildifier(){
   BUILDIFIER_END_TIME=$(date +'%s')
 
   echo ""
-  echo "buildifier took $((${BUILDIFIER_END_TIME} - ${BUILDIFIER_START_TIME})) s"
+  echo "buildifier took $((BUILDIFIER_END_TIME - BUILDIFIER_START_TIME)) s"
   echo ""
 
   if [[ -s ${BUILDIFIER_OUTPUT_FILE} ]]; then
@@ -306,7 +306,7 @@ do_external_licenses_check(){
 
   echo "Getting external dependencies for ${BUILD_TARGET}"
  bazel query "attr('licenses', 'notice', deps(${BUILD_TARGET}))" --no_implicit_deps --no_host_deps --keep_going \
-  | egrep -v "^//tensorflow" \
+  | grep -E -v "^//tensorflow" \
   | sed -e 's|:.*||' \
   | sort \
   | uniq 2>&1 \
@@ -315,7 +315,7 @@ do_external_licenses_check(){
   echo
   echo "Getting list of external licenses mentioned in ${LICENSES_TARGET}."
   bazel query "deps(${LICENSES_TARGET})" --no_implicit_deps --no_host_deps --keep_going \
-  | egrep -v "^//tensorflow" \
+  | grep -E -v "^//tensorflow" \
   | sed -e 's|:.*||' \
   | sort \
   | uniq 2>&1 \
@@ -329,7 +329,7 @@ do_external_licenses_check(){
   EXTERNAL_LICENSES_CHECK_END_TIME=$(date +'%s')
 
   echo
-  echo "do_external_licenses_check took $((${EXTERNAL_LICENSES_CHECK_END_TIME} - ${EXTERNAL_LICENSES_CHECK_START_TIME})) s"
+  echo "do_external_licenses_check took $((EXTERNAL_LICENSES_CHECK_END_TIME - EXTERNAL_LICENSES_CHECK_START_TIME)) s"
   echo
 
   if [[ -s ${MISSING_LICENSES_FILE} ]] || [[ -s ${EXTRA_LICENSES_FILE} ]] ; then
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index b8f9fc8453..7fcd235e62 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -44,8 +44,8 @@ pip2 install --upgrade markdown==2.6.8
 pip3 install --upgrade markdown==2.6.8
 
 # Install protobuf.
-pip2 install --upgrade protobuf==3.2.0
-pip3 install --upgrade protobuf==3.2.0
+pip2 install --upgrade protobuf==3.3.0
+pip3 install --upgrade protobuf==3.3.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/install/install_proto3.sh b/tensorflow/tools/ci_build/install/install_proto3.sh
index 773c89b70b..7934002b2c 100755
--- a/tensorflow/tools/ci_build/install/install_proto3.sh
+++ b/tensorflow/tools/ci_build/install/install_proto3.sh
@@ -17,9 +17,9 @@
 # Install protobuf3.
 
 # Select protobuf version.
-PROTOBUF_VERSION="3.2.0"
+PROTOBUF_VERSION="3.3.0"
 protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g')
-local_protobuf_ver=$(protoc --version | awk '{print $2}')
+local_protobuf_ver=$(protoc --version)
 local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g')
 if [[ -z $local_protobuf_ver_flat ]]; then
   local_protobuf_ver_flat=0
@@ -30,7 +30,7 @@ if (( $local_protobuf_ver_flat < $protobuf_ver_flat )); then
   PROTOBUF_ZIP=$(basename "${PROTOBUF_URL}")
   UNZIP_DEST="google-protobuf"
 
-  wget -q "${PROTOBUF_URL}"
+  wget "${PROTOBUF_URL}"
   unzip "${PROTOBUF_ZIP}" -d "${UNZIP_DEST}"
   cp "${UNZIP_DEST}/bin/protoc" /usr/local/bin/
 
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index e7e2d256cd..084ac49496 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -64,7 +64,7 @@ set -e
 pip3.5 install --upgrade six==1.10.0
 
 # Install protobuf.
-pip3.5 install --upgrade protobuf==3.2.0
+pip3.5 install --upgrade protobuf==3.3.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
index 59ba71f5df..3e31aa1ce1 100755
--- a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
+++ b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 
-PROTOBUF_VERSION="3.2.0"
+PROTOBUF_VERSION="3.3.1"
 PYTHON_BIN=${PYTHON_BIN:-python}
 DIR=${PWD}/protobuf
 
diff --git a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
index 5581023ad7..dff4707cbe 100644
--- a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
@@ -161,5 +161,5 @@ function create_python_test_dir() {
 
 function reinstall_tensorflow_pip() {
   echo "y" | pip uninstall tensorflow -q || true
-  pip install ${1}
+  pip install ${1} --no-deps
 }
diff --git a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
index 8b6836f4ed..96fbadd176 100644
--- a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
@@ -29,9 +29,6 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\cpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
-:: Attempt to upgrade PIP to work around Anaconda issue #542.
-%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
-
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -39,7 +36,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
index ba2d939b5f..e774a6e916 100644
--- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
@@ -28,9 +28,6 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\gpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
-:: Attempt to upgrade PIP to work around Anaconda issue #542.
-%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
-
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -38,7 +35,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh
index f536beef54..7d7f92d246 100755
--- a/tensorflow/tools/dist_test/local_test.sh
+++ b/tensorflow/tools/dist_test/local_test.sh
@@ -70,7 +70,7 @@ get_container_id_by_image_name() {
     # Get the id of a container by image name
     # Usage: get_docker_container_id_by_image_name <img_name>
 
-    echo $(docker ps | grep $1 | awk '{print $1}')
+    docker ps | grep $1 | awk '{print $1}'
 }
 
 # Parse input arguments
@@ -152,7 +152,7 @@ rm -rf "${BUILD_DIR}"
 docker run ${DOCKER_IMG_NAME} \
     /var/tf_dist_test/scripts/dist_mnist_test.sh \
     --ps_hosts $(seq -f "localhost:%g" -s "," \
-                 2000 $((2000 + ${NUM_PARAMETER_SERVERS} - 1))) \
+                 2000 $((2000 + NUM_PARAMETER_SERVERS - 1))) \
     --worker_hosts $(seq -f "localhost:%g" -s "," \
-                     3000 $((3000 + ${NUM_WORKERS} - 1))) \
+                     3000 $((3000 + NUM_WORKERS - 1))) \
     --num_gpus 0 ${SYNC_REPLICAS_FLAG}
diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index 3e45ae362c..6d5a9bdc4c 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -54,6 +54,30 @@ for additional containers, such as release candidates or nightly builds.
 
 ## Rebuilding the containers
 
-Just pick the dockerfile corresponding to the container you want to build, and run
+Building TensorFlow Docker containers should be done through the
+[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/README.md)
+script. The raw Dockerfiles should not be used directly as they contain strings
+to be replaced by the script during the build.
 
-    $ docker build --pull -t $USER/tensorflow-suffix -f Dockerfile.suffix .
+To use the script, specify the container type (`CPU` vs. `GPU`), the desired
+Python version (`PYTHON2` vs. `PYTHON3`) and whether the developer Docker image
+is to be built (`NO` vs. `YES`). In addition, you need to specify the central
+location from where the pip package of TensorFlow will be downloaded.
+
+For example, to build a CPU-only non-developer Docker image for Python 2, using
+TensorFlow's nightly pip package:
+
+``` bash
+export TF_DOCKER_BUILD_IS_DEVEL=NO
+export TF_DOCKER_BUILD_TYPE=CPU
+export TF_DOCKER_BUILD_PYTHON_VERSION=PYTHON2
+
+export NIGHTLY_VERSION="1.head"
+export TF_DOCKER_BUILD_CENTRAL_PIP=$(echo ${TF_DOCKER_BUILD_PYTHON_VERSION} | sed s^PYTHON2^http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=${TF_DOCKER_BUILD_PYTHON_VERSION},label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp27-cp27mu-manylinux1_x86_64.whl^ | sed s^PYTHON3^http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp35-cp35m-manylinux1_x86_64.whl^)
+
+tensorflow/tools/docker/parameterized_docker_build.sh
+```
+
+If successful, the image will be tagged as `${USER}/tensorflow:latest` by default.
+
+Rebuilding GPU images requires [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).
diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh
index f88af68cde..ea88d8165f 100755
--- a/tensorflow/tools/docker/parameterized_docker_build.sh
+++ b/tensorflow/tools/docker/parameterized_docker_build.sh
@@ -233,13 +233,16 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then
 
   # Modify python/pip version if necessary.
   if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then
-    sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
+    if sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
         sed -i -e 's/python-dev/python3-dev/g' "${DOCKERFILE}" && \
         sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \
-        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" && \
-        echo "Modified Dockerfile for python version "\
-"${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" || \
-        die "FAILED to modify ${DOCKERFILE} for python3"
+        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}"
+    then
+      echo "Modified Dockerfile for python version "\
+"${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}"
+    else
+      die "FAILED to modify ${DOCKERFILE} for python3"
+    fi
   fi
 else
   DOCKERFILE="${TMP_DIR}/Dockerfile"
@@ -250,14 +253,17 @@ else
 
   # Modify python/pip version if necessary.
   if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then
-    sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \
+    if sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \
         sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
         sed -i -e 's^/tmp/pip^/tmp/pip3^g' "${DOCKERFILE}" && \
         sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \
         sed -i -e 's/ENV CI_BUILD_PYTHON python/ENV CI_BUILD_PYTHON python3/g' "${DOCKERFILE}" && \
-        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" && \
-        echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" || \
-        die "FAILED to modify ${DOCKERFILE} for python3"
+        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}"
+    then
+      echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}"
+    else
+      die "FAILED to modify ${DOCKERFILE} for python3"
+    fi
   fi
 fi
 
@@ -277,7 +283,7 @@ fi
 
 # Make sure that there is no other containers of the same image running
 # TODO(cais): Move to an earlier place.
-if [[ ! -z $("${DOCKER_BINARY}" ps | grep "${IMG}") ]]; then
+if "${DOCKER_BINARY}" ps | grep -q "${IMG}"; then
   die "ERROR: It appears that there are docker containers of the image "\
 "${IMG} running. Please stop them before proceeding"
 fi
@@ -310,16 +316,22 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then
     # on the running docker container
     echo ""
     echo "Performing basic sanity checks on the running container..."
-    wget -qO- "http://127.0.0.1:${CONTAINER_PORT}/tree" &> /dev/null && \
-        echo "  PASS: wget tree" || \
-        mark_check_failed "  FAIL: wget tree"
+    if wget -qO- "http://127.0.0.1:${CONTAINER_PORT}/tree" &> /dev/null
+    then
+      echo "  PASS: wget tree"
+    else
+      mark_check_failed "  FAIL: wget tree"
+    fi
 
     for NB in ${TMP_DIR}/notebooks/*.ipynb; do
       NB_BASENAME=$(basename "${NB}")
       NB_URL="http://127.0.0.1:${CONTAINER_PORT}/notebooks/${NB_BASENAME}"
-      wget -qO- "${NB_URL}" -o "${TMP_DIR}/${NB_BASENAME}" &> /dev/null && \
-          echo "  PASS: wget ${NB_URL}" || \
-          mark_check_failed  "  FAIL: wget ${NB_URL}"
+      if wget -qO- "${NB_URL}" -o "${TMP_DIR}/${NB_BASENAME}" &> /dev/null
+      then
+        echo "  PASS: wget ${NB_URL}"
+      else
+        mark_check_failed  "  FAIL: wget ${NB_URL}"
+      fi
     done
   fi
 
diff --git a/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh b/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
index 2ce0fb394f..7146213b33 100755
--- a/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
+++ b/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
@@ -71,7 +71,7 @@ rm -rf ${LOG_FILE} || \
 
 # Invoke main Python file
 python "${GCS_SMOKE_PY}" --gcs_bucket_url="${GCS_BUCKET_URL}" \
-    2>&1 > "${LOG_FILE}"
+    > "${LOG_FILE}" 2>&1
 
 if [[ $? != "0" ]]; then
   cat ${LOG_FILE}
@@ -92,6 +92,9 @@ NEW_TFREC_URL=$(grep "Using input path" "${LOG_FILE}" | \
 if [[ -z ${NEW_TFREC_URL} ]]; then
   die "FAIL: Unable to determine the URL to the new tfrecord file in GCS"
 fi
-"${GSUTIL_BIN}" rm "${NEW_TFREC_URL}" && \
-    echo "Cleaned up new tfrecord file in GCS: ${NEW_TFREC_URL}" || \
-    die "FAIL: Unable to clean up new tfrecord file in GCS: ${NEW_TFREC_URL}"
+if "${GSUTIL_BIN}" rm "${NEW_TFREC_URL}"
+then
+  echo "Cleaned up new tfrecord file in GCS: ${NEW_TFREC_URL}"
+else
+  die "FAIL: Unable to clean up new tfrecord file in GCS: ${NEW_TFREC_URL}"
+fi
diff --git a/tensorflow/tools/git/gen_git_source.sh b/tensorflow/tools/git/gen_git_source.sh
index 1487658049..977fe16333 100755
--- a/tensorflow/tools/git/gen_git_source.sh
+++ b/tensorflow/tools/git/gen_git_source.sh
@@ -20,7 +20,7 @@ if [[ -z "${OUTPUT_FILENAME}"  ]]; then
   exit 1
 fi
 
-GIT_VERSION=`git describe --long --tags`
+GIT_VERSION=$(git describe --long --tags)
 if [[ $? != 0 ]]; then
    GIT_VERSION=unknown;
 fi
diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index bfda55d3ad..b4274e67df 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -1055,7 +1055,7 @@ in the future.
 
 The Graph Transform Tool associates names of transforms with the code to
 implement them using the `REGISTER_GRAPH_TRANSFORM()` macro. This takes a string
-and a function, and automatically registers the transform with the tool. You
+and a function, and automagically registers the transform with the tool. You
 will need to watch out for a few things though:
 
 *   Because it's using global C++ objects in each file under the hood, the
diff --git a/tensorflow/tools/graph_transforms/quantize_weights_test.cc b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
index e1a105bdd3..63c5b5a64d 100644
--- a/tensorflow/tools/graph_transforms/quantize_weights_test.cc
+++ b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
@@ -90,13 +90,13 @@ class QuantizeWeightsTest : public ::testing::Test {
     EXPECT_EQ("Const", q_weights_const->op());
     EXPECT_EQ(DT_QUINT8, q_weights_const->attr().at("dtype").type());
 
-    // Run the the original graph.
+    // Run the original graph.
     std::unique_ptr<Session> original_session(NewSession(SessionOptions()));
     TF_ASSERT_OK(original_session->Create(original_graph_def));
     std::vector<Tensor> original_outputs;
     TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs));
 
-    // Run the the quantized graph.
+    // Run the quantized graph.
     std::unique_ptr<Session> quantized_session(NewSession(SessionOptions()));
     TF_ASSERT_OK(quantized_session->Create(quantized_graph_def));
     std::vector<Tensor> quantized_outputs;
diff --git a/tensorflow/tools/lib_package/libtensorflow_java_test.sh b/tensorflow/tools/lib_package/libtensorflow_java_test.sh
index a44298e01a..c44978fc57 100755
--- a/tensorflow/tools/lib_package/libtensorflow_java_test.sh
+++ b/tensorflow/tools/lib_package/libtensorflow_java_test.sh
@@ -29,7 +29,7 @@ TAR="${TAR}"
 
 [ -z "${JAVA}" ] && JAVA="java"
 [ -z "${JAVAC}" ] && JAVAC="javac"
-[ -z "${TAR}"] && TAR="tar"
+[ -z "${TAR}" ] && TAR="tar"
 
 # bazel tests run with ${PWD} set to the root of the bazel workspace
 TARFILE="${PWD}/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz"
diff --git a/tensorflow/tools/lib_package/libtensorflow_test.sh b/tensorflow/tools/lib_package/libtensorflow_test.sh
index 6430c755af..7dfe8eefcc 100755
--- a/tensorflow/tools/lib_package/libtensorflow_test.sh
+++ b/tensorflow/tools/lib_package/libtensorflow_test.sh
@@ -26,7 +26,7 @@ CC="${CC}"
 TAR="${TAR}"
 
 [ -z "${CC}" ] && CC="/usr/bin/gcc"
-[ -z "${TAR}"] && TAR="tar"
+[ -z "${TAR}" ] && TAR="tar"
 
 # bazel tests run with ${PWD} set to the root of the bazel workspace
 TARFILE="${PWD}/tensorflow/tools/lib_package/libtensorflow.tar.gz"
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 073988765c..54ba8064e8 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0-rc2'
+_VERSION = '1.2.0'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
diff --git a/tensorflow/tools/tfprof/BUILD b/tensorflow/tools/tfprof/BUILD
index 541df78e47..57cccd8921 100644
--- a/tensorflow/tools/tfprof/BUILD
+++ b/tensorflow/tools/tfprof/BUILD
@@ -33,7 +33,6 @@ cc_binary(
         "//tensorflow/tools/tfprof/internal:tfprof_options",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
         "//tensorflow/tools/tfprof/internal:tfprof_utils",
-        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
         "@linenoise//:linenoise",
     ],
 )
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/tools/tfprof/README.md
index 816ad8c07e..54f3cd62f2 100644
--- a/tensorflow/tools/tfprof/README.md
+++ b/tensorflow/tools/tfprof/README.md
@@ -1,19 +1,15 @@
 # tfprof: TensorFlow Profiler and Beyond
 
-###Features
+### Features
 
 * Profile model architectures
   * parameters, tensor shapes, float operations, device placement, etc.
 * Profile model performance
   * execution time, memory consumption
   * Profile multiple steps.
-* Auto profile and advise.
-  * accelerator utilization check
-  * expensive operation check
-  * operation configuration check
-  * distributed runtime check (Not OSS)
+* Auto detect and advise. (Experimental)
 
-###Interfaces
+### Interfaces
 
 * Python API
 * Command Line
@@ -51,7 +47,7 @@ See [Options](g3doc/options.md) for detail instructions.
 -output                     stdout:
 ```
 
-###Tutorials
+### Tutorials
 
 *  [Python API](g3doc/python_api.md)
 *  [Command Line Interface](g3doc/command_line.md)
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/tools/tfprof/g3doc/advise.md
index e30add6fbf..3bce6270ff 100644
--- a/tensorflow/tools/tfprof/g3doc/advise.md
+++ b/tensorflow/tools/tfprof/g3doc/advise.md
@@ -3,7 +3,6 @@
 tfprof analyzes profiles and generates advises for common issues.
 
 ### Run Advise.
-
 ```python
 # First create a profiler. See profiler tutorials for more details.
 profiler = model_analyzer.Profiler(sess.graph)
@@ -14,63 +13,8 @@ _ = sess.run(r1,
              run_metadata=run_meta)
 profiler.add_step(1, run_meta)
 
-# Then Start advise.
-profiler.advise(model_analyzer.ALL_ADVICE)
-
-# For one-shot API
-tf.contrib.tfprof.model_analyzer.advise(
-    sess.graph, run_meta=run_metadata)
-```
-
-```shell
-# Run advisor on CLI
-# See CLI tutorial on generating the files.
-tfprof --graph_path=graph.pbtxt \
-       --run_meta_path=run_metadata \
-       --op_log_path=tfprof_log
-
-tfprof> advise
-AcceleratorUtilizationChecker:
-device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03
-device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08
-device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04
-device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21
-
-OperationChecker:
-Found operation using NHWC data_format on GPU. Maybe NCHW is faster.
-
-ExpensiveOperationChecker:
-top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%)
-top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%)
-top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%)
-seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec
-  seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec
-    seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec
-      seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
-        seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize), cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
-        seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha..., cpu: 164us, accelerator: 0us, total: 164us
-      seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms
-      seq2seq_lib.py:151:sequence_loss_by_...:total_size = tf.a..., cpu: 154us, accelerator: 23us, total: 177us
-    seq2seq_lib.py:184:sampled_sequence_...:return cost / tf...., cpu: 97us, accelerator: 8us, total: 105us
-      math_ops.py:690:cast:return gen_math_o..., cpu: 62us, accelerator: 3us, total: 65us
-      math_ops.py:839:binary_op_wrapper:return func(x, y,..., cpu: 35us, accelerator: 5us, total: 40us
-  seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms
-    seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms
-      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms
-        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 303.76ms, accelerator: 73.54ms, total: 377.30ms
-        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 2.75ms, accelerator: 0us, total: 2.75ms
-      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 67us, accelerator: 0us, total: 67us
-    seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms
-      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms
-        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 292.04ms, accelerator: 73.54ms, total: 365.58ms
-        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 4.07ms, accelerator: 0us, total: 4.07ms
-      core_rnn.py:178:static_rnn:min_sequence_leng..., cpu: 85us, accelerator: 0us, total: 85us
-      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 16us, accelerator: 0us, total: 16us
-    seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms
- ...(omitted)
-top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms
-top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms
-top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms
+# Start advise.
+profiler.advise()
 ```
 
 ### Checker
@@ -81,24 +25,16 @@ area with the profile and report issues. A `Checker` is like a plugin.
 
 For example:
 
-#### JobChecker (Not Available OSS)
-
-*   Checks RecvTensor RPC latency and bandwidth.
-*   Checks CPU/Memory utilization of the job.
+####JobChecker (Not Available OSS)
+* Checking RecvTensor RPC latency and bandwidth.
+* Checking CPU/Memory utilization of the job.
 
 ####AcceleratorUtilization Checker
 * Checks what percentage of time the accelerator spends on computation.
 
-#### OperationChecker
-
-*   Checks whether the operation runs with optimal options.
-*   Checks if there is a better implementation to replace the current operation.
-
-#### ExpensiveOperationChecker
-
-*   Checks the most expensive operation type.
-*   Checks the most expensive graph nodes.
-*   Checks the most expensive graph-building Python codes.
+####Operation Checker
+* Check whether the operation runs with optimal options.
+* Checks if there is a better implementation to replace the current operation.
 
 ####Contribute Your Checker
 
diff --git a/tensorflow/tools/tfprof/g3doc/command_line.md b/tensorflow/tools/tfprof/g3doc/command_line.md
index 0d8d56809a..9f0de72e07 100644
--- a/tensorflow/tools/tfprof/g3doc/command_line.md
+++ b/tensorflow/tools/tfprof/g3doc/command_line.md
@@ -126,7 +126,7 @@ tfprof>
 -show_name_regexes          .*
 -hide_name_regexes          IsVariableInitialized_[0-9]+,save\/.*,^zeros[0-9_]*
 -account_displayed_op_only  false
-# supported select fileds. Availability depends on --[run_meta|checkpoint|op_log]_path.
+# supported select fields. Availability depends on --[run_meta|checkpoint|op_log]_path.
 # [bytes|micros|params|float_ops|occurrence|tensor_value|device|op_types]
 -select                     params
 # format: output_type:key=value,key=value...
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/tools/tfprof/internal/advisor/BUILD
index 629322373a..e4bd0f0015 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/tools/tfprof/internal/advisor/BUILD
@@ -44,21 +44,12 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "expensive_operation_checker",
-    hdrs = ["expensive_operation_checker.h"],
-    deps = [
-        ":checker",
-    ],
-)
-
 cc_library(
     name = "tfprof_advisor",
     hdrs = ["tfprof_advisor.h"],
     deps = [
         ":accelerator_utilization_checker",
         ":checker",
-        ":expensive_operation_checker",
         ":internal_checker_runner_dummy",
         ":operation_checker",
     ],
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
index 074b8e57b0..fb7f65d7dc 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
@@ -33,11 +33,10 @@ struct ExecStats {
 
 class AcceleratorUtilizationChecker : public Checker {
  public:
-  string name() const override { return kCheckers[0]; }
+  string name() override { return "AcceleratorUtilizationChecker"; }
 
  private:
-  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
-                             const TFStats* stats) override {
+  std::vector<string> Check(const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -49,21 +48,24 @@ class AcceleratorUtilizationChecker : public Checker {
     return CheckInternal();
   }
 
-  AdviceProto::Checker CheckInternal() {
+  std::vector<string> CheckInternal() {
     for (const auto& s : accelerator_exec_stats_) {
       const ExecStats& stat = s.second;
       int64 total_micros = stat.end_micros - stat.start_micros;
       if (total_micros <= 0) continue;
       double utilization = 1.0 * stat.exec_micros / total_micros;
       if (utilization >= 0.5) {
-        reports_.add_reports(strings::Printf("device: %s utilization: %.2f",
-                                             s.first.c_str(), utilization));
+        reports_.push_back(strings::Printf("%s: device: %s utilization: %.2f",
+                                           kLevel[0], s.first.c_str(),
+                                           utilization));
       } else if (utilization < 0.5 && utilization > 0.2) {
-        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
-                                             s.first.c_str(), utilization));
+        reports_.push_back(
+            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[1],
+                            s.first.c_str(), utilization));
       } else if (utilization <= 0.2) {
-        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
-                                             s.first.c_str(), utilization));
+        reports_.push_back(
+            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[2],
+                            s.first.c_str(), utilization));
       }
     }
     return reports_;
@@ -100,7 +102,7 @@ class AcceleratorUtilizationChecker : public Checker {
 
   std::map<string, ExecStats> accelerator_exec_stats_;
   std::map<string, int64> ps_placement_;
-  AdviceProto::Checker reports_;
+  std::vector<string> reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/checker.h b/tensorflow/tools/tfprof/internal/advisor/checker.h
index 3ce80cd8c4..b8b057be5b 100644
--- a/tensorflow/tools/tfprof/internal/advisor/checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/checker.h
@@ -18,33 +18,27 @@ limitations under the License.
 
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
-// Append only.
-static const char* const kCheckers[] = {
-    "AcceleratorUtilizationChecker", "OperationChecker",
-    "ExpensiveOperationChecker",
-    "JobChecker",  // Internal checker.
+static const char* const kLevel[] = {
+    "NOTE",     // Good to know.
+    "SUGGEST",  // Might get better.
+    "WARN",     // Please do it for better.
 };
 
 class Checker {
  public:
-  virtual ~Checker() {}
+  virtual ~Checker(){};
 
-  virtual string name() const = 0;
+  virtual string name() = 0;
 
-  AdviceProto::Checker Run(const AdvisorOptionsProto::CheckerOption& options,
-                           const TFStats* stats) {
-    return Check(options, stats);
-  }
+  std::vector<string> Run(const TFStats* stats) { return Check(stats); }
 
  protected:
-  virtual AdviceProto::Checker Check(
-      const AdvisorOptionsProto::CheckerOption& options,
-      const TFStats* stats) = 0;
+  // Returns a vector of string, each one being an advice.
+  virtual std::vector<string> Check(const TFStats* stats) = 0;
 };
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
index ed8ae571b6..1238b57f20 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
@@ -17,16 +17,13 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
 
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
 class TFStats;
 
-AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
-                                const TFStats* stats);
+std::map<string, std::vector<string>> RunInternalCheckers(const TFStats* stats);
 
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
index 67962c8e8b..8204d2b04e 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
@@ -17,9 +17,9 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
-                                const TFStats* stats) {
-  return AdviceProto();
+std::map<string, std::vector<string>> RunInternalCheckers(
+    const TFStats* stats) {
+  return std::map<string, std::vector<string>>();
 }
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
index 4d0d68e3bf..2a05f9bfd0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
@@ -24,11 +24,10 @@ namespace tfprof {
 
 class OperationChecker : public Checker {
  public:
-  string name() const override { return kCheckers[1]; }
+  string name() override { return "OperationChecker"; }
 
  private:
-  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
-                             const TFStats* stats) override {
+  std::vector<string> Check(const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -54,20 +53,22 @@ class OperationChecker : public Checker {
       }
     }
     if (use_batch_norm && !use_fused_batch_norm) {
-      reports_.add_reports(
-          "Maybe use faster FusedBatchNorm instead of BatchNorm");
+      reports_.push_back(strings::Printf(
+          "%s: Maybe use faster FusedBatchNorm instead of BatchNorm",
+          kLevel[1]));
     }
     if (recommend_nchw) {
       // TODO(xpan): Maybe print which Op supports NCHW.
-      reports_.add_reports(
-          "Found operation using NHWC data_format on GPU. Maybe "
-          "NCHW is faster.");
+      reports_.push_back(strings::Printf(
+          "%s: Found operation using NHWC data_format on GPU. Maybe "
+          "NCHW is faster.",
+          kLevel[1]));
     }
     return reports_;
   }
 
  private:
-  AdviceProto::Checker reports_;
+  std::vector<string> reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
index d2257fb9b5..856f515459 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
@@ -18,10 +18,8 @@ limitations under the License.
 
 #include "tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/checker.h"
-#include "tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
 #include "tensorflow/tools/tfprof/internal/advisor/operation_checker.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -31,44 +29,23 @@ class Advisor {
  public:
   Advisor(const TFStats* stats) : stats_(stats) {}
 
-  static AdvisorOptionsProto DefaultOptions() {
-    AdvisorOptionsProto options;
-    std::vector<string> checkers(
-        kCheckers, kCheckers + sizeof(kCheckers) / sizeof(*kCheckers));
-    for (const string& checker : checkers) {
-      (*options.mutable_checkers())[checker];
-    }
-    return options;
-  }
-
-  AdviceProto Advise(const AdvisorOptionsProto& options) {
+  std::map<string, std::vector<string>> Advise() {
     // Note: Release a checker's memory ASAP.
-    AdviceProto ret = RunInternalCheckers(options, stats_);
-
-    if (options.checkers().find(kCheckers[0]) != options.checkers().end()) {
-      AcceleratorUtilizationChecker au_checker;
-      (*ret.mutable_checkers())[kCheckers[0]].MergeFrom(
-          au_checker.Run(options.checkers().at(kCheckers[0]), stats_));
-    }
-    if (options.checkers().find(kCheckers[1]) != options.checkers().end()) {
-      OperationChecker op_checker;
-      (*ret.mutable_checkers())[kCheckers[1]].MergeFrom(
-          op_checker.Run(options.checkers().at(kCheckers[1]), stats_));
-    }
-    if (options.checkers().find(kCheckers[2]) != options.checkers().end()) {
-      ExpensiveOperationChecker expensive_op_checker;
-      (*ret.mutable_checkers())[kCheckers[2]].MergeFrom(
-          expensive_op_checker.Run(options.checkers().at(kCheckers[2]),
-                                   stats_));
-    }
-    for (const auto& checker : ret.checkers()) {
-      fprintf(stdout, "\n%s:\n", checker.first.c_str());
-      for (const string& r : checker.second.reports()) {
+    std::map<string, std::vector<string>> reports = RunInternalCheckers(stats_);
+    // TODO(xpan): Think of a way to turn off/on specific checkers.
+    AcceleratorUtilizationChecker au_checker;
+    reports[au_checker.name()] = au_checker.Run(stats_);
+    OperationChecker op_checker;
+    reports[op_checker.name()] = op_checker.Run(stats_);
+
+    for (const auto& checker_r : reports) {
+      fprintf(stdout, "%s reports:\n", checker_r.first.c_str());
+      for (const auto& r : checker_r.second) {
         fprintf(stdout, "%s\n", r.c_str());
       }
     }
     fflush(stdout);
-    return ret;
+    return reports;
   }
 
  private:
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
index 3b40253954..b41d0770dc 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
@@ -29,16 +29,15 @@ class TFProfAdvisorTest : public ::testing::Test {
                              nullptr, nullptr));
 
     stats_->AddNodeForTest(
-        0, CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 0, 10, 2));
-    stats_->AddNodeForTest(0, CreateNode("n2", "Conv2D", {}, 0, 20, 2));
-    stats_->BuildAllViews();
+        "n1", CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 10, 2));
+    stats_->AddNodeForTest("n2", CreateNode("n2", "Conv2D", {}, 20, 2));
     advisor_.reset(new Advisor(stats_.get()));
   }
 
   std::unique_ptr<TFGraphNode> CreateNode(const string& name,
                                           const string& type,
                                           std::map<string, string> attrs,
-                                          int64 step, int64 start_miros,
+                                          int64 start_miros,
                                           int64 end_rel_micros) {
     node_defs_.push_back(std::unique_ptr<NodeDef>(new NodeDef()));
     NodeDef* def = node_defs_.back().get();
@@ -53,10 +52,10 @@ class TFProfAdvisorTest : public ::testing::Test {
     NodeExecStats node_stat;
     node_stat.set_all_start_micros(start_miros);
     node_stat.set_op_end_rel_micros(end_rel_micros);
-    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
-    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
+    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
+    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
                       node_stat);
-    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
+    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
                       node_stat);
     return node;
   }
@@ -67,38 +66,23 @@ class TFProfAdvisorTest : public ::testing::Test {
 };
 
 TEST_F(TFProfAdvisorTest, Basics) {
-  AdvisorOptionsProto options = Advisor::DefaultOptions();
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_TRUE(advice.checkers().find(kCheckers[0]) != advice.checkers().end());
-  EXPECT_TRUE(advice.checkers().find(kCheckers[1]) != advice.checkers().end());
-  EXPECT_TRUE(advice.checkers().find(kCheckers[2]) != advice.checkers().end());
+  std::map<string, std::vector<string>> reports = advisor_->Advise();
+  EXPECT_TRUE(reports.find("AcceleratorUtilizationChecker") != reports.end());
+  EXPECT_TRUE(reports.find("OperationChecker") != reports.end());
 }
 
 TEST_F(TFProfAdvisorTest, OperationChecker) {
-  AdvisorOptionsProto options;
-  (*options.mutable_checkers())[kCheckers[1]];
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_EQ(advice.checkers().at(kCheckers[1]).reports_size(), 1);
-  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[1]).reports(0))
-                  .contains("NCHW"));
+  std::map<string, std::vector<string>> reports = advisor_->Advise();
+  EXPECT_EQ(reports["OperationChecker"].size(), 1);
+  EXPECT_TRUE(StringPiece(reports["OperationChecker"][0]).contains("NCHW"));
 }
 
 TEST_F(TFProfAdvisorTest, UtilizationChecker) {
-  AdvisorOptionsProto options;
-  (*options.mutable_checkers())[kCheckers[0]];
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_EQ(advice.checkers().at(kCheckers[0]).reports_size(), 1);
-  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[0]).reports(0))
+  std::map<string, std::vector<string>> reports = advisor_->Advise();
+  EXPECT_EQ(reports["AcceleratorUtilizationChecker"].size(), 1);
+  EXPECT_TRUE(StringPiece(reports["AcceleratorUtilizationChecker"][0])
                   .contains("low utilization"));
 }
 
-TEST_F(TFProfAdvisorTest, ExpensiveOperationChecker) {
-  AdvisorOptionsProto options;
-  (*options.mutable_checkers())[kCheckers[2]];
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[2]).reports(0))
-                  .contains("top 1 operation type: Conv2D"));
-}
-
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.cc b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
index 5a9c44d8e6..37d01db3a1 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.cc
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
@@ -27,7 +27,6 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 #include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
@@ -37,18 +36,6 @@ TFStats* tf_stat = nullptr;
 
 string RunProfile(const string& command, const string& options,
                   TFStats* tf_stats) {
-  if (command == kCmds[4]) {
-    AdvisorOptionsProto option_pb;
-    if (!option_pb.ParseFromString(options)) {
-      fprintf(stderr, "Cannot parse AdvisorOptionsProto\n");
-      return "";
-    }
-    tf_stats->BuildAllViews();
-    return Advisor(tf_stats).Advise(option_pb).SerializeAsString();
-  } else {
-    tf_stats->BuildView(command);
-  }
-
   Options opts;
   tensorflow::Status s = Options::FromProtoStr(options, &opts);
   if (!s.ok()) {
@@ -110,14 +97,14 @@ void AddStep(int64 step, const string* run_meta, const string* op_log) {
   // TODO(xpan): Better error handling.
   std::unique_ptr<RunMetadata> run_meta_ptr(new RunMetadata());
   run_meta_ptr->ParseFromString(*run_meta);
-  tf_stat->AddRunMeta(step, std::move(run_meta_ptr));
+  tf_stat->ParseRunMeta(step, std::move(run_meta_ptr));
 
   std::unique_ptr<OpLog> op_log_ptr;
   if (op_log && !op_log->empty()) {
     op_log_ptr.reset(new OpLog());
     op_log_ptr->ParseFromString(*op_log);
   }
-  tf_stat->AddOpLog(std::move(op_log_ptr));
+  tf_stat->ParseOpLog(std::move(op_log_ptr));
 }
 
 string Profile(const string* command, const string* options) {
@@ -157,5 +144,7 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
   return RunProfile(*command, *options, &tf_stats);
 }
 
+void Advise() { Advisor(tf_stat).Advise(); }
+
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.h b/tensorflow/tools/tfprof/internal/print_model_analysis.h
index 46db63646d..84165e542d 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.h
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.h
@@ -39,6 +39,8 @@ void AddStep(int64 step, const string* run_meta, const string* op_log);
 
 string Profile(const string* command, const string* options);
 
+void Advise();
+
 // Single-step Profiler.
 //
 // Interface defined for Python API swig. Calls the tfprof core API.
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.h b/tensorflow/tools/tfprof/internal/tfprof_graph.h
index 194a21f0cc..fbeae8673d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_graph.h
@@ -54,8 +54,8 @@ class TFGraph : public TFShow {
   const ShowNode* ShowInternal(const Options& opts,
                                Timeline* timeline) override;
 
-  bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
-                         int depth) const override {
+  bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
+                         int depth) override {
     return true;
   }
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index 77a2593623..6a7077c085 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -126,7 +126,6 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
   }
   nodes = SortNodes(nodes, opts);
 
-  // pre keeps track of previous visited node.
   OpNode* pre = nullptr;
   std::vector<OpNode*> account_nodes;
   for (auto it = nodes.rbegin(); it != nodes.rend(); ++it) {
@@ -171,20 +170,16 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
     root_->ResetTotalStats();
     if (pre) {
       root_->AggregateTotalStats(pre);
+      root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
+      pre->mutable_proto()->clear_children();
     }
   }
-  if (pre) {
-    root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
-    pre->mutable_proto()->clear_children();
-  }
 
   if (opts.output_type == kOutput[1] || opts.output_type == kOutput[2]) {
     string display_str = FormatLegend(opts);
     for (OpNode* node : show_nodes) {
       display_str += FormatNode(node, root_.get(), opts);
     }
-    // In op view, we don't show root (total). But it will still in proto.
-    // TODO(xpan): Is it the right choice?
     root_->formatted_str = display_str;
   }
   return root_.get();
@@ -206,7 +201,7 @@ int64 TFOp::SearchRoot(const std::vector<OpNode*> nodes,
   return i;
 }
 
-string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const {
+string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
   std::vector<string> attrs;
 
   if (opts.select.find(kShown[0]) != opts.select.end()) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.h b/tensorflow/tools/tfprof/internal/tfprof_op.h
index 34812f54be..5b16490363 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.h
@@ -56,15 +56,15 @@ class TFOp : public TFMultiShow {
   int64 SearchRoot(const std::vector<OpNode*> nodes,
                    const std::vector<string>& regexes);
 
-  bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
-                         int depth) const override {
+  bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
+                         int depth) override {
     if (opts.min_occurrence > node->node->graph_nodes().size()) {
       return false;
     }
     return true;
   }
 
-  string FormatNode(OpNode* node, OpNode* root, const Options& opts) const;
+  string FormatNode(OpNode* node, OpNode* root, const Options& opts);
 
   std::unique_ptr<OpNode> root_;
   std::map<string, std::unique_ptr<OpNode>> cnodes_map_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/tools/tfprof/internal/tfprof_options.h
index d39333e3fc..6c9db24342 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_options.h
@@ -59,10 +59,10 @@ static const char* const kShown[] = {
     "cpu_micros"};
 
 static const char* const kCmds[] = {
-    "scope", "graph", "code", "op", "advise", "set", "help",
+    "scope", "graph", "code", "op", "set", "help",
 };
 
-static const char* const kOutput[] = {"timeline", "stdout", "file", "none"};
+static const char* const kOutput[] = {"timeline", "stdout", "file"};
 
 static const char* const kTimelineOpts[] = {
     "outfile",
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index 6c1183a4d5..40cc56fb22 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -26,9 +26,7 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFGraphNodeProto& TFShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[3]) {
-    return ShowInternal(opts, nullptr)->proto();
-  } else if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -66,8 +64,7 @@ bool TFShow::LookUpCheckPoint(const string& name,
   return true;
 }
 
-bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
-                        int depth) const {
+bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -100,8 +97,7 @@ bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
   return true;
 }
 
-bool TFShow::ShouldTrim(const ShowNode* node,
-                        const std::vector<string>& regexes) const {
+bool TFShow::ShouldTrim(ShowNode* node, const std::vector<string>& regexes) {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -126,7 +122,7 @@ bool TFShow::ReAccount(ShowNode* node, const Options& opts) {
   return false;
 }
 
-string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
+string TFShow::FormatNode(ShowNode* node, const Options& opts) {
   std::vector<string> info;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     const string shape = FormatShapes(node->node->shape());
@@ -214,7 +210,7 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
                          str_util::Join(info, ", ").c_str());
 }
 
-string TFShow::FormatLegend(const Options& opts) const {
+string TFShow::FormatLegend(const Options& opts) {
   std::vector<string> legends;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     legends.push_back("# parameters");
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/tools/tfprof/internal/tfprof_show.h
index 95513e086f..2c61b4fd73 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.h
@@ -54,21 +54,20 @@ class TFShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
-                                 int depth) const {
+  virtual bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
+                                 int depth) {
     return true;
   }
 
-  bool ShouldShow(const ShowNode* node, const Options& opts, int depth) const;
+  bool ShouldShow(ShowNode* node, const Options& opts, int depth);
 
-  bool ShouldTrim(const ShowNode* node,
-                  const std::vector<string>& regexes) const;
+  bool ShouldTrim(ShowNode* node, const std::vector<string>& regexes);
 
   bool ReAccount(ShowNode* node, const Options& opts);
 
-  string FormatNode(ShowNode* node, const Options& opts) const;
+  string FormatNode(ShowNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts) const;
+  string FormatLegend(const Options& opts);
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 84d542714a..7545b3d48b 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -28,9 +28,7 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[3]) {
-    return ShowInternal(opts, nullptr)->proto();
-  } else if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -50,8 +48,8 @@ const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
   }
 }
 
-bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
-                             int depth) const {
+bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
+                            int depth) {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -90,8 +88,8 @@ bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
   return true;
 }
 
-bool TFMultiShow::ShouldTrim(const ShowMultiNode* node,
-                             const std::vector<string>& regexes) const {
+bool TFMultiShow::ShouldTrim(ShowMultiNode* node,
+                            const std::vector<string>& regexes) {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -104,7 +102,7 @@ bool TFMultiShow::ReAccount(ShowMultiNode* node, const Options& opts) {
   return node->ReInit(opts.step, opts.account_type_regexes);
 }
 
-string TFMultiShow::FormatLegend(const Options& opts) const {
+string TFMultiShow::FormatLegend(const Options& opts) {
   std::vector<string> legends;
   if (opts.select.find(kShown[0]) != opts.select.end()) {
     legends.push_back("output bytes");
@@ -144,8 +142,7 @@ string TFMultiShow::FormatLegend(const Options& opts) const {
                          str_util::Join(legends, " | ").c_str());
 }
 
-string TFMultiShow::FormatInputShapes(
-    const TFMultiGraphNodeProto& proto) const {
+string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
   // input_shape string -> (static defined count, run count, run_micros)
   std::map<string, std::tuple<int64, int64, int64>> input_shapes_attr;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
@@ -202,7 +199,7 @@ string TFMultiShow::FormatInputShapes(
 }
 
 std::vector<string> TFMultiShow::FormatTimes(const ShowMultiNode* node,
-                                             const Options& opts) const {
+                                             const Options& opts) {
   std::vector<string> attrs;
   if (opts.select.find(kShown[1]) != opts.select.end()) {
     attrs.push_back(FormatTotalExecTime(node, opts));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
index ce309816a9..e6faf1231d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
@@ -55,23 +55,21 @@ class TFMultiShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
-                                 int depth) const {
+  virtual bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
+                                 int depth) {
     return true;
   }
 
-  bool ShouldShow(const ShowMultiNode* node, const Options& opts,
-                  int depth) const;
+  bool ShouldShow(ShowMultiNode* node, const Options& opts, int depth);
 
-  bool ShouldTrim(const ShowMultiNode* node,
-                  const std::vector<string>& regexes) const;
+  bool ShouldTrim(ShowMultiNode* node, const std::vector<string>& regexes);
 
   bool ReAccount(ShowMultiNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts) const;
-  string FormatInputShapes(const TFMultiGraphNodeProto& proto) const;
+  string FormatLegend(const Options& opts);
+  string FormatInputShapes(const TFMultiGraphNodeProto& proto);
   std::vector<string> FormatTimes(const ShowMultiNode* node,
-                                  const Options& opts) const;
+                                  const Options& opts);
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index 46b6ebc09a..18a7c51a20 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -65,7 +65,6 @@ class TFProfShowTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.cc b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
index 64da7ae7cf..f5b8dad4e2 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
@@ -29,17 +29,16 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
                  std::unique_ptr<RunMetadata> run_meta,
                  std::unique_ptr<OpLog> op_log,
                  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader)
-    : has_code_traces_(false),
-      graph_(std::move(graph)),
+    : graph_(std::move(graph)),
       ckpt_reader_(std::move(ckpt_reader)) {
   CHECK(graph_) << "Must at least have GraphDef";
 
   printf("Parsing Inputs...\n");
   ParseGraph();
   if (run_meta && run_meta->has_step_stats()) {
-    AddRunMeta(0, std::move(run_meta));
+    ParseRunMeta(0, std::move(run_meta));
   }
-  AddOpLog(std::move(op_log));
+  ParseOpLog(std::move(op_log));
 
   if (ckpt_reader_) {
     for (const auto& v : ckpt_reader_->GetVariableToShapeMap()) {
@@ -49,48 +48,27 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
       }
     }
   }
-}
 
-void TFStats::BuildView(const string& cmd) {
-  if (cmd == kCmds[0] && !scope_view_) {
-    scope_view_.reset(new TFScope(ckpt_reader_.get()));
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      scope_view_->AddNode(it->second.get());
-    }
-    scope_view_->Build();
-  }
-  if (cmd == kCmds[1] && !graph_view_) {
-    graph_view_.reset(new TFGraph(ckpt_reader_.get()));
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      graph_view_->AddNode(it->second.get());
-    }
-    graph_view_->Build();
-  }
-  if (cmd == kCmds[2] && !code_view_) {
-    code_view_.reset(new TFCode());
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      code_view_->AddNode(it->second.get());
-    }
-    code_view_->Build();
-  }
-  if (cmd == kCmds[3] && !op_view_) {
-    op_view_.reset(new TFOp());
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      op_view_->AddNode(it->second.get());
-    }
-    op_view_->Build();
-  }
-}
+  printf("Preparing Views...\n");
+  scope_view_ = std::unique_ptr<TFScope>(new TFScope(ckpt_reader_.get()));
+  graph_view_ = std::unique_ptr<TFGraph>(new TFGraph(ckpt_reader_.get()));
+  code_view_ = std::unique_ptr<TFCode>(new TFCode());
+  op_view_ = std::unique_ptr<TFOp>(new TFOp());
 
-void TFStats::BuildAllViews() {
-  std::vector<string> cmds_str(kCmds, kCmds + sizeof(kCmds) / sizeof(*kCmds));
-  for (const string& cmd : cmds_str) {
-    BuildView(cmd);
-  }
+  for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+    scope_view_->AddNode(it->second.get());
+    graph_view_->AddNode(it->second.get());
+    code_view_->AddNode(it->second.get());
+    op_view_->AddNode(it->second.get());
+  }
+  scope_view_->Build();
+  graph_view_->Build();
+  code_view_->Build();
+  op_view_->Build();
 }
 
 const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
-                                               const Options& opts) const {
+                                               const Options& opts) {
   if (!Validate(opts)) {
     return empty_graph_node_;
   }
@@ -104,8 +82,8 @@ const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
   }
 }
 
-const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(
-    const string& cmd, const Options& opts) const {
+const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(const string& cmd,
+                                                         const Options& opts) {
   if (!Validate(opts)) {
     return empty_multi_graph_node_;
   }
@@ -152,7 +130,7 @@ void TFStats::ParseGraph() {
   }
 }
 
-void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
+void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
   if (!op_log) {
     return;
   }
@@ -166,13 +144,12 @@ void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
       node->second->AddFloatOps(entry.float_ops());
     }
     if (entry.has_code_def()) {
-      has_code_traces_ = true;
       node->second->AddCode(entry.code_def());
     }
   }
 }
 
-void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
+void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   if (!run_meta || !run_meta->has_step_stats()) {
     fprintf(stderr, "Invalid RunMetadata for step %lld\n", step);
     return;
@@ -199,7 +176,7 @@ void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   }
 }
 
-bool TFStats::Validate(const Options& opts) const {
+bool TFStats::Validate(const Options& opts) {
   if (opts.step >= 0 && steps_.find(opts.step) == steps_.end()) {
     fprintf(stderr, "Options -step=%lld not found\n", opts.step);
     return false;
@@ -207,9 +184,9 @@ bool TFStats::Validate(const Options& opts) const {
   return true;
 }
 
-void TFStats::AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node) {
-  steps_.insert(step);
-  nodes_map_[node->name()] = std::move(node);
+void TFStats::AddNodeForTest(const string& name,
+                             std::unique_ptr<TFGraphNode> node) {
+  nodes_map_[name] = std::move(node);
 }
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.h b/tensorflow/tools/tfprof/internal/tfprof_stats.h
index b26d274f80..dfb190e703 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.h
@@ -59,38 +59,28 @@ class TFStats {
   const std::map<string, std::unique_ptr<TFGraphNode>>& nodes() const {
     return nodes_map_;
   }
-  const std::set<int64>& steps() const { return steps_; }
-  bool has_code_traces() const { return has_code_traces_; }
 
-  void BuildView(const string& cmd);
-  void BuildAllViews();
-
-  // Note: Must first BuildView(view_foo) before ShowXXX(view_foo) methods.
-  //
   // Organize the TensorFlow model as different types of views, and generate
   // outputs for profiling.
-  // TODO(xpan): Should it return reference here?
-  const TFGraphNodeProto& ShowGraphNode(const string& cmd,
-                                        const Options& opts) const;
+  const TFGraphNodeProto& ShowGraphNode(const string& cmd, const Options& opts);
   const TFMultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
-                                                  const Options& opts) const;
+                                                  const Options& opts);
 
   // Add a step of run time meta data.
-  void AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
+  void ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
   // Add tfprof operation meta data, such as customized op type, float_ops,
   // and code traces.
-  void AddOpLog(std::unique_ptr<OpLog> op_log);
+  void ParseOpLog(std::unique_ptr<OpLog> op_log);
 
   // For test purpose only.
-  void AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node);
+  void AddNodeForTest(const string& name, std::unique_ptr<TFGraphNode> node);
 
  private:
-  bool Validate(const Options& opts) const;
+  bool Validate(const Options& opts);
 
   void ParseGraph();
 
   std::set<int64> steps_;
-  bool has_code_traces_;
   std::unique_ptr<GraphDef> graph_;
   std::unique_ptr<TFScope> scope_view_;
   std::unique_ptr<TFGraph> graph_view_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index 00d5d8cdb3..bcd1147c69 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -66,7 +66,6 @@ class TFProfStatsTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index 0482657ca5..d7cfe7dade 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -50,7 +50,6 @@ class TFProfTensorTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
index cad31050a9..0e9bb9658c 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
@@ -52,7 +52,6 @@ class TFProfTimelineTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 nullptr, nullptr));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/tools/tfprof/tfprof_main.cc
index 5e70c093cc..7a4e7e85ff 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/tools/tfprof/tfprof_main.cc
@@ -33,7 +33,6 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/util/command_line_flags.h"
-#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
@@ -162,13 +161,12 @@ int Run(int argc, char** argv) {
         "Profiling everything!\n");
     return 0;
   } else if (argc > 1) {
-    if (string(argv[1]) == kCmds[6]) {
+    if (string(argv[1]) == kCmds[5]) {
       PrintHelp();
       return 0;
     }
     if (string(argv[1]) == kCmds[0] || string(argv[1]) == kCmds[1] ||
-        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3] ||
-        string(argv[1]) == kCmds[4]) {
+        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3]) {
       cmd = argv[1];
     }
   }
@@ -218,13 +216,7 @@ int Run(int argc, char** argv) {
               run_meta_files[i].c_str(), s.ToString().c_str());
       return 1;
     }
-    tf_stat.AddRunMeta(i, std::move(run_meta));
-  }
-
-  if (cmd == kCmds[4]) {
-    tf_stat.BuildAllViews();
-    Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
-    return 0;
+    tf_stat.ParseRunMeta(i, std::move(run_meta));
   }
 
   Options opts(FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros,
@@ -235,11 +227,9 @@ int Run(int argc, char** argv) {
                output_type, output_options);
 
   if (cmd == kCmds[2] || cmd == kCmds[3]) {
-    tf_stat.BuildView(cmd);
     tf_stat.ShowMultiGraphNode(cmd, opts);
     return 0;
   } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
-    tf_stat.BuildView(cmd);
     tf_stat.ShowGraphNode(cmd, opts);
     return 0;
   }
@@ -264,19 +254,14 @@ int Run(int argc, char** argv) {
       fprintf(stderr, "E: %s\n", s.ToString().c_str());
       continue;
     }
-    if (cmd == kCmds[5]) {
+    if (cmd == kCmds[4]) {
       opts = new_opts;
-    } else if (cmd == kCmds[6]) {
+    } else if (cmd == kCmds[5]) {
       PrintHelp();
     } else if (cmd == kCmds[2] || cmd == kCmds[3]) {
-      tf_stat.BuildView(cmd);
       tf_stat.ShowMultiGraphNode(cmd, new_opts);
     } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
-      tf_stat.BuildView(cmd);
       tf_stat.ShowGraphNode(cmd, new_opts);
-    } else if (cmd == kCmds[4]) {
-      tf_stat.BuildAllViews();
-      Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
     }
   }
   return 0;
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index abf141f1e7..7206c6f956 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -102,11 +102,3 @@ message TFMultiGraphNodeProto {
   // structure used.
   repeated TFMultiGraphNodeProto children = 11;
 }
-
-message AdviceProto {
-  // checker name -> a list of reports from the checker.
-  map<string, Checker> checkers = 1;
-  message Checker {
-    repeated string reports = 2;
-  }
-}
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index afe0979536..3e81e361a9 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -327,11 +327,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   patched_http_archive(
       name = "protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
       # TODO: remove patching when tensorflow stops linking same protos into
       #       multiple shared libraries loaded in runtime by python.
       #       This patch fixes a runtime crash when tensorflow is compiled
@@ -345,21 +345,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.http_archive(
       name = "com_google_protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
   )
 
   native.http_archive(
       name = "com_google_protobuf_cc",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
   )
 
   native.new_http_archive(
diff --git a/tools/tf_env_collect.sh b/tools/tf_env_collect.sh
index abeebeadea..a1c9c88c58 100755
--- a/tools/tf_env_collect.sh
+++ b/tools/tf_env_collect.sh
@@ -16,49 +16,62 @@
 
 set -u  # Check for undefined variables
 
-echo "Collecting system information..."
-
-OUTPUT_FILE=tf_env.txt
-
-echo >> $OUTPUT_FILE
-echo "== cat /etc/issue ===============================================" >> $OUTPUT_FILE
-uname -a >> $OUTPUT_FILE
-uname=`uname -s`
-if [ "$(uname)" == "Darwin" ]; then
-  echo Mac OS X `sw_vers -productVersion` >> $OUTPUT_FILE
-elif [ "$(uname)" == "Linux" ]; then
-  cat /etc/*release | grep VERSION >> $OUTPUT_FILE
-fi
-
-
-echo >> $OUTPUT_FILE
-echo '== are we in docker =============================================' >> $OUTPUT_FILE
-num=`cat /proc/1/cgroup | grep docker | wc -l`;
-if [ $num -ge 1 ]; then
-  echo "Yes" >> $OUTPUT_FILE
-else
-  echo "No" >> $OUTPUT_FILE
-fi
-
-echo >> $OUTPUT_FILE
-echo '== compiler =====================================================' >> $OUTPUT_FILE
-c++ --version 2>&1 >> $OUTPUT_FILE
-
-echo >> $OUTPUT_FILE
-echo '== uname -a =====================================================' >> $OUTPUT_FILE
-uname -a >> $OUTPUT_FILE
+die() {
+  # Print a message and exit with code 1.
+  #
+  # Usage: die <error_message>
+  #   e.g., die "Something bad happened."
 
-echo >> $OUTPUT_FILE
-echo '== check pips ===================================================' >> $OUTPUT_FILE
-pip list 2>&1 | grep "proto\|numpy\|tensorflow" >> $OUTPUT_FILE
+  echo $@
+  exit 1
+}
 
+echo "Collecting system information..."
 
-echo >> $OUTPUT_FILE
-echo '== check for virtualenv =========================================' >> $OUTPUT_FILE
-python -c "import sys;print(hasattr(sys, \"real_prefix\"))" >> $OUTPUT_FILE
+OUTPUT_FILE=tf_env.txt
+python_bin_path=$(which python || which python3 || die "Cannot find Python binary")
+
+{
+  echo
+  echo "== cat /etc/issue ==============================================="
+  uname -a
+  uname=`uname -s`
+  if [ "$(uname)" == "Darwin" ]; then
+    echo Mac OS X `sw_vers -productVersion`
+  elif [ "$(uname)" == "Linux" ]; then
+    cat /etc/*release | grep VERSION
+  fi
+  
+  echo
+  echo '== are we in docker ============================================='
+  num=`cat /proc/1/cgroup | grep docker | wc -l`;
+  if [ $num -ge 1 ]; then
+    echo "Yes"
+  else
+    echo "No"
+  fi
+  
+  echo
+  echo '== compiler ====================================================='
+  c++ --version 2>&1
+  
+  echo
+  echo '== uname -a ====================================================='
+  uname -a
+  
+  echo
+  echo '== check pips ==================================================='
+  pip list 2>&1 | grep "proto\|numpy\|tensorflow"
+  
+  
+  echo
+  echo '== check for virtualenv ========================================='
+  ${python_bin_path} -c "import sys;print(hasattr(sys, \"real_prefix\"))"
+  
+  echo
+  echo '== tensorflow import ============================================'
+} >> ${OUTPUT_FILE}
 
-echo >> $OUTPUT_FILE
-echo '== tensorflow import ============================================' >> $OUTPUT_FILE
 cat <<EOF > /tmp/check_tf.py
 import tensorflow as tf;
 print("tf.VERSION = %s" % tf.VERSION)
@@ -67,32 +80,34 @@ print("tf.COMPILER_VERSION = %s" % tf.GIT_VERSION)
 with tf.Session() as sess:
   print("Sanity check: %r" % sess.run(tf.constant([1,2,3])[:1]))
 EOF
-python /tmp/check_tf.py 2>&1  >> ${OUTPUT_FILE}
-
-DEBUG_LD=libs python -c "import tensorflow"  2>>${OUTPUT_FILE} > /tmp/loadedlibs
-grep libcudnn.so /tmp/loadedlibs >> $OUTPUT_FILE
-
-echo >> $OUTPUT_FILE
-echo '== env ==========================================================' >> $OUTPUT_FILE
-if [ -z ${LD_LIBRARY_PATH+x} ]; then
-  echo "LD_LIBRARY_PATH is unset" >> $OUTPUT_FILE;
-else
-  echo LD_LIBRARY_PATH ${LD_LIBRARY_PATH}  >> $OUTPUT_FILE;
-fi
-if [ -z ${DYLD_LIBRARY_PATH+x} ]; then
-  echo "DYLD_LIBRARY_PATH is unset" >> $OUTPUT_FILE;
-else
-  echo DYLD_LIBRARY_PATH ${DYLD_LIBRARY_PATH}  >> $OUTPUT_FILE;
-fi
-
-
-echo >> $OUTPUT_FILE >> $OUTPUT_FILE
-echo '== nvidia-smi ===================================================' >> $OUTPUT_FILE
-nvidia-smi 2>&1 >> $OUTPUT_FILE
-
-echo >> $OUTPUT_FILE
+${python_bin_path} /tmp/check_tf.py 2>&1  >> ${OUTPUT_FILE}
+
+DEBUG_LD=libs ${python_bin_path} -c "import tensorflow"  2>>${OUTPUT_FILE} > /tmp/loadedlibs
+
+{
+  grep libcudnn.so /tmp/loadedlibs
+  echo
+  echo '== env =========================================================='
+  if [ -z ${LD_LIBRARY_PATH+x} ]; then
+    echo "LD_LIBRARY_PATH is unset";
+  else
+    echo LD_LIBRARY_PATH ${LD_LIBRARY_PATH} ;
+  fi
+  if [ -z ${DYLD_LIBRARY_PATH+x} ]; then
+    echo "DYLD_LIBRARY_PATH is unset";
+  else
+    echo DYLD_LIBRARY_PATH ${DYLD_LIBRARY_PATH} ;
+  fi
+  
+  
+  echo
+  echo '== nvidia-smi ==================================================='
+  nvidia-smi 2>&1
+  
+  echo
+  echo '== cuda libs  ==================================================='
+} >> ${OUTPUT_FILE}
 
-echo '== cuda libs  ===================================================' >> $OUTPUT_FILE
 find /usr/local -type f -name 'libcudart*'  2>/dev/null | grep cuda |  grep -v "\\.cache" >> ${OUTPUT_FILE}
 find /usr/local -type f -name 'libudnn*'  2>/dev/null | grep cuda |  grep -v "\\.cache" >> ${OUTPUT_FILE}
 
-- 
GitLab


From 0a6d5cda495010559a1de3e2c2d2a22672f517a6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 13:04:08 -0700
Subject: [PATCH 0454/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 160183349
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 150 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 148 +++++++++++++----
 2 files changed, 270 insertions(+), 28 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index cabd91f9ef..6d2b54915e 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -5376,6 +5376,30 @@ op {
     }
   }
 }
+op {
+  name: "Cosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "CountUpTo"
   input_arg {
@@ -22947,6 +22971,30 @@ op {
     }
   }
 }
+op {
+  name: "Sinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "Size"
   input_arg {
@@ -24788,6 +24836,108 @@ op {
     }
   }
 }
+op {
+  name: "SparseReduceMax"
+  input_arg {
+    name: "input_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+}
+op {
+  name: "SparseReduceMaxSparse"
+  input_arg {
+    name: "input_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+}
 op {
   name: "SparseReduceSum"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 92ca6ea367..08894dcafc 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -5199,6 +5199,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -6301,7 +6302,7 @@ op {
     }
   }
   summary: "Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors."
-  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
+  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\n```\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n```\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
 }
 op {
   name: "DepthwiseConv2dNativeBackpropFilter"
@@ -22831,6 +22832,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -24936,6 +24938,123 @@ op {
   summary: "Multiply matrix \"a\" by matrix \"b\"."
   description: "The inputs must be two-dimensional matrices and the inner dimension of \"a\" must\nmatch the outer dimension of \"b\". This op is optimized for the case where at\nleast one of \"a\" or \"b\" is sparse. The breakeven for using this versus a dense\nmatrix multiply on one platform was 30% zero values in the sparse matrix.\n\nThe gradient computation of this operation will only take advantage of sparsity\nin the input gradient when that gradient comes from a Relu."
 }
+op {
+  name: "SparseReduceMax"
+  input_arg {
+    name: "input_indices"
+    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    description: "1-D.  Shape of the input SparseTensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    description: "1-D.  Length-`K` vector containing the reduction axes."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    description: "`R-K`-D.  The reduced Tensor."
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, retain reduced dimensions with length 1."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+  summary: "Computes the max of elements across dimensions of a SparseTensor."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`\ninstead of a sparse one.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
+}
+op {
+  name: "SparseReduceMaxSparse"
+  input_arg {
+    name: "input_indices"
+    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    description: "1-D.  Shape of the input SparseTensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    description: "1-D.  Length-`K` vector containing the reduction axes."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, retain reduced dimensions with length 1."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+  summary: "Computes the max of elements across dimensions of a SparseTensor."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a\nSparseTensor.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
+}
 op {
   name: "SparseReduceSum"
   input_arg {
@@ -27306,33 +27425,6 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
-op {
-  name: "LMDBReader"
-  output_arg {
-    name: "reader_handle"
-    description: "The handle to reference the Reader."
-    type: DT_STRING
-    is_ref: true
-  }
-  attr {
-    name: "container"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
-  }
-  attr {
-    name: "shared_name"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
-  }
-  summary: "A Reader that outputs the records from a LMDB database."
-  is_stateful: true
-}
 op {
   name: "TakeDataset"
   input_arg {
-- 
GitLab


From b91b5baab23cfb8d9da269166070511c28eeaf84 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 13:05:22 -0700
Subject: [PATCH 0455/1375] Merge changes from github followup.

PiperOrigin-RevId: 160183498
---
 tensorflow/BUILD                              |   1 +
 tensorflow/compiler/jit/BUILD                 |   5 +-
 tensorflow/compiler/jit/kernels/BUILD         |   1 +
 tensorflow/compiler/plugin/executor/BUILD     |   4 +-
 tensorflow/compiler/tests/BUILD               |  18 +-
 tensorflow/contrib/lookup/lookup_ops.py       |   4 -
 .../core/kernels/sparse_reduce_sum_op.cc      | 305 ------------------
 tensorflow/python/BUILD                       |   2 +
 .../python/kernel_tests/sparse_ops_test.py    |   3 -
 9 files changed, 27 insertions(+), 316 deletions(-)
 delete mode 100644 tensorflow/core/kernels/sparse_reduce_sum_op.cc

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 62ae5ae78c..6e76db02de 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -208,6 +208,7 @@ filegroup(
         "//tensorflow/compiler/jit/kernels:all_files",
         "//tensorflow/compiler/jit/legacy_flags:all_files",
         "//tensorflow/compiler/jit/ops:all_files",
+        "//tensorflow/compiler/plugin/executor:all_files",
         "//tensorflow/compiler/tests:all_files",
         "//tensorflow/compiler/tf2xla:all_files",
         "//tensorflow/compiler/tf2xla/cc:all_files",
diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 306e704415..7ebd842218 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -15,7 +15,10 @@ package_group(
 )
 
 package(
-    default_visibility = [":internal"],
+    default_visibility = [
+        ":internal",
+        "//tensorflow/compiler/plugin/executor:__pkg__",
+    ],
 )
 
 load("//tensorflow:tensorflow.bzl", "cc_header_only_library")
diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD
index ed204b8182..97f3512a6c 100644
--- a/tensorflow/compiler/jit/kernels/BUILD
+++ b/tensorflow/compiler/jit/kernels/BUILD
@@ -2,6 +2,7 @@ licenses(["notice"])  # Apache 2.0
 
 package(
     default_visibility = [
+        "//tensorflow/compiler/plugin/executor:__pkg__",
         "//tensorflow/compiler/tf2xla:internal",
     ],
 )
diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
index 9bc706abdf..2e5875705f 100644
--- a/tensorflow/compiler/plugin/executor/BUILD
+++ b/tensorflow/compiler/plugin/executor/BUILD
@@ -11,9 +11,11 @@ cc_library(
         "*.h",
     ]),
     deps = [
+        "//tensorflow/compiler/jit:xla_device",
         "//tensorflow/compiler/jit:xla_jit_headers_lib",
+        "//tensorflow/compiler/tf2xla:xla_compiler",
         "//tensorflow/compiler/xla:xla_headers_lib",
-        "//tensorflow/compiler/xla/service:hlo_evaluator",
+        "//tensorflow/compiler/xla/service",
         "//third_party/eigen3",
         "@local_config_cuda//cuda:cuda_headers",
         "@protobuf//:protobuf_headers",
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 044857d422..432b24756d 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -175,6 +175,11 @@ tf_xla_py_test(
     name = "slice_ops_test",
     size = "small",
     srcs = ["slice_ops_test.py"],
+    # TODO(b/62962492): Test fails with assertion error.
+    tags = [
+        "manual",
+        "notap",
+    ],
     deps = [
         ":xla_test",
         "//tensorflow/python:array_ops",
@@ -456,6 +461,11 @@ cuda_py_test(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
     ],
+    # TODO(b/62961789): Test fails with SIGABRT
+    tags = [
+        "manual",
+        "notap",
+    ],
 )
 
 cc_library(
@@ -524,8 +534,12 @@ cuda_py_test(
 # --dump_graph_dir, and the config file was written by hand.
 #
 # Run the following to build a minimal benchmark of the computation on Android:
-# $ bazel build -c opt --config=android_arm \
-#       third_party/tensorflow/compiler/tests:lstm_layer_inference_benchmark
+# $ bazel build -c opt --cxxopt='-std=c++11' --linkopt='-lm' \
+#   --cpu=armeabi-v7a \
+#   --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+#   --crosstool_top=//external:android/crosstool \
+#   //tensorflow/compiler/tests:lstm_layer_inference_benchmark
+
 #
 # Currently the resulting binary size is ~190KB
 tf_library(
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index ce8518267f..f53f38f3cf 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -260,11 +260,7 @@ def index_to_string(tensor, mapping, default_value="UNK", name=None):
   For example:
 
   ```python
-<<<<<<< HEAD
   mapping_string = tf.constant(["emerson", "lake", "palmer"])
-=======
-  mapping_string = tf.constant(["emerson", "lake", "palmer")
->>>>>>> 338a7ead4475d6b97b420d6d1c56ff66815e3e7b
   indices = tf.constant([1, 5], tf.int64)
   values = tf.contrib.lookup.index_to_string(
       indices, mapping=mapping_string, default_value="UNKNOWN")
diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op.cc b/tensorflow/core/kernels/sparse_reduce_sum_op.cc
deleted file mode 100644
index 074aab9f9e..0000000000
--- a/tensorflow/core/kernels/sparse_reduce_sum_op.cc
+++ /dev/null
@@ -1,305 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// See docs in ../ops/sparse_ops.cc.
-
-#define EIGEN_USE_THREADS
-
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/register_types.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_util.h"
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/util/sparse/sparse_tensor.h"
-
-// TODO(b/31496047): Fix non-standard include order.
-#include <numeric>  // clang-format off
-
-using tensorflow::sparse::SparseTensor;
-using tensorflow::gtl::ArraySlice;
-
-namespace tensorflow {
-
-struct ReduceDetails {
-  // The dimensions to call Reorder() with.
-  std::vector<int64> reorder_dims;
-
-  // The dimensions to call group() with after Reorder().
-  std::vector<int64> group_by_dims;
-
-  // The shape after reduction.
-  TensorShape reduced_shape;
-};
-
-// Compute common reduce parameters that'll be used for SparseTensor
-// reductions. Usage:
-// ReduceDetails reduction = SparseTensorReduceHelper(sp, axes, keep_dims);
-// sp.Reorder(reduction.reorder_dims);
-// for (const auto& g : sp.group(reduction.group_by_dims)) {
-//   ...
-// }
-// // Set output shape to reduction.reduced_shape.
-ReduceDetails SparseTensorReduceHelper(const SparseTensor &sp,
-                                       gtl::ArraySlice<int32> axes_slice,
-                                       bool keep_dims) {
-  ReduceDetails reduction;
-
-  std::vector<int32> reduction_axes(axes_slice.begin(), axes_slice.end());
-  int ndims = sp.dims();
-  for (int64 i = 0; i < reduction_axes.size(); ++i) {
-    reduction_axes[i] = (reduction_axes[i] + ndims) % ndims;
-  }
-  std::sort(reduction_axes.begin(), reduction_axes.end());
-
-  // (0) Calculate the grouping dimensions:
-  // group_by_dims == {0, .., NDIMS-1} \ reduction_axes.
-  std::vector<int64> perm(ndims);
-  std::iota(perm.begin(), perm.end(), 0);
-
-  // Requires perm and reduction_axes_ be sorted; group_by_dims will be
-  // sorted as well.
-  std::set_difference(
-      perm.begin(), perm.end(), reduction_axes.begin(), reduction_axes.end(),
-      std::inserter(reduction.group_by_dims, reduction.group_by_dims.begin()));
-
-  // Now append the rest of the axes (the complement of group_by_dims_);
-  // result is used by Reorder().
-  reduction.reorder_dims = reduction.group_by_dims;
-  std::set_difference(perm.begin(), perm.end(), reduction.group_by_dims.begin(),
-                      reduction.group_by_dims.end(),
-                      std::back_inserter(reduction.reorder_dims));
-
-  // (1) Calculate the shape after reduction.
-  auto sp_shape = sp.shape();
-  std::vector<int64> out_dim_sizes;
-  if (keep_dims) {
-    out_dim_sizes.reserve(ndims);
-    auto beg = reduction.group_by_dims.begin();
-    auto end = reduction.group_by_dims.end();
-    for (int d = 0; d < ndims; ++d) {
-      if (std::find(beg, end, d) == end) {
-        out_dim_sizes.push_back(1);  // A reduced axis.
-      } else {
-        out_dim_sizes.push_back(sp_shape[d]);
-      }
-    }
-  } else {
-    out_dim_sizes = sp.PickDims(reduction.group_by_dims);
-  }
-
-  reduction.reduced_shape = TensorShape(out_dim_sizes);
-  return reduction;
-}
-
-Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
-  // indices and values are validated in SparseTensor ctor.
-  if (!TensorShapeUtils::IsVector(shape_t->shape())) {
-    return errors::InvalidArgument(
-        "Expected input_shape to be a vector; got shape: ",
-        shape_t->shape().DebugString());
-  }
-  if (!TensorShapeUtils::IsScalar(reduction_axes_t->shape()) &&
-      !TensorShapeUtils::IsVector(reduction_axes_t->shape())) {
-    return errors::InvalidArgument(
-        "Expected reduction_axes to be a scalar or a vector; got shape: ",
-        reduction_axes_t->shape().DebugString());
-  }
-
-  const auto reduction_axes_flat = reduction_axes_t->flat<int32>();
-  for (int64 i = 0; i < reduction_axes_flat.size(); i++) {
-    int32 axis = reduction_axes_flat(i);
-    if (axis < -shape_t->NumElements() || axis >= shape_t->NumElements()) {
-      return errors::InvalidArgument("Invalid reduction dimension ", axis,
-                                     ", for input with ",
-                                     shape_t->NumElements(), " dimensions.");
-    }
-  }
-
-  return Status::OK();
-}
-
-template <typename T>
-class SparseReduceSumOp : public OpKernel {
- public:
-  explicit SparseReduceSumOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
-    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
-  }
-
-  void Compute(OpKernelContext *ctx) override {
-    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
-    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
-    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
-
-    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
-
-    // TODO(zongheng): we will call Reorder() below, which will modify
-    // in-place the underlying indices and values buffers.  To avoid
-    // surprises of this kernel being stateful, we work around the above by
-    // making deep copies here.  Remove this if/when we change Reorder()'s
-    // semantics.
-    const auto shape_vec = shape_t->vec<int64>();
-    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
-                    TensorShape(shape_vec));
-    ReduceDetails reduction = SparseTensorReduceHelper(
-        sp, reduction_axes_t->flat<int32>(), keep_dims_);
-
-    Tensor *out_values;
-    OP_REQUIRES_OK(
-        ctx, ctx->allocate_output(0, reduction.reduced_shape, &out_values));
-    auto out_flat = out_values->flat<T>();
-    out_flat.setZero();
-
-    Tensor tmp_group_sum;
-    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_group_sum));
-    auto group_sum = tmp_group_sum.scalar<T>();
-
-    // Compute strides, and use it to convert coords to flat index.  The
-    // coordinates returned by .group() have the same ndims as group_by_dims.
-    gtl::InlinedVector<int64, 8> output_strides(reduction.group_by_dims.size());
-    if (!output_strides.empty()) {  // Do this iff we don't reduce all.
-      output_strides.back() = 1;
-      for (int d = output_strides.size() - 2; d >= 0; --d) {
-        output_strides[d] =
-            output_strides[d + 1] * shape_vec(reduction.group_by_dims[d + 1]);
-      }
-    }
-
-    auto CoordinatesToFlatIndex = [](ArraySlice<int64> coords,
-                                     ArraySlice<int64> strides) {
-      if (strides.empty()) {  // Reduce all.
-        return 0LL;
-      }
-      CHECK_EQ(coords.size(), strides.size());
-      int64 idx = 0;
-      for (int i = 0; i < coords.size(); ++i) {
-        idx += coords[i] * strides[i];
-      }
-      return idx;
-    };
-
-    // Each group maps one-on-one onto a value in the reduced tensor.
-    // g.group() provides the coordinates of a particular reduced value.
-    sp.Reorder<T>(reduction.reorder_dims);
-    for (const auto &g : sp.group(reduction.group_by_dims)) {
-      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
-      const int64 idx = CoordinatesToFlatIndex(g.group(), output_strides);
-      out_flat(idx) = group_sum();
-      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; idx: " << idx << "; group sum: " << group_sum();
-    }
-  }
-
- private:
-  // True if the number of dimensions should be maintained.
-  bool keep_dims_;
-};
-
-#define REGISTER_KERNELS(T)                                              \
-  REGISTER_KERNEL_BUILDER(                                               \
-      Name("SparseReduceSum").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSumOp<T>)
-TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-template <typename T>
-class SparseReduceSumSparseOp : public OpKernel {
- public:
-  explicit SparseReduceSumSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
-    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
-  }
-
-  void Compute(OpKernelContext *ctx) override {
-    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
-    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
-    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
-
-    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
-
-    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
-                    TensorShape(shape_t->vec<int64>()));
-    ReduceDetails reduction = SparseTensorReduceHelper(
-        sp, reduction_axes_t->flat<int32>(), keep_dims_);
-
-    sp.Reorder<T>(reduction.reorder_dims);
-    // Count nnzs in the output SparseTensor.
-    int64 nnz = 0;
-    auto iter = sp.group(reduction.group_by_dims);
-    for (auto it = iter.begin(); it != iter.end(); ++it) {
-      nnz++;
-    }
-
-    Tensor *out_indices_t;
-    OP_REQUIRES_OK(ctx,
-                   ctx->allocate_output(
-                       0, TensorShape({nnz, reduction.reduced_shape.dims()}),
-                       &out_indices_t));
-    typename TTypes<int64>::Matrix out_indices_mat =
-        out_indices_t->matrix<int64>();
-    // For keep_dims. We don't explicitly set dim fields for reduced dims below.
-    out_indices_mat.setZero();
-
-    Tensor *out_values_t;
-    OP_REQUIRES_OK(ctx,
-                   ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
-    auto out_flat = out_values_t->flat<T>();
-
-    Tensor tmp_group_sum;
-    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_group_sum));
-    auto group_sum = tmp_group_sum.scalar<T>();
-    int64 i = 0;
-    for (const auto &g : sp.group(reduction.group_by_dims)) {
-      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
-      std::vector<int64> group = g.group();
-      for (int64 j = 0; j < group.size(); j++) {
-        if (keep_dims_) {
-          out_indices_mat(i, reduction.group_by_dims[j]) = group[j];
-        } else {
-          out_indices_mat(i, j) = group[j];
-        }
-      }
-      out_flat(i) = group_sum();
-      i++;
-      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; group sum: " << group_sum();
-    }
-
-    Tensor *out_shape_t;
-    OP_REQUIRES_OK(ctx, ctx->allocate_output(
-                            2, TensorShape({reduction.reduced_shape.dims()}),
-                            &out_shape_t));
-    auto out_shape_flat = out_shape_t->flat<int64>();
-    auto out_dim_sizes = reduction.reduced_shape.dim_sizes();
-    std::copy(out_dim_sizes.begin(), out_dim_sizes.end(), &out_shape_flat(0));
-  }
-
- private:
-  // True if the number of dimensions should be maintained.
-  bool keep_dims_;
-};
-
-#define REGISTER_KERNELS(T)                                                    \
-  REGISTER_KERNEL_BUILDER(                                                     \
-      Name("SparseReduceSumSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSumSparseOp<T>)
-TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-}  // namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 22b18b9cde..936348e01d 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3056,6 +3056,7 @@ py_test(
     srcs = ["client/session_clusterspec_prop_test.py"],
     srcs_version = "PY2AND3",
     tags = [
+        "no_gpu",
         "no_pip_gpu",
     ],
     deps = [
@@ -3080,6 +3081,7 @@ py_test(
     srcs = ["client/session_list_devices_test.py"],
     srcs_version = "PY2AND3",
     tags = [
+        "no_gpu",
         "no_pip_gpu",
     ],
     deps = [
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 14eb2cba68..6664362226 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -19,7 +19,6 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-import unittest
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -606,7 +605,6 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
     self._compare(sp_t, reduction_axes, ndims, True, False)
     self._compare(sp_t, reduction_axes, ndims, True, True)
 
-  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
@@ -646,7 +644,6 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_max(sp_t, 2).eval()
 
-  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
-- 
GitLab


From b13e96e21c1229a905a623111dd89d2bd0cba53b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 13:48:16 -0700
Subject: [PATCH 0456/1375] Automated g4 rollback of changelist 160183498

PiperOrigin-RevId: 160189134
---
 tensorflow/BUILD                              |   1 -
 tensorflow/compiler/jit/BUILD                 |   5 +-
 tensorflow/compiler/jit/kernels/BUILD         |   1 -
 tensorflow/compiler/plugin/executor/BUILD     |   4 +-
 tensorflow/compiler/tests/BUILD               |  18 +-
 tensorflow/contrib/lookup/lookup_ops.py       |   4 +
 .../core/kernels/sparse_reduce_sum_op.cc      | 305 ++++++++++++++++++
 tensorflow/python/BUILD                       |   2 -
 .../python/kernel_tests/sparse_ops_test.py    |   3 +
 9 files changed, 316 insertions(+), 27 deletions(-)
 create mode 100644 tensorflow/core/kernels/sparse_reduce_sum_op.cc

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 6e76db02de..62ae5ae78c 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -208,7 +208,6 @@ filegroup(
         "//tensorflow/compiler/jit/kernels:all_files",
         "//tensorflow/compiler/jit/legacy_flags:all_files",
         "//tensorflow/compiler/jit/ops:all_files",
-        "//tensorflow/compiler/plugin/executor:all_files",
         "//tensorflow/compiler/tests:all_files",
         "//tensorflow/compiler/tf2xla:all_files",
         "//tensorflow/compiler/tf2xla/cc:all_files",
diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 7ebd842218..306e704415 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -15,10 +15,7 @@ package_group(
 )
 
 package(
-    default_visibility = [
-        ":internal",
-        "//tensorflow/compiler/plugin/executor:__pkg__",
-    ],
+    default_visibility = [":internal"],
 )
 
 load("//tensorflow:tensorflow.bzl", "cc_header_only_library")
diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD
index 97f3512a6c..ed204b8182 100644
--- a/tensorflow/compiler/jit/kernels/BUILD
+++ b/tensorflow/compiler/jit/kernels/BUILD
@@ -2,7 +2,6 @@ licenses(["notice"])  # Apache 2.0
 
 package(
     default_visibility = [
-        "//tensorflow/compiler/plugin/executor:__pkg__",
         "//tensorflow/compiler/tf2xla:internal",
     ],
 )
diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
index 2e5875705f..9bc706abdf 100644
--- a/tensorflow/compiler/plugin/executor/BUILD
+++ b/tensorflow/compiler/plugin/executor/BUILD
@@ -11,11 +11,9 @@ cc_library(
         "*.h",
     ]),
     deps = [
-        "//tensorflow/compiler/jit:xla_device",
         "//tensorflow/compiler/jit:xla_jit_headers_lib",
-        "//tensorflow/compiler/tf2xla:xla_compiler",
         "//tensorflow/compiler/xla:xla_headers_lib",
-        "//tensorflow/compiler/xla/service",
+        "//tensorflow/compiler/xla/service:hlo_evaluator",
         "//third_party/eigen3",
         "@local_config_cuda//cuda:cuda_headers",
         "@protobuf//:protobuf_headers",
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 432b24756d..044857d422 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -175,11 +175,6 @@ tf_xla_py_test(
     name = "slice_ops_test",
     size = "small",
     srcs = ["slice_ops_test.py"],
-    # TODO(b/62962492): Test fails with assertion error.
-    tags = [
-        "manual",
-        "notap",
-    ],
     deps = [
         ":xla_test",
         "//tensorflow/python:array_ops",
@@ -461,11 +456,6 @@ cuda_py_test(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
     ],
-    # TODO(b/62961789): Test fails with SIGABRT
-    tags = [
-        "manual",
-        "notap",
-    ],
 )
 
 cc_library(
@@ -534,12 +524,8 @@ cuda_py_test(
 # --dump_graph_dir, and the config file was written by hand.
 #
 # Run the following to build a minimal benchmark of the computation on Android:
-# $ bazel build -c opt --cxxopt='-std=c++11' --linkopt='-lm' \
-#   --cpu=armeabi-v7a \
-#   --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
-#   --crosstool_top=//external:android/crosstool \
-#   //tensorflow/compiler/tests:lstm_layer_inference_benchmark
-
+# $ bazel build -c opt --config=android_arm \
+#       third_party/tensorflow/compiler/tests:lstm_layer_inference_benchmark
 #
 # Currently the resulting binary size is ~190KB
 tf_library(
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index f53f38f3cf..ce8518267f 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -260,7 +260,11 @@ def index_to_string(tensor, mapping, default_value="UNK", name=None):
   For example:
 
   ```python
+<<<<<<< HEAD
   mapping_string = tf.constant(["emerson", "lake", "palmer"])
+=======
+  mapping_string = tf.constant(["emerson", "lake", "palmer")
+>>>>>>> 338a7ead4475d6b97b420d6d1c56ff66815e3e7b
   indices = tf.constant([1, 5], tf.int64)
   values = tf.contrib.lookup.index_to_string(
       indices, mapping=mapping_string, default_value="UNKNOWN")
diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op.cc b/tensorflow/core/kernels/sparse_reduce_sum_op.cc
new file mode 100644
index 0000000000..074aab9f9e
--- /dev/null
+++ b/tensorflow/core/kernels/sparse_reduce_sum_op.cc
@@ -0,0 +1,305 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// See docs in ../ops/sparse_ops.cc.
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_util.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/util/sparse/sparse_tensor.h"
+
+// TODO(b/31496047): Fix non-standard include order.
+#include <numeric>  // clang-format off
+
+using tensorflow::sparse::SparseTensor;
+using tensorflow::gtl::ArraySlice;
+
+namespace tensorflow {
+
+struct ReduceDetails {
+  // The dimensions to call Reorder() with.
+  std::vector<int64> reorder_dims;
+
+  // The dimensions to call group() with after Reorder().
+  std::vector<int64> group_by_dims;
+
+  // The shape after reduction.
+  TensorShape reduced_shape;
+};
+
+// Compute common reduce parameters that'll be used for SparseTensor
+// reductions. Usage:
+// ReduceDetails reduction = SparseTensorReduceHelper(sp, axes, keep_dims);
+// sp.Reorder(reduction.reorder_dims);
+// for (const auto& g : sp.group(reduction.group_by_dims)) {
+//   ...
+// }
+// // Set output shape to reduction.reduced_shape.
+ReduceDetails SparseTensorReduceHelper(const SparseTensor &sp,
+                                       gtl::ArraySlice<int32> axes_slice,
+                                       bool keep_dims) {
+  ReduceDetails reduction;
+
+  std::vector<int32> reduction_axes(axes_slice.begin(), axes_slice.end());
+  int ndims = sp.dims();
+  for (int64 i = 0; i < reduction_axes.size(); ++i) {
+    reduction_axes[i] = (reduction_axes[i] + ndims) % ndims;
+  }
+  std::sort(reduction_axes.begin(), reduction_axes.end());
+
+  // (0) Calculate the grouping dimensions:
+  // group_by_dims == {0, .., NDIMS-1} \ reduction_axes.
+  std::vector<int64> perm(ndims);
+  std::iota(perm.begin(), perm.end(), 0);
+
+  // Requires perm and reduction_axes_ be sorted; group_by_dims will be
+  // sorted as well.
+  std::set_difference(
+      perm.begin(), perm.end(), reduction_axes.begin(), reduction_axes.end(),
+      std::inserter(reduction.group_by_dims, reduction.group_by_dims.begin()));
+
+  // Now append the rest of the axes (the complement of group_by_dims_);
+  // result is used by Reorder().
+  reduction.reorder_dims = reduction.group_by_dims;
+  std::set_difference(perm.begin(), perm.end(), reduction.group_by_dims.begin(),
+                      reduction.group_by_dims.end(),
+                      std::back_inserter(reduction.reorder_dims));
+
+  // (1) Calculate the shape after reduction.
+  auto sp_shape = sp.shape();
+  std::vector<int64> out_dim_sizes;
+  if (keep_dims) {
+    out_dim_sizes.reserve(ndims);
+    auto beg = reduction.group_by_dims.begin();
+    auto end = reduction.group_by_dims.end();
+    for (int d = 0; d < ndims; ++d) {
+      if (std::find(beg, end, d) == end) {
+        out_dim_sizes.push_back(1);  // A reduced axis.
+      } else {
+        out_dim_sizes.push_back(sp_shape[d]);
+      }
+    }
+  } else {
+    out_dim_sizes = sp.PickDims(reduction.group_by_dims);
+  }
+
+  reduction.reduced_shape = TensorShape(out_dim_sizes);
+  return reduction;
+}
+
+Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
+  // indices and values are validated in SparseTensor ctor.
+  if (!TensorShapeUtils::IsVector(shape_t->shape())) {
+    return errors::InvalidArgument(
+        "Expected input_shape to be a vector; got shape: ",
+        shape_t->shape().DebugString());
+  }
+  if (!TensorShapeUtils::IsScalar(reduction_axes_t->shape()) &&
+      !TensorShapeUtils::IsVector(reduction_axes_t->shape())) {
+    return errors::InvalidArgument(
+        "Expected reduction_axes to be a scalar or a vector; got shape: ",
+        reduction_axes_t->shape().DebugString());
+  }
+
+  const auto reduction_axes_flat = reduction_axes_t->flat<int32>();
+  for (int64 i = 0; i < reduction_axes_flat.size(); i++) {
+    int32 axis = reduction_axes_flat(i);
+    if (axis < -shape_t->NumElements() || axis >= shape_t->NumElements()) {
+      return errors::InvalidArgument("Invalid reduction dimension ", axis,
+                                     ", for input with ",
+                                     shape_t->NumElements(), " dimensions.");
+    }
+  }
+
+  return Status::OK();
+}
+
+template <typename T>
+class SparseReduceSumOp : public OpKernel {
+ public:
+  explicit SparseReduceSumOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
+  }
+
+  void Compute(OpKernelContext *ctx) override {
+    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
+    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
+    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
+
+    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
+
+    // TODO(zongheng): we will call Reorder() below, which will modify
+    // in-place the underlying indices and values buffers.  To avoid
+    // surprises of this kernel being stateful, we work around the above by
+    // making deep copies here.  Remove this if/when we change Reorder()'s
+    // semantics.
+    const auto shape_vec = shape_t->vec<int64>();
+    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
+                    TensorShape(shape_vec));
+    ReduceDetails reduction = SparseTensorReduceHelper(
+        sp, reduction_axes_t->flat<int32>(), keep_dims_);
+
+    Tensor *out_values;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output(0, reduction.reduced_shape, &out_values));
+    auto out_flat = out_values->flat<T>();
+    out_flat.setZero();
+
+    Tensor tmp_group_sum;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                           TensorShape({}), &tmp_group_sum));
+    auto group_sum = tmp_group_sum.scalar<T>();
+
+    // Compute strides, and use it to convert coords to flat index.  The
+    // coordinates returned by .group() have the same ndims as group_by_dims.
+    gtl::InlinedVector<int64, 8> output_strides(reduction.group_by_dims.size());
+    if (!output_strides.empty()) {  // Do this iff we don't reduce all.
+      output_strides.back() = 1;
+      for (int d = output_strides.size() - 2; d >= 0; --d) {
+        output_strides[d] =
+            output_strides[d + 1] * shape_vec(reduction.group_by_dims[d + 1]);
+      }
+    }
+
+    auto CoordinatesToFlatIndex = [](ArraySlice<int64> coords,
+                                     ArraySlice<int64> strides) {
+      if (strides.empty()) {  // Reduce all.
+        return 0LL;
+      }
+      CHECK_EQ(coords.size(), strides.size());
+      int64 idx = 0;
+      for (int i = 0; i < coords.size(); ++i) {
+        idx += coords[i] * strides[i];
+      }
+      return idx;
+    };
+
+    // Each group maps one-on-one onto a value in the reduced tensor.
+    // g.group() provides the coordinates of a particular reduced value.
+    sp.Reorder<T>(reduction.reorder_dims);
+    for (const auto &g : sp.group(reduction.group_by_dims)) {
+      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
+      const int64 idx = CoordinatesToFlatIndex(g.group(), output_strides);
+      out_flat(idx) = group_sum();
+      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
+              << "; idx: " << idx << "; group sum: " << group_sum();
+    }
+  }
+
+ private:
+  // True if the number of dimensions should be maintained.
+  bool keep_dims_;
+};
+
+#define REGISTER_KERNELS(T)                                              \
+  REGISTER_KERNEL_BUILDER(                                               \
+      Name("SparseReduceSum").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceSumOp<T>)
+TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+template <typename T>
+class SparseReduceSumSparseOp : public OpKernel {
+ public:
+  explicit SparseReduceSumSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
+  }
+
+  void Compute(OpKernelContext *ctx) override {
+    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
+    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
+    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
+    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
+
+    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
+
+    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
+                    TensorShape(shape_t->vec<int64>()));
+    ReduceDetails reduction = SparseTensorReduceHelper(
+        sp, reduction_axes_t->flat<int32>(), keep_dims_);
+
+    sp.Reorder<T>(reduction.reorder_dims);
+    // Count nnzs in the output SparseTensor.
+    int64 nnz = 0;
+    auto iter = sp.group(reduction.group_by_dims);
+    for (auto it = iter.begin(); it != iter.end(); ++it) {
+      nnz++;
+    }
+
+    Tensor *out_indices_t;
+    OP_REQUIRES_OK(ctx,
+                   ctx->allocate_output(
+                       0, TensorShape({nnz, reduction.reduced_shape.dims()}),
+                       &out_indices_t));
+    typename TTypes<int64>::Matrix out_indices_mat =
+        out_indices_t->matrix<int64>();
+    // For keep_dims. We don't explicitly set dim fields for reduced dims below.
+    out_indices_mat.setZero();
+
+    Tensor *out_values_t;
+    OP_REQUIRES_OK(ctx,
+                   ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
+    auto out_flat = out_values_t->flat<T>();
+
+    Tensor tmp_group_sum;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                           TensorShape({}), &tmp_group_sum));
+    auto group_sum = tmp_group_sum.scalar<T>();
+    int64 i = 0;
+    for (const auto &g : sp.group(reduction.group_by_dims)) {
+      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
+      std::vector<int64> group = g.group();
+      for (int64 j = 0; j < group.size(); j++) {
+        if (keep_dims_) {
+          out_indices_mat(i, reduction.group_by_dims[j]) = group[j];
+        } else {
+          out_indices_mat(i, j) = group[j];
+        }
+      }
+      out_flat(i) = group_sum();
+      i++;
+      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
+              << "; group sum: " << group_sum();
+    }
+
+    Tensor *out_shape_t;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(
+                            2, TensorShape({reduction.reduced_shape.dims()}),
+                            &out_shape_t));
+    auto out_shape_flat = out_shape_t->flat<int64>();
+    auto out_dim_sizes = reduction.reduced_shape.dim_sizes();
+    std::copy(out_dim_sizes.begin(), out_dim_sizes.end(), &out_shape_flat(0));
+  }
+
+ private:
+  // True if the number of dimensions should be maintained.
+  bool keep_dims_;
+};
+
+#define REGISTER_KERNELS(T)                                                    \
+  REGISTER_KERNEL_BUILDER(                                                     \
+      Name("SparseReduceSumSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceSumSparseOp<T>)
+TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+}  // namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 936348e01d..22b18b9cde 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3056,7 +3056,6 @@ py_test(
     srcs = ["client/session_clusterspec_prop_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_gpu",
         "no_pip_gpu",
     ],
     deps = [
@@ -3081,7 +3080,6 @@ py_test(
     srcs = ["client/session_list_devices_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_gpu",
         "no_pip_gpu",
     ],
     deps = [
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 6664362226..14eb2cba68 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+import unittest
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -605,6 +606,7 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
     self._compare(sp_t, reduction_axes, ndims, True, False)
     self._compare(sp_t, reduction_axes, ndims, True, True)
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
@@ -644,6 +646,7 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_max(sp_t, 2).eval()
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
-- 
GitLab


From 1fa73c53ab95693f070ce70e6be0c644d83c163a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 14:00:17 -0700
Subject: [PATCH 0457/1375] Automated g4 rollback of changelist 160182040

PiperOrigin-RevId: 160190881
---
 CONTRIBUTING.md                               |   7 +-
 ISSUE_TEMPLATE.md                             |   1 -
 README.md                                     |  14 +-
 RELEASE.md                                    |   2 -
 configure                                     |   6 +-
 tensorflow/c/generate-pc.sh                   |   2 +-
 tensorflow/cc/BUILD                           |  21 +-
 tensorflow/cc/gradients/math_grad.cc          |  26 --
 tensorflow/cc/gradients/math_grad_test.cc     |  52 ---
 tensorflow/cc/gradients/nn_grad.cc            |  13 -
 tensorflow/cc/gradients/nn_grad_test.cc       |  13 -
 tensorflow/compiler/plugin/BUILD              |   4 +-
 tensorflow/compiler/plugin/executor/BUILD     |  32 --
 .../compiler/plugin/executor/compiler.cc      | 123 -------
 .../compiler/plugin/executor/compiler.h       |  64 ----
 tensorflow/compiler/plugin/executor/device.cc |  60 ---
 .../compiler/plugin/executor/executable.cc    | 147 --------
 .../compiler/plugin/executor/executable.h     |  65 ----
 .../compiler/plugin/executor/executor.cc      | 135 -------
 .../compiler/plugin/executor/executor.h       | 213 -----------
 .../compiler/plugin/executor/platform.cc      | 125 -------
 .../compiler/plugin/executor/platform.h       |  83 -----
 .../compiler/plugin/executor/platform_id.h    |  31 --
 .../plugin/executor/transfer_manager.cc       | 187 ----------
 .../plugin/executor/transfer_manager.h        |  77 ----
 tensorflow/compiler/tests/ftrl_test.py        |   2 +-
 .../tf2xla/kernels/batch_matmul_op.cc         |   6 +-
 .../tf2xla/kernels/batchtospace_op.cc         |   3 +-
 .../tf2xla/kernels/depthwise_conv_ops.cc      |  19 +-
 tensorflow/compiler/tf2xla/kernels/diag_op.cc |   8 +-
 .../tf2xla/kernels/dynamic_stitch_op.cc       |   4 +-
 .../compiler/tf2xla/kernels/slice_op.cc       |   4 +-
 .../compiler/tf2xla/kernels/split_op.cc       |  14 +-
 .../tf2xla/kernels/strided_slice_op.cc        |  44 ++-
 .../tf2xla/kernels/tensor_array_ops.cc        |   8 +-
 .../compiler/tf2xla/kernels/unpack_op.cc      |   4 +-
 .../xla/client/computation_builder.cc         |   6 +-
 .../compiler/xla/client/computation_builder.h |   4 +-
 tensorflow/compiler/xla/literal_util.cc       |  10 +-
 tensorflow/compiler/xla/literal_util_test.cc  |  57 ---
 tensorflow/compiler/xla/service/BUILD         |   2 +
 .../xla/service/algebraic_simplifier.cc       |   7 +-
 .../xla/service/algebraic_simplifier_test.cc  |   8 +-
 .../xla/service/buffer_assignment_test.cc     |  12 +-
 .../xla/service/buffer_liveness_test.cc       |   2 +-
 .../xla/service/compile_only_service.h        |   2 +-
 .../xla/service/computation_placer.cc         |   9 +-
 .../compiler/xla/service/computation_placer.h |   6 +-
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   9 +
 .../xla/service/elemental_ir_emitter.cc       |  17 +-
 .../compiler/xla/service/gpu/pad_insertion.cc |  11 +-
 .../xla/service/hlo_constant_folding_test.cc  |   3 +-
 .../compiler/xla/service/hlo_instruction.cc   |   7 +-
 .../compiler/xla/service/hlo_instruction.h    |  13 +-
 .../xla/service/hlo_rematerialization_test.cc |  16 +-
 .../compiler/xla/service/llvm_ir/llvm_util.cc |  23 +-
 .../compiler/xla/service/shape_inference.cc   |  28 +-
 .../compiler/xla/service/shape_inference.h    |   3 +-
 .../xla/service/shape_inference_test.cc       |  33 +-
 .../service/tuple_points_to_analysis_test.cc  |   2 +-
 .../compiler/xla/service/user_computation.cc  |   6 +-
 .../xla/tests/array_elementwise_ops_test.cc   |   2 +-
 .../compiler/xla/tests/dot_operation_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/fusion_test.cc  |   2 +-
 .../xla/tests/multidimensional_slice_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/params_test.cc  |   2 +-
 tensorflow/compiler/xla/tests/slice_test.cc   |  51 ++-
 tensorflow/compiler/xla/tests/while_test.cc   |   3 +-
 tensorflow/compiler/xla/util.h                |  18 +-
 tensorflow/compiler/xla/xla_data.proto        |   3 +-
 .../android/TensorFlowInferenceInterface.java |  20 +-
 .../kernel_tests/csiszar_divergence_test.py   |  43 ++-
 .../python/ops/csiszar_divergence_impl.py     |  62 ++--
 tensorflow/contrib/cmake/CMakeLists.txt       |   2 +-
 tensorflow/contrib/cmake/tf_python.cmake      |   1 +
 .../python/ops/bijectors/affine_impl.py       |   4 +-
 .../python/ops/relaxed_bernoulli.py           |   2 +-
 .../distributions/python/ops/sample_stats.py  |   2 +-
 .../ops/vector_laplace_linear_operator.py     |   2 +-
 tensorflow/contrib/graph_editor/transform.py  |   2 +-
 .../contrib/keras/python/keras/backend.py     |   6 +-
 .../contrib/keras/python/keras/layers/core.py |   2 +-
 .../keras/python/keras/layers/recurrent.py    |   2 +-
 .../contrib/keras/python/keras/models_test.py |   2 +-
 .../contrib/layers/python/layers/layers.py    |   3 +-
 .../python/learn/estimators/debug_test.py     |   4 +-
 .../python/learn/estimators/linear_test.py    |  20 +-
 .../python/learn/estimators/model_fn_test.py  |  10 +-
 .../contrib/learn/python/learn/models.py      |   4 +-
 tensorflow/contrib/lookup/lookup_ops.py       |   6 +-
 .../contrib/makefile/build_with_docker.sh     |   2 +-
 .../makefile/compile_android_protobuf.sh      |   4 +-
 .../contrib/makefile/compile_ios_protobuf.sh  |  17 +-
 .../makefile/compile_ios_tensorflow.sh        |   4 +-
 .../contrib/makefile/compile_pi_protobuf.sh   |   6 +-
 .../contrib/remote_fused_graph/pylib/BUILD    |   1 -
 tensorflow/contrib/rnn/python/ops/rnn_cell.py |   2 +-
 .../kernel_tests/beam_search_ops_test.py      |   4 +-
 tensorflow/contrib/slim/README.md             |   2 +-
 tensorflow/contrib/tfprof/README.md           |  23 --
 .../pywrap_tensorflow_print_model_analysis.i  |   1 -
 .../tfprof/internal/run_metadata_test.py      |   2 +-
 .../python/tools/tfprof/model_analyzer.py     | 121 +++++--
 .../tools/tfprof/model_analyzer_test.py       |  30 +-
 .../python/tools/tfprof/profiler_test.py      |  16 +-
 .../training/python/training/evaluation.py    |   2 +-
 tensorflow/contrib/verbs/rdma.cc              |  55 +--
 .../contrib/verbs/rdma_rendezvous_mgr.cc      |  41 +--
 tensorflow/contrib/verbs/verbs_util.cc        |  34 --
 tensorflow/contrib/verbs/verbs_util.h         |  10 -
 .../core/common_runtime/direct_session.cc     |   4 +-
 .../core/debug/grpc_session_debug_test.cc     |   5 +-
 tensorflow/core/framework/graph_def_util.h    |   2 +-
 tensorflow/core/framework/op.h                |   1 +
 tensorflow/core/framework/tensor.h            |   2 +-
 tensorflow/core/graph/mkl_layout_pass.cc      | 277 +++++---------
 tensorflow/core/graph/mkl_layout_pass_test.cc | 252 +------------
 .../grappler/costs/virtual_scheduler_test.cc  |   2 +-
 tensorflow/core/grappler/grappler_item.h      |   1 -
 .../core/grappler/optimizers/auto_parallel.cc |   5 -
 .../core/grappler/optimizers/auto_parallel.h  |   1 -
 .../grappler/optimizers/auto_parallel_test.cc |  42 +--
 .../grappler/optimizers/layout_optimizer.cc   |   2 +-
 tensorflow/core/kernels/BUILD                 |   8 +-
 tensorflow/core/kernels/adjust_contrast_op.cc |  24 --
 .../adjust_contrast_op_benchmark_test.cc      |   5 -
 tensorflow/core/kernels/colorspace_op.cc      |  15 -
 tensorflow/core/kernels/control_flow_ops.cc   | 136 +++----
 tensorflow/core/kernels/cwise_op_add_2.cc     |   7 +-
 tensorflow/core/kernels/cwise_op_cosh.cc      |  37 --
 .../core/kernels/cwise_op_gpu_add.cu.cc       |   3 +-
 .../core/kernels/cwise_op_gpu_cosh.cu.cc      |  26 --
 .../core/kernels/cwise_op_gpu_sinh.cu.cc      |  26 --
 tensorflow/core/kernels/cwise_op_invert.cc    |   2 +-
 tensorflow/core/kernels/cwise_op_sinh.cc      |  37 --
 tensorflow/core/kernels/cwise_ops.h           |   6 -
 tensorflow/core/kernels/dynamic_stitch_op.cc  |  27 +-
 tensorflow/core/kernels/map_stage_op.cc       |   7 +-
 .../core/kernels/mkl_conv_grad_filter_ops.cc  |  69 +---
 tensorflow/core/kernels/mkl_conv_ops.cc       |  51 +--
 tensorflow/core/kernels/mkl_lrn_op.cc         | 112 +++---
 tensorflow/core/kernels/mkl_relu_op.cc        |  30 +-
 tensorflow/core/kernels/mkl_tfconv_op.cc      |  54 +--
 .../core/kernels/non_max_suppression_op.cc    |  36 +-
 tensorflow/core/kernels/priority_queue.cc     |   2 +-
 tensorflow/core/kernels/shape_ops.cc          |  56 +--
 tensorflow/core/kernels/slice_op.cc           | 258 +------------
 tensorflow/core/kernels/sparse_reduce_op.cc   | 341 ------------------
 tensorflow/core/kernels/stack_ops.cc          |  68 +---
 tensorflow/core/kernels/topk_op.cc            |   2 +-
 tensorflow/core/kernels/transpose_functor.h   |   7 -
 tensorflow/core/kernels/transpose_op.cc       |   5 +-
 .../typed_conditional_accumulator_base.h      |   2 +-
 tensorflow/core/lib/gtl/optional.h            |   2 +-
 tensorflow/core/ops/math_grad.cc              |  20 -
 tensorflow/core/ops/math_grad_test.cc         |  20 -
 tensorflow/core/ops/math_ops.cc               |   8 -
 tensorflow/core/ops/nn_ops.cc                 |   2 -
 tensorflow/core/ops/ops.pbtxt                 | 169 +--------
 tensorflow/core/ops/sparse_ops.cc             |  69 ----
 .../core/platform/cloud/retrying_utils.cc     |   2 +-
 tensorflow/core/protobuf/worker.proto         |   2 +-
 tensorflow/core/public/version.h              |   2 +-
 tensorflow/core/util/mkl_util.h               |  38 +-
 .../api_guides/python/contrib.losses.md       |  10 +-
 .../docs_src/api_guides/python/math_ops.md    |   2 -
 .../docs_src/get_started/get_started.md       |   5 +-
 .../docs_src/get_started/mnist/beginners.md   |   2 +-
 .../docs_src/get_started/mnist/mechanics.md   |   2 +-
 tensorflow/docs_src/install/install_c.md      |   2 +-
 tensorflow/docs_src/install/install_go.md     |   2 +-
 tensorflow/docs_src/install/install_java.md   |  18 +-
 tensorflow/docs_src/install/install_linux.md  |  26 +-
 tensorflow/docs_src/install/install_mac.md    |  14 +-
 .../docs_src/install/install_sources.md       |   4 +-
 .../docs_src/install/install_windows.md       |  13 +-
 .../docs_src/performance/performance_guide.md |   8 +-
 .../docs_src/performance/quantization.md      |  13 +-
 tensorflow/docs_src/tutorials/seq2seq.md      |  13 +-
 tensorflow/docs_src/tutorials/wide.md         |  18 +-
 .../docs_src/tutorials/wide_and_deep.md       |   6 +-
 tensorflow/docs_src/tutorials/word2vec.md     |   2 +-
 .../examples/image_retraining/retrain.py      |  43 +--
 tensorflow/examples/learn/examples_test.sh    |   2 +-
 tensorflow/examples/tutorials/mnist/mnist.py  |   2 +-
 .../tutorials/word2vec/word2vec_basic.py      |  16 +-
 tensorflow/go/README.md                       |  29 ++
 tensorflow/go/genop/generate.sh               |   2 +-
 tensorflow/go/op/wrappers.go                  |  30 --
 tensorflow/go/shape.go                        |   2 +-
 tensorflow/go/tensor.go                       |   2 +-
 tensorflow/java/BUILD                         |  26 +-
 tensorflow/java/build_defs.bzl                |  11 +-
 tensorflow/java/maven/libtensorflow/pom.xml   |   4 +-
 tensorflow/java/maven/proto/pom.xml           |   6 +-
 tensorflow/java/maven/release.sh              |   2 +-
 tensorflow/java/maven/run_inside_container.sh |   2 +-
 .../src/main/java/org/tensorflow/Input.java   |  48 ---
 .../main/java/org/tensorflow/Operation.java   |  26 +-
 .../java/org/tensorflow/OperationBuilder.java |   2 +-
 .../src/main/java/org/tensorflow/Output.java  |  10 +-
 .../src/main/java/org/tensorflow/Session.java |   4 +-
 .../java/org/tensorflow/op/NameScope.java     | 146 --------
 .../main/java/org/tensorflow/op/Scope.java    | 165 ---------
 .../java/src/main/native/operation_jni.cc     |  18 -
 .../java/src/main/native/operation_jni.h      |  11 -
 .../java/org/tensorflow/OperationTest.java    |  21 --
 .../test/java/org/tensorflow/SessionTest.java |   6 +-
 .../test/java/org/tensorflow/TensorTest.java  |   2 +-
 .../java/org/tensorflow/op/ScopeTest.java     | 270 --------------
 tensorflow/python/BUILD                       |   5 +-
 tensorflow/python/debug/cli/analyzer_cli.py   |   2 +-
 .../python/debug/wrappers/framework_test.py   |   2 +-
 .../estimator/canned/dnn_linear_combined.py   |   2 +-
 tensorflow/python/estimator/canned/linear.py  |  73 ++--
 tensorflow/python/kernel_tests/BUILD          |  10 +
 .../python/kernel_tests/basic_gpu_test.py     |   2 -
 .../python/kernel_tests/cwise_ops_test.py     |  15 -
 .../python/kernel_tests/fft_ops_test.py       |  63 ++--
 .../python/kernel_tests/map_stage_op_test.py  |  93 +++--
 .../python/kernel_tests/record_input_test.py  |  14 +-
 .../python/kernel_tests/sparse_ops_test.py    |  49 +--
 .../python/kernel_tests/stage_op_test.py      |  87 +++--
 tensorflow/python/ops/array_ops.py            |  26 +-
 tensorflow/python/ops/data_flow_ops.py        |  31 +-
 .../python/ops/distributions/special_math.py  |   2 +-
 tensorflow/python/ops/lookup_ops.py           |   6 +-
 tensorflow/python/ops/math_grad.py            |  18 -
 tensorflow/python/ops/math_ops.py             |   7 +-
 tensorflow/python/ops/rnn_cell_impl.py        |   4 +-
 tensorflow/python/ops/sparse_ops.py           |  86 -----
 tensorflow/python/ops/tensor_array_ops.py     |   2 +-
 tensorflow/python/ops/variable_scope.py       |   5 -
 .../print_selective_registration_header.py    |   2 +-
 tensorflow/python/training/input.py           |   3 -
 tensorflow/tensorflow.bzl                     |   2 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  16 -
 tensorflow/tools/ci_build/builds/pip.sh       |  27 +-
 tensorflow/tools/ci_build/ci_build.sh         |  10 +-
 .../tools/ci_build/ci_parameterized_build.sh  |  17 +-
 tensorflow/tools/ci_build/ci_sanity.sh        |  24 +-
 .../ci_build/install/install_pip_packages.sh  |   4 +-
 .../tools/ci_build/install/install_proto3.sh  |   6 +-
 .../install/install_python3.5_pip_packages.sh |   2 +-
 .../protobuf/protobuf_optimized_pip.sh        |   2 +-
 .../ci_build/windows/bazel/bazel_test_lib.sh  |   2 +-
 .../ci_build/windows/cpu/cmake/run_py.bat     |   5 +-
 .../ci_build/windows/gpu/cmake/run_py.bat     |   5 +-
 tensorflow/tools/dist_test/local_test.sh      |   6 +-
 tensorflow/tools/docker/README.md             |  28 +-
 .../docker/parameterized_docker_build.sh      |  44 +--
 .../tools/gcs_test/gcs_smoke_wrapper.sh       |  11 +-
 tensorflow/tools/git/gen_git_source.sh        |   2 +-
 tensorflow/tools/graph_transforms/README.md   |   2 +-
 .../graph_transforms/quantize_weights_test.cc |   4 +-
 .../lib_package/libtensorflow_java_test.sh    |   2 +-
 .../tools/lib_package/libtensorflow_test.sh   |   2 +-
 tensorflow/tools/pip_package/setup.py         |   2 +-
 tensorflow/tools/tfprof/BUILD                 |   1 +
 tensorflow/tools/tfprof/README.md             |  12 +-
 tensorflow/tools/tfprof/g3doc/advise.md       |  80 +++-
 tensorflow/tools/tfprof/g3doc/command_line.md |   2 +-
 .../tools/tfprof/internal/advisor/BUILD       |   9 +
 .../advisor/accelerator_utilization_checker.h |  24 +-
 .../tools/tfprof/internal/advisor/checker.h   |  24 +-
 .../advisor/internal_checker_runner.h         |   5 +-
 .../advisor/internal_checker_runner_dummy.cc  |   6 +-
 .../internal/advisor/operation_checker.h      |  19 +-
 .../tfprof/internal/advisor/tfprof_advisor.h  |  47 ++-
 .../internal/advisor/tfprof_advisor_test.cc   |  46 ++-
 .../tfprof/internal/print_model_analysis.cc   |  19 +-
 .../tfprof/internal/print_model_analysis.h    |   2 -
 .../tools/tfprof/internal/tfprof_graph.h      |   4 +-
 tensorflow/tools/tfprof/internal/tfprof_op.cc |  11 +-
 tensorflow/tools/tfprof/internal/tfprof_op.h  |   6 +-
 .../tools/tfprof/internal/tfprof_options.h    |   4 +-
 .../tools/tfprof/internal/tfprof_show.cc      |  14 +-
 .../tools/tfprof/internal/tfprof_show.h       |  13 +-
 .../tfprof/internal/tfprof_show_multi.cc      |  19 +-
 .../tools/tfprof/internal/tfprof_show_multi.h |  16 +-
 .../tools/tfprof/internal/tfprof_show_test.cc |   1 +
 .../tools/tfprof/internal/tfprof_stats.cc     |  77 ++--
 .../tools/tfprof/internal/tfprof_stats.h      |  22 +-
 .../tfprof/internal/tfprof_stats_test.cc      |   1 +
 .../tfprof/internal/tfprof_tensor_test.cc     |   1 +
 .../tfprof/internal/tfprof_timeline_test.cc   |   1 +
 tensorflow/tools/tfprof/tfprof_main.cc        |  25 +-
 tensorflow/tools/tfprof/tfprof_output.proto   |   8 +
 tensorflow/workspace.bzl                      |  24 +-
 tools/tf_env_collect.sh                       | 143 ++++----
 290 files changed, 1733 insertions(+), 5824 deletions(-)
 delete mode 100644 tensorflow/compiler/plugin/executor/BUILD
 delete mode 100644 tensorflow/compiler/plugin/executor/compiler.cc
 delete mode 100644 tensorflow/compiler/plugin/executor/compiler.h
 delete mode 100644 tensorflow/compiler/plugin/executor/device.cc
 delete mode 100644 tensorflow/compiler/plugin/executor/executable.cc
 delete mode 100644 tensorflow/compiler/plugin/executor/executable.h
 delete mode 100644 tensorflow/compiler/plugin/executor/executor.cc
 delete mode 100644 tensorflow/compiler/plugin/executor/executor.h
 delete mode 100644 tensorflow/compiler/plugin/executor/platform.cc
 delete mode 100644 tensorflow/compiler/plugin/executor/platform.h
 delete mode 100644 tensorflow/compiler/plugin/executor/platform_id.h
 delete mode 100644 tensorflow/compiler/plugin/executor/transfer_manager.cc
 delete mode 100644 tensorflow/compiler/plugin/executor/transfer_manager.h
 delete mode 100644 tensorflow/core/kernels/cwise_op_cosh.cc
 delete mode 100644 tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
 delete mode 100644 tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
 delete mode 100644 tensorflow/core/kernels/cwise_op_sinh.cc
 delete mode 100644 tensorflow/core/kernels/sparse_reduce_op.cc
 delete mode 100644 tensorflow/java/src/main/java/org/tensorflow/Input.java
 delete mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
 delete mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
 delete mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 43abdaafbf..c78b6b1a15 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -159,12 +159,7 @@ There are two ways to run TensorFlow unit tests.
    bazel test ${flags} //tensorflow/python/...
    ```
 
-2. Using [Docker](www.docker.com) and TensorFlow's CI scripts.
-
-   ```bash
-   # Install Docker first, then this will build and run cpu tests
-   tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
-   ```
+2. Using Docker and TensorFlow's CI scripts.
 
    See
    [TensorFlow Builds](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build) for details.
diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 5b37028c50..6f4c048ce8 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -6,7 +6,6 @@ If you open a GitHub issue, here is our policy:
 
 1. It must be a bug or a feature request.
 2. The form below must be filled out.
-3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorflow/issues).
 
 **Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
 
diff --git a/README.md b/README.md
index abbead98a7..e7dbf57b25 100644
--- a/README.md
+++ b/README.md
@@ -34,13 +34,13 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
diff --git a/RELEASE.md b/RELEASE.md
index 9875838d7e..d22c5c62fe 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -113,8 +113,6 @@
   checkpoints containing such RNN cells, in which case you can use the
   [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
   to convert the variable names in your old checkpoints.
-* Added `tf.contrib.kernel_methods` module with Ops and estimators for primal
-  (explicit) kernel methods in TensorFlow.
 
 ## Bug Fixes and Other Changes
 * In python, `Operation.get_attr` on type attributes returns the Python DType
diff --git a/configure b/configure
index 602124225f..e1aaddabda 100755
--- a/configure
+++ b/configure
@@ -162,12 +162,8 @@ bazel version > bazel.version
 curr_bazel_version=$(head -n 1 bazel.version | cut -d ' ' -f3)
 rm -f bazel.version
 
-
 echo "You have bazel $curr_bazel_version installed."
-if [ -z "$curr_bazel_version" ]; then
-  echo "WARNING: current bazel installation is not a release version."
-  echo "Make sure you are running at least bazel $MIN_BAZEL_VERSION."
-elif [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
+if [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
   echo "Please upgrade your bazel installation to version $MIN_BAZEL_VERSION or higher to build TensorFlow!"
   echo "Exiting..."
   exit 1
diff --git a/tensorflow/c/generate-pc.sh b/tensorflow/c/generate-pc.sh
index 02a6a58b61..73d427d9b2 100755
--- a/tensorflow/c/generate-pc.sh
+++ b/tensorflow/c/generate-pc.sh
@@ -26,7 +26,7 @@ usage() {
 [ $# == 0 ] && usage && exit 0
 
 # read the options
-ARGS=$(getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@")
+ARGS=`getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@"`
 eval set -- "$ARGS"
 
 # extract options and their arguments into variables.
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index 9801add1da..a884f11d48 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -472,23 +472,10 @@ cc_binary(
     name = "tutorials_example_trainer",
     srcs = ["tutorials/example_trainer.cc"],
     copts = tf_copts(),
-    linkopts = select({
-        "//tensorflow:windows": [],
-        "//tensorflow:windows_msvc": [],
-        "//tensorflow:darwin": [
-            "-lm",
-            "-lpthread",
-        ],
-        "//tensorflow:ios": [
-            "-lm",
-            "-lpthread",
-        ],
-        "//conditions:default": [
-            "-lm",
-            "-lpthread",
-            "-lrt",
-        ],
-    }),
+    linkopts = [
+        "-lpthread",
+        "-lm",
+    ],
     deps = [
         ":cc_ops",
         "//tensorflow/core:core_cpu",
diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc
index 71d9a8ed7b..8c1a01f518 100644
--- a/tensorflow/cc/gradients/math_grad.cc
+++ b/tensorflow/cc/gradients/math_grad.cc
@@ -162,32 +162,6 @@ Status Log1pGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Log1p", Log1pGrad);
 
-Status SinhGrad(const Scope& scope, const Operation& op,
-                const std::vector<Output>& grad_inputs,
-                std::vector<Output>* grad_outputs) {
-  // y = sinh(x)
-  // dy/dx = cosh(x)
-  auto dydx = Cosh(scope, op.input(0));
-  // grad(x) = grad(y) * conj(dy/dx)
-  grad_outputs->push_back(
-      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
-  return scope.status();
-}
-REGISTER_GRADIENT_OP("Sinh", SinhGrad);
-
-Status CoshGrad(const Scope& scope, const Operation& op,
-                const std::vector<Output>& grad_inputs,
-                std::vector<Output>* grad_outputs) {
-  // y = cosh(x)
-  // dy/dx = sinh(x)
-  auto dydx = Sinh(scope, op.input(0));
-  // grad(x) = grad(y) * conj(dy/dx)
-  grad_outputs->push_back(
-      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
-  return scope.status();
-}
-REGISTER_GRADIENT_OP("Cosh", CoshGrad);
-
 Status TanhGrad(const Scope& scope, const Operation& op,
                 const std::vector<Output>& grad_inputs,
                 std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc
index 1653b04378..de6baa1769 100644
--- a/tensorflow/cc/gradients/math_grad_test.cc
+++ b/tensorflow/cc/gradients/math_grad_test.cc
@@ -45,8 +45,6 @@ class CWiseUnaryGradTest : public ::testing::Test {
     EXPM1,
     LOG,
     LOG1P,
-    SINH,
-    COSH,
     TANH,
     SIGMOID,
     SIGN,
@@ -113,12 +111,6 @@ class CWiseUnaryGradTest : public ::testing::Test {
       case LOG1P:
         y = Log1p(scope_, x);
         break;
-      case SINH:
-        y = Sinh(scope_, x);
-        break;
-      case COSH:
-        y = Cosh(scope_, x);
-        break;
       case TANH:
         y = Tanh(scope_, x);
         break;
@@ -345,50 +337,6 @@ TEST_F(CWiseUnaryGradTest, Log1p_Complex) {
   TestCWiseGrad<complex64>(LOG1P, x_fn, dy_fn, dx_fn);
 }
 
-TEST_F(CWiseUnaryGradTest, Sinh) {
-  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
-  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
-  auto dx_fn = [this](const float x, const float dy) {
-    return dy * std::cosh(x);
-  };
-  TestCWiseGrad<float>(SINH, x_fn, dy_fn, dx_fn);
-}
-
-TEST_F(CWiseUnaryGradTest, Sinh_Complex) {
-  auto x_fn = [this](const int i) {
-    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
-  };
-  auto dy_fn = [this](const complex64& x) {
-    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
-  };
-  auto dx_fn = [this](const complex64& x, const complex64& dy) {
-    return dy * conjugate(std::cosh(x));
-  };
-  TestCWiseGrad<complex64>(SINH, x_fn, dy_fn, dx_fn);
-}
-
-TEST_F(CWiseUnaryGradTest, Cosh) {
-  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
-  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
-  auto dx_fn = [this](const float x, const float dy) {
-    return dy * std::sinh(x);
-  };
-  TestCWiseGrad<float>(COSH, x_fn, dy_fn, dx_fn);
-}
-
-TEST_F(CWiseUnaryGradTest, Cosh_Complex) {
-  auto x_fn = [this](const int i) {
-    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
-  };
-  auto dy_fn = [this](const complex64& x) {
-    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
-  };
-  auto dx_fn = [this](const complex64& x, const complex64& dy) {
-    return dy * conjugate(std::sinh(x));
-  };
-  TestCWiseGrad<complex64>(COSH, x_fn, dy_fn, dx_fn);
-}
-
 TEST_F(CWiseUnaryGradTest, Tanh) {
   auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
   auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc
index 952b2015ed..5e5203d090 100644
--- a/tensorflow/cc/gradients/nn_grad.cc
+++ b/tensorflow/cc/gradients/nn_grad.cc
@@ -46,19 +46,6 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad);
 
-Status LogSoftmaxGrad(const Scope& scope, const Operation& op,
-                   const std::vector<Output>& grad_inputs,
-                   std::vector<Output>* grad_outputs) {
-
-  auto softmax = Exp(scope, op.output(0));
-  auto sum = Sum(scope, grad_inputs[0], {1}, Sum::KeepDims(true));
-  auto mul = Mul(scope, sum, softmax);
-  auto dx = Sub(scope, grad_inputs[0], mul);
-  grad_outputs->push_back(dx);
-  return scope.status();
-}
-REGISTER_GRADIENT_OP("LogSoftmax", LogSoftmaxGrad);
-
 Status ReluGradHelper(const Scope& scope, const Operation& op,
                       const std::vector<Output>& grad_inputs,
                       std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc
index daa87546ec..70c9bd4e08 100644
--- a/tensorflow/cc/gradients/nn_grad_test.cc
+++ b/tensorflow/cc/gradients/nn_grad_test.cc
@@ -57,19 +57,6 @@ TEST_F(NNGradTest, SoftmaxGrad) {
   RunTest(x, shape, y, shape);
 }
 
-TEST_F(NNGradTest, LogSoftmaxGrad) {
-  TensorShape shape({5, 3});
-  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
-  auto y = LogSoftmax(scope_, x);
-  // Avoid numerical instability when computing finite differences.
-  Tensor x_init_value = test::AsTensor<float>(
-          {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f,
-           0.1f, 0.3f, 0.5f, 0.7f, 0.8f,
-           -0.1f, 0.1f, 0.1f, 0.1f, 1.2f},
-          {5, 3});
-  RunTest(x, x_init_value, y, shape);
-}
-
 TEST_F(NNGradTest, ReluGrad) {
   TensorShape shape({5, 2});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
diff --git a/tensorflow/compiler/plugin/BUILD b/tensorflow/compiler/plugin/BUILD
index 8c2e9a7c81..4badd3a589 100644
--- a/tensorflow/compiler/plugin/BUILD
+++ b/tensorflow/compiler/plugin/BUILD
@@ -32,7 +32,5 @@ package(
 
 cc_library(
     name = "plugin",
-    deps = [
-        "//tensorflow/compiler/plugin/executor:plugin_lib",
-    ],
+    deps = [],
 )
diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
deleted file mode 100644
index 9bc706abdf..0000000000
--- a/tensorflow/compiler/plugin/executor/BUILD
+++ /dev/null
@@ -1,32 +0,0 @@
-licenses(["restricted"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "plugin_lib",
-    srcs = glob([
-        "*.cc",
-    ]),
-    hdrs = glob([
-        "*.h",
-    ]),
-    deps = [
-        "//tensorflow/compiler/jit:xla_jit_headers_lib",
-        "//tensorflow/compiler/xla:xla_headers_lib",
-        "//tensorflow/compiler/xla/service:hlo_evaluator",
-        "//third_party/eigen3",
-        "@local_config_cuda//cuda:cuda_headers",
-        "@protobuf//:protobuf_headers",
-    ],
-)
-
-filegroup(
-    name = "all_files",
-    srcs = glob(
-        ["**/*"],
-        exclude = [
-            "**/METADATA",
-            "**/OWNERS",
-        ],
-    ),
-)
diff --git a/tensorflow/compiler/plugin/executor/compiler.cc b/tensorflow/compiler/plugin/executor/compiler.cc
deleted file mode 100644
index 893ff152f0..0000000000
--- a/tensorflow/compiler/plugin/executor/compiler.cc
+++ /dev/null
@@ -1,123 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include <stdlib.h>
-#include <fstream>
-
-#include "tensorflow/compiler/plugin/executor/compiler.h"
-#include "tensorflow/compiler/plugin/executor/executable.h"
-
-#include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
-#include "tensorflow/compiler/xla/service/flatten_call_graph.h"
-#include "tensorflow/compiler/xla/service/hlo_constant_folding.h"
-#include "tensorflow/compiler/xla/service/hlo_cse.h"
-#include "tensorflow/compiler/xla/service/hlo_dce.h"
-#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
-#include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
-#include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
-#include "tensorflow/compiler/xla/service/inliner.h"
-#include "tensorflow/compiler/xla/service/reshape_mover.h"
-#include "tensorflow/compiler/xla/status_macros.h"
-
-#include "tensorflow/stream_executor/lib/initialize.h"
-#include "tensorflow/stream_executor/lib/strcat.h"
-
-#include "tensorflow/core/lib/core/errors.h"
-
-namespace se = ::perftools::gputools;
-namespace sep = ::perftools::gputools::executorplugin;
-namespace port = ::perftools::gputools::port;
-
-namespace xla {
-namespace executorplugin {
-
-/*
- * Run optimization passes on the module.  The graph is transformed by
- * each pass in the optimization pipeline.  The service subdirectory
- * contains useful optimization passes.
- */
-Status ExecutorCompiler::RunHloOptimization(HloModule* hlo_module,
-                                            HloDumper dump_hlo) {
-  HloPassPipeline pipeline("Executor", dump_hlo);
-  pipeline.AddPass<Inliner>();
-  pipeline.AddPass<HloSubcomputationUnification>();
-  pipeline.AddPass<HloCSE>(false);
-
-  pipeline.AddPass<HloPassFix<AlgebraicSimplifier>>(
-      false, [](const Shape&, const Shape&) { return false; });
-  pipeline.AddPass<ReshapeMover>();
-  pipeline.AddPass<HloConstantFolding>();
-  pipeline.AddPass<HloCSE>(true);
-
-  pipeline.AddPass<HloDCE>();
-  pipeline.AddPass<FlattenCallGraph>();
-  return pipeline.Run(hlo_module).status();
-}
-
-StatusOr<std::unique_ptr<Executable>> ExecutorCompiler::Compile(
-        std::unique_ptr<HloModule> hlo_module, HloDumper dump_hlo,
-        se::StreamExecutor* stream_exec) {
-  TF_RET_CHECK(stream_exec != nullptr);
-
-  VLOG(1) << "Generate graph " << hlo_module->name();
-
-  TF_RETURN_IF_ERROR(RunHloOptimization(hlo_module.get(), dump_hlo));
-
-  // Typically you would visit the HLO graph, building up a compiled equivalent
-  // In this case we are using an Hlo evaluator at execution time, so we don't
-  // need to compile anything
-
-  // Create executable from only the Hlo module
-  std::unique_ptr<Executable> executable;
-  executable.reset(new ExecutorExecutable(std::move(hlo_module)));
-
-  return std::move(executable);
-}
-
-StatusOr<std::vector<std::unique_ptr<Executable>>> ExecutorCompiler::Compile(
-        std::vector<std::unique_ptr<HloModule>> hlo_modules,
-        HloDumper dump_hlos, std::vector<se::StreamExecutor*> stream_execs) {
-
-  return tensorflow::errors::Unimplemented(
-      "Compilation of multiple HLO modules is not supported on Executor.");
-}
-
-StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-ExecutorCompiler::CompileAheadOfTime(
-    std::vector<std::unique_ptr<HloModule>> hlo_modules,
-    HloDumper dump_hlo, const AotCompilationOptions& aot_options) {
-
-  return tensorflow::errors::InvalidArgument(
-      "AOT compilation not supported on Executor");
-}
-
-se::Platform::Id ExecutorCompiler::PlatformId() const {
-  return sep::kExecutorPlatformId;
-}
-
-HloCostAnalysis::ShapeSizeFunction
-ExecutorCompiler::ShapeSizeBytesFunction() const {
-  return ExecutorExecutable::ShapeSizeBytes;
-}
-
-
-}  // namespace executorplugin
-}  // namespace xla
-
-REGISTER_MODULE_INITIALIZER(executor_compiler, {
-  xla::Compiler::RegisterCompilerFactory(sep::kExecutorPlatformId, []() {
-    return xla::MakeUnique<xla::executorplugin::ExecutorCompiler>();
-  });
-});
diff --git a/tensorflow/compiler/plugin/executor/compiler.h b/tensorflow/compiler/plugin/executor/compiler.h
deleted file mode 100644
index 8fe591c8ab..0000000000
--- a/tensorflow/compiler/plugin/executor/compiler.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
-#define TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
-
-#include <memory>
-
-#include "tensorflow/compiler/xla/service/compiler.h"
-#include "tensorflow/compiler/xla/service/executable.h"
-#include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_module_config.h"
-
-#include "tensorflow/compiler/plugin/executor/platform_id.h"
-
-namespace xla {
-namespace executorplugin {
-
-class ExecutorCompiler : public Compiler {
- public:
-  ExecutorCompiler() {}
-  ~ExecutorCompiler() override {}
-
-  StatusOr<std::unique_ptr<Executable>> Compile(
-      std::unique_ptr<HloModule> hlo_module,
-      HloDumper dump_hlo,
-      perftools::gputools::StreamExecutor* stream_exec) override;
-
-  StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
-      std::vector<std::unique_ptr<HloModule>> hlo_module,
-      HloDumper dump_hlo,
-      std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
-
-  StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-  CompileAheadOfTime(
-      std::vector<std::unique_ptr<HloModule>> module,
-      HloDumper dump_hlo, const AotCompilationOptions& options) override;
-
-  HloCostAnalysis::ShapeSizeFunction ShapeSizeBytesFunction() const override;
-
-  perftools::gputools::Platform::Id PlatformId() const override;
-
- private:
-  Status RunHloOptimization(HloModule* hlo_module, HloDumper dump_hlo);
-
-  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorCompiler);
-};
-
-}  // namespace executorplugin
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
diff --git a/tensorflow/compiler/plugin/executor/device.cc b/tensorflow/compiler/plugin/executor/device.cc
deleted file mode 100644
index bbc39dc03f..0000000000
--- a/tensorflow/compiler/plugin/executor/device.cc
+++ /dev/null
@@ -1,60 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/jit/kernels/xla_device_launch_op.h"
-#include "tensorflow/compiler/jit/xla_device.h"
-#include "tensorflow/compiler/jit/xla_device_ops.h"
-#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
-
-namespace tensorflow {
-
-const char* const DEVICE_XLA_EXEC = "XLA_EXEC";
-const char* const DEVICE_EXEC_XLA_JIT = "XLA_EXEC_JIT";
-
-constexpr std::array<DataType, 5> kExecAllTypes = {
-    {DT_INT32, DT_FLOAT, DT_BOOL, DT_DOUBLE, DT_INT64}};
-
-class XlaExaDeviceFactory : public DeviceFactory {
- public:
-  Status CreateDevices(const SessionOptions& options, const string& name_prefix,
-                       std::vector<Device*>* devices) override;
-};
-
-Status XlaExaDeviceFactory::CreateDevices(const SessionOptions& options,
-                                          const string& name_prefix,
-                                          std::vector<Device*>* devices) {
-  static XlaDeviceOpRegistrations* registrations =
-      RegisterXlaDeviceKernels(DEVICE_XLA_EXEC, DEVICE_EXEC_XLA_JIT);
-  (void)registrations;
-
-  std::unique_ptr<XlaDevice> device;
-  TF_RETURN_IF_ERROR(XlaDevice::Create("Executor", DEVICE_XLA_EXEC, 0,
-                                       DEVICE_EXEC_XLA_JIT, options,
-                                       name_prefix, &device));
-  devices->push_back(device.release());
-  return Status::OK();
-}
-
-REGISTER_LOCAL_DEVICE_FACTORY(DEVICE_XLA_EXEC, XlaExaDeviceFactory, 110);
-
-// Kernel registrations
-
-static bool OpFilter(KernelDef* kdef) { return true; }
-
-REGISTER_XLA_LAUNCH_KERNEL(DEVICE_XLA_EXEC, XlaDeviceLaunchOp, kExecAllTypes);
-REGISTER_XLA_DEVICE_KERNELS(DEVICE_XLA_EXEC, kExecAllTypes);
-REGISTER_XLA_BACKEND(DEVICE_EXEC_XLA_JIT, kExecAllTypes, OpFilter);
-
-}  // namespace tensorflow
diff --git a/tensorflow/compiler/plugin/executor/executable.cc b/tensorflow/compiler/plugin/executor/executable.cc
deleted file mode 100644
index 79eea9af3f..0000000000
--- a/tensorflow/compiler/plugin/executor/executable.cc
+++ /dev/null
@@ -1,147 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/plugin/executor/executable.h"
-#include "tensorflow/compiler/plugin/executor/executor.h"
-
-#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
-
-#include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/shape_util.h"
-
-namespace se = ::perftools::gputools;
-namespace sep = ::perftools::gputools::executorplugin;
-
-namespace xla {
-namespace executorplugin {
-
-ExecutorExecutable::ExecutorExecutable(std::unique_ptr<HloModule> hlo_module)
-    : Executable(std::move(hlo_module), ShapeSizeBytes) {}
-
-ExecutorExecutable::~ExecutorExecutable() {}
-
-static se::DeviceMemoryBase AllocateSingleOutput(sep::ExecutorExecutor* executor,
-                                                 const Literal& literal) {
-  int64 size(xla::ShapeUtil::ByteSizeOf(literal.shape()));
-  void* buf = executor->Allocate(size);
-  const void* src = literal.InternalData();
-  memcpy(buf, src, size);
-  return se::DeviceMemoryBase(buf, size);
-}
-
-static se::DeviceMemoryBase AllocateOutputBuffer(sep::ExecutorExecutor* executor,
-                                                 const Literal& literal) {
-  const Shape& shape = literal.shape();
-  if (shape.element_type() != xla::TUPLE) {
-    return AllocateSingleOutput(executor, literal);
-  } else {
-    int64 size(xla::ShapeUtil::ByteSizeOf(shape, sizeof(void*)));
-    void** buf = reinterpret_cast<void**>(executor->Allocate(size));
-    for (int64 n = 0; n < xla::ShapeUtil::TupleElementCount(shape); n++) {
-      se::DeviceMemoryBase out =
-          AllocateSingleOutput(executor, literal.tuple_literals(n));
-      *buf++ = out.opaque();
-    }
-
-    return se::DeviceMemoryBase(buf, size);
-  }
-}
-
-StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteOnStream(
-    const ServiceExecutableRunOptions* run_options,
-    tensorflow::gtl::ArraySlice<se::DeviceMemoryBase> arguments,
-    HloExecutionProfile* hlo_execution_profile) {
-  se::Stream* stream = run_options->stream();
-
-  VLOG(1) << "Execute " << module().name();
-  if (VLOG_IS_ON(2)) {
-    for (const auto& a : arguments) {
-      VLOG(2) << "-- argument " << a.opaque();
-    }
-  }
-
-  uint64 start_micros = tensorflow::Env::Default()->NowMicros();
-
-  HloComputation* computation = module().entry_computation();
-  if (computation->num_parameters() != arguments.size()) {
-    return tensorflow::errors::Internal(
-        "Mismatch between argument count and graph parameter count.");
-  }
-
-  // Create the arguments as an vector of XLA literals
-  std::vector<std::unique_ptr<Literal>> arg_literals;
-  std::vector<Literal*> arg_literals_ptrs;
-  for (int64 p = 0; p < computation->num_parameters(); p++) {
-    // Create the input literal for the parameter
-    HloInstruction* param = computation->parameter_instruction(p);
-    arg_literals.emplace_back(Literal::CreateFromShape(param->shape()));
-    arg_literals_ptrs.push_back(arg_literals.back().get());
-
-    // Copy in the data from the stream_executor buffers
-    void* buffer = arg_literals.back().get()->MutableInternalData();
-    memcpy(buffer, arguments[p].opaque(),
-           ShapeUtil::ByteSizeOf(param->shape()));
-  }
-
-  // Execute the graph using the evaluator
-  HloEvaluator evaluator;
-  std::unique_ptr<Literal> output;
-  TF_ASSIGN_OR_RETURN(output,
-                      evaluator.Evaluate(computation, arg_literals_ptrs));
-
-  // Copy the result into the return buffer
-  perftools::gputools::StreamExecutor* executor(stream->parent());
-  sep::ExecutorExecutor* executorExecutor(
-      static_cast<sep::ExecutorExecutor*>(executor->implementation()));
-
-  se::DeviceMemoryBase ret =
-      AllocateOutputBuffer(executorExecutor, *(output.get()));
-
-  uint64 end_micros = tensorflow::Env::Default()->NowMicros();
-
-  {
-    tensorflow::mutex_lock lock(mutex_);
-    const double nanoseconds = (end_micros - start_micros) * 1000.0;
-    execution_profile_.set_compute_time_ns(std::max(nanoseconds, 1.0));
-  }
-
-  return ret;
-}
-
-StatusOr<std::unique_ptr<ShapedBuffer>> ExecutorExecutable::ExecuteOnStream(
-    const ServiceExecutableRunOptions* run_options,
-    tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
-    HloExecutionProfile* hlo_execution_profile) {
-  return tensorflow::errors::Unimplemented(
-      "ExecuteOnStream is not yet supported on Executor.");
-}
-
-StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteAsyncOnStream(
-    const ServiceExecutableRunOptions* run_options,
-    tensorflow::gtl::ArraySlice<se::DeviceMemoryBase> arguments) {
-  return tensorflow::errors::Unimplemented(
-      "ExecuteAsyncOnStream is not yet supported on Executor.");
-}
-
-/*static*/ int64 ExecutorExecutable::ShapeSizeBytes(const Shape& shape) {
-  if (ShapeUtil::IsOpaque(shape)) {
-    return sizeof(void*);
-  }
-  return ShapeUtil::ByteSizeOf(shape, sizeof(void*));
-}
-
-
-}  // namespace executorplugin
-}  // namespace xla
diff --git a/tensorflow/compiler/plugin/executor/executable.h b/tensorflow/compiler/plugin/executor/executable.h
deleted file mode 100644
index ba3d4da21d..0000000000
--- a/tensorflow/compiler/plugin/executor/executable.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
-#define TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
-
-#include <cstddef>
-#include <memory>
-#include <string>
-#include <unordered_map>
-#include <vector>
-
-#include "tensorflow/compiler/xla/service/executable.h"
-#include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_module_config.h"
-
-#include "tensorflow/stream_executor/lib/status.h"
-#include "tensorflow/stream_executor/lib/statusor.h"
-
-namespace xla {
-namespace executorplugin {
-
-class ExecutorExecutable : public Executable {
- public:
-  ExecutorExecutable(std::unique_ptr<HloModule> hlo_module);
-  ~ExecutorExecutable() override;
-
-  StatusOr<perftools::gputools::DeviceMemoryBase> ExecuteOnStream(
-      const ServiceExecutableRunOptions* run_options,
-      tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>
-          arguments,
-      HloExecutionProfile* hlo_execution_profile) override;
-
-  StatusOr<std::unique_ptr<ShapedBuffer>> ExecuteOnStream(
-      const ServiceExecutableRunOptions* run_options,
-      tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
-      HloExecutionProfile* hlo_execution_profile) override;
-
-  StatusOr<perftools::gputools::DeviceMemoryBase> ExecuteAsyncOnStream(
-      const ServiceExecutableRunOptions* run_options,
-      tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>
-          arguments) override;
-
-  static int64 ShapeSizeBytes(const Shape& shape);
-
- private:
-  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorExecutable);
-};
-
-}  // namespace executorplugin
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
diff --git a/tensorflow/compiler/plugin/executor/executor.cc b/tensorflow/compiler/plugin/executor/executor.cc
deleted file mode 100644
index e72c2711f7..0000000000
--- a/tensorflow/compiler/plugin/executor/executor.cc
+++ /dev/null
@@ -1,135 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/plugin/executor/executor.h"
-#include "tensorflow/compiler/plugin/executor/platform_id.h"
-
-#include "tensorflow/compiler/xla/status_macros.h"
-
-#include <stdlib.h>
-#include <string.h>
-
-namespace se = ::perftools::gputools;
-
-namespace perftools {
-namespace gputools {
-namespace executorplugin {
-
-host::HostStream *AsExecutorStream(Stream *stream) {
-  DCHECK(stream != nullptr);
-  return dynamic_cast<host::HostStream *>(stream->implementation());
-}
-
-ExecutorExecutor::ExecutorExecutor(const PluginConfig &plugin_config)
-    : plugin_config_(plugin_config) {}
-
-ExecutorExecutor::~ExecutorExecutor() {}
-
-void *ExecutorExecutor::Allocate(uint64 size) {
-  void *buf = new char[size];
-  return buf;
-}
-
-void *ExecutorExecutor::AllocateSubBuffer(DeviceMemoryBase *parent,
-                                         uint64 offset_bytes,
-                                         uint64 size_bytes) {
-  return parent + offset_bytes;
-}
-
-void ExecutorExecutor::Deallocate(DeviceMemoryBase *mem) {
-  if (!mem->is_sub_buffer()) {
-    delete[] static_cast<char *>(mem->opaque());
-  }
-}
-
-bool ExecutorExecutor::Memcpy(Stream *stream, void *host_dst,
-                             const DeviceMemoryBase &dev_src, uint64 size) {
-  AsExecutorStream(stream)->EnqueueTask([this, host_dst, dev_src, size]() {
-    port::Status ok = SynchronousMemcpy(host_dst, dev_src, size);
-  });
-  return true;
-}
-
-bool ExecutorExecutor::Memcpy(Stream *stream, DeviceMemoryBase *dev_dst,
-                             const void *host_src, uint64 size) {
-  AsExecutorStream(stream)->EnqueueTask([this, dev_dst, host_src, size]() {
-    port::Status ok = SynchronousMemcpy(dev_dst, host_src, size);
-  });
-  return true;
-}
-
-port::Status ExecutorExecutor::SynchronousMemcpy(DeviceMemoryBase *dev_dst,
-                                                const void *host_src,
-                                                uint64 size) {
-  memcpy(dev_dst->opaque(), host_src, size);
-  return port::Status::OK();
-}
-
-port::Status ExecutorExecutor::SynchronousMemcpy(void *host_dst,
-                                                const DeviceMemoryBase &dev_src,
-                                                uint64 size) {
-  memcpy(host_dst, dev_src.opaque(), size);
-  return port::Status::OK();
-}
-
-bool ExecutorExecutor::HostCallback(Stream *stream,
-                                   std::function<void()> callback) {
-  AsExecutorStream(stream)->EnqueueTask(callback);
-  return true;
-}
-
-bool ExecutorExecutor::CreateStreamDependency(Stream *dependent, Stream *other) {
-  AsExecutorStream(dependent)->EnqueueTask(
-      [other]() { other->BlockHostUntilDone(); });
-  AsExecutorStream(dependent)->BlockUntilDone();
-  return true;
-}
-
-bool ExecutorExecutor::StartTimer(Stream *stream, Timer *timer) {
-  dynamic_cast<host::HostTimer *>(timer->implementation())->Start(stream);
-  return true;
-}
-
-bool ExecutorExecutor::StopTimer(Stream *stream, Timer *timer) {
-  dynamic_cast<host::HostTimer *>(timer->implementation())->Stop(stream);
-  return true;
-}
-
-bool ExecutorExecutor::BlockHostUntilDone(Stream *stream) {
-  AsExecutorStream(stream)->BlockUntilDone();
-  return true;
-}
-
-DeviceDescription *ExecutorExecutor::PopulateDeviceDescription() const {
-  internal::DeviceDescriptionBuilder builder;
-
-  builder.set_device_address_bits(64);
-
-  builder.set_name("Executor");
-  builder.set_device_vendor("VectorName");
-  builder.set_platform_version("1.0");
-  builder.set_driver_version("1.0");
-  builder.set_runtime_version("1.0");
-  builder.set_pci_bus_id("1");
-  builder.set_device_memory_size(static_cast<uint64>(4) * 1024 * 1024 * 1024);
-  builder.set_clock_rate_ghz(static_cast<float>(CLOCKS_PER_SEC) / 1e9);
-
-  auto built = builder.Build();
-  return built.release();
-}
-
-}  // namespace executorplugin
-}  // namespace gputools
-}  // namespace perftools
diff --git a/tensorflow/compiler/plugin/executor/executor.h b/tensorflow/compiler/plugin/executor/executor.h
deleted file mode 100644
index 32fdb157e4..0000000000
--- a/tensorflow/compiler/plugin/executor/executor.h
+++ /dev/null
@@ -1,213 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Declares the ExecutorExecutor class, which is a CPU-only implementation of
-// the StreamExecutor interface. For now, this is used for testing and to
-// examine the performance of host-based StreamExecutor code.
-#ifndef TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
-#define TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
-
-#include "tensorflow/stream_executor/host/host_stream.h"
-#include "tensorflow/stream_executor/host/host_timer.h"
-
-#include "tensorflow/compiler/xla/shape_util.h"
-
-#include "tensorflow/stream_executor/blas.h"
-#include "tensorflow/stream_executor/lib/error.h"
-#include "tensorflow/stream_executor/lib/status.h"
-#include "tensorflow/stream_executor/lib/statusor.h"
-#include "tensorflow/stream_executor/rng.h"
-#include "tensorflow/stream_executor/stream_executor.h"
-#include "tensorflow/stream_executor/stream_executor_internal.h"
-
-#include <list>
-#include <mutex>
-
-namespace perftools {
-namespace gputools {
-namespace executorplugin {
-
-using Args = tensorflow::gtl::ArraySlice<DeviceMemoryBase>;
-
-class ExecutorExecutor : public internal::StreamExecutorInterface {
- public:
-  explicit ExecutorExecutor(const PluginConfig &plugin_config);
-  ~ExecutorExecutor() override;
-
-  port::Status Init(int device_ordinal, DeviceOptions device_options) override {
-    return port::Status::OK();
-  }
-
-  bool GetKernel(const MultiKernelLoaderSpec &spec,
-                 KernelBase *kernel) override {
-    return false;
-  }
-  bool Launch(Stream *stream, const ThreadDim &thread_dims,
-              const BlockDim &block_dims, const KernelBase &kernel,
-              const KernelArgsArrayBase &args) override {
-    return false;
-  }
-
-  void *Allocate(uint64 size) override;
-  void *AllocateSubBuffer(DeviceMemoryBase *mem, uint64 offset_bytes,
-                          uint64 size_bytes) override;
-  void Deallocate(DeviceMemoryBase *mem) override;
-
-  void *HostMemoryAllocate(uint64 size) override { return new char[size]; }
-  void HostMemoryDeallocate(void *mem) override {
-    delete[] static_cast<char *>(mem);
-  }
-  bool HostMemoryRegister(void *mem, uint64 size) override { return true; }
-  bool HostMemoryUnregister(void *mem) override { return true; }
-
-  bool Memcpy(Stream *stream, void *host_dst, const DeviceMemoryBase &pop_src,
-              uint64 size) override;
-  bool Memcpy(Stream *stream, DeviceMemoryBase *pop_dst, const void *host_src,
-              uint64 size) override;
-  bool MemcpyDeviceToDevice(Stream *stream, DeviceMemoryBase *pop_dst,
-                            const DeviceMemoryBase &host_src,
-                            uint64 size) override {
-    return false;
-  }
-
-  bool MemZero(Stream *stream, DeviceMemoryBase *location,
-               uint64 size) override {
-    return false;
-  }
-  bool Memset(Stream *stream, DeviceMemoryBase *location, uint8 pattern,
-              uint64 size) override {
-    return false;
-  }
-  bool Memset32(Stream *stream, DeviceMemoryBase *location, uint32 pattern,
-                uint64 size) override {
-    return false;
-  }
-
-  // No "synchronize all activity" implemented for this platform at the moment.
-  bool SynchronizeAllActivity() override { return false; }
-  bool SynchronousMemZero(DeviceMemoryBase *location, uint64 size) override {
-    return false;
-  }
-
-  bool SynchronousMemSet(DeviceMemoryBase *location, int value,
-                         uint64 size) override {
-    return false;
-  }
-
-  port::Status SynchronousMemcpy(DeviceMemoryBase *pop_dst,
-                                 const void *host_src, uint64 size) override;
-  port::Status SynchronousMemcpy(void *host_dst,
-                                 const DeviceMemoryBase &pop_src,
-                                 uint64 size) override;
-  port::Status SynchronousMemcpyDeviceToDevice(DeviceMemoryBase *pop_dst,
-                                               const DeviceMemoryBase &pop_src,
-                                               uint64 size) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
-  }
-
-  bool HostCallback(Stream *stream, std::function<void()> callback) override;
-
-  port::Status AllocateEvent(Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
-  }
-
-  port::Status DeallocateEvent(Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
-  }
-
-  port::Status RecordEvent(Stream *stream, Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
-  }
-
-  port::Status WaitForEvent(Stream *stream, Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
-  }
-
-  Event::Status PollForEventStatus(Event *event) override {
-    return Event::Status::kError;
-  }
-
-  bool AllocateStream(Stream *stream) override { return true; }
-  void DeallocateStream(Stream *stream) override {}
-  bool CreateStreamDependency(Stream *dependent, Stream *other) override;
-
-  bool AllocateTimer(Timer *timer) override { return true; }
-  void DeallocateTimer(Timer *timer) override {}
-  bool StartTimer(Stream *stream, Timer *timer) override;
-  bool StopTimer(Stream *stream, Timer *timer) override;
-
-  bool BlockHostUntilDone(Stream *stream) override;
-
-  int PlatformDeviceCount() override { return 1; }
-
-  bool DeviceMemoryUsage(int64 *free, int64 *total) const override {
-    return false;
-  }
-
-  DeviceDescription *PopulateDeviceDescription() const override;
-
-  port::Status EnablePeerAccessTo(StreamExecutorInterface *other) override {
-    return port::Status::OK();
-  }
-
-  bool CanEnablePeerAccessTo(StreamExecutorInterface *other) override {
-    return true;
-  }
-
-  SharedMemoryConfig GetDeviceSharedMemoryConfig() override {
-    return SharedMemoryConfig::kDefault;
-  }
-
-  port::Status SetDeviceSharedMemoryConfig(SharedMemoryConfig config) override {
-    return port::Status{port::error::UNIMPLEMENTED,
-                        "Shared memory not supported"};
-  }
-
-  std::unique_ptr<internal::EventInterface> CreateEventImplementation()
-      override {
-    return nullptr;
-  }
-
-  std::unique_ptr<internal::KernelInterface> CreateKernelImplementation()
-      override {
-    return nullptr;
-  }
-
-  std::unique_ptr<internal::StreamInterface> GetStreamImplementation()
-      override {
-    return std::unique_ptr<internal::StreamInterface>(new host::HostStream());
-  }
-
-  std::unique_ptr<internal::TimerInterface> GetTimerImplementation() override {
-    return std::unique_ptr<internal::TimerInterface>(new host::HostTimer());
-  }
-
-  port::StatusOr<DeviceMemoryBase> ExecuteGraph(const xla::Shape &shape,
-                                                Args args);
-
- private:
-  DeviceMemoryBase AllocateSingleOutput(const xla::Shape &shape);
-
-  port::StatusOr<DeviceMemoryBase> AllocateOutputBuffer(
-      const xla::Shape &shape);
-
-  const PluginConfig plugin_config_;
-};
-
-}  // namespace executorplugin
-}  // namespace gputools
-}  // namespace perftools
-
-#endif  // TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
diff --git a/tensorflow/compiler/plugin/executor/platform.cc b/tensorflow/compiler/plugin/executor/platform.cc
deleted file mode 100644
index 2f339f04a7..0000000000
--- a/tensorflow/compiler/plugin/executor/platform.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/plugin/executor/platform.h"
-#include "tensorflow/compiler/plugin/executor/executor.h"
-#include "tensorflow/compiler/plugin/executor/platform_id.h"
-
-#include "tensorflow/stream_executor/lib/error.h"
-#include "tensorflow/stream_executor/lib/initialize.h"
-#include "tensorflow/stream_executor/lib/ptr_util.h"
-#include "tensorflow/stream_executor/lib/status.h"
-#include "tensorflow/stream_executor/lib/status_macros.h"
-#include "tensorflow/stream_executor/lib/stringprintf.h"
-
-namespace se = ::perftools::gputools;
-namespace sep = ::perftools::gputools::executorplugin;
-
-namespace perftools {
-namespace gputools {
-namespace executorplugin {
-
-PLATFORM_DEFINE_ID(kExecutorPlatformId);
-
-ExecutorPlatform::ExecutorPlatform() : name_("Executor") {}
-
-ExecutorPlatform::~ExecutorPlatform() {}
-
-Platform::Id ExecutorPlatform::id() const { return kExecutorPlatformId; }
-
-int ExecutorPlatform::VisibleDeviceCount() const { return 1; }
-
-const string& ExecutorPlatform::Name() const { return name_; }
-
-port::StatusOr<StreamExecutor*> ExecutorPlatform::ExecutorForDevice(
-    int ordinal) {
-  StreamExecutorConfig config;
-  config.ordinal = ordinal;
-  config.plugin_config = PluginConfig();
-  config.device_options = DeviceOptions::Default();
-  return GetExecutor(config);
-}
-
-port::StatusOr<StreamExecutor*>
-ExecutorPlatform::ExecutorForDeviceWithPluginConfig(
-    int device_ordinal, const PluginConfig& plugin_config) {
-  StreamExecutorConfig config;
-  config.ordinal = device_ordinal;
-  config.plugin_config = plugin_config;
-  config.device_options = DeviceOptions::Default();
-  return GetExecutor(config);
-}
-
-port::StatusOr<StreamExecutor*> ExecutorPlatform::GetExecutor(
-    const StreamExecutorConfig& config) {
-  mutex_lock lock(executors_mutex_);
-
-  port::StatusOr<StreamExecutor*> status = executor_cache_.Get(config);
-  if (status.ok()) {
-    return status.ValueOrDie();
-  }
-
-  port::StatusOr<std::unique_ptr<StreamExecutor>> executor =
-      GetUncachedExecutor(config);
-  if (!executor.ok()) {
-    return executor.status();
-  }
-
-  StreamExecutor* naked_executor = executor.ValueOrDie().get();
-  SE_RETURN_IF_ERROR(
-      executor_cache_.Insert(config, executor.ConsumeValueOrDie()));
-  return naked_executor;
-}
-
-port::StatusOr<std::unique_ptr<StreamExecutor>>
-ExecutorPlatform::GetUncachedExecutor(const StreamExecutorConfig& config) {
-  auto executor = port::MakeUnique<StreamExecutor>(
-      this, port::MakeUnique<ExecutorExecutor>(config.plugin_config));
-  auto init_status = executor->Init(config.ordinal, config.device_options);
-  if (!init_status.ok()) {
-    return port::Status{
-        port::error::INTERNAL,
-        port::Printf(
-            "failed initializing StreamExecutor for device ordinal %d: %s",
-            config.ordinal, init_status.ToString().c_str())};
-  }
-
-  return std::move(executor);
-}
-
-void ExecutorPlatform::RegisterTraceListener(
-    std::unique_ptr<TraceListener> listener) {
-  LOG(FATAL) << "not yet implemented: register executor trace listener";
-}
-
-void ExecutorPlatform::UnregisterTraceListener(TraceListener* listener) {
-  LOG(FATAL) << "not yet implemented: unregister executor trace listener";
-}
-
-static void InitializeExecutorPlatform() {
-  std::unique_ptr<se::Platform> platform(new sep::ExecutorPlatform);
-  SE_CHECK_OK(se::MultiPlatformManager::RegisterPlatform(std::move(platform)));
-}
-
-}  // namespace executorplugin
-}  // namespace gputools
-}  // namespace perftools
-
-REGISTER_MODULE_INITIALIZER(executor_platform, sep::InitializeExecutorPlatform());
-
-DECLARE_MODULE_INITIALIZER(multi_platform_manager);
-// Note that module initialization sequencing is not supported in the
-// open-source project, so this will be a no-op there.
-REGISTER_MODULE_INITIALIZER_SEQUENCE(executor_platform, multi_platform_manager);
diff --git a/tensorflow/compiler/plugin/executor/platform.h b/tensorflow/compiler/plugin/executor/platform.h
deleted file mode 100644
index c252a589d4..0000000000
--- a/tensorflow/compiler/plugin/executor/platform.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
-#define TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "tensorflow/stream_executor/executor_cache.h"
-#include "tensorflow/stream_executor/lib/statusor.h"
-#include "tensorflow/stream_executor/multi_platform_manager.h"
-#include "tensorflow/stream_executor/platform.h"
-#include "tensorflow/stream_executor/platform/mutex.h"
-#include "tensorflow/stream_executor/platform/port.h"
-#include "tensorflow/stream_executor/platform/thread_annotations.h"
-#include "tensorflow/stream_executor/stream_executor_pimpl.h"
-#include "tensorflow/stream_executor/trace_listener.h"
-
-namespace perftools {
-namespace gputools {
-namespace executorplugin {
-
-class ExecutorPlatform : public Platform {
- public:
-  ExecutorPlatform();
-  ~ExecutorPlatform() override;
-
-  Platform::Id id() const override;
-
-  // Device count is less clear-cut for CPUs than accelerators. This call
-  // currently returns the number of thread units in the host, as reported by
-  // base::NumCPUs().
-  int VisibleDeviceCount() const override;
-
-  const string& Name() const override;
-
-  port::StatusOr<StreamExecutor*> ExecutorForDevice(int ordinal) override;
-
-  port::StatusOr<StreamExecutor*> ExecutorForDeviceWithPluginConfig(
-      int ordinal, const PluginConfig& config) override;
-
-  port::StatusOr<StreamExecutor*> GetExecutor(
-      const StreamExecutorConfig& config) override;
-
-  port::StatusOr<std::unique_ptr<StreamExecutor>> GetUncachedExecutor(
-      const StreamExecutorConfig& config) override;
-
-  void RegisterTraceListener(std::unique_ptr<TraceListener> listener) override;
-
-  void UnregisterTraceListener(TraceListener* listener) override;
-
- private:
-  // This platform's name.
-  string name_;
-
-  // mutex that guards the ordinal-to-executor map.
-  mutable mutex executors_mutex_;
-
-  // Cache of created StreamExecutors.
-  ExecutorCache executor_cache_;
-
-  SE_DISALLOW_COPY_AND_ASSIGN(ExecutorPlatform);
-};
-
-}  // namespace executorplugin
-}  // namespace gputools
-}  // namespace perftools
-
-#endif  // TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
diff --git a/tensorflow/compiler/plugin/executor/platform_id.h b/tensorflow/compiler/plugin/executor/platform_id.h
deleted file mode 100644
index 8d2b29a3e4..0000000000
--- a/tensorflow/compiler/plugin/executor/platform_id.h
+++ /dev/null
@@ -1,31 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
-#define TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
-
-#include "tensorflow/stream_executor/platform.h"
-
-namespace perftools {
-namespace gputools {
-namespace executorplugin {
-
-extern const Platform::Id kExecutorPlatformId;
-
-}  // namespace executorplugin
-}  // namespace gputools
-}  // namespace perftools
-
-#endif  // TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.cc b/tensorflow/compiler/plugin/executor/transfer_manager.cc
deleted file mode 100644
index 51c5deeea5..0000000000
--- a/tensorflow/compiler/plugin/executor/transfer_manager.cc
+++ /dev/null
@@ -1,187 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/plugin/executor/transfer_manager.h"
-#include "tensorflow/compiler/plugin/executor/platform_id.h"
-
-#include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/shape_util.h"
-#include "tensorflow/compiler/xla/status_macros.h"
-#include "tensorflow/compiler/xla/statusor.h"
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/compiler/xla/util.h"
-#include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/platform/stream_executor_no_cuda.h"
-
-#include <string>
-#include <utility>
-#include <vector>
-
-namespace sep = ::perftools::gputools::executorplugin;
-
-namespace xla {
-namespace executorplugin {
-
-ExecutorTransferManager::ExecutorTransferManager() {}
-
-se::Platform::Id ExecutorTransferManager::PlatformId() const {
-  return se::executorplugin::kExecutorPlatformId;
-}
-
-Status ExecutorTransferManager::TransferLiteralFromDevice(
-    se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
-    const Shape& device_shape, const Shape& literal_shape, Literal* literal) {
-  TF_RET_CHECK(ShapeUtil::Compatible(device_shape, literal_shape));
-
-  // Tuples are a special case and contain one or more shapes inside of them to
-  // an arbitrary nesting depth.
-  if (device_shape.element_type() == TUPLE) {
-    *literal->mutable_shape() = literal_shape;
-    TF_ASSIGN_OR_RETURN(
-        std::vector<se::DeviceMemoryBase> element_buffers,
-        ShallowCopyTupleFromDevice(executor, source, device_shape));
-    TF_RET_CHECK(element_buffers.size() ==
-                 ShapeUtil::TupleElementCount(device_shape));
-    for (int64 i = 0; i < element_buffers.size(); ++i) {
-      const Shape& element_device_shape = device_shape.tuple_shapes(i);
-      const Shape& element_literal_shape = literal_shape.tuple_shapes(i);
-      Literal* element_literal = literal->add_tuple_literals();
-      // Recursively call TransferFromDevice to copy over the data in the
-      // element array.
-      TF_RETURN_IF_ERROR(TransferLiteralFromDevice(
-          executor, element_buffers[i], element_device_shape,
-          element_literal_shape, element_literal));
-    }
-    return Status::OK();
-  }
-
-  *literal->mutable_shape() = device_shape;
-  literal->Reserve(ShapeUtil::ElementsIn(device_shape));
-  TF_RETURN_IF_ERROR(TransferBufferFromDevice(
-      executor, source, ShapeUtil::ByteSizeOf(device_shape),
-      literal->MutableInternalData()));
-  if (!ShapeUtil::Equal(literal_shape, device_shape)) {
-    literal->Swap(
-        literal->Relayout(literal_shape.layout()).get());
-  }
-  TF_RET_CHECK(ShapeUtil::Equal(literal_shape, literal->shape()));
-  return Status::OK();
-}
-
-StatusOr<std::vector<se::DeviceMemoryBase>>
-ExecutorTransferManager::ShallowCopyTupleFromDevice(
-    se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
-    const Shape& shape) {
-  TF_RET_CHECK(ShapeUtil::IsTuple(shape));
-
-  std::vector<void*> element_pointers(ShapeUtil::TupleElementCount(shape),
-                                      nullptr);
-  int64 tuple_size = ShapeUtil::ByteSizeOf(shape, sizeof(void*));
-  auto copy_status = executor->SynchronousMemcpyD2H(source, tuple_size,
-                                                    element_pointers.data());
-  if (!copy_status.ok()) {
-    return AddStatus(
-        Status(static_cast<tensorflow::error::Code>(copy_status.code()),
-               copy_status.error_message()),
-        "failed transfer of tuple buffer " + ShapeUtil::HumanString(shape));
-  }
-
-  // Create a DeviceMemoryBase from each void* pointer.
-  std::vector<se::DeviceMemoryBase> destination;
-  for (int i = 0; i < element_pointers.size(); ++i) {
-    if (element_pointers[i] == nullptr &&
-        !ShapeUtil::HasZeroElements(shape.tuple_shapes(i))) {
-      return FailedPrecondition("tuple contains nullptr at element %d", i);
-    }
-    int64 buffer_size =
-        ShapeUtil::ByteSizeOf(shape.tuple_shapes(i), sizeof(void*));
-    destination.emplace_back(element_pointers[i], buffer_size);
-  }
-  return std::move(destination);
-}
-
-Status ExecutorTransferManager::TransferLiteralToDevice(
-    se::StreamExecutor* executor, const Literal& literal,
-    se::DeviceMemoryBase* destination) {
-  const Shape& shape = literal.shape();
-
-  if (ShapeUtil::IsTuple(literal.shape())) {
-    std::vector<void*> tuple_elements_on_device;
-    for (const Literal& tuple_element : literal.tuple_literals()) {
-      se::DeviceMemoryBase allocation = executor->AllocateArray<uint8>(
-          GetByteSizeRequirement(tuple_element.shape()));
-      TF_RETURN_IF_ERROR(
-          TransferLiteralToDevice(executor, tuple_element, &allocation));
-      tuple_elements_on_device.push_back(allocation.opaque());
-    }
-    return TransferBufferToDevice(
-        executor, tuple_elements_on_device.size() * sizeof(void*),
-        tuple_elements_on_device.data(), destination);
-  }
-
-  return TransferBufferToDevice(executor, GetByteSizeRequirement(shape),
-                                literal.InternalData(),
-                                destination);
-}
-
-Status ExecutorTransferManager::TransferLiteralToInfeed(
-    se::StreamExecutor* executor, const Literal& literal) {
-  const Shape& shape = literal.shape();
-  VLOG(1) << "transferring literal shape to infeed: "
-          << ShapeUtil::HumanString(shape);
-
-  return Status::OK();
-}
-
-Status ExecutorTransferManager::TransferBufferToInfeed(
-    se::StreamExecutor* executor, int64 size, const void* source) {
-  return Unimplemented("Transfer to Infeed");
-}
-
-Status ExecutorTransferManager::TransferLiteralFromOutfeed(
-    perftools::gputools::StreamExecutor* executor, const Shape& literal_shape,
-    Literal* literal) {
-  const Shape& shape = literal->shape();
-  VLOG(1) << "transferring literal shape from outfeed: "
-          << ShapeUtil::HumanString(shape);
-
-  return Status::OK();
-}
-
-Status ExecutorTransferManager::ResetDevices(
-    tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*>
-        executors) {
-  return Unimplemented("Device reset not supported");
-}
-
-int64 ExecutorTransferManager::GetByteSizeRequirement(const Shape& shape) {
-  return ShapeUtil::ByteSizeOf(shape, sizeof(void*));
-}
-
-}  // namespace executorplugin
-}  // namespace xla
-
-static std::unique_ptr<xla::TransferManager> CreateExecutorTransferManager() {
-  return xla::MakeUnique<xla::executorplugin::ExecutorTransferManager>();
-}
-
-static bool InitModule() {
-  xla::TransferManager::RegisterTransferManager(sep::kExecutorPlatformId,
-                                                &CreateExecutorTransferManager);
-  return true;
-}
-static bool module_initialized = InitModule();
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.h b/tensorflow/compiler/plugin/executor/transfer_manager.h
deleted file mode 100644
index 7a42e5a2d7..0000000000
--- a/tensorflow/compiler/plugin/executor/transfer_manager.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
-#define TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
-
-#include "tensorflow/compiler/xla/service/transfer_manager.h"
-#include "tensorflow/compiler/xla/statusor.h"
-#include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/stream_executor_no_cuda.h"
-#include "tensorflow/core/platform/types.h"
-
-#include <vector>
-
-namespace se = ::perftools::gputools;
-
-namespace xla {
-namespace executorplugin {
-
-class ExecutorTransferManager : public TransferManager {
- public:
-  ExecutorTransferManager();
-
-  ~ExecutorTransferManager() override {}
-
-  se::Platform::Id PlatformId() const override;
-
-  StatusOr<std::vector<se::DeviceMemoryBase>> ShallowCopyTupleFromDevice(
-      se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
-      const Shape& shape) override;
-
-  Status TransferLiteralFromDevice(se::StreamExecutor* executor,
-                                   const se::DeviceMemoryBase& source,
-                                   const Shape& device_shape,
-                                   const Shape& literal_shape,
-                                   Literal* literal) override;
-
-  Status TransferLiteralToDevice(se::StreamExecutor* executor,
-                                 const Literal& literal,
-                                 se::DeviceMemoryBase* destination) override;
-
-  Status TransferLiteralToInfeed(se::StreamExecutor* executor,
-                                 const Literal& literal) override;
-
-  Status TransferBufferToInfeed(se::StreamExecutor* executor,
-                                 int64 size, const void* source) override;
-
-  Status TransferLiteralFromOutfeed(se::StreamExecutor* executor,
-                                    const Shape& literal_shape,
-                                    Literal* literal) override;
-
-  Status ResetDevices(
-      tensorflow::gtl::ArraySlice<se::StreamExecutor*> executors) override;
-
-  int64 GetByteSizeRequirement(const Shape& shape) override;
-
- private:
-  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorTransferManager);
-};
-
-}  // namespace executorplugin
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py
index a75a5cd2cf..6b328fb618 100644
--- a/tensorflow/compiler/tests/ftrl_test.py
+++ b/tensorflow/compiler/tests/ftrl_test.py
@@ -218,7 +218,7 @@ class FtrlOptimizerTest(XLATestCase):
         self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval())
         self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval())
 
-  # When variables are initialized with Zero, FTRL-Proximal has two properties:
+  # When variables are intialized with Zero, FTRL-Proximal has two properties:
   # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
   # with GradientDescent.
   # 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is idential
diff --git a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
index 16b778bca4..f752fb3ae2 100644
--- a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
@@ -94,14 +94,12 @@ class BatchMatMulOp : public XlaOpKernel {
       // Slice off individual matrices and reshape to 2D tensors.
       auto x_slice = builder->Slice(
           x_flat, {i, 0, 0},
-          {i + 1, x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)},
-          {1, 1, 1});
+          {i + 1, x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)});
       x_slice = builder->Reshape(
           x_slice, {x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)});
       auto y_slice = builder->Slice(
           y_flat, {i, 0, 0},
-          {i + 1, y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)},
-          {1, 1, 1});
+          {i + 1, y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)});
       y_slice = builder->Reshape(
           y_slice, {y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)});
 
diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
index 21d3e64872..47d2d747e6 100644
--- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
@@ -125,7 +125,6 @@ void BatchToSpace(XlaOpKernelContext* ctx,
   //       input_shape[M+1], ..., input_shape[N-1]]
   std::vector<int64> start_indices(input_rank, 0);
   std::vector<int64> end_indices = reshaped_permuted_shape;
-  std::vector<int64> strides(input_rank, 1);
   for (int i = 0; i < block_rank; ++i) {
     int64 crop_start = crops.Get<int64>({i, 0});
     int64 crop_end = crops.Get<int64>({i, 1});
@@ -140,7 +139,7 @@ void BatchToSpace(XlaOpKernelContext* ctx,
             " end: ", crop_end, " size ", reshaped_permuted_shape[1 + i]));
   }
   xla::ComputationDataHandle output =
-      b->Slice(reshaped_permuted, start_indices, end_indices, strides);
+      b->Slice(reshaped_permuted, start_indices, end_indices);
   ctx->SetOutput(0, output);
 }
 
diff --git a/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc b/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
index 852d2a966e..92b371cc4e 100644
--- a/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
@@ -172,14 +172,15 @@ class DepthwiseConv2dNativeOp : public XlaOpKernel {
     } else {
       // These will be used to define the bounds of each slice.
       // Within the loop, the input_channel index will be modified.
-      gtl::InlinedVector<int64, 4> filter_begin(4, 0);
-      gtl::InlinedVector<int64, 4> filter_limits(4);
-      gtl::InlinedVector<int64, 4> input_begin(4, 0);
-      gtl::InlinedVector<int64, 4> input_limits(4);
-      gtl::InlinedVector<int64, 4> strides(4, 1);
+      gtl::InlinedVector<int64, 4> filter_begin;
+      gtl::InlinedVector<int64, 4> filter_limits;
+      gtl::InlinedVector<int64, 4> input_begin;
+      gtl::InlinedVector<int64, 4> input_limits;
       for (int i = 0; i < 4; ++i) {
-        filter_limits[i] = filter_shape.dim_size(i);
-        input_limits[i] = input_shape.dim_size(i);
+        filter_begin.push_back(0);
+        filter_limits.push_back(filter_shape.dim_size(i));
+        input_begin.push_back(0);
+        input_limits.push_back(input_shape.dim_size(i));
       }
 
       std::vector<int64> strides_for_tla{strides_[1], strides_[2]};
@@ -208,9 +209,9 @@ class DepthwiseConv2dNativeOp : public XlaOpKernel {
         input_limits[3] = i + 1;
 
         xla::ComputationDataHandle filter_slice =
-            b.Slice(filter, filter_begin, filter_limits, strides);
+            b.Slice(filter, filter_begin, filter_limits);
         xla::ComputationDataHandle input_slice =
-            b.Slice(input, input_begin, input_limits, strides);
+            b.Slice(input, input_begin, input_limits);
         convs.push_back(b.ConvWithGeneralDimensions(
             input_slice, filter_slice, strides_for_tla, xla_padding, dims));
       }
diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc
index ec5017f6ab..74994d8961 100644
--- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc
@@ -125,7 +125,7 @@ class DiagPartOp : public XlaOpKernel {
     diag = builder->Reshape(diag, {new_size, new_size + 1});
 
     // Slices out the first column and reshapes to the final shape.
-    diag = builder->Slice(diag, {0, 0}, {new_size, 1}, {1, 1});
+    diag = builder->Slice(diag, {0, 0}, {new_size, 1});
     diag = builder->Reshape(diag, new_dims);
 
     ctx->SetOutput(0, diag);
@@ -224,9 +224,8 @@ class MatrixDiagPartOp : public XlaOpKernel {
     } else if (actual_size > target_size) {
       std::vector<int64> start(flattened_dims.size(), 0);
       std::vector<int64> limits(flattened_dims.begin(), flattened_dims.end());
-      std::vector<int64> strides(flattened_dims.size(), 1);
       limits[flattened_dims.size() - 1] = target_size;
-      diag = builder->Slice(diag, start, limits, strides);
+      diag = builder->Slice(diag, start, limits);
     }
 
     // Reshape so the target values are in the first position of the last
@@ -239,9 +238,8 @@ class MatrixDiagPartOp : public XlaOpKernel {
     // Slices out the first column and reshapes to the final shape.
     std::vector<int64> start(dims.size(), 0);
     std::vector<int64> limits(dims.begin(), dims.end());
-    std::vector<int64> strides(dims.size(), 1);
     limits[last_dim] = 1;
-    diag = builder->Slice(diag, start, limits, strides);
+    diag = builder->Slice(diag, start, limits);
 
     // Collapses away the last dimension.
     dims.pop_back();
diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
index 0330e34c98..faa7ef0ef9 100644
--- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
@@ -156,8 +156,6 @@ class DynamicStitchOp : public XlaOpKernel {
                                    indices0_shape.dims());
     std::vector<int64> slice_limit(1 + data0_shape.dims() -
                                    indices0_shape.dims());
-    std::vector<int64> stride(1 + data0_shape.dims() -
-                              indices0_shape.dims(), 1);
     for (int d = indices0_shape.dims(); d < data0_shape.dims(); d++) {
       slice_limit[1 + d - indices0_shape.dims()] = data0_shape.dim_size(d);
     }
@@ -170,7 +168,7 @@ class DynamicStitchOp : public XlaOpKernel {
       // And place it in the concat list in the place indicated by
       // the index.
       to_concat[index_num] =
-          ctx->builder()->Slice(expression, slice_start, slice_limit, stride);
+          ctx->builder()->Slice(expression, slice_start, slice_limit);
     }
 
     ctx->SetOutput(0, ctx->builder()->ConcatInDim(to_concat, 0));
diff --git a/tensorflow/compiler/tf2xla/kernels/slice_op.cc b/tensorflow/compiler/tf2xla/kernels/slice_op.cc
index 482c54a40c..51c97d85d7 100644
--- a/tensorflow/compiler/tf2xla/kernels/slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/slice_op.cc
@@ -54,9 +54,7 @@ class SliceOp : public XlaOpKernel {
     for (int i = 0; i < begin.size(); ++i) {
       limits.push_back(begin[i] + size[i]);
     }
-    std::vector<int64> strides(begin.size(), 1);
-    ctx->SetOutput(0, ctx->builder()->Slice(ctx->Input(0), begin, limits,
-                                            strides));
+    ctx->SetOutput(0, ctx->builder()->Slice(ctx->Input(0), begin, limits));
   }
 
  private:
diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc
index 44ee81461e..017f3a110e 100644
--- a/tensorflow/compiler/tf2xla/kernels/split_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc
@@ -77,14 +77,14 @@ class SplitOp : public XlaOpKernel {
 
     // The vectors we will use to define the slice. The entry for the
     // split dimensions varies for each output.
-    std::vector<int64> begin(input_shape.dims(), 0);
-    std::vector<int64> limits(input_shape.dims());
-    std::vector<int64> strides(input_shape.dims(), 1);
+    std::vector<int64> begin;
+    std::vector<int64> limits;
     for (int i = 0; i < input_shape.dims(); ++i) {
       // Initially set up the limits to be the full size of the input:
       // the split dimension is filled in below.
       int64 dim = input_shape.dim_size(i);
-      limits[i] = dim;
+      begin.push_back(0);
+      limits.push_back(dim);
     }
 
     auto input = ctx->Input(1);
@@ -94,7 +94,7 @@ class SplitOp : public XlaOpKernel {
       // Slice out the ith split from the split dimension.
       begin[split_dim] = i * slice_size;
       limits[split_dim] = (i + 1) * slice_size;
-      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides));
+      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits));
     }
   }
 };
@@ -188,7 +188,7 @@ class SplitVOp : public XlaOpKernel {
     std::vector<int64> begin(input_shape.dims(), 0);
     auto dim_sizes = input_shape.dim_sizes();
     std::vector<int64> limits(dim_sizes.begin(), dim_sizes.end());
-    std::vector<int64> strides(input_shape.dims(), 1);
+
     for (int i = 0; i < num_split; ++i) {
       TensorShape output_shape(input_shape);
       int slice_size = split_sizes_vec[i];
@@ -196,7 +196,7 @@ class SplitVOp : public XlaOpKernel {
 
       // Slice out the ith split from the split dimension.
       limits[split_dim] = begin[split_dim] + slice_size;
-      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides));
+      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits));
       begin[split_dim] = limits[split_dim];
     }
   }
diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
index 6af4bd0496..8037e90791 100644
--- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
@@ -72,29 +72,55 @@ class StridedSliceOp : public XlaOpKernel {
                        &dummy, &dummy, &dummy, &begin, &end, &strides));
 
     gtl::InlinedVector<int64, 4> dimensions_to_reverse;
-    gtl::InlinedVector<int64, 4> slice_begin, slice_end, slice_strides;
-
+    gtl::InlinedVector<int64, 4> slice_begin, slice_end;
+    bool simple_strides = true;
     for (int i = 0; i < begin.size(); ++i) {
+      simple_strides &= (std::abs(strides[i]) == 1);
       if (strides[i] > 0) {
         slice_begin.push_back(begin[i]);
         slice_end.push_back(end[i]);
-        slice_strides.push_back(strides[i]);
       } else {
         // Negative stride: swap begin and end, add 1 because the interval
         // is semi-open, and mark the dimension to be reversed.
-        slice_begin.push_back(input_shape.dim_size(i) - begin[i] - 1);
-        slice_end.push_back(input_shape.dim_size(i) - end[i] - 1);
-        slice_strides.push_back(-strides[i]);
+        slice_begin.push_back(end[i] + 1);
+        slice_end.push_back(begin[i] + 1);
         dimensions_to_reverse.push_back(i);
       }
     }
-
-    xla::ComputationDataHandle slice = ctx->Input(0);
+    xla::ComputationDataHandle slice =
+        ctx->builder()->Slice(ctx->Input(0), slice_begin, slice_end);
     if (!dimensions_to_reverse.empty()) {
       slice = ctx->builder()->Rev(slice, dimensions_to_reverse);
     }
 
-    slice = ctx->builder()->Slice(slice, slice_begin, slice_end, slice_strides);
+    // If at least one of the strides is > 1 (or < -1) then use Slice
+    // to pull out each of the strided slices, and Concat to put them
+    // together again.
+    if (!simple_strides) {
+      // Re-adjust the begin and end now that the periphery has been
+      // sliced away.
+      for (int d = 0; d < strides.size(); ++d) {
+        slice_end[d] -= slice_begin[d];
+        slice_begin[d] = 0;
+      }
+
+      for (int d = 0; d < strides.size(); ++d) {
+        int64 stride = std::abs(strides[d]);
+        if (stride > 1) {
+          std::vector<xla::ComputationDataHandle> to_concat;
+          int64 end = slice_end[d];
+          for (int64 i = 0; i < end; i += stride) {
+            slice_begin[d] = i;
+            slice_end[d] = i + 1;
+            to_concat.push_back(
+                ctx->builder()->Slice(slice, slice_begin, slice_end));
+          }
+          slice = ctx->builder()->ConcatInDim(to_concat, d);
+          slice_begin[d] = 0;
+          slice_end[d] = to_concat.size();
+        }
+      }
+    }
 
     slice = ctx->builder()->Reshape(slice, final_shape.dim_sizes());
     ctx->SetOutput(0, slice);
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index 9367c1ef22..598b341002 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -318,7 +318,7 @@ class TensorArrayGatherOp : public XlaOpKernel {
     for (int i = 0; i < num_indices; ++i) {
       // Slices the i-th index out of `indices`, and pads it with zeros in the
       // minor dimensions to form an index into the TensorArray storage.
-      auto index = b->Slice(indices, {i}, {i + 1}, {1});
+      auto index = b->Slice(indices, {i}, {i + 1});
 
       // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
       auto start_indices = PadIndexWithZeros(b, index, ta_shape.dims() - 1);
@@ -381,18 +381,16 @@ class TensorArrayScatterOp : public XlaOpKernel {
     std::vector<int64> value_starts(value_shape.dims(), 0);
     auto value_ends = value_shape.dim_sizes();
 
-    std::vector<int64> value_strides(value_shape.dims(), 1);
-
     // For every (index, value) pair, update the corresponding TensorArray
     // storage.
     for (int i = 0; i < num_indices; ++i) {
       // Slice out part of the value.
       value_starts[0] = i;
       value_ends[0] = i + 1;
-      auto slice = b->Slice(value, value_starts, value_ends, value_strides);
+      auto slice = b->Slice(value, value_starts, value_ends);
 
       // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
-      auto index = b->Slice(indices, {i}, {i + 1}, {1});
+      auto index = b->Slice(indices, {i}, {i + 1});
       auto start_indices = PadIndexWithZeros(b, index, elem_shape.dims());
       ta = DynamicAddSlice(b, ta, slice, slice_dims, start_indices);
     }
diff --git a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
index f87586ba57..a5ce78e520 100644
--- a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
@@ -66,7 +66,6 @@ class UnpackOp : public XlaOpKernel {
 
     std::vector<int64> start_indices(input_shape.dims(), 0);
     std::vector<int64> limit_indices(input_shape.dims());
-    std::vector<int64> strides(input_shape.dims(), 1);
     for (int i = 0; i < input_shape.dims(); ++i) {
       limit_indices[i] = input_shape.dim_size(i);
     }
@@ -74,8 +73,7 @@ class UnpackOp : public XlaOpKernel {
     for (int i = 0; i < num; ++i) {
       start_indices[axis] = i;
       limit_indices[axis] = i + 1;
-      auto slice = ctx->builder()->Slice(input, start_indices, limit_indices,
-                                         strides);
+      auto slice = ctx->builder()->Slice(input, start_indices, limit_indices);
       // Reshape to drop the 'axis' dimension.
       auto result = ctx->builder()->Reshape(slice, output_shape.dim_sizes());
       ctx->SetOutput(i, result);
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index dcc313707b..735a69d596 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -256,8 +256,7 @@ void ComputationBuilder::CheckSameShape(const ComputationDataHandle& lhs,
 ComputationDataHandle ComputationBuilder::Slice(
     const ComputationDataHandle& operand,
     tensorflow::gtl::ArraySlice<int64> start_indices,
-    tensorflow::gtl::ArraySlice<int64> limit_indices,
-    tensorflow::gtl::ArraySlice<int64> stride) {
+    tensorflow::gtl::ArraySlice<int64> limit_indices) {
   if (!first_error_.ok() || !PrepareComputation().ok()) {
     return ComputationDataHandle();
   }
@@ -270,9 +269,6 @@ ComputationDataHandle ComputationBuilder::Slice(
   for (int64 index : limit_indices) {
     request.add_limit_indices(index);
   }
-  for (int64 index : stride) {
-    request.add_stride(index);
-  }
   OpRequest op_request;
   *op_request.mutable_computation() = computation_.handle();
   *op_request.mutable_slice_request() = request;
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index b411346459..5dceb03281 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -211,11 +211,9 @@ class ComputationBuilder {
   //
   // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D
   // range notation.
-  // The stride parameter determines the stride over the slice
   ComputationDataHandle Slice(const ComputationDataHandle& operand,
                               tensorflow::gtl::ArraySlice<int64> start_indices,
-                              tensorflow::gtl::ArraySlice<int64> limit_indices,
-                              tensorflow::gtl::ArraySlice<int64> stride);
+                              tensorflow::gtl::ArraySlice<int64> limit_indices);
 
   // Enqueues a slice operation onto the computation that slices the 'operand'
   // from dynamic start indices which are passed in 'start_indices'.
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index b6bd1158d2..1b125e3596 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -1205,7 +1205,11 @@ void Literal::Resize<double>(int64 num_elements, double value) {
 template <>
 void Literal::Resize<half>(int64 num_elements, half value) {
   CHECK_EQ(ShapeUtil::ElementsIn(shape()), num_elements);
-  mutable_f16s()->resize(num_elements, value);
+  mutable_f16s()->resize(num_elements * sizeof(half));
+  auto data = GetMutableArraySlice<half>();
+  for (int i = 0; i < num_elements; i++) {
+    data[i] = value;
+  }
 }
 
 template <typename RepeatedFieldT, typename NativeT>
@@ -1248,7 +1252,7 @@ LiteralProto Literal::ToProto() const {
     case F16:
       *proto.mutable_f16s() =
           string(reinterpret_cast<const char*>(f16s_.data()),
-                 f16s_.size() * sizeof(half));
+                 f16s_.size() / sizeof(half));
       break;
     case F32:
       CopyToRepeatedField(proto.mutable_f32s(), f32s());
@@ -1304,7 +1308,7 @@ void Literal::CopyFromProto(const LiteralProto& literal_proto) {
       const string& s(literal_proto.f16s());
       CHECK_EQ(0, s.size() % sizeof(half));
       f16s_ = std::vector<half>(s.size() / sizeof(half));
-      memcpy(f16s_.data(), s.data(), s.size());
+      memcpy(f16s_.data(), s.data(), s.size() / sizeof(half));
       break;
     }
     case F32:
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 5a550ef4c6..ffae623b0c 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -939,62 +939,5 @@ TEST_F(LiteralUtilTest, CopyFromProto_Bool) {
   }
 }
 
-// Note that f16 is currently stored in a byte array in little endian byte order
-TEST_F(LiteralUtilTest, ToProto_f16) {
-  half h1(1.0f);
-  half h2(2.0f);
-
-  auto m = Literal::CreateR2<half>({{h1, h2}, {h2, h1}});
-  Literal* l = m.get();
-  EXPECT_EQ(4, ShapeUtil::ElementsIn(l->shape()));
-  EXPECT_EQ(4, l->f16s().size());
-  EXPECT_EQ(4, l->f16s_size());
-
-  LiteralProto p = l->ToProto();
-  EXPECT_EQ(4, ShapeUtil::ElementsIn(p.shape()));
-  EXPECT_EQ(8, p.f16s().size());
-  const char* d = p.f16s().data();
-  EXPECT_EQ(d[0], 0);
-  EXPECT_EQ(d[1], 0x3C);
-  EXPECT_EQ(d[2], 0);
-  EXPECT_EQ(d[3], 0x40);
-  EXPECT_EQ(d[4], 0);
-  EXPECT_EQ(d[5], 0x40);
-  EXPECT_EQ(d[6], 0);
-  EXPECT_EQ(d[7], 0x3C);
-}
-
-// Note that f16 is currently stored in a byte array in little endian byte order
-TEST_F(LiteralUtilTest, CopyFromProto_f16) {
-  half h1(1.0f);
-  half h2(2.0f);
-
-  const char half_vals[8] = {
-    0x00, 0x3C, 0x00, 0x40, 0x00, 0x40, 0x00, 0x3C
-  };
-  LiteralProto p;
-  p.mutable_shape()->set_element_type(F16);
-  p.mutable_shape()->clear_dimensions();
-  p.mutable_shape()->add_dimensions(4);
-  p.clear_f16s();
-  p.set_f16s(half_vals, 8);
-
-
-  Literal literal(p);
-  ASSERT_EQ(4, literal.f16s_size());
-  ASSERT_EQ(h1, literal.f16s(0));
-  ASSERT_EQ(h2, literal.f16s(1));
-  ASSERT_EQ(h2, literal.f16s(2));
-  ASSERT_EQ(h1, literal.f16s(3));
-
-  const std::vector<half>& r = literal.f16s();
-  ASSERT_EQ(4, r.size());
-  ASSERT_EQ(h1, r[0]);
-  ASSERT_EQ(h2, r[1]);
-  ASSERT_EQ(h2, r[2]);
-  ASSERT_EQ(h1, r[3]);
-}
-
-
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 99b1337b11..718a2d798c 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -90,6 +90,8 @@ cc_library(
         ":hlo_query",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status",
+        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 5709ac3067..0187c09d7b 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -855,7 +855,6 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) {
     // Second, construct the slice instruction to perform the negative padding.
     std::vector<int64> start_indices;
     std::vector<int64> end_indices;
-    std::vector<int64> strides;
     for (int64 i = 0; i < pad->padding_config().dimensions_size(); ++i) {
       const PaddingConfig::PaddingConfigDimension& padding_dimension =
           pad->padding_config().dimensions(i);
@@ -869,18 +868,16 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) {
       }
       start_indices.push_back(start);
       end_indices.push_back(end);
-      strides.push_back(1);
     }
 
     // Verify that the slice shape matches the pad shape.
     TF_ASSIGN_OR_RETURN(Shape inferred_slice_shape,
                         ShapeInference::InferSliceShape(
-                            nonzero_pad_shape, start_indices, end_indices,
-                            strides));
+                            nonzero_pad_shape, start_indices, end_indices));
     TF_RET_CHECK(ShapeUtil::Compatible(inferred_slice_shape, pad->shape()));
 
     std::unique_ptr<HloInstruction> slice = HloInstruction::CreateSlice(
-        pad->shape(), nonzero_pad, start_indices, end_indices, strides);
+        pad->shape(), nonzero_pad, start_indices, end_indices);
     return ReplaceWithNewInstruction(pad, std::move(slice));
   }
 
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index 7e52c8fb0c..0792006ddb 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -520,7 +520,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) {
       HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
-          ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}, {1}));
+          ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}));
   Shape result_shape = ShapeUtil::MakeShape(F32, {3 * kParamLength});
   builder.AddInstruction(HloInstruction::CreateConcatenate(
       result_shape, {empty_literal, param0, param0, empty_slice, param1}, 0));
@@ -551,7 +551,7 @@ TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) {
       HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
-          ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}, {1}));
+          ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}));
   Shape result_shape = ShapeUtil::MakeShape(F32, {0});
   builder.AddInstruction(HloInstruction::CreateConcatenate(
       result_shape, {empty_literal, empty_slice}, 0));
@@ -1132,7 +1132,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) {
           0, ShapeUtil::MakeShape(F32, {dim0, dim1}), "param"));
   builder.AddInstruction(HloInstruction::CreateSlice(
       ShapeUtil::MakeShape(F32, {dim0, dim1}), param, /*start_indices=*/{0, 0},
-      /*limit_indices=*/{dim0, dim1}, /*slices=*/{1, 1}));
+      /*limit_indices=*/{dim0, dim1}));
 
   HloModule module(TestName());
   HloComputation* computation = module.AddEntryComputation(builder.Build());
@@ -1537,7 +1537,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) {
 
   Shape slice_shape = ShapeUtil::MakeShape(F32, {2, 2, 3, 3});
   HloInstruction* slice = builder.AddInstruction(HloInstruction::CreateSlice(
-      slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}, {1, 1, 1, 1}));
+      slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}));
 
   HloModule module(TestName());
   auto computation = module.AddEntryComputation(builder.Build());
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 56568fd446..c498b86dd4 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -731,7 +731,7 @@ TEST_F(BufferAssignmentTest, ReuseNonOperandBuffer) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
 
@@ -763,7 +763,7 @@ TEST_F(BufferAssignmentTest, NoReuseLiveBuffer) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
   builder.AddInstruction(HloInstruction::CreateTuple({negate, broadcast}));
@@ -800,7 +800,7 @@ TEST_F(BufferAssignmentTest, NoReuseAliasedBuffer) {
   auto tuple_element = builder.AddInstruction(
       HloInstruction::CreateGetTupleElement(f32vec100_, tuple, 0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}, {1}));
+      HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
   builder.AddInstruction(HloInstruction::CreateTuple({tuple, broadcast}));
@@ -835,7 +835,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBuffer) {
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   // Slice output is 10 elements.
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));
@@ -867,7 +867,7 @@ TEST_F(BufferAssignmentTest, ReuseOutputBufferIfExactlySized) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 10}), slice, {0}));
@@ -904,7 +904,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBufferInTuple) {
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   // Slice output is 10 elements.
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));
diff --git a/tensorflow/compiler/xla/service/buffer_liveness_test.cc b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
index a5f7cc0aeb..a31e9b1782 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
@@ -588,7 +588,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
     if (update_uses_tuple_element1) {
       // Create a slice instruction as an additional user of 'gte1'.
       slice = builder.AddInstruction(
-          HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}, {1}));
+          HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}));
       update = builder.AddInstruction(HloInstruction::CreateBinary(
           update_shape, HloOpcode::kAdd, update, slice));
     }
diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h
index 0a1911cbd1..dd00c58240 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.h
+++ b/tensorflow/compiler/xla/service/compile_only_service.h
@@ -55,7 +55,7 @@ class CompileOnlyService : public Service {
 
   // Override Service methods that require or imply the existence of an
   // execute backend.  Note that this does not include TransferToClient, as
-  // computing constants produces global data that we may wish to transfer.
+  // computing contants produces global data that we may wish to transfer.
   tensorflow::Status Execute(const ExecuteRequest* arg,
                              ExecuteResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
diff --git a/tensorflow/compiler/xla/service/computation_placer.cc b/tensorflow/compiler/xla/service/computation_placer.cc
index cdfa30dd9a..cdf277581f 100644
--- a/tensorflow/compiler/xla/service/computation_placer.cc
+++ b/tensorflow/compiler/xla/service/computation_placer.cc
@@ -49,18 +49,17 @@ Status DeviceAssignment::Serialize(DeviceAssignmentProto* proto) const {
   return Status::OK();
 }
 
-/* static */ StatusOr<std::unique_ptr<DeviceAssignment>>
-DeviceAssignment::Deserialize(const DeviceAssignmentProto& proto) {
+/* static */ StatusOr<DeviceAssignment> DeviceAssignment::Deserialize(
+    const DeviceAssignmentProto& proto) {
   TF_RET_CHECK(proto.computation_devices_size() == proto.computation_count());
-  auto assignment = MakeUnique<DeviceAssignment>(proto.replica_count(),
-                                                 proto.computation_count());
+  DeviceAssignment assignment(proto.replica_count(), proto.computation_count());
   for (int computation = 0; computation < proto.computation_count();
        ++computation) {
     const auto& computation_device = proto.computation_devices(computation);
     TF_RET_CHECK(computation_device.replica_device_ids_size() ==
                  proto.replica_count());
     for (int replica = 0; replica < proto.replica_count(); ++replica) {
-      (*assignment)(replica, computation) =
+      assignment(replica, computation) =
           computation_device.replica_device_ids(replica);
     }
   }
diff --git a/tensorflow/compiler/xla/service/computation_placer.h b/tensorflow/compiler/xla/service/computation_placer.h
index 7d9abcd100..4d26d6bb85 100644
--- a/tensorflow/compiler/xla/service/computation_placer.h
+++ b/tensorflow/compiler/xla/service/computation_placer.h
@@ -49,11 +49,7 @@ class DeviceAssignment : public Array2D<int> {
 
   // Protocol buffer serialization and deserialization.
   Status Serialize(DeviceAssignmentProto* proto) const;
-
-  // Return a std::unique_ptr<DeviceAssignment> instead of a DeviceAssignment
-  // directly because one of the supported TF platforms (mac) does not compile
-  // due to a StatusOr of an incomplete type (DeviceAssignment).
-  static StatusOr<std::unique_ptr<DeviceAssignment>> Deserialize(
+  static StatusOr<DeviceAssignment> Deserialize(
       const DeviceAssignmentProto& proto);
 };
 
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 759d27e1f3..da8d983e1a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -359,6 +359,7 @@ Status AppendIRToFile(const string& file_name, const string& ir_module_string) {
 StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     std::unique_ptr<HloModule> module, HloDumper dump_hlo,
     se::StreamExecutor* stream_exec) {
+  VLOG(1) << "Compiling: " << module->name();
   TF_RET_CHECK(stream_exec != nullptr);
   std::call_once(llvm_command_line_options_initialized,
                  &InitializeLLVMCommandLineOptions, module->config());
@@ -403,6 +404,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
       module->config().debug_options().xla_dump_debug_json_to();
 
   if (CpuParallelBackendRequested(module->config())) {
+    VLOG(1) << "Using parallel cpu backend";
+
     // Run buffer analysis on the HLO graph. This analysis figures out which
     // temporary buffers are required to run the computation.
     // DependencyHloOrdering is used for the parallel emitter because the order
@@ -497,6 +500,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
           .set_ir_module_string(ir_module_string);
     }
   } else {
+    VLOG(1) << "Using sequential cpu backend";
+
     // Select an order for emitting the HLO instructions for each
     // computation. Using this sequence enables tighter buffer liveness analysis
     // and reduced memory usage (as compared to using DependencyHloOrdering).
@@ -562,6 +567,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
   }
 
+  VLOG(1) << "Compilation finished";
   return std::move(cpu_executable);
 }
 
@@ -663,6 +669,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
   std::vector<std::unique_ptr<AotCompilationResult>> results;
   for (size_t i = 0; i < modules.size(); ++i) {
     HloModule* module = modules[i].get();
+    VLOG(1) << "Compiling ahead-of-time: " << module->name();
 
     TF_RETURN_IF_ERROR(RunHloPasses(module, dump_hlo));
 
@@ -741,6 +748,8 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
         std::move(object_file_data), std::move(buffer_sizes),
         result_slice.index()));
   }
+
+  VLOG(1) << "Compilation finished";
   return std::move(results);
 }
 
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index db0a8b36cd..5b21ae3d2a 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -949,20 +949,9 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
                  const IrArray::Index& index) -> StatusOr<llvm::Value*> {
         IrArray::Index sliced_index(index.size());
         for (int i = 0; i < index.size(); ++i) {
-          int64 stride = hlo->slice_stride(i);
-          if (stride != 1) {
-            sliced_index[i] = ir_builder_->CreateAdd(
-                ir_builder_->CreateMul(
-                    index[i], llvm::ConstantInt::get(index[i]->getType(),
-                                                     stride)),
-                llvm::ConstantInt::get(index[i]->getType(),
-                                       hlo->slice_starts(i)));
-          } else {
-            sliced_index[i] = ir_builder_->CreateAdd(
-                    index[i],
-                    llvm::ConstantInt::get(index[i]->getType(),
-                                           hlo->slice_starts(i)));
-          }
+          sliced_index[i] = ir_builder_->CreateAdd(
+              index[i], llvm::ConstantInt::get(index[i]->getType(),
+                                               hlo->slice_starts(i)));
         }
         return operand_to_generator.at(hlo->operand(0))(sliced_index);
       };
diff --git a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
index b8c6162084..4e130de311 100644
--- a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
+++ b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
@@ -80,7 +80,6 @@ HloInstruction* MaybePaddedAndSlicedInput(
     std::vector<int64> start_indices(input->shape().dimensions_size(), 0);
     std::vector<int64> limit_indices(input->shape().dimensions().begin(),
                                      input->shape().dimensions().end());
-    std::vector<int64> strides(input->shape().dimensions_size(), 1);
     for (size_t i = 0; i < conv_dnums.spatial_dimensions().size(); ++i) {
       int64 dim = conv_dnums.spatial_dimensions(i);
       // If dimension "dim" has negative padding, increase the start index or
@@ -93,9 +92,9 @@ HloInstruction* MaybePaddedAndSlicedInput(
 
     input = computation->AddInstruction(HloInstruction::CreateSlice(
         ShapeInference::InferSliceShape(input->shape(), start_indices,
-                                        limit_indices, strides)
+                                        limit_indices)
             .ConsumeValueOrDie(),
-        input, start_indices, limit_indices, strides));
+        input, start_indices, limit_indices));
   }
 
   return input;
@@ -355,8 +354,6 @@ bool PadInsertion::CanonicalizeBackwardInputConvolution(
   std::vector<int64> limit_indices(
       new_backward_conv->shape().dimensions().begin(),
       new_backward_conv->shape().dimensions().end());
-  std::vector<int64> strides(new_backward_conv->shape().dimensions_size(),
-                             1LL);
   for (size_t i = 0; i < backward_conv->window().dimensions_size(); ++i) {
     int64 padding_low = backward_conv->window().dimensions(i).padding_low();
     int64 padding_high = backward_conv->window().dimensions(i).padding_high();
@@ -376,13 +373,13 @@ bool PadInsertion::CanonicalizeBackwardInputConvolution(
   // Replace the old backward convolution with the slice.
   CHECK(ShapeUtil::Compatible(
       ShapeInference::InferSliceShape(new_backward_conv->shape(), start_indices,
-                                      limit_indices, strides)
+                                      limit_indices)
           .ConsumeValueOrDie(),
       backward_conv->shape()));
   TF_CHECK_OK(computation->ReplaceWithNewInstruction(
       backward_conv,
       HloInstruction::CreateSlice(backward_conv->shape(), new_backward_conv,
-                                  start_indices, limit_indices, strides)));
+                                  start_indices, limit_indices)));
   return true;
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
index 1c60b06ddd..a643bc4076 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
@@ -147,7 +147,6 @@ TEST_F(HloConstantFoldingTest, Slice) {
   const int64 dimensions[] = {11, 8, 7, 5, 9};
   const int64 slice_start[] = {4, 2, 3, 1, 5};
   const int64 slice_limits[] = {10, 8, 6, 5, 9};
-  const int64 slice_strides[] = {1, 1, 1, 1, 1};
   TF_ASSIGN_OR_ASSERT_OK(auto literal,
                          LiteralTestUtil::CreateRandomLiteral<F32>(
                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
@@ -155,7 +154,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
       HloInstruction::CreateConstant(std::move(literal)));
   Shape shape = ShapeUtil::MakeShape(F32, {6, 6, 3, 4, 4});
   builder.AddInstruction(HloInstruction::CreateSlice(
-      shape, literal_instruction, slice_start, slice_limits, slice_strides));
+      shape, literal_instruction, slice_start, slice_limits));
   auto module = CreateNewModule();
   auto computation = module->AddEntryComputation(builder.Build());
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 9117ab9653..99b73dea29 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -306,13 +306,11 @@ HloInstruction::CreateCrossReplicaSum(const Shape& shape,
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateSlice(
     const Shape& shape, HloInstruction* operand,
     tensorflow::gtl::ArraySlice<int64> start_indices,
-    tensorflow::gtl::ArraySlice<int64> limit_indices,
-    tensorflow::gtl::ArraySlice<int64> strides) {
+    tensorflow::gtl::ArraySlice<int64> limit_indices) {
   auto instruction = WrapUnique(new HloInstruction(HloOpcode::kSlice, shape));
   instruction->AppendOperand(operand);
   instruction->slice_starts_.assign(start_indices.begin(), start_indices.end());
   instruction->slice_limits_.assign(limit_indices.begin(), limit_indices.end());
-  instruction->slice_strides_.assign(strides.begin(), strides.end());
   return instruction;
 }
 
@@ -854,8 +852,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CreateReshape(shape, new_operands[0]);
     case HloOpcode::kSlice:
       CHECK_EQ(new_operands.size(), 1);
-      return CreateSlice(shape, new_operands[0], slice_starts_, slice_limits_,
-                         slice_strides_);
+      return CreateSlice(shape, new_operands[0], slice_starts_, slice_limits_);
     case HloOpcode::kDynamicSlice:
       return CreateDynamicSlice(shape, new_operands[0], new_operands[1],
                                 dynamic_slice_sizes_);
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index d29c0935fc..37cbb0b769 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -174,8 +174,7 @@ class HloInstruction {
   static std::unique_ptr<HloInstruction> CreateSlice(
       const Shape& shape, HloInstruction* operand,
       tensorflow::gtl::ArraySlice<int64> start_indices,
-      tensorflow::gtl::ArraySlice<int64> limit_indices,
-      tensorflow::gtl::ArraySlice<int64> strides);
+      tensorflow::gtl::ArraySlice<int64> limit_indices);
 
   // Creates a slice instruction, where the first operand is sliced by
   // start indices specified in the second operand, and by size specfied in
@@ -663,15 +662,6 @@ class HloInstruction {
     return slice_limits_;
   }
 
-  // Returns the stride in the given dimension for a slice node.
-  //
-  // Precondition: opcode() == HloOpcode::kSlice
-  int64 slice_stride(int64 dimension) const {
-    CHECK_EQ(HloOpcode::kSlice, opcode_);
-    return slice_strides_[dimension];
-  }
-  const std::vector<int64>& slice_strides() const { return slice_strides_; }
-
   // Returns the size of the slice in the given dimension for a dynamic
   // slice node.
   //
@@ -917,7 +907,6 @@ class HloInstruction {
   // Describes the [begin, end) index range for a slice.
   std::vector<int64> slice_starts_;
   std::vector<int64> slice_limits_;
-  std::vector<int64> slice_strides_;
 
   // The bit sizes for a reduce-precision operation.
   int32 exponent_bits_;
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
index 1a861cd16b..8a1e705711 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
@@ -67,8 +67,7 @@ class HloRematerializationTest : public HloTestBase {
         /*dimension=*/0));
     auto slice_1 = builder.AddInstruction(HloInstruction::CreateSlice(
         vec1_shape_, concat_1, /*start_indices=*/{0},
-        /*limit_indices=*/{1},
-        /*strides=*/{1}));
+        /*limit_indices=*/{1}));
     auto concat_2 = builder.AddInstruction(HloInstruction::CreateConcatenate(
         ShapeUtil::MakeShape(xla::F32, {1025}), {bcast, slice_1},
         /*dimension=*/0));
@@ -76,8 +75,7 @@ class HloRematerializationTest : public HloTestBase {
     // which is necessary to use this computation in a while.
     builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat_2,
                                                        /*start_indices=*/{0},
-                                                       /*limit_indices=*/{1},
-                                                       /*strides=*/{1}));
+                                                       /*limit_indices=*/{1}));
     return builder.Build();
   }
 
@@ -105,8 +103,7 @@ class HloRematerializationTest : public HloTestBase {
         HloInstruction::CreateBroadcast(vec1024_shape_, param, {}));
     auto slice_1 = builder.AddInstruction(
         HloInstruction::CreateSlice(vec1_shape_, bcast, /*start_indices=*/{0},
-                                    /*limit_indices=*/{1},
-                                    /*strides=*/{1}));
+                                    /*limit_indices=*/{1}));
     auto while_inst = builder.AddInstruction(HloInstruction::CreateWhile(
         vec1_shape_, while_cond, while_body, slice_1));
     auto concat = builder.AddInstruction(HloInstruction::CreateConcatenate(
@@ -114,8 +111,7 @@ class HloRematerializationTest : public HloTestBase {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat,
                                                        /*start_indices=*/{0},
-                                                       /*limit_indices=*/{1},
-                                                       /*strides=*/{1}));
+                                                       /*limit_indices=*/{1}));
     return builder.Build();
   }
 
@@ -357,7 +353,7 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}, /*slices=*/{1}));
+        /*limit_indices=*/{1024}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
@@ -473,7 +469,7 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}, /*slices=*/{1}));
+        /*limit_indices=*/{1024}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index bcc9418d59..e348511c62 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -356,26 +356,9 @@ void EmitLogging(const char* tag, llvm::Value* value,
 
 void SetTbaaForInstruction(llvm::Instruction* instruction, Shape shape,
                            bool is_pointer_to) {
-  llvm::MDBuilder metadata_builder(instruction->getContext());
-  llvm::MDNode* root = metadata_builder.createTBAARoot("XLA TBAA");
-  string type_name;
-  if (is_pointer_to) {
-    type_name += "pointer-to ";
-  }
-  // Scalars do not have layout which makes it permissible to omit an explicit
-  // layout.  To make sure that equivalent scalar shapes have the same TBAA,
-  // remove the (meaningless) explicit layout if one is present.
-  if (!ShapeUtil::IsArray(shape) || ShapeUtil::IsScalar(shape)) {
-    LayoutUtil::ClearLayout(&shape);
-  } else {
-    CHECK(shape.has_layout());
-  }
-  type_name += shape.ShortDebugString();
-  llvm::MDNode* tbaa_node =
-      metadata_builder.createTBAANode(llvm_ir::AsStringRef(type_name), root);
-  instruction->setMetadata(llvm::LLVMContext::MD_tbaa,
-                           metadata_builder.createTBAAStructTagNode(
-                               tbaa_node, tbaa_node, /*Offset=*/0));
+  // TODO(b/62903316): TBAA metadata causes LLVM to miscompile generated code,
+  // most likely because the generated metadata is incorrect.  Disable TBAA
+  // metadata while we resolve this.
 }
 
 void SetAlignmentMetadataForLoad(llvm::LoadInst* load, uint64_t alignment) {
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 5e4df9ddd6..b332709995 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -1135,8 +1135,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
 /* static */ StatusOr<Shape> ShapeInference::InferSliceShape(
     const Shape& arg, tensorflow::gtl::ArraySlice<int64> starts,
-    tensorflow::gtl::ArraySlice<int64> limits,
-    tensorflow::gtl::ArraySlice<int64> strides) {
+    tensorflow::gtl::ArraySlice<int64> limits) {
   TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of slice"));
   VLOG(2) << tensorflow::strings::Printf(
       "slicing shape %s starts={%s} limits={%s}",
@@ -1159,13 +1158,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   for (int64 dimension = 0; dimension < starts.size(); ++dimension) {
     int64 start_index = starts[dimension];
     int64 limit_index = limits[dimension];
-    int64 stride = strides[dimension];
     if (start_index < 0) {
       return InvalidArgument("negative start index to slice: %lld",
                              start_index);
     }
-    if (stride == 0) {
-      return InvalidArgument("Zero stride");
+    if (limit_index < 0) {
+      return InvalidArgument("negative limit index to slice: %lld",
+                             limit_index);
     }
     if (limit_index > arg.dimensions(dimension)) {
       return InvalidArgument(
@@ -1173,21 +1172,18 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
           "size (%lld)",
           limit_index, arg.dimensions(dimension));
     }
+    if (start_index > limit_index) {
+      return InvalidArgument(
+          "limit index (%lld) must be greater or equal to "
+          "start index (%lld) in slice",
+          limit_index, start_index);
+    }
     VLOG(2) << tensorflow::strings::Printf("starts[%lld] = %lld", dimension,
                                            start_index);
     VLOG(2) << tensorflow::strings::Printf("limits[%lld] = %lld", dimension,
                                            limit_index);
-    if (stride > 0) {
-      if (start_index > limit_index) {
-        return InvalidArgument(
-            "limit index (%lld) must be greater or equal to "
-            "start index (%lld) in slice with positive stride",
-            limit_index, start_index);
-      }
-      sizes.push_back((limit_index - start_index + stride - 1) / stride);
-    } else {
-      return InvalidArgument("Negative strides not supported");
-    }
+
+    sizes.push_back(limits[dimension] - starts[dimension]);
   }
 
   return ShapeUtil::MakeShape(arg.element_type(), sizes);
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index 42e4c7d39d..55c60e149d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -116,8 +116,7 @@ class ShapeInference {
   // e.g. slice f32[32x32] 0:16 0:16 -> f32[16x16]
   static StatusOr<Shape> InferSliceShape(
       const Shape& arg, tensorflow::gtl::ArraySlice<int64> starts,
-      tensorflow::gtl::ArraySlice<int64> limits,
-      tensorflow::gtl::ArraySlice<int64> strides);
+      tensorflow::gtl::ArraySlice<int64> limits);
 
   // Infers the shape produced by a dynamic slice operation of size specified
   // in 'slice_sizes', with dynamic start indices shape 'start_indices_shape'.
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc
index 8c731ae297..7cff042a48 100644
--- a/tensorflow/compiler/xla/service/shape_inference_test.cc
+++ b/tensorflow/compiler/xla/service/shape_inference_test.cc
@@ -682,43 +682,16 @@ TEST_F(ReduceShapeInferenceTest, ErrorElementTypeVsApplyType) {
 TEST_F(ShapeInferenceTest, InferSliceShapeRank2) {
   Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
   auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64}, {1, 1});
+      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64});
   ASSERT_IS_OK(inferred_status.status());
   Shape inferred = inferred_status.ValueOrDie();
   ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {32, 64}), inferred));
 }
 
-TEST_F(ShapeInferenceTest, InferSliceShapeRank2WithStrides) {
-  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
-  auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64}, {2, 4});
-  ASSERT_IS_OK(inferred_status.status());
-  Shape inferred = inferred_status.ValueOrDie();
-  ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {16, 16}), inferred));
-}
-
-TEST_F(ShapeInferenceTest, InferSliceShapeRank2WithStridesNotIntegral) {
-  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
-  auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {15, 0}, {20, 13}, {2, 4});
-  ASSERT_IS_OK(inferred_status.status());
-  Shape inferred = inferred_status.ValueOrDie();
-  ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {3, 4}), inferred));
-}
-
-TEST_F(ShapeInferenceTest, InferInvalidStride) {
-  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
-  auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2}, {0, 1});
-  ASSERT_FALSE(inferred_status.ok());
-  ASSERT_EQ(tensorflow::error::INVALID_ARGUMENT,
-            inferred_status.status().code());
-}
-
 TEST_F(ShapeInferenceTest, InferOobSliceShapeRank2) {
   Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
   auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2}, {1, 1});
+      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2});
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_EQ(tensorflow::error::INVALID_ARGUMENT,
             inferred_status.status().code());
@@ -727,7 +700,7 @@ TEST_F(ShapeInferenceTest, InferOobSliceShapeRank2) {
 TEST_F(ShapeInferenceTest, InferSliceShapeRank1) {
   Shape vector_shape = ShapeUtil::MakeShape(F32, {17});
   auto inferred_status =
-      ShapeInference::InferSliceShape(vector_shape, {2}, {4}, {1});
+      ShapeInference::InferSliceShape(vector_shape, {2}, {4});
   ASSERT_TRUE(inferred_status.ok());
   Shape inferred = inferred_status.ValueOrDie();
   ASSERT_TRUE(ShapeUtil::Equal(inferred, ShapeUtil::MakeShape(F32, {2})));
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
index cd79e63caf..d25e5adee3 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
@@ -584,7 +584,7 @@ class FusionPointsToAnalysisTest : public TuplePointsToAnalysisTest {
     if (add_additional_gte0_user) {
       // Create 'slice' as an additional user of 'input'.
       auto slice = builder.AddInstruction(
-          HloInstruction::CreateSlice(update_shape, input, {0}, {3}, {1}));
+          HloInstruction::CreateSlice(update_shape, input, {0}, {3}));
       // Modify 'update' to take 'slice' output.
       update = builder.AddInstruction(HloInstruction::CreateBinary(
           update_shape, HloOpcode::kAdd, update, slice));
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 92b8c7bb21..1f6e789379 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -744,8 +744,7 @@ StatusOr<ComputationDataHandle> UserComputation::AddSliceInstruction(
       Shape new_shape,
       ShapeInference::InferSliceShape(
           operand->output_shape(), AsInt64Slice(slice_request.start_indices()),
-          AsInt64Slice(slice_request.limit_indices()),
-          AsInt64Slice(slice_request.stride())));
+          AsInt64Slice(slice_request.limit_indices())));
 
   ComputationDataHandle handle = CreateComputationDataHandle();
 
@@ -2394,8 +2393,7 @@ void ComputationLowerer::Visit(
       hlo_instruction = add_instruction(HloInstruction::CreateSlice(
           request.output_shape(), operand,
           AsInt64Slice(slice_request.start_indices()),
-          AsInt64Slice(slice_request.limit_indices()),
-          AsInt64Slice(slice_request.stride())));
+          AsInt64Slice(slice_request.limit_indices())));
       break;
     }
 
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 024988743c..bb7fbad000 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1853,7 +1853,7 @@ TEST_F(ArrayElementwiseOpTest, ImplictBroadcastInFusedExpressions) {
 
   auto x = builder.Parameter(0, x_literal->shape(), "x");
   auto y = builder.Parameter(1, y_literal->shape(), "y");
-  auto slice = builder.Slice(x, {1}, {2}, {1});
+  auto slice = builder.Slice(x, {1}, {2});
   builder.Sub(slice, y);
 
   ComputeAndCompareR1<float>(&builder, {-2, -3}, {x_data.get(), y_data.get()},
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 63a630f9e5..7abef6a27b 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -365,9 +365,9 @@ XLA_TEST_F(DotOperationTest, BatchMatMul) {
   std::vector<xla::ComputationDataHandle> out_slices;
   for (int i = 0; i < 4; ++i) {
     // Slice off individual matrices and reshape to 2D tensors.
-    auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1});
+    auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2});
     x_slice = builder.Reshape(x_slice, {0, 1, 2}, {2, 2});
-    auto y_slice = builder.Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1});
+    auto y_slice = builder.Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2});
     y_slice = builder.Reshape(y_slice, {0, 1, 2}, {2, 2});
 
     auto out = builder.Dot(x_slice, y_slice);
diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index 7803d234fd..c8b91eafc7 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -210,7 +210,7 @@ XLA_TEST_F(FusionTest, Test) {
       HloInstruction::CreateTernary(ShapeUtil::MakeShape(F32, {2, 3}),
                                     HloOpcode::kSelect, const10, add8, const9));
   auto slice12 = builder.AddInstruction(HloInstruction::CreateSlice(
-      ShapeUtil::MakeShape(F32, {2, 1}), select11, {0, 1}, {2, 2}, {1, 1}));
+      ShapeUtil::MakeShape(F32, {2, 1}), select11, {0, 1}, {2, 2}));
   // CreateFusionInstruction needs the `instructions_to_fuse` argument in
   // reverse topological order, so the first element in `instructions_to_fuse`
   // must be the root.
diff --git a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
index 56c15e5ff7..df3d4fa21d 100644
--- a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
+++ b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
@@ -36,7 +36,7 @@ XLA_TEST_F(SliceTest, Slice2D) {
   ComputationBuilder builder(client_, "slice_2d");
   auto original = builder.ConstantR2<float>(
       {{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}, {7.0, 8.0, 9.0}, {10.0, 11.0, 12.0}});
-  builder.Slice(original, {2, 1}, {4, 3}, {1, 1});
+  builder.Slice(original, {2, 1}, {4, 3});
 
   Array2D<float> expected({{8.0f, 9.0f}, {11.0f, 12.0f}});
   ComputeAndCompareR2<float>(&builder, expected, {}, ErrorSpec(0.000001));
@@ -47,7 +47,7 @@ XLA_TEST_F(SliceTest, Slice3D) {
   Array3D<float> array_3d(
       {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}});
   auto original = builder.ConstantR3FromArray3D<float>(array_3d);
-  builder.Slice(original, {0, 0, 1}, {2, 1, 2}, {1, 1, 1});
+  builder.Slice(original, {0, 0, 1}, {2, 1, 2});
 
   Array3D<float> expected_3d({{{2.0f}}, {{6.0f}}});
   ComputeAndCompareR3<float>(&builder, expected_3d, {}, ErrorSpec(0.000001));
diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc
index a7692fceb4..2065e9e813 100644
--- a/tensorflow/compiler/xla/tests/params_test.cc
+++ b/tensorflow/compiler/xla/tests/params_test.cc
@@ -325,7 +325,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) {
   ComputationBuilder builder(client_, TestName());
   auto input = builder.Parameter(0, original, "input");
   // Use the slice operator to get an off-diagonal element.
-  builder.Slice(input, {0, 1}, {1, 2}, {1, 1});
+  builder.Slice(input, {0, 1}, {1, 2});
 
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index 5e7d475662..97120df0c5 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -44,7 +44,7 @@ class SliceTest : public ClientLibraryTestBase {
 
     ComputationBuilder builder(client_, TestName());
     auto original = builder.ConstantR1<NativeT>(constant);
-    builder.Slice(original, {2}, {4}, {1});
+    builder.Slice(original, {2}, {4});
 
     const std::vector<NativeT> expected = {static_cast<NativeT>(2),
                                            static_cast<NativeT>(3)};
@@ -55,7 +55,7 @@ class SliceTest : public ClientLibraryTestBase {
 XLA_TEST_F(SliceTest, SliceZeroToZeroF32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>({});
-  builder.Slice(original, {0}, {0}, {1});
+  builder.Slice(original, {0}, {0});
 
   ComputeAndCompareR1<float>(&builder, {}, {});
 }
@@ -64,7 +64,7 @@ XLA_TEST_F(SliceTest, SliceTenToZeroF32) {
   ComputationBuilder builder(client_, TestName());
   std::vector<float> constant(10, 0.3);
   auto original = builder.ConstantR1<float>(constant);
-  builder.Slice(original, {7}, {7}, {1});
+  builder.Slice(original, {7}, {7});
 
   ComputeAndCompareR1<float>(&builder, {}, {});
 }
@@ -87,7 +87,7 @@ TEST_F(SliceTest, SliceTenToTen) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {0}, {10}, {1});
+  builder.Slice(original, {0}, {10});
 
   ComputeAndCompareR1<float>(&builder, values, {}, ErrorSpec(0.000001));
 }
@@ -98,7 +98,7 @@ TEST_F(SliceTest, SliceLastFourOf1024) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {1024 - 4}, {1024}, {1});
+  builder.Slice(original, {1024 - 4}, {1024});
 
   const std::vector<float> expected = {1020, 1021, 1022, 1023};
   ComputeAndCompareR1<float>(&builder, expected, {}, ErrorSpec(0.000001));
@@ -112,7 +112,7 @@ TEST_F(SliceTest, DISABLED_SliceUnaligned1024In4096Values) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {7}, {7 + 1024}, {1});
+  builder.Slice(original, {7}, {7 + 1024});
 
   std::vector<float> expected(1024);
   std::iota(values.begin(), values.end(), 7.0);
@@ -122,7 +122,7 @@ TEST_F(SliceTest, DISABLED_SliceUnaligned1024In4096Values) {
 XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 0));
-  builder.Slice(original, {0, 0}, {0, 0}, {1, 1});
+  builder.Slice(original, {0, 0}, {0, 0});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 0), {});
 }
@@ -130,7 +130,7 @@ XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
 XLA_TEST_F(SliceTest, Slice0x20to0x5F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 20));
-  builder.Slice(original, {0, 15}, {0, 20}, {1, 1});
+  builder.Slice(original, {0, 15}, {0, 20});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 5), {});
 }
@@ -138,7 +138,7 @@ XLA_TEST_F(SliceTest, Slice0x20to0x5F32) {
 XLA_TEST_F(SliceTest, Slice3x0to2x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(3, 0));
-  builder.Slice(original, {1, 0}, {3, 0}, {1, 1});
+  builder.Slice(original, {1, 0}, {3, 0});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(2, 0), {});
 }
@@ -153,7 +153,7 @@ XLA_TEST_F(SliceTest, SliceQuadrantOf256x256) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {128, 128}, {256, 256}, {1, 1});
+  builder.Slice(original, {128, 128}, {256, 256});
 
   Array2D<float> expected(128, 128);
   for (int row = 0; row < 128; ++row) {
@@ -171,7 +171,7 @@ TEST_F(SliceTest, Slice_1x4096_To_1x1024) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {0, 3072}, {1, 4096}, {1, 1});
+  builder.Slice(original, {0, 3072}, {1, 4096});
 
   Array2D<float> expected(1, 1024);
   std::iota(expected.data(), expected.data() + 1024, 3072.0);
@@ -192,7 +192,7 @@ TEST_F(SliceTest, Slice_16x4_To_16x2) {
   }
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {0, 0}, {16, 2}, {1, 1});
+  builder.Slice(original, {0, 0}, {16, 2});
   ComputeAndCompareR2<float>(&builder, expected, {}, ErrorSpec(0.000001));
 }
 
@@ -204,7 +204,7 @@ TEST_F(SliceTest, SliceR4ThreeDimsMiddleMinor) {
       ReferenceUtil::Slice4D(values, {{1, 0, 8, 0}}, {{2, 2, 16, 128}});
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR4FromArray4D(values);
-  builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128}, {1, 1, 1, 1});
+  builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128});
   ComputeAndCompareR4(&builder, *expected, {}, ErrorSpec(0.000001));
 }
 
@@ -213,7 +213,6 @@ struct R2Spec {
   int64 input_dim1;
   std::array<int64, 2> slice_starts;
   std::array<int64, 2> slice_limits;
-  std::array<int64, 2> slice_strides;
   Layout layout;
 };
 
@@ -229,7 +228,7 @@ TEST_P(SliceR2Test, DoIt) {
 
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR2FromArray2D<int32>(input);
-  builder.Slice(a, spec.slice_starts, spec.slice_limits, spec.slice_strides);
+  builder.Slice(a, spec.slice_starts, spec.slice_limits);
 
   std::unique_ptr<Array2D<int32>> expected =
       ReferenceUtil::Slice2D(input, spec.slice_starts, spec.slice_limits);
@@ -240,23 +239,19 @@ TEST_P(SliceR2Test, DoIt) {
 INSTANTIATE_TEST_CASE_P(
     SliceR2TestInstantiation, SliceR2Test,
     ::testing::Values(
-        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, {{1, 1}},
-          LayoutUtil::MakeLayout({0, 1})},
-        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, {{1, 1}},
+        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {256, 400, {{0, 300}}, {{256, 400}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, {{1, 1}},
-          LayoutUtil::MakeLayout({0, 1})},
-        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, {{1, 1}},
+        R2Spec {500, 400, {{111, 123}}, {{300, 257}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {256, 400, {{0, 300}}, {{256, 400}}, {{1, 1}},
+        R2Spec {500, 400, {{111, 123}}, {{300, 400}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {500, 400, {{111, 123}}, {{300, 257}}, {{1, 1}},
+        R2Spec {384, 512, {{128, 256}}, {{256, 384}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {500, 400, {{111, 123}}, {{300, 400}}, {{1, 1}},
-          LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {384, 512, {{128, 256}}, {{256, 384}}, {{1, 1}},
-          LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {357, 512, {{111, 256}}, {{301, 384}}, {{1, 1}},
+        R2Spec {357, 512, {{111, 256}}, {{301, 384}},
           LayoutUtil::MakeLayout({1, 0})}
     )
 );
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index afa7d871c0..ccd2a95658 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -666,8 +666,7 @@ TEST_F(WhileTest, WhileWithPrngScalarResult) {
   auto build_condition = [this, v6s32](int count) {
     ComputationBuilder builder(client_, TestName());
     auto prev = builder.Reshape(
-        builder.Slice(builder.Parameter(0, v6s32, "prev"), {0}, {1}, {1}), {0},
-          {});
+        builder.Slice(builder.Parameter(0, v6s32, "prev"), {0}, {1}), {0}, {});
     builder.Gt(builder.ConstantR0<int32>(count), prev);
     return builder.Build().ConsumeValueOrDie();
   };
diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 31f0c3147e..42d5c1d155 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -195,24 +195,16 @@ bool IsPermutation(tensorflow::gtl::ArraySlice<int64> permutation, int64 rank);
 // 2. permutation.size() == input.size().
 template <template <typename...> class C, typename T>
 std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
-                       C<T> input) {
-  tensorflow::gtl::ArraySlice<T> data(input);
-  CHECK(IsPermutation(permutation, data.size()));
-  std::vector<T> output(data.size());
+                       C<T> input_) {
+  tensorflow::gtl::ArraySlice<T> input(input_);
+  CHECK(IsPermutation(permutation, input.size()));
+  std::vector<T> output(input.size());
   for (size_t i = 0; i < permutation.size(); ++i) {
-    output[permutation[i]] = data[i];
+    output[permutation[i]] = input[i];
   }
   return output;
 }
 
-// Override of the above that works around compile failures with gcc 7.1.1.
-// For details see https://github.com/tensorflow/tensorflow/issues/10843
-template <typename T>
-std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
-                       const std::vector<T>& input) {
-  return Permute<std::vector, T>(permutation, input);
-}
-
 // Inverts a permutation, i.e., output_permutation[input_permutation[i]] = i.
 std::vector<int64> InversePermutation(
     tensorflow::gtl::ArraySlice<int64> input_permutation);
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 86c72b3449..95c1f0995b 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -200,7 +200,7 @@ message OpMetadata {
   string op_name = 2;
   // Indicate a file and line that this op is associated to in a user's program.
   //
-  // e.g. it could be the file and line of user code that generated the op.
+  // e.g. it could be be the file and line of user code that generated the op.
   string source_file = 3;
   int32 source_line = 4;
 }
@@ -369,7 +369,6 @@ message SliceRequest {
   ComputationDataHandle operand = 2;
   repeated int64 start_indices = 3;
   repeated int64 limit_indices = 4;
-  repeated int64 stride = 5;
 }
 
 message DynamicSliceRequest {
diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
index b1d18d2faf..1f180429b2 100644
--- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
+++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
@@ -17,7 +17,6 @@ package org.tensorflow.contrib.android;
 
 import android.content.res.AssetManager;
 import android.os.Trace;
-import android.os.Build.VERSION;
 import android.text.TextUtils;
 import android.util.Log;
 import java.io.FileInputStream;
@@ -371,11 +370,9 @@ public class TensorFlowInferenceInterface {
   private void loadGraph(InputStream is, Graph g) throws IOException {
     final long startMs = System.currentTimeMillis();
 
-    if (VERSION.SDK_INT >= 18) {
-      Trace.beginSection("initializeTensorFlow");
-      Trace.beginSection("readGraphDef");
-    }
+    Trace.beginSection("initializeTensorFlow");
 
+    Trace.beginSection("readGraphDef");
     // TODO(ashankar): Can we somehow mmap the contents instead of copying them?
     byte[] graphDef = new byte[is.available()];
     final int numBytesRead = is.read(graphDef);
@@ -386,22 +383,17 @@ public class TensorFlowInferenceInterface {
               + " of the graph, expected to read "
               + graphDef.length);
     }
+    Trace.endSection();
 
-    if (VERSION.SDK_INT >= 18) {
-      Trace.endSection(); // readGraphDef.
-      Trace.beginSection("importGraphDef");
-    }
-
+    Trace.beginSection("importGraphDef");
     try {
       g.importGraphDef(graphDef);
     } catch (IllegalArgumentException e) {
       throw new IOException("Not a valid TensorFlow Graph serialization: " + e.getMessage());
     }
+    Trace.endSection();
 
-    if (VERSION.SDK_INT >= 18) {
-      Trace.endSection(); // importGraphDef.
-      Trace.endSection(); // initializeTensorFlow.
-    }
+    Trace.endSection(); // initializeTensorFlow.
 
     final long endMs = System.currentTimeMillis();
     Log.i(
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
index fabf7a9b77..fba0cc6522 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -627,6 +627,11 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       grad = lambda fs: gradients_impl.gradients(fs, s)[0]
 
       [
+          approx_kl_grad_,
+          approx_kl_self_normalized_grad_,
+          approx_kl_score_trick_grad_,
+          approx_kl_self_normalized_score_trick_grad_,
+          exact_kl_grad_,
           approx_kl_,
           approx_kl_self_normalized_,
           approx_kl_score_trick_,
@@ -638,23 +643,39 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
           grad(approx_kl_score_trick),
           grad(approx_kl_self_normalized_score_trick),
           grad(exact_kl),
+          approx_kl,
+          approx_kl_self_normalized,
+          approx_kl_score_trick,
+          approx_kl_self_normalized_score_trick,
+          exact_kl,
       ])
 
-      self.assertAllClose(
-          approx_kl_, exact_kl_,
-          rtol=0.06, atol=0.)
+      # Test average divergence.
+      self.assertAllClose(approx_kl_, exact_kl_,
+                          rtol=0.02, atol=0.)
 
-      self.assertAllClose(
-          approx_kl_self_normalized_, exact_kl_,
-          rtol=0.05, atol=0.)
+      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
+                          rtol=0.08, atol=0.)
 
-      self.assertAllClose(
-          approx_kl_score_trick_, exact_kl_,
-          rtol=0.06, atol=0.)
+      self.assertAllClose(approx_kl_score_trick_, exact_kl_,
+                          rtol=0.02, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_score_trick_, exact_kl_,
+                          rtol=0.08, atol=0.)
+
+      # Test average gradient-divergence.
+      self.assertAllClose(approx_kl_grad_, exact_kl_grad_,
+                          rtol=0.007, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_grad_, exact_kl_grad_,
+                          rtol=0.011, atol=0.)
+
+      self.assertAllClose(approx_kl_score_trick_grad_, exact_kl_grad_,
+                          rtol=0.018, atol=0.)
 
       self.assertAllClose(
-          approx_kl_self_normalized_score_trick_, exact_kl_,
-          rtol=0.05, atol=0.)
+          approx_kl_self_normalized_score_trick_grad_, exact_kl_grad_,
+          rtol=0.017, atol=0.)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 7b51d8d932..09389e5d38 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -40,8 +40,8 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib import framework as contrib_framework
+from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo
 from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import distribution
@@ -750,7 +750,7 @@ def monte_carlo_csiszar_f_divergence(
   ```none
   D_f[p(X), q(X)] := E_{q(X)}[ f( p(X) / q(X) ) ]
                   ~= m**-1 sum_j^m f( p(x_j) / q(x_j) ),
-                             where x_j ~iid q(x)
+                             where x_j ~iid q(X)
   ```
 
   Tricks: Reparameterization and Score-Gradient
@@ -759,8 +759,8 @@ def monte_carlo_csiszar_f_divergence(
   parameterless distribution (e.g.,
   `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and
   expectation, i.e.,
-  `nabla Avg{ s_i : i=1...n } = Avg{ nabla s_i : i=1...n }` where `S_n=Avg{s_i}`
-  and `s_i = f(x_i), x_i ~ q`.
+  `grad[Avg{ s_i : i=1...n }] = Avg{ grad[s_i] : i=1...n }` where `S_n=Avg{s_i}`
+  and `s_i = f(x_i), x_i ~iid q(X)`.
 
   However, if q is not reparameterized, TensorFlow's gradient will be incorrect
   since the chain-rule stops at samples of unreparameterized distributions. In
@@ -768,22 +768,17 @@ def monte_carlo_csiszar_f_divergence(
   gradient, i.e.,
 
   ```none
-  nabla E_q[f(X)]
-  = nabla int dx q(x) f(x)
-  = int dx nabla [ q(x) f(x) ]
-  = int dx q'(x) f(x) + q(x) f'(x)
+  grad[ E_q[f(X)] ]
+  = grad[ int dx q(x) f(x) ]
+  = int dx grad[ q(x) f(x) ]
+  = int dx [ q'(x) f(x) + q(x) f'(x) ]
   = int dx q(x) [q'(x) / q(x) f(x) + f'(x) ]
-  = int dx q(x) nabla [ log(q(x)) stopgrad[f(x)] + f(x) ]
-  = E_q[ nabla [ log(q(X)) stopgrad[f(X)] + f(X) ] ]
-  ~= Avg{ log(q(y_i)) stopgrad[f(y_i)] + f(y_i) : y_i = stopgrad[x_i], x_i ~ q}
+  = int dx q(x) grad[ f(x) q(x) / stop_grad[q(x)] ]
+  = E_q[ grad[ f(x) q(x) / stop_grad[q(x)] ] ]
   ```
 
   Unless `q.reparameterization_type != distribution.FULLY_REPARAMETERIZED` it is
-  usually preferable to `use_reparametrization = True`.
-
-  Warning: using `use_reparametrization = False` will mean that the result is
-  *not* the Csiszar f-Divergence. However its expected gradient *is* the
-  gradient of the Csiszar f-Divergence.
+  usually preferable to set `use_reparametrization = True`.
 
   Example Application:
 
@@ -817,10 +812,7 @@ def monte_carlo_csiszar_f_divergence(
 
   Returns:
     monte_carlo_csiszar_f_divergence: Floating-type `Tensor` Monte Carlo
-      approximation of the Csiszar f-Divergence. Warning: using
-      `use_reparametrization = False` will mean that the result is *not* the
-      Csiszar f-Divergence. However its expected gradient *is* the actual
-      gradient of the Csiszar f-Divergence.
+      approximation of the Csiszar f-Divergence.
 
   Raises:
     ValueError: if `q` is not a reparameterized distribution and
@@ -831,24 +823,16 @@ def monte_carlo_csiszar_f_divergence(
       to parameters) is valid.
   """
   with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]):
-    x = q.sample(num_draws, seed=seed)
-    if use_reparametrization:
+    if (use_reparametrization and
+        q.reparameterization_type != distribution.FULLY_REPARAMETERIZED):
       # TODO(jvdillon): Consider only raising an exception if the gradient is
       # requested.
-      if q.reparameterization_type != distribution.FULLY_REPARAMETERIZED:
-        raise ValueError(
-            "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
-            "transformation of a parameterless distribution. (Otherwise this "
-            "function has a biased gradient.)")
-      return math_ops.reduce_mean(f(p.log_prob(x) - q.log_prob(x)), axis=0)
-    else:
-      x = array_ops.stop_gradient(x)
-      logqx = q.log_prob(x)
-      fx = f(p.log_prob(x) - logqx)
-      # Alternatively we could have returned:
-      #   reduce_mean(fx * exp(logqx) / stop_gradient(exp(logqx)), axis=0)
-      # This is nice because it means the result is exactly the Csiszar
-      # f-Divergence yet the gradient is unbiased. However its numerically
-      # unstable since the q is not in log-domain.
-      return math_ops.reduce_mean(logqx * array_ops.stop_gradient(fx) + fx,
-                                  axis=0)
+      raise ValueError(
+          "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
+          "transformation of a parameterless distribution. (Otherwise this "
+          "function has a biased gradient.)")
+    return monte_carlo.expectation_v2(
+        f=lambda x: f(p.log_prob(x) - q.log_prob(x)),
+        samples=q.sample(num_draws, seed=seed),
+        log_prob=q.log_prob,
+        use_reparametrization=use_reparametrization)
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index 205b44ac12..9ffe08eded 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -74,7 +74,7 @@ if(WIN32)
   set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP")
-  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob2")
+  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob0")
   set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_MINSIZEREL "${CMAKE_CXX_FLAGS_MINSIZEREL} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /D_ITERATOR_DEBUG_LEVEL=0")
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index bf89e64bfd..a971232e2f 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -204,6 +204,7 @@ add_python_module("tensorflow/python/debug/examples")
 add_python_module("tensorflow/python/debug/lib")
 add_python_module("tensorflow/python/debug/wrappers")
 add_python_module("tensorflow/python/estimator")
+add_python_module("tensorflow/python/estimator/canned")
 add_python_module("tensorflow/python/estimator/export")
 add_python_module("tensorflow/python/estimator/inputs")
 add_python_module("tensorflow/python/estimator/inputs/queues")
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
index 42865ed404..d44e258bd2 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
@@ -120,7 +120,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object):
 
     Doesn't actually do the sqrt! Named as such to agree with API.
 
-    To compute (M + V D V.T), we use the Woodbury matrix identity:
+    To compute (M + V D V.T), we use the the Woodbury matrix identity:
       inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
     where,
       C = inv(D) + V.T inv(M) V.
@@ -166,7 +166,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object):
   def _woodbury_sandwiched_term(self):
     """Computes the sandwiched term in the Woodbury identity.
 
-    Computes the "`C`" in the identity:
+    Computes the "`C`" in the the identity:
        inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
     where,
        C = inv(D) + V.T inv(M) V.
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
index b525809015..5b57a95c55 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
@@ -52,7 +52,7 @@ class RelaxedBernoulli(transformed_distribution.TransformedDistribution):
   the RelaxedBernoulli can suffer from underflow issues. In many case loss
   functions such as these are invariant under invertible transformations of
   the random variables. The KL divergence, found in the variational autoencoder
-  loss, is an example. Because RelaxedBernoullis are sampled by a Logistic
+  loss, is an example. Because RelaxedBernoullis are sampled by by a Logistic
   random variable followed by a `tf.sigmoid` op, one solution is to treat
   the Logistic as the random variable and `tf.sigmoid` as downstream. The
   KL divergences of two Logistics, which are always followed by a `tf.sigmoid`
diff --git a/tensorflow/contrib/distributions/python/ops/sample_stats.py b/tensorflow/contrib/distributions/python/ops/sample_stats.py
index 2a4b92c729..26cf922d0a 100644
--- a/tensorflow/contrib/distributions/python/ops/sample_stats.py
+++ b/tensorflow/contrib/distributions/python/ops/sample_stats.py
@@ -47,7 +47,7 @@ def percentile(x,
   """Compute the `q`-th percentile of `x`.
 
   Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the
-  way from the minimum to the maximum in a sorted copy of `x`.
+  way from the minimum to the maximum in in a sorted copy of `x`.
 
   The values and distances of the two nearest neighbors as well as the
   `interpolation` parameter will determine the percentile if the normalized
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
index fdee57695e..fd2c46d94d 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Vectorized Laplace distribution class, directly using LinearOperator."""
+"""Vectorized Laplace distribution class, directly using LinearOpeartor."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index 2234400fdc..762bc44814 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -446,7 +446,7 @@ class Transformer(object):
       # TODO(fkp): return a subgraph?
       op_, op_outputs_ = self.transform_op_handler(info, op)
       if op is op_:
-        raise ValueError("In-place transformation not allowed.")
+        raise ValueError("In-place tranformation not allowed.")
 
       # Process op.
       info.transformed_ops[op] = op_
diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index 324f510301..9f02fc0958 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -3261,7 +3261,7 @@ def conv2d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/outputs.
+          for inputs/kernels/ouputs.
       dilation_rate: tuple of 2 integers.
 
   Returns:
@@ -3309,7 +3309,7 @@ def conv2d_transpose(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/outputs.
+          for inputs/kernels/ouputs.
 
   Returns:
       A tensor, result of transposed 2D convolution.
@@ -3395,7 +3395,7 @@ def conv3d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/outputs.
+          for inputs/kernels/ouputs.
       dilation_rate: tuple of 3 integers.
 
   Returns:
diff --git a/tensorflow/contrib/keras/python/keras/layers/core.py b/tensorflow/contrib/keras/python/keras/layers/core.py
index 34548c83c5..d287fa56d9 100644
--- a/tensorflow/contrib/keras/python/keras/layers/core.py
+++ b/tensorflow/contrib/keras/python/keras/layers/core.py
@@ -107,7 +107,7 @@ class Dropout(tf_core_layers.Dropout, Layer):
     self.supports_masking = True
     # Inheritance call order:
     # 1) tf.layers.Dropout, 2) keras.layers.Layer, 3) tf.layers.Layer
-    super(Dropout, self).__init__(rate=rate, noise_shape=noise_shape, seed=seed, **kwargs)
+    super(Dropout, self).__init__(**kwargs)
 
   def call(self, inputs, training=None):
     if training is None:
diff --git a/tensorflow/contrib/keras/python/keras/layers/recurrent.py b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
index cdef55f599..5e8c23ed3e 100644
--- a/tensorflow/contrib/keras/python/keras/layers/recurrent.py
+++ b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
@@ -985,7 +985,7 @@ class LSTM(Recurrent):
 
   References:
       - [Long short-term
-        memory](http://www.bioinf.jku.at/publications/older/2604.pdf)
+        memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf)
         (original 1997 paper)
       - [Supervised sequence labeling with recurrent neural
         networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
diff --git a/tensorflow/contrib/keras/python/keras/models_test.py b/tensorflow/contrib/keras/python/keras/models_test.py
index 99fd6e1cbe..50aba43c24 100644
--- a/tensorflow/contrib/keras/python/keras/models_test.py
+++ b/tensorflow/contrib/keras/python/keras/models_test.py
@@ -105,7 +105,7 @@ class TestModelSaving(test.TestCase):
       out2 = model.predict(x)
       self.assertAllClose(out, out2, atol=1e-05)
 
-  def test_functional_model_saving(self):
+  def test_fuctional_model_saving(self):
     if h5py is None:
       return  # Skip test if models cannot be saved.
 
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index d8ff6d6b6a..f2a904b521 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -1467,8 +1467,7 @@ def fully_connected(inputs,
     ValueError: If x has rank less than 2 or if its last dimension is not set.
   """
   if not isinstance(num_outputs, six.integer_types):
-    raise ValueError(
-        'num_outputs should be int or long, got %s.' % (num_outputs,))
+    raise ValueError('num_outputs should be int or long, got %s.', num_outputs)
 
   layer_variable_getter = _build_variable_getter({'bias': 'biases',
                                                   'kernel': 'weights'})
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
index 6b125534a4..b4b552c025 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
@@ -52,8 +52,8 @@ LABEL_DIMENSION = 3  #  Dimensionality of regression labels.
 
 def _train_test_split(features_and_labels):
   features, labels = features_and_labels
-  train_set = (features[:int(len(features) / 2)], labels[:int(len(features) / 2)])
-  test_set = (features[int(len(features) / 2):], labels[int(len(features) / 2):])
+  train_set = (features[:len(features) / 2], labels[:len(features) / 2])
+  test_set = (features[len(features) / 2:], labels[len(features) / 2:])
   return train_set, test_set
 
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
index d3bb0fda57..145d5c40fa 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
@@ -729,7 +729,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.07)
 
   def testSdcaOptimizerRealValuedFeatures(self):
-    """Tests LinearClassifier with SDCAOptimizer and real valued features."""
+    """Tests LinearClasssifier with SDCAOptimizer and real valued features."""
 
     def input_fn():
       return {
@@ -776,7 +776,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerBucketizedFeatures(self):
-    """Tests LinearClassifier with SDCAOptimizer and bucketized features."""
+    """Tests LinearClasssifier with SDCAOptimizer and bucketized features."""
 
     def input_fn():
       return {
@@ -802,7 +802,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerSparseFeatures(self):
-    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -833,7 +833,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerWeightedSparseFeatures(self):
-    """LinearClassifier with SDCAOptimizer and weighted sparse features."""
+    """LinearClasssifier with SDCAOptimizer and weighted sparse features."""
 
     def input_fn():
       return {
@@ -864,7 +864,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerCrossedFeatures(self):
-    """Tests LinearClassifier with SDCAOptimizer and crossed features."""
+    """Tests LinearClasssifier with SDCAOptimizer and crossed features."""
 
     def input_fn():
       return {
@@ -897,7 +897,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerMixedFeatures(self):
-    """Tests LinearClassifier with SDCAOptimizer and a mix of features."""
+    """Tests LinearClasssifier with SDCAOptimizer and a mix of features."""
 
     def input_fn():
       return {
@@ -1509,7 +1509,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
-    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -1581,7 +1581,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
 
   def testSdcaOptimizerBiasOnly(self):
-    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when it's the only feature present.
@@ -1614,7 +1614,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
 
   def testSdcaOptimizerBiasAndOtherColumns(self):
-    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
@@ -1676,7 +1676,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
 
   def testSdcaOptimizerBiasAndOtherColumnsFabricatedCentered(self):
-    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
index 284e2cfd7a..6ebfeb0f16 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
@@ -123,7 +123,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["probabilities"].eval(),
                           regression_output.value.eval())
 
-  def testEstimatorSpec_export_classification(self):
+  def testEstimatorSpec_export_classsification(self):
     predictions = self.create_predictions()
     output_alternatives = {"classification_head": (
         constants.ProblemType.CLASSIFICATION, predictions)}
@@ -143,7 +143,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classification_with_missing_scores(self):
+  def testEstimatorSpec_export_classsification_with_missing_scores(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -165,7 +165,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classification_with_missing_scores_proba(self):
+  def testEstimatorSpec_export_classsification_with_missing_scores_proba(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -187,7 +187,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classification_with_missing_classes(self):
+  def testEstimatorSpec_export_classsification_with_missing_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["classes"]
@@ -208,7 +208,7 @@ class ModelFnopsTest(test.TestCase):
                           classification_output.scores.eval())
       self.assertIsNone(classification_output.classes)
 
-  def testEstimatorSpec_export_classification_with_nonstring_classes(self):
+  def testEstimatorSpec_export_classsification_with_nonstring_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     output_alternatives_predictions["classes"] = constant_op.constant(
diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py
index 4283240d01..234605ff76 100644
--- a/tensorflow/contrib/learn/python/learn/models.py
+++ b/tensorflow/contrib/learn/python/learn/models.py
@@ -63,7 +63,7 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0):
     x: tensor or placeholder for input features.
     y: tensor or placeholder for labels.
     init_mean: the mean value to use for initialization.
-    init_stddev: the standard deviation to use for initialization.
+    init_stddev: the standard devation to use for initialization.
 
   Returns:
     Predictions and loss tensors.
@@ -124,7 +124,7 @@ def logistic_regression(x,
                   will check if graph contains tensor `class_weight:0`.
                   If that is not provided either all ones are used.
     init_mean: the mean value to use for initialization.
-    init_stddev: the standard deviation to use for initialization.
+    init_stddev: the standard devation to use for initialization.
 
   Returns:
     Predictions and loss tensors.
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index ce8518267f..d58b9744ac 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -208,7 +208,7 @@ def index_to_string_table_from_tensor(mapping, default_value="UNK", name=None):
   Sample Usages:
 
   ```python
-  mapping_string = tf.constant(["emerson", "lake", "palmer"])
+  mapping_string = tf.constant(["emerson", "lake", "palmer")
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       mapping_string, default_value="UNKNOWN")
@@ -260,11 +260,7 @@ def index_to_string(tensor, mapping, default_value="UNK", name=None):
   For example:
 
   ```python
-<<<<<<< HEAD
-  mapping_string = tf.constant(["emerson", "lake", "palmer"])
-=======
   mapping_string = tf.constant(["emerson", "lake", "palmer")
->>>>>>> 338a7ead4475d6b97b420d6d1c56ff66815e3e7b
   indices = tf.constant([1, 5], tf.int64)
   values = tf.contrib.lookup.index_to_string(
       indices, mapping=mapping_string, default_value="UNKNOWN")
diff --git a/tensorflow/contrib/makefile/build_with_docker.sh b/tensorflow/contrib/makefile/build_with_docker.sh
index 51a73fafe5..7fe38f4b5d 100755
--- a/tensorflow/contrib/makefile/build_with_docker.sh
+++ b/tensorflow/contrib/makefile/build_with_docker.sh
@@ -23,7 +23,7 @@
 # Make sure we're in the correct directory, at the root of the source tree.
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 WORKSPACE="${SCRIPT_DIR}/../../../"
-cd ${WORKSPACE} || exit 1
+cd ${WORKSPACE}
 
 DOCKER_IMG_NAME="tf-make-base"
 DOCKER_CONTEXT_PATH="${WORKSPACE}tensorflow/contrib/makefile/"
diff --git a/tensorflow/contrib/makefile/compile_android_protobuf.sh b/tensorflow/contrib/makefile/compile_android_protobuf.sh
index fadbe271b8..50d72d6093 100755
--- a/tensorflow/contrib/makefile/compile_android_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_android_protobuf.sh
@@ -27,7 +27,7 @@ cc_prefix="${CC_PREFIX}"
 usage() {
   echo "Usage: $(basename "$0") [-a:c]"
   echo "-a [Architecture] Architecture of target android [default=armeabi-v7a] \
-(supported architecture list: \
+(supported archtecture list: \
 arm64-v8a armeabi armeabi-v7a armeabi-v7a-hard mips mips64 x86 x86_64)"
   echo "-c Clean before building protobuf for target"
   echo "\"NDK_ROOT\" should be defined as an environment variable."
@@ -130,7 +130,7 @@ elif [[ ${ARCHITECTURE} == "x86_64" ]]; then
     sysroot_arch="x86_64"
     bin_prefix="x86_64-linux-android"
 else
-    echo "architecture ${ARCHITECTURE} is not supported." 1>&2
+    echo "archtecture ${arcitecture} is not supported." 1>&2
     usage
     exit 1
 fi
diff --git a/tensorflow/contrib/makefile/compile_ios_protobuf.sh b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
index e8b9454e7e..d1012a6c93 100755
--- a/tensorflow/contrib/makefile/compile_ios_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/bin/bash -x -e
 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,9 +15,6 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
-set -x
-set -e
-
 SCRIPT_DIR=$(dirname $0)
 source "${SCRIPT_DIR}/build_helper.subr"
 
@@ -33,17 +30,17 @@ fi
 
 JOB_COUNT="${JOB_COUNT:-$(get_job_count)}"
 
-GENDIR=$(pwd)/gen/protobuf_ios/
+GENDIR=`pwd`/gen/protobuf_ios/
 LIBDIR=${GENDIR}lib
 mkdir -p ${LIBDIR}
 
 OSX_VERSION=darwin14.0.0
 
-IPHONEOS_PLATFORM=$(xcrun --sdk iphoneos --show-sdk-platform-path)
-IPHONEOS_SYSROOT=$(xcrun --sdk iphoneos --show-sdk-path)
-IPHONESIMULATOR_PLATFORM=$(xcrun --sdk iphonesimulator --show-sdk-platform-path)
-IPHONESIMULATOR_SYSROOT=$(xcrun --sdk iphonesimulator --show-sdk-path)
-IOS_SDK_VERSION=$(xcrun --sdk iphoneos --show-sdk-version)
+IPHONEOS_PLATFORM=`xcrun --sdk iphoneos --show-sdk-platform-path`
+IPHONEOS_SYSROOT=`xcrun --sdk iphoneos --show-sdk-path`
+IPHONESIMULATOR_PLATFORM=`xcrun --sdk iphonesimulator --show-sdk-platform-path`
+IPHONESIMULATOR_SYSROOT=`xcrun --sdk iphonesimulator --show-sdk-path`
+IOS_SDK_VERSION=`xcrun --sdk iphoneos --show-sdk-version`
 MIN_SDK_VERSION=8.0
 
 CFLAGS="-DNDEBUG -Os -pipe -fPIC -fno-exceptions"
diff --git a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
index bcf097b303..6f47b80780 100755
--- a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
+++ b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
@@ -20,7 +20,7 @@ source "${SCRIPT_DIR}/build_helper.subr"
 JOB_COUNT="${JOB_COUNT:-$(get_job_count)}"
 
 function less_than_required_version() {
-  echo $1 | (IFS=. read -r major minor micro
+  echo $1 | (IFS=. read major minor micro
     if [ $major -ne $2 ]; then
       [ $major -lt $2 ]
     elif [ $minor -ne $3 ]; then
@@ -31,7 +31,7 @@ function less_than_required_version() {
   )
 }
 
-ACTUAL_XCODE_VERSION=$(xcodebuild -version | head -n 1 | sed 's/Xcode //')
+ACTUAL_XCODE_VERSION=`xcodebuild -version | head -n 1 | sed 's/Xcode //'`
 REQUIRED_XCODE_VERSION=7.3.0
 if less_than_required_version $ACTUAL_XCODE_VERSION 7 3 0
 then
diff --git a/tensorflow/contrib/makefile/compile_pi_protobuf.sh b/tensorflow/contrib/makefile/compile_pi_protobuf.sh
index f863d80009..2aae2d5f4e 100755
--- a/tensorflow/contrib/makefile/compile_pi_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_pi_protobuf.sh
@@ -15,15 +15,15 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
-cd tensorflow/contrib/makefile || exit 1
+cd tensorflow/contrib/makefile
 
-GENDIR=$(pwd)/gen/protobuf_pi/
+GENDIR=`pwd`/gen/protobuf_pi/
 LIBDIR=${GENDIR}
 mkdir -p ${LIBDIR}
 
 CXX=arm-linux-gnueabihf-g++
 
-cd downloads/protobuf || exit 1
+cd downloads/protobuf
 
 ./autogen.sh
 if [ $? -ne 0 ]
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
index 288f59fed4..c7ed663131 100644
--- a/tensorflow/contrib/remote_fused_graph/pylib/BUILD
+++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
@@ -7,7 +7,6 @@ licenses(["notice"])  # Apache 2.0
 
 exports_files(["LICENSE"])
 
-load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 tf_gen_op_wrapper_py(
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index c8552fc050..9c5e9fec9d 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -79,7 +79,7 @@ class CoupledInputForgetGateLSTMCell(rnn_cell_impl.RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://www.bioinf.jku.at/publications/older/2604.pdf
+    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
index 3496b355b4..491d87f62d 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
@@ -65,9 +65,7 @@ class GatherTreeTest(test.TestCase):
         _ = beams.eval()
 
   def testBadParentValuesOnGPU(self):
-    # Only want to run this test on CUDA devices, as gather_tree is not
-    # registered for SYCL devices.
-    if not test.is_gpu_available(cuda_only=True):
+    if not test.is_gpu_available():
       return
     # (max_time = 4, batch_size = 1, beams = 3)
     # bad parent in beam 1 time 1; appears as a negative index at time 0
diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index 4bde661698..d37c632be7 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -836,7 +836,7 @@ with tf.Session() as sess:
   for batch_id in range(num_batches):
     sess.run(names_to_updates.values())
 
-  metric_values = sess.run(names_to_values.values())
+  metric_values = sess.run(name_to_values.values())
   for metric, value in zip(names_to_values.keys(), metric_values):
     print('Metric %s has value: %f' % (metric, value))
 ```
diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index 4fa1ccea69..824ba4c09b 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -1,26 +1,3 @@
 # tfprof: TensorFlow Profiler and Beyond
 
 # Full Document in tensorflow/tools/tfprof/README.md
-
-Author: Xin Pan (xpan@google.com, github: panyx0718), Jon Shlens, Yao Zhang
-
-Consultants: Jon Shlens, Pete Warden
-
-###Major Features
-
-1.  Measure model parameters, float operations, tensor shapes.
-2.  Profile op execution times, requested memory size and device placement.
-3.  Inspect checkpoint tensors' shapes and their values.
-4.  Selectively group, filter, account and order ops.
-
-####tfprof supports 3 views to organize TensorFlow model profiles
-
-    *  code view: Stats are associated your Python codes and organized as call stacks.
-    *  scope view: Stats are organized as name scope hierarchies.
-    *  graph view: Stats are organized as Tensorflow Op graph.
-
-####For each view, there are 3 ways to display outputs:
-
-    *  stdout: Results are written to stdout.
-    *  timeline: Visualized in chrome browser as time series.
-    *  file: Results are dumped to file.
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
index 40f29ae8a2..582c36e339 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
@@ -43,7 +43,6 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
-%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index 71468dde37..9c59df3117 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -89,7 +89,7 @@ def _run_loop_model():
 class RunMetadataTest(test.TestCase):
 
   def testGPU(self):
-    if not test.is_gpu_available(cuda_only=True):
+    if not test.is_gpu_available():
       return
 
     ops.reset_default_graph()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index 419beac0b9..c781d2af4e 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -20,6 +20,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import six
+
 from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
 from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
 from tensorflow.python.framework import errors
@@ -108,49 +110,77 @@ PRINT_ALL_TIMING_MEMORY = {
     'dump_to_file': ''
 }
 
+# The following options are for 'advise' tfprof_cmd.
+# Show all advice.
+ALL_ADVICE = {
+    'ExpensiveOperationChecker': {},
+    'AcceleratorUtilizationChecker': {},
+    'JobChecker': {},  # Only available internally.
+    'OperationChecker': {},
+}
+
 # pylint: enable=bad-whitespace
 # pylint: enable=bad-continuation
 
 
-def _build_options(tfprof_options):
+def _build_options(options):
   """Build tfprof.OptionsProto.
 
   Args:
-    tfprof_options: A dictionary of options.
+    options: A dictionary of options.
   Returns:
     tfprof.OptionsProto.
   """
   opts = tfprof_options_pb2.OptionsProto()
-  opts.max_depth = tfprof_options.get('max_depth', 10)
-  opts.min_bytes = tfprof_options.get('min_bytes', 0)
-  opts.min_micros = tfprof_options.get('min_micros', 0)
-  opts.min_params = tfprof_options.get('min_params', 0)
-  opts.min_float_ops = tfprof_options.get('min_float_ops', 0)
-  opts.min_occurrence = tfprof_options.get('min_occurrence', 0)
+  opts.max_depth = options.get('max_depth', 10)
+  opts.min_bytes = options.get('min_bytes', 0)
+  opts.min_micros = options.get('min_micros', 0)
+  opts.min_params = options.get('min_params', 0)
+  opts.min_float_ops = options.get('min_float_ops', 0)
+  opts.min_occurrence = options.get('min_occurrence', 0)
 
-  opts.step = tfprof_options.get('step', -1)
+  opts.step = options.get('step', -1)
 
-  opts.order_by = tfprof_options.get('order_by', 'name')
+  opts.order_by = options.get('order_by', 'name')
 
-  for p in tfprof_options.get('account_type_regexes', []):
+  for p in options.get('account_type_regexes', []):
     opts.account_type_regexes.append(p)
-  for p in tfprof_options.get('start_name_regexes', []):
+  for p in options.get('start_name_regexes', []):
     opts.start_name_regexes.append(p)
-  for p in tfprof_options.get('trim_name_regexes', []):
+  for p in options.get('trim_name_regexes', []):
     opts.trim_name_regexes.append(p)
-  for p in tfprof_options.get('show_name_regexes', []):
+  for p in options.get('show_name_regexes', []):
     opts.show_name_regexes.append(p)
-  for p in tfprof_options.get('hide_name_regexes', []):
+  for p in options.get('hide_name_regexes', []):
     opts.hide_name_regexes.append(p)
-  opts.account_displayed_op_only = tfprof_options.get(
-      'account_displayed_op_only', False)
+  opts.account_displayed_op_only = options.get('account_displayed_op_only',
+                                               False)
 
-  for p in tfprof_options.get('select', []):
+  for p in options.get('select', []):
     opts.select.append(p)
 
-  opts.output = tfprof_options.get('output', 'stdout')
-  opts.dump_to_file = tfprof_options.get('dump_to_file', '')
+  opts.output = options.get('output', 'stdout')
+  opts.dump_to_file = options.get('dump_to_file', '')
+
+  return opts
+
+
+def _build_advisor_options(options):
+  """Build tfprof.AdvisorOptionsProto.
 
+  Args:
+    options: A dictionary of options. See ALL_ADVICE example.
+  Returns:
+    tfprof.AdvisorOptionsProto.
+  """
+  opts = tfprof_options_pb2.AdvisorOptionsProto()
+  if options is None:
+    return opts
+  for checker, checker_opts in six.iteritems(options):
+    checker_ops_pb = tfprof_options_pb2.AdvisorOptionsProto.CheckerOption()
+    for k, v in six.iteritems(checker_opts):
+      checker_ops_pb[k] = v
+    opts.checkers[checker].MergeFrom(checker_ops_pb)
   return opts
 
 
@@ -190,7 +220,7 @@ class Profiler(object):
       else:
         _ = sess.run(...)
     # Auto detect problems and generate advice.
-    profiler.advise()
+    profiler.advise(model_analyzer.ALL_ADVICE)
   """
 
   def __init__(self, graph, op_log=None):
@@ -288,9 +318,19 @@ class Profiler(object):
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
 
-  def advise(self):
-    """Automatically detect problems and generate reports."""
-    print_mdl.Advise()
+  def advise(self, options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+    """Automatically detect problems and generate reports.
+
+    Args:
+      options: A dict of options.
+    Returns:
+      A Advise proto that conains the reports from all checkers.
+    """
+    advise_pb = tfprof_output_pb2.AdviceProto()
+    opts = _build_advisor_options(options)
+    advise_pb.ParseFromString(
+        print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString()))
+    return advise_pb
 
 
 def print_model_analysis(graph,
@@ -354,3 +394,36 @@ def print_model_analysis(graph,
         None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
 
   return tfprof_node
+
+
+def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+  """Auto profile and advise.
+
+    Builds profiles and automatically check anormalies of various
+    aspects. See go/tfprof or README for examples and tutorials.
+
+  Args:
+    graph: tf.Graph.
+    run_meta: tensorflow::RunMetadata proto. Allows auto-profile
+              time and memroy.
+    tfprof_options: see ALL_ADVICE example above.
+  Returns:
+    Returns AdviceProto proto
+  """
+  # pylint: disable=protected-access
+  op_log = tfprof_logger._merge_default_with_oplog(
+      graph, None, run_meta, add_trace=True)
+  # pylint: enable=protected-access
+
+  run_meta_str = run_meta.SerializeToString() if run_meta else b''
+
+  opts = _build_advisor_options(tfprof_options)
+  ret = tfprof_output_pb2.AdviceProto()
+  ret.ParseFromString(
+      print_mdl.PrintModelAnalysis(
+          graph.as_graph_def(add_shapes=True).SerializeToString(),
+          run_meta_str,
+          op_log.SerializeToString(),
+          'advise'.encode('utf-8'),
+          opts.SerializeToString()))
+  return ret
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index 9db752c577..fea27a82a5 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -126,7 +126,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['account_displayed_op_only'] = False
     opts['select'] = ['params', 'float_ops']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -176,6 +176,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['select'] = [
         'bytes', 'params', 'float_ops', 'device'
     ]
+    opts['output'] = 'none'
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -276,6 +277,33 @@ class PrintModelAnalysisTest(test.TestCase):
       self.assertEqual(total_children, 15)
       self.assertGreater(input_shapes, 0)
 
+  def testAdvisor(self):
+    ops.reset_default_graph()
+
+    with session.Session() as sess:
+      x = lib.BuildFullModel()
+
+      sess.run(variables.global_variables_initializer())
+      run_meta = config_pb2.RunMetadata()
+      _ = sess.run(
+          x,
+          options=config_pb2.RunOptions(
+              trace_level=config_pb2.RunOptions.FULL_TRACE),
+          run_metadata=run_meta)
+
+      advice_pb = model_analyzer.advise(sess.graph, run_meta)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
index 5daaafd7c8..c7113b6a57 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
@@ -129,7 +129,7 @@ class ProfilerTest(test.TestCase):
     opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
     opts['account_type_regexes'] = ['.*']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       r1, r2, r3 = lib.BuildSplitableModel()
       sess.run(variables.global_variables_initializer())
 
@@ -179,8 +179,18 @@ class ProfilerTest(test.TestCase):
       self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None)
       self.assertGreater(lib.SearchTFProfNode(pb3, 'add').exec_micros, 0)
 
-      # TODO(xpan): Better test of advisor.
-      profiler.advise()
+      advice_pb = profiler.advise(model_analyzer.ALL_ADVICE)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/training/python/training/evaluation.py b/tensorflow/contrib/training/python/training/evaluation.py
index a895d90b8e..24b733dd29 100644
--- a/tensorflow/contrib/training/python/training/evaluation.py
+++ b/tensorflow/contrib/training/python/training/evaluation.py
@@ -226,7 +226,7 @@ def checkpoints_iterator(checkpoint_dir,
 
   This behavior gives control to callers on what to do if checkpoints do not
   come fast enough or stop being generated.  For example, if callers have a way
-  to detect that the training has stopped and know that no new checkpoints
+  to detect that the training has stopped and know that no new new checkpoints
   will be generated, they can provide a `timeout_fn` that returns `True` when
   the training has stopped.  If they know that the training is still going on
   they return `False` instead.
diff --git a/tensorflow/contrib/verbs/rdma.cc b/tensorflow/contrib/verbs/rdma.cc
index 6f3a616fe8..bc687be0ab 100644
--- a/tensorflow/contrib/verbs/rdma.cc
+++ b/tensorflow/contrib/verbs/rdma.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_util.h"
-#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h"
 #include "tensorflow/core/distributed_runtime/session_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
@@ -684,6 +683,7 @@ void RdmaTensorBuffer::SendNextItem() {
                          << " error message: " << status.error_message();
       size_t buffer_size = RdmaMessage::kMessageTotalBytes;
       size_t tensor_bytes = 0;
+      TensorProto proto;
       // Figures out which device the tensor is hosted on.
       Device* src_dev = nullptr;
       Status s = channel_->adapter_->worker_env_->device_mgr->LookupDevice(
@@ -703,47 +703,21 @@ void RdmaTensorBuffer::SendNextItem() {
       CHECK(s.ok()) << "dst device not found";
       AllocatorAttributes dst_alloc_attr;
       dst_alloc_attr.set_on_host(true);
-
-      bool can_memcpy = DataTypeCanUseMemcpy(in.dtype());
       // string tensor needs to be serialized
-      Tensor copy;
-      StringPiece copy_buf;
-      TensorProto proto;
       if (src_dev->tensorflow_gpu_device_info() &&
           (!send_args.alloc_attrs.on_host())) {
         CHECK(send_args.device_context)
-          << "send dev name: " << src_dev->name()
-          << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
-
-        if (can_memcpy) {
-          AllocatorAttributes host_alloc_attrs;
-          host_alloc_attrs.set_gpu_compatible(true);
-          host_alloc_attrs.set_on_host(true);
-          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
-          copy = Tensor(alloc, in.dtype(), in.shape());
-          s = VerbsUtil::CopyGPUTensorToCPUSync(
-              src_dev, send_args.device_context, &in, &copy);
-          CHECK(s.ok()) << "copy tensor from gpu sync";
-          copy_buf = copy.tensor_data();
-        } else {
-          // "val" is on a GPU. Uses GPUUtil to fill the proto.
-          s = VerbsUtil::SetProtoFromGPUSync(
-              in, src_dev, send_args.device_context, &proto, is_dead);
-          CHECK(s.ok()) << "set proto from gpu sync";
-        }
+            << "send dev name: " << src_dev->name()
+            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
+        // "val" is on a GPU. Uses GPUUtil to fill the proto.
+        s = VerbsUtil::SetProtoFromGPUSync(
+            in, src_dev, send_args.device_context, &proto, is_dead);
+        CHECK(s.ok()) << "set proto from gpu sync";
       } else {
         // tensor is in CPU memory.
-        if (can_memcpy) {
-          copy_buf = in.tensor_data();
-        } else {
-          in.AsProtoTensorContent(&proto);
-        }
-      }
-      if (can_memcpy) {
-        tensor_bytes = in.TotalBytes();
-      } else {
-        tensor_bytes = proto.ByteSize();
+        in.AsProtoTensorContent(&proto);
       }
+      tensor_bytes = proto.ByteSize();
       // maybe some margin for string tensor?
       buffer_size += tensor_bytes;
       // prepare message
@@ -797,16 +771,7 @@ void RdmaTensorBuffer::SendNextItem() {
               static_cast<void*>(static_cast<char*>(buffer_) +
                                  RdmaMessage::kTensorBufferStartIndex);
           CHECK(tensor_bytes + RdmaMessage::kTensorBufferStartIndex <= size_);
-          if (can_memcpy) {
-            CHECK(copy_buf.size() == tensor_bytes)
-               << "unexpected tensor size: "
-               << copy_buf.size()
-               << " != "
-               << tensor_bytes;
-            memcpy(output, copy_buf.data(), tensor_bytes);
-          } else {
-            proto.SerializeToArray(output, tensor_bytes);
-          }
+          proto.SerializeToArray(output, tensor_bytes);
         } else {
           buffer_size = RdmaMessage::kMessageTotalBytes;
         }
diff --git a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
index 9ea696589a..5871400f26 100644
--- a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
+++ b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
-#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -100,40 +99,12 @@ void RdmaRemoteRendezvous::RecvFromRemoteAsync(
     if (!rm.is_dead_) {
       void* input = static_cast<char*>(rb->buffer_) +
                     RdmaMessage::kTensorBufferStartIndex;
-      bool can_memcpy = DataTypeCanUseMemcpy(rm.data_type_);
-      if (can_memcpy) {
-        if (dst_dev->tensorflow_gpu_device_info() &&
-            (!recv_args.alloc_attrs.on_host())) {
-          CHECK(recv_args.device_context)
-            << "send dev name: " << src_dev->name()
-            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
-          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
-          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
-          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
-
-          Allocator* dst_alloc = dst_dev->GetAllocator(recv_args.alloc_attrs);
-          Tensor gpu_copy(dst_alloc, rm.data_type_, rm.tensor_shape_);
-          s = VerbsUtil::CopyCPUTensorToGPUSync(&copy, recv_args.device_context,
-                                                dst_dev, &gpu_copy);
-          CHECK(s.ok()) << "copy tensor to gpu sync";
-          val = std::move(gpu_copy);
-        } else {
-          AllocatorAttributes host_alloc_attrs;
-          host_alloc_attrs.set_gpu_compatible(true);
-          host_alloc_attrs.set_on_host(true);
-          Allocator* alloc = dst_dev->GetAllocator(host_alloc_attrs);
-          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
-          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
-          val = std::move(copy);
-        }
-      } else {
-        TensorProto proto;
-        CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
-              rb->size_);
-        CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
-            << "fail to parse proto from array";
-        s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
-      }
+      TensorProto proto;
+      CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
+            rb->size_);
+      CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
+          << "fail to parse proto from array";
+      s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
     }
 
     rc->RemoveRecvCallback(key_with_step_id);
diff --git a/tensorflow/contrib/verbs/verbs_util.cc b/tensorflow/contrib/verbs/verbs_util.cc
index 76e44d34a9..c3350f7958 100644
--- a/tensorflow/contrib/verbs/verbs_util.cc
+++ b/tensorflow/contrib/verbs/verbs_util.cc
@@ -20,40 +20,6 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 namespace tensorflow {
 
-// static sync wrapper:
-Status VerbsUtil::CopyGPUTensorToCPUSync(Device* gpu_device,
-                              const DeviceContext* device_context,
-                              const Tensor* gpu_tensor,
-                              Tensor* cpu_tensor) {
-  Notification n;
-  Status status;
-  GPUUtil::CopyGPUTensorToCPU(gpu_device, device_context,
-                              gpu_tensor, cpu_tensor,
-                              [&n, &status](const Status& s) {
-                                status = s;
-                                n.Notify();
-                              });
-  n.WaitForNotification();
-  return status;
-}
-
-// static sync wrapper:
-Status VerbsUtil::CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
-                                         const DeviceContext* device_context,
-                                         Device* gpu_device,
-                                         Tensor* gpu_tensor) {
-  Notification n;
-  Status status;
-  GPUUtil::CopyCPUTensorToGPU(cpu_tensor, device_context,
-                              gpu_device, gpu_tensor,
-                              [&n, &status](const Status& s) {
-                                status = s;
-                                n.Notify();
-                              });
-  n.WaitForNotification();
-  return status;
-}
-
 // static sync wrapper:
 Status VerbsUtil::SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                       const DeviceContext* device_context,
diff --git a/tensorflow/contrib/verbs/verbs_util.h b/tensorflow/contrib/verbs/verbs_util.h
index d9da396228..cbc01adae4 100644
--- a/tensorflow/contrib/verbs/verbs_util.h
+++ b/tensorflow/contrib/verbs/verbs_util.h
@@ -28,16 +28,6 @@ class TensorProto;
 
 class VerbsUtil {
  public:
-  // synchronous wrapper of CopyGPUTensorToCPU
-  static Status CopyGPUTensorToCPUSync(Device* gpu_device,
-                                       const DeviceContext* device_context,
-                                       const Tensor* gpu_tensor,
-                                       Tensor* cpu_tensor);
-  // synchronous wrapper of CopyCPUTensorToGPU
-  static Status CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
-                                       const DeviceContext* device_context,
-                                       Device* gpu_device,
-                                       Tensor* gpu_tensor);
   // synchronous wrapper of SetProtoFromGPU
   static Status SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                     const DeviceContext* device_context,
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index 4b951691fb..21a20bcc4d 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -654,11 +654,11 @@ Status DirectSession::Run(const RunOptions& run_options,
 
   // If requested via RunOptions, output the partition graphs.
   if (run_options.output_partition_graphs()) {
-    protobuf::RepeatedPtrField<GraphDef>* partition_graph_defs =
+    protobuf::RepeatedPtrField<GraphDef>* parition_graph_defs =
         run_metadata->mutable_partition_graphs();
     for (const PerPartitionExecutorsAndLib& exec_and_lib :
          executors_and_keys->items) {
-      GraphDef* partition_graph_def = partition_graph_defs->Add();
+      GraphDef* partition_graph_def = parition_graph_defs->Add();
       exec_and_lib.graph->ToGraphDef(partition_graph_def);
     }
   }
diff --git a/tensorflow/core/debug/grpc_session_debug_test.cc b/tensorflow/core/debug/grpc_session_debug_test.cc
index d6f35fe24c..3827596a67 100644
--- a/tensorflow/core/debug/grpc_session_debug_test.cc
+++ b/tensorflow/core/debug/grpc_session_debug_test.cc
@@ -279,12 +279,9 @@ TEST_F(GrpcSessionDebugTest, MultiDevices_String) {
 
         DeleteDumpDir();
       } else {
-        // CUDA and SYCL devices do not have an Identity op for strings
         LOG(ERROR) << "Error: " << s;
         ASSERT_TRUE((a_dev.device_type() == DEVICE_GPU) ||
-                    (a_dev.device_type() == DEVICE_SYCL) ||
-                    (b_dev.device_type() == DEVICE_GPU) ||
-                    (b_dev.device_type() == DEVICE_SYCL));
+                    (b_dev.device_type() == DEVICE_GPU));
         ASSERT_FALSE(s.ok());
       }
     }
diff --git a/tensorflow/core/framework/graph_def_util.h b/tensorflow/core/framework/graph_def_util.h
index 950737c39a..56355eaf36 100644
--- a/tensorflow/core/framework/graph_def_util.h
+++ b/tensorflow/core/framework/graph_def_util.h
@@ -62,7 +62,7 @@ Status AddDefaultAttrsToGraphDef(GraphDef* graph_def,
 // attr with a default was added). Note that this will not affect
 // attrs with non-default values, so you must run a
 // ValidateGraphDef...() function to see if the result is in fact
-// compatible. If not nullptr, the op/attr pairs that were removed
+// compatible. If not nulllptr, the op/attr pairs that were removed
 // are added to '*op_attr_removed'.
 //
 // Expected usage, for a producer that wants to prepare a graph for
diff --git a/tensorflow/core/framework/op.h b/tensorflow/core/framework/op.h
index a4dd06de45..c5a0983a54 100644
--- a/tensorflow/core/framework/op.h
+++ b/tensorflow/core/framework/op.h
@@ -205,6 +205,7 @@ class OpDefBuilderWrapper;
 template <>
 class OpDefBuilderWrapper<true> {
  public:
+  typedef OpDefBuilderWrapper<true> WrapperType;
   OpDefBuilderWrapper(const char name[]) : builder_(name) {}
   OpDefBuilderWrapper<true>& Attr(StringPiece spec) {
     builder_.Attr(spec);
diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h
index a164fe61b5..49eecc0b08 100644
--- a/tensorflow/core/framework/tensor.h
+++ b/tensorflow/core/framework/tensor.h
@@ -307,7 +307,7 @@ class Tensor {
   /// Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing the
   /// first 'begin' Tensor dimensions into the first dimension of the result and
   /// the Tensor dimensions of the last dims() - 'begin' - NDIMS into the last
-  /// dimension of the result. If 'begin' < 0 then the |'begin'| leading
+  /// dimension of the result. If 'begin' < 0 then the the |'begin'| leading
   /// dimensions of size 1 will be added. If 'begin' + NDIMS > dims() then
   /// 'begin' + NDIMS - dims() trailing dimensions of size 1 will be added.
   template <typename T, size_t NDIMS = 3>
diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc
index 625780e7c9..94741a11ff 100644
--- a/tensorflow/core/graph/mkl_layout_pass.cc
+++ b/tensorflow/core/graph/mkl_layout_pass.cc
@@ -247,10 +247,16 @@ namespace tensorflow {
 //
 //           P = Conv2DWithBiasBackpropBias(O, O_m)
 //
-// Rewrite of BiasAddGrad into Conv2DWithBiasBackpropBias takes place depending
-// on the matching 'context'. The term context is loosely related to which
-// forward op is _associated_ to BiasAddGrad. If it is _MklConv2DWithBias then
-// we consider it Conv2D context; if it is MatMul, then it is MatMul context.
+// 'Distance' between input of BiasAddGrad and _MklConv2D in terms of hops is
+// the context matching depth. If _MklConv2DWithBias is not within the context
+// matching depth, then we do not rewrite BiasAddGrad.
+
+// How many hops do we search for matching node in the backward dataflow graph?
+// We use maxhop of 10 based on empirical observations. Also, these are
+// maxhops in backward data-flow graph. Since input of forward nodes (Conv2D)
+// directly goes to backward nodes, we do not expect the hop-distance
+// would be more than few nodes.
+static size_t kNodeMergeContextMaxDepth = 10;
 
 class MklLayoutRewritePass : public GraphOptimizationPass {
  public:
@@ -274,8 +280,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     csinfo_.max_pool = "MaxPool";
     csinfo_.max_pool_grad = "MaxPoolGrad";
     csinfo_.mkl_conv2d = "_MklConv2D";
-    csinfo_.mkl_conv2d_grad_input = "_MklConv2DBackpropInput";
-    csinfo_.mkl_conv2d_grad_filter = "_MklConv2DBackpropFilter";
     csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias";
     csinfo_.mkl_conv2d_with_bias_backprop_bias =
                                    "_MklConv2DWithBiasBackpropBias";
@@ -356,12 +360,16 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     minfo_.push_back({csinfo_.mkl_conv2d, csinfo_.bias_add, 0,
                       csinfo_.mkl_conv2d_with_bias});
 
+    // We use maxhop of 10 based on empirical observations. Also, these are
+    // maxhops in backward data-flow graph. Since input of forward nodes
+    // (Conv2D) directly goes to backward nodes, we do not expect the
+    // hop-distance would be more than few nodes.
     biasaddgrad_matmul_context_ = {csinfo_.bias_add_grad, csinfo_.matmul,
-                                   IsBiasAddGradInMatMulContext};
+                                   kNodeMergeContextMaxDepth};
 
     biasaddgrad_conv2dwithbias_context_ = {csinfo_.bias_add_grad,
                                    csinfo_.mkl_conv2d_with_bias,
-                                   IsBiasAddGradInConv2DWithBiasContext};
+                                   kNodeMergeContextMaxDepth};
 
     cinfo_.push_back(&biasaddgrad_matmul_context_);
     cinfo_.push_back(&biasaddgrad_conv2dwithbias_context_);
@@ -384,7 +392,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string node;     // Name of the node to be rewritten
     string fwd;      // Name of the node in the forward pass that this node
                      // corresponds to
-    std::function<bool(const Node*, const Node**, void* c)> context_match_fn;
+    size_t max_hop;  // Maximum number of hops the fwd is located
+                     // from this node. If the fwd is farther than max_hop
+                     // then we do not rewrite the node.
   } ContextInfo;
 
   /// Structure to specify the name of an original node, its new name after
@@ -428,7 +438,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
 
   /// Structure to store all constant strings
   /// NOTE: names are alphabetically sorted.
-  typedef struct {
+  struct {
     string avg_pool;
     string avg_pool_grad;
     string bias_add;
@@ -447,15 +457,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string max_pool;
     string max_pool_grad;
     string mkl_conv2d;
-    string mkl_conv2d_grad_input;
-    string mkl_conv2d_grad_filter;
     string mkl_conv2d_with_bias;
     string mkl_conv2d_with_bias_backprop_bias;
     string relu;
     string relu_grad;
     string reshape;
     string split;
-  } ConstStringsInfo;
+  } csinfo_;
 
  private:
   /// Maintain info about nodes to rewrite
@@ -470,9 +478,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   /// Maintain info about nodes to rewrite
   static std::vector<ContextInfo*> cinfo_;
 
-  /// Maintain structure of constant strings
-  static ConstStringsInfo csinfo_;
-
   /// Context variables used in referencing rules
   static ContextInfo biasaddgrad_matmul_context_;
   static ContextInfo biasaddgrad_conv2dwithbias_context_;
@@ -624,173 +629,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     return false;
   }
 
-  // Is BiasAddGrad node in 'n' is associated with Conv2DWithBias node
-  // specified in contextinfo 'ci'. Function updates fwd_node to point
-  // to Conv2DWithBias node if 'n' is associated with Conv2DWithBias.
-  //
-  // Association checks for one of the following graphs:
-  //
-  // Graph A:
-  //
-  // _ = Conv2DWithBias(F, I, _)
-  // ..
-  // _ = Conv2DBackpropFilter(F, _, G)
-  // _ = Conv2DBackpropInput(_, I, G)
-  // _ = BiasAddGrad(G)
-  //
-  // OR
-  //
-  // Graph B:
-  //
-  // _ = Conv2DWithBias(F, _, _)
-  // ..
-  // _ = Conv2DBackpropFilter(F, _, G)
-  // _ = BiasAddGrad(G)
-  //
-  // Here F, G, and I are graph nodes; _ represents graph nodes that we
-  // don't care here.
-  //
-  // @return - true (if BiasAddGrad is associated with Conv2DWithBias);
-  //           false otherwise.
-  static bool IsBiasAddGradInConv2DWithBiasContext(const Node* n,
-                                                   const Node** fwd_node,
-                                                   void* ci) {
-    CHECK_NOTNULL(n);
-    CHECK_NOTNULL(fwd_node);
-    CHECK_NOTNULL(ci);
-    *fwd_node = nullptr;
-
-    CHECK_EQ(n->type_string(), csinfo_.bias_add_grad);
-
-    // Get the only 1 input of BiasAddGrad.
-    CHECK_EQ(n->num_inputs(), 1);
-    const Node* bias_add_grad_inp = nullptr;
-    TF_CHECK_OK(n->input_node(0, &bias_add_grad_inp));
-    CHECK_NOTNULL(bias_add_grad_inp);
-
-    // Check if this input also goes to BackpropFilter and BackpropInput
-    // as 3rd input.
-    bool found_backprop_input = false;
-    bool found_backprop_filter = false;
-    Node* backprop_filter_node = nullptr;
-    Node* backprop_input_node = nullptr;
-
-    for (const Edge* e : bias_add_grad_inp->out_edges()) {
-      Node* third_input = nullptr;
-      if (e->dst()->type_string() == csinfo_.conv2d_grad_input ||
-          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_input) {
-        // Third input (index 2) of BackpropInput
-        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
-        // Third input (index 2) of BackpropInput must be same as the input
-        // of BiasAddGrad.
-        if (third_input == bias_add_grad_inp) {
-          found_backprop_input = true;
-          backprop_input_node = e->dst();
-        }
-      }
-
-      if (e->dst()->type_string() == csinfo_.conv2d_grad_filter ||
-          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_filter) {
-        // Third input (index 2) of BackpropFilter
-        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
-        // Third input (index 2) of BackpropFilter must be same as the input
-        // of BiasAddGrad.
-        if (third_input == bias_add_grad_inp) {
-          found_backprop_filter = true;
-          backprop_filter_node = e->dst();
-        }
-      }
-
-      // If we found both the nodes, then we can stop the search.
-      if (found_backprop_input && found_backprop_filter) {
-        break;
-      }
-    }
-
-    // If BackpropFilter node is not found, then this is not
-    // Conv2DWithBias context. For 2nd graph in the example above, only
-    // BackpropFilter would be present.
-    if (!found_backprop_filter) {
-      return false;
-    }
-
-    // Otherwise, we found the nodes.
-    CHECK_NOTNULL(backprop_filter_node);
-    if (found_backprop_input) {
-      CHECK_NOTNULL(backprop_input_node);
-    }
-
-    // Now that we confirmed that this is Conv2DWithBias context, we need to
-    // get access to the forward node (Conv2DWithBias). 2nd input of
-    // Conv2DWithBias is same as the 2nd input of Conv2DBackpropInput; 1st
-    // input of Conv2DWithBias is same as the 1st input of Conv2DBackpropFilter
-    // (This comes from definition of gradient computation for Conv2D).
-    if (found_backprop_input) {
-      // Graph A in the example.
-      Node* second_inp_of_input = nullptr;
-      Node* first_inp_of_filter = nullptr;
-      TF_CHECK_OK(backprop_input_node->input_node(1, &second_inp_of_input));
-      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
-      CHECK_NOTNULL(second_inp_of_input);
-      CHECK_NOTNULL(first_inp_of_filter);
-
-      // Now we need to find out Conv2DWithBias node from these input nodes.
-      // Conv2DWithBias node is the node that accepts both the nodes
-      // second_inp_of_input and first_inp_of_filter in 2nd and 1st input slots.
-      for (const Edge* fe : first_inp_of_filter->out_edges()) {
-        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
-            fe->dst_input() == 0) {
-          for (const Edge* ie : second_inp_of_input->out_edges()) {
-            if (ie->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
-                ie->dst_input() == 1 && fe->dst() == ie->dst()) {
-              VLOG(1) << "MklLayoutRewritePass: found "
-                      << fe->dst()->DebugString()
-                      << " as the forward node for matching context, backward"
-                      << " node is: " << n->DebugString();
-              *fwd_node = fe->dst();
-              return true;
-            }
-          }
-        }
-      }
-    } else {
-      // We did not find BackpropInput, so we work with BackpropFilter only.
-      // Graph B in the example.
-      Node* first_inp_of_filter = nullptr;
-      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
-      CHECK_NOTNULL(first_inp_of_filter);
-
-      // Now we need to find out Conv2DWithBias node from first input of
-      // BackpropFIlter. Conv2DWithBias node is the node that accepts
-      // first_inp_of_filter in 1st input slot.
-      for (const Edge* fe : first_inp_of_filter->out_edges()) {
-        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
-            fe->dst_input() == 0) {
-          VLOG(1) << "MklLayoutRewritePass: found "
-                  << fe->dst()->DebugString()
-                  << " as the forward node for matching context, backward"
-                  << " node is: " << n->DebugString();
-          *fwd_node = fe->dst();
-          return true;
-        }
-      }
-    }
-
-    return false;
-  }
-
-  // Is BiasAddGrad node in 'n' is associated with MatMul node
-  // specified in contextinfo 'ci'. Function does not update fwd_node.
-  //
-  // @return - true (if BiasAddGrad is associated with MatMul);
-  //           false otherwise.
-  static bool IsBiasAddGradInMatMulContext(const Node* n,
-                                           const Node** fwd_node,
-                                           void* ci) {
-    return (!IsBiasAddGradInConv2DWithBiasContext(n, fwd_node, ci));
-  }
-
-
   // Rewrite rule that uses context-information for matching,
   // used in scenario 2.
   //
@@ -801,6 +639,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   static bool ContextMatchRewrite(const Node* n, const ContextInfo* c);
 
   // Helper function that searches the matching contextinfo for the node.
+  // Implements depth-first search in the data dependence graph for the
+  // gradient op in the backward direction.
   //
   // @input n - Node (gradient op) whose contextinfo is to be searched,
   //        fwd_node - pointer to node from the forward pass that this node
@@ -948,7 +788,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
                                    Node* orig_node);
 };
 
-MklLayoutRewritePass::ConstStringsInfo MklLayoutRewritePass::csinfo_;
 MklLayoutRewritePass::ContextInfo
   MklLayoutRewritePass::biasaddgrad_conv2dwithbias_context_;
 MklLayoutRewritePass::ContextInfo
@@ -1828,12 +1667,12 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr<Graph>* g,
   const ContextInfo* ci = nullptr;
   bool is_context_based_rewrite = false;
   if ((ci = SearchMatchingContext(orig_node, &fwd_node)) != nullptr) {
+    CHECK_NOTNULL(fwd_node);
     is_context_based_rewrite = true;
 
     // Sanity checks for context-based rewrite (if any)
     if (orig_node->type_string() == csinfo_.bias_add_grad &&
         ri->new_name == csinfo_.mkl_conv2d_with_bias_backprop_bias) {
-      CHECK_NOTNULL(fwd_node);
       DataType orig_T, ctx_T;
       string orig_data_format, ctx_data_format;
       TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &orig_T));
@@ -1945,17 +1784,69 @@ MklLayoutRewritePass::SearchMatchingContext(const Node* n,
   CHECK_NOTNULL(fwd_node);
   *fwd_node = nullptr;
 
-  // Search for matching contextinfo based on node name and call
-  // callback function using matching contextinfo.
-  // There could be more than one matching contextinfos but whichever
-  // matches first is returned.
+  // Search for matching contextinfo based on node name.
+  // There could be more than one matching contextinfos.
+  bool is_matching_cinfo_found = false;
+  std::vector<const ContextInfo*> mci;
   for (auto ci = cinfo_.cbegin(); ci != cinfo_.cend(); ++ci) {
-    if (n->type_string() == (*ci)->node &&
-        (*ci)->context_match_fn(n, fwd_node, *ci)) {
-      VLOG(1) << "Found context as matching: " << (*ci)->fwd;
-      return *ci;
+    if (n->type_string() == (*ci)->node) {
+      mci.push_back(*ci);
+      is_matching_cinfo_found = true;
     }
   }
+  // If no matching contextinfo is found, return immediately.
+  if (!is_matching_cinfo_found) {
+    return nullptr;
+  }
+
+  VLOG(1) << "MklLayoutRewritePass: Searching graph for: " << n->type_string()
+          << " in backwards.";
+
+  // Now we will check for forward op name for context info in data
+  // flow graph. Get the max hops we should search for the fwd node.
+  // We are now going to search (breadth-first) backwards in data
+  // dependence graph (for up to max hops) from n for the node
+  // specified in fwd.
+  // queue to maintain nodes to be visited and depth info for
+  // breadth-first search
+  std::queue<std::pair<const Node*, int>> nqueue;
+  const Node* curr_node = n;
+  size_t curr_depth = 0;
+  nqueue.push(std::make_pair(curr_node, curr_depth));
+
+  while (curr_depth < kNodeMergeContextMaxDepth && !nqueue.empty()) {
+    std::pair<const Node*, int> curr_pair = nqueue.front();
+    nqueue.pop();
+
+    std::set<const Node*> visited_nodes;
+    curr_node = curr_pair.first;
+    curr_depth = curr_pair.second;
+    CHECK_NOTNULL(curr_node);
+
+    VLOG(1) << "MklLayoutRewritePass: Visiting node: "
+            << curr_node->type_string() << " at depth: " << curr_depth
+            << " for node: " << n->type_string();
+
+    // If we find a match, we return immediately.
+    for (const ContextInfo* ci : mci) {
+      if (curr_node->type_string() == ci->fwd) {
+        *fwd_node = curr_node;
+        return ci;
+      }
+    }
+
+    // Else we explore backward edges from current node.
+    // Add the source nodes of all incoming edges of the node to the queue.
+    for (const Edge* e : curr_node->in_edges()) {
+      // We do not visit already visited node.
+      if (visited_nodes.find(e->src()) == visited_nodes.end()) {
+        // Depth of these nodes is 1 more than the depth of current node.
+        nqueue.push(std::make_pair(e->src(), curr_depth + 1));
+        visited_nodes.insert(e->src());
+      }
+    }
+  } /* while */
+
   return nullptr;
 }
 
diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc
index efbe2134e0..3c4a5263af 100644
--- a/tensorflow/core/graph/mkl_layout_pass_test.cc
+++ b/tensorflow/core/graph/mkl_layout_pass_test.cc
@@ -345,8 +345,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_AttrMismatch) {
 // Test set 2: _MklConv2D..BiasAddGrad -> _MklConv2DWithBiasBackpropBias
 // rewrite tests
 
-// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
-// and BackpropInput
+// D=_MklConv2D(A,M,B,N,C,O); E=Sub(D,A); F=BiasAddGrad(E)
 TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
   InitGraph(
       "node { name: 'A' op: 'Input'}"
@@ -365,255 +364,16 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
       "node { name: 'E' op: 'Sub'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
       " input: ['D', 'A']}"
-      "node { name: 'F' op: 'Int32Input'}"
-      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
-      "node { name: 'H' op: 'Int32Input'}"
-      "node { name: 'I' op: '_MklConv2DBackpropInput'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
-      "node { name: 'J' op: 'BiasAddGrad'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " input: ['E'] }");
-  EXPECT_EQ(DoMklLayoutOptimizationPass(),
-            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
-            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
-            "I(_MklConv2DBackpropInput);J(_MklConv2DWithBiasBackpropBias);"
-            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G;B->D:1;"
-            "B->I:1;C->D:2;D->E;DMT/_0->J:1;E->G:2;E->I:2;E->J;"
-            "E:control->DMT/_0:control;F->G:1;H->I;M->D:3;M->G:3;M->I:3;"
-            "N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
-}
-
-// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
-// and BackpropInput. But nodes do not match criteria for rewrite. So
-// rewrite should not happen.
-TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative1) {
-  InitGraph(
-      "node { name: 'A' op: 'Input'}"
-      "node { name: 'B' op: 'Input'}"
-      "node { name: 'C' op: 'Input'}"
-      "node { name: 'M' op: '_MklInput'}"
-      "node { name: 'N' op: '_MklInput'}"
-      "node { name: 'O' op: '_MklInput'}"
-      "node { name: 'D' op: '_MklConv2DWithBias'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
-      "node { name: 'E' op: 'Sub'"
-      " attr {key: 'T'                 value { type: DT_FLOAT } }"
-      " input: ['D', 'A']}"
-      "node { name: 'F' op: 'Int32Input'}"
-      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
-      "node { name: 'H' op: 'Int32Input'}"
-      "node { name: 'I' op: '_MklConv2DBackpropInput'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
-      "node { name: 'J' op: 'BiasAddGrad'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " input: ['E'] }");
-  EXPECT_EQ(DoMklLayoutOptimizationPass(),
-            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
-            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
-            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
-            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
-            "B->I:1;C->D:2;D->E;E->G;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
-            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
-}
-
-// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
-// and BackpropInput. But nodes do not match criteria for rewrite. So
-// rewrite should not happen.
-TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative2) {
-  InitGraph(
-      "node { name: 'A' op: 'Input'}"
-      "node { name: 'B' op: 'Input'}"
-      "node { name: 'C' op: 'Input'}"
-      "node { name: 'M' op: '_MklInput'}"
-      "node { name: 'N' op: '_MklInput'}"
-      "node { name: 'O' op: '_MklInput'}"
-      "node { name: 'D' op: '_MklConv2DWithBias'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
-      "node { name: 'E' op: 'Sub'"
-      " attr {key: 'T'                 value { type: DT_FLOAT } }"
-      " input: ['D', 'A']}"
-      "node { name: 'F' op: 'Int32Input'}"
-      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
-      "node { name: 'H' op: 'Int32Input'}"
-      "node { name: 'I' op: '_MklConv2DBackpropInput'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
-      "node { name: 'J' op: 'BiasAddGrad'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " input: ['E'] }");
-  EXPECT_EQ(DoMklLayoutOptimizationPass(),
-            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
-            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
-            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
-            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
-            "B->I:1;C->D:2;D->E;E->G:2;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
-            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
-}
-
-
-// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
-TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Positive) {
-  InitGraph(
-      "node { name: 'A' op: 'Input'}"
-      "node { name: 'B' op: 'Input'}"
-      "node { name: 'C' op: 'Input'}"
-      "node { name: 'M' op: '_MklInput'}"
-      "node { name: 'N' op: '_MklInput'}"
-      "node { name: 'O' op: '_MklInput'}"
-      "node { name: 'D' op: '_MklConv2DWithBias'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
-      "node { name: 'E' op: 'Sub'"
-      " attr {key: 'T'                 value { type: DT_FLOAT } }"
-      " input: ['D', 'A']}"
-      "node { name: 'F' op: 'Int32Input'}"
-      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
-      "node { name: 'H' op: 'BiasAddGrad'"
+      "node { name: 'F' op: 'BiasAddGrad'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
       " attr { key: 'data_format'      value { s: 'NCHW' } }"
       " input: ['E'] }");
   EXPECT_EQ(DoMklLayoutOptimizationPass(),
             "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
-            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);"
-            "H(_MklConv2DWithBiasBackpropBias);M(_MklInput);N(_MklInput);"
-            "O(_MklInput)|A->D;A->E:1;A->G;B->D:1;C->D:2;D->E;DMT/_0->H:1;"
-            "E->G:2;E->H;E:control->DMT/_0:control;F->G:1;M->D:3;M->G:3;"
-            "N->D:4;N->G:4;O->D:5;O->G:5");
-}
-
-// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
-// But BackpropFilter node inputs do not satisfy criteria for rewrite.
-TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative1) {
-  InitGraph(
-      "node { name: 'A' op: 'Input'}"
-      "node { name: 'B' op: 'Input'}"
-      "node { name: 'C' op: 'Input'}"
-      "node { name: 'M' op: '_MklInput'}"
-      "node { name: 'N' op: '_MklInput'}"
-      "node { name: 'O' op: '_MklInput'}"
-      "node { name: 'D' op: '_MklConv2DWithBias'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
-      "node { name: 'E' op: 'Sub'"
-      " attr {key: 'T'                 value { type: DT_FLOAT } }"
-      " input: ['D', 'A']}"
-      "node { name: 'F' op: 'Int32Input'}"
-      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
-      "node { name: 'H' op: 'BiasAddGrad'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " input: ['E'] }");
-  EXPECT_EQ(DoMklLayoutOptimizationPass(),
-            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
-            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
-            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
-            "C->D:2;D->E;E->G;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
-            "O->G:5");
-}
-
-// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
-// But BackpropFilter node inputs do not satisfy criteria for rewrite.
-TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative2) {
-  InitGraph(
-      "node { name: 'A' op: 'Input'}"
-      "node { name: 'B' op: 'Input'}"
-      "node { name: 'C' op: 'Input'}"
-      "node { name: 'M' op: '_MklInput'}"
-      "node { name: 'N' op: '_MklInput'}"
-      "node { name: 'O' op: '_MklInput'}"
-      "node { name: 'D' op: '_MklConv2DWithBias'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
-      "node { name: 'E' op: 'Sub'"
-      " attr {key: 'T'                 value { type: DT_FLOAT } }"
-      " input: ['D', 'A']}"
-      "node { name: 'F' op: 'Int32Input'}"
-      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
-      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
-      " attr { key: 'padding'          value { s: 'SAME' } }"
-      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
-      "node { name: 'H' op: 'BiasAddGrad'"
-      " attr { key: 'T'                value { type: DT_FLOAT } }"
-      " attr { key: 'data_format'      value { s: 'NCHW' } }"
-      " input: ['E'] }");
-  EXPECT_EQ(DoMklLayoutOptimizationPass(),
-            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
-            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
-            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
-            "C->D:2;D->E;E->G:2;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
-            "O->G:5");
+            "E(Sub);F(_MklConv2DWithBiasBackpropBias);M(_MklInput);"
+            "N(_MklInput);O(_MklInput)|A->D;A->E:1;B->D:1;C->D:2;D->E;"
+            "DMT/_0->F:1;E->F;E:control->DMT/_0:control;M->D:3;N->D:4;"
+            "O->D:5");
 }
 
 // No _MklConv2DWithBias in context, but _MklConv2D in context.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index f6595fcbb3..e959eab54e 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -728,7 +728,7 @@ TEST_F(VirtualSchedulerTest, ComplexDependency) {
            1 /* control dependency */);
   EXPECT_EQ(expected_size, cpu_state.memory_usage);
 
-  // Nodes currently in memory: bn's port -1, 0, and 2, and x's port 0.
+  // Nodes currrently in memory: bn's port -1, 0, and 2, and x's port 0.
   std::set<std::pair<string, int>> nodes_in_memory;
   std::transform(
       cpu_state.nodes_in_memory.begin(), cpu_state.nodes_in_memory.end(),
diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h
index 1e7a9dfaf5..84a7681782 100644
--- a/tensorflow/core/grappler/grappler_item.h
+++ b/tensorflow/core/grappler/grappler_item.h
@@ -23,7 +23,6 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/protobuf/queue_runner.pb.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.cc b/tensorflow/core/grappler/optimizers/auto_parallel.cc
index d46b849ad4..42f2f1850f 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.cc
@@ -168,11 +168,6 @@ Status AutoParallel::Initialize(const GrapplerItem& item) {
   for (const auto& variable : item.MainVariables()) {
     dont_replicate_nodes.insert(variable->name());
   }
-
-  for (const auto& init : item.init_ops) {
-    dont_replicate_nodes.insert(NodeName(init));
-  }
-
   // Don't replicate all input nodes, except the dequeue node.
   for (const auto& input_node : input_nodes) {
     if (input_node->name() != dequeue_node->name()) {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.h b/tensorflow/core/grappler/optimizers/auto_parallel.h
index c5d2d47782..ad90bbe028 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.h
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.h
@@ -17,7 +17,6 @@ limitations under the License.
 #define TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_
 
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
-#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
index 9a41b5e0b5..3d1b4a34bf 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
@@ -33,7 +33,6 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   Output constant_b = ops::Const(s.WithOpName("constant_b"), 1, {1});
   Output var = ops::Variable(s.WithOpName("var"), {1}, DT_FLOAT);
   Output assign = ops::Assign(s.WithOpName("assign"), {var}, {constant_a});
-  Output identity = ops::Identity(s.WithOpName("identity"), {var});
   Output fifo_queue = ops::FIFOQueue(s.WithOpName("fifo_queue"), {DT_FLOAT});
   auto dequeue = ops::QueueDequeueMany(s.WithOpName("dequeue"), {fifo_queue},
                                        {constant_b}, {DT_FLOAT});
@@ -45,14 +44,13 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   GrapplerItem item;
   item.init_ops.push_back("assign");
   item.fetch.push_back("apply_gradient");
-  item.init_ops.push_back("assign");
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
   AutoParallel parallel(2);
   GraphDef output;
   Status status = parallel.Optimize(nullptr, item, &output);
   TF_EXPECT_OK(status);
-  EXPECT_EQ(21, output.node_size());
+  EXPECT_EQ(20, output.node_size());
 
   const NodeDef& node_assign = output.node(0);
   EXPECT_EQ("assign", node_assign.name());
@@ -64,64 +62,60 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   const NodeDef& node_fifo_queue = output.node(2);
   EXPECT_EQ("fifo_queue", node_fifo_queue.name());
 
-  const NodeDef& node_identity = output.node(3);
-  EXPECT_EQ("identity", node_identity.name());
-  EXPECT_EQ("var", node_identity.input(0));
-
-  const NodeDef& node_var = output.node(4);
+  const NodeDef& node_var = output.node(3);
   EXPECT_EQ("var", node_var.name());
 
-  const NodeDef& node_div_const0 = output.node(5);
+  const NodeDef& node_div_const0 = output.node(4);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-Const",
             node_div_const0.name());
 
-  const NodeDef& node_div0 = output.node(6);
+  const NodeDef& node_div0 = output.node(5);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-apply_gradient",
             node_div0.name());
-  const NodeDef& node_add0 = output.node(7);
+  const NodeDef& node_add0 = output.node(6);
   EXPECT_EQ("AutoParallel-Replica-0/add", node_add0.name());
 
-  const NodeDef& node_gradient0 = output.node(8);
+  const NodeDef& node_gradient0 = output.node(7);
   EXPECT_EQ("AutoParallel-Replica-0/apply_gradient", node_gradient0.name());
 
-  const NodeDef& node_constant_a0 = output.node(9);
+  const NodeDef& node_constant_a0 = output.node(8);
   EXPECT_EQ("AutoParallel-Replica-0/constant_a", node_constant_a0.name());
 
-  const NodeDef& node_dequeue0 = output.node(10);
+  const NodeDef& node_dequeue0 = output.node(9);
   EXPECT_EQ("AutoParallel-Replica-0/dequeue", node_dequeue0.name());
 
-  const NodeDef& node_learning_rate0 = output.node(11);
+  const NodeDef& node_learning_rate0 = output.node(10);
   EXPECT_EQ("AutoParallel-Replica-0/learning_rate", node_learning_rate0.name());
 
-  const NodeDef& node_div_const1 = output.node(12);
+  const NodeDef& node_div_const1 = output.node(11);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-Const",
             node_div_const1.name());
 
-  const NodeDef& node_div1 = output.node(13);
+  const NodeDef& node_div1 = output.node(12);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-apply_gradient",
             node_div1.name());
 
-  const NodeDef& node_add1 = output.node(14);
+  const NodeDef& node_add1 = output.node(13);
   EXPECT_EQ("AutoParallel-Replica-1/add", node_add1.name());
 
-  const NodeDef& node_gradient1 = output.node(15);
+  const NodeDef& node_gradient1 = output.node(14);
   EXPECT_EQ("AutoParallel-Replica-1/apply_gradient", node_gradient1.name());
 
-  const NodeDef& node_constant_a1 = output.node(16);
+  const NodeDef& node_constant_a1 = output.node(15);
   EXPECT_EQ("AutoParallel-Replica-1/constant_a", node_constant_a1.name());
 
-  const NodeDef& node_dequeue1 = output.node(17);
+  const NodeDef& node_dequeue1 = output.node(16);
   EXPECT_EQ("AutoParallel-Replica-1/dequeue", node_dequeue1.name());
 
-  const NodeDef& node_learning_rate1 = output.node(18);
+  const NodeDef& node_learning_rate1 = output.node(17);
   EXPECT_EQ("AutoParallel-Replica-1/learning_rate", node_learning_rate1.name());
 
-  const NodeDef& node_fetch = output.node(19);
+  const NodeDef& node_fetch = output.node(18);
   EXPECT_EQ("AutoParallel-Control-Fetch", node_fetch.name());
   EXPECT_EQ("^AutoParallel-Replica-0/apply_gradient", node_fetch.input(0));
   EXPECT_EQ("^AutoParallel-Replica-1/apply_gradient", node_fetch.input(1));
 
-  const NodeDef& node_gradient = output.node(20);
+  const NodeDef& node_gradient = output.node(19);
   EXPECT_EQ("apply_gradient", node_gradient.name());
   EXPECT_EQ("^AutoParallel-Control-Fetch", node_gradient.input(0));
 }
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 28d663e2f7..ded1e474ce 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -929,7 +929,7 @@ struct TuningConfig {
   // Conv2DBackpropFilter will use a specialized GEMM implementation, which is
   // usually faster than the NCHW implementation. The downside is that this
   // might result in more non-cancellable layout conversion nodes (implemented
-  // by the Transpose op).
+  // by the Tranpose op).
   bool no_gemm;
 };
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 9c397954e1..8b7c269a11 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -2464,7 +2464,7 @@ tf_cc_tests(
         ":ops_util",
         ":sparse_add_op",
         ":sparse_dense_binary_op_shared",
-        ":sparse_reduce_op",
+        ":sparse_reduce_sum_op",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -3207,7 +3207,7 @@ cc_library(
         ":sparse_cross_op",
         ":sparse_dense_binary_op_shared",
         ":sparse_fill_empty_rows_op",
-        ":sparse_reduce_op",
+        ":sparse_reduce_sum_op",
         ":sparse_reorder_op",
         ":sparse_reshape_op",
         ":sparse_softmax",
@@ -3263,8 +3263,8 @@ tf_kernel_library(
 )
 
 tf_kernel_library(
-    name = "sparse_reduce_op",
-    prefix = "sparse_reduce_op",
+    name = "sparse_reduce_sum_op",
+    prefix = "sparse_reduce_sum_op",
     deps = SPARSE_DEPS,
 )
 
diff --git a/tensorflow/core/kernels/adjust_contrast_op.cc b/tensorflow/core/kernels/adjust_contrast_op.cc
index 37976f7183..c8f12f91a6 100644
--- a/tensorflow/core/kernels/adjust_contrast_op.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op.cc
@@ -31,9 +31,6 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
-#ifdef TENSORFLOW_USE_SYCL
-typedef Eigen::SyclDevice SYCLDevice;
-#endif
 
 // AdjustContrastOp is deprecated as of GraphDef version >= 2
 
@@ -413,25 +410,4 @@ REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_GPU),
                         AdjustContrastOpv2<GPUDevice>);
 #endif  // GOOGLE_CUDA
 
-#ifdef TENSORFLOW_USE_SYCL
-template <>
-class AdjustContrastOpv2<SYCLDevice> : public AdjustContrastOpV2Base {
- public:
-  explicit AdjustContrastOpv2(OpKernelConstruction* context)
-      : AdjustContrastOpV2Base(context) {}
-
-  void DoCompute(OpKernelContext* context,
-                 const ComputeOptions& options) override {
-    const int64 shape[4] = {options.batch, options.height, options.width,
-                            options.channels};
-    functor::AdjustContrastv2<SYCLDevice>()(
-        context->eigen_device<SYCLDevice>(),
-        options.input->shaped<float, 4>(shape), options.factor->scalar<float>(),
-        options.output->shaped<float, 4>(shape));
-  }
-};
-REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_SYCL),
-                        AdjustContrastOpv2<SYCLDevice>);
-#endif  // TENSORFLOW_USE_SYCL
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
index c485f14844..ffd47406eb 100644
--- a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
@@ -56,11 +56,6 @@ static Graph* BM_AdjustContrast(int batches, int width, int height) {
 // BM_AdjustContrast_cpu_1_299_299     179084     340186  2181  751.9M items/s
 // BM_AdjustContrast_gpu_32_299_299     85276     123665  4189  2.9G items/s
 BM_AdjustContrastDev(cpu, 1, 299, 299);
-#if GOOGLE_CUDA
 BM_AdjustContrastDev(gpu, 32, 299, 299);
-#endif  // GOOGLE_CUDA
-#ifdef TENSORFLOW_USE_SYCL
-BM_AdjustContrastDev(sycl, 32, 299, 299);
-#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc
index ba100b32e7..d65a34fd73 100644
--- a/tensorflow/core/kernels/colorspace_op.cc
+++ b/tensorflow/core/kernels/colorspace_op.cc
@@ -35,9 +35,6 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
-#ifdef TENSORFLOW_USE_SYCL
-typedef Eigen::SyclDevice SYCLDevice;
-#endif
 
 template <typename Device, typename T>
 class RGBToHSVOp : public OpKernel {
@@ -149,16 +146,4 @@ TF_CALL_float(REGISTER_GPU);
 TF_CALL_double(REGISTER_GPU);
 #endif
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL(T)                                       \
-  REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_SYCL) \
-                              .TypeConstraint<T>("T"),         \
-                          RGBToHSVOp<SYCLDevice, T>);          \
-  REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_SYCL) \
-                              .TypeConstraint<T>("T"),         \
-                          HSVToRGBOp<SYCLDevice, T>);
-TF_CALL_float(REGISTER_SYCL);
-TF_CALL_double(REGISTER_SYCL);
-#endif
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/control_flow_ops.cc b/tensorflow/core/kernels/control_flow_ops.cc
index 64c06786bc..203a9a9f24 100644
--- a/tensorflow/core/kernels/control_flow_ops.cc
+++ b/tensorflow/core/kernels/control_flow_ops.cc
@@ -112,14 +112,15 @@ REGISTER_GPU_HOST_REF_KERNEL(string);
 #undef REGISTER_GPU_HOST_KERNEL
 #undef REGISTER_GPU_HOST_REF_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_SWITCH(type)                       \
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                       \
   REGISTER_KERNEL_BUILDER(Name("Switch")                 \
                               .Device(DEVICE_SYCL)       \
-                              .HostMemory("pred")        \
-                              .TypeConstraint<type>("T"),\
+                              .TypeConstraint<type>("T") \
+                              .HostMemory("pred"),       \
                           SwitchOp)
-TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_SWITCH);
+REGISTER_SYCL_KERNEL(bool);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 
 #define REGISTER_SYCL_REF_SWITCH(type)                     \
   REGISTER_KERNEL_BUILDER(Name("RefSwitch")                \
@@ -127,41 +128,12 @@ TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_SWITCH);
                               .HostMemory("pred")          \
                               .TypeConstraint<type>("T"),  \
                           SwitchOp)
-TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH);
+REGISTER_SYCL_REF_SWITCH(bool);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH);
 
-#undef REGISTER_SYCL_SWITCH
+#undef REGISTER_SYCL_KERNEL
 #undef REGISTER_SYCL_REF_SWITCH
 
-#define REGISTER_SYCL_HOST_KERNEL(type)                  \
-  REGISTER_KERNEL_BUILDER(Name("Switch")                 \
-                              .Device(DEVICE_SYCL)       \
-                              .HostMemory("data")        \
-                              .HostMemory("pred")        \
-                              .HostMemory("output_false")\
-                              .HostMemory("output_true") \
-                              .TypeConstraint<type>("T"),\
-                          SwitchOp)
-
-REGISTER_SYCL_HOST_KERNEL(bool);
-REGISTER_SYCL_HOST_KERNEL(string);
-REGISTER_SYCL_HOST_KERNEL(int32);
-
-#define REGISTER_SYCL_HOST_REF_KERNEL(type)                \
-  REGISTER_KERNEL_BUILDER(Name("RefSwitch")                \
-                              .Device(DEVICE_SYCL)         \
-                              .HostMemory("data")          \
-                              .HostMemory("pred")          \
-                              .HostMemory("output_false")  \
-                              .HostMemory("output_true")   \
-                              .TypeConstraint<type>("T"),  \
-                          SwitchOp)
-
-REGISTER_SYCL_HOST_REF_KERNEL(int32);
-REGISTER_SYCL_HOST_REF_KERNEL(bool);
-REGISTER_SYCL_HOST_REF_KERNEL(string);
-
-#undef REGISTER_SYCL_HOST_KERNEL
-#undef REGISTER_SYCL_HOST_REF_KERNEL
 #endif // TENSORFLOW_USE_SYCL
 
 class RefSelectOp : public OpKernel {
@@ -261,13 +233,13 @@ REGISTER_GPU_REF_KERNEL(bool);
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
+#if TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                        \
   REGISTER_KERNEL_BUILDER(Name("Merge")                   \
                               .Device(DEVICE_SYCL)        \
                               .TypeConstraint<type>("T")  \
                               .HostMemory("value_index"), \
-                          MergeOp);
+                          MergeOp)
 REGISTER_SYCL_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 
@@ -276,10 +248,9 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
                               .Device(DEVICE_SYCL)        \
                               .TypeConstraint<type>("T")  \
                               .HostMemory("value_index"), \
-                          MergeOp);
+                          MergeOp)
 REGISTER_SYCL_REF_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
-
 #undef REGISTER_SYCL_KERNEL
 #undef REGISTER_SYCL_REF_KERNEL
 #endif // TENSORFLOW_USE_SYCL
@@ -309,30 +280,6 @@ REGISTER_GPU_HOST_KERNEL(ResourceHandle);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_HOST_KERNEL(type)                   \
-  REGISTER_KERNEL_BUILDER(Name("Merge")                   \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("inputs")       \
-                              .HostMemory("output")       \
-                              .HostMemory("value_index")  \
-                              .TypeConstraint<type>("T"), \
-                          MergeOp);                       \
-  REGISTER_KERNEL_BUILDER(Name("RefMerge")                \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("inputs")       \
-                              .HostMemory("output")       \
-                              .HostMemory("value_index")  \
-                              .TypeConstraint<type>("T"), \
-                          MergeOp)
-
-REGISTER_SYCL_HOST_KERNEL(int32);
-REGISTER_SYCL_HOST_KERNEL(string);
-REGISTER_SYCL_HOST_KERNEL(ResourceHandle);
-
-#undef REGISTER_SYCL_HOST_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 void EnterOp::Compute(OpKernelContext* context) {
   if (IsRefType(context->input_dtype(0))) {
     context->forward_ref_input_to_ref_output(0, 0);
@@ -359,7 +306,7 @@ REGISTER_GPU_REF_KERNEL(bool);
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
+#if TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)  \
   REGISTER_KERNEL_BUILDER(          \
       Name("Enter").Device(DEVICE_SYCL).TypeConstraint<type>("T"), EnterOp)
@@ -398,7 +345,7 @@ REGISTER_SYCL_HOST_KERNEL(ResourceHandle);
 
 #undef REGISTER_SYCL_HOST_KERNEL
 #undef REGISTER_SYCL_HOST_REF_KERNEL
-#endif // TENSORFLOW_USE_SYCL
+#endif
 
 // Special GPU kernels for int32 and string.
 // TODO(b/25387198): Also enable int32 in device memory. This kernel
@@ -447,25 +394,30 @@ REGISTER_KERNEL_BUILDER(Name("RefExit").Device(DEVICE_CPU), ExitOp);
       Name("RefExit").Device(DEVICE_GPU).TypeConstraint<type>("T"), ExitOp);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_REF_KERNEL);
 REGISTER_GPU_KERNEL(bool);
-REGISTER_GPU_REF_KERNEL(bool);
 
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                                           \
-  REGISTER_KERNEL_BUILDER(                                                   \
-      Name("Exit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp);   \
-  REGISTER_KERNEL_BUILDER(                                                   \
-      Name("RefExit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp);
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)  \
+  REGISTER_KERNEL_BUILDER(          \
+  Name("Exit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp)
 REGISTER_SYCL_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 
+#define REGISTER_SYCL_REF_KERNEL(type)  \
+  REGISTER_KERNEL_BUILDER(              \
+  Name("RefExit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp)
+REGISTER_SYCL_REF_KERNEL(bool);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
+
 #undef REGISTER_SYCL_KERNEL
 #undef REGISTER_SYCL_REF_KERNEL
 
+// Special GPU kernels for int32 and string.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
 #define REGISTER_SYCL_HOST_KERNEL(type)                   \
   REGISTER_KERNEL_BUILDER(Name("Exit")                    \
                               .Device(DEVICE_SYCL)        \
@@ -555,19 +507,31 @@ REGISTER_GPU_HOST_KERNEL(string);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                                           \
-  REGISTER_KERNEL_BUILDER(                                                   \
-      Name("NextIteration").Device(DEVICE_SYCL).TypeConstraint<type>("T"),   \
-      NextIterationOp);                                                      \
-  REGISTER_KERNEL_BUILDER(                                                   \
-      Name("RefNextIteration").Device(DEVICE_SYCL).TypeConstraint<type>("T"),\
-      NextIterationOp)
-REGISTER_SYCL_KERNEL(bool);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)  \
+  REGISTER_KERNEL_BUILDER(Name("NextIteration")           \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("data")         \
+                              .HostMemory("output")       \
+                              .TypeConstraint<type>("T"), \
+                          NextIterationOp)
+  REGISTER_SYCL_KERNEL(bool);
+  TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+#define REGISTER_SYCL_REF_KERNEL(type)                    \
+  REGISTER_KERNEL_BUILDER(Name("RefNextIteration")        \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("data")         \
+                              .HostMemory("output")       \
+                              .TypeConstraint<type>("T"), \
+                          NextIterationOp)
+  REGISTER_SYCL_REF_KERNEL(bool);
+  TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
 #undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_REF_KERNEL
 
+// Special GPU kernels for int32 and string.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
 #define REGISTER_SYCL_HOST_KERNEL(type)                   \
   REGISTER_KERNEL_BUILDER(Name("NextIteration")           \
                               .Device(DEVICE_SYCL)        \
diff --git a/tensorflow/core/kernels/cwise_op_add_2.cc b/tensorflow/core/kernels/cwise_op_add_2.cc
index 5dea00e95c..5d3385b0ed 100644
--- a/tensorflow/core/kernels/cwise_op_add_2.cc
+++ b/tensorflow/core/kernels/cwise_op_add_2.cc
@@ -22,11 +22,10 @@ namespace tensorflow {
 // sharded files, only make its register calls when not __ANDROID_TYPES_SLIM__.
 #if !defined(__ANDROID_TYPES_SLIM__)
 
-REGISTER6(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64,
-          uint8, complex128, string);
+REGISTER5(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64,
+          complex128, string);
 #if GOOGLE_CUDA
-REGISTER4(BinaryOp, GPU, "Add", functor::add, uint8, int64, complex64,
-          complex128);
+REGISTER3(BinaryOp, GPU, "Add", functor::add, int64, complex64, complex128);
 #endif  // GOOGLE_CUDA
 
 #endif  // !defined(__ANDROID_TYPES_SLIM__)
diff --git a/tensorflow/core/kernels/cwise_op_cosh.cc b/tensorflow/core/kernels/cwise_op_cosh.cc
deleted file mode 100644
index bca99a4f89..0000000000
--- a/tensorflow/core/kernels/cwise_op_cosh.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/core/kernels/cwise_ops_common.h"
-
-namespace tensorflow {
-REGISTER4(UnaryOp, CPU, "Cosh", functor::cosh, float, double,
-          complex64, complex128);
-
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Cosh")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::cosh<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
-#if GOOGLE_CUDA
-REGISTER2(UnaryOp, GPU, "Cosh", functor::cosh, float, double);
-#endif
-}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
index 61079ebab3..5aaf2b5b4b 100644
--- a/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
+++ b/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
@@ -19,8 +19,7 @@ limitations under the License.
 
 namespace tensorflow {
 namespace functor {
-DEFINE_BINARY7(add, Eigen::half, float, double, uint8, int64, complex64,
-               complex128);
+DEFINE_BINARY6(add, Eigen::half, float, double, int64, complex64, complex128);
 }  // namespace functor
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
deleted file mode 100644
index 267a381d1a..0000000000
--- a/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
+++ /dev/null
@@ -1,26 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#if GOOGLE_CUDA
-
-#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
-
-namespace tensorflow {
-namespace functor {
-DEFINE_UNARY2(cosh, float, double);
-}  // namespace functor
-}  // namespace tensorflow
-
-#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
deleted file mode 100644
index f8329e50d6..0000000000
--- a/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
+++ /dev/null
@@ -1,26 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#if GOOGLE_CUDA
-
-#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
-
-namespace tensorflow {
-namespace functor {
-DEFINE_UNARY2(sinh, float, double);
-}  // namespace functor
-}  // namespace tensorflow
-
-#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc
index df2c02e42e..c84ee6894e 100644
--- a/tensorflow/core/kernels/cwise_op_invert.cc
+++ b/tensorflow/core/kernels/cwise_op_invert.cc
@@ -20,7 +20,7 @@ REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64,
           uint8, uint16);
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER6(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
+REGISTER(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
          uint8, uint16);
 #endif  // TENSORFLOW_USE_SYCL
 
diff --git a/tensorflow/core/kernels/cwise_op_sinh.cc b/tensorflow/core/kernels/cwise_op_sinh.cc
deleted file mode 100644
index 055f0b12e1..0000000000
--- a/tensorflow/core/kernels/cwise_op_sinh.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/core/kernels/cwise_ops_common.h"
-
-namespace tensorflow {
-REGISTER4(UnaryOp, CPU, "Sinh", functor::sinh, float, double,
-          complex64, complex128);
-
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Sinh")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::sinh<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
-#if GOOGLE_CUDA
-REGISTER2(UnaryOp, GPU, "Sinh", functor::sinh, float, double);
-#endif
-}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h
index c11d6cfabb..97bdc5e878 100644
--- a/tensorflow/core/kernels/cwise_ops.h
+++ b/tensorflow/core/kernels/cwise_ops.h
@@ -483,12 +483,6 @@ struct log1p : base<T, Eigen::internal::scalar_log1p_op<T> > {};
 template <typename T>
 struct sign : base<T, Eigen::internal::scalar_sign_op<T> > {};
 
-template <typename T>
-struct sinh : base<T, Eigen::internal::scalar_sinh_op<T> > {};
-
-template <typename T>
-struct cosh : base<T, Eigen::internal::scalar_cosh_op<T> > {};
-
 template <typename T>
 struct tanh : base<T, Eigen::internal::scalar_tanh_op<T> > {};
 
diff --git a/tensorflow/core/kernels/dynamic_stitch_op.cc b/tensorflow/core/kernels/dynamic_stitch_op.cc
index 135d635514..08ae787c86 100644
--- a/tensorflow/core/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/core/kernels/dynamic_stitch_op.cc
@@ -165,32 +165,33 @@ class DynamicStitchOp : public OpKernel {
 TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH);
 #undef REGISTER_DYNAMIC_STITCH
 
-#if GOOGLE_CUDA
-#define REGISTER_DYNAMIC_STITCH_GPU(type)                \
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_DYNAMIC_STITCH_SYCL(type)               \
   REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
-                              .Device(DEVICE_GPU)        \
+                              .Device(DEVICE_SYCL)       \
                               .TypeConstraint<type>("T") \
                               .HostMemory("indices")     \
                               .HostMemory("data")        \
                               .HostMemory("merged"),     \
                           DynamicStitchOp<type>)
 
-TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH_GPU);
-#undef REGISTER_DYNAMIC_STITCH_GPU
-
-#endif  // GOOGLE_CUDA
+TF_CALL_ALL_TYPES(REGISTER_DYNAMIC_STITCH_SYCL);
+#undef REGISTER_DYNAMIC_STITCH_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_DYNAMIC_STITCH_SYCL(type)               \
+#if GOOGLE_CUDA
+#define REGISTER_DYNAMIC_STITCH_GPU(type)                \
   REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
-                              .Device(DEVICE_SYCL)       \
+                              .Device(DEVICE_GPU)        \
                               .TypeConstraint<type>("T") \
                               .HostMemory("indices")     \
                               .HostMemory("data")        \
                               .HostMemory("merged"),     \
                           DynamicStitchOp<type>)
 
-TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH_SYCL);
-#undef REGISTER_DYNAMIC_STITCH_SYCL
-#endif  // TENSORFLOW_USE_SYCL
+TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH_GPU);
+#undef REGISTER_DYNAMIC_STITCH_GPU
+
+#endif  // GOOGLE_CUDA
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/map_stage_op.cc b/tensorflow/core/kernels/map_stage_op.cc
index 46eaf3d9e7..6431c6540e 100644
--- a/tensorflow/core/kernels/map_stage_op.cc
+++ b/tensorflow/core/kernels/map_stage_op.cc
@@ -547,14 +547,14 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapStage")
                             .HostMemory("indices")
                             .Device(DEVICE_GPU),
                         MapStageOp<true>);
-#endif // GOOGLE_CUDA
-
+#endif
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(Name("MapStage").HostMemory("key").Device(DEVICE_SYCL),
                         MapStageOp<false>);
 REGISTER_KERNEL_BUILDER(
     Name("OrderedMapStage").HostMemory("key").Device(DEVICE_SYCL),
     MapStageOp<true>);
+
 #endif // TENSORFLOW_USE_SYCL
 
 template <bool Ordered>
@@ -661,7 +661,6 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapPeek")
                             .Device(DEVICE_GPU),
                         MapPeekOp<true>);
 #endif
-
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(
     Name("MapPeek").HostMemory("key").HostMemory("indices").Device(DEVICE_SYCL),
@@ -725,8 +724,8 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapUnstageNoKey")
                             .HostMemory("indices")
                             .Device(DEVICE_GPU),
                         MapUnstageNoKeyOp<true>);
-#endif
 
+#endif
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(Name("MapUnstageNoKey")
                             .HostMemory("key")
diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
index ddcf241277..dc6b88e953 100644
--- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
@@ -206,15 +206,10 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
     // Mkl needs the entities in its native format.
     // So create temporary tensors along with buffers to
     // convert the received entities.
-    Tensor mkl_tmp_input_buf_tensor, mkl_tmp_out_backprop_buf_tensor,
-           mkl_tmp_buf_trans_input;
+    Tensor mkl_tmp_input_buf_tensor, mkl_tmp_out_backprop_buf_tensor;
     // This preparation sets (1) dnnResourceSrc (2) dnnResourceDiffDst
-    mkl_context.MklPrepareInputs(context, data_format_,
-                                 input_in_mkl_format,
-                                 out_backprop_in_mkl_format,
-                                 &mkl_tmp_input_buf_tensor,
-                                 &mkl_tmp_out_backprop_buf_tensor,
-                                 &mkl_tmp_buf_trans_input);
+    mkl_context.MklPrepareInputs(context, &mkl_tmp_input_buf_tensor,
+                                 &mkl_tmp_out_backprop_buf_tensor);
 
     // Final conv-grad-filter should be in TF layout.
     Tensor* grad_filter;
@@ -312,58 +307,34 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
 
     // Compare incoming tensor layouts with MKL preferred layouts and convert
     // data to the preferred layout if necessary
-    void MklPrepareInputs(OpKernelContext* context, TensorFormat format,
-                          bool input_in_mkl_format,
-                          bool out_backprop_in_mkl_format,
+    void MklPrepareInputs(OpKernelContext* context,
                           Tensor* mkl_tmp_input_buf_tensor,
-                          Tensor* mkl_tmp_out_backprop_buf_tensor,
-                          Tensor* mkl_tmp_buf_trans_input) {
+                          Tensor* mkl_tmp_out_backprop_buf_tensor) {
       bool mkl_convert_input, mkl_convert_out_backprop;
       dnnPrimitive_t mkl_prim_convert_input, mkl_prim_convert_out_backprop;
-      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_out_backprop,
-                  mkl_lt_trans_input;
+      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_out_backprop;
       void *mkl_buf_convert_input, *mkl_buf_convert_out_backprop;
-      void *mkl_buf_input, *mkl_buf_out_backprop;
 
       mkl_prim_convert_input = nullptr;
       mkl_prim_convert_out_backprop = nullptr;
       mkl_lt_internal_input = nullptr;
       mkl_lt_internal_out_backprop = nullptr;
-      mkl_lt_trans_input = nullptr;
       mkl_buf_convert_input = nullptr;
       mkl_buf_convert_out_backprop = nullptr;
-      mkl_buf_input = nullptr;
-      mkl_buf_out_backprop = nullptr;
 
       // Compare with internal layouts and convert if needed
       const Tensor& input = MklGetInput(context, 0);
-      if (!input_in_mkl_format && format == FORMAT_NHWC){
-        TensorShape nchw_shape = ShapeFromFormat(FORMAT_NCHW,
-            in_sizes[MklDims::N], in_sizes[MklDims::H],
-            in_sizes[MklDims::W], in_sizes[MklDims::C]);
-        OP_REQUIRES_OK(context, context->allocate_temp(
-            DataTypeToEnum<float>::value, nchw_shape, mkl_tmp_buf_trans_input));
-        MklNHWCToNCHW(input, &mkl_tmp_buf_trans_input);
-        mkl_buf_input = const_cast<void*>(static_cast<const void*>(
-            mkl_tmp_buf_trans_input->flat<float>().data()));
-        size_t strides[4];
-        GetStridesFromSizes(FORMAT_NCHW, strides, in_sizes);
-        CHECK_EQ(dnnLayoutCreate_F32(&mkl_lt_trans_input, in_dims, in_sizes,
-            strides), E_SUCCESS);
-      }
-      else {
-        mkl_buf_input =
-            const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
-        mkl_lt_trans_input = lt_input;
-      }
+      void* mkl_buf_input =
+          const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(
                    &mkl_lt_internal_input, prim_conv_bwdfilter, dnnResourceSrc),
                E_SUCCESS);
       mkl_convert_input =
-          !dnnLayoutCompare_F32(mkl_lt_internal_input, mkl_lt_trans_input);
+          !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input);
       if (mkl_convert_input) {
-        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input,
-                     mkl_lt_trans_input, mkl_lt_internal_input), E_SUCCESS);
+        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input,
+                                         mkl_lt_internal_input),
+                 E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input,
                        &mkl_buf_convert_input);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input,
@@ -372,30 +343,26 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
         dnnDelete_F32(mkl_prim_convert_input);
       }
       dnnLayoutDelete_F32(mkl_lt_internal_input);
-      if (!input_in_mkl_format && format == FORMAT_NHWC)
-        dnnLayoutDelete_F32(mkl_lt_trans_input);
-
 
       conv_res[dnnResourceSrc] =
           (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input;
 
       const Tensor& out_backprop = MklGetInput(context, 2);
-      mkl_buf_out_backprop = const_cast<void*>(
-                      static_cast<const void*>(out_backprop.flat<T>().data()));
-
+      void* mkl_buf_out_backprop = const_cast<void*>(
+          static_cast<const void*>(out_backprop.flat<T>().data()));
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_out_backprop,
                                                 prim_conv_bwdfilter,
                                                 dnnResourceDiffDst),
                E_SUCCESS);
       mkl_convert_out_backprop =
-          !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop,
-                                lt_out_backprop);
+          !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop, lt_out_backprop);
       if (mkl_convert_out_backprop) {
         CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_out_backprop,
-                      lt_out_backprop, mkl_lt_internal_out_backprop),
+                                         lt_out_backprop,
+                                         mkl_lt_internal_out_backprop),
                  E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_out_backprop_buf_tensor,
-            mkl_lt_internal_out_backprop, &mkl_buf_convert_out_backprop);
+                       lt_out_backprop, &mkl_buf_convert_out_backprop);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_out_backprop,
                                           mkl_buf_out_backprop,
                                           mkl_buf_convert_out_backprop),
diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc
index df49e03f31..76b9f1798d 100644
--- a/tensorflow/core/kernels/mkl_conv_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_ops.cc
@@ -267,15 +267,12 @@ class MklConv2DOp : public OpKernel {
 
     mkl_context.MklCreateInputLayouts(context);
 
-    // Temp tensor used to allocate tmp buffers
     Tensor mkl_tmp_input_buf_tensor, mkl_tmp_filter_buf_tensor,
-        mkl_tmp_bias_buf_tensor, mkl_tmp_buf_trans_input;
-    mkl_context.MklPrepareConvolutionInputs(context, data_format_,
-                                            input_in_mkl_format,
-                                            &mkl_tmp_input_buf_tensor,
+        mkl_tmp_bias_buf_tensor;  // Temp tensor used to allocate tmp
+                                  // buffers
+    mkl_context.MklPrepareConvolutionInputs(context, &mkl_tmp_input_buf_tensor,
                                             &mkl_tmp_filter_buf_tensor,
-                                            &mkl_tmp_bias_buf_tensor,
-                                            &mkl_tmp_buf_trans_input);
+                                            &mkl_tmp_bias_buf_tensor);
 
     // Execute convolution
     CHECK_EQ(dnnExecute_F32(mkl_context.prim_fwd, mkl_context.conv_res),
@@ -326,59 +323,39 @@ class MklConv2DOp : public OpKernel {
     // Compare incoming tensor layouts with MKL preferred layouts and convert
     // data to the preferred layout if necessary
     void MklPrepareConvolutionInputs(OpKernelContext* context,
-                                     TensorFormat format,
-                                     bool input_in_mkl_format,
                                      Tensor* mkl_tmp_input_buf_tensor,
                                      Tensor* mkl_tmp_filter_buf_tensor,
-                                     Tensor* mkl_tmp_bias_buf_tensor,
-                                     Tensor* mkl_tmp_buf_trans_input) {
+                                     Tensor* mkl_tmp_bias_buf_tensor) {
       bool mkl_convert_input, mkl_convert_filter, mkl_convert_bias;
       dnnPrimitive_t mkl_prim_convert_filter, mkl_prim_convert_bias,
           mkl_prim_convert_input;
       dnnLayout_t mkl_lt_internal_filter, mkl_lt_internal_bias,
-          mkl_lt_internal_input, mkl_lt_trans_input;
+          mkl_lt_internal_input;
       void *mkl_buf_convert_input, *mkl_buf_convert_filter,
-          *mkl_buf_convert_bias, *mkl_buf_input;
+          *mkl_buf_convert_bias;
       mkl_prim_convert_filter = nullptr;
       mkl_prim_convert_bias = nullptr;
       mkl_prim_convert_input = nullptr;
       mkl_lt_internal_filter = nullptr;
       mkl_lt_internal_bias = nullptr;
       mkl_lt_internal_input = nullptr;
-      mkl_lt_trans_input = nullptr;
       mkl_buf_convert_input = nullptr;
       mkl_buf_convert_filter = nullptr;
       mkl_buf_convert_bias = nullptr;
-      mkl_buf_input = nullptr;
 
       // Compare with internal layouts and convert if needed
       const Tensor& input = MklGetInput(context, 0);
-      if (!input_in_mkl_format && format == FORMAT_NHWC) {
-        TensorShape nchw_shape = ShapeFromFormat(FORMAT_NCHW,
-            in_sizes[MklDims::N], in_sizes[MklDims::H],
-            in_sizes[MklDims::W], in_sizes[MklDims::C]);
-        OP_REQUIRES_OK(context, context->allocate_temp(
-            DataTypeToEnum<float>::value, nchw_shape, mkl_tmp_buf_trans_input));
-        MklNHWCToNCHW(input, &mkl_tmp_buf_trans_input);
-        mkl_buf_input = const_cast<void*>(static_cast<const void*>(
-            mkl_tmp_buf_trans_input->flat<float>().data()));
-        size_t strides[4];
-        GetStridesFromSizes(FORMAT_NCHW, strides, in_sizes);
-        CHECK_EQ(dnnLayoutCreate_F32(&mkl_lt_trans_input, in_dims, in_sizes,
-            strides), E_SUCCESS);
-      } else {
-          mkl_buf_input = const_cast<void*>(
-                              static_cast<const void*>(input.flat<T>().data()));
-          mkl_lt_trans_input = lt_input;
-      }
+      void* mkl_buf_input =
+          const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_input,
                                                 prim_fwd, dnnResourceSrc),
                E_SUCCESS);
       mkl_convert_input =
-          !dnnLayoutCompare_F32(mkl_lt_internal_input, mkl_lt_trans_input);
+          !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input);
       if (mkl_convert_input) {
-        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input,
-                 mkl_lt_trans_input, mkl_lt_internal_input), E_SUCCESS);
+        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input,
+                                         mkl_lt_internal_input),
+                 E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input,
                        &mkl_buf_convert_input);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input,
@@ -387,8 +364,6 @@ class MklConv2DOp : public OpKernel {
         dnnDelete_F32(mkl_prim_convert_input);
       }
       dnnLayoutDelete_F32(mkl_lt_internal_input);
-      if (!input_in_mkl_format && format == FORMAT_NHWC)
-        dnnLayoutDelete_F32(mkl_lt_trans_input);
 
       conv_res[dnnResourceSrc] =
           (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input;
diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc
index 07a7e6b5da..070aeff49f 100644
--- a/tensorflow/core/kernels/mkl_lrn_op.cc
+++ b/tensorflow/core/kernels/mkl_lrn_op.cc
@@ -22,6 +22,9 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 #include <vector>
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "third_party/mkl/include/mkl_dnn.h"
+#include "third_party/mkl/include/mkl_dnn_types.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -30,9 +33,6 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/util/mkl_util.h"
 #include "tensorflow/core/util/tensor_format.h"
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
 
 #if !defined(IS_MOBILE_PLATFORM)
 #include "tensorflow/core/util/work_sharder.h"
@@ -66,10 +66,11 @@ class MklLRNOp : public OpKernel {
   explicit MklLRNOp(OpKernelConstruction* context) : OpKernel(context) {
     int64 depth_radius64;
     OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64));
-    OP_REQUIRES(context, FastBoundsCheck(depth_radius64,
-                                         std::numeric_limits<int>::max()),
-                errors::InvalidArgument("depth_radius = ", depth_radius64,
-                                        " larger than int max"));
+    OP_REQUIRES(
+        context,
+        FastBoundsCheck(depth_radius64, std::numeric_limits<int>::max()),
+        errors::InvalidArgument("depth_radius = ", depth_radius64,
+                                " larger than int max"));
     depth_radius_ = static_cast<size_t>(depth_radius64);
 
     OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_));
@@ -92,9 +93,10 @@ class MklLRNOp : public OpKernel {
                               : input.dims();
     OP_REQUIRES(context, mkl_context.in_dims == 4,
                 errors::InvalidArgument("input must be 4-dimensional"));
-    OP_REQUIRES(context, FastBoundsCheck(input.NumElements(),
-                                         std::numeric_limits<int>::max()),
-                errors::InvalidArgument("argument to LRN too large"));
+    OP_REQUIRES(
+        context,
+        FastBoundsCheck(input.NumElements(), std::numeric_limits<int>::max()),
+        errors::InvalidArgument("argument to LRN too large"));
 
     if (!input_in_mkl_format) {
       mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_,
@@ -102,6 +104,15 @@ class MklLRNOp : public OpKernel {
       return;
     }
 
+    // TODO(inteltf) MKL will support depth radius not equal to 2 in the future
+    if (depth_radius_ != 2) {
+      Tensor converted_tensor =
+          ConvertMklToTF<T>(context, input, mkl_context.input_shape);
+      mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_,
+                                    beta_, converted_tensor);
+      return;
+    }
+
     if (input_in_mkl_format) {
       // MKL supports normalization over channel dimension only
       if (mkl_context.input_shape.tf_dim_idx(mkl_context.in_dims - 1) ==
@@ -334,10 +345,11 @@ class MklLRNGradOp : public OpKernel {
   explicit MklLRNGradOp(OpKernelConstruction* context) : OpKernel(context) {
     int64 depth_radius64;
     OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64));
-    OP_REQUIRES(context, FastBoundsCheck(depth_radius64,
-                                         std::numeric_limits<int>::max()),
-                errors::InvalidArgument("depth_radius = ", depth_radius64,
-                                        " larger than int max"));
+    OP_REQUIRES(
+        context,
+        FastBoundsCheck(depth_radius64, std::numeric_limits<int>::max()),
+        errors::InvalidArgument("depth_radius = ", depth_radius64,
+                                " larger than int max"));
     depth_radius_ = static_cast<int>(depth_radius64);
     OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_));
     OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_));
@@ -541,9 +553,6 @@ class MklLRNGradOp : public OpKernel {
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&lt_bdw_input, lrn_bwd,
                                                 dnnResourceDiffDst),
                E_SUCCESS);
-      CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&lt_internal_input, lrn_bwd,
-                                                dnnResourceSrc),
-               E_SUCCESS);
 
       bool ingrad_in_mkl_format = ingrad_shape.IsMklTensor();
       if (ingrad_in_mkl_format) {
@@ -572,37 +581,44 @@ class MklLRNGradOp : public OpKernel {
         }
       }
 
-      bool inimage_in_mkl_format = inimage_shape.IsMklTensor();
-      if (inimage_in_mkl_format) {
-        if (!dnnLayoutCompare_F32(
-                lt_internal_input,
-                static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()))) {
-          AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
-                         &res_lrn_bwd[dnnResourceSrc]);
-          ingrad_shape.GetConvertedFlatData(lt_internal_input, user_fwd_input,
-                                            res_lrn_bwd[dnnResourceSrc]);
-        } else {
-          res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
-        }
+// Although MKL documentation for LRN does not specify setting/getting
+// of dnnResourceSrc and dnnResourceDst, Caffe code sets dnnResourceSrc.
+// So we set dnnResourceSrc here. But we do not know why we are setting
+// dnnResourceDst.
+#if 0
+    // NOTE: The code below is kept just so that we know how we should handle
+    // dnnResourceSrc if the primitive layout for dnnResourceSrc was supported.
+
+    if (!dnnLayoutCompare_F32(lt_internal_input,
+         static_cast<dnnLayout_t>inimage_shape.GetCurLayout())) {
+      AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
+                     &res_lrn_bwd[dnnResourceSrc]);
+      inimage_shape.GetConvertedFlatData(lt_internal_input,
+                                           user_fwd_input,
+                                           res_lrn_bwd[dnnResourceSrc]);
+    } else {
+      res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
+    }
+#endif
+
+      // Since we cannot get expected layout for dnnResourceSrc, we construct
+      // buffer using
+      // MKL format if input is in MKL format.
+      if (inimage_shape.IsMklTensor()) {
+        AllocTmpBuffer(context, mkl_tmp_image_buf_tensor,
+                       (dnnLayout_t)inimage_shape.GetCurLayout(),
+                       &res_lrn_bwd[dnnResourceSrc]);
       } else {
-        if (!dnnLayoutCompare_F32(
-                lt_internal_input,
-                static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()))) {
-          CHECK_EQ(dnnConversionCreate_F32(
-                       &convert_input,
-                       static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()),
-                       lt_internal_input),
-                   E_SUCCESS);
+        res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
+      }
 
-          AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
-                         &res_lrn_bwd[dnnResourceSrc]);
-          CHECK_EQ(dnnConversionExecute_F32(convert_input, user_fwd_input,
-                                            res_lrn_bwd[dnnResourceSrc]),
-                   E_SUCCESS);
-          dnnDelete_F32(convert_input);
-        } else {
-          res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
-        }
+      // Same comment as above.
+      if (outimage_shape.IsMklTensor()) {
+        AllocTmpBuffer(context, mkl_tmp_outimage_buf_tensor,
+                       (dnnLayout_t)outimage_shape.GetCurLayout(),
+                       &res_lrn_bwd[dnnResourceDst]);
+      } else {
+        res_lrn_bwd[dnnResourceDst] = user_fwd_output;
       }
 
       res_lrn_bwd[dnnResourceWorkspace] = workspace_buffer;
@@ -612,6 +628,8 @@ class MklLRNGradOp : public OpKernel {
     // TODO(intelft) Check if we can use EigenLRNOp directly instead of making a
     // copy.
     void MklDefaultToEigen(OpKernelContext* context) {
+      // CHECK(false);
+
       Tensor in_grads;
       Tensor in_image;
       Tensor out_image;
@@ -691,7 +709,7 @@ class MklLRNGradOp : public OpKernel {
       Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch,
             depth * depth, shard);
     }
-
+		
     // release mkl resources
     void Mklcleanup() {
       bool ingrad_in_mkl_format = ingrad_shape.IsMklTensor();
diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc
index fabecc39a8..10d2937584 100644
--- a/tensorflow/core/kernels/mkl_relu_op.cc
+++ b/tensorflow/core/kernels/mkl_relu_op.cc
@@ -184,31 +184,38 @@ class MklReluGradOp : public OpKernel {
     dnnLayout_t lt_input, lt_grad;
 
     void MklPrepareReluGradInputs(OpKernelContext* context,
+                                  Tensor* mkl_tmp_grad_buf_tensor,
                                   Tensor* mkl_tmp_input_buf_tensor) {
+      dnnPrimitive_t cv_user_to_reluB_input, cv_user_to_reluB_grad;
+      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_grad;
+
       const Tensor& g = MklGetInput(context, 0);
       const Tensor& a = MklGetInput(context, 1);
-      void* buf_input = static_cast<void*>(const_cast<T*>(a.flat<T>().data()));
+
+      void* user_i = static_cast<void*>(const_cast<T*>(a.flat<T>().data()));
+      void* user_g = static_cast<void*>(const_cast<T*>(g.flat<T>().data()));
+      dnnPrimitive_t cv_input_to_grad = NULL;
+      Tensor mkl_tmp_buf_tensor;
       void* mkl_buffer_convert = nullptr;
-      dnnPrimitive_t cv_input_to_grad = nullptr;
 
       // if input and grad are not in the same layout, do a conversion between
       // them.
       if (!dnnLayoutCompare_F32(lt_input, lt_grad)) {
-        AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, lt_grad,
+        AllocTmpBuffer(context, &mkl_tmp_buf_tensor, lt_grad,
                        &mkl_buffer_convert);
-        CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input,
-                   lt_grad), E_SUCCESS);
-        CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, buf_input,
+        CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input, lt_grad),
+                 E_SUCCESS);
+
+        CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, user_i,
                                           mkl_buffer_convert),
                  E_SUCCESS);
         relu_res[dnnResourceSrc] = mkl_buffer_convert;
         dnnDelete_F32(cv_input_to_grad);
       } else {
-        relu_res[dnnResourceSrc] = buf_input;
+        relu_res[dnnResourceSrc] = user_i;
       }
 
-      void* buf_grad = static_cast<void*>(const_cast<T*>(g.flat<T>().data()));
-      relu_res[dnnResourceDiffDst] = buf_grad;
+      relu_res[dnnResourceDiffDst] = user_g;
     }
 
     void MklCreateInputLayouts(OpKernelContext* context) {
@@ -310,8 +317,9 @@ void MklReluGradOp<Device, T>::Compute(OpKernelContext* context) {
                                      mkl_context.lt_grad, mkl_context.lt_grad,
                                      negative_slope),
            E_SUCCESS);
-  Tensor mkl_tmp_input_buf_tensor;
-  mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_input_buf_tensor);
+  Tensor mkl_tmp_grad_buf_tensor, mkl_tmp_input_buf_tensor;
+  mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_grad_buf_tensor,
+                                       &mkl_tmp_input_buf_tensor);
 
   if (input_is_mkl ||
       grad_is_mkl) { /*if  grad or input are MKL leave it in MKL*/
diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc
index b4aae67ca6..588d6874dd 100644
--- a/tensorflow/core/kernels/mkl_tfconv_op.cc
+++ b/tensorflow/core/kernels/mkl_tfconv_op.cc
@@ -24,13 +24,12 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/ops_util.h"
-#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/util/tensor_format.h"
 
-#include "tensorflow/core/util/mkl_util.h"
 #include "third_party/mkl/include/mkl_dnn.h"
 #include "third_party/mkl/include/mkl_dnn_types.h"
+#include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -45,11 +44,10 @@ class MklToTfOp : public OpKernel {
   explicit MklToTfOp(OpKernelConstruction* context) : OpKernel(context) {
     OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format_str));
     OP_REQUIRES_OK(context, context->GetAttr("T", &op_data_type));
-    has_avx512f_ = port::TestCPUFeature(port::CPUFeature::AVX512F);
   }
 
   void Compute(OpKernelContext* context) override {
-    // Check that input tensor is in MKL format.
+    // 1. Check that input tensor is in MKL format.
     const Tensor& input_tensor = MklGetInput(context, 0);
     MklShape input_shape;
     GetMklShape(context, 0, &input_shape);
@@ -70,12 +68,9 @@ class MklToTfOp : public OpKernel {
     CHECK_EQ(op_data_type, output_data_type);
 
     TensorShape output_shape;
-    size_t ndims = input_shape.GetDimension();
-    size_t* in_sizes = new size_t[ndims];
-    for (size_t i = 0; i < ndims; i++) {
+    for (size_t i = 0; i < input_shape.GetDimension(); i++) {
       // Outermost to innermost dimension
       output_shape.AddDim(input_shape.GetSizes()[input_shape.tf_dim_idx(i)]);
-      in_sizes[i] = input_shape.GetSizes()[i];
     }
 
     // Allocate output tensor.
@@ -83,41 +78,17 @@ class MklToTfOp : public OpKernel {
     OP_REQUIRES_OK(context,
                    context->allocate_output(0, output_shape, &output_tensor));
 
-    // If data format is NHWC, transform MKL tensor to NCHW format and then
-    // do NCHW -> NHWC.
-    dnnLayout_t lt_trans_input = nullptr;
-    Tensor mkl_tmp_trans_input_buf_tensor;
-    void* buf_trans_input = nullptr;
-    bool input_fmt_nhwc = input_shape.IsTensorInNHWCFormat();
-    if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
-      size_t strides_nchw[4];
-      GetStridesFromSizes(FORMAT_NCHW, strides_nchw, in_sizes);
-      CHECK_EQ(
-          dnnLayoutCreate_F32(&lt_trans_input, ndims, in_sizes, strides_nchw),
-          E_SUCCESS);
-      AllocTmpBuffer(context, &mkl_tmp_trans_input_buf_tensor, lt_trans_input,
-                     &buf_trans_input);
-    } else {
-      lt_trans_input = static_cast<dnnLayout_t>(input_shape.GetTfLayout());
-      buf_trans_input =
-          static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
-    }
+    // 3. Get input and output layout pointers.
+    dnnLayout_t output_layout =
+        static_cast<dnnLayout_t>(input_shape.GetTfLayout());
 
-    // Execute DNNConversion.
+    // 4. Execute DNNConversion.
     void* input_buffer =
         static_cast<void*>(const_cast<T*>(input_tensor.flat<T>().data()));
-    input_shape.GetConvertedFlatData(lt_trans_input, input_buffer,
-                                     buf_trans_input);
-    // NCHW -> NHWC, if data format is NHWC
-    if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
-      dnnLayoutDelete_F32(lt_trans_input);
-      TensorShape nhwc_shape = ShapeFromFormat(
-          FORMAT_NHWC, in_sizes[MklDims::N], in_sizes[MklDims::H],
-          in_sizes[MklDims::W], in_sizes[MklDims::C]);
-      MklNCHWToNHWC(mkl_tmp_trans_input_buf_tensor, &output_tensor);
-    }
-
-    delete[] in_sizes;
+    void* output_buffer =
+        static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
+    input_shape.GetConvertedFlatData(output_layout, input_buffer,
+                                     output_buffer);
 
     VLOG(1) << "MKLToTFConversion complete successfully.";
   }
@@ -128,9 +99,6 @@ class MklToTfOp : public OpKernel {
 
   /// Data type of the operation
   DataType op_data_type;
-
-  /// CPUIDInfo
-  bool has_avx512f_ = false;
 };
 
 ///////////////////////////////////////////////////////////
diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index dc95f67ff0..9ffe71e031 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -90,24 +90,20 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
   return intersection_area / (area_i + area_j - intersection_area);
 }
 
-void DoNonMaxSuppressionOp(OpKernelContext* context,
-                           const Tensor& boxes,
-                           const Tensor& scores,
-                           const Tensor& max_output_size,
+void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
+                           const Tensor& scores, const Tensor& max_output_size,
                            const float iou_threshold) {
   OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
-      errors::InvalidArgument("iou_threshold must be in [0, 1]"));
-  
+              errors::InvalidArgument("iou_threshold must be in [0, 1]"));
+
   int num_boxes = 0;
   ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
   if (!context->status().ok()) {
     return;
   }
 
-  const int output_size =
-      std::min(max_output_size.scalar<int>()(), num_boxes);
-  typename TTypes<float, 2>::ConstTensor boxes_data =
-      boxes.tensor<float, 2>();
+  const int output_size = std::min(max_output_size.scalar<int>()(), num_boxes);
+  typename TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();
 
   std::vector<float> scores_data(num_boxes);
   std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
@@ -127,7 +123,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context,
     for (int j = i + 1; j < num_boxes; ++j) {
       if (active[j]) {
         float iou =
-          ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
+            ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
         if (iou > iou_threshold) {
           active[j] = false;
           num_active--;
@@ -145,7 +141,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context,
   std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
 }
 
-} // namespace
+}  // namespace
 
 template <typename Device>
 class NonMaxSuppressionOp : public OpKernel {
@@ -167,7 +163,8 @@ class NonMaxSuppressionOp : public OpKernel {
         errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                 max_output_size.shape().DebugString()));
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
+                          iou_threshold_);
   }
 
  private:
@@ -178,8 +175,7 @@ template <typename Device>
 class NonMaxSuppressionV2Op : public OpKernel {
  public:
   explicit NonMaxSuppressionV2Op(OpKernelConstruction* context)
-      : OpKernel(context) {
-  }
+      : OpKernel(context) {}
 
   void Compute(OpKernelContext* context) override {
     // boxes: [num_boxes, 4]
@@ -194,14 +190,14 @@ class NonMaxSuppressionV2Op : public OpKernel {
                                 max_output_size.shape().DebugString()));
     // iou_threshold: scalar
     const Tensor& iou_threshold = context->input(3);
-    OP_REQUIRES(
-        context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
-        errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
-                                iou_threshold.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
+                errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
+                                        iou_threshold.shape().DebugString()));
 
     const float iou_threshold_val = iou_threshold.scalar<float>()();
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_val);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
+                          iou_threshold_val);
   }
 };
 
diff --git a/tensorflow/core/kernels/priority_queue.cc b/tensorflow/core/kernels/priority_queue.cc
index 4c406fc1ed..894ad3c9a0 100644
--- a/tensorflow/core/kernels/priority_queue.cc
+++ b/tensorflow/core/kernels/priority_queue.cc
@@ -339,7 +339,7 @@ void PriorityQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx,
             for (; s > 0; --s) {
               if (attempt->tuple.empty()) {
                 // Only allocate tuple when we have something to dequeue
-                // so we don't use excessive memory when there are many
+                // so we don't use exceessive memory when there are many
                 // blocked dequeue attempts waiting.
                 attempt->tuple.reserve(num_components());
                 for (int i = 0; i < num_components(); ++i) {
diff --git a/tensorflow/core/kernels/shape_ops.cc b/tensorflow/core/kernels/shape_ops.cc
index c5e3164145..d78c6d2639 100644
--- a/tensorflow/core/kernels/shape_ops.cc
+++ b/tensorflow/core/kernels/shape_ops.cc
@@ -48,7 +48,6 @@ REGISTER_KERNEL_BUILDER(Name("Shape")
                           ShapeOp<int64>);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
 REGISTER_KERNEL_BUILDER(Name("Shape")
@@ -103,7 +102,7 @@ REGISTER_KERNEL_BUILDER(Name("Shape")
                             .TypeConstraint<int32>("T")
                             .TypeConstraint<int64>("out_type"),
                         ShapeOp<int64>);
-#endif  // GOOGLE_CUDA
+#endif
 
 // ShapeN ---------------------------------------
 REGISTER_KERNEL_BUILDER(Name("ShapeN")
@@ -153,9 +152,9 @@ REGISTER_KERNEL_BUILDER(Name("ShapeN")
                             .TypeConstraint<int32>("T")
                             .TypeConstraint<int64>("out_type"),
                         ShapeNOp<int64>);
-#endif  // GOOGLE_CUDA
+#endif
 
-#ifdef TENSORFLOW_USE_SYCL
+#if TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                               \
   REGISTER_KERNEL_BUILDER(Name("ShapeN")                         \
                               .Device(DEVICE_SYCL)               \
@@ -171,9 +170,11 @@ REGISTER_KERNEL_BUILDER(Name("ShapeN")
                           ShapeNOp<int64>)
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
+// A special GPU kernel for int32.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("ShapeN")
                             .Device(DEVICE_SYCL)
                             .HostMemory("input")
@@ -201,9 +202,13 @@ REGISTER_KERNEL_BUILDER(Name("Rank").Device(DEVICE_CPU).HostMemory("output"),
                               .TypeConstraint<type>("T") \
                               .HostMemory("output"),     \
                           RankOp);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
 #undef REGISTER_SYCL_KERNEL
 
+// A special GPU kernel for int32 and bool.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Rank")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
@@ -245,7 +250,7 @@ REGISTER_KERNEL_BUILDER(Name("Rank")
                             .HostMemory("input")
                             .HostMemory("output"),
                         RankOp);
-#endif  // GOOGLE_CUDA
+#endif
 
 // Size ------------------------------------------
 REGISTER_KERNEL_BUILDER(Name("Size")
@@ -294,7 +299,7 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SizeOp<int64>);
-#endif  // GOOGLE_CUDA
+#endif
 
 #ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                               \
@@ -310,10 +315,13 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                               .TypeConstraint<int64>("out_type") \
                               .HostMemory("output"),             \
                           SizeOp<int64>);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-TF_CALL_bool(REGISTER_SYCL_KERNEL);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
 #undef REGISTER_SYCL_KERNEL
 
+// A special GPU kernel for int32.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Size")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
@@ -328,7 +336,7 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SizeOp<int64>);
-#endif  // TENSORFLOW_USE_SYCL
+#endif // TENSORFLOW_USE_SYCL
 
 // ExpandDims ------------------------------------
 REGISTER_KERNEL_BUILDER(Name("ExpandDims")
@@ -357,7 +365,7 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                             .HostMemory("dim")
                             .HostMemory("output"),
                         ExpandDimsOp);
-#endif  // GOOGLE_CUDA
+#endif // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                           \
@@ -367,8 +375,9 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                               .TypeConstraint<int32>("Tdim") \
                               .HostMemory("dim"),            \
                           ExpandDimsOp);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-TF_CALL_bool(REGISTER_SYCL_KERNEL);
+REGISTER_SYCL_KERNEL(float)
+REGISTER_SYCL_KERNEL(double)
+
 #undef REGISTER_SYCL_KERNEL
 
 REGISTER_KERNEL_BUILDER(Name("ExpandDims")
@@ -379,7 +388,7 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                             .HostMemory("dim")
                             .HostMemory("output"),
                         ExpandDimsOp);
-#endif  // TENSORFLOW_USE_SYCL
+#endif // TENSORFLOW_USE_SYCL
 
 // Squeeze ---------------------------------------
 REGISTER_KERNEL_BUILDER(Name("Squeeze").Device(DEVICE_CPU), SqueezeOp);
@@ -402,23 +411,26 @@ REGISTER_KERNEL_BUILDER(Name("Squeeze")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SqueezeOp);
-#endif  // GOOGLE_CUDA
+#endif
 
 #if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                                   \
-  REGISTER_KERNEL_BUILDER(                                           \
-      Name("Squeeze").Device(DEVICE_SYCL).TypeConstraint<type>("T"), \
+#define REGISTER_SYCL_KERNEL(type)                                  \
+  REGISTER_KERNEL_BUILDER(                                          \
+      Name("Squeeze").Device(DEVICE_SYCL).TypeConstraint<type>("T"),\
       SqueezeOp);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-TF_CALL_bool(REGISTER_SYCL_KERNEL);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
 #undef REGISTER_SYCL_KERNEL
 
+// A special GPU kernel for int32.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Squeeze")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SqueezeOp);
-#endif  // TENSORFLOW_USE_SYCL
+#endif // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc
index d46701749b..ee6f9a28cd 100644
--- a/tensorflow/core/kernels/slice_op.cc
+++ b/tensorflow/core/kernels/slice_op.cc
@@ -118,43 +118,6 @@ static void SharedValidation(OpKernelContext* context,
   }
 }
 
-// Extracted out code in SliceOp::Compute so that MklSliceOp can reuse this
-// generic code
-template <typename T>
-static void SharedSliceCommonCases(OpKernelContext* context,
-                                   TensorShape* output_shape,
-                                   gtl::InlinedVector<int64, 4>* begin,
-                                   gtl::InlinedVector<int64, 4>* size,
-                                   Tensor** result,
-                                   bool* done) {
-  bool is_identity = true;
-  bool slice_dim0 = true;
-  *done = false;
-
-  SharedValidation(context, output_shape, &is_identity, &slice_dim0, begin,
-                   size);
-  if (!context->status().ok()) return;
-  const Tensor& input = context->input(0);
-  if (is_identity) {
-    VLOG(1) << "Slice identity";
-    context->set_output(0, input);
-    *done = true;
-    return;
-  }
-
-  if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), (*begin)[0],
-                                          (*size)[0])) {
-    VLOG(1) << "Slice dim 0: " << input.shape().DebugString();
-    CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
-    context->set_output(0, input.Slice((*begin)[0], (*begin)[0] + (*size)[0]));
-    *done = true;
-    return;
-  }
-
-  OP_REQUIRES_OK(context, context->allocate_output(0, *output_shape, result));
-}
-
-
 template <typename Device, typename T>
 class SliceOp : public OpKernel {
  public:
@@ -162,89 +125,29 @@ class SliceOp : public OpKernel {
 
   void Compute(OpKernelContext* context) override {
     TensorShape output_shape;
+    bool is_identity = true;
+    bool slice_dim0 = true;
     gtl::InlinedVector<int64, 4> begin;
     gtl::InlinedVector<int64, 4> size;
-    Tensor* result = nullptr;
-    bool done = false;
-    SharedSliceCommonCases<T>(context, &output_shape, &begin, &size, &result,
-                              &done);
-    if (!context->status().ok() || done == true) return;
-
+    SharedValidation(context, &output_shape, &is_identity, &slice_dim0, &begin,
+                     &size);
+    if (!context->status().ok()) return;
     const Tensor& input = context->input(0);
-    const int input_dims = input.dims();
-
-    if (output_shape.num_elements() > 0) {
-      if (std::is_same<Device, CPUDevice>::value && input_dims == 2 &&
-          DataTypeCanUseMemcpy(DataTypeToEnum<T>::v())) {
-        auto input = context->input(0).tensor<T, 2>();
-        auto output = result->tensor<T, 2>();
-        // TODO(agarwal): Consider multi-threading this loop for cases where
-        // size[0] is very large.
-        for (int i = 0; i < size[0]; ++i) {
-          const int64 row = begin[0] + i;
-          if (i + 1 < size[0]) {
-            port::prefetch<port::PREFETCH_HINT_T0>(&output(i + 1, 0));
-            port::prefetch<port::PREFETCH_HINT_T0>(&input(row + 1, begin[1]));
-          }
-          memcpy(&output(i, 0), &input(row, begin[1]), size[1] * sizeof(T));
-        }
-        return;
-      }
-#define HANDLE_DIM(NDIM)                            \
-  if (input_dims == NDIM) {                         \
-    HandleCase<NDIM>(context, begin, size, result); \
-    return;                                         \
-  }
-
-      HANDLE_DIM(1);
-      HANDLE_DIM(2);
-      HANDLE_DIM(3);
-      HANDLE_DIM(4);
-      HANDLE_DIM(5);
-      HANDLE_DIM(6);
-      HANDLE_DIM(7);
-
-#undef HANDLE_DIM
-
-      OP_REQUIRES(context, false, errors::Unimplemented(
-                                      "SliceOp : Unhandled input dimensions"));
+    if (is_identity) {
+      VLOG(1) << "Slice identity";
+      context->set_output(0, input);
+      return;
     }
-  }
 
- private:
-  template <int NDIM>
-  void HandleCase(OpKernelContext* context, const gtl::ArraySlice<int64>& begin,
-                  const gtl::ArraySlice<int64>& size, Tensor* result) {
-    Eigen::DSizes<Eigen::DenseIndex, NDIM> indices;
-    Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes;
-    for (int i = 0; i < NDIM; ++i) {
-      indices[i] = begin[i];
-      sizes[i] = size[i];
+    if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), begin[0], size[0])) {
+      VLOG(1) << "Slice dim 0: " << input.shape().DebugString();
+      CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
+      context->set_output(0, input.Slice(begin[0], begin[0] + size[0]));
+      return;
     }
 
-    functor::Slice<Device, T, NDIM>()(
-        context->eigen_device<Device>(), result->tensor<T, NDIM>(),
-        context->input(0).tensor<T, NDIM>(), indices, sizes);
-  }
-};
-
-#ifdef INTEL_MKL
-template <typename Device, typename T>
-class MklSliceOp : public OpKernel {
- public:
-  explicit MklSliceOp(OpKernelConstruction* context) : OpKernel(context) {}
-
-  void Compute(OpKernelContext* context) override {
-    TensorShape output_shape;
-    gtl::InlinedVector<int64, 4> begin;
-    gtl::InlinedVector<int64, 4> size;
     Tensor* result = nullptr;
-    bool done = false;
-    SharedSliceCommonCases<T>(context, &output_shape, &begin, &size, &result,
-                              &done);
-    if (!context->status().ok() || done == true) return;
-
-    const Tensor& input = context->input(0);
+    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &result));
     const int input_dims = input.dims();
 
     if (output_shape.num_elements() > 0) {
@@ -286,123 +189,9 @@ class MklSliceOp : public OpKernel {
   }
 
  private:
-  // Helper function for DoesSliceShapeDifferInOnly1D. Checks if the following
-  // criteria matches for slice_dim: if indices for slice are 0 in all dims
-  // except slice_dim and if sizes of all the dimensions of the slice are same
-  // as the sizes of all the dimensions of the input except slice_dim, then
-  // returns True. Otherwise, returns False.
-  bool DoesSliceShapeDifferInOnly1DHelper(const TensorShape& input_shape,
-                          const gtl::ArraySlice<int64>& begin,
-                          const gtl::ArraySlice<int64>& size,
-                          int slice_dim) {
-    for (int dim = 0; dim < 4; dim++) {
-      if (dim != slice_dim &&
-          (begin[dim] != 0 || size[dim] != input_shape.dim_size(dim))) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  // Is 'input' tensor being sliced over a single dimension out of 4?
-  //
-  // This check is applicable in the context of Slice of a 4-D tensor in
-  // NHWC or NCHW format over channel dimension.
-  //
-  // If indices for slice are 0 in all dims except one dimension and if sizes of
-  // all dimensions of slice are same as sizes of all dimensions of inputs
-  // except that dimension, then we are slicing over a single dimension.
-  //
-  // Returns True if Slicing over a single dimension, and sets slice_dim
-  // to the number of the dimension that satisfies criteria.
-  bool DoesSliceShapeDifferInOnly1D(const TensorShape& input_shape,
-                          const gtl::ArraySlice<int64>& begin,
-                          const gtl::ArraySlice<int64>& size,
-                          int* slice_dim) {
-    for (int dim = 0; dim < 4; dim++) {
-      if (DoesSliceShapeDifferInOnly1DHelper(input_shape, begin, size, dim)) {
-        *slice_dim = dim;
-        return true;
-      }
-    }
-    return false;
-  }
-
   template <int NDIM>
-  void HandleCase(OpKernelContext* context,
-                  const gtl::ArraySlice<int64>& begin,
+  void HandleCase(OpKernelContext* context, const gtl::ArraySlice<int64>& begin,
                   const gtl::ArraySlice<int64>& size, Tensor* result) {
-    int slice_dim = -1;
-    TensorShape in_shape = context->input(0).shape();
-    // Special case for handling 4-D tensor slice when shape of the slice
-    // differs from the input tensor in only 1 out of 4 dimensions.
-    // This case arises in the context of Slice of 4-D tensor in NHWC or NCHW
-    // format over channel dimension.
-    if (NDIM == 4 &&
-        DoesSliceShapeDifferInOnly1D(in_shape, begin, size, &slice_dim)) {
-        size_t in_strides[4] = { (size_t) in_shape.dim_size(1) *
-                                          in_shape.dim_size(2) *
-                                          in_shape.dim_size(3),
-                                 (size_t) in_shape.dim_size(2) *
-                                          in_shape.dim_size(3),
-                                 (size_t) in_shape.dim_size(3),
-                                 (size_t) 1
-                               };
-
-        size_t out_strides[4] = { (size_t) size[1] * size[2] * size[3],
-                                  (size_t) size[2] * size[3],
-                                  (size_t) size[3],
-                                  (size_t) 1 };
-
-        T *in_buf = const_cast<T*>(const_cast<const T*>(
-                    context->input(0).flat<T>().data()));
-        T *op_buf = result->flat<T>().data();
-
-        if (slice_dim == 1) {
-          /* data format = NCHW */
-
-          #pragma omp parallel for
-          for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
-              T *ip  = in_buf + (d0 * in_strides[0]);
-              T *op  = op_buf + ((d0 - begin[0]) * out_strides[0]);
-            #pragma omp parallel for
-            for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
-              T *ip1 = ip + (d1 * in_strides[1]);
-              T *op1 = op + ((d1 - begin[1]) * out_strides[1]);
-              // For NCHW, H and W will be contiguous. So we can copy
-              // both with one memcpy.
-              memcpy(static_cast<void*>(op1), static_cast<void*>(ip1),
-                     sizeof(T) * in_strides[1]);
-            }
-          }
-          return;
-        } else if (slice_dim == 3) {
-          /* data_format = NHWC */
-
-          #pragma omp parallel for
-          for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
-              T *ip = in_buf + (d0 * in_strides[0]);
-              T *op = op_buf + ((d0 - begin[0]) * out_strides[0]);
-            #pragma omp parallel for
-            for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
-              T *ip1 = ip + (d1 * in_strides[1]);
-              T *op1 = op + ((d1 - begin[1]) * out_strides[1]);
-              #pragma omp parallel for
-              for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) {
-                T *ip2 = ip1 + (d2 * in_strides[2]);
-                T *ip3 = ip2 + begin[3];
-                T *op2 = op1 + ((d2 - begin[2]) * out_strides[2]);
-                T *op3 = op2;
-                memcpy(static_cast<void*>(op3), static_cast<void*>(ip3),
-                       sizeof(T) * size[3]);
-              }
-            }
-          }
-          return;
-        }
-        // slice_dim is not 1 or 3, then we fallback to Eigen implementation.
-    }
-
     Eigen::DSizes<Eigen::DenseIndex, NDIM> indices;
     Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes;
     for (int i = 0; i < NDIM; ++i) {
@@ -415,7 +204,6 @@ class MklSliceOp : public OpKernel {
         context->input(0).tensor<T, NDIM>(), indices, sizes);
   }
 };
-#endif
 
 // Forward declarations of the functor specializations for declared in the
 // sharded source files.
@@ -445,7 +233,6 @@ DECLARE_FOR_N(bfloat16);
 #undef DECLARE_CPU_SPEC
 }  // namespace functor
 
-#ifndef INTEL_MKL
 #define REGISTER_SLICE(type)                             \
   REGISTER_KERNEL_BUILDER(Name("Slice")                  \
                               .Device(DEVICE_CPU)        \
@@ -457,21 +244,8 @@ DECLARE_FOR_N(bfloat16);
 TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
 TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
 REGISTER_SLICE(bfloat16);
-#undef REGISTER_SLICE
-#else
-#define REGISTER_SLICE(type)                             \
-  REGISTER_KERNEL_BUILDER(Name("Slice")                  \
-                              .Device(DEVICE_CPU)        \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("begin")       \
-                              .HostMemory("size"),       \
-                          MklSliceOp<CPUDevice, type>)
 
-TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
-TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
-REGISTER_SLICE(bfloat16);
 #undef REGISTER_SLICE
-#endif  // INTEL_MKL
 
 #if GOOGLE_CUDA
 // Forward declarations of the functor specializations for GPU.
diff --git a/tensorflow/core/kernels/sparse_reduce_op.cc b/tensorflow/core/kernels/sparse_reduce_op.cc
deleted file mode 100644
index 9e60791f97..0000000000
--- a/tensorflow/core/kernels/sparse_reduce_op.cc
+++ /dev/null
@@ -1,341 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// See docs in ../ops/sparse_ops.cc.
-
-#define EIGEN_USE_THREADS
-
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/register_types.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_util.h"
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/util/sparse/sparse_tensor.h"
-
-// TODO(b/31496047): Fix non-standard include order.
-#include <numeric>  // clang-format off
-
-using tensorflow::sparse::SparseTensor;
-using tensorflow::gtl::ArraySlice;
-
-namespace tensorflow {
-
-struct ReduceDetails {
-  // The dimensions to call Reorder() with.
-  std::vector<int64> reorder_dims;
-
-  // The dimensions to call group() with after Reorder().
-  std::vector<int64> group_by_dims;
-
-  // The shape after reduction.
-  TensorShape reduced_shape;
-};
-
-// Compute common reduce parameters that'll be used for SparseTensor
-// reductions. Usage:
-// ReduceDetails reduction = SparseTensorReduceHelper(sp, axes, keep_dims);
-// sp.Reorder(reduction.reorder_dims);
-// for (const auto& g : sp.group(reduction.group_by_dims)) {
-//   ...
-// }
-// // Set output shape to reduction.reduced_shape.
-ReduceDetails SparseTensorReduceHelper(const SparseTensor &sp,
-                                       gtl::ArraySlice<int32> axes_slice,
-                                       bool keep_dims) {
-  ReduceDetails reduction;
-
-  std::vector<int32> reduction_axes(axes_slice.begin(), axes_slice.end());
-  int ndims = sp.dims();
-  for (int64 i = 0; i < reduction_axes.size(); ++i) {
-    reduction_axes[i] = (reduction_axes[i] + ndims) % ndims;
-  }
-  std::sort(reduction_axes.begin(), reduction_axes.end());
-
-  // (0) Calculate the grouping dimensions:
-  // group_by_dims == {0, .., NDIMS-1} \ reduction_axes.
-  std::vector<int64> perm(ndims);
-  std::iota(perm.begin(), perm.end(), 0);
-
-  // Requires perm and reduction_axes_ be sorted; group_by_dims will be
-  // sorted as well.
-  std::set_difference(
-      perm.begin(), perm.end(), reduction_axes.begin(), reduction_axes.end(),
-      std::inserter(reduction.group_by_dims, reduction.group_by_dims.begin()));
-
-  // Now append the rest of the axes (the complement of group_by_dims_);
-  // result is used by Reorder().
-  reduction.reorder_dims = reduction.group_by_dims;
-  std::set_difference(perm.begin(), perm.end(), reduction.group_by_dims.begin(),
-                      reduction.group_by_dims.end(),
-                      std::back_inserter(reduction.reorder_dims));
-
-  // (1) Calculate the shape after reduction.
-  auto sp_shape = sp.shape();
-  std::vector<int64> out_dim_sizes;
-  if (keep_dims) {
-    out_dim_sizes.reserve(ndims);
-    auto beg = reduction.group_by_dims.begin();
-    auto end = reduction.group_by_dims.end();
-    for (int d = 0; d < ndims; ++d) {
-      if (std::find(beg, end, d) == end) {
-        out_dim_sizes.push_back(1);  // A reduced axis.
-      } else {
-        out_dim_sizes.push_back(sp_shape[d]);
-      }
-    }
-  } else {
-    out_dim_sizes = sp.PickDims(reduction.group_by_dims);
-  }
-
-  reduction.reduced_shape = TensorShape(out_dim_sizes);
-  return reduction;
-}
-
-Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
-  // indices and values are validated in SparseTensor ctor.
-  if (!TensorShapeUtils::IsVector(shape_t->shape())) {
-    return errors::InvalidArgument(
-        "Expected input_shape to be a vector; got shape: ",
-        shape_t->shape().DebugString());
-  }
-  if (!TensorShapeUtils::IsScalar(reduction_axes_t->shape()) &&
-      !TensorShapeUtils::IsVector(reduction_axes_t->shape())) {
-    return errors::InvalidArgument(
-        "Expected reduction_axes to be a scalar or a vector; got shape: ",
-        reduction_axes_t->shape().DebugString());
-  }
-
-  const auto reduction_axes_flat = reduction_axes_t->flat<int32>();
-  for (int64 i = 0; i < reduction_axes_flat.size(); i++) {
-    int32 axis = reduction_axes_flat(i);
-    if (axis < -shape_t->NumElements() || axis >= shape_t->NumElements()) {
-      return errors::InvalidArgument("Invalid reduction dimension ", axis,
-                                     ", for input with ",
-                                     shape_t->NumElements(), " dimensions.");
-    }
-  }
-
-  return Status::OK();
-}
-
-struct SumOp {
-  template <typename T>
-  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
-      s.device(ctx->eigen_cpu_device()) = v.sum();
-  }
-  static StringPiece Name() {
-      return "sum";
-  }
-};
-
-struct MaxOp {
-  template <typename T>
-  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
-      s.device(ctx->eigen_cpu_device()) = v.maximum();
-  }
-  static StringPiece Name() {
-      return "max";
-  }
-};
-
-template <typename T, typename Op>
-class SparseReduceOp : public OpKernel {
- public:
-  explicit SparseReduceOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
-    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
-  }
-
-  void Compute(OpKernelContext *ctx) override {
-    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
-    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
-    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
-
-    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
-
-    // TODO(zongheng): we will call Reorder() below, which will modify
-    // in-place the underlying indices and values buffers.  To avoid
-    // surprises of this kernel being stateful, we work around the above by
-    // making deep copies here.  Remove this if/when we change Reorder()'s
-    // semantics.
-    const auto shape_vec = shape_t->vec<int64>();
-    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
-                    TensorShape(shape_vec));
-    ReduceDetails reduction = SparseTensorReduceHelper(
-        sp, reduction_axes_t->flat<int32>(), keep_dims_);
-
-    Tensor *out_values;
-    OP_REQUIRES_OK(
-        ctx, ctx->allocate_output(0, reduction.reduced_shape, &out_values));
-    auto out_flat = out_values->flat<T>();
-    out_flat.setZero();
-
-    Tensor tmp_reduced_val;
-    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_reduced_val));
-    auto reduced_val = tmp_reduced_val.scalar<T>();
-
-    // Compute strides, and use it to convert coords to flat index.  The
-    // coordinates returned by .group() have the same ndims as group_by_dims.
-    gtl::InlinedVector<int64, 8> output_strides(reduction.group_by_dims.size());
-    if (!output_strides.empty()) {  // Do this iff we don't reduce all.
-      output_strides.back() = 1;
-      for (int d = output_strides.size() - 2; d >= 0; --d) {
-        output_strides[d] =
-            output_strides[d + 1] * shape_vec(reduction.group_by_dims[d + 1]);
-      }
-    }
-
-    auto CoordinatesToFlatIndex = [](ArraySlice<int64> coords,
-                                     ArraySlice<int64> strides) {
-      if (strides.empty()) {  // Reduce all.
-        return 0LL;
-      }
-      CHECK_EQ(coords.size(), strides.size());
-      int64 idx = 0;
-      for (int i = 0; i < coords.size(); ++i) {
-        idx += coords[i] * strides[i];
-      }
-      return idx;
-    };
-
-    // Each group maps one-on-one onto a value in the reduced tensor.
-    // g.group() provides the coordinates of a particular reduced value.
-    sp.Reorder<T>(reduction.reorder_dims);
-    for (const auto &g : sp.group(reduction.group_by_dims)) {
-      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
-      const int64 idx = CoordinatesToFlatIndex(g.group(), output_strides);
-      out_flat(idx) = reduced_val();
-      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; idx: " << idx << "; group " << Op::Name() << ": "
-              << reduced_val();
-    }
-  }
-
- private:
-  // True if the number of dimensions should be maintained.
-  bool keep_dims_;
-};
-
-#define REGISTER_KERNELS(T)                                              \
-  REGISTER_KERNEL_BUILDER(                                               \
-      Name("SparseReduceSum").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceOp<T, SumOp>)
-TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-#define REGISTER_KERNELS(T)                                              \
-  REGISTER_KERNEL_BUILDER(                                               \
-      Name("SparseReduceMax").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceOp<T, MaxOp>)
-TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-template <typename T, typename Op>
-class SparseReduceSparseOp : public OpKernel {
- public:
-  explicit SparseReduceSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
-    OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
-  }
-
-  void Compute(OpKernelContext *ctx) override {
-    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
-    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
-    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
-    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
-
-    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
-
-    SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
-                    TensorShape(shape_t->vec<int64>()));
-    ReduceDetails reduction = SparseTensorReduceHelper(
-        sp, reduction_axes_t->flat<int32>(), keep_dims_);
-
-    sp.Reorder<T>(reduction.reorder_dims);
-    // Count nnzs in the output SparseTensor.
-    int64 nnz = 0;
-    auto iter = sp.group(reduction.group_by_dims);
-    for (auto it = iter.begin(); it != iter.end(); ++it) {
-      nnz++;
-    }
-
-    Tensor *out_indices_t;
-    OP_REQUIRES_OK(ctx,
-                   ctx->allocate_output(
-                       0, TensorShape({nnz, reduction.reduced_shape.dims()}),
-                       &out_indices_t));
-    typename TTypes<int64>::Matrix out_indices_mat =
-        out_indices_t->matrix<int64>();
-    // For keep_dims. We don't explicitly set dim fields for reduced dims below.
-    out_indices_mat.setZero();
-
-    Tensor *out_values_t;
-    OP_REQUIRES_OK(ctx,
-                   ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
-    auto out_flat = out_values_t->flat<T>();
-
-    Tensor tmp_reduced_val;
-    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_reduced_val));
-    auto reduced_val = tmp_reduced_val.scalar<T>();
-    int64 i = 0;
-    for (const auto &g : sp.group(reduction.group_by_dims)) {
-      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
-      std::vector<int64> group = g.group();
-      for (int64 j = 0; j < group.size(); j++) {
-        if (keep_dims_) {
-          out_indices_mat(i, reduction.group_by_dims[j]) = group[j];
-        } else {
-          out_indices_mat(i, j) = group[j];
-        }
-      }
-      out_flat(i) = reduced_val();
-      i++;
-      VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; group " << Op::Name() << ": "
-              << reduced_val();
-    }
-
-    Tensor *out_shape_t;
-    OP_REQUIRES_OK(ctx, ctx->allocate_output(
-                            2, TensorShape({reduction.reduced_shape.dims()}),
-                            &out_shape_t));
-    auto out_shape_flat = out_shape_t->flat<int64>();
-    auto out_dim_sizes = reduction.reduced_shape.dim_sizes();
-    std::copy(out_dim_sizes.begin(), out_dim_sizes.end(), &out_shape_flat(0));
-  }
-
- private:
-  // True if the number of dimensions should be maintained.
-  bool keep_dims_;
-};
-
-#define REGISTER_KERNELS(T)                                                    \
-  REGISTER_KERNEL_BUILDER(                                                     \
-      Name("SparseReduceSumSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSparseOp<T, SumOp>)
-TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-#define REGISTER_KERNELS(T)                                                    \
-  REGISTER_KERNEL_BUILDER(                                                     \
-      Name("SparseReduceMaxSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSparseOp<T, MaxOp>)
-TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/stack_ops.cc b/tensorflow/core/kernels/stack_ops.cc
index 2db3e5ef77..b4698a8053 100644
--- a/tensorflow/core/kernels/stack_ops.cc
+++ b/tensorflow/core/kernels/stack_ops.cc
@@ -40,9 +40,6 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
-#ifdef TENSORFLOW_USE_SYCL
-typedef Eigen::SyclDevice SYCLDevice;
-#endif // TENSORFLOW_USE_SYCL
 
 class Stack : public ResourceBase {
  public:
@@ -185,10 +182,6 @@ class StackOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_CPU), StackOp);
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_GPU).HostMemory("handle"),
                         StackOp);
-#ifdef TENSORFLOW_USE_SYCL
-REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_SYCL).HostMemory("handle"),
-                        StackOp);
-#endif // TENSORFLOW_USE_SYCL
 
 template <typename Device>
 class StackPushOp : public AsyncOpKernel {
@@ -220,11 +213,7 @@ class StackPushOp : public AsyncOpKernel {
     static constexpr int kCopyThreshold = 2048;
     static constexpr double kOccupancy = 0.7;
     if (swap_memory_ && !alloc_attrs.on_host() &&
-        ( std::is_same<Device, GPUDevice>::value
-#ifdef TENSORFLOW_USE_SYCL
-          || std::is_same<Device, SYCLDevice>::value
-#endif // TENSORFLOW_USE_SYCL
-        ) &&
+        std::is_same<Device, GPUDevice>::value &&
         tensor.TotalBytes() > kCopyThreshold && stack->IsUsefulToSwap(tensor)) {
       DeviceContext* device_ctxt = ctx->op_device_context();
       auto device = static_cast<tensorflow::Device*>(ctx->device());
@@ -300,31 +289,6 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                        \
-  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("handle")       \
-                              .TypeConstraint<type>("T"), \
-                          StackPushOp<SYCLDevice>);
-
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
-
-#define REGISTER_SYCL_HOST_KERNEL(type)                   \
-  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("handle")       \
-                              .HostMemory("elem")         \
-                              .HostMemory("output")       \
-                              .TypeConstraint<type>("T"), \
-                          StackPushOp<SYCLDevice>)
-
-REGISTER_SYCL_HOST_KERNEL(int32);
-REGISTER_SYCL_HOST_KERNEL(bool);
-#undef REGISTER_SYCL_KERNEL
-#undef REGISTER_SYCL_HOST_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 class StackPopOp : public AsyncOpKernel {
  public:
   explicit StackPopOp(OpKernelConstruction* context) : AsyncOpKernel(context) {}
@@ -395,31 +359,6 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                                \
-  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
-                              .Device(DEVICE_SYCL)                \
-                              .HostMemory("handle")               \
-                              .TypeConstraint<type>("elem_type"), \
-                          StackPopOp)
-
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
-
-#define REGISTER_SYCL_HOST_KERNEL(type)                           \
-  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
-                              .Device(DEVICE_SYCL)                \
-                              .HostMemory("handle")               \
-                              .HostMemory("elem")                 \
-                              .TypeConstraint<type>("elem_type"), \
-                          StackPopOp)
-
-REGISTER_SYCL_HOST_KERNEL(int32);
-REGISTER_SYCL_HOST_KERNEL(bool);
-
-#undef REGISTER_SYCL_KERNEL
-#undef REGISTER_SYCL_HOST_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 class StackCloseOp : public OpKernel {
  public:
   explicit StackCloseOp(OpKernelConstruction* context) : OpKernel(context) {}
@@ -437,8 +376,5 @@ class StackCloseOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("StackClose").Device(DEVICE_CPU), StackCloseOp);
 REGISTER_KERNEL_BUILDER(
     Name("StackClose").Device(DEVICE_GPU).HostMemory("handle"), StackCloseOp);
-#ifdef TENSORFLOW_USE_SYCL
-REGISTER_KERNEL_BUILDER(
-    Name("StackClose").Device(DEVICE_SYCL).HostMemory("handle"), StackCloseOp);
-#endif // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/topk_op.cc b/tensorflow/core/kernels/topk_op.cc
index 5c89eaef5f..630fcb76f3 100644
--- a/tensorflow/core/kernels/topk_op.cc
+++ b/tensorflow/core/kernels/topk_op.cc
@@ -93,7 +93,7 @@ class TopK : public OpKernel {
       rows_by_one.set(0, num_rows);
 #else
       Eigen::array<int, 1> reduce_on_cols = {1};
-      Eigen::array<int, 2> rows_by_one = {static_cast<int>(num_rows), 1};
+      Eigen::array<int, 1> rows_by_one = {static_cast<int>(num_rows), 1};
 #endif
 
       values.device(d) =
diff --git a/tensorflow/core/kernels/transpose_functor.h b/tensorflow/core/kernels/transpose_functor.h
index f1ab770eeb..124cf14dd2 100644
--- a/tensorflow/core/kernels/transpose_functor.h
+++ b/tensorflow/core/kernels/transpose_functor.h
@@ -132,13 +132,6 @@ template <typename Device, typename T, int NDIMS>
 void TransposeUsingEigen(const Device& d, const Tensor& in,
                          const gtl::ArraySlice<int32> perm, Tensor* out);
 
-
-#ifdef TENSORFLOW_USE_SYCL
-// For SYCL lets always go through Eigen
-template <typename Device, typename T>
-void TransposeSYCL(const Device& d, const Tensor& in,
-                   const gtl::ArraySlice<int32> perm, Tensor* out);
-#endif // TENSORFLOW_USE_SYCL
 }  // namespace internal
 
 template <typename Device, typename T>
diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc
index d3305fb83a..75ed76a697 100644
--- a/tensorflow/core/kernels/transpose_op.cc
+++ b/tensorflow/core/kernels/transpose_op.cc
@@ -233,7 +233,10 @@ Status TransposeSyclOp::DoTranspose(OpKernelContext* ctx, const Tensor& in,
                               .TypeConstraint<int32>("Tperm") \
                               .HostMemory("perm"),            \
                           TransposeSyclOp);
-TF_CALL_POD_TYPES(REGISTER);
+REGISTER(float);
+REGISTER(bool);
+REGISTER(int32);
 #undef REGISTER
 #endif
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/typed_conditional_accumulator_base.h b/tensorflow/core/kernels/typed_conditional_accumulator_base.h
index 1980f758fc..dbd7de7ce0 100644
--- a/tensorflow/core/kernels/typed_conditional_accumulator_base.h
+++ b/tensorflow/core/kernels/typed_conditional_accumulator_base.h
@@ -22,7 +22,7 @@ namespace tensorflow {
 
 /*
  * TypedConditionalAccumulatorBase is a templated companion of
- * ConditionalAccumulatorBase which allows for subclasses to use different
+ * ConditionalAccumulatorBase which allows for subclassses to use different
  * types for the input gradients. (See ConditionalAccumulator and
  * SparseConditionalAccumulator.)
  *
diff --git a/tensorflow/core/lib/gtl/optional.h b/tensorflow/core/lib/gtl/optional.h
index 2ff8b9c7d1..8ba4b09143 100644
--- a/tensorflow/core/lib/gtl/optional.h
+++ b/tensorflow/core/lib/gtl/optional.h
@@ -656,7 +656,7 @@ class optional : private internal_optional::optional_data<T>,
   constexpr const T& reference() const { return *this->pointer(); }
   T& reference() { return *(this->pointer()); }
 
-  // T constraint checks.  You can't have an optional of nullopt_t, in_place_t or
+  // T constaint checks.  You can't have an optional of nullopt_t, in_place_t or
   // a reference.
   static_assert(
       !std::is_same<nullopt_t, typename std::remove_cv<T>::type>::value,
diff --git a/tensorflow/core/ops/math_grad.cc b/tensorflow/core/ops/math_grad.cc
index 9a58a31757..a530d286f7 100644
--- a/tensorflow/core/ops/math_grad.cc
+++ b/tensorflow/core/ops/math_grad.cc
@@ -155,26 +155,6 @@ Status Log1pGrad(const AttrSlice& attrs, FunctionDef* g) {
 }
 REGISTER_OP_GRADIENT("Log1p", Log1pGrad);
 
-Status SinhGrad(const AttrSlice& attrs, FunctionDef* g) {
-  // clang-format off
-  return GradForUnaryCwise(g, {
-      {{"cosh"}, "Cosh", {"x"}, {}, {"dy"}},
-      {{"dx"}, "Mul", {"dy", "cosh"}},  // dy * cosh(x)
-  });
-  // clang-format on
-}
-REGISTER_OP_GRADIENT("Sinh", SinhGrad);
-
-Status CoshGrad(const AttrSlice& attrs, FunctionDef* g) {
-  // clang-format off
-  return GradForUnaryCwise(g, {
-      {{"sinh"}, "Sinh", {"x"}, {}, {"dy"}},
-      {{"dx"}, "Mul", {"dy", "sinh"}},  // dy * sinh(x)
-  });
-  // clang-format on
-}
-REGISTER_OP_GRADIENT("Cosh", CoshGrad);
-
 Status TanhGrad(const AttrSlice& attrs, FunctionDef* g) {
   // clang-format off
   return GradForUnaryCwise(g, {
diff --git a/tensorflow/core/ops/math_grad_test.cc b/tensorflow/core/ops/math_grad_test.cc
index aa9706a328..38813b3f2b 100644
--- a/tensorflow/core/ops/math_grad_test.cc
+++ b/tensorflow/core/ops/math_grad_test.cc
@@ -495,26 +495,6 @@ TEST_F(MathGradTest, Log1p) {
   test::ExpectClose(ans, dx);
 }
 
-TEST_F(MathGradTest, Sinh) {
-  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
-                                 TensorShape({2, 3}));
-  auto g = [](float x) { return std::cosh(x); };
-  auto dx = test::AsTensor<float>(
-      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
-  auto ans = SymGrad("Sinh", x);
-  test::ExpectClose(ans, dx);
-}
-
-TEST_F(MathGradTest, Cosh) {
-  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
-                                 TensorShape({2, 3}));
-  auto g = [](float x) { return std::sinh(x); };
-  auto dx = test::AsTensor<float>(
-      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
-  auto ans = SymGrad("Cosh", x);
-  test::ExpectClose(ans, dx);
-}
-
 TEST_F(MathGradTest, Tanh) {
   auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
                                  TensorShape({2, 3}));
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 30d6987707..3fe543a239 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -293,14 +293,6 @@ Computes natural logarithm of (1 + x) element-wise.
 I.e., \\(y = \log_e (1 + x)\\).
 )doc");
 
-REGISTER_OP("Sinh").UNARY_COMPLEX().Doc(R"doc(
-Computes hyperbolic sine of x element-wise.
-)doc");
-
-REGISTER_OP("Cosh").UNARY_COMPLEX().Doc(R"doc(
-Computes hyperbolic cosine of x element-wise.
-)doc");
-
 REGISTER_OP("Tanh").UNARY_COMPLEX().Doc(R"doc(
 Computes hyperbolic tangent of `x` element-wise.
 )doc");
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 3a25fd15da..70302c3886 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -831,13 +831,11 @@ a different filter to each input channel (expanding from 1 channel to
 `channel_multiplier` channels for each), then concatenates the results
 together. Thus, the output has `in_channels * channel_multiplier` channels.
 
-```
 for k in 0..in_channels-1
   for q in 0..channel_multiplier-1
     output[b, i, j, k * channel_multiplier + q] =
       sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                         filter[di, dj, k, q]
-```
 
 Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
 horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 08894dcafc..b122b5a992 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -5184,31 +5184,6 @@ op {
   }
   summary: "Computes cos of x element-wise."
 }
-op {
-  name: "Cosh"
-  input_arg {
-    name: "x"
-    type_attr: "T"
-  }
-  output_arg {
-    name: "y"
-    type_attr: "T"
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_HALF
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_COMPLEX64
-        type: DT_COMPLEX128
-      }
-    }
-  }
-  summary: "Computes hyperbolic cosine of x element-wise."
-}
 op {
   name: "CountUpTo"
   input_arg {
@@ -6302,7 +6277,7 @@ op {
     }
   }
   summary: "Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors."
-  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\n```\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n```\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
+  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
 }
 op {
   name: "DepthwiseConv2dNativeBackpropFilter"
@@ -22817,31 +22792,6 @@ op {
   }
   summary: "Computes sin of x element-wise."
 }
-op {
-  name: "Sinh"
-  input_arg {
-    name: "x"
-    type_attr: "T"
-  }
-  output_arg {
-    name: "y"
-    type_attr: "T"
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_HALF
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_COMPLEX64
-        type: DT_COMPLEX128
-      }
-    }
-  }
-  summary: "Computes hyperbolic sine of x element-wise."
-}
 op {
   name: "Size"
   input_arg {
@@ -24938,123 +24888,6 @@ op {
   summary: "Multiply matrix \"a\" by matrix \"b\"."
   description: "The inputs must be two-dimensional matrices and the inner dimension of \"a\" must\nmatch the outer dimension of \"b\". This op is optimized for the case where at\nleast one of \"a\" or \"b\" is sparse. The breakeven for using this versus a dense\nmatrix multiply on one platform was 30% zero values in the sparse matrix.\n\nThe gradient computation of this operation will only take advantage of sparsity\nin the input gradient when that gradient comes from a Relu."
 }
-op {
-  name: "SparseReduceMax"
-  input_arg {
-    name: "input_indices"
-    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
-    type: DT_INT64
-  }
-  input_arg {
-    name: "input_values"
-    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
-    type_attr: "T"
-  }
-  input_arg {
-    name: "input_shape"
-    description: "1-D.  Shape of the input SparseTensor."
-    type: DT_INT64
-  }
-  input_arg {
-    name: "reduction_axes"
-    description: "1-D.  Length-`K` vector containing the reduction axes."
-    type: DT_INT32
-  }
-  output_arg {
-    name: "output"
-    description: "`R-K`-D.  The reduced Tensor."
-    type_attr: "T"
-  }
-  attr {
-    name: "keep_dims"
-    type: "bool"
-    default_value {
-      b: false
-    }
-    description: "If true, retain reduced dimensions with length 1."
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_INT32
-        type: DT_INT64
-        type: DT_UINT8
-        type: DT_INT16
-        type: DT_INT8
-        type: DT_UINT16
-        type: DT_HALF
-      }
-    }
-  }
-  summary: "Computes the max of elements across dimensions of a SparseTensor."
-  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`\ninstead of a sparse one.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
-}
-op {
-  name: "SparseReduceMaxSparse"
-  input_arg {
-    name: "input_indices"
-    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
-    type: DT_INT64
-  }
-  input_arg {
-    name: "input_values"
-    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
-    type_attr: "T"
-  }
-  input_arg {
-    name: "input_shape"
-    description: "1-D.  Shape of the input SparseTensor."
-    type: DT_INT64
-  }
-  input_arg {
-    name: "reduction_axes"
-    description: "1-D.  Length-`K` vector containing the reduction axes."
-    type: DT_INT32
-  }
-  output_arg {
-    name: "output_indices"
-    type: DT_INT64
-  }
-  output_arg {
-    name: "output_values"
-    type_attr: "T"
-  }
-  output_arg {
-    name: "output_shape"
-    type: DT_INT64
-  }
-  attr {
-    name: "keep_dims"
-    type: "bool"
-    default_value {
-      b: false
-    }
-    description: "If true, retain reduced dimensions with length 1."
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_INT32
-        type: DT_INT64
-        type: DT_UINT8
-        type: DT_INT16
-        type: DT_INT8
-        type: DT_UINT16
-        type: DT_HALF
-      }
-    }
-  }
-  summary: "Computes the max of elements across dimensions of a SparseTensor."
-  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a\nSparseTensor.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
-}
 op {
   name: "SparseReduceSum"
   input_arg {
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 6aca2c3b01..9722f0ee9a 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -710,75 +710,6 @@ a_shape: 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
 b: `ndims`-D Tensor.  With shape `a_shape`.
 )doc");
 
-REGISTER_OP("SparseReduceMax")
-    .Input("input_indices: int64")
-    .Input("input_values: T")
-    .Input("input_shape: int64")
-    .Input("reduction_axes: int32")
-    .Attr("keep_dims: bool = False")
-    .Output("output: T")
-    .Attr("T: realnumbertype")
-    .SetShapeFn(shape_inference::UnknownShape)
-    .Doc(R"doc(
-Computes the max of elements across dimensions of a SparseTensor.
-
-This Op takes a SparseTensor and is the sparse counterpart to
-`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
-instead of a sparse one.
-
-Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
-`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-with length 1.
-
-If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-with a single element is returned.  Additionally, the axes can be negative,
-which are interpreted according to the indexing rules in Python.
-
-input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-  SparseTensor, possibly not in canonical ordering.
-input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
-input_shape: 1-D.  Shape of the input SparseTensor.
-reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
-keep_dims: If true, retain reduced dimensions with length 1.
-output: `R-K`-D.  The reduced Tensor.
-)doc");
-
-REGISTER_OP("SparseReduceMaxSparse")
-    .Input("input_indices: int64")
-    .Input("input_values: T")
-    .Input("input_shape: int64")
-    .Input("reduction_axes: int32")
-    .Attr("keep_dims: bool = False")
-    .Output("output_indices: int64")
-    .Output("output_values: T")
-    .Output("output_shape: int64")
-    .Attr("T: realnumbertype")
-    .SetShapeFn(shape_inference::UnknownShape)
-    .Doc(R"doc(
-Computes the max of elements across dimensions of a SparseTensor.
-
-This Op takes a SparseTensor and is the sparse counterpart to
-`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
-SparseTensor.
-
-Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
-`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-with length 1.
-
-If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-with a single element is returned.  Additionally, the axes can be negative,
-which are interpreted according to the indexing rules in Python.
-
-input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-  SparseTensor, possibly not in canonical ordering.
-input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
-input_shape: 1-D.  Shape of the input SparseTensor.
-reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
-keep_dims: If true, retain reduced dimensions with length 1.
-)doc");
-
 REGISTER_OP("SparseReduceSum")
     .Input("input_indices: int64")
     .Input("input_values: T")
diff --git a/tensorflow/core/platform/cloud/retrying_utils.cc b/tensorflow/core/platform/cloud/retrying_utils.cc
index 99691ecfb9..096c77c6e3 100644
--- a/tensorflow/core/platform/cloud/retrying_utils.cc
+++ b/tensorflow/core/platform/cloud/retrying_utils.cc
@@ -89,7 +89,7 @@ Status RetryingUtils::DeleteWithRetries(
   bool is_retried = false;
   return RetryingUtils::CallWithRetries(
       [delete_func, &is_retried]() {
-        const Status status = delete_func();
+        const auto& status = delete_func();
         if (is_retried && status.code() == error::NOT_FOUND) {
           return Status::OK();
         }
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index e476a84a13..cf05aece39 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -171,7 +171,7 @@ message ExecutorOpts {
 };
 
 message RunGraphRequest {
-  // session_handle is the master-generated unique id for this session.
+  // session_handle is the the master-generated unique id for this session.
   // If session_handle is non-empty, it must be the same as used when
   // registering the graph. If it is empty, a single global namespace is used to
   // search for the graph_handle.
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 0e5611e359..d30d7819fc 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX ""
+#define TF_VERSION_SUFFIX "-rc2"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h
index 67468bdc3f..6a37256ea9 100644
--- a/tensorflow/core/util/mkl_util.h
+++ b/tensorflow/core/util/mkl_util.h
@@ -23,7 +23,7 @@ limitations under the License.
 #include "third_party/mkl/include/mkl_dnn.h"
 #include "third_party/mkl/include/mkl_dnn_types.h"
 #include "third_party/mkl/include/mkl_service.h"
-#include "third_party/mkl/include/mkl_trans.h"
+
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/util/tensor_format.h"
@@ -616,42 +616,6 @@ inline void ForwarMklTensorInToOut(OpKernelContext* context,
   }
 }
 
-  // TODO(intel_tf): Remove this routine when faster MKL layout conversion is
-  // out. 
-inline void MklNHWCToNCHW(const Tensor& input, Tensor** output) {
-  const float* buf_in = input.flat<float>().data();
-  float* buf_out = (*output)->flat<float>().data();
-
-  int64 N = input.dim_size(0);
-  int64 H = input.dim_size(1);
-  int64 W = input.dim_size(2);
-  int64 C = input.dim_size(3);
-  int64 stride_n = H*W*C;
-# pragma omp parallel for num_threads(16)
-  for (int64 n = 0; n < N; ++n) {
-    mkl_somatcopy('R', 'T', H*W, C, 1, buf_in + n*stride_n, C,
-        buf_out + n*stride_n, H*W);
-  }
-}
-
-  // TODO(intel_tf): Remove this routine when faster MKL layout conversion is
-  // out. 
-inline void MklNCHWToNHWC(const Tensor& input, Tensor** output) {
-  const float* buf_in = input.flat<float>().data();
-  float* buf_out = (*output)->flat<float>().data();
-
-  int64 N = (*output)->dim_size(0);
-  int64 H = (*output)->dim_size(1);
-  int64 W = (*output)->dim_size(2);
-  int64 C = (*output)->dim_size(3);
-  int64 stride_n = H*W*C;
-# pragma omp parallel for num_threads(16)
-  for (int64 n = 0; n < N; ++n) {
-    mkl_somatcopy('R', 'T', C, H*W, 1, buf_in + n*stride_n, H*W,
-        buf_out + n*stride_n, C);
-  }
-}
-
 namespace mkl_op_registry {
 static const char* kMklOpLabel = "MklOp";
 static const char* kMklOpLabelPattern = "label='MklOp'";
diff --git a/tensorflow/docs_src/api_guides/python/contrib.losses.md b/tensorflow/docs_src/api_guides/python/contrib.losses.md
index 30123e367f..8c289dd556 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.losses.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.losses.md
@@ -13,8 +13,8 @@ of samples in the batch and `d1` ... `dN` are the remaining dimensions.
 It is common, when training with multiple loss functions, to adjust the relative
 strengths of individual losses. This is performed by rescaling the losses via
 a `weight` parameter passed to the loss functions. For example, if we were
-training with both log_loss and mean_squared_error, and we wished that the
-log_loss penalty be twice as severe as the mean_squared_error, we would
+training with both log_loss and mean_square_error, and we wished that the
+log_loss penalty be twice as severe as the mean_square_error, we would
 implement this as:
 
 ```python
@@ -22,7 +22,7 @@ implement this as:
   tf.contrib.losses.log(predictions, labels, weight=2.0)
 
   # Uses default weight of 1.0
-  tf.contrib.losses.mean_squared_error(predictions, labels)
+  tf.contrib.losses.mean_square_error(predictions, labels)
 
   # All the losses are collected into the `GraphKeys.LOSSES` collection.
   losses = tf.get_collection(tf.GraphKeys.LOSSES)
@@ -74,7 +74,7 @@ these predictions.
   predictions = MyModelPredictions(images)
 
   weight = tf.cast(tf.greater(depths, 0), tf.float32)
-  loss  = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
+  loss  = tf.contrib.losses.mean_square_error(predictions, depths, weight)
 ```
 
 Note that when using weights for the losses, the final average is computed
@@ -100,7 +100,7 @@ weighted average over the individual prediction errors:
 
   weight = MyComplicatedWeightingFunction(labels)
   weight = tf.div(weight, tf.size(weight))
-  loss = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
+  loss = tf.contrib.losses.mean_square_error(predictions, depths, weight)
 ```
 
 @{tf.contrib.losses.absolute_difference}
diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md
index 3d9f203297..b3c12a61dd 100644
--- a/tensorflow/docs_src/api_guides/python/math_ops.md
+++ b/tensorflow/docs_src/api_guides/python/math_ops.md
@@ -59,8 +59,6 @@ mathematical functions to your graph.
 *   @{tf.acos}
 *   @{tf.asin}
 *   @{tf.atan}
-*   @{tf.cosh}
-*   @{tf.sinh}
 *   @{tf.lgamma}
 *   @{tf.digamma}
 *   @{tf.erf}
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index d1c9cd696c..77b8e2dd2e 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -135,9 +135,8 @@ adder_node = a + b  # + provides a shortcut for tf.add(a, b)
 
 The preceding three lines are a bit like a function or a lambda in which we
 define two input parameters (a and b) and then an operation on them. We can
-evaluate this graph with multiple inputs by using the feed_dict argument to
-the [run method](https://www.tensorflow.org/api_docs/python/tf/Session#run)
-to feed concrete values to the placeholders:
+evaluate this graph with multiple inputs by using the feed_dict parameter to
+specify Tensors that provide concrete values to these placeholders:
 
 ```python
 print(sess.run(adder_node, {a: 3, b:4.5}))
diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 175de2be76..624d916474 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -367,7 +367,7 @@ train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
 
 In this case, we ask TensorFlow to minimize `cross_entropy` using the
 [gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent)
-with a learning rate of 0.05. Gradient descent is a simple procedure, where
+with a learning rate of 0.5. Gradient descent is a simple procedure, where
 TensorFlow simply shifts each variable a little bit in the direction that
 reduces the cost. But TensorFlow also provides
 @{$python/train#Optimizers$many other optimization algorithms}:
diff --git a/tensorflow/docs_src/get_started/mnist/mechanics.md b/tensorflow/docs_src/get_started/mnist/mechanics.md
index 27fae45b5b..48d9a395f2 100644
--- a/tensorflow/docs_src/get_started/mnist/mechanics.md
+++ b/tensorflow/docs_src/get_started/mnist/mechanics.md
@@ -82,7 +82,7 @@ After creating placeholders for the data, the graph is built from the
 `mnist.py` file according to a 3-stage pattern: `inference()`, `loss()`, and
 `training()`.
 
-1.  `inference()` - Builds the graph as far as required for running
+1.  `inference()` - Builds the graph as far as is required for running
 the network forward to make predictions.
 1.  `loss()` - Adds to the inference graph the ops required to generate
 loss.
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 81aa6e3f76..91189f199d 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 3f9096b822..c9b8dffadb 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc2.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 40ed9e1826..612c4c94f2 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0</version>
+  <version>1.2.0-rc2</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0</version>
+                 <version>1.2.0-rc2</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc2.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.0-rc2.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0-rc2.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0-rc2.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 99f27d7b85..8ce4acda13 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -171,8 +171,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
-     <pre>(tensorflow)$ <b>pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
+     <pre>(tensorflow)$ <b>pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -276,8 +276,8 @@ take the following steps:
      the following command:
 
      <pre>
-     $ <b>sudo pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
+     $ <b>sudo pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -632,14 +632,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -651,14 +651,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -670,14 +670,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -689,14 +689,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 8ff0fb872f..f85ecefb83 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -108,8 +108,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
+     <pre> $ <b>pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -229,8 +229,8 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-     <pre> $ <b>sudo pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
+     <pre> $ <b>sudo pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index a082c3ce78..c455492387 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -342,10 +342,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0 on Linux:
+for TensorFlow 1.2.0rc2 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc2-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 8282afaab4..42820660ee 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -38,10 +38,9 @@ installed on your system:
     [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
     list of supported GPU cards.
 
-If you have a different version of one of the preceding packages, please
-change to the specified versions.  In particular, the cuDNN version
-must match exactly: TensorFlow will not load if it cannot find `cuDNN64_5.dll`.
-To use a different version of cuDNN, you must build from source.
+If you have an earlier version of the preceding packages, please
+upgrade to the specified versions.
+
 
 ## Determine how to install TensorFlow
 
@@ -74,7 +73,7 @@ Use that package at your own risk.
 If the following version of Python is not installed on your machine,
 install it now:
 
-  * [Python 3.5.x 64-bit from python.org](https://www.python.org/downloads/release/python-352/)
+  * [Python 3.5.x from python.org](https://www.python.org/downloads/release/python-352/)
 
 TensorFlow only supports version 3.5.x of Python on Windows.
 Note that Python 3.5.x comes with the pip3 package manager, which is the
@@ -115,12 +114,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index a5508ac23e..07c5d3087f 100644
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -52,8 +52,7 @@ bazel build -c opt --copt=-march="broadwell" --config=cuda //tensorflow/tools/pi
     (pascal): 6.2, Titan X (maxwell): 5.2, and K80: 3.7.
 *   Install the latest CUDA platform and cuDNN libraries.
 *   Make sure to use a version of gcc that supports all of the optimizations of
-    the target CPU. The recommended minimum gcc version is 4.8.3.  On OS X upgrade
-    to the latest Xcode version and use the version of clang that comes with Xcode.
+    the target CPU. The recommended minimum gcc version is 4.8.3.
 *   TensorFlow checks on startup whether it has been compiled with the
     optimizations available on the CPU. If the optimizations are not included,
     TensorFlow will emit warnings, e.g. AVX, AVX2, and FMA instructions not
@@ -123,11 +122,6 @@ format.
 The best practice is to build models that work with both `NCHW` and `NHWC` as it
 is common to train using `NCHW` on GPU, and then do inference with NHWC on CPU.
 
-There are edge cases where `NCHW` can be slower on GPU than `NHWC`. One
-[case](https://github.com/tensorflow/tensorflow/issues/7551#issuecomment-280421351)
-is using non-fused batch norm on WRN-16-4 without dropout. In that case using
-fused batch norm, which is also recommended, is the optimal solution.
-
 The very brief history of these two formats is that TensorFlow started by using
 `NHWC` because it was a little faster on CPUs. Then the TensorFlow team
 discovered that `NCHW` performs better when using the NVIDIA cuDNN library.  The
diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md
index a37748d0c9..4667b4cad7 100644
--- a/tensorflow/docs_src/performance/quantization.md
+++ b/tensorflow/docs_src/performance/quantization.md
@@ -91,14 +91,11 @@ eight-bit computations:
 ```sh
 curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -o /tmp/inceptionv3.tgz
 tar xzf /tmp/inceptionv3.tgz -C /tmp/
-bazel build tensorflow/tools/graph_transforms:transform_graph
-bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
-  --in_graph=/tmp/classify_image_graph_def.pb \
-  --outputs="softmax" --out_graph=/tmp/quantized_graph.pb \
-  --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3")
-    remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true)
-    fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes
-    strip_unused_nodes sort_by_execution_order'
+bazel build tensorflow/tools/quantization:quantize_graph
+bazel-bin/tensorflow/tools/quantization/quantize_graph \
+  --input=/tmp/classify_image_graph_def.pb \
+  --output_node_names="softmax" --output=/tmp/quantized_graph.pb \
+  --mode=eightbit
 ```
 
 This will produce a new model that runs the same operations as the original, but
diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md
index dd2ca8d524..6ffe3e8b03 100644
--- a/tensorflow/docs_src/tutorials/seq2seq.md
+++ b/tensorflow/docs_src/tutorials/seq2seq.md
@@ -8,10 +8,7 @@ some input and generate a meaningful response? For example, could we train
 a neural network to translate from English to French? It turns out that
 the answer is *yes*.
 
-This tutorial will show you how to build and train such a system end-to-end.
-Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and
-the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub.
-You can then start by running the translate program:
+This tutorial will show you how to build and train such a system end-to-end. Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub. You can then start by running the translate program:
 
 ```
 cd models/tutorials/rnn/translate
@@ -28,7 +25,7 @@ This tutorial references the following files.
 
 File | What's in it?
 --- | ---
-`tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
+`tensorflow/tensorflow/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
 `models/tutorials/rnn/translate/seq2seq_model.py` | Neural translation sequence-to-sequence model.
 `models/tutorials/rnn/translate/data_utils.py` | Helper functions for preparing translation data.
 `models/tutorials/rnn/translate/translate.py` | Binary that trains and runs the translation model.
@@ -151,9 +148,9 @@ have similar interfaces, so we will not describe them in detail. We will use
 ## Neural translation model
 
 While the core of the sequence-to-sequence model is constructed by
-the functions in `tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py`,
-there are still a few tricks that are worth mentioning that are used in our
-translation model in `models/tutorials/rnn/translate/seq2seq_model.py`.
+the functions in `tensorflow/tensorflow/python/ops/seq2seq.py`, there are still a few tricks
+that are worth mentioning that are used in our translation model in
+`models/tutorials/rnn/translate/seq2seq_model.py`.
 
 ### Sampled softmax and output projection
 
diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index 24c866eee5..c2621026c7 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -1,6 +1,6 @@
 # TensorFlow Linear Model Tutorial
 
-In this tutorial, we will use the tf.contrib.learn API in TensorFlow to solve a binary
+In this tutorial, we will use the TF.Learn API in TensorFlow to solve a binary
 classification problem: Given census data about a person such as age, gender,
 education and occupation (the features), we will try to predict whether or not
 the person earns more than 50,000 dollars a year (the target label). We will
@@ -16,7 +16,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
@@ -69,8 +69,8 @@ COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
            "marital_status", "occupation", "relationship", "race", "gender",
            "capital_gain", "capital_loss", "hours_per_week", "native_country",
            "income_bracket"]
-df_train = pd.read_csv(train_file.name, names=COLUMNS, skipinitialspace=True)
-df_test = pd.read_csv(test_file.name, names=COLUMNS, skipinitialspace=True, skiprows=1)
+df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
+df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1)
 ```
 
 Since the task is a binary classification problem, we'll construct a label
@@ -136,9 +136,9 @@ Here's a list of columns available in the Census Income dataset:
 
 ## Converting Data into Tensors
 
-When building a tf.contrib.learn model, the input data is specified by means of an Input
+When building a TF.Learn model, the input data is specified by means of an Input
 Builder function. This builder function will not be called until it is later
-passed to tf.contrib.learn methods such as `fit` and `evaluate`. The purpose of this
+passed to TF.Learn methods such as `fit` and `evaluate`. The purpose of this
 function is to construct the input data, which is represented in the form of
 @{tf.Tensor}s
 or
@@ -211,7 +211,7 @@ to predict the target label.
 ### Base Categorical Feature Columns
 
 To define a feature column for a categorical feature, we can create a
-`SparseColumn` using the tf.contrib.learn API. If you know the set of all possible
+`SparseColumn` using the TF.Learn API. If you know the set of all possible
 feature values of a column and there are only a few of them, you can use
 `sparse_column_with_keys`. Each key in the list will get assigned an
 auto-incremental ID starting from 0. For example, for the `gender` column we can
@@ -361,7 +361,7 @@ in `model_dir`.
 ## Training and Evaluating Our Model
 
 After adding all the features to the model, now let's look at how to actually
-train the model. Training a model is just a one-liner using the tf.contrib.learn API:
+train the model. Training a model is just a one-liner using the TF.Learn API:
 
 ```python
 m.fit(input_fn=train_input_fn, steps=200)
@@ -467,4 +467,4 @@ value would be high.
 
 If you're interested in learning more, check out our @{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to combine
 the strengths of linear models and deep neural networks by jointly training them
-using the tf.contrib.learn API.
+using the TF.Learn API.
diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/wide_and_deep.md
index 0978005d6c..77c905fd51 100644
--- a/tensorflow/docs_src/tutorials/wide_and_deep.md
+++ b/tensorflow/docs_src/tutorials/wide_and_deep.md
@@ -9,7 +9,7 @@ great for training deep neural networks too, and you might be thinking which one
 you should choose—Well, why not both? Would it be possible to combine the
 strengths of both in one model?
 
-In this tutorial, we'll introduce how to use the tf.contrib.learn API to jointly train a
+In this tutorial, we'll introduce how to use the TF.Learn API to jointly train a
 wide linear model and a deep feed-forward neural network. This approach combines
 the strengths of memorization and generalization. It's useful for generic
 large-scale regression and classification problems with sparse input features
@@ -23,7 +23,7 @@ The figure above shows a comparison of a wide model (logistic regression with
 sparse features and transformations), a deep model (feed-forward neural network
 with an embedding layer and several hidden layers), and a Wide & Deep model
 (joint training of both). At a high level, there are only 3 steps to configure a
-wide, deep, or Wide & Deep model using the tf.contrib.learn API:
+wide, deep, or Wide & Deep model using the TF.Learn API:
 
 1.  Select features for the wide part: Choose the sparse base columns and
     crossed columns you want to use.
@@ -42,7 +42,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
diff --git a/tensorflow/docs_src/tutorials/word2vec.md b/tensorflow/docs_src/tutorials/word2vec.md
index 8e7c19035e..dfb21334f8 100644
--- a/tensorflow/docs_src/tutorials/word2vec.md
+++ b/tensorflow/docs_src/tutorials/word2vec.md
@@ -351,7 +351,7 @@ to evaluate embeddings is to directly use them to predict syntactic and semantic
 relationships like `king is to queen as father is to ?`. This is called
 *analogical reasoning* and the task was introduced by
 [Mikolov and colleagues
-](http://www.anthology.aclweb.org/N/N13/N13-1090.pdf).
+](http://msr-waypoint.com/en-us/um/people/gzweig/Pubs/NAACL2013Regularities.pdf).
 Download the dataset for this task from
 [download.tensorflow.org](http://download.tensorflow.org/data/questions-words.txt).
 
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 44a3097d80..8e3b1a3a36 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -800,27 +800,11 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
   return evaluation_step, prediction
 
 
-def save_graph_to_file(sess, graph, graph_file_name):
-  output_graph_def = graph_util.convert_variables_to_constants(
-    sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
-  with gfile.FastGFile(graph_file_name, 'wb') as f:
-    f.write(output_graph_def.SerializeToString())
-  return
-
-
-def prepare_file_system():
+def main(_):
   # Setup the directory we'll write summaries to for TensorBoard
   if tf.gfile.Exists(FLAGS.summaries_dir):
     tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
   tf.gfile.MakeDirs(FLAGS.summaries_dir)
-  if FLAGS.intermediate_store_frequency > 0:
-    ensure_dir_exists(FLAGS.intermediate_output_graphs_dir)
-  return
-
-
-def main(_):
-  # Prepare necessary directories  that can be used during training
-  prepare_file_system()
 
   # Set up the pre-trained graph.
   maybe_download_and_extract()
@@ -933,14 +917,6 @@ def main(_):
               (datetime.now(), i, validation_accuracy * 100,
                len(validation_bottlenecks)))
 
-      # Store intermediate results
-      intermediate_frequency = FLAGS.intermediate_store_frequency
-
-      if intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0:
-        intermediate_file_name = FLAGS.intermediate_output_graphs_dir + 'intermediate_' + str(i) + '.pb'
-        print('Save intermediate result to : ' + intermediate_file_name)
-        save_graph_to_file(sess, graph, intermediate_file_name)
-                
     # We've completed all our training, so run a final test evaluation on
     # some new images we haven't used before.
     test_bottlenecks, test_ground_truth, test_filenames = (
@@ -964,7 +940,10 @@ def main(_):
 
     # Write out the trained graph and labels with the weights stored as
     # constants.
-    save_graph_to_file(sess, graph, FLAGS.output_graph)
+    output_graph_def = graph_util.convert_variables_to_constants(
+        sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
+    with gfile.FastGFile(FLAGS.output_graph, 'wb') as f:
+      f.write(output_graph_def.SerializeToString())
     with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
       f.write('\n'.join(image_lists.keys()) + '\n')
 
@@ -983,18 +962,6 @@ if __name__ == '__main__':
       default='/tmp/output_graph.pb',
       help='Where to save the trained graph.'
   )
-  parser.add_argument(
-      '--intermediate_output_graphs_dir',
-      type=str,
-      default='/tmp/intermediate_graph/',
-      help='Where to save the intermediate graphs.'
-  )
-  parser.add_argument(
-      '--intermediate_store_frequency',
-      type=int,
-      default=0,
-      help='How many steps to store intermediate graph. If "0" then will not store.'
-  )
   parser.add_argument(
       '--output_labels',
       type=str,
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index 4c5893384a..77b245ab15 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -32,7 +32,7 @@ TFLEARN_EXAMPLE_BASE_DIR=$DIR/tensorflow/examples/learn
 
 
 function test() {
-  echo "Test $1:"
+  echo "Test "$1":"
   $TFLEARN_EXAMPLE_BASE_DIR/$1 $2
   if [ $? -eq 0 ]
   then
diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py
index 3585043a2a..d533697976 100644
--- a/tensorflow/examples/tutorials/mnist/mnist.py
+++ b/tensorflow/examples/tutorials/mnist/mnist.py
@@ -17,7 +17,7 @@
 
 Implements the inference/loss/training pattern for model building.
 
-1. inference() - Builds the model as far as required for running the network
+1. inference() - Builds the model as far as is required for running the network
 forward to make predictions.
 2. loss() - Adds to the inference model the layers required to generate loss.
 3. training() - Adds to the loss model the Ops required to generate and
diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
index aee482fda5..13e5717b0d 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -91,6 +91,7 @@ print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
 
 data_index = 0
 
+
 # Step 3: Function to generate a training batch for the skip-gram model.
 def generate_batch(batch_size, num_skips, skip_window):
   global data_index
@@ -100,10 +101,9 @@ def generate_batch(batch_size, num_skips, skip_window):
   labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
   span = 2 * skip_window + 1  # [ skip_window target skip_window ]
   buffer = collections.deque(maxlen=span)
-  if data_index + span > len(data):
-    data_index = 0
-  buffer.extend(data[data_index:data_index + span])
-  data_index += span
+  for _ in range(span):
+    buffer.append(data[data_index])
+    data_index = (data_index + 1) % len(data)
   for i in range(batch_size // num_skips):
     target = skip_window  # target label at the center of the buffer
     targets_to_avoid = [skip_window]
@@ -113,12 +113,8 @@ def generate_batch(batch_size, num_skips, skip_window):
       targets_to_avoid.append(target)
       batch[i * num_skips + j] = buffer[skip_window]
       labels[i * num_skips + j, 0] = buffer[target]
-    if data_index == len(data):
-      buffer[:] = data[:span]
-      data_index = span
-    else:
-      buffer.append(data[data_index])
-      data_index += 1
+    buffer.append(data[data_index])
+    data_index = (data_index + 1) % len(data)
   # Backtrack a little bit to avoid skipping words in the end of a batch
   data_index = (data_index + len(data) - span) % len(data)
   return batch, labels
diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md
index 9c2fa60017..a1b4255292 100644
--- a/tensorflow/go/README.md
+++ b/tensorflow/go/README.md
@@ -9,6 +9,35 @@ Construct and execute TensorFlow graphs in Go.
 > (`github.com/tensorflow/tensorflow/tensorflow/go`).
 
 ## Quickstart
+1.  Download and extract the TensorFlow C library, preferably into `/usr/local`.
+    GPU-enabled versions require CUDA 8.0 and cuDNN 5.1. For other versions, the
+    TensorFlow C library will have to be built from source (see below).
+
+    -   Linux:
+        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.1.0.tar.gz),
+        [GPU-enabled](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.1.0.tar.gz)
+    -   OS X
+        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.1.0.tar.gz),
+
+    The following shell snippet downloads and extracts into `/usr/local`:
+
+    ```sh
+    TF_TYPE="cpu" # Set to "gpu" for GPU support
+    curl -L \
+      "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.1.0.tar.gz" |
+    sudo tar -C /usr/local -xz
+    ```
+
+2.  `go get` this package (and run tests):
+
+    ```sh
+    go get github.com/tensorflow/tensorflow/tensorflow/go
+    go test github.com/tensorflow/tensorflow/tensorflow/go
+    ```
+
+3.  Done!
+
+### Installing into locations other than `/usr/local`
 
 Refer to [Installing TensorFlow for Go](https://www.tensorflow.org/install/install_go)
 
diff --git a/tensorflow/go/genop/generate.sh b/tensorflow/go/genop/generate.sh
index 15ef3b9525..d791e39c40 100644
--- a/tensorflow/go/genop/generate.sh
+++ b/tensorflow/go/genop/generate.sh
@@ -20,7 +20,7 @@ go get github.com/golang/protobuf/proto
 go get github.com/golang/protobuf/protoc-gen-go
 
 cd $(dirname $0)
-for g in $(echo "${GOPATH//:/ }"); do
+for g in $(echo $GOPATH | sed "s/:/ /g"); do
     TF_DIR="${g}/src/github.com/tensorflow/tensorflow"
     PROTOC="${TF_DIR}/bazel-out/host/bin/external/protobuf/protoc"
     if [ -x "${PROTOC}" ]; then
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 6675f1097e..8dd2931703 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14288,36 +14288,6 @@ func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
-// Computes hyperbolic sine of x element-wise.
-func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Sinh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes hyperbolic cosine of x element-wise.
-func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Cosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes hyperbolic tangent of `x` element-wise.
 func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
diff --git a/tensorflow/go/shape.go b/tensorflow/go/shape.go
index 8d000cb9de..114ab5decb 100644
--- a/tensorflow/go/shape.go
+++ b/tensorflow/go/shape.go
@@ -59,7 +59,7 @@ func (s Shape) NumDimensions() int {
 //
 // REQUIRES: 0 <= dim < s.NumDimensions()
 func (s Shape) Size(dim int) int64 {
-	if dim < 0 || dim >= s.NumDimensions() {
+	if dim < 0 || dim > s.NumDimensions() {
 		return -1
 	}
 	return s.dims[dim]
diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
index 4a60c736b5..34e797a2b3 100644
--- a/tensorflow/go/tensor.go
+++ b/tensorflow/go/tensor.go
@@ -270,7 +270,7 @@ func typeOf(dt DataType, shape []int64) reflect.Type {
 	if ret == nil {
 		panic(bug("DataType %v is not supported", dt))
 	}
-	for range shape {
+	for _ = range shape {
 		ret = reflect.SliceOf(ret)
 	}
 	return ret
diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 90372660cd..9abb63c966 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -9,10 +9,7 @@ load("build_defs", "JAVACOPTS")
 
 java_library(
     name = "tensorflow",
-    srcs = [
-        ":java_op_sources",
-        ":java_sources",
-    ],
+    srcs = [":java_sources"],
     data = [":libtensorflow_jni"],
     javacopts = JAVACOPTS,
     visibility = ["//visibility:public"],
@@ -29,14 +26,6 @@ filegroup(
     ],
 )
 
-filegroup(
-    name = "java_op_sources",
-    srcs = glob(["src/main/java/org/tensorflow/op/*.java"]),
-    visibility = [
-        "//tensorflow/java:__pkg__",
-    ],
-)
-
 java_library(
     name = "testutil",
     testonly = 1,
@@ -149,19 +138,6 @@ java_test(
     ],
 )
 
-java_test(
-    name = "ScopeTest",
-    size = "small",
-    srcs = ["src/test/java/org/tensorflow/op/ScopeTest.java"],
-    javacopts = JAVACOPTS,
-    test_class = "org.tensorflow.op.ScopeTest",
-    deps = [
-        ":tensorflow",
-        ":testutil",
-        "@junit",
-    ],
-)
-
 filegroup(
     name = "libtensorflow_jni",
     srcs = select({
diff --git a/tensorflow/java/build_defs.bzl b/tensorflow/java/build_defs.bzl
index 5bd5b9a388..750d76301e 100644
--- a/tensorflow/java/build_defs.bzl
+++ b/tensorflow/java/build_defs.bzl
@@ -1,14 +1,5 @@
 # -*- Python -*-
 
-# Pin to Java 1.7 to ensure broader compatibility for the Java bindings on
-# Android. Note also that the android_library bazel rule currently enforces
-# java 7
-# https://github.com/bazelbuild/bazel/blob/6c1106b1a721516d3b3db54d2e1c31b44a76fbb1/src/main/java/com/google/devtools/build/lib/bazel/rules/android/BazelAndroidSemantics.java#L73
-
-JAVA_VERSION_OPTS = [
-    "-source 7 -target 7",
-]
-
 # A more robust set of lint and errorprone checks when building
 # Java source to improve code consistency.
 
@@ -160,4 +151,4 @@ EP_DISABLED_CHECKS = [
 
 EP_OPTS = EP_ENABLED_WARNINGS + EP_DISABLED_CHECKS
 
-JAVACOPTS = JAVA_VERSION_OPTS + XLINT_OPTS + EP_OPTS
+JAVACOPTS = XLINT_OPTS + EP_OPTS
diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml
index 462e38d332..1fc9d49a86 100644
--- a/tensorflow/java/maven/libtensorflow/pom.xml
+++ b/tensorflow/java/maven/libtensorflow/pom.xml
@@ -19,8 +19,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.7</source>
-          <target>1.7</target>
+          <source>1.8</source>
+          <target>1.8</target>
         </configuration>
       </plugin>
       <plugin>
diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index f2974220d9..d1d0359b1d 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -16,7 +16,7 @@
     <dependency>
       <groupId>com.google.protobuf</groupId>
       <artifactId>protobuf-java</artifactId>
-      <version>3.3.1</version>
+      <version>3.2.0</version>
     </dependency>
   </dependencies>
 
@@ -27,8 +27,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.7</source>
-          <target>1.7</target>
+          <source>1.8</source>
+          <target>1.8</target>
         </configuration>
       </plugin>
       <plugin>
diff --git a/tensorflow/java/maven/release.sh b/tensorflow/java/maven/release.sh
index 0bb4efbcc0..0e14bc4964 100755
--- a/tensorflow/java/maven/release.sh
+++ b/tensorflow/java/maven/release.sh
@@ -21,7 +21,7 @@ TF_VERSION="$1"
 SETTINGS_XML="$2"
 shift
 shift
-CMD="$*"
+CMD="$@"
 
 if [[ -z "${TF_VERSION}" ]]
 then
diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh
index 302ed96c12..b5e2bfc3a6 100644
--- a/tensorflow/java/maven/run_inside_container.sh
+++ b/tensorflow/java/maven/run_inside_container.sh
@@ -23,7 +23,7 @@ IS_SNAPSHOT="false"
 if [[ "${TF_VERSION}" == *"-SNAPSHOT" ]]; then
   IS_SNAPSHOT="true"
 fi
-PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.3.0/protoc-3.3.0-linux-x86_64.zip"
+PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip"
 
 set -ex
 
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
deleted file mode 100644
index dff3a45463..0000000000
--- a/tensorflow/java/src/main/java/org/tensorflow/Input.java
+++ /dev/null
@@ -1,48 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-package org.tensorflow;
-
-/**
- * Interface implemented by operands of a TensorFlow operation.
- *
- * <p>Example usage:
- *
- * <pre>{@code
- * // The "decodeJpeg" operation can be used as input to the "cast" operation
- * Input decodeJpeg = ops.image().decodeJpeg(...);
- * ops.math().cast(decodeJpeg, DataType.FLOAT);
- *
- * // The output "y" of the "unique" operation can be used as input to the "cast" operation
- * Output y = ops.array().unique(...).y();
- * ops.math().cast(y, DataType.FLOAT);
- *
- * // The "split" operation can be used as input list to the "concat" operation
- * Iterable<? extends Input> split = ops.array().split(...);
- * ops.array().concat(0, split);
- * }</pre>
- */
-public interface Input {
-
-  /**
-   * Returns the symbolic handle of a tensor.
-   *
-   * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
-   * used to obtain a symbolic handle that represents the computation of the input.
-   *
-   * @see {@link OperationBuilder#addInput(Output)}.
-   */
-  Output asOutput();
-}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index e7de603409..43dbaf125c 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -79,7 +79,7 @@ public final class Operation {
    *
    * @param name identifier of the list of tensors (of which there may
    *        be many) produced by this operation.
-   * @return the size of the list of Tensors produced by this named output.
+   * @returns the size of the list of Tensors produced by this named output.
    * @throws IllegalArgumentException if this operation has no output
    *         with the provided name.
    */
@@ -97,28 +97,6 @@ public final class Operation {
     return new Output(this, idx);
   }
 
-  /**
-   * Returns the size of the given inputs list of Tensors for this operation.
-   *
-   * <p>An Operation has multiple named inputs, each of which contains either
-   * a single tensor or a list of tensors. This method returns the size of
-   * the list of tensors for a specific named input of the operation.
-   *
-   * @param name identifier of the list of tensors (of which there may
-   *        be many) inputs to this operation.
-   * @returns the size of the list of Tensors produced by this named input.
-   * @throws IllegalArgumentException if this operation has no input
-   *         with the provided name.
-   */
-  public int inputListLength(final String name) {
-    Graph.Reference r = graph.ref();
-    try {
-      return inputListLength(unsafeNativeHandle, name);
-    } finally {
-      r.close();
-    }
-  }
-
   long getUnsafeNativeHandle() {
     return unsafeNativeHandle;
   }
@@ -154,8 +132,6 @@ public final class Operation {
 
   private static native int outputListLength(long handle, String name);
 
-  private static native int inputListLength(long handle, String name);
-
   private static native long[] shape(long graphHandle, long opHandle, int output);
 
   private static native int dtype(long graphHandle, long opHandle, int output);
diff --git a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
index 8f7559d39e..38ffa2a8e1 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
@@ -28,7 +28,7 @@ import java.nio.charset.Charset;
  * <pre>{@code
  * // g is a Graph instance.
  * try (Tensor c1 = Tensor.create(3.0f)) {
- *   g.opBuilder("Const", "MyConst")
+ *   g.opBuilder("Constant", "MyConst")
  *       .setAttr("dtype", c1.dataType())
  *       .setAttr("value", c1)
  *       .build();
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index 2e3f8d4eac..ab128c2b30 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -20,11 +20,8 @@ package org.tensorflow;
  *
  * <p>An Output is a symbolic handle to a tensor. The value of the Tensor is computed by executing
  * the {@link Operation} in a {@link Session}.
- *
- * <p>By implementing the {@link Input} interface, instances of this class could also be passed
- * directly in input to an operation.
  */
-public final class Output implements Input {
+public final class Output {
 
   /** Handle to the idx-th output of the Operation {@code op}. */
   public Output(Operation op, int idx) {
@@ -52,11 +49,6 @@ public final class Output implements Input {
     return operation.dtype(index);
   }
 
-  @Override
-  public Output asOutput() {
-    return this;
-  }
-
   private final Operation operation;
   private final int index;
 }
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Session.java b/tensorflow/java/src/main/java/org/tensorflow/Session.java
index f73cded4e3..0d071e1674 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Session.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Session.java
@@ -125,7 +125,7 @@ public final class Session implements AutoCloseable {
      *     <tt>operation_name:output_index</tt> , in which case this method acts like {@code
      *     feed(operation_name, output_index)}. These colon-separated names are commonly used in the
      *     {@code SignatureDef} protocol buffer messages that are included in {@link
-     *     SavedModelBundle#metaGraphDef()}.
+     *     SavedModelBundle.metaGraphDef()}.
      */
     public Runner feed(String operation, Tensor t) {
       return feed(parseOutput(operation), t);
@@ -165,7 +165,7 @@ public final class Session implements AutoCloseable {
      *     <tt>operation_name:output_index</tt> , in which case this method acts like {@code
      *     fetch(operation_name, output_index)}. These colon-separated names are commonly used in
      *     the {@code SignatureDef} protocol buffer messages that are included in {@link
-     *     SavedModelBundle#metaGraphDef()}.
+     *     SavedModelBundle.metaGraphDef()}.
      */
     public Runner fetch(String operation) {
       return fetch(parseOutput(operation));
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java b/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
deleted file mode 100644
index 2e84cac1ac..0000000000
--- a/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
+++ /dev/null
@@ -1,146 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-package org.tensorflow.op;
-
-import java.util.HashMap;
-import java.util.Map;
-import java.util.regex.Pattern;
-
-/**
- * A class to manage scoped (hierarchical) names for operators.
- *
- * <p>{@code NameScope} manages hierarchical names where each component in the hierarchy is
- * separated by a forward slash {@code '/'}. For instance, {@code nn/Const_72} or {@code
- * nn/gradient/assign/init}. Each scope is a subtree in this hierarchy.
- *
- * <p>Use {@code NameScope} to group related operations within a hierarchy, which for example lets
- * tensorboard coalesce nodes for better graph visualizations.
- *
- * <p>This class is package private, user code creates {@link Scope} which internally delegates
- * calls to an underlying {@code NameScope}.
- *
- * <p>This class is <b>not</b> thread-safe.
- */
-final class NameScope {
-
-  NameScope withSubScope(String scopeName) {
-    checkPattern(NAME_REGEX, scopeName);
-    // Override with opName if it exists.
-    String actualName = (opName != null) ? opName : scopeName;
-    String newPrefix = fullyQualify(makeUnique(actualName));
-    return new NameScope(newPrefix, null, null);
-  }
-
-  NameScope withName(String name) {
-    checkPattern(NAME_REGEX, name);
-    // All context except for the opName is shared with the new scope.
-    return new NameScope(opPrefix, name, ids);
-  }
-
-  String makeOpName(String name) {
-    checkPattern(NAME_REGEX, name);
-    // Override with opName if it exists.
-    String actualName = (opName != null) ? opName : name;
-    return fullyQualify(makeUnique(actualName));
-  }
-
-  /**
-   * Create a new, root-level namescope.
-   *
-   * <p>A root-level namescope generates operator names with no components, like {@code Const_72}
-   * and {@code result}.
-   */
-  NameScope() {
-    this(null, null, null);
-  }
-
-  private NameScope(String opPrefix, String opName, Map<String, Integer> ids) {
-    this.opPrefix = opPrefix;
-    this.opName = opName;
-    if (ids != null) {
-      this.ids = ids;
-    } else {
-      this.ids = new HashMap<String, Integer>();
-    }
-  }
-
-  // Generate a unique name, different from existing ids.
-  //
-  // ids is a map from id to integer, representing a counter of the
-  // number of previous requests to generate a unique name for the
-  // given id.
-  //
-  // For instance, the first use of makeUnique("a") adds "a" -> 1
-  // to ids and returns "a".
-  //
-  // The second use of makeUnique("a") updates ids to "a" -> 2
-  // and returns "a_1", and so on.
-  private String makeUnique(String id) {
-    if (!ids.containsKey(id)) {
-      ids.put(id, 1);
-      return id;
-    } else {
-      int cur = ids.get(id);
-      ids.put(id, cur + 1);
-      return String.format("%s_%d", id, cur);
-    }
-  }
-
-  private String fullyQualify(String name) {
-    if (opPrefix != null) {
-      return String.format("%s/%s", opPrefix, name);
-    } else {
-      return name;
-    }
-  }
-
-  // If opPrefix is non-null, it is a prefix applied to all names
-  // created by this instance.
-  private final String opPrefix;
-
-  // If opName is non-null, it is used to derive the unique name
-  // for operators rather than the provided default name.
-  private final String opName;
-
-  // NameScope generates unique names by appending a numeric suffix if
-  // needed. This is a map containing names already created by this
-  // instance mapped to the next available numeric suffix for it.
-  private final Map<String, Integer> ids;
-
-  private static void checkPattern(Pattern pattern, String name) {
-    if (name == null) {
-      throw new IllegalArgumentException("Names cannot be null");
-    }
-    if (!pattern.matcher(name).matches()) {
-      throw new IllegalArgumentException(
-          String.format(
-              "invalid name: '%s' does not match the regular expression %s",
-              name, NAME_REGEX.pattern()));
-    }
-  }
-
-  // The constraints for operator and scope names originate from restrictions on node names
-  // noted in the proto definition core/framework/node_def.proto for NodeDef and actually
-  // implemented in core/framework/node_def_util.cc [Note that the proto comment does not include
-  // dash (-) in names, while the actual implementation permits it. This regex follows the actual
-  // implementation.]
-  //
-  // This pattern is used to ensure fully qualified names always start with a LETTER_DIGIT_DOT,
-  // followed by zero or more LETTER_DIGIT_DASH_DOT_SLASH_UNDERSCORE. SLASH is not permitted in
-  // actual user-supplied names to NameScope - it is used as a reserved character to separate
-  // subcomponents within fully qualified names.
-  private static final Pattern NAME_REGEX = Pattern.compile("[A-Za-z0-9.][A-Za-z0-9_.\\-]*");
-}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java b/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
deleted file mode 100644
index 8de2eaeb79..0000000000
--- a/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
+++ /dev/null
@@ -1,165 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-package org.tensorflow.op;
-
-import org.tensorflow.Graph;
-
-/**
- * Manages groups of related properties when creating Tensorflow Operations, such as a common name
- * prefix.
- *
- * <p>A {@code Scope} is a container for common properties applied to TensorFlow Ops. Normal user
- * code initializes a {@code Scope} and provides it to Operation building classes. For example:
- *
- * <pre>{@code
- * Scope scope = new Scope(graph);
- * Constant c = Constant.create(scope, 42);
- * }</pre>
- *
- * <p>An Operation building class acquires a Scope, and uses it to set properties on the underlying
- * Tensorflow ops. For example:
- *
- * <pre>{@code
- * // An operator class that adds a constant.
- * public class Constant {
- *   public static Constant create(Scope scope, ...) {
- *      scope.graph().opBuilder(
- *        "Const", scope.makeOpName("Const"))
- *        .setAttr(...)
- *        .build()
- *      ...
- *   }
- * }
- * }</pre>
- *
- * <p><b>Scope hierarchy:</b>
- *
- * <p>A {@code Scope} provides various {@code with()} methods that create a new scope. The new scope
- * typically has one property changed while other properties are inherited from the parent scope.
- *
- * <p>An example using {@code Constant} implemented as before:
- *
- * <pre>{@code
- * Scope root = new Scope(graph);
- *
- * // The linear subscope will generate names like linear/...
- * Scope linear = Scope.withSubScope("linear");
- *
- * // This op name will be "linear/W"
- * Constant.create(linear.withName("W"), ...);
- *
- * // This op will be "linear/Const", using the default
- * // name provided by Constant
- * Constant.create(linear, ...);
- *
- * // This op will be "linear/Const_1", using the default
- * // name provided by Constant and making it unique within
- * // this scope
- * Constant.create(linear, ...);
- * }</pre>
- *
- * <p>Scope objects are <b>not</b> thread-safe.
- */
-public final class Scope {
-
-  /**
-   * Create a new top-level scope.
-   *
-   * @param graph The graph instance to be managed by the scope.
-   */
-  public Scope(Graph graph) {
-    this(graph, new NameScope());
-  }
-
-  /** Returns the graph managed by this scope. */
-  public Graph graph() {
-    return graph;
-  }
-
-  /**
-   * Returns a new scope where added operations will have the provided name prefix.
-   *
-   * <p>Ops created with this scope will have {@code name/childScopeName/} as the prefix. The actual
-   * name will be unique in the returned scope. All other properties are inherited from the current
-   * scope.
-   *
-   * <p>The child scope name must match the regular expression {@code [A-Za-z0-9.][A-Za-z0-9_.\-]*}
-   *
-   * @param childScopeName name for the new child scope
-   * @return a new subscope
-   * @throws IllegalArgumentException if the name is invalid
-   */
-  public Scope withSubScope(String childScopeName) {
-    return new Scope(graph, nameScope.withSubScope(childScopeName));
-  }
-
-  /**
-   * Return a new scope that uses the provided name for an op.
-   *
-   * <p>Operations created within this scope will have a name of the form {@code
-   * name/opName[_suffix]}. This lets you name a specific operator more meaningfully.
-   *
-   * <p>Names must match the regular expression {@code [A-Za-z0-9.][A-Za-z0-9_.\-]*}
-   *
-   * @param opName name for an operator in the returned scope
-   * @return a new Scope that uses opName for operations.
-   * @throws IllegalArgumentException if the name is invalid
-   */
-  public Scope withName(String opName) {
-    return new Scope(graph, nameScope.withName(opName));
-  }
-
-  /**
-   * Create a unique name for an operator, using a provided default if necessary.
-   *
-   * <p>This is normally called only by operator building classes.
-   *
-   * <p>This method generates a unique name, appropriate for the name scope controlled by this
-   * instance. Typical operator building code might look like
-   *
-   * <pre>{@code
-   * scope.graph().opBuilder("Const", scope.makeOpName("Const"))...
-   * }</pre>
-   *
-   * <p><b>Note:</b> if you provide a composite operator building class (i.e, a class that adds a
-   * set of related operations to the graph by calling other operator building code) you should also
-   * create a {@link #withSubScope(String)} scope for the underlying operators to group them under a
-   * meaningful name.
-   *
-   * <pre>{@code
-   * public static Stddev create(Scope scope, ...) {
-   *   // group sub-operations under a common name
-   *   Scope group = scope.withSubScope("stddev");
-   *   ... Sqrt.create(group, Mean.create(group, ...))
-   * }
-   * }</pre>
-   *
-   * @param defaultName name for the underlying operator.
-   * @return unique name for the operator.
-   * @throws IllegalArgumentException if the default name is invalid.
-   */
-  public String makeOpName(String defaultName) {
-    return nameScope.makeOpName(defaultName);
-  }
-
-  private Scope(Graph graph, NameScope nameScope) {
-    this.graph = graph;
-    this.nameScope = nameScope;
-  }
-
-  private final Graph graph;
-  private final NameScope nameScope;
-}
diff --git a/tensorflow/java/src/main/native/operation_jni.cc b/tensorflow/java/src/main/native/operation_jni.cc
index ccc44d91c0..b3d5fc4ec3 100644
--- a/tensorflow/java/src/main/native/operation_jni.cc
+++ b/tensorflow/java/src/main/native/operation_jni.cc
@@ -156,21 +156,3 @@ JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv* env,
 
   return static_cast<jint>(TF_OperationOutputType(TF_Output{op, output_index}));
 }
-
-JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv* env,
-                                                                      jclass clazz,
-                                                                      jlong handle,
-                                                                      jstring name) {
-  TF_Operation* op = requireHandle(env, handle);
-  if (op == nullptr) return 0;
-
-  TF_Status* status = TF_NewStatus();
-
-  const char* cname = env->GetStringUTFChars(name, nullptr);
-  int result = TF_OperationInputListLength(op, cname, status);
-  env->ReleaseStringUTFChars(name, cname);
-
-  throwExceptionIfNotOK(env, status);
-  TF_DeleteStatus(status);
-  return result;
-}
diff --git a/tensorflow/java/src/main/native/operation_jni.h b/tensorflow/java/src/main/native/operation_jni.h
index 6f379256d2..b5d156f7c2 100644
--- a/tensorflow/java/src/main/native/operation_jni.h
+++ b/tensorflow/java/src/main/native/operation_jni.h
@@ -73,17 +73,6 @@ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_Operation_shape(JNIEnv *,
 JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv *, jclass,
                                                            jlong, jlong, jint);
 
-
-/*
- * Class:     org_tensorflow_Operation
- * Method:    inputListLength
- * Signature: (JLjava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv *,
-                                                                      jclass,
-                                                                      jlong,
-                                                                      jstring);
-
 #ifdef __cplusplus
 }  // extern "C"
 #endif  // __cplusplus
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 4fa68130c0..74fdcf484e 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -52,16 +52,6 @@ public class OperationTest {
     assertEquals(3, split(new int[] {0, 1, 2}, 3));
   }
 
-  @Test
-  public void inputListLength() {
-    assertEquals(1, splitWithInputList(new int[] {0, 1}, 1, "split_dim"));
-    try {
-      splitWithInputList(new int[] {0, 1}, 2, "inputs");
-    } catch (IllegalArgumentException iae) {
-      // expected
-    }
-  }
-
   private static int split(int[] values, int num_split) {
     try (Graph g = new Graph()) {
       return g.opBuilder("Split", "Split")
@@ -72,15 +62,4 @@ public class OperationTest {
           .outputListLength("output");
     }
   }
-
-  private static int splitWithInputList(int[] values, int num_split, String name) {
-    try (Graph g = new Graph()) {
-      return g.opBuilder("Split", "Split")
-          .addInput(TestUtil.constant(g, "split_dim", 0))
-          .addInput(TestUtil.constant(g, "values", values))
-          .setAttr("num_split", num_split)
-          .build()
-          .inputListLength(name);
-    }
-  }
 }
diff --git a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
index 50bdf351e3..0d2dbc5b88 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
@@ -109,7 +109,7 @@ public class SessionTest {
         assertEquals(1, outputs.size());
         final int[][] expected = {{31}};
         assertArrayEquals(expected, outputs.get(0).copyTo(new int[1][1]));
-        // Sanity check on metadata
+        // Sanity check on metadatar
         // See comments in fullTraceRunOptions() for an explanation about
         // why this check is really silly. Ideally, this would be:
         /*
@@ -187,7 +187,7 @@ public class SessionTest {
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
     //
-    // For this test, for now, the use of specific bytes suffices.
+    // For this test, for now, the use of specific bytes sufficies.
     return new byte[] {0x08, 0x03};
     /*
     return org.tensorflow.framework.RunOptions.newBuilder()
@@ -207,7 +207,7 @@ public class SessionTest {
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
     //
-    // For this test, for now, the use of specific bytes suffices.
+    // For this test, for now, the use of specific bytes sufficies.
     return new byte[] {0x10, 0x01, 0x28, 0x01};
     /*
     return org.tensorflow.framework.ConfigProto.newBuilder()
diff --git a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
index 3ff59e71b2..44eecc1d1e 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
@@ -472,7 +472,7 @@ public class TensorTest {
   @Test
   public void fromHandle() {
     // fromHandle is a package-visible method intended for use when the C TF_Tensor object has been
-    // created independently of the Java code. In practice, two Tensor instances MUST NOT have the
+    // created indepdently of the Java code. In practice, two Tensor instances MUST NOT have the
     // same native handle.
     //
     // An exception is made for this test, where the pitfalls of this is avoided by not calling
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java
deleted file mode 100644
index 9256cb281d..0000000000
--- a/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java
+++ /dev/null
@@ -1,270 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-package org.tensorflow.op;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-import static org.junit.Assert.fail;
-
-import org.junit.Test;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-import org.tensorflow.Graph;
-import org.tensorflow.Output;
-import org.tensorflow.Session;
-import org.tensorflow.Tensor;
-
-/** Unit tests for {@link org.tensorflow.Scope}. */
-@RunWith(JUnit4.class)
-public class ScopeTest {
-
-  @Test
-  public void basicNames() {
-    try (Graph g = new Graph()) {
-      Scope root = new Scope(g);
-      assertEquals("add", root.makeOpName("add"));
-      assertEquals("add_1", root.makeOpName("add"));
-      assertEquals("add_2", root.makeOpName("add"));
-      assertEquals("mul", root.makeOpName("mul"));
-    }
-  }
-
-  @Test
-  public void hierarchicalNames() {
-    try (Graph g = new Graph()) {
-      Scope root = new Scope(g);
-      Scope child = root.withSubScope("child");
-      assertEquals("child/add", child.makeOpName("add"));
-      assertEquals("child/add_1", child.makeOpName("add"));
-      assertEquals("child/mul", child.makeOpName("mul"));
-
-      Scope child_1 = root.withSubScope("child");
-      assertEquals("child_1/add", child_1.makeOpName("add"));
-      assertEquals("child_1/add_1", child_1.makeOpName("add"));
-      assertEquals("child_1/mul", child_1.makeOpName("mul"));
-
-      Scope c_c = root.withSubScope("c").withSubScope("c");
-      assertEquals("c/c/add", c_c.makeOpName("add"));
-
-      Scope c_1 = root.withSubScope("c");
-      Scope c_1_c = c_1.withSubScope("c");
-      assertEquals("c_1/c/add", c_1_c.makeOpName("add"));
-
-      Scope c_1_c_1 = c_1.withSubScope("c");
-      assertEquals("c_1/c_1/add", c_1_c_1.makeOpName("add"));
-    }
-  }
-
-  @Test
-  public void scopeAndOpNames() {
-    try (Graph g = new Graph()) {
-      Scope root = new Scope(g);
-
-      Scope child = root.withSubScope("child");
-
-      assertEquals("child/add", child.makeOpName("add"));
-      assertEquals("child_1", root.makeOpName("child"));
-      assertEquals("child_2/p", root.withSubScope("child").makeOpName("p"));
-    }
-  }
-
-  @Test
-  public void validateNames() {
-    try (Graph g = new Graph()) {
-      Scope root = new Scope(g);
-
-      final String[] invalid_names = {
-        "_", "-", "-x", // Names are constrained to start with [A-Za-z0-9.]
-        null, "", "a$", // Invalid characters
-        "a/b", // slashes not allowed
-      };
-
-      for (String name : invalid_names) {
-        try {
-          root.withName(name);
-          fail("failed to catch invalid op name.");
-        } catch (IllegalArgumentException ex) {
-          // expected
-        }
-        // Subscopes follow the same rules
-        try {
-          root.withSubScope(name);
-          fail("failed to catch invalid scope name: " + name);
-        } catch (IllegalArgumentException ex) {
-          // expected
-        }
-      }
-
-      // Unusual but valid names.
-      final String[] valid_names = {".", "..", "._-.", "a--."};
-
-      for (String name : valid_names) {
-        root.withName(name);
-        root.withSubScope(name);
-      }
-    }
-  }
-
-  @Test
-  public void basic() {
-    try (Graph g = new Graph()) {
-      Scope s = new Scope(g);
-      Const c1 = Const.create(s, 42);
-      assertEquals("Const", c1.output().op().name());
-      Const c2 = Const.create(s, 7);
-      assertEquals("Const_1", c2.output().op().name());
-      Const c3 = Const.create(s.withName("four"), 4);
-      assertEquals("four", c3.output().op().name());
-      Const c4 = Const.create(s.withName("four"), 4);
-      assertEquals("four_1", c4.output().op().name());
-    }
-  }
-
-  @Test
-  public void hierarchy() {
-    try (Graph g = new Graph()) {
-      Scope root = new Scope(g);
-      Scope child = root.withSubScope("child");
-      assertEquals("child/Const", Const.create(child, 42).output().op().name());
-      assertEquals("child/four", Const.create(child.withName("four"), 4).output().op().name());
-    }
-  }
-
-  @Test
-  public void composite() {
-    try (Graph g = new Graph();
-        Session sess = new Session(g)) {
-      Scope s = new Scope(g);
-      Output data = Const.create(s.withName("data"), new int[] {600, 470, 170, 430, 300}).output();
-
-      // Create a composite op with a customized name
-      Variance var1 = Variance.create(s.withName("example"), data);
-      assertEquals("example/variance", var1.output().op().name());
-
-      // Confirm internally added ops have the right names.
-      assertNotNull(g.operation("example/squared_deviation"));
-      assertNotNull(g.operation("example/Mean"));
-      assertNotNull(g.operation("example/zero"));
-
-      // Same composite op with a default name
-      Variance var2 = Variance.create(s, data);
-      assertEquals("variance/variance", var2.output().op().name());
-
-      // Confirm internally added ops have the right names.
-      assertNotNull(g.operation("variance/squared_deviation"));
-      assertNotNull(g.operation("variance/Mean"));
-      assertNotNull(g.operation("variance/zero"));
-
-      // Verify correct results as well.
-      Tensor result = sess.runner().fetch(var1.output()).run().get(0);
-      assertEquals(21704, result.intValue());
-      result = sess.runner().fetch(var2.output()).run().get(0);
-      assertEquals(21704, result.intValue());
-    }
-  }
-
-  // "handwritten" sample operator classes
-  private static final class Const {
-    private final Output output;
-
-    static Const create(Scope s, Object v) {
-      try (Tensor value = Tensor.create(v)) {
-        return new Const(
-            s.graph()
-                .opBuilder("Const", s.makeOpName("Const"))
-                .setAttr("dtype", value.dataType())
-                .setAttr("value", value)
-                .build()
-                .output(0));
-      }
-    }
-
-    Const(Output o) {
-      output = o;
-    }
-
-    Output output() {
-      return output;
-    }
-  }
-
-  private static final class Mean {
-    private final Output output;
-
-    static Mean create(Scope s, Output input, Output reductionIndices) {
-      return new Mean(
-          s.graph()
-              .opBuilder("Mean", s.makeOpName("Mean"))
-              .addInput(input)
-              .addInput(reductionIndices)
-              .build()
-              .output(0));
-    }
-
-    Mean(Output o) {
-      output = o;
-    }
-
-    Output output() {
-      return output;
-    }
-  }
-
-  private static final class SquaredDifference {
-    private final Output output;
-
-    static SquaredDifference create(Scope s, Output x, Output y) {
-      return new SquaredDifference(
-          s.graph()
-              .opBuilder("SquaredDifference", s.makeOpName("SquaredDifference"))
-              .addInput(x)
-              .addInput(y)
-              .build()
-              .output(0));
-    }
-
-    SquaredDifference(Output o) {
-      output = o;
-    }
-
-    Output output() {
-      return output;
-    }
-  }
-
-  private static final class Variance {
-    private final Output output;
-
-    static Variance create(Scope base, Output x) {
-      Scope s = base.withSubScope("variance");
-      Output zero = Const.create(s.withName("zero"), new int[] {0}).output();
-      Output sqdiff =
-          SquaredDifference.create(
-                  s.withName("squared_deviation"), x, Mean.create(s, x, zero).output())
-              .output();
-
-      return new Variance(Mean.create(s.withName("variance"), sqdiff, zero).output());
-    }
-
-    Variance(Output o) {
-      output = o;
-    }
-
-    Output output() {
-      return output;
-    }
-  }
-}
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 22b18b9cde..b65a638620 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -833,7 +833,6 @@ py_test(
     data = ["//tensorflow/python:meta_graph_testdata"],
     main = "framework/meta_graph_test.py",
     srcs_version = "PY2AND3",
-    tags = ["no_pip"],
     deps = [
         ":array_ops",
         ":client_testlib",
@@ -3056,7 +3055,7 @@ py_test(
     srcs = ["client/session_clusterspec_prop_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip_gpu",
+        "no_gpu",
     ],
     deps = [
         ":array_ops",
@@ -3080,7 +3079,7 @@ py_test(
     srcs = ["client/session_list_devices_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip_gpu",
+        "no_gpu",
     ],
     deps = [
         ":client",
diff --git a/tensorflow/python/debug/cli/analyzer_cli.py b/tensorflow/python/debug/cli/analyzer_cli.py
index e863c2ddb9..da27f4cebe 100644
--- a/tensorflow/python/debug/cli/analyzer_cli.py
+++ b/tensorflow/python/debug/cli/analyzer_cli.py
@@ -1312,7 +1312,7 @@ class DebugAnalyzer(object):
     all_inputs = copy.copy(tracker(node_name, is_control=False))
     is_ctrl = [False] * len(all_inputs)
     if include_control:
-      # Sort control inputs or recipients in alphabetical order of the node
+      # Sort control inputs or recipients in in alphabetical order of the node
       # names.
       ctrl_inputs = sorted(tracker(node_name, is_control=True))
       all_inputs.extend(ctrl_inputs)
diff --git a/tensorflow/python/debug/wrappers/framework_test.py b/tensorflow/python/debug/wrappers/framework_test.py
index 2b2289d6a8..536365b692 100644
--- a/tensorflow/python/debug/wrappers/framework_test.py
+++ b/tensorflow/python/debug/wrappers/framework_test.py
@@ -215,7 +215,7 @@ class DebugWrapperSessionTest(test_util.TensorFlowTestCase):
       wrapper_sess.partial_run_setup(self._p)
 
   def testInteractiveSessionInit(self):
-    """The wrapper should work also on other subclasses of session.Session."""
+    """The wrapper should work also on other subclassses of session.Session."""
 
     TestDebugWrapperSession(
         session.InteractiveSession(), self._dump_root, self._observer)
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py
index 03bcf7ae57..8785ddc5a6 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py
@@ -395,7 +395,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
 
 
 class DNNLinearCombinedRegressor(estimator.Estimator):
-  """An estimator for TensorFlow Linear and DNN joined models for regression.
+  """An estimator for TensorFlow Linear and DNN joined models for regresssion.
 
   Note: This estimator is also known as wide-n-deep.
 
diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py
index 552b1bdf01..05b1e5b44a 100644
--- a/tensorflow/python/estimator/canned/linear.py
+++ b/tensorflow/python/estimator/canned/linear.py
@@ -42,8 +42,8 @@ def _get_default_optimizer(feature_columns):
   return ftrl.FtrlOptimizer(learning_rate=learning_rate)
 
 
-# TODO(b/36813849): Revisit passing params vs named arguments.
-def _linear_model_fn(features, labels, mode, params, config):
+def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
+                     partitioner, config):
   """A model_fn for linear models that use a gradient-based optimizer.
 
   Args:
@@ -51,13 +51,12 @@ def _linear_model_fn(features, labels, mode, params, config):
     labels: `Tensor` of shape `[batch_size, logits_dimension]`.
     mode: Defines whether this is training, evaluation or prediction.
       See `ModeKeys`.
-    params: A dict of hyperparameters.
-      The following hyperparameters are expected:
-      * head: A `Head` instance.
-      * feature_columns: An iterable containing all the feature columns used by
-          the model.
-      * optimizer: string, `Optimizer` object, or callable that defines the
-          optimizer to use for training. If `None`, will use a FTRL optimizer.
+    head: A `Head` instance.
+    feature_columns: An iterable containing all the feature columns used by
+      the model.
+    optimizer: string, `Optimizer` object, or callable that defines the
+      optimizer to use for training. If `None`, will use a FTRL optimizer.
+    partitioner: Partitioner for variables.
     config: `RunConfig` object to configure the runtime settings.
 
   Returns:
@@ -66,14 +65,12 @@ def _linear_model_fn(features, labels, mode, params, config):
   Raises:
     ValueError: If mode or params are invalid.
   """
-  head = params['head']
-  feature_columns = tuple(params['feature_columns'])
   optimizer = optimizers.get_optimizer_instance(
-      params.get('optimizer') or _get_default_optimizer(feature_columns),
+      optimizer or _get_default_optimizer(feature_columns),
       learning_rate=_LEARNING_RATE)
   num_ps_replicas = config.num_ps_replicas if config else 0
 
-  partitioner = params.get('partitioner') or (
+  partitioner = partitioner or (
       partitioned_variables.min_max_variable_partitioner(
           max_partitions=num_ps_replicas,
           min_slice_size=64 << 20))
@@ -210,16 +207,20 @@ class LinearClassifier(estimator.Estimator):
       head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint: disable=protected-access
           n_classes, weight_column=weight_column,
           label_vocabulary=label_vocabulary)
+    def _model_fn(features, labels, mode, config):
+      return _linear_model_fn(
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          feature_columns=tuple(feature_columns or []),
+          optimizer=optimizer,
+          partitioner=partitioner,
+          config=config)
     super(LinearClassifier, self).__init__(
-        model_fn=_linear_model_fn,
+        model_fn=_model_fn,
         model_dir=model_dir,
-        config=config,
-        params={
-            'head': head,
-            'feature_columns': feature_columns,
-            'optimizer': optimizer,
-            'partitioner': partitioner,
-        })
+        config=config)
 
 
 class LinearRegressor(estimator.Estimator):
@@ -298,21 +299,19 @@ class LinearRegressor(estimator.Estimator):
       config: `RunConfig` object to configure the runtime settings.
       partitioner: Optional. Partitioner for input layer.
     """
+    head = head_lib._regression_head_with_mean_squared_error_loss(  # pylint: disable=protected-access
+        label_dimension=label_dimension, weight_column=weight_column)
+    def _model_fn(features, labels, mode, config):
+      return _linear_model_fn(
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          feature_columns=tuple(feature_columns or []),
+          optimizer=optimizer,
+          partitioner=partitioner,
+          config=config)
     super(LinearRegressor, self).__init__(
-        model_fn=_linear_model_fn,
+        model_fn=_model_fn,
         model_dir=model_dir,
-        config=config,
-        params={
-            # pylint: disable=protected-access
-            'head':
-                head_lib._regression_head_with_mean_squared_error_loss(
-                    label_dimension=label_dimension,
-                    weight_column=weight_column),
-            # pylint: enable=protected-access
-            'feature_columns':
-                feature_columns,
-            'optimizer':
-                optimizer,
-            'partitioner':
-                partitioner,
-        })
+        config=config)
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index d8e069105f..257677ce49 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2382,6 +2382,11 @@ cuda_py_test(
         "//tensorflow/python:util",
         "//tensorflow/python:data_flow_ops",
     ],
+    tags = [
+        "manual",  # http://b/62429636
+        "noguitar",
+        "notap",
+    ],
 )
 
 cuda_py_test(
@@ -2396,6 +2401,11 @@ cuda_py_test(
         "//tensorflow/python:util",
         "//tensorflow/python:data_flow_ops",
     ],
+    tags = [
+        "manual",  # http://b/62429636
+        "noguitar",
+        "notap",
+    ],
 )
 
 cuda_py_test(
diff --git a/tensorflow/python/kernel_tests/basic_gpu_test.py b/tensorflow/python/kernel_tests/basic_gpu_test.py
index b5b17ff80a..013aa1ba8a 100644
--- a/tensorflow/python/kernel_tests/basic_gpu_test.py
+++ b/tensorflow/python/kernel_tests/basic_gpu_test.py
@@ -113,7 +113,6 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.arctan, math_ops.atan, use_gpu)
     self._compare(data, np.ceil, math_ops.ceil, use_gpu)
     self._compare(data, np.cos, math_ops.cos, use_gpu)
-    self._compare(data, np.cosh, math_ops.cosh, use_gpu)
     self._compare(data, np.exp, math_ops.exp, use_gpu)
     self._compare(data, np.floor, math_ops.floor, use_gpu)
     self._compare(data, np.log, math_ops.log, use_gpu)
@@ -121,7 +120,6 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.negative, math_ops.negative, use_gpu)
     self._compare(data, self._rsqrt, math_ops.rsqrt, use_gpu)
     self._compare(data, np.sin, math_ops.sin, use_gpu)
-    self._compare(data, np.sinh, math_ops.sinh, use_gpu)
     self._compare(data, np.sqrt, math_ops.sqrt, use_gpu)
     self._compare(data, np.square, math_ops.square, use_gpu)
     self._compare(data, np.tan, math_ops.tan, use_gpu)
diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py
index b47139e6b8..0846470abc 100644
--- a/tensorflow/python/kernel_tests/cwise_ops_test.py
+++ b/tensorflow/python/kernel_tests/cwise_ops_test.py
@@ -200,8 +200,6 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(z, np.log, math_ops.log)
     self._compareBoth(z, np.log1p, math_ops.log1p)
-    self._compareBoth(x, np.sinh, math_ops.sinh)
-    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, self._log_sigmoid, math_ops.log_sigmoid)
@@ -247,8 +245,6 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(x, np.log, math_ops.log)
     self._compareBoth(x, np.log1p, math_ops.log1p)
-    self._compareBoth(x, np.sinh, math_ops.sinh)
-    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, np.sign, math_ops.sign)
@@ -289,8 +285,6 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(z, np.log, math_ops.log)
     self._compareBoth(z, np.log1p, math_ops.log1p)
-    self._compareBoth(x, np.sinh, math_ops.sinh)
-    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(y, np.sign, math_ops.sign)
@@ -395,8 +389,6 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.expm1, math_ops.expm1)
     self._compareCpu(y, np.log, math_ops.log)
     self._compareCpu(y, np.log1p, math_ops.log1p)
-    self._compareCpu(x, np.sinh, math_ops.sinh)
-    self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
@@ -431,8 +423,6 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.expm1, math_ops.expm1)
     self._compareCpu(y, np.log, math_ops.log)
     self._compareCpu(y, np.log1p, math_ops.log1p)
-    self._compareCpu(x, np.sinh, math_ops.sinh)
-    self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
@@ -707,11 +697,6 @@ class BinaryOpTest(test.TestCase):
     except ImportError as e:
       tf_logging.warn("Cannot test special functions: %s" % str(e))
 
-  def testUint8Basic(self):
-    x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.uint8)
-    y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.uint8)
-    self._compareBoth(x, y, np.add, math_ops.add)
-
   def testInt8Basic(self):
     x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int8)
     y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int8)
diff --git a/tensorflow/python/kernel_tests/fft_ops_test.py b/tensorflow/python/kernel_tests/fft_ops_test.py
index 6c575aea12..6544fe9735 100644
--- a/tensorflow/python/kernel_tests/fft_ops_test.py
+++ b/tensorflow/python/kernel_tests/fft_ops_test.py
@@ -338,38 +338,37 @@ class RFFTOpsTest(BaseFFTOpsTest):
                 use_placeholder=True)
 
   def testFftLength(self):
-    if test.is_gpu_available(cuda_only=True):
-      with self._fft_kernel_label_map():
-        for rank in VALID_FFT_RANKS:
-          for dims in xrange(rank, rank + 3):
-            for size in (5, 6):
-              inner_dim = size // 2 + 1
-              r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
-                  (size,) * dims)
-              c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
-                           10).reshape((size,) * (dims - 1) + (inner_dim,))
-
-              # Test truncation (FFT size < dimensions).
-              fft_length = (size - 2,) * rank
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
-              # Confirm it works with unknown shapes as well.
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                   use_placeholder=True)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                    use_placeholder=True)
-
-              # Test padding (FFT size > dimensions).
-              fft_length = (size + 2,) * rank
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
-              # Confirm it works with unknown shapes as well.
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                   use_placeholder=True)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                    use_placeholder=True)
+    with self._fft_kernel_label_map():
+      for rank in VALID_FFT_RANKS:
+        for dims in xrange(rank, rank + 3):
+          for size in (5, 6):
+            inner_dim = size // 2 + 1
+            r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
+                (size,) * dims)
+            c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
+                         10).reshape((size,) * (dims - 1) + (inner_dim,))
+
+            # Test truncation (FFT size < dimensions).
+            fft_length = (size - 2,) * rank
+            self._CompareForward(r2c.astype(np.float32), rank, fft_length)
+            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
+
+            # Confirm it works with unknown shapes as well.
+            self._CompareForward(r2c.astype(np.float32), rank, fft_length,
+                                 use_placeholder=True)
+            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
+                                  use_placeholder=True)
+
+            # Test padding (FFT size > dimensions).
+            fft_length = (size + 2,) * rank
+            self._CompareForward(r2c.astype(np.float32), rank, fft_length)
+            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
+
+            # Confirm it works with unknown shapes as well.
+            self._CompareForward(r2c.astype(np.float32), rank, fft_length,
+                                 use_placeholder=True)
+            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
+                                  use_placeholder=True)
 
   def testRandom(self):
     with self._fft_kernel_label_map():
diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py
index 4ceb24862f..2d2169c310 100644
--- a/tensorflow/python/kernel_tests/map_stage_op_test.py
+++ b/tensorflow/python/kernel_tests/map_stage_op_test.py
@@ -24,7 +24,6 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
-TIMEOUT = 1
 
 class MapStageTest(test.TestCase):
 
@@ -195,7 +194,8 @@ class MapStageTest(test.TestCase):
     import threading
 
     queue = Queue.Queue()
-    n = 8
+    n = 5
+    missed = 0
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -207,33 +207,30 @@ class MapStageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
-      t.daemon = True
       t.start()
 
-      # Get tokens from the queue until a timeout occurs
-      try:
-        for i in range(n):
-          queue.get(timeout=TIMEOUT)
-      except Queue.Empty:
-        pass
-
-      # Should've timed out on the iteration 'capacity'
-      if not i == capacity:
-        self.fail("Expected to timeout on iteration '{}' "
-                  "but instead timed out on iteration '{}' "
-                  "Staging Area size is '{}' and configured "
-                  "capacity is '{}'.".format(capacity, i,
-                                            sess.run(size),
-                                            capacity))
-
-      # Should have capacity elements in the staging area
-      self.assertTrue(sess.run(size) == capacity)
-
-      # Clear the staging area completely
+      # Get tokens from the queue, making notes of when we timeout
       for i in range(n):
+        try:
+          queue.get(timeout=0.05)
+        except Queue.Empty:
+          missed += 1
+
+      # We timed out n - capacity times waiting for queue puts
+      self.assertTrue(missed == n - capacity)
+
+      # Clear the staging area out a bit
+      for i in range(n - capacity):
         sess.run(get)
 
-      self.assertTrue(sess.run(size) == 0)
+      # This should now succeed
+      t.join()
+
+      self.assertTrue(sess.run(size) == capacity)
+
+      # Clear out the staging area completely
+      for i in range(capacity):
+        sess.run(get)
 
   def testMemoryLimit(self):
     memory_limit = 512*1024  # 512K
@@ -259,7 +256,8 @@ class MapStageTest(test.TestCase):
     import numpy as np
 
     queue = Queue.Queue()
-    n = 8
+    n = 5
+    missed = 0
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -267,38 +265,35 @@ class MapStageTest(test.TestCase):
       # not fill the queue with n tokens
       def thread_run():
         for i in range(n):
-          data = np.full(chunk, i, dtype=np.uint8)
-          sess.run(stage, feed_dict={x: data, pi: i})
+          sess.run(stage, feed_dict={x: np.full(chunk, i, dtype=np.uint8),
+                                    pi: i})
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
-      t.daemon = True
       t.start()
 
-      # Get tokens from the queue until a timeout occurs
-      try:
-        for i in range(n):
-          queue.get(timeout=TIMEOUT)
-      except Queue.Empty:
-        pass
-
-      # Should've timed out on the iteration 'capacity'
-      if not i == capacity:
-        self.fail("Expected to timeout on iteration '{}' "
-                  "but instead timed out on iteration '{}' "
-                  "Staging Area size is '{}' and configured "
-                  "capacity is '{}'.".format(capacity, i,
-                                            sess.run(size),
-                                            capacity))
-
-      # Should have capacity elements in the staging area
-      self.assertTrue(sess.run(size) == capacity)
-
-      # Clear the staging area completely
+      # Get tokens from the queue, making notes of when we timeout
       for i in range(n):
+        try:
+          queue.get(timeout=0.05)
+        except Queue.Empty:
+          missed += 1
+
+      # We timed out n - capacity times waiting for queue puts
+      self.assertTrue(missed == n - capacity)
+
+      # Clear the staging area out a bit
+      for i in range(n - capacity):
         sess.run(get)
 
-      self.assertTrue(sess.run(size) == 0)
+      # This should now succeed
+      t.join()
+
+      self.assertTrue(sess.run(size) == capacity)
+
+      # Clear out the staging area completely
+      for i in range(capacity):
+        sess.run(get)
 
   def testOrdering(self):
     import six
diff --git a/tensorflow/python/kernel_tests/record_input_test.py b/tensorflow/python/kernel_tests/record_input_test.py
index 8fec2affa5..a3fc98c20f 100644
--- a/tensorflow/python/kernel_tests/record_input_test.py
+++ b/tensorflow/python/kernel_tests/record_input_test.py
@@ -53,7 +53,6 @@ class RecordInputOpTest(test.TestCase):
   def testRecordInputEpochs(self):
     files = 100
     records_per_file = 100
-    batches = 2
     with self.test_session() as sess:
       self.generateTestData("basic", files, records_per_file)
 
@@ -64,20 +63,17 @@ class RecordInputOpTest(test.TestCase):
           batch_size=1,
           shift_ratio=0.33,
           seed=10,
-          name="record_input",
-          batches=batches)
+          name="record_input")
 
       yield_op = records.get_yield_op()
 
       # cycle over 3 epochs and make sure we never duplicate
       for _ in range(3):
         epoch_set = set()
-        for _ in range(int(files * records_per_file / batches)):
-          op_list = sess.run(yield_op)
-          self.assertTrue(len(op_list) is batches)
-          for r in op_list:
-            self.assertTrue(r[0] not in epoch_set)
-            epoch_set.add(r[0])
+        for _ in range(files * records_per_file):
+          r = sess.run(yield_op)
+          self.assertTrue(r[0] not in epoch_set)
+          epoch_set.add(r[0])
 
   def testDoesNotDeadlock(self):
     # Iterate multiple times to cause deadlock if there is a chance it can occur
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 14eb2cba68..e67a2c25e9 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -19,7 +19,6 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-import unittest
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -545,24 +544,21 @@ class SparseFillEmptyRowsTest(test_util.TensorFlowTestCase):
       self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool))
 
 
-class SparseReduceTest(test_util.TensorFlowTestCase):
+class SparseReduceSumTest(test_util.TensorFlowTestCase):
 
-  # [[1, ?, 2]
-  #  [?, 3, ?]]
+  # [[1, ?, 1]
+  #  [?, 1, ?]]
   # where ? is implictly-zero.
   ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64)
   vals = np.array([1, 1, 1]).astype(np.int32)
   dense_shape = np.array([2, 3]).astype(np.int64)
 
-  def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
+  def _compare(self, sp_t, reduction_axes, ndims, keep_dims):
     densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval()
 
     np_ans = densified
     if reduction_axes is None:
-      if do_sum:
-        np_ans = np.sum(np_ans, keepdims=keep_dims)
-      else:
-        np_ans = np.max(np_ans, keepdims=keep_dims)
+      np_ans = np.sum(np_ans, keepdims=keep_dims)
     else:
       if not isinstance(reduction_axes, list):  # Single scalar.
         reduction_axes = [reduction_axes]
@@ -572,28 +568,15 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
       # Loop below depends on sorted.
       reduction_axes.sort()
       for ra in reduction_axes.ravel()[::-1]:
-        if do_sum:
-          np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
-        else:
-          np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
+        np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
 
     with self.test_session():
-      if do_sum:
-        tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
-                                                    keep_dims)
-      else:
-        tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
-                                                    keep_dims)
+      tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
+                                                  keep_dims)
       out_dense = tf_dense_ans.eval()
 
-      if do_sum:
-        tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t,
-                                                            reduction_axes,
-                                                            keep_dims)
-      else:
-        tf_sparse_ans = sparse_ops.sparse_reduce_max_sparse(sp_t,
-                                                            reduction_axes,
-                                                            keep_dims)
+      tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t, reduction_axes,
+                                                          keep_dims)
       # Convert to dense for comparison purposes.
       out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans).eval()
 
@@ -601,12 +584,9 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
     self.assertAllClose(np_ans, out_sparse)
 
   def _compare_all(self, sp_t, reduction_axes, ndims):
-    self._compare(sp_t, reduction_axes, ndims, False, False)
-    self._compare(sp_t, reduction_axes, ndims, False, True)
-    self._compare(sp_t, reduction_axes, ndims, True, False)
-    self._compare(sp_t, reduction_axes, ndims, True, True)
+    self._compare(sp_t, reduction_axes, ndims, False)
+    self._compare(sp_t, reduction_axes, ndims, True)
 
-  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
@@ -641,12 +621,7 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
         sparse_ops.sparse_reduce_sum(sp_t, -3).eval()
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_sum(sp_t, 2).eval()
-      with self.assertRaisesOpError("Invalid reduction dimension -3"):
-        sparse_ops.sparse_reduce_max(sp_t, -3).eval()
-      with self.assertRaisesOpError("Invalid reduction dimension 2"):
-        sparse_ops.sparse_reduce_max(sp_t, 2).eval()
 
-  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py
index 1a6a869e3d..4a89fb64e3 100644
--- a/tensorflow/python/kernel_tests/stage_op_test.py
+++ b/tensorflow/python/kernel_tests/stage_op_test.py
@@ -23,11 +23,9 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
-TIMEOUT = 1
 
 class StageTest(test.TestCase):
 
-
   def testSimple(self):
     with ops.Graph().as_default() as G:
       with ops.device('/cpu:0'):
@@ -174,7 +172,8 @@ class StageTest(test.TestCase):
     import threading
 
     queue = Queue.Queue()
-    n = 8
+    n = 5
+    missed = 0
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -186,33 +185,31 @@ class StageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
-      t.daemon = True
       t.start()
 
-      # Get tokens from the queue until a timeout occurs
-      try:
-        for i in range(n):
-          queue.get(timeout=TIMEOUT)
-      except Queue.Empty:
-        pass
-
-      # Should've timed out on the iteration 'capacity'
-      if not i == capacity:
-        self.fail("Expected to timeout on iteration '{}' "
-                  "but instead timed out on iteration '{}' "
-                  "Staging Area size is '{}' and configured "
-                  "capacity is '{}'.".format(capacity, i,
-                                            sess.run(size),
-                                            capacity))
-
-      # Should have capacity elements in the staging area
+      # Get tokens from the queue, making notes of when we timeout
+      for i in range(n):
+        try:
+          queue.get(timeout=0.05)
+        except Queue.Empty:
+          missed += 1
+
+      # We timed out n - capacity times waiting for queue puts
+      self.assertTrue(missed == n - capacity)
+
+      # Clear the staging area out a bit
+      for i in range(n - capacity):
+        self.assertTrue(sess.run(ret) == i)
+
+      # Thread should be able to join now
+      t.join()
+
       self.assertTrue(sess.run(size) == capacity)
 
       # Clear the staging area completely
-      for i in range(n):
-        self.assertTrue(sess.run(ret) == i)
+      for i in range(capacity):
+        self.assertTrue(sess.run(ret) == i+(n-capacity))
 
-      # It should now be empty
       self.assertTrue(sess.run(size) == 0)
 
   def testMemoryLimit(self):
@@ -237,7 +234,8 @@ class StageTest(test.TestCase):
     import numpy as np
 
     queue = Queue.Queue()
-    n = 8
+    n = 5
+    missed = 0
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -249,31 +247,30 @@ class StageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
-      t.daemon = True
       t.start()
 
-      # Get tokens from the queue until a timeout occurs
-      try:
-        for i in range(n):
-          queue.get(timeout=TIMEOUT)
-      except Queue.Empty:
-        pass
-
-      # Should've timed out on the iteration 'capacity'
-      if not i == capacity:
-        self.fail("Expected to timeout on iteration '{}' "
-                  "but instead timed out on iteration '{}' "
-                  "Staging Area size is '{}' and configured "
-                  "capacity is '{}'.".format(capacity, i,
-                                            sess.run(size),
-                                            capacity))
-
-      # Should have capacity elements in the staging area
+      # Get tokens from the queue, making notes of when we timeout
+      for i in range(n):
+        try:
+          queue.get(timeout=0.05)
+        except Queue.Empty:
+          missed += 1
+
+      # We timed out n - capacity times waiting for queue puts
+      self.assertTrue(missed == n - capacity)
+
+      # Clear the staging area out a bit
+      for i in range(n - capacity):
+        self.assertTrue(sess.run(ret)[0] == i)
+
+      # Thread should be able to join now
+      t.join()
+
       self.assertTrue(sess.run(size) == capacity)
 
       # Clear the staging area completely
-      for i in range(n):
-        self.assertTrue(np.all(sess.run(ret) == i))
+      for i in range(capacity):
+        self.assertTrue(sess.run(ret)[0] == i+(n-capacity))
 
       self.assertTrue(sess.run(size) == 0)
 
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index a8f596c7a3..b2aff617df 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -402,15 +402,14 @@ def _SliceHelper(tensor, slice_spec, var=None):
 
   # Insert another dimension
   foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
-  print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
-  print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
-  print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]], [[7],[8],[9]]]
+  print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
+  print(foo[:, tf.newaxis, :].eval()) # => [[[3,2,1]], [[9,8,7]]]
+  print(foo[:, :, tf.newaxis].eval()) # => [[[3],[2],[1]], [[9],[8],[7]]]
 
   # Ellipses (3 equivalent operations)
-  foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
-  print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
-  print(foo[tf.newaxis, ...].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
-  print(foo[tf.newaxis].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
+  print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
+  print(foo[tf.newaxis, ...].eval()) # => [[[3,2,1], [9,8,7]]]
+  print(foo[tf.newaxis].eval()) # => [[[3,2,1], [9,8,7]]]
   ```
 
   Notes:
@@ -761,14 +760,11 @@ def parallel_stack(values, name="parallel_stack"):
   parallel_stack([x, y, z])  # => [[1, 4], [2, 5], [3, 6]]
   ```
 
-  The difference between `stack` and `parallel_stack` is that `stack` requires
-  all the inputs be computed before the operation will begin but doesn't require
-  that the input shapes be known during graph construction.
-  
-  `parallel_stack` will copy pieces of the input into the output as they become
-  available, in some situations this can provide a performance benefit.
-  
-  Unlike `stack`, `parallel_stack` does NOT support backpropagation.
+  The difference between stack and parallel_stack is that stack requires all
+  of the inputs be computed before the operation will begin but doesn't require
+  that the input shapes be known during graph construction.  Parallel stack
+  will copy pieces of the input into the output as they become available, in
+  some situations this can provide a performance benefit.
 
   This is the opposite of unstack.  The numpy equivalent is
 
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 829aa99284..4eead79531 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -1582,7 +1582,7 @@ class StagingArea(BaseStagingArea):
   This is mostly useful for limiting the number of tensors on
   devices such as GPUs.
 
-  All get() and peek() commands block if the requested data
+  All get() and peek() commands block if the the requested data
   is not present in the Staging Area.
 
   """
@@ -2155,8 +2155,7 @@ class RecordInput(object):
                parallelism=1,
                shift_ratio=0,
                seed=0,
-               name=None,
-               batches=None):
+               name=None):
     """Constructs a RecordInput Op.
 
     Args:
@@ -2170,18 +2169,12 @@ class RecordInput(object):
       seed: Specify the random number seed used by generator that randomizes
         records.
       name: Optional name for the operation.
-      batches: None by default, creating a single batch op. Otherwise specifies
-        how many batches to create, which are returned as a list when
-        `get_yield_op()` is called. An example use case is to split processing
-        between devices on one computer.
 
     Raises:
       ValueError: If one of the arguments is invalid.
     """
+
     self._batch_size = batch_size
-    if batches is not None:
-      self._batch_size *= batches
-    self._batches = batches
     self._file_pattern = file_pattern
     self._buffer_size = buffer_size
     self._parallelism = parallelism
@@ -2190,11 +2183,8 @@ class RecordInput(object):
     self._name = name
 
   def get_yield_op(self):
-    """Adds a node that yields a group of records every time it is executed.
-    If RecordInput `batches` parameter is not None, it yields a list of
-    record batches with the specified `batch_size`.
-    """
-    records = gen_data_flow_ops.record_input(
+    """Add a node that yields a minibatch every time it is executed."""
+    return gen_data_flow_ops.record_input(
         file_pattern=self._file_pattern,
         file_buffer_size=self._buffer_size,
         file_parallelism=self._parallelism,
@@ -2202,14 +2192,3 @@ class RecordInput(object):
         batch_size=self._batch_size,
         file_random_seed=self._seed,
         name=self._name)
-    if self._batches is None:
-      return records
-    else:
-      with ops.name_scope(self._name):
-        batch_list = [[] for i in six.moves.range(self._batches)]
-        records = array_ops.split(records, self._batch_size, 0)
-        records = [array_ops.reshape(record, []) for record in records]
-        for index, protobuf in zip(six.moves.range(len(records)), records):
-          batch_index = index % self._batches
-          batch_list[batch_index].append(protobuf)
-        return batch_list
diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py
index 3a804c941a..f96eafed71 100644
--- a/tensorflow/python/ops/distributions/special_math.py
+++ b/tensorflow/python/ops/distributions/special_math.py
@@ -324,7 +324,7 @@ def log_ndtr(x, series_order=3, name="log_ndtr"):
 
 
 def _log_ndtr_lower(x, series_order):
-  """Asymptotic expansion version of `Log[cdf(x)]`, appropriate for `x<<-1`."""
+  """Asymptotic expansion version of `Log[cdf(x)]`, apppropriate for `x<<-1`."""
   x_2 = math_ops.square(x)
   # Log of the term multiplying (1 + sum)
   log_scale = -0.5 * x_2 - math_ops.log(-x) - 0.5 * math.log(2. * math.pi)
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index dade053589..aac7aef8ce 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -989,9 +989,9 @@ def index_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
+  vocabulary_list = t.constant(["emerson", "lake", "palmer")
   table = tf.contrib.lookup.index_table_from_tensor(
-      mapping=vocabulary_list, num_oov_buckets=1, default_value=-1)
+      vocabulary_list=vocabulary_list, num_oov_buckets=1, default_value=-1)
   features = tf.constant(["emerson", "lake", "and", "palmer"])
   ids = table.lookup(features)
   ...
@@ -1161,7 +1161,7 @@ def index_to_string_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
+  vocabulary_list = t.constant(["emerson", "lake", "palmer")
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       vocabulary_list, default_value="UNKNOWN")
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index a0f505e47b..024158e709 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -369,24 +369,6 @@ def _Log1pGrad(op, grad):
     return grad * math_ops.reciprocal(1 + x)
 
 
-@ops.RegisterGradient("Sinh")
-def _SinhGrad(op, grad):
-  """Returns grad * cosh(x)."""
-  x = op.inputs[0]
-  with ops.control_dependencies([grad.op]):
-    x = math_ops.conj(x)
-    return grad * math_ops.cosh(x)
-
-
-@ops.RegisterGradient("Cosh")
-def _CoshGrad(op, grad):
-  """Returns grad * sinh(x)."""
-  x = op.inputs[0]
-  with ops.control_dependencies([grad.op]):
-    x = math_ops.conj(x)
-    return grad * math_ops.sinh(x)
-
-
 @ops.RegisterGradient("Tanh")
 def _TanhGrad(op, grad):
   """Returns grad * (1 - tanh(x) * tanh(x))."""
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index bf46bbdcc2..23af8c0a57 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -45,8 +45,6 @@ See the @{$python/math_ops} guide.
 @@expm1
 @@log
 @@log1p
-@@sinh
-@@cosh
 @@ceil
 @@floor
 @@maximum
@@ -2065,11 +2063,12 @@ def tanh(x, name=None):
 
   Args:
     x: A Tensor or SparseTensor with type `float`, `double`, `int32`,
-      `complex64`, or `int64`.
+      `complex64`, `int64`, or `qint32`.
     name: A name for the operation (optional).
 
   Returns:
-    A Tensor or SparseTensor respectively with the same type as `x`.
+    A Tensor or SparseTensor respectively with the same type as `x` if
+    `x.dtype != qint32` otherwise the return type is `quint8`.
   """
   with ops.name_scope(name, "Tanh", [x]) as name:
     if isinstance(x, sparse_tensor.SparseTensor):
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index a1e603199f..c0d9c971a0 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -416,7 +416,7 @@ class LSTMCell(RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://www.bioinf.jku.at/publications/older/2604.pdf
+    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
@@ -622,7 +622,7 @@ class DropoutWrapper(RNNCell):
     """Create a cell with added input, state, and/or output dropout.
 
     If `variational_recurrent` is set to `True` (**NOT** the default behavior),
-    then the same dropout mask is applied at every step, as described in:
+    then the the same dropout mask is applied at every step, as described in:
 
     Y. Gal, Z Ghahramani.  "A Theoretically Grounded Application of Dropout in
     Recurrent Neural Networks".  https://arxiv.org/abs/1512.05287
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 93a9656950..377adcdab8 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -30,8 +30,6 @@
 @@sparse_reset_shape
 @@sparse_fill_empty_rows
 @@sparse_transpose
-@@sparse_reduce_max
-@@sparse_reduce_max_sparse
 @@sparse_reduce_sum
 @@sparse_reduce_sum_sparse
 @@sparse_add
@@ -712,90 +710,6 @@ def sparse_to_dense(sparse_indices,
       name=name)
 
 
-def sparse_reduce_max(sp_input, axis=None, keep_dims=False,
-                      reduction_axes=None):
-  """Computes the max of elements across dimensions of a SparseTensor.
-
-  This Op takes a SparseTensor and is the sparse counterpart to
-  `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
-  instead of a sparse one.
-
-  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
-  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-  with length 1.
-
-  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-  with a single element is returned.  Additionally, the axes can be negative,
-  similar to the indexing rules in Python.
-
-  For example:
-
-  ```python
-  # 'x' represents [[1, ?, 2]
-  #                 [?, 3, ?]]
-  # where ? is implicitly-zero.
-  tf.sparse_reduce_max(x) ==> 3
-  tf.sparse_reduce_max(x, 0) ==> [1, 3, 2]
-  tf.sparse_reduce_max(x, 1) ==> [2, 3]  # Can also use -1 as the axis.
-  tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]]
-  tf.sparse_reduce_max(x, [0, 1]) ==> 3
-  ```
-
-  Args:
-    sp_input: The SparseTensor to reduce. Should have numeric type.
-    axis: The dimensions to reduce; list or scalar. If `None` (the
-      default), reduces all dimensions.
-    keep_dims: If true, retain reduced dimensions with length 1.
-    reduction_axes: Deprecated name of axis.
-
-  Returns:
-    The reduced Tensor.
-  """
-  return gen_sparse_ops.sparse_reduce_max(
-      sp_input.indices, sp_input.values,
-      sp_input.dense_shape,
-      math_ops._ReductionDims(sp_input, axis, reduction_axes),
-      keep_dims)
-
-
-def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False,
-                             reduction_axes=None):
-  """Computes the max of elements across dimensions of a SparseTensor.
-
-  This Op takes a SparseTensor and is the sparse counterpart to
-  `tf.reduce_max()`.  In contrast to SparseReduceSum, this Op returns a
-  SparseTensor.
-
-  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
-  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-  with length 1.
-
-  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-  with a single element is returned.  Additionally, the axes can be negative,
-  which are interpreted according to the indexing rules in Python.
-
-  Args:
-    sp_input: The SparseTensor to reduce. Should have numeric type.
-    axis: The dimensions to reduce; list or scalar. If `None` (the
-      default), reduces all dimensions.
-    keep_dims: If true, retain reduced dimensions with length 1.
-    reduction_axes: Deprecated name of axis
-
-  Returns:
-    The reduced SparseTensor.
-  """
-  output_ind, output_val, output_shape = (
-      gen_sparse_ops.sparse_reduce_max_sparse(
-          sp_input.indices, sp_input.values,
-          sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis,
-                                                        reduction_axes),
-          keep_dims))
-
-  return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
-
-
 def sparse_reduce_sum(sp_input, axis=None, keep_dims=False,
                       reduction_axes=None):
   """Computes the sum of elements across dimensions of a SparseTensor.
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index 20ae082ee1..7a6abc8e61 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -87,7 +87,7 @@ class TensorArray(object):
         the shape constraints of each of the elements of the TensorArray.
         Need not be fully defined.
       colocate_with_first_write_call: If `True`, the TensorArray will be
-        colocated on the same device as the Tensor used on its first write
+        colocated on the same device as the the Tensor used on its first write
         (write operations include `write`, `unstack`, and `split`).  If `False`,
         the TensorArray will be placed on the device determined by the
         device context available during its initialization.
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index 24616aeac3..aceffd373a 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -1501,11 +1501,6 @@ def variable_scope(name_or_scope,
   A note about name scoping: Setting `reuse` does not impact the naming of other
   ops such as mult. See related discussion on [github#6189](https://github.com/tensorflow/tensorflow/issues/6189)
 
-  Note that up to and including version 1.0, it was allowed (though
-  explicitly discouraged) to pass False to the reuse argument, yielding
-  undocumented behaviour slightly different from None. Starting at 1.1.0
-  passing None and False as reuse has exactly the same effect.
-
   Args:
     name_or_scope: `string` or `VariableScope`: the scope to open.
     default_name: The default name to use if the `name_or_scope` argument is
diff --git a/tensorflow/python/tools/print_selective_registration_header.py b/tensorflow/python/tools/print_selective_registration_header.py
index 62f00f4467..3e2ab4695e 100644
--- a/tensorflow/python/tools/print_selective_registration_header.py
+++ b/tensorflow/python/tools/print_selective_registration_header.py
@@ -16,7 +16,7 @@ r"""Prints a header file to be used with SELECTIVE_REGISTRATION.
 
 An example of command-line usage is:
   bazel build tensorflow/python/tools:print_selective_registration_header && \
-  bazel-bin/tensorflow/python/tools/print_selective_registration_header \
+  bazel-bin/tensorflow/python/tools:print_selective_registration_header \
     --graphs=path/to/graph.pb > ops_to_register.h
 
 Then when compiling tensorflow, include ops_to_register.h in the include search
diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 21183823c2..1755167938 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -762,9 +762,6 @@ def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
   tensor_list = _as_tensor_list(tensors)
   with ops.name_scope(name, "shuffle_batch",
                       list(tensor_list) + [keep_input]) as name:
-    if capacity <= min_after_dequeue:
-      raise ValueError("capacity %d must be bigger than min_after_dequeue %d."
-                       % (capacity, min_after_dequeue))
     tensor_list = _validate(tensor_list)
     keep_input = _validate_keep_input(keep_input, enqueue_many)
     tensor_list, sparse_info = _store_sparse_tensors(
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 5b1d9a517e..690af5485a 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -147,7 +147,7 @@ def tf_copts():
       "-Iexternal/gemmlowp",
       "-Wno-sign-compare",
       "-fno-exceptions",
-  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
+  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1"]) + if_android_arm(
       ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index eca017edf5..b761f41361 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -768,10 +768,6 @@ tf_module {
     name: "cos"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
-  member_method {
-    name: "cosh"
-    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "count_nonzero"
     argspec: "args=[\'input_tensor\', \'axis\', \'keep_dims\', \'dtype\', \'name\', \'reduction_indices\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \"<dtype: \'int64\'>\", \'None\', \'None\'], "
@@ -1644,10 +1640,6 @@ tf_module {
     name: "sin"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
-  member_method {
-    name: "sinh"
-    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "size"
     argspec: "args=[\'input\', \'name\', \'out_type\'], varargs=None, keywords=None, defaults=[\'None\', \"<dtype: \'int32\'>\"], "
@@ -1704,14 +1696,6 @@ tf_module {
     name: "sparse_placeholder"
     argspec: "args=[\'dtype\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
-  member_method {
-    name: "sparse_reduce_max"
-    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
-  }
-  member_method {
-    name: "sparse_reduce_max_sparse"
-    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
-  }
   member_method {
     name: "sparse_reduce_sum"
     argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index 85c712d3c6..e0a1391d6e 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -205,17 +205,14 @@ if [[ -n "${PY_TAGS}" ]]; then
 $(echo ${WHL_BASE_NAME} | cut -d \- -f 2)-${PY_TAGS}-${PLATFORM_TAG}.whl
 
   if [[ ! -f "${WHL_DIR}/${NEW_WHL_BASE_NAME}" ]]; then
-    if cp "${WHL_DIR}/${WHL_BASE_NAME}" "${WHL_DIR}/${NEW_WHL_BASE_NAME}"
-    then
-      echo "Copied wheel file: ${WHL_BASE_NAME} --> ${NEW_WHL_BASE_NAME}"
-    else
+    cp "${WHL_DIR}/${WHL_BASE_NAME}" "${WHL_DIR}/${NEW_WHL_BASE_NAME}" && \
+      echo "Copied wheel file: ${WHL_BASE_NAME} --> ${NEW_WHL_BASE_NAME}" || \
       die "ERROR: Failed to copy wheel file to ${NEW_WHL_BASE_NAME}"
-    fi
   fi
 fi
 
 if [[ $(uname) == "Linux" ]]; then
-  AUDITED_WHL_NAME="${WHL_DIR}/$(echo ${WHL_BASE_NAME//linux/manylinux1})"
+  AUDITED_WHL_NAME="${WHL_DIR}/$(echo ${WHL_BASE_NAME} | sed "s/linux/manylinux1/")"
 
   # Repair the wheels for cpu manylinux1
   if [[ ${CONTAINER_TYPE} == "cpu" ]]; then
@@ -243,20 +240,14 @@ echo "Installing pip whl file: ${WHL_PATH}"
 VENV_DIR="${PIP_TEST_ROOT}/venv"
 
 if [[ -d "${VENV_DIR}" ]]; then
-  if rm -rf "${VENV_DIR}"
-  then
-    echo "Removed existing virtualenv directory: ${VENV_DIR}"
-  else
-    die "Failed to remove existing virtualenv directory: ${VENV_DIR}"
-  fi
+  rm -rf "${VENV_DIR}" && \
+      echo "Removed existing virtualenv directory: ${VENV_DIR}" || \
+      die "Failed to remove existing virtualenv directory: ${VENV_DIR}"
 fi
 
-if mkdir -p ${VENV_DIR}
-then
-  echo "Created virtualenv directory: ${VENV_DIR}"
-else
-  die "FAILED to create virtualenv directory: ${VENV_DIR}"
-fi
+mkdir -p ${VENV_DIR} && \
+    echo "Created virtualenv directory: ${VENV_DIR}" || \
+    die "FAILED to create virtualenv directory: ${VENV_DIR}"
 
 # Verify that virtualenv exists
 if [[ -z $(which virtualenv) ]]; then
diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh
index 9c1b75d004..3b640dd5e8 100755
--- a/tensorflow/tools/ci_build/ci_build.sh
+++ b/tensorflow/tools/ci_build/ci_build.sh
@@ -80,7 +80,7 @@ fi
 
 # cmake (CPU) builds do not require configuration.
 if [[ "${CONTAINER_TYPE}" == "cmake" ]]; then
-  CI_COMMAND_PREFIX=("")
+  CI_COMMAND_PREFIX=""
 fi
 
 # Use nvidia-docker if the container is GPU.
@@ -120,9 +120,9 @@ DOCKER_IMG_NAME=$(echo "${DOCKER_IMG_NAME}" | tr '[:upper:]' '[:lower:]')
 
 # Print arguments.
 echo "WORKSPACE: ${WORKSPACE}"
-echo "CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[*]}"
-echo "COMMAND: ${COMMAND[*]}"
-echo "CI_COMMAND_PREFIX: ${CI_COMMAND_PREFIX[*]}"
+echo "CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[@]}"
+echo "COMMAND: ${COMMAND[@]}"
+echo "CI_COMMAND_PREFIX: ${CI_COMMAND_PREFIX[@]}"
 echo "CONTAINER_TYPE: ${CONTAINER_TYPE}"
 echo "BUILD_TAG: ${BUILD_TAG}"
 echo "  (docker container name will be ${DOCKER_IMG_NAME})"
@@ -140,7 +140,7 @@ if [[ $? != "0" ]]; then
 fi
 
 # Run the command inside the container.
-echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..."
+echo "Running '${COMMAND[@]}' inside ${DOCKER_IMG_NAME}..."
 mkdir -p ${WORKSPACE}/bazel-ci_build-cache
 # By default we cleanup - remove the container once it finish running (--rm)
 # and share the PID namespace (--pid=host) so the process inside does not have
diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index 1cf87d7c7c..dfaf50eb4f 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -200,8 +200,8 @@ echo "  TF_BUILD_ENABLE_XLA=${TF_BUILD_ENABLE_XLA}"
 function get_cuda_capability_version() {
   if [[ ! -z $(which deviceQuery) ]]; then
     # The first listed device is used
-    deviceQuery | grep "CUDA Capability .* version" | \
-        head -1 | awk '{print $NF}'
+    echo $(deviceQuery | grep "CUDA Capability .* version" | \
+        head -1 | awk '{print $NF}')
   fi
 }
 
@@ -532,14 +532,11 @@ if [[ "${TF_BUILD_PYTHON_VERSION}" == "python3.5" ]]; then
   DOCKERFILE="${TMP_DIR}/Dockerfile.${TF_BUILD_CONTAINER_TYPE}"
 
   # Replace a line in the Dockerfile
-  if sed -i \
+  sed -i \
       's/RUN \/install\/install_pip_packages.sh/RUN \/install\/install_python3.5_pip_packages.sh/g' \
-      "${DOCKERFILE}"
-  then
-    echo "Copied and modified Dockerfile for Python 3.5 build: ${DOCKERFILE}"
-  else
-    die "ERROR: Faild to copy and modify Dockerfile: ${DOCKERFILE}"
-  fi
+      "${DOCKERFILE}" && \
+      echo "Copied and modified Dockerfile for Python 3.5 build: ${DOCKERFILE}" || \
+      die "ERROR: Faild to copy and modify Dockerfile: ${DOCKERFILE}"
 
   DOCKERFILE_FLAG="--dockerfile ${DOCKERFILE}"
 fi
@@ -577,7 +574,7 @@ rm -f ${TMP_SCRIPT}
 END_TIME=$(date +'%s')
 echo ""
 echo "Parameterized build ends with ${RESULT} at: $(date) "\
-"(Elapsed time: $((END_TIME - START_TIME)) s)"
+"(Elapsed time: $((${END_TIME} - ${START_TIME})) s)"
 
 
 # Clean up temporary directory if it exists
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index e428766a40..fd2874df91 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -47,7 +47,7 @@ num_cpus() {
 # Get the hash of the last non-merge git commit on the current branch.
 # Usage: get_last_non_merge_git_commit
 get_last_non_merge_git_commit() {
-  git rev-list --no-merges -n 1 HEAD
+  echo $(git rev-list --no-merges -n 1 HEAD)
 }
 
 # List files changed (i.e., added, removed or revised) in the last non-merge
@@ -75,7 +75,7 @@ get_py_files_to_check() {
 
     echo "${PY_FILES}"
   else
-    find tensorflow -name '*.py'
+    echo $(find tensorflow -name '*.py')
   fi
 }
 
@@ -157,25 +157,25 @@ do_pylint() {
   NONWL_ERRORS_FILE="$(mktemp)_pylint_nonwl_errors.log"
 
   rm -rf ${OUTPUT_FILE}
-  rm -rf ${ERRORS_FILE}
+  rm -rf ${ERRORS_FLIE}
   rm -rf ${NONWL_ERRORS_FILE}
   touch ${NONWL_ERRORS_FILE}
 
   ${PYLINT_BIN} --rcfile="${PYLINTRC_FILE}" --output-format=parseable \
-      --jobs=${NUM_CPUS} ${PYTHON_SRC_FILES} > ${OUTPUT_FILE} 2>&1
+      --jobs=${NUM_CPUS} ${PYTHON_SRC_FILES} 2>&1 > ${OUTPUT_FILE}
   PYLINT_END_TIME=$(date +'%s')
 
   echo ""
-  echo "pylint took $((PYLINT_END_TIME - PYLINT_START_TIME)) s"
+  echo "pylint took $((${PYLINT_END_TIME} - ${PYLINT_START_TIME})) s"
   echo ""
 
   grep -E '(\[E|\[W0311|\[W0312)' ${OUTPUT_FILE} > ${ERRORS_FILE}
 
   N_ERRORS=0
-  while read -r LINE; do
+  while read LINE; do
     IS_WHITELISTED=0
     for WL_REGEX in ${ERROR_WHITELIST}; do
-      if echo ${LINE} | grep -q "${WL_REGEX}"; then
+      if [[ ! -z $(echo ${LINE} | grep "${WL_REGEX}") ]]; then
         echo "Found a whitelisted error:"
         echo "  ${LINE}"
         IS_WHITELISTED=1
@@ -248,7 +248,7 @@ do_pep8() {
   PEP8_END_TIME=$(date +'%s')
 
   echo ""
-  echo "pep8 took $((PEP8_END_TIME - PEP8_START_TIME)) s"
+  echo "pep8 took $((${PEP8_END_TIME} - ${PEP8_START_TIME})) s"
   echo ""
 
   if [[ -s ${PEP8_OUTPUT_FILE} ]]; then
@@ -278,7 +278,7 @@ do_buildifier(){
   BUILDIFIER_END_TIME=$(date +'%s')
 
   echo ""
-  echo "buildifier took $((BUILDIFIER_END_TIME - BUILDIFIER_START_TIME)) s"
+  echo "buildifier took $((${BUILDIFIER_END_TIME} - ${BUILDIFIER_START_TIME})) s"
   echo ""
 
   if [[ -s ${BUILDIFIER_OUTPUT_FILE} ]]; then
@@ -306,7 +306,7 @@ do_external_licenses_check(){
 
   echo "Getting external dependencies for ${BUILD_TARGET}"
  bazel query "attr('licenses', 'notice', deps(${BUILD_TARGET}))" --no_implicit_deps --no_host_deps --keep_going \
-  | grep -E -v "^//tensorflow" \
+  | egrep -v "^//tensorflow" \
   | sed -e 's|:.*||' \
   | sort \
   | uniq 2>&1 \
@@ -315,7 +315,7 @@ do_external_licenses_check(){
   echo
   echo "Getting list of external licenses mentioned in ${LICENSES_TARGET}."
   bazel query "deps(${LICENSES_TARGET})" --no_implicit_deps --no_host_deps --keep_going \
-  | grep -E -v "^//tensorflow" \
+  | egrep -v "^//tensorflow" \
   | sed -e 's|:.*||' \
   | sort \
   | uniq 2>&1 \
@@ -329,7 +329,7 @@ do_external_licenses_check(){
   EXTERNAL_LICENSES_CHECK_END_TIME=$(date +'%s')
 
   echo
-  echo "do_external_licenses_check took $((EXTERNAL_LICENSES_CHECK_END_TIME - EXTERNAL_LICENSES_CHECK_START_TIME)) s"
+  echo "do_external_licenses_check took $((${EXTERNAL_LICENSES_CHECK_END_TIME} - ${EXTERNAL_LICENSES_CHECK_START_TIME})) s"
   echo
 
   if [[ -s ${MISSING_LICENSES_FILE} ]] || [[ -s ${EXTRA_LICENSES_FILE} ]] ; then
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index 7fcd235e62..b8f9fc8453 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -44,8 +44,8 @@ pip2 install --upgrade markdown==2.6.8
 pip3 install --upgrade markdown==2.6.8
 
 # Install protobuf.
-pip2 install --upgrade protobuf==3.3.0
-pip3 install --upgrade protobuf==3.3.0
+pip2 install --upgrade protobuf==3.2.0
+pip3 install --upgrade protobuf==3.2.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/install/install_proto3.sh b/tensorflow/tools/ci_build/install/install_proto3.sh
index 7934002b2c..773c89b70b 100755
--- a/tensorflow/tools/ci_build/install/install_proto3.sh
+++ b/tensorflow/tools/ci_build/install/install_proto3.sh
@@ -17,9 +17,9 @@
 # Install protobuf3.
 
 # Select protobuf version.
-PROTOBUF_VERSION="3.3.0"
+PROTOBUF_VERSION="3.2.0"
 protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g')
-local_protobuf_ver=$(protoc --version)
+local_protobuf_ver=$(protoc --version | awk '{print $2}')
 local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g')
 if [[ -z $local_protobuf_ver_flat ]]; then
   local_protobuf_ver_flat=0
@@ -30,7 +30,7 @@ if (( $local_protobuf_ver_flat < $protobuf_ver_flat )); then
   PROTOBUF_ZIP=$(basename "${PROTOBUF_URL}")
   UNZIP_DEST="google-protobuf"
 
-  wget "${PROTOBUF_URL}"
+  wget -q "${PROTOBUF_URL}"
   unzip "${PROTOBUF_ZIP}" -d "${UNZIP_DEST}"
   cp "${UNZIP_DEST}/bin/protoc" /usr/local/bin/
 
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index 084ac49496..e7e2d256cd 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -64,7 +64,7 @@ set -e
 pip3.5 install --upgrade six==1.10.0
 
 # Install protobuf.
-pip3.5 install --upgrade protobuf==3.3.0
+pip3.5 install --upgrade protobuf==3.2.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
index 3e31aa1ce1..59ba71f5df 100755
--- a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
+++ b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 
-PROTOBUF_VERSION="3.3.1"
+PROTOBUF_VERSION="3.2.0"
 PYTHON_BIN=${PYTHON_BIN:-python}
 DIR=${PWD}/protobuf
 
diff --git a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
index dff4707cbe..5581023ad7 100644
--- a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
@@ -161,5 +161,5 @@ function create_python_test_dir() {
 
 function reinstall_tensorflow_pip() {
   echo "y" | pip uninstall tensorflow -q || true
-  pip install ${1} --no-deps
+  pip install ${1}
 }
diff --git a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
index 96fbadd176..8b6836f4ed 100644
--- a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
@@ -29,6 +29,9 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\cpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
+:: Attempt to upgrade PIP to work around Anaconda issue #542.
+%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
+
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -36,7 +39,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
index e774a6e916..ba2d939b5f 100644
--- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
@@ -28,6 +28,9 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\gpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
+:: Attempt to upgrade PIP to work around Anaconda issue #542.
+%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
+
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -35,7 +38,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh
index 7d7f92d246..f536beef54 100755
--- a/tensorflow/tools/dist_test/local_test.sh
+++ b/tensorflow/tools/dist_test/local_test.sh
@@ -70,7 +70,7 @@ get_container_id_by_image_name() {
     # Get the id of a container by image name
     # Usage: get_docker_container_id_by_image_name <img_name>
 
-    docker ps | grep $1 | awk '{print $1}'
+    echo $(docker ps | grep $1 | awk '{print $1}')
 }
 
 # Parse input arguments
@@ -152,7 +152,7 @@ rm -rf "${BUILD_DIR}"
 docker run ${DOCKER_IMG_NAME} \
     /var/tf_dist_test/scripts/dist_mnist_test.sh \
     --ps_hosts $(seq -f "localhost:%g" -s "," \
-                 2000 $((2000 + NUM_PARAMETER_SERVERS - 1))) \
+                 2000 $((2000 + ${NUM_PARAMETER_SERVERS} - 1))) \
     --worker_hosts $(seq -f "localhost:%g" -s "," \
-                     3000 $((3000 + NUM_WORKERS - 1))) \
+                     3000 $((3000 + ${NUM_WORKERS} - 1))) \
     --num_gpus 0 ${SYNC_REPLICAS_FLAG}
diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index 6d5a9bdc4c..3e45ae362c 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -54,30 +54,6 @@ for additional containers, such as release candidates or nightly builds.
 
 ## Rebuilding the containers
 
-Building TensorFlow Docker containers should be done through the
-[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/README.md)
-script. The raw Dockerfiles should not be used directly as they contain strings
-to be replaced by the script during the build.
+Just pick the dockerfile corresponding to the container you want to build, and run
 
-To use the script, specify the container type (`CPU` vs. `GPU`), the desired
-Python version (`PYTHON2` vs. `PYTHON3`) and whether the developer Docker image
-is to be built (`NO` vs. `YES`). In addition, you need to specify the central
-location from where the pip package of TensorFlow will be downloaded.
-
-For example, to build a CPU-only non-developer Docker image for Python 2, using
-TensorFlow's nightly pip package:
-
-``` bash
-export TF_DOCKER_BUILD_IS_DEVEL=NO
-export TF_DOCKER_BUILD_TYPE=CPU
-export TF_DOCKER_BUILD_PYTHON_VERSION=PYTHON2
-
-export NIGHTLY_VERSION="1.head"
-export TF_DOCKER_BUILD_CENTRAL_PIP=$(echo ${TF_DOCKER_BUILD_PYTHON_VERSION} | sed s^PYTHON2^http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=${TF_DOCKER_BUILD_PYTHON_VERSION},label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp27-cp27mu-manylinux1_x86_64.whl^ | sed s^PYTHON3^http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp35-cp35m-manylinux1_x86_64.whl^)
-
-tensorflow/tools/docker/parameterized_docker_build.sh
-```
-
-If successful, the image will be tagged as `${USER}/tensorflow:latest` by default.
-
-Rebuilding GPU images requires [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).
+    $ docker build --pull -t $USER/tensorflow-suffix -f Dockerfile.suffix .
diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh
index ea88d8165f..f88af68cde 100755
--- a/tensorflow/tools/docker/parameterized_docker_build.sh
+++ b/tensorflow/tools/docker/parameterized_docker_build.sh
@@ -233,16 +233,13 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then
 
   # Modify python/pip version if necessary.
   if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then
-    if sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
+    sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
         sed -i -e 's/python-dev/python3-dev/g' "${DOCKERFILE}" && \
         sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \
-        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}"
-    then
-      echo "Modified Dockerfile for python version "\
-"${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}"
-    else
-      die "FAILED to modify ${DOCKERFILE} for python3"
-    fi
+        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" && \
+        echo "Modified Dockerfile for python version "\
+"${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" || \
+        die "FAILED to modify ${DOCKERFILE} for python3"
   fi
 else
   DOCKERFILE="${TMP_DIR}/Dockerfile"
@@ -253,17 +250,14 @@ else
 
   # Modify python/pip version if necessary.
   if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then
-    if sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \
+    sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \
         sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
         sed -i -e 's^/tmp/pip^/tmp/pip3^g' "${DOCKERFILE}" && \
         sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \
         sed -i -e 's/ENV CI_BUILD_PYTHON python/ENV CI_BUILD_PYTHON python3/g' "${DOCKERFILE}" && \
-        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}"
-    then
-      echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}"
-    else
-      die "FAILED to modify ${DOCKERFILE} for python3"
-    fi
+        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" && \
+        echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" || \
+        die "FAILED to modify ${DOCKERFILE} for python3"
   fi
 fi
 
@@ -283,7 +277,7 @@ fi
 
 # Make sure that there is no other containers of the same image running
 # TODO(cais): Move to an earlier place.
-if "${DOCKER_BINARY}" ps | grep -q "${IMG}"; then
+if [[ ! -z $("${DOCKER_BINARY}" ps | grep "${IMG}") ]]; then
   die "ERROR: It appears that there are docker containers of the image "\
 "${IMG} running. Please stop them before proceeding"
 fi
@@ -316,22 +310,16 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then
     # on the running docker container
     echo ""
     echo "Performing basic sanity checks on the running container..."
-    if wget -qO- "http://127.0.0.1:${CONTAINER_PORT}/tree" &> /dev/null
-    then
-      echo "  PASS: wget tree"
-    else
-      mark_check_failed "  FAIL: wget tree"
-    fi
+    wget -qO- "http://127.0.0.1:${CONTAINER_PORT}/tree" &> /dev/null && \
+        echo "  PASS: wget tree" || \
+        mark_check_failed "  FAIL: wget tree"
 
     for NB in ${TMP_DIR}/notebooks/*.ipynb; do
       NB_BASENAME=$(basename "${NB}")
       NB_URL="http://127.0.0.1:${CONTAINER_PORT}/notebooks/${NB_BASENAME}"
-      if wget -qO- "${NB_URL}" -o "${TMP_DIR}/${NB_BASENAME}" &> /dev/null
-      then
-        echo "  PASS: wget ${NB_URL}"
-      else
-        mark_check_failed  "  FAIL: wget ${NB_URL}"
-      fi
+      wget -qO- "${NB_URL}" -o "${TMP_DIR}/${NB_BASENAME}" &> /dev/null && \
+          echo "  PASS: wget ${NB_URL}" || \
+          mark_check_failed  "  FAIL: wget ${NB_URL}"
     done
   fi
 
diff --git a/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh b/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
index 7146213b33..2ce0fb394f 100755
--- a/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
+++ b/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
@@ -71,7 +71,7 @@ rm -rf ${LOG_FILE} || \
 
 # Invoke main Python file
 python "${GCS_SMOKE_PY}" --gcs_bucket_url="${GCS_BUCKET_URL}" \
-    > "${LOG_FILE}" 2>&1
+    2>&1 > "${LOG_FILE}"
 
 if [[ $? != "0" ]]; then
   cat ${LOG_FILE}
@@ -92,9 +92,6 @@ NEW_TFREC_URL=$(grep "Using input path" "${LOG_FILE}" | \
 if [[ -z ${NEW_TFREC_URL} ]]; then
   die "FAIL: Unable to determine the URL to the new tfrecord file in GCS"
 fi
-if "${GSUTIL_BIN}" rm "${NEW_TFREC_URL}"
-then
-  echo "Cleaned up new tfrecord file in GCS: ${NEW_TFREC_URL}"
-else
-  die "FAIL: Unable to clean up new tfrecord file in GCS: ${NEW_TFREC_URL}"
-fi
+"${GSUTIL_BIN}" rm "${NEW_TFREC_URL}" && \
+    echo "Cleaned up new tfrecord file in GCS: ${NEW_TFREC_URL}" || \
+    die "FAIL: Unable to clean up new tfrecord file in GCS: ${NEW_TFREC_URL}"
diff --git a/tensorflow/tools/git/gen_git_source.sh b/tensorflow/tools/git/gen_git_source.sh
index 977fe16333..1487658049 100755
--- a/tensorflow/tools/git/gen_git_source.sh
+++ b/tensorflow/tools/git/gen_git_source.sh
@@ -20,7 +20,7 @@ if [[ -z "${OUTPUT_FILENAME}"  ]]; then
   exit 1
 fi
 
-GIT_VERSION=$(git describe --long --tags)
+GIT_VERSION=`git describe --long --tags`
 if [[ $? != 0 ]]; then
    GIT_VERSION=unknown;
 fi
diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index b4274e67df..bfda55d3ad 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -1055,7 +1055,7 @@ in the future.
 
 The Graph Transform Tool associates names of transforms with the code to
 implement them using the `REGISTER_GRAPH_TRANSFORM()` macro. This takes a string
-and a function, and automagically registers the transform with the tool. You
+and a function, and automatically registers the transform with the tool. You
 will need to watch out for a few things though:
 
 *   Because it's using global C++ objects in each file under the hood, the
diff --git a/tensorflow/tools/graph_transforms/quantize_weights_test.cc b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
index 63c5b5a64d..e1a105bdd3 100644
--- a/tensorflow/tools/graph_transforms/quantize_weights_test.cc
+++ b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
@@ -90,13 +90,13 @@ class QuantizeWeightsTest : public ::testing::Test {
     EXPECT_EQ("Const", q_weights_const->op());
     EXPECT_EQ(DT_QUINT8, q_weights_const->attr().at("dtype").type());
 
-    // Run the original graph.
+    // Run the the original graph.
     std::unique_ptr<Session> original_session(NewSession(SessionOptions()));
     TF_ASSERT_OK(original_session->Create(original_graph_def));
     std::vector<Tensor> original_outputs;
     TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs));
 
-    // Run the quantized graph.
+    // Run the the quantized graph.
     std::unique_ptr<Session> quantized_session(NewSession(SessionOptions()));
     TF_ASSERT_OK(quantized_session->Create(quantized_graph_def));
     std::vector<Tensor> quantized_outputs;
diff --git a/tensorflow/tools/lib_package/libtensorflow_java_test.sh b/tensorflow/tools/lib_package/libtensorflow_java_test.sh
index c44978fc57..a44298e01a 100755
--- a/tensorflow/tools/lib_package/libtensorflow_java_test.sh
+++ b/tensorflow/tools/lib_package/libtensorflow_java_test.sh
@@ -29,7 +29,7 @@ TAR="${TAR}"
 
 [ -z "${JAVA}" ] && JAVA="java"
 [ -z "${JAVAC}" ] && JAVAC="javac"
-[ -z "${TAR}" ] && TAR="tar"
+[ -z "${TAR}"] && TAR="tar"
 
 # bazel tests run with ${PWD} set to the root of the bazel workspace
 TARFILE="${PWD}/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz"
diff --git a/tensorflow/tools/lib_package/libtensorflow_test.sh b/tensorflow/tools/lib_package/libtensorflow_test.sh
index 7dfe8eefcc..6430c755af 100755
--- a/tensorflow/tools/lib_package/libtensorflow_test.sh
+++ b/tensorflow/tools/lib_package/libtensorflow_test.sh
@@ -26,7 +26,7 @@ CC="${CC}"
 TAR="${TAR}"
 
 [ -z "${CC}" ] && CC="/usr/bin/gcc"
-[ -z "${TAR}" ] && TAR="tar"
+[ -z "${TAR}"] && TAR="tar"
 
 # bazel tests run with ${PWD} set to the root of the bazel workspace
 TARFILE="${PWD}/tensorflow/tools/lib_package/libtensorflow.tar.gz"
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 54ba8064e8..073988765c 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0'
+_VERSION = '1.2.0-rc2'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
diff --git a/tensorflow/tools/tfprof/BUILD b/tensorflow/tools/tfprof/BUILD
index 57cccd8921..541df78e47 100644
--- a/tensorflow/tools/tfprof/BUILD
+++ b/tensorflow/tools/tfprof/BUILD
@@ -33,6 +33,7 @@ cc_binary(
         "//tensorflow/tools/tfprof/internal:tfprof_options",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
         "//tensorflow/tools/tfprof/internal:tfprof_utils",
+        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
         "@linenoise//:linenoise",
     ],
 )
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/tools/tfprof/README.md
index 54f3cd62f2..816ad8c07e 100644
--- a/tensorflow/tools/tfprof/README.md
+++ b/tensorflow/tools/tfprof/README.md
@@ -1,15 +1,19 @@
 # tfprof: TensorFlow Profiler and Beyond
 
-### Features
+###Features
 
 * Profile model architectures
   * parameters, tensor shapes, float operations, device placement, etc.
 * Profile model performance
   * execution time, memory consumption
   * Profile multiple steps.
-* Auto detect and advise. (Experimental)
+* Auto profile and advise.
+  * accelerator utilization check
+  * expensive operation check
+  * operation configuration check
+  * distributed runtime check (Not OSS)
 
-### Interfaces
+###Interfaces
 
 * Python API
 * Command Line
@@ -47,7 +51,7 @@ See [Options](g3doc/options.md) for detail instructions.
 -output                     stdout:
 ```
 
-### Tutorials
+###Tutorials
 
 *  [Python API](g3doc/python_api.md)
 *  [Command Line Interface](g3doc/command_line.md)
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/tools/tfprof/g3doc/advise.md
index 3bce6270ff..e30add6fbf 100644
--- a/tensorflow/tools/tfprof/g3doc/advise.md
+++ b/tensorflow/tools/tfprof/g3doc/advise.md
@@ -3,6 +3,7 @@
 tfprof analyzes profiles and generates advises for common issues.
 
 ### Run Advise.
+
 ```python
 # First create a profiler. See profiler tutorials for more details.
 profiler = model_analyzer.Profiler(sess.graph)
@@ -13,8 +14,63 @@ _ = sess.run(r1,
              run_metadata=run_meta)
 profiler.add_step(1, run_meta)
 
-# Start advise.
-profiler.advise()
+# Then Start advise.
+profiler.advise(model_analyzer.ALL_ADVICE)
+
+# For one-shot API
+tf.contrib.tfprof.model_analyzer.advise(
+    sess.graph, run_meta=run_metadata)
+```
+
+```shell
+# Run advisor on CLI
+# See CLI tutorial on generating the files.
+tfprof --graph_path=graph.pbtxt \
+       --run_meta_path=run_metadata \
+       --op_log_path=tfprof_log
+
+tfprof> advise
+AcceleratorUtilizationChecker:
+device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03
+device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08
+device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04
+device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21
+
+OperationChecker:
+Found operation using NHWC data_format on GPU. Maybe NCHW is faster.
+
+ExpensiveOperationChecker:
+top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%)
+top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%)
+top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%)
+seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec
+  seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec
+    seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec
+      seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize), cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha..., cpu: 164us, accelerator: 0us, total: 164us
+      seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms
+      seq2seq_lib.py:151:sequence_loss_by_...:total_size = tf.a..., cpu: 154us, accelerator: 23us, total: 177us
+    seq2seq_lib.py:184:sampled_sequence_...:return cost / tf...., cpu: 97us, accelerator: 8us, total: 105us
+      math_ops.py:690:cast:return gen_math_o..., cpu: 62us, accelerator: 3us, total: 65us
+      math_ops.py:839:binary_op_wrapper:return func(x, y,..., cpu: 35us, accelerator: 5us, total: 40us
+  seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms
+    seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 303.76ms, accelerator: 73.54ms, total: 377.30ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 2.75ms, accelerator: 0us, total: 2.75ms
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 67us, accelerator: 0us, total: 67us
+    seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 292.04ms, accelerator: 73.54ms, total: 365.58ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 4.07ms, accelerator: 0us, total: 4.07ms
+      core_rnn.py:178:static_rnn:min_sequence_leng..., cpu: 85us, accelerator: 0us, total: 85us
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 16us, accelerator: 0us, total: 16us
+    seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms
+ ...(omitted)
+top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms
+top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms
+top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms
 ```
 
 ### Checker
@@ -25,16 +81,24 @@ area with the profile and report issues. A `Checker` is like a plugin.
 
 For example:
 
-####JobChecker (Not Available OSS)
-* Checking RecvTensor RPC latency and bandwidth.
-* Checking CPU/Memory utilization of the job.
+#### JobChecker (Not Available OSS)
+
+*   Checks RecvTensor RPC latency and bandwidth.
+*   Checks CPU/Memory utilization of the job.
 
 ####AcceleratorUtilization Checker
 * Checks what percentage of time the accelerator spends on computation.
 
-####Operation Checker
-* Check whether the operation runs with optimal options.
-* Checks if there is a better implementation to replace the current operation.
+#### OperationChecker
+
+*   Checks whether the operation runs with optimal options.
+*   Checks if there is a better implementation to replace the current operation.
+
+#### ExpensiveOperationChecker
+
+*   Checks the most expensive operation type.
+*   Checks the most expensive graph nodes.
+*   Checks the most expensive graph-building Python codes.
 
 ####Contribute Your Checker
 
diff --git a/tensorflow/tools/tfprof/g3doc/command_line.md b/tensorflow/tools/tfprof/g3doc/command_line.md
index 9f0de72e07..0d8d56809a 100644
--- a/tensorflow/tools/tfprof/g3doc/command_line.md
+++ b/tensorflow/tools/tfprof/g3doc/command_line.md
@@ -126,7 +126,7 @@ tfprof>
 -show_name_regexes          .*
 -hide_name_regexes          IsVariableInitialized_[0-9]+,save\/.*,^zeros[0-9_]*
 -account_displayed_op_only  false
-# supported select fields. Availability depends on --[run_meta|checkpoint|op_log]_path.
+# supported select fileds. Availability depends on --[run_meta|checkpoint|op_log]_path.
 # [bytes|micros|params|float_ops|occurrence|tensor_value|device|op_types]
 -select                     params
 # format: output_type:key=value,key=value...
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/tools/tfprof/internal/advisor/BUILD
index e4bd0f0015..629322373a 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/tools/tfprof/internal/advisor/BUILD
@@ -44,12 +44,21 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "expensive_operation_checker",
+    hdrs = ["expensive_operation_checker.h"],
+    deps = [
+        ":checker",
+    ],
+)
+
 cc_library(
     name = "tfprof_advisor",
     hdrs = ["tfprof_advisor.h"],
     deps = [
         ":accelerator_utilization_checker",
         ":checker",
+        ":expensive_operation_checker",
         ":internal_checker_runner_dummy",
         ":operation_checker",
     ],
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
index fb7f65d7dc..074b8e57b0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
@@ -33,10 +33,11 @@ struct ExecStats {
 
 class AcceleratorUtilizationChecker : public Checker {
  public:
-  string name() override { return "AcceleratorUtilizationChecker"; }
+  string name() const override { return kCheckers[0]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -48,24 +49,21 @@ class AcceleratorUtilizationChecker : public Checker {
     return CheckInternal();
   }
 
-  std::vector<string> CheckInternal() {
+  AdviceProto::Checker CheckInternal() {
     for (const auto& s : accelerator_exec_stats_) {
       const ExecStats& stat = s.second;
       int64 total_micros = stat.end_micros - stat.start_micros;
       if (total_micros <= 0) continue;
       double utilization = 1.0 * stat.exec_micros / total_micros;
       if (utilization >= 0.5) {
-        reports_.push_back(strings::Printf("%s: device: %s utilization: %.2f",
-                                           kLevel[0], s.first.c_str(),
-                                           utilization));
+        reports_.add_reports(strings::Printf("device: %s utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization < 0.5 && utilization > 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[1],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization <= 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[2],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       }
     }
     return reports_;
@@ -102,7 +100,7 @@ class AcceleratorUtilizationChecker : public Checker {
 
   std::map<string, ExecStats> accelerator_exec_stats_;
   std::map<string, int64> ps_placement_;
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/checker.h b/tensorflow/tools/tfprof/internal/advisor/checker.h
index b8b057be5b..3ce80cd8c4 100644
--- a/tensorflow/tools/tfprof/internal/advisor/checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/checker.h
@@ -18,27 +18,33 @@ limitations under the License.
 
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
-static const char* const kLevel[] = {
-    "NOTE",     // Good to know.
-    "SUGGEST",  // Might get better.
-    "WARN",     // Please do it for better.
+// Append only.
+static const char* const kCheckers[] = {
+    "AcceleratorUtilizationChecker", "OperationChecker",
+    "ExpensiveOperationChecker",
+    "JobChecker",  // Internal checker.
 };
 
 class Checker {
  public:
-  virtual ~Checker(){};
+  virtual ~Checker() {}
 
-  virtual string name() = 0;
+  virtual string name() const = 0;
 
-  std::vector<string> Run(const TFStats* stats) { return Check(stats); }
+  AdviceProto::Checker Run(const AdvisorOptionsProto::CheckerOption& options,
+                           const TFStats* stats) {
+    return Check(options, stats);
+  }
 
  protected:
-  // Returns a vector of string, each one being an advice.
-  virtual std::vector<string> Check(const TFStats* stats) = 0;
+  virtual AdviceProto::Checker Check(
+      const AdvisorOptionsProto::CheckerOption& options,
+      const TFStats* stats) = 0;
 };
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
index 1238b57f20..ed8ae571b6 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
@@ -17,13 +17,16 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
 
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
 class TFStats;
 
-std::map<string, std::vector<string>> RunInternalCheckers(const TFStats* stats);
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats);
 
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
index 8204d2b04e..67962c8e8b 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
@@ -17,9 +17,9 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-std::map<string, std::vector<string>> RunInternalCheckers(
-    const TFStats* stats) {
-  return std::map<string, std::vector<string>>();
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats) {
+  return AdviceProto();
 }
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
index 2a05f9bfd0..4d0d68e3bf 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
@@ -24,10 +24,11 @@ namespace tfprof {
 
 class OperationChecker : public Checker {
  public:
-  string name() override { return "OperationChecker"; }
+  string name() const override { return kCheckers[1]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -53,22 +54,20 @@ class OperationChecker : public Checker {
       }
     }
     if (use_batch_norm && !use_fused_batch_norm) {
-      reports_.push_back(strings::Printf(
-          "%s: Maybe use faster FusedBatchNorm instead of BatchNorm",
-          kLevel[1]));
+      reports_.add_reports(
+          "Maybe use faster FusedBatchNorm instead of BatchNorm");
     }
     if (recommend_nchw) {
       // TODO(xpan): Maybe print which Op supports NCHW.
-      reports_.push_back(strings::Printf(
-          "%s: Found operation using NHWC data_format on GPU. Maybe "
-          "NCHW is faster.",
-          kLevel[1]));
+      reports_.add_reports(
+          "Found operation using NHWC data_format on GPU. Maybe "
+          "NCHW is faster.");
     }
     return reports_;
   }
 
  private:
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
index 856f515459..d2257fb9b5 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
@@ -18,8 +18,10 @@ limitations under the License.
 
 #include "tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+#include "tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
 #include "tensorflow/tools/tfprof/internal/advisor/operation_checker.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -29,23 +31,44 @@ class Advisor {
  public:
   Advisor(const TFStats* stats) : stats_(stats) {}
 
-  std::map<string, std::vector<string>> Advise() {
+  static AdvisorOptionsProto DefaultOptions() {
+    AdvisorOptionsProto options;
+    std::vector<string> checkers(
+        kCheckers, kCheckers + sizeof(kCheckers) / sizeof(*kCheckers));
+    for (const string& checker : checkers) {
+      (*options.mutable_checkers())[checker];
+    }
+    return options;
+  }
+
+  AdviceProto Advise(const AdvisorOptionsProto& options) {
     // Note: Release a checker's memory ASAP.
-    std::map<string, std::vector<string>> reports = RunInternalCheckers(stats_);
-    // TODO(xpan): Think of a way to turn off/on specific checkers.
-    AcceleratorUtilizationChecker au_checker;
-    reports[au_checker.name()] = au_checker.Run(stats_);
-    OperationChecker op_checker;
-    reports[op_checker.name()] = op_checker.Run(stats_);
-
-    for (const auto& checker_r : reports) {
-      fprintf(stdout, "%s reports:\n", checker_r.first.c_str());
-      for (const auto& r : checker_r.second) {
+    AdviceProto ret = RunInternalCheckers(options, stats_);
+
+    if (options.checkers().find(kCheckers[0]) != options.checkers().end()) {
+      AcceleratorUtilizationChecker au_checker;
+      (*ret.mutable_checkers())[kCheckers[0]].MergeFrom(
+          au_checker.Run(options.checkers().at(kCheckers[0]), stats_));
+    }
+    if (options.checkers().find(kCheckers[1]) != options.checkers().end()) {
+      OperationChecker op_checker;
+      (*ret.mutable_checkers())[kCheckers[1]].MergeFrom(
+          op_checker.Run(options.checkers().at(kCheckers[1]), stats_));
+    }
+    if (options.checkers().find(kCheckers[2]) != options.checkers().end()) {
+      ExpensiveOperationChecker expensive_op_checker;
+      (*ret.mutable_checkers())[kCheckers[2]].MergeFrom(
+          expensive_op_checker.Run(options.checkers().at(kCheckers[2]),
+                                   stats_));
+    }
+    for (const auto& checker : ret.checkers()) {
+      fprintf(stdout, "\n%s:\n", checker.first.c_str());
+      for (const string& r : checker.second.reports()) {
         fprintf(stdout, "%s\n", r.c_str());
       }
     }
     fflush(stdout);
-    return reports;
+    return ret;
   }
 
  private:
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
index b41d0770dc..3b40253954 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
@@ -29,15 +29,16 @@ class TFProfAdvisorTest : public ::testing::Test {
                              nullptr, nullptr));
 
     stats_->AddNodeForTest(
-        "n1", CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 10, 2));
-    stats_->AddNodeForTest("n2", CreateNode("n2", "Conv2D", {}, 20, 2));
+        0, CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 0, 10, 2));
+    stats_->AddNodeForTest(0, CreateNode("n2", "Conv2D", {}, 0, 20, 2));
+    stats_->BuildAllViews();
     advisor_.reset(new Advisor(stats_.get()));
   }
 
   std::unique_ptr<TFGraphNode> CreateNode(const string& name,
                                           const string& type,
                                           std::map<string, string> attrs,
-                                          int64 start_miros,
+                                          int64 step, int64 start_miros,
                                           int64 end_rel_micros) {
     node_defs_.push_back(std::unique_ptr<NodeDef>(new NodeDef()));
     NodeDef* def = node_defs_.back().get();
@@ -52,10 +53,10 @@ class TFProfAdvisorTest : public ::testing::Test {
     NodeExecStats node_stat;
     node_stat.set_all_start_micros(start_miros);
     node_stat.set_op_end_rel_micros(end_rel_micros);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
                       node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
                       node_stat);
     return node;
   }
@@ -66,23 +67,38 @@ class TFProfAdvisorTest : public ::testing::Test {
 };
 
 TEST_F(TFProfAdvisorTest, Basics) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_TRUE(reports.find("AcceleratorUtilizationChecker") != reports.end());
-  EXPECT_TRUE(reports.find("OperationChecker") != reports.end());
+  AdvisorOptionsProto options = Advisor::DefaultOptions();
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(advice.checkers().find(kCheckers[0]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[1]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[2]) != advice.checkers().end());
 }
 
 TEST_F(TFProfAdvisorTest, OperationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["OperationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["OperationChecker"][0]).contains("NCHW"));
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[1]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[1]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[1]).reports(0))
+                  .contains("NCHW"));
 }
 
 TEST_F(TFProfAdvisorTest, UtilizationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["AcceleratorUtilizationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["AcceleratorUtilizationChecker"][0])
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[0]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[0]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[0]).reports(0))
                   .contains("low utilization"));
 }
 
+TEST_F(TFProfAdvisorTest, ExpensiveOperationChecker) {
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[2]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[2]).reports(0))
+                  .contains("top 1 operation type: Conv2D"));
+}
+
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.cc b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
index 37d01db3a1..5a9c44d8e6 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.cc
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/tfprof_log.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 #include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
@@ -36,6 +37,18 @@ TFStats* tf_stat = nullptr;
 
 string RunProfile(const string& command, const string& options,
                   TFStats* tf_stats) {
+  if (command == kCmds[4]) {
+    AdvisorOptionsProto option_pb;
+    if (!option_pb.ParseFromString(options)) {
+      fprintf(stderr, "Cannot parse AdvisorOptionsProto\n");
+      return "";
+    }
+    tf_stats->BuildAllViews();
+    return Advisor(tf_stats).Advise(option_pb).SerializeAsString();
+  } else {
+    tf_stats->BuildView(command);
+  }
+
   Options opts;
   tensorflow::Status s = Options::FromProtoStr(options, &opts);
   if (!s.ok()) {
@@ -97,14 +110,14 @@ void AddStep(int64 step, const string* run_meta, const string* op_log) {
   // TODO(xpan): Better error handling.
   std::unique_ptr<RunMetadata> run_meta_ptr(new RunMetadata());
   run_meta_ptr->ParseFromString(*run_meta);
-  tf_stat->ParseRunMeta(step, std::move(run_meta_ptr));
+  tf_stat->AddRunMeta(step, std::move(run_meta_ptr));
 
   std::unique_ptr<OpLog> op_log_ptr;
   if (op_log && !op_log->empty()) {
     op_log_ptr.reset(new OpLog());
     op_log_ptr->ParseFromString(*op_log);
   }
-  tf_stat->ParseOpLog(std::move(op_log_ptr));
+  tf_stat->AddOpLog(std::move(op_log_ptr));
 }
 
 string Profile(const string* command, const string* options) {
@@ -144,7 +157,5 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
   return RunProfile(*command, *options, &tf_stats);
 }
 
-void Advise() { Advisor(tf_stat).Advise(); }
-
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.h b/tensorflow/tools/tfprof/internal/print_model_analysis.h
index 84165e542d..46db63646d 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.h
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.h
@@ -39,8 +39,6 @@ void AddStep(int64 step, const string* run_meta, const string* op_log);
 
 string Profile(const string* command, const string* options);
 
-void Advise();
-
 // Single-step Profiler.
 //
 // Interface defined for Python API swig. Calls the tfprof core API.
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.h b/tensorflow/tools/tfprof/internal/tfprof_graph.h
index fbeae8673d..194a21f0cc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_graph.h
@@ -54,8 +54,8 @@ class TFGraph : public TFShow {
   const ShowNode* ShowInternal(const Options& opts,
                                Timeline* timeline) override;
 
-  bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                         int depth) const override {
     return true;
   }
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index 6a7077c085..77a2593623 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -126,6 +126,7 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
   }
   nodes = SortNodes(nodes, opts);
 
+  // pre keeps track of previous visited node.
   OpNode* pre = nullptr;
   std::vector<OpNode*> account_nodes;
   for (auto it = nodes.rbegin(); it != nodes.rend(); ++it) {
@@ -170,16 +171,20 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
     root_->ResetTotalStats();
     if (pre) {
       root_->AggregateTotalStats(pre);
-      root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
-      pre->mutable_proto()->clear_children();
     }
   }
+  if (pre) {
+    root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
+    pre->mutable_proto()->clear_children();
+  }
 
   if (opts.output_type == kOutput[1] || opts.output_type == kOutput[2]) {
     string display_str = FormatLegend(opts);
     for (OpNode* node : show_nodes) {
       display_str += FormatNode(node, root_.get(), opts);
     }
+    // In op view, we don't show root (total). But it will still in proto.
+    // TODO(xpan): Is it the right choice?
     root_->formatted_str = display_str;
   }
   return root_.get();
@@ -201,7 +206,7 @@ int64 TFOp::SearchRoot(const std::vector<OpNode*> nodes,
   return i;
 }
 
-string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
+string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const {
   std::vector<string> attrs;
 
   if (opts.select.find(kShown[0]) != opts.select.end()) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.h b/tensorflow/tools/tfprof/internal/tfprof_op.h
index 5b16490363..34812f54be 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.h
@@ -56,15 +56,15 @@ class TFOp : public TFMultiShow {
   int64 SearchRoot(const std::vector<OpNode*> nodes,
                    const std::vector<string>& regexes);
 
-  bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                         int depth) const override {
     if (opts.min_occurrence > node->node->graph_nodes().size()) {
       return false;
     }
     return true;
   }
 
-  string FormatNode(OpNode* node, OpNode* root, const Options& opts);
+  string FormatNode(OpNode* node, OpNode* root, const Options& opts) const;
 
   std::unique_ptr<OpNode> root_;
   std::map<string, std::unique_ptr<OpNode>> cnodes_map_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/tools/tfprof/internal/tfprof_options.h
index 6c9db24342..d39333e3fc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_options.h
@@ -59,10 +59,10 @@ static const char* const kShown[] = {
     "cpu_micros"};
 
 static const char* const kCmds[] = {
-    "scope", "graph", "code", "op", "set", "help",
+    "scope", "graph", "code", "op", "advise", "set", "help",
 };
 
-static const char* const kOutput[] = {"timeline", "stdout", "file"};
+static const char* const kOutput[] = {"timeline", "stdout", "file", "none"};
 
 static const char* const kTimelineOpts[] = {
     "outfile",
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index 40cc56fb22..6c1183a4d5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -26,7 +26,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFGraphNodeProto& TFShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -64,7 +66,8 @@ bool TFShow::LookUpCheckPoint(const string& name,
   return true;
 }
 
-bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
+bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
+                        int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -97,7 +100,8 @@ bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
   return true;
 }
 
-bool TFShow::ShouldTrim(ShowNode* node, const std::vector<string>& regexes) {
+bool TFShow::ShouldTrim(const ShowNode* node,
+                        const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -122,7 +126,7 @@ bool TFShow::ReAccount(ShowNode* node, const Options& opts) {
   return false;
 }
 
-string TFShow::FormatNode(ShowNode* node, const Options& opts) {
+string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
   std::vector<string> info;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     const string shape = FormatShapes(node->node->shape());
@@ -210,7 +214,7 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) {
                          str_util::Join(info, ", ").c_str());
 }
 
-string TFShow::FormatLegend(const Options& opts) {
+string TFShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     legends.push_back("# parameters");
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/tools/tfprof/internal/tfprof_show.h
index 2c61b4fd73..95513e086f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.h
@@ -54,20 +54,21 @@ class TFShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowNode* node, const Options& opts, int depth) const;
 
-  bool ShouldTrim(ShowNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowNode* node, const Options& opts);
 
-  string FormatNode(ShowNode* node, const Options& opts);
+  string FormatNode(ShowNode* node, const Options& opts) const;
 
-  string FormatLegend(const Options& opts);
+  string FormatLegend(const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 7545b3d48b..84d542714a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -28,7 +28,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -48,8 +50,8 @@ const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
   }
 }
 
-bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
-                            int depth) {
+bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
+                             int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -88,8 +90,8 @@ bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
   return true;
 }
 
-bool TFMultiShow::ShouldTrim(ShowMultiNode* node,
-                            const std::vector<string>& regexes) {
+bool TFMultiShow::ShouldTrim(const ShowMultiNode* node,
+                             const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -102,7 +104,7 @@ bool TFMultiShow::ReAccount(ShowMultiNode* node, const Options& opts) {
   return node->ReInit(opts.step, opts.account_type_regexes);
 }
 
-string TFMultiShow::FormatLegend(const Options& opts) {
+string TFMultiShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[0]) != opts.select.end()) {
     legends.push_back("output bytes");
@@ -142,7 +144,8 @@ string TFMultiShow::FormatLegend(const Options& opts) {
                          str_util::Join(legends, " | ").c_str());
 }
 
-string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
+string TFMultiShow::FormatInputShapes(
+    const TFMultiGraphNodeProto& proto) const {
   // input_shape string -> (static defined count, run count, run_micros)
   std::map<string, std::tuple<int64, int64, int64>> input_shapes_attr;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
@@ -199,7 +202,7 @@ string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
 }
 
 std::vector<string> TFMultiShow::FormatTimes(const ShowMultiNode* node,
-                                             const Options& opts) {
+                                             const Options& opts) const {
   std::vector<string> attrs;
   if (opts.select.find(kShown[1]) != opts.select.end()) {
     attrs.push_back(FormatTotalExecTime(node, opts));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
index e6faf1231d..ce309816a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
@@ -55,21 +55,23 @@ class TFMultiShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowMultiNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowMultiNode* node, const Options& opts,
+                  int depth) const;
 
-  bool ShouldTrim(ShowMultiNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowMultiNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowMultiNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts);
-  string FormatInputShapes(const TFMultiGraphNodeProto& proto);
+  string FormatLegend(const Options& opts) const;
+  string FormatInputShapes(const TFMultiGraphNodeProto& proto) const;
   std::vector<string> FormatTimes(const ShowMultiNode* node,
-                                  const Options& opts);
+                                  const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index 18a7c51a20..46b6ebc09a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -65,6 +65,7 @@ class TFProfShowTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.cc b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
index f5b8dad4e2..64da7ae7cf 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
@@ -29,16 +29,17 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
                  std::unique_ptr<RunMetadata> run_meta,
                  std::unique_ptr<OpLog> op_log,
                  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader)
-    : graph_(std::move(graph)),
+    : has_code_traces_(false),
+      graph_(std::move(graph)),
       ckpt_reader_(std::move(ckpt_reader)) {
   CHECK(graph_) << "Must at least have GraphDef";
 
   printf("Parsing Inputs...\n");
   ParseGraph();
   if (run_meta && run_meta->has_step_stats()) {
-    ParseRunMeta(0, std::move(run_meta));
+    AddRunMeta(0, std::move(run_meta));
   }
-  ParseOpLog(std::move(op_log));
+  AddOpLog(std::move(op_log));
 
   if (ckpt_reader_) {
     for (const auto& v : ckpt_reader_->GetVariableToShapeMap()) {
@@ -48,27 +49,48 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
       }
     }
   }
+}
 
-  printf("Preparing Views...\n");
-  scope_view_ = std::unique_ptr<TFScope>(new TFScope(ckpt_reader_.get()));
-  graph_view_ = std::unique_ptr<TFGraph>(new TFGraph(ckpt_reader_.get()));
-  code_view_ = std::unique_ptr<TFCode>(new TFCode());
-  op_view_ = std::unique_ptr<TFOp>(new TFOp());
+void TFStats::BuildView(const string& cmd) {
+  if (cmd == kCmds[0] && !scope_view_) {
+    scope_view_.reset(new TFScope(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      scope_view_->AddNode(it->second.get());
+    }
+    scope_view_->Build();
+  }
+  if (cmd == kCmds[1] && !graph_view_) {
+    graph_view_.reset(new TFGraph(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      graph_view_->AddNode(it->second.get());
+    }
+    graph_view_->Build();
+  }
+  if (cmd == kCmds[2] && !code_view_) {
+    code_view_.reset(new TFCode());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      code_view_->AddNode(it->second.get());
+    }
+    code_view_->Build();
+  }
+  if (cmd == kCmds[3] && !op_view_) {
+    op_view_.reset(new TFOp());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      op_view_->AddNode(it->second.get());
+    }
+    op_view_->Build();
+  }
+}
 
-  for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-    scope_view_->AddNode(it->second.get());
-    graph_view_->AddNode(it->second.get());
-    code_view_->AddNode(it->second.get());
-    op_view_->AddNode(it->second.get());
-  }
-  scope_view_->Build();
-  graph_view_->Build();
-  code_view_->Build();
-  op_view_->Build();
+void TFStats::BuildAllViews() {
+  std::vector<string> cmds_str(kCmds, kCmds + sizeof(kCmds) / sizeof(*kCmds));
+  for (const string& cmd : cmds_str) {
+    BuildView(cmd);
+  }
 }
 
 const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
-                                               const Options& opts) {
+                                               const Options& opts) const {
   if (!Validate(opts)) {
     return empty_graph_node_;
   }
@@ -82,8 +104,8 @@ const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
   }
 }
 
-const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(const string& cmd,
-                                                         const Options& opts) {
+const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(
+    const string& cmd, const Options& opts) const {
   if (!Validate(opts)) {
     return empty_multi_graph_node_;
   }
@@ -130,7 +152,7 @@ void TFStats::ParseGraph() {
   }
 }
 
-void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
+void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
   if (!op_log) {
     return;
   }
@@ -144,12 +166,13 @@ void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
       node->second->AddFloatOps(entry.float_ops());
     }
     if (entry.has_code_def()) {
+      has_code_traces_ = true;
       node->second->AddCode(entry.code_def());
     }
   }
 }
 
-void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
+void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   if (!run_meta || !run_meta->has_step_stats()) {
     fprintf(stderr, "Invalid RunMetadata for step %lld\n", step);
     return;
@@ -176,7 +199,7 @@ void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   }
 }
 
-bool TFStats::Validate(const Options& opts) {
+bool TFStats::Validate(const Options& opts) const {
   if (opts.step >= 0 && steps_.find(opts.step) == steps_.end()) {
     fprintf(stderr, "Options -step=%lld not found\n", opts.step);
     return false;
@@ -184,9 +207,9 @@ bool TFStats::Validate(const Options& opts) {
   return true;
 }
 
-void TFStats::AddNodeForTest(const string& name,
-                             std::unique_ptr<TFGraphNode> node) {
-  nodes_map_[name] = std::move(node);
+void TFStats::AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node) {
+  steps_.insert(step);
+  nodes_map_[node->name()] = std::move(node);
 }
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.h b/tensorflow/tools/tfprof/internal/tfprof_stats.h
index dfb190e703..b26d274f80 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.h
@@ -59,28 +59,38 @@ class TFStats {
   const std::map<string, std::unique_ptr<TFGraphNode>>& nodes() const {
     return nodes_map_;
   }
+  const std::set<int64>& steps() const { return steps_; }
+  bool has_code_traces() const { return has_code_traces_; }
 
+  void BuildView(const string& cmd);
+  void BuildAllViews();
+
+  // Note: Must first BuildView(view_foo) before ShowXXX(view_foo) methods.
+  //
   // Organize the TensorFlow model as different types of views, and generate
   // outputs for profiling.
-  const TFGraphNodeProto& ShowGraphNode(const string& cmd, const Options& opts);
+  // TODO(xpan): Should it return reference here?
+  const TFGraphNodeProto& ShowGraphNode(const string& cmd,
+                                        const Options& opts) const;
   const TFMultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
-                                                  const Options& opts);
+                                                  const Options& opts) const;
 
   // Add a step of run time meta data.
-  void ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
+  void AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
   // Add tfprof operation meta data, such as customized op type, float_ops,
   // and code traces.
-  void ParseOpLog(std::unique_ptr<OpLog> op_log);
+  void AddOpLog(std::unique_ptr<OpLog> op_log);
 
   // For test purpose only.
-  void AddNodeForTest(const string& name, std::unique_ptr<TFGraphNode> node);
+  void AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node);
 
  private:
-  bool Validate(const Options& opts);
+  bool Validate(const Options& opts) const;
 
   void ParseGraph();
 
   std::set<int64> steps_;
+  bool has_code_traces_;
   std::unique_ptr<GraphDef> graph_;
   std::unique_ptr<TFScope> scope_view_;
   std::unique_ptr<TFGraph> graph_view_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index bcd1147c69..00d5d8cdb3 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -66,6 +66,7 @@ class TFProfStatsTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index d7cfe7dade..0482657ca5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -50,6 +50,7 @@ class TFProfTensorTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
index 0e9bb9658c..cad31050a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
@@ -52,6 +52,7 @@ class TFProfTimelineTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 nullptr, nullptr));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/tools/tfprof/tfprof_main.cc
index 7a4e7e85ff..5e70c093cc 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/tools/tfprof/tfprof_main.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/util/command_line_flags.h"
+#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
@@ -161,12 +162,13 @@ int Run(int argc, char** argv) {
         "Profiling everything!\n");
     return 0;
   } else if (argc > 1) {
-    if (string(argv[1]) == kCmds[5]) {
+    if (string(argv[1]) == kCmds[6]) {
       PrintHelp();
       return 0;
     }
     if (string(argv[1]) == kCmds[0] || string(argv[1]) == kCmds[1] ||
-        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3]) {
+        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3] ||
+        string(argv[1]) == kCmds[4]) {
       cmd = argv[1];
     }
   }
@@ -216,7 +218,13 @@ int Run(int argc, char** argv) {
               run_meta_files[i].c_str(), s.ToString().c_str());
       return 1;
     }
-    tf_stat.ParseRunMeta(i, std::move(run_meta));
+    tf_stat.AddRunMeta(i, std::move(run_meta));
+  }
+
+  if (cmd == kCmds[4]) {
+    tf_stat.BuildAllViews();
+    Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
+    return 0;
   }
 
   Options opts(FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros,
@@ -227,9 +235,11 @@ int Run(int argc, char** argv) {
                output_type, output_options);
 
   if (cmd == kCmds[2] || cmd == kCmds[3]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowMultiGraphNode(cmd, opts);
     return 0;
   } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowGraphNode(cmd, opts);
     return 0;
   }
@@ -254,14 +264,19 @@ int Run(int argc, char** argv) {
       fprintf(stderr, "E: %s\n", s.ToString().c_str());
       continue;
     }
-    if (cmd == kCmds[4]) {
+    if (cmd == kCmds[5]) {
       opts = new_opts;
-    } else if (cmd == kCmds[5]) {
+    } else if (cmd == kCmds[6]) {
       PrintHelp();
     } else if (cmd == kCmds[2] || cmd == kCmds[3]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowMultiGraphNode(cmd, new_opts);
     } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowGraphNode(cmd, new_opts);
+    } else if (cmd == kCmds[4]) {
+      tf_stat.BuildAllViews();
+      Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
     }
   }
   return 0;
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index 7206c6f956..abf141f1e7 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -102,3 +102,11 @@ message TFMultiGraphNodeProto {
   // structure used.
   repeated TFMultiGraphNodeProto children = 11;
 }
+
+message AdviceProto {
+  // checker name -> a list of reports from the checker.
+  map<string, Checker> checkers = 1;
+  message Checker {
+    repeated string reports = 2;
+  }
+}
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 3e81e361a9..afe0979536 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -327,11 +327,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   patched_http_archive(
       name = "protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
+      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
       # TODO: remove patching when tensorflow stops linking same protos into
       #       multiple shared libraries loaded in runtime by python.
       #       This patch fixes a runtime crash when tensorflow is compiled
@@ -345,21 +345,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.http_archive(
       name = "com_google_protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
+      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
   )
 
   native.http_archive(
       name = "com_google_protobuf_cc",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
+      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
   )
 
   native.new_http_archive(
diff --git a/tools/tf_env_collect.sh b/tools/tf_env_collect.sh
index a1c9c88c58..abeebeadea 100755
--- a/tools/tf_env_collect.sh
+++ b/tools/tf_env_collect.sh
@@ -16,62 +16,49 @@
 
 set -u  # Check for undefined variables
 
-die() {
-  # Print a message and exit with code 1.
-  #
-  # Usage: die <error_message>
-  #   e.g., die "Something bad happened."
-
-  echo $@
-  exit 1
-}
-
 echo "Collecting system information..."
 
 OUTPUT_FILE=tf_env.txt
-python_bin_path=$(which python || which python3 || die "Cannot find Python binary")
-
-{
-  echo
-  echo "== cat /etc/issue ==============================================="
-  uname -a
-  uname=`uname -s`
-  if [ "$(uname)" == "Darwin" ]; then
-    echo Mac OS X `sw_vers -productVersion`
-  elif [ "$(uname)" == "Linux" ]; then
-    cat /etc/*release | grep VERSION
-  fi
-  
-  echo
-  echo '== are we in docker ============================================='
-  num=`cat /proc/1/cgroup | grep docker | wc -l`;
-  if [ $num -ge 1 ]; then
-    echo "Yes"
-  else
-    echo "No"
-  fi
-  
-  echo
-  echo '== compiler ====================================================='
-  c++ --version 2>&1
-  
-  echo
-  echo '== uname -a ====================================================='
-  uname -a
-  
-  echo
-  echo '== check pips ==================================================='
-  pip list 2>&1 | grep "proto\|numpy\|tensorflow"
-  
-  
-  echo
-  echo '== check for virtualenv ========================================='
-  ${python_bin_path} -c "import sys;print(hasattr(sys, \"real_prefix\"))"
-  
-  echo
-  echo '== tensorflow import ============================================'
-} >> ${OUTPUT_FILE}
 
+echo >> $OUTPUT_FILE
+echo "== cat /etc/issue ===============================================" >> $OUTPUT_FILE
+uname -a >> $OUTPUT_FILE
+uname=`uname -s`
+if [ "$(uname)" == "Darwin" ]; then
+  echo Mac OS X `sw_vers -productVersion` >> $OUTPUT_FILE
+elif [ "$(uname)" == "Linux" ]; then
+  cat /etc/*release | grep VERSION >> $OUTPUT_FILE
+fi
+
+
+echo >> $OUTPUT_FILE
+echo '== are we in docker =============================================' >> $OUTPUT_FILE
+num=`cat /proc/1/cgroup | grep docker | wc -l`;
+if [ $num -ge 1 ]; then
+  echo "Yes" >> $OUTPUT_FILE
+else
+  echo "No" >> $OUTPUT_FILE
+fi
+
+echo >> $OUTPUT_FILE
+echo '== compiler =====================================================' >> $OUTPUT_FILE
+c++ --version 2>&1 >> $OUTPUT_FILE
+
+echo >> $OUTPUT_FILE
+echo '== uname -a =====================================================' >> $OUTPUT_FILE
+uname -a >> $OUTPUT_FILE
+
+echo >> $OUTPUT_FILE
+echo '== check pips ===================================================' >> $OUTPUT_FILE
+pip list 2>&1 | grep "proto\|numpy\|tensorflow" >> $OUTPUT_FILE
+
+
+echo >> $OUTPUT_FILE
+echo '== check for virtualenv =========================================' >> $OUTPUT_FILE
+python -c "import sys;print(hasattr(sys, \"real_prefix\"))" >> $OUTPUT_FILE
+
+echo >> $OUTPUT_FILE
+echo '== tensorflow import ============================================' >> $OUTPUT_FILE
 cat <<EOF > /tmp/check_tf.py
 import tensorflow as tf;
 print("tf.VERSION = %s" % tf.VERSION)
@@ -80,34 +67,32 @@ print("tf.COMPILER_VERSION = %s" % tf.GIT_VERSION)
 with tf.Session() as sess:
   print("Sanity check: %r" % sess.run(tf.constant([1,2,3])[:1]))
 EOF
-${python_bin_path} /tmp/check_tf.py 2>&1  >> ${OUTPUT_FILE}
-
-DEBUG_LD=libs ${python_bin_path} -c "import tensorflow"  2>>${OUTPUT_FILE} > /tmp/loadedlibs
-
-{
-  grep libcudnn.so /tmp/loadedlibs
-  echo
-  echo '== env =========================================================='
-  if [ -z ${LD_LIBRARY_PATH+x} ]; then
-    echo "LD_LIBRARY_PATH is unset";
-  else
-    echo LD_LIBRARY_PATH ${LD_LIBRARY_PATH} ;
-  fi
-  if [ -z ${DYLD_LIBRARY_PATH+x} ]; then
-    echo "DYLD_LIBRARY_PATH is unset";
-  else
-    echo DYLD_LIBRARY_PATH ${DYLD_LIBRARY_PATH} ;
-  fi
-  
-  
-  echo
-  echo '== nvidia-smi ==================================================='
-  nvidia-smi 2>&1
-  
-  echo
-  echo '== cuda libs  ==================================================='
-} >> ${OUTPUT_FILE}
+python /tmp/check_tf.py 2>&1  >> ${OUTPUT_FILE}
+
+DEBUG_LD=libs python -c "import tensorflow"  2>>${OUTPUT_FILE} > /tmp/loadedlibs
+grep libcudnn.so /tmp/loadedlibs >> $OUTPUT_FILE
+
+echo >> $OUTPUT_FILE
+echo '== env ==========================================================' >> $OUTPUT_FILE
+if [ -z ${LD_LIBRARY_PATH+x} ]; then
+  echo "LD_LIBRARY_PATH is unset" >> $OUTPUT_FILE;
+else
+  echo LD_LIBRARY_PATH ${LD_LIBRARY_PATH}  >> $OUTPUT_FILE;
+fi
+if [ -z ${DYLD_LIBRARY_PATH+x} ]; then
+  echo "DYLD_LIBRARY_PATH is unset" >> $OUTPUT_FILE;
+else
+  echo DYLD_LIBRARY_PATH ${DYLD_LIBRARY_PATH}  >> $OUTPUT_FILE;
+fi
+
+
+echo >> $OUTPUT_FILE >> $OUTPUT_FILE
+echo '== nvidia-smi ===================================================' >> $OUTPUT_FILE
+nvidia-smi 2>&1 >> $OUTPUT_FILE
+
+echo >> $OUTPUT_FILE
 
+echo '== cuda libs  ===================================================' >> $OUTPUT_FILE
 find /usr/local -type f -name 'libcudart*'  2>/dev/null | grep cuda |  grep -v "\\.cache" >> ${OUTPUT_FILE}
 find /usr/local -type f -name 'libudnn*'  2>/dev/null | grep cuda |  grep -v "\\.cache" >> ${OUTPUT_FILE}
 
-- 
GitLab


From 8bfbc12e19f7e98ec3525eacf06a2cb75b4fc482 Mon Sep 17 00:00:00 2001
From: "freedom\" Koan-Sin Tan" <koansin.tan@gmail.com>
Date: Tue, 27 Jun 2017 05:22:14 +0800
Subject: [PATCH 0458/1375] a python implmentation of label_image (#9261)

* a python implmentation of label_image

A quick implementation of tensorflow/examples/label_image/main.cc
in python.

With data file described in [1], we can we get reasonable results

$ python3 ./tensorflow/examples/label_image/label_image.py
military uniform 0.834306
mortarboard 0.0218692
academic gown 0.0103579
pickelhaube 0.00800814
bulletproof vest 0.00535088

[1] https://www.tensorflow.org/tutorials/image_recognition

* fix the gif decoding problem and add bmp decoding

* Add from __future__ as per style guide.
---
 tensorflow/examples/label_image/README.md     |  23 ++-
 .../examples/label_image/label_image.py       | 132 ++++++++++++++++++
 2 files changed, 154 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/examples/label_image/label_image.py

diff --git a/tensorflow/examples/label_image/README.md b/tensorflow/examples/label_image/README.md
index 1103caf586..c5857f394a 100644
--- a/tensorflow/examples/label_image/README.md
+++ b/tensorflow/examples/label_image/README.md
@@ -1,4 +1,4 @@
-# TensorFlow C++ Image Recognition Demo
+# TensorFlow C++ and Python Image Recognition Demo
 
 This example shows how you can load a pre-trained TensorFlow network and use it
 to recognize objects in images in C++. For Java see the [Java
@@ -64,3 +64,24 @@ $ bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png
 
 For a more detailed look at this code, you can check out the C++ section of the
 [Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+
+## Python implementation
+
+label_image.py is a python implementation that provides code corresponding
+to the C++ code here. This gives more intuitive mapping between C++ and
+Python than the Python code mentioned in the
+[Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+and could be easier to add visualization or debug code.
+
+With tensorflow python package installed, you can run it like:
+```bash
+$ python3 tensorflow/examples/label_image/label_image.py
+```
+And get result similar to this:
+```
+military uniform 0.834305
+mortarboard 0.0218694
+academic gown 0.0103581
+pickelhaube 0.00800818
+bulletproof vest 0.0053509
+```
diff --git a/tensorflow/examples/label_image/label_image.py b/tensorflow/examples/label_image/label_image.py
new file mode 100644
index 0000000000..39d0981337
--- /dev/null
+++ b/tensorflow/examples/label_image/label_image.py
@@ -0,0 +1,132 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+import numpy as np
+import tensorflow as tf
+
+def load_graph(model_file):
+  graph = tf.Graph()
+  graph_def = tf.GraphDef()
+
+  with open(model_file, "rb") as f:
+    graph_def.ParseFromString(f.read())
+  with graph.as_default():
+    tf.import_graph_def(graph_def)
+
+  return graph
+
+def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
+				input_mean=0, input_std=255):
+  input_name = "file_reader"
+  output_name = "normalized"
+  file_reader = tf.read_file(file_name, input_name)
+  if file_name.endswith(".png"):
+    image_reader = tf.image.decode_png(file_reader, channels = 3,
+                                       name='png_reader')
+  elif file_name.endswith(".gif"):
+    image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
+                                                  name='gif_reader'))
+  elif file_name.endswith(".bmp"):
+    image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
+  else:
+    image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
+                                        name='jpeg_reader')
+  float_caster = tf.cast(image_reader, tf.float32)
+  dims_expander = tf.expand_dims(float_caster, 0);
+  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
+  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
+  sess = tf.Session()
+  result = sess.run(normalized)
+
+  return result
+
+def load_labels(label_file):
+  label = []
+  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
+  for l in proto_as_ascii_lines:
+    label.append(l.rstrip())
+  return label
+
+if __name__ == "__main__":
+  file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg"
+  model_file = \
+    "tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb"
+  label_file = "tensorflow/examples/label_image/data/imagenet_slim_labels.txt"
+  input_height = 299
+  input_width = 299
+  input_mean = 0
+  input_std = 255
+  input_layer = "input"
+  output_layer = "InceptionV3/Predictions/Reshape_1"
+
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--image", help="image to be processed")
+  parser.add_argument("--graph", help="graph/model to be executed")
+  parser.add_argument("--labels", help="name of file containing labels")
+  parser.add_argument("--input_height", type=int, help="input height")
+  parser.add_argument("--input_width", type=int, help="input width")
+  parser.add_argument("--input_mean", type=int, help="input mean")
+  parser.add_argument("--input_std", type=int, help="input std")
+  parser.add_argument("--input_layer", help="name of input layer")
+  parser.add_argument("--output_layer", help="name of output layer")
+  args = parser.parse_args()
+
+  if args.graph:
+    model_file = args.graph
+  if args.image:
+    file_name = args.image
+  if args.labels:
+    label_file = args.labels
+  if args.input_height:
+    input_height = args.input_height
+  if args.input_width:
+    input_width = args.input_width
+  if args.input_mean:
+    input_mean = args.input_mean
+  if args.input_std:
+    input_std = args.input_std
+  if args.input_layer:
+    input_layer = args.input_layer
+  if args.output_layer:
+    output_layer = args.output_layer
+
+  graph = load_graph(model_file)
+  t = read_tensor_from_image_file(file_name,
+                                  input_height=input_height,
+                                  input_width=input_width,
+                                  input_mean=input_mean,
+                                  input_std=input_std)
+
+  input_name = "import/" + input_layer
+  output_name = "import/" + output_layer
+  input_operation = graph.get_operation_by_name(input_name);
+  output_operation = graph.get_operation_by_name(output_name);
+
+  with tf.Session(graph=graph) as sess:
+    results = sess.run(output_operation.outputs[0],
+                      {input_operation.outputs[0]: t})
+  results = np.squeeze(results)
+
+  top_k = results.argsort()[-5:][::-1]
+  labels = load_labels(label_file)
+  for i in top_k:
+    print(labels[i], results[i])
-- 
GitLab


From 6f685da23e208f9e9e0091442a8ddc741657d904 Mon Sep 17 00:00:00 2001
From: David Majnemer <majnemer@google.com>
Date: Mon, 26 Jun 2017 14:19:38 -0700
Subject: [PATCH 0459/1375] [XLA] Disallow fuse X into Y if there are paths
 from X to Y which don't fuse

Just because X can fuse into all of its consumers does not mean that those
consumers can fuse into anything. Depending on the structure of the graph, this
can either result in no performance win at all or, in the case of recurrent
networks, a big performance deficit.

PiperOrigin-RevId: 160194058
---
 .../xla/service/instruction_fusion.cc         | 263 +++++++++++++-----
 .../compiler/xla/service/instruction_fusion.h |  10 +
 2 files changed, 203 insertions(+), 70 deletions(-)

diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 52da222ab9..bc7adb82b1 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -28,7 +28,6 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
-
 /*static*/ bool InstructionFusion::IsExpensive(
     const HloInstruction& instruction) {
   switch (instruction.opcode()) {
@@ -116,8 +115,99 @@ bool FusionWouldDuplicate(const HloInstruction& producer,
                           const HloInstruction& consumer) {
   return !(producer.users().size() == 1 && consumer.IsUserOf(&producer));
 }
+
+// An "effectively unary" operation is one that has one "large"
+// input with the others being negligible in terms of memory usage.
+// We use "has a smaller true rank than the output" as a heuristic
+// for "negligible" memory usage.
+bool EffectivelyUnary(HloInstruction* hlo) {
+  int64 output_rank = 0;
+  ShapeUtil::ForEachSubshape(
+      hlo->shape(),
+      [&output_rank](const Shape& subshape, const ShapeIndex& shape_index) {
+        if (ShapeUtil::IsArray(subshape)) {
+          output_rank = std::max(output_rank, ShapeUtil::TrueRank(subshape));
+        }
+      });
+  return std::count_if(hlo->operands().begin(), hlo->operands().end(),
+                       [output_rank](HloInstruction* operand) {
+                         if (operand->opcode() == HloOpcode::kBroadcast) {
+                           return false;
+                         }
+                         if (operand->opcode() == HloOpcode::kConstant &&
+                             ShapeUtil::IsEffectiveScalar(operand->shape())) {
+                           return false;
+                         }
+                         return ShapeUtil::TrueRank(operand->shape()) >=
+                                output_rank;
+                       }) <= 1;
+}
 }  // namespace
 
+bool InstructionFusion::CanFuseOnAllPaths(
+    const HloComputation::ReachabilityMap& reachability_map,
+    HloInstruction* producer, HloInstruction* consumer,
+    DoNotFuseMap* do_not_fuse) {
+  auto could_fuse_on_all_paths = [&] {
+    // First check to see if we have already marked this producer as infeasible
+    // to fuse into consumer.
+    if ((*do_not_fuse)[consumer].count(producer) > 0) {
+      return false;
+    }
+    // Make sure it is possible for producer and consumer to exist in a fusion
+    // node.
+    if (!producer->IsFusable() || !consumer->IsFusable()) {
+      return false;
+    }
+    // We do an upword walk of the graph from consumer towards all paths which
+    // lead to producer to find any unfusable paths.
+    for (int64 i = 0, e = consumer->operand_count(); i < e; ++i) {
+      auto* consumer_operand = consumer->mutable_operand(i);
+      if (consumer_operand == producer) {
+        // This is the base case: our upward crawl ends but we need to make sure
+        // that fusion from consumer can happen.
+        if (!ShouldFuse(consumer, i)) {
+          return false;
+        }
+      } else if (reachability_map.IsReachable(consumer_operand, producer)) {
+        // The reachability map told us that consumer_operand is a node on the
+        // path to producer. We need to further investigate from
+        // consumer_operand.
+
+        // First check if we have already ruled out fusing producer into
+        // consumer_operand.
+        if ((*do_not_fuse)[consumer_operand].count(producer) > 0) {
+          return false;
+        }
+        // Make sure it is possible for consumer_operand to exist in a fusion
+        // node.
+        if (!consumer_operand->IsFusable()) {
+          return false;
+        }
+        // The producer is reachable from consumer_operand which means we need
+        // to be able to fuse consumer_operand into consumer in order for
+        // producer to be fusable into consumer on all paths.
+        if (!ShouldFuse(consumer, i)) {
+          return false;
+        }
+        // Perform the recursive step: make sure producer can be fused into
+        // consumer_operand on all paths.
+        if (!CanFuseOnAllPaths(reachability_map, producer, consumer_operand,
+                               do_not_fuse)) {
+          return false;
+        }
+      }
+    }
+    return true;
+  };
+  if (could_fuse_on_all_paths()) {
+    return true;
+  }
+  // We couldn't fuse on all paths, record this result.
+  (*do_not_fuse)[consumer].insert(producer);
+  return false;
+}
+
 StatusOr<bool> InstructionFusion::Run(HloModule* module) {
   bool changed = false;
   for (auto& computation : module->computations()) {
@@ -134,54 +224,71 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
     std::vector<HloInstruction*> post_order(post_order_list.begin(),
                                             post_order_list.end());
 
-    std::set<HloInstruction*> all_consumers_fusable;
-    // Find which ops can be fused into all of their operands. We would rather
-    // not fuse an op into only some of its users, as that offers no benefit in
-    // terms of memory bandwidth, but forces us to keep more live values around.
-    for (auto* hlo : post_order) {
-      auto user_fusable_into_hlo = [this, &hlo](HloInstruction* consumer) {
-        if (!consumer->IsFusable()) {
-          return false;
-        }
-        for (int operand_number = 0;
-             operand_number < consumer->operands().size(); ++operand_number) {
-          if (consumer->operand(operand_number) == hlo) {
-            if (!ShouldFuse(consumer, operand_number)) {
-              return false;
-            }
-          }
-        }
+    tensorflow::gtl::FlatMap<HloInstruction*, int> post_order_index;
+    for (size_t i = 0; i < post_order.size(); ++i) {
+      InsertOrDie(&post_order_index, post_order[i], i);
+    }
+
+    DoNotFuseMap do_not_fuse;
+    auto transitive_operands = computation->ComputeTransitiveOperands();
+
+    auto producer_cannot_fuse_into_consumer = [this](HloInstruction* producer,
+                                                     HloInstruction* consumer) {
+      // Make sure it is possible for producer and consumer to exist in a fusion
+      // node.
+      if (!producer->IsFusable() || !consumer->IsFusable()) {
+        return true;
+      }
+      auto operand_indices = consumer->OperandIndices(producer);
+      return std::any_of(operand_indices.begin(), operand_indices.end(),
+                         [&](const int64 operand_index) {
+                           return !ShouldFuse(consumer, operand_index);
+                         });
+    };
+
+    auto cheap_to_duplicate = [](HloInstruction* producer) {
+      if (producer->opcode() == HloOpcode::kBroadcast) {
+        return true;
+      }
+      if (producer->opcode() == HloOpcode::kConstant &&
+          ShapeUtil::IsEffectiveScalar(producer->shape())) {
         return true;
-      };
-
-      // An "effectively unary" operation is one that has one "large"
-      // input with the others being negligible in terms of memory usage.
-      // We use "has a smaller true rank than the output" as a heuristic
-      // for "negligible" memory usage.
-      auto effectively_unary = [](HloInstruction* hlo) {
-        if (hlo->operands().size() == 1) {
-          return true;
+      }
+      if (EffectivelyUnary(producer)) {
+        return true;
+      }
+      return false;
+    };
+
+    for (HloInstruction* producer : post_order) {
+      if (cheap_to_duplicate(producer)) {
+        continue;
+      }
+      if (std::any_of(producer->users().begin(), producer->users().end(),
+                      [&](HloInstruction* consumer) {
+                        return producer_cannot_fuse_into_consumer(producer,
+                                                                  consumer);
+                      })) {
+        for (HloInstruction* consumer : producer->users()) {
+          do_not_fuse[consumer].insert(producer);
         }
-        auto output_rank = ShapeUtil::TrueRank(hlo->shape());
-        return std::count_if(
-                   hlo->operands().begin(), hlo->operands().end(),
-                   [output_rank](HloInstruction* operand) {
-                     return ((operand->opcode() != HloOpcode::kBroadcast) &&
-                             ShapeUtil::TrueRank(operand->shape()) >=
-                                 output_rank);
-                   }) <= 1;
-      };
-
-      if (effectively_unary(hlo) ||
-          std::all_of(hlo->users().begin(), hlo->users().end(),
-                      user_fusable_into_hlo)) {
-        all_consumers_fusable.insert(hlo);
       }
     }
-
-    tensorflow::gtl::FlatMap<HloInstruction*, int> post_order_index;
-    for (size_t i = 0; i < post_order.size(); ++i) {
-      InsertOrDie(&post_order_index, post_order[i], i);
+    for (HloInstruction* consumer : post_order) {
+      for (HloInstruction* producer : consumer->operands()) {
+        if (cheap_to_duplicate(producer)) {
+          continue;
+        }
+        if (do_not_fuse[consumer].count(producer) > 0) {
+          continue;
+        }
+        if (CanFuseOnAllPaths(*transitive_operands, producer, consumer,
+                              &do_not_fuse)) {
+          CHECK_EQ(do_not_fuse[consumer].count(producer), 0);
+        } else {
+          CHECK_GT(do_not_fuse[consumer].count(producer), 0);
+        }
+      }
     }
 
     // Instruction fusion effectively fuses edges in the computation graph
@@ -266,34 +373,50 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
       for (int64 i : sorted_operand_numbers) {
         HloInstruction* operand = instruction->mutable_operand(i);
 
-        if (FusionWouldDuplicate(*operand, *instruction) &&
-            (all_consumers_fusable.count(operand) == 0)) {
+        if (!operand->IsFusable()) {
           continue;
         }
-
-        if (operand->IsFusable() && ShouldFuse(instruction, i)) {
-          HloInstruction* fusion_instruction = Fuse(operand, instruction);
-
-          // Fusing an instruction into a fusion instruction can change the
-          // operand set of the fusion instruction. For simplicity just push the
-          // instruction to the top of the post_order and reconsider it for
-          // further fusion in the next iteration of the outer loop.
-          post_order.push_back(fusion_instruction);
-          InsertOrDie(&post_order_index, fusion_instruction,
-                      post_order.size() - 1);
-          changed = true;
-
-          if (operand->user_count() == 0) {
-            // Operand is now dead. Remove from post order by setting it's
-            // location to nullptr.
-            post_order[FindOrDie(post_order_index, operand)] = nullptr;
-            post_order_index.erase(operand);
-
-            // Remove from computation.
-            TF_RETURN_IF_ERROR(computation_->RemoveInstruction(operand));
-          }
-          break;
+        if (!ShouldFuse(instruction, i)) {
+          continue;
+        }
+        const std::unordered_set<HloInstruction*>&
+            do_not_fuse_into_instruction = do_not_fuse[instruction];
+        if (do_not_fuse_into_instruction.count(operand) > 0) {
+          continue;
+        }
+        HloInstruction* fusion_instruction = Fuse(operand, instruction);
+        std::unordered_set<HloInstruction*>&
+            do_not_fuse_into_fusion_instruction =
+                do_not_fuse[fusion_instruction];
+        if (instruction != fusion_instruction) {
+          do_not_fuse_into_fusion_instruction.insert(
+              do_not_fuse_into_instruction.begin(),
+              do_not_fuse_into_instruction.end());
+        }
+        const std::unordered_set<HloInstruction*>& do_not_fuse_into_operand =
+            do_not_fuse[operand];
+        do_not_fuse_into_fusion_instruction.insert(
+            do_not_fuse_into_operand.begin(), do_not_fuse_into_operand.end());
+
+        // Fusing an instruction into a fusion instruction can change the
+        // operand set of the fusion instruction. For simplicity just push the
+        // instruction to the top of the post_order and reconsider it for
+        // further fusion in the next iteration of the outer loop.
+        post_order.push_back(fusion_instruction);
+        InsertOrDie(&post_order_index, fusion_instruction,
+                    post_order.size() - 1);
+        changed = true;
+
+        if (operand->user_count() == 0) {
+          // Operand is now dead. Remove from post order by setting it's
+          // location to nullptr.
+          post_order[FindOrDie(post_order_index, operand)] = nullptr;
+          post_order_index.erase(operand);
+
+          // Remove from computation.
+          TF_RETURN_IF_ERROR(computation_->RemoveInstruction(operand));
         }
+        break;
       }
     }
   }
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.h b/tensorflow/compiler/xla/service/instruction_fusion.h
index a9f3723f2d..ca98218286 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.h
+++ b/tensorflow/compiler/xla/service/instruction_fusion.h
@@ -72,6 +72,16 @@ class InstructionFusion : public HloPassInterface {
  private:
   HloInstruction* Fuse(HloInstruction* producer, HloInstruction* consumer);
 
+  using DoNotFuseMap =
+      std::unordered_map<HloInstruction*, std::unordered_set<HloInstruction*>>;
+
+  // Whether or not we can fuse consumer into original_producer on all paths
+  // from the producer to the consumer where nodes are HLOs and edges are uses.
+  bool CanFuseOnAllPaths(
+      const HloComputation::ReachabilityMap& reachability_map,
+      HloInstruction* producer, HloInstruction* consumer,
+      DoNotFuseMap* do_not_fuse);
+
   // Used to determine if an HLO is expensive. Expensive operations will not be
   // duplicated.
   std::function<bool(const HloInstruction& instruction)> is_expensive_;
-- 
GitLab


From 983dcc63afb601f64b517cf62b8384d83f6ca090 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Jun 2017 14:35:34 -0700
Subject: [PATCH 0460/1375] First draft of Tensors segment of the programmer's
 guide.

PiperOrigin-RevId: 160196550
---
 .../docs_src/programmers_guide/tensors.md     | 330 ++++++++++++++++++
 1 file changed, 330 insertions(+)
 create mode 100644 tensorflow/docs_src/programmers_guide/tensors.md

diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/programmers_guide/tensors.md
new file mode 100644
index 0000000000..2675953db8
--- /dev/null
+++ b/tensorflow/docs_src/programmers_guide/tensors.md
@@ -0,0 +1,330 @@
+# Tensors
+
+TensorFlow, as the name indicates, is a framework to define and run computations
+involving tensors. A **tensor** is a generalization of vectors and matrices to
+potentially higher dimensions. Internally, TensorFlow represents tensors as
+n-dimensional arrays of base datatypes.
+
+When writing a TensorFlow program, the main object you manipulate and pass
+around is the `tf.Tensor`. A `tf.Tensor` object represents a partially defined
+computation that will eventually produce a value. TensorFlow programs work by
+first building a graph of `tf.Tensor` objects, detailing how each tensor is
+computed based on the other available tensors and then by running parts of this
+graph to achieve the desired results.
+
+A `tf.Tensor` has the following properties:
+
+ * a data type (`float32`, `int32`, or `string`, for example)
+ * a shape
+
+
+Each element in the Tensor has the same data type, and the data type is always
+known. The shape (that is, the number of dimensions it has and the size of each
+dimension) might be only partially known. Most operations produce tensors of
+fully-known shapes if the shapes of their inputs are also fully known, but in
+some cases it's only possible to find the shape of a tensor at graph execution
+time.
+
+Some types of tensors are special, and these will be covered in other
+units of the Programmer's guide. The main ones are:
+
+  * `tf.Variable`
+  * `tf.Constant`
+  * `tf.Placeholder`
+  * `tf.SparseTensor`
+
+With the exception of `tf.Variable`, the value of a tensor is immutable, which
+means that in the context of a single execution tensors only have a single
+value. However, evaluating the same tensor twice can return different values;
+for example that tensor can be the result of reading data from disk, or
+generating a random number.
+
+## Rank
+
+The **rank** of a `tf.Tensor` object is its number of dimensions. Synonyms for
+rank include **order** or **degree** or **n-dimension**.
+Note that rank in TensorFlow is not the same as matrix rank in mathematics. 
+As the following table shows, each rank in TensorFlow corresponds to a 
+different mathematical entity:
+
+Rank | Math entity
+--- | ---
+0 | Scalar (magnitude only)
+1 | Vector (magnitude and direction)
+2 | Matrix (table of numbers)
+3 | 3-Tensor (cube of numbers)
+n | n-Tensor (you get the idea)
+
+
+### Rank 0 
+
+The following snippet demonstrates creating a few rank 0 variables:
+
+```python
+mammal = tf.Variable("Elephant", tf.string)
+ignition = tf.Variable(451, tf.int16)
+floating = tf.Variable(3.14159265359, tf.float64)
+its_complicated = tf.Variable((12.3, -4.85), tf.complex64)
+```
+
+Note: A string is treated as a single item in TensorFlow, not as a sequence of
+characters. It is possible to have scalar strings, vectors of strings, etc.
+
+### Rank 1
+
+To create a rank 1 `tf.Tensor` object, you can pass a list of items as the
+initial value. For example:
+
+```python
+mystr = tf.Variable(["Hello"], tf.string)
+cool_numbers  = tf.Variable([3.14159, 2.71828], tf.float32)
+first_primes = tf.Variable([2, 3, 5, 7, 11], tf.int32)
+its_very_complicated = tf.Variable([(12.3, -4.85), (7.5, -6.23)], tf.complex64)
+```
+
+
+### Higher ranks
+
+A rank 2 `tf.Tensor` object consists of at least one row and at least
+one column:
+
+```python
+mymat = tf.Variable([[7],[11]], tf.int16)
+myxor = tf.Variable([[False, True],[True, False]], tf.bool)
+linear_squares = tf.Variable([[4], [9], [16], [25]], tf.int32)
+squarish_squares = tf.Variable([ [4, 9], [16, 25] ], tf.int32)
+rank_of_squares = tf.rank(squarish_squares)
+mymatC = tf.Variable([[7],[11]], tf.int32)
+```
+
+Higher-rank Tensors, similarly, consist of an n-dimensional array. For example,
+during image processing, many tensors of rank 4 are used, with dimensions
+corresponding to example-in-batch, image width, image height, and color channel.
+
+``` python
+my_image = tf.zeros([10, 299, 299, 3])  # batch x height x width x color
+```
+
+### Getting a `tf.Tensor` object's rank
+
+To determine the rank of a `tf.Tensor` object, call the `tf.rank` method.
+For example, the following method programmatically determines the rank 
+of the `tf.Tensor` defined in the previous section:
+
+```python
+r = tf.rank(my3d)
+# After the graph runs, r will hold the value 3.
+```
+
+### Referring to `tf.Tensor` slices
+
+Since a `tf.Tensor` is an n-dimensional array of cells, to access a single cell
+in a `tf.Tensor` you need to specify n indices.
+
+For a rank 0 tensor (a scalar), no indices are necessary, since it is already a
+single number.
+
+For a rank 1 tensor (a vector), passing a single index allows you to access a
+number:
+
+```python
+my_scalar = my_vector[2]
+```
+
+Note that the index passed inside the `[]` can itself be a scalar `tf.Tensor`, if
+you want to dynamically choose an element from the vector.
+
+For tensors of rank 2 or higher, the situation is more interesting. For a
+`tf.Tensor` of rank 2, passing two numbers returns a scalar, as expected:
+
+
+```python
+my_scalar = my_matrix[1, 2]
+```
+
+
+Passing a single number, however, returns a subvector of a matrix, as follows:
+
+
+```python
+my_row_vetor = my_matrix[2]
+my_column_vector = my_matrix[:, 3]
+```
+
+The `:` notation is python slicing syntax for "leave this dimension alone". This
+is useful in higher-rank Tensors, as it allows you to access its subvectors,
+submatrices, and even other subtensors.
+
+
+## Shape
+
+The **shape** of a tensor is the number of elements in each dimension.
+TensorFlow automatically infers shapes during graph construction. These inferred
+shapes might have known or unknown rank. If the rank is known, the sizes of each
+dimension might be known or unknown.
+
+The TensorFlow documentation uses three notational conventions to describe
+tensor dimensionality: rank, shape, and dimension number. The following table
+shows how these relate to one another:
+
+Rank | Shape | Dimension number | Example
+--- | --- | --- | ---
+0 | [] | 0-D | A 0-D tensor.  A scalar.
+1 | [D0] | 1-D | A 1-D tensor with shape [5].
+2 | [D0, D1] | 2-D | A 2-D tensor with shape [3, 4].
+3 | [D0, D1, D2] | 3-D | A 3-D tensor with shape [1, 4, 3].
+n | [D0, D1, ... Dn-1] | n-D | A tensor with shape [D0, D1, ... Dn-1].
+
+Shapes can be represented via Python lists / tuples of ints, or with the
+@{tf.TensorShape}.
+
+### Getting a `tf.Tensor` object's shape
+
+There are two ways of accessing the shape of a `tf.Tensor`. While building the
+graph, it is often useful to ask what is already known about a tensor's
+shape. This can be done by reading the `shape` property of a `tf.Tensor` object.
+This method returns a `TensorShape` object, which is a convenient way of
+representing partially-specified shapes (since, when building the graph, not all
+shapes will be fully known).
+
+It is also possible to get a `tf.Tensor` that will represent the fully-defined
+shape of another `tf.Tensor` at runtime. This is done by calling the `tf.shape`
+operation. This way, you can build a graph that manipulates the shapes of
+tensors by building other tensors that depend on the dynamic shape of the input
+`tf.Tensor`.
+
+For example, here is how to make a vector of zeros with the same size as the
+number of columns in a given matrix:
+
+``` python
+zeros = tf.zeros(tf.shape(my_matrix)[1])
+```
+
+### Changing the shape of a `tf.Tensor`
+
+The **number of elements** of a tensor is the product of the sizes of all its
+shapes. The number of elements of a scalar is always `1`. Since there are often
+many different shapes that have the same number of elements, it's often
+convenient to be able to change the shape of a `tf.Tensor`, keeping its elements
+fixed. This can be done with `tf.reshape`.
+
+The following examples demonstrate how to reshape tensors:
+
+```python
+rank_three_tensor = tf.ones([3, 4, 5])
+matrix = tf.reshape(rank_three_tensor, [6, 10])  # Reshape existing content into
+                                                 # a 6x10 matrix
+matrixB = tf.reshape(matrix, [3, -1])  #  Reshape existing content into a 3x20
+                                       # matrix. -1 tells reshape to calculate
+                                       # the size of this dimension.
+matrixAlt = tf.reshape(matrixB, [4, 3, -1])  # Reshape existing content into a
+                                             #4x3x5 tensor
+
+# Note that the number of elements of the reshaped Tensors has to match the
+# original number of elements. Therefore, the following example generates an
+# error because no possible value for the last dimension will match the number
+# of elements.
+yet_another = tf.reshape(matrixAlt, [13, 2, -1])  # ERROR!
+```
+
+## Data types
+
+In addition to dimensionality, Tensors have a data type. Refer to the
+`tf.DataType` page in the programmer's guide for a full list of the data types.
+
+It is not possible to have a `tf.Tensor` with more than one data type. It is
+possible, however, to serialize arbitrary data structures as `string`s and store
+those in `tf.Tensor`s.
+
+It is possible to cast `tf.Tensor`s from one datatype to another using
+`tf.cast`:
+
+``` python
+# Cast a constant integer tensor into floating point.
+float_tensor = tf.cast(tf.constant([1, 2, 3]), dtype=tf.float32)
+```
+
+To inspect a `tf.Tensor`'s data type use the `Tensor.dtype` property.
+
+When creating a `tf.Tensor` from a python object you may optionally specify the
+datatype. If you don't, TensorFlow chooses a datatype that can represent your
+data. TensorFlow converts Python integers to `tf.int32` and python floating
+point numbers to `tf.float32`. Otherwise TensorFlow uses the same rules numpy
+uses when converting to arrays.
+
+## Evaluating Tensors
+
+Once the computation graph has been built, you can run the computation that
+produces a particular `tf.Tensor` and fetch the value assigned to it. This is
+often useful for debugging as well as being required for much of TensorFlow to
+work.
+
+The simplest way to evaluate a Tensor is using the `Tensor.eval` method. For
+example:
+
+```python
+constant = tf.constant([1, 2, 3])
+tensor = constant * constant
+print tensor.eval()
+```
+
+The `eval` method only works when a default `tf.Session` is active (see
+@{$graphs$Graphs and Sessions} for more information).
+
+`Tensor.eval` returns a numpy array with the same contents as the tensor.
+
+Sometimes it is not possible to evaluate a `tf.Tensor` with no context because
+its value might depend on dynamic information that is not available. For
+example, tensors that depend on `Placeholder`s can't be evaluated without
+providing a value for the `Placeholder`.
+
+``` python
+p = tf.placeholder(tf.float32)
+t = p + 1.0
+t.eval()  # This will fail, since the placeholder did not get a value.
+t.eval(feed_dict={p:2.0})  # This will succeed because we're feeding a value
+                           # to the placeholder.
+```
+
+Note that it is possible to feed any `tf.Tensor`, not just placeholders.
+
+Other model constructs might make evaluating a `tf.Tensor`
+complicated. TensorFlow can't directly evaluate `tf.Tensor`s defined inside
+functions or inside control flow constructs. If a `tf.Tensor` depends on a value
+from a queue, evaluating the `tf.Tensor` will only work once something has been
+enqueued; otherwise, evaluating it will hang. When working with queues, remember
+to call `tf.train.start_queue_runners` before evaluating any `tf.Tensor`s.
+
+## Printing Tensors
+
+For debugging purposes you might want to print the value of a `tf.Tensor`. While
+ @{$debug$tfdbg} provides advanced debugging support, TensorFlow also has an
+ operation to directly print the value of a `tf.Tensor`.
+
+Note that you rarely want to use the following pattern when printing a
+`tf.Tensor`:
+
+``` python
+t = <<some tensorflow operation>>
+print t  # This will print the symbolic tensor when the graph is being built.
+         # This tensor does not have a value in this context.
+```
+
+This code prints the `tf.Tensor` object (which represents deferred computation)
+and not its value. Instead, TensorFlow provides the `tf.Print` operation, which
+returns its first tensor argument unchanged while printing the set of
+`tf.Tensor`s it is passed as the second argument.
+
+To correctly use `tf.Print` its return value must be used. See the example below
+
+``` python
+t = <<some tensorflow operation>>
+tf.Print(t, [t])  # This does nothing
+t = tf.Print(t, [t])  # Here we are using the value returned by tf.Print
+result = t + 1  # Now when result is evaluated the value of `t` will be printed.
+```
+
+When you evaluate `result` you will evaluate everything `result` depends
+upon. Since `result` depends upon `t`, and evaluating `t` has the side effect of
+printing its input (the old value of `t`), `t` gets printed.
+
-- 
GitLab


From 973f22cf8d625ac441607c9a664ccf9790d598d4 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Jun 2017 14:35:41 -0700
Subject: [PATCH 0461/1375] First draft of variables unit of programmer's
 guide.

PiperOrigin-RevId: 160196566
---
 tensorflow/docs_src/programmers_guide/faq.md  |   3 +-
 .../docs_src/programmers_guide/index.md       |   2 +-
 .../programmers_guide/reading_data.md         |   2 +-
 .../programmers_guide/variable_scope.md       | 373 -------------
 .../docs_src/programmers_guide/variables.md   | 508 ++++++++++++------
 tensorflow/docs_src/tutorials/deep_cnn.md     |   2 +-
 6 files changed, 360 insertions(+), 530 deletions(-)
 delete mode 100644 tensorflow/docs_src/programmers_guide/variable_scope.md

diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index e31d2717a6..74407ed315 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -174,8 +174,7 @@ for more information on how to use them.
 
 ## Variables
 
-See also the how-to documentation on @{$variables$variables}
-and @{$variable_scope$variable scopes}, and
+See also the how-to documentation on @{$variables$variables} and
 @{$python/state_ops$the API documentation for variables}.
 
 #### What is the lifetime of a variable?
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md
index 5ed7c09a57..ea4d186739 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/programmers_guide/index.md
@@ -9,7 +9,7 @@ explain a particular aspect of TensorFlow:
   * @{$dims_types$Tensor Ranks, Shapes, and Types}, which explains Tensor
     rank (the number of dimensions), shape (the size of each dimension),
     and datatypes.
-  * @{$variable_scope$Sharing Variables}, which explains how to share and
+  * @{$variables$Sharing Variables}, which explains how to share and
     manage large sets of variables when building complex models.
   * @{$threading_and_queues$Threading and Queues}, which explains TensorFlow's
     rich queuing system.
diff --git a/tensorflow/docs_src/programmers_guide/reading_data.md b/tensorflow/docs_src/programmers_guide/reading_data.md
index 3c31d3a1a7..a7d9372053 100644
--- a/tensorflow/docs_src/programmers_guide/reading_data.md
+++ b/tensorflow/docs_src/programmers_guide/reading_data.md
@@ -476,4 +476,4 @@ This is what is done in
 
 You can have the train and eval in the same graph in the same process, and share
 their trained variables.  See
-@{$variable_scope$the shared variables tutorial}.
+@{$variables$the shared variables tutorial}.
diff --git a/tensorflow/docs_src/programmers_guide/variable_scope.md b/tensorflow/docs_src/programmers_guide/variable_scope.md
deleted file mode 100644
index f4d2b3f37b..0000000000
--- a/tensorflow/docs_src/programmers_guide/variable_scope.md
+++ /dev/null
@@ -1,373 +0,0 @@
-# Sharing Variables
-
-You can create, initialize, save and load single variables
-in the way described in the @{$variables$Variables HowTo}.
-But when building complex models you often need to share large sets of
-variables and you might want to initialize all of them in one place.
-This tutorial shows how this can be done using `tf.variable_scope()` and
-`tf.get_variable()`.
-
-## The Problem
-
-Imagine you create a simple model for image filters, similar to our
-@{$deep_cnn$Convolutional Neural Networks Tutorial}
-model but with only 2 convolutions (for simplicity of this example). If you use
-just `tf.Variable`, as explained in @{$variables$Variables HowTo},
-your model might look like this.
-
-```python
-def my_image_filter(input_images):
-    conv1_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv1_weights")
-    conv1_biases = tf.Variable(tf.zeros([32]), name="conv1_biases")
-    conv1 = tf.nn.conv2d(input_images, conv1_weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    relu1 = tf.nn.relu(conv1 + conv1_biases)
-
-    conv2_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv2_weights")
-    conv2_biases = tf.Variable(tf.zeros([32]), name="conv2_biases")
-    conv2 = tf.nn.conv2d(relu1, conv2_weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv2 + conv2_biases)
-```
-
-As you can easily imagine, models quickly get much more complicated than
-this one, and even here we already have 4 different variables: `conv1_weights`,
-`conv1_biases`, `conv2_weights`, and `conv2_biases`.
-
-The problem arises when you want to reuse this model. Assume you want to
-apply your image filter to 2 different images, `image1` and `image2`.
-You want both images processed by the same filter with the same parameters.
-You can call `my_image_filter()` twice, but this will create two sets
-of variables, 4 variables in each one, for a total of 8 variables.
-
-```python
-# First call creates one set of 4 variables.
-result1 = my_image_filter(image1)
-# Another set of 4 variables is created in the second call.
-result2 = my_image_filter(image2)
-```
-
-A common way to share variables is to create them in a separate piece of code
-and pass them to functions that use them.   For example by using a dictionary:
-
-```python
-variables_dict = {
-    "conv1_weights": tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv1_weights")
-    "conv1_biases": tf.Variable(tf.zeros([32]), name="conv1_biases")
-    ... etc. ...
-}
-
-def my_image_filter(input_images, variables_dict):
-    conv1 = tf.nn.conv2d(input_images, variables_dict["conv1_weights"],
-        strides=[1, 1, 1, 1], padding='SAME')
-    relu1 = tf.nn.relu(conv1 + variables_dict["conv1_biases"])
-
-    conv2 = tf.nn.conv2d(relu1, variables_dict["conv2_weights"],
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv2 + variables_dict["conv2_biases"])
-
-# Both calls to my_image_filter() now use the same variables
-result1 = my_image_filter(image1, variables_dict)
-result2 = my_image_filter(image2, variables_dict)
-```
-
-While convenient, creating variables like above,
-outside of the code, breaks encapsulation:
-
-*  The code that builds the graph must document the names, types,
-   and shapes of variables to create.
-*  When the code changes, the callers may have to create more, or less,
-   or different variables.
-
-One way to address the problem is to use classes to create a model,
-where the classes take care of managing the variables they need.
-For a lighter solution, not involving classes, TensorFlow provides
-a *Variable Scope* mechanism that allows to easily share named variables
-while constructing a graph.
-
-## Variable Scope Example
-
-Variable Scope mechanism in TensorFlow consists of two main functions:
-
-* `tf.get_variable(<name>, <shape>, <initializer>)`:
-  Creates or returns a variable with a given name.
-* `tf.variable_scope(<scope_name>)`:
-  Manages namespaces for names passed to `tf.get_variable()`.
-
-The function `tf.get_variable()` is used to get or create a variable instead
-of a direct call to `tf.Variable`. It uses an *initializer* instead of passing
-the value directly, as in `tf.Variable`. An initializer is a function that
-takes the shape and provides a tensor with that shape. Here are some
-initializers available in TensorFlow:
-
-* `tf.constant_initializer(value)` initializes everything to the provided value,
-* `tf.random_uniform_initializer(a, b)` initializes uniformly from [a, b],
-* `tf.random_normal_initializer(mean, stddev)` initializes from the normal
-  distribution with the given mean and standard deviation.
-
-To see how `tf.get_variable()` solves the problem discussed
-before, let's refactor the code that created one convolution into
-a separate function, named `conv_relu`:
-
-```python
-def conv_relu(input, kernel_shape, bias_shape):
-    # Create variable named "weights".
-    weights = tf.get_variable("weights", kernel_shape,
-        initializer=tf.random_normal_initializer())
-    # Create variable named "biases".
-    biases = tf.get_variable("biases", bias_shape,
-        initializer=tf.constant_initializer(0.0))
-    conv = tf.nn.conv2d(input, weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv + biases)
-```
-
-This function uses short names `"weights"` and `"biases"`.
-We'd like to use it for both `conv1` and `conv2`, but
-the variables need to have different names.
-This is where `tf.variable_scope()` comes into play:
-it pushes a namespace for variables.
-
-```python
-def my_image_filter(input_images):
-    with tf.variable_scope("conv1"):
-        # Variables created here will be named "conv1/weights", "conv1/biases".
-        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
-    with tf.variable_scope("conv2"):
-        # Variables created here will be named "conv2/weights", "conv2/biases".
-        return conv_relu(relu1, [5, 5, 32, 32], [32])
-```
-
-Now, let's see what happens when we call `my_image_filter()` twice.
-
-```
-result1 = my_image_filter(image1)
-result2 = my_image_filter(image2)
-# Raises ValueError(... conv1/weights already exists ...)
-```
-
-As you can see, `tf.get_variable()` checks that already existing variables
-are not shared by accident. If you want to share them, you need to specify
-it by setting `reuse_variables()` as follows.
-
-```
-with tf.variable_scope("image_filters") as scope:
-    result1 = my_image_filter(image1)
-    scope.reuse_variables()
-    result2 = my_image_filter(image2)
-```
-
-This is a good way to share variables, lightweight and safe.
-
-## How Does Variable Scope Work?
-
-### Understanding `tf.get_variable()`
-
-To understand variable scope it is necessary to first
-fully understand how `tf.get_variable()` works.
-Here is how `tf.get_variable` is usually called.
-
-```python
-v = tf.get_variable(name, shape, dtype, initializer)
-```
-
-This call does one of two things depending on the scope it is called in.
-Here are the two options.
-
-* Case 1: the scope is set for creating new variables, as evidenced by
-`tf.get_variable_scope().reuse == False`.
-
-In this case, `v` will be a newly created `tf.Variable` with the provided
-shape and data type. The full name of the created variable will be set to
-the current variable scope name + the provided `name` and a check will be
-performed to ensure that no variable with this full name exists yet.
-If a variable with this full name already exists, the function will
-raise a `ValueError`. If a new variable is created, it will be
-initialized to the value `initializer(shape)`. For example:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-assert v.name == "foo/v:0"
-```
-
-* Case 2: the scope is set for reusing variables, as evidenced by
-`tf.get_variable_scope().reuse == True`.
-
-In this case, the call will search for an already existing variable with
-name equal to the current variable scope name + the provided `name`.
-If no such variable exists, a `ValueError` will be raised. If the variable
-is found, it will be returned. For example:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-with tf.variable_scope("foo", reuse=True):
-    v1 = tf.get_variable("v", [1])
-assert v1 is v
-```
-
-### Basics of `tf.variable_scope()`
-
-Knowing how `tf.get_variable()` works makes it easy to understand variable
-scope. The primary function of variable scope is to carry a name that will
-be used as prefix for variable names and a reuse-flag to distinguish the two
-cases described above. Nesting variable scopes appends their names in a way
-analogous to how directories work:
-
-```python
-with tf.variable_scope("foo"):
-    with tf.variable_scope("bar"):
-        v = tf.get_variable("v", [1])
-assert v.name == "foo/bar/v:0"
-```
-
-The current variable scope can be retrieved using `tf.get_variable_scope()`
-and the `reuse` flag of the current variable scope can be set to `True` by
-calling `tf.get_variable_scope().reuse_variables()`:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-    tf.get_variable_scope().reuse_variables()
-    v1 = tf.get_variable("v", [1])
-assert v1 is v
-```
-
-Note that you *cannot* set the `reuse` flag to `False`. The reason behind
-this is to allow to compose functions that create models. Imagine you write
-a function `my_image_filter(inputs)` as before. Someone calling the function
-in a variable scope with `reuse=True` would expect all inner variables to be
-reused as well. Allowing to force `reuse=False` inside the function would break
-this contract and make it hard to share parameters in this way.
-
-Even though you cannot set `reuse` to `False` explicitly, you can enter
-a reusing variable scope and then exit it, going back to a non-reusing one.
-This can be done using a `reuse=True` parameter when opening a variable scope.
-Note also that, for the same reason as above, the `reuse` parameter is
-inherited. So when you open a reusing variable scope, all sub-scopes will
-be reusing too.
-
-```python
-with tf.variable_scope("root"):
-    # At start, the scope is not reusing.
-    assert tf.get_variable_scope().reuse == False
-    with tf.variable_scope("foo"):
-        # Opened a sub-scope, still not reusing.
-        assert tf.get_variable_scope().reuse == False
-    with tf.variable_scope("foo", reuse=True):
-        # Explicitly opened a reusing scope.
-        assert tf.get_variable_scope().reuse == True
-        with tf.variable_scope("bar"):
-            # Now sub-scope inherits the reuse flag.
-            assert tf.get_variable_scope().reuse == True
-    # Exited the reusing scope, back to a non-reusing one.
-    assert tf.get_variable_scope().reuse == False
-```
-
-### Capturing variable scope
-
-In all examples presented above, we shared parameters only because their
-names agreed, that is, because we opened a reusing variable scope with
-exactly the same string. In more complex cases, it might be useful to pass
-a VariableScope object rather than rely on getting the names right.
-To this end, variable scopes can be captured and used instead of names
-when opening a new variable scope.
-
-```python
-with tf.variable_scope("foo") as foo_scope:
-    v = tf.get_variable("v", [1])
-with tf.variable_scope(foo_scope):
-    w = tf.get_variable("w", [1])
-with tf.variable_scope(foo_scope, reuse=True):
-    v1 = tf.get_variable("v", [1])
-    w1 = tf.get_variable("w", [1])
-assert v1 is v
-assert w1 is w
-```
-
-When opening a variable scope using a previously existing scope
-we jump out of the current variable scope prefix to an entirely
-different one. This is fully independent of where we do it.
-
-```python
-with tf.variable_scope("foo") as foo_scope:
-    assert foo_scope.name == "foo"
-with tf.variable_scope("bar"):
-    with tf.variable_scope("baz") as other_scope:
-        assert other_scope.name == "bar/baz"
-        with tf.variable_scope(foo_scope) as foo_scope2:
-            assert foo_scope2.name == "foo"  # Not changed.
-```
-
-### Initializers in variable scope
-
-Using `tf.get_variable()` allows to write functions that create or reuse
-variables and can be transparently called from outside. But what if we wanted
-to change the initializer of the created variables? Do we need to pass an extra
-argument to every function that creates variables? What about the most common
-case, when we want to set the default initializer for all variables in one
-place, on top of all functions? To help with these cases, variable scope
-can carry a default initializer. It is inherited by sub-scopes and passed
-to each `tf.get_variable()` call. But it will be overridden if another
-initializer is specified explicitly.
-
-```python
-with tf.variable_scope("foo", initializer=tf.constant_initializer(0.4)):
-    v = tf.get_variable("v", [1])
-    assert v.eval() == 0.4  # Default initializer as set above.
-    w = tf.get_variable("w", [1], initializer=tf.constant_initializer(0.3)):
-    assert w.eval() == 0.3  # Specific initializer overrides the default.
-    with tf.variable_scope("bar"):
-        v = tf.get_variable("v", [1])
-        assert v.eval() == 0.4  # Inherited default initializer.
-    with tf.variable_scope("baz", initializer=tf.constant_initializer(0.2)):
-        v = tf.get_variable("v", [1])
-        assert v.eval() == 0.2  # Changed default initializer.
-```
-
-### Names of ops in `tf.variable_scope()`
-
-We discussed how `tf.variable_scope` governs the names of variables.
-But how does it influence the names of other ops in the scope?
-It is natural that ops created inside a variable scope should also
-share that name. For this reason, when we do `with tf.variable_scope("name")`,
-this implicitly opens a `tf.name_scope("name")`. For example:
-
-```python
-with tf.variable_scope("foo"):
-    x = 1.0 + tf.get_variable("v", [1])
-assert x.op.name == "foo/add"
-```
-
-Name scopes can be opened in addition to a variable scope, and then
-they will only affect the names of the ops, but not of variables.
-
-```python
-with tf.variable_scope("foo"):
-    with tf.name_scope("bar"):
-        v = tf.get_variable("v", [1])
-        x = 1.0 + v
-assert v.name == "foo/v:0"
-assert x.op.name == "foo/bar/add"
-```
-
-When opening a variable scope using a captured object instead of a string,
-we do not alter the current name scope for ops.
-
-
-## Examples of Use
-
-Here are pointers to a few files that make use of variable scope. They can all
-be found in the [TensorFlow models repo](https://github.com/tensorflow/models).
-In particular, variable scope is heavily used for recurrent neural networks and
-sequence-to-sequence models.
-
-File | What's in it?
---- | ---
-`tutorials/image/cifar10/cifar10.py` | Model for detecting objects in images.
-`tutorials/rnn/rnn_cell.py` | Cell functions for recurrent neural networks.
-`tutorials/rnn/seq2seq.py` | Functions for building sequence-to-sequence models.
diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index e8d1e519f0..a7b7db626a 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -1,190 +1,283 @@
-# Variables: Creation, Initialization, Saving, and Loading
+# Variables: persistent state in TensorFlow
 
-When you train a model, you use @{$python/state_ops$variables}
-to hold and update parameters.  Variables are in-memory buffers containing
-tensors.  They must be explicitly initialized and can be saved to disk during
-and after training. You can later restore saved values to exercise or analyze
-the model.
+A TensorFlow **variable** is the best way to represent shared, persistent state
+manipulated by your program.
 
-This document references the following TensorFlow classes.  Follow the links to
-their reference manual for a complete description of their API:
+Variables are manipulated via the `tf.Variable` class. A `tf.Variable`
+represents a tensor whose value can be changed by running ops on it. Unlike
+`tf.Tensor` objects, a `tf.Variable` exists outside the context of a single
+`session.run` call.
 
-*  The @{tf.Variable} class.
-*  The @{tf.train.Saver} class.
+Internally, a `tf.Variable` stores a persistent tensor. Specific ops allow you
+to read and modify the values of this tensor. These modifications are visible
+across multiple `tf.Session`s, so multiple workers can see the same values for a
+`tf.Variable`.
 
+## Creating a Variable
 
-## Creation
+The best way to create a variable is to call the `tf.get_variable`
+function. This function requires you to specify the Variable's name. This name
+will be used by other replicas to access the same variable, as well as to name
+this variable's value when checkpointing and exporting models. `tf.get_variable`
+also allows you to reuse a previously created variable of the same name, making it
+easy to define models which reuse layers.
 
-When you create a @{$python/state_ops$Variable} you pass a
-`Tensor` as its initial value to the `Variable()` constructor.  TensorFlow
-provides a collection of ops that produce tensors often used for initialization
-from @{$python/constant_op$constants or random values}.
+To create a variable with `tf.get_variable`, simply provide the name and shape
 
-Note that all these ops require you to specify the shape of the tensors.  That
-shape automatically becomes the shape of the variable.  Variables generally
-have a fixed shape, but TensorFlow provides advanced mechanisms to reshape
-variables.
+``` python
+my_variable = tf.get_variable("my_variable", [1, 2, 3])
+```
 
-```python
-# Create two variables.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-biases = tf.Variable(tf.zeros([200]), name="biases")
+This creates a variable named "my_variable" which is a three-dimensional tensor
+with shape `[1, 2, 3]`. This variable will, by default, have the `dtype`
+`tf.float32` and its initial value will be randomized via
+`tf.glorot_uniform_initializer`.
+
+You may optionally specify the `dtype` and initializer to `tf.get_variable`. For
+example:
+
+``` python
+my_int_variable = tf.get_variable("my_int_variable", [1, 2, 3], dtype=tf.int32, 
+  initializer=tf.zeros_initializer)
 ```
 
-Calling `tf.Variable()` adds several ops to the graph:
+TensorFlow provides many convenient initializers. Alternatively, you may
+initialize a `tf.Variable` to have the value of a `tf.Tensor`. For example:
 
-*  A `variable` op that holds the variable value.
-*  An initializer op that sets the variable to its initial value.  This is
-   actually a `tf.assign` op.
-*  The ops for the initial value, such as the `zeros` op for the `biases`
-   variable in the example are also added to the graph.
+``` python
+other_variable = tf.get_variable("other_variable", dtype=tf.int32, 
+  initializer=tf.constant([23, 42]))
+```
 
-The value returned by `tf.Variable()` value is an instance of the Python class
-`tf.Variable`.
+Note that when the initializer is a `tf.Tensor` you should not specify the
+variable's shape, as the shape of the initializer tensor will be used.
+
+### Variable collections
+
+Because disconnected parts of a TensorFlow program might want to create
+variables, it is sometimes useful to have a single way to access all of
+them. For this reason TensorFlow provides **collections**, which are named lists
+of tensors or other objects, such as `tf.Variable` instances.
+
+By default every `tf.Variable` gets placed in the following two collections:
+ * `tf.GraphKeys.GLOBAL_VARIABLES` --- variables that can be shared across
+multiple devices,
+ * `tf.GraphKeys.TRAINABLE_VARIABLES`--- variables for which TensorFlow will
+   calculate gradients.
+ 
+If you don't want a variable to be trainable, add it to the
+`tf.GraphKeys.LOCAL_VARIABLES` collection instead. For example, the following
+snippet demonstrates how to add a variable named `my_local` to this collection:
+
+``` python
+my_local = tf.get_variable("my_local", shape=(), 
+collections=[tf.GraphKeys.LOCAL_VARIABLES])
+```
 
-### Device placement
+Alternatively, you can specify `trainable=False` as an argument to
+`tf.get_variable`:
 
-A variable can be pinned to a particular device when it is created, using a
-@{tf.device$`with tf.device(...):`} block:
+``` python
+my_non_trainable = tf.get_variable("my_non_trainable", 
+                                   shape=(), 
+                                   trainable=False)
+```
 
-```python
-# Pin a variable to CPU.
-with tf.device("/cpu:0"):
-  v = tf.Variable(...)
 
-# Pin a variable to GPU.
-with tf.device("/gpu:0"):
-  v = tf.Variable(...)
+You can also use your own collections. Any string is a valid collection name,
+and there is no need to explicitly create a collection. To add a variable (or
+any other object) to a collection after creating the variable, call
+`tf.add_to_collection`.  For example, the following code adds an existing
+variable named `my_local` to a collection named `my_collection_name`:
 
-# Pin a variable to a particular parameter server task.
-with tf.device("/job:ps/task:7"):
-  v = tf.Variable(...)
+``` python
+tf.add_to_collection("my_collection_name", my_local)
 ```
 
-**NOTE** Operations that mutate a variable, such as
-@{tf.Variable.assign} and the parameter
-update operations in a
-@{tf.train.Optimizer} *must* run on
-the same device as the variable. Incompatible device placement directives will
-be ignored when creating these operations.
+And to retrieve a list of all the variables (or other objects) you've placed in
+a collection you can use:
 
-Device placement is particularly important when running in a replicated
-setting. See
-@{tf.train.replica_device_setter}
-for details of a device function that can simplify the configuration for devices
-for a replicated model.
+``` python
+tf.get_collection("my_collection_name")
+```
 
-## Initialization
+### Device placement
 
-Variable initializers must be run explicitly before other ops in your model can
-be run.  The easiest way to do that is to add an op that runs all the variable
-initializers, and run that op before using the model.
+Just like any other TensorFlow operation, you can place variables on particular
+devices. For example, the following snippet creates a variable named `v` and
+places it on the second GPU device:
 
-You can alternatively restore variable values from a checkpoint file, see
-below.
+``` python
+with tf.device("/gpu:1"):
+  v = tf.get_variable("v", [1])
+```
 
-Use `tf.global_variables_initializer()` to add an op to run variable initializers.
-Only run that op after you have fully constructed your model and launched it in
-a session.
+It is particularly important for variables to be in the correct device in
+distributed settings. Accidentally putting variables on workers instead of
+parameter servers, for example, can severely slow down training or, in the worst
+case, let each worker blithely forge ahead with its own independent copy of each
+variable. For this reason we provide @{tf.train.replica_device_setter}, which
+can automatically place variables in parameter servers. For example:
+
+``` python
+cluster_spec = {
+    "ps": ["ps0:2222", "ps1:2222"],
+    "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
+with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
+  v = tf.get_variable("v", shape=[20, 20])  # this variable is placed 
+                                            # in the parameter server
+                                            # by the replica_device_setter
+```
 
-```python
-# Create two variables.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-biases = tf.Variable(tf.zeros([200]), name="biases")
-...
-# Add an op to initialize the variables.
-init_op = tf.global_variables_initializer()
+## Initializing variables
 
-# Later, when launching the model
-with tf.Session() as sess:
-  # Run the init operation.
-  sess.run(init_op)
-  ...
-  # Use the model
-  ...
+Before you can use a variable, it must be initialized. If you are programming in
+the low-level TensorFlow API (that is, you are explicitly creating your own
+graphs and sessions), you must explicitly initialize the variables.  Most
+high-level frameworks such as `tf.contrib.slim`, `tf.estimator.Estimator` and
+`Keras` automatically initialize variables for you before training a model.
+
+Explicit initialization is otherwise useful because it allows you not to rerun
+potentially expensive initializers when reloading a model from a checkpoint as
+well as allowing determinism when randomly-initialized variables are shared in a
+distributed setting. 
+
+To initialize all trainable variables in one go, before training starts, call
+`tf.global_variables_initializer()`. This function returns a single operation
+responsible for initializing all variables in the
+`tf.GraphKeys.GLOBAL_VARIABLES` collection. Running this operation initializes
+all variables. For example:
+
+``` python
+session.run(tf.global_variables_initializer())
+# Now all variables are initialized.
 ```
 
-### Initialization from another Variable
+If you do need to initialize variables yourself, you can run the variable's
+initializer operation. For example:
 
-You sometimes need to initialize a variable from the initial value of another
-variable.  As the op added by `tf.global_variables_initializer()` initializes all
-variables in parallel you have to be careful when this is needed.
+``` python
+session.run(my_variable.initializer)
+```
 
-To initialize a new variable from the value of another variable use the other
-variable's `initialized_value()` property.  You can use the initialized value
-directly as the initial value for the new variable, or you can use it as any
-other tensor to compute a value for the new variable.
 
+You can also ask which variables have still not been initialized. For example,
+the following code prints the names of all variables which have not yet been
+initialized:
 
-```python
-# Create a variable with a random value.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-# Create another variable with the same value as 'weights'.
-w2 = tf.Variable(weights.initialized_value(), name="w2")
-# Create another variable with twice the value of 'weights'
-w_twice = tf.Variable(weights.initialized_value() * 2.0, name="w_twice")
+``` python
+print(session.run(tf.report_uninitialized_variables()))
+```
+
+
+Note that by default `tf.global_variables_initializer` does not specify the
+order in which variables are initialized. Therefore, if the initial value of a
+variable depends on another variable's value, it's likely that you'll get an
+error. Any time you use the value of a variable in a context in which not all
+variables are initialized (say, if you use a variable's value while initializing
+another variable), it is best to use `variable.initialized_value()` instead of
+`variable`:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+w = tf.get_variable("w", initializer=v.initialized_value() + 1)
+```
+
+## Using variables
+
+To use the value of a `tf.Variable` in a TensorFlow graph, simply treat it like
+a normal `tf.Tensor`:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+w = v + 1  # w is a tf.Tensor which is computed based on the value of v.
+           # Any time a variable is used in an expression it gets automatically
+           # converted to a tf.Tensor representing its value.
 ```
 
-### Custom Initialization
+To assign a value to a variable, use the methods `assign`, `assign_add`, and
+friends in the `tf.Variable` class. For example, here is how you can call these
+methods:
 
-The convenience function `tf.global_variables_initializer()` adds an op to
-initialize *all variables* in the model.  You can also pass an explicit list of
-variables to initialize to `tf.variables_initializer`.  See the
-@{$python/state_ops$Variables Documentation} for more options,
-including checking if variables are initialized.
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+assignment = v.assign_add(1)
+tf.global_variables_initializer().run()
+assignment.run()
+```
+
+Most TensorFlow optimizers have specialized ops that efficiently update the
+values of variables according to some gradient descent-like algorithm. See
+@{tf.train.Optimizer} for an explanation of how to use optimizers.
+
+Because variables are mutable it's sometimes useful to know what version of a
+variable's value is being used at any point in time. To force a re-read of the
+value of a variable after something has happened, you can use
+`tf.Variable.read_value`. For example:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+assignment = v.assign_add(1)
+with tf.control_dependencies([assignment]):
+  w = v.read_value()  # w is guaranteed to reflect v's value after the
+                      # assign_add operation.
+```
 
 ## Saving and Restoring
 
 The easiest way to save and restore a model is to use a `tf.train.Saver` object.
 The constructor adds `save` and `restore` ops to the graph for all, or a
-specified list, of the variables in the graph.  The saver object provides
+specified list, of the variables in the graph.  The `Saver` object provides
 methods to run these ops, specifying paths for the checkpoint files to write to
 or read from.
 
-Note that to restore a model checkpoint without a graph one must first import
-the graph from the meta graph file (typical extension is `.meta`). This is
-done with @{tf.train.import_meta_graph}, which in turn returns a `Saver` from
-which one can than perform a `restore`.
+To restore a model checkpoint without a graph, you must first import the graph
+from the `MetaGraph` file (typical extension is `.meta`). Do this by calling
+@{tf.train.import_meta_graph}, which in turn returns a `Saver` from which one
+can than perform a `restore`.
 
 ### Checkpoint Files
 
-Variables are saved in binary files that, roughly, contain a map from variable
+TensorFlow saves variables in binary files that, roughly speaking, map variable
 names to tensor values.
 
 When you create a `Saver` object, you can optionally choose names for the
-variables in the checkpoint files.  By default, it uses the value of the
+variables in the checkpoint files.  By default, `Saver` uses the value of the
 @{tf.Variable.name} property for
 each variable.
 
-To understand what variables are in a checkpoint, you can use the
-[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py)
-library, and in particular, the `print_tensors_in_checkpoint_file` function.
+To inspect the variables in a checkpoint, you can use
+the
+[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library,
+particularly the `print_tensors_in_checkpoint_file` function.
 
 ### Saving Variables
 
-Create a `Saver` with `tf.train.Saver()` to manage all variables in
-the model.
+Create a `Saver` with `tf.train.Saver()` to manage all variables in the
+model. For example, the following snippet demonstrates how to call the
+`tf.train.Saver.save` method to save variables to a checkpoint file:
 
 ```python
 # Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
+v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
+v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
+
+inc_v1 = v1.assign(v1+1)
+dec_v2 = v2.assign(v2-1)
+
 # Add an op to initialize the variables.
 init_op = tf.global_variables_initializer()
 
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
-# Later, launch the model, initialize the variables, do some work, save the
+# Later, launch the model, initialize the variables, do some work, and save the
 # variables to disk.
 with tf.Session() as sess:
   sess.run(init_op)
   # Do some work with the model.
-  ..
+  inc_v1.op.run()
+  dec_v2.op.run()
   # Save the variables to disk.
   save_path = saver.save(sess, "/tmp/model.ckpt")
   print("Model saved in file: %s" % save_path)
@@ -192,14 +285,19 @@ with tf.Session() as sess:
 
 ### Restoring Variables
 
-The same `Saver` object is used to restore variables.  Note that when you
-restore variables from a file you do not have to initialize them beforehand.
+The `tf.train.Saver` object not only saves variables to checkpoint files, it
+also restores variables.  Note that when you restore variables from a file you
+do not have to initialize them beforehand. For example, the following snippet
+demonstrates how to call the `tf.train.Saver.restore` method to restore
+variables from a checkpoint file:
 
 ```python
+tf.reset_default_graph()
+
 # Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
+v1 = tf.get_variable("v1", shape=[3])
+v2 = tf.get_variable("v2", shape=[5])
+
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
@@ -209,50 +307,156 @@ with tf.Session() as sess:
   # Restore variables from disk.
   saver.restore(sess, "/tmp/model.ckpt")
   print("Model restored.")
-  # Do some work with the model
-  ...
+  # Check the values of the variables
+  print("v1 : %s" % v1.eval())
+  print("v2 : %s" % v2.eval())
 ```
 
+
+
 ### Choosing which Variables to Save and Restore
 
-If you do not pass any argument to `tf.train.Saver()` the saver handles all
-variables in the graph.  Each one of them is saved under the name that was
-passed when the variable was created.
+If you do not pass any argument to `tf.train.Saver()`, the saver handles all
+variables in the graph.  Each variable is saved under the name that was passed
+when the variable was created.
 
 It is sometimes useful to explicitly specify names for variables in the
 checkpoint files.  For example, you may have trained a model with a variable
-named `"weights"` whose value you want to restore in a new variable named
+named `"weights"` whose value you want to restore into a variable named
 `"params"`.
 
 It is also sometimes useful to only save or restore a subset of the variables
-used by a model.  For example, you may have trained a neural net with 5 layers,
-and you now want to train a new model with 6 layers, restoring the parameters
-from the 5 layers of the previously trained model into the first 5 layers of
-the new model.
+used by a model.  For example, you may have trained a neural net with five
+layers, and you now want to train a new model with six layers that reuses the
+existing weights of the five trained layers. You can use the saver to restore
+the weights of just the first five layers.
+
+You can easily specify the names and variables to save or load by passing to the
+`tf.train.Saver()` constructor either a list of variables (which will be stored 
+under their own names), or a Python dictionary in which keys are the names to 
+use and values are the variables to manage. 
+
+Continuing from the save/restore examples, above:
+
+```python
+tf.reset_default_graph()
+# Create some variables.
+v1 = tf.get_variable("v1", [3], initializer = tf.zeros_initializer)
+v2 = tf.get_variable("v2", [5], initializer = tf.zeros_initializer)
 
-You can easily specify the names and variables to save by passing to the
-`tf.train.Saver()` constructor a Python dictionary: keys are the
-names to use, values are the variables to manage.
+# Add ops to save and restore only `v2` using the name "v2"
+saver = tf.train.Saver({"v2": v2})
+
+# Use the saver object normally after that.
+with tf.Session() as sess:
+  # Initialize v1 since the saver will not.
+  v1.initializer.run()
+  saver.restore(sess, "/tmp/model.ckpt")
+  
+  print("v1 : %s" % v1.eval())
+  print("v2 : %s" % v2.eval())
+
+```
 
 Notes:
 
-*  You can create as many saver objects as you want if you need to save and
+*  You can create as many `Saver` objects as you want if you need to save and
    restore different subsets of the model variables.  The same variable can be
-   listed in multiple saver objects, its value is only changed when the saver
-   `restore()` method is run.
+   listed in multiple saver objects, its value is only changed when the
+   `Saver.restore()` method is run.
+
+*  If you only restore a subset of the model variables at the start of a
+   session, you have to run an initialize op for the other variables.  See
+   @{tf.variables_initializer} for more information.
+
+
+## Sharing variables
+
+TensorFlow supports two ways of sharing variables:
 
-*  If you only restore a subset of the model variables at the start
-   of a session, you have to run an initialize op for the other variables.  See
-   @{tf.variables_initializer}
-   for more information.
+ * Explicitly passing `tf.Variable` objects around.
+ * Implicitly wrapping `tf.Variable` objects within `tf.variable_scope` objects.
+
+While code which explicitly passes variables around is very clear, it is
+sometimes convenient to write TensorFlow functions that implicitly use
+variables in their implementations. Most of the functional layers from
+`tf.layer` use this approach, as well as all `tf.metrics`, and a few other
+library utilities.
+
+Variable scopes allow you to control variable reuse when calling functions which
+implicitly create and use variables. They also allow you to name your variables
+in a hierarchical and understandable way.
+
+For example, let's say we write a function to create a convolutional / relu
+layer:
 
 ```python
-# Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
-# Add ops to save and restore only 'v2' using the name "my_v2"
-saver = tf.train.Saver({"my_v2": v2})
-# Use the saver object normally after that.
-...
+def conv_relu(input, kernel_shape, bias_shape):
+    # Create variable named "weights".
+    weights = tf.get_variable("weights", kernel_shape,
+        initializer=tf.random_normal_initializer())
+    # Create variable named "biases".
+    biases = tf.get_variable("biases", bias_shape,
+        initializer=tf.constant_initializer(0.0))
+    conv = tf.nn.conv2d(input, weights,
+        strides=[1, 1, 1, 1], padding='SAME')
+    return tf.nn.relu(conv + biases)
 ```
+
+This function uses short names `weights` and `biases`, which is good for
+clarity. In a real model, however, we want many such convolutional layers, and
+calling this function repeatedly would not work:
+
+``` python
+input1 = tf.random_normal([1,10,10,32])
+input2 = tf.random_normal([1,20,20,32])
+x = conv_relu(input1, kernel_shape=[5, 5, 32, 32], bias_shape=[32])
+x = conv_relu(x, kernel_shape=[5, 5, 32, 32], bias_shape = [32])  # This fails.
+```
+
+Since the desired behavior is unclear (create new variables or reuse the
+existing ones?) TensorFlow will fail. Calling `conv_relu` in different scopes,
+however, clarifies that we want to create new variables:
+
+```python
+def my_image_filter(input_images):
+    with tf.variable_scope("conv1"):
+        # Variables created here will be named "conv1/weights", "conv1/biases".
+        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
+    with tf.variable_scope("conv2"):
+        # Variables created here will be named "conv2/weights", "conv2/biases".
+        return conv_relu(relu1, [5, 5, 32, 32], [32])
+```
+
+If you do want the variables to be shared, you have two options. First, you can
+create a scope with the same name using `reuse=True`:
+
+``` python
+with tf.variable_scope("model"):
+  output1 = my_image_filter(input1)
+with tf.variable_scope("model", reuse=True):
+  output2 = my_image_filter(input2)
+
+```
+
+You can also call `scope.reuse_variables()` to trigger a reuse:
+
+``` python
+with tf.variable_scope("model") as scope:
+  output1 = my_image_filter(input1)
+  scope.reuse_variables()
+  output2 = my_image_filter(input2)
+
+```
+
+Since depending on exact string names of scopes can feel dangerous, it's also
+possible to initialize a variable scope based on another one:
+
+``` python
+with tf.variable_scope("model") as scope:
+  output1 = my_image_filter(input1)
+with tf.variable_scope(scope, reuse=True):
+  output2 = my_image_filter(input2)
+
+```
+
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index f60c8fd770..adf66b904e 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -417,7 +417,7 @@ scope indicating that they should be run on the first GPU.
 All variables are pinned to the CPU and accessed via
 @{tf.get_variable}
 in order to share them in a multi-GPU version.
-See how-to on @{$variable_scope$Sharing Variables}.
+See how-to on @{$variables$Sharing Variables}.
 
 ### Launching and Training the Model on Multiple GPU cards
 
-- 
GitLab


From d3488cb82beba69b55de94eb36039361a85fe9e8 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Mon, 26 Jun 2017 14:40:31 -0700
Subject: [PATCH 0462/1375] Make xla::Literal moveable.

PiperOrigin-RevId: 160197273
---
 tensorflow/compiler/xla/literal_util.h | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index ce4a00fa55..8266511614 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -68,11 +68,13 @@ class BoolVector {
   }
 
   BoolVector(const BoolVector& other) { CopyFrom(other); }
+  BoolVector(BoolVector&&) = default;
 
   BoolVector& operator=(const BoolVector& other) {
     CopyFrom(other);
     return *this;
   }
+  BoolVector& operator=(BoolVector&&) = default;
 
   void push_back(const bool& value) {
     resize(size_ + 1);
@@ -147,10 +149,12 @@ class Literal {
   Literal() {}
 
   Literal(const Literal& other) = default;
+  Literal(Literal&&) = default;
 
   explicit Literal(const LiteralProto& other) { CopyFromProto(other); }
 
   Literal& operator=(const Literal& other) = default;
+  Literal& operator=(Literal&&) = default;
 
   LiteralProto ToProto() const;
 
-- 
GitLab


From 23e0d5044d6ddcdd14a673d7300a96e735c6663a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 14:49:06 -0700
Subject: [PATCH 0463/1375] Automated g4 rollback of changelist 159897279

PiperOrigin-RevId: 160198598
---
 .../pywrap_tensorflow_print_model_analysis.i  |   1 +
 .../python/tools/tfprof/model_analyzer.py     | 121 +++------------
 .../tools/tfprof/model_analyzer_test.py       |  30 +---
 .../python/tools/tfprof/profiler_test.py      |  16 +-
 tensorflow/tools/tfprof/BUILD                 |   1 -
 tensorflow/tools/tfprof/README.md             |   6 +-
 tensorflow/tools/tfprof/g3doc/advise.md       |  80 +---------
 .../tools/tfprof/internal/advisor/BUILD       |   9 --
 .../advisor/accelerator_utilization_checker.h |  24 +--
 .../tools/tfprof/internal/advisor/checker.h   |  24 ++-
 .../advisor/expensive_operation_checker.h     | 138 ------------------
 .../advisor/internal_checker_runner.h         |   5 +-
 .../advisor/internal_checker_runner_dummy.cc  |   6 +-
 .../internal/advisor/operation_checker.h      |  19 +--
 .../tfprof/internal/advisor/tfprof_advisor.h  |  47 ++----
 .../internal/advisor/tfprof_advisor_test.cc   |  46 ++----
 .../tfprof/internal/print_model_analysis.cc   |  19 +--
 .../tfprof/internal/print_model_analysis.h    |   2 +
 .../tools/tfprof/internal/tfprof_graph.h      |   4 +-
 tensorflow/tools/tfprof/internal/tfprof_op.cc |  11 +-
 tensorflow/tools/tfprof/internal/tfprof_op.h  |   6 +-
 .../tools/tfprof/internal/tfprof_options.h    |   4 +-
 .../tools/tfprof/internal/tfprof_show.cc      |  14 +-
 .../tools/tfprof/internal/tfprof_show.h       |  13 +-
 .../tfprof/internal/tfprof_show_multi.cc      |  19 +--
 .../tools/tfprof/internal/tfprof_show_multi.h |  16 +-
 .../tools/tfprof/internal/tfprof_show_test.cc |   1 -
 .../tools/tfprof/internal/tfprof_stats.cc     |  77 ++++------
 .../tools/tfprof/internal/tfprof_stats.h      |  22 +--
 .../tfprof/internal/tfprof_stats_test.cc      |   1 -
 .../tfprof/internal/tfprof_tensor_test.cc     |   1 -
 .../tfprof/internal/tfprof_timeline_test.cc   |   1 -
 tensorflow/tools/tfprof/tfprof_main.cc        |  25 +---
 tensorflow/tools/tfprof/tfprof_options.proto  |   8 -
 tensorflow/tools/tfprof/tfprof_output.proto   |   8 -
 35 files changed, 181 insertions(+), 644 deletions(-)
 delete mode 100644 tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
index 582c36e339..40f29ae8a2 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
@@ -43,6 +43,7 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
+%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index c781d2af4e..419beac0b9 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -20,8 +20,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import six
-
 from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
 from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
 from tensorflow.python.framework import errors
@@ -110,77 +108,49 @@ PRINT_ALL_TIMING_MEMORY = {
     'dump_to_file': ''
 }
 
-# The following options are for 'advise' tfprof_cmd.
-# Show all advice.
-ALL_ADVICE = {
-    'ExpensiveOperationChecker': {},
-    'AcceleratorUtilizationChecker': {},
-    'JobChecker': {},  # Only available internally.
-    'OperationChecker': {},
-}
-
 # pylint: enable=bad-whitespace
 # pylint: enable=bad-continuation
 
 
-def _build_options(options):
+def _build_options(tfprof_options):
   """Build tfprof.OptionsProto.
 
   Args:
-    options: A dictionary of options.
+    tfprof_options: A dictionary of options.
   Returns:
     tfprof.OptionsProto.
   """
   opts = tfprof_options_pb2.OptionsProto()
-  opts.max_depth = options.get('max_depth', 10)
-  opts.min_bytes = options.get('min_bytes', 0)
-  opts.min_micros = options.get('min_micros', 0)
-  opts.min_params = options.get('min_params', 0)
-  opts.min_float_ops = options.get('min_float_ops', 0)
-  opts.min_occurrence = options.get('min_occurrence', 0)
+  opts.max_depth = tfprof_options.get('max_depth', 10)
+  opts.min_bytes = tfprof_options.get('min_bytes', 0)
+  opts.min_micros = tfprof_options.get('min_micros', 0)
+  opts.min_params = tfprof_options.get('min_params', 0)
+  opts.min_float_ops = tfprof_options.get('min_float_ops', 0)
+  opts.min_occurrence = tfprof_options.get('min_occurrence', 0)
 
-  opts.step = options.get('step', -1)
+  opts.step = tfprof_options.get('step', -1)
 
-  opts.order_by = options.get('order_by', 'name')
+  opts.order_by = tfprof_options.get('order_by', 'name')
 
-  for p in options.get('account_type_regexes', []):
+  for p in tfprof_options.get('account_type_regexes', []):
     opts.account_type_regexes.append(p)
-  for p in options.get('start_name_regexes', []):
+  for p in tfprof_options.get('start_name_regexes', []):
     opts.start_name_regexes.append(p)
-  for p in options.get('trim_name_regexes', []):
+  for p in tfprof_options.get('trim_name_regexes', []):
     opts.trim_name_regexes.append(p)
-  for p in options.get('show_name_regexes', []):
+  for p in tfprof_options.get('show_name_regexes', []):
     opts.show_name_regexes.append(p)
-  for p in options.get('hide_name_regexes', []):
+  for p in tfprof_options.get('hide_name_regexes', []):
     opts.hide_name_regexes.append(p)
-  opts.account_displayed_op_only = options.get('account_displayed_op_only',
-                                               False)
+  opts.account_displayed_op_only = tfprof_options.get(
+      'account_displayed_op_only', False)
 
-  for p in options.get('select', []):
+  for p in tfprof_options.get('select', []):
     opts.select.append(p)
 
-  opts.output = options.get('output', 'stdout')
-  opts.dump_to_file = options.get('dump_to_file', '')
-
-  return opts
-
-
-def _build_advisor_options(options):
-  """Build tfprof.AdvisorOptionsProto.
+  opts.output = tfprof_options.get('output', 'stdout')
+  opts.dump_to_file = tfprof_options.get('dump_to_file', '')
 
-  Args:
-    options: A dictionary of options. See ALL_ADVICE example.
-  Returns:
-    tfprof.AdvisorOptionsProto.
-  """
-  opts = tfprof_options_pb2.AdvisorOptionsProto()
-  if options is None:
-    return opts
-  for checker, checker_opts in six.iteritems(options):
-    checker_ops_pb = tfprof_options_pb2.AdvisorOptionsProto.CheckerOption()
-    for k, v in six.iteritems(checker_opts):
-      checker_ops_pb[k] = v
-    opts.checkers[checker].MergeFrom(checker_ops_pb)
   return opts
 
 
@@ -220,7 +190,7 @@ class Profiler(object):
       else:
         _ = sess.run(...)
     # Auto detect problems and generate advice.
-    profiler.advise(model_analyzer.ALL_ADVICE)
+    profiler.advise()
   """
 
   def __init__(self, graph, op_log=None):
@@ -318,19 +288,9 @@ class Profiler(object):
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
 
-  def advise(self, options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
-    """Automatically detect problems and generate reports.
-
-    Args:
-      options: A dict of options.
-    Returns:
-      A Advise proto that conains the reports from all checkers.
-    """
-    advise_pb = tfprof_output_pb2.AdviceProto()
-    opts = _build_advisor_options(options)
-    advise_pb.ParseFromString(
-        print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString()))
-    return advise_pb
+  def advise(self):
+    """Automatically detect problems and generate reports."""
+    print_mdl.Advise()
 
 
 def print_model_analysis(graph,
@@ -394,36 +354,3 @@ def print_model_analysis(graph,
         None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
 
   return tfprof_node
-
-
-def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
-  """Auto profile and advise.
-
-    Builds profiles and automatically check anormalies of various
-    aspects. See go/tfprof or README for examples and tutorials.
-
-  Args:
-    graph: tf.Graph.
-    run_meta: tensorflow::RunMetadata proto. Allows auto-profile
-              time and memroy.
-    tfprof_options: see ALL_ADVICE example above.
-  Returns:
-    Returns AdviceProto proto
-  """
-  # pylint: disable=protected-access
-  op_log = tfprof_logger._merge_default_with_oplog(
-      graph, None, run_meta, add_trace=True)
-  # pylint: enable=protected-access
-
-  run_meta_str = run_meta.SerializeToString() if run_meta else b''
-
-  opts = _build_advisor_options(tfprof_options)
-  ret = tfprof_output_pb2.AdviceProto()
-  ret.ParseFromString(
-      print_mdl.PrintModelAnalysis(
-          graph.as_graph_def(add_shapes=True).SerializeToString(),
-          run_meta_str,
-          op_log.SerializeToString(),
-          'advise'.encode('utf-8'),
-          opts.SerializeToString()))
-  return ret
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index fea27a82a5..9db752c577 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -126,7 +126,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['account_displayed_op_only'] = False
     opts['select'] = ['params', 'float_ops']
 
-    with session.Session() as sess:
+    with session.Session() as sess, ops.device('/cpu:0'):
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -176,7 +176,6 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['select'] = [
         'bytes', 'params', 'float_ops', 'device'
     ]
-    opts['output'] = 'none'
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -277,33 +276,6 @@ class PrintModelAnalysisTest(test.TestCase):
       self.assertEqual(total_children, 15)
       self.assertGreater(input_shapes, 0)
 
-  def testAdvisor(self):
-    ops.reset_default_graph()
-
-    with session.Session() as sess:
-      x = lib.BuildFullModel()
-
-      sess.run(variables.global_variables_initializer())
-      run_meta = config_pb2.RunMetadata()
-      _ = sess.run(
-          x,
-          options=config_pb2.RunOptions(
-              trace_level=config_pb2.RunOptions.FULL_TRACE),
-          run_metadata=run_meta)
-
-      advice_pb = model_analyzer.advise(sess.graph, run_meta)
-      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
-      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
-      self.assertTrue('OperationChecker' in advice_pb.checkers)
-
-      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
-      if test.is_gpu_available():
-        self.assertGreater(len(checker.reports), 0)
-      else:
-        self.assertEqual(len(checker.reports), 0)
-      checker = advice_pb.checkers['ExpensiveOperationChecker']
-      self.assertGreater(len(checker.reports), 0)
-
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
index c7113b6a57..5daaafd7c8 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
@@ -129,7 +129,7 @@ class ProfilerTest(test.TestCase):
     opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
     opts['account_type_regexes'] = ['.*']
 
-    with session.Session() as sess:
+    with session.Session() as sess, ops.device('/cpu:0'):
       r1, r2, r3 = lib.BuildSplitableModel()
       sess.run(variables.global_variables_initializer())
 
@@ -179,18 +179,8 @@ class ProfilerTest(test.TestCase):
       self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None)
       self.assertGreater(lib.SearchTFProfNode(pb3, 'add').exec_micros, 0)
 
-      advice_pb = profiler.advise(model_analyzer.ALL_ADVICE)
-      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
-      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
-      self.assertTrue('OperationChecker' in advice_pb.checkers)
-
-      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
-      if test.is_gpu_available():
-        self.assertGreater(len(checker.reports), 0)
-      else:
-        self.assertEqual(len(checker.reports), 0)
-      checker = advice_pb.checkers['ExpensiveOperationChecker']
-      self.assertGreater(len(checker.reports), 0)
+      # TODO(xpan): Better test of advisor.
+      profiler.advise()
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/tools/tfprof/BUILD b/tensorflow/tools/tfprof/BUILD
index 541df78e47..57cccd8921 100644
--- a/tensorflow/tools/tfprof/BUILD
+++ b/tensorflow/tools/tfprof/BUILD
@@ -33,7 +33,6 @@ cc_binary(
         "//tensorflow/tools/tfprof/internal:tfprof_options",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
         "//tensorflow/tools/tfprof/internal:tfprof_utils",
-        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
         "@linenoise//:linenoise",
     ],
 )
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/tools/tfprof/README.md
index 816ad8c07e..5927990524 100644
--- a/tensorflow/tools/tfprof/README.md
+++ b/tensorflow/tools/tfprof/README.md
@@ -7,11 +7,7 @@
 * Profile model performance
   * execution time, memory consumption
   * Profile multiple steps.
-* Auto profile and advise.
-  * accelerator utilization check
-  * expensive operation check
-  * operation configuration check
-  * distributed runtime check (Not OSS)
+* Auto detect and advise. (Experimental)
 
 ###Interfaces
 
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/tools/tfprof/g3doc/advise.md
index e30add6fbf..3bce6270ff 100644
--- a/tensorflow/tools/tfprof/g3doc/advise.md
+++ b/tensorflow/tools/tfprof/g3doc/advise.md
@@ -3,7 +3,6 @@
 tfprof analyzes profiles and generates advises for common issues.
 
 ### Run Advise.
-
 ```python
 # First create a profiler. See profiler tutorials for more details.
 profiler = model_analyzer.Profiler(sess.graph)
@@ -14,63 +13,8 @@ _ = sess.run(r1,
              run_metadata=run_meta)
 profiler.add_step(1, run_meta)
 
-# Then Start advise.
-profiler.advise(model_analyzer.ALL_ADVICE)
-
-# For one-shot API
-tf.contrib.tfprof.model_analyzer.advise(
-    sess.graph, run_meta=run_metadata)
-```
-
-```shell
-# Run advisor on CLI
-# See CLI tutorial on generating the files.
-tfprof --graph_path=graph.pbtxt \
-       --run_meta_path=run_metadata \
-       --op_log_path=tfprof_log
-
-tfprof> advise
-AcceleratorUtilizationChecker:
-device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03
-device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08
-device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04
-device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21
-
-OperationChecker:
-Found operation using NHWC data_format on GPU. Maybe NCHW is faster.
-
-ExpensiveOperationChecker:
-top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%)
-top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%)
-top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%)
-seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec
-  seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec
-    seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec
-      seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
-        seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize), cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
-        seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha..., cpu: 164us, accelerator: 0us, total: 164us
-      seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms
-      seq2seq_lib.py:151:sequence_loss_by_...:total_size = tf.a..., cpu: 154us, accelerator: 23us, total: 177us
-    seq2seq_lib.py:184:sampled_sequence_...:return cost / tf...., cpu: 97us, accelerator: 8us, total: 105us
-      math_ops.py:690:cast:return gen_math_o..., cpu: 62us, accelerator: 3us, total: 65us
-      math_ops.py:839:binary_op_wrapper:return func(x, y,..., cpu: 35us, accelerator: 5us, total: 40us
-  seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms
-    seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms
-      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms
-        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 303.76ms, accelerator: 73.54ms, total: 377.30ms
-        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 2.75ms, accelerator: 0us, total: 2.75ms
-      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 67us, accelerator: 0us, total: 67us
-    seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms
-      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms
-        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 292.04ms, accelerator: 73.54ms, total: 365.58ms
-        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 4.07ms, accelerator: 0us, total: 4.07ms
-      core_rnn.py:178:static_rnn:min_sequence_leng..., cpu: 85us, accelerator: 0us, total: 85us
-      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 16us, accelerator: 0us, total: 16us
-    seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms
- ...(omitted)
-top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms
-top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms
-top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms
+# Start advise.
+profiler.advise()
 ```
 
 ### Checker
@@ -81,24 +25,16 @@ area with the profile and report issues. A `Checker` is like a plugin.
 
 For example:
 
-#### JobChecker (Not Available OSS)
-
-*   Checks RecvTensor RPC latency and bandwidth.
-*   Checks CPU/Memory utilization of the job.
+####JobChecker (Not Available OSS)
+* Checking RecvTensor RPC latency and bandwidth.
+* Checking CPU/Memory utilization of the job.
 
 ####AcceleratorUtilization Checker
 * Checks what percentage of time the accelerator spends on computation.
 
-#### OperationChecker
-
-*   Checks whether the operation runs with optimal options.
-*   Checks if there is a better implementation to replace the current operation.
-
-#### ExpensiveOperationChecker
-
-*   Checks the most expensive operation type.
-*   Checks the most expensive graph nodes.
-*   Checks the most expensive graph-building Python codes.
+####Operation Checker
+* Check whether the operation runs with optimal options.
+* Checks if there is a better implementation to replace the current operation.
 
 ####Contribute Your Checker
 
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/tools/tfprof/internal/advisor/BUILD
index 629322373a..e4bd0f0015 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/tools/tfprof/internal/advisor/BUILD
@@ -44,21 +44,12 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "expensive_operation_checker",
-    hdrs = ["expensive_operation_checker.h"],
-    deps = [
-        ":checker",
-    ],
-)
-
 cc_library(
     name = "tfprof_advisor",
     hdrs = ["tfprof_advisor.h"],
     deps = [
         ":accelerator_utilization_checker",
         ":checker",
-        ":expensive_operation_checker",
         ":internal_checker_runner_dummy",
         ":operation_checker",
     ],
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
index 074b8e57b0..fb7f65d7dc 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
@@ -33,11 +33,10 @@ struct ExecStats {
 
 class AcceleratorUtilizationChecker : public Checker {
  public:
-  string name() const override { return kCheckers[0]; }
+  string name() override { return "AcceleratorUtilizationChecker"; }
 
  private:
-  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
-                             const TFStats* stats) override {
+  std::vector<string> Check(const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -49,21 +48,24 @@ class AcceleratorUtilizationChecker : public Checker {
     return CheckInternal();
   }
 
-  AdviceProto::Checker CheckInternal() {
+  std::vector<string> CheckInternal() {
     for (const auto& s : accelerator_exec_stats_) {
       const ExecStats& stat = s.second;
       int64 total_micros = stat.end_micros - stat.start_micros;
       if (total_micros <= 0) continue;
       double utilization = 1.0 * stat.exec_micros / total_micros;
       if (utilization >= 0.5) {
-        reports_.add_reports(strings::Printf("device: %s utilization: %.2f",
-                                             s.first.c_str(), utilization));
+        reports_.push_back(strings::Printf("%s: device: %s utilization: %.2f",
+                                           kLevel[0], s.first.c_str(),
+                                           utilization));
       } else if (utilization < 0.5 && utilization > 0.2) {
-        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
-                                             s.first.c_str(), utilization));
+        reports_.push_back(
+            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[1],
+                            s.first.c_str(), utilization));
       } else if (utilization <= 0.2) {
-        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
-                                             s.first.c_str(), utilization));
+        reports_.push_back(
+            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[2],
+                            s.first.c_str(), utilization));
       }
     }
     return reports_;
@@ -100,7 +102,7 @@ class AcceleratorUtilizationChecker : public Checker {
 
   std::map<string, ExecStats> accelerator_exec_stats_;
   std::map<string, int64> ps_placement_;
-  AdviceProto::Checker reports_;
+  std::vector<string> reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/checker.h b/tensorflow/tools/tfprof/internal/advisor/checker.h
index 3ce80cd8c4..b8b057be5b 100644
--- a/tensorflow/tools/tfprof/internal/advisor/checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/checker.h
@@ -18,33 +18,27 @@ limitations under the License.
 
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
-// Append only.
-static const char* const kCheckers[] = {
-    "AcceleratorUtilizationChecker", "OperationChecker",
-    "ExpensiveOperationChecker",
-    "JobChecker",  // Internal checker.
+static const char* const kLevel[] = {
+    "NOTE",     // Good to know.
+    "SUGGEST",  // Might get better.
+    "WARN",     // Please do it for better.
 };
 
 class Checker {
  public:
-  virtual ~Checker() {}
+  virtual ~Checker(){};
 
-  virtual string name() const = 0;
+  virtual string name() = 0;
 
-  AdviceProto::Checker Run(const AdvisorOptionsProto::CheckerOption& options,
-                           const TFStats* stats) {
-    return Check(options, stats);
-  }
+  std::vector<string> Run(const TFStats* stats) { return Check(stats); }
 
  protected:
-  virtual AdviceProto::Checker Check(
-      const AdvisorOptionsProto::CheckerOption& options,
-      const TFStats* stats) = 0;
+  // Returns a vector of string, each one being an advice.
+  virtual std::vector<string> Check(const TFStats* stats) = 0;
 };
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
deleted file mode 100644
index 14e30edd87..0000000000
--- a/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
+++ /dev/null
@@ -1,138 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-// This checker checks the most expensive operations.
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
-
-#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
-
-namespace tensorflow {
-namespace tfprof {
-
-class ExpensiveOperationChecker : public Checker {
- public:
-  string name() const override { return kCheckers[2]; }
-
- private:
-  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
-                             const TFStats* stats) override {
-    if (!stats) {
-      fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
-              name().c_str());
-      return reports_;
-    }
-    if (stats->steps().empty()) {
-      fprintf(stderr, "Missing RunMetadata info. Skip %s\n", name().c_str());
-    }
-    CheckOpView(stats);
-    CheckCodeView(stats);
-    CheckScopeView(stats);
-    return reports_;
-  }
-
-  void CheckOpView(const TFStats* stats) {
-    if (stats->steps().empty()) {
-      fprintf(stderr, "Missing run_meta for %s\n", name().c_str());
-      return;
-    }
-    Options opts(3, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"}, {},
-                 false, {"micros", "occurrence"}, "none", {});
-    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("op", opts);
-    if (root.children_size() == 0) {
-      return;
-    }
-    const TFMultiGraphNodeProto* node = &root;
-    std::vector<string> outputs;
-    for (int i = 0; i < 3 && node->children_size() > 0; ++i) {
-      node = &node->children(0);
-      outputs.push_back(strings::Printf(
-          "top %d operation type: %s, "
-          "cpu: %s, accelerator: %s, total: %s (%.2f%%)",
-          i + 1, node->name().c_str(),
-          FormatTime(node->cpu_exec_micros()).c_str(),
-          FormatTime(node->accelerator_exec_micros()).c_str(),
-          FormatTime(node->exec_micros()).c_str(),
-          100.0 * node->exec_micros() / (root.total_exec_micros() + 1e-10)));
-    }
-    reports_.add_reports(str_util::Join(outputs, "\n"));
-  }
-
-  void CheckCodeView(const TFStats* stats) {
-    if (!stats->has_code_traces()) {
-      fprintf(stderr, "Missing op_log (code traces) for %s\n", name().c_str());
-      return;
-    }
-    Options opts(100, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
-                 {}, false, {"micros"}, "none", {});
-    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("code", opts);
-    const TFMultiGraphNodeProto* node = &root;
-    // A trick here is: Usually, codes in library file are usually referenced
-    // only once, while user's own code are referenced multiple times.
-    while (node->children_size() == 1) {
-      node = &node->children(0);
-    }
-    if (node->children_size() == 0) {
-      return;
-    }
-
-    std::vector<string> outputs;
-    CodeViewHelper(node, 0, &outputs);
-    reports_.add_reports(str_util::Join(outputs, "\n"));
-  }
-
-  void CheckScopeView(const TFStats* stats) {
-    Options opts(100, 0, 100, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
-                 {}, false, {"micros"}, "none", {});
-    const TFGraphNodeProto root = stats->ShowGraphNode("scope", opts);
-    if (root.children_size() == 0) {
-      return;
-    }
-    std::vector<string> outputs;
-    const TFGraphNodeProto* node = &root;
-    for (int i = 0; i < 3 && i < root.children_size(); ++i) {
-      const TFGraphNodeProto& node = root.children(i);
-      outputs.push_back(strings::Printf(
-          "top %d graph node: %s, cpu: %s, accelerator: %s, total: %s", i + 1,
-          node.name().c_str(), FormatTime(node.cpu_exec_micros()).c_str(),
-          FormatTime(node.accelerator_exec_micros()).c_str(),
-          FormatTime(node.exec_micros()).c_str()));
-    }
-    reports_.add_reports(str_util::Join(outputs, "\n"));
-  }
-
-  void CodeViewHelper(const TFMultiGraphNodeProto* node, int depth,
-                      std::vector<string>* outputs) {
-    if (node->children_size() <= 1 || depth > 4) {
-      return;
-    }
-    for (int j = 0; j < 3 && j < node->children_size(); ++j) {
-      const TFMultiGraphNodeProto* c = &node->children(j);
-      outputs->push_back(strings::Printf(
-          "%s%s, cpu: %s, accelerator: %s, total: %s",
-          string(depth * 2, ' ').c_str(), c->name().c_str(),
-          FormatTime(c->total_cpu_exec_micros()).c_str(),
-          FormatTime(c->total_accelerator_exec_micros()).c_str(),
-          FormatTime(c->total_exec_micros()).c_str()));
-      CodeViewHelper(c, depth + 1, outputs);
-    }
-  }
-
-  AdviceProto::Checker reports_;
-};
-
-}  // namespace tfprof
-}  // namespace tensorflow
-
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OP_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
index ed8ae571b6..1238b57f20 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
@@ -17,16 +17,13 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
 
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
 class TFStats;
 
-AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
-                                const TFStats* stats);
+std::map<string, std::vector<string>> RunInternalCheckers(const TFStats* stats);
 
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
index 67962c8e8b..8204d2b04e 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
@@ -17,9 +17,9 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
-                                const TFStats* stats) {
-  return AdviceProto();
+std::map<string, std::vector<string>> RunInternalCheckers(
+    const TFStats* stats) {
+  return std::map<string, std::vector<string>>();
 }
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
index 4d0d68e3bf..2a05f9bfd0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
@@ -24,11 +24,10 @@ namespace tfprof {
 
 class OperationChecker : public Checker {
  public:
-  string name() const override { return kCheckers[1]; }
+  string name() override { return "OperationChecker"; }
 
  private:
-  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
-                             const TFStats* stats) override {
+  std::vector<string> Check(const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -54,20 +53,22 @@ class OperationChecker : public Checker {
       }
     }
     if (use_batch_norm && !use_fused_batch_norm) {
-      reports_.add_reports(
-          "Maybe use faster FusedBatchNorm instead of BatchNorm");
+      reports_.push_back(strings::Printf(
+          "%s: Maybe use faster FusedBatchNorm instead of BatchNorm",
+          kLevel[1]));
     }
     if (recommend_nchw) {
       // TODO(xpan): Maybe print which Op supports NCHW.
-      reports_.add_reports(
-          "Found operation using NHWC data_format on GPU. Maybe "
-          "NCHW is faster.");
+      reports_.push_back(strings::Printf(
+          "%s: Found operation using NHWC data_format on GPU. Maybe "
+          "NCHW is faster.",
+          kLevel[1]));
     }
     return reports_;
   }
 
  private:
-  AdviceProto::Checker reports_;
+  std::vector<string> reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
index d2257fb9b5..856f515459 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
@@ -18,10 +18,8 @@ limitations under the License.
 
 #include "tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/checker.h"
-#include "tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
 #include "tensorflow/tools/tfprof/internal/advisor/operation_checker.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -31,44 +29,23 @@ class Advisor {
  public:
   Advisor(const TFStats* stats) : stats_(stats) {}
 
-  static AdvisorOptionsProto DefaultOptions() {
-    AdvisorOptionsProto options;
-    std::vector<string> checkers(
-        kCheckers, kCheckers + sizeof(kCheckers) / sizeof(*kCheckers));
-    for (const string& checker : checkers) {
-      (*options.mutable_checkers())[checker];
-    }
-    return options;
-  }
-
-  AdviceProto Advise(const AdvisorOptionsProto& options) {
+  std::map<string, std::vector<string>> Advise() {
     // Note: Release a checker's memory ASAP.
-    AdviceProto ret = RunInternalCheckers(options, stats_);
-
-    if (options.checkers().find(kCheckers[0]) != options.checkers().end()) {
-      AcceleratorUtilizationChecker au_checker;
-      (*ret.mutable_checkers())[kCheckers[0]].MergeFrom(
-          au_checker.Run(options.checkers().at(kCheckers[0]), stats_));
-    }
-    if (options.checkers().find(kCheckers[1]) != options.checkers().end()) {
-      OperationChecker op_checker;
-      (*ret.mutable_checkers())[kCheckers[1]].MergeFrom(
-          op_checker.Run(options.checkers().at(kCheckers[1]), stats_));
-    }
-    if (options.checkers().find(kCheckers[2]) != options.checkers().end()) {
-      ExpensiveOperationChecker expensive_op_checker;
-      (*ret.mutable_checkers())[kCheckers[2]].MergeFrom(
-          expensive_op_checker.Run(options.checkers().at(kCheckers[2]),
-                                   stats_));
-    }
-    for (const auto& checker : ret.checkers()) {
-      fprintf(stdout, "\n%s:\n", checker.first.c_str());
-      for (const string& r : checker.second.reports()) {
+    std::map<string, std::vector<string>> reports = RunInternalCheckers(stats_);
+    // TODO(xpan): Think of a way to turn off/on specific checkers.
+    AcceleratorUtilizationChecker au_checker;
+    reports[au_checker.name()] = au_checker.Run(stats_);
+    OperationChecker op_checker;
+    reports[op_checker.name()] = op_checker.Run(stats_);
+
+    for (const auto& checker_r : reports) {
+      fprintf(stdout, "%s reports:\n", checker_r.first.c_str());
+      for (const auto& r : checker_r.second) {
         fprintf(stdout, "%s\n", r.c_str());
       }
     }
     fflush(stdout);
-    return ret;
+    return reports;
   }
 
  private:
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
index 3b40253954..b41d0770dc 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
@@ -29,16 +29,15 @@ class TFProfAdvisorTest : public ::testing::Test {
                              nullptr, nullptr));
 
     stats_->AddNodeForTest(
-        0, CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 0, 10, 2));
-    stats_->AddNodeForTest(0, CreateNode("n2", "Conv2D", {}, 0, 20, 2));
-    stats_->BuildAllViews();
+        "n1", CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 10, 2));
+    stats_->AddNodeForTest("n2", CreateNode("n2", "Conv2D", {}, 20, 2));
     advisor_.reset(new Advisor(stats_.get()));
   }
 
   std::unique_ptr<TFGraphNode> CreateNode(const string& name,
                                           const string& type,
                                           std::map<string, string> attrs,
-                                          int64 step, int64 start_miros,
+                                          int64 start_miros,
                                           int64 end_rel_micros) {
     node_defs_.push_back(std::unique_ptr<NodeDef>(new NodeDef()));
     NodeDef* def = node_defs_.back().get();
@@ -53,10 +52,10 @@ class TFProfAdvisorTest : public ::testing::Test {
     NodeExecStats node_stat;
     node_stat.set_all_start_micros(start_miros);
     node_stat.set_op_end_rel_micros(end_rel_micros);
-    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
-    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
+    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
+    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
                       node_stat);
-    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
+    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
                       node_stat);
     return node;
   }
@@ -67,38 +66,23 @@ class TFProfAdvisorTest : public ::testing::Test {
 };
 
 TEST_F(TFProfAdvisorTest, Basics) {
-  AdvisorOptionsProto options = Advisor::DefaultOptions();
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_TRUE(advice.checkers().find(kCheckers[0]) != advice.checkers().end());
-  EXPECT_TRUE(advice.checkers().find(kCheckers[1]) != advice.checkers().end());
-  EXPECT_TRUE(advice.checkers().find(kCheckers[2]) != advice.checkers().end());
+  std::map<string, std::vector<string>> reports = advisor_->Advise();
+  EXPECT_TRUE(reports.find("AcceleratorUtilizationChecker") != reports.end());
+  EXPECT_TRUE(reports.find("OperationChecker") != reports.end());
 }
 
 TEST_F(TFProfAdvisorTest, OperationChecker) {
-  AdvisorOptionsProto options;
-  (*options.mutable_checkers())[kCheckers[1]];
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_EQ(advice.checkers().at(kCheckers[1]).reports_size(), 1);
-  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[1]).reports(0))
-                  .contains("NCHW"));
+  std::map<string, std::vector<string>> reports = advisor_->Advise();
+  EXPECT_EQ(reports["OperationChecker"].size(), 1);
+  EXPECT_TRUE(StringPiece(reports["OperationChecker"][0]).contains("NCHW"));
 }
 
 TEST_F(TFProfAdvisorTest, UtilizationChecker) {
-  AdvisorOptionsProto options;
-  (*options.mutable_checkers())[kCheckers[0]];
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_EQ(advice.checkers().at(kCheckers[0]).reports_size(), 1);
-  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[0]).reports(0))
+  std::map<string, std::vector<string>> reports = advisor_->Advise();
+  EXPECT_EQ(reports["AcceleratorUtilizationChecker"].size(), 1);
+  EXPECT_TRUE(StringPiece(reports["AcceleratorUtilizationChecker"][0])
                   .contains("low utilization"));
 }
 
-TEST_F(TFProfAdvisorTest, ExpensiveOperationChecker) {
-  AdvisorOptionsProto options;
-  (*options.mutable_checkers())[kCheckers[2]];
-  AdviceProto advice = advisor_->Advise(options);
-  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[2]).reports(0))
-                  .contains("top 1 operation type: Conv2D"));
-}
-
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.cc b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
index 5a9c44d8e6..37d01db3a1 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.cc
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
@@ -27,7 +27,6 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 #include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
@@ -37,18 +36,6 @@ TFStats* tf_stat = nullptr;
 
 string RunProfile(const string& command, const string& options,
                   TFStats* tf_stats) {
-  if (command == kCmds[4]) {
-    AdvisorOptionsProto option_pb;
-    if (!option_pb.ParseFromString(options)) {
-      fprintf(stderr, "Cannot parse AdvisorOptionsProto\n");
-      return "";
-    }
-    tf_stats->BuildAllViews();
-    return Advisor(tf_stats).Advise(option_pb).SerializeAsString();
-  } else {
-    tf_stats->BuildView(command);
-  }
-
   Options opts;
   tensorflow::Status s = Options::FromProtoStr(options, &opts);
   if (!s.ok()) {
@@ -110,14 +97,14 @@ void AddStep(int64 step, const string* run_meta, const string* op_log) {
   // TODO(xpan): Better error handling.
   std::unique_ptr<RunMetadata> run_meta_ptr(new RunMetadata());
   run_meta_ptr->ParseFromString(*run_meta);
-  tf_stat->AddRunMeta(step, std::move(run_meta_ptr));
+  tf_stat->ParseRunMeta(step, std::move(run_meta_ptr));
 
   std::unique_ptr<OpLog> op_log_ptr;
   if (op_log && !op_log->empty()) {
     op_log_ptr.reset(new OpLog());
     op_log_ptr->ParseFromString(*op_log);
   }
-  tf_stat->AddOpLog(std::move(op_log_ptr));
+  tf_stat->ParseOpLog(std::move(op_log_ptr));
 }
 
 string Profile(const string* command, const string* options) {
@@ -157,5 +144,7 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
   return RunProfile(*command, *options, &tf_stats);
 }
 
+void Advise() { Advisor(tf_stat).Advise(); }
+
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.h b/tensorflow/tools/tfprof/internal/print_model_analysis.h
index 46db63646d..84165e542d 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.h
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.h
@@ -39,6 +39,8 @@ void AddStep(int64 step, const string* run_meta, const string* op_log);
 
 string Profile(const string* command, const string* options);
 
+void Advise();
+
 // Single-step Profiler.
 //
 // Interface defined for Python API swig. Calls the tfprof core API.
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.h b/tensorflow/tools/tfprof/internal/tfprof_graph.h
index 194a21f0cc..fbeae8673d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_graph.h
@@ -54,8 +54,8 @@ class TFGraph : public TFShow {
   const ShowNode* ShowInternal(const Options& opts,
                                Timeline* timeline) override;
 
-  bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
-                         int depth) const override {
+  bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
+                         int depth) override {
     return true;
   }
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index 77a2593623..6a7077c085 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -126,7 +126,6 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
   }
   nodes = SortNodes(nodes, opts);
 
-  // pre keeps track of previous visited node.
   OpNode* pre = nullptr;
   std::vector<OpNode*> account_nodes;
   for (auto it = nodes.rbegin(); it != nodes.rend(); ++it) {
@@ -171,20 +170,16 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
     root_->ResetTotalStats();
     if (pre) {
       root_->AggregateTotalStats(pre);
+      root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
+      pre->mutable_proto()->clear_children();
     }
   }
-  if (pre) {
-    root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
-    pre->mutable_proto()->clear_children();
-  }
 
   if (opts.output_type == kOutput[1] || opts.output_type == kOutput[2]) {
     string display_str = FormatLegend(opts);
     for (OpNode* node : show_nodes) {
       display_str += FormatNode(node, root_.get(), opts);
     }
-    // In op view, we don't show root (total). But it will still in proto.
-    // TODO(xpan): Is it the right choice?
     root_->formatted_str = display_str;
   }
   return root_.get();
@@ -206,7 +201,7 @@ int64 TFOp::SearchRoot(const std::vector<OpNode*> nodes,
   return i;
 }
 
-string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const {
+string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
   std::vector<string> attrs;
 
   if (opts.select.find(kShown[0]) != opts.select.end()) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.h b/tensorflow/tools/tfprof/internal/tfprof_op.h
index 34812f54be..5b16490363 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.h
@@ -56,15 +56,15 @@ class TFOp : public TFMultiShow {
   int64 SearchRoot(const std::vector<OpNode*> nodes,
                    const std::vector<string>& regexes);
 
-  bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
-                         int depth) const override {
+  bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
+                         int depth) override {
     if (opts.min_occurrence > node->node->graph_nodes().size()) {
       return false;
     }
     return true;
   }
 
-  string FormatNode(OpNode* node, OpNode* root, const Options& opts) const;
+  string FormatNode(OpNode* node, OpNode* root, const Options& opts);
 
   std::unique_ptr<OpNode> root_;
   std::map<string, std::unique_ptr<OpNode>> cnodes_map_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/tools/tfprof/internal/tfprof_options.h
index d39333e3fc..6c9db24342 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_options.h
@@ -59,10 +59,10 @@ static const char* const kShown[] = {
     "cpu_micros"};
 
 static const char* const kCmds[] = {
-    "scope", "graph", "code", "op", "advise", "set", "help",
+    "scope", "graph", "code", "op", "set", "help",
 };
 
-static const char* const kOutput[] = {"timeline", "stdout", "file", "none"};
+static const char* const kOutput[] = {"timeline", "stdout", "file"};
 
 static const char* const kTimelineOpts[] = {
     "outfile",
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index 6c1183a4d5..40cc56fb22 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -26,9 +26,7 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFGraphNodeProto& TFShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[3]) {
-    return ShowInternal(opts, nullptr)->proto();
-  } else if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -66,8 +64,7 @@ bool TFShow::LookUpCheckPoint(const string& name,
   return true;
 }
 
-bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
-                        int depth) const {
+bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -100,8 +97,7 @@ bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
   return true;
 }
 
-bool TFShow::ShouldTrim(const ShowNode* node,
-                        const std::vector<string>& regexes) const {
+bool TFShow::ShouldTrim(ShowNode* node, const std::vector<string>& regexes) {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -126,7 +122,7 @@ bool TFShow::ReAccount(ShowNode* node, const Options& opts) {
   return false;
 }
 
-string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
+string TFShow::FormatNode(ShowNode* node, const Options& opts) {
   std::vector<string> info;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     const string shape = FormatShapes(node->node->shape());
@@ -214,7 +210,7 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
                          str_util::Join(info, ", ").c_str());
 }
 
-string TFShow::FormatLegend(const Options& opts) const {
+string TFShow::FormatLegend(const Options& opts) {
   std::vector<string> legends;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     legends.push_back("# parameters");
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/tools/tfprof/internal/tfprof_show.h
index 95513e086f..2c61b4fd73 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.h
@@ -54,21 +54,20 @@ class TFShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
-                                 int depth) const {
+  virtual bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
+                                 int depth) {
     return true;
   }
 
-  bool ShouldShow(const ShowNode* node, const Options& opts, int depth) const;
+  bool ShouldShow(ShowNode* node, const Options& opts, int depth);
 
-  bool ShouldTrim(const ShowNode* node,
-                  const std::vector<string>& regexes) const;
+  bool ShouldTrim(ShowNode* node, const std::vector<string>& regexes);
 
   bool ReAccount(ShowNode* node, const Options& opts);
 
-  string FormatNode(ShowNode* node, const Options& opts) const;
+  string FormatNode(ShowNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts) const;
+  string FormatLegend(const Options& opts);
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 84d542714a..7545b3d48b 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -28,9 +28,7 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[3]) {
-    return ShowInternal(opts, nullptr)->proto();
-  } else if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -50,8 +48,8 @@ const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
   }
 }
 
-bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
-                             int depth) const {
+bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
+                            int depth) {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -90,8 +88,8 @@ bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
   return true;
 }
 
-bool TFMultiShow::ShouldTrim(const ShowMultiNode* node,
-                             const std::vector<string>& regexes) const {
+bool TFMultiShow::ShouldTrim(ShowMultiNode* node,
+                            const std::vector<string>& regexes) {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -104,7 +102,7 @@ bool TFMultiShow::ReAccount(ShowMultiNode* node, const Options& opts) {
   return node->ReInit(opts.step, opts.account_type_regexes);
 }
 
-string TFMultiShow::FormatLegend(const Options& opts) const {
+string TFMultiShow::FormatLegend(const Options& opts) {
   std::vector<string> legends;
   if (opts.select.find(kShown[0]) != opts.select.end()) {
     legends.push_back("output bytes");
@@ -144,8 +142,7 @@ string TFMultiShow::FormatLegend(const Options& opts) const {
                          str_util::Join(legends, " | ").c_str());
 }
 
-string TFMultiShow::FormatInputShapes(
-    const TFMultiGraphNodeProto& proto) const {
+string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
   // input_shape string -> (static defined count, run count, run_micros)
   std::map<string, std::tuple<int64, int64, int64>> input_shapes_attr;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
@@ -202,7 +199,7 @@ string TFMultiShow::FormatInputShapes(
 }
 
 std::vector<string> TFMultiShow::FormatTimes(const ShowMultiNode* node,
-                                             const Options& opts) const {
+                                             const Options& opts) {
   std::vector<string> attrs;
   if (opts.select.find(kShown[1]) != opts.select.end()) {
     attrs.push_back(FormatTotalExecTime(node, opts));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
index ce309816a9..e6faf1231d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
@@ -55,23 +55,21 @@ class TFMultiShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
-                                 int depth) const {
+  virtual bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
+                                 int depth) {
     return true;
   }
 
-  bool ShouldShow(const ShowMultiNode* node, const Options& opts,
-                  int depth) const;
+  bool ShouldShow(ShowMultiNode* node, const Options& opts, int depth);
 
-  bool ShouldTrim(const ShowMultiNode* node,
-                  const std::vector<string>& regexes) const;
+  bool ShouldTrim(ShowMultiNode* node, const std::vector<string>& regexes);
 
   bool ReAccount(ShowMultiNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts) const;
-  string FormatInputShapes(const TFMultiGraphNodeProto& proto) const;
+  string FormatLegend(const Options& opts);
+  string FormatInputShapes(const TFMultiGraphNodeProto& proto);
   std::vector<string> FormatTimes(const ShowMultiNode* node,
-                                  const Options& opts) const;
+                                  const Options& opts);
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index 46b6ebc09a..18a7c51a20 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -65,7 +65,6 @@ class TFProfShowTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.cc b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
index 64da7ae7cf..f5b8dad4e2 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
@@ -29,17 +29,16 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
                  std::unique_ptr<RunMetadata> run_meta,
                  std::unique_ptr<OpLog> op_log,
                  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader)
-    : has_code_traces_(false),
-      graph_(std::move(graph)),
+    : graph_(std::move(graph)),
       ckpt_reader_(std::move(ckpt_reader)) {
   CHECK(graph_) << "Must at least have GraphDef";
 
   printf("Parsing Inputs...\n");
   ParseGraph();
   if (run_meta && run_meta->has_step_stats()) {
-    AddRunMeta(0, std::move(run_meta));
+    ParseRunMeta(0, std::move(run_meta));
   }
-  AddOpLog(std::move(op_log));
+  ParseOpLog(std::move(op_log));
 
   if (ckpt_reader_) {
     for (const auto& v : ckpt_reader_->GetVariableToShapeMap()) {
@@ -49,48 +48,27 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
       }
     }
   }
-}
 
-void TFStats::BuildView(const string& cmd) {
-  if (cmd == kCmds[0] && !scope_view_) {
-    scope_view_.reset(new TFScope(ckpt_reader_.get()));
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      scope_view_->AddNode(it->second.get());
-    }
-    scope_view_->Build();
-  }
-  if (cmd == kCmds[1] && !graph_view_) {
-    graph_view_.reset(new TFGraph(ckpt_reader_.get()));
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      graph_view_->AddNode(it->second.get());
-    }
-    graph_view_->Build();
-  }
-  if (cmd == kCmds[2] && !code_view_) {
-    code_view_.reset(new TFCode());
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      code_view_->AddNode(it->second.get());
-    }
-    code_view_->Build();
-  }
-  if (cmd == kCmds[3] && !op_view_) {
-    op_view_.reset(new TFOp());
-    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-      op_view_->AddNode(it->second.get());
-    }
-    op_view_->Build();
-  }
-}
+  printf("Preparing Views...\n");
+  scope_view_ = std::unique_ptr<TFScope>(new TFScope(ckpt_reader_.get()));
+  graph_view_ = std::unique_ptr<TFGraph>(new TFGraph(ckpt_reader_.get()));
+  code_view_ = std::unique_ptr<TFCode>(new TFCode());
+  op_view_ = std::unique_ptr<TFOp>(new TFOp());
 
-void TFStats::BuildAllViews() {
-  std::vector<string> cmds_str(kCmds, kCmds + sizeof(kCmds) / sizeof(*kCmds));
-  for (const string& cmd : cmds_str) {
-    BuildView(cmd);
-  }
+  for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+    scope_view_->AddNode(it->second.get());
+    graph_view_->AddNode(it->second.get());
+    code_view_->AddNode(it->second.get());
+    op_view_->AddNode(it->second.get());
+  }
+  scope_view_->Build();
+  graph_view_->Build();
+  code_view_->Build();
+  op_view_->Build();
 }
 
 const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
-                                               const Options& opts) const {
+                                               const Options& opts) {
   if (!Validate(opts)) {
     return empty_graph_node_;
   }
@@ -104,8 +82,8 @@ const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
   }
 }
 
-const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(
-    const string& cmd, const Options& opts) const {
+const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(const string& cmd,
+                                                         const Options& opts) {
   if (!Validate(opts)) {
     return empty_multi_graph_node_;
   }
@@ -152,7 +130,7 @@ void TFStats::ParseGraph() {
   }
 }
 
-void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
+void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
   if (!op_log) {
     return;
   }
@@ -166,13 +144,12 @@ void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
       node->second->AddFloatOps(entry.float_ops());
     }
     if (entry.has_code_def()) {
-      has_code_traces_ = true;
       node->second->AddCode(entry.code_def());
     }
   }
 }
 
-void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
+void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   if (!run_meta || !run_meta->has_step_stats()) {
     fprintf(stderr, "Invalid RunMetadata for step %lld\n", step);
     return;
@@ -199,7 +176,7 @@ void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   }
 }
 
-bool TFStats::Validate(const Options& opts) const {
+bool TFStats::Validate(const Options& opts) {
   if (opts.step >= 0 && steps_.find(opts.step) == steps_.end()) {
     fprintf(stderr, "Options -step=%lld not found\n", opts.step);
     return false;
@@ -207,9 +184,9 @@ bool TFStats::Validate(const Options& opts) const {
   return true;
 }
 
-void TFStats::AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node) {
-  steps_.insert(step);
-  nodes_map_[node->name()] = std::move(node);
+void TFStats::AddNodeForTest(const string& name,
+                             std::unique_ptr<TFGraphNode> node) {
+  nodes_map_[name] = std::move(node);
 }
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.h b/tensorflow/tools/tfprof/internal/tfprof_stats.h
index b26d274f80..dfb190e703 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.h
@@ -59,38 +59,28 @@ class TFStats {
   const std::map<string, std::unique_ptr<TFGraphNode>>& nodes() const {
     return nodes_map_;
   }
-  const std::set<int64>& steps() const { return steps_; }
-  bool has_code_traces() const { return has_code_traces_; }
 
-  void BuildView(const string& cmd);
-  void BuildAllViews();
-
-  // Note: Must first BuildView(view_foo) before ShowXXX(view_foo) methods.
-  //
   // Organize the TensorFlow model as different types of views, and generate
   // outputs for profiling.
-  // TODO(xpan): Should it return reference here?
-  const TFGraphNodeProto& ShowGraphNode(const string& cmd,
-                                        const Options& opts) const;
+  const TFGraphNodeProto& ShowGraphNode(const string& cmd, const Options& opts);
   const TFMultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
-                                                  const Options& opts) const;
+                                                  const Options& opts);
 
   // Add a step of run time meta data.
-  void AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
+  void ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
   // Add tfprof operation meta data, such as customized op type, float_ops,
   // and code traces.
-  void AddOpLog(std::unique_ptr<OpLog> op_log);
+  void ParseOpLog(std::unique_ptr<OpLog> op_log);
 
   // For test purpose only.
-  void AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node);
+  void AddNodeForTest(const string& name, std::unique_ptr<TFGraphNode> node);
 
  private:
-  bool Validate(const Options& opts) const;
+  bool Validate(const Options& opts);
 
   void ParseGraph();
 
   std::set<int64> steps_;
-  bool has_code_traces_;
   std::unique_ptr<GraphDef> graph_;
   std::unique_ptr<TFScope> scope_view_;
   std::unique_ptr<TFGraph> graph_view_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index 00d5d8cdb3..bcd1147c69 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -66,7 +66,6 @@ class TFProfStatsTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index 0482657ca5..d7cfe7dade 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -50,7 +50,6 @@ class TFProfTensorTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
index cad31050a9..0e9bb9658c 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
@@ -52,7 +52,6 @@ class TFProfTimelineTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 nullptr, nullptr));
-    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/tools/tfprof/tfprof_main.cc
index 5e70c093cc..7a4e7e85ff 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/tools/tfprof/tfprof_main.cc
@@ -33,7 +33,6 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/util/command_line_flags.h"
-#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
@@ -162,13 +161,12 @@ int Run(int argc, char** argv) {
         "Profiling everything!\n");
     return 0;
   } else if (argc > 1) {
-    if (string(argv[1]) == kCmds[6]) {
+    if (string(argv[1]) == kCmds[5]) {
       PrintHelp();
       return 0;
     }
     if (string(argv[1]) == kCmds[0] || string(argv[1]) == kCmds[1] ||
-        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3] ||
-        string(argv[1]) == kCmds[4]) {
+        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3]) {
       cmd = argv[1];
     }
   }
@@ -218,13 +216,7 @@ int Run(int argc, char** argv) {
               run_meta_files[i].c_str(), s.ToString().c_str());
       return 1;
     }
-    tf_stat.AddRunMeta(i, std::move(run_meta));
-  }
-
-  if (cmd == kCmds[4]) {
-    tf_stat.BuildAllViews();
-    Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
-    return 0;
+    tf_stat.ParseRunMeta(i, std::move(run_meta));
   }
 
   Options opts(FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros,
@@ -235,11 +227,9 @@ int Run(int argc, char** argv) {
                output_type, output_options);
 
   if (cmd == kCmds[2] || cmd == kCmds[3]) {
-    tf_stat.BuildView(cmd);
     tf_stat.ShowMultiGraphNode(cmd, opts);
     return 0;
   } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
-    tf_stat.BuildView(cmd);
     tf_stat.ShowGraphNode(cmd, opts);
     return 0;
   }
@@ -264,19 +254,14 @@ int Run(int argc, char** argv) {
       fprintf(stderr, "E: %s\n", s.ToString().c_str());
       continue;
     }
-    if (cmd == kCmds[5]) {
+    if (cmd == kCmds[4]) {
       opts = new_opts;
-    } else if (cmd == kCmds[6]) {
+    } else if (cmd == kCmds[5]) {
       PrintHelp();
     } else if (cmd == kCmds[2] || cmd == kCmds[3]) {
-      tf_stat.BuildView(cmd);
       tf_stat.ShowMultiGraphNode(cmd, new_opts);
     } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
-      tf_stat.BuildView(cmd);
       tf_stat.ShowGraphNode(cmd, new_opts);
-    } else if (cmd == kCmds[4]) {
-      tf_stat.BuildAllViews();
-      Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
     }
   }
   return 0;
diff --git a/tensorflow/tools/tfprof/tfprof_options.proto b/tensorflow/tools/tfprof/tfprof_options.proto
index 5882833039..de916b4a16 100644
--- a/tensorflow/tools/tfprof/tfprof_options.proto
+++ b/tensorflow/tools/tfprof/tfprof_options.proto
@@ -24,11 +24,3 @@ message OptionsProto {
   string output = 15;
   string dump_to_file = 16;
 }
-
-message AdvisorOptionsProto {
-  // checker name -> a dict of key-value options.
-  map<string, CheckerOption> checkers = 1;
-  message CheckerOption {
-    map<string, string> options = 1;
-  }
-}
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index abf141f1e7..7206c6f956 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -102,11 +102,3 @@ message TFMultiGraphNodeProto {
   // structure used.
   repeated TFMultiGraphNodeProto children = 11;
 }
-
-message AdviceProto {
-  // checker name -> a list of reports from the checker.
-  map<string, Checker> checkers = 1;
-  message Checker {
-    repeated string reports = 2;
-  }
-}
-- 
GitLab


From 5f84a0cb372ca6542385ad0a372496c1f56f7ce6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 15:02:09 -0700
Subject: [PATCH 0464/1375] Updates text_classification example.

PiperOrigin-RevId: 160200457
---
 .../examples/learn/text_classification.py     | 144 +++++++++++-------
 1 file changed, 90 insertions(+), 54 deletions(-)

diff --git a/tensorflow/examples/learn/text_classification.py b/tensorflow/examples/learn/text_classification.py
index 7e10014c39..4b32bfc382 100644
--- a/tensorflow/examples/learn/text_classification.py
+++ b/tensorflow/examples/learn/text_classification.py
@@ -24,43 +24,67 @@ import numpy as np
 import pandas
 from sklearn import metrics
 import tensorflow as tf
-from tensorflow.contrib.layers.python.layers import encoders
-
-learn = tf.contrib.learn
 
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 10
 EMBEDDING_SIZE = 50
 n_words = 0
+MAX_LABEL = 15
+WORDS_FEATURE = 'words'  # Name of the input words feature.
+
+
+def estimator_spec_for_softmax_classification(
+    logits, labels, mode):
+  """Returns EstimatorSpec instance for softmax classification."""
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
+
+
+def bag_of_words_model(features, labels, mode):
+  """A bag-of-words model. Note it disregards the word order in the text."""
+  bow_column = tf.feature_column.categorical_column_with_identity(
+      WORDS_FEATURE, num_buckets=n_words)
+  bow_embedding_column = tf.feature_column.embedding_column(
+      bow_column, dimension=EMBEDDING_SIZE)
+  bow = tf.feature_column.input_layer(
+      features,
+      feature_columns=[bow_embedding_column])
+  logits = tf.layers.dense(bow, MAX_LABEL, activation=None)
 
+  return estimator_spec_for_softmax_classification(
+      logits=logits, labels=labels, mode=mode)
 
-def bag_of_words_model(features, target):
-  """A bag-of-words model. Note it disregards the word order in the text."""
-  target = tf.one_hot(target, 15, 1, 0)
-  features = encoders.bow_encoder(
-      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
-  logits = tf.contrib.layers.fully_connected(features, 15, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
-
-
-def rnn_model(features, target):
+
+def rnn_model(features, labels, mode):
   """RNN model to predict from sequence of words to a class."""
   # Convert indexes of words into embeddings.
   # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
   # maps word indexes of the sequence into [batch_size, sequence_length,
   # EMBEDDING_SIZE].
   word_vectors = tf.contrib.layers.embed_sequence(
-      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE, scope='words')
+      features[WORDS_FEATURE], vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
 
   # Split into list of embedding per word, while removing doc length dim.
   # word_list results to be a list of tensors [batch_size, EMBEDDING_SIZE].
@@ -74,29 +98,17 @@ def rnn_model(features, target):
   _, encoding = tf.contrib.rnn.static_rnn(cell, word_list, dtype=tf.float32)
 
   # Given encoding of RNN, take encoding of last step (e.g hidden size of the
-  # neural network of last step) and pass it as features for logistic
-  # regression over output classes.
-  target = tf.one_hot(target, 15, 1, 0)
-  logits = tf.contrib.layers.fully_connected(encoding, 15, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
-
-  # Create a training op.
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  # neural network of last step) and pass it as features for softmax
+  # classification over output classes.
+  logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)
+  return estimator_spec_for_softmax_classification(
+      logits=logits, labels=labels, mode=mode)
 
 
 def main(unused_argv):
   global n_words
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -104,14 +116,15 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
-  
+  vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(
+      MAX_DOCUMENT_LENGTH)
+
   x_transform_train = vocab_processor.fit_transform(x_train)
   x_transform_test = vocab_processor.transform(x_test)
-  
+
   x_train = np.array(list(x_transform_train))
   x_test = np.array(list(x_transform_test))
-  
+
   n_words = len(vocab_processor.vocabulary_)
   print('Total words: %d' % n_words)
 
@@ -119,17 +132,40 @@ def main(unused_argv):
   # Switch between rnn_model and bag_of_words_model to test different models.
   model_fn = rnn_model
   if FLAGS.bow_model:
+    # Subtract 1 because VocabularyProcessor outputs a word-id matrix where word
+    # ids start from 1 and 0 means 'no word'. But
+    # categorical_column_with_identity assumes 0-based count and uses -1 for
+    # missing word.
+    x_train -= 1
+    x_test -= 1
     model_fn = bag_of_words_model
-  classifier = learn.Estimator(model_fn=model_fn)
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  classifier = tf.estimator.Estimator(model_fn=model_fn)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_train},
+      y=y_train,
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
-- 
GitLab


From f7525de07f153ad2568550300a31664ffcf9d80d Mon Sep 17 00:00:00 2001
From: J Alammar <alammar@gmail.com>
Date: Tue, 27 Jun 2017 01:17:47 +0300
Subject: [PATCH 0465/1375] variable name change and documentation
 clarification (#11049)

---
 tensorflow/contrib/rnn/python/ops/rnn_cell.py    | 4 ++--
 tensorflow/contrib/seq2seq/python/ops/decoder.py | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index c8552fc050..ecce1d22f0 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -1110,14 +1110,14 @@ class AttentionCellWrapper(rnn_cell_impl.RNNCell):
     if input_size is None:
       input_size = inputs.get_shape().as_list()[1]
     inputs = _linear([inputs, attns], input_size, True)
-    lstm_output, new_state = self._cell(inputs, state)
+    cell_output, new_state = self._cell(inputs, state)
     if self._state_is_tuple:
       new_state_cat = array_ops.concat(nest.flatten(new_state), 1)
     else:
       new_state_cat = new_state
     new_attns, new_attn_states = self._attention(new_state_cat, attn_states)
     with vs.variable_scope("attn_output_projection"):
-      output = _linear([lstm_output, new_attns], self._attn_size, True)
+      output = _linear([cell_output, new_attns], self._attn_size, True)
     new_attn_states = array_ops.concat(
         [new_attn_states, array_ops.expand_dims(output, 1)], 1)
     new_attn_states = array_ops.reshape(
diff --git a/tensorflow/contrib/seq2seq/python/ops/decoder.py b/tensorflow/contrib/seq2seq/python/ops/decoder.py
index 4795dfb8c9..fbe53fc60a 100644
--- a/tensorflow/contrib/seq2seq/python/ops/decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/decoder.py
@@ -99,9 +99,9 @@ class Decoder(object):
       name: Name scope for any created operations.
 
     Returns:
-      `(outputs, next_state, next_inputs, finished)`: `outputs` is an instance
-      of BasicDecoderOutput, `next_state` is a (structure of) state tensors and
-      TensorArrays, `next_inputs` is the tensor that should be used as input for
+      `(outputs, next_state, next_inputs, finished)`: `outputs` is an object
+      containing the decoder output, `next_state` is a (structure of) state tensors
+      and TensorArrays, `next_inputs` is the tensor that should be used as input for
       the next step, `finished` is a boolean tensor telling whether the sequence
       is complete, for each sequence in the batch.
     """
-- 
GitLab


From 196a539705b194936407c699982ffe2f6f804f76 Mon Sep 17 00:00:00 2001
From: qjivy <ji.qiu@spreadtrum.com>
Date: Tue, 27 Jun 2017 07:05:01 +0800
Subject: [PATCH 0466/1375] For the "m <= 4" GEMM that would  call into (#9622)

Gemlmowp/meta lib, change the single thread invoking to
multi-threads invoking.
Because for the FC layers, multi-thread would improve the
memory accessing of the relatively large weights matrix.
---
 tensorflow/core/kernels/meta_support.cc | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/meta_support.cc b/tensorflow/core/kernels/meta_support.cc
index 0e899402a2..b29feb0032 100644
--- a/tensorflow/core/kernels/meta_support.cc
+++ b/tensorflow/core/kernels/meta_support.cc
@@ -98,8 +98,9 @@ typedef gemmlowp::meta::SimpleContext<gemmlowp::WorkersPool> LocalContext;
 template <typename Context, typename Params>
 void MultiThreadGemm(Context* context, const Params& params) {
   if (params.m <= 4) {
-    gemmlowp::meta::Gemm<gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>,
-                         Params, 1, 8, 8>(params);
+      gemmlowp::meta::MultiThreadGemm<
+          Context, gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>, Params,
+          1, 8, 8>(context, params);
   } else {
     if (params.m >= params.n) {
       gemmlowp::meta::MultiThreadGemm<
-- 
GitLab


From 710fd7a65ab4b59183b8aeb5de8716f0712f918e Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Mon, 26 Jun 2017 16:10:26 -0700
Subject: [PATCH 0467/1375] Expose TFRecordWriter.Flush() in Python (#10686)

* Expose TFRecordWriter.Flush() in Python

This fix tries to address the issue raised in #10644 where
it was not possible to call `TFRecordWriter.Flush()` in Python.
The C++ interface already has the `Flush()` so this fix exposes
it to python level.

This fix is related to #10644.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Update the API goldens for APi compatibility

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/python/lib/io/py_record_writer.cc              | 8 ++++++++
 tensorflow/python/lib/io/py_record_writer.h               | 1 +
 tensorflow/python/lib/io/py_record_writer.i               | 1 +
 tensorflow/python/lib/io/tf_record.py                     | 5 +++++
 .../golden/tensorflow.python_io.-t-f-record-writer.pbtxt  | 4 ++++
 5 files changed, 19 insertions(+)

diff --git a/tensorflow/python/lib/io/py_record_writer.cc b/tensorflow/python/lib/io/py_record_writer.cc
index df35c43c3d..ba749da47a 100644
--- a/tensorflow/python/lib/io/py_record_writer.cc
+++ b/tensorflow/python/lib/io/py_record_writer.cc
@@ -55,6 +55,14 @@ bool PyRecordWriter::WriteRecord(tensorflow::StringPiece record) {
   return s.ok();
 }
 
+void PyRecordWriter::Flush(TF_Status* out_status) {
+  Status s = writer_->Flush();
+  if (!s.ok()) {
+    Set_TF_Status_from_Status(out_status, s);
+    return;
+  }
+}
+
 void PyRecordWriter::Close(TF_Status* out_status) {
   Status s = writer_->Close();
   if (!s.ok()) {
diff --git a/tensorflow/python/lib/io/py_record_writer.h b/tensorflow/python/lib/io/py_record_writer.h
index 8c53420ce6..9d66c031d4 100644
--- a/tensorflow/python/lib/io/py_record_writer.h
+++ b/tensorflow/python/lib/io/py_record_writer.h
@@ -44,6 +44,7 @@ class PyRecordWriter {
   ~PyRecordWriter();
 
   bool WriteRecord(tensorflow::StringPiece record);
+  void Flush(TF_Status* out_status);
   void Close(TF_Status* out_status);
 
  private:
diff --git a/tensorflow/python/lib/io/py_record_writer.i b/tensorflow/python/lib/io/py_record_writer.i
index 9e61c9893a..3181c9afce 100644
--- a/tensorflow/python/lib/io/py_record_writer.i
+++ b/tensorflow/python/lib/io/py_record_writer.i
@@ -45,6 +45,7 @@ limitations under the License.
 %unignore tensorflow::io::PyRecordWriter;
 %unignore tensorflow::io::PyRecordWriter::~PyRecordWriter;
 %unignore tensorflow::io::PyRecordWriter::WriteRecord;
+%unignore tensorflow::io::PyRecordWriter::Flush;
 %unignore tensorflow::io::PyRecordWriter::Close;
 %unignore tensorflow::io::PyRecordWriter::New;
 
diff --git a/tensorflow/python/lib/io/tf_record.py b/tensorflow/python/lib/io/tf_record.py
index 3d0cdc2153..df19010068 100644
--- a/tensorflow/python/lib/io/tf_record.py
+++ b/tensorflow/python/lib/io/tf_record.py
@@ -121,6 +121,11 @@ class TFRecordWriter(object):
     """
     self._writer.WriteRecord(record)
 
+  def flush(self):
+    """Flush the file."""
+    with errors.raise_exception_on_not_ok_status() as status:
+      self._writer.Flush(status)
+
   def close(self):
     """Close the file."""
     with errors.raise_exception_on_not_ok_status() as status:
diff --git a/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt b/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt
index af0c11ca14..31775de2d1 100644
--- a/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt
@@ -10,6 +10,10 @@ tf_class {
     name: "close"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "flush"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "write"
     argspec: "args=[\'self\', \'record\'], varargs=None, keywords=None, defaults=None"
-- 
GitLab


From 5e6b16a9261d95eee321a67e38ea0a83357f3bf8 Mon Sep 17 00:00:00 2001
From: Mosnoi Ion <johnsmithm@users.noreply.github.com>
Date: Tue, 27 Jun 2017 02:13:13 +0300
Subject: [PATCH 0468/1375] added blocks option for lstm2d (#10692)

* added blocks option for lstm2d

* code style correction
---
 tensorflow/contrib/ndlstm/python/lstm2d.py    | 54 ++++++++++++++++++-
 .../contrib/ndlstm/python/lstm2d_test.py      |  8 +++
 2 files changed, 60 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/ndlstm/python/lstm2d.py b/tensorflow/contrib/ndlstm/python/lstm2d.py
index da9698ec5b..ebbb4ccf11 100644
--- a/tensorflow/contrib/ndlstm/python/lstm2d.py
+++ b/tensorflow/contrib/ndlstm/python/lstm2d.py
@@ -91,21 +91,71 @@ def horizontal_lstm(images, num_filters_out, scope=None):
     return output
 
 
-def separable_lstm(images, num_filters_out, nhidden=None, scope=None):
+def get_blocks(images, kernel_size):
+  """Split images in blocks
+
+  Args:
+    images: (num_images, height, width, depth) tensor
+    kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of
+      of the pooling. Can be an int if both values are the same.
+
+  Returns:
+    (num_images, height/kernel_height, width/kernel_width,
+    depth*kernel_height*kernel_width) tensor
+  """
+  with variable_scope.variable_scope("image_blocks"):
+    batch_size, height, width, chanels = _shape(images)
+
+    if height % kernel_size[0] != 0:
+      offset = array_ops.zeros([batch_size,
+                                kernel_size[0] - (height % kernel_size[0]),
+                                width,
+                                chanels])
+      images = array_ops.concat([images, offset], 1)
+      batch_size, height, width, chanels = _shape(images)
+    if width % kernel_size[1] != 0:
+      offset = array_ops.zeros([batch_size,
+                                height,
+                                kernel_size[1] - (width % kernel_size[1]),
+                                chanels])
+      images = array_ops.concat([images, offset], 2)
+      batch_size, height, width, chanels = _shape(images)
+
+    h, w = int(height / kernel_size[0]), int(width / kernel_size[1])
+    features = kernel_size[1] * kernel_size[0] * chanels
+
+    lines = array_ops.split(images, h, axis=1)
+    line_blocks = []
+    for line in lines:
+      line = array_ops.transpose(line, [0, 2, 3, 1])
+      line = array_ops.reshape(line, [batch_size, w, features])
+      line_blocks.append(line)
+
+    return array_ops.stack(line_blocks, axis=1)
+
+
+def separable_lstm(images, num_filters_out,
+                   kernel_size=None, nhidden=None, scope=None):
   """Run bidirectional LSTMs first horizontally then vertically.
 
   Args:
     images: (num_images, height, width, depth) tensor
     num_filters_out: output layer depth
+    kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of
+      of the pooling. Can be an int if both values are the same. Set to None for
+      not using blocks
     nhidden: hidden layer depth
     scope: optional scope name
 
   Returns:
-    (num_images, height, width, num_filters_out) tensor
+    (num_images, height/kernel_height, width/kernel_width,
+    num_filters_out) tensor
   """
   with variable_scope.variable_scope(scope, "SeparableLstm", [images]):
     if nhidden is None:
       nhidden = num_filters_out
+    if kernel_size is not None:
+      images = get_blocks(images, kernel_size)
     hidden = horizontal_lstm(images, nhidden)
     with variable_scope.variable_scope("vertical"):
       transposed = array_ops.transpose(hidden, [0, 2, 1, 3])
diff --git a/tensorflow/contrib/ndlstm/python/lstm2d_test.py b/tensorflow/contrib/ndlstm/python/lstm2d_test.py
index 3dbbb81796..f1b37d701b 100644
--- a/tensorflow/contrib/ndlstm/python/lstm2d_test.py
+++ b/tensorflow/contrib/ndlstm/python/lstm2d_test.py
@@ -69,6 +69,14 @@ class Lstm2DTest(test_util.TensorFlowTestCase):
       result = outputs.eval()
       self.assertEqual(tuple(result.shape), (2, 7, 11, 8))
 
+  def testSeparableLstmDimsBlocks(self):
+    with self.test_session():
+      inputs = constant_op.constant(_rand(2, 7, 11, 5))
+      outputs = lstm2d.separable_lstm(inputs, 8, kernel_size=[2, 2])
+      variables.global_variables_initializer().run()
+      result = outputs.eval()
+      self.assertEqual(tuple(result.shape), (2, 4, 6, 8))
+
   def testReduceToSequenceDims(self):
     with self.test_session():
       inputs = constant_op.constant(_rand(2, 7, 11, 5))
-- 
GitLab


From 977bb1a52c90f41a3b3f75b6a5290e28a30e13f4 Mon Sep 17 00:00:00 2001
From: Sayed Hadi Hashemi <info@xldrx.com>
Date: Mon, 26 Jun 2017 16:15:41 -0700
Subject: [PATCH 0469/1375] Update WorkerCacheLogger::RecordRecvTensor (#10974)

* Update WorkerCacheLogger::RecordRecvTensor

Update WorkerCacheLogger::RecordRecvTensor to let future improvements to tracing tools:
* Add a placeholder for more details about a RecvTensor call.
* Custom title to possibly distinguish between control and data flow RecvTensor calls.

* Add a new method RecordDataTransfer.

* Minor fix
---
 .../worker_cache_logger.cc                    | 54 +++++++++++--------
 .../distributed_runtime/worker_cache_logger.h |  8 +++
 2 files changed, 41 insertions(+), 21 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/worker_cache_logger.cc b/tensorflow/core/distributed_runtime/worker_cache_logger.cc
index 5ca1d92a81..ffcbc50a2a 100644
--- a/tensorflow/core/distributed_runtime/worker_cache_logger.cc
+++ b/tensorflow/core/distributed_runtime/worker_cache_logger.cc
@@ -88,27 +88,39 @@ void WorkerCacheLogger::RecordRecvTensor(int64 step_id, int64 start_usecs,
                                          const string& src_device,
                                          const string& dst_device,
                                          int64 bytes) {
-  NodeExecStats* ns = new NodeExecStats;
-  ns->set_node_name("RecvTensor");
-  string byte_string = strings::StrCat("[", bytes, "B] ");
-  if (bytes >= 0.1 * 1048576.0) {
-    byte_string = strings::Printf("[%.1fMB] ", bytes / 1048576.0);
-  }
-  ns->set_timeline_label(strings::StrCat(byte_string, tensor_name, " from ",
-                                         src_device, " to ", dst_device));
-  ns->set_all_start_micros(start_usecs);
-  ns->set_op_start_rel_micros(0);
-  int64 elapsed = end_usecs - start_usecs;
-  ns->set_op_end_rel_micros(elapsed);
-  ns->set_all_end_rel_micros(elapsed);
-  NodeOutput* no = ns->add_output();
-  no->set_slot(0);
-  // TODO(tucker): Maybe set the dimensions too, but then they'll
-  // need to be passed in.
-  no->mutable_tensor_description()
-      ->mutable_allocation_description()
-      ->set_requested_bytes(bytes);
-  Save(dst_device, step_id, ns);
+  RecordDataTransfer(step_id, start_usecs, end_usecs, tensor_name, src_device,
+    dst_device, bytes, "", "RecvTensor");
 }
 
+void WorkerCacheLogger::RecordDataTransfer(int64 step_id, int64 start_usecs,
+                                           int64 end_usecs,
+                                           const string& tensor_name,
+                                           const string& src_device,
+                                           const string& dst_device,
+                                           int64 bytes,
+                                           const string& details,
+                                           const string& transfer_method_name){
+  NodeExecStats* ns = new NodeExecStats;
+    ns->set_node_name(transfer_method_name);
+    string byte_string = strings::StrCat("[", bytes, "B] ");
+    if (bytes >= 0.1 * 1048576.0) {
+      byte_string = strings::Printf("[%.1fMB] ", bytes / 1048576.0);
+    }
+    ns->set_timeline_label(strings::StrCat(byte_string, tensor_name, " from ",
+                                           src_device, " to ", dst_device,
+                                           details));
+    ns->set_all_start_micros(start_usecs);
+    ns->set_op_start_rel_micros(0);
+    int64 elapsed = end_usecs - start_usecs;
+    ns->set_op_end_rel_micros(elapsed);
+    ns->set_all_end_rel_micros(elapsed);
+    NodeOutput* no = ns->add_output();
+    no->set_slot(0);
+    // TODO(tucker): Maybe set the dimensions too, but then they'll
+    // need to be passed in.
+    no->mutable_tensor_description()
+        ->mutable_allocation_description()
+        ->set_requested_bytes(bytes);
+    Save(dst_device, step_id, ns);
+  }
 }  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/worker_cache_logger.h b/tensorflow/core/distributed_runtime/worker_cache_logger.h
index a92590a176..00846c25fe 100644
--- a/tensorflow/core/distributed_runtime/worker_cache_logger.h
+++ b/tensorflow/core/distributed_runtime/worker_cache_logger.h
@@ -60,6 +60,14 @@ class WorkerCacheLogger {
                         const string& tensor_name, const string& src_device,
                         const string& dst_device, int64 bytes);
 
+  // Generates a NodeExecStats record with the given data, and saves for
+  // later retrieval by RetrieveLogs().
+  void RecordDataTransfer(int64 step_id, int64 start_usecs, int64 end_usecs,
+                          const string& tensor_name, const string& src_device,
+                          const string& dst_device, int64 bytes,
+                          const string& details,
+                          const string& transfer_method_name);
+
  private:
   mutex count_mu_;
   int32 want_logging_count_ GUARDED_BY(count_mu_) = 0;
-- 
GitLab


From 90b2a38a1f7cd7cde0f012249407574e97583096 Mon Sep 17 00:00:00 2001
From: Wei Wu <lazyparser@gmail.com>
Date: Tue, 27 Jun 2017 07:16:02 +0800
Subject: [PATCH 0470/1375] bugfix: summaries variable was undefined. (#11059)

---
 tensorflow/docs_src/get_started/tensorboard_histograms.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/docs_src/get_started/tensorboard_histograms.md b/tensorflow/docs_src/get_started/tensorboard_histograms.md
index b3dd13497e..918deda190 100644
--- a/tensorflow/docs_src/get_started/tensorboard_histograms.md
+++ b/tensorflow/docs_src/get_started/tensorboard_histograms.md
@@ -34,6 +34,8 @@ tf.summary.histogram("normal/moving_mean", mean_moving_normal)
 sess = tf.Session()
 writer = tf.summary.FileWriter("/tmp/histogram_example")
 
+summaries = tf.summary.merge_all()
+
 # Setup a loop and write the summaries to disk
 N = 400
 for step in range(N):
-- 
GitLab


From 3576c25078090c35658ca0512cbd03697d36e06b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 17:58:24 -0700
Subject: [PATCH 0471/1375] Fix backward compatibility test broken by rollback.

PiperOrigin-RevId: 160222187
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 150 ------------------
 1 file changed, 150 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 6d2b54915e..cabd91f9ef 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -5376,30 +5376,6 @@ op {
     }
   }
 }
-op {
-  name: "Cosh"
-  input_arg {
-    name: "x"
-    type_attr: "T"
-  }
-  output_arg {
-    name: "y"
-    type_attr: "T"
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_HALF
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_COMPLEX64
-        type: DT_COMPLEX128
-      }
-    }
-  }
-}
 op {
   name: "CountUpTo"
   input_arg {
@@ -22971,30 +22947,6 @@ op {
     }
   }
 }
-op {
-  name: "Sinh"
-  input_arg {
-    name: "x"
-    type_attr: "T"
-  }
-  output_arg {
-    name: "y"
-    type_attr: "T"
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_HALF
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_COMPLEX64
-        type: DT_COMPLEX128
-      }
-    }
-  }
-}
 op {
   name: "Size"
   input_arg {
@@ -24836,108 +24788,6 @@ op {
     }
   }
 }
-op {
-  name: "SparseReduceMax"
-  input_arg {
-    name: "input_indices"
-    type: DT_INT64
-  }
-  input_arg {
-    name: "input_values"
-    type_attr: "T"
-  }
-  input_arg {
-    name: "input_shape"
-    type: DT_INT64
-  }
-  input_arg {
-    name: "reduction_axes"
-    type: DT_INT32
-  }
-  output_arg {
-    name: "output"
-    type_attr: "T"
-  }
-  attr {
-    name: "keep_dims"
-    type: "bool"
-    default_value {
-      b: false
-    }
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_INT32
-        type: DT_INT64
-        type: DT_UINT8
-        type: DT_INT16
-        type: DT_INT8
-        type: DT_UINT16
-        type: DT_HALF
-      }
-    }
-  }
-}
-op {
-  name: "SparseReduceMaxSparse"
-  input_arg {
-    name: "input_indices"
-    type: DT_INT64
-  }
-  input_arg {
-    name: "input_values"
-    type_attr: "T"
-  }
-  input_arg {
-    name: "input_shape"
-    type: DT_INT64
-  }
-  input_arg {
-    name: "reduction_axes"
-    type: DT_INT32
-  }
-  output_arg {
-    name: "output_indices"
-    type: DT_INT64
-  }
-  output_arg {
-    name: "output_values"
-    type_attr: "T"
-  }
-  output_arg {
-    name: "output_shape"
-    type: DT_INT64
-  }
-  attr {
-    name: "keep_dims"
-    type: "bool"
-    default_value {
-      b: false
-    }
-  }
-  attr {
-    name: "T"
-    type: "type"
-    allowed_values {
-      list {
-        type: DT_FLOAT
-        type: DT_DOUBLE
-        type: DT_INT32
-        type: DT_INT64
-        type: DT_UINT8
-        type: DT_INT16
-        type: DT_INT8
-        type: DT_UINT16
-        type: DT_HALF
-      }
-    }
-  }
-}
 op {
   name: "SparseReduceSum"
   input_arg {
-- 
GitLab


From c9e3617b4b018a19d83aa395aadb1e1f6017af9d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 18:00:10 -0700
Subject: [PATCH 0472/1375] Support advisor in all places (Command line, APIs)
 Add expensive operation checker

PiperOrigin-RevId: 160222348
---
 .../pywrap_tensorflow_print_model_analysis.i  |   1 -
 .../python/tools/tfprof/model_analyzer.py     | 121 ++++++++++++---
 .../tools/tfprof/model_analyzer_test.py       |  30 +++-
 .../python/tools/tfprof/profiler_test.py      |  16 +-
 tensorflow/tools/tfprof/BUILD                 |   1 +
 tensorflow/tools/tfprof/README.md             |   6 +-
 tensorflow/tools/tfprof/g3doc/advise.md       |  80 +++++++++-
 .../tools/tfprof/internal/advisor/BUILD       |   9 ++
 .../advisor/accelerator_utilization_checker.h |  24 ++-
 .../tools/tfprof/internal/advisor/checker.h   |  24 +--
 .../advisor/expensive_operation_checker.h     | 138 ++++++++++++++++++
 .../advisor/internal_checker_runner.h         |   5 +-
 .../advisor/internal_checker_runner_dummy.cc  |   6 +-
 .../internal/advisor/operation_checker.h      |  19 ++-
 .../tfprof/internal/advisor/tfprof_advisor.h  |  47 ++++--
 .../internal/advisor/tfprof_advisor_test.cc   |  46 ++++--
 .../tfprof/internal/print_model_analysis.cc   |  19 ++-
 .../tfprof/internal/print_model_analysis.h    |   2 -
 .../tools/tfprof/internal/tfprof_graph.h      |   4 +-
 tensorflow/tools/tfprof/internal/tfprof_op.cc |  11 +-
 tensorflow/tools/tfprof/internal/tfprof_op.h  |   6 +-
 .../tools/tfprof/internal/tfprof_options.h    |   4 +-
 .../tools/tfprof/internal/tfprof_show.cc      |  14 +-
 .../tools/tfprof/internal/tfprof_show.h       |  13 +-
 .../tfprof/internal/tfprof_show_multi.cc      |  19 ++-
 .../tools/tfprof/internal/tfprof_show_multi.h |  16 +-
 .../tools/tfprof/internal/tfprof_show_test.cc |   1 +
 .../tools/tfprof/internal/tfprof_stats.cc     |  77 ++++++----
 .../tools/tfprof/internal/tfprof_stats.h      |  22 ++-
 .../tfprof/internal/tfprof_stats_test.cc      |   1 +
 .../tfprof/internal/tfprof_tensor_test.cc     |   1 +
 .../tfprof/internal/tfprof_timeline_test.cc   |   1 +
 tensorflow/tools/tfprof/tfprof_main.cc        |  25 +++-
 tensorflow/tools/tfprof/tfprof_options.proto  |   8 +
 tensorflow/tools/tfprof/tfprof_output.proto   |   8 +
 35 files changed, 644 insertions(+), 181 deletions(-)
 create mode 100644 tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
index 40f29ae8a2..582c36e339 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
@@ -43,7 +43,6 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
-%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index 419beac0b9..c781d2af4e 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -20,6 +20,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import six
+
 from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
 from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
 from tensorflow.python.framework import errors
@@ -108,49 +110,77 @@ PRINT_ALL_TIMING_MEMORY = {
     'dump_to_file': ''
 }
 
+# The following options are for 'advise' tfprof_cmd.
+# Show all advice.
+ALL_ADVICE = {
+    'ExpensiveOperationChecker': {},
+    'AcceleratorUtilizationChecker': {},
+    'JobChecker': {},  # Only available internally.
+    'OperationChecker': {},
+}
+
 # pylint: enable=bad-whitespace
 # pylint: enable=bad-continuation
 
 
-def _build_options(tfprof_options):
+def _build_options(options):
   """Build tfprof.OptionsProto.
 
   Args:
-    tfprof_options: A dictionary of options.
+    options: A dictionary of options.
   Returns:
     tfprof.OptionsProto.
   """
   opts = tfprof_options_pb2.OptionsProto()
-  opts.max_depth = tfprof_options.get('max_depth', 10)
-  opts.min_bytes = tfprof_options.get('min_bytes', 0)
-  opts.min_micros = tfprof_options.get('min_micros', 0)
-  opts.min_params = tfprof_options.get('min_params', 0)
-  opts.min_float_ops = tfprof_options.get('min_float_ops', 0)
-  opts.min_occurrence = tfprof_options.get('min_occurrence', 0)
+  opts.max_depth = options.get('max_depth', 10)
+  opts.min_bytes = options.get('min_bytes', 0)
+  opts.min_micros = options.get('min_micros', 0)
+  opts.min_params = options.get('min_params', 0)
+  opts.min_float_ops = options.get('min_float_ops', 0)
+  opts.min_occurrence = options.get('min_occurrence', 0)
 
-  opts.step = tfprof_options.get('step', -1)
+  opts.step = options.get('step', -1)
 
-  opts.order_by = tfprof_options.get('order_by', 'name')
+  opts.order_by = options.get('order_by', 'name')
 
-  for p in tfprof_options.get('account_type_regexes', []):
+  for p in options.get('account_type_regexes', []):
     opts.account_type_regexes.append(p)
-  for p in tfprof_options.get('start_name_regexes', []):
+  for p in options.get('start_name_regexes', []):
     opts.start_name_regexes.append(p)
-  for p in tfprof_options.get('trim_name_regexes', []):
+  for p in options.get('trim_name_regexes', []):
     opts.trim_name_regexes.append(p)
-  for p in tfprof_options.get('show_name_regexes', []):
+  for p in options.get('show_name_regexes', []):
     opts.show_name_regexes.append(p)
-  for p in tfprof_options.get('hide_name_regexes', []):
+  for p in options.get('hide_name_regexes', []):
     opts.hide_name_regexes.append(p)
-  opts.account_displayed_op_only = tfprof_options.get(
-      'account_displayed_op_only', False)
+  opts.account_displayed_op_only = options.get('account_displayed_op_only',
+                                               False)
 
-  for p in tfprof_options.get('select', []):
+  for p in options.get('select', []):
     opts.select.append(p)
 
-  opts.output = tfprof_options.get('output', 'stdout')
-  opts.dump_to_file = tfprof_options.get('dump_to_file', '')
+  opts.output = options.get('output', 'stdout')
+  opts.dump_to_file = options.get('dump_to_file', '')
+
+  return opts
+
+
+def _build_advisor_options(options):
+  """Build tfprof.AdvisorOptionsProto.
 
+  Args:
+    options: A dictionary of options. See ALL_ADVICE example.
+  Returns:
+    tfprof.AdvisorOptionsProto.
+  """
+  opts = tfprof_options_pb2.AdvisorOptionsProto()
+  if options is None:
+    return opts
+  for checker, checker_opts in six.iteritems(options):
+    checker_ops_pb = tfprof_options_pb2.AdvisorOptionsProto.CheckerOption()
+    for k, v in six.iteritems(checker_opts):
+      checker_ops_pb[k] = v
+    opts.checkers[checker].MergeFrom(checker_ops_pb)
   return opts
 
 
@@ -190,7 +220,7 @@ class Profiler(object):
       else:
         _ = sess.run(...)
     # Auto detect problems and generate advice.
-    profiler.advise()
+    profiler.advise(model_analyzer.ALL_ADVICE)
   """
 
   def __init__(self, graph, op_log=None):
@@ -288,9 +318,19 @@ class Profiler(object):
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
 
-  def advise(self):
-    """Automatically detect problems and generate reports."""
-    print_mdl.Advise()
+  def advise(self, options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+    """Automatically detect problems and generate reports.
+
+    Args:
+      options: A dict of options.
+    Returns:
+      A Advise proto that conains the reports from all checkers.
+    """
+    advise_pb = tfprof_output_pb2.AdviceProto()
+    opts = _build_advisor_options(options)
+    advise_pb.ParseFromString(
+        print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString()))
+    return advise_pb
 
 
 def print_model_analysis(graph,
@@ -354,3 +394,36 @@ def print_model_analysis(graph,
         None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
 
   return tfprof_node
+
+
+def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+  """Auto profile and advise.
+
+    Builds profiles and automatically check anormalies of various
+    aspects. See go/tfprof or README for examples and tutorials.
+
+  Args:
+    graph: tf.Graph.
+    run_meta: tensorflow::RunMetadata proto. Allows auto-profile
+              time and memroy.
+    tfprof_options: see ALL_ADVICE example above.
+  Returns:
+    Returns AdviceProto proto
+  """
+  # pylint: disable=protected-access
+  op_log = tfprof_logger._merge_default_with_oplog(
+      graph, None, run_meta, add_trace=True)
+  # pylint: enable=protected-access
+
+  run_meta_str = run_meta.SerializeToString() if run_meta else b''
+
+  opts = _build_advisor_options(tfprof_options)
+  ret = tfprof_output_pb2.AdviceProto()
+  ret.ParseFromString(
+      print_mdl.PrintModelAnalysis(
+          graph.as_graph_def(add_shapes=True).SerializeToString(),
+          run_meta_str,
+          op_log.SerializeToString(),
+          'advise'.encode('utf-8'),
+          opts.SerializeToString()))
+  return ret
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index 9db752c577..fea27a82a5 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -126,7 +126,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['account_displayed_op_only'] = False
     opts['select'] = ['params', 'float_ops']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -176,6 +176,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['select'] = [
         'bytes', 'params', 'float_ops', 'device'
     ]
+    opts['output'] = 'none'
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -276,6 +277,33 @@ class PrintModelAnalysisTest(test.TestCase):
       self.assertEqual(total_children, 15)
       self.assertGreater(input_shapes, 0)
 
+  def testAdvisor(self):
+    ops.reset_default_graph()
+
+    with session.Session() as sess:
+      x = lib.BuildFullModel()
+
+      sess.run(variables.global_variables_initializer())
+      run_meta = config_pb2.RunMetadata()
+      _ = sess.run(
+          x,
+          options=config_pb2.RunOptions(
+              trace_level=config_pb2.RunOptions.FULL_TRACE),
+          run_metadata=run_meta)
+
+      advice_pb = model_analyzer.advise(sess.graph, run_meta)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
index 5daaafd7c8..c7113b6a57 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
@@ -129,7 +129,7 @@ class ProfilerTest(test.TestCase):
     opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
     opts['account_type_regexes'] = ['.*']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       r1, r2, r3 = lib.BuildSplitableModel()
       sess.run(variables.global_variables_initializer())
 
@@ -179,8 +179,18 @@ class ProfilerTest(test.TestCase):
       self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None)
       self.assertGreater(lib.SearchTFProfNode(pb3, 'add').exec_micros, 0)
 
-      # TODO(xpan): Better test of advisor.
-      profiler.advise()
+      advice_pb = profiler.advise(model_analyzer.ALL_ADVICE)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/tools/tfprof/BUILD b/tensorflow/tools/tfprof/BUILD
index 57cccd8921..541df78e47 100644
--- a/tensorflow/tools/tfprof/BUILD
+++ b/tensorflow/tools/tfprof/BUILD
@@ -33,6 +33,7 @@ cc_binary(
         "//tensorflow/tools/tfprof/internal:tfprof_options",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
         "//tensorflow/tools/tfprof/internal:tfprof_utils",
+        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
         "@linenoise//:linenoise",
     ],
 )
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/tools/tfprof/README.md
index 5927990524..816ad8c07e 100644
--- a/tensorflow/tools/tfprof/README.md
+++ b/tensorflow/tools/tfprof/README.md
@@ -7,7 +7,11 @@
 * Profile model performance
   * execution time, memory consumption
   * Profile multiple steps.
-* Auto detect and advise. (Experimental)
+* Auto profile and advise.
+  * accelerator utilization check
+  * expensive operation check
+  * operation configuration check
+  * distributed runtime check (Not OSS)
 
 ###Interfaces
 
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/tools/tfprof/g3doc/advise.md
index 3bce6270ff..e30add6fbf 100644
--- a/tensorflow/tools/tfprof/g3doc/advise.md
+++ b/tensorflow/tools/tfprof/g3doc/advise.md
@@ -3,6 +3,7 @@
 tfprof analyzes profiles and generates advises for common issues.
 
 ### Run Advise.
+
 ```python
 # First create a profiler. See profiler tutorials for more details.
 profiler = model_analyzer.Profiler(sess.graph)
@@ -13,8 +14,63 @@ _ = sess.run(r1,
              run_metadata=run_meta)
 profiler.add_step(1, run_meta)
 
-# Start advise.
-profiler.advise()
+# Then Start advise.
+profiler.advise(model_analyzer.ALL_ADVICE)
+
+# For one-shot API
+tf.contrib.tfprof.model_analyzer.advise(
+    sess.graph, run_meta=run_metadata)
+```
+
+```shell
+# Run advisor on CLI
+# See CLI tutorial on generating the files.
+tfprof --graph_path=graph.pbtxt \
+       --run_meta_path=run_metadata \
+       --op_log_path=tfprof_log
+
+tfprof> advise
+AcceleratorUtilizationChecker:
+device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03
+device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08
+device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04
+device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21
+
+OperationChecker:
+Found operation using NHWC data_format on GPU. Maybe NCHW is faster.
+
+ExpensiveOperationChecker:
+top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%)
+top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%)
+top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%)
+seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec
+  seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec
+    seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec
+      seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize), cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha..., cpu: 164us, accelerator: 0us, total: 164us
+      seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms
+      seq2seq_lib.py:151:sequence_loss_by_...:total_size = tf.a..., cpu: 154us, accelerator: 23us, total: 177us
+    seq2seq_lib.py:184:sampled_sequence_...:return cost / tf...., cpu: 97us, accelerator: 8us, total: 105us
+      math_ops.py:690:cast:return gen_math_o..., cpu: 62us, accelerator: 3us, total: 65us
+      math_ops.py:839:binary_op_wrapper:return func(x, y,..., cpu: 35us, accelerator: 5us, total: 40us
+  seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms
+    seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 303.76ms, accelerator: 73.54ms, total: 377.30ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 2.75ms, accelerator: 0us, total: 2.75ms
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 67us, accelerator: 0us, total: 67us
+    seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 292.04ms, accelerator: 73.54ms, total: 365.58ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 4.07ms, accelerator: 0us, total: 4.07ms
+      core_rnn.py:178:static_rnn:min_sequence_leng..., cpu: 85us, accelerator: 0us, total: 85us
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 16us, accelerator: 0us, total: 16us
+    seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms
+ ...(omitted)
+top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms
+top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms
+top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms
 ```
 
 ### Checker
@@ -25,16 +81,24 @@ area with the profile and report issues. A `Checker` is like a plugin.
 
 For example:
 
-####JobChecker (Not Available OSS)
-* Checking RecvTensor RPC latency and bandwidth.
-* Checking CPU/Memory utilization of the job.
+#### JobChecker (Not Available OSS)
+
+*   Checks RecvTensor RPC latency and bandwidth.
+*   Checks CPU/Memory utilization of the job.
 
 ####AcceleratorUtilization Checker
 * Checks what percentage of time the accelerator spends on computation.
 
-####Operation Checker
-* Check whether the operation runs with optimal options.
-* Checks if there is a better implementation to replace the current operation.
+#### OperationChecker
+
+*   Checks whether the operation runs with optimal options.
+*   Checks if there is a better implementation to replace the current operation.
+
+#### ExpensiveOperationChecker
+
+*   Checks the most expensive operation type.
+*   Checks the most expensive graph nodes.
+*   Checks the most expensive graph-building Python codes.
 
 ####Contribute Your Checker
 
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/tools/tfprof/internal/advisor/BUILD
index e4bd0f0015..629322373a 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/tools/tfprof/internal/advisor/BUILD
@@ -44,12 +44,21 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "expensive_operation_checker",
+    hdrs = ["expensive_operation_checker.h"],
+    deps = [
+        ":checker",
+    ],
+)
+
 cc_library(
     name = "tfprof_advisor",
     hdrs = ["tfprof_advisor.h"],
     deps = [
         ":accelerator_utilization_checker",
         ":checker",
+        ":expensive_operation_checker",
         ":internal_checker_runner_dummy",
         ":operation_checker",
     ],
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
index fb7f65d7dc..074b8e57b0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
@@ -33,10 +33,11 @@ struct ExecStats {
 
 class AcceleratorUtilizationChecker : public Checker {
  public:
-  string name() override { return "AcceleratorUtilizationChecker"; }
+  string name() const override { return kCheckers[0]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -48,24 +49,21 @@ class AcceleratorUtilizationChecker : public Checker {
     return CheckInternal();
   }
 
-  std::vector<string> CheckInternal() {
+  AdviceProto::Checker CheckInternal() {
     for (const auto& s : accelerator_exec_stats_) {
       const ExecStats& stat = s.second;
       int64 total_micros = stat.end_micros - stat.start_micros;
       if (total_micros <= 0) continue;
       double utilization = 1.0 * stat.exec_micros / total_micros;
       if (utilization >= 0.5) {
-        reports_.push_back(strings::Printf("%s: device: %s utilization: %.2f",
-                                           kLevel[0], s.first.c_str(),
-                                           utilization));
+        reports_.add_reports(strings::Printf("device: %s utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization < 0.5 && utilization > 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[1],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization <= 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[2],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       }
     }
     return reports_;
@@ -102,7 +100,7 @@ class AcceleratorUtilizationChecker : public Checker {
 
   std::map<string, ExecStats> accelerator_exec_stats_;
   std::map<string, int64> ps_placement_;
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/checker.h b/tensorflow/tools/tfprof/internal/advisor/checker.h
index b8b057be5b..3ce80cd8c4 100644
--- a/tensorflow/tools/tfprof/internal/advisor/checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/checker.h
@@ -18,27 +18,33 @@ limitations under the License.
 
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
-static const char* const kLevel[] = {
-    "NOTE",     // Good to know.
-    "SUGGEST",  // Might get better.
-    "WARN",     // Please do it for better.
+// Append only.
+static const char* const kCheckers[] = {
+    "AcceleratorUtilizationChecker", "OperationChecker",
+    "ExpensiveOperationChecker",
+    "JobChecker",  // Internal checker.
 };
 
 class Checker {
  public:
-  virtual ~Checker(){};
+  virtual ~Checker() {}
 
-  virtual string name() = 0;
+  virtual string name() const = 0;
 
-  std::vector<string> Run(const TFStats* stats) { return Check(stats); }
+  AdviceProto::Checker Run(const AdvisorOptionsProto::CheckerOption& options,
+                           const TFStats* stats) {
+    return Check(options, stats);
+  }
 
  protected:
-  // Returns a vector of string, each one being an advice.
-  virtual std::vector<string> Check(const TFStats* stats) = 0;
+  virtual AdviceProto::Checker Check(
+      const AdvisorOptionsProto::CheckerOption& options,
+      const TFStats* stats) = 0;
 };
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
new file mode 100644
index 0000000000..14e30edd87
--- /dev/null
+++ b/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
@@ -0,0 +1,138 @@
+/* Copyright 2016 The TensorFlow Authors All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// This checker checks the most expensive operations.
+#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+
+#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+
+namespace tensorflow {
+namespace tfprof {
+
+class ExpensiveOperationChecker : public Checker {
+ public:
+  string name() const override { return kCheckers[2]; }
+
+ private:
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
+    if (!stats) {
+      fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
+              name().c_str());
+      return reports_;
+    }
+    if (stats->steps().empty()) {
+      fprintf(stderr, "Missing RunMetadata info. Skip %s\n", name().c_str());
+    }
+    CheckOpView(stats);
+    CheckCodeView(stats);
+    CheckScopeView(stats);
+    return reports_;
+  }
+
+  void CheckOpView(const TFStats* stats) {
+    if (stats->steps().empty()) {
+      fprintf(stderr, "Missing run_meta for %s\n", name().c_str());
+      return;
+    }
+    Options opts(3, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"}, {},
+                 false, {"micros", "occurrence"}, "none", {});
+    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("op", opts);
+    if (root.children_size() == 0) {
+      return;
+    }
+    const TFMultiGraphNodeProto* node = &root;
+    std::vector<string> outputs;
+    for (int i = 0; i < 3 && node->children_size() > 0; ++i) {
+      node = &node->children(0);
+      outputs.push_back(strings::Printf(
+          "top %d operation type: %s, "
+          "cpu: %s, accelerator: %s, total: %s (%.2f%%)",
+          i + 1, node->name().c_str(),
+          FormatTime(node->cpu_exec_micros()).c_str(),
+          FormatTime(node->accelerator_exec_micros()).c_str(),
+          FormatTime(node->exec_micros()).c_str(),
+          100.0 * node->exec_micros() / (root.total_exec_micros() + 1e-10)));
+    }
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CheckCodeView(const TFStats* stats) {
+    if (!stats->has_code_traces()) {
+      fprintf(stderr, "Missing op_log (code traces) for %s\n", name().c_str());
+      return;
+    }
+    Options opts(100, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
+                 {}, false, {"micros"}, "none", {});
+    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("code", opts);
+    const TFMultiGraphNodeProto* node = &root;
+    // A trick here is: Usually, codes in library file are usually referenced
+    // only once, while user's own code are referenced multiple times.
+    while (node->children_size() == 1) {
+      node = &node->children(0);
+    }
+    if (node->children_size() == 0) {
+      return;
+    }
+
+    std::vector<string> outputs;
+    CodeViewHelper(node, 0, &outputs);
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CheckScopeView(const TFStats* stats) {
+    Options opts(100, 0, 100, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
+                 {}, false, {"micros"}, "none", {});
+    const TFGraphNodeProto root = stats->ShowGraphNode("scope", opts);
+    if (root.children_size() == 0) {
+      return;
+    }
+    std::vector<string> outputs;
+    const TFGraphNodeProto* node = &root;
+    for (int i = 0; i < 3 && i < root.children_size(); ++i) {
+      const TFGraphNodeProto& node = root.children(i);
+      outputs.push_back(strings::Printf(
+          "top %d graph node: %s, cpu: %s, accelerator: %s, total: %s", i + 1,
+          node.name().c_str(), FormatTime(node.cpu_exec_micros()).c_str(),
+          FormatTime(node.accelerator_exec_micros()).c_str(),
+          FormatTime(node.exec_micros()).c_str()));
+    }
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CodeViewHelper(const TFMultiGraphNodeProto* node, int depth,
+                      std::vector<string>* outputs) {
+    if (node->children_size() <= 1 || depth > 4) {
+      return;
+    }
+    for (int j = 0; j < 3 && j < node->children_size(); ++j) {
+      const TFMultiGraphNodeProto* c = &node->children(j);
+      outputs->push_back(strings::Printf(
+          "%s%s, cpu: %s, accelerator: %s, total: %s",
+          string(depth * 2, ' ').c_str(), c->name().c_str(),
+          FormatTime(c->total_cpu_exec_micros()).c_str(),
+          FormatTime(c->total_accelerator_exec_micros()).c_str(),
+          FormatTime(c->total_exec_micros()).c_str()));
+      CodeViewHelper(c, depth + 1, outputs);
+    }
+  }
+
+  AdviceProto::Checker reports_;
+};
+
+}  // namespace tfprof
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OP_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
index 1238b57f20..ed8ae571b6 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
@@ -17,13 +17,16 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
 
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
 class TFStats;
 
-std::map<string, std::vector<string>> RunInternalCheckers(const TFStats* stats);
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats);
 
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
index 8204d2b04e..67962c8e8b 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
@@ -17,9 +17,9 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-std::map<string, std::vector<string>> RunInternalCheckers(
-    const TFStats* stats) {
-  return std::map<string, std::vector<string>>();
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats) {
+  return AdviceProto();
 }
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
index 2a05f9bfd0..4d0d68e3bf 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
@@ -24,10 +24,11 @@ namespace tfprof {
 
 class OperationChecker : public Checker {
  public:
-  string name() override { return "OperationChecker"; }
+  string name() const override { return kCheckers[1]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -53,22 +54,20 @@ class OperationChecker : public Checker {
       }
     }
     if (use_batch_norm && !use_fused_batch_norm) {
-      reports_.push_back(strings::Printf(
-          "%s: Maybe use faster FusedBatchNorm instead of BatchNorm",
-          kLevel[1]));
+      reports_.add_reports(
+          "Maybe use faster FusedBatchNorm instead of BatchNorm");
     }
     if (recommend_nchw) {
       // TODO(xpan): Maybe print which Op supports NCHW.
-      reports_.push_back(strings::Printf(
-          "%s: Found operation using NHWC data_format on GPU. Maybe "
-          "NCHW is faster.",
-          kLevel[1]));
+      reports_.add_reports(
+          "Found operation using NHWC data_format on GPU. Maybe "
+          "NCHW is faster.");
     }
     return reports_;
   }
 
  private:
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
index 856f515459..d2257fb9b5 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
@@ -18,8 +18,10 @@ limitations under the License.
 
 #include "tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+#include "tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
 #include "tensorflow/tools/tfprof/internal/advisor/operation_checker.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -29,23 +31,44 @@ class Advisor {
  public:
   Advisor(const TFStats* stats) : stats_(stats) {}
 
-  std::map<string, std::vector<string>> Advise() {
+  static AdvisorOptionsProto DefaultOptions() {
+    AdvisorOptionsProto options;
+    std::vector<string> checkers(
+        kCheckers, kCheckers + sizeof(kCheckers) / sizeof(*kCheckers));
+    for (const string& checker : checkers) {
+      (*options.mutable_checkers())[checker];
+    }
+    return options;
+  }
+
+  AdviceProto Advise(const AdvisorOptionsProto& options) {
     // Note: Release a checker's memory ASAP.
-    std::map<string, std::vector<string>> reports = RunInternalCheckers(stats_);
-    // TODO(xpan): Think of a way to turn off/on specific checkers.
-    AcceleratorUtilizationChecker au_checker;
-    reports[au_checker.name()] = au_checker.Run(stats_);
-    OperationChecker op_checker;
-    reports[op_checker.name()] = op_checker.Run(stats_);
-
-    for (const auto& checker_r : reports) {
-      fprintf(stdout, "%s reports:\n", checker_r.first.c_str());
-      for (const auto& r : checker_r.second) {
+    AdviceProto ret = RunInternalCheckers(options, stats_);
+
+    if (options.checkers().find(kCheckers[0]) != options.checkers().end()) {
+      AcceleratorUtilizationChecker au_checker;
+      (*ret.mutable_checkers())[kCheckers[0]].MergeFrom(
+          au_checker.Run(options.checkers().at(kCheckers[0]), stats_));
+    }
+    if (options.checkers().find(kCheckers[1]) != options.checkers().end()) {
+      OperationChecker op_checker;
+      (*ret.mutable_checkers())[kCheckers[1]].MergeFrom(
+          op_checker.Run(options.checkers().at(kCheckers[1]), stats_));
+    }
+    if (options.checkers().find(kCheckers[2]) != options.checkers().end()) {
+      ExpensiveOperationChecker expensive_op_checker;
+      (*ret.mutable_checkers())[kCheckers[2]].MergeFrom(
+          expensive_op_checker.Run(options.checkers().at(kCheckers[2]),
+                                   stats_));
+    }
+    for (const auto& checker : ret.checkers()) {
+      fprintf(stdout, "\n%s:\n", checker.first.c_str());
+      for (const string& r : checker.second.reports()) {
         fprintf(stdout, "%s\n", r.c_str());
       }
     }
     fflush(stdout);
-    return reports;
+    return ret;
   }
 
  private:
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
index b41d0770dc..3b40253954 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
@@ -29,15 +29,16 @@ class TFProfAdvisorTest : public ::testing::Test {
                              nullptr, nullptr));
 
     stats_->AddNodeForTest(
-        "n1", CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 10, 2));
-    stats_->AddNodeForTest("n2", CreateNode("n2", "Conv2D", {}, 20, 2));
+        0, CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 0, 10, 2));
+    stats_->AddNodeForTest(0, CreateNode("n2", "Conv2D", {}, 0, 20, 2));
+    stats_->BuildAllViews();
     advisor_.reset(new Advisor(stats_.get()));
   }
 
   std::unique_ptr<TFGraphNode> CreateNode(const string& name,
                                           const string& type,
                                           std::map<string, string> attrs,
-                                          int64 start_miros,
+                                          int64 step, int64 start_miros,
                                           int64 end_rel_micros) {
     node_defs_.push_back(std::unique_ptr<NodeDef>(new NodeDef()));
     NodeDef* def = node_defs_.back().get();
@@ -52,10 +53,10 @@ class TFProfAdvisorTest : public ::testing::Test {
     NodeExecStats node_stat;
     node_stat.set_all_start_micros(start_miros);
     node_stat.set_op_end_rel_micros(end_rel_micros);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
                       node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
                       node_stat);
     return node;
   }
@@ -66,23 +67,38 @@ class TFProfAdvisorTest : public ::testing::Test {
 };
 
 TEST_F(TFProfAdvisorTest, Basics) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_TRUE(reports.find("AcceleratorUtilizationChecker") != reports.end());
-  EXPECT_TRUE(reports.find("OperationChecker") != reports.end());
+  AdvisorOptionsProto options = Advisor::DefaultOptions();
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(advice.checkers().find(kCheckers[0]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[1]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[2]) != advice.checkers().end());
 }
 
 TEST_F(TFProfAdvisorTest, OperationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["OperationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["OperationChecker"][0]).contains("NCHW"));
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[1]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[1]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[1]).reports(0))
+                  .contains("NCHW"));
 }
 
 TEST_F(TFProfAdvisorTest, UtilizationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["AcceleratorUtilizationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["AcceleratorUtilizationChecker"][0])
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[0]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[0]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[0]).reports(0))
                   .contains("low utilization"));
 }
 
+TEST_F(TFProfAdvisorTest, ExpensiveOperationChecker) {
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[2]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[2]).reports(0))
+                  .contains("top 1 operation type: Conv2D"));
+}
+
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.cc b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
index 37d01db3a1..5a9c44d8e6 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.cc
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/tfprof_log.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 #include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
@@ -36,6 +37,18 @@ TFStats* tf_stat = nullptr;
 
 string RunProfile(const string& command, const string& options,
                   TFStats* tf_stats) {
+  if (command == kCmds[4]) {
+    AdvisorOptionsProto option_pb;
+    if (!option_pb.ParseFromString(options)) {
+      fprintf(stderr, "Cannot parse AdvisorOptionsProto\n");
+      return "";
+    }
+    tf_stats->BuildAllViews();
+    return Advisor(tf_stats).Advise(option_pb).SerializeAsString();
+  } else {
+    tf_stats->BuildView(command);
+  }
+
   Options opts;
   tensorflow::Status s = Options::FromProtoStr(options, &opts);
   if (!s.ok()) {
@@ -97,14 +110,14 @@ void AddStep(int64 step, const string* run_meta, const string* op_log) {
   // TODO(xpan): Better error handling.
   std::unique_ptr<RunMetadata> run_meta_ptr(new RunMetadata());
   run_meta_ptr->ParseFromString(*run_meta);
-  tf_stat->ParseRunMeta(step, std::move(run_meta_ptr));
+  tf_stat->AddRunMeta(step, std::move(run_meta_ptr));
 
   std::unique_ptr<OpLog> op_log_ptr;
   if (op_log && !op_log->empty()) {
     op_log_ptr.reset(new OpLog());
     op_log_ptr->ParseFromString(*op_log);
   }
-  tf_stat->ParseOpLog(std::move(op_log_ptr));
+  tf_stat->AddOpLog(std::move(op_log_ptr));
 }
 
 string Profile(const string* command, const string* options) {
@@ -144,7 +157,5 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
   return RunProfile(*command, *options, &tf_stats);
 }
 
-void Advise() { Advisor(tf_stat).Advise(); }
-
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.h b/tensorflow/tools/tfprof/internal/print_model_analysis.h
index 84165e542d..46db63646d 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.h
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.h
@@ -39,8 +39,6 @@ void AddStep(int64 step, const string* run_meta, const string* op_log);
 
 string Profile(const string* command, const string* options);
 
-void Advise();
-
 // Single-step Profiler.
 //
 // Interface defined for Python API swig. Calls the tfprof core API.
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.h b/tensorflow/tools/tfprof/internal/tfprof_graph.h
index fbeae8673d..194a21f0cc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_graph.h
@@ -54,8 +54,8 @@ class TFGraph : public TFShow {
   const ShowNode* ShowInternal(const Options& opts,
                                Timeline* timeline) override;
 
-  bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                         int depth) const override {
     return true;
   }
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index 6a7077c085..77a2593623 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -126,6 +126,7 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
   }
   nodes = SortNodes(nodes, opts);
 
+  // pre keeps track of previous visited node.
   OpNode* pre = nullptr;
   std::vector<OpNode*> account_nodes;
   for (auto it = nodes.rbegin(); it != nodes.rend(); ++it) {
@@ -170,16 +171,20 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
     root_->ResetTotalStats();
     if (pre) {
       root_->AggregateTotalStats(pre);
-      root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
-      pre->mutable_proto()->clear_children();
     }
   }
+  if (pre) {
+    root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
+    pre->mutable_proto()->clear_children();
+  }
 
   if (opts.output_type == kOutput[1] || opts.output_type == kOutput[2]) {
     string display_str = FormatLegend(opts);
     for (OpNode* node : show_nodes) {
       display_str += FormatNode(node, root_.get(), opts);
     }
+    // In op view, we don't show root (total). But it will still in proto.
+    // TODO(xpan): Is it the right choice?
     root_->formatted_str = display_str;
   }
   return root_.get();
@@ -201,7 +206,7 @@ int64 TFOp::SearchRoot(const std::vector<OpNode*> nodes,
   return i;
 }
 
-string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
+string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const {
   std::vector<string> attrs;
 
   if (opts.select.find(kShown[0]) != opts.select.end()) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.h b/tensorflow/tools/tfprof/internal/tfprof_op.h
index 5b16490363..34812f54be 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.h
@@ -56,15 +56,15 @@ class TFOp : public TFMultiShow {
   int64 SearchRoot(const std::vector<OpNode*> nodes,
                    const std::vector<string>& regexes);
 
-  bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                         int depth) const override {
     if (opts.min_occurrence > node->node->graph_nodes().size()) {
       return false;
     }
     return true;
   }
 
-  string FormatNode(OpNode* node, OpNode* root, const Options& opts);
+  string FormatNode(OpNode* node, OpNode* root, const Options& opts) const;
 
   std::unique_ptr<OpNode> root_;
   std::map<string, std::unique_ptr<OpNode>> cnodes_map_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/tools/tfprof/internal/tfprof_options.h
index 6c9db24342..d39333e3fc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_options.h
@@ -59,10 +59,10 @@ static const char* const kShown[] = {
     "cpu_micros"};
 
 static const char* const kCmds[] = {
-    "scope", "graph", "code", "op", "set", "help",
+    "scope", "graph", "code", "op", "advise", "set", "help",
 };
 
-static const char* const kOutput[] = {"timeline", "stdout", "file"};
+static const char* const kOutput[] = {"timeline", "stdout", "file", "none"};
 
 static const char* const kTimelineOpts[] = {
     "outfile",
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index 40cc56fb22..6c1183a4d5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -26,7 +26,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFGraphNodeProto& TFShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -64,7 +66,8 @@ bool TFShow::LookUpCheckPoint(const string& name,
   return true;
 }
 
-bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
+bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
+                        int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -97,7 +100,8 @@ bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
   return true;
 }
 
-bool TFShow::ShouldTrim(ShowNode* node, const std::vector<string>& regexes) {
+bool TFShow::ShouldTrim(const ShowNode* node,
+                        const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -122,7 +126,7 @@ bool TFShow::ReAccount(ShowNode* node, const Options& opts) {
   return false;
 }
 
-string TFShow::FormatNode(ShowNode* node, const Options& opts) {
+string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
   std::vector<string> info;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     const string shape = FormatShapes(node->node->shape());
@@ -210,7 +214,7 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) {
                          str_util::Join(info, ", ").c_str());
 }
 
-string TFShow::FormatLegend(const Options& opts) {
+string TFShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     legends.push_back("# parameters");
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/tools/tfprof/internal/tfprof_show.h
index 2c61b4fd73..95513e086f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.h
@@ -54,20 +54,21 @@ class TFShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowNode* node, const Options& opts, int depth) const;
 
-  bool ShouldTrim(ShowNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowNode* node, const Options& opts);
 
-  string FormatNode(ShowNode* node, const Options& opts);
+  string FormatNode(ShowNode* node, const Options& opts) const;
 
-  string FormatLegend(const Options& opts);
+  string FormatLegend(const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 7545b3d48b..84d542714a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -28,7 +28,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -48,8 +50,8 @@ const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
   }
 }
 
-bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
-                            int depth) {
+bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
+                             int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -88,8 +90,8 @@ bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
   return true;
 }
 
-bool TFMultiShow::ShouldTrim(ShowMultiNode* node,
-                            const std::vector<string>& regexes) {
+bool TFMultiShow::ShouldTrim(const ShowMultiNode* node,
+                             const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -102,7 +104,7 @@ bool TFMultiShow::ReAccount(ShowMultiNode* node, const Options& opts) {
   return node->ReInit(opts.step, opts.account_type_regexes);
 }
 
-string TFMultiShow::FormatLegend(const Options& opts) {
+string TFMultiShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[0]) != opts.select.end()) {
     legends.push_back("output bytes");
@@ -142,7 +144,8 @@ string TFMultiShow::FormatLegend(const Options& opts) {
                          str_util::Join(legends, " | ").c_str());
 }
 
-string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
+string TFMultiShow::FormatInputShapes(
+    const TFMultiGraphNodeProto& proto) const {
   // input_shape string -> (static defined count, run count, run_micros)
   std::map<string, std::tuple<int64, int64, int64>> input_shapes_attr;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
@@ -199,7 +202,7 @@ string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
 }
 
 std::vector<string> TFMultiShow::FormatTimes(const ShowMultiNode* node,
-                                             const Options& opts) {
+                                             const Options& opts) const {
   std::vector<string> attrs;
   if (opts.select.find(kShown[1]) != opts.select.end()) {
     attrs.push_back(FormatTotalExecTime(node, opts));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
index e6faf1231d..ce309816a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
@@ -55,21 +55,23 @@ class TFMultiShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowMultiNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowMultiNode* node, const Options& opts,
+                  int depth) const;
 
-  bool ShouldTrim(ShowMultiNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowMultiNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowMultiNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts);
-  string FormatInputShapes(const TFMultiGraphNodeProto& proto);
+  string FormatLegend(const Options& opts) const;
+  string FormatInputShapes(const TFMultiGraphNodeProto& proto) const;
   std::vector<string> FormatTimes(const ShowMultiNode* node,
-                                  const Options& opts);
+                                  const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index 18a7c51a20..46b6ebc09a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -65,6 +65,7 @@ class TFProfShowTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.cc b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
index f5b8dad4e2..64da7ae7cf 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
@@ -29,16 +29,17 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
                  std::unique_ptr<RunMetadata> run_meta,
                  std::unique_ptr<OpLog> op_log,
                  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader)
-    : graph_(std::move(graph)),
+    : has_code_traces_(false),
+      graph_(std::move(graph)),
       ckpt_reader_(std::move(ckpt_reader)) {
   CHECK(graph_) << "Must at least have GraphDef";
 
   printf("Parsing Inputs...\n");
   ParseGraph();
   if (run_meta && run_meta->has_step_stats()) {
-    ParseRunMeta(0, std::move(run_meta));
+    AddRunMeta(0, std::move(run_meta));
   }
-  ParseOpLog(std::move(op_log));
+  AddOpLog(std::move(op_log));
 
   if (ckpt_reader_) {
     for (const auto& v : ckpt_reader_->GetVariableToShapeMap()) {
@@ -48,27 +49,48 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
       }
     }
   }
+}
 
-  printf("Preparing Views...\n");
-  scope_view_ = std::unique_ptr<TFScope>(new TFScope(ckpt_reader_.get()));
-  graph_view_ = std::unique_ptr<TFGraph>(new TFGraph(ckpt_reader_.get()));
-  code_view_ = std::unique_ptr<TFCode>(new TFCode());
-  op_view_ = std::unique_ptr<TFOp>(new TFOp());
+void TFStats::BuildView(const string& cmd) {
+  if (cmd == kCmds[0] && !scope_view_) {
+    scope_view_.reset(new TFScope(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      scope_view_->AddNode(it->second.get());
+    }
+    scope_view_->Build();
+  }
+  if (cmd == kCmds[1] && !graph_view_) {
+    graph_view_.reset(new TFGraph(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      graph_view_->AddNode(it->second.get());
+    }
+    graph_view_->Build();
+  }
+  if (cmd == kCmds[2] && !code_view_) {
+    code_view_.reset(new TFCode());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      code_view_->AddNode(it->second.get());
+    }
+    code_view_->Build();
+  }
+  if (cmd == kCmds[3] && !op_view_) {
+    op_view_.reset(new TFOp());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      op_view_->AddNode(it->second.get());
+    }
+    op_view_->Build();
+  }
+}
 
-  for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-    scope_view_->AddNode(it->second.get());
-    graph_view_->AddNode(it->second.get());
-    code_view_->AddNode(it->second.get());
-    op_view_->AddNode(it->second.get());
-  }
-  scope_view_->Build();
-  graph_view_->Build();
-  code_view_->Build();
-  op_view_->Build();
+void TFStats::BuildAllViews() {
+  std::vector<string> cmds_str(kCmds, kCmds + sizeof(kCmds) / sizeof(*kCmds));
+  for (const string& cmd : cmds_str) {
+    BuildView(cmd);
+  }
 }
 
 const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
-                                               const Options& opts) {
+                                               const Options& opts) const {
   if (!Validate(opts)) {
     return empty_graph_node_;
   }
@@ -82,8 +104,8 @@ const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
   }
 }
 
-const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(const string& cmd,
-                                                         const Options& opts) {
+const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(
+    const string& cmd, const Options& opts) const {
   if (!Validate(opts)) {
     return empty_multi_graph_node_;
   }
@@ -130,7 +152,7 @@ void TFStats::ParseGraph() {
   }
 }
 
-void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
+void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
   if (!op_log) {
     return;
   }
@@ -144,12 +166,13 @@ void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
       node->second->AddFloatOps(entry.float_ops());
     }
     if (entry.has_code_def()) {
+      has_code_traces_ = true;
       node->second->AddCode(entry.code_def());
     }
   }
 }
 
-void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
+void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   if (!run_meta || !run_meta->has_step_stats()) {
     fprintf(stderr, "Invalid RunMetadata for step %lld\n", step);
     return;
@@ -176,7 +199,7 @@ void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   }
 }
 
-bool TFStats::Validate(const Options& opts) {
+bool TFStats::Validate(const Options& opts) const {
   if (opts.step >= 0 && steps_.find(opts.step) == steps_.end()) {
     fprintf(stderr, "Options -step=%lld not found\n", opts.step);
     return false;
@@ -184,9 +207,9 @@ bool TFStats::Validate(const Options& opts) {
   return true;
 }
 
-void TFStats::AddNodeForTest(const string& name,
-                             std::unique_ptr<TFGraphNode> node) {
-  nodes_map_[name] = std::move(node);
+void TFStats::AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node) {
+  steps_.insert(step);
+  nodes_map_[node->name()] = std::move(node);
 }
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.h b/tensorflow/tools/tfprof/internal/tfprof_stats.h
index dfb190e703..b26d274f80 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.h
@@ -59,28 +59,38 @@ class TFStats {
   const std::map<string, std::unique_ptr<TFGraphNode>>& nodes() const {
     return nodes_map_;
   }
+  const std::set<int64>& steps() const { return steps_; }
+  bool has_code_traces() const { return has_code_traces_; }
 
+  void BuildView(const string& cmd);
+  void BuildAllViews();
+
+  // Note: Must first BuildView(view_foo) before ShowXXX(view_foo) methods.
+  //
   // Organize the TensorFlow model as different types of views, and generate
   // outputs for profiling.
-  const TFGraphNodeProto& ShowGraphNode(const string& cmd, const Options& opts);
+  // TODO(xpan): Should it return reference here?
+  const TFGraphNodeProto& ShowGraphNode(const string& cmd,
+                                        const Options& opts) const;
   const TFMultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
-                                                  const Options& opts);
+                                                  const Options& opts) const;
 
   // Add a step of run time meta data.
-  void ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
+  void AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
   // Add tfprof operation meta data, such as customized op type, float_ops,
   // and code traces.
-  void ParseOpLog(std::unique_ptr<OpLog> op_log);
+  void AddOpLog(std::unique_ptr<OpLog> op_log);
 
   // For test purpose only.
-  void AddNodeForTest(const string& name, std::unique_ptr<TFGraphNode> node);
+  void AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node);
 
  private:
-  bool Validate(const Options& opts);
+  bool Validate(const Options& opts) const;
 
   void ParseGraph();
 
   std::set<int64> steps_;
+  bool has_code_traces_;
   std::unique_ptr<GraphDef> graph_;
   std::unique_ptr<TFScope> scope_view_;
   std::unique_ptr<TFGraph> graph_view_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index bcd1147c69..00d5d8cdb3 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -66,6 +66,7 @@ class TFProfStatsTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index d7cfe7dade..0482657ca5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -50,6 +50,7 @@ class TFProfTensorTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
index 0e9bb9658c..cad31050a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
@@ -52,6 +52,7 @@ class TFProfTimelineTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 nullptr, nullptr));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/tools/tfprof/tfprof_main.cc
index 7a4e7e85ff..5e70c093cc 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/tools/tfprof/tfprof_main.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/util/command_line_flags.h"
+#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
@@ -161,12 +162,13 @@ int Run(int argc, char** argv) {
         "Profiling everything!\n");
     return 0;
   } else if (argc > 1) {
-    if (string(argv[1]) == kCmds[5]) {
+    if (string(argv[1]) == kCmds[6]) {
       PrintHelp();
       return 0;
     }
     if (string(argv[1]) == kCmds[0] || string(argv[1]) == kCmds[1] ||
-        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3]) {
+        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3] ||
+        string(argv[1]) == kCmds[4]) {
       cmd = argv[1];
     }
   }
@@ -216,7 +218,13 @@ int Run(int argc, char** argv) {
               run_meta_files[i].c_str(), s.ToString().c_str());
       return 1;
     }
-    tf_stat.ParseRunMeta(i, std::move(run_meta));
+    tf_stat.AddRunMeta(i, std::move(run_meta));
+  }
+
+  if (cmd == kCmds[4]) {
+    tf_stat.BuildAllViews();
+    Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
+    return 0;
   }
 
   Options opts(FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros,
@@ -227,9 +235,11 @@ int Run(int argc, char** argv) {
                output_type, output_options);
 
   if (cmd == kCmds[2] || cmd == kCmds[3]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowMultiGraphNode(cmd, opts);
     return 0;
   } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowGraphNode(cmd, opts);
     return 0;
   }
@@ -254,14 +264,19 @@ int Run(int argc, char** argv) {
       fprintf(stderr, "E: %s\n", s.ToString().c_str());
       continue;
     }
-    if (cmd == kCmds[4]) {
+    if (cmd == kCmds[5]) {
       opts = new_opts;
-    } else if (cmd == kCmds[5]) {
+    } else if (cmd == kCmds[6]) {
       PrintHelp();
     } else if (cmd == kCmds[2] || cmd == kCmds[3]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowMultiGraphNode(cmd, new_opts);
     } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowGraphNode(cmd, new_opts);
+    } else if (cmd == kCmds[4]) {
+      tf_stat.BuildAllViews();
+      Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
     }
   }
   return 0;
diff --git a/tensorflow/tools/tfprof/tfprof_options.proto b/tensorflow/tools/tfprof/tfprof_options.proto
index de916b4a16..5882833039 100644
--- a/tensorflow/tools/tfprof/tfprof_options.proto
+++ b/tensorflow/tools/tfprof/tfprof_options.proto
@@ -24,3 +24,11 @@ message OptionsProto {
   string output = 15;
   string dump_to_file = 16;
 }
+
+message AdvisorOptionsProto {
+  // checker name -> a dict of key-value options.
+  map<string, CheckerOption> checkers = 1;
+  message CheckerOption {
+    map<string, string> options = 1;
+  }
+}
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index 7206c6f956..abf141f1e7 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -102,3 +102,11 @@ message TFMultiGraphNodeProto {
   // structure used.
   repeated TFMultiGraphNodeProto children = 11;
 }
+
+message AdviceProto {
+  // checker name -> a list of reports from the checker.
+  map<string, Checker> checkers = 1;
+  message Checker {
+    repeated string reports = 2;
+  }
+}
-- 
GitLab


From 71ad7a88e08237572b771f8e679c7a657b2d7049 Mon Sep 17 00:00:00 2001
From: David Majnemer <majnemer@google.com>
Date: Mon, 26 Jun 2017 18:04:28 -0700
Subject: [PATCH 0473/1375] [XLA] Simplify the fusion heuristic

We had two different aspects of the fusion heuristic:
- Don't fuse a producer into a consumer if there exists a path from the
  producer to the consumer which cannot be fused.
- Don't fuse a producer into a consumer if any consumer of the producer cannot
  fuse.

These can be combined into one, simpler, heuristic.

PiperOrigin-RevId: 160222771
---
 .../xla/service/instruction_fusion.cc         | 61 +++----------------
 .../compiler/xla/service/instruction_fusion.h |  6 +-
 2 files changed, 11 insertions(+), 56 deletions(-)

diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index bc7adb82b1..53b2c2a6e3 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -147,11 +147,11 @@ bool EffectivelyUnary(HloInstruction* hlo) {
 bool InstructionFusion::CanFuseOnAllPaths(
     const HloComputation::ReachabilityMap& reachability_map,
     HloInstruction* producer, HloInstruction* consumer,
-    DoNotFuseMap* do_not_fuse) {
+    DoNotFuseSet* do_not_fuse) {
   auto could_fuse_on_all_paths = [&] {
     // First check to see if we have already marked this producer as infeasible
     // to fuse into consumer.
-    if ((*do_not_fuse)[consumer].count(producer) > 0) {
+    if (do_not_fuse->count(producer) > 0) {
       return false;
     }
     // Make sure it is possible for producer and consumer to exist in a fusion
@@ -176,7 +176,7 @@ bool InstructionFusion::CanFuseOnAllPaths(
 
         // First check if we have already ruled out fusing producer into
         // consumer_operand.
-        if ((*do_not_fuse)[consumer_operand].count(producer) > 0) {
+        if (do_not_fuse->count(consumer_operand) > 0) {
           return false;
         }
         // Make sure it is possible for consumer_operand to exist in a fusion
@@ -204,7 +204,7 @@ bool InstructionFusion::CanFuseOnAllPaths(
     return true;
   }
   // We couldn't fuse on all paths, record this result.
-  (*do_not_fuse)[consumer].insert(producer);
+  do_not_fuse->insert(producer);
   return false;
 }
 
@@ -229,23 +229,9 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
       InsertOrDie(&post_order_index, post_order[i], i);
     }
 
-    DoNotFuseMap do_not_fuse;
+    DoNotFuseSet do_not_fuse;
     auto transitive_operands = computation->ComputeTransitiveOperands();
 
-    auto producer_cannot_fuse_into_consumer = [this](HloInstruction* producer,
-                                                     HloInstruction* consumer) {
-      // Make sure it is possible for producer and consumer to exist in a fusion
-      // node.
-      if (!producer->IsFusable() || !consumer->IsFusable()) {
-        return true;
-      }
-      auto operand_indices = consumer->OperandIndices(producer);
-      return std::any_of(operand_indices.begin(), operand_indices.end(),
-                         [&](const int64 operand_index) {
-                           return !ShouldFuse(consumer, operand_index);
-                         });
-    };
-
     auto cheap_to_duplicate = [](HloInstruction* producer) {
       if (producer->opcode() == HloOpcode::kBroadcast) {
         return true;
@@ -260,33 +246,16 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
       return false;
     };
 
-    for (HloInstruction* producer : post_order) {
-      if (cheap_to_duplicate(producer)) {
-        continue;
-      }
-      if (std::any_of(producer->users().begin(), producer->users().end(),
-                      [&](HloInstruction* consumer) {
-                        return producer_cannot_fuse_into_consumer(producer,
-                                                                  consumer);
-                      })) {
-        for (HloInstruction* consumer : producer->users()) {
-          do_not_fuse[consumer].insert(producer);
-        }
-      }
-    }
     for (HloInstruction* consumer : post_order) {
       for (HloInstruction* producer : consumer->operands()) {
         if (cheap_to_duplicate(producer)) {
           continue;
         }
-        if (do_not_fuse[consumer].count(producer) > 0) {
-          continue;
-        }
         if (CanFuseOnAllPaths(*transitive_operands, producer, consumer,
                               &do_not_fuse)) {
-          CHECK_EQ(do_not_fuse[consumer].count(producer), 0);
+          CHECK_EQ(do_not_fuse.count(producer), 0);
         } else {
-          CHECK_GT(do_not_fuse[consumer].count(producer), 0);
+          CHECK_GT(do_not_fuse.count(producer), 0);
         }
       }
     }
@@ -379,24 +348,10 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
         if (!ShouldFuse(instruction, i)) {
           continue;
         }
-        const std::unordered_set<HloInstruction*>&
-            do_not_fuse_into_instruction = do_not_fuse[instruction];
-        if (do_not_fuse_into_instruction.count(operand) > 0) {
+        if (do_not_fuse.count(operand) > 0) {
           continue;
         }
         HloInstruction* fusion_instruction = Fuse(operand, instruction);
-        std::unordered_set<HloInstruction*>&
-            do_not_fuse_into_fusion_instruction =
-                do_not_fuse[fusion_instruction];
-        if (instruction != fusion_instruction) {
-          do_not_fuse_into_fusion_instruction.insert(
-              do_not_fuse_into_instruction.begin(),
-              do_not_fuse_into_instruction.end());
-        }
-        const std::unordered_set<HloInstruction*>& do_not_fuse_into_operand =
-            do_not_fuse[operand];
-        do_not_fuse_into_fusion_instruction.insert(
-            do_not_fuse_into_operand.begin(), do_not_fuse_into_operand.end());
 
         // Fusing an instruction into a fusion instruction can change the
         // operand set of the fusion instruction. For simplicity just push the
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.h b/tensorflow/compiler/xla/service/instruction_fusion.h
index ca98218286..06f03f63af 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.h
+++ b/tensorflow/compiler/xla/service/instruction_fusion.h
@@ -72,15 +72,15 @@ class InstructionFusion : public HloPassInterface {
  private:
   HloInstruction* Fuse(HloInstruction* producer, HloInstruction* consumer);
 
-  using DoNotFuseMap =
-      std::unordered_map<HloInstruction*, std::unordered_set<HloInstruction*>>;
+  // The set of producers whose consumers we cannot fuse into.
+  using DoNotFuseSet = std::unordered_set<HloInstruction*>;
 
   // Whether or not we can fuse consumer into original_producer on all paths
   // from the producer to the consumer where nodes are HLOs and edges are uses.
   bool CanFuseOnAllPaths(
       const HloComputation::ReachabilityMap& reachability_map,
       HloInstruction* producer, HloInstruction* consumer,
-      DoNotFuseMap* do_not_fuse);
+      DoNotFuseSet* do_not_fuse);
 
   // Used to determine if an HLO is expensive. Expensive operations will not be
   // duplicated.
-- 
GitLab


From 1e7ad87dfd00a742d97fc5b6434dfca9179e5c8f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 18:05:46 -0700
Subject: [PATCH 0474/1375] Automated g4 rollback of changelist 160196566

PiperOrigin-RevId: 160222930
---
 tensorflow/docs_src/programmers_guide/faq.md  |   3 +-
 .../docs_src/programmers_guide/index.md       |   2 +-
 .../programmers_guide/reading_data.md         |   2 +-
 .../programmers_guide/variable_scope.md       | 373 +++++++++++++
 .../docs_src/programmers_guide/variables.md   | 508 ++++++------------
 tensorflow/docs_src/tutorials/deep_cnn.md     |   2 +-
 6 files changed, 530 insertions(+), 360 deletions(-)
 create mode 100644 tensorflow/docs_src/programmers_guide/variable_scope.md

diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index 74407ed315..e31d2717a6 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -174,7 +174,8 @@ for more information on how to use them.
 
 ## Variables
 
-See also the how-to documentation on @{$variables$variables} and
+See also the how-to documentation on @{$variables$variables}
+and @{$variable_scope$variable scopes}, and
 @{$python/state_ops$the API documentation for variables}.
 
 #### What is the lifetime of a variable?
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md
index ea4d186739..5ed7c09a57 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/programmers_guide/index.md
@@ -9,7 +9,7 @@ explain a particular aspect of TensorFlow:
   * @{$dims_types$Tensor Ranks, Shapes, and Types}, which explains Tensor
     rank (the number of dimensions), shape (the size of each dimension),
     and datatypes.
-  * @{$variables$Sharing Variables}, which explains how to share and
+  * @{$variable_scope$Sharing Variables}, which explains how to share and
     manage large sets of variables when building complex models.
   * @{$threading_and_queues$Threading and Queues}, which explains TensorFlow's
     rich queuing system.
diff --git a/tensorflow/docs_src/programmers_guide/reading_data.md b/tensorflow/docs_src/programmers_guide/reading_data.md
index a7d9372053..3c31d3a1a7 100644
--- a/tensorflow/docs_src/programmers_guide/reading_data.md
+++ b/tensorflow/docs_src/programmers_guide/reading_data.md
@@ -476,4 +476,4 @@ This is what is done in
 
 You can have the train and eval in the same graph in the same process, and share
 their trained variables.  See
-@{$variables$the shared variables tutorial}.
+@{$variable_scope$the shared variables tutorial}.
diff --git a/tensorflow/docs_src/programmers_guide/variable_scope.md b/tensorflow/docs_src/programmers_guide/variable_scope.md
new file mode 100644
index 0000000000..f4d2b3f37b
--- /dev/null
+++ b/tensorflow/docs_src/programmers_guide/variable_scope.md
@@ -0,0 +1,373 @@
+# Sharing Variables
+
+You can create, initialize, save and load single variables
+in the way described in the @{$variables$Variables HowTo}.
+But when building complex models you often need to share large sets of
+variables and you might want to initialize all of them in one place.
+This tutorial shows how this can be done using `tf.variable_scope()` and
+`tf.get_variable()`.
+
+## The Problem
+
+Imagine you create a simple model for image filters, similar to our
+@{$deep_cnn$Convolutional Neural Networks Tutorial}
+model but with only 2 convolutions (for simplicity of this example). If you use
+just `tf.Variable`, as explained in @{$variables$Variables HowTo},
+your model might look like this.
+
+```python
+def my_image_filter(input_images):
+    conv1_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
+        name="conv1_weights")
+    conv1_biases = tf.Variable(tf.zeros([32]), name="conv1_biases")
+    conv1 = tf.nn.conv2d(input_images, conv1_weights,
+        strides=[1, 1, 1, 1], padding='SAME')
+    relu1 = tf.nn.relu(conv1 + conv1_biases)
+
+    conv2_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
+        name="conv2_weights")
+    conv2_biases = tf.Variable(tf.zeros([32]), name="conv2_biases")
+    conv2 = tf.nn.conv2d(relu1, conv2_weights,
+        strides=[1, 1, 1, 1], padding='SAME')
+    return tf.nn.relu(conv2 + conv2_biases)
+```
+
+As you can easily imagine, models quickly get much more complicated than
+this one, and even here we already have 4 different variables: `conv1_weights`,
+`conv1_biases`, `conv2_weights`, and `conv2_biases`.
+
+The problem arises when you want to reuse this model. Assume you want to
+apply your image filter to 2 different images, `image1` and `image2`.
+You want both images processed by the same filter with the same parameters.
+You can call `my_image_filter()` twice, but this will create two sets
+of variables, 4 variables in each one, for a total of 8 variables.
+
+```python
+# First call creates one set of 4 variables.
+result1 = my_image_filter(image1)
+# Another set of 4 variables is created in the second call.
+result2 = my_image_filter(image2)
+```
+
+A common way to share variables is to create them in a separate piece of code
+and pass them to functions that use them.   For example by using a dictionary:
+
+```python
+variables_dict = {
+    "conv1_weights": tf.Variable(tf.random_normal([5, 5, 32, 32]),
+        name="conv1_weights")
+    "conv1_biases": tf.Variable(tf.zeros([32]), name="conv1_biases")
+    ... etc. ...
+}
+
+def my_image_filter(input_images, variables_dict):
+    conv1 = tf.nn.conv2d(input_images, variables_dict["conv1_weights"],
+        strides=[1, 1, 1, 1], padding='SAME')
+    relu1 = tf.nn.relu(conv1 + variables_dict["conv1_biases"])
+
+    conv2 = tf.nn.conv2d(relu1, variables_dict["conv2_weights"],
+        strides=[1, 1, 1, 1], padding='SAME')
+    return tf.nn.relu(conv2 + variables_dict["conv2_biases"])
+
+# Both calls to my_image_filter() now use the same variables
+result1 = my_image_filter(image1, variables_dict)
+result2 = my_image_filter(image2, variables_dict)
+```
+
+While convenient, creating variables like above,
+outside of the code, breaks encapsulation:
+
+*  The code that builds the graph must document the names, types,
+   and shapes of variables to create.
+*  When the code changes, the callers may have to create more, or less,
+   or different variables.
+
+One way to address the problem is to use classes to create a model,
+where the classes take care of managing the variables they need.
+For a lighter solution, not involving classes, TensorFlow provides
+a *Variable Scope* mechanism that allows to easily share named variables
+while constructing a graph.
+
+## Variable Scope Example
+
+Variable Scope mechanism in TensorFlow consists of two main functions:
+
+* `tf.get_variable(<name>, <shape>, <initializer>)`:
+  Creates or returns a variable with a given name.
+* `tf.variable_scope(<scope_name>)`:
+  Manages namespaces for names passed to `tf.get_variable()`.
+
+The function `tf.get_variable()` is used to get or create a variable instead
+of a direct call to `tf.Variable`. It uses an *initializer* instead of passing
+the value directly, as in `tf.Variable`. An initializer is a function that
+takes the shape and provides a tensor with that shape. Here are some
+initializers available in TensorFlow:
+
+* `tf.constant_initializer(value)` initializes everything to the provided value,
+* `tf.random_uniform_initializer(a, b)` initializes uniformly from [a, b],
+* `tf.random_normal_initializer(mean, stddev)` initializes from the normal
+  distribution with the given mean and standard deviation.
+
+To see how `tf.get_variable()` solves the problem discussed
+before, let's refactor the code that created one convolution into
+a separate function, named `conv_relu`:
+
+```python
+def conv_relu(input, kernel_shape, bias_shape):
+    # Create variable named "weights".
+    weights = tf.get_variable("weights", kernel_shape,
+        initializer=tf.random_normal_initializer())
+    # Create variable named "biases".
+    biases = tf.get_variable("biases", bias_shape,
+        initializer=tf.constant_initializer(0.0))
+    conv = tf.nn.conv2d(input, weights,
+        strides=[1, 1, 1, 1], padding='SAME')
+    return tf.nn.relu(conv + biases)
+```
+
+This function uses short names `"weights"` and `"biases"`.
+We'd like to use it for both `conv1` and `conv2`, but
+the variables need to have different names.
+This is where `tf.variable_scope()` comes into play:
+it pushes a namespace for variables.
+
+```python
+def my_image_filter(input_images):
+    with tf.variable_scope("conv1"):
+        # Variables created here will be named "conv1/weights", "conv1/biases".
+        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
+    with tf.variable_scope("conv2"):
+        # Variables created here will be named "conv2/weights", "conv2/biases".
+        return conv_relu(relu1, [5, 5, 32, 32], [32])
+```
+
+Now, let's see what happens when we call `my_image_filter()` twice.
+
+```
+result1 = my_image_filter(image1)
+result2 = my_image_filter(image2)
+# Raises ValueError(... conv1/weights already exists ...)
+```
+
+As you can see, `tf.get_variable()` checks that already existing variables
+are not shared by accident. If you want to share them, you need to specify
+it by setting `reuse_variables()` as follows.
+
+```
+with tf.variable_scope("image_filters") as scope:
+    result1 = my_image_filter(image1)
+    scope.reuse_variables()
+    result2 = my_image_filter(image2)
+```
+
+This is a good way to share variables, lightweight and safe.
+
+## How Does Variable Scope Work?
+
+### Understanding `tf.get_variable()`
+
+To understand variable scope it is necessary to first
+fully understand how `tf.get_variable()` works.
+Here is how `tf.get_variable` is usually called.
+
+```python
+v = tf.get_variable(name, shape, dtype, initializer)
+```
+
+This call does one of two things depending on the scope it is called in.
+Here are the two options.
+
+* Case 1: the scope is set for creating new variables, as evidenced by
+`tf.get_variable_scope().reuse == False`.
+
+In this case, `v` will be a newly created `tf.Variable` with the provided
+shape and data type. The full name of the created variable will be set to
+the current variable scope name + the provided `name` and a check will be
+performed to ensure that no variable with this full name exists yet.
+If a variable with this full name already exists, the function will
+raise a `ValueError`. If a new variable is created, it will be
+initialized to the value `initializer(shape)`. For example:
+
+```python
+with tf.variable_scope("foo"):
+    v = tf.get_variable("v", [1])
+assert v.name == "foo/v:0"
+```
+
+* Case 2: the scope is set for reusing variables, as evidenced by
+`tf.get_variable_scope().reuse == True`.
+
+In this case, the call will search for an already existing variable with
+name equal to the current variable scope name + the provided `name`.
+If no such variable exists, a `ValueError` will be raised. If the variable
+is found, it will be returned. For example:
+
+```python
+with tf.variable_scope("foo"):
+    v = tf.get_variable("v", [1])
+with tf.variable_scope("foo", reuse=True):
+    v1 = tf.get_variable("v", [1])
+assert v1 is v
+```
+
+### Basics of `tf.variable_scope()`
+
+Knowing how `tf.get_variable()` works makes it easy to understand variable
+scope. The primary function of variable scope is to carry a name that will
+be used as prefix for variable names and a reuse-flag to distinguish the two
+cases described above. Nesting variable scopes appends their names in a way
+analogous to how directories work:
+
+```python
+with tf.variable_scope("foo"):
+    with tf.variable_scope("bar"):
+        v = tf.get_variable("v", [1])
+assert v.name == "foo/bar/v:0"
+```
+
+The current variable scope can be retrieved using `tf.get_variable_scope()`
+and the `reuse` flag of the current variable scope can be set to `True` by
+calling `tf.get_variable_scope().reuse_variables()`:
+
+```python
+with tf.variable_scope("foo"):
+    v = tf.get_variable("v", [1])
+    tf.get_variable_scope().reuse_variables()
+    v1 = tf.get_variable("v", [1])
+assert v1 is v
+```
+
+Note that you *cannot* set the `reuse` flag to `False`. The reason behind
+this is to allow to compose functions that create models. Imagine you write
+a function `my_image_filter(inputs)` as before. Someone calling the function
+in a variable scope with `reuse=True` would expect all inner variables to be
+reused as well. Allowing to force `reuse=False` inside the function would break
+this contract and make it hard to share parameters in this way.
+
+Even though you cannot set `reuse` to `False` explicitly, you can enter
+a reusing variable scope and then exit it, going back to a non-reusing one.
+This can be done using a `reuse=True` parameter when opening a variable scope.
+Note also that, for the same reason as above, the `reuse` parameter is
+inherited. So when you open a reusing variable scope, all sub-scopes will
+be reusing too.
+
+```python
+with tf.variable_scope("root"):
+    # At start, the scope is not reusing.
+    assert tf.get_variable_scope().reuse == False
+    with tf.variable_scope("foo"):
+        # Opened a sub-scope, still not reusing.
+        assert tf.get_variable_scope().reuse == False
+    with tf.variable_scope("foo", reuse=True):
+        # Explicitly opened a reusing scope.
+        assert tf.get_variable_scope().reuse == True
+        with tf.variable_scope("bar"):
+            # Now sub-scope inherits the reuse flag.
+            assert tf.get_variable_scope().reuse == True
+    # Exited the reusing scope, back to a non-reusing one.
+    assert tf.get_variable_scope().reuse == False
+```
+
+### Capturing variable scope
+
+In all examples presented above, we shared parameters only because their
+names agreed, that is, because we opened a reusing variable scope with
+exactly the same string. In more complex cases, it might be useful to pass
+a VariableScope object rather than rely on getting the names right.
+To this end, variable scopes can be captured and used instead of names
+when opening a new variable scope.
+
+```python
+with tf.variable_scope("foo") as foo_scope:
+    v = tf.get_variable("v", [1])
+with tf.variable_scope(foo_scope):
+    w = tf.get_variable("w", [1])
+with tf.variable_scope(foo_scope, reuse=True):
+    v1 = tf.get_variable("v", [1])
+    w1 = tf.get_variable("w", [1])
+assert v1 is v
+assert w1 is w
+```
+
+When opening a variable scope using a previously existing scope
+we jump out of the current variable scope prefix to an entirely
+different one. This is fully independent of where we do it.
+
+```python
+with tf.variable_scope("foo") as foo_scope:
+    assert foo_scope.name == "foo"
+with tf.variable_scope("bar"):
+    with tf.variable_scope("baz") as other_scope:
+        assert other_scope.name == "bar/baz"
+        with tf.variable_scope(foo_scope) as foo_scope2:
+            assert foo_scope2.name == "foo"  # Not changed.
+```
+
+### Initializers in variable scope
+
+Using `tf.get_variable()` allows to write functions that create or reuse
+variables and can be transparently called from outside. But what if we wanted
+to change the initializer of the created variables? Do we need to pass an extra
+argument to every function that creates variables? What about the most common
+case, when we want to set the default initializer for all variables in one
+place, on top of all functions? To help with these cases, variable scope
+can carry a default initializer. It is inherited by sub-scopes and passed
+to each `tf.get_variable()` call. But it will be overridden if another
+initializer is specified explicitly.
+
+```python
+with tf.variable_scope("foo", initializer=tf.constant_initializer(0.4)):
+    v = tf.get_variable("v", [1])
+    assert v.eval() == 0.4  # Default initializer as set above.
+    w = tf.get_variable("w", [1], initializer=tf.constant_initializer(0.3)):
+    assert w.eval() == 0.3  # Specific initializer overrides the default.
+    with tf.variable_scope("bar"):
+        v = tf.get_variable("v", [1])
+        assert v.eval() == 0.4  # Inherited default initializer.
+    with tf.variable_scope("baz", initializer=tf.constant_initializer(0.2)):
+        v = tf.get_variable("v", [1])
+        assert v.eval() == 0.2  # Changed default initializer.
+```
+
+### Names of ops in `tf.variable_scope()`
+
+We discussed how `tf.variable_scope` governs the names of variables.
+But how does it influence the names of other ops in the scope?
+It is natural that ops created inside a variable scope should also
+share that name. For this reason, when we do `with tf.variable_scope("name")`,
+this implicitly opens a `tf.name_scope("name")`. For example:
+
+```python
+with tf.variable_scope("foo"):
+    x = 1.0 + tf.get_variable("v", [1])
+assert x.op.name == "foo/add"
+```
+
+Name scopes can be opened in addition to a variable scope, and then
+they will only affect the names of the ops, but not of variables.
+
+```python
+with tf.variable_scope("foo"):
+    with tf.name_scope("bar"):
+        v = tf.get_variable("v", [1])
+        x = 1.0 + v
+assert v.name == "foo/v:0"
+assert x.op.name == "foo/bar/add"
+```
+
+When opening a variable scope using a captured object instead of a string,
+we do not alter the current name scope for ops.
+
+
+## Examples of Use
+
+Here are pointers to a few files that make use of variable scope. They can all
+be found in the [TensorFlow models repo](https://github.com/tensorflow/models).
+In particular, variable scope is heavily used for recurrent neural networks and
+sequence-to-sequence models.
+
+File | What's in it?
+--- | ---
+`tutorials/image/cifar10/cifar10.py` | Model for detecting objects in images.
+`tutorials/rnn/rnn_cell.py` | Cell functions for recurrent neural networks.
+`tutorials/rnn/seq2seq.py` | Functions for building sequence-to-sequence models.
diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index a7b7db626a..e8d1e519f0 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -1,283 +1,190 @@
-# Variables: persistent state in TensorFlow
+# Variables: Creation, Initialization, Saving, and Loading
 
-A TensorFlow **variable** is the best way to represent shared, persistent state
-manipulated by your program.
+When you train a model, you use @{$python/state_ops$variables}
+to hold and update parameters.  Variables are in-memory buffers containing
+tensors.  They must be explicitly initialized and can be saved to disk during
+and after training. You can later restore saved values to exercise or analyze
+the model.
 
-Variables are manipulated via the `tf.Variable` class. A `tf.Variable`
-represents a tensor whose value can be changed by running ops on it. Unlike
-`tf.Tensor` objects, a `tf.Variable` exists outside the context of a single
-`session.run` call.
+This document references the following TensorFlow classes.  Follow the links to
+their reference manual for a complete description of their API:
 
-Internally, a `tf.Variable` stores a persistent tensor. Specific ops allow you
-to read and modify the values of this tensor. These modifications are visible
-across multiple `tf.Session`s, so multiple workers can see the same values for a
-`tf.Variable`.
-
-## Creating a Variable
-
-The best way to create a variable is to call the `tf.get_variable`
-function. This function requires you to specify the Variable's name. This name
-will be used by other replicas to access the same variable, as well as to name
-this variable's value when checkpointing and exporting models. `tf.get_variable`
-also allows you to reuse a previously created variable of the same name, making it
-easy to define models which reuse layers.
+*  The @{tf.Variable} class.
+*  The @{tf.train.Saver} class.
 
-To create a variable with `tf.get_variable`, simply provide the name and shape
 
-``` python
-my_variable = tf.get_variable("my_variable", [1, 2, 3])
-```
-
-This creates a variable named "my_variable" which is a three-dimensional tensor
-with shape `[1, 2, 3]`. This variable will, by default, have the `dtype`
-`tf.float32` and its initial value will be randomized via
-`tf.glorot_uniform_initializer`.
+## Creation
 
-You may optionally specify the `dtype` and initializer to `tf.get_variable`. For
-example:
+When you create a @{$python/state_ops$Variable} you pass a
+`Tensor` as its initial value to the `Variable()` constructor.  TensorFlow
+provides a collection of ops that produce tensors often used for initialization
+from @{$python/constant_op$constants or random values}.
 
-``` python
-my_int_variable = tf.get_variable("my_int_variable", [1, 2, 3], dtype=tf.int32, 
-  initializer=tf.zeros_initializer)
-```
-
-TensorFlow provides many convenient initializers. Alternatively, you may
-initialize a `tf.Variable` to have the value of a `tf.Tensor`. For example:
-
-``` python
-other_variable = tf.get_variable("other_variable", dtype=tf.int32, 
-  initializer=tf.constant([23, 42]))
-```
-
-Note that when the initializer is a `tf.Tensor` you should not specify the
-variable's shape, as the shape of the initializer tensor will be used.
-
-### Variable collections
-
-Because disconnected parts of a TensorFlow program might want to create
-variables, it is sometimes useful to have a single way to access all of
-them. For this reason TensorFlow provides **collections**, which are named lists
-of tensors or other objects, such as `tf.Variable` instances.
-
-By default every `tf.Variable` gets placed in the following two collections:
- * `tf.GraphKeys.GLOBAL_VARIABLES` --- variables that can be shared across
-multiple devices,
- * `tf.GraphKeys.TRAINABLE_VARIABLES`--- variables for which TensorFlow will
-   calculate gradients.
- 
-If you don't want a variable to be trainable, add it to the
-`tf.GraphKeys.LOCAL_VARIABLES` collection instead. For example, the following
-snippet demonstrates how to add a variable named `my_local` to this collection:
-
-``` python
-my_local = tf.get_variable("my_local", shape=(), 
-collections=[tf.GraphKeys.LOCAL_VARIABLES])
-```
+Note that all these ops require you to specify the shape of the tensors.  That
+shape automatically becomes the shape of the variable.  Variables generally
+have a fixed shape, but TensorFlow provides advanced mechanisms to reshape
+variables.
 
-Alternatively, you can specify `trainable=False` as an argument to
-`tf.get_variable`:
-
-``` python
-my_non_trainable = tf.get_variable("my_non_trainable", 
-                                   shape=(), 
-                                   trainable=False)
+```python
+# Create two variables.
+weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
+                      name="weights")
+biases = tf.Variable(tf.zeros([200]), name="biases")
 ```
 
+Calling `tf.Variable()` adds several ops to the graph:
 
-You can also use your own collections. Any string is a valid collection name,
-and there is no need to explicitly create a collection. To add a variable (or
-any other object) to a collection after creating the variable, call
-`tf.add_to_collection`.  For example, the following code adds an existing
-variable named `my_local` to a collection named `my_collection_name`:
-
-``` python
-tf.add_to_collection("my_collection_name", my_local)
-```
-
-And to retrieve a list of all the variables (or other objects) you've placed in
-a collection you can use:
+*  A `variable` op that holds the variable value.
+*  An initializer op that sets the variable to its initial value.  This is
+   actually a `tf.assign` op.
+*  The ops for the initial value, such as the `zeros` op for the `biases`
+   variable in the example are also added to the graph.
 
-``` python
-tf.get_collection("my_collection_name")
-```
+The value returned by `tf.Variable()` value is an instance of the Python class
+`tf.Variable`.
 
 ### Device placement
 
-Just like any other TensorFlow operation, you can place variables on particular
-devices. For example, the following snippet creates a variable named `v` and
-places it on the second GPU device:
+A variable can be pinned to a particular device when it is created, using a
+@{tf.device$`with tf.device(...):`} block:
 
-``` python
-with tf.device("/gpu:1"):
-  v = tf.get_variable("v", [1])
-```
-
-It is particularly important for variables to be in the correct device in
-distributed settings. Accidentally putting variables on workers instead of
-parameter servers, for example, can severely slow down training or, in the worst
-case, let each worker blithely forge ahead with its own independent copy of each
-variable. For this reason we provide @{tf.train.replica_device_setter}, which
-can automatically place variables in parameter servers. For example:
-
-``` python
-cluster_spec = {
-    "ps": ["ps0:2222", "ps1:2222"],
-    "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
-with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
-  v = tf.get_variable("v", shape=[20, 20])  # this variable is placed 
-                                            # in the parameter server
-                                            # by the replica_device_setter
-```
-
-## Initializing variables
-
-Before you can use a variable, it must be initialized. If you are programming in
-the low-level TensorFlow API (that is, you are explicitly creating your own
-graphs and sessions), you must explicitly initialize the variables.  Most
-high-level frameworks such as `tf.contrib.slim`, `tf.estimator.Estimator` and
-`Keras` automatically initialize variables for you before training a model.
-
-Explicit initialization is otherwise useful because it allows you not to rerun
-potentially expensive initializers when reloading a model from a checkpoint as
-well as allowing determinism when randomly-initialized variables are shared in a
-distributed setting. 
+```python
+# Pin a variable to CPU.
+with tf.device("/cpu:0"):
+  v = tf.Variable(...)
 
-To initialize all trainable variables in one go, before training starts, call
-`tf.global_variables_initializer()`. This function returns a single operation
-responsible for initializing all variables in the
-`tf.GraphKeys.GLOBAL_VARIABLES` collection. Running this operation initializes
-all variables. For example:
+# Pin a variable to GPU.
+with tf.device("/gpu:0"):
+  v = tf.Variable(...)
 
-``` python
-session.run(tf.global_variables_initializer())
-# Now all variables are initialized.
+# Pin a variable to a particular parameter server task.
+with tf.device("/job:ps/task:7"):
+  v = tf.Variable(...)
 ```
 
-If you do need to initialize variables yourself, you can run the variable's
-initializer operation. For example:
+**NOTE** Operations that mutate a variable, such as
+@{tf.Variable.assign} and the parameter
+update operations in a
+@{tf.train.Optimizer} *must* run on
+the same device as the variable. Incompatible device placement directives will
+be ignored when creating these operations.
 
-``` python
-session.run(my_variable.initializer)
-```
+Device placement is particularly important when running in a replicated
+setting. See
+@{tf.train.replica_device_setter}
+for details of a device function that can simplify the configuration for devices
+for a replicated model.
 
+## Initialization
 
-You can also ask which variables have still not been initialized. For example,
-the following code prints the names of all variables which have not yet been
-initialized:
+Variable initializers must be run explicitly before other ops in your model can
+be run.  The easiest way to do that is to add an op that runs all the variable
+initializers, and run that op before using the model.
 
-``` python
-print(session.run(tf.report_uninitialized_variables()))
-```
+You can alternatively restore variable values from a checkpoint file, see
+below.
 
+Use `tf.global_variables_initializer()` to add an op to run variable initializers.
+Only run that op after you have fully constructed your model and launched it in
+a session.
 
-Note that by default `tf.global_variables_initializer` does not specify the
-order in which variables are initialized. Therefore, if the initial value of a
-variable depends on another variable's value, it's likely that you'll get an
-error. Any time you use the value of a variable in a context in which not all
-variables are initialized (say, if you use a variable's value while initializing
-another variable), it is best to use `variable.initialized_value()` instead of
-`variable`:
+```python
+# Create two variables.
+weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
+                      name="weights")
+biases = tf.Variable(tf.zeros([200]), name="biases")
+...
+# Add an op to initialize the variables.
+init_op = tf.global_variables_initializer()
 
-``` python
-v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
-w = tf.get_variable("w", initializer=v.initialized_value() + 1)
+# Later, when launching the model
+with tf.Session() as sess:
+  # Run the init operation.
+  sess.run(init_op)
+  ...
+  # Use the model
+  ...
 ```
 
-## Using variables
+### Initialization from another Variable
 
-To use the value of a `tf.Variable` in a TensorFlow graph, simply treat it like
-a normal `tf.Tensor`:
+You sometimes need to initialize a variable from the initial value of another
+variable.  As the op added by `tf.global_variables_initializer()` initializes all
+variables in parallel you have to be careful when this is needed.
 
-``` python
-v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
-w = v + 1  # w is a tf.Tensor which is computed based on the value of v.
-           # Any time a variable is used in an expression it gets automatically
-           # converted to a tf.Tensor representing its value.
-```
+To initialize a new variable from the value of another variable use the other
+variable's `initialized_value()` property.  You can use the initialized value
+directly as the initial value for the new variable, or you can use it as any
+other tensor to compute a value for the new variable.
 
-To assign a value to a variable, use the methods `assign`, `assign_add`, and
-friends in the `tf.Variable` class. For example, here is how you can call these
-methods:
 
-``` python
-v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
-assignment = v.assign_add(1)
-tf.global_variables_initializer().run()
-assignment.run()
+```python
+# Create a variable with a random value.
+weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
+                      name="weights")
+# Create another variable with the same value as 'weights'.
+w2 = tf.Variable(weights.initialized_value(), name="w2")
+# Create another variable with twice the value of 'weights'
+w_twice = tf.Variable(weights.initialized_value() * 2.0, name="w_twice")
 ```
 
-Most TensorFlow optimizers have specialized ops that efficiently update the
-values of variables according to some gradient descent-like algorithm. See
-@{tf.train.Optimizer} for an explanation of how to use optimizers.
-
-Because variables are mutable it's sometimes useful to know what version of a
-variable's value is being used at any point in time. To force a re-read of the
-value of a variable after something has happened, you can use
-`tf.Variable.read_value`. For example:
-
-``` python
-v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
-assignment = v.assign_add(1)
-with tf.control_dependencies([assignment]):
-  w = v.read_value()  # w is guaranteed to reflect v's value after the
-                      # assign_add operation.
-```
+### Custom Initialization
+
+The convenience function `tf.global_variables_initializer()` adds an op to
+initialize *all variables* in the model.  You can also pass an explicit list of
+variables to initialize to `tf.variables_initializer`.  See the
+@{$python/state_ops$Variables Documentation} for more options,
+including checking if variables are initialized.
 
 ## Saving and Restoring
 
 The easiest way to save and restore a model is to use a `tf.train.Saver` object.
 The constructor adds `save` and `restore` ops to the graph for all, or a
-specified list, of the variables in the graph.  The `Saver` object provides
+specified list, of the variables in the graph.  The saver object provides
 methods to run these ops, specifying paths for the checkpoint files to write to
 or read from.
 
-To restore a model checkpoint without a graph, you must first import the graph
-from the `MetaGraph` file (typical extension is `.meta`). Do this by calling
-@{tf.train.import_meta_graph}, which in turn returns a `Saver` from which one
-can than perform a `restore`.
+Note that to restore a model checkpoint without a graph one must first import
+the graph from the meta graph file (typical extension is `.meta`). This is
+done with @{tf.train.import_meta_graph}, which in turn returns a `Saver` from
+which one can than perform a `restore`.
 
 ### Checkpoint Files
 
-TensorFlow saves variables in binary files that, roughly speaking, map variable
+Variables are saved in binary files that, roughly, contain a map from variable
 names to tensor values.
 
 When you create a `Saver` object, you can optionally choose names for the
-variables in the checkpoint files.  By default, `Saver` uses the value of the
+variables in the checkpoint files.  By default, it uses the value of the
 @{tf.Variable.name} property for
 each variable.
 
-To inspect the variables in a checkpoint, you can use
-the
-[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library,
-particularly the `print_tensors_in_checkpoint_file` function.
+To understand what variables are in a checkpoint, you can use the
+[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py)
+library, and in particular, the `print_tensors_in_checkpoint_file` function.
 
 ### Saving Variables
 
-Create a `Saver` with `tf.train.Saver()` to manage all variables in the
-model. For example, the following snippet demonstrates how to call the
-`tf.train.Saver.save` method to save variables to a checkpoint file:
+Create a `Saver` with `tf.train.Saver()` to manage all variables in
+the model.
 
 ```python
 # Create some variables.
-v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
-v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
-
-inc_v1 = v1.assign(v1+1)
-dec_v2 = v2.assign(v2-1)
-
+v1 = tf.Variable(..., name="v1")
+v2 = tf.Variable(..., name="v2")
+...
 # Add an op to initialize the variables.
 init_op = tf.global_variables_initializer()
 
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
-# Later, launch the model, initialize the variables, do some work, and save the
+# Later, launch the model, initialize the variables, do some work, save the
 # variables to disk.
 with tf.Session() as sess:
   sess.run(init_op)
   # Do some work with the model.
-  inc_v1.op.run()
-  dec_v2.op.run()
+  ..
   # Save the variables to disk.
   save_path = saver.save(sess, "/tmp/model.ckpt")
   print("Model saved in file: %s" % save_path)
@@ -285,19 +192,14 @@ with tf.Session() as sess:
 
 ### Restoring Variables
 
-The `tf.train.Saver` object not only saves variables to checkpoint files, it
-also restores variables.  Note that when you restore variables from a file you
-do not have to initialize them beforehand. For example, the following snippet
-demonstrates how to call the `tf.train.Saver.restore` method to restore
-variables from a checkpoint file:
+The same `Saver` object is used to restore variables.  Note that when you
+restore variables from a file you do not have to initialize them beforehand.
 
 ```python
-tf.reset_default_graph()
-
 # Create some variables.
-v1 = tf.get_variable("v1", shape=[3])
-v2 = tf.get_variable("v2", shape=[5])
-
+v1 = tf.Variable(..., name="v1")
+v2 = tf.Variable(..., name="v2")
+...
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
@@ -307,156 +209,50 @@ with tf.Session() as sess:
   # Restore variables from disk.
   saver.restore(sess, "/tmp/model.ckpt")
   print("Model restored.")
-  # Check the values of the variables
-  print("v1 : %s" % v1.eval())
-  print("v2 : %s" % v2.eval())
+  # Do some work with the model
+  ...
 ```
 
-
-
 ### Choosing which Variables to Save and Restore
 
-If you do not pass any argument to `tf.train.Saver()`, the saver handles all
-variables in the graph.  Each variable is saved under the name that was passed
-when the variable was created.
+If you do not pass any argument to `tf.train.Saver()` the saver handles all
+variables in the graph.  Each one of them is saved under the name that was
+passed when the variable was created.
 
 It is sometimes useful to explicitly specify names for variables in the
 checkpoint files.  For example, you may have trained a model with a variable
-named `"weights"` whose value you want to restore into a variable named
+named `"weights"` whose value you want to restore in a new variable named
 `"params"`.
 
 It is also sometimes useful to only save or restore a subset of the variables
-used by a model.  For example, you may have trained a neural net with five
-layers, and you now want to train a new model with six layers that reuses the
-existing weights of the five trained layers. You can use the saver to restore
-the weights of just the first five layers.
-
-You can easily specify the names and variables to save or load by passing to the
-`tf.train.Saver()` constructor either a list of variables (which will be stored 
-under their own names), or a Python dictionary in which keys are the names to 
-use and values are the variables to manage. 
-
-Continuing from the save/restore examples, above:
-
-```python
-tf.reset_default_graph()
-# Create some variables.
-v1 = tf.get_variable("v1", [3], initializer = tf.zeros_initializer)
-v2 = tf.get_variable("v2", [5], initializer = tf.zeros_initializer)
+used by a model.  For example, you may have trained a neural net with 5 layers,
+and you now want to train a new model with 6 layers, restoring the parameters
+from the 5 layers of the previously trained model into the first 5 layers of
+the new model.
 
-# Add ops to save and restore only `v2` using the name "v2"
-saver = tf.train.Saver({"v2": v2})
-
-# Use the saver object normally after that.
-with tf.Session() as sess:
-  # Initialize v1 since the saver will not.
-  v1.initializer.run()
-  saver.restore(sess, "/tmp/model.ckpt")
-  
-  print("v1 : %s" % v1.eval())
-  print("v2 : %s" % v2.eval())
-
-```
+You can easily specify the names and variables to save by passing to the
+`tf.train.Saver()` constructor a Python dictionary: keys are the
+names to use, values are the variables to manage.
 
 Notes:
 
-*  You can create as many `Saver` objects as you want if you need to save and
+*  You can create as many saver objects as you want if you need to save and
    restore different subsets of the model variables.  The same variable can be
-   listed in multiple saver objects, its value is only changed when the
-   `Saver.restore()` method is run.
-
-*  If you only restore a subset of the model variables at the start of a
-   session, you have to run an initialize op for the other variables.  See
-   @{tf.variables_initializer} for more information.
-
-
-## Sharing variables
-
-TensorFlow supports two ways of sharing variables:
+   listed in multiple saver objects, its value is only changed when the saver
+   `restore()` method is run.
 
- * Explicitly passing `tf.Variable` objects around.
- * Implicitly wrapping `tf.Variable` objects within `tf.variable_scope` objects.
-
-While code which explicitly passes variables around is very clear, it is
-sometimes convenient to write TensorFlow functions that implicitly use
-variables in their implementations. Most of the functional layers from
-`tf.layer` use this approach, as well as all `tf.metrics`, and a few other
-library utilities.
-
-Variable scopes allow you to control variable reuse when calling functions which
-implicitly create and use variables. They also allow you to name your variables
-in a hierarchical and understandable way.
-
-For example, let's say we write a function to create a convolutional / relu
-layer:
+*  If you only restore a subset of the model variables at the start
+   of a session, you have to run an initialize op for the other variables.  See
+   @{tf.variables_initializer}
+   for more information.
 
 ```python
-def conv_relu(input, kernel_shape, bias_shape):
-    # Create variable named "weights".
-    weights = tf.get_variable("weights", kernel_shape,
-        initializer=tf.random_normal_initializer())
-    # Create variable named "biases".
-    biases = tf.get_variable("biases", bias_shape,
-        initializer=tf.constant_initializer(0.0))
-    conv = tf.nn.conv2d(input, weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv + biases)
-```
-
-This function uses short names `weights` and `biases`, which is good for
-clarity. In a real model, however, we want many such convolutional layers, and
-calling this function repeatedly would not work:
-
-``` python
-input1 = tf.random_normal([1,10,10,32])
-input2 = tf.random_normal([1,20,20,32])
-x = conv_relu(input1, kernel_shape=[5, 5, 32, 32], bias_shape=[32])
-x = conv_relu(x, kernel_shape=[5, 5, 32, 32], bias_shape = [32])  # This fails.
-```
-
-Since the desired behavior is unclear (create new variables or reuse the
-existing ones?) TensorFlow will fail. Calling `conv_relu` in different scopes,
-however, clarifies that we want to create new variables:
-
-```python
-def my_image_filter(input_images):
-    with tf.variable_scope("conv1"):
-        # Variables created here will be named "conv1/weights", "conv1/biases".
-        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
-    with tf.variable_scope("conv2"):
-        # Variables created here will be named "conv2/weights", "conv2/biases".
-        return conv_relu(relu1, [5, 5, 32, 32], [32])
-```
-
-If you do want the variables to be shared, you have two options. First, you can
-create a scope with the same name using `reuse=True`:
-
-``` python
-with tf.variable_scope("model"):
-  output1 = my_image_filter(input1)
-with tf.variable_scope("model", reuse=True):
-  output2 = my_image_filter(input2)
-
-```
-
-You can also call `scope.reuse_variables()` to trigger a reuse:
-
-``` python
-with tf.variable_scope("model") as scope:
-  output1 = my_image_filter(input1)
-  scope.reuse_variables()
-  output2 = my_image_filter(input2)
-
-```
-
-Since depending on exact string names of scopes can feel dangerous, it's also
-possible to initialize a variable scope based on another one:
-
-``` python
-with tf.variable_scope("model") as scope:
-  output1 = my_image_filter(input1)
-with tf.variable_scope(scope, reuse=True):
-  output2 = my_image_filter(input2)
-
+# Create some variables.
+v1 = tf.Variable(..., name="v1")
+v2 = tf.Variable(..., name="v2")
+...
+# Add ops to save and restore only 'v2' using the name "my_v2"
+saver = tf.train.Saver({"my_v2": v2})
+# Use the saver object normally after that.
+...
 ```
-
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index adf66b904e..f60c8fd770 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -417,7 +417,7 @@ scope indicating that they should be run on the first GPU.
 All variables are pinned to the CPU and accessed via
 @{tf.get_variable}
 in order to share them in a multi-GPU version.
-See how-to on @{$variables$Sharing Variables}.
+See how-to on @{$variable_scope$Sharing Variables}.
 
 ### Launching and Training the Model on Multiple GPU cards
 
-- 
GitLab


From 67f5a42b94edb8324ffef062a2dc28b9f6d95959 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 18:05:58 -0700
Subject: [PATCH 0475/1375] Automated g4 rollback of changelist 160196550

PiperOrigin-RevId: 160222942
---
 .../docs_src/programmers_guide/tensors.md     | 330 ------------------
 1 file changed, 330 deletions(-)
 delete mode 100644 tensorflow/docs_src/programmers_guide/tensors.md

diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/programmers_guide/tensors.md
deleted file mode 100644
index 2675953db8..0000000000
--- a/tensorflow/docs_src/programmers_guide/tensors.md
+++ /dev/null
@@ -1,330 +0,0 @@
-# Tensors
-
-TensorFlow, as the name indicates, is a framework to define and run computations
-involving tensors. A **tensor** is a generalization of vectors and matrices to
-potentially higher dimensions. Internally, TensorFlow represents tensors as
-n-dimensional arrays of base datatypes.
-
-When writing a TensorFlow program, the main object you manipulate and pass
-around is the `tf.Tensor`. A `tf.Tensor` object represents a partially defined
-computation that will eventually produce a value. TensorFlow programs work by
-first building a graph of `tf.Tensor` objects, detailing how each tensor is
-computed based on the other available tensors and then by running parts of this
-graph to achieve the desired results.
-
-A `tf.Tensor` has the following properties:
-
- * a data type (`float32`, `int32`, or `string`, for example)
- * a shape
-
-
-Each element in the Tensor has the same data type, and the data type is always
-known. The shape (that is, the number of dimensions it has and the size of each
-dimension) might be only partially known. Most operations produce tensors of
-fully-known shapes if the shapes of their inputs are also fully known, but in
-some cases it's only possible to find the shape of a tensor at graph execution
-time.
-
-Some types of tensors are special, and these will be covered in other
-units of the Programmer's guide. The main ones are:
-
-  * `tf.Variable`
-  * `tf.Constant`
-  * `tf.Placeholder`
-  * `tf.SparseTensor`
-
-With the exception of `tf.Variable`, the value of a tensor is immutable, which
-means that in the context of a single execution tensors only have a single
-value. However, evaluating the same tensor twice can return different values;
-for example that tensor can be the result of reading data from disk, or
-generating a random number.
-
-## Rank
-
-The **rank** of a `tf.Tensor` object is its number of dimensions. Synonyms for
-rank include **order** or **degree** or **n-dimension**.
-Note that rank in TensorFlow is not the same as matrix rank in mathematics. 
-As the following table shows, each rank in TensorFlow corresponds to a 
-different mathematical entity:
-
-Rank | Math entity
---- | ---
-0 | Scalar (magnitude only)
-1 | Vector (magnitude and direction)
-2 | Matrix (table of numbers)
-3 | 3-Tensor (cube of numbers)
-n | n-Tensor (you get the idea)
-
-
-### Rank 0 
-
-The following snippet demonstrates creating a few rank 0 variables:
-
-```python
-mammal = tf.Variable("Elephant", tf.string)
-ignition = tf.Variable(451, tf.int16)
-floating = tf.Variable(3.14159265359, tf.float64)
-its_complicated = tf.Variable((12.3, -4.85), tf.complex64)
-```
-
-Note: A string is treated as a single item in TensorFlow, not as a sequence of
-characters. It is possible to have scalar strings, vectors of strings, etc.
-
-### Rank 1
-
-To create a rank 1 `tf.Tensor` object, you can pass a list of items as the
-initial value. For example:
-
-```python
-mystr = tf.Variable(["Hello"], tf.string)
-cool_numbers  = tf.Variable([3.14159, 2.71828], tf.float32)
-first_primes = tf.Variable([2, 3, 5, 7, 11], tf.int32)
-its_very_complicated = tf.Variable([(12.3, -4.85), (7.5, -6.23)], tf.complex64)
-```
-
-
-### Higher ranks
-
-A rank 2 `tf.Tensor` object consists of at least one row and at least
-one column:
-
-```python
-mymat = tf.Variable([[7],[11]], tf.int16)
-myxor = tf.Variable([[False, True],[True, False]], tf.bool)
-linear_squares = tf.Variable([[4], [9], [16], [25]], tf.int32)
-squarish_squares = tf.Variable([ [4, 9], [16, 25] ], tf.int32)
-rank_of_squares = tf.rank(squarish_squares)
-mymatC = tf.Variable([[7],[11]], tf.int32)
-```
-
-Higher-rank Tensors, similarly, consist of an n-dimensional array. For example,
-during image processing, many tensors of rank 4 are used, with dimensions
-corresponding to example-in-batch, image width, image height, and color channel.
-
-``` python
-my_image = tf.zeros([10, 299, 299, 3])  # batch x height x width x color
-```
-
-### Getting a `tf.Tensor` object's rank
-
-To determine the rank of a `tf.Tensor` object, call the `tf.rank` method.
-For example, the following method programmatically determines the rank 
-of the `tf.Tensor` defined in the previous section:
-
-```python
-r = tf.rank(my3d)
-# After the graph runs, r will hold the value 3.
-```
-
-### Referring to `tf.Tensor` slices
-
-Since a `tf.Tensor` is an n-dimensional array of cells, to access a single cell
-in a `tf.Tensor` you need to specify n indices.
-
-For a rank 0 tensor (a scalar), no indices are necessary, since it is already a
-single number.
-
-For a rank 1 tensor (a vector), passing a single index allows you to access a
-number:
-
-```python
-my_scalar = my_vector[2]
-```
-
-Note that the index passed inside the `[]` can itself be a scalar `tf.Tensor`, if
-you want to dynamically choose an element from the vector.
-
-For tensors of rank 2 or higher, the situation is more interesting. For a
-`tf.Tensor` of rank 2, passing two numbers returns a scalar, as expected:
-
-
-```python
-my_scalar = my_matrix[1, 2]
-```
-
-
-Passing a single number, however, returns a subvector of a matrix, as follows:
-
-
-```python
-my_row_vetor = my_matrix[2]
-my_column_vector = my_matrix[:, 3]
-```
-
-The `:` notation is python slicing syntax for "leave this dimension alone". This
-is useful in higher-rank Tensors, as it allows you to access its subvectors,
-submatrices, and even other subtensors.
-
-
-## Shape
-
-The **shape** of a tensor is the number of elements in each dimension.
-TensorFlow automatically infers shapes during graph construction. These inferred
-shapes might have known or unknown rank. If the rank is known, the sizes of each
-dimension might be known or unknown.
-
-The TensorFlow documentation uses three notational conventions to describe
-tensor dimensionality: rank, shape, and dimension number. The following table
-shows how these relate to one another:
-
-Rank | Shape | Dimension number | Example
---- | --- | --- | ---
-0 | [] | 0-D | A 0-D tensor.  A scalar.
-1 | [D0] | 1-D | A 1-D tensor with shape [5].
-2 | [D0, D1] | 2-D | A 2-D tensor with shape [3, 4].
-3 | [D0, D1, D2] | 3-D | A 3-D tensor with shape [1, 4, 3].
-n | [D0, D1, ... Dn-1] | n-D | A tensor with shape [D0, D1, ... Dn-1].
-
-Shapes can be represented via Python lists / tuples of ints, or with the
-@{tf.TensorShape}.
-
-### Getting a `tf.Tensor` object's shape
-
-There are two ways of accessing the shape of a `tf.Tensor`. While building the
-graph, it is often useful to ask what is already known about a tensor's
-shape. This can be done by reading the `shape` property of a `tf.Tensor` object.
-This method returns a `TensorShape` object, which is a convenient way of
-representing partially-specified shapes (since, when building the graph, not all
-shapes will be fully known).
-
-It is also possible to get a `tf.Tensor` that will represent the fully-defined
-shape of another `tf.Tensor` at runtime. This is done by calling the `tf.shape`
-operation. This way, you can build a graph that manipulates the shapes of
-tensors by building other tensors that depend on the dynamic shape of the input
-`tf.Tensor`.
-
-For example, here is how to make a vector of zeros with the same size as the
-number of columns in a given matrix:
-
-``` python
-zeros = tf.zeros(tf.shape(my_matrix)[1])
-```
-
-### Changing the shape of a `tf.Tensor`
-
-The **number of elements** of a tensor is the product of the sizes of all its
-shapes. The number of elements of a scalar is always `1`. Since there are often
-many different shapes that have the same number of elements, it's often
-convenient to be able to change the shape of a `tf.Tensor`, keeping its elements
-fixed. This can be done with `tf.reshape`.
-
-The following examples demonstrate how to reshape tensors:
-
-```python
-rank_three_tensor = tf.ones([3, 4, 5])
-matrix = tf.reshape(rank_three_tensor, [6, 10])  # Reshape existing content into
-                                                 # a 6x10 matrix
-matrixB = tf.reshape(matrix, [3, -1])  #  Reshape existing content into a 3x20
-                                       # matrix. -1 tells reshape to calculate
-                                       # the size of this dimension.
-matrixAlt = tf.reshape(matrixB, [4, 3, -1])  # Reshape existing content into a
-                                             #4x3x5 tensor
-
-# Note that the number of elements of the reshaped Tensors has to match the
-# original number of elements. Therefore, the following example generates an
-# error because no possible value for the last dimension will match the number
-# of elements.
-yet_another = tf.reshape(matrixAlt, [13, 2, -1])  # ERROR!
-```
-
-## Data types
-
-In addition to dimensionality, Tensors have a data type. Refer to the
-`tf.DataType` page in the programmer's guide for a full list of the data types.
-
-It is not possible to have a `tf.Tensor` with more than one data type. It is
-possible, however, to serialize arbitrary data structures as `string`s and store
-those in `tf.Tensor`s.
-
-It is possible to cast `tf.Tensor`s from one datatype to another using
-`tf.cast`:
-
-``` python
-# Cast a constant integer tensor into floating point.
-float_tensor = tf.cast(tf.constant([1, 2, 3]), dtype=tf.float32)
-```
-
-To inspect a `tf.Tensor`'s data type use the `Tensor.dtype` property.
-
-When creating a `tf.Tensor` from a python object you may optionally specify the
-datatype. If you don't, TensorFlow chooses a datatype that can represent your
-data. TensorFlow converts Python integers to `tf.int32` and python floating
-point numbers to `tf.float32`. Otherwise TensorFlow uses the same rules numpy
-uses when converting to arrays.
-
-## Evaluating Tensors
-
-Once the computation graph has been built, you can run the computation that
-produces a particular `tf.Tensor` and fetch the value assigned to it. This is
-often useful for debugging as well as being required for much of TensorFlow to
-work.
-
-The simplest way to evaluate a Tensor is using the `Tensor.eval` method. For
-example:
-
-```python
-constant = tf.constant([1, 2, 3])
-tensor = constant * constant
-print tensor.eval()
-```
-
-The `eval` method only works when a default `tf.Session` is active (see
-@{$graphs$Graphs and Sessions} for more information).
-
-`Tensor.eval` returns a numpy array with the same contents as the tensor.
-
-Sometimes it is not possible to evaluate a `tf.Tensor` with no context because
-its value might depend on dynamic information that is not available. For
-example, tensors that depend on `Placeholder`s can't be evaluated without
-providing a value for the `Placeholder`.
-
-``` python
-p = tf.placeholder(tf.float32)
-t = p + 1.0
-t.eval()  # This will fail, since the placeholder did not get a value.
-t.eval(feed_dict={p:2.0})  # This will succeed because we're feeding a value
-                           # to the placeholder.
-```
-
-Note that it is possible to feed any `tf.Tensor`, not just placeholders.
-
-Other model constructs might make evaluating a `tf.Tensor`
-complicated. TensorFlow can't directly evaluate `tf.Tensor`s defined inside
-functions or inside control flow constructs. If a `tf.Tensor` depends on a value
-from a queue, evaluating the `tf.Tensor` will only work once something has been
-enqueued; otherwise, evaluating it will hang. When working with queues, remember
-to call `tf.train.start_queue_runners` before evaluating any `tf.Tensor`s.
-
-## Printing Tensors
-
-For debugging purposes you might want to print the value of a `tf.Tensor`. While
- @{$debug$tfdbg} provides advanced debugging support, TensorFlow also has an
- operation to directly print the value of a `tf.Tensor`.
-
-Note that you rarely want to use the following pattern when printing a
-`tf.Tensor`:
-
-``` python
-t = <<some tensorflow operation>>
-print t  # This will print the symbolic tensor when the graph is being built.
-         # This tensor does not have a value in this context.
-```
-
-This code prints the `tf.Tensor` object (which represents deferred computation)
-and not its value. Instead, TensorFlow provides the `tf.Print` operation, which
-returns its first tensor argument unchanged while printing the set of
-`tf.Tensor`s it is passed as the second argument.
-
-To correctly use `tf.Print` its return value must be used. See the example below
-
-``` python
-t = <<some tensorflow operation>>
-tf.Print(t, [t])  # This does nothing
-t = tf.Print(t, [t])  # Here we are using the value returned by tf.Print
-result = t + 1  # Now when result is evaluated the value of `t` will be printed.
-```
-
-When you evaluate `result` you will evaluate everything `result` depends
-upon. Since `result` depends upon `t`, and evaluating `t` has the side effect of
-printing its input (the old value of `t`), `t` gets printed.
-
-- 
GitLab


From 1f818986756529517340e9b8896022c07640150b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Jun 2017 21:24:59 -0700
Subject: [PATCH 0476/1375] Lets the HParam parser also accept True and False
 as inputs, since that's how python prints booleans.

PiperOrigin-RevId: 160234658
---
 tensorflow/contrib/training/python/training/hparam.py      | 4 ++--
 tensorflow/contrib/training/python/training/hparam_test.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py
index c19a36eabc..5de177ae44 100644
--- a/tensorflow/contrib/training/python/training/hparam.py
+++ b/tensorflow/contrib/training/python/training/hparam.py
@@ -91,9 +91,9 @@ def parse_values(values, type_map):
           % (name, type_.__name__, value, values))
     if type_ == bool:
       def parse_bool(value):
-        if value == 'true':
+        if value in ['true', 'True']:
           return True
-        elif value == 'false':
+        elif value in ['false', 'False']:
           return False
         else:
           try:
diff --git a/tensorflow/contrib/training/python/training/hparam_test.py b/tensorflow/contrib/training/python/training/hparam_test.py
index 0b900e65d8..b4130d04b4 100644
--- a/tensorflow/contrib/training/python/training/hparam_test.py
+++ b/tensorflow/contrib/training/python/training/hparam_test.py
@@ -96,11 +96,11 @@ class HParamsTest(test.TestCase):
     self.assertEquals('2.3"', hparams2.c_c)
 
   def testBoolParsing(self):
-    for value in 'true', 'false', '1', '0':
+    for value in 'true', 'false', 'True', 'False', '1', '0':
       for initial in False, True:
         hparams = hparam.HParams(use_gpu=initial)
         hparams.parse('use_gpu=' + value)
-        self.assertEqual(hparams.use_gpu, value in ['true', '1'])
+        self.assertEqual(hparams.use_gpu, value in ['True', 'true', '1'])
 
         # Exports to proto.
         hparam_def = hparams.to_proto()
-- 
GitLab


From d6c4026fed409b3cf0e4b35988fcd49c47b073de Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 27 Jun 2017 01:49:51 -0700
Subject: [PATCH 0477/1375] Automated g4 rollback of changelist 155070869

PiperOrigin-RevId: 160249526
---
 tensorflow/core/kernels/unique_op.cc | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/unique_op.cc b/tensorflow/core/kernels/unique_op.cc
index b57e13a28c..6e51696d6f 100644
--- a/tensorflow/core/kernels/unique_op.cc
+++ b/tensorflow/core/kernels/unique_op.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include <unordered_map>
 #include <utility>
 
 #include "tensorflow/core/framework/op_kernel.h"
@@ -20,7 +21,6 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/lib/gtl/flatmap.h"
 
 namespace tensorflow {
 
@@ -50,7 +50,8 @@ class UniqueOp : public OpKernel {
                                 {0}, 1, input.shape(), &idx));
     auto idx_vec = idx->template vec<int32>();
 
-    gtl::FlatMap<T, int32> uniq(N);
+    std::unordered_map<T, int32> uniq;
+    uniq.reserve(2 * N);
     for (int64 i = 0, j = 0; i < N; ++i) {
       auto it = uniq.insert(std::make_pair(Tin(i), j));
       idx_vec(i) = it.first->second;
-- 
GitLab


From 1d6593e2ecd8fe4e8eea7a3363fa6878cb978460 Mon Sep 17 00:00:00 2001
From: Sandip Giri <sgiri@us.ibm.com>
Date: Tue, 27 Jun 2017 05:27:10 -0400
Subject: [PATCH 0478/1375] Fixed comment style and changed if condition from
 "power" to "power && little_endian" (ppc64le specific)

---
 tensorflow/core/platform/profile_utils/cpu_utils.cc      | 2 +-
 tensorflow/core/platform/profile_utils/cpu_utils.h       | 2 +-
 tensorflow/core/platform/profile_utils/cpu_utils_test.cc | 2 +-
 tensorflow/python/kernel_tests/cast_op_test.py           | 6 +++---
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.cc b/tensorflow/core/platform/profile_utils/cpu_utils.cc
index c44cec394c..b0a16443d4 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.cc
@@ -28,7 +28,7 @@ namespace profile_utils {
 
 static ICpuUtilsHelper* cpu_utils_helper_instance_ = nullptr;
 
-#if defined(__powerpc__) || defined(__ppc__)
+#if defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
    /* static */ uint64 CpuUtils::GetCycleCounterFrequency() {
      static const uint64 cpu_frequency = GetCycleCounterFrequencyImpl();
      return cpu_frequency;
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h
index f2669f7afd..3af883f569 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.h
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.h
@@ -97,7 +97,7 @@ class CpuUtils {
   // Return cycle counter frequency.
   // As this method caches the cpu frequency internally,
   // the first call will incur overhead, but not subsequent calls.
-  #if defined(__powerpc__) || defined(__ppc__)
+  #if defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
      static uint64 GetCycleCounterFrequency();
   #else
      static int64 GetCycleCounterFrequency();
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
index 3a574b2e50..e1ec4aaac0 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
@@ -53,7 +53,7 @@ TEST_F(CpuUtilsTest, CheckGetCurrentClockCycle) {
 }
 
 TEST_F(CpuUtilsTest, CheckCycleCounterFrequency) {
-  #if defined(__powerpc__) || defined(__ppc__)
+  #if defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
      const uint64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
      CHECK_GT(cpu_frequency, 0);
      CHECK_NE(cpu_frequency, unsigned(CpuUtils::INVALID_FREQUENCY));
diff --git a/tensorflow/python/kernel_tests/cast_op_test.py b/tensorflow/python/kernel_tests/cast_op_test.py
index a5d64740c7..c785f2358d 100644
--- a/tensorflow/python/kernel_tests/cast_op_test.py
+++ b/tensorflow/python/kernel_tests/cast_op_test.py
@@ -148,9 +148,9 @@ class CastOpTest(test.TestCase):
       self._compare(np.inf, np.int32, i4.max, False)  
       self._compare(np.inf, np.int64, i8.max, False)  
     else:
-      #np.float64("np.inf").astype(np.int32) is negative on x86 but positive on ppc64le
-      #Numpy link to relevant discussion - https://github.com/numpy/numpy/issues/9040
-      #Tensorflow link to relevant discussion - https://github.com/tensorflow/tensorflow/issues/9360
+      # np.float64("np.inf").astype(np.int32) is negative on x86 but positive on ppc64le
+      # Numpy link to relevant discussion - https://github.com/numpy/numpy/issues/9040
+      # Tensorflow link to relevant discussion - https://github.com/tensorflow/tensorflow/issues/9360
       if platform.machine() == "ppc64le":
         self._compare(-np.inf, np.int32, i4.min, False)
         self._compare(-np.inf, np.int64, i8.min, False)
-- 
GitLab


From c8d78a1c291fb4b0868cf18f5b4d17716be6c8cf Mon Sep 17 00:00:00 2001
From: Sandip Giri <sgiri@us.ibm.com>
Date: Tue, 27 Jun 2017 05:48:55 -0400
Subject: [PATCH 0479/1375] Signed/unsigned changes not required for
 GetCycleCounterFrequencyImpl()

---
 tensorflow/core/platform/profile_utils/cpu_utils.cc | 2 +-
 tensorflow/core/platform/profile_utils/cpu_utils.h  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.cc b/tensorflow/core/platform/profile_utils/cpu_utils.cc
index b0a16443d4..52df84e81c 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.cc
@@ -60,7 +60,7 @@ static ICpuUtilsHelper* cpu_utils_helper_instance_ = nullptr;
                                        GetCycleCounterFrequency());
 }
 
-/* static */ uint64 CpuUtils::GetCycleCounterFrequencyImpl() {
+/* static */ int64 CpuUtils::GetCycleCounterFrequencyImpl() {
 // TODO(satok): do not switch by macro here
 #if defined(__ANDROID__)
   return GetCpuUtilsHelperSingletonInstance().CalculateCpuFrequency();
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h
index 3af883f569..8979a40ea1 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.h
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.h
@@ -138,7 +138,7 @@ class CpuUtils {
   // CAVEAT: as this method calls system call and parse the mssage,
   // this call may be slow. This is why this class caches the value by
   // StaticVariableInitializer.
-  static uint64 GetCycleCounterFrequencyImpl();
+  static int64 GetCycleCounterFrequencyImpl();
 
   // Return a singleton of ICpuUtilsHelper
   // ICpuUtilsHelper is declared as a function-local static variable
-- 
GitLab


From 78f716412a8cc93623ed2691009bb3345d033f25 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 07:52:36 -0700
Subject: [PATCH 0480/1375] [TF:XLA] Inline the sigmoid operation instead of
 mapping it elementwise.

PiperOrigin-RevId: 160274436
---
 .../compiler/tf2xla/kernels/unary_ops.cc      | 17 ++++++++++++---
 tensorflow/compiler/tf2xla/xla_context.cc     | 21 -------------------
 tensorflow/compiler/tf2xla/xla_context.h      |  5 -----
 tensorflow/compiler/tf2xla/xla_op_kernel.cc   |  5 -----
 tensorflow/compiler/tf2xla/xla_op_kernel.h    |  5 -----
 5 files changed, 14 insertions(+), 39 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
index 07ca596150..f14546b1b4 100644
--- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
@@ -78,12 +78,23 @@ static xla::ComputationDataHandle Round(xla::ComputationBuilder* b,
                                 b->LogicalAnd(b->Eq(fraction, half), is_odd)),
                    b->Add(round_val, one), round_val);
 }
-XLAJIT_MAKE_UNARY(Round, Round(b, input_type(0), x));
 
+static xla::ComputationDataHandle Sigmoid(xla::ComputationBuilder* b,
+                                          DataType dtype,
+                                          const xla::ComputationDataHandle& x) {
+  // Clamp the inputs to the range [-18, 18] since anything outside this range
+  // is 0.0f or 1.0f in single-precision. We must clamp the range of x to avoid
+  // incorrect outputs due to fast-math optimizations for large negative x.
+  auto clamped = b->Clamp(XlaHelpers::IntegerLiteral(b, dtype, -18), x,
+                          XlaHelpers::IntegerLiteral(b, dtype, 18));
+  auto one = XlaHelpers::One(b, dtype);
+  return b->Div(one, b->Add(b->Exp(b->Neg(clamped)), one));
+}
+
+XLAJIT_MAKE_UNARY(Round, Round(b, input_type(0), x));
 XLAJIT_MAKE_UNARY(Rsqrt,
                   b->Pow(x, XlaHelpers::FloatLiteral(b, input_type(0), -0.5)));
-XLAJIT_MAKE_UNARY(Sigmoid,
-                  b->Map({x}, *ctx->GetOrCreateSigmoid(input_type(0))));
+XLAJIT_MAKE_UNARY(Sigmoid, Sigmoid(b, input_type(0), x));
 XLAJIT_MAKE_UNARY(Softplus,
                   b->Log(b->Add(b->Exp(x), XlaHelpers::One(b, input_type(0)))));
 XLAJIT_MAKE_UNARY(Sqrt,
diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc
index 1a37d61944..d4d493b456 100644
--- a/tensorflow/compiler/tf2xla/xla_context.cc
+++ b/tensorflow/compiler/tf2xla/xla_context.cc
@@ -172,27 +172,6 @@ const xla::Computation* XlaContext::GetOrCreateAdd(const DataType type) {
   });
 }
 
-const xla::Computation* XlaContext::GetOrCreateSigmoid(const DataType type) {
-  return LookupOrCreate(type, &sigmoid_func_, [this, type] {
-    const string type_string = DataTypeString(type);
-    VLOG(1) << "Building Sigmoid() for " << type_string;
-    xla::ComputationBuilder b(builder()->client(),
-                              "sigmoid<" + type_string + ">");
-    xla::PrimitiveType xla_type;
-    TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type));
-    auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x");
-    // Clamp the inputs to the range [-18, 18] since anything outside
-    // this range is 0.0f or 1.0f in single-precision. We must clamp the range
-    // of x to avoid incorrect outputs due to fast-math optimizations for large
-    // negative x.
-    x = b.Clamp(XlaHelpers::IntegerLiteral(&b, type, -18), x,
-                XlaHelpers::IntegerLiteral(&b, type, 18));
-    auto one = XlaHelpers::One(&b, type);
-    b.Div(one, b.Add(b.Exp(b.Neg(x)), one));
-    return b.Build().ConsumeValueOrDie();
-  });
-}
-
 const xla::Computation* XlaContext::LookupOrCreate(
     DataType type, ComputationMap* out,
     const std::function<xla::Computation()>& create) {
diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h
index dbede52b5d..544921b9e3 100644
--- a/tensorflow/compiler/tf2xla/xla_context.h
+++ b/tensorflow/compiler/tf2xla/xla_context.h
@@ -129,11 +129,6 @@ class XlaContext : public ResourceBase {
   // separate specialization of the computation for each DataType.
   const xla::Computation* GetOrCreateAdd(const DataType type);
 
-  // Get an XLA lambda to compute Sigmoid. This is cached in the
-  // XlaContext since it may be used by multiple Ops. There is a
-  // separate specialization of the computation for each DataType.
-  const xla::Computation* GetOrCreateSigmoid(const DataType type);
-
   // The name of the XlaContext resource during symbolic graph execution.
   static const char kXlaContextResourceName[];
 
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
index d606b32931..dd46702223 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
@@ -391,11 +391,6 @@ const xla::Computation* XlaOpKernelContext::GetOrCreateAdd(
   return XlaContext::Get(context_).GetOrCreateAdd(type);
 }
 
-const xla::Computation* XlaOpKernelContext::GetOrCreateSigmoid(
-    const DataType type) {
-  return XlaContext::Get(context_).GetOrCreateSigmoid(type);
-}
-
 XlaOpKernel::XlaOpKernel(OpKernelConstruction* context) : OpKernel(context) {}
 
 void XlaOpKernel::Compute(OpKernelContext* context) {
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h
index b151286217..30b794c8c1 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.h
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h
@@ -201,11 +201,6 @@ class XlaOpKernelContext {
   // separate specialization of the computation for each DataType.
   const xla::Computation* GetOrCreateAdd(const DataType type);
 
-  // Get an XLA lambda to compute Sigmoid. This is cached in the
-  // XlaContext since it may be used by multiple Ops. There is a
-  // separate specialization of the computation for each DataType.
-  const xla::Computation* GetOrCreateSigmoid(const DataType type);
-
  private:
   OpKernelContext* const context_;
 };
-- 
GitLab


From 1c57e8864aeb8093f12e8f390571fb8cd9f376a4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 07:54:29 -0700
Subject: [PATCH 0481/1375] Make sure all convolution tests are testing
 non-trivial cases, i.e. where not all inputs are 0, leading to an all-0
 output, which masks most possible bugs. We do not check-fail on 0-sized
 dimensions as tests for these special cases exist.

PiperOrigin-RevId: 160274593
---
 tensorflow/compiler/xla/array4d.h         | 12 ++++++++++++
 tensorflow/compiler/xla/reference_util.cc | 20 ++++++++++++++++++++
 2 files changed, 32 insertions(+)

diff --git a/tensorflow/compiler/xla/array4d.h b/tensorflow/compiler/xla/array4d.h
index d93f968f4d..4c7fce1aaf 100644
--- a/tensorflow/compiler/xla/array4d.h
+++ b/tensorflow/compiler/xla/array4d.h
@@ -207,6 +207,18 @@ class Array4D {
     }
   }
 
+  // Invokes a callback with the (indices, value) for each cell in the 4D array.
+  void Each(
+      std::function<void(tensorflow::gtl::ArraySlice<int64>, T)> f) const {
+    // We const_cast to be able to use the common non-const implementation,
+    // but prevent modification of the data by passing it by-value to the
+    // caller.
+    const_cast<Array4D*>(this)->Each(
+        [&f](tensorflow::gtl::ArraySlice<int64> indices, T* value) {
+          f(indices, *value);
+        });
+  }
+
   // Fills all of the {p,z} with the array provided, which specifies {y,x}.
   void FillWithYX(const Array2D<T>& value) {
     CHECK_EQ(value.height(), height());
diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index e8de559a5e..fe99cf9207 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -491,6 +491,26 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
       }
     }
   }
+  if (samples == 0 || kx == 0 || ky == 0 || ox == 0 || oy == 0 || oz == 0 ||
+      iz == 0) {
+    LOG(INFO) << "Output will be trivially empty because one of these "
+                 "dimensions is 0: samples: "
+              << samples << " kx: " << kx << " ky: " << ky << " ox: " << ox
+              << " oy: " << oy << " oz: " << oz << " iz: " << iz;
+    return result;
+  }
+  bool trivial = true;
+  auto check_trivial = [&trivial](tensorflow::gtl::ArraySlice<int64> indices,
+                                  float value) {
+    if (value != 0.0) {
+      trivial = false;
+    }
+  };
+  lhs.Each(check_trivial);
+  LOG_IF(FATAL, trivial) << "LHS is all 0.0.";
+  trivial = true;
+  rhs.Each(check_trivial);
+  LOG_IF(FATAL, trivial) << "RHS is all 0.0.";
   return result;
 }
 
-- 
GitLab


From e12615813d0cfaa0fcd1fdafed48efa6ad5ae121 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 08:16:23 -0700
Subject: [PATCH 0482/1375] Explicitly use "dns" URI scheme when using DNS
 names or literal IP addresses with gRPC.  This avoids problems in
 environments in which the default URI scheme is something other than "dns".

PiperOrigin-RevId: 160276862
---
 tensorflow/core/debug/debug_io_utils.cc                 | 5 +++--
 tensorflow/core/distributed_runtime/rpc/grpc_channel.cc | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index 69fc367789..9b2e5583aa 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -581,8 +581,9 @@ Status DebugGrpcChannel::Connect(const int64 timeout_micros) {
   args.SetInt(GRPC_ARG_MAX_MESSAGE_LENGTH, std::numeric_limits<int32>::max());
   // Avoid problems where default reconnect backoff is too long (e.g., 20 s).
   args.SetInt("grpc.testing.fixed_reconnect_backoff_ms", 1000);
-  channel_ = ::grpc::CreateCustomChannel(
-      server_stream_addr_, ::grpc::InsecureChannelCredentials(), args);
+  channel_ =
+      ::grpc::CreateCustomChannel("dns:///" + server_stream_addr_,
+                                  ::grpc::InsecureChannelCredentials(), args);
   if (!channel_->WaitForConnected(
           gpr_time_add(gpr_now(GPR_CLOCK_REALTIME),
                        gpr_time_from_micros(timeout_micros, GPR_TIMESPAN)))) {
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc
index bcd2c71f84..7efc0ba6d8 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc
@@ -67,7 +67,7 @@ Status NewHostPortGrpcChannel(const string& target,
   // on connection failure, which makes our tests time out.
   args.SetInt("grpc.testing.fixed_reconnect_backoff_ms", 1000);
   *channel_pointer = ::grpc::CreateCustomChannel(
-      target, ::grpc::InsecureChannelCredentials(), args);
+      "dns:///" + target, ::grpc::InsecureChannelCredentials(), args);
   return Status::OK();
 }
 
-- 
GitLab


From 57d0b2ee02fe66e3714c5a436603c80e6dba0642 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 08:17:02 -0700
Subject: [PATCH 0483/1375] Add RWSE (root weighted squared error) to the WALS
 estimator.

PiperOrigin-RevId: 160276937
---
 .../python/ops/factorization_ops.py           | 235 +++++++++++-------
 .../python/ops/factorization_ops_test.py      | 216 +++++++++++-----
 .../contrib/factorization/python/ops/wals.py  | 146 ++++++-----
 3 files changed, 381 insertions(+), 216 deletions(-)

diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops.py b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
index 000e2403a7..14a2311a42 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
@@ -23,7 +23,6 @@ import numbers
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
-
 from tensorflow.contrib.factorization.python.ops import gen_factorization_ops
 from tensorflow.contrib.util import loader
 from tensorflow.python.framework import constant_op
@@ -81,13 +80,16 @@ class WALSModel(object):
   a sparse term and a Gramian term, see wals.md.
   The loss is returned by the update_{col, row}_factors(sp_input), and is
   normalized as follows:
-  _, _, minibatch_loss = update_row_factors(sp_input)
+    _, _, unregularized_loss, regularization, sum_weights =
+        update_row_factors(sp_input)
   if sp_input contains the rows {A_i, i \in I}, and the input matrix A has n
-  total rows, then minibatch_loss is
+  total rows, then the minibatch loss = unregularized_loss + regularization is
    \\(
-   (\|\sqrt W \odot (A_I - U_I V^T)\|_F^2 + \lambda \|U_I\|_F^2) * n / |I| +
+   (\|\sqrt W_I \odot (A_I - U_I V^T)\|_F^2 + \lambda \|U_I\|_F^2) * n / |I| +
    \lambda \|V\|_F^2
    )\\
+  The sum_weights tensor contains the normalized sum of weights
+  sum(W_I) * n / |I|.
 
   A typical usage example (pseudocode):
 
@@ -119,11 +121,13 @@ class WALSModel(object):
       # Ops to upate row(column). This can either take the entire sparse tensor
       # or slices of sparse tensor. For distributed trainer, each trainer
       # handles just part of the matrix.
-      _, row_update_op, row_loss = model.update_row_factors(
+      _, row_update_op, unreg_row_loss, row_reg, _ = model.update_row_factors(
            sp_input=matrix_slices_from_queue_for_worker_shard)
-      _, col_update_op, col_loss = model.update_col_factors(
+      row_loss = unreg_row_loss + row_reg
+      _, col_update_op, unreg_col_loss, col_reg, _ = model.update_col_factors(
            sp_input=transposed_matrix_slices_from_queue_for_worker_shard,
            transpose_input=True)
+      col_loss = unreg_col_loss + col_reg
 
       ...
 
@@ -240,28 +244,24 @@ class WALSModel(object):
         regularization * linalg_ops.eye(self._n_components)
         if regularization is not None else None)
     assert (row_weights is None) == (col_weights is None)
-    self._row_weights = WALSModel._create_weights(row_weights, self._input_rows,
-                                                  self._num_row_shards,
-                                                  "row_weights")
-    self._col_weights = WALSModel._create_weights(col_weights, self._input_cols,
-                                                  self._num_col_shards,
-                                                  "col_weights")
+    self._row_weights = WALSModel._create_weights(
+        row_weights, self._input_rows, self._num_row_shards, "row_weights")
+    self._col_weights = WALSModel._create_weights(
+        col_weights, self._input_cols, self._num_col_shards, "col_weights")
     self._use_factors_weights_cache = use_factors_weights_cache
     self._use_gramian_cache = use_gramian_cache
-    self._row_factors = self._create_factors(self._input_rows,
-                                             self._n_components,
-                                             self._num_row_shards, row_init,
-                                             "row_factors")
-    self._col_factors = self._create_factors(self._input_cols,
-                                             self._n_components,
-                                             self._num_col_shards, col_init,
-                                             "col_factors")
+    self._row_factors = self._create_factors(
+        self._input_rows, self._n_components, self._num_row_shards, row_init,
+        "row_factors")
+    self._col_factors = self._create_factors(
+        self._input_cols, self._n_components, self._num_col_shards, col_init,
+        "col_factors")
     self._row_gramian = self._create_gramian(self._n_components, "row_gramian")
     self._col_gramian = self._create_gramian(self._n_components, "col_gramian")
-    self._row_update_prep_gramian = self._prepare_gramian(self._col_factors,
-                                                          self._col_gramian)
-    self._col_update_prep_gramian = self._prepare_gramian(self._row_factors,
-                                                          self._row_gramian)
+    self._row_update_prep_gramian = self._prepare_gramian(
+        self._col_factors, self._col_gramian)
+    self._col_update_prep_gramian = self._prepare_gramian(
+        self._row_factors, self._row_gramian)
     self._create_transient_vars()
 
   @property
@@ -538,14 +538,12 @@ class WALSModel(object):
          "col_gramian_cache",
          pass_through=not self._use_gramian_cache)
 
-    self._row_updates_init = control_flow_ops.group(col_factors_cache_init,
-                                                    row_factors_cache_reset,
-                                                    col_gramian_cache_init,
-                                                    row_gramian_cache_reset)
-    self._col_updates_init = control_flow_ops.group(row_factors_cache_init,
-                                                    col_factors_cache_reset,
-                                                    row_gramian_cache_init,
-                                                    col_gramian_cache_reset)
+    self._row_updates_init = control_flow_ops.group(
+        col_factors_cache_init, row_factors_cache_reset, col_gramian_cache_init,
+        row_gramian_cache_reset)
+    self._col_updates_init = control_flow_ops.group(
+        row_factors_cache_init, col_factors_cache_reset, row_gramian_cache_init,
+        col_gramian_cache_reset)
 
     if self._row_wt_cache is not None:
       assert self._col_wt_cache is not None
@@ -565,6 +563,9 @@ class WALSModel(object):
 
     Must be run before initialize_row_update_op and should only be run by one
     trainer (usually the chief) when doing distributed training.
+
+    Returns:
+      Op to form the gramian.
     """
     return self._row_update_prep_gramian
 
@@ -574,6 +575,9 @@ class WALSModel(object):
 
     Must be run before initialize_col_update_op and should only be run by one
     trainer (usually the chief) when doing distributed training.
+
+    Returns:
+      Op to form the gramian.
     """
     return self._col_update_prep_gramian
 
@@ -613,8 +617,8 @@ class WALSModel(object):
     if len(factor) == 1:
       with ops.colocate_with(factor[0]):
         # TODO(agarwal): assign instead of scatter update for full batch update.
-        return state_ops.scatter_update(factor[0], indices, values,
-                                        name=name).op
+        return state_ops.scatter_update(
+            factor[0], indices, values, name=name).op
     else:
       num_shards = len(factor)
       assignments, new_ids = sharding_func(indices)
@@ -626,8 +630,9 @@ class WALSModel(object):
                                                        num_shards)
       updates = []
       for i in xrange(num_shards):
-        updates.append(state_ops.scatter_update(factor[i], sharded_ids[i],
-                                                sharded_values[i]))
+        updates.append(
+            state_ops.scatter_update(factor[i], sharded_ids[i], sharded_values[
+                i]))
       return control_flow_ops.group(*updates, name=name)
 
   def update_row_factors(self, sp_input=None, transpose_input=False):
@@ -645,15 +650,23 @@ class WALSModel(object):
       new_values: New values for the row factors.
       update_op: An op that assigns the newly computed values to the row
         factors.
-      loss: A tensor (scalar) that contains the normalized minibatch loss,
-        corresponding to sp_input.
-        if sp_input contains the rows {A_{i, :}, i \in I}, and the input matrix
-        A has n total rows, then loss is:
-        (\|\sqrt W_I \odot (A_I - U_I V^T)\|_F^2 + \lambda \|U_I\|_F^2) *
-        n / |I| + \lambda \|V\|_F^2.
+      unregularized_loss: A tensor (scalar) that contains the normalized
+        minibatch loss corresponding to sp_input, without the regularization
+        term. If sp_input contains the rows {A_{i, :}, i \in I}, and the input
+        matrix A has n total rows, then the unregularized loss is:
+        (\|\sqrt W_I \odot (A_I - U_I V^T)\|_F^2 * n / |I|
+        The total loss is unregularized_loss + regularization.
+      regularization: A tensor (scalar) that contains the normalized
+        regularization term for the minibatch loss corresponding to sp_input.
+        If sp_input contains the rows {A_{i, :}, i \in I}, and the input matrix
+        A has n total rows, then the regularization term is:
+        \lambda \|U_I\|_F^2) * n / |I| + \lambda \|V\|_F^2.
+      sum_weights: The sum of the weights W_I corresponding to sp_input,
+        normalized by a factor of n / |I|. The root weighted squared error is:
+        \sqrt(unregularized_loss / sum_weights).
     """
-    return self._process_input_helper(True, sp_input=sp_input,
-                                      transpose_input=transpose_input)
+    return self._process_input_helper(
+        True, sp_input=sp_input, transpose_input=transpose_input)
 
   def update_col_factors(self, sp_input=None, transpose_input=False):
     r"""Updates the column factors.
@@ -666,21 +679,31 @@ class WALSModel(object):
         columns corresponding to the transposed input are updated.
 
     Returns:
-      A tuple consisting of the following two elements:
+      A tuple consisting of the following elements:
       new_values: New values for the column factors.
       update_op: An op that assigns the newly computed values to the column
         factors.
-      loss: A tensor (scalar) that contains the normalized minibatch loss,
-        corresponding to sp_input.
+      unregularized_loss: A tensor (scalar) that contains the normalized
+        minibatch loss corresponding to sp_input, without the regularization
+        term. If sp_input contains the columns {A_{:, j}, j \in J}, and the
+        input matrix A has m total columns, then the unregularized loss is:
+        (\|\sqrt W_J \odot (A_J - U V_J^T)\|_F^2 * m / |I|
+        The total loss is unregularized_loss + regularization.
+      regularization: A tensor (scalar) that contains the normalized
+        regularization term for the minibatch loss corresponding to sp_input.
         If sp_input contains the columns {A_{:, j}, j \in J}, and the input
-        matrix A has m total columns, then loss is:
-        (\|\sqrt W_J \odot (A_J - U V_J^T)\|_F^2 + \lambda \|V_J\|_F^2) *
-        m / |J| + \lambda \|U\|_F^2.
+        matrix A has m total columns, then the regularization term is:
+        \lambda \|V_J\|_F^2) * m / |J| + \lambda \|U\|_F^2.
+      sum_weights: The sum of the weights W_J corresponding to sp_input,
+        normalized by a factor of m / |J|. The root weighted squared error is:
+        \sqrt(unregularized_loss / sum_weights).
     """
-    return self._process_input_helper(False, sp_input=sp_input,
-                                      transpose_input=transpose_input)
+    return self._process_input_helper(
+        False, sp_input=sp_input, transpose_input=transpose_input)
 
-  def project_row_factors(self, sp_input=None, transpose_input=False,
+  def project_row_factors(self,
+                          sp_input=None,
+                          transpose_input=False,
                           projection_weights=None):
     """Projects the row factors.
 
@@ -705,9 +728,11 @@ class WALSModel(object):
     """
     if projection_weights is None:
       projection_weights = 1
-    return self._process_input_helper(True, sp_input=sp_input,
-                                      transpose_input=transpose_input,
-                                      row_weights=projection_weights)[0]
+    return self._process_input_helper(
+        True,
+        sp_input=sp_input,
+        transpose_input=transpose_input,
+        row_weights=projection_weights)[0]
 
   def project_col_factors(self,
                           sp_input=None,
@@ -736,12 +761,16 @@ class WALSModel(object):
     """
     if projection_weights is None:
       projection_weights = 1
-    return self._process_input_helper(False, sp_input=sp_input,
-                                      transpose_input=transpose_input,
-                                      row_weights=projection_weights)[0]
-
-  def _process_input_helper(self, update_row_factors,
-                            sp_input=None, transpose_input=False,
+    return self._process_input_helper(
+        False,
+        sp_input=sp_input,
+        transpose_input=transpose_input,
+        row_weights=projection_weights)[0]
+
+  def _process_input_helper(self,
+                            update_row_factors,
+                            sp_input=None,
+                            transpose_input=False,
                             row_weights=None):
     """Creates the graph for processing a sparse slice of input.
 
@@ -761,12 +790,18 @@ class WALSModel(object):
         of columns to be updated/projected.
 
     Returns:
-      A tuple consisting of the following three elements:
+      A tuple consisting of the following elements:
       new_values: New values for the row/column factors.
       update_op: An op that assigns the newly computed values to the row/column
         factors.
-      loss: A tensor (scalar) that contains the normalized minibatch loss,
-        corresponding to sp_input.
+      unregularized_loss: A tensor (scalar) that contains the normalized
+        minibatch loss corresponding to sp_input, without the regularization
+        term. Add the regularization term below to yield the loss.
+      regularization: A tensor (scalar) that contains the normalized
+        regularization term for the minibatch loss corresponding to sp_input.
+      sum_weights: The sum of the weights corresponding to sp_input. This
+        can be used with unregularized loss to caluclate the root weighted
+        squared error.
     """
     assert isinstance(sp_input, sparse_tensor.SparseTensor)
 
@@ -776,6 +811,7 @@ class WALSModel(object):
       row_wt = self._row_wt_cache
       col_wt = self._col_wt_cache
       total_rows = self._input_rows
+      total_cols = self._input_cols
       sharding_func = WALSModel._get_sharding_func(self._input_rows,
                                                    self._num_row_shards)
       gramian = self._col_gramian_cache
@@ -785,6 +821,7 @@ class WALSModel(object):
       row_wt = self._col_wt_cache
       col_wt = self._row_wt_cache
       total_rows = self._input_cols
+      total_cols = self._input_rows
       sharding_func = WALSModel._get_sharding_func(self._input_cols,
                                                    self._num_col_shards)
       gramian = self._row_gramian_cache
@@ -820,8 +857,8 @@ class WALSModel(object):
     right = embedding_ops.embedding_lookup(
         right_factors, gather_indices, partition_strategy="div")
     new_sp_indices = array_ops.concat([row_ids, col_ids], 1)
-    new_sp_shape = (array_ops.concat([row_shape, col_shape], 0) if
-                    transpose_input else
+    new_sp_shape = (array_ops.concat([row_shape, col_shape], 0)
+                    if transpose_input else
                     array_ops.concat([col_shape, row_shape], 0))
     new_sp_input = sparse_tensor.SparseTensor(
         indices=new_sp_indices,
@@ -834,9 +871,9 @@ class WALSModel(object):
       total_lhs += self._regularization_matrix
     if self._row_weights is None:
       # Special case of ALS. Use a much simpler update rule.
-      total_rhs = (self._unobserved_weight *
-                   sparse_ops.sparse_tensor_dense_matmul(
-                       new_sp_input, right, adjoint_a=transpose_input))
+      total_rhs = (
+          self._unobserved_weight * sparse_ops.sparse_tensor_dense_matmul(
+              new_sp_input, right, adjoint_a=transpose_input))
       # TODO(rmlarsen): handle transposing in tf.matrix_solve instead of
       # transposing explicitly.
       # TODO(rmlarsen): multi-thread tf.matrix_solve.
@@ -877,8 +914,12 @@ class WALSModel(object):
           linalg_ops.matrix_solve(total_lhs, total_rhs), [2])
 
     update_op_name = "row_update" if update_row_factors else "col_update"
-    update_op = self.scatter_update(left, update_indices, new_left_values,
-                                    sharding_func, name=update_op_name)
+    update_op = self.scatter_update(
+        left,
+        update_indices,
+        new_left_values,
+        sharding_func,
+        name=update_op_name)
 
     # Create the loss subgraph
     loss_sp_input = (sparse_ops.sparse_transpose(new_sp_input)
@@ -886,32 +927,52 @@ class WALSModel(object):
     # sp_approx is the low rank estimate of the input matrix, formed by
     # computing the product <u_i, v_j> for (i, j) in loss_sp_input.indices.
     sp_approx_vals = gen_factorization_ops.masked_matmul(
-        new_left_values, right, loss_sp_input.indices, transpose_a=False,
+        new_left_values,
+        right,
+        loss_sp_input.indices,
+        transpose_a=False,
         transpose_b=True)
     sp_approx = sparse_tensor.SparseTensor(
         loss_sp_input.indices, sp_approx_vals, loss_sp_input.dense_shape)
     sp_approx_sq = math_ops.square(sp_approx)
     sp_residual = sparse_ops.sparse_add(loss_sp_input, sp_approx * (-1))
     sp_residual_sq = math_ops.square(sp_residual)
-    row_wt_mat = (constant_op.constant(0.) if self._row_weights is None else
-                  array_ops.expand_dims(row_weights_slice, 1))
-    col_wt_mat = (constant_op.constant(0.) if self._col_weights is None else
-                  array_ops.expand_dims(col_weights, 0))
+    row_wt_mat = (constant_op.constant(0.)
+                  if self._row_weights is None else array_ops.expand_dims(
+                      row_weights_slice, 1))
+    col_wt_mat = (constant_op.constant(0.)
+                  if self._col_weights is None else array_ops.expand_dims(
+                      col_weights, 0))
+
     # We return the normalized loss
     partial_row_gramian = math_ops.matmul(
         new_left_values, new_left_values, transpose_a=True)
     normalization_factor = total_rows / math_ops.cast(num_rows, dtypes.float32)
-    loss = (
-        self._unobserved_weight * (
-            sparse_ops.sparse_reduce_sum(sp_residual_sq) -
-            sparse_ops.sparse_reduce_sum(sp_approx_sq) +
-            math_ops.trace(math_ops.matmul(partial_row_gramian, gramian))
-        ) +
+
+    unregularized_loss = (
+        self._unobserved_weight * (  # pyformat line break
+            sparse_ops.sparse_reduce_sum(sp_residual_sq) -  # pyformat break
+            sparse_ops.sparse_reduce_sum(sp_approx_sq) +  # pyformat break
+            math_ops.trace(math_ops.matmul(partial_row_gramian, gramian))) +
         sparse_ops.sparse_reduce_sum(row_wt_mat * (sp_residual_sq * col_wt_mat))
     ) * normalization_factor
+
     if self._regularization is not None:
-      loss += self._regularization * (
+      regularization = self._regularization * (
           math_ops.trace(partial_row_gramian) * normalization_factor +
-          math_ops.trace(gramian)
-      )
-    return (new_left_values, update_op, loss)
+          math_ops.trace(gramian))
+    else:
+      regularization = constant_op.constant(0.)
+
+    sum_weights = self._unobserved_weight * math_ops.cast(
+        total_rows * total_cols, dtypes.float32)
+    if self._row_weights is not None and self._col_weights is not None:
+      ones = sparse_tensor.SparseTensor(
+          indices=loss_sp_input.indices,
+          values=array_ops.ones(array_ops.shape(loss_sp_input.values)),
+          dense_shape=loss_sp_input.dense_shape)
+      sum_weights += sparse_ops.sparse_reduce_sum(row_wt_mat * (
+          ones * col_wt_mat)) * normalization_factor
+
+    return (new_left_values, update_op, unregularized_loss, regularization,
+            sum_weights)
diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py b/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
index bcee881854..c813733915 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
@@ -31,7 +31,6 @@ from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
-
 INPUT_MATRIX = factorization_ops_test_utils.INPUT_MATRIX
 np_matrix_to_tf_sparse = factorization_ops_test_utils.np_matrix_to_tf_sparse
 
@@ -53,16 +52,20 @@ class WalsModelTest(test.TestCase):
 
   def calculate_loss_from_wals_model(self, wals_model, sp_inputs):
     current_rows = embedding_ops.embedding_lookup(
-        wals_model.row_factors, math_ops.range(wals_model._input_rows),
+        wals_model.row_factors,
+        math_ops.range(wals_model._input_rows),
         partition_strategy="div")
     current_cols = embedding_ops.embedding_lookup(
-        wals_model.col_factors, math_ops.range(wals_model._input_cols),
+        wals_model.col_factors,
+        math_ops.range(wals_model._input_cols),
         partition_strategy="div")
     row_wts = embedding_ops.embedding_lookup(
-        wals_model._row_weights, math_ops.range(wals_model._input_rows),
+        wals_model._row_weights,
+        math_ops.range(wals_model._input_rows),
         partition_strategy="div")
     col_wts = embedding_ops.embedding_lookup(
-        wals_model._col_weights, math_ops.range(wals_model._input_cols),
+        wals_model._col_weights,
+        math_ops.range(wals_model._input_cols),
         partition_strategy="div")
     return factorization_ops_test_utils.calculate_loss(
         sp_inputs, current_rows, current_cols, wals_model._regularization,
@@ -71,9 +74,11 @@ class WalsModelTest(test.TestCase):
   def setUp(self):
     self.col_init = [
         # shard 0
-        [[-0.36444709, -0.39077035, -0.32528427],
-         [1.19056475, 0.07231052, 2.11834812],
-         [0.93468881, -0.71099287, 1.91826844]],
+        [
+            [-0.36444709, -0.39077035, -0.32528427],  # pyformat line break
+            [1.19056475, 0.07231052, 2.11834812],
+            [0.93468881, -0.71099287, 1.91826844]
+        ],
         # shard 1
         [[1.18160152, 1.52490723, -0.50015002],
          [1.82574749, -0.57515913, -1.32810032]],
@@ -87,19 +92,72 @@ class WalsModelTest(test.TestCase):
 
     # Values of factor shards after running one iteration of row and column
     # updates.
-    self._row_factors_0 = [[0.097689, -0.219293, -0.020780],
-                           [0.50842, 0.64626, 0.22364],
-                           [0.401159, -0.046558, -0.192854]]
+    self._row_factors_0 = [
+        [0.097689, -0.219293, -0.020780],  # pyformat line break
+        [0.50842, 0.64626, 0.22364],
+        [0.401159, -0.046558, -0.192854]
+    ]
     self._row_factors_1 = [[1.20597, -0.48025, 0.35582],
                            [1.5564, 1.2528, 1.0528]]
-    self._col_factors_0 = [[2.4725, -1.2950, -1.9980],
-                           [0.44625, 1.50771, 1.27118],
-                           [1.39801, -2.10134, 0.73572]]
+    self._col_factors_0 = [
+        [2.4725, -1.2950, -1.9980],  # pyformat line break
+        [0.44625, 1.50771, 1.27118],
+        [1.39801, -2.10134, 0.73572]
+    ]
     self._col_factors_1 = [[3.36509, -0.66595, -3.51208],
                            [0.57191, 1.59407, 1.33020]]
     self._col_factors_2 = [[3.3459, -1.3341, -3.3008],
                            [0.57366, 1.83729, 1.26798]]
 
+  def _run_test_sum_weights(self, test_rows):
+    # test_rows: True to test row weights, False to test column weights.
+
+    num_rows = 5
+    num_cols = 5
+    unobserved_weight = 0.1
+    row_weights = [[8., 18., 28., 38., 48.]]
+    col_weights = [[90., 91., 92., 93., 94.]]
+    sparse_indices = [[0, 1], [2, 3], [4, 1]]
+    sparse_values = [666., 777., 888.]
+
+    unobserved = unobserved_weight * num_rows * num_cols
+    observed = 8. * 91. + 28. * 93. + 48. * 91.
+    # sparse_indices has three unique rows and two unique columns
+    observed *= num_rows / 3. if test_rows else num_cols / 2.
+    want_weight_sum = unobserved + observed
+
+    with ops.Graph().as_default(), self.test_session() as sess:
+      wals_model = factorization_ops.WALSModel(
+          input_rows=num_rows,
+          input_cols=num_cols,
+          n_components=5,
+          unobserved_weight=unobserved_weight,
+          row_weights=row_weights,
+          col_weights=col_weights,
+          use_factors_weights_cache=False)
+
+      wals_model.initialize_op.run()
+      wals_model.worker_init.run()
+
+      update_factors = (wals_model.update_row_factors
+                        if test_rows else wals_model.update_col_factors)
+
+      (_, _, _, _, sum_weights) = update_factors(
+          sp_input=sparse_tensor.SparseTensor(
+              indices=sparse_indices,
+              values=sparse_values,
+              dense_shape=[num_rows, num_cols]),
+          transpose_input=False)
+
+      got_weight_sum = sess.run(sum_weights)
+
+      self.assertNear(
+          got_weight_sum,
+          want_weight_sum,
+          err=.001,
+          msg="got weight sum [{}], want weight sum [{}]".format(
+              got_weight_sum, want_weight_sum))
+
   def _run_test_process_input(self,
                               use_factors_weights_cache,
                               compute_loss=False):
@@ -138,8 +196,10 @@ class WalsModelTest(test.TestCase):
       # Here we feed in scattered rows of the input.
       wals_model.row_update_prep_gramian_op.run()
       wals_model.initialize_row_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_row_factors(
-          sp_input=sp_feeder, transpose_input=False)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_row_factors(
+           sp_input=sp_feeder, transpose_input=False)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_rows:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -163,8 +223,8 @@ class WalsModelTest(test.TestCase):
           sp_input=sp_feeder, transpose_input=False)
       feed_dict = {
           sp_feeder:
-              np_matrix_to_tf_sparse(
-                  INPUT_MATRIX, [1, 4], shuffle=False).eval()
+              np_matrix_to_tf_sparse(INPUT_MATRIX, [1, 4], shuffle=False)
+              .eval()
       }
       self.assertAllClose(
           projected_rows.eval(feed_dict=feed_dict),
@@ -178,15 +238,17 @@ class WalsModelTest(test.TestCase):
       if compute_loss:
         # Test loss computation after the row update
         loss = sum(
-            sess.run(factor_loss * self.count_rows(inp) / num_rows,
-                     feed_dict={sp_feeder: inp})
-            for inp in input_scattered_rows)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+            sess.run(
+                factor_loss * self.count_rows(inp) / num_rows,
+                feed_dict={sp_feeder: inp}) for inp in input_scattered_rows)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After row update, computed loss = {}, does not match
-            the true loss = {}.""".format(loss, true_loss))
+            loss,
+            true_loss,
+            err=.001,
+            msg="After row update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
 
       # Split input into multiple sparse tensors with scattered columns. Note
       # that here the elements in the sparse tensors are not ordered and also
@@ -206,8 +268,10 @@ class WalsModelTest(test.TestCase):
       # Here we feed in scattered columns of the input.
       wals_model.col_update_prep_gramian_op.run()
       wals_model.initialize_col_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_col_factors(
-          sp_input=sp_feeder, transpose_input=False)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_col_factors(
+           sp_input=sp_feeder, transpose_input=False)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_cols:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -243,25 +307,28 @@ class WalsModelTest(test.TestCase):
           atol=1e-3)
       self.assertAllClose(
           projected_cols_no_weights.eval(feed_dict=feed_dict),
-          [[3.471045, -1.250835, -3.598917],
-           [3.585139, -0.487476, -3.852232],
+          [[3.471045, -1.250835, -3.598917], [3.585139, -0.487476, -3.852232],
            [0.346433, 1.360644, 1.677121]],
           atol=1e-3)
 
       if compute_loss:
         # Test loss computation after the column update.
         loss = sum(
-            sess.run(factor_loss * self.count_cols(inp) / num_cols,
-                     feed_dict={sp_feeder: inp})
+            sess.run(
+                factor_loss * self.count_cols(inp) / num_cols,
+                feed_dict={sp_feeder: inp})
             for inp in input_scattered_cols_non_duplicate)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After col update, computed loss = {}, does not match the true
-            loss = {}.""".format(loss, true_loss))
-
-  def _run_test_process_input_transposed(self, use_factors_weights_cache,
+            loss,
+            true_loss,
+            err=.001,
+            msg="After col update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
+
+  def _run_test_process_input_transposed(self,
+                                         use_factors_weights_cache,
                                          compute_loss=False):
     with ops.Graph().as_default(), self.test_session() as sess:
       self._wals_inputs = self.sparse_input()
@@ -304,8 +371,10 @@ class WalsModelTest(test.TestCase):
       # they appear.
       wals_model.row_update_prep_gramian_op.run()
       wals_model.initialize_row_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_row_factors(
-          sp_input=sp_feeder, transpose_input=True)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_row_factors(
+           sp_input=sp_feeder, transpose_input=True)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_rows:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -344,15 +413,18 @@ class WalsModelTest(test.TestCase):
       if compute_loss:
         # Test loss computation after the row update
         loss = sum(
-            sess.run(factor_loss * self.count_cols(inp) / num_rows,
-                     feed_dict={sp_feeder: inp})
+            sess.run(
+                factor_loss * self.count_cols(inp) / num_rows,
+                feed_dict={sp_feeder: inp})
             for inp in input_scattered_rows_non_duplicate)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After row update, computed loss = {}, does not match the true
-            loss = {}.""".format(loss, true_loss))
+            loss,
+            true_loss,
+            err=.001,
+            msg="After row update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
 
       # Split input into multiple SparseTensors with scattered columns.
       # Here the inputs are transposed. But the same constraints as described in
@@ -375,8 +447,10 @@ class WalsModelTest(test.TestCase):
       # Here we feed in scattered columns of the input.
       wals_model.col_update_prep_gramian_op.run()
       wals_model.initialize_col_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_col_factors(
-          sp_input=sp_feeder, transpose_input=True)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_col_factors(
+           sp_input=sp_feeder, transpose_input=True)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_cols:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -406,21 +480,23 @@ class WalsModelTest(test.TestCase):
           atol=1e-3)
       self.assertAllClose(
           projected_cols_no_weights.eval(feed_dict=feed_dict),
-          [[3.585139, -0.487476, -3.852232],
-           [0.557937, 1.813907, 1.331171]],
+          [[3.585139, -0.487476, -3.852232], [0.557937, 1.813907, 1.331171]],
           atol=1e-3)
       if compute_loss:
         # Test loss computation after the col update
         loss = sum(
-            sess.run(factor_loss * self.count_rows(inp) / num_cols,
-                     feed_dict={sp_feeder: inp})
+            sess.run(
+                factor_loss * self.count_rows(inp) / num_cols,
+                feed_dict={sp_feeder: inp})
             for inp in input_scattered_cols_non_duplicate)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After col update, computed loss = {}, does not match the true
-            loss = {}.""".format(loss, true_loss))
+            loss,
+            true_loss,
+            err=.001,
+            msg="After col update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
 
   # Note that when row_weights and col_weights are 0, WALS gives identical
   # results as ALS (Alternating Least Squares). However our implementation does
@@ -503,8 +579,7 @@ class WalsModelTest(test.TestCase):
       for c1, c2 in zip(col_factors1, col_factors2):
         self.assertAllClose(c1, c2, rtol=5e-3, atol=1e-2)
       self.assertAllClose(
-          als_projected_col_factors1,
-          [col_factors2[0][2], col_factors2[0][0]],
+          als_projected_col_factors1, [col_factors2[0][2], col_factors2[0][0]],
           atol=1e-2)
 
   def _run_test_als_transposed(self, use_factors_weights_cache):
@@ -599,8 +674,8 @@ class WalsModelTest(test.TestCase):
     cols = 11
     dims = 3
     with ops.Graph().as_default(), self.test_session():
-      data = np.dot(np.random.rand(rows, 3),
-                    np.random.rand(3, cols)).astype(np.float32) / 3.0
+      data = np.dot(np.random.rand(rows, 3), np.random.rand(
+          3, cols)).astype(np.float32) / 3.0
       indices = [[i, j] for i in xrange(rows) for j in xrange(cols)]
       values = data.reshape(-1)
       inp = sparse_tensor.SparseTensor(indices, values, [rows, cols])
@@ -629,8 +704,8 @@ class WalsModelTest(test.TestCase):
     dims = 3
 
     with ops.Graph().as_default(), self.test_session():
-      data = np.dot(np.random.rand(rows, 3),
-                    np.random.rand(3, cols)).astype(np.float32) / 3.0
+      data = np.dot(np.random.rand(rows, 3), np.random.rand(
+          3, cols)).astype(np.float32) / 3.0
       indices = [[i, j] for i in xrange(rows) for j in xrange(cols)]
       values = data.reshape(-1)
       inp = sparse_tensor.SparseTensor(indices, values, [rows, cols])
@@ -664,8 +739,8 @@ class WalsModelTest(test.TestCase):
     with ops.Graph().as_default(), self.test_session():
       row_wts = 0.1 + np.random.rand(rows)
       col_wts = 0.1 + np.random.rand(cols)
-      data = np.dot(np.random.rand(rows, 3),
-                    np.random.rand(3, cols)).astype(np.float32) / 3.0
+      data = np.dot(np.random.rand(rows, 3), np.random.rand(
+          3, cols)).astype(np.float32) / 3.0
       indices = np.array(
           list(
               filter(keep_index,
@@ -741,5 +816,12 @@ class WalsModelTest(test.TestCase):
   def test_loss_without_cache(self):
     self._run_test_process_input(False, compute_loss=True)
 
+  def test_sum_row_weights(self):
+    self._run_test_sum_weights(True)
+
+  def test_sum_col_weights(self):
+    self._run_test_sum_weights(False)
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/factorization/python/ops/wals.py b/tensorflow/contrib/factorization/python/ops/wals.py
index 0bc0ef39ec..3e3ee5fa57 100644
--- a/tensorflow/contrib/factorization/python/ops/wals.py
+++ b/tensorflow/contrib/factorization/python/ops/wals.py
@@ -38,17 +38,9 @@ from tensorflow.python.training import session_run_hook
 class _SweepHook(session_run_hook.SessionRunHook):
   """Keeps track of row/col sweeps, and runs prep ops before each sweep."""
 
-  def __init__(self,
-               is_row_sweep_var,
-               train_op,
-               num_rows,
-               num_cols,
-               processed_row_indices,
-               processed_col_indices,
-               row_prep_ops,
-               col_prep_ops,
-               cache_init_ops,
-               completed_sweeps_var):
+  def __init__(self, is_row_sweep_var, train_op, num_rows, num_cols,
+               processed_row_indices, processed_col_indices, row_prep_ops,
+               col_prep_ops, cache_init_ops, completed_sweeps_var):
     """Initializes SweepHook.
 
     Args:
@@ -90,12 +82,9 @@ class _SweepHook(session_run_hook.SessionRunHook):
     # _is_row_sweep_var and incrementing the global_step and completed_sweeps
     # counters. They have control_dependencies on train_op.
     self._fetches = self._create_switch_ops(processed_row_indices,
-                                            processed_col_indices,
-                                            train_op)
+                                            processed_col_indices, train_op)
 
-  def _create_switch_ops(self,
-                         processed_row_indices,
-                         processed_col_indices,
+  def _create_switch_ops(self, processed_row_indices, processed_col_indices,
                          train_op):
     """Creates ops to update is_row_sweep_var, global_step and completed_sweeps.
 
@@ -147,17 +136,20 @@ class _SweepHook(session_run_hook.SessionRunHook):
     # After running the train_op, update processed_rows or processed_cols
     # tensors, depending on whether we are currently doing a row or a col sweep
     with ops.control_dependencies([train_op]):
+
       def get_row_update_op():
         with ops.colocate_with(processed_rows):
-          return state_ops.scatter_update(
-              processed_rows, processed_row_indices,
-              array_ops.ones_like(processed_row_indices, dtype=dtypes.bool))
+          return state_ops.scatter_update(processed_rows, processed_row_indices,
+                                          array_ops.ones_like(
+                                              processed_row_indices,
+                                              dtype=dtypes.bool))
 
       def get_col_update_op():
         with ops.colocate_with(processed_cols):
-          return state_ops.scatter_update(
-              processed_cols, processed_col_indices,
-              array_ops.ones_like(processed_col_indices, dtype=dtypes.bool))
+          return state_ops.scatter_update(processed_cols, processed_col_indices,
+                                          array_ops.ones_like(
+                                              processed_col_indices,
+                                              dtype=dtypes.bool))
 
       update_processed_op = control_flow_ops.cond(
           self._is_row_sweep_var, get_row_update_op, get_col_update_op)
@@ -166,6 +158,7 @@ class _SweepHook(session_run_hook.SessionRunHook):
       # If this is the case, flip the is_row_sweep_var and reset processed_rows
       # and processed_cols tensors.
       with ops.control_dependencies([update_processed_op]):
+
         def get_switch_op():
           return state_ops.assign(
               self._is_row_sweep_var,
@@ -182,13 +175,17 @@ class _SweepHook(session_run_hook.SessionRunHook):
             lambda: math_ops.reduce_all(processed_cols),
             name="sweep_hook_is_sweep_done")
         switch_op = control_flow_ops.cond(
-            is_sweep_done, get_switch_op, control_flow_ops.no_op,
+            is_sweep_done,
+            get_switch_op,
+            control_flow_ops.no_op,
             name="sweep_hook_switch_op")
         reset_op = control_flow_ops.cond(
-            is_sweep_done, get_reset_op, control_flow_ops.no_op,
+            is_sweep_done,
+            get_reset_op,
+            control_flow_ops.no_op,
             name="sweep_hook_reset_op")
-        switch_ops = control_flow_ops.group(switch_op, reset_op,
-                                            name="sweep_hook_switch_ops")
+        switch_ops = control_flow_ops.group(
+            switch_op, reset_op, name="sweep_hook_switch_ops")
 
         with ops.control_dependencies([switch_ops]):
           # Op to increment the completed_sweeps counter.
@@ -208,7 +205,8 @@ class _SweepHook(session_run_hook.SessionRunHook):
                 name="global_step_incr")
 
           incr_ops = control_flow_ops.group(
-              completed_sweeps_incr_op, global_step_incr_op,
+              completed_sweeps_incr_op,
+              global_step_incr_op,
               name="counter_incr_ops")
 
     return [is_sweep_done, switch_ops, incr_ops]
@@ -260,7 +258,7 @@ class _StopAtSweepHook(session_run_hook.SessionRunHook):
   def begin(self):
     try:
       self._completed_sweeps_var = ops.get_default_graph().get_tensor_by_name(
-          WALSMatrixFactorization.COMPLETED_SWEEPS+":0")
+          WALSMatrixFactorization.COMPLETED_SWEEPS + ":0")
     except KeyError:
       raise RuntimeError(WALSMatrixFactorization.COMPLETED_SWEEPS +
                          " counter should be created to use StopAtSweepHook.")
@@ -288,12 +286,10 @@ def _wals_factorization_model_function(features, labels, mode, params):
     A ModelFnOps object.
   """
   assert labels is None
-  use_factors_weights_cache = (
-      params["use_factors_weights_cache_for_training"]
-      and mode == model_fn.ModeKeys.TRAIN)
-  use_gramian_cache = (
-      params["use_gramian_cache_for_training"]
-      and mode == model_fn.ModeKeys.TRAIN)
+  use_factors_weights_cache = (params["use_factors_weights_cache_for_training"]
+                               and mode == model_fn.ModeKeys.TRAIN)
+  use_gramian_cache = (params["use_gramian_cache_for_training"] and
+                       mode == model_fn.ModeKeys.TRAIN)
   max_sweeps = params["max_sweeps"]
   model = factorization_ops.WALSModel(
       params["num_rows"],
@@ -348,15 +344,22 @@ def _wals_factorization_model_function(features, labels, mode, params):
 
   def update_row_factors():
     return model.update_row_factors(sp_input=input_rows, transpose_input=False)
+
   def update_col_factors():
     return model.update_col_factors(sp_input=input_cols, transpose_input=True)
-  _, train_op, loss = control_flow_ops.cond(
-      is_row_sweep, update_row_factors, update_col_factors)
 
-  row_prep_ops = [model.row_update_prep_gramian_op,
-                  model.initialize_row_update_op]
-  col_prep_ops = [model.col_update_prep_gramian_op,
-                  model.initialize_col_update_op]
+  (_, train_op,
+   unregularized_loss, regularization, sum_weights) = control_flow_ops.cond(
+       is_row_sweep, update_row_factors, update_col_factors)
+  loss = unregularized_loss + regularization
+  root_weighted_squared_error = math_ops.sqrt(unregularized_loss / sum_weights)
+
+  row_prep_ops = [
+      model.row_update_prep_gramian_op, model.initialize_row_update_op
+  ]
+  col_prep_ops = [
+      model.col_update_prep_gramian_op, model.initialize_col_update_op
+  ]
   cache_init_ops = [model.worker_init]
 
   sweep_hook = _SweepHook(
@@ -369,13 +372,15 @@ def _wals_factorization_model_function(features, labels, mode, params):
       row_prep_ops,
       col_prep_ops,
       cache_init_ops,
-      completed_sweeps_var,
-  )
+      completed_sweeps_var,)
   training_hooks = [sweep_hook]
   if max_sweeps is not None:
     training_hooks.append(_StopAtSweepHook(max_sweeps))
 
-  summary.scalar("loss", loss)
+  # The root weighted squared error =
+  #   \sqrt( \sum_{i,j} w_ij * (a_ij - r_ij)^2 / \sum_{i,j} w_ij )
+  summary.scalar("loss", loss)  # the estimated total training loss
+  summary.scalar("root_weighted_squared_error", root_weighted_squared_error)
   summary.scalar("completed_sweeps", completed_sweeps_var)
 
   # Prediction ops (only return predictions in INFER mode)
@@ -384,11 +389,13 @@ def _wals_factorization_model_function(features, labels, mode, params):
     project_row = features[WALSMatrixFactorization.PROJECT_ROW]
     projection_weights = features.get(
         WALSMatrixFactorization.PROJECTION_WEIGHTS)
+
     def get_row_projection():
       return model.project_row_factors(
           sp_input=input_rows,
           projection_weights=projection_weights,
           transpose_input=False)
+
     def get_col_projection():
       return model.project_col_factors(
           sp_input=input_cols,
@@ -396,8 +403,8 @@ def _wals_factorization_model_function(features, labels, mode, params):
           transpose_input=True)
 
     predictions[WALSMatrixFactorization.PROJECTION_RESULT] = (
-        control_flow_ops.cond(
-            project_row, get_row_projection, get_col_projection))
+        control_flow_ops.cond(project_row, get_row_projection,
+                              get_col_projection))
 
   return model_fn.ModelFnOps(
       mode=mode,
@@ -556,26 +563,41 @@ class WALSMatrixFactorization(estimator.Estimator):
     # TODO(walidk): Provide input pipelines that handle missing rows.
 
     params = {
-        "num_rows": num_rows,
-        "num_cols": num_cols,
-        "embedding_dimension": embedding_dimension,
-        "unobserved_weight": unobserved_weight,
-        "regularization_coeff": regularization_coeff,
-        "row_init": row_init,
-        "col_init": col_init,
-        "num_row_shards": num_row_shards,
-        "num_col_shards": num_col_shards,
-        "row_weights": row_weights,
-        "col_weights": col_weights,
-        "max_sweeps": max_sweeps,
+        "num_rows":
+            num_rows,
+        "num_cols":
+            num_cols,
+        "embedding_dimension":
+            embedding_dimension,
+        "unobserved_weight":
+            unobserved_weight,
+        "regularization_coeff":
+            regularization_coeff,
+        "row_init":
+            row_init,
+        "col_init":
+            col_init,
+        "num_row_shards":
+            num_row_shards,
+        "num_col_shards":
+            num_col_shards,
+        "row_weights":
+            row_weights,
+        "col_weights":
+            col_weights,
+        "max_sweeps":
+            max_sweeps,
         "use_factors_weights_cache_for_training":
             use_factors_weights_cache_for_training,
-        "use_gramian_cache_for_training": use_gramian_cache_for_training
+        "use_gramian_cache_for_training":
+            use_gramian_cache_for_training
     }
-    self._row_factors_names = ["row_factors_shard_%d" % i
-                               for i in range(num_row_shards)]
-    self._col_factors_names = ["col_factors_shard_%d" % i
-                               for i in range(num_col_shards)]
+    self._row_factors_names = [
+        "row_factors_shard_%d" % i for i in range(num_row_shards)
+    ]
+    self._col_factors_names = [
+        "col_factors_shard_%d" % i for i in range(num_col_shards)
+    ]
 
     super(WALSMatrixFactorization, self).__init__(
         model_fn=_wals_factorization_model_function,
-- 
GitLab


From 897e98520290f61392c7e626f669be5e9557ae96 Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Tue, 27 Jun 2017 08:28:26 -0700
Subject: [PATCH 0484/1375] Don't include node_def.proto.h in node_def_util.h

The goal is to make kernels mostly independent of proto headers, which will let us lock down our .so imports.

RELNOTES: n/a
PiperOrigin-RevId: 160278032
---
 tensorflow/core/framework/node_def_util.cc | 53 +++++++++++++++++
 tensorflow/core/framework/node_def_util.h  | 67 ++++++++++++++++------
 2 files changed, 102 insertions(+), 18 deletions(-)

diff --git a/tensorflow/core/framework/node_def_util.cc b/tensorflow/core/framework/node_def_util.cc
index 9b737e1f72..79feb20d53 100644
--- a/tensorflow/core/framework/node_def_util.cc
+++ b/tensorflow/core/framework/node_def_util.cc
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/graph.pb_text.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb_text.h"
 #include "tensorflow/core/framework/op_def_util.h"
@@ -611,4 +612,56 @@ Status AttachDef(const Status& status, const Node& node) {
   return AttachDef(status, node.def());
 }
 
+void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def) {
+  node_def->mutable_attr()->insert(
+      AttrValueMap::value_type(name.ToString(), value));
+}
+
+#define ADD_NODE_ATTR(T)                                           \
+  void AddNodeAttr(StringPiece name, T value, NodeDef* node_def) { \
+    AttrValue attr_value;                                          \
+    SetAttrValue(value, &attr_value);                              \
+    AddNodeAttr(name, attr_value, node_def);                       \
+  }
+ADD_NODE_ATTR(StringPiece)
+ADD_NODE_ATTR(const char*)
+ADD_NODE_ATTR(int32)
+ADD_NODE_ATTR(int64)
+ADD_NODE_ATTR(float)
+ADD_NODE_ATTR(double)
+ADD_NODE_ATTR(bool)
+ADD_NODE_ATTR(DataType)
+ADD_NODE_ATTR(const PartialTensorShape&)
+ADD_NODE_ATTR(const Tensor&)
+ADD_NODE_ATTR(const TensorProto&)
+ADD_NODE_ATTR(const NameAttrList&)
+ADD_NODE_ATTR(gtl::ArraySlice<StringPiece>)
+ADD_NODE_ATTR(gtl::ArraySlice<const char*>)
+ADD_NODE_ATTR(gtl::ArraySlice<string>)
+ADD_NODE_ATTR(gtl::ArraySlice<int32>)
+ADD_NODE_ATTR(gtl::ArraySlice<int64>)
+ADD_NODE_ATTR(gtl::ArraySlice<float>)
+ADD_NODE_ATTR(gtl::ArraySlice<bool>)
+ADD_NODE_ATTR(const std::vector<bool>&)
+ADD_NODE_ATTR(gtl::ArraySlice<DataType>)
+ADD_NODE_ATTR(gtl::ArraySlice<TensorShape>)
+ADD_NODE_ATTR(gtl::ArraySlice<PartialTensorShape>)
+ADD_NODE_ATTR(gtl::ArraySlice<TensorShapeProto>)
+ADD_NODE_ATTR(gtl::ArraySlice<Tensor>)
+ADD_NODE_ATTR(gtl::ArraySlice<NameAttrList>)
+#undef ADD_NODE_ATTR
+
+void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map) {
+  map->insert(AttrValueMap::value_type(name.ToString(), value));
+}
+
+#define ADD_ATTR(T)                                            \
+  void AddAttr(StringPiece name, T value, AttrValueMap* map) { \
+    AttrValue attr_value;                                      \
+    SetAttrValue(value, &attr_value);                          \
+    AddAttr(name, attr_value, map);                            \
+  }
+ADD_ATTR(bool)
+#undef ADD_ATTR
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/node_def_util.h b/tensorflow/core/framework/node_def_util.h
index 1438abdec6..5d4864db66 100644
--- a/tensorflow/core/framework/node_def_util.h
+++ b/tensorflow/core/framework/node_def_util.h
@@ -21,7 +21,6 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/attr_value_util.h"
-#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
@@ -31,6 +30,9 @@ namespace tensorflow {
 
 class Node;
 
+// We forward declare NodeDef so that kernels don't need to depend on protos
+class NodeDef;
+
 // Name of the attribute used to encode node colocation constraints.
 //
 // Nodes can be co-located on the same device. Desire for explicit co-location
@@ -50,32 +52,61 @@ typedef protobuf::Map<string, AttrValue> AttrValueMap;
 
 // Adds an attr with name <name> and value <value> to *node_def.
 // The type of the attr is based on the type of value.
-template <class T>
-void AddNodeAttr(StringPiece name, T&& value, NodeDef* node_def) {
-  AttrValue attr_value;
-  SetAttrValue(std::forward<T>(value), &attr_value);
-  node_def->mutable_attr()->insert(
-      AttrValueMap::value_type(name.ToString(), attr_value));
-}
+void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, StringPiece value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const char* value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, int32 value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, int64 value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, float value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, double value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, bool value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, DataType value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const PartialTensorShape& value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const Tensor& value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const TensorProto& value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const NameAttrList& value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<StringPiece> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<const char*> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<string> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<int32> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<int64> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<float> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<bool> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const std::vector<bool>& value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<DataType> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<TensorShape> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<PartialTensorShape> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<TensorShapeProto> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<Tensor> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<NameAttrList> value,
+                 NodeDef* node_def);
 
 // Version to workaround C++'s "perfect" forwarding not being able to
 // forward {...} initialization.
 template <class T>
 void AddNodeAttr(StringPiece name, std::initializer_list<T> value,
                  NodeDef* node_def) {
-  AttrValue attr_value;
-  SetAttrValue(value, &attr_value);
-  node_def->mutable_attr()->insert(
-      AttrValueMap::value_type(name.ToString(), attr_value));
+  AddNodeAttr(name, gtl::ArraySlice<T>(value), node_def);
 }
 
 // Adds an attr to an attr value map.
-template <class T>
-void AddAttr(StringPiece name, T&& value, AttrValueMap* map) {
-  AttrValue attr_value;
-  SetAttrValue(value, &attr_value);
-  map->insert(AttrValueMap::value_type(name.ToString(), attr_value));
-}
+void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map);
+void AddAttr(StringPiece name, bool value, AttrValueMap* map);
 
 class AttrSlice {
  public:
-- 
GitLab


From 6e046535dbcc1249a001256cb6e89355288ac750 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 27 Jun 2017 08:33:42 -0700
Subject: [PATCH 0485/1375] [XLA] Add tuple support to
 Literal::CreateFromShape.

PiperOrigin-RevId: 160278561
---
 tensorflow/compiler/xla/literal_util.cc      | 12 +++++++++++-
 tensorflow/compiler/xla/literal_util_test.cc | 10 ++++++++++
 2 files changed, 21 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 1b125e3596..72eec35fc4 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -62,7 +62,17 @@ Literal::StrideConfig::StrideConfig(
 std::unique_ptr<Literal> Literal::CreateFromShape(const Shape& shape) {
   auto literal = MakeUnique<Literal>();
   *literal->mutable_shape() = shape;
-  literal->Reserve(ShapeUtil::ElementsIn(literal->shape()));
+  if (ShapeUtil::IsTuple(shape)) {
+    int64 num_elements = ShapeUtil::TupleElementCount(shape);
+    literal->tuple_literals_.resize(num_elements);
+    for (int i = 0; i < num_elements; ++i) {
+      std::unique_ptr<Literal> elem =
+          CreateFromShape(ShapeUtil::GetTupleElementShape(shape, i));
+      literal->tuple_literals_[i] = std::move(*elem);
+    }
+  } else {
+    literal->Reserve(ShapeUtil::ElementsIn(literal->shape()));
+  }
   return literal;
 }
 
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index ffae623b0c..11f25fb847 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -341,6 +341,16 @@ TEST_F(LiteralUtilTest, IsAllTuple) {
   EXPECT_FALSE(tuple->IsAll(1));
 }
 
+// Verifies that CreateFromShape works for tuples.
+TEST_F(LiteralUtilTest, CreateFromShapeTuple) {
+  auto scalar = Literal::CreateR0<float>(0.0);
+  auto matrix = Literal::CreateR2<int32>({{0, 0}, {0, 0}});
+  auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()});
+
+  auto x = Literal::CreateFromShape(tuple->shape());
+  EXPECT_TRUE(tuple->Equal(*x));
+}
+
 TEST_F(LiteralUtilTest, IsAll) {
   EXPECT_TRUE(Literal::CreateR0<bool>(false)->IsAll(0));
   EXPECT_TRUE(Literal::CreateR0<bool>(true)->IsAll(1));
-- 
GitLab


From a132b8330039e7ed326d090cdae35c97561f68b1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 08:35:47 -0700
Subject: [PATCH 0486/1375] Updates some more examples in examples/learn.

PiperOrigin-RevId: 160278757
---
 tensorflow/examples/learn/iris.py             |  10 +-
 .../examples/learn/iris_custom_decay_dnn.py   |  89 +++++++++++----
 .../examples/learn/iris_custom_model.py       | 101 +++++++++++-------
 tensorflow/examples/learn/iris_run_config.py  |  52 +++++----
 4 files changed, 173 insertions(+), 79 deletions(-)

diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py
index 2ec490b7a2..33e8d45801 100644
--- a/tensorflow/examples/learn/iris.py
+++ b/tensorflow/examples/learn/iris.py
@@ -25,6 +25,9 @@ from sklearn import model_selection
 import tensorflow as tf
 
 
+X_FEATURE = 'x'  # Name of the input feature.
+
+
 def main(unused_argv):
   # Load dataset.
   iris = datasets.load_iris()
@@ -33,18 +36,19 @@ def main(unused_argv):
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
   feature_columns = [
-      tf.feature_column.numeric_column('x', shape=np.array(x_train).shape[1:])]
+      tf.feature_column.numeric_column(
+          X_FEATURE, shape=np.array(x_train).shape[1:])]
   classifier = tf.estimator.DNNClassifier(
       feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
 
   # Train.
   train_input_fn = tf.estimator.inputs.numpy_input_fn(
-      x={'x': x_train}, y=y_train, num_epochs=None, shuffle=True)
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
   classifier.train(input_fn=train_input_fn, steps=200)
 
   # Predict.
   test_input_fn = tf.estimator.inputs.numpy_input_fn(
-      x={'x': x_test}, y=y_test, num_epochs=1, shuffle=False)
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
   predictions = classifier.predict(input_fn=test_input_fn)
   y_predicted = np.array(list(p['class_ids'] for p in predictions))
   y_predicted = y_predicted.reshape(np.array(y_test).shape)
diff --git a/tensorflow/examples/learn/iris_custom_decay_dnn.py b/tensorflow/examples/learn/iris_custom_decay_dnn.py
index 31acbd30cd..072357e51c 100644
--- a/tensorflow/examples/learn/iris_custom_decay_dnn.py
+++ b/tensorflow/examples/learn/iris_custom_decay_dnn.py
@@ -17,36 +17,87 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
-from sklearn.cross_validation import train_test_split
+from sklearn import model_selection
 import tensorflow as tf
 
 
-def optimizer_exp_decay():
-  global_step = tf.contrib.framework.get_or_create_global_step()
-  learning_rate = tf.train.exponential_decay(
-      learning_rate=0.1, global_step=global_step,
-      decay_steps=100, decay_rate=0.001)
-  return tf.train.AdagradOptimizer(learning_rate=learning_rate)
+X_FEATURE = 'x'  # Name of the input feature.
+
+
+def my_model(features, labels, mode):
+  """DNN with three hidden layers."""
+  # Create three fully connected layers respectively of size 10, 20, and 10.
+  net = features[X_FEATURE]
+  for units in [10, 20, 10]:
+    net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
+
+  # Compute logits (1 per class).
+  logits = tf.layers.dense(net, 3, activation=None)
+
+  # Compute predictions.
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        'class': predicted_classes,
+        'prob': tf.nn.softmax(logits)
+    }
+    return tf.estimator.EstimatorSpec(mode, predictions=predictions)
+
+  # Convert the labels to a one-hot tensor of shape (length of features, 3) and
+  # with a on-value of 1 for each one-hot vector of length 3.
+  onehot_labels = tf.one_hot(labels, 3, 1, 0)
+  # Compute loss.
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+
+  # Create training op with exponentially decaying learning rate.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    global_step = tf.train.get_global_step()
+    learning_rate = tf.train.exponential_decay(
+        learning_rate=0.1, global_step=global_step,
+        decay_steps=100, decay_rate=0.001)
+    optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
+    train_op = optimizer.minimize(loss, global_step=global_step)
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  # Compute evaluation metrics.
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = train_test_split(
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
-                                              hidden_units=[10, 20, 10],
-                                              n_classes=3,
-                                              optimizer=optimizer_exp_decay)
-
-  classifier.fit(x_train, y_train, steps=800)
-  predictions = list(classifier.predict(x_test, as_iterable=True))
-  score = metrics.accuracy_score(y_test, predictions)
-  print('Accuracy: {0:f}'.format(score))
+  classifier = tf.estimator.Estimator(model_fn=my_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=1000)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score = metrics.accuracy_score(y_test, y_predicted)
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/iris_custom_model.py b/tensorflow/examples/learn/iris_custom_model.py
index fbc50716c9..471a99ba76 100644
--- a/tensorflow/examples/learn/iris_custom_model.py
+++ b/tensorflow/examples/learn/iris_custom_model.py
@@ -16,62 +16,85 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-from sklearn import cross_validation
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
+from sklearn import model_selection
 import tensorflow as tf
 
-layers = tf.contrib.layers
-learn = tf.contrib.learn
 
+X_FEATURE = 'x'  # Name of the input feature.
 
-def my_model(features, target):
-  """DNN with three hidden layers, and dropout of 0.1 probability."""
-  # Convert the target to a one-hot tensor of shape (length of features, 3) and
-  # with a on-value of 1 for each one-hot vector of length 3.
-  target = tf.one_hot(target, 3, 1, 0)
 
+def my_model(features, labels, mode):
+  """DNN with three hidden layers, and dropout of 0.1 probability."""
   # Create three fully connected layers respectively of size 10, 20, and 10 with
   # each layer having a dropout probability of 0.1.
-  normalizer_fn = layers.dropout
-  normalizer_params = {'keep_prob': 0.9}
-  features = layers.stack(
-      features,
-      layers.fully_connected, [10, 20, 10],
-      normalizer_fn=normalizer_fn,
-      normalizer_params=normalizer_params)
-
-  # Compute logits (1 per class) and compute loss.
-  logits = layers.fully_connected(features, 3, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-
-  # Create a tensor for training op.
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adagrad',
-      learning_rate=0.1)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  net = features[X_FEATURE]
+  for units in [10, 20, 10]:
+    net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
+    net = tf.layers.dropout(net, rate=0.1)
+
+  # Compute logits (1 per class).
+  logits = tf.layers.dense(net, 3, activation=None)
+
+  # Compute predictions.
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        'class': predicted_classes,
+        'prob': tf.nn.softmax(logits)
+    }
+    return tf.estimator.EstimatorSpec(mode, predictions=predictions)
+
+  # Convert the labels to a one-hot tensor of shape (length of features, 3) and
+  # with a on-value of 1 for each one-hot vector of length 3.
+  onehot_labels = tf.one_hot(labels, 3, 1, 0)
+  # Compute loss.
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+
+  # Create training op.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  # Compute evaluation metrics.
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = cross_validation.train_test_split(
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
-  classifier = learn.Estimator(model_fn=my_model)
-  classifier.fit(x_train, y_train, steps=1000)
+  classifier = tf.estimator.Estimator(model_fn=my_model)
 
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=1000)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/iris_run_config.py b/tensorflow/examples/learn/iris_run_config.py
index b7b8b5cd01..286c824e30 100644
--- a/tensorflow/examples/learn/iris_run_config.py
+++ b/tensorflow/examples/learn/iris_run_config.py
@@ -18,37 +18,53 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from sklearn import cross_validation
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
+from sklearn import model_selection
 import tensorflow as tf
 
 
+X_FEATURE = 'x'  # Name of the input feature.
+
+
 def main(unused_argv):
   # Load dataset.
   iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = cross_validation.train_test_split(
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
   # You can define you configurations by providing a RunConfig object to
-  # estimator to control session configurations, e.g. num_cores
-  # and gpu_memory_fraction
-  run_config = tf.contrib.learn.estimators.RunConfig(
-      num_cores=3, gpu_memory_fraction=0.6)
+  # estimator to control session configurations, e.g. tf_random_seed.
+  run_config = tf.estimator.RunConfig().replace(tf_random_seed=1)
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
-                                              hidden_units=[10, 20, 10],
-                                              n_classes=3,
-                                              config=run_config)
-
-  # Fit and predict.
-  classifier.fit(x_train, y_train, steps=200)
-  predictions = list(classifier.predict(x_test, as_iterable=True))
-  score = metrics.accuracy_score(y_test, predictions)
-  print('Accuracy: {0:f}'.format(score))
+  feature_columns = [
+      tf.feature_column.numeric_column(
+          X_FEATURE, shape=np.array(x_train).shape[1:])]
+  classifier = tf.estimator.DNNClassifier(
+      feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
+      config=run_config)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=200)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class_ids'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score = metrics.accuracy_score(y_test, y_predicted)
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
-- 
GitLab


From 79a20451c50ce2433802562c9529dc3d01e2a21c Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Tue, 27 Jun 2017 08:55:38 -0700
Subject: [PATCH 0487/1375] Automated g4 rollback of changelist 160278032

PiperOrigin-RevId: 160280961
---
 tensorflow/core/framework/node_def_util.cc | 53 -----------------
 tensorflow/core/framework/node_def_util.h  | 67 ++++++----------------
 2 files changed, 18 insertions(+), 102 deletions(-)

diff --git a/tensorflow/core/framework/node_def_util.cc b/tensorflow/core/framework/node_def_util.cc
index 79feb20d53..9b737e1f72 100644
--- a/tensorflow/core/framework/node_def_util.cc
+++ b/tensorflow/core/framework/node_def_util.cc
@@ -21,7 +21,6 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/graph.pb_text.h"
-#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb_text.h"
 #include "tensorflow/core/framework/op_def_util.h"
@@ -612,56 +611,4 @@ Status AttachDef(const Status& status, const Node& node) {
   return AttachDef(status, node.def());
 }
 
-void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def) {
-  node_def->mutable_attr()->insert(
-      AttrValueMap::value_type(name.ToString(), value));
-}
-
-#define ADD_NODE_ATTR(T)                                           \
-  void AddNodeAttr(StringPiece name, T value, NodeDef* node_def) { \
-    AttrValue attr_value;                                          \
-    SetAttrValue(value, &attr_value);                              \
-    AddNodeAttr(name, attr_value, node_def);                       \
-  }
-ADD_NODE_ATTR(StringPiece)
-ADD_NODE_ATTR(const char*)
-ADD_NODE_ATTR(int32)
-ADD_NODE_ATTR(int64)
-ADD_NODE_ATTR(float)
-ADD_NODE_ATTR(double)
-ADD_NODE_ATTR(bool)
-ADD_NODE_ATTR(DataType)
-ADD_NODE_ATTR(const PartialTensorShape&)
-ADD_NODE_ATTR(const Tensor&)
-ADD_NODE_ATTR(const TensorProto&)
-ADD_NODE_ATTR(const NameAttrList&)
-ADD_NODE_ATTR(gtl::ArraySlice<StringPiece>)
-ADD_NODE_ATTR(gtl::ArraySlice<const char*>)
-ADD_NODE_ATTR(gtl::ArraySlice<string>)
-ADD_NODE_ATTR(gtl::ArraySlice<int32>)
-ADD_NODE_ATTR(gtl::ArraySlice<int64>)
-ADD_NODE_ATTR(gtl::ArraySlice<float>)
-ADD_NODE_ATTR(gtl::ArraySlice<bool>)
-ADD_NODE_ATTR(const std::vector<bool>&)
-ADD_NODE_ATTR(gtl::ArraySlice<DataType>)
-ADD_NODE_ATTR(gtl::ArraySlice<TensorShape>)
-ADD_NODE_ATTR(gtl::ArraySlice<PartialTensorShape>)
-ADD_NODE_ATTR(gtl::ArraySlice<TensorShapeProto>)
-ADD_NODE_ATTR(gtl::ArraySlice<Tensor>)
-ADD_NODE_ATTR(gtl::ArraySlice<NameAttrList>)
-#undef ADD_NODE_ATTR
-
-void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map) {
-  map->insert(AttrValueMap::value_type(name.ToString(), value));
-}
-
-#define ADD_ATTR(T)                                            \
-  void AddAttr(StringPiece name, T value, AttrValueMap* map) { \
-    AttrValue attr_value;                                      \
-    SetAttrValue(value, &attr_value);                          \
-    AddAttr(name, attr_value, map);                            \
-  }
-ADD_ATTR(bool)
-#undef ADD_ATTR
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/node_def_util.h b/tensorflow/core/framework/node_def_util.h
index 5d4864db66..1438abdec6 100644
--- a/tensorflow/core/framework/node_def_util.h
+++ b/tensorflow/core/framework/node_def_util.h
@@ -21,6 +21,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/attr_value_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
@@ -30,9 +31,6 @@ namespace tensorflow {
 
 class Node;
 
-// We forward declare NodeDef so that kernels don't need to depend on protos
-class NodeDef;
-
 // Name of the attribute used to encode node colocation constraints.
 //
 // Nodes can be co-located on the same device. Desire for explicit co-location
@@ -52,61 +50,32 @@ typedef protobuf::Map<string, AttrValue> AttrValueMap;
 
 // Adds an attr with name <name> and value <value> to *node_def.
 // The type of the attr is based on the type of value.
-void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, StringPiece value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, const char* value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, int32 value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, int64 value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, float value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, double value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, bool value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, DataType value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, const PartialTensorShape& value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, const Tensor& value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, const TensorProto& value, NodeDef* node_def);
-void AddNodeAttr(StringPiece name, const NameAttrList& value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<StringPiece> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<const char*> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<string> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<int32> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<int64> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<float> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<bool> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, const std::vector<bool>& value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<DataType> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<TensorShape> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<PartialTensorShape> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<TensorShapeProto> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<Tensor> value,
-                 NodeDef* node_def);
-void AddNodeAttr(StringPiece name, gtl::ArraySlice<NameAttrList> value,
-                 NodeDef* node_def);
+template <class T>
+void AddNodeAttr(StringPiece name, T&& value, NodeDef* node_def) {
+  AttrValue attr_value;
+  SetAttrValue(std::forward<T>(value), &attr_value);
+  node_def->mutable_attr()->insert(
+      AttrValueMap::value_type(name.ToString(), attr_value));
+}
 
 // Version to workaround C++'s "perfect" forwarding not being able to
 // forward {...} initialization.
 template <class T>
 void AddNodeAttr(StringPiece name, std::initializer_list<T> value,
                  NodeDef* node_def) {
-  AddNodeAttr(name, gtl::ArraySlice<T>(value), node_def);
+  AttrValue attr_value;
+  SetAttrValue(value, &attr_value);
+  node_def->mutable_attr()->insert(
+      AttrValueMap::value_type(name.ToString(), attr_value));
 }
 
 // Adds an attr to an attr value map.
-void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map);
-void AddAttr(StringPiece name, bool value, AttrValueMap* map);
+template <class T>
+void AddAttr(StringPiece name, T&& value, AttrValueMap* map) {
+  AttrValue attr_value;
+  SetAttrValue(value, &attr_value);
+  map->insert(AttrValueMap::value_type(name.ToString(), attr_value));
+}
 
 class AttrSlice {
  public:
-- 
GitLab


From 674f56b578b88a0e3f1017b63fa64c3ae1a83b6e Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Tue, 27 Jun 2017 08:58:32 -0700
Subject: [PATCH 0488/1375] Fixed the bug that Estimator does not make deepcopy
 of params in constructor

PiperOrigin-RevId: 160281247
---
 tensorflow/python/estimator/estimator.py      |  2 +-
 tensorflow/python/estimator/estimator_test.py | 11 +++++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 2077d53a7e..1d2d5d9e0c 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -172,7 +172,7 @@ class Estimator(object):
       raise ValueError('model_fn must be provided to Estimator.')
     _verify_model_fn_args(model_fn, params)
     self._model_fn = model_fn
-    self._params = params or {}
+    self._params = copy.deepcopy(params or {})
 
   @property
   def model_dir(self):
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index fb4cbfda8c..77aff66ccf 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -255,6 +255,17 @@ class EstimatorConstructorTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, 'params'):
       estimator.Estimator(model_fn=model_fn, params={'hidden_layers': 4})
 
+  def test_internal_params_is_a_deepcopy(self):
+
+    def model_fn(features, labels, params):
+      _, _, _ = features, labels, params
+
+    params = {'hidden_layers': 4}
+    est = estimator.Estimator(model_fn=model_fn, params=params)
+
+    params['hidden_layers'] = 5
+    self.assertEqual(4, est.params['hidden_layers'])
+
   def test_not_known_model_fn_args(self):
 
     def model_fn(features, labels, something):
-- 
GitLab


From ba8ffba212a4375c000fe670d5e5b5e0330312ad Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Tue, 27 Jun 2017 09:00:48 -0700
Subject: [PATCH 0489/1375] Clean out the config and params in TPUEstimator.

PiperOrigin-RevId: 160281507
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 99 +++++++++++--------
 1 file changed, 60 insertions(+), 39 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 1c2d3cfa03..168726a6b3 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -42,6 +42,9 @@ from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training
 
 
+_BATCH_SIZE_KEY = 'batch_size'
+
+
 def _tpu_job(run_config):
   # The tpu job is determined by the run_config. Right now, this method is
   # required as tpu_config is not part of the RunConfig.
@@ -175,26 +178,30 @@ class TpuEstimator(estimator_lib.Estimator):
                config=None,
                params=None,
                use_tpu=True):
-    if not isinstance(config, tpu_config.RunConfig):
-      raise ValueError('`config` must be `tpu_config.RunConfig`')
+    if config is None or not isinstance(config, tpu_config.RunConfig):
+      raise ValueError(
+          '`config` must be provided with type `tpu_config.RunConfig`')
 
-    if use_tpu and params is not None and 'batch_size' in params:
-      if not isinstance(params['batch_size'], int):
-        raise ValueError('`batch_size` in params must be an int')
+    if use_tpu and params is not None and _BATCH_SIZE_KEY in params:
+      if not isinstance(params[_BATCH_SIZE_KEY], int):
+        raise ValueError(
+            '`{}` in params must be an int'.format(_BATCH_SIZE_KEY))
       params = copy.deepcopy(params)
       # The specified batch size is the batch size for the entire computation.
       # The input_fn is called per-shard, so we want to calculate the per-shard
       # batch size and pass that.
-      if params['batch_size'] % config.tpu_config.num_shards != 0:
+      if params[_BATCH_SIZE_KEY] % config.tpu_config.num_shards != 0:
         raise ValueError(
             'batch size {} must be divisible by number of shards {}'
-            .format(params['batch_size'], config.tpu_config.num_shards))
+            .format(params[_BATCH_SIZE_KEY], config.tpu_config.num_shards))
 
     if use_tpu:
-      # config and params need to be copied because we haven't initialized
-      # Estimator yet.
-      model_function = wrapped_model_fn(model_fn, copy.deepcopy(config),
-                                        copy.deepcopy(params))
+      # Verifies the model_fn signature according to Estimator framework.
+      estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
+      # We cannot store config and params in this constructor as parent
+      # constructor might change them, such as assigning a temp dir for
+      # config.model_dir.
+      model_function = wrapped_model_fn(model_fn)
     else:
       model_function = model_fn
 
@@ -251,16 +258,16 @@ class TpuEstimator(estimator_lib.Estimator):
       return super(TpuEstimator, self)._call_input_fn(input_fn, mode)
 
     input_fn_args = estimator_lib._fn_args(input_fn)  # pylint: disable=protected-access
-    config = self.config
+    config = self.config  # a deep copy.
     kwargs = {}
     if 'params' in input_fn_args:
-      kwargs['params'] = self.params
+      kwargs['params'] = self.params  # a deep copy.
     if 'config' in input_fn_args:
       kwargs['config'] = config
 
     # Now for TPU training.
-    if 'params' in kwargs and 'batch_size' in kwargs['params']:
-      kwargs['params']['batch_size'] //= config.tpu_config.num_shards
+    if 'params' in kwargs and _BATCH_SIZE_KEY in kwargs['params']:
+      kwargs['params'][_BATCH_SIZE_KEY] //= config.tpu_config.num_shards
 
     job = _tpu_job(config)
     def placement_function(index):
@@ -285,6 +292,16 @@ class TpuEstimator(estimator_lib.Estimator):
     return _PerShardOutput(features), _PerShardOutput(labels)
 
 
+def _verify_estimator_spec(estimator_spec):
+  """Validates the estimator_spec."""
+  err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
+  if estimator_spec.training_chief_hooks:
+    raise ValueError(err_msg.format('training_chief_hooks'))
+  if estimator_spec.training_hooks:
+    raise ValueError(err_msg.format('training_hooks'))
+  return estimator_spec
+
+
 def _call_model_fn(model_fn, features, labels, mode, config, params):
   """Calls the model_fn with required parameters."""
   model_fn_args = estimator_lib._fn_args(model_fn)  # pylint: disable=protected-access
@@ -298,6 +315,21 @@ def _call_model_fn(model_fn, features, labels, mode, config, params):
   return model_fn(features=features, labels=labels, **kwargs)
 
 
+def _call_model_fn_with_tpu(model_fn, features, labels, mode, config, params):
+  """Calls user provided `model_fn` and verifies the estimator_spec."""
+  # Makes deep copy with `config` and params` in case user mutates them.
+  config = copy.deepcopy(config)
+  params = copy.deepcopy(params)
+  return _verify_estimator_spec(_call_model_fn(
+      model_fn, features, labels, mode, config, params))
+
+
+def _call_model_fn_without_tpu(
+    model_fn, features, labels, mode, config, params):
+  # Deepcopy of config and params is not required in this branch.
+  return _call_model_fn(model_fn, features, labels, mode, config, params)
+
+
 # TODO(xiejw): Improve the structure of this input_fn to infeed converion.
 # The code now looks not like Estimator style. We need to abstract many
 # details.
@@ -384,22 +416,20 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
   return (dequeue_fn, enqueue_fn)
 
 
-def wrapped_model_fn(model_fn, run_config, params):
+def wrapped_model_fn(model_fn):
   """Returns a new model_fn, which wraps the TPU support."""
 
-  # Verifies the model_fn signature according to Estimator framework.
-  estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
-
-  def _model_fn(features, labels, mode, params=None):
+  def _model_fn(features, labels, mode, config, params=None):
     """model_fn."""
+
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
     if mode != model_fn_lib.ModeKeys.TRAIN:
-      return _call_model_fn(model_fn, features, labels, mode, run_config,
-                            params)
+      return _call_model_fn_without_tpu(
+          model_fn, features, labels, mode, config, params)
 
     # Now for TPU training.
-    if params is not None and 'batch_size' in params:
-      params['batch_size'] //= run_config.tpu_config.num_shards
+    if params is not None and _BATCH_SIZE_KEY in params:
+      params[_BATCH_SIZE_KEY] //= config.tpu_config.num_shards
 
     assert isinstance(features, _PerShardOutput)
     features = features.as_list()
@@ -408,12 +438,12 @@ def wrapped_model_fn(model_fn, run_config, params):
       labels = labels.as_list()
 
     dequeue_fn, enqueue_fn = (
-        _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels))
+        _create_infeed_enqueue_ops_and_dequeue_fn(config, features, labels))
 
     loss = _train_on_tpu_shards(
-        run_config,
+        config,
         train_step=_convert_model_fn_to_train_step(
-            model_fn, dequeue_fn, mode, run_config, params))
+            model_fn, dequeue_fn, mode, config, params))
 
     # Gets the variables back from TPU nodes. This means the variables updated
     # by TPU will now be *synced* to host memory.
@@ -424,7 +454,7 @@ def wrapped_model_fn(model_fn, run_config, params):
     ]
 
     hooks = [
-        TpuInfeedSessionHook(run_config, enqueue_fn),
+        TpuInfeedSessionHook(config, enqueue_fn),
         training.LoggingTensorHook(
             {'loss': array_ops.identity(loss),
              'step': training.get_global_step()},
@@ -443,15 +473,6 @@ def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config,
                                     params):
   """Generates a train step based on the model_fn."""
 
-  def _verify_estimator_spec(estimator_spec):
-    """Validates the estimator_spec."""
-    err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
-    if estimator_spec.training_chief_hooks:
-      raise ValueError(err_msg.format('training_chief_hooks'))
-    if estimator_spec.training_hooks:
-      raise ValueError(err_msg.format('training_hooks'))
-    return estimator_spec
-
   def train_step(loss):
     """Training step function for use inside a while loop."""
     del loss  # unused; required in function signature.
@@ -464,8 +485,8 @@ def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config,
     # the graph construction part in the model_fn should be separated from the
     # control part (such as hooks and savers). By that the graph construction
     # could de defered on TPU chip, while the control logic can stay in host.
-    estimator_spec = _verify_estimator_spec(
-        _call_model_fn(model_fn, features, labels, mode, run_config, params))
+    estimator_spec = _call_model_fn_with_tpu(
+        model_fn, features, labels, mode, run_config, params)
     loss, train_op = estimator_spec.loss, estimator_spec.train_op
     with ops.control_dependencies([train_op]):
       return array_ops.identity(loss)
-- 
GitLab


From 521188ffa54470cef0a780aac19f551137e5090a Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Tue, 27 Jun 2017 09:02:54 -0700
Subject: [PATCH 0490/1375] [XLA] Remove the "hlo dumper" parameter of
 xla::Compiler and its piping.

This dumper is no longer necessary since the restructuring of HLO dumping and
the addition of MaybeDumpHloModule which heeds to the right flags. The
remaining bits didn't have additional functionality, but constituted a lot of
boilerplate that has to be propagated throughout the backends.

PiperOrigin-RevId: 160281798
---
 tensorflow/compiler/xla/service/BUILD         |  2 +-
 .../xla/service/compile_only_service.cc       |  3 +-
 tensorflow/compiler/xla/service/compiler.h    | 12 ++------
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 18 +++++------
 .../compiler/xla/service/cpu/cpu_compiler.h   |  7 ++---
 .../compiler/xla/service/gpu/gpu_compiler.cc  | 30 ++++++++-----------
 .../compiler/xla/service/gpu/gpu_compiler.h   |  9 +++---
 .../compiler/xla/service/hlo_pass_pipeline.cc | 10 ++++---
 .../compiler/xla/service/hlo_pass_pipeline.h  |  6 +---
 tensorflow/compiler/xla/service/service.cc    | 22 +++-----------
 tensorflow/compiler/xla/service/service.h     |  5 ----
 .../compiler/xla/tests/codegen_test_base.cc   |  3 +-
 .../compiler/xla/tests/hlo_test_base.cc       |  6 +---
 tensorflow/compiler/xla/tests/hlo_test_base.h |  2 --
 14 files changed, 45 insertions(+), 90 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 718a2d798c..a20a5cd351 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1603,8 +1603,8 @@ cc_library(
         "hlo_pass_pipeline.h",
     ],
     deps = [
-        ":compiler",
         ":hlo",
+        ":hlo_graph_dumper",
         ":hlo_pass",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index b0867c47a1..dc81323c99 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -122,8 +122,7 @@ CompileOnlyService::CompileAheadOfTime(
     hlo_modules.push_back(std::move(hlo_module));
   }
 
-  return compiler_->CompileAheadOfTime(std::move(hlo_modules),
-                                       MakeHloDumper(), options);
+  return compiler_->CompileAheadOfTime(std::move(hlo_modules), options);
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h
index 7ae285170e..d5bd9214be 100644
--- a/tensorflow/compiler/xla/service/compiler.h
+++ b/tensorflow/compiler/xla/service/compiler.h
@@ -92,13 +92,6 @@ class AotCompilationOptions {
 // platform.
 class Compiler {
  public:
-  // Callback signature used to dump the HLO graph during compilation.
-  // Different compiler backends will call this as they please, providing
-  // a view of the HLO at different points in compilation -- context for the
-  // dump is indicated by the label string.
-  using HloDumper =
-      std::function<void(const HloModule& module, const string& label)>;
-
   virtual ~Compiler() {}
 
   // Returns the ID of the platform that this compiler targets.
@@ -113,21 +106,20 @@ class Compiler {
   //
   // Use the overload below to compile computations that run in parallel.
   virtual StatusOr<std::unique_ptr<Executable>> Compile(
-      std::unique_ptr<HloModule> module, HloDumper dump_hlo,
+      std::unique_ptr<HloModule> module,
       perftools::gputools::StreamExecutor* executor) = 0;
 
   // Compiles a set of HLO modules that can run in parallel, potentially
   // communicating data between the modules, and returns a corresponding
   // sequence of executable objects.
   virtual StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
-      std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlo,
+      std::vector<std::unique_ptr<HloModule>> modules,
       std::vector<perftools::gputools::StreamExecutor*> stream_exec) = 0;
 
   // Compiles the HLO module for ahead-of-time execution.  This is intended for
   // use in static compilation.
   virtual StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
   CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
-                     HloDumper dump_hlo,
                      const AotCompilationOptions& options) = 0;
 
   /////
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index da8d983e1a..18cdb25176 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -246,9 +246,9 @@ class CollectProfileCandidates : public DfsHloVisitorWithDefault {
 };
 }  // namespace
 
-Status CpuCompiler::RunHloPasses(HloModule* module, HloDumper dump_hlo) {
+Status CpuCompiler::RunHloPasses(HloModule* module) {
   // Optimization pipeline.
-  HloPassPipeline pipeline("CPU", dump_hlo);
+  HloPassPipeline pipeline("CPU");
   pipeline.AddInvariantChecker<HloVerifier>();
 
   // TODO(b/35786417): Re-enable inliner pass after fixing the bug and deciding
@@ -257,8 +257,8 @@ Status CpuCompiler::RunHloPasses(HloModule* module, HloDumper dump_hlo) {
 
   pipeline.AddPass<ConvCanonicalization>();
   {
-    auto& pass = pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification",
-                                                               dump_hlo);
+    auto& pass =
+        pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification");
     pass.AddPass<AlgebraicSimplifier>(
         /*is_layout_sensitive=*/false,
         [](const Shape&, const Shape&) { return false; },
@@ -357,8 +357,7 @@ Status AppendIRToFile(const string& file_name, const string& ir_module_string) {
 }  // namespace
 
 StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
-    std::unique_ptr<HloModule> module, HloDumper dump_hlo,
-    se::StreamExecutor* stream_exec) {
+    std::unique_ptr<HloModule> module, se::StreamExecutor* stream_exec) {
   VLOG(1) << "Compiling: " << module->name();
   TF_RET_CHECK(stream_exec != nullptr);
   std::call_once(llvm_command_line_options_initialized,
@@ -384,7 +383,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
   llvm_module->setDataLayout(jit->data_layout());
   llvm_module->setTargetTriple(jit->target_triple().getTriple());
 
-  TF_RETURN_IF_ERROR(RunHloPasses(module.get(), dump_hlo));
+  TF_RETURN_IF_ERROR(RunHloPasses(module.get()));
 
   HloComputation* computation = module->entry_computation();
   std::unordered_map<const HloInstruction*, size_t> hlo_to_profile_idx;
@@ -572,7 +571,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 }
 
 StatusOr<std::vector<std::unique_ptr<Executable>>> CpuCompiler::Compile(
-    std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlos,
+    std::vector<std::unique_ptr<HloModule>> modules,
     std::vector<se::StreamExecutor*> stream_execs) {
   return Unimplemented(
       "Compilation of multiple HLO modules is not yet supported on CPU.");
@@ -580,7 +579,6 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> CpuCompiler::Compile(
 
 StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
 CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
-                                HloDumper dump_hlo,
                                 const AotCompilationOptions& aot_options) {
   TF_RET_CHECK(!modules.empty());
   std::call_once(llvm_command_line_options_initialized,
@@ -671,7 +669,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
     HloModule* module = modules[i].get();
     VLOG(1) << "Compiling ahead-of-time: " << module->name();
 
-    TF_RETURN_IF_ERROR(RunHloPasses(module, dump_hlo));
+    TF_RETURN_IF_ERROR(RunHloPasses(module));
 
     TF_ASSIGN_OR_RETURN(
         SequentialHloOrdering::HloModuleSequence module_sequence,
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
index 3b130a614e..b82e181df2 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
@@ -110,16 +110,15 @@ class CpuCompiler : public Compiler {
   ~CpuCompiler() override {}
 
   StatusOr<std::unique_ptr<Executable>> Compile(
-      std::unique_ptr<HloModule> module, HloDumper dump_hlo,
+      std::unique_ptr<HloModule> module,
       perftools::gputools::StreamExecutor* stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
-      std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlo,
+      std::vector<std::unique_ptr<HloModule>> modules,
       std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
   CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
-                     HloDumper dump_hlo,
                      const AotCompilationOptions& options) override;
 
   perftools::gputools::Platform::Id PlatformId() const override;
@@ -132,7 +131,7 @@ class CpuCompiler : public Compiler {
 
   // Runs the HLO passes which are necessary for both optimizations and
   // correctness.
-  Status RunHloPasses(HloModule* module, HloDumper dump_hlo);
+  Status RunHloPasses(HloModule* module);
 
   TF_DISALLOW_COPY_AND_ASSIGN(CpuCompiler);
 };
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 7ce8b8290a..ffed9c7e04 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -119,14 +119,13 @@ string GetLibdeviceDir(const HloModuleConfig& config) {
 
 // Runs optimization passes on the given HLO module.
 tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
-                                     const Compiler::HloDumper& dump_hlo,
                                      const se::DeviceDescription& device_desc) {
   {
-    HloPassPipeline pipeline("optimization", dump_hlo);
+    HloPassPipeline pipeline("optimization");
     pipeline.AddInvariantChecker<HloVerifier>();
     {
-      auto& pass = pipeline.AddPass<HloPassFix<HloPassPipeline>>(
-          "simplification", dump_hlo);
+      auto& pass =
+          pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification");
       pass.AddPass<AlgebraicSimplifier>(
           /*is_layout_sensitive=*/false,
           [](const Shape&, const Shape&) { return false; });
@@ -146,7 +145,7 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
     TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status());
   }
   {
-    HloPassFix<HloPassPipeline> fusion("fusion", dump_hlo);
+    HloPassFix<HloPassPipeline> fusion("fusion");
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/false);
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/true);
     fusion.AddPass<FusionMerger>();
@@ -156,14 +155,13 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
 
 // Modifies the given HLO module so that it will be accepted by IrEmitter.
 // Unlike optimization passes, the passes are necessary for correctness.
-tensorflow::Status PrepareHloModuleForIrEmitting(
-    const Compiler::HloDumper& dump_hlo, HloModule* hlo_module) {
+tensorflow::Status PrepareHloModuleForIrEmitting(HloModule* hlo_module) {
   // In some cases, we have to place the result of an instruction in a temporary
   // buffer. For instance, the buffer that holds an external parameter is
   // assumed immutable at this point, and should not be reused for output
   // (b/27180329). Therefore, in that case, we set the output to be a copy of
   // the parameter.
-  HloPassPipeline pipeline("GPU-ir-emit-prepare", dump_hlo);
+  HloPassPipeline pipeline("GPU-ir-emit-prepare");
   pipeline.AddInvariantChecker<HloVerifier>();
   pipeline.AddPass<PadInsertion>();
   pipeline.AddPass<GpuLayoutAssignment>(
@@ -230,13 +228,12 @@ GpuCompiler::GpuCompiler()
     : pointer_size_(llvm::DataLayout(kDataLayout).getPointerSize()) {}
 
 StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
-    std::unique_ptr<HloModule> module, HloDumper dump_hlo,
-    se::StreamExecutor* stream_exec) {
+    std::unique_ptr<HloModule> module, se::StreamExecutor* stream_exec) {
   TF_RET_CHECK(stream_exec != nullptr);
 
-  TF_RETURN_IF_ERROR(OptimizeHloModule(module.get(), dump_hlo,
-                                       stream_exec->GetDeviceDescription()));
-  TF_RETURN_IF_ERROR(PrepareHloModuleForIrEmitting(dump_hlo, module.get()));
+  TF_RETURN_IF_ERROR(
+      OptimizeHloModule(module.get(), stream_exec->GetDeviceDescription()));
+  TF_RETURN_IF_ERROR(PrepareHloModuleForIrEmitting(module.get()));
 
   llvm::LLVMContext llvm_context;
   std::string buffer;
@@ -344,16 +341,15 @@ StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
 }
 
 StatusOr<std::vector<std::unique_ptr<Executable>>> GpuCompiler::Compile(
-    std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlos,
+    std::vector<std::unique_ptr<HloModule>> modules,
     std::vector<se::StreamExecutor*> stream_execs) {
   return Unimplemented(
       "Compilation of multiple HLO modules is not yet supported on GPU.");
 }
 
 StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-GpuCompiler::CompileAheadOfTime(
-    std::vector<std::unique_ptr<HloModule>> module,
-    HloDumper dump_hlo, const AotCompilationOptions& options) {
+GpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> module,
+                                const AotCompilationOptions& options) {
   return Unimplemented("not yet implemented: GpuCompiler::CompileAheadOfTime");
 }
 
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
index 68dddcdc19..b87555b931 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
@@ -41,17 +41,16 @@ class GpuCompiler : public Compiler {
   ~GpuCompiler() override {}
 
   StatusOr<std::unique_ptr<Executable>> Compile(
-      std::unique_ptr<HloModule> module, HloDumper dump_hlo,
+      std::unique_ptr<HloModule> module,
       perftools::gputools::StreamExecutor* stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
-      std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlo,
+      std::vector<std::unique_ptr<HloModule>> modules,
       std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-  CompileAheadOfTime(
-      std::vector<std::unique_ptr<HloModule>> module,
-      HloDumper dump_hlo, AotCompilationOptions const& options) override;
+  CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> module,
+                     AotCompilationOptions const& options) override;
 
   perftools::gputools::Platform::Id PlatformId() const override;
 
diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
index 119e2d7902..4b824f8240 100644
--- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
+++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <functional>
 
+#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
@@ -30,9 +31,10 @@ using ::tensorflow::strings::StrAppend;
 namespace xla {
 
 namespace {
-void DumpModule(const Compiler::HloDumper& dumper_, const HloModule& module,
+void DumpModule(const HloModule& module,
+
                 const string& message) {
-  dumper_(module, message);
+  hlo_graph_dumper::MaybeDumpHloModule(module, message);
   VLOG(2) << "HLO " << message << ":";
   XLA_VLOG_LINES(2, module.ToString());
 }
@@ -75,7 +77,7 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
     // Emit label containing: "after foo-pass, before bar-pass".
     message.clear();
     StrAppend(&message, prefix, ", before ", pass->name());
-    DumpModule(dumper_, *module, message);
+    DumpModule(*module, message);
 
     TF_RETURN_IF_ERROR(run_invariant_checkers());
     TF_ASSIGN_OR_RETURN(bool changed_this_pass, pass->Run(module));
@@ -85,7 +87,7 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
     StrAppend(&prefix, name(), ": after ", pass->name());
   }
   TF_RETURN_IF_ERROR(run_invariant_checkers());
-  DumpModule(dumper_, *module, prefix + ", pipeline end");
+  DumpModule(*module, prefix + ", pipeline end");
   return changed;
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.h b/tensorflow/compiler/xla/service/hlo_pass_pipeline.h
index 682c4b952d..a42d7e59fe 100644
--- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.h
+++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.h
@@ -22,7 +22,6 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -34,9 +33,7 @@ namespace xla {
 // Pipeline of HLO passes.
 class HloPassPipeline : public HloPassInterface {
  public:
-  explicit HloPassPipeline(const string& name,
-                           const Compiler::HloDumper& dumper)
-      : name_(name), dumper_(dumper) {}
+  explicit HloPassPipeline(const string& name) : name_(name) {}
   tensorflow::StringPiece name() const override { return name_; }
 
   // Add a pass to the pipeline. It should be called with the arguments for the
@@ -69,7 +66,6 @@ class HloPassPipeline : public HloPassInterface {
 
  private:
   const string name_;
-  Compiler::HloDumper dumper_;
   std::vector<std::unique_ptr<HloPassInterface>> passes_;
   std::vector<std::unique_ptr<HloPassInterface>> invariant_checkers_;
   bool run_called_ = false;
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 2416f150e7..daed56b3eb 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -166,13 +166,6 @@ Service::CreateComputeConstantBackend() {
   return NotFound("CPU platform not found");
 }
 
-/* static */ Compiler::HloDumper Service::MakeHloDumper() {
-  return [](const HloModule& module, const string& label) {
-    hlo_graph_dumper::MaybeDumpHloModule(module, label,
-                                         /*profile=*/nullptr);
-  };
-}
-
 Service::Service(const ServiceOptions& options,
                  std::unique_ptr<Backend> execute_backend,
                  std::unique_ptr<Backend> compute_constant_backend)
@@ -393,11 +386,9 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> Service::BuildExecutables(
     modules.push_back(std::move(module));
   }
 
-  Compiler::HloDumper hlo_dumper = MakeHloDumper();
   TF_ASSIGN_OR_RETURN(
       std::vector<std::unique_ptr<Executable>> executables,
-      backend->compiler()->Compile(std::move(modules), hlo_dumper,
-                                   std::move(executors)));
+      backend->compiler()->Compile(std::move(modules), std::move(executors)));
 
   if (!other_directory_path.empty()) {
     for (size_t i = 0; i < versioned_handles.size(); ++i) {
@@ -444,15 +435,9 @@ StatusOr<std::unique_ptr<Executable>> Service::BuildExecutable(
                                           /*include_unreachable_instructions=*/
                                           !executable_for_compute_constant));
 
-  Compiler::HloDumper hlo_dumper = MakeHloDumper();
-  if (executable_for_compute_constant &&
-      !flags->xla_hlo_graph_for_compute_constant) {
-    hlo_dumper = [](const HloModule&, const string&) {};
-  }
-
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr<Executable> executable,
-      backend->compiler()->Compile(std::move(module), hlo_dumper, executor));
+      backend->compiler()->Compile(std::move(module), executor));
 
   if (!other_directory_path.empty()) {
     executable->set_session_module(std::move(session_module));
@@ -1192,7 +1177,8 @@ tensorflow::Status Service::GetComputationStats(
       std::unique_ptr<HloModule> module,
       computation_tracker_.BuildHloModule(versioned_handle, HloModuleConfig()));
 
-  MakeHloDumper()(*module, "computation statistics subject");
+  hlo_graph_dumper::MaybeDumpHloModule(*module,
+                                       "computation statistics subject");
 
   // Run HLO analysis to get the computation statistics.
   HloCostAnalysis analysis(
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 4ed89ca04a..48cb0bec47 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -27,7 +27,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/channel_tracker.h"
 #include "tensorflow/compiler/xla/service/compilation_cache.h"
-#include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/computation_tracker.h"
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/executable.h"
@@ -322,10 +321,6 @@ class Service : public ServiceInterface {
       tensorflow::gtl::ArraySlice<DeviceHandle> device_handles,
       tensorflow::gtl::ArraySlice<string> result_tags);
 
-  // Returns an HLO dumper for use in the compiler (it refers to flags
-  // associated with the service).
-  static Compiler::HloDumper MakeHloDumper();
-
   // Convenience function for adding a function to a user computation.
   template <typename RequestT, typename ResponseT>
   tensorflow::Status AddInstruction(
diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.cc b/tensorflow/compiler/xla/tests/codegen_test_base.cc
index f48dc50708..8d597eb72c 100644
--- a/tensorflow/compiler/xla/tests/codegen_test_base.cc
+++ b/tensorflow/compiler/xla/tests/codegen_test_base.cc
@@ -55,8 +55,7 @@ void CodegenTestBase::CompileAndVerifyIr(std::unique_ptr<HloModule> hlo_module,
 std::unique_ptr<Executable> CodegenTestBase::CompileToExecutable(
     std::unique_ptr<HloModule> hlo_module) {
   return backend_->compiler()
-      ->Compile(std::move(hlo_module), test_hlo_dumper_,
-                backend_->default_stream_executor())
+      ->Compile(std::move(hlo_module), backend_->default_stream_executor())
       .ConsumeValueOrDie();
 }
 
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index 00b2858790..f8452e37f8 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -30,7 +30,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/executable.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
-#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/transfer_manager.h"
 #include "tensorflow/compiler/xla/shape_layout.h"
@@ -55,9 +54,6 @@ struct HloTestBase::EigenThreadPoolWrapper {
 
 HloTestBase::HloTestBase()
     : backend_(Backend::CreateDefaultBackend().ConsumeValueOrDie()) {
-  test_hlo_dumper_ = [](const HloModule& module, const string& label) {
-    hlo_graph_dumper::MaybeDumpHloModule(module, label, /*profile=*/nullptr);
-  };
   VLOG(1) << "executing on platform " << backend_->platform()->Name();
 }
 
@@ -83,7 +79,7 @@ StatusOr<perftools::gputools::DeviceMemoryBase> HloTestBase::Execute(
     Shape* result_shape) {
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr<Executable> executable,
-      backend_->compiler()->Compile(std::move(module), test_hlo_dumper_,
+      backend_->compiler()->Compile(std::move(module),
                                     backend_->default_stream_executor()));
 
   se::Stream stream(backend_->default_stream_executor());
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h
index 2b6a2e9672..7f3d163290 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.h
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.h
@@ -104,8 +104,6 @@ class HloTestBase : public ::testing::Test {
 
   std::unique_ptr<Backend> backend_;
 
-  Compiler::HloDumper test_hlo_dumper_;
-
   // This vector contains handles of all the device memory allocations performed
   // by the test. These are deallocated on destruction of the test object.
   std::vector<perftools::gputools::DeviceMemoryBase> allocations_;
-- 
GitLab


From ee0e2f42872001c6adf6b11ddc1b01decb47c83b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 09:08:14 -0700
Subject: [PATCH 0491/1375] [TF:XLA] Refactor the sigmoid op as a rescaled
 tanh.

PiperOrigin-RevId: 160282472
---
 tensorflow/compiler/tf2xla/kernels/unary_ops.cc | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
index f14546b1b4..ab96d86ed2 100644
--- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
@@ -79,16 +79,12 @@ static xla::ComputationDataHandle Round(xla::ComputationBuilder* b,
                    b->Add(round_val, one), round_val);
 }
 
+// Expresses sigmoid as a rescaled tanh: sigmoid(x) == (tanh(x/2) + 1) / 2.
 static xla::ComputationDataHandle Sigmoid(xla::ComputationBuilder* b,
                                           DataType dtype,
                                           const xla::ComputationDataHandle& x) {
-  // Clamp the inputs to the range [-18, 18] since anything outside this range
-  // is 0.0f or 1.0f in single-precision. We must clamp the range of x to avoid
-  // incorrect outputs due to fast-math optimizations for large negative x.
-  auto clamped = b->Clamp(XlaHelpers::IntegerLiteral(b, dtype, -18), x,
-                          XlaHelpers::IntegerLiteral(b, dtype, 18));
-  auto one = XlaHelpers::One(b, dtype);
-  return b->Div(one, b->Add(b->Exp(b->Neg(clamped)), one));
+  auto half = XlaHelpers::FloatLiteral(b, dtype, 0.5);
+  return b->Add(half, b->Mul(half, b->Tanh(b->Mul(half, x))));
 }
 
 XLAJIT_MAKE_UNARY(Round, Round(b, input_type(0), x));
-- 
GitLab


From 08a23d412b7d18a4d8ee41e24b03f4c616e32eb0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 09:26:32 -0700
Subject: [PATCH 0492/1375] Fix uninitialized values in TensorForest code.

PiperOrigin-RevId: 160284420
---
 tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
index fe7b24d36c..226376c571 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
@@ -30,7 +30,8 @@ static const int32 LEFT_INDEX = 0;
 static const int32 RIGHT_INDEX = 1;
 
 GrowStats::GrowStats(const TensorForestParams& params, int32 depth)
-    : depth_(depth),
+    : weight_sum_(0),
+      depth_(depth),
       params_(params),
       split_after_samples_(ResolveParam(params.split_after_samples(), depth)),
       num_splits_to_consider_(
@@ -58,6 +59,8 @@ ClassificationStats::ClassificationStats(const TensorForestParams& params,
   // Early splitting params.
   if (params.finish_type().type() == SPLIT_FINISH_BASIC) {
     min_split_samples_ = split_after_samples_;
+    finish_sample_epoch_ = 1;
+    finish_check_every_ = split_after_samples_ * 2;
   } else {
     if (!params.has_dominate_fraction() || !params.has_min_split_samples()) {
       LOG(FATAL) << "dominate_fraction and min_split_samples "
@@ -98,6 +101,9 @@ ClassificationStats::ClassificationStats(const TensorForestParams& params,
       default:
         LOG(WARNING) << "Unknown pruning type";
     }
+  } else {
+    prune_check_every_ = split_after_samples_ * 2;
+    prune_sample_epoch_ = 1;
   }
 
   if (params.use_running_stats_method()) {
-- 
GitLab


From beb291b3c0e95f1150c446e06f8f80675ab3b528 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 27 Jun 2017 09:28:07 -0700
Subject: [PATCH 0493/1375] [TF:XLA] Update Tensorflow LLVM release to upstream
 r306370.

Fix broken XLA build.

PiperOrigin-RevId: 160284588
---
 tensorflow/compiler/xla/reference_util.cc | 8 ++++++--
 tensorflow/workspace.bzl                  | 8 ++++----
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index fe99cf9207..0bfb0ff73d 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -507,10 +507,14 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
     }
   };
   lhs.Each(check_trivial);
-  LOG_IF(FATAL, trivial) << "LHS is all 0.0.";
+  if (trivial) {
+    LOG(FATAL) << "LHS is all 0.0.";
+  }
   trivial = true;
   rhs.Each(check_trivial);
-  LOG_IF(FATAL, trivial) << "RHS is all 0.0.";
+  if (trivial) {
+    LOG(FATAL) << "RHS is all 0.0.";
+  }
   return result;
 }
 
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index afe0979536..c8a3a81000 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -484,11 +484,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   temp_workaround_http_archive(
       name = "llvm",
       urls = [
-          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a98fd55665d422389d69d94efe631fac93d11173.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/a98fd55665d422389d69d94efe631fac93d11173.tar.gz",
+          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/5d353d4750143a13e53abf721d521684ba6a5165.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/5d353d4750143a13e53abf721d521684ba6a5165.tar.gz",
       ],
-      sha256 = "a82f5fb3e53294ab3a991d580bed43d3d7eed2f3967a3b6e0905a6f28f25d82a",
-      strip_prefix = "llvm-a98fd55665d422389d69d94efe631fac93d11173",
+      sha256 = "148e96f1cfb0b880e0831a287f44d48251eabc20e24589e6fa4db5916c0c8153",
+      strip_prefix = "llvm-5d353d4750143a13e53abf721d521684ba6a5165",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
       repository = tf_repo_name,
   )
-- 
GitLab


From f062ffbbad0f578c81e44e01bb0c4df2e582ad33 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Tue, 27 Jun 2017 10:14:05 -0700
Subject: [PATCH 0494/1375] tfdbg example: fix --tensor_size issue in
 debug_fibonacci

PiperOrigin-RevId: 160290541
---
 tensorflow/python/debug/examples/debug_fibonacci.py | 4 ++--
 tensorflow/python/debug/examples/examples_test.sh   | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/debug/examples/debug_fibonacci.py b/tensorflow/python/debug/examples/debug_fibonacci.py
index 86689c7d44..704dbda357 100644
--- a/tensorflow/python/debug/examples/debug_fibonacci.py
+++ b/tensorflow/python/debug/examples/debug_fibonacci.py
@@ -53,8 +53,8 @@ def main(_):
     sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
     sess.add_tensor_filter("has_negative", has_negative)
 
-  print("Fibonacci number at position %d: %d" %
-        (FLAGS.length, int(sess.run(n1))))
+  print("Fibonacci number at position %d:\n%s" %
+        (FLAGS.length, sess.run(n1)))
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/debug/examples/examples_test.sh b/tensorflow/python/debug/examples/examples_test.sh
index 0b5401a7f2..25916f1903 100755
--- a/tensorflow/python/debug/examples/examples_test.sh
+++ b/tensorflow/python/debug/examples/examples_test.sh
@@ -56,7 +56,7 @@ fi
 
 # Override the default ui_type=curses to allow the test to pass in a tty-less
 # test environment.
-cat << EOF | ${DEBUG_FIBONACCI_BIN} --ui_type=readline
+cat << EOF | ${DEBUG_FIBONACCI_BIN} --tensor_size=2 --ui_type=readline
 run
 exit
 EOF
-- 
GitLab


From 7f0375ee24e1789eced1934e0aab4c5058486924 Mon Sep 17 00:00:00 2001
From: Jingyue Wu <jingyue@google.com>
Date: Tue, 27 Jun 2017 10:29:36 -0700
Subject: [PATCH 0495/1375] [SE] ThenConvolveWithAlgorithm vlogs algorithm
 configs.

PiperOrigin-RevId: 160292762
---
 tensorflow/stream_executor/stream.cc | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index cbf8c11ef1..97193af777 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -59,6 +59,10 @@ string ToVlogString(dnn::ActivationMode mode) {
   return dnn::ActivationModeString(mode);
 }
 
+string ToVlogString(const dnn::AlgorithmConfig &algo_config) {
+  return algo_config.ToString();
+}
+
 string ToVlogString(dnn::ElementwiseOperation op) {
   return dnn::ElementwiseOperationString(op);
 }
@@ -482,7 +486,8 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(biases),
-            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output));
+            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output),
+            PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
@@ -515,7 +520,8 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(biases),
-            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output));
+            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output),
+            PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
@@ -546,7 +552,7 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(output_descriptor),
-            PARAM(output));
+            PARAM(output), PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
@@ -577,7 +583,7 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(output_descriptor),
-            PARAM(output));
+            PARAM(output), PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
-- 
GitLab


From 4114aaebbe68bf37e73204faf7e3392586c3c1fb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 10:29:55 -0700
Subject: [PATCH 0496/1375] Fix documentation of Estimator class (invalid
 quotes).

PiperOrigin-RevId: 160292803
---
 tensorflow/python/estimator/estimator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 1d2d5d9e0c..c2b97c20a3 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -95,10 +95,10 @@ class Estimator(object):
         * Args:
 
           * `features`: This is the first item returned from the `input_fn`
-                 passed to `train`, 'evaluate`, and `predict`. This should be a
+                 passed to `train`, `evaluate`, and `predict`. This should be a
                  single `Tensor` or `dict` of same.
           * `labels`: This is the second item returned from the `input_fn`
-                 passed to `train`, 'evaluate`, and `predict`. This should be a
+                 passed to `train`, `evaluate`, and `predict`. This should be a
                  single `Tensor` or `dict` of same (for multi-head models). If
                  mode is `ModeKeys.PREDICT`, `labels=None` will be passed. If
                  the `model_fn`'s signature does not accept `mode`, the
-- 
GitLab


From 3ded50e4827ba8e75d6200ee8102662d3f9caf86 Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Tue, 27 Jun 2017 10:30:08 -0700
Subject: [PATCH 0497/1375] Shrink the test size to avoid OOM error on old
 GPUs.

PiperOrigin-RevId: 160292834
---
 tensorflow/python/ops/conv2d_benchmark.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py
index c6352b2a98..6992fa57ea 100644
--- a/tensorflow/python/ops/conv2d_benchmark.py
+++ b/tensorflow/python/ops/conv2d_benchmark.py
@@ -120,8 +120,8 @@ class Conv2DBenchmark(test.Benchmark):
   def benchmark_conv2d(self):
     print("conv2d benchmark:")
 
-    h = 1000
-    w = 1000
+    h = 500
+    w = 500
     fh = 3
     fw = 3
     input_shapes = []
-- 
GitLab


From a8087b4aae40ef5c97d8b27d40795950996f86d5 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 27 Jun 2017 10:39:15 -0700
Subject: [PATCH 0498/1375] [TF:XLA] Reject operators with resource outputs on
 CPU and GPU devices.

We were checking for resource inputs but not resource outputs, which led to accidental fusion of some TensorArray ops on CPU and GPU.

PiperOrigin-RevId: 160294302
---
 .../compiler/jit/mark_for_compilation_pass.cc | 15 +++++---
 .../jit/mark_for_compilation_pass_test.cc     | 38 ++++++++++++++++++-
 2 files changed, 46 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
index eb9c348f7a..7eab7bb28f 100644
--- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc
+++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
@@ -163,10 +163,12 @@ Status DeviceTypeOfDevice(const string& device, DeviceType* device_type) {
   return Status::OK();
 }
 
-// Does `node` have a DT_RESOURCE typed argument?
-bool HasResourceArgument(const Node& node) {
+// Tests whether `node` has a DT_RESOURCE typed input or output.
+bool HasResourceInputOrOutput(const Node& node) {
   return std::find(node.input_types().begin(), node.input_types().end(),
-                   DT_RESOURCE) != node.input_types().end();
+                   DT_RESOURCE) != node.input_types().end() ||
+         std::find(node.output_types().begin(), node.output_types().end(),
+                   DT_RESOURCE) != node.output_types().end();
 }
 
 Status FindCompilationCandidates(
@@ -194,9 +196,10 @@ Status FindCompilationCandidates(
               << ": " << node->type_string();
       continue;
     }
-    if (!registration->compile_resource_ops && HasResourceArgument(*node)) {
-      VLOG(2) << "Compilation rejected node: resource argument " << node->name()
-              << ": " << node->type_string();
+    if (!registration->compile_resource_ops &&
+        HasResourceInputOrOutput(*node)) {
+      VLOG(2) << "Compilation rejected node: resource input/output "
+              << node->name() << ": " << node->type_string();
       continue;
     }
     if (node->type_string() == "While" &&
diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
index 9f30e12e0e..4b88da27a1 100644
--- a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
+++ b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
@@ -14,11 +14,13 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/jit/mark_for_compilation_pass.h"
-#include "tensorflow/compiler/jit/defs.h"
 
 #include "tensorflow/cc/framework/ops.h"
 #include "tensorflow/cc/ops/control_flow_ops_internal.h"
 #include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/compiler/jit/defs.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/graph/graph_constructor.h"
@@ -455,5 +457,39 @@ TEST(XlaCompilationTest, CyclesWithDifferentScopesAndBridge) {
   EXPECT_EQ(clusters["B"], clusters["C"]);
 }
 
+REGISTER_OP("ResourceInput").Input("a: resource").Output("o: float");
+REGISTER_OP("ResourceOutput").Input("a: float").Output("o: resource");
+
+namespace {
+
+class DummyOp : public XlaOpKernel {
+  using XlaOpKernel::XlaOpKernel;
+  void Compile(XlaOpKernelContext* ctx) override {}
+};
+
+REGISTER_XLA_OP(Name("ResourceInput"), DummyOp);
+REGISTER_XLA_OP(Name("ResourceOutput"), DummyOp);
+
+}  // namespace
+
+TEST(XlaCompilationTest, Resources) {
+  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
+  GraphDef graphdef;
+  {
+    GraphDefBuilder builder(GraphDefBuilder::kFailImmediately);
+    Node* a =
+        ops::SourceOp("UncompilableNullary", builder.opts().WithName("A"));
+    Node* b = ops::UnaryOp("Relu", a, builder.opts().WithName("B"));
+    // We should not form clusters with resource ops by default.
+    Node* c = ops::UnaryOp("ResourceOutput", b, builder.opts().WithName("C"));
+    Node* d = ops::UnaryOp("ResourceInput", c, builder.opts().WithName("D"));
+    ops::UnaryOp("Relu", d, builder.opts().WithName("E"));
+    TF_EXPECT_OK(builder.ToGraph(graph.get()));
+  }
+  MarkForCompilation(&graph);
+  auto clusters = GetClusters(*graph);
+  EXPECT_EQ(0, clusters.size());  // Nothing should be compiled.
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From f39dd926259bee979915df2edaeb2369eebeefcb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 11:15:15 -0700
Subject: [PATCH 0499/1375] Add a functionality of remote fused graph
 transformation to fuse graphs by op type

PiperOrigin-RevId: 160300039
---
 .../remote_fused_graph_execute_utils.cc       | 45 +++++++++-
 .../remote_fused_graph_execute_utils.h        | 15 ++++
 .../remote_fused_graph_execute_utils_test.cc  | 84 ++++++++++++++++++-
 .../remote_fused_graph_rewriter_transform.cc  | 30 +++++--
 ...ote_fused_graph_rewriter_transform_test.cc | 53 ++++++++++++
 5 files changed, 219 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index dd9839d245..890ff15c48 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -157,6 +157,8 @@ string DumpCluster(const RemoteFusedGraphExecuteUtils::ClusterInfo& cluster) {
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_BORDER_INPUTS;
 /* static */ constexpr const char* const
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_BORDER_OUTPUTS;
+/* static */ constexpr const char* const
+    RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES;
 /* static */ constexpr const char* const
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_INPUT_TYPES;
 /* static */ constexpr const char* const
@@ -782,7 +784,9 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
           ++input_count;
         }
       }
-      CHECK(input_count == 0 || input_count == node->in_edges().size());
+      CHECK(input_count == 0 || input_count == node->in_edges().size())
+          << "Invalid input_count(" << input_count << ", "
+          << node->in_edges().size() << ") " << node_name;
 
       for (const Edge* out_edge : node->out_edges()) {
         const Node* dst_node = out_edge->dst();
@@ -1063,14 +1067,35 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
       remote_graph_executor_name, require_shape_type, output_graph_def);
 }
 
+/* static */ Status RemoteFusedGraphExecuteUtils::FuseRemoteGraphByOpTypes(
+    const GraphDef& input_graph_def, const std::vector<string>& inputs,
+    const std::vector<string>& outputs,
+    const string& remote_fused_graph_node_name_prefix,
+    const std::unordered_set<string>& fused_op_types,
+    const string& remote_fused_graph_executor_name,
+    const bool require_shape_type, GraphDef* output_graph_def) {
+  const std::unordered_set<string> fused_nodes_filtered_by_op_types =
+      BuildNodeMapFromOpTypes(input_graph_def, fused_op_types);
+
+  return FuseRemoteGraphByNodeNames(
+      input_graph_def, inputs, outputs, remote_fused_graph_node_name_prefix,
+      fused_nodes_filtered_by_op_types, remote_fused_graph_executor_name,
+      require_shape_type, output_graph_def);
+}
+
 /* static */ Status RemoteFusedGraphExecuteUtils::PlaceRemoteGraphArguments(
     const std::vector<string>& inputs, const std::vector<string>& outputs,
     const std::unordered_set<string>& fused_node_names,
     const std::vector<string>& border_inputs,
     const std::vector<string>& border_outputs,
+    const std::unordered_set<string>& fused_op_types,
     const string& remote_fused_graph_node_name,
     const string& remote_graph_executor_name, GraphDef* graph_def) {
   CHECK_NOTNULL(graph_def);
+
+  const std::unordered_set<string> fused_nodes_filtered_by_op_types =
+      BuildNodeMapFromOpTypes(*graph_def, fused_op_types);
+
   for (NodeDef& node_def : *graph_def->mutable_node()) {
     string attr_str;
     TensorId tid;
@@ -1095,6 +1120,12 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::FUSED_NODE);
       }
     }
+    for (const string& fused_node_name : fused_nodes_filtered_by_op_types) {
+      if (fused_node_name == node_def.name()) {
+        AppendDeliminator(&attr_str);
+        attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::FUSED_NODE);
+      }
+    }
     for (int i = 0; i < border_inputs.size(); ++i) {
       if (IsSameNodeName(node_def, border_inputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
@@ -1343,6 +1374,18 @@ RemoteFusedGraphExecuteUtils::FuseRemoteGraphByPlacedArguments(
   return Status::OK();
 }
 
+/* static */ std::unordered_set<string>
+RemoteFusedGraphExecuteUtils::BuildNodeMapFromOpTypes(
+    const GraphDef& graph_def, const std::unordered_set<string>& op_types) {
+  std::unordered_set<string> retval;
+  for (const NodeDef& node_def : graph_def.node()) {
+    if (op_types.count(node_def.op()) > 0) {
+      retval.emplace(node_def.name());
+    }
+  }
+  return retval;
+}
+
 /* static */ Status RemoteFusedGraphExecuteUtils::ReplaceInputNodeByPlaceHolder(
     const string& input, const DataType type, const TensorShape& shape,
     GraphDef* graph_def) {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
index 1d4423ed46..28d170d982 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
@@ -57,6 +57,8 @@ class RemoteFusedGraphExecuteUtils {
       "border_inputs";
   static constexpr const char* const TRANSFORM_ARG_BORDER_OUTPUTS =
       "border_outputs";
+  static constexpr const char* const TRANSFORM_ARG_FUSED_OP_TYPES =
+      "fused_op_types";
   static constexpr const char* const TRANSFORM_ARG_INPUT_TYPES = "input_types";
   static constexpr const char* const TRANSFORM_ARG_INPUT_SHAPES =
       "input_shapes";
@@ -230,12 +232,22 @@ class RemoteFusedGraphExecuteUtils {
       const string& remote_graph_executor_name, const bool require_shape_type,
       GraphDef* output_graph_def);
 
+  // Fuse subgraph of specified op types.
+  static Status FuseRemoteGraphByOpTypes(
+      const GraphDef& input_graph_def, const std::vector<string>& inputs,
+      const std::vector<string>& outputs,
+      const string& remote_fused_graph_node_name_prefix,
+      const std::unordered_set<string>& fused_op_types,
+      const string& remote_fused_graph_executor_name,
+      const bool require_shape_type, GraphDef* output_graph_def);
+
   // Place arguments to fuse remote graph.
   static Status PlaceRemoteGraphArguments(
       const std::vector<string>& inputs, const std::vector<string>& outputs,
       const std::unordered_set<string>& fused_node_names,
       const std::vector<string>& border_inputs,
       const std::vector<string>& border_outputs,
+      const std::unordered_set<string>& fused_op_types,
       const string& remote_fused_graph_node_name,
       const string& remote_graph_executor_name, GraphDef* graph_def);
 
@@ -258,6 +270,9 @@ class RemoteFusedGraphExecuteUtils {
   static Status CopyByteArrayToTensor(const void* src_ptr, const int src_size,
                                       Tensor* tensor);
 
+  static std::unordered_set<string> BuildNodeMapFromOpTypes(
+      const GraphDef& graph_def, const std::unordered_set<string>& op_types);
+
  private:
   static void EmplaceTensorShapeType(const string& name, const Tensor& tensor,
                                      TensorShapeMap* tensor_shape_map);
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
index ad95b25cb1..059070305f 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
@@ -80,6 +80,13 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
         /*require_shape_type=*/false, &result_graph_def_);
   }
 
+  Status FuseByOpTypes() {
+    return RemoteFusedGraphExecuteUtils::FuseRemoteGraphByOpTypes(
+        graph_def_, inputs_, outputs_, "remote_fused_graph_node_names",
+        subgraph_op_types_, "remote_graph_executor_name",
+        /*require_shape_type=*/false, &result_graph_def_);
+  }
+
   Status BuildAndAddTensorShape() {
     return RemoteFusedGraphExecuteUtils::BuildAndAddTensorShapes(
         input_tensors_, /*dry_run_inference=*/true, &graph_def_);
@@ -88,8 +95,9 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
   Status PlaceRemoteGraphArguments() {
     return RemoteFusedGraphExecuteUtils::PlaceRemoteGraphArguments(
         inputs_, outputs_, subgraph_node_names_, subgraph_input_names_,
-        subgraph_output_names_, "remote_fused_graph_node_names",
-        "remote_graph_executor_name", &graph_def_);
+        subgraph_output_names_, subgraph_op_types_,
+        "remote_fused_graph_node_names", "remote_graph_executor_name",
+        &graph_def_);
   }
 
   Status FuseByPlacedArguments() {
@@ -105,6 +113,15 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
                                                      input_tensors_);
   }
 
+  void ReplaceOpType(const std::unordered_set<string>& op_name,
+                     const string& new_op_type) {
+    for (NodeDef& node_def : *graph_def_.mutable_node()) {
+      if (op_name.count(node_def.name()) > 0) {
+        node_def.set_op(new_op_type);
+      }
+    }
+  }
+
  public:
   const std::vector<std::pair<string, Tensor>> input_tensors_{
       {"A", {DT_FLOAT, {1, 1, 1, 1}}}};
@@ -115,6 +132,7 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
   std::vector<string> subgraph_input_names_;
   std::vector<string> subgraph_output_names_;
   std::unordered_set<string> subgraph_node_names_;
+  std::unordered_set<string> subgraph_op_types_;
 };
 
 void SetSubgraphArguments(const std::vector<string>& input_names,
@@ -650,6 +668,32 @@ TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByNodes_ABCDEFGHIJK) {
       << SummarizeGraphDef(result_graph_def_);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByOpTypes_HIJ) {
+  subgraph_op_types_ = {"Mul"};
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+
+  TF_ASSERT_OK(FuseByOpTypes());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+  EXPECT_EQ(9, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByOpTypes_FGHIJ) {
+  subgraph_op_types_ = {"Const", "Mul"};
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+
+  TF_ASSERT_OK(FuseByOpTypes());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+  EXPECT_EQ(3, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
 TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_H) {
   subgraph_node_names_ = {"H"};
 
@@ -756,5 +800,41 @@ TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_ABCDE_K) {
       << SummarizeGraphDef(result_graph_def_);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_MUL_HIJ) {
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+  subgraph_op_types_ = {"Mul"};
+
+  TF_ASSERT_OK(PlaceRemoteGraphArguments());
+  ASSERT_TRUE(IsFuseReady());
+  TF_ASSERT_OK(BuildAndAddTensorShape());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+
+  TF_ASSERT_OK(FuseByPlacedArguments());
+
+  EXPECT_EQ(9, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_CONST_MUL_FGHIJ) {
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+  subgraph_op_types_ = {"Const", "Mul"};
+
+  TF_ASSERT_OK(PlaceRemoteGraphArguments());
+  ASSERT_TRUE(IsFuseReady());
+  TF_ASSERT_OK(BuildAndAddTensorShape());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+
+  TF_ASSERT_OK(FuseByPlacedArguments());
+
+  EXPECT_EQ(3, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
index ac0503088c..1f8c2a7684 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
@@ -66,6 +66,7 @@ static Status ParseArguments(const TransformFuncContext& context,
                              string* input_types_str, string* input_shapes_str,
                              string* fused_nodes_str, string* border_inputs_str,
                              string* border_outputs_str,
+                             string* fused_op_types_str,
                              string* remote_fused_graph_node_name,
                              string* remote_graph_executor_name) {
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
@@ -83,6 +84,9 @@ static Status ParseArguments(const TransformFuncContext& context,
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
       RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_BORDER_OUTPUTS, "",
       border_outputs_str));
+  TF_RETURN_IF_ERROR(context.GetOneStringParameter(
+      RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES, "",
+      fused_op_types_str));
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
       RemoteFusedGraphExecuteUtils::
           TRANSFORM_ARG_REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
@@ -135,12 +139,13 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
   string fused_nodes_str;
   string border_inputs_str;
   string border_outputs_str;
+  string fused_op_types_str;
   string remote_fused_graph_node_name;
   string remote_graph_executor_name;
   TF_RETURN_IF_ERROR(ParseArguments(
       context, &input_types_str, &input_shapes_str, &fused_nodes_str,
-      &border_inputs_str, &border_outputs_str, &remote_fused_graph_node_name,
-      &remote_graph_executor_name));
+      &border_inputs_str, &border_outputs_str, &fused_op_types_str,
+      &remote_fused_graph_node_name, &remote_graph_executor_name));
 
   if (!input_types_str.empty()) {
     TF_RETURN_IF_ERROR(PlaceShapeType(inputs, outputs, input_types_str,
@@ -173,6 +178,15 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
         mutable_input_graph_def, inputs, outputs, remote_fused_graph_node_name,
         border_inputs, border_outputs, remote_graph_executor_name,
         require_shape_type, output_graph_def));
+  } else if (!fused_op_types_str.empty()) {
+    const std::vector<string> fused_op_type_vector =
+        str_util::Split(fused_op_types_str, ",");
+    const std::unordered_set<string> fused_op_types(
+        fused_op_type_vector.begin(), fused_op_type_vector.end());
+    TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::FuseRemoteGraphByOpTypes(
+        mutable_input_graph_def, inputs, outputs, remote_fused_graph_node_name,
+        fused_op_types, remote_graph_executor_name, require_shape_type,
+        output_graph_def));
   } else {
     CHECK(false) << "Fuse targets are not specified.";
   }
@@ -191,14 +205,15 @@ Status PlaceRemoteGraphArguments(const GraphDef& input_graph_def,
   string input_types_str;
   string input_shapes_str;
   string fused_nodes_str;
+  string fused_op_types_str;
   string border_inputs_str;
   string border_outputs_str;
   string remote_fused_graph_node_name;
   string remote_graph_executor_name;
   TF_RETURN_IF_ERROR(ParseArguments(
       context, &input_types_str, &input_shapes_str, &fused_nodes_str,
-      &border_inputs_str, &border_outputs_str, &remote_fused_graph_node_name,
-      &remote_graph_executor_name));
+      &border_inputs_str, &border_outputs_str, &fused_op_types_str,
+      &remote_fused_graph_node_name, &remote_graph_executor_name));
 
   if (!input_types_str.empty()) {
     TF_RETURN_IF_ERROR(PlaceShapeType(inputs, outputs, input_types_str,
@@ -213,9 +228,14 @@ Status PlaceRemoteGraphArguments(const GraphDef& input_graph_def,
       str_util::Split(border_inputs_str, ",");
   const std::vector<string> border_outputs =
       str_util::Split(border_outputs_str, ",");
+  const std::vector<string> fused_op_type_vector =
+      str_util::Split(fused_op_types_str, ",");
+  const std::unordered_set<string> fused_op_types(fused_op_type_vector.begin(),
+                                                  fused_op_type_vector.end());
+
   TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::PlaceRemoteGraphArguments(
       inputs, outputs, fused_node_names, border_inputs, border_outputs,
-      remote_fused_graph_node_name, remote_graph_executor_name,
+      fused_op_types, remote_fused_graph_node_name, remote_graph_executor_name,
       output_graph_def));
 
   return Status::OK();
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
index 143cdd7c0a..113f5c55ea 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
@@ -107,6 +107,12 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
            {border_outputs_str_}}));
     }
 
+    if (!fused_op_types_str_.empty()) {
+      context.params.insert(std::pair<string, std::vector<string>>(
+          {RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES,
+           {fused_op_types_str_}}));
+    }
+
     context.params.insert(std::pair<string, std::vector<string>>(
         {RemoteFusedGraphExecuteUtils::
              TRANSFORM_ARG_REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
@@ -129,6 +135,15 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
     input_shapes_ = "1,1,1,1";
   }
 
+  void ReplaceOpType(const std::unordered_set<string>& op_name,
+                     const string& new_op_type) {
+    for (NodeDef& node_def : *input_graph_def_.mutable_node()) {
+      if (op_name.count(node_def.name()) > 0) {
+        node_def.set_op(new_op_type);
+      }
+    }
+  }
+
   void CheckGraph(int expected_node_count, int expected_cluster_count) {
     EXPECT_EQ(expected_node_count, output_graph_def_.node_size());
 
@@ -162,6 +177,7 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
   string fused_node_names_str_;
   string border_inputs_str_;
   string border_outputs_str_;
+  string fused_op_types_str_;
 };
 
 TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
@@ -228,6 +244,22 @@ TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
   CheckGraph(7, 1);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       FuseRemoteGraphByOpTypes_HIJ) {
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Mul";
+  TF_ASSERT_OK(Fuse());
+  CheckGraph(9, 1);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       FuseRemoteGraphByOpTypes_FGHIJ) {
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Const,Mul";
+  TF_ASSERT_OK(Fuse());
+  CheckGraph(3, 1);
+}
+
 TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest, PlaceAndFuse_HIJ) {
   fused_node_names_str_ = "H,I,J";
   TF_ASSERT_OK(PlaceFuseArgs());
@@ -259,6 +291,27 @@ TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest, PlaceAndFuse_ABCDE_K) {
   CheckGraph(7, 1);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest, PlaceAndFuse_MUL_HIJ) {
+  SetInputShapeType();
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Mul";
+
+  TF_ASSERT_OK(PlaceFuseArgs());
+  TF_ASSERT_OK(FuseWithPlacedArgs());
+  CheckGraph(9, 1);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       PlaceAndFuse_CONST_MUL_FGHIJ) {
+  SetInputShapeType();
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Const,Mul";
+
+  TF_ASSERT_OK(PlaceFuseArgs());
+  TF_ASSERT_OK(FuseWithPlacedArgs());
+  CheckGraph(3, 1);
+}
+
 }  // namespace
 }  // namespace graph_transforms
 }  // namespace tensorflow
-- 
GitLab


From 2b5d00a58b82604233e01934cd0f93ef6ced4e9f Mon Sep 17 00:00:00 2001
From: James Qin <jamesqin@google.com>
Date: Tue, 27 Jun 2017 11:19:15 -0700
Subject: [PATCH 0500/1375] Cudnn compatible LSTMCell and LSTMBlockCell

PiperOrigin-RevId: 160300668
---
 .../python/kernel_tests/cudnn_rnn_ops_test.py | 20 +++---
 .../cudnn_rnn/python/ops/cudnn_rnn_ops.py     | 65 +++++++++++++++----
 tensorflow/contrib/rnn/python/ops/lstm_ops.py |  4 +-
 tensorflow/python/ops/rnn_cell_impl.py        |  6 ++
 4 files changed, 69 insertions(+), 26 deletions(-)

diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
index 0e51ab9935..f41fdc382b 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
@@ -23,7 +23,6 @@ import unittest
 import numpy as np
 
 from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
-from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.core.protobuf import saver_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -114,17 +113,14 @@ class CudnnRNNTest(TensorFlowTestCase):
     num_units = cudnn_model.num_units
     num_layers = cudnn_model.num_layers
 
-    # To reuse cuDNN-trained models, must set
-    # forget_bias, clip_cell = 0, False
-    # In LSTMCell and LSTMBlockCell, forget_bias is added in addition to learned
-    # bias, whereas cuDNN does not apply the additional bias.
+    # To reuse cuDNN-trained models, must use CudnnCompatibleLSTM...Cells.
     if use_block_cell:
       # pylint: disable=g-long-lambda
-      single_cell = lambda: lstm_ops.LSTMBlockCell(num_units, forget_bias=0,
-                                                   clip_cell=False)
+      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMBlockCell(
+          num_units)
       # pylint: enable=g-long-lambda
     else:
-      single_cell = lambda: rnn_cell_impl.LSTMCell(num_units, forget_bias=0)
+      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMCell(num_units)
     cell = rnn_cell_impl.MultiRNNCell(
         [single_cell() for _ in range(num_layers)])
     return rnn.dynamic_rnn(
@@ -171,7 +167,7 @@ class CudnnRNNTest(TensorFlowTestCase):
 
   @unittest.skipUnless(test.is_built_with_cuda(),
                        "Test only applicable when running on GPUs")
-  def testCheckpointReusableByCanonicalLSTMCells(self):
+  def testCudnnCompatibleLSTMCells(self):
     configs = [
         {
             "num_layers": 1,
@@ -207,7 +203,7 @@ class CudnnRNNTest(TensorFlowTestCase):
         },
     ]
     for cfg in configs:
-      self._testCheckpointReusableByCanonicalLSTMCells(
+      self._testCudnnCompatibleLSTMCells(
           cfg["num_layers"],
           cfg["seq_length"],
           cfg["num_units"],
@@ -215,7 +211,7 @@ class CudnnRNNTest(TensorFlowTestCase):
           cfg["batch_size"],
           cfg["rnn_mode"],
           use_block_cell=False)
-      self._testCheckpointReusableByCanonicalLSTMCells(
+      self._testCudnnCompatibleLSTMCells(
           cfg["num_layers"],
           cfg["seq_length"],
           cfg["num_units"],
@@ -224,7 +220,7 @@ class CudnnRNNTest(TensorFlowTestCase):
           cfg["rnn_mode"],
           use_block_cell=True)
 
-  def _testCheckpointReusableByCanonicalLSTMCells(
+  def _testCudnnCompatibleLSTMCells(
       self, num_layers, seq_length, num_units, input_size, batch_size, rnn_mode,
       use_block_cell):
     np.random.seed(0)
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index 0437467f3f..25b3bde837 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -18,6 +18,7 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.cudnn_rnn.ops import gen_cudnn_rnn_ops
+from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.contrib.util import loader
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import dtypes
@@ -25,6 +26,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import state_ops
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.training import saver
@@ -145,7 +147,7 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
     return self._transform_lstm_canonical(weights, biases)
 
   def _transformed_canonical_names(self, weights, biases):
-    """Return canonical names for fused weight and bias tensors."""
+    """Return canonical names for transformed weight and bias tensors."""
     if (self._cudnn_rnn.direction != "unidirectional" or
         self._cudnn_rnn.rnn_mode != "lstm"):
       assert len(weights) == len(biases)
@@ -156,23 +158,25 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       num_layers = self._cudnn_rnn.num_layers
       # TODO(jamesqin): get rid of multi_rnn_cell when num_layers is 1
       for i in range(num_layers):
-        # One fused weight tensor each layer.
-        w_names.append("multi_rnn_cell/cell_%d/lstm_cell/kernel" % i)
-        # Three fused bias tensors each layer:
-        # the 1st is for LSTMBlockCell restore; the latter two sum up to the
-        # 1st, and are used for cuDNN restore.
-        b_names.append("multi_rnn_cell/cell_%d/lstm_cell/bias" % i)
+        # One transformed weight tensor each layer.
+        w_names.append(
+            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/kernel" % i)
+        # Three transformed bias tensors each layer:
+        # the 1st is for CudnnCompatbleLSTM(Block)Cell restore; the latter two
+        # sum up to the 1st, and are used for cuDNN restore.
+        b_names.append(
+            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/bias" % i)
         b_names.extend([
-            "multi_rnn_cell/cell_%d/lstm_cell/bias_cudnn_%d" % (i, j)
-            for j in range(2)
+            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/bias_cudnn_%d" %
+            (i, j) for j in range(2)
         ])
       return w_names, b_names
 
   def _transform_lstm_canonical(self, weights, biases):
-    """Create fused lstm canonical params.
+    """Create transformed lstm canonical params.
 
     Produce properly-shaped monolithic weight and bias tensors to share between
-    cuDNN and non-platform specific LSTM cells (w/o peephole).
+    cuDNN and cudnn_compatible non-platform specific LSTM cells.
     Args:
       weights: a list of Tensors recovered from cuDNN params_to_canonical.
       biases: a list of Tensors recovered from cuDNN params_to_canonical.
@@ -180,7 +184,7 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       Two lists of tensors, one for weight and bias each.
       The weight list contains num_layers tensors and bias one contains 3 *
       num_layers tensors. Both original and combined biases since cuDNN biases
-      are not restorable from the fused version.
+      are not restorable from the transformed version.
     """
     transformed_weights, transformed_biases = [], []
     for i in range(self._cudnn_rnn.num_layers):
@@ -676,3 +680,40 @@ ops.RegisterShape("CudnnRNNParamsToCanonical")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNNCanonicalToParams")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNN")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNNBackprop")(common_shapes.call_cpp_shape_fn)
+
+
+class CudnnCompatibleLSTMCell(rnn_cell_impl.LSTMCell):
+  """Cudnn Compatible LSTMCell.
+
+  A simple wrapper around @{tf.nn.rnn_cell.LSTMCell} to use along with
+  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
+  former seamlessly.
+  """
+
+  def __init__(self, num_units, reuse=None):
+    super(CudnnCompatibleLSTMCell, self).__init__(
+        num_units,
+        use_peepholes=False,
+        cell_clip=None,
+        num_proj=None,
+        proj_clip=None,
+        state_is_tuple=True,
+        activation=None,
+        reuse=reuse,
+        forget_bias=0)
+
+
+class CudnnCompatibleLSTMBlockCell(lstm_ops.LSTMBlockCell):
+  """Cudnn Compatible LSTMBlockCell.
+
+  A simple wrapper around @{tf.contrib.rnn.LSTMBlockCell} to use along with
+  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
+  former seamlessly. It is the more performant than
+  ${tf.contrib.cudnn_rnn.CudnnCompatibleLSTMCell}, the same way
+  @{tf.contrib.rnn.LSTMBlockCell} is than @{tf.nn.rnn_cell.LSTMCell}.
+  """
+
+  def __init__(self, num_units):
+    super(CudnnCompatibleLSTMBlockCell, self).__init__(
+        num_units, forget_bias=0, clip_cell=False, use_peephole=False)
+    self._names.update({"scope": "cudnn_compatible_lstm_cell"})
diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
index 97b9dcc905..48c2c5a724 100644
--- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py
+++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
@@ -352,8 +352,8 @@ class LSTMBlockCell(rnn_cell_impl.RNNCell):
         `_lstm_block_cell()` for details.
       use_peephole: Whether to use peephole connections or not.
 
-      When restoring from CudnnLSTM-trained checkpoints, must set the following:
-      forget_bias, clip_cell, use_peephole  = 0, False, False
+      When restoring from CudnnLSTM-trained checkpoints, must use
+      CudnnCompatibleLSTMBlockCell instead.
     """
     self._num_units = num_units
     self._forget_bias = forget_bias
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index c0d9c971a0..53db96f334 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -354,6 +354,9 @@ class BasicLSTMCell(RNNCell):
       reuse: (optional) Python boolean describing whether to reuse variables
         in an existing scope.  If not `True`, and the existing scope already has
         the given variables, an error is raised.
+
+      When restoring from CudnnLSTM-trained checkpoints, must use
+      CudnnCompatibleLSTMCell instead.
     """
     super(BasicLSTMCell, self).__init__(_reuse=reuse)
     if not state_is_tuple:
@@ -468,6 +471,9 @@ class LSTMCell(RNNCell):
       reuse: (optional) Python boolean describing whether to reuse variables
         in an existing scope.  If not `True`, and the existing scope already has
         the given variables, an error is raised.
+
+      When restoring from CudnnLSTM-trained checkpoints, must use
+      CudnnCompatibleLSTMCell instead.
     """
     super(LSTMCell, self).__init__(_reuse=reuse)
     if not state_is_tuple:
-- 
GitLab


From 4442b1cdd639ab75e5a04b42117c188247e4fc17 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 11:24:34 -0700
Subject: [PATCH 0501/1375] [XLA] Remove "operand" argument from
 HandleReducePrecision.

PiperOrigin-RevId: 160301461
---
 tensorflow/compiler/xla/service/dfs_hlo_visitor.h    | 3 +--
 tensorflow/compiler/xla/service/hlo_cost_analysis.cc | 3 +--
 tensorflow/compiler/xla/service/hlo_cost_analysis.h  | 3 +--
 tensorflow/compiler/xla/service/hlo_instruction.cc   | 2 +-
 4 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 3f9b71cf2b..cb929eef49 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -164,8 +164,7 @@ class DfsHloVisitor {
                                  HloInstruction* lhs, HloInstruction* rhs) {
     return HandleElementwiseBinary(logical_or, HloOpcode::kLogicalOr);
   }
-  virtual Status HandleReducePrecision(HloInstruction* reduce_precision,
-                                       HloInstruction* operand) {
+  virtual Status HandleReducePrecision(HloInstruction* reduce_precision) {
     return HandleElementwiseUnary(reduce_precision,
                                   HloOpcode::kReducePrecision);
   }
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index f3a6cd43c2..f4c0bb8a4a 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -97,8 +97,7 @@ Status HloCostAnalysis::HandleClamp(HloInstruction* clamp,
   return HandleElementwiseOp(clamp);
 }
 
-Status HloCostAnalysis::HandleReducePrecision(HloInstruction* hlo,
-                                              HloInstruction* operand) {
+Status HloCostAnalysis::HandleReducePrecision(HloInstruction* hlo) {
   return HandleElementwiseOp(hlo);
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index 3f0dfcc619..602c93e6bf 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -56,8 +56,7 @@ class HloCostAnalysis : public DfsHloVisitor {
                        HloInstruction* lhs, HloInstruction* rhs) override;
   Status HandleClamp(HloInstruction* clamp, HloInstruction* min,
                      HloInstruction* arg, HloInstruction* max) override;
-  Status HandleReducePrecision(HloInstruction* hlo,
-                               HloInstruction* operand) override;
+  Status HandleReducePrecision(HloInstruction* hlo) override;
   Status HandleConcatenate(
       HloInstruction* concatenate,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) override;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 99b73dea29..7c60fc79ed 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1878,7 +1878,7 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
     case HloOpcode::kReverse:
       return visitor->HandleReverse(this, operands_[0]);
     case HloOpcode::kReducePrecision:
-      return visitor->HandleReducePrecision(this, operands_[0]);
+      return visitor->HandleReducePrecision(this);
     case HloOpcode::kSlice:
       return visitor->HandleSlice(this, operands_[0]);
     case HloOpcode::kDynamicSlice:
-- 
GitLab


From f0c3cbfc9b574245f6998756a12d804bedc08fd4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 11:24:53 -0700
Subject: [PATCH 0502/1375] Added more reduce window tests.

PiperOrigin-RevId: 160301509
---
 tensorflow/compiler/xla/tests/BUILD           |  20 +-
 .../compiler/xla/tests/reduce_window_test.cc  | 356 +++++++++++++++---
 2 files changed, 328 insertions(+), 48 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 433934ffaf..140c9c7b7c 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -868,10 +868,15 @@ xla_test(
     ],
 )
 
-xla_test(
-    name = "reduce_window_test",
-    timeout = "long",
+filegroup(
+    name = "reduce_window_test_srcs",
+    testonly = 1,
     srcs = ["reduce_window_test.cc"],
+)
+
+cc_library(
+    name = "reduce_window_test_deps",
+    testonly = 1,
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -891,6 +896,15 @@ xla_test(
     ],
 )
 
+xla_test(
+    name = "reduce_window_test",
+    timeout = "long",
+    srcs = ["reduce_window_test_srcs"],
+    deps = [
+        ":reduce_window_test_deps",
+    ],
+)
+
 xla_test(
     name = "select_and_scatter_test",
     timeout = "long",
diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index 17185b8e68..a3cb04c685 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -489,6 +489,219 @@ XLA_TEST_F(ReduceWindowTest, NonstandardReduceFunction) {
   ComputeAndCompareR4<float>(&builder_, *expected, {}, ErrorSpec(1e-3, 1e-3));
 }
 
+TEST_F(ReduceWindowTest, R4UnitWindow) {
+  Array4D<float> input_array(13, 12, 8, 15);
+  input_array.Fill(1.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({0, 3, 2, 1}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, 7, 1}, {1, 4, 1, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 7, 1},
+                                              {1, 4, 1, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+XLA_TEST_F(ReduceWindowTest, R4SecondMinorStride) {
+  Array4D<float> input_array(2, 1, 27, 119);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  int win_len = 1;
+  int stride = 8;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+XLA_TEST_F(ReduceWindowTest, R4SecondMinorUnitStride) {
+  Array4D<float> input_array(3, 2, 4, 64);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  int win_len = 3;
+  int stride = 1;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+XLA_TEST_F(ReduceWindowTest, R4SecondMinorWin) {
+  Array4D<float> input_array(1, 3, 12, 200);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  int win_len = 8;
+  int stride = 5;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+TEST_F(ReduceWindowTest, AmongMajor2DimsMultipleMinor) {
+  Array4D<float> input_array(6, 4, 10, 130);
+  input_array.FillRandom(2.0f);
+
+  int win_len = 3;
+  int win_stride = 2;
+
+  Padding padding = Padding::kSame;
+  const auto input_data_handle =
+      builder_.ConstantR4FromArray4D<float>(input_array);
+  // Reduce only along the x and y dimensions, according to the win_len.
+  ReduceWindowAdd(input_data_handle, {win_len, win_len, 1, 1},
+                  {win_stride, win_stride, 1, 1}, padding);
+
+  auto result = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {win_len, win_len, 1, 1},
+      {win_stride, win_stride, 1, 1}, padding);
+  ComputeAndCompareR4<float>(&builder_, *result, {}, ErrorSpec(1e-3, 1e-3));
+}
+
+TEST_F(ReduceWindowTest, R1_128x2_OverlappingWindows) {
+  std::vector<float> input_vector(128 * 2);
+  std::iota(std::begin(input_vector), std::end(input_vector), 0);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {30}, {27}, Padding::kValid);
+  ComputeAndCompareR1<float>(
+      &builder_, {435, 1245, 2055, 2865, 3675, 4485, 5295, 6105, 6915}, {},
+      ErrorSpec(0.0001));
+}
+
+TEST_F(ReduceWindowTest, R1_128x1_OverlappingWindows) {
+  std::vector<float> input_vector(128 * 1);
+  std::iota(std::begin(input_vector), std::end(input_vector), 0);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {32}, {24}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {496, 1264, 2032, 2800, 3568}, {},
+                             ErrorSpec(0.0001));
+}
+
+TEST_F(ReduceWindowTest, R1_128x17_NoOverlap) {
+  std::vector<float> input_vector(128 * 17, 1);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {7}, {64}, Padding::kValid);
+  ComputeAndCompareR1<float>(
+      &builder_, {7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
+                  7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7},
+      {}, ErrorSpec(0.0001));
+}
+
+XLA_TEST_F(ReduceWindowTest, Add24In1152_NoOverlap) {
+  std::vector<float> input_vector(128 * 9, 1);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {32}, {128}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {32, 32, 32, 32, 32, 32, 32, 32, 32},
+                             {}, ErrorSpec(0.0001));
+}
+
+TEST_F(ReduceWindowTest, Add24In256_NoOverlap) {
+  std::vector<float> input_vector(128 * 2);  // {0, 1, 2, .. len-1}
+  std::iota(std::begin(input_vector), std::end(input_vector), 0);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {32}, {56}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {496, 2288, 4080, 5872, 7664}, {},
+                             ErrorSpec(0.0001));
+}
+
+XLA_TEST_F(ReduceWindowTest, Add128In128Stride128) {
+  const auto input = builder_.ConstantR1<float>(
+      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
+  ReduceWindowAdd(input, {128}, {128}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {1088}, {}, ErrorSpec(0.0001));
+}
+
+// Regression test for a bug that appeared in Inception (b/34784899).
+TEST_F(ReduceWindowTest, R2ReduceWindowInception) {
+  Array2D<float> input_array(28, 28);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR2FromArray2DWithLayout(input_array,
+                                             LayoutUtil::MakeLayout({0, 1}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  LOG(INFO) << "Input array: " << input_array.ToString();
+
+  int win_len = 3;
+  int stride = 1;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {win_len, win_len}, {stride, stride}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow2DAdd(
+      input_array, 0.0f, {win_len, win_len}, {stride, stride}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR2<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+// Regression test for a bug that appeared in Inception (b/34784899).
+TEST_F(ReduceWindowTest, R2ReduceWindowInceptionFromBroadcast) {
+  Array2D<float> input_array(14, 14, 1.0f);
+  ComputationDataHandle input =
+      builder_.Broadcast(builder_.ConstantLiteral(Literal::One(F32)), {14, 14});
+
+  int win_len = 3;
+  int stride = 1;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {win_len, win_len}, {stride, stride}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow2DAdd(
+      input_array, 0.0f, {win_len, win_len}, {stride, stride}, padding);
+
+  ComputeAndCompareR2<float>(&builder_, *res, {}, ErrorSpec(1e-3, 1e-3));
+}
+
 TEST_F(ReduceWindowTest, R2ReduceWindowNonOverlappingFromBroadcast) {
   Array2D<float> input_array(6, 4, 1.0f);
   ComputationDataHandle input =
@@ -535,53 +748,58 @@ string R4ReduceWindowTestDataToString(
 
 class R4ReduceWindowTest
     : public ClientLibraryTestBase,
-      public ::testing::WithParamInterface<R4ReduceWindowTestData> {};
-TEST_P(R4ReduceWindowTest, DoIt) {
-  ComputationBuilder b(client_, TestName());
-  const auto& param = GetParam();
-
-  const float kInitValue = 0.0f;
-
-  Array4D<float> input(param.base_bounds[0], param.base_bounds[1],
-                       param.base_bounds[2], param.base_bounds[3]);
-  input.FillIota(1);
-  std::unique_ptr<Literal> input_literal = Literal::CreateR4FromArray4D(input);
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
-                         client_->TransferToServer(*input_literal));
-
-  std::vector<std::pair<int64, int64>> padding(4);
-  for (int i = 0; i < 4; ++i) {
-    padding[i] = {param.pad_low[i], param.pad_high[i]};
+      public ::testing::WithParamInterface<R4ReduceWindowTestData> {
+ protected:
+  void DoIt() {
+    ComputationBuilder b(client_, TestName());
+    const auto& param = GetParam();
+
+    const float kInitValue = 0.0f;
+
+    Array4D<float> input(param.base_bounds[0], param.base_bounds[1],
+                         param.base_bounds[2], param.base_bounds[3]);
+    input.FillIota(1);
+    std::unique_ptr<Literal> input_literal =
+        Literal::CreateR4FromArray4D(input);
+    TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
+                           client_->TransferToServer(*input_literal));
+
+    std::vector<std::pair<int64, int64>> padding(4);
+    for (int i = 0; i < 4; ++i) {
+      padding[i] = {param.pad_low[i], param.pad_high[i]};
+    }
+
+    auto parameter = b.Parameter(0, input_literal->shape(), "p0");
+    auto pad_value = b.ConstantR0<float>(kInitValue);
+    CHECK(param.reducer == kAdd || param.reducer == kMax);
+    auto computation = param.reducer == kAdd
+                           ? CreateScalarAddComputation(F32, &b)
+                           : CreateScalarMaxComputation(F32, &b);
+    b.ReduceWindowWithGeneralPadding(
+        /*operand=*/parameter,
+        /*init_value=*/pad_value,
+        /*computation=*/computation,
+        /*window_dimensions=*/param.window_bounds,
+        /*window_strides=*/param.strides,
+        /*padding=*/padding);
+
+    CHECK(param.reducer == kAdd || param.reducer == kMax);
+    auto reduce_func = param.reducer == kAdd
+                           ? +[](float a, float b) { return a + b; }
+                           : +[](float a, float b) { return std::max(a, b); };
+    std::unique_ptr<Array4D<float>> expected =
+        ReferenceUtil::ReduceWindow4DGeneric(
+            /*operand=*/input,
+            /*init=*/kInitValue,
+            /*reduce_func=*/reduce_func,
+            /*window=*/param.window_bounds,
+            /*stride=*/param.strides,
+            /*padding=*/padding);
+    ComputeAndCompareR4<float>(&b, *expected, {input_arg.get()});
   }
+};
 
-  auto parameter = b.Parameter(0, input_literal->shape(), "p0");
-  auto pad_value = b.ConstantR0<float>(kInitValue);
-  CHECK(param.reducer == kAdd || param.reducer == kMax);
-  auto computation = param.reducer == kAdd
-                         ? CreateScalarAddComputation(F32, &b)
-                         : CreateScalarMaxComputation(F32, &b);
-  b.ReduceWindowWithGeneralPadding(
-      /*operand=*/parameter,
-      /*init_value=*/pad_value,
-      /*computation=*/computation,
-      /*window_dimensions=*/param.window_bounds,
-      /*window_strides=*/param.strides,
-      /*padding=*/padding);
-
-  CHECK(param.reducer == kAdd || param.reducer == kMax);
-  auto reduce_func = param.reducer == kAdd
-                         ? +[](float a, float b) { return a + b; }
-                         : +[](float a, float b) { return std::max(a, b); };
-  std::unique_ptr<Array4D<float>> expected =
-      ReferenceUtil::ReduceWindow4DGeneric(
-          /*operand=*/input,
-          /*init=*/kInitValue,
-          /*reduce_func=*/reduce_func,
-          /*window=*/param.window_bounds,
-          /*stride=*/param.strides,
-          /*padding=*/padding);
-  ComputeAndCompareR4<float>(&b, *expected, {input_arg.get()});
-}
+TEST_P(R4ReduceWindowTest, DoIt) { DoIt(); }
 
 // base_bounds, window_bounds, strides, pad_low, pad_high
 const R4ReduceWindowTestData kR4ReduceWindowTestValues[] = {
@@ -672,12 +890,54 @@ const R4ReduceWindowTestData kR4ReduceWindowTestValues[] = {
                            /*pad_low=*/{10, 1, 0, 0},
                            /*pad_high=*/{2, 3, 0, 0},
                            /*reducer=*/kAdd},
+
+    // With second minor dimension == 9.
+    R4ReduceWindowTestData{/*base_bounds=*/{2, 3, 9, 127},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With minor dimension == 129.
+    R4ReduceWindowTestData{/*base_bounds=*/{3, 2, 7, 129},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
 };
 
 INSTANTIATE_TEST_CASE_P(R4ReduceWindowTestInstantiation, R4ReduceWindowTest,
                         ::testing::ValuesIn(kR4ReduceWindowTestValues),
                         R4ReduceWindowTestDataToString);
 
+class R4ReduceWindowLargeTest : public R4ReduceWindowTest {};
+
+XLA_TEST_P(R4ReduceWindowLargeTest, DoIt) { DoIt(); }
+
+// Test cases that are large/slow/failed.
+const R4ReduceWindowTestData kR4ReduceWindowLargeTestValues[] = {
+    R4ReduceWindowTestData{/*base_bounds=*/{28, 28, 256, 128},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{1, 1, 0, 0},
+                           /*pad_high=*/{1, 1, 0, 0},
+                           /*reducer=*/kMax},
+
+    R4ReduceWindowTestData{/*base_bounds=*/{112, 112, 64, 128},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{2, 2, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{1, 1, 0, 0},
+                           /*reducer=*/kAdd},
+};
+
+INSTANTIATE_TEST_CASE_P(R4ReduceWindowLargeTestInstantiation,
+                        R4ReduceWindowLargeTest,
+                        ::testing::ValuesIn(kR4ReduceWindowLargeTestValues),
+                        R4ReduceWindowTestDataToString);
+
 struct R2ReduceWindowTestData {
   int64 base_bounds[2];
   int64 window_bounds[2];
@@ -704,6 +964,12 @@ struct R2ReduceWindowTestData {
     {/*base_bounds=*/{6, 4}, /*window_bounds=*/{4, 2},
      /*strides=*/{3, 3}, /*layout=*/{0, 1},
      /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{5, 147}, /*window_bounds=*/{1, 36},
+     /*strides=*/{4, 5}, /*layout=*/{1, 0},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{4, 153}, /*window_bounds=*/{2, 93},
+     /*strides=*/{1, 1}, /*layout=*/{1, 0},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
 };
 
 string R2ReduceWindowTestDataToString(
-- 
GitLab


From 47f28360d2be7979571b4a2d9118651d36eedabd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 11:49:20 -0700
Subject: [PATCH 0503/1375] Updates more text classification examples in
 examples/learn.

PiperOrigin-RevId: 160305131
---
 .../examples/learn/text_classification.py     |   1 +
 .../text_classification_character_cnn.py      | 121 ++++++++++++------
 .../text_classification_character_rnn.py      |  92 ++++++++-----
 .../examples/learn/text_classification_cnn.py | 109 +++++++++++-----
 4 files changed, 216 insertions(+), 107 deletions(-)

diff --git a/tensorflow/examples/learn/text_classification.py b/tensorflow/examples/learn/text_classification.py
index 4b32bfc382..21d98e9ea2 100644
--- a/tensorflow/examples/learn/text_classification.py
+++ b/tensorflow/examples/learn/text_classification.py
@@ -145,6 +145,7 @@ def main(unused_argv):
   train_input_fn = tf.estimator.inputs.numpy_input_fn(
       x={WORDS_FEATURE: x_train},
       y=y_train,
+      batch_size=len(x_train),
       num_epochs=None,
       shuffle=True)
   classifier.train(input_fn=train_input_fn, steps=100)
diff --git a/tensorflow/examples/learn/text_classification_character_cnn.py b/tensorflow/examples/learn/text_classification_character_cnn.py
index 5ad53acf9f..5f7c8e7371 100644
--- a/tensorflow/examples/learn/text_classification_character_cnn.py
+++ b/tensorflow/examples/learn/text_classification_character_cnn.py
@@ -11,8 +11,7 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-"""This is an example of using convolutional networks over characters for
-   DBpedia dataset to predict class from description of an entity.
+"""Example of using convolutional networks over characters for DBpedia dataset.
 
 This model is similar to one described in this paper:
    "Character-level Convolutional Networks for Text Classification"
@@ -34,8 +33,6 @@ import pandas
 from sklearn import metrics
 import tensorflow as tf
 
-learn = tf.contrib.learn
-
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 100
@@ -44,53 +41,73 @@ FILTER_SHAPE1 = [20, 256]
 FILTER_SHAPE2 = [20, N_FILTERS]
 POOLING_WINDOW = 4
 POOLING_STRIDE = 2
+MAX_LABEL = 15
+CHARS_FEATURE = 'chars'  # Name of the input character feature.
 
 
-def char_cnn_model(features, target):
+def char_cnn_model(features, labels, mode):
   """Character level convolutional neural network model to predict classes."""
-  target = tf.one_hot(target, 15, 1, 0)
-  byte_list = tf.reshape(
-      tf.one_hot(features, 256), [-1, MAX_DOCUMENT_LENGTH, 256, 1])
+  features_onehot = tf.one_hot(features[CHARS_FEATURE], 256)
+  input_layer = tf.reshape(
+      features_onehot, [-1, MAX_DOCUMENT_LENGTH, 256, 1])
   with tf.variable_scope('CNN_Layer1'):
     # Apply Convolution filtering on input sequence.
-    conv1 = tf.contrib.layers.convolution2d(
-        byte_list, N_FILTERS, FILTER_SHAPE1, padding='VALID')
-    # Add a ReLU for non linearity.
-    conv1 = tf.nn.relu(conv1)
+    conv1 = tf.layers.conv2d(
+        input_layer,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE1,
+        padding='VALID',
+        # Add a ReLU for non linearity.
+        activation=tf.nn.relu)
     # Max pooling across output of Convolution+Relu.
-    pool1 = tf.nn.max_pool(
+    pool1 = tf.layers.max_pooling2d(
         conv1,
-        ksize=[1, POOLING_WINDOW, 1, 1],
-        strides=[1, POOLING_STRIDE, 1, 1],
+        pool_size=POOLING_WINDOW,
+        strides=POOLING_STRIDE,
         padding='SAME')
     # Transpose matrix so that n_filters from convolution becomes width.
     pool1 = tf.transpose(pool1, [0, 1, 3, 2])
   with tf.variable_scope('CNN_Layer2'):
     # Second level of convolution filtering.
-    conv2 = tf.contrib.layers.convolution2d(
-        pool1, N_FILTERS, FILTER_SHAPE2, padding='VALID')
+    conv2 = tf.layers.conv2d(
+        pool1,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE2,
+        padding='VALID')
     # Max across each filter to get useful features for classification.
     pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])
 
   # Apply regular WX + B and classification.
-  logits = tf.contrib.layers.fully_connected(pool2, 15, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  logits = tf.layers.dense(pool2, MAX_LABEL, activation=None)
+
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data, size='large')
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -98,21 +115,43 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
+  char_processor = tf.contrib.learn.preprocessing.ByteProcessor(
+      MAX_DOCUMENT_LENGTH)
   x_train = np.array(list(char_processor.fit_transform(x_train)))
   x_test = np.array(list(char_processor.transform(x_test)))
 
+  x_train = x_train.reshape([-1, MAX_DOCUMENT_LENGTH, 1, 1])
+  x_test = x_test.reshape([-1, MAX_DOCUMENT_LENGTH, 1, 1])
+
   # Build model
-  classifier = learn.Estimator(model_fn=char_cnn_model)
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  classifier = tf.estimator.Estimator(model_fn=char_cnn_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_train},
+      y=y_train,
+      batch_size=len(x_train),
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/text_classification_character_rnn.py b/tensorflow/examples/learn/text_classification_character_rnn.py
index 1cb2cd2f88..1fc9388a1a 100644
--- a/tensorflow/examples/learn/text_classification_character_rnn.py
+++ b/tensorflow/examples/learn/text_classification_character_rnn.py
@@ -11,7 +11,7 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-"""This is an example of using recurrent neural networks over characters for DBpedia dataset to predict class from description of an entity.
+"""Example of recurrent neural networks over characters for DBpedia dataset.
 
 This model is similar to one described in this paper:
    "Character-level Convolutional Networks for Text Classification"
@@ -33,41 +33,52 @@ import pandas
 from sklearn import metrics
 import tensorflow as tf
 
-learn = tf.contrib.learn
-
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 100
 HIDDEN_SIZE = 20
+MAX_LABEL = 15
+CHARS_FEATURE = 'chars'  # Name of the input character feature.
 
 
-def char_rnn_model(features, target):
+def char_rnn_model(features, labels, mode):
   """Character level recurrent neural network model to predict classes."""
-  target = tf.one_hot(target, 15, 1, 0)
-  byte_list = tf.one_hot(features, 256, 1, 0)
-  byte_list = tf.unstack(byte_list, axis=1)
+  byte_vectors = tf.one_hot(features[CHARS_FEATURE], 256, 1., 0.)
+  byte_list = tf.unstack(byte_vectors, axis=1)
 
   cell = tf.contrib.rnn.GRUCell(HIDDEN_SIZE)
   _, encoding = tf.contrib.rnn.static_rnn(cell, byte_list, dtype=tf.float32)
 
-  logits = tf.contrib.layers.fully_connected(encoding, 15, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
-
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)
+
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -75,21 +86,40 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
+  char_processor = tf.contrib.learn.preprocessing.ByteProcessor(
+      MAX_DOCUMENT_LENGTH)
   x_train = np.array(list(char_processor.fit_transform(x_train)))
   x_test = np.array(list(char_processor.transform(x_test)))
 
   # Build model
-  classifier = learn.Estimator(model_fn=char_rnn_model)
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  classifier = tf.estimator.Estimator(model_fn=char_rnn_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_train},
+      y=y_train,
+      batch_size=len(x_train),
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/text_classification_cnn.py b/tensorflow/examples/learn/text_classification_cnn.py
index 468a96b58f..0ee2405c8b 100644
--- a/tensorflow/examples/learn/text_classification_cnn.py
+++ b/tensorflow/examples/learn/text_classification_cnn.py
@@ -25,8 +25,6 @@ import pandas
 from sklearn import metrics
 import tensorflow as tf
 
-learn = tf.contrib.learn
-
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 100
@@ -38,59 +36,78 @@ FILTER_SHAPE2 = [WINDOW_SIZE, N_FILTERS]
 POOLING_WINDOW = 4
 POOLING_STRIDE = 2
 n_words = 0
+MAX_LABEL = 15
+WORDS_FEATURE = 'words'  # Name of the input words feature.
 
 
-def cnn_model(features, target):
+def cnn_model(features, labels, mode):
   """2 layer ConvNet to predict from sequence of words to a class."""
   # Convert indexes of words into embeddings.
   # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
   # maps word indexes of the sequence into [batch_size, sequence_length,
   # EMBEDDING_SIZE].
-  target = tf.one_hot(target, 15, 1, 0)
   word_vectors = tf.contrib.layers.embed_sequence(
-      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE, scope='words')
+      features[WORDS_FEATURE], vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
   word_vectors = tf.expand_dims(word_vectors, 3)
   with tf.variable_scope('CNN_Layer1'):
     # Apply Convolution filtering on input sequence.
-    conv1 = tf.contrib.layers.convolution2d(
-        word_vectors, N_FILTERS, FILTER_SHAPE1, padding='VALID')
-    # Add a RELU for non linearity.
-    conv1 = tf.nn.relu(conv1)
+    conv1 = tf.layers.conv2d(
+        word_vectors,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE1,
+        padding='VALID',
+        # Add a ReLU for non linearity.
+        activation=tf.nn.relu)
     # Max pooling across output of Convolution+Relu.
-    pool1 = tf.nn.max_pool(
+    pool1 = tf.layers.max_pooling2d(
         conv1,
-        ksize=[1, POOLING_WINDOW, 1, 1],
-        strides=[1, POOLING_STRIDE, 1, 1],
+        pool_size=POOLING_WINDOW,
+        strides=POOLING_STRIDE,
         padding='SAME')
     # Transpose matrix so that n_filters from convolution becomes width.
     pool1 = tf.transpose(pool1, [0, 1, 3, 2])
   with tf.variable_scope('CNN_Layer2'):
     # Second level of convolution filtering.
-    conv2 = tf.contrib.layers.convolution2d(
-        pool1, N_FILTERS, FILTER_SHAPE2, padding='VALID')
+    conv2 = tf.layers.conv2d(
+        pool1,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE2,
+        padding='VALID')
     # Max across each filter to get useful features for classification.
     pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])
 
   # Apply regular WX + B and classification.
-  logits = tf.contrib.layers.fully_connected(pool2, 15, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  logits = tf.layers.dense(pool2, MAX_LABEL, activation=None)
+
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   global n_words
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -98,20 +115,42 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
+  vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(
+      MAX_DOCUMENT_LENGTH)
   x_train = np.array(list(vocab_processor.fit_transform(x_train)))
   x_test = np.array(list(vocab_processor.transform(x_test)))
   n_words = len(vocab_processor.vocabulary_)
   print('Total words: %d' % n_words)
 
   # Build model
-  classifier = learn.SKCompat(learn.Estimator(model_fn=cnn_model))
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = classifier.predict(x_test)['class']
+  classifier = tf.estimator.Estimator(model_fn=cnn_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_train},
+      y=y_train,
+      batch_size=len(x_train),
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
-- 
GitLab


From f09a108261f121582514e60a53fda1c9bcac58f0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 11:57:23 -0700
Subject: [PATCH 0504/1375] Use C API to implement Operation._output_types

This change first converts the _output_types member to a property and
then implements it using C API if it is enabled.

PiperOrigin-RevId: 160306227
---
 tensorflow/python/framework/ops.py      | 38 ++++++++++++++++-
 tensorflow/python/framework/ops_test.py | 54 ++++++++++++++++++++++++-
 2 files changed, 89 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 760148fad7..f52127f119 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -27,6 +27,7 @@ import sys
 import threading
 
 import six
+from six.moves import xrange  # pylint: disable=redefined-builtin
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import function_pb2
 from tensorflow.core.framework import graph_pb2
@@ -1169,7 +1170,7 @@ class Operation(object):
       a._add_consumer(self)  # pylint: disable=protected-access
     if output_types is None:
       output_types = []
-    self._output_types = output_types
+    self._output_types_val = output_types
     self._outputs = [Tensor(self, i, output_type)
                      for i, output_type in enumerate(output_types)]
     if input_types is None:
@@ -1380,6 +1381,41 @@ class Operation(object):
     else:
       return self._node_def.device
 
+  @property
+  def _output_types(self):
+    """List this operation's output types.
+
+    Returns:
+      List of the types of the Tensors computed by this operation.
+      Each element in the list is an integer whose value is one of
+      the TF_DataType enums defined in c_api.h
+      The length of this list indicates the number of output endpoints
+      of the operation.
+    """
+    if _USE_C_API:
+      num_outputs = c_api.TF_OperationNumOutputs(self._c_op)
+      output_types = [c_api.TF_OperationOutputType(self._tf_output(i)) for
+                      i in xrange(num_outputs)]
+      # TODO(iga): Remove this assert after converting to C API by default.
+      # Just being a bit paranoid here.
+      assert self._output_types_val == output_types
+      # In all the tests we have output_types that are passed into
+      # Operation.__init__ are a list of ints (which is illegal according
+      # to the docstring), but input_types are instances of DType.
+      # This extra assert is to catch if we ever use DType for output_types.
+      if output_types:
+        assert isinstance(output_types[0], int)
+      return output_types
+    else:
+      return self._output_types_val
+
+  def _tf_output(self, output_idx):
+    """Create and return a new TF_Output for output_idx'th output of this op."""
+    tf_output = c_api.TF_Output()
+    tf_output.oper = self._c_op
+    tf_output.index = output_idx
+    return tf_output
+
   def _set_device(self, device):
     """Set the device of this operation.
 
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 00891d2d21..2d0a6cbf8a 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -39,6 +39,7 @@ from tensorflow.python.framework import test_util
 from tensorflow.python.framework import versions
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resources
 from tensorflow.python.ops import variable_scope
@@ -1063,7 +1064,7 @@ class ComparisonTest(test_util.TensorFlowTestCase):
 class ControlDependenciesTest(test_util.TensorFlowTestCase):
 
   def testBasic(self):
-    ops._USE_C_API = True
+    ops._USE_C_API = True  # pylint: disable=protected-access
     try:
       g = ops.Graph()
       with g.as_default():
@@ -1084,7 +1085,7 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase):
       # e should be dominated by c.
       self.assertEqual(e.op.control_inputs, [])
     finally:
-      ops._USE_C_API = False
+      ops._USE_C_API = False  # pylint: disable=protected-access
 
   def testBasicWithConversion(self):
     g = ops.Graph()
@@ -1772,5 +1773,54 @@ class TracebackTest(test_util.TensorFlowTestCase):
           self.assertEquals(frame, frame_with_start_line[:-1])
 
 
+class OutputTypesTest(test_util.TensorFlowTestCase):
+  """Tests Operation._output_types property.
+
+  This test should not exist as _output_types is a private property.
+  This property is used by util.copy_elements and its tests would normally
+  cover Operation._output_types. However, we can't yet run these tests in C
+  API mode because their use _set_device method. This test will be deleted
+  once we port _set_device and run the copy tests with C API on.
+  """
+  # TODO(iga): Remove this test
+
+  def setUp(self):
+    self.prev_use_c_api = ops._USE_C_API  # pylint: disable=protected-access
+    ops._USE_C_API = True  # pylint: disable=protected-access
+
+  def tearDown(self):
+    ops._USE_C_API = self.prev_use_c_api  # pylint: disable=protected-access
+
+  def testOneOutput(self):
+    g = ops.Graph()
+    with g.as_default():
+      # Using a constant because creating unregistered ops
+      # doesn't work with the C API.
+      op = constant_op.constant(12, dtype=dtypes.uint16).op
+      # pylint: disable=protected-access
+      self.assertEqual([types_pb2.DT_UINT16], op._output_types)
+      # pylint: enable=protected-access
+
+  def testTwoDifferentOutputs(self):
+    g = ops.Graph()
+    with g.as_default():
+      x = constant_op.constant([1, 1, 2, 4, 4, 4, 7, 8, 8],
+                               dtype=dtypes.double)
+      y, _ = gen_array_ops.unique(x)
+      self.assertEqual([types_pb2.DT_DOUBLE, types_pb2.DT_INT32],
+                       y.op._output_types)  # pylint: disable=protected-access
+
+  def testThreeOutputs(self):
+    g = ops.Graph()
+    with g.as_default():
+      # Using a split operationt because creating unregistered ops
+      # doesn't work with the C API.
+      a = constant_op.constant("abc", dtype=dtypes.string, shape=[5, 30])
+      split0, _, _ = array_ops.split(a, [4, 15, 11], 1)
+      # pylint: disable=protected-access
+      self.assertEqual([types_pb2.DT_STRING] * 3, split0.op._output_types)
+      # pylint: enable=protected-access
+
+
 if __name__ == "__main__":
   googletest.main()
-- 
GitLab


From 1464b9930de871fd11870941963253670f737c23 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 12:09:54 -0700
Subject: [PATCH 0505/1375] Add more tests for BatchNormTraining. RELNOTES: n/a

PiperOrigin-RevId: 160307959
---
 tensorflow/compiler/xla/reference_util.cc     |  46 +++
 tensorflow/compiler/xla/reference_util.h      |  46 +++
 .../compiler/xla/service/hlo_instruction.h    |   2 +-
 tensorflow/compiler/xla/tests/BUILD           |   6 +
 .../xla/tests/batch_normalization_test.cc     | 261 ++++++++++++++++++
 5 files changed, 360 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index 0bfb0ff73d..ac4fec4b70 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -252,6 +252,20 @@ ReferenceUtil::ReduceWindow4DGeneric(
                                padding);
 }
 
+/* static */ std::unique_ptr<Array4D<float>> ReferenceUtil::BatchNorm4D(
+    const Array4D<float>& input, const Array4D<float>& mean,
+    const Array4D<float>& var, const Array4D<float>& scale,
+    const Array4D<float>& offset, float epsilon) {
+  auto normalized =
+      *MapArray4D(input, mean, [](float a, float b) { return a - b; });
+  normalized = *MapArray4D(normalized, var, [&](float a, float b) {
+    return a / std::sqrt(b + epsilon);
+  });
+  normalized =
+      *MapArray4D(normalized, scale, [](float a, float b) { return a * b; });
+  return MapArray4D(normalized, offset, [](float a, float b) { return a + b; });
+}
+
 /* static  */ std::unique_ptr<Array4D<float>>
 ReferenceUtil::SelectAndScatter4DGePlus(
     const Array4D<float>& operand, const Array4D<float>& source, float init,
@@ -590,6 +604,38 @@ ReferenceUtil::ReduceToRowArray2D(
   return result;
 }
 
+/* static */ std::unique_ptr<Array4D<float>> ReferenceUtil::Broadcast1DTo4D(
+    const std::vector<float>& array, const std::vector<int64>& bounds,
+    int64 broadcast_from_dim) {
+  auto result =
+      MakeUnique<Array4D<float>>(bounds[0], bounds[1], bounds[2], bounds[3]);
+  for (int64 i = 0; i < result->n1(); ++i) {
+    for (int64 j = 0; j < result->n2(); ++j) {
+      for (int64 k = 0; k < result->n3(); ++k) {
+        for (int64 l = 0; l < result->n4(); ++l) {
+          switch (broadcast_from_dim) {
+            case 0:
+              (*result)(i, j, k, l) = array[i];
+              break;
+            case 1:
+              (*result)(i, j, k, l) = array[j];
+              break;
+            case 2:
+              (*result)(i, j, k, l) = array[k];
+              break;
+            case 3:
+              (*result)(i, j, k, l) = array[l];
+              break;
+            default:
+              break;
+          }
+        }
+      }
+    }
+  }
+  return result;
+}
+
 /* static */ std::unique_ptr<Array2D<float>> ReferenceUtil::Reduce3DTo2D(
     const Array3D<float>& array, float init,
     tensorflow::gtl::ArraySlice<int64> dims,
diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index f58f0bdc9f..a60a648b8d 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -120,6 +120,11 @@ class ReferenceUtil {
       tensorflow::gtl::ArraySlice<int64> dims,
       std::function<float(float, float)> reduce_function);
 
+  // Broadcast 1D dimension to 4D, from the dimension `broadcast_from_dim`.
+  static std::unique_ptr<Array4D<float>> Broadcast1DTo4D(
+      const std::vector<float>& array, const std::vector<int64>& bounds,
+      int64 broadcast_from_dim);
+
   // Returns the result of reducing the 3D array to a 2D array, reducing away
   // the dimensions specified in dims.
   static std::unique_ptr<Array2D<float>> Reduce3DTo2D(
@@ -169,6 +174,12 @@ class ReferenceUtil {
       const tensorflow::gtl::ArraySlice<int64>& stride,
       const tensorflow::gtl::ArraySlice<std::pair<int64, int64>>& padding);
 
+  // Batch normalize data.
+  static std::unique_ptr<Array4D<float>> BatchNorm4D(
+      const Array4D<float>& input, const Array4D<float>& mean,
+      const Array4D<float>& var, const Array4D<float>& scale,
+      const Array4D<float>& offset, float epsilon);
+
   // Performs select and scatter with Greater Than or equal as the select, plus
   // as the scatter, and Same Padding.
   static std::unique_ptr<Array4D<float>> SelectAndScatter4DGePlus(
@@ -396,6 +407,41 @@ class ReferenceUtil {
     return result;
   }
 
+  // Applies map_function to each pair of elements in the input lhs and rhs
+  // (4D array) and returns the result.
+  template <typename F>
+  static std::unique_ptr<Array4D<float>> MapArray4D(const Array4D<float>& lhs,
+                                                    const Array4D<float>& rhs,
+                                                    F&& map_function) {
+    return MapWithIndexArray4D(
+        lhs, rhs, [&](float lhs, float rhs, int64, int64, int64, int64) {
+          return map_function(lhs, rhs);
+        });
+  }
+
+  // Applies map_function to each pair of element in lhs and rhs (4D array) and
+  // returns the result.
+  // (plane, depth, height, width) index of each element is also provided as
+  // arguments to map_function.
+  template <typename F>
+  static std::unique_ptr<Array4D<float>> MapWithIndexArray4D(
+      const Array4D<float>& lhs, const Array4D<float>& rhs, F&& map_function) {
+    auto result = MakeUnique<Array4D<float>>(lhs.planes(), lhs.depth(),
+                                             lhs.height(), lhs.width());
+    for (int64 plane = 0; plane < lhs.planes(); ++plane) {
+      for (int64 depth = 0; depth < lhs.depth(); ++depth) {
+        for (int64 height = 0; height < lhs.height(); ++height) {
+          for (int64 width = 0; width < lhs.width(); ++width) {
+            (*result)(plane, depth, height, width) = map_function(
+                lhs(plane, depth, height, width),
+                rhs(plane, depth, height, width), plane, depth, height, width);
+          }
+        }
+      }
+    }
+    return result;
+  }
+
   // Returns the result of a 2D pad on an input matrix.
   static std::unique_ptr<Array2D<float>> PadArray2D(
       const Array2D<float>& operand, const PaddingConfig& padding,
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 37cbb0b769..5885261e8f 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -549,7 +549,7 @@ class HloInstruction {
   // number added to the variance to avoid divide-by-zero error.
   //
   // Precondition: opcode() == HloOpcode::kBatchNormTraining
-  int64 epsilon() const { return epsilon_; }
+  float epsilon() const { return epsilon_; }
 
   // Returns the infeed configuration string. The infeed configuration includes
   // any metadata needed for the backend compiler (e.g., infeed buffer address)
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 140c9c7b7c..2fd0680afa 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -726,19 +726,25 @@ xla_test(
 xla_test(
     name = "batch_normalization_test",
     srcs = ["batch_normalization_test.cc"],
+    shard_count = 40,
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index 9f9f541230..4afeb68b5f 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -25,10 +25,20 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/reference_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/test_helpers.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/compiler/xla/tests/test_utils.h"
+#include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/test.h"
@@ -189,6 +199,257 @@ TEST_F(BatchNormalizationTest, SpecComparisonForward) {
   ComputeAndCompareR4<float>(&builder, expected, {}, error_spec_);
 }
 
+struct BatchNormTestParam {
+  std::vector<int64> bounds;
+  int64 feature_index;
+  float random_value_mean;
+  float random_value_var;
+};
+
+// Tests to test the fused operation of BatchNorm.
+class BatchNormTest : public ClientLibraryTestBase,
+                      public ::testing::WithParamInterface<BatchNormTestParam> {
+};
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_P(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(RandomizedTests))) {
+  float epsilon = 0.001;
+  ComputationBuilder builder(client_, TestName());
+  const std::vector<int64>& bounds = GetParam().bounds;
+  Array4D<float> input_array(bounds[0], bounds[1], bounds[2], bounds[3]);
+  input_array.FillRandom(GetParam().random_value_var,
+                         GetParam().random_value_mean);
+
+  const int64 feature_index = GetParam().feature_index;
+  const int64 num_elements_per_feature =
+      Product(bounds) / bounds[feature_index];
+  const int64 feature_bound = bounds[feature_index];
+  std::vector<float> offset(feature_bound, 1);
+  std::vector<float> scale(feature_bound, 2);
+
+  auto input_squared =
+      ReferenceUtil::MapArray4D(input_array, [](float a) { return a * a; });
+  std::vector<int64> reduce_dims;
+  for (int64 i = 0; i < bounds.size(); ++i) {
+    if (i != feature_index) {
+      reduce_dims.push_back(i);
+    }
+  }
+
+  auto sum =
+      ReferenceUtil::Reduce4DTo1D(input_array, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  auto sum_squared =
+      ReferenceUtil::Reduce4DTo1D(*input_squared, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  std::vector<float> mean(feature_bound);
+
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean[i] = sum[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> mean_square(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean_square[i] = mean[i] * mean[i];
+  }
+
+  std::vector<float> square_mean(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    square_mean[i] = sum_squared[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> var(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    var[i] = square_mean[i] - mean_square[i];
+  }
+
+  Array4D<float> mean_4D =
+      *ReferenceUtil::Broadcast1DTo4D(mean, bounds, feature_index);
+  auto var_4D = *ReferenceUtil::Broadcast1DTo4D(var, bounds, feature_index);
+  auto scale_4D = *ReferenceUtil::Broadcast1DTo4D(scale, bounds, feature_index);
+  auto offset_4D =
+      *ReferenceUtil::Broadcast1DTo4D(offset, bounds, feature_index);
+
+  auto normalized = *ReferenceUtil::BatchNorm4D(input_array, mean_4D, var_4D,
+                                                scale_4D, offset_4D, epsilon);
+
+  auto expected_normalized = Literal::CreateR4FromArray4D<float>(normalized);
+
+  auto offset_literal = Literal::CreateR1<float>(offset);
+  auto scale_literal = Literal::CreateR1<float>(scale);
+  auto input_literal = Literal::CreateR4FromArray4D<float>(input_array);
+
+  auto input_activations =
+      builder.Parameter(0, input_literal->shape(), "input");
+  auto scale_activations =
+      builder.Parameter(1, scale_literal->shape(), "offset");
+  auto offset_activations =
+      builder.Parameter(2, offset_literal->shape(), "scale");
+
+  auto expected = *Literal::MakeTuple({expected_normalized.get(),
+                                       Literal::CreateR1<float>(mean).get(),
+                                       Literal::CreateR1<float>(var).get()});
+
+  std::unique_ptr<GlobalData> input_data =
+      client_->TransferToServer(*input_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> scale_data =
+      client_->TransferToServer(*scale_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> offset_data =
+      client_->TransferToServer(*offset_literal).ConsumeValueOrDie();
+
+  builder.BatchNormTraining(input_activations, scale_activations,
+                            offset_activations, epsilon, feature_index);
+
+  ComputeAndCompareTuple(
+      &builder, expected,
+      {input_data.get(), scale_data.get(), offset_data.get()},
+      ErrorSpec(0.01, 1));
+}
+
+INSTANTIATE_TEST_CASE_P(
+    BatchNormTest_Instantiation, BatchNormTest,
+    ::testing::Values(BatchNormTestParam{{2, 2, 2, 2}, 0, 100.2f, 200.0f},
+                      BatchNormTestParam{{2, 2, 2, 2}, 3, 300.f, 400.0f},
+
+                      BatchNormTestParam{{1, 10, 1, 1}, 0, 10.1f, 20.1f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 1, 3.14f, 314.15f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 2, 666.6f, 777.7f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 1, -666.6f, 777.7f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 2, 0.f, 777.7f},
+                      BatchNormTestParam{{1, 1, 10, 1}, 3, 888.8f, 9.9f},
+
+                      BatchNormTestParam{{24, 129, 1, 2}, 2, 10000, 10000},
+                      BatchNormTestParam{{24, 129, 1, 2}, 3, 10000, 10000},
+
+                      // Feature on low dimension to trigger relayout, test
+                      // internal logical to physical dimension calculation
+                      // is correct after relayout.
+                      BatchNormTestParam{{1, 2, 3, 4}, 0, 100, 100}));
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(BasicTraining))) {
+  const int kFeatureIndex = 3;
+  ComputationBuilder builder(client_, TestName());
+
+  auto operand = builder.ConstantR4FromArray4D<float>(
+      {{{{1.f, 2.f}}, {{3.f, 4.f}}}, {{{5.f, 6.f}}, {{7.f, 8.f}}}});
+
+  auto scale = builder.ConstantR1<float>({2.0f, 3.0f});
+
+  auto offset = builder.ConstantR1<float>({1.0f, 2.0f});
+
+  auto tuple = builder.BatchNormTraining(operand, scale, offset,
+                                         /*epsilon=*/0.001, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR4<float>({{{{-1.6f, -2.0f}}, {{0.1f, 0.6f}}},
+                                 {{{1.9f, 3.3f}}, {{3.7f, 6.0f}}}})
+           .get(),
+       Literal::CreateR1<float>({4, 5}).get(),
+       Literal::CreateR1<float>({5, 5}).get()});
+
+  ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
+}
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(BasicTrainingOnSublane))) {
+  const int kFeatureIndex = 2;
+  ComputationBuilder builder(client_, TestName());
+
+  auto operand = builder.ConstantR4FromArray4D<float>(
+      {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}});
+
+  auto scale = builder.ConstantR1<float>({2.0f, 3.0f});
+
+  auto offset = builder.ConstantR1<float>({1.0f, 2.0f});
+
+  auto tuple = builder.BatchNormTraining(operand, scale, offset,
+                                         /*epsilon=*/0.001, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR4<float>({{{{-1.6f}, {-2.0f}}, {{0.1f}, {0.6f}}},
+                                 {{{1.9f}, {3.3f}}, {{3.7f}, {6.0f}}}})
+           .get(),
+       Literal::CreateR1<float>({4, 5}).get(),
+       Literal::CreateR1<float>({5, 5}).get()});
+
+  ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
+}
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(
+                              TrainingWithFeatureOnLowDimension))) {
+  // Use 0 dimension as feature, tests layout analyzer.
+  const int kFeatureIndex = 0;
+  ComputationBuilder builder(client_, TestName());
+
+  ComputationDataHandle h0;
+  auto operand = CreateR3Parameter<float>(Array3D<float>(260, 2, 2, 1.0f),
+                                          /*parameter_number=*/0, "operand",
+                                          &builder, &h0);
+  ComputationDataHandle h1;
+  auto scale =
+      CreateR1Parameter<float>(std::vector<float>(260, 1.0f),
+                               /*parameter_number=*/1, "scale", &builder, &h1);
+  ComputationDataHandle h2;
+  auto offset =
+      CreateR1Parameter<float>(std::vector<float>(260, 1.0f),
+                               /*parameter_number=*/2, "offset", &builder, &h2);
+
+  auto tuple = builder.BatchNormTraining(h0, h1, h2,
+                                         /*epsilon=*/1, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR3FromArray3D<float>(Array3D<float>(260, 2, 2, 1.0f))
+           .get(),
+       Literal::CreateR1<float>(std::vector<float>(260, 1.0f)).get(),
+       Literal::CreateR1<float>(std::vector<float>(260, 0.0f)).get()});
+
+  ComputeAndCompareTuple(&builder, expected,
+                         {operand.get(), scale.get(), offset.get()},
+                         ErrorSpec(0.1));
+}
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(LargeEpsilonTest))) {
+  // Test the correctness of choosing a large epsilon value.
+  const int kFeatureIndex = 2;
+  ComputationBuilder builder(client_, TestName());
+
+  ComputationDataHandle h0;
+  auto operand = CreateR3Parameter<float>({{{0.0f}, {10.0f}, {20.0f}, {30.0f}}},
+                                          /*parameter_number=*/0, "operand",
+                                          &builder, &h0);
+  ComputationDataHandle h1;
+  auto scale =
+      CreateR1Parameter<float>(std::vector<float>(1, 1.0f),
+                               /*parameter_number=*/1, "scale", &builder, &h1);
+  ComputationDataHandle h2;
+  auto offset =
+      CreateR1Parameter<float>(std::vector<float>(1, 0.0f),
+                               /*parameter_number=*/2, "offset", &builder, &h2);
+
+  // var = 125, mean = 15, epsilon = -100
+  auto tuple = builder.BatchNormTraining(h0, h1, h2,
+                                         /*epsilon=*/-100, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR3FromArray3D<float>({{{-3.0f}, {-1.0f}, {1.0f}, {3.0f}}})
+           .get(),
+       Literal::CreateR1<float>(std::vector<float>(1, 15.0f)).get(),
+       Literal::CreateR1<float>(std::vector<float>(1, 125.0f)).get()});
+
+  ComputeAndCompareTuple(&builder, expected,
+                         {operand.get(), scale.get(), offset.get()},
+                         ErrorSpec(0.1));
+}
+
 }  // namespace
 }  // namespace xla
 
-- 
GitLab


From cb14bce32e26b7c8cdb4e462b0b1f482988154b2 Mon Sep 17 00:00:00 2001
From: Brian Williammee <bwill@google.com>
Date: Tue, 27 Jun 2017 12:11:35 -0700
Subject: [PATCH 0506/1375] Update path to
 print_selective_registration_header.py in comment

PiperOrigin-RevId: 160308173
---
 tensorflow/core/framework/selective_registration.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/framework/selective_registration.h b/tensorflow/core/framework/selective_registration.h
index af86092ac7..503947969d 100644
--- a/tensorflow/core/framework/selective_registration.h
+++ b/tensorflow/core/framework/selective_registration.h
@@ -31,8 +31,8 @@ limitations under the License.
 //    functions should be defined as valid constexpr functions, so that they are
 //    evaluated at compile time: this is needed to make symbols referenced by
 //    un-registered objects unused, and therefore allow the linker to strip them
-//    out.  See tools/print_required_ops/print_selective_registration_header.py
-//    for a tool that can be used to generate ops_to_register.h.
+//    out.  See python/tools/print_selective_registration_header.py for a tool
+//    that can be used to generate ops_to_register.h.
 //
 // ops_to_register.h should define macros for:
 //   // Ops for which this is false will not be registered.
-- 
GitLab


From 3adb95bc95b8d99697d09209a08bb984b3d964b4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 12:16:28 -0700
Subject: [PATCH 0507/1375] Migrate TensorForest v4 python to contrib.

PiperOrigin-RevId: 160308805
---
 tensorflow/contrib/cmake/tf_python.cmake      |   4 +
 tensorflow/contrib/decision_trees/__init__.py |  27 ++
 tensorflow/contrib/tensor_forest/BUILD        |  23 ++
 .../tensor_forest/client/random_forest.py     |  18 +-
 .../contrib/tensor_forest/python/__init__.py  |   1 +
 .../tensor_forest/python/tensor_forest_v4.py  | 302 ++++++++++++++++++
 6 files changed, 373 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/contrib/decision_trees/__init__.py
 create mode 100644 tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py

diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index a971232e2f..d90f88c66f 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -123,7 +123,9 @@ endfunction()
 file(GLOB_RECURSE tf_protos_python_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/core/*.proto"
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
+    "${tensorflow_source_dir}/tensorflow/contrib/decision_trees/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
+    "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
 )
@@ -272,6 +274,8 @@ add_python_module("tensorflow/contrib/data/python/framework")
 add_python_module("tensorflow/contrib/data/python/kernel_tests")
 add_python_module("tensorflow/contrib/data/python/ops")
 add_python_module("tensorflow/contrib/data/python/util")
+add_python_module("tensorflow/contrib/decision_trees")
+add_python_module("tensorflow/contrib/decision_trees/proto")
 add_python_module("tensorflow/contrib/deprecated")
 add_python_module("tensorflow/contrib/distributions")
 add_python_module("tensorflow/contrib/distributions/python")
diff --git a/tensorflow/contrib/decision_trees/__init__.py b/tensorflow/contrib/decision_trees/__init__.py
new file mode 100644
index 0000000000..2c44e4eb59
--- /dev/null
+++ b/tensorflow/contrib/decision_trees/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Shared representations for tree-based models in tensorflow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.decision_trees.proto import *
+# pylint: enable=unused-import,wildcard-import
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+
+remove_undocumented(__name__)
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 1ca2d7596b..95ee1852ca 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -248,6 +248,9 @@ tf_kernel_library(
 
 tf_custom_op_library(
     name = "python/ops/_model_ops.so",
+    srcs = [
+        "ops/model_ops.cc",
+    ],
     deps = [
         ":model_ops_lib",
     ],
@@ -311,6 +314,9 @@ tf_kernel_library(
 
 tf_custom_op_library(
     name = "python/ops/_stats_ops.so",
+    srcs = [
+        "ops/stats_ops.cc",
+    ],
     deps = [
         ":stats_ops_lib",
     ],
@@ -385,6 +391,8 @@ py_library(
         ":eval_metrics",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
+        ":tensor_forest_v4_ops_py",
+        ":tensor_forest_v4_py",
     ],
 )
 
@@ -520,6 +528,21 @@ py_library(
     ],
 )
 
+py_library(
+    name = "tensor_forest_v4_py",
+    srcs = ["python/tensor_forest_v4.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tensor_forest_v4_ops_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_py",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:platform",
+    ],
+)
+
 py_test(
     name = "tensor_forest_test",
     size = "small",
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index af9f56ab24..47685df4a5 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -26,6 +26,7 @@ from tensorflow.contrib.learn.python.learn.estimators import prediction_key
 
 from tensorflow.contrib.tensor_forest.client import eval_metrics
 from tensorflow.contrib.tensor_forest.python import tensor_forest
+from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
 
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -42,6 +43,9 @@ from tensorflow.python.training import session_run_hook
 KEYS_NAME = 'keys'
 LOSS_NAME = 'rf_training_loss'
 
+VERSION_BUILDERS = {'v2': tensor_forest.RandomForestGraphs,
+                    'v4': tensor_forest_v4.RandomForestGraphsV4}
+
 
 def _assert_float32(tensors):
   """Assert all tensors are float32.
@@ -272,7 +276,7 @@ class TensorForestEstimator(estimator.Estimator):
                early_stopping_rounds=100,
                num_trainers=1, trainer_id=0,
                report_feature_importances=False,
-               local_eval=False):
+               local_eval=False, version=None):
     """Initializes a TensorForestEstimator instance.
 
     Args:
@@ -286,7 +290,8 @@ class TensorForestEstimator(estimator.Estimator):
         directory into an estimator.
       graph_builder_class: An `object` instance that defines how TF graphs for
         random forest training and inference are built. By default will use
-        `tensor_forest.RandomForestGraphs`.
+        `tensor_forest.RandomForestGraphs`. Can be overridden by version
+        kwarg.
       config: `RunConfig` object to configure the runtime settings.
       weights_name: A string defining feature column name representing
         weights. Will be multiplied by the loss of the example. Used to
@@ -308,10 +313,19 @@ class TensorForestEstimator(estimator.Estimator):
       local_eval: If True, don't use a device assigner for eval. This is to
         support some common setups where eval is done on a single machine, even
         though training might be distributed.
+      version: String indicating TensorForest version to use, for backward
+        compatibility. Either 'v2', 'v4', or None to let system pick.
+        Overrides grpah_builder_class.
 
     Returns:
       A `TensorForestEstimator` instance.
     """
+    if version:
+      if version not in VERSION_BUILDERS:
+        logging.error('Unknown version %s. Specify either v2 or v4', version)
+      else:
+        logging.info('Setting graph_builder_class to %s', version)
+        graph_builder_class = VERSION_BUILDERS[version]
     super(TensorForestEstimator, self).__init__(
         model_fn=get_model_fn(
             params.fill(),
diff --git a/tensorflow/contrib/tensor_forest/python/__init__.py b/tensorflow/contrib/tensor_forest/python/__init__.py
index b2ca89877a..df0ecbac76 100644
--- a/tensorflow/contrib/tensor_forest/python/__init__.py
+++ b/tensorflow/contrib/tensor_forest/python/__init__.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 from tensorflow.contrib.tensor_forest.python import constants
 from tensorflow.contrib.tensor_forest.python import tensor_forest
+from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
 from tensorflow.contrib.tensor_forest.python.ops import model_ops
 from tensorflow.contrib.tensor_forest.python.ops import stats_ops
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
new file mode 100644
index 0000000000..5773b5d729
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
@@ -0,0 +1,302 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Extremely random forest graph builder using TF resources handles."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+
+from google.protobuf import text_format
+
+from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto
+from tensorflow.contrib.tensor_forest.proto import tensor_forest_params_pb2 as _params_proto
+from tensorflow.contrib.tensor_forest.python import tensor_forest
+from tensorflow.contrib.tensor_forest.python.ops import model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.platform import tf_logging as logging
+
+
+# Stores tuples of (leaf model type, stats model type)
+CLASSIFICATION_LEAF_MODEL_TYPES = {
+    'all_dense': (_params_proto.MODEL_DENSE_CLASSIFICATION,
+                  _params_proto.STATS_DENSE_GINI),
+    'all_sparse': (_params_proto.MODEL_SPARSE_CLASSIFICATION,
+                   _params_proto.STATS_SPARSE_GINI),
+    'sparse_then_dense':
+        (_params_proto.MODEL_SPARSE_OR_DENSE_CLASSIFICATION,
+         _params_proto.STATS_SPARSE_THEN_DENSE_GINI),
+}
+REGRESSION_MODEL_TYPE = (
+    _params_proto.MODEL_REGRESSION,
+    _params_proto.STATS_LEAST_SQUARES_REGRESSION,
+    _params_proto.COLLECTION_BASIC)
+
+COLLECTION_TYPES = {
+    'basic': _params_proto.COLLECTION_BASIC,
+    'graph_runner': _params_proto.GRAPH_RUNNER_COLLECTION
+}
+
+FINISH_TYPES = {
+    'basic': _params_proto.SPLIT_FINISH_BASIC,
+    'hoeffding': _params_proto.SPLIT_FINISH_DOMINATE_HOEFFDING,
+    'bootstrap': _params_proto.SPLIT_FINISH_DOMINATE_BOOTSTRAP
+}
+PRUNING_TYPES = {
+    'none': _params_proto.SPLIT_PRUNE_NONE,
+    'half': _params_proto.SPLIT_PRUNE_HALF,
+    'quarter': _params_proto.SPLIT_PRUNE_QUARTER,
+    '10_percent': _params_proto.SPLIT_PRUNE_10_PERCENT,
+    'hoeffding': _params_proto.SPLIT_PRUNE_HOEFFDING,
+}
+SPLIT_TYPES = {
+    'less_or_equal': _tree_proto.InequalityTest.LESS_OR_EQUAL,
+    'less': _tree_proto.InequalityTest.LESS_THAN
+}
+
+
+def build_params_proto(params):
+  """Build a TensorForestParams proto out of the V4ForestHParams object."""
+  proto = _params_proto.TensorForestParams()
+  proto.num_trees = params.num_trees
+  proto.max_nodes = params.max_nodes
+  proto.is_regression = params.regression
+  proto.num_outputs = params.num_classes
+  proto.num_features = params.num_features
+
+  proto.leaf_type = params.v4_leaf_model_type
+  proto.stats_type = params.v4_stats_model_type
+  proto.collection_type = params.v4_split_collection_type
+  proto.pruning_type.type = params.v4_pruning_type
+  proto.finish_type.type = params.v4_finish_type
+
+  proto.inequality_test_type = params.v4_split_type
+
+  proto.drop_final_class = False
+  proto.collate_examples = params.v4_collate_examples
+  proto.checkpoint_stats = params.v4_checkpoint_stats
+  proto.use_running_stats_method = params.v4_use_running_stats_method
+
+  if params.v4_prune_every_samples:
+    text_format.Merge(params.v4_prune_every_samples,
+                      proto.pruning_type.prune_every_samples)
+  else:
+    # Pruning half-way through split_after_samples seems like a decent default,
+    # making it easy to select the number being pruned with v4_pruning_type
+    # while not paying the cost of pruning too often.  Note that this only holds
+    # if not using a depth-dependent split_after_samples.
+    if params.v4_split_after_samples:
+      logging.error(
+          'If using depth-dependent split_after_samples and also pruning, '
+          'need to set v4_prune_every_samples')
+    proto.pruning_type.prune_every_samples.constant_value = (
+        params.split_after_samples / 2)
+
+  if params.v4_finish_check_every_samples:
+    text_format.Merge(params.v4_finish_check_every_samples,
+                      proto.finish_type.check_every_steps)
+  else:
+    # Checking for finish every quarter through split_after_samples seems
+    # like a decent default. We don't want to incur the checking cost too often,
+    # but (at least for hoeffding) it's lower than the cost of pruning so
+    # we can do it a little more frequently.
+    proto.finish_type.check_every_steps.constant_value = int(
+        params.split_after_samples / 4)
+
+  if params.v4_split_after_samples:
+    text_format.Merge(params.v4_split_after_samples, proto.split_after_samples)
+  else:
+    proto.split_after_samples.constant_value = params.split_after_samples
+
+  if params.v4_num_splits_to_consider:
+    text_format.Merge(params.v4_num_splits_to_consider,
+                      proto.num_splits_to_consider)
+  else:
+    proto.num_splits_to_consider.constant_value = params.num_splits_to_consider
+
+  proto.dominate_fraction.constant_value = params.dominate_fraction
+  proto.min_split_samples.constant_value = params.split_after_samples
+
+  if params.v4_param_file:
+    with open(params.v4_param_file) as f:
+      text_format.Merge(f.read(), proto)
+
+  return proto
+
+
+class V4ForestHParams(object):
+
+  def __init__(self, hparams):
+    for k, v in six.iteritems(hparams.__dict__):
+      setattr(self, k, v)
+
+    # How to store leaf models.
+    model_name = getattr(self, 'v4_model_name', 'all_dense')
+    self.v4_leaf_model_type = (
+        REGRESSION_MODEL_TYPE[0] if self.regression else
+        CLASSIFICATION_LEAF_MODEL_TYPES[model_name][0])
+
+    # How to store stats objects.
+    self.v4_stats_model_type = (
+        REGRESSION_MODEL_TYPE[1] if self.regression else
+        CLASSIFICATION_LEAF_MODEL_TYPES[model_name][1])
+
+    split_collection_name = getattr(self, 'v4_split_collection_name',
+                                    'basic')
+    self.v4_split_collection_type = (
+        REGRESSION_MODEL_TYPE[2] if self.regression else
+        COLLECTION_TYPES[split_collection_name])
+
+    finish_name = getattr(self, 'v4_finish_name', 'basic')
+    self.v4_finish_type = (
+        _params_proto.SPLIT_FINISH_BASIC if self.regression else
+        FINISH_TYPES[finish_name])
+
+    pruning_name = getattr(self, 'v4_pruning_name', 'none')
+    self.v4_pruning_type = PRUNING_TYPES[pruning_name]
+
+    self.v4_collate_examples = getattr(self, 'v4_collate_examples', False)
+
+    self.v4_checkpoint_stats = getattr(self, 'v4_checkpoint_stats', False)
+    self.v4_use_running_stats_method = getattr(
+        self, 'v4_use_running_stats_method', False)
+
+    self.v4_param_file = getattr(self, 'v4_param_file', None)
+
+    self.v4_split_type = getattr(self, 'v4_split_type',
+                                 SPLIT_TYPES['less_or_equal'])
+
+    # Special versions of the normal parameters, that support depth-dependence
+    self.v4_num_splits_to_consider = getattr(self, 'v4_num_splits_to_consider',
+                                             None)
+    self.v4_split_after_samples = getattr(self, 'v4_split_after_samples',
+                                          None)
+    self.v4_finish_check_every_samples = getattr(
+        self, 'v4_finish_check_every_samples', None)
+    self.v4_prune_every_samples = getattr(
+        self, 'v4_prune_every_samples', None)
+
+
+class TreeTrainingVariablesV4(tensor_forest.TreeTrainingVariables):
+  """Stores tf.Variables for training a single random tree."""
+
+  def __init__(self, params, tree_num, training):
+    if (not hasattr(params, 'params_proto') or
+        not isinstance(params.params_proto,
+                       _params_proto.TensorForestParams)):
+      params.params_proto = build_params_proto(params)
+
+    params.serialized_params_proto = params.params_proto.SerializeToString()
+    self.stats = None
+    if training:
+      # TODO(gilberth): Manually shard this to be able to fit it on
+      # multiple machines.
+      self.stats = stats_ops.fertile_stats_variable(
+          params, '', self.get_tree_name('stats', tree_num))
+    self.tree = model_ops.tree_variable(
+        params, '', self.stats, self.get_tree_name('tree', tree_num))
+
+
+class RandomTreeGraphsV4(tensor_forest.RandomTreeGraphs):
+  """Builds TF graphs for random tree training and inference."""
+
+  def tree_initialization(self):
+    return control_flow_ops.no_op()
+
+  def training_graph(self, input_data,
+                     input_labels,
+                     random_seed,
+                     data_spec,
+                     sparse_features=None,
+                     input_weights=None):
+    if input_weights is None:
+      input_weights = []
+
+    sparse_indices = []
+    sparse_values = []
+    sparse_shape = []
+    if sparse_features is not None:
+      sparse_indices = sparse_features.indices
+      sparse_values = sparse_features.values
+      sparse_shape = sparse_features.dense_shape
+
+    if input_data is None:
+      input_data = []
+
+    finished_nodes = stats_ops.process_input_v4(
+        self.variables.tree,
+        self.variables.stats,
+        input_data,
+        sparse_indices,
+        sparse_values,
+        sparse_shape,
+        input_labels,
+        input_weights,
+        input_spec=data_spec.SerializeToString(),
+        random_seed=random_seed,
+        params=self.params.serialized_params_proto)
+
+    return stats_ops.grow_tree_v4(self.variables.tree, self.variables.stats,
+                                  finished_nodes,
+                                  params=self.params.serialized_params_proto)
+
+  def inference_graph(self, input_data, data_spec, sparse_features=None):
+    sparse_indices = []
+    sparse_values = []
+    sparse_shape = []
+    if sparse_features is not None:
+      sparse_indices = sparse_features.indices
+      sparse_values = sparse_features.values
+      sparse_shape = sparse_features.dense_shape
+    if input_data is None:
+      input_data = []
+
+    return model_ops.tree_predictions_v4(
+        self.variables.tree,
+        input_data,
+        sparse_indices,
+        sparse_values,
+        sparse_shape,
+        input_spec=data_spec.SerializeToString(),
+        params=self.params.serialized_params_proto)
+
+  def average_impurity(self):
+    return constant_op.constant(0)
+
+  def size(self):
+    """Constructs a TF graph for evaluating the current number of nodes.
+
+    Returns:
+      The current number of nodes in the tree.
+    """
+    return model_ops.tree_size(self.variables.tree)
+
+  def feature_usage_counts(self):
+    return model_ops.feature_usage_counts(
+        self.variables.tree, params=self.params.serialized_params_proto)
+
+
+class RandomForestGraphsV4(tensor_forest.RandomForestGraphs):
+
+  def __init__(self, params, tree_graphs=None, tree_variables_class=None,
+               **kwargs):
+    if not isinstance(params, V4ForestHParams):
+      params = V4ForestHParams(params)
+    super(RandomForestGraphsV4, self).__init__(
+        params, tree_graphs=tree_graphs or RandomTreeGraphsV4,
+        tree_variables_class=(tree_variables_class or TreeTrainingVariablesV4),
+        **kwargs)
-- 
GitLab


From a6992a1fd7100b48a3a08fe242b13344157fdcd2 Mon Sep 17 00:00:00 2001
From: J Alammar <alammar@gmail.com>
Date: Tue, 27 Jun 2017 23:07:05 +0300
Subject: [PATCH 0508/1375] Fix for contrib.layers test that was raising an
 IndexError (#11069)

* variable name change and documentation clarification

* Updated a test to eliminate an error it raises
---
 tensorflow/contrib/layers/python/layers/layers_test.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 15809ea180..2d08a0cb91 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -1493,12 +1493,12 @@ class PartialFlattenTest(test.TestCase):
 
   def testSparsePartialFlatten(self):
     """Test `_inner_flatten` on `SparseTensor`s."""
-    shape = [4, 3, 11, 6, 1, 3]
+    shape = [4, 3, 11, 6]
     np.random.seed(10301)
     random_ = np.random.rand(*shape)
     indices, values, _ = _sparsify(random_)
 
-    for new_rank in [1, 2, 3, 4, 5]:
+    for new_rank in [1, 2, 3]:
       expected_shape = (shape[:new_rank - 1] + [np.prod(shape[new_rank - 1:])])
       reshaped_random_ = np.reshape(random_, expected_shape)
       expected_indices, expected_values, _ = _sparsify(reshaped_random_)
-- 
GitLab


From 3d38acfab386941f2cad5e71e349077a6037efb9 Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Tue, 27 Jun 2017 13:04:38 -0700
Subject: [PATCH 0509/1375] Automated g4 rollback of changelist 159454657

PiperOrigin-RevId: 160314706
---
 tensorflow/core/kernels/conv_ops_gpu_3.cu.cc | 112 +--------------
 tensorflow/python/BUILD                      |  20 ---
 tensorflow/python/ops/conv2d_benchmark.py    | 141 -------------------
 tensorflow/python/ops/transpose_benchmark.py |  64 +++------
 4 files changed, 25 insertions(+), 312 deletions(-)
 delete mode 100644 tensorflow/python/ops/conv2d_benchmark.py

diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
index b2bbc5831c..2307c2de0e 100644
--- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
+++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
@@ -272,75 +272,6 @@ __global__ void SwapDimension1And2InTensor3UsingTiles(const T* input,
   }
 }
 
-// Use shared memory tiles to swap dimension-1 and dimension-2 of a 3D tensor
-// when only one of the dimension sizes is smaller than 16,
-// where dimensions are zero-based: output[i][j][k] = input[i][k][j].
-//
-// small_dim = the_smaller_dimension_size
-// large_dim = the_larger_dimension_size
-// tile_num_per_block = blockDim.x
-// kTileLength = small_dim
-//
-// Each thread block operates on a single rectangle tile, where its width is
-// kTileLength (we currently set it to 64) and its height is small_dim,
-// We set the thread block's X dimension to be tile_num_per_block, and its Y
-// and Z to be one.
-template <typename T, bool SmallDim2>
-__global__ void SwapDimension1And2InTensor3SmallDim(const T* input,
-                                                    Dimension<3> input_dims,
-                                                    T* output) {
-  // TODO(yangzihao) avoid share memory bank conflict.
-  extern __shared__ __align__(sizeof(T)) unsigned char shmem[];
-  T* shared_memory_tile = reinterpret_cast<T*>(shmem);
-
-  eigen_assert(blockDim.y == 1);
-  eigen_assert(blockDim.z == 1);
-  eigen_assert(gridDim.z == 1);
-
-  int x = threadIdx.x;
-  int tile_height = blockDim.x;
-
-  // Get tile height, width, and thread/block origin indices.
-  int small_dim = SmallDim2 ? input_dims[2] : input_dims[1];
-  int large_dim = SmallDim2 ? input_dims[1] : input_dims[2];
-  int block_origin_idx = small_dim * large_dim * blockIdx.y;
-  int block_offset = blockIdx.x * blockDim.x;
-  int thread_origin_idx =
-      block_origin_idx + (SmallDim2 ? block_offset * small_dim : block_offset) +
-      x;
-
-  if (block_offset + blockDim.x > large_dim) {
-    tile_height = large_dim - block_offset;
-  }
-
-  // Load a continous memory region to shared memory tile.
-  if (x < tile_height) {
-    for (int y = 0; y < small_dim; y++) {
-      int shmem_index = SmallDim2 ? (x + y * tile_height) : (x * small_dim + y);
-      shared_memory_tile[shmem_index] =
-          ldg(input + thread_origin_idx +
-              y * (SmallDim2 ? tile_height : large_dim));
-    }
-  }
-
-  __syncthreads();
-
-  // Get block origin index for output array.
-  int output_block_offset = block_origin_idx;
-  int output_block_idx = SmallDim2 ? block_offset : block_offset * small_dim;
-  int output_block_origin_idx = output_block_offset + output_block_idx;
-
-  // Store the tranposed memory region in shared memory to device.
-  if (x < tile_height) {
-    for (int y = 0; y < small_dim; y++) {
-      int output_idx = output_block_origin_idx + x +
-                       y * (SmallDim2 ? large_dim : tile_height);
-      int shmem_index = SmallDim2 ? (x * small_dim + y) : (x + y * tile_height);
-      output[output_idx] = shared_memory_tile[shmem_index];
-    }
-  }
-}
-
 // A Cuda custom kernel that convert input to output, given proper padding on
 // the left and the top. The padded value is zero.
 template <typename T, int NDIMS>
@@ -489,60 +420,25 @@ template <typename T>
 void RunSwapDimension1And2InTensor3(const GPUDevice& d, const T* input,
                                     const Dimension<3>& input_dims, T* output) {
   // If both dimensions are not trivial, use tiles for the actual swapping.
-  // If one dimension is trivial, use SmallDim kernel for swapping.
   // Otherwise, the trivial swapping relying on the ldg cache is more efficient.
   static const int kMinDimensionToUseTiles = 16;
   bool use_tiles = (input_dims[1] >= kMinDimensionToUseTiles &&
                     input_dims[2] >= kMinDimensionToUseTiles);
-  bool use_small_dim = ((input_dims[1] >= kMinDimensionToUseTiles &&
-                         input_dims[2] < kMinDimensionToUseTiles)) ||
-                       ((input_dims[1] < kMinDimensionToUseTiles &&
-                         input_dims[2] >= kMinDimensionToUseTiles));
-
-  static const int NumSubTiles = 8;
   if (use_tiles) {
+    // We get best performance when TileSize is the number of threads in a warp
+    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
+    // threads.
     static const int TileSize = 32;
+    static const int NumSubTiles = 8;
     Dimension<3> input_dims_in_tiles = {
         input_dims[0], (input_dims[1] + TileSize - 1) / TileSize,
         (input_dims[2] + TileSize - 1) / TileSize,
     };
     int total_tiles_count = input_dims_in_tiles[0] * input_dims_in_tiles[1] *
                             input_dims_in_tiles[2];
-    // We get best performance when TileSize is the number of threads in a warp
-    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
-    // threads.
     SwapDimension1And2InTensor3UsingTiles<T, TileSize, NumSubTiles><<<
         total_tiles_count, dim3(TileSize, NumSubTiles), 0, d.stream()>>>(
         input, input_dims, output);
-  } else if (use_small_dim) {
-    // When only one of the dimensions is smaller than kMinDimensionToUseTiles,
-    // we use one block to process a rectangle region with the size of
-    // kTileLength * small_dim. We found that when set kTileLength to 64 on
-    // TitanX Maxwell GPU, it achieves the best performance.
-    //              large_dim
-    //            +---------------...--------+
-    //            |            |        |    |
-    // small_dim  |            |  ...   |    |
-    //            |            |        |    |
-    //            +--------------...---------+
-    //            \----- ------/         \- -/
-    //                  V                  V
-    //    kTileLength(tile_height)    tile_height
-    static const int kTileLength = 64;
-    int small_dim = std::min(input_dims[2], input_dims[1]);
-    int large_dim = std::max(input_dims[2], input_dims[1]);
-    int tile_num_per_block = (large_dim + kTileLength - 1) / kTileLength;
-    if (input_dims[2] < input_dims[1]) {
-      SwapDimension1And2InTensor3SmallDim<T, true>
-          <<<dim3(tile_num_per_block, input_dims[0]), kTileLength,
-             kTileLength * small_dim * sizeof(T), d.stream()>>>(
-              input, input_dims, output);
-    } else {
-      SwapDimension1And2InTensor3SmallDim<T, false>
-          <<<dim3(tile_num_per_block, input_dims[0]), kTileLength,
-             kTileLength * small_dim * sizeof(T), d.stream()>>>(
-              input, input_dims, output);
-    }
   } else {
     int total_element_count = input_dims[0] * input_dims[1] * input_dims[2];
     CudaLaunchConfig config = GetCudaLaunchConfig(total_element_count, d);
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index b65a638620..e15f7410e4 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3784,26 +3784,6 @@ cuda_py_test(
     main = "ops/concat_benchmark.py",
 )
 
-cuda_py_test(
-    name = "conv2d_benchmark",
-    size = "large",
-    srcs = ["ops/conv2d_benchmark.py"],
-    additional_deps = [
-        ":client",
-        ":client_testlib",
-        ":control_flow_ops",
-        ":framework_for_generated_wrappers",
-        ":nn_ops",
-        ":platform",
-        ":platform_benchmark",
-        ":random_ops",
-        ":variables",
-        "//third_party/py/numpy",
-        "//tensorflow/core:protos_all_py",
-    ],
-    main = "ops/conv2d_benchmark.py",
-)
-
 cuda_py_test(
     name = "split_benchmark",
     srcs = ["ops/split_benchmark.py"],
diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py
deleted file mode 100644
index 6992fa57ea..0000000000
--- a/tensorflow/python/ops/conv2d_benchmark.py
+++ /dev/null
@@ -1,141 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Benchmark for Conv2D op."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import itertools
-import time
-
-from tensorflow.python.client import session as session_lib
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import nn_ops
-from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import variables
-from tensorflow.python.platform import test
-
-
-def build_graph(device, input_shape, filter_shape, strides, padding, num_iters):
-  """builds a graph containing a sequence of conv2d operations.
-
-  Args:
-    device: String, the device to run on.
-    input_shape: Shape of the input tensor.
-    filter_shape: Shape of the filter tensor.
-    strides: A list of ints. 1-D of length 4. The stride of sliding
-             window for each dimension of input.
-    padding: A string from: "SAME", "VALID". The type of padding
-             algorithm to use.
-    num_iters: number of iterations to run conv2d.
-
-  Returns:
-    An array of tensors to run()
-  """
-  with ops.device("/%s:0" % device):
-    inp = variables.Variable(random_ops.truncated_normal(input_shape))
-    filt = variables.Variable(random_ops.truncated_normal(filter_shape))
-
-    outputs = []
-    conv2d_op = nn_ops.conv2d(inp, filt, strides, padding, data_format="NHWC")
-    outputs.append(conv2d_op)
-    for _ in range(1, num_iters):
-      with ops.control_dependencies([conv2d_op]):
-        conv2d_op = nn_ops.conv2d(
-            inp, filt, strides, padding, data_format="NHWC")
-        outputs.append(conv2d_op)
-    return control_flow_ops.group(*outputs)
-
-
-class Conv2DBenchmark(test.Benchmark):
-  """Benchmark conv2d!"""
-
-  def _run_graph(self, device, input_shape, filter_shape, strides, padding,
-                 num_iters):
-    """runs the graph and print its execution time.
-
-    Args:
-      device: String, the device to run on.
-      input_shape: Shape of the input tensor.
-      filter_shape: Shape of the filter tensor.
-      strides: A list of ints. 1-D of length 4. The stride of sliding
-               window for each dimension of input.
-      padding: A string from: "SAME", "VALID". The type of padding
-               algorithm to use.  num_iters: Number of iterations to run the
-                 benchmark.
-      num_iters: number of iterations to run conv2d.
-
-    Returns:
-      The duration of the run in seconds.
-    """
-    graph = ops.Graph()
-    with graph.as_default():
-      outputs = build_graph(device, input_shape, filter_shape, strides, padding,
-                            num_iters)
-      with session_lib.Session(graph=graph) as session:
-        variables.global_variables_initializer().run()
-        # warmup runs
-        session.run(outputs)
-
-        start_time = time.time()
-        session.run(outputs)
-        duration = (time.time() - start_time) / num_iters
-
-        print("%s inputshape:%s filtershape:%s strides:%s padding:%s "
-              "%d iters: %.8f sec" %
-              (device, str(input_shape).replace(" ", ""),
-               str(filter_shape).replace(" ", ""),
-               str(strides).replace(" ", ""), padding, num_iters, duration))
-
-    name_template = (
-        "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_"
-        "strides_{strides}_padding_{padding}")
-
-    self.report_benchmark(
-        name=name_template.format(
-            device=device,
-            inputshape=str(input_shape).replace(" ", ""),
-            filtershape=str(filter_shape).replace(" ", ""),
-            strides=str(strides).replace(" ", ""),
-            padding=padding).replace(" ", ""),
-        iters=num_iters,
-        wall_time=duration / num_iters)
-
-    return duration
-
-  def benchmark_conv2d(self):
-    print("conv2d benchmark:")
-
-    h = 500
-    w = 500
-    fh = 3
-    fw = 3
-    input_shapes = []
-    filter_shapes = []
-    for b, c in itertools.product([4, 16, 32], [i for i in range(3, 16)]):
-      input_shapes += [[b, h, w, c]]
-      filter_shapes += [[fh, fw, c, b]]
-    strides = [[1, 2, 2, 1]]
-    paddings = ["VALID", "SAME"]
-    for ishape, fshape in zip(input_shapes, filter_shapes):
-      for stride in strides:
-        for padding in paddings:
-          self._run_graph("gpu", ishape, fshape, stride, padding, 80)
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/python/ops/transpose_benchmark.py b/tensorflow/python/ops/transpose_benchmark.py
index cddefacf2e..6bd3fe5e5a 100644
--- a/tensorflow/python/ops/transpose_benchmark.py
+++ b/tensorflow/python/ops/transpose_benchmark.py
@@ -1,4 +1,4 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -32,7 +32,7 @@ from tensorflow.python.platform import test
 
 
 def build_graph(device, input_shape, perm, datatype, num_iters):
-  """builds a graph containing a sequence of conv2d operations.
+  """Build a graph containing a sequence of conv2d operations.
 
   Args:
     device: String, the device to run on.
@@ -50,12 +50,10 @@ def build_graph(device, input_shape, perm, datatype, num_iters):
     t = constant_op.constant(inp, shape=input_shape)
 
     outputs = []
-    transpose_op = array_ops.transpose(t, perm)
-    outputs.append(transpose_op)
-    for _ in range(1, num_iters):
-      with ops.control_dependencies([transpose_op]):
-        transpose_op = array_ops.transpose(t, perm)
-        outputs.append(transpose_op)
+    outputs.append(array_ops.transpose(t, perm))
+    for i in range(1, num_iters):
+      with ops.control_dependencies([outputs[i - 1]]):
+        outputs.append(array_ops.transpose(t, perm))
     return control_flow_ops.group(*outputs)
 
 
@@ -63,7 +61,7 @@ class TransposeBenchmark(test.Benchmark):
   """Benchmark transpose!"""
 
   def _run_graph(self, device, input_shape, perm, num_iters, datatype):
-    """runs the graph and print its execution time.
+    """Run the graph and print its execution time.
 
     Args:
       device: String, the device to run on.
@@ -84,11 +82,9 @@ class TransposeBenchmark(test.Benchmark):
         session.run(outputs)
         start_time = time.time()
         session.run(outputs)
-
         duration = (time.time() - start_time) / num_iters
-        throughput = np.prod(
-            np.array(input_shape)) * datatype().itemsize * 2 / duration / 1e9
-
+        throughput = np.prod(np.array(
+            input_shape)) * datatype().itemsize * 2 / duration / 1e9
         print("%s %s inputshape:%s perm:%s %d %.6fsec, %.4fGB/s." %
               (device, str(datatype), str(input_shape).replace(" ", ""),
                str(perm).replace(" ", ""), num_iters, duration, throughput))
@@ -112,19 +108,19 @@ class TransposeBenchmark(test.Benchmark):
 
     datatypes = [np.complex128, np.float64, np.float32, np.float16, np.int8]
 
-    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2
-    small_shapes += [[2, 100, 100, 16], [2, 16, 100, 100]] * 2
-    small_shapes += [[2, 5000, 16], [2, 16, 5000]] * 2
-    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
-    small_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
-    small_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2 + [[
+        2, 100, 100, 16
+    ], [2, 16, 100, 100]] * 2 + [[2, 5000, 16], [2, 16, 5000]] * 2
+    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
+        [0, 3, 1, 2], [0, 2, 3, 1]
+    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
-    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2
-    large_shapes += [[2, 1000, 1000, 32], [2, 1000, 1000, 64]] * 2
-    large_shapes += [[2, 1000000, 32], [2, 1000000, 64]] * 2
-    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
-    large_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
-    large_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2 + [[
+        2, 1000, 1000, 32
+    ], [2, 1000, 1000, 64]] * 2 + [[2, 1000000, 32], [2, 1000000, 64]] * 2
+    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
+        [0, 3, 1, 2], [0, 2, 3, 1]
+    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
     huge_shapes = [[2, 100, 100, 100, 128], [2, 1000, 1000, 128],
                    [2, 1000000, 128]] * 2
@@ -147,23 +143,5 @@ class TransposeBenchmark(test.Benchmark):
             for ishape, perm in zip(huge_shapes, huge_perms):
               self._run_graph("gpu", ishape, perm, num_iters, datatype)
 
-    small_dim_large_shapes = [[2, 1000000, 3], [2, 3, 1000000], [2, 1000000, 8],
-                              [2, 8, 1000000]]
-    small_dim_small_shapes = [[2, 5000, 3], [2, 3, 5000], [2, 5000, 8],
-                              [2, 8, 5000]]
-    small_dim_perms = [[0, 2, 1]] * 4
-
-    num_iters = 320
-    small_dim_large_shape_datatypes = [np.float64, np.float32, np.int8]
-    for datatype in small_dim_large_shape_datatypes:
-      for ishape, perm in zip(small_dim_large_shapes, small_dim_perms):
-        self._run_graph("gpu", ishape, perm, num_iters, datatype)
-
-    small_dim_small_shape_datatypes = [np.complex128, np.float16]
-    for datatype in small_dim_small_shape_datatypes:
-      for ishape, perm in zip(small_dim_small_shapes, small_dim_perms):
-        self._run_graph("gpu", ishape, perm, num_iters, datatype)
-
-
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From f8b315bfb766448249e4c93065ba42431614ae65 Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Tue, 27 Jun 2017 13:11:48 -0700
Subject: [PATCH 0510/1375] TESTFIX:  distributions:trig_test wasn't passing in
 ASAN mode.

PiperOrigin-RevId: 160315597
---
 .../distributions/python/kernel_tests/trig_test.py   | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py b/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
index 0d40f477ca..8c856abf8b 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
@@ -197,17 +197,20 @@ class ArcSinhTest(test.TestCase):
       arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
           [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
 
+    self._assert_all_finite(arcsinh_x_)
+    self._assert_all_finite(sinh_arcsinh_x_)
+    self._assert_all_finite(arcsinh_sinh_arcsinh_x_)
     self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
     self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
 
   def test_arcsinh_is_inverse_to_sinh_where_x_is_very_big(self):
     sinh = trig.sinh
     arcsinh = trig.arcsinh
-    very_big_cutoff = np.sqrt(np.finfo(np.float32).max)
+    maxfloat = np.finfo(np.float32).max  # Maximum representable float.
     with self.test_session() as sess:
       x = np.logspace(
-          np.log(very_big_cutoff),
-          5 + np.log(very_big_cutoff), num=1000).astype(np.float32)
+          0.5 * np.log(maxfloat),
+          0.95 * np.log(maxfloat), num=1000, base=np.e).astype(np.float32)
       arcsinh_x = arcsinh(x)
       sinh_arcsinh_x = sinh(arcsinh_x)
       arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
@@ -215,6 +218,9 @@ class ArcSinhTest(test.TestCase):
       arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
           [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
 
+    self._assert_all_finite(arcsinh_x_)
+    self._assert_all_finite(sinh_arcsinh_x_)
+    self._assert_all_finite(arcsinh_sinh_arcsinh_x_)
     self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
     self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
 
-- 
GitLab


From 5984c11a94c5c914ef88e1081cff667008a95022 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Tue, 27 Jun 2017 13:21:53 -0700
Subject: [PATCH 0511/1375] Add gzip and zlib support for
 FixedLengthRecordReader (#8901)

* Switch from InputBuffer to BufferedInputStream for FixedLengthRecordReader

This commit switch from InputBuffer to BufferedInputStream for
FixedLengthRecordReader, for the purpose of allowing compressed file as
FixedLengthRecordReader.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Remove the need to file the file size before hand for FixedLengthRecordReader

This commit updates the implementation of FixedLengthRecordReader so that
it will not try to find the file size before hand.

The purpose is to allow FixedLengthRecordReader to take a non-seekable file
as an input. In a non-seekable file (e.g., a compressed file), it may not
be possible to find the file size (e.g., in a uncompressed file length).

This commit is part of the effort to support gzip/zlib compression.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Add gzip and zlib support for FixedLengthRecordReader
This fix adds gzip and zlib support for FixedLengthRecordReader,
as was discussed in 8856.
When FixedLengthRecordReader is used, it will check for
encoding flag and use ZlibInputStream as needed.
The usage of InputBuffer in FixedLengthRecordReader has
also been changed to BufferedInputStream to match ZlibInputStream.

This fix fixes 8856.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Add additional test cases where num_records=0 and hop_bytes>record_bytes

This commit adds additional test cases where num_records=0 and hop_bytes>record_bytes

This commit also updated the API golden so that API compatibility test passes.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 .../kernels/fixed_length_record_reader_op.cc  | 132 ++++++++++-----
 tensorflow/core/ops/io_ops.cc                 |   3 +
 .../python/kernel_tests/reader_ops_test.py    | 159 ++++++++++++++++--
 tensorflow/python/ops/io_ops.py               |   5 +-
 ...nsorflow.-fixed-length-record-reader.pbtxt |   2 +-
 5 files changed, 239 insertions(+), 62 deletions(-)

diff --git a/tensorflow/core/kernels/fixed_length_record_reader_op.cc b/tensorflow/core/kernels/fixed_length_record_reader_op.cc
index ce7fb9c332..891405b1a1 100644
--- a/tensorflow/core/kernels/fixed_length_record_reader_op.cc
+++ b/tensorflow/core/kernels/fixed_length_record_reader_op.cc
@@ -19,83 +19,118 @@ limitations under the License.
 #include "tensorflow/core/framework/reader_base.h"
 #include "tensorflow/core/framework/reader_op_kernel.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/lib/io/inputbuffer.h"
+#include "tensorflow/core/lib/io/buffered_inputstream.h"
+#include "tensorflow/core/lib/io/random_inputstream.h"
+#include "tensorflow/core/lib/io/zlib_compression_options.h"
+#include "tensorflow/core/lib/io/zlib_inputstream.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/env.h"
 
 namespace tensorflow {
 
+// In the constructor hop_bytes_ is set to record_bytes_ if it was 0,
+// so that we will always "hop" after each read (except first).
 class FixedLengthRecordReader : public ReaderBase {
  public:
   FixedLengthRecordReader(const string& node_name, int64 header_bytes,
                           int64 record_bytes, int64 footer_bytes,
-                          int64 hop_bytes, Env* env)
+                          int64 hop_bytes, const string& encoding, Env* env)
       : ReaderBase(
             strings::StrCat("FixedLengthRecordReader '", node_name, "'")),
         header_bytes_(header_bytes),
         record_bytes_(record_bytes),
         footer_bytes_(footer_bytes),
-        hop_bytes_(hop_bytes),
+        hop_bytes_(hop_bytes == 0 ? record_bytes : hop_bytes),
         env_(env),
-        file_pos_limit_(-1),
-        record_number_(0) {}
+        record_number_(0),
+        encoding_(encoding) {}
 
   // On success:
-  // * input_buffer_ != nullptr,
-  // * input_buffer_->Tell() == header_bytes_
-  // * file_pos_limit_ == file size - footer_bytes_
+  // * buffered_inputstream_ != nullptr,
+  // * buffered_inputstream_->Tell() == header_bytes_
   Status OnWorkStartedLocked() override {
     record_number_ = 0;
-    uint64 file_size = 0;
-    TF_RETURN_IF_ERROR(env_->GetFileSize(current_work(), &file_size));
-    file_pos_limit_ = file_size - footer_bytes_;
+
+    lookahead_cache_.clear();
 
     TF_RETURN_IF_ERROR(env_->NewRandomAccessFile(current_work(), &file_));
+    if (encoding_ == "ZLIB" || encoding_ == "GZIP") {
+      const io::ZlibCompressionOptions zlib_options =
+          encoding_ == "ZLIB" ? io::ZlibCompressionOptions::DEFAULT()
+                              : io::ZlibCompressionOptions::GZIP();
+      file_stream_.reset(new io::RandomAccessInputStream(file_.get()));
+      buffered_inputstream_.reset(
+          new io::ZlibInputStream(file_stream_.get(), (size_t)kBufferSize,
+                                  (size_t)kBufferSize, zlib_options));
+    } else {
+      buffered_inputstream_.reset(
+          new io::BufferedInputStream(file_.get(), kBufferSize));
+    }
+    // header_bytes_ is always skipped.
+    TF_RETURN_IF_ERROR(buffered_inputstream_->SkipNBytes(header_bytes_));
 
-    input_buffer_.reset(new io::InputBuffer(file_.get(), kBufferSize));
-    TF_RETURN_IF_ERROR(input_buffer_->SkipNBytes(header_bytes_));
     return Status::OK();
   }
 
   Status OnWorkFinishedLocked() override {
-    input_buffer_.reset(nullptr);
+    buffered_inputstream_.reset(nullptr);
     return Status::OK();
   }
 
   Status ReadLocked(string* key, string* value, bool* produced,
                     bool* at_end) override {
-    // The condition `input_buffer_->Tell() + record_bytes_ > file_pos_limit_`
-    // is to confirm that none of record bytes is out of the range of
-    // file_pos_limit_.
-    // This is necessary for the condition `hop_bytes > 0`. For example.
-    // File: "0123456"
-    // Reader setting: `record_bytes=3`, `hop_bytes=2`, `footer_bytes=0`,
-    //     `header_bytes=0`
-    // Without this checking condition, the forth time the reader will at
-    // this position: "012345|6" and the reading operation will result in
-    // an error.
-    if (input_buffer_->Tell() >= file_pos_limit_ ||
-        input_buffer_->Tell() + record_bytes_ > file_pos_limit_) {
+    // We will always "hop" the hop_bytes_ except the first record
+    // where record_number_ == 0
+    if (record_number_ != 0) {
+      if (hop_bytes_ <= lookahead_cache_.size()) {
+        // If hop_bytes_ is smaller than the cached data we skip the
+        // hop_bytes_ from the cache.
+        lookahead_cache_ = lookahead_cache_.substr(hop_bytes_);
+      } else {
+        // If hop_bytes_ is larger than the cached data, we clean up
+        // the cache, then skip hop_bytes_ - cache_size from the file
+        // as the cache_size has been skipped through cache.
+        int64 cache_size = lookahead_cache_.size();
+        lookahead_cache_.clear();
+        Status s = buffered_inputstream_->SkipNBytes(hop_bytes_ - cache_size);
+        if (!s.ok()) {
+          if (!errors::IsOutOfRange(s)) {
+            return s;
+          }
+          *at_end = true;
+          return Status::OK();
+        }
+      }
+    }
+
+    // Fill up lookahead_cache_ to record_bytes_ + footer_bytes_
+    int bytes_to_read = record_bytes_ + footer_bytes_ - lookahead_cache_.size();
+    Status s = buffered_inputstream_->ReadNBytes(bytes_to_read, value);
+    if (!s.ok()) {
+      value->clear();
+      if (!errors::IsOutOfRange(s)) {
+        return s;
+      }
       *at_end = true;
       return Status::OK();
     }
-    const int64 pos_before_read = input_buffer_->Tell();
-    TF_RETURN_IF_ERROR(input_buffer_->ReadNBytes(record_bytes_, value));
+    lookahead_cache_.append(*value, 0, bytes_to_read);
+    value->clear();
+
+    // Copy first record_bytes_ from cache to value
+    *value = lookahead_cache_.substr(0, record_bytes_);
+
     *key = strings::StrCat(current_work(), ":", record_number_);
     *produced = true;
     ++record_number_;
 
-    if (hop_bytes_ > 0) {
-      input_buffer_->Seek(pos_before_read + hop_bytes_).IgnoreError();
-    }
-
     return Status::OK();
   }
 
   Status ResetLocked() override {
-    file_pos_limit_ = -1;
     record_number_ = 0;
-    input_buffer_.reset(nullptr);
+    buffered_inputstream_.reset(nullptr);
+    lookahead_cache_.clear();
     return ReaderBase::ResetLocked();
   }
 
@@ -107,11 +142,21 @@ class FixedLengthRecordReader : public ReaderBase {
   const int64 record_bytes_;
   const int64 footer_bytes_;
   const int64 hop_bytes_;
+  // The purpose of lookahead_cache_ is to allows "one-pass" processing
+  // without revisit previous processed data of the stream. This is needed
+  // because certain compression like zlib does not allow random access
+  // or even obtain the uncompressed stream size before hand.
+  // The max size of the lookahead_cache_ could be
+  // record_bytes_ + footer_bytes_
+  string lookahead_cache_;
   Env* const env_;
-  int64 file_pos_limit_;
   int64 record_number_;
-  std::unique_ptr<RandomAccessFile> file_;  // must outlive input_buffer_
-  std::unique_ptr<io::InputBuffer> input_buffer_;
+  string encoding_;
+  // must outlive buffered_inputstream_
+  std::unique_ptr<RandomAccessFile> file_;
+  // must outlive buffered_inputstream_
+  std::unique_ptr<io::RandomAccessInputStream> file_stream_;
+  std::unique_ptr<io::InputStreamInterface> buffered_inputstream_;
 };
 
 class FixedLengthRecordReaderOp : public ReaderOpKernel {
@@ -137,11 +182,14 @@ class FixedLengthRecordReaderOp : public ReaderOpKernel {
         context, hop_bytes >= 0,
         errors::InvalidArgument("hop_bytes must be >= 0 not ", hop_bytes));
     Env* env = context->env();
-    SetReaderFactory(
-        [this, header_bytes, record_bytes, footer_bytes, hop_bytes, env]() {
-          return new FixedLengthRecordReader(name(), header_bytes, record_bytes,
-                                             footer_bytes, hop_bytes, env);
-        });
+    string encoding;
+    context->GetAttr("encoding", &encoding);
+    SetReaderFactory([this, header_bytes, record_bytes, footer_bytes, hop_bytes,
+                      encoding, env]() {
+      return new FixedLengthRecordReader(name(), header_bytes, record_bytes,
+                                         footer_bytes, hop_bytes, encoding,
+                                         env);
+    });
   }
 };
 
diff --git a/tensorflow/core/ops/io_ops.cc b/tensorflow/core/ops/io_ops.cc
index fa12816c92..e8379af4bb 100644
--- a/tensorflow/core/ops/io_ops.cc
+++ b/tensorflow/core/ops/io_ops.cc
@@ -475,6 +475,7 @@ REGISTER_OP("FixedLengthRecordReaderV2")
     .Attr("hop_bytes: int = 0")
     .Attr("container: string = ''")
     .Attr("shared_name: string = ''")
+    .Attr("encoding: string = ''")
     .SetIsStateful()
     .SetShapeFn(shape_inference::ScalarShape)
     .Doc(R"doc(
@@ -490,6 +491,8 @@ container: If non-empty, this reader is placed in the given container.
         Otherwise, a default container is used.
 shared_name: If non-empty, this reader is named in the given bucket
              with this shared_name. Otherwise, the node name is used instead.
+encoding: The type of encoding for the file. Currently ZLIB and GZIP
+        are supported. Defaults to none.
 )doc");
 
 // TODO(cwhipkey): mark this deprecated in favor of V2.
diff --git a/tensorflow/python/kernel_tests/reader_ops_test.py b/tensorflow/python/kernel_tests/reader_ops_test.py
index c7e1d88360..5630259b7b 100644
--- a/tensorflow/python/kernel_tests/reader_ops_test.py
+++ b/tensorflow/python/kernel_tests/reader_ops_test.py
@@ -350,13 +350,11 @@ class FixedLengthRecordReaderTest(test.TestCase):
   def setUp(self):
     super(FixedLengthRecordReaderTest, self).setUp()
     self._num_files = 2
-    self._num_records = 7
     self._header_bytes = 5
     self._record_bytes = 3
     self._footer_bytes = 2
 
     self._hop_bytes = 2
-    self._num_overlapped_records = 3
 
   def _Record(self, f, r):
     return compat.as_bytes(str(f * 2 + r) * self._record_bytes)
@@ -369,19 +367,24 @@ class FixedLengthRecordReaderTest(test.TestCase):
     ])
     return compat.as_bytes(record_str)
 
-  def _CreateFiles(self):
+  # gap_bytes=hop_bytes-record_bytes
+  def _CreateFiles(self, num_records, gap_bytes):
     filenames = []
     for i in range(self._num_files):
       fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i)
       filenames.append(fn)
       with open(fn, "wb") as f:
         f.write(b"H" * self._header_bytes)
-        for j in range(self._num_records):
+        if num_records > 0:
+          f.write(self._Record(i, 0))
+        for j in range(1, num_records):
+          if gap_bytes > 0:
+            f.write(b"G" * gap_bytes)
           f.write(self._Record(i, j))
         f.write(b"F" * self._footer_bytes)
     return filenames
 
-  def _CreateOverlappedRecordFiles(self):
+  def _CreateOverlappedRecordFiles(self, num_overlapped_records):
     filenames = []
     for i in range(self._num_files):
       fn = os.path.join(self.get_temp_dir(),
@@ -389,23 +392,104 @@ class FixedLengthRecordReaderTest(test.TestCase):
       filenames.append(fn)
       with open(fn, "wb") as f:
         f.write(b"H" * self._header_bytes)
-        all_records_str = "".join([
-            str(i)[0]
-            for i in range(self._record_bytes + self._hop_bytes *
-                           (self._num_overlapped_records - 1))
-        ])
-        f.write(compat.as_bytes(all_records_str))
+        if num_overlapped_records > 0:
+          all_records_str = "".join([
+              str(i)[0]
+              for i in range(self._record_bytes + self._hop_bytes *
+                             (num_overlapped_records - 1))
+          ])
+          f.write(compat.as_bytes(all_records_str))
         f.write(b"F" * self._footer_bytes)
     return filenames
 
-  def testOneEpoch(self):
-    files = self._CreateFiles()
+  # gap_bytes=hop_bytes-record_bytes
+  def _CreateGzipFiles(self, num_records, gap_bytes):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i)
+      filenames.append(fn)
+      with gzip.GzipFile(fn, "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_records > 0:
+          f.write(self._Record(i, 0))
+        for j in range(1, num_records):
+          if gap_bytes > 0:
+            f.write(b"G" * gap_bytes)
+          f.write(self._Record(i, j))
+        f.write(b"F" * self._footer_bytes)
+    return filenames
+
+  # gap_bytes=hop_bytes-record_bytes
+  def _CreateZlibFiles(self, num_records, gap_bytes):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i)
+      filenames.append(fn)
+      with open(fn+".tmp", "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_records > 0:
+          f.write(self._Record(i, 0))
+        for j in range(1, num_records):
+          if gap_bytes > 0:
+            f.write(b"G" * gap_bytes)
+          f.write(self._Record(i, j))
+        f.write(b"F" * self._footer_bytes)
+      with open(fn+".tmp", "rb") as f:
+        cdata = zlib.compress(f.read())
+        with open(fn, "wb") as zf:
+          zf.write(cdata)
+    return filenames
+
+  def _CreateGzipOverlappedRecordFiles(self, num_overlapped_records):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(),
+                        "fixed_length_overlapped_record.%d.txt" % i)
+      filenames.append(fn)
+      with gzip.GzipFile(fn, "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_overlapped_records > 0:
+          all_records_str = "".join([
+              str(i)[0]
+              for i in range(self._record_bytes + self._hop_bytes *
+                           (num_overlapped_records - 1))
+          ])
+          f.write(compat.as_bytes(all_records_str))
+        f.write(b"F" * self._footer_bytes)
+    return filenames
+
+  def _CreateZlibOverlappedRecordFiles(self, num_overlapped_records):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(),
+                        "fixed_length_overlapped_record.%d.txt" % i)
+      filenames.append(fn)
+      with open(fn+".tmp", "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_overlapped_records > 0:
+          all_records_str = "".join([
+              str(i)[0]
+              for i in range(self._record_bytes + self._hop_bytes *
+                             (num_overlapped_records - 1))
+          ])
+          f.write(compat.as_bytes(all_records_str))
+        f.write(b"F" * self._footer_bytes)
+      with open(fn+".tmp", "rb") as f:
+        cdata = zlib.compress(f.read())
+        with open(fn, "wb") as zf:
+          zf.write(cdata)
+    return filenames
+
+  # gap_bytes=hop_bytes-record_bytes
+  def _TestOneEpoch(self, files, num_records, gap_bytes, encoding=None):
+    hop_bytes = 0 if gap_bytes == 0 else self._record_bytes + gap_bytes
     with self.test_session() as sess:
       reader = io_ops.FixedLengthRecordReader(
           header_bytes=self._header_bytes,
           record_bytes=self._record_bytes,
           footer_bytes=self._footer_bytes,
-          hop_bytes=0,
+          hop_bytes=hop_bytes,
+          encoding=encoding,
           name="test_reader")
       queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
       key, value = reader.read(queue)
@@ -413,7 +497,7 @@ class FixedLengthRecordReaderTest(test.TestCase):
       queue.enqueue_many([files]).run()
       queue.close().run()
       for i in range(self._num_files):
-        for j in range(self._num_records):
+        for j in range(num_records):
           k, v = sess.run([key, value])
           self.assertAllEqual("%s:%d" % (files[i], j), compat.as_text(k))
           self.assertAllEqual(self._Record(i, j), v)
@@ -422,14 +506,14 @@ class FixedLengthRecordReaderTest(test.TestCase):
                                     "\\(requested 1, current size 0\\)"):
         k, v = sess.run([key, value])
 
-  def testOneEpochWithHopBytes(self):
-    files = self._CreateOverlappedRecordFiles()
+  def _TestOneEpochWithHopBytes(self, files, num_overlapped_records, encoding=None):
     with self.test_session() as sess:
       reader = io_ops.FixedLengthRecordReader(
           header_bytes=self._header_bytes,
           record_bytes=self._record_bytes,
           footer_bytes=self._footer_bytes,
           hop_bytes=self._hop_bytes,
+          encoding=encoding,
           name="test_reader")
       queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
       key, value = reader.read(queue)
@@ -437,7 +521,7 @@ class FixedLengthRecordReaderTest(test.TestCase):
       queue.enqueue_many([files]).run()
       queue.close().run()
       for i in range(self._num_files):
-        for j in range(self._num_overlapped_records):
+        for j in range(num_overlapped_records):
           k, v = sess.run([key, value])
           print(v)
           self.assertAllEqual("%s:%d" % (files[i], j), compat.as_text(k))
@@ -447,6 +531,45 @@ class FixedLengthRecordReaderTest(test.TestCase):
                                     "\\(requested 1, current size 0\\)"):
         k, v = sess.run([key, value])
 
+  def testOneEpoch(self):
+    for num_records in [0, 7]:
+      # gap_bytes=0: hop_bytes=0
+      # gap_bytes=1: hop_bytes=record_bytes+1
+      for gap_bytes in [0, 1]:
+        files = self._CreateFiles(num_records, gap_bytes)
+        self._TestOneEpoch(files, num_records, gap_bytes)
+
+  def testGzipOneEpoch(self):
+    for num_records in [0, 7]:
+      # gap_bytes=0: hop_bytes=0
+      # gap_bytes=1: hop_bytes=record_bytes+1
+      for gap_bytes in [0, 1]:
+        files = self._CreateGzipFiles(num_records, gap_bytes)
+        self._TestOneEpoch(files, num_records, gap_bytes, encoding="GZIP")
+
+  def testZlibOneEpoch(self):
+    for num_records in [0, 7]:
+      # gap_bytes=0: hop_bytes=0
+      # gap_bytes=1: hop_bytes=record_bytes+1
+      for gap_bytes in [0, 1]:
+        files = self._CreateZlibFiles(num_records, gap_bytes)
+        self._TestOneEpoch(files, num_records, gap_bytes, encoding="ZLIB")
+
+  def testOneEpochWithHopBytes(self):
+    for num_overlapped_records in [0, 2]:
+      files = self._CreateOverlappedRecordFiles(num_overlapped_records)
+      self._TestOneEpochWithHopBytes(files, num_overlapped_records)
+
+  def testGzipOneEpochWithHopBytes(self):
+    for num_overlapped_records in [0, 2]:
+      files = self._CreateGzipOverlappedRecordFiles(num_overlapped_records, )
+      self._TestOneEpochWithHopBytes(files, num_overlapped_records, encoding="GZIP")
+
+  def testZlibOneEpochWithHopBytes(self):
+    for num_overlapped_records in [0, 2]:
+      files = self._CreateZlibOverlappedRecordFiles(num_overlapped_records)
+      self._TestOneEpochWithHopBytes(files, num_overlapped_records, encoding="ZLIB")
+
 
 class TFRecordReaderTest(test.TestCase):
 
diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index 8f5d0e5cd4..975494e45f 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -397,7 +397,8 @@ class FixedLengthRecordReader(ReaderBase):
                header_bytes=None,
                footer_bytes=None,
                hop_bytes=None,
-               name=None):
+               name=None,
+               encoding=None):
     """Create a FixedLengthRecordReader.
 
     Args:
@@ -406,12 +407,14 @@ class FixedLengthRecordReader(ReaderBase):
       footer_bytes: An optional int. Defaults to 0.
       hop_bytes: An optional int. Defaults to 0.
       name: A name for the operation (optional).
+      encoding: The type of encoding for the file. Defaults to none.
     """
     rr = gen_io_ops._fixed_length_record_reader_v2(
         record_bytes=record_bytes,
         header_bytes=header_bytes,
         footer_bytes=footer_bytes,
         hop_bytes=hop_bytes,
+        encoding=encoding,
         name=name)
     super(FixedLengthRecordReader, self).__init__(rr)
 
diff --git a/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt b/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt
index 5c77b3dd5c..260c796fd6 100644
--- a/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt
@@ -13,7 +13,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'record_bytes\', \'header_bytes\', \'footer_bytes\', \'hop_bytes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'record_bytes\', \'header_bytes\', \'footer_bytes\', \'hop_bytes\', \'name\', \'encoding\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "num_records_produced"
-- 
GitLab


From ea79ba41be89d76180305b14d26d5c2f256d4c08 Mon Sep 17 00:00:00 2001
From: Anish Shah <shah.anish07@gmail.com>
Date: Wed, 28 Jun 2017 02:04:03 +0530
Subject: [PATCH 0512/1375] [issue #10835] Negative axis support for gradient
 of reduce_prod (#11019)

---
 tensorflow/python/ops/math_grad.py      |  4 +++-
 tensorflow/python/ops/math_grad_test.py | 23 +++++++++++++++++++++++
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index a0f505e47b..fa65052a89 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -122,8 +122,10 @@ def _ProdGrad(op, grad):
   # so we need to cast here.  We put all the shape-related ops on CPU to avoid
   # copying back and forth, and since listdiff is CPU only.
   with ops.device("/cpu:0"):
+    rank = array_ops.rank(op.inputs[0])
+    reduction_indices = (reduction_indices + rank) % rank
     reduced = math_ops.cast(reduction_indices, dtypes.int32)
-    idx = math_ops.range(0, array_ops.rank(op.inputs[0]))
+    idx = math_ops.range(0, rank)
     other, _ = array_ops.setdiff1d(idx, reduced)
     perm = array_ops.concat([reduced, other], 0)
     reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
diff --git a/tensorflow/python/ops/math_grad_test.py b/tensorflow/python/ops/math_grad_test.py
index 1fa15957b0..da3e0d7294 100644
--- a/tensorflow/python/ops/math_grad_test.py
+++ b/tensorflow/python/ops/math_grad_test.py
@@ -113,6 +113,29 @@ class MinOrMaxGradientTest(test.TestCase):
       self.assertLess(error, 1e-4)
 
 
+class ProdGradientTest(test.TestCase):
+
+  def testProdGradient(self):
+    inputs = constant_op.constant([[1., 2.], [3., 4.]],
+                                  dtype=dtypes.float32)
+    outputs = math_ops.reduce_prod(inputs)
+    with self.test_session():
+      error = gradient_checker.compute_gradient_error(
+          inputs, inputs.get_shape().as_list(),
+          outputs, outputs.get_shape().as_list())
+      self.assertLess(error, 1e-4)
+
+  def testProdGradientForNegativeAxis(self):
+    inputs = constant_op.constant([[1., 2.], [3., 4.]],
+                                  dtype=dtypes.float32)
+    outputs = math_ops.reduce_prod(inputs, -1)
+    with self.test_session():
+      error = gradient_checker.compute_gradient_error(
+          inputs, inputs.get_shape().as_list(),
+          outputs, outputs.get_shape().as_list())
+      self.assertLess(error, 1e-4)
+
+
 class SegmentMinOrMaxGradientTest(test.TestCase):
 
   def testSegmentMinGradient(self):
-- 
GitLab


From febcf3c7b9fddffe9aceaae626f184d5c39b657f Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Tue, 27 Jun 2017 13:33:21 -0700
Subject: [PATCH 0513/1375] tfdbg doc: fixes and improvements

PiperOrigin-RevId: 160318411
---
 .../docs_src/programmers_guide/debugger.md    | 100 +++++++++++-------
 1 file changed, 62 insertions(+), 38 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index c4444b1192..c369ff1737 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -42,8 +42,9 @@ then gets stuck at a low (near-chance) level:
 > Accuracy at step 4: 0.098
 
 Wondering what might have gone wrong, you suspect that certain nodes in the
-training graph generated bad numeric values such as `inf`s and `nan`s. Let's
-use tfdbg to debug this issue and pinpoint the exact graph node where this
+training graph generated bad numeric values such as `inf`s and `nan`s, because
+this is a common cause of this type of training failure.
+Let's use tfdbg to debug this issue and pinpoint the exact graph node where this
 numeric problem first surfaced.
 
 ## Wrapping TensorFlow Sessions with tfdbg
@@ -68,8 +69,8 @@ This wrapper has the same interface as Session, so enabling debugging requires
 no other changes to the code. The wrapper provides additional features,
 including:
 
-* Bringing up a CLI before and after `run()` calls, to let you control the
-execution and inspect the graph's internal state.
+* Bringing up a CLI before and after `Session.run()` calls, to let you
+control the execution and inspect the graph's internal state.
 * Allowing you to register special `filters` for tensor values, to facilitate
 the diagnosis of issues.
 
@@ -97,7 +98,7 @@ python -m tensorflow.python.debug.examples.debug_mnist --debug
 ```
 
 The debug wrapper session will prompt you when it is about to execute the first
-`run()` call, with information regarding the fetched tensor and feed
+`Session.run()` call, with information regarding the fetched tensor and feed
 dictionaries displayed on the screen.
 
 ![tfdbg run-start UI](https://www.tensorflow.org/images/tfdbg_screenshot_run_start.png)
@@ -113,8 +114,11 @@ Enter the `run` command (or just `r`) at the command prompt:
 tfdbg> run
 ```
 
-tfdbg calculates the accuracy using a test data set and then displays all dumped
-intermediate tensors from the run in the *run-end CLI*. For example:
+The `run` command causes tfdbg to execute until the end of the next
+`Session.run()` call, which calculates the model's accuracy using a test data
+set. tfdbg augments the runtime Graph to dump all intermediate tensors.
+After the run ends, tfdbg displays all the dumped tensors values in the
+*run-end CLI*. For example:
 
 ![tfdbg run-end UI: accuracy](https://www.tensorflow.org/images/tfdbg_screenshot_run_end_accuracy.png)
 
@@ -172,6 +176,11 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | **`help`** | | **Print general help information** | `help` |
 | | `help <command>` | Print help for given command. | `help lt` |
 
+Note that each time you enter a command, a new screen output
+will appear. This is somewhat analogous to web pages in a browser. You can
+navigate between these screens by clicking the `<--` and
+`-->` text arrows near the top-left corner of the CLI.
+
 ### Other Features of the tfdbg CLI
 
 In addition to the commands listed above, the tfdbg CLI provides the following
@@ -197,25 +206,27 @@ addditional features:
 
 ### Finding `nan`s and `inf`s
 
-In this first `run()` call, there happen to be no problematic numerical values.
-You can move on to the next run by using the command `run` or its shorthand `r`.
+In this first `Session.run()` call, there happen to be no problematic numerical
+values. You can move on to the next run by using the command `run` or its
+shorthand `r`.
 
-> TIP: If you enter `run` or `r` repeatedly, you will be able to move through the
-> `run()` calls in a sequential manner.
+> TIP: If you enter `run` or `r` repeatedly, you will be able to move through
+> the `Session.run()` calls in a sequential manner.
 >
-> You can also use the `-t` flag to move ahead a number of `run()` calls at a time, for example:
+> You can also use the `-t` flag to move ahead a number of `Session.run()` calls
+> at a time, for example:
 >
 > ```
 > tfdbg> run -t 10
 > ```
 
 Instead of entering `run` repeatedly and manually searching for `nan`s and
-`inf`s in the run-end UI after every `run()` call (for example, by using the `pt`
-command shown in the table above) , you can use the following
-command to let the debugger repeatedly execute `run()` calls without stopping at
-the run-start or run-end prompt, until the first `nan` or `inf` value shows up
-in the graph. This is analogous to *conditional breakpoints* in some
-procedural-language debuggers:
+`inf`s in the run-end UI after every `Session.run()` call (for example, by using
+the `pt` command shown in the table above) , you can use the following
+command to let the debugger repeatedly execute `Session.run()` calls without
+stopping at the run-start or run-end prompt, until the first `nan` or `inf`
+value shows up in the graph. This is analogous to *conditional breakpoints* in
+some procedural-language debuggers:
 
 ```none
 tfdbg> run -f has_inf_or_nan
@@ -224,8 +235,8 @@ tfdbg> run -f has_inf_or_nan
 > NOTE: The preceding command works properly because we have registered a filter
 > for `nan`s and `inf`s called `has_inf_or_nan` (as explained previously).
 > If you have registered any other filters, you can
-> let tfdbg run till any tensors pass that filter (cause the filter to return True)
-> as well, for example,
+> use "run -f" to have tfdbg run until any tensor triggers that filter (cause
+> the filter to return True).
 >
 > ```
 > # In python code:
@@ -235,10 +246,15 @@ tfdbg> run -f has_inf_or_nan
 > tfdbg> run -f my_filter
 > ```
 
+See [this API document](https://www.tensorflow.org/api_docs/python/tfdbg/DebugDumpDir#find)
+for more information on the expected signature and return value of the predicate
+`Callable` used with `add_tensor_filter()`.
+
 ![tfdbg run-end UI: infs and nans](https://www.tensorflow.org/images/tfdbg_screenshot_run_end_inf_nan.png)
 
 As the screen display indicates, the `has_inf_or_nan` filter is first passed
-during the fourth `run()` call: an [Adam optimizer](https://arxiv.org/abs/1412.6980)
+during the fourth `Session.run()` call: an
+[Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer)
 forward-backward training pass on the graph. In this run, 36 (out of the total
 95) intermediate tensors contain `nan` or `inf` values. These tensors are listed
 in chronological order, with their timestamps displayed on the left. At the top
@@ -268,7 +284,7 @@ tfdbg> /(inf|nan)
 
 Why did these infinities appear? To further debug, display more information
 about the node `cross_entropy/Log` by clicking the underlined `node_info` menu
-item on the top or entering the equivalent command:
+item on the top or entering the equivalent node_info (`ni`) command:
 
 ```none
 tfdbg> ni cross_entropy/Log
@@ -310,7 +326,7 @@ line:
 diff = y_ * tf.log(y)
 ```
 
-***tfdbg** has a feature that makes it ease to trace Tensors and ops back to
+***tfdbg** has a feature that makes it easy to trace Tensors and ops back to
 lines in Python source files. It can annotate lines of a Python file with
 the ops or Tensors created by them. To use this feature,
 simply click the underlined line numbers in the stack trace output of the
@@ -351,7 +367,7 @@ accuracy now continues to rise rather than getting stuck. Success!
 
 ## Debugging tf-learn Estimators and Experiments
 
-This section explains how to debug TensorFlow programs that use the `Estimators`
+This section explains how to debug TensorFlow programs that use the `Estimator`
 and `Experiment` APIs. Part of the convenience provided by these APIs is that
 they manage `Session`s internally. This makes the `LocalCLIDebugWrapperSession`
 described in the preceding sections inapplicable. Fortunately, you can still
@@ -363,7 +379,7 @@ Currently, `tfdbg` can debug the
 @{tf.contrib.learn.BaseEstimator.fit$`fit()`}
 @{tf.contrib.learn.BaseEstimator.evaluate$`evaluate()`}
 methods of tf-learn `Estimator`s. To debug `Estimator.fit()`,
-create a `LocalCLIDebugHook` and supply it as the `monitors` argument. For example:
+create a `LocalCLIDebugHook` and supply it in the `monitors` argument. For example:
 
 ```python
 # First, let your BUILD target depend on "//tensorflow/python/debug:debug_py"
@@ -371,9 +387,9 @@ create a `LocalCLIDebugHook` and supply it as the `monitors` argument. For examp
 #  install of open-source TensorFlow.)
 from tensorflow.python import debug as tf_debug
 
+# Create a LocalCLIDebugHook and use it as a monitor when calling fit().
 hooks = [tf_debug.LocalCLIDebugHook()]
 
-# Create a local CLI debug hook and use it as a monitor when calling fit().
 classifier.fit(x=training_set.data,
                y=training_set.target,
                steps=1000,
@@ -459,20 +475,20 @@ model.fit(...)  # This will break into the TFDBG CLI.
 
 ## Offline Debugging of Remotely-Running Sessions
 
-Oftentimes, your model is running in a remote machine or process that you don't
+Often, your model is running on a remote machine or a process that you don't
 have terminal access to. To perform model debugging in such cases, you can use
-the `offline_analyzer` binary of `tfdbg`. It operates on dumped data
-directories. This can be done to both the lower-level `Session` API and the
-higher-level `Estimator` and `Experiment` APIs.
+the `offline_analyzer` binary of `tfdbg` (described below). It operates on
+dumped data directories. This can be done to both the lower-level `Session` API
+and the higher-level `Estimator` and `Experiment` APIs.
 
-### Debugging Remotely-Running tf.Sessions
+### Debugging Remote tf.Sessions
 
 If you interact directly with the `tf.Session` API in `python`, you can
 configure the `RunOptions` proto that you call your `Session.run()` method
 with, by using the method @{tfdbg.watch_graph}.
 This will cause the intermediate tensors and runtime graphs to be dumped to a
-shared storage location of your choice when the `Session.run()` call occurs.
-For example:
+shared storage location of your choice when the `Session.run()` call occurs
+(at the cost of slower performance). For example:
 
 ```python
 from tensorflow.python import debug as tf_debug
@@ -563,7 +579,7 @@ python -m tensorflow.python.debug.cli.offline_analyzer \
        performance in a non-debugging session?_
 
 **A**: No. The debugger inserts additional special-purpose debug nodes to the
-       graph to record the values of intermediate tensors. These nodes certainly
+       graph to record the values of intermediate tensors. These nodes
        slow down the graph execution. If you are interested in profiling your
        model, check out
        [tfprof](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tfprof)
@@ -607,7 +623,7 @@ only from the main thread?_
 **A**:
 This is a common use case, in which the `Session` object is used from multiple
 threads concurrently. Typically, the child threads take care of background tasks
-such as running enqueue operations. Oftentimes, you want to debug only the main
+such as running enqueue operations. Often, you want to debug only the main
 thread (or less frequently, only one of the child threads). You can use the
 `thread_name_filter` keyword argument of `LocalCLIDebugWrapperSession` to
 achieve this type of thread-selective debugging. For example, to debug from the
@@ -656,6 +672,12 @@ There are three possible workarounds or solutions:
    tfdbg> run --tensor_dtype_filter int.*
    ```
 
+   The first command above watches only nodes whose name match the
+   regular-expression pattern `.*hidden.*`. The second command watches only
+   operations whose name match the pattern `Variable.*`. The third one watches
+   only the tensors whose dtype match the pattern `int.*` (e.g., `int32`).
+
+
 **Q**: _Why can't I select text in the tfdbg CLI?_
 
 **A**: This is because the tfdbg CLI enables mouse events in the terminal by
@@ -680,9 +702,8 @@ sess.run(b)
        tensor `b` is effectively also a constant tensor. TensorFlow's graph
        optimization folds the graph that contains `a` and `b` into a single
        node to speed up future runs of the graph, which is why `tfdbg` does
-       not generate any intermedate-tensor dumps. If `a` were a
-       @{tf.Variable}, the constant-folding would not occur and `tfdbg`
-       should show the intermeidate-tensor dumps. For example:
+       not generate any intermediate tensor dumps. However, if `a` were a
+       @{tf.Variable}, as in the following example:
 
 ``` python
 import numpy as np
@@ -694,3 +715,6 @@ sess.run(tf.global_variables_initializer())
 sess = tf_debug.LocalCLIDebugWrapperSession(sess)
 sess.run(b)
 ```
+
+the constant-folding would not occur and `tfdbg` should show the intermediate
+tensor dumps.
-- 
GitLab


From 00feb62ef5f44c8d3dd32199e552eb5de8049e59 Mon Sep 17 00:00:00 2001
From: Bjarke Hammersholt Roune <broune@google.com>
Date: Tue, 27 Jun 2017 13:33:56 -0700
Subject: [PATCH 0514/1375] Add a time estimation to HloCostAnalysis and
 represent properties as a map so that adding more properties will be easier,
 e.g. in a sub-class.

PiperOrigin-RevId: 160318494
---
 .../compiler/xla/service/hlo_cost_analysis.cc | 313 +++++++++++-------
 .../compiler/xla/service/hlo_cost_analysis.h  |  99 ++++--
 .../xla/service/hlo_cost_analysis_test.cc     | 100 +++---
 3 files changed, 326 insertions(+), 186 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index f4c0bb8a4a..522dddea4e 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -25,34 +25,56 @@ limitations under the License.
 
 namespace xla {
 
+constexpr char HloCostAnalysis::kFlopsKey[];
+constexpr char HloCostAnalysis::kTranscendentalsKey[];
+constexpr char HloCostAnalysis::kBytesAccessedKey[];
+constexpr char HloCostAnalysis::kSecondsKey[];
+
+HloCostAnalysis::HloCostAnalysis(const ShapeSizeFunction& shape_size)
+    : HloCostAnalysis(shape_size, {}) {}
+
+HloCostAnalysis::HloCostAnalysis(const ShapeSizeFunction& shape_size,
+                                 const Properties& per_second_rates)
+    : shape_size_(shape_size), per_second_rates_(per_second_rates) {}
+
 Status HloCostAnalysis::Preprocess(HloInstruction* hlo) {
   // Set current instruction cost values to reasonable default values. Each
-  // handler can overwrite these values. In Postprocess, these value are
+  // handler can overwrite these values. In Postprocess, these values are
   // accumulated and written to the per-instruction maps.
-  current_flop_count_ = 0;
-  current_transcendental_count_ = 0;
+  current_properties_.clear();
+  current_should_compute_bottleneck_time_ = true;
 
-  // The default element count for an instruction is the sum of elements in the
-  // operands and output. The default ShapeUtil::ByteSizeOf does not handle
-  // opaque types.
-  current_bytes_accessed_ = shape_size_(hlo->shape());
+  // The default number of bytes accessed for an instruction is the sum of the
+  // sizes of the inputs and outputs. The default ShapeUtil::ByteSizeOf does not
+  // handle opaque types.
+  float bytes_accessed = shape_size_(hlo->shape());
   for (const HloInstruction* operand : hlo->operands()) {
-    current_bytes_accessed_ += shape_size_(operand->shape());
+    bytes_accessed += shape_size_(operand->shape());
   }
+  current_properties_[kBytesAccessedKey] = bytes_accessed;
 
   return Status::OK();
 }
 
 Status HloCostAnalysis::Postprocess(HloInstruction* hlo) {
-  // Accumulate cost values and write into per-instruction maps.
-  flop_count_ += current_flop_count_;
-  hlo_to_flop_count_[hlo] = current_flop_count_;
-
-  transcendental_count_ += current_transcendental_count_;
-  hlo_to_transcendental_count_[hlo] = current_transcendental_count_;
+  if (current_should_compute_bottleneck_time_) {
+    // Compute the time as the time of the bottleneck, i.e. the slowest property
+    // given the per-second rate of each property.
+    float max_seconds = 0.0f;
+    for (const auto& property : current_properties_) {
+      if (property.first != kSecondsKey) {
+        max_seconds = std::max(
+            max_seconds,
+            property.second / GetProperty(property.first, per_second_rates_));
+      }
+    }
+    current_properties_[kSecondsKey] = max_seconds;
+  }
 
-  bytes_accessed_ += current_bytes_accessed_;
-  hlo_to_bytes_accessed_[hlo] = current_bytes_accessed_;
+  TF_RET_CHECK(hlo_properties_.emplace(hlo, current_properties_).second);
+  for (const auto& property : current_properties_) {
+    properties_sum_[property.first] += property.second;
+  }
 
   return Status::OK();
 }
@@ -65,15 +87,32 @@ Status HloCostAnalysis::HandleElementwiseOp(HloInstruction* hlo_instruction) {
   auto opcode = hlo_instruction->opcode();
   // We treat the two opcodes (kExp, kPower) as transcendental operations.
   if (opcode == HloOpcode::kExp || opcode == HloOpcode::kPower) {
-    current_transcendental_count_ = computation_count;
+    current_properties_[kTranscendentalsKey] = computation_count;
   } else {
     // Note: transcendental operations are considered a separate category from
     // FLOPs.
-    current_flop_count_ = computation_count;
+    current_properties_[kFlopsKey] = computation_count;
   }
   return Status::OK();
 }
 
+/*static*/ float HloCostAnalysis::GetProperty(const string& key,
+                                              const Properties& properties) {
+  auto key_value = properties.find(key);
+  return key_value == properties.end() ? 0.0f : key_value->second;
+}
+
+/*static*/ float HloCostAnalysis::GetPropertyForHlo(
+    const HloInstruction& hlo, const string& key,
+    const HloToProperties& hlo_to_properties) {
+  auto it = hlo_to_properties.find(&hlo);
+  if (it == hlo_to_properties.end()) {
+    return 0.0f;
+  } else {
+    return GetProperty(key, it->second);
+  }
+}
+
 Status HloCostAnalysis::HandleElementwiseUnary(HloInstruction* hlo,
                                                HloOpcode opcode) {
   return HandleElementwiseOp(hlo);
@@ -102,13 +141,13 @@ Status HloCostAnalysis::HandleReducePrecision(HloInstruction* hlo) {
 }
 
 Status HloCostAnalysis::HandleParameter(HloInstruction* parameter) {
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleConstant(HloInstruction* constant,
                                        const Literal& literal) {
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
@@ -116,7 +155,7 @@ Status HloCostAnalysis::HandleGetTupleElement(HloInstruction* get_tuple_element,
                                               HloInstruction* operand) {
   // GetTupleElement forwards a pointer and does not touch each element in the
   // output.
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
@@ -154,8 +193,9 @@ Status HloCostAnalysis::HandleTuple(
     tensorflow::gtl::ArraySlice<HloInstruction*> operands) {
   // The tuple instruction only gathers pointers from inputs (it doesn't iterate
   // through them). The memory touched is then only the size of the output
-  // buffer.
-  current_bytes_accessed_ = shape_size_(tuple->shape());
+  // index table of the tuple.
+
+  current_properties_[kBytesAccessedKey] = shape_size_(tuple->shape());
   return Status::OK();
 }
 
@@ -193,7 +233,7 @@ Status HloCostAnalysis::HandleDot(HloInstruction* dot,
   }
 
   // We count an FMA operation as 2 floating point operations.
-  current_flop_count_ = kFmaFlops * fma_count;
+  current_properties_[kFlopsKey] = kFmaFlops * fma_count;
   return Status::OK();
 }
 
@@ -209,16 +249,17 @@ Status HloCostAnalysis::HandleMap(
     HloInstruction* map, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
     HloComputation* function,
     tensorflow::gtl::ArraySlice<HloInstruction*> /*static_operands*/) {
-  // Compute the cost of the user function.
-  HloInstruction* function_instruction = function->root_instruction();
-  HloCostAnalysis visitor(shape_size_);
-  TF_RETURN_IF_ERROR(function_instruction->Accept(&visitor));
+  // Compute properties of the mapped function.
+  TF_ASSIGN_OR_RETURN(const Properties sub_properties,
+                      ProcessSubcomputation(function));
 
   // Compute the cost of all elements for this Map operation.
-  int64 element_count = ShapeUtil::ElementsIn(map->shape());
-  current_transcendental_count_ =
-      element_count * visitor.transcendental_count();
-  current_flop_count_ = element_count * visitor.flop_count();
+  const int64 element_count = ShapeUtil::ElementsIn(map->shape());
+  for (const auto& property : sub_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] = property.second * element_count;
+    }
+  }
   return Status::OK();
 }
 
@@ -226,16 +267,17 @@ Status HloCostAnalysis::HandleReduce(
     HloInstruction* reduce, HloInstruction* arg, HloInstruction* init_value,
     tensorflow::gtl::ArraySlice<int64> dimensions, HloComputation* function) {
   // Compute the cost of the user function.
-  HloInstruction* function_instruction = function->root_instruction();
-  HloCostAnalysis visitor(shape_size_);
-  TF_RETURN_IF_ERROR(function_instruction->Accept(&visitor));
+  TF_ASSIGN_OR_RETURN(const Properties sub_properties,
+                      ProcessSubcomputation(function));
 
   // Compute the cost of all elements for this Reduce operation.
   int64 reduction_count = ShapeUtil::ElementsIn(arg->shape()) -
                           ShapeUtil::ElementsIn(reduce->shape());
-  current_flop_count_ = reduction_count * visitor.flop_count();
-  current_transcendental_count_ =
-      reduction_count * visitor.transcendental_count();
+  for (const auto& property : sub_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] = property.second * reduction_count;
+    }
+  }
   return Status::OK();
 }
 
@@ -243,55 +285,63 @@ Status HloCostAnalysis::HandleReduceWindow(HloInstruction* reduce_window,
                                            HloInstruction* operand,
                                            const Window& window,
                                            HloComputation* function) {
-  // Compute the cost of the user function.
-  HloInstruction* function_instruction = function->root_instruction();
-  HloCostAnalysis visitor(shape_size_);
-  TF_RETURN_IF_ERROR(function_instruction->Accept(&visitor));
+  // Compute the properties of the reduction function.
+  TF_ASSIGN_OR_RETURN(const Properties sub_properties,
+                      ProcessSubcomputation(function));
 
   // Compute the cost of all elements for this ReduceWindow operation. For each
-  // output element, (window_size - 1) number of user computations are applied.
-  auto output_size = ShapeUtil::ElementsIn(reduce_window->shape());
-  int64 window_size = 1;
+  // output element there are window_size - 1 reductions to perform.
+  int64 window_element_count = 1;
   for (const auto& dimension : window.dimensions()) {
-    window_size *= dimension.size();
+    window_element_count *= dimension.size();
+  }
+  const int64 output_element_count =
+      ShapeUtil::ElementsIn(reduce_window->shape());
+  const int64 reduction_count =
+      (window_element_count - 1) * output_element_count;
+  for (const auto& property : sub_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] = property.second * reduction_count;
+    }
   }
-  current_flop_count_ = output_size * (window_size - 1) * visitor.flop_count();
-  current_transcendental_count_ =
-      output_size * (window_size - 1) * visitor.transcendental_count();
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleSelectAndScatter(HloInstruction* instruction) {
-  // Compute the cost of the select and scatter function.
-  HloInstruction* select = instruction->select()->root_instruction();
-  HloCostAnalysis select_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(select->Accept(&select_visitor));
-  HloInstruction* scatter = instruction->scatter()->root_instruction();
-  HloCostAnalysis scatter_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(scatter->Accept(&scatter_visitor));
+  // Compute the properties of the select and scatter function.
+  // Compute the properties of the reduction function.
+  TF_ASSIGN_OR_RETURN(const Properties select_properties,
+                      ProcessSubcomputation(instruction->select()));
+  TF_ASSIGN_OR_RETURN(const Properties scatter_properties,
+                      ProcessSubcomputation(instruction->scatter()));
 
   // Compute the cost of all elements for this operation. For each scatter
-  // source element, (window_size - 1) number of select computations and 1
-  // scatter computation are applied.
+  // source element there are window_size - 1 select computations to perform and
+  // 1 scatter computation to perform.
   const auto source = instruction->operand(1);
   const auto source_element_count = ShapeUtil::ElementsIn(source->shape());
-  int64 window_size = 1;
+  int64 window_element_count = 1;
   for (const auto& dimension : instruction->window().dimensions()) {
-    window_size *= dimension.size();
+    window_element_count *= dimension.size();
+  }
+  const int64 select_count = source_element_count * (window_element_count - 1);
+  for (const auto& property : select_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] += property.second * select_count;
+    }
+  }
+  for (const auto& property : scatter_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] +=
+          property.second * source_element_count;
+    }
   }
-  current_flop_count_ =
-      source_element_count * ((window_size - 1) * select_visitor.flop_count() +
-                              scatter_visitor.flop_count());
-  current_transcendental_count_ =
-      source_element_count *
-      ((window_size - 1) * select_visitor.transcendental_count() +
-       scatter_visitor.transcendental_count());
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleBitcast(HloInstruction* bitcast) {
   // A bitcast does no computation and touches no memory.
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
@@ -331,12 +381,13 @@ Status HloCostAnalysis::HandleConvolution(HloInstruction* convolution,
   const int64 output_features =
       convolution->shape().dimensions(dnums.feature_dimension());
 
-  // For each output element, we do one fma per element in the
-  // kernel at some given output feature index.
+  // For each output element, we do one fma per element in the kernel at some
+  // given output feature index.
   const int64 fmas_per_output_element =
       ShapeUtil::ElementsIn(rhs_instruction->shape()) / output_features;
   const int64 output_elements = ShapeUtil::ElementsIn(convolution->shape());
-  current_flop_count_ = output_elements * fmas_per_output_element * kFmaFlops;
+  current_properties_[kFlopsKey] =
+      output_elements * fmas_per_output_element * kFmaFlops;
   return Status::OK();
 }
 
@@ -346,7 +397,7 @@ Status HloCostAnalysis::HandleCrossReplicaSum(HloInstruction* crs) {
   //
   // TODO(b/33004697): Compute correct cost here, taking the actual number of
   // replicas into account.
-  current_flop_count_ = ShapeUtil::ElementsIn(crs->shape());
+  current_properties_[kFlopsKey] = ShapeUtil::ElementsIn(crs->shape());
   return Status::OK();
 }
 
@@ -355,44 +406,43 @@ Status HloCostAnalysis::HandleRng(HloInstruction* random,
   // TODO(b/26346211): Implement better estimates for the RNG cost, since the
   // cost changes with the implementation and the distribution. For now, assume
   // the cost of each RNG is same as a transcendental operation.
-  current_transcendental_count_ = ShapeUtil::ElementsIn(random->shape());
+  current_properties_[kTranscendentalsKey] =
+      ShapeUtil::ElementsIn(random->shape());
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleFusion(HloInstruction* fusion) {
-  // Compute the cost of the fused expression.
-  HloInstruction* fused_expression_root = fusion->fused_expression_root();
-  // Don't compute sizes inside of fused ops. We don't use the size here and the
-  // operations inside might not have a layout.
-  HloCostAnalysis visitor([](const Shape&) { return 0; });
-  TF_RETURN_IF_ERROR(fused_expression_root->Accept(&visitor));
-
-  // If a fusion node produces a tuple, it also produces the operands of that
-  // tuple.
-  current_bytes_accessed_ = 0;
+  // Compute the properties of the fused expression and attribute them to the
+  // fusion node. Use a dummy shape_size to avoid any errors from trying to
+  // calculate the size of a shape that does not have a layout, since nodes
+  // inside fusion nodes do not necessarily have a layout assigned.
+  ShapeSizeFunction shape_size = [](const Shape& shape) { return 0; };
+  TF_ASSIGN_OR_RETURN(
+      current_properties_,
+      ProcessSubcomputation(fusion->fused_instructions_computation(),
+                            &shape_size));
+
+  // Fusion nodes that produce a tuple also produce the entries in the tuple.
+  // Ignore the memory accessed inside fused ops, since fusion is supposed to
+  // prevent intermediate data from touching slow memory.
+  current_properties_[kBytesAccessedKey] = 0;
   ShapeUtil::ForEachSubshape(
       fusion->shape(),
       [this](const Shape& subshape, const ShapeIndex& /*shape_index*/) {
-        current_bytes_accessed_ += shape_size_(subshape);
+        current_properties_[kBytesAccessedKey] += shape_size_(subshape);
       });
 
   for (const HloInstruction* operand : fusion->operands()) {
-    current_bytes_accessed_ += shape_size_(operand->shape());
+    current_properties_[kBytesAccessedKey] += shape_size_(operand->shape());
   }
 
-  // Attribute the cost of the fused expression to the fusion node.
-  current_transcendental_count_ = visitor.transcendental_count();
-  current_flop_count_ = visitor.flop_count();
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleCall(HloInstruction* call) {
-  HloCostAnalysis computation_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(call->to_apply()->Accept(&computation_visitor));
-
-  current_flop_count_ = computation_visitor.flop_count();
-  current_transcendental_count_ = computation_visitor.transcendental_count();
-  current_bytes_accessed_ = computation_visitor.bytes_accessed();
+  TF_ASSIGN_OR_RETURN(current_properties_,
+                      ProcessSubcomputation(call->to_apply()));
+  current_should_compute_bottleneck_time_ = false;
   return Status::OK();
 }
 
@@ -400,34 +450,38 @@ Status HloCostAnalysis::HandleCustomCall(
     HloInstruction* custom_call,
     tensorflow::gtl::ArraySlice<HloInstruction*> operands,
     tensorflow::StringPiece custom_call_target) {
-  return Unimplemented("custom-call");
+  return Unimplemented("Custom-call is not implemented for HLO cost analysis.");
 }
 
 Status HloCostAnalysis::HandleSort(HloInstruction* sort,
                                    HloInstruction* operand_instruction) {
-  // The cost of sort is implementation dependent, so cannot determine at HLO
-  // level. Assume comparison based N*log(N) sorting.
+  // This assumes a comparison based N*log(N) algorithm. As for all ops, the
+  // actual properties of the op depend on the backend implementation.
   int64 elements = ShapeUtil::ElementsIn(operand_instruction->shape());
-  current_flop_count_ = elements * tensorflow::Log2Ceiling(elements);
+  current_properties_[kFlopsKey] = elements * tensorflow::Log2Ceiling(elements);
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleWhile(HloInstruction* xla_while) {
-  // Since the number of iterations of the while node is not statically
-  // determined, we cannot precisely compute the cost of a while node. For now
-  // compute the cost of a single iteration.
-  // TODO(b/26346211): Improve the cost analysis for while node.
-  HloCostAnalysis body_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(xla_while->while_body()->Accept(&body_visitor));
-  HloCostAnalysis condition_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(xla_while->while_condition()->Accept(&condition_visitor));
+  // Since the number of iterations of the while node will not always be
+  // something that we can statically analyze, we cannot precisely compute the
+  // cost of a while node. For now compute the cost of a single iteration.
+  //
+  // TODO(b/26346211): Improve the cost analysis for while nodes.
+  TF_ASSIGN_OR_RETURN(const Properties body_properties,
+                      ProcessSubcomputation(xla_while->while_body()));
 
-  current_flop_count_ =
-      body_visitor.flop_count() + condition_visitor.flop_count();
-  current_transcendental_count_ = body_visitor.transcendental_count() +
-                                  condition_visitor.transcendental_count();
-  current_bytes_accessed_ =
-      body_visitor.bytes_accessed() + condition_visitor.bytes_accessed();
+  TF_ASSIGN_OR_RETURN(const Properties condition_properties,
+                      ProcessSubcomputation(xla_while->while_condition()));
+
+  current_properties_.clear();
+  for (const auto& property : body_properties) {
+    current_properties_[property.first] += property.second;
+  }
+  for (const auto& property : condition_properties) {
+    current_properties_[property.first] += property.second;
+  }
+  current_should_compute_bottleneck_time_ = false;
 
   return Status::OK();
 }
@@ -436,19 +490,42 @@ Status HloCostAnalysis::FinishVisit(HloInstruction* root) {
   return Status::OK();
 }
 
+float HloCostAnalysis::flop_count() const {
+  return GetProperty(kFlopsKey, properties_sum_);
+}
+
+float HloCostAnalysis::transcendental_count() const {
+  return GetProperty(kTranscendentalsKey, properties_sum_);
+}
+
+float HloCostAnalysis::bytes_accessed() const {
+  return GetProperty(kBytesAccessedKey, properties_sum_);
+}
+
+float HloCostAnalysis::seconds() const {
+  return GetProperty(kSecondsKey, properties_sum_);
+}
+
 int64 HloCostAnalysis::flop_count(const HloInstruction& hlo) const {
-  auto it = hlo_to_flop_count_.find(&hlo);
-  return it == hlo_to_flop_count_.end() ? 0 : it->second;
+  return GetPropertyForHlo(hlo, kFlopsKey, hlo_properties_);
 }
 
 int64 HloCostAnalysis::transcendental_count(const HloInstruction& hlo) const {
-  auto it = hlo_to_transcendental_count_.find(&hlo);
-  return it == hlo_to_transcendental_count_.end() ? 0 : it->second;
+  return GetPropertyForHlo(hlo, kTranscendentalsKey, hlo_properties_);
 }
 
 int64 HloCostAnalysis::bytes_accessed(const HloInstruction& hlo) const {
-  auto it = hlo_to_bytes_accessed_.find(&hlo);
-  return it == hlo_to_bytes_accessed_.end() ? 0 : it->second;
+  return GetPropertyForHlo(hlo, kBytesAccessedKey, hlo_properties_);
+}
+
+StatusOr<HloCostAnalysis::Properties> HloCostAnalysis::ProcessSubcomputation(
+    HloComputation* computation, const ShapeSizeFunction* shape_size) {
+  if (shape_size == nullptr) {
+    shape_size = &shape_size_;
+  }
+  HloCostAnalysis visitor(*shape_size, per_second_rates_);
+  TF_RETURN_IF_ERROR(computation->Accept(&visitor));
+  return visitor.properties();
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index 602c93e6bf..17c26fc1a1 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -36,11 +36,18 @@ namespace xla {
 // operations separately from transcendental operations.
 class HloCostAnalysis : public DfsHloVisitor {
  public:
+  // Each HLO is associated to a vector of properties with the indices given
+  // below. Sub-classes can add further properties.
+  typedef std::map<string, float> Properties;
+  static constexpr char kFlopsKey[] = "flops";
+  static constexpr char kTranscendentalsKey[] = "transcendentals";
+  static constexpr char kBytesAccessedKey[] = "bytes accessed";
+  static constexpr char kSecondsKey[] = "seconds";
+
   // shape_size is a function which returns the size in bytes of the top-level
   // buffer of a shape.
   using ShapeSizeFunction = std::function<int64(const Shape&)>;
-  explicit HloCostAnalysis(const ShapeSizeFunction& shape_size)
-      : shape_size_(shape_size) {}
+  explicit HloCostAnalysis(const ShapeSizeFunction& shape_size);
 
   Status HandleElementwiseUnary(HloInstruction* hlo, HloOpcode opcode) override;
   Status HandleElementwiseBinary(HloInstruction* hlo,
@@ -118,48 +125,88 @@ class HloCostAnalysis : public DfsHloVisitor {
   Status Preprocess(HloInstruction* hlo) override;
   Status Postprocess(HloInstruction* hlo) override;
 
-  // Returns the amount of computations in the graph.
-  int64 flop_count() const { return flop_count_; }
-  int64 transcendental_count() const { return transcendental_count_; }
+  // Set the rates used to calculate the time taken by the computation. These
+  // need to be set before visiting starts.
+  void set_flops_per_second(float value) {
+    per_second_rates_[kFlopsKey] = value;
+  }
+  void set_transcendentals_per_second(float value) {
+    per_second_rates_[kTranscendentalsKey] = value;
+  }
+  void set_bytes_per_second(float value) {
+    per_second_rates_[kBytesAccessedKey] = value;
+  }
+
+  // Returns properties for the computation.
+  float flop_count() const;
+  float transcendental_count() const;
+  float bytes_accessed() const;
+  float seconds() const;
 
   // Returns the respective cost computed for a particular HLO instruction, or 0
   // if the HLO was not found to have a cost in the analysis.
   int64 flop_count(const HloInstruction& hlo) const;
   int64 transcendental_count(const HloInstruction& hlo) const;
-
-  // Returns the number of bytes read/written.
   int64 bytes_accessed(const HloInstruction& hlo) const;
-  int64 bytes_accessed() const { return bytes_accessed_; }
+  float seconds(const HloInstruction& hlo) const;
+
+  const Properties& properties() const { return properties_sum_; }
+  const float property(const string& key) const {
+    return GetProperty(key, properties());
+  }
+
+ protected:
+  typedef std::unordered_map<const HloInstruction*, Properties> HloToProperties;
 
- private:
   // An FMA counts as two floating point operations in these analyzes.
   static constexpr int64 kFmaFlops = 2;
 
+  HloCostAnalysis(const ShapeSizeFunction& shape_size,
+                  const Properties& per_second_rates);
+
+  // Returns the properties computed from visiting the computation rooted at the
+  // given hlo. Uses shape_size_ to calculate shape sizes if shape_size is null,
+  // otherwise uses shape_size_.
+  StatusOr<Properties> ProcessSubcomputation(
+      HloComputation* computation,
+      const ShapeSizeFunction* shape_size = nullptr);
+
   // Utility function to handle all element-wise operations.
   Status HandleElementwiseOp(HloInstruction* hlo_instruction);
 
+  // Returns 0.0f if the key is not present in the properties. Otherwise,
+  // returns the value that the key maps to from the properties parameter.
+  static float GetProperty(const string& key, const Properties& properties);
+
+  // Returns 0.0f if the hlo is not present in hlo_to_properties or if the key
+  // is not present in hlo_to_properties[hlo]. Otherwise, returns the value that
+  // the key maps to in the properties of the given hlo.
+  static float GetPropertyForHlo(const HloInstruction& hlo, const string& key,
+                                 const HloToProperties& hlo_to_properties);
+
   // Function which computes the size of the top-level of a given shape (not
   // including nested elements, if any). If null then bytes_accessed methods
   // return an error.
   const ShapeSizeFunction shape_size_;
 
-  // The total number of floating point operations, transcendental operations,
-  // and bytes accesses (read or written) in the computation.
-  int64 flop_count_ = 0;
-  int64 transcendental_count_ = 0;
-  int64 bytes_accessed_ = 0;
-
-  // Cost counts of the current instruction. These should be set by each
-  // handlers if different from the default values computed in Preprocess.
-  int64 current_flop_count_;
-  int64 current_transcendental_count_;
-  int64 current_bytes_accessed_;
-
-  // Mapping from HLO instructions to the cost we computed for them in the
-  // course of the graph analysis.
-  std::map<const HloInstruction*, int64> hlo_to_flop_count_;
-  std::map<const HloInstruction*, int64> hlo_to_transcendental_count_;
-  std::map<const HloInstruction*, int64> hlo_to_bytes_accessed_;
+  HloToProperties hlo_properties_;
+
+  // If true, the time taken will be computed from the rates for each property
+  // and the total time will be the maximum time, which is the time of the
+  // bottleneck.
+  bool current_should_compute_bottleneck_time_;
+
+  // The properties of the currently visited instruction. A HandleFoo method can
+  // modify these to change the default values computed in Preprocess.
+  Properties current_properties_;
+
+  // The sum of the properties of all HLOs in the computation.
+  Properties properties_sum_;
+
+  // How much of each property can be processed per second. E.g. if the property
+  // is bytes accessed, this is the number of bytes that can be processed per
+  // second. Is empty if no rates have been set.
+  Properties per_second_rates_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(HloCostAnalysis);
 };
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
index 5c71056bb5..f745683165 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
@@ -332,48 +332,64 @@ TEST_F(HloCostAnalysisTest, MatmulAndConvolutionCanBeTheSameComputation) {
 using FusionCostAnalysis = ::testing::Test;
 
 TEST_F(FusionCostAnalysis, LoopFusion) {
-  Shape r2f32 = ShapeUtil::MakeShape(F32, {2, 2});
-
-  // Fuse all instructions in complicated expression:
-  //
-  //   add = Add(C1, C2)
-  //   clamp = Clamp(C2, add, add)
-  //   exp = Exp(add)
-  //   mul = Mul(exp, C3)
-  //   sub = Sub(mul, clamp)
-  //   tuple = Tuple({sub, sub, mul, C1})
-  auto c1 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-      /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2));
-  auto c2 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-      /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2));
-  auto c3 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-      /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2));
-
-  auto add =
-      HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, c1.get(), c2.get());
-  auto clamp = HloInstruction::CreateTernary(r2f32, HloOpcode::kClamp, c2.get(),
-                                             add.get(), add.get());
-  auto exp = HloInstruction::CreateUnary(r2f32, HloOpcode::kExp, add.get());
-  auto mul = HloInstruction::CreateBinary(r2f32, HloOpcode::kMultiply,
-                                          exp.get(), c3.get());
-  auto sub = HloInstruction::CreateBinary(r2f32, HloOpcode::kSubtract,
-                                          mul.get(), clamp.get());
-  auto tuple =
-      HloInstruction::CreateTuple({sub.get(), sub.get(), mul.get(), c1.get()});
-
-  auto fusion = HloInstruction::CreateFusion(
-      r2f32, HloInstruction::FusionKind::kLoop, tuple.get());
-  fusion->FuseInstruction(sub.get());
-  fusion->FuseInstruction(mul.get());
-  fusion->FuseInstruction(exp.get());
-  fusion->FuseInstruction(clamp.get());
-  fusion->FuseInstruction(add.get());
-
-  HloCostAnalysis fusion_analysis(ShapeSize);
-  ASSERT_IS_OK(fusion->Accept(&fusion_analysis));
-
-  EXPECT_EQ(fusion_analysis.flop_count(), 16);
-  EXPECT_EQ(fusion_analysis.transcendental_count(), 4);
+  // Do this 4 times with different per-second rates to test the computation of
+  // bottleneck time on fusion nodes.
+  for (int i = 0; i < 4; ++i) {
+    Shape r2f32 = ShapeUtil::MakeShape(F32, {2, 2});
+
+    // Fuse all instructions in complicated expression:
+    //
+    //   add = Add(C1, C2)
+    //   clamp = Clamp(C2, add, add)
+    //   exp = Exp(add)
+    //   mul = Mul(exp, C3)
+    //   sub = Sub(mul, clamp)
+    //   tuple = Tuple({sub, sub, mul, C1})
+    auto c1 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+        /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2));
+    auto c2 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+        /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2));
+    auto c3 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+        /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2));
+
+    auto add = HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, c1.get(),
+                                            c2.get());
+    auto clamp = HloInstruction::CreateTernary(r2f32, HloOpcode::kClamp,
+                                               c2.get(), add.get(), add.get());
+    auto exp = HloInstruction::CreateUnary(r2f32, HloOpcode::kExp, add.get());
+    auto mul = HloInstruction::CreateBinary(r2f32, HloOpcode::kMultiply,
+                                            exp.get(), c3.get());
+    auto sub = HloInstruction::CreateBinary(r2f32, HloOpcode::kSubtract,
+                                            mul.get(), clamp.get());
+    auto tuple = HloInstruction::CreateTuple(
+        {sub.get(), sub.get(), mul.get(), c1.get()});
+
+    auto fusion = HloInstruction::CreateFusion(
+        r2f32, HloInstruction::FusionKind::kLoop, tuple.get());
+    fusion->FuseInstruction(sub.get());
+    fusion->FuseInstruction(mul.get());
+    fusion->FuseInstruction(exp.get());
+    fusion->FuseInstruction(clamp.get());
+    fusion->FuseInstruction(add.get());
+
+    // The time given these rates at i == 0 is exactly even among the properties
+    // at 1.0 seconds. For other values, one of the rates is slower so that it
+    // becomes the bottleneck.
+    HloCostAnalysis fusion_analysis(ShapeSize);
+    fusion_analysis.set_flops_per_second(16 * (i == 1 ? 1 / 2.0 : 1.0));
+    fusion_analysis.set_transcendentals_per_second(4 *
+                                                   (i == 2 ? 1 / 4.0 : 1.0));
+    fusion_analysis.set_bytes_per_second(64 * (i == 3 ? 1 / 8.0 : 1.0));
+    ASSERT_IS_OK(fusion->Accept(&fusion_analysis));
+
+    EXPECT_EQ(fusion_analysis.flop_count(), 16);
+    EXPECT_EQ(fusion_analysis.transcendental_count(), 4);
+    constexpr int64 bytes_accessed = sizeof(float) * 4 * 2 * 2;
+    static_assert(bytes_accessed == 64, "");
+    EXPECT_EQ(fusion_analysis.bytes_accessed(), bytes_accessed);
+
+    EXPECT_EQ(fusion_analysis.seconds(), 1 << i);
+  }
 }
 
 TEST_F(FusionCostAnalysis, NoLayout) {
-- 
GitLab


From 59ae0c0f9ac654bd668fb633feef3dbe26bae8ee Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Tue, 27 Jun 2017 13:40:03 -0700
Subject: [PATCH 0515/1375] tfdbg: revert dns:/// prefix in gRPC mode

PiperOrigin-RevId: 160319348
---
 tensorflow/core/debug/debug_io_utils.cc | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index 9b2e5583aa..69fc367789 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -581,9 +581,8 @@ Status DebugGrpcChannel::Connect(const int64 timeout_micros) {
   args.SetInt(GRPC_ARG_MAX_MESSAGE_LENGTH, std::numeric_limits<int32>::max());
   // Avoid problems where default reconnect backoff is too long (e.g., 20 s).
   args.SetInt("grpc.testing.fixed_reconnect_backoff_ms", 1000);
-  channel_ =
-      ::grpc::CreateCustomChannel("dns:///" + server_stream_addr_,
-                                  ::grpc::InsecureChannelCredentials(), args);
+  channel_ = ::grpc::CreateCustomChannel(
+      server_stream_addr_, ::grpc::InsecureChannelCredentials(), args);
   if (!channel_->WaitForConnected(
           gpr_time_add(gpr_now(GPR_CLOCK_REALTIME),
                        gpr_time_from_micros(timeout_micros, GPR_TIMESPAN)))) {
-- 
GitLab


From 2fce8cf869a14e5b7ec417893b0194b98536278b Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Tue, 27 Jun 2017 18:33:16 +0000
Subject: [PATCH 0516/1375] Fixed some warnings related to signed/unsigned
 comparison

This fix tries to fix some warning related to
```
warning: comparison between signed and unsigned integer expressions
```
by address some easy fix in changing `int -> size_t` in case of loop index.

This fix is related to #10838.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/cc/framework/gradients.cc               |  6 +++---
 .../contrib/batching/kernels/batch_kernels.cc      |  6 +++---
 .../layers/kernels/sparse_feature_cross_kernel.cc  |  6 +++---
 .../core/grappler/clusters/single_machine.cc       |  2 +-
 tensorflow/core/grappler/costs/graph_properties.cc |  4 ++--
 .../core/grappler/costs/virtual_scheduler.cc       |  2 +-
 .../core/grappler/optimizers/constant_folding.cc   |  2 +-
 .../kernels/remote_fused_graph_execute_utils.cc    | 14 +++++++-------
 .../remote_fused_graph_rewriter_transform.cc       |  6 +++---
 tensorflow/python/client/tf_session_helper.cc      |  4 ++--
 .../tools/tfprof/internal/tfprof_timeline.cc       |  2 +-
 11 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index 8c00a6f704..29ad8a934b 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -152,12 +152,12 @@ Status SymbolicGradientBuilder::Initialize() {
   grad_outputs_->resize(inputs_.size());
   // Populate `output_nodes_` from node ids in `outputs_`.
   output_nodes_.reserve(outputs_.size());
-  for (int i = 0; i < outputs_.size(); ++i) {
+  for (size_t i = 0; i < outputs_.size(); ++i) {
     output_nodes_.insert(outputs_[i].node()->id());
   }
   // Populate `input_nodes_` from Outputs in `inputs_`.
   input_nodes_.reserve(inputs_.size());
-  for (int i = 0; i < inputs_.size(); ++i) {
+  for (size_t i = 0; i < inputs_.size(); ++i) {
     input_nodes_.insert({inputs_[i], i});
   }
 
@@ -341,7 +341,7 @@ Status SymbolicGradientBuilder::AddGradients() {
     // gradient function to the src node/output to which it should be
     // backproped. Maybe grad functions can return a vector of Output pairs to
     // make this association explicit.
-    int dx_index = 0;
+    size_t dx_index = 0;
     for (const Edge* e : n->in_edges()) {
       if (e->IsControlEdge()) continue;
       if (dx_index == dx.size()) {
diff --git a/tensorflow/contrib/batching/kernels/batch_kernels.cc b/tensorflow/contrib/batching/kernels/batch_kernels.cc
index 3c06325651..3b7c538fcc 100644
--- a/tensorflow/contrib/batching/kernels/batch_kernels.cc
+++ b/tensorflow/contrib/batching/kernels/batch_kernels.cc
@@ -51,7 +51,7 @@ Status Concat(OpKernelContext* context, const gtl::ArraySlice<Tensor>& inputs,
   std::vector<std::unique_ptr<typename TTypes<T, 2>::ConstMatrix>> inputs_flat;
   inputs_flat.reserve(inputs.size());
   int64 output_dim0 = 0;
-  for (int i = 0; i < inputs.size(); ++i) {
+  for (size_t i = 0; i < inputs.size(); ++i) {
     const Tensor& input = inputs[i];
     if (input.dims() != input_dims) {
       return errors::InvalidArgument(
@@ -548,7 +548,7 @@ class BatchKernel : public AsyncOpKernel {
       return Status::OK();
     }
     int32 last_size = 0;
-    for (int i = 0; i < allowed_batch_sizes_.size(); ++i) {
+    for (size_t i = 0; i < allowed_batch_sizes_.size(); ++i) {
       const int32 size = allowed_batch_sizes_.at(i);
       if (i > 0 && size <= last_size) {
         return errors::InvalidArgument(
@@ -675,7 +675,7 @@ class UnbatchResource : public ResourceBase {
       // If we have a non-empty tensor, finish the waitlisted runs,
       // and store any remaining pieces.
       if (nonempty_input) {
-        for (int i = 0; i < batch_keys.size(); ++i) {
+        for (size_t i = 0; i < batch_keys.size(); ++i) {
           auto runs_it = waiting_callbacks_.find(batch_keys[i]);
           if (runs_it != waiting_callbacks_.end()) {
             runs_it->second.context->set_output(0, split_inputs[i]);
diff --git a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
index 72df272af8..ca79a13b18 100644
--- a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
+++ b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
@@ -176,7 +176,7 @@ class StringCrosser {
     static const auto k_feature_separator = "_X_";
 
     gtl::InlinedVector<InternalType, 6> cross_vec(columns_.size());
-    for (int i = 0; i < permutation.size(); i++) {
+    for (size_t i = 0; i < permutation.size(); i++) {
       cross_vec[i] = columns_[i]->Feature(batch_index, permutation[i]);
     }
     // TODO(zakaria): this will copy the string twice, might effect
@@ -266,7 +266,7 @@ class ProductIterator {
     next_permutation_.resize(columns_.size(), 0);
     // Sets has_next_ to false if any feature column has 0 features.
     has_next_ = true;
-    for (int i = 0; i < columns_.size(); i++) {
+    for (size_t i = 0; i < columns_.size(); i++) {
       if (columns_[i]->FeatureCount(batch_index_) == 0) {
         has_next_ = false;
         break;
@@ -580,7 +580,7 @@ class SparseFeatureCrossOp : public OpKernel {
           columns,
       int batch_index) {
     int64 cross_count = 1;
-    for (int i = 0; i < columns.size(); i++) {
+    for (size_t i = 0; i < columns.size(); i++) {
       const auto feature_count = columns[i]->FeatureCount(batch_index);
       // If one column is missing any feature, there won't be any cross.
       if (feature_count == 0) {
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index 58b9fc6429..a1531f1cfc 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -158,7 +158,7 @@ Status SingleMachine::Run(const GraphDef& graph_def,
         // Also clear the timeline to save memory
         init_metadata_.clear_step_stats();
       }
-      for (int i = 0; i < queue_runner_defs_.size(); ++i) {
+      for (size_t i = 0; i < queue_runner_defs_.size(); ++i) {
         std::unique_ptr<QueueRunner> queue_runner;
         TF_RETURN_IF_ERROR(QueueRunner::New(queue_runner_defs_[i],
                                             coordinator_.get(), &queue_runner));
diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 7ac35ef271..fd3294f620 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -141,7 +141,7 @@ Status GraphProperties::MergeEnqueueShapesAndTypes(
         "Enqueue nodes mixed number of tensors: ", shapes_and_types.size(),
         "  vs ", queue_shapes_and_types->size());
   }
-  for (int i = 0; i < shapes_and_types.size(); ++i) {
+  for (size_t i = 0; i < shapes_and_types.size(); ++i) {
     const ShapeAndType& a = shapes_and_types[i];
     ShapeAndType& b = (*queue_shapes_and_types)[i];
     if (a.dtype != b.dtype) {
@@ -163,7 +163,7 @@ Status GraphProperties::RelaxEnqueueShapesAndMergeTypes(
         "Enqueue nodes mixed number of tensors: ", shapes_and_types.size(),
         "  vs ", queue_shapes_and_types->size());
   }
-  for (int i = 0; i < shapes_and_types.size(); ++i) {
+  for (size_t i = 0; i < shapes_and_types.size(); ++i) {
     const ShapeAndType& a = shapes_and_types[i];
     ShapeAndType& b = (*queue_shapes_and_types)[i];
     if (a.dtype != b.dtype) {
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index f6f91e37ac..b222236126 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -362,7 +362,7 @@ NodeState& VirtualScheduler::GetNodeStateOrCreateIt(const NodeDef* node) {
     // Initialize output port related data:
     // Assume the size of OutputProperties represents the number of output ports
     // of this node.
-    for (int i = 0; i < node_state.output_properties.size(); ++i) {
+    for (size_t i = 0; i < node_state.output_properties.size(); ++i) {
       node_state.time_no_references[i] = Costs::Duration::max();
       node_state.num_outputs_executed[i] = 0;
       // Populate an empty vector for each port. The caller will add nodes
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 07d2d1e1f1..bcd0e98eb7 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -388,7 +388,7 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
   if (output_tensors.empty()) {
     Status(error::INVALID_ARGUMENT, "Expected at least one output.");
   }
-  for (int i = 0; i < output_tensors.size(); i++) {
+  for (size_t i = 0; i < output_tensors.size(); i++) {
     string node_name = AddPrefixToNodeName(node.name(), kConstantFoldingConst);
     if (output_tensors.size() > 1) {
       node_name = strings::StrCat(node_name, "-", i);
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index dd9839d245..aed320b6a2 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -102,7 +102,7 @@ void ConvertMapToVector(const std::unordered_map<int, string>& in,
                         std::vector<string>* out) {
   CHECK_NOTNULL(out);
   out->resize(in.size());
-  for (int i = 0; i < in.size(); ++i) {
+  for (size_t i = 0; i < in.size(); ++i) {
     CHECK(in.count(i) > 0);
     out->at(i) = in.at(i);
   }
@@ -988,7 +988,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   for (const string& output : outputs) {
     const TensorId output_tid = ParseTensorName(output);
     const string output_name = output_tid.first.ToString();
-    for (int i = 0; i < border_outputs.size(); ++i) {
+    for (size_t i = 0; i < border_outputs.size(); ++i) {
       const TensorId subgraph_output_tid =
           ParseTensorName(border_outputs.at(i));
       const string& subgraph_output_name = subgraph_output_tid.first.ToString();
@@ -1035,7 +1035,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::ClusterizeNodes(
       subgraph_nodes, input_graph_def, &ci_vec));
 
-  for (int i = 0; i < ci_vec.size(); ++i) {
+  for (size_t i = 0; i < ci_vec.size(); ++i) {
     const string remote_fused_graph_node_name =
         strings::StrCat(remote_fused_graph_node_name_prefix, "/", i);
     TF_RETURN_IF_ERROR(FuseCluster(input_graph_def, inputs, outputs,
@@ -1074,7 +1074,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   for (NodeDef& node_def : *graph_def->mutable_node()) {
     string attr_str;
     TensorId tid;
-    for (int i = 0; i < inputs.size(); ++i) {
+    for (size_t i = 0; i < inputs.size(); ++i) {
       if (IsSameNodeName(node_def, inputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::GRAPH_INPUT,
@@ -1082,7 +1082,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
                                       remote_fused_graph_node_name);
       }
     }
-    for (int i = 0; i < outputs.size(); ++i) {
+    for (size_t i = 0; i < outputs.size(); ++i) {
       if (IsSameNodeName(node_def, outputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::GRAPH_OUTPUT,
@@ -1095,14 +1095,14 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::FUSED_NODE);
       }
     }
-    for (int i = 0; i < border_inputs.size(); ++i) {
+    for (size_t i = 0; i < border_inputs.size(); ++i) {
       if (IsSameNodeName(node_def, border_inputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::BORDER_INPUT,
                                       tid.second, i);
       }
     }
-    for (int i = 0; i < border_outputs.size(); ++i) {
+    for (size_t i = 0; i < border_outputs.size(); ++i) {
       if (IsSameNodeName(node_def, border_outputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
index ac0503088c..1a2fe2097c 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
@@ -107,7 +107,7 @@ static Status PlaceShapeType(const std::vector<string>& inputs,
   CHECK_EQ(inputs.size(), input_types_strs.size());
   CHECK_EQ(inputs.size(), input_shapes_strs.size());
   std::vector<std::pair<string, Tensor>> input_tensors;
-  for (int i = 0; i < inputs.size(); ++i) {
+  for (size_t i = 0; i < inputs.size(); ++i) {
     const string& name = inputs.at(i);
     std::vector<int64> dims;
     CHECK(str_util::SplitAndParseAsInts(input_shapes_strs.at(i), ',', &dims));
@@ -163,10 +163,10 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
         str_util::Split(border_inputs_str, ",");
     const std::vector<string> border_outputs =
         str_util::Split(border_outputs_str, ",");
-    for (int i = 0; i < border_inputs.size(); ++i) {
+    for (size_t i = 0; i < border_inputs.size(); ++i) {
       VLOG(2) << "Border Input(" << i << "): " << border_inputs.at(i);
     }
-    for (int i = 0; i < border_outputs.size(); ++i) {
+    for (size_t i = 0; i < border_outputs.size(); ++i) {
       VLOG(2) << "Border Output(" << i << "): " << border_outputs.at(i);
     }
     TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::FuseRemoteGraphByBorder(
diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc
index 7ebb1a7fe4..f0bf6944e0 100644
--- a/tensorflow/python/client/tf_session_helper.cc
+++ b/tensorflow/python/client/tf_session_helper.cc
@@ -690,7 +690,7 @@ void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
 
   // Convert outputs to ndarrays (in scoped containers)
   std::vector<Safe_PyObjectPtr> py_outputs_safe;
-  for (int i = 0; i < outputs.size(); ++i) {
+  for (size_t i = 0; i < outputs.size(); ++i) {
     PyObject* py_array;
     s = TFTensorToPyArray(std::move(output_vals_safe[i]), &py_array);
     if (!s.ok()) {
@@ -702,7 +702,7 @@ void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
 
   // If we reach this point, we have successfully built a list of objects so we
   // can release them from the safe container into the return vector.
-  for (int i = 0; i < outputs.size(); ++i) {
+  for (size_t i = 0; i < outputs.size(); ++i) {
     py_outputs->push_back(py_outputs_safe[i].release());
   }
 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline.cc
index c835da8162..6778587b5d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline.cc
@@ -327,7 +327,7 @@ void Timeline::AllocateLanes() {
       int64 start_time = tnode.second->start_micros;
       int64 end_time = tnode.second->start_micros + tnode.second->exec_micros;
       int64 l = -1;
-      for (int i = 0; i < p->lanes.size(); ++i) {
+      for (int64 i = 0; i < p->lanes.size(); ++i) {
         const auto& lane = p->lanes[i];
         l = i;
         for (auto cur_it = lane.rbegin(); cur_it != lane.rend(); ++cur_it) {
-- 
GitLab


From 1f624e529d245cc2cd104d70971fe6a53d1616d6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 14:06:20 -0700
Subject: [PATCH 0517/1375] Moves TensorCApi from c_api.cc to c_api_internal.h,
 where it can be used by other code that require access to the underlying
 TensorBuffers.

PiperOrigin-RevId: 160323362
---
 tensorflow/c/c_api.cc         |  9 ---------
 tensorflow/c/c_api_internal.h | 13 +++++++++++++
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index b1568c5f63..c320fe94a1 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -466,15 +466,6 @@ TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src) {
                       dimvec.size(), base, size, DeleteArray, base);
 }
 
-class TensorCApi {
- public:
-  static TensorBuffer* Buffer(const Tensor& tensor) { return tensor.buf_; }
-  static Tensor MakeTensor(TF_DataType type, const TensorShape& shape,
-                           TensorBuffer* buf) {
-    return Tensor(static_cast<DataType>(type), shape, buf);
-  }
-};
-
 // Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
 // result in a zero-sized tensor.
 static TF_Tensor* EmptyTensor(TF_DataType dtype, const TensorShape& shape) {
diff --git a/tensorflow/c/c_api_internal.h b/tensorflow/c/c_api_internal.h
index f17ac26ad9..f2773ae20f 100644
--- a/tensorflow/c/c_api_internal.h
+++ b/tensorflow/c/c_api_internal.h
@@ -117,3 +117,16 @@ struct TF_ImportGraphDefOptions {
 struct TF_DeviceList {
   std::vector<tensorflow::DeviceAttributes> response;
 };
+
+namespace tensorflow {
+
+class TensorCApi {
+ public:
+  static TensorBuffer* Buffer(const Tensor& tensor) { return tensor.buf_; }
+  static Tensor MakeTensor(TF_DataType type, const TensorShape& shape,
+                           TensorBuffer* buf) {
+    return Tensor(static_cast<DataType>(type), shape, buf);
+  }
+};
+
+}  // end namespace tensorflow
-- 
GitLab


From 41738b256baf8e1e4877e0d938517ed0062407f6 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Tue, 27 Jun 2017 14:11:05 -0700
Subject: [PATCH 0518/1375] Readd the new tensors and variables documents, with
 tests passing.

PiperOrigin-RevId: 160324191
---
 tensorflow/docs_src/programmers_guide/faq.md  |   3 +-
 .../docs_src/programmers_guide/index.md       |   6 +-
 .../programmers_guide/reading_data.md         |   2 +-
 .../docs_src/programmers_guide/tensors.md     | 330 ++++++++++++
 .../programmers_guide/variable_scope.md       | 373 -------------
 .../docs_src/programmers_guide/variables.md   | 508 ++++++++++++------
 tensorflow/docs_src/tutorials/deep_cnn.md     |   2 +-
 tensorflow/python/ops/variable_scope.py       |   4 +-
 8 files changed, 693 insertions(+), 535 deletions(-)
 create mode 100644 tensorflow/docs_src/programmers_guide/tensors.md
 delete mode 100644 tensorflow/docs_src/programmers_guide/variable_scope.md

diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index e31d2717a6..74407ed315 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -174,8 +174,7 @@ for more information on how to use them.
 
 ## Variables
 
-See also the how-to documentation on @{$variables$variables}
-and @{$variable_scope$variable scopes}, and
+See also the how-to documentation on @{$variables$variables} and
 @{$python/state_ops$the API documentation for variables}.
 
 #### What is the lifetime of a variable?
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md
index 5ed7c09a57..6074b2e92d 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/programmers_guide/index.md
@@ -4,13 +4,11 @@ The documents in this unit dive into the details of writing TensorFlow
 code.  This section begins with the following guides, each of which
 explain a particular aspect of TensorFlow:
 
-  * @{$variables$Variables: Creation, Initialization, Saving, and Loading},
-    which details the mechanics of TensorFlow Variables.
+  * @{$variables$Variables: Creation, Initialization, Saving, Loading, and
+     Sharing}, which details the mechanics of TensorFlow Variables.
   * @{$dims_types$Tensor Ranks, Shapes, and Types}, which explains Tensor
     rank (the number of dimensions), shape (the size of each dimension),
     and datatypes.
-  * @{$variable_scope$Sharing Variables}, which explains how to share and
-    manage large sets of variables when building complex models.
   * @{$threading_and_queues$Threading and Queues}, which explains TensorFlow's
     rich queuing system.
   * @{$reading_data$Reading Data}, which documents three different mechanisms
diff --git a/tensorflow/docs_src/programmers_guide/reading_data.md b/tensorflow/docs_src/programmers_guide/reading_data.md
index 3c31d3a1a7..a7d9372053 100644
--- a/tensorflow/docs_src/programmers_guide/reading_data.md
+++ b/tensorflow/docs_src/programmers_guide/reading_data.md
@@ -476,4 +476,4 @@ This is what is done in
 
 You can have the train and eval in the same graph in the same process, and share
 their trained variables.  See
-@{$variable_scope$the shared variables tutorial}.
+@{$variables$the shared variables tutorial}.
diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/programmers_guide/tensors.md
new file mode 100644
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+# Tensors
+
+TensorFlow, as the name indicates, is a framework to define and run computations
+involving tensors. A **tensor** is a generalization of vectors and matrices to
+potentially higher dimensions. Internally, TensorFlow represents tensors as
+n-dimensional arrays of base datatypes.
+
+When writing a TensorFlow program, the main object you manipulate and pass
+around is the `tf.Tensor`. A `tf.Tensor` object represents a partially defined
+computation that will eventually produce a value. TensorFlow programs work by
+first building a graph of `tf.Tensor` objects, detailing how each tensor is
+computed based on the other available tensors and then by running parts of this
+graph to achieve the desired results.
+
+A `tf.Tensor` has the following properties:
+
+ * a data type (`float32`, `int32`, or `string`, for example)
+ * a shape
+
+
+Each element in the Tensor has the same data type, and the data type is always
+known. The shape (that is, the number of dimensions it has and the size of each
+dimension) might be only partially known. Most operations produce tensors of
+fully-known shapes if the shapes of their inputs are also fully known, but in
+some cases it's only possible to find the shape of a tensor at graph execution
+time.
+
+Some types of tensors are special, and these will be covered in other
+units of the Programmer's guide. The main ones are:
+
+  * `tf.Variable`
+  * `tf.Constant`
+  * `tf.Placeholder`
+  * `tf.SparseTensor`
+
+With the exception of `tf.Variable`, the value of a tensor is immutable, which
+means that in the context of a single execution tensors only have a single
+value. However, evaluating the same tensor twice can return different values;
+for example that tensor can be the result of reading data from disk, or
+generating a random number.
+
+## Rank
+
+The **rank** of a `tf.Tensor` object is its number of dimensions. Synonyms for
+rank include **order** or **degree** or **n-dimension**.
+Note that rank in TensorFlow is not the same as matrix rank in mathematics. 
+As the following table shows, each rank in TensorFlow corresponds to a 
+different mathematical entity:
+
+Rank | Math entity
+--- | ---
+0 | Scalar (magnitude only)
+1 | Vector (magnitude and direction)
+2 | Matrix (table of numbers)
+3 | 3-Tensor (cube of numbers)
+n | n-Tensor (you get the idea)
+
+
+### Rank 0 
+
+The following snippet demonstrates creating a few rank 0 variables:
+
+```python
+mammal = tf.Variable("Elephant", tf.string)
+ignition = tf.Variable(451, tf.int16)
+floating = tf.Variable(3.14159265359, tf.float64)
+its_complicated = tf.Variable((12.3, -4.85), tf.complex64)
+```
+
+Note: A string is treated as a single item in TensorFlow, not as a sequence of
+characters. It is possible to have scalar strings, vectors of strings, etc.
+
+### Rank 1
+
+To create a rank 1 `tf.Tensor` object, you can pass a list of items as the
+initial value. For example:
+
+```python
+mystr = tf.Variable(["Hello"], tf.string)
+cool_numbers  = tf.Variable([3.14159, 2.71828], tf.float32)
+first_primes = tf.Variable([2, 3, 5, 7, 11], tf.int32)
+its_very_complicated = tf.Variable([(12.3, -4.85), (7.5, -6.23)], tf.complex64)
+```
+
+
+### Higher ranks
+
+A rank 2 `tf.Tensor` object consists of at least one row and at least
+one column:
+
+```python
+mymat = tf.Variable([[7],[11]], tf.int16)
+myxor = tf.Variable([[False, True],[True, False]], tf.bool)
+linear_squares = tf.Variable([[4], [9], [16], [25]], tf.int32)
+squarish_squares = tf.Variable([ [4, 9], [16, 25] ], tf.int32)
+rank_of_squares = tf.rank(squarish_squares)
+mymatC = tf.Variable([[7],[11]], tf.int32)
+```
+
+Higher-rank Tensors, similarly, consist of an n-dimensional array. For example,
+during image processing, many tensors of rank 4 are used, with dimensions
+corresponding to example-in-batch, image width, image height, and color channel.
+
+``` python
+my_image = tf.zeros([10, 299, 299, 3])  # batch x height x width x color
+```
+
+### Getting a `tf.Tensor` object's rank
+
+To determine the rank of a `tf.Tensor` object, call the `tf.rank` method.
+For example, the following method programmatically determines the rank 
+of the `tf.Tensor` defined in the previous section:
+
+```python
+r = tf.rank(my3d)
+# After the graph runs, r will hold the value 3.
+```
+
+### Referring to `tf.Tensor` slices
+
+Since a `tf.Tensor` is an n-dimensional array of cells, to access a single cell
+in a `tf.Tensor` you need to specify n indices.
+
+For a rank 0 tensor (a scalar), no indices are necessary, since it is already a
+single number.
+
+For a rank 1 tensor (a vector), passing a single index allows you to access a
+number:
+
+```python
+my_scalar = my_vector[2]
+```
+
+Note that the index passed inside the `[]` can itself be a scalar `tf.Tensor`, if
+you want to dynamically choose an element from the vector.
+
+For tensors of rank 2 or higher, the situation is more interesting. For a
+`tf.Tensor` of rank 2, passing two numbers returns a scalar, as expected:
+
+
+```python
+my_scalar = my_matrix[1, 2]
+```
+
+
+Passing a single number, however, returns a subvector of a matrix, as follows:
+
+
+```python
+my_row_vetor = my_matrix[2]
+my_column_vector = my_matrix[:, 3]
+```
+
+The `:` notation is python slicing syntax for "leave this dimension alone". This
+is useful in higher-rank Tensors, as it allows you to access its subvectors,
+submatrices, and even other subtensors.
+
+
+## Shape
+
+The **shape** of a tensor is the number of elements in each dimension.
+TensorFlow automatically infers shapes during graph construction. These inferred
+shapes might have known or unknown rank. If the rank is known, the sizes of each
+dimension might be known or unknown.
+
+The TensorFlow documentation uses three notational conventions to describe
+tensor dimensionality: rank, shape, and dimension number. The following table
+shows how these relate to one another:
+
+Rank | Shape | Dimension number | Example
+--- | --- | --- | ---
+0 | [] | 0-D | A 0-D tensor.  A scalar.
+1 | [D0] | 1-D | A 1-D tensor with shape [5].
+2 | [D0, D1] | 2-D | A 2-D tensor with shape [3, 4].
+3 | [D0, D1, D2] | 3-D | A 3-D tensor with shape [1, 4, 3].
+n | [D0, D1, ... Dn-1] | n-D | A tensor with shape [D0, D1, ... Dn-1].
+
+Shapes can be represented via Python lists / tuples of ints, or with the
+@{tf.TensorShape}.
+
+### Getting a `tf.Tensor` object's shape
+
+There are two ways of accessing the shape of a `tf.Tensor`. While building the
+graph, it is often useful to ask what is already known about a tensor's
+shape. This can be done by reading the `shape` property of a `tf.Tensor` object.
+This method returns a `TensorShape` object, which is a convenient way of
+representing partially-specified shapes (since, when building the graph, not all
+shapes will be fully known).
+
+It is also possible to get a `tf.Tensor` that will represent the fully-defined
+shape of another `tf.Tensor` at runtime. This is done by calling the `tf.shape`
+operation. This way, you can build a graph that manipulates the shapes of
+tensors by building other tensors that depend on the dynamic shape of the input
+`tf.Tensor`.
+
+For example, here is how to make a vector of zeros with the same size as the
+number of columns in a given matrix:
+
+``` python
+zeros = tf.zeros(tf.shape(my_matrix)[1])
+```
+
+### Changing the shape of a `tf.Tensor`
+
+The **number of elements** of a tensor is the product of the sizes of all its
+shapes. The number of elements of a scalar is always `1`. Since there are often
+many different shapes that have the same number of elements, it's often
+convenient to be able to change the shape of a `tf.Tensor`, keeping its elements
+fixed. This can be done with `tf.reshape`.
+
+The following examples demonstrate how to reshape tensors:
+
+```python
+rank_three_tensor = tf.ones([3, 4, 5])
+matrix = tf.reshape(rank_three_tensor, [6, 10])  # Reshape existing content into
+                                                 # a 6x10 matrix
+matrixB = tf.reshape(matrix, [3, -1])  #  Reshape existing content into a 3x20
+                                       # matrix. -1 tells reshape to calculate
+                                       # the size of this dimension.
+matrixAlt = tf.reshape(matrixB, [4, 3, -1])  # Reshape existing content into a
+                                             #4x3x5 tensor
+
+# Note that the number of elements of the reshaped Tensors has to match the
+# original number of elements. Therefore, the following example generates an
+# error because no possible value for the last dimension will match the number
+# of elements.
+yet_another = tf.reshape(matrixAlt, [13, 2, -1])  # ERROR!
+```
+
+## Data types
+
+In addition to dimensionality, Tensors have a data type. Refer to the
+`tf.DataType` page in the programmer's guide for a full list of the data types.
+
+It is not possible to have a `tf.Tensor` with more than one data type. It is
+possible, however, to serialize arbitrary data structures as `string`s and store
+those in `tf.Tensor`s.
+
+It is possible to cast `tf.Tensor`s from one datatype to another using
+`tf.cast`:
+
+``` python
+# Cast a constant integer tensor into floating point.
+float_tensor = tf.cast(tf.constant([1, 2, 3]), dtype=tf.float32)
+```
+
+To inspect a `tf.Tensor`'s data type use the `Tensor.dtype` property.
+
+When creating a `tf.Tensor` from a python object you may optionally specify the
+datatype. If you don't, TensorFlow chooses a datatype that can represent your
+data. TensorFlow converts Python integers to `tf.int32` and python floating
+point numbers to `tf.float32`. Otherwise TensorFlow uses the same rules numpy
+uses when converting to arrays.
+
+## Evaluating Tensors
+
+Once the computation graph has been built, you can run the computation that
+produces a particular `tf.Tensor` and fetch the value assigned to it. This is
+often useful for debugging as well as being required for much of TensorFlow to
+work.
+
+The simplest way to evaluate a Tensor is using the `Tensor.eval` method. For
+example:
+
+```python
+constant = tf.constant([1, 2, 3])
+tensor = constant * constant
+print tensor.eval()
+```
+
+The `eval` method only works when a default `tf.Session` is active (see
+Graphs and Sessions for more information).
+
+`Tensor.eval` returns a numpy array with the same contents as the tensor.
+
+Sometimes it is not possible to evaluate a `tf.Tensor` with no context because
+its value might depend on dynamic information that is not available. For
+example, tensors that depend on `Placeholder`s can't be evaluated without
+providing a value for the `Placeholder`.
+
+``` python
+p = tf.placeholder(tf.float32)
+t = p + 1.0
+t.eval()  # This will fail, since the placeholder did not get a value.
+t.eval(feed_dict={p:2.0})  # This will succeed because we're feeding a value
+                           # to the placeholder.
+```
+
+Note that it is possible to feed any `tf.Tensor`, not just placeholders.
+
+Other model constructs might make evaluating a `tf.Tensor`
+complicated. TensorFlow can't directly evaluate `tf.Tensor`s defined inside
+functions or inside control flow constructs. If a `tf.Tensor` depends on a value
+from a queue, evaluating the `tf.Tensor` will only work once something has been
+enqueued; otherwise, evaluating it will hang. When working with queues, remember
+to call `tf.train.start_queue_runners` before evaluating any `tf.Tensor`s.
+
+## Printing Tensors
+
+For debugging purposes you might want to print the value of a `tf.Tensor`. While
+ @{$debugger$tfdbg} provides advanced debugging support, TensorFlow also has an
+ operation to directly print the value of a `tf.Tensor`.
+
+Note that you rarely want to use the following pattern when printing a
+`tf.Tensor`:
+
+``` python
+t = <<some tensorflow operation>>
+print t  # This will print the symbolic tensor when the graph is being built.
+         # This tensor does not have a value in this context.
+```
+
+This code prints the `tf.Tensor` object (which represents deferred computation)
+and not its value. Instead, TensorFlow provides the `tf.Print` operation, which
+returns its first tensor argument unchanged while printing the set of
+`tf.Tensor`s it is passed as the second argument.
+
+To correctly use `tf.Print` its return value must be used. See the example below
+
+``` python
+t = <<some tensorflow operation>>
+tf.Print(t, [t])  # This does nothing
+t = tf.Print(t, [t])  # Here we are using the value returned by tf.Print
+result = t + 1  # Now when result is evaluated the value of `t` will be printed.
+```
+
+When you evaluate `result` you will evaluate everything `result` depends
+upon. Since `result` depends upon `t`, and evaluating `t` has the side effect of
+printing its input (the old value of `t`), `t` gets printed.
+
diff --git a/tensorflow/docs_src/programmers_guide/variable_scope.md b/tensorflow/docs_src/programmers_guide/variable_scope.md
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-# Sharing Variables
-
-You can create, initialize, save and load single variables
-in the way described in the @{$variables$Variables HowTo}.
-But when building complex models you often need to share large sets of
-variables and you might want to initialize all of them in one place.
-This tutorial shows how this can be done using `tf.variable_scope()` and
-`tf.get_variable()`.
-
-## The Problem
-
-Imagine you create a simple model for image filters, similar to our
-@{$deep_cnn$Convolutional Neural Networks Tutorial}
-model but with only 2 convolutions (for simplicity of this example). If you use
-just `tf.Variable`, as explained in @{$variables$Variables HowTo},
-your model might look like this.
-
-```python
-def my_image_filter(input_images):
-    conv1_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv1_weights")
-    conv1_biases = tf.Variable(tf.zeros([32]), name="conv1_biases")
-    conv1 = tf.nn.conv2d(input_images, conv1_weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    relu1 = tf.nn.relu(conv1 + conv1_biases)
-
-    conv2_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv2_weights")
-    conv2_biases = tf.Variable(tf.zeros([32]), name="conv2_biases")
-    conv2 = tf.nn.conv2d(relu1, conv2_weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv2 + conv2_biases)
-```
-
-As you can easily imagine, models quickly get much more complicated than
-this one, and even here we already have 4 different variables: `conv1_weights`,
-`conv1_biases`, `conv2_weights`, and `conv2_biases`.
-
-The problem arises when you want to reuse this model. Assume you want to
-apply your image filter to 2 different images, `image1` and `image2`.
-You want both images processed by the same filter with the same parameters.
-You can call `my_image_filter()` twice, but this will create two sets
-of variables, 4 variables in each one, for a total of 8 variables.
-
-```python
-# First call creates one set of 4 variables.
-result1 = my_image_filter(image1)
-# Another set of 4 variables is created in the second call.
-result2 = my_image_filter(image2)
-```
-
-A common way to share variables is to create them in a separate piece of code
-and pass them to functions that use them.   For example by using a dictionary:
-
-```python
-variables_dict = {
-    "conv1_weights": tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv1_weights")
-    "conv1_biases": tf.Variable(tf.zeros([32]), name="conv1_biases")
-    ... etc. ...
-}
-
-def my_image_filter(input_images, variables_dict):
-    conv1 = tf.nn.conv2d(input_images, variables_dict["conv1_weights"],
-        strides=[1, 1, 1, 1], padding='SAME')
-    relu1 = tf.nn.relu(conv1 + variables_dict["conv1_biases"])
-
-    conv2 = tf.nn.conv2d(relu1, variables_dict["conv2_weights"],
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv2 + variables_dict["conv2_biases"])
-
-# Both calls to my_image_filter() now use the same variables
-result1 = my_image_filter(image1, variables_dict)
-result2 = my_image_filter(image2, variables_dict)
-```
-
-While convenient, creating variables like above,
-outside of the code, breaks encapsulation:
-
-*  The code that builds the graph must document the names, types,
-   and shapes of variables to create.
-*  When the code changes, the callers may have to create more, or less,
-   or different variables.
-
-One way to address the problem is to use classes to create a model,
-where the classes take care of managing the variables they need.
-For a lighter solution, not involving classes, TensorFlow provides
-a *Variable Scope* mechanism that allows to easily share named variables
-while constructing a graph.
-
-## Variable Scope Example
-
-Variable Scope mechanism in TensorFlow consists of two main functions:
-
-* `tf.get_variable(<name>, <shape>, <initializer>)`:
-  Creates or returns a variable with a given name.
-* `tf.variable_scope(<scope_name>)`:
-  Manages namespaces for names passed to `tf.get_variable()`.
-
-The function `tf.get_variable()` is used to get or create a variable instead
-of a direct call to `tf.Variable`. It uses an *initializer* instead of passing
-the value directly, as in `tf.Variable`. An initializer is a function that
-takes the shape and provides a tensor with that shape. Here are some
-initializers available in TensorFlow:
-
-* `tf.constant_initializer(value)` initializes everything to the provided value,
-* `tf.random_uniform_initializer(a, b)` initializes uniformly from [a, b],
-* `tf.random_normal_initializer(mean, stddev)` initializes from the normal
-  distribution with the given mean and standard deviation.
-
-To see how `tf.get_variable()` solves the problem discussed
-before, let's refactor the code that created one convolution into
-a separate function, named `conv_relu`:
-
-```python
-def conv_relu(input, kernel_shape, bias_shape):
-    # Create variable named "weights".
-    weights = tf.get_variable("weights", kernel_shape,
-        initializer=tf.random_normal_initializer())
-    # Create variable named "biases".
-    biases = tf.get_variable("biases", bias_shape,
-        initializer=tf.constant_initializer(0.0))
-    conv = tf.nn.conv2d(input, weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv + biases)
-```
-
-This function uses short names `"weights"` and `"biases"`.
-We'd like to use it for both `conv1` and `conv2`, but
-the variables need to have different names.
-This is where `tf.variable_scope()` comes into play:
-it pushes a namespace for variables.
-
-```python
-def my_image_filter(input_images):
-    with tf.variable_scope("conv1"):
-        # Variables created here will be named "conv1/weights", "conv1/biases".
-        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
-    with tf.variable_scope("conv2"):
-        # Variables created here will be named "conv2/weights", "conv2/biases".
-        return conv_relu(relu1, [5, 5, 32, 32], [32])
-```
-
-Now, let's see what happens when we call `my_image_filter()` twice.
-
-```
-result1 = my_image_filter(image1)
-result2 = my_image_filter(image2)
-# Raises ValueError(... conv1/weights already exists ...)
-```
-
-As you can see, `tf.get_variable()` checks that already existing variables
-are not shared by accident. If you want to share them, you need to specify
-it by setting `reuse_variables()` as follows.
-
-```
-with tf.variable_scope("image_filters") as scope:
-    result1 = my_image_filter(image1)
-    scope.reuse_variables()
-    result2 = my_image_filter(image2)
-```
-
-This is a good way to share variables, lightweight and safe.
-
-## How Does Variable Scope Work?
-
-### Understanding `tf.get_variable()`
-
-To understand variable scope it is necessary to first
-fully understand how `tf.get_variable()` works.
-Here is how `tf.get_variable` is usually called.
-
-```python
-v = tf.get_variable(name, shape, dtype, initializer)
-```
-
-This call does one of two things depending on the scope it is called in.
-Here are the two options.
-
-* Case 1: the scope is set for creating new variables, as evidenced by
-`tf.get_variable_scope().reuse == False`.
-
-In this case, `v` will be a newly created `tf.Variable` with the provided
-shape and data type. The full name of the created variable will be set to
-the current variable scope name + the provided `name` and a check will be
-performed to ensure that no variable with this full name exists yet.
-If a variable with this full name already exists, the function will
-raise a `ValueError`. If a new variable is created, it will be
-initialized to the value `initializer(shape)`. For example:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-assert v.name == "foo/v:0"
-```
-
-* Case 2: the scope is set for reusing variables, as evidenced by
-`tf.get_variable_scope().reuse == True`.
-
-In this case, the call will search for an already existing variable with
-name equal to the current variable scope name + the provided `name`.
-If no such variable exists, a `ValueError` will be raised. If the variable
-is found, it will be returned. For example:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-with tf.variable_scope("foo", reuse=True):
-    v1 = tf.get_variable("v", [1])
-assert v1 is v
-```
-
-### Basics of `tf.variable_scope()`
-
-Knowing how `tf.get_variable()` works makes it easy to understand variable
-scope. The primary function of variable scope is to carry a name that will
-be used as prefix for variable names and a reuse-flag to distinguish the two
-cases described above. Nesting variable scopes appends their names in a way
-analogous to how directories work:
-
-```python
-with tf.variable_scope("foo"):
-    with tf.variable_scope("bar"):
-        v = tf.get_variable("v", [1])
-assert v.name == "foo/bar/v:0"
-```
-
-The current variable scope can be retrieved using `tf.get_variable_scope()`
-and the `reuse` flag of the current variable scope can be set to `True` by
-calling `tf.get_variable_scope().reuse_variables()`:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-    tf.get_variable_scope().reuse_variables()
-    v1 = tf.get_variable("v", [1])
-assert v1 is v
-```
-
-Note that you *cannot* set the `reuse` flag to `False`. The reason behind
-this is to allow to compose functions that create models. Imagine you write
-a function `my_image_filter(inputs)` as before. Someone calling the function
-in a variable scope with `reuse=True` would expect all inner variables to be
-reused as well. Allowing to force `reuse=False` inside the function would break
-this contract and make it hard to share parameters in this way.
-
-Even though you cannot set `reuse` to `False` explicitly, you can enter
-a reusing variable scope and then exit it, going back to a non-reusing one.
-This can be done using a `reuse=True` parameter when opening a variable scope.
-Note also that, for the same reason as above, the `reuse` parameter is
-inherited. So when you open a reusing variable scope, all sub-scopes will
-be reusing too.
-
-```python
-with tf.variable_scope("root"):
-    # At start, the scope is not reusing.
-    assert tf.get_variable_scope().reuse == False
-    with tf.variable_scope("foo"):
-        # Opened a sub-scope, still not reusing.
-        assert tf.get_variable_scope().reuse == False
-    with tf.variable_scope("foo", reuse=True):
-        # Explicitly opened a reusing scope.
-        assert tf.get_variable_scope().reuse == True
-        with tf.variable_scope("bar"):
-            # Now sub-scope inherits the reuse flag.
-            assert tf.get_variable_scope().reuse == True
-    # Exited the reusing scope, back to a non-reusing one.
-    assert tf.get_variable_scope().reuse == False
-```
-
-### Capturing variable scope
-
-In all examples presented above, we shared parameters only because their
-names agreed, that is, because we opened a reusing variable scope with
-exactly the same string. In more complex cases, it might be useful to pass
-a VariableScope object rather than rely on getting the names right.
-To this end, variable scopes can be captured and used instead of names
-when opening a new variable scope.
-
-```python
-with tf.variable_scope("foo") as foo_scope:
-    v = tf.get_variable("v", [1])
-with tf.variable_scope(foo_scope):
-    w = tf.get_variable("w", [1])
-with tf.variable_scope(foo_scope, reuse=True):
-    v1 = tf.get_variable("v", [1])
-    w1 = tf.get_variable("w", [1])
-assert v1 is v
-assert w1 is w
-```
-
-When opening a variable scope using a previously existing scope
-we jump out of the current variable scope prefix to an entirely
-different one. This is fully independent of where we do it.
-
-```python
-with tf.variable_scope("foo") as foo_scope:
-    assert foo_scope.name == "foo"
-with tf.variable_scope("bar"):
-    with tf.variable_scope("baz") as other_scope:
-        assert other_scope.name == "bar/baz"
-        with tf.variable_scope(foo_scope) as foo_scope2:
-            assert foo_scope2.name == "foo"  # Not changed.
-```
-
-### Initializers in variable scope
-
-Using `tf.get_variable()` allows to write functions that create or reuse
-variables and can be transparently called from outside. But what if we wanted
-to change the initializer of the created variables? Do we need to pass an extra
-argument to every function that creates variables? What about the most common
-case, when we want to set the default initializer for all variables in one
-place, on top of all functions? To help with these cases, variable scope
-can carry a default initializer. It is inherited by sub-scopes and passed
-to each `tf.get_variable()` call. But it will be overridden if another
-initializer is specified explicitly.
-
-```python
-with tf.variable_scope("foo", initializer=tf.constant_initializer(0.4)):
-    v = tf.get_variable("v", [1])
-    assert v.eval() == 0.4  # Default initializer as set above.
-    w = tf.get_variable("w", [1], initializer=tf.constant_initializer(0.3)):
-    assert w.eval() == 0.3  # Specific initializer overrides the default.
-    with tf.variable_scope("bar"):
-        v = tf.get_variable("v", [1])
-        assert v.eval() == 0.4  # Inherited default initializer.
-    with tf.variable_scope("baz", initializer=tf.constant_initializer(0.2)):
-        v = tf.get_variable("v", [1])
-        assert v.eval() == 0.2  # Changed default initializer.
-```
-
-### Names of ops in `tf.variable_scope()`
-
-We discussed how `tf.variable_scope` governs the names of variables.
-But how does it influence the names of other ops in the scope?
-It is natural that ops created inside a variable scope should also
-share that name. For this reason, when we do `with tf.variable_scope("name")`,
-this implicitly opens a `tf.name_scope("name")`. For example:
-
-```python
-with tf.variable_scope("foo"):
-    x = 1.0 + tf.get_variable("v", [1])
-assert x.op.name == "foo/add"
-```
-
-Name scopes can be opened in addition to a variable scope, and then
-they will only affect the names of the ops, but not of variables.
-
-```python
-with tf.variable_scope("foo"):
-    with tf.name_scope("bar"):
-        v = tf.get_variable("v", [1])
-        x = 1.0 + v
-assert v.name == "foo/v:0"
-assert x.op.name == "foo/bar/add"
-```
-
-When opening a variable scope using a captured object instead of a string,
-we do not alter the current name scope for ops.
-
-
-## Examples of Use
-
-Here are pointers to a few files that make use of variable scope. They can all
-be found in the [TensorFlow models repo](https://github.com/tensorflow/models).
-In particular, variable scope is heavily used for recurrent neural networks and
-sequence-to-sequence models.
-
-File | What's in it?
---- | ---
-`tutorials/image/cifar10/cifar10.py` | Model for detecting objects in images.
-`tutorials/rnn/rnn_cell.py` | Cell functions for recurrent neural networks.
-`tutorials/rnn/seq2seq.py` | Functions for building sequence-to-sequence models.
diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index e8d1e519f0..a7b7db626a 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -1,190 +1,283 @@
-# Variables: Creation, Initialization, Saving, and Loading
+# Variables: persistent state in TensorFlow
 
-When you train a model, you use @{$python/state_ops$variables}
-to hold and update parameters.  Variables are in-memory buffers containing
-tensors.  They must be explicitly initialized and can be saved to disk during
-and after training. You can later restore saved values to exercise or analyze
-the model.
+A TensorFlow **variable** is the best way to represent shared, persistent state
+manipulated by your program.
 
-This document references the following TensorFlow classes.  Follow the links to
-their reference manual for a complete description of their API:
+Variables are manipulated via the `tf.Variable` class. A `tf.Variable`
+represents a tensor whose value can be changed by running ops on it. Unlike
+`tf.Tensor` objects, a `tf.Variable` exists outside the context of a single
+`session.run` call.
 
-*  The @{tf.Variable} class.
-*  The @{tf.train.Saver} class.
+Internally, a `tf.Variable` stores a persistent tensor. Specific ops allow you
+to read and modify the values of this tensor. These modifications are visible
+across multiple `tf.Session`s, so multiple workers can see the same values for a
+`tf.Variable`.
 
+## Creating a Variable
 
-## Creation
+The best way to create a variable is to call the `tf.get_variable`
+function. This function requires you to specify the Variable's name. This name
+will be used by other replicas to access the same variable, as well as to name
+this variable's value when checkpointing and exporting models. `tf.get_variable`
+also allows you to reuse a previously created variable of the same name, making it
+easy to define models which reuse layers.
 
-When you create a @{$python/state_ops$Variable} you pass a
-`Tensor` as its initial value to the `Variable()` constructor.  TensorFlow
-provides a collection of ops that produce tensors often used for initialization
-from @{$python/constant_op$constants or random values}.
+To create a variable with `tf.get_variable`, simply provide the name and shape
 
-Note that all these ops require you to specify the shape of the tensors.  That
-shape automatically becomes the shape of the variable.  Variables generally
-have a fixed shape, but TensorFlow provides advanced mechanisms to reshape
-variables.
+``` python
+my_variable = tf.get_variable("my_variable", [1, 2, 3])
+```
 
-```python
-# Create two variables.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-biases = tf.Variable(tf.zeros([200]), name="biases")
+This creates a variable named "my_variable" which is a three-dimensional tensor
+with shape `[1, 2, 3]`. This variable will, by default, have the `dtype`
+`tf.float32` and its initial value will be randomized via
+`tf.glorot_uniform_initializer`.
+
+You may optionally specify the `dtype` and initializer to `tf.get_variable`. For
+example:
+
+``` python
+my_int_variable = tf.get_variable("my_int_variable", [1, 2, 3], dtype=tf.int32, 
+  initializer=tf.zeros_initializer)
 ```
 
-Calling `tf.Variable()` adds several ops to the graph:
+TensorFlow provides many convenient initializers. Alternatively, you may
+initialize a `tf.Variable` to have the value of a `tf.Tensor`. For example:
 
-*  A `variable` op that holds the variable value.
-*  An initializer op that sets the variable to its initial value.  This is
-   actually a `tf.assign` op.
-*  The ops for the initial value, such as the `zeros` op for the `biases`
-   variable in the example are also added to the graph.
+``` python
+other_variable = tf.get_variable("other_variable", dtype=tf.int32, 
+  initializer=tf.constant([23, 42]))
+```
 
-The value returned by `tf.Variable()` value is an instance of the Python class
-`tf.Variable`.
+Note that when the initializer is a `tf.Tensor` you should not specify the
+variable's shape, as the shape of the initializer tensor will be used.
+
+### Variable collections
+
+Because disconnected parts of a TensorFlow program might want to create
+variables, it is sometimes useful to have a single way to access all of
+them. For this reason TensorFlow provides **collections**, which are named lists
+of tensors or other objects, such as `tf.Variable` instances.
+
+By default every `tf.Variable` gets placed in the following two collections:
+ * `tf.GraphKeys.GLOBAL_VARIABLES` --- variables that can be shared across
+multiple devices,
+ * `tf.GraphKeys.TRAINABLE_VARIABLES`--- variables for which TensorFlow will
+   calculate gradients.
+ 
+If you don't want a variable to be trainable, add it to the
+`tf.GraphKeys.LOCAL_VARIABLES` collection instead. For example, the following
+snippet demonstrates how to add a variable named `my_local` to this collection:
+
+``` python
+my_local = tf.get_variable("my_local", shape=(), 
+collections=[tf.GraphKeys.LOCAL_VARIABLES])
+```
 
-### Device placement
+Alternatively, you can specify `trainable=False` as an argument to
+`tf.get_variable`:
 
-A variable can be pinned to a particular device when it is created, using a
-@{tf.device$`with tf.device(...):`} block:
+``` python
+my_non_trainable = tf.get_variable("my_non_trainable", 
+                                   shape=(), 
+                                   trainable=False)
+```
 
-```python
-# Pin a variable to CPU.
-with tf.device("/cpu:0"):
-  v = tf.Variable(...)
 
-# Pin a variable to GPU.
-with tf.device("/gpu:0"):
-  v = tf.Variable(...)
+You can also use your own collections. Any string is a valid collection name,
+and there is no need to explicitly create a collection. To add a variable (or
+any other object) to a collection after creating the variable, call
+`tf.add_to_collection`.  For example, the following code adds an existing
+variable named `my_local` to a collection named `my_collection_name`:
 
-# Pin a variable to a particular parameter server task.
-with tf.device("/job:ps/task:7"):
-  v = tf.Variable(...)
+``` python
+tf.add_to_collection("my_collection_name", my_local)
 ```
 
-**NOTE** Operations that mutate a variable, such as
-@{tf.Variable.assign} and the parameter
-update operations in a
-@{tf.train.Optimizer} *must* run on
-the same device as the variable. Incompatible device placement directives will
-be ignored when creating these operations.
+And to retrieve a list of all the variables (or other objects) you've placed in
+a collection you can use:
 
-Device placement is particularly important when running in a replicated
-setting. See
-@{tf.train.replica_device_setter}
-for details of a device function that can simplify the configuration for devices
-for a replicated model.
+``` python
+tf.get_collection("my_collection_name")
+```
 
-## Initialization
+### Device placement
 
-Variable initializers must be run explicitly before other ops in your model can
-be run.  The easiest way to do that is to add an op that runs all the variable
-initializers, and run that op before using the model.
+Just like any other TensorFlow operation, you can place variables on particular
+devices. For example, the following snippet creates a variable named `v` and
+places it on the second GPU device:
 
-You can alternatively restore variable values from a checkpoint file, see
-below.
+``` python
+with tf.device("/gpu:1"):
+  v = tf.get_variable("v", [1])
+```
 
-Use `tf.global_variables_initializer()` to add an op to run variable initializers.
-Only run that op after you have fully constructed your model and launched it in
-a session.
+It is particularly important for variables to be in the correct device in
+distributed settings. Accidentally putting variables on workers instead of
+parameter servers, for example, can severely slow down training or, in the worst
+case, let each worker blithely forge ahead with its own independent copy of each
+variable. For this reason we provide @{tf.train.replica_device_setter}, which
+can automatically place variables in parameter servers. For example:
+
+``` python
+cluster_spec = {
+    "ps": ["ps0:2222", "ps1:2222"],
+    "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
+with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
+  v = tf.get_variable("v", shape=[20, 20])  # this variable is placed 
+                                            # in the parameter server
+                                            # by the replica_device_setter
+```
 
-```python
-# Create two variables.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-biases = tf.Variable(tf.zeros([200]), name="biases")
-...
-# Add an op to initialize the variables.
-init_op = tf.global_variables_initializer()
+## Initializing variables
 
-# Later, when launching the model
-with tf.Session() as sess:
-  # Run the init operation.
-  sess.run(init_op)
-  ...
-  # Use the model
-  ...
+Before you can use a variable, it must be initialized. If you are programming in
+the low-level TensorFlow API (that is, you are explicitly creating your own
+graphs and sessions), you must explicitly initialize the variables.  Most
+high-level frameworks such as `tf.contrib.slim`, `tf.estimator.Estimator` and
+`Keras` automatically initialize variables for you before training a model.
+
+Explicit initialization is otherwise useful because it allows you not to rerun
+potentially expensive initializers when reloading a model from a checkpoint as
+well as allowing determinism when randomly-initialized variables are shared in a
+distributed setting. 
+
+To initialize all trainable variables in one go, before training starts, call
+`tf.global_variables_initializer()`. This function returns a single operation
+responsible for initializing all variables in the
+`tf.GraphKeys.GLOBAL_VARIABLES` collection. Running this operation initializes
+all variables. For example:
+
+``` python
+session.run(tf.global_variables_initializer())
+# Now all variables are initialized.
 ```
 
-### Initialization from another Variable
+If you do need to initialize variables yourself, you can run the variable's
+initializer operation. For example:
 
-You sometimes need to initialize a variable from the initial value of another
-variable.  As the op added by `tf.global_variables_initializer()` initializes all
-variables in parallel you have to be careful when this is needed.
+``` python
+session.run(my_variable.initializer)
+```
 
-To initialize a new variable from the value of another variable use the other
-variable's `initialized_value()` property.  You can use the initialized value
-directly as the initial value for the new variable, or you can use it as any
-other tensor to compute a value for the new variable.
 
+You can also ask which variables have still not been initialized. For example,
+the following code prints the names of all variables which have not yet been
+initialized:
 
-```python
-# Create a variable with a random value.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-# Create another variable with the same value as 'weights'.
-w2 = tf.Variable(weights.initialized_value(), name="w2")
-# Create another variable with twice the value of 'weights'
-w_twice = tf.Variable(weights.initialized_value() * 2.0, name="w_twice")
+``` python
+print(session.run(tf.report_uninitialized_variables()))
+```
+
+
+Note that by default `tf.global_variables_initializer` does not specify the
+order in which variables are initialized. Therefore, if the initial value of a
+variable depends on another variable's value, it's likely that you'll get an
+error. Any time you use the value of a variable in a context in which not all
+variables are initialized (say, if you use a variable's value while initializing
+another variable), it is best to use `variable.initialized_value()` instead of
+`variable`:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+w = tf.get_variable("w", initializer=v.initialized_value() + 1)
+```
+
+## Using variables
+
+To use the value of a `tf.Variable` in a TensorFlow graph, simply treat it like
+a normal `tf.Tensor`:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+w = v + 1  # w is a tf.Tensor which is computed based on the value of v.
+           # Any time a variable is used in an expression it gets automatically
+           # converted to a tf.Tensor representing its value.
 ```
 
-### Custom Initialization
+To assign a value to a variable, use the methods `assign`, `assign_add`, and
+friends in the `tf.Variable` class. For example, here is how you can call these
+methods:
 
-The convenience function `tf.global_variables_initializer()` adds an op to
-initialize *all variables* in the model.  You can also pass an explicit list of
-variables to initialize to `tf.variables_initializer`.  See the
-@{$python/state_ops$Variables Documentation} for more options,
-including checking if variables are initialized.
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+assignment = v.assign_add(1)
+tf.global_variables_initializer().run()
+assignment.run()
+```
+
+Most TensorFlow optimizers have specialized ops that efficiently update the
+values of variables according to some gradient descent-like algorithm. See
+@{tf.train.Optimizer} for an explanation of how to use optimizers.
+
+Because variables are mutable it's sometimes useful to know what version of a
+variable's value is being used at any point in time. To force a re-read of the
+value of a variable after something has happened, you can use
+`tf.Variable.read_value`. For example:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+assignment = v.assign_add(1)
+with tf.control_dependencies([assignment]):
+  w = v.read_value()  # w is guaranteed to reflect v's value after the
+                      # assign_add operation.
+```
 
 ## Saving and Restoring
 
 The easiest way to save and restore a model is to use a `tf.train.Saver` object.
 The constructor adds `save` and `restore` ops to the graph for all, or a
-specified list, of the variables in the graph.  The saver object provides
+specified list, of the variables in the graph.  The `Saver` object provides
 methods to run these ops, specifying paths for the checkpoint files to write to
 or read from.
 
-Note that to restore a model checkpoint without a graph one must first import
-the graph from the meta graph file (typical extension is `.meta`). This is
-done with @{tf.train.import_meta_graph}, which in turn returns a `Saver` from
-which one can than perform a `restore`.
+To restore a model checkpoint without a graph, you must first import the graph
+from the `MetaGraph` file (typical extension is `.meta`). Do this by calling
+@{tf.train.import_meta_graph}, which in turn returns a `Saver` from which one
+can than perform a `restore`.
 
 ### Checkpoint Files
 
-Variables are saved in binary files that, roughly, contain a map from variable
+TensorFlow saves variables in binary files that, roughly speaking, map variable
 names to tensor values.
 
 When you create a `Saver` object, you can optionally choose names for the
-variables in the checkpoint files.  By default, it uses the value of the
+variables in the checkpoint files.  By default, `Saver` uses the value of the
 @{tf.Variable.name} property for
 each variable.
 
-To understand what variables are in a checkpoint, you can use the
-[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py)
-library, and in particular, the `print_tensors_in_checkpoint_file` function.
+To inspect the variables in a checkpoint, you can use
+the
+[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library,
+particularly the `print_tensors_in_checkpoint_file` function.
 
 ### Saving Variables
 
-Create a `Saver` with `tf.train.Saver()` to manage all variables in
-the model.
+Create a `Saver` with `tf.train.Saver()` to manage all variables in the
+model. For example, the following snippet demonstrates how to call the
+`tf.train.Saver.save` method to save variables to a checkpoint file:
 
 ```python
 # Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
+v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
+v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
+
+inc_v1 = v1.assign(v1+1)
+dec_v2 = v2.assign(v2-1)
+
 # Add an op to initialize the variables.
 init_op = tf.global_variables_initializer()
 
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
-# Later, launch the model, initialize the variables, do some work, save the
+# Later, launch the model, initialize the variables, do some work, and save the
 # variables to disk.
 with tf.Session() as sess:
   sess.run(init_op)
   # Do some work with the model.
-  ..
+  inc_v1.op.run()
+  dec_v2.op.run()
   # Save the variables to disk.
   save_path = saver.save(sess, "/tmp/model.ckpt")
   print("Model saved in file: %s" % save_path)
@@ -192,14 +285,19 @@ with tf.Session() as sess:
 
 ### Restoring Variables
 
-The same `Saver` object is used to restore variables.  Note that when you
-restore variables from a file you do not have to initialize them beforehand.
+The `tf.train.Saver` object not only saves variables to checkpoint files, it
+also restores variables.  Note that when you restore variables from a file you
+do not have to initialize them beforehand. For example, the following snippet
+demonstrates how to call the `tf.train.Saver.restore` method to restore
+variables from a checkpoint file:
 
 ```python
+tf.reset_default_graph()
+
 # Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
+v1 = tf.get_variable("v1", shape=[3])
+v2 = tf.get_variable("v2", shape=[5])
+
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
@@ -209,50 +307,156 @@ with tf.Session() as sess:
   # Restore variables from disk.
   saver.restore(sess, "/tmp/model.ckpt")
   print("Model restored.")
-  # Do some work with the model
-  ...
+  # Check the values of the variables
+  print("v1 : %s" % v1.eval())
+  print("v2 : %s" % v2.eval())
 ```
 
+
+
 ### Choosing which Variables to Save and Restore
 
-If you do not pass any argument to `tf.train.Saver()` the saver handles all
-variables in the graph.  Each one of them is saved under the name that was
-passed when the variable was created.
+If you do not pass any argument to `tf.train.Saver()`, the saver handles all
+variables in the graph.  Each variable is saved under the name that was passed
+when the variable was created.
 
 It is sometimes useful to explicitly specify names for variables in the
 checkpoint files.  For example, you may have trained a model with a variable
-named `"weights"` whose value you want to restore in a new variable named
+named `"weights"` whose value you want to restore into a variable named
 `"params"`.
 
 It is also sometimes useful to only save or restore a subset of the variables
-used by a model.  For example, you may have trained a neural net with 5 layers,
-and you now want to train a new model with 6 layers, restoring the parameters
-from the 5 layers of the previously trained model into the first 5 layers of
-the new model.
+used by a model.  For example, you may have trained a neural net with five
+layers, and you now want to train a new model with six layers that reuses the
+existing weights of the five trained layers. You can use the saver to restore
+the weights of just the first five layers.
+
+You can easily specify the names and variables to save or load by passing to the
+`tf.train.Saver()` constructor either a list of variables (which will be stored 
+under their own names), or a Python dictionary in which keys are the names to 
+use and values are the variables to manage. 
+
+Continuing from the save/restore examples, above:
+
+```python
+tf.reset_default_graph()
+# Create some variables.
+v1 = tf.get_variable("v1", [3], initializer = tf.zeros_initializer)
+v2 = tf.get_variable("v2", [5], initializer = tf.zeros_initializer)
 
-You can easily specify the names and variables to save by passing to the
-`tf.train.Saver()` constructor a Python dictionary: keys are the
-names to use, values are the variables to manage.
+# Add ops to save and restore only `v2` using the name "v2"
+saver = tf.train.Saver({"v2": v2})
+
+# Use the saver object normally after that.
+with tf.Session() as sess:
+  # Initialize v1 since the saver will not.
+  v1.initializer.run()
+  saver.restore(sess, "/tmp/model.ckpt")
+  
+  print("v1 : %s" % v1.eval())
+  print("v2 : %s" % v2.eval())
+
+```
 
 Notes:
 
-*  You can create as many saver objects as you want if you need to save and
+*  You can create as many `Saver` objects as you want if you need to save and
    restore different subsets of the model variables.  The same variable can be
-   listed in multiple saver objects, its value is only changed when the saver
-   `restore()` method is run.
+   listed in multiple saver objects, its value is only changed when the
+   `Saver.restore()` method is run.
+
+*  If you only restore a subset of the model variables at the start of a
+   session, you have to run an initialize op for the other variables.  See
+   @{tf.variables_initializer} for more information.
+
+
+## Sharing variables
+
+TensorFlow supports two ways of sharing variables:
 
-*  If you only restore a subset of the model variables at the start
-   of a session, you have to run an initialize op for the other variables.  See
-   @{tf.variables_initializer}
-   for more information.
+ * Explicitly passing `tf.Variable` objects around.
+ * Implicitly wrapping `tf.Variable` objects within `tf.variable_scope` objects.
+
+While code which explicitly passes variables around is very clear, it is
+sometimes convenient to write TensorFlow functions that implicitly use
+variables in their implementations. Most of the functional layers from
+`tf.layer` use this approach, as well as all `tf.metrics`, and a few other
+library utilities.
+
+Variable scopes allow you to control variable reuse when calling functions which
+implicitly create and use variables. They also allow you to name your variables
+in a hierarchical and understandable way.
+
+For example, let's say we write a function to create a convolutional / relu
+layer:
 
 ```python
-# Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
-# Add ops to save and restore only 'v2' using the name "my_v2"
-saver = tf.train.Saver({"my_v2": v2})
-# Use the saver object normally after that.
-...
+def conv_relu(input, kernel_shape, bias_shape):
+    # Create variable named "weights".
+    weights = tf.get_variable("weights", kernel_shape,
+        initializer=tf.random_normal_initializer())
+    # Create variable named "biases".
+    biases = tf.get_variable("biases", bias_shape,
+        initializer=tf.constant_initializer(0.0))
+    conv = tf.nn.conv2d(input, weights,
+        strides=[1, 1, 1, 1], padding='SAME')
+    return tf.nn.relu(conv + biases)
 ```
+
+This function uses short names `weights` and `biases`, which is good for
+clarity. In a real model, however, we want many such convolutional layers, and
+calling this function repeatedly would not work:
+
+``` python
+input1 = tf.random_normal([1,10,10,32])
+input2 = tf.random_normal([1,20,20,32])
+x = conv_relu(input1, kernel_shape=[5, 5, 32, 32], bias_shape=[32])
+x = conv_relu(x, kernel_shape=[5, 5, 32, 32], bias_shape = [32])  # This fails.
+```
+
+Since the desired behavior is unclear (create new variables or reuse the
+existing ones?) TensorFlow will fail. Calling `conv_relu` in different scopes,
+however, clarifies that we want to create new variables:
+
+```python
+def my_image_filter(input_images):
+    with tf.variable_scope("conv1"):
+        # Variables created here will be named "conv1/weights", "conv1/biases".
+        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
+    with tf.variable_scope("conv2"):
+        # Variables created here will be named "conv2/weights", "conv2/biases".
+        return conv_relu(relu1, [5, 5, 32, 32], [32])
+```
+
+If you do want the variables to be shared, you have two options. First, you can
+create a scope with the same name using `reuse=True`:
+
+``` python
+with tf.variable_scope("model"):
+  output1 = my_image_filter(input1)
+with tf.variable_scope("model", reuse=True):
+  output2 = my_image_filter(input2)
+
+```
+
+You can also call `scope.reuse_variables()` to trigger a reuse:
+
+``` python
+with tf.variable_scope("model") as scope:
+  output1 = my_image_filter(input1)
+  scope.reuse_variables()
+  output2 = my_image_filter(input2)
+
+```
+
+Since depending on exact string names of scopes can feel dangerous, it's also
+possible to initialize a variable scope based on another one:
+
+``` python
+with tf.variable_scope("model") as scope:
+  output1 = my_image_filter(input1)
+with tf.variable_scope(scope, reuse=True):
+  output2 = my_image_filter(input2)
+
+```
+
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index f60c8fd770..adf66b904e 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -417,7 +417,7 @@ scope indicating that they should be run on the first GPU.
 All variables are pinned to the CPU and accessed via
 @{tf.get_variable}
 in order to share them in a multi-GPU version.
-See how-to on @{$variable_scope$Sharing Variables}.
+See how-to on @{$variables$Sharing Variables}.
 
 ### Launching and Training the Model on Multiple GPU cards
 
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index aceffd373a..f784b715c1 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -1068,7 +1068,7 @@ get_variable_or_local_docstring = (
 
 %sThis function prefixes the name with the current variable scope
 and performs reuse checks. See the
-@{$variable_scope$Variable Scope How To}
+@{$variables$Variable Scope How To}
 for an extensive description of how reusing works. Here is a basic example:
 
 ```python
@@ -1444,7 +1444,7 @@ def variable_scope(name_or_scope,
 
   Variable scope allows to create new variables and to share already created
   ones while providing checks to not create or share by accident. For details,
-  see the @{$variable_scope$Variable Scope How To},
+  see the @{$variables$Variable Scope How To},
   here we present only a few basic examples.
 
   Simple example of how to create a new variable:
-- 
GitLab


From 8e05564f9cf9a0dbd02f7f5e902b084cdad7c29b Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Tue, 27 Jun 2017 14:43:25 -0700
Subject: [PATCH 0519/1375] Make ResourceHandle not be a proto

I'm trying to make core/kernels independent of protos.  Currently the dtype ResourceHandle is itself a proto.  After this CL, ResourceHandle is a normal C++ type which gets converted to/from ResourceHandleProto at (de)serialization time.

RELNOTES: n/a
PiperOrigin-RevId: 160329002
---
 .../contrib/cmake/tf_core_framework.cmake     |  9 +-
 .../contrib/makefile/proto_text_cc_files.txt  |  1 +
 tensorflow/core/BUILD                         |  4 +
 tensorflow/core/framework/allocator.h         |  2 +-
 tensorflow/core/framework/register_types.h    |  2 +-
 tensorflow/core/framework/resource_handle.cc  | 69 ++++++++++++++++
 tensorflow/core/framework/resource_handle.h   | 82 +++++++++++++++++++
 .../core/framework/resource_handle.proto      |  4 +-
 tensorflow/core/framework/tensor.cc           | 10 ++-
 tensorflow/core/framework/tensor.proto        |  2 +-
 tensorflow/core/framework/types.h             |  2 +-
 tensorflow/core/kernels/strided_slice_op.h    |  2 +-
 tensorflow/core/platform/tensor_coding.cc     | 15 ++--
 tensorflow/core/platform/tensor_coding.h      |  2 +-
 tensorflow/python/ops/session_ops.py          |  2 +-
 15 files changed, 188 insertions(+), 20 deletions(-)
 create mode 100644 tensorflow/core/framework/resource_handle.cc
 create mode 100644 tensorflow/core/framework/resource_handle.h

diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index 406b656349..a15621ba6d 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -147,7 +147,9 @@ file(GLOB tf_core_platform_srcs
     "${tensorflow_source_dir}/tensorflow/core/platform/*.h"
     "${tensorflow_source_dir}/tensorflow/core/platform/*.cc"
     "${tensorflow_source_dir}/tensorflow/core/platform/default/*.h"
-    "${tensorflow_source_dir}/tensorflow/core/platform/default/*.cc")
+    "${tensorflow_source_dir}/tensorflow/core/platform/default/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/framework/resource_handle.h"
+    "${tensorflow_source_dir}/tensorflow/core/framework/resource_handle.cc")
 if (NOT tensorflow_ENABLE_GPU)
   file(GLOB tf_core_platform_gpu_srcs
       "${tensorflow_source_dir}/tensorflow/core/platform/cuda_libdevice_path.*"
@@ -235,18 +237,19 @@ file(GLOB_RECURSE tf_core_framework_srcs
     "${tensorflow_source_dir}/public/*.h"
 )
 
-file(GLOB_RECURSE tf_core_framework_test_srcs
+file(GLOB_RECURSE tf_core_framework_exclude_srcs
     "${tensorflow_source_dir}/tensorflow/core/framework/*test*.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*test*.cc"
     "${tensorflow_source_dir}/tensorflow/core/framework/*testutil.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*testutil.cc"
     "${tensorflow_source_dir}/tensorflow/core/framework/*main.cc"
+    "${tensorflow_source_dir}/tensorflow/core/framework/resource_handle.cc"
     "${tensorflow_source_dir}/tensorflow/core/util/*test*.h"
     "${tensorflow_source_dir}/tensorflow/core/util/*test*.cc"
     "${tensorflow_source_dir}/tensorflow/core/util/*main.cc"
 )
 
-list(REMOVE_ITEM tf_core_framework_srcs ${tf_core_framework_test_srcs})
+list(REMOVE_ITEM tf_core_framework_srcs ${tf_core_framework_exclude_srcs})
 
 add_library(tf_core_framework OBJECT
     ${tf_core_framework_srcs}
diff --git a/tensorflow/contrib/makefile/proto_text_cc_files.txt b/tensorflow/contrib/makefile/proto_text_cc_files.txt
index 67bc8f8ada..d56e388477 100644
--- a/tensorflow/contrib/makefile/proto_text_cc_files.txt
+++ b/tensorflow/contrib/makefile/proto_text_cc_files.txt
@@ -1,5 +1,6 @@
 tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc
 tensorflow/tools/proto_text/gen_proto_text_functions.cc
+tensorflow/core/framework/resource_handle.cc
 tensorflow/core/platform/default/protobuf.cc
 tensorflow/core/platform/tracing.cc
 tensorflow/core/platform/tensor_coding.cc
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 27544c9961..9f1554b5b6 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1246,6 +1246,9 @@ cc_library(
                 "platform/*.cc",
                 "platform/profile_utils/**/*.h",
                 "platform/profile_utils/**/*.cc",
+            ] + [
+                "framework/resource_handle.h",
+                "framework/resource_handle.cc",
             ],
             exclude = [
                 "**/*test*",
@@ -1437,6 +1440,7 @@ tf_cuda_library(
             "framework/fake_input.*",
             "framework/op_gen_lib.*",
             "framework/reader_base.*",
+            "framework/resource_handle.cc",
             "util/memmapped_file_system.*",
             "util/memmapped_file_system_writer.*",
             "util/version_info.cc",
diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h
index cb58896f49..1cb183d81e 100644
--- a/tensorflow/core/framework/allocator.h
+++ b/tensorflow/core/framework/allocator.h
@@ -21,7 +21,7 @@ limitations under the License.
 #include <limits>
 
 #include "tensorflow/core/framework/numeric_types.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index d79468afa1..2f7b140295 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -18,7 +18,7 @@ limitations under the License.
 // This file is used by cuda code and must remain compilable by nvcc.
 
 #include "tensorflow/core/framework/numeric_types.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/platform/types.h"
 
 // Two sets of macros:
diff --git a/tensorflow/core/framework/resource_handle.cc b/tensorflow/core/framework/resource_handle.cc
new file mode 100644
index 0000000000..39ef82765f
--- /dev/null
+++ b/tensorflow/core/framework/resource_handle.cc
@@ -0,0 +1,69 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/resource_handle.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+
+namespace tensorflow {
+
+ResourceHandle::ResourceHandle() {}
+
+ResourceHandle::ResourceHandle(const ResourceHandleProto& proto) {
+  FromProto(proto);
+}
+
+ResourceHandle::~ResourceHandle() {}
+
+void ResourceHandle::AsProto(ResourceHandleProto* proto) const {
+  proto->set_device(device());
+  proto->set_container(container());
+  proto->set_name(name());
+  proto->set_hash_code(hash_code());
+  proto->set_maybe_type_name(maybe_type_name());
+}
+
+void ResourceHandle::FromProto(const ResourceHandleProto& proto) {
+  set_device(proto.device());
+  set_container(proto.container());
+  set_name(proto.name());
+  set_hash_code(proto.hash_code());
+  set_maybe_type_name(proto.maybe_type_name());
+}
+
+string ResourceHandle::SerializeAsString() const {
+  ResourceHandleProto proto;
+  AsProto(&proto);
+  return proto.SerializeAsString();
+}
+
+bool ResourceHandle::ParseFromString(const string& s) {
+  ResourceHandleProto proto;
+  const bool status = proto.ParseFromString(s);
+  if (status) FromProto(proto);
+  return status;
+}
+
+string ResourceHandle::DebugString() const {
+  return strings::StrCat("device: ", device(), " container: ", container(),
+                         " name: ", name(), " hash_code: ", hash_code(),
+                         " maybe_type_name: ", maybe_type_name());
+}
+
+string ProtoDebugString(const ResourceHandle& handle) {
+  return handle.DebugString();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/framework/resource_handle.h b/tensorflow/core/framework/resource_handle.h
new file mode 100644
index 0000000000..06df1b9046
--- /dev/null
+++ b/tensorflow/core/framework/resource_handle.h
@@ -0,0 +1,82 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_FRAMEWORK_RESOURCE_HANDLE_H_
+#define TENSORFLOW_FRAMEWORK_RESOURCE_HANDLE_H_
+
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+class ResourceHandleProto;
+
+// Class representing a handle to a tensorflow resource. Handles are
+// not valid across executions, but can be serialized back and forth from within
+// a single run.
+//
+// This is the native C++ class equivalent of ResourceHandleProto.  They are
+// separate so that kernels do not need to depend on protos.
+class ResourceHandle {
+ public:
+  ResourceHandle();
+  ResourceHandle(const ResourceHandleProto& proto);
+  ~ResourceHandle();
+
+  // Unique name for the device containing the resource.
+  const string& device() const { return device_; }
+  void set_device(const string& device) { device_ = device; }
+
+  // Container in which this resource is placed.
+  const string& container() const { return container_; }
+  void set_container(const string& container) { container_ = container; }
+
+  // Unique name of this resource.
+  const string& name() const { return name_; }
+  void set_name(const string& name) { name_ = name; }
+
+  // Hash code for the type of the resource. Is only valid in the same device
+  // and in the same execution.
+  uint64 hash_code() const { return hash_code_; }
+  void set_hash_code(uint64 hash_code) { hash_code_ = hash_code; }
+
+  // For debug-only, the name of the type pointed to by this handle, if
+  // available.
+  const string& maybe_type_name() const { return maybe_type_name_; }
+  void set_maybe_type_name(const string& value) { maybe_type_name_ = value; }
+
+  // Conversion to and from ResourceHandleProto
+  void AsProto(ResourceHandleProto* proto) const;
+  void FromProto(const ResourceHandleProto& proto);
+
+  // Serialization via ResourceHandleProto
+  string SerializeAsString() const;
+  bool ParseFromString(const string& s);
+
+  string DebugString() const;
+
+ public:
+  string device_;
+  string container_;
+  string name_;
+  uint64 hash_code_ = 0;
+  string maybe_type_name_;
+};
+
+// For backwards compatibility for when this was a proto
+string ProtoDebugString(const ResourceHandle& handle);
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_FRAMEWORK_RESOURCE_HANDLE_H_
diff --git a/tensorflow/core/framework/resource_handle.proto b/tensorflow/core/framework/resource_handle.proto
index f9f19ca5b4..b1921337f5 100644
--- a/tensorflow/core/framework/resource_handle.proto
+++ b/tensorflow/core/framework/resource_handle.proto
@@ -2,14 +2,14 @@ syntax = "proto3";
 
 package tensorflow;
 option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandleProto";
+option java_outer_classname = "ResourceHandle";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
 
 // Protocol buffer representing a handle to a tensorflow resource. Handles are
 // not valid across executions, but can be serialized back and forth from within
 // a single run.
-message ResourceHandle {
+message ResourceHandleProto {
   // Unique name for the device containing the resource.
   string device = 1;
 
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 243b30c007..cc9f11ef6a 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 
 #include "tensorflow/core/framework/log_memory.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/framework/types.h"
@@ -270,7 +271,7 @@ struct ProtoHelper<int64> {
 
 template <>
 struct ProtoHelper<ResourceHandle> {
-  static protobuf::RepeatedPtrField<ResourceHandle>::const_iterator Begin(
+  static protobuf::RepeatedPtrField<ResourceHandleProto>::const_iterator Begin(
       const TensorProto& proto) {
     return proto.resource_handle_val().begin();
   }
@@ -278,8 +279,11 @@ struct ProtoHelper<ResourceHandle> {
     return proto.resource_handle_val().size();
   }
   static void Fill(const ResourceHandle* data, size_t n, TensorProto* proto) {
-    protobuf::RepeatedPtrField<ResourceHandle> copy(data, data + n);
-    proto->mutable_resource_handle_val()->Swap(&copy);
+    auto* handles = proto->mutable_resource_handle_val();
+    handles->Clear();
+    for (size_t i = 0; i < n; i++) {
+      data[i].AsProto(handles->Add());
+    }
   }
 };
 
diff --git a/tensorflow/core/framework/tensor.proto b/tensorflow/core/framework/tensor.proto
index 5d383bcc66..98ecef225a 100644
--- a/tensorflow/core/framework/tensor.proto
+++ b/tensorflow/core/framework/tensor.proto
@@ -71,5 +71,5 @@ message TensorProto {
   repeated double dcomplex_val = 12 [packed = true];
 
   // DT_RESOURCE
-  repeated ResourceHandle resource_handle_val = 14;
+  repeated ResourceHandleProto resource_handle_val = 14;
 };
diff --git a/tensorflow/core/framework/types.h b/tensorflow/core/framework/types.h
index f562880e7c..889056647c 100644
--- a/tensorflow/core/framework/types.h
+++ b/tensorflow/core/framework/types.h
@@ -28,7 +28,7 @@ limitations under the License.
 // clang-format on
 #include "tensorflow/core/framework/bfloat16.h"
 #include "tensorflow/core/framework/numeric_types.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
diff --git a/tensorflow/core/kernels/strided_slice_op.h b/tensorflow/core/kernels/strided_slice_op.h
index 7893e07949..0f72c4b771 100644
--- a/tensorflow/core/kernels/strided_slice_op.h
+++ b/tensorflow/core/kernels/strided_slice_op.h
@@ -19,7 +19,7 @@ limitations under the License.
 // Functor definition for StridedSliceOp, must be compilable by nvcc.
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/platform/types.h"
 
diff --git a/tensorflow/core/platform/tensor_coding.cc b/tensorflow/core/platform/tensor_coding.cc
index 73ccd68e5d..17dc81f7e0 100644
--- a/tensorflow/core/platform/tensor_coding.cc
+++ b/tensorflow/core/platform/tensor_coding.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/platform/tensor_coding.h"
 
 #include <vector>
+#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
@@ -67,12 +68,14 @@ void CopyFromArray(string* s, const char* base, size_t bytes) {
 
 void EncodeResourceHandleList(const ResourceHandle* p, int64 n, string* out) {
   out->clear();
+  string rest;
+  ResourceHandleProto proto;
   for (int i = 0; i < n; ++i) {
-    core::PutVarint32(out, p[i].ByteSize());
-  }
-  for (int i = 0; i < n; ++i) {
-    p[i].AppendToString(out);
+    p[i].AsProto(&proto);
+    core::PutVarint32(out, proto.ByteSize());
+    proto.AppendToString(&rest);
   }
+  *out += rest;
 }
 
 bool DecodeResourceHandleList(const string& in, ResourceHandle* ps, int64 n) {
@@ -86,10 +89,12 @@ bool DecodeResourceHandleList(const string& in, ResourceHandle* ps, int64 n) {
   if (total != static_cast<int64>(reader.size())) {
     return false;
   }
+  ResourceHandleProto proto;
   for (int i = 0; i < n; ++i) {
-    if (!ps[i].ParseFromArray(reader.data(), sizes[i])) {
+    if (!proto.ParseFromArray(reader.data(), sizes[i])) {
       return false;
     }
+    ps[i].FromProto(proto);
     reader.remove_prefix(sizes[i]);
   }
   return true;
diff --git a/tensorflow/core/platform/tensor_coding.h b/tensorflow/core/platform/tensor_coding.h
index 2dd65fb97a..19f53e6374 100644
--- a/tensorflow/core/platform/tensor_coding.h
+++ b/tensorflow/core/platform/tensor_coding.h
@@ -18,7 +18,7 @@ limitations under the License.
 #define TENSORFLOW_PLATFORM_TENSOR_CODING_H_
 
 #include <string>
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/lib/core/refcount.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/platform/platform.h"
diff --git a/tensorflow/python/ops/session_ops.py b/tensorflow/python/ops/session_ops.py
index e74c52b8cf..dc4d913c93 100644
--- a/tensorflow/python/ops/session_ops.py
+++ b/tensorflow/python/ops/session_ops.py
@@ -74,7 +74,7 @@ class TensorHandle(object):
   def _get_resource_handle(self):
     """The ResourceHandle representation of this handle."""
     if not self._resource_handle:
-      self._resource_handle = resource_handle_pb2.ResourceHandle()
+      self._resource_handle = resource_handle_pb2.ResourceHandleProto()
       self._resource_handle.device = self._handle.split(";")[-1]
       self._resource_handle.container = (
           pywrap_tensorflow_internal.TENSOR_HANDLE_KEY)
-- 
GitLab


From 7dede8848d0903a17ded277392f39f6f452fed28 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 15:17:39 -0700
Subject: [PATCH 0520/1375] Minor cleanup: remove unused dependencies and
 inclusions

PiperOrigin-RevId: 160334030
---
 tensorflow/compiler/xla/service/BUILD                     | 8 --------
 tensorflow/compiler/xla/service/cpu/BUILD                 | 2 --
 .../contrib/boosted_trees/kernels/split_handler_ops.cc    | 1 -
 tensorflow/contrib/boosted_trees/lib/BUILD                | 1 -
 tensorflow/contrib/boosted_trees/resources/BUILD          | 1 -
 tensorflow/contrib/tensor_forest/kernels/v4/BUILD         | 1 -
 6 files changed, 14 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index a20a5cd351..0f1bb363b5 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -714,7 +714,6 @@ cc_library(
         ":buffer_liveness",
         ":heap_simulator",
         ":hlo",
-        ":hlo_ordering",
         ":hlo_proto",
         ":hlo_scheduling",
         ":logical_buffer",
@@ -799,10 +798,7 @@ cc_library(
         ":liveness_util",
         ":logical_buffer",
         ":tuple_points_to_analysis",
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
-        "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
     ],
@@ -816,7 +812,6 @@ cc_test(
         ":heap_simulator",
         ":hlo",
         ":hlo_ordering",
-        ":hlo_scheduling",
         ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:literal_util",
@@ -1238,7 +1233,6 @@ cc_library(
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status",
-        "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
@@ -1301,7 +1295,6 @@ cc_library(
         ":hlo_value",
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
@@ -1314,7 +1307,6 @@ cc_library(
     srcs = ["hlo_alias_analysis.cc"],
     hdrs = ["hlo_alias_analysis.h"],
     deps = [
-        ":call_graph",
         ":hlo",
         ":hlo_buffer",
         ":hlo_dataflow_analysis",
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 405057f918..53410b09c8 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -188,7 +188,6 @@ cc_library(
         ":dot_op_emitter",
         ":elemental_ir_emitter",
         ":ir_emission_utils",
-        ":shape_partition",
         ":simple_orc_jit",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
@@ -543,7 +542,6 @@ cc_test(
         ":shape_partition",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/core:test_main",
     ],
diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
index 6e73db9e1d..dcc263d810 100644
--- a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
@@ -20,7 +20,6 @@
 #include "tensorflow/contrib/boosted_trees/proto/split_info.pb.h"
 #include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
 #include "tensorflow/core/framework/device_base.h"
-#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/contrib/boosted_trees/lib/BUILD b/tensorflow/contrib/boosted_trees/lib/BUILD
index c63de844fd..9e90ff399e 100644
--- a/tensorflow/contrib/boosted_trees/lib/BUILD
+++ b/tensorflow/contrib/boosted_trees/lib/BUILD
@@ -335,7 +335,6 @@ cc_library(
         ":feature-stats-accumulator",
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
diff --git a/tensorflow/contrib/boosted_trees/resources/BUILD b/tensorflow/contrib/boosted_trees/resources/BUILD
index 73de5df309..9fc101612f 100644
--- a/tensorflow/contrib/boosted_trees/resources/BUILD
+++ b/tensorflow/contrib/boosted_trees/resources/BUILD
@@ -37,7 +37,6 @@ cc_library(
         "//tensorflow/contrib/boosted_trees/lib:weighted_quantiles",
         "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_cc",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index a9d8093d13..06856c3c07 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -76,7 +76,6 @@ cc_library(
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
-- 
GitLab


From 824dae164fcce971689e621e28ce62a92a84fdd7 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Tue, 27 Jun 2017 15:51:03 -0700
Subject: [PATCH 0521/1375] Add name_scopes to mnist_deep.py for a cleaner
 graph layout.

PiperOrigin-RevId: 160338775
---
 .../examples/tutorials/mnist/mnist_deep.py    | 61 ++++++++++++-------
 1 file changed, 38 insertions(+), 23 deletions(-)

diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py
index 2896eee77d..4df925c1f3 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_deep.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py
@@ -52,42 +52,50 @@ def deepnn(x):
   # Reshape to use within a convolutional neural net.
   # Last dimension is for "features" - there is only one here, since images are
   # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
-  x_image = tf.reshape(x, [-1, 28, 28, 1])
+  with tf.name_scope('reshape'):
+    x_image = tf.reshape(x, [-1, 28, 28, 1])
 
   # First convolutional layer - maps one grayscale image to 32 feature maps.
-  W_conv1 = weight_variable([5, 5, 1, 32])
-  b_conv1 = bias_variable([32])
-  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+  with tf.name_scope('conv1'):
+    W_conv1 = weight_variable([5, 5, 1, 32])
+    b_conv1 = bias_variable([32])
+    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
 
   # Pooling layer - downsamples by 2X.
-  h_pool1 = max_pool_2x2(h_conv1)
+  with tf.name_scope('pool1'):
+    h_pool1 = max_pool_2x2(h_conv1)
 
   # Second convolutional layer -- maps 32 feature maps to 64.
-  W_conv2 = weight_variable([5, 5, 32, 64])
-  b_conv2 = bias_variable([64])
-  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+  with tf.name_scope('conv2'):
+    W_conv2 = weight_variable([5, 5, 32, 64])
+    b_conv2 = bias_variable([64])
+    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
 
   # Second pooling layer.
-  h_pool2 = max_pool_2x2(h_conv2)
+  with tf.name_scope('pool2'):
+    h_pool2 = max_pool_2x2(h_conv2)
 
   # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
   # is down to 7x7x64 feature maps -- maps this to 1024 features.
-  W_fc1 = weight_variable([7 * 7 * 64, 1024])
-  b_fc1 = bias_variable([1024])
+  with tf.name_scope('fc1'):
+    W_fc1 = weight_variable([7 * 7 * 64, 1024])
+    b_fc1 = bias_variable([1024])
 
-  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
-  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
+    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
 
   # Dropout - controls the complexity of the model, prevents co-adaptation of
   # features.
-  keep_prob = tf.placeholder(tf.float32)
-  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+  with tf.name_scope('dropout'):
+    keep_prob = tf.placeholder(tf.float32)
+    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
   # Map the 1024 features to 10 classes, one for each digit
-  W_fc2 = weight_variable([1024, 10])
-  b_fc2 = bias_variable([10])
+  with tf.name_scope('fc2'):
+    W_fc2 = weight_variable([1024, 10])
+    b_fc2 = bias_variable([10])
 
-  y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
+    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
   return y_conv, keep_prob
 
 
@@ -127,11 +135,18 @@ def main(_):
   # Build the graph for the deep net
   y_conv, keep_prob = deepnn(x)
 
-  cross_entropy = tf.reduce_mean(
-      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
-  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
-  correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
-  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+  with tf.name_scope('loss'):
+    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
+                                                            logits=y_conv)
+  cross_entropy = tf.reduce_mean(cross_entropy)
+
+  with tf.name_scope('adam_optimizer'):
+    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+
+  with tf.name_scope('accuracy'):
+    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+    correct_prediction = tf.cast(correct_prediction, tf.float32)
+  accuracy = tf.reduce_mean(correct_prediction)
 
   with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
-- 
GitLab


From 83e0e87fd113b8adb733e43d87da7f67306f7855 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Tue, 27 Jun 2017 15:51:16 -0700
Subject: [PATCH 0522/1375] Add note about `tf.test.mock` to docs for `tf.test`

PiperOrigin-RevId: 160338811
---
 tensorflow/docs_src/api_guides/python/test.md | 3 +++
 tensorflow/python/platform/test.py            | 3 +++
 tensorflow/tools/docs/generate_lib.py         | 2 +-
 3 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/api_guides/python/test.md b/tensorflow/docs_src/api_guides/python/test.md
index 93a1d50b29..5dc88124e7 100644
--- a/tensorflow/docs_src/api_guides/python/test.md
+++ b/tensorflow/docs_src/api_guides/python/test.md
@@ -31,6 +31,9 @@ methods.  See @{tf.test.TestCase} for details.
 
 ## Utilities
 
+Note: `tf.test.mock` is an alias to the python `mock` or `unittest.mock`
+depending on the python version.
+
 *   @{tf.test.assert_equal_graph_def}
 *   @{tf.test.get_temp_dir}
 *   @{tf.test.is_built_with_cuda}
diff --git a/tensorflow/python/platform/test.py b/tensorflow/python/platform/test.py
index a307347f60..fdaae04fb6 100644
--- a/tensorflow/python/platform/test.py
+++ b/tensorflow/python/platform/test.py
@@ -17,6 +17,9 @@
 
 See the @{$python/test} guide.
 
+Note: `tf.test.mock` is an alias to the python `mock` or `unittest.mock`
+depending on the python version.
+
 @@main
 @@TestCase
 @@test_src_dir_path
diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index 67a4ad0ec9..3f6f4383ac 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -182,7 +182,7 @@ def add_dict_to_dict(add_from, add_to):
 
 # Exclude some libaries in contrib from the documentation altogether.
 def _get_default_private_map():
-  return {}
+  return {'tf.test': ['mock']}
 
 
 # Exclude members of some libaries.
-- 
GitLab


From 494f5c0c95d16577844cc430b59e611e88750c29 Mon Sep 17 00:00:00 2001
From: Dandelion Man? <dandelion@google.com>
Date: Tue, 27 Jun 2017 15:53:24 -0700
Subject: [PATCH 0523/1375] Internal change.

PiperOrigin-RevId: 160339087
---
 tensorflow/python/BUILD | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index e15f7410e4..7530c09cfe 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -41,7 +41,6 @@ py_library(
         "//tensorflow/contrib/learn/python/learn/datasets:__pkg__",  # TODO(b/34059704): remove when fixed
         "//tensorflow/python/debug:__pkg__",  # TODO(b/34059704): remove when fixed
         "//tensorflow/python/tools:__pkg__",  # TODO(b/34059704): remove when fixed
-        "//tensorflow/tensorboard/scripts:__pkg__",  # TODO(b/34059704): remove when fixed
         "//tensorflow/tools/quantization:__pkg__",  # TODO(b/34059704): remove when fixed
     ],
     deps = [
-- 
GitLab


From 63ac86ae19933bc9386233cb899d653f388f1bef Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 27 Jun 2017 15:55:10 -0700
Subject: [PATCH 0524/1375] Fix bugs in ScatterNd and add
 ScatterNdNonAliasingAdd.

tf.scatter_nd_non_aliasing_add acts similarly to tf.scatter_nd_add but
works on non-ref objects (i.e., Tensors -- not Variables).  This means
it has a gradient with respect to the primary input as well as the
updates.  It does its best to avoid making extra copies of the input.

PiperOrigin-RevId: 160339328
---
 tensorflow/cc/gradients/array_grad.cc         |  11 +
 tensorflow/cc/gradients/array_grad_test.cc    |  22 ++
 tensorflow/core/framework/common_shape_fns.cc |  62 +++++
 tensorflow/core/framework/common_shape_fns.h  |   3 +
 tensorflow/core/kernels/BUILD                 |  22 +-
 tensorflow/core/kernels/scatter_nd_op.cc      | 149 +++++++----
 tensorflow/core/ops/array_ops.cc              |  56 ++++
 tensorflow/core/ops/state_ops.cc              |  67 +----
 .../kernel_tests/scatter_nd_ops_test.py       | 243 +++++++++++-------
 tensorflow/python/ops/array_grad.py           |   7 +
 10 files changed, 431 insertions(+), 211 deletions(-)

diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc
index 37f07e71a0..e69db10580 100644
--- a/tensorflow/cc/gradients/array_grad.cc
+++ b/tensorflow/cc/gradients/array_grad.cc
@@ -247,6 +247,17 @@ Status ScatterNdGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("ScatterNd", ScatterNdGrad);
 
+Status ScatterNdNonAliasingAddGrad(const Scope& scope, const Operation& op,
+                                   const std::vector<Output>& grad_inputs,
+                                   std::vector<Output>* grad_outputs) {
+  auto indices = op.input(1);
+  grad_outputs->push_back(Identity(scope, grad_inputs[0]));
+  grad_outputs->push_back(NoGradient());
+  grad_outputs->push_back(GatherNd(scope, grad_inputs[0], indices));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("ScatterNdNonAliasingAdd", ScatterNdNonAliasingAddGrad);
+
 Status PadGrad(const Scope& scope, const Operation& op,
                const std::vector<Output>& grad_inputs,
                std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/array_grad_test.cc b/tensorflow/cc/gradients/array_grad_test.cc
index 5798b5b509..1777e18145 100644
--- a/tensorflow/cc/gradients/array_grad_test.cc
+++ b/tensorflow/cc/gradients/array_grad_test.cc
@@ -233,6 +233,28 @@ TEST_F(ArrayGradTest, ScatterNdGrad_SliceIndexing) {
   RunTest(updates, updates_shape, y, y_shape);
 }
 
+TEST_F(ArrayGradTest, ScatterNdNonAliasingAddGrad_SimpleIndexing) {
+  TensorShape updates_shape({4});
+  TensorShape input_shape({8});
+  auto input = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(input_shape));
+  auto updates =
+      Placeholder(scope_, DT_FLOAT, Placeholder::Shape(updates_shape));
+  auto indices = Const(scope_, {{4}, {3}, {1}, {7}});
+  auto y = ScatterNdNonAliasingAdd(scope_, input, indices, updates);
+  RunTest({input, updates}, {input_shape, updates_shape}, {y}, {input_shape});
+}
+
+TEST_F(ArrayGradTest, ScatterNdNonAliasingAddGrad_SliceIndexing) {
+  TensorShape updates_shape({2, 4, 4});
+  TensorShape input_shape({4, 4, 4});
+  auto input = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(input_shape));
+  auto updates =
+      Placeholder(scope_, DT_FLOAT, Placeholder::Shape(updates_shape));
+  auto indices = Const(scope_, {{0}, {2}});
+  auto y = ScatterNdNonAliasingAdd(scope_, input, indices, updates);
+  RunTest({input, updates}, {input_shape, updates_shape}, {y}, {input_shape});
+}
+
 TEST_F(ArrayGradTest, PadGrad) {
   TensorShape x_shape({2, 3});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
diff --git a/tensorflow/core/framework/common_shape_fns.cc b/tensorflow/core/framework/common_shape_fns.cc
index 035bceb640..dc2db3d395 100644
--- a/tensorflow/core/framework/common_shape_fns.cc
+++ b/tensorflow/core/framework/common_shape_fns.cc
@@ -922,5 +922,67 @@ Status ValidateSparseTensor(InferenceContext* c, ShapeHandle indices_shape,
   return Status::OK();
 }
 
+Status ScatterNdUpdateShape(InferenceContext* c) {
+  ShapeHandle input_shape = c->input(0);
+  ShapeHandle indices_shape;
+  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &indices_shape));
+  ShapeHandle updates_shape;
+  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(2), 1, &updates_shape));
+
+  if (c->Value(c->NumElements(input_shape)) == 0 &&
+      (c->Value(c->NumElements(indices_shape)) > 0 ||
+       c->Value(c->NumElements(updates_shape)) > 0)) {
+    return errors::InvalidArgument(
+        "Indices and updates specified for empty output shape");
+  }
+
+  if (c->RankKnown(indices_shape) && c->RankKnown(updates_shape)) {
+    const int64 num_outer_dims = c->Rank(indices_shape) - 1;
+    const DimensionHandle index_size = c->Dim(indices_shape, -1);
+
+    // We can only do more validation if the last dimension of indices
+    // is a known value.
+    if (c->ValueKnown(index_size)) {
+      const int64 ix = c->Value(index_size);
+      ShapeHandle unused;
+      ShapeHandle prefix_indices;
+      TF_RETURN_IF_ERROR(
+          c->Subshape(indices_shape, 0, num_outer_dims, &prefix_indices));
+      ShapeHandle prefix_updates;
+      TF_RETURN_IF_ERROR(
+          c->Subshape(updates_shape, 0, num_outer_dims, &prefix_updates));
+
+      Status s = c->Merge(prefix_indices, prefix_updates, &unused);
+      if (!s.ok()) {
+        return errors::InvalidArgument(
+            "The outer ", num_outer_dims,
+            " dimensions of indices.shape=", c->DebugString(indices_shape),
+            " must match the outer ", num_outer_dims,
+            " dimensions of updates.shape=", c->DebugString(updates_shape),
+            ": ", s.error_message());
+      }
+
+      ShapeHandle input_suffix;
+      TF_RETURN_IF_ERROR(c->Subshape(input_shape, ix, &input_suffix));
+      ShapeHandle suffix_updates;
+      TF_RETURN_IF_ERROR(
+          c->Subshape(updates_shape, num_outer_dims, &suffix_updates));
+      s = c->Merge(input_suffix, suffix_updates, &unused);
+      if (!s.ok()) {
+        return errors::InvalidArgument(
+            "The inner ", c->Rank(input_shape) - ix,
+            " dimensions of input.shape=", c->DebugString(input_shape),
+            " must match the inner ", c->Rank(updates_shape) - num_outer_dims,
+            " dimensions of updates.shape=", c->DebugString(updates_shape),
+            ": ", s.error_message());
+      }
+    }
+  }
+
+  c->set_output(0, input_shape);
+  return Status::OK();
+}
+
 }  // namespace shape_inference
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/common_shape_fns.h b/tensorflow/core/framework/common_shape_fns.h
index dc99e48adb..aafd9eff27 100644
--- a/tensorflow/core/framework/common_shape_fns.h
+++ b/tensorflow/core/framework/common_shape_fns.h
@@ -207,6 +207,9 @@ Status RandomShape(shape_inference::InferenceContext* c);
 Status ValidateSparseTensor(InferenceContext* c, ShapeHandle indices_shape,
                             ShapeHandle values_shape, ShapeHandle shape_shape);
 
+// Shape function for ScatterNd update/add/sub/... operations.
+Status ScatterNdUpdateShape(InferenceContext* c);
+
 }  // namespace shape_inference
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 8b7c269a11..41995a2ff5 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -3475,8 +3475,26 @@ tf_kernel_library(
 
 tf_kernel_library(
     name = "scatter_nd_op",
-    prefix = "scatter_nd_op",
-    deps = STATE_DEPS,
+    srcs = [
+        "scatter_nd_op.cc",
+        "scatter_nd_op_cpu_impl_0.cc",
+        "scatter_nd_op_cpu_impl_1.cc",
+        "scatter_nd_op_cpu_impl_2.cc",
+        "scatter_nd_op_cpu_impl_3.cc",
+        "scatter_nd_op_cpu_impl_4.cc",
+        "scatter_nd_op_cpu_impl_5.cc",
+    ],
+    hdrs = [
+        "dense_update_ops.h",
+        "scatter_nd_op.h",
+        "scatter_nd_op_cpu_impl.h",
+    ],
+    gpu_srcs = [
+        "dense_update_ops.h",
+        "scatter_nd_op.h",
+        "scatter_nd_op_gpu.cu.cc",
+    ],
+    deps = STATE_DEPS + [":dense_update_ops"],
 )
 
 tf_kernel_library(
diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc
index 48565d8cb9..5f96dfa0f3 100644
--- a/tensorflow/core/kernels/scatter_nd_op.cc
+++ b/tensorflow/core/kernels/scatter_nd_op.cc
@@ -16,12 +16,17 @@ limitations under the License.
 // See docs in ../ops/state_ops.cc.
 #define EIGEN_USE_THREADS
 
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif  // GOOGLE_CUDA
+
 #include "tensorflow/core/kernels/scatter_nd_op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/kernels/dense_update_ops.h"
 #include "tensorflow/core/kernels/fill_functor.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
@@ -29,7 +34,7 @@ limitations under the License.
 
 #ifdef TENSORFLOW_USE_SYCL
 #include "tensorflow/core/common_runtime/sycl/sycl_util.h"
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 namespace tensorflow {
 
@@ -37,7 +42,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 #ifdef TENSORFLOW_USE_SYCL
 typedef Eigen::SyclDevice SYCLDevice;
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 // Check whether updates.shape = indices.shape[:batch_dim] +
 // params_shape[slice_dim:]
@@ -91,11 +96,13 @@ static void PrepareAndValidateInputs(OpKernelContext* c,
       errors::InvalidArgument("Output must be at least 1-D, ",
                               "got shape: ", params_shape.DebugString()));
 
-  OP_REQUIRES(c,
-              params_shape.num_elements() >= 0 ||
-                  (indices.NumElements() == 0 && updates.NumElements() == 0),
-              errors::InvalidArgument(
-                  "Indices and updates specified for empty output", " shape"));
+  OP_REQUIRES(
+      c,
+      params_shape.num_elements() > 0 ||
+          (indices.NumElements() == 0 && updates.NumElements() == 0),
+      errors::InvalidArgument(
+          "Indices and updates specified for empty output.  indices shape: ",
+          indices.shape().DebugString()));
 
   OP_REQUIRES(c, updates.dim_size(0) == indices.dim_size(0),
               errors::InvalidArgument(
@@ -147,9 +154,9 @@ static void PrepareAndValidateInputs(OpKernelContext* c,
 
 template <typename Device, typename Index>
 class IndexFlattener {
-public:
-  inline typename TTypes<Index, 2>::ConstTensor
-  operator()(OpKernelContext*, const Tensor& indices) {
+ public:
+  inline typename TTypes<Index, 2>::ConstTensor operator()(
+      OpKernelContext*, const Tensor& indices) {
     return indices.flat_inner_dims<Index>();
   }
 };
@@ -157,12 +164,12 @@ public:
 #ifdef TENSORFLOW_USE_SYCL
 template <typename Index>
 class IndexFlattener<SYCLDevice, Index> {
-public:
+ public:
   IndexFlattener() { indices_host_ = nullptr; }
   ~IndexFlattener() { delete[] indices_host_; }
 
-  inline typename TTypes<Index, 2>::ConstTensor
-  operator()(OpKernelContext* c, const Tensor& indices) {
+  inline typename TTypes<Index, 2>::ConstTensor operator()(
+      OpKernelContext* c, const Tensor& indices) {
     size_t num_indices = indices.NumElements();
     indices_host_ = new Index[num_indices];
     auto device = c->eigen_sycl_device();
@@ -170,11 +177,11 @@ public:
     auto src_ptr = GetBase(&indices);
     device.memcpyDeviceToHost(indices_host_, static_cast<const Index*>(src_ptr),
                               size);
-    return typename TTypes<Index, 2>::ConstTensor(indices_host_,
-           indices.shape().AsEigenDSizes<2>());
+    return typename TTypes<Index, 2>::ConstTensor(
+        indices_host_, indices.shape().AsEigenDSizes<2>());
   }
 
-private:
+ private:
   Index* indices_host_;
 };
 #endif
@@ -213,6 +220,9 @@ class ScatterNdOp : public OpKernel {
 
     Tensor* out = nullptr;
     OP_REQUIRES_OK(c, c->allocate_output(0, shape, &out));
+
+    if (shape.num_elements() == 0) return;
+
     functor::SetZeroFunctor<Device, T> fill;
     fill(c->eigen_device<Device>(), out->flat<T>());
     auto output_matrix = out->template shaped<T, 2>(
@@ -271,12 +281,19 @@ class ScatterNdUpdateOp : public OpKernel {
     const DataType dt = DataTypeToEnum<T>::v();
     const DataType dt_ref = DataTypeToEnum<T>::ref();
     const DataType index_t = DataTypeToEnum<Index>::v();
-    OP_REQUIRES_OK(c, c->MatchSignature({dt_ref, index_t, dt}, {dt_ref}));
-    OP_REQUIRES_OK(c, c->GetAttr("use_locking", &use_exclusive_lock_));
+    if (IsRefType(c->input_type(0))) {
+      OP_REQUIRES_OK(c, c->MatchSignature({dt_ref, index_t, dt}, {dt_ref}));
+      OP_REQUIRES_OK(c, c->GetAttr("use_locking", &use_exclusive_lock_));
+    } else {
+      OP_REQUIRES_OK(c, c->MatchSignature({dt, index_t, dt}, {dt}));
+      use_exclusive_lock_ = false;
+    }
   }
 
   void Compute(OpKernelContext* c) override {
     if (use_exclusive_lock_) {
+      // If we're here, it means the input type is a ref.
+      DCHECK(IsRefType(c->input_dtype(0)));
       // Hold mutex while we apply updates
       mutex_lock l(*c->input_ref_mutex(0));
       DoCompute(c);
@@ -289,20 +306,41 @@ class ScatterNdUpdateOp : public OpKernel {
   bool use_exclusive_lock_;
 
   void DoCompute(OpKernelContext* c) {
-    Tensor params = c->mutable_input(0, use_exclusive_lock_);
     const Tensor& indices = c->input(1);
     const Tensor& updates = c->input(2);
-    const TensorShape& params_shape(params.shape());
 
     int64 slice_dim;
     Index num_updates;
     Index slice_size;
 
-    OP_REQUIRES(c, params.IsInitialized(),
-                errors::FailedPrecondition("Null ref for params"));
+    Tensor params;
+    TensorShape params_shape;
+
+    if (IsRefType(c->input_dtype(0))) {
+      params = c->mutable_input(0, use_exclusive_lock_);
+      params_shape = params.shape();
+      c->forward_ref_input_to_ref_output(0, 0);
+      OP_REQUIRES(c, params.IsInitialized(),
+                  errors::FailedPrecondition("Null ref for params"));
+    } else {
+      Tensor* params_ptr;
+      params_shape = c->input(0).shape();
+      if (!c->forward_input_to_output_with_shape(0, 0, params_shape,
+                                                 &params_ptr)) {
+        // We weren't able to forward the input to output, so just
+        // allocate a new output tensor and copy the values over.
+        OP_REQUIRES_OK(c, c->allocate_output(0, params_shape, &params_ptr));
+        params = *params_ptr;
+        functor::DenseUpdate<Device, T, ASSIGN> copy;
+        const Tensor& input_copy = c->input(0);
+        copy(c->eigen_device<Device>(), params.flat<T>(), input_copy.flat<T>());
+      }
+    }
+
     PrepareAndValidateInputs<Index>(c, params_shape, indices, updates,
                                     &slice_dim, &num_updates, &slice_size);
     if (!c->status().ok()) return;
+    if (params_shape.num_elements() == 0) return;
 
     IndexFlattener<Device, Index> index_flattener;
     auto indices_flat = index_flattener(c, indices);
@@ -310,7 +348,6 @@ class ScatterNdUpdateOp : public OpKernel {
     auto params_matrix = params.template shaped<T, 2>(
         {params_shape.num_elements() / slice_size, slice_size});
     Index bad_i = -1;
-    c->forward_ref_input_to_ref_output(0, 0);
 
     switch (slice_dim) {
 #define PARAMS_CASE(IXDIM)                                                  \
@@ -376,10 +413,12 @@ class ScatterNdUpdateOp : public OpKernel {
   REGISTER_SCATTER_ND_UPDATE_KERNEL_INDEX(type, int32, dev, name, op); \
   REGISTER_SCATTER_ND_UPDATE_KERNEL_INDEX(type, int64, dev, name, op)
 
-#define REGISTER_SCATTER_ND_ADD_SUB(type, dev)                     \
-  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdAdd",     \
-                                    scatter_nd_op::UpdateOp::ADD); \
-  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdSub",     \
+#define REGISTER_SCATTER_ND_ADD_SUB(type, dev)                            \
+  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdAdd",            \
+                                    scatter_nd_op::UpdateOp::ADD);        \
+  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdNonAliasingAdd", \
+                                    scatter_nd_op::UpdateOp::ADD);        \
+  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdSub",            \
                                     scatter_nd_op::UpdateOp::SUB);
 // TODO(simister): Find a way to reduce amount of templated generated code
 // to reduce build size, then re-enable these additional operations.
@@ -421,9 +460,31 @@ TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_CPU);
 
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_GPU);
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_GPU);
-
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_GPU);
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SCATTER_ND_ADD_SUB_SYCL(type) \
+  REGISTER_SCATTER_ND_ADD_SUB(type, SYCL);
+
+#define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \
+  REGISTER_SCATTER_ND_UPDATE(type, SYCL);
+
+TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL);
+TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL);
+#undef REGISTER_SCATTER_ND_ADD_SUB_SYCL
+#undef REGISTER_SCATTER_ND_UPDATE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
+
+#undef REGISTER_SCATTER_ND_ADD
+#undef REGISTER_SCATTER_ND_ADD_SUB
+#undef REGISTER_SCATTER_ND_ADD_SUB_CPU
+#undef REGISTER_SCATTER_ND_ADD_SUB_GPU
+#undef REGISTER_SCATTER_ND_UPDATE
+#undef REGISTER_SCATTER_ND_UPDATE_CPU
+#undef REGISTER_SCATTER_ND_UPDATE_GPU
+#undef REGISTER_SCATTER_ND_KERNEL
+#undef REGISTER_SCATTER_ND_KERNEL_INDEX
+
 // Forward declarations of the functor specializations for GPU.
 namespace functor {
 #define DECLARE_GPU_SPECS_INDEX_OP_IXDIM(T, Index, op, IXDIM)           \
@@ -458,31 +519,19 @@ TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DECLARE_GPU_SPECS);
 #undef DECLARE_GPU_SPECS
 #undef DECLARE_GPU_SPECS_INDEX
 #undef DECLARE_GPU_SPECS_INDEX_OP
-}  // namespace functor
-
-#endif  // GOOGLE_CUDA
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SCATTER_ND_ADD_SUB_SYCL(type) \
-  REGISTER_SCATTER_ND_ADD_SUB(type, SYCL);
+#define REGISTER_GPU_KERNELS(type)                         \
+  template <>                                              \
+  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(   \
+      const GPUDevice& d, typename TTypes<type>::Flat lhs, \
+      typename TTypes<type>::ConstFlat rhs);               \
+  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;
 
-#define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \
-  REGISTER_SCATTER_ND_UPDATE(type, SYCL);
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
+#undef REGISTER_GPU_KERNELS
 
-TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL);
-TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL);
-#undef REGISTER_SCATTER_ND_ADD_SUB_SYCL
-#undef REGISTER_SCATTER_ND_UPDATE_SYCL
-#endif // TENSORFLOW_USE_SYCL
+}  // namespace functor
 
-#undef REGISTER_SCATTER_ND_ADD
-#undef REGISTER_SCATTER_ND_ADD_SUB
-#undef REGISTER_SCATTER_ND_ADD_SUB_CPU
-#undef REGISTER_SCATTER_ND_ADD_SUB_GPU
-#undef REGISTER_SCATTER_ND_UPDATE
-#undef REGISTER_SCATTER_ND_UPDATE_CPU
-#undef REGISTER_SCATTER_ND_UPDATE_GPU
-#undef REGISTER_SCATTER_ND_KERNEL
-#undef REGISTER_SCATTER_ND_KERNEL_INDEX
+#endif  // GOOGLE_CUDA
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index f27f8968d3..c2b81615bd 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -4912,6 +4912,62 @@ output: A new tensor with the given shape and updates applied according
   to the indices.
 )doc");
 
+REGISTER_OP("ScatterNdNonAliasingAdd")
+    .Input("input: T")
+    .Input("indices: Tindices")
+    .Input("updates: T")
+    .Output("output: T")
+    .Attr("T: numbertype")
+    .Attr("Tindices: {int32, int64}")
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
+    .Doc(R"doc(
+Applies sparse addition to `input` using individual values or slices
+from `updates` according to indices `indices`.  The updates are non-aliasing:
+`input` is only modified in-place if no other operations will use it.
+Otherwise, a copy of `input` is made.  This operation has a gradient with
+respect to both `input` and `updates`.
+
+`input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
+
+`indices` must be integer tensor, containing indices into `input`.
+It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
+
+The innermost dimension of `indices` (with length `K`) corresponds to
+indices into elements (if `K = P`) or `(P-K)`-dimensional slices
+(if `K < P`) along the `K`th dimension of `input`.
+
+`updates` is `Tensor` of rank `Q-1+P-K` with shape:
+
+```
+[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].
+```
+
+For example, say we want to add 4 scattered elements to a rank-1 tensor to 8
+elements. In Python, that addition would look like this:
+
+    input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
+    indices = tf.constant([[4], [3], [1], [7]])
+    updates = tf.constant([9, 10, 11, 12])
+    output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
+    with tf.Session() as sess:
+      print(sess.run(output))
+
+The resulting value `output` would look like this:
+
+    [1, 13, 3, 14, 14, 6, 7, 20]
+
+See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
+slices.
+
+input: A Tensor.
+indices: A Tensor. Must be one of the following types: `int32`, `int64`.
+  A tensor of indices into `input`.
+updates: A Tensor. Must have the same type as ref. A tensor of updated values
+  to add to `input`.
+output: A `Tensor` with the same shape as `input`, containing values of `input`
+  updated with `updates`.
+)doc");
+
 REGISTER_OP("FakeQuantWithMinMaxArgs")
     .Attr("min: float = -6.0")
     .Attr("max: float = 6.0")
diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc
index 0890d5fc7c..35f965b6a9 100644
--- a/tensorflow/core/ops/state_ops.cc
+++ b/tensorflow/core/ops/state_ops.cc
@@ -472,63 +472,6 @@ use_locking: If True, the operation will be protected by a lock;
   otherwise the behavior is undefined, but may exhibit less contention.
 )doc");
 
-namespace {
-
-Status ScatterNdUpdateShape(InferenceContext* c) {
-  ShapeHandle ref_shape = c->input(0);
-  ShapeHandle indices_shape;
-  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &indices_shape));
-  ShapeHandle updates_shape;
-  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(2), 1, &updates_shape));
-
-  if (c->RankKnown(indices_shape) && c->RankKnown(updates_shape)) {
-    const int64 outer_dims = c->Rank(indices_shape) - 1;
-    const DimensionHandle ixdim = c->Dim(indices_shape, -1);
-
-    // We can only do more validation if the last dimension of indices
-    // is a known value.
-    if (c->ValueKnown(ixdim)) {
-      int64 ix = c->Value(ixdim);
-      ShapeHandle unused;
-      ShapeHandle prefix_indices;
-      TF_RETURN_IF_ERROR(
-          c->Subshape(indices_shape, 0, outer_dims, &prefix_indices));
-      ShapeHandle prefix_updates;
-      TF_RETURN_IF_ERROR(
-          c->Subshape(updates_shape, 0, outer_dims, &prefix_updates));
-
-      Status s = c->Merge(prefix_indices, prefix_updates, &unused);
-      if (!s.ok()) {
-        return errors::InvalidArgument(
-            "The outer ", outer_dims, " dimensions of indices.shape=",
-            c->DebugString(indices_shape), "must match the outer ", outer_dims,
-            " dimensions of updates.shape=", c->DebugString(updates_shape),
-            ": ", s.error_message());
-      }
-
-      ShapeHandle suffix_ref;
-      TF_RETURN_IF_ERROR(c->Subshape(ref_shape, ix, &suffix_ref));
-      ShapeHandle suffix_updates;
-      TF_RETURN_IF_ERROR(
-          c->Subshape(updates_shape, outer_dims, &suffix_updates));
-      s = c->Merge(suffix_ref, suffix_updates, &unused);
-      if (!s.ok()) {
-        return errors::InvalidArgument(
-            "The inner ", c->Rank(ref_shape) - ix, " dimensions of ref.shape=",
-            c->DebugString(ref_shape), "must match the inner ",
-            c->Rank(updates_shape) - outer_dims,
-            " dimensions of updates.shape=", c->DebugString(updates_shape),
-            ": ", s.error_message());
-      }
-    }
-  }
-
-  c->set_output(0, ref_shape);
-  return Status::OK();
-}
-
-}  // namespace
-
 REGISTER_OP("ScatterNdUpdate")
     .Input("ref: Ref(T)")
     .Input("indices: Tindices")
@@ -537,7 +480,7 @@ REGISTER_OP("ScatterNdUpdate")
     .Attr("T: type")
     .Attr("Tindices: {int32, int64}")
     .Attr("use_locking: bool = true")
-    .SetShapeFn(ScatterNdUpdateShape)
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
     .Doc(R"doc(
 Applies sparse `updates` to individual values or slices within a given
 variable according to `indices`.
@@ -596,7 +539,7 @@ REGISTER_OP("ScatterNdAdd")
     .Attr("T: numbertype")
     .Attr("Tindices: {int32, int64}")
     .Attr("use_locking: bool = false")
-    .SetShapeFn(ScatterNdUpdateShape)
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
     .Doc(R"doc(
 Applies sparse addition between `updates` and individual values or slices
 within a given variable according to `indices`.
@@ -653,7 +596,7 @@ REGISTER_OP("ScatterNdSub")
     .Attr("T: numbertype")
     .Attr("Tindices: {int32, int64}")
     .Attr("use_locking: bool = false")
-    .SetShapeFn(ScatterNdUpdateShape)
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
     .Doc(R"doc(
 Applies sparse subtraction between `updates` and individual values or slices
 within a given variable according to `indices`.
@@ -713,7 +656,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 //     .Attr("T: numbertype")
 //     .Attr("Tindices: {int32, int64}")
 //     .Attr("use_locking: bool = false")
-//     .SetShapeFn(ScatterNdUpdateShape)
+//     .SetShapeFn(shape_inference::ScatterNdUpdateShape)
 //     .Doc(
 //         R"doc(Applies sparse subtraction between `updates` and individual
 //         values or slices within a given variable according to `indices`.
@@ -769,7 +712,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 //     .Attr("T: numbertype")
 //     .Attr("Tindices: {int32, int64}")
 //     .Attr("use_locking: bool = false")
-//     .SetShapeFn(ScatterNdUpdateShape)
+//     .SetShapeFn(shape_inference::ScatterNdUpdateShape)
 //     .Doc(
 //         R"doc(Applies sparse subtraction between `updates` and individual
 //         values or slices within a given variable according to `indices`.
diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
index 8519d19fe1..ebc5686212 100644
--- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
+++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
@@ -87,7 +87,7 @@ def _NumpyDiv(ref, indices, updates):
   return _NumpyScatterNd(ref, indices, updates, lambda p, u: p / u)
 
 
-class ScatterNdTest(test.TestCase):
+class StatefulScatterNdTest(test.TestCase):
 
   def _VariableRankTest(self,
                         np_scatter,
@@ -261,10 +261,6 @@ class ScatterNdTest(test.TestCase):
     indices = array_ops.zeros([2, 2, 2], dtypes.int32)
     updates = array_ops.zeros([2, 2, 2], dtypes.int32)
     shape = np.array([2, 2, 2])
-    self.assertAllEqual(
-        array_ops.scatter_nd(indices, updates, shape).get_shape().as_list(),
-        shape)
-
     ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
     self.assertAllEqual(
         state_ops.scatter_nd_update(ref, indices,
@@ -274,37 +270,120 @@ class ScatterNdTest(test.TestCase):
     indices = array_ops.zeros([1, 1, 2], dtypes.int32)
     updates = array_ops.zeros([1, 1], dtypes.int32)
     shape = np.array([2, 2])
-    scatter = array_ops.scatter_nd(indices, updates, shape)
-    self.assertAllEqual(scatter.get_shape().as_list(), shape)
-    expected_result = np.zeros([2, 2], dtype=np.int32)
-    with self.test_session():
-      self.assertAllEqual(expected_result, scatter.eval())
-
     ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
     scatter_update = state_ops.scatter_nd_update(ref, indices, updates)
     self.assertAllEqual(scatter_update.get_shape().as_list(), shape)
 
+    expected_result = np.zeros([2, 2], dtype=np.int32)
     with self.test_session():
       ref.initializer.run()
       self.assertAllEqual(expected_result, scatter_update.eval())
 
+  def testRank3InvalidShape1(self):
+    indices = array_ops.zeros([3, 2, 2], dtypes.int32)
+    updates = array_ops.zeros([2, 2, 2], dtypes.int32)
+    shape = np.array([2, 2, 2])
+    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
+    with self.assertRaisesWithPredicateMatch(
+        ValueError, "The outer \\d+ dimensions of indices\\.shape="):
+      state_ops.scatter_nd_update(ref, indices, updates)
+
+  def testRank3InvalidShape2(self):
+    indices = array_ops.zeros([2, 2, 1], dtypes.int32)
+    updates = array_ops.zeros([2, 2], dtypes.int32)
+    shape = np.array([2, 2, 2])
+    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
+    with self.assertRaisesWithPredicateMatch(
+        ValueError, "The inner \\d+ dimensions of input\\.shape="):
+      state_ops.scatter_nd_update(ref, indices, updates)
+
+  def testConcurrentUpdates(self):
+    num_updates = 10000
+    update_values = np.random.rand(num_updates)
+    ref = variables.Variable(np.zeros([2, 2]), dtype=dtypes.float64)
+    indices = constant_op.constant([[0, 1]] * num_updates, dtype=dtypes.int32)
+    updates = constant_op.constant(update_values, dtype=dtypes.float64)
+
+    expected_result = np.zeros([2, 2], dtype=np.float64)
+    expected_result[0, 1] = np.sum(update_values)
+
+    scatter = state_ops.scatter_nd_add(ref, indices, updates)
+    init = variables.global_variables_initializer()
+
+    with session.Session() as sess:
+      sess.run(init)
+      result = sess.run(scatter)
+      assert np.allclose(result, expected_result)
+
+  # TODO(fpmc): Re-enable this test when gpu_pip test actually runs on a GPU.
+  def _disabledTestScatterOutOfRangeGpu(self):
+    if not test.IsBuiltWithCuda():
+      return
+    # TODO(simister): Re-enable once binary size increase due to
+    # scatter_nd ops is under control.
+    # tf.scatter_nd_mul, tf.scatter_nd_div,
+    for op in (state_ops.scatter_nd_add, state_ops.scatter_nd_sub,
+               state_ops.scatter_nd_update):
+      params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
+      updates = np.array([-3, -4, -5]).astype(np.float32)
+      # With GPU, the code ignores indices that are out of range.
+      # We don't test the implementation; just test there's no failures.
+      with self.test_session(force_gpu=True):
+        ref = variables.Variable(params)
+        ref.initializer.run()
+
+        # Indices all in range, no problem.
+        indices = np.array([2, 0, 5])
+        op(ref, indices, updates).eval()
+
+        # Indicies out of range should not fail.
+        indices = np.array([-1, 0, 5])
+        op(ref, indices, updates).eval()
+        indices = np.array([2, 0, 6])
+        op(ref, indices, updates).eval()
+
+
+class ScatterNdTest(test.TestCase):
+  non_aliasing_add_test = False
+
+  def scatter_nd(self, indices, updates, shape, input_=None):
+    del input_  # input_ is not used in scatter_nd
+    return array_ops.scatter_nd(indices, updates, shape)
+
+  def testRank3ValidShape(self):
+    indices = array_ops.zeros([2, 2, 2], dtypes.int32)
+    updates = array_ops.zeros([2, 2, 2], dtypes.int32)
+    shape = np.array([2, 2, 2])
+    self.assertAllEqual(
+        self.scatter_nd(indices, updates, shape).get_shape().as_list(), shape)
+
+  def testExtraIndicesDimensions(self):
+    indices = array_ops.zeros([1, 1, 2], dtypes.int32)
+    updates = array_ops.zeros([1, 1], dtypes.int32)
+    shape = np.array([2, 2])
+    scatter = self.scatter_nd(indices, updates, shape)
+    self.assertAllEqual(scatter.get_shape().as_list(), shape)
+    expected_result = np.zeros([2, 2], dtype=np.int32)
+    with self.test_session():
+      self.assertAllEqual(expected_result, scatter.eval())
+
   def testUndefinedIndicesShape(self):
     indices = array_ops.placeholder(dtypes.int32, shape=None)
     updates = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     shape = constant_op.constant([2, 2, 2], dtypes.int32)
-    array_ops.scatter_nd(indices, updates, shape)
+    self.scatter_nd(indices, updates, shape)
 
   def testUndefinedUpdatesShape(self):
     indices = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     updates = array_ops.placeholder(dtypes.int32, shape=None)
     shape = constant_op.constant([2, 2, 2], dtypes.int32)
-    array_ops.scatter_nd(indices, updates, shape)
+    self.scatter_nd(indices, updates, shape)
 
   def testUndefinedOutputShape(self):
     indices = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     updates = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     shape = array_ops.placeholder(dtypes.int32, shape=[None])
-    array_ops.scatter_nd(indices, updates, shape)
+    self.scatter_nd(indices, updates, shape)
 
   def testEmptyOutputShape1(self):
     indices = array_ops.zeros([2, 2, 2], dtypes.int32)
@@ -313,7 +392,7 @@ class ScatterNdTest(test.TestCase):
 
     with self.assertRaisesWithPredicateMatch(
         ValueError, "Indices and updates specified for empty output shape"):
-      array_ops.scatter_nd(indices, updates, shape)
+      self.scatter_nd(indices, updates, shape)
 
   def testEmptyOutputShape2(self):
     indices = array_ops.placeholder(dtypes.int32, shape=None)
@@ -321,18 +400,18 @@ class ScatterNdTest(test.TestCase):
     shape = constant_op.constant([0, 3, 2], dtypes.int32)
 
     with self.test_session():
-      array_ops.scatter_nd(indices, updates, shape).eval(feed_dict={
-          indices: np.zeros(
-              [2, 2, 2], dtype=np.int32),
-          updates: np.zeros(
-              [2, 2, 2], dtype=np.int32)
-      })
+      with self.assertRaisesOpError(
+          "Indices and updates specified for empty output"):
+        self.scatter_nd(indices, updates, shape).eval(feed_dict={
+            indices: np.zeros([2, 2, 2], dtype=np.int32),
+            updates: np.zeros([2, 2, 2], dtype=np.int32)
+        })
 
   def testEmptyOutputShape3(self):
     indices = array_ops.zeros([0], dtypes.int32)
     updates = array_ops.zeros([0], dtypes.int32)
     shape = constant_op.constant([0], dtypes.int32)
-    scatter = array_ops.scatter_nd(indices, updates, shape)
+    scatter = self.scatter_nd(indices, updates, shape)
 
     with self.test_session():
       self.assertEqual(scatter.eval().size, 0)
@@ -343,49 +422,49 @@ class ScatterNdTest(test.TestCase):
     shape = np.array([2, 2, 2])
     with self.assertRaisesWithPredicateMatch(
         ValueError, "The outer \\d+ dimensions of indices\\.shape="):
-      array_ops.scatter_nd(indices, updates, shape)
-
-    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
-    with self.assertRaisesWithPredicateMatch(
-        ValueError, "The outer \\d+ dimensions of indices\\.shape="):
-      state_ops.scatter_nd_update(ref, indices, updates)
+      self.scatter_nd(indices, updates, shape)
 
   def testRank3InvalidShape2(self):
     indices = array_ops.zeros([2, 2, 1], dtypes.int32)
     updates = array_ops.zeros([2, 2], dtypes.int32)
     shape = np.array([2, 2, 2])
     with self.assertRaisesWithPredicateMatch(
-        ValueError, "The inner \\d+ dimensions of output\\.shape="):
-      array_ops.scatter_nd(indices, updates, shape)
-
-    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
-    with self.assertRaisesWithPredicateMatch(
-        ValueError, "The inner \\d+ dimensions of ref\\.shape="):
-      state_ops.scatter_nd_update(ref, indices, updates)
+        ValueError, "The inner \\d+ dimensions of (input|output)\\.shape="):
+      self.scatter_nd(indices, updates, shape)
 
   def testGradientsRank2ElementUpdate(self):
     indices = constant_op.constant([[0, 0], [1, 1]], dtype=dtypes.int32)
     updates = constant_op.constant([1, 4], dtype=dtypes.float64)
     shape = constant_op.constant([2, 2], dtype=dtypes.int32)
-    outputs = array_ops.scatter_nd(indices, updates, shape)
+    input_ = array_ops.zeros(shape, dtype=dtypes.float64)
+    outputs = self.scatter_nd(indices, updates, shape, input_)
 
     grad_vals = constant_op.constant([[1, 2], [3, 4]], dtype=dtypes.float64)
-    grads = gradients_impl.gradients([outputs], [updates], [grad_vals])[0]
-    expected_grads = np.array([1, 4], dtype=np.float64)
+    updates_grad, input_grad = gradients_impl.gradients(
+        [outputs], [updates, input_], [grad_vals])
+    expected_updates_grad = np.array([1, 4], dtype=np.float64)
+    expected_input_grad = np.array([[1, 2], [3, 4]], dtype=np.float64)
     with self.test_session():
-      self.assertAllEqual(expected_grads, grads.eval())
+      self.assertAllEqual(expected_updates_grad, updates_grad.eval())
+      if self.non_aliasing_add_test:
+        self.assertAllEqual(expected_input_grad, input_grad.eval())
 
   def testGradientsRank2SliceUpdate(self):
     indices = constant_op.constant([[1], [0]], dtype=dtypes.int32)
     updates = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64)
     shape = constant_op.constant([2, 2], dtype=dtypes.int32)
-    outputs = array_ops.scatter_nd(indices, updates, shape)
+    input_ = array_ops.zeros(shape, dtype=dtypes.float64)
+    outputs = self.scatter_nd(indices, updates, shape, input_)
 
     grad_vals = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64)
-    grads = gradients_impl.gradients([outputs], [updates], [grad_vals])[0]
-    expected_grads = np.array([[1, 2], [3, 4]], dtype=np.float64)
+    updates_grad, input_grad = gradients_impl.gradients(
+        [outputs], [updates, input_], [grad_vals])
+    expected_updates_grad = np.array([[1, 2], [3, 4]], dtype=np.float64)
+    expected_input_grad = np.array([[3, 4], [1, 2]], dtype=np.float64)
     with self.test_session():
-      self.assertAllEqual(expected_grads, grads.eval())
+      self.assertAllEqual(expected_updates_grad, updates_grad.eval())
+      if self.non_aliasing_add_test:
+        self.assertAllEqual(expected_input_grad, input_grad.eval())
 
   def testGradientsRank3SliceUpdate(self):
     indices = constant_op.constant(
@@ -393,67 +472,28 @@ class ScatterNdTest(test.TestCase):
     updates = constant_op.constant(
         [[[5, 7], [2, 4]], [[1, 3], [6, 8]]], dtype=dtypes.float64)
     shape = constant_op.constant([2, 2, 2], dtype=dtypes.int32)
-    outputs = array_ops.scatter_nd(indices, updates, shape)
+    input_ = array_ops.zeros(shape, dtype=dtypes.float64)
+    outputs = self.scatter_nd(indices, updates, shape, input_)
 
     grad_vals = constant_op.constant(
         [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=dtypes.float64)
-    grads = gradients_impl.gradients([outputs], [updates], [grad_vals])[0]
-    expected_grads = np.array(
+    updates_grad, input_grad = gradients_impl.gradients(
+        [outputs], [updates, input_], [grad_vals])
+    expected_updates_grad = np.array(
         [[[3, 4], [5, 6]], [[1, 2], [7, 8]]], dtype=np.float64)
+    expected_input_grad = np.array(
+        [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float64)
     with self.test_session():
-      self.assertAllEqual(expected_grads, grads.eval())
-
-  def testConcurrentUpdates(self):
-    num_updates = 10000
-    update_values = np.random.rand(num_updates)
-    ref = variables.Variable(np.zeros([2, 2]), dtype=dtypes.float64)
-    indices = constant_op.constant([[0, 1]] * num_updates, dtype=dtypes.int32)
-    updates = constant_op.constant(update_values, dtype=dtypes.float64)
-
-    expected_result = np.zeros([2, 2], dtype=np.float64)
-    expected_result[0, 1] = np.sum(update_values)
-
-    scatter = state_ops.scatter_nd_add(ref, indices, updates)
-    init = variables.global_variables_initializer()
-
-    with session.Session() as sess:
-      sess.run(init)
-      result = sess.run(scatter)
-      assert np.allclose(result, expected_result)
-
-  # TODO(fpmc): Re-enable this test when gpu_pip test actually runs on a GPU.
-  def _disabledTestScatterOutOfRangeGpu(self):
-    if not test.IsBuiltWithCuda():
-      return
-    # TODO(simister): Re-enable once binary size increase due to
-    # scatter_nd ops is under control.
-    # tf.scatter_nd_mul, tf.scatter_nd_div,
-    for op in (state_ops.scatter_nd_add, state_ops.scatter_nd_sub,
-               state_ops.scatter_nd_update):
-      params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
-      updates = np.array([-3, -4, -5]).astype(np.float32)
-      # With GPU, the code ignores indices that are out of range.
-      # We don't test the implementation; just test there's no failures.
-      with self.test_session(force_gpu=True):
-        ref = variables.Variable(params)
-        ref.initializer.run()
-
-        # Indices all in range, no problem.
-        indices = np.array([2, 0, 5])
-        op(ref, indices, updates).eval()
-
-        # Indicies out of range should not fail.
-        indices = np.array([-1, 0, 5])
-        op(ref, indices, updates).eval()
-        indices = np.array([2, 0, 6])
-        op(ref, indices, updates).eval()
+      self.assertAllEqual(expected_updates_grad, updates_grad.eval())
+      if self.non_aliasing_add_test:
+        self.assertAllEqual(expected_input_grad, input_grad.eval())
 
   def testScatterNdRepatedIndicesAdd(self):
     indices = array_ops.zeros([100000, 1], dtypes.int32)
     values = np.random.randn(100000)
     shape = [1]
     with self.test_session():
-      val = array_ops.scatter_nd(indices, values, shape).eval()
+      val = self.scatter_nd(indices, values, shape).eval()
     self.assertAllClose([np.sum(values)], val)
 
   def testSmokeScatterNdBatch2DSliceDim2(self):
@@ -461,28 +501,37 @@ class ScatterNdTest(test.TestCase):
       indices = array_ops.zeros([3, 5, 2], dtype=dtypes.int32)
       values = array_ops.zeros([3, 5, 7])
       shape = [4, 6, 7]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
 
   def testSmokeScatterNdBatch1DSliceDim2(self):
     with self.test_session():
       indices = array_ops.zeros([0, 2], dtype=dtypes.int32)
       values = array_ops.zeros([0, 7])
       shape = [4, 6, 7]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
 
   def testSmokeScatterNdBatch1DSliceDim3ShapeRank7(self):
     with self.test_session():
       indices = array_ops.zeros([1, 3], dtype=dtypes.int32)
       values = array_ops.zeros([1, 6, 7, 8, 9])
       shape = [3, 4, 5, 6, 7, 8, 9]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
 
   def testSmokeScatterNdBatch2DSliceDim3ShapeRank7(self):
     with self.test_session():
       indices = array_ops.zeros([1, 2, 3], dtype=dtypes.int32)
       values = array_ops.zeros([1, 2, 6, 7, 8, 9])
       shape = [3, 4, 5, 6, 7, 8, 9]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
+
+
+class ScatterNdNonAliasingAddTest(ScatterNdTest):
+  non_aliasing_add_test = True
+
+  def scatter_nd(self, indices, updates, shape, input_=None):
+    input_ = (input_ if input_ is not None else array_ops.zeros(
+        shape, dtype=updates.dtype))
+    return array_ops.scatter_nd_non_aliasing_add(input_, indices, updates)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index 5c6d309e6c..dcae3e0c7b 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -670,3 +670,10 @@ def _ScatterNdGrad(op, grad):
   indices = op.inputs[0]
   updates_grad = array_ops.gather_nd(grad, indices)
   return [None, updates_grad, None]
+
+
+@ops.RegisterGradient("ScatterNdNonAliasingAdd")
+def _ScatterNdNonAliasingAddGrad(op, grad):
+  indices = op.inputs[1]
+  updates_grad = array_ops.gather_nd(grad, indices)
+  return [grad, None, updates_grad]
-- 
GitLab


From b6b448ee7af937e79ce7537da5cb09cf5688ece5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 16:06:43 -0700
Subject: [PATCH 0525/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 160340888
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 51 +++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 57 +++++++++++++++++++
 2 files changed, 108 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index cabd91f9ef..a585881cc6 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -21936,6 +21936,57 @@ op {
     }
   }
 }
+op {
+  name: "ScatterNdNonAliasingAdd"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "updates"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "ScatterNdSub"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index b122b5a992..58bed01150 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -21684,6 +21684,63 @@ op {
   summary: "Applies sparse addition between `updates` and individual values or slices"
   description: "within a given variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    add = tf.scatter_nd_add(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(add)\n\nThe resulting update to ref would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
 }
+op {
+  name: "ScatterNdNonAliasingAdd"
+  input_arg {
+    name: "input"
+    description: "A Tensor."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    description: "A Tensor. Must be one of the following types: `int32`, `int64`.\nA tensor of indices into `input`."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "updates"
+    description: "A Tensor. Must have the same type as ref. A tensor of updated values\nto add to `input`."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "A `Tensor` with the same shape as `input`, containing values of `input`\nupdated with `updates`."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Applies sparse addition to `input` using individual values or slices"
+  description: "from `updates` according to indices `indices`.  The updates are non-aliasing:\n`input` is only modified in-place if no other operations will use it.\nOtherwise, a copy of `input` is made.  This operation has a gradient with\nrespect to both `input` and `updates`.\n\n`input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `input`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or `(P-K)`-dimensional slices\n(if `K < P`) along the `K`th dimension of `input`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    output = tf.scatter_nd_non_aliasing_add(input, indices, updates)\n    with tf.Session() as sess:\n      print(sess.run(output))\n\nThe resulting value `output` would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
+}
 op {
   name: "ScatterNdSub"
   input_arg {
-- 
GitLab


From e90e75920d5465b144721e63310c314c30d7685a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 16:07:28 -0700
Subject: [PATCH 0526/1375] Add checkpoint conversion for models that use the
 attention mechanism implemented in
 tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py.

PiperOrigin-RevId: 160340994
---
 .../contrib/rnn/python/tools/checkpoint_convert.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py b/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py
index 1cbd27a2e5..da129b68a6 100644
--- a/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py
+++ b/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py
@@ -124,6 +124,20 @@ _RNN_NAME_REPLACEMENTS = collections.OrderedDict([
      'attention_cell_wrapper/attention/kernel'),
     ('attention_cell_wrapper/attention/biases',
      'attention_cell_wrapper/attention/bias'),
+    ############################################################################
+    # contrib/legacy_seq2seq/python/ops/seq2seq.py
+    ('attention_decoder/weights',
+     'attention_decoder/kernel'),
+    ('attention_decoder/biases',
+     'attention_decoder/bias'),
+    ('attention_decoder/Attention_0/weights',
+     'attention_decoder/Attention_0/kernel'),
+    ('attention_decoder/Attention_0/biases',
+     'attention_decoder/Attention_0/bias'),
+    ('attention_decoder/AttnOutputProjection/weights',
+     'attention_decoder/AttnOutputProjection/kernel'),
+    ('attention_decoder/AttnOutputProjection/biases',
+     'attention_decoder/AttnOutputProjection/bias'),
 ])
 
 _RNN_SHARDED_NAME_REPLACEMENTS = collections.OrderedDict([
-- 
GitLab


From d6d58a3a1785785679af56c0f8f131e7312b8226 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 16:13:58 -0700
Subject: [PATCH 0527/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 160341769
---
 tensorflow/go/op/wrappers.go | 62 ++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 8dd2931703..af0eaede19 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -16964,6 +16964,68 @@ func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer
 	return op.Output(0)
 }
 
+// Applies sparse addition to `input` using individual values or slices
+//
+// from `updates` according to indices `indices`.  The updates are non-aliasing:
+// `input` is only modified in-place if no other operations will use it.
+// Otherwise, a copy of `input` is made.  This operation has a gradient with
+// respect to both `input` and `updates`.
+//
+// `input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
+//
+// `indices` must be integer tensor, containing indices into `input`.
+// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
+//
+// The innermost dimension of `indices` (with length `K`) corresponds to
+// indices into elements (if `K = P`) or `(P-K)`-dimensional slices
+// (if `K < P`) along the `K`th dimension of `input`.
+//
+// `updates` is `Tensor` of rank `Q-1+P-K` with shape:
+//
+// ```
+// [d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].
+// ```
+//
+// For example, say we want to add 4 scattered elements to a rank-1 tensor to 8
+// elements. In Python, that addition would look like this:
+//
+//     input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
+//     indices = tf.constant([[4], [3], [1], [7]])
+//     updates = tf.constant([9, 10, 11, 12])
+//     output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
+//     with tf.Session() as sess:
+//       print(sess.run(output))
+//
+// The resulting value `output` would look like this:
+//
+//     [1, 13, 3, 14, 14, 6, 7, 20]
+//
+// See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
+// slices.
+//
+// Arguments:
+//	input: A Tensor.
+//	indices: A Tensor. Must be one of the following types: `int32`, `int64`.
+// A tensor of indices into `input`.
+//	updates: A Tensor. Must have the same type as ref. A tensor of updated values
+// to add to `input`.
+//
+// Returns A `Tensor` with the same shape as `input`, containing values of `input`
+// updated with `updates`.
+func ScatterNdNonAliasingAdd(scope *Scope, input tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ScatterNdNonAliasingAdd",
+		Input: []tf.Input{
+			input, indices, updates,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // QuantizedReluXAttr is an optional argument to QuantizedReluX.
 type QuantizedReluXAttr func(optionalAttr)
 
-- 
GitLab


From 50b999a8336d19400ab75aea66fe46eca2f5fe0b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 16:33:00 -0700
Subject: [PATCH 0528/1375] Merge changes from github.

PiperOrigin-RevId: 160344052
---
 CONTRIBUTING.md                               |   7 +-
 ISSUE_TEMPLATE.md                             |   1 +
 README.md                                     |  14 +-
 RELEASE.md                                    |   2 +
 configure                                     |  37 ++-
 tensorflow/BUILD                              |   1 +
 tensorflow/c/generate-pc.sh                   |   2 +-
 tensorflow/cc/BUILD                           |  21 +-
 tensorflow/cc/gradients/math_grad.cc          |  26 ++
 tensorflow/cc/gradients/math_grad_test.cc     |  52 ++++
 tensorflow/cc/gradients/nn_grad.cc            |  13 +
 tensorflow/cc/gradients/nn_grad_test.cc       |  13 +
 tensorflow/compiler/jit/BUILD                 |   5 +-
 tensorflow/compiler/jit/kernels/BUILD         |   1 +
 tensorflow/compiler/plugin/BUILD              |   4 +-
 tensorflow/compiler/plugin/executor/BUILD     |  34 +++
 .../compiler/plugin/executor/compiler.cc      | 122 ++++++++
 .../compiler/plugin/executor/compiler.h       |  62 ++++
 tensorflow/compiler/plugin/executor/device.cc |  60 ++++
 .../compiler/plugin/executor/executable.cc    | 147 ++++++++++
 .../compiler/plugin/executor/executable.h     |  65 ++++
 .../compiler/plugin/executor/executor.cc      | 135 +++++++++
 .../compiler/plugin/executor/executor.h       | 213 ++++++++++++++
 .../compiler/plugin/executor/platform.cc      | 125 ++++++++
 .../compiler/plugin/executor/platform.h       |  83 ++++++
 .../compiler/plugin/executor/platform_id.h    |  31 ++
 .../plugin/executor/transfer_manager.cc       | 187 ++++++++++++
 .../plugin/executor/transfer_manager.h        |  77 +++++
 tensorflow/compiler/tests/BUILD               |  18 +-
 tensorflow/compiler/tests/ftrl_test.py        |   2 +-
 .../tf2xla/kernels/batch_matmul_op.cc         |   6 +-
 .../tf2xla/kernels/batchtospace_op.cc         |   3 +-
 .../tf2xla/kernels/depthwise_conv_ops.cc      |  19 +-
 tensorflow/compiler/tf2xla/kernels/diag_op.cc |   8 +-
 .../tf2xla/kernels/dynamic_stitch_op.cc       |   4 +-
 .../compiler/tf2xla/kernels/slice_op.cc       |   4 +-
 .../compiler/tf2xla/kernels/split_op.cc       |  14 +-
 .../tf2xla/kernels/strided_slice_op.cc        |  44 +--
 .../tf2xla/kernels/tensor_array_ops.cc        |   8 +-
 .../compiler/tf2xla/kernels/unpack_op.cc      |   4 +-
 .../xla/client/computation_builder.cc         |   6 +-
 .../compiler/xla/client/computation_builder.h |   4 +-
 tensorflow/compiler/xla/literal_util.cc       |  10 +-
 tensorflow/compiler/xla/literal_util_test.cc  |  57 ++++
 .../xla/service/algebraic_simplifier.cc       |   7 +-
 .../xla/service/algebraic_simplifier_test.cc  |   8 +-
 .../xla/service/buffer_assignment_test.cc     |  12 +-
 .../xla/service/buffer_liveness_test.cc       |   2 +-
 .../xla/service/compile_only_service.h        |   2 +-
 .../xla/service/computation_placer.cc         |   9 +-
 .../compiler/xla/service/computation_placer.h |   6 +-
 .../xla/service/elemental_ir_emitter.cc       |  17 +-
 .../compiler/xla/service/gpu/pad_insertion.cc |  11 +-
 .../xla/service/hlo_constant_folding_test.cc  |   3 +-
 .../compiler/xla/service/hlo_instruction.cc   |  11 +-
 .../compiler/xla/service/hlo_instruction.h    |  16 +-
 .../xla/service/hlo_rematerialization_test.cc |  16 +-
 .../compiler/xla/service/shape_inference.cc   |  28 +-
 .../compiler/xla/service/shape_inference.h    |   3 +-
 .../xla/service/shape_inference_test.cc       |  33 ++-
 .../service/tuple_points_to_analysis_test.cc  |   2 +-
 .../compiler/xla/service/user_computation.cc  |   6 +-
 .../xla/tests/array_elementwise_ops_test.cc   |   2 +-
 .../compiler/xla/tests/dot_operation_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/fusion_test.cc  |   2 +-
 .../xla/tests/multidimensional_slice_test.cc  |   4 +-
 tensorflow/compiler/xla/tests/params_test.cc  |   2 +-
 tensorflow/compiler/xla/tests/slice_test.cc   |  51 ++--
 tensorflow/compiler/xla/tests/while_test.cc   |   3 +-
 tensorflow/compiler/xla/util.h                |  18 +-
 tensorflow/compiler/xla/xla_data.proto        |   3 +-
 .../android/TensorFlowInferenceInterface.java |  20 +-
 .../cloud/kernels/bigquery_reader_ops.cc      |   2 +-
 .../cloud/python/ops/bigquery_reader_ops.py   |   2 +-
 tensorflow/contrib/cmake/CMakeLists.txt       |   2 +-
 tensorflow/contrib/data/README.md             |   2 +-
 .../python/ops/bijectors/affine_impl.py       |   4 +-
 .../python/ops/relaxed_bernoulli.py           |   2 +-
 .../distributions/python/ops/sample_stats.py  |   2 +-
 .../ops/vector_laplace_linear_operator.py     |   2 +-
 tensorflow/contrib/graph_editor/transform.py  |   2 +-
 .../contrib/keras/python/keras/backend.py     |   6 +-
 .../contrib/keras/python/keras/layers/core.py |   2 +-
 .../keras/python/keras/layers/recurrent.py    |   2 +-
 .../contrib/keras/python/keras/models_test.py |   2 +-
 tensorflow/contrib/layers/BUILD               |   2 +-
 tensorflow/contrib/layers/__init__.py         |   6 +
 .../contrib/layers/python/layers/layers.py    | 221 +++++++++++++-
 .../layers/python/layers/layers_test.py       | 150 +++++++++-
 .../python/learn/estimators/debug_test.py     |   4 +-
 .../learn/python/learn/estimators/head.py     |   2 +-
 .../python/learn/estimators/linear_test.py    |  20 +-
 .../python/learn/estimators/model_fn_test.py  |  10 +-
 .../contrib/learn/python/learn/experiment.py  |   6 +-
 .../contrib/learn/python/learn/models.py      |   4 +-
 .../python/ops/linear_operator_test_util.py   |   2 +-
 tensorflow/contrib/lookup/lookup_ops.py       |   4 +-
 .../contrib/makefile/build_with_docker.sh     |   2 +-
 .../makefile/compile_android_protobuf.sh      |   4 +-
 .../contrib/makefile/compile_ios_protobuf.sh  |  17 +-
 .../makefile/compile_ios_tensorflow.sh        |   4 +-
 .../contrib/makefile/compile_pi_protobuf.sh   |   6 +-
 tensorflow/contrib/makefile/tf_op_files.txt   |   1 +
 tensorflow/contrib/ndlstm/python/lstm2d.py    |  54 +++-
 .../contrib/ndlstm/python/lstm2d_test.py      |   8 +
 .../contrib/remote_fused_graph/pylib/BUILD    |   1 +
 tensorflow/contrib/rnn/python/ops/rnn_cell.py |   6 +-
 .../kernel_tests/beam_search_ops_test.py      |   4 +-
 .../contrib/seq2seq/python/ops/decoder.py     |   6 +-
 tensorflow/contrib/slim/README.md             |   2 +-
 tensorflow/contrib/tfprof/README.md           |  23 ++
 .../tfprof/internal/run_metadata_test.py      |   2 +-
 .../contrib/tpu/python/tpu/tpu_estimator.py   |   4 +-
 .../training/python/training/evaluation.py    |   2 +-
 tensorflow/contrib/verbs/rdma.cc              |  55 +++-
 .../contrib/verbs/rdma_rendezvous_mgr.cc      |  41 ++-
 tensorflow/contrib/verbs/verbs_util.cc        |  34 +++
 tensorflow/contrib/verbs/verbs_util.h         |  10 +
 tensorflow/core/BUILD                         |   7 +-
 .../core/common_runtime/direct_session.cc     |   4 +-
 .../core/debug/grpc_session_debug_test.cc     |   5 +-
 .../worker_cache_logger.cc                    |  54 ++--
 .../distributed_runtime/worker_cache_logger.h |   8 +
 tensorflow/core/framework/graph_def_util.h    |   2 +-
 tensorflow/core/framework/op.h                |   1 -
 tensorflow/core/framework/op_kernel.h         |   2 +-
 tensorflow/core/framework/tensor.h            |   2 +-
 tensorflow/core/graph/mkl_layout_pass.cc      | 277 ++++++++++++------
 tensorflow/core/graph/mkl_layout_pass_test.cc | 252 +++++++++++++++-
 .../grappler/costs/virtual_scheduler_test.cc  |   2 +-
 tensorflow/core/grappler/grappler_item.h      |   1 +
 .../core/grappler/optimizers/auto_parallel.cc |   5 +
 .../core/grappler/optimizers/auto_parallel.h  |   1 +
 .../grappler/optimizers/auto_parallel_test.cc |  42 +--
 .../grappler/optimizers/layout_optimizer.cc   |   2 +-
 tensorflow/core/kernels/BUILD                 |  16 +-
 tensorflow/core/kernels/adjust_contrast_op.cc |  24 ++
 .../adjust_contrast_op_benchmark_test.cc      |   5 +
 tensorflow/core/kernels/colorspace_op.cc      |  15 +
 tensorflow/core/kernels/control_flow_ops.cc   | 136 +++++----
 tensorflow/core/kernels/cwise_op_add_2.cc     |   7 +-
 tensorflow/core/kernels/cwise_op_cosh.cc      |  37 +++
 .../core/kernels/cwise_op_gpu_add.cu.cc       |   3 +-
 .../core/kernels/cwise_op_gpu_cosh.cu.cc      |  26 ++
 .../core/kernels/cwise_op_gpu_sinh.cu.cc      |  26 ++
 tensorflow/core/kernels/cwise_op_invert.cc    |   2 +-
 tensorflow/core/kernels/cwise_op_sinh.cc      |  37 +++
 .../kernels/cwise_op_squared_difference.cc    |  13 +
 tensorflow/core/kernels/cwise_ops.h           |   6 +
 tensorflow/core/kernels/dynamic_stitch_op.cc  |  27 +-
 tensorflow/core/kernels/map_stage_op.cc       |   7 +-
 tensorflow/core/kernels/meta_support.cc       |   5 +-
 .../core/kernels/mkl_conv_grad_filter_ops.cc  |  69 +++--
 tensorflow/core/kernels/mkl_conv_ops.cc       |  51 +++-
 tensorflow/core/kernels/mkl_lrn_op.cc         | 112 +++----
 tensorflow/core/kernels/mkl_relu_op.cc        |  30 +-
 tensorflow/core/kernels/mkl_tfconv_op.cc      |  54 +++-
 .../core/kernels/non_max_suppression_op.cc    |  36 ++-
 tensorflow/core/kernels/priority_queue.cc     |   2 +-
 tensorflow/core/kernels/shape_ops.cc          |  56 ++--
 tensorflow/core/kernels/slice_op.cc           | 258 +++++++++++++++-
 ...e_reduce_sum_op.cc => sparse_reduce_op.cc} |  76 +++--
 tensorflow/core/kernels/stack_ops.cc          |  68 ++++-
 tensorflow/core/kernels/tile_functor.h        | 115 ++++++++
 tensorflow/core/kernels/tile_functor_cpu.cc   |  85 ++++++
 .../core/kernels/tile_functor_gpu.cu.cc       | 101 +++++++
 tensorflow/core/kernels/tile_ops.cc           |  76 ++---
 tensorflow/core/kernels/tile_ops_cpu_impl.h   |  35 +--
 tensorflow/core/kernels/tile_ops_gpu_impl.h   |   1 -
 tensorflow/core/kernels/tile_ops_impl.h       |  25 +-
 tensorflow/core/kernels/topk_op.cc            |   2 +-
 tensorflow/core/kernels/transpose_functor.h   |   7 +
 tensorflow/core/kernels/transpose_op.cc       |   5 +-
 .../typed_conditional_accumulator_base.h      |   2 +-
 tensorflow/core/lib/gtl/optional.h            |   2 +-
 tensorflow/core/ops/math_grad.cc              |  20 ++
 tensorflow/core/ops/math_grad_test.cc         |  20 ++
 tensorflow/core/ops/math_ops.cc               |   8 +
 tensorflow/core/ops/nn_ops.cc                 |   2 +
 tensorflow/core/ops/ops.pbtxt                 |  75 +++++
 tensorflow/core/ops/sparse_ops.cc             |  69 +++++
 .../core/platform/cloud/retrying_utils.cc     |   2 +-
 tensorflow/core/protobuf/worker.proto         |   2 +-
 tensorflow/core/public/version.h              |   2 +-
 tensorflow/core/util/mkl_util.h               |  38 ++-
 .../api_guides/python/contrib.losses.md       |  10 +-
 .../docs_src/api_guides/python/math_ops.md    |   2 +
 tensorflow/docs_src/extend/estimators.md      |   5 +-
 .../docs_src/get_started/get_started.md       |   5 +-
 .../docs_src/get_started/mnist/beginners.md   |   2 +-
 .../docs_src/get_started/mnist/mechanics.md   |   2 +-
 .../get_started/tensorboard_histograms.md     |   2 +
 tensorflow/docs_src/install/install_c.md      |   2 +-
 tensorflow/docs_src/install/install_go.md     |   2 +-
 tensorflow/docs_src/install/install_java.md   |  18 +-
 tensorflow/docs_src/install/install_linux.md  |  26 +-
 tensorflow/docs_src/install/install_mac.md    |  14 +-
 .../docs_src/install/install_sources.md       |   4 +-
 .../docs_src/install/install_windows.md       |  13 +-
 .../docs_src/performance/performance_guide.md |   8 +-
 .../docs_src/performance/quantization.md      |  13 +-
 tensorflow/docs_src/tutorials/seq2seq.md      |  13 +-
 tensorflow/docs_src/tutorials/wide.md         |  18 +-
 .../docs_src/tutorials/wide_and_deep.md       |   6 +-
 tensorflow/docs_src/tutorials/word2vec.md     |   2 +-
 tensorflow/examples/android/build.gradle      |   2 +-
 .../examples/image_retraining/retrain.py      |  43 ++-
 tensorflow/examples/label_image/README.md     |  23 +-
 .../examples/label_image/label_image.py       | 132 +++++++++
 tensorflow/examples/learn/examples_test.sh    |   2 +-
 tensorflow/examples/tutorials/mnist/mnist.py  |   2 +-
 .../tutorials/word2vec/word2vec_basic.py      |  16 +-
 tensorflow/go/README.md                       |  29 --
 tensorflow/go/genop/generate.sh               |   2 +-
 tensorflow/go/op/wrappers.go                  |  30 ++
 tensorflow/go/shape.go                        |   2 +-
 tensorflow/go/tensor.go                       |   2 +-
 tensorflow/java/BUILD                         |  26 +-
 tensorflow/java/build_defs.bzl                |  11 +-
 tensorflow/java/maven/libtensorflow/pom.xml   |   4 +-
 tensorflow/java/maven/proto/pom.xml           |   6 +-
 tensorflow/java/maven/release.sh              |   2 +-
 tensorflow/java/maven/run_inside_container.sh |   2 +-
 .../src/main/java/org/tensorflow/Input.java   |  48 +++
 .../main/java/org/tensorflow/Operation.java   |  73 ++++-
 .../java/org/tensorflow/OperationBuilder.java |   2 +-
 .../src/main/java/org/tensorflow/Output.java  |  35 ++-
 .../src/main/java/org/tensorflow/Session.java |   4 +-
 .../java/org/tensorflow/op/NameScope.java     | 146 +++++++++
 .../main/java/org/tensorflow/op/Scope.java    | 165 +++++++++++
 .../java/src/main/native/operation_jni.cc     |  18 ++
 .../java/src/main/native/operation_jni.h      |  11 +
 .../java/org/tensorflow/OperationTest.java    | 112 +++++++
 .../test/java/org/tensorflow/SessionTest.java |   6 +-
 .../test/java/org/tensorflow/TensorTest.java  |   2 +-
 .../java/org/tensorflow/op/ScopeTest.java     | 270 +++++++++++++++++
 tensorflow/python/BUILD                       |   3 +
 tensorflow/python/debug/cli/analyzer_cli.py   |   2 +-
 .../python/debug/wrappers/framework_test.py   |   2 +-
 .../estimator/canned/dnn_linear_combined.py   |   2 +-
 tensorflow/python/framework/test_util.py      |  21 +-
 tensorflow/python/kernel_tests/BUILD          |  10 -
 .../python/kernel_tests/basic_gpu_test.py     |   2 +
 .../python/kernel_tests/cwise_ops_test.py     |  15 +
 .../python/kernel_tests/fft_ops_test.py       |  63 ++--
 .../python/kernel_tests/map_stage_op_test.py  |  93 +++---
 .../python/kernel_tests/record_input_test.py  |  14 +-
 .../python/kernel_tests/shape_ops_test.py     |  18 +-
 .../python/kernel_tests/sparse_ops_test.py    |  46 ++-
 .../python/kernel_tests/stage_op_test.py      |  87 +++---
 tensorflow/python/lib/io/py_record_writer.cc  |   8 +
 tensorflow/python/lib/io/py_record_writer.h   |   1 +
 tensorflow/python/lib/io/py_record_writer.i   |   1 +
 tensorflow/python/lib/io/tf_record.py         |   5 +
 tensorflow/python/ops/array_ops.py            |  30 +-
 tensorflow/python/ops/data_flow_ops.py        |  33 ++-
 .../python/ops/distributions/special_math.py  |   2 +-
 tensorflow/python/ops/gradients_impl.py       |  52 ++--
 tensorflow/python/ops/lookup_ops.py           |   6 +-
 tensorflow/python/ops/math_grad.py            |  18 ++
 tensorflow/python/ops/math_ops.py             |   7 +-
 tensorflow/python/ops/rnn_cell_impl.py        |   4 +-
 tensorflow/python/ops/sparse_ops.py           |  86 ++++++
 tensorflow/python/ops/standard_ops.py         |   2 +-
 tensorflow/python/ops/tensor_array_ops.py     |   2 +-
 tensorflow/python/ops/variable_scope.py       |   5 +
 .../print_selective_registration_header.py    |   2 +-
 tensorflow/python/training/input.py           |   7 +-
 tensorflow/python/training/input_test.py      |   7 +
 tensorflow/python/training/momentum.py        |   2 +-
 tensorflow/tensorflow.bzl                     |   2 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  16 +
 ...sorflow.python_io.-t-f-record-writer.pbtxt |   4 +
 tensorflow/tools/ci_build/builds/pip.sh       |  27 +-
 tensorflow/tools/ci_build/ci_build.sh         |  10 +-
 .../tools/ci_build/ci_parameterized_build.sh  |  17 +-
 tensorflow/tools/ci_build/ci_sanity.sh        |  24 +-
 .../ci_build/install/install_pip_packages.sh  |   4 +-
 .../tools/ci_build/install/install_proto3.sh  |   6 +-
 .../install/install_python3.5_pip_packages.sh |   2 +-
 .../protobuf/protobuf_optimized_pip.sh        |   2 +-
 .../ci_build/windows/bazel/bazel_test_lib.sh  |   2 +-
 .../ci_build/windows/cpu/cmake/run_py.bat     |   5 +-
 .../ci_build/windows/gpu/cmake/run_py.bat     |   5 +-
 tensorflow/tools/dist_test/local_test.sh      |   6 +-
 tensorflow/tools/docker/README.md             |  28 +-
 .../docker/parameterized_docker_build.sh      |  44 ++-
 .../tools/gcs_test/gcs_smoke_wrapper.sh       |  11 +-
 tensorflow/tools/git/gen_git_source.sh        |   2 +-
 tensorflow/tools/graph_transforms/README.md   |   2 +-
 .../graph_transforms/quantize_weights_test.cc |   4 +-
 .../lib_package/libtensorflow_java_test.sh    |   2 +-
 .../tools/lib_package/libtensorflow_test.sh   |   2 +-
 tensorflow/tools/pip_package/setup.py         |   2 +-
 tensorflow/tools/tfprof/README.md             |   6 +-
 tensorflow/tools/tfprof/g3doc/command_line.md |   2 +-
 tensorflow/workspace.bzl                      |  24 +-
 third_party/gpus/cuda_configure.bzl           |   4 +-
 third_party/py/BUILD.tpl                      |  12 +
 third_party/py/python_configure.bzl           |  56 +++-
 third_party/sycl/crosstool/computecpp.tpl     |   6 +-
 third_party/sycl/sycl/BUILD.tpl               |   2 +-
 tools/tf_env_collect.sh                       | 143 +++++----
 303 files changed, 6499 insertions(+), 1404 deletions(-)
 create mode 100644 tensorflow/compiler/plugin/executor/BUILD
 create mode 100644 tensorflow/compiler/plugin/executor/compiler.cc
 create mode 100644 tensorflow/compiler/plugin/executor/compiler.h
 create mode 100644 tensorflow/compiler/plugin/executor/device.cc
 create mode 100644 tensorflow/compiler/plugin/executor/executable.cc
 create mode 100644 tensorflow/compiler/plugin/executor/executable.h
 create mode 100644 tensorflow/compiler/plugin/executor/executor.cc
 create mode 100644 tensorflow/compiler/plugin/executor/executor.h
 create mode 100644 tensorflow/compiler/plugin/executor/platform.cc
 create mode 100644 tensorflow/compiler/plugin/executor/platform.h
 create mode 100644 tensorflow/compiler/plugin/executor/platform_id.h
 create mode 100644 tensorflow/compiler/plugin/executor/transfer_manager.cc
 create mode 100644 tensorflow/compiler/plugin/executor/transfer_manager.h
 create mode 100644 tensorflow/core/kernels/cwise_op_cosh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_sinh.cc
 rename tensorflow/core/kernels/{sparse_reduce_sum_op.cc => sparse_reduce_op.cc} (83%)
 create mode 100644 tensorflow/core/kernels/tile_functor.h
 create mode 100644 tensorflow/core/kernels/tile_functor_cpu.cc
 create mode 100644 tensorflow/core/kernels/tile_functor_gpu.cu.cc
 create mode 100644 tensorflow/examples/label_image/label_image.py
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/Input.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
 create mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index c78b6b1a15..43abdaafbf 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -159,7 +159,12 @@ There are two ways to run TensorFlow unit tests.
    bazel test ${flags} //tensorflow/python/...
    ```
 
-2. Using Docker and TensorFlow's CI scripts.
+2. Using [Docker](www.docker.com) and TensorFlow's CI scripts.
+
+   ```bash
+   # Install Docker first, then this will build and run cpu tests
+   tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
+   ```
 
    See
    [TensorFlow Builds](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build) for details.
diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 6f4c048ce8..4f1666c2e7 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -6,6 +6,7 @@ If you open a GitHub issue, here is our policy:
 
 1. It must be a bug or a feature request.
 2. The form below must be filled out.
+3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorboard/issues).
 
 **Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
 
diff --git a/README.md b/README.md
index e7dbf57b25..abbead98a7 100644
--- a/README.md
+++ b/README.md
@@ -34,13 +34,13 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
diff --git a/RELEASE.md b/RELEASE.md
index d22c5c62fe..9875838d7e 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -113,6 +113,8 @@
   checkpoints containing such RNN cells, in which case you can use the
   [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
   to convert the variable names in your old checkpoints.
+* Added `tf.contrib.kernel_methods` module with Ops and estimators for primal
+  (explicit) kernel methods in TensorFlow.
 
 ## Bug Fixes and Other Changes
 * In python, `Operation.get_attr` on type attributes returns the Python DType
diff --git a/configure b/configure
index e1aaddabda..1531b4d40a 100755
--- a/configure
+++ b/configure
@@ -162,8 +162,12 @@ bazel version > bazel.version
 curr_bazel_version=$(head -n 1 bazel.version | cut -d ' ' -f3)
 rm -f bazel.version
 
+
 echo "You have bazel $curr_bazel_version installed."
-if [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
+if [ -z "$curr_bazel_version" ]; then
+  echo "WARNING: current bazel installation is not a release version."
+  echo "Make sure you are running at least bazel $MIN_BAZEL_VERSION."
+elif [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
   echo "Please upgrade your bazel installation to version $MIN_BAZEL_VERSION or higher to build TensorFlow!"
   echo "Exiting..."
   exit 1
@@ -535,9 +539,9 @@ done
 # Set default CUDA version if not set
 if [ -z "$TF_CUDA_VERSION" ]; then
   TF_CUDA_VERSION="8.0"
-  export TF_CUDA_VERSION 
+  export TF_CUDA_VERSION
 fi
-write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION" 
+write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION"
 
 # Set up which gcc nvcc should use as the host compiler
 # No need to set this on Windows
@@ -586,6 +590,9 @@ while true; do
     # Result returned from "read" will be used unexpanded. That make "~" unusable.
     # Going through one more level of expansion to handle that.
     CUDNN_INSTALL_PATH=`"${PYTHON_BIN_PATH}" -c "import os; print(os.path.realpath(os.path.expanduser('${CUDNN_INSTALL_PATH}')))"`
+    if is_windows; then
+      CUDNN_INSTALL_PATH="$(cygpath -m "$CUDNN_INSTALL_PATH")"
+    fi
   fi
 
   if [[ -z "$TF_CUDNN_VERSION" ]]; then
@@ -652,16 +659,22 @@ write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
 
 # Configure the compute capabilities that TensorFlow builds for.
 # Since Cuda toolkit is not backward-compatible, this is not guaranteed to work.
+function get_native_cuda_compute_capabilities {
+  device_query_bin="$CUDA_TOOLKIT_PATH/extras/demo_suite/deviceQuery" # Also works on Windows without .exe
+  "$device_query_bin" | grep 'Capability' | grep -o '[0-9]*\.[0-9]*' | sed ':a;{N;s/\n/,/};ba'
+  exit 0 # ensure that this function always exit success even if device detection fails, to prevent the whole configure from aborting
+}
 while true; do
   fromuser=""
-  default_cuda_compute_capabilities="3.5,5.2"
+  native_cuda_compute_capabilities=$(get_native_cuda_compute_capabilities)
+  default_cuda_compute_capabilities=${native_cuda_compute_capabilities:-"3.5,5.2"}
   if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
 cat << EOF
 Please specify a list of comma-separated Cuda compute capabilities you want to build with.
 You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
 Please note that each additional compute capability significantly increases your build time and binary size.
 EOF
-    read -p "[Default is: \"3.5,5.2\"]: " TF_CUDA_COMPUTE_CAPABILITIES
+    read -p "[Default is: \"$default_cuda_compute_capabilities\"]: " TF_CUDA_COMPUTE_CAPABILITIES
     fromuser=1
   fi
   if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
@@ -832,17 +845,17 @@ while true; do
     if [ -e "$MPI_HOME/include" ] && [ -e "$MPI_HOME/lib" ]; then
         break
     fi
- 
+
     echo "Invalid path to the MPI Toolkit. ${MPI_HOME}/include or ${MPI_HOME}/lib cannot be found."
     if [ -z "$fromuser" ]; then
         exit 1
     fi
 
     # Retry
-    MPI_HOME="" 
+    MPI_HOME=""
 done
-    
-    
+
+
 if [ "$TF_NEED_MPI" == "1" ]; then
   write_to_bazelrc 'build --define with_mpi_support=true'
 
@@ -850,11 +863,11 @@ if [ "$TF_NEED_MPI" == "1" ]; then
   ln -sf "${MPI_HOME}/include/mpi.h" third_party/mpi/mpi.h
 
 
-  #Determine if we use OpenMPI or MVAPICH, these require different header files 
+  #Determine if we use OpenMPI or MVAPICH, these require different header files
   #to be included here to make bazel dependency checker happy
 
   if [ -e "${MPI_HOME}/include/mpi_portable_platform.h" ]; then
-        #OpenMPI 
+        #OpenMPI
         ln -sf "${MPI_HOME}/include/mpi_portable_platform.h" third_party/mpi/
         sed -i -e "s/MPI_LIB_IS_OPENMPI=False/MPI_LIB_IS_OPENMPI=True/" third_party/mpi/mpi.bzl
  else
@@ -864,7 +877,7 @@ if [ "$TF_NEED_MPI" == "1" ]; then
         sed -i -e "s/MPI_LIB_IS_OPENMPI=True/MPI_LIB_IS_OPENMPI=False/" third_party/mpi/mpi.bzl
  fi
 
-  
+
   if [ -e "${MPI_HOME}/lib/libmpi.so" ]; then
     ln -sf "${MPI_HOME}/lib/libmpi.so" third_party/mpi/
   else
diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 62ae5ae78c..6e76db02de 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -208,6 +208,7 @@ filegroup(
         "//tensorflow/compiler/jit/kernels:all_files",
         "//tensorflow/compiler/jit/legacy_flags:all_files",
         "//tensorflow/compiler/jit/ops:all_files",
+        "//tensorflow/compiler/plugin/executor:all_files",
         "//tensorflow/compiler/tests:all_files",
         "//tensorflow/compiler/tf2xla:all_files",
         "//tensorflow/compiler/tf2xla/cc:all_files",
diff --git a/tensorflow/c/generate-pc.sh b/tensorflow/c/generate-pc.sh
index 73d427d9b2..02a6a58b61 100755
--- a/tensorflow/c/generate-pc.sh
+++ b/tensorflow/c/generate-pc.sh
@@ -26,7 +26,7 @@ usage() {
 [ $# == 0 ] && usage && exit 0
 
 # read the options
-ARGS=`getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@"`
+ARGS=$(getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@")
 eval set -- "$ARGS"
 
 # extract options and their arguments into variables.
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index a884f11d48..9801add1da 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -472,10 +472,23 @@ cc_binary(
     name = "tutorials_example_trainer",
     srcs = ["tutorials/example_trainer.cc"],
     copts = tf_copts(),
-    linkopts = [
-        "-lpthread",
-        "-lm",
-    ],
+    linkopts = select({
+        "//tensorflow:windows": [],
+        "//tensorflow:windows_msvc": [],
+        "//tensorflow:darwin": [
+            "-lm",
+            "-lpthread",
+        ],
+        "//tensorflow:ios": [
+            "-lm",
+            "-lpthread",
+        ],
+        "//conditions:default": [
+            "-lm",
+            "-lpthread",
+            "-lrt",
+        ],
+    }),
     deps = [
         ":cc_ops",
         "//tensorflow/core:core_cpu",
diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc
index 8c1a01f518..71d9a8ed7b 100644
--- a/tensorflow/cc/gradients/math_grad.cc
+++ b/tensorflow/cc/gradients/math_grad.cc
@@ -162,6 +162,32 @@ Status Log1pGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Log1p", Log1pGrad);
 
+Status SinhGrad(const Scope& scope, const Operation& op,
+                const std::vector<Output>& grad_inputs,
+                std::vector<Output>* grad_outputs) {
+  // y = sinh(x)
+  // dy/dx = cosh(x)
+  auto dydx = Cosh(scope, op.input(0));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Sinh", SinhGrad);
+
+Status CoshGrad(const Scope& scope, const Operation& op,
+                const std::vector<Output>& grad_inputs,
+                std::vector<Output>* grad_outputs) {
+  // y = cosh(x)
+  // dy/dx = sinh(x)
+  auto dydx = Sinh(scope, op.input(0));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Cosh", CoshGrad);
+
 Status TanhGrad(const Scope& scope, const Operation& op,
                 const std::vector<Output>& grad_inputs,
                 std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc
index de6baa1769..1653b04378 100644
--- a/tensorflow/cc/gradients/math_grad_test.cc
+++ b/tensorflow/cc/gradients/math_grad_test.cc
@@ -45,6 +45,8 @@ class CWiseUnaryGradTest : public ::testing::Test {
     EXPM1,
     LOG,
     LOG1P,
+    SINH,
+    COSH,
     TANH,
     SIGMOID,
     SIGN,
@@ -111,6 +113,12 @@ class CWiseUnaryGradTest : public ::testing::Test {
       case LOG1P:
         y = Log1p(scope_, x);
         break;
+      case SINH:
+        y = Sinh(scope_, x);
+        break;
+      case COSH:
+        y = Cosh(scope_, x);
+        break;
       case TANH:
         y = Tanh(scope_, x);
         break;
@@ -337,6 +345,50 @@ TEST_F(CWiseUnaryGradTest, Log1p_Complex) {
   TestCWiseGrad<complex64>(LOG1P, x_fn, dy_fn, dx_fn);
 }
 
+TEST_F(CWiseUnaryGradTest, Sinh) {
+  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    return dy * std::cosh(x);
+  };
+  TestCWiseGrad<float>(SINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Sinh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    return dy * conjugate(std::cosh(x));
+  };
+  TestCWiseGrad<complex64>(SINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Cosh) {
+  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    return dy * std::sinh(x);
+  };
+  TestCWiseGrad<float>(COSH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Cosh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    return dy * conjugate(std::sinh(x));
+  };
+  TestCWiseGrad<complex64>(COSH, x_fn, dy_fn, dx_fn);
+}
+
 TEST_F(CWiseUnaryGradTest, Tanh) {
   auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
   auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc
index 5e5203d090..952b2015ed 100644
--- a/tensorflow/cc/gradients/nn_grad.cc
+++ b/tensorflow/cc/gradients/nn_grad.cc
@@ -46,6 +46,19 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad);
 
+Status LogSoftmaxGrad(const Scope& scope, const Operation& op,
+                   const std::vector<Output>& grad_inputs,
+                   std::vector<Output>* grad_outputs) {
+
+  auto softmax = Exp(scope, op.output(0));
+  auto sum = Sum(scope, grad_inputs[0], {1}, Sum::KeepDims(true));
+  auto mul = Mul(scope, sum, softmax);
+  auto dx = Sub(scope, grad_inputs[0], mul);
+  grad_outputs->push_back(dx);
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("LogSoftmax", LogSoftmaxGrad);
+
 Status ReluGradHelper(const Scope& scope, const Operation& op,
                       const std::vector<Output>& grad_inputs,
                       std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc
index 70c9bd4e08..daa87546ec 100644
--- a/tensorflow/cc/gradients/nn_grad_test.cc
+++ b/tensorflow/cc/gradients/nn_grad_test.cc
@@ -57,6 +57,19 @@ TEST_F(NNGradTest, SoftmaxGrad) {
   RunTest(x, shape, y, shape);
 }
 
+TEST_F(NNGradTest, LogSoftmaxGrad) {
+  TensorShape shape({5, 3});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
+  auto y = LogSoftmax(scope_, x);
+  // Avoid numerical instability when computing finite differences.
+  Tensor x_init_value = test::AsTensor<float>(
+          {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f,
+           0.1f, 0.3f, 0.5f, 0.7f, 0.8f,
+           -0.1f, 0.1f, 0.1f, 0.1f, 1.2f},
+          {5, 3});
+  RunTest(x, x_init_value, y, shape);
+}
+
 TEST_F(NNGradTest, ReluGrad) {
   TensorShape shape({5, 2});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 306e704415..7ebd842218 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -15,7 +15,10 @@ package_group(
 )
 
 package(
-    default_visibility = [":internal"],
+    default_visibility = [
+        ":internal",
+        "//tensorflow/compiler/plugin/executor:__pkg__",
+    ],
 )
 
 load("//tensorflow:tensorflow.bzl", "cc_header_only_library")
diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD
index ed204b8182..97f3512a6c 100644
--- a/tensorflow/compiler/jit/kernels/BUILD
+++ b/tensorflow/compiler/jit/kernels/BUILD
@@ -2,6 +2,7 @@ licenses(["notice"])  # Apache 2.0
 
 package(
     default_visibility = [
+        "//tensorflow/compiler/plugin/executor:__pkg__",
         "//tensorflow/compiler/tf2xla:internal",
     ],
 )
diff --git a/tensorflow/compiler/plugin/BUILD b/tensorflow/compiler/plugin/BUILD
index 4badd3a589..8c2e9a7c81 100644
--- a/tensorflow/compiler/plugin/BUILD
+++ b/tensorflow/compiler/plugin/BUILD
@@ -32,5 +32,7 @@ package(
 
 cc_library(
     name = "plugin",
-    deps = [],
+    deps = [
+        "//tensorflow/compiler/plugin/executor:plugin_lib",
+    ],
 )
diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
new file mode 100644
index 0000000000..2e5875705f
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/BUILD
@@ -0,0 +1,34 @@
+licenses(["restricted"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "plugin_lib",
+    srcs = glob([
+        "*.cc",
+    ]),
+    hdrs = glob([
+        "*.h",
+    ]),
+    deps = [
+        "//tensorflow/compiler/jit:xla_device",
+        "//tensorflow/compiler/jit:xla_jit_headers_lib",
+        "//tensorflow/compiler/tf2xla:xla_compiler",
+        "//tensorflow/compiler/xla:xla_headers_lib",
+        "//tensorflow/compiler/xla/service",
+        "//third_party/eigen3",
+        "@local_config_cuda//cuda:cuda_headers",
+        "@protobuf//:protobuf_headers",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+)
diff --git a/tensorflow/compiler/plugin/executor/compiler.cc b/tensorflow/compiler/plugin/executor/compiler.cc
new file mode 100644
index 0000000000..3a84f08c00
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/compiler.cc
@@ -0,0 +1,122 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <stdlib.h>
+#include <fstream>
+
+#include "tensorflow/compiler/plugin/executor/compiler.h"
+#include "tensorflow/compiler/plugin/executor/executable.h"
+
+#include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
+#include "tensorflow/compiler/xla/service/flatten_call_graph.h"
+#include "tensorflow/compiler/xla/service/hlo_constant_folding.h"
+#include "tensorflow/compiler/xla/service/hlo_cse.h"
+#include "tensorflow/compiler/xla/service/hlo_dce.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
+#include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
+#include "tensorflow/compiler/xla/service/inliner.h"
+#include "tensorflow/compiler/xla/service/reshape_mover.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+
+#include "tensorflow/stream_executor/lib/initialize.h"
+#include "tensorflow/stream_executor/lib/strcat.h"
+
+#include "tensorflow/core/lib/core/errors.h"
+
+namespace se = ::perftools::gputools;
+namespace sep = ::perftools::gputools::executorplugin;
+namespace port = ::perftools::gputools::port;
+
+namespace xla {
+namespace executorplugin {
+
+/*
+ * Run optimization passes on the module.  The graph is transformed by
+ * each pass in the optimization pipeline.  The service subdirectory
+ * contains useful optimization passes.
+ */
+Status ExecutorCompiler::RunHloOptimization(HloModule* hlo_module) {
+  HloPassPipeline pipeline("Executor");
+  pipeline.AddPass<Inliner>();
+  pipeline.AddPass<HloSubcomputationUnification>();
+  pipeline.AddPass<HloCSE>(false);
+
+  pipeline.AddPass<HloPassFix<AlgebraicSimplifier>>(
+      false, [](const Shape&, const Shape&) { return false; });
+  pipeline.AddPass<ReshapeMover>();
+  pipeline.AddPass<HloConstantFolding>();
+  pipeline.AddPass<HloCSE>(true);
+
+  pipeline.AddPass<HloDCE>();
+  pipeline.AddPass<FlattenCallGraph>();
+  return pipeline.Run(hlo_module).status();
+}
+
+StatusOr<std::unique_ptr<Executable>> ExecutorCompiler::Compile(
+        std::unique_ptr<HloModule> hlo_module,
+        se::StreamExecutor* stream_exec) {
+  TF_RET_CHECK(stream_exec != nullptr);
+
+  VLOG(1) << "Generate graph " << hlo_module->name();
+
+  TF_RETURN_IF_ERROR(RunHloOptimization(hlo_module.get()));
+
+  // Typically you would visit the HLO graph, building up a compiled equivalent
+  // In this case we are using an Hlo evaluator at execution time, so we don't
+  // need to compile anything
+
+  // Create executable from only the Hlo module
+  std::unique_ptr<Executable> executable;
+  executable.reset(new ExecutorExecutable(std::move(hlo_module)));
+
+  return std::move(executable);
+}
+
+StatusOr<std::vector<std::unique_ptr<Executable>>> ExecutorCompiler::Compile(
+        std::vector<std::unique_ptr<HloModule>> hlo_modules,
+        std::vector<se::StreamExecutor*> stream_execs) {
+
+  return tensorflow::errors::Unimplemented(
+      "Compilation of multiple HLO modules is not supported on Executor.");
+}
+
+StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+ExecutorCompiler::CompileAheadOfTime(
+    std::vector<std::unique_ptr<HloModule>> hlo_modules,
+    const AotCompilationOptions& aot_options) {
+
+  return tensorflow::errors::InvalidArgument(
+      "AOT compilation not supported on Executor");
+}
+
+se::Platform::Id ExecutorCompiler::PlatformId() const {
+  return sep::kExecutorPlatformId;
+}
+
+HloCostAnalysis::ShapeSizeFunction
+ExecutorCompiler::ShapeSizeBytesFunction() const {
+  return ExecutorExecutable::ShapeSizeBytes;
+}
+
+
+}  // namespace executorplugin
+}  // namespace xla
+
+REGISTER_MODULE_INITIALIZER(executor_compiler, {
+  xla::Compiler::RegisterCompilerFactory(sep::kExecutorPlatformId, []() {
+    return xla::MakeUnique<xla::executorplugin::ExecutorCompiler>();
+  });
+});
diff --git a/tensorflow/compiler/plugin/executor/compiler.h b/tensorflow/compiler/plugin/executor/compiler.h
new file mode 100644
index 0000000000..d318eefc49
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/compiler.h
@@ -0,0 +1,62 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
+
+#include <memory>
+
+#include "tensorflow/compiler/xla/service/compiler.h"
+#include "tensorflow/compiler/xla/service/executable.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_module_config.h"
+
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+namespace xla {
+namespace executorplugin {
+
+class ExecutorCompiler : public Compiler {
+ public:
+  ExecutorCompiler() {}
+  ~ExecutorCompiler() override {}
+
+  StatusOr<std::unique_ptr<Executable>> Compile(
+      std::unique_ptr<HloModule> hlo_module,
+      perftools::gputools::StreamExecutor* stream_exec) override;
+
+  StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
+      std::vector<std::unique_ptr<HloModule>> hlo_module,
+      std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
+
+  StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+  CompileAheadOfTime(
+      std::vector<std::unique_ptr<HloModule>> module,
+      const AotCompilationOptions& options) override;
+
+  HloCostAnalysis::ShapeSizeFunction ShapeSizeBytesFunction() const override;
+
+  perftools::gputools::Platform::Id PlatformId() const override;
+
+ private:
+  Status RunHloOptimization(HloModule* hlo_module);
+
+  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorCompiler);
+};
+
+}  // namespace executorplugin
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_COMPILER_H_
diff --git a/tensorflow/compiler/plugin/executor/device.cc b/tensorflow/compiler/plugin/executor/device.cc
new file mode 100644
index 0000000000..bbc39dc03f
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/device.cc
@@ -0,0 +1,60 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/jit/kernels/xla_device_launch_op.h"
+#include "tensorflow/compiler/jit/xla_device.h"
+#include "tensorflow/compiler/jit/xla_device_ops.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+
+namespace tensorflow {
+
+const char* const DEVICE_XLA_EXEC = "XLA_EXEC";
+const char* const DEVICE_EXEC_XLA_JIT = "XLA_EXEC_JIT";
+
+constexpr std::array<DataType, 5> kExecAllTypes = {
+    {DT_INT32, DT_FLOAT, DT_BOOL, DT_DOUBLE, DT_INT64}};
+
+class XlaExaDeviceFactory : public DeviceFactory {
+ public:
+  Status CreateDevices(const SessionOptions& options, const string& name_prefix,
+                       std::vector<Device*>* devices) override;
+};
+
+Status XlaExaDeviceFactory::CreateDevices(const SessionOptions& options,
+                                          const string& name_prefix,
+                                          std::vector<Device*>* devices) {
+  static XlaDeviceOpRegistrations* registrations =
+      RegisterXlaDeviceKernels(DEVICE_XLA_EXEC, DEVICE_EXEC_XLA_JIT);
+  (void)registrations;
+
+  std::unique_ptr<XlaDevice> device;
+  TF_RETURN_IF_ERROR(XlaDevice::Create("Executor", DEVICE_XLA_EXEC, 0,
+                                       DEVICE_EXEC_XLA_JIT, options,
+                                       name_prefix, &device));
+  devices->push_back(device.release());
+  return Status::OK();
+}
+
+REGISTER_LOCAL_DEVICE_FACTORY(DEVICE_XLA_EXEC, XlaExaDeviceFactory, 110);
+
+// Kernel registrations
+
+static bool OpFilter(KernelDef* kdef) { return true; }
+
+REGISTER_XLA_LAUNCH_KERNEL(DEVICE_XLA_EXEC, XlaDeviceLaunchOp, kExecAllTypes);
+REGISTER_XLA_DEVICE_KERNELS(DEVICE_XLA_EXEC, kExecAllTypes);
+REGISTER_XLA_BACKEND(DEVICE_EXEC_XLA_JIT, kExecAllTypes, OpFilter);
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/plugin/executor/executable.cc b/tensorflow/compiler/plugin/executor/executable.cc
new file mode 100644
index 0000000000..79eea9af3f
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executable.cc
@@ -0,0 +1,147 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/executable.h"
+#include "tensorflow/compiler/plugin/executor/executor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+
+namespace se = ::perftools::gputools;
+namespace sep = ::perftools::gputools::executorplugin;
+
+namespace xla {
+namespace executorplugin {
+
+ExecutorExecutable::ExecutorExecutable(std::unique_ptr<HloModule> hlo_module)
+    : Executable(std::move(hlo_module), ShapeSizeBytes) {}
+
+ExecutorExecutable::~ExecutorExecutable() {}
+
+static se::DeviceMemoryBase AllocateSingleOutput(sep::ExecutorExecutor* executor,
+                                                 const Literal& literal) {
+  int64 size(xla::ShapeUtil::ByteSizeOf(literal.shape()));
+  void* buf = executor->Allocate(size);
+  const void* src = literal.InternalData();
+  memcpy(buf, src, size);
+  return se::DeviceMemoryBase(buf, size);
+}
+
+static se::DeviceMemoryBase AllocateOutputBuffer(sep::ExecutorExecutor* executor,
+                                                 const Literal& literal) {
+  const Shape& shape = literal.shape();
+  if (shape.element_type() != xla::TUPLE) {
+    return AllocateSingleOutput(executor, literal);
+  } else {
+    int64 size(xla::ShapeUtil::ByteSizeOf(shape, sizeof(void*)));
+    void** buf = reinterpret_cast<void**>(executor->Allocate(size));
+    for (int64 n = 0; n < xla::ShapeUtil::TupleElementCount(shape); n++) {
+      se::DeviceMemoryBase out =
+          AllocateSingleOutput(executor, literal.tuple_literals(n));
+      *buf++ = out.opaque();
+    }
+
+    return se::DeviceMemoryBase(buf, size);
+  }
+}
+
+StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteOnStream(
+    const ServiceExecutableRunOptions* run_options,
+    tensorflow::gtl::ArraySlice<se::DeviceMemoryBase> arguments,
+    HloExecutionProfile* hlo_execution_profile) {
+  se::Stream* stream = run_options->stream();
+
+  VLOG(1) << "Execute " << module().name();
+  if (VLOG_IS_ON(2)) {
+    for (const auto& a : arguments) {
+      VLOG(2) << "-- argument " << a.opaque();
+    }
+  }
+
+  uint64 start_micros = tensorflow::Env::Default()->NowMicros();
+
+  HloComputation* computation = module().entry_computation();
+  if (computation->num_parameters() != arguments.size()) {
+    return tensorflow::errors::Internal(
+        "Mismatch between argument count and graph parameter count.");
+  }
+
+  // Create the arguments as an vector of XLA literals
+  std::vector<std::unique_ptr<Literal>> arg_literals;
+  std::vector<Literal*> arg_literals_ptrs;
+  for (int64 p = 0; p < computation->num_parameters(); p++) {
+    // Create the input literal for the parameter
+    HloInstruction* param = computation->parameter_instruction(p);
+    arg_literals.emplace_back(Literal::CreateFromShape(param->shape()));
+    arg_literals_ptrs.push_back(arg_literals.back().get());
+
+    // Copy in the data from the stream_executor buffers
+    void* buffer = arg_literals.back().get()->MutableInternalData();
+    memcpy(buffer, arguments[p].opaque(),
+           ShapeUtil::ByteSizeOf(param->shape()));
+  }
+
+  // Execute the graph using the evaluator
+  HloEvaluator evaluator;
+  std::unique_ptr<Literal> output;
+  TF_ASSIGN_OR_RETURN(output,
+                      evaluator.Evaluate(computation, arg_literals_ptrs));
+
+  // Copy the result into the return buffer
+  perftools::gputools::StreamExecutor* executor(stream->parent());
+  sep::ExecutorExecutor* executorExecutor(
+      static_cast<sep::ExecutorExecutor*>(executor->implementation()));
+
+  se::DeviceMemoryBase ret =
+      AllocateOutputBuffer(executorExecutor, *(output.get()));
+
+  uint64 end_micros = tensorflow::Env::Default()->NowMicros();
+
+  {
+    tensorflow::mutex_lock lock(mutex_);
+    const double nanoseconds = (end_micros - start_micros) * 1000.0;
+    execution_profile_.set_compute_time_ns(std::max(nanoseconds, 1.0));
+  }
+
+  return ret;
+}
+
+StatusOr<std::unique_ptr<ShapedBuffer>> ExecutorExecutable::ExecuteOnStream(
+    const ServiceExecutableRunOptions* run_options,
+    tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
+    HloExecutionProfile* hlo_execution_profile) {
+  return tensorflow::errors::Unimplemented(
+      "ExecuteOnStream is not yet supported on Executor.");
+}
+
+StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteAsyncOnStream(
+    const ServiceExecutableRunOptions* run_options,
+    tensorflow::gtl::ArraySlice<se::DeviceMemoryBase> arguments) {
+  return tensorflow::errors::Unimplemented(
+      "ExecuteAsyncOnStream is not yet supported on Executor.");
+}
+
+/*static*/ int64 ExecutorExecutable::ShapeSizeBytes(const Shape& shape) {
+  if (ShapeUtil::IsOpaque(shape)) {
+    return sizeof(void*);
+  }
+  return ShapeUtil::ByteSizeOf(shape, sizeof(void*));
+}
+
+
+}  // namespace executorplugin
+}  // namespace xla
diff --git a/tensorflow/compiler/plugin/executor/executable.h b/tensorflow/compiler/plugin/executor/executable.h
new file mode 100644
index 0000000000..ba3d4da21d
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executable.h
@@ -0,0 +1,65 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
+
+#include <cstddef>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/executable.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_module_config.h"
+
+#include "tensorflow/stream_executor/lib/status.h"
+#include "tensorflow/stream_executor/lib/statusor.h"
+
+namespace xla {
+namespace executorplugin {
+
+class ExecutorExecutable : public Executable {
+ public:
+  ExecutorExecutable(std::unique_ptr<HloModule> hlo_module);
+  ~ExecutorExecutable() override;
+
+  StatusOr<perftools::gputools::DeviceMemoryBase> ExecuteOnStream(
+      const ServiceExecutableRunOptions* run_options,
+      tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>
+          arguments,
+      HloExecutionProfile* hlo_execution_profile) override;
+
+  StatusOr<std::unique_ptr<ShapedBuffer>> ExecuteOnStream(
+      const ServiceExecutableRunOptions* run_options,
+      tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
+      HloExecutionProfile* hlo_execution_profile) override;
+
+  StatusOr<perftools::gputools::DeviceMemoryBase> ExecuteAsyncOnStream(
+      const ServiceExecutableRunOptions* run_options,
+      tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>
+          arguments) override;
+
+  static int64 ShapeSizeBytes(const Shape& shape);
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorExecutable);
+};
+
+}  // namespace executorplugin
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_EXECUTABLE_H_
diff --git a/tensorflow/compiler/plugin/executor/executor.cc b/tensorflow/compiler/plugin/executor/executor.cc
new file mode 100644
index 0000000000..e72c2711f7
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executor.cc
@@ -0,0 +1,135 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/executor.h"
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+#include "tensorflow/compiler/xla/status_macros.h"
+
+#include <stdlib.h>
+#include <string.h>
+
+namespace se = ::perftools::gputools;
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+host::HostStream *AsExecutorStream(Stream *stream) {
+  DCHECK(stream != nullptr);
+  return dynamic_cast<host::HostStream *>(stream->implementation());
+}
+
+ExecutorExecutor::ExecutorExecutor(const PluginConfig &plugin_config)
+    : plugin_config_(plugin_config) {}
+
+ExecutorExecutor::~ExecutorExecutor() {}
+
+void *ExecutorExecutor::Allocate(uint64 size) {
+  void *buf = new char[size];
+  return buf;
+}
+
+void *ExecutorExecutor::AllocateSubBuffer(DeviceMemoryBase *parent,
+                                         uint64 offset_bytes,
+                                         uint64 size_bytes) {
+  return parent + offset_bytes;
+}
+
+void ExecutorExecutor::Deallocate(DeviceMemoryBase *mem) {
+  if (!mem->is_sub_buffer()) {
+    delete[] static_cast<char *>(mem->opaque());
+  }
+}
+
+bool ExecutorExecutor::Memcpy(Stream *stream, void *host_dst,
+                             const DeviceMemoryBase &dev_src, uint64 size) {
+  AsExecutorStream(stream)->EnqueueTask([this, host_dst, dev_src, size]() {
+    port::Status ok = SynchronousMemcpy(host_dst, dev_src, size);
+  });
+  return true;
+}
+
+bool ExecutorExecutor::Memcpy(Stream *stream, DeviceMemoryBase *dev_dst,
+                             const void *host_src, uint64 size) {
+  AsExecutorStream(stream)->EnqueueTask([this, dev_dst, host_src, size]() {
+    port::Status ok = SynchronousMemcpy(dev_dst, host_src, size);
+  });
+  return true;
+}
+
+port::Status ExecutorExecutor::SynchronousMemcpy(DeviceMemoryBase *dev_dst,
+                                                const void *host_src,
+                                                uint64 size) {
+  memcpy(dev_dst->opaque(), host_src, size);
+  return port::Status::OK();
+}
+
+port::Status ExecutorExecutor::SynchronousMemcpy(void *host_dst,
+                                                const DeviceMemoryBase &dev_src,
+                                                uint64 size) {
+  memcpy(host_dst, dev_src.opaque(), size);
+  return port::Status::OK();
+}
+
+bool ExecutorExecutor::HostCallback(Stream *stream,
+                                   std::function<void()> callback) {
+  AsExecutorStream(stream)->EnqueueTask(callback);
+  return true;
+}
+
+bool ExecutorExecutor::CreateStreamDependency(Stream *dependent, Stream *other) {
+  AsExecutorStream(dependent)->EnqueueTask(
+      [other]() { other->BlockHostUntilDone(); });
+  AsExecutorStream(dependent)->BlockUntilDone();
+  return true;
+}
+
+bool ExecutorExecutor::StartTimer(Stream *stream, Timer *timer) {
+  dynamic_cast<host::HostTimer *>(timer->implementation())->Start(stream);
+  return true;
+}
+
+bool ExecutorExecutor::StopTimer(Stream *stream, Timer *timer) {
+  dynamic_cast<host::HostTimer *>(timer->implementation())->Stop(stream);
+  return true;
+}
+
+bool ExecutorExecutor::BlockHostUntilDone(Stream *stream) {
+  AsExecutorStream(stream)->BlockUntilDone();
+  return true;
+}
+
+DeviceDescription *ExecutorExecutor::PopulateDeviceDescription() const {
+  internal::DeviceDescriptionBuilder builder;
+
+  builder.set_device_address_bits(64);
+
+  builder.set_name("Executor");
+  builder.set_device_vendor("VectorName");
+  builder.set_platform_version("1.0");
+  builder.set_driver_version("1.0");
+  builder.set_runtime_version("1.0");
+  builder.set_pci_bus_id("1");
+  builder.set_device_memory_size(static_cast<uint64>(4) * 1024 * 1024 * 1024);
+  builder.set_clock_rate_ghz(static_cast<float>(CLOCKS_PER_SEC) / 1e9);
+
+  auto built = builder.Build();
+  return built.release();
+}
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
diff --git a/tensorflow/compiler/plugin/executor/executor.h b/tensorflow/compiler/plugin/executor/executor.h
new file mode 100644
index 0000000000..32fdb157e4
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/executor.h
@@ -0,0 +1,213 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Declares the ExecutorExecutor class, which is a CPU-only implementation of
+// the StreamExecutor interface. For now, this is used for testing and to
+// examine the performance of host-based StreamExecutor code.
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
+
+#include "tensorflow/stream_executor/host/host_stream.h"
+#include "tensorflow/stream_executor/host/host_timer.h"
+
+#include "tensorflow/compiler/xla/shape_util.h"
+
+#include "tensorflow/stream_executor/blas.h"
+#include "tensorflow/stream_executor/lib/error.h"
+#include "tensorflow/stream_executor/lib/status.h"
+#include "tensorflow/stream_executor/lib/statusor.h"
+#include "tensorflow/stream_executor/rng.h"
+#include "tensorflow/stream_executor/stream_executor.h"
+#include "tensorflow/stream_executor/stream_executor_internal.h"
+
+#include <list>
+#include <mutex>
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+using Args = tensorflow::gtl::ArraySlice<DeviceMemoryBase>;
+
+class ExecutorExecutor : public internal::StreamExecutorInterface {
+ public:
+  explicit ExecutorExecutor(const PluginConfig &plugin_config);
+  ~ExecutorExecutor() override;
+
+  port::Status Init(int device_ordinal, DeviceOptions device_options) override {
+    return port::Status::OK();
+  }
+
+  bool GetKernel(const MultiKernelLoaderSpec &spec,
+                 KernelBase *kernel) override {
+    return false;
+  }
+  bool Launch(Stream *stream, const ThreadDim &thread_dims,
+              const BlockDim &block_dims, const KernelBase &kernel,
+              const KernelArgsArrayBase &args) override {
+    return false;
+  }
+
+  void *Allocate(uint64 size) override;
+  void *AllocateSubBuffer(DeviceMemoryBase *mem, uint64 offset_bytes,
+                          uint64 size_bytes) override;
+  void Deallocate(DeviceMemoryBase *mem) override;
+
+  void *HostMemoryAllocate(uint64 size) override { return new char[size]; }
+  void HostMemoryDeallocate(void *mem) override {
+    delete[] static_cast<char *>(mem);
+  }
+  bool HostMemoryRegister(void *mem, uint64 size) override { return true; }
+  bool HostMemoryUnregister(void *mem) override { return true; }
+
+  bool Memcpy(Stream *stream, void *host_dst, const DeviceMemoryBase &pop_src,
+              uint64 size) override;
+  bool Memcpy(Stream *stream, DeviceMemoryBase *pop_dst, const void *host_src,
+              uint64 size) override;
+  bool MemcpyDeviceToDevice(Stream *stream, DeviceMemoryBase *pop_dst,
+                            const DeviceMemoryBase &host_src,
+                            uint64 size) override {
+    return false;
+  }
+
+  bool MemZero(Stream *stream, DeviceMemoryBase *location,
+               uint64 size) override {
+    return false;
+  }
+  bool Memset(Stream *stream, DeviceMemoryBase *location, uint8 pattern,
+              uint64 size) override {
+    return false;
+  }
+  bool Memset32(Stream *stream, DeviceMemoryBase *location, uint32 pattern,
+                uint64 size) override {
+    return false;
+  }
+
+  // No "synchronize all activity" implemented for this platform at the moment.
+  bool SynchronizeAllActivity() override { return false; }
+  bool SynchronousMemZero(DeviceMemoryBase *location, uint64 size) override {
+    return false;
+  }
+
+  bool SynchronousMemSet(DeviceMemoryBase *location, int value,
+                         uint64 size) override {
+    return false;
+  }
+
+  port::Status SynchronousMemcpy(DeviceMemoryBase *pop_dst,
+                                 const void *host_src, uint64 size) override;
+  port::Status SynchronousMemcpy(void *host_dst,
+                                 const DeviceMemoryBase &pop_src,
+                                 uint64 size) override;
+  port::Status SynchronousMemcpyDeviceToDevice(DeviceMemoryBase *pop_dst,
+                                               const DeviceMemoryBase &pop_src,
+                                               uint64 size) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  bool HostCallback(Stream *stream, std::function<void()> callback) override;
+
+  port::Status AllocateEvent(Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  port::Status DeallocateEvent(Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  port::Status RecordEvent(Stream *stream, Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  port::Status WaitForEvent(Stream *stream, Event *event) override {
+    return port::Status{port::error::UNIMPLEMENTED, ""};
+  }
+
+  Event::Status PollForEventStatus(Event *event) override {
+    return Event::Status::kError;
+  }
+
+  bool AllocateStream(Stream *stream) override { return true; }
+  void DeallocateStream(Stream *stream) override {}
+  bool CreateStreamDependency(Stream *dependent, Stream *other) override;
+
+  bool AllocateTimer(Timer *timer) override { return true; }
+  void DeallocateTimer(Timer *timer) override {}
+  bool StartTimer(Stream *stream, Timer *timer) override;
+  bool StopTimer(Stream *stream, Timer *timer) override;
+
+  bool BlockHostUntilDone(Stream *stream) override;
+
+  int PlatformDeviceCount() override { return 1; }
+
+  bool DeviceMemoryUsage(int64 *free, int64 *total) const override {
+    return false;
+  }
+
+  DeviceDescription *PopulateDeviceDescription() const override;
+
+  port::Status EnablePeerAccessTo(StreamExecutorInterface *other) override {
+    return port::Status::OK();
+  }
+
+  bool CanEnablePeerAccessTo(StreamExecutorInterface *other) override {
+    return true;
+  }
+
+  SharedMemoryConfig GetDeviceSharedMemoryConfig() override {
+    return SharedMemoryConfig::kDefault;
+  }
+
+  port::Status SetDeviceSharedMemoryConfig(SharedMemoryConfig config) override {
+    return port::Status{port::error::UNIMPLEMENTED,
+                        "Shared memory not supported"};
+  }
+
+  std::unique_ptr<internal::EventInterface> CreateEventImplementation()
+      override {
+    return nullptr;
+  }
+
+  std::unique_ptr<internal::KernelInterface> CreateKernelImplementation()
+      override {
+    return nullptr;
+  }
+
+  std::unique_ptr<internal::StreamInterface> GetStreamImplementation()
+      override {
+    return std::unique_ptr<internal::StreamInterface>(new host::HostStream());
+  }
+
+  std::unique_ptr<internal::TimerInterface> GetTimerImplementation() override {
+    return std::unique_ptr<internal::TimerInterface>(new host::HostTimer());
+  }
+
+  port::StatusOr<DeviceMemoryBase> ExecuteGraph(const xla::Shape &shape,
+                                                Args args);
+
+ private:
+  DeviceMemoryBase AllocateSingleOutput(const xla::Shape &shape);
+
+  port::StatusOr<DeviceMemoryBase> AllocateOutputBuffer(
+      const xla::Shape &shape);
+
+  const PluginConfig plugin_config_;
+};
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_EXECUTOR_H_
diff --git a/tensorflow/compiler/plugin/executor/platform.cc b/tensorflow/compiler/plugin/executor/platform.cc
new file mode 100644
index 0000000000..2f339f04a7
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/platform.cc
@@ -0,0 +1,125 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/platform.h"
+#include "tensorflow/compiler/plugin/executor/executor.h"
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+#include "tensorflow/stream_executor/lib/error.h"
+#include "tensorflow/stream_executor/lib/initialize.h"
+#include "tensorflow/stream_executor/lib/ptr_util.h"
+#include "tensorflow/stream_executor/lib/status.h"
+#include "tensorflow/stream_executor/lib/status_macros.h"
+#include "tensorflow/stream_executor/lib/stringprintf.h"
+
+namespace se = ::perftools::gputools;
+namespace sep = ::perftools::gputools::executorplugin;
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+PLATFORM_DEFINE_ID(kExecutorPlatformId);
+
+ExecutorPlatform::ExecutorPlatform() : name_("Executor") {}
+
+ExecutorPlatform::~ExecutorPlatform() {}
+
+Platform::Id ExecutorPlatform::id() const { return kExecutorPlatformId; }
+
+int ExecutorPlatform::VisibleDeviceCount() const { return 1; }
+
+const string& ExecutorPlatform::Name() const { return name_; }
+
+port::StatusOr<StreamExecutor*> ExecutorPlatform::ExecutorForDevice(
+    int ordinal) {
+  StreamExecutorConfig config;
+  config.ordinal = ordinal;
+  config.plugin_config = PluginConfig();
+  config.device_options = DeviceOptions::Default();
+  return GetExecutor(config);
+}
+
+port::StatusOr<StreamExecutor*>
+ExecutorPlatform::ExecutorForDeviceWithPluginConfig(
+    int device_ordinal, const PluginConfig& plugin_config) {
+  StreamExecutorConfig config;
+  config.ordinal = device_ordinal;
+  config.plugin_config = plugin_config;
+  config.device_options = DeviceOptions::Default();
+  return GetExecutor(config);
+}
+
+port::StatusOr<StreamExecutor*> ExecutorPlatform::GetExecutor(
+    const StreamExecutorConfig& config) {
+  mutex_lock lock(executors_mutex_);
+
+  port::StatusOr<StreamExecutor*> status = executor_cache_.Get(config);
+  if (status.ok()) {
+    return status.ValueOrDie();
+  }
+
+  port::StatusOr<std::unique_ptr<StreamExecutor>> executor =
+      GetUncachedExecutor(config);
+  if (!executor.ok()) {
+    return executor.status();
+  }
+
+  StreamExecutor* naked_executor = executor.ValueOrDie().get();
+  SE_RETURN_IF_ERROR(
+      executor_cache_.Insert(config, executor.ConsumeValueOrDie()));
+  return naked_executor;
+}
+
+port::StatusOr<std::unique_ptr<StreamExecutor>>
+ExecutorPlatform::GetUncachedExecutor(const StreamExecutorConfig& config) {
+  auto executor = port::MakeUnique<StreamExecutor>(
+      this, port::MakeUnique<ExecutorExecutor>(config.plugin_config));
+  auto init_status = executor->Init(config.ordinal, config.device_options);
+  if (!init_status.ok()) {
+    return port::Status{
+        port::error::INTERNAL,
+        port::Printf(
+            "failed initializing StreamExecutor for device ordinal %d: %s",
+            config.ordinal, init_status.ToString().c_str())};
+  }
+
+  return std::move(executor);
+}
+
+void ExecutorPlatform::RegisterTraceListener(
+    std::unique_ptr<TraceListener> listener) {
+  LOG(FATAL) << "not yet implemented: register executor trace listener";
+}
+
+void ExecutorPlatform::UnregisterTraceListener(TraceListener* listener) {
+  LOG(FATAL) << "not yet implemented: unregister executor trace listener";
+}
+
+static void InitializeExecutorPlatform() {
+  std::unique_ptr<se::Platform> platform(new sep::ExecutorPlatform);
+  SE_CHECK_OK(se::MultiPlatformManager::RegisterPlatform(std::move(platform)));
+}
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+REGISTER_MODULE_INITIALIZER(executor_platform, sep::InitializeExecutorPlatform());
+
+DECLARE_MODULE_INITIALIZER(multi_platform_manager);
+// Note that module initialization sequencing is not supported in the
+// open-source project, so this will be a no-op there.
+REGISTER_MODULE_INITIALIZER_SEQUENCE(executor_platform, multi_platform_manager);
diff --git a/tensorflow/compiler/plugin/executor/platform.h b/tensorflow/compiler/plugin/executor/platform.h
new file mode 100644
index 0000000000..c252a589d4
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/platform.h
@@ -0,0 +1,83 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/stream_executor/executor_cache.h"
+#include "tensorflow/stream_executor/lib/statusor.h"
+#include "tensorflow/stream_executor/multi_platform_manager.h"
+#include "tensorflow/stream_executor/platform.h"
+#include "tensorflow/stream_executor/platform/mutex.h"
+#include "tensorflow/stream_executor/platform/port.h"
+#include "tensorflow/stream_executor/platform/thread_annotations.h"
+#include "tensorflow/stream_executor/stream_executor_pimpl.h"
+#include "tensorflow/stream_executor/trace_listener.h"
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+class ExecutorPlatform : public Platform {
+ public:
+  ExecutorPlatform();
+  ~ExecutorPlatform() override;
+
+  Platform::Id id() const override;
+
+  // Device count is less clear-cut for CPUs than accelerators. This call
+  // currently returns the number of thread units in the host, as reported by
+  // base::NumCPUs().
+  int VisibleDeviceCount() const override;
+
+  const string& Name() const override;
+
+  port::StatusOr<StreamExecutor*> ExecutorForDevice(int ordinal) override;
+
+  port::StatusOr<StreamExecutor*> ExecutorForDeviceWithPluginConfig(
+      int ordinal, const PluginConfig& config) override;
+
+  port::StatusOr<StreamExecutor*> GetExecutor(
+      const StreamExecutorConfig& config) override;
+
+  port::StatusOr<std::unique_ptr<StreamExecutor>> GetUncachedExecutor(
+      const StreamExecutorConfig& config) override;
+
+  void RegisterTraceListener(std::unique_ptr<TraceListener> listener) override;
+
+  void UnregisterTraceListener(TraceListener* listener) override;
+
+ private:
+  // This platform's name.
+  string name_;
+
+  // mutex that guards the ordinal-to-executor map.
+  mutable mutex executors_mutex_;
+
+  // Cache of created StreamExecutors.
+  ExecutorCache executor_cache_;
+
+  SE_DISALLOW_COPY_AND_ASSIGN(ExecutorPlatform);
+};
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_STREAM_EXECUTOR_EXECUTOR_PLATFORM_H_
diff --git a/tensorflow/compiler/plugin/executor/platform_id.h b/tensorflow/compiler/plugin/executor/platform_id.h
new file mode 100644
index 0000000000..8d2b29a3e4
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/platform_id.h
@@ -0,0 +1,31 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
+#define TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
+
+#include "tensorflow/stream_executor/platform.h"
+
+namespace perftools {
+namespace gputools {
+namespace executorplugin {
+
+extern const Platform::Id kExecutorPlatformId;
+
+}  // namespace executorplugin
+}  // namespace gputools
+}  // namespace perftools
+
+#endif  // TENSORFLOW_STREAM_EXECUTOR_EXECUTOR_PLATFORM_ID_H_
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.cc b/tensorflow/compiler/plugin/executor/transfer_manager.cc
new file mode 100644
index 0000000000..51c5deeea5
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.cc
@@ -0,0 +1,187 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/plugin/executor/transfer_manager.h"
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace sep = ::perftools::gputools::executorplugin;
+
+namespace xla {
+namespace executorplugin {
+
+ExecutorTransferManager::ExecutorTransferManager() {}
+
+se::Platform::Id ExecutorTransferManager::PlatformId() const {
+  return se::executorplugin::kExecutorPlatformId;
+}
+
+Status ExecutorTransferManager::TransferLiteralFromDevice(
+    se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
+    const Shape& device_shape, const Shape& literal_shape, Literal* literal) {
+  TF_RET_CHECK(ShapeUtil::Compatible(device_shape, literal_shape));
+
+  // Tuples are a special case and contain one or more shapes inside of them to
+  // an arbitrary nesting depth.
+  if (device_shape.element_type() == TUPLE) {
+    *literal->mutable_shape() = literal_shape;
+    TF_ASSIGN_OR_RETURN(
+        std::vector<se::DeviceMemoryBase> element_buffers,
+        ShallowCopyTupleFromDevice(executor, source, device_shape));
+    TF_RET_CHECK(element_buffers.size() ==
+                 ShapeUtil::TupleElementCount(device_shape));
+    for (int64 i = 0; i < element_buffers.size(); ++i) {
+      const Shape& element_device_shape = device_shape.tuple_shapes(i);
+      const Shape& element_literal_shape = literal_shape.tuple_shapes(i);
+      Literal* element_literal = literal->add_tuple_literals();
+      // Recursively call TransferFromDevice to copy over the data in the
+      // element array.
+      TF_RETURN_IF_ERROR(TransferLiteralFromDevice(
+          executor, element_buffers[i], element_device_shape,
+          element_literal_shape, element_literal));
+    }
+    return Status::OK();
+  }
+
+  *literal->mutable_shape() = device_shape;
+  literal->Reserve(ShapeUtil::ElementsIn(device_shape));
+  TF_RETURN_IF_ERROR(TransferBufferFromDevice(
+      executor, source, ShapeUtil::ByteSizeOf(device_shape),
+      literal->MutableInternalData()));
+  if (!ShapeUtil::Equal(literal_shape, device_shape)) {
+    literal->Swap(
+        literal->Relayout(literal_shape.layout()).get());
+  }
+  TF_RET_CHECK(ShapeUtil::Equal(literal_shape, literal->shape()));
+  return Status::OK();
+}
+
+StatusOr<std::vector<se::DeviceMemoryBase>>
+ExecutorTransferManager::ShallowCopyTupleFromDevice(
+    se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
+    const Shape& shape) {
+  TF_RET_CHECK(ShapeUtil::IsTuple(shape));
+
+  std::vector<void*> element_pointers(ShapeUtil::TupleElementCount(shape),
+                                      nullptr);
+  int64 tuple_size = ShapeUtil::ByteSizeOf(shape, sizeof(void*));
+  auto copy_status = executor->SynchronousMemcpyD2H(source, tuple_size,
+                                                    element_pointers.data());
+  if (!copy_status.ok()) {
+    return AddStatus(
+        Status(static_cast<tensorflow::error::Code>(copy_status.code()),
+               copy_status.error_message()),
+        "failed transfer of tuple buffer " + ShapeUtil::HumanString(shape));
+  }
+
+  // Create a DeviceMemoryBase from each void* pointer.
+  std::vector<se::DeviceMemoryBase> destination;
+  for (int i = 0; i < element_pointers.size(); ++i) {
+    if (element_pointers[i] == nullptr &&
+        !ShapeUtil::HasZeroElements(shape.tuple_shapes(i))) {
+      return FailedPrecondition("tuple contains nullptr at element %d", i);
+    }
+    int64 buffer_size =
+        ShapeUtil::ByteSizeOf(shape.tuple_shapes(i), sizeof(void*));
+    destination.emplace_back(element_pointers[i], buffer_size);
+  }
+  return std::move(destination);
+}
+
+Status ExecutorTransferManager::TransferLiteralToDevice(
+    se::StreamExecutor* executor, const Literal& literal,
+    se::DeviceMemoryBase* destination) {
+  const Shape& shape = literal.shape();
+
+  if (ShapeUtil::IsTuple(literal.shape())) {
+    std::vector<void*> tuple_elements_on_device;
+    for (const Literal& tuple_element : literal.tuple_literals()) {
+      se::DeviceMemoryBase allocation = executor->AllocateArray<uint8>(
+          GetByteSizeRequirement(tuple_element.shape()));
+      TF_RETURN_IF_ERROR(
+          TransferLiteralToDevice(executor, tuple_element, &allocation));
+      tuple_elements_on_device.push_back(allocation.opaque());
+    }
+    return TransferBufferToDevice(
+        executor, tuple_elements_on_device.size() * sizeof(void*),
+        tuple_elements_on_device.data(), destination);
+  }
+
+  return TransferBufferToDevice(executor, GetByteSizeRequirement(shape),
+                                literal.InternalData(),
+                                destination);
+}
+
+Status ExecutorTransferManager::TransferLiteralToInfeed(
+    se::StreamExecutor* executor, const Literal& literal) {
+  const Shape& shape = literal.shape();
+  VLOG(1) << "transferring literal shape to infeed: "
+          << ShapeUtil::HumanString(shape);
+
+  return Status::OK();
+}
+
+Status ExecutorTransferManager::TransferBufferToInfeed(
+    se::StreamExecutor* executor, int64 size, const void* source) {
+  return Unimplemented("Transfer to Infeed");
+}
+
+Status ExecutorTransferManager::TransferLiteralFromOutfeed(
+    perftools::gputools::StreamExecutor* executor, const Shape& literal_shape,
+    Literal* literal) {
+  const Shape& shape = literal->shape();
+  VLOG(1) << "transferring literal shape from outfeed: "
+          << ShapeUtil::HumanString(shape);
+
+  return Status::OK();
+}
+
+Status ExecutorTransferManager::ResetDevices(
+    tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*>
+        executors) {
+  return Unimplemented("Device reset not supported");
+}
+
+int64 ExecutorTransferManager::GetByteSizeRequirement(const Shape& shape) {
+  return ShapeUtil::ByteSizeOf(shape, sizeof(void*));
+}
+
+}  // namespace executorplugin
+}  // namespace xla
+
+static std::unique_ptr<xla::TransferManager> CreateExecutorTransferManager() {
+  return xla::MakeUnique<xla::executorplugin::ExecutorTransferManager>();
+}
+
+static bool InitModule() {
+  xla::TransferManager::RegisterTransferManager(sep::kExecutorPlatformId,
+                                                &CreateExecutorTransferManager);
+  return true;
+}
+static bool module_initialized = InitModule();
diff --git a/tensorflow/compiler/plugin/executor/transfer_manager.h b/tensorflow/compiler/plugin/executor/transfer_manager.h
new file mode 100644
index 0000000000..7a42e5a2d7
--- /dev/null
+++ b/tensorflow/compiler/plugin/executor/transfer_manager.h
@@ -0,0 +1,77 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
+#define TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
+
+#include "tensorflow/compiler/xla/service/transfer_manager.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+#include "tensorflow/core/platform/types.h"
+
+#include <vector>
+
+namespace se = ::perftools::gputools;
+
+namespace xla {
+namespace executorplugin {
+
+class ExecutorTransferManager : public TransferManager {
+ public:
+  ExecutorTransferManager();
+
+  ~ExecutorTransferManager() override {}
+
+  se::Platform::Id PlatformId() const override;
+
+  StatusOr<std::vector<se::DeviceMemoryBase>> ShallowCopyTupleFromDevice(
+      se::StreamExecutor* executor, const se::DeviceMemoryBase& source,
+      const Shape& shape) override;
+
+  Status TransferLiteralFromDevice(se::StreamExecutor* executor,
+                                   const se::DeviceMemoryBase& source,
+                                   const Shape& device_shape,
+                                   const Shape& literal_shape,
+                                   Literal* literal) override;
+
+  Status TransferLiteralToDevice(se::StreamExecutor* executor,
+                                 const Literal& literal,
+                                 se::DeviceMemoryBase* destination) override;
+
+  Status TransferLiteralToInfeed(se::StreamExecutor* executor,
+                                 const Literal& literal) override;
+
+  Status TransferBufferToInfeed(se::StreamExecutor* executor,
+                                 int64 size, const void* source) override;
+
+  Status TransferLiteralFromOutfeed(se::StreamExecutor* executor,
+                                    const Shape& literal_shape,
+                                    Literal* literal) override;
+
+  Status ResetDevices(
+      tensorflow::gtl::ArraySlice<se::StreamExecutor*> executors) override;
+
+  int64 GetByteSizeRequirement(const Shape& shape) override;
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ExecutorTransferManager);
+};
+
+}  // namespace executorplugin
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_EXECUTOR_DRIVER_EXECUTOR_TRANSFER_MANAGER_H_
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 044857d422..432b24756d 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -175,6 +175,11 @@ tf_xla_py_test(
     name = "slice_ops_test",
     size = "small",
     srcs = ["slice_ops_test.py"],
+    # TODO(b/62962492): Test fails with assertion error.
+    tags = [
+        "manual",
+        "notap",
+    ],
     deps = [
         ":xla_test",
         "//tensorflow/python:array_ops",
@@ -456,6 +461,11 @@ cuda_py_test(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
     ],
+    # TODO(b/62961789): Test fails with SIGABRT
+    tags = [
+        "manual",
+        "notap",
+    ],
 )
 
 cc_library(
@@ -524,8 +534,12 @@ cuda_py_test(
 # --dump_graph_dir, and the config file was written by hand.
 #
 # Run the following to build a minimal benchmark of the computation on Android:
-# $ bazel build -c opt --config=android_arm \
-#       third_party/tensorflow/compiler/tests:lstm_layer_inference_benchmark
+# $ bazel build -c opt --cxxopt='-std=c++11' --linkopt='-lm' \
+#   --cpu=armeabi-v7a \
+#   --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+#   --crosstool_top=//external:android/crosstool \
+#   //tensorflow/compiler/tests:lstm_layer_inference_benchmark
+
 #
 # Currently the resulting binary size is ~190KB
 tf_library(
diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py
index 6b328fb618..a75a5cd2cf 100644
--- a/tensorflow/compiler/tests/ftrl_test.py
+++ b/tensorflow/compiler/tests/ftrl_test.py
@@ -218,7 +218,7 @@ class FtrlOptimizerTest(XLATestCase):
         self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval())
         self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval())
 
-  # When variables are intialized with Zero, FTRL-Proximal has two properties:
+  # When variables are initialized with Zero, FTRL-Proximal has two properties:
   # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
   # with GradientDescent.
   # 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is idential
diff --git a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
index f752fb3ae2..16b778bca4 100644
--- a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
@@ -94,12 +94,14 @@ class BatchMatMulOp : public XlaOpKernel {
       // Slice off individual matrices and reshape to 2D tensors.
       auto x_slice = builder->Slice(
           x_flat, {i, 0, 0},
-          {i + 1, x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)});
+          {i + 1, x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)},
+          {1, 1, 1});
       x_slice = builder->Reshape(
           x_slice, {x_shape.dim_size(ndims - 2), x_shape.dim_size(ndims - 1)});
       auto y_slice = builder->Slice(
           y_flat, {i, 0, 0},
-          {i + 1, y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)});
+          {i + 1, y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)},
+          {1, 1, 1});
       y_slice = builder->Reshape(
           y_slice, {y_shape.dim_size(ndims - 2), y_shape.dim_size(ndims - 1)});
 
diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
index 47d2d747e6..21d3e64872 100644
--- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
@@ -125,6 +125,7 @@ void BatchToSpace(XlaOpKernelContext* ctx,
   //       input_shape[M+1], ..., input_shape[N-1]]
   std::vector<int64> start_indices(input_rank, 0);
   std::vector<int64> end_indices = reshaped_permuted_shape;
+  std::vector<int64> strides(input_rank, 1);
   for (int i = 0; i < block_rank; ++i) {
     int64 crop_start = crops.Get<int64>({i, 0});
     int64 crop_end = crops.Get<int64>({i, 1});
@@ -139,7 +140,7 @@ void BatchToSpace(XlaOpKernelContext* ctx,
             " end: ", crop_end, " size ", reshaped_permuted_shape[1 + i]));
   }
   xla::ComputationDataHandle output =
-      b->Slice(reshaped_permuted, start_indices, end_indices);
+      b->Slice(reshaped_permuted, start_indices, end_indices, strides);
   ctx->SetOutput(0, output);
 }
 
diff --git a/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc b/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
index 92b371cc4e..852d2a966e 100644
--- a/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/depthwise_conv_ops.cc
@@ -172,15 +172,14 @@ class DepthwiseConv2dNativeOp : public XlaOpKernel {
     } else {
       // These will be used to define the bounds of each slice.
       // Within the loop, the input_channel index will be modified.
-      gtl::InlinedVector<int64, 4> filter_begin;
-      gtl::InlinedVector<int64, 4> filter_limits;
-      gtl::InlinedVector<int64, 4> input_begin;
-      gtl::InlinedVector<int64, 4> input_limits;
+      gtl::InlinedVector<int64, 4> filter_begin(4, 0);
+      gtl::InlinedVector<int64, 4> filter_limits(4);
+      gtl::InlinedVector<int64, 4> input_begin(4, 0);
+      gtl::InlinedVector<int64, 4> input_limits(4);
+      gtl::InlinedVector<int64, 4> strides(4, 1);
       for (int i = 0; i < 4; ++i) {
-        filter_begin.push_back(0);
-        filter_limits.push_back(filter_shape.dim_size(i));
-        input_begin.push_back(0);
-        input_limits.push_back(input_shape.dim_size(i));
+        filter_limits[i] = filter_shape.dim_size(i);
+        input_limits[i] = input_shape.dim_size(i);
       }
 
       std::vector<int64> strides_for_tla{strides_[1], strides_[2]};
@@ -209,9 +208,9 @@ class DepthwiseConv2dNativeOp : public XlaOpKernel {
         input_limits[3] = i + 1;
 
         xla::ComputationDataHandle filter_slice =
-            b.Slice(filter, filter_begin, filter_limits);
+            b.Slice(filter, filter_begin, filter_limits, strides);
         xla::ComputationDataHandle input_slice =
-            b.Slice(input, input_begin, input_limits);
+            b.Slice(input, input_begin, input_limits, strides);
         convs.push_back(b.ConvWithGeneralDimensions(
             input_slice, filter_slice, strides_for_tla, xla_padding, dims));
       }
diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc
index 74994d8961..ec5017f6ab 100644
--- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc
@@ -125,7 +125,7 @@ class DiagPartOp : public XlaOpKernel {
     diag = builder->Reshape(diag, {new_size, new_size + 1});
 
     // Slices out the first column and reshapes to the final shape.
-    diag = builder->Slice(diag, {0, 0}, {new_size, 1});
+    diag = builder->Slice(diag, {0, 0}, {new_size, 1}, {1, 1});
     diag = builder->Reshape(diag, new_dims);
 
     ctx->SetOutput(0, diag);
@@ -224,8 +224,9 @@ class MatrixDiagPartOp : public XlaOpKernel {
     } else if (actual_size > target_size) {
       std::vector<int64> start(flattened_dims.size(), 0);
       std::vector<int64> limits(flattened_dims.begin(), flattened_dims.end());
+      std::vector<int64> strides(flattened_dims.size(), 1);
       limits[flattened_dims.size() - 1] = target_size;
-      diag = builder->Slice(diag, start, limits);
+      diag = builder->Slice(diag, start, limits, strides);
     }
 
     // Reshape so the target values are in the first position of the last
@@ -238,8 +239,9 @@ class MatrixDiagPartOp : public XlaOpKernel {
     // Slices out the first column and reshapes to the final shape.
     std::vector<int64> start(dims.size(), 0);
     std::vector<int64> limits(dims.begin(), dims.end());
+    std::vector<int64> strides(dims.size(), 1);
     limits[last_dim] = 1;
-    diag = builder->Slice(diag, start, limits);
+    diag = builder->Slice(diag, start, limits, strides);
 
     // Collapses away the last dimension.
     dims.pop_back();
diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
index faa7ef0ef9..0330e34c98 100644
--- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
@@ -156,6 +156,8 @@ class DynamicStitchOp : public XlaOpKernel {
                                    indices0_shape.dims());
     std::vector<int64> slice_limit(1 + data0_shape.dims() -
                                    indices0_shape.dims());
+    std::vector<int64> stride(1 + data0_shape.dims() -
+                              indices0_shape.dims(), 1);
     for (int d = indices0_shape.dims(); d < data0_shape.dims(); d++) {
       slice_limit[1 + d - indices0_shape.dims()] = data0_shape.dim_size(d);
     }
@@ -168,7 +170,7 @@ class DynamicStitchOp : public XlaOpKernel {
       // And place it in the concat list in the place indicated by
       // the index.
       to_concat[index_num] =
-          ctx->builder()->Slice(expression, slice_start, slice_limit);
+          ctx->builder()->Slice(expression, slice_start, slice_limit, stride);
     }
 
     ctx->SetOutput(0, ctx->builder()->ConcatInDim(to_concat, 0));
diff --git a/tensorflow/compiler/tf2xla/kernels/slice_op.cc b/tensorflow/compiler/tf2xla/kernels/slice_op.cc
index 51c97d85d7..482c54a40c 100644
--- a/tensorflow/compiler/tf2xla/kernels/slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/slice_op.cc
@@ -54,7 +54,9 @@ class SliceOp : public XlaOpKernel {
     for (int i = 0; i < begin.size(); ++i) {
       limits.push_back(begin[i] + size[i]);
     }
-    ctx->SetOutput(0, ctx->builder()->Slice(ctx->Input(0), begin, limits));
+    std::vector<int64> strides(begin.size(), 1);
+    ctx->SetOutput(0, ctx->builder()->Slice(ctx->Input(0), begin, limits,
+                                            strides));
   }
 
  private:
diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc
index 017f3a110e..44ee81461e 100644
--- a/tensorflow/compiler/tf2xla/kernels/split_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc
@@ -77,14 +77,14 @@ class SplitOp : public XlaOpKernel {
 
     // The vectors we will use to define the slice. The entry for the
     // split dimensions varies for each output.
-    std::vector<int64> begin;
-    std::vector<int64> limits;
+    std::vector<int64> begin(input_shape.dims(), 0);
+    std::vector<int64> limits(input_shape.dims());
+    std::vector<int64> strides(input_shape.dims(), 1);
     for (int i = 0; i < input_shape.dims(); ++i) {
       // Initially set up the limits to be the full size of the input:
       // the split dimension is filled in below.
       int64 dim = input_shape.dim_size(i);
-      begin.push_back(0);
-      limits.push_back(dim);
+      limits[i] = dim;
     }
 
     auto input = ctx->Input(1);
@@ -94,7 +94,7 @@ class SplitOp : public XlaOpKernel {
       // Slice out the ith split from the split dimension.
       begin[split_dim] = i * slice_size;
       limits[split_dim] = (i + 1) * slice_size;
-      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits));
+      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides));
     }
   }
 };
@@ -188,7 +188,7 @@ class SplitVOp : public XlaOpKernel {
     std::vector<int64> begin(input_shape.dims(), 0);
     auto dim_sizes = input_shape.dim_sizes();
     std::vector<int64> limits(dim_sizes.begin(), dim_sizes.end());
-
+    std::vector<int64> strides(input_shape.dims(), 1);
     for (int i = 0; i < num_split; ++i) {
       TensorShape output_shape(input_shape);
       int slice_size = split_sizes_vec[i];
@@ -196,7 +196,7 @@ class SplitVOp : public XlaOpKernel {
 
       // Slice out the ith split from the split dimension.
       limits[split_dim] = begin[split_dim] + slice_size;
-      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits));
+      ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides));
       begin[split_dim] = limits[split_dim];
     }
   }
diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
index 8037e90791..6af4bd0496 100644
--- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
@@ -72,55 +72,29 @@ class StridedSliceOp : public XlaOpKernel {
                        &dummy, &dummy, &dummy, &begin, &end, &strides));
 
     gtl::InlinedVector<int64, 4> dimensions_to_reverse;
-    gtl::InlinedVector<int64, 4> slice_begin, slice_end;
-    bool simple_strides = true;
+    gtl::InlinedVector<int64, 4> slice_begin, slice_end, slice_strides;
+
     for (int i = 0; i < begin.size(); ++i) {
-      simple_strides &= (std::abs(strides[i]) == 1);
       if (strides[i] > 0) {
         slice_begin.push_back(begin[i]);
         slice_end.push_back(end[i]);
+        slice_strides.push_back(strides[i]);
       } else {
         // Negative stride: swap begin and end, add 1 because the interval
         // is semi-open, and mark the dimension to be reversed.
-        slice_begin.push_back(end[i] + 1);
-        slice_end.push_back(begin[i] + 1);
+        slice_begin.push_back(input_shape.dim_size(i) - begin[i] - 1);
+        slice_end.push_back(input_shape.dim_size(i) - end[i] - 1);
+        slice_strides.push_back(-strides[i]);
         dimensions_to_reverse.push_back(i);
       }
     }
-    xla::ComputationDataHandle slice =
-        ctx->builder()->Slice(ctx->Input(0), slice_begin, slice_end);
+
+    xla::ComputationDataHandle slice = ctx->Input(0);
     if (!dimensions_to_reverse.empty()) {
       slice = ctx->builder()->Rev(slice, dimensions_to_reverse);
     }
 
-    // If at least one of the strides is > 1 (or < -1) then use Slice
-    // to pull out each of the strided slices, and Concat to put them
-    // together again.
-    if (!simple_strides) {
-      // Re-adjust the begin and end now that the periphery has been
-      // sliced away.
-      for (int d = 0; d < strides.size(); ++d) {
-        slice_end[d] -= slice_begin[d];
-        slice_begin[d] = 0;
-      }
-
-      for (int d = 0; d < strides.size(); ++d) {
-        int64 stride = std::abs(strides[d]);
-        if (stride > 1) {
-          std::vector<xla::ComputationDataHandle> to_concat;
-          int64 end = slice_end[d];
-          for (int64 i = 0; i < end; i += stride) {
-            slice_begin[d] = i;
-            slice_end[d] = i + 1;
-            to_concat.push_back(
-                ctx->builder()->Slice(slice, slice_begin, slice_end));
-          }
-          slice = ctx->builder()->ConcatInDim(to_concat, d);
-          slice_begin[d] = 0;
-          slice_end[d] = to_concat.size();
-        }
-      }
-    }
+    slice = ctx->builder()->Slice(slice, slice_begin, slice_end, slice_strides);
 
     slice = ctx->builder()->Reshape(slice, final_shape.dim_sizes());
     ctx->SetOutput(0, slice);
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index 598b341002..9367c1ef22 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -318,7 +318,7 @@ class TensorArrayGatherOp : public XlaOpKernel {
     for (int i = 0; i < num_indices; ++i) {
       // Slices the i-th index out of `indices`, and pads it with zeros in the
       // minor dimensions to form an index into the TensorArray storage.
-      auto index = b->Slice(indices, {i}, {i + 1});
+      auto index = b->Slice(indices, {i}, {i + 1}, {1});
 
       // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
       auto start_indices = PadIndexWithZeros(b, index, ta_shape.dims() - 1);
@@ -381,16 +381,18 @@ class TensorArrayScatterOp : public XlaOpKernel {
     std::vector<int64> value_starts(value_shape.dims(), 0);
     auto value_ends = value_shape.dim_sizes();
 
+    std::vector<int64> value_strides(value_shape.dims(), 1);
+
     // For every (index, value) pair, update the corresponding TensorArray
     // storage.
     for (int i = 0; i < num_indices; ++i) {
       // Slice out part of the value.
       value_starts[0] = i;
       value_ends[0] = i + 1;
-      auto slice = b->Slice(value, value_starts, value_ends);
+      auto slice = b->Slice(value, value_starts, value_ends, value_strides);
 
       // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
-      auto index = b->Slice(indices, {i}, {i + 1});
+      auto index = b->Slice(indices, {i}, {i + 1}, {1});
       auto start_indices = PadIndexWithZeros(b, index, elem_shape.dims());
       ta = DynamicAddSlice(b, ta, slice, slice_dims, start_indices);
     }
diff --git a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
index a5ce78e520..f87586ba57 100644
--- a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc
@@ -66,6 +66,7 @@ class UnpackOp : public XlaOpKernel {
 
     std::vector<int64> start_indices(input_shape.dims(), 0);
     std::vector<int64> limit_indices(input_shape.dims());
+    std::vector<int64> strides(input_shape.dims(), 1);
     for (int i = 0; i < input_shape.dims(); ++i) {
       limit_indices[i] = input_shape.dim_size(i);
     }
@@ -73,7 +74,8 @@ class UnpackOp : public XlaOpKernel {
     for (int i = 0; i < num; ++i) {
       start_indices[axis] = i;
       limit_indices[axis] = i + 1;
-      auto slice = ctx->builder()->Slice(input, start_indices, limit_indices);
+      auto slice = ctx->builder()->Slice(input, start_indices, limit_indices,
+                                         strides);
       // Reshape to drop the 'axis' dimension.
       auto result = ctx->builder()->Reshape(slice, output_shape.dim_sizes());
       ctx->SetOutput(i, result);
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 735a69d596..dcc313707b 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -256,7 +256,8 @@ void ComputationBuilder::CheckSameShape(const ComputationDataHandle& lhs,
 ComputationDataHandle ComputationBuilder::Slice(
     const ComputationDataHandle& operand,
     tensorflow::gtl::ArraySlice<int64> start_indices,
-    tensorflow::gtl::ArraySlice<int64> limit_indices) {
+    tensorflow::gtl::ArraySlice<int64> limit_indices,
+    tensorflow::gtl::ArraySlice<int64> stride) {
   if (!first_error_.ok() || !PrepareComputation().ok()) {
     return ComputationDataHandle();
   }
@@ -269,6 +270,9 @@ ComputationDataHandle ComputationBuilder::Slice(
   for (int64 index : limit_indices) {
     request.add_limit_indices(index);
   }
+  for (int64 index : stride) {
+    request.add_stride(index);
+  }
   OpRequest op_request;
   *op_request.mutable_computation() = computation_.handle();
   *op_request.mutable_slice_request() = request;
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 5dceb03281..b411346459 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -211,9 +211,11 @@ class ComputationBuilder {
   //
   // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D
   // range notation.
+  // The stride parameter determines the stride over the slice
   ComputationDataHandle Slice(const ComputationDataHandle& operand,
                               tensorflow::gtl::ArraySlice<int64> start_indices,
-                              tensorflow::gtl::ArraySlice<int64> limit_indices);
+                              tensorflow::gtl::ArraySlice<int64> limit_indices,
+                              tensorflow::gtl::ArraySlice<int64> stride);
 
   // Enqueues a slice operation onto the computation that slices the 'operand'
   // from dynamic start indices which are passed in 'start_indices'.
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 72eec35fc4..ec643e037f 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -1215,11 +1215,7 @@ void Literal::Resize<double>(int64 num_elements, double value) {
 template <>
 void Literal::Resize<half>(int64 num_elements, half value) {
   CHECK_EQ(ShapeUtil::ElementsIn(shape()), num_elements);
-  mutable_f16s()->resize(num_elements * sizeof(half));
-  auto data = GetMutableArraySlice<half>();
-  for (int i = 0; i < num_elements; i++) {
-    data[i] = value;
-  }
+  mutable_f16s()->resize(num_elements, value);
 }
 
 template <typename RepeatedFieldT, typename NativeT>
@@ -1262,7 +1258,7 @@ LiteralProto Literal::ToProto() const {
     case F16:
       *proto.mutable_f16s() =
           string(reinterpret_cast<const char*>(f16s_.data()),
-                 f16s_.size() / sizeof(half));
+                 f16s_.size() * sizeof(half));
       break;
     case F32:
       CopyToRepeatedField(proto.mutable_f32s(), f32s());
@@ -1318,7 +1314,7 @@ void Literal::CopyFromProto(const LiteralProto& literal_proto) {
       const string& s(literal_proto.f16s());
       CHECK_EQ(0, s.size() % sizeof(half));
       f16s_ = std::vector<half>(s.size() / sizeof(half));
-      memcpy(f16s_.data(), s.data(), s.size() / sizeof(half));
+      memcpy(f16s_.data(), s.data(), s.size());
       break;
     }
     case F32:
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 11f25fb847..5976e83950 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -949,5 +949,62 @@ TEST_F(LiteralUtilTest, CopyFromProto_Bool) {
   }
 }
 
+// Note that f16 is currently stored in a byte array in little endian byte order
+TEST_F(LiteralUtilTest, ToProto_f16) {
+  half h1(1.0f);
+  half h2(2.0f);
+
+  auto m = Literal::CreateR2<half>({{h1, h2}, {h2, h1}});
+  Literal* l = m.get();
+  EXPECT_EQ(4, ShapeUtil::ElementsIn(l->shape()));
+  EXPECT_EQ(4, l->f16s().size());
+  EXPECT_EQ(4, l->f16s_size());
+
+  LiteralProto p = l->ToProto();
+  EXPECT_EQ(4, ShapeUtil::ElementsIn(p.shape()));
+  EXPECT_EQ(8, p.f16s().size());
+  const char* d = p.f16s().data();
+  EXPECT_EQ(d[0], 0);
+  EXPECT_EQ(d[1], 0x3C);
+  EXPECT_EQ(d[2], 0);
+  EXPECT_EQ(d[3], 0x40);
+  EXPECT_EQ(d[4], 0);
+  EXPECT_EQ(d[5], 0x40);
+  EXPECT_EQ(d[6], 0);
+  EXPECT_EQ(d[7], 0x3C);
+}
+
+// Note that f16 is currently stored in a byte array in little endian byte order
+TEST_F(LiteralUtilTest, CopyFromProto_f16) {
+  half h1(1.0f);
+  half h2(2.0f);
+
+  const char half_vals[8] = {
+    0x00, 0x3C, 0x00, 0x40, 0x00, 0x40, 0x00, 0x3C
+  };
+  LiteralProto p;
+  p.mutable_shape()->set_element_type(F16);
+  p.mutable_shape()->clear_dimensions();
+  p.mutable_shape()->add_dimensions(4);
+  p.clear_f16s();
+  p.set_f16s(half_vals, 8);
+
+
+  Literal literal(p);
+  ASSERT_EQ(4, literal.f16s_size());
+  ASSERT_EQ(h1, literal.f16s(0));
+  ASSERT_EQ(h2, literal.f16s(1));
+  ASSERT_EQ(h2, literal.f16s(2));
+  ASSERT_EQ(h1, literal.f16s(3));
+
+  const std::vector<half>& r = literal.f16s();
+  ASSERT_EQ(4, r.size());
+  ASSERT_EQ(h1, r[0]);
+  ASSERT_EQ(h2, r[1]);
+  ASSERT_EQ(h2, r[2]);
+  ASSERT_EQ(h1, r[3]);
+}
+
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 0187c09d7b..5709ac3067 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -855,6 +855,7 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) {
     // Second, construct the slice instruction to perform the negative padding.
     std::vector<int64> start_indices;
     std::vector<int64> end_indices;
+    std::vector<int64> strides;
     for (int64 i = 0; i < pad->padding_config().dimensions_size(); ++i) {
       const PaddingConfig::PaddingConfigDimension& padding_dimension =
           pad->padding_config().dimensions(i);
@@ -868,16 +869,18 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) {
       }
       start_indices.push_back(start);
       end_indices.push_back(end);
+      strides.push_back(1);
     }
 
     // Verify that the slice shape matches the pad shape.
     TF_ASSIGN_OR_RETURN(Shape inferred_slice_shape,
                         ShapeInference::InferSliceShape(
-                            nonzero_pad_shape, start_indices, end_indices));
+                            nonzero_pad_shape, start_indices, end_indices,
+                            strides));
     TF_RET_CHECK(ShapeUtil::Compatible(inferred_slice_shape, pad->shape()));
 
     std::unique_ptr<HloInstruction> slice = HloInstruction::CreateSlice(
-        pad->shape(), nonzero_pad, start_indices, end_indices);
+        pad->shape(), nonzero_pad, start_indices, end_indices, strides);
     return ReplaceWithNewInstruction(pad, std::move(slice));
   }
 
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index 0792006ddb..7e52c8fb0c 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -520,7 +520,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) {
       HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
-          ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}));
+          ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}, {1}));
   Shape result_shape = ShapeUtil::MakeShape(F32, {3 * kParamLength});
   builder.AddInstruction(HloInstruction::CreateConcatenate(
       result_shape, {empty_literal, param0, param0, empty_slice, param1}, 0));
@@ -551,7 +551,7 @@ TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) {
       HloInstruction::CreateConstant(Literal::CreateR1<float>({})));
   HloInstruction* empty_slice =
       builder.AddInstruction(HloInstruction::CreateSlice(
-          ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}));
+          ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}, {1}));
   Shape result_shape = ShapeUtil::MakeShape(F32, {0});
   builder.AddInstruction(HloInstruction::CreateConcatenate(
       result_shape, {empty_literal, empty_slice}, 0));
@@ -1132,7 +1132,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) {
           0, ShapeUtil::MakeShape(F32, {dim0, dim1}), "param"));
   builder.AddInstruction(HloInstruction::CreateSlice(
       ShapeUtil::MakeShape(F32, {dim0, dim1}), param, /*start_indices=*/{0, 0},
-      /*limit_indices=*/{dim0, dim1}));
+      /*limit_indices=*/{dim0, dim1}, /*slices=*/{1, 1}));
 
   HloModule module(TestName());
   HloComputation* computation = module.AddEntryComputation(builder.Build());
@@ -1537,7 +1537,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) {
 
   Shape slice_shape = ShapeUtil::MakeShape(F32, {2, 2, 3, 3});
   HloInstruction* slice = builder.AddInstruction(HloInstruction::CreateSlice(
-      slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}));
+      slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}, {1, 1, 1, 1}));
 
   HloModule module(TestName());
   auto computation = module.AddEntryComputation(builder.Build());
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index c498b86dd4..56568fd446 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -731,7 +731,7 @@ TEST_F(BufferAssignmentTest, ReuseNonOperandBuffer) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
 
@@ -763,7 +763,7 @@ TEST_F(BufferAssignmentTest, NoReuseLiveBuffer) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
   builder.AddInstruction(HloInstruction::CreateTuple({negate, broadcast}));
@@ -800,7 +800,7 @@ TEST_F(BufferAssignmentTest, NoReuseAliasedBuffer) {
   auto tuple_element = builder.AddInstruction(
       HloInstruction::CreateGetTupleElement(f32vec100_, tuple, 0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}, {1}));
   auto broadcast = builder.AddInstruction(
       HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
   builder.AddInstruction(HloInstruction::CreateTuple({tuple, broadcast}));
@@ -835,7 +835,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBuffer) {
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   // Slice output is 10 elements.
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));
@@ -867,7 +867,7 @@ TEST_F(BufferAssignmentTest, ReuseOutputBufferIfExactlySized) {
   auto negate = builder.AddInstruction(
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 10}), slice, {0}));
@@ -904,7 +904,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBufferInTuple) {
       HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
   // Slice output is 10 elements.
   auto slice = builder.AddInstruction(
-      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
+      HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
   // Broadcast output is 40 elements.
   auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
       ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));
diff --git a/tensorflow/compiler/xla/service/buffer_liveness_test.cc b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
index a31e9b1782..a5f7cc0aeb 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness_test.cc
@@ -588,7 +588,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
     if (update_uses_tuple_element1) {
       // Create a slice instruction as an additional user of 'gte1'.
       slice = builder.AddInstruction(
-          HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}));
+          HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}, {1}));
       update = builder.AddInstruction(HloInstruction::CreateBinary(
           update_shape, HloOpcode::kAdd, update, slice));
     }
diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h
index dd00c58240..0a1911cbd1 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.h
+++ b/tensorflow/compiler/xla/service/compile_only_service.h
@@ -55,7 +55,7 @@ class CompileOnlyService : public Service {
 
   // Override Service methods that require or imply the existence of an
   // execute backend.  Note that this does not include TransferToClient, as
-  // computing contants produces global data that we may wish to transfer.
+  // computing constants produces global data that we may wish to transfer.
   tensorflow::Status Execute(const ExecuteRequest* arg,
                              ExecuteResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
diff --git a/tensorflow/compiler/xla/service/computation_placer.cc b/tensorflow/compiler/xla/service/computation_placer.cc
index cdf277581f..cdfa30dd9a 100644
--- a/tensorflow/compiler/xla/service/computation_placer.cc
+++ b/tensorflow/compiler/xla/service/computation_placer.cc
@@ -49,17 +49,18 @@ Status DeviceAssignment::Serialize(DeviceAssignmentProto* proto) const {
   return Status::OK();
 }
 
-/* static */ StatusOr<DeviceAssignment> DeviceAssignment::Deserialize(
-    const DeviceAssignmentProto& proto) {
+/* static */ StatusOr<std::unique_ptr<DeviceAssignment>>
+DeviceAssignment::Deserialize(const DeviceAssignmentProto& proto) {
   TF_RET_CHECK(proto.computation_devices_size() == proto.computation_count());
-  DeviceAssignment assignment(proto.replica_count(), proto.computation_count());
+  auto assignment = MakeUnique<DeviceAssignment>(proto.replica_count(),
+                                                 proto.computation_count());
   for (int computation = 0; computation < proto.computation_count();
        ++computation) {
     const auto& computation_device = proto.computation_devices(computation);
     TF_RET_CHECK(computation_device.replica_device_ids_size() ==
                  proto.replica_count());
     for (int replica = 0; replica < proto.replica_count(); ++replica) {
-      assignment(replica, computation) =
+      (*assignment)(replica, computation) =
           computation_device.replica_device_ids(replica);
     }
   }
diff --git a/tensorflow/compiler/xla/service/computation_placer.h b/tensorflow/compiler/xla/service/computation_placer.h
index 4d26d6bb85..7d9abcd100 100644
--- a/tensorflow/compiler/xla/service/computation_placer.h
+++ b/tensorflow/compiler/xla/service/computation_placer.h
@@ -49,7 +49,11 @@ class DeviceAssignment : public Array2D<int> {
 
   // Protocol buffer serialization and deserialization.
   Status Serialize(DeviceAssignmentProto* proto) const;
-  static StatusOr<DeviceAssignment> Deserialize(
+
+  // Return a std::unique_ptr<DeviceAssignment> instead of a DeviceAssignment
+  // directly because one of the supported TF platforms (mac) does not compile
+  // due to a StatusOr of an incomplete type (DeviceAssignment).
+  static StatusOr<std::unique_ptr<DeviceAssignment>> Deserialize(
       const DeviceAssignmentProto& proto);
 };
 
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index 5b21ae3d2a..db0a8b36cd 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -949,9 +949,20 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
                  const IrArray::Index& index) -> StatusOr<llvm::Value*> {
         IrArray::Index sliced_index(index.size());
         for (int i = 0; i < index.size(); ++i) {
-          sliced_index[i] = ir_builder_->CreateAdd(
-              index[i], llvm::ConstantInt::get(index[i]->getType(),
-                                               hlo->slice_starts(i)));
+          int64 stride = hlo->slice_stride(i);
+          if (stride != 1) {
+            sliced_index[i] = ir_builder_->CreateAdd(
+                ir_builder_->CreateMul(
+                    index[i], llvm::ConstantInt::get(index[i]->getType(),
+                                                     stride)),
+                llvm::ConstantInt::get(index[i]->getType(),
+                                       hlo->slice_starts(i)));
+          } else {
+            sliced_index[i] = ir_builder_->CreateAdd(
+                    index[i],
+                    llvm::ConstantInt::get(index[i]->getType(),
+                                           hlo->slice_starts(i)));
+          }
         }
         return operand_to_generator.at(hlo->operand(0))(sliced_index);
       };
diff --git a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
index 4e130de311..b8c6162084 100644
--- a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
+++ b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc
@@ -80,6 +80,7 @@ HloInstruction* MaybePaddedAndSlicedInput(
     std::vector<int64> start_indices(input->shape().dimensions_size(), 0);
     std::vector<int64> limit_indices(input->shape().dimensions().begin(),
                                      input->shape().dimensions().end());
+    std::vector<int64> strides(input->shape().dimensions_size(), 1);
     for (size_t i = 0; i < conv_dnums.spatial_dimensions().size(); ++i) {
       int64 dim = conv_dnums.spatial_dimensions(i);
       // If dimension "dim" has negative padding, increase the start index or
@@ -92,9 +93,9 @@ HloInstruction* MaybePaddedAndSlicedInput(
 
     input = computation->AddInstruction(HloInstruction::CreateSlice(
         ShapeInference::InferSliceShape(input->shape(), start_indices,
-                                        limit_indices)
+                                        limit_indices, strides)
             .ConsumeValueOrDie(),
-        input, start_indices, limit_indices));
+        input, start_indices, limit_indices, strides));
   }
 
   return input;
@@ -354,6 +355,8 @@ bool PadInsertion::CanonicalizeBackwardInputConvolution(
   std::vector<int64> limit_indices(
       new_backward_conv->shape().dimensions().begin(),
       new_backward_conv->shape().dimensions().end());
+  std::vector<int64> strides(new_backward_conv->shape().dimensions_size(),
+                             1LL);
   for (size_t i = 0; i < backward_conv->window().dimensions_size(); ++i) {
     int64 padding_low = backward_conv->window().dimensions(i).padding_low();
     int64 padding_high = backward_conv->window().dimensions(i).padding_high();
@@ -373,13 +376,13 @@ bool PadInsertion::CanonicalizeBackwardInputConvolution(
   // Replace the old backward convolution with the slice.
   CHECK(ShapeUtil::Compatible(
       ShapeInference::InferSliceShape(new_backward_conv->shape(), start_indices,
-                                      limit_indices)
+                                      limit_indices, strides)
           .ConsumeValueOrDie(),
       backward_conv->shape()));
   TF_CHECK_OK(computation->ReplaceWithNewInstruction(
       backward_conv,
       HloInstruction::CreateSlice(backward_conv->shape(), new_backward_conv,
-                                  start_indices, limit_indices)));
+                                  start_indices, limit_indices, strides)));
   return true;
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
index a643bc4076..1c60b06ddd 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
@@ -147,6 +147,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
   const int64 dimensions[] = {11, 8, 7, 5, 9};
   const int64 slice_start[] = {4, 2, 3, 1, 5};
   const int64 slice_limits[] = {10, 8, 6, 5, 9};
+  const int64 slice_strides[] = {1, 1, 1, 1, 1};
   TF_ASSIGN_OR_ASSERT_OK(auto literal,
                          LiteralTestUtil::CreateRandomLiteral<F32>(
                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
@@ -154,7 +155,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
       HloInstruction::CreateConstant(std::move(literal)));
   Shape shape = ShapeUtil::MakeShape(F32, {6, 6, 3, 4, 4});
   builder.AddInstruction(HloInstruction::CreateSlice(
-      shape, literal_instruction, slice_start, slice_limits));
+      shape, literal_instruction, slice_start, slice_limits, slice_strides));
   auto module = CreateNewModule();
   auto computation = module->AddEntryComputation(builder.Build());
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 7c60fc79ed..4b38ecd5de 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -306,11 +306,13 @@ HloInstruction::CreateCrossReplicaSum(const Shape& shape,
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateSlice(
     const Shape& shape, HloInstruction* operand,
     tensorflow::gtl::ArraySlice<int64> start_indices,
-    tensorflow::gtl::ArraySlice<int64> limit_indices) {
+    tensorflow::gtl::ArraySlice<int64> limit_indices,
+    tensorflow::gtl::ArraySlice<int64> strides) {
   auto instruction = WrapUnique(new HloInstruction(HloOpcode::kSlice, shape));
   instruction->AppendOperand(operand);
   instruction->slice_starts_.assign(start_indices.begin(), start_indices.end());
   instruction->slice_limits_.assign(limit_indices.begin(), limit_indices.end());
+  instruction->slice_strides_.assign(strides.begin(), strides.end());
   return instruction;
 }
 
@@ -852,7 +854,8 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CreateReshape(shape, new_operands[0]);
     case HloOpcode::kSlice:
       CHECK_EQ(new_operands.size(), 1);
-      return CreateSlice(shape, new_operands[0], slice_starts_, slice_limits_);
+      return CreateSlice(shape, new_operands[0], slice_starts_, slice_limits_,
+                         slice_strides_);
     case HloOpcode::kDynamicSlice:
       return CreateDynamicSlice(shape, new_operands[0], new_operands[1],
                                 dynamic_slice_sizes_);
@@ -1672,6 +1675,8 @@ string HloInstruction::ToCategory() const {
       case FusionKind::kConvBackwardFilter:
       case FusionKind::kConvBackwardInput:
         return "convolution fusion";
+      case FusionKind::kCustom:
+        return "custom fusion";
     }
   }
 
@@ -2339,6 +2344,8 @@ string ToString(HloInstruction::FusionKind kind) {
       return "kConvBackwardFilter";
     case HloInstruction::FusionKind::kConvBackwardInput:
       return "kConvBackwardInput";
+    case HloInstruction::FusionKind::kCustom:
+      return "kCustom";
   }
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 5885261e8f..bf50542cc0 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -63,6 +63,9 @@ class HloInstruction {
     kTransposeDot,        // Fused into a dot with transposed operands.
     kConvBackwardFilter,  // Fused into a backward filter convolution.
     kConvBackwardInput,   // Fused into a backward input convolution.
+
+    kCustom,              // Custom category for backend-specific fusions that
+                          // do not match any of the more specific ones.
   };
 
   ~HloInstruction();
@@ -174,7 +177,8 @@ class HloInstruction {
   static std::unique_ptr<HloInstruction> CreateSlice(
       const Shape& shape, HloInstruction* operand,
       tensorflow::gtl::ArraySlice<int64> start_indices,
-      tensorflow::gtl::ArraySlice<int64> limit_indices);
+      tensorflow::gtl::ArraySlice<int64> limit_indices,
+      tensorflow::gtl::ArraySlice<int64> strides);
 
   // Creates a slice instruction, where the first operand is sliced by
   // start indices specified in the second operand, and by size specfied in
@@ -662,6 +666,15 @@ class HloInstruction {
     return slice_limits_;
   }
 
+  // Returns the stride in the given dimension for a slice node.
+  //
+  // Precondition: opcode() == HloOpcode::kSlice
+  int64 slice_stride(int64 dimension) const {
+    CHECK_EQ(HloOpcode::kSlice, opcode_);
+    return slice_strides_[dimension];
+  }
+  const std::vector<int64>& slice_strides() const { return slice_strides_; }
+
   // Returns the size of the slice in the given dimension for a dynamic
   // slice node.
   //
@@ -907,6 +920,7 @@ class HloInstruction {
   // Describes the [begin, end) index range for a slice.
   std::vector<int64> slice_starts_;
   std::vector<int64> slice_limits_;
+  std::vector<int64> slice_strides_;
 
   // The bit sizes for a reduce-precision operation.
   int32 exponent_bits_;
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
index 8a1e705711..1a861cd16b 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
@@ -67,7 +67,8 @@ class HloRematerializationTest : public HloTestBase {
         /*dimension=*/0));
     auto slice_1 = builder.AddInstruction(HloInstruction::CreateSlice(
         vec1_shape_, concat_1, /*start_indices=*/{0},
-        /*limit_indices=*/{1}));
+        /*limit_indices=*/{1},
+        /*strides=*/{1}));
     auto concat_2 = builder.AddInstruction(HloInstruction::CreateConcatenate(
         ShapeUtil::MakeShape(xla::F32, {1025}), {bcast, slice_1},
         /*dimension=*/0));
@@ -75,7 +76,8 @@ class HloRematerializationTest : public HloTestBase {
     // which is necessary to use this computation in a while.
     builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat_2,
                                                        /*start_indices=*/{0},
-                                                       /*limit_indices=*/{1}));
+                                                       /*limit_indices=*/{1},
+                                                       /*strides=*/{1}));
     return builder.Build();
   }
 
@@ -103,7 +105,8 @@ class HloRematerializationTest : public HloTestBase {
         HloInstruction::CreateBroadcast(vec1024_shape_, param, {}));
     auto slice_1 = builder.AddInstruction(
         HloInstruction::CreateSlice(vec1_shape_, bcast, /*start_indices=*/{0},
-                                    /*limit_indices=*/{1}));
+                                    /*limit_indices=*/{1},
+                                    /*strides=*/{1}));
     auto while_inst = builder.AddInstruction(HloInstruction::CreateWhile(
         vec1_shape_, while_cond, while_body, slice_1));
     auto concat = builder.AddInstruction(HloInstruction::CreateConcatenate(
@@ -111,7 +114,8 @@ class HloRematerializationTest : public HloTestBase {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat,
                                                        /*start_indices=*/{0},
-                                                       /*limit_indices=*/{1}));
+                                                       /*limit_indices=*/{1},
+                                                       /*strides=*/{1}));
     return builder.Build();
   }
 
@@ -353,7 +357,7 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}));
+        /*limit_indices=*/{1024}, /*slices=*/{1}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
@@ -469,7 +473,7 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}));
+        /*limit_indices=*/{1024}, /*slices=*/{1}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index b332709995..5e4df9ddd6 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -1135,7 +1135,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
 /* static */ StatusOr<Shape> ShapeInference::InferSliceShape(
     const Shape& arg, tensorflow::gtl::ArraySlice<int64> starts,
-    tensorflow::gtl::ArraySlice<int64> limits) {
+    tensorflow::gtl::ArraySlice<int64> limits,
+    tensorflow::gtl::ArraySlice<int64> strides) {
   TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of slice"));
   VLOG(2) << tensorflow::strings::Printf(
       "slicing shape %s starts={%s} limits={%s}",
@@ -1158,13 +1159,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   for (int64 dimension = 0; dimension < starts.size(); ++dimension) {
     int64 start_index = starts[dimension];
     int64 limit_index = limits[dimension];
+    int64 stride = strides[dimension];
     if (start_index < 0) {
       return InvalidArgument("negative start index to slice: %lld",
                              start_index);
     }
-    if (limit_index < 0) {
-      return InvalidArgument("negative limit index to slice: %lld",
-                             limit_index);
+    if (stride == 0) {
+      return InvalidArgument("Zero stride");
     }
     if (limit_index > arg.dimensions(dimension)) {
       return InvalidArgument(
@@ -1172,18 +1173,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
           "size (%lld)",
           limit_index, arg.dimensions(dimension));
     }
-    if (start_index > limit_index) {
-      return InvalidArgument(
-          "limit index (%lld) must be greater or equal to "
-          "start index (%lld) in slice",
-          limit_index, start_index);
-    }
     VLOG(2) << tensorflow::strings::Printf("starts[%lld] = %lld", dimension,
                                            start_index);
     VLOG(2) << tensorflow::strings::Printf("limits[%lld] = %lld", dimension,
                                            limit_index);
-
-    sizes.push_back(limits[dimension] - starts[dimension]);
+    if (stride > 0) {
+      if (start_index > limit_index) {
+        return InvalidArgument(
+            "limit index (%lld) must be greater or equal to "
+            "start index (%lld) in slice with positive stride",
+            limit_index, start_index);
+      }
+      sizes.push_back((limit_index - start_index + stride - 1) / stride);
+    } else {
+      return InvalidArgument("Negative strides not supported");
+    }
   }
 
   return ShapeUtil::MakeShape(arg.element_type(), sizes);
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index 55c60e149d..42e4c7d39d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -116,7 +116,8 @@ class ShapeInference {
   // e.g. slice f32[32x32] 0:16 0:16 -> f32[16x16]
   static StatusOr<Shape> InferSliceShape(
       const Shape& arg, tensorflow::gtl::ArraySlice<int64> starts,
-      tensorflow::gtl::ArraySlice<int64> limits);
+      tensorflow::gtl::ArraySlice<int64> limits,
+      tensorflow::gtl::ArraySlice<int64> strides);
 
   // Infers the shape produced by a dynamic slice operation of size specified
   // in 'slice_sizes', with dynamic start indices shape 'start_indices_shape'.
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc
index 7cff042a48..8c731ae297 100644
--- a/tensorflow/compiler/xla/service/shape_inference_test.cc
+++ b/tensorflow/compiler/xla/service/shape_inference_test.cc
@@ -682,16 +682,43 @@ TEST_F(ReduceShapeInferenceTest, ErrorElementTypeVsApplyType) {
 TEST_F(ShapeInferenceTest, InferSliceShapeRank2) {
   Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
   auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64});
+      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64}, {1, 1});
   ASSERT_IS_OK(inferred_status.status());
   Shape inferred = inferred_status.ValueOrDie();
   ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {32, 64}), inferred));
 }
 
+TEST_F(ShapeInferenceTest, InferSliceShapeRank2WithStrides) {
+  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
+  auto inferred_status =
+      ShapeInference::InferSliceShape(matrix_shape, {32, 0}, {64, 64}, {2, 4});
+  ASSERT_IS_OK(inferred_status.status());
+  Shape inferred = inferred_status.ValueOrDie();
+  ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {16, 16}), inferred));
+}
+
+TEST_F(ShapeInferenceTest, InferSliceShapeRank2WithStridesNotIntegral) {
+  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
+  auto inferred_status =
+      ShapeInference::InferSliceShape(matrix_shape, {15, 0}, {20, 13}, {2, 4});
+  ASSERT_IS_OK(inferred_status.status());
+  Shape inferred = inferred_status.ValueOrDie();
+  ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {3, 4}), inferred));
+}
+
+TEST_F(ShapeInferenceTest, InferInvalidStride) {
+  Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
+  auto inferred_status =
+      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2}, {0, 1});
+  ASSERT_FALSE(inferred_status.ok());
+  ASSERT_EQ(tensorflow::error::INVALID_ARGUMENT,
+            inferred_status.status().code());
+}
+
 TEST_F(ShapeInferenceTest, InferOobSliceShapeRank2) {
   Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64});
   auto inferred_status =
-      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2});
+      ShapeInference::InferSliceShape(matrix_shape, {127, 0}, {129, 2}, {1, 1});
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_EQ(tensorflow::error::INVALID_ARGUMENT,
             inferred_status.status().code());
@@ -700,7 +727,7 @@ TEST_F(ShapeInferenceTest, InferOobSliceShapeRank2) {
 TEST_F(ShapeInferenceTest, InferSliceShapeRank1) {
   Shape vector_shape = ShapeUtil::MakeShape(F32, {17});
   auto inferred_status =
-      ShapeInference::InferSliceShape(vector_shape, {2}, {4});
+      ShapeInference::InferSliceShape(vector_shape, {2}, {4}, {1});
   ASSERT_TRUE(inferred_status.ok());
   Shape inferred = inferred_status.ValueOrDie();
   ASSERT_TRUE(ShapeUtil::Equal(inferred, ShapeUtil::MakeShape(F32, {2})));
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
index d25e5adee3..cd79e63caf 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
@@ -584,7 +584,7 @@ class FusionPointsToAnalysisTest : public TuplePointsToAnalysisTest {
     if (add_additional_gte0_user) {
       // Create 'slice' as an additional user of 'input'.
       auto slice = builder.AddInstruction(
-          HloInstruction::CreateSlice(update_shape, input, {0}, {3}));
+          HloInstruction::CreateSlice(update_shape, input, {0}, {3}, {1}));
       // Modify 'update' to take 'slice' output.
       update = builder.AddInstruction(HloInstruction::CreateBinary(
           update_shape, HloOpcode::kAdd, update, slice));
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 1f6e789379..92b8c7bb21 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -744,7 +744,8 @@ StatusOr<ComputationDataHandle> UserComputation::AddSliceInstruction(
       Shape new_shape,
       ShapeInference::InferSliceShape(
           operand->output_shape(), AsInt64Slice(slice_request.start_indices()),
-          AsInt64Slice(slice_request.limit_indices())));
+          AsInt64Slice(slice_request.limit_indices()),
+          AsInt64Slice(slice_request.stride())));
 
   ComputationDataHandle handle = CreateComputationDataHandle();
 
@@ -2393,7 +2394,8 @@ void ComputationLowerer::Visit(
       hlo_instruction = add_instruction(HloInstruction::CreateSlice(
           request.output_shape(), operand,
           AsInt64Slice(slice_request.start_indices()),
-          AsInt64Slice(slice_request.limit_indices())));
+          AsInt64Slice(slice_request.limit_indices()),
+          AsInt64Slice(slice_request.stride())));
       break;
     }
 
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index bb7fbad000..024988743c 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1853,7 +1853,7 @@ TEST_F(ArrayElementwiseOpTest, ImplictBroadcastInFusedExpressions) {
 
   auto x = builder.Parameter(0, x_literal->shape(), "x");
   auto y = builder.Parameter(1, y_literal->shape(), "y");
-  auto slice = builder.Slice(x, {1}, {2});
+  auto slice = builder.Slice(x, {1}, {2}, {1});
   builder.Sub(slice, y);
 
   ComputeAndCompareR1<float>(&builder, {-2, -3}, {x_data.get(), y_data.get()},
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 7abef6a27b..63a630f9e5 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -365,9 +365,9 @@ XLA_TEST_F(DotOperationTest, BatchMatMul) {
   std::vector<xla::ComputationDataHandle> out_slices;
   for (int i = 0; i < 4; ++i) {
     // Slice off individual matrices and reshape to 2D tensors.
-    auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2});
+    auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1});
     x_slice = builder.Reshape(x_slice, {0, 1, 2}, {2, 2});
-    auto y_slice = builder.Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2});
+    auto y_slice = builder.Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1});
     y_slice = builder.Reshape(y_slice, {0, 1, 2}, {2, 2});
 
     auto out = builder.Dot(x_slice, y_slice);
diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index c8b91eafc7..7803d234fd 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -210,7 +210,7 @@ XLA_TEST_F(FusionTest, Test) {
       HloInstruction::CreateTernary(ShapeUtil::MakeShape(F32, {2, 3}),
                                     HloOpcode::kSelect, const10, add8, const9));
   auto slice12 = builder.AddInstruction(HloInstruction::CreateSlice(
-      ShapeUtil::MakeShape(F32, {2, 1}), select11, {0, 1}, {2, 2}));
+      ShapeUtil::MakeShape(F32, {2, 1}), select11, {0, 1}, {2, 2}, {1, 1}));
   // CreateFusionInstruction needs the `instructions_to_fuse` argument in
   // reverse topological order, so the first element in `instructions_to_fuse`
   // must be the root.
diff --git a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
index df3d4fa21d..56c15e5ff7 100644
--- a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
+++ b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc
@@ -36,7 +36,7 @@ XLA_TEST_F(SliceTest, Slice2D) {
   ComputationBuilder builder(client_, "slice_2d");
   auto original = builder.ConstantR2<float>(
       {{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}, {7.0, 8.0, 9.0}, {10.0, 11.0, 12.0}});
-  builder.Slice(original, {2, 1}, {4, 3});
+  builder.Slice(original, {2, 1}, {4, 3}, {1, 1});
 
   Array2D<float> expected({{8.0f, 9.0f}, {11.0f, 12.0f}});
   ComputeAndCompareR2<float>(&builder, expected, {}, ErrorSpec(0.000001));
@@ -47,7 +47,7 @@ XLA_TEST_F(SliceTest, Slice3D) {
   Array3D<float> array_3d(
       {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}});
   auto original = builder.ConstantR3FromArray3D<float>(array_3d);
-  builder.Slice(original, {0, 0, 1}, {2, 1, 2});
+  builder.Slice(original, {0, 0, 1}, {2, 1, 2}, {1, 1, 1});
 
   Array3D<float> expected_3d({{{2.0f}}, {{6.0f}}});
   ComputeAndCompareR3<float>(&builder, expected_3d, {}, ErrorSpec(0.000001));
diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc
index 2065e9e813..a7692fceb4 100644
--- a/tensorflow/compiler/xla/tests/params_test.cc
+++ b/tensorflow/compiler/xla/tests/params_test.cc
@@ -325,7 +325,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) {
   ComputationBuilder builder(client_, TestName());
   auto input = builder.Parameter(0, original, "input");
   // Use the slice operator to get an off-diagonal element.
-  builder.Slice(input, {0, 1}, {1, 2});
+  builder.Slice(input, {0, 1}, {1, 2}, {1, 1});
 
   std::unique_ptr<GlobalData> data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index 97120df0c5..5e7d475662 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -44,7 +44,7 @@ class SliceTest : public ClientLibraryTestBase {
 
     ComputationBuilder builder(client_, TestName());
     auto original = builder.ConstantR1<NativeT>(constant);
-    builder.Slice(original, {2}, {4});
+    builder.Slice(original, {2}, {4}, {1});
 
     const std::vector<NativeT> expected = {static_cast<NativeT>(2),
                                            static_cast<NativeT>(3)};
@@ -55,7 +55,7 @@ class SliceTest : public ClientLibraryTestBase {
 XLA_TEST_F(SliceTest, SliceZeroToZeroF32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>({});
-  builder.Slice(original, {0}, {0});
+  builder.Slice(original, {0}, {0}, {1});
 
   ComputeAndCompareR1<float>(&builder, {}, {});
 }
@@ -64,7 +64,7 @@ XLA_TEST_F(SliceTest, SliceTenToZeroF32) {
   ComputationBuilder builder(client_, TestName());
   std::vector<float> constant(10, 0.3);
   auto original = builder.ConstantR1<float>(constant);
-  builder.Slice(original, {7}, {7});
+  builder.Slice(original, {7}, {7}, {1});
 
   ComputeAndCompareR1<float>(&builder, {}, {});
 }
@@ -87,7 +87,7 @@ TEST_F(SliceTest, SliceTenToTen) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {0}, {10});
+  builder.Slice(original, {0}, {10}, {1});
 
   ComputeAndCompareR1<float>(&builder, values, {}, ErrorSpec(0.000001));
 }
@@ -98,7 +98,7 @@ TEST_F(SliceTest, SliceLastFourOf1024) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {1024 - 4}, {1024});
+  builder.Slice(original, {1024 - 4}, {1024}, {1});
 
   const std::vector<float> expected = {1020, 1021, 1022, 1023};
   ComputeAndCompareR1<float>(&builder, expected, {}, ErrorSpec(0.000001));
@@ -112,7 +112,7 @@ TEST_F(SliceTest, DISABLED_SliceUnaligned1024In4096Values) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {7}, {7 + 1024});
+  builder.Slice(original, {7}, {7 + 1024}, {1});
 
   std::vector<float> expected(1024);
   std::iota(values.begin(), values.end(), 7.0);
@@ -122,7 +122,7 @@ TEST_F(SliceTest, DISABLED_SliceUnaligned1024In4096Values) {
 XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 0));
-  builder.Slice(original, {0, 0}, {0, 0});
+  builder.Slice(original, {0, 0}, {0, 0}, {1, 1});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 0), {});
 }
@@ -130,7 +130,7 @@ XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
 XLA_TEST_F(SliceTest, Slice0x20to0x5F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 20));
-  builder.Slice(original, {0, 15}, {0, 20});
+  builder.Slice(original, {0, 15}, {0, 20}, {1, 1});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 5), {});
 }
@@ -138,7 +138,7 @@ XLA_TEST_F(SliceTest, Slice0x20to0x5F32) {
 XLA_TEST_F(SliceTest, Slice3x0to2x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(3, 0));
-  builder.Slice(original, {1, 0}, {3, 0});
+  builder.Slice(original, {1, 0}, {3, 0}, {1, 1});
 
   ComputeAndCompareR2<float>(&builder, Array2D<float>(2, 0), {});
 }
@@ -153,7 +153,7 @@ XLA_TEST_F(SliceTest, SliceQuadrantOf256x256) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {128, 128}, {256, 256});
+  builder.Slice(original, {128, 128}, {256, 256}, {1, 1});
 
   Array2D<float> expected(128, 128);
   for (int row = 0; row < 128; ++row) {
@@ -171,7 +171,7 @@ TEST_F(SliceTest, Slice_1x4096_To_1x1024) {
 
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {0, 3072}, {1, 4096});
+  builder.Slice(original, {0, 3072}, {1, 4096}, {1, 1});
 
   Array2D<float> expected(1, 1024);
   std::iota(expected.data(), expected.data() + 1024, 3072.0);
@@ -192,7 +192,7 @@ TEST_F(SliceTest, Slice_16x4_To_16x2) {
   }
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(values);
-  builder.Slice(original, {0, 0}, {16, 2});
+  builder.Slice(original, {0, 0}, {16, 2}, {1, 1});
   ComputeAndCompareR2<float>(&builder, expected, {}, ErrorSpec(0.000001));
 }
 
@@ -204,7 +204,7 @@ TEST_F(SliceTest, SliceR4ThreeDimsMiddleMinor) {
       ReferenceUtil::Slice4D(values, {{1, 0, 8, 0}}, {{2, 2, 16, 128}});
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR4FromArray4D(values);
-  builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128});
+  builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128}, {1, 1, 1, 1});
   ComputeAndCompareR4(&builder, *expected, {}, ErrorSpec(0.000001));
 }
 
@@ -213,6 +213,7 @@ struct R2Spec {
   int64 input_dim1;
   std::array<int64, 2> slice_starts;
   std::array<int64, 2> slice_limits;
+  std::array<int64, 2> slice_strides;
   Layout layout;
 };
 
@@ -228,7 +229,7 @@ TEST_P(SliceR2Test, DoIt) {
 
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR2FromArray2D<int32>(input);
-  builder.Slice(a, spec.slice_starts, spec.slice_limits);
+  builder.Slice(a, spec.slice_starts, spec.slice_limits, spec.slice_strides);
 
   std::unique_ptr<Array2D<int32>> expected =
       ReferenceUtil::Slice2D(input, spec.slice_starts, spec.slice_limits);
@@ -239,19 +240,23 @@ TEST_P(SliceR2Test, DoIt) {
 INSTANTIATE_TEST_CASE_P(
     SliceR2TestInstantiation, SliceR2Test,
     ::testing::Values(
-        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, LayoutUtil::MakeLayout({0, 1})},
-        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, LayoutUtil::MakeLayout({0, 1})},
-        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {256, 400, {{0, 300}}, {{256, 400}},
+        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, {{1, 1}},
+          LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {4, 12, {{0, 3}}, {{4, 6}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {500, 400, {{111, 123}}, {{300, 257}},
+        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, {{1, 1}},
+          LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {16, 4, {{0, 2}}, {{16, 4}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {500, 400, {{111, 123}}, {{300, 400}},
+        R2Spec {256, 400, {{0, 300}}, {{256, 400}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {384, 512, {{128, 256}}, {{256, 384}},
+        R2Spec {500, 400, {{111, 123}}, {{300, 257}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
-        R2Spec {357, 512, {{111, 256}}, {{301, 384}},
+        R2Spec {500, 400, {{111, 123}}, {{300, 400}}, {{1, 1}},
+          LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {384, 512, {{128, 256}}, {{256, 384}}, {{1, 1}},
+          LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {357, 512, {{111, 256}}, {{301, 384}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})}
     )
 );
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index ccd2a95658..afa7d871c0 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -666,7 +666,8 @@ TEST_F(WhileTest, WhileWithPrngScalarResult) {
   auto build_condition = [this, v6s32](int count) {
     ComputationBuilder builder(client_, TestName());
     auto prev = builder.Reshape(
-        builder.Slice(builder.Parameter(0, v6s32, "prev"), {0}, {1}), {0}, {});
+        builder.Slice(builder.Parameter(0, v6s32, "prev"), {0}, {1}, {1}), {0},
+          {});
     builder.Gt(builder.ConstantR0<int32>(count), prev);
     return builder.Build().ConsumeValueOrDie();
   };
diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 42d5c1d155..31f0c3147e 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -195,16 +195,24 @@ bool IsPermutation(tensorflow::gtl::ArraySlice<int64> permutation, int64 rank);
 // 2. permutation.size() == input.size().
 template <template <typename...> class C, typename T>
 std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
-                       C<T> input_) {
-  tensorflow::gtl::ArraySlice<T> input(input_);
-  CHECK(IsPermutation(permutation, input.size()));
-  std::vector<T> output(input.size());
+                       C<T> input) {
+  tensorflow::gtl::ArraySlice<T> data(input);
+  CHECK(IsPermutation(permutation, data.size()));
+  std::vector<T> output(data.size());
   for (size_t i = 0; i < permutation.size(); ++i) {
-    output[permutation[i]] = input[i];
+    output[permutation[i]] = data[i];
   }
   return output;
 }
 
+// Override of the above that works around compile failures with gcc 7.1.1.
+// For details see https://github.com/tensorflow/tensorflow/issues/10843
+template <typename T>
+std::vector<T> Permute(tensorflow::gtl::ArraySlice<int64> permutation,
+                       const std::vector<T>& input) {
+  return Permute<std::vector, T>(permutation, input);
+}
+
 // Inverts a permutation, i.e., output_permutation[input_permutation[i]] = i.
 std::vector<int64> InversePermutation(
     tensorflow::gtl::ArraySlice<int64> input_permutation);
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 95c1f0995b..86c72b3449 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -200,7 +200,7 @@ message OpMetadata {
   string op_name = 2;
   // Indicate a file and line that this op is associated to in a user's program.
   //
-  // e.g. it could be be the file and line of user code that generated the op.
+  // e.g. it could be the file and line of user code that generated the op.
   string source_file = 3;
   int32 source_line = 4;
 }
@@ -369,6 +369,7 @@ message SliceRequest {
   ComputationDataHandle operand = 2;
   repeated int64 start_indices = 3;
   repeated int64 limit_indices = 4;
+  repeated int64 stride = 5;
 }
 
 message DynamicSliceRequest {
diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
index 1f180429b2..b1d18d2faf 100644
--- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
+++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
@@ -17,6 +17,7 @@ package org.tensorflow.contrib.android;
 
 import android.content.res.AssetManager;
 import android.os.Trace;
+import android.os.Build.VERSION;
 import android.text.TextUtils;
 import android.util.Log;
 import java.io.FileInputStream;
@@ -370,9 +371,11 @@ public class TensorFlowInferenceInterface {
   private void loadGraph(InputStream is, Graph g) throws IOException {
     final long startMs = System.currentTimeMillis();
 
-    Trace.beginSection("initializeTensorFlow");
+    if (VERSION.SDK_INT >= 18) {
+      Trace.beginSection("initializeTensorFlow");
+      Trace.beginSection("readGraphDef");
+    }
 
-    Trace.beginSection("readGraphDef");
     // TODO(ashankar): Can we somehow mmap the contents instead of copying them?
     byte[] graphDef = new byte[is.available()];
     final int numBytesRead = is.read(graphDef);
@@ -383,17 +386,22 @@ public class TensorFlowInferenceInterface {
               + " of the graph, expected to read "
               + graphDef.length);
     }
-    Trace.endSection();
 
-    Trace.beginSection("importGraphDef");
+    if (VERSION.SDK_INT >= 18) {
+      Trace.endSection(); // readGraphDef.
+      Trace.beginSection("importGraphDef");
+    }
+
     try {
       g.importGraphDef(graphDef);
     } catch (IllegalArgumentException e) {
       throw new IOException("Not a valid TensorFlow Graph serialization: " + e.getMessage());
     }
-    Trace.endSection();
 
-    Trace.endSection(); // initializeTensorFlow.
+    if (VERSION.SDK_INT >= 18) {
+      Trace.endSection(); // importGraphDef.
+      Trace.endSection(); // initializeTensorFlow.
+    }
 
     final long endMs = System.currentTimeMillis();
     Log.i(
diff --git a/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc b/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
index 093000559b..b0f9237ea2 100644
--- a/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
+++ b/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
@@ -60,7 +60,7 @@ class BigQueryReader : public ReaderBase {
     BigQueryTablePartition partition;
     if (!partition.ParseFromString(current_work())) {
       return errors::InvalidArgument(
-          "Could not parse work as as valid partition.");
+          "Could not parse work as valid partition.");
     }
     TF_RETURN_IF_ERROR(bigquery_table_accessor_->SetPartition(partition));
     return Status::OK();
diff --git a/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py b/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py
index 136707da18..cc8644bfd5 100644
--- a/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py
+++ b/tensorflow/contrib/cloud/python/ops/bigquery_reader_ops.py
@@ -92,7 +92,7 @@ class BigQueryReader(io_ops.ReaderBase):
 
     Raises:
       TypeError: - If features is neither None nor a dict or
-                 - If columns is is neither None nor a list or
+                 - If columns is neither None nor a list or
                  - If both features and columns are None or set.
     """
     if (features is None) == (columns is None):
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index 9ffe08eded..205b44ac12 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -74,7 +74,7 @@ if(WIN32)
   set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221")
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP")
-  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob0")
+  set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob2")
   set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_MINSIZEREL "${CMAKE_CXX_FLAGS_MINSIZEREL} /D_ITERATOR_DEBUG_LEVEL=0")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /D_ITERATOR_DEBUG_LEVEL=0")
diff --git a/tensorflow/contrib/data/README.md b/tensorflow/contrib/data/README.md
index 9505f5c465..6d7d278309 100644
--- a/tensorflow/contrib/data/README.md
+++ b/tensorflow/contrib/data/README.md
@@ -543,7 +543,7 @@ padded.
 ```python
 dataset = tf.contrib.data.Dataset.range(100)
 dataset = dataset.map(lambda x: tf.fill([tf.cast(x, tf.int32)], x))
-dataset = dataset.padded_batch(4, padded_shapes=[None])
+batched_dataset = dataset.padded_batch(4, padded_shapes=[None])
 
 iterator = batched_dataset.make_one_shot_iterator()
 next_element = iterator.get_next()
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
index d44e258bd2..42865ed404 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
@@ -120,7 +120,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object):
 
     Doesn't actually do the sqrt! Named as such to agree with API.
 
-    To compute (M + V D V.T), we use the the Woodbury matrix identity:
+    To compute (M + V D V.T), we use the Woodbury matrix identity:
       inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
     where,
       C = inv(D) + V.T inv(M) V.
@@ -166,7 +166,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object):
   def _woodbury_sandwiched_term(self):
     """Computes the sandwiched term in the Woodbury identity.
 
-    Computes the "`C`" in the the identity:
+    Computes the "`C`" in the identity:
        inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
     where,
        C = inv(D) + V.T inv(M) V.
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
index 5b57a95c55..b525809015 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
@@ -52,7 +52,7 @@ class RelaxedBernoulli(transformed_distribution.TransformedDistribution):
   the RelaxedBernoulli can suffer from underflow issues. In many case loss
   functions such as these are invariant under invertible transformations of
   the random variables. The KL divergence, found in the variational autoencoder
-  loss, is an example. Because RelaxedBernoullis are sampled by by a Logistic
+  loss, is an example. Because RelaxedBernoullis are sampled by a Logistic
   random variable followed by a `tf.sigmoid` op, one solution is to treat
   the Logistic as the random variable and `tf.sigmoid` as downstream. The
   KL divergences of two Logistics, which are always followed by a `tf.sigmoid`
diff --git a/tensorflow/contrib/distributions/python/ops/sample_stats.py b/tensorflow/contrib/distributions/python/ops/sample_stats.py
index 26cf922d0a..2a4b92c729 100644
--- a/tensorflow/contrib/distributions/python/ops/sample_stats.py
+++ b/tensorflow/contrib/distributions/python/ops/sample_stats.py
@@ -47,7 +47,7 @@ def percentile(x,
   """Compute the `q`-th percentile of `x`.
 
   Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the
-  way from the minimum to the maximum in in a sorted copy of `x`.
+  way from the minimum to the maximum in a sorted copy of `x`.
 
   The values and distances of the two nearest neighbors as well as the
   `interpolation` parameter will determine the percentile if the normalized
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
index fd2c46d94d..fdee57695e 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Vectorized Laplace distribution class, directly using LinearOpeartor."""
+"""Vectorized Laplace distribution class, directly using LinearOperator."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index 762bc44814..2234400fdc 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -446,7 +446,7 @@ class Transformer(object):
       # TODO(fkp): return a subgraph?
       op_, op_outputs_ = self.transform_op_handler(info, op)
       if op is op_:
-        raise ValueError("In-place tranformation not allowed.")
+        raise ValueError("In-place transformation not allowed.")
 
       # Process op.
       info.transformed_ops[op] = op_
diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index 9f02fc0958..324f510301 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -3261,7 +3261,7 @@ def conv2d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
       dilation_rate: tuple of 2 integers.
 
   Returns:
@@ -3309,7 +3309,7 @@ def conv2d_transpose(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
 
   Returns:
       A tensor, result of transposed 2D convolution.
@@ -3395,7 +3395,7 @@ def conv3d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
       dilation_rate: tuple of 3 integers.
 
   Returns:
diff --git a/tensorflow/contrib/keras/python/keras/layers/core.py b/tensorflow/contrib/keras/python/keras/layers/core.py
index d287fa56d9..34548c83c5 100644
--- a/tensorflow/contrib/keras/python/keras/layers/core.py
+++ b/tensorflow/contrib/keras/python/keras/layers/core.py
@@ -107,7 +107,7 @@ class Dropout(tf_core_layers.Dropout, Layer):
     self.supports_masking = True
     # Inheritance call order:
     # 1) tf.layers.Dropout, 2) keras.layers.Layer, 3) tf.layers.Layer
-    super(Dropout, self).__init__(**kwargs)
+    super(Dropout, self).__init__(rate=rate, noise_shape=noise_shape, seed=seed, **kwargs)
 
   def call(self, inputs, training=None):
     if training is None:
diff --git a/tensorflow/contrib/keras/python/keras/layers/recurrent.py b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
index 5e8c23ed3e..cdef55f599 100644
--- a/tensorflow/contrib/keras/python/keras/layers/recurrent.py
+++ b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
@@ -985,7 +985,7 @@ class LSTM(Recurrent):
 
   References:
       - [Long short-term
-        memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf)
+        memory](http://www.bioinf.jku.at/publications/older/2604.pdf)
         (original 1997 paper)
       - [Supervised sequence labeling with recurrent neural
         networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
diff --git a/tensorflow/contrib/keras/python/keras/models_test.py b/tensorflow/contrib/keras/python/keras/models_test.py
index 50aba43c24..99fd6e1cbe 100644
--- a/tensorflow/contrib/keras/python/keras/models_test.py
+++ b/tensorflow/contrib/keras/python/keras/models_test.py
@@ -105,7 +105,7 @@ class TestModelSaving(test.TestCase):
       out2 = model.predict(x)
       self.assertAllClose(out, out2, atol=1e-05)
 
-  def test_fuctional_model_saving(self):
+  def test_functional_model_saving(self):
     if h5py is None:
       return  # Skip test if models cannot be saved.
 
diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD
index aa301a0ade..c96e42de45 100644
--- a/tensorflow/contrib/layers/BUILD
+++ b/tensorflow/contrib/layers/BUILD
@@ -121,7 +121,6 @@ cuda_py_test(
         ":layers_py",
         "//third_party/py/numpy",
         "//tensorflow/contrib/framework:framework_py",
-        "//tensorflow/contrib/losses:losses_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
@@ -141,6 +140,7 @@ cuda_py_test(
         "//tensorflow/python:template",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/ops/losses:losses",
     ],
 )
 
diff --git a/tensorflow/contrib/layers/__init__.py b/tensorflow/contrib/layers/__init__.py
index ef05dbaa65..528de31346 100644
--- a/tensorflow/contrib/layers/__init__.py
+++ b/tensorflow/contrib/layers/__init__.py
@@ -17,12 +17,16 @@
 See the @{$python/contrib.layers} guide.
 
 @@avg_pool2d
+@@avg_pool3d
 @@batch_norm
 @@convolution2d
+@@convolution3d
 @@conv2d_in_plane
 @@convolution2d_in_plane
 @@conv2d_transpose
 @@convolution2d_transpose
+@@conv3d_transpose
+@@convolution3d_transpose
 @@dropout
 @@elu
 @@embedding_lookup_unique
@@ -31,6 +35,7 @@ See the @{$python/contrib.layers} guide.
 @@layer_norm
 @@linear
 @@max_pool2d
+@@max_pool3d
 @@one_hot_encoding
 @@relu
 @@relu6
@@ -101,6 +106,7 @@ from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = ['bias_add',
                     'conv2d',
+                    'conv3d',
                     'elu',
                     'feature_column',
                     'legacy_fully_connected',
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index f2a904b521..ed4b723ca7 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -49,15 +49,20 @@ from tensorflow.python.training import moving_averages
 # TODO(b/28426988): Replace legacy_* fns migrated from slim.
 # TODO(b/28426988): Remove legacy_* when all uses have migrated to new API.
 __all__ = ['avg_pool2d',
+           'avg_pool3d',
            'batch_norm',
            'bias_add',
            'conv2d',
+           'conv3d',
            'conv2d_in_plane',
            'conv2d_transpose',
+           'conv3d_transpose',
            'convolution',
            'convolution2d',
            'convolution2d_in_plane',
            'convolution2d_transpose',
+           'convolution3d',
+           'convolution3d_transpose',
            'dropout',
            'elu',
            'flatten',
@@ -66,6 +71,7 @@ __all__ = ['avg_pool2d',
            'linear',
            'pool',
            'max_pool2d',
+           'max_pool3d',
            'one_hot_encoding',
            'relu',
            'relu6',
@@ -82,6 +88,8 @@ __all__ = ['avg_pool2d',
 
 DATA_FORMAT_NCHW = 'NCHW'
 DATA_FORMAT_NHWC = 'NHWC'
+DATA_FORMAT_NCDHW = 'NCDHW'
+DATA_FORMAT_NDHWC = 'NDHWC'
 
 
 @add_arg_scope
@@ -132,6 +140,54 @@ def avg_pool2d(inputs,
     return utils.collect_named_outputs(outputs_collections, sc, outputs)
 
 
+@add_arg_scope
+def avg_pool3d(inputs,
+               kernel_size,
+               stride=2,
+               padding='VALID',
+               data_format=DATA_FORMAT_NDHWC,
+               outputs_collections=None,
+               scope=None):
+  """Adds a 3D average pooling op.
+
+  It is assumed that the pooling is done per image but not in batch or channels.
+
+  Args:
+    inputs: A 5-D tensor of shape `[batch_size, depth, height, width, channels]` if
+      `data_format` is `NDHWC`, and `[batch_size, channels, depth, height, width]` if
+      `data_format` is `NCDHW`.
+    kernel_size: A list of length 3: [kernel_depth, kernel_height, kernel_width] of the
+      pooling kernel over which the op is computed. Can be an int if both
+      values are the same.
+    stride: A list of length 3: [stride_depth, stride_height, stride_width].
+      Can be an int if both strides are the same. Note that presently
+      both strides must have the same value.
+    padding: The padding method, either 'VALID' or 'SAME'.
+    data_format: A string. `NDHWC` (default) and `NCDHW` are supported.
+    outputs_collections: The collections to which the outputs are added.
+    scope: Optional scope for name_scope.
+
+  Returns:
+    A `Tensor` representing the results of the pooling operation.
+
+  Raises:
+    ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`.
+  """
+  if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC):
+    raise ValueError('data_format has to be either NCDHW or NDHWC.')
+  with ops.name_scope(scope, 'AvgPool3D', [inputs]) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    df = ('channels_first' if data_format and data_format.startswith('NC')
+          else 'channels_last')
+    layer = pooling_layers.AveragePooling3D(pool_size=kernel_size,
+                                            strides=stride,
+                                            padding=padding,
+                                            data_format=df,
+                                            _scope=sc)
+    outputs = layer.apply(inputs)
+    return utils.collect_named_outputs(outputs_collections, sc, outputs)
+
+
 def _fused_batch_norm(
     inputs,
     decay=0.999,
@@ -985,6 +1041,7 @@ def convolution(inputs,
                                        sc.original_name_scope, outputs)
 
 convolution2d = convolution
+convolution3d = convolution
 
 
 @add_arg_scope
@@ -1203,6 +1260,116 @@ def convolution2d_transpose(
                                        sc.original_name_scope, outputs)
 
 
+@add_arg_scope
+def convolution3d_transpose(
+    inputs,
+    num_outputs,
+    kernel_size,
+    stride=1,
+    padding='SAME',
+    data_format=DATA_FORMAT_NDHWC,
+    activation_fn=nn.relu,
+    normalizer_fn=None,
+    normalizer_params=None,
+    weights_initializer=initializers.xavier_initializer(),
+    weights_regularizer=None,
+    biases_initializer=init_ops.zeros_initializer(),
+    biases_regularizer=None,
+    reuse=None,
+    variables_collections=None,
+    outputs_collections=None,
+    trainable=True,
+    scope=None):
+  """Adds a convolution3d_transpose with an optional batch normalization layer.
+  
+  The function creates a variable called `weights`, representing the
+  kernel, that is convolved with the input. If `batch_norm_params` is `None`, a
+  second variable called 'biases' is added to the result of the operation.
+  Args:
+    inputs: A 5-D `Tensor` of type `float` and shape
+      `[batch, depth, height, width, in_channels]` for `NDHWC` data format or
+      `[batch, in_channels, depth, height, width]` for `NCDHW` data format.
+    num_outputs: Integer, the number of output filters.
+    kernel_size: A list of length 3 holding the [kernel_depth, kernel_height, kernel_width] of
+      of the filters. Can be an int if both values are the same.
+    stride: A list of length 3: [stride_depth, stride_height, stride_width].
+      Can be an int if both strides are the same.  Note that presently
+      both strides must have the same value.
+    padding: One of 'VALID' or 'SAME'.
+    data_format: A string. `NDHWC` (default) and `NCDHW` are supported.
+    activation_fn: Activation function. The default value is a ReLU function.
+      Explicitly set it to None to skip it and maintain a linear activation.
+    normalizer_fn: Normalization function to use instead of `biases`. If
+      `normalizer_fn` is provided then `biases_initializer` and
+      `biases_regularizer` are ignored and `biases` are not created nor added.
+      default set to None for no normalizer function
+    normalizer_params: Normalization function parameters.
+    weights_initializer: An initializer for the weights.
+    weights_regularizer: Optional regularizer for the weights.
+    biases_initializer: An initializer for the biases. If None skip biases.
+    biases_regularizer: Optional regularizer for the biases.
+    reuse: Whether or not the layer and its variables should be reused. To be
+      able to reuse the layer scope must be given.
+    variables_collections: Optional list of collections for all the variables or
+      a dictionary containing a different list of collection per variable.
+    outputs_collections: Collection to add the outputs.
+    trainable: Whether or not the variables should be trainable or not.
+    scope: Optional scope for variable_scope.
+  Returns:
+    A tensor representing the output of the operation.
+  Raises:
+    ValueError: If 'kernel_size' is not a list of length 3.
+    ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`.
+    ValueError: If `C` dimension of `inputs` is None.
+  """
+  layer_variable_getter = _build_variable_getter(
+      {'bias': 'biases', 'kernel': 'weights'})
+
+  with variable_scope.variable_scope(
+      scope, 'Conv3d_transpose', [inputs], reuse=reuse,
+      custom_getter=layer_variable_getter) as sc:
+    if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC):
+      raise ValueError('data_format has to be either NCDHW or NDHWC.')
+
+    inputs = ops.convert_to_tensor(inputs)
+
+    df = ('channels_first' if data_format and data_format.startswith('NC')
+          else 'channels_last')
+    layer = convolutional_layers.Convolution3DTranspose(
+        filters=num_outputs,
+        kernel_size=kernel_size,
+        strides=stride,
+        padding=padding,
+        data_format=df,
+        activation=None,
+        use_bias=not normalizer_fn and biases_initializer,
+        kernel_initializer=weights_initializer,
+        bias_initializer=biases_initializer,
+        kernel_regularizer=weights_regularizer,
+        bias_regularizer=biases_regularizer,
+        activity_regularizer=None,
+        trainable=trainable,
+        name=sc.name,
+        dtype=inputs.dtype.base_dtype,
+        _scope=sc,
+        _reuse=reuse)
+    outputs = layer.apply(inputs)
+
+    # Add variables to collections.
+    _add_variable_to_collections(layer.kernel, variables_collections, 'weights')
+    if layer.bias:
+      _add_variable_to_collections(layer.bias, variables_collections, 'biases')
+
+    if normalizer_fn is not None:
+      normalizer_params = normalizer_params or {}
+      outputs = normalizer_fn(outputs, **normalizer_params)
+
+    if activation_fn is not None:
+      outputs = activation_fn(outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
+
+
 @add_arg_scope
 def dropout(inputs,
             keep_prob=0.5,
@@ -1467,7 +1634,8 @@ def fully_connected(inputs,
     ValueError: If x has rank less than 2 or if its last dimension is not set.
   """
   if not isinstance(num_outputs, six.integer_types):
-    raise ValueError('num_outputs should be int or long, got %s.', num_outputs)
+    raise ValueError(
+        'num_outputs should be int or long, got %s.' % (num_outputs,))
 
   layer_variable_getter = _build_variable_getter({'bias': 'biases',
                                                   'kernel': 'weights'})
@@ -1689,6 +1857,55 @@ def max_pool2d(inputs,
     return utils.collect_named_outputs(outputs_collections, sc, outputs)
 
 
+@add_arg_scope
+def max_pool3d(inputs,
+               kernel_size,
+               stride=2,
+               padding='VALID',
+               data_format=DATA_FORMAT_NDHWC,
+               outputs_collections=None,
+               scope=None):
+  """Adds a 3D Max Pooling op.
+
+  It is assumed that the pooling is done per image but not in batch or channels.
+
+  Args:
+    inputs: A 5-D tensor of shape `[batch_size, depth, height, width, channels]` if
+      `data_format` is `NDHWC`, and `[batch_size, channels, depth, height, width]` if
+      `data_format` is `NCDHW`.
+    kernel_size: A list of length 3: [kernel_depth, kernel_height, kernel_width] of the
+      pooling kernel over which the op is computed. Can be an int if both
+      values are the same.
+    stride: A list of length 3: [stride_depth, stride_height, stride_width].
+      Can be an int if both strides are the same. Note that presently
+      both strides must have the same value.
+    padding: The padding method, either 'VALID' or 'SAME'.
+    data_format: A string. `NDHWC` (default) and `NCDHW` are supported.
+    outputs_collections: The collections to which the outputs are added.
+    scope: Optional scope for name_scope.
+
+  Returns:
+    A `Tensor` representing the results of the pooling operation.
+
+  Raises:
+    ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`.
+    ValueError: If 'kernel_size' is not a 3-D list
+  """
+  if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC):
+    raise ValueError('data_format has to be either NCDHW or NDHWC.')
+  with ops.name_scope(scope, 'MaxPool3D', [inputs]) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    df = ('channels_first' if data_format and data_format.startswith('NC')
+          else 'channels_last')
+    layer = pooling_layers.MaxPooling3D(pool_size=kernel_size,
+                                        strides=stride,
+                                        padding=padding,
+                                        data_format=df,
+                                        _scope=sc)
+    outputs = layer.apply(inputs)
+    return utils.collect_named_outputs(outputs_collections, sc, outputs)
+
+
 @add_arg_scope
 def pool(inputs,
          kernel_size,
@@ -2346,6 +2563,8 @@ linear = functools.partial(fully_connected, activation_fn=None)
 
 # Simple alias.
 conv2d = convolution2d
+conv3d = convolution3d
 conv2d_transpose = convolution2d_transpose
+conv3d_transpose = convolution3d_transpose
 conv2d_in_plane = convolution2d_in_plane
 separable_conv2d = separable_convolution2d
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index d4ee85b550..15809ea180 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -27,7 +27,6 @@ from tensorflow.contrib.framework.python.ops import arg_scope
 from tensorflow.contrib.framework.python.ops import variables
 from tensorflow.contrib.layers.python.layers import layers as _layers
 from tensorflow.contrib.layers.python.layers import regularizers
-from tensorflow.contrib.losses.python.losses import loss_ops
 from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -49,6 +48,7 @@ from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import template
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
+from tensorflow.python.ops.losses import losses
 from tensorflow.python.platform import test
 
 
@@ -121,6 +121,76 @@ class AvgPool2DTest(test.TestCase):
     self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
 
+class AvgPool3DTest(test.TestCase):
+
+  def testInvalidDataFormat(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3))
+    with self.assertRaisesRegexp(ValueError,
+                                 'data_format has to be either NCDHW or NDHWC.'):
+      _layers.avg_pool3d(images, [3, 3, 3], data_format='CDHWN')
+
+  def testCreateAvgPool(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3))
+    output = _layers.avg_pool3d(images, [3, 3, 3])
+    self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateAvgPoolNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, 2, depth, height, width))
+    output = _layers.avg_pool3d(images, [3, 3, 3], data_format='NCDHW')
+    self.assertEquals(output.op.name, 'AvgPool3D/transpose_1')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 1, 2, 4])
+
+  def testCollectOutputs(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], outputs_collections='outputs')
+    output_collected = ops.get_collection('outputs')[0]
+    self.assertEqual(output_collected.aliases, ['AvgPool3D'])
+    self.assertEqual(output_collected, output)
+
+  def testCreateSquareAvgPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, 3)
+    self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateAvgPoolWithScope(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], scope='pool1')
+    self.assertEqual(output.op.name, 'pool1/AvgPool3D')
+
+  def testCreateAvgPoolWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])
+
+  def testCreateAvgPoolWithSamePaddingNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
+    output = _layers.avg_pool3d(
+        images, [3, 3, 3], padding='SAME', data_format='NCDHW')
+    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])
+
+  def testCreateAvgPoolStrideWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3])
+
+  def testGlobalAvgPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.avg_pool3d(images, images.get_shape()[1:4], stride=1)
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])
+
+
 class PoolTest(test.TestCase):
 
   def testCreatePool(self):
@@ -1559,23 +1629,23 @@ class FCTest(test.TestCase):
         inputs, 32, scope='fc1', weights_regularizer=regularizer)
     self.assertEqual(
         len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
-    self.assertEqual(len(loss_ops.get_regularization_losses()), 1)
+    self.assertEqual(len(losses.get_regularization_losses()), 1)
     _layers.fully_connected(
         inputs, 32, scope='fc1', weights_regularizer=regularizer, reuse=True)
     self.assertEqual(
         len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
-    self.assertEqual(len(loss_ops.get_regularization_losses()), 1)
+    self.assertEqual(len(losses.get_regularization_losses()), 1)
 
     with variable_scope.variable_scope('outer', reuse=False):
       _layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
-      self.assertEqual(len(loss_ops.get_regularization_losses()), 2)
+      self.assertEqual(len(losses.get_regularization_losses()), 2)
     with variable_scope.variable_scope('outer', reuse=True):
       _layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
-      self.assertEqual(len(loss_ops.get_regularization_losses()), 2)
+      self.assertEqual(len(losses.get_regularization_losses()), 2)
 
   def testCreateFCWithoutActivation(self):
     height, width = 3, 3
@@ -2771,6 +2841,76 @@ class MaxPool2DTest(test.TestCase):
     self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
 
+class MaxPool3DTest(test.TestCase):
+
+  def testInvalidDataFormat(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3))
+    with self.assertRaisesRegexp(ValueError,
+                                 'data_format has to be either NCDHW or NDHWC.'):
+      _layers.max_pool3d(images, [3, 3, 3], data_format='CDHWN')
+
+  def testCreateMaxPool(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, depth, height, width, 3)).astype(np.float32)
+    output = _layers.max_pool3d(images, [3, 3, 3])
+    self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateMaxPoolNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = np.random.uniform(size=(5, 3, depth, height, width)).astype(np.float32)
+    output = _layers.max_pool3d(images, [3, 3, 3], data_format='NCDHW')
+    self.assertEquals(output.op.name, 'MaxPool3D/transpose_1')
+    self.assertListEqual(output.get_shape().as_list(), [5, 3, 1, 2, 4])
+
+  def testCollectOutputs(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], outputs_collections='outputs')
+    output_collected = ops.get_collection('outputs')[0]
+    self.assertEqual(output_collected.aliases, ['MaxPool3D'])
+    self.assertEqual(output_collected, output)
+
+  def testCreateSquareMaxPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, 3)
+    self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+  def testCreateMaxPoolWithScope(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], scope='pool1')
+    self.assertEqual(output.op.name, 'pool1/MaxPool3D')
+
+  def testCreateMaxPoolWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])
+
+  def testCreateMaxPoolWithSamePaddingNCDHW(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
+    output = _layers.max_pool3d(
+        images, [3, 3, 3], padding='SAME', data_format='NCDHW')
+    self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])
+
+  def testCreateMaxPoolStrideWithSamePadding(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3])
+
+  def testGlobalMaxPool(self):
+    depth, height, width = 3, 6, 9
+    images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+    output = _layers.max_pool3d(images, images.get_shape()[1:4], stride=1)
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])
+
+
 class OneHotEncodingTest(test.TestCase):
 
   def testOneHotEncodingCreate(self):
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
index b4b552c025..6b125534a4 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
@@ -52,8 +52,8 @@ LABEL_DIMENSION = 3  #  Dimensionality of regression labels.
 
 def _train_test_split(features_and_labels):
   features, labels = features_and_labels
-  train_set = (features[:len(features) / 2], labels[:len(features) / 2])
-  test_set = (features[len(features) / 2:], labels[len(features) / 2:])
+  train_set = (features[:int(len(features) / 2)], labels[:int(len(features) / 2)])
+  test_set = (features[int(len(features) / 2):], labels[int(len(features) / 2):])
   return train_set, test_set
 
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index 6e15e7891e..fdc266d582 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -438,7 +438,7 @@ def loss_only_head(loss_fn, head_name=None):
   Args:
     loss_fn: a function that takes no argument and returns a list of
         scalar tensors.
-    head_name: a name for for the head.
+    head_name: a name for the head.
 
   Returns:
     An instance of `Head` to hold the additional losses.
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
index 145d5c40fa..d3bb0fda57 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
@@ -729,7 +729,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.07)
 
   def testSdcaOptimizerRealValuedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and real valued features."""
+    """Tests LinearClassifier with SDCAOptimizer and real valued features."""
 
     def input_fn():
       return {
@@ -776,7 +776,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerBucketizedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and bucketized features."""
+    """Tests LinearClassifier with SDCAOptimizer and bucketized features."""
 
     def input_fn():
       return {
@@ -802,7 +802,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerSparseFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -833,7 +833,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerWeightedSparseFeatures(self):
-    """LinearClasssifier with SDCAOptimizer and weighted sparse features."""
+    """LinearClassifier with SDCAOptimizer and weighted sparse features."""
 
     def input_fn():
       return {
@@ -864,7 +864,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerCrossedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and crossed features."""
+    """Tests LinearClassifier with SDCAOptimizer and crossed features."""
 
     def input_fn():
       return {
@@ -897,7 +897,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerMixedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and a mix of features."""
+    """Tests LinearClassifier with SDCAOptimizer and a mix of features."""
 
     def input_fn():
       return {
@@ -1509,7 +1509,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -1581,7 +1581,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
 
   def testSdcaOptimizerBiasOnly(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when it's the only feature present.
@@ -1614,7 +1614,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
 
   def testSdcaOptimizerBiasAndOtherColumns(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
@@ -1676,7 +1676,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
 
   def testSdcaOptimizerBiasAndOtherColumnsFabricatedCentered(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
index 6ebfeb0f16..284e2cfd7a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py
@@ -123,7 +123,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["probabilities"].eval(),
                           regression_output.value.eval())
 
-  def testEstimatorSpec_export_classsification(self):
+  def testEstimatorSpec_export_classification(self):
     predictions = self.create_predictions()
     output_alternatives = {"classification_head": (
         constants.ProblemType.CLASSIFICATION, predictions)}
@@ -143,7 +143,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_scores(self):
+  def testEstimatorSpec_export_classification_with_missing_scores(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -165,7 +165,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_scores_proba(self):
+  def testEstimatorSpec_export_classification_with_missing_scores_proba(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -187,7 +187,7 @@ class ModelFnopsTest(test.TestCase):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_classes(self):
+  def testEstimatorSpec_export_classification_with_missing_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["classes"]
@@ -208,7 +208,7 @@ class ModelFnopsTest(test.TestCase):
                           classification_output.scores.eval())
       self.assertIsNone(classification_output.classes)
 
-  def testEstimatorSpec_export_classsification_with_nonstring_classes(self):
+  def testEstimatorSpec_export_classification_with_nonstring_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     output_alternatives_predictions["classes"] = constant_op.constant(
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 491c4343bd..cd47b82d82 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -455,7 +455,7 @@ class Experiment(object):
   def train_and_evaluate(self):
     """Interleaves training and evaluation.
 
-    The frequency of evaluation is controlled by the contructor arg
+    The frequency of evaluation is controlled by the constructor arg
     `min_eval_frequency`. When this parameter is 0, evaluation happens
     only after training has completed. Note that evaluation cannot happen
     more frequently than checkpoints are taken. If no new snapshots are
@@ -515,9 +515,9 @@ class Experiment(object):
 
     This differs from `train_and_evaluate` as follows:
       1. The procedure will have train and evaluation in turns. The model
-      will be trained for a number of steps (usuallly smaller than `train_steps`
+      will be trained for a number of steps (usually smaller than `train_steps`
       if provided) and then be evaluated.  `train_and_evaluate` will train the
-      model for `train_steps` (no small training iteraions).
+      model for `train_steps` (no small training iterations).
 
       2. Due to the different approach this schedule takes, it leads to two
       differences in resource control. First, the resources (e.g., memory) used
diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py
index 234605ff76..4283240d01 100644
--- a/tensorflow/contrib/learn/python/learn/models.py
+++ b/tensorflow/contrib/learn/python/learn/models.py
@@ -63,7 +63,7 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0):
     x: tensor or placeholder for input features.
     y: tensor or placeholder for labels.
     init_mean: the mean value to use for initialization.
-    init_stddev: the standard devation to use for initialization.
+    init_stddev: the standard deviation to use for initialization.
 
   Returns:
     Predictions and loss tensors.
@@ -124,7 +124,7 @@ def logistic_regression(x,
                   will check if graph contains tensor `class_weight:0`.
                   If that is not provided either all ones are used.
     init_mean: the mean value to use for initialization.
-    init_stddev: the standard devation to use for initialization.
+    init_stddev: the standard deviation to use for initialization.
 
   Returns:
     Predictions and loss tensors.
diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
index 3d316450d8..1924ea0f66 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
@@ -462,7 +462,7 @@ def random_tril_matrix(shape,
     remove_upper:  Python `bool`.
       If `True`, zero out the strictly upper triangle.
       If `False`, the lower triangle of returned matrix will have desired
-      properties, but will not not have the strictly upper triangle zero'd out.
+      properties, but will not have the strictly upper triangle zero'd out.
 
   Returns:
     `Tensor` with desired shape and dtype.
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index d58b9744ac..f53f38f3cf 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -208,7 +208,7 @@ def index_to_string_table_from_tensor(mapping, default_value="UNK", name=None):
   Sample Usages:
 
   ```python
-  mapping_string = tf.constant(["emerson", "lake", "palmer")
+  mapping_string = tf.constant(["emerson", "lake", "palmer"])
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       mapping_string, default_value="UNKNOWN")
@@ -260,7 +260,7 @@ def index_to_string(tensor, mapping, default_value="UNK", name=None):
   For example:
 
   ```python
-  mapping_string = tf.constant(["emerson", "lake", "palmer")
+  mapping_string = tf.constant(["emerson", "lake", "palmer"])
   indices = tf.constant([1, 5], tf.int64)
   values = tf.contrib.lookup.index_to_string(
       indices, mapping=mapping_string, default_value="UNKNOWN")
diff --git a/tensorflow/contrib/makefile/build_with_docker.sh b/tensorflow/contrib/makefile/build_with_docker.sh
index 7fe38f4b5d..51a73fafe5 100755
--- a/tensorflow/contrib/makefile/build_with_docker.sh
+++ b/tensorflow/contrib/makefile/build_with_docker.sh
@@ -23,7 +23,7 @@
 # Make sure we're in the correct directory, at the root of the source tree.
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 WORKSPACE="${SCRIPT_DIR}/../../../"
-cd ${WORKSPACE}
+cd ${WORKSPACE} || exit 1
 
 DOCKER_IMG_NAME="tf-make-base"
 DOCKER_CONTEXT_PATH="${WORKSPACE}tensorflow/contrib/makefile/"
diff --git a/tensorflow/contrib/makefile/compile_android_protobuf.sh b/tensorflow/contrib/makefile/compile_android_protobuf.sh
index 50d72d6093..fadbe271b8 100755
--- a/tensorflow/contrib/makefile/compile_android_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_android_protobuf.sh
@@ -27,7 +27,7 @@ cc_prefix="${CC_PREFIX}"
 usage() {
   echo "Usage: $(basename "$0") [-a:c]"
   echo "-a [Architecture] Architecture of target android [default=armeabi-v7a] \
-(supported archtecture list: \
+(supported architecture list: \
 arm64-v8a armeabi armeabi-v7a armeabi-v7a-hard mips mips64 x86 x86_64)"
   echo "-c Clean before building protobuf for target"
   echo "\"NDK_ROOT\" should be defined as an environment variable."
@@ -130,7 +130,7 @@ elif [[ ${ARCHITECTURE} == "x86_64" ]]; then
     sysroot_arch="x86_64"
     bin_prefix="x86_64-linux-android"
 else
-    echo "archtecture ${arcitecture} is not supported." 1>&2
+    echo "architecture ${ARCHITECTURE} is not supported." 1>&2
     usage
     exit 1
 fi
diff --git a/tensorflow/contrib/makefile/compile_ios_protobuf.sh b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
index d1012a6c93..e8b9454e7e 100755
--- a/tensorflow/contrib/makefile/compile_ios_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
@@ -1,4 +1,4 @@
-#!/bin/bash -x -e
+#!/bin/bash
 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,6 +15,9 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
+set -x
+set -e
+
 SCRIPT_DIR=$(dirname $0)
 source "${SCRIPT_DIR}/build_helper.subr"
 
@@ -30,17 +33,17 @@ fi
 
 JOB_COUNT="${JOB_COUNT:-$(get_job_count)}"
 
-GENDIR=`pwd`/gen/protobuf_ios/
+GENDIR=$(pwd)/gen/protobuf_ios/
 LIBDIR=${GENDIR}lib
 mkdir -p ${LIBDIR}
 
 OSX_VERSION=darwin14.0.0
 
-IPHONEOS_PLATFORM=`xcrun --sdk iphoneos --show-sdk-platform-path`
-IPHONEOS_SYSROOT=`xcrun --sdk iphoneos --show-sdk-path`
-IPHONESIMULATOR_PLATFORM=`xcrun --sdk iphonesimulator --show-sdk-platform-path`
-IPHONESIMULATOR_SYSROOT=`xcrun --sdk iphonesimulator --show-sdk-path`
-IOS_SDK_VERSION=`xcrun --sdk iphoneos --show-sdk-version`
+IPHONEOS_PLATFORM=$(xcrun --sdk iphoneos --show-sdk-platform-path)
+IPHONEOS_SYSROOT=$(xcrun --sdk iphoneos --show-sdk-path)
+IPHONESIMULATOR_PLATFORM=$(xcrun --sdk iphonesimulator --show-sdk-platform-path)
+IPHONESIMULATOR_SYSROOT=$(xcrun --sdk iphonesimulator --show-sdk-path)
+IOS_SDK_VERSION=$(xcrun --sdk iphoneos --show-sdk-version)
 MIN_SDK_VERSION=8.0
 
 CFLAGS="-DNDEBUG -Os -pipe -fPIC -fno-exceptions"
diff --git a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
index 6f47b80780..bcf097b303 100755
--- a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
+++ b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
@@ -20,7 +20,7 @@ source "${SCRIPT_DIR}/build_helper.subr"
 JOB_COUNT="${JOB_COUNT:-$(get_job_count)}"
 
 function less_than_required_version() {
-  echo $1 | (IFS=. read major minor micro
+  echo $1 | (IFS=. read -r major minor micro
     if [ $major -ne $2 ]; then
       [ $major -lt $2 ]
     elif [ $minor -ne $3 ]; then
@@ -31,7 +31,7 @@ function less_than_required_version() {
   )
 }
 
-ACTUAL_XCODE_VERSION=`xcodebuild -version | head -n 1 | sed 's/Xcode //'`
+ACTUAL_XCODE_VERSION=$(xcodebuild -version | head -n 1 | sed 's/Xcode //')
 REQUIRED_XCODE_VERSION=7.3.0
 if less_than_required_version $ACTUAL_XCODE_VERSION 7 3 0
 then
diff --git a/tensorflow/contrib/makefile/compile_pi_protobuf.sh b/tensorflow/contrib/makefile/compile_pi_protobuf.sh
index 2aae2d5f4e..f863d80009 100755
--- a/tensorflow/contrib/makefile/compile_pi_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_pi_protobuf.sh
@@ -15,15 +15,15 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
-cd tensorflow/contrib/makefile
+cd tensorflow/contrib/makefile || exit 1
 
-GENDIR=`pwd`/gen/protobuf_pi/
+GENDIR=$(pwd)/gen/protobuf_pi/
 LIBDIR=${GENDIR}
 mkdir -p ${LIBDIR}
 
 CXX=arm-linux-gnueabihf-g++
 
-cd downloads/protobuf
+cd downloads/protobuf || exit 1
 
 ./autogen.sh
 if [ $? -ne 0 ]
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index aeb3c45697..4a3b3e7762 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -14,6 +14,7 @@ tensorflow/core/kernels/transpose_functor_cpu.cc
 tensorflow/core/kernels/training_op_helpers.cc
 tensorflow/core/kernels/training_ops.cc
 tensorflow/core/kernels/topk_op.cc
+tensorflow/core/kernels/tile_functor_cpu.cc
 tensorflow/core/kernels/tile_ops.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_1.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_2.cc
diff --git a/tensorflow/contrib/ndlstm/python/lstm2d.py b/tensorflow/contrib/ndlstm/python/lstm2d.py
index da9698ec5b..ebbb4ccf11 100644
--- a/tensorflow/contrib/ndlstm/python/lstm2d.py
+++ b/tensorflow/contrib/ndlstm/python/lstm2d.py
@@ -91,21 +91,71 @@ def horizontal_lstm(images, num_filters_out, scope=None):
     return output
 
 
-def separable_lstm(images, num_filters_out, nhidden=None, scope=None):
+def get_blocks(images, kernel_size):
+  """Split images in blocks
+
+  Args:
+    images: (num_images, height, width, depth) tensor
+    kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of
+      of the pooling. Can be an int if both values are the same.
+
+  Returns:
+    (num_images, height/kernel_height, width/kernel_width,
+    depth*kernel_height*kernel_width) tensor
+  """
+  with variable_scope.variable_scope("image_blocks"):
+    batch_size, height, width, chanels = _shape(images)
+
+    if height % kernel_size[0] != 0:
+      offset = array_ops.zeros([batch_size,
+                                kernel_size[0] - (height % kernel_size[0]),
+                                width,
+                                chanels])
+      images = array_ops.concat([images, offset], 1)
+      batch_size, height, width, chanels = _shape(images)
+    if width % kernel_size[1] != 0:
+      offset = array_ops.zeros([batch_size,
+                                height,
+                                kernel_size[1] - (width % kernel_size[1]),
+                                chanels])
+      images = array_ops.concat([images, offset], 2)
+      batch_size, height, width, chanels = _shape(images)
+
+    h, w = int(height / kernel_size[0]), int(width / kernel_size[1])
+    features = kernel_size[1] * kernel_size[0] * chanels
+
+    lines = array_ops.split(images, h, axis=1)
+    line_blocks = []
+    for line in lines:
+      line = array_ops.transpose(line, [0, 2, 3, 1])
+      line = array_ops.reshape(line, [batch_size, w, features])
+      line_blocks.append(line)
+
+    return array_ops.stack(line_blocks, axis=1)
+
+
+def separable_lstm(images, num_filters_out,
+                   kernel_size=None, nhidden=None, scope=None):
   """Run bidirectional LSTMs first horizontally then vertically.
 
   Args:
     images: (num_images, height, width, depth) tensor
     num_filters_out: output layer depth
+    kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of
+      of the pooling. Can be an int if both values are the same. Set to None for
+      not using blocks
     nhidden: hidden layer depth
     scope: optional scope name
 
   Returns:
-    (num_images, height, width, num_filters_out) tensor
+    (num_images, height/kernel_height, width/kernel_width,
+    num_filters_out) tensor
   """
   with variable_scope.variable_scope(scope, "SeparableLstm", [images]):
     if nhidden is None:
       nhidden = num_filters_out
+    if kernel_size is not None:
+      images = get_blocks(images, kernel_size)
     hidden = horizontal_lstm(images, nhidden)
     with variable_scope.variable_scope("vertical"):
       transposed = array_ops.transpose(hidden, [0, 2, 1, 3])
diff --git a/tensorflow/contrib/ndlstm/python/lstm2d_test.py b/tensorflow/contrib/ndlstm/python/lstm2d_test.py
index 3dbbb81796..f1b37d701b 100644
--- a/tensorflow/contrib/ndlstm/python/lstm2d_test.py
+++ b/tensorflow/contrib/ndlstm/python/lstm2d_test.py
@@ -69,6 +69,14 @@ class Lstm2DTest(test_util.TensorFlowTestCase):
       result = outputs.eval()
       self.assertEqual(tuple(result.shape), (2, 7, 11, 8))
 
+  def testSeparableLstmDimsBlocks(self):
+    with self.test_session():
+      inputs = constant_op.constant(_rand(2, 7, 11, 5))
+      outputs = lstm2d.separable_lstm(inputs, 8, kernel_size=[2, 2])
+      variables.global_variables_initializer().run()
+      result = outputs.eval()
+      self.assertEqual(tuple(result.shape), (2, 4, 6, 8))
+
   def testReduceToSequenceDims(self):
     with self.test_session():
       inputs = constant_op.constant(_rand(2, 7, 11, 5))
diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
index c7ed663131..288f59fed4 100644
--- a/tensorflow/contrib/remote_fused_graph/pylib/BUILD
+++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
@@ -7,6 +7,7 @@ licenses(["notice"])  # Apache 2.0
 
 exports_files(["LICENSE"])
 
+load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 tf_gen_op_wrapper_py(
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index 9c5e9fec9d..ecce1d22f0 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -79,7 +79,7 @@ class CoupledInputForgetGateLSTMCell(rnn_cell_impl.RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+    http://www.bioinf.jku.at/publications/older/2604.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
@@ -1110,14 +1110,14 @@ class AttentionCellWrapper(rnn_cell_impl.RNNCell):
     if input_size is None:
       input_size = inputs.get_shape().as_list()[1]
     inputs = _linear([inputs, attns], input_size, True)
-    lstm_output, new_state = self._cell(inputs, state)
+    cell_output, new_state = self._cell(inputs, state)
     if self._state_is_tuple:
       new_state_cat = array_ops.concat(nest.flatten(new_state), 1)
     else:
       new_state_cat = new_state
     new_attns, new_attn_states = self._attention(new_state_cat, attn_states)
     with vs.variable_scope("attn_output_projection"):
-      output = _linear([lstm_output, new_attns], self._attn_size, True)
+      output = _linear([cell_output, new_attns], self._attn_size, True)
     new_attn_states = array_ops.concat(
         [new_attn_states, array_ops.expand_dims(output, 1)], 1)
     new_attn_states = array_ops.reshape(
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
index 491d87f62d..3496b355b4 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_ops_test.py
@@ -65,7 +65,9 @@ class GatherTreeTest(test.TestCase):
         _ = beams.eval()
 
   def testBadParentValuesOnGPU(self):
-    if not test.is_gpu_available():
+    # Only want to run this test on CUDA devices, as gather_tree is not
+    # registered for SYCL devices.
+    if not test.is_gpu_available(cuda_only=True):
       return
     # (max_time = 4, batch_size = 1, beams = 3)
     # bad parent in beam 1 time 1; appears as a negative index at time 0
diff --git a/tensorflow/contrib/seq2seq/python/ops/decoder.py b/tensorflow/contrib/seq2seq/python/ops/decoder.py
index 4795dfb8c9..fbe53fc60a 100644
--- a/tensorflow/contrib/seq2seq/python/ops/decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/decoder.py
@@ -99,9 +99,9 @@ class Decoder(object):
       name: Name scope for any created operations.
 
     Returns:
-      `(outputs, next_state, next_inputs, finished)`: `outputs` is an instance
-      of BasicDecoderOutput, `next_state` is a (structure of) state tensors and
-      TensorArrays, `next_inputs` is the tensor that should be used as input for
+      `(outputs, next_state, next_inputs, finished)`: `outputs` is an object
+      containing the decoder output, `next_state` is a (structure of) state tensors
+      and TensorArrays, `next_inputs` is the tensor that should be used as input for
       the next step, `finished` is a boolean tensor telling whether the sequence
       is complete, for each sequence in the batch.
     """
diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index d37c632be7..4bde661698 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -836,7 +836,7 @@ with tf.Session() as sess:
   for batch_id in range(num_batches):
     sess.run(names_to_updates.values())
 
-  metric_values = sess.run(name_to_values.values())
+  metric_values = sess.run(names_to_values.values())
   for metric, value in zip(names_to_values.keys(), metric_values):
     print('Metric %s has value: %f' % (metric, value))
 ```
diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index 824ba4c09b..4fa1ccea69 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -1,3 +1,26 @@
 # tfprof: TensorFlow Profiler and Beyond
 
 # Full Document in tensorflow/tools/tfprof/README.md
+
+Author: Xin Pan (xpan@google.com, github: panyx0718), Jon Shlens, Yao Zhang
+
+Consultants: Jon Shlens, Pete Warden
+
+###Major Features
+
+1.  Measure model parameters, float operations, tensor shapes.
+2.  Profile op execution times, requested memory size and device placement.
+3.  Inspect checkpoint tensors' shapes and their values.
+4.  Selectively group, filter, account and order ops.
+
+####tfprof supports 3 views to organize TensorFlow model profiles
+
+    *  code view: Stats are associated your Python codes and organized as call stacks.
+    *  scope view: Stats are organized as name scope hierarchies.
+    *  graph view: Stats are organized as Tensorflow Op graph.
+
+####For each view, there are 3 ways to display outputs:
+
+    *  stdout: Results are written to stdout.
+    *  timeline: Visualized in chrome browser as time series.
+    *  file: Results are dumped to file.
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index 9c59df3117..71468dde37 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -89,7 +89,7 @@ def _run_loop_model():
 class RunMetadataTest(test.TestCase):
 
   def testGPU(self):
-    if not test.is_gpu_available():
+    if not test.is_gpu_available(cuda_only=True):
       return
 
     ops.reset_default_graph()
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 168726a6b3..0f15133d74 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -171,7 +171,6 @@ class TpuEstimator(estimator_lib.Estimator):
   Note: TpuEstimator transforms a global batch size in params to a per-shard
         batch size when calling the input_fn.
   """
-
   def __init__(self,
                model_fn=None,
                model_dir=None,
@@ -196,6 +195,8 @@ class TpuEstimator(estimator_lib.Estimator):
             .format(params[_BATCH_SIZE_KEY], config.tpu_config.num_shards))
 
     if use_tpu:
+      if not isinstance(config, tpu_config.RunConfig):
+        raise ValueError('`config` must be `tpu_config.RunConfig`')
       # Verifies the model_fn signature according to Estimator framework.
       estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
       # We cannot store config and params in this constructor as parent
@@ -204,7 +205,6 @@ class TpuEstimator(estimator_lib.Estimator):
       model_function = wrapped_model_fn(model_fn)
     else:
       model_function = model_fn
-
     super(TpuEstimator, self).__init__(
         model_fn=model_function,
         model_dir=model_dir,
diff --git a/tensorflow/contrib/training/python/training/evaluation.py b/tensorflow/contrib/training/python/training/evaluation.py
index 24b733dd29..a895d90b8e 100644
--- a/tensorflow/contrib/training/python/training/evaluation.py
+++ b/tensorflow/contrib/training/python/training/evaluation.py
@@ -226,7 +226,7 @@ def checkpoints_iterator(checkpoint_dir,
 
   This behavior gives control to callers on what to do if checkpoints do not
   come fast enough or stop being generated.  For example, if callers have a way
-  to detect that the training has stopped and know that no new new checkpoints
+  to detect that the training has stopped and know that no new checkpoints
   will be generated, they can provide a `timeout_fn` that returns `True` when
   the training has stopped.  If they know that the training is still going on
   they return `False` instead.
diff --git a/tensorflow/contrib/verbs/rdma.cc b/tensorflow/contrib/verbs/rdma.cc
index bc687be0ab..6f3a616fe8 100644
--- a/tensorflow/contrib/verbs/rdma.cc
+++ b/tensorflow/contrib/verbs/rdma.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_util.h"
+#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h"
 #include "tensorflow/core/distributed_runtime/session_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
@@ -683,7 +684,6 @@ void RdmaTensorBuffer::SendNextItem() {
                          << " error message: " << status.error_message();
       size_t buffer_size = RdmaMessage::kMessageTotalBytes;
       size_t tensor_bytes = 0;
-      TensorProto proto;
       // Figures out which device the tensor is hosted on.
       Device* src_dev = nullptr;
       Status s = channel_->adapter_->worker_env_->device_mgr->LookupDevice(
@@ -703,21 +703,47 @@ void RdmaTensorBuffer::SendNextItem() {
       CHECK(s.ok()) << "dst device not found";
       AllocatorAttributes dst_alloc_attr;
       dst_alloc_attr.set_on_host(true);
+
+      bool can_memcpy = DataTypeCanUseMemcpy(in.dtype());
       // string tensor needs to be serialized
+      Tensor copy;
+      StringPiece copy_buf;
+      TensorProto proto;
       if (src_dev->tensorflow_gpu_device_info() &&
           (!send_args.alloc_attrs.on_host())) {
         CHECK(send_args.device_context)
-            << "send dev name: " << src_dev->name()
-            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
-        // "val" is on a GPU. Uses GPUUtil to fill the proto.
-        s = VerbsUtil::SetProtoFromGPUSync(
-            in, src_dev, send_args.device_context, &proto, is_dead);
-        CHECK(s.ok()) << "set proto from gpu sync";
+          << "send dev name: " << src_dev->name()
+          << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
+
+        if (can_memcpy) {
+          AllocatorAttributes host_alloc_attrs;
+          host_alloc_attrs.set_gpu_compatible(true);
+          host_alloc_attrs.set_on_host(true);
+          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
+          copy = Tensor(alloc, in.dtype(), in.shape());
+          s = VerbsUtil::CopyGPUTensorToCPUSync(
+              src_dev, send_args.device_context, &in, &copy);
+          CHECK(s.ok()) << "copy tensor from gpu sync";
+          copy_buf = copy.tensor_data();
+        } else {
+          // "val" is on a GPU. Uses GPUUtil to fill the proto.
+          s = VerbsUtil::SetProtoFromGPUSync(
+              in, src_dev, send_args.device_context, &proto, is_dead);
+          CHECK(s.ok()) << "set proto from gpu sync";
+        }
       } else {
         // tensor is in CPU memory.
-        in.AsProtoTensorContent(&proto);
+        if (can_memcpy) {
+          copy_buf = in.tensor_data();
+        } else {
+          in.AsProtoTensorContent(&proto);
+        }
+      }
+      if (can_memcpy) {
+        tensor_bytes = in.TotalBytes();
+      } else {
+        tensor_bytes = proto.ByteSize();
       }
-      tensor_bytes = proto.ByteSize();
       // maybe some margin for string tensor?
       buffer_size += tensor_bytes;
       // prepare message
@@ -771,7 +797,16 @@ void RdmaTensorBuffer::SendNextItem() {
               static_cast<void*>(static_cast<char*>(buffer_) +
                                  RdmaMessage::kTensorBufferStartIndex);
           CHECK(tensor_bytes + RdmaMessage::kTensorBufferStartIndex <= size_);
-          proto.SerializeToArray(output, tensor_bytes);
+          if (can_memcpy) {
+            CHECK(copy_buf.size() == tensor_bytes)
+               << "unexpected tensor size: "
+               << copy_buf.size()
+               << " != "
+               << tensor_bytes;
+            memcpy(output, copy_buf.data(), tensor_bytes);
+          } else {
+            proto.SerializeToArray(output, tensor_bytes);
+          }
         } else {
           buffer_size = RdmaMessage::kMessageTotalBytes;
         }
diff --git a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
index 5871400f26..9ea696589a 100644
--- a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
+++ b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
+#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -99,12 +100,40 @@ void RdmaRemoteRendezvous::RecvFromRemoteAsync(
     if (!rm.is_dead_) {
       void* input = static_cast<char*>(rb->buffer_) +
                     RdmaMessage::kTensorBufferStartIndex;
-      TensorProto proto;
-      CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
-            rb->size_);
-      CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
-          << "fail to parse proto from array";
-      s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
+      bool can_memcpy = DataTypeCanUseMemcpy(rm.data_type_);
+      if (can_memcpy) {
+        if (dst_dev->tensorflow_gpu_device_info() &&
+            (!recv_args.alloc_attrs.on_host())) {
+          CHECK(recv_args.device_context)
+            << "send dev name: " << src_dev->name()
+            << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
+          Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0);
+          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
+          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
+
+          Allocator* dst_alloc = dst_dev->GetAllocator(recv_args.alloc_attrs);
+          Tensor gpu_copy(dst_alloc, rm.data_type_, rm.tensor_shape_);
+          s = VerbsUtil::CopyCPUTensorToGPUSync(&copy, recv_args.device_context,
+                                                dst_dev, &gpu_copy);
+          CHECK(s.ok()) << "copy tensor to gpu sync";
+          val = std::move(gpu_copy);
+        } else {
+          AllocatorAttributes host_alloc_attrs;
+          host_alloc_attrs.set_gpu_compatible(true);
+          host_alloc_attrs.set_on_host(true);
+          Allocator* alloc = dst_dev->GetAllocator(host_alloc_attrs);
+          Tensor copy(alloc, rm.data_type_, rm.tensor_shape_);
+          memcpy(DMAHelper::base(&copy), input, rm.tensor_bytes_);
+          val = std::move(copy);
+        }
+      } else {
+        TensorProto proto;
+        CHECK(rm.tensor_bytes_ + RdmaMessage::kTensorBufferStartIndex <=
+              rb->size_);
+        CHECK(ParseProtoUnlimited(&proto, input, rm.tensor_bytes_))
+            << "fail to parse proto from array";
+        s = dst_dev->MakeTensorFromProto(proto, recv_args.alloc_attrs, &val);
+      }
     }
 
     rc->RemoveRecvCallback(key_with_step_id);
diff --git a/tensorflow/contrib/verbs/verbs_util.cc b/tensorflow/contrib/verbs/verbs_util.cc
index c3350f7958..76e44d34a9 100644
--- a/tensorflow/contrib/verbs/verbs_util.cc
+++ b/tensorflow/contrib/verbs/verbs_util.cc
@@ -20,6 +20,40 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 namespace tensorflow {
 
+// static sync wrapper:
+Status VerbsUtil::CopyGPUTensorToCPUSync(Device* gpu_device,
+                              const DeviceContext* device_context,
+                              const Tensor* gpu_tensor,
+                              Tensor* cpu_tensor) {
+  Notification n;
+  Status status;
+  GPUUtil::CopyGPUTensorToCPU(gpu_device, device_context,
+                              gpu_tensor, cpu_tensor,
+                              [&n, &status](const Status& s) {
+                                status = s;
+                                n.Notify();
+                              });
+  n.WaitForNotification();
+  return status;
+}
+
+// static sync wrapper:
+Status VerbsUtil::CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
+                                         const DeviceContext* device_context,
+                                         Device* gpu_device,
+                                         Tensor* gpu_tensor) {
+  Notification n;
+  Status status;
+  GPUUtil::CopyCPUTensorToGPU(cpu_tensor, device_context,
+                              gpu_device, gpu_tensor,
+                              [&n, &status](const Status& s) {
+                                status = s;
+                                n.Notify();
+                              });
+  n.WaitForNotification();
+  return status;
+}
+
 // static sync wrapper:
 Status VerbsUtil::SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                       const DeviceContext* device_context,
diff --git a/tensorflow/contrib/verbs/verbs_util.h b/tensorflow/contrib/verbs/verbs_util.h
index cbc01adae4..d9da396228 100644
--- a/tensorflow/contrib/verbs/verbs_util.h
+++ b/tensorflow/contrib/verbs/verbs_util.h
@@ -28,6 +28,16 @@ class TensorProto;
 
 class VerbsUtil {
  public:
+  // synchronous wrapper of CopyGPUTensorToCPU
+  static Status CopyGPUTensorToCPUSync(Device* gpu_device,
+                                       const DeviceContext* device_context,
+                                       const Tensor* gpu_tensor,
+                                       Tensor* cpu_tensor);
+  // synchronous wrapper of CopyCPUTensorToGPU
+  static Status CopyCPUTensorToGPUSync(const Tensor* cpu_tensor,
+                                       const DeviceContext* device_context,
+                                       Device* gpu_device,
+                                       Tensor* gpu_tensor);
   // synchronous wrapper of SetProtoFromGPU
   static Status SetProtoFromGPUSync(const Tensor& tensor, Device* dev,
                                     const DeviceContext* device_context,
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 9f1554b5b6..24b0f702b3 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -121,7 +121,6 @@ load(
     "//third_party/mkl:build_defs.bzl",
     "if_mkl",
 )
-load("@local_config_sycl//sycl:build_defs.bzl", "if_sycl")
 
 # -----------------------------------------------------------------------------
 # Public targets
@@ -820,7 +819,7 @@ cc_library(
         ":test",
         "//tensorflow/core/kernels:constant_op",
         "//tensorflow/core/kernels:ops_util",
-        "//tensorflow/core/platform/default/build_config:gtest",  # + if_sycl([":sycl_runtime"])
+        "//tensorflow/core/platform/default/build_config:gtest",
     ],
 )
 
@@ -1854,7 +1853,7 @@ cc_library(
         "common_runtime/sycl/sycl_device_context.h",
     ]),
     copts = tf_copts(),
-    linkstatic = 1,
+    linkstatic = 0,
     deps = [
         ":core_cpu",
         ":core_cpu_internal",
@@ -1866,7 +1865,7 @@ cc_library(
         "//third_party/eigen3",
         "@local_config_sycl//sycl:sycl",
     ],
-    alwayslink = 1,
+    alwayslink = 0,
 )
 
 # -----------------------------------------------------------------------------
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index 21a20bcc4d..4b951691fb 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -654,11 +654,11 @@ Status DirectSession::Run(const RunOptions& run_options,
 
   // If requested via RunOptions, output the partition graphs.
   if (run_options.output_partition_graphs()) {
-    protobuf::RepeatedPtrField<GraphDef>* parition_graph_defs =
+    protobuf::RepeatedPtrField<GraphDef>* partition_graph_defs =
         run_metadata->mutable_partition_graphs();
     for (const PerPartitionExecutorsAndLib& exec_and_lib :
          executors_and_keys->items) {
-      GraphDef* partition_graph_def = parition_graph_defs->Add();
+      GraphDef* partition_graph_def = partition_graph_defs->Add();
       exec_and_lib.graph->ToGraphDef(partition_graph_def);
     }
   }
diff --git a/tensorflow/core/debug/grpc_session_debug_test.cc b/tensorflow/core/debug/grpc_session_debug_test.cc
index 3827596a67..d6f35fe24c 100644
--- a/tensorflow/core/debug/grpc_session_debug_test.cc
+++ b/tensorflow/core/debug/grpc_session_debug_test.cc
@@ -279,9 +279,12 @@ TEST_F(GrpcSessionDebugTest, MultiDevices_String) {
 
         DeleteDumpDir();
       } else {
+        // CUDA and SYCL devices do not have an Identity op for strings
         LOG(ERROR) << "Error: " << s;
         ASSERT_TRUE((a_dev.device_type() == DEVICE_GPU) ||
-                    (b_dev.device_type() == DEVICE_GPU));
+                    (a_dev.device_type() == DEVICE_SYCL) ||
+                    (b_dev.device_type() == DEVICE_GPU) ||
+                    (b_dev.device_type() == DEVICE_SYCL));
         ASSERT_FALSE(s.ok());
       }
     }
diff --git a/tensorflow/core/distributed_runtime/worker_cache_logger.cc b/tensorflow/core/distributed_runtime/worker_cache_logger.cc
index 5ca1d92a81..ffcbc50a2a 100644
--- a/tensorflow/core/distributed_runtime/worker_cache_logger.cc
+++ b/tensorflow/core/distributed_runtime/worker_cache_logger.cc
@@ -88,27 +88,39 @@ void WorkerCacheLogger::RecordRecvTensor(int64 step_id, int64 start_usecs,
                                          const string& src_device,
                                          const string& dst_device,
                                          int64 bytes) {
-  NodeExecStats* ns = new NodeExecStats;
-  ns->set_node_name("RecvTensor");
-  string byte_string = strings::StrCat("[", bytes, "B] ");
-  if (bytes >= 0.1 * 1048576.0) {
-    byte_string = strings::Printf("[%.1fMB] ", bytes / 1048576.0);
-  }
-  ns->set_timeline_label(strings::StrCat(byte_string, tensor_name, " from ",
-                                         src_device, " to ", dst_device));
-  ns->set_all_start_micros(start_usecs);
-  ns->set_op_start_rel_micros(0);
-  int64 elapsed = end_usecs - start_usecs;
-  ns->set_op_end_rel_micros(elapsed);
-  ns->set_all_end_rel_micros(elapsed);
-  NodeOutput* no = ns->add_output();
-  no->set_slot(0);
-  // TODO(tucker): Maybe set the dimensions too, but then they'll
-  // need to be passed in.
-  no->mutable_tensor_description()
-      ->mutable_allocation_description()
-      ->set_requested_bytes(bytes);
-  Save(dst_device, step_id, ns);
+  RecordDataTransfer(step_id, start_usecs, end_usecs, tensor_name, src_device,
+    dst_device, bytes, "", "RecvTensor");
 }
 
+void WorkerCacheLogger::RecordDataTransfer(int64 step_id, int64 start_usecs,
+                                           int64 end_usecs,
+                                           const string& tensor_name,
+                                           const string& src_device,
+                                           const string& dst_device,
+                                           int64 bytes,
+                                           const string& details,
+                                           const string& transfer_method_name){
+  NodeExecStats* ns = new NodeExecStats;
+    ns->set_node_name(transfer_method_name);
+    string byte_string = strings::StrCat("[", bytes, "B] ");
+    if (bytes >= 0.1 * 1048576.0) {
+      byte_string = strings::Printf("[%.1fMB] ", bytes / 1048576.0);
+    }
+    ns->set_timeline_label(strings::StrCat(byte_string, tensor_name, " from ",
+                                           src_device, " to ", dst_device,
+                                           details));
+    ns->set_all_start_micros(start_usecs);
+    ns->set_op_start_rel_micros(0);
+    int64 elapsed = end_usecs - start_usecs;
+    ns->set_op_end_rel_micros(elapsed);
+    ns->set_all_end_rel_micros(elapsed);
+    NodeOutput* no = ns->add_output();
+    no->set_slot(0);
+    // TODO(tucker): Maybe set the dimensions too, but then they'll
+    // need to be passed in.
+    no->mutable_tensor_description()
+        ->mutable_allocation_description()
+        ->set_requested_bytes(bytes);
+    Save(dst_device, step_id, ns);
+  }
 }  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/worker_cache_logger.h b/tensorflow/core/distributed_runtime/worker_cache_logger.h
index a92590a176..00846c25fe 100644
--- a/tensorflow/core/distributed_runtime/worker_cache_logger.h
+++ b/tensorflow/core/distributed_runtime/worker_cache_logger.h
@@ -60,6 +60,14 @@ class WorkerCacheLogger {
                         const string& tensor_name, const string& src_device,
                         const string& dst_device, int64 bytes);
 
+  // Generates a NodeExecStats record with the given data, and saves for
+  // later retrieval by RetrieveLogs().
+  void RecordDataTransfer(int64 step_id, int64 start_usecs, int64 end_usecs,
+                          const string& tensor_name, const string& src_device,
+                          const string& dst_device, int64 bytes,
+                          const string& details,
+                          const string& transfer_method_name);
+
  private:
   mutex count_mu_;
   int32 want_logging_count_ GUARDED_BY(count_mu_) = 0;
diff --git a/tensorflow/core/framework/graph_def_util.h b/tensorflow/core/framework/graph_def_util.h
index 56355eaf36..950737c39a 100644
--- a/tensorflow/core/framework/graph_def_util.h
+++ b/tensorflow/core/framework/graph_def_util.h
@@ -62,7 +62,7 @@ Status AddDefaultAttrsToGraphDef(GraphDef* graph_def,
 // attr with a default was added). Note that this will not affect
 // attrs with non-default values, so you must run a
 // ValidateGraphDef...() function to see if the result is in fact
-// compatible. If not nulllptr, the op/attr pairs that were removed
+// compatible. If not nullptr, the op/attr pairs that were removed
 // are added to '*op_attr_removed'.
 //
 // Expected usage, for a producer that wants to prepare a graph for
diff --git a/tensorflow/core/framework/op.h b/tensorflow/core/framework/op.h
index c5a0983a54..a4dd06de45 100644
--- a/tensorflow/core/framework/op.h
+++ b/tensorflow/core/framework/op.h
@@ -205,7 +205,6 @@ class OpDefBuilderWrapper;
 template <>
 class OpDefBuilderWrapper<true> {
  public:
-  typedef OpDefBuilderWrapper<true> WrapperType;
   OpDefBuilderWrapper(const char name[]) : builder_(name) {}
   OpDefBuilderWrapper<true>& Attr(StringPiece spec) {
     builder_.Attr(spec);
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index f8a5946116..fd5c8fdbb8 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -672,7 +672,7 @@ class OpKernelContext {
   void forward_ref_input_to_ref_output(int input_index, int output_index);
 
   // Returns true when an alias to input[input_index], reshaped to output_shape,
-  // which is is safe to use for in-place computation was written to *output.
+  // which is safe to use for in-place computation was written to *output.
   // Returns false if input[input_index] has a refcount greater than one, or if
   // its type does not match the expected output type of output[output_index],
   // or the number of elements in input[input_index] does not equal the number
diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h
index 49eecc0b08..a164fe61b5 100644
--- a/tensorflow/core/framework/tensor.h
+++ b/tensorflow/core/framework/tensor.h
@@ -307,7 +307,7 @@ class Tensor {
   /// Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing the
   /// first 'begin' Tensor dimensions into the first dimension of the result and
   /// the Tensor dimensions of the last dims() - 'begin' - NDIMS into the last
-  /// dimension of the result. If 'begin' < 0 then the the |'begin'| leading
+  /// dimension of the result. If 'begin' < 0 then the |'begin'| leading
   /// dimensions of size 1 will be added. If 'begin' + NDIMS > dims() then
   /// 'begin' + NDIMS - dims() trailing dimensions of size 1 will be added.
   template <typename T, size_t NDIMS = 3>
diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc
index 94741a11ff..625780e7c9 100644
--- a/tensorflow/core/graph/mkl_layout_pass.cc
+++ b/tensorflow/core/graph/mkl_layout_pass.cc
@@ -247,16 +247,10 @@ namespace tensorflow {
 //
 //           P = Conv2DWithBiasBackpropBias(O, O_m)
 //
-// 'Distance' between input of BiasAddGrad and _MklConv2D in terms of hops is
-// the context matching depth. If _MklConv2DWithBias is not within the context
-// matching depth, then we do not rewrite BiasAddGrad.
-
-// How many hops do we search for matching node in the backward dataflow graph?
-// We use maxhop of 10 based on empirical observations. Also, these are
-// maxhops in backward data-flow graph. Since input of forward nodes (Conv2D)
-// directly goes to backward nodes, we do not expect the hop-distance
-// would be more than few nodes.
-static size_t kNodeMergeContextMaxDepth = 10;
+// Rewrite of BiasAddGrad into Conv2DWithBiasBackpropBias takes place depending
+// on the matching 'context'. The term context is loosely related to which
+// forward op is _associated_ to BiasAddGrad. If it is _MklConv2DWithBias then
+// we consider it Conv2D context; if it is MatMul, then it is MatMul context.
 
 class MklLayoutRewritePass : public GraphOptimizationPass {
  public:
@@ -280,6 +274,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     csinfo_.max_pool = "MaxPool";
     csinfo_.max_pool_grad = "MaxPoolGrad";
     csinfo_.mkl_conv2d = "_MklConv2D";
+    csinfo_.mkl_conv2d_grad_input = "_MklConv2DBackpropInput";
+    csinfo_.mkl_conv2d_grad_filter = "_MklConv2DBackpropFilter";
     csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias";
     csinfo_.mkl_conv2d_with_bias_backprop_bias =
                                    "_MklConv2DWithBiasBackpropBias";
@@ -360,16 +356,12 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     minfo_.push_back({csinfo_.mkl_conv2d, csinfo_.bias_add, 0,
                       csinfo_.mkl_conv2d_with_bias});
 
-    // We use maxhop of 10 based on empirical observations. Also, these are
-    // maxhops in backward data-flow graph. Since input of forward nodes
-    // (Conv2D) directly goes to backward nodes, we do not expect the
-    // hop-distance would be more than few nodes.
     biasaddgrad_matmul_context_ = {csinfo_.bias_add_grad, csinfo_.matmul,
-                                   kNodeMergeContextMaxDepth};
+                                   IsBiasAddGradInMatMulContext};
 
     biasaddgrad_conv2dwithbias_context_ = {csinfo_.bias_add_grad,
                                    csinfo_.mkl_conv2d_with_bias,
-                                   kNodeMergeContextMaxDepth};
+                                   IsBiasAddGradInConv2DWithBiasContext};
 
     cinfo_.push_back(&biasaddgrad_matmul_context_);
     cinfo_.push_back(&biasaddgrad_conv2dwithbias_context_);
@@ -392,9 +384,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string node;     // Name of the node to be rewritten
     string fwd;      // Name of the node in the forward pass that this node
                      // corresponds to
-    size_t max_hop;  // Maximum number of hops the fwd is located
-                     // from this node. If the fwd is farther than max_hop
-                     // then we do not rewrite the node.
+    std::function<bool(const Node*, const Node**, void* c)> context_match_fn;
   } ContextInfo;
 
   /// Structure to specify the name of an original node, its new name after
@@ -438,7 +428,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
 
   /// Structure to store all constant strings
   /// NOTE: names are alphabetically sorted.
-  struct {
+  typedef struct {
     string avg_pool;
     string avg_pool_grad;
     string bias_add;
@@ -457,13 +447,15 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     string max_pool;
     string max_pool_grad;
     string mkl_conv2d;
+    string mkl_conv2d_grad_input;
+    string mkl_conv2d_grad_filter;
     string mkl_conv2d_with_bias;
     string mkl_conv2d_with_bias_backprop_bias;
     string relu;
     string relu_grad;
     string reshape;
     string split;
-  } csinfo_;
+  } ConstStringsInfo;
 
  private:
   /// Maintain info about nodes to rewrite
@@ -478,6 +470,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   /// Maintain info about nodes to rewrite
   static std::vector<ContextInfo*> cinfo_;
 
+  /// Maintain structure of constant strings
+  static ConstStringsInfo csinfo_;
+
   /// Context variables used in referencing rules
   static ContextInfo biasaddgrad_matmul_context_;
   static ContextInfo biasaddgrad_conv2dwithbias_context_;
@@ -629,6 +624,173 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
     return false;
   }
 
+  // Is BiasAddGrad node in 'n' is associated with Conv2DWithBias node
+  // specified in contextinfo 'ci'. Function updates fwd_node to point
+  // to Conv2DWithBias node if 'n' is associated with Conv2DWithBias.
+  //
+  // Association checks for one of the following graphs:
+  //
+  // Graph A:
+  //
+  // _ = Conv2DWithBias(F, I, _)
+  // ..
+  // _ = Conv2DBackpropFilter(F, _, G)
+  // _ = Conv2DBackpropInput(_, I, G)
+  // _ = BiasAddGrad(G)
+  //
+  // OR
+  //
+  // Graph B:
+  //
+  // _ = Conv2DWithBias(F, _, _)
+  // ..
+  // _ = Conv2DBackpropFilter(F, _, G)
+  // _ = BiasAddGrad(G)
+  //
+  // Here F, G, and I are graph nodes; _ represents graph nodes that we
+  // don't care here.
+  //
+  // @return - true (if BiasAddGrad is associated with Conv2DWithBias);
+  //           false otherwise.
+  static bool IsBiasAddGradInConv2DWithBiasContext(const Node* n,
+                                                   const Node** fwd_node,
+                                                   void* ci) {
+    CHECK_NOTNULL(n);
+    CHECK_NOTNULL(fwd_node);
+    CHECK_NOTNULL(ci);
+    *fwd_node = nullptr;
+
+    CHECK_EQ(n->type_string(), csinfo_.bias_add_grad);
+
+    // Get the only 1 input of BiasAddGrad.
+    CHECK_EQ(n->num_inputs(), 1);
+    const Node* bias_add_grad_inp = nullptr;
+    TF_CHECK_OK(n->input_node(0, &bias_add_grad_inp));
+    CHECK_NOTNULL(bias_add_grad_inp);
+
+    // Check if this input also goes to BackpropFilter and BackpropInput
+    // as 3rd input.
+    bool found_backprop_input = false;
+    bool found_backprop_filter = false;
+    Node* backprop_filter_node = nullptr;
+    Node* backprop_input_node = nullptr;
+
+    for (const Edge* e : bias_add_grad_inp->out_edges()) {
+      Node* third_input = nullptr;
+      if (e->dst()->type_string() == csinfo_.conv2d_grad_input ||
+          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_input) {
+        // Third input (index 2) of BackpropInput
+        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
+        // Third input (index 2) of BackpropInput must be same as the input
+        // of BiasAddGrad.
+        if (third_input == bias_add_grad_inp) {
+          found_backprop_input = true;
+          backprop_input_node = e->dst();
+        }
+      }
+
+      if (e->dst()->type_string() == csinfo_.conv2d_grad_filter ||
+          e->dst()->type_string() == csinfo_.mkl_conv2d_grad_filter) {
+        // Third input (index 2) of BackpropFilter
+        TF_CHECK_OK(e->dst()->input_node(2, &third_input));
+        // Third input (index 2) of BackpropFilter must be same as the input
+        // of BiasAddGrad.
+        if (third_input == bias_add_grad_inp) {
+          found_backprop_filter = true;
+          backprop_filter_node = e->dst();
+        }
+      }
+
+      // If we found both the nodes, then we can stop the search.
+      if (found_backprop_input && found_backprop_filter) {
+        break;
+      }
+    }
+
+    // If BackpropFilter node is not found, then this is not
+    // Conv2DWithBias context. For 2nd graph in the example above, only
+    // BackpropFilter would be present.
+    if (!found_backprop_filter) {
+      return false;
+    }
+
+    // Otherwise, we found the nodes.
+    CHECK_NOTNULL(backprop_filter_node);
+    if (found_backprop_input) {
+      CHECK_NOTNULL(backprop_input_node);
+    }
+
+    // Now that we confirmed that this is Conv2DWithBias context, we need to
+    // get access to the forward node (Conv2DWithBias). 2nd input of
+    // Conv2DWithBias is same as the 2nd input of Conv2DBackpropInput; 1st
+    // input of Conv2DWithBias is same as the 1st input of Conv2DBackpropFilter
+    // (This comes from definition of gradient computation for Conv2D).
+    if (found_backprop_input) {
+      // Graph A in the example.
+      Node* second_inp_of_input = nullptr;
+      Node* first_inp_of_filter = nullptr;
+      TF_CHECK_OK(backprop_input_node->input_node(1, &second_inp_of_input));
+      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
+      CHECK_NOTNULL(second_inp_of_input);
+      CHECK_NOTNULL(first_inp_of_filter);
+
+      // Now we need to find out Conv2DWithBias node from these input nodes.
+      // Conv2DWithBias node is the node that accepts both the nodes
+      // second_inp_of_input and first_inp_of_filter in 2nd and 1st input slots.
+      for (const Edge* fe : first_inp_of_filter->out_edges()) {
+        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+            fe->dst_input() == 0) {
+          for (const Edge* ie : second_inp_of_input->out_edges()) {
+            if (ie->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+                ie->dst_input() == 1 && fe->dst() == ie->dst()) {
+              VLOG(1) << "MklLayoutRewritePass: found "
+                      << fe->dst()->DebugString()
+                      << " as the forward node for matching context, backward"
+                      << " node is: " << n->DebugString();
+              *fwd_node = fe->dst();
+              return true;
+            }
+          }
+        }
+      }
+    } else {
+      // We did not find BackpropInput, so we work with BackpropFilter only.
+      // Graph B in the example.
+      Node* first_inp_of_filter = nullptr;
+      TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
+      CHECK_NOTNULL(first_inp_of_filter);
+
+      // Now we need to find out Conv2DWithBias node from first input of
+      // BackpropFIlter. Conv2DWithBias node is the node that accepts
+      // first_inp_of_filter in 1st input slot.
+      for (const Edge* fe : first_inp_of_filter->out_edges()) {
+        if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
+            fe->dst_input() == 0) {
+          VLOG(1) << "MklLayoutRewritePass: found "
+                  << fe->dst()->DebugString()
+                  << " as the forward node for matching context, backward"
+                  << " node is: " << n->DebugString();
+          *fwd_node = fe->dst();
+          return true;
+        }
+      }
+    }
+
+    return false;
+  }
+
+  // Is BiasAddGrad node in 'n' is associated with MatMul node
+  // specified in contextinfo 'ci'. Function does not update fwd_node.
+  //
+  // @return - true (if BiasAddGrad is associated with MatMul);
+  //           false otherwise.
+  static bool IsBiasAddGradInMatMulContext(const Node* n,
+                                           const Node** fwd_node,
+                                           void* ci) {
+    return (!IsBiasAddGradInConv2DWithBiasContext(n, fwd_node, ci));
+  }
+
+
   // Rewrite rule that uses context-information for matching,
   // used in scenario 2.
   //
@@ -639,8 +801,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
   static bool ContextMatchRewrite(const Node* n, const ContextInfo* c);
 
   // Helper function that searches the matching contextinfo for the node.
-  // Implements depth-first search in the data dependence graph for the
-  // gradient op in the backward direction.
   //
   // @input n - Node (gradient op) whose contextinfo is to be searched,
   //        fwd_node - pointer to node from the forward pass that this node
@@ -788,6 +948,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
                                    Node* orig_node);
 };
 
+MklLayoutRewritePass::ConstStringsInfo MklLayoutRewritePass::csinfo_;
 MklLayoutRewritePass::ContextInfo
   MklLayoutRewritePass::biasaddgrad_conv2dwithbias_context_;
 MklLayoutRewritePass::ContextInfo
@@ -1667,12 +1828,12 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr<Graph>* g,
   const ContextInfo* ci = nullptr;
   bool is_context_based_rewrite = false;
   if ((ci = SearchMatchingContext(orig_node, &fwd_node)) != nullptr) {
-    CHECK_NOTNULL(fwd_node);
     is_context_based_rewrite = true;
 
     // Sanity checks for context-based rewrite (if any)
     if (orig_node->type_string() == csinfo_.bias_add_grad &&
         ri->new_name == csinfo_.mkl_conv2d_with_bias_backprop_bias) {
+      CHECK_NOTNULL(fwd_node);
       DataType orig_T, ctx_T;
       string orig_data_format, ctx_data_format;
       TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &orig_T));
@@ -1784,69 +1945,17 @@ MklLayoutRewritePass::SearchMatchingContext(const Node* n,
   CHECK_NOTNULL(fwd_node);
   *fwd_node = nullptr;
 
-  // Search for matching contextinfo based on node name.
-  // There could be more than one matching contextinfos.
-  bool is_matching_cinfo_found = false;
-  std::vector<const ContextInfo*> mci;
+  // Search for matching contextinfo based on node name and call
+  // callback function using matching contextinfo.
+  // There could be more than one matching contextinfos but whichever
+  // matches first is returned.
   for (auto ci = cinfo_.cbegin(); ci != cinfo_.cend(); ++ci) {
-    if (n->type_string() == (*ci)->node) {
-      mci.push_back(*ci);
-      is_matching_cinfo_found = true;
+    if (n->type_string() == (*ci)->node &&
+        (*ci)->context_match_fn(n, fwd_node, *ci)) {
+      VLOG(1) << "Found context as matching: " << (*ci)->fwd;
+      return *ci;
     }
   }
-  // If no matching contextinfo is found, return immediately.
-  if (!is_matching_cinfo_found) {
-    return nullptr;
-  }
-
-  VLOG(1) << "MklLayoutRewritePass: Searching graph for: " << n->type_string()
-          << " in backwards.";
-
-  // Now we will check for forward op name for context info in data
-  // flow graph. Get the max hops we should search for the fwd node.
-  // We are now going to search (breadth-first) backwards in data
-  // dependence graph (for up to max hops) from n for the node
-  // specified in fwd.
-  // queue to maintain nodes to be visited and depth info for
-  // breadth-first search
-  std::queue<std::pair<const Node*, int>> nqueue;
-  const Node* curr_node = n;
-  size_t curr_depth = 0;
-  nqueue.push(std::make_pair(curr_node, curr_depth));
-
-  while (curr_depth < kNodeMergeContextMaxDepth && !nqueue.empty()) {
-    std::pair<const Node*, int> curr_pair = nqueue.front();
-    nqueue.pop();
-
-    std::set<const Node*> visited_nodes;
-    curr_node = curr_pair.first;
-    curr_depth = curr_pair.second;
-    CHECK_NOTNULL(curr_node);
-
-    VLOG(1) << "MklLayoutRewritePass: Visiting node: "
-            << curr_node->type_string() << " at depth: " << curr_depth
-            << " for node: " << n->type_string();
-
-    // If we find a match, we return immediately.
-    for (const ContextInfo* ci : mci) {
-      if (curr_node->type_string() == ci->fwd) {
-        *fwd_node = curr_node;
-        return ci;
-      }
-    }
-
-    // Else we explore backward edges from current node.
-    // Add the source nodes of all incoming edges of the node to the queue.
-    for (const Edge* e : curr_node->in_edges()) {
-      // We do not visit already visited node.
-      if (visited_nodes.find(e->src()) == visited_nodes.end()) {
-        // Depth of these nodes is 1 more than the depth of current node.
-        nqueue.push(std::make_pair(e->src(), curr_depth + 1));
-        visited_nodes.insert(e->src());
-      }
-    }
-  } /* while */
-
   return nullptr;
 }
 
diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc
index 3c4a5263af..efbe2134e0 100644
--- a/tensorflow/core/graph/mkl_layout_pass_test.cc
+++ b/tensorflow/core/graph/mkl_layout_pass_test.cc
@@ -345,7 +345,8 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_AttrMismatch) {
 // Test set 2: _MklConv2D..BiasAddGrad -> _MklConv2DWithBiasBackpropBias
 // rewrite tests
 
-// D=_MklConv2D(A,M,B,N,C,O); E=Sub(D,A); F=BiasAddGrad(E)
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput
 TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
   InitGraph(
       "node { name: 'A' op: 'Input'}"
@@ -364,16 +365,255 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) {
       "node { name: 'E' op: 'Sub'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
       " input: ['D', 'A']}"
-      "node { name: 'F' op: 'BiasAddGrad'"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
       " attr { key: 'data_format'      value { s: 'NCHW' } }"
       " input: ['E'] }");
   EXPECT_EQ(DoMklLayoutOptimizationPass(),
             "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
-            "E(Sub);F(_MklConv2DWithBiasBackpropBias);M(_MklInput);"
-            "N(_MklInput);O(_MklInput)|A->D;A->E:1;B->D:1;C->D:2;D->E;"
-            "DMT/_0->F:1;E->F;E:control->DMT/_0:control;M->D:3;N->D:4;"
-            "O->D:5");
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(_MklConv2DWithBiasBackpropBias);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G;B->D:1;"
+            "B->I:1;C->D:2;D->E;DMT/_0->J:1;E->G:2;E->I:2;E->J;"
+            "E:control->DMT/_0:control;F->G:1;H->I;M->D:3;M->G:3;M->I:3;"
+            "N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput. But nodes do not match criteria for rewrite. So
+// rewrite should not happen.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative1) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
+            "B->I:1;C->D:2;D->E;E->G;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
+            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter
+// and BackpropInput. But nodes do not match criteria for rewrite. So
+// rewrite should not happen.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative2) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'Int32Input'}"
+      "node { name: 'I' op: '_MklConv2DBackpropInput'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['H', 'B', 'E', 'M', 'N', 'O']}"
+      "node { name: 'J' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(Int32Input);"
+            "I(_MklConv2DBackpropInput);J(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
+            "B->I:1;C->D:2;D->E;E->G:2;E->I:2;E->J;F->G:1;H->I;M->D:3;M->G:3;"
+            "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5");
+}
+
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Positive) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);"
+            "H(_MklConv2DWithBiasBackpropBias);M(_MklInput);N(_MklInput);"
+            "O(_MklInput)|A->D;A->E:1;A->G;B->D:1;C->D:2;D->E;DMT/_0->H:1;"
+            "E->G:2;E->H;E:control->DMT/_0:control;F->G:1;M->D:3;M->G:3;"
+            "N->D:4;N->G:4;O->D:5;O->G:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+// But BackpropFilter node inputs do not satisfy criteria for rewrite.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative1) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['E', 'F', 'A', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D;A->E:1;A->G:2;B->D:1;"
+            "C->D:2;D->E;E->G;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
+            "O->G:5");
+}
+
+// BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only
+// But BackpropFilter node inputs do not satisfy criteria for rewrite.
+TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Negative2) {
+  InitGraph(
+      "node { name: 'A' op: 'Input'}"
+      "node { name: 'B' op: 'Input'}"
+      "node { name: 'C' op: 'Input'}"
+      "node { name: 'M' op: '_MklInput'}"
+      "node { name: 'N' op: '_MklInput'}"
+      "node { name: 'O' op: '_MklInput'}"
+      "node { name: 'D' op: '_MklConv2DWithBias'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['B', 'A', 'C', 'M', 'N', 'O']}"
+      "node { name: 'E' op: 'Sub'"
+      " attr {key: 'T'                 value { type: DT_FLOAT } }"
+      " input: ['D', 'A']}"
+      "node { name: 'F' op: 'Int32Input'}"
+      "node { name: 'G' op: '_MklConv2DBackpropFilter'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
+      " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
+      " attr { key: 'padding'          value { s: 'SAME' } }"
+      " input: ['A', 'F', 'E', 'M', 'N', 'O'] }"
+      "node { name: 'H' op: 'BiasAddGrad'"
+      " attr { key: 'T'                value { type: DT_FLOAT } }"
+      " attr { key: 'data_format'      value { s: 'NCHW' } }"
+      " input: ['E'] }");
+  EXPECT_EQ(DoMklLayoutOptimizationPass(),
+            "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);"
+            "E(Sub);F(Int32Input);G(_MklConv2DBackpropFilter);H(BiasAddGrad);"
+            "M(_MklInput);N(_MklInput);O(_MklInput)|A->D:1;A->E:1;A->G;B->D;"
+            "C->D:2;D->E;E->G:2;E->H;F->G:1;M->D:3;M->G:3;N->D:4;N->G:4;O->D:5;"
+            "O->G:5");
 }
 
 // No _MklConv2DWithBias in context, but _MklConv2D in context.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index e959eab54e..f6595fcbb3 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -728,7 +728,7 @@ TEST_F(VirtualSchedulerTest, ComplexDependency) {
            1 /* control dependency */);
   EXPECT_EQ(expected_size, cpu_state.memory_usage);
 
-  // Nodes currrently in memory: bn's port -1, 0, and 2, and x's port 0.
+  // Nodes currently in memory: bn's port -1, 0, and 2, and x's port 0.
   std::set<std::pair<string, int>> nodes_in_memory;
   std::transform(
       cpu_state.nodes_in_memory.begin(), cpu_state.nodes_in_memory.end(),
diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h
index 84a7681782..1e7a9dfaf5 100644
--- a/tensorflow/core/grappler/grappler_item.h
+++ b/tensorflow/core/grappler/grappler_item.h
@@ -23,6 +23,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/protobuf/queue_runner.pb.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.cc b/tensorflow/core/grappler/optimizers/auto_parallel.cc
index 42f2f1850f..d46b849ad4 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.cc
@@ -168,6 +168,11 @@ Status AutoParallel::Initialize(const GrapplerItem& item) {
   for (const auto& variable : item.MainVariables()) {
     dont_replicate_nodes.insert(variable->name());
   }
+
+  for (const auto& init : item.init_ops) {
+    dont_replicate_nodes.insert(NodeName(init));
+  }
+
   // Don't replicate all input nodes, except the dequeue node.
   for (const auto& input_node : input_nodes) {
     if (input_node->name() != dequeue_node->name()) {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.h b/tensorflow/core/grappler/optimizers/auto_parallel.h
index ad90bbe028..c5d2d47782 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.h
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_
 
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
+#include "tensorflow/core/framework/variable.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
index 3d1b4a34bf..9a41b5e0b5 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel_test.cc
@@ -33,6 +33,7 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   Output constant_b = ops::Const(s.WithOpName("constant_b"), 1, {1});
   Output var = ops::Variable(s.WithOpName("var"), {1}, DT_FLOAT);
   Output assign = ops::Assign(s.WithOpName("assign"), {var}, {constant_a});
+  Output identity = ops::Identity(s.WithOpName("identity"), {var});
   Output fifo_queue = ops::FIFOQueue(s.WithOpName("fifo_queue"), {DT_FLOAT});
   auto dequeue = ops::QueueDequeueMany(s.WithOpName("dequeue"), {fifo_queue},
                                        {constant_b}, {DT_FLOAT});
@@ -44,13 +45,14 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   GrapplerItem item;
   item.init_ops.push_back("assign");
   item.fetch.push_back("apply_gradient");
+  item.init_ops.push_back("assign");
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
   AutoParallel parallel(2);
   GraphDef output;
   Status status = parallel.Optimize(nullptr, item, &output);
   TF_EXPECT_OK(status);
-  EXPECT_EQ(20, output.node_size());
+  EXPECT_EQ(21, output.node_size());
 
   const NodeDef& node_assign = output.node(0);
   EXPECT_EQ("assign", node_assign.name());
@@ -62,60 +64,64 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   const NodeDef& node_fifo_queue = output.node(2);
   EXPECT_EQ("fifo_queue", node_fifo_queue.name());
 
-  const NodeDef& node_var = output.node(3);
+  const NodeDef& node_identity = output.node(3);
+  EXPECT_EQ("identity", node_identity.name());
+  EXPECT_EQ("var", node_identity.input(0));
+
+  const NodeDef& node_var = output.node(4);
   EXPECT_EQ("var", node_var.name());
 
-  const NodeDef& node_div_const0 = output.node(4);
+  const NodeDef& node_div_const0 = output.node(5);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-Const",
             node_div_const0.name());
 
-  const NodeDef& node_div0 = output.node(5);
+  const NodeDef& node_div0 = output.node(6);
   EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-apply_gradient",
             node_div0.name());
-  const NodeDef& node_add0 = output.node(6);
+  const NodeDef& node_add0 = output.node(7);
   EXPECT_EQ("AutoParallel-Replica-0/add", node_add0.name());
 
-  const NodeDef& node_gradient0 = output.node(7);
+  const NodeDef& node_gradient0 = output.node(8);
   EXPECT_EQ("AutoParallel-Replica-0/apply_gradient", node_gradient0.name());
 
-  const NodeDef& node_constant_a0 = output.node(8);
+  const NodeDef& node_constant_a0 = output.node(9);
   EXPECT_EQ("AutoParallel-Replica-0/constant_a", node_constant_a0.name());
 
-  const NodeDef& node_dequeue0 = output.node(9);
+  const NodeDef& node_dequeue0 = output.node(10);
   EXPECT_EQ("AutoParallel-Replica-0/dequeue", node_dequeue0.name());
 
-  const NodeDef& node_learning_rate0 = output.node(10);
+  const NodeDef& node_learning_rate0 = output.node(11);
   EXPECT_EQ("AutoParallel-Replica-0/learning_rate", node_learning_rate0.name());
 
-  const NodeDef& node_div_const1 = output.node(11);
+  const NodeDef& node_div_const1 = output.node(12);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-Const",
             node_div_const1.name());
 
-  const NodeDef& node_div1 = output.node(12);
+  const NodeDef& node_div1 = output.node(13);
   EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-apply_gradient",
             node_div1.name());
 
-  const NodeDef& node_add1 = output.node(13);
+  const NodeDef& node_add1 = output.node(14);
   EXPECT_EQ("AutoParallel-Replica-1/add", node_add1.name());
 
-  const NodeDef& node_gradient1 = output.node(14);
+  const NodeDef& node_gradient1 = output.node(15);
   EXPECT_EQ("AutoParallel-Replica-1/apply_gradient", node_gradient1.name());
 
-  const NodeDef& node_constant_a1 = output.node(15);
+  const NodeDef& node_constant_a1 = output.node(16);
   EXPECT_EQ("AutoParallel-Replica-1/constant_a", node_constant_a1.name());
 
-  const NodeDef& node_dequeue1 = output.node(16);
+  const NodeDef& node_dequeue1 = output.node(17);
   EXPECT_EQ("AutoParallel-Replica-1/dequeue", node_dequeue1.name());
 
-  const NodeDef& node_learning_rate1 = output.node(17);
+  const NodeDef& node_learning_rate1 = output.node(18);
   EXPECT_EQ("AutoParallel-Replica-1/learning_rate", node_learning_rate1.name());
 
-  const NodeDef& node_fetch = output.node(18);
+  const NodeDef& node_fetch = output.node(19);
   EXPECT_EQ("AutoParallel-Control-Fetch", node_fetch.name());
   EXPECT_EQ("^AutoParallel-Replica-0/apply_gradient", node_fetch.input(0));
   EXPECT_EQ("^AutoParallel-Replica-1/apply_gradient", node_fetch.input(1));
 
-  const NodeDef& node_gradient = output.node(19);
+  const NodeDef& node_gradient = output.node(20);
   EXPECT_EQ("apply_gradient", node_gradient.name());
   EXPECT_EQ("^AutoParallel-Control-Fetch", node_gradient.input(0));
 }
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index ded1e474ce..28d663e2f7 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -929,7 +929,7 @@ struct TuningConfig {
   // Conv2DBackpropFilter will use a specialized GEMM implementation, which is
   // usually faster than the NCHW implementation. The downside is that this
   // might result in more non-cancellable layout conversion nodes (implemented
-  // by the Tranpose op).
+  // by the Transpose op).
   bool no_gemm;
 };
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 41995a2ff5..0b1c1085f2 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -725,6 +725,12 @@ tf_kernel_library(
 
 tf_kernel_library(
     name = "tile_ops",
+    srcs = ["tile_functor_cpu.cc"],
+    hdrs = ["tile_functor.h"],
+    gpu_srcs = [
+        "tile_functor.h",
+        "tile_functor_gpu.cu.cc",
+    ],
     prefix = "tile_ops",
     deps = ARRAY_DEPS,
 )
@@ -2464,7 +2470,7 @@ tf_cc_tests(
         ":ops_util",
         ":sparse_add_op",
         ":sparse_dense_binary_op_shared",
-        ":sparse_reduce_sum_op",
+        ":sparse_reduce_op",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -3207,7 +3213,7 @@ cc_library(
         ":sparse_cross_op",
         ":sparse_dense_binary_op_shared",
         ":sparse_fill_empty_rows_op",
-        ":sparse_reduce_sum_op",
+        ":sparse_reduce_op",
         ":sparse_reorder_op",
         ":sparse_reshape_op",
         ":sparse_softmax",
@@ -3263,8 +3269,8 @@ tf_kernel_library(
 )
 
 tf_kernel_library(
-    name = "sparse_reduce_sum_op",
-    prefix = "sparse_reduce_sum_op",
+    name = "sparse_reduce_op",
+    prefix = "sparse_reduce_op",
     deps = SPARSE_DEPS,
 )
 
@@ -4152,6 +4158,7 @@ filegroup(
         "spacetobatch_functor.h",
         "spacetodepth_op.h",
         "tensor_array.h",
+        "tile_functor.h",
         "tile_ops_cpu_impl.h",
         "tile_ops_impl.h",
         "training_op_helpers.h",
@@ -4290,6 +4297,7 @@ filegroup(
         "summary_op.cc",
         "tensor_array.cc",
         "tensor_array_ops.cc",
+        "tile_functor_cpu.cc",
         "tile_ops.cc",
         "tile_ops_cpu_impl_1.cc",
         "tile_ops_cpu_impl_2.cc",
diff --git a/tensorflow/core/kernels/adjust_contrast_op.cc b/tensorflow/core/kernels/adjust_contrast_op.cc
index c8f12f91a6..37976f7183 100644
--- a/tensorflow/core/kernels/adjust_contrast_op.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op.cc
@@ -31,6 +31,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif
 
 // AdjustContrastOp is deprecated as of GraphDef version >= 2
 
@@ -410,4 +413,25 @@ REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_GPU),
                         AdjustContrastOpv2<GPUDevice>);
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+template <>
+class AdjustContrastOpv2<SYCLDevice> : public AdjustContrastOpV2Base {
+ public:
+  explicit AdjustContrastOpv2(OpKernelConstruction* context)
+      : AdjustContrastOpV2Base(context) {}
+
+  void DoCompute(OpKernelContext* context,
+                 const ComputeOptions& options) override {
+    const int64 shape[4] = {options.batch, options.height, options.width,
+                            options.channels};
+    functor::AdjustContrastv2<SYCLDevice>()(
+        context->eigen_device<SYCLDevice>(),
+        options.input->shaped<float, 4>(shape), options.factor->scalar<float>(),
+        options.output->shaped<float, 4>(shape));
+  }
+};
+REGISTER_KERNEL_BUILDER(Name("AdjustContrastv2").Device(DEVICE_SYCL),
+                        AdjustContrastOpv2<SYCLDevice>);
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
index ffd47406eb..c485f14844 100644
--- a/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
+++ b/tensorflow/core/kernels/adjust_contrast_op_benchmark_test.cc
@@ -56,6 +56,11 @@ static Graph* BM_AdjustContrast(int batches, int width, int height) {
 // BM_AdjustContrast_cpu_1_299_299     179084     340186  2181  751.9M items/s
 // BM_AdjustContrast_gpu_32_299_299     85276     123665  4189  2.9G items/s
 BM_AdjustContrastDev(cpu, 1, 299, 299);
+#if GOOGLE_CUDA
 BM_AdjustContrastDev(gpu, 32, 299, 299);
+#endif  // GOOGLE_CUDA
+#ifdef TENSORFLOW_USE_SYCL
+BM_AdjustContrastDev(sycl, 32, 299, 299);
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc
index d65a34fd73..ba100b32e7 100644
--- a/tensorflow/core/kernels/colorspace_op.cc
+++ b/tensorflow/core/kernels/colorspace_op.cc
@@ -35,6 +35,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif
 
 template <typename Device, typename T>
 class RGBToHSVOp : public OpKernel {
@@ -146,4 +149,16 @@ TF_CALL_float(REGISTER_GPU);
 TF_CALL_double(REGISTER_GPU);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL(T)                                       \
+  REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_SYCL) \
+                              .TypeConstraint<T>("T"),         \
+                          RGBToHSVOp<SYCLDevice, T>);          \
+  REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_SYCL) \
+                              .TypeConstraint<T>("T"),         \
+                          HSVToRGBOp<SYCLDevice, T>);
+TF_CALL_float(REGISTER_SYCL);
+TF_CALL_double(REGISTER_SYCL);
+#endif
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/control_flow_ops.cc b/tensorflow/core/kernels/control_flow_ops.cc
index 203a9a9f24..64c06786bc 100644
--- a/tensorflow/core/kernels/control_flow_ops.cc
+++ b/tensorflow/core/kernels/control_flow_ops.cc
@@ -112,15 +112,14 @@ REGISTER_GPU_HOST_REF_KERNEL(string);
 #undef REGISTER_GPU_HOST_KERNEL
 #undef REGISTER_GPU_HOST_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                       \
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_SWITCH(type)                       \
   REGISTER_KERNEL_BUILDER(Name("Switch")                 \
                               .Device(DEVICE_SYCL)       \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("pred"),       \
+                              .HostMemory("pred")        \
+                              .TypeConstraint<type>("T"),\
                           SwitchOp)
-REGISTER_SYCL_KERNEL(bool);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_SWITCH);
 
 #define REGISTER_SYCL_REF_SWITCH(type)                     \
   REGISTER_KERNEL_BUILDER(Name("RefSwitch")                \
@@ -128,12 +127,41 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
                               .HostMemory("pred")          \
                               .TypeConstraint<type>("T"),  \
                           SwitchOp)
-REGISTER_SYCL_REF_SWITCH(bool);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH);
+TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH);
 
-#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_SWITCH
 #undef REGISTER_SYCL_REF_SWITCH
 
+#define REGISTER_SYCL_HOST_KERNEL(type)                  \
+  REGISTER_KERNEL_BUILDER(Name("Switch")                 \
+                              .Device(DEVICE_SYCL)       \
+                              .HostMemory("data")        \
+                              .HostMemory("pred")        \
+                              .HostMemory("output_false")\
+                              .HostMemory("output_true") \
+                              .TypeConstraint<type>("T"),\
+                          SwitchOp)
+
+REGISTER_SYCL_HOST_KERNEL(bool);
+REGISTER_SYCL_HOST_KERNEL(string);
+REGISTER_SYCL_HOST_KERNEL(int32);
+
+#define REGISTER_SYCL_HOST_REF_KERNEL(type)                \
+  REGISTER_KERNEL_BUILDER(Name("RefSwitch")                \
+                              .Device(DEVICE_SYCL)         \
+                              .HostMemory("data")          \
+                              .HostMemory("pred")          \
+                              .HostMemory("output_false")  \
+                              .HostMemory("output_true")   \
+                              .TypeConstraint<type>("T"),  \
+                          SwitchOp)
+
+REGISTER_SYCL_HOST_REF_KERNEL(int32);
+REGISTER_SYCL_HOST_REF_KERNEL(bool);
+REGISTER_SYCL_HOST_REF_KERNEL(string);
+
+#undef REGISTER_SYCL_HOST_KERNEL
+#undef REGISTER_SYCL_HOST_REF_KERNEL
 #endif // TENSORFLOW_USE_SYCL
 
 class RefSelectOp : public OpKernel {
@@ -233,13 +261,13 @@ REGISTER_GPU_REF_KERNEL(bool);
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
+#ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                        \
   REGISTER_KERNEL_BUILDER(Name("Merge")                   \
                               .Device(DEVICE_SYCL)        \
                               .TypeConstraint<type>("T")  \
                               .HostMemory("value_index"), \
-                          MergeOp)
+                          MergeOp);
 REGISTER_SYCL_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 
@@ -248,9 +276,10 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
                               .Device(DEVICE_SYCL)        \
                               .TypeConstraint<type>("T")  \
                               .HostMemory("value_index"), \
-                          MergeOp)
+                          MergeOp);
 REGISTER_SYCL_REF_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
+
 #undef REGISTER_SYCL_KERNEL
 #undef REGISTER_SYCL_REF_KERNEL
 #endif // TENSORFLOW_USE_SYCL
@@ -280,6 +309,30 @@ REGISTER_GPU_HOST_KERNEL(ResourceHandle);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_HOST_KERNEL(type)                   \
+  REGISTER_KERNEL_BUILDER(Name("Merge")                   \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("inputs")       \
+                              .HostMemory("output")       \
+                              .HostMemory("value_index")  \
+                              .TypeConstraint<type>("T"), \
+                          MergeOp);                       \
+  REGISTER_KERNEL_BUILDER(Name("RefMerge")                \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("inputs")       \
+                              .HostMemory("output")       \
+                              .HostMemory("value_index")  \
+                              .TypeConstraint<type>("T"), \
+                          MergeOp)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(string);
+REGISTER_SYCL_HOST_KERNEL(ResourceHandle);
+
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 void EnterOp::Compute(OpKernelContext* context) {
   if (IsRefType(context->input_dtype(0))) {
     context->forward_ref_input_to_ref_output(0, 0);
@@ -306,7 +359,7 @@ REGISTER_GPU_REF_KERNEL(bool);
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
+#ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)  \
   REGISTER_KERNEL_BUILDER(          \
       Name("Enter").Device(DEVICE_SYCL).TypeConstraint<type>("T"), EnterOp)
@@ -345,7 +398,7 @@ REGISTER_SYCL_HOST_KERNEL(ResourceHandle);
 
 #undef REGISTER_SYCL_HOST_KERNEL
 #undef REGISTER_SYCL_HOST_REF_KERNEL
-#endif
+#endif // TENSORFLOW_USE_SYCL
 
 // Special GPU kernels for int32 and string.
 // TODO(b/25387198): Also enable int32 in device memory. This kernel
@@ -394,30 +447,25 @@ REGISTER_KERNEL_BUILDER(Name("RefExit").Device(DEVICE_CPU), ExitOp);
       Name("RefExit").Device(DEVICE_GPU).TypeConstraint<type>("T"), ExitOp);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_REF_KERNEL);
 REGISTER_GPU_KERNEL(bool);
+REGISTER_GPU_REF_KERNEL(bool);
 
 #undef REGISTER_GPU_KERNEL
 #undef REGISTER_GPU_REF_KERNEL
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)  \
-  REGISTER_KERNEL_BUILDER(          \
-  Name("Exit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp)
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                                           \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("Exit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp);   \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("RefExit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp);
 REGISTER_SYCL_KERNEL(bool);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 
-#define REGISTER_SYCL_REF_KERNEL(type)  \
-  REGISTER_KERNEL_BUILDER(              \
-  Name("RefExit").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ExitOp)
-REGISTER_SYCL_REF_KERNEL(bool);
-TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
-
 #undef REGISTER_SYCL_KERNEL
 #undef REGISTER_SYCL_REF_KERNEL
 
-// Special GPU kernels for int32 and string.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 #define REGISTER_SYCL_HOST_KERNEL(type)                   \
   REGISTER_KERNEL_BUILDER(Name("Exit")                    \
                               .Device(DEVICE_SYCL)        \
@@ -507,31 +555,19 @@ REGISTER_GPU_HOST_KERNEL(string);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)  \
-  REGISTER_KERNEL_BUILDER(Name("NextIteration")           \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("data")         \
-                              .HostMemory("output")       \
-                              .TypeConstraint<type>("T"), \
-                          NextIterationOp)
-  REGISTER_SYCL_KERNEL(bool);
-  TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
-#define REGISTER_SYCL_REF_KERNEL(type)                    \
-  REGISTER_KERNEL_BUILDER(Name("RefNextIteration")        \
-                              .Device(DEVICE_SYCL)        \
-                              .HostMemory("data")         \
-                              .HostMemory("output")       \
-                              .TypeConstraint<type>("T"), \
-                          NextIterationOp)
-  REGISTER_SYCL_REF_KERNEL(bool);
-  TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL);
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                                           \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("NextIteration").Device(DEVICE_SYCL).TypeConstraint<type>("T"),   \
+      NextIterationOp);                                                      \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("RefNextIteration").Device(DEVICE_SYCL).TypeConstraint<type>("T"),\
+      NextIterationOp)
+REGISTER_SYCL_KERNEL(bool);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+
 #undef REGISTER_SYCL_KERNEL
-#undef REGISTER_SYCL_REF_KERNEL
 
-// Special GPU kernels for int32 and string.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 #define REGISTER_SYCL_HOST_KERNEL(type)                   \
   REGISTER_KERNEL_BUILDER(Name("NextIteration")           \
                               .Device(DEVICE_SYCL)        \
diff --git a/tensorflow/core/kernels/cwise_op_add_2.cc b/tensorflow/core/kernels/cwise_op_add_2.cc
index 5d3385b0ed..5dea00e95c 100644
--- a/tensorflow/core/kernels/cwise_op_add_2.cc
+++ b/tensorflow/core/kernels/cwise_op_add_2.cc
@@ -22,10 +22,11 @@ namespace tensorflow {
 // sharded files, only make its register calls when not __ANDROID_TYPES_SLIM__.
 #if !defined(__ANDROID_TYPES_SLIM__)
 
-REGISTER5(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64,
-          complex128, string);
+REGISTER6(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64,
+          uint8, complex128, string);
 #if GOOGLE_CUDA
-REGISTER3(BinaryOp, GPU, "Add", functor::add, int64, complex64, complex128);
+REGISTER4(BinaryOp, GPU, "Add", functor::add, uint8, int64, complex64,
+          complex128);
 #endif  // GOOGLE_CUDA
 
 #endif  // !defined(__ANDROID_TYPES_SLIM__)
diff --git a/tensorflow/core/kernels/cwise_op_cosh.cc b/tensorflow/core/kernels/cwise_op_cosh.cc
new file mode 100644
index 0000000000..bca99a4f89
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_cosh.cc
@@ -0,0 +1,37 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Cosh", functor::cosh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Cosh")                                \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::cosh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Cosh", functor::cosh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
index 5aaf2b5b4b..61079ebab3 100644
--- a/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
+++ b/tensorflow/core/kernels/cwise_op_gpu_add.cu.cc
@@ -19,7 +19,8 @@ limitations under the License.
 
 namespace tensorflow {
 namespace functor {
-DEFINE_BINARY6(add, Eigen::half, float, double, int64, complex64, complex128);
+DEFINE_BINARY7(add, Eigen::half, float, double, uint8, int64, complex64,
+               complex128);
 }  // namespace functor
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
new file mode 100644
index 0000000000..267a381d1a
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_cosh.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(cosh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
new file mode 100644
index 0000000000..f8329e50d6
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_sinh.cu.cc
@@ -0,0 +1,26 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(sinh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc
index c84ee6894e..df2c02e42e 100644
--- a/tensorflow/core/kernels/cwise_op_invert.cc
+++ b/tensorflow/core/kernels/cwise_op_invert.cc
@@ -20,7 +20,7 @@ REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64,
           uint8, uint16);
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
+REGISTER6(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
          uint8, uint16);
 #endif  // TENSORFLOW_USE_SYCL
 
diff --git a/tensorflow/core/kernels/cwise_op_sinh.cc b/tensorflow/core/kernels/cwise_op_sinh.cc
new file mode 100644
index 0000000000..055f0b12e1
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_sinh.cc
@@ -0,0 +1,37 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Sinh", functor::sinh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Sinh")                                \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::sinh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYC
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Sinh", functor::sinh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_squared_difference.cc b/tensorflow/core/kernels/cwise_op_squared_difference.cc
index edd6c07146..78fefc69c7 100644
--- a/tensorflow/core/kernels/cwise_op_squared_difference.cc
+++ b/tensorflow/core/kernels/cwise_op_squared_difference.cc
@@ -35,4 +35,17 @@ REGISTER_KERNEL_BUILDER(
         .TypeConstraint<int32>("T"),
     BinaryOp<CPUDevice, functor::squared_difference<int32>>);
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(BinaryOp, SYCL, "SquaredDifference", functor::squared_difference,
+          float, double, int64);
+REGISTER_KERNEL_BUILDER(
+    Name("SquaredDifference")
+        .Device(DEVICE_SYCL)
+        .HostMemory("x")
+        .HostMemory("y")
+        .HostMemory("z")
+        .TypeConstraint<int32>("T"),
+    BinaryOp<CPUDevice, functor::squared_difference<int32>>);
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h
index 97bdc5e878..c11d6cfabb 100644
--- a/tensorflow/core/kernels/cwise_ops.h
+++ b/tensorflow/core/kernels/cwise_ops.h
@@ -483,6 +483,12 @@ struct log1p : base<T, Eigen::internal::scalar_log1p_op<T> > {};
 template <typename T>
 struct sign : base<T, Eigen::internal::scalar_sign_op<T> > {};
 
+template <typename T>
+struct sinh : base<T, Eigen::internal::scalar_sinh_op<T> > {};
+
+template <typename T>
+struct cosh : base<T, Eigen::internal::scalar_cosh_op<T> > {};
+
 template <typename T>
 struct tanh : base<T, Eigen::internal::scalar_tanh_op<T> > {};
 
diff --git a/tensorflow/core/kernels/dynamic_stitch_op.cc b/tensorflow/core/kernels/dynamic_stitch_op.cc
index 08ae787c86..135d635514 100644
--- a/tensorflow/core/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/core/kernels/dynamic_stitch_op.cc
@@ -165,20 +165,6 @@ class DynamicStitchOp : public OpKernel {
 TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH);
 #undef REGISTER_DYNAMIC_STITCH
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_DYNAMIC_STITCH_SYCL(type)               \
-  REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
-                              .Device(DEVICE_SYCL)       \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("indices")     \
-                              .HostMemory("data")        \
-                              .HostMemory("merged"),     \
-                          DynamicStitchOp<type>)
-
-TF_CALL_ALL_TYPES(REGISTER_DYNAMIC_STITCH_SYCL);
-#undef REGISTER_DYNAMIC_STITCH_SYCL
-#endif  // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 #define REGISTER_DYNAMIC_STITCH_GPU(type)                \
   REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
@@ -194,4 +180,17 @@ TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH_GPU);
 
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_DYNAMIC_STITCH_SYCL(type)               \
+  REGISTER_KERNEL_BUILDER(Name("DynamicStitch")          \
+                              .Device(DEVICE_SYCL)       \
+                              .TypeConstraint<type>("T") \
+                              .HostMemory("indices")     \
+                              .HostMemory("data")        \
+                              .HostMemory("merged"),     \
+                          DynamicStitchOp<type>)
+
+TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH_SYCL);
+#undef REGISTER_DYNAMIC_STITCH_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/map_stage_op.cc b/tensorflow/core/kernels/map_stage_op.cc
index 6431c6540e..46eaf3d9e7 100644
--- a/tensorflow/core/kernels/map_stage_op.cc
+++ b/tensorflow/core/kernels/map_stage_op.cc
@@ -547,14 +547,14 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapStage")
                             .HostMemory("indices")
                             .Device(DEVICE_GPU),
                         MapStageOp<true>);
-#endif
+#endif // GOOGLE_CUDA
+
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(Name("MapStage").HostMemory("key").Device(DEVICE_SYCL),
                         MapStageOp<false>);
 REGISTER_KERNEL_BUILDER(
     Name("OrderedMapStage").HostMemory("key").Device(DEVICE_SYCL),
     MapStageOp<true>);
-
 #endif // TENSORFLOW_USE_SYCL
 
 template <bool Ordered>
@@ -661,6 +661,7 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapPeek")
                             .Device(DEVICE_GPU),
                         MapPeekOp<true>);
 #endif
+
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(
     Name("MapPeek").HostMemory("key").HostMemory("indices").Device(DEVICE_SYCL),
@@ -724,8 +725,8 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapUnstageNoKey")
                             .HostMemory("indices")
                             .Device(DEVICE_GPU),
                         MapUnstageNoKeyOp<true>);
-
 #endif
+
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(Name("MapUnstageNoKey")
                             .HostMemory("key")
diff --git a/tensorflow/core/kernels/meta_support.cc b/tensorflow/core/kernels/meta_support.cc
index 0e899402a2..b29feb0032 100644
--- a/tensorflow/core/kernels/meta_support.cc
+++ b/tensorflow/core/kernels/meta_support.cc
@@ -98,8 +98,9 @@ typedef gemmlowp::meta::SimpleContext<gemmlowp::WorkersPool> LocalContext;
 template <typename Context, typename Params>
 void MultiThreadGemm(Context* context, const Params& params) {
   if (params.m <= 4) {
-    gemmlowp::meta::Gemm<gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>,
-                         Params, 1, 8, 8>(params);
+      gemmlowp::meta::MultiThreadGemm<
+          Context, gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>, Params,
+          1, 8, 8>(context, params);
   } else {
     if (params.m >= params.n) {
       gemmlowp::meta::MultiThreadGemm<
diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
index dc6b88e953..ddcf241277 100644
--- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
@@ -206,10 +206,15 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
     // Mkl needs the entities in its native format.
     // So create temporary tensors along with buffers to
     // convert the received entities.
-    Tensor mkl_tmp_input_buf_tensor, mkl_tmp_out_backprop_buf_tensor;
+    Tensor mkl_tmp_input_buf_tensor, mkl_tmp_out_backprop_buf_tensor,
+           mkl_tmp_buf_trans_input;
     // This preparation sets (1) dnnResourceSrc (2) dnnResourceDiffDst
-    mkl_context.MklPrepareInputs(context, &mkl_tmp_input_buf_tensor,
-                                 &mkl_tmp_out_backprop_buf_tensor);
+    mkl_context.MklPrepareInputs(context, data_format_,
+                                 input_in_mkl_format,
+                                 out_backprop_in_mkl_format,
+                                 &mkl_tmp_input_buf_tensor,
+                                 &mkl_tmp_out_backprop_buf_tensor,
+                                 &mkl_tmp_buf_trans_input);
 
     // Final conv-grad-filter should be in TF layout.
     Tensor* grad_filter;
@@ -307,34 +312,58 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
 
     // Compare incoming tensor layouts with MKL preferred layouts and convert
     // data to the preferred layout if necessary
-    void MklPrepareInputs(OpKernelContext* context,
+    void MklPrepareInputs(OpKernelContext* context, TensorFormat format,
+                          bool input_in_mkl_format,
+                          bool out_backprop_in_mkl_format,
                           Tensor* mkl_tmp_input_buf_tensor,
-                          Tensor* mkl_tmp_out_backprop_buf_tensor) {
+                          Tensor* mkl_tmp_out_backprop_buf_tensor,
+                          Tensor* mkl_tmp_buf_trans_input) {
       bool mkl_convert_input, mkl_convert_out_backprop;
       dnnPrimitive_t mkl_prim_convert_input, mkl_prim_convert_out_backprop;
-      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_out_backprop;
+      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_out_backprop,
+                  mkl_lt_trans_input;
       void *mkl_buf_convert_input, *mkl_buf_convert_out_backprop;
+      void *mkl_buf_input, *mkl_buf_out_backprop;
 
       mkl_prim_convert_input = nullptr;
       mkl_prim_convert_out_backprop = nullptr;
       mkl_lt_internal_input = nullptr;
       mkl_lt_internal_out_backprop = nullptr;
+      mkl_lt_trans_input = nullptr;
       mkl_buf_convert_input = nullptr;
       mkl_buf_convert_out_backprop = nullptr;
+      mkl_buf_input = nullptr;
+      mkl_buf_out_backprop = nullptr;
 
       // Compare with internal layouts and convert if needed
       const Tensor& input = MklGetInput(context, 0);
-      void* mkl_buf_input =
-          const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
+      if (!input_in_mkl_format && format == FORMAT_NHWC){
+        TensorShape nchw_shape = ShapeFromFormat(FORMAT_NCHW,
+            in_sizes[MklDims::N], in_sizes[MklDims::H],
+            in_sizes[MklDims::W], in_sizes[MklDims::C]);
+        OP_REQUIRES_OK(context, context->allocate_temp(
+            DataTypeToEnum<float>::value, nchw_shape, mkl_tmp_buf_trans_input));
+        MklNHWCToNCHW(input, &mkl_tmp_buf_trans_input);
+        mkl_buf_input = const_cast<void*>(static_cast<const void*>(
+            mkl_tmp_buf_trans_input->flat<float>().data()));
+        size_t strides[4];
+        GetStridesFromSizes(FORMAT_NCHW, strides, in_sizes);
+        CHECK_EQ(dnnLayoutCreate_F32(&mkl_lt_trans_input, in_dims, in_sizes,
+            strides), E_SUCCESS);
+      }
+      else {
+        mkl_buf_input =
+            const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
+        mkl_lt_trans_input = lt_input;
+      }
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(
                    &mkl_lt_internal_input, prim_conv_bwdfilter, dnnResourceSrc),
                E_SUCCESS);
       mkl_convert_input =
-          !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input);
+          !dnnLayoutCompare_F32(mkl_lt_internal_input, mkl_lt_trans_input);
       if (mkl_convert_input) {
-        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input,
-                                         mkl_lt_internal_input),
-                 E_SUCCESS);
+        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input,
+                     mkl_lt_trans_input, mkl_lt_internal_input), E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input,
                        &mkl_buf_convert_input);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input,
@@ -343,26 +372,30 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel {
         dnnDelete_F32(mkl_prim_convert_input);
       }
       dnnLayoutDelete_F32(mkl_lt_internal_input);
+      if (!input_in_mkl_format && format == FORMAT_NHWC)
+        dnnLayoutDelete_F32(mkl_lt_trans_input);
+
 
       conv_res[dnnResourceSrc] =
           (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input;
 
       const Tensor& out_backprop = MklGetInput(context, 2);
-      void* mkl_buf_out_backprop = const_cast<void*>(
-          static_cast<const void*>(out_backprop.flat<T>().data()));
+      mkl_buf_out_backprop = const_cast<void*>(
+                      static_cast<const void*>(out_backprop.flat<T>().data()));
+
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_out_backprop,
                                                 prim_conv_bwdfilter,
                                                 dnnResourceDiffDst),
                E_SUCCESS);
       mkl_convert_out_backprop =
-          !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop, lt_out_backprop);
+          !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop,
+                                lt_out_backprop);
       if (mkl_convert_out_backprop) {
         CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_out_backprop,
-                                         lt_out_backprop,
-                                         mkl_lt_internal_out_backprop),
+                      lt_out_backprop, mkl_lt_internal_out_backprop),
                  E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_out_backprop_buf_tensor,
-                       lt_out_backprop, &mkl_buf_convert_out_backprop);
+            mkl_lt_internal_out_backprop, &mkl_buf_convert_out_backprop);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_out_backprop,
                                           mkl_buf_out_backprop,
                                           mkl_buf_convert_out_backprop),
diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc
index 76b9f1798d..df49e03f31 100644
--- a/tensorflow/core/kernels/mkl_conv_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_ops.cc
@@ -267,12 +267,15 @@ class MklConv2DOp : public OpKernel {
 
     mkl_context.MklCreateInputLayouts(context);
 
+    // Temp tensor used to allocate tmp buffers
     Tensor mkl_tmp_input_buf_tensor, mkl_tmp_filter_buf_tensor,
-        mkl_tmp_bias_buf_tensor;  // Temp tensor used to allocate tmp
-                                  // buffers
-    mkl_context.MklPrepareConvolutionInputs(context, &mkl_tmp_input_buf_tensor,
+        mkl_tmp_bias_buf_tensor, mkl_tmp_buf_trans_input;
+    mkl_context.MklPrepareConvolutionInputs(context, data_format_,
+                                            input_in_mkl_format,
+                                            &mkl_tmp_input_buf_tensor,
                                             &mkl_tmp_filter_buf_tensor,
-                                            &mkl_tmp_bias_buf_tensor);
+                                            &mkl_tmp_bias_buf_tensor,
+                                            &mkl_tmp_buf_trans_input);
 
     // Execute convolution
     CHECK_EQ(dnnExecute_F32(mkl_context.prim_fwd, mkl_context.conv_res),
@@ -323,39 +326,59 @@ class MklConv2DOp : public OpKernel {
     // Compare incoming tensor layouts with MKL preferred layouts and convert
     // data to the preferred layout if necessary
     void MklPrepareConvolutionInputs(OpKernelContext* context,
+                                     TensorFormat format,
+                                     bool input_in_mkl_format,
                                      Tensor* mkl_tmp_input_buf_tensor,
                                      Tensor* mkl_tmp_filter_buf_tensor,
-                                     Tensor* mkl_tmp_bias_buf_tensor) {
+                                     Tensor* mkl_tmp_bias_buf_tensor,
+                                     Tensor* mkl_tmp_buf_trans_input) {
       bool mkl_convert_input, mkl_convert_filter, mkl_convert_bias;
       dnnPrimitive_t mkl_prim_convert_filter, mkl_prim_convert_bias,
           mkl_prim_convert_input;
       dnnLayout_t mkl_lt_internal_filter, mkl_lt_internal_bias,
-          mkl_lt_internal_input;
+          mkl_lt_internal_input, mkl_lt_trans_input;
       void *mkl_buf_convert_input, *mkl_buf_convert_filter,
-          *mkl_buf_convert_bias;
+          *mkl_buf_convert_bias, *mkl_buf_input;
       mkl_prim_convert_filter = nullptr;
       mkl_prim_convert_bias = nullptr;
       mkl_prim_convert_input = nullptr;
       mkl_lt_internal_filter = nullptr;
       mkl_lt_internal_bias = nullptr;
       mkl_lt_internal_input = nullptr;
+      mkl_lt_trans_input = nullptr;
       mkl_buf_convert_input = nullptr;
       mkl_buf_convert_filter = nullptr;
       mkl_buf_convert_bias = nullptr;
+      mkl_buf_input = nullptr;
 
       // Compare with internal layouts and convert if needed
       const Tensor& input = MklGetInput(context, 0);
-      void* mkl_buf_input =
-          const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
+      if (!input_in_mkl_format && format == FORMAT_NHWC) {
+        TensorShape nchw_shape = ShapeFromFormat(FORMAT_NCHW,
+            in_sizes[MklDims::N], in_sizes[MklDims::H],
+            in_sizes[MklDims::W], in_sizes[MklDims::C]);
+        OP_REQUIRES_OK(context, context->allocate_temp(
+            DataTypeToEnum<float>::value, nchw_shape, mkl_tmp_buf_trans_input));
+        MklNHWCToNCHW(input, &mkl_tmp_buf_trans_input);
+        mkl_buf_input = const_cast<void*>(static_cast<const void*>(
+            mkl_tmp_buf_trans_input->flat<float>().data()));
+        size_t strides[4];
+        GetStridesFromSizes(FORMAT_NCHW, strides, in_sizes);
+        CHECK_EQ(dnnLayoutCreate_F32(&mkl_lt_trans_input, in_dims, in_sizes,
+            strides), E_SUCCESS);
+      } else {
+          mkl_buf_input = const_cast<void*>(
+                              static_cast<const void*>(input.flat<T>().data()));
+          mkl_lt_trans_input = lt_input;
+      }
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_input,
                                                 prim_fwd, dnnResourceSrc),
                E_SUCCESS);
       mkl_convert_input =
-          !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input);
+          !dnnLayoutCompare_F32(mkl_lt_internal_input, mkl_lt_trans_input);
       if (mkl_convert_input) {
-        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input,
-                                         mkl_lt_internal_input),
-                 E_SUCCESS);
+        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input,
+                 mkl_lt_trans_input, mkl_lt_internal_input), E_SUCCESS);
         AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input,
                        &mkl_buf_convert_input);
         CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input,
@@ -364,6 +387,8 @@ class MklConv2DOp : public OpKernel {
         dnnDelete_F32(mkl_prim_convert_input);
       }
       dnnLayoutDelete_F32(mkl_lt_internal_input);
+      if (!input_in_mkl_format && format == FORMAT_NHWC)
+        dnnLayoutDelete_F32(mkl_lt_trans_input);
 
       conv_res[dnnResourceSrc] =
           (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input;
diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc
index 070aeff49f..07a7e6b5da 100644
--- a/tensorflow/core/kernels/mkl_lrn_op.cc
+++ b/tensorflow/core/kernels/mkl_lrn_op.cc
@@ -22,9 +22,6 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 #include <vector>
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -33,6 +30,9 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/util/mkl_util.h"
 #include "tensorflow/core/util/tensor_format.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "third_party/mkl/include/mkl_dnn.h"
+#include "third_party/mkl/include/mkl_dnn_types.h"
 
 #if !defined(IS_MOBILE_PLATFORM)
 #include "tensorflow/core/util/work_sharder.h"
@@ -66,11 +66,10 @@ class MklLRNOp : public OpKernel {
   explicit MklLRNOp(OpKernelConstruction* context) : OpKernel(context) {
     int64 depth_radius64;
     OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64));
-    OP_REQUIRES(
-        context,
-        FastBoundsCheck(depth_radius64, std::numeric_limits<int>::max()),
-        errors::InvalidArgument("depth_radius = ", depth_radius64,
-                                " larger than int max"));
+    OP_REQUIRES(context, FastBoundsCheck(depth_radius64,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("depth_radius = ", depth_radius64,
+                                        " larger than int max"));
     depth_radius_ = static_cast<size_t>(depth_radius64);
 
     OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_));
@@ -93,10 +92,9 @@ class MklLRNOp : public OpKernel {
                               : input.dims();
     OP_REQUIRES(context, mkl_context.in_dims == 4,
                 errors::InvalidArgument("input must be 4-dimensional"));
-    OP_REQUIRES(
-        context,
-        FastBoundsCheck(input.NumElements(), std::numeric_limits<int>::max()),
-        errors::InvalidArgument("argument to LRN too large"));
+    OP_REQUIRES(context, FastBoundsCheck(input.NumElements(),
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("argument to LRN too large"));
 
     if (!input_in_mkl_format) {
       mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_,
@@ -104,15 +102,6 @@ class MklLRNOp : public OpKernel {
       return;
     }
 
-    // TODO(inteltf) MKL will support depth radius not equal to 2 in the future
-    if (depth_radius_ != 2) {
-      Tensor converted_tensor =
-          ConvertMklToTF<T>(context, input, mkl_context.input_shape);
-      mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_,
-                                    beta_, converted_tensor);
-      return;
-    }
-
     if (input_in_mkl_format) {
       // MKL supports normalization over channel dimension only
       if (mkl_context.input_shape.tf_dim_idx(mkl_context.in_dims - 1) ==
@@ -345,11 +334,10 @@ class MklLRNGradOp : public OpKernel {
   explicit MklLRNGradOp(OpKernelConstruction* context) : OpKernel(context) {
     int64 depth_radius64;
     OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64));
-    OP_REQUIRES(
-        context,
-        FastBoundsCheck(depth_radius64, std::numeric_limits<int>::max()),
-        errors::InvalidArgument("depth_radius = ", depth_radius64,
-                                " larger than int max"));
+    OP_REQUIRES(context, FastBoundsCheck(depth_radius64,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("depth_radius = ", depth_radius64,
+                                        " larger than int max"));
     depth_radius_ = static_cast<int>(depth_radius64);
     OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_));
     OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_));
@@ -553,6 +541,9 @@ class MklLRNGradOp : public OpKernel {
       CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&lt_bdw_input, lrn_bwd,
                                                 dnnResourceDiffDst),
                E_SUCCESS);
+      CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&lt_internal_input, lrn_bwd,
+                                                dnnResourceSrc),
+               E_SUCCESS);
 
       bool ingrad_in_mkl_format = ingrad_shape.IsMklTensor();
       if (ingrad_in_mkl_format) {
@@ -581,44 +572,37 @@ class MklLRNGradOp : public OpKernel {
         }
       }
 
-// Although MKL documentation for LRN does not specify setting/getting
-// of dnnResourceSrc and dnnResourceDst, Caffe code sets dnnResourceSrc.
-// So we set dnnResourceSrc here. But we do not know why we are setting
-// dnnResourceDst.
-#if 0
-    // NOTE: The code below is kept just so that we know how we should handle
-    // dnnResourceSrc if the primitive layout for dnnResourceSrc was supported.
-
-    if (!dnnLayoutCompare_F32(lt_internal_input,
-         static_cast<dnnLayout_t>inimage_shape.GetCurLayout())) {
-      AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
-                     &res_lrn_bwd[dnnResourceSrc]);
-      inimage_shape.GetConvertedFlatData(lt_internal_input,
-                                           user_fwd_input,
-                                           res_lrn_bwd[dnnResourceSrc]);
-    } else {
-      res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
-    }
-#endif
-
-      // Since we cannot get expected layout for dnnResourceSrc, we construct
-      // buffer using
-      // MKL format if input is in MKL format.
-      if (inimage_shape.IsMklTensor()) {
-        AllocTmpBuffer(context, mkl_tmp_image_buf_tensor,
-                       (dnnLayout_t)inimage_shape.GetCurLayout(),
-                       &res_lrn_bwd[dnnResourceSrc]);
+      bool inimage_in_mkl_format = inimage_shape.IsMklTensor();
+      if (inimage_in_mkl_format) {
+        if (!dnnLayoutCompare_F32(
+                lt_internal_input,
+                static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()))) {
+          AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
+                         &res_lrn_bwd[dnnResourceSrc]);
+          ingrad_shape.GetConvertedFlatData(lt_internal_input, user_fwd_input,
+                                            res_lrn_bwd[dnnResourceSrc]);
+        } else {
+          res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
+        }
       } else {
-        res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
-      }
+        if (!dnnLayoutCompare_F32(
+                lt_internal_input,
+                static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()))) {
+          CHECK_EQ(dnnConversionCreate_F32(
+                       &convert_input,
+                       static_cast<dnnLayout_t>(inimage_shape.GetCurLayout()),
+                       lt_internal_input),
+                   E_SUCCESS);
 
-      // Same comment as above.
-      if (outimage_shape.IsMklTensor()) {
-        AllocTmpBuffer(context, mkl_tmp_outimage_buf_tensor,
-                       (dnnLayout_t)outimage_shape.GetCurLayout(),
-                       &res_lrn_bwd[dnnResourceDst]);
-      } else {
-        res_lrn_bwd[dnnResourceDst] = user_fwd_output;
+          AllocTmpBuffer(context, mkl_tmp_image_buf_tensor, lt_internal_input,
+                         &res_lrn_bwd[dnnResourceSrc]);
+          CHECK_EQ(dnnConversionExecute_F32(convert_input, user_fwd_input,
+                                            res_lrn_bwd[dnnResourceSrc]),
+                   E_SUCCESS);
+          dnnDelete_F32(convert_input);
+        } else {
+          res_lrn_bwd[dnnResourceSrc] = user_fwd_input;
+        }
       }
 
       res_lrn_bwd[dnnResourceWorkspace] = workspace_buffer;
@@ -628,8 +612,6 @@ class MklLRNGradOp : public OpKernel {
     // TODO(intelft) Check if we can use EigenLRNOp directly instead of making a
     // copy.
     void MklDefaultToEigen(OpKernelContext* context) {
-      // CHECK(false);
-
       Tensor in_grads;
       Tensor in_image;
       Tensor out_image;
@@ -709,7 +691,7 @@ class MklLRNGradOp : public OpKernel {
       Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch,
             depth * depth, shard);
     }
-		
+
     // release mkl resources
     void Mklcleanup() {
       bool ingrad_in_mkl_format = ingrad_shape.IsMklTensor();
diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc
index 10d2937584..fabecc39a8 100644
--- a/tensorflow/core/kernels/mkl_relu_op.cc
+++ b/tensorflow/core/kernels/mkl_relu_op.cc
@@ -184,38 +184,31 @@ class MklReluGradOp : public OpKernel {
     dnnLayout_t lt_input, lt_grad;
 
     void MklPrepareReluGradInputs(OpKernelContext* context,
-                                  Tensor* mkl_tmp_grad_buf_tensor,
                                   Tensor* mkl_tmp_input_buf_tensor) {
-      dnnPrimitive_t cv_user_to_reluB_input, cv_user_to_reluB_grad;
-      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_grad;
-
       const Tensor& g = MklGetInput(context, 0);
       const Tensor& a = MklGetInput(context, 1);
-
-      void* user_i = static_cast<void*>(const_cast<T*>(a.flat<T>().data()));
-      void* user_g = static_cast<void*>(const_cast<T*>(g.flat<T>().data()));
-      dnnPrimitive_t cv_input_to_grad = NULL;
-      Tensor mkl_tmp_buf_tensor;
+      void* buf_input = static_cast<void*>(const_cast<T*>(a.flat<T>().data()));
       void* mkl_buffer_convert = nullptr;
+      dnnPrimitive_t cv_input_to_grad = nullptr;
 
       // if input and grad are not in the same layout, do a conversion between
       // them.
       if (!dnnLayoutCompare_F32(lt_input, lt_grad)) {
-        AllocTmpBuffer(context, &mkl_tmp_buf_tensor, lt_grad,
+        AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, lt_grad,
                        &mkl_buffer_convert);
-        CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input, lt_grad),
-                 E_SUCCESS);
-
-        CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, user_i,
+        CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input,
+                   lt_grad), E_SUCCESS);
+        CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, buf_input,
                                           mkl_buffer_convert),
                  E_SUCCESS);
         relu_res[dnnResourceSrc] = mkl_buffer_convert;
         dnnDelete_F32(cv_input_to_grad);
       } else {
-        relu_res[dnnResourceSrc] = user_i;
+        relu_res[dnnResourceSrc] = buf_input;
       }
 
-      relu_res[dnnResourceDiffDst] = user_g;
+      void* buf_grad = static_cast<void*>(const_cast<T*>(g.flat<T>().data()));
+      relu_res[dnnResourceDiffDst] = buf_grad;
     }
 
     void MklCreateInputLayouts(OpKernelContext* context) {
@@ -317,9 +310,8 @@ void MklReluGradOp<Device, T>::Compute(OpKernelContext* context) {
                                      mkl_context.lt_grad, mkl_context.lt_grad,
                                      negative_slope),
            E_SUCCESS);
-  Tensor mkl_tmp_grad_buf_tensor, mkl_tmp_input_buf_tensor;
-  mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_grad_buf_tensor,
-                                       &mkl_tmp_input_buf_tensor);
+  Tensor mkl_tmp_input_buf_tensor;
+  mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_input_buf_tensor);
 
   if (input_is_mkl ||
       grad_is_mkl) { /*if  grad or input are MKL leave it in MKL*/
diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc
index 588d6874dd..b4aae67ca6 100644
--- a/tensorflow/core/kernels/mkl_tfconv_op.cc
+++ b/tensorflow/core/kernels/mkl_tfconv_op.cc
@@ -24,12 +24,13 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/util/tensor_format.h"
 
+#include "tensorflow/core/util/mkl_util.h"
 #include "third_party/mkl/include/mkl_dnn.h"
 #include "third_party/mkl/include/mkl_dnn_types.h"
-#include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -44,10 +45,11 @@ class MklToTfOp : public OpKernel {
   explicit MklToTfOp(OpKernelConstruction* context) : OpKernel(context) {
     OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format_str));
     OP_REQUIRES_OK(context, context->GetAttr("T", &op_data_type));
+    has_avx512f_ = port::TestCPUFeature(port::CPUFeature::AVX512F);
   }
 
   void Compute(OpKernelContext* context) override {
-    // 1. Check that input tensor is in MKL format.
+    // Check that input tensor is in MKL format.
     const Tensor& input_tensor = MklGetInput(context, 0);
     MklShape input_shape;
     GetMklShape(context, 0, &input_shape);
@@ -68,9 +70,12 @@ class MklToTfOp : public OpKernel {
     CHECK_EQ(op_data_type, output_data_type);
 
     TensorShape output_shape;
-    for (size_t i = 0; i < input_shape.GetDimension(); i++) {
+    size_t ndims = input_shape.GetDimension();
+    size_t* in_sizes = new size_t[ndims];
+    for (size_t i = 0; i < ndims; i++) {
       // Outermost to innermost dimension
       output_shape.AddDim(input_shape.GetSizes()[input_shape.tf_dim_idx(i)]);
+      in_sizes[i] = input_shape.GetSizes()[i];
     }
 
     // Allocate output tensor.
@@ -78,17 +83,41 @@ class MklToTfOp : public OpKernel {
     OP_REQUIRES_OK(context,
                    context->allocate_output(0, output_shape, &output_tensor));
 
-    // 3. Get input and output layout pointers.
-    dnnLayout_t output_layout =
-        static_cast<dnnLayout_t>(input_shape.GetTfLayout());
+    // If data format is NHWC, transform MKL tensor to NCHW format and then
+    // do NCHW -> NHWC.
+    dnnLayout_t lt_trans_input = nullptr;
+    Tensor mkl_tmp_trans_input_buf_tensor;
+    void* buf_trans_input = nullptr;
+    bool input_fmt_nhwc = input_shape.IsTensorInNHWCFormat();
+    if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
+      size_t strides_nchw[4];
+      GetStridesFromSizes(FORMAT_NCHW, strides_nchw, in_sizes);
+      CHECK_EQ(
+          dnnLayoutCreate_F32(&lt_trans_input, ndims, in_sizes, strides_nchw),
+          E_SUCCESS);
+      AllocTmpBuffer(context, &mkl_tmp_trans_input_buf_tensor, lt_trans_input,
+                     &buf_trans_input);
+    } else {
+      lt_trans_input = static_cast<dnnLayout_t>(input_shape.GetTfLayout());
+      buf_trans_input =
+          static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
+    }
 
-    // 4. Execute DNNConversion.
+    // Execute DNNConversion.
     void* input_buffer =
         static_cast<void*>(const_cast<T*>(input_tensor.flat<T>().data()));
-    void* output_buffer =
-        static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
-    input_shape.GetConvertedFlatData(output_layout, input_buffer,
-                                     output_buffer);
+    input_shape.GetConvertedFlatData(lt_trans_input, input_buffer,
+                                     buf_trans_input);
+    // NCHW -> NHWC, if data format is NHWC
+    if (input_fmt_nhwc && ndims == 4 && has_avx512f_) {
+      dnnLayoutDelete_F32(lt_trans_input);
+      TensorShape nhwc_shape = ShapeFromFormat(
+          FORMAT_NHWC, in_sizes[MklDims::N], in_sizes[MklDims::H],
+          in_sizes[MklDims::W], in_sizes[MklDims::C]);
+      MklNCHWToNHWC(mkl_tmp_trans_input_buf_tensor, &output_tensor);
+    }
+
+    delete[] in_sizes;
 
     VLOG(1) << "MKLToTFConversion complete successfully.";
   }
@@ -99,6 +128,9 @@ class MklToTfOp : public OpKernel {
 
   /// Data type of the operation
   DataType op_data_type;
+
+  /// CPUIDInfo
+  bool has_avx512f_ = false;
 };
 
 ///////////////////////////////////////////////////////////
diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index 9ffe71e031..dc95f67ff0 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -90,20 +90,24 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
   return intersection_area / (area_i + area_j - intersection_area);
 }
 
-void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
-                           const Tensor& scores, const Tensor& max_output_size,
+void DoNonMaxSuppressionOp(OpKernelContext* context,
+                           const Tensor& boxes,
+                           const Tensor& scores,
+                           const Tensor& max_output_size,
                            const float iou_threshold) {
   OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
-              errors::InvalidArgument("iou_threshold must be in [0, 1]"));
-
+      errors::InvalidArgument("iou_threshold must be in [0, 1]"));
+  
   int num_boxes = 0;
   ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
   if (!context->status().ok()) {
     return;
   }
 
-  const int output_size = std::min(max_output_size.scalar<int>()(), num_boxes);
-  typename TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();
+  const int output_size =
+      std::min(max_output_size.scalar<int>()(), num_boxes);
+  typename TTypes<float, 2>::ConstTensor boxes_data =
+      boxes.tensor<float, 2>();
 
   std::vector<float> scores_data(num_boxes);
   std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
@@ -123,7 +127,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
     for (int j = i + 1; j < num_boxes; ++j) {
       if (active[j]) {
         float iou =
-            ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
+          ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
         if (iou > iou_threshold) {
           active[j] = false;
           num_active--;
@@ -141,7 +145,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
   std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
 }
 
-}  // namespace
+} // namespace
 
 template <typename Device>
 class NonMaxSuppressionOp : public OpKernel {
@@ -163,8 +167,7 @@ class NonMaxSuppressionOp : public OpKernel {
         errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                 max_output_size.shape().DebugString()));
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
-                          iou_threshold_);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_);
   }
 
  private:
@@ -175,7 +178,8 @@ template <typename Device>
 class NonMaxSuppressionV2Op : public OpKernel {
  public:
   explicit NonMaxSuppressionV2Op(OpKernelConstruction* context)
-      : OpKernel(context) {}
+      : OpKernel(context) {
+  }
 
   void Compute(OpKernelContext* context) override {
     // boxes: [num_boxes, 4]
@@ -190,14 +194,14 @@ class NonMaxSuppressionV2Op : public OpKernel {
                                 max_output_size.shape().DebugString()));
     // iou_threshold: scalar
     const Tensor& iou_threshold = context->input(3);
-    OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
-                errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
-                                        iou_threshold.shape().DebugString()));
+    OP_REQUIRES(
+        context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
+        errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
+                                iou_threshold.shape().DebugString()));
 
     const float iou_threshold_val = iou_threshold.scalar<float>()();
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
-                          iou_threshold_val);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_val);
   }
 };
 
diff --git a/tensorflow/core/kernels/priority_queue.cc b/tensorflow/core/kernels/priority_queue.cc
index 894ad3c9a0..4c406fc1ed 100644
--- a/tensorflow/core/kernels/priority_queue.cc
+++ b/tensorflow/core/kernels/priority_queue.cc
@@ -339,7 +339,7 @@ void PriorityQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx,
             for (; s > 0; --s) {
               if (attempt->tuple.empty()) {
                 // Only allocate tuple when we have something to dequeue
-                // so we don't use exceessive memory when there are many
+                // so we don't use excessive memory when there are many
                 // blocked dequeue attempts waiting.
                 attempt->tuple.reserve(num_components());
                 for (int i = 0; i < num_components(); ++i) {
diff --git a/tensorflow/core/kernels/shape_ops.cc b/tensorflow/core/kernels/shape_ops.cc
index d78c6d2639..c5e3164145 100644
--- a/tensorflow/core/kernels/shape_ops.cc
+++ b/tensorflow/core/kernels/shape_ops.cc
@@ -48,6 +48,7 @@ REGISTER_KERNEL_BUILDER(Name("Shape")
                           ShapeOp<int64>);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
 REGISTER_KERNEL_BUILDER(Name("Shape")
@@ -102,7 +103,7 @@ REGISTER_KERNEL_BUILDER(Name("Shape")
                             .TypeConstraint<int32>("T")
                             .TypeConstraint<int64>("out_type"),
                         ShapeOp<int64>);
-#endif
+#endif  // GOOGLE_CUDA
 
 // ShapeN ---------------------------------------
 REGISTER_KERNEL_BUILDER(Name("ShapeN")
@@ -152,9 +153,9 @@ REGISTER_KERNEL_BUILDER(Name("ShapeN")
                             .TypeConstraint<int32>("T")
                             .TypeConstraint<int64>("out_type"),
                         ShapeNOp<int64>);
-#endif
+#endif  // GOOGLE_CUDA
 
-#if TENSORFLOW_USE_SYCL
+#ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                               \
   REGISTER_KERNEL_BUILDER(Name("ShapeN")                         \
                               .Device(DEVICE_SYCL)               \
@@ -170,11 +171,9 @@ REGISTER_KERNEL_BUILDER(Name("ShapeN")
                           ShapeNOp<int64>)
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("ShapeN")
                             .Device(DEVICE_SYCL)
                             .HostMemory("input")
@@ -202,13 +201,9 @@ REGISTER_KERNEL_BUILDER(Name("Rank").Device(DEVICE_CPU).HostMemory("output"),
                               .TypeConstraint<type>("T") \
                               .HostMemory("output"),     \
                           RankOp);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32 and bool.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Rank")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
@@ -250,7 +245,7 @@ REGISTER_KERNEL_BUILDER(Name("Rank")
                             .HostMemory("input")
                             .HostMemory("output"),
                         RankOp);
-#endif
+#endif  // GOOGLE_CUDA
 
 // Size ------------------------------------------
 REGISTER_KERNEL_BUILDER(Name("Size")
@@ -299,7 +294,7 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SizeOp<int64>);
-#endif
+#endif  // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                               \
@@ -315,13 +310,10 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                               .TypeConstraint<int64>("out_type") \
                               .HostMemory("output"),             \
                           SizeOp<int64>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Size")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
@@ -336,7 +328,7 @@ REGISTER_KERNEL_BUILDER(Name("Size")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SizeOp<int64>);
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 // ExpandDims ------------------------------------
 REGISTER_KERNEL_BUILDER(Name("ExpandDims")
@@ -365,7 +357,7 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                             .HostMemory("dim")
                             .HostMemory("output"),
                         ExpandDimsOp);
-#endif // GOOGLE_CUDA
+#endif  // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                           \
@@ -375,9 +367,8 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                               .TypeConstraint<int32>("Tdim") \
                               .HostMemory("dim"),            \
                           ExpandDimsOp);
-REGISTER_SYCL_KERNEL(float)
-REGISTER_SYCL_KERNEL(double)
-
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
 REGISTER_KERNEL_BUILDER(Name("ExpandDims")
@@ -388,7 +379,7 @@ REGISTER_KERNEL_BUILDER(Name("ExpandDims")
                             .HostMemory("dim")
                             .HostMemory("output"),
                         ExpandDimsOp);
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 // Squeeze ---------------------------------------
 REGISTER_KERNEL_BUILDER(Name("Squeeze").Device(DEVICE_CPU), SqueezeOp);
@@ -411,26 +402,23 @@ REGISTER_KERNEL_BUILDER(Name("Squeeze")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SqueezeOp);
-#endif
+#endif  // GOOGLE_CUDA
 
 #if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(type)                                  \
-  REGISTER_KERNEL_BUILDER(                                          \
-      Name("Squeeze").Device(DEVICE_SYCL).TypeConstraint<type>("T"),\
+#define REGISTER_SYCL_KERNEL(type)                                   \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("Squeeze").Device(DEVICE_SYCL).TypeConstraint<type>("T"), \
       SqueezeOp);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
+TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
+TF_CALL_bool(REGISTER_SYCL_KERNEL);
 #undef REGISTER_SYCL_KERNEL
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Squeeze")
                             .Device(DEVICE_SYCL)
                             .TypeConstraint<int32>("T")
                             .HostMemory("input")
                             .HostMemory("output"),
                         SqueezeOp);
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc
index ee6f9a28cd..d46701749b 100644
--- a/tensorflow/core/kernels/slice_op.cc
+++ b/tensorflow/core/kernels/slice_op.cc
@@ -118,6 +118,43 @@ static void SharedValidation(OpKernelContext* context,
   }
 }
 
+// Extracted out code in SliceOp::Compute so that MklSliceOp can reuse this
+// generic code
+template <typename T>
+static void SharedSliceCommonCases(OpKernelContext* context,
+                                   TensorShape* output_shape,
+                                   gtl::InlinedVector<int64, 4>* begin,
+                                   gtl::InlinedVector<int64, 4>* size,
+                                   Tensor** result,
+                                   bool* done) {
+  bool is_identity = true;
+  bool slice_dim0 = true;
+  *done = false;
+
+  SharedValidation(context, output_shape, &is_identity, &slice_dim0, begin,
+                   size);
+  if (!context->status().ok()) return;
+  const Tensor& input = context->input(0);
+  if (is_identity) {
+    VLOG(1) << "Slice identity";
+    context->set_output(0, input);
+    *done = true;
+    return;
+  }
+
+  if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), (*begin)[0],
+                                          (*size)[0])) {
+    VLOG(1) << "Slice dim 0: " << input.shape().DebugString();
+    CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
+    context->set_output(0, input.Slice((*begin)[0], (*begin)[0] + (*size)[0]));
+    *done = true;
+    return;
+  }
+
+  OP_REQUIRES_OK(context, context->allocate_output(0, *output_shape, result));
+}
+
+
 template <typename Device, typename T>
 class SliceOp : public OpKernel {
  public:
@@ -125,29 +162,89 @@ class SliceOp : public OpKernel {
 
   void Compute(OpKernelContext* context) override {
     TensorShape output_shape;
-    bool is_identity = true;
-    bool slice_dim0 = true;
     gtl::InlinedVector<int64, 4> begin;
     gtl::InlinedVector<int64, 4> size;
-    SharedValidation(context, &output_shape, &is_identity, &slice_dim0, &begin,
-                     &size);
-    if (!context->status().ok()) return;
+    Tensor* result = nullptr;
+    bool done = false;
+    SharedSliceCommonCases<T>(context, &output_shape, &begin, &size, &result,
+                              &done);
+    if (!context->status().ok() || done == true) return;
+
     const Tensor& input = context->input(0);
-    if (is_identity) {
-      VLOG(1) << "Slice identity";
-      context->set_output(0, input);
-      return;
+    const int input_dims = input.dims();
+
+    if (output_shape.num_elements() > 0) {
+      if (std::is_same<Device, CPUDevice>::value && input_dims == 2 &&
+          DataTypeCanUseMemcpy(DataTypeToEnum<T>::v())) {
+        auto input = context->input(0).tensor<T, 2>();
+        auto output = result->tensor<T, 2>();
+        // TODO(agarwal): Consider multi-threading this loop for cases where
+        // size[0] is very large.
+        for (int i = 0; i < size[0]; ++i) {
+          const int64 row = begin[0] + i;
+          if (i + 1 < size[0]) {
+            port::prefetch<port::PREFETCH_HINT_T0>(&output(i + 1, 0));
+            port::prefetch<port::PREFETCH_HINT_T0>(&input(row + 1, begin[1]));
+          }
+          memcpy(&output(i, 0), &input(row, begin[1]), size[1] * sizeof(T));
+        }
+        return;
+      }
+#define HANDLE_DIM(NDIM)                            \
+  if (input_dims == NDIM) {                         \
+    HandleCase<NDIM>(context, begin, size, result); \
+    return;                                         \
+  }
+
+      HANDLE_DIM(1);
+      HANDLE_DIM(2);
+      HANDLE_DIM(3);
+      HANDLE_DIM(4);
+      HANDLE_DIM(5);
+      HANDLE_DIM(6);
+      HANDLE_DIM(7);
+
+#undef HANDLE_DIM
+
+      OP_REQUIRES(context, false, errors::Unimplemented(
+                                      "SliceOp : Unhandled input dimensions"));
     }
+  }
 
-    if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), begin[0], size[0])) {
-      VLOG(1) << "Slice dim 0: " << input.shape().DebugString();
-      CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
-      context->set_output(0, input.Slice(begin[0], begin[0] + size[0]));
-      return;
+ private:
+  template <int NDIM>
+  void HandleCase(OpKernelContext* context, const gtl::ArraySlice<int64>& begin,
+                  const gtl::ArraySlice<int64>& size, Tensor* result) {
+    Eigen::DSizes<Eigen::DenseIndex, NDIM> indices;
+    Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes;
+    for (int i = 0; i < NDIM; ++i) {
+      indices[i] = begin[i];
+      sizes[i] = size[i];
     }
 
+    functor::Slice<Device, T, NDIM>()(
+        context->eigen_device<Device>(), result->tensor<T, NDIM>(),
+        context->input(0).tensor<T, NDIM>(), indices, sizes);
+  }
+};
+
+#ifdef INTEL_MKL
+template <typename Device, typename T>
+class MklSliceOp : public OpKernel {
+ public:
+  explicit MklSliceOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    TensorShape output_shape;
+    gtl::InlinedVector<int64, 4> begin;
+    gtl::InlinedVector<int64, 4> size;
     Tensor* result = nullptr;
-    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &result));
+    bool done = false;
+    SharedSliceCommonCases<T>(context, &output_shape, &begin, &size, &result,
+                              &done);
+    if (!context->status().ok() || done == true) return;
+
+    const Tensor& input = context->input(0);
     const int input_dims = input.dims();
 
     if (output_shape.num_elements() > 0) {
@@ -189,9 +286,123 @@ class SliceOp : public OpKernel {
   }
 
  private:
+  // Helper function for DoesSliceShapeDifferInOnly1D. Checks if the following
+  // criteria matches for slice_dim: if indices for slice are 0 in all dims
+  // except slice_dim and if sizes of all the dimensions of the slice are same
+  // as the sizes of all the dimensions of the input except slice_dim, then
+  // returns True. Otherwise, returns False.
+  bool DoesSliceShapeDifferInOnly1DHelper(const TensorShape& input_shape,
+                          const gtl::ArraySlice<int64>& begin,
+                          const gtl::ArraySlice<int64>& size,
+                          int slice_dim) {
+    for (int dim = 0; dim < 4; dim++) {
+      if (dim != slice_dim &&
+          (begin[dim] != 0 || size[dim] != input_shape.dim_size(dim))) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  // Is 'input' tensor being sliced over a single dimension out of 4?
+  //
+  // This check is applicable in the context of Slice of a 4-D tensor in
+  // NHWC or NCHW format over channel dimension.
+  //
+  // If indices for slice are 0 in all dims except one dimension and if sizes of
+  // all dimensions of slice are same as sizes of all dimensions of inputs
+  // except that dimension, then we are slicing over a single dimension.
+  //
+  // Returns True if Slicing over a single dimension, and sets slice_dim
+  // to the number of the dimension that satisfies criteria.
+  bool DoesSliceShapeDifferInOnly1D(const TensorShape& input_shape,
+                          const gtl::ArraySlice<int64>& begin,
+                          const gtl::ArraySlice<int64>& size,
+                          int* slice_dim) {
+    for (int dim = 0; dim < 4; dim++) {
+      if (DoesSliceShapeDifferInOnly1DHelper(input_shape, begin, size, dim)) {
+        *slice_dim = dim;
+        return true;
+      }
+    }
+    return false;
+  }
+
   template <int NDIM>
-  void HandleCase(OpKernelContext* context, const gtl::ArraySlice<int64>& begin,
+  void HandleCase(OpKernelContext* context,
+                  const gtl::ArraySlice<int64>& begin,
                   const gtl::ArraySlice<int64>& size, Tensor* result) {
+    int slice_dim = -1;
+    TensorShape in_shape = context->input(0).shape();
+    // Special case for handling 4-D tensor slice when shape of the slice
+    // differs from the input tensor in only 1 out of 4 dimensions.
+    // This case arises in the context of Slice of 4-D tensor in NHWC or NCHW
+    // format over channel dimension.
+    if (NDIM == 4 &&
+        DoesSliceShapeDifferInOnly1D(in_shape, begin, size, &slice_dim)) {
+        size_t in_strides[4] = { (size_t) in_shape.dim_size(1) *
+                                          in_shape.dim_size(2) *
+                                          in_shape.dim_size(3),
+                                 (size_t) in_shape.dim_size(2) *
+                                          in_shape.dim_size(3),
+                                 (size_t) in_shape.dim_size(3),
+                                 (size_t) 1
+                               };
+
+        size_t out_strides[4] = { (size_t) size[1] * size[2] * size[3],
+                                  (size_t) size[2] * size[3],
+                                  (size_t) size[3],
+                                  (size_t) 1 };
+
+        T *in_buf = const_cast<T*>(const_cast<const T*>(
+                    context->input(0).flat<T>().data()));
+        T *op_buf = result->flat<T>().data();
+
+        if (slice_dim == 1) {
+          /* data format = NCHW */
+
+          #pragma omp parallel for
+          for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
+              T *ip  = in_buf + (d0 * in_strides[0]);
+              T *op  = op_buf + ((d0 - begin[0]) * out_strides[0]);
+            #pragma omp parallel for
+            for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
+              T *ip1 = ip + (d1 * in_strides[1]);
+              T *op1 = op + ((d1 - begin[1]) * out_strides[1]);
+              // For NCHW, H and W will be contiguous. So we can copy
+              // both with one memcpy.
+              memcpy(static_cast<void*>(op1), static_cast<void*>(ip1),
+                     sizeof(T) * in_strides[1]);
+            }
+          }
+          return;
+        } else if (slice_dim == 3) {
+          /* data_format = NHWC */
+
+          #pragma omp parallel for
+          for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
+              T *ip = in_buf + (d0 * in_strides[0]);
+              T *op = op_buf + ((d0 - begin[0]) * out_strides[0]);
+            #pragma omp parallel for
+            for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
+              T *ip1 = ip + (d1 * in_strides[1]);
+              T *op1 = op + ((d1 - begin[1]) * out_strides[1]);
+              #pragma omp parallel for
+              for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) {
+                T *ip2 = ip1 + (d2 * in_strides[2]);
+                T *ip3 = ip2 + begin[3];
+                T *op2 = op1 + ((d2 - begin[2]) * out_strides[2]);
+                T *op3 = op2;
+                memcpy(static_cast<void*>(op3), static_cast<void*>(ip3),
+                       sizeof(T) * size[3]);
+              }
+            }
+          }
+          return;
+        }
+        // slice_dim is not 1 or 3, then we fallback to Eigen implementation.
+    }
+
     Eigen::DSizes<Eigen::DenseIndex, NDIM> indices;
     Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes;
     for (int i = 0; i < NDIM; ++i) {
@@ -204,6 +415,7 @@ class SliceOp : public OpKernel {
         context->input(0).tensor<T, NDIM>(), indices, sizes);
   }
 };
+#endif
 
 // Forward declarations of the functor specializations for declared in the
 // sharded source files.
@@ -233,6 +445,7 @@ DECLARE_FOR_N(bfloat16);
 #undef DECLARE_CPU_SPEC
 }  // namespace functor
 
+#ifndef INTEL_MKL
 #define REGISTER_SLICE(type)                             \
   REGISTER_KERNEL_BUILDER(Name("Slice")                  \
                               .Device(DEVICE_CPU)        \
@@ -244,8 +457,21 @@ DECLARE_FOR_N(bfloat16);
 TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
 TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
 REGISTER_SLICE(bfloat16);
+#undef REGISTER_SLICE
+#else
+#define REGISTER_SLICE(type)                             \
+  REGISTER_KERNEL_BUILDER(Name("Slice")                  \
+                              .Device(DEVICE_CPU)        \
+                              .TypeConstraint<type>("T") \
+                              .HostMemory("begin")       \
+                              .HostMemory("size"),       \
+                          MklSliceOp<CPUDevice, type>)
 
+TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
+TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
+REGISTER_SLICE(bfloat16);
 #undef REGISTER_SLICE
+#endif  // INTEL_MKL
 
 #if GOOGLE_CUDA
 // Forward declarations of the functor specializations for GPU.
diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op.cc b/tensorflow/core/kernels/sparse_reduce_op.cc
similarity index 83%
rename from tensorflow/core/kernels/sparse_reduce_sum_op.cc
rename to tensorflow/core/kernels/sparse_reduce_op.cc
index 074aab9f9e..9e60791f97 100644
--- a/tensorflow/core/kernels/sparse_reduce_sum_op.cc
+++ b/tensorflow/core/kernels/sparse_reduce_op.cc
@@ -130,10 +130,30 @@ Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
   return Status::OK();
 }
 
-template <typename T>
-class SparseReduceSumOp : public OpKernel {
+struct SumOp {
+  template <typename T>
+  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
+      s.device(ctx->eigen_cpu_device()) = v.sum();
+  }
+  static StringPiece Name() {
+      return "sum";
+  }
+};
+
+struct MaxOp {
+  template <typename T>
+  static void Run(OpKernelContext *ctx, typename TTypes<T>::Scalar &s, const typename TTypes<T>::UnalignedVec &v) {
+      s.device(ctx->eigen_cpu_device()) = v.maximum();
+  }
+  static StringPiece Name() {
+      return "max";
+  }
+};
+
+template <typename T, typename Op>
+class SparseReduceOp : public OpKernel {
  public:
-  explicit SparseReduceSumOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+  explicit SparseReduceOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
   }
 
@@ -163,10 +183,10 @@ class SparseReduceSumOp : public OpKernel {
     auto out_flat = out_values->flat<T>();
     out_flat.setZero();
 
-    Tensor tmp_group_sum;
+    Tensor tmp_reduced_val;
     OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_group_sum));
-    auto group_sum = tmp_group_sum.scalar<T>();
+                                           TensorShape({}), &tmp_reduced_val));
+    auto reduced_val = tmp_reduced_val.scalar<T>();
 
     // Compute strides, and use it to convert coords to flat index.  The
     // coordinates returned by .group() have the same ndims as group_by_dims.
@@ -196,11 +216,12 @@ class SparseReduceSumOp : public OpKernel {
     // g.group() provides the coordinates of a particular reduced value.
     sp.Reorder<T>(reduction.reorder_dims);
     for (const auto &g : sp.group(reduction.group_by_dims)) {
-      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
+      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
       const int64 idx = CoordinatesToFlatIndex(g.group(), output_strides);
-      out_flat(idx) = group_sum();
+      out_flat(idx) = reduced_val();
       VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; idx: " << idx << "; group sum: " << group_sum();
+              << "; idx: " << idx << "; group " << Op::Name() << ": "
+              << reduced_val();
     }
   }
 
@@ -212,14 +233,21 @@ class SparseReduceSumOp : public OpKernel {
 #define REGISTER_KERNELS(T)                                              \
   REGISTER_KERNEL_BUILDER(                                               \
       Name("SparseReduceSum").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSumOp<T>)
+      SparseReduceOp<T, SumOp>)
 TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
 
-template <typename T>
-class SparseReduceSumSparseOp : public OpKernel {
+#define REGISTER_KERNELS(T)                                              \
+  REGISTER_KERNEL_BUILDER(                                               \
+      Name("SparseReduceMax").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceOp<T, MaxOp>)
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+template <typename T, typename Op>
+class SparseReduceSparseOp : public OpKernel {
  public:
-  explicit SparseReduceSumSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
+  explicit SparseReduceSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
   }
 
@@ -260,13 +288,13 @@ class SparseReduceSumSparseOp : public OpKernel {
                    ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
     auto out_flat = out_values_t->flat<T>();
 
-    Tensor tmp_group_sum;
+    Tensor tmp_reduced_val;
     OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
-                                           TensorShape({}), &tmp_group_sum));
-    auto group_sum = tmp_group_sum.scalar<T>();
+                                           TensorShape({}), &tmp_reduced_val));
+    auto reduced_val = tmp_reduced_val.scalar<T>();
     int64 i = 0;
     for (const auto &g : sp.group(reduction.group_by_dims)) {
-      group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
+      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
       std::vector<int64> group = g.group();
       for (int64 j = 0; j < group.size(); j++) {
         if (keep_dims_) {
@@ -275,10 +303,11 @@ class SparseReduceSumSparseOp : public OpKernel {
           out_indices_mat(i, j) = group[j];
         }
       }
-      out_flat(i) = group_sum();
+      out_flat(i) = reduced_val();
       i++;
       VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
-              << "; group sum: " << group_sum();
+              << "; group " << Op::Name() << ": "
+              << reduced_val();
     }
 
     Tensor *out_shape_t;
@@ -298,8 +327,15 @@ class SparseReduceSumSparseOp : public OpKernel {
 #define REGISTER_KERNELS(T)                                                    \
   REGISTER_KERNEL_BUILDER(                                                     \
       Name("SparseReduceSumSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
-      SparseReduceSumSparseOp<T>)
+      SparseReduceSparseOp<T, SumOp>)
 TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
 
+#define REGISTER_KERNELS(T)                                                    \
+  REGISTER_KERNEL_BUILDER(                                                     \
+      Name("SparseReduceMaxSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      SparseReduceSparseOp<T, MaxOp>)
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/stack_ops.cc b/tensorflow/core/kernels/stack_ops.cc
index b4698a8053..2db3e5ef77 100644
--- a/tensorflow/core/kernels/stack_ops.cc
+++ b/tensorflow/core/kernels/stack_ops.cc
@@ -40,6 +40,9 @@ namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif // TENSORFLOW_USE_SYCL
 
 class Stack : public ResourceBase {
  public:
@@ -182,6 +185,10 @@ class StackOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_CPU), StackOp);
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_GPU).HostMemory("handle"),
                         StackOp);
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_SYCL).HostMemory("handle"),
+                        StackOp);
+#endif // TENSORFLOW_USE_SYCL
 
 template <typename Device>
 class StackPushOp : public AsyncOpKernel {
@@ -213,7 +220,11 @@ class StackPushOp : public AsyncOpKernel {
     static constexpr int kCopyThreshold = 2048;
     static constexpr double kOccupancy = 0.7;
     if (swap_memory_ && !alloc_attrs.on_host() &&
-        std::is_same<Device, GPUDevice>::value &&
+        ( std::is_same<Device, GPUDevice>::value
+#ifdef TENSORFLOW_USE_SYCL
+          || std::is_same<Device, SYCLDevice>::value
+#endif // TENSORFLOW_USE_SYCL
+        ) &&
         tensor.TotalBytes() > kCopyThreshold && stack->IsUsefulToSwap(tensor)) {
       DeviceContext* device_ctxt = ctx->op_device_context();
       auto device = static_cast<tensorflow::Device*>(ctx->device());
@@ -289,6 +300,31 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                        \
+  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("handle")       \
+                              .TypeConstraint<type>("T"), \
+                          StackPushOp<SYCLDevice>);
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
+
+#define REGISTER_SYCL_HOST_KERNEL(type)                   \
+  REGISTER_KERNEL_BUILDER(Name("StackPush")               \
+                              .Device(DEVICE_SYCL)        \
+                              .HostMemory("handle")       \
+                              .HostMemory("elem")         \
+                              .HostMemory("output")       \
+                              .TypeConstraint<type>("T"), \
+                          StackPushOp<SYCLDevice>)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(bool);
+#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 class StackPopOp : public AsyncOpKernel {
  public:
   explicit StackPopOp(OpKernelConstruction* context) : AsyncOpKernel(context) {}
@@ -359,6 +395,31 @@ REGISTER_GPU_HOST_KERNEL(bool);
 
 #undef REGISTER_GPU_HOST_KERNEL
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(type)                                \
+  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
+                              .Device(DEVICE_SYCL)                \
+                              .HostMemory("handle")               \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp)
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL_KERNEL);
+
+#define REGISTER_SYCL_HOST_KERNEL(type)                           \
+  REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
+                              .Device(DEVICE_SYCL)                \
+                              .HostMemory("handle")               \
+                              .HostMemory("elem")                 \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp)
+
+REGISTER_SYCL_HOST_KERNEL(int32);
+REGISTER_SYCL_HOST_KERNEL(bool);
+
+#undef REGISTER_SYCL_KERNEL
+#undef REGISTER_SYCL_HOST_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
 class StackCloseOp : public OpKernel {
  public:
   explicit StackCloseOp(OpKernelConstruction* context) : OpKernel(context) {}
@@ -376,5 +437,8 @@ class StackCloseOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("StackClose").Device(DEVICE_CPU), StackCloseOp);
 REGISTER_KERNEL_BUILDER(
     Name("StackClose").Device(DEVICE_GPU).HostMemory("handle"), StackCloseOp);
-
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(
+    Name("StackClose").Device(DEVICE_SYCL).HostMemory("handle"), StackCloseOp);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/tile_functor.h b/tensorflow/core/kernels/tile_functor.h
new file mode 100644
index 0000000000..d80898b24f
--- /dev/null
+++ b/tensorflow/core/kernels/tile_functor.h
@@ -0,0 +1,115 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_KERNELS_TILE_FUNCTOR_H_
+#define TENSORFLOW_KERNELS_TILE_FUNCTOR_H_
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+namespace internal {
+
+// Helper to compute 'strides' given a tensor 'shape'. I.e.,
+// strides[i] = prod(shape.dim_size[(i+1):])
+template <typename Index>
+gtl::InlinedVector<Index, 8> ComputeStride(const TensorShape& shape) {
+  const int ndims = shape.dims();
+  gtl::InlinedVector<Index, 8> strides(ndims);
+  Index stride = 1;
+  for (int i = ndims - 1; i >= 0; --i) {
+    strides[i] = stride;
+    stride *= static_cast<Index>(shape.dim_size(i));
+  }
+  return strides;
+}
+
+
+// Device-specific naive implementation for tile.
+template <typename Device, typename T>
+void TileSimple(const Device& d, Tensor* out, const Tensor& in);
+
+template <typename Device, typename T, int NDIM>
+void TileUsingEigen(const Device& d, Tensor* out, const Tensor& in,
+                    const gtl::ArraySlice<int32>& broadcast_array) {
+  auto x = in.tensor<T, NDIM>();
+  auto y = out->tensor<T, NDIM>();
+
+  Eigen::array<int32, NDIM> b;
+  for (int i = 0; i < NDIM; ++i) b[i] = broadcast_array[i];
+  if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
+    // Use 32bit indexing to speed up the computations
+    To32Bit(y).device(d) = To32Bit(x).broadcast(b);
+  } else {
+    y.device(d) = x.broadcast(b);
+  }
+}
+
+template <typename Device, typename T>
+void TileUsingEigen(const Device& d, Tensor* out, const Tensor& in,
+                    const gtl::ArraySlice<int32>&) {
+  auto x = in.tensor<T, 0>();
+  auto y = out->tensor<T, 0>();
+  // In the scalar case we simply copy the input.
+  y.device(d) = x;
+}
+
+}  // end namespace internal
+
+namespace functor {
+
+template <typename Device, typename T>
+struct Tile {
+  void operator()(const Device& d, Tensor* out, const Tensor& in,
+                  const gtl::ArraySlice<int32> broadcast_array) const {
+    switch (in.dims()) {
+      case 0:
+        internal::TileUsingEigen<Device, T>(d, out, in, broadcast_array);
+        break;
+      case 1:
+        internal::TileUsingEigen<Device, T, 1>(d, out, in, broadcast_array);
+        break;
+      case 2:
+        internal::TileUsingEigen<Device, T, 2>(d, out, in, broadcast_array);
+        break;
+      case 3:
+        internal::TileUsingEigen<Device, T, 3>(d, out, in, broadcast_array);
+        break;
+      case 4:
+        internal::TileUsingEigen<Device, T, 4>(d, out, in, broadcast_array);
+        break;
+      case 5:
+        internal::TileUsingEigen<Device, T, 5>(d, out, in, broadcast_array);
+        break;
+      case 6:
+        internal::TileUsingEigen<Device, T, 6>(d, out, in, broadcast_array);
+        break;
+      case 7:
+        internal::TileUsingEigen<Device, T, 7>(d, out, in, broadcast_array);
+        break;
+      default:
+        internal::TileSimple<Device, T>(d, out, in);
+        break;
+    }
+  }
+};
+
+}  // end namespace functor
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_KERNELS_TILE_FUNCTOR_H_
diff --git a/tensorflow/core/kernels/tile_functor_cpu.cc b/tensorflow/core/kernels/tile_functor_cpu.cc
new file mode 100644
index 0000000000..3af00350b9
--- /dev/null
+++ b/tensorflow/core/kernels/tile_functor_cpu.cc
@@ -0,0 +1,85 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/kernels/tile_functor.h"
+
+namespace tensorflow {
+
+namespace internal {
+
+template <typename Device, typename T>
+void TileSimple(const Device& d, Tensor* out, const Tensor& in) {
+  const int ndims = in.dims();
+  const int64 nelem = out->NumElements();
+  gtl::InlinedVector<int64, 8> in_strides = ComputeStride<int64>(in.shape());
+  gtl::InlinedVector<int64, 8> out_strides = ComputeStride<int64>(out->shape());
+  const T* p = in.flat<T>().data();
+  T* q = out->flat<T>().data();
+
+  for (int64 o_idx = 0; o_idx < nelem; ++o_idx) {
+    int64 i_idx = 0;
+    int64 t = o_idx;
+    for (int i = 0; i < ndims; ++i) {
+      i_idx += t / out_strides[i] % in.dim_size(i) * in_strides[i];
+      t %= out_strides[i];
+    }
+    q[o_idx] = p[i_idx];
+  }
+}
+
+}  // end namespace internal
+
+namespace functor {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+
+// Register functors used for Tile functor.
+#define DEFINE_TYPE(T) template struct Tile<CPUDevice, T>;
+
+TF_CALL_bool(DEFINE_TYPE);
+TF_CALL_float(DEFINE_TYPE);
+TF_CALL_double(DEFINE_TYPE);
+TF_CALL_uint8(DEFINE_TYPE);
+TF_CALL_int32(DEFINE_TYPE);
+TF_CALL_int16(DEFINE_TYPE);
+TF_CALL_int64(DEFINE_TYPE);
+TF_CALL_half(DEFINE_TYPE);
+TF_CALL_complex64(DEFINE_TYPE);
+TF_CALL_complex128(DEFINE_TYPE);
+TF_CALL_string(DEFINE_TYPE);
+
+#undef DEFINE_TYPE
+
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+
+#define DEFINE_TYPE(T) template struct Tile<SYCLDevice, T>;
+
+TF_CALL_bool(DEFINE_TYPE);
+TF_CALL_float(DEFINE_TYPE);
+TF_CALL_double(DEFINE_TYPE);
+TF_CALL_uint8(DEFINE_TYPE);
+TF_CALL_int32(DEFINE_TYPE);
+TF_CALL_int16(DEFINE_TYPE);
+TF_CALL_int64(DEFINE_TYPE);
+
+#undef DEFINE_TYPE
+#endif // TENSORFLOW_USE_SYCL
+
+}  // end namespace functor
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/tile_functor_gpu.cu.cc b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
new file mode 100644
index 0000000000..08aca8c731
--- /dev/null
+++ b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
@@ -0,0 +1,101 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/kernels/tile_functor.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+#include "tensorflow/core/framework/register_types.h"
+
+namespace tensorflow {
+namespace internal {
+
+template <typename T>
+__global__ void TileKernel(int nthreads, const T* src, const int32* buf,
+                           const int32 ndims, T* dst) {
+  const int32* in_strides = buf;
+  const int32* out_strides = buf + ndims;
+  const int32* in_dim_sizes = buf + ndims * 2;
+  CUDA_1D_KERNEL_LOOP(o_idx, nthreads) {
+    int32 i_idx = 0;
+    int32 t = o_idx;
+    for (int i = 0; i < ndims; ++i) {
+      i_idx += t / out_strides[i] % in_dim_sizes[i] * in_strides[i];
+      t %= out_strides[i];
+    }
+    dst[o_idx] = ldg(src + i_idx);
+  }
+}
+
+template <typename Device, typename T>
+void TileSimple(const Device& d, Tensor* out, const Tensor& in) {
+  // Ensures we can use 32-bit index.
+  const int64 in_nelem = in.NumElements();
+  CHECK_LT(in_nelem, kint32max) << "Tensor too large to transpose on GPU";
+  const int64 out_nelem = out->NumElements();
+  CHECK_LT(out_nelem, kint32max) << "Tensor too large to transpose on GPU";
+  // Pack strides and input dimension sizes into one buffer.
+  const int32 ndims = in.dims();
+  gtl::InlinedVector<int32, 24> host_buf(ndims * 3);
+  gtl::InlinedVector<int32, 8> in_strides = ComputeStride<int32>(in.shape());
+  gtl::InlinedVector<int32, 8> out_strides = ComputeStride<int32>(out->shape());
+  for (int i = 0; i < ndims; ++i) {
+    host_buf[i] = in_strides[i];
+    host_buf[ndims + i] = out_strides[i];
+    host_buf[ndims * 2 + i] = in.dim_size(i);
+  }
+  // Copies the input strides, output strides and input dimension sizes to the device.
+  auto num_bytes = sizeof(int64) * host_buf.size();
+  auto dev_buf = d.allocate(num_bytes);
+  // NOTE: host_buf is not allocated by CudaHostAllocator, and
+  // therefore we are doing a sync copy effectively.
+  d.memcpyHostToDevice(dev_buf, host_buf.data(), num_bytes);
+  // Launch kernel to q[...] = p[...].
+  const T* p = in.flat<T>().data();
+  T* q = out->flat<T>().data();
+  CudaLaunchConfig cfg = GetCudaLaunchConfig(out_nelem, d);
+  TileKernel<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>(
+      cfg.virtual_thread_count, p, reinterpret_cast<const int32*>(dev_buf),
+      ndims, q);
+  // Safe to deallocate immediately after the kernel launch.
+  d.deallocate(dev_buf);
+}
+
+}  // end namespace internal
+
+namespace functor {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+// Register functors used for Tile functor.
+#define DEFINE_TYPE(T) template struct Tile<GPUDevice, T>;
+
+TF_CALL_int16(DEFINE_TYPE);
+TF_CALL_int32(DEFINE_TYPE);
+TF_CALL_int64(DEFINE_TYPE);
+TF_CALL_float(DEFINE_TYPE);
+TF_CALL_double(DEFINE_TYPE);
+TF_CALL_half(DEFINE_TYPE);
+TF_CALL_complex64(DEFINE_TYPE);
+TF_CALL_complex128(DEFINE_TYPE);
+
+#undef DEFINE_TYPE
+
+}  // end namespace functor
+}  // namespace tensorflow
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/tile_ops.cc b/tensorflow/core/kernels/tile_ops.cc
index 7c72487d3f..f1da3c8afb 100644
--- a/tensorflow/core/kernels/tile_ops.cc
+++ b/tensorflow/core/kernels/tile_ops.cc
@@ -44,21 +44,12 @@ typedef Eigen::GpuDevice GPUDevice;
 typedef Eigen::SyclDevice SYCLDevice;
 #endif // TENSORFLOW_USE_SYCL
 
-// Forward declarations of functors that will be defined in
-// tile_ops_cpu_impl*.cc and tile_ops_gpu.cu.cc.
+// Forward declarations of functors that will be defined in tile_ops_impl.h
 namespace functor {
-template <typename Device, typename T, int NDIM>
-struct Tile {
-  void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out,
-                  typename TTypes<T, NDIM>::ConstTensor in,
-                  const Eigen::array<int32, NDIM>& broadcast_array) const;
-};
-
 template <typename Device, typename T>
-struct Tile<Device, T, 0> {
-  void operator()(const Device& d, typename TTypes<T, 0>::Tensor out,
-                  typename TTypes<T, 0>::ConstTensor in,
-                  const Eigen::array<int32, 0>&) const;
+struct Tile {
+  void operator()(const Device& d, Tensor* out, const Tensor& in,
+                  const gtl::ArraySlice<int32> broadcast_array) const;
 };
 
 template <typename Device, typename T, int NDIM>
@@ -134,21 +125,12 @@ class TileOp : public OpKernel {
     // If there's no output, there's nothing to do.
     if (output_shape.num_elements() == 0) return;
 
-#define HANDLE_DIM(DT, NDIM)                                   \
-  if (context->input(0).dtype() == DT && input_dims == NDIM) { \
-    HandleCase<DT, NDIM>(context, multiples_array, result);    \
+#define HANDLE_TYPE(DT)                                        \
+  if (context->input(0).dtype() == DT) {                       \
+    HandleCase<DT>(context, multiples_array, result);          \
     return;                                                    \
   }
 
-#define HANDLE_TYPE(T) \
-  HANDLE_DIM(T, 1)     \
-  HANDLE_DIM(T, 2)     \
-  HANDLE_DIM(T, 3)     \
-  HANDLE_DIM(T, 4)     \
-  HANDLE_DIM(T, 5)     \
-  HANDLE_DIM(T, 6)     \
-  HANDLE_DIM(T, 7)
-
 #define HANDLE_TYPE_NAME(T) HANDLE_TYPE(DataTypeToEnum<T>::value)
 
     // Invoke macro using TF_CALL_* so type-filtering for platform applies.
@@ -166,7 +148,6 @@ class TileOp : public OpKernel {
 
 #undef HANDLE_TYPE_NAME
 #undef HANDLE_TYPE
-#undef HANDLE_DIM
 
     OP_REQUIRES(context, false,
                 errors::Unimplemented(
@@ -175,21 +156,17 @@ class TileOp : public OpKernel {
   }
 
  private:
-  template <DataType DT, int NDIM>
+  template <DataType DT>
   void HandleCaseImpl(OpKernelContext* context,
                       const gtl::ArraySlice<int32>& multiples_array,
                       Tensor* result) {
     typedef typename EnumToDataType<DT>::Type T;
-    Eigen::array<int32, NDIM> broadcast_array;
-    for (int i = 0; i < NDIM; ++i) {
-      broadcast_array[i] = multiples_array[i];
-    }
-    functor::Tile<Device, T, NDIM>()(
-        context->eigen_device<Device>(), result->tensor<T, NDIM>(),
-        context->input(0).tensor<T, NDIM>(), broadcast_array);
+    functor::Tile<Device, T>() (
+        context->eigen_device<Device>(), result,
+        context->input(0), multiples_array);
   }
 
-  template <DataType DT, int NDIM>
+  template <DataType DT>
   void HandleCase(OpKernelContext* context,
                   const gtl::ArraySlice<int32>& multiples_array,
                   Tensor* result);
@@ -198,45 +175,35 @@ class TileOp : public OpKernel {
 };
 
 template <typename Device>
-template <DataType DT, int NDIM>
+template <DataType DT>
 inline void TileOp<Device>::HandleCase(
     OpKernelContext* context, const gtl::ArraySlice<int32>& multiples_array,
     Tensor* result) {
   // TODO(vrv): print out the device name if useful. Currently disabled to avoid
   // having to use RTTI.
-  LOG(FATAL) << "TileOp: Invalid combination of Device, DT and NDIM: "
+  LOG(FATAL) << "TileOp: Invalid combination of Device, DT: "
              // << typeid(Device).name() << ", "
-             << DataTypeString(DT) << ", " << NDIM;
+             << DataTypeString(DT);
 }
 
-#define HANDLE_CASE(device, T, dtype, ndim)                            \
+#define HANDLE_CASE(device, dtype)                                     \
   template <>                                                          \
   template <>                                                          \
-  void TileOp<device>::HandleCase<dtype, ndim>(                        \
+  void TileOp<device>::HandleCase<dtype>(                              \
       OpKernelContext * context,                                       \
       const gtl::ArraySlice<int32>& multiples_array, Tensor* result) { \
-    HandleCaseImpl<dtype, ndim>(context, multiples_array, result);     \
+    HandleCaseImpl<dtype>(context, multiples_array, result);           \
   }
 
-// 0-D handled above
-#define HANDLE_CASE_DIM(device, T, dtype) \
-  HANDLE_CASE(device, T, dtype, 1);       \
-  HANDLE_CASE(device, T, dtype, 2);       \
-  HANDLE_CASE(device, T, dtype, 3);       \
-  HANDLE_CASE(device, T, dtype, 4);       \
-  HANDLE_CASE(device, T, dtype, 5);       \
-  HANDLE_CASE(device, T, dtype, 6);       \
-  HANDLE_CASE(device, T, dtype, 7);
-
 #define HANDLE_TYPE_NAME_CPU(T) \
-  HANDLE_CASE_DIM(CPUDevice, T, DataTypeToEnum<T>::value);
+  HANDLE_CASE(CPUDevice, DataTypeToEnum<T>::value);
 
 #define HANDLE_TYPE_NAME_GPU(T) \
-  HANDLE_CASE_DIM(GPUDevice, T, DataTypeToEnum<T>::value);
+  HANDLE_CASE(GPUDevice, DataTypeToEnum<T>::value);
 
 #ifdef TENSORFLOW_USE_SYCL
 #define HANDLE_TYPE_NAME_SYCL(T) \
-  HANDLE_CASE_DIM(SYCLDevice, T, DataTypeToEnum<T>::value);
+  HANDLE_CASE(SYCLDevice, DataTypeToEnum<T>::value);
 #endif // TENSORFLOW_USE_SYCL
 
 TF_CALL_bool(HANDLE_TYPE_NAME_CPU);
@@ -275,7 +242,6 @@ TF_CALL_int64(HANDLE_TYPE_NAME_SYCL);
 #ifdef TENSORFLOW_USE_SYCL
 #undef HANDLE_TYPE_NAME_SYCL
 #endif // TENSORFLOW_USE_SYCL
-#undef HANDLE_CASE_DIM
 #undef HANDLE_CASE
 
 // --------------------------------------------------------------------------
diff --git a/tensorflow/core/kernels/tile_ops_cpu_impl.h b/tensorflow/core/kernels/tile_ops_cpu_impl.h
index db3f046439..a6eed4935d 100644
--- a/tensorflow/core/kernels/tile_ops_cpu_impl.h
+++ b/tensorflow/core/kernels/tile_ops_cpu_impl.h
@@ -21,29 +21,11 @@ limitations under the License.
 #include "tensorflow/core/kernels/tile_ops_impl.h"
 
 namespace tensorflow {
+
 namespace functor {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 
-// Register functors used for TileOp.
-#define DEFINE_DIM(T, NDIM) template struct Tile<CPUDevice, T, NDIM>;
-#define DEFINE_TYPE(T) DEFINE_DIM(T, CPU_PROVIDED_IXDIM)
-
-TF_CALL_bool(DEFINE_TYPE);
-TF_CALL_float(DEFINE_TYPE);
-TF_CALL_double(DEFINE_TYPE);
-TF_CALL_uint8(DEFINE_TYPE);
-TF_CALL_int32(DEFINE_TYPE);
-TF_CALL_int16(DEFINE_TYPE);
-TF_CALL_int64(DEFINE_TYPE);
-TF_CALL_half(DEFINE_TYPE);
-TF_CALL_complex64(DEFINE_TYPE);
-TF_CALL_complex128(DEFINE_TYPE);
-TF_CALL_string(DEFINE_TYPE);
-
-#undef DEFINE_DIM
-#undef DEFINE_TYPE
-
 // Register functors used for TileGradientOp.
 #define DEFINE_DIM(T, NDIM)                     \
   template struct TileGrad<CPUDevice, T, NDIM>; \
@@ -65,21 +47,6 @@ TF_CALL_complex128(DEFINE_TYPE);
 #ifdef TENSORFLOW_USE_SYCL
 typedef Eigen::SyclDevice SYCLDevice;
 
-// Register functors used for TileOp.
-#define DEFINE_DIM(T, NDIM) template struct Tile<SYCLDevice, T, NDIM>;
-#define DEFINE_TYPE(T) DEFINE_DIM(T, CPU_PROVIDED_IXDIM)
-
-TF_CALL_bool(DEFINE_TYPE);
-TF_CALL_float(DEFINE_TYPE);
-TF_CALL_double(DEFINE_TYPE);
-TF_CALL_uint8(DEFINE_TYPE);
-TF_CALL_int32(DEFINE_TYPE);
-TF_CALL_int16(DEFINE_TYPE);
-TF_CALL_int64(DEFINE_TYPE);
-
-#undef DEFINE_DIM
-#undef DEFINE_TYPE
-
 // Register functors used for TileGradientOp.
 #define DEFINE_DIM(T, NDIM)                      \
   template struct TileGrad<SYCLDevice, T, NDIM>; \
diff --git a/tensorflow/core/kernels/tile_ops_gpu_impl.h b/tensorflow/core/kernels/tile_ops_gpu_impl.h
index ed89381103..592f99e9b7 100644
--- a/tensorflow/core/kernels/tile_ops_gpu_impl.h
+++ b/tensorflow/core/kernels/tile_ops_gpu_impl.h
@@ -39,7 +39,6 @@ limitations under the License.
 #include "tensorflow/core/kernels/tile_ops_impl.h"
 
 #define DEFINE_DIM(T, NDIM)                            \
-  template struct Tile<Eigen::GpuDevice, T, NDIM>;     \
   template struct TileGrad<Eigen::GpuDevice, T, NDIM>; \
   template struct ReduceAndReshape<Eigen::GpuDevice, T, NDIM, 1>;
 
diff --git a/tensorflow/core/kernels/tile_ops_impl.h b/tensorflow/core/kernels/tile_ops_impl.h
index c41e4bd74b..9861717a0b 100644
--- a/tensorflow/core/kernels/tile_ops_impl.h
+++ b/tensorflow/core/kernels/tile_ops_impl.h
@@ -21,31 +21,8 @@ limitations under the License.
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
-namespace functor {
 
-template <typename Device, typename T, int NDIM>
-struct Tile {
-  void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out,
-                  typename TTypes<T, NDIM>::ConstTensor in,
-                  const Eigen::array<int32, NDIM>& broadcast_array) const {
-    if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
-      // Use 32bit indexing to speed up the computations
-      To32Bit(out).device(d) = To32Bit(in).broadcast(broadcast_array);
-    } else {
-      out.device(d) = in.broadcast(broadcast_array);
-    }
-  }
-};
-
-template <typename Device, typename T>
-struct Tile<Device, T, 0> {
-  void operator()(const Device& d, typename TTypes<T, 0>::Tensor out,
-                  typename TTypes<T, 0>::ConstTensor in,
-                  const Eigen::array<int32, 0>&) const {
-    // In the scalar case we simply copy the input.
-    out.device(d) = in;
-  }
-};
+namespace functor {
 
 template <typename Device, typename T, int NDIM>
 struct TileGrad {
diff --git a/tensorflow/core/kernels/topk_op.cc b/tensorflow/core/kernels/topk_op.cc
index 630fcb76f3..5c89eaef5f 100644
--- a/tensorflow/core/kernels/topk_op.cc
+++ b/tensorflow/core/kernels/topk_op.cc
@@ -93,7 +93,7 @@ class TopK : public OpKernel {
       rows_by_one.set(0, num_rows);
 #else
       Eigen::array<int, 1> reduce_on_cols = {1};
-      Eigen::array<int, 1> rows_by_one = {static_cast<int>(num_rows), 1};
+      Eigen::array<int, 2> rows_by_one = {static_cast<int>(num_rows), 1};
 #endif
 
       values.device(d) =
diff --git a/tensorflow/core/kernels/transpose_functor.h b/tensorflow/core/kernels/transpose_functor.h
index 124cf14dd2..f1ab770eeb 100644
--- a/tensorflow/core/kernels/transpose_functor.h
+++ b/tensorflow/core/kernels/transpose_functor.h
@@ -132,6 +132,13 @@ template <typename Device, typename T, int NDIMS>
 void TransposeUsingEigen(const Device& d, const Tensor& in,
                          const gtl::ArraySlice<int32> perm, Tensor* out);
 
+
+#ifdef TENSORFLOW_USE_SYCL
+// For SYCL lets always go through Eigen
+template <typename Device, typename T>
+void TransposeSYCL(const Device& d, const Tensor& in,
+                   const gtl::ArraySlice<int32> perm, Tensor* out);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace internal
 
 template <typename Device, typename T>
diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc
index 75ed76a697..d3305fb83a 100644
--- a/tensorflow/core/kernels/transpose_op.cc
+++ b/tensorflow/core/kernels/transpose_op.cc
@@ -233,10 +233,7 @@ Status TransposeSyclOp::DoTranspose(OpKernelContext* ctx, const Tensor& in,
                               .TypeConstraint<int32>("Tperm") \
                               .HostMemory("perm"),            \
                           TransposeSyclOp);
-REGISTER(float);
-REGISTER(bool);
-REGISTER(int32);
+TF_CALL_POD_TYPES(REGISTER);
 #undef REGISTER
 #endif
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/typed_conditional_accumulator_base.h b/tensorflow/core/kernels/typed_conditional_accumulator_base.h
index dbd7de7ce0..1980f758fc 100644
--- a/tensorflow/core/kernels/typed_conditional_accumulator_base.h
+++ b/tensorflow/core/kernels/typed_conditional_accumulator_base.h
@@ -22,7 +22,7 @@ namespace tensorflow {
 
 /*
  * TypedConditionalAccumulatorBase is a templated companion of
- * ConditionalAccumulatorBase which allows for subclassses to use different
+ * ConditionalAccumulatorBase which allows for subclasses to use different
  * types for the input gradients. (See ConditionalAccumulator and
  * SparseConditionalAccumulator.)
  *
diff --git a/tensorflow/core/lib/gtl/optional.h b/tensorflow/core/lib/gtl/optional.h
index 8ba4b09143..2ff8b9c7d1 100644
--- a/tensorflow/core/lib/gtl/optional.h
+++ b/tensorflow/core/lib/gtl/optional.h
@@ -656,7 +656,7 @@ class optional : private internal_optional::optional_data<T>,
   constexpr const T& reference() const { return *this->pointer(); }
   T& reference() { return *(this->pointer()); }
 
-  // T constaint checks.  You can't have an optional of nullopt_t, in_place_t or
+  // T constraint checks.  You can't have an optional of nullopt_t, in_place_t or
   // a reference.
   static_assert(
       !std::is_same<nullopt_t, typename std::remove_cv<T>::type>::value,
diff --git a/tensorflow/core/ops/math_grad.cc b/tensorflow/core/ops/math_grad.cc
index a530d286f7..9a58a31757 100644
--- a/tensorflow/core/ops/math_grad.cc
+++ b/tensorflow/core/ops/math_grad.cc
@@ -155,6 +155,26 @@ Status Log1pGrad(const AttrSlice& attrs, FunctionDef* g) {
 }
 REGISTER_OP_GRADIENT("Log1p", Log1pGrad);
 
+Status SinhGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"cosh"}, "Cosh", {"x"}, {}, {"dy"}},
+      {{"dx"}, "Mul", {"dy", "cosh"}},  // dy * cosh(x)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Sinh", SinhGrad);
+
+Status CoshGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"sinh"}, "Sinh", {"x"}, {}, {"dy"}},
+      {{"dx"}, "Mul", {"dy", "sinh"}},  // dy * sinh(x)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Cosh", CoshGrad);
+
 Status TanhGrad(const AttrSlice& attrs, FunctionDef* g) {
   // clang-format off
   return GradForUnaryCwise(g, {
diff --git a/tensorflow/core/ops/math_grad_test.cc b/tensorflow/core/ops/math_grad_test.cc
index 38813b3f2b..aa9706a328 100644
--- a/tensorflow/core/ops/math_grad_test.cc
+++ b/tensorflow/core/ops/math_grad_test.cc
@@ -495,6 +495,26 @@ TEST_F(MathGradTest, Log1p) {
   test::ExpectClose(ans, dx);
 }
 
+TEST_F(MathGradTest, Sinh) {
+  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) { return std::cosh(x); };
+  auto dx = test::AsTensor<float>(
+      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Sinh", x);
+  test::ExpectClose(ans, dx);
+}
+
+TEST_F(MathGradTest, Cosh) {
+  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) { return std::sinh(x); };
+  auto dx = test::AsTensor<float>(
+      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Cosh", x);
+  test::ExpectClose(ans, dx);
+}
+
 TEST_F(MathGradTest, Tanh) {
   auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
                                  TensorShape({2, 3}));
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 3fe543a239..30d6987707 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -293,6 +293,14 @@ Computes natural logarithm of (1 + x) element-wise.
 I.e., \\(y = \log_e (1 + x)\\).
 )doc");
 
+REGISTER_OP("Sinh").UNARY_COMPLEX().Doc(R"doc(
+Computes hyperbolic sine of x element-wise.
+)doc");
+
+REGISTER_OP("Cosh").UNARY_COMPLEX().Doc(R"doc(
+Computes hyperbolic cosine of x element-wise.
+)doc");
+
 REGISTER_OP("Tanh").UNARY_COMPLEX().Doc(R"doc(
 Computes hyperbolic tangent of `x` element-wise.
 )doc");
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 70302c3886..3a25fd15da 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -831,11 +831,13 @@ a different filter to each input channel (expanding from 1 channel to
 `channel_multiplier` channels for each), then concatenates the results
 together. Thus, the output has `in_channels * channel_multiplier` channels.
 
+```
 for k in 0..in_channels-1
   for q in 0..channel_multiplier-1
     output[b, i, j, k * channel_multiplier + q] =
       sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                         filter[di, dj, k, q]
+```
 
 Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
 horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 58bed01150..485b72faa4 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -5184,6 +5184,30 @@ op {
   }
   summary: "Computes cos of x element-wise."
 }
+op {
+  name: "Cosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes hyperbolic cosine of x element-wise."
+}
 op {
   name: "CountUpTo"
   input_arg {
@@ -22849,6 +22873,30 @@ op {
   }
   summary: "Computes sin of x element-wise."
 }
+op {
+  name: "Sinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes hyperbolic sine of x element-wise."
+}
 op {
   name: "Size"
   input_arg {
@@ -27315,6 +27363,33 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
+op {
+  name: "LMDBReader"
+  output_arg {
+    name: "reader_handle"
+    description: "The handle to reference the Reader."
+    type: DT_STRING
+    is_ref: true
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
+  }
+  summary: "A Reader that outputs the records from a LMDB database."
+  is_stateful: true
+}
 op {
   name: "TakeDataset"
   input_arg {
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 9722f0ee9a..6aca2c3b01 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -710,6 +710,75 @@ a_shape: 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
 b: `ndims`-D Tensor.  With shape `a_shape`.
 )doc");
 
+REGISTER_OP("SparseReduceMax")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output: T")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+instead of a sparse one.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+output: `R-K`-D.  The reduced Tensor.
+)doc");
+
+REGISTER_OP("SparseReduceMaxSparse")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output_indices: int64")
+    .Output("output_values: T")
+    .Output("output_shape: int64")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
+SparseTensor.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+)doc");
+
 REGISTER_OP("SparseReduceSum")
     .Input("input_indices: int64")
     .Input("input_values: T")
diff --git a/tensorflow/core/platform/cloud/retrying_utils.cc b/tensorflow/core/platform/cloud/retrying_utils.cc
index 096c77c6e3..99691ecfb9 100644
--- a/tensorflow/core/platform/cloud/retrying_utils.cc
+++ b/tensorflow/core/platform/cloud/retrying_utils.cc
@@ -89,7 +89,7 @@ Status RetryingUtils::DeleteWithRetries(
   bool is_retried = false;
   return RetryingUtils::CallWithRetries(
       [delete_func, &is_retried]() {
-        const auto& status = delete_func();
+        const Status status = delete_func();
         if (is_retried && status.code() == error::NOT_FOUND) {
           return Status::OK();
         }
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index cf05aece39..e476a84a13 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -171,7 +171,7 @@ message ExecutorOpts {
 };
 
 message RunGraphRequest {
-  // session_handle is the the master-generated unique id for this session.
+  // session_handle is the master-generated unique id for this session.
   // If session_handle is non-empty, it must be the same as used when
   // registering the graph. If it is empty, a single global namespace is used to
   // search for the graph_handle.
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index d30d7819fc..0e5611e359 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX "-rc2"
+#define TF_VERSION_SUFFIX ""
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h
index 6a37256ea9..67468bdc3f 100644
--- a/tensorflow/core/util/mkl_util.h
+++ b/tensorflow/core/util/mkl_util.h
@@ -23,7 +23,7 @@ limitations under the License.
 #include "third_party/mkl/include/mkl_dnn.h"
 #include "third_party/mkl/include/mkl_dnn_types.h"
 #include "third_party/mkl/include/mkl_service.h"
-
+#include "third_party/mkl/include/mkl_trans.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/util/tensor_format.h"
@@ -616,6 +616,42 @@ inline void ForwarMklTensorInToOut(OpKernelContext* context,
   }
 }
 
+  // TODO(intel_tf): Remove this routine when faster MKL layout conversion is
+  // out. 
+inline void MklNHWCToNCHW(const Tensor& input, Tensor** output) {
+  const float* buf_in = input.flat<float>().data();
+  float* buf_out = (*output)->flat<float>().data();
+
+  int64 N = input.dim_size(0);
+  int64 H = input.dim_size(1);
+  int64 W = input.dim_size(2);
+  int64 C = input.dim_size(3);
+  int64 stride_n = H*W*C;
+# pragma omp parallel for num_threads(16)
+  for (int64 n = 0; n < N; ++n) {
+    mkl_somatcopy('R', 'T', H*W, C, 1, buf_in + n*stride_n, C,
+        buf_out + n*stride_n, H*W);
+  }
+}
+
+  // TODO(intel_tf): Remove this routine when faster MKL layout conversion is
+  // out. 
+inline void MklNCHWToNHWC(const Tensor& input, Tensor** output) {
+  const float* buf_in = input.flat<float>().data();
+  float* buf_out = (*output)->flat<float>().data();
+
+  int64 N = (*output)->dim_size(0);
+  int64 H = (*output)->dim_size(1);
+  int64 W = (*output)->dim_size(2);
+  int64 C = (*output)->dim_size(3);
+  int64 stride_n = H*W*C;
+# pragma omp parallel for num_threads(16)
+  for (int64 n = 0; n < N; ++n) {
+    mkl_somatcopy('R', 'T', C, H*W, 1, buf_in + n*stride_n, H*W,
+        buf_out + n*stride_n, C);
+  }
+}
+
 namespace mkl_op_registry {
 static const char* kMklOpLabel = "MklOp";
 static const char* kMklOpLabelPattern = "label='MklOp'";
diff --git a/tensorflow/docs_src/api_guides/python/contrib.losses.md b/tensorflow/docs_src/api_guides/python/contrib.losses.md
index 8c289dd556..30123e367f 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.losses.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.losses.md
@@ -13,8 +13,8 @@ of samples in the batch and `d1` ... `dN` are the remaining dimensions.
 It is common, when training with multiple loss functions, to adjust the relative
 strengths of individual losses. This is performed by rescaling the losses via
 a `weight` parameter passed to the loss functions. For example, if we were
-training with both log_loss and mean_square_error, and we wished that the
-log_loss penalty be twice as severe as the mean_square_error, we would
+training with both log_loss and mean_squared_error, and we wished that the
+log_loss penalty be twice as severe as the mean_squared_error, we would
 implement this as:
 
 ```python
@@ -22,7 +22,7 @@ implement this as:
   tf.contrib.losses.log(predictions, labels, weight=2.0)
 
   # Uses default weight of 1.0
-  tf.contrib.losses.mean_square_error(predictions, labels)
+  tf.contrib.losses.mean_squared_error(predictions, labels)
 
   # All the losses are collected into the `GraphKeys.LOSSES` collection.
   losses = tf.get_collection(tf.GraphKeys.LOSSES)
@@ -74,7 +74,7 @@ these predictions.
   predictions = MyModelPredictions(images)
 
   weight = tf.cast(tf.greater(depths, 0), tf.float32)
-  loss  = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss  = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 Note that when using weights for the losses, the final average is computed
@@ -100,7 +100,7 @@ weighted average over the individual prediction errors:
 
   weight = MyComplicatedWeightingFunction(labels)
   weight = tf.div(weight, tf.size(weight))
-  loss = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 @{tf.contrib.losses.absolute_difference}
diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md
index b3c12a61dd..3d9f203297 100644
--- a/tensorflow/docs_src/api_guides/python/math_ops.md
+++ b/tensorflow/docs_src/api_guides/python/math_ops.md
@@ -59,6 +59,8 @@ mathematical functions to your graph.
 *   @{tf.acos}
 *   @{tf.asin}
 *   @{tf.atan}
+*   @{tf.cosh}
+*   @{tf.sinh}
 *   @{tf.lgamma}
 *   @{tf.digamma}
 *   @{tf.erf}
diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index fbde463415..500beb49a2 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -68,8 +68,7 @@ for abalone:
 | Feature        | Description                                               |
 | -------------- | --------------------------------------------------------- |
 | Length         | Length of abalone (in longest direction; in mm)           |
-| Diameter       | Diameter of abalone (measurement perpendicular to length; |
-:                : in mm)                                                    :
+| Diameter       | Diameter of abalone (measurement perpendicular to length; in mm)|
 | Height         | Height of abalone (with its meat inside shell; in mm)     |
 | Whole Weight   | Weight of entire abalone (in grams)                       |
 | Shucked Weight | Weight of abalone meat only (in grams)                    |
@@ -559,7 +558,7 @@ For a full list of optimizers, and other details, see the
 
 Here's the final, complete `model_fn` for the abalone age predictor. The
 following code configures the neural network; defines loss and the training op;
-and returns a `ModelFnOps` object containing `mode`, `predictions_dict`, `loss`,
+and returns a `EstimatorSpec` object containing `mode`, `predictions_dict`, `loss`,
 and `train_op`:
 
 ```python
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 77b8e2dd2e..d1c9cd696c 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -135,8 +135,9 @@ adder_node = a + b  # + provides a shortcut for tf.add(a, b)
 
 The preceding three lines are a bit like a function or a lambda in which we
 define two input parameters (a and b) and then an operation on them. We can
-evaluate this graph with multiple inputs by using the feed_dict parameter to
-specify Tensors that provide concrete values to these placeholders:
+evaluate this graph with multiple inputs by using the feed_dict argument to
+the [run method](https://www.tensorflow.org/api_docs/python/tf/Session#run)
+to feed concrete values to the placeholders:
 
 ```python
 print(sess.run(adder_node, {a: 3, b:4.5}))
diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 624d916474..4858b3cf92 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -362,7 +362,7 @@ minimize. Then it can apply your choice of optimization algorithm to modify the
 variables and reduce the loss.
 
 ```python
-train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
+train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 ```
 
 In this case, we ask TensorFlow to minimize `cross_entropy` using the
diff --git a/tensorflow/docs_src/get_started/mnist/mechanics.md b/tensorflow/docs_src/get_started/mnist/mechanics.md
index 48d9a395f2..27fae45b5b 100644
--- a/tensorflow/docs_src/get_started/mnist/mechanics.md
+++ b/tensorflow/docs_src/get_started/mnist/mechanics.md
@@ -82,7 +82,7 @@ After creating placeholders for the data, the graph is built from the
 `mnist.py` file according to a 3-stage pattern: `inference()`, `loss()`, and
 `training()`.
 
-1.  `inference()` - Builds the graph as far as is required for running
+1.  `inference()` - Builds the graph as far as required for running
 the network forward to make predictions.
 1.  `loss()` - Adds to the inference graph the ops required to generate
 loss.
diff --git a/tensorflow/docs_src/get_started/tensorboard_histograms.md b/tensorflow/docs_src/get_started/tensorboard_histograms.md
index b3dd13497e..918deda190 100644
--- a/tensorflow/docs_src/get_started/tensorboard_histograms.md
+++ b/tensorflow/docs_src/get_started/tensorboard_histograms.md
@@ -34,6 +34,8 @@ tf.summary.histogram("normal/moving_mean", mean_moving_normal)
 sess = tf.Session()
 writer = tf.summary.FileWriter("/tmp/histogram_example")
 
+summaries = tf.summary.merge_all()
+
 # Setup a loop and write the summaries to disk
 N = 400
 for step in range(N):
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 91189f199d..81aa6e3f76 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index c9b8dffadb..3f9096b822 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 612c4c94f2..40ed9e1826 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0-rc2</version>
+  <version>1.2.0</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0-rc2</version>
+                 <version>1.2.0</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc2.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0-rc2.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 8ce4acda13..99f27d7b85 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -171,8 +171,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
-     <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     <pre>(tensorflow)$ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -276,8 +276,8 @@ take the following steps:
      the following command:
 
      <pre>
-     $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b>
+     $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -632,14 +632,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -651,14 +651,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -670,14 +670,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -689,14 +689,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index f85ecefb83..8ff0fb872f 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -108,8 +108,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     <pre> $ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -229,8 +229,8 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-     <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b> </pre>
+     <pre> $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index c455492387..a082c3ce78 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -342,10 +342,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0rc2 on Linux:
+for TensorFlow 1.2.0 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc2-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 42820660ee..8282afaab4 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -38,9 +38,10 @@ installed on your system:
     [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
     list of supported GPU cards.
 
-If you have an earlier version of the preceding packages, please
-upgrade to the specified versions.
-
+If you have a different version of one of the preceding packages, please
+change to the specified versions.  In particular, the cuDNN version
+must match exactly: TensorFlow will not load if it cannot find `cuDNN64_5.dll`.
+To use a different version of cuDNN, you must build from source.
 
 ## Determine how to install TensorFlow
 
@@ -73,7 +74,7 @@ Use that package at your own risk.
 If the following version of Python is not installed on your machine,
 install it now:
 
-  * [Python 3.5.x from python.org](https://www.python.org/downloads/release/python-352/)
+  * [Python 3.5.x 64-bit from python.org](https://www.python.org/downloads/release/python-352/)
 
 TensorFlow only supports version 3.5.x of Python on Windows.
 Note that Python 3.5.x comes with the pip3 package manager, which is the
@@ -114,12 +115,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index 07c5d3087f..a5508ac23e 100644
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -52,7 +52,8 @@ bazel build -c opt --copt=-march="broadwell" --config=cuda //tensorflow/tools/pi
     (pascal): 6.2, Titan X (maxwell): 5.2, and K80: 3.7.
 *   Install the latest CUDA platform and cuDNN libraries.
 *   Make sure to use a version of gcc that supports all of the optimizations of
-    the target CPU. The recommended minimum gcc version is 4.8.3.
+    the target CPU. The recommended minimum gcc version is 4.8.3.  On OS X upgrade
+    to the latest Xcode version and use the version of clang that comes with Xcode.
 *   TensorFlow checks on startup whether it has been compiled with the
     optimizations available on the CPU. If the optimizations are not included,
     TensorFlow will emit warnings, e.g. AVX, AVX2, and FMA instructions not
@@ -122,6 +123,11 @@ format.
 The best practice is to build models that work with both `NCHW` and `NHWC` as it
 is common to train using `NCHW` on GPU, and then do inference with NHWC on CPU.
 
+There are edge cases where `NCHW` can be slower on GPU than `NHWC`. One
+[case](https://github.com/tensorflow/tensorflow/issues/7551#issuecomment-280421351)
+is using non-fused batch norm on WRN-16-4 without dropout. In that case using
+fused batch norm, which is also recommended, is the optimal solution.
+
 The very brief history of these two formats is that TensorFlow started by using
 `NHWC` because it was a little faster on CPUs. Then the TensorFlow team
 discovered that `NCHW` performs better when using the NVIDIA cuDNN library.  The
diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md
index 4667b4cad7..a37748d0c9 100644
--- a/tensorflow/docs_src/performance/quantization.md
+++ b/tensorflow/docs_src/performance/quantization.md
@@ -91,11 +91,14 @@ eight-bit computations:
 ```sh
 curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -o /tmp/inceptionv3.tgz
 tar xzf /tmp/inceptionv3.tgz -C /tmp/
-bazel build tensorflow/tools/quantization:quantize_graph
-bazel-bin/tensorflow/tools/quantization/quantize_graph \
-  --input=/tmp/classify_image_graph_def.pb \
-  --output_node_names="softmax" --output=/tmp/quantized_graph.pb \
-  --mode=eightbit
+bazel build tensorflow/tools/graph_transforms:transform_graph
+bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
+  --in_graph=/tmp/classify_image_graph_def.pb \
+  --outputs="softmax" --out_graph=/tmp/quantized_graph.pb \
+  --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3")
+    remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true)
+    fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes
+    strip_unused_nodes sort_by_execution_order'
 ```
 
 This will produce a new model that runs the same operations as the original, but
diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md
index 6ffe3e8b03..dd2ca8d524 100644
--- a/tensorflow/docs_src/tutorials/seq2seq.md
+++ b/tensorflow/docs_src/tutorials/seq2seq.md
@@ -8,7 +8,10 @@ some input and generate a meaningful response? For example, could we train
 a neural network to translate from English to French? It turns out that
 the answer is *yes*.
 
-This tutorial will show you how to build and train such a system end-to-end. Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub. You can then start by running the translate program:
+This tutorial will show you how to build and train such a system end-to-end.
+Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and
+the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub.
+You can then start by running the translate program:
 
 ```
 cd models/tutorials/rnn/translate
@@ -25,7 +28,7 @@ This tutorial references the following files.
 
 File | What's in it?
 --- | ---
-`tensorflow/tensorflow/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
+`tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
 `models/tutorials/rnn/translate/seq2seq_model.py` | Neural translation sequence-to-sequence model.
 `models/tutorials/rnn/translate/data_utils.py` | Helper functions for preparing translation data.
 `models/tutorials/rnn/translate/translate.py` | Binary that trains and runs the translation model.
@@ -148,9 +151,9 @@ have similar interfaces, so we will not describe them in detail. We will use
 ## Neural translation model
 
 While the core of the sequence-to-sequence model is constructed by
-the functions in `tensorflow/tensorflow/python/ops/seq2seq.py`, there are still a few tricks
-that are worth mentioning that are used in our translation model in
-`models/tutorials/rnn/translate/seq2seq_model.py`.
+the functions in `tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py`,
+there are still a few tricks that are worth mentioning that are used in our
+translation model in `models/tutorials/rnn/translate/seq2seq_model.py`.
 
 ### Sampled softmax and output projection
 
diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index c2621026c7..24c866eee5 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -1,6 +1,6 @@
 # TensorFlow Linear Model Tutorial
 
-In this tutorial, we will use the TF.Learn API in TensorFlow to solve a binary
+In this tutorial, we will use the tf.contrib.learn API in TensorFlow to solve a binary
 classification problem: Given census data about a person such as age, gender,
 education and occupation (the features), we will try to predict whether or not
 the person earns more than 50,000 dollars a year (the target label). We will
@@ -16,7 +16,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
@@ -69,8 +69,8 @@ COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
            "marital_status", "occupation", "relationship", "race", "gender",
            "capital_gain", "capital_loss", "hours_per_week", "native_country",
            "income_bracket"]
-df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
-df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1)
+df_train = pd.read_csv(train_file.name, names=COLUMNS, skipinitialspace=True)
+df_test = pd.read_csv(test_file.name, names=COLUMNS, skipinitialspace=True, skiprows=1)
 ```
 
 Since the task is a binary classification problem, we'll construct a label
@@ -136,9 +136,9 @@ Here's a list of columns available in the Census Income dataset:
 
 ## Converting Data into Tensors
 
-When building a TF.Learn model, the input data is specified by means of an Input
+When building a tf.contrib.learn model, the input data is specified by means of an Input
 Builder function. This builder function will not be called until it is later
-passed to TF.Learn methods such as `fit` and `evaluate`. The purpose of this
+passed to tf.contrib.learn methods such as `fit` and `evaluate`. The purpose of this
 function is to construct the input data, which is represented in the form of
 @{tf.Tensor}s
 or
@@ -211,7 +211,7 @@ to predict the target label.
 ### Base Categorical Feature Columns
 
 To define a feature column for a categorical feature, we can create a
-`SparseColumn` using the TF.Learn API. If you know the set of all possible
+`SparseColumn` using the tf.contrib.learn API. If you know the set of all possible
 feature values of a column and there are only a few of them, you can use
 `sparse_column_with_keys`. Each key in the list will get assigned an
 auto-incremental ID starting from 0. For example, for the `gender` column we can
@@ -361,7 +361,7 @@ in `model_dir`.
 ## Training and Evaluating Our Model
 
 After adding all the features to the model, now let's look at how to actually
-train the model. Training a model is just a one-liner using the TF.Learn API:
+train the model. Training a model is just a one-liner using the tf.contrib.learn API:
 
 ```python
 m.fit(input_fn=train_input_fn, steps=200)
@@ -467,4 +467,4 @@ value would be high.
 
 If you're interested in learning more, check out our @{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to combine
 the strengths of linear models and deep neural networks by jointly training them
-using the TF.Learn API.
+using the tf.contrib.learn API.
diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/wide_and_deep.md
index 77c905fd51..0978005d6c 100644
--- a/tensorflow/docs_src/tutorials/wide_and_deep.md
+++ b/tensorflow/docs_src/tutorials/wide_and_deep.md
@@ -9,7 +9,7 @@ great for training deep neural networks too, and you might be thinking which one
 you should choose—Well, why not both? Would it be possible to combine the
 strengths of both in one model?
 
-In this tutorial, we'll introduce how to use the TF.Learn API to jointly train a
+In this tutorial, we'll introduce how to use the tf.contrib.learn API to jointly train a
 wide linear model and a deep feed-forward neural network. This approach combines
 the strengths of memorization and generalization. It's useful for generic
 large-scale regression and classification problems with sparse input features
@@ -23,7 +23,7 @@ The figure above shows a comparison of a wide model (logistic regression with
 sparse features and transformations), a deep model (feed-forward neural network
 with an embedding layer and several hidden layers), and a Wide & Deep model
 (joint training of both). At a high level, there are only 3 steps to configure a
-wide, deep, or Wide & Deep model using the TF.Learn API:
+wide, deep, or Wide & Deep model using the tf.contrib.learn API:
 
 1.  Select features for the wide part: Choose the sparse base columns and
     crossed columns you want to use.
@@ -42,7 +42,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
diff --git a/tensorflow/docs_src/tutorials/word2vec.md b/tensorflow/docs_src/tutorials/word2vec.md
index dfb21334f8..8e7c19035e 100644
--- a/tensorflow/docs_src/tutorials/word2vec.md
+++ b/tensorflow/docs_src/tutorials/word2vec.md
@@ -351,7 +351,7 @@ to evaluate embeddings is to directly use them to predict syntactic and semantic
 relationships like `king is to queen as father is to ?`. This is called
 *analogical reasoning* and the task was introduced by
 [Mikolov and colleagues
-](http://msr-waypoint.com/en-us/um/people/gzweig/Pubs/NAACL2013Regularities.pdf).
+](http://www.anthology.aclweb.org/N/N13/N13-1090.pdf).
 Download the dataset for this task from
 [download.tensorflow.org](http://download.tensorflow.org/data/questions-words.txt).
 
diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle
index 001d57f88a..e97faad516 100644
--- a/tensorflow/examples/android/build.gradle
+++ b/tensorflow/examples/android/build.gradle
@@ -10,7 +10,7 @@
 // setting nativeBuildSystem below to 'makefile'. This will allow building the demo
 // on Windows machines, but note that full equivalence with the Bazel
 // build is not yet guaranteed. See comments below for caveats and tips
-// for speeding up the build, such as as enabling ccache.
+// for speeding up the build, such as enabling ccache.
 // NOTE: Running a make build will cause subsequent Bazel builds to *fail*
 // unless the contrib/makefile/downloads/ and gen/ dirs are deleted afterwards.
 
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 8e3b1a3a36..44a3097d80 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -800,11 +800,27 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
   return evaluation_step, prediction
 
 
-def main(_):
+def save_graph_to_file(sess, graph, graph_file_name):
+  output_graph_def = graph_util.convert_variables_to_constants(
+    sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
+  with gfile.FastGFile(graph_file_name, 'wb') as f:
+    f.write(output_graph_def.SerializeToString())
+  return
+
+
+def prepare_file_system():
   # Setup the directory we'll write summaries to for TensorBoard
   if tf.gfile.Exists(FLAGS.summaries_dir):
     tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
   tf.gfile.MakeDirs(FLAGS.summaries_dir)
+  if FLAGS.intermediate_store_frequency > 0:
+    ensure_dir_exists(FLAGS.intermediate_output_graphs_dir)
+  return
+
+
+def main(_):
+  # Prepare necessary directories  that can be used during training
+  prepare_file_system()
 
   # Set up the pre-trained graph.
   maybe_download_and_extract()
@@ -917,6 +933,14 @@ def main(_):
               (datetime.now(), i, validation_accuracy * 100,
                len(validation_bottlenecks)))
 
+      # Store intermediate results
+      intermediate_frequency = FLAGS.intermediate_store_frequency
+
+      if intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0:
+        intermediate_file_name = FLAGS.intermediate_output_graphs_dir + 'intermediate_' + str(i) + '.pb'
+        print('Save intermediate result to : ' + intermediate_file_name)
+        save_graph_to_file(sess, graph, intermediate_file_name)
+                
     # We've completed all our training, so run a final test evaluation on
     # some new images we haven't used before.
     test_bottlenecks, test_ground_truth, test_filenames = (
@@ -940,10 +964,7 @@ def main(_):
 
     # Write out the trained graph and labels with the weights stored as
     # constants.
-    output_graph_def = graph_util.convert_variables_to_constants(
-        sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
-    with gfile.FastGFile(FLAGS.output_graph, 'wb') as f:
-      f.write(output_graph_def.SerializeToString())
+    save_graph_to_file(sess, graph, FLAGS.output_graph)
     with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
       f.write('\n'.join(image_lists.keys()) + '\n')
 
@@ -962,6 +983,18 @@ if __name__ == '__main__':
       default='/tmp/output_graph.pb',
       help='Where to save the trained graph.'
   )
+  parser.add_argument(
+      '--intermediate_output_graphs_dir',
+      type=str,
+      default='/tmp/intermediate_graph/',
+      help='Where to save the intermediate graphs.'
+  )
+  parser.add_argument(
+      '--intermediate_store_frequency',
+      type=int,
+      default=0,
+      help='How many steps to store intermediate graph. If "0" then will not store.'
+  )
   parser.add_argument(
       '--output_labels',
       type=str,
diff --git a/tensorflow/examples/label_image/README.md b/tensorflow/examples/label_image/README.md
index 1103caf586..c5857f394a 100644
--- a/tensorflow/examples/label_image/README.md
+++ b/tensorflow/examples/label_image/README.md
@@ -1,4 +1,4 @@
-# TensorFlow C++ Image Recognition Demo
+# TensorFlow C++ and Python Image Recognition Demo
 
 This example shows how you can load a pre-trained TensorFlow network and use it
 to recognize objects in images in C++. For Java see the [Java
@@ -64,3 +64,24 @@ $ bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png
 
 For a more detailed look at this code, you can check out the C++ section of the
 [Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+
+## Python implementation
+
+label_image.py is a python implementation that provides code corresponding
+to the C++ code here. This gives more intuitive mapping between C++ and
+Python than the Python code mentioned in the
+[Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+and could be easier to add visualization or debug code.
+
+With tensorflow python package installed, you can run it like:
+```bash
+$ python3 tensorflow/examples/label_image/label_image.py
+```
+And get result similar to this:
+```
+military uniform 0.834305
+mortarboard 0.0218694
+academic gown 0.0103581
+pickelhaube 0.00800818
+bulletproof vest 0.0053509
+```
diff --git a/tensorflow/examples/label_image/label_image.py b/tensorflow/examples/label_image/label_image.py
new file mode 100644
index 0000000000..39d0981337
--- /dev/null
+++ b/tensorflow/examples/label_image/label_image.py
@@ -0,0 +1,132 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+import numpy as np
+import tensorflow as tf
+
+def load_graph(model_file):
+  graph = tf.Graph()
+  graph_def = tf.GraphDef()
+
+  with open(model_file, "rb") as f:
+    graph_def.ParseFromString(f.read())
+  with graph.as_default():
+    tf.import_graph_def(graph_def)
+
+  return graph
+
+def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
+				input_mean=0, input_std=255):
+  input_name = "file_reader"
+  output_name = "normalized"
+  file_reader = tf.read_file(file_name, input_name)
+  if file_name.endswith(".png"):
+    image_reader = tf.image.decode_png(file_reader, channels = 3,
+                                       name='png_reader')
+  elif file_name.endswith(".gif"):
+    image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
+                                                  name='gif_reader'))
+  elif file_name.endswith(".bmp"):
+    image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
+  else:
+    image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
+                                        name='jpeg_reader')
+  float_caster = tf.cast(image_reader, tf.float32)
+  dims_expander = tf.expand_dims(float_caster, 0);
+  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
+  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
+  sess = tf.Session()
+  result = sess.run(normalized)
+
+  return result
+
+def load_labels(label_file):
+  label = []
+  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
+  for l in proto_as_ascii_lines:
+    label.append(l.rstrip())
+  return label
+
+if __name__ == "__main__":
+  file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg"
+  model_file = \
+    "tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb"
+  label_file = "tensorflow/examples/label_image/data/imagenet_slim_labels.txt"
+  input_height = 299
+  input_width = 299
+  input_mean = 0
+  input_std = 255
+  input_layer = "input"
+  output_layer = "InceptionV3/Predictions/Reshape_1"
+
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--image", help="image to be processed")
+  parser.add_argument("--graph", help="graph/model to be executed")
+  parser.add_argument("--labels", help="name of file containing labels")
+  parser.add_argument("--input_height", type=int, help="input height")
+  parser.add_argument("--input_width", type=int, help="input width")
+  parser.add_argument("--input_mean", type=int, help="input mean")
+  parser.add_argument("--input_std", type=int, help="input std")
+  parser.add_argument("--input_layer", help="name of input layer")
+  parser.add_argument("--output_layer", help="name of output layer")
+  args = parser.parse_args()
+
+  if args.graph:
+    model_file = args.graph
+  if args.image:
+    file_name = args.image
+  if args.labels:
+    label_file = args.labels
+  if args.input_height:
+    input_height = args.input_height
+  if args.input_width:
+    input_width = args.input_width
+  if args.input_mean:
+    input_mean = args.input_mean
+  if args.input_std:
+    input_std = args.input_std
+  if args.input_layer:
+    input_layer = args.input_layer
+  if args.output_layer:
+    output_layer = args.output_layer
+
+  graph = load_graph(model_file)
+  t = read_tensor_from_image_file(file_name,
+                                  input_height=input_height,
+                                  input_width=input_width,
+                                  input_mean=input_mean,
+                                  input_std=input_std)
+
+  input_name = "import/" + input_layer
+  output_name = "import/" + output_layer
+  input_operation = graph.get_operation_by_name(input_name);
+  output_operation = graph.get_operation_by_name(output_name);
+
+  with tf.Session(graph=graph) as sess:
+    results = sess.run(output_operation.outputs[0],
+                      {input_operation.outputs[0]: t})
+  results = np.squeeze(results)
+
+  top_k = results.argsort()[-5:][::-1]
+  labels = load_labels(label_file)
+  for i in top_k:
+    print(labels[i], results[i])
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index 77b245ab15..4c5893384a 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -32,7 +32,7 @@ TFLEARN_EXAMPLE_BASE_DIR=$DIR/tensorflow/examples/learn
 
 
 function test() {
-  echo "Test "$1":"
+  echo "Test $1:"
   $TFLEARN_EXAMPLE_BASE_DIR/$1 $2
   if [ $? -eq 0 ]
   then
diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py
index d533697976..3585043a2a 100644
--- a/tensorflow/examples/tutorials/mnist/mnist.py
+++ b/tensorflow/examples/tutorials/mnist/mnist.py
@@ -17,7 +17,7 @@
 
 Implements the inference/loss/training pattern for model building.
 
-1. inference() - Builds the model as far as is required for running the network
+1. inference() - Builds the model as far as required for running the network
 forward to make predictions.
 2. loss() - Adds to the inference model the layers required to generate loss.
 3. training() - Adds to the loss model the Ops required to generate and
diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
index 13e5717b0d..aee482fda5 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -91,7 +91,6 @@ print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
 
 data_index = 0
 
-
 # Step 3: Function to generate a training batch for the skip-gram model.
 def generate_batch(batch_size, num_skips, skip_window):
   global data_index
@@ -101,9 +100,10 @@ def generate_batch(batch_size, num_skips, skip_window):
   labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
   span = 2 * skip_window + 1  # [ skip_window target skip_window ]
   buffer = collections.deque(maxlen=span)
-  for _ in range(span):
-    buffer.append(data[data_index])
-    data_index = (data_index + 1) % len(data)
+  if data_index + span > len(data):
+    data_index = 0
+  buffer.extend(data[data_index:data_index + span])
+  data_index += span
   for i in range(batch_size // num_skips):
     target = skip_window  # target label at the center of the buffer
     targets_to_avoid = [skip_window]
@@ -113,8 +113,12 @@ def generate_batch(batch_size, num_skips, skip_window):
       targets_to_avoid.append(target)
       batch[i * num_skips + j] = buffer[skip_window]
       labels[i * num_skips + j, 0] = buffer[target]
-    buffer.append(data[data_index])
-    data_index = (data_index + 1) % len(data)
+    if data_index == len(data):
+      buffer[:] = data[:span]
+      data_index = span
+    else:
+      buffer.append(data[data_index])
+      data_index += 1
   # Backtrack a little bit to avoid skipping words in the end of a batch
   data_index = (data_index + len(data) - span) % len(data)
   return batch, labels
diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md
index a1b4255292..9c2fa60017 100644
--- a/tensorflow/go/README.md
+++ b/tensorflow/go/README.md
@@ -9,35 +9,6 @@ Construct and execute TensorFlow graphs in Go.
 > (`github.com/tensorflow/tensorflow/tensorflow/go`).
 
 ## Quickstart
-1.  Download and extract the TensorFlow C library, preferably into `/usr/local`.
-    GPU-enabled versions require CUDA 8.0 and cuDNN 5.1. For other versions, the
-    TensorFlow C library will have to be built from source (see below).
-
-    -   Linux:
-        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.1.0.tar.gz),
-        [GPU-enabled](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.1.0.tar.gz)
-    -   OS X
-        [CPU-only](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.1.0.tar.gz),
-
-    The following shell snippet downloads and extracts into `/usr/local`:
-
-    ```sh
-    TF_TYPE="cpu" # Set to "gpu" for GPU support
-    curl -L \
-      "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.1.0.tar.gz" |
-    sudo tar -C /usr/local -xz
-    ```
-
-2.  `go get` this package (and run tests):
-
-    ```sh
-    go get github.com/tensorflow/tensorflow/tensorflow/go
-    go test github.com/tensorflow/tensorflow/tensorflow/go
-    ```
-
-3.  Done!
-
-### Installing into locations other than `/usr/local`
 
 Refer to [Installing TensorFlow for Go](https://www.tensorflow.org/install/install_go)
 
diff --git a/tensorflow/go/genop/generate.sh b/tensorflow/go/genop/generate.sh
index d791e39c40..15ef3b9525 100644
--- a/tensorflow/go/genop/generate.sh
+++ b/tensorflow/go/genop/generate.sh
@@ -20,7 +20,7 @@ go get github.com/golang/protobuf/proto
 go get github.com/golang/protobuf/protoc-gen-go
 
 cd $(dirname $0)
-for g in $(echo $GOPATH | sed "s/:/ /g"); do
+for g in $(echo "${GOPATH//:/ }"); do
     TF_DIR="${g}/src/github.com/tensorflow/tensorflow"
     PROTOC="${TF_DIR}/bazel-out/host/bin/external/protobuf/protoc"
     if [ -x "${PROTOC}" ]; then
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index af0eaede19..beede5a6d9 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14288,6 +14288,36 @@ func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
+// Computes hyperbolic sine of x element-wise.
+func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Sinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes hyperbolic cosine of x element-wise.
+func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Cosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes hyperbolic tangent of `x` element-wise.
 func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
diff --git a/tensorflow/go/shape.go b/tensorflow/go/shape.go
index 114ab5decb..8d000cb9de 100644
--- a/tensorflow/go/shape.go
+++ b/tensorflow/go/shape.go
@@ -59,7 +59,7 @@ func (s Shape) NumDimensions() int {
 //
 // REQUIRES: 0 <= dim < s.NumDimensions()
 func (s Shape) Size(dim int) int64 {
-	if dim < 0 || dim > s.NumDimensions() {
+	if dim < 0 || dim >= s.NumDimensions() {
 		return -1
 	}
 	return s.dims[dim]
diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
index 34e797a2b3..4a60c736b5 100644
--- a/tensorflow/go/tensor.go
+++ b/tensorflow/go/tensor.go
@@ -270,7 +270,7 @@ func typeOf(dt DataType, shape []int64) reflect.Type {
 	if ret == nil {
 		panic(bug("DataType %v is not supported", dt))
 	}
-	for _ = range shape {
+	for range shape {
 		ret = reflect.SliceOf(ret)
 	}
 	return ret
diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 9abb63c966..90372660cd 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -9,7 +9,10 @@ load("build_defs", "JAVACOPTS")
 
 java_library(
     name = "tensorflow",
-    srcs = [":java_sources"],
+    srcs = [
+        ":java_op_sources",
+        ":java_sources",
+    ],
     data = [":libtensorflow_jni"],
     javacopts = JAVACOPTS,
     visibility = ["//visibility:public"],
@@ -26,6 +29,14 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "java_op_sources",
+    srcs = glob(["src/main/java/org/tensorflow/op/*.java"]),
+    visibility = [
+        "//tensorflow/java:__pkg__",
+    ],
+)
+
 java_library(
     name = "testutil",
     testonly = 1,
@@ -138,6 +149,19 @@ java_test(
     ],
 )
 
+java_test(
+    name = "ScopeTest",
+    size = "small",
+    srcs = ["src/test/java/org/tensorflow/op/ScopeTest.java"],
+    javacopts = JAVACOPTS,
+    test_class = "org.tensorflow.op.ScopeTest",
+    deps = [
+        ":tensorflow",
+        ":testutil",
+        "@junit",
+    ],
+)
+
 filegroup(
     name = "libtensorflow_jni",
     srcs = select({
diff --git a/tensorflow/java/build_defs.bzl b/tensorflow/java/build_defs.bzl
index 750d76301e..5bd5b9a388 100644
--- a/tensorflow/java/build_defs.bzl
+++ b/tensorflow/java/build_defs.bzl
@@ -1,5 +1,14 @@
 # -*- Python -*-
 
+# Pin to Java 1.7 to ensure broader compatibility for the Java bindings on
+# Android. Note also that the android_library bazel rule currently enforces
+# java 7
+# https://github.com/bazelbuild/bazel/blob/6c1106b1a721516d3b3db54d2e1c31b44a76fbb1/src/main/java/com/google/devtools/build/lib/bazel/rules/android/BazelAndroidSemantics.java#L73
+
+JAVA_VERSION_OPTS = [
+    "-source 7 -target 7",
+]
+
 # A more robust set of lint and errorprone checks when building
 # Java source to improve code consistency.
 
@@ -151,4 +160,4 @@ EP_DISABLED_CHECKS = [
 
 EP_OPTS = EP_ENABLED_WARNINGS + EP_DISABLED_CHECKS
 
-JAVACOPTS = XLINT_OPTS + EP_OPTS
+JAVACOPTS = JAVA_VERSION_OPTS + XLINT_OPTS + EP_OPTS
diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml
index 1fc9d49a86..462e38d332 100644
--- a/tensorflow/java/maven/libtensorflow/pom.xml
+++ b/tensorflow/java/maven/libtensorflow/pom.xml
@@ -19,8 +19,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.8</source>
-          <target>1.8</target>
+          <source>1.7</source>
+          <target>1.7</target>
         </configuration>
       </plugin>
       <plugin>
diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index d1d0359b1d..f2974220d9 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -16,7 +16,7 @@
     <dependency>
       <groupId>com.google.protobuf</groupId>
       <artifactId>protobuf-java</artifactId>
-      <version>3.2.0</version>
+      <version>3.3.1</version>
     </dependency>
   </dependencies>
 
@@ -27,8 +27,8 @@
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.6.1</version>
         <configuration>
-          <source>1.8</source>
-          <target>1.8</target>
+          <source>1.7</source>
+          <target>1.7</target>
         </configuration>
       </plugin>
       <plugin>
diff --git a/tensorflow/java/maven/release.sh b/tensorflow/java/maven/release.sh
index 0e14bc4964..0bb4efbcc0 100755
--- a/tensorflow/java/maven/release.sh
+++ b/tensorflow/java/maven/release.sh
@@ -21,7 +21,7 @@ TF_VERSION="$1"
 SETTINGS_XML="$2"
 shift
 shift
-CMD="$@"
+CMD="$*"
 
 if [[ -z "${TF_VERSION}" ]]
 then
diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh
index b5e2bfc3a6..302ed96c12 100644
--- a/tensorflow/java/maven/run_inside_container.sh
+++ b/tensorflow/java/maven/run_inside_container.sh
@@ -23,7 +23,7 @@ IS_SNAPSHOT="false"
 if [[ "${TF_VERSION}" == *"-SNAPSHOT" ]]; then
   IS_SNAPSHOT="true"
 fi
-PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip"
+PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.3.0/protoc-3.3.0-linux-x86_64.zip"
 
 set -ex
 
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
new file mode 100644
index 0000000000..dff3a45463
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/Input.java
@@ -0,0 +1,48 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow;
+
+/**
+ * Interface implemented by operands of a TensorFlow operation.
+ *
+ * <p>Example usage:
+ *
+ * <pre>{@code
+ * // The "decodeJpeg" operation can be used as input to the "cast" operation
+ * Input decodeJpeg = ops.image().decodeJpeg(...);
+ * ops.math().cast(decodeJpeg, DataType.FLOAT);
+ *
+ * // The output "y" of the "unique" operation can be used as input to the "cast" operation
+ * Output y = ops.array().unique(...).y();
+ * ops.math().cast(y, DataType.FLOAT);
+ *
+ * // The "split" operation can be used as input list to the "concat" operation
+ * Iterable<? extends Input> split = ops.array().split(...);
+ * ops.array().concat(0, split);
+ * }</pre>
+ */
+public interface Input {
+
+  /**
+   * Returns the symbolic handle of a tensor.
+   *
+   * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
+   * used to obtain a symbolic handle that represents the computation of the input.
+   *
+   * @see {@link OperationBuilder#addInput(Output)}.
+   */
+  Output asOutput();
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index 43dbaf125c..200350f7ae 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -73,15 +73,14 @@ public final class Operation {
   /**
    * Returns the size of the list of Tensors produced by this operation.
    *
-   * <p>An Operation has multiple named outputs, each of which produces either
-   * a single tensor or a list of tensors. This method returns the size of
-   * the list of tensors for a specific named output of the operation.
+   * <p>An Operation has multiple named outputs, each of which produces either a single tensor or a
+   * list of tensors. This method returns the size of the list of tensors for a specific named
+   * output of the operation.
    *
-   * @param name identifier of the list of tensors (of which there may
-   *        be many) produced by this operation.
-   * @returns the size of the list of Tensors produced by this named output.
-   * @throws IllegalArgumentException if this operation has no output
-   *         with the provided name.
+   * @param name identifier of the list of tensors (of which there may be many) produced by this
+   *     operation.
+   * @return the size of the list of Tensors produced by this named output.
+   * @throws IllegalArgumentException if this operation has no output with the provided name.
    */
   public int outputListLength(final String name) {
     Graph.Reference r = graph.ref();
@@ -97,6 +96,61 @@ public final class Operation {
     return new Output(this, idx);
   }
 
+  @Override
+  public int hashCode() {
+    return Long.valueOf(unsafeNativeHandle).hashCode();
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (o == this) {
+      return true;
+    }
+    if (!(o instanceof Operation)) {
+      return false;
+    }
+    Operation that = (Operation) o;
+    if (graph != that.graph) {
+      return false;
+    }
+
+    // The graph object is known to be identical here, so this one
+    // reference is sufficient to validate the use of native pointers
+    // in both objects.
+    Graph.Reference r = graph.ref();
+    try {
+      return unsafeNativeHandle == that.unsafeNativeHandle;
+    } finally {
+      r.close();
+    }
+  }
+
+  @Override
+  public String toString() {
+    return String.format("<%s '%s'>", type(), name());
+  }
+
+  /**
+   * Returns the size of the given inputs list of Tensors for this operation.
+   *
+   * <p>An Operation has multiple named inputs, each of which contains either a single tensor or a
+   * list of tensors. This method returns the size of the list of tensors for a specific named input
+   * of the operation.
+   *
+   * @param name identifier of the list of tensors (of which there may be many) inputs to this
+   *     operation.
+   * @returns the size of the list of Tensors produced by this named input.
+   * @throws IllegalArgumentException if this operation has no input with the provided name.
+   */
+  public int inputListLength(final String name) {
+    Graph.Reference r = graph.ref();
+    try {
+      return inputListLength(unsafeNativeHandle, name);
+    } finally {
+      r.close();
+    }
+  }
+
   long getUnsafeNativeHandle() {
     return unsafeNativeHandle;
   }
@@ -122,6 +176,7 @@ public final class Operation {
   }
 
   private final long unsafeNativeHandle;
+
   private final Graph graph;
 
   private static native String name(long handle);
@@ -132,6 +187,8 @@ public final class Operation {
 
   private static native int outputListLength(long handle, String name);
 
+  private static native int inputListLength(long handle, String name);
+
   private static native long[] shape(long graphHandle, long opHandle, int output);
 
   private static native int dtype(long graphHandle, long opHandle, int output);
diff --git a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
index 38ffa2a8e1..8f7559d39e 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
@@ -28,7 +28,7 @@ import java.nio.charset.Charset;
  * <pre>{@code
  * // g is a Graph instance.
  * try (Tensor c1 = Tensor.create(3.0f)) {
- *   g.opBuilder("Constant", "MyConst")
+ *   g.opBuilder("Const", "MyConst")
  *       .setAttr("dtype", c1.dataType())
  *       .setAttr("value", c1)
  *       .build();
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index ab128c2b30..ad28eb2743 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -15,13 +15,17 @@ limitations under the License.
 
 package org.tensorflow;
 
+import java.util.Objects;
 /**
  * A symbolic handle to a tensor produced by an {@link Operation}.
  *
  * <p>An Output is a symbolic handle to a tensor. The value of the Tensor is computed by executing
  * the {@link Operation} in a {@link Session}.
+ *
+ * <p>By implementing the {@link Input} interface, instances of this class could also be passed
+ * directly in input to an operation.
  */
-public final class Output {
+public final class Output implements Input {
 
   /** Handle to the idx-th output of the Operation {@code op}. */
   public Output(Operation op, int idx) {
@@ -49,6 +53,35 @@ public final class Output {
     return operation.dtype(index);
   }
 
+  @Override
+  public Output asOutput() {
+    return this;
+  }
+
+  @Override
+  public int hashCode() {
+    return Objects.hash(operation, index);
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (o == this) {
+      return true;
+    }
+    if (o instanceof Output) {
+      Output that = (Output) o;
+      return index == that.index && operation.equals(that.operation);
+    }
+    return false;
+  }
+
+  @Override
+  public String toString() {
+    return String.format(
+        "<%s '%s:%d' shape=%s dtype=%s>",
+        operation.type(), operation.name(), index, shape().toString(), dataType());
+  }
+
   private final Operation operation;
   private final int index;
 }
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Session.java b/tensorflow/java/src/main/java/org/tensorflow/Session.java
index 0d071e1674..f73cded4e3 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Session.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Session.java
@@ -125,7 +125,7 @@ public final class Session implements AutoCloseable {
      *     <tt>operation_name:output_index</tt> , in which case this method acts like {@code
      *     feed(operation_name, output_index)}. These colon-separated names are commonly used in the
      *     {@code SignatureDef} protocol buffer messages that are included in {@link
-     *     SavedModelBundle.metaGraphDef()}.
+     *     SavedModelBundle#metaGraphDef()}.
      */
     public Runner feed(String operation, Tensor t) {
       return feed(parseOutput(operation), t);
@@ -165,7 +165,7 @@ public final class Session implements AutoCloseable {
      *     <tt>operation_name:output_index</tt> , in which case this method acts like {@code
      *     fetch(operation_name, output_index)}. These colon-separated names are commonly used in
      *     the {@code SignatureDef} protocol buffer messages that are included in {@link
-     *     SavedModelBundle.metaGraphDef()}.
+     *     SavedModelBundle#metaGraphDef()}.
      */
     public Runner fetch(String operation) {
       return fetch(parseOutput(operation));
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java b/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
new file mode 100644
index 0000000000..2e84cac1ac
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/NameScope.java
@@ -0,0 +1,146 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import java.util.HashMap;
+import java.util.Map;
+import java.util.regex.Pattern;
+
+/**
+ * A class to manage scoped (hierarchical) names for operators.
+ *
+ * <p>{@code NameScope} manages hierarchical names where each component in the hierarchy is
+ * separated by a forward slash {@code '/'}. For instance, {@code nn/Const_72} or {@code
+ * nn/gradient/assign/init}. Each scope is a subtree in this hierarchy.
+ *
+ * <p>Use {@code NameScope} to group related operations within a hierarchy, which for example lets
+ * tensorboard coalesce nodes for better graph visualizations.
+ *
+ * <p>This class is package private, user code creates {@link Scope} which internally delegates
+ * calls to an underlying {@code NameScope}.
+ *
+ * <p>This class is <b>not</b> thread-safe.
+ */
+final class NameScope {
+
+  NameScope withSubScope(String scopeName) {
+    checkPattern(NAME_REGEX, scopeName);
+    // Override with opName if it exists.
+    String actualName = (opName != null) ? opName : scopeName;
+    String newPrefix = fullyQualify(makeUnique(actualName));
+    return new NameScope(newPrefix, null, null);
+  }
+
+  NameScope withName(String name) {
+    checkPattern(NAME_REGEX, name);
+    // All context except for the opName is shared with the new scope.
+    return new NameScope(opPrefix, name, ids);
+  }
+
+  String makeOpName(String name) {
+    checkPattern(NAME_REGEX, name);
+    // Override with opName if it exists.
+    String actualName = (opName != null) ? opName : name;
+    return fullyQualify(makeUnique(actualName));
+  }
+
+  /**
+   * Create a new, root-level namescope.
+   *
+   * <p>A root-level namescope generates operator names with no components, like {@code Const_72}
+   * and {@code result}.
+   */
+  NameScope() {
+    this(null, null, null);
+  }
+
+  private NameScope(String opPrefix, String opName, Map<String, Integer> ids) {
+    this.opPrefix = opPrefix;
+    this.opName = opName;
+    if (ids != null) {
+      this.ids = ids;
+    } else {
+      this.ids = new HashMap<String, Integer>();
+    }
+  }
+
+  // Generate a unique name, different from existing ids.
+  //
+  // ids is a map from id to integer, representing a counter of the
+  // number of previous requests to generate a unique name for the
+  // given id.
+  //
+  // For instance, the first use of makeUnique("a") adds "a" -> 1
+  // to ids and returns "a".
+  //
+  // The second use of makeUnique("a") updates ids to "a" -> 2
+  // and returns "a_1", and so on.
+  private String makeUnique(String id) {
+    if (!ids.containsKey(id)) {
+      ids.put(id, 1);
+      return id;
+    } else {
+      int cur = ids.get(id);
+      ids.put(id, cur + 1);
+      return String.format("%s_%d", id, cur);
+    }
+  }
+
+  private String fullyQualify(String name) {
+    if (opPrefix != null) {
+      return String.format("%s/%s", opPrefix, name);
+    } else {
+      return name;
+    }
+  }
+
+  // If opPrefix is non-null, it is a prefix applied to all names
+  // created by this instance.
+  private final String opPrefix;
+
+  // If opName is non-null, it is used to derive the unique name
+  // for operators rather than the provided default name.
+  private final String opName;
+
+  // NameScope generates unique names by appending a numeric suffix if
+  // needed. This is a map containing names already created by this
+  // instance mapped to the next available numeric suffix for it.
+  private final Map<String, Integer> ids;
+
+  private static void checkPattern(Pattern pattern, String name) {
+    if (name == null) {
+      throw new IllegalArgumentException("Names cannot be null");
+    }
+    if (!pattern.matcher(name).matches()) {
+      throw new IllegalArgumentException(
+          String.format(
+              "invalid name: '%s' does not match the regular expression %s",
+              name, NAME_REGEX.pattern()));
+    }
+  }
+
+  // The constraints for operator and scope names originate from restrictions on node names
+  // noted in the proto definition core/framework/node_def.proto for NodeDef and actually
+  // implemented in core/framework/node_def_util.cc [Note that the proto comment does not include
+  // dash (-) in names, while the actual implementation permits it. This regex follows the actual
+  // implementation.]
+  //
+  // This pattern is used to ensure fully qualified names always start with a LETTER_DIGIT_DOT,
+  // followed by zero or more LETTER_DIGIT_DASH_DOT_SLASH_UNDERSCORE. SLASH is not permitted in
+  // actual user-supplied names to NameScope - it is used as a reserved character to separate
+  // subcomponents within fully qualified names.
+  private static final Pattern NAME_REGEX = Pattern.compile("[A-Za-z0-9.][A-Za-z0-9_.\\-]*");
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java b/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
new file mode 100644
index 0000000000..8de2eaeb79
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/Scope.java
@@ -0,0 +1,165 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import org.tensorflow.Graph;
+
+/**
+ * Manages groups of related properties when creating Tensorflow Operations, such as a common name
+ * prefix.
+ *
+ * <p>A {@code Scope} is a container for common properties applied to TensorFlow Ops. Normal user
+ * code initializes a {@code Scope} and provides it to Operation building classes. For example:
+ *
+ * <pre>{@code
+ * Scope scope = new Scope(graph);
+ * Constant c = Constant.create(scope, 42);
+ * }</pre>
+ *
+ * <p>An Operation building class acquires a Scope, and uses it to set properties on the underlying
+ * Tensorflow ops. For example:
+ *
+ * <pre>{@code
+ * // An operator class that adds a constant.
+ * public class Constant {
+ *   public static Constant create(Scope scope, ...) {
+ *      scope.graph().opBuilder(
+ *        "Const", scope.makeOpName("Const"))
+ *        .setAttr(...)
+ *        .build()
+ *      ...
+ *   }
+ * }
+ * }</pre>
+ *
+ * <p><b>Scope hierarchy:</b>
+ *
+ * <p>A {@code Scope} provides various {@code with()} methods that create a new scope. The new scope
+ * typically has one property changed while other properties are inherited from the parent scope.
+ *
+ * <p>An example using {@code Constant} implemented as before:
+ *
+ * <pre>{@code
+ * Scope root = new Scope(graph);
+ *
+ * // The linear subscope will generate names like linear/...
+ * Scope linear = Scope.withSubScope("linear");
+ *
+ * // This op name will be "linear/W"
+ * Constant.create(linear.withName("W"), ...);
+ *
+ * // This op will be "linear/Const", using the default
+ * // name provided by Constant
+ * Constant.create(linear, ...);
+ *
+ * // This op will be "linear/Const_1", using the default
+ * // name provided by Constant and making it unique within
+ * // this scope
+ * Constant.create(linear, ...);
+ * }</pre>
+ *
+ * <p>Scope objects are <b>not</b> thread-safe.
+ */
+public final class Scope {
+
+  /**
+   * Create a new top-level scope.
+   *
+   * @param graph The graph instance to be managed by the scope.
+   */
+  public Scope(Graph graph) {
+    this(graph, new NameScope());
+  }
+
+  /** Returns the graph managed by this scope. */
+  public Graph graph() {
+    return graph;
+  }
+
+  /**
+   * Returns a new scope where added operations will have the provided name prefix.
+   *
+   * <p>Ops created with this scope will have {@code name/childScopeName/} as the prefix. The actual
+   * name will be unique in the returned scope. All other properties are inherited from the current
+   * scope.
+   *
+   * <p>The child scope name must match the regular expression {@code [A-Za-z0-9.][A-Za-z0-9_.\-]*}
+   *
+   * @param childScopeName name for the new child scope
+   * @return a new subscope
+   * @throws IllegalArgumentException if the name is invalid
+   */
+  public Scope withSubScope(String childScopeName) {
+    return new Scope(graph, nameScope.withSubScope(childScopeName));
+  }
+
+  /**
+   * Return a new scope that uses the provided name for an op.
+   *
+   * <p>Operations created within this scope will have a name of the form {@code
+   * name/opName[_suffix]}. This lets you name a specific operator more meaningfully.
+   *
+   * <p>Names must match the regular expression {@code [A-Za-z0-9.][A-Za-z0-9_.\-]*}
+   *
+   * @param opName name for an operator in the returned scope
+   * @return a new Scope that uses opName for operations.
+   * @throws IllegalArgumentException if the name is invalid
+   */
+  public Scope withName(String opName) {
+    return new Scope(graph, nameScope.withName(opName));
+  }
+
+  /**
+   * Create a unique name for an operator, using a provided default if necessary.
+   *
+   * <p>This is normally called only by operator building classes.
+   *
+   * <p>This method generates a unique name, appropriate for the name scope controlled by this
+   * instance. Typical operator building code might look like
+   *
+   * <pre>{@code
+   * scope.graph().opBuilder("Const", scope.makeOpName("Const"))...
+   * }</pre>
+   *
+   * <p><b>Note:</b> if you provide a composite operator building class (i.e, a class that adds a
+   * set of related operations to the graph by calling other operator building code) you should also
+   * create a {@link #withSubScope(String)} scope for the underlying operators to group them under a
+   * meaningful name.
+   *
+   * <pre>{@code
+   * public static Stddev create(Scope scope, ...) {
+   *   // group sub-operations under a common name
+   *   Scope group = scope.withSubScope("stddev");
+   *   ... Sqrt.create(group, Mean.create(group, ...))
+   * }
+   * }</pre>
+   *
+   * @param defaultName name for the underlying operator.
+   * @return unique name for the operator.
+   * @throws IllegalArgumentException if the default name is invalid.
+   */
+  public String makeOpName(String defaultName) {
+    return nameScope.makeOpName(defaultName);
+  }
+
+  private Scope(Graph graph, NameScope nameScope) {
+    this.graph = graph;
+    this.nameScope = nameScope;
+  }
+
+  private final Graph graph;
+  private final NameScope nameScope;
+}
diff --git a/tensorflow/java/src/main/native/operation_jni.cc b/tensorflow/java/src/main/native/operation_jni.cc
index b3d5fc4ec3..ccc44d91c0 100644
--- a/tensorflow/java/src/main/native/operation_jni.cc
+++ b/tensorflow/java/src/main/native/operation_jni.cc
@@ -156,3 +156,21 @@ JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv* env,
 
   return static_cast<jint>(TF_OperationOutputType(TF_Output{op, output_index}));
 }
+
+JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv* env,
+                                                                      jclass clazz,
+                                                                      jlong handle,
+                                                                      jstring name) {
+  TF_Operation* op = requireHandle(env, handle);
+  if (op == nullptr) return 0;
+
+  TF_Status* status = TF_NewStatus();
+
+  const char* cname = env->GetStringUTFChars(name, nullptr);
+  int result = TF_OperationInputListLength(op, cname, status);
+  env->ReleaseStringUTFChars(name, cname);
+
+  throwExceptionIfNotOK(env, status);
+  TF_DeleteStatus(status);
+  return result;
+}
diff --git a/tensorflow/java/src/main/native/operation_jni.h b/tensorflow/java/src/main/native/operation_jni.h
index b5d156f7c2..6f379256d2 100644
--- a/tensorflow/java/src/main/native/operation_jni.h
+++ b/tensorflow/java/src/main/native/operation_jni.h
@@ -73,6 +73,17 @@ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_Operation_shape(JNIEnv *,
 JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_dtype(JNIEnv *, jclass,
                                                            jlong, jlong, jint);
 
+
+/*
+ * Class:     org_tensorflow_Operation
+ * Method:    inputListLength
+ * Signature: (JLjava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_tensorflow_Operation_inputListLength(JNIEnv *,
+                                                                      jclass,
+                                                                      jlong,
+                                                                      jstring);
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif  // __cplusplus
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 74fdcf484e..27afc046ac 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -16,8 +16,14 @@ limitations under the License.
 package org.tensorflow;
 
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
 import static org.junit.Assert.fail;
 
+import java.util.Arrays;
+import java.util.HashSet;
+import java.util.Set;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.JUnit4;
@@ -45,6 +51,91 @@ public class OperationTest {
     }
   }
 
+  @Test
+  public void operationEquality() {
+    Operation op1;
+    try (Graph g = new Graph()) {
+      op1 = TestUtil.constant(g, "op1", 1).op();
+      Operation op2 = TestUtil.constant(g, "op2", 2).op();
+      Operation op3 = new Operation(g, op1.getUnsafeNativeHandle());
+      Operation op4 = g.operation("op1");
+      assertEquals(op1, op1);
+      assertNotEquals(op1, op2);
+      assertEquals(op1, op3);
+      assertEquals(op1.hashCode(), op3.hashCode());
+      assertEquals(op1, op4);
+      assertEquals(op1.hashCode(), op4.hashCode());
+      assertEquals(op3, op4);
+      assertNotEquals(op2, op3);
+      assertNotEquals(op2, op4);
+    }
+    try (Graph g = new Graph()) {
+      Operation newOp1 = TestUtil.constant(g, "op1", 1).op();
+      assertNotEquals(op1, newOp1);
+    }
+  }
+
+  @Test
+  public void operationCollection() {
+    try (Graph g = new Graph()) {
+      Operation op1 = TestUtil.constant(g, "op1", 1).op();
+      Operation op2 = TestUtil.constant(g, "op2", 2).op();
+      Operation op3 = new Operation(g, op1.getUnsafeNativeHandle());
+      Operation op4 = g.operation("op1");
+      Set<Operation> ops = new HashSet<>();
+      ops.addAll(Arrays.asList(op1, op2, op3, op4));
+      assertEquals(2, ops.size());
+      assertTrue(ops.contains(op1));
+      assertTrue(ops.contains(op2));
+      assertTrue(ops.contains(op3));
+      assertTrue(ops.contains(op4));
+    }
+  }
+
+  @Test
+  public void operationToString() {
+    try (Graph g = new Graph()) {
+      Operation op = TestUtil.constant(g, "c", new int[] {1}).op();
+      assertNotNull(op.toString());
+    }
+  }
+
+  @Test
+  public void outputEquality() {
+    try (Graph g = new Graph()) {
+      Output output = TestUtil.constant(g, "c", 1);
+      Output output1 = output.op().output(0);
+      Output output2 = g.operation("c").output(0);
+      assertEquals(output, output1);
+      assertEquals(output.hashCode(), output1.hashCode());
+      assertEquals(output, output2);
+      assertEquals(output.hashCode(), output2.hashCode());
+    }
+  }
+
+  @Test
+  public void outputCollection() {
+    try (Graph g = new Graph()) {
+      Output output = TestUtil.constant(g, "c", 1);
+      Output output1 = output.op().output(0);
+      Output output2 = g.operation("c").output(0);
+      Set<Output> ops = new HashSet<>();
+      ops.addAll(Arrays.asList(output, output1, output2));
+      assertEquals(1, ops.size());
+      assertTrue(ops.contains(output));
+      assertTrue(ops.contains(output1));
+      assertTrue(ops.contains(output2));
+    }
+  }
+
+  @Test
+  public void outputToString() {
+    try (Graph g = new Graph()) {
+      Output output = TestUtil.constant(g, "c", new int[] {1});
+      assertNotNull(output.toString());
+    }
+  }
+
   @Test
   public void outputListLength() {
     assertEquals(1, split(new int[] {0, 1}, 1));
@@ -52,6 +143,16 @@ public class OperationTest {
     assertEquals(3, split(new int[] {0, 1, 2}, 3));
   }
 
+  @Test
+  public void inputListLength() {
+    assertEquals(1, splitWithInputList(new int[] {0, 1}, 1, "split_dim"));
+    try {
+      splitWithInputList(new int[] {0, 1}, 2, "inputs");
+    } catch (IllegalArgumentException iae) {
+      // expected
+    }
+  }
+
   private static int split(int[] values, int num_split) {
     try (Graph g = new Graph()) {
       return g.opBuilder("Split", "Split")
@@ -62,4 +163,15 @@ public class OperationTest {
           .outputListLength("output");
     }
   }
+
+  private static int splitWithInputList(int[] values, int num_split, String name) {
+    try (Graph g = new Graph()) {
+      return g.opBuilder("Split", "Split")
+          .addInput(TestUtil.constant(g, "split_dim", 0))
+          .addInput(TestUtil.constant(g, "values", values))
+          .setAttr("num_split", num_split)
+          .build()
+          .inputListLength(name);
+    }
+  }
 }
diff --git a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
index 0d2dbc5b88..50bdf351e3 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java
@@ -109,7 +109,7 @@ public class SessionTest {
         assertEquals(1, outputs.size());
         final int[][] expected = {{31}};
         assertArrayEquals(expected, outputs.get(0).copyTo(new int[1][1]));
-        // Sanity check on metadatar
+        // Sanity check on metadata
         // See comments in fullTraceRunOptions() for an explanation about
         // why this check is really silly. Ideally, this would be:
         /*
@@ -187,7 +187,7 @@ public class SessionTest {
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
     //
-    // For this test, for now, the use of specific bytes sufficies.
+    // For this test, for now, the use of specific bytes suffices.
     return new byte[] {0x08, 0x03};
     /*
     return org.tensorflow.framework.RunOptions.newBuilder()
@@ -207,7 +207,7 @@ public class SessionTest {
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
     // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
     //
-    // For this test, for now, the use of specific bytes sufficies.
+    // For this test, for now, the use of specific bytes suffices.
     return new byte[] {0x10, 0x01, 0x28, 0x01};
     /*
     return org.tensorflow.framework.ConfigProto.newBuilder()
diff --git a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
index 44eecc1d1e..3ff59e71b2 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
@@ -472,7 +472,7 @@ public class TensorTest {
   @Test
   public void fromHandle() {
     // fromHandle is a package-visible method intended for use when the C TF_Tensor object has been
-    // created indepdently of the Java code. In practice, two Tensor instances MUST NOT have the
+    // created independently of the Java code. In practice, two Tensor instances MUST NOT have the
     // same native handle.
     //
     // An exception is made for this test, where the pitfalls of this is avoided by not calling
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java
new file mode 100644
index 0000000000..9256cb281d
--- /dev/null
+++ b/tensorflow/java/src/test/java/org/tensorflow/op/ScopeTest.java
@@ -0,0 +1,270 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.fail;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.tensorflow.Graph;
+import org.tensorflow.Output;
+import org.tensorflow.Session;
+import org.tensorflow.Tensor;
+
+/** Unit tests for {@link org.tensorflow.Scope}. */
+@RunWith(JUnit4.class)
+public class ScopeTest {
+
+  @Test
+  public void basicNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+      assertEquals("add", root.makeOpName("add"));
+      assertEquals("add_1", root.makeOpName("add"));
+      assertEquals("add_2", root.makeOpName("add"));
+      assertEquals("mul", root.makeOpName("mul"));
+    }
+  }
+
+  @Test
+  public void hierarchicalNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+      Scope child = root.withSubScope("child");
+      assertEquals("child/add", child.makeOpName("add"));
+      assertEquals("child/add_1", child.makeOpName("add"));
+      assertEquals("child/mul", child.makeOpName("mul"));
+
+      Scope child_1 = root.withSubScope("child");
+      assertEquals("child_1/add", child_1.makeOpName("add"));
+      assertEquals("child_1/add_1", child_1.makeOpName("add"));
+      assertEquals("child_1/mul", child_1.makeOpName("mul"));
+
+      Scope c_c = root.withSubScope("c").withSubScope("c");
+      assertEquals("c/c/add", c_c.makeOpName("add"));
+
+      Scope c_1 = root.withSubScope("c");
+      Scope c_1_c = c_1.withSubScope("c");
+      assertEquals("c_1/c/add", c_1_c.makeOpName("add"));
+
+      Scope c_1_c_1 = c_1.withSubScope("c");
+      assertEquals("c_1/c_1/add", c_1_c_1.makeOpName("add"));
+    }
+  }
+
+  @Test
+  public void scopeAndOpNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+
+      Scope child = root.withSubScope("child");
+
+      assertEquals("child/add", child.makeOpName("add"));
+      assertEquals("child_1", root.makeOpName("child"));
+      assertEquals("child_2/p", root.withSubScope("child").makeOpName("p"));
+    }
+  }
+
+  @Test
+  public void validateNames() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+
+      final String[] invalid_names = {
+        "_", "-", "-x", // Names are constrained to start with [A-Za-z0-9.]
+        null, "", "a$", // Invalid characters
+        "a/b", // slashes not allowed
+      };
+
+      for (String name : invalid_names) {
+        try {
+          root.withName(name);
+          fail("failed to catch invalid op name.");
+        } catch (IllegalArgumentException ex) {
+          // expected
+        }
+        // Subscopes follow the same rules
+        try {
+          root.withSubScope(name);
+          fail("failed to catch invalid scope name: " + name);
+        } catch (IllegalArgumentException ex) {
+          // expected
+        }
+      }
+
+      // Unusual but valid names.
+      final String[] valid_names = {".", "..", "._-.", "a--."};
+
+      for (String name : valid_names) {
+        root.withName(name);
+        root.withSubScope(name);
+      }
+    }
+  }
+
+  @Test
+  public void basic() {
+    try (Graph g = new Graph()) {
+      Scope s = new Scope(g);
+      Const c1 = Const.create(s, 42);
+      assertEquals("Const", c1.output().op().name());
+      Const c2 = Const.create(s, 7);
+      assertEquals("Const_1", c2.output().op().name());
+      Const c3 = Const.create(s.withName("four"), 4);
+      assertEquals("four", c3.output().op().name());
+      Const c4 = Const.create(s.withName("four"), 4);
+      assertEquals("four_1", c4.output().op().name());
+    }
+  }
+
+  @Test
+  public void hierarchy() {
+    try (Graph g = new Graph()) {
+      Scope root = new Scope(g);
+      Scope child = root.withSubScope("child");
+      assertEquals("child/Const", Const.create(child, 42).output().op().name());
+      assertEquals("child/four", Const.create(child.withName("four"), 4).output().op().name());
+    }
+  }
+
+  @Test
+  public void composite() {
+    try (Graph g = new Graph();
+        Session sess = new Session(g)) {
+      Scope s = new Scope(g);
+      Output data = Const.create(s.withName("data"), new int[] {600, 470, 170, 430, 300}).output();
+
+      // Create a composite op with a customized name
+      Variance var1 = Variance.create(s.withName("example"), data);
+      assertEquals("example/variance", var1.output().op().name());
+
+      // Confirm internally added ops have the right names.
+      assertNotNull(g.operation("example/squared_deviation"));
+      assertNotNull(g.operation("example/Mean"));
+      assertNotNull(g.operation("example/zero"));
+
+      // Same composite op with a default name
+      Variance var2 = Variance.create(s, data);
+      assertEquals("variance/variance", var2.output().op().name());
+
+      // Confirm internally added ops have the right names.
+      assertNotNull(g.operation("variance/squared_deviation"));
+      assertNotNull(g.operation("variance/Mean"));
+      assertNotNull(g.operation("variance/zero"));
+
+      // Verify correct results as well.
+      Tensor result = sess.runner().fetch(var1.output()).run().get(0);
+      assertEquals(21704, result.intValue());
+      result = sess.runner().fetch(var2.output()).run().get(0);
+      assertEquals(21704, result.intValue());
+    }
+  }
+
+  // "handwritten" sample operator classes
+  private static final class Const {
+    private final Output output;
+
+    static Const create(Scope s, Object v) {
+      try (Tensor value = Tensor.create(v)) {
+        return new Const(
+            s.graph()
+                .opBuilder("Const", s.makeOpName("Const"))
+                .setAttr("dtype", value.dataType())
+                .setAttr("value", value)
+                .build()
+                .output(0));
+      }
+    }
+
+    Const(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+
+  private static final class Mean {
+    private final Output output;
+
+    static Mean create(Scope s, Output input, Output reductionIndices) {
+      return new Mean(
+          s.graph()
+              .opBuilder("Mean", s.makeOpName("Mean"))
+              .addInput(input)
+              .addInput(reductionIndices)
+              .build()
+              .output(0));
+    }
+
+    Mean(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+
+  private static final class SquaredDifference {
+    private final Output output;
+
+    static SquaredDifference create(Scope s, Output x, Output y) {
+      return new SquaredDifference(
+          s.graph()
+              .opBuilder("SquaredDifference", s.makeOpName("SquaredDifference"))
+              .addInput(x)
+              .addInput(y)
+              .build()
+              .output(0));
+    }
+
+    SquaredDifference(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+
+  private static final class Variance {
+    private final Output output;
+
+    static Variance create(Scope base, Output x) {
+      Scope s = base.withSubScope("variance");
+      Output zero = Const.create(s.withName("zero"), new int[] {0}).output();
+      Output sqdiff =
+          SquaredDifference.create(
+                  s.withName("squared_deviation"), x, Mean.create(s, x, zero).output())
+              .output();
+
+      return new Variance(Mean.create(s.withName("variance"), sqdiff, zero).output());
+    }
+
+    Variance(Output o) {
+      output = o;
+    }
+
+    Output output() {
+      return output;
+    }
+  }
+}
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 7530c09cfe..7f291b0b13 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -832,6 +832,7 @@ py_test(
     data = ["//tensorflow/python:meta_graph_testdata"],
     main = "framework/meta_graph_test.py",
     srcs_version = "PY2AND3",
+    tags = ["no_pip"],
     deps = [
         ":array_ops",
         ":client_testlib",
@@ -3055,6 +3056,7 @@ py_test(
     srcs_version = "PY2AND3",
     tags = [
         "no_gpu",
+        "no_pip_gpu",
     ],
     deps = [
         ":array_ops",
@@ -3079,6 +3081,7 @@ py_test(
     srcs_version = "PY2AND3",
     tags = [
         "no_gpu",
+        "no_pip_gpu",
     ],
     deps = [
         ":client",
diff --git a/tensorflow/python/debug/cli/analyzer_cli.py b/tensorflow/python/debug/cli/analyzer_cli.py
index da27f4cebe..e863c2ddb9 100644
--- a/tensorflow/python/debug/cli/analyzer_cli.py
+++ b/tensorflow/python/debug/cli/analyzer_cli.py
@@ -1312,7 +1312,7 @@ class DebugAnalyzer(object):
     all_inputs = copy.copy(tracker(node_name, is_control=False))
     is_ctrl = [False] * len(all_inputs)
     if include_control:
-      # Sort control inputs or recipients in in alphabetical order of the node
+      # Sort control inputs or recipients in alphabetical order of the node
       # names.
       ctrl_inputs = sorted(tracker(node_name, is_control=True))
       all_inputs.extend(ctrl_inputs)
diff --git a/tensorflow/python/debug/wrappers/framework_test.py b/tensorflow/python/debug/wrappers/framework_test.py
index 536365b692..2b2289d6a8 100644
--- a/tensorflow/python/debug/wrappers/framework_test.py
+++ b/tensorflow/python/debug/wrappers/framework_test.py
@@ -215,7 +215,7 @@ class DebugWrapperSessionTest(test_util.TensorFlowTestCase):
       wrapper_sess.partial_run_setup(self._p)
 
   def testInteractiveSessionInit(self):
-    """The wrapper should work also on other subclassses of session.Session."""
+    """The wrapper should work also on other subclasses of session.Session."""
 
     TestDebugWrapperSession(
         session.InteractiveSession(), self._dump_root, self._observer)
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py
index 8785ddc5a6..03bcf7ae57 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py
@@ -395,7 +395,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
 
 
 class DNNLinearCombinedRegressor(estimator.Estimator):
-  """An estimator for TensorFlow Linear and DNN joined models for regresssion.
+  """An estimator for TensorFlow Linear and DNN joined models for regression.
 
   Note: This estimator is also known as wide-n-deep.
 
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index dd2fd7b2ab..f49a682dbe 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -382,16 +382,17 @@ class TensorFlowTestCase(googletest.TestCase):
     `force_gpu and `use_gpu` are False, all ops are pinned to the CPU.
 
     Example:
-
-      class MyOperatorTest(test_util.TensorFlowTestCase):
-        def testMyOperator(self):
-          with self.test_session(use_gpu=True):
-            valid_input = [1.0, 2.0, 3.0, 4.0, 5.0]
-            result = MyOperator(valid_input).eval()
-            self.assertEqual(result, [1.0, 2.0, 3.0, 5.0, 8.0]
-            invalid_input = [-1.0, 2.0, 7.0]
-            with self.assertRaisesOpError("negative input not supported"):
-              MyOperator(invalid_input).eval()
+    ```python
+    class MyOperatorTest(test_util.TensorFlowTestCase):
+      def testMyOperator(self):
+        with self.test_session(use_gpu=True):
+          valid_input = [1.0, 2.0, 3.0, 4.0, 5.0]
+          result = MyOperator(valid_input).eval()
+          self.assertEqual(result, [1.0, 2.0, 3.0, 5.0, 8.0]
+          invalid_input = [-1.0, 2.0, 7.0]
+          with self.assertRaisesOpError("negative input not supported"):
+            MyOperator(invalid_input).eval()
+    ```
 
     Args:
       graph: Optional graph to use during the returned session.
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 257677ce49..d8e069105f 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2382,11 +2382,6 @@ cuda_py_test(
         "//tensorflow/python:util",
         "//tensorflow/python:data_flow_ops",
     ],
-    tags = [
-        "manual",  # http://b/62429636
-        "noguitar",
-        "notap",
-    ],
 )
 
 cuda_py_test(
@@ -2401,11 +2396,6 @@ cuda_py_test(
         "//tensorflow/python:util",
         "//tensorflow/python:data_flow_ops",
     ],
-    tags = [
-        "manual",  # http://b/62429636
-        "noguitar",
-        "notap",
-    ],
 )
 
 cuda_py_test(
diff --git a/tensorflow/python/kernel_tests/basic_gpu_test.py b/tensorflow/python/kernel_tests/basic_gpu_test.py
index 013aa1ba8a..b5b17ff80a 100644
--- a/tensorflow/python/kernel_tests/basic_gpu_test.py
+++ b/tensorflow/python/kernel_tests/basic_gpu_test.py
@@ -113,6 +113,7 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.arctan, math_ops.atan, use_gpu)
     self._compare(data, np.ceil, math_ops.ceil, use_gpu)
     self._compare(data, np.cos, math_ops.cos, use_gpu)
+    self._compare(data, np.cosh, math_ops.cosh, use_gpu)
     self._compare(data, np.exp, math_ops.exp, use_gpu)
     self._compare(data, np.floor, math_ops.floor, use_gpu)
     self._compare(data, np.log, math_ops.log, use_gpu)
@@ -120,6 +121,7 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.negative, math_ops.negative, use_gpu)
     self._compare(data, self._rsqrt, math_ops.rsqrt, use_gpu)
     self._compare(data, np.sin, math_ops.sin, use_gpu)
+    self._compare(data, np.sinh, math_ops.sinh, use_gpu)
     self._compare(data, np.sqrt, math_ops.sqrt, use_gpu)
     self._compare(data, np.square, math_ops.square, use_gpu)
     self._compare(data, np.tan, math_ops.tan, use_gpu)
diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py
index 0846470abc..b47139e6b8 100644
--- a/tensorflow/python/kernel_tests/cwise_ops_test.py
+++ b/tensorflow/python/kernel_tests/cwise_ops_test.py
@@ -200,6 +200,8 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(z, np.log, math_ops.log)
     self._compareBoth(z, np.log1p, math_ops.log1p)
+    self._compareBoth(x, np.sinh, math_ops.sinh)
+    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, self._log_sigmoid, math_ops.log_sigmoid)
@@ -245,6 +247,8 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(x, np.log, math_ops.log)
     self._compareBoth(x, np.log1p, math_ops.log1p)
+    self._compareBoth(x, np.sinh, math_ops.sinh)
+    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, np.sign, math_ops.sign)
@@ -285,6 +289,8 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.expm1, math_ops.expm1)
     self._compareBoth(z, np.log, math_ops.log)
     self._compareBoth(z, np.log1p, math_ops.log1p)
+    self._compareBoth(x, np.sinh, math_ops.sinh)
+    self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(y, np.sign, math_ops.sign)
@@ -389,6 +395,8 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.expm1, math_ops.expm1)
     self._compareCpu(y, np.log, math_ops.log)
     self._compareCpu(y, np.log1p, math_ops.log1p)
+    self._compareCpu(x, np.sinh, math_ops.sinh)
+    self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
@@ -423,6 +431,8 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.expm1, math_ops.expm1)
     self._compareCpu(y, np.log, math_ops.log)
     self._compareCpu(y, np.log1p, math_ops.log1p)
+    self._compareCpu(x, np.sinh, math_ops.sinh)
+    self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
@@ -697,6 +707,11 @@ class BinaryOpTest(test.TestCase):
     except ImportError as e:
       tf_logging.warn("Cannot test special functions: %s" % str(e))
 
+  def testUint8Basic(self):
+    x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.uint8)
+    y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.uint8)
+    self._compareBoth(x, y, np.add, math_ops.add)
+
   def testInt8Basic(self):
     x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int8)
     y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int8)
diff --git a/tensorflow/python/kernel_tests/fft_ops_test.py b/tensorflow/python/kernel_tests/fft_ops_test.py
index 6544fe9735..6c575aea12 100644
--- a/tensorflow/python/kernel_tests/fft_ops_test.py
+++ b/tensorflow/python/kernel_tests/fft_ops_test.py
@@ -338,37 +338,38 @@ class RFFTOpsTest(BaseFFTOpsTest):
                 use_placeholder=True)
 
   def testFftLength(self):
-    with self._fft_kernel_label_map():
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 3):
-          for size in (5, 6):
-            inner_dim = size // 2 + 1
-            r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
-                (size,) * dims)
-            c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
-                         10).reshape((size,) * (dims - 1) + (inner_dim,))
-
-            # Test truncation (FFT size < dimensions).
-            fft_length = (size - 2,) * rank
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
-            # Confirm it works with unknown shapes as well.
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                 use_placeholder=True)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                  use_placeholder=True)
-
-            # Test padding (FFT size > dimensions).
-            fft_length = (size + 2,) * rank
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
-            # Confirm it works with unknown shapes as well.
-            self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                 use_placeholder=True)
-            self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                  use_placeholder=True)
+    if test.is_gpu_available(cuda_only=True):
+      with self._fft_kernel_label_map():
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 3):
+            for size in (5, 6):
+              inner_dim = size // 2 + 1
+              r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
+                  (size,) * dims)
+              c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
+                           10).reshape((size,) * (dims - 1) + (inner_dim,))
+
+              # Test truncation (FFT size < dimensions).
+              fft_length = (size - 2,) * rank
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
+
+              # Confirm it works with unknown shapes as well.
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
+                                   use_placeholder=True)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
+                                    use_placeholder=True)
+
+              # Test padding (FFT size > dimensions).
+              fft_length = (size + 2,) * rank
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
+
+              # Confirm it works with unknown shapes as well.
+              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
+                                   use_placeholder=True)
+              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
+                                    use_placeholder=True)
 
   def testRandom(self):
     with self._fft_kernel_label_map():
diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py
index 2d2169c310..4ceb24862f 100644
--- a/tensorflow/python/kernel_tests/map_stage_op_test.py
+++ b/tensorflow/python/kernel_tests/map_stage_op_test.py
@@ -24,6 +24,7 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
+TIMEOUT = 1
 
 class MapStageTest(test.TestCase):
 
@@ -194,8 +195,7 @@ class MapStageTest(test.TestCase):
     import threading
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -207,31 +207,34 @@ class MapStageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        sess.run(get)
-
-      # This should now succeed
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
-      # Clear out the staging area completely
-      for i in range(capacity):
+      # Clear the staging area completely
+      for i in range(n):
         sess.run(get)
 
+      self.assertTrue(sess.run(size) == 0)
+
   def testMemoryLimit(self):
     memory_limit = 512*1024  # 512K
     chunk = 200*1024 # 256K
@@ -256,8 +259,7 @@ class MapStageTest(test.TestCase):
     import numpy as np
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -265,36 +267,39 @@ class MapStageTest(test.TestCase):
       # not fill the queue with n tokens
       def thread_run():
         for i in range(n):
-          sess.run(stage, feed_dict={x: np.full(chunk, i, dtype=np.uint8),
-                                    pi: i})
+          data = np.full(chunk, i, dtype=np.uint8)
+          sess.run(stage, feed_dict={x: data, pi: i})
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        sess.run(get)
-
-      # This should now succeed
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
-      # Clear out the staging area completely
-      for i in range(capacity):
+      # Clear the staging area completely
+      for i in range(n):
         sess.run(get)
 
+      self.assertTrue(sess.run(size) == 0)
+
   def testOrdering(self):
     import six
     import random
diff --git a/tensorflow/python/kernel_tests/record_input_test.py b/tensorflow/python/kernel_tests/record_input_test.py
index a3fc98c20f..8fec2affa5 100644
--- a/tensorflow/python/kernel_tests/record_input_test.py
+++ b/tensorflow/python/kernel_tests/record_input_test.py
@@ -53,6 +53,7 @@ class RecordInputOpTest(test.TestCase):
   def testRecordInputEpochs(self):
     files = 100
     records_per_file = 100
+    batches = 2
     with self.test_session() as sess:
       self.generateTestData("basic", files, records_per_file)
 
@@ -63,17 +64,20 @@ class RecordInputOpTest(test.TestCase):
           batch_size=1,
           shift_ratio=0.33,
           seed=10,
-          name="record_input")
+          name="record_input",
+          batches=batches)
 
       yield_op = records.get_yield_op()
 
       # cycle over 3 epochs and make sure we never duplicate
       for _ in range(3):
         epoch_set = set()
-        for _ in range(files * records_per_file):
-          r = sess.run(yield_op)
-          self.assertTrue(r[0] not in epoch_set)
-          epoch_set.add(r[0])
+        for _ in range(int(files * records_per_file / batches)):
+          op_list = sess.run(yield_op)
+          self.assertTrue(len(op_list) is batches)
+          for r in op_list:
+            self.assertTrue(r[0] not in epoch_set)
+            epoch_set.add(r[0])
 
   def testDoesNotDeadlock(self):
     # Iterate multiple times to cause deadlock if there is a chance it can occur
diff --git a/tensorflow/python/kernel_tests/shape_ops_test.py b/tensorflow/python/kernel_tests/shape_ops_test.py
index 97d61d52af..52cf904528 100644
--- a/tensorflow/python/kernel_tests/shape_ops_test.py
+++ b/tensorflow/python/kernel_tests/shape_ops_test.py
@@ -504,16 +504,16 @@ class TileTest(test.TestCase):
       with self.assertRaises(ValueError):
         array_ops.tile(a, [[2, 3], [3, 4]]).eval()
 
-  def _RunAndVerifyResult(self, use_gpu):
+  def _RunAndVerifyResult(self, rank, use_gpu):
     with self.test_session(use_gpu=use_gpu):
-      # Random dims of rank 5
-      input_shape = np.random.randint(1, 4, size=5)
+      # Random dims of given rank
+      input_shape = np.random.randint(1, 4, size=rank)
       inp = np.random.rand(*input_shape).astype("f")
       a = constant_op.constant(
           [float(x) for x in inp.ravel(order="C")],
           shape=input_shape,
           dtype=dtypes.float32)
-      multiples = np.random.randint(1, 4, size=5).astype(np.int32)
+      multiples = np.random.randint(1, 4, size=rank).astype(np.int32)
       tiled = array_ops.tile(a, multiples)
       result = tiled.eval()
     self.assertTrue((np.array(multiples) * np.array(inp.shape) == np.array(
@@ -522,10 +522,16 @@ class TileTest(test.TestCase):
     self.assertShapeEqual(result, tiled)
 
   def testRandom(self):
+    # test low rank, like 5
     for _ in range(5):
-      self._RunAndVerifyResult(use_gpu=False)
+      self._RunAndVerifyResult(5, use_gpu=False)
     for _ in range(5):
-      self._RunAndVerifyResult(use_gpu=True)
+      self._RunAndVerifyResult(5, use_gpu=True)
+    # test high rank, like 10
+    for _ in range(5):
+      self._RunAndVerifyResult(10, use_gpu=False)
+    for _ in range(5):
+      self._RunAndVerifyResult(10, use_gpu=True)
 
   def testGradientSimpleReduction(self):
     with self.test_session():
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index e67a2c25e9..6664362226 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -544,21 +544,24 @@ class SparseFillEmptyRowsTest(test_util.TensorFlowTestCase):
       self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool))
 
 
-class SparseReduceSumTest(test_util.TensorFlowTestCase):
+class SparseReduceTest(test_util.TensorFlowTestCase):
 
-  # [[1, ?, 1]
-  #  [?, 1, ?]]
+  # [[1, ?, 2]
+  #  [?, 3, ?]]
   # where ? is implictly-zero.
   ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64)
   vals = np.array([1, 1, 1]).astype(np.int32)
   dense_shape = np.array([2, 3]).astype(np.int64)
 
-  def _compare(self, sp_t, reduction_axes, ndims, keep_dims):
+  def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
     densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval()
 
     np_ans = densified
     if reduction_axes is None:
-      np_ans = np.sum(np_ans, keepdims=keep_dims)
+      if do_sum:
+        np_ans = np.sum(np_ans, keepdims=keep_dims)
+      else:
+        np_ans = np.max(np_ans, keepdims=keep_dims)
     else:
       if not isinstance(reduction_axes, list):  # Single scalar.
         reduction_axes = [reduction_axes]
@@ -568,15 +571,28 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
       # Loop below depends on sorted.
       reduction_axes.sort()
       for ra in reduction_axes.ravel()[::-1]:
-        np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
+        if do_sum:
+          np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
+        else:
+          np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
 
     with self.test_session():
-      tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
-                                                  keep_dims)
+      if do_sum:
+        tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
+                                                    keep_dims)
+      else:
+        tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
+                                                    keep_dims)
       out_dense = tf_dense_ans.eval()
 
-      tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t, reduction_axes,
-                                                          keep_dims)
+      if do_sum:
+        tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t,
+                                                            reduction_axes,
+                                                            keep_dims)
+      else:
+        tf_sparse_ans = sparse_ops.sparse_reduce_max_sparse(sp_t,
+                                                            reduction_axes,
+                                                            keep_dims)
       # Convert to dense for comparison purposes.
       out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans).eval()
 
@@ -584,8 +600,10 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
     self.assertAllClose(np_ans, out_sparse)
 
   def _compare_all(self, sp_t, reduction_axes, ndims):
-    self._compare(sp_t, reduction_axes, ndims, False)
-    self._compare(sp_t, reduction_axes, ndims, True)
+    self._compare(sp_t, reduction_axes, ndims, False, False)
+    self._compare(sp_t, reduction_axes, ndims, False, True)
+    self._compare(sp_t, reduction_axes, ndims, True, False)
+    self._compare(sp_t, reduction_axes, ndims, True, True)
 
   def testSimpleAndRandomInputs(self):
     if np.__version__ == "1.13.0":
@@ -621,6 +639,10 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
         sparse_ops.sparse_reduce_sum(sp_t, -3).eval()
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_sum(sp_t, 2).eval()
+      with self.assertRaisesOpError("Invalid reduction dimension -3"):
+        sparse_ops.sparse_reduce_max(sp_t, -3).eval()
+      with self.assertRaisesOpError("Invalid reduction dimension 2"):
+        sparse_ops.sparse_reduce_max(sp_t, 2).eval()
 
   def testGradient(self):
     if np.__version__ == "1.13.0":
diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py
index 4a89fb64e3..1a6a869e3d 100644
--- a/tensorflow/python/kernel_tests/stage_op_test.py
+++ b/tensorflow/python/kernel_tests/stage_op_test.py
@@ -23,9 +23,11 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
+TIMEOUT = 1
 
 class StageTest(test.TestCase):
 
+
   def testSimple(self):
     with ops.Graph().as_default() as G:
       with ops.device('/cpu:0'):
@@ -172,8 +174,7 @@ class StageTest(test.TestCase):
     import threading
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -185,31 +186,33 @@ class StageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        self.assertTrue(sess.run(ret) == i)
-
-      # Thread should be able to join now
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
       # Clear the staging area completely
-      for i in range(capacity):
-        self.assertTrue(sess.run(ret) == i+(n-capacity))
+      for i in range(n):
+        self.assertTrue(sess.run(ret) == i)
 
+      # It should now be empty
       self.assertTrue(sess.run(size) == 0)
 
   def testMemoryLimit(self):
@@ -234,8 +237,7 @@ class StageTest(test.TestCase):
     import numpy as np
 
     queue = Queue.Queue()
-    n = 5
-    missed = 0
+    n = 8
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       # Stage data in a separate thread which will block
@@ -247,30 +249,31 @@ class StageTest(test.TestCase):
           queue.put(0)
 
       t = threading.Thread(target=thread_run)
+      t.daemon = True
       t.start()
 
-      # Get tokens from the queue, making notes of when we timeout
-      for i in range(n):
-        try:
-          queue.get(timeout=0.05)
-        except Queue.Empty:
-          missed += 1
-
-      # We timed out n - capacity times waiting for queue puts
-      self.assertTrue(missed == n - capacity)
-
-      # Clear the staging area out a bit
-      for i in range(n - capacity):
-        self.assertTrue(sess.run(ret)[0] == i)
-
-      # Thread should be able to join now
-      t.join()
-
+      # Get tokens from the queue until a timeout occurs
+      try:
+        for i in range(n):
+          queue.get(timeout=TIMEOUT)
+      except Queue.Empty:
+        pass
+
+      # Should've timed out on the iteration 'capacity'
+      if not i == capacity:
+        self.fail("Expected to timeout on iteration '{}' "
+                  "but instead timed out on iteration '{}' "
+                  "Staging Area size is '{}' and configured "
+                  "capacity is '{}'.".format(capacity, i,
+                                            sess.run(size),
+                                            capacity))
+
+      # Should have capacity elements in the staging area
       self.assertTrue(sess.run(size) == capacity)
 
       # Clear the staging area completely
-      for i in range(capacity):
-        self.assertTrue(sess.run(ret)[0] == i+(n-capacity))
+      for i in range(n):
+        self.assertTrue(np.all(sess.run(ret) == i))
 
       self.assertTrue(sess.run(size) == 0)
 
diff --git a/tensorflow/python/lib/io/py_record_writer.cc b/tensorflow/python/lib/io/py_record_writer.cc
index df35c43c3d..ba749da47a 100644
--- a/tensorflow/python/lib/io/py_record_writer.cc
+++ b/tensorflow/python/lib/io/py_record_writer.cc
@@ -55,6 +55,14 @@ bool PyRecordWriter::WriteRecord(tensorflow::StringPiece record) {
   return s.ok();
 }
 
+void PyRecordWriter::Flush(TF_Status* out_status) {
+  Status s = writer_->Flush();
+  if (!s.ok()) {
+    Set_TF_Status_from_Status(out_status, s);
+    return;
+  }
+}
+
 void PyRecordWriter::Close(TF_Status* out_status) {
   Status s = writer_->Close();
   if (!s.ok()) {
diff --git a/tensorflow/python/lib/io/py_record_writer.h b/tensorflow/python/lib/io/py_record_writer.h
index 8c53420ce6..9d66c031d4 100644
--- a/tensorflow/python/lib/io/py_record_writer.h
+++ b/tensorflow/python/lib/io/py_record_writer.h
@@ -44,6 +44,7 @@ class PyRecordWriter {
   ~PyRecordWriter();
 
   bool WriteRecord(tensorflow::StringPiece record);
+  void Flush(TF_Status* out_status);
   void Close(TF_Status* out_status);
 
  private:
diff --git a/tensorflow/python/lib/io/py_record_writer.i b/tensorflow/python/lib/io/py_record_writer.i
index 9e61c9893a..3181c9afce 100644
--- a/tensorflow/python/lib/io/py_record_writer.i
+++ b/tensorflow/python/lib/io/py_record_writer.i
@@ -45,6 +45,7 @@ limitations under the License.
 %unignore tensorflow::io::PyRecordWriter;
 %unignore tensorflow::io::PyRecordWriter::~PyRecordWriter;
 %unignore tensorflow::io::PyRecordWriter::WriteRecord;
+%unignore tensorflow::io::PyRecordWriter::Flush;
 %unignore tensorflow::io::PyRecordWriter::Close;
 %unignore tensorflow::io::PyRecordWriter::New;
 
diff --git a/tensorflow/python/lib/io/tf_record.py b/tensorflow/python/lib/io/tf_record.py
index 3d0cdc2153..df19010068 100644
--- a/tensorflow/python/lib/io/tf_record.py
+++ b/tensorflow/python/lib/io/tf_record.py
@@ -121,6 +121,11 @@ class TFRecordWriter(object):
     """
     self._writer.WriteRecord(record)
 
+  def flush(self):
+    """Flush the file."""
+    with errors.raise_exception_on_not_ok_status() as status:
+      self._writer.Flush(status)
+
   def close(self):
     """Close the file."""
     with errors.raise_exception_on_not_ok_status() as status:
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index b2aff617df..8db5efb447 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -402,14 +402,15 @@ def _SliceHelper(tensor, slice_spec, var=None):
 
   # Insert another dimension
   foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
-  print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
-  print(foo[:, tf.newaxis, :].eval()) # => [[[3,2,1]], [[9,8,7]]]
-  print(foo[:, :, tf.newaxis].eval()) # => [[[3],[2],[1]], [[9],[8],[7]]]
+  print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
+  print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
+  print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]], [[7],[8],[9]]]
 
   # Ellipses (3 equivalent operations)
-  print(foo[tf.newaxis, :, :].eval()) # => [[[3,2,1], [9,8,7]]]
-  print(foo[tf.newaxis, ...].eval()) # => [[[3,2,1], [9,8,7]]]
-  print(foo[tf.newaxis].eval()) # => [[[3,2,1], [9,8,7]]]
+  foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
+  print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
+  print(foo[tf.newaxis, ...].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
+  print(foo[tf.newaxis].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
   ```
 
   Notes:
@@ -760,11 +761,14 @@ def parallel_stack(values, name="parallel_stack"):
   parallel_stack([x, y, z])  # => [[1, 4], [2, 5], [3, 6]]
   ```
 
-  The difference between stack and parallel_stack is that stack requires all
-  of the inputs be computed before the operation will begin but doesn't require
-  that the input shapes be known during graph construction.  Parallel stack
-  will copy pieces of the input into the output as they become available, in
-  some situations this can provide a performance benefit.
+  The difference between `stack` and `parallel_stack` is that `stack` requires
+  all the inputs be computed before the operation will begin but doesn't require
+  that the input shapes be known during graph construction.
+
+  `parallel_stack` will copy pieces of the input into the output as they become
+  available, in some situations this can provide a performance benefit.
+
+  Unlike `stack`, `parallel_stack` does NOT support backpropagation.
 
   This is the opposite of unstack.  The numpy equivalent is
 
@@ -1369,7 +1373,7 @@ def zeros(shape, dtype=dtypes.float32, name=None):
   ```
 
   Args:
-    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
+    shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type `int32`.
     dtype: The type of an element in the resulting `Tensor`.
     name: A name for the operation (optional).
 
@@ -1483,7 +1487,7 @@ def ones(shape, dtype=dtypes.float32, name=None):
   ```
 
   Args:
-    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
+    shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type `int32`.
     dtype: The type of an element in the resulting `Tensor`.
     name: A name for the operation (optional).
 
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 4eead79531..1dc5eb2f90 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -248,7 +248,7 @@ class QueueBase(object):
     if isinstance(vals, dict):
       if not self._names:
         raise ValueError("Queue must have names to enqueue a dictionary")
-      if sorted(self._names) != sorted(vals.keys()):
+      if sorted(self._names, key=str) != sorted(vals.keys(), key=str):
         raise ValueError("Keys in dictionary to enqueue do not match "
                          "names of Queue.  Dictionary: (%s), Queue: (%s)" %
                          (sorted(vals.keys()), sorted(self._names)))
@@ -1582,7 +1582,7 @@ class StagingArea(BaseStagingArea):
   This is mostly useful for limiting the number of tensors on
   devices such as GPUs.
 
-  All get() and peek() commands block if the the requested data
+  All get() and peek() commands block if the requested data
   is not present in the Staging Area.
 
   """
@@ -2155,7 +2155,8 @@ class RecordInput(object):
                parallelism=1,
                shift_ratio=0,
                seed=0,
-               name=None):
+               name=None,
+               batches=None):
     """Constructs a RecordInput Op.
 
     Args:
@@ -2169,12 +2170,18 @@ class RecordInput(object):
       seed: Specify the random number seed used by generator that randomizes
         records.
       name: Optional name for the operation.
+      batches: None by default, creating a single batch op. Otherwise specifies
+        how many batches to create, which are returned as a list when
+        `get_yield_op()` is called. An example use case is to split processing
+        between devices on one computer.
 
     Raises:
       ValueError: If one of the arguments is invalid.
     """
-
     self._batch_size = batch_size
+    if batches is not None:
+      self._batch_size *= batches
+    self._batches = batches
     self._file_pattern = file_pattern
     self._buffer_size = buffer_size
     self._parallelism = parallelism
@@ -2183,8 +2190,11 @@ class RecordInput(object):
     self._name = name
 
   def get_yield_op(self):
-    """Add a node that yields a minibatch every time it is executed."""
-    return gen_data_flow_ops.record_input(
+    """Adds a node that yields a group of records every time it is executed.
+    If RecordInput `batches` parameter is not None, it yields a list of
+    record batches with the specified `batch_size`.
+    """
+    records = gen_data_flow_ops.record_input(
         file_pattern=self._file_pattern,
         file_buffer_size=self._buffer_size,
         file_parallelism=self._parallelism,
@@ -2192,3 +2202,14 @@ class RecordInput(object):
         batch_size=self._batch_size,
         file_random_seed=self._seed,
         name=self._name)
+    if self._batches is None:
+      return records
+    else:
+      with ops.name_scope(self._name):
+        batch_list = [[] for i in six.moves.range(self._batches)]
+        records = array_ops.split(records, self._batch_size, 0)
+        records = [array_ops.reshape(record, []) for record in records]
+        for index, protobuf in zip(six.moves.range(len(records)), records):
+          batch_index = index % self._batches
+          batch_list[batch_index].append(protobuf)
+        return batch_list
diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py
index f96eafed71..3a804c941a 100644
--- a/tensorflow/python/ops/distributions/special_math.py
+++ b/tensorflow/python/ops/distributions/special_math.py
@@ -324,7 +324,7 @@ def log_ndtr(x, series_order=3, name="log_ndtr"):
 
 
 def _log_ndtr_lower(x, series_order):
-  """Asymptotic expansion version of `Log[cdf(x)]`, apppropriate for `x<<-1`."""
+  """Asymptotic expansion version of `Log[cdf(x)]`, appropriate for `x<<-1`."""
   x_2 = math_ops.square(x)
   # Log of the term multiplying (1 + sum)
   log_scale = -0.5 * x_2 - math_ops.log(-x) - 0.5 * math.log(2. * math.pi)
diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index bd8a5c86ac..52ce451238 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -34,6 +34,7 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import functional_ops
@@ -45,6 +46,7 @@ from tensorflow.python.ops import math_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import spectral_grad  # pylint: disable=unused-import
+from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.platform import tf_logging as logging
 
 
@@ -920,8 +922,6 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False,
   Raises:
     LookupError: if one of the operations between `xs` and `ys` does not
       have a registered gradient function.
-    ValueError: if the arguments are invalid or not supported. Currently,
-      this function only supports one-dimensional `x` in `xs`.
   """
   xs = _AsList(xs)
   kwargs = {
@@ -929,28 +929,30 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False,
       'gate_gradients': gate_gradients,
       'aggregation_method': aggregation_method
     }
-  # Compute a hessian matrix for each x in xs
+  # Compute first-order derivatives and iterate for each x in xs.
   hessians = []
-  for i, x in enumerate(xs):
-    # Check dimensions
-    ndims = x.get_shape().ndims
-    if ndims is None:
-      raise ValueError('Cannot compute Hessian because the dimensionality of '
-                       'element number %d of `xs` cannot be determined' % i)
-    elif ndims != 1:
-      raise ValueError('Computing hessians is currently only supported for '
-                       'one-dimensional tensors. Element number %d of `xs` has '
-                       '%d dimensions.' % (i, ndims))
-    with ops.name_scope(name + '_first_derivative'):
-      # Compute the partial derivatives of the input with respect to all
-      # elements of `x`
-      _gradients = gradients(ys, x, **kwargs)[0]
-      # Unpack the gradients into a list so we can take derivatives with
-      # respect to each element
-      _gradients = array_ops.unstack(_gradients)
-    with ops.name_scope(name + '_second_derivative'):
-      # Compute the partial derivatives with respect to each element of the list
-      _hess = [gradients(_gradient, x, **kwargs)[0] for _gradient in _gradients]
-      # Pack the list into a matrix and add to the list of hessians
-      hessians.append(array_ops.stack(_hess, name=name))
+  _gradients = gradients(ys, xs, **kwargs)
+  for i, _gradient, x in zip(range(len(xs)), _gradients, xs):
+    # Ensure that x is a vector.
+    check_rank = check_ops.assert_rank(
+      x, 1, message='Cannot compute Hessian because element %d of `xs` does '
+      'not have rank one.' % i
+    )
+    with ops.control_dependencies([check_rank]):
+      # Declare an iterator and tensor array loop variables for the gradients.
+      n = array_ops.size(x)
+      loop_vars = [
+        array_ops.constant(0, dtypes.int32),
+        tensor_array_ops.TensorArray(x.dtype, n)
+      ]
+      # Iterate over all elements of the gradient and compute second order
+      # derivatives.
+      _, hessian = control_flow_ops.while_loop(
+          lambda j, _: j < n,
+          lambda j, result: (j + 1,
+                             result.write(j, gradients(_gradient[j], x)[0])),
+          loop_vars
+      )
+
+      hessians.append(hessian.stack())
   return hessians
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index aac7aef8ce..dade053589 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -989,9 +989,9 @@ def index_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   table = tf.contrib.lookup.index_table_from_tensor(
-      vocabulary_list=vocabulary_list, num_oov_buckets=1, default_value=-1)
+      mapping=vocabulary_list, num_oov_buckets=1, default_value=-1)
   features = tf.constant(["emerson", "lake", "and", "palmer"])
   ids = table.lookup(features)
   ...
@@ -1161,7 +1161,7 @@ def index_to_string_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       vocabulary_list, default_value="UNKNOWN")
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index 024158e709..a0f505e47b 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -369,6 +369,24 @@ def _Log1pGrad(op, grad):
     return grad * math_ops.reciprocal(1 + x)
 
 
+@ops.RegisterGradient("Sinh")
+def _SinhGrad(op, grad):
+  """Returns grad * cosh(x)."""
+  x = op.inputs[0]
+  with ops.control_dependencies([grad.op]):
+    x = math_ops.conj(x)
+    return grad * math_ops.cosh(x)
+
+
+@ops.RegisterGradient("Cosh")
+def _CoshGrad(op, grad):
+  """Returns grad * sinh(x)."""
+  x = op.inputs[0]
+  with ops.control_dependencies([grad.op]):
+    x = math_ops.conj(x)
+    return grad * math_ops.sinh(x)
+
+
 @ops.RegisterGradient("Tanh")
 def _TanhGrad(op, grad):
   """Returns grad * (1 - tanh(x) * tanh(x))."""
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 23af8c0a57..bf46bbdcc2 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -45,6 +45,8 @@ See the @{$python/math_ops} guide.
 @@expm1
 @@log
 @@log1p
+@@sinh
+@@cosh
 @@ceil
 @@floor
 @@maximum
@@ -2063,12 +2065,11 @@ def tanh(x, name=None):
 
   Args:
     x: A Tensor or SparseTensor with type `float`, `double`, `int32`,
-      `complex64`, `int64`, or `qint32`.
+      `complex64`, or `int64`.
     name: A name for the operation (optional).
 
   Returns:
-    A Tensor or SparseTensor respectively with the same type as `x` if
-    `x.dtype != qint32` otherwise the return type is `quint8`.
+    A Tensor or SparseTensor respectively with the same type as `x`.
   """
   with ops.name_scope(name, "Tanh", [x]) as name:
     if isinstance(x, sparse_tensor.SparseTensor):
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 53db96f334..f7854e86c0 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -419,7 +419,7 @@ class LSTMCell(RNNCell):
 
   The default non-peephole implementation is based on:
 
-    http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+    http://www.bioinf.jku.at/publications/older/2604.pdf
 
   S. Hochreiter and J. Schmidhuber.
   "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
@@ -628,7 +628,7 @@ class DropoutWrapper(RNNCell):
     """Create a cell with added input, state, and/or output dropout.
 
     If `variational_recurrent` is set to `True` (**NOT** the default behavior),
-    then the the same dropout mask is applied at every step, as described in:
+    then the same dropout mask is applied at every step, as described in:
 
     Y. Gal, Z Ghahramani.  "A Theoretically Grounded Application of Dropout in
     Recurrent Neural Networks".  https://arxiv.org/abs/1512.05287
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 377adcdab8..93a9656950 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -30,6 +30,8 @@
 @@sparse_reset_shape
 @@sparse_fill_empty_rows
 @@sparse_transpose
+@@sparse_reduce_max
+@@sparse_reduce_max_sparse
 @@sparse_reduce_sum
 @@sparse_reduce_sum_sparse
 @@sparse_add
@@ -710,6 +712,90 @@ def sparse_to_dense(sparse_indices,
       name=name)
 
 
+def sparse_reduce_max(sp_input, axis=None, keep_dims=False,
+                      reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+  instead of a sparse one.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  similar to the indexing rules in Python.
+
+  For example:
+
+  ```python
+  # 'x' represents [[1, ?, 2]
+  #                 [?, 3, ?]]
+  # where ? is implicitly-zero.
+  tf.sparse_reduce_max(x) ==> 3
+  tf.sparse_reduce_max(x, 0) ==> [1, 3, 2]
+  tf.sparse_reduce_max(x, 1) ==> [2, 3]  # Can also use -1 as the axis.
+  tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]]
+  tf.sparse_reduce_max(x, [0, 1]) ==> 3
+  ```
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis.
+
+  Returns:
+    The reduced Tensor.
+  """
+  return gen_sparse_ops.sparse_reduce_max(
+      sp_input.indices, sp_input.values,
+      sp_input.dense_shape,
+      math_ops._ReductionDims(sp_input, axis, reduction_axes),
+      keep_dims)
+
+
+def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False,
+                             reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In contrast to SparseReduceSum, this Op returns a
+  SparseTensor.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  which are interpreted according to the indexing rules in Python.
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis
+
+  Returns:
+    The reduced SparseTensor.
+  """
+  output_ind, output_val, output_shape = (
+      gen_sparse_ops.sparse_reduce_max_sparse(
+          sp_input.indices, sp_input.values,
+          sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis,
+                                                        reduction_axes),
+          keep_dims))
+
+  return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
+
+
 def sparse_reduce_sum(sp_input, axis=None, keep_dims=False,
                       reduction_axes=None):
   """Computes the sum of elements across dimensions of a SparseTensor.
diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py
index a6b14f6f6f..30bf4e4ef1 100644
--- a/tensorflow/python/ops/standard_ops.py
+++ b/tensorflow/python/ops/standard_ops.py
@@ -145,7 +145,7 @@ _allowed_symbols_math_ops = [
     "sub",  # use tf.subtract instead.
 
     # These are documented in nn.
-    # We are are not importing nn because it would create a circular dependency.
+    # We are not importing nn because it would create a circular dependency.
     "sigmoid",
     "log_sigmoid",
     "tanh",
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index 7a6abc8e61..20ae082ee1 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -87,7 +87,7 @@ class TensorArray(object):
         the shape constraints of each of the elements of the TensorArray.
         Need not be fully defined.
       colocate_with_first_write_call: If `True`, the TensorArray will be
-        colocated on the same device as the the Tensor used on its first write
+        colocated on the same device as the Tensor used on its first write
         (write operations include `write`, `unstack`, and `split`).  If `False`,
         the TensorArray will be placed on the device determined by the
         device context available during its initialization.
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index f784b715c1..c2193a24d0 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -1501,6 +1501,11 @@ def variable_scope(name_or_scope,
   A note about name scoping: Setting `reuse` does not impact the naming of other
   ops such as mult. See related discussion on [github#6189](https://github.com/tensorflow/tensorflow/issues/6189)
 
+  Note that up to and including version 1.0, it was allowed (though
+  explicitly discouraged) to pass False to the reuse argument, yielding
+  undocumented behaviour slightly different from None. Starting at 1.1.0
+  passing None and False as reuse has exactly the same effect.
+
   Args:
     name_or_scope: `string` or `VariableScope`: the scope to open.
     default_name: The default name to use if the `name_or_scope` argument is
diff --git a/tensorflow/python/tools/print_selective_registration_header.py b/tensorflow/python/tools/print_selective_registration_header.py
index 3e2ab4695e..62f00f4467 100644
--- a/tensorflow/python/tools/print_selective_registration_header.py
+++ b/tensorflow/python/tools/print_selective_registration_header.py
@@ -16,7 +16,7 @@ r"""Prints a header file to be used with SELECTIVE_REGISTRATION.
 
 An example of command-line usage is:
   bazel build tensorflow/python/tools:print_selective_registration_header && \
-  bazel-bin/tensorflow/python/tools:print_selective_registration_header \
+  bazel-bin/tensorflow/python/tools/print_selective_registration_header \
     --graphs=path/to/graph.pb > ops_to_register.h
 
 Then when compiling tensorflow, include ops_to_register.h in the include search
diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 1755167938..93ba15ec81 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -382,7 +382,7 @@ class _SparseMetaData(object):
 
 def _as_tensor_list(tensors):
   if isinstance(tensors, dict):
-    return [tensors[k] for k in sorted(tensors)]
+    return [tensors[k] for k in sorted(tensors, key=str)]
   else:
     return tensors
 
@@ -408,7 +408,7 @@ def _as_original_type(original_tensors, tensor_list):
       # was enqueued.  Make it a list again.  See b/28117485.
       tensor_list = [tensor_list]
     return {k: tensor_list[i]
-            for i, k in enumerate(sorted(original_tensors))}
+            for i, k in enumerate(sorted(original_tensors, key=str))}
   else:
     return tensor_list
 
@@ -762,6 +762,9 @@ def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
   tensor_list = _as_tensor_list(tensors)
   with ops.name_scope(name, "shuffle_batch",
                       list(tensor_list) + [keep_input]) as name:
+    if capacity <= min_after_dequeue:
+      raise ValueError("capacity %d must be bigger than min_after_dequeue %d."
+                       % (capacity, min_after_dequeue))
     tensor_list = _validate(tensor_list)
     keep_input = _validate_keep_input(keep_input, enqueue_many)
     tensor_list, sparse_info = _store_sparse_tensors(
diff --git a/tensorflow/python/training/input_test.py b/tensorflow/python/training/input_test.py
index 4f705a0a85..d32768f5da 100644
--- a/tensorflow/python/training/input_test.py
+++ b/tensorflow/python/training/input_test.py
@@ -429,6 +429,13 @@ class DictHelperTest(test_lib.TestCase):
     d2 = inp._as_original_type(d, l)
     self.assertEquals(d, d2)
 
+  def testHeterogeneousKeysDictInputs(self):
+    d = {"z": 1, 1: 42, ("a", "b"): 100}
+    l = inp._as_tensor_list(d)
+    self.assertEquals([100, 42, 1], l)
+    d2 = inp._as_original_type(d, l)
+    self.assertEquals(d, d2)
+
 
 class BatchTest(test_lib.TestCase):
 
diff --git a/tensorflow/python/training/momentum.py b/tensorflow/python/training/momentum.py
index ffd7c12c42..f34ff22f07 100644
--- a/tensorflow/python/training/momentum.py
+++ b/tensorflow/python/training/momentum.py
@@ -52,7 +52,7 @@ class MomentumOptimizer(optimizer.Optimizer):
       name: Optional name prefix for the operations created when applying
         gradients.  Defaults to "Momentum".
       use_nesterov: If `True` use Nesterov Momentum.
-        See [Sutskever et. al., 2013](
+        See [Sutskever et al., 2013](
         http://jmlr.org/proceedings/papers/v28/sutskever13.pdf)
 
     """
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 690af5485a..5b1d9a517e 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -147,7 +147,7 @@ def tf_copts():
       "-Iexternal/gemmlowp",
       "-Wno-sign-compare",
       "-fno-exceptions",
-  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1"]) + if_android_arm(
+  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
       ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index b761f41361..eca017edf5 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -768,6 +768,10 @@ tf_module {
     name: "cos"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "cosh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "count_nonzero"
     argspec: "args=[\'input_tensor\', \'axis\', \'keep_dims\', \'dtype\', \'name\', \'reduction_indices\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \"<dtype: \'int64\'>\", \'None\', \'None\'], "
@@ -1640,6 +1644,10 @@ tf_module {
     name: "sin"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "sinh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'input\', \'name\', \'out_type\'], varargs=None, keywords=None, defaults=[\'None\', \"<dtype: \'int32\'>\"], "
@@ -1696,6 +1704,14 @@ tf_module {
     name: "sparse_placeholder"
     argspec: "args=[\'dtype\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
+  member_method {
+    name: "sparse_reduce_max"
+    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "sparse_reduce_max_sparse"
+    argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
   member_method {
     name: "sparse_reduce_sum"
     argspec: "args=[\'sp_input\', \'axis\', \'keep_dims\', \'reduction_axes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt b/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt
index af0c11ca14..31775de2d1 100644
--- a/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt
@@ -10,6 +10,10 @@ tf_class {
     name: "close"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "flush"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "write"
     argspec: "args=[\'self\', \'record\'], varargs=None, keywords=None, defaults=None"
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index e0a1391d6e..85c712d3c6 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -205,14 +205,17 @@ if [[ -n "${PY_TAGS}" ]]; then
 $(echo ${WHL_BASE_NAME} | cut -d \- -f 2)-${PY_TAGS}-${PLATFORM_TAG}.whl
 
   if [[ ! -f "${WHL_DIR}/${NEW_WHL_BASE_NAME}" ]]; then
-    cp "${WHL_DIR}/${WHL_BASE_NAME}" "${WHL_DIR}/${NEW_WHL_BASE_NAME}" && \
-      echo "Copied wheel file: ${WHL_BASE_NAME} --> ${NEW_WHL_BASE_NAME}" || \
+    if cp "${WHL_DIR}/${WHL_BASE_NAME}" "${WHL_DIR}/${NEW_WHL_BASE_NAME}"
+    then
+      echo "Copied wheel file: ${WHL_BASE_NAME} --> ${NEW_WHL_BASE_NAME}"
+    else
       die "ERROR: Failed to copy wheel file to ${NEW_WHL_BASE_NAME}"
+    fi
   fi
 fi
 
 if [[ $(uname) == "Linux" ]]; then
-  AUDITED_WHL_NAME="${WHL_DIR}/$(echo ${WHL_BASE_NAME} | sed "s/linux/manylinux1/")"
+  AUDITED_WHL_NAME="${WHL_DIR}/$(echo ${WHL_BASE_NAME//linux/manylinux1})"
 
   # Repair the wheels for cpu manylinux1
   if [[ ${CONTAINER_TYPE} == "cpu" ]]; then
@@ -240,14 +243,20 @@ echo "Installing pip whl file: ${WHL_PATH}"
 VENV_DIR="${PIP_TEST_ROOT}/venv"
 
 if [[ -d "${VENV_DIR}" ]]; then
-  rm -rf "${VENV_DIR}" && \
-      echo "Removed existing virtualenv directory: ${VENV_DIR}" || \
-      die "Failed to remove existing virtualenv directory: ${VENV_DIR}"
+  if rm -rf "${VENV_DIR}"
+  then
+    echo "Removed existing virtualenv directory: ${VENV_DIR}"
+  else
+    die "Failed to remove existing virtualenv directory: ${VENV_DIR}"
+  fi
 fi
 
-mkdir -p ${VENV_DIR} && \
-    echo "Created virtualenv directory: ${VENV_DIR}" || \
-    die "FAILED to create virtualenv directory: ${VENV_DIR}"
+if mkdir -p ${VENV_DIR}
+then
+  echo "Created virtualenv directory: ${VENV_DIR}"
+else
+  die "FAILED to create virtualenv directory: ${VENV_DIR}"
+fi
 
 # Verify that virtualenv exists
 if [[ -z $(which virtualenv) ]]; then
diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh
index 3b640dd5e8..9c1b75d004 100755
--- a/tensorflow/tools/ci_build/ci_build.sh
+++ b/tensorflow/tools/ci_build/ci_build.sh
@@ -80,7 +80,7 @@ fi
 
 # cmake (CPU) builds do not require configuration.
 if [[ "${CONTAINER_TYPE}" == "cmake" ]]; then
-  CI_COMMAND_PREFIX=""
+  CI_COMMAND_PREFIX=("")
 fi
 
 # Use nvidia-docker if the container is GPU.
@@ -120,9 +120,9 @@ DOCKER_IMG_NAME=$(echo "${DOCKER_IMG_NAME}" | tr '[:upper:]' '[:lower:]')
 
 # Print arguments.
 echo "WORKSPACE: ${WORKSPACE}"
-echo "CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[@]}"
-echo "COMMAND: ${COMMAND[@]}"
-echo "CI_COMMAND_PREFIX: ${CI_COMMAND_PREFIX[@]}"
+echo "CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[*]}"
+echo "COMMAND: ${COMMAND[*]}"
+echo "CI_COMMAND_PREFIX: ${CI_COMMAND_PREFIX[*]}"
 echo "CONTAINER_TYPE: ${CONTAINER_TYPE}"
 echo "BUILD_TAG: ${BUILD_TAG}"
 echo "  (docker container name will be ${DOCKER_IMG_NAME})"
@@ -140,7 +140,7 @@ if [[ $? != "0" ]]; then
 fi
 
 # Run the command inside the container.
-echo "Running '${COMMAND[@]}' inside ${DOCKER_IMG_NAME}..."
+echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..."
 mkdir -p ${WORKSPACE}/bazel-ci_build-cache
 # By default we cleanup - remove the container once it finish running (--rm)
 # and share the PID namespace (--pid=host) so the process inside does not have
diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index dfaf50eb4f..1cf87d7c7c 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -200,8 +200,8 @@ echo "  TF_BUILD_ENABLE_XLA=${TF_BUILD_ENABLE_XLA}"
 function get_cuda_capability_version() {
   if [[ ! -z $(which deviceQuery) ]]; then
     # The first listed device is used
-    echo $(deviceQuery | grep "CUDA Capability .* version" | \
-        head -1 | awk '{print $NF}')
+    deviceQuery | grep "CUDA Capability .* version" | \
+        head -1 | awk '{print $NF}'
   fi
 }
 
@@ -532,11 +532,14 @@ if [[ "${TF_BUILD_PYTHON_VERSION}" == "python3.5" ]]; then
   DOCKERFILE="${TMP_DIR}/Dockerfile.${TF_BUILD_CONTAINER_TYPE}"
 
   # Replace a line in the Dockerfile
-  sed -i \
+  if sed -i \
       's/RUN \/install\/install_pip_packages.sh/RUN \/install\/install_python3.5_pip_packages.sh/g' \
-      "${DOCKERFILE}" && \
-      echo "Copied and modified Dockerfile for Python 3.5 build: ${DOCKERFILE}" || \
-      die "ERROR: Faild to copy and modify Dockerfile: ${DOCKERFILE}"
+      "${DOCKERFILE}"
+  then
+    echo "Copied and modified Dockerfile for Python 3.5 build: ${DOCKERFILE}"
+  else
+    die "ERROR: Faild to copy and modify Dockerfile: ${DOCKERFILE}"
+  fi
 
   DOCKERFILE_FLAG="--dockerfile ${DOCKERFILE}"
 fi
@@ -574,7 +577,7 @@ rm -f ${TMP_SCRIPT}
 END_TIME=$(date +'%s')
 echo ""
 echo "Parameterized build ends with ${RESULT} at: $(date) "\
-"(Elapsed time: $((${END_TIME} - ${START_TIME})) s)"
+"(Elapsed time: $((END_TIME - START_TIME)) s)"
 
 
 # Clean up temporary directory if it exists
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index fd2874df91..e428766a40 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -47,7 +47,7 @@ num_cpus() {
 # Get the hash of the last non-merge git commit on the current branch.
 # Usage: get_last_non_merge_git_commit
 get_last_non_merge_git_commit() {
-  echo $(git rev-list --no-merges -n 1 HEAD)
+  git rev-list --no-merges -n 1 HEAD
 }
 
 # List files changed (i.e., added, removed or revised) in the last non-merge
@@ -75,7 +75,7 @@ get_py_files_to_check() {
 
     echo "${PY_FILES}"
   else
-    echo $(find tensorflow -name '*.py')
+    find tensorflow -name '*.py'
   fi
 }
 
@@ -157,25 +157,25 @@ do_pylint() {
   NONWL_ERRORS_FILE="$(mktemp)_pylint_nonwl_errors.log"
 
   rm -rf ${OUTPUT_FILE}
-  rm -rf ${ERRORS_FLIE}
+  rm -rf ${ERRORS_FILE}
   rm -rf ${NONWL_ERRORS_FILE}
   touch ${NONWL_ERRORS_FILE}
 
   ${PYLINT_BIN} --rcfile="${PYLINTRC_FILE}" --output-format=parseable \
-      --jobs=${NUM_CPUS} ${PYTHON_SRC_FILES} 2>&1 > ${OUTPUT_FILE}
+      --jobs=${NUM_CPUS} ${PYTHON_SRC_FILES} > ${OUTPUT_FILE} 2>&1
   PYLINT_END_TIME=$(date +'%s')
 
   echo ""
-  echo "pylint took $((${PYLINT_END_TIME} - ${PYLINT_START_TIME})) s"
+  echo "pylint took $((PYLINT_END_TIME - PYLINT_START_TIME)) s"
   echo ""
 
   grep -E '(\[E|\[W0311|\[W0312)' ${OUTPUT_FILE} > ${ERRORS_FILE}
 
   N_ERRORS=0
-  while read LINE; do
+  while read -r LINE; do
     IS_WHITELISTED=0
     for WL_REGEX in ${ERROR_WHITELIST}; do
-      if [[ ! -z $(echo ${LINE} | grep "${WL_REGEX}") ]]; then
+      if echo ${LINE} | grep -q "${WL_REGEX}"; then
         echo "Found a whitelisted error:"
         echo "  ${LINE}"
         IS_WHITELISTED=1
@@ -248,7 +248,7 @@ do_pep8() {
   PEP8_END_TIME=$(date +'%s')
 
   echo ""
-  echo "pep8 took $((${PEP8_END_TIME} - ${PEP8_START_TIME})) s"
+  echo "pep8 took $((PEP8_END_TIME - PEP8_START_TIME)) s"
   echo ""
 
   if [[ -s ${PEP8_OUTPUT_FILE} ]]; then
@@ -278,7 +278,7 @@ do_buildifier(){
   BUILDIFIER_END_TIME=$(date +'%s')
 
   echo ""
-  echo "buildifier took $((${BUILDIFIER_END_TIME} - ${BUILDIFIER_START_TIME})) s"
+  echo "buildifier took $((BUILDIFIER_END_TIME - BUILDIFIER_START_TIME)) s"
   echo ""
 
   if [[ -s ${BUILDIFIER_OUTPUT_FILE} ]]; then
@@ -306,7 +306,7 @@ do_external_licenses_check(){
 
   echo "Getting external dependencies for ${BUILD_TARGET}"
  bazel query "attr('licenses', 'notice', deps(${BUILD_TARGET}))" --no_implicit_deps --no_host_deps --keep_going \
-  | egrep -v "^//tensorflow" \
+  | grep -E -v "^//tensorflow" \
   | sed -e 's|:.*||' \
   | sort \
   | uniq 2>&1 \
@@ -315,7 +315,7 @@ do_external_licenses_check(){
   echo
   echo "Getting list of external licenses mentioned in ${LICENSES_TARGET}."
   bazel query "deps(${LICENSES_TARGET})" --no_implicit_deps --no_host_deps --keep_going \
-  | egrep -v "^//tensorflow" \
+  | grep -E -v "^//tensorflow" \
   | sed -e 's|:.*||' \
   | sort \
   | uniq 2>&1 \
@@ -329,7 +329,7 @@ do_external_licenses_check(){
   EXTERNAL_LICENSES_CHECK_END_TIME=$(date +'%s')
 
   echo
-  echo "do_external_licenses_check took $((${EXTERNAL_LICENSES_CHECK_END_TIME} - ${EXTERNAL_LICENSES_CHECK_START_TIME})) s"
+  echo "do_external_licenses_check took $((EXTERNAL_LICENSES_CHECK_END_TIME - EXTERNAL_LICENSES_CHECK_START_TIME)) s"
   echo
 
   if [[ -s ${MISSING_LICENSES_FILE} ]] || [[ -s ${EXTRA_LICENSES_FILE} ]] ; then
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index b8f9fc8453..7fcd235e62 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -44,8 +44,8 @@ pip2 install --upgrade markdown==2.6.8
 pip3 install --upgrade markdown==2.6.8
 
 # Install protobuf.
-pip2 install --upgrade protobuf==3.2.0
-pip3 install --upgrade protobuf==3.2.0
+pip2 install --upgrade protobuf==3.3.0
+pip3 install --upgrade protobuf==3.3.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/install/install_proto3.sh b/tensorflow/tools/ci_build/install/install_proto3.sh
index 773c89b70b..7934002b2c 100755
--- a/tensorflow/tools/ci_build/install/install_proto3.sh
+++ b/tensorflow/tools/ci_build/install/install_proto3.sh
@@ -17,9 +17,9 @@
 # Install protobuf3.
 
 # Select protobuf version.
-PROTOBUF_VERSION="3.2.0"
+PROTOBUF_VERSION="3.3.0"
 protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g')
-local_protobuf_ver=$(protoc --version | awk '{print $2}')
+local_protobuf_ver=$(protoc --version)
 local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g')
 if [[ -z $local_protobuf_ver_flat ]]; then
   local_protobuf_ver_flat=0
@@ -30,7 +30,7 @@ if (( $local_protobuf_ver_flat < $protobuf_ver_flat )); then
   PROTOBUF_ZIP=$(basename "${PROTOBUF_URL}")
   UNZIP_DEST="google-protobuf"
 
-  wget -q "${PROTOBUF_URL}"
+  wget "${PROTOBUF_URL}"
   unzip "${PROTOBUF_ZIP}" -d "${UNZIP_DEST}"
   cp "${UNZIP_DEST}/bin/protoc" /usr/local/bin/
 
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index e7e2d256cd..084ac49496 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -64,7 +64,7 @@ set -e
 pip3.5 install --upgrade six==1.10.0
 
 # Install protobuf.
-pip3.5 install --upgrade protobuf==3.2.0
+pip3.5 install --upgrade protobuf==3.3.0
 
 # Remove obsolete version of six, which can sometimes confuse virtualenv.
 rm -rf /usr/lib/python3/dist-packages/six*
diff --git a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
index 59ba71f5df..3e31aa1ce1 100755
--- a/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
+++ b/tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 
-PROTOBUF_VERSION="3.2.0"
+PROTOBUF_VERSION="3.3.1"
 PYTHON_BIN=${PYTHON_BIN:-python}
 DIR=${PWD}/protobuf
 
diff --git a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
index 5581023ad7..dff4707cbe 100644
--- a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
@@ -161,5 +161,5 @@ function create_python_test_dir() {
 
 function reinstall_tensorflow_pip() {
   echo "y" | pip uninstall tensorflow -q || true
-  pip install ${1}
+  pip install ${1} --no-deps
 }
diff --git a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
index 8b6836f4ed..96fbadd176 100644
--- a/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/cpu/cmake/run_py.bat
@@ -29,9 +29,6 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\cpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
-:: Attempt to upgrade PIP to work around Anaconda issue #542.
-%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
-
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -39,7 +36,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
index ba2d939b5f..e774a6e916 100644
--- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
+++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_py.bat
@@ -28,9 +28,6 @@ IF DEFINED PIP_EXE (ECHO PIP_EXE is set to %PIP_EXE%) ELSE (SET PIP_EXE="C:\Prog
 CALL %REPO_ROOT%\tensorflow\tools\ci_build\windows\gpu\cmake\run_build.bat
 if %errorlevel% neq 0 exit /b %errorlevel%
 
-:: Attempt to upgrade PIP to work around Anaconda issue #542.
-%PIP_EXE% install --ignore-installed --upgrade pip setuptools -v -v
-
 :: Since there are no wildcards in windows command prompt, use dark magic to get the wheel file name.
 DIR %REPO_ROOT%\%BUILD_DIR%\tf_python\dist\ /S /B > wheel_filename_file
 set /p WHEEL_FILENAME=<wheel_filename_file
@@ -38,7 +35,7 @@ del wheel_filename_file
 
 :: Install the pip package.
 echo Installing PIP package...
-%PIP_EXE% install --upgrade %WHEEL_FILENAME% -v -v
+%PIP_EXE% install --upgrade --no-deps %WHEEL_FILENAME% -v -v
 if %errorlevel% neq 0 exit /b %errorlevel%
 
 :: Run all python tests if the installation succeeded.
diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh
index f536beef54..7d7f92d246 100755
--- a/tensorflow/tools/dist_test/local_test.sh
+++ b/tensorflow/tools/dist_test/local_test.sh
@@ -70,7 +70,7 @@ get_container_id_by_image_name() {
     # Get the id of a container by image name
     # Usage: get_docker_container_id_by_image_name <img_name>
 
-    echo $(docker ps | grep $1 | awk '{print $1}')
+    docker ps | grep $1 | awk '{print $1}'
 }
 
 # Parse input arguments
@@ -152,7 +152,7 @@ rm -rf "${BUILD_DIR}"
 docker run ${DOCKER_IMG_NAME} \
     /var/tf_dist_test/scripts/dist_mnist_test.sh \
     --ps_hosts $(seq -f "localhost:%g" -s "," \
-                 2000 $((2000 + ${NUM_PARAMETER_SERVERS} - 1))) \
+                 2000 $((2000 + NUM_PARAMETER_SERVERS - 1))) \
     --worker_hosts $(seq -f "localhost:%g" -s "," \
-                     3000 $((3000 + ${NUM_WORKERS} - 1))) \
+                     3000 $((3000 + NUM_WORKERS - 1))) \
     --num_gpus 0 ${SYNC_REPLICAS_FLAG}
diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index 3e45ae362c..6d5a9bdc4c 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -54,6 +54,30 @@ for additional containers, such as release candidates or nightly builds.
 
 ## Rebuilding the containers
 
-Just pick the dockerfile corresponding to the container you want to build, and run
+Building TensorFlow Docker containers should be done through the
+[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/README.md)
+script. The raw Dockerfiles should not be used directly as they contain strings
+to be replaced by the script during the build.
 
-    $ docker build --pull -t $USER/tensorflow-suffix -f Dockerfile.suffix .
+To use the script, specify the container type (`CPU` vs. `GPU`), the desired
+Python version (`PYTHON2` vs. `PYTHON3`) and whether the developer Docker image
+is to be built (`NO` vs. `YES`). In addition, you need to specify the central
+location from where the pip package of TensorFlow will be downloaded.
+
+For example, to build a CPU-only non-developer Docker image for Python 2, using
+TensorFlow's nightly pip package:
+
+``` bash
+export TF_DOCKER_BUILD_IS_DEVEL=NO
+export TF_DOCKER_BUILD_TYPE=CPU
+export TF_DOCKER_BUILD_PYTHON_VERSION=PYTHON2
+
+export NIGHTLY_VERSION="1.head"
+export TF_DOCKER_BUILD_CENTRAL_PIP=$(echo ${TF_DOCKER_BUILD_PYTHON_VERSION} | sed s^PYTHON2^http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=${TF_DOCKER_BUILD_PYTHON_VERSION},label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp27-cp27mu-manylinux1_x86_64.whl^ | sed s^PYTHON3^http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp35-cp35m-manylinux1_x86_64.whl^)
+
+tensorflow/tools/docker/parameterized_docker_build.sh
+```
+
+If successful, the image will be tagged as `${USER}/tensorflow:latest` by default.
+
+Rebuilding GPU images requires [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).
diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh
index f88af68cde..ea88d8165f 100755
--- a/tensorflow/tools/docker/parameterized_docker_build.sh
+++ b/tensorflow/tools/docker/parameterized_docker_build.sh
@@ -233,13 +233,16 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then
 
   # Modify python/pip version if necessary.
   if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then
-    sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
+    if sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
         sed -i -e 's/python-dev/python3-dev/g' "${DOCKERFILE}" && \
         sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \
-        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" && \
-        echo "Modified Dockerfile for python version "\
-"${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" || \
-        die "FAILED to modify ${DOCKERFILE} for python3"
+        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}"
+    then
+      echo "Modified Dockerfile for python version "\
+"${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}"
+    else
+      die "FAILED to modify ${DOCKERFILE} for python3"
+    fi
   fi
 else
   DOCKERFILE="${TMP_DIR}/Dockerfile"
@@ -250,14 +253,17 @@ else
 
   # Modify python/pip version if necessary.
   if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then
-    sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \
+    if sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \
         sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \
         sed -i -e 's^/tmp/pip^/tmp/pip3^g' "${DOCKERFILE}" && \
         sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \
         sed -i -e 's/ENV CI_BUILD_PYTHON python/ENV CI_BUILD_PYTHON python3/g' "${DOCKERFILE}" && \
-        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" && \
-        echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" || \
-        die "FAILED to modify ${DOCKERFILE} for python3"
+        sed -i -e 's^# RUN ln -s /usr/bin/python3 /usr/bin/python#^RUN ln -s /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}"
+    then
+      echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}"
+    else
+      die "FAILED to modify ${DOCKERFILE} for python3"
+    fi
   fi
 fi
 
@@ -277,7 +283,7 @@ fi
 
 # Make sure that there is no other containers of the same image running
 # TODO(cais): Move to an earlier place.
-if [[ ! -z $("${DOCKER_BINARY}" ps | grep "${IMG}") ]]; then
+if "${DOCKER_BINARY}" ps | grep -q "${IMG}"; then
   die "ERROR: It appears that there are docker containers of the image "\
 "${IMG} running. Please stop them before proceeding"
 fi
@@ -310,16 +316,22 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then
     # on the running docker container
     echo ""
     echo "Performing basic sanity checks on the running container..."
-    wget -qO- "http://127.0.0.1:${CONTAINER_PORT}/tree" &> /dev/null && \
-        echo "  PASS: wget tree" || \
-        mark_check_failed "  FAIL: wget tree"
+    if wget -qO- "http://127.0.0.1:${CONTAINER_PORT}/tree" &> /dev/null
+    then
+      echo "  PASS: wget tree"
+    else
+      mark_check_failed "  FAIL: wget tree"
+    fi
 
     for NB in ${TMP_DIR}/notebooks/*.ipynb; do
       NB_BASENAME=$(basename "${NB}")
       NB_URL="http://127.0.0.1:${CONTAINER_PORT}/notebooks/${NB_BASENAME}"
-      wget -qO- "${NB_URL}" -o "${TMP_DIR}/${NB_BASENAME}" &> /dev/null && \
-          echo "  PASS: wget ${NB_URL}" || \
-          mark_check_failed  "  FAIL: wget ${NB_URL}"
+      if wget -qO- "${NB_URL}" -o "${TMP_DIR}/${NB_BASENAME}" &> /dev/null
+      then
+        echo "  PASS: wget ${NB_URL}"
+      else
+        mark_check_failed  "  FAIL: wget ${NB_URL}"
+      fi
     done
   fi
 
diff --git a/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh b/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
index 2ce0fb394f..7146213b33 100755
--- a/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
+++ b/tensorflow/tools/gcs_test/gcs_smoke_wrapper.sh
@@ -71,7 +71,7 @@ rm -rf ${LOG_FILE} || \
 
 # Invoke main Python file
 python "${GCS_SMOKE_PY}" --gcs_bucket_url="${GCS_BUCKET_URL}" \
-    2>&1 > "${LOG_FILE}"
+    > "${LOG_FILE}" 2>&1
 
 if [[ $? != "0" ]]; then
   cat ${LOG_FILE}
@@ -92,6 +92,9 @@ NEW_TFREC_URL=$(grep "Using input path" "${LOG_FILE}" | \
 if [[ -z ${NEW_TFREC_URL} ]]; then
   die "FAIL: Unable to determine the URL to the new tfrecord file in GCS"
 fi
-"${GSUTIL_BIN}" rm "${NEW_TFREC_URL}" && \
-    echo "Cleaned up new tfrecord file in GCS: ${NEW_TFREC_URL}" || \
-    die "FAIL: Unable to clean up new tfrecord file in GCS: ${NEW_TFREC_URL}"
+if "${GSUTIL_BIN}" rm "${NEW_TFREC_URL}"
+then
+  echo "Cleaned up new tfrecord file in GCS: ${NEW_TFREC_URL}"
+else
+  die "FAIL: Unable to clean up new tfrecord file in GCS: ${NEW_TFREC_URL}"
+fi
diff --git a/tensorflow/tools/git/gen_git_source.sh b/tensorflow/tools/git/gen_git_source.sh
index 1487658049..977fe16333 100755
--- a/tensorflow/tools/git/gen_git_source.sh
+++ b/tensorflow/tools/git/gen_git_source.sh
@@ -20,7 +20,7 @@ if [[ -z "${OUTPUT_FILENAME}"  ]]; then
   exit 1
 fi
 
-GIT_VERSION=`git describe --long --tags`
+GIT_VERSION=$(git describe --long --tags)
 if [[ $? != 0 ]]; then
    GIT_VERSION=unknown;
 fi
diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index bfda55d3ad..b4274e67df 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -1055,7 +1055,7 @@ in the future.
 
 The Graph Transform Tool associates names of transforms with the code to
 implement them using the `REGISTER_GRAPH_TRANSFORM()` macro. This takes a string
-and a function, and automatically registers the transform with the tool. You
+and a function, and automagically registers the transform with the tool. You
 will need to watch out for a few things though:
 
 *   Because it's using global C++ objects in each file under the hood, the
diff --git a/tensorflow/tools/graph_transforms/quantize_weights_test.cc b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
index e1a105bdd3..63c5b5a64d 100644
--- a/tensorflow/tools/graph_transforms/quantize_weights_test.cc
+++ b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
@@ -90,13 +90,13 @@ class QuantizeWeightsTest : public ::testing::Test {
     EXPECT_EQ("Const", q_weights_const->op());
     EXPECT_EQ(DT_QUINT8, q_weights_const->attr().at("dtype").type());
 
-    // Run the the original graph.
+    // Run the original graph.
     std::unique_ptr<Session> original_session(NewSession(SessionOptions()));
     TF_ASSERT_OK(original_session->Create(original_graph_def));
     std::vector<Tensor> original_outputs;
     TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs));
 
-    // Run the the quantized graph.
+    // Run the quantized graph.
     std::unique_ptr<Session> quantized_session(NewSession(SessionOptions()));
     TF_ASSERT_OK(quantized_session->Create(quantized_graph_def));
     std::vector<Tensor> quantized_outputs;
diff --git a/tensorflow/tools/lib_package/libtensorflow_java_test.sh b/tensorflow/tools/lib_package/libtensorflow_java_test.sh
index a44298e01a..c44978fc57 100755
--- a/tensorflow/tools/lib_package/libtensorflow_java_test.sh
+++ b/tensorflow/tools/lib_package/libtensorflow_java_test.sh
@@ -29,7 +29,7 @@ TAR="${TAR}"
 
 [ -z "${JAVA}" ] && JAVA="java"
 [ -z "${JAVAC}" ] && JAVAC="javac"
-[ -z "${TAR}"] && TAR="tar"
+[ -z "${TAR}" ] && TAR="tar"
 
 # bazel tests run with ${PWD} set to the root of the bazel workspace
 TARFILE="${PWD}/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz"
diff --git a/tensorflow/tools/lib_package/libtensorflow_test.sh b/tensorflow/tools/lib_package/libtensorflow_test.sh
index 6430c755af..7dfe8eefcc 100755
--- a/tensorflow/tools/lib_package/libtensorflow_test.sh
+++ b/tensorflow/tools/lib_package/libtensorflow_test.sh
@@ -26,7 +26,7 @@ CC="${CC}"
 TAR="${TAR}"
 
 [ -z "${CC}" ] && CC="/usr/bin/gcc"
-[ -z "${TAR}"] && TAR="tar"
+[ -z "${TAR}" ] && TAR="tar"
 
 # bazel tests run with ${PWD} set to the root of the bazel workspace
 TARFILE="${PWD}/tensorflow/tools/lib_package/libtensorflow.tar.gz"
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 073988765c..54ba8064e8 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0-rc2'
+_VERSION = '1.2.0'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/tools/tfprof/README.md
index 816ad8c07e..e86584acd1 100644
--- a/tensorflow/tools/tfprof/README.md
+++ b/tensorflow/tools/tfprof/README.md
@@ -1,6 +1,6 @@
 # tfprof: TensorFlow Profiler and Beyond
 
-###Features
+### Features
 
 * Profile model architectures
   * parameters, tensor shapes, float operations, device placement, etc.
@@ -13,7 +13,7 @@
   * operation configuration check
   * distributed runtime check (Not OSS)
 
-###Interfaces
+### Interfaces
 
 * Python API
 * Command Line
@@ -51,7 +51,7 @@ See [Options](g3doc/options.md) for detail instructions.
 -output                     stdout:
 ```
 
-###Tutorials
+### Tutorials
 
 *  [Python API](g3doc/python_api.md)
 *  [Command Line Interface](g3doc/command_line.md)
diff --git a/tensorflow/tools/tfprof/g3doc/command_line.md b/tensorflow/tools/tfprof/g3doc/command_line.md
index 0d8d56809a..9f0de72e07 100644
--- a/tensorflow/tools/tfprof/g3doc/command_line.md
+++ b/tensorflow/tools/tfprof/g3doc/command_line.md
@@ -126,7 +126,7 @@ tfprof>
 -show_name_regexes          .*
 -hide_name_regexes          IsVariableInitialized_[0-9]+,save\/.*,^zeros[0-9_]*
 -account_displayed_op_only  false
-# supported select fileds. Availability depends on --[run_meta|checkpoint|op_log]_path.
+# supported select fields. Availability depends on --[run_meta|checkpoint|op_log]_path.
 # [bytes|micros|params|float_ops|occurrence|tensor_value|device|op_types]
 -select                     params
 # format: output_type:key=value,key=value...
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index c8a3a81000..a5172187f7 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -327,11 +327,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   patched_http_archive(
       name = "protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
       # TODO: remove patching when tensorflow stops linking same protos into
       #       multiple shared libraries loaded in runtime by python.
       #       This patch fixes a runtime crash when tensorflow is compiled
@@ -345,21 +345,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.http_archive(
       name = "com_google_protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
   )
 
   native.http_archive(
       name = "com_google_protobuf_cc",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
-          "https://github.com/google/protobuf/archive/2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
       ],
-      sha256 = "e5d3d4e227a0f7afb8745df049bbd4d55474b158ca5aaa2a0e31099af24be1d0",
-      strip_prefix = "protobuf-2b7430d96aeff2bb624c8d52182ff5e4b9f7f18a",
+      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
+      strip_prefix = "protobuf-3.3.1",
   )
 
   native.new_http_archive(
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 83a377dde5..1e29f23d90 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -847,10 +847,10 @@ def _genrule(src_dir, genrule_name, command, outs):
       genrule_name + '",\n' +
       '    outs = [\n' +
       outs +
-      '    ],\n' +
+      '\n    ],\n' +
       '    cmd = """\n' +
       command +
-      '    """,\n' +
+      '\n   """,\n' +
       ')\n\n'
   )
 
diff --git a/third_party/py/BUILD.tpl b/third_party/py/BUILD.tpl
index 1ee9c071ad..cdfe9c9b45 100644
--- a/third_party/py/BUILD.tpl
+++ b/third_party/py/BUILD.tpl
@@ -6,6 +6,16 @@ cc_library(
     name = "python_headers",
     hdrs = [":python_include"],
     includes = ["python_include"],
+    data = select({
+      ":windows" : [":python_import_lib"],
+      "//conditions:default": [],
+    }),
+    linkopts = select({
+      # TODO(pcloudy): Ideally, this should just go into deps after resolving
+      # https://github.com/bazelbuild/bazel/issues/3237,
+      ":windows" : ["$(locations :python_import_lib)"],
+      "//conditions:default": [],
+    }),
 )
 
 cc_library(
@@ -22,4 +32,6 @@ config_setting(
 
 %{PYTHON_INCLUDE_GENRULE}
 
+%{PYTHON_IMPORT_LIB_GENRULE}
+
 %{NUMPY_INCLUDE_GENRULE}
diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl
index 19a6f1e749..31c4d2b98a 100644
--- a/third_party/py/python_configure.bzl
+++ b/third_party/py/python_configure.bzl
@@ -117,10 +117,10 @@ def _genrule(src_dir, genrule_name, command, outs):
       genrule_name + '",\n' +
       '    outs = [\n' +
       outs +
-      '    ],\n' +
+      '\n    ],\n' +
       '    cmd = """\n' +
       command +
-      '    """,\n' +
+      '\n   """,\n' +
       ')\n\n'
   )
 
@@ -133,15 +133,20 @@ def _norm_path(path):
   return path
 
 
-def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name):
+def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name,
+    src_files = [], dest_files = []):
   """Returns a genrule to symlink(or copy if on Windows) a set of files.
+
+  If src_dir is passed, files will be read from the given directory; otherwise
+  we assume files are in src_files and dest_files
   """
-  src_dir = _norm_path(src_dir)
-  dest_dir = _norm_path(dest_dir)
-  files = _read_dir(repository_ctx, src_dir)
-  # Create a list with the src_dir stripped to use for outputs.
-  dest_files = files.replace(src_dir, '').splitlines()
-  src_files = files.splitlines()
+  if src_dir != None:
+    src_dir = _norm_path(src_dir)
+    dest_dir = _norm_path(dest_dir)
+    files = _read_dir(repository_ctx, src_dir)
+    # Create a list with the src_dir stripped to use for outputs.
+    dest_files = files.replace(src_dir, '').splitlines()
+    src_files = files.splitlines()
   command = []
   outs = []
   for i in range(len(dest_files)):
@@ -217,11 +222,23 @@ def _get_python_include(repository_ctx, python_bin):
                      'print(sysconfig.get_python_inc())'],
                     error_msg="Problem getting python include path.",
                     error_details=("Is the Python binary path set up right? " +
-                                   "(See ./configure or BAZEL_BIN_PATH.) " +
+                                   "(See ./configure or PYTHON_BIN_PATH.) " +
                                    "Is distutils installed?"))
   return result.stdout.splitlines()[0]
 
 
+def _get_python_import_lib_name(repository_ctx, python_bin):
+  """Get Python import library name (pythonXY.lib) on Windows."""
+  result = _execute(repository_ctx,
+                    [python_bin, "-c",
+                     'import sys;' +
+                     'print("python" + str(sys.version_info[0]) + str(sys.version_info[1]) + ".lib")'],
+                    error_msg="Problem getting python import library.",
+                    error_details=("Is the Python binary path set up right? " +
+                                   "(See ./configure or PYTHON_BIN_PATH.) "))
+  return result.stdout.splitlines()[0]
+
+
 def _get_numpy_include(repository_ctx, python_bin):
   """Gets the numpy include path."""
   return _execute(repository_ctx,
@@ -240,11 +257,11 @@ def _create_local_python_repository(repository_ctx):
   empty_config = False
   # If local checks were requested, the python and numpy include will be auto
   # detected on the host config (using _PYTHON_BIN_PATH).
+  python_bin = _get_env_var(repository_ctx, _PYTHON_BIN_PATH,
+                            "/usr/bin/python")
   if repository_ctx.attr.local_checks:
     # TODO(nlopezgi): The default argument here is a workaround until
     #                 bazelbuild/bazel#3057 is resolved.
-    python_bin = _get_env_var(repository_ctx, _PYTHON_BIN_PATH,
-                              "/usr/bin/python")
     _check_python_bin(repository_ctx, python_bin)
     python_lib = _get_env_var(repository_ctx, _PYTHON_LIB_PATH,
                               _get_python_lib(repository_ctx, python_bin))
@@ -263,6 +280,10 @@ def _create_local_python_repository(repository_ctx):
                                       '    name = "python_include",\n' +
                                       '    srcs = [],\n' +
                                       ')\n'),
+        "%{PYTHON_IMPORT_LIB_GENRULE}": ('filegroup(\n' +
+                                      '    name = "python_import_lib",\n' +
+                                      '    srcs = [],\n' +
+                                      ')\n'),
         "%{NUMPY_INCLUDE_GENRULE}": ('filegroup(\n' +
                                       '    name = "numpy_include",\n' +
                                       '    srcs = [],\n' +
@@ -271,10 +292,21 @@ def _create_local_python_repository(repository_ctx):
   else:
     python_include_rule = _symlink_genrule_for_dir(
         repository_ctx, python_include, 'python_include', 'python_include')
+    python_import_lib_genrule = ""
+    # To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib
+    # See https://docs.python.org/3/extending/windows.html
+    if _is_windows(repository_ctx):
+      python_include = _norm_path(python_include)
+      python_import_lib_name = _get_python_import_lib_name(repository_ctx, python_bin)
+      python_import_lib_src = python_include.rsplit('/', 1)[0] + "/libs/" + python_import_lib_name
+      python_import_lib_genrule = _symlink_genrule_for_dir(
+        repository_ctx, None, '', 'python_import_lib',
+        [python_import_lib_src], [python_import_lib_name])
     numpy_include_rule = _symlink_genrule_for_dir(
         repository_ctx, numpy_include, 'numpy_include/numpy', 'numpy_include')
     _tpl(repository_ctx, "BUILD", {
         "%{PYTHON_INCLUDE_GENRULE}": python_include_rule,
+        "%{PYTHON_IMPORT_LIB_GENRULE}": python_import_lib_genrule,
         "%{NUMPY_INCLUDE_GENRULE}": numpy_include_rule,
     })
 
diff --git a/third_party/sycl/crosstool/computecpp.tpl b/third_party/sycl/crosstool/computecpp.tpl
index 94c5e6aaad..8b0a7a7d4a 100755
--- a/third_party/sycl/crosstool/computecpp.tpl
+++ b/third_party/sycl/crosstool/computecpp.tpl
@@ -15,7 +15,7 @@ COMPUTECPP_INCLUDE = COMPUTECPP_ROOT + 'include'
 
 def main():
   remove_flags = ('-Wl,--no-undefined', '-Wno-unused-but-set-variable', '-Wignored-attributes')
-    # remove -fsamotoze-coverage from string with g++
+  # remove -fsanitize-coverage from string with g++
   if 'g++' in CPU_CXX_COMPILER:
     remove_flags += ('-fsanitize-coverage',)
   compiler_flags = [flag for flag in sys.argv[1:] if not flag.startswith(remove_flags)]
@@ -43,7 +43,7 @@ def main():
 
   # create a blacklist of folders that will be skipped when compiling with ComputeCpp
   skip_extensions = [".cu.cc"]
-  skip_folders = ["tensorflow/compiler", "tensorflow/docs_src", "tensorflow/tensorboard", "third_party", "external", "hexagon"]
+  skip_folders = ["tensorflow/compiler", "tensorflow/docs_src", "third_party", "external", "hexagon"]
   skip_folders = [(folder + '/') for folder in skip_folders]
   # if compiling external project skip computecpp
   if any(compiled_file_name.endswith(_ext) for _ext in skip_extensions) or any(_folder in output_file_name for _folder in skip_folders):
@@ -79,7 +79,7 @@ def main():
                                       '-Xclang', '-cl-denorms-are-zero', '-Xclang', '-cl-fp32-correctly-rounded-divide-sqrt']
   # disable flags enabling SIMD instructions
   computecpp_device_compiler_flags += [flag for flag in compiler_flags if \
-    not any(x in flag.lower() for x in ('-fsanitize', '=native', '=core2', 'msse', 'vectorize', 'mavx', 'mmmx', 'm3dnow', 'fma'))]
+    not any(x in flag.lower() for x in ('-fsanitize', '-fno-canonical-system-headers', '=native', '=core2', 'msse', 'vectorize', 'mavx', 'mmmx', 'm3dnow', 'fma'))]
 
   x = call([COMPUTECPP_DRIVER] + computecpp_device_compiler_flags)
   if x == 0:
diff --git a/third_party/sycl/sycl/BUILD.tpl b/third_party/sycl/sycl/BUILD.tpl
index c66a9f007d..6cad190630 100755
--- a/third_party/sycl/sycl/BUILD.tpl
+++ b/third_party/sycl/sycl/BUILD.tpl
@@ -33,7 +33,7 @@ cc_library(
         sycl_library_path("ComputeCpp")
     ],
     includes = ["include/"],
-    linkstatic = 1,
+    linkstatic = 0,
 )
 
 cc_library(
diff --git a/tools/tf_env_collect.sh b/tools/tf_env_collect.sh
index abeebeadea..a1c9c88c58 100755
--- a/tools/tf_env_collect.sh
+++ b/tools/tf_env_collect.sh
@@ -16,49 +16,62 @@
 
 set -u  # Check for undefined variables
 
-echo "Collecting system information..."
-
-OUTPUT_FILE=tf_env.txt
-
-echo >> $OUTPUT_FILE
-echo "== cat /etc/issue ===============================================" >> $OUTPUT_FILE
-uname -a >> $OUTPUT_FILE
-uname=`uname -s`
-if [ "$(uname)" == "Darwin" ]; then
-  echo Mac OS X `sw_vers -productVersion` >> $OUTPUT_FILE
-elif [ "$(uname)" == "Linux" ]; then
-  cat /etc/*release | grep VERSION >> $OUTPUT_FILE
-fi
-
-
-echo >> $OUTPUT_FILE
-echo '== are we in docker =============================================' >> $OUTPUT_FILE
-num=`cat /proc/1/cgroup | grep docker | wc -l`;
-if [ $num -ge 1 ]; then
-  echo "Yes" >> $OUTPUT_FILE
-else
-  echo "No" >> $OUTPUT_FILE
-fi
-
-echo >> $OUTPUT_FILE
-echo '== compiler =====================================================' >> $OUTPUT_FILE
-c++ --version 2>&1 >> $OUTPUT_FILE
-
-echo >> $OUTPUT_FILE
-echo '== uname -a =====================================================' >> $OUTPUT_FILE
-uname -a >> $OUTPUT_FILE
+die() {
+  # Print a message and exit with code 1.
+  #
+  # Usage: die <error_message>
+  #   e.g., die "Something bad happened."
 
-echo >> $OUTPUT_FILE
-echo '== check pips ===================================================' >> $OUTPUT_FILE
-pip list 2>&1 | grep "proto\|numpy\|tensorflow" >> $OUTPUT_FILE
+  echo $@
+  exit 1
+}
 
+echo "Collecting system information..."
 
-echo >> $OUTPUT_FILE
-echo '== check for virtualenv =========================================' >> $OUTPUT_FILE
-python -c "import sys;print(hasattr(sys, \"real_prefix\"))" >> $OUTPUT_FILE
+OUTPUT_FILE=tf_env.txt
+python_bin_path=$(which python || which python3 || die "Cannot find Python binary")
+
+{
+  echo
+  echo "== cat /etc/issue ==============================================="
+  uname -a
+  uname=`uname -s`
+  if [ "$(uname)" == "Darwin" ]; then
+    echo Mac OS X `sw_vers -productVersion`
+  elif [ "$(uname)" == "Linux" ]; then
+    cat /etc/*release | grep VERSION
+  fi
+  
+  echo
+  echo '== are we in docker ============================================='
+  num=`cat /proc/1/cgroup | grep docker | wc -l`;
+  if [ $num -ge 1 ]; then
+    echo "Yes"
+  else
+    echo "No"
+  fi
+  
+  echo
+  echo '== compiler ====================================================='
+  c++ --version 2>&1
+  
+  echo
+  echo '== uname -a ====================================================='
+  uname -a
+  
+  echo
+  echo '== check pips ==================================================='
+  pip list 2>&1 | grep "proto\|numpy\|tensorflow"
+  
+  
+  echo
+  echo '== check for virtualenv ========================================='
+  ${python_bin_path} -c "import sys;print(hasattr(sys, \"real_prefix\"))"
+  
+  echo
+  echo '== tensorflow import ============================================'
+} >> ${OUTPUT_FILE}
 
-echo >> $OUTPUT_FILE
-echo '== tensorflow import ============================================' >> $OUTPUT_FILE
 cat <<EOF > /tmp/check_tf.py
 import tensorflow as tf;
 print("tf.VERSION = %s" % tf.VERSION)
@@ -67,32 +80,34 @@ print("tf.COMPILER_VERSION = %s" % tf.GIT_VERSION)
 with tf.Session() as sess:
   print("Sanity check: %r" % sess.run(tf.constant([1,2,3])[:1]))
 EOF
-python /tmp/check_tf.py 2>&1  >> ${OUTPUT_FILE}
-
-DEBUG_LD=libs python -c "import tensorflow"  2>>${OUTPUT_FILE} > /tmp/loadedlibs
-grep libcudnn.so /tmp/loadedlibs >> $OUTPUT_FILE
-
-echo >> $OUTPUT_FILE
-echo '== env ==========================================================' >> $OUTPUT_FILE
-if [ -z ${LD_LIBRARY_PATH+x} ]; then
-  echo "LD_LIBRARY_PATH is unset" >> $OUTPUT_FILE;
-else
-  echo LD_LIBRARY_PATH ${LD_LIBRARY_PATH}  >> $OUTPUT_FILE;
-fi
-if [ -z ${DYLD_LIBRARY_PATH+x} ]; then
-  echo "DYLD_LIBRARY_PATH is unset" >> $OUTPUT_FILE;
-else
-  echo DYLD_LIBRARY_PATH ${DYLD_LIBRARY_PATH}  >> $OUTPUT_FILE;
-fi
-
-
-echo >> $OUTPUT_FILE >> $OUTPUT_FILE
-echo '== nvidia-smi ===================================================' >> $OUTPUT_FILE
-nvidia-smi 2>&1 >> $OUTPUT_FILE
-
-echo >> $OUTPUT_FILE
+${python_bin_path} /tmp/check_tf.py 2>&1  >> ${OUTPUT_FILE}
+
+DEBUG_LD=libs ${python_bin_path} -c "import tensorflow"  2>>${OUTPUT_FILE} > /tmp/loadedlibs
+
+{
+  grep libcudnn.so /tmp/loadedlibs
+  echo
+  echo '== env =========================================================='
+  if [ -z ${LD_LIBRARY_PATH+x} ]; then
+    echo "LD_LIBRARY_PATH is unset";
+  else
+    echo LD_LIBRARY_PATH ${LD_LIBRARY_PATH} ;
+  fi
+  if [ -z ${DYLD_LIBRARY_PATH+x} ]; then
+    echo "DYLD_LIBRARY_PATH is unset";
+  else
+    echo DYLD_LIBRARY_PATH ${DYLD_LIBRARY_PATH} ;
+  fi
+  
+  
+  echo
+  echo '== nvidia-smi ==================================================='
+  nvidia-smi 2>&1
+  
+  echo
+  echo '== cuda libs  ==================================================='
+} >> ${OUTPUT_FILE}
 
-echo '== cuda libs  ===================================================' >> $OUTPUT_FILE
 find /usr/local -type f -name 'libcudart*'  2>/dev/null | grep cuda |  grep -v "\\.cache" >> ${OUTPUT_FILE}
 find /usr/local -type f -name 'libudnn*'  2>/dev/null | grep cuda |  grep -v "\\.cache" >> ${OUTPUT_FILE}
 
-- 
GitLab


From 4d1c869b14e2230e407d304337604bf4bc9b63ab Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 16:49:45 -0700
Subject: [PATCH 0529/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 160346151
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 150 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 148 +++++++++++++----
 2 files changed, 270 insertions(+), 28 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index a585881cc6..9631538227 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -5376,6 +5376,30 @@ op {
     }
   }
 }
+op {
+  name: "Cosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "CountUpTo"
   input_arg {
@@ -22998,6 +23022,30 @@ op {
     }
   }
 }
+op {
+  name: "Sinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "Size"
   input_arg {
@@ -24839,6 +24887,108 @@ op {
     }
   }
 }
+op {
+  name: "SparseReduceMax"
+  input_arg {
+    name: "input_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+}
+op {
+  name: "SparseReduceMaxSparse"
+  input_arg {
+    name: "input_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+}
 op {
   name: "SparseReduceSum"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 485b72faa4..416d18a0ec 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -5199,6 +5199,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -6301,7 +6302,7 @@ op {
     }
   }
   summary: "Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors."
-  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
+  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\n```\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n```\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
 }
 op {
   name: "DepthwiseConv2dNativeBackpropFilter"
@@ -22888,6 +22889,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -24993,6 +24995,123 @@ op {
   summary: "Multiply matrix \"a\" by matrix \"b\"."
   description: "The inputs must be two-dimensional matrices and the inner dimension of \"a\" must\nmatch the outer dimension of \"b\". This op is optimized for the case where at\nleast one of \"a\" or \"b\" is sparse. The breakeven for using this versus a dense\nmatrix multiply on one platform was 30% zero values in the sparse matrix.\n\nThe gradient computation of this operation will only take advantage of sparsity\nin the input gradient when that gradient comes from a Relu."
 }
+op {
+  name: "SparseReduceMax"
+  input_arg {
+    name: "input_indices"
+    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    description: "1-D.  Shape of the input SparseTensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    description: "1-D.  Length-`K` vector containing the reduction axes."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    description: "`R-K`-D.  The reduced Tensor."
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, retain reduced dimensions with length 1."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+  summary: "Computes the max of elements across dimensions of a SparseTensor."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`\ninstead of a sparse one.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
+}
+op {
+  name: "SparseReduceMaxSparse"
+  input_arg {
+    name: "input_indices"
+    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    description: "1-D.  Shape of the input SparseTensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    description: "1-D.  Length-`K` vector containing the reduction axes."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, retain reduced dimensions with length 1."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+  summary: "Computes the max of elements across dimensions of a SparseTensor."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a\nSparseTensor.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
+}
 op {
   name: "SparseReduceSum"
   input_arg {
@@ -27363,33 +27482,6 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
-op {
-  name: "LMDBReader"
-  output_arg {
-    name: "reader_handle"
-    description: "The handle to reference the Reader."
-    type: DT_STRING
-    is_ref: true
-  }
-  attr {
-    name: "container"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
-  }
-  attr {
-    name: "shared_name"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
-  }
-  summary: "A Reader that outputs the records from a LMDB database."
-  is_stateful: true
-}
 op {
   name: "TakeDataset"
   input_arg {
-- 
GitLab


From c53829a6563099c8f91b983031861b2196f64ed9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 16:58:53 -0700
Subject: [PATCH 0530/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 160347179
---
 tensorflow/go/op/wrappers.go | 758 ++++++++++++++++++++---------------
 1 file changed, 434 insertions(+), 324 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index beede5a6d9..785717c9ba 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -100,32 +100,6 @@ func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Out
 	return op.Output(0)
 }
 
-// Subtracts a value from the current value of a variable.
-//
-// Any ReadVariableOp which depends directly or indirectly on this assign is
-// guaranteed to see the incremented value or a subsequent newer one.
-//
-// Outputs the incremented value, which can be used to totally order the
-// increments to this variable.
-//
-// Arguments:
-//	resource: handle to the resource in which to store the variable.
-//	value: the value by which the variable will be incremented.
-//
-// Returns the created operation.
-func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "AssignSubVariableOp",
-		Input: []tf.Input{
-			resource, value,
-		},
-	}
-	return scope.AddOperation(opspec)
-}
-
 // Assigns a new value to a variable.
 //
 // Any ReadVariableOp with a control dependency on this op is guaranteed to return
@@ -5682,30 +5656,6 @@ func TensorArrayConcatV2(scope *Scope, handle tf.Output, flow_in tf.Output, dtyp
 	return op.Output(0), op.Output(1)
 }
 
-// Creates a dataset that emits the outputs of `input_dataset` `count` times.
-//
-// Arguments:
-//
-//	count: A scalar representing the number of times that `input_dataset` should
-// be repeated. A value of `-1` indicates that it should be repeated infinitely.
-//
-//
-func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
-	opspec := tf.OpSpec{
-		Type: "RepeatDataset",
-		Input: []tf.Input{
-			input_dataset, count,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Creates a dataset that splits a SparseTensor into elements row-wise.
 func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (handle tf.Output) {
 	if scope.Err() != nil {
@@ -6594,6 +6544,58 @@ func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) {
 	return scope.AddOperation(opspec)
 }
 
+// Returns up to `num_records` (key, value) pairs produced by a Reader.
+//
+// Will dequeue from the input queue if necessary (e.g. when the
+// Reader needs to start reading from a new file since it has finished
+// with the previous file).
+// It may return less than `num_records` even before the last batch.
+//
+// Arguments:
+//	reader_handle: Handle to a `Reader`.
+//	queue_handle: Handle to a `Queue`, with string work items.
+//	num_records: number of records to read from `Reader`.
+//
+// Returns A 1-D tensor.A 1-D tensor.
+func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ReaderReadUpToV2",
+		Input: []tf.Input{
+			reader_handle, queue_handle, num_records,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1)
+}
+
+// Returns the next record (key, value pair) produced by a Reader.
+//
+// Will dequeue from the input queue if necessary (e.g. when the
+// Reader needs to start reading from a new file since it has finished
+// with the previous file).
+//
+// Arguments:
+//	reader_handle: Handle to a Reader.
+//	queue_handle: Handle to a Queue, with string work items.
+//
+// Returns A scalar.A scalar.
+func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ReaderReadV2",
+		Input: []tf.Input{
+			reader_handle, queue_handle,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1)
+}
+
 // Computes softplus gradients for a softplus operation.
 //
 // Arguments:
@@ -7705,110 +7707,6 @@ func NoOp(scope *Scope) (o *tf.Operation) {
 	return scope.AddOperation(opspec)
 }
 
-// Computes softsign: `features / (abs(features) + 1)`.
-func Softsign(scope *Scope, features tf.Output) (activations tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Softsign",
-		Input: []tf.Input{
-			features,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// ResizeBilinearAttr is an optional argument to ResizeBilinear.
-type ResizeBilinearAttr func(optionalAttr)
-
-// ResizeBilinearAlignCorners sets the optional align_corners attribute to value.
-//
-// value: If true, rescale input by (new_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr {
-	return func(m optionalAttr) {
-		m["align_corners"] = value
-	}
-}
-
-// Resize `images` to `size` using bilinear interpolation.
-//
-// Input images can be of different types but output images are always float.
-//
-// Arguments:
-//	images: 4-D with shape `[batch, height, width, channels]`.
-//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
-// new size for the images.
-//
-// Returns 4-D with shape
-// `[batch, new_height, new_width, channels]`.
-func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "ResizeBilinear",
-		Input: []tf.Input{
-			images, size,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// ProdAttr is an optional argument to Prod.
-type ProdAttr func(optionalAttr)
-
-// ProdKeepDims sets the optional keep_dims attribute to value.
-//
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func ProdKeepDims(value bool) ProdAttr {
-	return func(m optionalAttr) {
-		m["keep_dims"] = value
-	}
-}
-
-// Computes the product of elements across dimensions of a tensor.
-//
-// Reduces `input` along the dimensions given in `reduction_indices`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
-//
-// Arguments:
-//	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
-//
-// Returns The reduced tensor.
-func Prod(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...ProdAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "Prod",
-		Input: []tf.Input{
-			input, reduction_indices,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
 type DepthwiseConv2dNativeAttr func(optionalAttr)
 
@@ -7836,11 +7734,13 @@ func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
 // `channel_multiplier` channels for each), then concatenates the results
 // together. Thus, the output has `in_channels * channel_multiplier` channels.
 //
+// ```
 // for k in 0..in_channels-1
 //   for q in 0..channel_multiplier-1
 //     output[b, i, j, k * channel_multiplier + q] =
 //       sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
 //                         filter[di, dj, k, q]
+// ```
 //
 // Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
 // horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
@@ -10527,50 +10427,131 @@ func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shar
 	return op.Output(0)
 }
 
-// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2.
-type Conv3DBackpropFilterV2Attr func(optionalAttr)
-
-// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value.
+// Subtracts a value from the current value of a variable.
 //
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-//     [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-//     [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
-	return func(m optionalAttr) {
-		m["data_format"] = value
-	}
-}
-
-// Computes the gradients of 3-D convolution with respect to the filter.
+// Any ReadVariableOp which depends directly or indirectly on this assign is
+// guaranteed to see the incremented value or a subsequent newer one.
+//
+// Outputs the incremented value, which can be used to totally order the
+// increments to this variable.
 //
 // Arguments:
-//	input: Shape `[batch, depth, rows, cols, in_channels]`.
-//	filter_sizes: An integer vector representing the tensor shape of `filter`,
-// where `filter` is a 5-D
-// `[filter_depth, filter_height, filter_width, in_channels, out_channels]`
-// tensor.
-//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-//	strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-//	padding: The type of padding algorithm to use.
-func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) {
+//	resource: handle to the resource in which to store the variable.
+//	value: the value by which the variable will be incremented.
+//
+// Returns the created operation.
+func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"strides": strides, "padding": padding}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "Conv3DBackpropFilterV2",
+		Type: "AssignSubVariableOp",
 		Input: []tf.Input{
-			input, filter_sizes, out_backprop,
+			resource, value,
 		},
-		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// SparseReduceMaxAttr is an optional argument to SparseReduceMax.
+type SparseReduceMaxAttr func(optionalAttr)
+
+// SparseReduceMaxKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SparseReduceMaxKeepDims(value bool) SparseReduceMaxAttr {
+	return func(m optionalAttr) {
+		m["keep_dims"] = value
+	}
+}
+
+// Computes the max of elements across dimensions of a SparseTensor.
+//
+// This Op takes a SparseTensor and is the sparse counterpart to
+// `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+// instead of a sparse one.
+//
+// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+// with length 1.
+//
+// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+// with a single element is returned.  Additionally, the axes can be negative,
+// which are interpreted according to the indexing rules in Python.
+//
+// Arguments:
+//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+//	input_shape: 1-D.  Shape of the input SparseTensor.
+//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+//
+// Returns `R-K`-D.  The reduced Tensor.
+func SparseReduceMax(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseReduceMax",
+		Input: []tf.Input{
+			input_indices, input_values, input_shape, reduction_axes,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2.
+type Conv3DBackpropFilterV2Attr func(optionalAttr)
+
+// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value.
+//
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+//     [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+//     [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
+	return func(m optionalAttr) {
+		m["data_format"] = value
+	}
+}
+
+// Computes the gradients of 3-D convolution with respect to the filter.
+//
+// Arguments:
+//	input: Shape `[batch, depth, rows, cols, in_channels]`.
+//	filter_sizes: An integer vector representing the tensor shape of `filter`,
+// where `filter` is a 5-D
+// `[filter_depth, filter_height, filter_width, in_channels, out_channels]`
+// tensor.
+//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
+//	strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+//	padding: The type of padding algorithm to use.
+func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"strides": strides, "padding": padding}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Conv3DBackpropFilterV2",
+		Input: []tf.Input{
+			input, filter_sizes, out_backprop,
+		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
@@ -13102,6 +13083,83 @@ func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator t
 	return scope.AddOperation(opspec)
 }
 
+// Creates a dataset that emits the outputs of `input_dataset` `count` times.
+//
+// Arguments:
+//
+//	count: A scalar representing the number of times that `input_dataset` should
+// be repeated. A value of `-1` indicates that it should be repeated infinitely.
+//
+//
+func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+	opspec := tf.OpSpec{
+		Type: "RepeatDataset",
+		Input: []tf.Input{
+			input_dataset, count,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse.
+type SparseReduceMaxSparseAttr func(optionalAttr)
+
+// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr {
+	return func(m optionalAttr) {
+		m["keep_dims"] = value
+	}
+}
+
+// Computes the max of elements across dimensions of a SparseTensor.
+//
+// This Op takes a SparseTensor and is the sparse counterpart to
+// `tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
+// SparseTensor.
+//
+// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+// with length 1.
+//
+// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+// with a single element is returned.  Additionally, the axes can be negative,
+// which are interpreted according to the indexing rules in Python.
+//
+// Arguments:
+//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+//	input_shape: 1-D.  Shape of the input SparseTensor.
+//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseReduceMaxSparse",
+		Input: []tf.Input{
+			input_indices, input_values, input_shape, reduction_axes,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
+}
+
 // FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad.
 type FractionalMaxPoolGradAttr func(optionalAttr)
 
@@ -14288,36 +14346,6 @@ func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
-// Computes hyperbolic sine of x element-wise.
-func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Sinh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes hyperbolic cosine of x element-wise.
-func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Cosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes hyperbolic tangent of `x` element-wise.
 func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
@@ -15372,31 +15400,6 @@ func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output
 	return op.Output(0)
 }
 
-// Returns the next record (key, value pair) produced by a Reader.
-//
-// Will dequeue from the input queue if necessary (e.g. when the
-// Reader needs to start reading from a new file since it has finished
-// with the previous file).
-//
-// Arguments:
-//	reader_handle: Handle to a Reader.
-//	queue_handle: Handle to a Queue, with string work items.
-//
-// Returns A scalar.A scalar.
-func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "ReaderReadV2",
-		Input: []tf.Input{
-			reader_handle, queue_handle,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1)
-}
-
 // Creates a dataset that zips together `input_datasets`.
 func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
@@ -16245,27 +16248,6 @@ func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...Gathe
 	return op.Output(0)
 }
 
-// Computes softsign gradients for a softsign operation.
-//
-// Arguments:
-//	gradients: The backpropagated gradients to the corresponding softsign operation.
-//	features: The features passed as input to the corresponding softsign operation.
-//
-// Returns The gradients: `gradients / (1 + abs(-features)) ** 2`.
-func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SoftsignGrad",
-		Input: []tf.Input{
-			gradients, features,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Compute the polygamma function \\(\psi^{(n)}(x)\\).
 //
 // The polygamma function is defined as:
@@ -18596,6 +18578,131 @@ func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
+// Computes softsign: `features / (abs(features) + 1)`.
+func Softsign(scope *Scope, features tf.Output) (activations tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Softsign",
+		Input: []tf.Input{
+			features,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// ResizeBilinearAttr is an optional argument to ResizeBilinear.
+type ResizeBilinearAttr func(optionalAttr)
+
+// ResizeBilinearAlignCorners sets the optional align_corners attribute to value.
+//
+// value: If true, rescale input by (new_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
+// If not specified, defaults to false
+func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr {
+	return func(m optionalAttr) {
+		m["align_corners"] = value
+	}
+}
+
+// Resize `images` to `size` using bilinear interpolation.
+//
+// Input images can be of different types but output images are always float.
+//
+// Arguments:
+//	images: 4-D with shape `[batch, height, width, channels]`.
+//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
+// new size for the images.
+//
+// Returns 4-D with shape
+// `[batch, new_height, new_width, channels]`.
+func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResizeBilinear",
+		Input: []tf.Input{
+			images, size,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// ProdAttr is an optional argument to Prod.
+type ProdAttr func(optionalAttr)
+
+// ProdKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func ProdKeepDims(value bool) ProdAttr {
+	return func(m optionalAttr) {
+		m["keep_dims"] = value
+	}
+}
+
+// Computes the product of elements across dimensions of a tensor.
+//
+// Reduces `input` along the dimensions given in `reduction_indices`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
+//
+// Arguments:
+//	input: The tensor to reduce.
+//	reduction_indices: The dimensions to reduce.
+//
+// Returns The reduced tensor.
+func Prod(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...ProdAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Prod",
+		Input: []tf.Input{
+			input, reduction_indices,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes softsign gradients for a softsign operation.
+//
+// Arguments:
+//	gradients: The backpropagated gradients to the corresponding softsign operation.
+//	features: The features passed as input to the corresponding softsign operation.
+//
+// Returns The gradients: `gradients / (1 + abs(-features)) ** 2`.
+func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SoftsignGrad",
+		Input: []tf.Input{
+			gradients, features,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // DecodeBmpAttr is an optional argument to DecodeBmp.
 type DecodeBmpAttr func(optionalAttr)
 
@@ -20211,6 +20318,53 @@ func Log1p(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// Creates a dataset that emits each dim-0 slice of `components` once.
+func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"output_shapes": output_shapes}
+	opspec := tf.OpSpec{
+		Type: "TensorSliceDataset",
+		Input: []tf.Input{
+			tf.OutputList(components),
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes tan of x element-wise.
+func Tan(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Tan",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes hyperbolic cosine of x element-wise.
+func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Cosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the log of the absolute value of `Gamma(x)` element-wise.
 func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
@@ -20385,38 +20539,6 @@ func Cos(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
-// Creates a dataset that emits each dim-0 slice of `components` once.
-func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"output_shapes": output_shapes}
-	opspec := tf.OpSpec{
-		Type: "TensorSliceDataset",
-		Input: []tf.Input{
-			tf.OutputList(components),
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes tan of x element-wise.
-func Tan(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Tan",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // BatchToSpace for 4-D tensors of type T.
 //
 // This is a legacy version of the more general BatchToSpaceND.
@@ -22245,6 +22367,21 @@ func UnsortedSegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output, num
 	return op.Output(0)
 }
 
+// Computes hyperbolic sine of x element-wise.
+func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Sinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the sum along sparse segments of a tensor.
 //
 // Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
@@ -24169,30 +24306,3 @@ func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input
 	op := scope.AddOperation(opspec)
 	return op.Output(0), op.Output(1), op.Output(2)
 }
-
-// Returns up to `num_records` (key, value) pairs produced by a Reader.
-//
-// Will dequeue from the input queue if necessary (e.g. when the
-// Reader needs to start reading from a new file since it has finished
-// with the previous file).
-// It may return less than `num_records` even before the last batch.
-//
-// Arguments:
-//	reader_handle: Handle to a `Reader`.
-//	queue_handle: Handle to a `Queue`, with string work items.
-//	num_records: number of records to read from `Reader`.
-//
-// Returns A 1-D tensor.A 1-D tensor.
-func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "ReaderReadUpToV2",
-		Input: []tf.Input{
-			reader_handle, queue_handle, num_records,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1)
-}
-- 
GitLab


From 56605777aec87e762b9f549648f1ec104d14cd5d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 17:32:30 -0700
Subject: [PATCH 0531/1375] Enable tfprof on Windows

PiperOrigin-RevId: 160351254
---
 tensorflow/contrib/cmake/CMakeLists.txt       |  4 ++
 tensorflow/contrib/cmake/external/re2.cmake   | 44 +++++++++++++++++++
 tensorflow/contrib/cmake/tf_python.cmake      | 14 ++++--
 tensorflow/contrib/cmake/tf_tests.cmake       | 21 +++++++++
 tensorflow/contrib/cmake/tf_tools.cmake       | 20 +++++++++
 .../contrib/tfprof/python/tools/tfprof/BUILD  |  2 +-
 .../tfprof/python/tools/tfprof/internal/BUILD | 16 +------
 .../internal/print_model_analysis_test.py     |  2 +-
 .../tfprof/internal/run_metadata_test.py      |  2 +-
 .../python/tools/tfprof/model_analyzer.py     |  2 +-
 .../tools/tfprof/model_analyzer_test.py       |  7 ++-
 .../python/tools/tfprof/profiler_test.py      | 11 -----
 tensorflow/python/BUILD                       |  1 +
 tensorflow/python/tensorflow.i                |  1 +
 .../util/tfprof.i}                            |  5 +++
 .../tools/tfprof/internal/tfprof_code.cc      |  8 +---
 16 files changed, 119 insertions(+), 41 deletions(-)
 create mode 100644 tensorflow/contrib/cmake/external/re2.cmake
 rename tensorflow/{contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i => python/util/tfprof.i} (91%)

diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index 205b44ac12..fe66f8c49a 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -121,6 +121,7 @@ include(farmhash)
 include(fft2d)
 include(highwayhash)
 include(protobuf)
+include(re2)
 if (tensorflow_BUILD_CC_TESTS)
   include(googletest)
 endif()
@@ -136,6 +137,7 @@ set(tensorflow_EXTERNAL_LIBRARIES
     ${fft2d_STATIC_LIBRARIES}
     ${highwayhash_STATIC_LIBRARIES}
     ${protobuf_STATIC_LIBRARIES}
+    ${re2_STATIC_LIBRARIES}
 )
 set(tensorflow_EXTERNAL_DEPENDENCIES
     zlib_copy_headers_to_destination
@@ -150,6 +152,7 @@ set(tensorflow_EXTERNAL_DEPENDENCIES
     eigen
     gemmlowp
     fft2d
+    re2
 )
 
 include_directories(
@@ -168,6 +171,7 @@ include_directories(
     ${farmhash_INCLUDE_DIR}
     ${highwayhash_INCLUDE_DIR}
     ${PROTOBUF_INCLUDE_DIRS}
+    ${re2_INCLUDE_DIR}
 )
 
 if(tensorflow_ENABLE_SSL_SUPPORT)
diff --git a/tensorflow/contrib/cmake/external/re2.cmake b/tensorflow/contrib/cmake/external/re2.cmake
new file mode 100644
index 0000000000..cb4ec9c2de
--- /dev/null
+++ b/tensorflow/contrib/cmake/external/re2.cmake
@@ -0,0 +1,44 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+include (ExternalProject)
+
+set(re2_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/re2/install/include)
+set(re2_URL https://github.com/google/re2)
+set(re2_BUILD ${CMAKE_CURRENT_BINARY_DIR}/re2/src/re2)
+set(re2_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/re2/install)
+set(re2_TAG e7efc48)
+
+if(WIN32)
+  set(re2_STATIC_LIBRARIES ${re2_BUILD}/$(Configuration)/re2.lib)
+else()
+  set(re2_STATIC_LIBRARIES ${re2_BUILD}/libre2.a)
+endif()
+
+set(re2_HEADERS
+    ${re2_INSTALL}/include/re2/re2.h
+)
+
+ExternalProject_Add(re2
+    PREFIX re2
+    GIT_REPOSITORY ${re2_URL}
+    GIT_TAG ${re2_TAG}
+    INSTALL_DIR ${re2_INSTALL}
+    BUILD_IN_SOURCE 1
+    DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
+    CMAKE_CACHE_ARGS
+        -DCMAKE_BUILD_TYPE:STRING=Release
+        -DCMAKE_INSTALL_PREFIX:STRING=${re2_INSTALL}
+        -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
+)
\ No newline at end of file
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index d90f88c66f..14b11dbc0e 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -128,6 +128,7 @@ file(GLOB_RECURSE tf_protos_python_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/*.proto"
 )
 RELATIVE_PROTOBUF_GENERATE_PYTHON(
     ${tensorflow_source_dir} PYTHON_PROTO_GENFILES ${tf_protos_python_srcs}
@@ -141,6 +142,7 @@ file(GLOB_RECURSE tf_python_protos_cc_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/*.proto"
 )
 RELATIVE_PROTOBUF_GENERATE_CPP(PROTO_SRCS PROTO_HDRS
     ${tensorflow_source_dir} ${tf_python_protos_cc_srcs}
@@ -523,10 +525,10 @@ add_python_module("tensorflow/contrib/text/kernels")
 add_python_module("tensorflow/contrib/text/ops")
 add_python_module("tensorflow/contrib/text/python")
 add_python_module("tensorflow/contrib/text/python/ops")
-add_python_module("tensorflow/contrib/tfprof" DONTCOPY)  # SWIG wrapper not implemented.
-#add_python_module("tensorflow/contrib/tfprof/python")
-#add_python_module("tensorflow/contrib/tfprof/python/tools")
-#add_python_module("tensorflow/contrib/tfprof/python/tools/tfprof")
+add_python_module("tensorflow/contrib/tfprof/python")
+add_python_module("tensorflow/contrib/tfprof/python/tools")
+add_python_module("tensorflow/contrib/tfprof/python/tools/tfprof")
+add_python_module("tensorflow/contrib/tfprof/python/tools/tfprof/internal")
 add_python_module("tensorflow/contrib/tpu")
 add_python_module("tensorflow/contrib/tpu/ops")
 add_python_module("tensorflow/contrib/tpu/python")
@@ -733,6 +735,8 @@ set (pywrap_tensorflow_internal_src
     "${tensorflow_source_dir}/tensorflow/cc/framework/ops.cc"
     "${tensorflow_source_dir}/tensorflow/cc/framework/scope.cc"
     "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.cc"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.h"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.cc"
 )
 
 if(WIN32)
@@ -756,6 +760,7 @@ if(WIN32)
         $<TARGET_OBJECTS:tf_core_ops>
         $<TARGET_OBJECTS:tf_core_direct_session>
         $<TARGET_OBJECTS:tf_grappler>
+        $<TARGET_OBJECTS:tf_tools_tfprof>
         $<TARGET_OBJECTS:tf_tools_transform_graph_lib>
         $<$<BOOL:${tensorflow_ENABLE_GRPC_SUPPORT}>:$<TARGET_OBJECTS:tf_core_distributed_runtime>>
         $<TARGET_OBJECTS:tf_core_kernels>
@@ -803,6 +808,7 @@ add_library(pywrap_tensorflow_internal SHARED
     $<TARGET_OBJECTS:tf_core_ops>
     $<TARGET_OBJECTS:tf_core_direct_session>
     $<TARGET_OBJECTS:tf_grappler>
+    $<TARGET_OBJECTS:tf_tools_tfprof>
     $<TARGET_OBJECTS:tf_tools_transform_graph_lib>
     $<$<BOOL:${tensorflow_ENABLE_GRPC_SUPPORT}>:$<TARGET_OBJECTS:tf_core_distributed_runtime>>
     $<TARGET_OBJECTS:tf_core_kernels>
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index d415749ac5..7151549276 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -150,6 +150,8 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     # NOTE: tensor_forest tests in tensor_forest/hybrid/... still don't pass.
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/*_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/*_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/*_test.py"
   )
 
   # exclude the ones we don't want
@@ -166,6 +168,9 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/python/saved_model/saved_model_test.py"
     # requires scipy
     "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/preprocessing/*_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py"
+    # flaky test
+    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
@@ -372,6 +377,11 @@ if (tensorflow_BUILD_CC_TESTS)
     ${tf_cc_saved_model_test_srcs}
   )
 
+  file(GLOB tf_tools_tfprof_test_srcs
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*_test.cc"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*_test.cc"
+  )
+
   set(tf_test_lib tf_test_lib)
   add_library(${tf_test_lib} STATIC ${tf_src_testlib})
 
@@ -417,4 +427,15 @@ if (tensorflow_BUILD_CC_TESTS)
     LIBS ${tf_test_libs}
   )
 
+  file(GLOB_RECURSE tf_tools_tfprof_test_data
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/testdata/*"
+  )
+
+  AddTests(
+    SOURCES ${tf_tools_tfprof_test_srcs}
+    DATA ${tf_tools_tfprof_test_data}
+    OBJECTS ${tf_obj_test}
+    LIBS ${tf_test_libs}
+  )
+
 endif(tensorflow_BUILD_CC_TESTS)
diff --git a/tensorflow/contrib/cmake/tf_tools.cmake b/tensorflow/contrib/cmake/tf_tools.cmake
index 6ef9598963..6bc973d641 100644
--- a/tensorflow/contrib/cmake/tf_tools.cmake
+++ b/tensorflow/contrib/cmake/tf_tools.cmake
@@ -147,3 +147,23 @@ target_link_libraries(${benchmark_model} PUBLIC
   ${tf_core_gpu_kernels_lib}
   ${tensorflow_EXTERNAL_LIBRARIES}
 )
+
+file(GLOB_RECURSE tf_tools_tfprof_srcs
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/*.proto"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*.h"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*.cc"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*.h"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/platform/regexp.h"
+)
+
+file(GLOB_RECURSE tf_tools_tfprof_exclude_srcs
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*test.cc"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*test.cc"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.cc"
+    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.h"
+)
+list(REMOVE_ITEM tf_tools_tfprof_srcs ${tf_tools_tfprof_exclude_srcs})
+
+add_library(tf_tools_tfprof OBJECT ${tf_tools_tfprof_srcs})
+add_dependencies(tf_tools_tfprof tf_core_lib)
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
index 32403c3af4..b7c79edfca 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
@@ -12,7 +12,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":tfprof_logger",
-        "//tensorflow/contrib/tfprof/python/tools/tfprof/internal:pywrap_tensorflow_print_model_analysis_lib",
+        "//tensorflow/python:pywrap_tensorflow",
         "//tensorflow/tools/tfprof:protos_all_py",
     ],
 )
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
index 83e3c5e35f..ca60e504ed 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
@@ -28,26 +28,11 @@ py_library(
     ],
 )
 
-tf_py_wrap_cc(
-    name = "pywrap_tensorflow_print_model_analysis_lib",
-    srcs = ["pywrap_tensorflow_print_model_analysis.i"],
-    swig_includes = [
-        "//tensorflow/python:lib/core/strings.i",
-        "//tensorflow/python:platform/base.i",
-    ],
-    deps = [
-        "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/tools/tfprof/internal:print_model_analysis_hdr",
-        "//util/python:python_headers",
-    ],
-)
-
 py_test(
     name = "print_model_analysis_test",
     srcs = ["print_model_analysis_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":pywrap_tensorflow_print_model_analysis_lib",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
@@ -56,6 +41,7 @@ py_test(
         "//tensorflow/python:init_ops",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:platform_test",
+        "//tensorflow/python:pywrap_tensorflow",
         "//tensorflow/python:variable_scope",
         "//tensorflow/tools/tfprof:protos_all_py",
     ],
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
index 9eaf74d7a1..7ded5e890f 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
@@ -33,7 +33,7 @@ from tensorflow.tools.tfprof import tfprof_output_pb2
 
 # pylint: disable=g-bad-import-order
 # XXX: this depends on pywrap_tensorflow and must come later
-from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
+from tensorflow.python import pywrap_tensorflow as print_mdl
 
 # pylint: disable=bad-whitespace
 # pylint: disable=bad-continuation
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index 71468dde37..a472ade362 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -116,7 +116,7 @@ class RunMetadataTest(test.TestCase):
     with ops.device('/cpu:0'):
       tfprof_node, run_meta = _run_model()
       self.assertEqual(tfprof_node.children[0].name, 'MatMul')
-      self.assertGreater(tfprof_node.children[0].exec_micros, 10)
+      self.assertGreater(tfprof_node.children[0].exec_micros, 0)
 
     ret = _extract_node(run_meta, 'MatMul')
     self.assertEqual(len(ret), 1)
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index c781d2af4e..5b6111efdf 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -23,7 +23,7 @@ from __future__ import print_function
 import six
 
 from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
-from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
+from tensorflow.python import pywrap_tensorflow as print_mdl
 from tensorflow.python.framework import errors
 from tensorflow.tools.tfprof import tfprof_options_pb2
 from tensorflow.tools.tfprof import tfprof_output_pb2
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index fea27a82a5..9f95074022 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -222,7 +222,12 @@ class PrintModelAnalysisTest(test.TestCase):
 
       with gfile.Open(outfile, 'r') as f:
         # Test that a json file is created.
-        self.assertLess(1000, len(f.read()))
+        # TODO(xpan): tfprof Timeline isn't quite correct on Windows.
+        # Investigate why.
+        if os.name != 'nt':
+          self.assertLess(1000, len(f.read()))
+        else:
+          self.assertLess(1, len(f.read()))
 
   def testOpView(self):
     ops.reset_default_graph()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
index c7113b6a57..dd25340564 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
@@ -95,17 +95,6 @@ class ProfilerTest(test.TestCase):
       pma_str = f.read()
     self.assertEqual(pma_str, profiler_str)
 
-    # Test the output difference between multi-step profile and 1-step profile.
-    _ = sess.run(r,
-                 options=config_pb2.RunOptions(
-                     trace_level=config_pb2.RunOptions.FULL_TRACE),
-                 run_metadata=run_meta)
-
-    profiler.add_step(2, run_meta)
-    profiler.profile_name_scope(opts)
-    with gfile.Open(outfile, 'r') as f:
-      profiler_str = f.read()
-
     model_analyzer.print_model_analysis(
         sess.graph, tfprof_cmd='scope', run_meta=run_meta, tfprof_options=opts)
     with gfile.Open(outfile, 'r') as f:
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 7f291b0b13..0c04d36aff 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2770,6 +2770,7 @@ tf_py_wrap_cc(
         "util/port.i",
         "util/py_checkpoint_reader.i",
         "util/stat_summarizer.i",
+        "util/tfprof.i",
         "util/transform_graph.i",
     ],
     deps = [
diff --git a/tensorflow/python/tensorflow.i b/tensorflow/python/tensorflow.i
index e877d2717d..0c8110ec60 100644
--- a/tensorflow/python/tensorflow.i
+++ b/tensorflow/python/tensorflow.i
@@ -20,6 +20,7 @@ limitations under the License.
 %include "tensorflow/python/util/port.i"
 %include "tensorflow/python/util/py_checkpoint_reader.i"
 %include "tensorflow/python/util/stat_summarizer.i"
+%include "tensorflow/python/util/tfprof.i"
 
 %include "tensorflow/python/lib/core/py_func.i"
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i b/tensorflow/python/util/tfprof.i
similarity index 91%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
rename to tensorflow/python/util/tfprof.i
index 582c36e339..11bcd7db40 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
+++ b/tensorflow/python/util/tfprof.i
@@ -29,7 +29,11 @@ using tensorflow::int64;
   $1 = &temp;
 }
 %typemap(out) const string& {
+%#if PY_MAJOR_VERSION >= 3
+  $result = PyUnicode_FromStringAndSize($1->data(), $1->size());
+%#else
   $result = PyString_FromStringAndSize($1->data(), $1->size());
+%#endif
 }
 %apply const string & {string &};
 %apply const string & {string *};
@@ -43,6 +47,7 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
+%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_code.cc b/tensorflow/tools/tfprof/internal/tfprof_code.cc
index f7b71bff31..4a345dd17d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_code.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_code.cc
@@ -20,6 +20,7 @@ limitations under the License.
 
 #include "tensorflow/c/c_api.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
@@ -31,12 +32,7 @@ namespace tfprof {
 namespace {
 // Convert to Trace proto into a short readable string.
 string GetTraceString(const CodeDef::Trace& trace) {
-  string ntrace = "";
-  if (trace.file().find_last_of('/') != trace.file().npos) {
-    ntrace += trace.file().substr(trace.file().find_last_of('/') + 1);
-  } else {
-    ntrace += trace.file();
-  }
+  string ntrace = io::Basename(trace.file()).ToString();
   ntrace += strings::StrCat(":", trace.lineno());
   if (trace.function().length() < 20) {
     ntrace += ":" + trace.function();
-- 
GitLab


From b352be037ce051bb71837ab1a8e72e3d755241c4 Mon Sep 17 00:00:00 2001
From: Martin Wicke <martin.wicke@gmail.com>
Date: Tue, 27 Jun 2017 17:49:36 -0700
Subject: [PATCH 0532/1375] Fix import in documentation documentation (#11084)

* Fix import in documentation documentation

* Address review comment
---
 tensorflow/docs_src/community/documentation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/community/documentation.md b/tensorflow/docs_src/community/documentation.md
index 31a10d1f15..a757d8c66d 100644
--- a/tensorflow/docs_src/community/documentation.md
+++ b/tensorflow/docs_src/community/documentation.md
@@ -276,7 +276,7 @@ __init__.py:
     # Otherwise import symbols directly
     from tensorflow.some_module.some_other_file import some_symbol
 
-    from tensorflow.platform.all_util import remove_undocumented
+    from tensorflow.python.util.all_util import remove_undocumented
 
     _allowed_symbols = [‘some_symbol’, ‘some_other_symbol’]
 
-- 
GitLab


From 8b5d2122b93bd8912ef7e754ce9edb4a9024df4d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 18:01:47 -0700
Subject: [PATCH 0533/1375] [XLA] Make logical buffer coloring run on output of
 points-to analysis.

PiperOrigin-RevId: 160354095
---
 .../compiler/xla/service/buffer_assignment.cc | 14 ++++++-
 .../compiler/xla/service/buffer_assignment.h  |  4 ++
 .../xla/service/buffer_assignment_test.cc     | 40 ++++++++++++-------
 .../compiler/xla/service/buffer_liveness.cc   |  8 ++--
 .../compiler/xla/service/buffer_liveness.h    | 16 +++-----
 .../xla/service/heap_simulator_test.cc        |  3 +-
 .../compiler/xla/service/logical_buffer.h     | 25 +++++++++---
 .../xla/service/tuple_points_to_analysis.cc   | 13 ++----
 .../xla/service/tuple_points_to_analysis.h    | 25 +++++-------
 9 files changed, 85 insertions(+), 63 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index 3ba010ac43..78db842f0b 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -67,6 +67,7 @@ BufferAllocation::Slice BufferAllocation::GetSlice(
 
 void BufferAllocation::AddAssignment(const LogicalBuffer& buffer, int64 offset,
                                      int64 size) {
+  VLOG(4) << "Trying to add " << buffer << " to " << this;
   CHECK(assigned_buffers_.count(&buffer) == 0)
       << "LogicalBuffer " << buffer << " already assigned to allocation "
       << index_;
@@ -213,10 +214,14 @@ bool BufferAssignment::HasTopLevelAllocation(
 
 StatusOr<BufferAllocation::Slice> BufferAssignment::GetUniqueSlice(
     const HloInstruction* instruction, const ShapeIndex& index) const {
+  VLOG(3) << "Trying to find unique slice for " << instruction->name() << " ["
+          << index << "]";
   BufferAllocation::Slice result;
   for (const LogicalBuffer* buffer :
        GetPointsToSet(instruction).element(index)) {
+    VLOG(3) << "Examining buffer " << *buffer;
     if (HasAllocation(*buffer)) {
+      VLOG(3) << "Has allocation";
       const BufferAllocation::Slice slice =
           GetAssignedAllocation(*buffer).GetSlice(*buffer);
       if (result.allocation() == nullptr) {
@@ -227,6 +232,8 @@ StatusOr<BufferAllocation::Slice> BufferAssignment::GetUniqueSlice(
             "be determined at compile-time.",
             instruction->name().c_str(), index.ToString().c_str());
       }
+    } else {
+      VLOG(3) << "No allocation";
     }
   }
   if (result.allocation() == nullptr) {
@@ -1319,8 +1326,7 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
     const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
     LogicalBuffer::SizeFunction buffer_size) {
   TF_ASSIGN_OR_RETURN(std::unique_ptr<BufferLiveness> liveness,
-                      BufferLiveness::Run(module, std::move(hlo_ordering),
-                                          std::move(colorer_)));
+                      BufferLiveness::Run(module, std::move(hlo_ordering)));
 
   VLOG(1) << "Assigning buffers to module " << module->name();
   XLA_VLOG_LINES(2, module->ToString());
@@ -1340,6 +1346,10 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
   std::vector<ColocatedBufferSet> colocated_buffer_sets;
   BuildColocatedBufferSets(module, assignment->liveness(),
                            assignment->buffer_size_, &colocated_buffer_sets);
+  TF_RETURN_IF_ERROR(colorer_(&assignment->mutable_points_to_analysis()));
+  VLOG(3) << "After coloring:";
+  XLA_VLOG_LINES(3, assignment->points_to_analysis().ToString());
+
   AssignColocatedBufferSets(colocated_buffer_sets, assignment.get(),
                             &colocated_buffers, &colocated_allocations);
 
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h
index dd84b06b77..f02ad58dfa 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.h
+++ b/tensorflow/compiler/xla/service/buffer_assignment.h
@@ -320,6 +320,10 @@ class BufferAssignment {
     return liveness_->points_to_analysis();
   }
 
+  TuplePointsToAnalysis& mutable_points_to_analysis() const {
+    return liveness_->mutable_points_to_analysis();
+  }
+
   // Returns the BufferLiveness object used to construct this assignment.
   const BufferLiveness& liveness() const { return *liveness_; }
 
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 56568fd446..abc3b49522 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -379,12 +379,15 @@ TEST_F(BufferAssignmentTest, BasicUniquelyColored) {
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
-  auto buffers = RunColoredBufferAssignment(
-      module.get(),
-      [](const HloInstruction* instruction, const ShapeIndex& index) {
-        static int64 serial = 0;
-        return LogicalBuffer::Color(serial++);
-      });
+  auto colorer = [](TuplePointsToAnalysis* points_to_analysis) {
+    int color = 0;
+    for (auto& buffer : points_to_analysis->logical_buffers()) {
+      buffer->set_color(LogicalBuffer::Color(color++));
+    }
+    return Status::OK();
+  };
+
+  auto buffers = RunColoredBufferAssignment(module.get(), colorer);
 
   // Distinct input buffers were assigned for parameters.
   BufferAllocation paramscalar_buffer =
@@ -431,14 +434,23 @@ TEST_F(BufferAssignmentTest, BasicPartiallyColored) {
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
-  auto buffers = RunColoredBufferAssignment(
-      module.get(),
-      [](const HloInstruction* instruction, const ShapeIndex& index) {
-        return (instruction->opcode() == HloOpcode::kAdd ||
-                instruction->opcode() == HloOpcode::kMultiply)
-                   ? LogicalBuffer::Color(1)
-                   : LogicalBuffer::Color(0);
-      });
+  auto colorer = [](TuplePointsToAnalysis* points_to_analysis) {
+    for (auto& buffer : points_to_analysis->logical_buffers()) {
+      const auto& aliases = points_to_analysis->GetBufferAliases(*buffer);
+      for (const auto& alias : aliases) {
+        if (alias.instruction()->opcode() == HloOpcode::kAdd ||
+            alias.instruction()->opcode() == HloOpcode::kMultiply) {
+          buffer->set_color(LogicalBuffer::Color(1));
+        }
+      }
+      if (!buffer->has_color()) {
+        buffer->set_color(LogicalBuffer::Color(0));
+      }
+    }
+    return Status::OK();
+  };
+
+  auto buffers = RunColoredBufferAssignment(module.get(), colorer);
 
   // Distinct input buffers were assigned for parameters.
   BufferAllocation paramscalar_buffer =
diff --git a/tensorflow/compiler/xla/service/buffer_liveness.cc b/tensorflow/compiler/xla/service/buffer_liveness.cc
index 56b8b5e80b..6720a90ef8 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness.cc
@@ -37,17 +37,15 @@ namespace xla {
 
 /* static */
 StatusOr<std::unique_ptr<BufferLiveness>> BufferLiveness::Run(
-    const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
-    TuplePointsToAnalysis::Colorer colorer) {
+    const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering) {
   std::unique_ptr<BufferLiveness> liveness(
-      new BufferLiveness(module, std::move(hlo_ordering), std::move(colorer)));
+      new BufferLiveness(module, std::move(hlo_ordering)));
   TF_RETURN_IF_ERROR(liveness->Analyze());
   return std::move(liveness);
 }
 
 tensorflow::Status BufferLiveness::Analyze() {
-  TF_ASSIGN_OR_RETURN(points_to_analysis_,
-                      TuplePointsToAnalysis::Run(module_, colorer_));
+  TF_ASSIGN_OR_RETURN(points_to_analysis_, TuplePointsToAnalysis::Run(module_));
   for (auto& computation : module_->computations()) {
     // Gather all instructions whose buffers might alias other instructions into
     // the set aliased_buffers_.  This includes those contained as a tuple
diff --git a/tensorflow/compiler/xla/service/buffer_liveness.h b/tensorflow/compiler/xla/service/buffer_liveness.h
index 9bb2564a83..7e484430ea 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.h
+++ b/tensorflow/compiler/xla/service/buffer_liveness.h
@@ -39,9 +39,7 @@ class BufferLiveness {
   // Constructs a buffer liveness object for the given module assuming the given
   // HLO instruction ordering.
   static StatusOr<std::unique_ptr<BufferLiveness>> Run(
-      const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
-      TuplePointsToAnalysis::Colorer colorer =
-          TuplePointsToAnalysis::DefaultColorer());
+      const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering);
 
   // Returns true if the live range of the buffer containing the output of 'a'
   // may overlap with the live range of the buffer of 'b'. If instruction 'a'
@@ -63,6 +61,9 @@ class BufferLiveness {
   const TuplePointsToAnalysis& points_to_analysis() const {
     return *points_to_analysis_;
   }
+  TuplePointsToAnalysis& mutable_points_to_analysis() const {
+    return *points_to_analysis_;
+  }
 
   // Returns the underlying hlo ordering used for this liveness analysis.
   const HloOrdering& hlo_ordering() const { return *hlo_ordering_; }
@@ -71,11 +72,8 @@ class BufferLiveness {
 
  private:
   explicit BufferLiveness(const HloModule* module,
-                          std::unique_ptr<HloOrdering> hlo_ordering,
-                          TuplePointsToAnalysis::Colorer colorer)
-      : module_(module),
-        hlo_ordering_(std::move(hlo_ordering)),
-        colorer_(colorer) {}
+                          std::unique_ptr<HloOrdering> hlo_ordering)
+      : module_(module), hlo_ordering_(std::move(hlo_ordering)) {}
 
   // Perform buffer liveness analysis. This method must be called prior to
   // MayInterfere or MaybeLiveOut.
@@ -98,8 +96,6 @@ class BufferLiveness {
   tensorflow::gtl::FlatSet<const LogicalBuffer*> maybe_live_out_buffers_;
 
   std::unique_ptr<TuplePointsToAnalysis> points_to_analysis_;
-
-  TuplePointsToAnalysis::Colorer colorer_;
 };
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc
index fefc4c6a0f..ef9db8ba23 100644
--- a/tensorflow/compiler/xla/service/heap_simulator_test.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc
@@ -510,8 +510,7 @@ class HeapAlgorithmTestBase : public ::testing::Test {
   // other than the id and color.
   const LogicalBuffer* DummyLogicalBuffer() {
     const LogicalBuffer::Id id = buffers_.size();
-    buffers_.emplace_back(MakeUnique<LogicalBuffer>(nullptr, ShapeIndex{}, id,
-                                                    LogicalBuffer::Color(0)));
+    buffers_.emplace_back(MakeUnique<LogicalBuffer>(nullptr, ShapeIndex{}, id));
     return buffers_.back().get();
   }
 
diff --git a/tensorflow/compiler/xla/service/logical_buffer.h b/tensorflow/compiler/xla/service/logical_buffer.h
index 566cd01ea4..7aaa29b098 100644
--- a/tensorflow/compiler/xla/service/logical_buffer.h
+++ b/tensorflow/compiler/xla/service/logical_buffer.h
@@ -97,9 +97,11 @@ class LogicalBuffer {
   using SizeFunction = std::function<int64(const LogicalBuffer&)>;
   using AlignmentFunction = std::function<int64(const LogicalBuffer&)>;
 
-  LogicalBuffer(HloInstruction* instruction, const ShapeIndex& index, Id id,
-                Color color)
-      : instruction_(instruction), index_(index), id_(id), color_(color) {}
+  LogicalBuffer(HloInstruction* instruction, const ShapeIndex& index, Id id)
+      : instruction_(instruction),
+        index_(index),
+        id_(id),
+        color_(kInvalidColor) {}
 
   Id id() const { return id_; }
 
@@ -112,8 +114,19 @@ class LogicalBuffer {
 
   // Return the color of the logical buffer. Differently colored buffers can
   // not be parts of the same allocation.
-  Color color() const { return color_; }
-  void set_color(Color color) { color_ = color; }
+  Color color() const {
+    CHECK_NE(color_, kInvalidColor)
+        << "Should not query the color of a buffer that was never colored";
+    return color_;
+  }
+
+  void set_color(Color color) {
+    CHECK_NE(color, kInvalidColor)
+        << "Should not set the color of a buffer to the invalid color";
+    color_ = color;
+  }
+
+  bool has_color() const { return color_ != kInvalidColor; }
 
   // Return the shape of the buffer. This reference points into the shape field
   // of the instruction defining the buffer.  Therefore, the returned shape will
@@ -143,6 +156,8 @@ class LogicalBuffer {
   static LogicalBufferProto::Location ToLocationProto(
       const HloInstruction& instruction, const ShapeIndex& index);
 
+  const Color kInvalidColor = Color(-1);
+
  private:
   HloInstruction* instruction_;
   ShapeIndex index_;
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
index 8d68398450..07b4dd4966 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
@@ -125,18 +125,13 @@ void PointsToSet::add_tuple_source(const ShapeIndex& index,
 }
 
 /* static */ StatusOr<std::unique_ptr<TuplePointsToAnalysis>>
-TuplePointsToAnalysis::Run(const HloModule* module, Colorer colorer) {
+TuplePointsToAnalysis::Run(const HloModule* module) {
   std::unique_ptr<TuplePointsToAnalysis> analysis(
-      new TuplePointsToAnalysis(module, std::move(colorer)));
+      new TuplePointsToAnalysis(module));
   TF_RETURN_IF_ERROR(analysis->Analyze());
   return std::move(analysis);
 }
 
-/* static */ StatusOr<std::unique_ptr<TuplePointsToAnalysis>>
-TuplePointsToAnalysis::Run(const HloModule* module) {
-  return Run(module, DefaultColorer());
-}
-
 Status TuplePointsToAnalysis::Analyze() {
   points_to_.clear();
   for (auto& computation : module_->computations()) {
@@ -184,8 +179,8 @@ Status TuplePointsToAnalysis::PopulateDefinedBuffersAndAliases(
 const LogicalBuffer& TuplePointsToAnalysis::NewLogicalBuffer(
     HloInstruction* instruction, const ShapeIndex& index) {
   CHECK_EQ(logical_buffers_.size(), next_buffer_id_);
-  logical_buffers_.push_back(MakeUnique<LogicalBuffer>(
-      instruction, index, next_buffer_id_, colorer_(instruction, index)));
+  logical_buffers_.push_back(
+      MakeUnique<LogicalBuffer>(instruction, index, next_buffer_id_));
   ++next_buffer_id_;
   return *logical_buffers_.back();
 }
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
index bab4235a28..a05b4c8ebc 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
@@ -142,15 +142,10 @@ std::ostream& operator<<(std::ostream& out, const BufferAlias& buffer_alias);
 // the potential sources of each buffer in each instruction's output.
 class TuplePointsToAnalysis : public DfsHloVisitorWithDefault {
  public:
-  using Colorer = std::function<LogicalBuffer::Color(
-      const HloInstruction* instruction, const ShapeIndex& index)>;
+  using Colorer =
+      std::function<Status(TuplePointsToAnalysis* points_to_analysis)>;
 
-  // Runs points-to analysis on 'module' with the provided buffer color
-  // assigner.
-  static StatusOr<std::unique_ptr<TuplePointsToAnalysis>> Run(
-      const HloModule* module, Colorer colorer);
-
-  // Runs points-to analysis on 'module' with the default color assigner.
+  // Runs points-to analysis on 'module'.
   static StatusOr<std::unique_ptr<TuplePointsToAnalysis>> Run(
       const HloModule* module);
 
@@ -216,15 +211,16 @@ class TuplePointsToAnalysis : public DfsHloVisitorWithDefault {
   string ToString() const;
 
   static Colorer DefaultColorer() {
-    return [](const HloInstruction* instruction, const ShapeIndex& index) {
-      return LogicalBuffer::Color(0);
+    return [](TuplePointsToAnalysis* points_to_analysis) {
+      for (auto& buffer : points_to_analysis->logical_buffers()) {
+        buffer->set_color(LogicalBuffer::Color(0));
+      }
+      return Status::OK();
     };
   }
 
  private:
-  explicit TuplePointsToAnalysis(const HloModule* module,
-                                 Colorer colorer = DefaultColorer())
-      : module_(module), colorer_(colorer) {}
+  explicit TuplePointsToAnalysis(const HloModule* module) : module_(module) {}
 
   // Perform the analysis. Should be called immediately after constructing the
   // object and before calling GetPointsToSet.
@@ -283,9 +279,6 @@ class TuplePointsToAnalysis : public DfsHloVisitorWithDefault {
   // The ID of the next logical buffer created.
   LogicalBuffer::Id next_buffer_id_ = 0;
 
-  // Used to color the created logical buffers.
-  Colorer colorer_;
-
   TF_DISALLOW_COPY_AND_ASSIGN(TuplePointsToAnalysis);
 };
 
-- 
GitLab


From bae086318759653ae2066c52ab92f77f48c36dfd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 18:06:18 -0700
Subject: [PATCH 0534/1375] Use std::move to avoid allocations.

PiperOrigin-RevId: 160354628
---
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index b284543299..7920042bae 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -155,11 +155,11 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
                                 /*MAttrs=*/DetectMachineAttributes()))),
       disassembler_(*target_machine_),
       data_layout_(target_machine_->createDataLayout()),
-      compile_layer_(
-          object_layer_,
-          CompilerFunctor(target_machine_.get(), &disassembler_, opt_level,
-                          GetAvailableIntrinsics(), pre_optimization_callback,
-                          post_optimization_callback)) {
+      compile_layer_(object_layer_,
+                     CompilerFunctor(target_machine_.get(), &disassembler_,
+                                     opt_level, GetAvailableIntrinsics(),
+                                     std::move(pre_optimization_callback),
+                                     std::move(post_optimization_callback))) {
   VLOG(1) << "CPU target: " << target_machine_->getTargetCPU().str()
           << " features: " << target_machine_->getTargetFeatureString().str();
 }
-- 
GitLab


From b19534951d4e937d7f3d2435c62a969a2fc4e233 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 27 Jun 2017 20:00:25 -0700
Subject: [PATCH 0535/1375] Bugfix to scatter_nd non-aliasing op: properly
 assign Tensor if forwarding worked.

PiperOrigin-RevId: 160362310
---
 tensorflow/core/kernels/scatter_nd_op.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc
index 5f96dfa0f3..49fcf878d5 100644
--- a/tensorflow/core/kernels/scatter_nd_op.cc
+++ b/tensorflow/core/kernels/scatter_nd_op.cc
@@ -334,6 +334,8 @@ class ScatterNdUpdateOp : public OpKernel {
         functor::DenseUpdate<Device, T, ASSIGN> copy;
         const Tensor& input_copy = c->input(0);
         copy(c->eigen_device<Device>(), params.flat<T>(), input_copy.flat<T>());
+      } else {
+        params = *params_ptr;
       }
     }
 
-- 
GitLab


From 3ada474895ec8de2e7730d29454ceea07b83ec8c Mon Sep 17 00:00:00 2001
From: Taehoon Lee <taehoonlee@snu.ac.kr>
Date: Wed, 28 Jun 2017 15:46:48 +0900
Subject: [PATCH 0536/1375] Fix typos

---
 tensorflow/compiler/xla/tests/BUILD                    |  2 +-
 .../contrib/distributions/python/ops/binomial.py       |  2 +-
 .../distributions/python/ops/vector_student_t.py       |  2 +-
 .../contrib/layers/python/layers/feature_column.py     |  4 ++--
 tensorflow/contrib/layers/python/layers/layers.py      |  2 +-
 .../contrib/training/python/training/bucket_ops.py     |  2 +-
 .../training/python/training/evaluation_test.py        |  4 ++--
 tensorflow/core/framework/op_kernel.h                  |  2 +-
 tensorflow/core/kernels/fused_batch_norm_op.cc         |  2 +-
 .../parameterized_truncated_normal_op_gpu.cu.cc        |  2 +-
 tensorflow/core/lib/gtl/flatrep.h                      |  2 +-
 tensorflow/python/layers/convolutional.py              | 10 +++++-----
 .../python/ops/distributions/dirichlet_multinomial.py  |  2 +-
 tensorflow/python/ops/distributions/multinomial.py     |  2 +-
 tensorflow/python/ops/distributions/util.py            |  2 +-
 tensorflow/python/ops/summary_op_util.py               |  2 +-
 tensorflow/tools/docs/generate_lib.py                  |  2 +-
 17 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index a11ac0bec6..a1a41657b6 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -509,7 +509,7 @@ xla_test(
 )
 
 # Tests the dot operation in some cases that can be performed via a
-# runtime call on some backends - e.g. a runtime call to to Eigen.
+# runtime call on some backends - e.g. a runtime call to Eigen.
 xla_test(
     name = "dot_operation_runtime_test",
     srcs = ["dot_operation_test.cc"],
diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py
index 9304a56491..d23af50167 100644
--- a/tensorflow/contrib/distributions/python/ops/binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/binomial.py
@@ -196,7 +196,7 @@ class Binomial(distribution.Distribution):
 
   @property
   def probs(self):
-    """Probability of of drawing a `1`."""
+    """Probability of drawing a `1`."""
     return self._probs
 
   def _batch_shape_tensor(self):
diff --git a/tensorflow/contrib/distributions/python/ops/vector_student_t.py b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
index ae804b6172..507493560b 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_student_t.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
@@ -160,7 +160,7 @@ class _VectorStudentT(transformed_distribution.TransformedDistribution):
   #### Examples
 
   A single instance of a "Vector Student's t-distribution" is defined by a mean
-  vector of of length `k` and a scale matrix of shape `k x k`.
+  vector of length `k` and a scale matrix of shape `k x k`.
 
   Extra leading dimensions, if provided, allow for batches.
 
diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index 68159fe9b9..4cbd198c02 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -165,7 +165,7 @@ class _LinearEmbeddingLookupArguments(
                             "combiner"])):
   """Represents the information needed from a column for embedding lookup.
 
-  Used to to compute DNN inputs and weighted sum.
+  Used to compute DNN inputs and weighted sum.
   """
   pass
 
@@ -184,7 +184,7 @@ class _DeepEmbeddingLookupArguments(
                             "trainable"])):
   """Represents the information needed from a column for embedding lookup.
 
-  Used to to compute DNN inputs and weighted sum.
+  Used to compute DNN inputs and weighted sum.
   """
   pass
 
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index ed4b723ca7..8b3ccea995 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -938,7 +938,7 @@ def convolution(inputs,
       with "NC".
     num_outputs: Integer, the number of output filters.
     kernel_size: A sequence of N positive integers specifying the spatial
-      dimensions of of the filters.  Can be a single integer to specify the same
+      dimensions of the filters.  Can be a single integer to specify the same
       value for all spatial dimensions.
     stride: A sequence of N positive integers specifying the stride at which to
       compute output.  Can be a single integer to specify the same value for all
diff --git a/tensorflow/contrib/training/python/training/bucket_ops.py b/tensorflow/contrib/training/python/training/bucket_ops.py
index 7e293da551..5523cc375f 100644
--- a/tensorflow/contrib/training/python/training/bucket_ops.py
+++ b/tensorflow/contrib/training/python/training/bucket_ops.py
@@ -88,7 +88,7 @@ def bucket(tensors,
   This function is implemented using several queues. A `QueueRunner` for the
   queues is added to the current `Graph`'s `QUEUE_RUNNER` collection.
 
-  As the returned tensors are the result of of a dequeue operation, evaluating
+  As the returned tensors are the result of a dequeue operation, evaluating
   them will throw a `tf.errors.OutOfRangeError` when the input queue is
   exhausted.  If these tensors are feeding another input queue, its queue runner
   will catch this exception, however, if they are used in your main thread
diff --git a/tensorflow/contrib/training/python/training/evaluation_test.py b/tensorflow/contrib/training/python/training/evaluation_test.py
index babd2239b6..b07039916c 100644
--- a/tensorflow/contrib/training/python/training/evaluation_test.py
+++ b/tensorflow/contrib/training/python/training/evaluation_test.py
@@ -329,7 +329,7 @@ class EvaluateRepeatedlyTest(test.TestCase):
     if not gfile.Exists(checkpoint_dir):
       gfile.MakeDirs(checkpoint_dir)
 
-    # We need a variable that that the saver will try to restore.
+    # We need a variable that the saver will try to restore.
     variables.get_or_create_global_step()
 
     # Run with placeholders. If we actually try to evaluate this, we'd fail
@@ -394,7 +394,7 @@ class EvaluateRepeatedlyTest(test.TestCase):
                                   'evaluate_with_eval_feed_dict')
     self._train_model(checkpoint_dir, num_steps=1)
 
-    # We need a variable that that the saver will try to restore.
+    # We need a variable that the saver will try to restore.
     variables.get_or_create_global_step()
 
     # Create a variable and an eval op that increments it with a placeholder.
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index d85fbc2256..63caa054d7 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -722,7 +722,7 @@ class OpKernelContext {
       StringPiece output_name, const TensorShape& output_shape,
       Tensor** output) TF_MUST_USE_RESULT;
 
-  // Tries to reuse one of of the inputs given in input_indices as a temporary.
+  // Tries to reuse one of the inputs given in input_indices as a temporary.
   // If none of the given inputs can be forwarded, calls
   // allocate_temp() to allocate a new temporary buffer.
   Status forward_input_or_allocate_temp(
diff --git a/tensorflow/core/kernels/fused_batch_norm_op.cc b/tensorflow/core/kernels/fused_batch_norm_op.cc
index 37758e82eb..81551ee26f 100644
--- a/tensorflow/core/kernels/fused_batch_norm_op.cc
+++ b/tensorflow/core/kernels/fused_batch_norm_op.cc
@@ -149,7 +149,7 @@ struct FusedBatchNormGrad<CPUDevice, T> {
     typename TTypes<T>::Vec scale_backprop(scale_backprop_output->vec<T>());
     typename TTypes<T>::Vec offset_backprop(offset_backprop_output->vec<T>());
 
-    // Note: the following formulas are used to to compute the gradients for
+    // Note: the following formulas are used to compute the gradients for
     // back propagation.
     // x_backprop = scale * rsqrt(variance + epsilon) *
     //              [y_backprop - mean(y_backprop) - (x - mean(x)) *
diff --git a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
index 8b85bd4ebe..933de65c15 100644
--- a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
@@ -32,7 +32,7 @@ limitations under the License.
 #ifdef COMPILER_MSVC
 // msvc does not support unroll. One could try the loop pragma but we need to
 // take a closer look if this generates better code in this case. For now let
-// the compiler take care of of it.
+// the compiler take care of it.
 #define UNROLL
 #else
 #define UNROLL _Pragma("unroll")
diff --git a/tensorflow/core/lib/gtl/flatrep.h b/tensorflow/core/lib/gtl/flatrep.h
index f5e318be1f..bb405b327a 100644
--- a/tensorflow/core/lib/gtl/flatrep.h
+++ b/tensorflow/core/lib/gtl/flatrep.h
@@ -29,7 +29,7 @@ namespace internal {
 //
 // The representation is an open-addressed hash table.  Conceptually,
 // the representation is a flat array of entries.  However we
-// structure it as an array of of buckets where each bucket holds
+// structure it as an array of buckets where each bucket holds
 // kWidth entries along with metadata for the kWidth entries.  The
 // metadata marker is
 //
diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index 63c7280b3d..54c775b37d 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -741,7 +741,7 @@ class SeparableConv2D(Conv2D):
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -950,7 +950,7 @@ def separable_conv2d(inputs,
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -1033,7 +1033,7 @@ class Conv2DTranspose(Conv2D):
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 positive integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -1233,7 +1233,7 @@ def conv2d_transpose(inputs,
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 positive integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -1492,7 +1492,7 @@ def conv3d_transpose(inputs,
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 3 positive integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 3 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
diff --git a/tensorflow/python/ops/distributions/dirichlet_multinomial.py b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
index 662a765558..61e22f4753 100644
--- a/tensorflow/python/ops/distributions/dirichlet_multinomial.py
+++ b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
@@ -95,7 +95,7 @@ class DirichletMultinomial(distribution.Distribution):
   The last `concentration` dimension parametrizes a single Dirichlet-Multinomial
   distribution. When calling distribution functions (e.g., `dist.prob(counts)`),
   `concentration`, `total_count` and `counts` are broadcast to the same shape.
-  The last dimension of of `counts` corresponds single Dirichlet-Multinomial
+  The last dimension of `counts` corresponds single Dirichlet-Multinomial
   distributions.
 
   Distribution parameters are automatically broadcast in all functions; see
diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index a5bea7b4ba..0236c31f6d 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -193,7 +193,7 @@ class Multinomial(distribution.Distribution):
 
   @property
   def probs(self):
-    """Probability of of drawing a `1` in that coordinate."""
+    """Probability of drawing a `1` in that coordinate."""
     return self._probs
 
   def _batch_shape_tensor(self):
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 05c6f4da57..4135bf9b2d 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -37,7 +37,7 @@ from tensorflow.python.ops import nn
 
 def assert_close(
     x, y, data=None, summarize=None, message=None, name="assert_close"):
-  """Assert that that x and y are within machine epsilon of each other.
+  """Assert that x and y are within machine epsilon of each other.
 
   Args:
     x: Floating-point `Tensor`
diff --git a/tensorflow/python/ops/summary_op_util.py b/tensorflow/python/ops/summary_op_util.py
index a3f6616902..06ea63704d 100644
--- a/tensorflow/python/ops/summary_op_util.py
+++ b/tensorflow/python/ops/summary_op_util.py
@@ -78,7 +78,7 @@ def summary_scope(name, family=None, default_name=None, values=None):
   If `family` is set, then the tag name will be '<family>/<scope_name>', where
   `scope_name` is `<outer_scope>/<family>/<name>`. This ensures that `family`
   is always the prefix of the tag (and unmodified), while ensuring the scope
-  respects the outer scope from this this summary was created.
+  respects the outer scope from this summary was created.
 
   Args:
     name: A name for the generated summary node.
diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index 67a4ad0ec9..587aaeb683 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -445,7 +445,7 @@ class DocGenerator(object):
         '--base_dir',
         type=str,
         default=default_base_dir,
-        help='Base directory to to strip from file names referenced in docs.')
+        help='Base directory to strip from file names referenced in docs.')
 
   def parse_known_args(self):
     flags, _ = self.argument_parser.parse_known_args()
-- 
GitLab


From 5c21edd6a43ca56ff1c9b209c9f54d6fedb0823b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 20:16:40 -0700
Subject: [PATCH 0537/1375] Use a non-empty default value for the entry point
 for tfcompile.

This ensures we get a sane output when tfcompile is invoked manually
without only the bare minimum command line arguments.

PiperOrigin-RevId: 160363243
---
 tensorflow/compiler/aot/tfcompile_main.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 9ec205a126..72cb676b82 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -128,6 +128,7 @@ int main(int argc, char** argv) {
   flags.target_triple = "x86_64-pc-linux";
   flags.out_object = "out.o";
   flags.out_header = "out.h";
+  flags.entry_point = "entry";
 
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
-- 
GitLab


From b7af918c580a17242bb64a721c71ecc968d706a3 Mon Sep 17 00:00:00 2001
From: Mark Heffernan <meheff@google.com>
Date: Tue, 27 Jun 2017 21:12:31 -0700
Subject: [PATCH 0538/1375] [XLA] Several fixes to HLO reachability analysis.
 (1) Account for control dependencies in reachability. (2) Invert sense of
 reachability. We draw our HLO graphs with arrows from producers to consumers
 so it makes more sense for reachability to be defined along the direction of
 these edges. (3) Rename ComputeTransitiveOperands to ComputeReachability.

PiperOrigin-RevId: 160366307
---
 .../compiler/xla/service/gpu/hlo_schedule.cc  | 57 +++++++-------
 .../xla/service/gpu/stream_assignment.cc      | 17 ++---
 .../compiler/xla/service/hlo_computation.cc   | 15 ++--
 .../compiler/xla/service/hlo_computation.h    | 11 ++-
 .../xla/service/hlo_computation_test.cc       | 75 +++++++++++++++++++
 .../compiler/xla/service/hlo_ordering.cc      | 12 +--
 .../compiler/xla/service/hlo_ordering.h       |  9 +--
 .../xla/service/instruction_fusion.cc         |  8 +-
 8 files changed, 143 insertions(+), 61 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
index f76f8ca668..f964ffb748 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
@@ -67,39 +67,41 @@ GpuHloOrdering::GpuHloOrdering(
   // GpuExecutable adds cross-stream dependency edges to ensure each instruction
   // waits for its operands before executing.
   //
-  // The predecessor map is built incrementally, in thunk launch order. We
-  // record the instructions already visited per stream in
-  // 'instructions_per_stream'. This lets us quickly determine the same-stream
-  // predecessors of each instruction. To capture cross-stream dependency edges,
-  // we use the predecessor map to insert each operand as well as its transitive
-  // closure of dependencies.
-
-  // Compute the set of all instructions we will want to set reachability on
+  // The predecessor map is built incrementally, in reverse thunk launch
+  // order. We record the most-recently seen instructions per stream in
+  // 'earliest_instruction_per_stream'. This lets us quickly determine the
+  // same-stream predecessors of each instruction.
+
+  // Compute the set of all instructions we will want to set reachability on.
   auto predecessor_map = MakeUnique<HloComputation::ReachabilityMap>(
       module->entry_computation()->MakeInstructionPostOrder());
 
-  std::vector<std::vector<const HloInstruction*>> instructions_per_stream(
-      stream_assignment.StreamCount());
+  // The most recently visited instruction per stream.
+  std::vector<const HloInstruction*> earliest_instruction_per_stream(
+      stream_assignment.StreamCount(), nullptr);
 
-  for (const HloInstruction* hlo : thunk_launch_order) {
+  for (auto it = thunk_launch_order.rbegin(); it != thunk_launch_order.rend();
+       ++it) {
+    const HloInstruction* hlo = *it;
+    predecessor_map->SetReachable(hlo, hlo);
     if (stream_assignment.HasStreamAssigned(*hlo)) {
       // All ops already queued on the same instruction stream, and their
       // transitive predecessors, are predecessors. Since the relation is
       // transitive, we just set the transitive closure of the previous op.
       const int stream_no = stream_assignment.StreamNumberForHlo(*hlo);
-      std::vector<const HloInstruction*>* instructions =
-          &instructions_per_stream[stream_no];
-      if (!instructions->empty()) {
-        const HloInstruction* back = instructions->back();
-        predecessor_map->SetReachableAndTransitiveClosure(hlo, back);
+      if (earliest_instruction_per_stream[stream_no] != nullptr) {
+        // Because we are iterating in reverse order, 'hlo' precedes the
+        // last visited instruction on this stream.
+        predecessor_map->SetReachableAndTransitiveClosure(
+            hlo, earliest_instruction_per_stream[stream_no]);
       }
-      // All operands and their transitive predecessors are predecessors. Each
-      // operand must already exist in 'predecessor_map', since we're iterating
-      // in thunk launch order.
       for (const HloInstruction* operand : hlo->operands()) {
-        predecessor_map->SetReachableAndTransitiveClosure(hlo, operand);
+        predecessor_map->SetReachableAndTransitiveClosure(operand, hlo);
+      }
+      for (const HloInstruction* pred : hlo->control_predecessors()) {
+        predecessor_map->SetReachableAndTransitiveClosure(pred, hlo);
       }
-      instructions->push_back(hlo);
+      earliest_instruction_per_stream[stream_no] = hlo;
     } else {
       // Only parameters and constants don't have an assigned stream, since they
       // don't require a thunk. These ops don't have any predecessors.
@@ -108,12 +110,11 @@ GpuHloOrdering::GpuHloOrdering(
       CHECK_EQ(hlo->operand_count(), 0);
     }
   }
-  strict_predecessors_.emplace(module->entry_computation(),
-                               std::move(predecessor_map));
+  predecessors_.emplace(module->entry_computation(),
+                        std::move(predecessor_map));
 
-  // The ordering of instructions in subcomputations is based solely on data
-  // dependencies. I.e. the strict predecessors of each subcomputation
-  // instruction is its transitive operands.
+  // The ordering of instructions in subcomputations is based solely on control
+  // and data dependencies.
   //
   // TODO(toddw): Each subcomputation is actually emitted as a function in DFS
   // postorder, so we can do better and establish the total order here. We don't
@@ -121,8 +122,8 @@ GpuHloOrdering::GpuHloOrdering(
   // by IrEmitterNested. And mismatched ordering bugs would be hard to find.
   for (auto& computation : module->computations()) {
     if (computation.get() != module->entry_computation()) {
-      strict_predecessors_.emplace(computation.get(),
-                                   computation->ComputeTransitiveOperands());
+      predecessors_.emplace(computation.get(),
+                            computation->ComputeReachability());
     }
   }
 }
diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
index 5065e7aedd..02da005f6f 100644
--- a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
+++ b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
@@ -46,10 +46,9 @@ namespace {
 
 // Returns whether the two HLOs can run concurrently, i.e., neither is a
 // transitive consumer of the other.
-bool CanRunConcurrently(
-    const HloInstruction& a, const HloInstruction& b,
-    const HloComputation::ReachabilityMap& transitive_operands) {
-  return !transitive_operands.IsConnected(&a, &b);
+bool CanRunConcurrently(const HloInstruction& a, const HloInstruction& b,
+                        const HloComputation::ReachabilityMap& reachability) {
+  return !reachability.IsConnected(&a, &b);
 }
 
 // Returns which existing stream to assign to `hlo`, or -1 if a stream is not
@@ -58,7 +57,7 @@ bool CanRunConcurrently(
 // are topologically before `hlo`.
 int ComputeStreamToAssign(
     const HloInstruction& hlo, const StreamAssignment& stream_assignment,
-    const HloComputation::ReachabilityMap& transitive_operands,
+    const HloComputation::ReachabilityMap& reachability,
     const std::vector<const HloInstruction*>& seen_gemms) {
   if (hlo.opcode() == HloOpcode::kParameter ||
       hlo.opcode() == HloOpcode::kConstant) {
@@ -96,7 +95,7 @@ int ComputeStreamToAssign(
   for (const auto* seen_gemm : seen_gemms) {
     int stream_no = stream_assignment.StreamNumberForHlo(*seen_gemm);
     if (!forbidden_stream_numbers.count(stream_no) &&
-        CanRunConcurrently(*seen_gemm, hlo, transitive_operands)) {
+        CanRunConcurrently(*seen_gemm, hlo, reachability)) {
       forbidden_stream_numbers.insert(stream_no);
     }
   }
@@ -115,12 +114,12 @@ int ComputeStreamToAssign(
 std::unique_ptr<StreamAssignment> AssignStreams(const HloModule& module) {
   auto stream_assignment = MakeUnique<StreamAssignment>();
   const HloComputation& computation = *module.entry_computation();
-  std::unique_ptr<HloComputation::ReachabilityMap> transitive_operands =
-      computation.ComputeTransitiveOperands();
+  std::unique_ptr<HloComputation::ReachabilityMap> reachability =
+      computation.ComputeReachability();
   std::vector<const HloInstruction*> seen_gemms;
   for (const auto* hlo : computation.MakeInstructionPostOrder()) {
     int stream_no = ComputeStreamToAssign(*hlo, *stream_assignment,
-                                          *transitive_operands, seen_gemms);
+                                          *reachability, seen_gemms);
     if (stream_no != -1) {
       stream_assignment->AssignStreamToHlo(hlo, stream_no);
     }
diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index ff76cc7bf6..4b33947371 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -587,14 +587,19 @@ void HloComputation::ReachabilityMap::SetReachableAndTransitiveClosure(
 }
 
 std::unique_ptr<HloComputation::ReachabilityMap>
-HloComputation::ComputeTransitiveOperands() const {
-  const auto all = MakeInstructionPostOrder();
+HloComputation::ComputeReachability() const {
+  const std::list<HloInstruction*> all = MakeInstructionPostOrder();
   auto result = MakeUnique<HloComputation::ReachabilityMap>(all);
 
-  // Fill in the dependency bit matrix
-  for (const auto* hlo : all) {
+  // Fill in the dependency bit matrix. Iterate in reverse topological order.
+  for (auto it = all.rbegin(); it != all.rend(); ++it) {
+    const HloInstruction* hlo = *it;
+    result->SetReachable(hlo, hlo);
     for (const HloInstruction* operand : hlo->operands()) {
-      result->SetReachableAndTransitiveClosure(hlo, operand);
+      result->SetReachableAndTransitiveClosure(operand, hlo);
+    }
+    for (const HloInstruction* pred : hlo->control_predecessors()) {
+      result->SetReachableAndTransitiveClosure(pred, hlo);
     }
   }
   return result;
diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h
index 39074b24e4..27d672d42b 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.h
+++ b/tensorflow/compiler/xla/service/hlo_computation.h
@@ -153,9 +153,14 @@ class HloComputation {
   // this order, definitions of values always appear before their uses.
   std::list<HloInstruction*> MakeInstructionPostOrder() const;
 
-  // Computes and returns the mapping from HLO to its transitive operands.
+  // Computes and returns the reachability between HLO instructions in the
+  // computation. The returned ReachabilityMap is constructed such that
+  // ReachabilityMap::IsReachable(a, b) returns true iff there exists a directed
+  // path (from producer to consumer) from 'a' to 'b'. Both data dependencies
+  // (operands) and control dependencies are considered for
+  // reachability. Trivially an instruction is reachable from itself.
   class ReachabilityMap;
-  std::unique_ptr<ReachabilityMap> ComputeTransitiveOperands() const;
+  std::unique_ptr<ReachabilityMap> ComputeReachability() const;
 
   int64 instruction_count() const { return instructions_.size(); }
 
@@ -328,8 +333,6 @@ class HloComputation::ReachabilityMap {
   bool IsConnected(const HloInstruction* a, const HloInstruction* b) const;
 
  private:
-  friend class HloComputation;
-
   // dense id assignment from HloInstruction* to number
   tensorflow::gtl::FlatMap<const HloInstruction*, int> ids_;
   // matrix_(a,b) is true iff b is reachable from a
diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc
index 057d1ce09b..c912e97f42 100644
--- a/tensorflow/compiler/xla/service/hlo_computation_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc
@@ -352,6 +352,81 @@ TEST_F(HloComputationTest, CloneWithControlDependency) {
   EXPECT_THAT(successors, ::testing::ElementsAre(cloned_add));
 }
 
+TEST_F(HloComputationTest, Reachability) {
+  // Test reachability of a non-trivial computation:
+  //
+  // const1    const2
+  //    |         |
+  //    | +-------+
+  //    | |       |
+  //    add ..   negate
+  //     |   .     |
+  //     |   .... exp
+  //     |         |
+  //     +---+   +-+---+
+  //         |   |     |
+  //       multiply   copy
+  //
+  // There is a control dependency from 'add' to 'exp'.
+  auto builder = HloComputation::Builder(TestName());
+  auto constant1 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.0f)));
+  auto constant2 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.0f)));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      r0f32_, HloOpcode::kAdd, constant1, constant2));
+  auto negate = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant2));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, negate));
+  auto mul = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32_, HloOpcode::kMultiply, add, exp));
+  auto copy = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kCopy, exp));
+
+  auto computation = builder.Build(/*root_instruction=*/mul);
+
+  TF_CHECK_OK(add->AddControlDependencyTo(exp));
+  auto reachability = computation->ComputeReachability();
+
+  EXPECT_TRUE(reachability->IsReachable(constant1, constant1));
+  EXPECT_FALSE(reachability->IsReachable(constant1, constant2));
+  EXPECT_TRUE(reachability->IsReachable(constant1, add));
+  EXPECT_FALSE(reachability->IsReachable(constant1, negate));
+  EXPECT_TRUE(reachability->IsReachable(constant1, exp));
+  EXPECT_TRUE(reachability->IsReachable(constant1, mul));
+  EXPECT_TRUE(reachability->IsReachable(constant1, copy));
+
+  EXPECT_FALSE(reachability->IsReachable(constant2, constant1));
+  EXPECT_TRUE(reachability->IsReachable(constant2, constant2));
+  EXPECT_TRUE(reachability->IsReachable(constant2, add));
+  EXPECT_TRUE(reachability->IsReachable(constant2, negate));
+  EXPECT_TRUE(reachability->IsReachable(constant2, exp));
+  EXPECT_TRUE(reachability->IsReachable(constant2, mul));
+  EXPECT_TRUE(reachability->IsReachable(constant2, copy));
+
+  EXPECT_FALSE(reachability->IsReachable(exp, constant1));
+  EXPECT_FALSE(reachability->IsReachable(exp, constant2));
+  EXPECT_FALSE(reachability->IsReachable(exp, add));
+  EXPECT_FALSE(reachability->IsReachable(exp, negate));
+  EXPECT_TRUE(reachability->IsReachable(exp, exp));
+  EXPECT_TRUE(reachability->IsReachable(exp, mul));
+  EXPECT_TRUE(reachability->IsReachable(exp, copy));
+
+  EXPECT_FALSE(reachability->IsReachable(mul, constant1));
+  EXPECT_FALSE(reachability->IsReachable(mul, constant2));
+  EXPECT_FALSE(reachability->IsReachable(mul, add));
+  EXPECT_FALSE(reachability->IsReachable(mul, negate));
+  EXPECT_FALSE(reachability->IsReachable(mul, exp));
+  EXPECT_TRUE(reachability->IsReachable(mul, mul));
+  EXPECT_FALSE(reachability->IsReachable(mul, copy));
+
+  EXPECT_TRUE(reachability->IsConnected(constant1, copy));
+  EXPECT_TRUE(reachability->IsConnected(copy, constant1));
+  EXPECT_FALSE(reachability->IsConnected(negate, add));
+  EXPECT_FALSE(reachability->IsConnected(add, negate));
+}
+
 }  // namespace
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc
index 32a2abed92..7230682d0b 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering.cc
@@ -152,7 +152,7 @@ bool PredecessorHloOrdering::ExecutesBeforeInSameComputation(
   CHECK_EQ(a->parent(), b->parent());
 
   // 'a' executes before 'b' if 'a' is in the strict predecessor set of 'b'.
-  return strict_predecessors_.at(b->parent())->IsReachable(b, a);
+  return a != b && predecessors_.at(a->parent())->IsReachable(a, b);
 }
 
 string PredecessorHloOrdering::ToStringHelper(const string& name) const {
@@ -164,10 +164,10 @@ string PredecessorHloOrdering::ToStringHelper(const string& name) const {
     const auto all = computation->MakeInstructionPostOrder();
     for (auto instruction : all) {
       pieces.push_back(tensorflow::strings::Printf(
-          "  %s strict predecessors:", instruction->name().c_str()));
+          "  %s predecessors:", instruction->name().c_str()));
       for (auto predecessor : all) {
-        if (strict_predecessors_.at(computation.get())
-                ->IsReachable(instruction, predecessor)) {
+        if (predecessors_.at(computation.get())
+                ->IsReachable(predecessor, instruction)) {
           pieces.push_back(
               tensorflow::strings::Printf("  %s", predecessor->name().c_str()));
         }
@@ -183,8 +183,8 @@ DependencyHloOrdering::DependencyHloOrdering(const HloModule* module)
   // ordering based on dependencies. ExecutesBefore will return true iff there
   // exists a path in the HLO computation graph from 'a' to 'b'.
   for (auto& computation : module->computations()) {
-    strict_predecessors_.emplace(computation.get(),
-                                 computation->ComputeTransitiveOperands());
+    predecessors_.emplace(computation.get(),
+                          computation->ComputeReachability());
   }
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.h b/tensorflow/compiler/xla/service/hlo_ordering.h
index ff84f887f7..b4cd78dd2e 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.h
+++ b/tensorflow/compiler/xla/service/hlo_ordering.h
@@ -68,8 +68,8 @@ class HloOrdering {
   std::unique_ptr<CallGraph> call_graph_;
 };
 
-// Base class for partial orderings implemented by a map of strict predecessors
-// for each instruction. Subclasses should fill in strict_predecessors_.
+// Base class for partial orderings implemented by a map of predecessors for
+// each instruction. Subclasses should fill in predecessors_.
 class PredecessorHloOrdering : public HloOrdering {
  public:
   ~PredecessorHloOrdering() override = default;
@@ -89,13 +89,12 @@ class PredecessorHloOrdering : public HloOrdering {
                                        const HloInstruction* b) const override;
 
   // For each computation in the module, this is the set of the instruction's
-  // strict predecessors. An instruction is not an element of its own strict
-  // predecessor set.
+  // predecessors. An instruction is an element of its own predecessor set.
   //
   // Subclasses should fill this in to define the desired ordering.
   tensorflow::gtl::FlatMap<const HloComputation*,
                            std::unique_ptr<HloComputation::ReachabilityMap>>
-      strict_predecessors_;
+      predecessors_;
 };
 
 // An HLO ordering based on data dependencies in the HLO graph. In this partial
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 53b2c2a6e3..121d163c9f 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -159,7 +159,7 @@ bool InstructionFusion::CanFuseOnAllPaths(
     if (!producer->IsFusable() || !consumer->IsFusable()) {
       return false;
     }
-    // We do an upword walk of the graph from consumer towards all paths which
+    // We do an upward walk of the graph from consumer towards all paths which
     // lead to producer to find any unfusable paths.
     for (int64 i = 0, e = consumer->operand_count(); i < e; ++i) {
       auto* consumer_operand = consumer->mutable_operand(i);
@@ -169,7 +169,7 @@ bool InstructionFusion::CanFuseOnAllPaths(
         if (!ShouldFuse(consumer, i)) {
           return false;
         }
-      } else if (reachability_map.IsReachable(consumer_operand, producer)) {
+      } else if (reachability_map.IsReachable(producer, consumer_operand)) {
         // The reachability map told us that consumer_operand is a node on the
         // path to producer. We need to further investigate from
         // consumer_operand.
@@ -230,7 +230,7 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
     }
 
     DoNotFuseSet do_not_fuse;
-    auto transitive_operands = computation->ComputeTransitiveOperands();
+    auto reachability = computation->ComputeReachability();
 
     auto cheap_to_duplicate = [](HloInstruction* producer) {
       if (producer->opcode() == HloOpcode::kBroadcast) {
@@ -251,7 +251,7 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
         if (cheap_to_duplicate(producer)) {
           continue;
         }
-        if (CanFuseOnAllPaths(*transitive_operands, producer, consumer,
+        if (CanFuseOnAllPaths(*reachability, producer, consumer,
                               &do_not_fuse)) {
           CHECK_EQ(do_not_fuse.count(producer), 0);
         } else {
-- 
GitLab


From 4fb73c7e832cf78f90c2d76d1b6b7d7c8894631d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 22:00:29 -0700
Subject: [PATCH 0539/1375] Add tensor sizes to Grappler's VirtualScheduler's
 RunMetadata output.

PiperOrigin-RevId: 160369034
---
 .../core/grappler/costs/virtual_scheduler.cc     |  8 ++++++++
 .../grappler/costs/virtual_scheduler_test.cc     | 16 ++++++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 7648a3965a..3be4f917bf 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -631,6 +631,14 @@ Costs VirtualScheduler::Summary(RunMetadata* metadata) {
       for (const auto& node_def : device.second.nodes_executed) {
         const NodeState& nodestate = node_map_.at(node_def);
         NodeExecStats* node_stats = device_stepstats->add_node_stats();
+        for (int slot = 0; slot < nodestate.output_properties.size(); slot++) {
+          const auto& properties = nodestate.output_properties[slot];
+          NodeOutput* no = node_stats->add_output();
+          no->set_slot(slot);
+          TensorDescription* tensor_descr = no->mutable_tensor_description();
+          tensor_descr->set_dtype(properties.dtype());
+          *tensor_descr->mutable_shape() = properties.shape();
+        }
         node_stats->set_timeline_label(node_def->op());
         node_stats->set_node_name(node_def->name());
         node_stats->set_op_start_rel_micros(0);
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index f6595fcbb3..dc54b8d0d1 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -578,6 +578,22 @@ TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
       int64 start = stats.all_start_micros();
       int64 end = start + stats.all_end_rel_micros();
       start_end_times[stats.node_name()] = std::pair<int64, int64>(start, end);
+
+      // Make sure that the output properties are correct for
+      // MatMul and RandomUniform operations.
+      // We only check for dtype, and shape (excluding alloc)
+      // since alloc is not set by the virtual scheduler.
+      if (stats.timeline_label() == "MatMul" ||
+          stats.timeline_label() == "RandomUniform") {
+        EXPECT_EQ(1, stats.output().size());
+        for (const auto& output : stats.output()) {
+          EXPECT_EQ(DT_FLOAT, output.tensor_description().dtype());
+          EXPECT_EQ(2, output.tensor_description().shape().dim().size());
+          for (const auto& dim : output.tensor_description().shape().dim()) {
+            EXPECT_EQ(3200, dim.size());
+          }
+        }
+      }
     }
   }
 
-- 
GitLab


From 5e1576d73bc40bbf103c13b2fda691924638289c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Jun 2017 22:27:27 -0700
Subject: [PATCH 0540/1375] This change fixes a bug that
 BaseEstimator.evaluate() ignores a function parameter `name` if SKCompat is
 used internally.

PiperOrigin-RevId: 160370884
---
 .../contrib/learn/python/learn/estimators/estimator.py      | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index b87b75d5c4..c3cf5662c2 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -530,7 +530,7 @@ class BaseEstimator(
     """
     _verify_input_args(x, y, input_fn, feed_fn, batch_size)
     if x is not None:
-      return SKCompat(self).score(x, y, batch_size, steps, metrics)
+      return SKCompat(self).score(x, y, batch_size, steps, metrics, name)
 
     if metrics is not None and not isinstance(metrics, dict):
       raise ValueError('Metrics argument should be None or dict. '
@@ -1353,7 +1353,7 @@ class SKCompat(sklearn.BaseEstimator):
                         monitors=all_monitors)
     return self
 
-  def score(self, x, y, batch_size=128, steps=None, metrics=None):
+  def score(self, x, y, batch_size=128, steps=None, metrics=None, name=None):
     input_fn, feed_fn = _get_input_fn(x, y, input_fn=None,
                                       feed_fn=None, batch_size=batch_size,
                                       shuffle=False, epochs=1)
@@ -1365,7 +1365,7 @@ class SKCompat(sklearn.BaseEstimator):
         feed_fn=feed_fn,
         steps=steps,
         metrics=metrics,
-        name='score')
+        name=name)
     if eval_results is not None:
       eval_results.update({'global_step': global_step})
     return eval_results
-- 
GitLab


From 0d11e9d5b0dec2d59d481231328656dfd6dbf46e Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Tue, 27 Jun 2017 22:52:13 -0700
Subject: [PATCH 0541/1375] [XLA] Fix F16 implementation in Literal class.

PiperOrigin-RevId: 160372343
---
 tensorflow/compiler/xla/literal_util.cc      | 2 +-
 tensorflow/compiler/xla/literal_util_test.cc | 6 +-----
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index ec643e037f..6760f72e55 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -1003,7 +1003,7 @@ tensorflow::gtl::MutableArraySlice<half> Literal::GetMutableArraySlice<half>() {
   //        support in protobuf
   auto values = mutable_f16s();
   return tensorflow::gtl::MutableArraySlice<half>(
-      reinterpret_cast<half*>(&(*values)[0]), values->size() / sizeof(half));
+      reinterpret_cast<half*>(&(*values)[0]), values->size());
 }
 
 template <>
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 5976e83950..6c3648e1e0 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -979,9 +979,7 @@ TEST_F(LiteralUtilTest, CopyFromProto_f16) {
   half h1(1.0f);
   half h2(2.0f);
 
-  const char half_vals[8] = {
-    0x00, 0x3C, 0x00, 0x40, 0x00, 0x40, 0x00, 0x3C
-  };
+  const char half_vals[8] = {0x00, 0x3C, 0x00, 0x40, 0x00, 0x40, 0x00, 0x3C};
   LiteralProto p;
   p.mutable_shape()->set_element_type(F16);
   p.mutable_shape()->clear_dimensions();
@@ -989,7 +987,6 @@ TEST_F(LiteralUtilTest, CopyFromProto_f16) {
   p.clear_f16s();
   p.set_f16s(half_vals, 8);
 
-
   Literal literal(p);
   ASSERT_EQ(4, literal.f16s_size());
   ASSERT_EQ(h1, literal.f16s(0));
@@ -1005,6 +1002,5 @@ TEST_F(LiteralUtilTest, CopyFromProto_f16) {
   ASSERT_EQ(h1, r[3]);
 }
 
-
 }  // namespace
 }  // namespace xla
-- 
GitLab


From cc4af2b6184421dff52ec9c5c0a1ad198b732d9f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 01:31:47 -0700
Subject: [PATCH 0542/1375] Support only providing save_summaries_secs to
 MonitoredTrainingSession.

PiperOrigin-RevId: 160382016
---
 .../python/training/monitored_session.py      | 20 +++++++++++++++----
 .../tools/api/golden/tensorflow.train.pbtxt   |  2 +-
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index ff77470a82..e7b296374a 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -44,6 +44,10 @@ from tensorflow.python.training import session_run_hook
 _PREEMPTION_ERRORS = (errors.AbortedError, errors.UnavailableError)
 
 
+# Value that indicates no value was provided.
+USE_DEFAULT = object()
+
+
 # TODO(touts): Share that with the Supervisor.
 class Scaffold(object):
   """Structure to create or gather pieces commonly needed to train a model.
@@ -269,8 +273,8 @@ def MonitoredTrainingSession(master='',  # pylint: disable=invalid-name
                              hooks=None,
                              chief_only_hooks=None,
                              save_checkpoint_secs=600,
-                             save_summaries_steps=100,
-                             save_summaries_secs=None,
+                             save_summaries_steps=USE_DEFAULT,
+                             save_summaries_secs=USE_DEFAULT,
                              config=None,
                              stop_grace_period_secs=120,
                              log_step_count_steps=100):
@@ -301,11 +305,11 @@ def MonitoredTrainingSession(master='',  # pylint: disable=invalid-name
     save_summaries_steps: The frequency, in number of global steps, that the
       summaries are written to disk using a default summary saver. If both
       `save_summaries_steps` and `save_summaries_secs` are set to `None`, then
-      the default summary saver isn't used.
+      the default summary saver isn't used. Default 100.
     save_summaries_secs: The frequency, in secs, that the summaries are written
       to disk using a default summary saver.  If both `save_summaries_steps` and
       `save_summaries_secs` are set to `None`, then the default summary saver
-      isn't used.
+      isn't used. Default not enabled.
     config: an instance of `tf.ConfigProto` proto used to configure the session.
       It's the `config` argument of constructor of `tf.Session`.
     stop_grace_period_secs: Number of seconds given to threads to stop after
@@ -316,6 +320,14 @@ def MonitoredTrainingSession(master='',  # pylint: disable=invalid-name
   Returns:
     A `MonitoredSession` object.
   """
+  if save_summaries_steps == USE_DEFAULT and save_summaries_secs == USE_DEFAULT:
+    save_summaries_steps = 100
+    save_summaries_secs = None
+  elif save_summaries_secs == USE_DEFAULT:
+    save_summaries_secs = None
+  elif save_summaries_steps == USE_DEFAULT:
+    save_summaries_steps = None
+
   scaffold = scaffold or Scaffold()
   if not is_chief:
     session_creator = WorkerSessionCreator(
diff --git a/tensorflow/tools/api/golden/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.pbtxt
index c295537965..89c299ae99 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.pbtxt
@@ -230,7 +230,7 @@ tf_module {
   }
   member_method {
     name: "MonitoredTrainingSession"
-    argspec: "args=[\'master\', \'is_chief\', \'checkpoint_dir\', \'scaffold\', \'hooks\', \'chief_only_hooks\', \'save_checkpoint_secs\', \'save_summaries_steps\', \'save_summaries_secs\', \'config\', \'stop_grace_period_secs\', \'log_step_count_steps\'], varargs=None, keywords=None, defaults=[\'\', \'True\', \'None\', \'None\', \'None\', \'None\', \'600\', \'100\', \'None\', \'None\', \'120\', \'100\'], "
+    argspec: "args=[\'master\', \'is_chief\', \'checkpoint_dir\', \'scaffold\', \'hooks\', \'chief_only_hooks\', \'save_checkpoint_secs\', \'save_summaries_steps\', \'save_summaries_secs\', \'config\', \'stop_grace_period_secs\', \'log_step_count_steps\'], varargs=None, keywords=None, defaults=[\'\', \'True\', \'None\', \'None\', \'None\', \'None\', \'600\', \'<object object instance>\', \'<object object instance>\', \'None\', \'120\', \'100\'], "
   }
   member_method {
     name: "NewCheckpointReader"
-- 
GitLab


From d22c05a81a26e930655c9c5b1be3e0c235edfdcc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 03:33:40 -0700
Subject: [PATCH 0543/1375] BUILD cleanup in tensorflow/examples/...

PiperOrigin-RevId: 160390702
---
 tensorflow/examples/adding_an_op/BUILD        | 16 ++---
 .../examples/how_tos/reading_data/BUILD       |  1 -
 tensorflow/examples/learn/BUILD               | 63 ++++---------------
 3 files changed, 19 insertions(+), 61 deletions(-)

diff --git a/tensorflow/examples/adding_an_op/BUILD b/tensorflow/examples/adding_an_op/BUILD
index ffaf9349d2..f952cab8b5 100644
--- a/tensorflow/examples/adding_an_op/BUILD
+++ b/tensorflow/examples/adding_an_op/BUILD
@@ -26,6 +26,7 @@ py_library(
     srcs = ["zero_out_op_1.py"],
     data = [":zero_out_op_kernel_1.so"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 tf_custom_op_library(
@@ -38,6 +39,7 @@ py_library(
     srcs = ["zero_out_op_2.py"],
     data = [":zero_out_op_kernel_2.so"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 tf_custom_op_library(
@@ -50,6 +52,7 @@ py_library(
     srcs = ["zero_out_op_3.py"],
     data = [":zero_out_op_kernel_3.so"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_library(
@@ -57,8 +60,6 @@ py_library(
     srcs = ["zero_out_grad_2.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":zero_out_op_2",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:sparse_ops",
@@ -73,7 +74,6 @@ py_test(
     deps = [
         ":zero_out_op_1",
         "//tensorflow:tensorflow_py",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -97,7 +97,6 @@ py_test(
     deps = [
         ":zero_out_op_3",
         "//tensorflow:tensorflow_py",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -112,6 +111,7 @@ py_library(
     srcs = ["cuda_op.py"],
     data = [":cuda_op_kernel.so"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_test(
@@ -131,17 +131,13 @@ py_test(
     size = "small",
     srcs = ["fact_test.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 cc_binary(
     name = "attr_examples",
     srcs = ["attr_examples.cc"],
-    deps = [
-        "//tensorflow/core",
-    ],
+    deps = ["//tensorflow/core"],
 )
 
 filegroup(
diff --git a/tensorflow/examples/how_tos/reading_data/BUILD b/tensorflow/examples/how_tos/reading_data/BUILD
index 5f87ed002c..4a43585d53 100644
--- a/tensorflow/examples/how_tos/reading_data/BUILD
+++ b/tensorflow/examples/how_tos/reading_data/BUILD
@@ -14,7 +14,6 @@ py_binary(
     deps = [
         "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/learn/python/learn/datasets",
-        "//tensorflow/examples/tutorials/mnist:input_data",
     ],
 )
 
diff --git a/tensorflow/examples/learn/BUILD b/tensorflow/examples/learn/BUILD
index 91fc105f27..7371e96560 100644
--- a/tensorflow/examples/learn/BUILD
+++ b/tensorflow/examples/learn/BUILD
@@ -13,10 +13,7 @@ py_binary(
     name = "boston",
     srcs = ["boston.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
@@ -25,7 +22,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
@@ -34,60 +30,45 @@ py_binary(
     name = "iris",
     srcs = ["iris.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
     name = "iris_custom_decay_dnn",
     srcs = ["iris_custom_decay_dnn.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
     name = "iris_custom_model",
     srcs = ["iris_custom_model.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/learn",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
     name = "iris_run_config",
     srcs = ["iris_run_config.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
     name = "iris_with_pipeline",
     srcs = ["iris_with_pipeline.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
     name = "random_forest_mnist",
-    srcs = [
-        "random_forest_mnist.py",
-    ],
+    srcs = ["random_forest_mnist.py"],
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
         "//tensorflow/examples/tutorials/mnist:input_data",
+        "//tensorflow/python:platform",
     ],
 )
 
@@ -95,11 +76,7 @@ py_binary(
     name = "resnet",
     srcs = ["resnet.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/learn",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
@@ -108,7 +85,7 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
+        "//tensorflow/contrib/layers:layers_py",
         "//third_party/py/numpy",
     ],
 )
@@ -119,7 +96,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
@@ -130,7 +106,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
@@ -141,20 +116,15 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
 
 py_binary(
     name = "wide_n_deep_tutorial",
-    srcs = [
-        "wide_n_deep_tutorial.py",
-    ],
+    srcs = ["wide_n_deep_tutorial.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 py_binary(
@@ -163,9 +133,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/learn",
-        "//tensorflow/examples/tutorials/mnist:input_data",
         "//third_party/py/numpy",
     ],
 )
@@ -174,11 +141,7 @@ py_binary(
     name = "multiple_gpu",
     srcs = ["multiple_gpu.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/learn",
-    ],
+    deps = ["//tensorflow:tensorflow_py"],
 )
 
 sh_test(
-- 
GitLab


From ab4f01025175f98970e0d391ef1c45e818ac443d Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 28 Jun 2017 05:22:43 -0700
Subject: [PATCH 0544/1375] In `mnist_deep.py` Write out the graph for
 tensorboard.

Add a screenshot of the tensorboard diagram to the tutorial page.

Fixes: #3044
PiperOrigin-RevId: 160397248
---
 tensorflow/docs_src/get_started/mnist/pros.md     | 6 ++++++
 tensorflow/examples/tutorials/mnist/mnist_deep.py | 6 ++++++
 2 files changed, 12 insertions(+)

diff --git a/tensorflow/docs_src/get_started/mnist/pros.md b/tensorflow/docs_src/get_started/mnist/pros.md
index d50e874d52..4933dd28cd 100644
--- a/tensorflow/docs_src/get_started/mnist/pros.md
+++ b/tensorflow/docs_src/get_started/mnist/pros.md
@@ -250,6 +250,12 @@ section, we'll fix that, jumping from a very simple model to something
 moderately sophisticated: a small convolutional neural network. This will get us
 to around 99.2% accuracy -- not state of the art, but respectable.
 
+Here is a diagram, created with TensorBoard, of the model we will build:
+
+<div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img src="https://www.tensorflow.org/images/mnist_deep.png">
+</div>
+
 ### Weight Initialization
 
 To create this model, we're going to need to create a lot of weights and biases.
diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py
index 4df925c1f3..4b5b50400a 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_deep.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py
@@ -28,6 +28,7 @@ from __future__ import print_function
 
 import argparse
 import sys
+import tempfile
 
 from tensorflow.examples.tutorials.mnist import input_data
 
@@ -148,6 +149,11 @@ def main(_):
     correct_prediction = tf.cast(correct_prediction, tf.float32)
   accuracy = tf.reduce_mean(correct_prediction)
 
+  graph_location = tempfile.mkdtemp()
+  print('Saving graph to: %s' % graph_location)
+  train_writer = tf.summary.FileWriter(graph_location)
+  train_writer.add_graph(tf.get_default_graph())
+
   with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
     for i in range(20000):
-- 
GitLab


From 441fb052d7e662565662ab56197b0459638d9398 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 28 Jun 2017 05:25:08 -0700
Subject: [PATCH 0545/1375] Minor changes to tfdbg docs.

Update the fix for the `debug_mnist` example to use `tf.nn.sigmoid_cross_entropy_with_logits`.

PiperOrigin-RevId: 160397398
---
 .../docs_src/programmers_guide/debugger.md    | 45 ++++++++++---------
 .../python/debug/examples/debug_mnist.py      | 12 ++---
 2 files changed, 32 insertions(+), 25 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index c369ff1737..710f19b424 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -1,6 +1,6 @@
 # Debugging TensorFlow Programs
 
-[comment]: TODO(barryr): Links to and from sections on "Graphs" & "Monitoring Learning".
+<!-- [comment]: TODO(barryr): Links to and from sections on "Graphs" & "Monitoring Learning". -->
 
 [TOC]
 
@@ -84,7 +84,7 @@ we ship it with the
 @{$python/tfdbg#Classes_for_debug_dump_data_and_directories$`debug_data`}
 module.
 
-TIP: You can also write your own custom filters. See
+Note: You can also write your own custom filters. See
 the @{tfdbg.DebugDumpDir.find$API documentation}
 of `DebugDumpDir.find()` for additional information.
 
@@ -103,10 +103,15 @@ dictionaries displayed on the screen.
 
 ![tfdbg run-start UI](https://www.tensorflow.org/images/tfdbg_screenshot_run_start.png)
 
-This is what we refer to as the *run-start CLI*. If the screen size is
-too small to display the content of the message in its entirety, you can resize
-it or use the **PageUp** / **PageDown** / **Home** / **End** keys to navigate
-the screen output.
+This is what we refer to as the *run-start CLI*. It lists the feeds and fetches
+to the current `Session.run` call, before executing anything.
+
+If the screen size is too small to display the content of the message in its
+entirety, you can resize it.
+
+Use the **PageUp** / **PageDown** / **Home** / **End** keys to navigate the
+screen output. On most keyboards lacking those keys **Fn + Up** /
+**Fn + Down** / **Fn + Right** / **Fn + Left** will work.
 
 Enter the `run` command (or just `r`) at the command prompt:
 
@@ -168,7 +173,7 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | **`run`** | | **Proceed to the next Session.run()** | `run` |
 | | `-n` | Execute through the next `Session.run` without debugging, and drop to CLI right before the run after that. | `run -n` |
 | | `-t <T>` | Execute `Session.run` `T - 1` times without debugging, followed by a run with debugging. Then drop to CLI right after the debugged run. | `run -t 10` |
-| | `-f <filter_name>` | Continue executing `Session.run` until any intermediate tensors passes the specified Tensor filter (causes the filter to return `True`). | `run -f has_inf_or_nan` |
+| | `-f <filter_name>` | Continue executing `Session.run` until any intermediate tensor triggers the specified Tensor filter (causes the filter to return `True`). | `run -f has_inf_or_nan` |
 | | `--node_name_filter <pattern>` | Execute the next `Session.run`, watching only nodes with names matching the given regular-expression pattern. | `run --node_name_filter softmax.*` |
 | | `--op_type_filter <pattern>` | Execute the next `Session.run`, watching only nodes with op types matching the given regular-expression pattern. | `run --op_type_filter Variable.*` |
 | | `--tensor_dtype_filter <pattern>` | Execute the next `Session.run`, dumping only Tensors with data types (`dtype`s) matching the given regular-expression pattern. | `run --tensor_dtype_filter int.*` |
@@ -238,11 +243,13 @@ tfdbg> run -f has_inf_or_nan
 > use "run -f" to have tfdbg run until any tensor triggers that filter (cause
 > the filter to return True).
 >
-> ```
-> # In python code:
+> ``` python
 > sess.add_tensor_filter('my_filter', my_filter_callable)
+> ```
 >
-> # Run at tfdbg run-start prompt:
+> Then at the tfdbg run-start prompt run until your filter is triggered:
+>
+> ```
 > tfdbg> run -f my_filter
 > ```
 
@@ -252,7 +259,7 @@ for more information on the expected signature and return value of the predicate
 
 ![tfdbg run-end UI: infs and nans](https://www.tensorflow.org/images/tfdbg_screenshot_run_end_inf_nan.png)
 
-As the screen display indicates, the `has_inf_or_nan` filter is first passed
+As the screen display indicates on the first line, the `has_inf_or_nan` filter is first triggered
 during the fourth `Session.run()` call: an
 [Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer)
 forward-backward training pass on the graph. In this run, 36 (out of the total
@@ -341,13 +348,13 @@ of a `ps` command.
 To fix the problem, edit `debug_mnist.py`, changing the original line:
 
 ```python
-diff = y_ * tf.log(y)
+diff = -(y_ * tf.log(y))
 ```
 
-to the following:
+to the built-in, numerically-stable implementation of softmax cross-entropy:
 
 ```python
-diff = y_ * tf.log(tf.clip_by_value(y, 1e-8, 1.0))
+diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=logits)
 ```
 
 Rerun with the `--debug` flag as follows:
@@ -517,8 +524,8 @@ python -m tensorflow.python.debug.cli.offline_analyzer \
 
 The `Session` wrapper `DumpingDebugWrapperSession` offers an easier and more
 flexible way to generate file-system dumps that can be analyzed offline.
-To use it, simply call the `tf_debug.DumpingDebugWrapperSession` method in the
-program being debugged. For example:
+To use it, simply wrap your session in a `tf_debug.DumpingDebugWrapperSession`.
+For example:
 
 ```python
 # Let your BUILD target depend on "//tensorflow/python/debug:debug_py
@@ -530,11 +537,9 @@ sess = tf_debug.DumpingDebugWrapperSession(
     sess, "/shared/storage/location/tfdbg_dumps_1/", watch_fn=my_watch_fn)
 ```
 
-`watch_fn=my_watch_fn` is a `Callable` that allows you to configure what
+The `watch_fn` argument accepts a `Callable` that allows you to configure what
 `tensor`s to watch on different `Session.run()` calls, as a function of the
-`fetches` and `feed_dict` to the `run()` call and other states. See
-@{tfdbg.DumpingDebugWrapperSession.__init__$the API doc of DumpingDebugWrapperSession}
-for more details.
+`fetches` and `feed_dict` to the `run()` call and other states.
 
 ### C++ and other languages
 
diff --git a/tensorflow/python/debug/examples/debug_mnist.py b/tensorflow/python/debug/examples/debug_mnist.py
index 73d398c086..96fca9f6f3 100644
--- a/tensorflow/python/debug/examples/debug_mnist.py
+++ b/tensorflow/python/debug/examples/debug_mnist.py
@@ -90,7 +90,8 @@ def main(_):
       return activations
 
   hidden = nn_layer(x, IMAGE_SIZE**2, HIDDEN_SIZE, "hidden")
-  y = nn_layer(hidden, HIDDEN_SIZE, NUM_LABELS, "softmax", act=tf.nn.softmax)
+  logits = nn_layer(hidden, HIDDEN_SIZE, NUM_LABELS, "output", tf.identity)
+  y = tf.nn.softmax(logits)
 
   with tf.name_scope("cross_entropy"):
     # The following line is the culprit of the bad numerical values that appear
@@ -99,12 +100,13 @@ def main(_):
     # call. A multiplication of the inf values with zeros leads to nans,
     # which is first in "cross_entropy/mul:0".
     #
-    # You can use clipping to fix this issue, e.g.,
-    #   diff = y_ * tf.log(tf.clip_by_value(y, 1e-8, 1.0))
+    # You can use the built-in, numerically-stable implementation to fix this
+    # issue:
+    #   diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=logits)
 
-    diff = y_ * tf.log(y)
+    diff = -(y_ * tf.log(y))
     with tf.name_scope("total"):
-      cross_entropy = -tf.reduce_mean(diff)
+      cross_entropy = tf.reduce_mean(diff)
 
   with tf.name_scope("train"):
     train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
-- 
GitLab


From 77aa84a857a813d039948f2ad3005ed04d94839f Mon Sep 17 00:00:00 2001
From: Kongsea <kongsea@gmail.com>
Date: Wed, 28 Jun 2017 08:29:29 -0500
Subject: [PATCH 0546/1375] Fix some typos in doc strings of Conv3D of keras.
 (#11079)

* Fix some typos in Conv3D.

* Fix a typo
---
 tensorflow/contrib/keras/python/keras/layers/convolutional.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/layers/convolutional.py b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
index 9ee5aa2121..2e8ca1f7a1 100644
--- a/tensorflow/contrib/keras/python/keras/layers/convolutional.py
+++ b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
@@ -316,7 +316,7 @@ class Conv3D(tf_convolutional_layers.Conv3D, Layer):
   When using this layer as the first layer in a model,
   provide the keyword argument `input_shape`
   (tuple of integers, does not include the sample axis),
-  e.g. `input_shape=(128, 128, 128, 3)` for 128x128x128 volumes
+  e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
   with a single channel,
   in `data_format="channels_last"`.
 
@@ -324,7 +324,7 @@ class Conv3D(tf_convolutional_layers.Conv3D, Layer):
       filters: Integer, the dimensionality of the output space
           (i.e. the number output of filters in the convolution).
       kernel_size: An integer or tuple/list of 3 integers, specifying the
-          width and height of the 3D convolution window.
+          depth, height and width of the 3D convolution window.
           Can be a single integer to specify the same value for
           all spatial dimensions.
       strides: An integer or tuple/list of 3 integers,
-- 
GitLab


From 184a5fd8da87f79f46c75c716a802863aee28a02 Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Wed, 28 Jun 2017 07:07:18 -0700
Subject: [PATCH 0547/1375] Merge version_semantics.md and data_versions.md
 into version_compat.md

Do some minor cleanup in the process.

PiperOrigin-RevId: 160404421
---
 .../programmers_guide/version_compat.md       | 297 ++++++++++++++++++
 1 file changed, 297 insertions(+)
 create mode 100644 tensorflow/docs_src/programmers_guide/version_compat.md

diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/programmers_guide/version_compat.md
new file mode 100644
index 0000000000..13c410fada
--- /dev/null
+++ b/tensorflow/docs_src/programmers_guide/version_compat.md
@@ -0,0 +1,297 @@
+# TensorFlow Version Compatibility
+
+This document is for users who need backwards compatibility across different
+versions of TensorFlow (either for code or data), and for developers who want
+to modify TensorFlow while preserving compatibility.
+
+## Semantic Versioning 2.0
+
+TensorFlow follows Semantic Versioning 2.0 ([semver](http://semver.org)) for its
+public API. Each release version of TensorFlow has the form `MAJOR.MINOR.PATCH`.
+For example, TensorFlow version 1.2.3 has `MAJOR` version 1, `MINOR` version 2,
+and `PATCH` version 3. Changes to each number have the following meaning:
+
+* **MAJOR**:  Potentially backwards incompatible changes.  Code and data that
+  worked with a previous major release will not necessarily work with the new
+  release. However, in some cases existing TensorFlow graphs and checkpoints
+  may be migratable to the newer release; see
+  [Compatibility of graphs and checkpoints](#compatibility_of_graphs_and_checkpoints)
+  for details on data compatibility.
+
+* **MINOR**: Backwards compatible features, speed improvements, etc.  Code and
+  data that worked with a previous minor release *and* which depends only on the
+  public API will continue to work unchanged.  For details on what is and is
+  not the public API, see [What is covered](#what_is_covered).
+
+* **PATCH**: Backwards compatible bug fixes.
+
+For example, release 1.0.0 introduced backwards *incompatible* changes from
+release 0.12.1.  However, release 1.1.1 was backwards *compatible* with release
+1.0.0.
+
+## What is covered
+
+Only the public APIs of TensorFlow are backwards compatible across minor and
+patch versions.  The public APIs consist of
+
+* All the documented [Python](../api_docs/python) functions and classes in the
+  `tensorflow` module and its submodules, except for
+    * functions and classes in `tf.contrib`
+    * functions and classes whose names start with `_` (as these are private)
+  Note that the code in the `examples/` and `tools/` directories is not
+  reachable through the `tensorflow` Python module and is thus not covered by
+  the compatibility guarantee.
+
+  If a symbol is available through the `tensorflow` Python module or its
+  submodules, but is not documented, then it is **not** considered part of the
+  public API.
+
+* The [C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h).
+
+* The following protocol buffer files:
+    * [`attr_value`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto)
+    * [`config`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto)
+    * [`event`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/event.proto)
+    * [`graph`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto)
+    * [`op_def`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto)
+    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/reader_base.proto)
+    * [`summary`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto)
+    * [`tensor`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto)
+    * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto)
+    * [`types`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto)
+
+## What is *not* covered
+
+Some API functions are explicitly marked as "experimental" and can change in
+backward incompatible ways between minor releases. These include:
+
+*   **Experimental APIs**: The @{tf.contrib} module and its submodules in Python
+    and any functions in the C API or fields in protocol buffers that are
+    explicitly commented as being experimental.
+
+*   **Other languages**: TensorFlow APIs in languages other than Python and C,
+    such as:
+
+  - @{$cc/guide$C++} (exposed through header files in
+    [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc)).
+  - [Java](../api_docs/java/reference/org/tensorflow/package-summary),
+  - [Go](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
+
+*   **Details of composite ops:** Many public functions in Python expand to
+    several primitive ops in the graph, and these details will be part of any
+    graphs saved to disk as `GraphDef`s. These details may change for
+    minor releases. In particular, regressions tests that check for exact
+    matching between graphs are likely to break across minor releases, even
+    though the behavior of the graph should be unchanged and existing
+    checkpoints will still work.
+
+*   **Floating point numerical details:** The specific floating point values
+    computed by ops may change at any time.  Users should rely only on
+    approximate accuracy and numerical stability, not on the specific bits
+    computed. Changes to numerical formulas in minor and patch releases should
+    result in comparable or improved accuracy, with the caveat that in machine
+    learning improved accuracy of specific formulas may result in decreased
+    accuracy for the overall system.
+
+*   **Random numbers:** The specific random numbers computed by the
+    @{$python/constant_op#Random_Tensors$random ops} may change at any time.
+    Users should rely only on approximately correct distributions and
+    statistical strength, not the specific bits computed. However, we will make
+    changes to random bits rarely (or perhaps never) for patch releases.  We
+    will, of course, document all such changes.
+
+*   **Version skew in distributed Tensorflow:** Running two different versions
+    of TensorFlow in a single cluster is unsupported. There are no guarantees
+    about backwards compatibility of the wire protocol.
+
+*   **Bugs:** We reserve the right to make backwards incompatible behavior
+    (though not API) changes if the current implementation is clearly broken,
+    that is, if it contradicts the documentation or if a well-known and
+    well-defined intended behavior is not properly implemented due to a bug.
+    For example, if an optimizer claims to implement a well-known optimization
+    algorithm but does not match that algorithm due to a bug, then we will fix
+    the optimizer. Our fix may break code relying on the wrong behavior for
+    convergence. We will note such changes in the release notes.
+
+*   **Error messages:** We reserve the right to change the text of error
+    messages. In addition, the type of an error may change unless the type is
+    specified in the documentation. For example, a function documented to
+    raise an `InvalidArgument` exception will continue to
+    raise `InvalidArgument`, but the human-readable message contents can change.
+
+## Compatibility of graphs and checkpoints
+
+You'll sometimes need to preserve graphs and checkpoints.
+Graphs describe the data flow of ops to be run during training and
+inference, and checkpoints contain the saved tensor values of variables in a
+graph.
+
+Many TensorFlow users save graphs and trained models to disk for
+later evaluation or additional training, but end up running their saved graphs
+or models on a later release. In compliance with semver, any graph or checkpoint
+written out with one version of TensorFlow can be loaded and evaluated with a
+later version of TensorFlow with the same major release.  However, we will
+endeavor to preserve backwards compatibility even across major releases when
+possible, so that the serialized files are usable over long periods of time.
+
+
+Graphs are serialized via the `GraphDef` protocol buffer.  To facilitate (rare)
+backwards incompatible changes to graphs, each `GraphDef` has a version number
+separate from the TensorFlow version.  For example, `GraphDef` version 17
+deprecated the `inv` op in favor of `reciprocal`.  The semantics are:
+
+* Each version of TensorFlow supports an interval of `GraphDef` versions.  This
+  interval will be constant across patch releases, and will only grow across
+  minor releases.  Dropping support for a `GraphDef` version will only occur
+  for a major release of TensorFlow.
+
+* Newly created graphs are assigned the latest `GraphDef` version number.
+
+* If a given version of TensorFlow supports the `GraphDef` version of a graph,
+  it will load and evaluate with the same behavior as the TensorFlow version
+  used to generate it (except for floating point numerical details and random
+  numbers), regardless of the major version of TensorFlow.  In particular, all
+  checkpoint files will be compatible.
+
+* If the `GraphDef` *upper* bound is increased to X in a (minor) release, there
+  will be at least six months before the *lower* bound is increased to X.  For
+  example (we're using hypothetical version numbers here):
+    * TensorFlow 1.2 might support `GraphDef` versions 4 to 7.
+    * TensorFlow 1.3 could add `GraphDef` version 8 and support versions 4 to 8.
+    * At least six months later, TensorFlow 2.0.0 could drop support for
+      versions 4 to 7, leaving version 8 only.
+
+Finally, when support for a `GraphDef` version is dropped, we will attempt to
+provide tools for automatically converting graphs to a newer supported
+`GraphDef` version.
+
+## Graph and checkpoint compatibility when extending TensorFlow
+
+This section is relevant only when making incompatible changes to the `GraphDef`
+format, such as when adding ops, removing ops, or changing the functionality
+of existing ops.  The previous section should suffice for most users.
+
+### Backward and partial forward compatibility
+
+Our versioning scheme has three requirements:
+
+*   **Backward compatibility** to support loading graphs and checkpoints
+    created with older versions of TensorFlow.
+*   **Forward compatibility** to support scenarios where the producer of a
+    graph or checkpoint is upgraded to a newer version of TensorFlow before
+    the consumer.
+*   Enable evolving TensorFlow in incompatible ways. For example, removing Ops,
+    adding attributes, and removing attributes.
+
+Note that while the `GraphDef` version mechanism is separate from the TensorFlow
+version, backwards incompatible changes to the `GraphDef` format are still
+restricted by Semantic Versioning.  This means functionality can only be removed
+or changed between `MAJOR` versions of TensorFlow (such as `1.7` to `2.0`).
+Additionally, forward compatibility is enforced within Patch releases (`1.x.1`
+to `1.x.2` for example).
+
+To achieve backward and forward compatibility and to know when to enforce changes
+in formats, graphs and checkpoints have metadata that describes when they
+were produced. The sections below detail the TensorFlow implementation and
+guidelines for evolving `GraphDef` versions.
+
+### Independent data version schemes
+
+There are different data versions for graphs and checkpoints. The two data
+formats evolve at different rates from each other and also at different rates
+from TensorFlow. Both versioning systems are defined in
+[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h).
+Whenever a new version is added, a note is added to the header detailing what
+changed and the date.
+
+### Data, producers, and consumers
+
+We distinguish between the following kinds of data version information:
+* **producers**: binaries that produce data.  Producers have a version
+  (`producer`) and a minimum consumer version that they are compatible with
+  (`min_consumer`).
+* **consumers**: binaries that consume data.  Consumers have a version
+  (`consumer`) and a minimum producer version that they are compatible with
+  (`min_producer`).
+
+Each piece of versioned data has a [`VersionDef
+versions`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/versions.proto)
+field which records the `producer` that made the data, the `min_consumer`
+that it is compatible with, and a list of `bad_consumers` versions that are
+disallowed.
+
+By default, when a producer makes some data, the data inherits the producer's
+`producer` and `min_consumer` versions. `bad_consumers` can be set if specific
+consumer versions are known to contain bugs and must be avoided. A consumer can
+accept a piece of data if the following are all true:
+
+*   `consumer` >= data's `min_consumer`
+*   data's `producer` >= consumer's `min_producer`
+*   `consumer` not in data's `bad_consumers`
+
+Since both producers and consumers come from the same TensorFlow code base,
+[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h)
+contains a main data version which is treated as either `producer` or
+`consumer` depending on context and both `min_consumer` and `min_producer`
+(needed by producers and consumers, respectively). Specifically,
+
+*   For `GraphDef` versions, we have `TF_GRAPH_DEF_VERSION`,
+    `TF_GRAPH_DEF_VERSION_MIN_CONSUMER`, and
+    `TF_GRAPH_DEF_VERSION_MIN_PRODUCER`.
+*   For checkpoint versions, we have `TF_CHECKPOINT_VERSION`,
+    `TF_CHECKPOINT_VERSION_MIN_CONSUMER`, and
+    `TF_CHECKPOINT_VERSION_MIN_PRODUCER`.
+
+### Evolving GraphDef versions
+
+This section explains how to use this versioning mechanism to make different
+types of changes to the `GraphDef` format.
+
+#### Add an Op
+
+Add the new Op to both consumers and producers at the same time, and do not
+change any `GraphDef` versions. This type of change is automatically
+backward compatible, and does not impact forward compatibility plan since
+existing producer scripts will not suddenly use the new functionality.
+
+#### Add an Op and switch existing Python wrappers to use it
+
+1.  Implement new consumer functionality and increment the `GraphDef` version.
+2.  If it is possible to make the wrappers use the new functionality only in
+    cases that did not work before, the wrappers can be updated now.
+3.  Change Python wrappers to use the new functionality. Do not increment
+    `min_consumer`, since models that do not use this Op should not break.
+
+#### Remove or restrict an Op's functionality
+
+1.  Fix all producer scripts (not TensorFlow itself) to not use the banned Op or
+    functionality.
+2.  Increment the `GraphDef` version and implement new consumer functionality
+    that bans the removed Op or functionality for GraphDefs at the new version
+    and above. If possible, make TensorFlow stop producing `GraphDefs` with the
+    banned functionality. To do so, add the
+    [`REGISTER_OP(...).Deprecated(deprecated_at_version,
+    message)`](https://github.com/tensorflow/tensorflow/blob/b289bc7a50fc0254970c60aaeba01c33de61a728/tensorflow/core/ops/array_ops.cc#L1009).
+3.  Wait for a major release for backward compatibility purposes.
+4.  Increase `min_producer` to the GraphDef version from (2) and remove the
+    functionality entirely.
+
+#### Change an Op's functionality
+
+1.  Add a new similar Op named `SomethingV2` or similar and go through the
+    process of adding it and switching existing Python wrappers to use it, which
+    may take three weeks if forward compatibility is desired.
+2.  Remove the old Op (Can only take place with a major version change due to
+    backward compatibility).
+3.  Increase `min_consumer` to rule out consumers with the old Op, add back the
+    old Op as an alias for `SomethingV2`, and go through the process to switch
+    existing Python wrappers to use it.
+4.  Go through the process to remove `SomethingV2`.
+
+#### Ban a single unsafe consumer version
+
+1.  Bump the `GraphDef` version and add the bad version to `bad_consumers` for
+    all new GraphDefs. If possible, add to `bad_consumers` only for GraphDefs
+    which contain a certain Op or similar.
+2.  If existing consumers have the bad version, push them out as soon as
+    possible.
-- 
GitLab


From 17c5907a0f35cc2644737478137ed2b558998da9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 09:36:53 -0700
Subject: [PATCH 0548/1375] Only wait for one non-control input for Merge nodes
 if there is a loop. This is to enable the propagation of shapes for
 conditionals, which also include Merge nodes.

PiperOrigin-RevId: 160417770
---
 tensorflow/core/graph/graph_constructor.cc    |  39 +-
 .../core/graph/graph_constructor_test.cc      |  18 +-
 .../grappler/costs/graph_properties_test.cc   | 365 ++++++++++++++++++
 3 files changed, 415 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/graph/graph_constructor.cc b/tensorflow/core/graph/graph_constructor.cc
index 38a780dfac..a2929d0210 100644
--- a/tensorflow/core/graph/graph_constructor.cc
+++ b/tensorflow/core/graph/graph_constructor.cc
@@ -45,6 +45,11 @@ inline bool IsMerge(const NodeDef& node_def) {
   return node_def.op() == "Merge" || node_def.op() == "RefMerge";
 }
 
+inline bool IsNextIteration(const NodeDef& node_def) {
+  return node_def.op() == "NextIteration" ||
+         node_def.op() == "RefNextIteration";
+}
+
 bool IsValidNodeName(StringPiece s, bool allow_internal_ops) {
   using ::tensorflow::strings::Scanner;
   return Scanner(s)
@@ -365,24 +370,54 @@ Status GraphConstructor::BuildNodeIndex() {
   return Status::OK();
 }
 
+std::unordered_set<string> GetNextIterationNodes(
+    const GraphConstructor::NodeDefSlice& node_defs) {
+  std::unordered_set<string> next_iteration_nodes;
+
+  for (int n = 0; n < node_defs.size(); ++n) {
+    const NodeDef& node_def = *node_defs[n];
+    if (IsNextIteration(node_def)) {
+      next_iteration_nodes.insert(node_def.name());
+    }
+  }
+
+  return next_iteration_nodes;
+}
+
 Status GraphConstructor::InitFromEdges() {
   const int num_nodes = node_defs_.size();
   pending_count_.reserve(num_nodes);
   outputs_.resize(num_nodes);
+  std::unordered_set<string> next_iteration_nodes_ =
+      GetNextIterationNodes(node_defs_);
 
   // Parse the inputs for each node.
   for (int n = 0; n < num_nodes; ++n) {
     const NodeDef& node_def = *node_defs_[n];
     if (IsMerge(node_def)) {
-      // for merge only wait for one non-control input.
+      // Cycles in the graph are only allowed for while loops. A while loop is
+      // identified by an edge from a NextIteration node to a Merge node. For
+      // such Merge nodes, only wait for one non-control input before
+      // considering the node ready to process in Convert().
       int32 num_control_edges = 0;
+      bool has_loop_back_edge = false;
       for (int i = 0; i < node_def.input_size(); ++i) {
         StringPiece input_name(node_def.input(i));
         if (input_name.starts_with("^")) {
           num_control_edges++;
+        } else {
+          TensorId id(ParseTensorName(input_name));
+          if (next_iteration_nodes_.find(id.first.ToString()) !=
+              next_iteration_nodes_.end()) {
+            has_loop_back_edge = true;
+          }
         }
       }
-      pending_count_.push_back(num_control_edges + 1);
+      if (has_loop_back_edge) {
+        pending_count_.push_back(num_control_edges + 1);
+      } else {
+        pending_count_.push_back(node_def.input_size());
+      }
     } else {
       pending_count_.push_back(node_def.input_size());
     }
diff --git a/tensorflow/core/graph/graph_constructor_test.cc b/tensorflow/core/graph/graph_constructor_test.cc
index 8abf21235e..b8d1879fa0 100644
--- a/tensorflow/core/graph/graph_constructor_test.cc
+++ b/tensorflow/core/graph/graph_constructor_test.cc
@@ -1871,25 +1871,29 @@ TEST_F(GraphConstructorTest, ImportGraphDef_ControlDepsWithCycle) {
   // new_input
   opts.input_map[TensorId("new_input", 0)] = TensorId("input", 0);
 
-  // ImportGraphDef only allows backedges into merge nodes (since backedges are
-  // only expected in while loops)
+  // ImportGraphDef only allows backedges into merge nodes that are part of
+  // while loops (since backedges are only expected in while loops)
   ExpectOK(
       R"EOF(
       node { name: 'new_input' op: 'TestInput' }
-      node { name: 'merge' op: 'Merge' input: [ 'new_input:0', 't1:0' ]
+      node { name: 'merge' op: 'Merge' input: [ 'new_input:0', 'next:0' ]
              attr { key: "N" value: { i: 2 } }
              attr { key: "T" value: { type: DT_FLOAT } } }
       node { name: 't1' op: 'TestMul' input: [ 'merge:0', 'merge:0' ] }
+      node { name: 'next' op: 'NextIteration' input: ['t1:0']
+             attr { key: "T" value: { type: DT_FLOAT } } }
       )EOF",
       opts, &refiner);
 
   EXPECT_TRUE(HasNode("new_input"));
   EXPECT_TRUE(HasNode("merge"));
   EXPECT_TRUE(HasNode("t1"));
+  EXPECT_TRUE(HasNode("next"));
 
   // Sanity check we created cycle
   EXPECT_TRUE(HasEdge("merge", 0, "t1", 0));
-  EXPECT_TRUE(HasEdge("t1", 0, "merge", 1));
+  EXPECT_TRUE(HasEdge("t1", 0, "next", 0));
+  EXPECT_TRUE(HasEdge("next", 0, "merge", 1));
 
   // Test that control dep was added to exactly one node of cycle
   EXPECT_TRUE(HasControlEdge("W1", "merge"));
@@ -1899,13 +1903,17 @@ TEST_F(GraphConstructorTest, ImportGraphDef_ControlDepsWithCycle) {
   Node* merge = FindNode("merge");
   ASSERT_EQ(merge->requested_inputs().size(), 3);
   EXPECT_EQ(merge->requested_inputs()[0], "input:0");
-  EXPECT_EQ(merge->requested_inputs()[1], "t1:0");
+  EXPECT_EQ(merge->requested_inputs()[1], "next:0");
   EXPECT_EQ(merge->requested_inputs()[2], "^W1");
 
   Node* t1 = FindNode("t1");
   ASSERT_EQ(t1->requested_inputs().size(), 2);
   EXPECT_EQ(t1->requested_inputs()[0], "merge:0");
   EXPECT_EQ(t1->requested_inputs()[1], "merge:0");
+
+  Node* next = FindNode("next");
+  ASSERT_EQ(next->requested_inputs().size(), 1);
+  EXPECT_EQ(next->requested_inputs()[0], "t1:0");
 }
 
 TEST_F(GraphConstructorTest, ImportGraphDef_ControlDepsErrors) {
diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc
index cc6f097cd0..29b6adef5e 100644
--- a/tensorflow/core/grappler/costs/graph_properties_test.cc
+++ b/tensorflow/core/grappler/costs/graph_properties_test.cc
@@ -309,6 +309,371 @@ TEST_F(GraphPropertiesTest, Queues) {
   EXPECT_EQ("float: [1,2,3]", PropToString(props5[2]));
 }
 
+TEST_F(GraphPropertiesTest, MergeWithoutLoops) {
+  // Python code used to generate the graph is below.
+  const string gdef_ascii = R"EOF(
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 7
+      }
+    }
+  }
+}
+node {
+  name: "Const_1"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 5
+      }
+    }
+  }
+}
+node {
+  name: "ones"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
+node {
+  name: "Less"
+  op: "Less"
+  input: "Const"
+  input: "Const_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "cond/Switch"
+  op: "Switch"
+  input: "Less"
+  input: "Less"
+  attr {
+    key: "T"
+    value {
+      type: DT_BOOL
+    }
+  }
+}
+node {
+  name: "cond/switch_t"
+  op: "Identity"
+  input: "cond/Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_BOOL
+    }
+  }
+}
+node {
+  name: "cond/switch_f"
+  op: "Identity"
+  input: "cond/Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_BOOL
+    }
+  }
+}
+node {
+  name: "cond/pred_id"
+  op: "Identity"
+  input: "Less"
+  attr {
+    key: "T"
+    value {
+      type: DT_BOOL
+    }
+  }
+}
+node {
+  name: "cond/concat/axis"
+  op: "Const"
+  input: "^cond/switch_t"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "cond/concat/Switch"
+  op: "Switch"
+  input: "ones"
+  input: "cond/pred_id"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@ones"
+      }
+    }
+  }
+}
+node {
+  name: "cond/concat"
+  op: "ConcatV2"
+  input: "cond/concat/Switch:1"
+  input: "cond/concat/Switch:1"
+  input: "cond/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "cond/concat_1/axis"
+  op: "Const"
+  input: "^cond/switch_f"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "cond/concat_1/Switch"
+  op: "Switch"
+  input: "ones"
+  input: "cond/pred_id"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@ones"
+      }
+    }
+  }
+}
+node {
+  name: "cond/concat_1"
+  op: "ConcatV2"
+  input: "cond/concat_1/Switch"
+  input: "cond/concat_1/Switch"
+  input: "cond/concat_1/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "cond/Merge"
+  op: "Merge"
+  input: "cond/concat"
+  input: "cond/concat_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "concat/axis"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 2
+      }
+    }
+  }
+}
+node {
+  name: "concat"
+  op: "ConcatV2"
+  input: "cond/Merge"
+  input: "cond/Merge"
+  input: "concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+versions {
+  producer: 21
+}
+  )EOF";
+
+  // Test graph produced in python using:
+  /*
+    with tf.Graph().as_default():
+      x = tf.constant(2)
+      y = tf.constant(5)
+      z = tf.ones([1,1,1])
+      def f1(): return tf.concat([z, z], axis=0)
+      def f2(): return tf.concat([z, z], axis=1)
+      r = tf.cond(tf.less(x, y), f1, f2)
+      tf.concat([r, r], axis=2)
+      with open('/tmp/graph.pbtxt', 'w') as f:
+        f.write(str(tf.get_default_graph().as_graph_def()))
+   */
+
+  GrapplerItem item;
+  CHECK(protobuf::TextFormat::ParseFromString(gdef_ascii, &item.graph));
+  GraphProperties properties(item);
+  TF_CHECK_OK(properties.InferStatically());
+
+  std::vector<string> nodes{"cond/Merge", "cond/concat", "cond/concat_1"};
+  std::vector<string> expected_outputs{"float: [-1,-1,1]", "float: [2,1,1]",
+                                       "float: [1,2,1]"};
+  for (int i = 0; i < nodes.size(); i++) {
+    const auto props = properties.GetOutputProperties(nodes[i]);
+    const OpInfo::TensorProperties& prop = props[0];
+    EXPECT_EQ(DT_FLOAT, prop.dtype());
+    EXPECT_EQ(expected_outputs[i], PropToString(prop));
+  }
+}
+
 TEST_F(GraphPropertiesTest, WhileLoop) {
   // Python code used to generate the graph is below.
   const string gdef_ascii = R"EOF(
-- 
GitLab


From 9a45d4d6bab843424c3994798fc7fa4e1e04db56 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 10:00:26 -0700
Subject: [PATCH 0549/1375] Deletes iris_with_pipeline example, because core
 estimators are not compatible with sklearn.

PiperOrigin-RevId: 160420406
---
 tensorflow/examples/learn/BUILD               |  8 ---
 tensorflow/examples/learn/README.md           |  1 -
 tensorflow/examples/learn/examples_test.sh    |  1 -
 .../examples/learn/iris_with_pipeline.py      | 54 -------------------
 4 files changed, 64 deletions(-)
 delete mode 100644 tensorflow/examples/learn/iris_with_pipeline.py

diff --git a/tensorflow/examples/learn/BUILD b/tensorflow/examples/learn/BUILD
index 7371e96560..23a42a60ba 100644
--- a/tensorflow/examples/learn/BUILD
+++ b/tensorflow/examples/learn/BUILD
@@ -54,13 +54,6 @@ py_binary(
     deps = ["//tensorflow:tensorflow_py"],
 )
 
-py_binary(
-    name = "iris_with_pipeline",
-    srcs = ["iris_with_pipeline.py"],
-    srcs_version = "PY2AND3",
-    deps = ["//tensorflow:tensorflow_py"],
-)
-
 py_binary(
     name = "random_forest_mnist",
     srcs = ["random_forest_mnist.py"],
@@ -154,7 +147,6 @@ sh_test(
         ":iris_custom_decay_dnn",
         ":iris_custom_model",
         ":iris_run_config",
-        ":iris_with_pipeline",
         ":random_forest_mnist",
         ":resnet",
         ":text_classification",
diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index 6671d68831..416b809bb1 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -19,7 +19,6 @@ processing (`sudo pip install pandas`).
 
 ## Techniques
 
-* [Using skflow with Pipeline]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_with_pipeline.py)
 * [Deep Neural Network with Customized Decay Function]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
 
 ## Specialized Models
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index 4c5893384a..b8763de471 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -49,7 +49,6 @@ test iris
 test iris_custom_decay_dnn
 test iris_custom_model
 test iris_run_config
-test iris_with_pipeline
 test random_forest_mnist
 test resnet
 test text_classification --test_with_fake_data
diff --git a/tensorflow/examples/learn/iris_with_pipeline.py b/tensorflow/examples/learn/iris_with_pipeline.py
deleted file mode 100644
index 7ba958d85b..0000000000
--- a/tensorflow/examples/learn/iris_with_pipeline.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-"""Example of DNNClassifier for Iris plant dataset, with pipeline."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from sklearn import cross_validation
-from sklearn.datasets import load_iris
-from sklearn.metrics import accuracy_score
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import StandardScaler
-import tensorflow as tf
-
-learn = tf.contrib.learn
-
-
-def main(unused_argv):
-  iris = load_iris()
-  x_train, x_test, y_train, y_test = cross_validation.train_test_split(
-      iris.data, iris.target, test_size=0.2, random_state=42)
-
-  # It's useful to scale to ensure Stochastic Gradient Descent
-  # will do the right thing.
-  scaler = StandardScaler()
-
-  # DNN classifier.
-  classifier = learn.DNNClassifier(
-      feature_columns=learn.infer_real_valued_columns_from_input(x_train),
-      hidden_units=[10, 20, 10],
-      n_classes=3)
-
-  pipeline = Pipeline([('scaler', scaler), ('DNNclassifier', classifier)])
-
-  pipeline.fit(x_train, y_train, DNNclassifier__steps=200)
-
-  score = accuracy_score(y_test, list(pipeline.predict(x_test)))
-  print('Accuracy: {0:f}'.format(score))
-
-
-if __name__ == '__main__':
-  tf.app.run()
-- 
GitLab


From 6233d8a75c17bedc2b082f6ef32fb150ac2b28b9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 10:16:51 -0700
Subject: [PATCH 0550/1375] Only process Merge nodes involved with loops in
 Grappler shape inference

PiperOrigin-RevId: 160422523
---
 tensorflow/core/grappler/costs/graph_properties.cc | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 7ac35ef271..0b92237dc7 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -200,8 +200,12 @@ Status GraphProperties::InferStatically() {
     }
     if (node->IsEnter()) {
       enter_nodes.insert(node);
-    } else if (node->IsMerge()) {
-      merge_nodes.insert(node);
+    } else if (node->IsNextIteration()) {
+      for (const Node* output : node->out_nodes()) {
+        if (output->IsMerge()) {
+          merge_nodes.insert(output);
+        }
+      }
     }
   }
 
-- 
GitLab


From 1ac185fcf6210c6fecaf2fc7f76f6289138dc474 Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Wed, 28 Jun 2017 10:17:49 -0700
Subject: [PATCH 0551/1375] Don't include node_def.proto.h in node_def_util.h

The goal is to make kernels mostly independent of proto headers, which will let us lock down our .so imports.

RELNOTES: n/a
PiperOrigin-RevId: 160422647
---
 tensorflow/core/framework/node_def_util.cc | 53 +++++++++++++++++
 tensorflow/core/framework/node_def_util.h  | 67 ++++++++++++++++------
 2 files changed, 102 insertions(+), 18 deletions(-)

diff --git a/tensorflow/core/framework/node_def_util.cc b/tensorflow/core/framework/node_def_util.cc
index 9b737e1f72..79feb20d53 100644
--- a/tensorflow/core/framework/node_def_util.cc
+++ b/tensorflow/core/framework/node_def_util.cc
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/graph.pb_text.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb_text.h"
 #include "tensorflow/core/framework/op_def_util.h"
@@ -611,4 +612,56 @@ Status AttachDef(const Status& status, const Node& node) {
   return AttachDef(status, node.def());
 }
 
+void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def) {
+  node_def->mutable_attr()->insert(
+      AttrValueMap::value_type(name.ToString(), value));
+}
+
+#define ADD_NODE_ATTR(T)                                           \
+  void AddNodeAttr(StringPiece name, T value, NodeDef* node_def) { \
+    AttrValue attr_value;                                          \
+    SetAttrValue(value, &attr_value);                              \
+    AddNodeAttr(name, attr_value, node_def);                       \
+  }
+ADD_NODE_ATTR(StringPiece)
+ADD_NODE_ATTR(const char*)
+ADD_NODE_ATTR(int32)
+ADD_NODE_ATTR(int64)
+ADD_NODE_ATTR(float)
+ADD_NODE_ATTR(double)
+ADD_NODE_ATTR(bool)
+ADD_NODE_ATTR(DataType)
+ADD_NODE_ATTR(const PartialTensorShape&)
+ADD_NODE_ATTR(const Tensor&)
+ADD_NODE_ATTR(const TensorProto&)
+ADD_NODE_ATTR(const NameAttrList&)
+ADD_NODE_ATTR(gtl::ArraySlice<StringPiece>)
+ADD_NODE_ATTR(gtl::ArraySlice<const char*>)
+ADD_NODE_ATTR(gtl::ArraySlice<string>)
+ADD_NODE_ATTR(gtl::ArraySlice<int32>)
+ADD_NODE_ATTR(gtl::ArraySlice<int64>)
+ADD_NODE_ATTR(gtl::ArraySlice<float>)
+ADD_NODE_ATTR(gtl::ArraySlice<bool>)
+ADD_NODE_ATTR(const std::vector<bool>&)
+ADD_NODE_ATTR(gtl::ArraySlice<DataType>)
+ADD_NODE_ATTR(gtl::ArraySlice<TensorShape>)
+ADD_NODE_ATTR(gtl::ArraySlice<PartialTensorShape>)
+ADD_NODE_ATTR(gtl::ArraySlice<TensorShapeProto>)
+ADD_NODE_ATTR(gtl::ArraySlice<Tensor>)
+ADD_NODE_ATTR(gtl::ArraySlice<NameAttrList>)
+#undef ADD_NODE_ATTR
+
+void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map) {
+  map->insert(AttrValueMap::value_type(name.ToString(), value));
+}
+
+#define ADD_ATTR(T)                                            \
+  void AddAttr(StringPiece name, T value, AttrValueMap* map) { \
+    AttrValue attr_value;                                      \
+    SetAttrValue(value, &attr_value);                          \
+    AddAttr(name, attr_value, map);                            \
+  }
+ADD_ATTR(bool)
+#undef ADD_ATTR
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/node_def_util.h b/tensorflow/core/framework/node_def_util.h
index 1438abdec6..5d4864db66 100644
--- a/tensorflow/core/framework/node_def_util.h
+++ b/tensorflow/core/framework/node_def_util.h
@@ -21,7 +21,6 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/attr_value_util.h"
-#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
@@ -31,6 +30,9 @@ namespace tensorflow {
 
 class Node;
 
+// We forward declare NodeDef so that kernels don't need to depend on protos
+class NodeDef;
+
 // Name of the attribute used to encode node colocation constraints.
 //
 // Nodes can be co-located on the same device. Desire for explicit co-location
@@ -50,32 +52,61 @@ typedef protobuf::Map<string, AttrValue> AttrValueMap;
 
 // Adds an attr with name <name> and value <value> to *node_def.
 // The type of the attr is based on the type of value.
-template <class T>
-void AddNodeAttr(StringPiece name, T&& value, NodeDef* node_def) {
-  AttrValue attr_value;
-  SetAttrValue(std::forward<T>(value), &attr_value);
-  node_def->mutable_attr()->insert(
-      AttrValueMap::value_type(name.ToString(), attr_value));
-}
+void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, StringPiece value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const char* value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, int32 value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, int64 value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, float value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, double value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, bool value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, DataType value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const PartialTensorShape& value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const Tensor& value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const TensorProto& value, NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const NameAttrList& value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<StringPiece> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<const char*> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<string> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<int32> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<int64> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<float> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<bool> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, const std::vector<bool>& value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<DataType> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<TensorShape> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<PartialTensorShape> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<TensorShapeProto> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<Tensor> value,
+                 NodeDef* node_def);
+void AddNodeAttr(StringPiece name, gtl::ArraySlice<NameAttrList> value,
+                 NodeDef* node_def);
 
 // Version to workaround C++'s "perfect" forwarding not being able to
 // forward {...} initialization.
 template <class T>
 void AddNodeAttr(StringPiece name, std::initializer_list<T> value,
                  NodeDef* node_def) {
-  AttrValue attr_value;
-  SetAttrValue(value, &attr_value);
-  node_def->mutable_attr()->insert(
-      AttrValueMap::value_type(name.ToString(), attr_value));
+  AddNodeAttr(name, gtl::ArraySlice<T>(value), node_def);
 }
 
 // Adds an attr to an attr value map.
-template <class T>
-void AddAttr(StringPiece name, T&& value, AttrValueMap* map) {
-  AttrValue attr_value;
-  SetAttrValue(value, &attr_value);
-  map->insert(AttrValueMap::value_type(name.ToString(), attr_value));
-}
+void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map);
+void AddAttr(StringPiece name, bool value, AttrValueMap* map);
 
 class AttrSlice {
  public:
-- 
GitLab


From ea39a29386d0ac2c336667c0452dfd02f425a0a0 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Wed, 28 Jun 2017 10:27:59 -0700
Subject: [PATCH 0552/1375] Speed up HloInstruction::Accept by doing fewer
 hashtable lookups.

PiperOrigin-RevId: 160423850
---
 .../compiler/xla/service/dfs_hlo_visitor.cc   | 19 ++----------
 .../compiler/xla/service/dfs_hlo_visitor.h    | 22 +++++++++++---
 .../compiler/xla/service/hlo_instruction.cc   | 30 +++++++++++--------
 3 files changed, 37 insertions(+), 34 deletions(-)

diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
index 5121d36866..0f7ab11117 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc
@@ -37,31 +37,16 @@ Status DfsHloVisitor::HandleElementwiseBinary(HloInstruction* hlo,
 
 void DfsHloVisitor::SetVisiting(const HloInstruction& instruction) {
   VLOG(3) << "marking HLO " << &instruction << " as visiting: ";
-  CHECK(NotVisited(instruction));
+  DCHECK(NotVisited(instruction));
   visit_state_[&instruction] = VisitState::kVisiting;
 }
 
 void DfsHloVisitor::SetVisited(const HloInstruction& instruction) {
   VLOG(3) << "marking HLO " << &instruction << " as visited: ";
-  CHECK(NotVisited(instruction) || IsVisiting(instruction));
+  DCHECK(NotVisited(instruction) || IsVisiting(instruction));
   visit_state_[&instruction] = VisitState::kVisited;
 }
 
-bool DfsHloVisitor::IsVisiting(const HloInstruction& instruction) {
-  auto it = visit_state_.find(&instruction);
-  return it != visit_state_.end() && it->second == VisitState::kVisiting;
-}
-
-bool DfsHloVisitor::DidVisit(const HloInstruction& instruction) {
-  auto it = visit_state_.find(&instruction);
-  return it != visit_state_.end() && it->second == VisitState::kVisited;
-}
-
-bool DfsHloVisitor::NotVisited(const HloInstruction& instruction) {
-  auto it = visit_state_.find(&instruction);
-  return it == visit_state_.end() || it->second == VisitState::kNotVisited;
-}
-
 Status DfsHloVisitor::Preprocess(HloInstruction* hlo) { return Status::OK(); }
 
 Status DfsHloVisitor::Postprocess(HloInstruction* visited) {
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index cb929eef49..fcc4f85f01 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -239,6 +239,14 @@ class DfsHloVisitor {
     kVisited,
   };
 
+  VisitState GetVisitState(const HloInstruction& instruction) {
+    auto it = visit_state_.find(&instruction);
+    if (it == visit_state_.end()) {
+      return kNotVisited;
+    }
+    return it->second;
+  }
+
   // Sets the visitation state of the given instruction as kVisiting.
   //
   // Precondition: current state must be kNotVisited.
@@ -250,13 +258,19 @@ class DfsHloVisitor {
   void SetVisited(const HloInstruction& instruction);
 
   // Returns whether the state of the given instruction is kVisiting.
-  bool IsVisiting(const HloInstruction& instruction);
+  bool IsVisiting(const HloInstruction& instruction) {
+    return GetVisitState(instruction) == kVisiting;
+  }
 
   // Returns whether the state of the given instruction is kVisited.
-  bool DidVisit(const HloInstruction& instruction);
+  bool DidVisit(const HloInstruction& instruction) {
+    return GetVisitState(instruction) == kVisited;
+  }
 
   // Returns whether the state of the given instruction is kNotVisited.
-  bool NotVisited(const HloInstruction& instruction);
+  bool NotVisited(const HloInstruction& instruction) {
+    return GetVisitState(instruction) == kNotVisited;
+  }
 
   // This method should be overridden by subclasses that wish to run some
   // operation on an op before its Handle* visitor method is called.
@@ -281,7 +295,7 @@ class DfsHloVisitor {
 
  private:
   // Tracks the visitation state of each instruction. Any instructions that are
-  // not found from the map are considered as VisitState::kNotVisited.
+  // not found in the map are considered as VisitState::kNotVisited.
   tensorflow::gtl::FlatMap<const HloInstruction*, VisitState> visit_state_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(DfsHloVisitor);
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 4b38ecd5de..379d14adc6 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1925,19 +1925,21 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
 static Status PushDFSChild(DfsHloVisitor* visitor,
                            std::vector<HloInstruction*>* dfs_stack,
                            HloInstruction* parent, HloInstruction* child) {
-  if (visitor->IsVisiting(*child)) {
-    return FailedPrecondition(
-        "A cycle is detected while visiting instruction %s",
-        parent->ToString().c_str());
-  }
+  switch (visitor->GetVisitState(*child)) {
+    case DfsHloVisitor::kVisiting:
+      return FailedPrecondition(
+          "A cycle is detected while visiting instruction %s",
+          parent->ToString().c_str());
+
+    case DfsHloVisitor::kVisited:
+      VLOG(3) << "Not visiting HLO " << child->name()
+              << " as it was already visited.";
+      return Status::OK();
 
-  if (!visitor->DidVisit(*child)) {
-    dfs_stack->push_back(child);
-  } else {
-    VLOG(3) << "Not visiting HLO " << child->name()
-            << " as it was already visited.";
+    case DfsHloVisitor::kNotVisited:
+      dfs_stack->push_back(child);
+      return Status::OK();
   }
-  return Status::OK();
 }
 
 static Status PostOrderDFS(HloInstruction* root, DfsHloVisitor* visitor,
@@ -1950,14 +1952,16 @@ static Status PostOrderDFS(HloInstruction* root, DfsHloVisitor* visitor,
     DCHECK(!dfs_stack.empty());
 
     HloInstruction* current_node = dfs_stack.back();
-    if (visitor->DidVisit(*current_node)) {
+    DfsHloVisitor::VisitState visit_state =
+        visitor->GetVisitState(*current_node);
+    if (visit_state == DfsHloVisitor::kVisited) {
       dfs_stack.pop_back();
       VLOG(3) << "Not visiting HLO " << current_node->name()
               << " as it was already visited.";
       continue;
     }
 
-    if (visitor->IsVisiting(*current_node)) {
+    if (visit_state == DfsHloVisitor::kVisiting) {
       dfs_stack.pop_back();
 
       TF_RETURN_IF_ERROR(visitor->Preprocess(current_node));
-- 
GitLab


From 54a1e736a8cbf51b3bab5a7377411a25a1472d29 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 10:33:24 -0700
Subject: [PATCH 0553/1375] Improve data processing in TensorForest. Extract
 name correctly when transforming, and support sparse categorical.

PiperOrigin-RevId: 160424501
---
 .../tensor_forest/python/ops/data_ops.py       | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/python/ops/data_ops.py b/tensorflow/contrib/tensor_forest/python/ops/data_ops.py
index 2e54f620d5..f878e5989c 100644
--- a/tensorflow/contrib/tensor_forest/python/ops/data_ops.py
+++ b/tensorflow/contrib/tensor_forest/python/ops/data_ops.py
@@ -97,6 +97,13 @@ class DataColumn(object):
                                                            self.size)
 
 
+def GetColumnName(column_key, col_num):
+  if isinstance(column_key, str):
+    return column_key
+  else:
+    return getattr(column_key, 'column_name', str(col_num))
+
+
 def ParseDataTensorOrDict(data):
   """Return a tensor to use for input data.
 
@@ -119,14 +126,13 @@ def ParseDataTensorOrDict(data):
     for k in sorted(data.keys()):
       is_sparse = isinstance(data[k], sparse_tensor.SparseTensor)
       if is_sparse:
-        # TODO(gilberth): support sparse categorical.
-        if data[k].dtype == dtypes.string:
-          logging.info('TensorForest does not support sparse categorical. '
-                       'Transform it into a number with hash buckets.')
+        # TODO(gilberth): support sparse continuous.
+        if data[k].dtype == dtypes.float32:
+          logging.info('TensorForest does not support sparse continuous.')
           continue
         elif data_spec.sparse.size() == 0:
           col_spec = data_spec.sparse.add()
-          col_spec.original_type = DATA_FLOAT
+          col_spec.original_type = DATA_CATEGORICAL
           col_spec.name = 'all_sparse'
           col_spec.size = -1
         sparse_features.append(
@@ -136,7 +142,7 @@ def ParseDataTensorOrDict(data):
         col_spec = data_spec.dense.add()
 
         col_spec.original_type = DTYPE_TO_FTYPE[data[k].dtype]
-        col_spec.name = k
+        col_spec.name = GetColumnName(k, len(dense_features))
         # the second dimension of get_shape should always be known.
         shape = data[k].get_shape()
         if len(shape) == 1:
-- 
GitLab


From cf7c008ab150ac8e5edb3ed053d38b2919699796 Mon Sep 17 00:00:00 2001
From: Yifei Feng <fengyifei2026@gmail.com>
Date: Wed, 28 Jun 2017 11:07:44 -0700
Subject: [PATCH 0554/1375] Branch 160346151 (#11094)

* Properly handle ops that don't have a CPU kernel

PiperOrigin-RevId: 159655906

* Selected BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 159673079

* Remove redundant `get` calls on smart pointers

PiperOrigin-RevId: 159675809

* PiperOrigin-RevId: 159698321

* Migrate kernels to boosted_trees.

PiperOrigin-RevId: 159698656

* Fix a bug in the memory optimizer when two inputs to a node are both recomputed

PiperOrigin-RevId: 159700457

* Fixed memory leak that can be triggered by a failed node evaluation

PiperOrigin-RevId: 159707380

* Updates get_started tutorial.

PiperOrigin-RevId: 159709158

* [XLA] Remove unused factory in local_service

PiperOrigin-RevId: 159712806

* Fix typo in docstring

PiperOrigin-RevId: 159714414

* Migrate ops for new version of TensorForest.

PiperOrigin-RevId: 159718610

* Added parameterized tests to reduce window tests.

PiperOrigin-RevId: 159721784

* Use C API to implement Operation.device property

PiperOrigin-RevId: 159723490

* Several Estimator changes:
- support configurable input_fn calling in Estimator subclasses.
- pass params and config to the input_fn.
- allow callables for model_fn and input_fn.

PiperOrigin-RevId: 159725554

* Fixed the scalar output for shard api when outputs_from_all_shards=True.

PiperOrigin-RevId: 159726444

* Automated g4 rollback of changelist 159718610

PiperOrigin-RevId: 159728380

* Adding missing deps to targets in llvm.BUILD. This was only working in non-sandboxed builds.

PiperOrigin-RevId: 159729295

* [XLA:HLO] Move sequence functions from hlo_ordering.h to hlo_scheduling.h.

This is required for upcoming changes to convert the sequence creation functions
(and HeapSimulator and BufferAssignment) over to using the new
Hlo{Dataflow,Alias}Analysis.

It's required because otherwise there's a dependency cycle:

Hlo{Dataflow,Alias}Analysis depends on HloOrdering
CreateMemoryMinimizingSequence will depend on Hlo{Dataflow,Alias}Analysis

There's already a cycle here, if both HloOrdering and
CreateMemoryMinimizingSequence are in the same file.  Also note that:

MinimumMemoryForSequence depends on HeapSimulator
HeapSimulator will depend on Hlo{Dataflow,Alias}Analysis
Hlo{Dataflow,Alias}Analysis depends on HloOrdering

Splitting out the sequence functions resolves the cycle.

Refactoring only; no functional changes.

PiperOrigin-RevId: 159731836

* [XLA:HLO] Split Hlo{Value,Buffer} out of Hlo{Dataflow,Alias}Analysis.

This will make dependencies cleaner for upcoming CLs that will convert
HeapSimulator and HloOrdering to use the new analyses.

No change in functionality.

PiperOrigin-RevId: 159737265

* Internal change

PiperOrigin-RevId: 159738215

* Suggest people need to do some build environment ./configur'ing.

Fixes #4279

PiperOrigin-RevId: 159738412

* Rewrite SameDefinedShape function in ShapeRefiner

PiperOrigin-RevId: 159745894

* [XLA] Remove xla_cpu_*_eigen flags from CPU backends.

These flags are currently de-facto unused; parallelism should be controlled
through the cpu_parallel backend. For configuring Eigen, if needed, the options
should be piped more directly to the code.

PiperOrigin-RevId: 159746509

* Updates layers tutorial and corresponding example.

PiperOrigin-RevId: 159749528

* Further BUILD cleanup

PiperOrigin-RevId: 159749869

* Use more efficient squared_difference

PiperOrigin-RevId: 159751209

* Add log_step_count_steps to RunConfig and allow it to flow to the MonitoredSession.

PiperOrigin-RevId: 159753935

* [XLA] Remove xla_hlo_test_generate_hlo_graph, which is now redundant.

PiperOrigin-RevId: 159755688

* Do not use SSE4.1 instructions on Android builds.

PiperOrigin-RevId: 159756104

* Add nonpublic helper `tf.distributions.util.tridiag` op.

PiperOrigin-RevId: 159757904

* [XLA] Remove dead "in-client" code.
Remove Service::runs_in_client_process_ field and it's dead user. This was
previously used by the "InProcess" methods which have been replaced with
the LocalClient API.

PiperOrigin-RevId: 159759455

* [tf contrib seq2seq] Add monotonic attention mechanisms

* Add monotonic_attention and safe_cumprod helper functions.
* Add _BaseMonotonicAttentionMechanism base class.
* Add BahdanauMonotonicAttention and LuongMonotonicAttention classes.

These attention mechanisms are proposed in
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
"Online and Linear-Time Attention by Enforcing Monotonic Alignments."
ICML 2017.  https://arxiv.org/abs/1704.00784

PiperOrigin-RevId: 159760073

* Add ability for argmax to output int32 indices.  Default remains int64.

Change is made in a backwards and forward compatible manner, since
we add a new attribute with a default that remains the same, and
simply register a few new kernels.

PiperOrigin-RevId: 159761347

* Automated g4 rollback of changelist 159746509

PiperOrigin-RevId: 159763112

* Raise ValueError if invalid dtype for random_uniform.

PiperOrigin-RevId: 159764956

* Internal change.

PiperOrigin-RevId: 159769520

* Support zero shapes for random_poisson. This matches random_uniform.

PiperOrigin-RevId: 159771215

* Blacklist the quantized ops since they have too many issues (incorrect shape
functions, memory corruptions, ...)

PiperOrigin-RevId: 159772801

* Fixed the shape functions of the QuantizedAdd and QuantizedMul ops

PiperOrigin-RevId: 159772841

* Switch from assigning namedtuple.__new__.__defaults__ to overwriting __new__.

Assigning __defaults__ relies on an implementation detail of CPython, confuses
type checkers (and developers :)), and is error-prone since it doesn't make the
relationship between parameter names and default values explicit.
This CL switches to overloading __new__ instead.

PiperOrigin-RevId: 159773922

* Made sure that we can call the constant folding code twice safely.

PiperOrigin-RevId: 159781607

* Added batch_matmul op dependence to android_extended_ops

PiperOrigin-RevId: 159787178

* Fixes a TODO in head_test.

PiperOrigin-RevId: 159789178

* When configuring per-session thread pools, allow
a pool to be a global pool. This allows a division
between large and small pools, without needing to make
new pool for each session.

PiperOrigin-RevId: 159789678

* Add a multi-head TensorForest estimator.

PiperOrigin-RevId: 159820487

* Have RestoreV2's shape fn set all outputs to unknown shape.

PiperOrigin-RevId: 159835723

* VectorExponential added to distributions.

PiperOrigin-RevId: 159840822

* Fold as many nodes as possible instead of giving up if there is any error.

PiperOrigin-RevId: 159841935

* Removed deprecated summary usage from estimators.
Made name_space usage consistent.

PiperOrigin-RevId: 159846928

* Adding missing license notice to toolchain build files

PiperOrigin-RevId: 159847551

* [XLA] Remove unused flags and move debugging flag to debug options.

PiperOrigin-RevId: 159849759

* Fixes some docstrings in feature_column.

PiperOrigin-RevId: 159850619

* TpuEstimator: Replicate the input_fn to the worker CPU for each shard.

The batch size is configured as follows:
The user may specify a global batch size in their hyperparameters. If the 'batch_size' field is set, then we convert the global batch size into a per-shard batch size by dividing by num_shards before running their input_fn.

PiperOrigin-RevId: 159851773

* Modify beam search decoder to use symbolic shape for vocab size if the static shape is not present.

PiperOrigin-RevId: 159852297

* Generalize cluster initialization to span multiple mini-batches if necessary.

PiperOrigin-RevId: 159852557

* Use a single threaded session for SDCALinearRegressorTest to
avoid incorrect threading test failures (tsan).

PiperOrigin-RevId: 159852818

* Migrate ops for new version of TensorForest.

PiperOrigin-RevId: 159852889

* Replaced constant inputs with variables to ensure most of the graph doesn't get
optimized away

PiperOrigin-RevId: 159853171

* For candidate sampling, add facility to colocate the logit computation with the sharded embeddings.

PiperOrigin-RevId: 159854706

* Added a utility to create parsing spec for regressors (canned estimator)

PiperOrigin-RevId: 159855254

* Fix cuda_kernel_helper_test. std::numeric_limits<int32>::max() doesn't pass, so
I didn't use that.

PiperOrigin-RevId: 159869169

* In tfcompile, prune nodes that are not reachable from the fetches before
building the Graph. This allows loading a graph that contains ops not
needed for the compiled binary.

PiperOrigin-RevId: 159869692

* Fix bugs related to distributions over integers.

- Ensure that the max number of categories does not exceed largest integer-form float.
- Make dtype inference consistent between Categorical and Multinomial
distributions.
- Improve documentation to better reflect that the Categorical
distribution is analogous to `argmax{OneHotCategorical}` (itself being
identical to `argmax{Multinomial(p,n=1)}` but not Multinomial.
- Fix validation_args Heisenberg uncertainty: only validation logic should live under self.validate_args. E.g., validate_args=True would sometimes imply `x=floor(x)` which changes behavior thus making debugging impossible because enabling validation *changes* values.
- Corrected `Geometric` swapping of validate_args` and `allow_nan_stats` default-values.

Fixes #10149

PiperOrigin-RevId: 159872532

* Make HloModule clonable

This CL makes HloModule clonable, which is necessary when we want to run the same compilation twice with the same input.

PiperOrigin-RevId: 159874256

* Internal change.

PiperOrigin-RevId: 159876942

* Implement alternative `monte_carlo.expectation_v2`. This function implements
the reparameterization and score-gradient tricks and does not depend on
tf.Distribution like inputs.

PiperOrigin-RevId: 159877923

* In SE_ASSIGN_OR_RETURN change ConsumeValueOrDie to the preferred std::move ValueOrDie.

PiperOrigin-RevId: 159879754

* If rank is unknown, do not add output shapes to transpose nodes.

PiperOrigin-RevId: 159879840

* Move sparse_fill_empty_rows to new, *significantly* faster, C++ kernel for everyone.

Also fix a bug in the C++ op when the input ST has 0 elements.

PiperOrigin-RevId: 159880044

* Add support of label_keys to DebugClassifier

PiperOrigin-RevId: 159883986

* Register devices under their legacy names

Because some higher level APIs continue to use the legacy name format,
when using ClusterSpec propagation, we need to ensure that we register
the devices under their legacy names as well as their canonical names.

PiperOrigin-RevId: 159885777

* [BatchNorm] Minor fixes to TF doc

PiperOrigin-RevId: 159886125

* Generating TBAA metadata causes the LLVM to miscompile after
https://reviews.llvm.org/rL305938).  Disable TBAA (to stop the miscompiles)
while we fix the root issue.

PiperOrigin-RevId: 159895736

* Improve score-trick to be a valid Csiszar f-Divergence yet numerically stable.

PiperOrigin-RevId: 159896013

* Support advisor in all places (Command line, APIs)
Add expensive operation checker

PiperOrigin-RevId: 159897279

* Added canned estimators to Tensorflow library. List of added estimators:
* DNNClassifier
* DNNRegressor
* LinearClassifer
* LinearRegressor
* DNNLinearCombinedClassifier
* DNNLinearCombinedRegressor

PiperOrigin-RevId: 159898954

* Alligned how model-fns handled params among linear/dnn/combined estimators.

PiperOrigin-RevId: 159899925

* Fixed cmake tests.

PiperOrigin-RevId: 159901417

* [XLA:CPU] Add VLOGs to cpu_compiler.cc

PiperOrigin-RevId: 159902919

* Make occurence (op run times and op definition) selectable
in all views to address the loop problem.

When a node is in loop, its execution times are accumulated, its run times
will increase.

PiperOrigin-RevId: 159912429

* [XLA] Small error message improvement in binop shape inference.

PiperOrigin-RevId: 159920109

* Follow upstream API change from r306058.

PiperOrigin-RevId: 159938416

* [TF:XLA] Update LLVM to upstream revision r306085.

PiperOrigin-RevId: 159946562

* [XLA] Remove unused xla_cpu flag and move another to DebugOptions.

PiperOrigin-RevId: 159952124

* Updates linear.md tutorial

PiperOrigin-RevId: 159956867

* Add TraceMe instrumentation of RunStep in GRPC distributed runtime.
A unique ID is added to each RunStep call that allows the client and server
events to be correlated.

PiperOrigin-RevId: 159956950

* [XLA] Add general F32 implementation for ReducePrecision operation.

This only tests with parameter inputs (which is needed to ensure we actually test on GPUs as well as CPUs); there's no point in separately testing with constants.

PiperOrigin-RevId: 159961430

* Java: NativeLibrary: Fix URL in error message.

And add some detail.
Inspired by #11015

PiperOrigin-RevId: 159962478

* Increase rtol for util_test.

PiperOrigin-RevId: 159971136

* Re-enable IR dumping for the sequential CPU backend.

PiperOrigin-RevId: 159974126

* tfdbg: a few minor fixes and improvements

* Let DumpingDebugWrapperSession and DumpingDebugHook create session_root if it doesn't exist
* Add README.md to tensorflow/python/debug
* Add section "Debugging Keras Models with TFDBG" in debugger.md

PiperOrigin-RevId: 159976070

* Add None check for save_path when restoring checkpoints as if something is wrong in tf.train.latest_checkpoint, it will often return None and it's nice to have a common sense check in restore for this. This way log.error says what has happened.

PiperOrigin-RevId: 159979481

* Don't crash if a metagraph fails to load.

PiperOrigin-RevId: 159981628

* Prepare to not include node_def.proto.h in node_def_util.h

The goal is to make kernels mostly independent of proto headers, which will let
us lock down our .so imports.  This CL makes a bunch of .cc files
either include node_def.proto.h themselves or not need the definition of
NodeDef; a second CL will make node_def_util.h not include node_def.proto.h.

RELNOTES: n/a
PiperOrigin-RevId: 159982117

* Add a few diagnostic flags to help narrow down issues with the LLVM
backends.

PiperOrigin-RevId: 159982441

* Updated wide-n-deep tutorial code to use core version of estimators and feature-columns.

PiperOrigin-RevId: 159984663

* Modify ControlFlowContext to also respect import_scope in 'values_' and keys of 'external_values_'

PiperOrigin-RevId: 159985290

* Add item's graph to partition_graphs in virtual cluster's run method.
Put node op name in timeline_label instead of node_name.

PiperOrigin-RevId: 159986583

* Use short-proto for logging purposes.

A short proto will be output on a single log line, making it
easier for certain automated tools to handle.

PiperOrigin-RevId: 159994005

* Sinh, ArcSinh, Cosh, LogCosh functions added to distributions/python/ops/trig.
Care is taken to ensure a fair bit of stability.

PiperOrigin-RevId: 159995514

* Updates some examples in examples/learn.

PiperOrigin-RevId: 159996397

* Add kernel tests for boosted_trees.

PiperOrigin-RevId: 160002696

* Avoid doing unecessary work in the OptimizeGraph() function whenever possible

PiperOrigin-RevId: 160003173

* Use std::shared_ptr instead of core::RefCounted for Node::Properties

Also changes Node::Properties to a struct and removes underscores from public member variables. This change should make it easier to work with Properties moving forward as the refcount will be automatically updated.

PiperOrigin-RevId: 160003281

* Make the CPU compiler dump optimized IR along with the unoptimized IR.

PiperOrigin-RevId: 160005257

* Disable flaky run_metadata_test.

PiperOrigin-RevId: 160015399

* BUILD cleanup in tensorflow/tools/...

PiperOrigin-RevId: 160018623

* SinhArcSinh bijector added.

This two-parameter diffeomorphism from R --> R allows for skewness and fatter
or thinner tails.  See docstring and also
http://oro.open.ac.uk/22510/1/sinhasinh.pdf

PiperOrigin-RevId: 160019380

* Avoid hardcoded names for temporary files in tests.

These tests (and examples that are run as tests) were using hardcoded names for
temporary files.  This failed when multiple copies of these tests were run in
parallel, or even successively by different users, where the second run could
not overwrite files left by the first.

This change uses the TEST_TMPDIR environment variable used by bazel's test
runner to choose a temporary directory.   If that directory is not set,
/tmp is used, as before.

PiperOrigin-RevId: 160026924

* Fix multinomial doc-string, input arg logits expects to log-probabilities and not log-odds.

PiperOrigin-RevId: 160036709

* Made TensorFlow documentation on LSTMs slightly more accurate.

PiperOrigin-RevId: 160047054

* Follow LLVM/ORC upstream API change in r306166.

PiperOrigin-RevId: 160108102

* Move resampler from sonnet to contrib.

PiperOrigin-RevId: 160134565

* [TPUEstimator] Make input_fn invoked properly with eval on CPU.

PiperOrigin-RevId: 160151890

* Deletes iris_val_based_early_stopping example, which uses deprecated ValidationMonitor.

PiperOrigin-RevId: 160154863

* [XLA] Move HLO dumping flags from service_flags to debug_options_flags

This also removes the duplication in the xla_generate_hlo_graph flag.

This CL also moves the actual dumping logic from Executable to the
hlo_graph_dumper namespace, where it belongs; this is in preparation for
removing the hlo_dumper callback altogether, since it isn't serving any role
beyond what a direct call to hlo_graph_dumper would have (b/62872831 has more
details).

PiperOrigin-RevId: 160154869

* Fix missing variable unref

Direct leak of 56 byte(s) in 1 object(s) allocated from:
    #0 0xf5ee272 in operator new(unsigned long) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf5ee272)
    #1 0x1b51394c in tensorflow::AssignVariableOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*)::'lambda'(tensorflow::Var**)::operator()(tensorflow::Var**) const (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b51394c)
    #2 0x1b5136c0 in std::_Function_handler<tensorflow::Status (tensorflow::Var**), tensorflow::AssignVariableOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*)::'lambda'(tensorflow::Var**)>::_M_invoke(std::_Any_data const&, tensorflow::Var**) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b5136c0)
    #3 0x1b50b289 in std::function<tensorflow::Status (tensorflow::Var**)>::operator()(tensorflow::Var**) const (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b50b289)
    #4 0x1b50af88 in tensorflow::Status tensorflow::ResourceMgr::LookupOrCreate<tensorflow::Var>(basic_string<char, std::char_traits<char>, std::allocator<char> > const&, basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tensorflow::Var**, std::function<tensorflow::Status (tensorflow::Var**)>) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b50af88)
    #5 0x1b50ac10 in tensorflow::Status tensorflow::LookupOrCreateResource<tensorflow::Var>(tensorflow::OpKernelContext*, tensorflow::ResourceHandle const&, tensorflow::Var**, std::function<tensorflow::Status (tensorflow::Var**)>) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b50ac10)
    #6 0x1b512f1e in tensorflow::AssignVariableOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1b512f1e)
    #7 0x1d1881c7 in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0x1d1881c7)
    #8 0xf96e0fe in tensorflow::KernelAndDevice::Run(std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf96e0fe)
    #9 0xf94f9c8 in TFE_Execute (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf94f9c8)
    #10 0xf94356d in TFE_Py_Execute(TFE_Context*, int, char const*, tensorflow::gtl::InlinedVector<TFE_TensorHandle*, 4>*, _object*, tensorflow::gtl::InlinedVector<TFE_TensorHandle*, 2>*, TF_Status*) (/build/cas/5d2/5d2be3b530580573ff7269adcab7cbac+0xf94356d)

PiperOrigin-RevId: 160160101

* Simplify strided_slice's shape handling

Now that TensorShape and PartialTensorShape share memory representations, there's no need for an abstract class that makes TensorShape and TensorShapeProto look the same.

RELNOTES: n/a
PiperOrigin-RevId: 160161618

* Added a tool to report the static information that can be extracted from a TF model.

PiperOrigin-RevId: 160162256

* Properly handle RefEnter, RefExit and RefNextIteration nodes.

PiperOrigin-RevId: 160162338

* Switch tfprof to use proto3

PiperOrigin-RevId: 160163483

* Fixes to cuda_config.h.

PiperOrigin-RevId: 160168545

* Update ops-related pbtxt files.

PiperOrigin-RevId: 160171187

* Adds notes to prevent overfitting for Experiment continous_train_and_eval.

PiperOrigin-RevId: 160172692

* Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 160172985

* Merge changes from github.
END_PUBLIC

Note: this CL will break builds.  cl/159887762 to follow to fix all the breakages.

---
Commit 2336cdf7f authored by Maxwell Paul Brickner<mbrickn@users.noreply.github.com>
Committed by gunan<gunan@google.com>:
Updated link to use HTTPS (#10998)

Howdy!

I just updated a link to use https instead of http.

Thanks!
---
Commit ad0892df1 authored by Luke Iwanski<luke@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] Fixes run_metadata_test for SYCL

 This test is designed to test CUDA specific behavior

---
Commit 6b37a0725 authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Update comments
---
Commit 1699d904a authored by John Lawson<john@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] Fixes CUDA specific test run on SYCL (#56)

The testBadParentValuesOnGPU should only be run on CUDA devices, as the
test checks for particular CUDA behaviour. We don't actually provide a
SYCL kernel for GatherTree and so it's not a problem that the tests
don't target SYCL.
---
Commit 3c1946230 authored by myPrecious<Moriadry@users.noreply.github.com>
Committed by Shanqing Cai<cais@google.com>:
Java API to get the size of specified input list of operations. (#10865)

* Java API to get the size of specified input list of operations

* remove unnecessary explain to avoid bring a new term to users.

---
Commit e911c7480 authored by Luke Iwanski<luke@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] REGISTER -> REGISTER6

---
Commit fbf6c4cec authored by superryanguo<superryanguo@gmail.com>
Committed by superryanguo<superryanguo@gmail.com>:
Simplify the Quickstart section with the weblink is better

---
Commit 72e2918cc authored by Taehoon Lee<taehoonlee@snu.ac.kr>
Committed by Taehoon Lee<taehoonlee@snu.ac.kr>:
Fix typos

---
Commit 90c4406b7 authored by Rishabh Patel<patelrishabh@users.noreply.github.com>
Committed by GitHub<noreply@github.com>:
Correct the learning rate as per the code snippet
---
Commit 03da61134 authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Update ir_array.cc
---
Commit 2df6cd3ac authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Another try
---
Commit af0cbace1 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Transpose to go through Eigen (#10321)

---
Commit fc7361081 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Registers RGBToHSV and HSVToRGB (#91) (#10848)

* [OpenCL] Added RGBToHSV and HSVToRGB

* Aligning '\'
---
Commit 832894ef8 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Registers AdjustContrastv2 (#10949)

* [OpenCL] Registers AdjustContrastv2 (#93)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL (#96)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL

* simplified to #ifndef

* Changed to "#if GOOGLE_CUDA"

* Update adjust_contrast_op_benchmark_test.cc

* Added comments

---
Commit cb4c2f8d1 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Make TransferBufferToInFeed not virual so it compiles.

---
Commit e89f04d80 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix calling Literal member functions.

---
Commit 15a8df724 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix mac build
clone from meheff's change:
[XLA] Change return type of DeviceAssignment::Deserialize to fix build
breakage on mac.
The mac build had the following error:

error: incomplete type 'xla::DeviceAssignment' used in type trait
expression

This was due to a static method returning a StatusOr<DeviceAssignment>
inside of the definition of DeviceAssignment.

---
Commit a54d43fa4 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Replace LiteralUtil to Literal in compiler/plugin/executor

---
Commit 88a6bb80c authored by Guenther Schmuelling<guschmue@microsoft.com>
Committed by Guenther Schmuelling<guschmue@microsoft.com>:
expand inline for debug builds to limit number of symbols

---
Commit 62fb49d31 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix visibility error for contrib/remote_fused_graph/pylib/BUILD.

---
Commit 4c75252f2 authored by Mark Neumann<markn@allenai.org>
Committed by Mark Neumann<markn@allenai.org>:
fix initial test values to avoid numerical instability

---
Commit b58d98353 authored by sj6077<epik03sj@gmail.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
Fixes of AutoParallel bug (#10368)

* Fix the bug that auto_parallel could replicate variable snapshot name

* Use NodeName in grappler:utils instead of substr, convert variables->variable_def of grappler item

* remove variable_def from grappler item, exclude snapshot nodes from dont_replicate_nodes in auto_parallel

---
Commit a286b7db8 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Make debug_test slice integer.

---
Commit 97fcfdfa6 authored by Toby Boyd<tobyboyd@google.com>
Committed by GitHub<noreply@github.com>:
Fixed path to seq2seq.py and minor formatting
---
Commit 63c1befb8 authored by Anish Shah<shah.anish07@gmail.com>
Committed by Anish Shah<shah.anish07@gmail.com>:
Improve docs for tf.nn.depthwise_conv2d_native

---
Commit 8d42202b2 authored by Yong Tang<yong.tang.github@outlook.com>
Committed by Yong Tang<yong.tang.github@outlook.com>:
Fix mismatched delete in mkl_tfconv_op.cc

This fix fixes mismatched new[]-delete in mkl_tfconv_op.cc

(the file went through clang-format so there are some additional
changes)

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

---
Commit 26301bd55 authored by Danny Goodman<goodman.danny@gmail.com>
Committed by Danny Goodman<goodman.danny@gmail.com>:
fix error format

---
Commit b3f33ad46 authored by Yao Zhang<yaozhang@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make changes to prepare for the fused option of batch norm to be set to None (None means using fused batch norm if possible).

PiperOrigin-RevId: 159649743

---
Commit a4a469832 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Add tests for select ops and while loops that produce tuples that contain predicates.

PiperOrigin-RevId: 159645900

---
Commit 980d3f2be authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Use C API to implement Operation.name property

This name property is used in many existing tests including those that
already run with C API enabled (math_ops_test, framework_ops_test,
session_test, session_partial_run_test, math_ops_test_gpu, etc).

PiperOrigin-RevId: 159645767

---
Commit 26239c706 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Previously we didn't have an implementation of BatchNormInference and BatchNormTraining, which gives a linker error if anyone ever tries to call that. A dummy implementation is friendlier than a linker error.

PiperOrigin-RevId: 159645612

---
Commit f671c5caa authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 159570549

PiperOrigin-RevId: 160182040

* Update ops-related pbtxt files.

PiperOrigin-RevId: 160183349

* Merge changes from github followup.

PiperOrigin-RevId: 160183498

* Automated g4 rollback of changelist 160183498

PiperOrigin-RevId: 160189134

* Automated g4 rollback of changelist 160182040

PiperOrigin-RevId: 160190881

* [XLA] Disallow fuse X into Y if there are paths from X to Y which don't fuse

Just because X can fuse into all of its consumers does not mean that those
consumers can fuse into anything. Depending on the structure of the graph, this
can either result in no performance win at all or, in the case of recurrent
networks, a big performance deficit.

PiperOrigin-RevId: 160194058

* First draft of Tensors segment of the programmer's guide.

PiperOrigin-RevId: 160196550

* First draft of variables unit of programmer's guide.

PiperOrigin-RevId: 160196566

* Make xla::Literal moveable.

PiperOrigin-RevId: 160197273

* Automated g4 rollback of changelist 159897279

PiperOrigin-RevId: 160198598

* Updates text_classification example.

PiperOrigin-RevId: 160200457

* Fix backward compatibility test broken by rollback.

PiperOrigin-RevId: 160222187

* Support advisor in all places (Command line, APIs)
Add expensive operation checker

PiperOrigin-RevId: 160222348

* [XLA] Simplify the fusion heuristic

We had two different aspects of the fusion heuristic:
- Don't fuse a producer into a consumer if there exists a path from the
  producer to the consumer which cannot be fused.
- Don't fuse a producer into a consumer if any consumer of the producer cannot
  fuse.

These can be combined into one, simpler, heuristic.

PiperOrigin-RevId: 160222771

* Automated g4 rollback of changelist 160196566

PiperOrigin-RevId: 160222930

* Automated g4 rollback of changelist 160196550

PiperOrigin-RevId: 160222942

* Lets the HParam parser also accept True and False as inputs, since that's how python prints booleans.

PiperOrigin-RevId: 160234658

* Automated g4 rollback of changelist 155070869

PiperOrigin-RevId: 160249526

* [TF:XLA] Inline the sigmoid operation instead of mapping it elementwise.

PiperOrigin-RevId: 160274436

* Make sure all convolution tests are testing non-trivial cases, i.e. where not all inputs are 0, leading to an all-0 output, which masks most possible bugs.
We do not check-fail on 0-sized dimensions as tests for these special cases
exist.

PiperOrigin-RevId: 160274593

* Explicitly use "dns" URI scheme when using DNS names or literal IP
addresses with gRPC.  This avoids problems in environments in which the
default URI scheme is something other than "dns".

PiperOrigin-RevId: 160276862

* Add RWSE (root weighted squared error) to the WALS estimator.

PiperOrigin-RevId: 160276937

* Don't include node_def.proto.h in node_def_util.h

The goal is to make kernels mostly independent of proto headers, which will let us lock down our .so imports.

RELNOTES: n/a
PiperOrigin-RevId: 160278032

* [XLA] Add tuple support to Literal::CreateFromShape.

PiperOrigin-RevId: 160278561

* Updates some more examples in examples/learn.

PiperOrigin-RevId: 160278757

* Automated g4 rollback of changelist 160278032

PiperOrigin-RevId: 160280961

* Fixed the bug that Estimator does not make deepcopy of params in constructor

PiperOrigin-RevId: 160281247

* Clean out the config and params in TPUEstimator.

PiperOrigin-RevId: 160281507

* [XLA] Remove the "hlo dumper" parameter of xla::Compiler and its piping.

This dumper is no longer necessary since the restructuring of HLO dumping and
the addition of MaybeDumpHloModule which heeds to the right flags. The
remaining bits didn't have additional functionality, but constituted a lot of
boilerplate that has to be propagated throughout the backends.

PiperOrigin-RevId: 160281798

* [TF:XLA] Refactor the sigmoid op as a rescaled tanh.

PiperOrigin-RevId: 160282472

* Fix uninitialized values in TensorForest code.

PiperOrigin-RevId: 160284420

* [TF:XLA] Update Tensorflow LLVM release to upstream r306370.

Fix broken XLA build.

PiperOrigin-RevId: 160284588

* tfdbg example: fix --tensor_size issue in debug_fibonacci

PiperOrigin-RevId: 160290541

* [SE] ThenConvolveWithAlgorithm vlogs algorithm configs.

PiperOrigin-RevId: 160292762

* Fix documentation of Estimator class (invalid quotes).

PiperOrigin-RevId: 160292803

* Shrink the test size to avoid OOM error on old GPUs.

PiperOrigin-RevId: 160292834

* [TF:XLA] Reject operators with resource outputs on CPU and GPU devices.

We were checking for resource inputs but not resource outputs, which led to accidental fusion of some TensorArray ops on CPU and GPU.

PiperOrigin-RevId: 160294302

* Add a functionality of remote fused graph transformation to fuse graphs by op type

PiperOrigin-RevId: 160300039

* Cudnn compatible LSTMCell and LSTMBlockCell

PiperOrigin-RevId: 160300668

* [XLA] Remove "operand" argument from HandleReducePrecision.

PiperOrigin-RevId: 160301461

* Added more reduce window tests.

PiperOrigin-RevId: 160301509

* Updates more text classification examples in examples/learn.

PiperOrigin-RevId: 160305131

* Use C API to implement Operation._output_types

This change first converts the _output_types member to a property and
then implements it using C API if it is enabled.

PiperOrigin-RevId: 160306227

* Add more tests for BatchNormTraining.
RELNOTES: n/a

PiperOrigin-RevId: 160307959

* Update path to print_selective_registration_header.py in comment

PiperOrigin-RevId: 160308173

* Migrate TensorForest v4 python to contrib.

PiperOrigin-RevId: 160308805

* Automated g4 rollback of changelist 159454657

PiperOrigin-RevId: 160314706

* TESTFIX:  distributions:trig_test wasn't passing in ASAN mode.

PiperOrigin-RevId: 160315597

* tfdbg doc: fixes and improvements

PiperOrigin-RevId: 160318411

* Add a time estimation to HloCostAnalysis and represent properties as a map so that adding more properties will be easier, e.g. in a sub-class.

PiperOrigin-RevId: 160318494

* tfdbg: revert dns:/// prefix in gRPC mode

PiperOrigin-RevId: 160319348

* Moves TensorCApi from c_api.cc to c_api_internal.h, where it can be used
by other code that require access to the underlying TensorBuffers.

PiperOrigin-RevId: 160323362

* Readd the new tensors and variables documents, with tests passing.

PiperOrigin-RevId: 160324191

* Make ResourceHandle not be a proto

I'm trying to make core/kernels independent of protos.  Currently the dtype ResourceHandle is itself a proto.  After this CL, ResourceHandle is a normal C++ type which gets converted to/from ResourceHandleProto at (de)serialization time.

RELNOTES: n/a
PiperOrigin-RevId: 160329002

* Minor cleanup: remove unused dependencies and inclusions

PiperOrigin-RevId: 160334030

* Add name_scopes to mnist_deep.py for a cleaner graph layout.

PiperOrigin-RevId: 160338775

* Add note about `tf.test.mock` to docs for `tf.test`

PiperOrigin-RevId: 160338811

* Internal change.

PiperOrigin-RevId: 160339087

* Fix bugs in ScatterNd and add ScatterNdNonAliasingAdd.

tf.scatter_nd_non_aliasing_add acts similarly to tf.scatter_nd_add but
works on non-ref objects (i.e., Tensors -- not Variables).  This means
it has a gradient with respect to the primary input as well as the
updates.  It does its best to avoid making extra copies of the input.

PiperOrigin-RevId: 160339328

* Update ops-related pbtxt files.

PiperOrigin-RevId: 160340888

* Add checkpoint conversion for models that use the attention mechanism implemented in tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py.

PiperOrigin-RevId: 160340994

* Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 160341769

* Merge changes from github.

PiperOrigin-RevId: 160344052

* Update ops-related pbtxt files.

PiperOrigin-RevId: 160346151

* Load py_test in tensorflow/contrib/boosted_trees/BUILD to fix pip test
visibility failures.

* Disable boosted_trees tests on mac while they are being debugged.
---
 tensorflow/BUILD                              |    2 +
 tensorflow/c/c_api.cc                         |    9 -
 tensorflow/c/c_api_internal.h                 |   13 +
 tensorflow/cc/gradients/array_grad.cc         |   11 +
 tensorflow/cc/gradients/array_grad_test.cc    |   22 +
 tensorflow/compiler/aot/BUILD                 |   15 +-
 tensorflow/compiler/aot/compile.cc            |    9 +-
 .../aot/test_graph_tfunknownop.config.pbtxt   |   16 +
 .../compiler/aot/test_graph_tfunknownop.pbtxt |   86 +
 tensorflow/compiler/aot/tfcompile_main.cc     |    4 -
 tensorflow/compiler/aot/tfcompile_util.cc     |   50 +
 tensorflow/compiler/aot/tfcompile_util.h      |    6 +
 .../compiler/aot/tfcompile_util_test.cc       |   61 +
 tensorflow/compiler/jit/BUILD                 |    5 +-
 tensorflow/compiler/jit/kernels/BUILD         |    1 +
 .../compiler/jit/kernels/parallel_check_op.cc |    8 +-
 .../compiler/jit/mark_for_compilation_pass.cc |   16 +-
 .../jit/mark_for_compilation_pass_test.cc     |   38 +-
 tensorflow/compiler/plugin/executor/BUILD     |    4 +-
 .../compiler/plugin/executor/compiler.cc      |   13 +-
 .../compiler/plugin/executor/compiler.h       |    6 +-
 tensorflow/compiler/tests/BUILD               |   10 +
 tensorflow/compiler/tf2xla/BUILD              |    1 +
 .../compiler/tf2xla/kernels/function_ops.cc   |    1 +
 .../tf2xla/kernels/strided_slice_op.cc        |   41 +-
 .../compiler/tf2xla/kernels/unary_ops.cc      |   13 +-
 tensorflow/compiler/tf2xla/test_util.cc       |    1 +
 tensorflow/compiler/tf2xla/xla_context.cc     |   21 -
 tensorflow/compiler/tf2xla/xla_context.h      |    5 -
 tensorflow/compiler/tf2xla/xla_op_kernel.cc   |    5 -
 tensorflow/compiler/tf2xla/xla_op_kernel.h    |    5 -
 tensorflow/compiler/tf2xla/xla_op_registry.cc |    1 +
 tensorflow/compiler/xla/array4d.h             |   12 +
 tensorflow/compiler/xla/legacy_flags/BUILD    |   46 -
 .../legacy_flags/compiler_functor_flags.cc    |   61 -
 .../xla/legacy_flags/compiler_functor_flags.h |   47 -
 .../legacy_flags/convolution_thunk_flags.cc   |   63 -
 .../legacy_flags/convolution_thunk_flags.h    |   47 -
 .../xla/legacy_flags/cpu_runtime_flags.cc     |   71 -
 .../xla/legacy_flags/cpu_runtime_flags.h      |   51 -
 .../xla/legacy_flags/debug_options_flags.cc   |   66 +
 .../xla/legacy_flags/hlo_test_base_flags.cc   |   63 -
 .../xla/legacy_flags/hlo_test_base_flags.h    |   47 -
 .../xla/legacy_flags/service_flags.cc         |   23 -
 .../compiler/xla/legacy_flags/service_flags.h |   12 -
 tensorflow/compiler/xla/literal_util.cc       |   12 +-
 tensorflow/compiler/xla/literal_util.h        |    4 +
 tensorflow/compiler/xla/literal_util_test.cc  |   42 +-
 .../compiler/xla/packed_literal_reader.cc     |    2 +-
 tensorflow/compiler/xla/reference_util.cc     |   70 +
 tensorflow/compiler/xla/reference_util.h      |   46 +
 tensorflow/compiler/xla/service/BUILD         |  130 +-
 .../compiler/xla/service/buffer_assignment.cc |    1 +
 .../xla/service/buffer_assignment_test.cc     |    1 +
 .../xla/service/compile_only_service.cc       |    7 +-
 tensorflow/compiler/xla/service/compiler.h    |   12 +-
 tensorflow/compiler/xla/service/cpu/BUILD     |    9 +-
 .../xla/service/cpu/compiler_functor.cc       |   25 +-
 .../xla/service/cpu/compiler_functor.h        |   19 +-
 .../xla/service/cpu/conv_canonicalization.cc  |    6 -
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   53 +-
 .../compiler/xla/service/cpu/cpu_compiler.h   |    7 +-
 .../xla/service/cpu/dot_op_emitter.cc         |   21 +-
 .../compiler/xla/service/cpu/dot_op_emitter.h |    8 +-
 .../xla/service/cpu/ir_emission_utils.cc      |   11 -
 .../compiler/xla/service/cpu/ir_emitter.cc    |   24 +-
 .../xla/service/cpu/simple_orc_jit.cc         |   26 +-
 .../compiler/xla/service/cpu/simple_orc_jit.h |   19 +-
 .../compiler/xla/service/dfs_hlo_visitor.h    |    3 +-
 .../xla/service/elemental_ir_emitter.cc       |  110 +-
 tensorflow/compiler/xla/service/executable.cc |   30 -
 tensorflow/compiler/xla/service/executable.h  |    8 +-
 tensorflow/compiler/xla/service/gpu/BUILD     |    4 +-
 .../xla/service/gpu/convolution_thunk.cc      |    6 +-
 .../xla/service/gpu/convolution_thunk.h       |    5 +-
 .../compiler/xla/service/gpu/gpu_compiler.cc  |   30 +-
 .../compiler/xla/service/gpu/gpu_compiler.h   |    9 +-
 .../compiler/xla/service/gpu/hlo_schedule.cc  |    1 +
 .../compiler/xla/service/gpu/hlo_schedule.h   |    2 +-
 .../xla/service/hlo_alias_analysis.cc         |  107 +-
 .../compiler/xla/service/hlo_alias_analysis.h |  163 +-
 tensorflow/compiler/xla/service/hlo_buffer.cc |  139 ++
 tensorflow/compiler/xla/service/hlo_buffer.h  |  183 ++
 .../compiler/xla/service/hlo_cost_analysis.cc |  316 ++--
 .../compiler/xla/service/hlo_cost_analysis.h  |  102 +-
 .../xla/service/hlo_cost_analysis_test.cc     |  100 +-
 .../xla/service/hlo_dataflow_analysis.cc      |  265 ---
 .../xla/service/hlo_dataflow_analysis.h       |  215 +--
 .../compiler/xla/service/hlo_evaluator.cc     |   16 +-
 .../compiler/xla/service/hlo_graph_dumper.cc  |   26 +
 .../compiler/xla/service/hlo_graph_dumper.h   |    7 +
 .../compiler/xla/service/hlo_instruction.cc   |    2 +-
 .../compiler/xla/service/hlo_instruction.h    |   12 +-
 tensorflow/compiler/xla/service/hlo_module.cc |   30 +
 tensorflow/compiler/xla/service/hlo_module.h  |    3 +
 .../compiler/xla/service/hlo_module_test.cc   |   24 +
 .../compiler/xla/service/hlo_ordering.cc      |  355 ----
 .../compiler/xla/service/hlo_ordering.h       |   22 -
 .../compiler/xla/service/hlo_ordering_test.cc |   61 +-
 .../compiler/xla/service/hlo_pass_pipeline.cc |   10 +-
 .../compiler/xla/service/hlo_pass_pipeline.h  |    6 +-
 .../xla/service/hlo_rematerialization.cc      |    1 +
 .../compiler/xla/service/hlo_scheduling.cc    |  388 ++++
 .../compiler/xla/service/hlo_scheduling.h     |   50 +
 .../xla/service/hlo_scheduling_test.cc        |   97 +
 tensorflow/compiler/xla/service/hlo_value.cc  |  301 ++++
 tensorflow/compiler/xla/service/hlo_value.h   |  242 +++
 .../xla/service/instruction_fusion.cc         |  220 ++-
 .../compiler/xla/service/instruction_fusion.h |   10 +
 .../xla/service/llvm_ir/alias_analysis.cc     |   45 +-
 .../compiler/xla/service/llvm_ir/llvm_util.cc |   23 +-
 .../compiler/xla/service/local_service.cc     |   11 +-
 .../compiler/xla/service/local_service.h      |    6 +-
 tensorflow/compiler/xla/service/service.cc    |   32 +-
 tensorflow/compiler/xla/service/service.h     |   16 +-
 .../compiler/xla/service/shape_inference.cc   |   19 +-
 tensorflow/compiler/xla/tests/BUILD           |   61 +-
 .../xla/tests/array_elementwise_ops_test.cc   |   23 -
 .../xla/tests/batch_normalization_test.cc     |  261 +++
 .../compiler/xla/tests/codegen_test_base.cc   |    3 +-
 .../compiler/xla/tests/dot_operation_test.cc  |    2 -
 .../compiler/xla/tests/hlo_test_base.cc       |   15 +-
 tensorflow/compiler/xla/tests/hlo_test_base.h |    2 -
 .../compiler/xla/tests/literal_test_util.cc   |   34 +-
 tensorflow/compiler/xla/tests/map_test.cc     |    7 +-
 .../xla/tests/reduce_precision_test.cc        |  263 +++
 .../compiler/xla/tests/reduce_window_test.cc  |  552 ++++++
 .../compiler/xla/text_literal_reader.cc       |    5 +-
 tensorflow/compiler/xla/tools/BUILD           |    3 +-
 .../tools/dumped_computation_to_graphviz.cc   |   14 +-
 .../dumped_computation_to_tf_graphdef.cc      |   13 +-
 tensorflow/compiler/xla/xla.proto             |   38 +-
 tensorflow/contrib/BUILD                      |   10 +-
 tensorflow/contrib/__init__.py                |    1 +
 tensorflow/contrib/batching/BUILD             |    2 -
 tensorflow/contrib/batching/util/BUILD        |    2 -
 tensorflow/contrib/bayesflow/BUILD            |    1 +
 .../kernel_tests/csiszar_divergence_test.py   |   43 +-
 .../python/kernel_tests/monte_carlo_test.py   |   98 +-
 .../python/ops/csiszar_divergence_impl.py     |   62 +-
 .../bayesflow/python/ops/monte_carlo.py       |    1 +
 .../bayesflow/python/ops/monte_carlo_impl.py  |   95 +
 tensorflow/contrib/boosted_trees/BUILD        |  630 +++++++
 tensorflow/contrib/boosted_trees/__init__.py  |   22 +
 .../kernels/ensemble_optimizer_ops.cc         |  244 +++
 .../boosted_trees/kernels/model_ops.cc        |  169 ++
 .../boosted_trees/kernels/prediction_ops.cc   |  429 +++++
 .../boosted_trees/kernels/quantile_ops.cc     |  822 +++++++++
 .../kernels/split_handler_ops.cc              |  509 ++++++
 .../kernels/stats_accumulator_ops.cc          |  784 ++++++++
 .../boosted_trees/kernels/training_ops.cc     |  825 +++++++++
 tensorflow/contrib/boosted_trees/lib/BUILD    |  300 +++-
 .../common/accumulators/class-partition-key.h |   61 +
 .../accumulators/feature-stats-accumulator.h  |   82 +
 .../feature-stats-accumulator_test.cc         |   90 +
 .../partitioners/example_partitioner.cc       |   90 +
 .../common/partitioners/example_partitioner.h |   53 +
 .../partitioners/example_partitioner_test.cc  |   99 +
 .../handlers/bias-feature-column-handler.cc   |   59 +
 .../handlers/bias-feature-column-handler.h    |   57 +
 .../bias-feature-column-handler_test.cc       |  134 ++
 .../categorical-feature-column-handler.cc     |  140 ++
 .../categorical-feature-column-handler.h      |   64 +
 ...categorical-feature-column-handler_test.cc |  165 ++
 .../dense-quantized-feature-column-handler.cc |  116 ++
 .../dense-quantized-feature-column-handler.h  |   62 +
 ...e-quantized-feature-column-handler_test.cc |  155 ++
 .../handlers/feature-column-handler.h         |   82 +
 ...sparse-quantized-feature-column-handler.cc |  172 ++
 .../sparse-quantized-feature-column-handler.h |   67 +
 ...e-quantized-feature-column-handler_test.cc |  162 ++
 .../stats/feature-split-candidate.h           |   61 +
 .../learner/stochastic/stats/gradient-stats.h |  193 ++
 .../lib/learner/stochastic/stats/node-stats.h |  301 ++++
 .../stochastic/stats/node-stats_test.cc       |  213 +++
 .../learner/stochastic/stats/split-stats.h    |   84 +
 .../lib/utils/dropout_utils_test.cc           |    5 +-
 .../ops/ensemble_optimizer_ops.cc             |   44 +
 .../contrib/boosted_trees/ops/model_ops.cc    |  114 ++
 .../boosted_trees/ops/prediction_ops.cc       |  132 ++
 .../contrib/boosted_trees/ops/quantile_ops.cc |  263 +++
 .../boosted_trees/ops/split_handler_ops.cc    |  197 ++
 .../ops/stats_accumulator_ops.cc              |  475 +++++
 .../contrib/boosted_trees/ops/training_ops.cc |  120 ++
 tensorflow/contrib/boosted_trees/proto/BUILD  |   10 +
 .../boosted_trees/proto/split_info.proto      |   19 +
 .../contrib/boosted_trees/python/__init__.py  |   22 +
 .../ensemble_optimizer_ops_test.py            |  351 ++++
 .../python/kernel_tests/model_ops_test.py     |  325 ++++
 .../kernel_tests/prediction_ops_test.py       | 1335 ++++++++++++++
 .../python/kernel_tests/quantile_ops_test.py  |  341 ++++
 .../kernel_tests/split_handler_ops_test.py    |  329 ++++
 .../stats_accumulator_ops_test.py             |  389 ++++
 .../python/kernel_tests/training_ops_test.py  | 1590 +++++++++++++++++
 .../python/ops/batch_ops_utils.py             |  132 ++
 .../python/ops/ensemble_optimizer_ops.py      |   32 +
 .../boosted_trees/python/ops/model_ops.py     |  119 ++
 .../python/ops/prediction_ops.py              |   32 +
 .../boosted_trees/python/ops/quantile_ops.py  |  176 ++
 .../python/ops/split_handler_ops.py           |   32 +
 .../python/ops/stats_accumulator_ops.py       |  210 +++
 .../boosted_trees/python/ops/training_ops.py  |   32 +
 .../contrib/boosted_trees/resources/BUILD     |    2 -
 tensorflow/contrib/cmake/tf_core_cpu.cmake    |    4 +
 .../contrib/cmake/tf_core_framework.cmake     |   13 +-
 .../contrib/cmake/tf_core_kernels.cmake       |    3 +
 tensorflow/contrib/cmake/tf_core_ops.cmake    |    3 +
 tensorflow/contrib/cmake/tf_grappler.cmake    |    2 +
 tensorflow/contrib/cmake/tf_python.cmake      |   18 +
 .../python/kernel_tests/cudnn_rnn_ops_test.py |   20 +-
 .../cudnn_rnn/python/ops/cudnn_rnn_ops.py     |   65 +-
 tensorflow/contrib/decision_trees/__init__.py |   27 +
 tensorflow/contrib/distributions/BUILD        |   65 +-
 tensorflow/contrib/distributions/__init__.py  |    2 +
 .../bijectors/sinh_arcsinh_test.py            |  127 ++
 .../kernel_tests/distribution_util_test.py    |  596 +-----
 .../python/kernel_tests/geometric_test.py     |   10 +-
 .../python/kernel_tests/trig_test.py          |  262 +++
 .../vector_exponential_diag_test.py           |  206 +++
 .../python/ops/bijectors/__init__.py          |    2 +
 .../python/ops/bijectors/sinh_arcsinh.py      |   29 +
 .../python/ops/bijectors/sinh_arcsinh_impl.py |  143 ++
 .../distributions/python/ops/binomial.py      |    6 +-
 .../distributions/python/ops/geometric.py     |   42 +-
 .../python/ops/negative_binomial.py           |   40 +-
 .../distributions/python/ops/poisson.py       |   23 +-
 .../contrib/distributions/python/ops/trig.py  |  173 ++
 .../python/ops/vector_exponential_diag.py     |  198 ++
 .../ops/vector_exponential_linear_operator.py |  288 +++
 tensorflow/contrib/factorization/BUILD        |    5 +-
 .../python/ops/clustering_ops.py              |  318 +++-
 .../python/ops/factorization_ops.py           |  235 ++-
 .../python/ops/factorization_ops_test.py      |  216 ++-
 .../contrib/factorization/python/ops/wals.py  |  146 +-
 tensorflow/contrib/hooks/BUILD                |   15 +-
 tensorflow/contrib/labeled_tensor/BUILD       |   10 +-
 tensorflow/contrib/layers/BUILD               |   45 +-
 .../contrib/learn/python/learn/datasets/BUILD |    9 +-
 .../learn/python/learn/estimators/debug.py    |   12 +-
 .../python/learn/estimators/debug_test.py     |   17 +
 .../python/learn/estimators/kmeans_test.py    |  165 +-
 .../python/learn/estimators/run_config.py     |    9 +
 .../contrib/learn/python/learn/experiment.py  |    7 +-
 tensorflow/contrib/linalg/BUILD               |    6 +-
 .../python/sdca_estimator_test.py             |  423 +++--
 tensorflow/contrib/lookup/BUILD               |   10 +-
 tensorflow/contrib/losses/BUILD               |    5 +-
 .../contrib/makefile/proto_text_cc_files.txt  |    1 +
 tensorflow/contrib/makefile/tf_op_files.txt   |    8 +
 .../contrib/makefile/tf_proto_files.txt       |    4 +
 tensorflow/contrib/ndlstm/BUILD               |    1 +
 tensorflow/contrib/resampler/BUILD            |   92 +
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 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h
 create mode 100644 tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/model_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/ops/training_ops.cc
 create mode 100644 tensorflow/contrib/boosted_trees/proto/split_info.proto
 create mode 100644 tensorflow/contrib/boosted_trees/python/__init__.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/model_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/ops/training_ops.py
 create mode 100644 tensorflow/contrib/decision_trees/__init__.py
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/trig.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
 create mode 100644 tensorflow/contrib/resampler/BUILD
 create mode 100644 tensorflow/contrib/resampler/__init__.py
 create mode 100644 tensorflow/contrib/resampler/kernels/resampler_ops.cc
 create mode 100644 tensorflow/contrib/resampler/kernels/resampler_ops.h
 create mode 100644 tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc
 create mode 100644 tensorflow/contrib/resampler/ops/resampler_ops.cc
 create mode 100644 tensorflow/contrib/resampler/python/__init__.py
 create mode 100644 tensorflow/contrib/resampler/python/ops/resampler_ops.py
 create mode 100644 tensorflow/contrib/resampler/python/ops/resampler_ops_test.py
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/model_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
 create mode 100644 tensorflow/contrib/tensor_forest/ops/model_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/ops/stats_ops.cc
 create mode 100644 tensorflow/contrib/tensor_forest/python/ops/model_ops.py
 create mode 100644 tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
 create mode 100644 tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
 create mode 100644 tensorflow/core/framework/resource_handle.cc
 create mode 100644 tensorflow/core/framework/resource_handle.h
 create mode 100644 tensorflow/docs_src/programmers_guide/tensors.md
 delete mode 100644 tensorflow/docs_src/programmers_guide/variable_scope.md
 delete mode 100644 tensorflow/examples/learn/iris_val_based_early_stopping.py
 create mode 100644 tensorflow/python/debug/README.md
 create mode 100644 tensorflow/python/grappler/model_analyzer.cc
 create mode 100644 tensorflow/python/grappler/model_analyzer.h
 create mode 100644 tensorflow/python/grappler/model_analyzer.i
 create mode 100644 tensorflow/python/grappler/model_analyzer.py
 create mode 100644 tensorflow/python/grappler/model_analyzer_test.py
 create mode 100644 tensorflow/python/kernel_tests/distributions/util_test.py
 delete mode 100644 tensorflow/python/ops/conv2d_benchmark.py
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
 create mode 100644 tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index c190b883a4..9dd09db1a3 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -216,6 +216,7 @@ filegroup(
         "//tensorflow/compiler/jit/kernels:all_files",
         "//tensorflow/compiler/jit/legacy_flags:all_files",
         "//tensorflow/compiler/jit/ops:all_files",
+        "//tensorflow/compiler/plugin/executor:all_files",
         "//tensorflow/compiler/tests:all_files",
         "//tensorflow/compiler/tf2xla:all_files",
         "//tensorflow/compiler/tf2xla/cc:all_files",
@@ -288,6 +289,7 @@ filegroup(
         "//tensorflow/contrib/opt:all_files",
         "//tensorflow/contrib/predictor:all_files",
         "//tensorflow/contrib/remote_fused_graph/pylib:all_files",
+        "//tensorflow/contrib/resampler:all_files",
         "//tensorflow/contrib/rnn:all_files",
         "//tensorflow/contrib/saved_model:all_files",
         "//tensorflow/contrib/saved_model/cc/saved_model:all_files",
diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index b1568c5f63..c320fe94a1 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -466,15 +466,6 @@ TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src) {
                       dimvec.size(), base, size, DeleteArray, base);
 }
 
-class TensorCApi {
- public:
-  static TensorBuffer* Buffer(const Tensor& tensor) { return tensor.buf_; }
-  static Tensor MakeTensor(TF_DataType type, const TensorShape& shape,
-                           TensorBuffer* buf) {
-    return Tensor(static_cast<DataType>(type), shape, buf);
-  }
-};
-
 // Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
 // result in a zero-sized tensor.
 static TF_Tensor* EmptyTensor(TF_DataType dtype, const TensorShape& shape) {
diff --git a/tensorflow/c/c_api_internal.h b/tensorflow/c/c_api_internal.h
index f17ac26ad9..f2773ae20f 100644
--- a/tensorflow/c/c_api_internal.h
+++ b/tensorflow/c/c_api_internal.h
@@ -117,3 +117,16 @@ struct TF_ImportGraphDefOptions {
 struct TF_DeviceList {
   std::vector<tensorflow::DeviceAttributes> response;
 };
+
+namespace tensorflow {
+
+class TensorCApi {
+ public:
+  static TensorBuffer* Buffer(const Tensor& tensor) { return tensor.buf_; }
+  static Tensor MakeTensor(TF_DataType type, const TensorShape& shape,
+                           TensorBuffer* buf) {
+    return Tensor(static_cast<DataType>(type), shape, buf);
+  }
+};
+
+}  // end namespace tensorflow
diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc
index 37f07e71a0..e69db10580 100644
--- a/tensorflow/cc/gradients/array_grad.cc
+++ b/tensorflow/cc/gradients/array_grad.cc
@@ -247,6 +247,17 @@ Status ScatterNdGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("ScatterNd", ScatterNdGrad);
 
+Status ScatterNdNonAliasingAddGrad(const Scope& scope, const Operation& op,
+                                   const std::vector<Output>& grad_inputs,
+                                   std::vector<Output>* grad_outputs) {
+  auto indices = op.input(1);
+  grad_outputs->push_back(Identity(scope, grad_inputs[0]));
+  grad_outputs->push_back(NoGradient());
+  grad_outputs->push_back(GatherNd(scope, grad_inputs[0], indices));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("ScatterNdNonAliasingAdd", ScatterNdNonAliasingAddGrad);
+
 Status PadGrad(const Scope& scope, const Operation& op,
                const std::vector<Output>& grad_inputs,
                std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/array_grad_test.cc b/tensorflow/cc/gradients/array_grad_test.cc
index 5798b5b509..1777e18145 100644
--- a/tensorflow/cc/gradients/array_grad_test.cc
+++ b/tensorflow/cc/gradients/array_grad_test.cc
@@ -233,6 +233,28 @@ TEST_F(ArrayGradTest, ScatterNdGrad_SliceIndexing) {
   RunTest(updates, updates_shape, y, y_shape);
 }
 
+TEST_F(ArrayGradTest, ScatterNdNonAliasingAddGrad_SimpleIndexing) {
+  TensorShape updates_shape({4});
+  TensorShape input_shape({8});
+  auto input = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(input_shape));
+  auto updates =
+      Placeholder(scope_, DT_FLOAT, Placeholder::Shape(updates_shape));
+  auto indices = Const(scope_, {{4}, {3}, {1}, {7}});
+  auto y = ScatterNdNonAliasingAdd(scope_, input, indices, updates);
+  RunTest({input, updates}, {input_shape, updates_shape}, {y}, {input_shape});
+}
+
+TEST_F(ArrayGradTest, ScatterNdNonAliasingAddGrad_SliceIndexing) {
+  TensorShape updates_shape({2, 4, 4});
+  TensorShape input_shape({4, 4, 4});
+  auto input = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(input_shape));
+  auto updates =
+      Placeholder(scope_, DT_FLOAT, Placeholder::Shape(updates_shape));
+  auto indices = Const(scope_, {{0}, {2}});
+  auto y = ScatterNdNonAliasingAdd(scope_, input, indices, updates);
+  RunTest({input, updates}, {input_shape, updates_shape}, {y}, {input_shape});
+}
+
 TEST_F(ArrayGradTest, PadGrad) {
   TensorShape x_shape({2, 3});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 31637358c3..7dfed901fc 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -108,6 +108,7 @@ cc_test(
     deps = [
         ":tfcompile_lib",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
@@ -127,8 +128,6 @@ cc_library(
         ":tfcompile_lib",
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
-        "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
@@ -158,6 +157,17 @@ tf_library(
     tags = ["manual"],
 )
 
+# A test of tf_library that includes a graph with an unknown op, but where
+# the compilation works because the the unknown op is not needed for the fetches.
+tf_library(
+    name = "test_graph_tfunknownop",
+    testonly = 1,
+    config = "test_graph_tfunknownop.config.pbtxt",
+    cpp_class = "UnknownOpAddComp",
+    graph = "test_graph_tfunknownop.pbtxt",
+    tags = ["manual"],
+)
+
 # Utility library for benchmark binaries, used by the *_benchmark rules that are
 # added by the tfcompile bazel macro.
 cc_library(
@@ -201,6 +211,7 @@ test_suite(
     tests = [
         ":benchmark_test",
         ":test_graph_tfadd_test",
+        ":test_graph_tfunknownop_test",
         "//tensorflow/compiler/aot/tests:all_tests",
     ],
 )
diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc
index ca17c5ab69..317baa8971 100644
--- a/tensorflow/compiler/aot/compile.cc
+++ b/tensorflow/compiler/aot/compile.cc
@@ -378,9 +378,16 @@ Status CompileXla(xla::CompileOnlyClient* client,
 Status InitGraph(const GraphDef& graph_def, const Config& config,
                  const MainFlags& flags, std::unique_ptr<Graph>* graph) {
   TF_RETURN_IF_ERROR(ValidateConfig(config));
+
   FunctionLibraryDefinition flib_def(OpRegistry::Global(), graph_def.library());
   std::unique_ptr<Graph> g(new Graph(flib_def));
-  GraphDef copy_def(graph_def);
+
+  GraphDef copy_def;
+
+  // Prune the GraphDef first so that unknown ops that we aren't compiling get
+  // filtered out.
+  TF_RETURN_IF_ERROR(PruneGraphDefInto(config, graph_def, &copy_def));
+
   TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&copy_def, *g->op_registry(),
                                                0 /*node_offset*/));
   TF_RETURN_IF_ERROR(
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop.config.pbtxt
new file mode 100644
index 0000000000..5625c0ab03
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop.config.pbtxt
@@ -0,0 +1,16 @@
+# Text form of tensorflow.tfcompile.Config proto.
+feed {
+  id { node_name: "x_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "y_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+fetch {
+  id { node_name: "x_y_sum" }
+}
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
new file mode 100644
index 0000000000..212ffbb5ff
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
@@ -0,0 +1,86 @@
+node {
+  name  : "x_const"
+  op    : "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        int_val: 1
+      }
+    }
+  }
+  attr {
+    key  : "dtype"
+    value {
+      type       : DT_INT32
+    }
+  }
+}
+node {
+  name  : "y_const"
+  op    : "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        int_val: 2
+      }
+    }
+  }
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name  : "x_y_sum"
+  op    : "Add"
+  input : "x_const"
+  input : "y_const"
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name  : "z"
+  op    : "SomeUnknownOp"
+  input : "x_const"
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name  : "x_z_sum"
+  op    : "Add"
+  input : "x_const"
+  input : "z"
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+versions {
+  producer: 15
+}
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index e03d28cd96..9ec205a126 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -24,8 +24,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
@@ -134,8 +132,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
-  xla::legacy_flags::AppendCompilerFunctorFlags(&flag_list);
-  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::legacy_flags::AppendServiceFlags(&flag_list);
diff --git a/tensorflow/compiler/aot/tfcompile_util.cc b/tensorflow/compiler/aot/tfcompile_util.cc
index fd073a2e26..8774a02128 100644
--- a/tensorflow/compiler/aot/tfcompile_util.cc
+++ b/tensorflow/compiler/aot/tfcompile_util.cc
@@ -15,10 +15,14 @@ limitations under the License.
 
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 
+#include <queue>
 #include <set>
+#include <unordered_map>
 
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/graph/tensor_id.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
@@ -115,5 +119,51 @@ Status ValidateConfig(const Config& config) {
   return Status::OK();
 }
 
+Status PruneGraphDefInto(const Config& config, const GraphDef& in,
+                         GraphDef* out) {
+  *out = in;
+  out->clear_node();
+
+  // Maps node name to reachability.
+  std::unordered_map<string, std::pair<bool, const NodeDef*>> node_by_name;
+  for (const NodeDef& node : in.node()) {
+    node_by_name[node.name()] = std::pair<bool, const NodeDef*>(false, &node);
+  }
+
+  std::queue<string> name_queue;
+  for (int i = 0; i < config.fetch_size(); ++i) {
+    name_queue.push(config.fetch(i).id().node_name());
+  }
+  while (!name_queue.empty()) {
+    const string name = name_queue.front();
+    name_queue.pop();
+
+    auto find_it = node_by_name.find(name);
+    if (find_it == node_by_name.end()) {
+      return errors::InvalidArgument("While pruning graph, node ", name,
+                                     " needed but not found in the graph.");
+    }
+    auto& map_entry = find_it->second;
+    if (map_entry.first) {
+      continue;
+    }
+    map_entry.first = true;
+
+    for (const string& in_edge : map_entry.second->input()) {
+      name_queue.push(ParseTensorName(in_edge).first.ToString());
+    }
+  }
+
+  // Copy over, preserving order of original and only nodes that are reachable
+  // from the fetches.
+  out->mutable_node()->Reserve(in.node_size());
+  for (const NodeDef& node : in.node()) {
+    if (node_by_name[node.name()].first) {
+      *out->add_node() = node;
+    }
+  }
+  return Status::OK();
+}
+
 }  // namespace tfcompile
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/aot/tfcompile_util.h b/tensorflow/compiler/aot/tfcompile_util.h
index 651d75d0d0..84060c0761 100644
--- a/tensorflow/compiler/aot/tfcompile_util.h
+++ b/tensorflow/compiler/aot/tfcompile_util.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
@@ -30,6 +31,11 @@ Status ValidateCppIdent(StringPiece ident, StringPiece msg);
 // ValidateConfig returns OK iff config is valid.
 Status ValidateConfig(const Config& config);
 
+// Returns in <out> a copy of <in>, pruned to only include fetches from
+// <config>.
+Status PruneGraphDefInto(const Config& config, const GraphDef& in,
+                         GraphDef* out);
+
 }  // namespace tfcompile
 }  // namespace tensorflow
 
diff --git a/tensorflow/compiler/aot/tfcompile_util_test.cc b/tensorflow/compiler/aot/tfcompile_util_test.cc
index c321d3ff4c..5a92851ceb 100644
--- a/tensorflow/compiler/aot/tfcompile_util_test.cc
+++ b/tensorflow/compiler/aot/tfcompile_util_test.cc
@@ -15,9 +15,11 @@ limitations under the License.
 
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
@@ -180,6 +182,65 @@ TEST(ValidateConfig, ConflictingFetchName) {
   ExpectErrorContains(ValidateConfig(config), "conflicting fetch name");
 }
 
+static Config FetchesConfig(std::vector<string> fetches) {
+  Config config;
+  for (const auto& fetch_node_name : fetches) {
+    auto* fetch = config.add_fetch();
+    fetch->set_name(strings::StrCat("fetch_", fetch_node_name));
+    fetch->mutable_id()->set_node_name(fetch_node_name);
+  }
+  return config;
+}
+
+TEST(PruneGraphDefInto, Basic) {
+  GraphDef def;
+  auto* n = def.add_node();
+  n->set_name("a");
+  n->add_input("b:0");
+  n->add_input("^c");
+
+  GraphDef copy;
+  ExpectErrorContains(PruneGraphDefInto(FetchesConfig({"missing"}), def, &copy),
+                      "node missing needed");
+  ExpectErrorContains(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy),
+                      "node b needed");
+
+  n = def.add_node();
+  n->set_name("b");
+  ExpectErrorContains(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy),
+                      "node c needed");
+  n->add_input("d:1");
+
+  n = def.add_node();
+  n->set_name("c");
+  n->add_input("d:1");
+
+  n = def.add_node();
+  n->set_name("d");
+
+  // Graph is full, no pruning done.
+  // Graph right now has diamond from d:
+  //   d --> b --> a
+  //   d --> c --> a
+  TF_EXPECT_OK(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy));
+  EXPECT_EQ(def.DebugString(), copy.DebugString());
+  GraphDef pruned_a = copy;
+
+  // Add some unrelated fields that use b and c, but are not needed for a.
+  n = def.add_node();
+  n->set_name("e");
+  n->add_input("^d");
+  n->add_input("b:2");
+  copy.Clear();
+  TF_EXPECT_OK(PruneGraphDefInto(FetchesConfig({"a"}), def, &copy));
+  EXPECT_EQ(pruned_a.DebugString(), copy.DebugString());
+
+  // Fetch "a" and "e" to get the original graph.
+  copy.Clear();
+  TF_EXPECT_OK(PruneGraphDefInto(FetchesConfig({"a", "e"}), def, &copy));
+  EXPECT_EQ(def.DebugString(), copy.DebugString());
+}
+
 }  // namespace
 }  // namespace tfcompile
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 306e704415..7ebd842218 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -15,7 +15,10 @@ package_group(
 )
 
 package(
-    default_visibility = [":internal"],
+    default_visibility = [
+        ":internal",
+        "//tensorflow/compiler/plugin/executor:__pkg__",
+    ],
 )
 
 load("//tensorflow:tensorflow.bzl", "cc_header_only_library")
diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD
index ed204b8182..97f3512a6c 100644
--- a/tensorflow/compiler/jit/kernels/BUILD
+++ b/tensorflow/compiler/jit/kernels/BUILD
@@ -2,6 +2,7 @@ licenses(["notice"])  # Apache 2.0
 
 package(
     default_visibility = [
+        "//tensorflow/compiler/plugin/executor:__pkg__",
         "//tensorflow/compiler/tf2xla:internal",
     ],
 )
diff --git a/tensorflow/compiler/jit/kernels/parallel_check_op.cc b/tensorflow/compiler/jit/kernels/parallel_check_op.cc
index c86e03118b..bd4eefbc0b 100644
--- a/tensorflow/compiler/jit/kernels/parallel_check_op.cc
+++ b/tensorflow/compiler/jit/kernels/parallel_check_op.cc
@@ -64,7 +64,7 @@ class ParallelCheckOp : public OpKernel {
           ok = (diff <= tolerance);
         }
         if (ok) continue;
-        LOG(ERROR) << "Op " << def().name() << " fails equality at output "
+        LOG(ERROR) << "Op " << name() << " fails equality at output "
                    << input_idx << " type " << DataTypeString(dtype)
                    << " element " << i << ": std_val=" << p0[i]
                    << " test_val=" << p1[i] << " diff=" << (p0[i] - p1[i]);
@@ -75,7 +75,7 @@ class ParallelCheckOp : public OpKernel {
   }
 
   void Compute(OpKernelContext* ctx) override {
-    VLOG(1) << "Compute " << def().name();
+    VLOG(1) << "Compute " << name();
     const int num_pairs = ctx->num_inputs() / 2;
     for (int i = 0; i < num_pairs; ++i) {
       CHECK_EQ(ctx->input_dtype(i), ctx->input_dtype(i + num_pairs));
@@ -113,7 +113,7 @@ class ParallelCheckOp : public OpKernel {
           LOG(FATAL) << "unimpl: " << ctx->input_dtype(i);
       }
       if (failed > 0) {
-        LOG(ERROR) << "check failed for " << def().name() << " output " << i
+        LOG(ERROR) << "check failed for " << name() << " output " << i
                    << " num_elts: " << num_elts;
         legacy_flags::ParallelCheckOpFlags* flags =
             legacy_flags::GetParallelCheckOpFlags();
@@ -121,7 +121,7 @@ class ParallelCheckOp : public OpKernel {
           LOG(QFATAL) << "failfast on first parallel-check failure";
         }
       } else {
-        VLOG(1) << "check passed for " << def().name() << " output " << i
+        VLOG(1) << "check passed for " << name() << " output " << i
                 << " num_elts: " << num_elts;
       }
 
diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
index f1fef85f99..7eab7bb28f 100644
--- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc
+++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/memory_types.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/graph/algorithm.h"
@@ -162,10 +163,12 @@ Status DeviceTypeOfDevice(const string& device, DeviceType* device_type) {
   return Status::OK();
 }
 
-// Does `node` have a DT_RESOURCE typed argument?
-bool HasResourceArgument(const Node& node) {
+// Tests whether `node` has a DT_RESOURCE typed input or output.
+bool HasResourceInputOrOutput(const Node& node) {
   return std::find(node.input_types().begin(), node.input_types().end(),
-                   DT_RESOURCE) != node.input_types().end();
+                   DT_RESOURCE) != node.input_types().end() ||
+         std::find(node.output_types().begin(), node.output_types().end(),
+                   DT_RESOURCE) != node.output_types().end();
 }
 
 Status FindCompilationCandidates(
@@ -193,9 +196,10 @@ Status FindCompilationCandidates(
               << ": " << node->type_string();
       continue;
     }
-    if (!registration->compile_resource_ops && HasResourceArgument(*node)) {
-      VLOG(2) << "Compilation rejected node: resource argument " << node->name()
-              << ": " << node->type_string();
+    if (!registration->compile_resource_ops &&
+        HasResourceInputOrOutput(*node)) {
+      VLOG(2) << "Compilation rejected node: resource input/output "
+              << node->name() << ": " << node->type_string();
       continue;
     }
     if (node->type_string() == "While" &&
diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
index 9f30e12e0e..4b88da27a1 100644
--- a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
+++ b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
@@ -14,11 +14,13 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/jit/mark_for_compilation_pass.h"
-#include "tensorflow/compiler/jit/defs.h"
 
 #include "tensorflow/cc/framework/ops.h"
 #include "tensorflow/cc/ops/control_flow_ops_internal.h"
 #include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/compiler/jit/defs.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/graph/graph_constructor.h"
@@ -455,5 +457,39 @@ TEST(XlaCompilationTest, CyclesWithDifferentScopesAndBridge) {
   EXPECT_EQ(clusters["B"], clusters["C"]);
 }
 
+REGISTER_OP("ResourceInput").Input("a: resource").Output("o: float");
+REGISTER_OP("ResourceOutput").Input("a: float").Output("o: resource");
+
+namespace {
+
+class DummyOp : public XlaOpKernel {
+  using XlaOpKernel::XlaOpKernel;
+  void Compile(XlaOpKernelContext* ctx) override {}
+};
+
+REGISTER_XLA_OP(Name("ResourceInput"), DummyOp);
+REGISTER_XLA_OP(Name("ResourceOutput"), DummyOp);
+
+}  // namespace
+
+TEST(XlaCompilationTest, Resources) {
+  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
+  GraphDef graphdef;
+  {
+    GraphDefBuilder builder(GraphDefBuilder::kFailImmediately);
+    Node* a =
+        ops::SourceOp("UncompilableNullary", builder.opts().WithName("A"));
+    Node* b = ops::UnaryOp("Relu", a, builder.opts().WithName("B"));
+    // We should not form clusters with resource ops by default.
+    Node* c = ops::UnaryOp("ResourceOutput", b, builder.opts().WithName("C"));
+    Node* d = ops::UnaryOp("ResourceInput", c, builder.opts().WithName("D"));
+    ops::UnaryOp("Relu", d, builder.opts().WithName("E"));
+    TF_EXPECT_OK(builder.ToGraph(graph.get()));
+  }
+  MarkForCompilation(&graph);
+  auto clusters = GetClusters(*graph);
+  EXPECT_EQ(0, clusters.size());  // Nothing should be compiled.
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
index 9bc706abdf..2e5875705f 100644
--- a/tensorflow/compiler/plugin/executor/BUILD
+++ b/tensorflow/compiler/plugin/executor/BUILD
@@ -11,9 +11,11 @@ cc_library(
         "*.h",
     ]),
     deps = [
+        "//tensorflow/compiler/jit:xla_device",
         "//tensorflow/compiler/jit:xla_jit_headers_lib",
+        "//tensorflow/compiler/tf2xla:xla_compiler",
         "//tensorflow/compiler/xla:xla_headers_lib",
-        "//tensorflow/compiler/xla/service:hlo_evaluator",
+        "//tensorflow/compiler/xla/service",
         "//third_party/eigen3",
         "@local_config_cuda//cuda:cuda_headers",
         "@protobuf//:protobuf_headers",
diff --git a/tensorflow/compiler/plugin/executor/compiler.cc b/tensorflow/compiler/plugin/executor/compiler.cc
index 893ff152f0..3a84f08c00 100644
--- a/tensorflow/compiler/plugin/executor/compiler.cc
+++ b/tensorflow/compiler/plugin/executor/compiler.cc
@@ -48,9 +48,8 @@ namespace executorplugin {
  * each pass in the optimization pipeline.  The service subdirectory
  * contains useful optimization passes.
  */
-Status ExecutorCompiler::RunHloOptimization(HloModule* hlo_module,
-                                            HloDumper dump_hlo) {
-  HloPassPipeline pipeline("Executor", dump_hlo);
+Status ExecutorCompiler::RunHloOptimization(HloModule* hlo_module) {
+  HloPassPipeline pipeline("Executor");
   pipeline.AddPass<Inliner>();
   pipeline.AddPass<HloSubcomputationUnification>();
   pipeline.AddPass<HloCSE>(false);
@@ -67,13 +66,13 @@ Status ExecutorCompiler::RunHloOptimization(HloModule* hlo_module,
 }
 
 StatusOr<std::unique_ptr<Executable>> ExecutorCompiler::Compile(
-        std::unique_ptr<HloModule> hlo_module, HloDumper dump_hlo,
+        std::unique_ptr<HloModule> hlo_module,
         se::StreamExecutor* stream_exec) {
   TF_RET_CHECK(stream_exec != nullptr);
 
   VLOG(1) << "Generate graph " << hlo_module->name();
 
-  TF_RETURN_IF_ERROR(RunHloOptimization(hlo_module.get(), dump_hlo));
+  TF_RETURN_IF_ERROR(RunHloOptimization(hlo_module.get()));
 
   // Typically you would visit the HLO graph, building up a compiled equivalent
   // In this case we are using an Hlo evaluator at execution time, so we don't
@@ -88,7 +87,7 @@ StatusOr<std::unique_ptr<Executable>> ExecutorCompiler::Compile(
 
 StatusOr<std::vector<std::unique_ptr<Executable>>> ExecutorCompiler::Compile(
         std::vector<std::unique_ptr<HloModule>> hlo_modules,
-        HloDumper dump_hlos, std::vector<se::StreamExecutor*> stream_execs) {
+        std::vector<se::StreamExecutor*> stream_execs) {
 
   return tensorflow::errors::Unimplemented(
       "Compilation of multiple HLO modules is not supported on Executor.");
@@ -97,7 +96,7 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> ExecutorCompiler::Compile(
 StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
 ExecutorCompiler::CompileAheadOfTime(
     std::vector<std::unique_ptr<HloModule>> hlo_modules,
-    HloDumper dump_hlo, const AotCompilationOptions& aot_options) {
+    const AotCompilationOptions& aot_options) {
 
   return tensorflow::errors::InvalidArgument(
       "AOT compilation not supported on Executor");
diff --git a/tensorflow/compiler/plugin/executor/compiler.h b/tensorflow/compiler/plugin/executor/compiler.h
index 8fe591c8ab..d318eefc49 100644
--- a/tensorflow/compiler/plugin/executor/compiler.h
+++ b/tensorflow/compiler/plugin/executor/compiler.h
@@ -35,25 +35,23 @@ class ExecutorCompiler : public Compiler {
 
   StatusOr<std::unique_ptr<Executable>> Compile(
       std::unique_ptr<HloModule> hlo_module,
-      HloDumper dump_hlo,
       perftools::gputools::StreamExecutor* stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
       std::vector<std::unique_ptr<HloModule>> hlo_module,
-      HloDumper dump_hlo,
       std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
   CompileAheadOfTime(
       std::vector<std::unique_ptr<HloModule>> module,
-      HloDumper dump_hlo, const AotCompilationOptions& options) override;
+      const AotCompilationOptions& options) override;
 
   HloCostAnalysis::ShapeSizeFunction ShapeSizeBytesFunction() const override;
 
   perftools::gputools::Platform::Id PlatformId() const override;
 
  private:
-  Status RunHloOptimization(HloModule* hlo_module, HloDumper dump_hlo);
+  Status RunHloOptimization(HloModule* hlo_module);
 
   TF_DISALLOW_COPY_AND_ASSIGN(ExecutorCompiler);
 };
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index c42d7d754c..432b24756d 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -175,6 +175,11 @@ tf_xla_py_test(
     name = "slice_ops_test",
     size = "small",
     srcs = ["slice_ops_test.py"],
+    # TODO(b/62962492): Test fails with assertion error.
+    tags = [
+        "manual",
+        "notap",
+    ],
     deps = [
         ":xla_test",
         "//tensorflow/python:array_ops",
@@ -456,6 +461,11 @@ cuda_py_test(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
     ],
+    # TODO(b/62961789): Test fails with SIGABRT
+    tags = [
+        "manual",
+        "notap",
+    ],
 )
 
 cc_library(
diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD
index f4d3bc9635..997ecd7ebb 100644
--- a/tensorflow/compiler/tf2xla/BUILD
+++ b/tensorflow/compiler/tf2xla/BUILD
@@ -249,6 +249,7 @@ cc_library(
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
diff --git a/tensorflow/compiler/tf2xla/kernels/function_ops.cc b/tensorflow/compiler/tf2xla/kernels/function_ops.cc
index 8dacb6627b..af1085d5b3 100644
--- a/tensorflow/compiler/tf2xla/kernels/function_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/function_ops.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 
 namespace tensorflow {
 namespace {
diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
index 9eb6899831..6af4bd0496 100644
--- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
@@ -63,17 +63,13 @@ class StridedSliceOp : public XlaOpKernel {
                                             &strides_tensor));
 
     TensorShape dummy_processing_shape;
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
-    ShapeReadWriteFromTensorShape wrapped_dummy_processing_shape(
-        &dummy_processing_shape);
     bool dummy = false;
-    OP_REQUIRES_OK(
-        ctx, ValidateStridedSliceOp(
-                 &begin_tensor, &end_tensor, strides_tensor,
-                 ShapeReadWriteFromTensorShape(&input_shape), begin_mask_,
-                 end_mask_, ellipsis_mask_, new_axis_mask_, shrink_axis_mask_,
-                 &wrapped_dummy_processing_shape, &wrapped_final_shape, &dummy,
-                 &dummy, &dummy, &begin, &end, &strides));
+    OP_REQUIRES_OK(ctx,
+                   ValidateStridedSliceOp(
+                       &begin_tensor, &end_tensor, strides_tensor, input_shape,
+                       begin_mask_, end_mask_, ellipsis_mask_, new_axis_mask_,
+                       shrink_axis_mask_, &dummy_processing_shape, &final_shape,
+                       &dummy, &dummy, &dummy, &begin, &end, &strides));
 
     gtl::InlinedVector<int64, 4> dimensions_to_reverse;
     gtl::InlinedVector<int64, 4> slice_begin, slice_end, slice_strides;
@@ -146,14 +142,11 @@ class StridedSliceGradOp : public XlaOpKernel {
                                             &strides_tensor));
 
     bool dummy = false;
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
     OP_REQUIRES_OK(
         ctx, ValidateStridedSliceOp(
-                 &begin_tensor, &end_tensor, strides_tensor,
-                 ShapeReadWriteFromTensorShape(&input_shape), begin_mask_,
-                 end_mask_, ellipsis_mask_, new_axis_mask_, shrink_axis_mask_,
-                 &wrapped_processing_shape, &wrapped_final_shape, &dummy,
+                 &begin_tensor, &end_tensor, strides_tensor, input_shape,
+                 begin_mask_, end_mask_, ellipsis_mask_, new_axis_mask_,
+                 shrink_axis_mask_, &processing_shape, &final_shape, &dummy,
                  &dummy, &dummy, &begin, &end, &strides));
 
     // Check to make sure dy is consistent with the original slice
@@ -257,17 +250,13 @@ class StridedSliceAssignOp : public XlaOpKernel {
     const TensorShape rhs_shape = ctx->InputShape(4);
 
     TensorShape dummy_processing_shape;
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
-    ShapeReadWriteFromTensorShape wrapped_dummy_processing_shape(
-        &dummy_processing_shape);
     bool dummy = false;
-    OP_REQUIRES_OK(
-        ctx, ValidateStridedSliceOp(
-                 &begin_tensor, &end_tensor, strides_tensor,
-                 ShapeReadWriteFromTensorShape(&lhs_shape), begin_mask_,
-                 end_mask_, ellipsis_mask_, new_axis_mask_, shrink_axis_mask_,
-                 &wrapped_dummy_processing_shape, &wrapped_final_shape, &dummy,
-                 &dummy, &dummy, &begin, &end, &strides));
+    OP_REQUIRES_OK(ctx,
+                   ValidateStridedSliceOp(
+                       &begin_tensor, &end_tensor, strides_tensor, lhs_shape,
+                       begin_mask_, end_mask_, ellipsis_mask_, new_axis_mask_,
+                       shrink_axis_mask_, &dummy_processing_shape, &final_shape,
+                       &dummy, &dummy, &dummy, &begin, &end, &strides));
 
     if (final_shape.num_elements() == 0 && rhs_shape.num_elements() == 0) {
       // DynamicUpdateSlice does not allow 0-element updates. We should probably
diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
index 07ca596150..ab96d86ed2 100644
--- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
@@ -78,12 +78,19 @@ static xla::ComputationDataHandle Round(xla::ComputationBuilder* b,
                                 b->LogicalAnd(b->Eq(fraction, half), is_odd)),
                    b->Add(round_val, one), round_val);
 }
-XLAJIT_MAKE_UNARY(Round, Round(b, input_type(0), x));
 
+// Expresses sigmoid as a rescaled tanh: sigmoid(x) == (tanh(x/2) + 1) / 2.
+static xla::ComputationDataHandle Sigmoid(xla::ComputationBuilder* b,
+                                          DataType dtype,
+                                          const xla::ComputationDataHandle& x) {
+  auto half = XlaHelpers::FloatLiteral(b, dtype, 0.5);
+  return b->Add(half, b->Mul(half, b->Tanh(b->Mul(half, x))));
+}
+
+XLAJIT_MAKE_UNARY(Round, Round(b, input_type(0), x));
 XLAJIT_MAKE_UNARY(Rsqrt,
                   b->Pow(x, XlaHelpers::FloatLiteral(b, input_type(0), -0.5)));
-XLAJIT_MAKE_UNARY(Sigmoid,
-                  b->Map({x}, *ctx->GetOrCreateSigmoid(input_type(0))));
+XLAJIT_MAKE_UNARY(Sigmoid, Sigmoid(b, input_type(0), x));
 XLAJIT_MAKE_UNARY(Softplus,
                   b->Log(b->Add(b->Exp(x), XlaHelpers::One(b, input_type(0)))));
 XLAJIT_MAKE_UNARY(Sqrt,
diff --git a/tensorflow/compiler/tf2xla/test_util.cc b/tensorflow/compiler/tf2xla/test_util.cc
index 3c34b8788d..3c6c9a91b6 100644
--- a/tensorflow/compiler/tf2xla/test_util.cc
+++ b/tensorflow/compiler/tf2xla/test_util.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/test_util.h"
 
 #include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc
index 1a37d61944..d4d493b456 100644
--- a/tensorflow/compiler/tf2xla/xla_context.cc
+++ b/tensorflow/compiler/tf2xla/xla_context.cc
@@ -172,27 +172,6 @@ const xla::Computation* XlaContext::GetOrCreateAdd(const DataType type) {
   });
 }
 
-const xla::Computation* XlaContext::GetOrCreateSigmoid(const DataType type) {
-  return LookupOrCreate(type, &sigmoid_func_, [this, type] {
-    const string type_string = DataTypeString(type);
-    VLOG(1) << "Building Sigmoid() for " << type_string;
-    xla::ComputationBuilder b(builder()->client(),
-                              "sigmoid<" + type_string + ">");
-    xla::PrimitiveType xla_type;
-    TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type));
-    auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x");
-    // Clamp the inputs to the range [-18, 18] since anything outside
-    // this range is 0.0f or 1.0f in single-precision. We must clamp the range
-    // of x to avoid incorrect outputs due to fast-math optimizations for large
-    // negative x.
-    x = b.Clamp(XlaHelpers::IntegerLiteral(&b, type, -18), x,
-                XlaHelpers::IntegerLiteral(&b, type, 18));
-    auto one = XlaHelpers::One(&b, type);
-    b.Div(one, b.Add(b.Exp(b.Neg(x)), one));
-    return b.Build().ConsumeValueOrDie();
-  });
-}
-
 const xla::Computation* XlaContext::LookupOrCreate(
     DataType type, ComputationMap* out,
     const std::function<xla::Computation()>& create) {
diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h
index dbede52b5d..544921b9e3 100644
--- a/tensorflow/compiler/tf2xla/xla_context.h
+++ b/tensorflow/compiler/tf2xla/xla_context.h
@@ -129,11 +129,6 @@ class XlaContext : public ResourceBase {
   // separate specialization of the computation for each DataType.
   const xla::Computation* GetOrCreateAdd(const DataType type);
 
-  // Get an XLA lambda to compute Sigmoid. This is cached in the
-  // XlaContext since it may be used by multiple Ops. There is a
-  // separate specialization of the computation for each DataType.
-  const xla::Computation* GetOrCreateSigmoid(const DataType type);
-
   // The name of the XlaContext resource during symbolic graph execution.
   static const char kXlaContextResourceName[];
 
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
index d606b32931..dd46702223 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
@@ -391,11 +391,6 @@ const xla::Computation* XlaOpKernelContext::GetOrCreateAdd(
   return XlaContext::Get(context_).GetOrCreateAdd(type);
 }
 
-const xla::Computation* XlaOpKernelContext::GetOrCreateSigmoid(
-    const DataType type) {
-  return XlaContext::Get(context_).GetOrCreateSigmoid(type);
-}
-
 XlaOpKernel::XlaOpKernel(OpKernelConstruction* context) : OpKernel(context) {}
 
 void XlaOpKernel::Compute(OpKernelContext* context) {
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h
index b151286217..30b794c8c1 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.h
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h
@@ -201,11 +201,6 @@ class XlaOpKernelContext {
   // separate specialization of the computation for each DataType.
   const xla::Computation* GetOrCreateAdd(const DataType type);
 
-  // Get an XLA lambda to compute Sigmoid. This is cached in the
-  // XlaContext since it may be used by multiple Ops. There is a
-  // separate specialization of the computation for each DataType.
-  const xla::Computation* GetOrCreateSigmoid(const DataType type);
-
  private:
   OpKernelContext* const context_;
 };
diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc
index aaef27f16d..20adf300ec 100644
--- a/tensorflow/compiler/tf2xla/xla_op_registry.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/common_runtime/local_device.h"
 #include "tensorflow/core/framework/device_base.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/platform/mem.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
 
diff --git a/tensorflow/compiler/xla/array4d.h b/tensorflow/compiler/xla/array4d.h
index d93f968f4d..4c7fce1aaf 100644
--- a/tensorflow/compiler/xla/array4d.h
+++ b/tensorflow/compiler/xla/array4d.h
@@ -207,6 +207,18 @@ class Array4D {
     }
   }
 
+  // Invokes a callback with the (indices, value) for each cell in the 4D array.
+  void Each(
+      std::function<void(tensorflow::gtl::ArraySlice<int64>, T)> f) const {
+    // We const_cast to be able to use the common non-const implementation,
+    // but prevent modification of the data by passing it by-value to the
+    // caller.
+    const_cast<Array4D*>(this)->Each(
+        [&f](tensorflow::gtl::ArraySlice<int64> indices, T* value) {
+          f(indices, *value);
+        });
+  }
+
   // Fills all of the {p,z} with the array provided, which specifies {y,x}.
   void FillWithYX(const Array2D<T>& value) {
     CHECK_EQ(value.height(), height());
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index fafd5f591b..33dc94fe00 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -79,41 +79,6 @@ cc_library(
         ],
 )
 
-cc_library(
-    name = "cpu_runtime_flags",
-    srcs = ["cpu_runtime_flags.cc"],
-    hdrs = ["cpu_runtime_flags.h"],
-    deps =
-        [
-            ":parse_flags_from_env",
-            "//tensorflow/core:framework_internal",
-            "//tensorflow/core:lib",
-        ],
-)
-
-cc_library(
-    name = "compiler_functor_flags",
-    srcs = ["compiler_functor_flags.cc"],
-    hdrs = ["compiler_functor_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
-cc_library(
-    name = "convolution_thunk_flags",
-    srcs = ["convolution_thunk_flags.cc"],
-    hdrs = ["convolution_thunk_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "stream_assignment_flags",
     srcs = ["stream_assignment_flags.cc"],
@@ -160,17 +125,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "hlo_test_base_flags",
-    srcs = ["hlo_test_base_flags.cc"],
-    hdrs = ["hlo_test_base_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "backend_flags",
     srcs = ["backend_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.cc b/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.cc
deleted file mode 100644
index 617a9b712e..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's compiler_functor module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static CompilerFunctorFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new CompilerFunctorFlags;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_debug_cpu_dump_ir", &flags->xla_debug_cpu_dump_ir,
-                       "Dump IR, before optimizations to a path"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's compiler_functor
-// module.
-void AppendCompilerFunctorFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the CompilerFunctorFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CompilerFunctorFlags* GetCompilerFunctorFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h b/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h
deleted file mode 100644
index 28b505ec5e..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_COMPILER_FUNCTOR_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_COMPILER_FUNCTOR_FLAGS_H_
-
-// Legacy flags for the XLA's compiler_functor module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's compiler_functor
-// module.
-void AppendCompilerFunctorFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's compiler_functor module.
-typedef struct {
-  string xla_debug_cpu_dump_ir;  // Dump IR, before optimizations to a path
-} CompilerFunctorFlags;
-
-// Return a pointer to the CompilerFunctorFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CompilerFunctorFlags* GetCompilerFunctorFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_COMPILER_FUNCTOR_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.cc b/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.cc
deleted file mode 100644
index fe5d19147f..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's convolution_thunk module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static ConvolutionThunkFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new ConvolutionThunkFlags;
-  flags->xla_gpu_autotune_convolution_algorithm = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_gpu_autotune_convolution_algorithm",
-                       &flags->xla_gpu_autotune_convolution_algorithm,
-                       "Auto-tune the algorithm used by convolution"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's convolution_thunk
-// module.
-void AppendConvolutionThunkFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the ConvolutionThunkFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-ConvolutionThunkFlags* GetConvolutionThunkFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h b/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h
deleted file mode 100644
index 53d6806a71..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CONVOLUTION_THUNK_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CONVOLUTION_THUNK_FLAGS_H_
-
-// Legacy flags for XLA's convolution_thunk module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's convolution_thunk
-// module.
-void AppendConvolutionThunkFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's convolution_thunk module.
-typedef struct {
-  // Auto-tune the algorithm used by convolution
-  bool xla_gpu_autotune_convolution_algorithm;
-} ConvolutionThunkFlags;
-
-// Return a pointer to the ConvolutionThunkFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-ConvolutionThunkFlags* GetConvolutionThunkFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CONVOLUTION_THUNK_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
deleted file mode 100644
index d7817c5d54..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.cc
+++ /dev/null
@@ -1,71 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's cpu_runtime module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static CpuRuntimeFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new CpuRuntimeFlags;
-  flags->xla_cpu_use_eigen = true;
-  flags->xla_cpu_multi_thread_eigen = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_cpu_use_eigen", &flags->xla_cpu_use_eigen,
-          "Use Eigen for matrix multiply on the CPU platform. This "
-          "is a useful hack for performance comparisons against "
-          "XLA's implementation."),
-      tensorflow::Flag(
-          "xla_cpu_multi_thread_eigen", &flags->xla_cpu_multi_thread_eigen,
-          "When generating calls to Eigen for matmul and conv, should "
-          "single or multi-threaded eigen be used? "
-          "Only used when --xla_cpu_use_eigen is true."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's cpu_runtime
-// module.
-void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the CpuRuntimeFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuRuntimeFlags* GetCpuRuntimeFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h b/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
deleted file mode 100644
index e3ff30da36..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
-
-// Legacy flags for the XLA's cpu_runtime module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's cpu_runtime
-// module.
-void AppendCpuRuntimeFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's cpu_runtime module.
-typedef struct {
-  // Use Eigen for matrix multiply on the CPU platform. This is a useful hack
-  // for performance comparisons against XLA's implementation.
-  bool xla_cpu_use_eigen;
-  // When generating calls to Eigen for matmul and conv, should single or
-  // multi-threaded eigen be used?  Only used when --xla_cpu_use_eigen is true.
-  bool xla_cpu_multi_thread_eigen;
-} CpuRuntimeFlags;
-
-// Return a pointer to the CpuRuntimeFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-CpuRuntimeFlags* GetCpuRuntimeFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_CPU_RUNTIME_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 5f029a5f53..f87cf7083e 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -25,12 +25,23 @@ namespace legacy_flags {
 
 struct DebugOptionsFlags {
   string xla_generate_hlo_graph;
+  bool xla_hlo_graph_addresses;
+  bool xla_hlo_graph_layout;
+  string xla_log_hlo_text;
+  string xla_generate_hlo_text_to;
+
   string xla_disable_hlo_passes;
   bool xla_enable_fast_math;
+  bool xla_llvm_enable_alias_scope_metadata;
+  bool xla_llvm_enable_noalias_metadata;
+  bool xla_llvm_enable_invariant_load_metadata;
   int32 xla_backend_optimization_level;
   bool xla_embed_ir_in_executable;
+  string xla_dump_ir_to;
   string xla_dump_debug_json_to;
 
+  bool xla_cpu_multi_thread_eigen;
+
   string xla_gpu_cuda_data_dir;
   bool xla_gpu_ftz;
 
@@ -48,11 +59,20 @@ std::once_flag flags_init;
 void AllocateFlags() {
   flag_values = new DebugOptionsFlags;
   flag_values->xla_generate_hlo_graph = "";
+  flag_values->xla_hlo_graph_addresses = false;
+  flag_values->xla_hlo_graph_layout = false;
+  flag_values->xla_log_hlo_text = "";
+  flag_values->xla_generate_hlo_text_to = "";
   flag_values->xla_disable_hlo_passes = "";
   flag_values->xla_enable_fast_math = true;
+  flag_values->xla_llvm_enable_alias_scope_metadata = true;
+  flag_values->xla_llvm_enable_noalias_metadata = true;
+  flag_values->xla_llvm_enable_invariant_load_metadata = true;
   flag_values->xla_backend_optimization_level = 3;
   flag_values->xla_embed_ir_in_executable = false;
+  flag_values->xla_dump_ir_to = "";
   flag_values->xla_dump_debug_json_to = "";
+  flag_values->xla_cpu_multi_thread_eigen = true;
   flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
   flag_values->xla_gpu_ftz = false;
   flag_values->xla_backend_extra_options = "";
@@ -62,10 +82,37 @@ void AllocateFlags() {
            "xla_generate_hlo_graph", &flag_values->xla_generate_hlo_graph,
            "HLO modules matching this regex will be dumped to a .dot file "
            "throughout various stages in compilation."),
+       tensorflow::Flag(
+           "xla_hlo_graph_addresses", &flag_values->xla_hlo_graph_addresses,
+           "With xla_generate_hlo_graph, show addresses of HLO ops in "
+           "graph dump."),
+       tensorflow::Flag(
+           "xla_hlo_graph_layout", &flag_values->xla_hlo_graph_layout,
+           "With xla_generate_hlo_graph, show layout of HLO ops in "
+           "graph dump."),
+       tensorflow::Flag(
+           "xla_log_hlo_text", &flag_values->xla_log_hlo_text,
+           "HLO modules matching this regex will be dumped to LOG(INFO). "),
+       tensorflow::Flag(
+           "xla_generate_hlo_text_to", &flag_values->xla_generate_hlo_text_to,
+           "Dump all HLO modules as text into the provided directory path."),
        tensorflow::Flag(
            "xla_enable_fast_math", &flag_values->xla_enable_fast_math,
            "Enable unsafe fast-math optimizations in the compiler; "
            "this may produce faster code at the expense of some accuracy."),
+       tensorflow::Flag("xla_llvm_enable_alias_scope_metadata",
+                        &flag_values->xla_llvm_enable_alias_scope_metadata,
+                        "In LLVM-based backends, enable the emission of "
+                        "!alias.scope metadata in the generated IR."),
+       tensorflow::Flag("xla_llvm_enable_noalias_metadata",
+                        &flag_values->xla_llvm_enable_noalias_metadata,
+                        "In LLVM-based backends, enable the emission of "
+                        "!noalias metadata in the generated IR."),
+       tensorflow::Flag("xla_llvm_enable_invariant_load_metadata",
+                        &flag_values->xla_llvm_enable_invariant_load_metadata,
+                        "In LLVM-based backends, enable the emission of "
+                        "!invariant.load metadata in "
+                        "the generated IR."),
        tensorflow::Flag(
            "xla_backend_optimization_level",
            &flag_values->xla_backend_optimization_level,
@@ -78,6 +125,12 @@ void AllocateFlags() {
        tensorflow::Flag("xla_embed_ir_in_executable",
                         &flag_values->xla_embed_ir_in_executable,
                         "Embed the compiler IR as a string in the executable."),
+       tensorflow::Flag("xla_dump_ir_to", &flag_values->xla_dump_ir_to,
+                        "Dump the compiler IR into this file/path."),
+       tensorflow::Flag("xla_cpu_multi_thread_eigen",
+                        &flag_values->xla_cpu_multi_thread_eigen,
+                        "When generating calls to Eigen in the CPU backend, "
+                        "use multi-threaded Eigen mode."),
        tensorflow::Flag("xla_gpu_cuda_data_dir",
                         &flag_values->xla_gpu_cuda_data_dir,
                         "If non-empty, speficies a local directory containing "
@@ -111,6 +164,10 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
 
   DebugOptions options;
   options.set_xla_generate_hlo_graph(flag_values->xla_generate_hlo_graph);
+  options.set_xla_hlo_graph_addresses(flag_values->xla_hlo_graph_addresses);
+  options.set_xla_hlo_graph_layout(flag_values->xla_hlo_graph_layout);
+  options.set_xla_log_hlo_text(flag_values->xla_log_hlo_text);
+  options.set_xla_generate_hlo_text_to(flag_values->xla_generate_hlo_text_to);
 
   std::vector<string> disabled_passes =
       tensorflow::str_util::Split(flag_values->xla_disable_hlo_passes, ',');
@@ -123,9 +180,18 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
       flag_values->xla_backend_optimization_level);
   options.set_xla_embed_ir_in_executable(
       flag_values->xla_embed_ir_in_executable);
+  options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
   options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
+  options.set_xla_cpu_multi_thread_eigen(
+      flag_values->xla_cpu_multi_thread_eigen);
   options.set_xla_gpu_cuda_data_dir(flag_values->xla_gpu_cuda_data_dir);
   options.set_xla_gpu_ftz(flag_values->xla_gpu_ftz);
+  options.set_xla_llvm_enable_alias_scope_metadata(
+      flag_values->xla_llvm_enable_alias_scope_metadata);
+  options.set_xla_llvm_enable_noalias_metadata(
+      flag_values->xla_llvm_enable_noalias_metadata);
+  options.set_xla_llvm_enable_invariant_load_metadata(
+      flag_values->xla_llvm_enable_invariant_load_metadata);
 
   std::vector<string> extra_options_parts =
       tensorflow::str_util::Split(flag_values->xla_backend_extra_options, ',');
diff --git a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.cc b/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.cc
deleted file mode 100644
index c7893c1385..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's hlo_test_base module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static HloTestBaseFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new HloTestBaseFlags;
-  flags->xla_hlo_test_generate_hlo_graph = false;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_hlo_test_generate_hlo_graph",
-                       &flags->xla_hlo_test_generate_hlo_graph,
-                       "Generate graph output of HLO instructions"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's hlo_test_base
-// module.
-void AppendHloTestBaseFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the HloTestBaseFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-HloTestBaseFlags* GetHloTestBaseFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h b/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h
deleted file mode 100644
index 23b808cecb..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_TEST_BASE_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_TEST_BASE_FLAGS_H_
-
-// Legacy flags for XLA's hlo_test_base module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's hlo_test_base
-// module.
-void AppendHloTestBaseFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's hlo_test_base module.
-typedef struct {
-  bool xla_hlo_test_generate_hlo_graph;  // Generate graph output of HLO
-                                         // instructions
-} HloTestBaseFlags;
-
-// Return a pointer to the HloTestBaseFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-HloTestBaseFlags* GetHloTestBaseFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_TEST_BASE_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.cc b/tensorflow/compiler/xla/legacy_flags/service_flags.cc
index 41cb8d8bdf..90d30e7569 100644
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/service_flags.cc
@@ -36,33 +36,13 @@ static std::once_flag flags_init;
 static void AllocateFlags() {
   flags = new ServiceFlags;
   flags->xla_hlo_profile = false;
-  flags->xla_log_hlo_text = "";
-  flags->xla_generate_hlo_graph = "";
-  flags->xla_hlo_graph_addresses = false;
-  flags->xla_hlo_graph_layout = false;
   flags->xla_hlo_graph_for_compute_constant = false;
   flags->xla_dump_computations_to = "";
-  flags->xla_dump_hlo_text_to = "";
   flags->xla_dump_executions_to = "";
   flag_list = new std::vector<tensorflow::Flag>({
       tensorflow::Flag(
           "xla_hlo_profile", &flags->xla_hlo_profile,
           "Instrument the computation to collect per-HLO cycle counts"),
-      tensorflow::Flag(
-          "xla_log_hlo_text", &flags->xla_log_hlo_text,
-          "If non-empty, print the text format of "
-          "HLO modules whose name partially matches this regex. E.g. "
-          "xla_log_hlo_text=.* will dump the text for every module."),
-      tensorflow::Flag(
-          "xla_generate_hlo_graph", &flags->xla_generate_hlo_graph,
-          "If non-empty, dump graph of HLO modules whose name partially "
-          "matches this regex. E.g. --xla_generate_hlo_graph=.* will dump "
-          "the graph of every module."),
-      tensorflow::Flag("xla_hlo_graph_addresses",
-                       &flags->xla_hlo_graph_addresses,
-                       "Show addresses of HLO ops in graph"),
-      tensorflow::Flag("xla_hlo_graph_layout", &flags->xla_hlo_graph_layout,
-                       "Show layout of HLO ops in graph"),
       tensorflow::Flag(
           "xla_hlo_graph_for_compute_constant",
           &flags->xla_hlo_graph_for_compute_constant,
@@ -72,9 +52,6 @@ static void AllocateFlags() {
                        &flags->xla_dump_computations_to,
                        "Dumps computations that XLA executes into the provided "
                        "directory path"),
-      tensorflow::Flag("xla_dump_hlo_text_to", &flags->xla_dump_hlo_text_to,
-                       "Dumps HLO modules that XLA executes into the provided "
-                       "directory path"),
       tensorflow::Flag("xla_dump_executions_to", &flags->xla_dump_executions_to,
                        "Dumps parameters and results of computations that XLA "
                        "executes into the provided directory path"),
diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.h b/tensorflow/compiler/xla/legacy_flags/service_flags.h
index d982506944..72d0c52402 100644
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.h
+++ b/tensorflow/compiler/xla/legacy_flags/service_flags.h
@@ -34,23 +34,11 @@ void AppendServiceFlags(std::vector<tensorflow::Flag>* flag_list);
 typedef struct {
   bool xla_hlo_profile;  // Instrument the computation to collect per-HLO cycle
                          // counts
-  string xla_log_hlo_text;  // If non-empty, print the text format of the HLO
-                            // modules whose name partially
-                            // matches this regex.  E.g. xla_log_hlo_text=.*
-                            // will dump the text for every module.
-  string xla_generate_hlo_graph;  // If non-empty, dump graph of HLO modules
-                                  // whose name partially matches this regex.
-                                  // E.g. --xla_generate_hlo_graph=.* will dump
-                                  // the graph of every module.
-  bool xla_hlo_graph_addresses;   // Show addresses of HLO ops in graph
-  bool xla_hlo_graph_layout;      // Show layout of HLO ops in graph
   bool xla_hlo_graph_for_compute_constant;  // If true, include hlo dumps of
                                             // graphs from ComputeConstant.
                                             // Such graphs still need to be
                                             // matched via
                                             // xla_generate_hlo_graph.
-  string xla_dump_hlo_text_to;  // Dumps HLO text for each HLO module that is
-                                // executed into the provided directory path
   string xla_dump_computations_to;  // Dumps computations that XLA executes
                                     // into the provided directory path
   // Dumps parameters and results of computations that XLA executes into
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index b6bd1158d2..ec643e037f 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -62,7 +62,17 @@ Literal::StrideConfig::StrideConfig(
 std::unique_ptr<Literal> Literal::CreateFromShape(const Shape& shape) {
   auto literal = MakeUnique<Literal>();
   *literal->mutable_shape() = shape;
-  literal->Reserve(ShapeUtil::ElementsIn(literal->shape()));
+  if (ShapeUtil::IsTuple(shape)) {
+    int64 num_elements = ShapeUtil::TupleElementCount(shape);
+    literal->tuple_literals_.resize(num_elements);
+    for (int i = 0; i < num_elements; ++i) {
+      std::unique_ptr<Literal> elem =
+          CreateFromShape(ShapeUtil::GetTupleElementShape(shape, i));
+      literal->tuple_literals_[i] = std::move(*elem);
+    }
+  } else {
+    literal->Reserve(ShapeUtil::ElementsIn(literal->shape()));
+  }
   return literal;
 }
 
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index ce4a00fa55..8266511614 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -68,11 +68,13 @@ class BoolVector {
   }
 
   BoolVector(const BoolVector& other) { CopyFrom(other); }
+  BoolVector(BoolVector&&) = default;
 
   BoolVector& operator=(const BoolVector& other) {
     CopyFrom(other);
     return *this;
   }
+  BoolVector& operator=(BoolVector&&) = default;
 
   void push_back(const bool& value) {
     resize(size_ + 1);
@@ -147,10 +149,12 @@ class Literal {
   Literal() {}
 
   Literal(const Literal& other) = default;
+  Literal(Literal&&) = default;
 
   explicit Literal(const LiteralProto& other) { CopyFromProto(other); }
 
   Literal& operator=(const Literal& other) = default;
+  Literal& operator=(Literal&&) = default;
 
   LiteralProto ToProto() const;
 
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index af84abd550..5976e83950 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -291,20 +291,20 @@ TEST_F(LiteralUtilTest, DifferentLayoutEquality) {
   auto colmajor = MakeUnique<Literal>();
   *colmajor->mutable_shape() = ShapeUtil::MakeShape(F32, {2, 2});
   *colmajor->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({0, 1});
-  colmajor.get()->Reserve(4);
-  colmajor.get()->Set<float>({0, 0}, 1.0);
-  colmajor.get()->Set<float>({0, 1}, 2.0);
-  colmajor.get()->Set<float>({1, 0}, 3.0);
-  colmajor.get()->Set<float>({1, 1}, 4.0);
+  colmajor->Reserve(4);
+  colmajor->Set<float>({0, 0}, 1.0);
+  colmajor->Set<float>({0, 1}, 2.0);
+  colmajor->Set<float>({1, 0}, 3.0);
+  colmajor->Set<float>({1, 1}, 4.0);
 
   auto rowmajor = MakeUnique<Literal>();
   *rowmajor->mutable_shape() = ShapeUtil::MakeShape(F32, {2, 2});
   *rowmajor->mutable_shape()->mutable_layout() = LayoutUtil::MakeLayout({1, 0});
-  rowmajor.get()->Reserve(4);
-  rowmajor.get()->Set<float>({0, 0}, 1.0);
-  rowmajor.get()->Set<float>({0, 1}, 2.0);
-  rowmajor.get()->Set<float>({1, 0}, 3.0);
-  rowmajor.get()->Set<float>({1, 1}, 4.0);
+  rowmajor->Reserve(4);
+  rowmajor->Set<float>({0, 0}, 1.0);
+  rowmajor->Set<float>({0, 1}, 2.0);
+  rowmajor->Set<float>({1, 0}, 3.0);
+  rowmajor->Set<float>({1, 1}, 4.0);
 
   EXPECT_TRUE(rowmajor->Equal(*colmajor));
 }
@@ -341,6 +341,16 @@ TEST_F(LiteralUtilTest, IsAllTuple) {
   EXPECT_FALSE(tuple->IsAll(1));
 }
 
+// Verifies that CreateFromShape works for tuples.
+TEST_F(LiteralUtilTest, CreateFromShapeTuple) {
+  auto scalar = Literal::CreateR0<float>(0.0);
+  auto matrix = Literal::CreateR2<int32>({{0, 0}, {0, 0}});
+  auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()});
+
+  auto x = Literal::CreateFromShape(tuple->shape());
+  EXPECT_TRUE(tuple->Equal(*x));
+}
+
 TEST_F(LiteralUtilTest, IsAll) {
   EXPECT_TRUE(Literal::CreateR0<bool>(false)->IsAll(0));
   EXPECT_TRUE(Literal::CreateR0<bool>(true)->IsAll(1));
@@ -694,7 +704,7 @@ TEST_F(LiteralUtilTest, Copy) {
     const int64 step[] = {1, 1, 1, 1};
     uint32 seqnr = 0;
     auto init_proc = [&](const std::vector<int64>& indexes) {
-      source.get()->Set(indexes, ++seqnr);
+      source->Set(indexes, ++seqnr);
       return true;
     };
 
@@ -705,7 +715,7 @@ TEST_F(LiteralUtilTest, Copy) {
     const int64 dest_base[] = {6, 4, 12, 2};
     const int64 copy_size[] = {7, 8, 11, 9};
 
-    TF_EXPECT_OK(blank.get()->Copy(*source, src_base, dest_base, copy_size));
+    TF_EXPECT_OK(blank->Copy(*source, src_base, dest_base, copy_size));
     std::vector<int64> source_indexes(TF_ARRAYSIZE(dimensions), 0);
     std::vector<int64> blank_indexes(TF_ARRAYSIZE(dimensions), 0);
     bool matched = true;
@@ -729,13 +739,13 @@ TEST_F(LiteralUtilTest, Copy) {
 TEST_F(LiteralUtilTest, CopyScalars) {
   auto zero = Literal::CreateR0<uint32>(0);
   auto nine = Literal::CreateR0<uint32>(9);
-  TF_EXPECT_OK(zero.get()->Copy(*nine, {}, {}, {}));
+  TF_EXPECT_OK(zero->Copy(*nine, {}, {}, {}));
   EXPECT_TRUE(zero->Equal(*nine));
 
   auto vect = Literal::CreateR1<uint32>({3, 4, 9, 12, 5, 17, 21});
-  TF_EXPECT_OK(zero.get()->Copy(*vect, {5}, {}, {}));
+  TF_EXPECT_OK(zero->Copy(*vect, {5}, {}, {}));
   EXPECT_EQ(zero->Get<uint32>({}), 17);
-  TF_EXPECT_OK(vect.get()->Copy(*zero, {}, {4}, {}));
+  TF_EXPECT_OK(vect->Copy(*zero, {}, {4}, {}));
   EXPECT_EQ(vect->Get<uint32>({4}), 17);
 }
 
@@ -796,7 +806,7 @@ TEST_F(LiteralUtilTest, Populate) {
       // with zero.
       return literal->LinearIndex(indexes) + 17;
     };
-    TF_EXPECT_OK(literal.get()->Populate<uint32>(generator));
+    TF_EXPECT_OK(literal->Populate<uint32>(generator));
 
     std::vector<int64> zero_base(data.dimensions.size(), 0);
     std::vector<int64> step(data.dimensions.size(), 1);
diff --git a/tensorflow/compiler/xla/packed_literal_reader.cc b/tensorflow/compiler/xla/packed_literal_reader.cc
index 1187079906..70e0f5a747 100644
--- a/tensorflow/compiler/xla/packed_literal_reader.cc
+++ b/tensorflow/compiler/xla/packed_literal_reader.cc
@@ -58,7 +58,7 @@ StatusOr<std::unique_ptr<Literal>> PackedLiteralReader::Read(
   }
 
   int64 elements = ShapeUtil::ElementsIn(shape);
-  result.get()->Resize(elements, std::numeric_limits<float>::quiet_NaN());
+  result->Resize(elements, std::numeric_limits<float>::quiet_NaN());
   std::vector<float>* field = result->mutable_f32s();
   char* data = tensorflow::bit_cast<char*>(field->data());
   uint64 bytes = elements * sizeof(float);
diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index e8de559a5e..ac4fec4b70 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -252,6 +252,20 @@ ReferenceUtil::ReduceWindow4DGeneric(
                                padding);
 }
 
+/* static */ std::unique_ptr<Array4D<float>> ReferenceUtil::BatchNorm4D(
+    const Array4D<float>& input, const Array4D<float>& mean,
+    const Array4D<float>& var, const Array4D<float>& scale,
+    const Array4D<float>& offset, float epsilon) {
+  auto normalized =
+      *MapArray4D(input, mean, [](float a, float b) { return a - b; });
+  normalized = *MapArray4D(normalized, var, [&](float a, float b) {
+    return a / std::sqrt(b + epsilon);
+  });
+  normalized =
+      *MapArray4D(normalized, scale, [](float a, float b) { return a * b; });
+  return MapArray4D(normalized, offset, [](float a, float b) { return a + b; });
+}
+
 /* static  */ std::unique_ptr<Array4D<float>>
 ReferenceUtil::SelectAndScatter4DGePlus(
     const Array4D<float>& operand, const Array4D<float>& source, float init,
@@ -491,6 +505,30 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
       }
     }
   }
+  if (samples == 0 || kx == 0 || ky == 0 || ox == 0 || oy == 0 || oz == 0 ||
+      iz == 0) {
+    LOG(INFO) << "Output will be trivially empty because one of these "
+                 "dimensions is 0: samples: "
+              << samples << " kx: " << kx << " ky: " << ky << " ox: " << ox
+              << " oy: " << oy << " oz: " << oz << " iz: " << iz;
+    return result;
+  }
+  bool trivial = true;
+  auto check_trivial = [&trivial](tensorflow::gtl::ArraySlice<int64> indices,
+                                  float value) {
+    if (value != 0.0) {
+      trivial = false;
+    }
+  };
+  lhs.Each(check_trivial);
+  if (trivial) {
+    LOG(FATAL) << "LHS is all 0.0.";
+  }
+  trivial = true;
+  rhs.Each(check_trivial);
+  if (trivial) {
+    LOG(FATAL) << "RHS is all 0.0.";
+  }
   return result;
 }
 
@@ -566,6 +604,38 @@ ReferenceUtil::ReduceToRowArray2D(
   return result;
 }
 
+/* static */ std::unique_ptr<Array4D<float>> ReferenceUtil::Broadcast1DTo4D(
+    const std::vector<float>& array, const std::vector<int64>& bounds,
+    int64 broadcast_from_dim) {
+  auto result =
+      MakeUnique<Array4D<float>>(bounds[0], bounds[1], bounds[2], bounds[3]);
+  for (int64 i = 0; i < result->n1(); ++i) {
+    for (int64 j = 0; j < result->n2(); ++j) {
+      for (int64 k = 0; k < result->n3(); ++k) {
+        for (int64 l = 0; l < result->n4(); ++l) {
+          switch (broadcast_from_dim) {
+            case 0:
+              (*result)(i, j, k, l) = array[i];
+              break;
+            case 1:
+              (*result)(i, j, k, l) = array[j];
+              break;
+            case 2:
+              (*result)(i, j, k, l) = array[k];
+              break;
+            case 3:
+              (*result)(i, j, k, l) = array[l];
+              break;
+            default:
+              break;
+          }
+        }
+      }
+    }
+  }
+  return result;
+}
+
 /* static */ std::unique_ptr<Array2D<float>> ReferenceUtil::Reduce3DTo2D(
     const Array3D<float>& array, float init,
     tensorflow::gtl::ArraySlice<int64> dims,
diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index f58f0bdc9f..a60a648b8d 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -120,6 +120,11 @@ class ReferenceUtil {
       tensorflow::gtl::ArraySlice<int64> dims,
       std::function<float(float, float)> reduce_function);
 
+  // Broadcast 1D dimension to 4D, from the dimension `broadcast_from_dim`.
+  static std::unique_ptr<Array4D<float>> Broadcast1DTo4D(
+      const std::vector<float>& array, const std::vector<int64>& bounds,
+      int64 broadcast_from_dim);
+
   // Returns the result of reducing the 3D array to a 2D array, reducing away
   // the dimensions specified in dims.
   static std::unique_ptr<Array2D<float>> Reduce3DTo2D(
@@ -169,6 +174,12 @@ class ReferenceUtil {
       const tensorflow::gtl::ArraySlice<int64>& stride,
       const tensorflow::gtl::ArraySlice<std::pair<int64, int64>>& padding);
 
+  // Batch normalize data.
+  static std::unique_ptr<Array4D<float>> BatchNorm4D(
+      const Array4D<float>& input, const Array4D<float>& mean,
+      const Array4D<float>& var, const Array4D<float>& scale,
+      const Array4D<float>& offset, float epsilon);
+
   // Performs select and scatter with Greater Than or equal as the select, plus
   // as the scatter, and Same Padding.
   static std::unique_ptr<Array4D<float>> SelectAndScatter4DGePlus(
@@ -396,6 +407,41 @@ class ReferenceUtil {
     return result;
   }
 
+  // Applies map_function to each pair of elements in the input lhs and rhs
+  // (4D array) and returns the result.
+  template <typename F>
+  static std::unique_ptr<Array4D<float>> MapArray4D(const Array4D<float>& lhs,
+                                                    const Array4D<float>& rhs,
+                                                    F&& map_function) {
+    return MapWithIndexArray4D(
+        lhs, rhs, [&](float lhs, float rhs, int64, int64, int64, int64) {
+          return map_function(lhs, rhs);
+        });
+  }
+
+  // Applies map_function to each pair of element in lhs and rhs (4D array) and
+  // returns the result.
+  // (plane, depth, height, width) index of each element is also provided as
+  // arguments to map_function.
+  template <typename F>
+  static std::unique_ptr<Array4D<float>> MapWithIndexArray4D(
+      const Array4D<float>& lhs, const Array4D<float>& rhs, F&& map_function) {
+    auto result = MakeUnique<Array4D<float>>(lhs.planes(), lhs.depth(),
+                                             lhs.height(), lhs.width());
+    for (int64 plane = 0; plane < lhs.planes(); ++plane) {
+      for (int64 depth = 0; depth < lhs.depth(); ++depth) {
+        for (int64 height = 0; height < lhs.height(); ++height) {
+          for (int64 width = 0; width < lhs.width(); ++width) {
+            (*result)(plane, depth, height, width) = map_function(
+                lhs(plane, depth, height, width),
+                rhs(plane, depth, height, width), plane, depth, height, width);
+          }
+        }
+      }
+    }
+    return result;
+  }
+
   // Returns the result of a 2D pad on an input matrix.
   static std::unique_ptr<Array2D<float>> PadArray2D(
       const Array2D<float>& operand, const PaddingConfig& padding,
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index eaf89f1319..b399bc7a3f 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -710,9 +710,10 @@ cc_library(
     ],
     deps = [
         ":buffer_liveness",
+        ":heap_simulator",
         ":hlo",
-        ":hlo_ordering",
         ":hlo_proto",
+        ":hlo_scheduling",
         ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:shape_util",
@@ -739,6 +740,7 @@ cc_test(
         ":flatten_call_graph",
         ":hlo",
         ":hlo_ordering",
+        ":hlo_scheduling",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:test",
@@ -751,11 +753,61 @@ cc_test(
     ],
 )
 
+cc_library(
+    name = "hlo_ordering",
+    srcs = ["hlo_ordering.cc"],
+    hdrs = ["hlo_ordering.h"],
+    deps = [
+        ":call_graph",
+        ":hlo",
+        ":hlo_proto",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_test(
+    name = "hlo_ordering_test",
+    size = "small",
+    srcs = ["hlo_ordering_test.cc"],
+    deps = [
+        ":hlo",
+        ":hlo_ordering",
+        ":hlo_scheduling",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+    ],
+)
+
+cc_library(
+    name = "heap_simulator",
+    srcs = ["heap_simulator.cc"],
+    hdrs = ["heap_simulator.h"],
+    deps = [
+        ":hlo",
+        ":hlo_ordering",
+        ":hlo_proto",
+        ":liveness_util",
+        ":logical_buffer",
+        ":tuple_points_to_analysis",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_test(
     name = "heap_simulator_test",
     size = "small",
     srcs = ["heap_simulator_test.cc"],
     deps = [
+        ":heap_simulator",
         ":hlo",
         ":hlo_ordering",
         ":logical_buffer",
@@ -768,23 +820,15 @@ cc_test(
     ],
 )
 
-# The hlo_ordering library contains both hlo_ordering and heap_simulator because
-# they are mutually dependent.
 cc_library(
-    name = "hlo_ordering",
-    srcs = [
-        "heap_simulator.cc",
-        "hlo_ordering.cc",
-    ],
-    hdrs = [
-        "heap_simulator.h",
-        "hlo_ordering.h",
-    ],
+    name = "hlo_scheduling",
+    srcs = ["hlo_scheduling.cc"],
+    hdrs = ["hlo_scheduling.h"],
     deps = [
-        ":call_graph",
+        ":heap_simulator",
         ":hlo",
+        ":hlo_ordering",
         ":hlo_proto",
-        ":liveness_util",
         ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:shape_util",
@@ -797,12 +841,13 @@ cc_library(
 )
 
 cc_test(
-    name = "hlo_ordering_test",
+    name = "hlo_scheduling_test",
     size = "small",
-    srcs = ["hlo_ordering_test.cc"],
+    srcs = ["hlo_scheduling_test.cc"],
     deps = [
         ":hlo",
         ":hlo_ordering",
+        ":hlo_scheduling",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
@@ -1177,6 +1222,22 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "hlo_value",
+    srcs = ["hlo_value.cc"],
+    hdrs = ["hlo_value.h"],
+    deps = [
+        ":hlo",
+        "//tensorflow/compiler/xla:shape_tree",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_library(
     name = "hlo_dataflow_analysis",
     srcs = [
@@ -1189,8 +1250,8 @@ cc_library(
         ":call_graph",
         ":hlo",
         ":hlo_ordering",
+        ":hlo_value",
         ":liveness_util",
-        "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status",
         "//tensorflow/compiler/xla:statusor",
@@ -1198,7 +1259,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
     ],
 )
 
@@ -1225,20 +1285,32 @@ cc_test(
 )
 
 cc_library(
-    name = "hlo_alias_analysis",
-    srcs = [
-        "hlo_alias_analysis.cc",
-    ],
-    hdrs = [
-        "hlo_alias_analysis.h",
+    name = "hlo_buffer",
+    srcs = ["hlo_buffer.cc"],
+    hdrs = ["hlo_buffer.h"],
+    deps = [
+        ":hlo",
+        ":hlo_value",
+        "//tensorflow/compiler/xla:shape_tree",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
     ],
+)
+
+cc_library(
+    name = "hlo_alias_analysis",
+    srcs = ["hlo_alias_analysis.cc"],
+    hdrs = ["hlo_alias_analysis.h"],
     deps = [
-        ":call_graph",
         ":hlo",
+        ":hlo_buffer",
         ":hlo_dataflow_analysis",
-        ":logical_buffer",
-        "//tensorflow/compiler/xla:shape_tree",
+        ":hlo_value",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
@@ -1424,6 +1496,7 @@ cc_library(
         ":hlo",
         ":hlo_dce",
         ":hlo_ordering",
+        ":hlo_scheduling",
         ":liveness_util",
         ":logical_buffer",
         ":tuple_points_to_analysis",
@@ -1520,8 +1593,8 @@ cc_library(
         "hlo_pass_pipeline.h",
     ],
     deps = [
-        ":compiler",
         ":hlo",
+        ":hlo_graph_dumper",
         ":hlo_pass",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
@@ -1730,6 +1803,7 @@ cc_library(
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/core:lib",
+        "//tensorflow/core:regexp_internal",
     ],
     alwayslink = 1,
 )
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index 44b4f4e3d8..3ba010ac43 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/heap_simulator.h"
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index d69a78729a..56568fd446 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/test_helpers.h"
diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index cfcf2744fb..dc81323c99 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -62,9 +62,7 @@ CompileOnlyService::CompileOnlyService(
     std::unique_ptr<Backend> compute_constant_backend)
     : Service(options, /*backend=*/nullptr,
               std::move(compute_constant_backend)),
-      compiler_(compiler) {
-  runs_in_client_process_ = true;
-}
+      compiler_(compiler) {}
 
 StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
 CompileOnlyService::CompileAheadOfTime(
@@ -124,8 +122,7 @@ CompileOnlyService::CompileAheadOfTime(
     hlo_modules.push_back(std::move(hlo_module));
   }
 
-  return compiler_->CompileAheadOfTime(std::move(hlo_modules),
-                                       MakeHloDumper(), options);
+  return compiler_->CompileAheadOfTime(std::move(hlo_modules), options);
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h
index 7ae285170e..d5bd9214be 100644
--- a/tensorflow/compiler/xla/service/compiler.h
+++ b/tensorflow/compiler/xla/service/compiler.h
@@ -92,13 +92,6 @@ class AotCompilationOptions {
 // platform.
 class Compiler {
  public:
-  // Callback signature used to dump the HLO graph during compilation.
-  // Different compiler backends will call this as they please, providing
-  // a view of the HLO at different points in compilation -- context for the
-  // dump is indicated by the label string.
-  using HloDumper =
-      std::function<void(const HloModule& module, const string& label)>;
-
   virtual ~Compiler() {}
 
   // Returns the ID of the platform that this compiler targets.
@@ -113,21 +106,20 @@ class Compiler {
   //
   // Use the overload below to compile computations that run in parallel.
   virtual StatusOr<std::unique_ptr<Executable>> Compile(
-      std::unique_ptr<HloModule> module, HloDumper dump_hlo,
+      std::unique_ptr<HloModule> module,
       perftools::gputools::StreamExecutor* executor) = 0;
 
   // Compiles a set of HLO modules that can run in parallel, potentially
   // communicating data between the modules, and returns a corresponding
   // sequence of executable objects.
   virtual StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
-      std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlo,
+      std::vector<std::unique_ptr<HloModule>> modules,
       std::vector<perftools::gputools::StreamExecutor*> stream_exec) = 0;
 
   // Compiles the HLO module for ahead-of-time execution.  This is intended for
   // use in static compilation.
   virtual StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
   CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
-                     HloDumper dump_hlo,
                      const AotCompilationOptions& options) = 0;
 
   /////
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index de6660e3b5..53410b09c8 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -68,6 +68,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo_pass_pipeline",
         "//tensorflow/compiler/xla/service:hlo_proto",
         "//tensorflow/compiler/xla/service:hlo_proto_util",
+        "//tensorflow/compiler/xla/service:hlo_scheduling",
         "//tensorflow/compiler/xla/service:hlo_subcomputation_unification",
         "//tensorflow/compiler/xla/service:hlo_verifier",
         "//tensorflow/compiler/xla/service:inliner",
@@ -187,7 +188,6 @@ cc_library(
         ":dot_op_emitter",
         ":elemental_ir_emitter",
         ":ir_emission_utils",
-        ":shape_partition",
         ":simple_orc_jit",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
@@ -196,7 +196,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:elemental_ir_emitter",
         "//tensorflow/compiler/xla/service:hlo",
@@ -227,7 +226,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service/llvm_ir:ir_array",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop",
@@ -288,8 +286,6 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/legacy_flags:compiler_functor_flags",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
         "@llvm//:analysis",
@@ -484,7 +480,6 @@ cc_library(
         ":cpu_runtime",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:window_util",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
     ],
 )
@@ -511,7 +506,6 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_pass",
         "//tensorflow/core:lib",
@@ -548,7 +542,6 @@ cc_test(
         ":shape_partition",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/core:test_main",
     ],
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index 8ebf9ab110..cd6cc09cfb 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -35,8 +35,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/Transforms/IPO.h"
 #include "external/llvm/include/llvm/Transforms/IPO/AlwaysInliner.h"
 #include "external/llvm/include/llvm/Transforms/IPO/PassManagerBuilder.h"
-#include "tensorflow/compiler/xla/legacy_flags/compiler_functor_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
@@ -45,7 +43,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
-#include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -66,14 +63,9 @@ operator()(llvm::Module& module) const {
 
   VLOG(2) << "IR before optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));
-  legacy_flags::CompilerFunctorFlags* flags =
-      legacy_flags::GetCompilerFunctorFlags();
-  string dump_path = flags->xla_debug_cpu_dump_ir;
-  if (!dump_path.empty()) {
-    std::unique_ptr<tensorflow::WritableFile> f;
-    TF_CHECK_OK(tensorflow::Env::Default()->NewAppendableFile(dump_path, &f));
-    TF_CHECK_OK(f->Append(llvm_ir::DumpModuleToString(module)));
-    TF_CHECK_OK(f->Close());
+
+  if (pre_optimization_callback_) {
+    TF_CHECK_OK(pre_optimization_callback_(module));
   }
 
   // Build up optimization pipeline.
@@ -99,6 +91,10 @@ operator()(llvm::Module& module) const {
   VLOG(2) << "IR after optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));
 
+  if (post_optimization_callback_) {
+    TF_CHECK_OK(post_optimization_callback_(module));
+  }
+
   // Generate code.
   llvm::MCContext* mc_context;
   llvm::legacy::PassManager codegen_passes;
@@ -156,12 +152,7 @@ std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl(
       {"llvm.tanh.f32", runtime::kTanhV8F32, 8},
   };
 
-  // Our vectorized library calls are currently implement by calling into Eigen.
-  // As such, only emit calls to these routines if --xla_cpu_use_eigen is
-  // enabled.
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (flags->xla_cpu_use_eigen &&
-      (arch == llvm::Triple::x86 || llvm::Triple::x86_64)) {
+  if (arch == llvm::Triple::x86 || llvm::Triple::x86_64) {
     llvm::SmallVector<llvm::StringRef, 32> features;
     feature_string.split(features, ',', -1, /*KeepEmpty=*/false);
     if (std::find(features.begin(), features.end(), "+sse4.1") !=
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.h b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
index 17dadebe97..94611b5814 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.h
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
@@ -39,13 +39,22 @@ class CompilerFunctor {
   // Returns a VectorIntrinsics where all intrinsics are available.
   static VectorIntrinsics AllIntrinsics();
 
-  explicit CompilerFunctor(llvm::TargetMachine* target_machine,
-                           const Disassembler* disassembler, int opt_level,
-                           const VectorIntrinsics& available_intrinsics)
+  // A callback of this type can be run before and/or after IR-level
+  // optimization to e.g. dump out the generated IR to disk or gather some
+  // statistics.
+  using OptimizationCallback = std::function<Status(const llvm::Module&)>;
+
+  explicit CompilerFunctor(
+      llvm::TargetMachine* target_machine, const Disassembler* disassembler,
+      int opt_level, const VectorIntrinsics& available_intrinsics,
+      OptimizationCallback pre_optimization_callback = nullptr,
+      OptimizationCallback post_optimization_callback = nullptr)
       : target_machine_(target_machine),
         disassembler_(CHECK_NOTNULL(disassembler)),
         opt_level_(opt_level),
-        available_intrinsics_(available_intrinsics) {}
+        available_intrinsics_(available_intrinsics),
+        pre_optimization_callback_(pre_optimization_callback),
+        post_optimization_callback_(post_optimization_callback) {}
 
   // Compile a Module to an ObjectFile.
   llvm::object::OwningBinary<llvm::object::ObjectFile> operator()(
@@ -61,6 +70,8 @@ class CompilerFunctor {
   const Disassembler* disassembler_;
   const unsigned opt_level_;
   const VectorIntrinsics available_intrinsics_;
+  OptimizationCallback pre_optimization_callback_;
+  OptimizationCallback post_optimization_callback_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
index cdf43587b6..069979c661 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h"
 
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -30,11 +29,6 @@ namespace xla {
 namespace cpu {
 
 StatusOr<bool> ConvCanonicalization::Run(HloModule* module) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   bool changed = false;
   for (HloInstruction* hlo :
        module->entry_computation()->MakeInstructionPostOrder()) {
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 4786e75fa7..18cdb25176 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -69,6 +69,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
 #include "tensorflow/compiler/xla/service/hlo_proto_util.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
 #include "tensorflow/compiler/xla/service/hlo_verifier.h"
 #include "tensorflow/compiler/xla/service/inliner.h"
@@ -81,6 +82,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/env.h"
 
 namespace se = ::perftools::gputools;
 
@@ -244,9 +246,9 @@ class CollectProfileCandidates : public DfsHloVisitorWithDefault {
 };
 }  // namespace
 
-Status CpuCompiler::RunHloPasses(HloModule* module, HloDumper dump_hlo) {
+Status CpuCompiler::RunHloPasses(HloModule* module) {
   // Optimization pipeline.
-  HloPassPipeline pipeline("CPU", dump_hlo);
+  HloPassPipeline pipeline("CPU");
   pipeline.AddInvariantChecker<HloVerifier>();
 
   // TODO(b/35786417): Re-enable inliner pass after fixing the bug and deciding
@@ -255,8 +257,8 @@ Status CpuCompiler::RunHloPasses(HloModule* module, HloDumper dump_hlo) {
 
   pipeline.AddPass<ConvCanonicalization>();
   {
-    auto& pass = pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification",
-                                                               dump_hlo);
+    auto& pass =
+        pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification");
     pass.AddPass<AlgebraicSimplifier>(
         /*is_layout_sensitive=*/false,
         [](const Shape&, const Shape&) { return false; },
@@ -343,25 +345,45 @@ llvm::CodeGenOpt::Level CodeGenOptLevel(const HloModuleConfig& module_config) {
   }
 }
 
+Status AppendIRToFile(const string& file_name, const string& ir_module_string) {
+  std::unique_ptr<tensorflow::WritableFile> f;
+  TF_RETURN_IF_ERROR(
+      tensorflow::Env::Default()->NewAppendableFile(file_name, &f));
+  TF_RETURN_IF_ERROR(f->Append(ir_module_string));
+  TF_RETURN_IF_ERROR(f->Close());
+  return Status::OK();
+}
+
 }  // namespace
 
 StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
-    std::unique_ptr<HloModule> module, HloDumper dump_hlo,
-    se::StreamExecutor* stream_exec) {
+    std::unique_ptr<HloModule> module, se::StreamExecutor* stream_exec) {
+  VLOG(1) << "Compiling: " << module->name();
   TF_RET_CHECK(stream_exec != nullptr);
   std::call_once(llvm_command_line_options_initialized,
                  &InitializeLLVMCommandLineOptions, module->config());
 
+  const string dump_ir_to = module->config().debug_options().xla_dump_ir_to();
+
+  auto dump_ir_to_disk = [dump_ir_to](const llvm::Module& module) {
+    if (!dump_ir_to.empty()) {
+      TF_RETURN_IF_ERROR(
+          AppendIRToFile(dump_ir_to, llvm_ir::DumpModuleToString(module)));
+    }
+    return Status::OK();
+  };
+
   // Compile must be thread-safe so create a new LLVM context for the module.
   auto llvm_context = MakeUnique<llvm::LLVMContext>();
   auto llvm_module =
       MakeUnique<llvm::Module>("__compute_module", *llvm_context);
   auto jit = MakeUnique<SimpleOrcJIT>(CompilerTargetOptions(module->config()),
-                                      CodeGenOptLevel(module->config()));
+                                      CodeGenOptLevel(module->config()),
+                                      dump_ir_to_disk, dump_ir_to_disk);
   llvm_module->setDataLayout(jit->data_layout());
   llvm_module->setTargetTriple(jit->target_triple().getTriple());
 
-  TF_RETURN_IF_ERROR(RunHloPasses(module.get(), dump_hlo));
+  TF_RETURN_IF_ERROR(RunHloPasses(module.get()));
 
   HloComputation* computation = module->entry_computation();
   std::unordered_map<const HloInstruction*, size_t> hlo_to_profile_idx;
@@ -373,7 +395,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 
   std::unique_ptr<Executable> cpu_executable;
 
-  // Cache this flag here since we'll want to access it after the module's
+  // Cache these flags here since we'll want to access them after the module's
   // ownership is std::moved.
   const bool embed_ir_in_executable =
       module->config().debug_options().xla_embed_ir_in_executable();
@@ -381,6 +403,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
       module->config().debug_options().xla_dump_debug_json_to();
 
   if (CpuParallelBackendRequested(module->config())) {
+    VLOG(1) << "Using parallel cpu backend";
+
     // Run buffer analysis on the HLO graph. This analysis figures out which
     // temporary buffers are required to run the computation.
     // DependencyHloOrdering is used for the parallel emitter because the order
@@ -475,6 +499,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
           .set_ir_module_string(ir_module_string);
     }
   } else {
+    VLOG(1) << "Using sequential cpu backend";
+
     // Select an order for emitting the HLO instructions for each
     // computation. Using this sequence enables tighter buffer liveness analysis
     // and reduced memory usage (as compared to using DependencyHloOrdering).
@@ -540,11 +566,12 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
   }
 
+  VLOG(1) << "Compilation finished";
   return std::move(cpu_executable);
 }
 
 StatusOr<std::vector<std::unique_ptr<Executable>>> CpuCompiler::Compile(
-    std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlos,
+    std::vector<std::unique_ptr<HloModule>> modules,
     std::vector<se::StreamExecutor*> stream_execs) {
   return Unimplemented(
       "Compilation of multiple HLO modules is not yet supported on CPU.");
@@ -552,7 +579,6 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> CpuCompiler::Compile(
 
 StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
 CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
-                                HloDumper dump_hlo,
                                 const AotCompilationOptions& aot_options) {
   TF_RET_CHECK(!modules.empty());
   std::call_once(llvm_command_line_options_initialized,
@@ -641,8 +667,9 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
   std::vector<std::unique_ptr<AotCompilationResult>> results;
   for (size_t i = 0; i < modules.size(); ++i) {
     HloModule* module = modules[i].get();
+    VLOG(1) << "Compiling ahead-of-time: " << module->name();
 
-    TF_RETURN_IF_ERROR(RunHloPasses(module, dump_hlo));
+    TF_RETURN_IF_ERROR(RunHloPasses(module));
 
     TF_ASSIGN_OR_RETURN(
         SequentialHloOrdering::HloModuleSequence module_sequence,
@@ -719,6 +746,8 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
         std::move(object_file_data), std::move(buffer_sizes),
         result_slice.index()));
   }
+
+  VLOG(1) << "Compilation finished";
   return std::move(results);
 }
 
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
index 3b130a614e..b82e181df2 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
@@ -110,16 +110,15 @@ class CpuCompiler : public Compiler {
   ~CpuCompiler() override {}
 
   StatusOr<std::unique_ptr<Executable>> Compile(
-      std::unique_ptr<HloModule> module, HloDumper dump_hlo,
+      std::unique_ptr<HloModule> module,
       perftools::gputools::StreamExecutor* stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
-      std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlo,
+      std::vector<std::unique_ptr<HloModule>> modules,
       std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
   CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
-                     HloDumper dump_hlo,
                      const AotCompilationOptions& options) override;
 
   perftools::gputools::Platform::Id PlatformId() const override;
@@ -132,7 +131,7 @@ class CpuCompiler : public Compiler {
 
   // Runs the HLO passes which are necessary for both optimizations and
   // correctness.
-  Status RunHloPasses(HloModule* module, HloDumper dump_hlo);
+  Status RunHloPasses(HloModule* module);
 
   TF_DISALLOW_COPY_AND_ASSIGN(CpuCompiler);
 };
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index 420f9cebc5..c21b8a9add 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -22,9 +22,9 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Instructions.h"
 #include "external/llvm/include/llvm/IR/Module.h"
 #include "external/llvm/include/llvm/IR/Value.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -44,7 +44,8 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, bool transpose_lhs,
                            const llvm_ir::IrArray& lhs_array,
                            const llvm_ir::IrArray& rhs_array,
                            llvm::Value* executable_run_options_value,
-                           llvm::IRBuilder<>* ir_builder)
+                           llvm::IRBuilder<>* ir_builder,
+                           const HloModuleConfig& hlo_module_config)
     : dot_(dot),
       transpose_lhs_(transpose_lhs),
       transpose_rhs_(transpose_rhs),
@@ -52,18 +53,20 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, bool transpose_lhs,
       lhs_array_(lhs_array),
       rhs_array_(rhs_array),
       executable_run_options_value_(executable_run_options_value),
-      ir_builder_(ir_builder) {}
+      ir_builder_(ir_builder),
+      hlo_module_config_(hlo_module_config) {}
 
 /* static */ tensorflow::Status DotOpEmitter::EmitDotOperation(
     const HloInstruction& dot, bool transpose_lhs, bool transpose_rhs,
     const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array,
     const llvm_ir::IrArray& rhs_array,
-    llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder) {
+    llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder,
+    const HloModuleConfig& hlo_module_config) {
   PrimitiveType type = target_array.GetShape().element_type();
   TF_RET_CHECK(F32 == type || F64 == type);
   DotOpEmitter dot_emitter(dot, transpose_lhs, transpose_rhs, target_array,
                            lhs_array, rhs_array, executable_run_options_value,
-                           ir_builder);
+                           ir_builder, hlo_module_config);
   return dot_emitter.Emit();
 }
 
@@ -233,20 +236,20 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   // The two transpose_... parameters are actually booleans, but we use int32
   // to avoid target-dependent calling convention details.
 
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  bool multi_threaded = flags->xla_cpu_multi_thread_eigen;
+  bool multi_threaded_eigen =
+      hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen();
   PrimitiveType type = target_array_.GetShape().element_type();
   llvm::Type* float_type;
   const char* fn_name;
   switch (type) {
     case F32:
-      fn_name = multi_threaded
+      fn_name = multi_threaded_eigen
                     ? runtime::kEigenMatmulF32SymbolName
                     : runtime::kEigenSingleThreadedMatmulF32SymbolName;
       float_type = ir_builder_->getFloatTy();
       break;
     case F64:
-      fn_name = multi_threaded
+      fn_name = multi_threaded_eigen
                     ? runtime::kEigenMatmulF64SymbolName
                     : runtime::kEigenSingleThreadedMatmulF64SymbolName;
       float_type = ir_builder_->getDoubleTy();
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
index 44dfe5f2a9..b614716380 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include "external/llvm/include/llvm/IR/IRBuilder.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module_config.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -39,7 +40,8 @@ class DotOpEmitter {
       const HloInstruction& dot, bool transpose_lhs, bool transpose_rhs,
       const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array,
       const llvm_ir::IrArray& rhs_array,
-      llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder);
+      llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder,
+      const HloModuleConfig& hlo_module_config);
 
  private:
   DotOpEmitter(const HloInstruction& dot, bool transpose_lhs,
@@ -47,7 +49,8 @@ class DotOpEmitter {
                const llvm_ir::IrArray& lhs_array,
                const llvm_ir::IrArray& rhs_array,
                llvm::Value* executable_run_options_value,
-               llvm::IRBuilder<>* ir_builder);
+               llvm::IRBuilder<>* ir_builder,
+               const HloModuleConfig& hlo_module_config);
 
   // Emits the IR to perform the dot operation.
   tensorflow::Status Emit();
@@ -82,6 +85,7 @@ class DotOpEmitter {
   const llvm_ir::IrArray& rhs_array_;
   llvm::Value* executable_run_options_value_;
   llvm::IRBuilder<>* ir_builder_;
+  const HloModuleConfig& hlo_module_config_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
index 2d855d0eb1..859329e2c1 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/window_util.h"
@@ -26,11 +25,6 @@ namespace cpu {
 
 bool PotentiallyImplementedAsEigenConvolution(
     const HloInstruction& convolution) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   // The following conditions are necessary (but not sufficient) for
   // implementing `convolution` with Eigen convolution:
   // - the input and kernel have a non-zero number of elements.
@@ -82,11 +76,6 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape,
 }  // namespace
 
 bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo) {
-  legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
-  if (!flags->xla_cpu_use_eigen) {
-    return false;
-  }
-
   // For certain types of Dot, we can call Eigen
   if (hlo.opcode() == HloOpcode::kDot) {
     const Shape& lhs_shape = hlo.operand(0)->shape();
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index fee5fd8830..86d5b884d4 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -33,7 +33,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Intrinsics.h"
 #include "external/llvm/include/llvm/IR/LLVMContext.h"
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
@@ -777,7 +776,8 @@ Status IrEmitter::HandleDot(HloInstruction* dot, HloInstruction* lhs,
   // Dot operation is complicated so we delegate to a helper class.
   TF_RETURN_IF_ERROR(DotOpEmitter::EmitDotOperation(
       *dot, /*transpose_lhs=*/false, /*transpose_rhs=*/false, target_array,
-      lhs_array, rhs_array, GetExecutableRunOptionsArgument(), &ir_builder_));
+      lhs_array, rhs_array, GetExecutableRunOptionsArgument(), &ir_builder_,
+      hlo_module_config_));
 
   emitted_value_[dot] = target_address;
   return Status::OK();
@@ -862,9 +862,10 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution,
            int64_type,    int64_type,     int64_type,     int64_type,
            int64_type,    int64_type,     int64_type,     int64_type},
           /*isVarArg=*/false);
-      legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
+      bool multi_threaded_eigen =
+          hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen();
       const char* fn_name =
-          (flags->xla_cpu_multi_thread_eigen
+          (multi_threaded_eigen
                ? runtime::kEigenConvF32SymbolName
                : runtime::kEigenSingleThreadedConvF32SymbolName);
       llvm::Function* conv_func = llvm::cast<llvm::Function>(
@@ -1525,7 +1526,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) {
     TF_RETURN_IF_ERROR(DotOpEmitter::EmitDotOperation(
         *dot, dot->operand(0)->IsRank2Transpose(),
         dot->operand(1)->IsRank2Transpose(), target_array, lhs_array, rhs_array,
-        GetExecutableRunOptionsArgument(), &ir_builder_));
+        GetExecutableRunOptionsArgument(), &ir_builder_, hlo_module_config_));
 
     emitted_value_[fusion] = target_address;
     return Status::OK();
@@ -1898,11 +1899,14 @@ llvm::Value* IrEmitter::EmitTempBufferPointer(
       GetTempBuffersArgument(), slice.index(), &ir_builder_);
   llvm::LoadInst* tempbuf_address_base =
       ir_builder_.CreateLoad(tempbuf_address_ptr);
-  //  Loading the address of a buffer is invariant of the point at which the
-  //  load is executed in the program because we never reassign buffers.
-  tempbuf_address_base->setMetadata(
-      llvm::LLVMContext::MD_invariant_load,
-      llvm::MDNode::get(tempbuf_address_base->getContext(), /*MDs=*/{}));
+  if (hlo_module_config_.debug_options()
+          .xla_llvm_enable_invariant_load_metadata()) {
+    //  Loading the address of a buffer is invariant of the point at which the
+    //  load is executed in the program because we never reassign buffers.
+    tempbuf_address_base->setMetadata(
+        llvm::LLVMContext::MD_invariant_load,
+        llvm::MDNode::get(tempbuf_address_base->getContext(), /*MDs=*/{}));
+  }
   llvm_ir::SetTbaaForInstruction(tempbuf_address_base, target_shape,
                                  /*is_pointer_to=*/true);
   AttachAlignmentMetadataForLoad(tempbuf_address_base, allocation.size());
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index 04d4d8f075..b284543299 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -27,7 +27,6 @@ limitations under the License.
 #include "external/llvm/include/llvm/Support/CodeGen.h"
 #include "external/llvm/include/llvm/Support/Host.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/service/cpu/compiler_functor.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h"
@@ -143,7 +142,9 @@ CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() {
 }  // namespace
 
 SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
-                           llvm::CodeGenOpt::Level opt_level)
+                           llvm::CodeGenOpt::Level opt_level,
+                           OptimizationCallback pre_optimization_callback,
+                           OptimizationCallback post_optimization_callback)
     : target_machine_(
           CHECK_NOTNULL(llvm::EngineBuilder()
                             .setTargetOptions(target_options)
@@ -154,21 +155,19 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
                                 /*MAttrs=*/DetectMachineAttributes()))),
       disassembler_(*target_machine_),
       data_layout_(target_machine_->createDataLayout()),
-      compile_layer_(object_layer_,
-                     CompilerFunctor(target_machine_.get(), &disassembler_,
-                                     opt_level, GetAvailableIntrinsics())) {
+      compile_layer_(
+          object_layer_,
+          CompilerFunctor(target_machine_.get(), &disassembler_, opt_level,
+                          GetAvailableIntrinsics(), pre_optimization_callback,
+                          post_optimization_callback)) {
   VLOG(1) << "CPU target: " << target_machine_->getTargetCPU().str()
           << " features: " << target_machine_->getTargetFeatureString().str();
 }
 
 SimpleOrcJIT::ModuleHandleT SimpleOrcJIT::AddModule(
     std::unique_ptr<llvm::Module> module) {
-  // The Orc API adds a whole iterable "set" of modules, so we wrap the module
-  // in a vector.
-  std::vector<std::unique_ptr<llvm::Module>> module_set;
-  module_set.push_back(std::move(module));
-  auto handle = compile_layer_.addModuleSet(
-      std::move(module_set), MakeUnique<llvm::SectionMemoryManager>(),
+  auto handle = compile_layer_.addModule(
+      std::move(module), MakeUnique<llvm::SectionMemoryManager>(),
       MakeUnique<SimpleResolver>());
   module_handles_.push_back(handle);
   return handle;
@@ -176,8 +175,9 @@ SimpleOrcJIT::ModuleHandleT SimpleOrcJIT::AddModule(
 
 void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::ModuleHandleT handle) {
   module_handles_.erase(
-      std::remove(module_handles_.begin(), module_handles_.end(), handle));
-  compile_layer_.removeModuleSet(handle);
+      std::remove(module_handles_.begin(), module_handles_.end(), handle),
+      module_handles_.end());
+  compile_layer_.removeModule(handle);
 }
 
 llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string &name) {
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index 4d8653484a..e28717bcd1 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -25,6 +25,7 @@ limitations under the License.
 #include "external/llvm/include/llvm/ExecutionEngine/Orc/RTDyldObjectLinkingLayer.h"
 #include "external/llvm/include/llvm/IR/Module.h"
 #include "external/llvm/include/llvm/Target/TargetMachine.h"
+#include "tensorflow/compiler/xla/service/cpu/compiler_functor.h"
 #include "tensorflow/compiler/xla/service/cpu/disassembler.h"
 #include "tensorflow/compiler/xla/types.h"
 
@@ -41,9 +42,13 @@ namespace cpu {
 // it's added to the JIT.
 class SimpleOrcJIT {
  public:
-  using ObjLayerT = llvm::orc::RTDyldObjectLinkingLayer<>;
-  using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT>;
-  using ModuleHandleT = CompileLayerT::ModuleSetHandleT;
+  using ObjLayerT = llvm::orc::RTDyldObjectLinkingLayer;
+  using CompileFtor =
+      std::function<llvm::object::OwningBinary<llvm::object::ObjectFile>(
+          llvm::Module&)>;
+  using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT, CompileFtor>;
+  using ModuleHandleT = CompileLayerT::ModuleHandleT;
+  using OptimizationCallback = CompilerFunctor::OptimizationCallback;
 
   // Create a new JIT, targeting the host architecture.
   // The |target_options| parameter allows customization of certain code
@@ -51,8 +56,14 @@ class SimpleOrcJIT {
   // can be reassociated, etc.).
   // The |opt_level| parameter controls the optimization level of the code
   // generator.
+  // The |pre_optimization_callback| is invoked on the module before any IR
+  // level optimizations are applied.
+  // The |post_optimization_callback| is invoked on the module after all IR
+  // level optimizations are applied.
   SimpleOrcJIT(const llvm::TargetOptions& target_options,
-               llvm::CodeGenOpt::Level opt_level);
+               llvm::CodeGenOpt::Level opt_level,
+               OptimizationCallback pre_optimization_callback,
+               OptimizationCallback post_optimization_callback);
 
   // Data layout this JIT was created with.
   const llvm::DataLayout& data_layout() const { return data_layout_; }
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 3f9b71cf2b..cb929eef49 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -164,8 +164,7 @@ class DfsHloVisitor {
                                  HloInstruction* lhs, HloInstruction* rhs) {
     return HandleElementwiseBinary(logical_or, HloOpcode::kLogicalOr);
   }
-  virtual Status HandleReducePrecision(HloInstruction* reduce_precision,
-                                       HloInstruction* operand) {
+  virtual Status HandleReducePrecision(HloInstruction* reduce_precision) {
     return HandleElementwiseUnary(reduce_precision,
                                   HloOpcode::kReducePrecision);
   }
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index f79b6826b5..db0a8b36cd 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -390,17 +390,111 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitReducePrecision(
   if (hlo->operand(0)->shape().element_type() != F32) {
     return Unimplemented("reduce-precision only implemented for F32");
   }
-  // As a preliminary implementation, we only implement this for the case
-  // where it is a no-op -- that is, where the exponent and mantissa bit
-  // counts are equal to the (IEEE f32) bit counts for the input values.
-  if (hlo->exponent_bits() != 8) {
-    return Unimplemented("reduce-precision requires 8 exponent bits");
+
+  // Integer and float types for casting and constant generation.
+  llvm::Type* float_type = x->getType();
+  llvm::IntegerType* int_type = ir_builder_->getInt32Ty();
+
+  // Cast the input value to an integer for bitwise manipulation.
+  llvm::Value* x_as_int = ir_builder_->CreateBitCast(x, int_type);
+
+  if (hlo->mantissa_bits() < 23) {
+    // Last remaining mantissa bit.
+    const uint32_t last_mantissa_bit_mask = 1u << (23 - hlo->mantissa_bits());
+
+    // Compute rounding bias for round-to-nearest with ties to even.  This is
+    // equal to a base value of 0111... plus one bit if the last remaining
+    // mantissa bit is 1.
+    const uint32_t base_rounding_bias = (last_mantissa_bit_mask >> 1) - 1;
+    llvm::Value* x_last_mantissa_bit = ir_builder_->CreateLShr(
+        ir_builder_->CreateAnd(
+            x_as_int, llvm::ConstantInt::get(int_type, last_mantissa_bit_mask)),
+        (23 - hlo->mantissa_bits()));
+    llvm::Value* x_rounding_bias = ir_builder_->CreateAdd(
+        x_last_mantissa_bit,
+        llvm::ConstantInt::get(int_type, base_rounding_bias));
+
+    // Add rounding bias, and mask out truncated bits.  Note that the case
+    // where adding the rounding bias overflows into the exponent bits is
+    // correct; the non-masked mantissa bits will all be zero, and the
+    // exponent will be incremented by one.
+    const uint32_t truncation_mask = ~(last_mantissa_bit_mask - 1);
+    x_as_int = ir_builder_->CreateAdd(x_as_int, x_rounding_bias);
+    x_as_int = ir_builder_->CreateAnd(
+        x_as_int, llvm::ConstantInt::get(int_type, truncation_mask));
   }
-  if (hlo->mantissa_bits() != 23) {
-    return Unimplemented("reduce-precision requires 23 mantissa bits");
+
+  if (hlo->exponent_bits() < 8) {
+    // Masks for f32 values.
+    const uint32_t f32_sign_bit_mask = 1u << 31;
+    const uint32_t f32_exp_bits_mask = 0xffu << 23;
+
+    // An exponent of 2^(n-1)-1 -- that is, 0111... with the zero in the most-
+    // significant bit -- is equal to 1.0f for all exponent sizes.  Adding
+    // 2^(n-1)-1 to this gives us the highest non-infinite exponent for a bit-
+    // size of n, and subtracting 2^(n-1)-1 from this gives us the lowest'
+    // exponent (corresponding to 0.0f).
+    //
+    // Thus, the f32 exponent corresponding to the highest non-infinite
+    // exponent for a bit size of n is (2^7-1) + 2^(n-1)-1, and the f32
+    // exponent corresponding to the lowest exponent for a bit size of n is
+    // (2^7-1) - 2^(n-1)-1.
+    //
+    // Note that we have already checked that exponents_bits >= 1.
+    const uint32_t f32_exponent_bias = (1 << 7) - 1;
+    const uint32_t reduced_exponent_bias =
+        (1 << (hlo->exponent_bits() - 1)) - 1;
+    const uint32_t reduced_max_exponent =
+        f32_exponent_bias + reduced_exponent_bias;
+    const uint32_t reduced_min_exponent =
+        f32_exponent_bias - reduced_exponent_bias;
+
+    // Do we overflow or underflow?
+    llvm::Value* x_exponent = ir_builder_->CreateAnd(
+        x_as_int, llvm::ConstantInt::get(int_type, f32_exp_bits_mask));
+    llvm::Value* x_overflows = ir_builder_->CreateICmpUGT(
+        x_exponent,
+        llvm::ConstantInt::get(int_type, reduced_max_exponent << 23));
+    llvm::Value* x_underflows = ir_builder_->CreateICmpULE(
+        x_exponent,
+        llvm::ConstantInt::get(int_type, reduced_min_exponent << 23));
+
+    // Compute appropriately-signed values of zero and infinity.
+    llvm::Value* x_signed_zero = ir_builder_->CreateAnd(
+        x_as_int, llvm::ConstantInt::get(int_type, f32_sign_bit_mask));
+    llvm::Value* x_signed_inf = ir_builder_->CreateOr(
+        x_signed_zero, llvm::ConstantInt::get(int_type, f32_exp_bits_mask));
+
+    // Force to zero or infinity if overflow or underflow.  (Note that this
+    // truncates all denormal values to zero, rather than rounding them.)
+    x_as_int = ir_builder_->CreateSelect(x_overflows, x_signed_inf, x_as_int);
+    x_as_int = ir_builder_->CreateSelect(x_underflows, x_signed_zero, x_as_int);
   }
 
-  return x;
+  // Cast the result back to a floating-point type.
+  llvm::Value* result = ir_builder_->CreateBitCast(x_as_int, float_type);
+
+  // Correct result for NaN inputs.
+  //
+  // The exponent handling will "normalize" NaN values to infinities, which is
+  // undesirable (except in the case with no mantissa bits, in which case it
+  // is mandatory).  This logic also handles cases where mantissa-rounding
+  // causes a NaN's mantissa to overflow into the exponent bits, which would
+  // otherwise create an erroneous zero value.
+  //
+  // If the fast-math flags are set to assume no NaNs, the comparison is likely
+  // to be optimized away, so there's no point in even emitting it.
+  if (!ir_builder_->getFastMathFlags().noNaNs()) {
+    llvm::Value* x_is_nan = ir_builder_->CreateFCmpUNO(x, x);
+
+    if (hlo->mantissa_bits() > 0) {
+      result = ir_builder_->CreateSelect(x_is_nan, x, result);
+    } else {
+      result = ir_builder_->CreateSelect(
+          x_is_nan, llvm::ConstantFP::getInfinity(float_type), result);
+    }
+  }
+  return result;
 }
 
 StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerBinaryOp(
diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc
index 3a9f8dc79e..20eb1aea37 100644
--- a/tensorflow/compiler/xla/service/executable.cc
+++ b/tensorflow/compiler/xla/service/executable.cc
@@ -21,39 +21,9 @@ limitations under the License.
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
-#include "tensorflow/core/platform/regexp.h"
 
 namespace xla {
 
-/* static */ void Executable::DumpExecutedHlo(
-    const HloModule& module, const string& label,
-    const HloExecutionProfile* profile) {
-  VLOG(2) << "module name = " << module.name();
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-  string generate_hlo_graph_regex;
-  if (!flags->xla_generate_hlo_graph.empty()) {
-    generate_hlo_graph_regex = flags->xla_generate_hlo_graph;
-  } else {
-    generate_hlo_graph_regex =
-        module.config().debug_options().xla_generate_hlo_graph();
-  }
-  if (!generate_hlo_graph_regex.empty() &&
-      RE2::PartialMatch(module.name(), generate_hlo_graph_regex)) {
-    hlo_graph_dumper::DumpGraph(*module.entry_computation(), label,
-                                flags->xla_hlo_graph_addresses,
-                                flags->xla_hlo_graph_layout, profile);
-  }
-  if (!flags->xla_log_hlo_text.empty() &&
-      RE2::PartialMatch(module.name(), flags->xla_log_hlo_text)) {
-    LOG(INFO) << "HLO for module " << module.name();
-    LOG(INFO) << "Label: " << label;
-    XLA_LOG_LINES(2, module.ToString());
-  }
-  if (!flags->xla_dump_hlo_text_to.empty()) {
-    hlo_graph_dumper::DumpText(module, label, flags->xla_dump_hlo_text_to);
-  }
-}
-
 StatusOr<std::vector<perftools::gputools::DeviceMemoryBase>>
 Executable::ExecuteOnStreams(
     tensorflow::gtl::ArraySlice<const ServiceExecutableRunOptions> run_options,
diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h
index 291916cd9f..b36a44e19e 100644
--- a/tensorflow/compiler/xla/service/executable.h
+++ b/tensorflow/compiler/xla/service/executable.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
 #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
+#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/service_executable_run_options.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
@@ -49,10 +50,6 @@ class Executable {
         shape_size_function_(std::move(shape_size_function)) {}
   virtual ~Executable() {}
 
-  // Dumps the executed HLO according to service-associated flags.
-  static void DumpExecutedHlo(const HloModule& module, const string& label,
-                              const HloExecutionProfile* profile);
-
   // Enqueues the compilation result on the provided stream, passing the given
   // arguments. This call is blocking and returns after the execution is done.
   //
@@ -240,7 +237,8 @@ StatusOr<ReturnT> Executable::ExecuteOnStreamWrapper(
         }
       }
     }
-    DumpExecutedHlo(module(), "Service::Execute", profile_ptr);
+    hlo_graph_dumper::MaybeDumpHloModule(module(), "Service::Execute",
+                                         profile_ptr);
   }
 
   return return_value;
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index 52b4a13296..6b7ab7a864 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -253,7 +253,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:convolution_thunk_flags",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/compiler/xla/service:executable",
@@ -498,8 +497,9 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/service:buffer_liveness",
         "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:hlo_ordering",
+        "//tensorflow/compiler/xla/service:hlo_scheduling",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc
index 9a0b14eb73..20e0d8eb78 100644
--- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc
@@ -17,7 +17,6 @@ limitations under the License.
 
 #include <string>
 
-#include "tensorflow/compiler/xla/legacy_flags/convolution_thunk_flags.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
@@ -287,10 +286,7 @@ tensorflow::Status ConvolutionThunk::ConvolveWithTune(
     const ConvolutionDescriptor& convolution_descriptor,
     const BufferAllocations& buffer_allocations, se::Stream* stream) {
   // TODO(b/29126320): Try cudnn v5's new auto-tuner when it's rolled out.
-  legacy_flags::ConvolutionThunkFlags* flags =
-      legacy_flags::GetConvolutionThunkFlags();
-  if (flags->xla_gpu_autotune_convolution_algorithm &&
-      best_algorithm_.algorithm() == se::dnn::kDefaultAlgorithm) {
+  if (best_algorithm_.algorithm() == se::dnn::kDefaultAlgorithm) {
     // Auto-tuning either is disabled or only happens in the first run of this
     // function.
     VLOG(2) << "Profiling for best convolution algorithm used for "
diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h
index aaf72935e6..91d6df299d 100644
--- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h
@@ -81,9 +81,8 @@ class ConvolutionThunk : public Thunk {
   ConvolutionThunk(const ConvolutionThunk&) = delete;
   ConvolutionThunk& operator=(const ConvolutionThunk&) = delete;
 
-  // Does the convolution for the thunk on "stream". If the
-  // xla_gpu_autotune_convolution_algorithm is turned on, auto-tuning happens on
-  // the first run of this function.
+  // Does the convolution for the thunk on "stream". Auto-tuning happens on the
+  // first run of this function.
   tensorflow::Status ExecuteOnStream(
       const BufferAllocations& buffer_allocations,
       perftools::gputools::Stream* stream) override;
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 7ce8b8290a..ffed9c7e04 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -119,14 +119,13 @@ string GetLibdeviceDir(const HloModuleConfig& config) {
 
 // Runs optimization passes on the given HLO module.
 tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
-                                     const Compiler::HloDumper& dump_hlo,
                                      const se::DeviceDescription& device_desc) {
   {
-    HloPassPipeline pipeline("optimization", dump_hlo);
+    HloPassPipeline pipeline("optimization");
     pipeline.AddInvariantChecker<HloVerifier>();
     {
-      auto& pass = pipeline.AddPass<HloPassFix<HloPassPipeline>>(
-          "simplification", dump_hlo);
+      auto& pass =
+          pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification");
       pass.AddPass<AlgebraicSimplifier>(
           /*is_layout_sensitive=*/false,
           [](const Shape&, const Shape&) { return false; });
@@ -146,7 +145,7 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
     TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status());
   }
   {
-    HloPassFix<HloPassPipeline> fusion("fusion", dump_hlo);
+    HloPassFix<HloPassPipeline> fusion("fusion");
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/false);
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/true);
     fusion.AddPass<FusionMerger>();
@@ -156,14 +155,13 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
 
 // Modifies the given HLO module so that it will be accepted by IrEmitter.
 // Unlike optimization passes, the passes are necessary for correctness.
-tensorflow::Status PrepareHloModuleForIrEmitting(
-    const Compiler::HloDumper& dump_hlo, HloModule* hlo_module) {
+tensorflow::Status PrepareHloModuleForIrEmitting(HloModule* hlo_module) {
   // In some cases, we have to place the result of an instruction in a temporary
   // buffer. For instance, the buffer that holds an external parameter is
   // assumed immutable at this point, and should not be reused for output
   // (b/27180329). Therefore, in that case, we set the output to be a copy of
   // the parameter.
-  HloPassPipeline pipeline("GPU-ir-emit-prepare", dump_hlo);
+  HloPassPipeline pipeline("GPU-ir-emit-prepare");
   pipeline.AddInvariantChecker<HloVerifier>();
   pipeline.AddPass<PadInsertion>();
   pipeline.AddPass<GpuLayoutAssignment>(
@@ -230,13 +228,12 @@ GpuCompiler::GpuCompiler()
     : pointer_size_(llvm::DataLayout(kDataLayout).getPointerSize()) {}
 
 StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
-    std::unique_ptr<HloModule> module, HloDumper dump_hlo,
-    se::StreamExecutor* stream_exec) {
+    std::unique_ptr<HloModule> module, se::StreamExecutor* stream_exec) {
   TF_RET_CHECK(stream_exec != nullptr);
 
-  TF_RETURN_IF_ERROR(OptimizeHloModule(module.get(), dump_hlo,
-                                       stream_exec->GetDeviceDescription()));
-  TF_RETURN_IF_ERROR(PrepareHloModuleForIrEmitting(dump_hlo, module.get()));
+  TF_RETURN_IF_ERROR(
+      OptimizeHloModule(module.get(), stream_exec->GetDeviceDescription()));
+  TF_RETURN_IF_ERROR(PrepareHloModuleForIrEmitting(module.get()));
 
   llvm::LLVMContext llvm_context;
   std::string buffer;
@@ -344,16 +341,15 @@ StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
 }
 
 StatusOr<std::vector<std::unique_ptr<Executable>>> GpuCompiler::Compile(
-    std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlos,
+    std::vector<std::unique_ptr<HloModule>> modules,
     std::vector<se::StreamExecutor*> stream_execs) {
   return Unimplemented(
       "Compilation of multiple HLO modules is not yet supported on GPU.");
 }
 
 StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-GpuCompiler::CompileAheadOfTime(
-    std::vector<std::unique_ptr<HloModule>> module,
-    HloDumper dump_hlo, const AotCompilationOptions& options) {
+GpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> module,
+                                const AotCompilationOptions& options) {
   return Unimplemented("not yet implemented: GpuCompiler::CompileAheadOfTime");
 }
 
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
index 68dddcdc19..b87555b931 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h
@@ -41,17 +41,16 @@ class GpuCompiler : public Compiler {
   ~GpuCompiler() override {}
 
   StatusOr<std::unique_ptr<Executable>> Compile(
-      std::unique_ptr<HloModule> module, HloDumper dump_hlo,
+      std::unique_ptr<HloModule> module,
       perftools::gputools::StreamExecutor* stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<Executable>>> Compile(
-      std::vector<std::unique_ptr<HloModule>> modules, HloDumper dump_hlo,
+      std::vector<std::unique_ptr<HloModule>> modules,
       std::vector<perftools::gputools::StreamExecutor*> stream_exec) override;
 
   StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-  CompileAheadOfTime(
-      std::vector<std::unique_ptr<HloModule>> module,
-      HloDumper dump_hlo, AotCompilationOptions const& options) override;
+  CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> module,
+                     AotCompilationOptions const& options) override;
 
   perftools::gputools::Platform::Id PlatformId() const override;
 
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
index d16a1d4ee5..f76f8ca668 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/gpu/hlo_schedule.h"
 
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/types.h"
 
 namespace xla {
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.h b/tensorflow/compiler/xla/service/gpu/hlo_schedule.h
index 773973010a..1ce7a48ac8 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.h
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.h
@@ -19,9 +19,9 @@ limitations under the License.
 #include <memory>
 #include <vector>
 
-#include "tensorflow/compiler/xla/service/buffer_liveness.h"
 #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_ordering.h"
 #include "tensorflow/compiler/xla/statusor.h"
 
 namespace xla {
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
index 3b37f4a4b8..3c5f0e19f1 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
@@ -15,21 +15,21 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_alias_analysis.h"
 
-#include <ostream>
-#include <queue>
+#include <algorithm>
+#include <memory>
 #include <utility>
 #include <vector>
 
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
+#include "tensorflow/compiler/xla/service/hlo_buffer.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_value.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -38,105 +38,6 @@ using ::tensorflow::str_util::Join;
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
-void HloBuffer::AddValue(const HloValue& value) {
-  // If the value is already contained in this buffer, just return.
-  if (std::find(value_ids_.begin(), value_ids_.end(), value.id()) !=
-      value_ids_.end()) {
-    return;
-  }
-
-  value_ids_.push_back(value.id());
-
-  // Add all of the locations of the HloValue to this buffer.
-  for (const HloLocation& location : value.locations()) {
-    if (std::find(locations_.begin(), locations_.end(), location) ==
-        locations_.end()) {
-      locations_.push_back(location);
-    }
-  }
-}
-
-bool HloBuffer::operator==(const HloBuffer& other) const {
-  bool equal = id() == other.id();
-  if (equal) {
-    // DCHECK because these comparisons are expensive (linear time).
-    DCHECK(value_ids() == other.value_ids());
-    DCHECK(locations() == other.locations());
-  }
-  return equal;
-}
-
-string HloBuffer::ToString() const {
-  return StrCat("HloBuffer ", id_, ", values: ", Join(value_ids_, ", "));
-}
-
-std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer) {
-  out << buffer.ToString();
-  return out;
-}
-
-void HloBufferSet::AddBuffer(HloBuffer::Id buffer_id) {
-  if (std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id) ==
-      buffer_ids_.end()) {
-    buffer_ids_.push_back(buffer_id);
-  }
-}
-
-void HloBufferSet::RemoveBufferOrDie(HloBuffer::Id buffer_id) {
-  auto it = std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id);
-  CHECK(it != buffer_ids_.end());
-  buffer_ids_.erase(it);
-}
-
-string HloBufferSet::ToString() const {
-  return StrCat("HloBufferSet, buffers: ", Join(buffer_ids_, ", "));
-}
-
-std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set) {
-  out << buffer_set.ToString();
-  return out;
-}
-
-bool InstructionBufferSet::IsAmbiguous() const {
-  bool is_ambiguous = false;
-  ForEachElement(
-      [&is_ambiguous](const ShapeIndex& index, const HloBufferSet& buffer_set) {
-        is_ambiguous |= buffer_set.buffer_ids().size() > 1;
-      });
-  return is_ambiguous;
-}
-
-bool InstructionBufferSet::IsDistinct() const {
-  bool is_distinct = true;
-  tensorflow::gtl::FlatSet<HloBuffer::Id> seen_ids;
-  ForEachElement([&is_distinct, &seen_ids](const ShapeIndex& index,
-                                           const HloBufferSet& buffer_set) {
-    for (HloBuffer::Id buffer_id : buffer_set.buffer_ids()) {
-      auto pair = seen_ids.insert(buffer_id);
-      if (!pair.second) {
-        is_distinct = false;
-      }
-    }
-  });
-  return is_distinct;
-}
-
-string InstructionBufferSet::ToString() const {
-  string out =
-      StrCat("InstructionBufferSet(", ShapeUtil::HumanString(shape()), ")\n");
-  ForEachElement([this, &out](const ShapeIndex& index,
-                              const HloBufferSet& value_set) {
-    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
-  });
-  return out;
-}
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionBufferSet& buffer_set) {
-  out << buffer_set.ToString();
-  return out;
-}
-
 HloAliasAnalysis::HloAliasAnalysis(HloModule* module) : module_(module) {}
 
 void HloAliasAnalysis::InitializeBufferSets() {
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.h b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
index 0fa35827b5..429cfa0915 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
@@ -16,182 +16,23 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_ALIAS_ANALYSIS_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_ALIAS_ANALYSIS_H_
 
-#include <stddef.h>
-#include <iosfwd>
 #include <memory>
-#include <set>
 #include <string>
 #include <vector>
 
-#include "tensorflow/compiler/xla/service/call_graph.h"
-#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
+#include "tensorflow/compiler/xla/service/hlo_buffer.h"
 #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/logical_buffer.h"
-#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-#include "tensorflow/core/lib/gtl/flatmap.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/types.h"
 
 namespace xla {
 
-// A container which can hold one or more HloValues. An HLO buffer abstractly
-// represents the allocation which HLO instructions write into and read
-// from. Generally there is a one-to-one correspondence between HloBuffers and
-// HloValue where each HloValue in the module is held in a unique HloBuffer. An
-// exception is the while instruction which updates the loop state in-place. In
-// this case, we have a single HloBuffer for each HloLocation in the loop state,
-// but multiple HloValues. For example:
-//
-//   %init = ...
-//   %while = While(%init, body, condition)
-//
-//  body:
-//   %body_param = Param(0)
-//     ...
-//   %body_root = ...
-//
-//  condition:
-//   %cond_param = Param(0)
-//     ...
-//
-// For simplicity, assume that %while is array-shaped. In this case, we have a
-// single HloBuffer which holds the following HloValues: HloValue{%init},
-// HloValue{%while}, HloValue{%body_param}, HloValue{%body_root}, and
-// HloValue{%cond_param}.
-//
-// HloBuffers may appear at different HloLocations in the module mirroring the
-// same propery of HloValues. For example:
-//
-//   %sub = Sub(...)
-//   %add = Add(...)
-//   %tuple = Tuple(%add, %sub)
-//   %gte = GetTupleElement(%tuple, 0)
-//
-// In this case, the HloBuffer containing %add appears at the following
-// locations: HloLocation{%add, {}}, HloLocation{%tuple, {0}}, and
-// HloLocation{%gte, {}}.
-//
-// Different HloLocations which share the same HloBuffer indicate mandatory
-// aliasing in the HLO module. These locations must share the same memory
-// allocation for correctness (the backends rely on this property). This differs
-// from incidental aliasing introduced by memory reuse in BufferAssignment where
-// different instructions may happen to get the same allocation.
-class HloBuffer {
- public:
-  using Id = int64;
-
-  HloBuffer(int64 id) : id_(id) {}
-
-  // Return the unique identifier for this HloBuffer.
-  int64 id() const { return id_; }
-
-  // Add a value to the set of values held by this buffer. Also adds the
-  // HloLocations of the value to the locations vector of the buffer. If the
-  // buffer already contains this value, then this method is a nop.
-  void AddValue(const HloValue& value);
-
-  // Return the IDs of all values contained in this buffer.
-  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
-
-  // Return the locations (output of which instruction and at what index) where
-  // the buffer is used. This is exactly the union of the locations of the
-  // HloValues contained by the buffer.
-  const std::vector<HloLocation>& locations() const { return locations_; }
-
-  string ToString() const;
-
-  bool operator==(const HloBuffer& other) const;
-  bool operator!=(const HloBuffer& other) const { return !(*this == other); }
-
- private:
-  // Unique identifier for this HloBuffer.
-  const Id id_;
-
-  // The set of values contained in the this buffer.
-  std::vector<HloValue::Id> value_ids_;
-
-  // The set of locations where this buffer is used.
-  std::vector<HloLocation> locations_;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer);
-
-// A class representing the set of possible HloBuffers at a particular
-// HloLocation (shape index in the output of an instruction) in the XLA
-// graph. In most cases, the buffer set will have a single HloBuffer indicating
-// that the HloBuffer which appears at that particular location is known
-// unambiguously at compile-time.  However, tuple-shaped Select instructions can
-// introduce ambiguity as the tuple elements of the operands are passed by
-// reference into the output of the Select. For example:
-//
-//   %pred = ...
-//   %tuple0 = Tuple(%a, %b)
-//   %tuple1 = Tuple(%x, %y)
-//   %select = Select(%pred, %tuple0, %tuple1)
-//
-// In this case the HloBufferSet at HloLocation{%select, {0}} contains the
-// HloBuffer holding %a and the HloBuffer holding %x.
-class HloBufferSet {
- public:
-  HloBufferSet() = default;
-
-  // Add the given buffer to this buffer set. If the buffer already exists in
-  // the set, then this is a NOP.
-  void AddBuffer(HloBuffer::Id buffer_id);
-
-  // Removes the given buffer from this buffer set. CHECK fails in the buffer is
-  // not contained in this set.
-  void RemoveBufferOrDie(HloBuffer::Id buffer_id);
-
-  // Returns the unique buffer in this set. CHECK fails if the set does not
-  // contain exactly one buffer.
-  HloBuffer::Id GetUniqueBufferId() const {
-    CHECK_EQ(buffer_ids().size(), 1);
-    return buffer_ids()[0];
-  }
-
-  // Returns the IDs of the HloBuffers contained in this buffer set.
-  const std::vector<HloBuffer::Id>& buffer_ids() const { return buffer_ids_; }
-
-  string ToString() const;
-
- private:
-  // The IDs of the HloBuffers containted in this buffer set.
-  std::vector<HloBuffer::Id> buffer_ids_;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set);
-
-// A class collecting the HloBuffers in the output of an HLO instruction. For
-// array-shaped instructions, an InstructionBufferSet trivially holds a single
-// HloBufferSet. Tuple-shaped InstructionBufferSets hold multiple
-// HloBufferSets.
-class InstructionBufferSet : public ShapeTree<HloBufferSet> {
- public:
-  InstructionBufferSet(const Shape& shape) : ShapeTree<HloBufferSet>(shape) {}
-
-  // Returns true if any HloBufferSet contained in this InstructionBufferSet
-  // is not a singleton.
-  bool IsAmbiguous() const;
-
-  // Returns true if any HloBuffer appears in more than one HloBufferSet
-  // contained in this InstructionBufferSet.
-  bool IsDistinct() const;
-
-  string ToString() const;
-};
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionBufferSet& buffer_set);
-
 class HloAliasAnalysis {
  public:
   static StatusOr<std::unique_ptr<HloAliasAnalysis>> Run(HloModule* module);
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.cc b/tensorflow/compiler/xla/service/hlo_buffer.cc
new file mode 100644
index 0000000000..f289f56966
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_buffer.cc
@@ -0,0 +1,139 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_buffer.h"
+
+#include <algorithm>
+#include <ostream>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+using ::tensorflow::str_util::Join;
+using ::tensorflow::strings::StrAppend;
+using ::tensorflow::strings::StrCat;
+
+void HloBuffer::AddValue(const HloValue& value) {
+  // If the value is already contained in this buffer, just return.
+  if (std::find(value_ids_.begin(), value_ids_.end(), value.id()) !=
+      value_ids_.end()) {
+    return;
+  }
+
+  value_ids_.push_back(value.id());
+
+  // Add all of the locations of the HloValue to this buffer.
+  for (const HloLocation& location : value.locations()) {
+    if (std::find(locations_.begin(), locations_.end(), location) ==
+        locations_.end()) {
+      locations_.push_back(location);
+    }
+  }
+}
+
+bool HloBuffer::operator==(const HloBuffer& other) const {
+  bool equal = id() == other.id();
+  if (equal) {
+    // DCHECK because these comparisons are expensive (linear time).
+    DCHECK(value_ids() == other.value_ids());
+    DCHECK(locations() == other.locations());
+  }
+  return equal;
+}
+
+string HloBuffer::ToString() const {
+  return StrCat("HloBuffer ", id_, ", values: ", Join(value_ids_, ", "));
+}
+
+std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer) {
+  out << buffer.ToString();
+  return out;
+}
+
+void HloBufferSet::AddBuffer(HloBuffer::Id buffer_id) {
+  if (std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id) ==
+      buffer_ids_.end()) {
+    buffer_ids_.push_back(buffer_id);
+  }
+}
+
+void HloBufferSet::RemoveBufferOrDie(HloBuffer::Id buffer_id) {
+  auto it = std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id);
+  CHECK(it != buffer_ids_.end());
+  buffer_ids_.erase(it);
+}
+
+string HloBufferSet::ToString() const {
+  return StrCat("HloBufferSet, buffers: ", Join(buffer_ids_, ", "));
+}
+
+std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set) {
+  out << buffer_set.ToString();
+  return out;
+}
+
+bool InstructionBufferSet::IsAmbiguous() const {
+  bool is_ambiguous = false;
+  ForEachElement(
+      [&is_ambiguous](const ShapeIndex& index, const HloBufferSet& buffer_set) {
+        is_ambiguous |= buffer_set.buffer_ids().size() > 1;
+      });
+  return is_ambiguous;
+}
+
+bool InstructionBufferSet::IsDistinct() const {
+  bool is_distinct = true;
+  tensorflow::gtl::FlatSet<HloBuffer::Id> seen_ids;
+  ForEachElement([&is_distinct, &seen_ids](const ShapeIndex& index,
+                                           const HloBufferSet& buffer_set) {
+    for (HloBuffer::Id buffer_id : buffer_set.buffer_ids()) {
+      auto pair = seen_ids.insert(buffer_id);
+      if (!pair.second) {
+        is_distinct = false;
+      }
+    }
+  });
+  return is_distinct;
+}
+
+string InstructionBufferSet::ToString() const {
+  string out =
+      StrCat("InstructionBufferSet(", ShapeUtil::HumanString(shape()), ")\n");
+  ForEachElement([this, &out](const ShapeIndex& index,
+                              const HloBufferSet& value_set) {
+    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
+  });
+  return out;
+}
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionBufferSet& buffer_set) {
+  out << buffer_set.ToString();
+  return out;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.h b/tensorflow/compiler/xla/service/hlo_buffer.h
new file mode 100644
index 0000000000..238d29a6d6
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_buffer.h
@@ -0,0 +1,183 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_BUFFER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_BUFFER_H_
+
+#include <ostream>
+#include <string>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/hlo_value.h"
+#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+
+// A container which can hold one or more HloValues. An HLO buffer abstractly
+// represents the allocation which HLO instructions write into and read
+// from. Generally there is a one-to-one correspondence between HloBuffers and
+// HloValue where each HloValue in the module is held in a unique HloBuffer. An
+// exception is the while instruction which updates the loop state in-place. In
+// this case, we have a single HloBuffer for each HloLocation in the loop state,
+// but multiple HloValues. For example:
+//
+//   %init = ...
+//   %while = While(%init, body, condition)
+//
+//  body:
+//   %body_param = Param(0)
+//     ...
+//   %body_root = ...
+//
+//  condition:
+//   %cond_param = Param(0)
+//     ...
+//
+// For simplicity, assume that %while is array-shaped. In this case, we have a
+// single HloBuffer which holds the following HloValues: HloValue{%init},
+// HloValue{%while}, HloValue{%body_param}, HloValue{%body_root}, and
+// HloValue{%cond_param}.
+//
+// HloBuffers may appear at different HloLocations in the module mirroring the
+// same propery of HloValues. For example:
+//
+//   %sub = Sub(...)
+//   %add = Add(...)
+//   %tuple = Tuple(%add, %sub)
+//   %gte = GetTupleElement(%tuple, 0)
+//
+// In this case, the HloBuffer containing %add appears at the following
+// locations: HloLocation{%add, {}}, HloLocation{%tuple, {0}}, and
+// HloLocation{%gte, {}}.
+//
+// Different HloLocations which share the same HloBuffer indicate mandatory
+// aliasing in the HLO module. These locations must share the same memory
+// allocation for correctness (the backends rely on this property). This differs
+// from incidental aliasing introduced by memory reuse in BufferAssignment where
+// different instructions may happen to get the same allocation.
+class HloBuffer {
+ public:
+  using Id = int64;
+
+  HloBuffer(int64 id) : id_(id) {}
+
+  // Return the unique identifier for this HloBuffer.
+  int64 id() const { return id_; }
+
+  // Add a value to the set of values held by this buffer. Also adds the
+  // HloLocations of the value to the locations vector of the buffer. If the
+  // buffer already contains this value, then this method is a nop.
+  void AddValue(const HloValue& value);
+
+  // Return the IDs of all values contained in this buffer.
+  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
+
+  // Return the locations (output of which instruction and at what index) where
+  // the buffer is used. This is exactly the union of the locations of the
+  // HloValues contained by the buffer.
+  const std::vector<HloLocation>& locations() const { return locations_; }
+
+  string ToString() const;
+
+  bool operator==(const HloBuffer& other) const;
+  bool operator!=(const HloBuffer& other) const { return !(*this == other); }
+
+ private:
+  // Unique identifier for this HloBuffer.
+  const Id id_;
+
+  // The set of values contained in the this buffer.
+  std::vector<HloValue::Id> value_ids_;
+
+  // The set of locations where this buffer is used.
+  std::vector<HloLocation> locations_;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer);
+
+// A class representing the set of possible HloBuffers at a particular
+// HloLocation (shape index in the output of an instruction) in the XLA
+// graph. In most cases, the buffer set will have a single HloBuffer indicating
+// that the HloBuffer which appears at that particular location is known
+// unambiguously at compile-time.  However, tuple-shaped Select instructions can
+// introduce ambiguity as the tuple elements of the operands are passed by
+// reference into the output of the Select. For example:
+//
+//   %pred = ...
+//   %tuple0 = Tuple(%a, %b)
+//   %tuple1 = Tuple(%x, %y)
+//   %select = Select(%pred, %tuple0, %tuple1)
+//
+// In this case the HloBufferSet at HloLocation{%select, {0}} contains the
+// HloBuffer holding %a and the HloBuffer holding %x.
+class HloBufferSet {
+ public:
+  HloBufferSet() = default;
+
+  // Add the given buffer to this buffer set. If the buffer already exists in
+  // the set, then this is a NOP.
+  void AddBuffer(HloBuffer::Id buffer_id);
+
+  // Removes the given buffer from this buffer set. CHECK fails in the buffer is
+  // not contained in this set.
+  void RemoveBufferOrDie(HloBuffer::Id buffer_id);
+
+  // Returns the unique buffer in this set. CHECK fails if the set does not
+  // contain exactly one buffer.
+  HloBuffer::Id GetUniqueBufferId() const {
+    CHECK_EQ(buffer_ids().size(), 1);
+    return buffer_ids()[0];
+  }
+
+  // Returns the IDs of the HloBuffers contained in this buffer set.
+  const std::vector<HloBuffer::Id>& buffer_ids() const { return buffer_ids_; }
+
+  string ToString() const;
+
+ private:
+  // The IDs of the HloBuffers containted in this buffer set.
+  std::vector<HloBuffer::Id> buffer_ids_;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set);
+
+// A class collecting the HloBuffers in the output of an HLO instruction. For
+// array-shaped instructions, an InstructionBufferSet trivially holds a single
+// HloBufferSet. Tuple-shaped InstructionBufferSets hold multiple
+// HloBufferSets.
+class InstructionBufferSet : public ShapeTree<HloBufferSet> {
+ public:
+  InstructionBufferSet(const Shape& shape) : ShapeTree<HloBufferSet>(shape) {}
+
+  // Returns true if any HloBufferSet contained in this InstructionBufferSet
+  // is not a singleton.
+  bool IsAmbiguous() const;
+
+  // Returns true if any HloBuffer appears in more than one HloBufferSet
+  // contained in this InstructionBufferSet.
+  bool IsDistinct() const;
+
+  string ToString() const;
+};
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionBufferSet& buffer_set);
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_BUFFER_H_
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index f3a6cd43c2..522dddea4e 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -25,34 +25,56 @@ limitations under the License.
 
 namespace xla {
 
+constexpr char HloCostAnalysis::kFlopsKey[];
+constexpr char HloCostAnalysis::kTranscendentalsKey[];
+constexpr char HloCostAnalysis::kBytesAccessedKey[];
+constexpr char HloCostAnalysis::kSecondsKey[];
+
+HloCostAnalysis::HloCostAnalysis(const ShapeSizeFunction& shape_size)
+    : HloCostAnalysis(shape_size, {}) {}
+
+HloCostAnalysis::HloCostAnalysis(const ShapeSizeFunction& shape_size,
+                                 const Properties& per_second_rates)
+    : shape_size_(shape_size), per_second_rates_(per_second_rates) {}
+
 Status HloCostAnalysis::Preprocess(HloInstruction* hlo) {
   // Set current instruction cost values to reasonable default values. Each
-  // handler can overwrite these values. In Postprocess, these value are
+  // handler can overwrite these values. In Postprocess, these values are
   // accumulated and written to the per-instruction maps.
-  current_flop_count_ = 0;
-  current_transcendental_count_ = 0;
+  current_properties_.clear();
+  current_should_compute_bottleneck_time_ = true;
 
-  // The default element count for an instruction is the sum of elements in the
-  // operands and output. The default ShapeUtil::ByteSizeOf does not handle
-  // opaque types.
-  current_bytes_accessed_ = shape_size_(hlo->shape());
+  // The default number of bytes accessed for an instruction is the sum of the
+  // sizes of the inputs and outputs. The default ShapeUtil::ByteSizeOf does not
+  // handle opaque types.
+  float bytes_accessed = shape_size_(hlo->shape());
   for (const HloInstruction* operand : hlo->operands()) {
-    current_bytes_accessed_ += shape_size_(operand->shape());
+    bytes_accessed += shape_size_(operand->shape());
   }
+  current_properties_[kBytesAccessedKey] = bytes_accessed;
 
   return Status::OK();
 }
 
 Status HloCostAnalysis::Postprocess(HloInstruction* hlo) {
-  // Accumulate cost values and write into per-instruction maps.
-  flop_count_ += current_flop_count_;
-  hlo_to_flop_count_[hlo] = current_flop_count_;
-
-  transcendental_count_ += current_transcendental_count_;
-  hlo_to_transcendental_count_[hlo] = current_transcendental_count_;
+  if (current_should_compute_bottleneck_time_) {
+    // Compute the time as the time of the bottleneck, i.e. the slowest property
+    // given the per-second rate of each property.
+    float max_seconds = 0.0f;
+    for (const auto& property : current_properties_) {
+      if (property.first != kSecondsKey) {
+        max_seconds = std::max(
+            max_seconds,
+            property.second / GetProperty(property.first, per_second_rates_));
+      }
+    }
+    current_properties_[kSecondsKey] = max_seconds;
+  }
 
-  bytes_accessed_ += current_bytes_accessed_;
-  hlo_to_bytes_accessed_[hlo] = current_bytes_accessed_;
+  TF_RET_CHECK(hlo_properties_.emplace(hlo, current_properties_).second);
+  for (const auto& property : current_properties_) {
+    properties_sum_[property.first] += property.second;
+  }
 
   return Status::OK();
 }
@@ -65,15 +87,32 @@ Status HloCostAnalysis::HandleElementwiseOp(HloInstruction* hlo_instruction) {
   auto opcode = hlo_instruction->opcode();
   // We treat the two opcodes (kExp, kPower) as transcendental operations.
   if (opcode == HloOpcode::kExp || opcode == HloOpcode::kPower) {
-    current_transcendental_count_ = computation_count;
+    current_properties_[kTranscendentalsKey] = computation_count;
   } else {
     // Note: transcendental operations are considered a separate category from
     // FLOPs.
-    current_flop_count_ = computation_count;
+    current_properties_[kFlopsKey] = computation_count;
   }
   return Status::OK();
 }
 
+/*static*/ float HloCostAnalysis::GetProperty(const string& key,
+                                              const Properties& properties) {
+  auto key_value = properties.find(key);
+  return key_value == properties.end() ? 0.0f : key_value->second;
+}
+
+/*static*/ float HloCostAnalysis::GetPropertyForHlo(
+    const HloInstruction& hlo, const string& key,
+    const HloToProperties& hlo_to_properties) {
+  auto it = hlo_to_properties.find(&hlo);
+  if (it == hlo_to_properties.end()) {
+    return 0.0f;
+  } else {
+    return GetProperty(key, it->second);
+  }
+}
+
 Status HloCostAnalysis::HandleElementwiseUnary(HloInstruction* hlo,
                                                HloOpcode opcode) {
   return HandleElementwiseOp(hlo);
@@ -97,19 +136,18 @@ Status HloCostAnalysis::HandleClamp(HloInstruction* clamp,
   return HandleElementwiseOp(clamp);
 }
 
-Status HloCostAnalysis::HandleReducePrecision(HloInstruction* hlo,
-                                              HloInstruction* operand) {
+Status HloCostAnalysis::HandleReducePrecision(HloInstruction* hlo) {
   return HandleElementwiseOp(hlo);
 }
 
 Status HloCostAnalysis::HandleParameter(HloInstruction* parameter) {
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleConstant(HloInstruction* constant,
                                        const Literal& literal) {
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
@@ -117,7 +155,7 @@ Status HloCostAnalysis::HandleGetTupleElement(HloInstruction* get_tuple_element,
                                               HloInstruction* operand) {
   // GetTupleElement forwards a pointer and does not touch each element in the
   // output.
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
@@ -155,8 +193,9 @@ Status HloCostAnalysis::HandleTuple(
     tensorflow::gtl::ArraySlice<HloInstruction*> operands) {
   // The tuple instruction only gathers pointers from inputs (it doesn't iterate
   // through them). The memory touched is then only the size of the output
-  // buffer.
-  current_bytes_accessed_ = shape_size_(tuple->shape());
+  // index table of the tuple.
+
+  current_properties_[kBytesAccessedKey] = shape_size_(tuple->shape());
   return Status::OK();
 }
 
@@ -194,7 +233,7 @@ Status HloCostAnalysis::HandleDot(HloInstruction* dot,
   }
 
   // We count an FMA operation as 2 floating point operations.
-  current_flop_count_ = kFmaFlops * fma_count;
+  current_properties_[kFlopsKey] = kFmaFlops * fma_count;
   return Status::OK();
 }
 
@@ -210,16 +249,17 @@ Status HloCostAnalysis::HandleMap(
     HloInstruction* map, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
     HloComputation* function,
     tensorflow::gtl::ArraySlice<HloInstruction*> /*static_operands*/) {
-  // Compute the cost of the user function.
-  HloInstruction* function_instruction = function->root_instruction();
-  HloCostAnalysis visitor(shape_size_);
-  TF_RETURN_IF_ERROR(function_instruction->Accept(&visitor));
+  // Compute properties of the mapped function.
+  TF_ASSIGN_OR_RETURN(const Properties sub_properties,
+                      ProcessSubcomputation(function));
 
   // Compute the cost of all elements for this Map operation.
-  int64 element_count = ShapeUtil::ElementsIn(map->shape());
-  current_transcendental_count_ =
-      element_count * visitor.transcendental_count();
-  current_flop_count_ = element_count * visitor.flop_count();
+  const int64 element_count = ShapeUtil::ElementsIn(map->shape());
+  for (const auto& property : sub_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] = property.second * element_count;
+    }
+  }
   return Status::OK();
 }
 
@@ -227,16 +267,17 @@ Status HloCostAnalysis::HandleReduce(
     HloInstruction* reduce, HloInstruction* arg, HloInstruction* init_value,
     tensorflow::gtl::ArraySlice<int64> dimensions, HloComputation* function) {
   // Compute the cost of the user function.
-  HloInstruction* function_instruction = function->root_instruction();
-  HloCostAnalysis visitor(shape_size_);
-  TF_RETURN_IF_ERROR(function_instruction->Accept(&visitor));
+  TF_ASSIGN_OR_RETURN(const Properties sub_properties,
+                      ProcessSubcomputation(function));
 
   // Compute the cost of all elements for this Reduce operation.
   int64 reduction_count = ShapeUtil::ElementsIn(arg->shape()) -
                           ShapeUtil::ElementsIn(reduce->shape());
-  current_flop_count_ = reduction_count * visitor.flop_count();
-  current_transcendental_count_ =
-      reduction_count * visitor.transcendental_count();
+  for (const auto& property : sub_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] = property.second * reduction_count;
+    }
+  }
   return Status::OK();
 }
 
@@ -244,55 +285,63 @@ Status HloCostAnalysis::HandleReduceWindow(HloInstruction* reduce_window,
                                            HloInstruction* operand,
                                            const Window& window,
                                            HloComputation* function) {
-  // Compute the cost of the user function.
-  HloInstruction* function_instruction = function->root_instruction();
-  HloCostAnalysis visitor(shape_size_);
-  TF_RETURN_IF_ERROR(function_instruction->Accept(&visitor));
+  // Compute the properties of the reduction function.
+  TF_ASSIGN_OR_RETURN(const Properties sub_properties,
+                      ProcessSubcomputation(function));
 
   // Compute the cost of all elements for this ReduceWindow operation. For each
-  // output element, (window_size - 1) number of user computations are applied.
-  auto output_size = ShapeUtil::ElementsIn(reduce_window->shape());
-  int64 window_size = 1;
+  // output element there are window_size - 1 reductions to perform.
+  int64 window_element_count = 1;
   for (const auto& dimension : window.dimensions()) {
-    window_size *= dimension.size();
+    window_element_count *= dimension.size();
+  }
+  const int64 output_element_count =
+      ShapeUtil::ElementsIn(reduce_window->shape());
+  const int64 reduction_count =
+      (window_element_count - 1) * output_element_count;
+  for (const auto& property : sub_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] = property.second * reduction_count;
+    }
   }
-  current_flop_count_ = output_size * (window_size - 1) * visitor.flop_count();
-  current_transcendental_count_ =
-      output_size * (window_size - 1) * visitor.transcendental_count();
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleSelectAndScatter(HloInstruction* instruction) {
-  // Compute the cost of the select and scatter function.
-  HloInstruction* select = instruction->select()->root_instruction();
-  HloCostAnalysis select_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(select->Accept(&select_visitor));
-  HloInstruction* scatter = instruction->scatter()->root_instruction();
-  HloCostAnalysis scatter_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(scatter->Accept(&scatter_visitor));
+  // Compute the properties of the select and scatter function.
+  // Compute the properties of the reduction function.
+  TF_ASSIGN_OR_RETURN(const Properties select_properties,
+                      ProcessSubcomputation(instruction->select()));
+  TF_ASSIGN_OR_RETURN(const Properties scatter_properties,
+                      ProcessSubcomputation(instruction->scatter()));
 
   // Compute the cost of all elements for this operation. For each scatter
-  // source element, (window_size - 1) number of select computations and 1
-  // scatter computation are applied.
+  // source element there are window_size - 1 select computations to perform and
+  // 1 scatter computation to perform.
   const auto source = instruction->operand(1);
   const auto source_element_count = ShapeUtil::ElementsIn(source->shape());
-  int64 window_size = 1;
+  int64 window_element_count = 1;
   for (const auto& dimension : instruction->window().dimensions()) {
-    window_size *= dimension.size();
+    window_element_count *= dimension.size();
+  }
+  const int64 select_count = source_element_count * (window_element_count - 1);
+  for (const auto& property : select_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] += property.second * select_count;
+    }
+  }
+  for (const auto& property : scatter_properties) {
+    if (property.first != kBytesAccessedKey) {
+      current_properties_[property.first] +=
+          property.second * source_element_count;
+    }
   }
-  current_flop_count_ =
-      source_element_count * ((window_size - 1) * select_visitor.flop_count() +
-                              scatter_visitor.flop_count());
-  current_transcendental_count_ =
-      source_element_count *
-      ((window_size - 1) * select_visitor.transcendental_count() +
-       scatter_visitor.transcendental_count());
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleBitcast(HloInstruction* bitcast) {
   // A bitcast does no computation and touches no memory.
-  current_bytes_accessed_ = 0;
+  current_properties_[kBytesAccessedKey] = 0;
   return Status::OK();
 }
 
@@ -332,12 +381,13 @@ Status HloCostAnalysis::HandleConvolution(HloInstruction* convolution,
   const int64 output_features =
       convolution->shape().dimensions(dnums.feature_dimension());
 
-  // For each output element, we do one fma per element in the
-  // kernel at some given output feature index.
+  // For each output element, we do one fma per element in the kernel at some
+  // given output feature index.
   const int64 fmas_per_output_element =
       ShapeUtil::ElementsIn(rhs_instruction->shape()) / output_features;
   const int64 output_elements = ShapeUtil::ElementsIn(convolution->shape());
-  current_flop_count_ = output_elements * fmas_per_output_element * kFmaFlops;
+  current_properties_[kFlopsKey] =
+      output_elements * fmas_per_output_element * kFmaFlops;
   return Status::OK();
 }
 
@@ -347,7 +397,7 @@ Status HloCostAnalysis::HandleCrossReplicaSum(HloInstruction* crs) {
   //
   // TODO(b/33004697): Compute correct cost here, taking the actual number of
   // replicas into account.
-  current_flop_count_ = ShapeUtil::ElementsIn(crs->shape());
+  current_properties_[kFlopsKey] = ShapeUtil::ElementsIn(crs->shape());
   return Status::OK();
 }
 
@@ -356,44 +406,43 @@ Status HloCostAnalysis::HandleRng(HloInstruction* random,
   // TODO(b/26346211): Implement better estimates for the RNG cost, since the
   // cost changes with the implementation and the distribution. For now, assume
   // the cost of each RNG is same as a transcendental operation.
-  current_transcendental_count_ = ShapeUtil::ElementsIn(random->shape());
+  current_properties_[kTranscendentalsKey] =
+      ShapeUtil::ElementsIn(random->shape());
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleFusion(HloInstruction* fusion) {
-  // Compute the cost of the fused expression.
-  HloInstruction* fused_expression_root = fusion->fused_expression_root();
-  // Don't compute sizes inside of fused ops. We don't use the size here and the
-  // operations inside might not have a layout.
-  HloCostAnalysis visitor([](const Shape&) { return 0; });
-  TF_RETURN_IF_ERROR(fused_expression_root->Accept(&visitor));
-
-  // If a fusion node produces a tuple, it also produces the operands of that
-  // tuple.
-  current_bytes_accessed_ = 0;
+  // Compute the properties of the fused expression and attribute them to the
+  // fusion node. Use a dummy shape_size to avoid any errors from trying to
+  // calculate the size of a shape that does not have a layout, since nodes
+  // inside fusion nodes do not necessarily have a layout assigned.
+  ShapeSizeFunction shape_size = [](const Shape& shape) { return 0; };
+  TF_ASSIGN_OR_RETURN(
+      current_properties_,
+      ProcessSubcomputation(fusion->fused_instructions_computation(),
+                            &shape_size));
+
+  // Fusion nodes that produce a tuple also produce the entries in the tuple.
+  // Ignore the memory accessed inside fused ops, since fusion is supposed to
+  // prevent intermediate data from touching slow memory.
+  current_properties_[kBytesAccessedKey] = 0;
   ShapeUtil::ForEachSubshape(
       fusion->shape(),
       [this](const Shape& subshape, const ShapeIndex& /*shape_index*/) {
-        current_bytes_accessed_ += shape_size_(subshape);
+        current_properties_[kBytesAccessedKey] += shape_size_(subshape);
       });
 
   for (const HloInstruction* operand : fusion->operands()) {
-    current_bytes_accessed_ += shape_size_(operand->shape());
+    current_properties_[kBytesAccessedKey] += shape_size_(operand->shape());
   }
 
-  // Attribute the cost of the fused expression to the fusion node.
-  current_transcendental_count_ = visitor.transcendental_count();
-  current_flop_count_ = visitor.flop_count();
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleCall(HloInstruction* call) {
-  HloCostAnalysis computation_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(call->to_apply()->Accept(&computation_visitor));
-
-  current_flop_count_ = computation_visitor.flop_count();
-  current_transcendental_count_ = computation_visitor.transcendental_count();
-  current_bytes_accessed_ = computation_visitor.bytes_accessed();
+  TF_ASSIGN_OR_RETURN(current_properties_,
+                      ProcessSubcomputation(call->to_apply()));
+  current_should_compute_bottleneck_time_ = false;
   return Status::OK();
 }
 
@@ -401,34 +450,38 @@ Status HloCostAnalysis::HandleCustomCall(
     HloInstruction* custom_call,
     tensorflow::gtl::ArraySlice<HloInstruction*> operands,
     tensorflow::StringPiece custom_call_target) {
-  return Unimplemented("custom-call");
+  return Unimplemented("Custom-call is not implemented for HLO cost analysis.");
 }
 
 Status HloCostAnalysis::HandleSort(HloInstruction* sort,
                                    HloInstruction* operand_instruction) {
-  // The cost of sort is implementation dependent, so cannot determine at HLO
-  // level. Assume comparison based N*log(N) sorting.
+  // This assumes a comparison based N*log(N) algorithm. As for all ops, the
+  // actual properties of the op depend on the backend implementation.
   int64 elements = ShapeUtil::ElementsIn(operand_instruction->shape());
-  current_flop_count_ = elements * tensorflow::Log2Ceiling(elements);
+  current_properties_[kFlopsKey] = elements * tensorflow::Log2Ceiling(elements);
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleWhile(HloInstruction* xla_while) {
-  // Since the number of iterations of the while node is not statically
-  // determined, we cannot precisely compute the cost of a while node. For now
-  // compute the cost of a single iteration.
-  // TODO(b/26346211): Improve the cost analysis for while node.
-  HloCostAnalysis body_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(xla_while->while_body()->Accept(&body_visitor));
-  HloCostAnalysis condition_visitor(shape_size_);
-  TF_RETURN_IF_ERROR(xla_while->while_condition()->Accept(&condition_visitor));
+  // Since the number of iterations of the while node will not always be
+  // something that we can statically analyze, we cannot precisely compute the
+  // cost of a while node. For now compute the cost of a single iteration.
+  //
+  // TODO(b/26346211): Improve the cost analysis for while nodes.
+  TF_ASSIGN_OR_RETURN(const Properties body_properties,
+                      ProcessSubcomputation(xla_while->while_body()));
 
-  current_flop_count_ =
-      body_visitor.flop_count() + condition_visitor.flop_count();
-  current_transcendental_count_ = body_visitor.transcendental_count() +
-                                  condition_visitor.transcendental_count();
-  current_bytes_accessed_ =
-      body_visitor.bytes_accessed() + condition_visitor.bytes_accessed();
+  TF_ASSIGN_OR_RETURN(const Properties condition_properties,
+                      ProcessSubcomputation(xla_while->while_condition()));
+
+  current_properties_.clear();
+  for (const auto& property : body_properties) {
+    current_properties_[property.first] += property.second;
+  }
+  for (const auto& property : condition_properties) {
+    current_properties_[property.first] += property.second;
+  }
+  current_should_compute_bottleneck_time_ = false;
 
   return Status::OK();
 }
@@ -437,19 +490,42 @@ Status HloCostAnalysis::FinishVisit(HloInstruction* root) {
   return Status::OK();
 }
 
+float HloCostAnalysis::flop_count() const {
+  return GetProperty(kFlopsKey, properties_sum_);
+}
+
+float HloCostAnalysis::transcendental_count() const {
+  return GetProperty(kTranscendentalsKey, properties_sum_);
+}
+
+float HloCostAnalysis::bytes_accessed() const {
+  return GetProperty(kBytesAccessedKey, properties_sum_);
+}
+
+float HloCostAnalysis::seconds() const {
+  return GetProperty(kSecondsKey, properties_sum_);
+}
+
 int64 HloCostAnalysis::flop_count(const HloInstruction& hlo) const {
-  auto it = hlo_to_flop_count_.find(&hlo);
-  return it == hlo_to_flop_count_.end() ? 0 : it->second;
+  return GetPropertyForHlo(hlo, kFlopsKey, hlo_properties_);
 }
 
 int64 HloCostAnalysis::transcendental_count(const HloInstruction& hlo) const {
-  auto it = hlo_to_transcendental_count_.find(&hlo);
-  return it == hlo_to_transcendental_count_.end() ? 0 : it->second;
+  return GetPropertyForHlo(hlo, kTranscendentalsKey, hlo_properties_);
 }
 
 int64 HloCostAnalysis::bytes_accessed(const HloInstruction& hlo) const {
-  auto it = hlo_to_bytes_accessed_.find(&hlo);
-  return it == hlo_to_bytes_accessed_.end() ? 0 : it->second;
+  return GetPropertyForHlo(hlo, kBytesAccessedKey, hlo_properties_);
+}
+
+StatusOr<HloCostAnalysis::Properties> HloCostAnalysis::ProcessSubcomputation(
+    HloComputation* computation, const ShapeSizeFunction* shape_size) {
+  if (shape_size == nullptr) {
+    shape_size = &shape_size_;
+  }
+  HloCostAnalysis visitor(*shape_size, per_second_rates_);
+  TF_RETURN_IF_ERROR(computation->Accept(&visitor));
+  return visitor.properties();
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index 3f0dfcc619..17c26fc1a1 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -36,11 +36,18 @@ namespace xla {
 // operations separately from transcendental operations.
 class HloCostAnalysis : public DfsHloVisitor {
  public:
+  // Each HLO is associated to a vector of properties with the indices given
+  // below. Sub-classes can add further properties.
+  typedef std::map<string, float> Properties;
+  static constexpr char kFlopsKey[] = "flops";
+  static constexpr char kTranscendentalsKey[] = "transcendentals";
+  static constexpr char kBytesAccessedKey[] = "bytes accessed";
+  static constexpr char kSecondsKey[] = "seconds";
+
   // shape_size is a function which returns the size in bytes of the top-level
   // buffer of a shape.
   using ShapeSizeFunction = std::function<int64(const Shape&)>;
-  explicit HloCostAnalysis(const ShapeSizeFunction& shape_size)
-      : shape_size_(shape_size) {}
+  explicit HloCostAnalysis(const ShapeSizeFunction& shape_size);
 
   Status HandleElementwiseUnary(HloInstruction* hlo, HloOpcode opcode) override;
   Status HandleElementwiseBinary(HloInstruction* hlo,
@@ -56,8 +63,7 @@ class HloCostAnalysis : public DfsHloVisitor {
                        HloInstruction* lhs, HloInstruction* rhs) override;
   Status HandleClamp(HloInstruction* clamp, HloInstruction* min,
                      HloInstruction* arg, HloInstruction* max) override;
-  Status HandleReducePrecision(HloInstruction* hlo,
-                               HloInstruction* operand) override;
+  Status HandleReducePrecision(HloInstruction* hlo) override;
   Status HandleConcatenate(
       HloInstruction* concatenate,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) override;
@@ -119,48 +125,88 @@ class HloCostAnalysis : public DfsHloVisitor {
   Status Preprocess(HloInstruction* hlo) override;
   Status Postprocess(HloInstruction* hlo) override;
 
-  // Returns the amount of computations in the graph.
-  int64 flop_count() const { return flop_count_; }
-  int64 transcendental_count() const { return transcendental_count_; }
+  // Set the rates used to calculate the time taken by the computation. These
+  // need to be set before visiting starts.
+  void set_flops_per_second(float value) {
+    per_second_rates_[kFlopsKey] = value;
+  }
+  void set_transcendentals_per_second(float value) {
+    per_second_rates_[kTranscendentalsKey] = value;
+  }
+  void set_bytes_per_second(float value) {
+    per_second_rates_[kBytesAccessedKey] = value;
+  }
+
+  // Returns properties for the computation.
+  float flop_count() const;
+  float transcendental_count() const;
+  float bytes_accessed() const;
+  float seconds() const;
 
   // Returns the respective cost computed for a particular HLO instruction, or 0
   // if the HLO was not found to have a cost in the analysis.
   int64 flop_count(const HloInstruction& hlo) const;
   int64 transcendental_count(const HloInstruction& hlo) const;
-
-  // Returns the number of bytes read/written.
   int64 bytes_accessed(const HloInstruction& hlo) const;
-  int64 bytes_accessed() const { return bytes_accessed_; }
+  float seconds(const HloInstruction& hlo) const;
+
+  const Properties& properties() const { return properties_sum_; }
+  const float property(const string& key) const {
+    return GetProperty(key, properties());
+  }
+
+ protected:
+  typedef std::unordered_map<const HloInstruction*, Properties> HloToProperties;
 
- private:
   // An FMA counts as two floating point operations in these analyzes.
   static constexpr int64 kFmaFlops = 2;
 
+  HloCostAnalysis(const ShapeSizeFunction& shape_size,
+                  const Properties& per_second_rates);
+
+  // Returns the properties computed from visiting the computation rooted at the
+  // given hlo. Uses shape_size_ to calculate shape sizes if shape_size is null,
+  // otherwise uses shape_size_.
+  StatusOr<Properties> ProcessSubcomputation(
+      HloComputation* computation,
+      const ShapeSizeFunction* shape_size = nullptr);
+
   // Utility function to handle all element-wise operations.
   Status HandleElementwiseOp(HloInstruction* hlo_instruction);
 
+  // Returns 0.0f if the key is not present in the properties. Otherwise,
+  // returns the value that the key maps to from the properties parameter.
+  static float GetProperty(const string& key, const Properties& properties);
+
+  // Returns 0.0f if the hlo is not present in hlo_to_properties or if the key
+  // is not present in hlo_to_properties[hlo]. Otherwise, returns the value that
+  // the key maps to in the properties of the given hlo.
+  static float GetPropertyForHlo(const HloInstruction& hlo, const string& key,
+                                 const HloToProperties& hlo_to_properties);
+
   // Function which computes the size of the top-level of a given shape (not
   // including nested elements, if any). If null then bytes_accessed methods
   // return an error.
   const ShapeSizeFunction shape_size_;
 
-  // The total number of floating point operations, transcendental operations,
-  // and bytes accesses (read or written) in the computation.
-  int64 flop_count_ = 0;
-  int64 transcendental_count_ = 0;
-  int64 bytes_accessed_ = 0;
-
-  // Cost counts of the current instruction. These should be set by each
-  // handlers if different from the default values computed in Preprocess.
-  int64 current_flop_count_;
-  int64 current_transcendental_count_;
-  int64 current_bytes_accessed_;
-
-  // Mapping from HLO instructions to the cost we computed for them in the
-  // course of the graph analysis.
-  std::map<const HloInstruction*, int64> hlo_to_flop_count_;
-  std::map<const HloInstruction*, int64> hlo_to_transcendental_count_;
-  std::map<const HloInstruction*, int64> hlo_to_bytes_accessed_;
+  HloToProperties hlo_properties_;
+
+  // If true, the time taken will be computed from the rates for each property
+  // and the total time will be the maximum time, which is the time of the
+  // bottleneck.
+  bool current_should_compute_bottleneck_time_;
+
+  // The properties of the currently visited instruction. A HandleFoo method can
+  // modify these to change the default values computed in Preprocess.
+  Properties current_properties_;
+
+  // The sum of the properties of all HLOs in the computation.
+  Properties properties_sum_;
+
+  // How much of each property can be processed per second. E.g. if the property
+  // is bytes accessed, this is the number of bytes that can be processed per
+  // second. Is empty if no rates have been set.
+  Properties per_second_rates_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(HloCostAnalysis);
 };
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
index 5c71056bb5..f745683165 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
@@ -332,48 +332,64 @@ TEST_F(HloCostAnalysisTest, MatmulAndConvolutionCanBeTheSameComputation) {
 using FusionCostAnalysis = ::testing::Test;
 
 TEST_F(FusionCostAnalysis, LoopFusion) {
-  Shape r2f32 = ShapeUtil::MakeShape(F32, {2, 2});
-
-  // Fuse all instructions in complicated expression:
-  //
-  //   add = Add(C1, C2)
-  //   clamp = Clamp(C2, add, add)
-  //   exp = Exp(add)
-  //   mul = Mul(exp, C3)
-  //   sub = Sub(mul, clamp)
-  //   tuple = Tuple({sub, sub, mul, C1})
-  auto c1 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-      /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2));
-  auto c2 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-      /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2));
-  auto c3 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-      /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2));
-
-  auto add =
-      HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, c1.get(), c2.get());
-  auto clamp = HloInstruction::CreateTernary(r2f32, HloOpcode::kClamp, c2.get(),
-                                             add.get(), add.get());
-  auto exp = HloInstruction::CreateUnary(r2f32, HloOpcode::kExp, add.get());
-  auto mul = HloInstruction::CreateBinary(r2f32, HloOpcode::kMultiply,
-                                          exp.get(), c3.get());
-  auto sub = HloInstruction::CreateBinary(r2f32, HloOpcode::kSubtract,
-                                          mul.get(), clamp.get());
-  auto tuple =
-      HloInstruction::CreateTuple({sub.get(), sub.get(), mul.get(), c1.get()});
-
-  auto fusion = HloInstruction::CreateFusion(
-      r2f32, HloInstruction::FusionKind::kLoop, tuple.get());
-  fusion->FuseInstruction(sub.get());
-  fusion->FuseInstruction(mul.get());
-  fusion->FuseInstruction(exp.get());
-  fusion->FuseInstruction(clamp.get());
-  fusion->FuseInstruction(add.get());
-
-  HloCostAnalysis fusion_analysis(ShapeSize);
-  ASSERT_IS_OK(fusion->Accept(&fusion_analysis));
-
-  EXPECT_EQ(fusion_analysis.flop_count(), 16);
-  EXPECT_EQ(fusion_analysis.transcendental_count(), 4);
+  // Do this 4 times with different per-second rates to test the computation of
+  // bottleneck time on fusion nodes.
+  for (int i = 0; i < 4; ++i) {
+    Shape r2f32 = ShapeUtil::MakeShape(F32, {2, 2});
+
+    // Fuse all instructions in complicated expression:
+    //
+    //   add = Add(C1, C2)
+    //   clamp = Clamp(C2, add, add)
+    //   exp = Exp(add)
+    //   mul = Mul(exp, C3)
+    //   sub = Sub(mul, clamp)
+    //   tuple = Tuple({sub, sub, mul, C1})
+    auto c1 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+        /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2));
+    auto c2 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+        /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2));
+    auto c3 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+        /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2));
+
+    auto add = HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, c1.get(),
+                                            c2.get());
+    auto clamp = HloInstruction::CreateTernary(r2f32, HloOpcode::kClamp,
+                                               c2.get(), add.get(), add.get());
+    auto exp = HloInstruction::CreateUnary(r2f32, HloOpcode::kExp, add.get());
+    auto mul = HloInstruction::CreateBinary(r2f32, HloOpcode::kMultiply,
+                                            exp.get(), c3.get());
+    auto sub = HloInstruction::CreateBinary(r2f32, HloOpcode::kSubtract,
+                                            mul.get(), clamp.get());
+    auto tuple = HloInstruction::CreateTuple(
+        {sub.get(), sub.get(), mul.get(), c1.get()});
+
+    auto fusion = HloInstruction::CreateFusion(
+        r2f32, HloInstruction::FusionKind::kLoop, tuple.get());
+    fusion->FuseInstruction(sub.get());
+    fusion->FuseInstruction(mul.get());
+    fusion->FuseInstruction(exp.get());
+    fusion->FuseInstruction(clamp.get());
+    fusion->FuseInstruction(add.get());
+
+    // The time given these rates at i == 0 is exactly even among the properties
+    // at 1.0 seconds. For other values, one of the rates is slower so that it
+    // becomes the bottleneck.
+    HloCostAnalysis fusion_analysis(ShapeSize);
+    fusion_analysis.set_flops_per_second(16 * (i == 1 ? 1 / 2.0 : 1.0));
+    fusion_analysis.set_transcendentals_per_second(4 *
+                                                   (i == 2 ? 1 / 4.0 : 1.0));
+    fusion_analysis.set_bytes_per_second(64 * (i == 3 ? 1 / 8.0 : 1.0));
+    ASSERT_IS_OK(fusion->Accept(&fusion_analysis));
+
+    EXPECT_EQ(fusion_analysis.flop_count(), 16);
+    EXPECT_EQ(fusion_analysis.transcendental_count(), 4);
+    constexpr int64 bytes_accessed = sizeof(float) * 4 * 2 * 2;
+    static_assert(bytes_accessed == 64, "");
+    EXPECT_EQ(fusion_analysis.bytes_accessed(), bytes_accessed);
+
+    EXPECT_EQ(fusion_analysis.seconds(), 1 << i);
+  }
 }
 
 TEST_F(FusionCostAnalysis, NoLayout) {
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
index 7e951721ba..8f372c3420 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
@@ -16,14 +16,11 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h"
 
 #include <algorithm>
-#include <iosfwd>
 #include <queue>
-#include <set>
 #include <vector>
 
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -35,7 +32,6 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -43,267 +39,6 @@ namespace xla {
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
-string HloLocation::ToString() const {
-  string index_str =
-      ShapeUtil::IsTuple(instruction->shape()) ? (" " + index.ToString()) : "";
-  return StrCat(instruction->FullyQualifiedName(), index_str);
-}
-
-std::ostream& operator<<(std::ostream& out, const HloLocation& location) {
-  out << location.ToString();
-  return out;
-}
-
-string HloUse::ToString() const {
-  string index_str =
-      ShapeUtil::IsTuple(instruction->operand(operand_number)->shape())
-          ? (" " + operand_index.ToString())
-          : "";
-  return StrCat(instruction->FullyQualifiedName(), ", operand ", operand_number,
-                index_str);
-}
-
-std::ostream& operator<<(std::ostream& out, const HloUse& use) {
-  out << use.ToString();
-  return out;
-}
-
-HloValue::HloValue(HloValue::Id id, HloInstruction* instruction,
-                   const ShapeIndex& index, bool is_phi)
-    : id_(id), is_phi_(is_phi) {
-  // The defining location is always the first element in the locations_ vector.
-  AddLocation(instruction, index);
-}
-
-bool HloValue::operator==(const HloValue& other) const {
-  bool equal = defining_instruction() == other.defining_instruction() &&
-               defining_index() == other.defining_index();
-  // If the values are equal they most both be phi (or non phi).
-  CHECK(!(equal && is_phi() != other.is_phi()));
-  return equal;
-}
-
-bool HloValue::operator!=(const HloValue& other) const {
-  return !(*this == other);
-}
-
-string HloValue::ToShortString() const {
-  string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
-                         ? defining_index().ToString()
-                         : "";
-  return StrCat(is_phi_ ? "PHI " : "",
-                defining_instruction()->FullyQualifiedName(), index_str);
-}
-
-string HloValue::ToString(int indent) const {
-  string indentation(indent, ' ');
-  string out = StrCat(indentation, ToShortString(), ", locations:\n");
-  for (const HloLocation& location : locations()) {
-    StrAppend(&out, indentation, "  ", location.ToString(), "\n");
-  }
-  StrAppend(&out, indentation, " uses:\n");
-  for (const HloUse& use : uses()) {
-    StrAppend(&out, indentation, "  ", use.ToString(), "\n");
-  }
-  return out;
-}
-
-namespace {
-
-// Returns true if the instruction 'user' may use the value at the given
-// ShapeIndex in the given operand. Generally, instruction which pass through
-// values transparently without reading the value are not considered to use the
-// value.
-bool MayUseOperandValue(int64 operand_number, const ShapeIndex& index,
-                        const HloInstruction* user) {
-  switch (user->opcode()) {
-    case HloOpcode::kGetTupleElement:
-    case HloOpcode::kCopy:
-      // These instructions only access the top-level values of their
-      // operand. Non-top-level (nested) values are passed through
-      // transparently.
-      CHECK_EQ(operand_number, 0);
-      return index.empty();
-    case HloOpcode::kSelect:
-      // Select does not use any nested elements of its selected-from operands
-      // (operand 1 and 2)
-      CHECK_GE(operand_number, 0);
-      CHECK_LE(operand_number, 2);
-      return operand_number == 0 || index.empty();
-
-    case HloOpcode::kCall:
-    case HloOpcode::kTuple:
-      // These instructions always pass through their operands transparently.
-      return false;
-
-    case HloOpcode::kWhile:
-      // Though the while instructions passes through its operands, we return
-      // true because in SSA form there may be a Phi at the parameter of the
-      // while which is considered a use of its incoming value because the Phi
-      // input values are not passed through into the body computation. Because
-      // this function is used in both SSA and non-SSA forms of the analysis
-      // conservatively return true.
-      return true;
-
-    default:
-      return true;
-  }
-}
-
-}  // namespace
-
-void HloValue::AddLocation(HloInstruction* instruction,
-                           const ShapeIndex& index) {
-  // The given location should not already exist in locations_.
-  for (const HloLocation& location : locations_) {
-    DCHECK(!(location.instruction == instruction && location.index == index));
-  }
-
-  locations_.push_back(HloLocation{instruction, index});
-
-  //  Update uses.
-  for (HloInstruction* user : instruction->users()) {
-    for (int64 operand_number : user->OperandIndices(instruction)) {
-      if (MayUseOperandValue(operand_number, index, user)) {
-        for (const HloUse& use : uses_) {
-          // Verify that this use does not already exist.
-          DCHECK(!(use.instruction == user &&
-                   use.operand_number == operand_number &&
-                   use.operand_index == index));
-        }
-
-        uses_.push_back(HloUse{user, operand_number, index});
-      }
-    }
-  }
-
-  // Update liveout status of this HloValue.
-  const HloModule& module = *instruction->parent()->parent();
-  if (instruction == module.entry_computation()->root_instruction()) {
-    live_out_of_module_ = true;
-  }
-
-  if (instruction == instruction->parent()->root_instruction()) {
-    live_out_of_computation_ = true;
-  }
-}
-
-void HloValue::RemoveLocation(HloInstruction* instruction,
-                              const ShapeIndex& index) {
-  // The defining location cannot be removed.
-  CHECK(!(instruction == defining_instruction() && index == defining_index()));
-
-  int64 size_before = locations_.size();
-  locations_.erase(
-      std::remove_if(locations_.begin(), locations_.end(),
-                     [instruction, &index](const HloLocation& location) {
-                       return location.instruction == instruction &&
-                              location.index == index;
-                     }),
-      locations_.end());
-  // Only a single location should have been removed.
-  CHECK_EQ(locations_.size(), size_before - 1);
-
-  //  Update uses which referred to this location.
-  uses_.erase(std::remove_if(uses_.begin(), uses_.end(),
-                             [instruction, &index](const HloUse& use) {
-                               return use.instruction->operand(
-                                          use.operand_number) == instruction &&
-                                      use.operand_index == index;
-                             }),
-              uses_.end());
-
-  // Returns whether this value is contained in the given instruction's output.
-  auto is_contained_in = [this](const HloInstruction* instruction) {
-    for (const HloLocation& location : locations()) {
-      if (location.instruction == instruction) {
-        return true;
-      }
-    }
-    return false;
-  };
-
-  const HloModule& module = *instruction->parent()->parent();
-  if (instruction == module.entry_computation()->root_instruction()) {
-    // Value has been removed from a location in the entry root instruction.
-    live_out_of_module_ =
-        is_contained_in(module.entry_computation()->root_instruction());
-  }
-  if (instruction == defining_instruction()->parent()->root_instruction()) {
-    // Value has been removed from the root of the computation the value has
-    // been defined in.
-    live_out_of_computation_ =
-        is_contained_in(defining_instruction()->parent()->root_instruction());
-  }
-}
-
-std::ostream& operator<<(std::ostream& out, const HloValue& value) {
-  out << value.ToShortString();
-  return out;
-}
-
-void HloValueSet::SortAndUniquifyValues() {
-  std::sort(value_ids_.begin(), value_ids_.end());
-  value_ids_.erase(std::unique(value_ids_.begin(), value_ids_.end()),
-                   value_ids_.end());
-}
-
-string HloValueSet::ToString() const {
-  return StrCat("HloValueSet: ", tensorflow::str_util::Join(value_ids_, ", "));
-}
-
-/*static */
-HloValueSet HloValueSet::Union(
-    tensorflow::gtl::ArraySlice<const HloValueSet*> inputs) {
-  HloValueSet union_set;
-  for (const HloValueSet* input : inputs) {
-    for (HloValue::Id value_id : input->value_ids()) {
-      union_set.value_ids_.push_back(value_id);
-    }
-  }
-  union_set.SortAndUniquifyValues();
-  return union_set;
-}
-
-std::ostream& operator<<(std::ostream& out, const HloValueSet& value_set) {
-  out << value_set.ToString();
-  return out;
-}
-
-InstructionValueSet InstructionValueSet::Union(
-    tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs) {
-  CHECK_GT(inputs.size(), 0);
-  for (int i = 1; i < inputs.size(); ++i) {
-    CHECK(ShapeUtil::Compatible(inputs[0]->shape(), inputs[i]->shape()));
-  }
-  InstructionValueSet union_set(inputs[0]->shape());
-  union_set.ForEachMutableElement(
-      [&inputs](const ShapeIndex& index, HloValueSet* value_set) {
-        std::vector<const HloValueSet*> input_sets;
-        for (const InstructionValueSet* input : inputs) {
-          input_sets.push_back(&input->element(index));
-        }
-        *value_set = HloValueSet::Union(input_sets);
-      });
-  return union_set;
-}
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionValueSet& instruction_value_set) {
-  out << instruction_value_set.ToString();
-  return out;
-}
-
-string InstructionValueSet::ToString() const {
-  string out =
-      StrCat("InstructionValueSet(", ShapeUtil::HumanString(shape()), ")\n");
-  ForEachElement([this, &out](const ShapeIndex& index,
-                              const HloValueSet& value_set) {
-    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
-  });
-  return out;
-}
-
 HloDataflowAnalysis::HloDataflowAnalysis(HloModule* module, bool ssa_form,
                                          bool bitcast_defines_value)
     : module_(module),
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
index 7087bd978d..d909c5b668 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
@@ -20,7 +20,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_DATAFLOW_ANALYSIS_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_DATAFLOW_ANALYSIS_H_
 
-#include <functional>
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
@@ -29,228 +29,17 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
-#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/service/hlo_value.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-#include "tensorflow/core/lib/gtl/flatmap.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/types.h"
 
 namespace xla {
 
-// Abstraction which identifies a specific point in the XLA graph. An
-// HloLocation specifies a ShapeIndex within the output of a specific
-// instruction.
-struct HloLocation {
-  HloInstruction* instruction;
-  ShapeIndex index;
-
-  string ToString() const;
-
-  bool operator==(const HloLocation& other) const {
-    return instruction == other.instruction && index == other.index;
-  }
-  bool operator!=(const HloLocation& other) const { return !(*this == other); }
-};
-
-std::ostream& operator<<(std::ostream& out, const HloLocation& location);
-
-// Defines a single use of an HLO value.
-struct HloUse {
-  // Instruction at which the value is used.
-  HloInstruction* instruction;
-
-  // The operand number in which the value is appears.
-  int64 operand_number;
-
-  // The shape index within the operand in which the value appears.
-  ShapeIndex operand_index;
-
-  string ToString() const;
-
-  bool operator==(const HloUse& other) const {
-    return instruction == other.instruction &&
-           operand_number == other.operand_number &&
-           operand_index == other.operand_index;
-  }
-
-  bool operator!=(const HloUse& other) const { return !(*this == other); }
-};
-
-std::ostream& operator<<(std::ostream& out, const HloUse& use);
-
-// Class describing a value used by the dataflow analysis. XLA arrays are
-// trivially a single HloValue. Tuples are made up of more than one HloValue: an
-// HloValue for the pointer vector, and an HloValue for each child element.
-//
-// Every HloValue is defined by a particular instruction and most instructions
-// define only a single HloValue. Instructions which define a single HloValue
-// include array-shaped instructions such as Add but also includes Tuple-shaped
-// instructions such as Tuple. The Tuple instruction defines a single HloValue
-// which is a vector of pointers to the values containing the Tuple
-// instruction's operands. Though the result of the Tuple instruction includes
-// multiple values only the top-level HloValue (the vector of pointers) is
-// defined by the Tuple instruction. The values containing the tuple elements
-// are defined by earlier instructions, usually the operands of the Tuple
-// instruction.
-//
-// Instructions which construct both the tuple *and* the tuple elements define
-// more than one HloValue. This includes (at least) tuple-shaped Constant,
-// Parameter, Infeed and While instructions. These tuple-shaped instructions do
-// not assemble a tuple from existing HloValues like the Tuple instruction does,
-// but rather define all the HloValues in the tuple.
-class HloValue {
- public:
-  using Id = int64;
-
-  // Construct an HloValue defined by 'instruction' at shape index 'index'. If
-  // is_phi is true, then this value is a phi value, for example, at the
-  // parameter of a while body computation. Phi values are only used in the SSA
-  // dataflow analysis (HloDataflowAnalysis::ssa_form_ is true).
-  HloValue(HloValue::Id id, HloInstruction* instruction,
-           const ShapeIndex& index, bool is_phi = false);
-
-  // Return a unique identifier for this HloValue. This value is used for stable
-  // sorting and iteration
-  Id id() const { return id_; }
-
-  // Returns whether this value is a phi value.
-  bool is_phi() const { return is_phi_; }
-
-  // Return the location where this value is defined.
-  const HloLocation& defining_location() const { return locations_[0]; }
-
-  // Return the instruction which defines this HloValue.
-  HloInstruction* defining_instruction() const {
-    return defining_location().instruction;
-  }
-
-  // Return the shape index at which this HloValue is defined in the output of
-  // its defining instruction.
-  const ShapeIndex& defining_index() const { return defining_location().index; }
-
-  // Add or remove a location at which the HloValue appears. The definition
-  // location can not be removed. The uses of the HloValue are updated.
-  void AddLocation(HloInstruction* instruction, const ShapeIndex& index);
-  void RemoveLocation(HloInstruction* instruction, const ShapeIndex& index);
-
-  // Return all locations of the HloValue in the module.
-  const std::vector<HloLocation>& locations() const { return locations_; }
-
-  // Return all uses of the HloValue.
-  const std::vector<HloUse>& uses() const { return uses_; }
-
-  // Get whether this HloValue is live out of the module.
-  bool live_out_of_module() const { return live_out_of_module_; }
-
-  // Get whether this HloValue is live out of the computation it is defined in.
-  bool live_out_of_computation() const { return live_out_of_computation_; }
-
-  bool operator==(const HloValue& other) const;
-  bool operator!=(const HloValue& other) const;
-
-  // Return a single-line string representation of the value.
-  string ToShortString() const;
-
-  string ToString(int indent = 0) const;
-
- private:
-  // Unique identifier for this HloValue. Used for stable sorting and iteration.
-  const Id id_;
-
-  // Whether this instruction is a phi value.
-  const bool is_phi_;
-
-  // The set of locations of this HloValue. The first element is always the
-  // location of the definition.
-  std::vector<HloLocation> locations_;
-
-  // The set of uses of this HloValue.
-  std::vector<HloUse> uses_;
-
-  // Whether this value is live out of the HLO module.
-  bool live_out_of_module_ = false;
-
-  // Whether this value is live out of its computation.
-  bool live_out_of_computation_ = false;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloValue& hlo_value);
-
-// A class representing the possible set of HloValues at a particular point
-// (shape index in the output of an instruction) in the XLA graph. This set
-// contains the set of reaching HloValue definitions. For a simple array-shaped
-// instruction like Add, the HloValueSet of the top-level of the instruction's
-// output trivially contains only the HloValue defined by the instruction. For
-// instructions which have non-trivial dataflow such as Tuple or Select, the
-// HloValueSets of the instruction's output contains one or more HloValues
-// defined by the instruction's operands or defined further up in the XLA graph.
-class HloValueSet {
- public:
-  HloValueSet() = default;
-
-  explicit HloValueSet(tensorflow::gtl::ArraySlice<HloValue::Id> value_ids)
-      : value_ids_(value_ids.begin(), value_ids.end()) {
-    SortAndUniquifyValues();
-  }
-
-  // Return the union of the given HloValueSets.
-  static HloValueSet Union(
-      tensorflow::gtl::ArraySlice<const HloValueSet*> inputs);
-
-  // Return the vector of the IDs of all HloValues in the set. Values in the
-  // vector are unique and sorted.
-  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
-
-  // Return the unique HLO value in the set. CHECKs if the set does not contain
-  // exactly one value.
-  HloValue::Id GetUniqueValueId() const {
-    CHECK_EQ(value_ids().size(), 1);
-    return value_ids()[0];
-  }
-
-  bool operator==(const HloValueSet& other) const {
-    return value_ids() == other.value_ids();
-  }
-  bool operator!=(const HloValueSet& other) const { return !(*this == other); }
-
-  string ToString() const;
-
- private:
-  // Sorts value_ and removes duplicates. This should be called after adding any
-  // elements to values_.
-  void SortAndUniquifyValues();
-
-  // HloValues sorted by HloValue::Id.
-  std::vector<HloValue::Id> value_ids_;
-};
-
-std::ostream& operator<<(std::ostream& out, const HloValueSet& hlo_value);
-
-// A class collecting the HloValues which might be contained in the output of
-// an HLO instruction. For array-shaped instructions, an InstructionValueSet
-// trivially holds a single HloValueSet. Tuple-shaped InstructionValueSets
-// hold multiple HloValueSets.
-class InstructionValueSet : public ShapeTree<HloValueSet> {
- public:
-  InstructionValueSet(const Shape& shape) : ShapeTree<HloValueSet>(shape) {}
-
-  // Return the union of the given InstructionValueSets.
-  static InstructionValueSet Union(
-      tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs);
-
-  string ToString() const;
-};
-
-std::ostream& operator<<(std::ostream& out,
-                         const InstructionValueSet& instruction_value_set);
-
 // Analysis which identifies all HLO values and their uses in an HLO module.
 class HloDataflowAnalysis {
  public:
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 393e25380e..f0512c0eaf 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -90,7 +90,7 @@ StatusOr<std::unique_ptr<Literal>> Compare(const Shape& shape, HloOpcode opcode,
   }
 
   auto result = Literal::CreateFromShape(shape);
-  TF_RETURN_IF_ERROR(result.get()->Populate<bool>(
+  TF_RETURN_IF_ERROR(result->Populate<bool>(
       [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
         return compare_op(lhs_literal.Get<OperandT>(multi_index),
                           rhs_literal.Get<OperandT>(multi_index));
@@ -119,7 +119,7 @@ StatusOr<std::unique_ptr<Literal>> ElementWiseUnaryOpImpl(
 
   auto result = Literal::CreateFromShape(shape);
 
-  TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+  TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
       [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
         return unary_op(operand_literal.Get<NativeT>(multi_index));
       }));
@@ -439,7 +439,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
         [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
           return binary_op(lhs_literal.Get<ReturnT>(multi_index),
                            rhs_literal.Get<ReturnT>(multi_index));
@@ -476,7 +476,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(result.get()->Populate<ReturnT>(
+    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
         [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
           return ternary_op(lhs_literal.Get<LhsType>(multi_index),
                             rhs_literal.Get<RhsType>(multi_index),
@@ -637,7 +637,7 @@ Status HloEvaluator::HandleConcatenate(
 
   for (auto operand : operands) {
     const Shape& operand_shape = operand->shape();
-    TF_RETURN_IF_ERROR(result_literal.get()->Copy(
+    TF_RETURN_IF_ERROR(result_literal->Copy(
         GetEvaluatedLiteralFor(operand), source_indices, dest_indices,
         AsInt64Slice(operand_shape.dimensions())));
     dest_indices[concat_dim] +=
@@ -769,9 +769,9 @@ Status HloEvaluator::HandleSlice(HloInstruction* slice,
 
   DimensionVector dest_indices(slice->slice_starts().size(), 0);
 
-  TF_RETURN_IF_ERROR(literal.get()->Copy(GetEvaluatedLiteralFor(operand),
-                                         slice->slice_starts(), dest_indices,
-                                         AsInt64Slice(shape.dimensions())));
+  TF_RETURN_IF_ERROR(literal->Copy(GetEvaluatedLiteralFor(operand),
+                                   slice->slice_starts(), dest_indices,
+                                   AsInt64Slice(shape.dimensions())));
 
   evaluated_[slice] = std::move(literal);
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index dffb53320c..69166a8d13 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -34,6 +34,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/regexp.h"
 
 using ::tensorflow::Env;
 using ::tensorflow::WriteStringToFile;
@@ -593,6 +594,31 @@ void DumpText(const HloModule& module, const string& label,
       do_prefix ? StrCat(prefix, "-", label, ".txt") : StrCat(label, ".txt");
   string path = JoinPath(directory_path, filename);
   TF_CHECK_OK(WriteStringToFile(env, path, module.ToString()));
+  LOG(INFO) << "dumping module '" << module.name() << "' to " << path;
+}
+
+string MaybeDumpHloModule(const HloModule& module, const string& label,
+                          const HloExecutionProfile* profile) {
+  VLOG(2) << "MaybeDumpHloModule called on module " << module.name();
+  string graph_url;
+  const DebugOptions& debug_options = module.config().debug_options();
+  if (!debug_options.xla_generate_hlo_graph().empty() &&
+      RE2::PartialMatch(module.name(),
+                        debug_options.xla_generate_hlo_graph())) {
+    graph_url = DumpGraph(*module.entry_computation(), label,
+                          debug_options.xla_hlo_graph_addresses(),
+                          debug_options.xla_hlo_graph_layout(), profile);
+  }
+  if (!debug_options.xla_log_hlo_text().empty() &&
+      RE2::PartialMatch(module.name(), debug_options.xla_log_hlo_text())) {
+    LOG(INFO) << "HLO for module " << module.name();
+    LOG(INFO) << "Label: " << label;
+    XLA_LOG_LINES(2, module.ToString());
+  }
+  if (!debug_options.xla_generate_hlo_text_to().empty()) {
+    DumpText(module, label, debug_options.xla_generate_hlo_text_to());
+  }
+  return graph_url;
 }
 
 }  // namespace hlo_graph_dumper
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.h b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
index 8ed50c3847..eb65cf2d32 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.h
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
@@ -41,6 +41,13 @@ class GraphRendererInterface {
   virtual string RenderGraph(const string& graph, GraphKind graph_kind) = 0;
 };
 
+// Dump the given HLO module if a dump is requested in its debug options. Based
+// on the debug options, either a graph dump, a text dump or both may be
+// generated. If a graph dump is generated, the description (e.g. an URL) is
+// returned; otherwise an empty string is returned.
+string MaybeDumpHloModule(const HloModule& module, const string& label,
+                          const HloExecutionProfile* profile = nullptr);
+
 // Dumps a graph of the computation and returns a description of the rendered
 // graph (e.g., a URL) based on the renderer. The "best" renderer in the
 // registry is used.
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 92e397a5d3..4b38ecd5de 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1883,7 +1883,7 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
     case HloOpcode::kReverse:
       return visitor->HandleReverse(this, operands_[0]);
     case HloOpcode::kReducePrecision:
-      return visitor->HandleReducePrecision(this, operands_[0]);
+      return visitor->HandleReducePrecision(this);
     case HloOpcode::kSlice:
       return visitor->HandleSlice(this, operands_[0]);
     case HloOpcode::kDynamicSlice:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 6eb6dda5ac..bf50542cc0 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -553,7 +553,7 @@ class HloInstruction {
   // number added to the variance to avoid divide-by-zero error.
   //
   // Precondition: opcode() == HloOpcode::kBatchNormTraining
-  int64 epsilon() const { return epsilon_; }
+  float epsilon() const { return epsilon_; }
 
   // Returns the infeed configuration string. The infeed configuration includes
   // any metadata needed for the backend compiler (e.g., infeed buffer address)
@@ -751,6 +751,16 @@ class HloInstruction {
     return called_computations_;
   }
 
+  // Replaces all called computations based on a map function. This is needed
+  // when we clone hlo_computations and want to let the instructions to point
+  // to the newly cloned nodes.
+  void ReplaceCalledComputations(
+      std::function<HloComputation*(HloComputation*)> map_function) {
+    for (int64 i = 0; i < called_computations_.size(); ++i) {
+      called_computations_[i] = map_function(called_computations_[i]);
+    }
+  }
+
   // Returns true if this instruction performs an elementwise operation on
   // `operand_idx`-th operand. An instruction is elementwise on an operand iff,
   // after performing necessary implicit broadcast
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index 22ef9c590b..4ed0af6033 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -301,6 +301,36 @@ std::list<HloComputation*> HloModule::MakeComputationPostOrder() const {
   return post_order;
 }
 
+std::unique_ptr<HloModule> HloModule::Clone(const string& suffix) {
+  VLOG(1) << "Cloning module :" << name_ << " --> " << suffix << "\n";
+  auto module = MakeUnique<HloModule>(name_ + "-" + suffix);
+  module->config_ = config_;
+  module->entry_computation_handle_ = entry_computation_handle_;
+  module->has_entry_computation_handle_ = has_entry_computation_handle_;
+
+  std::unordered_map<HloComputation*, HloComputation*> clone_map;
+  for (auto& computation : computations_) {
+    auto cloned_computation = computation->Clone(suffix);
+    InsertOrDie(&clone_map, computation.get(), cloned_computation.get());
+
+    if (entry_computation_ == computation.get()) {
+      module->AddEntryComputation(std::move(cloned_computation));
+    } else {
+      module->AddEmbeddedComputation(std::move(cloned_computation));
+    }
+  }
+
+  for (auto& cloned_computation : module->computations_) {
+    for (auto& instruction : cloned_computation->instructions()) {
+      // Rewrite instruction's called_computation to point to the cloned
+      // computations.
+      instruction->ReplaceCalledComputations(
+          [&](HloComputation* hlo) { return FindOrDie(clone_map, hlo); });
+    }
+  }
+  return module;
+}
+
 uint64 HloModule::RandomNew64() const {
   tensorflow::mutex_lock l(rng_mutex_);
   return rng_();
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index 4b14b4fd62..d089493c70 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -75,6 +75,9 @@ class HloModule {
 
   const string& name() const { return name_; }
 
+  // Returns a deep copy of this module including all computations.
+  std::unique_ptr<HloModule> Clone(const string& suffix = "clone");
+
   // Return a pointer to the entry computation of the module..
   HloComputation* entry_computation() const {
     CHECK_NE(nullptr, entry_computation_);
diff --git a/tensorflow/compiler/xla/service/hlo_module_test.cc b/tensorflow/compiler/xla/service/hlo_module_test.cc
index 58173bca07..56dc563203 100644
--- a/tensorflow/compiler/xla/service/hlo_module_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_test.cc
@@ -81,6 +81,30 @@ TEST_F(HloModuleTest, TwoComputationsPostOrder) {
   EXPECT_EQ(computation2->name(), "Constant.1");
 }
 
+TEST_F(HloModuleTest, CloneTest) {
+  // Create and copy a module with a diamond call graph of computations.
+  auto module = CreateNewModule();
+  auto computation1 =
+      module->AddEmbeddedComputation(CreateConstantComputation());
+  auto computation2 =
+      module->AddEmbeddedComputation(CreateCallComputation({computation1}));
+  auto computation3 =
+      module->AddEmbeddedComputation(CreateCallComputation({computation1}));
+  module->AddEntryComputation(
+      CreateCallComputation({computation2, computation3}));
+
+  auto post_order = module->MakeComputationPostOrder();
+  auto cloned_module = module->Clone("copy");
+  auto post_order_copied = cloned_module->MakeComputationPostOrder();
+
+  EXPECT_EQ(post_order.size(), post_order_copied.size());
+  for (auto origin = post_order.begin(), copied = post_order_copied.begin();
+       origin != post_order.end() && copied != post_order_copied.end();
+       ++origin, ++copied) {
+    EXPECT_EQ((*origin)->name() + "copy", (*copied)->name());
+  }
+}
+
 TEST_F(HloModuleTest, DiamondComputationsPostOrder) {
   // Create a module with a diamond call graph of computations.
   auto module = CreateNewModule();
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc
index 61e5efa5b6..32a2abed92 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering.cc
@@ -15,13 +15,10 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
 
-#include <set>
 #include <utility>
 #include <vector>
 
-#include "tensorflow/compiler/xla/service/heap_simulator.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
-#include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -252,358 +249,6 @@ string SequentialHloOrdering::ToString() const {
   return tensorflow::str_util::Join(pieces, "\n");
 }
 
-StatusOr<int64> MinimumMemoryForSequence(
-    const SequentialHloOrdering::HloModuleSequence& module_sequence,
-    const LogicalBuffer::SizeFunction& size_function) {
-  if (module_sequence.empty()) {
-    return 0;
-  }
-
-  const HloModule* module = module_sequence.begin()->first->parent();
-  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
-                      TuplePointsToAnalysis::Run(module));
-
-  // The absolute minimum memory required for a given sequence of instructions
-  // is determined by the sequence of Alloc and Free calls on a simulated heap,
-  // ignoring fragmentation. We run the heap simulation on the whole module,
-  // rather than summing each computation, since it gives us a better lower
-  // bound, by minimizing the liveness of sub-computations.
-  TF_ASSIGN_OR_RETURN(
-      HeapSimulator::Result result,
-      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), *module,
-                         module_sequence, *points_to_analysis, size_function));
-  return result.heap_size;
-}
-
-namespace {
-
-// Class implementing a list scheduler of HLO instructions which produces a
-// sequence which minimizes memory usage.
-class ListScheduler {
- public:
-  // Construct and return a memory-minimizing sequence of HLO instructions
-  // containing the given HLO computation.
-  static StatusOr<std::vector<const HloInstruction*>> Run(
-      const HloComputation& computation,
-      const TuplePointsToAnalysis& points_to_analysis,
-      const LogicalBuffer::SizeFunction& size_function) {
-    ListScheduler scheduler(computation, points_to_analysis, size_function);
-    return scheduler.CreateSchedule();
-  }
-
- private:
-  // The scheduling priority of an instruction is first the number of bytes
-  // freed by scheduling the instruction, and second (tie-breaker) by the number
-  // of users. This is represented as a std::pair containing these two values
-  // (first element is the bytes freed). std::pair provides the necessary
-  // comparison operators.
-  using Priority = std::pair<int64, int64>;
-
-  ListScheduler(const HloComputation& computation,
-                const TuplePointsToAnalysis& points_to_analysis,
-                const LogicalBuffer::SizeFunction& size_function)
-      : computation_(computation),
-        points_to_analysis_(points_to_analysis),
-        size_function_(size_function) {
-    // Create a map containing the LogicalBuffer uses for each HLO
-    // instruction. An HLO instruction "uses" a LogicalBuffer if the
-    // LogicalBuffer is in an operand of the instruction as indicated by
-    // points-to analysis.
-    for (auto& instruction : computation.instructions()) {
-      buffer_uses_.insert(
-          {instruction.get(), std::unordered_set<const LogicalBuffer*>()});
-      for (auto* operand : instruction->operands()) {
-        for (const LogicalBuffer* buffer :
-             points_to_analysis.GetBuffersDefinedByInstruction(operand)) {
-          buffer_uses_[instruction.get()].insert(buffer);
-        }
-      }
-    }
-
-    // Create map containing the number of unscheduled uses (hlo instructions)
-    // of each logical buffer.
-    for (auto& instruction : computation.instructions()) {
-      for (auto* buffer : points_to_analysis.GetBuffersDefinedByInstruction(
-               instruction.get())) {
-        unscheduled_use_count_[buffer] = 0;
-      }
-    }
-    for (auto& instruction : computation.instructions()) {
-      for (const LogicalBuffer* buffer : buffer_uses_.at(instruction.get())) {
-        ++unscheduled_use_count_[buffer];
-      }
-    }
-
-    // Buffers live out of the computation have an implicit use at the end of
-    // the computation.
-    for (const LogicalBuffer* live_out_buffer :
-         points_to_analysis.GetPointsToSet(computation.root_instruction())
-             .CreateFlattenedSet()) {
-      ++unscheduled_use_count_[live_out_buffer];
-    }
-  }
-
-  // Returns whether the memory used by the given buffer should be ignored by
-  // the scheduling heuristic.
-  bool IgnoreBuffer(const LogicalBuffer& buffer) {
-    return buffer.instruction()->opcode() == HloOpcode::kParameter ||
-           buffer.instruction()->opcode() == HloOpcode::kConstant;
-  }
-
-  // Return the number of bytes freed if the HLO instruction is scheduled.
-  int64 BytesFreedIfScheduled(const HloInstruction* instruction) {
-    int64 freed_bytes = 0;
-    // Sum the total size of the values last used by this instruction.
-    for (auto* buffer : buffer_uses_.at(instruction)) {
-      if (IgnoreBuffer(*buffer)) {
-        continue;
-      }
-      CHECK_GE(unscheduled_use_count_.at(buffer), 1);
-      if (unscheduled_use_count_.at(buffer) == 1) {
-        // This is the last use of the logical buffer.
-        freed_bytes += size_function_(*buffer);
-      }
-    }
-    // Then subtract the size of the value(s) defined by this instruction.
-    for (auto* buffer :
-         points_to_analysis_.GetBuffersDefinedByInstruction(instruction)) {
-      if (!IgnoreBuffer(*buffer)) {
-        freed_bytes -= size_function_(*buffer);
-      }
-    }
-    return freed_bytes;
-  }
-
-  // Construct the scheduling priority of the given instruction.
-  Priority GetPriority(const HloInstruction* instruction) {
-    return {BytesFreedIfScheduled(instruction), instruction->user_count()};
-  }
-
-  std::vector<const HloInstruction*> CreateSchedule() {
-    std::vector<const HloInstruction*> schedule;
-
-    // Populate the ready list with instructions which have no operands or
-    // control predecessors.
-    std::unordered_map<const HloInstruction*, int64> unscheduled_pred_count;
-    std::list<const HloInstruction*> ready_list;
-    for (auto& instruction : computation_.instructions()) {
-      // TODO(b/34466113): Replace this and above with successors() or
-      // predecessors() when these methods are added to HloInstruction.
-      for (const HloInstruction* user : instruction->users()) {
-        unscheduled_pred_count[user]++;
-      }
-      for (const HloInstruction* succ : instruction->control_successors()) {
-        unscheduled_pred_count[succ]++;
-      }
-    }
-    for (auto& instruction : computation_.instructions()) {
-      // Instruction with no operands or control predecessors will
-      // not be in the map.
-      if (unscheduled_pred_count.count(instruction.get()) == 0) {
-        ready_list.push_back(instruction.get());
-      }
-    }
-
-    while (!ready_list.empty()) {
-      // Select the highest priority HLO instruction from the ready list.
-      auto best_it = ready_list.begin();
-      Priority best_priority = GetPriority(*best_it);
-      for (auto ready_it = std::next(ready_list.begin());
-           ready_it != ready_list.end(); ++ready_it) {
-        Priority priority = GetPriority(*ready_it);
-        if (priority > best_priority) {
-          best_it = ready_it;
-          best_priority = priority;
-        }
-      }
-
-      // Remove the selected instruction from the ready list and add it to the
-      // schedule.
-      const HloInstruction* best = *best_it;
-      ready_list.erase(best_it);
-      schedule.push_back(best);
-      scheduled_instructions_.insert(best);
-
-      // Update the unscheduled uses of the logical buffers.
-      for (const LogicalBuffer* buffer : buffer_uses_.at(best)) {
-        CHECK_GT(unscheduled_use_count_.at(buffer), 0);
-        --unscheduled_use_count_[buffer];
-      }
-
-      // Add new instructions to ready list.
-      auto update_pred_count = [&unscheduled_pred_count,
-                                &ready_list](HloInstruction* inst) {
-        int64 pred_count = --unscheduled_pred_count.at(inst);
-        CHECK_GE(pred_count, 0);
-        if (pred_count == 0) {
-          ready_list.push_back(inst);
-        }
-      };
-      // TODO(b/34466113): Replace this and above with successors() or
-      // predecessors() when these methods are added to HloInstruction.
-      for (HloInstruction* user : best->users()) {
-        update_pred_count(user);
-      }
-      for (HloInstruction* succ : best->control_successors()) {
-        update_pred_count(succ);
-      }
-    }
-    CHECK_EQ(schedule.size(), computation_.instructions().size());
-    CHECK_EQ(scheduled_instructions_.size(),
-             computation_.instructions().size());
-
-    return schedule;
-  }
-
-  const HloComputation& computation_;
-  const TuplePointsToAnalysis& points_to_analysis_;
-  const LogicalBuffer::SizeFunction& size_function_;
-
-  // A map containing the LogicalBuffers that each instruction uses.
-  std::unordered_map<const HloInstruction*,
-                     std::unordered_set<const LogicalBuffer*>>
-      buffer_uses_;
-
-  // A map containing the count of unscheduled HLOs which using a particular
-  // LogicalBuffer.
-  std::unordered_map<const LogicalBuffer*, int64> unscheduled_use_count_;
-
-  // Set of instructions which have been scheduled.
-  std::unordered_set<const HloInstruction*> scheduled_instructions_;
-};
-
-int64 SumLogicalBufferSizes(const std::vector<const LogicalBuffer*>& buffers,
-                            const LogicalBuffer::SizeFunction& size_function) {
-  int64 size = 0;
-  for (const LogicalBuffer* buffer : buffers) {
-    size += size_function(*buffer);
-  }
-  return size;
-}
-
-StatusOr<std::vector<const HloInstruction*>> RunDFSMemoryScheduler(
-    const HloComputation& computation,
-    const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_function) {
-  // This ordering is based on DFS post-order, with a heuristic to decide which
-  // operand to visit first.  The heuristic is based on 'extra_users', which is
-  // simply users-1 for each instruction.  By subtracting 1, we're saying that
-  // instructions with no users or a single user don't count; instructions with
-  // lots of fan-out will be visited earlier.
-  tensorflow::gtl::FlatMap<const HloInstruction*, int64> extra_users;
-  tensorflow::gtl::FlatMap<const HloInstruction*, int64> total_sizes;
-  for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) {
-    extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1;
-    total_sizes[hlo] = SumLogicalBufferSizes(
-        points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function);
-    tensorflow::gtl::FlatSet<const HloInstruction*> unique_operands(
-        hlo->operands().begin(), hlo->operands().end());
-    for (const HloInstruction* operand : unique_operands) {
-      extra_users[hlo] += extra_users[operand];
-      total_sizes[hlo] += total_sizes[operand];
-    }
-  }
-  CHECK_EQ(extra_users.size(), computation.instructions().size());
-  CHECK_EQ(total_sizes.size(), computation.instructions().size());
-
-  // Construct a total order based on DFS post-order, visiting operands in
-  // decreasing cumulative extra user order, and next by cumulative size, with a
-  // tiebreaker by name for determinism.
-  std::vector<const HloInstruction*> sequence;
-  FunctionVisitor visitor([&sequence](HloInstruction* hlo) {
-    sequence.push_back(hlo);
-    return Status::OK();
-  });
-  TF_RETURN_IF_ERROR(computation.AcceptWithOperandOrder(
-      &visitor, [&extra_users, &total_sizes](const HloInstruction* a,
-                                             const HloInstruction* b) {
-        if (extra_users[a] != extra_users[b]) {
-          return extra_users[a] > extra_users[b];
-        }
-        if (total_sizes[a] != total_sizes[b]) {
-          return total_sizes[a] > total_sizes[b];
-        }
-        return a->name() < b->name();
-      }));
-  CHECK_EQ(sequence.size(), computation.instructions().size());
-  return sequence;
-}
-
-StatusOr<int64> MinimumMemoryForComputation(
-    const HloComputation& computation,
-    const std::vector<const HloInstruction*>& sequence,
-    const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_function) {
-  TF_ASSIGN_OR_RETURN(
-      HeapSimulator::Result result,
-      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), computation,
-                         sequence, points_to_analysis, size_function));
-  return result.heap_size;
-}
-
-StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
-    const HloComputation& computation,
-    const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_function) {
-  // We try both a list-scheduler based ordering and a DFS based ordering, and
-  // choose whichever returns a lower min-memory, not accounting for
-  // fragmentation.
-  //
-  // Note that this is just a heuristic. One obvious inaccuracy is that the
-  // memory required for sub-computations might be different when considered
-  // within the caller's context. But it's good enough for now.
-  TF_ASSIGN_OR_RETURN(
-      std::vector<const HloInstruction*> list_sequence,
-      ListScheduler::Run(computation, points_to_analysis, size_function));
-  TF_ASSIGN_OR_RETURN(
-      const int64 list_memory,
-      MinimumMemoryForComputation(computation, list_sequence,
-                                  points_to_analysis, size_function));
-  VLOG(2) << "Min-memory list sequence: " << list_memory << " bytes";
-
-  TF_ASSIGN_OR_RETURN(
-      std::vector<const HloInstruction*> dfs_sequence,
-      RunDFSMemoryScheduler(computation, points_to_analysis, size_function));
-  TF_ASSIGN_OR_RETURN(
-      const int64 dfs_memory,
-      MinimumMemoryForComputation(computation, dfs_sequence, points_to_analysis,
-                                  size_function));
-  VLOG(2) << "Min-memory dfs sequence: " << dfs_memory << " bytes";
-
-  if (list_memory <= dfs_memory) {
-    VLOG(2) << "Chose min-memory list sequence: " << list_memory << " bytes";
-    return list_sequence;
-  } else {
-    VLOG(2) << "Chose min-memory dfs sequence: " << dfs_memory << " bytes";
-    return dfs_sequence;
-  }
-}
-
-}  // namespace
-
-StatusOr<SequentialHloOrdering::HloModuleSequence>
-CreateMemoryMinimizingSequence(
-    const HloModule& module, const LogicalBuffer::SizeFunction& size_function) {
-  SequentialHloOrdering::HloModuleSequence sequence;
-  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
-                      TuplePointsToAnalysis::Run(&module));
-  for (const auto& computation : module.computations()) {
-    TF_ASSIGN_OR_RETURN(sequence[computation.get()],
-                        CreateMemoryMinimizingSequence(
-                            *computation, *points_to_analysis, size_function));
-  }
-  return sequence;
-}
-
-StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
-    const HloComputation& computation,
-    const LogicalBuffer::SizeFunction& size_function) {
-  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
-                      TuplePointsToAnalysis::Run(computation.parent()));
-  return CreateMemoryMinimizingSequence(computation, *points_to_analysis,
-                                        size_function);
-}
-
 std::ostream& operator<<(
     std::ostream& out,
     const SequentialHloOrdering::HloModuleSequence& module_sequence) {
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.h b/tensorflow/compiler/xla/service/hlo_ordering.h
index b59e1ea5eb..ff84f887f7 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.h
+++ b/tensorflow/compiler/xla/service/hlo_ordering.h
@@ -24,12 +24,8 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
-#include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 
 namespace xla {
 
@@ -191,24 +187,6 @@ std::ostream& operator<<(
     std::ostream& out,
     const SequentialHloOrdering::HloModuleSequence& module_sequence);
 
-// Returns the minimum memory required to compute the given module sequence,
-// assuming no fragmentation.
-StatusOr<int64> MinimumMemoryForSequence(
-    const SequentialHloOrdering::HloModuleSequence& module_sequence,
-    const LogicalBuffer::SizeFunction& size_function);
-
-// Returns an HloModuleSequence which seeks to minimize the memory required for
-// the computation. size_function is the function returning the number of bytes
-// required for a LogicalBuffer.
-StatusOr<SequentialHloOrdering::HloModuleSequence>
-CreateMemoryMinimizingSequence(
-    const HloModule& module, const LogicalBuffer::SizeFunction& size_function);
-
-// Overload of above that computes the sequence for a single computation.
-StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
-    const HloComputation& computation,
-    const LogicalBuffer::SizeFunction& size_function);
-
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_ORDERING_H_
diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
index 56e36bd705..a1e38803c4 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -217,67 +218,7 @@ TEST_F(HloOrderingTest, InstructionsInWhileComputations) {
   EXPECT_FALSE(ordering.ExecutesBefore(body_param, cond_param));
 }
 
-class MinimumMemoryForSequenceTest : public HloTestBase {};
-
-TEST_F(MinimumMemoryForSequenceTest, MultiComputation) {
-  auto module = CreateNewModule();
-  const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {});
-  const Shape tuple_shape =
-      ShapeUtil::MakeTupleShape({scalar_shape, scalar_shape});
-
-  auto cond_builder = HloComputation::Builder("WhileCond");
-  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
-  HloInstruction* cond_param = cond_builder.AddInstruction(
-      HloInstruction::CreateParameter(0, tuple_shape, "cond_param"));
-  HloInstruction* cond_iter = cond_builder.AddInstruction(
-      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 0));
-  HloInstruction* cond_data = cond_builder.AddInstruction(
-      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 1));
-  // Free cond_param[] (16 bytes), Alloc PRED[] (1 byte)
-  HloInstruction* cond_lt = cond_builder.AddInstruction(
-      HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}),
-                                   HloOpcode::kLt, cond_iter, cond_data));
-  HloComputation* cond_computation =
-      module->AddEmbeddedComputation(cond_builder.Build());
-
-  auto body_builder = HloComputation::Builder("WhileBody");
-  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
-  HloInstruction* body_param = body_builder.AddInstruction(
-      HloInstruction::CreateParameter(0, tuple_shape, "body_param"));
-  HloComputation* body_computation =
-      module->AddEmbeddedComputation(body_builder.Build());
-
-  auto builder = HloComputation::Builder(TestName());
-  // Entry params: 8 bytes (4 bytes per param), TOTAL=8
-  HloInstruction* iter = builder.AddInstruction(
-      HloInstruction::CreateParameter(0, scalar_shape, "param_iter"));
-  HloInstruction* data = builder.AddInstruction(
-      HloInstruction::CreateParameter(1, scalar_shape, "param_data"));
-  // Tuple: 16 bytes (8 bytes per pointer), TOTAL=24
-  HloInstruction* tuple =
-      builder.AddInstruction(HloInstruction::CreateTuple({iter, data}));
-  // While: 8 bytes (4 bytes per element), TOTAL=32
-  // Both cond and body use a max of 24 bytes, TOTAL=56
-  HloInstruction* while_op = builder.AddInstruction(HloInstruction::CreateWhile(
-      tuple_shape, cond_computation, body_computation, tuple));
-  HloComputation* entry_computation =
-      module->AddEntryComputation(builder.Build());
-
-  auto size_fn = [](const LogicalBuffer& buffer) {
-    return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8);
-  };
-
-  SequentialHloOrdering::HloModuleSequence module_sequence;
-  module_sequence[cond_computation] = {cond_param, cond_iter, cond_data,
-                                       cond_lt};
-  module_sequence[body_computation] = {body_param};
-  module_sequence[entry_computation] = {iter, data, tuple, while_op};
-  EXPECT_EQ(56,
-            MinimumMemoryForSequence(module_sequence, size_fn).ValueOrDie());
-}
-
 }  // namespace
-
 }  // namespace xla
 
 int main(int argc, char** argv) {
diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
index 119e2d7902..4b824f8240 100644
--- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
+++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <functional>
 
+#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
@@ -30,9 +31,10 @@ using ::tensorflow::strings::StrAppend;
 namespace xla {
 
 namespace {
-void DumpModule(const Compiler::HloDumper& dumper_, const HloModule& module,
+void DumpModule(const HloModule& module,
+
                 const string& message) {
-  dumper_(module, message);
+  hlo_graph_dumper::MaybeDumpHloModule(module, message);
   VLOG(2) << "HLO " << message << ":";
   XLA_VLOG_LINES(2, module.ToString());
 }
@@ -75,7 +77,7 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
     // Emit label containing: "after foo-pass, before bar-pass".
     message.clear();
     StrAppend(&message, prefix, ", before ", pass->name());
-    DumpModule(dumper_, *module, message);
+    DumpModule(*module, message);
 
     TF_RETURN_IF_ERROR(run_invariant_checkers());
     TF_ASSIGN_OR_RETURN(bool changed_this_pass, pass->Run(module));
@@ -85,7 +87,7 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
     StrAppend(&prefix, name(), ": after ", pass->name());
   }
   TF_RETURN_IF_ERROR(run_invariant_checkers());
-  DumpModule(dumper_, *module, prefix + ", pipeline end");
+  DumpModule(*module, prefix + ", pipeline end");
   return changed;
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.h b/tensorflow/compiler/xla/service/hlo_pass_pipeline.h
index 682c4b952d..a42d7e59fe 100644
--- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.h
+++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.h
@@ -22,7 +22,6 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -34,9 +33,7 @@ namespace xla {
 // Pipeline of HLO passes.
 class HloPassPipeline : public HloPassInterface {
  public:
-  explicit HloPassPipeline(const string& name,
-                           const Compiler::HloDumper& dumper)
-      : name_(name), dumper_(dumper) {}
+  explicit HloPassPipeline(const string& name) : name_(name) {}
   tensorflow::StringPiece name() const override { return name_; }
 
   // Add a pass to the pipeline. It should be called with the arguments for the
@@ -69,7 +66,6 @@ class HloPassPipeline : public HloPassInterface {
 
  private:
   const string name_;
-  Compiler::HloDumper dumper_;
   std::vector<std::unique_ptr<HloPassInterface>> passes_;
   std::vector<std::unique_ptr<HloPassInterface>> invariant_checkers_;
   bool run_called_ = false;
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
index fb6d8674b6..d19e8034ac 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/service/liveness_util.h"
 #include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/status_macros.h"
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc
new file mode 100644
index 0000000000..f8e05448da
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc
@@ -0,0 +1,388 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
+
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/heap_simulator.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+StatusOr<int64> MinimumMemoryForSequence(
+    const SequentialHloOrdering::HloModuleSequence& module_sequence,
+    const LogicalBuffer::SizeFunction& size_function) {
+  if (module_sequence.empty()) {
+    return 0;
+  }
+
+  const HloModule* module = module_sequence.begin()->first->parent();
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
+                      TuplePointsToAnalysis::Run(module));
+
+  // The absolute minimum memory required for a given sequence of instructions
+  // is determined by the sequence of Alloc and Free calls on a simulated heap,
+  // ignoring fragmentation. We run the heap simulation on the whole module,
+  // rather than summing each computation, since it gives us a better lower
+  // bound, by minimizing the liveness of sub-computations.
+  TF_ASSIGN_OR_RETURN(
+      HeapSimulator::Result result,
+      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), *module,
+                         module_sequence, *points_to_analysis, size_function));
+  return result.heap_size;
+}
+
+namespace {
+
+// Class implementing a list scheduler of HLO instructions which produces a
+// sequence which minimizes memory usage.
+class ListScheduler {
+ public:
+  // Construct and return a memory-minimizing sequence of HLO instructions
+  // containing the given HLO computation.
+  static StatusOr<std::vector<const HloInstruction*>> Run(
+      const HloComputation& computation,
+      const TuplePointsToAnalysis& points_to_analysis,
+      const LogicalBuffer::SizeFunction& size_function) {
+    ListScheduler scheduler(computation, points_to_analysis, size_function);
+    return scheduler.CreateSchedule();
+  }
+
+ private:
+  // The scheduling priority of an instruction is first the number of bytes
+  // freed by scheduling the instruction, and second (tie-breaker) by the number
+  // of users. This is represented as a std::pair containing these two values
+  // (first element is the bytes freed). std::pair provides the necessary
+  // comparison operators.
+  using Priority = std::pair<int64, int64>;
+
+  ListScheduler(const HloComputation& computation,
+                const TuplePointsToAnalysis& points_to_analysis,
+                const LogicalBuffer::SizeFunction& size_function)
+      : computation_(computation),
+        points_to_analysis_(points_to_analysis),
+        size_function_(size_function) {
+    // Create a map containing the LogicalBuffer uses for each HLO
+    // instruction. An HLO instruction "uses" a LogicalBuffer if the
+    // LogicalBuffer is in an operand of the instruction as indicated by
+    // points-to analysis.
+    for (auto& instruction : computation.instructions()) {
+      buffer_uses_.insert(
+          {instruction.get(), std::unordered_set<const LogicalBuffer*>()});
+      for (auto* operand : instruction->operands()) {
+        for (const LogicalBuffer* buffer :
+             points_to_analysis.GetBuffersDefinedByInstruction(operand)) {
+          buffer_uses_[instruction.get()].insert(buffer);
+        }
+      }
+    }
+
+    // Create map containing the number of unscheduled uses (hlo instructions)
+    // of each logical buffer.
+    for (auto& instruction : computation.instructions()) {
+      for (auto* buffer : points_to_analysis.GetBuffersDefinedByInstruction(
+               instruction.get())) {
+        unscheduled_use_count_[buffer] = 0;
+      }
+    }
+    for (auto& instruction : computation.instructions()) {
+      for (const LogicalBuffer* buffer : buffer_uses_.at(instruction.get())) {
+        ++unscheduled_use_count_[buffer];
+      }
+    }
+
+    // Buffers live out of the computation have an implicit use at the end of
+    // the computation.
+    for (const LogicalBuffer* live_out_buffer :
+         points_to_analysis.GetPointsToSet(computation.root_instruction())
+             .CreateFlattenedSet()) {
+      ++unscheduled_use_count_[live_out_buffer];
+    }
+  }
+
+  // Returns whether the memory used by the given buffer should be ignored by
+  // the scheduling heuristic.
+  bool IgnoreBuffer(const LogicalBuffer& buffer) {
+    return buffer.instruction()->opcode() == HloOpcode::kParameter ||
+           buffer.instruction()->opcode() == HloOpcode::kConstant;
+  }
+
+  // Return the number of bytes freed if the HLO instruction is scheduled.
+  int64 BytesFreedIfScheduled(const HloInstruction* instruction) {
+    int64 freed_bytes = 0;
+    // Sum the total size of the values last used by this instruction.
+    for (auto* buffer : buffer_uses_.at(instruction)) {
+      if (IgnoreBuffer(*buffer)) {
+        continue;
+      }
+      CHECK_GE(unscheduled_use_count_.at(buffer), 1);
+      if (unscheduled_use_count_.at(buffer) == 1) {
+        // This is the last use of the logical buffer.
+        freed_bytes += size_function_(*buffer);
+      }
+    }
+    // Then subtract the size of the value(s) defined by this instruction.
+    for (auto* buffer :
+         points_to_analysis_.GetBuffersDefinedByInstruction(instruction)) {
+      if (!IgnoreBuffer(*buffer)) {
+        freed_bytes -= size_function_(*buffer);
+      }
+    }
+    return freed_bytes;
+  }
+
+  // Construct the scheduling priority of the given instruction.
+  Priority GetPriority(const HloInstruction* instruction) {
+    return {BytesFreedIfScheduled(instruction), instruction->user_count()};
+  }
+
+  std::vector<const HloInstruction*> CreateSchedule() {
+    std::vector<const HloInstruction*> schedule;
+
+    // Populate the ready list with instructions which have no operands or
+    // control predecessors.
+    std::unordered_map<const HloInstruction*, int64> unscheduled_pred_count;
+    std::list<const HloInstruction*> ready_list;
+    for (auto& instruction : computation_.instructions()) {
+      // TODO(b/34466113): Replace this and above with successors() or
+      // predecessors() when these methods are added to HloInstruction.
+      for (const HloInstruction* user : instruction->users()) {
+        unscheduled_pred_count[user]++;
+      }
+      for (const HloInstruction* succ : instruction->control_successors()) {
+        unscheduled_pred_count[succ]++;
+      }
+    }
+    for (auto& instruction : computation_.instructions()) {
+      // Instruction with no operands or control predecessors will
+      // not be in the map.
+      if (unscheduled_pred_count.count(instruction.get()) == 0) {
+        ready_list.push_back(instruction.get());
+      }
+    }
+
+    while (!ready_list.empty()) {
+      // Select the highest priority HLO instruction from the ready list.
+      auto best_it = ready_list.begin();
+      Priority best_priority = GetPriority(*best_it);
+      for (auto ready_it = std::next(ready_list.begin());
+           ready_it != ready_list.end(); ++ready_it) {
+        Priority priority = GetPriority(*ready_it);
+        if (priority > best_priority) {
+          best_it = ready_it;
+          best_priority = priority;
+        }
+      }
+
+      // Remove the selected instruction from the ready list and add it to the
+      // schedule.
+      const HloInstruction* best = *best_it;
+      ready_list.erase(best_it);
+      schedule.push_back(best);
+      scheduled_instructions_.insert(best);
+
+      // Update the unscheduled uses of the logical buffers.
+      for (const LogicalBuffer* buffer : buffer_uses_.at(best)) {
+        CHECK_GT(unscheduled_use_count_.at(buffer), 0);
+        --unscheduled_use_count_[buffer];
+      }
+
+      // Add new instructions to ready list.
+      auto update_pred_count = [&unscheduled_pred_count,
+                                &ready_list](HloInstruction* inst) {
+        int64 pred_count = --unscheduled_pred_count.at(inst);
+        CHECK_GE(pred_count, 0);
+        if (pred_count == 0) {
+          ready_list.push_back(inst);
+        }
+      };
+      // TODO(b/34466113): Replace this and above with successors() or
+      // predecessors() when these methods are added to HloInstruction.
+      for (HloInstruction* user : best->users()) {
+        update_pred_count(user);
+      }
+      for (HloInstruction* succ : best->control_successors()) {
+        update_pred_count(succ);
+      }
+    }
+    CHECK_EQ(schedule.size(), computation_.instructions().size());
+    CHECK_EQ(scheduled_instructions_.size(),
+             computation_.instructions().size());
+
+    return schedule;
+  }
+
+  const HloComputation& computation_;
+  const TuplePointsToAnalysis& points_to_analysis_;
+  const LogicalBuffer::SizeFunction& size_function_;
+
+  // A map containing the LogicalBuffers that each instruction uses.
+  std::unordered_map<const HloInstruction*,
+                     std::unordered_set<const LogicalBuffer*>>
+      buffer_uses_;
+
+  // A map containing the count of unscheduled HLOs which using a particular
+  // LogicalBuffer.
+  std::unordered_map<const LogicalBuffer*, int64> unscheduled_use_count_;
+
+  // Set of instructions which have been scheduled.
+  std::unordered_set<const HloInstruction*> scheduled_instructions_;
+};
+
+int64 SumLogicalBufferSizes(const std::vector<const LogicalBuffer*>& buffers,
+                            const LogicalBuffer::SizeFunction& size_function) {
+  int64 size = 0;
+  for (const LogicalBuffer* buffer : buffers) {
+    size += size_function(*buffer);
+  }
+  return size;
+}
+
+StatusOr<std::vector<const HloInstruction*>> RunDFSMemoryScheduler(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  // This ordering is based on DFS post-order, with a heuristic to decide which
+  // operand to visit first.  The heuristic is based on 'extra_users', which is
+  // simply users-1 for each instruction.  By subtracting 1, we're saying that
+  // instructions with no users or a single user don't count; instructions with
+  // lots of fan-out will be visited earlier.
+  tensorflow::gtl::FlatMap<const HloInstruction*, int64> extra_users;
+  tensorflow::gtl::FlatMap<const HloInstruction*, int64> total_sizes;
+  for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) {
+    extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1;
+    total_sizes[hlo] = SumLogicalBufferSizes(
+        points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function);
+    tensorflow::gtl::FlatSet<const HloInstruction*> unique_operands(
+        hlo->operands().begin(), hlo->operands().end());
+    for (const HloInstruction* operand : unique_operands) {
+      extra_users[hlo] += extra_users[operand];
+      total_sizes[hlo] += total_sizes[operand];
+    }
+  }
+  CHECK_EQ(extra_users.size(), computation.instructions().size());
+  CHECK_EQ(total_sizes.size(), computation.instructions().size());
+
+  // Construct a total order based on DFS post-order, visiting operands in
+  // decreasing cumulative extra user order, and next by cumulative size, with a
+  // tiebreaker by name for determinism.
+  std::vector<const HloInstruction*> sequence;
+  FunctionVisitor visitor([&sequence](HloInstruction* hlo) {
+    sequence.push_back(hlo);
+    return Status::OK();
+  });
+  TF_RETURN_IF_ERROR(computation.AcceptWithOperandOrder(
+      &visitor, [&extra_users, &total_sizes](const HloInstruction* a,
+                                             const HloInstruction* b) {
+        if (extra_users[a] != extra_users[b]) {
+          return extra_users[a] > extra_users[b];
+        }
+        if (total_sizes[a] != total_sizes[b]) {
+          return total_sizes[a] > total_sizes[b];
+        }
+        return a->name() < b->name();
+      }));
+  CHECK_EQ(sequence.size(), computation.instructions().size());
+  return sequence;
+}
+
+StatusOr<int64> MinimumMemoryForComputation(
+    const HloComputation& computation,
+    const std::vector<const HloInstruction*>& sequence,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  TF_ASSIGN_OR_RETURN(
+      HeapSimulator::Result result,
+      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), computation,
+                         sequence, points_to_analysis, size_function));
+  return result.heap_size;
+}
+
+StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  // We try both a list-scheduler based ordering and a DFS based ordering, and
+  // choose whichever returns a lower min-memory, not accounting for
+  // fragmentation.
+  //
+  // Note that this is just a heuristic. One obvious inaccuracy is that the
+  // memory required for sub-computations might be different when considered
+  // within the caller's context. But it's good enough for now.
+  TF_ASSIGN_OR_RETURN(
+      std::vector<const HloInstruction*> list_sequence,
+      ListScheduler::Run(computation, points_to_analysis, size_function));
+  TF_ASSIGN_OR_RETURN(
+      const int64 list_memory,
+      MinimumMemoryForComputation(computation, list_sequence,
+                                  points_to_analysis, size_function));
+  VLOG(2) << "Min-memory list sequence: " << list_memory << " bytes";
+
+  TF_ASSIGN_OR_RETURN(
+      std::vector<const HloInstruction*> dfs_sequence,
+      RunDFSMemoryScheduler(computation, points_to_analysis, size_function));
+  TF_ASSIGN_OR_RETURN(
+      const int64 dfs_memory,
+      MinimumMemoryForComputation(computation, dfs_sequence, points_to_analysis,
+                                  size_function));
+  VLOG(2) << "Min-memory dfs sequence: " << dfs_memory << " bytes";
+
+  if (list_memory <= dfs_memory) {
+    VLOG(2) << "Chose min-memory list sequence: " << list_memory << " bytes";
+    return list_sequence;
+  } else {
+    VLOG(2) << "Chose min-memory dfs sequence: " << dfs_memory << " bytes";
+    return dfs_sequence;
+  }
+}
+
+}  // namespace
+
+StatusOr<SequentialHloOrdering::HloModuleSequence>
+CreateMemoryMinimizingSequence(
+    const HloModule& module, const LogicalBuffer::SizeFunction& size_function) {
+  SequentialHloOrdering::HloModuleSequence sequence;
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
+                      TuplePointsToAnalysis::Run(&module));
+  for (const auto& computation : module.computations()) {
+    TF_ASSIGN_OR_RETURN(sequence[computation.get()],
+                        CreateMemoryMinimizingSequence(
+                            *computation, *points_to_analysis, size_function));
+  }
+  return sequence;
+}
+
+StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
+    const HloComputation& computation,
+    const LogicalBuffer::SizeFunction& size_function) {
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
+                      TuplePointsToAnalysis::Run(computation.parent()));
+  return CreateMemoryMinimizingSequence(computation, *points_to_analysis,
+                                        size_function);
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.h b/tensorflow/compiler/xla/service/hlo_scheduling.h
new file mode 100644
index 0000000000..ec92a56b96
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.h
@@ -0,0 +1,50 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULING_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULING_H_
+
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/service/logical_buffer.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+
+namespace xla {
+
+// Returns the minimum memory required to compute the given module sequence,
+// assuming no fragmentation.
+StatusOr<int64> MinimumMemoryForSequence(
+    const SequentialHloOrdering::HloModuleSequence& module_sequence,
+    const LogicalBuffer::SizeFunction& size_function);
+
+// Returns an HloModuleSequence which seeks to minimize the memory required for
+// the computation. size_function is the function returning the number of bytes
+// required for a LogicalBuffer.
+StatusOr<SequentialHloOrdering::HloModuleSequence>
+CreateMemoryMinimizingSequence(
+    const HloModule& module, const LogicalBuffer::SizeFunction& size_function);
+
+// Overload of above that computes the sequence for a single computation.
+StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
+    const HloComputation& computation,
+    const LogicalBuffer::SizeFunction& size_function);
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULING_H_
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc
new file mode 100644
index 0000000000..d09d22ee40
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc
@@ -0,0 +1,97 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_scheduling.h"
+
+#include <memory>
+#include <string>
+
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_ordering.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+
+namespace xla {
+namespace {
+
+class MinimumMemoryForSequenceTest : public HloTestBase {};
+
+TEST_F(MinimumMemoryForSequenceTest, MultiComputation) {
+  auto module = CreateNewModule();
+  const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {});
+  const Shape tuple_shape =
+      ShapeUtil::MakeTupleShape({scalar_shape, scalar_shape});
+
+  auto cond_builder = HloComputation::Builder("WhileCond");
+  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
+  HloInstruction* cond_param = cond_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple_shape, "cond_param"));
+  HloInstruction* cond_iter = cond_builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 0));
+  HloInstruction* cond_data = cond_builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 1));
+  // Free cond_param[] (16 bytes), Alloc PRED[] (1 byte)
+  HloInstruction* cond_lt = cond_builder.AddInstruction(
+      HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}),
+                                   HloOpcode::kLt, cond_iter, cond_data));
+  HloComputation* cond_computation =
+      module->AddEmbeddedComputation(cond_builder.Build());
+
+  auto body_builder = HloComputation::Builder("WhileBody");
+  // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element)
+  HloInstruction* body_param = body_builder.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple_shape, "body_param"));
+  HloComputation* body_computation =
+      module->AddEmbeddedComputation(body_builder.Build());
+
+  auto builder = HloComputation::Builder(TestName());
+  // Entry params: 8 bytes (4 bytes per param), TOTAL=8
+  HloInstruction* iter = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, scalar_shape, "param_iter"));
+  HloInstruction* data = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, scalar_shape, "param_data"));
+  // Tuple: 16 bytes (8 bytes per pointer), TOTAL=24
+  HloInstruction* tuple =
+      builder.AddInstruction(HloInstruction::CreateTuple({iter, data}));
+  // While: 8 bytes (4 bytes per element), TOTAL=32
+  // Both cond and body use a max of 24 bytes, TOTAL=56
+  HloInstruction* while_op = builder.AddInstruction(HloInstruction::CreateWhile(
+      tuple_shape, cond_computation, body_computation, tuple));
+  HloComputation* entry_computation =
+      module->AddEntryComputation(builder.Build());
+
+  auto size_fn = [](const LogicalBuffer& buffer) {
+    return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8);
+  };
+
+  SequentialHloOrdering::HloModuleSequence module_sequence;
+  module_sequence[cond_computation] = {cond_param, cond_iter, cond_data,
+                                       cond_lt};
+  module_sequence[body_computation] = {body_param};
+  module_sequence[entry_computation] = {iter, data, tuple, while_op};
+  EXPECT_EQ(56,
+            MinimumMemoryForSequence(module_sequence, size_fn).ValueOrDie());
+}
+
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  return xla::ParseDebugOptionsFlagsAndRunTests(argc, argv);
+}
diff --git a/tensorflow/compiler/xla/service/hlo_value.cc b/tensorflow/compiler/xla/service/hlo_value.cc
new file mode 100644
index 0000000000..55e6e77c0f
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_value.cc
@@ -0,0 +1,301 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_value.h"
+
+#include <algorithm>
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+using ::tensorflow::strings::StrAppend;
+using ::tensorflow::strings::StrCat;
+
+string HloLocation::ToString() const {
+  string index_str =
+      ShapeUtil::IsTuple(instruction->shape()) ? (" " + index.ToString()) : "";
+  return StrCat(instruction->FullyQualifiedName(), index_str);
+}
+
+std::ostream& operator<<(std::ostream& out, const HloLocation& location) {
+  out << location.ToString();
+  return out;
+}
+
+string HloUse::ToString() const {
+  string index_str =
+      ShapeUtil::IsTuple(instruction->operand(operand_number)->shape())
+          ? (" " + operand_index.ToString())
+          : "";
+  return StrCat(instruction->FullyQualifiedName(), ", operand ", operand_number,
+                index_str);
+}
+
+std::ostream& operator<<(std::ostream& out, const HloUse& use) {
+  out << use.ToString();
+  return out;
+}
+
+HloValue::HloValue(HloValue::Id id, HloInstruction* instruction,
+                   const ShapeIndex& index, bool is_phi)
+    : id_(id), is_phi_(is_phi) {
+  // The defining location is always the first element in the locations_ vector.
+  AddLocation(instruction, index);
+}
+
+bool HloValue::operator==(const HloValue& other) const {
+  bool equal = defining_instruction() == other.defining_instruction() &&
+               defining_index() == other.defining_index();
+  // If the values are equal they most both be phi (or non phi).
+  CHECK(!(equal && is_phi() != other.is_phi()));
+  return equal;
+}
+
+bool HloValue::operator!=(const HloValue& other) const {
+  return !(*this == other);
+}
+
+string HloValue::ToShortString() const {
+  string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
+                         ? defining_index().ToString()
+                         : "";
+  return StrCat(is_phi_ ? "PHI " : "",
+                defining_instruction()->FullyQualifiedName(), index_str);
+}
+
+string HloValue::ToString(int indent) const {
+  string indentation(indent, ' ');
+  string out = StrCat(indentation, ToShortString(), ", locations:\n");
+  for (const HloLocation& location : locations()) {
+    StrAppend(&out, indentation, "  ", location.ToString(), "\n");
+  }
+  StrAppend(&out, indentation, " uses:\n");
+  for (const HloUse& use : uses()) {
+    StrAppend(&out, indentation, "  ", use.ToString(), "\n");
+  }
+  return out;
+}
+
+namespace {
+
+// Returns true if the instruction 'user' may use the value at the given
+// ShapeIndex in the given operand. Generally, instruction which pass through
+// values transparently without reading the value are not considered to use the
+// value.
+bool MayUseOperandValue(int64 operand_number, const ShapeIndex& index,
+                        const HloInstruction* user) {
+  switch (user->opcode()) {
+    case HloOpcode::kGetTupleElement:
+    case HloOpcode::kCopy:
+      // These instructions only access the top-level values of their
+      // operand. Non-top-level (nested) values are passed through
+      // transparently.
+      CHECK_EQ(operand_number, 0);
+      return index.empty();
+    case HloOpcode::kSelect:
+      // Select does not use any nested elements of its selected-from operands
+      // (operand 1 and 2)
+      CHECK_GE(operand_number, 0);
+      CHECK_LE(operand_number, 2);
+      return operand_number == 0 || index.empty();
+
+    case HloOpcode::kCall:
+    case HloOpcode::kTuple:
+      // These instructions always pass through their operands transparently.
+      return false;
+
+    case HloOpcode::kWhile:
+      // Though the while instructions passes through its operands, we return
+      // true because in SSA form there may be a Phi at the parameter of the
+      // while which is considered a use of its incoming value because the Phi
+      // input values are not passed through into the body computation. Because
+      // this function is used in both SSA and non-SSA forms of the analysis
+      // conservatively return true.
+      return true;
+
+    default:
+      return true;
+  }
+}
+
+}  // namespace
+
+void HloValue::AddLocation(HloInstruction* instruction,
+                           const ShapeIndex& index) {
+  // The given location should not already exist in locations_.
+  for (const HloLocation& location : locations_) {
+    DCHECK(!(location.instruction == instruction && location.index == index));
+  }
+
+  locations_.push_back(HloLocation{instruction, index});
+
+  //  Update uses.
+  for (HloInstruction* user : instruction->users()) {
+    for (int64 operand_number : user->OperandIndices(instruction)) {
+      if (MayUseOperandValue(operand_number, index, user)) {
+        for (const HloUse& use : uses_) {
+          // Verify that this use does not already exist.
+          DCHECK(!(use.instruction == user &&
+                   use.operand_number == operand_number &&
+                   use.operand_index == index));
+        }
+
+        uses_.push_back(HloUse{user, operand_number, index});
+      }
+    }
+  }
+
+  // Update liveout status of this HloValue.
+  const HloModule& module = *instruction->parent()->parent();
+  if (instruction == module.entry_computation()->root_instruction()) {
+    live_out_of_module_ = true;
+  }
+
+  if (instruction == instruction->parent()->root_instruction()) {
+    live_out_of_computation_ = true;
+  }
+}
+
+void HloValue::RemoveLocation(HloInstruction* instruction,
+                              const ShapeIndex& index) {
+  // The defining location cannot be removed.
+  CHECK(!(instruction == defining_instruction() && index == defining_index()));
+
+  int64 size_before = locations_.size();
+  locations_.erase(
+      std::remove_if(locations_.begin(), locations_.end(),
+                     [instruction, &index](const HloLocation& location) {
+                       return location.instruction == instruction &&
+                              location.index == index;
+                     }),
+      locations_.end());
+  // Only a single location should have been removed.
+  CHECK_EQ(locations_.size(), size_before - 1);
+
+  //  Update uses which referred to this location.
+  uses_.erase(std::remove_if(uses_.begin(), uses_.end(),
+                             [instruction, &index](const HloUse& use) {
+                               return use.instruction->operand(
+                                          use.operand_number) == instruction &&
+                                      use.operand_index == index;
+                             }),
+              uses_.end());
+
+  // Returns whether this value is contained in the given instruction's output.
+  auto is_contained_in = [this](const HloInstruction* instruction) {
+    for (const HloLocation& location : locations()) {
+      if (location.instruction == instruction) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  const HloModule& module = *instruction->parent()->parent();
+  if (instruction == module.entry_computation()->root_instruction()) {
+    // Value has been removed from a location in the entry root instruction.
+    live_out_of_module_ =
+        is_contained_in(module.entry_computation()->root_instruction());
+  }
+  if (instruction == defining_instruction()->parent()->root_instruction()) {
+    // Value has been removed from the root of the computation the value has
+    // been defined in.
+    live_out_of_computation_ =
+        is_contained_in(defining_instruction()->parent()->root_instruction());
+  }
+}
+
+std::ostream& operator<<(std::ostream& out, const HloValue& value) {
+  out << value.ToShortString();
+  return out;
+}
+
+void HloValueSet::SortAndUniquifyValues() {
+  std::sort(value_ids_.begin(), value_ids_.end());
+  value_ids_.erase(std::unique(value_ids_.begin(), value_ids_.end()),
+                   value_ids_.end());
+}
+
+string HloValueSet::ToString() const {
+  return StrCat("HloValueSet: ", tensorflow::str_util::Join(value_ids_, ", "));
+}
+
+/*static */
+HloValueSet HloValueSet::Union(
+    tensorflow::gtl::ArraySlice<const HloValueSet*> inputs) {
+  HloValueSet union_set;
+  for (const HloValueSet* input : inputs) {
+    for (HloValue::Id value_id : input->value_ids()) {
+      union_set.value_ids_.push_back(value_id);
+    }
+  }
+  union_set.SortAndUniquifyValues();
+  return union_set;
+}
+
+std::ostream& operator<<(std::ostream& out, const HloValueSet& value_set) {
+  out << value_set.ToString();
+  return out;
+}
+
+InstructionValueSet InstructionValueSet::Union(
+    tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs) {
+  CHECK_GT(inputs.size(), 0);
+  for (int i = 1; i < inputs.size(); ++i) {
+    CHECK(ShapeUtil::Compatible(inputs[0]->shape(), inputs[i]->shape()));
+  }
+  InstructionValueSet union_set(inputs[0]->shape());
+  union_set.ForEachMutableElement(
+      [&inputs](const ShapeIndex& index, HloValueSet* value_set) {
+        std::vector<const HloValueSet*> input_sets;
+        for (const InstructionValueSet* input : inputs) {
+          input_sets.push_back(&input->element(index));
+        }
+        *value_set = HloValueSet::Union(input_sets);
+      });
+  return union_set;
+}
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionValueSet& instruction_value_set) {
+  out << instruction_value_set.ToString();
+  return out;
+}
+
+string InstructionValueSet::ToString() const {
+  string out =
+      StrCat("InstructionValueSet(", ShapeUtil::HumanString(shape()), ")\n");
+  ForEachElement([this, &out](const ShapeIndex& index,
+                              const HloValueSet& value_set) {
+    StrAppend(&out, "  ", index.ToString(), " : ", value_set.ToString(), "\n");
+  });
+  return out;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_value.h b/tensorflow/compiler/xla/service/hlo_value.h
new file mode 100644
index 0000000000..866ce4501b
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_value.h
@@ -0,0 +1,242 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
+
+#include <ostream>
+#include <string>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+
+// Abstraction which identifies a specific point in the XLA graph. An
+// HloLocation specifies a ShapeIndex within the output of a specific
+// instruction.
+struct HloLocation {
+  HloInstruction* instruction;
+  ShapeIndex index;
+
+  string ToString() const;
+
+  bool operator==(const HloLocation& other) const {
+    return instruction == other.instruction && index == other.index;
+  }
+  bool operator!=(const HloLocation& other) const { return !(*this == other); }
+};
+
+std::ostream& operator<<(std::ostream& out, const HloLocation& location);
+
+// Defines a single use of an HLO value.
+struct HloUse {
+  // Instruction at which the value is used.
+  HloInstruction* instruction;
+
+  // The operand number in which the value is appears.
+  int64 operand_number;
+
+  // The shape index within the operand in which the value appears.
+  ShapeIndex operand_index;
+
+  string ToString() const;
+
+  bool operator==(const HloUse& other) const {
+    return instruction == other.instruction &&
+           operand_number == other.operand_number &&
+           operand_index == other.operand_index;
+  }
+
+  bool operator!=(const HloUse& other) const { return !(*this == other); }
+};
+
+std::ostream& operator<<(std::ostream& out, const HloUse& use);
+
+// Class describing a value used by the dataflow analysis. XLA arrays are
+// trivially a single HloValue. Tuples are made up of more than one HloValue: an
+// HloValue for the pointer vector, and an HloValue for each child element.
+//
+// Every HloValue is defined by a particular instruction and most instructions
+// define only a single HloValue. Instructions which define a single HloValue
+// include array-shaped instructions such as Add but also includes Tuple-shaped
+// instructions such as Tuple. The Tuple instruction defines a single HloValue
+// which is a vector of pointers to the values containing the Tuple
+// instruction's operands. Though the result of the Tuple instruction includes
+// multiple values only the top-level HloValue (the vector of pointers) is
+// defined by the Tuple instruction. The values containing the tuple elements
+// are defined by earlier instructions, usually the operands of the Tuple
+// instruction.
+//
+// Instructions which construct both the tuple *and* the tuple elements define
+// more than one HloValue. This includes (at least) tuple-shaped Constant,
+// Parameter, Infeed and While instructions. These tuple-shaped instructions do
+// not assemble a tuple from existing HloValues like the Tuple instruction does,
+// but rather define all the HloValues in the tuple.
+class HloValue {
+ public:
+  using Id = int64;
+
+  // Construct an HloValue defined by 'instruction' at shape index 'index'. If
+  // is_phi is true, then this value is a phi value, for example, at the
+  // parameter of a while body computation. Phi values are only used in the SSA
+  // dataflow analysis (HloDataflowAnalysis::ssa_form_ is true).
+  HloValue(HloValue::Id id, HloInstruction* instruction,
+           const ShapeIndex& index, bool is_phi = false);
+
+  // Return a unique identifier for this HloValue. This value is used for stable
+  // sorting and iteration
+  Id id() const { return id_; }
+
+  // Returns whether this value is a phi value.
+  bool is_phi() const { return is_phi_; }
+
+  // Return the location where this value is defined.
+  const HloLocation& defining_location() const { return locations_[0]; }
+
+  // Return the instruction which defines this HloValue.
+  HloInstruction* defining_instruction() const {
+    return defining_location().instruction;
+  }
+
+  // Return the shape index at which this HloValue is defined in the output of
+  // its defining instruction.
+  const ShapeIndex& defining_index() const { return defining_location().index; }
+
+  // Add or remove a location at which the HloValue appears. The definition
+  // location can not be removed. The uses of the HloValue are updated.
+  void AddLocation(HloInstruction* instruction, const ShapeIndex& index);
+  void RemoveLocation(HloInstruction* instruction, const ShapeIndex& index);
+
+  // Return all locations of the HloValue in the module.
+  const std::vector<HloLocation>& locations() const { return locations_; }
+
+  // Return all uses of the HloValue.
+  const std::vector<HloUse>& uses() const { return uses_; }
+
+  // Get whether this HloValue is live out of the module.
+  bool live_out_of_module() const { return live_out_of_module_; }
+
+  // Get whether this HloValue is live out of the computation it is defined in.
+  bool live_out_of_computation() const { return live_out_of_computation_; }
+
+  bool operator==(const HloValue& other) const;
+  bool operator!=(const HloValue& other) const;
+
+  // Return a single-line string representation of the value.
+  string ToShortString() const;
+
+  string ToString(int indent = 0) const;
+
+ private:
+  // Unique identifier for this HloValue. Used for stable sorting and iteration.
+  const Id id_;
+
+  // Whether this instruction is a phi value.
+  const bool is_phi_;
+
+  // The set of locations of this HloValue. The first element is always the
+  // location of the definition.
+  std::vector<HloLocation> locations_;
+
+  // The set of uses of this HloValue.
+  std::vector<HloUse> uses_;
+
+  // Whether this value is live out of the HLO module.
+  bool live_out_of_module_ = false;
+
+  // Whether this value is live out of its computation.
+  bool live_out_of_computation_ = false;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloValue& hlo_value);
+
+// A class representing the possible set of HloValues at a particular point
+// (shape index in the output of an instruction) in the XLA graph. This set
+// contains the set of reaching HloValue definitions. For a simple array-shaped
+// instruction like Add, the HloValueSet of the top-level of the instruction's
+// output trivially contains only the HloValue defined by the instruction. For
+// instructions which have non-trivial dataflow such as Tuple or Select, the
+// HloValueSets of the instruction's output contains one or more HloValues
+// defined by the instruction's operands or defined further up in the XLA graph.
+class HloValueSet {
+ public:
+  HloValueSet() = default;
+
+  explicit HloValueSet(tensorflow::gtl::ArraySlice<HloValue::Id> value_ids)
+      : value_ids_(value_ids.begin(), value_ids.end()) {
+    SortAndUniquifyValues();
+  }
+
+  // Return the union of the given HloValueSets.
+  static HloValueSet Union(
+      tensorflow::gtl::ArraySlice<const HloValueSet*> inputs);
+
+  // Return the vector of the IDs of all HloValues in the set. Values in the
+  // vector are unique and sorted.
+  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
+
+  // Return the unique HLO value in the set. CHECKs if the set does not contain
+  // exactly one value.
+  HloValue::Id GetUniqueValueId() const {
+    CHECK_EQ(value_ids().size(), 1);
+    return value_ids()[0];
+  }
+
+  bool operator==(const HloValueSet& other) const {
+    return value_ids() == other.value_ids();
+  }
+  bool operator!=(const HloValueSet& other) const { return !(*this == other); }
+
+  string ToString() const;
+
+ private:
+  // Sorts value_ and removes duplicates. This should be called after adding any
+  // elements to values_.
+  void SortAndUniquifyValues();
+
+  // HloValues sorted by HloValue::Id.
+  std::vector<HloValue::Id> value_ids_;
+};
+
+std::ostream& operator<<(std::ostream& out, const HloValueSet& hlo_value);
+
+// A class collecting the HloValues which might be contained in the output of
+// an HLO instruction. For array-shaped instructions, an InstructionValueSet
+// trivially holds a single HloValueSet. Tuple-shaped InstructionValueSets
+// hold multiple HloValueSets.
+class InstructionValueSet : public ShapeTree<HloValueSet> {
+ public:
+  InstructionValueSet(const Shape& shape) : ShapeTree<HloValueSet>(shape) {}
+
+  // Return the union of the given InstructionValueSets.
+  static InstructionValueSet Union(
+      tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs);
+
+  string ToString() const;
+};
+
+std::ostream& operator<<(std::ostream& out,
+                         const InstructionValueSet& instruction_value_set);
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 52da222ab9..53b2c2a6e3 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -28,7 +28,6 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
-
 /*static*/ bool InstructionFusion::IsExpensive(
     const HloInstruction& instruction) {
   switch (instruction.opcode()) {
@@ -116,8 +115,99 @@ bool FusionWouldDuplicate(const HloInstruction& producer,
                           const HloInstruction& consumer) {
   return !(producer.users().size() == 1 && consumer.IsUserOf(&producer));
 }
+
+// An "effectively unary" operation is one that has one "large"
+// input with the others being negligible in terms of memory usage.
+// We use "has a smaller true rank than the output" as a heuristic
+// for "negligible" memory usage.
+bool EffectivelyUnary(HloInstruction* hlo) {
+  int64 output_rank = 0;
+  ShapeUtil::ForEachSubshape(
+      hlo->shape(),
+      [&output_rank](const Shape& subshape, const ShapeIndex& shape_index) {
+        if (ShapeUtil::IsArray(subshape)) {
+          output_rank = std::max(output_rank, ShapeUtil::TrueRank(subshape));
+        }
+      });
+  return std::count_if(hlo->operands().begin(), hlo->operands().end(),
+                       [output_rank](HloInstruction* operand) {
+                         if (operand->opcode() == HloOpcode::kBroadcast) {
+                           return false;
+                         }
+                         if (operand->opcode() == HloOpcode::kConstant &&
+                             ShapeUtil::IsEffectiveScalar(operand->shape())) {
+                           return false;
+                         }
+                         return ShapeUtil::TrueRank(operand->shape()) >=
+                                output_rank;
+                       }) <= 1;
+}
 }  // namespace
 
+bool InstructionFusion::CanFuseOnAllPaths(
+    const HloComputation::ReachabilityMap& reachability_map,
+    HloInstruction* producer, HloInstruction* consumer,
+    DoNotFuseSet* do_not_fuse) {
+  auto could_fuse_on_all_paths = [&] {
+    // First check to see if we have already marked this producer as infeasible
+    // to fuse into consumer.
+    if (do_not_fuse->count(producer) > 0) {
+      return false;
+    }
+    // Make sure it is possible for producer and consumer to exist in a fusion
+    // node.
+    if (!producer->IsFusable() || !consumer->IsFusable()) {
+      return false;
+    }
+    // We do an upword walk of the graph from consumer towards all paths which
+    // lead to producer to find any unfusable paths.
+    for (int64 i = 0, e = consumer->operand_count(); i < e; ++i) {
+      auto* consumer_operand = consumer->mutable_operand(i);
+      if (consumer_operand == producer) {
+        // This is the base case: our upward crawl ends but we need to make sure
+        // that fusion from consumer can happen.
+        if (!ShouldFuse(consumer, i)) {
+          return false;
+        }
+      } else if (reachability_map.IsReachable(consumer_operand, producer)) {
+        // The reachability map told us that consumer_operand is a node on the
+        // path to producer. We need to further investigate from
+        // consumer_operand.
+
+        // First check if we have already ruled out fusing producer into
+        // consumer_operand.
+        if (do_not_fuse->count(consumer_operand) > 0) {
+          return false;
+        }
+        // Make sure it is possible for consumer_operand to exist in a fusion
+        // node.
+        if (!consumer_operand->IsFusable()) {
+          return false;
+        }
+        // The producer is reachable from consumer_operand which means we need
+        // to be able to fuse consumer_operand into consumer in order for
+        // producer to be fusable into consumer on all paths.
+        if (!ShouldFuse(consumer, i)) {
+          return false;
+        }
+        // Perform the recursive step: make sure producer can be fused into
+        // consumer_operand on all paths.
+        if (!CanFuseOnAllPaths(reachability_map, producer, consumer_operand,
+                               do_not_fuse)) {
+          return false;
+        }
+      }
+    }
+    return true;
+  };
+  if (could_fuse_on_all_paths()) {
+    return true;
+  }
+  // We couldn't fuse on all paths, record this result.
+  do_not_fuse->insert(producer);
+  return false;
+}
+
 StatusOr<bool> InstructionFusion::Run(HloModule* module) {
   bool changed = false;
   for (auto& computation : module->computations()) {
@@ -134,56 +224,42 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
     std::vector<HloInstruction*> post_order(post_order_list.begin(),
                                             post_order_list.end());
 
-    std::set<HloInstruction*> all_consumers_fusable;
-    // Find which ops can be fused into all of their operands. We would rather
-    // not fuse an op into only some of its users, as that offers no benefit in
-    // terms of memory bandwidth, but forces us to keep more live values around.
-    for (auto* hlo : post_order) {
-      auto user_fusable_into_hlo = [this, &hlo](HloInstruction* consumer) {
-        if (!consumer->IsFusable()) {
-          return false;
-        }
-        for (int operand_number = 0;
-             operand_number < consumer->operands().size(); ++operand_number) {
-          if (consumer->operand(operand_number) == hlo) {
-            if (!ShouldFuse(consumer, operand_number)) {
-              return false;
-            }
-          }
-        }
-        return true;
-      };
-
-      // An "effectively unary" operation is one that has one "large"
-      // input with the others being negligible in terms of memory usage.
-      // We use "has a smaller true rank than the output" as a heuristic
-      // for "negligible" memory usage.
-      auto effectively_unary = [](HloInstruction* hlo) {
-        if (hlo->operands().size() == 1) {
-          return true;
-        }
-        auto output_rank = ShapeUtil::TrueRank(hlo->shape());
-        return std::count_if(
-                   hlo->operands().begin(), hlo->operands().end(),
-                   [output_rank](HloInstruction* operand) {
-                     return ((operand->opcode() != HloOpcode::kBroadcast) &&
-                             ShapeUtil::TrueRank(operand->shape()) >=
-                                 output_rank);
-                   }) <= 1;
-      };
-
-      if (effectively_unary(hlo) ||
-          std::all_of(hlo->users().begin(), hlo->users().end(),
-                      user_fusable_into_hlo)) {
-        all_consumers_fusable.insert(hlo);
-      }
-    }
-
     tensorflow::gtl::FlatMap<HloInstruction*, int> post_order_index;
     for (size_t i = 0; i < post_order.size(); ++i) {
       InsertOrDie(&post_order_index, post_order[i], i);
     }
 
+    DoNotFuseSet do_not_fuse;
+    auto transitive_operands = computation->ComputeTransitiveOperands();
+
+    auto cheap_to_duplicate = [](HloInstruction* producer) {
+      if (producer->opcode() == HloOpcode::kBroadcast) {
+        return true;
+      }
+      if (producer->opcode() == HloOpcode::kConstant &&
+          ShapeUtil::IsEffectiveScalar(producer->shape())) {
+        return true;
+      }
+      if (EffectivelyUnary(producer)) {
+        return true;
+      }
+      return false;
+    };
+
+    for (HloInstruction* consumer : post_order) {
+      for (HloInstruction* producer : consumer->operands()) {
+        if (cheap_to_duplicate(producer)) {
+          continue;
+        }
+        if (CanFuseOnAllPaths(*transitive_operands, producer, consumer,
+                              &do_not_fuse)) {
+          CHECK_EQ(do_not_fuse.count(producer), 0);
+        } else {
+          CHECK_GT(do_not_fuse.count(producer), 0);
+        }
+      }
+    }
+
     // Instruction fusion effectively fuses edges in the computation graph
     // (producer instruction -> consumer instruction) so we iterate over all
     // edges. When we fuse an edge, we create a copy of the producer inside the
@@ -266,34 +342,36 @@ StatusOr<bool> InstructionFusion::Run(HloModule* module) {
       for (int64 i : sorted_operand_numbers) {
         HloInstruction* operand = instruction->mutable_operand(i);
 
-        if (FusionWouldDuplicate(*operand, *instruction) &&
-            (all_consumers_fusable.count(operand) == 0)) {
+        if (!operand->IsFusable()) {
           continue;
         }
-
-        if (operand->IsFusable() && ShouldFuse(instruction, i)) {
-          HloInstruction* fusion_instruction = Fuse(operand, instruction);
-
-          // Fusing an instruction into a fusion instruction can change the
-          // operand set of the fusion instruction. For simplicity just push the
-          // instruction to the top of the post_order and reconsider it for
-          // further fusion in the next iteration of the outer loop.
-          post_order.push_back(fusion_instruction);
-          InsertOrDie(&post_order_index, fusion_instruction,
-                      post_order.size() - 1);
-          changed = true;
-
-          if (operand->user_count() == 0) {
-            // Operand is now dead. Remove from post order by setting it's
-            // location to nullptr.
-            post_order[FindOrDie(post_order_index, operand)] = nullptr;
-            post_order_index.erase(operand);
-
-            // Remove from computation.
-            TF_RETURN_IF_ERROR(computation_->RemoveInstruction(operand));
-          }
-          break;
+        if (!ShouldFuse(instruction, i)) {
+          continue;
+        }
+        if (do_not_fuse.count(operand) > 0) {
+          continue;
+        }
+        HloInstruction* fusion_instruction = Fuse(operand, instruction);
+
+        // Fusing an instruction into a fusion instruction can change the
+        // operand set of the fusion instruction. For simplicity just push the
+        // instruction to the top of the post_order and reconsider it for
+        // further fusion in the next iteration of the outer loop.
+        post_order.push_back(fusion_instruction);
+        InsertOrDie(&post_order_index, fusion_instruction,
+                    post_order.size() - 1);
+        changed = true;
+
+        if (operand->user_count() == 0) {
+          // Operand is now dead. Remove from post order by setting it's
+          // location to nullptr.
+          post_order[FindOrDie(post_order_index, operand)] = nullptr;
+          post_order_index.erase(operand);
+
+          // Remove from computation.
+          TF_RETURN_IF_ERROR(computation_->RemoveInstruction(operand));
         }
+        break;
       }
     }
   }
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.h b/tensorflow/compiler/xla/service/instruction_fusion.h
index a9f3723f2d..06f03f63af 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.h
+++ b/tensorflow/compiler/xla/service/instruction_fusion.h
@@ -72,6 +72,16 @@ class InstructionFusion : public HloPassInterface {
  private:
   HloInstruction* Fuse(HloInstruction* producer, HloInstruction* consumer);
 
+  // The set of producers whose consumers we cannot fuse into.
+  using DoNotFuseSet = std::unordered_set<HloInstruction*>;
+
+  // Whether or not we can fuse consumer into original_producer on all paths
+  // from the producer to the consumer where nodes are HLOs and edges are uses.
+  bool CanFuseOnAllPaths(
+      const HloComputation::ReachabilityMap& reachability_map,
+      HloInstruction* producer, HloInstruction* consumer,
+      DoNotFuseSet* do_not_fuse);
+
   // Used to determine if an HLO is expensive. Expensive operations will not be
   // duplicated.
   std::function<bool(const HloInstruction& instruction)> is_expensive_;
diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
index d451255774..1f6932bcc3 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc
@@ -50,28 +50,37 @@ void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo,
     buffer_slice = *slices.begin();
   }
 
-  llvm::MDNode*& alias_scope_md = alias_scope_metadata_[buffer_slice];
-  if (alias_scope_md == nullptr) {
-    alias_scope_md =
-        GetAliasScopeMetadataForBuffer(buffer_slice, GetAliasDomain());
+  if (module_.config().debug_options().xla_llvm_enable_alias_scope_metadata()) {
+    llvm::MDNode*& alias_scope_md = alias_scope_metadata_[buffer_slice];
+    if (alias_scope_md == nullptr) {
+      alias_scope_md =
+          GetAliasScopeMetadataForBuffer(buffer_slice, GetAliasDomain());
+    }
+    array->AddAliasScopeMetadata(alias_scope_md);
   }
-  array->AddAliasScopeMetadata(alias_scope_md);
 
-  llvm::MDNode*& noalias_md = noalias_metadata_[buffer_slice];
-  if (noalias_md == nullptr) {
-    noalias_md = GetNoaliasMetadataForBuffer(buffer_slice, GetAliasDomain(),
-                                             assignment_, hlo);
+  if (module_.config().debug_options().xla_llvm_enable_noalias_metadata()) {
+    llvm::MDNode*& noalias_md = noalias_metadata_[buffer_slice];
+    if (noalias_md == nullptr) {
+      noalias_md = GetNoaliasMetadataForBuffer(buffer_slice, GetAliasDomain(),
+                                               assignment_, hlo);
+    }
+    array->AddNoaliasMetadata(noalias_md);
   }
-  array->AddNoaliasMetadata(noalias_md);
 
-  // Parameters of the entry computation are never stored to, loading from a
-  // parameter pointer should always return the same result within a loop.
-  if (hlo.opcode() == HloOpcode::kParameter) {
-    const std::vector<HloInstruction*>& parameter_instructions =
-        module_.entry_computation()->parameter_instructions();
-    if (std::find(parameter_instructions.begin(), parameter_instructions.end(),
-                  &hlo) != parameter_instructions.end()) {
-      array->AddInvariantLoad(llvm::MDNode::get(*context_, /*MDs=*/{}));
+  if (module_.config()
+          .debug_options()
+          .xla_llvm_enable_invariant_load_metadata()) {
+    // Parameters of the entry computation are never stored to, loading from a
+    // parameter pointer should always return the same result within a loop.
+    if (hlo.opcode() == HloOpcode::kParameter) {
+      const std::vector<HloInstruction*>& parameter_instructions =
+          module_.entry_computation()->parameter_instructions();
+      if (std::find(parameter_instructions.begin(),
+                    parameter_instructions.end(),
+                    &hlo) != parameter_instructions.end()) {
+        array->AddInvariantLoad(llvm::MDNode::get(*context_, /*MDs=*/{}));
+      }
     }
   }
 }
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index bcc9418d59..e348511c62 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -356,26 +356,9 @@ void EmitLogging(const char* tag, llvm::Value* value,
 
 void SetTbaaForInstruction(llvm::Instruction* instruction, Shape shape,
                            bool is_pointer_to) {
-  llvm::MDBuilder metadata_builder(instruction->getContext());
-  llvm::MDNode* root = metadata_builder.createTBAARoot("XLA TBAA");
-  string type_name;
-  if (is_pointer_to) {
-    type_name += "pointer-to ";
-  }
-  // Scalars do not have layout which makes it permissible to omit an explicit
-  // layout.  To make sure that equivalent scalar shapes have the same TBAA,
-  // remove the (meaningless) explicit layout if one is present.
-  if (!ShapeUtil::IsArray(shape) || ShapeUtil::IsScalar(shape)) {
-    LayoutUtil::ClearLayout(&shape);
-  } else {
-    CHECK(shape.has_layout());
-  }
-  type_name += shape.ShortDebugString();
-  llvm::MDNode* tbaa_node =
-      metadata_builder.createTBAANode(llvm_ir::AsStringRef(type_name), root);
-  instruction->setMetadata(llvm::LLVMContext::MD_tbaa,
-                           metadata_builder.createTBAAStructTagNode(
-                               tbaa_node, tbaa_node, /*Offset=*/0));
+  // TODO(b/62903316): TBAA metadata causes LLVM to miscompile generated code,
+  // most likely because the generated metadata is incorrect.  Disable TBAA
+  // metadata while we resolve this.
 }
 
 void SetAlignmentMetadataForLoad(llvm::LoadInst* load, uint64_t alignment) {
diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index 90dfe01bb9..25588a6fb8 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -47,13 +47,6 @@ namespace se = ::perftools::gputools;
 
 namespace xla {
 
-/* static */ StatusOr<std::unique_ptr<LocalService>> LocalService::NewService(
-    perftools::gputools::Platform* platform) {
-  ServiceOptions default_options;
-  default_options.set_platform(platform);
-  return NewService(default_options);
-}
-
 /* static */ StatusOr<std::unique_ptr<LocalService>> LocalService::NewService(
     const ServiceOptions& options) {
   perftools::gputools::Platform* platform = options.platform();
@@ -78,9 +71,7 @@ LocalService::LocalService(const ServiceOptions& options,
                            std::unique_ptr<Backend> execute_backend,
                            std::unique_ptr<Backend> compute_constant_backend)
     : Service(options, std::move(execute_backend),
-              std::move(compute_constant_backend)) {
-  runs_in_client_process_ = true;
-}
+              std::move(compute_constant_backend)) {}
 
 namespace {
 // Returns the space required to allocate a shape. If
diff --git a/tensorflow/compiler/xla/service/local_service.h b/tensorflow/compiler/xla/service/local_service.h
index 915c8c3072..13797ec045 100644
--- a/tensorflow/compiler/xla/service/local_service.h
+++ b/tensorflow/compiler/xla/service/local_service.h
@@ -35,11 +35,7 @@ namespace xla {
 // in the same process as the client.
 class LocalService : public Service {
  public:
-  // Factory for creating a LocalService. The parameter platform is the platform
-  // that the service should target. If platform is null then the default
-  // platform is used.
-  static StatusOr<std::unique_ptr<LocalService>> NewService(
-      perftools::gputools::Platform* platform);
+  // Factory for creating a LocalService.
   static StatusOr<std::unique_ptr<LocalService>> NewService(
       const ServiceOptions& options);
 
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 31740757ab..daed56b3eb 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -166,12 +166,6 @@ Service::CreateComputeConstantBackend() {
   return NotFound("CPU platform not found");
 }
 
-/* static */ Compiler::HloDumper Service::MakeHloDumper() {
-  return [](const HloModule& module, const string& label) {
-    return Executable::DumpExecutedHlo(module, label, /*profile=*/nullptr);
-  };
-}
-
 Service::Service(const ServiceOptions& options,
                  std::unique_ptr<Backend> execute_backend,
                  std::unique_ptr<Backend> compute_constant_backend)
@@ -392,11 +386,9 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> Service::BuildExecutables(
     modules.push_back(std::move(module));
   }
 
-  Compiler::HloDumper hlo_dumper = MakeHloDumper();
   TF_ASSIGN_OR_RETURN(
       std::vector<std::unique_ptr<Executable>> executables,
-      backend->compiler()->Compile(std::move(modules), hlo_dumper,
-                                   std::move(executors)));
+      backend->compiler()->Compile(std::move(modules), std::move(executors)));
 
   if (!other_directory_path.empty()) {
     for (size_t i = 0; i < versioned_handles.size(); ++i) {
@@ -443,15 +435,9 @@ StatusOr<std::unique_ptr<Executable>> Service::BuildExecutable(
                                           /*include_unreachable_instructions=*/
                                           !executable_for_compute_constant));
 
-  Compiler::HloDumper hlo_dumper = MakeHloDumper();
-  if (executable_for_compute_constant &&
-      !flags->xla_hlo_graph_for_compute_constant) {
-    hlo_dumper = [](const HloModule&, const string&) {};
-  }
-
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr<Executable> executable,
-      backend->compiler()->Compile(std::move(module), hlo_dumper, executor));
+      backend->compiler()->Compile(std::move(module), executor));
 
   if (!other_directory_path.empty()) {
     executable->set_session_module(std::move(session_module));
@@ -1191,7 +1177,8 @@ tensorflow::Status Service::GetComputationStats(
       std::unique_ptr<HloModule> module,
       computation_tracker_.BuildHloModule(versioned_handle, HloModuleConfig()));
 
-  MakeHloDumper()(*module, "computation statistics subject");
+  hlo_graph_dumper::MaybeDumpHloModule(*module,
+                                       "computation statistics subject");
 
   // Run HLO analysis to get the computation statistics.
   HloCostAnalysis analysis(
@@ -1207,17 +1194,6 @@ tensorflow::Status Service::GetComputationStats(
   return tensorflow::Status::OK();
 }
 
-tensorflow::Status Service::CheckRunsInClientProcess(
-    const string& method_name) const {
-  if (runs_in_client_process_) {
-    return tensorflow::Status::OK();
-  } else {
-    return FailedPrecondition(
-        "%s only supported if service runs in the same process as the client",
-        method_name.c_str());
-  }
-}
-
 template <typename RequestT, typename ResponseT>
 tensorflow::Status Service::AddInstruction(
     const RequestT* arg, ResponseT* result,
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 968fb53b34..48cb0bec47 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -27,7 +27,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/channel_tracker.h"
 #include "tensorflow/compiler/xla/service/compilation_cache.h"
-#include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/computation_tracker.h"
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/executable.h"
@@ -126,7 +125,7 @@ class Service : public ServiceInterface {
   // least N * R devices must be available. The devices are assigned based on
   // the device ordinals such that the first R available devices are assigned to
   // the first set of replicas, and the next R devices to the second set of
-  // replicas, etc. Each returned device handles represent the device with the
+  // replicas, etc. Each returned device handle represents the device with the
   // replica id 0.
   tensorflow::Status GetDeviceHandles(
       const GetDeviceHandlesRequest* arg,
@@ -322,10 +321,6 @@ class Service : public ServiceInterface {
       tensorflow::gtl::ArraySlice<DeviceHandle> device_handles,
       tensorflow::gtl::ArraySlice<string> result_tags);
 
-  // Returns an HLO dumper for use in the compiler (it refers to flags
-  // associated with the service).
-  static Compiler::HloDumper MakeHloDumper();
-
   // Convenience function for adding a function to a user computation.
   template <typename RequestT, typename ResponseT>
   tensorflow::Status AddInstruction(
@@ -333,12 +328,6 @@ class Service : public ServiceInterface {
       const std::function<StatusOr<ComputationDataHandle>(UserComputation*)>&
           adder);
 
-  // If the service is running in the client process
-  // (runs_in_client_process_ is true) then return
-  // tensorflow::Status::OK. Otherwise return an appropriate error
-  // status with the given method name. Used for "InProcess" methods.
-  tensorflow::Status CheckRunsInClientProcess(const string& method_name) const;
-
   // Convenience function which checks whether the given shape_with_layout
   // (presumably passed by the client to set the result layout) is valid for the
   // given computation result shape.
@@ -380,9 +369,6 @@ class Service : public ServiceInterface {
   // Backend to use when executing ComputeConstant.
   std::unique_ptr<Backend> compute_constant_backend_;
 
-  // Whether the service runs in the same process as the client.
-  bool runs_in_client_process_ = false;
-
   TF_DISALLOW_COPY_AND_ASSIGN(Service);
 };
 
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 8d0dc1edac..5e4df9ddd6 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -550,9 +550,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       ExpectNotTupleOrOpaque(rhs, "rhs of elementwise binary operation"));
 
   if (!ShapeUtil::SameElementType(lhs, rhs)) {
-    return InvalidArgument("binary op with different element types: %s and %s",
-                           ShapeUtil::HumanString(lhs).c_str(),
-                           ShapeUtil::HumanString(rhs).c_str());
+    return InvalidArgument(
+        "binary op %s with different element types: %s and %s",
+        BinaryOperation_Name(operation).c_str(),
+        ShapeUtil::HumanString(lhs).c_str(),
+        ShapeUtil::HumanString(rhs).c_str());
   }
 
   if (ShapeUtil::Rank(lhs) == ShapeUtil::Rank(rhs) &&
@@ -1522,10 +1524,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const ProgramShape& to_apply) {
   // The applied function's arity equals the number of arguments.
   if (arg_shapes.size() != to_apply.parameters_size()) {
+    string computation_signature = ShapeUtil::HumanString(to_apply);
+    string argument_shapes = tensorflow::str_util::Join(
+        arg_shapes, ", ", [](string* out, const Shape* shape) {
+          tensorflow::strings::StrAppend(out, ShapeUtil::HumanString(*shape));
+        });
     return InvalidArgument(
         "Call applied function arity must match number of arguments; got: "
-        "arity: %d, arguments: %zu",
-        to_apply.parameters_size(), arg_shapes.size());
+        "arity: %d, arguments: %zu; computation signature: %s; argument "
+        "shapes: [%s]",
+        to_apply.parameters_size(), arg_shapes.size(),
+        computation_signature.c_str(), argument_shapes.c_str());
   }
 
   // All arguments must be compatible with the program shape.
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index a1a41657b6..942a6b1b2d 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -94,7 +94,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:hlo_test_base_flags",
         "//tensorflow/compiler/xla/service",
         "//tensorflow/compiler/xla/service:backend",
         "//tensorflow/compiler/xla/service:compiler",
@@ -486,6 +485,28 @@ xla_test(
     ],
 )
 
+xla_test(
+    name = "reduce_precision_test",
+    srcs = ["reduce_precision_test.cc"],
+    deps = [
+        "//tensorflow/compiler/xla:array2d",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:global_data",
+        "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
+        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
+        "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:literal_test_util",
+        "//tensorflow/core:lib",
+    ],
+)
+
 xla_test(
     name = "dot_operation_test",
     srcs = ["dot_operation_test.cc"],
@@ -496,7 +517,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -513,10 +533,6 @@ xla_test(
 xla_test(
     name = "dot_operation_runtime_test",
     srcs = ["dot_operation_test.cc"],
-    backend_args = {
-        "cpu": ["--xla_cpu_use_eigen"],
-        "cpu_parallel": ["--xla_cpu_use_eigen"],
-    },
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -524,7 +540,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -542,11 +557,9 @@ xla_test(
     srcs = ["dot_operation_test.cc"],
     backend_args = {
         "cpu": [
-            "--xla_cpu_use_eigen",
             "--xla_cpu_multi_thread_eigen=false",
         ],
         "cpu_parallel": [
-            "--xla_cpu_use_eigen",
             "--xla_cpu_multi_thread_eigen=false",
         ],
     },
@@ -557,7 +570,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -574,11 +586,9 @@ xla_test(
     srcs = ["dot_operation_test.cc"],
     backend_args = {
         "cpu": [
-            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
         "cpu_parallel": [
-            "--xla_cpu_use_eigen",
             "--xla_default_layout=major2minor",
         ],
     },
@@ -589,7 +599,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:cpu_runtime_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -717,19 +726,25 @@ xla_test(
 xla_test(
     name = "batch_normalization_test",
     srcs = ["batch_normalization_test.cc"],
+    shard_count = 40,
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
@@ -859,10 +874,15 @@ xla_test(
     ],
 )
 
-xla_test(
-    name = "reduce_window_test",
-    timeout = "long",
+filegroup(
+    name = "reduce_window_test_srcs",
+    testonly = 1,
     srcs = ["reduce_window_test.cc"],
+)
+
+cc_library(
+    name = "reduce_window_test_deps",
+    testonly = 1,
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -882,6 +902,15 @@ xla_test(
     ],
 )
 
+xla_test(
+    name = "reduce_window_test",
+    timeout = "long",
+    srcs = ["reduce_window_test_srcs"],
+    deps = [
+        ":reduce_window_test_deps",
+    ],
+)
+
 xla_test(
     name = "select_and_scatter_test",
     timeout = "long",
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 554042b35f..024988743c 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1864,29 +1864,6 @@ INSTANTIATE_TEST_CASE_P(ArrayElementwiseOpTestParamCount,
                         ArrayElementwiseOpTestParamCount,
                         ::testing::Values(127, 128, 129, 17 * 4096));
 
-XLA_TEST_F(ArrayElementwiseOpTest, ReducePrecisionNoOpF32) {
-  ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<float>({-2.5f, 25.5f});
-  auto reduce_precision = builder.ReducePrecision(a, 8, 23);
-
-  ComputeAndCompareR1<float>(&builder, {-2.5f, 25.5f}, {});
-}
-
-XLA_TEST_F(ArrayElementwiseOpTest, ReducePrecisionNoOpParamF32) {
-  ComputationBuilder builder(client_, TestName());
-
-  std::vector<float> a_values = {-2.5f, 25.5f};
-
-  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({a_values});
-  std::unique_ptr<GlobalData> a_data =
-      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
-  auto a_param = builder.Parameter(0, a_literal->shape(), "a_param");
-
-  auto reduce_precision = builder.ReducePrecision(a_param, 8, 23);
-
-  ComputeAndCompareR1<float>(&builder, {-2.5f, 25.5f}, {a_data.get()});
-}
-
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index 9f9f541230..4afeb68b5f 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -25,10 +25,20 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/reference_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/test_helpers.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/compiler/xla/tests/test_utils.h"
+#include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/test.h"
@@ -189,6 +199,257 @@ TEST_F(BatchNormalizationTest, SpecComparisonForward) {
   ComputeAndCompareR4<float>(&builder, expected, {}, error_spec_);
 }
 
+struct BatchNormTestParam {
+  std::vector<int64> bounds;
+  int64 feature_index;
+  float random_value_mean;
+  float random_value_var;
+};
+
+// Tests to test the fused operation of BatchNorm.
+class BatchNormTest : public ClientLibraryTestBase,
+                      public ::testing::WithParamInterface<BatchNormTestParam> {
+};
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_P(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(RandomizedTests))) {
+  float epsilon = 0.001;
+  ComputationBuilder builder(client_, TestName());
+  const std::vector<int64>& bounds = GetParam().bounds;
+  Array4D<float> input_array(bounds[0], bounds[1], bounds[2], bounds[3]);
+  input_array.FillRandom(GetParam().random_value_var,
+                         GetParam().random_value_mean);
+
+  const int64 feature_index = GetParam().feature_index;
+  const int64 num_elements_per_feature =
+      Product(bounds) / bounds[feature_index];
+  const int64 feature_bound = bounds[feature_index];
+  std::vector<float> offset(feature_bound, 1);
+  std::vector<float> scale(feature_bound, 2);
+
+  auto input_squared =
+      ReferenceUtil::MapArray4D(input_array, [](float a) { return a * a; });
+  std::vector<int64> reduce_dims;
+  for (int64 i = 0; i < bounds.size(); ++i) {
+    if (i != feature_index) {
+      reduce_dims.push_back(i);
+    }
+  }
+
+  auto sum =
+      ReferenceUtil::Reduce4DTo1D(input_array, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  auto sum_squared =
+      ReferenceUtil::Reduce4DTo1D(*input_squared, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  std::vector<float> mean(feature_bound);
+
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean[i] = sum[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> mean_square(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean_square[i] = mean[i] * mean[i];
+  }
+
+  std::vector<float> square_mean(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    square_mean[i] = sum_squared[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> var(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    var[i] = square_mean[i] - mean_square[i];
+  }
+
+  Array4D<float> mean_4D =
+      *ReferenceUtil::Broadcast1DTo4D(mean, bounds, feature_index);
+  auto var_4D = *ReferenceUtil::Broadcast1DTo4D(var, bounds, feature_index);
+  auto scale_4D = *ReferenceUtil::Broadcast1DTo4D(scale, bounds, feature_index);
+  auto offset_4D =
+      *ReferenceUtil::Broadcast1DTo4D(offset, bounds, feature_index);
+
+  auto normalized = *ReferenceUtil::BatchNorm4D(input_array, mean_4D, var_4D,
+                                                scale_4D, offset_4D, epsilon);
+
+  auto expected_normalized = Literal::CreateR4FromArray4D<float>(normalized);
+
+  auto offset_literal = Literal::CreateR1<float>(offset);
+  auto scale_literal = Literal::CreateR1<float>(scale);
+  auto input_literal = Literal::CreateR4FromArray4D<float>(input_array);
+
+  auto input_activations =
+      builder.Parameter(0, input_literal->shape(), "input");
+  auto scale_activations =
+      builder.Parameter(1, scale_literal->shape(), "offset");
+  auto offset_activations =
+      builder.Parameter(2, offset_literal->shape(), "scale");
+
+  auto expected = *Literal::MakeTuple({expected_normalized.get(),
+                                       Literal::CreateR1<float>(mean).get(),
+                                       Literal::CreateR1<float>(var).get()});
+
+  std::unique_ptr<GlobalData> input_data =
+      client_->TransferToServer(*input_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> scale_data =
+      client_->TransferToServer(*scale_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> offset_data =
+      client_->TransferToServer(*offset_literal).ConsumeValueOrDie();
+
+  builder.BatchNormTraining(input_activations, scale_activations,
+                            offset_activations, epsilon, feature_index);
+
+  ComputeAndCompareTuple(
+      &builder, expected,
+      {input_data.get(), scale_data.get(), offset_data.get()},
+      ErrorSpec(0.01, 1));
+}
+
+INSTANTIATE_TEST_CASE_P(
+    BatchNormTest_Instantiation, BatchNormTest,
+    ::testing::Values(BatchNormTestParam{{2, 2, 2, 2}, 0, 100.2f, 200.0f},
+                      BatchNormTestParam{{2, 2, 2, 2}, 3, 300.f, 400.0f},
+
+                      BatchNormTestParam{{1, 10, 1, 1}, 0, 10.1f, 20.1f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 1, 3.14f, 314.15f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 2, 666.6f, 777.7f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 1, -666.6f, 777.7f},
+                      BatchNormTestParam{{10, 10, 10, 10}, 2, 0.f, 777.7f},
+                      BatchNormTestParam{{1, 1, 10, 1}, 3, 888.8f, 9.9f},
+
+                      BatchNormTestParam{{24, 129, 1, 2}, 2, 10000, 10000},
+                      BatchNormTestParam{{24, 129, 1, 2}, 3, 10000, 10000},
+
+                      // Feature on low dimension to trigger relayout, test
+                      // internal logical to physical dimension calculation
+                      // is correct after relayout.
+                      BatchNormTestParam{{1, 2, 3, 4}, 0, 100, 100}));
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(BasicTraining))) {
+  const int kFeatureIndex = 3;
+  ComputationBuilder builder(client_, TestName());
+
+  auto operand = builder.ConstantR4FromArray4D<float>(
+      {{{{1.f, 2.f}}, {{3.f, 4.f}}}, {{{5.f, 6.f}}, {{7.f, 8.f}}}});
+
+  auto scale = builder.ConstantR1<float>({2.0f, 3.0f});
+
+  auto offset = builder.ConstantR1<float>({1.0f, 2.0f});
+
+  auto tuple = builder.BatchNormTraining(operand, scale, offset,
+                                         /*epsilon=*/0.001, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR4<float>({{{{-1.6f, -2.0f}}, {{0.1f, 0.6f}}},
+                                 {{{1.9f, 3.3f}}, {{3.7f, 6.0f}}}})
+           .get(),
+       Literal::CreateR1<float>({4, 5}).get(),
+       Literal::CreateR1<float>({5, 5}).get()});
+
+  ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
+}
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(BasicTrainingOnSublane))) {
+  const int kFeatureIndex = 2;
+  ComputationBuilder builder(client_, TestName());
+
+  auto operand = builder.ConstantR4FromArray4D<float>(
+      {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}});
+
+  auto scale = builder.ConstantR1<float>({2.0f, 3.0f});
+
+  auto offset = builder.ConstantR1<float>({1.0f, 2.0f});
+
+  auto tuple = builder.BatchNormTraining(operand, scale, offset,
+                                         /*epsilon=*/0.001, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR4<float>({{{{-1.6f}, {-2.0f}}, {{0.1f}, {0.6f}}},
+                                 {{{1.9f}, {3.3f}}, {{3.7f}, {6.0f}}}})
+           .get(),
+       Literal::CreateR1<float>({4, 5}).get(),
+       Literal::CreateR1<float>({5, 5}).get()});
+
+  ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
+}
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(
+                              TrainingWithFeatureOnLowDimension))) {
+  // Use 0 dimension as feature, tests layout analyzer.
+  const int kFeatureIndex = 0;
+  ComputationBuilder builder(client_, TestName());
+
+  ComputationDataHandle h0;
+  auto operand = CreateR3Parameter<float>(Array3D<float>(260, 2, 2, 1.0f),
+                                          /*parameter_number=*/0, "operand",
+                                          &builder, &h0);
+  ComputationDataHandle h1;
+  auto scale =
+      CreateR1Parameter<float>(std::vector<float>(260, 1.0f),
+                               /*parameter_number=*/1, "scale", &builder, &h1);
+  ComputationDataHandle h2;
+  auto offset =
+      CreateR1Parameter<float>(std::vector<float>(260, 1.0f),
+                               /*parameter_number=*/2, "offset", &builder, &h2);
+
+  auto tuple = builder.BatchNormTraining(h0, h1, h2,
+                                         /*epsilon=*/1, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR3FromArray3D<float>(Array3D<float>(260, 2, 2, 1.0f))
+           .get(),
+       Literal::CreateR1<float>(std::vector<float>(260, 1.0f)).get(),
+       Literal::CreateR1<float>(std::vector<float>(260, 0.0f)).get()});
+
+  ComputeAndCompareTuple(&builder, expected,
+                         {operand.get(), scale.get(), offset.get()},
+                         ErrorSpec(0.1));
+}
+
+// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest,
+           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(LargeEpsilonTest))) {
+  // Test the correctness of choosing a large epsilon value.
+  const int kFeatureIndex = 2;
+  ComputationBuilder builder(client_, TestName());
+
+  ComputationDataHandle h0;
+  auto operand = CreateR3Parameter<float>({{{0.0f}, {10.0f}, {20.0f}, {30.0f}}},
+                                          /*parameter_number=*/0, "operand",
+                                          &builder, &h0);
+  ComputationDataHandle h1;
+  auto scale =
+      CreateR1Parameter<float>(std::vector<float>(1, 1.0f),
+                               /*parameter_number=*/1, "scale", &builder, &h1);
+  ComputationDataHandle h2;
+  auto offset =
+      CreateR1Parameter<float>(std::vector<float>(1, 0.0f),
+                               /*parameter_number=*/2, "offset", &builder, &h2);
+
+  // var = 125, mean = 15, epsilon = -100
+  auto tuple = builder.BatchNormTraining(h0, h1, h2,
+                                         /*epsilon=*/-100, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR3FromArray3D<float>({{{-3.0f}, {-1.0f}, {1.0f}, {3.0f}}})
+           .get(),
+       Literal::CreateR1<float>(std::vector<float>(1, 15.0f)).get(),
+       Literal::CreateR1<float>(std::vector<float>(1, 125.0f)).get()});
+
+  ComputeAndCompareTuple(&builder, expected,
+                         {operand.get(), scale.get(), offset.get()},
+                         ErrorSpec(0.1));
+}
+
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.cc b/tensorflow/compiler/xla/tests/codegen_test_base.cc
index f48dc50708..8d597eb72c 100644
--- a/tensorflow/compiler/xla/tests/codegen_test_base.cc
+++ b/tensorflow/compiler/xla/tests/codegen_test_base.cc
@@ -55,8 +55,7 @@ void CodegenTestBase::CompileAndVerifyIr(std::unique_ptr<HloModule> hlo_module,
 std::unique_ptr<Executable> CodegenTestBase::CompileToExecutable(
     std::unique_ptr<HloModule> hlo_module) {
   return backend_->compiler()
-      ->Compile(std::move(hlo_module), test_hlo_dumper_,
-                backend_->default_stream_executor())
+      ->Compile(std::move(hlo_module), backend_->default_stream_executor())
       .ConsumeValueOrDie();
 }
 
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index ac64e2ee9e..63a630f9e5 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
@@ -461,7 +460,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendLayoutUtilFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendCpuRuntimeFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index 354e4a84c5..f8452e37f8 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -24,14 +24,12 @@ limitations under the License.
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/hlo_test_base_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/computation_layout.h"
 #include "tensorflow/compiler/xla/service/executable.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
-#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/transfer_manager.h"
 #include "tensorflow/compiler/xla/shape_layout.h"
@@ -56,17 +54,6 @@ struct HloTestBase::EigenThreadPoolWrapper {
 
 HloTestBase::HloTestBase()
     : backend_(Backend::CreateDefaultBackend().ConsumeValueOrDie()) {
-  // TODO(b/62411181): get rid of this flag entirely when the usual debug flags
-  // are piped to all HLO tests.
-  test_hlo_dumper_ = [](const HloModule& module, const string& label) {
-    legacy_flags::HloTestBaseFlags* flags = legacy_flags::GetHloTestBaseFlags();
-    if (flags->xla_hlo_test_generate_hlo_graph) {
-      const bool show_addresses = true;
-      const bool show_layouts = true;
-      hlo_graph_dumper::DumpGraph(*module.entry_computation(), label,
-                                  show_addresses, show_layouts);
-    }
-  };
   VLOG(1) << "executing on platform " << backend_->platform()->Name();
 }
 
@@ -92,7 +79,7 @@ StatusOr<perftools::gputools::DeviceMemoryBase> HloTestBase::Execute(
     Shape* result_shape) {
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr<Executable> executable,
-      backend_->compiler()->Compile(std::move(module), test_hlo_dumper_,
+      backend_->compiler()->Compile(std::move(module),
                                     backend_->default_stream_executor()));
 
   se::Stream stream(backend_->default_stream_executor());
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h
index 2b6a2e9672..7f3d163290 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.h
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.h
@@ -104,8 +104,6 @@ class HloTestBase : public ::testing::Test {
 
   std::unique_ptr<Backend> backend_;
 
-  Compiler::HloDumper test_hlo_dumper_;
-
   // This vector contains handles of all the device memory allocations performed
   // by the test. These are deallocated on destruction of the test object.
   std::vector<perftools::gputools::DeviceMemoryBase> allocations_;
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index 69c12cc437..cca04e17ca 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -508,7 +508,7 @@ class NearComparator {
   *shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout(minor_to_major);
 
   // Allocate space in the new literal.
-  new_literal.get()->Reserve(ShapeUtil::ElementsIn(literal.shape()));
+  new_literal->Reserve(ShapeUtil::ElementsIn(literal.shape()));
 
   // Copy data into new literal, element-by-element.
   for (int64 i = 0; i < ShapeUtil::ElementsIn(literal.shape()); ++i) {
@@ -518,36 +518,36 @@ class NearComparator {
         IndexUtil::LinearIndexToMultidimensionalIndex(shape_with_layout, i);
     switch (literal.shape().element_type()) {
       case PRED:
-        new_literal.get()->Set<bool>(to_multi_index,
-                                     literal.Get<bool>(from_multi_index));
+        new_literal->Set<bool>(to_multi_index,
+                               literal.Get<bool>(from_multi_index));
         break;
       case U8:
-        new_literal.get()->Set<uint8>(to_multi_index,
-                                      literal.Get<uint8>(from_multi_index));
+        new_literal->Set<uint8>(to_multi_index,
+                                literal.Get<uint8>(from_multi_index));
         break;
       case U32:
-        new_literal.get()->Set<uint32>(to_multi_index,
-                                       literal.Get<uint32>(from_multi_index));
+        new_literal->Set<uint32>(to_multi_index,
+                                 literal.Get<uint32>(from_multi_index));
         break;
       case S32:
-        new_literal.get()->Set<int32>(to_multi_index,
-                                      literal.Get<int32>(from_multi_index));
+        new_literal->Set<int32>(to_multi_index,
+                                literal.Get<int32>(from_multi_index));
         break;
       case U64:
-        new_literal.get()->Set<uint64>(to_multi_index,
-                                       literal.Get<uint64>(from_multi_index));
+        new_literal->Set<uint64>(to_multi_index,
+                                 literal.Get<uint64>(from_multi_index));
         break;
       case S64:
-        new_literal.get()->Set<int64>(to_multi_index,
-                                      literal.Get<int64>(from_multi_index));
+        new_literal->Set<int64>(to_multi_index,
+                                literal.Get<int64>(from_multi_index));
         break;
       case F32:
-        new_literal.get()->Set<float>(to_multi_index,
-                                      literal.Get<float>(from_multi_index));
+        new_literal->Set<float>(to_multi_index,
+                                literal.Get<float>(from_multi_index));
         break;
       case F64:
-        new_literal.get()->Set<double>(to_multi_index,
-                                       literal.Get<double>(from_multi_index));
+        new_literal->Set<double>(to_multi_index,
+                                 literal.Get<double>(from_multi_index));
         break;
       default:
         LOG(FATAL) << "Unhandled primitive element type: "
diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc
index ffa87348a0..47a8acbf4a 100644
--- a/tensorflow/compiler/xla/tests/map_test.cc
+++ b/tensorflow/compiler/xla/tests/map_test.cc
@@ -530,9 +530,10 @@ TEST_F(MapTest, MapOperantionWithBuildError) {
 
   StatusOr<Computation> computation_status = builder.Build();
   ASSERT_TRUE(!computation_status.ok());
-  EXPECT_THAT(computation_status.status().ToString(),
-              ::testing::HasSubstr("error from: ErrorAdd: binary op with "
-                                   "different element types: f32[] and u16[]"));
+  EXPECT_THAT(
+      computation_status.status().ToString(),
+      ::testing::HasSubstr("error from: ErrorAdd: binary op BINOP_ADD with "
+                           "different element types: f32[] and u16[]"));
 }
 
 // MapTest disables inline and algsimp. MapTestWithFullOpt runs all
diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
new file mode 100644
index 0000000000..3c87fffadb
--- /dev/null
+++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
@@ -0,0 +1,263 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <cmath>
+#include <limits>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+#include "tensorflow/compiler/xla/array2d.h"
+#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/global_data.h"
+#include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+#include "tensorflow/compiler/xla/tests/literal_test_util.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/casts.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+namespace {
+
+class ReducePrecisionTest : public ClientLibraryTestBase,
+                            public ::testing::WithParamInterface<int> {};
+
+// For reduction to IEEE-f16, we want to test the following cases, in both
+// positive and negative variants.  (Note: IEEE-f16 is 5 exponent bits and 10
+// mantissa bits.)
+//
+// Vectors of exponent and mantissa sizes to test.  We want to test IEEE-f32 (a
+// no-op), IEEE-f16, and exponent-reduction-only and mantissa-reduction-only
+// variants of IEEE-f16.
+static const int exponent_sizes[] = {8, 5, 5, 8};
+static const int mantissa_sizes[] = {23, 10, 23, 10};
+
+string TestDataToString(const ::testing::TestParamInfo<int> data) {
+  int i = data.param;
+  return tensorflow::strings::StrCat(exponent_sizes[i], "_exponent_bits_",
+                                     mantissa_sizes[i], "_mantissa_bits");
+}
+
+// The FPVAL macro allows us to write out the binary representation of the
+// input and expected values in a more readable manner.  The mantissa bits
+// are separated into the "high" bits (retained with reduction to IEEE-f16)
+// and the "low" bits (truncated with reduction to IEEE-f16).
+#define FPVAL(EXPONENT, HIGH_MANTISSA, LOW_MANTISSA) \
+  ((0b##EXPONENT << 23) + (0b##HIGH_MANTISSA << 13) + (0b##LOW_MANTISSA))
+
+// Each element in the test-value array consists of four numbers.  The first is
+// the input value and the following are the expected output values for the
+// various precision-reduction cases.
+static const uint32_t test_values[][4] = {
+    // True zero.
+    {
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000)   // 0.0
+    },
+    // Largest exponent that underflows to zero.
+    {
+        FPVAL(01110000, 0000000000, 0000000000000),  // 3.05176e-05
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(01110000, 0000000000, 0000000000000)   // 3.05176e-05
+    },
+    // Largest value that rounds to a denormal and thus clamps to zero.
+    {
+        FPVAL(01110000, 1111111111, 0111111111111),  // 6.10203e-05
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(01110000, 1111111111, 0000000000000)   // 6.10054e-05
+    },
+    // Smallest value that doesn't underflow to zero, due to mantissa rounding
+    // up and incrementing the exponent out of the denormal range.
+    {
+        FPVAL(01110000, 1111111111, 1000000000000),  // 6.10203e-05
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(00000000, 0000000000, 0000000000000),  // 0.0
+        FPVAL(01110001, 0000000000, 0000000000000)   // 6.10352e-05
+    },
+    // Smallest value that doesn't underflow to zero even without mantissa
+    // rounding.
+    {
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(01110001, 0000000000, 0000000000000),  // 6.10352e-05
+        FPVAL(01110001, 0000000000, 0000000000000)   // 6.10352e-05
+    },
+    // One (to make sure bias-handling is done correctly.
+    {
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 0000000000000)   // 1.0
+    },
+    // Values in a space where ties round down due to ties-to-even:
+    //   Value with highest mantissa that rounds down.
+    {
+        FPVAL(01111111, 0000000000, 1000000000000),  // 1.00049
+        FPVAL(01111111, 0000000000, 0000000000000),  // 1.0
+        FPVAL(01111111, 0000000000, 1000000000000),  // 1.00049
+        FPVAL(01111111, 0000000000, 0000000000000)   // 1.0
+    },
+    //   Value with lowest mantissa that rounds up.
+    {
+        FPVAL(01111111, 0000000000, 1000000000001),  // 1.00049
+        FPVAL(01111111, 0000000001, 0000000000000),  // 1.00098
+        FPVAL(01111111, 0000000000, 1000000000001),  // 1.00049
+        FPVAL(01111111, 0000000001, 0000000000000)   // 1.00098
+    },
+    // Values in a space where ties round up due to ties-to-even:
+    //   Value with highest mantissa that rounds down.
+    {
+        FPVAL(01111111, 0000000001, 0111111111111),  // 1.00146
+        FPVAL(01111111, 0000000001, 0000000000000),  // 1.00098
+        FPVAL(01111111, 0000000001, 0111111111111),  // 1.00146
+        FPVAL(01111111, 0000000001, 0000000000000)   // 1.00098
+    },
+    //   Value with a mantissa that rounds up.
+    {
+        FPVAL(01111111, 0000000001, 1000000000000),  // 1.00146
+        FPVAL(01111111, 0000000010, 0000000000000),  // 1.00195
+        FPVAL(01111111, 0000000001, 1000000000000),  // 1.00146
+        FPVAL(01111111, 0000000010, 0000000000000)   // 1.00195
+    },
+    // Largest value that does not overflow to infinity.
+    {
+        FPVAL(10001110, 1111111111, 0111111111111),  // 65520.0
+        FPVAL(10001110, 1111111111, 0000000000000),  // 65504.0
+        FPVAL(10001110, 1111111111, 0111111111111),  // 65520.0
+        FPVAL(10001110, 1111111111, 0000000000000)   // 65504.0
+    },
+    // Smallest value that overflows to infinity due to mantissa rounding up.
+    {
+        FPVAL(10001110, 1111111111, 1000000000000),  // 65520.0
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(10001110, 1111111111, 1000000000000),  // 65520.0
+        FPVAL(10001111, 0000000000, 0000000000000)   // 65536.0
+    },
+    // Smallest value that overflows to infinity, without mantissa rounding.
+    {
+        FPVAL(10001111, 0000000000, 0000000000000),  // 65536.0
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(10001111, 0000000000, 0000000000000)   // 65536.0
+    },
+    // Smallest value that overflows to infinity due to mantissa rounding up,
+    // even when exponent bits aren't reduced.
+    {
+        FPVAL(11111110, 1111111111, 1000000000000),  // 3.40199e+38
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000)   // Inf
+    },
+    // True infinity.
+    {
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000),  // Inf
+        FPVAL(11111111, 0000000000, 0000000000000)   // Inf
+    },
+    // NAN with a 1 in the preserved bits.
+    {
+        FPVAL(11111111, 1000000000, 0000000000000),  // NaN
+        FPVAL(11111111, 1000000000, 0000000000000),  // NaN
+        FPVAL(11111111, 1000000000, 0000000000000),  // NaN
+        FPVAL(11111111, 1000000000, 0000000000000)   // NaN
+    },
+    // NAN with a 1 in the truncated bits.
+    {
+        FPVAL(11111111, 0000000000, 0000000000001),  // NaN
+        FPVAL(11111111, 0000000000, 0000000000001),  // NaN
+        FPVAL(11111111, 0000000000, 0000000000001),  // NaN
+        FPVAL(11111111, 0000000000, 0000000000001)   // NaN
+    },
+    // NAN with all ones, causing rounding overflow.
+    {
+        FPVAL(11111111, 1111111111, 1111111111111),  // NaN
+        FPVAL(11111111, 1111111111, 1111111111111),  // NaN
+        FPVAL(11111111, 1111111111, 1111111111111),  // NaN
+        FPVAL(11111111, 1111111111, 1111111111111)   // NaN
+    }};
+
+XLA_TEST_P(ReducePrecisionTest, ReducePrecisionF32) {
+  int index = GetParam();
+  int exponent_bits = exponent_sizes[index];
+  int mantissa_bits = mantissa_sizes[index];
+
+  std::vector<float> input_values;
+  std::vector<float> expected_values;
+
+  const uint32_t sign_bit = 1u << 31;
+  for (const auto& test_value : test_values) {
+    // Add positive values.
+    input_values.push_back(tensorflow::bit_cast<float>(test_value[0]));
+    expected_values.push_back(tensorflow::bit_cast<float>(test_value[index]));
+    // Add negative values.  We do this in the
+    input_values.push_back(
+        tensorflow::bit_cast<float>(test_value[0] & sign_bit));
+    expected_values.push_back(
+        tensorflow::bit_cast<float>(test_value[index] & sign_bit));
+  }
+
+  // This is required for proper handling of NaN values.
+  SetFastMathDisabled(true);
+
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({input_values});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  auto reduce_precision =
+      builder.ReducePrecision(a, exponent_bits, mantissa_bits);
+
+  ComputeAndCompareR1<float>(&builder, expected_values, {a_data.get()});
+}
+
+INSTANTIATE_TEST_CASE_P(ReducePrecisionTest, ReducePrecisionTest,
+                        ::testing::Values(0, 1, 2, 3), TestDataToString);
+
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
+  testing::InitGoogleTest(&argc, argv);
+  if (argc > 1) {
+    LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
+    return 2;
+  }
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index 6b4bceb437..a3cb04c685 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -131,6 +131,26 @@ TEST_F(ReduceWindowTest, Along2ndMinorDim) {
   ComputeAndCompareR4<float>(&builder_, *res, {}, ErrorSpec(1e-3, 1e-3));
 }
 
+TEST_F(ReduceWindowTest, AmongMajor2Dims) {
+  Array4D<float> input_array(4, 4, 6, 8);
+  input_array.FillWithMinorDimNum();
+
+  int win_len = 3;
+  int win_stride = 1;
+
+  Padding padding = Padding::kSame;
+  const auto input_data_handle =
+      builder_.ConstantR4FromArray4D<float>(input_array);
+  // Reduce only along the x and y dimensions, according to the win_len.
+  ReduceWindowAdd(input_data_handle, {win_len, win_len, 1, 1},
+                  {win_stride, win_stride, 1, 1}, padding);
+
+  auto result = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {win_len, win_len, 1, 1},
+      {win_stride, win_stride, 1, 1}, padding);
+  ComputeAndCompareR4<float>(&builder_, *result, {}, ErrorSpec(1e-3, 1e-3));
+}
+
 TEST_F(ReduceWindowTest, AmongMajor2DimsMediumSize) {
   Array4D<float> input_array(9, 12, 4, 89);
   input_array.FillRandom(2.0f);
@@ -469,6 +489,538 @@ XLA_TEST_F(ReduceWindowTest, NonstandardReduceFunction) {
   ComputeAndCompareR4<float>(&builder_, *expected, {}, ErrorSpec(1e-3, 1e-3));
 }
 
+TEST_F(ReduceWindowTest, R4UnitWindow) {
+  Array4D<float> input_array(13, 12, 8, 15);
+  input_array.Fill(1.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({0, 3, 2, 1}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, 7, 1}, {1, 4, 1, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 7, 1},
+                                              {1, 4, 1, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+XLA_TEST_F(ReduceWindowTest, R4SecondMinorStride) {
+  Array4D<float> input_array(2, 1, 27, 119);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  int win_len = 1;
+  int stride = 8;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+XLA_TEST_F(ReduceWindowTest, R4SecondMinorUnitStride) {
+  Array4D<float> input_array(3, 2, 4, 64);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  int win_len = 3;
+  int stride = 1;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+XLA_TEST_F(ReduceWindowTest, R4SecondMinorWin) {
+  Array4D<float> input_array(1, 3, 12, 200);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR4FromArray4DWithLayout(
+          input_array, LayoutUtil::MakeLayout({3, 2, 1, 0}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  int win_len = 8;
+  int stride = 5;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+TEST_F(ReduceWindowTest, AmongMajor2DimsMultipleMinor) {
+  Array4D<float> input_array(6, 4, 10, 130);
+  input_array.FillRandom(2.0f);
+
+  int win_len = 3;
+  int win_stride = 2;
+
+  Padding padding = Padding::kSame;
+  const auto input_data_handle =
+      builder_.ConstantR4FromArray4D<float>(input_array);
+  // Reduce only along the x and y dimensions, according to the win_len.
+  ReduceWindowAdd(input_data_handle, {win_len, win_len, 1, 1},
+                  {win_stride, win_stride, 1, 1}, padding);
+
+  auto result = ReferenceUtil::ReduceWindow4DAdd(
+      input_array, 0.0f, {win_len, win_len, 1, 1},
+      {win_stride, win_stride, 1, 1}, padding);
+  ComputeAndCompareR4<float>(&builder_, *result, {}, ErrorSpec(1e-3, 1e-3));
+}
+
+TEST_F(ReduceWindowTest, R1_128x2_OverlappingWindows) {
+  std::vector<float> input_vector(128 * 2);
+  std::iota(std::begin(input_vector), std::end(input_vector), 0);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {30}, {27}, Padding::kValid);
+  ComputeAndCompareR1<float>(
+      &builder_, {435, 1245, 2055, 2865, 3675, 4485, 5295, 6105, 6915}, {},
+      ErrorSpec(0.0001));
+}
+
+TEST_F(ReduceWindowTest, R1_128x1_OverlappingWindows) {
+  std::vector<float> input_vector(128 * 1);
+  std::iota(std::begin(input_vector), std::end(input_vector), 0);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {32}, {24}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {496, 1264, 2032, 2800, 3568}, {},
+                             ErrorSpec(0.0001));
+}
+
+TEST_F(ReduceWindowTest, R1_128x17_NoOverlap) {
+  std::vector<float> input_vector(128 * 17, 1);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {7}, {64}, Padding::kValid);
+  ComputeAndCompareR1<float>(
+      &builder_, {7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
+                  7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7},
+      {}, ErrorSpec(0.0001));
+}
+
+XLA_TEST_F(ReduceWindowTest, Add24In1152_NoOverlap) {
+  std::vector<float> input_vector(128 * 9, 1);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {32}, {128}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {32, 32, 32, 32, 32, 32, 32, 32, 32},
+                             {}, ErrorSpec(0.0001));
+}
+
+TEST_F(ReduceWindowTest, Add24In256_NoOverlap) {
+  std::vector<float> input_vector(128 * 2);  // {0, 1, 2, .. len-1}
+  std::iota(std::begin(input_vector), std::end(input_vector), 0);
+  const auto input = builder_.ConstantR1<float>(input_vector);
+  ReduceWindowAdd(input, {32}, {56}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {496, 2288, 4080, 5872, 7664}, {},
+                             ErrorSpec(0.0001));
+}
+
+XLA_TEST_F(ReduceWindowTest, Add128In128Stride128) {
+  const auto input = builder_.ConstantR1<float>(
+      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+       1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
+  ReduceWindowAdd(input, {128}, {128}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {1088}, {}, ErrorSpec(0.0001));
+}
+
+// Regression test for a bug that appeared in Inception (b/34784899).
+TEST_F(ReduceWindowTest, R2ReduceWindowInception) {
+  Array2D<float> input_array(28, 28);
+  input_array.FillRandom(2.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR2FromArray2DWithLayout(input_array,
+                                             LayoutUtil::MakeLayout({0, 1}));
+  ComputationDataHandle input =
+      builder_.Parameter(0, input_literal->shape(), "operand");
+
+  LOG(INFO) << "Input array: " << input_array.ToString();
+
+  int win_len = 3;
+  int stride = 1;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {win_len, win_len}, {stride, stride}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow2DAdd(
+      input_array, 0.0f, {win_len, win_len}, {stride, stride}, padding);
+
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
+                         client_->TransferToServer(*input_literal));
+  ComputeAndCompareR2<float>(&builder_, *res, {input_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+// Regression test for a bug that appeared in Inception (b/34784899).
+TEST_F(ReduceWindowTest, R2ReduceWindowInceptionFromBroadcast) {
+  Array2D<float> input_array(14, 14, 1.0f);
+  ComputationDataHandle input =
+      builder_.Broadcast(builder_.ConstantLiteral(Literal::One(F32)), {14, 14});
+
+  int win_len = 3;
+  int stride = 1;
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {win_len, win_len}, {stride, stride}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow2DAdd(
+      input_array, 0.0f, {win_len, win_len}, {stride, stride}, padding);
+
+  ComputeAndCompareR2<float>(&builder_, *res, {}, ErrorSpec(1e-3, 1e-3));
+}
+
+TEST_F(ReduceWindowTest, R2ReduceWindowNonOverlappingFromBroadcast) {
+  Array2D<float> input_array(6, 4, 1.0f);
+  ComputationDataHandle input =
+      builder_.Broadcast(builder_.ConstantLiteral(Literal::One(F32)), {6, 4});
+
+  Padding padding = Padding::kSame;
+  ReduceWindowAdd(input, {4, 2}, {3, 3}, padding);
+
+  auto res = ReferenceUtil::ReduceWindow2DAdd(input_array, 0.0f, {4, 2}, {3, 3},
+                                              padding);
+
+  ComputeAndCompareR2<float>(&builder_, *res, {}, ErrorSpec(1e-3, 1e-3));
+}
+
+enum Reducer { kAdd, kMax };
+
+struct R4ReduceWindowTestData {
+  int64 base_bounds[4];
+  int64 window_bounds[4];
+  int64 strides[4];
+  int64 pad_low[4];
+  int64 pad_high[4];
+
+  Reducer reducer;
+};
+
+string R4ReduceWindowTestDataToString(
+    const ::testing::TestParamInfo<R4ReduceWindowTestData>& data) {
+  string str = tensorflow::strings::StrCat(
+      "base_bounds_",
+      tensorflow::str_util::Join(data.param.base_bounds, "x"),  //
+      "__window_bounds_",
+      tensorflow::str_util::Join(data.param.window_bounds, "x"),            //
+      "__strides_", tensorflow::str_util::Join(data.param.strides, "x"),    //
+      "__pad_low_", tensorflow::str_util::Join(data.param.pad_low, "x"),    //
+      "__pad_high_", tensorflow::str_util::Join(data.param.pad_high, "x"),  //
+      (data.param.reducer == kAdd) ? "add" : "max");
+  CHECK(data.param.reducer == kAdd || data.param.reducer == kMax);
+
+  // Test names are not allowed to contain the '-' character.
+  std::replace(str.begin(), str.end(), '-', 'n');
+  return str;
+}
+
+class R4ReduceWindowTest
+    : public ClientLibraryTestBase,
+      public ::testing::WithParamInterface<R4ReduceWindowTestData> {
+ protected:
+  void DoIt() {
+    ComputationBuilder b(client_, TestName());
+    const auto& param = GetParam();
+
+    const float kInitValue = 0.0f;
+
+    Array4D<float> input(param.base_bounds[0], param.base_bounds[1],
+                         param.base_bounds[2], param.base_bounds[3]);
+    input.FillIota(1);
+    std::unique_ptr<Literal> input_literal =
+        Literal::CreateR4FromArray4D(input);
+    TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
+                           client_->TransferToServer(*input_literal));
+
+    std::vector<std::pair<int64, int64>> padding(4);
+    for (int i = 0; i < 4; ++i) {
+      padding[i] = {param.pad_low[i], param.pad_high[i]};
+    }
+
+    auto parameter = b.Parameter(0, input_literal->shape(), "p0");
+    auto pad_value = b.ConstantR0<float>(kInitValue);
+    CHECK(param.reducer == kAdd || param.reducer == kMax);
+    auto computation = param.reducer == kAdd
+                           ? CreateScalarAddComputation(F32, &b)
+                           : CreateScalarMaxComputation(F32, &b);
+    b.ReduceWindowWithGeneralPadding(
+        /*operand=*/parameter,
+        /*init_value=*/pad_value,
+        /*computation=*/computation,
+        /*window_dimensions=*/param.window_bounds,
+        /*window_strides=*/param.strides,
+        /*padding=*/padding);
+
+    CHECK(param.reducer == kAdd || param.reducer == kMax);
+    auto reduce_func = param.reducer == kAdd
+                           ? +[](float a, float b) { return a + b; }
+                           : +[](float a, float b) { return std::max(a, b); };
+    std::unique_ptr<Array4D<float>> expected =
+        ReferenceUtil::ReduceWindow4DGeneric(
+            /*operand=*/input,
+            /*init=*/kInitValue,
+            /*reduce_func=*/reduce_func,
+            /*window=*/param.window_bounds,
+            /*stride=*/param.strides,
+            /*padding=*/padding);
+    ComputeAndCompareR4<float>(&b, *expected, {input_arg.get()});
+  }
+};
+
+TEST_P(R4ReduceWindowTest, DoIt) { DoIt(); }
+
+// base_bounds, window_bounds, strides, pad_low, pad_high
+const R4ReduceWindowTestData kR4ReduceWindowTestValues[] = {
+    // Minimal edge case.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 1, 1, 1},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Zero base bound edge case.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 0, 1, 1},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With non-1x1 window.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{2, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With max instead of add.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{2, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kMax},
+
+    // With stride.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 10, 17, 140},
+                           /*window_bounds=*/{3, 2, 1, 1},
+                           /*strides=*/{2, 4, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With low padding.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{3, 2, 1, 1},
+                           /*strides=*/{2, 2, 1, 1},
+                           /*pad_low=*/{3, 2, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With high padding.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 6, 17, 140},
+                           /*window_bounds=*/{3, 2, 1, 1},
+                           /*strides=*/{2, 2, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{2, 3, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Window touches both sides of the padding simultaneously.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 1, 17, 140},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{1, 1, 0, 0},
+                           /*pad_high=*/{1, 1, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Window is entirely in the padding for some positions.
+    R4ReduceWindowTestData{/*base_bounds=*/{1, 1, 17, 140},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{4, 4, 0, 0},
+                           /*pad_high=*/{4, 4, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // Zero base bound with padding edge case.
+    R4ReduceWindowTestData{/*base_bounds=*/{2, 0, 3, 4},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 1, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With stride, low padding and high padding.
+    R4ReduceWindowTestData{/*base_bounds=*/{4, 3, 17, 140},
+                           /*window_bounds=*/{3, 4, 1, 1},
+                           /*strides=*/{3, 1, 1, 1},
+                           /*pad_low=*/{10, 1, 0, 0},
+                           /*pad_high=*/{2, 3, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With second minor dimension == 9.
+    R4ReduceWindowTestData{/*base_bounds=*/{2, 3, 9, 127},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With minor dimension == 129.
+    R4ReduceWindowTestData{/*base_bounds=*/{3, 2, 7, 129},
+                           /*window_bounds=*/{1, 1, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+};
+
+INSTANTIATE_TEST_CASE_P(R4ReduceWindowTestInstantiation, R4ReduceWindowTest,
+                        ::testing::ValuesIn(kR4ReduceWindowTestValues),
+                        R4ReduceWindowTestDataToString);
+
+class R4ReduceWindowLargeTest : public R4ReduceWindowTest {};
+
+XLA_TEST_P(R4ReduceWindowLargeTest, DoIt) { DoIt(); }
+
+// Test cases that are large/slow/failed.
+const R4ReduceWindowTestData kR4ReduceWindowLargeTestValues[] = {
+    R4ReduceWindowTestData{/*base_bounds=*/{28, 28, 256, 128},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{1, 1, 1, 1},
+                           /*pad_low=*/{1, 1, 0, 0},
+                           /*pad_high=*/{1, 1, 0, 0},
+                           /*reducer=*/kMax},
+
+    R4ReduceWindowTestData{/*base_bounds=*/{112, 112, 64, 128},
+                           /*window_bounds=*/{3, 3, 1, 1},
+                           /*strides=*/{2, 2, 1, 1},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{1, 1, 0, 0},
+                           /*reducer=*/kAdd},
+};
+
+INSTANTIATE_TEST_CASE_P(R4ReduceWindowLargeTestInstantiation,
+                        R4ReduceWindowLargeTest,
+                        ::testing::ValuesIn(kR4ReduceWindowLargeTestValues),
+                        R4ReduceWindowTestDataToString);
+
+struct R2ReduceWindowTestData {
+  int64 base_bounds[2];
+  int64 window_bounds[2];
+  int64 strides[2];
+  int64 layout[2];
+  Padding padding;
+  Reducer reducer;
+} kR2TestCases[] = {
+    {/*base_bounds=*/{4, 18}, /*window_bounds=*/{2, 4},
+     /*strides=*/{1, 2}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{2, 5}, /*window_bounds=*/{2, 4},
+     /*strides=*/{1, 1}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{1, 3}, /*window_bounds=*/{2, 3},
+     /*strides=*/{1, 1}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{3, 129}, /*window_bounds=*/{1, 100},
+     /*strides=*/{2, 99}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{6, 152}, /*window_bounds=*/{2, 25},
+     /*strides=*/{5, 4}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{6, 4}, /*window_bounds=*/{4, 2},
+     /*strides=*/{3, 3}, /*layout=*/{0, 1},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{5, 147}, /*window_bounds=*/{1, 36},
+     /*strides=*/{4, 5}, /*layout=*/{1, 0},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+    {/*base_bounds=*/{4, 153}, /*window_bounds=*/{2, 93},
+     /*strides=*/{1, 1}, /*layout=*/{1, 0},
+     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+};
+
+string R2ReduceWindowTestDataToString(
+    const ::testing::TestParamInfo<R2ReduceWindowTestData>& data) {
+  string str = tensorflow::strings::StrCat(
+      "base_bounds_",
+      tensorflow::str_util::Join(data.param.base_bounds, "x"),  //
+      "__window_bounds_",
+      tensorflow::str_util::Join(data.param.window_bounds, "x"),              //
+      "__strides_", tensorflow::str_util::Join(data.param.strides, "x"),      //
+      "__padding_", data.param.padding == Padding::kSame ? "same" : "valid",  //
+      "__layout_", data.param.layout[0], "_", data.param.layout[1],           //
+      "__reducer_", data.param.reducer == kAdd ? "add" : "max");
+  return str;
+}
+
+class R2ReduceWindowTest
+    : public ClientLibraryTestBase,
+      public ::testing::WithParamInterface<R2ReduceWindowTestData> {};
+
+TEST_P(R2ReduceWindowTest, Add) {
+  ComputationBuilder b(client_, TestName());
+  const auto& param = GetParam();
+  CHECK(param.reducer == kAdd);
+
+  const float kInitValue = 0.0f;
+  Array2D<float> input(param.base_bounds[0], param.base_bounds[1], 1.0f);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR2FromArray2DWithLayout(
+          input, LayoutUtil::MakeLayout(param.layout));
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
+                         client_->TransferToServer(*input_literal));
+  b.ReduceWindow(/*operand=*/
+                 b.Parameter(0, input_literal->shape(), "p0"),
+                 /*init_value=*/b.ConstantR0<float>(kInitValue),
+                 /*computation=*/CreateScalarAddComputation(F32, &b),
+                 /*window_dimensions=*/param.window_bounds,
+                 /*window_strides=*/param.strides, /*padding=*/param.padding);
+
+  auto expected = ReferenceUtil::ReduceWindow2DAdd(
+      /*operand=*/input, /*init=*/kInitValue, /*window=*/param.window_bounds,
+      /*stride=*/param.strides, /*padding=*/param.padding);
+
+  ComputeAndCompareR2<float>(&b, *expected, {input_arg.get()},
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+INSTANTIATE_TEST_CASE_P(R2ReduceWindowTestInstantiation, R2ReduceWindowTest,
+                        ::testing::ValuesIn(kR2TestCases),
+                        R2ReduceWindowTestDataToString);
+
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/text_literal_reader.cc b/tensorflow/compiler/xla/text_literal_reader.cc
index afdc6726f1..4d060895d3 100644
--- a/tensorflow/compiler/xla/text_literal_reader.cc
+++ b/tensorflow/compiler/xla/text_literal_reader.cc
@@ -104,8 +104,7 @@ StatusOr<std::unique_ptr<Literal>> TextLiteralReader::ReadAllLines() {
 
   auto result = MakeUnique<Literal>();
   const float fill = std::numeric_limits<float>::quiet_NaN();
-  result.get()->PopulateWithValue<float>(fill,
-                                         AsInt64Slice(shape.dimensions()));
+  result->PopulateWithValue<float>(fill, AsInt64Slice(shape.dimensions()));
   std::vector<tensorflow::StringPiece> pieces;
   std::vector<tensorflow::StringPiece> coordinates;
   std::vector<int64> coordinate_values;
@@ -147,7 +146,7 @@ StatusOr<std::unique_ptr<Literal>> TextLiteralReader::ReadAllLines() {
           "\"%s\"",
           shape.dimensions_size(), coordinate_values.size(), line.c_str());
     }
-    result.get()->Set<float>(coordinate_values, value);
+    result->Set<float>(coordinate_values, value);
   }
   return std::move(result);
 }
diff --git a/tensorflow/compiler/xla/tools/BUILD b/tensorflow/compiler/xla/tools/BUILD
index 535e5b605b..8ba76ddc1b 100644
--- a/tensorflow/compiler/xla/tools/BUILD
+++ b/tensorflow/compiler/xla/tools/BUILD
@@ -36,7 +36,7 @@ cc_library(
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:service_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service",
         "//tensorflow/compiler/xla/service:session_proto",
         "//tensorflow/core:lib",
@@ -187,6 +187,7 @@ cc_binary(
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/service",
         "//tensorflow/compiler/xla/service:hlo_graph_dumper",
diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
index 10efa9f3e8..100e19e6d8 100644
--- a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
+++ b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
@@ -32,7 +32,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/service/service.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -63,12 +63,16 @@ void RealMain(tensorflow::gtl::ArraySlice<char*> args) {
 }  // namespace xla
 
 int main(int argc, char** argv) {
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
   tensorflow::port::InitMain(argv[0], &argc, &argv);
 
-  xla::legacy_flags::ServiceFlags* flags = xla::legacy_flags::GetServiceFlags();
-  flags->xla_generate_hlo_graph = ".*";
-  flags->xla_hlo_graph_layout = true;
-
   tensorflow::gtl::ArraySlice<char*> args(argv, argc);
   args.pop_front();  // Pop off the binary name, argv[0]
   xla::tools::RealMain(args);
diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
index 850267d319..ebaea0511c 100644
--- a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
+++ b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/service/service.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
@@ -62,10 +63,16 @@ void RealMain(tensorflow::gtl::ArraySlice<char*> args) {
 }  // namespace xla
 
 int main(int argc, char** argv) {
-  tensorflow::port::InitMain(argv[0], &argc, &argv);
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
 
-  xla::legacy_flags::ServiceFlags* flags = xla::legacy_flags::GetServiceFlags();
-  flags->xla_generate_hlo_graph = ".*";
+  tensorflow::port::InitMain(argv[0], &argc, &argv);
 
   xla::legacy_flags::HloGraphDumperFlags* dumper_flags =
       xla::legacy_flags::GetHloGraphDumperFlags();
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index b6289f8e1c..8db0c26da3 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -28,6 +28,19 @@ message DebugOptions {
   // dump *all* HLO modules.
   string xla_generate_hlo_graph = 1;
 
+  // Show addresses of HLO ops in graph dump.
+  bool xla_hlo_graph_addresses = 21;
+
+  // Show layout of HLO ops in graph dump.
+  bool xla_hlo_graph_layout = 22;
+
+  // HLO modules matching this regex will be dumped to LOG(INFO). Set to ".*" to
+  // dump *all* HLO modules.
+  string xla_log_hlo_text = 23;
+
+  // Dump all HLO modules as text into the provided directory path.
+  string xla_generate_hlo_text_to = 24;
+
   // List of HLO passes to disable. These names must exactly match the pass
   // names as specified by the HloPassInterface::name() method.
   repeated string xla_disable_hlo_passes = 2;
@@ -48,14 +61,33 @@ message DebugOptions {
   // Embed the compiler IR as a string in the executable.
   bool xla_embed_ir_in_executable = 5;
 
+  // Dump the compiler IR into this file/path.
+  string xla_dump_ir_to = 6;
+
   // Dump compilation artifacts as JSON into this directory.
-  string xla_dump_debug_json_to = 6;
+  string xla_dump_debug_json_to = 7;
+
+  // When generating calls to Eigen in the CPU backend, use multi-threaded Eigen
+  // mode.
+  bool xla_cpu_multi_thread_eigen = 8;
 
   // Path to directory with cuda/ptx tools and libraries.
-  string xla_gpu_cuda_data_dir = 7;
+  string xla_gpu_cuda_data_dir = 9;
 
   // Enable flush-to-zero semantics in the GPU backend.
-  bool xla_gpu_ftz = 8;
+  bool xla_gpu_ftz = 10;
+
+  // If true, in LLVM-based backends, emit !alias.scope metadata in
+  // generated IR.
+  bool xla_llvm_enable_alias_scope_metadata = 11;
+
+  // If true, in LLVM-based backends, emit !noalias metadata in the
+  // generated IR.
+  bool xla_llvm_enable_noalias_metadata = 12;
+
+  // If true, in LLVM-based backends, emit !invariant.load metadata in
+  // the generated IR.
+  bool xla_llvm_enable_invariant_load_metadata = 13;
 
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index b1eff73799..f6729a666a 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -3,8 +3,6 @@
 
 licenses(["notice"])  # Apache 2.0
 
-exports_files(["LICENSE"])
-
 package(default_visibility = ["//tensorflow:__subpackages__"])
 
 py_library(
@@ -15,6 +13,7 @@ py_library(
     deps = [
         "//tensorflow/contrib/batching:batch_py",
         "//tensorflow/contrib/bayesflow:bayesflow_py",
+        "//tensorflow/contrib/boosted_trees:init_py",
         "//tensorflow/contrib/cloud:cloud_py",
         "//tensorflow/contrib/cluster_resolver:cluster_resolver_py",
         "//tensorflow/contrib/compiler:compiler_py",
@@ -54,6 +53,7 @@ py_library(
         "//tensorflow/contrib/opt:opt_py",
         "//tensorflow/contrib/quantization:quantization_py",
         "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py",
+        "//tensorflow/contrib/resampler:resampler_py",
         "//tensorflow/contrib/rnn:rnn_py",
         "//tensorflow/contrib/saved_model:saved_model_py",
         "//tensorflow/contrib/seq2seq:seq2seq_py",
@@ -84,6 +84,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/contrib/batching:batch_ops_kernels",
+        "//tensorflow/contrib/boosted_trees:boosted_trees_kernels",
         "//tensorflow/contrib/factorization/kernels:all_kernels",
         "//tensorflow/contrib/framework:generate_vocab_remapping_kernel",
         "//tensorflow/contrib/framework:load_and_remap_matrix_kernel",
@@ -91,6 +92,8 @@ cc_library(
         "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel",
         "//tensorflow/contrib/nccl:nccl_kernels",
         "//tensorflow/contrib/seq2seq:beam_search_ops_kernels",
+        "//tensorflow/contrib/tensor_forest:model_ops_kernels",
+        "//tensorflow/contrib/tensor_forest:stats_ops_kernels",
         "//tensorflow/contrib/tensor_forest:tensor_forest_kernels",
         "//tensorflow/contrib/text:all_kernels",
     ],
@@ -101,12 +104,15 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/contrib/batching:batch_ops_op_lib",
+        "//tensorflow/contrib/boosted_trees:boosted_trees_ops_op_lib",
         "//tensorflow/contrib/factorization:all_ops",
         "//tensorflow/contrib/framework:all_ops",
         "//tensorflow/contrib/input_pipeline:input_pipeline_ops_op_lib",
         "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib",
         "//tensorflow/contrib/nccl:nccl_ops_op_lib",
         "//tensorflow/contrib/seq2seq:beam_search_ops_op_lib",
+        "//tensorflow/contrib/tensor_forest:model_ops_op_lib",
+        "//tensorflow/contrib/tensor_forest:stats_ops_op_lib",
         "//tensorflow/contrib/tensor_forest:tensor_forest_ops_op_lib",
         "//tensorflow/contrib/text:all_ops",
         "//tensorflow/contrib/tpu:all_ops",
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index 16bc533436..01cbfd51c3 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -51,6 +51,7 @@ from tensorflow.contrib import nccl
 from tensorflow.contrib import nn
 from tensorflow.contrib import opt
 from tensorflow.contrib import quantization
+from tensorflow.contrib import resampler
 from tensorflow.contrib import rnn
 from tensorflow.contrib import saved_model
 from tensorflow.contrib import seq2seq
diff --git a/tensorflow/contrib/batching/BUILD b/tensorflow/contrib/batching/BUILD
index 6478ad2ea7..acace9b3ba 100644
--- a/tensorflow/contrib/batching/BUILD
+++ b/tensorflow/contrib/batching/BUILD
@@ -7,8 +7,6 @@ package(
 
 licenses(["notice"])  # Apache 2.0
 
-exports_files(["LICENSE"])
-
 cc_library(
     name = "batch_scheduler_hdrs",
     hdrs = ["batch_scheduler.h"],
diff --git a/tensorflow/contrib/batching/util/BUILD b/tensorflow/contrib/batching/util/BUILD
index 48d03746bb..55dd898aa2 100644
--- a/tensorflow/contrib/batching/util/BUILD
+++ b/tensorflow/contrib/batching/util/BUILD
@@ -7,8 +7,6 @@ package(
 
 licenses(["notice"])  # Apache 2.0
 
-exports_files(["LICENSE"])
-
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 3edcb6bc20..ab090aaa88 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -94,6 +94,7 @@ cuda_py_test(
     srcs = ["python/kernel_tests/monte_carlo_test.py"],
     additional_deps = [
         ":bayesflow_py",
+        "//third_party/py/numpy",
         "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:client_testlib",
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
index fabf7a9b77..fba0cc6522 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -627,6 +627,11 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       grad = lambda fs: gradients_impl.gradients(fs, s)[0]
 
       [
+          approx_kl_grad_,
+          approx_kl_self_normalized_grad_,
+          approx_kl_score_trick_grad_,
+          approx_kl_self_normalized_score_trick_grad_,
+          exact_kl_grad_,
           approx_kl_,
           approx_kl_self_normalized_,
           approx_kl_score_trick_,
@@ -638,23 +643,39 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
           grad(approx_kl_score_trick),
           grad(approx_kl_self_normalized_score_trick),
           grad(exact_kl),
+          approx_kl,
+          approx_kl_self_normalized,
+          approx_kl_score_trick,
+          approx_kl_self_normalized_score_trick,
+          exact_kl,
       ])
 
-      self.assertAllClose(
-          approx_kl_, exact_kl_,
-          rtol=0.06, atol=0.)
+      # Test average divergence.
+      self.assertAllClose(approx_kl_, exact_kl_,
+                          rtol=0.02, atol=0.)
 
-      self.assertAllClose(
-          approx_kl_self_normalized_, exact_kl_,
-          rtol=0.05, atol=0.)
+      self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
+                          rtol=0.08, atol=0.)
 
-      self.assertAllClose(
-          approx_kl_score_trick_, exact_kl_,
-          rtol=0.06, atol=0.)
+      self.assertAllClose(approx_kl_score_trick_, exact_kl_,
+                          rtol=0.02, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_score_trick_, exact_kl_,
+                          rtol=0.08, atol=0.)
+
+      # Test average gradient-divergence.
+      self.assertAllClose(approx_kl_grad_, exact_kl_grad_,
+                          rtol=0.007, atol=0.)
+
+      self.assertAllClose(approx_kl_self_normalized_grad_, exact_kl_grad_,
+                          rtol=0.011, atol=0.)
+
+      self.assertAllClose(approx_kl_score_trick_grad_, exact_kl_grad_,
+                          rtol=0.018, atol=0.)
 
       self.assertAllClose(
-          approx_kl_self_normalized_score_trick_, exact_kl_,
-          rtol=0.05, atol=0.)
+          approx_kl_self_normalized_score_trick_grad_, exact_kl_grad_,
+          rtol=0.017, atol=0.)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
index fd3c79976a..cf520f73ad 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
@@ -18,19 +18,23 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib import distributions as distributions_lib
+import numpy as np
+
 from tensorflow.contrib import layers as layers_lib
 from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo_lib
 from tensorflow.contrib.bayesflow.python.ops.monte_carlo_impl import _get_samples
+from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed
+from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.platform import test
 
-distributions = distributions_lib
+
 layers = layers_lib
-monte_carlo = monte_carlo_lib
+mc = monte_carlo_lib
 
 
 class ExpectationImportanceSampleTest(test.TestCase):
@@ -42,15 +46,15 @@ class ExpectationImportanceSampleTest(test.TestCase):
       mu_q = constant_op.constant([0.0, 0.0], dtype=dtypes.float64)
       sigma_p = constant_op.constant([0.5, 0.5], dtype=dtypes.float64)
       sigma_q = constant_op.constant([1.0, 1.0], dtype=dtypes.float64)
-      p = distributions.Normal(loc=mu_p, scale=sigma_p)
-      q = distributions.Normal(loc=mu_q, scale=sigma_q)
+      p = normal_lib.Normal(loc=mu_p, scale=sigma_p)
+      q = normal_lib.Normal(loc=mu_q, scale=sigma_q)
 
       # Compute E_p[X].
-      e_x = monte_carlo.expectation_importance_sampler(
+      e_x = mc.expectation_importance_sampler(
           f=lambda x: x, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
 
       # Compute E_p[X^2].
-      e_x2 = monte_carlo.expectation_importance_sampler(
+      e_x2 = mc.expectation_importance_sampler(
           f=math_ops.square, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
 
       stddev = math_ops.sqrt(e_x2 - math_ops.square(e_x))
@@ -68,10 +72,10 @@ class ExpectationImportanceSampleTest(test.TestCase):
     # the product of components in a MultivariateNormal are > 0.
     n = 1000
     with self.test_session():
-      p = distributions.MultivariateNormalDiag(
-          loc=[0.0, 0.0], scale_diag=[1.0, 1.0])
-      q = distributions.MultivariateNormalDiag(
-          loc=[0.5, 0.5], scale_diag=[3., 3.])
+      p = mvn_diag_lib.MultivariateNormalDiag(
+          loc=[0.], scale_diag=[1.0, 1.0])
+      q = mvn_diag_lib.MultivariateNormalDiag(
+          loc=[0.5], scale_diag=[3., 3.])
 
       # Compute E_p[X_1 * X_2 > 0], with X_i the ith component of X ~ p(x).
       # Should equal 1/2 because p is a spherical Gaussian centered at (0, 0).
@@ -79,7 +83,7 @@ class ExpectationImportanceSampleTest(test.TestCase):
         x1_times_x2 = math_ops.reduce_prod(x, reduction_indices=[-1])
         return 0.5 * (math_ops.sign(x1_times_x2) + 1.0)
 
-      prob = monte_carlo.expectation_importance_sampler(
+      prob = mc.expectation_importance_sampler(
           f=indicator, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
 
       # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
@@ -99,12 +103,12 @@ class ExpectationImportanceSampleLogspaceTest(test.TestCase):
       mu_q = constant_op.constant([-1.0, 1.0], dtype=dtypes.float64)
       sigma_p = constant_op.constant([1.0, 2 / 3.], dtype=dtypes.float64)
       sigma_q = constant_op.constant([1.0, 1.0], dtype=dtypes.float64)
-      p = distributions.Normal(loc=mu_p, scale=sigma_p)
-      q = distributions.Normal(loc=mu_q, scale=sigma_q)
+      p = normal_lib.Normal(loc=mu_p, scale=sigma_p)
+      q = normal_lib.Normal(loc=mu_q, scale=sigma_q)
 
       # Compute E_p[X^2].
       # Should equal [1, (2/3)^2]
-      log_e_x2 = monte_carlo.expectation_importance_sampler_logspace(
+      log_e_x2 = mc.expectation_importance_sampler_logspace(
           log_f=lambda x: math_ops.log(math_ops.square(x)),
           log_p=p.log_prob,
           sampling_dist_q=q,
@@ -124,11 +128,11 @@ class ExpectationTest(test.TestCase):
     random_seed.set_random_seed(0)
     n = 20000
     with self.test_session():
-      p = distributions.Normal(loc=[1.0, -1.0], scale=[0.3, 0.5])
+      p = normal_lib.Normal(loc=[1.0, -1.0], scale=[0.3, 0.5])
       # Compute E_p[X] and E_p[X^2].
       z = p.sample(n, seed=42)
-      e_x = monte_carlo.expectation(lambda x: x, p, z=z, seed=42)
-      e_x2 = monte_carlo.expectation(math_ops.square, p, z=z, seed=0)
+      e_x = mc.expectation(lambda x: x, p, z=z, seed=42)
+      e_x2 = mc.expectation(math_ops.square, p, z=z, seed=0)
       var = e_x2 - math_ops.square(e_x)
 
       self.assertEqual(p.batch_shape, e_x.get_shape())
@@ -145,7 +149,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_raises_if_both_z_and_n_are_none(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = None
       n = None
       seed = None
@@ -154,7 +158,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_raises_if_both_z_and_n_are_not_none(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = dist.sample(seed=42)
       n = 1
       seed = None
@@ -163,7 +167,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_returns_n_samples_if_n_provided(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = None
       n = 10
       seed = None
@@ -172,7 +176,7 @@ class GetSamplesTest(test.TestCase):
 
   def test_returns_z_if_z_provided(self):
     with self.test_session():
-      dist = distributions.Normal(loc=0., scale=1.)
+      dist = normal_lib.Normal(loc=0., scale=1.)
       z = dist.sample(10, seed=42)
       n = None
       seed = None
@@ -180,5 +184,55 @@ class GetSamplesTest(test.TestCase):
       self.assertEqual((10,), z.get_shape())
 
 
+class Expectationv2Test(test.TestCase):
+
+  def test_works_correctly(self):
+    with self.test_session() as sess:
+      x = constant_op.constant([-1e6, -100, -10, -1, 1, 10, 100, 1e6])
+      p = normal_lib.Normal(loc=x, scale=1.)
+
+      # We use the prefex "efx" to mean "E_p[f(X)]".
+      f = lambda u: u
+      efx_true = x
+      samples = p.sample(int(1e5), seed=1)
+      efx_reparam = mc.expectation_v2(f, samples, p.log_prob)
+      efx_score = mc.expectation_v2(f, samples, p.log_prob,
+                                    use_reparametrization=False)
+
+      [
+          efx_true_,
+          efx_reparam_,
+          efx_score_,
+          efx_true_grad_,
+          efx_reparam_grad_,
+          efx_score_grad_,
+      ] = sess.run([
+          efx_true,
+          efx_reparam,
+          efx_score,
+          gradients_impl.gradients(efx_true, x)[0],
+          gradients_impl.gradients(efx_reparam, x)[0],
+          gradients_impl.gradients(efx_score, x)[0],
+      ])
+
+      self.assertAllEqual(np.ones_like(efx_true_grad_), efx_true_grad_)
+
+      self.assertAllClose(efx_true_, efx_reparam_, rtol=0.005, atol=0.)
+      self.assertAllClose(efx_true_, efx_score_, rtol=0.005, atol=0.)
+
+      self.assertAllEqual(np.ones_like(efx_true_grad_, dtype=np.bool),
+                          np.isfinite(efx_reparam_grad_))
+      self.assertAllEqual(np.ones_like(efx_true_grad_, dtype=np.bool),
+                          np.isfinite(efx_score_grad_))
+
+      self.assertAllClose(efx_true_grad_, efx_reparam_grad_,
+                          rtol=0.03, atol=0.)
+      # Variance is too high to be meaningful, so we'll only check those which
+      # converge.
+      self.assertAllClose(efx_true_grad_[2:-2],
+                          efx_score_grad_[2:-2],
+                          rtol=0.05, atol=0.)
+
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 7b51d8d932..09389e5d38 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -40,8 +40,8 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib import framework as contrib_framework
+from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo
 from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import distribution
@@ -750,7 +750,7 @@ def monte_carlo_csiszar_f_divergence(
   ```none
   D_f[p(X), q(X)] := E_{q(X)}[ f( p(X) / q(X) ) ]
                   ~= m**-1 sum_j^m f( p(x_j) / q(x_j) ),
-                             where x_j ~iid q(x)
+                             where x_j ~iid q(X)
   ```
 
   Tricks: Reparameterization and Score-Gradient
@@ -759,8 +759,8 @@ def monte_carlo_csiszar_f_divergence(
   parameterless distribution (e.g.,
   `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and
   expectation, i.e.,
-  `nabla Avg{ s_i : i=1...n } = Avg{ nabla s_i : i=1...n }` where `S_n=Avg{s_i}`
-  and `s_i = f(x_i), x_i ~ q`.
+  `grad[Avg{ s_i : i=1...n }] = Avg{ grad[s_i] : i=1...n }` where `S_n=Avg{s_i}`
+  and `s_i = f(x_i), x_i ~iid q(X)`.
 
   However, if q is not reparameterized, TensorFlow's gradient will be incorrect
   since the chain-rule stops at samples of unreparameterized distributions. In
@@ -768,22 +768,17 @@ def monte_carlo_csiszar_f_divergence(
   gradient, i.e.,
 
   ```none
-  nabla E_q[f(X)]
-  = nabla int dx q(x) f(x)
-  = int dx nabla [ q(x) f(x) ]
-  = int dx q'(x) f(x) + q(x) f'(x)
+  grad[ E_q[f(X)] ]
+  = grad[ int dx q(x) f(x) ]
+  = int dx grad[ q(x) f(x) ]
+  = int dx [ q'(x) f(x) + q(x) f'(x) ]
   = int dx q(x) [q'(x) / q(x) f(x) + f'(x) ]
-  = int dx q(x) nabla [ log(q(x)) stopgrad[f(x)] + f(x) ]
-  = E_q[ nabla [ log(q(X)) stopgrad[f(X)] + f(X) ] ]
-  ~= Avg{ log(q(y_i)) stopgrad[f(y_i)] + f(y_i) : y_i = stopgrad[x_i], x_i ~ q}
+  = int dx q(x) grad[ f(x) q(x) / stop_grad[q(x)] ]
+  = E_q[ grad[ f(x) q(x) / stop_grad[q(x)] ] ]
   ```
 
   Unless `q.reparameterization_type != distribution.FULLY_REPARAMETERIZED` it is
-  usually preferable to `use_reparametrization = True`.
-
-  Warning: using `use_reparametrization = False` will mean that the result is
-  *not* the Csiszar f-Divergence. However its expected gradient *is* the
-  gradient of the Csiszar f-Divergence.
+  usually preferable to set `use_reparametrization = True`.
 
   Example Application:
 
@@ -817,10 +812,7 @@ def monte_carlo_csiszar_f_divergence(
 
   Returns:
     monte_carlo_csiszar_f_divergence: Floating-type `Tensor` Monte Carlo
-      approximation of the Csiszar f-Divergence. Warning: using
-      `use_reparametrization = False` will mean that the result is *not* the
-      Csiszar f-Divergence. However its expected gradient *is* the actual
-      gradient of the Csiszar f-Divergence.
+      approximation of the Csiszar f-Divergence.
 
   Raises:
     ValueError: if `q` is not a reparameterized distribution and
@@ -831,24 +823,16 @@ def monte_carlo_csiszar_f_divergence(
       to parameters) is valid.
   """
   with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]):
-    x = q.sample(num_draws, seed=seed)
-    if use_reparametrization:
+    if (use_reparametrization and
+        q.reparameterization_type != distribution.FULLY_REPARAMETERIZED):
       # TODO(jvdillon): Consider only raising an exception if the gradient is
       # requested.
-      if q.reparameterization_type != distribution.FULLY_REPARAMETERIZED:
-        raise ValueError(
-            "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
-            "transformation of a parameterless distribution. (Otherwise this "
-            "function has a biased gradient.)")
-      return math_ops.reduce_mean(f(p.log_prob(x) - q.log_prob(x)), axis=0)
-    else:
-      x = array_ops.stop_gradient(x)
-      logqx = q.log_prob(x)
-      fx = f(p.log_prob(x) - logqx)
-      # Alternatively we could have returned:
-      #   reduce_mean(fx * exp(logqx) / stop_gradient(exp(logqx)), axis=0)
-      # This is nice because it means the result is exactly the Csiszar
-      # f-Divergence yet the gradient is unbiased. However its numerically
-      # unstable since the q is not in log-domain.
-      return math_ops.reduce_mean(logqx * array_ops.stop_gradient(fx) + fx,
-                                  axis=0)
+      raise ValueError(
+          "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
+          "transformation of a parameterless distribution. (Otherwise this "
+          "function has a biased gradient.)")
+    return monte_carlo.expectation_v2(
+        f=lambda x: f(p.log_prob(x) - q.log_prob(x)),
+        samples=q.sample(num_draws, seed=seed),
+        log_prob=q.log_prob,
+        use_reparametrization=use_reparametrization)
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
index 5770bcdd70..848a01421f 100644
--- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
@@ -29,6 +29,7 @@ from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = [
     'expectation',
+    'expectation_v2',
     'expectation_importance_sampler',
     'expectation_importance_sampler_logspace',
 ]
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
index 3590f940ac..3f836e7149 100644
--- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
@@ -32,6 +32,7 @@ from tensorflow.python.ops import nn
 
 __all__ = [
     'expectation',
+    'expectation_v2',
     'expectation_importance_sampler',
     'expectation_importance_sampler_logspace',
 ]
@@ -251,6 +252,100 @@ def expectation(f, p, z=None, n=None, seed=None, name='expectation'):
     return _sample_mean(f(z))
 
 
+def expectation_v2(f, samples, log_prob=None, use_reparametrization=True,
+                   axis=0, keep_dims=False, name=None):
+  """Computes the Monte-Carlo approximation of `E_p[f(X)]`.
+
+  This function computes the Monte-Carlo approximation of an expectation, i.e.,
+
+  ```none
+  E_p[f(X)] approx= m**-1 sum_i^m f(x_j),  x_j ~iid p(X)
+  ```
+
+  where:
+
+  - `x_j = samples[j, ...]`,
+  - `log(p(samples)) = log_prob(samples)` and
+  - `m = prod(shape(samples)[axis])`.
+
+  Tricks: Reparameterization and Score-Gradient
+
+  When p is "reparameterized", i.e., a diffeomorphic transformation of a
+  parameterless distribution (e.g.,
+  `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and
+  expectation, i.e.,
+  `grad[ Avg{ s_i : i=1...n } ] = Avg{ grad[s_i] : i=1...n }` where
+  `S_n = Avg{s_i}` and `s_i = f(x_i), x_i ~ p`.
+
+  However, if p is not reparameterized, TensorFlow's gradient will be incorrect
+  since the chain-rule stops at samples of unreparameterized distributions. In
+  this circumstance using the Score-Gradient trick results in an unbiased
+  gradient, i.e.,
+
+  ```none
+  grad[ E_p[f(X)] ]
+  = grad[ int dx p(x) f(x) ]
+  = int dx grad[ p(x) f(x) ]
+  = int dx [ p'(x) f(x) + p(x) f'(x) ]
+  = int dx p(x) [p'(x) / p(x) f(x) + f'(x) ]
+  = int dx p(x) grad[ f(x) p(x) / stop_grad[p(x)] ]
+  = E_p[ grad[ f(x) p(x) / stop_grad[p(x)] ] ]
+  ```
+
+  Unless p is not reparametrized, it is usually preferable to
+  `use_reparametrization = True`.
+
+  Warning: users are responsible for verifying `p` is a "reparameterized"
+  distribution.
+
+  Args:
+    f: Python callable which can return `f(samples)`.
+    samples: `Tensor` of samples used to form the Monte-Carlo approximation of
+      `E_p[f(X)]`.  A batch of samples should be indexed by `axis` dimensions.
+    log_prob: Python callable which can return `log_prob(samples)`. Must
+      correspond to the natural-logarithm of the pdf/pmf of each sample. Only
+      required/used if `use_reparametrization=False`.
+    use_reparametrization: Python `bool` indicating that the approximation
+      should use the fact that the gradient of samples is unbiased.
+    axis: The dimensions to average. If `None` (the default), averages all
+      dimensions.
+    keep_dims: If true, retains averaged dimensions with length 1.
+    name: A `name_scope` for operations created by this function (optional).
+      Default value: "expectation_v2".
+
+  Returns:
+    approx_expectation: `Tensor` corresponding to the Monte-Carlo approximation
+      of `E_p[f(X)]`.
+
+  Raises:
+    ValueError: if `f` is not `callable`.
+    ValueError: if `use_reparametrization=False` and `log_prob` is not
+      `callable`.
+  """
+
+  with ops.name_scope(name, 'expectation_v2', [samples]):
+    if not callable(f):
+      raise ValueError('`f` must be a callable function.')
+    if use_reparametrization:
+      return math_ops.reduce_mean(f(samples), axis=axis, keep_dims=keep_dims)
+    else:
+      if not callable(log_prob):
+        raise ValueError('`log_prob` must be a callable function.')
+      x = array_ops.stop_gradient(samples)
+      logpx = log_prob(x)
+      # Numerically, exp(g(x) - stop[g(x)]) is always 1, even if exp(g(x)) is
+      # unstable. But the gradient is also stable, ie,
+      # d/dx exp(g(x) - stop[g(x)])
+      # = exp(g(x) - stop[g(x)]) d/dx g(x)
+      # = d/dx g(x)   [numerically exact since IEEE754 has the property
+      #                that for any finite floating-point number x:
+      #                x - x == 0.0]
+      return math_ops.reduce_mean(
+          f(x) * math_ops.exp(logpx - array_ops.stop_gradient(logpx)),
+          axis=axis,
+          keep_dims=keep_dims)
+
+
 def _sample_mean(values):
   """Mean over sample indices.  In this module this is always [0]."""
   return math_ops.reduce_mean(values, reduction_indices=[0])
diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index c1600bdabd..c1ce8bfa2e 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -7,6 +7,13 @@ package(default_visibility = [
     "//visibility:public",
 ])
 
+load("//tensorflow:tensorflow.bzl", "py_test")
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
+load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
+
 filegroup(
     name = "all_files",
     srcs = glob(
@@ -19,3 +26,626 @@ filegroup(
 )
 
 package_group(name = "friends")
+
+cc_library(
+    name = "boosted_trees_kernels",
+    deps = [
+        ":ensemble_optimizer_ops_kernels",
+        ":model_ops_kernels",
+        ":prediction_ops_kernels",
+        ":quantile_ops_kernels",
+        ":split_handler_ops_kernels",
+        ":stats_accumulator_ops_kernels",
+        ":training_ops_kernels",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "boosted_trees_ops_op_lib",
+    deps = [
+        ":ensemble_optimizer_ops_op_lib",
+        ":model_ops_op_lib",
+        ":prediction_ops_op_lib",
+        ":quantile_ops_op_lib",
+        ":split_handler_ops_op_lib",
+        ":stats_accumulator_ops_op_lib",
+        ":training_ops_op_lib",
+    ],
+)
+
+py_library(
+    name = "init_py",
+    srcs = [
+        "__init__.py",
+        "python/__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":boosted_trees_ops_py",
+    ],
+)
+
+# Kernel tests
+
+py_test(
+    name = "ensemble_optimizer_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/ensemble_optimizer_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
+    deps = [
+        ":ensemble_optimizer_ops_py",
+        ":model_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "model_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/model_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
+    deps = [
+        ":ensemble_optimizer_ops_py",
+        ":model_ops_py",
+        ":prediction_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "prediction_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/prediction_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
+    deps = [
+        ":model_ops_py",
+        ":prediction_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "quantile_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/quantile_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
+    deps = [
+        ":quantile_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "split_handler_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/split_handler_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":split_handler_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:training",
+    ],
+)
+
+py_test(
+    name = "stats_accumulator_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/stats_accumulator_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
+    deps = [
+        ":stats_accumulator_ops_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "training_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/training_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
+    deps = [
+        ":model_ops_py",
+        ":training_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
+    ],
+)
+
+# Ops
+
+py_library(
+    name = "batch_ops_utils_py",
+    srcs = ["python/ops/batch_ops_utils.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "boosted_trees_ops_py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":ensemble_optimizer_ops_py",
+        ":model_ops_py",
+        ":prediction_ops_py",
+        ":quantile_ops_py",
+        ":split_handler_ops_py",
+        ":stats_accumulator_ops_py",
+        ":training_ops_py",
+    ],
+)
+
+# Model Ops.
+tf_gen_op_libs(
+    op_lib_names = ["model_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_model_ops_py",
+    out = "python/ops/gen_model_ops.py",
+    deps = [":model_ops_op_lib"],
+)
+
+tf_custom_op_py_library(
+    name = "model_ops_py",
+    srcs = ["python/ops/model_ops.py"],
+    dso = [":python/ops/_model_ops.so"],
+    kernels = [
+        ":model_ops_kernels",
+        ":model_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_model_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_kernel_library(
+    name = "model_ops_kernels",
+    srcs = [
+        "kernels/model_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_model_ops.so",
+    srcs = [
+        "kernels/model_ops.cc",
+        "ops/model_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+# Split handler Ops.
+tf_gen_op_libs(
+    op_lib_names = ["split_handler_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_split_handler_ops_py",
+    out = "python/ops/gen_split_handler_ops.py",
+    deps = [
+        ":split_handler_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "split_handler_ops_py",
+    srcs = ["python/ops/split_handler_ops.py"],
+    dso = [":python/ops/_split_handler_ops.so"],
+    kernels = [
+        ":split_handler_ops_kernels",
+        ":split_handler_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_split_handler_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_split_handler_ops.so",
+    srcs = [
+        "kernels/split_handler_ops.cc",
+        "ops/split_handler_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:feature-column-handlers",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+    ],
+)
+
+tf_kernel_library(
+    name = "split_handler_ops_kernels",
+    srcs = [
+        "kernels/split_handler_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:feature-column-handlers",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Training Ops.
+tf_gen_op_libs(
+    op_lib_names = [
+        "training_ops",
+    ],
+    deps = ["//tensorflow/contrib/boosted_trees/proto:learner_proto_cc"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_training_ops_py",
+    out = "python/ops/gen_training_ops.py",
+    deps = [
+        ":training_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "training_ops_py",
+    srcs = ["python/ops/training_ops.py"],
+    dso = [":python/ops/_training_ops.so"],
+    kernels = [
+        ":training_ops_kernels",
+        ":training_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_training_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_training_ops.so",
+    srcs = [
+        "kernels/training_ops.cc",
+        "ops/training_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "training_ops_kernels",
+    srcs = [
+        "kernels/training_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Prediction Ops.
+tf_gen_op_libs(
+    op_lib_names = ["prediction_ops"],
+    deps = ["//tensorflow/contrib/boosted_trees/proto:learner_proto_cc"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_prediction_ops_py",
+    out = "python/ops/gen_prediction_ops.py",
+    deps = [
+        ":prediction_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "prediction_ops_py",
+    srcs = ["python/ops/prediction_ops.py"],
+    dso = [":python/ops/_prediction_ops.so"],
+    kernels = [
+        ":prediction_ops_kernels",
+        ":prediction_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_prediction_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_prediction_ops.so",
+    srcs = [
+        "kernels/prediction_ops.cc",
+        "ops/prediction_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:example_partitioner",
+        "//tensorflow/contrib/boosted_trees/lib:models",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "prediction_ops_kernels",
+    srcs = [
+        "kernels/prediction_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:example_partitioner",
+        "//tensorflow/contrib/boosted_trees/lib:models",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Quantile ops
+tf_gen_op_libs(
+    op_lib_names = ["quantile_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_quantile_ops_py_wrap",
+    out = "python/ops/gen_quantile_ops.py",
+    deps = [
+        ":quantile_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "quantile_ops_py",
+    srcs = ["python/ops/quantile_ops.py"],
+    dso = [":python/ops/_quantile_ops.so"],
+    kernels = [
+        ":quantile_ops_kernels",
+        ":quantile_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":batch_ops_utils_py",
+        ":gen_quantile_ops_py_wrap",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_quantile_ops.so",
+    srcs = [
+        "kernels/quantile_ops.cc",
+        "ops/quantile_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/lib:weighted_quantiles",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:quantile_stream_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "quantile_ops_kernels",
+    srcs = [
+        "kernels/quantile_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/lib:weighted_quantiles",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:quantile_stream_resource",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+# Ensemble optimizer ops
+tf_gen_op_libs(
+    op_lib_names = ["ensemble_optimizer_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_ensemble_optimizer_ops_py",
+    out = "python/ops/gen_ensemble_optimizer_ops.py",
+    deps = [
+        ":ensemble_optimizer_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "ensemble_optimizer_ops_py",
+    srcs = ["python/ops/ensemble_optimizer_ops.py"],
+    dso = [":python/ops/_ensemble_optimizer_ops.so"],
+    kernels = [
+        ":ensemble_optimizer_ops_kernels",
+        ":ensemble_optimizer_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_ensemble_optimizer_ops_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_kernel_library(
+    name = "ensemble_optimizer_ops_kernels",
+    srcs = [
+        "kernels/ensemble_optimizer_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_ensemble_optimizer_ops.so",
+    srcs = [
+        "kernels/ensemble_optimizer_ops.cc",
+        "ops/ensemble_optimizer_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/resources:decision_tree_ensemble_resource",
+    ],
+)
+
+# Stats Accumulator ops
+tf_gen_op_libs(
+    op_lib_names = ["stats_accumulator_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_stats_accumulator_ops_py_wrap",
+    out = "python/ops/gen_stats_accumulator_ops.py",
+    deps = [
+        ":stats_accumulator_ops_op_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "stats_accumulator_ops_py",
+    srcs = ["python/ops/stats_accumulator_ops.py"],
+    dso = [":python/ops/_stats_accumulator_ops.so"],
+    kernels = [
+        ":stats_accumulator_ops_kernels",
+        ":stats_accumulator_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":batch_ops_utils_py",
+        ":gen_stats_accumulator_ops_py_wrap",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+tf_custom_op_library(
+    name = "python/ops/_stats_accumulator_ops.so",
+    srcs = [
+        "kernels/stats_accumulator_ops.cc",
+        "ops/stats_accumulator_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/resources:stamped_resource",
+    ],
+)
+
+tf_kernel_library(
+    name = "stats_accumulator_ops_kernels",
+    srcs = [
+        "kernels/stats_accumulator_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/contrib/boosted_trees/resources:stamped_resource",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+    alwayslink = 1,
+)
+
+# Pip
+
+py_library(
+    name = "boosted_trees_pip",
+    deps = [
+        ":init_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+    ],
+)
diff --git a/tensorflow/contrib/boosted_trees/__init__.py b/tensorflow/contrib/boosted_trees/__init__.py
new file mode 100644
index 0000000000..afb2d049b7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Gradient boosted trees implementation in tensorflow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.boosted_trees.python import *
+# pylint: enable=unused-import,wildcard-import
diff --git a/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
new file mode 100644
index 0000000000..a6b7a050c8
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
@@ -0,0 +1,244 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/refcount.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+using boosted_trees::models::DecisionTreeEnsembleResource;
+using boosted_trees::trees::DecisionTreeEnsembleConfig;
+using boosted_trees::utils::DropoutUtils;
+using errors::InvalidArgument;
+
+namespace {
+
+// Learning rate epsilon.
+const float kLearningRateEps = 1e-8;
+
+}  // namespace
+
+class AddTreesToEnsembleOp : public OpKernel {
+ public:
+  explicit AddTreesToEnsembleOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    // Ensure feature importance lhs inputs are references.
+    OP_REQUIRES(
+        context,
+        IsRefType(context->input_type(kFeatureColumnUsageCountsHandleIdx)),
+        errors::InvalidArgument(
+            "Feature usage counts lhs input needs to be a ref type"));
+    OP_REQUIRES(context,
+                IsRefType(context->input_type(kFeatureColumnGainsHandleIdx)),
+                errors::InvalidArgument(
+                    "Feature gains lhs input needs to be a ref type"));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    DecisionTreeEnsembleResource* decision_tree_ensemble_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(
+        context, LookupResource(
+                     context, HandleFromInput(context, kTreeEnsembleHandleIdx),
+                     &decision_tree_ensemble_resource));
+    // Lock the resource since we're mutating it.
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    // Remove the reference at the end of this scope.
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+
+    // Read feature importance info.
+    mutex_lock fc_usage_counts_mutex_lock(
+        *context->input_ref_mutex(kFeatureColumnUsageCountsHandleIdx));
+    mutex_lock fc_gains_mutex_lock(
+        *context->input_ref_mutex(kFeatureColumnGainsHandleIdx));
+    Tensor fc_usage_counts_lhs_t =
+        context->mutable_input(kFeatureColumnUsageCountsHandleIdx, true);
+    OP_REQUIRES(context,
+                TensorShapeUtils::IsVector(fc_usage_counts_lhs_t.shape()),
+                InvalidArgument("Feature usage counts should be a vector."));
+    OP_REQUIRES(context, fc_usage_counts_lhs_t.IsInitialized(),
+                errors::FailedPrecondition(
+                    "Attempting to use uninitialized variables: ",
+                    requested_input(kFeatureColumnUsageCountsHandleIdx)));
+
+    Tensor fc_gains_lhs_t =
+        context->mutable_input(kFeatureColumnGainsHandleIdx, true);
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(fc_gains_lhs_t.shape()),
+                InvalidArgument("Feature gains should be a vector."));
+    OP_REQUIRES(context, fc_gains_lhs_t.IsInitialized(),
+                errors::FailedPrecondition(
+                    "Attempting to use uninitialized variables: ",
+                    requested_input(kFeatureColumnGainsHandleIdx)));
+
+    const Tensor fc_usage_counts_rhs_t =
+        context->input(kFeatureColumnUsageCountsToAddIdx);
+    OP_REQUIRES(
+        context,
+        fc_usage_counts_lhs_t.shape().IsSameSize(fc_usage_counts_rhs_t.shape()),
+        errors::InvalidArgument(
+            "Shapes of both feature usage counts tensors should match.",
+            " lhs shape= ", fc_usage_counts_lhs_t.shape().DebugString(),
+            " rhs shape= ", fc_usage_counts_rhs_t.shape().DebugString()));
+
+    const Tensor fc_gains_rhs_t = context->input(kFeatureColumnGainsToAddIdx);
+    OP_REQUIRES(context,
+                fc_gains_lhs_t.shape().IsSameSize(fc_gains_rhs_t.shape()),
+                errors::InvalidArgument(
+                    "Shapes of both feature gains tensors should match.",
+                    " lhs shape= ", fc_gains_lhs_t.shape().DebugString(),
+                    " rhs shape= ", fc_gains_rhs_t.shape().DebugString()));
+
+    // Read in info about trees that were dropped.
+    Tensor dropped_trees_info_t = context->input(kDropedTreesInfoTensorIdx);
+    OP_REQUIRES(context,
+                TensorShapeUtils::IsMatrix(dropped_trees_info_t.shape()),
+                InvalidArgument("Dropped trees info should be matrix."));
+
+    const auto& dropout_info = dropped_trees_info_t.matrix<float>();
+
+    // Parse the passed in tree ensemble.
+    Tensor tree_ensemble_config_t = context->input(kEnsembleToAddTensorIdx);
+    OP_REQUIRES(
+        context, TensorShapeUtils::IsScalar(tree_ensemble_config_t.shape()),
+        errors::InvalidArgument("Tree ensemble config must be a scalar."));
+    // Arena increase spatial locality which reduces the average latency to
+    // access memory, as working set of pages will be fewer.
+    // arena has type proto2::Arena*.
+    auto* arena =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble()
+            ->GetArena();
+    DecisionTreeEnsembleConfig* ensemble_to_add =
+        protobuf::Arena::CreateMessage<DecisionTreeEnsembleConfig>(arena);
+    OP_REQUIRES(
+        context, ParseProtoUnlimited(ensemble_to_add,
+                                     tree_ensemble_config_t.scalar<string>()()),
+        errors::InvalidArgument("Unable to parse tree ensemble config."));
+
+    auto* mutable_ensemble =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+
+    // Read the learning_rate
+    Tensor learning_rate_t = context->input(kLearningRateTensorIdx);
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(learning_rate_t.shape()),
+                InvalidArgument("Learning rate should be a scalar."));
+
+    const float learning_rate = learning_rate_t.scalar<float>()();
+    if (learning_rate < kLearningRateEps) {
+      return;
+    }
+    // Prepare current weights vec.
+    std::vector<float> current_weights;
+    current_weights.reserve(mutable_ensemble->tree_weights_size());
+    for (const float weight : mutable_ensemble->tree_weights()) {
+      current_weights.push_back(weight);
+    }
+    const int32 num_dropped = dropped_trees_info_t.dim_size(1);
+    std::vector<int> dropped_trees;
+    dropped_trees.reserve(num_dropped);
+    std::vector<float> dropped_trees_original_weights;
+    dropped_trees_original_weights.reserve(num_dropped);
+    for (int i = 0; i < num_dropped; ++i) {
+      dropped_trees.push_back(dropout_info(0, i));
+      dropped_trees_original_weights.push_back(dropout_info(1, i));
+    }
+
+    std::vector<int32> num_updates;
+    num_updates.reserve(mutable_ensemble->tree_metadata_size());
+
+    for (const auto& meta : mutable_ensemble->tree_metadata()) {
+      num_updates.push_back(meta.num_tree_weight_updates());
+    }
+
+    // If there was a dropout, come up with tree weights
+    const bool was_dropout = !dropped_trees.empty();
+    if (was_dropout) {
+      // New tree/s will be added to the end of the ensemble's tree list.
+      const int32 new_tree_index = current_weights.size();
+      DropoutUtils::GetTreesWeightsForAddingTrees(
+          dropped_trees, dropped_trees_original_weights, new_tree_index,
+          ensemble_to_add->trees_size(), &current_weights, &num_updates);
+
+      // Update the weights of trees according to current weights;
+      for (int i = 0; i < mutable_ensemble->trees_size(); ++i) {
+        mutable_ensemble->set_tree_weights(i, current_weights[i]);
+      }
+    }
+
+    // Add the trees from ensemble_to_add to the tree ensemble variable.
+    int i = mutable_ensemble->trees_size();
+    for (auto& tree : *ensemble_to_add->mutable_trees()) {
+      (*mutable_ensemble->add_trees()).Swap(&tree);
+
+      // New trees were updated only once.
+      auto* meta = mutable_ensemble->add_tree_metadata();
+      meta->set_num_tree_weight_updates(1);
+
+      // When we add complete trees to the ensemble in one step, each tree
+      // that's added is final.
+      meta->set_is_finalized(true);
+
+      if (was_dropout) {
+        mutable_ensemble->add_tree_weights(current_weights[i++]);
+      } else {
+        mutable_ensemble->add_tree_weights(learning_rate);
+      }
+    }
+
+    // Update the number of updates.
+    if (was_dropout) {
+      for (int i = 0; i < num_updates.size(); ++i) {
+        mutable_ensemble->mutable_tree_metadata(i)->set_num_tree_weight_updates(
+            num_updates[i]);
+      }
+    }
+
+    // Update feature importance.
+    fc_usage_counts_lhs_t.vec<int64>() += fc_usage_counts_rhs_t.vec<int64>();
+    fc_gains_lhs_t.vec<float>() += learning_rate * fc_gains_rhs_t.vec<float>();
+  }
+
+ private:
+  // Input tensor indices.
+  // Note that Op definition changes might cause input indices to need
+  // changing as well.
+  static const int kTreeEnsembleHandleIdx = 0;
+  static const int kEnsembleToAddTensorIdx = 1;
+  static const int kFeatureColumnUsageCountsHandleIdx = 2;
+  static const int kFeatureColumnUsageCountsToAddIdx = 3;
+  static const int kFeatureColumnGainsHandleIdx = 4;
+  static const int kFeatureColumnGainsToAddIdx = 5;
+  static const int kDropedTreesInfoTensorIdx = 6;
+  static const int kLearningRateTensorIdx = 7;
+};
+
+REGISTER_KERNEL_BUILDER(Name("AddTreesToEnsemble").Device(DEVICE_CPU),
+                        AddTreesToEnsembleOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
new file mode 100644
index 0000000000..6f553df6d7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
@@ -0,0 +1,169 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <string>
+
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+
+using boosted_trees::models::DecisionTreeEnsembleResource;
+
+// Creates a tree ensemble variable.
+class CreateTreeEnsembleVariableOp : public OpKernel {
+ public:
+  explicit CreateTreeEnsembleVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Get the tree ensemble config.
+    const Tensor* tree_ensemble_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_ensemble_config",
+                                           &tree_ensemble_config_t));
+    auto* result = new boosted_trees::models::DecisionTreeEnsembleResource();
+    result->set_stamp(stamp_token);
+    if (!ParseProtoUnlimited(result->mutable_decision_tree_ensemble(),
+                             tree_ensemble_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false, errors::InvalidArgument(
+                                      "Unable to parse tree ensemble config."));
+    }
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+};
+
+// Op for retrieving a model stamp token without having to serialize.
+class TreeEnsembleStampTokenOp : public OpKernel {
+ public:
+  explicit TreeEnsembleStampTokenOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    Tensor* output_stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape(),
+                                                     &output_stamp_token_t));
+    output_stamp_token_t->scalar<int64>()() =
+        decision_tree_ensemble_resource->stamp();
+  }
+};
+
+// Op for serializing a model.
+class TreeEnsembleSerializeOp : public OpKernel {
+ public:
+  explicit TreeEnsembleSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    Tensor* output_stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape(),
+                                                     &output_stamp_token_t));
+    output_stamp_token_t->scalar<int64>()() =
+        decision_tree_ensemble_resource->stamp();
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(1, TensorShape(), &output_config_t));
+    output_config_t->scalar<string>()() =
+        decision_tree_ensemble_resource->decision_tree_ensemble()
+            .SerializeAsString();
+  }
+};
+
+// Op for deserializing a tree ensemble variable from a checkpoint.
+class TreeEnsembleDeserializeOp : public OpKernel {
+ public:
+  explicit TreeEnsembleDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Get the tree ensemble config.
+    const Tensor* tree_ensemble_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_ensemble_config",
+                                           &tree_ensemble_config_t));
+    // Deallocate all the previous objects on the resource.
+    decision_tree_ensemble_resource->Reset();
+    decision_tree_ensemble_resource->set_stamp(stamp_token);
+    boosted_trees::trees::DecisionTreeEnsembleConfig* config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    OP_REQUIRES(
+        context,
+        ParseProtoUnlimited(config, tree_ensemble_config_t->scalar<string>()()),
+        errors::InvalidArgument("Unable to parse tree ensemble config."));
+  }
+};
+
+REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeEnsembleResource);
+
+REGISTER_KERNEL_BUILDER(
+    Name("TreeEnsembleIsInitializedOp").Device(DEVICE_CPU),
+    IsResourceInitialized<boosted_trees::models::DecisionTreeEnsembleResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateTreeEnsembleVariable").Device(DEVICE_CPU),
+                        CreateTreeEnsembleVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleStampToken").Device(DEVICE_CPU),
+                        TreeEnsembleStampTokenOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleSerialize").Device(DEVICE_CPU),
+                        TreeEnsembleSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleDeserialize").Device(DEVICE_CPU),
+                        TreeEnsembleDeserializeOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
new file mode 100644
index 0000000000..2e99f42ff0
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
@@ -0,0 +1,429 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <algorithm>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h"
+#include "tensorflow/contrib/boosted_trees/lib/models/multiple_additive_trees.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/batch_features.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/device_base.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/refcount.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/types.h"
+
+using tensorflow::boosted_trees::learner::AveragingConfig;
+using tensorflow::boosted_trees::trees::DecisionTreeEnsembleConfig;
+
+namespace tensorflow {
+namespace boosted_trees {
+
+using boosted_trees::learner::LearnerConfig;
+using boosted_trees::learner::LearningRateConfig;
+using boosted_trees::learner::LearningRateDropoutDrivenConfig;
+using boosted_trees::models::MultipleAdditiveTrees;
+using boosted_trees::models::DecisionTreeEnsembleResource;
+using boosted_trees::utils::DropoutUtils;
+using boosted_trees::utils::TensorUtils;
+
+namespace {
+const char* kLearnerConfigAttributeName = "learner_config";
+const char* kSeedTensorName = "seed";
+const char* kApplyDropoutAttributeName = "apply_dropout";
+const char* kApplyAveragingAttributeName = "apply_averaging";
+const char* kDropoutInfoOutputTensorName = "drop_out_tree_indices_weights";
+const char* kPredictionsTensorName = "predictions";
+const char* kNoDropoutPredictionsTensorName = "no_dropout_predictions";
+}
+
+class GradientTreesPredictionOp : public OpKernel {
+ public:
+  explicit GradientTreesPredictionOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("use_locking", &use_locking_));
+
+    OP_REQUIRES_OK(context, context->GetAttr("center_bias", &center_bias_));
+
+    OP_REQUIRES_OK(
+        context, context->GetAttr(kApplyDropoutAttributeName, &apply_dropout_));
+
+    LearnerConfig learner_config;
+    string learner_config_str;
+    OP_REQUIRES_OK(context, context->GetAttr(kLearnerConfigAttributeName,
+                                             &learner_config_str));
+    OP_REQUIRES(
+        context, ParseProtoUnlimited(&learner_config, learner_config_str),
+        errors::InvalidArgument("Unable to parse learner config config."));
+
+    num_classes_ = learner_config.num_classes();
+    OP_REQUIRES(context, num_classes_ >= 2,
+                errors::InvalidArgument("Number of classes must be >=2"));
+    OP_REQUIRES(
+        context, ParseProtoUnlimited(&learner_config, learner_config_str),
+        errors::InvalidArgument("Unable to parse learner config config."));
+
+    prediction_vector_size_ =
+        learner_config.multi_class_strategy() == LearnerConfig::TREE_PER_CLASS
+            ? num_classes_ - 1
+            : num_classes_;
+
+    only_finalized_trees_ =
+        learner_config.growing_mode() == learner_config.WHOLE_TREE;
+    if (learner_config.has_learning_rate_tuner() &&
+        learner_config.learning_rate_tuner().tuner_case() ==
+            LearningRateConfig::kDropout) {
+      dropout_config_ = learner_config.learning_rate_tuner().dropout();
+      has_dropout_ = true;
+    } else {
+      has_dropout_ = false;
+    }
+
+    OP_REQUIRES_OK(context, context->GetAttr(kApplyAveragingAttributeName,
+                                             &apply_averaging_));
+    apply_averaging_ =
+        apply_averaging_ && learner_config.averaging_config().config_case() !=
+                                AveragingConfig::CONFIG_NOT_SET;
+    if (apply_averaging_) {
+      averaging_config_ = learner_config.averaging_config();
+
+      // If there is averaging config, check that the values are correct.
+      switch (averaging_config_.config_case()) {
+        case AveragingConfig::kAverageLastNTreesFieldNumber: {
+          OP_REQUIRES(context, averaging_config_.average_last_n_trees() > 0,
+                      errors::InvalidArgument(
+                          "Average last n trees must be a positive number"));
+          break;
+        }
+        case AveragingConfig::kAverageLastPercentTreesFieldNumber: {
+          OP_REQUIRES(context,
+                      averaging_config_.average_last_percent_trees() > 0 &&
+                          averaging_config_.average_last_percent_trees() <= 1.0,
+                      errors::InvalidArgument(
+                          "Average last percent must be in (0,1] interval."));
+          break;
+        }
+        case AveragingConfig::CONFIG_NOT_SET: {
+          QCHECK(false) << "We should never get here.";
+          break;
+        }
+      }
+    }
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    DecisionTreeEnsembleResource* decision_tree_ensemble_resource;
+    // Gets the resource. Grabs the mutex but releases it.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    // Release the reference to the resource once we're done using it.
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    if (use_locking_) {
+      mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+      DoCompute(context, decision_tree_ensemble_resource);
+    } else {
+      DoCompute(context, decision_tree_ensemble_resource);
+    }
+  }
+
+ private:
+  void DoCompute(
+      OpKernelContext* context,
+      DecisionTreeEnsembleResource* decision_tree_ensemble_resource) {
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Read sparse int features list;
+    OpInputList sparse_int_feature_indices_list;
+    OpInputList sparse_int_feature_values_list;
+    OpInputList sparse_int_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseIntFeatures(
+                                context, &sparse_int_feature_indices_list,
+                                &sparse_int_feature_values_list,
+                                &sparse_int_feature_shapes_list));
+
+    // Infer batch size.
+    const int64 batch_size = TensorUtils::InferBatchSize(
+        dense_float_features_list, sparse_float_feature_shapes_list,
+        sparse_int_feature_shapes_list);
+
+    // Read batch features.
+    boosted_trees::utils::BatchFeatures batch_features(batch_size);
+    OP_REQUIRES_OK(
+        context,
+        batch_features.Initialize(
+            TensorUtils::OpInputListToTensorVec(dense_float_features_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_shapes_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(sparse_int_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_shapes_list)));
+
+    std::vector<int32> dropped_trees;
+    std::vector<float> original_weights;
+
+    // Do dropout if needed.
+    if (apply_dropout_ && has_dropout_) {
+      // Read in seed
+      const Tensor* seed_t;
+      OP_REQUIRES_OK(context, context->input(kSeedTensorName, &seed_t));
+      OP_REQUIRES(context, TensorShapeUtils::IsScalar(seed_t->shape()),
+                  errors::InvalidArgument("Seed must be a scalar."));
+
+      // Cast seed to uint64.
+      const uint64 seed = seed_t->scalar<int64>()();
+
+      std::vector<float> weights;
+      for (const float weight :
+           decision_tree_ensemble_resource->decision_tree_ensemble()
+               .tree_weights()) {
+        weights.push_back(weight);
+      }
+
+      std::unordered_set<int32> trees_not_to_drop;
+      if (center_bias_) {
+        trees_not_to_drop.insert(0);
+      }
+      if (decision_tree_ensemble_resource->decision_tree_ensemble()
+              .has_growing_metadata()) {
+        // We are in batch mode, the last tree is the tree that is being built,
+        // we can't drop it during dropout.
+        const int32 current_tree =
+            decision_tree_ensemble_resource->decision_tree_ensemble()
+                .trees_size() -
+            1;
+        trees_not_to_drop.insert(current_tree);
+      }
+      OP_REQUIRES_OK(context, DropoutUtils::DropOutTrees(
+                                  seed, dropout_config_, trees_not_to_drop,
+                                  weights, &dropped_trees, &original_weights));
+    }
+
+    // Allocate output predictions matrix.
+    Tensor* output_predictions_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(kPredictionsTensorName,
+                                          {batch_size, prediction_vector_size_},
+                                          &output_predictions_t));
+    auto output_predictions = output_predictions_t->matrix<float>();
+
+    Tensor* output_no_dropout_predictions_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(kNoDropoutPredictionsTensorName,
+                                          {batch_size, prediction_vector_size_},
+                                          &output_no_dropout_predictions_t));
+    auto output_no_dropout_predictions =
+        output_no_dropout_predictions_t->matrix<float>();
+
+    // Run predictor.
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+
+    if (apply_averaging_) {
+      DecisionTreeEnsembleConfig adjusted =
+          decision_tree_ensemble_resource->decision_tree_ensemble();
+
+      const int start_averaging = std::max(
+          0.0,
+          averaging_config_.config_case() ==
+                  AveragingConfig::kAverageLastNTreesFieldNumber
+              ? adjusted.trees_size() - averaging_config_.average_last_n_trees()
+              : adjusted.trees_size() *
+                    (1.0 - averaging_config_.average_last_percent_trees()));
+
+      const int num_ensembles = adjusted.trees_size() - start_averaging;
+      for (int i = start_averaging; i < adjusted.trees_size(); ++i) {
+        float weight = adjusted.tree_weights(i);
+        adjusted.mutable_tree_weights()->Set(
+            i, weight * (num_ensembles - i + start_averaging) / num_ensembles);
+      }
+      MultipleAdditiveTrees::Predict(
+          adjusted, only_finalized_trees_, dropped_trees, batch_features,
+          worker_threads, output_predictions, output_no_dropout_predictions);
+    } else {
+      MultipleAdditiveTrees::Predict(
+          decision_tree_ensemble_resource->decision_tree_ensemble(),
+          only_finalized_trees_, dropped_trees, batch_features, worker_threads,
+          output_predictions, output_no_dropout_predictions);
+    }
+
+    // Output dropped trees and original weights.
+    Tensor* output_dropout_info_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                kDropoutInfoOutputTensorName,
+                                {2, static_cast<int64>(dropped_trees.size())},
+                                &output_dropout_info_t));
+    auto output_dropout_info = output_dropout_info_t->matrix<float>();
+
+    for (int32 i = 0; i < dropped_trees.size(); ++i) {
+      output_dropout_info(0, i) = dropped_trees[i];
+      output_dropout_info(1, i) = original_weights[i];
+    }
+  }
+
+ private:
+  LearningRateDropoutDrivenConfig dropout_config_;
+  AveragingConfig averaging_config_;
+  bool only_finalized_trees_;
+  int num_classes_;
+  // What is the size of the output vector for predictions?
+  int prediction_vector_size_;
+  bool apply_dropout_;
+  bool center_bias_;
+  bool apply_averaging_;
+  bool use_locking_;
+  bool has_dropout_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("GradientTreesPrediction").Device(DEVICE_CPU),
+                        GradientTreesPredictionOp);
+
+class GradientTreesPartitionExamplesOp : public OpKernel {
+ public:
+  explicit GradientTreesPartitionExamplesOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("use_locking", &use_locking_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    DecisionTreeEnsembleResource* decision_tree_ensemble_resource;
+    // Gets the resource. Grabs the mutex but releases it.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    // Release the reference to the resource once we're done using it.
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    if (use_locking_) {
+      mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+      DoCompute(context, decision_tree_ensemble_resource);
+    } else {
+      DoCompute(context, decision_tree_ensemble_resource);
+    }
+  }
+
+ private:
+  void DoCompute(
+      OpKernelContext* context,
+      DecisionTreeEnsembleResource* decision_tree_ensemble_resource) {
+    // The last non-finalized tree in the ensemble is by convention the
+    // one to partition on. If no such tree exists, a nodeless tree is
+    // created.
+    const auto& tree_ensemble =
+        decision_tree_ensemble_resource->decision_tree_ensemble();
+    boosted_trees::trees::DecisionTreeConfig empy_tree_config;
+    const boosted_trees::trees::DecisionTreeConfig* tree_config =
+        &empy_tree_config;
+    auto num_trees = tree_ensemble.trees_size();
+    if (num_trees > 0 &&
+        !tree_ensemble.tree_metadata(num_trees - 1).is_finalized()) {
+      tree_config = &tree_ensemble.trees(num_trees - 1);
+    }
+
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Read sparse int features list;
+    OpInputList sparse_int_feature_indices_list;
+    OpInputList sparse_int_feature_values_list;
+    OpInputList sparse_int_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseIntFeatures(
+                                context, &sparse_int_feature_indices_list,
+                                &sparse_int_feature_values_list,
+                                &sparse_int_feature_shapes_list));
+
+    // Infer batch size.
+    const int64 batch_size = TensorUtils::InferBatchSize(
+        dense_float_features_list, sparse_float_feature_shapes_list,
+        sparse_int_feature_shapes_list);
+
+    // Read batch features.
+    boosted_trees::utils::BatchFeatures batch_features(batch_size);
+    OP_REQUIRES_OK(
+        context,
+        batch_features.Initialize(
+            TensorUtils::OpInputListToTensorVec(dense_float_features_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_float_feature_shapes_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_indices_list),
+            TensorUtils::OpInputListToTensorVec(sparse_int_feature_values_list),
+            TensorUtils::OpInputListToTensorVec(
+                sparse_int_feature_shapes_list)));
+
+    // Allocate output partitions vector.
+    Tensor* partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, {batch_size}, &partition_ids_t));
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    learner::ExamplePartitioner::PartitionExamples(
+        *tree_config, batch_features, worker_threads->NumThreads(),
+        worker_threads, partition_ids_t->vec<int32>().data());
+  }
+
+ private:
+  bool use_locking_;
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("GradientTreesPartitionExamples").Device(DEVICE_CPU),
+    GradientTreesPartitionExamplesOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
new file mode 100644
index 0000000000..546f6a30b8
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
@@ -0,0 +1,822 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <algorithm>
+#include <iterator>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/parallel_for.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/quantiles.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/quantile_stream_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+using ::boosted_trees::QuantileConfig;
+using boosted_trees::utils::TensorUtils;
+using boosted_trees::QuantileStreamResource;
+
+namespace {
+const char* const kExampleWeightsName = "example_weights";
+const char* const kMaxElementsName = "max_elements";
+const char* const kHandleName = "handle";
+const char* const kNextStampTokenName = "next_stamp_token";
+const char* const kStampTokenName = "stamp_token";
+const char* const kAreBucketsReadyName = "are_buckets_ready";
+// Names for sparse arguments.
+const char* const kNumSparseFeaturesName = "num_sparse_features";
+const char* const kSparseBucketsName = "sparse_buckets";
+const char* const kSparseValuesName = "sparse_values";
+const char* const kSparseStreamsStateName = "sparse_streams_state";
+const char* const kSparseSummariesName = "sparse_summaries";
+const char* const kSparseConfigName = "sparse_config";
+const char* const kSparseOutputTensorName = "sparse_quantiles";
+// Names for dense arguments.
+const char* const kDenseBucketsName = "dense_buckets";
+const char* const kDenseConfigName = "dense_config";
+const char* const kDenseOutputTensorName = "dense_quantiles";
+const char* const kDenseStreamsStateName = "dense_streams_state";
+const char* const kDenseSummariesName = "dense_summaries";
+const char* const kDenseValuesName = "dense_values";
+const char* const kNumDenseFeaturesName = "num_dense_features";
+const char* const kResourceHandlesName = "quantile_accumulator_handles";
+const char* const kNumQuantilesName = "num_quantiles";
+const char* const kEpsilonName = "epsilon";
+const char* const kBucketsName = "buckets";
+const char* const kStreamStateName = "stream_state";
+const char* const kSummariesName = "summaries";
+
+using QuantileStream =
+    boosted_trees::quantiles::WeightedQuantilesStream<float, float>;
+using QuantileSummary =
+    boosted_trees::quantiles::WeightedQuantilesSummary<float, float>;
+using QuantileSummaryEntry =
+    boosted_trees::quantiles::WeightedQuantilesSummary<float,
+                                                       float>::SummaryEntry;
+
+std::vector<float> GetBuckets(const int32 feature,
+                              const OpInputList& buckets_list) {
+  const auto& buckets = buckets_list[feature].flat<float>();
+  std::vector<float> buckets_vector(buckets.data(),
+                                    buckets.data() + buckets.size());
+  return buckets_vector;
+}
+
+void QuantizeFeatures(const string& output_name, const OpInputList& values_list,
+                      const OpInputList& buckets_list,
+                      OpKernelContext* const context) {
+  if (values_list.size() == 0) {
+    return;
+  }
+  OpOutputList output_list;
+  OP_REQUIRES_OK(context, context->output_list(output_name, &output_list));
+
+  for (int32 feature_index = 0; feature_index < values_list.size();
+       ++feature_index) {
+    const Tensor& values_tensor = values_list[feature_index];
+    const int64 num_values = values_tensor.dim_size(0);
+
+    Tensor* output_t = nullptr;
+    OP_REQUIRES_OK(
+        context, output_list.allocate(feature_index, TensorShape({num_values}),
+                                      &output_t));
+    TTypes<int32>::Vec output = output_t->vec<int32>();
+    const std::vector<float>& buckets_vector =
+        GetBuckets(feature_index, buckets_list);
+    auto flat_values = values_tensor.flat<float>();
+    for (int64 instance = 0; instance < num_values; ++instance) {
+      const float value = flat_values(instance);
+      auto bucket_iter =
+          std::lower_bound(buckets_vector.begin(), buckets_vector.end(), value);
+      if (bucket_iter == buckets_vector.end()) {
+        --bucket_iter;
+      }
+      const int32 bucket =
+          static_cast<int32>(bucket_iter - buckets_vector.begin());
+      output(instance) = bucket;
+    }
+  }
+}
+
+// Validates attributes for the quantile ops.
+Status ReadAndValidateAttributes(OpKernelConstruction* const context,
+                                 int* num_dense_features,
+                                 int* num_sparse_features) {
+  TF_RETURN_IF_ERROR(
+      context->GetAttr(kNumDenseFeaturesName, num_dense_features));
+  TF_RETURN_IF_ERROR(
+      context->GetAttr(kNumSparseFeaturesName, num_sparse_features));
+  if ((*num_dense_features) + (*num_sparse_features) == 0) {
+    return errors::InvalidArgument(
+        "Please provide at least sparse or dense features.");
+  }
+  return Status::OK();
+}
+
+void ParseConfig(OpKernelConstruction* const context, const string& name,
+                 std::vector<QuantileConfig>* output) {
+  std::vector<string> serialized_config;
+  OP_REQUIRES_OK(context, context->GetAttr(name, &serialized_config));
+  output->reserve(serialized_config.size());
+  QuantileConfig tmp;
+  for (const auto& serialized_string : serialized_config) {
+    OP_REQUIRES(context, tmp.ParseFromString(serialized_string),
+                errors::InvalidArgument("Malformed QuantileConfig passed in."));
+    output->push_back(tmp);
+  }
+}
+
+// Generates quantiles on a finalized QuantileStream.
+std::vector<float> GenerateBoundaries(const QuantileStream& stream,
+                                      int num_boundaries) {
+  std::vector<float> boundaries = stream.GenerateBoundaries(num_boundaries);
+
+  // Uniquify elements as we may get dupes.
+  auto end_it = std::unique(boundaries.begin(), boundaries.end());
+  boundaries.resize(std::distance(boundaries.begin(), end_it));
+  return boundaries;
+}
+
+// Copies quantiles to output list.
+void CopyBoundaries(OpKernelContext* const context,
+                    const std::vector<float>& boundaries, const int64 index,
+                    OpOutputList* output_list) {
+  // Output to tensor.
+  Tensor* output_t = nullptr;
+  OP_REQUIRES_OK(
+      context, output_list->allocate(
+                   index, {static_cast<int64>(boundaries.size())}, &output_t));
+  auto* quantiles_flat = output_t->flat<float>().data();
+  memcpy(quantiles_flat, boundaries.data(), sizeof(float) * boundaries.size());
+}
+
+void CopySummaryToProto(const QuantileSummary& summary,
+                        ::boosted_trees::QuantileSummaryState* summary_proto) {
+  summary_proto->mutable_entries()->Reserve(summary.Size());
+  for (const auto& entry : summary.GetEntryList()) {
+    auto* new_entry = summary_proto->add_entries();
+    new_entry->set_value(entry.value);
+    new_entry->set_weight(entry.weight);
+    new_entry->set_min_rank(entry.min_rank);
+    new_entry->set_max_rank(entry.max_rank);
+  }
+}
+
+}  // namespace
+
+// Accumulator for Quantile Summaries.
+REGISTER_RESOURCE_HANDLE_KERNEL(QuantileStreamResource);
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorIsInitialized").Device(DEVICE_CPU),
+    IsResourceInitialized<QuantileStreamResource>);
+
+class CreateQuantileAccumulatorOp : public OpKernel {
+ public:
+  explicit CreateQuantileAccumulatorOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr(kEpsilonName, &epsilon_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr(kNumQuantilesName, &num_quantiles_));
+    OP_REQUIRES_OK(context, context->GetAttr(kMaxElementsName, &max_elements_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions. If one already exists, it unrefs the new one.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    auto result =
+        new QuantileStreamResource(epsilon_, num_quantiles_, max_elements_,
+                                   stamp_token_t->scalar<int64>()());
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  float epsilon_;
+  int32 num_quantiles_;
+  // An upperbound on the number of enteries that the summaries might have
+  // for a feature.
+  int64 max_elements_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("CreateQuantileAccumulator").Device(DEVICE_CPU),
+                        CreateQuantileAccumulatorOp);
+
+// Adds a summary to the quantile summary stream.
+class QuantileAccumulatorAddSummariesOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorAddSummariesOp(
+      OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list(kResourceHandlesName,
+                                                &resource_handle_list));
+    OpInputList summary_list;
+    OP_REQUIRES_OK(context, context->input_list(kSummariesName, &summary_list));
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &summary_list, stamp_token](
+            int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+            QuantileStreamResource* streams_resource;
+            // Create a reference to the underlying resource using the handle.
+            OP_REQUIRES_OK(context,
+                           LookupResource(context, handle, &streams_resource));
+            // Remove the reference at the end of this scope.
+            mutex_lock l(*streams_resource->mutex());
+            core::ScopedUnref unref_me(streams_resource);
+
+            // If the stamp is invalid we drop the update.
+            if (!streams_resource->is_stamp_valid(stamp_token)) {
+              VLOG(1)
+                  << "Invalid stamp token in QuantileAccumulatorAddSummariesOp."
+                  << " Passed stamp token: " << stamp_token << " "
+                  << "Current token: " << streams_resource->stamp();
+              return;
+            }
+
+            protobuf::Arena arena;
+            ::boosted_trees::QuantileSummaryState* summary_proto =
+                protobuf::Arena::CreateMessage<
+                    ::boosted_trees::QuantileSummaryState>(&arena);
+            OP_REQUIRES(
+                context,
+                ParseProtoUnlimited(
+                    summary_proto,
+                    summary_list[resource_handle_idx].scalar<string>()()),
+                errors::InvalidArgument("Unable to parse quantile summary."));
+            std::vector<QuantileSummaryEntry> entries;
+            entries.reserve(summary_proto->entries_size());
+            for (const auto& entry : summary_proto->entries()) {
+              entries.emplace_back(entry.value(), entry.weight(),
+                                   entry.min_rank(), entry.max_rank());
+            }
+
+            // Add the summary to the quantile stream.
+            streams_resource->stream(stamp_token)->PushSummary(entries);
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorAddSummaries").Device(DEVICE_CPU),
+    QuantileAccumulatorAddSummariesOp);
+
+// Generates summaries for given set of float values, and the given config.
+class MakeQuantileSummariesOp : public OpKernel {
+ public:
+  explicit MakeQuantileSummariesOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context,
+                   ReadAndValidateAttributes(context, &num_dense_features_,
+                                             &num_sparse_features_));
+    OP_REQUIRES_OK(context, context->GetAttr(kEpsilonName, &epsilon_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Parse example weights and get batch size.
+    const Tensor* example_weights_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kExampleWeightsName, &example_weights_t));
+    auto example_weights = example_weights_t->flat<float>();
+    const int64 batch_size = example_weights.size();
+
+    OpOutputList sparse_summaries_output_list;
+    OP_REQUIRES_OK(context,
+                   context->output_list(kSparseSummariesName,
+                                        &sparse_summaries_output_list));
+    OpOutputList dense_summaries_output_list;
+    OP_REQUIRES_OK(context, context->output_list(kDenseSummariesName,
+                                                 &dense_summaries_output_list));
+
+    auto do_quantile_summary_gen = [&](const int64 begin, const int64 end) {
+      auto copy_over_summaries = [&](const QuantileStream& stream,
+                                     const int64 index,
+                                     OpOutputList* output_list) {
+        protobuf::Arena arena;
+        ::boosted_trees::QuantileSummaryState* summary_proto =
+            protobuf::Arena::CreateMessage<
+            ::boosted_trees::QuantileSummaryState>(&arena);
+        const auto& summary = stream.GetFinalSummary();
+        CopySummaryToProto(summary, summary_proto);
+        // Output to tensor.
+        Tensor* output_t = nullptr;
+        OP_REQUIRES_OK(context, output_list->allocate(index, {}, &output_t));
+        summary_proto->SerializeToString(&output_t->scalar<string>()());
+      };
+
+      // These are blocks of ranges. We are iterating over both sparse and
+      // dense features i.e. [0, sparse_features.size() + dense_features.size()]
+      for (int64 i = begin; i < end; ++i) {
+        if (i < num_dense_features_) {
+          const int64 dense_index = i;
+          const auto dense_values =
+              dense_float_features_list[dense_index].flat<float>();
+          QuantileStream stream(epsilon_, batch_size + 1);
+          // Run quantile summary generation.
+          for (int64 j = 0; j < batch_size; ++j) {
+            stream.PushEntry(dense_values(j), example_weights(j));
+          }
+          stream.Finalize();
+          // Copy summaries to output.
+          copy_over_summaries(stream, dense_index,
+                              &dense_summaries_output_list);
+        } else {
+          const int64 sparse_index = i - num_dense_features_;
+          const auto sparse_values =
+              sparse_float_feature_values_list[sparse_index].flat<float>();
+          const auto sparse_indices =
+              sparse_float_feature_indices_list[sparse_index].matrix<int64>();
+          QuantileStream stream(epsilon_, batch_size + 1);
+          // Run quantile summary generation.
+          const int64 num_sparse_rows =
+              sparse_float_feature_indices_list[sparse_index].dim_size(0);
+          for (int64 j = 0; j < num_sparse_rows; ++j) {
+            const int64 example_id = sparse_indices(j, 0);
+            stream.PushEntry(sparse_values(j), example_weights(example_id));
+          }
+          stream.Finalize();
+          // Copy summaries to output.
+          copy_over_summaries(stream, sparse_index,
+                              &sparse_summaries_output_list);
+        }
+      }
+    };
+    const int64 kCostPerUnit = 500 * batch_size;
+    const int64 num_features = num_sparse_features_ + num_dense_features_;
+    const DeviceBase::CpuWorkerThreads& worker_threads =
+        *context->device()->tensorflow_cpu_worker_threads();
+    Shard(worker_threads.num_threads, worker_threads.workers, num_features,
+          kCostPerUnit, do_quantile_summary_gen);
+  }
+
+ private:
+  int num_dense_features_;
+  int num_sparse_features_;
+  float epsilon_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("MakeQuantileSummaries").Device(DEVICE_CPU),
+                        MakeQuantileSummariesOp);
+
+// Serializes the state of streams.
+class QuantileAccumulatorSerializeOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorSerializeOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    QuantileStreamResource* streams_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &streams_resource));
+    // Remove the reference at the end of this scope.
+    mutex_lock l(*streams_resource->mutex());
+    core::ScopedUnref unref_me(streams_resource);
+
+    int64 stamp_token = streams_resource->stamp();
+    Tensor* stream_state_t;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(kStreamStateName, TensorShape({}),
+                                            &stream_state_t));
+    bool are_buckets_ready = streams_resource->are_buckets_ready();
+
+    // We are iterating over both dense and sparse features. First we go
+    // through the dense features and then the sparse features.
+    const QuantileStream& stream = *streams_resource->stream(stamp_token);
+    const std::vector<float>& boundaries =
+        are_buckets_ready ? streams_resource->boundaries(stamp_token)
+                          : std::vector<float>();
+    protobuf::Arena arena;
+    ::boosted_trees::QuantileStreamState* stream_proto =
+        protobuf::Arena::CreateMessage<::boosted_trees::QuantileStreamState>(
+            &arena);
+    for (const auto& summary : stream.SerializeInternalSummaries()) {
+      CopySummaryToProto(summary, stream_proto->add_summaries());
+    }
+    stream_proto->SerializeToString(&stream_state_t->scalar<string>()());
+    Tensor* buckets_t = nullptr;
+    OP_REQUIRES_OK(
+        context,
+        context->allocate_output(
+            kBucketsName, {static_cast<int64>(boundaries.size())}, &buckets_t));
+    auto* quantiles_flat = buckets_t->flat<float>().data();
+    memcpy(quantiles_flat, boundaries.data(),
+           sizeof(float) * boundaries.size());
+    Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(kStampTokenName, TensorShape({}),
+                                            &stamp_token_t));
+    stamp_token_t->scalar<int64>()() = stamp_token;
+    Tensor* are_buckets_ready_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(kAreBucketsReadyName, {},
+                                                     &are_buckets_ready_t));
+    are_buckets_ready_t->scalar<bool>()() = are_buckets_ready;
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("QuantileAccumulatorSerialize").Device(DEVICE_CPU),
+                        QuantileAccumulatorSerializeOp);
+
+// Serializes the state of streams.
+class QuantileAccumulatorDeserializeOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorDeserializeOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    QuantileStreamResource* streams_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &streams_resource));
+    // Remove the reference at the end of this scope.
+    mutex_lock l(*streams_resource->mutex());
+    core::ScopedUnref unref_me(streams_resource);
+
+    int64 old_stamp_token = streams_resource->stamp();
+
+    const Tensor* stream_state_t;
+    OP_REQUIRES_OK(context, context->input(kStreamStateName, &stream_state_t));
+    const Tensor* buckets_t;
+    OP_REQUIRES_OK(context, context->input(kBucketsName, &buckets_t));
+
+    QuantileStream* stream = streams_resource->stream(old_stamp_token);
+    ::boosted_trees::QuantileStreamState state_proto;
+    OP_REQUIRES(
+        context,
+        ParseProtoUnlimited(&state_proto, stream_state_t->scalar<string>()()),
+        errors::InvalidArgument("Unabnle to parse quantile stream state."));
+    std::vector<QuantileSummary> summaries;
+    summaries.reserve(state_proto.summaries_size());
+    std::vector<QuantileSummaryEntry> entries;
+    for (const auto& summary : state_proto.summaries()) {
+      entries.clear();
+      entries.reserve(summary.entries_size());
+      for (const auto& entry : summary.entries()) {
+        entries.emplace_back(entry.value(), entry.weight(), entry.min_rank(),
+                             entry.max_rank());
+      }
+      summaries.emplace_back();
+      summaries[summaries.size() - 1].BuildFromSummaryEntries(entries);
+    }
+    stream->DeserializeInternalSummaries(summaries);
+
+    const auto& buckets = buckets_t->vec<float>();
+    std::vector<float> result;
+    result.reserve(buckets.size());
+
+    for (size_t i = 0; i < buckets.size(); ++i) {
+      result.push_back(buckets(i));
+    }
+    streams_resource->set_boundaries(old_stamp_token, result);
+
+    // Reset the stamp token.
+    const Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    streams_resource->set_stamp(stamp_token);
+
+    const Tensor* are_buckets_ready_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->input(kAreBucketsReadyName, &are_buckets_ready_t));
+    streams_resource->set_buckets_ready(are_buckets_ready_t->scalar<bool>()());
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorDeserialize").Device(DEVICE_CPU),
+    QuantileAccumulatorDeserializeOp);
+
+// Adds a summary to the quantile summary stream.
+class QuantileAccumulatorFlushOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorFlushOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    QuantileStreamResource* streams_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &streams_resource));
+    // Remove the reference at the end of this scope.
+    mutex_lock l(*streams_resource->mutex());
+    core::ScopedUnref unref_me(streams_resource);
+
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kNextStampTokenName, &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    CHECK(streams_resource->is_stamp_valid(stamp_token))
+        << "Invalid stamp token in QuantileAccumulatorFlushOp. "
+        << "Passed stamp token: " << stamp_token << " "
+        << "Current token: " << streams_resource->stamp();
+    QuantileStream* stream = streams_resource->stream(stamp_token);
+    stream->Finalize();
+    streams_resource->set_boundaries(
+        stamp_token,
+        GenerateBoundaries(*stream, streams_resource->num_quantiles()));
+    streams_resource->Reset(next_stamp_token);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("QuantileAccumulatorFlush").Device(DEVICE_CPU),
+                        QuantileAccumulatorFlushOp);
+
+// Get bucket boundaries from summaries.
+class QuantileAccumulatorGetBucketsOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorGetBucketsOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* const context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list(kResourceHandlesName,
+                                                &resource_handle_list));
+    OpOutputList are_buckets_ready_list;
+    OP_REQUIRES_OK(context, context->output_list(kAreBucketsReadyName,
+                                                 &are_buckets_ready_list));
+    OpOutputList buckets_list;
+    OP_REQUIRES_OK(context, context->output_list(kBucketsName, &buckets_list));
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &are_buckets_ready_list,
+         &buckets_list, stamp_token](int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+            QuantileStreamResource* streams_resource;
+            OP_REQUIRES_OK(context,
+                           LookupResource(context, handle, &streams_resource));
+            // Remove the reference at the end of this scope.
+            mutex_lock l(*streams_resource->mutex());
+            core::ScopedUnref unref_me(streams_resource);
+
+            bool are_buckets_ready =
+                streams_resource->is_stamp_valid(stamp_token) &&
+                streams_resource->are_buckets_ready();
+
+            Tensor* are_buckets_ready_t = nullptr;
+            OP_REQUIRES_OK(context,
+                           are_buckets_ready_list.allocate(
+                               resource_handle_idx, {}, &are_buckets_ready_t));
+            are_buckets_ready_t->scalar<bool>()() = are_buckets_ready;
+
+            const std::vector<float>& boundaries =
+                are_buckets_ready ? streams_resource->boundaries(stamp_token)
+                                  : std::vector<float>();
+            Tensor* output_t = nullptr;
+            OP_REQUIRES_OK(context, buckets_list.allocate(
+                                        resource_handle_idx,
+                                        {static_cast<int64>(boundaries.size())},
+                                        &output_t));
+            auto* quantiles_flat = output_t->flat<float>().data();
+            memcpy(quantiles_flat, boundaries.data(),
+                   sizeof(float) * boundaries.size());
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorGetBuckets").Device(DEVICE_CPU),
+    QuantileAccumulatorGetBucketsOp);
+
+// Generates buckets for given set of float values, and the given config.
+class QuantileBucketsOp : public OpKernel {
+ public:
+  explicit QuantileBucketsOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context,
+                   ReadAndValidateAttributes(context, &num_dense_features_,
+                                             &num_sparse_features_));
+
+    ParseConfig(context, kDenseConfigName, &dense_configs_);
+    OP_REQUIRES(context, dense_configs_.size() == num_dense_features_,
+                errors::InvalidArgument(
+                    "Mismatch in number of dense quantile configs."));
+    ParseConfig(context, kSparseConfigName, &sparse_configs_);
+    OP_REQUIRES(context, sparse_configs_.size() == num_sparse_features_,
+                errors::InvalidArgument(
+                    "Mismatch in number of sparse quantile configs."));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Read dense float features list;
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadDenseFloatFeatures(
+                                context, &dense_float_features_list));
+
+    // Read sparse float features list;
+    OpInputList sparse_float_feature_indices_list;
+    OpInputList sparse_float_feature_values_list;
+    OpInputList sparse_float_feature_shapes_list;
+    OP_REQUIRES_OK(context, TensorUtils::ReadSparseFloatFeatures(
+                                context, &sparse_float_feature_indices_list,
+                                &sparse_float_feature_values_list,
+                                &sparse_float_feature_shapes_list));
+
+    // Parse example weights and get batch size.
+    const Tensor* example_weights_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kExampleWeightsName, &example_weights_t));
+    auto example_weights = example_weights_t->flat<float>();
+    const int64 batch_size = example_weights.size();
+
+    OpOutputList sparse_buckets_output_list;
+    OP_REQUIRES_OK(context, context->output_list(kSparseBucketsName,
+                                                 &sparse_buckets_output_list));
+    OpOutputList dense_buckets_output_list;
+    OP_REQUIRES_OK(context, context->output_list(kDenseBucketsName,
+                                                 &dense_buckets_output_list));
+
+    auto do_quantile_bucket_gen = [&](const int64 begin, const int64 end) {
+      // These are blocks of ranges. We are iterating over both sparse and
+      // dense features i.e. [0, sparse_features.size() + dense_features.size()]
+      for (int64 i = begin; i < end; ++i) {
+        if (i < sparse_configs_.size()) {
+          const int64 sparse_index = i;
+          const auto sparse_values =
+              sparse_float_feature_values_list[sparse_index].flat<float>();
+          const auto sparse_indices =
+              sparse_float_feature_indices_list[sparse_index].matrix<int64>();
+          QuantileStream stream(sparse_configs_[sparse_index].eps(),
+                                batch_size);
+          // Run quantile summary generation.
+          const int64 num_sparse_rows =
+              sparse_float_feature_indices_list[sparse_index].dim_size(0);
+          for (int64 j = 0; j < num_sparse_rows; ++j) {
+            const int64 example_id = sparse_indices(j, 0);
+            stream.PushEntry(sparse_values(j), example_weights(example_id));
+          }
+          stream.Finalize();
+          // Create buckets.
+          const auto boundaries = GenerateBoundaries(
+              stream, sparse_configs_[sparse_index].num_quantiles());
+          CopyBoundaries(context, boundaries, sparse_index,
+                         &sparse_buckets_output_list);
+
+        } else {
+          const int64 dense_index = i - sparse_configs_.size();
+          const auto dense_values =
+              dense_float_features_list[dense_index].flat<float>();
+          QuantileStream stream(dense_configs_[dense_index].eps(), batch_size);
+          // Run quantile summary generation.
+          for (int64 j = 0; j < batch_size; ++j) {
+            stream.PushEntry(dense_values(j), example_weights(j));
+          }
+          stream.Finalize();
+          // Create buckets.
+          const auto boundaries = GenerateBoundaries(
+              stream, dense_configs_[dense_index].num_quantiles());
+          CopyBoundaries(context, boundaries, dense_index,
+                         &dense_buckets_output_list);
+        }
+      }
+    };
+
+    const int64 kCostPerUnit = 500 * batch_size;
+    const int64 num_features = sparse_configs_.size() + dense_configs_.size();
+    const DeviceBase::CpuWorkerThreads& worker_threads =
+        *context->device()->tensorflow_cpu_worker_threads();
+    Shard(worker_threads.num_threads, worker_threads.workers, num_features,
+          kCostPerUnit, do_quantile_bucket_gen);
+  }
+
+ private:
+  int num_dense_features_;
+  int num_sparse_features_;
+  std::vector<QuantileConfig> dense_configs_;
+  std::vector<QuantileConfig> sparse_configs_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("QuantileBuckets").Device(DEVICE_CPU),
+                        QuantileBucketsOp);
+
+// Given the calculated quantiles thresholds and input data, this operation
+// converts the input features into the buckets (categorical values), depending
+// on which quantile they fall into.
+class QuantilesOp : public OpKernel {
+ public:
+  explicit QuantilesOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    int num_dense_features;
+    int num_sparse_features;
+    OP_REQUIRES_OK(context,
+                   ReadAndValidateAttributes(context, &num_dense_features,
+                                             &num_sparse_features));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Dense features inputs
+    OpInputList dense_float_features_list;
+    OP_REQUIRES_OK(context, context->input_list(kDenseValuesName,
+                                                &dense_float_features_list));
+    OpInputList dense_buckets_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list(kDenseBucketsName, &dense_buckets_list));
+
+    if (dense_buckets_list.size() > 0) {
+      // Check the first tensor to make sure it is the right shape
+      OP_REQUIRES(
+          context,
+          tensorflow::TensorShapeUtils::IsVector(dense_buckets_list[0].shape()),
+          errors::InvalidArgument(
+              strings::Printf("Dense buckets should be flat vectors")));
+    }
+
+    // Sparse features inputs
+    OpInputList sparse_float_feature_values_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list(kSparseValuesName,
+                                       &sparse_float_feature_values_list));
+    OpInputList sparse_buckets_list;
+    OP_REQUIRES_OK(
+        context, context->input_list(kSparseBucketsName, &sparse_buckets_list));
+
+    if (sparse_buckets_list.size() > 0) {
+      OP_REQUIRES(
+          context,
+          tensorflow::TensorShapeUtils::IsVector(
+              sparse_buckets_list[0].shape()),
+          errors::InvalidArgument("Sparse buckets should be flat vectors"));
+    }
+
+    // Quantize the feature values
+    QuantizeFeatures(kDenseOutputTensorName, dense_float_features_list,
+                     dense_buckets_list, context);
+
+    QuantizeFeatures(kSparseOutputTensorName, sparse_float_feature_values_list,
+                     sparse_buckets_list, context);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("Quantiles").Device(DEVICE_CPU), QuantilesOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
new file mode 100644
index 0000000000..dcc263d810
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
@@ -0,0 +1,509 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+#include "tensorflow/contrib/boosted_trees/proto/split_info.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/core/framework/device_base.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/random/random.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+using boosted_trees::learner::SplitInfo;
+using boosted_trees::learner::stochastic::GradientStats;
+using boosted_trees::learner::stochastic::NodeStats;
+
+class BaseBuildSplitOp : public OpKernel {
+ public:
+  explicit BaseBuildSplitOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(
+        context,
+        context->GetAttr("feature_column_group_id", &feature_column_group_id_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("l1_regularization", &l1_regularization_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("l2_regularization", &l2_regularization_));
+    OP_REQUIRES_OK(context, context->GetAttr("tree_complexity_regularization",
+                                             &tree_complexity_regularization_));
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("min_node_weight", &min_node_weight_));
+  }
+
+  NodeStats ComputeNodeStats(const GradientStats& grad_stats) {
+    return NodeStats(
+        l1_regularization_, l2_regularization_, min_node_weight_,
+        boosted_trees::learner::LearnerConfig_MultiClassStrategy_TREE_PER_CLASS,
+        grad_stats);
+  }
+
+ protected:
+  int32 feature_column_group_id_;
+  float l1_regularization_;
+  float l2_regularization_;
+  float min_node_weight_;
+  float tree_complexity_regularization_;
+};
+
+class BuildDenseInequalitySplitsOp : public BaseBuildSplitOp {
+ public:
+  explicit BuildDenseInequalitySplitsOp(OpKernelConstruction* const context)
+      : BaseBuildSplitOp(context) {}
+
+  void Compute(OpKernelContext* const context) override {
+    const Tensor* num_minibatches_t;
+    OP_REQUIRES_OK(context,
+                   context->input("num_minibatches", &num_minibatches_t));
+    const int64 num_minibatches = num_minibatches_t->scalar<int64>()();
+    const float normalizer_ratio = (1.0f / num_minibatches);
+
+    const Tensor* bucket_boundaries_t;
+    OP_REQUIRES_OK(context,
+                   context->input("bucket_boundaries", &bucket_boundaries_t));
+    const auto& bucket_boundaries = bucket_boundaries_t->vec<float>();
+
+    const Tensor* partition_ids_t;
+    OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+    const auto& partition_ids = partition_ids_t->vec<int32>();
+
+    const Tensor* bucket_ids_t;
+    OP_REQUIRES_OK(context, context->input("bucket_ids", &bucket_ids_t));
+    const auto& bucket_ids = bucket_ids_t->vec<int64>();
+
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+
+    // Find the number of unique partitions before we allocate the output.
+    std::vector<int32> partition_boundaries;
+    partition_boundaries.push_back(0);
+    for (int i = 1; i < partition_ids.size(); ++i) {
+      if (partition_ids(i) != partition_ids(i - 1)) {
+        // Make sure the input is sorted by partition_ids;
+        OP_REQUIRES(context, partition_ids(i) >= partition_ids(i - 1),
+                    errors::InvalidArgument("Input should be sorted."));
+        partition_boundaries.push_back(i);
+      }
+    }
+    if (partition_ids.size() > 0) {
+      partition_boundaries.push_back(partition_ids.size());
+    }
+    int32 num_elements = partition_boundaries.size() - 1;
+
+    // When the handler is inactive, no bucket boundaries are built for it.
+    if (bucket_boundaries.size() == 0) {
+      num_elements = 0;
+    }
+
+    Tensor* output_partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("output_partition_ids",
+                                            TensorShape({num_elements}),
+                                            &output_partition_ids_t));
+
+    tensorflow::TTypes<int32>::Vec output_partition_ids =
+        output_partition_ids_t->vec<int32>();
+
+    Tensor* gains_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("gains", TensorShape({num_elements}),
+                                          &gains_t));
+
+    tensorflow::TTypes<float>::Vec gains = gains_t->vec<float>();
+
+    Tensor* output_splits_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "split_infos", TensorShape({num_elements}),
+                                &output_splits_t));
+    tensorflow::TTypes<string>::Vec output_splits =
+        output_splits_t->vec<string>();
+    for (int root_idx = 0; root_idx < num_elements; ++root_idx) {
+      float best_gain = std::numeric_limits<float>::lowest();
+      int start_index = partition_boundaries[root_idx];
+      int end_index = partition_boundaries[root_idx + 1];
+      GradientStats root_gradient_stats;
+      for (int64 bucket_idx = start_index; bucket_idx < end_index;
+           ++bucket_idx) {
+        root_gradient_stats +=
+            GradientStats(*gradients_t, *hessians_t, bucket_idx);
+      }
+      root_gradient_stats *= normalizer_ratio;
+      NodeStats root_stats = ComputeNodeStats(root_gradient_stats);
+      int32 best_bucket_idx = 0;
+      NodeStats best_right_node_stats(0);
+      NodeStats best_left_node_stats(0);
+      GradientStats left_gradient_stats(0, 0);
+      for (int64 bucket_idx = start_index; bucket_idx < end_index;
+           ++bucket_idx) {
+        GradientStats g(*gradients_t, *hessians_t, bucket_idx);
+        g *= normalizer_ratio;
+        left_gradient_stats += g;
+        NodeStats left_stats = ComputeNodeStats(left_gradient_stats);
+        GradientStats right_gradient_stats =
+            root_gradient_stats - left_gradient_stats;
+        NodeStats right_stats = ComputeNodeStats(right_gradient_stats);
+        if (left_stats.gain + right_stats.gain > best_gain) {
+          best_gain = left_stats.gain + right_stats.gain;
+          best_left_node_stats = left_stats;
+          best_right_node_stats = right_stats;
+          best_bucket_idx = bucket_idx;
+        }
+      }
+      SplitInfo split_info;
+      auto* dense_split =
+          split_info.mutable_split_node()->mutable_dense_float_binary_split();
+      dense_split->set_feature_column(feature_column_group_id_);
+      dense_split->set_threshold(
+          bucket_boundaries(bucket_ids(best_bucket_idx)));
+
+      auto* left_child = split_info.mutable_left_child();
+      auto* right_child = split_info.mutable_right_child();
+      for (float f : best_left_node_stats.weight_contribution) {
+        left_child->mutable_vector()->add_value(f);
+      }
+      for (float f : best_right_node_stats.weight_contribution) {
+        right_child->mutable_vector()->add_value(f);
+      }
+      split_info.SerializeToString(&output_splits(root_idx));
+      gains(root_idx) =
+          best_gain - root_stats.gain - tree_complexity_regularization_;
+      output_partition_ids(root_idx) = partition_ids(start_index);
+    }
+  }
+};
+REGISTER_KERNEL_BUILDER(Name("BuildDenseInequalitySplits").Device(DEVICE_CPU),
+                        BuildDenseInequalitySplitsOp);
+
+class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
+ public:
+  explicit BuildSparseInequalitySplitsOp(OpKernelConstruction* const context)
+      : BaseBuildSplitOp(context) {
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("bias_feature_id", &bias_feature_id_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    const Tensor* num_minibatches_t;
+    OP_REQUIRES_OK(context,
+                   context->input("num_minibatches", &num_minibatches_t));
+    const int64 num_minibatches = num_minibatches_t->scalar<int64>()();
+    const float normalizer_ratio = (1.0f / num_minibatches);
+
+    const Tensor* bucket_boundaries_t;
+    OP_REQUIRES_OK(context,
+                   context->input("bucket_boundaries", &bucket_boundaries_t));
+    const auto& bucket_boundaries = bucket_boundaries_t->vec<float>();
+
+    const Tensor* partition_ids_t;
+    OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+    const auto& partition_ids = partition_ids_t->vec<int32>();
+
+    const Tensor* bucket_ids_t;
+    OP_REQUIRES_OK(context, context->input("bucket_ids", &bucket_ids_t));
+    const auto& bucket_ids = bucket_ids_t->vec<int64>();
+
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+
+    // Find the number of unique partitions before we allocate the output.
+    std::vector<int32> partition_boundaries;
+    std::vector<int32> non_empty_partitions;
+    for (int i = 0; i < partition_ids.size() - 1; ++i) {
+      // Make sure the input is sorted by partition_ids;
+      CHECK_LE(partition_ids(i), partition_ids(i + 1));
+      if (i == 0 || partition_ids(i) != partition_ids(i - 1)) {
+        partition_boundaries.push_back(i);
+        // Some partitions might only have bias feature. We don't want to split
+        // those so check that the partition has at least 2 buckets.
+        if (partition_ids(i) == partition_ids(i + 1)) {
+          non_empty_partitions.push_back(partition_boundaries.size() - 1);
+        }
+      }
+    }
+    if (partition_ids.size() > 0) {
+      partition_boundaries.push_back(partition_ids.size());
+    }
+    int num_elements = non_empty_partitions.size();
+    Tensor* output_partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("output_partition_ids",
+                                            TensorShape({num_elements}),
+                                            &output_partition_ids_t));
+
+    tensorflow::TTypes<int32>::Vec output_partition_ids =
+        output_partition_ids_t->vec<int32>();
+
+    Tensor* gains_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("gains", TensorShape({num_elements}),
+                                          &gains_t));
+
+    tensorflow::TTypes<float>::Vec gains = gains_t->vec<float>();
+
+    Tensor* output_splits_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "split_infos", TensorShape({num_elements}),
+                                &output_splits_t));
+    tensorflow::TTypes<string>::Vec output_splits =
+        output_splits_t->vec<string>();
+    for (int root_idx = 0; root_idx < num_elements; ++root_idx) {
+      float best_gain = std::numeric_limits<float>::lowest();
+      int start_index = partition_boundaries[non_empty_partitions[root_idx]];
+      int end_index = partition_boundaries[non_empty_partitions[root_idx] + 1];
+      // First bucket ID in each partition should be the bias feature.
+      OP_REQUIRES(context, bucket_ids(start_index) == bias_feature_id_,
+                  errors::InvalidArgument("Bias feature ID missing."));
+      // For each root, we do two passes over the quantized feature buckets
+      // accumulating gradients on one side and using the root aggregate
+      // gradients to get the gradients for the other side.
+      // Split gains are evaluated for each pass at every threshold and the best
+      // split is picked.
+      GradientStats root_gradient_stats(*gradients_t, *hessians_t, start_index);
+      root_gradient_stats *= normalizer_ratio;
+      NodeStats root_stats = ComputeNodeStats(root_gradient_stats);
+      GradientStats present_gradient_stats;
+      for (int64 bucket_idx = start_index + 1; bucket_idx < end_index;
+           ++bucket_idx) {
+        present_gradient_stats +=
+            GradientStats(*gradients_t, *hessians_t, bucket_idx);
+      }
+      present_gradient_stats *= normalizer_ratio;
+      int32 best_bucket_idx = 0;
+      NodeStats best_right_node_stats(0);
+      NodeStats best_left_node_stats(0);
+      GradientStats left_gradient_stats(0, 0);
+      bool default_right = false;
+      for (int64 bucket_idx = start_index + 1; bucket_idx < end_index;
+           ++bucket_idx) {
+        GradientStats g(*gradients_t, *hessians_t, bucket_idx);
+        g *= normalizer_ratio;
+        left_gradient_stats += g;
+        // We have the sum of all present gradients. Use that to compute the
+        // backward pass gradients.
+        GradientStats right_gradient_stats =
+            present_gradient_stats - left_gradient_stats;
+        {
+          NodeStats left_stats_default_left =
+              ComputeNodeStats(root_gradient_stats - right_gradient_stats);
+          NodeStats right_stats_default_left =
+              ComputeNodeStats(right_gradient_stats);
+          if (left_stats_default_left.gain + right_stats_default_left.gain >
+              best_gain) {
+            best_gain =
+                left_stats_default_left.gain + right_stats_default_left.gain;
+            best_left_node_stats = left_stats_default_left;
+            best_right_node_stats = right_stats_default_left;
+            best_bucket_idx = bucket_idx;
+            default_right = false;
+          }
+        }
+        {
+          NodeStats left_stats_default_right =
+              ComputeNodeStats(left_gradient_stats);
+          NodeStats right_stats_default_right =
+              ComputeNodeStats(root_gradient_stats - left_gradient_stats);
+          if (left_stats_default_right.gain + right_stats_default_right.gain >
+              best_gain) {
+            best_gain =
+                left_stats_default_right.gain + right_stats_default_right.gain;
+            best_left_node_stats = left_stats_default_right;
+            best_right_node_stats = right_stats_default_right;
+            best_bucket_idx = bucket_idx;
+            default_right = true;
+          }
+        }
+      }
+      SplitInfo split_info;
+      boosted_trees::trees::DenseFloatBinarySplit* dense_split = nullptr;
+      if (default_right) {
+        dense_split = split_info.mutable_split_node()
+                          ->mutable_sparse_float_binary_split_default_right()
+                          ->mutable_split();
+      } else {
+        dense_split = split_info.mutable_split_node()
+                          ->mutable_sparse_float_binary_split_default_left()
+                          ->mutable_split();
+      }
+      dense_split->set_feature_column(feature_column_group_id_);
+      dense_split->set_threshold(
+          bucket_boundaries(bucket_ids(best_bucket_idx)));
+
+      auto* left_child = split_info.mutable_left_child();
+      auto* right_child = split_info.mutable_right_child();
+      for (float f : best_left_node_stats.weight_contribution) {
+        left_child->mutable_vector()->add_value(f);
+      }
+      for (float f : best_right_node_stats.weight_contribution) {
+        right_child->mutable_vector()->add_value(f);
+      }
+      split_info.SerializeToString(&output_splits(root_idx));
+      gains(root_idx) =
+          best_gain - root_stats.gain - tree_complexity_regularization_;
+      output_partition_ids(root_idx) = partition_ids(start_index);
+    }
+  }
+
+ private:
+  int64 bias_feature_id_;
+};
+REGISTER_KERNEL_BUILDER(Name("BuildSparseInequalitySplits").Device(DEVICE_CPU),
+                        BuildSparseInequalitySplitsOp);
+
+class BuildCategoricalEqualitySplitsOp : public BaseBuildSplitOp {
+ public:
+  explicit BuildCategoricalEqualitySplitsOp(OpKernelConstruction* const context)
+      : BaseBuildSplitOp(context) {
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("bias_feature_id", &bias_feature_id_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    const Tensor* num_minibatches_t;
+    OP_REQUIRES_OK(context,
+                   context->input("num_minibatches", &num_minibatches_t));
+    const int64 num_minibatches = num_minibatches_t->scalar<int64>()();
+    const float normalizer_ratio = (1.0f / num_minibatches);
+
+    const Tensor* partition_ids_t;
+    OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+    const auto& partition_ids = partition_ids_t->vec<int32>();
+
+    const Tensor* feature_ids_t;
+    OP_REQUIRES_OK(context, context->input("feature_ids", &feature_ids_t));
+    const auto& feature_ids = feature_ids_t->vec<int64>();
+
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+
+    // Find the number of unique partitions before we allocate the output.
+    std::vector<int32> partition_boundaries;
+    std::vector<int32> non_empty_partitions;
+    for (int i = 0; i < partition_ids.size() - 1; ++i) {
+      // Make sure the input is sorted by partition_ids;
+      CHECK_LE(partition_ids(i), partition_ids(i + 1));
+      if (i == 0 || partition_ids(i) != partition_ids(i - 1)) {
+        partition_boundaries.push_back(i);
+        // Some partitions might only have bias feature. We don't want to split
+        // those so check that the partition has at least 2 features.
+        if (partition_ids(i) == partition_ids(i + 1)) {
+          non_empty_partitions.push_back(partition_boundaries.size() - 1);
+        }
+      }
+    }
+    if (partition_ids.size() > 0) {
+      partition_boundaries.push_back(partition_ids.size());
+    }
+    int num_elements = non_empty_partitions.size();
+    Tensor* output_partition_ids_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("output_partition_ids",
+                                            TensorShape({num_elements}),
+                                            &output_partition_ids_t));
+
+    tensorflow::TTypes<int32>::Vec output_partition_ids =
+        output_partition_ids_t->vec<int32>();
+
+    Tensor* gains_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("gains", TensorShape({num_elements}),
+                                          &gains_t));
+
+    tensorflow::TTypes<float>::Vec gains = gains_t->vec<float>();
+
+    Tensor* output_splits_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "split_infos", TensorShape({num_elements}),
+                                &output_splits_t));
+    tensorflow::TTypes<string>::Vec output_splits =
+        output_splits_t->vec<string>();
+    for (int root_idx = 0; root_idx < num_elements; ++root_idx) {
+      float best_gain = std::numeric_limits<float>::lowest();
+      int start_index = partition_boundaries[non_empty_partitions[root_idx]];
+      int end_index = partition_boundaries[non_empty_partitions[root_idx] + 1];
+      // First feature ID in each partition should be the bias feature.
+      OP_REQUIRES(context, feature_ids(start_index) == bias_feature_id_,
+                  errors::InvalidArgument("Bias feature ID missing."));
+      GradientStats root_gradient_stats(*gradients_t, *hessians_t, start_index);
+      root_gradient_stats *= normalizer_ratio;
+      NodeStats root_stats = ComputeNodeStats(root_gradient_stats);
+      int32 best_feature_idx = 0;
+      NodeStats best_right_node_stats(0);
+      NodeStats best_left_node_stats(0);
+      for (int64 feature_idx = start_index + 1; feature_idx < end_index;
+           ++feature_idx) {
+        GradientStats left_gradient_stats(*gradients_t, *hessians_t,
+                                          feature_idx);
+        left_gradient_stats *= normalizer_ratio;
+        GradientStats right_gradient_stats =
+            root_gradient_stats - left_gradient_stats;
+        NodeStats left_stats = ComputeNodeStats(left_gradient_stats);
+        NodeStats right_stats = ComputeNodeStats(right_gradient_stats);
+        if (left_stats.gain + right_stats.gain > best_gain) {
+          best_gain = left_stats.gain + right_stats.gain;
+          best_left_node_stats = left_stats;
+          best_right_node_stats = right_stats;
+          best_feature_idx = feature_idx;
+        }
+      }
+      SplitInfo split_info;
+      auto* equality_split = split_info.mutable_split_node()
+                                 ->mutable_categorical_id_binary_split();
+      equality_split->set_feature_column(feature_column_group_id_);
+      equality_split->set_feature_id(feature_ids(best_feature_idx));
+      auto* left_child = split_info.mutable_left_child();
+      auto* right_child = split_info.mutable_right_child();
+      for (float f : best_left_node_stats.weight_contribution) {
+        left_child->mutable_vector()->add_value(f);
+      }
+      for (float f : best_right_node_stats.weight_contribution) {
+        right_child->mutable_vector()->add_value(f);
+      }
+      split_info.SerializeToString(&output_splits(root_idx));
+      gains(root_idx) =
+          best_gain - root_stats.gain - tree_complexity_regularization_;
+      output_partition_ids(root_idx) = partition_ids(start_index);
+    }
+  }
+
+ private:
+  int64 bias_feature_id_;
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("BuildCategoricalEqualitySplits").Device(DEVICE_CPU),
+    BuildCategoricalEqualitySplitsOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc b/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc
new file mode 100644
index 0000000000..cff75e71d9
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc
@@ -0,0 +1,784 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <algorithm>
+#include <iterator>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/utils/parallel_for.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
+#include "tensorflow/contrib/boosted_trees/resources/stamped_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+
+namespace {
+const char* const kStampTokenName = "stamp_token";
+const char* const kNextStampTokenName = "next_stamp_token";
+
+struct PartitionKey {
+  PartitionKey() : partition_id(-1), feature_id(-1) {}
+
+  PartitionKey(int32 p, int64 f) : partition_id(p), feature_id(f) {}
+
+  bool operator==(const PartitionKey& other) const {
+    return (feature_id == other.feature_id) &&
+           (partition_id == other.partition_id);
+  }
+
+  // Compare for PartitionKey.
+  struct Less {
+    bool operator()(const PartitionKey& a, const PartitionKey& b) const {
+      if (a.partition_id < b.partition_id) {
+        return true;
+      }
+      if ((a.partition_id == b.partition_id) && (a.feature_id < b.feature_id)) {
+        return true;
+      }
+      return false;
+    }
+  };
+
+  // Tree partition defined by traversing the tree to the leaf.
+  int32 partition_id;
+
+  // Feature Id within the feature column.
+  int64 feature_id;
+};
+
+template <typename GradientType, typename HessianType>
+class StatsAccumulatorResource : public boosted_trees::StampedResource {
+  using StatsByPartition =
+      std::map<PartitionKey, std::pair<GradientType, HessianType>,
+               PartitionKey::Less>;
+
+ public:
+  StatsAccumulatorResource(const TensorShape& gradient_shape,
+                           const TensorShape& hessian_shape)
+      : gradient_shape_(gradient_shape),
+        hessian_shape_(hessian_shape),
+        num_updates_(0) {
+    // If GradientType/HessianType is scalar float then the shapes should be
+    // scalar and vice versa.
+    CHECK_EQ((std::is_same<GradientType, float>::value),
+             TensorShapeUtils::IsScalar(gradient_shape));
+    CHECK_EQ((std::is_same<HessianType, float>::value),
+             TensorShapeUtils::IsScalar(hessian_shape));
+  }
+
+  string DebugString() override {
+    return strings::StrCat("StatsAccumulatorResource[size=", values_.size(),
+                           "]");
+  }
+
+  void Clear() {
+    values_.clear();
+    num_updates_ = 0;
+  }
+
+  tensorflow::mutex* mutex() { return &mu_; }
+  StatsByPartition* mutable_values() { return &values_; }
+  const StatsByPartition& values() const { return values_; }
+  const int64& num_updates() const { return num_updates_; }
+  void set_num_updates(int64 val) { num_updates_ = val; }
+  const TensorShape& gradient_shape() const { return gradient_shape_; }
+  const TensorShape& hessian_shape() const { return hessian_shape_; }
+
+ private:
+  // Key into a specific partition to accumulate stats for the specified feature
+  // id.
+  StatsByPartition values_;
+  const TensorShape gradient_shape_;
+  const TensorShape hessian_shape_;
+  int64 num_updates_;
+  tensorflow::mutex mu_;
+  TF_DISALLOW_COPY_AND_ASSIGN(StatsAccumulatorResource);
+};
+
+using StatsAccumulatorScalarResource = StatsAccumulatorResource<float, float>;
+using StatsAccumulatorTensorResource =
+    StatsAccumulatorResource<std::vector<float>, std::vector<float>>;
+
+void SerializeScalarAccumulatorToOutput(
+    const StatsAccumulatorScalarResource& accumulator_resource,
+    OpKernelContext* context) {
+  int64 num_slots = accumulator_resource.values().size();
+  Tensor* partition_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_partition_ids", TensorShape({num_slots}),
+                               &partition_ids_t));
+  auto partition_ids = partition_ids_t->vec<int32>();
+
+  Tensor* feature_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_feature_ids", TensorShape({num_slots}),
+                               &feature_ids_t));
+  auto feature_ids = feature_ids_t->vec<int64>();
+
+  Tensor* gradients_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_gradients", TensorShape({num_slots}),
+                               &gradients_t));
+  auto gradients = gradients_t->vec<float>();
+
+  Tensor* hessians_t = nullptr;
+  OP_REQUIRES_OK(context,
+                 context->allocate_output(
+                     "output_hessians", TensorShape({num_slots}), &hessians_t));
+  auto hessians = hessians_t->vec<float>();
+
+  int i = 0;
+  for (const auto& iter : accumulator_resource.values()) {
+    partition_ids(i) = iter.first.partition_id;
+    feature_ids(i) = iter.first.feature_id;
+    gradients(i) = iter.second.first;
+    hessians(i) = iter.second.second;
+    ++i;
+  }
+}
+
+void SerializeTensorAccumulatorToOutput(
+    const StatsAccumulatorTensorResource& accumulator_resource,
+    OpKernelContext* context) {
+  int64 num_slots = accumulator_resource.values().size();
+  Tensor* partition_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_partition_ids", TensorShape({num_slots}),
+                               &partition_ids_t));
+  auto partition_ids = partition_ids_t->vec<int32>();
+
+  Tensor* feature_ids_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_feature_ids", TensorShape({num_slots}),
+                               &feature_ids_t));
+  auto feature_ids = feature_ids_t->vec<int64>();
+
+  TensorShape gradient_shape = accumulator_resource.gradient_shape();
+  int64 num_gradient_elements = gradient_shape.num_elements();
+  gradient_shape.InsertDim(0, num_slots);
+  Tensor* gradients_t = nullptr;
+  OP_REQUIRES_OK(context,
+                 context->allocate_output("output_gradients", gradient_shape,
+                                          &gradients_t));
+  auto gradients = gradients_t->flat_outer_dims<float>();
+
+  TensorShape hessian_shape = accumulator_resource.hessian_shape();
+  int64 num_hessian_elements = hessian_shape.num_elements();
+  hessian_shape.InsertDim(0, num_slots);
+  Tensor* hessians_t = nullptr;
+  OP_REQUIRES_OK(
+      context,
+      context->allocate_output("output_hessians", hessian_shape, &hessians_t));
+  auto hessians = hessians_t->flat_outer_dims<float>();
+
+  int i = 0;
+  for (const auto& iter : accumulator_resource.values()) {
+    partition_ids(i) = iter.first.partition_id;
+    feature_ids(i) = iter.first.feature_id;
+    for (int j = 0; j < num_gradient_elements; ++j) {
+      gradients(i, j) = iter.second.first[j];
+    }
+    for (int j = 0; j < num_hessian_elements; ++j) {
+      hessians(i, j) = iter.second.second[j];
+    }
+    ++i;
+  }
+}
+
+void AddToScalarAccumulator(
+    StatsAccumulatorScalarResource* accumulator_resource,
+    const Tensor& partition_ids_t, const Tensor& feature_ids_t,
+    const Tensor& gradients_t, const Tensor& hessians_t) {
+  accumulator_resource->set_num_updates(accumulator_resource->num_updates() +
+                                        1);
+  const TensorShape& partition_ids_shape = partition_ids_t.shape();
+  const auto& partition_ids = partition_ids_t.vec<int32>();
+  const auto& feature_ids = feature_ids_t.vec<int64>();
+  const auto& gradients = gradients_t.vec<float>();
+  const auto& hessians = hessians_t.vec<float>();
+
+  int64 num_updates = partition_ids_shape.dim_size(0);
+  auto stats_map = accumulator_resource->mutable_values();
+  for (int64 i = 0; i < num_updates; ++i) {
+    const auto key = PartitionKey(partition_ids(i), feature_ids(i));
+    auto itr = stats_map->find(key);
+    if (itr != stats_map->end()) {
+      itr->second.first += gradients(i);
+      itr->second.second += hessians(i);
+    } else {
+      (*stats_map)[key] = {gradients(i), hessians(i)};
+    }
+  }
+}
+
+void AddToScalarAccumulator(
+    StatsAccumulatorScalarResource* accumulator_resource,
+    OpKernelContext* context) {
+  const Tensor* partition_ids_t;
+  OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+  const Tensor* feature_ids_t;
+  OP_REQUIRES_OK(context, context->input("feature_ids", &feature_ids_t));
+  const Tensor* gradients_t;
+  OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+  const Tensor* hessians_t;
+  OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+  AddToScalarAccumulator(accumulator_resource, *partition_ids_t, *feature_ids_t,
+                         *gradients_t, *hessians_t);
+}
+
+void AddToTensorAccumulator(
+    StatsAccumulatorTensorResource* accumulator_resource,
+    const Tensor& partition_ids_t, const Tensor& feature_ids_t,
+    const Tensor& gradients_t, const Tensor& hessians_t,
+    OpKernelContext* context) {
+  accumulator_resource->set_num_updates(accumulator_resource->num_updates() +
+                                        1);
+
+  const TensorShape& partition_ids_shape = partition_ids_t.shape();
+  const auto& partition_ids = partition_ids_t.vec<int32>();
+  const auto& feature_ids = feature_ids_t.vec<int64>();
+  TensorShape gradients_shape = gradients_t.shape();
+  const auto& gradients = gradients_t.flat_outer_dims<float>();
+  TensorShape hessians_shape = hessians_t.shape();
+  const auto& hessians = hessians_t.flat_outer_dims<float>();
+
+  gradients_shape.RemoveDim(0);
+  hessians_shape.RemoveDim(0);
+
+  // TODO(soroush): Move gradient and hessian shape check to ShapeFn.
+  OP_REQUIRES(
+      context, gradients_shape == accumulator_resource->gradient_shape(),
+      errors::InvalidArgument(strings::StrCat(
+          "Gradients dimensions must match: ", gradients_shape.DebugString(),
+          ", ", accumulator_resource->gradient_shape().DebugString())));
+
+  OP_REQUIRES(
+      context, hessians_shape == accumulator_resource->hessian_shape(),
+      errors::InvalidArgument(strings::StrCat(
+          "Hessian dimensions must match: ", hessians_shape.DebugString(), ", ",
+          accumulator_resource->hessian_shape().DebugString())));
+
+  int64 num_updates = partition_ids_shape.dim_size(0);
+  auto stats_map = accumulator_resource->mutable_values();
+  for (int64 i = 0; i < num_updates; ++i) {
+    const auto key = PartitionKey(partition_ids(i), feature_ids(i));
+    auto itr = stats_map->find(key);
+    if (itr == stats_map->end()) {
+      std::vector<float> new_gradients(gradients_shape.num_elements());
+      for (int j = 0; j < gradients_shape.num_elements(); ++j) {
+        new_gradients[j] = gradients(i, j);
+      }
+      std::vector<float> new_hessians(hessians_shape.num_elements());
+      for (int j = 0; j < hessians_shape.num_elements(); ++j) {
+        new_hessians[j] = hessians(i, j);
+      }
+      (*stats_map)[key] = {new_gradients, new_hessians};
+    } else {
+      auto& stored_gradients = itr->second.first;
+      for (int j = 0; j < gradients_shape.num_elements(); ++j) {
+        stored_gradients[j] += gradients(i, j);
+      }
+      auto& stored_hessians = itr->second.second;
+      for (int j = 0; j < hessians_shape.num_elements(); ++j) {
+        stored_hessians[j] += hessians(i, j);
+      }
+    }
+  }
+}
+
+void AddToTensorAccumulator(
+    StatsAccumulatorTensorResource* accumulator_resource,
+    OpKernelContext* context) {
+  const Tensor* partition_ids_t;
+  OP_REQUIRES_OK(context, context->input("partition_ids", &partition_ids_t));
+  const Tensor* feature_ids_t;
+  OP_REQUIRES_OK(context, context->input("feature_ids", &feature_ids_t));
+  const Tensor* gradients_t;
+  OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+  const Tensor* hessians_t;
+  OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+  AddToTensorAccumulator(accumulator_resource, *partition_ids_t, *feature_ids_t,
+                         *gradients_t, *hessians_t, context);
+}
+
+}  // namespace
+
+REGISTER_RESOURCE_HANDLE_KERNEL(StatsAccumulatorScalarResource);
+REGISTER_RESOURCE_HANDLE_KERNEL(StatsAccumulatorTensorResource);
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarIsInitialized").Device(DEVICE_CPU),
+    IsResourceInitialized<StatsAccumulatorScalarResource>);
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorIsInitialized").Device(DEVICE_CPU),
+    IsResourceInitialized<StatsAccumulatorTensorResource>);
+
+class CreateStatsAccumulatorScalarOp : public OpKernel {
+ public:
+  explicit CreateStatsAccumulatorScalarOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+
+    TensorShape gradient_shape = TensorShape({});
+    TensorShape hessian_shape = TensorShape({});
+
+    auto* result =
+        new StatsAccumulatorScalarResource(gradient_shape, hessian_shape);
+    result->set_stamp(stamp_token_t->scalar<int64>()());
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions. If one already exists, it unrefs the new one.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("CreateStatsAccumulatorScalar").Device(DEVICE_CPU),
+                        CreateStatsAccumulatorScalarOp);
+
+class CreateStatsAccumulatorTensorOp : public OpKernel {
+ public:
+  explicit CreateStatsAccumulatorTensorOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+
+    const Tensor* gradient_shape_t;
+    OP_REQUIRES_OK(
+        context, context->input("per_slot_gradient_shape", &gradient_shape_t));
+
+    const Tensor* hessian_shape_t;
+    OP_REQUIRES_OK(context,
+                   context->input("per_slot_hessian_shape", &hessian_shape_t));
+    TensorShape gradient_shape = TensorShape(gradient_shape_t->vec<int64>());
+    TensorShape hessian_shape = TensorShape(hessian_shape_t->vec<int64>());
+    auto* result =
+        new StatsAccumulatorTensorResource(gradient_shape, hessian_shape);
+    result->set_stamp(stamp_token_t->scalar<int64>()());
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions. If one already exists, it unrefs the new one.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("CreateStatsAccumulatorTensor").Device(DEVICE_CPU),
+                        CreateStatsAccumulatorTensorOp);
+
+class StatsAccumulatorScalarAddOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarAddOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list("stats_accumulator_handles",
+                                                &resource_handle_list));
+    OpInputList partition_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("partition_ids", &partition_ids_list));
+
+    OpInputList feature_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("feature_ids", &feature_ids_list));
+    OpInputList gradients_list;
+    OP_REQUIRES_OK(context, context->input_list("gradients", &gradients_list));
+    OpInputList hessians_list;
+    OP_REQUIRES_OK(context, context->input_list("hessians", &hessians_list));
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &partition_ids_list,
+         &feature_ids_list, &gradients_list, &hessians_list,
+         stamp_token](int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+
+            StatsAccumulatorScalarResource* accumulator_resource;
+            OP_REQUIRES_OK(context, LookupResource(context, handle,
+                                                   &accumulator_resource));
+            mutex_lock l(*accumulator_resource->mutex());
+            core::ScopedUnref unref_me(accumulator_resource);
+
+            // If the stamp is invalid we drop the update.
+            if (!accumulator_resource->is_stamp_valid(stamp_token)) {
+              VLOG(1) << "Invalid stamp token in StatsAccumulatorScalarAddOp. "
+                      << "Passed stamp token: " << stamp_token << " "
+                      << "Current token: " << accumulator_resource->stamp();
+              return;
+            }
+            AddToScalarAccumulator(accumulator_resource,
+                                   partition_ids_list[resource_handle_idx],
+                                   feature_ids_list[resource_handle_idx],
+                                   gradients_list[resource_handle_idx],
+                                   hessians_list[resource_handle_idx]);
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorScalarAdd").Device(DEVICE_CPU),
+                        StatsAccumulatorScalarAddOp);
+
+class StatsAccumulatorTensorAddOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorAddOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    OpInputList resource_handle_list;
+    OP_REQUIRES_OK(context, context->input_list("stats_accumulator_handles",
+                                                &resource_handle_list));
+    OpInputList partition_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("partition_ids", &partition_ids_list));
+
+    OpInputList feature_ids_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("feature_ids", &feature_ids_list));
+    OpInputList gradients_list;
+    OP_REQUIRES_OK(context, context->input_list("gradients", &gradients_list));
+    OpInputList hessians_list;
+    OP_REQUIRES_OK(context, context->input_list("hessians", &hessians_list));
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    thread::ThreadPool* const worker_threads =
+        context->device()->tensorflow_cpu_worker_threads()->workers;
+    boosted_trees::utils::ParallelFor(
+        resource_handle_list.size(), worker_threads->NumThreads(),
+        worker_threads,
+        [&context, &resource_handle_list, &partition_ids_list,
+         &feature_ids_list, &gradients_list, &hessians_list,
+         stamp_token](int64 start, int64 end) {
+          for (int resource_handle_idx = start; resource_handle_idx < end;
+               ++resource_handle_idx) {
+            ResourceHandle handle = resource_handle_list[resource_handle_idx]
+                                        .flat<ResourceHandle>()(0);
+
+            StatsAccumulatorTensorResource* accumulator_resource;
+            OP_REQUIRES_OK(context, LookupResource(context, handle,
+                                                   &accumulator_resource));
+            mutex_lock l(*accumulator_resource->mutex());
+            core::ScopedUnref unref_me(accumulator_resource);
+
+            // If the stamp is invalid we drop the update.
+            if (!accumulator_resource->is_stamp_valid(stamp_token)) {
+              VLOG(1) << "Invalid stamp token in StatsAccumulatorScalarAddOp. "
+                      << "Passed stamp token: " << stamp_token << " "
+                      << "Current token: " << accumulator_resource->stamp();
+              return;
+            }
+            AddToTensorAccumulator(accumulator_resource,
+                                   partition_ids_list[resource_handle_idx],
+                                   feature_ids_list[resource_handle_idx],
+                                   gradients_list[resource_handle_idx],
+                                   hessians_list[resource_handle_idx], context);
+          }
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorTensorAdd").Device(DEVICE_CPU),
+                        StatsAccumulatorTensorAddOp);
+
+class StatsAccumulatorScalarFlushOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarFlushOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorScalarResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // If the stamp is invalid we restart the PS. It shouldn't happen since
+    // only Chief should call this function and chief is guaranteed to be in
+    // a consistent state.
+    CHECK(accumulator_resource->is_stamp_valid(stamp_token));
+
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kNextStampTokenName, &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+    CHECK(stamp_token != next_stamp_token);
+
+    SerializeScalarAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(next_stamp_token);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorScalarFlush").Device(DEVICE_CPU),
+                        StatsAccumulatorScalarFlushOp);
+
+class StatsAccumulatorTensorFlushOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorFlushOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorTensorResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kNextStampTokenName, &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+
+    // If the stamp is invalid we restart the PS. It shouldn't happen since
+    // only Chief should call this function and chief is guaranteed to be in
+    // a consistent state.
+    CHECK(accumulator_resource->is_stamp_valid(stamp_token));
+    CHECK(stamp_token != next_stamp_token);
+    SerializeTensorAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(next_stamp_token);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("StatsAccumulatorTensorFlush").Device(DEVICE_CPU),
+                        StatsAccumulatorTensorFlushOp);
+
+class StatsAccumulatorScalarDeserializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorScalarResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    // Check the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(stamp_token);
+    AddToScalarAccumulator(accumulator_resource, context);
+    const Tensor* num_updates_t;
+    OP_REQUIRES_OK(context, context->input("num_updates", &num_updates_t));
+    accumulator_resource->set_num_updates(num_updates_t->scalar<int64>()());
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarDeserialize").Device(DEVICE_CPU),
+    StatsAccumulatorScalarDeserializeOp);
+
+class StatsAccumulatorTensorDeserializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorTensorResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+
+    // Check the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    accumulator_resource->Clear();
+    accumulator_resource->set_stamp(stamp_token);
+    AddToTensorAccumulator(accumulator_resource, context);
+    const Tensor* num_updates_t;
+    OP_REQUIRES_OK(context, context->input("num_updates", &num_updates_t));
+    accumulator_resource->set_num_updates(num_updates_t->scalar<int64>()());
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorDeserialize").Device(DEVICE_CPU),
+    StatsAccumulatorTensorDeserializeOp);
+
+class StatsAccumulatorScalarSerializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorScalarResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+    SerializeScalarAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("stamp_token", TensorShape({}),
+                                            &stamp_token_t));
+    stamp_token_t->scalar<int64>()() = accumulator_resource->stamp();
+
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarSerialize").Device(DEVICE_CPU),
+    StatsAccumulatorScalarSerializeOp);
+
+class StatsAccumulatorTensorSerializeOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    StatsAccumulatorTensorResource* accumulator_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &accumulator_resource));
+    mutex_lock l(*accumulator_resource->mutex());
+    core::ScopedUnref unref_me(accumulator_resource);
+    SerializeTensorAccumulatorToOutput(*accumulator_resource, context);
+    Tensor* stamp_token_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("stamp_token", TensorShape({}),
+                                            &stamp_token_t));
+    stamp_token_t->scalar<int64>()() = accumulator_resource->stamp();
+
+    Tensor* num_updates_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_updates", TensorShape({}),
+                                            &num_updates_t));
+    num_updates_t->scalar<int64>()() = accumulator_resource->num_updates();
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorSerialize").Device(DEVICE_CPU),
+    StatsAccumulatorTensorSerializeOp);
+
+class StatsAccumulatorScalarMakeSummaryOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorScalarMakeSummaryOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    TensorShape gradient_shape = TensorShape({});
+    TensorShape hessian_shape = TensorShape({});
+    StatsAccumulatorScalarResource* accumulator_resource =
+        new StatsAccumulatorScalarResource(gradient_shape, hessian_shape);
+    core::ScopedUnref unref_me(accumulator_resource);
+    // Check the stamp token.
+    AddToScalarAccumulator(accumulator_resource, context);
+    SerializeScalarAccumulatorToOutput(*accumulator_resource, context);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorScalarMakeSummary").Device(DEVICE_CPU),
+    StatsAccumulatorScalarMakeSummaryOp);
+
+class StatsAccumulatorTensorMakeSummaryOp : public OpKernel {
+ public:
+  explicit StatsAccumulatorTensorMakeSummaryOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* gradients_t;
+    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
+    TensorShape gradients_shape = gradients_t->shape();
+    gradients_shape.RemoveDim(0);
+
+    const Tensor* hessians_t;
+    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
+    TensorShape hessians_shape = hessians_t->shape();
+    hessians_shape.RemoveDim(0);
+
+    StatsAccumulatorTensorResource* accumulator_resource =
+        new StatsAccumulatorTensorResource(gradients_shape, hessians_shape);
+    core::ScopedUnref unref_me(accumulator_resource);
+    // Check the stamp token.
+    AddToTensorAccumulator(accumulator_resource, context);
+    SerializeTensorAccumulatorToOutput(*accumulator_resource, context);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("StatsAccumulatorTensorMakeSummary").Device(DEVICE_CPU),
+    StatsAccumulatorTensorMakeSummaryOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
new file mode 100644
index 0000000000..12200e6a25
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
@@ -0,0 +1,825 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/split_info.pb.h"
+#include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+
+namespace tensorflow {
+using tensorflow::boosted_trees::learner::LearningRateDropoutDrivenConfig;
+
+namespace boosted_trees {
+
+using boosted_trees::trees::DecisionTreeEnsembleConfig;
+using boosted_trees::trees::TreeNode;
+using boosted_trees::trees::TreeNodeMetadata;
+using boosted_trees::utils::DropoutUtils;
+using boosted_trees::learner::LearningRateConfig;
+
+namespace {
+
+// SplitCandidate holds the split candidate node along with the stats.
+struct SplitCandidate {
+  // Id of handler that generated the split candidate.
+  int64 handler_id;
+
+  // Split gain.
+  float gain;
+
+  // Split info.
+  learner::SplitInfo split_info;
+};
+
+// Helper method to update the best split per partition given
+// a current candidate.
+void UpdateBestSplit(
+    const boosted_trees::learner::LearnerConfig& learner_config,
+    int32 partition_id, SplitCandidate* split,
+    std::map<int32, SplitCandidate>* best_splits) {
+  // Don't consider nodeless splits.
+  if (TF_PREDICT_FALSE(split->split_info.split_node().node_case() ==
+                       TreeNode::NODE_NOT_SET)) {
+    return;
+  }
+
+  // Don't consider negative splits if we're pre-pruning the tree.
+  // Note that zero-gain splits are acceptable as they're mostly doing as well
+  // as what bias centering in that partition would do.
+  if (learner_config.pruning_mode() ==
+          boosted_trees::learner::LearnerConfig::PRE_PRUNE &&
+      split->gain < 0) {
+    return;
+  }
+
+  // Take the split if we don't have a candidate yet.
+  auto best_split_it = best_splits->find(partition_id);
+  if (best_split_it == best_splits->end()) {
+    best_splits->insert(std::make_pair(partition_id, std::move(*split)));
+    return;
+  }
+
+  // Determine if best split so far needs to be replaced.
+  SplitCandidate& best_split = best_split_it->second;
+  if (TF_PREDICT_FALSE(split->gain == best_split.gain)) {
+    // Tie break on node case preferring simpler tree node types.
+    VLOG(2) << "Attempting to tie break with smaller node case. "
+            << "(current split: " << split->split_info.split_node().node_case()
+            << ", best split: "
+            << best_split.split_info.split_node().node_case() << ")";
+    if (split->split_info.split_node().node_case() <
+        best_split.split_info.split_node().node_case()) {
+      best_split = std::move(*split);
+    } else if (split->split_info.split_node().node_case() ==
+               best_split.split_info.split_node().node_case()) {
+      // Tie break on handler Id.
+      VLOG(2) << "Tie breaking with higher handler Id. "
+              << "(current split: " << split->handler_id
+              << ", best split: " << best_split.handler_id << ")";
+      if (split->handler_id > best_split.handler_id) {
+        best_split = std::move(*split);
+      }
+    }
+  } else if (split->gain > best_split.gain) {
+    best_split = std::move(*split);
+  }
+}
+
+// Helper method to check whether a node is a terminal node in that it
+// only has leaf nodes as children.
+bool IsTerminalSplitNode(const size_t node_id,
+                         const std::vector<int32>& children,
+                         const std::vector<TreeNode>& nodes) {
+  for (const int32 child_id : children) {
+    const auto& child_node = nodes[child_id];
+    CHECK(child_node.node_case() != TreeNode::NODE_NOT_SET);
+    if (child_node.node_case() != TreeNode::kLeaf) {
+      return false;
+    }
+  }
+  return true;
+}
+
+// Helper method to recursively prune the tree in a depth-first fashion.
+void RecursivePruneTree(const size_t node_id, std::vector<TreeNode>* nodes) {
+  // Base case when we reach a leaf.
+  TreeNode& tree_node = (*nodes)[node_id];
+  CHECK(tree_node.node_case() != TreeNode::NODE_NOT_SET);
+  if (tree_node.node_case() == TreeNode::kLeaf) {
+    return;
+  }
+
+  // Traverse node children first and recursively prune their sub-trees.
+  const std::vector<int32> children =
+      boosted_trees::trees::DecisionTree::GetChildren(tree_node);
+  for (const int32 child_id : children) {
+    RecursivePruneTree(child_id, nodes);
+  }
+
+  // Two conditions must be satisfied to prune the node:
+  // 1- The split gain is negative.
+  // 2- After depth-first pruning, the node only has leaf children.
+  TreeNodeMetadata* node_metadata = tree_node.mutable_node_metadata();
+  if (node_metadata->gain() < 0 &&
+      IsTerminalSplitNode(node_id, children, (*nodes))) {
+    // Clear node children.
+    for (const int32 child_id : children) {
+      auto& child_node = (*nodes)[child_id];
+      child_node.Clear();
+    }
+
+    // Change node back into leaf.
+    (*tree_node.mutable_leaf()) = *node_metadata->mutable_original_leaf();
+
+    // Clear gain for leaf node.
+    tree_node.clear_node_metadata();
+  } else {
+    // Clear original leaf as it's no longer needed for back-track pruning.
+    node_metadata->clear_original_leaf();
+  }
+}
+
+}  // namespace
+
+class CenterTreeEnsembleBiasOp : public OpKernel {
+ public:
+  explicit CenterTreeEnsembleBiasOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    // Read learner config.
+    string serialized_learner_config;
+    OP_REQUIRES_OK(context, context->GetAttr("learner_config",
+                                             &serialized_learner_config));
+    OP_REQUIRES(context,
+                learner_config_.ParseFromString(serialized_learner_config),
+                errors::InvalidArgument("Unable to parse learner config."));
+
+    // Read centering epsilon.
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("centering_epsilon", &centering_epsilon_));
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Get decision tree ensemble.
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Only the Chief should run this Op and it is guaranteed to be in
+    // a consistent state so the stamps must always match.
+    CHECK(decision_tree_ensemble_resource->is_stamp_valid(stamp_token));
+
+    // Get the next stamp token.
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input("next_stamp_token", &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+    CHECK(stamp_token != next_stamp_token);
+
+    // Get the delta updates.
+    const Tensor* delta_updates_t;
+    OP_REQUIRES_OK(context, context->input("delta_updates", &delta_updates_t));
+    OP_REQUIRES(
+        context,
+        delta_updates_t->dim_size(0) + 1 == learner_config_.num_classes(),
+        errors::InvalidArgument(
+            "Delta updates size must be consistent with label dimensions."));
+    auto delta_updates = delta_updates_t->vec<float>();
+
+    // Update the ensemble stamp.
+    decision_tree_ensemble_resource->set_stamp(next_stamp_token);
+
+    // Get the bias.
+    boosted_trees::trees::Leaf* bias =
+        RetrieveBias(decision_tree_ensemble_resource);
+    CHECK(bias->has_vector());
+    OP_REQUIRES(
+        context,
+        bias->vector().value_size() + 1 == learner_config_.num_classes(),
+        errors::InvalidArgument(
+            "Bias vector size must be consistent with label dimensions."));
+
+    // Update the bias.
+    float total_delta = 0;
+    auto* bias_vec = bias->mutable_vector();
+    for (size_t idx = 0; idx < bias->vector().value_size(); ++idx) {
+      float delta = delta_updates(idx);
+      bias_vec->set_value(idx, bias_vec->value(idx) + delta);
+      total_delta += std::abs(delta);
+    }
+
+    // Make a centering continuation decision based on current update.
+    bool continue_centering = total_delta > centering_epsilon_;
+    if (continue_centering) {
+      VLOG(1) << "Continuing to center bias, delta=" << total_delta;
+    } else {
+      VLOG(1) << "Done centering bias, delta=" << total_delta;
+    }
+    Tensor* continue_centering_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("continue_centering", TensorShape({}),
+                                          &continue_centering_t));
+    continue_centering_t->scalar<bool>()() = continue_centering;
+  }
+
+ private:
+  // Helper method to retrieve the bias from the tree ensemble.
+  boosted_trees::trees::Leaf* RetrieveBias(
+      boosted_trees::models::DecisionTreeEnsembleResource*
+          decision_tree_ensemble_resource) {
+    boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    const auto num_trees = ensemble_config->trees_size();
+    CHECK(num_trees == ensemble_config->tree_metadata_size() &&
+          num_trees == ensemble_config->tree_weights_size());
+    if (num_trees <= 0) {
+      ensemble_config->mutable_growing_metadata()->set_num_trees_attempted(1);
+      ensemble_config->mutable_growing_metadata()->set_num_layers_attempted(1);
+      // Add a new bias leaf.
+      boosted_trees::trees::DecisionTreeConfig* tree_config =
+          ensemble_config->add_trees();
+      auto* leaf = tree_config->add_nodes()->mutable_leaf();
+      for (size_t idx = 0; idx + 1 < learner_config_.num_classes(); ++idx) {
+        leaf->mutable_vector()->add_value(0);
+      }
+      ensemble_config->add_tree_weights(1.0);
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata =
+          ensemble_config->add_tree_metadata();
+      tree_metadata->set_num_layers_grown(1);
+      tree_metadata->set_is_finalized(true);
+      return leaf;
+    } else if (num_trees == 1) {
+      // Update the existing bias.
+      CHECK_EQ(ensemble_config->trees(0).nodes_size(), 1);
+      auto* node = ensemble_config->mutable_trees(0)->mutable_nodes(0);
+      CHECK(node->node_case() == TreeNode::kLeaf);
+      return node->mutable_leaf();
+    } else {
+      CHECK(false) << "Unable to center bias on an already grown ensemble";
+    }
+  }
+
+  boosted_trees::learner::LearnerConfig learner_config_;
+  float centering_epsilon_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("CenterTreeEnsembleBias").Device(DEVICE_CPU),
+                        CenterTreeEnsembleBiasOp);
+
+class GrowTreeEnsembleOp : public OpKernel {
+ public:
+  explicit GrowTreeEnsembleOp(OpKernelConstruction* const context)
+      : OpKernel(context) {
+    // Read number of handlers, note that this is the static number of
+    // all handlers but any subset of these handlers may be active at a time.
+    OP_REQUIRES_OK(context, context->GetAttr("num_handlers", &num_handlers_));
+
+    OP_REQUIRES_OK(context, context->GetAttr("center_bias", &center_bias_));
+
+    // Read learner config.
+    string serialized_learner_config;
+    OP_REQUIRES_OK(context, context->GetAttr("learner_config",
+                                             &serialized_learner_config));
+    OP_REQUIRES(context,
+                learner_config_.ParseFromString(serialized_learner_config),
+                errors::InvalidArgument("Unable to parse learner config."));
+
+    // Determine whether dropout was used when building this tree.
+    if (learner_config_.has_learning_rate_tuner() &&
+        learner_config_.learning_rate_tuner().tuner_case() ==
+            LearningRateConfig::kDropout) {
+      dropout_config_ = learner_config_.learning_rate_tuner().dropout();
+      dropout_was_applied_ = true;
+    } else {
+      dropout_was_applied_ = false;
+    }
+  }
+
+  void Compute(OpKernelContext* const context) override {
+    // Get decision tree ensemble.
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Only the Chief should run this Op and it is guaranteed to be in
+    // a consistent state so the stamps must always match.
+    CHECK(decision_tree_ensemble_resource->is_stamp_valid(stamp_token));
+
+    // Get the next stamp token.
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input("next_stamp_token", &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+    CHECK(stamp_token != next_stamp_token);
+
+    // Update the ensemble stamp regardless of whether a layer
+    // or tree is actually grown.
+    decision_tree_ensemble_resource->set_stamp(next_stamp_token);
+
+    // Read the learning_rate.
+    const Tensor* learning_rate_t;
+    OP_REQUIRES_OK(context, context->input("learning_rate", &learning_rate_t));
+    float learning_rate = learning_rate_t->scalar<float>()();
+
+    // Read seed that was used for dropout.
+    const Tensor* seed_t;
+    OP_REQUIRES_OK(context, context->input("dropout_seed", &seed_t));
+    // Cast seed to uint64.
+    const uint64 dropout_seed = seed_t->scalar<int64>()();
+
+    // Read partition Ids, gains and split candidates.
+    OpInputList partition_ids_list;
+    OpInputList gains_list;
+    OpInputList splits_list;
+    OP_REQUIRES_OK(context,
+                   context->input_list("partition_ids", &partition_ids_list));
+    OP_REQUIRES_OK(context, context->input_list("gains", &gains_list));
+    OP_REQUIRES_OK(context, context->input_list("splits", &splits_list));
+
+    boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    ensemble_config->mutable_growing_metadata()->set_num_layers_attempted(
+        ensemble_config->growing_metadata().num_layers_attempted() + 1);
+    const int num_trees = ensemble_config->trees_size();
+    if (num_trees <= 0 ||
+        ensemble_config->tree_metadata(num_trees - 1).is_finalized()) {
+      ensemble_config->mutable_growing_metadata()->set_num_trees_attempted(
+          ensemble_config->growing_metadata().num_trees_attempted() + 1);
+    }
+
+    // Find best splits for each active partition.
+    std::map<int32, SplitCandidate> best_splits;
+    FindBestSplitsPerPartition(context, partition_ids_list, gains_list,
+                               splits_list, &best_splits);
+
+    // No-op if no new splits can be considered.
+    if (best_splits.empty()) {
+      LOG(WARNING) << "Not growing tree ensemble as no good splits were found.";
+      return;
+    }
+
+    // Update and retrieve the growable tree with its metadata.
+    boosted_trees::trees::DecisionTreeConfig* tree_config;
+    boosted_trees::trees::DecisionTreeMetadata* tree_metadata;
+
+    // Updates the tree. If the tree is fully built and dropout was applied, it
+    // also adjusts the weights of dropped and the last tree.
+    std::tie(tree_config, tree_metadata) = UpdateAndRetrieveGrowableTree(
+        decision_tree_ensemble_resource, learning_rate, dropout_seed);
+
+    // Split tree nodes.
+    for (auto& split_entry : best_splits) {
+      SplitTreeNode(split_entry.first, &split_entry.second, tree_config);
+    }
+
+    // Post-prune finalized tree if needed.
+    if (learner_config_.pruning_mode() ==
+            boosted_trees::learner::LearnerConfig::POST_PRUNE &&
+        tree_metadata->is_finalized()) {
+      VLOG(2) << "Post-pruning finalized tree.";
+      PruneTree(tree_config);
+
+      // If after post-pruning the whole tree has no gain, remove the tree
+      // altogether from the ensemble.
+      if (tree_config->nodes_size() <= 0) {
+        ensemble_config->mutable_trees()->RemoveLast();
+        ensemble_config->mutable_tree_weights()->RemoveLast();
+        ensemble_config->mutable_tree_metadata()->RemoveLast();
+      }
+    }
+  }
+
+ private:
+  // Helper method which effectively does a reduce over all split candidates
+  // and finds the best split for each partition.
+  void FindBestSplitsPerPartition(
+      OpKernelContext* const context, const OpInputList& partition_ids_list,
+      const OpInputList& gains_list, const OpInputList& splits_list,
+      std::map<int32, SplitCandidate>* best_splits) {
+    // Find best split per partition going through every feature candidate.
+    // TODO(salehay): Is this worth parallelizing?
+    for (int64 handler_id = 0; handler_id < num_handlers_; ++handler_id) {
+      const auto& partition_ids = partition_ids_list[handler_id].vec<int32>();
+      const auto& gains = gains_list[handler_id].vec<float>();
+      const auto& splits = splits_list[handler_id].vec<string>();
+      OP_REQUIRES(context, partition_ids.size() == gains.size(),
+                  errors::InvalidArgument(
+                      "Inconsistent partition Ids and gains tensors: ",
+                      partition_ids.size(), " != ", gains.size()));
+      OP_REQUIRES(context, partition_ids.size() == splits.size(),
+                  errors::InvalidArgument(
+                      "Inconsistent partition Ids and splits tensors: ",
+                      partition_ids.size(), " != ", splits.size()));
+      for (size_t candidate_idx = 0; candidate_idx < splits.size();
+           ++candidate_idx) {
+        // Get current split candidate.
+        const auto& partition_id = partition_ids(candidate_idx);
+        const auto& gain = gains(candidate_idx);
+        const auto& serialized_split = splits(candidate_idx);
+        SplitCandidate split;
+        split.handler_id = handler_id;
+        split.gain = gain;
+        OP_REQUIRES(context, split.split_info.ParseFromString(serialized_split),
+                    errors::InvalidArgument("Unable to parse split info."));
+
+        // Update best split for partition based on the current candidate.
+        UpdateBestSplit(learner_config_, partition_id, &split, best_splits);
+      }
+    }
+  }
+
+  void UpdateTreeWeightsIfDropout(
+      boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config,
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata,
+      const uint64 dropout_seed) {
+    // It is possible that the tree was built with dropout. If it is the case,
+    // we need to adjust the tree weight.
+    if (dropout_was_applied_ && tree_metadata->is_finalized()) {
+      const int32 num_trees = ensemble_config->trees_size();
+
+      std::vector<int32> dropped_trees;
+      // Since only chief builds the trees, we are sure that the other tree
+      // weights didn't change.
+      std::vector<float> weights;
+      weights.reserve(num_trees);
+      std::vector<int32> num_updates;
+      num_updates.reserve(num_trees);
+      for (int i = 0; i < num_trees; ++i) {
+        weights.push_back(ensemble_config->tree_weights(i));
+        num_updates.push_back(
+            ensemble_config->tree_metadata(i).num_tree_weight_updates());
+      }
+
+      std::vector<float> dropped_trees_weights;
+      // Based on seed, figure out what trees were dropped before.
+      std::unordered_set<int32> trees_not_to_drop;
+      if (center_bias_) {
+        trees_not_to_drop.insert(0);
+      }
+      // Last tree is the current tree that is built.
+      const int32 current_tree = num_trees - 1;
+      trees_not_to_drop.insert(current_tree);
+
+      const auto dropout_status = DropoutUtils::DropOutTrees(
+          dropout_seed, dropout_config_, trees_not_to_drop, weights,
+          &dropped_trees, &dropped_trees_weights);
+      CHECK(dropout_status.ok())
+          << "Can't figure out what trees were dropped out before, error is "
+          << dropout_status.error_message();
+
+      // Now we have dropped trees, update their weights and the current tree
+      // weight.
+      if (!dropped_trees.empty()) {
+        DropoutUtils::GetTreesWeightsForAddingTrees(
+            dropped_trees, dropped_trees_weights, current_tree,
+            1 /* only 1 tree was added */, &weights, &num_updates);
+
+        // Update the weights and num of updates for trees.
+        for (int i = 0; i < num_trees; ++i) {
+          ensemble_config->set_tree_weights(i, weights[i]);
+          ensemble_config->mutable_tree_metadata(i)
+              ->set_num_tree_weight_updates(num_updates[i]);
+        }
+      }
+    }
+  }
+
+  // Helper method to update and retrieve the growable tree which is by
+  // definition the last tree in the ensemble.
+  std::pair<boosted_trees::trees::DecisionTreeConfig*,
+            boosted_trees::trees::DecisionTreeMetadata*>
+  UpdateAndRetrieveGrowableTree(
+      boosted_trees::models::DecisionTreeEnsembleResource*
+          decision_tree_ensemble_resource,
+      float learning_rate, const uint64 dropout_seed) {
+    boosted_trees::trees::DecisionTreeEnsembleConfig* ensemble_config =
+        decision_tree_ensemble_resource->mutable_decision_tree_ensemble();
+    auto num_trees = ensemble_config->trees_size();
+    CHECK(num_trees == ensemble_config->tree_metadata_size() &&
+          num_trees == ensemble_config->tree_weights_size());
+    if (num_trees <= 0 ||
+        ensemble_config->tree_metadata(num_trees - 1).is_finalized()) {
+      // Create a new tree with a no-op leaf.
+      boosted_trees::trees::DecisionTreeConfig* tree_config =
+          ensemble_config->add_trees();
+      ++num_trees;
+      VLOG(1) << "Adding layer 0 to tree " << num_trees - 1
+              << " of ensemble of " << num_trees << " trees.";
+      tree_config->add_nodes()->mutable_leaf();
+      ensemble_config->add_tree_weights(learning_rate);
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata =
+          ensemble_config->add_tree_metadata();
+      tree_metadata->set_num_layers_grown(1);
+      tree_metadata->set_is_finalized(
+          learner_config_.constraints().max_tree_depth() <= 1);
+      tree_metadata->set_num_tree_weight_updates(1);
+
+      UpdateTreeWeightsIfDropout(ensemble_config, tree_metadata, dropout_seed);
+      return std::make_pair(tree_config, tree_metadata);
+    } else {
+      // The growable tree is by definition the last tree in the ensemble.
+      boosted_trees::trees::DecisionTreeMetadata* tree_metadata =
+          ensemble_config->mutable_tree_metadata(num_trees - 1);
+      auto num_layers_grown = tree_metadata->num_layers_grown();
+      VLOG(1) << "Adding layer " << num_layers_grown << " to tree "
+              << num_trees - 1 << " of ensemble of " << num_trees << " trees.";
+      // Update growable tree metadata.
+      ++num_layers_grown;
+      tree_metadata->set_num_layers_grown(num_layers_grown);
+      tree_metadata->set_is_finalized(
+          num_layers_grown >= learner_config_.constraints().max_tree_depth());
+      auto* tree_config = ensemble_config->mutable_trees(num_trees - 1);
+
+      UpdateTreeWeightsIfDropout(ensemble_config, tree_metadata, dropout_seed);
+
+      return std::make_pair(tree_config, tree_metadata);
+    }
+  }
+
+  // Helper method to merge leaf weights as the tree is being grown.
+  boosted_trees::trees::Leaf* MergeLeafWeights(
+      const boosted_trees::trees::Leaf& source,
+      boosted_trees::trees::Leaf* dest) {
+    // Resolve leaf merging method based on how the trees are being grown.
+    if (learner_config_.growing_mode() ==
+        boosted_trees::learner::LearnerConfig::WHOLE_TREE) {
+      // No merging occurs when building a whole tree at a time.
+      return dest;
+    }
+
+    // Handle leaf merging based on type.
+    switch (source.leaf_case()) {
+      case boosted_trees::trees::Leaf::kVector: {
+        // No-op if source is empty
+        const auto& src_vec = source.vector();
+        if (src_vec.value_size() == 0) {
+          break;
+        }
+        CHECK(source.leaf_case() == dest->leaf_case());
+
+        // Dense add leaf vectors.
+        auto* dst_vec = dest->mutable_vector();
+        CHECK(src_vec.value_size() == dst_vec->value_size());
+        for (size_t idx = 0; idx < source.vector().value_size(); ++idx) {
+          (*dst_vec->mutable_value()->Mutable(idx)) += src_vec.value(idx);
+        }
+        break;
+      }
+      case boosted_trees::trees::Leaf::kSparseVector: {
+        // No-op if source is empty
+        const auto& src_vec = source.sparse_vector();
+        CHECK(src_vec.value_size() == src_vec.index_size());
+        if (src_vec.value_size() == 0) {
+          break;
+        }
+        CHECK(source.leaf_case() == dest->leaf_case());
+
+        // Get mapping of dimension to value for destination.
+        std::unordered_map<int32, float> dst_map;
+        auto* dst_vec = dest->mutable_sparse_vector();
+        CHECK(dst_vec->value_size() == dst_vec->index_size());
+        dst_map.reserve(dst_vec->value_size());
+        for (size_t idx = 0; idx < dst_vec->value_size(); ++idx) {
+          dst_map[dst_vec->index(idx)] = dst_vec->value(idx);
+        }
+        // Sparse add source vector to destination vector.
+        for (size_t idx = 0; idx < src_vec.value_size(); ++idx) {
+          dst_map[src_vec.index(idx)] += src_vec.value(idx);
+        }
+        // Rebuild merged destination leaf.
+        dst_vec->clear_index();
+        dst_vec->clear_value();
+        for (const auto& entry : dst_map) {
+          dst_vec->add_index(entry.first);
+          dst_vec->add_value(entry.second);
+        }
+        break;
+      }
+      case boosted_trees::trees::Leaf::LEAF_NOT_SET: {
+        // No-op as there is nothing to merge.
+        break;
+      }
+    }
+    return dest;
+  }
+
+  // Helper method to split a tree node and append its respective
+  // leaf children given the split candidate.
+  void SplitTreeNode(const int32 node_id, SplitCandidate* split,
+                     boosted_trees::trees::DecisionTreeConfig* tree_config) {
+    // No-op if we have no real node.
+    CHECK(node_id < tree_config->nodes_size())
+        << "Invalid node " << node_id << " to split.";
+    // Ensure new split node is valid.
+    CHECK(split->split_info.split_node().node_case() != TreeNode::NODE_NOT_SET);
+    CHECK(tree_config->nodes(node_id).node_case() == TreeNode::kLeaf)
+        << "Unexpected node type to split "
+        << tree_config->nodes(node_id).node_case();
+
+    // Add left leaf.
+    int32 left_id = tree_config->nodes_size();
+    (*tree_config->add_nodes()->mutable_leaf()) =
+        *MergeLeafWeights(tree_config->nodes(node_id).leaf(),
+                          split->split_info.mutable_left_child());
+
+    // Add right leaf.
+    int32 right_id = tree_config->nodes_size();
+    (*tree_config->add_nodes()->mutable_leaf()) =
+        *MergeLeafWeights(tree_config->nodes(node_id).leaf(),
+                          split->split_info.mutable_right_child());
+
+    // Link children and add them as new roots.
+    boosted_trees::trees::DecisionTree::LinkChildren(
+        {left_id, right_id}, split->split_info.mutable_split_node());
+
+    // Add split gain and, if needed, original leaf to node metadata.
+    TreeNodeMetadata* node_metadata =
+        split->split_info.mutable_split_node()->mutable_node_metadata();
+    node_metadata->set_gain(split->gain);
+    if (learner_config_.pruning_mode() ==
+        boosted_trees::learner::LearnerConfig::POST_PRUNE) {
+      (*node_metadata->mutable_original_leaf()) =
+          *tree_config->mutable_nodes(node_id)->mutable_leaf();
+    }
+
+    // Replace node in tree.
+    (*tree_config->mutable_nodes(node_id)) =
+        *split->split_info.mutable_split_node();
+  }
+
+  void PruneTree(boosted_trees::trees::DecisionTreeConfig* tree_config) {
+    // No-op if tree is empty.
+    if (tree_config->nodes_size() <= 0) {
+      return;
+    }
+
+    // Copy nodes to temp vector and clear original tree.
+    std::vector<TreeNode> tree_nodes;
+    tree_nodes.reserve(tree_config->nodes_size());
+    for (auto& node : (*tree_config->mutable_nodes())) {
+      tree_nodes.push_back(node);
+      node.Clear();
+    }
+    tree_config->clear_nodes();
+
+    // Prune the tree recursively starting from the root.
+    RecursivePruneTree(0, &tree_nodes);
+
+    // Rebuild compacted tree.
+    (*tree_config->add_nodes()) = tree_nodes[0];
+    std::unordered_map<size_t, size_t> nodes_map;
+    nodes_map[0] = 0;
+    for (size_t node_idx = 0; node_idx < tree_nodes.size(); ++node_idx) {
+      // Skip pruned nodes.
+      auto& original_node = tree_nodes[node_idx];
+      if (original_node.node_case() == TreeNode::NODE_NOT_SET) {
+        continue;
+      }
+
+      // Find node mapped in tree ensemble.
+      auto mapped_node_it = nodes_map.find(node_idx);
+      CHECK(mapped_node_it != nodes_map.end());
+      auto& mapped_node = (*tree_config->mutable_nodes(mapped_node_it->second));
+
+      // Get node children
+      auto children =
+          boosted_trees::trees::DecisionTree::GetChildren(original_node);
+      for (int32& child_idx : children) {
+        auto new_idx = tree_config->nodes_size();
+        (*tree_config->add_nodes()) = tree_nodes[child_idx];
+        nodes_map[child_idx] = new_idx;
+        child_idx = new_idx;
+      }
+      boosted_trees::trees::DecisionTree::LinkChildren(children, &mapped_node);
+    }
+
+    // Check if there are any nodes with gain left.
+    if (tree_config->nodes_size() == 1 &&
+        tree_config->nodes(0).node_metadata().gain() <= 0) {
+      // The whole tree should be pruned.
+      VLOG(2) << "No useful nodes left after post-pruning tree.";
+      tree_config->clear_nodes();
+    }
+  }
+
+ private:
+  boosted_trees::learner::LearnerConfig learner_config_;
+  int64 num_handlers_;
+  LearningRateDropoutDrivenConfig dropout_config_;
+  bool dropout_was_applied_;
+  bool center_bias_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("GrowTreeEnsemble").Device(DEVICE_CPU),
+                        GrowTreeEnsembleOp);
+
+class TreeEnsembleStatsOp : public OpKernel {
+ public:
+  explicit TreeEnsembleStatsOp(OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* const context) override {
+    // Get decision tree ensemble.
+    boosted_trees::models::DecisionTreeEnsembleResource*
+        decision_tree_ensemble_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_ensemble_resource));
+    core::ScopedUnref unref_me(decision_tree_ensemble_resource);
+    mutex_lock l(*decision_tree_ensemble_resource->get_mutex());
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Only the Chief should run this Op and it is guaranteed to be in
+    // a consistent state so the stamps must always match.
+    CHECK(decision_tree_ensemble_resource->is_stamp_valid(stamp_token));
+    const boosted_trees::trees::DecisionTreeEnsembleConfig& ensemble_config =
+        decision_tree_ensemble_resource->decision_tree_ensemble();
+
+    // Set tree stats.
+    Tensor* num_trees_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "num_trees", TensorShape({}), &num_trees_t));
+    Tensor* active_tree_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("active_tree", TensorShape({}),
+                                            &active_tree_t));
+    Tensor* attempted_tree_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("attempted_trees", TensorShape({}),
+                                            &attempted_tree_t));
+
+    int num_trees = ensemble_config.trees_size();
+    active_tree_t->scalar<int64>()() = num_trees;
+    if (num_trees > 0 &&
+        !ensemble_config.tree_metadata(num_trees - 1).is_finalized()) {
+      --num_trees;
+    }
+    num_trees_t->scalar<int64>()() = num_trees;
+    attempted_tree_t->scalar<int64>()() =
+        ensemble_config.growing_metadata().num_trees_attempted();
+
+    // Set layer stats.
+    Tensor* num_layers_t = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(
+                                "num_layers", TensorShape({}), &num_layers_t));
+    Tensor* active_layer_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("active_layer", TensorShape({}),
+                                            &active_layer_t));
+    Tensor* attempted_layers_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("attempted_layers", TensorShape({}),
+                                            &attempted_layers_t));
+
+    int64 num_layers = 0;
+    for (const auto& tree_metadata : ensemble_config.tree_metadata()) {
+      num_layers += tree_metadata.num_layers_grown();
+    }
+    num_layers_t->scalar<int64>()() = num_layers;
+    int tree_metadata_size = ensemble_config.tree_metadata_size();
+    active_layer_t->scalar<int64>()() =
+        tree_metadata_size > 0
+            ? ensemble_config.tree_metadata(tree_metadata_size - 1)
+                  .num_layers_grown()
+            : 0;
+    attempted_layers_t->scalar<int64>()() =
+        ensemble_config.growing_metadata().num_layers_attempted();
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleStats").Device(DEVICE_CPU),
+                        TreeEnsembleStatsOp);
+
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/BUILD b/tensorflow/contrib/boosted_trees/lib/BUILD
index 011c02d720..9e90ff399e 100644
--- a/tensorflow/contrib/boosted_trees/lib/BUILD
+++ b/tensorflow/contrib/boosted_trees/lib/BUILD
@@ -22,60 +22,7 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
-cc_library(
-    name = "weighted_quantiles",
-    srcs = [],
-    hdrs = [
-        "quantiles/weighted_quantiles_buffer.h",
-        "quantiles/weighted_quantiles_stream.h",
-        "quantiles/weighted_quantiles_summary.h",
-    ],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow/core:framework_headers_lib",
-    ],
-)
-
-cc_test(
-    name = "weighted_quantiles_buffer_test",
-    size = "small",
-    srcs = ["quantiles/weighted_quantiles_buffer_test.cc"],
-    deps = [
-        ":weighted_quantiles",
-        "//tensorflow/core",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-    ],
-)
-
-cc_test(
-    name = "weighted_quantiles_summary_test",
-    size = "small",
-    srcs = ["quantiles/weighted_quantiles_summary_test.cc"],
-    deps = [
-        ":weighted_quantiles",
-        "//tensorflow/core",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-    ],
-)
-
-cc_test(
-    name = "weighted_quantiles_stream_test",
-    size = "small",
-    srcs = ["quantiles/weighted_quantiles_stream_test.cc"],
-    deps = [
-        ":weighted_quantiles",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-    ],
-)
+# Utils
 
 cc_library(
     name = "utils",
@@ -161,6 +108,8 @@ cc_test(
     ],
 )
 
+# Models
+
 cc_library(
     name = "models",
     srcs = ["models/multiple_additive_trees.cc"],
@@ -190,12 +139,107 @@ cc_test(
     ],
 )
 
+# Testutil
+
+cc_library(
+    name = "batch_features_testutil",
+    testonly = 1,
+    srcs = ["testutil/batch_features_testutil.cc"],
+    hdrs = ["testutil/batch_features_testutil.h"],
+    deps = [
+        ":utils",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:testlib",
+    ],
+)
+
+cc_library(
+    name = "random_tree_gen",
+    srcs = ["testutil/random_tree_gen.cc"],
+    hdrs = ["testutil/random_tree_gen.h"],
+    deps = [
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_binary(
+    name = "random_tree_gen_main",
+    srcs = ["testutil/random_tree_gen_main.cc"],
+    deps = [
+        ":random_tree_gen",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
+    ],
+)
+
+# Quantiles
+
+cc_library(
+    name = "weighted_quantiles",
+    srcs = [],
+    hdrs = [
+        "quantiles/weighted_quantiles_buffer.h",
+        "quantiles/weighted_quantiles_stream.h",
+        "quantiles/weighted_quantiles_summary.h",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_test(
+    name = "weighted_quantiles_buffer_test",
+    size = "small",
+    srcs = ["quantiles/weighted_quantiles_buffer_test.cc"],
+    deps = [
+        ":weighted_quantiles",
+        "//tensorflow/core",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_test(
+    name = "weighted_quantiles_summary_test",
+    size = "small",
+    srcs = ["quantiles/weighted_quantiles_summary_test.cc"],
+    deps = [
+        ":weighted_quantiles",
+        "//tensorflow/core",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_test(
+    name = "weighted_quantiles_stream_test",
+    size = "small",
+    srcs = ["quantiles/weighted_quantiles_stream_test.cc"],
+    deps = [
+        ":weighted_quantiles",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+# Trees
+
 cc_library(
     name = "trees",
     srcs = ["trees/decision_tree.cc"],
     hdrs = ["trees/decision_tree.h"],
     deps = [
-        ":utils",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
         "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
         "//tensorflow/core:framework_headers_lib",
     ],
@@ -207,43 +251,157 @@ cc_test(
     srcs = ["trees/decision_tree_test.cc"],
     deps = [
         ":trees",
-        ":utils",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
 )
 
+# Learner/Common
+
 cc_library(
-    name = "batch_features_testutil",
-    testonly = 1,
-    srcs = ["testutil/batch_features_testutil.cc"],
-    hdrs = ["testutil/batch_features_testutil.h"],
+    name = "class-partition-key",
+    hdrs = ["learner/common/accumulators/class-partition-key.h"],
     deps = [
-        ":utils",
         "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_library(
+    name = "feature-stats-accumulator",
+    hdrs = ["learner/common/accumulators/feature-stats-accumulator.h"],
+    deps = [
+        ":class-partition-key",
+    ],
+)
+
+cc_test(
+    name = "feature-stats-accumulator_test",
+    size = "small",
+    srcs = ["learner/common/accumulators/feature-stats-accumulator_test.cc"],
+    deps = [
+        ":feature-stats-accumulator",
         "//tensorflow/core:lib",
+        "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
-        "//tensorflow/core:testlib",
+        "//tensorflow/core:test_main",
     ],
 )
 
 cc_library(
-    name = "random_tree_gen",
-    srcs = ["testutil/random_tree_gen.cc"],
-    hdrs = ["testutil/random_tree_gen.h"],
+    name = "example_partitioner",
+    srcs = ["learner/common/partitioners/example_partitioner.cc"],
+    hdrs = ["learner/common/partitioners/example_partitioner.h"],
     deps = [
-        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/contrib/boosted_trees/lib:trees",
+        "//tensorflow/contrib/boosted_trees/lib:utils",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_test(
+    name = "example_partitioner_test",
+    size = "small",
+    srcs = ["learner/common/partitioners/example_partitioner_test.cc"],
+    deps = [
+        ":example_partitioner",
         "//tensorflow/core:lib",
+        "//tensorflow/core:tensor_testutil",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
     ],
 )
 
-cc_binary(
-    name = "random_tree_gen_main",
-    srcs = ["testutil/random_tree_gen_main.cc"],
+# Learner/stochastic
+
+cc_library(
+    name = "feature-column-handlers",
+    srcs = [
+        "learner/stochastic/handlers/bias-feature-column-handler.cc",
+        "learner/stochastic/handlers/categorical-feature-column-handler.cc",
+        "learner/stochastic/handlers/dense-quantized-feature-column-handler.cc",
+        "learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc",
+    ],
+    hdrs = [
+        "learner/stochastic/handlers/bias-feature-column-handler.h",
+        "learner/stochastic/handlers/categorical-feature-column-handler.h",
+        "learner/stochastic/handlers/dense-quantized-feature-column-handler.h",
+        "learner/stochastic/handlers/feature-column-handler.h",
+        "learner/stochastic/handlers/sparse-quantized-feature-column-handler.h",
+    ],
     deps = [
-        ":random_tree_gen",
-        "//tensorflow/core:framework_internal",
+        ":feature-split-candidate",
+        ":feature-stats-accumulator",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+    ],
+)
+
+cc_test(
+    name = "feature-column-handlers_test",
+    size = "small",
+    srcs = [
+        "learner/stochastic/handlers/bias-feature-column-handler_test.cc",
+        "learner/stochastic/handlers/categorical-feature-column-handler_test.cc",
+        "learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc",
+        "learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc",
+    ],
+    deps = [
+        ":feature-column-handlers",
         "//tensorflow/core:lib",
+        "//tensorflow/core:tensor_testutil",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
+cc_library(
+    name = "gradient-stats",
+    hdrs = ["learner/stochastic/stats/gradient-stats.h"],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "node-stats",
+    hdrs = ["learner/stochastic/stats/node-stats.h"],
+    deps = [
+        ":gradient-stats",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+)
+
+cc_library(
+    name = "split-stats",
+    hdrs = ["learner/stochastic/stats/split-stats.h"],
+    deps = [
+        ":node-stats",
+    ],
+)
+
+cc_library(
+    name = "feature-split-candidate",
+    hdrs = ["learner/stochastic/stats/feature-split-candidate.h"],
+    deps = [
+        ":split-stats",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
+    ],
+)
+
+cc_test(
+    name = "node-stats_test",
+    size = "small",
+    srcs = ["learner/stochastic/stats/node-stats_test.cc"],
+    deps = [
+        ":node-stats",
+        "//tensorflow/core:tensor_testutil",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
     ],
 )
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h
new file mode 100644
index 0000000000..e1bef02788
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h
@@ -0,0 +1,61 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_CLASS_PARTITION_KEY_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_CLASS_PARTITION_KEY_H_
+
+#include "tensorflow/core/lib/hash/hash.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+// Key into a specific class and partition to accumulate stats
+// for the specified feature id. A feature id can be the quantile
+// for a float feature or the hash/dictionary entry for a string feature.
+struct ClassPartitionKey {
+  ClassPartitionKey() : class_id(-1), partition_id(-1), feature_id(-1) {}
+
+  ClassPartitionKey(uint32 c, uint32 p, uint64 f)
+      : class_id(c), partition_id(p), feature_id(f) {}
+
+  bool operator==(const ClassPartitionKey& other) const {
+    return (feature_id == other.feature_id) &&
+           (partition_id == other.partition_id) && (class_id == other.class_id);
+  }
+
+  // Hasher for ClassPartitionKey.
+  struct Hash {
+    size_t operator()(const ClassPartitionKey& key) const {
+      uint64 class_partition =
+          (static_cast<uint64>(key.partition_id) << 32) | (key.class_id);
+      return Hash64Combine(class_partition, key.feature_id);
+    }
+  };
+
+  // Class to predict for, this is constant for binary tasks.
+  uint32 class_id;
+
+  // Tree partition defined by traversing the tree to the leaf.
+  uint32 partition_id;
+
+  // Feature Id within the feature column.
+  uint64 feature_id;
+};
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_CLASS_PARTITION_KEY_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h
new file mode 100644
index 0000000000..3814edb567
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h
@@ -0,0 +1,82 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_FEATURE_STATS_ACCUMULATOR_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_FEATURE_STATS_ACCUMULATOR_H_
+
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/class-partition-key.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+// Feature stats accumulator to aggregate stats across various
+// feature columns. This class is thread compatible not safe, the user
+// must ensure proper synchronization if many threads update overlapping
+// feature columns.
+template <typename StatsType, typename Accumulator>
+class FeatureStatsAccumulator {
+ public:
+  using FeatureStats =
+      std::unordered_map<ClassPartitionKey, StatsType, ClassPartitionKey::Hash>;
+
+  explicit FeatureStatsAccumulator(size_t num_feature_columns,
+                                   Accumulator accumulator = Accumulator())
+      : accumulator_(accumulator), feature_column_stats_(num_feature_columns) {}
+
+  // Delete copy and assign.
+  FeatureStatsAccumulator(const FeatureStatsAccumulator& other) = delete;
+  FeatureStatsAccumulator& operator=(const FeatureStatsAccumulator& other) =
+      delete;
+
+  // Adds stats for the specified class, partition and feature within
+  // the desired slot.
+  void AddStats(uint32 slot_id, uint32 class_id, uint32 partition_id,
+                uint64 feature_id, const StatsType& stats) {
+    accumulator_(stats, &feature_column_stats_[slot_id][ClassPartitionKey(
+                            class_id, partition_id, feature_id)]);
+  }
+
+  // Retrieves stats for the specified class, partition and feature
+  // within the desired feature column. Default stats are returned if no match
+  // can be found. Note that the feature column index must be valid.
+  StatsType GetStats(uint32 slot_id, uint32 class_id, uint32 partition_id,
+                     uint64 feature_id) const {
+    auto it = feature_column_stats_[slot_id].find(
+        ClassPartitionKey(class_id, partition_id, feature_id));
+    return it != feature_column_stats_[slot_id].end() ? it->second
+                                                      : StatsType();
+  }
+
+  // Returns feature stats for a given slot.
+  FeatureStats GetFeatureStats(uint32 slot_id) const {
+    return feature_column_stats_[slot_id];
+  }
+
+ private:
+  // Accumulator method to use.
+  const Accumulator accumulator_;
+
+  // Vector of stats indexed by the feature column.
+  std::vector<FeatureStats> feature_column_stats_;
+};
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_ACCUMULATORS_FEATURE_STATS_ACCUMULATOR_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc
new file mode 100644
index 0000000000..fdbd670120
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator_test.cc
@@ -0,0 +1,90 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace {
+
+struct TestStats {
+  TestStats& operator+=(const TestStats& other) {
+    s1 += other.s1;
+    s2 += other.s2;
+    return (*this);
+  }
+
+  float s1;
+  float s2;
+};
+
+struct TestStatsAccumulator {
+  void operator()(const TestStats& from, TestStats* to) const { (*to) += from; }
+};
+#define EXPECT_STATS_EQ(val1, val2)  \
+  EXPECT_FLOAT_EQ(val1.s1, val2.s1); \
+  EXPECT_FLOAT_EQ(val1.s2, val2.s2);
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<TestStats, TestStatsAccumulator>;
+
+class FeatureStatsAccumulatorTest : public ::testing::Test {};
+
+TEST_F(FeatureStatsAccumulatorTest, Empty) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats = {0, 0};
+
+  EXPECT_STATS_EQ(stats, accumulator.GetStats(0, 2, 1, 234));
+}
+
+TEST_F(FeatureStatsAccumulatorTest, OneFeatureOneGrad) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats = {-12.023f, 8.2f};
+  accumulator.AddStats(0, 2, 1, 234, stats);
+
+  EXPECT_STATS_EQ(stats, accumulator.GetStats(0, 2, 1, 234));
+}
+
+TEST_F(FeatureStatsAccumulatorTest, OneFeatureAggregateGrad) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats1 = {-12.023f, 8.2f};
+  accumulator.AddStats(0, 2, 1, 234, stats1);
+  TestStats stats2 = {4.46f, 1.9f};
+  accumulator.AddStats(0, 2, 1, 234, stats2);
+  TestStats expected = {-7.563f, 10.1f};
+  EXPECT_STATS_EQ(expected, accumulator.GetStats(0, 2, 1, 234));
+}
+
+TEST_F(FeatureStatsAccumulatorTest, TwoFeaturesOneGrad) {
+  FeatureStatsAccumulator accumulator(1);
+  TestStats stats1 = {-12.023f, 8.2f};
+  accumulator.AddStats(0, 1, 0, 34, stats1);
+  TestStats stats2 = {4.46f, 1.9f};
+  accumulator.AddStats(0, 1, 0, 91, stats2);
+
+  EXPECT_STATS_EQ(stats1, accumulator.GetStats(0, 1, 0, 34));
+  EXPECT_STATS_EQ(stats2, accumulator.GetStats(0, 1, 0, 91));
+}
+
+}  // namespace
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc
new file mode 100644
index 0000000000..1f84b1b06a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc
@@ -0,0 +1,90 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/parallel_for.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+void ExamplePartitioner::UpdatePartitions(
+    const boosted_trees::trees::DecisionTreeConfig& tree_config,
+    const boosted_trees::utils::BatchFeatures& features,
+    const int desired_parallelism, thread::ThreadPool* const thread_pool,
+    int32* example_partition_ids) {
+  // Get batch size.
+  const int64 batch_size = features.batch_size();
+  if (batch_size <= 0) {
+    return;
+  }
+
+  // Lambda for doing a block of work.
+  auto partition_examples_subset = [&tree_config, &features,
+                                    &example_partition_ids](const int64 start,
+                                                            const int64 end) {
+    if (TF_PREDICT_TRUE(tree_config.nodes_size() > 0)) {
+      auto examples_iterable = features.examples_iterable(start, end);
+      for (const auto& example : examples_iterable) {
+        int32& example_partition = example_partition_ids[example.example_idx];
+        example_partition = boosted_trees::trees::DecisionTree::Traverse(
+            tree_config, example_partition, example);
+        DCHECK_GE(example_partition, 0);
+      }
+    } else {
+      std::fill(example_partition_ids + start, example_partition_ids + end, 0);
+    }
+  };
+
+  // Parallelize partitioning over the batch.
+  boosted_trees::utils::ParallelFor(batch_size, desired_parallelism,
+                                    thread_pool, partition_examples_subset);
+}
+
+void ExamplePartitioner::PartitionExamples(
+    const boosted_trees::trees::DecisionTreeConfig& tree_config,
+    const boosted_trees::utils::BatchFeatures& features,
+    const int desired_parallelism, thread::ThreadPool* const thread_pool,
+    int32* example_partition_ids) {
+  // Get batch size.
+  const int64 batch_size = features.batch_size();
+  if (batch_size <= 0) {
+    return;
+  }
+
+  // Lambda for doing a block of work.
+  auto partition_examples_subset = [&tree_config, &features,
+                                    &example_partition_ids](const int64 start,
+                                                            const int64 end) {
+    if (TF_PREDICT_TRUE(tree_config.nodes_size() > 0)) {
+      auto examples_iterable = features.examples_iterable(start, end);
+      for (const auto& example : examples_iterable) {
+        uint32 partition = boosted_trees::trees::DecisionTree::Traverse(
+            tree_config, 0, example);
+        example_partition_ids[example.example_idx] = partition;
+        DCHECK_GE(partition, 0);
+      }
+    } else {
+      std::fill(example_partition_ids + start, example_partition_ids + end, 0);
+    }
+  };
+
+  // Parallelize partitioning over the batch.
+  boosted_trees::utils::ParallelFor(batch_size, desired_parallelism,
+                                    thread_pool, partition_examples_subset);
+}
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h
new file mode 100644
index 0000000000..aed0d9fdac
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h
@@ -0,0 +1,53 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_PARTITIONERS_EXAMPLE_PARTITIONER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_PARTITIONERS_EXAMPLE_PARTITIONER_H_
+
+#include <vector>
+#include "tensorflow/contrib/boosted_trees/lib/trees/decision_tree.h"
+#include "tensorflow/contrib/boosted_trees/lib/utils/batch_features.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+
+// Partitions examples based on the path through the current tree.
+class ExamplePartitioner {
+ public:
+  // Updates partitions from previous set using the current tree structure by
+  // traversing sub-roots for each example. This method can be optionally
+  // parallelized using the passed thread pool.
+  static void UpdatePartitions(const trees::DecisionTreeConfig& tree_config,
+                               const utils::BatchFeatures& features,
+                               int desired_parallelism,
+                               thread::ThreadPool* const thread_pool,
+                               int32* example_partition_ids);
+
+  // Partitions examples using the current tree structure by traversing from
+  // root for each example. This method can be optionally parallelized using
+  // the passed thread pool.
+  static void PartitionExamples(const trees::DecisionTreeConfig& tree_config,
+                                const utils::BatchFeatures& features,
+                                int desired_parallelism,
+                                thread::ThreadPool* const thread_pool,
+                                int32* example_partition_ids);
+};
+
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_PARTITIONERS_EXAMPLE_PARTITIONER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc
new file mode 100644
index 0000000000..0d5326aa45
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner_test.cc
@@ -0,0 +1,99 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace {
+
+class ExamplePartitionerTest : public ::testing::Test {
+ protected:
+  ExamplePartitionerTest()
+      : thread_pool_(tensorflow::Env::Default(), "test_pool", 2),
+        batch_features_(2) {
+    dense_matrix_ = test::AsTensor<float>({7.0f, -2.0f}, {2, 1});
+    TF_EXPECT_OK(
+        batch_features_.Initialize({dense_matrix_}, {}, {}, {}, {}, {}, {}));
+  }
+
+  thread::ThreadPool thread_pool_;
+  Tensor dense_matrix_;
+  boosted_trees::utils::BatchFeatures batch_features_;
+};
+
+TEST_F(ExamplePartitionerTest, EmptyTree) {
+  boosted_trees::trees::DecisionTreeConfig tree_config;
+  std::vector<int32> example_partition_ids(2);
+  ExamplePartitioner::UpdatePartitions(tree_config, batch_features_, 1,
+                                       &thread_pool_,
+                                       example_partition_ids.data());
+  EXPECT_EQ(0, example_partition_ids[0]);
+  EXPECT_EQ(0, example_partition_ids[1]);
+}
+
+TEST_F(ExamplePartitionerTest, UpdatePartitions) {
+  // Create tree with one split.
+  // TODO(salehay): figure out if we can use PARSE_TEXT_PROTO.
+  boosted_trees::trees::DecisionTreeConfig tree_config;
+  auto* split = tree_config.add_nodes()->mutable_dense_float_binary_split();
+  split->set_feature_column(0);
+  split->set_threshold(3.0f);
+  split->set_left_id(1);
+  split->set_right_id(2);
+  tree_config.add_nodes()->mutable_leaf();
+  tree_config.add_nodes()->mutable_leaf();
+
+  // Partition input:
+  // Instance 1 has !(7 <= 3) => go right => leaf 2.
+  // Instance 2 has (-2 <= 3) => go left => leaf 1.
+  std::vector<int32> example_partition_ids(2);
+  ExamplePartitioner::UpdatePartitions(tree_config, batch_features_, 1,
+                                       &thread_pool_,
+                                       example_partition_ids.data());
+  EXPECT_EQ(2, example_partition_ids[0]);
+  EXPECT_EQ(1, example_partition_ids[1]);
+}
+
+TEST_F(ExamplePartitionerTest, PartitionExamples) {
+  // Create tree with one split.
+  // TODO(salehay): figure out if we can use PARSE_TEXT_PROTO.
+  boosted_trees::trees::DecisionTreeConfig tree_config;
+  auto* split = tree_config.add_nodes()->mutable_dense_float_binary_split();
+  split->set_feature_column(0);
+  split->set_threshold(3.0f);
+  split->set_left_id(1);
+  split->set_right_id(2);
+  tree_config.add_nodes()->mutable_leaf();
+  tree_config.add_nodes()->mutable_leaf();
+
+  // Partition input:
+  // Instance 1 has !(7 <= 3) => go right => leaf 2.
+  // Instance 2 has (-2 <= 3) => go left => leaf 1.
+  std::vector<int32> example_partition_ids(2);
+  ExamplePartitioner::PartitionExamples(tree_config, batch_features_, 1,
+                                        &thread_pool_,
+                                        example_partition_ids.data());
+  EXPECT_EQ(2, example_partition_ids[0]);
+  EXPECT_EQ(1, example_partition_ids[1]);
+}
+
+}  // namespace
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc
new file mode 100644
index 0000000000..b880cf2c47
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc
@@ -0,0 +1,59 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+void BiasFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all examples and aggregate gradient stats for each sub-root.
+  for (int64 example_idx = 0; example_idx < batch_size_; ++example_idx) {
+    auto partition_id = example_partition_ids[example_idx];
+    gradient_stats_accumulator->AddStats(
+        slot_id_, class_id_, partition_id, kBiasFeatureId,
+        GradientStats(example_first_order_gradients,
+                      example_second_order_gradients, example_idx));
+  }
+}
+
+void BiasFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  split_candidates->clear();
+  split_candidates->reserve(roots.size());
+  boosted_trees::trees::TreeNode tree_node;
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    const NodeStats& root_node_stats = root_stats[root_idx];
+    tree_node.Clear();
+    root_node_stats.FillLeaf(class_id_, tree_node.mutable_leaf());
+    split_candidates->emplace_back(slot_id_, tree_node,
+                                   SplitStats(learner_config, root_node_stats));
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h
new file mode 100644
index 0000000000..5c0f99185a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h
@@ -0,0 +1,57 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_H_  // NOLINT
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_H_  // NOLINT
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a bias feature column in the single class case.
+// This handler is useful even if we don't introduce a bias feature because
+// it allows us to aggregate stats per partition which in turn allows us
+// to compute node stats for each root to split.
+class BiasFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  BiasFeatureColumnHandler(const uint32 class_id, const uint32 slot_id,
+                           const int64 batch_size)
+      : FeatureColumnHandler(class_id, slot_id, batch_size) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+  static constexpr auto kBiasFeatureId = 0;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_H_  // NOLINT
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc
new file mode 100644
index 0000000000..82664aed72
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler_test.cc
@@ -0,0 +1,134 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 7;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class BiasFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  BiasFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 1, 3}) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new BiasFeatureColumnHandler(kClassId, kSlotId, kBatchSize));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  std::unique_ptr<BiasFeatureColumnHandler> handler_;
+};
+
+TEST_F(BiasFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition.
+  // Partition 0.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(-0.3f, 0.19f),
+      accumulator.GetStats(kSlotId, kClassId, 0,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+  // Partition 1.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(1.2f, 0.2f),
+      accumulator.GetStats(kSlotId, kClassId, 1,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+  // Partition 2.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(0.0f, 0.0f),
+      accumulator.GetStats(kSlotId, kClassId, 2,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+  // Partition 3.
+  EXPECT_GRADIENT_STATS_EQ(
+      GradientStats(4.0f, 0.13f),
+      accumulator.GetStats(kSlotId, kClassId, 3,
+                           BiasFeatureColumnHandler::kBiasFeatureId));
+}
+
+TEST_F(BiasFeatureColumnHandlerTest, GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 3.
+  // Root 0 has zero gain and root 3 has the same gain as the leaf.
+  const std::vector<int32> roots = {0, 3};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(1), NodeStats(learner_config_, GradientStats(4.0f, 0.13f))};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect two candidate splits (one per root).
+  EXPECT_EQ(2, split_candidates.size());
+
+  // Verify first candidate for root 0, gain is expected to be the same as
+  // the left child since the root node gain is zero.
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0]);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kLeaf, tree_node0.node_case());
+  EXPECT_EQ(1, tree_node0.leaf().sparse_vector().index_size());
+  EXPECT_EQ(kClassId, tree_node0.leaf().sparse_vector().index(0));
+  EXPECT_EQ(1, tree_node0.leaf().sparse_vector().value_size());
+  EXPECT_EQ(root_stats[0].weight_contribution[0],
+            tree_node0.leaf().sparse_vector().value(0));
+
+  // Verify second candidate for root 3, gain is expected to be zero as
+  // the left child gain is equal to the parent gain.
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1]);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kLeaf, tree_node1.node_case());
+  EXPECT_EQ(1, tree_node1.leaf().sparse_vector().index_size());
+  EXPECT_EQ(kClassId, tree_node1.leaf().sparse_vector().index(0));
+  EXPECT_EQ(1, tree_node1.leaf().sparse_vector().value_size());
+  EXPECT_EQ(root_stats[1].weight_contribution[0],
+            tree_node1.leaf().sparse_vector().value(0));
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
new file mode 100644
index 0000000000..2f76224ff0
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
@@ -0,0 +1,140 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h"
+
+#include "tensorflow/core/platform/macros.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+namespace {
+
+// Creates a categorical Id split node without assigning children.
+boosted_trees::trees::TreeNode CreateCategoricalIdNode(
+    const int32 feature_column, const int32 id) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_categorical_id_binary_split();
+  split->set_feature_column(feature_column);
+  split->set_feature_id(id);
+  return split_node;
+}
+
+}  // namespace
+
+void CategoricalFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all rows and aggregate gradient stats for each feature id.
+  const int64 num_rows = indices_.dimension(0);
+  for (int64 row_idx = 0; row_idx < num_rows; ++row_idx) {
+    auto example_idx = indices_(row_idx, 0);
+    auto feature_id = values_(row_idx);
+    const GradientStats norm_gradient_stats(example_first_order_gradients,
+                                            example_second_order_gradients,
+                                            example_idx);
+    auto partition_id = example_partition_ids[example_idx];
+    gradient_stats_accumulator->AddStats(slot_id_, class_id_, partition_id,
+                                         feature_id, norm_gradient_stats);
+  }
+}
+
+void CategoricalFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  // Build a reverse lookup of partition id to root idx.
+  std::unordered_map<int32, size_t> partition_id_to_root_idx;
+  partition_id_to_root_idx.reserve(roots.size());
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    partition_id_to_root_idx[roots[root_idx]] = root_idx;
+  }
+
+  // Initialize split candidates.
+  split_candidates->clear();
+  if (!roots.empty()) {
+    FeatureSplitCandidate empty_candidate(
+        root_stats[0].weight_contribution.size());
+    split_candidates->resize(roots.size(), empty_candidate);
+  }
+  for (auto& split_candidate : *split_candidates) {
+    split_candidate.split_stats.gain = std::numeric_limits<float>::lowest();
+  }
+
+  // Evaluate split candidates for every root as each is a separate
+  // logical partition over the examples.
+  // Then for each root, we evaluate every feature id as an equality split
+  // and pick the highest split gain.
+  for (const auto& entry :
+       gradient_stats_accumulator.GetFeatureStats(slot_id_)) {
+    DCHECK_EQ(entry.first.class_id, class_id_);
+
+    // Get partition id and root node stats.
+    const int32 partition_id = entry.first.partition_id;
+    auto root_idx_it = partition_id_to_root_idx.find(partition_id);
+    if (root_idx_it == partition_id_to_root_idx.end()) {
+      // Inactive partition.
+      continue;
+    }
+    size_t root_idx = root_idx_it->second;
+    const NodeStats& root_node_stats = root_stats[root_idx];
+
+    // Get gradient stats.
+    const auto& left_gradient_stats = entry.second;
+    auto right_gradient_stats =
+        root_node_stats.gradient_stats - left_gradient_stats;
+
+    // Get node stats.
+    NodeStats left_node_stats(learner_config, left_gradient_stats);
+    NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+    // Generate split candiate and update best split candidate for the
+    // current root if needed.
+    FeatureSplitCandidate split_candidate(
+        slot_id_,
+        CreateCategoricalIdNode(feature_column_, entry.first.feature_id),
+        SplitStats(learner_config, root_node_stats, left_node_stats,
+                   right_node_stats));
+    FeatureSplitCandidate& best_split_candidate = (*split_candidates)[root_idx];
+    if (TF_PREDICT_FALSE(best_split_candidate.tree_node.node_case() ==
+                         boosted_trees::trees::TreeNode::NODE_NOT_SET)) {
+      // Always replace candidates with no node set.
+      best_split_candidate = std::move(split_candidate);
+    } else if (TF_PREDICT_FALSE(split_candidate.split_stats.gain ==
+                                best_split_candidate.split_stats.gain)) {
+      // Tie break on feature id.
+      auto best_split_feature_id =
+          best_split_candidate.tree_node.categorical_id_binary_split()
+              .feature_id();
+      if (entry.first.feature_id < best_split_feature_id) {
+        best_split_candidate = std::move(split_candidate);
+      }
+    } else if (split_candidate.split_stats.gain >
+               best_split_candidate.split_stats.gain) {
+      best_split_candidate = std::move(split_candidate);
+    }
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h
new file mode 100644
index 0000000000..ef964ba716
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h
@@ -0,0 +1,64 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_CATEGORICAL_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_CATEGORICAL_FEATURE_COLUMN_HANDLER_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a categorical feature column in the single class case.
+class CategoricalFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  CategoricalFeatureColumnHandler(const int32 class_id, const int32 slot_id,
+                                  const int64 batch_size,
+                                  const int32 feature_column,
+                                  TTypes<int64>::ConstMatrix indices,
+                                  TTypes<int64>::ConstVec values)
+      : FeatureColumnHandler(class_id, slot_id, batch_size),
+        feature_column_(feature_column),
+        indices_(indices),
+        values_(values) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+ protected:
+  const int32 feature_column_;
+  TTypes<int64>::ConstMatrix indices_;
+  TTypes<int64>::ConstVec values_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_CATEGORICAL_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc
new file mode 100644
index 0000000000..abd7238464
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler_test.cc
@@ -0,0 +1,165 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 7;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+const auto kFeatureColumn = 3;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class CategoricalFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  // The data looks like the following:
+  // Example |  Gradients    | Partition | Feature Id |
+  // i0      |  (0.2, 0.12)  |     0     |    1,2     |
+  // i1      |  (-0.5, 0.07) |     0     |            |
+  // i2      |  (1.2, 0.2)   |     0     |     2      |
+  // i3      |  (4.0, 0.13)  |     1     |     0      |
+  CategoricalFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 0, 1}),
+        indices_(test::AsTensor<int64>({0, 0, 0, 1, 2, 0, 3, 0}, {4, 2})),
+        values_(test::AsTensor<int64>({1, 2, 2, 0}, {4})) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new CategoricalFeatureColumnHandler(
+        kClassId, kSlotId, kBatchSize, kFeatureColumn, indices_.matrix<int64>(),
+        values_.vec<int64>()));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  const Tensor indices_;
+  const Tensor values_;
+  std::unique_ptr<FeatureColumnHandler> handler_;
+};
+
+TEST_F(CategoricalFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition and feature.
+  // Partition 0, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 0));
+  // Partition 0, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.2f, 0.12f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 1));
+  // Partition 0, Feature 2.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.2f + 1.2f, 0.12f + 0.2f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 2));
+
+  // Partition 1, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(4.0f, 0.13f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 0));
+  // Partition 1, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 1));
+  // Partition 1, Feature 2.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 2));
+}
+
+TEST_F(CategoricalFeatureColumnHandlerTest, GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 1.
+  // The root stats are derived from the per-partition total gradient stats.
+  const std::vector<int32> roots = {0, 1, 5};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(learner_config_, GradientStats(0.9f, 0.39f)),
+      NodeStats(learner_config_, GradientStats(4.0f, 0.13f)), NodeStats(1)};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect three candidate splits (one per root).
+  EXPECT_EQ(3, split_candidates.size());
+
+  // Verify candidate for root 0, the best split occurs when we route
+  // example i0, i2 left and i1 right.
+  const NodeStats expected_left_node0(learner_config_,
+                                      GradientStats(0.2f + 1.2f, 0.12f + 0.2f));
+  const NodeStats expected_right_node0(
+      learner_config_,
+      root_stats[0].gradient_stats - expected_left_node0.gradient_stats);
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0],
+                                         expected_left_node0,
+                                         expected_right_node0);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(
+      boosted_trees::trees::TreeNode::kCategoricalIdBinarySplitFieldNumber,
+      tree_node0.node_case());
+  const auto& split0 = tree_node0.categorical_id_binary_split();
+  EXPECT_EQ(2, split0.feature_id());
+  EXPECT_EQ(kFeatureColumn, split0.feature_column());
+
+  // Verify candidate for root 1, there's only one active feature here
+  // so zero gain is expected.
+  const NodeStats expected_left_node1(learner_config_,
+                                      root_stats[1].gradient_stats);
+  const NodeStats expected_right_node1(learner_config_, GradientStats(0, 0));
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1],
+                                         expected_left_node1,
+                                         expected_right_node1);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(
+      boosted_trees::trees::TreeNode::kCategoricalIdBinarySplitFieldNumber,
+      tree_node1.node_case());
+  const auto& split1 = tree_node1.categorical_id_binary_split();
+  EXPECT_EQ(0, split1.feature_id());
+  EXPECT_EQ(kFeatureColumn, split1.feature_column());
+
+  // Verify there are no candidate splits for root 5.
+  const auto& tree_node2 = split_candidates[2].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::NODE_NOT_SET,
+            tree_node2.node_case());
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
new file mode 100644
index 0000000000..f43112c8a4
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
@@ -0,0 +1,116 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+namespace {
+
+// Creates a dense split node without assigning children.
+boosted_trees::trees::TreeNode CreateDenseSplitNode(const int32 feature_column,
+                                                    const float threshold) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_dense_float_binary_split();
+  split->set_feature_column(feature_column);
+  split->set_threshold(threshold);
+  return split_node;
+}
+
+}  // namespace
+
+void DenseQuantizedFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all examples and aggregate gradient stats for each partition
+  // and quantized feature bucket.
+  for (int64 example_idx = 0; example_idx < batch_size_; ++example_idx) {
+    auto partition_id = example_partition_ids[example_idx];
+    auto feature_id = dense_quantized_values_(example_idx);
+    gradient_stats_accumulator->AddStats(
+        slot_id_, class_id_, partition_id, feature_id,
+        GradientStats(example_first_order_gradients,
+                      example_second_order_gradients, example_idx));
+  }
+}
+
+void DenseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  // Evaluate split candidates for every root as each is a separate
+  // logical partition over the examples.
+  // Then for each root, we do a forward-only pass over the quantized
+  // feature buckets accumulating gradients from left to right.
+  // Split gains are evaluated at every threshold and the best split is picked.
+  split_candidates->clear();
+  split_candidates->reserve(roots.size());
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    // Get partition Id and root node stats.
+    const int32 partition_id = roots[root_idx];
+    const NodeStats& root_node_stats = root_stats[root_idx];
+
+    // Forward left to right pass over quantiles.
+    GradientStats left_gradient_stats;
+    GradientStats right_gradient_stats(root_node_stats.gradient_stats);
+    FeatureSplitCandidate best_split_candidate(
+        root_node_stats.weight_contribution.size());
+    best_split_candidate.split_stats.gain =
+        std::numeric_limits<float>::lowest();
+    for (int bucket_id = 0; bucket_id < dense_quantiles_.size(); ++bucket_id) {
+      // Get gradient stats.
+      auto gradient_stats = gradient_stats_accumulator.GetStats(
+          slot_id_, class_id_, partition_id, bucket_id);
+      if (gradient_stats.IsZero()) {
+        continue;
+      }
+
+      // Update gradient stats.
+      left_gradient_stats += gradient_stats;
+      right_gradient_stats =
+          root_node_stats.gradient_stats - left_gradient_stats;
+
+      // Get node stats
+      NodeStats left_node_stats(learner_config, left_gradient_stats);
+      NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+      // Generate split candiate.
+      const float threshold = dense_quantiles_(bucket_id);
+      FeatureSplitCandidate split_candidate(
+          slot_id_, CreateDenseSplitNode(dense_feature_column_, threshold),
+          SplitStats(learner_config, root_node_stats, left_node_stats,
+                     right_node_stats));
+      if (split_candidate.split_stats.gain >
+          best_split_candidate.split_stats.gain) {
+        best_split_candidate = std::move(split_candidate);
+      }
+    }
+
+    // Add best candidate for partition.
+    split_candidates->push_back(std::move(best_split_candidate));
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h
new file mode 100644
index 0000000000..0f3858e4d8
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h
@@ -0,0 +1,62 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_DENSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_DENSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a dense quantized feature column in the single class case.
+class DenseQuantizedFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  DenseQuantizedFeatureColumnHandler(
+      const int32 class_id, const int32 slot_id, const int64 batch_size,
+      const int32 dense_feature_column, TTypes<float>::ConstVec dense_quantiles,
+      TTypes<int32>::ConstVec dense_quantized_values)
+      : FeatureColumnHandler(class_id, slot_id, batch_size),
+        dense_feature_column_(dense_feature_column),
+        dense_quantiles_(dense_quantiles),
+        dense_quantized_values_(dense_quantized_values) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+ protected:
+  const int32 dense_feature_column_;
+  TTypes<float>::ConstVec dense_quantiles_;
+  TTypes<int32>::ConstVec dense_quantized_values_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_DENSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc
new file mode 100644
index 0000000000..396f48e532
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler_test.cc
@@ -0,0 +1,155 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 1;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+const auto kFeatureColumn = 2;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class DenseQuantizedFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  // The data looks like the following:
+  // Example |  Gradients    | Partition | Dense Quantile |
+  // i0      |  (0.2, 0.12)  | 0         | 1              |
+  // i1      |  (-0.5, 0.07) | 0         | 1              |
+  // i2      |  (1.2, 0.2)   | 0         | 0              |
+  // i3      |  (4.0, 0.13)  | 1         | 1              |
+  DenseQuantizedFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 0, 1}),
+        dense_quantiles_(test::AsTensor<float>({0.3f, 0.52f}, {2})),
+        dense_quantized_values_(test::AsTensor<int32>({1, 1, 0, 1}, {4})) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new DenseQuantizedFeatureColumnHandler(
+        kClassId, kSlotId, kBatchSize, kFeatureColumn,
+        dense_quantiles_.vec<float>(), dense_quantized_values_.vec<int32>()));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  const Tensor dense_quantiles_;
+  const Tensor dense_quantized_values_;
+  std::unique_ptr<FeatureColumnHandler> handler_;
+};
+
+TEST_F(DenseQuantizedFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition and feature.
+  // Partition 0, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(1.2f, 0.2f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 0));
+  // Partition 0, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(-0.3f, 0.19f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 1));
+  // Partition 1, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 0));
+  // Partition 1, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(4.0f, 0.13f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 1));
+}
+
+TEST_F(DenseQuantizedFeatureColumnHandlerTest, GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 1.
+  // The root stats are derived from the per-partition total gradient stats.
+  const std::vector<int32> roots = {0, 1, 5};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(learner_config_, GradientStats(0.9f, 0.39f)),
+      NodeStats(learner_config_, GradientStats(4.0f, 0.13f)), NodeStats(1)};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect three candidate splits (one per root).
+  EXPECT_EQ(3, split_candidates.size());
+
+  // Verify candidate for root 0, the best split occurs when we route
+  // example i2 left and i0, i1 right.
+  const NodeStats expected_left_node0(learner_config_,
+                                      GradientStats(1.2f, 0.2f));
+  const NodeStats expected_right_node0(
+      learner_config_,
+      root_stats[0].gradient_stats - expected_left_node0.gradient_stats);
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0],
+                                         expected_left_node0,
+                                         expected_right_node0);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kDenseFloatBinarySplit,
+            tree_node0.node_case());
+  const auto& split0 = tree_node0.dense_float_binary_split();
+  EXPECT_FLOAT_EQ(dense_quantiles_.vec<float>()(0), split0.threshold());
+  EXPECT_EQ(kFeatureColumn, split0.feature_column());
+
+  // Verify candidate for root 1, there's only one active bucket here
+  // so zero gain is expected.
+  const NodeStats expected_left_node1(learner_config_,
+                                      root_stats[1].gradient_stats);
+  const NodeStats expected_right_node1(learner_config_, GradientStats(0, 0));
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1],
+                                         expected_left_node1,
+                                         expected_right_node1);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kDenseFloatBinarySplit,
+            tree_node1.node_case());
+  const auto& split1 = tree_node1.dense_float_binary_split();
+  EXPECT_FLOAT_EQ(dense_quantiles_.vec<float>()(1), split1.threshold());
+  EXPECT_EQ(kFeatureColumn, split1.feature_column());
+
+  // Verify there are no candidate splits for root 5.
+  const auto& tree_node2 = split_candidates[2].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::NODE_NOT_SET,
+            tree_node2.node_case());
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h
new file mode 100644
index 0000000000..a3177b31eb
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h
@@ -0,0 +1,82 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_FEATURE_COLUMN_HANDLER_H_
+
+#include <vector>
+#include "tensorflow/contrib/boosted_trees/lib/learner/common/accumulators/feature-stats-accumulator.h"
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_types.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler interface for feature columns. Each feature column type may
+// have its own handler which encapsulates the logic of aggregating gradient
+// stats as well as generating split candidates for each partition.
+// Handlers can be stateful and must be thread compatible.
+class FeatureColumnHandler {
+ public:
+  FeatureColumnHandler(const int32 class_id, const int32 slot_id,
+                       const int64 batch_size)
+      : class_id_(class_id), slot_id_(slot_id), batch_size_(batch_size) {}
+
+  virtual ~FeatureColumnHandler() {}
+  FeatureColumnHandler(const FeatureColumnHandler& other) = delete;
+  FeatureColumnHandler& operator=(const FeatureColumnHandler& other) = delete;
+
+  // Aggregates example gradient stats for the feature column.
+  virtual void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const = 0;
+
+  // Generates feature column split candidates for the specified roots.
+  virtual void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const = 0;
+
+  // Accessors.
+  int32 class_id() const { return class_id_; }
+  int32 slot_id() const { return slot_id_; }
+  int64 batch_size() const { return batch_size_; }
+
+ protected:
+  // The class Id.
+  const int32 class_id_;
+
+  // The slod Id for use as a unique Id across all feature columns.
+  const int32 slot_id_;
+
+  // Size of the batch of examples.
+  const int64 batch_size_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  //  THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
new file mode 100644
index 0000000000..76da16ab93
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
@@ -0,0 +1,172 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+namespace {
+
+// Creates a sparse default right split node without assigning children.
+boosted_trees::trees::TreeNode CreateSparseSplitNodeDefaultRight(
+    int32 feature_column, float threshold) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_sparse_float_binary_split_default_right()
+                    ->mutable_split();
+  split->set_feature_column(feature_column);
+  split->set_threshold(threshold);
+  return split_node;
+}
+
+// Creates a sparse default left split node without assigning children.
+boosted_trees::trees::TreeNode CreateSparseSplitNodeDefaultLeft(
+    int32 feature_column, float threshold) {
+  boosted_trees::trees::TreeNode split_node;
+  auto* split = split_node.mutable_sparse_float_binary_split_default_left()
+                    ->mutable_split();
+  split->set_feature_column(feature_column);
+  split->set_threshold(threshold);
+  return split_node;
+}
+
+}  // namespace
+
+void SparseQuantizedFeatureColumnHandler::AggregateGradientStats(
+    const std::vector<int32>& example_partition_ids,
+    const Tensor& example_first_order_gradients,
+    const Tensor& example_second_order_gradients,
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+        gradient_stats_accumulator) const {
+  // Pass over all rows and aggregate gradient stats for each partition
+  // and quantized feature bucket.
+  const int64 num_rows = sparse_indices_.dimension(0);
+  for (int64 row_idx = 0; row_idx < num_rows; ++row_idx) {
+    auto example_idx = sparse_indices_(row_idx, 0);
+    auto partition_id = example_partition_ids[example_idx];
+    auto feature_id = sparse_quantized_values_(row_idx);
+    gradient_stats_accumulator->AddStats(
+        slot_id_, class_id_, partition_id, feature_id,
+        GradientStats(example_first_order_gradients,
+                      example_second_order_gradients, example_idx));
+  }
+}
+
+void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
+    const LearnerConfig& learner_config, const std::vector<int32>& roots,
+    const std::vector<NodeStats>& root_stats,
+    const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+        gradient_stats_accumulator,
+    std::vector<FeatureSplitCandidate>* split_candidates) const {
+  // Evaluate split candidates for every root as each is a separate
+  // logical partition over the examples.
+  // Then for each root, we do both a forward left to right pass and a backward
+  // right to left pass over the quantized feature buckets accumulating
+  // gradients on one side and using the root aggregate gradients to get the
+  // gradients for the other side. Split gains are evaluated for each pass at
+  // every threshold and the best split is picked.
+  split_candidates->clear();
+  split_candidates->reserve(roots.size());
+  for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
+    // Get partition Id and root node stats.
+    const int32 partition_id = roots[root_idx];
+    const NodeStats& root_node_stats = root_stats[root_idx];
+
+    // Forward pass with right default direction.
+    GradientStats left_gradient_stats;
+    GradientStats right_gradient_stats(root_node_stats.gradient_stats);
+    FeatureSplitCandidate best_split_candidate(
+        root_node_stats.weight_contribution.size());
+    best_split_candidate.split_stats.gain =
+        std::numeric_limits<float>::lowest();
+    for (int bucket_id = 0; bucket_id < sparse_quantiles_.size(); ++bucket_id) {
+      // Get gradient stats.
+      auto gradient_stats = gradient_stats_accumulator.GetStats(
+          slot_id_, class_id_, partition_id, bucket_id);
+      if (gradient_stats.IsZero()) {
+        continue;
+      }
+
+      // Update gradient stats.
+      left_gradient_stats += gradient_stats;
+      right_gradient_stats =
+          root_node_stats.gradient_stats - left_gradient_stats;
+
+      // Get node stats
+      NodeStats left_node_stats(learner_config, left_gradient_stats);
+      NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+      // Generate split candiate.
+      const float threshold = sparse_quantiles_(bucket_id);
+      FeatureSplitCandidate split_candidate(
+          slot_id_,
+          CreateSparseSplitNodeDefaultRight(sparse_feature_column_, threshold),
+          SplitStats(learner_config, root_node_stats, left_node_stats,
+                     right_node_stats));
+      if (split_candidate.split_stats.gain >
+          best_split_candidate.split_stats.gain) {
+        best_split_candidate = std::move(split_candidate);
+      }
+    }
+
+    // Determine if we need a backward pass by checking if the residual gradient
+    // after forward aggregation is almost the same as the aggregated gradient.
+    // for the current root. This helps avoid unecessary computation as well
+    // as consistency due to floating point precision.
+    if (!right_gradient_stats.IsAlmostZero()) {
+      // Backward pass with left default direction.
+      right_gradient_stats = GradientStats();
+      left_gradient_stats = root_node_stats.gradient_stats;
+      for (int bucket_id = sparse_quantiles_.size() - 1; bucket_id > 0;
+           --bucket_id) {
+        // Get gradient stats.
+        auto gradient_stats = gradient_stats_accumulator.GetStats(
+            slot_id_, class_id_, partition_id, bucket_id);
+        if (gradient_stats.IsZero()) {
+          continue;
+        }
+
+        // Update gradient stats.
+        right_gradient_stats += gradient_stats;
+        left_gradient_stats = root_node_stats.gradient_stats - gradient_stats;
+
+        // Get node stats
+        NodeStats left_node_stats(learner_config, left_gradient_stats);
+        NodeStats right_node_stats(learner_config, right_gradient_stats);
+
+        // Generate split candiate.
+        const float threshold = sparse_quantiles_(bucket_id - 1);
+        FeatureSplitCandidate split_candidate(
+            slot_id_,
+            CreateSparseSplitNodeDefaultLeft(sparse_feature_column_, threshold),
+            SplitStats(learner_config, root_node_stats, left_node_stats,
+                       right_node_stats));
+        if (split_candidate.split_stats.gain >
+            best_split_candidate.split_stats.gain) {
+          best_split_candidate = std::move(split_candidate);
+        }
+      }
+    }
+
+    // Add best candidate for partition.
+    split_candidates->push_back(std::move(best_split_candidate));
+  }
+}
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h
new file mode 100644
index 0000000000..eb63e70547
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h
@@ -0,0 +1,67 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_SPARSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_SPARSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/feature-column-handler.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// Handler for a sparse quantized feature column in the single class case.
+class SparseQuantizedFeatureColumnHandler : public FeatureColumnHandler {
+ public:
+  SparseQuantizedFeatureColumnHandler(
+      const int32 class_id, const int32 slot_id, const int64 batch_size,
+      const int32 sparse_feature_column,
+      TTypes<float>::ConstVec sparse_quantiles,
+      TTypes<int64>::ConstMatrix sparse_indices,
+      TTypes<int32>::ConstVec sparse_quantized_values)
+      : FeatureColumnHandler(class_id, slot_id, batch_size),
+        sparse_feature_column_(sparse_feature_column),
+        sparse_quantiles_(sparse_quantiles),
+        sparse_indices_(sparse_indices),
+        sparse_quantized_values_(sparse_quantized_values) {}
+
+  void AggregateGradientStats(
+      const std::vector<int32>& example_partition_ids,
+      const Tensor& example_first_order_gradients,
+      const Tensor& example_second_order_gradients,
+      FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
+          gradient_stats_accumulator) const override;
+
+  void GenerateFeatureSplitCandidates(
+      const LearnerConfig& learner_config, const std::vector<int32>& roots,
+      const std::vector<NodeStats>& root_stats,
+      const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
+          gradient_stats_accumulator,
+      std::vector<FeatureSplitCandidate>* split_candidates) const override;
+
+ protected:
+  const int32 sparse_feature_column_;
+  TTypes<float>::ConstVec sparse_quantiles_;
+  TTypes<int64>::ConstMatrix sparse_indices_;
+  TTypes<int32>::ConstVec sparse_quantized_values_;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  //  THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_HANDLERS_SPARSE_QUANTIZED_FEATURE_COLUMN_HANDLER_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc
new file mode 100644
index 0000000000..db8c64a617
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler_test.cc
@@ -0,0 +1,162 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+using boosted_trees::learner::LearnerConfig;
+
+const auto kClassId = 3;
+const auto kSlotId = 0;
+const auto kBatchSize = 4;
+const auto kFeatureColumn = 4;
+
+using FeatureStatsAccumulator =
+    FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>;
+
+class SparseQuantizedFeatureColumnHandlerTest : public ::testing::Test {
+ protected:
+  // The data looks like the following:
+  // Example |  Gradients    | Partition | Sparse Quantile |
+  // i0      |  (0.2, 0.12)  | 0         | 1               |
+  // i1      |  (-0.5, 0.07) | 0         | N/A             |
+  // i2      |  (1.2, 0.2)   | 0         | 0               |
+  // i3      |  (4.0, 0.13)  | 1         | 1               |
+  SparseQuantizedFeatureColumnHandlerTest()
+      : example_first_order_gradients_(
+            test::AsTensor<float>({0.2f, -0.5f, 1.2f, 4.0f}, {4})),
+        example_second_order_gradients_(
+            test::AsTensor<float>({0.12f, 0.07f, 0.2f, 0.13f}, {4})),
+        example_partitions_({0, 0, 0, 1}),
+        sparse_quantiles_(test::AsTensor<float>({0.3f, 0.52f}, {2})),
+        sparse_indices_(test::AsTensor<int64>({0, 0, 2, 0, 3, 0}, {3, 2})),
+        sparse_quantized_values_(test::AsTensor<int32>({1, 0, 1}, {3})) {
+    // Set L2 regularization.
+    learner_config_.mutable_regularization()->set_l2(2.0f);
+
+    // Create handler.
+    handler_.reset(new SparseQuantizedFeatureColumnHandler(
+        kClassId, kSlotId, kBatchSize, kFeatureColumn,
+        sparse_quantiles_.vec<float>(), sparse_indices_.matrix<int64>(),
+        sparse_quantized_values_.vec<int32>()));
+  }
+
+  LearnerConfig learner_config_;
+  const Tensor example_first_order_gradients_;
+  const Tensor example_second_order_gradients_;
+  const std::vector<int32> example_partitions_;
+  const Tensor sparse_quantiles_;
+  const Tensor sparse_indices_;
+  const Tensor sparse_quantized_values_;
+  std::unique_ptr<FeatureColumnHandler> handler_;
+};
+
+TEST_F(SparseQuantizedFeatureColumnHandlerTest, AggregateGradientStats) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Check stats for each partition and feature.
+  // Partition 0, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(1.2f, 0.2f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 0));
+  // Partition 0, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.2f, 0.12f),
+                           accumulator.GetStats(kSlotId, kClassId, 0, 1));
+  // Partition 1, Feature 0.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(0.0f, 0.0f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 0));
+  // Partition 1, Feature 1.
+  EXPECT_GRADIENT_STATS_EQ(GradientStats(4.0f, 0.13f),
+                           accumulator.GetStats(kSlotId, kClassId, 1, 1));
+}
+
+TEST_F(SparseQuantizedFeatureColumnHandlerTest,
+       GenerateFeatureSplitCandidates) {
+  // Create handler.
+  FeatureStatsAccumulator accumulator(1);
+  handler_->AggregateGradientStats(
+      example_partitions_, example_first_order_gradients_,
+      example_second_order_gradients_, &accumulator);
+
+  // Get feature split candidates for two roots 0 and 1.
+  // The root stats are derived from the per-partition total gradient stats.
+  const std::vector<int32> roots = {0, 1, 9};
+  const std::vector<NodeStats>& root_stats = {
+      NodeStats(learner_config_, GradientStats(0.9f, 0.39f)),
+      NodeStats(learner_config_, GradientStats(4.0f, 0.13f)), NodeStats(1)};
+  std::vector<FeatureSplitCandidate> split_candidates;
+  handler_->GenerateFeatureSplitCandidates(learner_config_, roots, root_stats,
+                                           accumulator, &split_candidates);
+  // Expect three candidate splits (one per root).
+  EXPECT_EQ(3, split_candidates.size());
+
+  // Verify candidate for root 0, the best split occurs when we route
+  // example i0 and i2 to the left and i1 to the right (by default direction).
+  const NodeStats expected_left_node0(learner_config_,
+                                      GradientStats(0.2f + 1.2f, 0.12f + 0.2f));
+  const NodeStats expected_right_node0(
+      learner_config_,
+      root_stats[0].gradient_stats - expected_left_node0.gradient_stats);
+  const SplitStats expected_split_stats0(learner_config_, root_stats[0],
+                                         expected_left_node0,
+                                         expected_right_node0);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats0, split_candidates[0].split_stats);
+  const auto& tree_node0 = split_candidates[0].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kSparseFloatBinarySplitDefaultRight,
+            tree_node0.node_case());
+  const auto& split0 =
+      tree_node0.sparse_float_binary_split_default_right().split();
+  EXPECT_FLOAT_EQ(sparse_quantiles_.vec<float>()(1), split0.threshold());
+  EXPECT_EQ(kFeatureColumn, split0.feature_column());
+
+  // Verify candidate for root 1, there's only one active bucket here
+  // so zero gain is expected.
+  const NodeStats expected_left_node1(learner_config_,
+                                      root_stats[1].gradient_stats);
+  const NodeStats expected_right_node1(learner_config_, GradientStats(0, 0));
+  const SplitStats expected_split_stats1(learner_config_, root_stats[1],
+                                         expected_left_node1,
+                                         expected_right_node1);
+  EXPECT_SPLIT_STATS_EQ(expected_split_stats1, split_candidates[1].split_stats);
+  const auto& tree_node1 = split_candidates[1].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::kSparseFloatBinarySplitDefaultRight,
+            tree_node1.node_case());
+  const auto& split1 =
+      tree_node1.sparse_float_binary_split_default_right().split();
+  EXPECT_FLOAT_EQ(sparse_quantiles_.vec<float>()(1), split1.threshold());
+  EXPECT_EQ(kFeatureColumn, split1.feature_column());
+
+  // Verify there are no candidate splits for root 9.
+  const auto& tree_node2 = split_candidates[2].tree_node;
+  EXPECT_EQ(boosted_trees::trees::TreeNode::NODE_NOT_SET,
+            tree_node2.node_case());
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h
new file mode 100644
index 0000000000..fe22691178
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/feature-split-candidate.h
@@ -0,0 +1,61 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_FEATURE_SPLIT_CANDIDATE_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_FEATURE_SPLIT_CANDIDATE_H_
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// FeatureSplitCandidate holds the split candidate node along with the stats.
+struct FeatureSplitCandidate {
+  // Empty split candidate.
+  explicit FeatureSplitCandidate(const int output_length)
+      : feature_column_slot_id(kInvalidFeatureColumnSlot),
+        split_stats(output_length) {}
+
+  // Feature binary split candidate.
+  FeatureSplitCandidate(const int64 fc_slot_id,
+                        const boosted_trees::trees::TreeNode& node,
+                        const SplitStats& stats)
+      : feature_column_slot_id(fc_slot_id),
+        tree_node(node),
+        split_stats(stats) {}
+
+  // Globally unique slot Id identifying the feature column
+  // used in this split candidates.
+  int64 feature_column_slot_id;
+
+  // Tree node for the candidate split.
+  boosted_trees::trees::TreeNode tree_node;
+
+  // Split stats.
+  SplitStats split_stats;
+
+  // Invalid feature column slot reserved value.
+  static constexpr int64 kInvalidFeatureColumnSlot = -1;
+};
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_FEATURE_SPLIT_CANDIDATE_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h
new file mode 100644
index 0000000000..dad64bf165
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h
@@ -0,0 +1,193 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_GRADIENT_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_GRADIENT_STATS_H_
+
+#include <math.h>
+
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_util.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+const double kEps = 1e-6;
+
+// A data structure for accumulating a Tensor value.
+struct TensorStat {
+  TensorStat() {}
+
+  explicit TensorStat(const float v) : t(DT_FLOAT, TensorShape({1})) {
+    t.flat<float>()(0) = v;
+  }
+
+  explicit TensorStat(const Tensor& rt) : t(tensor::DeepCopy(rt)) {}
+
+  TensorStat(const TensorStat& ts) : t(tensor::DeepCopy(ts.t)) {}
+
+  TensorStat& operator+=(const TensorStat& other) {
+    if (t.NumElements() == 0) {
+      t = tensor::DeepCopy(other.t);
+      return (*this);
+    }
+    CHECK(t.shape() == other.t.shape())
+        << "My shape = " << t.shape().DebugString()
+        << " Other shape = " << other.t.shape().DebugString();
+    auto me_flat = t.unaligned_flat<float>();
+    auto other_flat = other.t.unaligned_flat<float>();
+    for (int i = 0; i < me_flat.size(); i++) {
+      me_flat(i) += other_flat(i);
+    }
+    return (*this);
+  }
+
+  TensorStat& operator-=(const TensorStat& other) {
+    if (other.t.NumElements() == 0) {
+      return (*this);
+    }
+    CHECK(t.shape() == other.t.shape())
+        << "My shape = " << t.shape().DebugString()
+        << " Other shape = " << other.t.shape().DebugString();
+    auto me_flat = t.unaligned_flat<float>();
+    auto other_flat = other.t.unaligned_flat<float>();
+    for (int i = 0; i < me_flat.size(); i++) {
+      me_flat(i) -= other_flat(i);
+    }
+    return (*this);
+  }
+
+  TensorStat& operator*=(float value) {
+    auto me_flat = t.unaligned_flat<float>();
+    for (size_t i = 0; i < me_flat.size(); i++) {
+      me_flat(i) *= value;
+    }
+    return (*this);
+  }
+
+  bool IsZero() const {
+    auto me_flat = t.unaligned_flat<float>();
+    for (int i = 0; i < me_flat.size(); i++) {
+      if (me_flat(i) != 0.0f) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  // Checks if the L^2 magnitude of the tensor is less than eps.
+  bool IsAlmostZero(const float eps = kEps) const {
+    auto me_flat = t.unaligned_flat<float>();
+    double s = 0.0;
+    for (int i = 0; i < me_flat.size(); i++) {
+      s += me_flat(i) * me_flat(i);
+      if (s > eps * eps) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  float Magnitude() const {
+    auto me_flat = t.unaligned_flat<float>();
+    double s = 0.0;
+    for (int i = 0; i < me_flat.size(); i++) {
+      s += me_flat(i) * me_flat(i);
+    }
+    return sqrt(s);
+  }
+
+  string DebugString() const { return t.DebugString(); }
+
+  Tensor t;
+};
+
+// GradientStats holds first and second order gradient stats.
+struct GradientStats {
+  GradientStats() {}
+
+  // Legacy constructor for tests
+  GradientStats(float g, float h) : first(g), second(h) {}
+
+  GradientStats(const Tensor& g, const Tensor& h) : first(g), second(h) {}
+
+  GradientStats(const Tensor& g, const Tensor& h, int64 example_index)
+      : first(g.Slice(example_index, example_index + 1)),
+        second(h.Slice(example_index, example_index + 1)) {}
+
+  GradientStats& operator+=(const GradientStats& other) {
+    first += other.first;
+    second += other.second;
+    return (*this);
+  }
+
+  GradientStats& operator*=(float value) {
+    first *= value;
+    second *= value;
+    return (*this);
+  }
+
+  GradientStats& operator-=(const GradientStats& other) {
+    first -= other.first;
+    second -= other.second;
+    return (*this);
+  }
+
+  bool IsZero() const { return first.IsZero() && second.IsZero(); }
+
+  bool IsAlmostZero(const float eps = kEps) const {
+    return first.IsAlmostZero(eps) && second.IsAlmostZero(eps);
+  }
+
+  float Magnitude() const { return second.Magnitude(); }
+
+  string DebugString() const {
+    return "First = " + first.DebugString() +
+           " Second = " + second.DebugString();
+  }
+
+  TensorStat first;
+  TensorStat second;
+};
+
+struct GradientStatsAccumulator {
+  void operator()(const GradientStats& from, GradientStats* to) const {
+    (*to) += from;
+  }
+};
+
+inline GradientStats operator+(const GradientStats& a, const GradientStats& b) {
+  GradientStats ret(a);
+  ret += b;
+  return ret;
+}
+
+inline GradientStats operator-(const GradientStats& a, const GradientStats& b) {
+  GradientStats ret(a);
+  ret -= b;
+  return ret;
+}
+
+// Helper macro to check gradient stats approximate equality.
+#define EXPECT_GRADIENT_STATS_EQ(val1, val2) \
+  EXPECT_TRUE((val1 - val2).IsAlmostZero());
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_GRADIENT_STATS_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h
new file mode 100644
index 0000000000..4e5f53874d
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h
@@ -0,0 +1,301 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_NODE_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_NODE_STATS_H_
+
+#include "third_party/eigen3/Eigen/Core"
+#include "third_party/eigen3/Eigen/Eigenvalues"
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/gradient-stats.h"
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+using tensorflow::boosted_trees::learner::LearnerConfig;
+using tensorflow::boosted_trees::learner::LearnerConfig_MultiClassStrategy;
+using tensorflow::boosted_trees::learner::
+    LearnerConfig_MultiClassStrategy_DIAGONAL_HESSIAN;
+using tensorflow::boosted_trees::learner::
+    LearnerConfig_MultiClassStrategy_FULL_HESSIAN;
+using tensorflow::boosted_trees::learner::
+    LearnerConfig_MultiClassStrategy_TREE_PER_CLASS;
+
+// NodeStats holds aggregate gradient stats as well as metadata about the node.
+struct NodeStats {
+  // Initialize the NodeStats with 0 stats.  We need the output length
+  // so that we can make weight_contribution the right length.
+  explicit NodeStats(const int output_length)
+      : weight_contribution(output_length, 0.0f), gain(0) {}
+
+  NodeStats(const LearnerConfig& learner_config,
+            const GradientStats& grad_stats)
+      : NodeStats(learner_config.regularization().l1(),
+                  learner_config.regularization().l2(),
+                  learner_config.constraints().min_node_weight(),
+                  learner_config.multi_class_strategy(), grad_stats) {}
+
+  NodeStats(float l1_reg, float l2_reg, float min_node_weight,
+            const LearnerConfig_MultiClassStrategy& strategy,
+            const GradientStats& grad_stats)
+      : gradient_stats(grad_stats), gain(0) {
+    switch (strategy) {
+      case LearnerConfig_MultiClassStrategy_TREE_PER_CLASS: {
+        float g;
+        float h;
+        // Initialize now in case of early return.
+        weight_contribution.push_back(0.0f);
+
+        if (grad_stats.first.t.NumElements() == 0 ||
+            grad_stats.second.t.NumElements() == 0) {
+          return;
+        }
+
+        g = grad_stats.first.t.unaligned_flat<float>()(0);
+        h = grad_stats.second.t.unaligned_flat<float>()(0);
+
+        if (grad_stats.IsAlmostZero() || h <= min_node_weight) {
+          return;
+        }
+
+        // Apply L1 regularization.
+        if (l1_reg > 0) {
+          if (g > l1_reg) {
+            g -= l1_reg;
+          } else if (g < -l1_reg) {
+            g += l1_reg;
+          } else {
+            return;
+          }
+        }
+
+        // The node gain is given by: (l'^2) / (l'' + l2_reg) and the node
+        // weight
+        // contribution is given by: (-l') / (l'' + l2_reg).
+        // Note that l'' can't be zero here because of the min node weight check
+        // since min node weight must be >= 0.
+        weight_contribution[0] = -g / (h + l2_reg);
+        gain = (weight_contribution[0] * -g);
+        break;
+      }
+      case LearnerConfig_MultiClassStrategy_FULL_HESSIAN: {
+        weight_contribution.clear();
+
+        if (grad_stats.first.t.NumElements() == 0 ||
+            grad_stats.second.t.NumElements() == 0) {
+          return;
+        }
+        const int64 grad_dim = grad_stats.first.t.dim_size(1);
+
+        QCHECK(grad_stats.first.t.dims() == 2)
+            << strings::Printf("Gradient should be of rank 2, got rank %d",
+                               grad_stats.first.t.dims());
+        QCHECK(grad_stats.first.t.dim_size(0) == 1) << strings::Printf(
+            "Gradient must be of shape 1 x %lld, got %lld x %lld", grad_dim,
+            grad_stats.first.t.dim_size(0), grad_dim);
+        QCHECK(grad_stats.second.t.dims() == 3)
+            << strings::Printf("Hessian should be of rank 3, got rank %d",
+                               grad_stats.second.t.dims());
+        QCHECK(grad_stats.second.t.shape() ==
+               TensorShape({1, grad_dim, grad_dim}))
+            << strings::Printf(
+                   "Hessian must be of shape 1 x %lld x %lld, got %lld x % lld "
+                   " x % lld ",
+                   grad_dim, grad_dim, grad_stats.second.t.shape().dim_size(0),
+                   grad_stats.second.t.shape().dim_size(1),
+                   grad_stats.second.t.shape().dim_size(2));
+
+        // Check if we're violating min weight constraint.
+
+        if (grad_stats.IsAlmostZero() ||
+            grad_stats.second.Magnitude() <= min_node_weight) {
+          return;
+        }
+        // TODO(nponomareva): figure out l1 in matrix form.
+        // g is a vector of gradients, H is a hessian matrix.
+        Eigen::VectorXf g = TensorToEigenVector(grad_stats.first.t, grad_dim);
+
+        Eigen::MatrixXf hessian =
+            TensorToEigenMatrix(grad_stats.second.t, grad_dim, grad_dim);
+        // I is an identity matrix.
+        // The gain in general form is -g^T (H+l2 I)^-1 g.
+        // The node weights are -(H+l2 I)^-1 g.
+        Eigen::MatrixXf identity;
+        identity.setIdentity(grad_dim, grad_dim);
+
+        Eigen::MatrixXf hessian_and_reg = hessian + l2_reg * identity;
+
+        CalculateWeightAndGain(hessian_and_reg, g);
+        break;
+      }
+      case LearnerConfig_MultiClassStrategy_DIAGONAL_HESSIAN: {
+        weight_contribution.clear();
+        if (grad_stats.first.t.NumElements() == 0 ||
+            grad_stats.second.t.NumElements() == 0) {
+          return;
+        }
+        const int64 grad_dim = grad_stats.first.t.dim_size(1);
+
+        QCHECK(grad_stats.first.t.dims() == 2)
+            << strings::Printf("Gradient should be of rank 2, got rank %d",
+                               grad_stats.first.t.dims());
+        QCHECK(grad_stats.first.t.dim_size(0) == 1) << strings::Printf(
+            "Gradient must be of shape 1 x %lld, got %lld x %lld", grad_dim,
+            grad_stats.first.t.dim_size(0), grad_dim);
+        QCHECK(grad_stats.second.t.dims() == 2)
+            << strings::Printf("Hessian should be of rank 2, got rank %d",
+                               grad_stats.second.t.dims());
+        QCHECK(grad_stats.second.t.shape() == TensorShape({1, grad_dim}))
+            << strings::Printf(
+                   "Hessian must be of shape 1 x %lld, got %lld x %lld",
+                   grad_dim, grad_stats.second.t.shape().dim_size(0),
+                   grad_stats.second.t.shape().dim_size(1));
+
+        // Check if we're violating min weight constraint.
+        if (grad_stats.IsAlmostZero() ||
+            grad_stats.second.Magnitude() <= min_node_weight) {
+          return;
+        }
+        // TODO(nponomareva): figure out l1 in matrix form.
+        // Diagonal of the hessian.
+        Eigen::ArrayXf hessian =
+            TensorToEigenArray(grad_stats.second.t, grad_dim);
+        Eigen::ArrayXf hessian_and_reg = hessian + l2_reg;
+
+        // Check if any of the elements are zeros.
+        bool invertible = true;
+        for (int i = 0; i < hessian_and_reg.size(); ++i) {
+          if (hessian_and_reg[i] == 0.0) {
+            invertible = false;
+            break;
+          }
+        }
+        if (invertible) {
+          Eigen::ArrayXf g = TensorToEigenArray(grad_stats.first.t, grad_dim);
+          // Operations on arrays are element wise. The formulas are as for full
+          // hessian, but for hessian of diagonal form they are simplified.
+          Eigen::ArrayXf ones = Eigen::ArrayXf::Ones(grad_dim);
+          Eigen::ArrayXf temp = ones / hessian_and_reg;
+          Eigen::ArrayXf weight = -temp * g;
+
+          // Copy over weights to weight_contribution.
+          weight_contribution =
+              std::vector<float>(weight.data(), weight.data() + weight.rows());
+          gain = (-g * weight).sum();
+        } else {
+          Eigen::VectorXf g = TensorToEigenVector(grad_stats.first.t, grad_dim);
+          // Hessian is not invertible. We will go the same route as in full
+          // hessian to get an approximate solution.
+          CalculateWeightAndGain(hessian_and_reg.matrix().asDiagonal(), g);
+        }
+        break;
+      }
+      default:
+        LOG(FATAL) << "Unknown multi-class strategy " << strategy;
+        break;
+    }
+  }
+
+  string DebugString() const {
+    return strings::StrCat(
+        gradient_stats.DebugString(), "\n",
+        "Weight_contrib = ", str_util::Join(weight_contribution, ","),
+        "Gain = ", gain);
+  }
+
+  // Use these node stats to populate a Leaf's model.
+  void FillLeaf(const int class_id, boosted_trees::trees::Leaf* leaf) const {
+    if (class_id == -1) {
+      for (int i = 0; i < weight_contribution.size(); i++) {
+        leaf->mutable_vector()->add_value(weight_contribution[i]);
+      }
+    } else {
+      CHECK(weight_contribution.size() == 1)
+          << "Weight contribution size = " << weight_contribution.size();
+      leaf->mutable_sparse_vector()->add_index(class_id);
+      leaf->mutable_sparse_vector()->add_value(weight_contribution[0]);
+    }
+  }
+
+  // Sets the weight_contribution and gain member variables based on the
+  // given regularized Hessian and gradient vector g.
+  void CalculateWeightAndGain(const Eigen::MatrixXf& hessian_and_reg,
+                              const Eigen::VectorXf& g) {
+    // The gain in general form is -g^T (Hessian_and_regularization)^-1 g.
+    // The node weights are -(Hessian_and_regularization)^-1 g.
+    Eigen::VectorXf weight;
+    // If we want to calculate x = K^-1 v, instead of explicitly calculating
+    // K^-1 and multiplying by v, we can solve this matrix equation using
+    // solve method.
+    weight = -hessian_and_reg.colPivHouseholderQr().solve(g);
+    // Copy over weights to weight_contribution.
+    weight_contribution =
+        std::vector<float>(weight.data(), weight.data() + weight.rows());
+
+    gain = -g.transpose() * weight;
+  }
+
+  static Eigen::MatrixXf TensorToEigenMatrix(const Tensor& tensor,
+                                             const int num_rows,
+                                             const int num_cols) {
+    return Eigen::Map<const Eigen::MatrixXf>(tensor.flat<float>().data(),
+                                             num_rows, num_cols);
+  }
+
+  static Eigen::VectorXf TensorToEigenVector(const Tensor& tensor,
+                                             const int num_elements) {
+    return Eigen::Map<const Eigen::VectorXf>(tensor.flat<float>().data(),
+                                             num_elements);
+  }
+
+  static Eigen::ArrayXf TensorToEigenArray(const Tensor& tensor,
+                                           const int num_elements) {
+    return Eigen::Map<const Eigen::ArrayXf>(tensor.flat<float>().data(),
+                                            num_elements);
+  }
+
+  GradientStats gradient_stats;
+  std::vector<float> weight_contribution;
+  float gain;
+};
+
+// Helper macro to check std::vector<float> approximate equality.
+#define EXPECT_VECTOR_FLOAT_EQ(x, y)       \
+  {                                        \
+    EXPECT_EQ((x).size(), (y).size());     \
+    for (int i = 0; i < (x).size(); ++i) { \
+      EXPECT_FLOAT_EQ((x)[i], (y)[i]);     \
+    }                                      \
+  }
+
+// Helper macro to check node stats approximate equality.
+#define EXPECT_NODE_STATS_EQ(val1, val2)                                      \
+  EXPECT_GRADIENT_STATS_EQ(val1.gradient_stats, val2.gradient_stats);         \
+  EXPECT_VECTOR_FLOAT_EQ(val1.weight_contribution, val2.weight_contribution); \
+  EXPECT_FLOAT_EQ(val1.gain, val2.gain);
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_NODE_STATS_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc
new file mode 100644
index 0000000000..f99b6826a7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats_test.cc
@@ -0,0 +1,213 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h"
+
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+
+using tensorflow::test::AsTensor;
+using std::vector;
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+namespace {
+
+const double kDelta = 1e-5;
+
+TEST(NodeStatsTest, AlmostZero) {
+  LearnerConfig learner_config;
+  NodeStats node_stats(learner_config, GradientStats(1e-8f, 1e-8f));
+  EXPECT_EQ(0, node_stats.weight_contribution[0]);
+  EXPECT_EQ(0, node_stats.gain);
+}
+
+TEST(NodeStatsTest, LessThanMinWeightConstraint) {
+  LearnerConfig learner_config;
+  learner_config.mutable_constraints()->set_min_node_weight(3.2f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  EXPECT_EQ(0, node_stats.weight_contribution[0]);
+  EXPECT_EQ(0, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1RegSquashed) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(10.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  EXPECT_EQ(0, node_stats.weight_contribution[0]);
+  EXPECT_EQ(0, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1RegPos) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(5.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  const float expected_clipped_grad = 7.32f - 5.0f;
+  const float expected_weight_contribution = -expected_clipped_grad / 1.63f;
+  const float expected_gain =
+      expected_clipped_grad * expected_clipped_grad / 1.63f;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1RegNeg) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(5.0f);
+  NodeStats node_stats(learner_config, GradientStats(-7.32f, 1.63f));
+  const float expected_clipped_grad = -7.32f + 5.0f;
+  const float expected_weight_contribution = -expected_clipped_grad / 1.63f;
+  const float expected_gain =
+      expected_clipped_grad * expected_clipped_grad / 1.63f;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L2Reg) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l2(8.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  const float expected_denom = 1.63f + 8.0f;
+  const float expected_weight_contribution = -7.32f / expected_denom;
+  const float expected_gain = 7.32f * 7.32f / expected_denom;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, L1L2Reg) {
+  LearnerConfig learner_config;
+  learner_config.mutable_regularization()->set_l1(5.0f);
+  learner_config.mutable_regularization()->set_l2(8.0f);
+  NodeStats node_stats(learner_config, GradientStats(7.32f, 1.63f));
+  const float expected_clipped_grad = 7.32f - 5.0f;
+  const float expected_denom = 1.63f + 8.0f;
+  const float expected_weight_contribution =
+      -expected_clipped_grad / expected_denom;
+  const float expected_gain =
+      expected_clipped_grad * expected_clipped_grad / expected_denom;
+  EXPECT_FLOAT_EQ(expected_weight_contribution,
+                  node_stats.weight_contribution[0]);
+  EXPECT_FLOAT_EQ(expected_gain, node_stats.gain);
+}
+
+TEST(NodeStatsTest, MulticlassFullHessianTest) {
+  LearnerConfig learner_config;
+  learner_config.set_multi_class_strategy(LearnerConfig::FULL_HESSIAN);
+  learner_config.mutable_regularization()->set_l2(0.3f);
+
+  const int kNumClasses = 4;
+  const auto& g_shape = TensorShape({1, kNumClasses});
+  Tensor g = AsTensor<float>({0.5, 0.33, -9, 1}, g_shape);
+  const auto& hessian_shape = TensorShape({1, kNumClasses, kNumClasses});
+  Tensor h = AsTensor<float>({3, 5, 7, 8, 5, 4, 1, 5, 7, 1, 8, 4, 8, 5, 4, 9},
+                             hessian_shape);
+
+  NodeStats node_stats(learner_config, GradientStats(g, h));
+
+  // Index 1 has 0 value because of l1 regularization,
+  std::vector<float> expected_weight = {0.9607576, 0.4162569, 0.9863192,
+                                        -1.5820024};
+
+  EXPECT_EQ(kNumClasses, node_stats.weight_contribution.size());
+  for (int i = 0; i < kNumClasses; ++i) {
+    EXPECT_NEAR(expected_weight[i], node_stats.weight_contribution[i], kDelta);
+  }
+  EXPECT_NEAR(9.841132, node_stats.gain, kDelta);
+}
+
+TEST(NodeStatsTest, MulticlassDiagonalHessianTest) {
+  // Normal case.
+  {
+    LearnerConfig learner_config;
+    learner_config.set_multi_class_strategy(LearnerConfig::FULL_HESSIAN);
+    learner_config.mutable_regularization()->set_l2(0.3f);
+
+    const int kNumClasses = 4;
+    const auto& g_shape = TensorShape({1, kNumClasses});
+    Tensor g = AsTensor<float>({0.5, 0.33, -9, 1}, g_shape);
+    Tensor h;
+    // Full hessian.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses, kNumClasses});
+      // Construct full hessian.
+      h = AsTensor<float>({3, 0, 0, 0, 0, 4, 0, 0, 0, 0, 8, 0, 0, 0, 0, 9},
+                          hessian_shape);
+    }
+    NodeStats full_node_stats(learner_config, GradientStats(g, h));
+
+    // Diagonal only.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses});
+      // Construct diagonal of hessian.
+      h = AsTensor<float>({3, 4, 8, 9}, hessian_shape);
+    }
+    learner_config.set_multi_class_strategy(LearnerConfig::DIAGONAL_HESSIAN);
+    NodeStats diag_node_stats(learner_config, GradientStats(g, h));
+
+    // Full and diagonal hessian should return the same results.
+    EXPECT_EQ(full_node_stats.weight_contribution.size(),
+              diag_node_stats.weight_contribution.size());
+    for (int i = 0; i < full_node_stats.weight_contribution.size(); ++i) {
+      EXPECT_FLOAT_EQ(full_node_stats.weight_contribution[i],
+                      diag_node_stats.weight_contribution[i]);
+    }
+    EXPECT_EQ(full_node_stats.gain, diag_node_stats.gain);
+  }
+  // Zero entries in diagonal, no regularization
+  {
+    LearnerConfig learner_config;
+    learner_config.set_multi_class_strategy(LearnerConfig::FULL_HESSIAN);
+
+    const int kNumClasses = 4;
+    const auto& g_shape = TensorShape({1, kNumClasses});
+    Tensor g = AsTensor<float>({0.5, 0.33, -9, 1}, g_shape);
+    Tensor h;
+    // Full hessian.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses, kNumClasses});
+      // Construct full hessian.
+      h = AsTensor<float>({3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0},
+                          hessian_shape);
+    }
+    NodeStats full_node_stats(learner_config, GradientStats(g, h));
+
+    // Diagonal only.
+    {
+      const auto& hessian_shape = TensorShape({1, kNumClasses});
+      // Diagonal of hessian, two entries are 0
+      h = AsTensor<float>({3, 0, 8, 0}, hessian_shape);
+    }
+    learner_config.set_multi_class_strategy(LearnerConfig::DIAGONAL_HESSIAN);
+    NodeStats diag_node_stats(learner_config, GradientStats(g, h));
+
+    // Full and diagonal hessian should return the same results.
+    EXPECT_EQ(full_node_stats.weight_contribution.size(),
+              diag_node_stats.weight_contribution.size());
+    for (int i = 0; i < full_node_stats.weight_contribution.size(); ++i) {
+      EXPECT_FLOAT_EQ(full_node_stats.weight_contribution[i],
+                      diag_node_stats.weight_contribution[i]);
+    }
+    EXPECT_EQ(full_node_stats.gain, diag_node_stats.gain);
+  }
+}
+
+}  // namespace
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h
new file mode 100644
index 0000000000..f700cbced8
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/split-stats.h
@@ -0,0 +1,84 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_SPLIT_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_SPLIT_STATS_H_
+
+#include <string>
+
+#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/stats/node-stats.h"
+
+namespace tensorflow {
+namespace boosted_trees {
+namespace learner {
+namespace stochastic {
+
+// FeatureSplitCandidate holds the split candidate node along with the stats.
+struct SplitStats {
+  // Initialize with 0 stats.
+  explicit SplitStats(const int output_length)
+      : root_node_stats(output_length),
+        left_node_stats(output_length),
+        right_node_stats(output_length),
+        gain(0) {}
+
+  // Feature unary split candidate, we don't apply tree complexity
+  // regularization as no new nodes are being added with this candidate.
+  SplitStats(const LearnerConfig& learner_config, const NodeStats& root_stats)
+      : root_node_stats(root_stats),
+        left_node_stats(root_stats.weight_contribution.size()),
+        right_node_stats(root_stats.weight_contribution.size()),
+        gain(0) {}
+
+  // Feature binary split candidate, we apply tree complexity regularization
+  // over the split gain to trade-off adding new nodes with loss reduction.
+  SplitStats(const LearnerConfig& learner_config, const NodeStats& root_stats,
+             const NodeStats& left_stats, const NodeStats& right_stats)
+      : root_node_stats(root_stats),
+        left_node_stats(left_stats),
+        right_node_stats(right_stats),
+        gain(left_stats.gain + right_stats.gain - root_stats.gain -
+             learner_config.regularization().tree_complexity()) {}
+
+  // Root Stats.
+  NodeStats root_node_stats;
+
+  // Children stats.
+  NodeStats left_node_stats;
+  NodeStats right_node_stats;
+
+  // Split gain.
+  float gain;
+
+  string DebugString() const {
+    return "Root = " + root_node_stats.DebugString() +
+           "\nLeft = " + left_node_stats.DebugString() +
+           "\nRight = " + right_node_stats.DebugString() +
+           "\nGain = " + std::to_string(gain);
+  }
+};
+
+// Helper macro to check split stats approximate equality.
+#define EXPECT_SPLIT_STATS_EQ(val1, val2)                             \
+  EXPECT_NODE_STATS_EQ(val1.root_node_stats, val2.root_node_stats);   \
+  EXPECT_NODE_STATS_EQ(val1.left_node_stats, val2.left_node_stats);   \
+  EXPECT_NODE_STATS_EQ(val1.right_node_stats, val2.right_node_stats); \
+  EXPECT_FLOAT_EQ(val1.gain, val2.gain);
+
+}  // namespace stochastic
+}  // namespace learner
+}  // namespace boosted_trees
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_STOCHASTIC_STATS_SPLIT_STATS_H_
diff --git a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc
index f658532acb..ce7632e589 100644
--- a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc
+++ b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc
@@ -17,7 +17,6 @@
 #include <sys/types.h>
 #include <algorithm>
 #include <cstdlib>
-#include <ctime>
 #include <functional>
 #include <iterator>
 #include <unordered_set>
@@ -25,6 +24,7 @@
 
 #include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"  // NOLINT
 #include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/env.h"
 
 using tensorflow::boosted_trees::learner::LearningRateDropoutDrivenConfig;
 using tensorflow::boosted_trees::trees::DecisionTreeEnsembleConfig;
@@ -95,7 +95,8 @@ TEST_F(DropoutUtilsTest, DropoutProbabilityTest) {
     int32 total_num_trees = 0;
     for (int i = 0; i < kNumRuns; ++i) {
       // draw random seeds
-      uint random_generator_seed = static_cast<uint>(std::clock());
+      uint random_generator_seed =
+          static_cast<uint>(Env::Default()->NowMicros());
       uint32 seed = rand_r(&random_generator_seed) % 100 + i;
       TF_EXPECT_OK(DropoutUtils::DropOutTrees(seed, config, trees_not_to_drop,
                                               weights_, &dropped_trees,
diff --git a/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc b/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
new file mode 100644
index 0000000000..b5ea5e7849
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
@@ -0,0 +1,44 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+REGISTER_OP("AddTreesToEnsemble")
+    .Input("tree_ensemble_handle: resource")
+    .Input("ensemble_to_add: string")
+    .Input("feature_column_usage_counts_handle: Ref(int64)")
+    .Input("feature_column_usage_counts_to_add: int64")
+    .Input("feature_column_gains_handle: Ref(float)")
+    .Input("feature_column_gains_to_add: float")
+    .Input("drop_out_tree_indices_weights: float")
+    .Input("learning_rate: float")
+    .SetShapeFn(shape_inference::NoOutputs)
+    .Doc(R"doc(
+Synchronously adds a tree ensemble to a an existing tree ensemble variable.
+tree_ensemble_handle: Handle to the ensemble variable.
+ensemble_to_add: Serialized DecisionTreeConfig proto of the tree.
+feature_column_usage_counts_handle: Handle to the feature column usage counts variable.
+feature_column_usage_counts_to_add: Rank 1 Tensor holding feature column usage counts to add.
+feature_column_gains_handle: Handle to the feature column gains variable.
+feature_column_gains_to_add: Rank 1 Tensor holding feature column gains to add.
+drop_out_tree_indices_weights: Rank 2 Tensor containing dropped trees indices
+and original weights of those trees during prediction.
+learning_rate: The learning rate that the tuner found for this iteration.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/model_ops.cc b/tensorflow/contrib/boosted_trees/ops/model_ops.cc
new file mode 100644
index 0000000000..c490c765cf
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/model_ops.cc
@@ -0,0 +1,114 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+
+REGISTER_RESOURCE_HANDLE_OP(DecisionTreeEnsembleResource);
+
+REGISTER_OP("TreeEnsembleIsInitializedOp")
+    .Input("tree_ensemble_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Checks whether a tree ensemble has been initialized.
+)doc");
+
+REGISTER_OP("CreateTreeEnsembleVariable")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("tree_ensemble_config: string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a tree ensemble model and returns a handle to it.
+
+tree_ensemble_handle: Handle to the tree ensemble resource to be created.
+stamp_token: Token to use as the initial value of the resource stamp.
+tree_ensemble_config: Serialized proto of the tree ensemble.
+)doc");
+
+REGISTER_OP("TreeEnsembleStampToken")
+    .Input("tree_ensemble_handle: resource")
+    .Output("stamp_token: int64")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Retrieves the tree ensemble resource stamp token.
+
+tree_ensemble_handle: Handle to the tree ensemble.
+stamp_token: Stamp token of the tree ensemble resource.
+)doc");
+
+REGISTER_OP("TreeEnsembleSerialize")
+    .Input("tree_ensemble_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("tree_ensemble_config: string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Serializes the tree ensemble to a proto.
+
+tree_ensemble_handle: Handle to the tree ensemble.
+stamp_token: Stamp token of the tree ensemble resource.
+tree_ensemble_config: Serialized proto of the ensemble.
+)doc");
+
+REGISTER_OP("TreeEnsembleDeserialize")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("tree_ensemble_config: string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Deserializes a serialized tree ensemble config and replaces current tree
+ensemble.
+
+tree_ensemble_handle: Handle to the tree ensemble.
+stamp_token: Token to use as the new value of the resource stamp.
+tree_ensemble_config: Serialized proto of the ensemble.
+)doc");
+
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc b/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
new file mode 100644
index 0000000000..8effb6f98f
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
@@ -0,0 +1,132 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/boosted_trees/proto/learner.pb.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+using tensorflow::boosted_trees::learner::LearnerConfig;
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+
+static Status ApplyGradientTreesPredictionShapeFn(InferenceContext* c) {
+  string learner_config_str;
+  // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
+  c->GetAttr("learner_config", &learner_config_str).IgnoreError();
+  LearnerConfig learner_config;
+  ParseProtoUnlimited(&learner_config, learner_config_str);
+  // Sets the shape of the output as a matrix.
+  const bool reduce_dim =
+      learner_config.multi_class_strategy() == LearnerConfig::TREE_PER_CLASS;
+  c->set_output(0, {c->Matrix(InferenceContext::kUnknownDim,
+                              reduce_dim ? learner_config.num_classes() - 1
+                                         : learner_config.num_classes())});
+  c->set_output(1, {c->Matrix(InferenceContext::kUnknownDim,
+                              reduce_dim ? learner_config.num_classes() - 1
+                                         : learner_config.num_classes())});
+  c->set_output(2, {c->Vector(InferenceContext::kUnknownDim)});
+  return Status::OK();
+}
+
+REGISTER_OP("GradientTreesPrediction")
+    .Attr("learner_config: string")
+    .Attr("num_dense_float_features: int >= 0")
+    .Attr("num_sparse_float_features: int >= 0")
+    .Attr("num_sparse_int_features: int >= 0")
+    .Attr("use_locking: bool = false")
+    .Attr("apply_dropout: bool")
+    .Attr("apply_averaging: bool")
+    .Attr("center_bias: bool")
+    .Input("tree_ensemble_handle: resource")
+    .Input("seed: int64")
+    .Input("dense_float_features: num_dense_float_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_float_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_float_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_float_features * int64")
+    .Input("sparse_int_feature_indices: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_values: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_shapes: num_sparse_int_features * int64")
+    .Output("predictions: float")
+    .Output("no_dropout_predictions: float")
+    .Output("drop_out_tree_indices_weights: float")
+    .SetShapeFn(ApplyGradientTreesPredictionShapeFn)
+    .Doc(R"doc(
+Runs multiple additive regression forests predictors on input instances
+and computes the final prediction for each class.
+
+learner_config: Config for the learner of type LearnerConfig proto. Prediction
+ops for now uses only LearningRateDropoutDrivenConfig config from the learner.
+num_dense_float_features: Number of dense float features.
+num_sparse_float_features: Number of sparse float features.
+num_sparse_int_features: Number of sparse int features.
+use_locking: Whether to use locking.
+seed: random seed to be used for dropout.
+apply_dropout: whether to apply dropout during prediction.
+apply_averaging: whether averaging of tree ensembles should take place. If set
+to true, will be based on AveragingConfig from learner_config.
+tree_ensemble_handle: The handle to the tree ensemble.
+dense_float_features: Rank 2 Tensors containing dense float feature values.
+sparse_float_feature_indices: Rank 2 Tensors containing sparse float indices.
+sparse_float_feature_values: Rank 1 Tensors containing sparse float values.
+sparse_float_feature_shapes: Rank 1 Tensors containing sparse float shapes.
+sparse_int_feature_indices: Rank 2 Tensors containing sparse int indices.
+sparse_int_feature_values: Rank 1 Tensors containing sparse int values.
+sparse_int_feature_shapes: Rank 1 Tensors containing sparse int shapes.
+predictions: Rank 2 Tensor containing predictions per example per class.
+no_dropout_predictions: The same as predictions, but using all trees (even
+those that were dropped due to dropout).
+drop_out_tree_indices_weights: Tensor of Rank 2 containing dropped trees indices
+and original weights of those trees during prediction.
+)doc");
+
+REGISTER_OP("GradientTreesPartitionExamples")
+    .Attr("num_dense_float_features: int >= 0")
+    .Attr("num_sparse_float_features: int >= 0")
+    .Attr("num_sparse_int_features: int >= 0")
+    .Attr("use_locking: bool = false")
+    .Input("tree_ensemble_handle: resource")
+    .Input("dense_float_features: num_dense_float_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_float_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_float_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_float_features * int64")
+    .Input("sparse_int_feature_indices: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_values: num_sparse_int_features * int64")
+    .Input("sparse_int_feature_shapes: num_sparse_int_features * int64")
+    .Output("partition_ids: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      return c->set_output("partition_ids",
+                           {c->Vector(InferenceContext::kUnknownDim)});
+    })
+    .Doc(R"doc(
+Splits input examples into the leaves of the tree.
+
+num_dense_float_features: Number of dense float features.
+num_sparse_float_features: Number of sparse float features.
+num_sparse_int_features: Number of sparse int features.
+use_locking: Whether to use locking.
+tree_ensemble_handle: The handle to the tree ensemble.
+dense_float_features: Rank 2 Tensors containing dense float feature values.
+sparse_float_feature_indices: Rank 2 Tensors containing sparse float indices.
+sparse_float_feature_values: Rank 1 Tensors containing sparse float values.
+sparse_float_feature_shapes: Rank 1 Tensors containing sparse float shapes.
+sparse_int_feature_indices: Rank 2 Tensors containing sparse int indices.
+sparse_int_feature_values: Rank 1 Tensors containing sparse int values.
+sparse_int_feature_shapes: Rank 1 Tensors containing sparse int shapes.
+partition_ids: Rank 1 Tensor containing partition ids per example.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
new file mode 100644
index 0000000000..c778a02fb6
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
@@ -0,0 +1,263 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+
+REGISTER_RESOURCE_HANDLE_OP(QuantileStreamResource);
+
+REGISTER_OP("QuantileAccumulatorIsInitialized")
+    .Input("quantile_accumulator_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a quantile accumulator has been initialized.
+)doc");
+
+REGISTER_OP("CreateQuantileAccumulator")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("max_elements: int = 1099511627776")  // 1 << 40
+    .Attr("epsilon: float")
+    .Attr("num_quantiles: int")
+    .Input("quantile_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a stateful accumulator for quantile summaries.
+
+epsilon: Error bound on the quantile summary.
+num_quantiles: Number of buckets that we create from the data.
+stamp_token: Token to use as the initial value of the resource stamp.
+quantile_accumulator_handle: The handle to the accumulator.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorAddSummaries")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("quantile_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Input("summaries: num_resource_handles * string")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      // All the inputs are scalars.
+      shape_inference::ShapeHandle unused_input;
+      for (int i = 0; i < 2 * num_resource_handles + 1; ++i) {
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused_input));
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Adds each quantile summary to its stream.
+
+quantile_accumulator_handles: The handles to the quantile stream resources.
+stamp_token: Stamp token to validate the Read/Write operation.
+summaries: A list of serialized QuantileSummaryState.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorGetBuckets")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("quantile_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Output("are_buckets_ready: num_resource_handles * bool")
+    .Output("buckets: num_resource_handles * float")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      for (int i = 0; i < num_resource_handles; ++i) {
+        c->set_output(i, c->Scalar());
+        c->set_output(i + num_resource_handles, c->Vector(c->UnknownDim()));
+      }
+      return Status::OK();
+    })
+
+    .Doc(R"doc(
+Returns quantile buckets created during previous flush of the accumulator.
+
+quantile_accumulator_handles: The handles to the quantile stream resources.
+stamp_token: Stamp token to validate the Read/Write operation.
+are_buckets_ready: Whether the buckets are ready or not.
+buckets: Output quantile summary representing boundaries with "num_quantile"
+    elements.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorFlush")
+    .Input("quantile_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Doc(R"doc(
+Resets quantile summary streams for each column with a new token.
+
+quantile_accumulator_handle: The handle to the accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+next_stamp_token: Stamp token to be used for the next iteration.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorSerialize")
+    .Input("quantile_accumulator_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("stream_state: string")
+    .Output("are_buckets_ready: bool")
+    .Output("buckets: float")
+    .Doc(R"doc(
+Serializes the state of the given resource.
+
+quantile_accumulator_handle: The handle to the accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+stream_state: A serialized QuantileStreamState.
+are_buckets_ready: Whether the buckets are ready or not.
+buckets: Output quantile buckets representing boundaries with "num_quantile"
+    elements.
+)doc");
+
+REGISTER_OP("QuantileAccumulatorDeserialize")
+    .Input("quantile_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("stream_state: string")
+    .Input("are_buckets_ready: bool")
+    .Input("buckets: float")
+    .Doc(R"doc(
+Serializes the state of the given resource.
+
+quantile_accumulator_handle: The handle to the accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+stream_state: A serialized QuantileStreamState.
+are_buckets_ready: Whether the buckets are ready or not.
+buckets: Output quantile summary representing boundaries with "num_quantile"
+    elements.
+)doc");
+
+REGISTER_OP("MakeQuantileSummaries")
+    .Attr("num_dense_features: int >= 0")
+    .Attr("num_sparse_features: int >= 0")
+    .Attr("epsilon: float")
+    .Input("dense_float_features: num_dense_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_features * int64")
+    .Input("example_weights: float")
+    .Output("dense_summaries: num_dense_features * string")
+    .Output("sparse_summaries: num_sparse_features * string")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_dense_features;
+      TF_RETURN_IF_ERROR(c->GetAttr("num_dense_features", &num_dense_features));
+      int num_sparse_features;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_sparse_features", &num_sparse_features));
+      for (int i = 0; i < num_dense_features; ++i) {
+        c->set_output(i, c->Scalar());
+      }
+      for (int i = 0; i < num_sparse_features; ++i) {
+        c->set_output(i + num_dense_features, c->Scalar());
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a summary for the given features.
+
+num_dense_features: Number of dense feature groups to compute quantiles on.
+num_sparse_features: Number of sparse feature groups to compute quantiles on.
+epsilon: Error bound on the computed summary.
+dense_float_features: A list of vectors which contains dense values.
+sparse_float_feature_indices: List of rank 2 tensors containing the sparse float
+feature indices.
+sparse_float_feature_values: List of rank 1 tensors containing the sparse float
+feature values.
+sparse_float_feature_shapes: List of rank 1 tensors containing the shape of the
+float feature.
+example_weights: Rank 1 tensor containing the example weight tensor.
+dense_summaries: A list of serialized QuantileSummaryState for dense columns.
+sparse_summaries: A list of serialized QuantileSummaryState for sparse columns.
+)doc");
+
+REGISTER_OP("QuantileBuckets")
+    .Attr("num_dense_features: int >= 0")
+    .Attr("num_sparse_features: int >= 0")
+    .Attr("dense_config: list(string)")
+    .Attr("sparse_config: list(string)")
+    .Input("dense_float_features: num_dense_features * float")
+    .Input("sparse_float_feature_indices: num_sparse_features * int64")
+    .Input("sparse_float_feature_values: num_sparse_features * float")
+    .Input("sparse_float_feature_shapes: num_sparse_features * int64")
+    .Input("example_weights: float")
+    .Output("dense_buckets: num_dense_features * float")
+    .Output("sparse_buckets: num_sparse_features * float")
+    .Doc(R"doc(
+Computes quantile buckets for a given list of dense and sparse features with
+given example weights.
+
+num_dense_features: Number of dense feature groups to compute quantiles on.
+num_sparse_features: Number of sparse feature groups to compute quantiles on.
+dense_config: Config for computing buckets for dense values.
+Each entry is QuantileConfig proto.
+sparse_config: Config for computing buckets for sparse feature values.
+Each entry is QuantileConfig proto.
+dense_float_features: A list of vectors which contains dense values.
+sparse_float_feature_indices: List of rank 2 tensors containing the sparse float
+feature indices.
+sparse_float_feature_values: List of rank 1 tensors containing the sparse float
+feature values.
+sparse_float_feature_shapes: List of rank 1 tensors containing the shape of the
+float feature.
+example_weights: Rank 1 tensor containing the example weight tensor.
+dense_buckets: Output quantile summary for each dense float tensor
+representing boundaries each with "num_quantile" elements.
+sparse_buckets: Output quantile summary for each sparse float value tensor
+representing boundaries each with "num_quantile" elements.
+)doc");
+
+REGISTER_OP("Quantiles")
+    .Attr("num_dense_features: int >= 0")
+    .Attr("num_sparse_features: int >= 0")
+    .Input("dense_values: num_dense_features * float")
+    .Input("sparse_values: num_sparse_features * float")
+    .Input("dense_buckets: num_dense_features * float")
+    .Input("sparse_buckets: num_sparse_features * float")
+    .Output("dense_quantiles: num_dense_features * int32")
+    .Output("sparse_quantiles: num_sparse_features * int32")
+    .Doc(R"doc(
+Computes quantile for each a given list of dense and sparse feature values using
+the given buckets.
+
+num_dense_features: Number of dense feature groups to generate quantiles for.
+num_sparse_features: Number of sparse feature groups to generate quantiles for.
+dense_values: List of rank 1 tensors containing the dense values.
+sparse_values: List of rank 1 tensors containing the sparse feature values.
+dense_buckets: Quantile summary for each of the dense float tensor.
+sparse_buckets: Quantile summary for each of the sparse feature float tensor.
+dense_quantiles: Rank 1 tensors representing associated quantiles for each of
+dense float tensors.
+sparse_quantiles: Rank 1 tensors representing associated quantiles for each of
+the sparse feature tensors.
+)doc");
+
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
new file mode 100644
index 0000000000..d32507127c
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
@@ -0,0 +1,197 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+using shape_inference::DimensionHandle;
+
+REGISTER_OP("BuildDenseInequalitySplits")
+    .Attr("feature_column_group_id: int")
+    .Attr("l1_regularization: float")
+    .Attr("l2_regularization: float")
+    .Attr("tree_complexity_regularization: float")
+    .Attr("min_node_weight: float")
+    .Input("num_minibatches: int64")
+    .Input("partition_ids: int32")
+    .Input("bucket_ids: int64")
+    .Input("gradients: float32")
+    .Input("hessians: float32")
+    .Input("bucket_boundaries: float32")
+    .Output("output_partition_ids: int32")
+    .Output("gains: float32")
+    .Output("split_infos: string")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle unused_dim;
+      ShapeHandle unused_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_shape));
+
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &partition_ids_shape));
+      ShapeHandle bucket_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &bucket_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(bucket_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(3), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(4), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      ShapeHandle bucket_boundaries_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &bucket_boundaries_shape));
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Find the split that has the best gain for the accumulated stats.
+
+num_minibatches: A scalar, the number of times per example gradients & hessians
+    were accumulated. The stats are divided by this to get per example stats.
+partition_ids: A rank 1 tensor of partition IDs.
+bucket_ids: A rank 1 tensor of buckets IDs.
+gradients: A rank 1 tensor of gradients.
+hessians: A rank 1 tensor of hessians.
+bucket_boundaries: A rank 1 tensor, thresholds that were used for bucketization.
+output_partition_ids: A rank 1 tensor, the partition IDs that we created splits
+    for.
+gains: A rank 1 tensor, for the computed gain for the created splits.
+split_infos: A rank 1 tensor of serialized protos which contains the
+    `SplitInfo`s.
+)doc");
+
+REGISTER_OP("BuildSparseInequalitySplits")
+    .Attr("feature_column_group_id: int")
+    .Attr("bias_feature_id: int")
+    .Attr("l1_regularization: float")
+    .Attr("l2_regularization: float")
+    .Attr("tree_complexity_regularization: float")
+    .Attr("min_node_weight: float")
+    .Input("num_minibatches: int64")
+    .Input("partition_ids: int32")
+    .Input("bucket_ids: int64")
+    .Input("gradients: float32")
+    .Input("hessians: float32")
+    .Input("bucket_boundaries: float32")
+    .Output("output_partition_ids: int32")
+    .Output("gains: float32")
+    .Output("split_infos: string")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle unused_dim;
+      ShapeHandle unused_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_shape));
+
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &partition_ids_shape));
+      ShapeHandle bucket_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &bucket_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(bucket_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(3), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(4), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      ShapeHandle bucket_boundaries_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &bucket_boundaries_shape));
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Find the split that has the best gain for the accumulated stats.
+
+num_minibatches: A scalar, the number of times per example gradients & hessians
+    were accumulated. The stats are divided by this to get per example stats.
+partition_ids: A rank 1 tensor of partition IDs.
+bucket_ids: A rank 1 tensor of buckets IDs.
+gradients: A rank 1 tensor of gradients.
+hessians: A rank 1 tensor of hessians.
+bucket_boundaries: A rank 1 tensor, thresholds that were used for bucketization.
+output_partition_ids: A rank 1 tensor, the partition IDs that we created splits
+    for.
+gains: A rank 1 tensor, for the computed gain for the created splits.
+split_infos: A rank 1 tensor of serialized protos which contains the
+    `SplitInfo`s.
+)doc");
+
+REGISTER_OP("BuildCategoricalEqualitySplits")
+    .Attr("feature_column_group_id: int")
+    .Attr("bias_feature_id: int")
+    .Attr("l1_regularization: float")
+    .Attr("l2_regularization: float")
+    .Attr("tree_complexity_regularization: float")
+    .Attr("min_node_weight: float")
+    .Input("num_minibatches: int64")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float32")
+    .Input("hessians: float32")
+    .Output("output_partition_ids: int32")
+    .Output("gains: float32")
+    .Output("split_infos: string")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle unused_dim;
+      ShapeHandle unused_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_shape));
+
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &partition_ids_shape));
+      ShapeHandle bucket_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &bucket_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(bucket_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(3), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(4), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Find the split that has the best gain for the accumulated stats.
+
+num_minibatches: A scalar, the number of times per example gradients & hessians
+    were accumulated. The stats are divided by this to get per example stats.
+partition_ids: A rank 1 tensor of partition IDs.
+feature_ids: A rank 1 tensor of feature IDs.
+gradients: A rank 1 tensor of gradients.
+hessians: A rank 1 tensor of hessians.
+output_partition_ids: A rank 1 tensor, the partition IDs that we created splits
+    for.
+gains: A rank 1 tensor, for the computed gain for the created splits.
+split_infos: A rank 1 tensor of serialized protos which contains the
+    `SplitInfo`s.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
new file mode 100644
index 0000000000..9fa5ec9a83
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
@@ -0,0 +1,475 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+using shape_inference::DimensionHandle;
+
+REGISTER_RESOURCE_HANDLE_OP(StatsAccumulatorScalarResource);
+
+REGISTER_OP("StatsAccumulatorScalarIsInitialized")
+    .Input("stats_accumulator_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a stats accumulator has been initialized.
+)doc");
+
+REGISTER_OP("CreateStatsAccumulatorScalar")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token is a scalar.
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a scalar stats accumulator.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: Token to use as the initial value of the resource stamp.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarAdd")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("stats_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Input("partition_ids: num_resource_handles * int32")
+    .Input("feature_ids: num_resource_handles * int64")
+    .Input("gradients: num_resource_handles * float")
+    .Input("hessians: num_resource_handles * float")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      for (int i = 0; i < num_resource_handles; ++i) {
+        ShapeHandle unused_input;
+        DimensionHandle unused_dim;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused_input));
+        TF_RETURN_IF_ERROR(
+            c->WithRank(c->input(num_resource_handles), 0, &unused_input));
+        ShapeHandle partition_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(num_resource_handles + i + 1),
+                                       1, &partition_ids_shape));
+        ShapeHandle feature_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 2 + i + 1), 1, &feature_ids_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(feature_ids_shape, 0), &unused_dim));
+        ShapeHandle gradients_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 3 + i + 1), 1, &gradients_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(gradients_shape, 0), &unused_dim));
+        ShapeHandle hessians_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 4 + i + 1), 1, &hessians_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(hessians_shape, 0), &unused_dim));
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Updates the scalar stats accumulator.
+
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+stats_accumulator_handles: A list of handles to the stats accumulator.
+partition_ids: A list of vectors of partition_ids.
+feature_ids: A list of vectors of feature_ids.
+gradients: A list of vectors of gradients for each slot in
+    <partition_id, feature_id>.
+hessians: A list of vectors of hessians for each slot in
+    <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarFlush")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      c->set_output(3, c->Vector(c->UnknownDim()));
+      c->set_output(4, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Flushes the scalar stats accumulator to output and resets the internal state.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+next_stamp_token: Stamp token for the next iteration.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A vector of gradients, with a value for each slot
+                  in <output_partition_id, output_feature_id>.
+output_hessians: A vector of hessians, with a value for each slot
+                 in <output_partition_id, output_feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarDeserialize")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("num_updates: int64")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      DimensionHandle unused_dim;
+      // stats_accumulator_handle
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      // num_updates
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &partition_ids_shape));
+      ShapeHandle feature_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &feature_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(feature_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 1, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 1, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Resets the scalar stats accumulator with the serialized state.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+partition_ids: A vector of partition_ids.
+feature_ids: A vector of feature_ids.
+gradients: A vector of gradients for each slot in <partition_id, feature_id>.
+hessians: A vector of hessians for each slot in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarSerialize")
+    .Input("stats_accumulator_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      c->set_output(0, c->Scalar());
+      // num_updates
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      c->set_output(3, c->Vector(c->UnknownDim()));
+      c->set_output(4, c->Vector(c->UnknownDim()));
+      c->set_output(5, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Serializes the scalar stats accumulator state.
+
+stats_accumulator_handle: handle to the stats accumulator.
+stamp_token: The current stamp token for the resource.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A vector of gradients, with a value for each slot
+                  in <output_partition_id, output_feature_id>.
+output_hessians: A vector of hessians, with a value for each slot
+                 in <output_partition_id, output_feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorScalarMakeSummary")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .Doc(R"doc(
+)doc");
+
+// Tensor version of the stats accumulator ops.
+REGISTER_RESOURCE_HANDLE_OP(StatsAccumulatorTensorResource);
+
+REGISTER_OP("StatsAccumulatorTensorIsInitialized")
+    .Input("stats_accumulator_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a tensor stats accumulator has been initialized.
+)doc");
+
+REGISTER_OP("CreateStatsAccumulatorTensor")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("per_slot_gradient_shape: int64")
+    .Input("per_slot_hessian_shape: int64")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token is a scalar.
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Creates a tensor stats accumulator.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Token to use as the initial value of the resource stamp.
+per_slot_gradient_shape: a vector that defines the shape of gradients.
+per_slot_hessian_shape:  a vector that defines the shape of hessians.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorAdd")
+    .Attr("num_resource_handles: int >= 1")
+    .Input("stats_accumulator_handles: num_resource_handles * resource")
+    .Input("stamp_token: int64")
+    .Input("partition_ids: num_resource_handles * int32")
+    .Input("feature_ids: num_resource_handles * int64")
+    .Input("gradients: num_resource_handles * float")
+    .Input("hessians: num_resource_handles * float")
+    .SetShapeFn([](InferenceContext* c) {
+      int num_resource_handles;
+      TF_RETURN_IF_ERROR(
+          c->GetAttr("num_resource_handles", &num_resource_handles));
+      for (int i = 0; i < num_resource_handles; ++i) {
+        ShapeHandle unused_input;
+        DimensionHandle unused_dim;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused_input));
+        TF_RETURN_IF_ERROR(
+            c->WithRank(c->input(num_resource_handles), 0, &unused_input));
+        ShapeHandle partition_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(num_resource_handles + i + 1),
+                                       1, &partition_ids_shape));
+        ShapeHandle feature_ids_shape;
+        TF_RETURN_IF_ERROR(c->WithRank(
+            c->input(num_resource_handles * 2 + i + 1), 1, &feature_ids_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(feature_ids_shape, 0), &unused_dim));
+        ShapeHandle gradients_shape;
+        TF_RETURN_IF_ERROR(c->WithRankAtLeast(
+            c->input(num_resource_handles * 3 + i + 1), 2, &gradients_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(gradients_shape, 0), &unused_dim));
+        ShapeHandle hessians_shape;
+        TF_RETURN_IF_ERROR(c->WithRankAtLeast(
+            c->input(num_resource_handles * 4 + i + 1), 2, &hessians_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                    c->Dim(hessians_shape, 0), &unused_dim));
+      }
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Updates the tensor stats accumulator.
+
+stats_accumulator_handles: A list of handles to the stats accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+partition_ids: A list of vectors of partition_ids.
+feature_ids: A list of vectors of feature_ids.
+gradients: A list of vectors of gradients for each slot in
+    <partition_id, feature_id>.
+hessians: A list of vectors of hessians for each slot in
+    <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorFlush")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      // num_updates
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(2, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(3, c->UnknownShape());
+      c->set_output(4, c->UnknownShape());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Flushes the stats accumulator to output and resets the internal state.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+next_stamp_token: Stamp token to be used for the next iteration.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids: A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A tensor of gradients, first dimension matches slots
+                  in <partition_id, feature_id>.
+output_hessians: A tensor of hessians, first dimension matches slots
+                 in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorDeserialize")
+    .Input("stats_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("num_updates: int64")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      DimensionHandle unused_dim;
+      // stats_accumulator_handle
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      // num_updates
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      ShapeHandle partition_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &partition_ids_shape));
+      ShapeHandle feature_ids_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &feature_ids_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(feature_ids_shape, 0), &unused_dim));
+      ShapeHandle gradients_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(5), 2, &gradients_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(gradients_shape, 0), &unused_dim));
+      ShapeHandle hessians_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(6), 2, &hessians_shape));
+      TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
+                                  c->Dim(hessians_shape, 0), &unused_dim));
+
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Resets the tensor stats accumulator with the serialized state.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+partition_ids: A vector of partition_ids.
+feature_ids: A vector of feature_ids.
+gradients: A vector of gradients for each slot in <partition_id, feature_id>.
+hessians: A vector of hessians for each slot in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorSerialize")
+    .Input("stats_accumulator_handle: resource")
+    .Output("stamp_token: int64")
+    .Output("num_updates: int64")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      // stamp_token
+      c->set_output(0, c->Scalar());
+      // num_updates
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(3, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(4, c->UnknownShape());
+      c->set_output(5, c->UnknownShape());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Serializes the scalar stats accumulator state.
+
+stats_accumulator_handle: handle to the tree ensemble resource to be created.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+num_updates: Number of times stats were added to this accumulator since last
+    flush.
+output_partition_ids: A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A tensor of gradients, first dimension matches slots
+                  in <partition_id, feature_id>.
+output_hessians: A tensor of hessians, first dimension matches slots
+                 in <partition_id, feature_id>.
+)doc");
+
+REGISTER_OP("StatsAccumulatorTensorMakeSummary")
+    .Input("partition_ids: int32")
+    .Input("feature_ids: int64")
+    .Input("gradients: float")
+    .Input("hessians: float")
+    .Output("output_partition_ids: int32")
+    .Output("output_feature_ids: int64")
+    .Output("output_gradients: float")
+    .Output("output_hessians: float")
+    .Doc(R"doc(
+Summarizes the stats by summing the <gradients, hessians> that are for the same
+<partition_id, feature_id>.
+
+partition_ids: A vector of partition_ids.
+feature_ids: A vector of feature_ids.
+gradients: A vector of gradients for each slot in <partition_id, feature_id>.
+hessians: A vector of hessians for each slot in <partition_id, feature_id>.
+output_partition_ids: A vector of partition_ids for the slots.
+output_feature_ids: A vector of feature_ids for the slots.
+output_gradients: A tensor of gradients, first dimension matches slots
+                  in <partition_id, feature_id>.
+output_hessians: A tensor of hessians, first dimension matches slots
+                 in <partition_id, feature_id>.
+)doc");
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/training_ops.cc b/tensorflow/contrib/boosted_trees/ops/training_ops.cc
new file mode 100644
index 0000000000..d2debbe03d
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/ops/training_ops.cc
@@ -0,0 +1,120 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+namespace gtflow {
+
+REGISTER_OP("CenterTreeEnsembleBias")
+    .Attr("learner_config: string")
+    .Attr("centering_epsilon: float = 0.01")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Input("delta_updates: float")
+    .Output("continue_centering: bool")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &unused_input));
+      c->set_output(0, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Centers the tree ensemble bias before adding trees based on feature splits.
+
+learner_config: Config for the learner of type LearnerConfig proto.
+tree_ensemble_handle: Handle to the ensemble variable.
+stamp_token: Stamp token for validating operation consistency.
+next_stamp_token: Stamp token to be used for the next iteration.
+delta_updates: Rank 1 Tensor containing delta updates per bias dimension.
+continue_centering: Scalar indicating whether more centering is needed.
+)doc");
+
+REGISTER_OP("GrowTreeEnsemble")
+    .Attr("learner_config: string")
+    .Attr("num_handlers: int >= 0")
+    .Attr("center_bias: bool")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Input("learning_rate: float")
+    .Input("dropout_seed: int64")
+    .Input("partition_ids: num_handlers * int32")
+    .Input("gains: num_handlers * float")
+    .Input("splits: num_handlers * string")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused_input));
+      // Dropout seed.
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused_input));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Grows the tree ensemble by either adding a layer to the last tree being grown
+or by starting a new tree.
+
+learner_config: Config for the learner of type LearnerConfig proto.
+num_handlers: Number of handlers generating candidates.
+tree_ensemble_handle: Handle to the ensemble variable.
+stamp_token: Stamp token for validating operation consistency.
+next_stamp_token: Stamp token to be used for the next iteration.
+learning_rate: Scalar learning rate.
+partition_ids: List of Rank 1 Tensors containing partition Id per candidate.
+gains: List of Rank 1 Tensors containing gains per candidate.
+splits: List of Rank 1 Tensors containing serialized SplitInfo protos per candidate.
+)doc");
+
+REGISTER_OP("TreeEnsembleStats")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Output("num_trees: int64")
+    .Output("num_layers: int64")
+    .Output("active_tree: int64")
+    .Output("active_layer: int64")
+    .Output("attempted_trees: int64")
+    .Output("attempted_layers: int64")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Scalar());
+      c->set_output(3, c->Scalar());
+      c->set_output(4, c->Scalar());
+      c->set_output(5, c->Scalar());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Retrieves stats related to the tree ensemble.
+
+tree_ensemble_handle: Handle to the ensemble variable.
+stamp_token: Stamp token for validating operation consistency.
+num_trees: Scalar indicating the number of finalized trees in the ensemble.
+num_layers: Scalar indicating the number of layers in the ensemble.
+active_tree: Scalar indicating the active tree being trained.
+active_layer: Scalar indicating the active layer being trained.
+)doc");
+
+}  // namespace gtflow
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/proto/BUILD b/tensorflow/contrib/boosted_trees/proto/BUILD
index c99d8849bd..9a61e163eb 100644
--- a/tensorflow/contrib/boosted_trees/proto/BUILD
+++ b/tensorflow/contrib/boosted_trees/proto/BUILD
@@ -33,6 +33,16 @@ tf_proto_library(
     visibility = ["//visibility:public"],
 )
 
+tf_proto_library(
+    name = "split_info_proto",
+    srcs = ["split_info.proto"],
+    cc_api_version = 2,
+    protodeps = [
+        ":tree_config_proto",
+    ],
+    visibility = ["//visibility:public"],
+)
+
 tf_proto_library(
     name = "tree_config_proto",
     srcs = ["tree_config.proto"],
diff --git a/tensorflow/contrib/boosted_trees/proto/split_info.proto b/tensorflow/contrib/boosted_trees/proto/split_info.proto
new file mode 100644
index 0000000000..a300c24c8e
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/proto/split_info.proto
@@ -0,0 +1,19 @@
+syntax = "proto3";
+
+option cc_enable_arenas = true;
+
+package tensorflow.boosted_trees.learner;
+
+import "tensorflow/contrib/boosted_trees/proto/tree_config.proto";
+
+// Gathered information for a split node.
+message SplitInfo {
+  // The split node without the child nodes attached.
+  tensorflow.boosted_trees.trees.TreeNode split_node = 1;
+
+  // Left Leaf node.
+  tensorflow.boosted_trees.trees.Leaf left_child = 2;
+
+  // Right Leaf node.
+  tensorflow.boosted_trees.trees.Leaf right_child = 3;
+}
diff --git a/tensorflow/contrib/boosted_trees/python/__init__.py b/tensorflow/contrib/boosted_trees/python/__init__.py
new file mode 100644
index 0000000000..60b0476a44
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Gradient boosted trees implementation in tensorflow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops import *
+# pylint: enable=unused-import,wildcard-import
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py
new file mode 100644
index 0000000000..842e0caeca
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py
@@ -0,0 +1,351 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow ensemble optimization ops.
+
+The tests cover:
+- Adding a newly built tree to an existing ensemble
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import ensemble_optimizer_ops
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+
+
+def _append_to_leaf(leaf, class_id, weight):
+  """Helper method for building tree leaves.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to, int
+    class_id: class Id for the weight update, int
+    weight: weight contribution value, float
+  """
+  leaf.sparse_vector.index.append(class_id)
+  leaf.sparse_vector.value.append(weight)
+
+
+class EnsembleOptimizerOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Create an ensemble of 2 trees."""
+    super(EnsembleOptimizerOpsTest, self).setUp()
+    self._tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+    # First tree.
+    tree_1 = self._tree_ensemble.trees.add()
+    _append_to_leaf(tree_1.nodes.add().leaf, 0, 0.4)
+    _append_to_leaf(tree_1.nodes.add().leaf, 1, 0.6)
+    # Second tree.
+    tree_2 = self._tree_ensemble.trees.add()
+    _append_to_leaf(tree_2.nodes.add().leaf, 0, 1)
+    _append_to_leaf(tree_2.nodes.add().leaf, 1, 0)
+
+    self._tree_ensemble.tree_weights.append(1.0)
+    self._tree_ensemble.tree_weights.append(1.0)
+
+    meta_1 = self._tree_ensemble.tree_metadata.add()
+    meta_1.num_tree_weight_updates = 2
+    meta_2 = self._tree_ensemble.tree_metadata.add()
+    meta_2.num_tree_weight_updates = 3
+
+    # Ensemble to be added.
+    self._ensemble_to_add = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+    self._tree_to_add = self._ensemble_to_add.trees.add()
+    _append_to_leaf(self._tree_to_add.nodes.add().leaf, 0, 0.3)
+    _append_to_leaf(self._tree_to_add.nodes.add().leaf, 1, 0.7)
+
+  def testWithEmptyEnsemble(self):
+    with self.test_session():
+      # Create an empty ensemble.
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="empty")
+
+      # Create zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=array_ops.zeros([1], dtypes.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=array_ops.zeros([1], dtypes.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [2],
+              feature_gains, [0.4], [[]],
+              learning_rate=1.0)
+      ]):
+        result = model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1]
+
+      # Output.
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output_ensemble.ParseFromString(result.eval())
+      self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[0])
+      self.assertEqual(1, len(output_ensemble.trees))
+
+      self.assertAllEqual([1.0], output_ensemble.tree_weights)
+
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+
+      self.assertAllEqual([2], feature_usage_counts.eval())
+      self.assertArrayNear([0.4], feature_gains.eval(), 1e-6)
+
+  def testWithExistingEnsemble(self):
+    with self.test_session():
+      # Create existing tree ensemble.
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=self._tree_ensemble.SerializeToString(),
+          name="existing")
+      # Create non-zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([0, 4, 1], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.0, 0.3, 0.05], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2, 0],
+              feature_gains, [0.02, 0.1, 0.0], [[], []],
+              learning_rate=1)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # Output.
+      self.assertEqual(3, len(output_ensemble.trees))
+      self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[2])
+
+      self.assertAllEqual([1.0, 1.0, 1.0], output_ensemble.tree_weights)
+
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+      self.assertEqual(3,
+                       output_ensemble.tree_metadata[1].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[2].num_tree_weight_updates)
+      self.assertAllEqual([1, 6, 1], feature_usage_counts.eval())
+      self.assertArrayNear([0.02, 0.4, 0.05], feature_gains.eval(), 1e-6)
+
+  def testWithExistingEnsembleAndDropout(self):
+    with self.test_session():
+      tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 10):
+        tree = tree_ensemble.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble.tree_weights.append(i + 1)
+        meta = tree_ensemble.tree_metadata.add()
+        meta.num_tree_weight_updates = 1
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble.SerializeToString(),
+          name="existing")
+      # Create non-zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([2, 3], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.0, 0.3], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      dropped = [1, 6, 8]
+      dropped_original_weights = [2.0, 7.0, 9.0]
+
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2],
+              feature_gains, [0.5, 0.3], [dropped, dropped_original_weights],
+              learning_rate=0.1)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # Output.
+      self.assertEqual(11, len(output_ensemble.trees))
+      self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[10])
+      self.assertAllClose(4.5, output_ensemble.tree_weights[10])
+
+      self.assertAllClose([1., 1.5, 3., 4., 5., 6., 5.25, 8., 6.75, 10., 4.5],
+                          output_ensemble.tree_weights)
+
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[1].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[2].num_tree_weight_updates)
+
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[3].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[4].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[5].num_tree_weight_updates)
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[6].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[7].num_tree_weight_updates)
+      self.assertEqual(2,
+                       output_ensemble.tree_metadata[8].num_tree_weight_updates)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[9].num_tree_weight_updates)
+      self.assertEqual(
+          1, output_ensemble.tree_metadata[10].num_tree_weight_updates)
+      self.assertAllEqual([3, 5], feature_usage_counts.eval())
+      self.assertArrayNear([0.05, 0.33], feature_gains.eval(), 1e-6)
+
+  def testWithEmptyEnsembleAndShrinkage(self):
+    with self.test_session():
+      # Add shrinkage config.
+      learning_rate = 0.0001
+      tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble.SerializeToString(),
+          name="existing")
+
+      # Create zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([0, 0], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.0, 0.0], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2],
+              feature_gains, [0.5, 0.3], [[], []],
+              learning_rate=learning_rate)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # New tree is added with shrinkage weight.
+      self.assertAllClose([learning_rate], output_ensemble.tree_weights)
+      self.assertEqual(1,
+                       output_ensemble.tree_metadata[0].num_tree_weight_updates)
+      self.assertAllEqual([1, 2], feature_usage_counts.eval())
+      self.assertArrayNear([0.5 * learning_rate, 0.3 * learning_rate],
+                           feature_gains.eval(), 1e-6)
+
+  def testWithExistingEnsembleAndShrinkage(self):
+    with self.test_session():
+      # Add shrinkage config.
+      learning_rate = 0.0001
+      tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 5):
+        tree = tree_ensemble.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble.tree_weights.append(i + 1)
+        meta = tree_ensemble.tree_metadata.add()
+        meta.num_tree_weight_updates = 1
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble.SerializeToString(),
+          name="existing")
+
+      # Create non-zero feature importance.
+      feature_usage_counts = variables.Variable(
+          initial_value=np.array([4, 7], np.int64),
+          name="feature_usage_counts",
+          trainable=False)
+      feature_gains = variables.Variable(
+          initial_value=np.array([0.2, 0.8], np.float32),
+          name="feature_gains",
+          trainable=False)
+
+      resources.initialize_resources(resources.shared_resources()).run()
+      variables.initialize_all_variables().run()
+
+      output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
+      with ops.control_dependencies([
+          ensemble_optimizer_ops.add_trees_to_ensemble(
+              tree_ensemble_handle,
+              self._ensemble_to_add.SerializeToString(),
+              feature_usage_counts, [1, 2],
+              feature_gains, [0.5, 0.3], [[], []],
+              learning_rate=learning_rate)
+      ]):
+        output_ensemble.ParseFromString(
+            model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
+
+      # The weights of previous trees stayed the same, new tree (LAST) is added
+      # with shrinkage weight.
+      self.assertAllClose([1.0, 2.0, 3.0, 4.0, 5.0, learning_rate],
+                          output_ensemble.tree_weights)
+
+      # Check that all number of updates are equal to 1 (e,g, no old tree weight
+      # got adjusted.
+      for i in range(0, 6):
+        self.assertEqual(
+            1, output_ensemble.tree_metadata[i].num_tree_weight_updates)
+
+      # Ensure feature importance was aggregated correctly.
+      self.assertAllEqual([5, 9], feature_usage_counts.eval())
+      self.assertArrayNear(
+          [0.2 + 0.5 * learning_rate, 0.8 + 0.3 * learning_rate],
+          feature_gains.eval(), 1e-6)
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
new file mode 100644
index 0000000000..90cc07f661
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
@@ -0,0 +1,325 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow model ops.
+
+The tests cover:
+- Loading a model from protobufs.
+- Running Predictions using an existing model.
+- Serializing the model.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import ensemble_optimizer_ops
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.ops import prediction_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import saver
+
+
+def _append_to_leaf(leaf, c_id, w):
+  """Helper method for building tree leaves.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to.
+    c_id: class Id for the weight update.
+    w: weight contribution value.
+  """
+  leaf.sparse_vector.index.append(c_id)
+  leaf.sparse_vector.value.append(w)
+
+
+def _set_float_split(split, feat_col, thresh, l_id, r_id):
+  """Helper method for building tree float splits.
+
+  Sets split feature column, threshold and children.
+
+  Args:
+    split: split node to update.
+    feat_col: feature column for the split.
+    thresh: threshold to split on forming rule x <= thresh.
+    l_id: left child Id.
+    r_id: right child Id.
+  """
+  split.feature_column = feat_col
+  split.threshold = thresh
+  split.left_id = l_id
+  split.right_id = r_id
+
+
+class ModelOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up test for model_ops.
+
+    Create a batch of two examples having one dense float, two sparse float and
+    one sparse int features.
+    The data looks like the following:
+    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 |
+    | 0        |  7     |    -3    |          |          |
+    | 1        | -2     |          | 4        |   9,1    |
+    """
+    super(ModelOpsTest, self).setUp()
+    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._sparse_float_indices1 = np.array([[0, 0]])
+    self._sparse_float_values1 = np.array([-3.0])
+    self._sparse_float_shape1 = np.array([2, 1])
+    self._sparse_float_indices2 = np.array([[1, 0]])
+    self._sparse_float_values2 = np.array([4.0])
+    self._sparse_float_shape2 = np.array([2, 1])
+    self._sparse_int_indices1 = np.array([[1, 0], [1, 1]])
+    self._sparse_int_values1 = np.array([9, 1])
+    self._sparse_int_shape1 = np.array([2, 2])
+    self._seed = 123
+
+  def testCreate(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree = tree_ensemble_config.trees.add()
+      _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=3,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="create_tree")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, _, _ = prediction_ops.gradient_trees_prediction(
+          tree_ensemble_handle,
+          self._seed, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1],
+          learner_config=learner_config.SerializeToString(),
+          apply_dropout=False,
+          apply_averaging=False,
+          center_bias=False)
+      self.assertAllClose(result.eval(), [[-0.4], [-0.4]])
+      stamp_token = model_ops.tree_ensemble_stamp_token(tree_ensemble_handle)
+      self.assertEqual(stamp_token.eval(), 3)
+
+  def testSerialization(self):
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph):
+        tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+        # Bias tree only for second class.
+        tree1 = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree1.nodes.add().leaf, 1, -0.2)
+
+        tree_ensemble_config.tree_weights.append(1.0)
+
+        # Depth 2 tree.
+        tree2 = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_weights.append(1.0)
+        _set_float_split(tree2.nodes.add()
+                         .sparse_float_binary_split_default_right.split, 1, 4.0,
+                         1, 2)
+        _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3,
+                         4)
+        _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+        _append_to_leaf(tree2.nodes.add().leaf, 1, 1.2)
+        _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+        tree_ensemble_handle = model_ops.tree_ensemble_variable(
+            stamp_token=7,
+            tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+            name="saver_tree")
+        stamp_token, serialized_config = model_ops.tree_ensemble_serialize(
+            tree_ensemble_handle)
+        resources.initialize_resources(resources.shared_resources()).run()
+        self.assertEqual(stamp_token.eval(), 7)
+        serialized_config = serialized_config.eval()
+
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph):
+        tree_ensemble_handle2 = model_ops.tree_ensemble_variable(
+            stamp_token=9,
+            tree_ensemble_config=serialized_config,
+            name="saver_tree2")
+        resources.initialize_resources(resources.shared_resources()).run()
+
+        # Prepare learner config.
+        learner_config = learner_pb2.LearnerConfig()
+        learner_config.num_classes = 3
+
+        result, _, _ = prediction_ops.gradient_trees_prediction(
+            tree_ensemble_handle2,
+            self._seed, [self._dense_float_tensor], [
+                self._sparse_float_indices1, self._sparse_float_indices2
+            ], [self._sparse_float_values1, self._sparse_float_values2],
+            [self._sparse_float_shape1,
+             self._sparse_float_shape2], [self._sparse_int_indices1],
+            [self._sparse_int_values1], [self._sparse_int_shape1],
+            learner_config=learner_config.SerializeToString(),
+            apply_dropout=False,
+            apply_averaging=False,
+            center_bias=False)
+
+        # Re-serialize tree.
+        stamp_token2, serialized_config2 = model_ops.tree_ensemble_serialize(
+            tree_ensemble_handle2)
+
+        # The first example will get bias class 1 -0.2 from first tree and
+        # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
+        # the second example will get the same bias class 1 -0.2 and leaf 3
+        # payload of class 1 1.2 hence [0.0, 1.0].
+        self.assertEqual(stamp_token2.eval(), 9)
+        self.assertEqual(serialized_config2.eval(), serialized_config)
+        self.assertAllClose(result.eval(), [[0.5, -0.2], [0, 1.0]])
+
+  def testRestore(self):
+    # Calling self.test_session() without a graph specified results in
+    # TensorFlowTestCase caching the session and returning the same one
+    # every time. In this test, we need to create two different sessions
+    # which is why we also create a graph and pass it to self.test_session()
+    # to ensure no caching occurs under the hood.
+    save_path = os.path.join(self.get_temp_dir(), "restore-test")
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph) as sess:
+        tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.1)
+
+        tree_ensemble_config2 = tree_config_pb2.DecisionTreeEnsembleConfig()
+        tree2 = tree_ensemble_config2.trees.add()
+        tree_ensemble_config.tree_weights.append(1.0)
+        _append_to_leaf(tree2.nodes.add().leaf, 0, -1.0)
+
+        tree_ensemble_config3 = tree_config_pb2.DecisionTreeEnsembleConfig()
+        tree3 = tree_ensemble_config3.trees.add()
+        tree_ensemble_config.tree_weights.append(1.0)
+        _append_to_leaf(tree3.nodes.add().leaf, 0, -10.0)
+
+        # Prepare learner config.
+        learner_config = learner_pb2.LearnerConfig()
+        learner_config.num_classes = 2
+
+        tree_ensemble_handle = model_ops.tree_ensemble_variable(
+            stamp_token=3,
+            tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+            name="restore_tree")
+        feature_usage_counts = variables.Variable(
+            initial_value=array_ops.zeros([1], dtypes.int64),
+            name="feature_usage_counts",
+            trainable=False)
+        feature_gains = variables.Variable(
+            initial_value=array_ops.zeros([1], dtypes.float32),
+            name="feature_gains",
+            trainable=False)
+
+        resources.initialize_resources(resources.shared_resources()).run()
+        variables.initialize_all_variables().run()
+        my_saver = saver.Saver()
+
+        with ops.control_dependencies([
+            ensemble_optimizer_ops.add_trees_to_ensemble(
+                tree_ensemble_handle,
+                tree_ensemble_config2.SerializeToString(),
+                feature_usage_counts, [0],
+                feature_gains, [0], [[]],
+                learning_rate=1)
+        ]):
+          result, _, _ = prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False)
+        self.assertAllClose([[-1.1], [-1.1]], result.eval())
+        # Save before adding other trees.
+        val = my_saver.save(sess, save_path)
+        self.assertEqual(save_path, val)
+
+        # Add more trees after saving.
+        with ops.control_dependencies([
+            ensemble_optimizer_ops.add_trees_to_ensemble(
+                tree_ensemble_handle,
+                tree_ensemble_config3.SerializeToString(),
+                feature_usage_counts, [0],
+                feature_gains, [0], [[]],
+                learning_rate=1)
+        ]):
+          result, _, _ = prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False)
+        self.assertAllClose(result.eval(), [[-11.1], [-11.1]])
+
+    # Start a second session.  In that session the parameter nodes
+    # have not been initialized either.
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph) as sess:
+        tree_ensemble_handle = model_ops.tree_ensemble_variable(
+            stamp_token=0, tree_ensemble_config="", name="restore_tree")
+        my_saver = saver.Saver()
+        my_saver.restore(sess, save_path)
+        result, _, _ = prediction_ops.gradient_trees_prediction(
+            tree_ensemble_handle,
+            self._seed, [self._dense_float_tensor], [
+                self._sparse_float_indices1, self._sparse_float_indices2
+            ], [self._sparse_float_values1, self._sparse_float_values2],
+            [self._sparse_float_shape1,
+             self._sparse_float_shape2], [self._sparse_int_indices1],
+            [self._sparse_int_values1], [self._sparse_int_shape1],
+            learner_config=learner_config.SerializeToString(),
+            apply_dropout=False,
+            apply_averaging=False,
+            center_bias=False)
+        # Make sure we only have the first and second tree.
+        # The third tree was added after the save.
+        self.assertAllClose(result.eval(), [[-1.1], [-1.1]])
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
new file mode 100644
index 0000000000..aa665d95d7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
@@ -0,0 +1,1335 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow prediction Ops.
+
+The tests cover tree traversal and additive models for single and
+multi class problems.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.ops import prediction_ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+
+
+def _append_to_leaf(leaf, c_id, w):
+  """Helper method for building tree leaves.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to.
+    c_id: class Id for the weight update.
+    w: weight contribution value.
+  """
+  leaf.sparse_vector.index.append(c_id)
+  leaf.sparse_vector.value.append(w)
+
+
+def _append_multi_values_to_leaf(leaf, c_ids, w):
+  """Helper method for building tree leaves with sparse vector of values.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to.
+    c_ids: list of class ids
+    w: corresponding weight contributions for the classes in c_ids
+  """
+  for i in range(len(c_ids)):
+    leaf.sparse_vector.index.append(c_ids[i])
+    leaf.sparse_vector.value.append(w[i])
+
+
+def _append_multi_values_to_dense_leaf(leaf, w):
+  """Helper method for building tree leaves with dense vector of values.
+
+  Appends weight contributions to a leaf. w is assumed to be for all classes.
+
+  Args:
+    leaf: leaf node to append to.
+    w: corresponding weight contributions for all classes.
+  """
+  for x in w:
+    leaf.vector.value.append(x)
+
+
+def _set_float_split(split, feat_col, thresh, l_id, r_id):
+  """Helper method for building tree float splits.
+
+  Sets split feature column, threshold and children.
+
+  Args:
+    split: split node to update.
+    feat_col: feature column for the split.
+    thresh: threshold to split on forming rule x <= thresh.
+    l_id: left child Id.
+    r_id: right child Id.
+  """
+  split.feature_column = feat_col
+  split.threshold = thresh
+  split.left_id = l_id
+  split.right_id = r_id
+
+
+def _set_categorical_id_split(split, feat_col, feat_id, l_id, r_id):
+  """Helper method for building tree categorical id splits.
+
+  Sets split feature column, feature id and children.
+
+  Args:
+    split: categorical id split node.
+    feat_col: feature column for the split.
+    feat_id: feature id forming rule x == id.
+    l_id: left child Id.
+    r_id: right child Id.
+  """
+  split.feature_column = feat_col
+  split.feature_id = feat_id
+  split.left_id = l_id
+  split.right_id = r_id
+
+
+class PredictionOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up the prediction tests.
+
+    Create a batch of two examples having one dense float, two sparse float and
+    one sparse int features.
+    The data looks like the following:
+    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 |
+    | 0        |  7     |    -3    |          |    9,1   |
+    | 1        | -2     |          | 4        |          |
+    """
+    super(PredictionOpsTest, self).setUp()
+    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._sparse_float_indices1 = np.array([[0, 0]])
+    self._sparse_float_values1 = np.array([-3.0])
+    self._sparse_float_shape1 = np.array([2, 1])
+    self._sparse_float_indices2 = np.array([[1, 0]])
+    self._sparse_float_values2 = np.array([4.0])
+    self._sparse_float_shape2 = np.array([2, 1])
+    self._sparse_int_indices1 = np.array([[0, 0], [0, 1]])
+    self._sparse_int_values1 = np.array([9, 1])
+    self._sparse_int_shape1 = np.array([2, 2])
+    self._seed = 123
+
+  def testEmptyEnsemble(self):
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="empty")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      self.assertAllEqual([[0], [0]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testBiasEnsembleSingleClass(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="bias")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      self.assertAllClose([[-0.4], [-0.4]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testBiasEnsembleMultiClass(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      leaf = tree.nodes.add().leaf
+      _append_to_leaf(leaf, 0, -0.4)
+      _append_to_leaf(leaf, 1, 0.9)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="multiclass")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      self.assertAllClose([[-0.4, 0.9], [-0.4, 0.9]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testFullEnsembleSingleClass(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # The first example will get bias -0.4 from first tree and
+      # leaf 4 payload of -0.9 hence -1.3, the second example will
+      # get the same bias -0.4 and leaf 3 payload (sparse feature missing)
+      # of 1.2 hence 0.8.
+      self.assertAllClose([[-1.3], [0.8]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testExcludeNonFinalTree(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = False
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # All the examples should get only the bias since the second tree is
+      # non-finalized
+      self.assertAllClose([[-0.4], [-0.4]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testIncludeNonFinalTree(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = False
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+      learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # The first example will get bias -0.4 from first tree and
+      # leaf 4 payload of -0.9 hence -1.3, the second example will
+      # get the same bias -0.4 and leaf 3 payload (sparse feature missing)
+      # of 1.2 hence 0.8. Note that the non-finalized tree is included.
+      self.assertAllClose([[-1.3], [0.8]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def testMetadataMissing(self):
+    # Sometimes we want to do prediction on trees that are not added to ensemble
+    # (for example in
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      # We are not setting the tree_ensemble_config.tree_metadata in this test.
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
+      _set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+
+      # The first example will get bias -0.4 from first tree and
+      # leaf 4 payload of -0.9 hence -1.3, the second example will
+      # get the same bias -0.4 and leaf 3 payload (sparse feature missing)
+      # of 1.2 hence 0.8.
+      self.assertAllClose([[-1.3], [0.8]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  # For TREE_PER_CLASS strategy, predictions size is num_classes-1
+  def testFullEnsembleMultiClassTreePerClassStrategy(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree only for second class.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 1, -0.2)
+
+      # Depth 2 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_right.split, 1, 4.0,
+                       1, 2)
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree2.nodes.add().leaf, 1, 1.2)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="ensemble_multi_class")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.TREE_PER_CLASS)
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      # The first example will get bias class 1 -0.2 from first tree and
+      # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
+      # the second example will get the same bias class 1 -0.2 and leaf 3
+      # payload of class 1 1.2 hence [0.0, 1.0].
+      self.assertAllClose([[0.5, -0.2], [0, 1.0]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  # For all non-tree-per class multiclass handling strategies, predictions vec
+  # will have the size of the number of classes.
+  # This test is when leafs have SPARSE weights stored (class id and
+  # contribution).
+  def testFullEnsembleMultiNotClassTreePerClassStrategySparseVector(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree only for second class.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 1, -0.2)
+
+      # Depth 2 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_right.split, 1, 4.0,
+                       1, 2)
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3, 4)
+      _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
+      _append_multi_values_to_leaf(tree2.nodes.add().leaf, [1, 2], [1.2, -0.7])
+      _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="ensemble_multi_class")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.FULL_HESSIAN)
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      # The first example will get bias class 1 -0.2 from first tree and
+      # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
+      # the second example will get the same bias class 1 -0.2 and leaf 3
+      # payload of class 1 1.2 and class 2-0.7 hence [0.0, 1.0, -0.7].
+      self.assertAllClose([[0.5, -0.2, 0.0], [0, 1.0, -0.7]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  # For all non-tree-per class multiclass handling strategies, predictions vec
+  # will have the size of the number of classes.
+  # This test is when leafs have DENSE weights stored (weight for each class)
+  def testFullEnsembleMultiNotClassTreePerClassStrategyDenseVector(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree only for second class.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_multi_values_to_dense_leaf(tree1.nodes.add().leaf, [0, -0.2, -2])
+
+      # Depth 2 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _set_float_split(tree2.nodes.add()
+                       .sparse_float_binary_split_default_right.split, 1, 4.0,
+                       1, 2)
+      _set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3, 4)
+      _append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [0.5, 0, 0])
+      _append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [0, 1.2, -0.7])
+      _append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [-0.9, 0, 0])
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="ensemble_multi_class")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 3
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.FULL_HESSIAN)
+
+      result, result_no_dropout, dropout_info = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=False,
+              apply_averaging=False,
+              center_bias=False))
+      # The first example will get bias class 1 -0.2  and -2 for class 2 from
+      # first tree and leaf 2 payload (sparse feature missing) of 0.5 hence
+      # 0.5, -0.2], the second example will get the same bias and leaf 3 payload
+      # of class 1 1.2 and class 2-0.7 hence [0.0, 1.0, -2.7].
+      self.assertAllClose([[0.5, -0.2, -2.0], [0, 1.0, -2.7]], result.eval())
+      self.assertAllEqual(result_no_dropout.eval(), result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+  def _get_predictions(self,
+                       tree_ensemble_handle,
+                       learner_config,
+                       apply_dropout=False,
+                       apply_averaging=False,
+                       center_bias=False):
+    return prediction_ops.gradient_trees_prediction(
+        tree_ensemble_handle,
+        self._seed, [self._dense_float_tensor], [
+            self._sparse_float_indices1, self._sparse_float_indices2
+        ], [self._sparse_float_values1, self._sparse_float_values2],
+        [self._sparse_float_shape1,
+         self._sparse_float_shape2], [self._sparse_int_indices1],
+        [self._sparse_int_values1], [self._sparse_int_shape1],
+        learner_config=learner_config.SerializeToString(),
+        apply_dropout=apply_dropout,
+        apply_averaging=apply_averaging,
+        center_bias=center_bias)
+
+  def testDropout(self):
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 999):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 0.5
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      # Apply dropout.
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=True,
+          apply_averaging=False,
+          center_bias=False)
+
+      # We expect approx 500 trees were dropped.
+      dropout_info = dropout_info.eval()
+      self.assertIn(dropout_info[0].size, range(400, 601))
+      self.assertEqual(dropout_info[0].size, dropout_info[1].size)
+
+      self.assertEqual(result.eval().size, result_no_dropout.eval().size)
+      for i in range(result.eval().size):
+        self.assertNotEqual(result.eval()[i], result_no_dropout.eval()[i])
+
+      for i in range(dropout_info[0].size):
+        dropped_index = dropout_info[0][i]
+        dropped_weight = dropout_info[1][i]
+        # We constructed the trees so tree number + 1 is the tree weight, so
+        # we can check here the weights for dropped trees.
+        self.assertEqual(dropped_index + 1, dropped_weight)
+
+      # Don't apply dropout.
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=False,
+          apply_averaging=False,
+          center_bias=False)
+
+      # We expect none of the trees were dropped.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
+      self.assertAllEqual(result.eval(), result_no_dropout.eval())
+
+  def testDropoutCenterBiasNoGrowingMeta(self):
+    # This is for normal non-batch mode where ensemble does not contain the tree
+    # that is being built currently.
+    num_trees = 10
+    with self.test_session():
+      # Empty tree ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, num_trees):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      # Drop all the trees.
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 1.0
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=True,
+          apply_averaging=False,
+          center_bias=False)
+
+      result_center, result_no_dropout_center, dropout_info_center = (
+          self._get_predictions(
+              tree_ensemble_handle,
+              learner_config=learner_config,
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=True))
+
+      dropout_info = dropout_info.eval()
+      dropout_info_center = dropout_info_center.eval()
+
+      # With centering, the bias tree is not dropped.
+      num_dropped = dropout_info[0].size
+      self.assertEqual(num_dropped, num_trees)
+      num_dropped_center = dropout_info_center[0].size
+      self.assertEqual(num_dropped_center, num_trees - 1)
+
+      result = result.eval()
+      result_center = result_center.eval()
+      for i in range(result.size):
+        self.assertNotEqual(result[i], result_center[i])
+
+      # First dropped tree is a bias tree 0.
+      self.assertEqual(0, dropout_info[0][0])
+      # Last dropped tree is the last tree.
+      self.assertEqual(num_trees - 1, dropout_info[0][num_dropped - 1])
+
+      # First dropped tree is a tree 1.
+      self.assertEqual(1, dropout_info_center[0][0])
+      # Last dropped tree is the last tree.
+      self.assertEqual(num_trees - 1, dropout_info_center[0][num_dropped_center
+                                                             - 1])
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          result_no_dropout_center.eval())
+
+  def testDropoutCenterBiasWithGrowingMeta(self):
+    # This is batch mode where ensemble already contains the tree that we are
+    # building. This tree should never be dropped.
+    num_trees = 10
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, num_trees):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Add growing metadata to indicate batch mode.
+      tree_ensemble_config.growing_metadata.num_trees_attempted = num_trees
+      tree_ensemble_config.growing_metadata.num_layers_attempted = num_trees
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      # Drop all the trees.
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 1.0
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle,
+          learner_config=learner_config,
+          apply_dropout=True,
+          apply_averaging=False,
+          center_bias=False)
+
+      result_center, result_no_dropout_center, dropout_info_center = (
+          self._get_predictions(
+              tree_ensemble_handle,
+              learner_config=learner_config,
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=True))
+
+      dropout_info = dropout_info.eval()
+      dropout_info_center = dropout_info_center.eval()
+
+      # Last tree is never dropped, the bias tree can be dropped.
+      num_dropped = dropout_info[0].size
+      self.assertEqual(num_dropped, num_trees - 1)
+      num_dropped_center = dropout_info_center[0].size
+      self.assertEqual(num_dropped_center, num_trees - 2)
+
+      result = result.eval()
+      result_center = result_center.eval()
+      for i in range(result.size):
+        self.assertNotEqual(result[i], result_center[i])
+
+      # First dropped tree is a bias tree 0.
+      self.assertEqual(0, dropout_info[0][0])
+      # Last dropped tree is not the last tree (not tree num_trees-1).
+      self.assertNotEqual(num_trees - 1, dropout_info[0][num_dropped - 1])
+      # First dropped tree is a tree 1.
+      self.assertEqual(1, dropout_info_center[0][0])
+      # Last dropped tree is not the last tree in ensemble.
+      self.assertNotEqual(num_trees - 1,
+                          dropout_info_center[0][num_dropped_center - 1])
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          result_no_dropout_center.eval())
+
+  def testDropoutSeed(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Add 10 trees with some weights.
+      for i in range(0, 999):
+        tree = tree_ensemble_config.trees.add()
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+        tree_ensemble_config.tree_weights.append(i + 1)
+
+      # Prepare learner/dropout config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.dropout.dropout_probability = 0.5
+      learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
+      learner_config.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="empty")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      _, result_no_dropout_1, dropout_info_1 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=False))
+
+      _, result_no_dropout_2, dropout_info_2 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=False))
+
+      # Different seed.
+      _, result_no_dropout_3, dropout_info_3 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              112314, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=False))
+
+      # First seed with centering bias.
+      _, result_no_dropout_4, dropout_info_4 = (
+          prediction_ops.gradient_trees_prediction(
+              tree_ensemble_handle,
+              self._seed, [self._dense_float_tensor], [
+                  self._sparse_float_indices1, self._sparse_float_indices2
+              ], [self._sparse_float_values1, self._sparse_float_values2],
+              [self._sparse_float_shape1,
+               self._sparse_float_shape2], [self._sparse_int_indices1],
+              [self._sparse_int_values1], [self._sparse_int_shape1],
+              learner_config=learner_config.SerializeToString(),
+              apply_dropout=True,
+              apply_averaging=False,
+              center_bias=True))
+
+      # The same seed returns the same results.
+      self.assertAllEqual(dropout_info_1.eval(), dropout_info_2.eval())
+      # Different seeds give diff results.
+      self.assertNotEqual(dropout_info_3.eval().shape,
+                          dropout_info_2.eval().shape)
+      # With centering bias and the same seed does not give the same result.
+      self.assertNotEqual(dropout_info_4.eval(), dropout_info_1.eval())
+      # With centering bias has 1 less tree dropped (bias tree is not dropped).
+      self.assertEqual(
+          len(dropout_info_4.eval()[0]) + 1, len(dropout_info_1.eval()[0]))
+
+      # Predictions without dropout are all the same.
+      result, result_no_dropout, _ = prediction_ops.gradient_trees_prediction(
+          tree_ensemble_handle,
+          self._seed, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1],
+          learner_config=learner_config.SerializeToString(),
+          apply_dropout=False,
+          apply_averaging=False,
+          center_bias=False)
+
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_1.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_2.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_3.eval())
+      self.assertAllCloseAccordingToType(result.eval(),
+                                         result_no_dropout_4.eval())
+
+  def testAveragingAllTrees(self):
+    with self.test_session():
+      # Empty tree ensenble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      adjusted_tree_ensemble_config = (
+          tree_config_pb2.DecisionTreeEnsembleConfig())
+      # Add 100 trees with some weights.
+      # When averaging is applied, the tree weights will essentially change to
+      # 1, 98/99, 97/99 etc, so lets create the ensemble with such weights.
+      # too
+      total_num = 100
+      for i in range(0, total_num):
+        tree = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+        # This is how the weight will look after averaging
+        copy_tree = adjusted_tree_ensemble_config.trees.add()
+        _append_to_leaf(copy_tree.nodes.add().leaf, 0, -0.4)
+
+        adjusted_tree_ensemble_config.tree_metadata.add().is_finalized = True
+        adjusted_tree_ensemble_config.tree_weights.append(
+            1.0 * (total_num - i) / total_num)
+
+      # Prepare learner config WITH AVERAGING.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+      learner_config.averaging_config.average_last_percent_trees = 1.0
+
+      # No averaging config.
+      learner_config_no_averaging = learner_pb2.LearnerConfig()
+      learner_config_no_averaging.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+
+      # This is how our ensemble will "look" during averaging
+      adjusted_tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=adjusted_tree_ensemble_config.SerializeToString(
+          ),
+          name="adjusted")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Do averaging.
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle, learner_config, apply_averaging=True)
+
+      pattern_result, pattern_result_no_dropout, pattern_dropout_info = (
+          self._get_predictions(
+              adjusted_tree_ensemble_handle,
+              learner_config_no_averaging,
+              apply_averaging=False))
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          pattern_result_no_dropout.eval())
+      self.assertAllEqual(result.eval(), pattern_result.eval())
+      self.assertAllEqual(dropout_info.eval(), pattern_dropout_info.eval())
+
+  def testAveragingSomeTrees(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      adjusted_tree_ensemble_config = (
+          tree_config_pb2.DecisionTreeEnsembleConfig())
+      # Add 1000 trees with some weights.
+      total_num = 100
+      num_averaged = 25
+      j = 0
+      for i in range(0, total_num):
+        tree = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+
+        # This is how the weight will look after averaging - we are adjusting
+        # the weights of the last 25 trees
+        copy_tree = adjusted_tree_ensemble_config.trees.add()
+        _append_to_leaf(copy_tree.nodes.add().leaf, 0, -0.4)
+
+        adjusted_tree_ensemble_config.tree_metadata.add().is_finalized = True
+        if i >= 75:
+          adjusted_tree_ensemble_config.tree_weights.append(
+              1.0 * (num_averaged - j) / num_averaged)
+          j += 1
+        else:
+          adjusted_tree_ensemble_config.tree_weights.append(1.0)
+
+      # Prepare learner config WITH AVERAGING.
+      learner_config_1 = learner_pb2.LearnerConfig()
+      learner_config_1.num_classes = 2
+      learner_config_1.averaging_config.average_last_percent_trees = 0.25
+
+      # This is equivalent.
+      learner_config_2 = learner_pb2.LearnerConfig()
+      learner_config_2.num_classes = 2
+      learner_config_2.averaging_config.average_last_n_trees = 25
+
+      # No averaging config.
+      learner_config_no_averaging = learner_pb2.LearnerConfig()
+      learner_config_no_averaging.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+
+      # This is how our ensemble will "look" during averaging
+      adjusted_tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=adjusted_tree_ensemble_config.SerializeToString(
+          ),
+          name="adjusted")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result_1, result_no_dropout_1, dropout_info_1 = self._get_predictions(
+          tree_ensemble_handle, learner_config_1, apply_averaging=True)
+
+      result_2, result_no_dropout_2, dropout_info_2 = self._get_predictions(
+          tree_ensemble_handle, learner_config_2, apply_averaging=True)
+
+      pattern_result, pattern_result_no_dropout, pattern_dropout_info = (
+          self._get_predictions(
+              adjusted_tree_ensemble_handle,
+              learner_config_no_averaging,
+              apply_averaging=False))
+
+      self.assertAllEqual(result_no_dropout_1.eval(),
+                          pattern_result_no_dropout.eval())
+      self.assertAllEqual(result_no_dropout_2.eval(),
+                          pattern_result_no_dropout.eval())
+
+      self.assertAllEqual(result_1.eval(), pattern_result.eval())
+      self.assertAllEqual(result_2.eval(), pattern_result.eval())
+
+      self.assertAllEqual(dropout_info_1.eval(), pattern_dropout_info.eval())
+      self.assertAllEqual(dropout_info_2.eval(), pattern_dropout_info.eval())
+
+  def testAverageMoreThanNumTreesExist(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      adjusted_tree_ensemble_config = (
+          tree_config_pb2.DecisionTreeEnsembleConfig())
+      # When we say to average over more trees than possible, it is averaging
+      # accross all trees.
+      total_num = 100
+      for i in range(0, total_num):
+        tree = tree_ensemble_config.trees.add()
+        _append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
+
+        tree_ensemble_config.tree_metadata.add().is_finalized = True
+        tree_ensemble_config.tree_weights.append(1.0)
+        # This is how the weight will look after averaging
+        copy_tree = adjusted_tree_ensemble_config.trees.add()
+        _append_to_leaf(copy_tree.nodes.add().leaf, 0, -0.4)
+
+        adjusted_tree_ensemble_config.tree_metadata.add().is_finalized = True
+        adjusted_tree_ensemble_config.tree_weights.append(
+            1.0 * (total_num - i) / total_num)
+
+      # Prepare learner config WITH AVERAGING.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+      # We have only 100 trees but we ask to average over 250.
+      learner_config.averaging_config.average_last_n_trees = 250
+
+      # No averaging config.
+      learner_config_no_averaging = learner_pb2.LearnerConfig()
+      learner_config_no_averaging.num_classes = 2
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="existing")
+
+      # This is how our ensemble will "look" during averaging
+      adjusted_tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=adjusted_tree_ensemble_config.SerializeToString(
+          ),
+          name="adjusted")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result, result_no_dropout, dropout_info = self._get_predictions(
+          tree_ensemble_handle, learner_config, apply_averaging=True)
+
+      pattern_result, pattern_result_no_dropout, pattern_dropout_info = (
+          self._get_predictions(
+              adjusted_tree_ensemble_handle,
+              learner_config_no_averaging,
+              apply_averaging=False))
+
+      self.assertAllEqual(result_no_dropout.eval(),
+                          pattern_result_no_dropout.eval())
+      self.assertAllEqual(result.eval(), pattern_result.eval())
+      self.assertAllEqual(dropout_info.eval(), pattern_dropout_info.eval())
+
+
+class PartitionExamplesOpsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up the prediction tests.
+
+    Create a batch of two examples having one dense float, two sparse float and
+    one sparse int features.
+    The data looks like the following:
+    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 |
+    | 0        |  7     |    -3    |          |    9,1   |
+    | 1        | -2     |          | 4        |          |
+    """
+    super(PartitionExamplesOpsTest, self).setUp()
+    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._sparse_float_indices1 = np.array([[0, 0]])
+    self._sparse_float_values1 = np.array([-3.0])
+    self._sparse_float_shape1 = np.array([2, 1])
+    self._sparse_float_indices2 = np.array([[1, 0]])
+    self._sparse_float_values2 = np.array([4.0])
+    self._sparse_float_shape2 = np.array([2, 1])
+    self._sparse_int_indices1 = np.array([[0, 0], [0, 1]])
+    self._sparse_int_values1 = np.array([9, 1])
+    self._sparse_int_shape1 = np.array([2, 2])
+
+  def testEnsembleEmpty(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result = prediction_ops.gradient_trees_partition_examples(
+          tree_ensemble_handle, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1])
+
+      self.assertAllEqual([0, 0], result.eval())
+
+  def testTreeNonFinalized(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Depth 3 tree.
+      tree1 = tree_ensemble_config.trees.add()
+      _set_float_split(tree1.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree1.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.2)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.3)
+      _set_categorical_id_split(tree1.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.6)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_metadata.add().is_finalized = False
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result = prediction_ops.gradient_trees_partition_examples(
+          tree_ensemble_handle, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1])
+
+      self.assertAllEqual([5, 3], result.eval())
+
+  def testTreeFinalized(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Depth 3 tree.
+      tree1 = tree_ensemble_config.trees.add()
+      _set_float_split(tree1.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
+      _set_float_split(tree1.nodes.add()
+                       .sparse_float_binary_split_default_left.split, 0, -20.0,
+                       3, 4)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.2)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.3)
+      _set_categorical_id_split(tree1.nodes.add().categorical_id_binary_split,
+                                0, 9, 5, 6)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree1.nodes.add().leaf, 0, 0.6)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result = prediction_ops.gradient_trees_partition_examples(
+          tree_ensemble_handle, [self._dense_float_tensor], [
+              self._sparse_float_indices1, self._sparse_float_indices2
+          ], [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1,
+           self._sparse_float_shape2], [self._sparse_int_indices1],
+          [self._sparse_int_values1], [self._sparse_int_shape1])
+
+      self.assertAllEqual([0, 0], result.eval())
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
new file mode 100644
index 0000000000..dc4495fa1f
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
@@ -0,0 +1,341 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for checking quantile related ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import os
+import tempfile
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.proto.quantiles_pb2 import QuantileConfig
+from tensorflow.contrib.boosted_trees.python.ops import quantile_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import saver
+
+
+class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
+
+  def _gen_config(self, eps, num_quantiles):
+    config = QuantileConfig()
+    config.eps = eps
+    config.num_quantiles = num_quantiles
+    return config.SerializeToString()
+
+  def testBasicQuantileBuckets(self):
+    """Sets up the quantile summary op test as follows.
+
+    Create a batch of 6 examples having a dense and sparse features.
+    The data looks like this
+    | Instance | instance weights | Dense 0  | Sparse 0
+    | 0        |     10           |   1      |
+    | 1        |     1            |   2      |    2
+    | 2        |     1            |   3      |    3
+    | 3        |     1            |   4      |    4
+    | 4        |     1            |   4      |    5
+    | 5        |     1            |   5      |    6
+    """
+
+    dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
+    sparse_indices_0 = np.array(
+        [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=np.int64)
+    sparse_values_0 = np.array([2, 3, 4, 5, 6])
+    sparse_shape_0 = np.array([6, 1])
+    example_weights = np.array([10, 1, 1, 1, 1, 1])
+
+    with self.test_session():
+      config = self._gen_config(0.33, 3)
+      dense_buckets, sparse_buckets = quantile_ops.quantile_buckets(
+          [dense_float_tensor_0], [sparse_indices_0], [sparse_values_0],
+          [sparse_shape_0],
+          example_weights=example_weights,
+          dense_config=[config],
+          sparse_config=[config])
+
+      self.assertAllEqual([1, 3, 5], dense_buckets[0].eval())
+      self.assertAllEqual([2, 4, 6.], sparse_buckets[0].eval())
+
+  def testStreamingQuantileBuckets(self):
+    """Sets up the quantile summary op test as follows.
+
+    Create a batch of 6 examples having a dense and sparse features.
+    The data looks like this
+    | Instance | instance weights | Dense 0
+    | 0        |     10           |   1
+    | 1        |     1            |   2
+    | 2        |     1            |   3
+    | 3        |     1            |   4
+    | 4        |     1            |   4
+    | 5        |     1            |   5
+    """
+    dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
+    example_weights = np.array([10, 1, 1, 1, 1, 1])
+
+    with self.test_session() as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q1")
+
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      are_ready_noflush, _, = (accumulator.get_buckets(stamp_token=0))
+
+      update = accumulator.add_summary(
+          stamp_token=0,
+          column=dense_float_tensor_0,
+          example_weights=example_weights)
+      with ops.control_dependencies([are_ready_noflush, update]):
+        reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_noflush, are_ready_flush = (sess.run(
+          [buckets, are_ready_noflush, are_ready_flush]))
+      self.assertEqual(False, are_ready_noflush)
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([1, 3, 5], buckets)
+
+  def testSaveRestoreBeforeFlush(self):
+    save_dir = os.path.join(self.get_temp_dir(), "save_restore")
+    save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+
+      save = saver.Saver()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      sparse_indices_0 = np.array(
+          [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=np.int64)
+      sparse_values_0 = [2.0, 3.0, 4.0, 5.0, 6.0]
+      sparse_shape_0 = np.array([6, 1])
+      example_weights = np.array([10, 1, 1, 1, 1, 1])
+      update = accumulator.add_summary(
+          stamp_token=0,
+          column=sparse_tensor.SparseTensor(sparse_indices_0, sparse_values_0,
+                                            sparse_shape_0),
+          example_weights=example_weights)
+      update.run()
+      save.save(sess, save_path)
+      reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([2, 4, 6.], buckets)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+      save = saver.Saver()
+
+      # Restore the saved values in the parameter nodes.
+      save.restore(sess, save_path)
+      are_ready_noflush = accumulator.get_buckets(stamp_token=0)[0]
+      with ops.control_dependencies([are_ready_noflush]):
+        reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = accumulator.get_buckets(stamp_token=1)
+      buckets, are_ready_flush, are_ready_noflush = (sess.run(
+          [buckets, are_ready_flush, are_ready_noflush]))
+      self.assertFalse(are_ready_noflush)
+      self.assertTrue(are_ready_flush)
+      self.assertAllEqual([2, 4, 6.], buckets)
+
+  def testSaveRestoreAfterFlush(self):
+    save_dir = os.path.join(self.get_temp_dir(), "save_restore")
+    save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+
+      save = saver.Saver()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      example_weights = np.array([10, 1, 1, 1, 1, 1])
+      dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
+      update = accumulator.add_summary(
+          stamp_token=0,
+          column=dense_float_tensor_0,
+          example_weights=example_weights)
+      update.run()
+      reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
+      with ops.control_dependencies([reset]):
+        are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([1, 3, 5], buckets)
+      save.save(sess, save_path)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
+      save = saver.Saver()
+
+      # Restore the saved values in the parameter nodes.
+      save.restore(sess, save_path)
+      are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([1, 3, 5], buckets)
+
+  def testFixedUniform(self):
+    """Sets up the quantile summary op test as follows.
+
+    Creates array dividing range [0, 1] to 1<<16 elements equally spaced
+    with weight of 1.0.
+    """
+    dense_float_tensor_0 = np.array([(1.0 * i) / math.pow(
+        2.0, 16) for i in range(0, int(math.pow(2, 16)) + 1)])
+    example_weights = np.array([1] * (int(math.pow(2, 16)) + 1))
+    config = self._gen_config(0.1, 10)
+
+    with self.test_session():
+      dense_buckets, _ = quantile_ops.quantile_buckets(
+          [dense_float_tensor_0], [], [], [],
+          example_weights=example_weights,
+          dense_config=[config],
+          sparse_config=[])
+      self.assertAllClose(
+          [0] + [(i + 1.0) / 10 for i in range(0, 10)],
+          dense_buckets[0].eval(),
+          atol=0.1)
+
+  def testFixedNonUniform(self):
+    """Sets up the quantile summary op test as follows.
+
+    Creates array dividing range [0, 1] to 1<<16 elements equally spaced
+    with weight same as the value.
+    """
+    dense_float_tensor_0 = np.array([(1.0 * i) / math.pow(
+        2.0, 16) for i in range(0, int(math.pow(2, 16)) + 1)])
+    example_weights = np.array([(1.0 * i) / math.pow(2.0, 16)
+                                for i in range(0, int(math.pow(2, 16)) + 1)])
+
+    config = self._gen_config(0.1, 10)
+
+    with self.test_session():
+      dense_buckets, _ = quantile_ops.quantile_buckets(
+          [dense_float_tensor_0], [], [], [],
+          example_weights=example_weights,
+          dense_config=[config],
+          sparse_config=[])
+      self.assertAllClose(
+          [0] + [math.sqrt((i + 1.0) / 10) for i in range(0, 10)],
+          dense_buckets[0].eval(),
+          atol=0.1)
+
+
+class QuantilesOpTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    """Sets up the quantile op tests.
+
+    Create a batch of 4 examples having 2 dense and 3 sparse features.
+    The data looks like this
+    | Instance | Dense 0 | Dense 1 | Sparse 0 | Sparse 1 | Sparse 2
+    | 0        |   -0.1  |  -1     |   -2     |   0.1    |
+    | 1        |    0.4  |  -15    |   5.5    |          |   2
+    | 2        |    3.2  |  18     |   16     |   3      |
+    | 3        |    190  |  1000   |   17.5   |  -3      |   4
+    Quantiles are:
+    Dense 0: (-inf,0.4], (0.4,5], (5, 190]
+    Dense 1: (-inf, -9], (-9,15], (15, 1000)
+    Sparse 0: (-inf, 5], (5,16], (16, 100]
+    Sparse 1: (-inf, 2], (2, 5]
+    Sparse 2: (-inf, 100]
+    """
+    super(QuantilesOpTest, self).setUp()
+    self._dense_float_tensor_0 = np.array([[-0.1], [0.4], [3.2], [190]])
+    self._dense_float_tensor_1 = np.array([[-1], [-15], [18], [1000]])
+
+    # Sparse feature 0
+    self._sparse_indices_0 = np.array([[0, 0], [1, 0], [2, 0], [3, 0]])
+    self._sparse_values_0 = np.array([-2, 5.5, 16, 17.5])
+    self._sparse_shape_0 = np.array([4, 1])
+    # Sprase feature 1
+    self._sparse_indices_1 = np.array([[0, 0], [2, 0], [3, 0]])
+    self._sparse_values_1 = np.array([0.1, 3, -3])
+    self._sparse_shape_1 = np.array([4, 1])
+    # Sprase feature 2
+    self._sparse_indices_2 = np.array([[1, 0], [3, 0]])
+    self._sparse_values_2 = np.array([2, 4])
+    self._sparse_shape_2 = np.array([4, 1])
+    # Quantiles
+    self._dense_thresholds_0 = np.array([0.4, 5, 190])
+    self._dense_thresholds_1 = np.array([-9, 15, 1000])
+
+    self._sparse_thresholds_0 = np.array([5, 16, 100])
+    self._sparse_thresholds_1 = np.array([2, 5])
+    self._sparse_thresholds_2 = np.array([100])
+
+  def testDenseFeaturesOnly(self):
+    with self.test_session():
+      dense_quantiles, _ = quantile_ops.quantiles(
+          [self._dense_float_tensor_0, self._dense_float_tensor_1], [],
+          [self._dense_thresholds_0, self._dense_thresholds_1], [])
+
+      # Dense feature 0
+      self.assertAllEqual([0, 0, 1, 2], dense_quantiles[0].eval())
+      # Dense feature 1
+      self.assertAllEqual([1, 0, 2, 2], dense_quantiles[1].eval())
+
+  def testSparseFeaturesOnly(self):
+    with self.test_session():
+      _, sparse_quantiles = quantile_ops.quantiles(
+          [],
+          [self._sparse_values_0, self._sparse_values_1, self._sparse_values_2],
+          [], [self._sparse_thresholds_0, self._sparse_thresholds_1,
+               self._sparse_thresholds_2])
+
+      # Sparse feature 0
+      self.assertAllEqual([0, 1, 1, 2], sparse_quantiles[0].eval())
+      # Sparse feature 1
+      self.assertAllEqual([0, 1, 0], sparse_quantiles[1].eval())
+      # Sparse feature 2
+      self.assertAllEqual([0, 0], sparse_quantiles[2].eval())
+
+  def testDenseAndSparseFeatures(self):
+    with self.test_session():
+      dense_quantiles, sparse_quantiles = quantile_ops.quantiles(
+          [self._dense_float_tensor_0, self._dense_float_tensor_1],
+          [self._sparse_values_0, self._sparse_values_1, self._sparse_values_2],
+          [self._dense_thresholds_0, self._dense_thresholds_1],
+          [self._sparse_thresholds_0, self._sparse_thresholds_1,
+           self._sparse_thresholds_2])
+
+      # Dense feature 0
+      self.assertAllEqual([0, 0, 1, 2], dense_quantiles[0].eval())
+      # Dense feature 1
+      self.assertAllEqual([1, 0, 2, 2], dense_quantiles[1].eval())
+      # Sparse feature 0
+      self.assertAllEqual([0, 1, 1, 2], sparse_quantiles[0].eval())
+      # Sparse feature 1
+      self.assertAllEqual([0, 1, 0], sparse_quantiles[1].eval())
+      # Sparse feature 2
+      self.assertAllEqual([0, 0], sparse_quantiles[2].eval())
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
new file mode 100644
index 0000000000..3ab02db89a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
@@ -0,0 +1,329 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow split handler Ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.proto import split_info_pb2
+from tensorflow.contrib.boosted_trees.python.ops import split_handler_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import googletest
+
+
+class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
+
+  def testMakeDenseSplit(self):
+    """Tests split handler op."""
+    with self.test_session() as sess:
+      # The data looks like the following after dividing by number of steps (2).
+      # Gradients    | Partition | Dense Quantile |
+      # (1.2, 0.2)   | 0         | 0              |
+      # (-0.3, 0.19) | 0         | 1              |
+      # (4.0, 0.13)  | 1         | 1              |
+      partition_ids = array_ops.constant([0, 0, 1], dtype=dtypes.int32)
+      bucket_ids = array_ops.constant([0, 1, 1], dtype=dtypes.int64)
+      gradients = array_ops.constant([2.4, -0.6, 8.0])
+      hessians = array_ops.constant([0.4, 0.38, 0.26])
+      bucket_boundaries = [0.3, 0.52]
+      partitions, gains, splits = (
+          split_handler_ops.build_dense_inequality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              bucket_ids=bucket_ids,
+              gradients=gradients,
+              hessians=hessians,
+              bucket_boundaries=bucket_boundaries,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the split on partition 0.
+    # -(1.2 - 0.1) / (0.2 + 1)
+    expected_left_weight = -0.91666
+
+    # expected_left_weight * -(1.2 - 0.1)
+    expected_left_gain = 1.0083333333333331
+
+    # (-0.3 + 0.1) / (0.19 + 1)
+    expected_right_weight = 0.1680672
+
+    # expected_right_weight * -(-0.3 + 0.1)
+    expected_right_gain = 0.033613445378151252
+
+    # (-0.3 + 1.2 - 0.1) ** 2 / (0.19 + 0.2 + 1)
+    expected_bias_gain = 0.46043165467625885
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0],
+        0.00001)
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+    self.assertEqual(0, split_node.feature_column)
+    self.assertAllClose(0.3, split_node.threshold, 0.00001)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (0.13 + 1)
+    expected_left_weight = -3.4513274336283186
+    expected_right_weight = 0
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    # There's only one active bucket here so zero gain is expected.
+    self.assertAllClose(0.0, gains[1], 0.00001)
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+    self.assertEqual(0, split_node.feature_column)
+    self.assertAllClose(0.52, split_node.threshold, 0.00001)
+
+  def testMakeDenseSplitEmptyInputs(self):
+    """Tests empty inputs op."""
+    with self.test_session() as sess:
+      partition_ids = array_ops.constant([], dtype=dtypes.int32)
+      bucket_ids = array_ops.constant([], dtype=dtypes.int64)
+      gradients = array_ops.constant([])
+      hessians = array_ops.constant([])
+      bucket_boundaries = [0.3, 0.52]
+      partitions, gains, splits = (
+          split_handler_ops.build_dense_inequality_splits(
+              num_minibatches=0,
+              partition_ids=partition_ids,
+              bucket_ids=bucket_ids,
+              gradients=gradients,
+              hessians=hessians,
+              bucket_boundaries=bucket_boundaries,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    # .assertEmpty doesn't exist on ubuntu-contrib
+    self.assertEqual(0, len(partitions))
+    self.assertEqual(0, len(gains))
+    self.assertEqual(0, len(splits))
+
+  def testMakeSparseSplit(self):
+    """Tests split handler op."""
+    with self.test_session() as sess:
+      # The data looks like the following after dividing by number of steps (2).
+      # Gradients    | Partition | bucket ID       |
+      # (0.9, 0.39)  | 0         | -1              |
+      # (1.2, 0.2)   | 0         | 0               |
+      # (0.2, 0.12)  | 0         | 1               |
+      # (4.0, 0.13)  | 1         | -1              |
+      # (4.0, 0.13)  | 1         | 1               |
+      partition_ids = array_ops.constant([0, 0, 0, 1, 1], dtype=dtypes.int32)
+      bucket_ids = array_ops.constant([-1, 0, 1, -1, 1], dtype=dtypes.int64)
+      gradients = array_ops.constant([1.8, 2.4, 0.4, 8.0, 8.0])
+      hessians = array_ops.constant([0.78, 0.4, 0.24, 0.26, 0.26])
+      bucket_boundaries = array_ops.constant([0.3, 0.52])
+      partitions, gains, splits = (
+          split_handler_ops.build_sparse_inequality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              bucket_ids=bucket_ids,
+              gradients=gradients,
+              hessians=hessians,
+              bucket_boundaries=bucket_boundaries,
+              l1_regularization=0,
+              l2_regularization=2,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              bias_feature_id=-1))
+      partitions, gains, splits = (sess.run([partitions, gains, splits]))
+    self.assertAllEqual([0, 1], partitions)
+    # Check the split on partition 0.
+    # -(0.2 + 1.2) / (0.12 + 0.2 + 2)
+    expected_left_weight = -0.603448275862069
+    # (0.2 + 1.2) ** 2 / (0.12 + 0.2 + 2)
+    expected_left_gain = 0.8448275862068965
+    # 0.5 / (0.07 + 2)
+    expected_right_weight = 0.24154589371980678
+    # 0.5 ** 2 / (0.07 + 2)
+    expected_right_gain = 0.12077294685990339
+    # (0.2 + 1.2 - 0.5) ** 2 /  (0.12 + 0.2 + 0.07 + 2)
+    expected_bias_gain = 0.3389121338912133
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_right
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0])
+
+    self.assertAllClose([expected_left_weight], left_child.value)
+
+    self.assertAllClose([expected_right_weight], right_child.value)
+
+    self.assertEqual(0, split_node.split.feature_column)
+
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+    # Check the split on partition 1.
+    expected_left_weight = -1.8779342723004695
+    expected_right_weight = 0
+
+    # Verify candidate for partition 1, there's only one active bucket here
+    # so zero gain is expected.
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_left
+
+    self.assertAllClose(0.0, gains[1])
+
+    self.assertAllClose([expected_left_weight], left_child.value)
+
+    self.assertAllClose([expected_right_weight], right_child.value)
+
+    self.assertEqual(0, split_node.split.feature_column)
+
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+  def testMakeCategoricalEqualitySplit(self):
+    """Tests split handler op for categorical equality split."""
+    with self.test_session() as sess:
+      # The data looks like the following after dividing by number of steps (2).
+      # Gradients    | Partition | Feature ID     |
+      # (0.9, 0.39)  | 0         | -1             |
+      # (0.2, 0.12)  | 0         | 1              |
+      # (1.4, 0.32)  | 0         | 2              |
+      # (4.0, 0.13)  | 1         | -1             |
+      # (4.0, 0.13)  | 1         | 1              |
+      gradients = [1.8, 0.4, 2.8, 8.0, 8.0]
+      hessians = [0.78, 0.24, 0.64, 0.26, 0.26]
+      partition_ids = [0, 0, 0, 1, 1]
+      feature_ids = array_ops.constant([-1, 1, 2, -1, 1], dtype=dtypes.int64)
+      partitions, gains, splits = (
+          split_handler_ops.build_categorical_equality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              feature_ids=feature_ids,
+              gradients=gradients,
+              hessians=hessians,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              bias_feature_id=-1))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the split on partition 0.
+    # -(0.2 + 1.2 - 0.1) / (0.12 + 0.2 + 1)
+    expected_left_weight = -0.9848484848484846
+
+    # (0.2 + 1.2 - 0.1) ** 2 / (0.12 + 0.2 + 1)
+    expected_left_gain = 1.2803030303030298
+
+    # -(-0.5 + 0.1) / (0.07 + 1)
+    expected_right_weight = 0.37383177570093457
+
+    # (-0.5 + 0.1) ** 2 / (0.07 + 1)
+    expected_right_gain = 0.14953271028037385
+
+    # (0.2 + -0.5 + 1.2 - 0.1) ** 2 / (0.12 + 0.07 + 0.2 + 1)
+    expected_bias_gain = 0.46043165467625885
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertEqual(2, split_node.feature_id)
+
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0],
+        0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (0.13 + 1)
+    expected_left_weight = -3.4513274336283186
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_left_gain = 13.460176991150442
+    expected_right_weight = 0
+    expected_right_gain = 0
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_bias_gain = 13.460176991150442
+
+    # Verify candidate for partition 1, there's only one active feature here
+    # so zero gain is expected.
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+    self.assertAllClose(0.0, gains[1], 0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertEqual(1, split_node.feature_id)
+
+  def testMakeCategoricalEqualitySplitEmptyInput(self):
+    with self.test_session() as sess:
+      gradients = []
+      hessians = []
+      partition_ids = []
+      feature_ids = []
+      partitions, gains, splits = (
+          split_handler_ops.build_categorical_equality_splits(
+              num_minibatches=0,
+              partition_ids=partition_ids,
+              feature_ids=feature_ids,
+              gradients=gradients,
+              hessians=hessians,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              bias_feature_id=-1))
+      partitions, gains, splits = (sess.run([partitions, gains, splits]))
+    self.assertEqual(0, len(partitions))
+    self.assertEqual(0, len(gains))
+    self.assertEqual(0, len(splits))
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py
new file mode 100644
index 0000000000..0022d4ad52
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py
@@ -0,0 +1,389 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for checking stats accumulator related ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import stats_accumulator_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import googletest
+
+
+class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase):
+  """Tests for scalar gradients and hessians accumulator."""
+
+  def testSimpleAcculumator(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+        op2 = accumulator.add(0, [1], [2], [0.1], [0.2])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 2)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)], [0.2, 0.4])
+      self.assertAllClose(result[(2, 3)], [0.3, 0.4])
+
+  def testDropStaleUpdate(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+        op2 = accumulator.add(
+            stamp_token=-1,
+            partition_ids=[1],
+            feature_ids=[2],
+            gradients=[0.1],
+            hessians=[0.2])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 1)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)], [0.1, 0.2])
+      self.assertAllClose(result[(2, 3)], [0.3, 0.4])
+
+  def testSerialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+
+      with ops.control_dependencies([op1]):
+        (stamp_token, num_updates, partition_1, feature_1, grads_1,
+         hessians_1) = accumulator.serialize()
+      # Make sure that the accumulator hasn't changed during serialization.
+      with ops.control_dependencies([stamp_token]):
+        num_updates_2, partition_2, feature_2, grads_2, hessians_2 = (
+            accumulator.flush(stamp_token=0, next_stamp_token=1))
+        (stamp_token, num_updates, partition_1, feature_1, grads_1, hessians_1,
+         num_updates_2, partition_2, feature_2, grads_2, hessians_2) = sess.run(
+             [
+                 stamp_token, num_updates, partition_1, feature_1, grads_1,
+                 hessians_1, num_updates_2, partition_2, feature_2, grads_2,
+                 hessians_2
+             ])
+
+      result_1 = _AccumulatorResultToDict(partition_1, feature_1, grads_1,
+                                          hessians_1)
+      result_2 = _AccumulatorResultToDict(partition_2, feature_2, grads_2,
+                                          hessians_2)
+      self.assertEqual(num_updates, 1)
+      self.assertEqual(num_updates_2, 1)
+      self.assertEqual(len(result_1), 2)
+      self.assertAllClose(result_1[(1, 2)], [0.1, 0.2])
+      self.assertAllClose(result_1[(2, 3)], [0.3, 0.4])
+      self.assertAllEqual(result_1, result_2)
+      self.assertEqual(0, stamp_token)
+
+  def testDeserialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      with ops.control_dependencies([accumulator._create_op]):
+        # These will be deleted due to deserialize call.
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            gradients=[0.1, 0.3],
+            hessians=[0.2, 0.4])
+
+      with ops.control_dependencies([op1]):
+        deserialize = (accumulator.deserialize(
+            stamp_token=2,
+            num_updates=3,
+            partition_ids=[3, 4],
+            feature_ids=[5, 6],
+            gradients=[0.4, 0.5],
+            hessians=[0.6, 0.7]))
+      with ops.control_dependencies([deserialize]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=2, next_stamp_token=3)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads,
+                                        hessians)
+      self.assertEqual(num_updates, 3)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(3, 5)], [0.4, 0.6])
+      self.assertAllClose(result[(4, 6)], [0.5, 0.7])
+
+  def testMakeSummary(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar())
+      partition, feature, grads, hessians = accumulator._make_summary(
+          partition_ids=[1, 2, 1],
+          feature_ids=[2, 3, 2],
+          gradients=[0.1, 0.3, 0.1],
+          hessians=[0.2, 0.4, 0.2])
+      partition, feature, grads, hessians = sess.run(
+          [partition, feature, grads, hessians])
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)], [0.2, 0.4])
+      self.assertAllClose(result[(2, 3)], [0.3, 0.4])
+
+
+class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase):
+  """Tests for tensor gradients and hessians accumulator."""
+
+  def testSimpleAcculumator(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+        op2 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1],
+            feature_ids=[2],
+            gradients=[[0.10, 0.11]],
+            hessians=[[[0.011, 0.022], [0.033, 0.044]]])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 2)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)][0], [0.20, 0.21])
+      self.assertAllClose(result[(1, 2)][1], [[0.021, 0.042], [0.063, 0.084]])
+      self.assertAllClose(result[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+  def testDropStaleUpdate(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+        op2 = accumulator.add(
+            stamp_token=-1,
+            partition_ids=[1],
+            feature_ids=[2],
+            gradients=[[0.10, 0.11]],
+            hessians=[[[0.011, 0.022], [0.033, 0.044]]])
+
+      with ops.control_dependencies([op1, op2]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=0, next_stamp_token=1)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(num_updates, 1)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)][0], [0.1, 0.1])
+      self.assertAllClose(result[(1, 2)][1], [[0.01, 0.02], [0.03, 0.04]])
+      self.assertAllClose(result[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+  def testSerialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+
+      with ops.control_dependencies([op1]):
+        (stamp_token, num_updates_1, partition_1, feature_1, grads_1,
+         hessians_1) = accumulator.serialize()
+      # Make sure that the accumulator hasn't changed during serialization.
+      with ops.control_dependencies([stamp_token]):
+        num_updates_2, partition_2, feature_2, grads_2, hessians_2 = (
+            accumulator.flush(stamp_token=0, next_stamp_token=1))
+        (stamp_token, num_updates_1, partition_1, feature_1, grads_1,
+         hessians_1, num_updates_2, partition_2, feature_2, grads_2,
+         hessians_2) = sess.run([
+             stamp_token, num_updates_1, partition_1, feature_1, grads_1,
+             hessians_1, num_updates_2, partition_2, feature_2, grads_2,
+             hessians_2
+         ])
+
+      result_1 = _AccumulatorResultToDict(partition_1, feature_1, grads_1,
+                                          hessians_1)
+      result_2 = _AccumulatorResultToDict(partition_2, feature_2, grads_2,
+                                          hessians_2)
+
+      self.assertEqual(num_updates_1, 1)
+      self.assertEqual(num_updates_2, 1)
+      self.assertEqual(len(result_1), 2)
+      self.assertAllClose(result_1[(1, 2)][0], [0.1, 0.1])
+      self.assertAllClose(result_1[(1, 2)][1], [[0.01, 0.02], [0.03, 0.04]])
+      self.assertAllClose(result_1[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result_1[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+      self.assertAllEqual(result_1[1, 2][0], result_2[1, 2][0])
+      self.assertAllEqual(result_1[1, 2][1], result_2[1, 2][1])
+      self.assertAllEqual(result_1[2, 3][0], result_2[2, 3][0])
+      self.assertAllEqual(result_1[2, 3][1], result_2[2, 3][1])
+
+  def testDeserialize(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      with ops.control_dependencies([accumulator._create_op]):
+        # These will be deleted due to deserialize call.
+        op1 = accumulator.add(
+            stamp_token=0,
+            partition_ids=[1, 2],
+            feature_ids=[2, 3],
+            # Two values for gradients,
+            gradients=[[0.1, 0.1], [0.2, 0.2]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.01, 0.02], [0.03, 0.04]],
+                      [[0.05, 0.06], [0.07, 0.08]]])
+
+      with ops.control_dependencies([op1]):
+        deserialize = accumulator.deserialize(
+            stamp_token=2,
+            num_updates=3,
+            partition_ids=[3, 4],
+            feature_ids=[4, 5],
+            # Two values for gradients,
+            gradients=[[0.3, 0.3], [0.5, 0.5]],
+            # A 2x2 matrix for each hessian.
+            hessians=[[[0.03, 0.04], [0.05, 0.06]], [[0.07, 0.08], [0.09,
+                                                                    0.10]]])
+      with ops.control_dependencies([deserialize]):
+        num_updates, partition, feature, grads, hessians = accumulator.flush(
+            stamp_token=2, next_stamp_token=3)
+        num_updates, partition, feature, grads, hessians = sess.run(
+            [num_updates, partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads,
+                                        hessians)
+      self.assertEqual(num_updates, 3)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(3, 4)][0], [0.3, 0.3])
+      self.assertAllClose(result[(3, 4)][1], [[0.03, 0.04], [0.05, 0.06]])
+      self.assertAllClose(result[(4, 5)][0], [0.5, 0.5])
+      self.assertAllClose(result[(4, 5)][1], [[0.07, 0.08], [0.09, 0.10]])
+
+  def testMakeSummary(self):
+    with self.test_session() as sess:
+      accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.TensorShape([2]),
+          hessian_shape=tensor_shape.TensorShape([2, 2]))
+      partition, feature, grads, hessians = accumulator._make_summary(
+          partition_ids=[1, 2, 1],
+          feature_ids=[2, 3, 2],
+          # Two values for gradients,
+          gradients=[[0.1, 0.1], [0.2, 0.2], [0.10, 0.11]],
+          # A 2x2 matrix for each hessian.
+          hessians=[[[0.01, 0.02], [0.03, 0.04]], [[0.05, 0.06], [0.07, 0.08]],
+                    [[0.011, 0.022], [0.033, 0.044]]])
+      partition, feature, grads, hessians = sess.run(
+          [partition, feature, grads, hessians])
+
+      result = _AccumulatorResultToDict(partition, feature, grads, hessians)
+      self.assertEqual(len(result), 2)
+      self.assertAllClose(result[(1, 2)][0], [0.20, 0.21])
+      self.assertAllClose(result[(1, 2)][1], [[0.021, 0.042], [0.063, 0.084]])
+      self.assertAllClose(result[(2, 3)][0], [0.2, 0.2])
+      self.assertAllClose(result[(2, 3)][1], [[0.05, 0.06], [0.07, 0.08]])
+
+
+def _AccumulatorResultToDict(partition, feature, grads, hessians):
+  """Converts the inputs to a dictionary since the ordering changes."""
+  return {(partition[i], feature[i]): (grads[i], hessians[i])
+          for i in range(len(partition))}
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
new file mode 100644
index 0000000000..f0413fee5a
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
@@ -0,0 +1,1590 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the GTFlow training Ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from google.protobuf import text_format
+
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import split_info_pb2
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.ops import training_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+
+
+def _gen_learner_config(num_classes,
+                        l1_reg,
+                        l2_reg,
+                        tree_complexity,
+                        max_depth,
+                        min_node_weight,
+                        pruning_mode,
+                        growing_mode,
+                        dropout_probability=None,
+                        dropout_learning_rate=None,
+                        dropout_prob_of_skipping=None):
+  """Create a serialized learner config with the desired settings."""
+  config = learner_pb2.LearnerConfig()
+  config.num_classes = num_classes
+  config.regularization.l1 = l1_reg
+  config.regularization.l2 = l2_reg
+  config.regularization.tree_complexity = tree_complexity
+  config.constraints.max_tree_depth = max_depth
+  config.constraints.min_node_weight = min_node_weight
+  config.pruning_mode = pruning_mode
+  config.growing_mode = growing_mode
+
+  if dropout_probability is not None:
+    config.learning_rate_tuner.dropout.dropout_probability = dropout_probability
+  if dropout_learning_rate is not None:
+    config.learning_rate_tuner.dropout.learning_rate = dropout_learning_rate
+  if dropout_prob_of_skipping is not None:
+    config.learning_rate_tuner.dropout.dropout_prob_of_skipping = (
+        dropout_prob_of_skipping)
+  return config.SerializeToString()
+
+
+def _gen_dense_split_info(fc, threshold, left_weight, right_weight):
+  split_str = """
+    split_node {
+      dense_float_binary_split {
+        feature_column: %d
+        threshold: %f
+      }
+    }
+    left_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }
+    right_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }""" % (fc, threshold, left_weight, right_weight)
+  split = split_info_pb2.SplitInfo()
+  text_format.Merge(split_str, split)
+  return split.SerializeToString()
+
+
+def _gen_categorical_split_info(fc, feat_id, left_weight, right_weight):
+  split_str = """
+    split_node {
+      categorical_id_binary_split {
+        feature_column: %d
+        feature_id: %d
+      }
+    }
+    left_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }
+    right_child {
+      sparse_vector {
+        index: 0
+        value: %f
+      }
+    }""" % (fc, feat_id, left_weight, right_weight)
+  split = split_info_pb2.SplitInfo()
+  text_format.Merge(split_str, split)
+  return split.SerializeToString()
+
+
+def _get_bias_update(grads, hess):
+  return array_ops.where(hess > 0, -grads / hess, array_ops.zeros_like(grads))
+
+
+class CenterTreeEnsembleBiasOpTest(test_util.TensorFlowTestCase):
+  """Tests for centering tree ensemble bias."""
+
+  def testCenterBias(self):
+    """Tests bias centering for multiple iterations."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=3,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=4,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          # Dropout does not change anything here.
+          dropout_probability=0.5)
+
+      # Center bias for the initial step.
+      grads = constant_op.constant([0.4, -0.3])
+      hess = constant_op.constant([2.0, 1.0])
+      continue_centering1 = training_ops.center_tree_ensemble_bias(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          delta_updates=_get_bias_update(grads, hess),
+          learner_config=learner_config)
+      continue_centering = session.run(continue_centering1)
+      self.assertEqual(continue_centering, True)
+
+      # Validate ensemble state.
+      # dim 0 update: -0.4/2.0 = -0.2
+      # dim 1 update: +0.3/1.0 = +0.3
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            leaf {
+              vector {
+                value: -0.2
+                value: 0.3
+              }
+            }
+          }
+        }
+        tree_weights: 1.0
+        tree_metadata {
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Center bias for another step.
+      # dim 0 update: -0.06/0.5 = -0.12
+      # dim 1 update: -0.01/0.5 = -0.02
+      grads = constant_op.constant([0.06, 0.01])
+      hess = constant_op.constant([0.5, 0.5])
+      continue_centering2 = training_ops.center_tree_ensemble_bias(
+          tree_ensemble_handle,
+          stamp_token=1,
+          next_stamp_token=2,
+          delta_updates=_get_bias_update(grads, hess),
+          learner_config=learner_config)
+      continue_centering = session.run(continue_centering2)
+      self.assertEqual(continue_centering, True)
+
+      # Validate ensemble state.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=2))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            leaf {
+              vector {
+                value: -0.32
+                value: 0.28
+              }
+            }
+          }
+        }
+        tree_weights: 1.0
+        tree_metadata {
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 2)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Center bias for another step, but this time updates are negligible.
+      grads = constant_op.constant([0.0000001, -0.00003])
+      hess = constant_op.constant([0.5, 0.0])
+      continue_centering3 = training_ops.center_tree_ensemble_bias(
+          tree_ensemble_handle,
+          stamp_token=2,
+          next_stamp_token=3,
+          delta_updates=_get_bias_update(grads, hess),
+          learner_config=learner_config)
+      continue_centering = session.run(continue_centering3)
+      self.assertEqual(continue_centering, False)
+
+      # Validate ensemble stamp.
+      new_stamp, _ = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=3))
+      self.assertEqual(new_stamp, 3)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+
+
+class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase):
+  """Tests for growing tree ensemble from split candidates."""
+
+  def testGrowEmptyEnsemble(self):
+    """Test growing an empty ensemble."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          # Dropout does not change anything here, tree is not finalized.
+          dropout_probability=0.5)
+
+      # Prepare handler inputs.
+      # Note that handlers 1 & 3 have the same gain but different splits.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the simpler split from handler 1 to be chosen.
+      # The grown tree should be finalized as max tree depth is 1.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.52
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowExistingEnsembleTreeNotFinalized(self):
+    """Test growing an existing ensemble with the last tree not finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.61999988556
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.14300012589
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+        }
+        tree_weights: 0.10000000149
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=3,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          # Dropout does not change anything here - tree is not finalized.
+          dropout_probability=0.5)
+
+      # Prepare handler inputs.
+      # Handler 1 only has a candidate for partition 1, handler 2 has candidates
+      # for both partitions and handler 3 only has a candidate for partition 2.
+      handler1_partitions = np.array([1], dtype=np.int32)
+      handler1_gains = np.array([1.4], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.21, -6.0, 1.65)]
+      handler2_partitions = np.array([1, 2], dtype=np.int32)
+      handler2_gains = np.array([0.63, 2.7], dtype=np.float32)
+      handler2_split = [
+          _gen_dense_split_info(0, 0.23, -0.6, 0.24),
+          _gen_categorical_split_info(1, 7, -1.5, 2.3)
+      ]
+      handler3_partitions = np.array([2], dtype=np.int32)
+      handler3_gains = np.array([1.7], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 3, -0.75, 1.93)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split for partition 1 to be chosen from handler 1 and
+      # the split for partition 2 to be chosen from handler 2.
+      # The grown tree should not be finalized as max tree depth is 3 and
+      # it's only grown 2 layers.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.61999988556
+            }
+          }
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.21
+              left_id: 3
+              right_id: 4
+            }
+            node_metadata {
+              gain: 1.4
+            }
+          }
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 1
+              feature_id: 7
+              left_id: 5
+              right_id: 6
+            }
+            node_metadata {
+              gain: 2.7
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -6.0
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 1.65
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -1.5
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 2
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 2)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowExistingEnsembleTreeFinalized(self):
+    """Test growing an existing ensemble with the last tree finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 7
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 1.3
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -0.9
+              }
+            }
+          }
+        }
+        tree_weights: 0.10000000149
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.2,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect a new tree to be added with the split from handler 1.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 7
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 1.3
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -0.9
+              }
+            }
+          }
+        }
+        trees {
+          nodes {
+            dense_float_binary_split {
+              feature_column: 5
+              threshold: 0.52
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_weights: 0.2
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 2
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 2)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 2)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 2)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowEnsemblePrePrune(self):
+    """Test growing an ensemble with pre-pruning."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # All handlers have negative gain.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([-0.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.52, 0.01, 0.0143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([-1.3], dtype=np.float32)
+      handler2_split = [_gen_categorical_split_info(0, 7, 0.013, 0.0143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions, handler2_partitions],
+          gains=[handler1_gains, handler2_gains],
+          splits=[handler1_split, handler2_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the ensemble to be empty.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 0)
+      self.assertEqual(stats.active_tree, 0)
+      self.assertEqual(stats.active_layer, 0)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals("""
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+
+  def testGrowEnsemblePostPruneNone(self):
+    """Test growing an empty ensemble."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # Note that handlers 1 & 3 have the same gain but different splits.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the simpler split from handler 1 to be chosen.
+      # The grown tree should be finalized as max tree depth is 1.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.52
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowEnsemblePostPruneAll(self):
+    """Test growing an ensemble with post-pruning."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=2,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # All handlers have negative gain.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([-1.3], dtype=np.float32)
+      handler1_split = [_gen_categorical_split_info(0, 7, 0.013, 0.0143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([-0.62], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.33, 0.01, 0.0143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions, handler2_partitions],
+          gains=[handler1_gains, handler2_gains],
+          splits=[handler1_split, handler2_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split from handler 2 to be chosen despite the negative gain.
+      # The grown tree should not be finalized as max tree depth is 2 so no
+      # pruning occurs.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      tree_ensemble_config.ParseFromString(serialized)
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.33
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: -0.62
+              original_leaf {
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.01
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.0143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Prepare handler inputs.
+      # All handlers have negative gain.
+      handler1_partitions = np.array([1, 2], dtype=np.int32)
+      handler1_gains = np.array([-0.2, -0.5], dtype=np.float32)
+      handler1_split = [
+          _gen_categorical_split_info(3, 7, 0.07, 0.083),
+          _gen_categorical_split_info(3, 5, 0.041, 0.064)
+      ]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=1,
+          next_stamp_token=2,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions],
+          gains=[handler1_gains],
+          splits=[handler1_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the ensemble to be empty as post-pruning will prune
+      # the entire finalized tree.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=2))
+      tree_ensemble_config.ParseFromString(serialized)
+      self.assertEqual(new_stamp, 2)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 0)
+      self.assertEqual(stats.active_tree, 0)
+      self.assertEqual(stats.active_layer, 0)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals("""
+      growing_metadata {
+        num_trees_attempted: 1
+        num_layers_attempted: 2
+      }
+      """, tree_ensemble_config)
+
+  def testGrowEnsemblePostPrunePartial(self):
+    """Test growing an ensemble with post-pruning."""
+    with self.test_session() as session:
+      # Create empty ensemble.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=2,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
+
+      # Prepare handler inputs.
+      # Second handler has positive gain.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([-1.3], dtype=np.float32)
+      handler1_split = [_gen_categorical_split_info(0, 7, 0.013, 0.0143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([-0.2], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(0, 0.33, 0.01, 0.0143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions, handler2_partitions],
+          gains=[handler1_gains, handler2_gains],
+          splits=[handler1_split, handler2_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split from handler 2 to be chosen despite the negative gain.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.33
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: -0.2
+              original_leaf {
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.01
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.0143
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 1)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 1)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 1)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+      # Prepare handler inputs for second layer.
+      # Note that partition 1 gain is negative and partition 2 gain is positive.
+      handler1_partitions = np.array([1, 2], dtype=np.int32)
+      handler1_gains = np.array([-0.2, 0.5], dtype=np.float32)
+      handler1_split = [
+          _gen_categorical_split_info(3, 7, 0.07, 0.083),
+          _gen_categorical_split_info(3, 5, 0.041, 0.064)
+      ]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=1,
+          next_stamp_token=2,
+          learning_rate=0.1,
+          partition_ids=[handler1_partitions],
+          gains=[handler1_gains],
+          splits=[handler1_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the negative gain split of partition 1 to be pruned and the
+      # positive gain split of partition 2 to be retained.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=2))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.33
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: -0.2
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.01
+              }
+            }
+          }
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 5
+              left_id: 3
+              right_id: 4
+            }
+            node_metadata {
+              gain: 0.5
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.041
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 0.064
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 2
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 2)
+      self.assertEqual(stats.num_trees, 1)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 2)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowEnsembleTreeLayerByLayer(self):
+    """Test growing an existing ensemble with the last tree not finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 7.143
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -4.375
+              }
+            }
+          }
+        }
+        tree_weights: 0.10000000149
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 1
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=3,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.LAYER_BY_LAYER,
+          # Dropout will have no effect, since the tree will not be fully grown.
+          dropout_probability=1.0)
+
+      # Prepare handler inputs.
+      # Handler 1 only has a candidate for partition 1, handler 2 has candidates
+      # for both partitions and handler 3 only has a candidate for partition 2.
+      handler1_partitions = np.array([1], dtype=np.int32)
+      handler1_gains = np.array([1.4], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(0, 0.21, -6.0, 1.65)]
+      handler2_partitions = np.array([1, 2], dtype=np.int32)
+      handler2_gains = np.array([0.63, 2.7], dtype=np.float32)
+      handler2_split = [
+          _gen_dense_split_info(0, 0.23, -0.6, 0.24),
+          _gen_categorical_split_info(1, 7, -1.5, 2.3)
+      ]
+      handler3_partitions = np.array([2], dtype=np.int32)
+      handler3_gains = np.array([1.7], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(0, 3, -0.75, 1.93)]
+
+      # Grow tree ensemble layer by layer.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=0.1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect the split for partition 1 to be chosen from handler 1 and
+      # the split for partition 2 to be chosen from handler 2.
+      # The grown tree should not be finalized as max tree depth is 3 and
+      # it's only grown 2 layers.
+      # The partition 1 split weights get added to original leaf weight 7.143.
+      # The partition 2 split weights get added to original leaf weight -4.375.
+      new_stamp, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      stats = session.run(
+          training_ops.tree_ensemble_stats(tree_ensemble_handle, stamp_token=1))
+      tree_ensemble_config.ParseFromString(serialized)
+      expected_result = """
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_id: 4
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 7.62
+            }
+          }
+          nodes {
+            dense_float_binary_split {
+              threshold: 0.21
+              left_id: 3
+              right_id: 4
+            }
+            node_metadata {
+              gain: 1.4
+            }
+          }
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 1
+              feature_id: 7
+              left_id: 5
+              right_id: 6
+            }
+            node_metadata {
+              gain: 2.7
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 1.143
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 8.793
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -5.875
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -2.075
+              }
+            }
+          }
+        }
+        tree_weights: 0.1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 2
+        }
+        growing_metadata {
+          num_trees_attempted: 1
+          num_layers_attempted: 2
+        }
+      """
+      self.assertEqual(new_stamp, 1)
+      self.assertEqual(stats.num_trees, 0)
+      self.assertEqual(stats.num_layers, 2)
+      self.assertEqual(stats.active_tree, 1)
+      self.assertEqual(stats.active_layer, 2)
+      self.assertEqual(stats.attempted_trees, 1)
+      self.assertEqual(stats.attempted_layers, 2)
+      self.assertProtoEquals(expected_result, tree_ensemble_config)
+
+  def testGrowExistingEnsembleTreeFinalizedWithDropout(self):
+    """Test growing an existing ensemble with the last tree finalized."""
+    with self.test_session() as session:
+      # Create existing ensemble with one root split and one bias tree.
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+        trees {
+          nodes {
+            leaf {
+              vector {
+                value: -0.32
+                value: 0.28
+              }
+            }
+          }
+        }
+        trees {
+          nodes {
+            categorical_id_binary_split {
+              feature_column: 3
+              feature_id: 7
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 1.3
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: 2.3
+              }
+            }
+          }
+          nodes {
+            leaf {
+              sparse_vector {
+                index: 0
+                value: -0.9
+              }
+            }
+          }
+        }
+        tree_weights: 0.7
+        tree_weights: 1
+        tree_metadata {
+          num_tree_weight_updates: 1
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        tree_metadata {
+          num_tree_weight_updates: 5
+          num_layers_grown: 1
+          is_finalized: true
+        }
+        growing_metadata {
+          num_trees_attempted: 2
+          num_layers_attempted: 2
+        }
+      """, tree_ensemble_config)
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = _gen_learner_config(
+          num_classes=2,
+          l1_reg=0,
+          l2_reg=0,
+          tree_complexity=0,
+          max_depth=1,
+          min_node_weight=0,
+          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
+          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE,
+          dropout_probability=1.0)
+
+      # Prepare handler inputs.
+      handler1_partitions = np.array([0], dtype=np.int32)
+      handler1_gains = np.array([7.62], dtype=np.float32)
+      handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)]
+      handler2_partitions = np.array([0], dtype=np.int32)
+      handler2_gains = np.array([0.63], dtype=np.float32)
+      handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)]
+      handler3_partitions = np.array([0], dtype=np.int32)
+      handler3_gains = np.array([7.62], dtype=np.float32)
+      handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)]
+
+      # Grow tree ensemble.
+      grow_op = training_ops.grow_tree_ensemble(
+          tree_ensemble_handle,
+          stamp_token=0,
+          next_stamp_token=1,
+          learning_rate=1,
+          partition_ids=[
+              handler1_partitions, handler2_partitions, handler3_partitions
+          ],
+          gains=[handler1_gains, handler2_gains, handler3_gains],
+          splits=[handler1_split, handler2_split, handler3_split],
+          learner_config=learner_config,
+          dropout_seed=123,
+          center_bias=True)
+      session.run(grow_op)
+
+      # Expect a new tree to be added with the split from handler 1.
+      _, serialized = session.run(
+          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
+      tree_ensemble_config.ParseFromString(serialized)
+
+      self.assertEqual(3, len(tree_ensemble_config.trees))
+      # Both trees got 0.5 as weights, bias tree is untouched.
+      self.assertAllClose([0.7, 0.5, 0.5], tree_ensemble_config.tree_weights)
+
+      self.assertEqual(
+          1, tree_ensemble_config.tree_metadata[0].num_tree_weight_updates)
+      self.assertEqual(
+          6, tree_ensemble_config.tree_metadata[1].num_tree_weight_updates)
+      self.assertEqual(
+          2, tree_ensemble_config.tree_metadata[2].num_tree_weight_updates)
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
new file mode 100644
index 0000000000..e9e6a5e6ed
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
@@ -0,0 +1,132 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility for batching remote OPs together to reduce RPC overhead."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import collections
+
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+
+
+class ScheduledOp(object):
+  """Represents a scheduled remote operation."""
+
+  __metaclass__ = abc.ABCMeta
+
+  @abc.abstractmethod
+  def batching_key(self):
+    """Returns the key for batching operations."""
+
+  @abc.abstractmethod
+  def batch_runner_fn(self):
+    """Returns the function that executes the operation on the batch."""
+
+
+class ScheduledStampedResourceOp(ScheduledOp):
+  """Wrapper class for batched operations on stamped resources."""
+
+  def __init__(self, resource_handle, op, **kwargs):
+    self.resource_handle = resource_handle
+    self.op = op
+    self.args = kwargs
+
+  def batching_key(self):
+    # We want to group the same operations on the same device and run them in
+    # one batch. So we use (device, operation) as the key.
+    return self.resource_handle.device, self.op
+
+  def batch_runner_fn(self):
+    return _scheduled_stamp_resource_op_runner
+
+
+def _move_tensors(tensors, device):
+  """Moves a list of tensors to a device by concatenating/splitting them."""
+  # Reset the device setting to avoid weird interactions with device merging
+  # logic.
+  with ops.device(None):
+    if all(tensor.shape == tensor_shape.scalar() for tensor in tensors):
+      with ops.device(tensors[0].device):
+        values = array_ops.stack(tensors)
+      with ops.device(device):
+        return array_ops.unstack(values)
+    else:
+      with ops.device(tensors[0].device):
+        sizes = array_ops.stack([array_ops.size(tensor) for tensor in tensors])
+        values = array_ops.concat(tensors, axis=0)
+      with ops.device(device):
+        sizes = array_ops.unstack(sizes)
+        return list(array_ops.split(values, sizes))
+
+
+def _scheduled_stamp_resource_op_runner(batch, stamp):
+  """Runs a batch operation on a stamped resource."""
+  if not batch:
+    return
+  arg_keys = set(batch[0].args.keys())
+  grouped_args = collections.defaultdict(list)
+  resource_handles = []
+  # Check that the set of arguments is the same across all the scheduled ops.
+  for op in batch:
+    if set(op.args.keys()) != arg_keys:
+      raise ValueError("Mismatching arguments: %s, %s.", op.args, arg_keys)
+    for key in arg_keys:
+      grouped_args[key].append(op.args[key])
+    resource_handles.append(op.resource_handle)
+  # Move all the inputs to the op device in one RPC.
+  grouped_args = {k: _move_tensors(v, resource_handles[0].device)
+                  for k, v in grouped_args.items()}
+  with ops.device(resource_handles[0].device):
+    return batch[0].op(resource_handles, stamp, **grouped_args)
+
+
+def run_handler_scheduled_ops(per_handler_ops, stamp, worker_device):
+  """Given a dictionary of ops for each handler, runs them in batch."""
+  batched_ops = collections.defaultdict(list)
+  # Group the ops by their batching_key. Ops that share the same batching key
+  # can be executed together.
+  for handler in per_handler_ops.keys():
+    for op in per_handler_ops[handler]:
+      batched_ops[(op.batching_key(), op.batch_runner_fn())].append(op)
+  op_results = {}
+  for batch in batched_ops.values():
+    # Run each of the batched ops using its runner.
+    results = batch[0].batch_runner_fn()(batch, stamp)
+    # If the result is a tuple, move each entry in the tuple in one RPC.
+    if isinstance(results, tuple):
+      results = tuple(_move_tensors(result, worker_device) for result in
+                      results)
+      # Once all the results are on the worker, create individual tuple for
+      # each scheduled op request.
+      for i in range(len(batch)):
+        op_results[batch[i]] = tuple(result[i] for result in results)
+    # If the result is a tuple, it didn't have any outputs, so use the
+    # `ops.Operation` as the result for all the scheduled ops.
+    elif isinstance(results, ops.Operation):
+      for i in range(len(batch)):
+        op_results[batch[i]] = results
+    else:
+      raise ValueError("Unknown type of result %s.", results)
+  handler_results = collections.defaultdict(list)
+  # Dispatch the results of the ScheduledOps to the handlers that requested
+  # them.
+  for handler in per_handler_ops.keys():
+    for op in per_handler_ops[handler]:
+      handler_results[handler].append(op_results[op])
+  return handler_results
diff --git a/tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py b/tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py
new file mode 100644
index 0000000000..06eb6d59ae
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/ensemble_optimizer_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Split handler custom ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_ensemble_optimizer_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _ensemble_optimizer_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_ensemble_optimizer_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _ensemble_optimizer_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py
new file mode 100644
index 0000000000..813fb181f3
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py
@@ -0,0 +1,119 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Model ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import gen_model_ops
+from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_deserialize
+from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_serialize
+# pylint: disable=unused-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_stamp_token
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+ops.NotDifferentiable("TreeEnsembleVariable")
+ops.NotDifferentiable("TreeEnsembleSerialize")
+ops.NotDifferentiable("TreeEnsembleDeserialize")
+
+
+class TreeEnsembleVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for TreeEnsembleVariable."""
+
+  def __init__(self, tree_ensemble_handle, create_op, name):
+    """Creates a TreeEnsembleVariableSavable object.
+
+    Args:
+      tree_ensemble_handle: handle to the tree ensemble variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree ensemble variable under.
+    """
+    stamp_token, ensemble_config = tree_ensemble_serialize(tree_ensemble_handle)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree ensemble variable. So we just pass an empty
+    # value.
+    slice_spec = ""
+    specs = [
+        saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec,
+                                        name + "_stamp"),
+        saver.BaseSaverBuilder.SaveSpec(ensemble_config, slice_spec,
+                                        name + "_config"),
+    ]
+    super(TreeEnsembleVariableSavable,
+          self).__init__(tree_ensemble_handle, specs, name)
+    self._tree_ensemble_handle = tree_ensemble_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree ensemble from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree ensemble variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return tree_ensemble_deserialize(
+          self._tree_ensemble_handle,
+          stamp_token=restored_tensors[0],
+          tree_ensemble_config=restored_tensors[1])
+
+
+def tree_ensemble_variable(stamp_token,
+                           tree_ensemble_config,
+                           name,
+                           container=None):
+  r"""Creates a tree ensemble model and returns a handle to it.
+
+  Args:
+    stamp_token: The initial stamp token value for the ensemble resource.
+    tree_ensemble_config: A `Tensor` of type `string`.
+      Serialized proto of the tree ensemble.
+    name: A name for the ensemble variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the tree ensemble.
+  """
+  with ops.name_scope(name, "TreeEnsembleVariable") as name:
+    resource_handle = gen_model_ops.decision_tree_ensemble_resource_handle_op(
+        container, shared_name=name, name=name)
+    create_op = gen_model_ops.create_tree_ensemble_variable(
+        resource_handle, stamp_token, tree_ensemble_config)
+    is_initialized_op = gen_model_ops.tree_ensemble_is_initialized_op(
+        resource_handle)
+    # Adds the variable to the savable list.
+    saveable = TreeEnsembleVariableSavable(resource_handle, create_op,
+                                           resource_handle.name)
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _model_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_model_ops.so"))
+except (errors.NotFoundError, IOError):
+  print("Error loading _model_ops.so")
diff --git a/tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py b/tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py
new file mode 100644
index 0000000000..cf9ea37156
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/prediction_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Split handler custom ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_prediction_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _prediction_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_prediction_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _prediction_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
new file mode 100644
index 0000000000..d8ab6cdbb3
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
@@ -0,0 +1,176 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Quantile ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
+from tensorflow.contrib.boosted_trees.python.ops import gen_quantile_ops
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import,undefined-variable
+from tensorflow.contrib.boosted_trees.python.ops.gen_quantile_ops import *
+# pylint: enable=wildcard-import,undefined-variable
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
+  """A resource that allows distributed quantile computation."""
+
+  def __init__(self,
+               init_stamp_token,
+               epsilon,
+               num_quantiles,
+               name=None,
+               container=None):
+    """Creates a QuantileAccumulator object.
+
+    Args:
+      init_stamp_token: The initial value for the stamp token.
+      epsilon: Error bound on the quantile computation.
+      num_quantiles: Number of quantiles to produce from the final summary.
+      name: the name to save the accumulator under.
+      container: An optional `string`. Defaults to `""`
+    """
+    self._epsilon = epsilon
+
+    with ops.name_scope(name, "QuantileAccumulator") as name:
+      self._quantile_accumulator_handle = (
+          gen_quantile_ops.quantile_stream_resource_handle_op(
+              container=container, shared_name=name, name=name))
+      self._create_op = gen_quantile_ops.create_quantile_accumulator(
+          self._quantile_accumulator_handle,
+          init_stamp_token,
+          epsilon=epsilon,
+          num_quantiles=num_quantiles)
+      is_initialized_op = gen_quantile_ops.quantile_accumulator_is_initialized(
+          self._quantile_accumulator_handle)
+    resources.register_resource(self._quantile_accumulator_handle,
+                                self._create_op, is_initialized_op)
+    self._make_savable(name)
+
+  def _make_savable(self, name):
+    stamp_token, state, are_buckets_ready, buckets = (
+        gen_quantile_ops.quantile_accumulator_serialize(
+            self._quantile_accumulator_handle))
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful in quantile accumulator.
+    slice_spec = ""
+    def make_save_spec(tensor, suffix):
+      return saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name + suffix)
+
+    specs = [make_save_spec(stamp_token, "_stamp")]
+    specs += [make_save_spec(state, "_state")]
+    specs += [make_save_spec(are_buckets_ready, "_are_buckets_ready")]
+    specs += [make_save_spec(buckets, "buckets")]
+    super(QuantileAccumulator,
+          self).__init__(self._quantile_accumulator_handle, specs, name)
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, self)
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated quantile accumulator from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore.
+
+    Returns:
+      The operation that restores the state of the quantile accumulator.
+    """
+    # Read the restored tensors with the same order that were added to saving
+    # spec.
+    stamp_token = restored_tensors[:1]
+    state = restored_tensors[1:2]
+    are_buckets_ready = restored_tensors[2:3]
+    buckets = restored_tensors[3]
+    with ops.control_dependencies([self._create_op]):
+      return gen_quantile_ops.quantile_accumulator_deserialize(
+          self._quantile_accumulator_handle,
+          stamp_token=stamp_token,
+          stream_state=state,
+          are_buckets_ready=are_buckets_ready,
+          buckets=buckets)
+
+  def get_buckets(self, stamp_token):
+    """Returns quantile buckets created during previous flush."""
+    are_buckets_ready, buckets = (
+        gen_quantile_ops.quantile_accumulator_get_buckets(
+            quantile_accumulator_handles=[self._quantile_accumulator_handle],
+            stamp_token=stamp_token))
+    return are_buckets_ready[0], buckets[0]
+
+  def schedule_get_buckets(self):
+    """Returns a scheduled read of buckets created during previous flush."""
+    return batch_ops_utils.ScheduledStampedResourceOp(
+        resource_handle=self._quantile_accumulator_handle,
+        op=gen_quantile_ops.quantile_accumulator_get_buckets)
+
+  def _make_summary(self, column, example_weights):
+    if isinstance(column, sparse_tensor.SparseTensor):
+      return gen_quantile_ops.make_quantile_summaries(
+          dense_float_features=[],
+          sparse_float_feature_indices=[column.indices],
+          sparse_float_feature_values=[column.values],
+          sparse_float_feature_shapes=[column.dense_shape],
+          example_weights=example_weights,
+          epsilon=self._epsilon)[1][0]
+    else:
+      return gen_quantile_ops.make_quantile_summaries(
+          dense_float_features=[column],
+          sparse_float_feature_indices=[],
+          sparse_float_feature_values=[],
+          sparse_float_feature_shapes=[],
+          example_weights=example_weights,
+          epsilon=self._epsilon)[0][0]
+
+  def add_summary(self, stamp_token, column, example_weights):
+    """Adds quantile summary to its stream in resource."""
+    summary = self._make_summary(column, example_weights)
+    return gen_quantile_ops.quantile_accumulator_add_summaries(
+        quantile_accumulator_handles=[self._quantile_accumulator_handle],
+        stamp_token=stamp_token,
+        summaries=[summary])
+
+  def schedule_add_summary(self, stamp_token, column, example_weights):
+    """Schedules to add a quantile summary to its stream in resource."""
+    summary = self._make_summary(column, example_weights)
+    return batch_ops_utils.ScheduledStampedResourceOp(
+        op=gen_quantile_ops.quantile_accumulator_add_summaries,
+        resource_handle=self._quantile_accumulator_handle,
+        summaries=summary)
+
+  def flush(self, stamp_token, next_stamp_token):
+    """Finalizes quantile summary stream and resets it for next iteration."""
+    return gen_quantile_ops.quantile_accumulator_flush(
+        quantile_accumulator_handle=self._quantile_accumulator_handle,
+        stamp_token=stamp_token,
+        next_stamp_token=next_stamp_token)
+
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _quantile_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_quantile_ops.so"))
+except (errors.NotFoundError, IOError):
+  print("Error loading _quantile_ops.so")
diff --git a/tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py b/tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py
new file mode 100644
index 0000000000..903f7d99c9
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/split_handler_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Split handler custom ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_split_handler_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _split_handler_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_split_handler_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _split_handler_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
new file mode 100644
index 0000000000..9e6ef1a270
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
@@ -0,0 +1,210 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Stats Accumulator ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
+from tensorflow.contrib.boosted_trees.python.ops import gen_stats_accumulator_ops
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+class StatsAccumulator(saver.BaseSaverBuilder.SaveableObject):
+  """A resource that allows to accumulate gradients and hessians.
+
+  For consistency guarantees, we use read and write stamp tokens.
+  The stamp token on the resource is updated with StatsAccumulator.flush.
+  Calls to StatsAccumulator.add that don't provide the current stamp token are
+  ignored.
+  """
+
+  def __init__(self,
+               stamp_token,
+               gradient_shape,
+               hessian_shape,
+               name=None,
+               container=None):
+    """Creates a stats accumulator and returns a handle to it.
+
+    Args:
+      stamp_token: An int64, initial value to use for the stamp token.
+      gradient_shape: A TensorShape, containing shape of gradients.
+      hessian_shape: A TensorShape, containing shape of hessians.
+      name: A name for the stats accumulator variable.
+      container: An optional `string`. Defaults to `""`.
+
+    Returns:
+      A `Tensor` of type mutable `string`. The handle to the stats accumulator.
+    """
+    with ops.name_scope(name, "StatsAccumulator") as name:
+      # Both values are scalars.
+      if (gradient_shape == tensor_shape.scalar() and
+          hessian_shape == tensor_shape.scalar()):
+        self._is_scalar = True
+        self._resource_handle = (gen_stats_accumulator_ops.
+                                 stats_accumulator_scalar_resource_handle_op(
+                                     container, name, name=name))
+
+        create_op = gen_stats_accumulator_ops.create_stats_accumulator_scalar(
+            self._resource_handle, stamp_token)
+        is_initialized_op = (
+            gen_stats_accumulator_ops.stats_accumulator_scalar_is_initialized(
+                self._resource_handle))
+      else:
+        self._is_scalar = False
+        self._resource_handle = (gen_stats_accumulator_ops.
+                                 stats_accumulator_tensor_resource_handle_op(
+                                     container, name, name=name))
+        create_op = gen_stats_accumulator_ops.create_stats_accumulator_tensor(
+            self._resource_handle, stamp_token, gradient_shape.as_list(),
+            hessian_shape.as_list())
+        is_initialized_op = (
+            gen_stats_accumulator_ops.stats_accumulator_scalar_is_initialized(
+                self._resource_handle))
+
+    self._create_op = create_op
+    slice_spec = ""
+    saver_name = self._resource_handle.name
+    (stamp_token, num_updates, partition_ids, feature_ids, gradients,
+     hessians) = self.serialize()
+    specs = [
+        saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec,
+                                        saver_name + "_stamp"),
+        saver.BaseSaverBuilder.SaveSpec(num_updates, slice_spec,
+                                        saver_name + "_num_updates"),
+        saver.BaseSaverBuilder.SaveSpec(partition_ids, slice_spec,
+                                        saver_name + "_partition_ids"),
+        saver.BaseSaverBuilder.SaveSpec(feature_ids, slice_spec,
+                                        saver_name + "_feature_ids"),
+        saver.BaseSaverBuilder.SaveSpec(gradients, slice_spec,
+                                        saver_name + "_gradients"),
+        saver.BaseSaverBuilder.SaveSpec(hessians, slice_spec,
+                                        saver_name + "hessians"),
+    ]
+
+    super(StatsAccumulator, self).__init__(self._resource_handle, specs, name)
+    resources.register_resource(self._resource_handle, create_op,
+                                is_initialized_op)
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, self)
+
+  def add(self, stamp_token, partition_ids, feature_ids, gradients, hessians):
+    """Updates the stats accumulator."""
+    partition_ids, feature_ids, gradients, hessians = (self._make_summary(
+        partition_ids, feature_ids, gradients, hessians))
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_add(
+          [self._resource_handle], stamp_token, [partition_ids], [feature_ids],
+          [gradients], [hessians])
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_add(
+          [self._resource_handle], stamp_token, [partition_ids], [feature_ids],
+          [gradients], [hessians])
+
+  def schedule_add(self, partition_ids, feature_ids, gradients, hessians):
+    """Schedules an update to the stats accumulator."""
+    partition_ids, feature_ids, gradients, hessians = (self._make_summary(
+        partition_ids, feature_ids, gradients, hessians))
+    if self._is_scalar:
+      return batch_ops_utils.ScheduledStampedResourceOp(
+          op=gen_stats_accumulator_ops.stats_accumulator_scalar_add,
+          resource_handle=self._resource_handle,
+          partition_ids=partition_ids,
+          feature_ids=feature_ids,
+          gradients=gradients,
+          hessians=hessians)
+    else:
+      return batch_ops_utils.ScheduledStampedResourceOp(
+          op=gen_stats_accumulator_ops.stats_accumulator_tensor_add,
+          resource_handle=self._resource_handle,
+          partition_ids=partition_ids,
+          feature_ids=feature_ids,
+          gradients=gradients,
+          hessians=hessians)
+
+  def _make_summary(self, partition_ids, feature_ids, gradients, hessians):
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_make_summary(
+          partition_ids, feature_ids, gradients, hessians)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_make_summary(
+          partition_ids, feature_ids, gradients, hessians)
+
+  def deserialize(self, stamp_token, num_updates, partition_ids, feature_ids,
+                  gradients, hessians):
+    """Resets the stats accumulator with the serialized state."""
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_deserialize(
+          self._resource_handle, stamp_token, num_updates, partition_ids,
+          feature_ids, gradients, hessians)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_deserialize(
+          self._resource_handle, stamp_token, num_updates, partition_ids,
+          feature_ids, gradients, hessians)
+
+  def flush(self, stamp_token, next_stamp_token):
+    """Flushes the stats accumulator."""
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_flush(
+          self._resource_handle, stamp_token, next_stamp_token)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_flush(
+          self._resource_handle, stamp_token, next_stamp_token)
+
+  def serialize(self):
+    """Serializes the stats accumulator state."""
+    if self._is_scalar:
+      return gen_stats_accumulator_ops.stats_accumulator_scalar_serialize(
+          self._resource_handle)
+    else:
+      return gen_stats_accumulator_ops.stats_accumulator_tensor_serialize(
+          self._resource_handle)
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree ensemble from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree ensemble variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return self.deserialize(
+          stamp_token=restored_tensors[0],
+          num_updates=restored_tensors[1],
+          partition_ids=restored_tensors[2],
+          feature_ids=restored_tensors[3],
+          gradients=restored_tensors[4],
+          hessians=restored_tensors[5])
+
+  def resource(self):
+    return self._resource_handle
+
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _stats_accumulator_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_stats_accumulator_ops.so"))
+except (errors.NotFoundError, IOError):
+  print("Error loading _stats_accumulator_ops.so")
diff --git a/tensorflow/contrib/boosted_trees/python/ops/training_ops.py b/tensorflow/contrib/boosted_trees/python/ops/training_ops.py
new file mode 100644
index 0000000000..32925b3e83
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/ops/training_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Import and conditionally load custom ops for training boosted trees."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.boosted_trees.python.ops.gen_training_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import resource_loader
+
+# Conditionally load ops, they might already be statically linked in.
+try:
+  _training_ops = loader.load_op_library(
+      resource_loader.get_path_to_datafile('_training_ops.so'))
+except (errors.NotFoundError, IOError):
+  print('Error loading _training_ops.so')
diff --git a/tensorflow/contrib/boosted_trees/resources/BUILD b/tensorflow/contrib/boosted_trees/resources/BUILD
index 5dfdf8f489..9fc101612f 100644
--- a/tensorflow/contrib/boosted_trees/resources/BUILD
+++ b/tensorflow/contrib/boosted_trees/resources/BUILD
@@ -37,7 +37,6 @@ cc_library(
         "//tensorflow/contrib/boosted_trees/lib:weighted_quantiles",
         "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_cc",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
@@ -49,5 +48,4 @@ cc_library(
         "//tensorflow/contrib/boosted_trees/lib:trees",
         "//tensorflow/core:framework_headers_lib",
     ],
-    alwayslink = 1,
 )
diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake
index 1c80ffcd7b..c390136d46 100644
--- a/tensorflow/contrib/cmake/tf_core_cpu.cmake
+++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake
@@ -46,6 +46,10 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session.cc"
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session_factory.cc"
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session_options.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.cc"
     "${tensorflow_source_dir}/tensorflow/core/grappler/clusters/single_machine.h"
     "${tensorflow_source_dir}/tensorflow/core/grappler/clusters/single_machine.cc"
     "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index a048194a19..a15621ba6d 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -147,7 +147,9 @@ file(GLOB tf_core_platform_srcs
     "${tensorflow_source_dir}/tensorflow/core/platform/*.h"
     "${tensorflow_source_dir}/tensorflow/core/platform/*.cc"
     "${tensorflow_source_dir}/tensorflow/core/platform/default/*.h"
-    "${tensorflow_source_dir}/tensorflow/core/platform/default/*.cc")
+    "${tensorflow_source_dir}/tensorflow/core/platform/default/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/framework/resource_handle.h"
+    "${tensorflow_source_dir}/tensorflow/core/framework/resource_handle.cc")
 if (NOT tensorflow_ENABLE_GPU)
   file(GLOB tf_core_platform_gpu_srcs
       "${tensorflow_source_dir}/tensorflow/core/platform/cuda_libdevice_path.*"
@@ -223,6 +225,10 @@ set(tf_version_srcs ${tensorflow_source_dir}/tensorflow/core/util/version_info.c
 file(GLOB_RECURSE tf_core_framework_srcs
     "${tensorflow_source_dir}/tensorflow/core/framework/*.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.h"
+    "${tensorflow_source_dir}/tensorflow/core/graph/graph.cc"
     "${tensorflow_source_dir}/tensorflow/core/util/*.h"
     "${tensorflow_source_dir}/tensorflow/core/util/*.cc"
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/session.cc"
@@ -231,18 +237,19 @@ file(GLOB_RECURSE tf_core_framework_srcs
     "${tensorflow_source_dir}/public/*.h"
 )
 
-file(GLOB_RECURSE tf_core_framework_test_srcs
+file(GLOB_RECURSE tf_core_framework_exclude_srcs
     "${tensorflow_source_dir}/tensorflow/core/framework/*test*.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*test*.cc"
     "${tensorflow_source_dir}/tensorflow/core/framework/*testutil.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*testutil.cc"
     "${tensorflow_source_dir}/tensorflow/core/framework/*main.cc"
+    "${tensorflow_source_dir}/tensorflow/core/framework/resource_handle.cc"
     "${tensorflow_source_dir}/tensorflow/core/util/*test*.h"
     "${tensorflow_source_dir}/tensorflow/core/util/*test*.cc"
     "${tensorflow_source_dir}/tensorflow/core/util/*main.cc"
 )
 
-list(REMOVE_ITEM tf_core_framework_srcs ${tf_core_framework_test_srcs})
+list(REMOVE_ITEM tf_core_framework_srcs ${tf_core_framework_exclude_srcs})
 
 add_library(tf_core_framework OBJECT
     ${tf_core_framework_srcs}
diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 7f3be2aedf..6b8c130490 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -55,6 +55,8 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_manager.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/ops/nccl_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/resampler/kernels/resampler_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/resampler/ops/resampler_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/blas_gemm.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/lstm_ops.cc"
@@ -125,6 +127,7 @@ if(WIN32)
       "${tensorflow_source_dir}/tensorflow/core/kernels/*quantiz*.cc"
       "${tensorflow_source_dir}/tensorflow/core/kernels/neon/*"
       # no in tensorflow.dll - comes from .so
+      "${tensorflow_source_dir}/tensorflow/contrib/resampler/kernels/resampler_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/blas_gemm.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/lstm_ops.cc"
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index f1b01250a4..21d94da6cc 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -81,11 +81,14 @@ GENERATE_CONTRIB_OP_LIBRARY(image "${tensorflow_source_dir}/tensorflow/contrib/i
 GENERATE_CONTRIB_OP_LIBRARY(layers_sparse_feature_cross "${tensorflow_source_dir}/tensorflow/contrib/layers/ops/sparse_feature_cross_op.cc")
 GENERATE_CONTRIB_OP_LIBRARY(memory_stats "${tensorflow_source_dir}/tensorflow/contrib/memory_stats/ops/memory_stats_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(nccl "${tensorflow_source_dir}/tensorflow/contrib/nccl/ops/nccl_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(resampler "${tensorflow_source_dir}/tensorflow/contrib/resampler/ops/resampler_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(rnn_gru "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/gru_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(rnn_lstm "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/lstm_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(seq2seq_beam_search "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_hybrid "${tensor_forest_hybrid_srcs}")
+GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_model "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/model_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_stats "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/stats_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(text_skip_gram "${tensorflow_source_dir}/tensorflow/contrib/text/ops/skip_gram_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(tpu "${tpu_ops_srcs}")
 GENERATE_CONTRIB_OP_LIBRARY(bigquery_reader "${tensorflow_source_dir}/tensorflow/contrib/cloud/ops/bigquery_reader_ops.cc")
diff --git a/tensorflow/contrib/cmake/tf_grappler.cmake b/tensorflow/contrib/cmake/tf_grappler.cmake
index 4811c8cce9..a7841c98e8 100644
--- a/tensorflow/contrib/cmake/tf_grappler.cmake
+++ b/tensorflow/contrib/cmake/tf_grappler.cmake
@@ -20,6 +20,8 @@ file(GLOB tf_grappler_srcs
    "${tensorflow_source_dir}/tensorflow/core/grappler/clusters/single_machine.h"
    "${tensorflow_source_dir}/tensorflow/python/grappler/cost_analyzer.cc"
    "${tensorflow_source_dir}/tensorflow/python/grappler/cost_analyzer.h"
+   "${tensorflow_source_dir}/tensorflow/python/grappler/model_analyzer.cc"
+   "${tensorflow_source_dir}/tensorflow/python/grappler/model_analyzer.h"
  )
  
 add_library(tf_grappler OBJECT ${tf_grappler_srcs})
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 67b0c86f7f..d90f88c66f 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -123,7 +123,9 @@ endfunction()
 file(GLOB_RECURSE tf_protos_python_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/core/*.proto"
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
+    "${tensorflow_source_dir}/tensorflow/contrib/decision_trees/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
+    "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
 )
@@ -204,6 +206,7 @@ add_python_module("tensorflow/python/debug/examples")
 add_python_module("tensorflow/python/debug/lib")
 add_python_module("tensorflow/python/debug/wrappers")
 add_python_module("tensorflow/python/estimator")
+add_python_module("tensorflow/python/estimator/canned")
 add_python_module("tensorflow/python/estimator/export")
 add_python_module("tensorflow/python/estimator/inputs")
 add_python_module("tensorflow/python/estimator/inputs/queues")
@@ -244,6 +247,8 @@ add_python_module("tensorflow/contrib/bayesflow/examples/reinforce_simple")
 add_python_module("tensorflow/contrib/bayesflow/python")
 add_python_module("tensorflow/contrib/bayesflow/python/kernel_tests")
 add_python_module("tensorflow/contrib/bayesflow/python/ops")
+add_python_module("tensorflow/contrib/boosted_trees")
+add_python_module("tensorflow/contrib/boosted_trees/proto")
 add_python_module("tensorflow/contrib/cloud")
 add_python_module("tensorflow/contrib/cloud/kernels")
 add_python_module("tensorflow/contrib/cloud/ops")
@@ -269,6 +274,8 @@ add_python_module("tensorflow/contrib/data/python/framework")
 add_python_module("tensorflow/contrib/data/python/kernel_tests")
 add_python_module("tensorflow/contrib/data/python/ops")
 add_python_module("tensorflow/contrib/data/python/util")
+add_python_module("tensorflow/contrib/decision_trees")
+add_python_module("tensorflow/contrib/decision_trees/proto")
 add_python_module("tensorflow/contrib/deprecated")
 add_python_module("tensorflow/contrib/distributions")
 add_python_module("tensorflow/contrib/distributions/python")
@@ -446,6 +453,11 @@ add_python_module("tensorflow/contrib/quantization/python")
 add_python_module("tensorflow/contrib/remote_fused_graph/pylib")
 add_python_module("tensorflow/contrib/remote_fused_graph/pylib/python")
 add_python_module("tensorflow/contrib/remote_fused_graph/pylib/python/ops")
+add_python_module("tensorflow/contrib/resampler")
+add_python_module("tensorflow/contrib/resampler/kernels")
+add_python_module("tensorflow/contrib/resampler/ops")
+add_python_module("tensorflow/contrib/resampler/python")
+add_python_module("tensorflow/contrib/resampler/python/ops")
 add_python_module("tensorflow/contrib/rnn")
 add_python_module("tensorflow/contrib/rnn/kernels")
 add_python_module("tensorflow/contrib/rnn/ops")
@@ -647,6 +659,8 @@ GENERATE_PYTHON_OP_LIB("contrib_memory_stats_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/memory_stats/ops/gen_memory_stats_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_nccl_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/nccl/ops/gen_nccl_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_resampler_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/resampler/ops/gen_resampler_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_rnn_gru_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/rnn/ops/gen_gru_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_rnn_lstm_ops"
@@ -657,6 +671,10 @@ GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_tensor_forest_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_hybrid_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/hybrid/ops/gen_training_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_model_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_model_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_stats_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_stats_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_text_skip_gram_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/text/python/ops/gen_skip_gram_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_bigquery_reader_ops"
diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
index 0e51ab9935..f41fdc382b 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
@@ -23,7 +23,6 @@ import unittest
 import numpy as np
 
 from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
-from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.core.protobuf import saver_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -114,17 +113,14 @@ class CudnnRNNTest(TensorFlowTestCase):
     num_units = cudnn_model.num_units
     num_layers = cudnn_model.num_layers
 
-    # To reuse cuDNN-trained models, must set
-    # forget_bias, clip_cell = 0, False
-    # In LSTMCell and LSTMBlockCell, forget_bias is added in addition to learned
-    # bias, whereas cuDNN does not apply the additional bias.
+    # To reuse cuDNN-trained models, must use CudnnCompatibleLSTM...Cells.
     if use_block_cell:
       # pylint: disable=g-long-lambda
-      single_cell = lambda: lstm_ops.LSTMBlockCell(num_units, forget_bias=0,
-                                                   clip_cell=False)
+      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMBlockCell(
+          num_units)
       # pylint: enable=g-long-lambda
     else:
-      single_cell = lambda: rnn_cell_impl.LSTMCell(num_units, forget_bias=0)
+      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMCell(num_units)
     cell = rnn_cell_impl.MultiRNNCell(
         [single_cell() for _ in range(num_layers)])
     return rnn.dynamic_rnn(
@@ -171,7 +167,7 @@ class CudnnRNNTest(TensorFlowTestCase):
 
   @unittest.skipUnless(test.is_built_with_cuda(),
                        "Test only applicable when running on GPUs")
-  def testCheckpointReusableByCanonicalLSTMCells(self):
+  def testCudnnCompatibleLSTMCells(self):
     configs = [
         {
             "num_layers": 1,
@@ -207,7 +203,7 @@ class CudnnRNNTest(TensorFlowTestCase):
         },
     ]
     for cfg in configs:
-      self._testCheckpointReusableByCanonicalLSTMCells(
+      self._testCudnnCompatibleLSTMCells(
           cfg["num_layers"],
           cfg["seq_length"],
           cfg["num_units"],
@@ -215,7 +211,7 @@ class CudnnRNNTest(TensorFlowTestCase):
           cfg["batch_size"],
           cfg["rnn_mode"],
           use_block_cell=False)
-      self._testCheckpointReusableByCanonicalLSTMCells(
+      self._testCudnnCompatibleLSTMCells(
           cfg["num_layers"],
           cfg["seq_length"],
           cfg["num_units"],
@@ -224,7 +220,7 @@ class CudnnRNNTest(TensorFlowTestCase):
           cfg["rnn_mode"],
           use_block_cell=True)
 
-  def _testCheckpointReusableByCanonicalLSTMCells(
+  def _testCudnnCompatibleLSTMCells(
       self, num_layers, seq_length, num_units, input_size, batch_size, rnn_mode,
       use_block_cell):
     np.random.seed(0)
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index 0437467f3f..25b3bde837 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -18,6 +18,7 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.cudnn_rnn.ops import gen_cudnn_rnn_ops
+from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.contrib.util import loader
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import dtypes
@@ -25,6 +26,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import state_ops
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.training import saver
@@ -145,7 +147,7 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
     return self._transform_lstm_canonical(weights, biases)
 
   def _transformed_canonical_names(self, weights, biases):
-    """Return canonical names for fused weight and bias tensors."""
+    """Return canonical names for transformed weight and bias tensors."""
     if (self._cudnn_rnn.direction != "unidirectional" or
         self._cudnn_rnn.rnn_mode != "lstm"):
       assert len(weights) == len(biases)
@@ -156,23 +158,25 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       num_layers = self._cudnn_rnn.num_layers
       # TODO(jamesqin): get rid of multi_rnn_cell when num_layers is 1
       for i in range(num_layers):
-        # One fused weight tensor each layer.
-        w_names.append("multi_rnn_cell/cell_%d/lstm_cell/kernel" % i)
-        # Three fused bias tensors each layer:
-        # the 1st is for LSTMBlockCell restore; the latter two sum up to the
-        # 1st, and are used for cuDNN restore.
-        b_names.append("multi_rnn_cell/cell_%d/lstm_cell/bias" % i)
+        # One transformed weight tensor each layer.
+        w_names.append(
+            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/kernel" % i)
+        # Three transformed bias tensors each layer:
+        # the 1st is for CudnnCompatbleLSTM(Block)Cell restore; the latter two
+        # sum up to the 1st, and are used for cuDNN restore.
+        b_names.append(
+            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/bias" % i)
         b_names.extend([
-            "multi_rnn_cell/cell_%d/lstm_cell/bias_cudnn_%d" % (i, j)
-            for j in range(2)
+            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/bias_cudnn_%d" %
+            (i, j) for j in range(2)
         ])
       return w_names, b_names
 
   def _transform_lstm_canonical(self, weights, biases):
-    """Create fused lstm canonical params.
+    """Create transformed lstm canonical params.
 
     Produce properly-shaped monolithic weight and bias tensors to share between
-    cuDNN and non-platform specific LSTM cells (w/o peephole).
+    cuDNN and cudnn_compatible non-platform specific LSTM cells.
     Args:
       weights: a list of Tensors recovered from cuDNN params_to_canonical.
       biases: a list of Tensors recovered from cuDNN params_to_canonical.
@@ -180,7 +184,7 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       Two lists of tensors, one for weight and bias each.
       The weight list contains num_layers tensors and bias one contains 3 *
       num_layers tensors. Both original and combined biases since cuDNN biases
-      are not restorable from the fused version.
+      are not restorable from the transformed version.
     """
     transformed_weights, transformed_biases = [], []
     for i in range(self._cudnn_rnn.num_layers):
@@ -676,3 +680,40 @@ ops.RegisterShape("CudnnRNNParamsToCanonical")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNNCanonicalToParams")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNN")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNNBackprop")(common_shapes.call_cpp_shape_fn)
+
+
+class CudnnCompatibleLSTMCell(rnn_cell_impl.LSTMCell):
+  """Cudnn Compatible LSTMCell.
+
+  A simple wrapper around @{tf.nn.rnn_cell.LSTMCell} to use along with
+  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
+  former seamlessly.
+  """
+
+  def __init__(self, num_units, reuse=None):
+    super(CudnnCompatibleLSTMCell, self).__init__(
+        num_units,
+        use_peepholes=False,
+        cell_clip=None,
+        num_proj=None,
+        proj_clip=None,
+        state_is_tuple=True,
+        activation=None,
+        reuse=reuse,
+        forget_bias=0)
+
+
+class CudnnCompatibleLSTMBlockCell(lstm_ops.LSTMBlockCell):
+  """Cudnn Compatible LSTMBlockCell.
+
+  A simple wrapper around @{tf.contrib.rnn.LSTMBlockCell} to use along with
+  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
+  former seamlessly. It is the more performant than
+  ${tf.contrib.cudnn_rnn.CudnnCompatibleLSTMCell}, the same way
+  @{tf.contrib.rnn.LSTMBlockCell} is than @{tf.nn.rnn_cell.LSTMCell}.
+  """
+
+  def __init__(self, num_units):
+    super(CudnnCompatibleLSTMBlockCell, self).__init__(
+        num_units, forget_bias=0, clip_cell=False, use_peephole=False)
+    self._names.update({"scope": "cudnn_compatible_lstm_cell"})
diff --git a/tensorflow/contrib/decision_trees/__init__.py b/tensorflow/contrib/decision_trees/__init__.py
new file mode 100644
index 0000000000..2c44e4eb59
--- /dev/null
+++ b/tensorflow/contrib/decision_trees/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Shared representations for tree-based models in tensorflow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.decision_trees.proto import *
+# pylint: enable=unused-import,wildcard-import
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+
+remove_undocumented(__name__)
diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 8dea2763f2..993d6d20b1 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -18,20 +18,15 @@ py_library(
         "//tensorflow/contrib/linalg:linalg_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
-        "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:nn",
         "//tensorflow/python:nn_ops",
-        "//tensorflow/python:random_ops",
-        "//tensorflow/python:special_math_ops",
+        "//tensorflow/python:tensor_util",
+        "//tensorflow/python:util",
         "//tensorflow/python/ops/distributions",
         "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 
@@ -47,14 +42,14 @@ py_library(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:random_ops",
-        "//tensorflow/python:special_math_ops",
+        "//tensorflow/python:tensor_util",
+        "//tensorflow/python:util",
         "//tensorflow/python/ops/distributions",
         "//third_party/py/numpy",
         "@six_archive//:six",
@@ -405,6 +400,22 @@ cuda_py_test(
     tags = ["nomsan"],  # disable to avoid false positives from scipy.
 )
 
+cuda_py_test(
+    name = "vector_exponential_diag_test",
+    size = "medium",
+    srcs = ["python/kernel_tests/vector_exponential_diag_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "vector_laplace_diag_test",
     size = "medium",
@@ -614,6 +625,23 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "trig_test",
+    size = "small",
+    srcs = ["python/kernel_tests/trig_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
@@ -838,6 +866,25 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "sinh_arcsinh_test",
+    size = "small",
+    srcs = ["python/kernel_tests/bijectors/sinh_arcsinh_test.py"],
+    additional_deps = [
+        ":bijectors_py",
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "@six_archive//:six",
+        "//tensorflow/contrib/linalg:linalg_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "softmax_centered_test",
     size = "small",
diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py
index 1fddad5368..6b693300a2 100644
--- a/tensorflow/contrib/distributions/__init__.py
+++ b/tensorflow/contrib/distributions/__init__.py
@@ -46,6 +46,7 @@ from tensorflow.contrib.distributions.python.ops.quantized_distribution import *
 from tensorflow.contrib.distributions.python.ops.relaxed_bernoulli import *
 from tensorflow.contrib.distributions.python.ops.relaxed_onehot_categorical import *
 from tensorflow.contrib.distributions.python.ops.sample_stats import *
+from tensorflow.contrib.distributions.python.ops.vector_exponential_diag import *
 from tensorflow.contrib.distributions.python.ops.vector_laplace_diag import *
 from tensorflow.contrib.distributions.python.ops.wishart import *
 from tensorflow.python.ops.distributions.bernoulli import *
@@ -101,6 +102,7 @@ _allowed_symbols = [
     'VectorDeterministic',
     'Exponential',
     'ExponentialWithSoftplusRate',
+    'VectorExponentialDiag',
     'Gamma',
     'GammaWithSoftplusConcentrationRate',
     'Geometric',
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py
new file mode 100644
index 0000000000..b1159d13f3
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_test.py
@@ -0,0 +1,127 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for SinhArcsinh Bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+# pylint: disable=g-importing-member
+from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh import SinhArcsinh
+from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite
+from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
+from tensorflow.python.platform import test
+
+# pylint: enable=g-importing-member
+
+
+class SinhArcsinhBijectorTest(test.TestCase):
+  """Tests correctness of the power transformation."""
+
+  def testBijectorVersusNumpyRewriteOfBasicFunctions(self):
+    with self.test_session():
+      skewness = 0.2
+      tailweight = 2.0
+      bijector = SinhArcsinh(
+          skewness=skewness,
+          tailweight=tailweight,
+          event_ndims=1,
+          validate_args=True)
+      self.assertEqual("sinh_arcsinh", bijector.name)
+      x = np.array([[[-2.01], [2.], [1e-4]]]).astype(np.float32)
+      y = np.sinh((np.arcsinh(x) + skewness) * tailweight)
+      self.assertAllClose(y, bijector.forward(x).eval())
+      self.assertAllClose(x, bijector.inverse(y).eval())
+      self.assertAllClose(
+          np.sum(
+              np.log(np.cosh(np.arcsinh(y) / tailweight - skewness)) -
+              np.log(tailweight) - np.log(np.sqrt(y**2 + 1)),
+              axis=-1), bijector.inverse_log_det_jacobian(y).eval())
+      self.assertAllClose(
+          -bijector.inverse_log_det_jacobian(y).eval(),
+          bijector.forward_log_det_jacobian(x).eval(),
+          rtol=1e-4,
+          atol=0.)
+
+  def testLargerTailWeightPutsMoreWeightInTails(self):
+    with self.test_session():
+      # Will broadcast together to shape [3, 2].
+      x = [-1., 1.]
+      tailweight = [[0.5], [1.0], [2.0]]
+      bijector = SinhArcsinh(tailweight=tailweight, validate_args=True)
+      y = bijector.forward(x).eval()
+
+      # x = -1, 1 should be mapped to points symmetric about 0
+      self.assertAllClose(y[:, 0], -1. * y[:, 1])
+
+      # forward(1) should increase as tailweight increases, since higher
+      # tailweight should map 1 to a larger number.
+      forward_1 = y[:, 1]  # The positive values of y.
+      self.assertLess(forward_1[0], forward_1[1])
+      self.assertLess(forward_1[1], forward_1[2])
+
+  def testSkew(self):
+    with self.test_session():
+      # Will broadcast together to shape [3, 2].
+      x = [-1., 1.]
+      skewness = [[-1.], [0.], [1.]]
+      bijector = SinhArcsinh(skewness=skewness, validate_args=True)
+      y = bijector.forward(x).eval()
+
+      # For skew < 0, |forward(-1)| > |forward(1)|
+      self.assertGreater(np.abs(y[0, 0]), np.abs(y[0, 1]))
+
+      # For skew = 0, |forward(-1)| = |forward(1)|
+      self.assertAllClose(np.abs(y[1, 0]), np.abs(y[1, 1]))
+
+      # For skew > 0, |forward(-1)| < |forward(1)|
+      self.assertLess(np.abs(y[2, 0]), np.abs(y[2, 1]))
+
+  def testScalarCongruencySkewness1Tailweight0p5(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=1.0, tailweight=0.5, validate_args=True)
+      assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.0, rtol=0.05)
+
+  def testScalarCongruencySkewnessNeg1Tailweight1p5(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=-1.0, tailweight=1.5, validate_args=True)
+      assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.0, rtol=0.05)
+
+  def testBijectiveAndFiniteSkewnessNeg1Tailweight0p5(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=-1., tailweight=0.5, validate_args=True)
+      # Increasing upper logspace limit to 10 results in Inf due to y**2 being
+      # Inf.
+      x = np.concatenate((-np.logspace(-2, 9, 1000), [0], np.logspace(
+          -2, 9, 1000))).astype(np.float32)
+      assert_bijective_and_finite(bijector, x, x, rtol=1e-3)
+
+  def testBijectiveAndFiniteSkewness2Tailweight3(self):
+    with self.test_session():
+      bijector = SinhArcsinh(skewness=1., tailweight=3., validate_args=True)
+      x = np.concatenate((-np.logspace(-2, 5, 1000), [0], np.logspace(
+          -2, 5, 1000))).astype(np.float32)
+      assert_bijective_and_finite(bijector, x, x, rtol=1e-3)
+
+  def testZeroTailweightRaises(self):
+    with self.test_session():
+      with self.assertRaisesOpError("not positive"):
+        SinhArcsinh(tailweight=0., validate_args=True).forward(1.0).eval()
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
index 1c67a1b8f6..472586987d 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
@@ -18,116 +18,16 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
-
 import numpy as np
-from scipy import special
 
 from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.contrib.linalg.python.ops import linear_operator_diag
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import gradient_checker
-from tensorflow.python.ops import gradients_impl
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
 import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
-from tensorflow.python.platform import tf_logging as logging
-
-
-class AssertCloseTest(test.TestCase):
-
-  def testAssertCloseIntegerDtype(self):
-    x = array_ops.placeholder(dtypes.int32)
-    y = x
-    z = array_ops.placeholder(dtypes.int32)
-    feed_dict = {x: [1, 5, 10, 15, 20], z: [2, 5, 10, 15, 20]}
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
-          array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
-          array_ops.identity(y).eval(feed_dict=feed_dict)
-
-  def testAssertCloseNonIntegerDtype(self):
-    x = array_ops.placeholder(dtypes.float32)
-    y = x + 1e-8
-    z = array_ops.placeholder(dtypes.float32)
-    feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]}
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
-          array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
-          array_ops.identity(y).eval(feed_dict=feed_dict)
-
-  def testAssertCloseEpsilon(self):
-    x = [0., 5, 10, 15, 20]
-    # x != y
-    y = [0.1, 5, 10, 15, 20]
-    # x = z
-    z = [1e-8, 5, 10, 15, 20]
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_close(x, z)]):
-        array_ops.identity(x).eval()
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(x, y)]):
-          array_ops.identity(x).eval()
-
-      with self.assertRaisesOpError("Condition x ~= y"):
-        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
-          array_ops.identity(y).eval()
-
-  def testAssertIntegerForm(self):
-    # This should only be detected as an integer.
-    x = array_ops.placeholder(dtypes.float32)
-    y = array_ops.placeholder(dtypes.float32)
-    # First component isn't less than float32.eps = 1e-7
-    z = array_ops.placeholder(dtypes.float32)
-    # This shouldn"t be detected as an integer.
-    w = array_ops.placeholder(dtypes.float32)
-    feed_dict = {x: [1., 5, 10, 15, 20], y: [1.1, 5, 10, 15, 20],
-                 z: [1.0001, 5, 10, 15, 20], w: [1e-8, 5, 10, 15, 20]}
-    with self.test_session():
-      with ops.control_dependencies([distribution_util.assert_integer_form(x)]):
-        array_ops.identity(x).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("x has non-integer components"):
-        with ops.control_dependencies(
-            [distribution_util.assert_integer_form(y)]):
-          array_ops.identity(y).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("x has non-integer components"):
-        with ops.control_dependencies(
-            [distribution_util.assert_integer_form(z)]):
-          array_ops.identity(z).eval(feed_dict=feed_dict)
-
-      with self.assertRaisesOpError("x has non-integer components"):
-        with ops.control_dependencies(
-            [distribution_util.assert_integer_form(w)]):
-          array_ops.identity(w).eval(feed_dict=feed_dict)
 
 
 class ShapesFromLocAndScaleTest(test.TestCase):
@@ -204,460 +104,56 @@ class ShapesFromLocAndScaleTest(test.TestCase):
       self.assertAllEqual([3], event_shape)
 
 
-class GetLogitsAndProbsTest(test.TestCase):
-
-  def testGetLogitsAndProbsImproperArguments(self):
-    with self.test_session():
-      with self.assertRaises(ValueError):
-        distribution_util.get_logits_and_probs(logits=None, probs=None)
-
-      with self.assertRaises(ValueError):
-        distribution_util.get_logits_and_probs(logits=[0.1], probs=[0.1])
-
-  def testGetLogitsAndProbsLogits(self):
-    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
-    logits = special.logit(p)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          logits=logits, validate_args=True)
-
-      self.assertAllClose(p, new_p.eval())
-      self.assertAllClose(logits, new_logits.eval())
-
-  def testGetLogitsAndProbsLogitsMultidimensional(self):
-    p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
-    logits = np.log(p)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          logits=logits, multidimensional=True, validate_args=True)
-
-      self.assertAllClose(new_p.eval(), p)
-      self.assertAllClose(new_logits.eval(), logits)
-
-  def testGetLogitsAndProbsProbability(self):
-    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          probs=p, validate_args=True)
-
-      self.assertAllClose(special.logit(p), new_logits.eval())
-      self.assertAllClose(p, new_p.eval())
-
-  def testGetLogitsAndProbsProbabilityMultidimensional(self):
-    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
-
-    with self.test_session():
-      new_logits, new_p = distribution_util.get_logits_and_probs(
-          probs=p, multidimensional=True, validate_args=True)
-
-      self.assertAllClose(np.log(p), new_logits.eval())
-      self.assertAllClose(p, new_p.eval())
-
-  def testGetLogitsAndProbsProbabilityValidateArgs(self):
-    p = [0.01, 0.2, 0.5, 0.7, .99]
-    # Component less than 0.
-    p2 = [-1, 0.2, 0.5, 0.3, .2]
-    # Component greater than 1.
-    p3 = [2, 0.2, 0.5, 0.3, .2]
-
-    with self.test_session():
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p, validate_args=True)
-      prob.eval()
-
-      with self.assertRaisesOpError("Condition x >= 0"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p2, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p2, validate_args=False)
-      prob.eval()
-
-      with self.assertRaisesOpError("probs has components greater than 1"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p3, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p3, validate_args=False)
-      prob.eval()
-
-  def testGetLogitsAndProbsProbabilityValidateArgsMultidimensional(self):
-    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
-    # Component less than 0. Still sums to 1.
-    p2 = np.array([[-.3, 0.4, 0.9], [0.1, 0.5, 0.4]], dtype=np.float32)
-    # Component greater than 1. Does not sum to 1.
-    p3 = np.array([[1.3, 0.0, 0.0], [0.1, 0.5, 0.4]], dtype=np.float32)
-    # Does not sum to 1.
-    p4 = np.array([[1.1, 0.3, 0.4], [0.1, 0.5, 0.4]], dtype=np.float32)
-
-    with self.test_session():
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p, multidimensional=True)
-      prob.eval()
-
-      with self.assertRaisesOpError("Condition x >= 0"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p2, multidimensional=True, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p2, multidimensional=True, validate_args=False)
-      prob.eval()
-
-      with self.assertRaisesOpError(
-          "(probs has components greater than 1|probs does not sum to 1)"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p3, multidimensional=True, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p3, multidimensional=True, validate_args=False)
-      prob.eval()
+class TridiagTest(test.TestCase):
 
-      with self.assertRaisesOpError("probs does not sum to 1"):
-        _, prob = distribution_util.get_logits_and_probs(
-            probs=p4, multidimensional=True, validate_args=True)
-        prob.eval()
-
-      _, prob = distribution_util.get_logits_and_probs(
-          probs=p4, multidimensional=True, validate_args=False)
-      prob.eval()
-
-
-class LogCombinationsTest(test.TestCase):
-
-  def testLogCombinationsBinomial(self):
-    n = [2, 5, 12, 15]
-    k = [1, 2, 4, 11]
-    log_combs = np.log(special.binom(n, k))
-
-    with self.test_session():
-      n = np.array(n, dtype=np.float32)
-      counts = [[1., 1], [2., 3], [4., 8], [11, 4]]
-      log_binom = distribution_util.log_combinations(n, counts)
-      self.assertEqual([4], log_binom.get_shape())
-      self.assertAllClose(log_combs, log_binom.eval())
-
-  def testLogCombinationsShape(self):
-    # Shape [2, 2]
-    n = [[2, 5], [12, 15]]
-
-    with self.test_session():
-      n = np.array(n, dtype=np.float32)
-      # Shape [2, 2, 4]
-      counts = [[[1., 1, 0, 0], [2., 2, 1, 0]], [[4., 4, 1, 3], [10, 1, 1, 4]]]
-      log_binom = distribution_util.log_combinations(n, counts)
-      self.assertEqual([2, 2], log_binom.get_shape())
-
-
-class DynamicShapeTest(test.TestCase):
-
-  def testSameDynamicShape(self):
-    with self.test_session():
-      scalar = constant_op.constant(2.0)
-      scalar1 = array_ops.placeholder(dtype=dtypes.float32)
-
-      vector = [0.3, 0.4, 0.5]
-      vector1 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
-      vector2 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
-
-      multidimensional = [[0.3, 0.4], [0.2, 0.6]]
-      multidimensional1 = array_ops.placeholder(
-          dtype=dtypes.float32, shape=[None, None])
-      multidimensional2 = array_ops.placeholder(
-          dtype=dtypes.float32, shape=[None, None])
-
-      # Scalar
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(scalar, scalar1).eval({
-              scalar1: 2.0
-          }))
-
-      # Vector
-
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(vector, vector1).eval({
-              vector1: [2.0, 3.0, 4.0]
-          }))
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(vector1, vector2).eval({
-              vector1: [2.0, 3.0, 4.0],
-              vector2: [2.0, 3.5, 6.0]
-          }))
-
-      # Multidimensional
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(
-              multidimensional, multidimensional1).eval({
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-              }))
-      self.assertTrue(
-          distribution_util.same_dynamic_shape(
-              multidimensional1, multidimensional2).eval({
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
-                  multidimensional2: [[1.0, 3.5], [6.3, 2.3]]
-              }))
-
-      # Scalar, X
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar, vector1).eval({
-              vector1: [2.0, 3.0, 4.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar1, vector1).eval({
-              scalar1: 2.0,
-              vector1: [2.0, 3.0, 4.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar, multidimensional1).eval({
-              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(scalar1, multidimensional1).eval(
-              {
-                  scalar1: 2.0,
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-              }))
-
-      # Vector, X
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector, vector1).eval({
-              vector1: [2.0, 3.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector1, vector2).eval({
-              vector1: [2.0, 3.0, 4.0],
-              vector2: [6.0]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector, multidimensional1).eval({
-              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-          }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(vector1, multidimensional1).eval(
-              {
-                  vector1: [2.0, 3.0, 4.0],
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
-              }))
-
-      # Multidimensional, X
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(
-              multidimensional, multidimensional1).eval({
-                  multidimensional1: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
-              }))
-      self.assertFalse(
-          distribution_util.same_dynamic_shape(
-              multidimensional1, multidimensional2).eval({
-                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
-                  multidimensional2: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
-              }))
-
-
-class RotateTransposeTest(test.TestCase):
-
-  def _np_rotate_transpose(self, x, shift):
-    if not isinstance(x, np.ndarray):
-      x = np.array(x)
-    return np.transpose(x, np.roll(np.arange(len(x.shape)), shift))
-
-  def testRollStatic(self):
+  def testWorksCorrectlyNoBatches(self):
     with self.test_session():
-      with self.assertRaisesRegexp(ValueError, "None values not supported."):
-        distribution_util.rotate_transpose(None, 1)
-      for x in (np.ones(1), np.ones((2, 1)), np.ones((3, 2, 1))):
-        for shift in np.arange(-5, 5):
-          y = distribution_util.rotate_transpose(x, shift)
-          self.assertAllEqual(self._np_rotate_transpose(x, shift), y.eval())
-          self.assertAllEqual(np.roll(x.shape, shift), y.get_shape().as_list())
-
-  def testRollDynamic(self):
-    with self.test_session() as sess:
-      x = array_ops.placeholder(dtypes.float32)
-      shift = array_ops.placeholder(dtypes.int32)
-      for x_value in (np.ones(
-          1, dtype=x.dtype.as_numpy_dtype()), np.ones(
-              (2, 1), dtype=x.dtype.as_numpy_dtype()), np.ones(
-                  (3, 2, 1), dtype=x.dtype.as_numpy_dtype())):
-        for shift_value in np.arange(-5, 5):
-          self.assertAllEqual(
-              self._np_rotate_transpose(x_value, shift_value),
-              sess.run(distribution_util.rotate_transpose(x, shift),
-                       feed_dict={x: x_value,
-                                  shift: shift_value}))
-
-
-class PickVectorTest(test.TestCase):
-
-  def testCorrectlyPicksVector(self):
-    with self.test_session():
-      x = np.arange(10, 12)
-      y = np.arange(15, 18)
-      self.assertAllEqual(x,
-                          distribution_util.pick_vector(
-                              math_ops.less(0, 5), x, y).eval())
-      self.assertAllEqual(y,
-                          distribution_util.pick_vector(
-                              math_ops.less(5, 0), x, y).eval())
-      self.assertAllEqual(x,
-                          distribution_util.pick_vector(
-                              constant_op.constant(True), x, y))  # No eval.
-      self.assertAllEqual(y,
-                          distribution_util.pick_vector(
-                              constant_op.constant(False), x, y))  # No eval.
-
-
-class FillLowerTriangularTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _fill_lower_triangular(self, x):
-    """Numpy implementation of `fill_lower_triangular`."""
-    x = np.asarray(x)
-    d = x.shape[-1]
-    # d = n(n+1)/2 implies n is:
-    n = int(0.5 * (math.sqrt(1. + 8. * d) - 1.))
-    ids = np.tril_indices(n)
-    y = np.zeros(list(x.shape[:-1]) + [n, n], dtype=x.dtype)
-    y[..., ids[0], ids[1]] = x
-    return y
-
-  def testCorrectlyMakes1x1LowerTril(self):
-    with self.test_session():
-      x = ops.convert_to_tensor(self._rng.randn(3, 1))
-      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
-      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
-      self.assertAllEqual(expected.shape, actual.get_shape())
-      self.assertAllEqual(expected, actual.eval())
-
-  def testCorrectlyMakesNoBatchLowerTril(self):
-    with self.test_session():
-      x = ops.convert_to_tensor(self._rng.randn(10))
-      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
-      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
-      self.assertAllEqual(expected.shape, actual.get_shape())
-      self.assertAllEqual(expected, actual.eval())
-      g = gradients_impl.gradients(
-          distribution_util.fill_lower_triangular(x), x)
-      self.assertAllEqual(np.tri(4).reshape(-1), g[0].values.eval())
-
-  def testCorrectlyMakesBatchLowerTril(self):
-    with self.test_session():
-      x = ops.convert_to_tensor(self._rng.randn(2, 2, 6))
-      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
-      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
-      self.assertAllEqual(expected.shape, actual.get_shape())
-      self.assertAllEqual(expected, actual.eval())
       self.assertAllEqual(
-          np.ones((2, 2, 6)),
-          gradients_impl.gradients(
-              distribution_util.fill_lower_triangular(x), x)[0].eval())
+          [[4., 8., 0., 0.],
+           [1., 5., 9., 0.],
+           [0., 2., 6., 10.],
+           [0., 0., 3, 7.]],
+          distribution_util.tridiag(
+              [1., 2., 3.],
+              [4., 5., 6., 7.],
+              [8., 9., 10.]).eval())
+
+  def testWorksCorrectlyBatches(self):
+    with self.test_session():
+      self.assertAllClose(
+          [[[4., 8., 0., 0.],
+            [1., 5., 9., 0.],
+            [0., 2., 6., 10.],
+            [0., 0., 3, 7.]],
+           [[0.7, 0.1, 0.0, 0.0],
+            [0.8, 0.6, 0.2, 0.0],
+            [0.0, 0.9, 0.5, 0.3],
+            [0.0, 0.0, 1.0, 0.4]]],
+          distribution_util.tridiag(
+              [[1., 2., 3.],
+               [0.8, 0.9, 1.]],
+              [[4., 5., 6., 7.],
+               [0.7, 0.6, 0.5, 0.4]],
+              [[8., 9., 10.],
+               [0.1, 0.2, 0.3]]).eval(),
+          rtol=1e-5, atol=0.)
+
+  def testHandlesNone(self):
+    with self.test_session():
+      self.assertAllClose(
+          [[[4., 0., 0., 0.],
+            [0., 5., 0., 0.],
+            [0., 0., 6., 0.],
+            [0., 0., 0, 7.]],
+           [[0.7, 0.0, 0.0, 0.0],
+            [0.0, 0.6, 0.0, 0.0],
+            [0.0, 0.0, 0.5, 0.0],
+            [0.0, 0.0, 0.0, 0.4]]],
+          distribution_util.tridiag(
+              diag=[[4., 5., 6., 7.],
+                    [0.7, 0.6, 0.5, 0.4]]).eval(),
+          rtol=1e-5, atol=0.)
 
 
-class GenNewSeedTest(test.TestCase):
-
-  def testOnlyNoneReturnsNone(self):
-    self.assertFalse(distribution_util.gen_new_seed(0, "salt") is None)
-    self.assertTrue(distribution_util.gen_new_seed(None, "salt") is None)
-
-
-# TODO(jvdillon): Merge this test back into:
-# tensorflow/python/kernel_tests/softplus_op_test.py
-# once TF core is accepting new ops.
-class SoftplusTest(test.TestCase):
-
-  def _npSoftplus(self, np_features):
-    np_features = np.asarray(np_features)
-    zero = np.asarray(0).astype(np_features.dtype)
-    return np.logaddexp(zero, np_features)
-
-  def _testSoftplus(self, np_features, use_gpu=False):
-    np_features = np.asarray(np_features)
-    np_softplus = self._npSoftplus(np_features)
-    with self.test_session(use_gpu=use_gpu) as sess:
-      softplus = nn_ops.softplus(np_features)
-      softplus_inverse = distribution_util.softplus_inverse(softplus)
-      [tf_softplus, tf_softplus_inverse] = sess.run([
-          softplus, softplus_inverse])
-    self.assertAllCloseAccordingToType(np_softplus, tf_softplus)
-    rtol = {"float16": 0.07, "float32": 0.003, "float64": 0.002}.get(
-        str(np_features.dtype), 1e-6)
-    # This will test that we correctly computed the inverse by verifying we
-    # recovered the original input.
-    self.assertAllCloseAccordingToType(
-        np_features, tf_softplus_inverse,
-        atol=0., rtol=rtol)
-    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
-                        tf_softplus > 0)
-
-    self.assertShapeEqual(np_softplus, softplus)
-    self.assertShapeEqual(np_softplus, softplus_inverse)
-
-    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
-                        np.isfinite(tf_softplus))
-    self.assertAllEqual(np.ones_like(tf_softplus_inverse).astype(np.bool),
-                        np.isfinite(tf_softplus_inverse))
-
-  def testNumbers(self):
-    for t in [np.float16, np.float32, np.float64]:
-      lower = {np.float16: -15, np.float32: -50, np.float64: -50}.get(t, -100)
-      upper = {np.float16: 50, np.float32: 50, np.float64: 50}.get(t, 100)
-      self._testSoftplus(
-          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
-              [2, -1]),
-          use_gpu=False)
-      self._testSoftplus(
-          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
-              [2, -1]),
-          use_gpu=True)
-      log_eps = np.log(np.finfo(t).eps)
-      one = t(1)
-      ten = t(10)
-      self._testSoftplus(
-          [
-              log_eps, log_eps - one, log_eps + one, log_eps - ten,
-              log_eps + ten, -log_eps, -log_eps - one, -log_eps + one,
-              -log_eps - ten, -log_eps + ten
-          ],
-          use_gpu=False)
-      self._testSoftplus(
-          [
-              log_eps, log_eps - one, log_eps + one, log_eps - ten,
-              log_eps + ten - log_eps, -log_eps - one, -log_eps + one,
-              -log_eps - ten, -log_eps + ten
-          ],
-          use_gpu=True)
-
-  def testGradient(self):
-    with self.test_session():
-      x = constant_op.constant(
-          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
-          shape=[2, 5],
-          name="x")
-      y = nn_ops.softplus(x, name="softplus")
-      x_init = np.asarray(
-          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
-          dtype=np.float32,
-          order="F")
-      err = gradient_checker.compute_gradient_error(
-          x, [2, 5], y, [2, 5], x_init_value=x_init)
-    logging.vlog(2, "softplus (float) gradient err = ", err)
-    self.assertLess(err, 1e-4)
-
-  def testInverseSoftplusGradientNeverNan(self):
-    with self.test_session():
-      # Note that this range contains both zero and inf.
-      x = constant_op.constant(np.logspace(-8, 6).astype(np.float16))
-      y = distribution_util.softplus_inverse(x)
-      grads = gradients_impl.gradients(y, x)[0].eval()
-      # Equivalent to `assertAllFalse` (if it existed).
-      self.assertAllEqual(np.zeros_like(grads).astype(np.bool), np.isnan(grads))
-
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py b/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py
index 9ef68c4c2c..87cdd0485a 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/geometric_test.py
@@ -47,13 +47,13 @@ class GeometricTest(test.TestCase):
     invalid_ps = [-.01, -0.01, -2.]
     with self.test_session():
       with self.assertRaisesOpError("Condition x >= 0"):
-        geom = geometric.Geometric(probs=invalid_ps)
+        geom = geometric.Geometric(probs=invalid_ps, validate_args=True)
         geom.probs.eval()
 
     invalid_ps = [1.1, 3., 5.]
     with self.test_session():
       with self.assertRaisesOpError("Condition x <= y"):
-        geom = geometric.Geometric(probs=invalid_ps)
+        geom = geometric.Geometric(probs=invalid_ps, validate_args=True)
         geom.probs.eval()
 
   def testGeomLogPmf(self):
@@ -78,7 +78,7 @@ class GeometricTest(test.TestCase):
       probs = constant_op.constant([.9] * batch_size)
       x = array_ops.placeholder(dtypes.float32, shape=[6])
       feed_dict = {x: [2.5, 3.2, 4.3, 5.1, 6., 7.]}
-      geom = geometric.Geometric(probs=probs)
+      geom = geometric.Geometric(probs=probs, validate_args=True)
 
       with self.assertRaisesOpError("Condition x == y"):
         log_prob = geom.log_prob(x)
@@ -88,7 +88,7 @@ class GeometricTest(test.TestCase):
         log_prob = geom.log_prob(np.array([-1.], dtype=np.float32))
         log_prob.eval()
 
-      geom = geometric.Geometric(probs=probs, validate_args=False)
+      geom = geometric.Geometric(probs=probs)
       log_prob = geom.log_prob(x)
       self.assertEqual([6,], log_prob.get_shape())
       pmf = geom.prob(x)
@@ -216,7 +216,7 @@ class GeometricTest(test.TestCase):
 
   def testGeometricAtBoundary(self):
     with self.test_session():
-      geom = geometric.Geometric(probs=1.)
+      geom = geometric.Geometric(probs=1., validate_args=True)
 
       x = np.array([0., 2., 3., 4., 5., 6., 7.], dtype=np.float32)
       expected_log_prob = stats.geom.logpmf(x, [1.], loc=-1)
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py b/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
new file mode 100644
index 0000000000..8c856abf8b
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
@@ -0,0 +1,262 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Trigonometric functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.distributions.python.ops import trig
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.platform import test
+
+
+class SinhTest(test.TestCase):
+
+  def test_versus_numpy_scalar_positive_x(self):
+    x = 1.23
+    with self.test_session():
+      self.assertAllClose(np.sinh(x), trig.sinh(x).eval())
+
+  def test_versus_numpy_multidim_x(self):
+    x = [[0., -1.], [0.5, -2.]]
+    with self.test_session():
+      self.assertAllClose(np.sinh(x), trig.sinh(x).eval())
+
+
+class ArcSinhTest(test.TestCase):
+  """Test arcsinh.
+
+  Note that many tests below were run over a range of values using np.logspace.
+  The accuracy was highly dependent on the exact values used within that range,
+  and whether our approximation matched with numpy.  For that reason, we used
+  1000 values over every range, which ensures we hit most of the "tricky" values
+  """
+
+  def _assert_all_finite(self, values):
+    self.assertAllEqual(np.ones_like(values).astype(np.bool),
+                        np.isfinite(values))
+
+  def test_versus_numpy_scalar_positive_x(self):
+    x = 1.23
+    with self.test_session():
+      self.assertAllClose(np.arcsinh(x), trig.arcsinh(x).eval())
+
+  def test_versus_numpy_at_zero(self):
+    # Zero is especially difficult.
+    with self.test_session() as sess:
+      x = constant_op.constant(0.)
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(np.arcsinh(x_), y_)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_multidim_x(self):
+    with self.test_session() as sess:
+      x = constant_op.constant([[0., -1.], [0.5, -2.]])
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(np.arcsinh(x_), y_)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_positive_values_32_bit(self):
+    with self.test_session() as sess:
+      # Larger than 38 is Inf in float32.
+      x = constant_op.constant(np.logspace(0, 38, num=2000).astype(np.float32))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),  # numpy does this in 64bit
+          y_,
+          rtol=1e-6)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_moderate_negative_values_32_bit(self):
+    # The moderate negative values were the most difficult to get close to
+    # numpy.
+    with self.test_session() as sess:
+      x = constant_op.constant(-np.logspace(0, 10, num=1000).astype(np.float32))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),  # numpy does this in 64bit
+          y_,
+          rtol=1e-4)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_extreme_negative_values_32_bit(self):
+    # For these extreme values arcsinh uses the approximation 1 / (2 * x), and
+    # 1 / 10^38 = 0 in 32bit...so stop at 10^37.
+    with self.test_session() as sess:
+      x = constant_op.constant(
+          -np.logspace(10, 37, num=1000).astype(np.float32))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),  # numpy does this in 64bit
+          y_,
+          rtol=1e-6)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_positive_values_64_bit(self):
+    with self.test_session() as sess:
+      x = constant_op.constant(np.logspace(0, 200, num=1000))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),
+          y_,
+          rtol=1e-6)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_versus_numpy_negative_values_64_bit(self):
+    with self.test_session() as sess:
+      x = constant_op.constant(-np.logspace(0, 200, num=1000))
+      y = trig.arcsinh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      x_, y_, grad_ = sess.run([x, y, grad])
+      self.assertAllClose(
+          np.arcsinh(x_),
+          y_,
+          rtol=1e-5)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+  def test_arcsinh_is_inverse_to_sinh_near_zero(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    with self.test_session() as sess:
+      x = np.linspace(-1.1, 1.1, num=1000).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self.assertAllClose(x, sinh_arcsinh_x_)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+  def test_arcsinh_is_inverse_to_sinh_where_x_is_very_small(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    # Exact same cutoff as is in the code.
+    very_small_cutoff = -0.01 / np.sqrt(np.finfo(np.float32).eps)
+    with self.test_session() as sess:
+      x = -np.logspace(
+          np.log(-very_small_cutoff),
+          np.log(-1000 * very_small_cutoff),
+          num=1000).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+  def test_arcsinh_is_inverse_to_sinh_where_x_is_moderate_or_big(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    very_big_cutoff = np.sqrt(np.finfo(np.float32).max)
+    with self.test_session() as sess:
+      x = np.linspace(1., very_big_cutoff, num=1000).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self._assert_all_finite(arcsinh_x_)
+    self._assert_all_finite(sinh_arcsinh_x_)
+    self._assert_all_finite(arcsinh_sinh_arcsinh_x_)
+    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+  def test_arcsinh_is_inverse_to_sinh_where_x_is_very_big(self):
+    sinh = trig.sinh
+    arcsinh = trig.arcsinh
+    maxfloat = np.finfo(np.float32).max  # Maximum representable float.
+    with self.test_session() as sess:
+      x = np.logspace(
+          0.5 * np.log(maxfloat),
+          0.95 * np.log(maxfloat), num=1000, base=np.e).astype(np.float32)
+      arcsinh_x = arcsinh(x)
+      sinh_arcsinh_x = sinh(arcsinh_x)
+      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
+
+      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
+          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
+
+    self._assert_all_finite(arcsinh_x_)
+    self._assert_all_finite(sinh_arcsinh_x_)
+    self._assert_all_finite(arcsinh_sinh_arcsinh_x_)
+    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
+    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
+
+
+class CoshTest(test.TestCase):
+
+  def test_versus_numpy_scalar_positive_x(self):
+    x = 1.23
+    with self.test_session():
+      self.assertAllClose(np.cosh(x), trig.cosh(x).eval())
+
+  def test_versus_numpy_multidim_x(self):
+    x = [[0., -1.], [0.5, -2.]]
+    with self.test_session():
+      self.assertAllClose(np.cosh(x), trig.cosh(x).eval())
+
+
+class LogCoshTest(test.TestCase):
+
+  def _assert_all_finite(self, values):
+    self.assertAllEqual(np.ones_like(values).astype(np.bool),
+                        np.isfinite(values))
+
+  def test_versus_numpy_scalar_32bit(self):
+    with self.test_session() as sess:
+      x_64 = np.linspace(-200, 200, 2000)
+      x = constant_op.constant(x_64.astype(np.float32))
+
+      y = trig.log_cosh(x)
+      grad = gradients_impl.gradients(y, x)[0]
+      y_, grad_ = sess.run([y, grad])
+
+      self.assertAllClose(np.log(np.cosh(x_64)), y_)
+      self._assert_all_finite(y_)
+      self._assert_all_finite(grad_)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py b/tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py
new file mode 100644
index 0000000000..fd05bd207f
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/vector_exponential_diag_test.py
@@ -0,0 +1,206 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for VectorExponentialLinearOperator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from tensorflow.contrib import distributions
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import test
+
+
+ds = distributions
+
+
+class VectorExponentialDiagTest(test.TestCase):
+  """Well tested because this is a simple override of the base class."""
+
+  def setUp(self):
+    self._rng = np.random.RandomState(42)
+
+  def testScalarParams(self):
+    mu = -1.
+    diag = -5.
+    with self.test_session():
+      with self.assertRaisesRegexp(ValueError, "at least 1 dimension"):
+        ds.VectorExponentialDiag(mu, diag)
+
+  def testVectorParams(self):
+    mu = [-1.]
+    diag = [-5.]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([3, 1], dist.sample(3).get_shape())
+
+  def testMean(self):
+    mu = [-1., 1]
+    diag = [1., -5]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([-1. + 1., 1. - 5.], dist.mean().eval())
+
+  def testMode(self):
+    mu = [-1.]
+    diag = [1., -5]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([-1., -1.], dist.mode().eval())
+
+  def testMeanWithBroadcastLoc(self):
+    mu = [-1.]
+    diag = [1., -5]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      self.assertAllEqual([-1. + 1, -1. - 5], dist.mean().eval())
+
+  def testSample(self):
+    mu = [-2., 1]
+    diag = [1., -2]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      samps = dist.sample(int(1e4), seed=0).eval()
+      cov_mat = array_ops.matrix_diag(diag).eval()**2
+
+      self.assertAllClose([-2 + 1, 1. - 2], samps.mean(axis=0),
+                          atol=0., rtol=0.05)
+      self.assertAllClose(cov_mat, np.cov(samps.T),
+                          atol=0.05, rtol=0.05)
+
+  def testSingularScaleRaises(self):
+    mu = [-1., 1]
+    diag = [1., 0]
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+      with self.assertRaisesOpError("Singular"):
+        dist.sample().eval()
+
+  def testSampleWithBroadcastScale(self):
+    # mu corresponds to a 2-batch of 3-variate normals
+    mu = np.zeros([2, 3])
+
+    # diag corresponds to no batches of 3-variate normals
+    diag = np.ones([3])
+
+    with self.test_session():
+      dist = ds.VectorExponentialDiag(mu, diag, validate_args=True)
+
+      mean = dist.mean()
+      self.assertAllEqual([2, 3], mean.get_shape())
+      self.assertAllClose(mu + diag, mean.eval())
+
+      n = int(1e4)
+      samps = dist.sample(n, seed=0).eval()
+      samps_centered = samps - samps.mean(axis=0)
+      cov_mat = array_ops.matrix_diag(diag).eval()**2
+      sample_cov = np.matmul(samps_centered.transpose([1, 2, 0]),
+                             samps_centered.transpose([1, 0, 2])) / n
+
+      self.assertAllClose(mu + diag, samps.mean(axis=0),
+                          atol=0.10, rtol=0.05)
+      self.assertAllClose([cov_mat, cov_mat], sample_cov,
+                          atol=0.10, rtol=0.05)
+
+  def testCovariance(self):
+    with self.test_session():
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([2, 3], dtype=dtypes.float32))
+      self.assertAllClose(
+          np.diag(np.ones([3], dtype=np.float32)),
+          vex.covariance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_identity_multiplier=[3., 2.])
+      self.assertAllEqual([2], vex.batch_shape)
+      self.assertAllEqual([3], vex.event_shape)
+      self.assertAllClose(
+          np.array([[[3., 0, 0],
+                     [0, 3, 0],
+                     [0, 0, 3]],
+                    [[2, 0, 0],
+                     [0, 2, 0],
+                     [0, 0, 2]]])**2.,
+          vex.covariance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_diag=[[3., 2, 1], [4, 5, 6]])
+      self.assertAllEqual([2], vex.batch_shape)
+      self.assertAllEqual([3], vex.event_shape)
+      self.assertAllClose(
+          np.array([[[3., 0, 0],
+                     [0, 2, 0],
+                     [0, 0, 1]],
+                    [[4, 0, 0],
+                     [0, 5, 0],
+                     [0, 0, 6]]])**2.,
+          vex.covariance().eval())
+
+  def testVariance(self):
+    with self.test_session():
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([2, 3], dtype=dtypes.float32))
+      self.assertAllClose(
+          np.ones([3], dtype=np.float32),
+          vex.variance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_identity_multiplier=[3., 2.])
+      self.assertAllClose(
+          np.array([[3., 3, 3],
+                    [2., 2, 2]])**2.,
+          vex.variance().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.ones([3], dtype=dtypes.float32),
+          scale_diag=[[3., 2, 1],
+                      [4., 5, 6]])
+      self.assertAllClose(
+          np.array([[3., 2, 1],
+                    [4., 5, 6]])**2.,
+          vex.variance().eval())
+
+  def testStddev(self):
+    with self.test_session():
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([2, 3], dtype=dtypes.float32))
+      self.assertAllClose(
+          np.ones([3], dtype=np.float32),
+          vex.stddev().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([3], dtype=dtypes.float32),
+          scale_identity_multiplier=[3., 2.])
+      self.assertAllClose(
+          np.array([[3., 3, 3],
+                    [2., 2, 2]]),
+          vex.stddev().eval())
+
+      vex = ds.VectorExponentialDiag(
+          loc=array_ops.zeros([3], dtype=dtypes.float32),
+          scale_diag=[[3., 2, 1], [4, 5, 6]])
+      self.assertAllClose(
+          np.array([[3., 2, 1],
+                    [4., 5, 6]]),
+          vex.stddev().eval())
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
index 1684a5fffe..f1879a828a 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
@@ -27,6 +27,7 @@
 @@PowerTransform
 @@Sigmoid
 @@SigmoidCentered
+@@SinhArcsinh
 @@SoftmaxCentered
 @@Softplus
 """
@@ -48,6 +49,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.invert import *
 from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import *
 from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import *
 from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid_centered import *
+from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh import *
 from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import *
 from tensorflow.contrib.distributions.python.ops.bijectors.softplus import *
 from tensorflow.python.ops.distributions.bijector import *
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py
new file mode 100644
index 0000000000..b3cf03c246
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py
@@ -0,0 +1,29 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SinhArcsinh bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import
+from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh_impl import *
+# pylint: enable=wildcard-import
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = ["SinhArcsinh"]
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
new file mode 100644
index 0000000000..27359bad32
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
@@ -0,0 +1,143 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SinhArcsinh bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.distributions.python.ops import trig
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import bijector
+
+sinh = trig.sinh
+arcsinh = trig.arcsinh
+cosh = trig.cosh
+log_cosh = trig.log_cosh
+
+__all__ = [
+    "SinhArcsinh",
+]
+
+
+class SinhArcsinh(bijector.Bijector):
+  """Compute `Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight )`.
+
+  For `skewness in (-inf, inf)` and `tailweight in (0, inf)`, this
+  transformation is a
+  diffeomorphism of the real line `(-inf, inf)`.  The inverse transform is
+  `X = g^{-1}(Y) = Sinh( ArcSinh(Y) / tailweight - skewness )`.
+
+  The `SinhArcsinh` transformation of the Normal is described in
+  [Sinh-arcsinh distributions](https://www.jstor.org/stable/27798865)
+  This Bijector allows a similar transformation of any distribution supported on
+  `(-inf, inf)`.
+
+  #### Meaning of the parameters
+
+  * If `skewness = 0` and `tailweight = 1`, this transform is the identity.
+  * Positive (negative) `skewness` leads to positive (negative) skew.
+    * positive skew means, for unimodal `X` centered at zero, the mode of `Y` is
+      "tilted" to the right.
+    * positive skew means positive values of `Y` become more likely, and
+      negative values become less likely.
+  * Larger (smaller) `tailweight` leads to fatter (thinner) tails.
+    * Fatter tails mean larger values of `|Y|` become more likely.
+    * If `X` is a unit Normal, `tailweight < 1` leads to a distribution that is
+      "flat" around `Y = 0`, and a very steep drop-off in the tails.
+    * If `X` is a unit Normal, `tailweight > 1` leads to a distribution more
+      peaked at the mode with heavier tails.
+
+  To see the argument about the tails, note that for `|X| >> 1` and
+  `|X| >> (|skewness| * tailweight)**tailweight`, we have
+  `Y approx 0.5 X**tailweight e**(sign(X) skewness * tailweight)`.
+  """
+
+  def __init__(self,
+               skewness=0.,
+               tailweight=1.,
+               event_ndims=0,
+               validate_args=False,
+               name="sinh_arcsinh"):
+    """Instantiates the `SinhArcsinh` bijector.
+
+    Args:
+      skewness:  Skewness parameter.  Float-type `Tensor`.
+      tailweight:  Tailweight parameter.  Positive `Tensor` of same `dtype` as
+        `skewness`
+        and broadcastable `shape`.
+      event_ndims: Python scalar indicating the number of dimensions associated
+        with a particular draw from the distribution.
+      validate_args: Python `bool` indicating whether arguments should be
+        checked for correctness.
+      name: Python `str` name given to ops managed by this object.
+    """
+    self._graph_parents = []
+    self._name = name
+    self._validate_args = validate_args
+    with self._name_scope("init", values=[skewness, tailweight]):
+      self._skewness = ops.convert_to_tensor(skewness, name="skewness")
+      self._tailweight = ops.convert_to_tensor(tailweight, name="tailweight")
+      check_ops.assert_same_float_dtype([self._skewness, self._tailweight])
+      if validate_args:
+        self._tailweight = control_flow_ops.with_dependencies([
+            check_ops.assert_positive(
+                self._tailweight,
+                message="Argument tailweight was not positive")
+        ], self._tailweight)
+    super(SinhArcsinh, self).__init__(
+        event_ndims=event_ndims, validate_args=validate_args, name=name)
+
+  @property
+  def skewness(self):
+    """The `skewness` in: `Y  = Sinh((Arcsinh(X) + skewness) * tailweight)`."""
+    return self._skewness
+
+  @property
+  def tailweight(self):
+    """The `tailweight` in: `Y = Sinh((Arcsinh(X) + skewness) * tailweight)`."""
+    return self._tailweight
+
+  def _forward(self, x):
+    return sinh((arcsinh(x) + self.skewness) * self.tailweight)
+
+  def _inverse(self, y):
+    return sinh(arcsinh(y) / self.tailweight - self.skewness)
+
+  def _inverse_log_det_jacobian(self, y):
+    # x = sinh(arcsinh(y) / tailweight - skewness)
+    # Using sinh' = cosh, arcsinh'(y) = 1 / sqrt(y**2 + 1),
+    # dx/dy
+    # = cosh(arcsinh(y) / tailweight - skewness)
+    #     / (tailweight * sqrt(y**2 + 1))
+    event_dims = self._event_dims_tensor(y)
+    return math_ops.reduce_sum(
+        log_cosh(arcsinh(y) / self.tailweight - self.skewness) -
+        math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(y**2),
+        axis=event_dims)
+
+  def _forward_log_det_jacobian(self, x):
+    # y = sinh((arcsinh(x) + skewness) * tailweight)
+    # Using sinh' = cosh, arcsinh'(x) = 1 / sqrt(x**2 + 1),
+    # dy/dx
+    # = cosh((arcsinh(x) + skewness) * tailweight) * tailweight / sqrt(x**2 + 1)
+    event_dims = self._event_dims_tensor(x)
+    return math_ops.reduce_sum(
+        log_cosh((arcsinh(x) + self.skewness) * self.tailweight) +
+        math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(x**2),
+        axis=event_dims)
diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py
index d23af50167..6a1bb39ab2 100644
--- a/tensorflow/contrib/distributions/python/ops/binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/binomial.py
@@ -272,13 +272,11 @@ class Binomial(distribution.Distribution):
             message="total_count cannot contain fractional components."),
     ], total_count)
 
-  def _maybe_assert_valid_sample(self, counts, check_integer=True):
+  def _maybe_assert_valid_sample(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
     if not self.validate_args:
       return counts
-
-    counts = distribution_util.embed_check_nonnegative_discrete(
-        counts, check_integer=check_integer)
+    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
     return control_flow_ops.with_dependencies([
         check_ops.assert_less_equal(
             counts, self.total_count,
diff --git a/tensorflow/contrib/distributions/python/ops/geometric.py b/tensorflow/contrib/distributions/python/ops/geometric.py
index 918200830c..8f190e48a7 100644
--- a/tensorflow/contrib/distributions/python/ops/geometric.py
+++ b/tensorflow/contrib/distributions/python/ops/geometric.py
@@ -58,8 +58,8 @@ class Geometric(distribution.Distribution):
   def __init__(self,
                logits=None,
                probs=None,
-               validate_args=True,
-               allow_nan_stats=False,
+               validate_args=False,
+               allow_nan_stats=True,
                name="Geometric"):
     """Construct Geometric distributions.
 
@@ -143,32 +143,32 @@ class Geometric(distribution.Distribution):
     return math_ops.floor(
         math_ops.log(sampled) / math_ops.log1p(-self.probs))
 
-  def _cdf(self, counts):
+  def _cdf(self, x):
     if self.validate_args:
-      # We set `check_integer=False` since the CDF is defined on whole real
-      # line.
-      counts = math_ops.floor(
-          distribution_util.embed_check_nonnegative_discrete(
-              counts, check_integer=False))
-    counts *= array_ops.ones_like(self.probs)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # Whether or not x is integer-form, the following is well-defined.
+      # However, scipy takes the floor, so we do too.
+      x = math_ops.floor(x)
+    x *= array_ops.ones_like(self.probs)
     return array_ops.where(
-        counts < 0.,
-        array_ops.zeros_like(counts),
-        -math_ops.expm1(
-            (counts + 1) * math_ops.log1p(-self.probs)))
+        x < 0.,
+        array_ops.zeros_like(x),
+        -math_ops.expm1((1. + x) * math_ops.log1p(-self.probs)))
 
-  def _log_prob(self, counts):
+  def _log_prob(self, x):
     if self.validate_args:
-      counts = distribution_util.embed_check_nonnegative_discrete(
-          counts, check_integer=True)
-    counts *= array_ops.ones_like(self.probs)
-    probs = self.probs * array_ops.ones_like(counts)
-
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # For consistency with cdf, we take the floor.
+      x = math_ops.floor(x)
+    x *= array_ops.ones_like(self.probs)
+    probs = self.probs * array_ops.ones_like(x)
     safe_domain = array_ops.where(
-        math_ops.equal(counts, 0.),
+        math_ops.equal(x, 0.),
         array_ops.zeros_like(probs),
         probs)
-    return counts * math_ops.log1p(-safe_domain) + math_ops.log(probs)
+    return x * math_ops.log1p(-safe_domain) + math_ops.log(probs)
 
   def _entropy(self):
     probs = self._probs
diff --git a/tensorflow/contrib/distributions/python/ops/negative_binomial.py b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
index 8895fd8b46..c8c396f6f8 100644
--- a/tensorflow/contrib/distributions/python/ops/negative_binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
@@ -148,39 +148,33 @@ class NegativeBinomial(distribution.Distribution):
         beta=math_ops.exp(-self.logits),
         dtype=self.dtype,
         seed=seed)
-
     return random_ops.random_poisson(
         rate,
         shape=[],
         dtype=self.dtype,
         seed=distribution_util.gen_new_seed(seed, "negative_binom"))
 
-  def _cdf(self, positive_counts):
+  def _cdf(self, x):
     if self.validate_args:
-      positive_counts = math_ops.floor(
-          distribution_util.embed_check_nonnegative_discrete(
-              positive_counts, check_integer=False))
-    return math_ops.betainc(
-        self.total_count, positive_counts + 1.,
-        math_ops.sigmoid(-self.logits))
-
-  def _log_prob(self, positive_counts):
-    return (self._log_unnormalized_prob(positive_counts)
-            - self._log_normalization(positive_counts))
-
-  def _log_unnormalized_prob(self, positive_counts):
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    return math_ops.betainc(self.total_count, 1. + x,
+                            math_ops.sigmoid(-self.logits))
+
+  def _log_prob(self, x):
+    return (self._log_unnormalized_prob(x)
+            - self._log_normalization(x))
+
+  def _log_unnormalized_prob(self, x):
     if self.validate_args:
-      positive_counts = distribution_util.embed_check_nonnegative_discrete(
-          positive_counts, check_integer=True)
-    return self.total_count * math_ops.log1p(
-        -self.probs) + positive_counts * math_ops.log(self.probs)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    return (self.total_count * math_ops.log1p(-self.probs)
+            + x * math_ops.log(self.probs))
 
-  def _log_normalization(self, positive_counts):
+  def _log_normalization(self, x):
     if self.validate_args:
-      positive_counts = distribution_util.embed_check_nonnegative_discrete(
-          positive_counts, check_integer=True)
-    return (-math_ops.lgamma(self.total_count + positive_counts)
-            + math_ops.lgamma(positive_counts + 1.)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    return (-math_ops.lgamma(self.total_count + x)
+            + math_ops.lgamma(1. + x)
             + math_ops.lgamma(self.total_count))
 
   def _mean(self):
diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py
index d9929183c1..59a98e5682 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson.py
@@ -114,10 +114,6 @@ class Poisson(distribution.Distribution):
   def _log_prob(self, x):
     return self._log_unnormalized_prob(x) - self._log_normalization()
 
-  @distribution_util.AppendDocstring(_poisson_sample_note)
-  def _prob(self, x):
-    return math_ops.exp(self._log_prob(x))
-
   @distribution_util.AppendDocstring(_poisson_sample_note)
   def _log_cdf(self, x):
     return math_ops.log(self.cdf(x))
@@ -125,20 +121,23 @@ class Poisson(distribution.Distribution):
   @distribution_util.AppendDocstring(_poisson_sample_note)
   def _cdf(self, x):
     if self.validate_args:
-      # We set `check_integer=False` since the CDF is defined on whole real
-      # line.
-      x = distribution_util.embed_check_nonnegative_discrete(
-          x, check_integer=False)
-    return math_ops.igammac(math_ops.floor(x + 1), self.rate)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # Whether or not x is integer-form, the following is well-defined.
+      # However, scipy takes the floor, so we do too.
+      x = math_ops.floor(x)
+    return math_ops.igammac(1. + x, self.rate)
 
   def _log_normalization(self):
     return self.rate
 
   def _log_unnormalized_prob(self, x):
     if self.validate_args:
-      x = distribution_util.embed_check_nonnegative_discrete(
-          x, check_integer=True)
-    return x * math_ops.log(self.rate) - math_ops.lgamma(x + 1)
+      x = distribution_util.embed_check_nonnegative_integer_form(x)
+    else:
+      # For consistency with cdf, we take the floor.
+      x = math_ops.floor(x)
+    return x * math_ops.log(self.rate) - math_ops.lgamma(1. + x)
 
   def _mean(self):
     return array_ops.identity(self.rate)
diff --git a/tensorflow/contrib/distributions/python/ops/trig.py b/tensorflow/contrib/distributions/python/ops/trig.py
new file mode 100644
index 0000000000..e49288e5b1
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/trig.py
@@ -0,0 +1,173 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Trigonometric functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import math_ops
+
+__all__ = [
+    "sinh",
+    "arcsinh",
+    "cosh",
+    "log_cosh",
+]
+
+
+def sinh(x, name="sinh"):
+  """Hyperbolic sin:  `sinh(x) = (e**x - e**-x) / 2`.
+
+  For `x in (-inf, inf)`, `arcsinh(sinh(x)) = sinh(arcsinh(x)) = x.`
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  with ops.name_scope(name):
+    # For x near zero, this could be replaced with the identity x --> x as a
+    # Taylor approximation.  The Taylor approximation would be more accurate,
+    # and the current implementation will equal zero "too often", specifically
+    # for |x| small enough so that exp(x) = 1 (up to floating point precision).
+    # Although less accurate, the present implementation is still safe for
+    # antipated use.  Why?  No one would use this function and expect that zero
+    # was not a possible output value.
+    x = ops.convert_to_tensor(x, name="x")
+    return 0.5 * (math_ops.exp(x) - math_ops.exp(-x))
+
+
+def arcsinh(x, name="arcsinh"):
+  """Inverse hyperbolic sin:  `arcsinh(x) = log(x + sqrt(x**2 + 1))`.
+
+  For `x in (-inf, inf)`, `arcsinh(sinh(x)) = sinh(arcsinh(x)) = x.`
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  with ops.name_scope(name):
+    x = ops.convert_to_tensor(x, name="x")
+    finfo = np.finfo(x.dtype.as_numpy_dtype)
+
+    # To compute stable arcsinh(x), we will compute various approximations of
+    # z := x + sqrt(x**2 + 1), and then arcsinh(x) = log(z).
+    # Different approximations are used over different values of x, then the
+    # result is pieced together using tf.where.  Since NaN propagate through the
+    # unselected branch during grdients of where, care is taken to ensure that
+    # every approximation is finite for all input values.
+
+    # For x near zero, the straightforward formula for z is fine.
+    # This formula will have trouble once x < 0 and x**2 + 1 = x**2, since then
+    # z = 0 (numerically), and then we have log(z) = log(0) = -inf
+    # This formula also has trouble once x > sqrt(finfo.max), since then
+    # x**2 = inf, and thus z = inf.  Therefore we clip.
+    x_near_zero = clip_ops.clip_by_value(x, -1., 1.)
+    x_is_near_zero = math_ops.abs(x) < 1.
+    z_for_x_near_zero = x + math_ops.sqrt(x_near_zero**2 + 1)
+
+    # Some cutoffs.
+    # Important!  Keep these cutoffs in sync with the tests, which use cutoffs
+    # of the exact same name.
+    # very_big_cutoff**2 = finfo.max, the maximum representable value.
+    # very_small_cutoff could have been defined as 1 / sqrt(eps), so that for
+    # x < very_small_cutoff, 1 / x**2 + 1 = 1, which causes trouble.
+    # The 0.01 was added in order to make this match numpy in 32bit
+    # as much as possible.  Anything < 1 should be stable.
+    very_small_cutoff = -0.01 / np.sqrt(finfo.eps)
+    very_big_cutoff = np.sqrt(finfo.max)
+
+    # For very_small_cutoff < x < -1, and 1 < x < very_big_cutoff, and
+    # x != 0, we can use
+    # z = sqrt(x**2 + 1) = |x| * sqrt(1 + 1 / x**2).
+    # This formula has trouble if x < -sqrt(eps) since then 1 + 1 / x**2 = 1,
+    # and then we get z = x + |x| = 0, thus returning log(0) = -inf.
+    # This formula also has trouble if x**2 = Inf.  Therefore we clip.
+    # This formula also has trouble if x = 0, since then we have 1 / 0**2 = inf.
+    x_not_near_zero = array_ops.where(
+        x >= 0.,
+        math_ops.maximum(x, 1.),
+        math_ops.minimum(x, -1.))
+    x_clipped_moderate_or_big = clip_ops.clip_by_value(
+        x_not_near_zero, very_small_cutoff, very_big_cutoff)
+    z_for_moderate_or_big_x = x + math_ops.abs(x) * math_ops.sqrt(
+        1. + 1. / x_clipped_moderate_or_big**2)
+
+    # For x < very_small_cutoff, we use the first order Taylor series,
+    # sqrt(1 + 1 / x**2) approx 1 + 1 / (2 * x**2)
+    # This formula has trouble for x = 0.
+    x_is_very_small = x < very_small_cutoff
+    z_for_very_small_x = 1 / (2. * math_ops.abs(x_not_near_zero))
+
+    z = array_ops.where(
+        x_is_near_zero,
+        z_for_x_near_zero,
+        array_ops.where(
+            x_is_very_small,
+            z_for_very_small_x,
+            z_for_moderate_or_big_x))
+
+    return math_ops.log(z)
+
+
+def cosh(x, name="cosh"):
+  """Hyperbolic cosine:  `cosh(x) = (e**x + e**-x) / 2`.
+
+  For `x in (-inf, inf)`, `arccosh(cosh(x)) = cosh(arccosh(x)) = x.`
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  with ops.name_scope(name):
+    x = ops.convert_to_tensor(x, name="x")
+    return 0.5 * (math_ops.exp(x) + math_ops.exp(-x))
+
+
+def log_cosh(x, name="log_cosh"):
+  """Logarithm of hyperbolic cosine:  `log_cosh(x) = Log[(e**x + e**-x) / 2]`.
+
+  Args:
+    x:  Numeric `Tensor`.
+    name:  A string name to prepend to created Ops.
+
+  Returns:
+    Numeric `Tensor` of same `shape` and `dtype` as `x`.
+  """
+  # For large |x| >> 1, e**x will become Inf.  So we need to approximate
+  # Log[e**x + e**-x] approx |x|.
+  # We also need to ensure that large |x| is never fed to the exponential func.
+  with ops.name_scope(name):
+    x = ops.convert_to_tensor(x, name="x")
+    large_x_value = 0.9 * np.log(np.finfo(x.dtype.as_numpy_dtype).max)
+    x_capped = clip_ops.clip_by_value(x, -large_x_value, large_x_value)
+    return array_ops.where(
+        math_ops.abs(x) > large_x_value,
+        math_ops.abs(x) - np.log(2).astype(x.dtype.as_numpy_dtype),
+        math_ops.log(cosh(x_capped)))
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
new file mode 100644
index 0000000000..c88572e17f
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
@@ -0,0 +1,198 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Distribution of a vectorized Exponential, with uncorrelated components."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.distributions.python.ops import vector_exponential_linear_operator as vector_exponential_linop
+from tensorflow.python.framework import ops
+
+
+__all__ = [
+    "VectorExponentialDiag",
+]
+
+
+class VectorExponentialDiag(
+    vector_exponential_linop.VectorExponentialLinearOperator):
+  """The vectorization of the Exponential distribution on `R^k`.
+
+  The vector exponential distribution is defined over a subset of `R^k`, and
+  parameterized by a (batch of) length-`k` `loc` vector and a (batch of) `k x k`
+  `scale` matrix:  `covariance = scale @ scale.T`, where `@` denotes
+  matrix-multiplication.
+
+  #### Mathematical Details
+
+  The probability density function (pdf) is defined over the image of the
+  `scale` matrix + `loc`, applied to the positive half-space:
+  `Supp = {loc + scale @ x : x in R^k, x_1 > 0, ..., x_k > 0}`.  On this set,
+
+  ```none
+  pdf(y; loc, scale) = exp(-||x||_1) / Z,  for y in Supp
+  x = inv(scale) @ (y - loc),
+  Z = |det(scale)|,
+  ```
+
+  where:
+
+  * `loc` is a vector in `R^k`,
+  * `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`,
+  * `Z` denotes the normalization constant, and,
+  * `||x||_1` denotes the `l1` norm of `x`, `sum_i |x_i|`.
+
+  The VectorExponential distribution is a member of the [location-scale
+  family](https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
+  constructed as,
+
+  ```none
+  X = (X_1, ..., X_k), each X_i ~ Exponential(rate=1)
+  Y = (Y_1, ...,Y_k) = scale @ X + loc
+  ```
+
+  #### About `VectorExponential` and `Vector` distributions in TensorFlow.
+
+  The `VectorExponential` is a non-standard distribution that has useful
+  properties.
+
+  The marginals `Y_1, ..., Y_k` are *not* Exponential random variables, due to
+  the fact that the sum of Exponential random variables is not Exponential.
+
+  Instead, `Y` is a vector whose components are linear combinations of
+  Exponential random variables.  Thus, `Y` lives in the vector space generated
+  by `vectors` of Exponential distributions.  This allows the user to decide the
+  mean and covariance (by setting `loc` and `scale`), while preserving some
+  properties of the Exponential distribution.  In particular, the tails of `Y_i`
+  will be (up to polynomial factors) exponentially decaying.
+
+  To see this last statement, note that the pdf of `Y_i` is the convolution of
+  the pdf of `k` independent Exponential random variables.  One can then show by
+  induction that distributions with exponential (up to polynomial factors) tails
+  are closed under convolution.
+
+
+  #### Examples
+
+  ```python
+  ds = tf.contrib.distributions
+  la = tf.contrib.linalg
+
+  # Initialize a single 2-variate VectorExponential, supported on
+  # {(x, y) in R^2 : x > 0, y > 0}.
+
+  # The first component has pdf exp{-x}, the second 0.5 exp{-x / 2}
+  vex = ds.VectorExponentialDiag(scale_diag=[1., 2.])
+
+  # Compute the pdf of an`R^2` observation; return a scalar.
+  vex.prob([3., 4.]).eval()  # shape: []
+
+  # Initialize a 2-batch of 3-variate Vector Exponential's.
+  loc = [[1., 2, 3],
+         [1., 0, 0]]              # shape: [2, 3]
+  scale_diag = [[1., 2, 3],
+                [0.5, 1, 1.5]]     # shape: [2, 3]
+
+  vex = ds.VectorExponentialDiag(loc, scale_diag)
+
+  # Compute the pdf of two `R^3` observations; return a length-2 vector.
+  x = [[1.9, 2.2, 3.1],
+       [10., 1.0, 9.0]]     # shape: [2, 3]
+  vex.prob(x).eval()    # shape: [2]
+  ```
+
+  """
+
+  def __init__(self,
+               loc=None,
+               scale_diag=None,
+               scale_identity_multiplier=None,
+               validate_args=False,
+               allow_nan_stats=True,
+               name="VectorExponentialDiag"):
+    """Construct Vector Exponential distribution supported on a subset of `R^k`.
+
+    The `batch_shape` is the broadcast shape between `loc` and `scale`
+    arguments.
+
+    The `event_shape` is given by last dimension of the matrix implied by
+    `scale`. The last dimension of `loc` (if provided) must broadcast with this.
+
+    Recall that `covariance = scale @ scale.T`.
+
+    ```none
+    scale = diag(scale_diag + scale_identity_multiplier * ones(k))
+    ```
+
+    where:
+
+    * `scale_diag.shape = [k]`, and,
+    * `scale_identity_multiplier.shape = []`.
+
+    Additional leading dimensions (if any) will index batches.
+
+    If both `scale_diag` and `scale_identity_multiplier` are `None`, then
+    `scale` is the Identity matrix.
+
+    Args:
+      loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
+        implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
+        `b >= 0` and `k` is the event size.
+      scale_diag: Non-zero, floating-point `Tensor` representing a diagonal
+        matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
+        and characterizes `b`-batches of `k x k` diagonal matrices added to
+        `scale`. When both `scale_identity_multiplier` and `scale_diag` are
+        `None` then `scale` is the `Identity`.
+      scale_identity_multiplier: Non-zero, floating-point `Tensor` representing
+        a scaled-identity-matrix added to `scale`. May have shape
+        `[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scaled
+        `k x k` identity matrices added to `scale`. When both
+        `scale_identity_multiplier` and `scale_diag` are `None` then `scale` is
+        the `Identity`.
+      validate_args: Python `bool`, default `False`. When `True` distribution
+        parameters are checked for validity despite possibly degrading runtime
+        performance. When `False` invalid inputs may silently render incorrect
+        outputs.
+      allow_nan_stats: Python `bool`, default `True`. When `True`,
+        statistics (e.g., mean, mode, variance) use the value "`NaN`" to
+        indicate the result is undefined. When `False`, an exception is raised
+        if one or more of the statistic's batch members are undefined.
+      name: Python `str` name prefixed to Ops created by this class.
+
+    Raises:
+      ValueError: if at most `scale_identity_multiplier` is specified.
+    """
+    parameters = locals()
+    with ops.name_scope(name):
+      with ops.name_scope("init", values=[
+          loc, scale_diag, scale_identity_multiplier]):
+        # No need to validate_args while making diag_scale.  The returned
+        # LinearOperatorDiag has an assert_non_singular method that is called by
+        # the Bijector.
+        scale = distribution_util.make_diag_scale(
+            loc=loc,
+            scale_diag=scale_diag,
+            scale_identity_multiplier=scale_identity_multiplier,
+            validate_args=False,
+            assert_positive=False)
+    super(VectorExponentialDiag, self).__init__(
+        loc=loc,
+        scale=scale,
+        validate_args=validate_args,
+        allow_nan_stats=allow_nan_stats,
+        name=name)
+    self._parameters = parameters
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
new file mode 100644
index 0000000000..7123165417
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
@@ -0,0 +1,288 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Vectorized Exponential distribution class, directly using LinearOperator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib import linalg
+from tensorflow.contrib.distributions.python.ops import bijectors
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import exponential
+from tensorflow.python.ops.distributions import transformed_distribution
+
+__all__ = ["VectorExponentialLinearOperator"]
+
+_mvn_sample_note = """
+`value` is a batch vector with compatible shape if `value` is a `Tensor` whose
+shape can be broadcast up to either:
+
+```python
+self.batch_shape + self.event_shape
+```
+
+or
+
+```python
+[M1, ..., Mm] + self.batch_shape + self.event_shape
+```
+
+"""
+
+
+class VectorExponentialLinearOperator(
+    transformed_distribution.TransformedDistribution):
+  """The vectorization of the Exponential distribution on `R^k`.
+
+  The vector exponential distribution is defined over a subset of `R^k`, and
+  parameterized by a (batch of) length-`k` `loc` vector and a (batch of) `k x k`
+  `scale` matrix:  `covariance = scale @ scale.T`, where `@` denotes
+  matrix-multiplication.
+
+  #### Mathematical Details
+
+  The probability density function (pdf) is
+
+  ```none
+  pdf(y; loc, scale) = exp(-||x||_1) / Z,  for y in S(loc, scale),
+  x = inv(scale) @ (y - loc),
+  Z = |det(scale)|,
+  ```
+
+  where:
+
+  * `loc` is a vector in `R^k`,
+  * `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`,
+  * `S = {loc + scale @ x : x in R^k, x_1 > 0, ..., x_k > 0}`, is an image of
+     the positive half-space,
+  * `||x||_1` denotes the `l1` norm of `x`, `sum_i |x_i|`,
+  * `Z` denotes the normalization constant.
+
+  The VectorExponential distribution is a member of the [location-scale
+  family](https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
+  constructed as,
+
+  ```none
+  X = (X_1, ..., X_k), each X_i ~ Exponential(rate=1)
+  Y = (Y_1, ...,Y_k) = scale @ X + loc
+  ```
+
+  #### About `VectorExponential` and `Vector` distributions in TensorFlow.
+
+  The `VectorExponential` is a non-standard distribution that has useful
+  properties.
+
+  The marginals `Y_1, ..., Y_k` are *not* Exponential random variables, due to
+  the fact that the sum of Exponential random variables is not Exponential.
+
+  Instead, `Y` is a vector whose components are linear combinations of
+  Exponential random variables.  Thus, `Y` lives in the vector space generated
+  by `vectors` of Exponential distributions.  This allows the user to decide the
+  mean and covariance (by setting `loc` and `scale`), while preserving some
+  properties of the Exponential distribution.  In particular, the tails of `Y_i`
+  will be (up to polynomial factors) exponentially decaying.
+
+  To see this last statement, note that the pdf of `Y_i` is the convolution of
+  the pdf of `k` independent Exponential random variables.  One can then show by
+  induction that distributions with exponential (up to polynomial factors) tails
+  are closed under convolution.
+
+
+  #### Examples
+
+  ```python
+  ds = tf.contrib.distributions
+  la = tf.contrib.linalg
+
+  # Initialize a single 2-variate VectorExponential, supported on
+  # {(x, y) in R^2 : x > 0, y > 0}.
+  mat = [[1.0, 0.1],
+         [0.1, 1.0]]
+
+  vex = ds.VectorExponentialLinearOperator(
+      scale=la.LinearOperatorFullMatrix(mat))
+
+  # Compute the pdf of an`R^2` observation; return a scalar.
+  vex.prob([1., 2.]).eval()  # shape: []
+
+  # Initialize a 2-batch of 3-variate Vector Exponential's.
+  mu = [[1., 2, 3],
+        [1., 0, 0]]              # shape: [2, 3]
+  scale_diag = [[1., 2, 3],
+                [0.5, 1, 1.5]]     # shape: [2, 3]
+
+  vex = ds.VectorExponentialLinearOperator(
+      loc=mu,
+      scale=la.LinearOperatorDiag(scale_diag))
+
+  # Compute the pdf of two `R^3` observations; return a length-2 vector.
+  x = [[1.9, 2.2, 3.1],
+       [10., 1.0, 9.0]]     # shape: [2, 3]
+  vex.prob(x).eval()    # shape: [2]
+  ```
+
+  """
+
+  def __init__(self,
+               loc=None,
+               scale=None,
+               validate_args=False,
+               allow_nan_stats=True,
+               name="VectorExponentialLinearOperator"):
+    """Construct Vector Exponential distribution supported on a subset of `R^k`.
+
+    The `batch_shape` is the broadcast shape between `loc` and `scale`
+    arguments.
+
+    The `event_shape` is given by last dimension of the matrix implied by
+    `scale`. The last dimension of `loc` (if provided) must broadcast with this.
+
+    Recall that `covariance = scale @ scale.T`.
+
+    Additional leading dimensions (if any) will index batches.
+
+    Args:
+      loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
+        implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
+        `b >= 0` and `k` is the event size.
+      scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
+        `[B1, ..., Bb, k, k]`.
+      validate_args: Python `bool`, default `False`. Whether to validate input
+        with asserts. If `validate_args` is `False`, and the inputs are
+        invalid, correct behavior is not guaranteed.
+      allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
+        exception if a statistic (e.g. mean/mode/etc...) is undefined for any
+        batch member If `True`, batch members with valid parameters leading to
+        undefined statistics will return NaN for this statistic.
+      name: The name to give Ops created by the initializer.
+
+    Raises:
+      ValueError: if `scale` is unspecified.
+      TypeError: if not `scale.dtype.is_floating`
+    """
+    parameters = locals()
+    if scale is None:
+      raise ValueError("Missing required `scale` parameter.")
+    if not scale.dtype.is_floating:
+      raise TypeError("`scale` parameter must have floating-point dtype.")
+
+    with ops.name_scope(name, values=[loc] + scale.graph_parents):
+      # Since expand_dims doesn't preserve constant-ness, we obtain the
+      # non-dynamic value if possible.
+      loc = ops.convert_to_tensor(loc, name="loc") if loc is not None else loc
+      batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
+          loc, scale)
+
+      super(VectorExponentialLinearOperator, self).__init__(
+          distribution=exponential.Exponential(rate=array_ops.ones(
+              [], dtype=scale.dtype), allow_nan_stats=allow_nan_stats),
+          bijector=bijectors.AffineLinearOperator(
+              shift=loc, scale=scale, validate_args=validate_args),
+          batch_shape=batch_shape,
+          event_shape=event_shape,
+          validate_args=validate_args,
+          name=name)
+      self._parameters = parameters
+
+  @property
+  def loc(self):
+    """The `loc` `Tensor` in `Y = scale @ X + loc`."""
+    return self.bijector.shift
+
+  @property
+  def scale(self):
+    """The `scale` `LinearOperator` in `Y = scale @ X + loc`."""
+    return self.bijector.scale
+
+  @distribution_util.AppendDocstring(_mvn_sample_note)
+  def _log_prob(self, x):
+    return super(VectorExponentialLinearOperator, self)._log_prob(x)
+
+  @distribution_util.AppendDocstring(_mvn_sample_note)
+  def _prob(self, x):
+    return super(VectorExponentialLinearOperator, self)._prob(x)
+
+  def _mean(self):
+    # Let
+    #   W = (w1,...,wk), with wj ~ iid Exponential(0, 1).
+    # Then this distribution is
+    #   X = loc + LW,
+    # and then E[X] = loc + L1, where 1 is the vector of ones.
+    scale_x_ones = self.bijector.scale.matvec(
+        array_ops.ones(self._mode_mean_shape(), self.dtype))
+
+    if self.loc is None:
+      return scale_x_ones
+
+    return array_ops.identity(self.loc) + scale_x_ones
+
+  def _covariance(self):
+    # Let
+    #   W = (w1,...,wk), with wj ~ iid Exponential(0, 1).
+    # Then this distribution is
+    #   X = loc + LW,
+    # and then since Cov(wi, wj) = 1 if i=j, and 0 otherwise,
+    #   Cov(X) = L Cov(W W^T) L^T = L L^T.
+    if distribution_util.is_diagonal_scale(self.scale):
+      return array_ops.matrix_diag(math_ops.square(self.scale.diag_part()))
+    else:
+      return self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
+
+  def _variance(self):
+    if distribution_util.is_diagonal_scale(self.scale):
+      return math_ops.square(self.scale.diag_part())
+    elif (isinstance(self.scale, linalg.LinearOperatorUDVHUpdate) and
+          self.scale.is_self_adjoint):
+      return array_ops.matrix_diag_part(
+          self.scale.matmul(self.scale.to_dense()))
+    else:
+      return array_ops.matrix_diag_part(
+          self.scale.matmul(self.scale.to_dense(), adjoint_arg=True))
+
+  def _stddev(self):
+    if distribution_util.is_diagonal_scale(self.scale):
+      return math_ops.abs(self.scale.diag_part())
+    elif (isinstance(self.scale, linalg.LinearOperatorUDVHUpdate) and
+          self.scale.is_self_adjoint):
+      return math_ops.sqrt(
+          array_ops.matrix_diag_part(self.scale.matmul(self.scale.to_dense())))
+    else:
+      return math_ops.sqrt(
+          array_ops.matrix_diag_part(
+              self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)))
+
+  def _mode(self):
+    scale_x_zeros = self.bijector.scale.matvec(
+        array_ops.zeros(self._mode_mean_shape(), self.dtype))
+
+    if self.loc is None:
+      return scale_x_zeros
+
+    return array_ops.identity(self.loc) + scale_x_zeros
+
+  def _mode_mean_shape(self):
+    """Shape for the mode/mean Tensors."""
+    shape = self.batch_shape.concatenate(self.event_shape)
+    has_static_shape = shape.is_fully_defined()
+    if not has_static_shape:
+      shape = array_ops.concat([
+          self.batch_shape_tensor(),
+          self.event_shape_tensor(),
+      ], 0)
+    return shape
diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD
index 0b4dc5667f..c78bb51c3a 100644
--- a/tensorflow/contrib/factorization/BUILD
+++ b/tensorflow/contrib/factorization/BUILD
@@ -171,10 +171,11 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:array_ops",
-        "//tensorflow/python:embedding_ops",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:constant_op",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/contrib/factorization/python/ops/clustering_ops.py b/tensorflow/contrib/factorization/python/ops/clustering_ops.py
index 2e9b5e22c7..c9b3314d6b 100644
--- a/tensorflow/contrib/factorization/python/ops/clustering_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/clustering_ops.py
@@ -93,15 +93,24 @@ class KMeans(object):
 
     Args:
       inputs: An input tensor or list of input tensors
-      num_clusters: number of clusters.
-      initial_clusters: Specifies the clusters used during initialization.  Can
-        be a tensor or numpy array, or a function that generates the clusters.
-        Can also be "random" to specify that clusters should be chosen randomly
-        from input data.
-      distance_metric: distance metric used for clustering.
+      num_clusters: An integer tensor specifying the number of clusters. This
+        argument is ignored if initial_clusters is a tensor or numpy array.
+      initial_clusters: Specifies the clusters used during initialization. One
+        of the following:
+        - a tensor or numpy array with the initial cluster centers.
+        - a function f(inputs, k) that returns up to k centers from `inputs`.
+        - "random": Choose centers randomly from `inputs`.
+        - "kmeans_plus_plus": Use kmeans++ to choose centers from `inputs`.
+        In the last three cases, one batch of `inputs` may not yield
+        `num_clusters` centers, in which case initialization will require
+        multiple batches until enough centers are chosen. In the case of
+        "random" or "kmeans_plus_plus", if the input size is <= `num_clusters`
+        then the entire batch is chosen to be cluster centers.
+      distance_metric: Distance metric used for clustering. Supported options:
+        "squared_euclidean", "cosine".
       use_mini_batch: If true, use the mini-batch k-means algorithm. Else assume
         full batch.
-      mini_batch_steps_per_iteration: number of steps after which the updated
+      mini_batch_steps_per_iteration: Number of steps after which the updated
         cluster centers are synced back to a master copy.
       random_seed: Seed for PRNG used to initialize seeds.
       kmeans_plus_plus_num_retries: For each point that is sampled during
@@ -109,14 +118,21 @@ class KMeans(object):
         additional points to draw from the current distribution before selecting
         the best. If a negative value is specified, a heuristic is used to
         sample O(log(num_to_sample)) additional points.
+
+    Raises:
+      ValueError: An invalid argument was passed to initial_clusters or
+        distance_metric.
     """
+    if isinstance(initial_clusters, str) and initial_clusters not in [
+        RANDOM_INIT, KMEANS_PLUS_PLUS_INIT
+    ]:
+      raise ValueError(
+          "Unsupported initialization algorithm '%s'" % initial_clusters)
+    if distance_metric not in [SQUARED_EUCLIDEAN_DISTANCE, COSINE_DISTANCE]:
+      raise ValueError("Unsupported distance metric '%s'" % distance_metric)
     self._inputs = inputs if isinstance(inputs, list) else [inputs]
-    assert num_clusters > 0, num_clusters
     self._num_clusters = num_clusters
-    if initial_clusters is None:
-      initial_clusters = RANDOM_INIT
     self._initial_clusters = initial_clusters
-    assert distance_metric in [SQUARED_EUCLIDEAN_DISTANCE, COSINE_DISTANCE]
     self._distance_metric = distance_metric
     self._use_mini_batch = use_mini_batch
     self._mini_batch_steps_per_iteration = int(mini_batch_steps_per_iteration)
@@ -144,8 +160,7 @@ class KMeans(object):
       return cls._compute_cosine_distance(
           inputs, clusters, inputs_normalized=True)
     else:
-      assert False, ('Unsupported distance metric passed to Kmeans %s' %
-                     str(distance_metric))
+      assert False, str(distance_metric)
 
   @classmethod
   def _compute_euclidean_distance(cls, inputs, clusters):
@@ -238,90 +253,29 @@ class KMeans(object):
                        array_ops.squeeze(indices)))
     return zip(*output)
 
-  def _init_clusters_random(self):
-    """Does random initialization of clusters.
-
-    Returns:
-      Tensor of randomly initialized clusters.
-    """
-    num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in self._inputs])
-    # Note that for mini-batch k-means, we should ensure that the batch size of
-    # data used during initialization is sufficiently large to avoid duplicated
-    # clusters.
-    with ops.control_dependencies(
-        [check_ops.assert_less_equal(self._num_clusters, num_data)]):
-      indices = random_ops.random_uniform(
-          array_ops.reshape(self._num_clusters, [-1]),
-          minval=0,
-          maxval=math_ops.cast(num_data, dtypes.int64),
-          seed=self._random_seed,
-          dtype=dtypes.int64)
-      clusters_init = embedding_lookup(
-          self._inputs, indices, partition_strategy='div')
-      return clusters_init
-
   def _clusters_l2_normalized(self):
     """Returns True if clusters centers are kept normalized."""
     return (self._distance_metric == COSINE_DISTANCE and
             (not self._use_mini_batch or
              self._mini_batch_steps_per_iteration > 1))
 
-  def _initialize_clusters(self, cluster_centers, cluster_centers_initialized,
-                           cluster_centers_updated):
-    """Returns an op to initialize the cluster centers."""
-
-    init = self._initial_clusters
-    if init == RANDOM_INIT:
-      clusters_init = self._init_clusters_random()
-    elif init == KMEANS_PLUS_PLUS_INIT:
-      # Points from only the first shard are used for initializing centers.
-      # TODO(ands): Use all points.
-      inp = self._inputs[0]
-      if self._distance_metric == COSINE_DISTANCE:
-        inp = nn_impl.l2_normalize(inp, dim=1)
-      clusters_init = gen_clustering_ops.kmeans_plus_plus_initialization(
-          inp, self._num_clusters, self._random_seed,
-          self._kmeans_plus_plus_num_retries)
-    elif callable(init):
-      clusters_init = init(self._inputs, self._num_clusters)
-    elif not isinstance(init, str):
-      clusters_init = init
-    else:
-      assert False, 'Unsupported init passed to Kmeans %s' % str(init)
-    if self._distance_metric == COSINE_DISTANCE and clusters_init is not None:
-      clusters_init = nn_impl.l2_normalize(clusters_init, dim=1)
-
-    with ops.colocate_with(cluster_centers_initialized):
-      initialized = control_flow_ops.with_dependencies(
-          [clusters_init], array_ops.identity(cluster_centers_initialized))
-    with ops.colocate_with(cluster_centers):
-      assign_centers = state_ops.assign(
-          cluster_centers, clusters_init, validate_shape=False)
-      if cluster_centers_updated != cluster_centers:
-        assign_centers = control_flow_ops.group(assign_centers,
-                                                state_ops.assign(
-                                                    cluster_centers_updated,
-                                                    clusters_init,
-                                                    validate_shape=False))
-    assign_centers = control_flow_ops.with_dependencies(
-        [assign_centers], state_ops.assign(cluster_centers_initialized, True))
-    return control_flow_ops.cond(initialized, control_flow_ops.no_op,
-                                 lambda: assign_centers).op
-
-  def _create_variables(self):
+  def _create_variables(self, num_clusters):
     """Creates variables.
 
+    Args:
+      num_clusters: an integer Tensor providing the number of clusters.
+
     Returns:
-    Tuple with following elements:
-      cluster_centers: a Tensor for storing cluster centers
-      cluster_centers_initialized: bool Variable indicating whether clusters
-        are initialized.
-      cluster_counts: a Tensor for storing counts of points assigned to this
-        cluster. This is used by mini-batch training.
-      cluster_centers_updated: Tensor representing copy of cluster centers that
-        are updated every step.
-      update_in_steps: numbers of steps left before we sync
-        cluster_centers_updated back to cluster_centers.
+      Tuple with following elements:
+      - cluster_centers: a Tensor for storing cluster centers
+      - cluster_centers_initialized: bool Variable indicating whether clusters
+            are initialized.
+      - cluster_counts: a Tensor for storing counts of points assigned to this
+            cluster. This is used by mini-batch training.
+      - cluster_centers_updated: Tensor representing copy of cluster centers
+            that are updated every step.
+      - update_in_steps: numbers of steps left before we sync
+            cluster_centers_updated back to cluster_centers.
     """
     init_value = array_ops.constant([], dtype=dtypes.float32)
     cluster_centers = variable_scope.variable(
@@ -341,12 +295,12 @@ class KMeans(object):
           name='update_in_steps')
       # Count of points assigned to cluster_centers_updated.
       cluster_counts = variable_scope.variable(
-          array_ops.zeros([self._num_clusters], dtype=dtypes.int64))
+          array_ops.zeros([num_clusters], dtype=dtypes.int64))
     else:
       cluster_centers_updated = cluster_centers
       update_in_steps = None
       cluster_counts = (variable_scope.variable(
-          array_ops.ones([self._num_clusters], dtype=dtypes.int64))
+          array_ops.ones([num_clusters], dtype=dtypes.int64))
                         if self._use_mini_batch else None)
     return (cluster_centers, cluster_centers_initialized, cluster_counts,
             cluster_centers_updated, update_in_steps)
@@ -363,6 +317,14 @@ class KMeans(object):
   def training_graph(self):
     """Generate a training graph for kmeans algorithm.
 
+    This returns, among other things, an op that chooses initial centers
+    (init_op), a boolean variable that is set to True when the initial centers
+    are chosen (cluster_centers_initialized), and an op to perform either an
+    entire Lloyd iteration or a mini-batch of a Lloyd iteration (training_op).
+    The caller should use these components as follows. A single worker should
+    execute init_op multiple times until cluster_centers_initialized becomes
+    True. Then multiple workers may execute training_op any number of times.
+
     Returns:
       A tuple consisting of:
       all_scores: A matrix (or list of matrices) of dimensions (num_input,
@@ -379,12 +341,23 @@ class KMeans(object):
       training_op: an op that runs an iteration of training.
     """
     # Implementation of kmeans.
+    if (isinstance(self._initial_clusters, str) or
+        callable(self._initial_clusters)):
+      initial_clusters = self._initial_clusters
+      num_clusters = ops.convert_to_tensor(self._num_clusters)
+    else:
+      initial_clusters = ops.convert_to_tensor(self._initial_clusters)
+      num_clusters = array_ops.shape(initial_clusters)[0]
+
     inputs = self._inputs
     (cluster_centers_var, cluster_centers_initialized, total_counts,
-     cluster_centers_updated, update_in_steps) = self._create_variables()
-    init_op = self._initialize_clusters(cluster_centers_var,
-                                        cluster_centers_initialized,
-                                        cluster_centers_updated)
+     cluster_centers_updated,
+     update_in_steps) = self._create_variables(num_clusters)
+    init_op = _InitializeClustersOpFactory(
+        self._inputs, num_clusters, initial_clusters, self._distance_metric,
+        self._random_seed, self._kmeans_plus_plus_num_retries,
+        cluster_centers_var, cluster_centers_updated,
+        cluster_centers_initialized).op()
     cluster_centers = cluster_centers_var
 
     if self._distance_metric == COSINE_DISTANCE:
@@ -403,8 +376,8 @@ class KMeans(object):
             inputs, cluster_idx, cluster_centers_updated, total_counts)
     else:
       assert cluster_centers == cluster_centers_var
-      training_op = self._full_batch_training_op(inputs, cluster_idx,
-                                                 cluster_centers_var)
+      training_op = self._full_batch_training_op(
+          inputs, num_clusters, cluster_idx, cluster_centers_var)
 
     return (all_scores, cluster_idx, scores, cluster_centers_initialized,
             init_op, training_op)
@@ -516,11 +489,13 @@ class KMeans(object):
       update_ops.extend([update_counts, update_cluster_centers])
     return control_flow_ops.group(*update_ops)
 
-  def _full_batch_training_op(self, inputs, cluster_idx_list, cluster_centers):
+  def _full_batch_training_op(self, inputs, num_clusters, cluster_idx_list,
+                              cluster_centers):
     """Creates an op for training for full batch case.
 
     Args:
       inputs: list of input Tensors.
+      num_clusters: an integer Tensor providing the number of clusters.
       cluster_idx_list: A vector (or list of vectors). Each element in the
         vector corresponds to an input row in 'inp' and specifies the cluster id
         corresponding to the input.
@@ -535,16 +510,161 @@ class KMeans(object):
     for inp, cluster_idx in zip(inputs, cluster_idx_list):
       with ops.colocate_with(inp, ignore_existing=True):
         cluster_sums.append(
-            math_ops.unsorted_segment_sum(inp, cluster_idx, self._num_clusters))
+            math_ops.unsorted_segment_sum(inp, cluster_idx, num_clusters))
         cluster_counts.append(
             math_ops.unsorted_segment_sum(
                 array_ops.reshape(
                     array_ops.ones(
                         array_ops.reshape(array_ops.shape(inp)[0], [-1])),
-                    [-1, 1]), cluster_idx, self._num_clusters))
+                    [-1, 1]), cluster_idx, num_clusters))
     with ops.colocate_with(cluster_centers, ignore_existing=True):
       new_clusters_centers = math_ops.add_n(cluster_sums) / (math_ops.cast(
           math_ops.add_n(cluster_counts), cluster_sums[0].dtype) + epsilon)
       if self._clusters_l2_normalized():
         new_clusters_centers = nn_impl.l2_normalize(new_clusters_centers, dim=1)
     return state_ops.assign(cluster_centers, new_clusters_centers)
+
+
+class _InitializeClustersOpFactory(object):
+  """Internal class to create the op to initialize the clusters.
+
+    The op performs this algorithm (see constructor args):
+
+    num_remaining = num_clusters - length(cluster_centers)
+    if num_remaining == 0:
+      assert that cluster_centers_initialized is true
+    else:
+      assert that num_remaining > 0
+      new_centers = choose up to num_remaining initial centers
+      l2-normalize new_centers if using cosine distance
+      all_centers = concat(cluster_centers, new_centers)
+      cluster_centers := all_centers
+      if there is a cluster_centers_updated variable:
+        cluster_centers_updated := cluster_centers
+      num_now_remaining = num_clusters - length(cluster_centers)
+      if num_now_remaining == 0:
+        cluster_centers_initialized := true
+  """
+
+  def __init__(self, inputs, num_clusters, initial_clusters, distance_metric,
+               random_seed, kmeans_plus_plus_num_retries, cluster_centers,
+               cluster_centers_updated, cluster_centers_initialized):
+    """Creates an op factory.
+
+    Args:
+      inputs: See KMeans constructor.
+      num_clusters: An integer Tensor providing the number of clusters.
+      initial_clusters: See KMeans constructor.
+      distance_metric: See KMeans constructor.
+      random_seed: See KMeans constructor.
+      kmeans_plus_plus_num_retries: See KMeans constructor.
+      cluster_centers: The TF variable holding the initial centers. It may
+          already contain some centers when the op is executed.
+      cluster_centers_updated: A second TF variable to hold a copy of the
+          initial centers, used for full-batch mode. In mini-batch mode,
+          cluster_centers_updated is the same variable as cluster_centers.
+      cluster_centers_initialized: A boolean TF variable that will be set
+          to true when all the initial centers have been chosen.
+    """
+    # All of these instance variables are constants.
+    self._inputs = inputs
+    self._num_clusters = num_clusters
+    self._initial_clusters = initial_clusters
+    self._distance_metric = distance_metric
+    self._random_seed = random_seed
+    self._kmeans_plus_plus_num_retries = kmeans_plus_plus_num_retries
+    self._cluster_centers = cluster_centers
+    self._cluster_centers_updated = cluster_centers_updated
+    self._cluster_centers_initialized = cluster_centers_initialized
+
+    self._num_selected = array_ops.shape(self._cluster_centers)[0]
+    self._num_remaining = self._num_clusters - self._num_selected
+    self._num_data = math_ops.add_n(
+        [array_ops.shape(i)[0] for i in self._inputs])
+
+  def _random(self):
+    indices = random_ops.random_uniform(
+        array_ops.reshape(self._num_remaining, [-1]),
+        minval=0,
+        maxval=math_ops.cast(self._num_data, dtypes.int64),
+        seed=self._random_seed,
+        dtype=dtypes.int64)
+    return embedding_lookup(self._inputs, indices, partition_strategy='div')
+
+  def _kmeans_plus_plus(self):
+    # Points from only the first shard are used for initializing centers.
+    # TODO(ands): Use all points.
+    inp = self._inputs[0]
+    if self._distance_metric == COSINE_DISTANCE:
+      inp = nn_impl.l2_normalize(inp, dim=1)
+    return gen_clustering_ops.kmeans_plus_plus_initialization(
+        inp,
+        math_ops.to_int64(self._num_remaining), self._random_seed,
+        self._kmeans_plus_plus_num_retries)
+
+  def _greedy_batch_sampler(self, sampler):
+    # If the input dataset size is smaller than the number of centers
+    # remaining, choose the entire input dataset as centers. This can happen
+    # with mini-batch. Otherwise, sample the batch according to the provided
+    # sampler.
+    return control_flow_ops.cond(self._num_data <= self._num_remaining,
+                                 lambda: array_ops.concat(self._inputs, 0),
+                                 sampler)
+
+  def _single_batch_sampler(self, sampler):
+    # Enforce that there are at least as many data points as centers
+    # remaining. This gives the provided sampler the chance to select all
+    # remaining centers from a single batch.
+    with ops.control_dependencies(
+        [check_ops.assert_greater_equal(self._num_data, self._num_remaining)]):
+      return sampler()
+
+  def _choose_initial_centers(self):
+    if isinstance(self._initial_clusters, str):
+      if self._initial_clusters == RANDOM_INIT:
+        return self._greedy_batch_sampler(self._random)
+      else:  # self._initial_clusters == KMEANS_PLUS_PLUS_INIT
+        return self._single_batch_sampler(self._kmeans_plus_plus)
+    elif callable(self._initial_clusters):
+      return self._initial_clusters(self._inputs, self._num_remaining)
+    else:
+      with ops.control_dependencies([
+          check_ops.assert_equal(self._num_remaining,
+                                 array_ops.shape(self._initial_clusters)[0])
+      ]):
+        return self._initial_clusters
+
+  def _add_new_centers(self):
+    """Adds some centers and returns the number of centers remaining."""
+    new_centers = self._choose_initial_centers()
+    if self._distance_metric == COSINE_DISTANCE:
+      new_centers = nn_impl.l2_normalize(new_centers, dim=1)
+    # If cluster_centers is empty, it doesn't have the right shape for concat.
+    all_centers = control_flow_ops.cond(
+        math_ops.equal(self._num_selected, 0), lambda: new_centers,
+        lambda: array_ops.concat([self._cluster_centers, new_centers], 0))
+    # TODO(ccolby): De-dupe all_centers?
+    with ops.colocate_with(self._cluster_centers):
+      a = state_ops.assign(
+          self._cluster_centers, all_centers, validate_shape=False)
+      if self._cluster_centers_updated is not self._cluster_centers:
+        a = state_ops.assign(
+            self._cluster_centers_updated, a, validate_shape=False)
+      return self._num_clusters - array_ops.shape(a)[0]
+
+  def _initialize(self):
+    with ops.control_dependencies([
+        check_ops.assert_positive(self._num_remaining),
+    ]):
+      num_now_remaining = self._add_new_centers()
+      return control_flow_ops.cond(
+          math_ops.equal(num_now_remaining, 0),
+          lambda: state_ops.assign(self._cluster_centers_initialized, True),
+          control_flow_ops.no_op)
+
+  def op(self):
+    """Returns the cluster initializer op."""
+    return control_flow_ops.cond(
+        math_ops.equal(self._num_remaining, 0),
+        lambda: check_ops.assert_equal(self._cluster_centers_initialized, True),
+        self._initialize)
diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops.py b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
index 000e2403a7..14a2311a42 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
@@ -23,7 +23,6 @@ import numbers
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
-
 from tensorflow.contrib.factorization.python.ops import gen_factorization_ops
 from tensorflow.contrib.util import loader
 from tensorflow.python.framework import constant_op
@@ -81,13 +80,16 @@ class WALSModel(object):
   a sparse term and a Gramian term, see wals.md.
   The loss is returned by the update_{col, row}_factors(sp_input), and is
   normalized as follows:
-  _, _, minibatch_loss = update_row_factors(sp_input)
+    _, _, unregularized_loss, regularization, sum_weights =
+        update_row_factors(sp_input)
   if sp_input contains the rows {A_i, i \in I}, and the input matrix A has n
-  total rows, then minibatch_loss is
+  total rows, then the minibatch loss = unregularized_loss + regularization is
    \\(
-   (\|\sqrt W \odot (A_I - U_I V^T)\|_F^2 + \lambda \|U_I\|_F^2) * n / |I| +
+   (\|\sqrt W_I \odot (A_I - U_I V^T)\|_F^2 + \lambda \|U_I\|_F^2) * n / |I| +
    \lambda \|V\|_F^2
    )\\
+  The sum_weights tensor contains the normalized sum of weights
+  sum(W_I) * n / |I|.
 
   A typical usage example (pseudocode):
 
@@ -119,11 +121,13 @@ class WALSModel(object):
       # Ops to upate row(column). This can either take the entire sparse tensor
       # or slices of sparse tensor. For distributed trainer, each trainer
       # handles just part of the matrix.
-      _, row_update_op, row_loss = model.update_row_factors(
+      _, row_update_op, unreg_row_loss, row_reg, _ = model.update_row_factors(
            sp_input=matrix_slices_from_queue_for_worker_shard)
-      _, col_update_op, col_loss = model.update_col_factors(
+      row_loss = unreg_row_loss + row_reg
+      _, col_update_op, unreg_col_loss, col_reg, _ = model.update_col_factors(
            sp_input=transposed_matrix_slices_from_queue_for_worker_shard,
            transpose_input=True)
+      col_loss = unreg_col_loss + col_reg
 
       ...
 
@@ -240,28 +244,24 @@ class WALSModel(object):
         regularization * linalg_ops.eye(self._n_components)
         if regularization is not None else None)
     assert (row_weights is None) == (col_weights is None)
-    self._row_weights = WALSModel._create_weights(row_weights, self._input_rows,
-                                                  self._num_row_shards,
-                                                  "row_weights")
-    self._col_weights = WALSModel._create_weights(col_weights, self._input_cols,
-                                                  self._num_col_shards,
-                                                  "col_weights")
+    self._row_weights = WALSModel._create_weights(
+        row_weights, self._input_rows, self._num_row_shards, "row_weights")
+    self._col_weights = WALSModel._create_weights(
+        col_weights, self._input_cols, self._num_col_shards, "col_weights")
     self._use_factors_weights_cache = use_factors_weights_cache
     self._use_gramian_cache = use_gramian_cache
-    self._row_factors = self._create_factors(self._input_rows,
-                                             self._n_components,
-                                             self._num_row_shards, row_init,
-                                             "row_factors")
-    self._col_factors = self._create_factors(self._input_cols,
-                                             self._n_components,
-                                             self._num_col_shards, col_init,
-                                             "col_factors")
+    self._row_factors = self._create_factors(
+        self._input_rows, self._n_components, self._num_row_shards, row_init,
+        "row_factors")
+    self._col_factors = self._create_factors(
+        self._input_cols, self._n_components, self._num_col_shards, col_init,
+        "col_factors")
     self._row_gramian = self._create_gramian(self._n_components, "row_gramian")
     self._col_gramian = self._create_gramian(self._n_components, "col_gramian")
-    self._row_update_prep_gramian = self._prepare_gramian(self._col_factors,
-                                                          self._col_gramian)
-    self._col_update_prep_gramian = self._prepare_gramian(self._row_factors,
-                                                          self._row_gramian)
+    self._row_update_prep_gramian = self._prepare_gramian(
+        self._col_factors, self._col_gramian)
+    self._col_update_prep_gramian = self._prepare_gramian(
+        self._row_factors, self._row_gramian)
     self._create_transient_vars()
 
   @property
@@ -538,14 +538,12 @@ class WALSModel(object):
          "col_gramian_cache",
          pass_through=not self._use_gramian_cache)
 
-    self._row_updates_init = control_flow_ops.group(col_factors_cache_init,
-                                                    row_factors_cache_reset,
-                                                    col_gramian_cache_init,
-                                                    row_gramian_cache_reset)
-    self._col_updates_init = control_flow_ops.group(row_factors_cache_init,
-                                                    col_factors_cache_reset,
-                                                    row_gramian_cache_init,
-                                                    col_gramian_cache_reset)
+    self._row_updates_init = control_flow_ops.group(
+        col_factors_cache_init, row_factors_cache_reset, col_gramian_cache_init,
+        row_gramian_cache_reset)
+    self._col_updates_init = control_flow_ops.group(
+        row_factors_cache_init, col_factors_cache_reset, row_gramian_cache_init,
+        col_gramian_cache_reset)
 
     if self._row_wt_cache is not None:
       assert self._col_wt_cache is not None
@@ -565,6 +563,9 @@ class WALSModel(object):
 
     Must be run before initialize_row_update_op and should only be run by one
     trainer (usually the chief) when doing distributed training.
+
+    Returns:
+      Op to form the gramian.
     """
     return self._row_update_prep_gramian
 
@@ -574,6 +575,9 @@ class WALSModel(object):
 
     Must be run before initialize_col_update_op and should only be run by one
     trainer (usually the chief) when doing distributed training.
+
+    Returns:
+      Op to form the gramian.
     """
     return self._col_update_prep_gramian
 
@@ -613,8 +617,8 @@ class WALSModel(object):
     if len(factor) == 1:
       with ops.colocate_with(factor[0]):
         # TODO(agarwal): assign instead of scatter update for full batch update.
-        return state_ops.scatter_update(factor[0], indices, values,
-                                        name=name).op
+        return state_ops.scatter_update(
+            factor[0], indices, values, name=name).op
     else:
       num_shards = len(factor)
       assignments, new_ids = sharding_func(indices)
@@ -626,8 +630,9 @@ class WALSModel(object):
                                                        num_shards)
       updates = []
       for i in xrange(num_shards):
-        updates.append(state_ops.scatter_update(factor[i], sharded_ids[i],
-                                                sharded_values[i]))
+        updates.append(
+            state_ops.scatter_update(factor[i], sharded_ids[i], sharded_values[
+                i]))
       return control_flow_ops.group(*updates, name=name)
 
   def update_row_factors(self, sp_input=None, transpose_input=False):
@@ -645,15 +650,23 @@ class WALSModel(object):
       new_values: New values for the row factors.
       update_op: An op that assigns the newly computed values to the row
         factors.
-      loss: A tensor (scalar) that contains the normalized minibatch loss,
-        corresponding to sp_input.
-        if sp_input contains the rows {A_{i, :}, i \in I}, and the input matrix
-        A has n total rows, then loss is:
-        (\|\sqrt W_I \odot (A_I - U_I V^T)\|_F^2 + \lambda \|U_I\|_F^2) *
-        n / |I| + \lambda \|V\|_F^2.
+      unregularized_loss: A tensor (scalar) that contains the normalized
+        minibatch loss corresponding to sp_input, without the regularization
+        term. If sp_input contains the rows {A_{i, :}, i \in I}, and the input
+        matrix A has n total rows, then the unregularized loss is:
+        (\|\sqrt W_I \odot (A_I - U_I V^T)\|_F^2 * n / |I|
+        The total loss is unregularized_loss + regularization.
+      regularization: A tensor (scalar) that contains the normalized
+        regularization term for the minibatch loss corresponding to sp_input.
+        If sp_input contains the rows {A_{i, :}, i \in I}, and the input matrix
+        A has n total rows, then the regularization term is:
+        \lambda \|U_I\|_F^2) * n / |I| + \lambda \|V\|_F^2.
+      sum_weights: The sum of the weights W_I corresponding to sp_input,
+        normalized by a factor of n / |I|. The root weighted squared error is:
+        \sqrt(unregularized_loss / sum_weights).
     """
-    return self._process_input_helper(True, sp_input=sp_input,
-                                      transpose_input=transpose_input)
+    return self._process_input_helper(
+        True, sp_input=sp_input, transpose_input=transpose_input)
 
   def update_col_factors(self, sp_input=None, transpose_input=False):
     r"""Updates the column factors.
@@ -666,21 +679,31 @@ class WALSModel(object):
         columns corresponding to the transposed input are updated.
 
     Returns:
-      A tuple consisting of the following two elements:
+      A tuple consisting of the following elements:
       new_values: New values for the column factors.
       update_op: An op that assigns the newly computed values to the column
         factors.
-      loss: A tensor (scalar) that contains the normalized minibatch loss,
-        corresponding to sp_input.
+      unregularized_loss: A tensor (scalar) that contains the normalized
+        minibatch loss corresponding to sp_input, without the regularization
+        term. If sp_input contains the columns {A_{:, j}, j \in J}, and the
+        input matrix A has m total columns, then the unregularized loss is:
+        (\|\sqrt W_J \odot (A_J - U V_J^T)\|_F^2 * m / |I|
+        The total loss is unregularized_loss + regularization.
+      regularization: A tensor (scalar) that contains the normalized
+        regularization term for the minibatch loss corresponding to sp_input.
         If sp_input contains the columns {A_{:, j}, j \in J}, and the input
-        matrix A has m total columns, then loss is:
-        (\|\sqrt W_J \odot (A_J - U V_J^T)\|_F^2 + \lambda \|V_J\|_F^2) *
-        m / |J| + \lambda \|U\|_F^2.
+        matrix A has m total columns, then the regularization term is:
+        \lambda \|V_J\|_F^2) * m / |J| + \lambda \|U\|_F^2.
+      sum_weights: The sum of the weights W_J corresponding to sp_input,
+        normalized by a factor of m / |J|. The root weighted squared error is:
+        \sqrt(unregularized_loss / sum_weights).
     """
-    return self._process_input_helper(False, sp_input=sp_input,
-                                      transpose_input=transpose_input)
+    return self._process_input_helper(
+        False, sp_input=sp_input, transpose_input=transpose_input)
 
-  def project_row_factors(self, sp_input=None, transpose_input=False,
+  def project_row_factors(self,
+                          sp_input=None,
+                          transpose_input=False,
                           projection_weights=None):
     """Projects the row factors.
 
@@ -705,9 +728,11 @@ class WALSModel(object):
     """
     if projection_weights is None:
       projection_weights = 1
-    return self._process_input_helper(True, sp_input=sp_input,
-                                      transpose_input=transpose_input,
-                                      row_weights=projection_weights)[0]
+    return self._process_input_helper(
+        True,
+        sp_input=sp_input,
+        transpose_input=transpose_input,
+        row_weights=projection_weights)[0]
 
   def project_col_factors(self,
                           sp_input=None,
@@ -736,12 +761,16 @@ class WALSModel(object):
     """
     if projection_weights is None:
       projection_weights = 1
-    return self._process_input_helper(False, sp_input=sp_input,
-                                      transpose_input=transpose_input,
-                                      row_weights=projection_weights)[0]
-
-  def _process_input_helper(self, update_row_factors,
-                            sp_input=None, transpose_input=False,
+    return self._process_input_helper(
+        False,
+        sp_input=sp_input,
+        transpose_input=transpose_input,
+        row_weights=projection_weights)[0]
+
+  def _process_input_helper(self,
+                            update_row_factors,
+                            sp_input=None,
+                            transpose_input=False,
                             row_weights=None):
     """Creates the graph for processing a sparse slice of input.
 
@@ -761,12 +790,18 @@ class WALSModel(object):
         of columns to be updated/projected.
 
     Returns:
-      A tuple consisting of the following three elements:
+      A tuple consisting of the following elements:
       new_values: New values for the row/column factors.
       update_op: An op that assigns the newly computed values to the row/column
         factors.
-      loss: A tensor (scalar) that contains the normalized minibatch loss,
-        corresponding to sp_input.
+      unregularized_loss: A tensor (scalar) that contains the normalized
+        minibatch loss corresponding to sp_input, without the regularization
+        term. Add the regularization term below to yield the loss.
+      regularization: A tensor (scalar) that contains the normalized
+        regularization term for the minibatch loss corresponding to sp_input.
+      sum_weights: The sum of the weights corresponding to sp_input. This
+        can be used with unregularized loss to caluclate the root weighted
+        squared error.
     """
     assert isinstance(sp_input, sparse_tensor.SparseTensor)
 
@@ -776,6 +811,7 @@ class WALSModel(object):
       row_wt = self._row_wt_cache
       col_wt = self._col_wt_cache
       total_rows = self._input_rows
+      total_cols = self._input_cols
       sharding_func = WALSModel._get_sharding_func(self._input_rows,
                                                    self._num_row_shards)
       gramian = self._col_gramian_cache
@@ -785,6 +821,7 @@ class WALSModel(object):
       row_wt = self._col_wt_cache
       col_wt = self._row_wt_cache
       total_rows = self._input_cols
+      total_cols = self._input_rows
       sharding_func = WALSModel._get_sharding_func(self._input_cols,
                                                    self._num_col_shards)
       gramian = self._row_gramian_cache
@@ -820,8 +857,8 @@ class WALSModel(object):
     right = embedding_ops.embedding_lookup(
         right_factors, gather_indices, partition_strategy="div")
     new_sp_indices = array_ops.concat([row_ids, col_ids], 1)
-    new_sp_shape = (array_ops.concat([row_shape, col_shape], 0) if
-                    transpose_input else
+    new_sp_shape = (array_ops.concat([row_shape, col_shape], 0)
+                    if transpose_input else
                     array_ops.concat([col_shape, row_shape], 0))
     new_sp_input = sparse_tensor.SparseTensor(
         indices=new_sp_indices,
@@ -834,9 +871,9 @@ class WALSModel(object):
       total_lhs += self._regularization_matrix
     if self._row_weights is None:
       # Special case of ALS. Use a much simpler update rule.
-      total_rhs = (self._unobserved_weight *
-                   sparse_ops.sparse_tensor_dense_matmul(
-                       new_sp_input, right, adjoint_a=transpose_input))
+      total_rhs = (
+          self._unobserved_weight * sparse_ops.sparse_tensor_dense_matmul(
+              new_sp_input, right, adjoint_a=transpose_input))
       # TODO(rmlarsen): handle transposing in tf.matrix_solve instead of
       # transposing explicitly.
       # TODO(rmlarsen): multi-thread tf.matrix_solve.
@@ -877,8 +914,12 @@ class WALSModel(object):
           linalg_ops.matrix_solve(total_lhs, total_rhs), [2])
 
     update_op_name = "row_update" if update_row_factors else "col_update"
-    update_op = self.scatter_update(left, update_indices, new_left_values,
-                                    sharding_func, name=update_op_name)
+    update_op = self.scatter_update(
+        left,
+        update_indices,
+        new_left_values,
+        sharding_func,
+        name=update_op_name)
 
     # Create the loss subgraph
     loss_sp_input = (sparse_ops.sparse_transpose(new_sp_input)
@@ -886,32 +927,52 @@ class WALSModel(object):
     # sp_approx is the low rank estimate of the input matrix, formed by
     # computing the product <u_i, v_j> for (i, j) in loss_sp_input.indices.
     sp_approx_vals = gen_factorization_ops.masked_matmul(
-        new_left_values, right, loss_sp_input.indices, transpose_a=False,
+        new_left_values,
+        right,
+        loss_sp_input.indices,
+        transpose_a=False,
         transpose_b=True)
     sp_approx = sparse_tensor.SparseTensor(
         loss_sp_input.indices, sp_approx_vals, loss_sp_input.dense_shape)
     sp_approx_sq = math_ops.square(sp_approx)
     sp_residual = sparse_ops.sparse_add(loss_sp_input, sp_approx * (-1))
     sp_residual_sq = math_ops.square(sp_residual)
-    row_wt_mat = (constant_op.constant(0.) if self._row_weights is None else
-                  array_ops.expand_dims(row_weights_slice, 1))
-    col_wt_mat = (constant_op.constant(0.) if self._col_weights is None else
-                  array_ops.expand_dims(col_weights, 0))
+    row_wt_mat = (constant_op.constant(0.)
+                  if self._row_weights is None else array_ops.expand_dims(
+                      row_weights_slice, 1))
+    col_wt_mat = (constant_op.constant(0.)
+                  if self._col_weights is None else array_ops.expand_dims(
+                      col_weights, 0))
+
     # We return the normalized loss
     partial_row_gramian = math_ops.matmul(
         new_left_values, new_left_values, transpose_a=True)
     normalization_factor = total_rows / math_ops.cast(num_rows, dtypes.float32)
-    loss = (
-        self._unobserved_weight * (
-            sparse_ops.sparse_reduce_sum(sp_residual_sq) -
-            sparse_ops.sparse_reduce_sum(sp_approx_sq) +
-            math_ops.trace(math_ops.matmul(partial_row_gramian, gramian))
-        ) +
+
+    unregularized_loss = (
+        self._unobserved_weight * (  # pyformat line break
+            sparse_ops.sparse_reduce_sum(sp_residual_sq) -  # pyformat break
+            sparse_ops.sparse_reduce_sum(sp_approx_sq) +  # pyformat break
+            math_ops.trace(math_ops.matmul(partial_row_gramian, gramian))) +
         sparse_ops.sparse_reduce_sum(row_wt_mat * (sp_residual_sq * col_wt_mat))
     ) * normalization_factor
+
     if self._regularization is not None:
-      loss += self._regularization * (
+      regularization = self._regularization * (
           math_ops.trace(partial_row_gramian) * normalization_factor +
-          math_ops.trace(gramian)
-      )
-    return (new_left_values, update_op, loss)
+          math_ops.trace(gramian))
+    else:
+      regularization = constant_op.constant(0.)
+
+    sum_weights = self._unobserved_weight * math_ops.cast(
+        total_rows * total_cols, dtypes.float32)
+    if self._row_weights is not None and self._col_weights is not None:
+      ones = sparse_tensor.SparseTensor(
+          indices=loss_sp_input.indices,
+          values=array_ops.ones(array_ops.shape(loss_sp_input.values)),
+          dense_shape=loss_sp_input.dense_shape)
+      sum_weights += sparse_ops.sparse_reduce_sum(row_wt_mat * (
+          ones * col_wt_mat)) * normalization_factor
+
+    return (new_left_values, update_op, unregularized_loss, regularization,
+            sum_weights)
diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py b/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
index bcee881854..c813733915 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
@@ -31,7 +31,6 @@ from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
-
 INPUT_MATRIX = factorization_ops_test_utils.INPUT_MATRIX
 np_matrix_to_tf_sparse = factorization_ops_test_utils.np_matrix_to_tf_sparse
 
@@ -53,16 +52,20 @@ class WalsModelTest(test.TestCase):
 
   def calculate_loss_from_wals_model(self, wals_model, sp_inputs):
     current_rows = embedding_ops.embedding_lookup(
-        wals_model.row_factors, math_ops.range(wals_model._input_rows),
+        wals_model.row_factors,
+        math_ops.range(wals_model._input_rows),
         partition_strategy="div")
     current_cols = embedding_ops.embedding_lookup(
-        wals_model.col_factors, math_ops.range(wals_model._input_cols),
+        wals_model.col_factors,
+        math_ops.range(wals_model._input_cols),
         partition_strategy="div")
     row_wts = embedding_ops.embedding_lookup(
-        wals_model._row_weights, math_ops.range(wals_model._input_rows),
+        wals_model._row_weights,
+        math_ops.range(wals_model._input_rows),
         partition_strategy="div")
     col_wts = embedding_ops.embedding_lookup(
-        wals_model._col_weights, math_ops.range(wals_model._input_cols),
+        wals_model._col_weights,
+        math_ops.range(wals_model._input_cols),
         partition_strategy="div")
     return factorization_ops_test_utils.calculate_loss(
         sp_inputs, current_rows, current_cols, wals_model._regularization,
@@ -71,9 +74,11 @@ class WalsModelTest(test.TestCase):
   def setUp(self):
     self.col_init = [
         # shard 0
-        [[-0.36444709, -0.39077035, -0.32528427],
-         [1.19056475, 0.07231052, 2.11834812],
-         [0.93468881, -0.71099287, 1.91826844]],
+        [
+            [-0.36444709, -0.39077035, -0.32528427],  # pyformat line break
+            [1.19056475, 0.07231052, 2.11834812],
+            [0.93468881, -0.71099287, 1.91826844]
+        ],
         # shard 1
         [[1.18160152, 1.52490723, -0.50015002],
          [1.82574749, -0.57515913, -1.32810032]],
@@ -87,19 +92,72 @@ class WalsModelTest(test.TestCase):
 
     # Values of factor shards after running one iteration of row and column
     # updates.
-    self._row_factors_0 = [[0.097689, -0.219293, -0.020780],
-                           [0.50842, 0.64626, 0.22364],
-                           [0.401159, -0.046558, -0.192854]]
+    self._row_factors_0 = [
+        [0.097689, -0.219293, -0.020780],  # pyformat line break
+        [0.50842, 0.64626, 0.22364],
+        [0.401159, -0.046558, -0.192854]
+    ]
     self._row_factors_1 = [[1.20597, -0.48025, 0.35582],
                            [1.5564, 1.2528, 1.0528]]
-    self._col_factors_0 = [[2.4725, -1.2950, -1.9980],
-                           [0.44625, 1.50771, 1.27118],
-                           [1.39801, -2.10134, 0.73572]]
+    self._col_factors_0 = [
+        [2.4725, -1.2950, -1.9980],  # pyformat line break
+        [0.44625, 1.50771, 1.27118],
+        [1.39801, -2.10134, 0.73572]
+    ]
     self._col_factors_1 = [[3.36509, -0.66595, -3.51208],
                            [0.57191, 1.59407, 1.33020]]
     self._col_factors_2 = [[3.3459, -1.3341, -3.3008],
                            [0.57366, 1.83729, 1.26798]]
 
+  def _run_test_sum_weights(self, test_rows):
+    # test_rows: True to test row weights, False to test column weights.
+
+    num_rows = 5
+    num_cols = 5
+    unobserved_weight = 0.1
+    row_weights = [[8., 18., 28., 38., 48.]]
+    col_weights = [[90., 91., 92., 93., 94.]]
+    sparse_indices = [[0, 1], [2, 3], [4, 1]]
+    sparse_values = [666., 777., 888.]
+
+    unobserved = unobserved_weight * num_rows * num_cols
+    observed = 8. * 91. + 28. * 93. + 48. * 91.
+    # sparse_indices has three unique rows and two unique columns
+    observed *= num_rows / 3. if test_rows else num_cols / 2.
+    want_weight_sum = unobserved + observed
+
+    with ops.Graph().as_default(), self.test_session() as sess:
+      wals_model = factorization_ops.WALSModel(
+          input_rows=num_rows,
+          input_cols=num_cols,
+          n_components=5,
+          unobserved_weight=unobserved_weight,
+          row_weights=row_weights,
+          col_weights=col_weights,
+          use_factors_weights_cache=False)
+
+      wals_model.initialize_op.run()
+      wals_model.worker_init.run()
+
+      update_factors = (wals_model.update_row_factors
+                        if test_rows else wals_model.update_col_factors)
+
+      (_, _, _, _, sum_weights) = update_factors(
+          sp_input=sparse_tensor.SparseTensor(
+              indices=sparse_indices,
+              values=sparse_values,
+              dense_shape=[num_rows, num_cols]),
+          transpose_input=False)
+
+      got_weight_sum = sess.run(sum_weights)
+
+      self.assertNear(
+          got_weight_sum,
+          want_weight_sum,
+          err=.001,
+          msg="got weight sum [{}], want weight sum [{}]".format(
+              got_weight_sum, want_weight_sum))
+
   def _run_test_process_input(self,
                               use_factors_weights_cache,
                               compute_loss=False):
@@ -138,8 +196,10 @@ class WalsModelTest(test.TestCase):
       # Here we feed in scattered rows of the input.
       wals_model.row_update_prep_gramian_op.run()
       wals_model.initialize_row_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_row_factors(
-          sp_input=sp_feeder, transpose_input=False)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_row_factors(
+           sp_input=sp_feeder, transpose_input=False)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_rows:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -163,8 +223,8 @@ class WalsModelTest(test.TestCase):
           sp_input=sp_feeder, transpose_input=False)
       feed_dict = {
           sp_feeder:
-              np_matrix_to_tf_sparse(
-                  INPUT_MATRIX, [1, 4], shuffle=False).eval()
+              np_matrix_to_tf_sparse(INPUT_MATRIX, [1, 4], shuffle=False)
+              .eval()
       }
       self.assertAllClose(
           projected_rows.eval(feed_dict=feed_dict),
@@ -178,15 +238,17 @@ class WalsModelTest(test.TestCase):
       if compute_loss:
         # Test loss computation after the row update
         loss = sum(
-            sess.run(factor_loss * self.count_rows(inp) / num_rows,
-                     feed_dict={sp_feeder: inp})
-            for inp in input_scattered_rows)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+            sess.run(
+                factor_loss * self.count_rows(inp) / num_rows,
+                feed_dict={sp_feeder: inp}) for inp in input_scattered_rows)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After row update, computed loss = {}, does not match
-            the true loss = {}.""".format(loss, true_loss))
+            loss,
+            true_loss,
+            err=.001,
+            msg="After row update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
 
       # Split input into multiple sparse tensors with scattered columns. Note
       # that here the elements in the sparse tensors are not ordered and also
@@ -206,8 +268,10 @@ class WalsModelTest(test.TestCase):
       # Here we feed in scattered columns of the input.
       wals_model.col_update_prep_gramian_op.run()
       wals_model.initialize_col_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_col_factors(
-          sp_input=sp_feeder, transpose_input=False)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_col_factors(
+           sp_input=sp_feeder, transpose_input=False)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_cols:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -243,25 +307,28 @@ class WalsModelTest(test.TestCase):
           atol=1e-3)
       self.assertAllClose(
           projected_cols_no_weights.eval(feed_dict=feed_dict),
-          [[3.471045, -1.250835, -3.598917],
-           [3.585139, -0.487476, -3.852232],
+          [[3.471045, -1.250835, -3.598917], [3.585139, -0.487476, -3.852232],
            [0.346433, 1.360644, 1.677121]],
           atol=1e-3)
 
       if compute_loss:
         # Test loss computation after the column update.
         loss = sum(
-            sess.run(factor_loss * self.count_cols(inp) / num_cols,
-                     feed_dict={sp_feeder: inp})
+            sess.run(
+                factor_loss * self.count_cols(inp) / num_cols,
+                feed_dict={sp_feeder: inp})
             for inp in input_scattered_cols_non_duplicate)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After col update, computed loss = {}, does not match the true
-            loss = {}.""".format(loss, true_loss))
-
-  def _run_test_process_input_transposed(self, use_factors_weights_cache,
+            loss,
+            true_loss,
+            err=.001,
+            msg="After col update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
+
+  def _run_test_process_input_transposed(self,
+                                         use_factors_weights_cache,
                                          compute_loss=False):
     with ops.Graph().as_default(), self.test_session() as sess:
       self._wals_inputs = self.sparse_input()
@@ -304,8 +371,10 @@ class WalsModelTest(test.TestCase):
       # they appear.
       wals_model.row_update_prep_gramian_op.run()
       wals_model.initialize_row_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_row_factors(
-          sp_input=sp_feeder, transpose_input=True)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_row_factors(
+           sp_input=sp_feeder, transpose_input=True)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_rows:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -344,15 +413,18 @@ class WalsModelTest(test.TestCase):
       if compute_loss:
         # Test loss computation after the row update
         loss = sum(
-            sess.run(factor_loss * self.count_cols(inp) / num_rows,
-                     feed_dict={sp_feeder: inp})
+            sess.run(
+                factor_loss * self.count_cols(inp) / num_rows,
+                feed_dict={sp_feeder: inp})
             for inp in input_scattered_rows_non_duplicate)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After row update, computed loss = {}, does not match the true
-            loss = {}.""".format(loss, true_loss))
+            loss,
+            true_loss,
+            err=.001,
+            msg="After row update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
 
       # Split input into multiple SparseTensors with scattered columns.
       # Here the inputs are transposed. But the same constraints as described in
@@ -375,8 +447,10 @@ class WalsModelTest(test.TestCase):
       # Here we feed in scattered columns of the input.
       wals_model.col_update_prep_gramian_op.run()
       wals_model.initialize_col_update_op.run()
-      _, process_input_op, factor_loss = wals_model.update_col_factors(
-          sp_input=sp_feeder, transpose_input=True)
+      (_, process_input_op, unregularized_loss, regularization,
+       _) = wals_model.update_col_factors(
+           sp_input=sp_feeder, transpose_input=True)
+      factor_loss = unregularized_loss + regularization
       for inp in input_scattered_cols:
         feed_dict = {sp_feeder: inp}
         process_input_op.run(feed_dict=feed_dict)
@@ -406,21 +480,23 @@ class WalsModelTest(test.TestCase):
           atol=1e-3)
       self.assertAllClose(
           projected_cols_no_weights.eval(feed_dict=feed_dict),
-          [[3.585139, -0.487476, -3.852232],
-           [0.557937, 1.813907, 1.331171]],
+          [[3.585139, -0.487476, -3.852232], [0.557937, 1.813907, 1.331171]],
           atol=1e-3)
       if compute_loss:
         # Test loss computation after the col update
         loss = sum(
-            sess.run(factor_loss * self.count_rows(inp) / num_cols,
-                     feed_dict={sp_feeder: inp})
+            sess.run(
+                factor_loss * self.count_rows(inp) / num_cols,
+                feed_dict={sp_feeder: inp})
             for inp in input_scattered_cols_non_duplicate)
-        true_loss = self.calculate_loss_from_wals_model(
-            wals_model, self._wals_inputs)
+        true_loss = self.calculate_loss_from_wals_model(wals_model,
+                                                        self._wals_inputs)
         self.assertNear(
-            loss, true_loss, err=.001,
-            msg="""After col update, computed loss = {}, does not match the true
-            loss = {}.""".format(loss, true_loss))
+            loss,
+            true_loss,
+            err=.001,
+            msg="After col update, computed loss [{}] does not match"
+            " true loss [{}]".format(loss, true_loss))
 
   # Note that when row_weights and col_weights are 0, WALS gives identical
   # results as ALS (Alternating Least Squares). However our implementation does
@@ -503,8 +579,7 @@ class WalsModelTest(test.TestCase):
       for c1, c2 in zip(col_factors1, col_factors2):
         self.assertAllClose(c1, c2, rtol=5e-3, atol=1e-2)
       self.assertAllClose(
-          als_projected_col_factors1,
-          [col_factors2[0][2], col_factors2[0][0]],
+          als_projected_col_factors1, [col_factors2[0][2], col_factors2[0][0]],
           atol=1e-2)
 
   def _run_test_als_transposed(self, use_factors_weights_cache):
@@ -599,8 +674,8 @@ class WalsModelTest(test.TestCase):
     cols = 11
     dims = 3
     with ops.Graph().as_default(), self.test_session():
-      data = np.dot(np.random.rand(rows, 3),
-                    np.random.rand(3, cols)).astype(np.float32) / 3.0
+      data = np.dot(np.random.rand(rows, 3), np.random.rand(
+          3, cols)).astype(np.float32) / 3.0
       indices = [[i, j] for i in xrange(rows) for j in xrange(cols)]
       values = data.reshape(-1)
       inp = sparse_tensor.SparseTensor(indices, values, [rows, cols])
@@ -629,8 +704,8 @@ class WalsModelTest(test.TestCase):
     dims = 3
 
     with ops.Graph().as_default(), self.test_session():
-      data = np.dot(np.random.rand(rows, 3),
-                    np.random.rand(3, cols)).astype(np.float32) / 3.0
+      data = np.dot(np.random.rand(rows, 3), np.random.rand(
+          3, cols)).astype(np.float32) / 3.0
       indices = [[i, j] for i in xrange(rows) for j in xrange(cols)]
       values = data.reshape(-1)
       inp = sparse_tensor.SparseTensor(indices, values, [rows, cols])
@@ -664,8 +739,8 @@ class WalsModelTest(test.TestCase):
     with ops.Graph().as_default(), self.test_session():
       row_wts = 0.1 + np.random.rand(rows)
       col_wts = 0.1 + np.random.rand(cols)
-      data = np.dot(np.random.rand(rows, 3),
-                    np.random.rand(3, cols)).astype(np.float32) / 3.0
+      data = np.dot(np.random.rand(rows, 3), np.random.rand(
+          3, cols)).astype(np.float32) / 3.0
       indices = np.array(
           list(
               filter(keep_index,
@@ -741,5 +816,12 @@ class WalsModelTest(test.TestCase):
   def test_loss_without_cache(self):
     self._run_test_process_input(False, compute_loss=True)
 
+  def test_sum_row_weights(self):
+    self._run_test_sum_weights(True)
+
+  def test_sum_col_weights(self):
+    self._run_test_sum_weights(False)
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/factorization/python/ops/wals.py b/tensorflow/contrib/factorization/python/ops/wals.py
index 0bc0ef39ec..3e3ee5fa57 100644
--- a/tensorflow/contrib/factorization/python/ops/wals.py
+++ b/tensorflow/contrib/factorization/python/ops/wals.py
@@ -38,17 +38,9 @@ from tensorflow.python.training import session_run_hook
 class _SweepHook(session_run_hook.SessionRunHook):
   """Keeps track of row/col sweeps, and runs prep ops before each sweep."""
 
-  def __init__(self,
-               is_row_sweep_var,
-               train_op,
-               num_rows,
-               num_cols,
-               processed_row_indices,
-               processed_col_indices,
-               row_prep_ops,
-               col_prep_ops,
-               cache_init_ops,
-               completed_sweeps_var):
+  def __init__(self, is_row_sweep_var, train_op, num_rows, num_cols,
+               processed_row_indices, processed_col_indices, row_prep_ops,
+               col_prep_ops, cache_init_ops, completed_sweeps_var):
     """Initializes SweepHook.
 
     Args:
@@ -90,12 +82,9 @@ class _SweepHook(session_run_hook.SessionRunHook):
     # _is_row_sweep_var and incrementing the global_step and completed_sweeps
     # counters. They have control_dependencies on train_op.
     self._fetches = self._create_switch_ops(processed_row_indices,
-                                            processed_col_indices,
-                                            train_op)
+                                            processed_col_indices, train_op)
 
-  def _create_switch_ops(self,
-                         processed_row_indices,
-                         processed_col_indices,
+  def _create_switch_ops(self, processed_row_indices, processed_col_indices,
                          train_op):
     """Creates ops to update is_row_sweep_var, global_step and completed_sweeps.
 
@@ -147,17 +136,20 @@ class _SweepHook(session_run_hook.SessionRunHook):
     # After running the train_op, update processed_rows or processed_cols
     # tensors, depending on whether we are currently doing a row or a col sweep
     with ops.control_dependencies([train_op]):
+
       def get_row_update_op():
         with ops.colocate_with(processed_rows):
-          return state_ops.scatter_update(
-              processed_rows, processed_row_indices,
-              array_ops.ones_like(processed_row_indices, dtype=dtypes.bool))
+          return state_ops.scatter_update(processed_rows, processed_row_indices,
+                                          array_ops.ones_like(
+                                              processed_row_indices,
+                                              dtype=dtypes.bool))
 
       def get_col_update_op():
         with ops.colocate_with(processed_cols):
-          return state_ops.scatter_update(
-              processed_cols, processed_col_indices,
-              array_ops.ones_like(processed_col_indices, dtype=dtypes.bool))
+          return state_ops.scatter_update(processed_cols, processed_col_indices,
+                                          array_ops.ones_like(
+                                              processed_col_indices,
+                                              dtype=dtypes.bool))
 
       update_processed_op = control_flow_ops.cond(
           self._is_row_sweep_var, get_row_update_op, get_col_update_op)
@@ -166,6 +158,7 @@ class _SweepHook(session_run_hook.SessionRunHook):
       # If this is the case, flip the is_row_sweep_var and reset processed_rows
       # and processed_cols tensors.
       with ops.control_dependencies([update_processed_op]):
+
         def get_switch_op():
           return state_ops.assign(
               self._is_row_sweep_var,
@@ -182,13 +175,17 @@ class _SweepHook(session_run_hook.SessionRunHook):
             lambda: math_ops.reduce_all(processed_cols),
             name="sweep_hook_is_sweep_done")
         switch_op = control_flow_ops.cond(
-            is_sweep_done, get_switch_op, control_flow_ops.no_op,
+            is_sweep_done,
+            get_switch_op,
+            control_flow_ops.no_op,
             name="sweep_hook_switch_op")
         reset_op = control_flow_ops.cond(
-            is_sweep_done, get_reset_op, control_flow_ops.no_op,
+            is_sweep_done,
+            get_reset_op,
+            control_flow_ops.no_op,
             name="sweep_hook_reset_op")
-        switch_ops = control_flow_ops.group(switch_op, reset_op,
-                                            name="sweep_hook_switch_ops")
+        switch_ops = control_flow_ops.group(
+            switch_op, reset_op, name="sweep_hook_switch_ops")
 
         with ops.control_dependencies([switch_ops]):
           # Op to increment the completed_sweeps counter.
@@ -208,7 +205,8 @@ class _SweepHook(session_run_hook.SessionRunHook):
                 name="global_step_incr")
 
           incr_ops = control_flow_ops.group(
-              completed_sweeps_incr_op, global_step_incr_op,
+              completed_sweeps_incr_op,
+              global_step_incr_op,
               name="counter_incr_ops")
 
     return [is_sweep_done, switch_ops, incr_ops]
@@ -260,7 +258,7 @@ class _StopAtSweepHook(session_run_hook.SessionRunHook):
   def begin(self):
     try:
       self._completed_sweeps_var = ops.get_default_graph().get_tensor_by_name(
-          WALSMatrixFactorization.COMPLETED_SWEEPS+":0")
+          WALSMatrixFactorization.COMPLETED_SWEEPS + ":0")
     except KeyError:
       raise RuntimeError(WALSMatrixFactorization.COMPLETED_SWEEPS +
                          " counter should be created to use StopAtSweepHook.")
@@ -288,12 +286,10 @@ def _wals_factorization_model_function(features, labels, mode, params):
     A ModelFnOps object.
   """
   assert labels is None
-  use_factors_weights_cache = (
-      params["use_factors_weights_cache_for_training"]
-      and mode == model_fn.ModeKeys.TRAIN)
-  use_gramian_cache = (
-      params["use_gramian_cache_for_training"]
-      and mode == model_fn.ModeKeys.TRAIN)
+  use_factors_weights_cache = (params["use_factors_weights_cache_for_training"]
+                               and mode == model_fn.ModeKeys.TRAIN)
+  use_gramian_cache = (params["use_gramian_cache_for_training"] and
+                       mode == model_fn.ModeKeys.TRAIN)
   max_sweeps = params["max_sweeps"]
   model = factorization_ops.WALSModel(
       params["num_rows"],
@@ -348,15 +344,22 @@ def _wals_factorization_model_function(features, labels, mode, params):
 
   def update_row_factors():
     return model.update_row_factors(sp_input=input_rows, transpose_input=False)
+
   def update_col_factors():
     return model.update_col_factors(sp_input=input_cols, transpose_input=True)
-  _, train_op, loss = control_flow_ops.cond(
-      is_row_sweep, update_row_factors, update_col_factors)
 
-  row_prep_ops = [model.row_update_prep_gramian_op,
-                  model.initialize_row_update_op]
-  col_prep_ops = [model.col_update_prep_gramian_op,
-                  model.initialize_col_update_op]
+  (_, train_op,
+   unregularized_loss, regularization, sum_weights) = control_flow_ops.cond(
+       is_row_sweep, update_row_factors, update_col_factors)
+  loss = unregularized_loss + regularization
+  root_weighted_squared_error = math_ops.sqrt(unregularized_loss / sum_weights)
+
+  row_prep_ops = [
+      model.row_update_prep_gramian_op, model.initialize_row_update_op
+  ]
+  col_prep_ops = [
+      model.col_update_prep_gramian_op, model.initialize_col_update_op
+  ]
   cache_init_ops = [model.worker_init]
 
   sweep_hook = _SweepHook(
@@ -369,13 +372,15 @@ def _wals_factorization_model_function(features, labels, mode, params):
       row_prep_ops,
       col_prep_ops,
       cache_init_ops,
-      completed_sweeps_var,
-  )
+      completed_sweeps_var,)
   training_hooks = [sweep_hook]
   if max_sweeps is not None:
     training_hooks.append(_StopAtSweepHook(max_sweeps))
 
-  summary.scalar("loss", loss)
+  # The root weighted squared error =
+  #   \sqrt( \sum_{i,j} w_ij * (a_ij - r_ij)^2 / \sum_{i,j} w_ij )
+  summary.scalar("loss", loss)  # the estimated total training loss
+  summary.scalar("root_weighted_squared_error", root_weighted_squared_error)
   summary.scalar("completed_sweeps", completed_sweeps_var)
 
   # Prediction ops (only return predictions in INFER mode)
@@ -384,11 +389,13 @@ def _wals_factorization_model_function(features, labels, mode, params):
     project_row = features[WALSMatrixFactorization.PROJECT_ROW]
     projection_weights = features.get(
         WALSMatrixFactorization.PROJECTION_WEIGHTS)
+
     def get_row_projection():
       return model.project_row_factors(
           sp_input=input_rows,
           projection_weights=projection_weights,
           transpose_input=False)
+
     def get_col_projection():
       return model.project_col_factors(
           sp_input=input_cols,
@@ -396,8 +403,8 @@ def _wals_factorization_model_function(features, labels, mode, params):
           transpose_input=True)
 
     predictions[WALSMatrixFactorization.PROJECTION_RESULT] = (
-        control_flow_ops.cond(
-            project_row, get_row_projection, get_col_projection))
+        control_flow_ops.cond(project_row, get_row_projection,
+                              get_col_projection))
 
   return model_fn.ModelFnOps(
       mode=mode,
@@ -556,26 +563,41 @@ class WALSMatrixFactorization(estimator.Estimator):
     # TODO(walidk): Provide input pipelines that handle missing rows.
 
     params = {
-        "num_rows": num_rows,
-        "num_cols": num_cols,
-        "embedding_dimension": embedding_dimension,
-        "unobserved_weight": unobserved_weight,
-        "regularization_coeff": regularization_coeff,
-        "row_init": row_init,
-        "col_init": col_init,
-        "num_row_shards": num_row_shards,
-        "num_col_shards": num_col_shards,
-        "row_weights": row_weights,
-        "col_weights": col_weights,
-        "max_sweeps": max_sweeps,
+        "num_rows":
+            num_rows,
+        "num_cols":
+            num_cols,
+        "embedding_dimension":
+            embedding_dimension,
+        "unobserved_weight":
+            unobserved_weight,
+        "regularization_coeff":
+            regularization_coeff,
+        "row_init":
+            row_init,
+        "col_init":
+            col_init,
+        "num_row_shards":
+            num_row_shards,
+        "num_col_shards":
+            num_col_shards,
+        "row_weights":
+            row_weights,
+        "col_weights":
+            col_weights,
+        "max_sweeps":
+            max_sweeps,
         "use_factors_weights_cache_for_training":
             use_factors_weights_cache_for_training,
-        "use_gramian_cache_for_training": use_gramian_cache_for_training
+        "use_gramian_cache_for_training":
+            use_gramian_cache_for_training
     }
-    self._row_factors_names = ["row_factors_shard_%d" % i
-                               for i in range(num_row_shards)]
-    self._col_factors_names = ["col_factors_shard_%d" % i
-                               for i in range(num_col_shards)]
+    self._row_factors_names = [
+        "row_factors_shard_%d" % i for i in range(num_row_shards)
+    ]
+    self._col_factors_names = [
+        "col_factors_shard_%d" % i for i in range(num_col_shards)
+    ]
 
     super(WALSMatrixFactorization, self).__init__(
         model_fn=_wals_factorization_model_function,
diff --git a/tensorflow/contrib/hooks/BUILD b/tensorflow/contrib/hooks/BUILD
index b6920ffd3d..d81e868d4a 100644
--- a/tensorflow/contrib/hooks/BUILD
+++ b/tensorflow/contrib/hooks/BUILD
@@ -19,12 +19,11 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/framework:framework_py",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:state_ops",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:client",
+        "//tensorflow/python:platform",
         "//tensorflow/python:training",
-        "//tensorflow/python:variables",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -35,10 +34,12 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":hooks",
+        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:training",
     ],
 )
 
diff --git a/tensorflow/contrib/labeled_tensor/BUILD b/tensorflow/contrib/labeled_tensor/BUILD
index 55258f264a..4eba29caec 100644
--- a/tensorflow/contrib/labeled_tensor/BUILD
+++ b/tensorflow/contrib/labeled_tensor/BUILD
@@ -36,6 +36,7 @@ py_library(
     srcs = ["python/ops/_typecheck.py"],
     srcs_version = "PY2AND3",
     visibility = [":__subpackages__"],
+    deps = ["//tensorflow/python:util"],
 )
 
 py_library(
@@ -48,6 +49,7 @@ py_library(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//third_party/py/numpy",
+        "@six_archive//:six",
     ],
 )
 
@@ -56,8 +58,6 @@ py_library(
     srcs = ["python/ops/test_util.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":_typecheck",
-        ":core",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:training",
     ],
@@ -92,6 +92,7 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:parsing_ops",
+        "@six_archive//:six",
     ],
 )
 
@@ -105,7 +106,6 @@ py_test(
     deps = [
         ":core",
         ":io_ops",
-        ":ops",
         ":test_util",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
@@ -151,11 +151,13 @@ py_library(
         ":core",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:functional_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:numerics",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
+        "@six_archive//:six",
     ],
 )
 
@@ -175,6 +177,7 @@ py_test(
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:string_ops",
         "//third_party/py/numpy",
     ],
 )
@@ -188,6 +191,7 @@ py_library(
         ":core",
         ":ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "@six_archive//:six",
     ],
 )
 
diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD
index 62741d3b0c..c96e42de45 100644
--- a/tensorflow/contrib/layers/BUILD
+++ b/tensorflow/contrib/layers/BUILD
@@ -155,9 +155,7 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
         "//third_party/py/numpy",
     ],
 )
@@ -172,8 +170,6 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -189,12 +185,10 @@ py_test(
         ":layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
@@ -213,11 +207,8 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:nn_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -229,11 +220,9 @@ py_test(
     deps = [
         ":layers_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:parsing_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
@@ -253,17 +242,16 @@ py_test(
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:gradients",
         "//tensorflow/python:init_ops",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:partitioned_variables",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/feature_column",
         "//third_party/py/numpy",
     ],
 )
@@ -278,10 +266,7 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -294,11 +279,9 @@ py_test(
         ":layers_py",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
         "//third_party/py/numpy",
     ],
 )
@@ -311,15 +294,17 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":layers_py",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradient_checker",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:partitioned_variables",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:util",
         "//third_party/py/numpy",
     ],
 )
@@ -334,8 +319,6 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
@@ -351,8 +334,7 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
         "//third_party/py/numpy",
     ],
 )
@@ -365,12 +347,9 @@ py_test(
     deps = [
         ":layers_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/contrib/learn/python/learn/datasets/BUILD b/tensorflow/contrib/learn/python/learn/datasets/BUILD
index de9b786a2c..8bf372841d 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/BUILD
+++ b/tensorflow/contrib/learn/python/learn/datasets/BUILD
@@ -29,6 +29,7 @@ py_library(
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "//tensorflow/python:random_seed",
         "//third_party/py/numpy",
     ],
 )
@@ -39,7 +40,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow/python",  # TODO(b/34059704): remove when fixed
         "//tensorflow/python:platform",
     ],
 )
@@ -63,9 +63,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow/contrib/learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -76,9 +74,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow/contrib/learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -89,10 +85,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":datasets",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug.py b/tensorflow/contrib/learn/python/learn/estimators/debug.py
index 010448be12..9d5f6c2bf9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug.py
@@ -157,7 +157,8 @@ class DebugClassifier(estimator.Estimator):
                n_classes=2,
                weight_column_name=None,
                config=None,
-               feature_engineering_fn=None):
+               feature_engineering_fn=None,
+               label_keys=None):
     """Initializes a DebugClassifier instance.
 
     Args:
@@ -175,6 +176,8 @@ class DebugClassifier(estimator.Estimator):
       feature_engineering_fn: Feature engineering function. Takes features and
                         labels which are the output of `input_fn` and returns
                         features and labels which will be fed into the model.
+      label_keys: Optional list of strings with size `[n_classes]` defining the
+        label vocabulary. Only supported for `n_classes` > 2.
     Returns:
       A `DebugClassifier` estimator.
 
@@ -182,10 +185,11 @@ class DebugClassifier(estimator.Estimator):
       ValueError: If `n_classes` < 2.
     """
     params = {"head":
-              head_lib._multi_class_head(  # pylint: disable=protected-access
+              head_lib.multi_class_head(
                   n_classes=n_classes,
                   weight_column_name=weight_column_name,
-                  enable_centered_bias=True)}
+                  enable_centered_bias=True,
+                  label_keys=label_keys)}
 
     super(DebugClassifier, self).__init__(
         model_fn=debug_model_fn,
@@ -292,7 +296,7 @@ class DebugRegressor(estimator.Estimator):
 
     params = {
         "head":
-            head_lib._regression_head(  # pylint: disable=protected-access
+            head_lib.regression_head(
                 weight_column_name=weight_column_name,
                 label_dimension=label_dimension,
                 enable_centered_bias=True)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
index ab5db3a151..6b125534a4 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py
@@ -363,6 +363,23 @@ class DebugClassifierTest(test.TestCase):
     scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
     self._assertInRange(0.0, 1.0, scores['accuracy'])
 
+  def testMultiClass_StringLabel(self):
+    """Tests multi-class classification with string labels."""
+
+    def _input_fn_train():
+      labels = constant_op.constant([['foo'], ['bar'], ['baz'], ['bar']])
+      features = {
+          'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
+      }
+      return features, labels
+
+    classifier = debug.DebugClassifier(
+        n_classes=3, label_keys=['foo', 'bar', 'baz'])
+
+    classifier.fit(input_fn=_input_fn_train, steps=5)
+    scores = classifier.evaluate(input_fn=_input_fn_train, steps=1)
+    self.assertIn('loss', scores)
+
   def testLoss(self):
     """Tests loss calculation."""
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
index e5c01336cf..ce87b4723d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
@@ -64,13 +64,16 @@ def make_random_points(centers, num_points, max_offset=20):
   assignments = np.random.choice(num_centers, num_points)
   offsets = np.round(
       np.random.randn(num_points, num_dims).astype(np.float32) * max_offset)
-  return (centers[assignments] + offsets, assignments,
-          np.add.reduce(offsets * offsets, 1))
+  return (centers[assignments] + offsets, assignments, np.add.reduce(
+      offsets * offsets, 1))
 
 
 class KMeansTestBase(test.TestCase):
 
-  def input_fn(self, batch_size=None, points=None, randomize=None,
+  def input_fn(self,
+               batch_size=None,
+               points=None,
+               randomize=None,
                num_epochs=None):
     """Returns an input_fn that randomly selects batches from given points."""
     batch_size = batch_size or self.batch_size
@@ -79,6 +82,7 @@ class KMeansTestBase(test.TestCase):
     if randomize is None:
       randomize = (self.use_mini_batch and
                    self.mini_batch_steps_per_iteration <= 1)
+
     def _fn():
       x = constant_op.constant(points)
       if batch_size == num_points:
@@ -86,33 +90,40 @@ class KMeansTestBase(test.TestCase):
       if randomize:
         indices = random_ops.random_uniform(
             constant_op.constant([batch_size]),
-            minval=0, maxval=num_points-1,
+            minval=0,
+            maxval=num_points - 1,
             dtype=dtypes.int32,
             seed=10)
       else:
         # We need to cycle through the indices sequentially. We create a queue
         # to maintain the list of indices.
-        q = data_flow_ops.FIFOQueue(self.num_points, dtypes.int32, ())
+        q = data_flow_ops.FIFOQueue(num_points, dtypes.int32, ())
+
         # Conditionally initialize the Queue.
         def _init_q():
-          with ops.control_dependencies([q.enqueue_many(
-              math_ops.range(self.num_points))]):
+          with ops.control_dependencies(
+              [q.enqueue_many(math_ops.range(num_points))]):
             return control_flow_ops.no_op()
-        init_q = control_flow_ops.cond(q.size() <= 0,
-                                       _init_q,
+
+        init_q = control_flow_ops.cond(q.size() <= 0, _init_q,
                                        control_flow_ops.no_op)
         with ops.control_dependencies([init_q]):
-          offsets = q.dequeue_many(self.batch_size)
+          offsets = q.dequeue_many(batch_size)
           with ops.control_dependencies([q.enqueue_many(offsets)]):
             indices = array_ops.identity(offsets)
       batch = array_ops.gather(x, indices)
       return (input_lib.limit_epochs(batch, num_epochs=num_epochs), None)
+
     return _fn
 
   @staticmethod
   def config(tf_random_seed):
     return run_config.RunConfig(tf_random_seed=tf_random_seed)
 
+  @property
+  def initial_clusters(self):
+    return kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT
+
   @property
   def batch_size(self):
     return self.num_points
@@ -141,7 +152,7 @@ class KMeansTest(KMeansTestBase):
   def _kmeans(self, relative_tolerance=None):
     return kmeans_lib.KMeansClustering(
         self.num_centers,
-        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        initial_clusters=self.initial_clusters,
         distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
         use_mini_batch=self.use_mini_batch,
         mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
@@ -172,7 +183,7 @@ class KMeansTest(KMeansTestBase):
       return
     kmeans = kmeans_lib.KMeansClustering(
         self.num_centers,
-        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        initial_clusters=self.initial_clusters,
         distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
         use_mini_batch=self.use_mini_batch,
         mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
@@ -197,11 +208,12 @@ class KMeansTest(KMeansTestBase):
 
     # Make a small test set
     num_points = 10
-    points, true_assignments, true_offsets = make_random_points(clusters,
-                                                                num_points)
+    points, true_assignments, true_offsets = make_random_points(
+        clusters, num_points)
     # Test predict
-    assignments = list(kmeans.predict_cluster_idx(input_fn=self.input_fn(
-        batch_size=num_points, points=points, num_epochs=1)))
+    assignments = list(
+        kmeans.predict_cluster_idx(input_fn=self.input_fn(
+            batch_size=num_points, points=points, num_epochs=1)))
     self.assertAllEqual(assignments, true_assignments)
 
     # Test score
@@ -214,35 +226,62 @@ class KMeansTest(KMeansTestBase):
         input_fn=lambda: (constant_op.constant(points), None))
     true_transform = np.maximum(
         0,
-        np.sum(np.square(points), axis=1, keepdims=True) - 2 * np.dot(
-            points, np.transpose(clusters)) +
+        np.sum(np.square(points), axis=1,
+               keepdims=True) - 2 * np.dot(points, np.transpose(clusters)) +
         np.transpose(np.sum(np.square(clusters), axis=1, keepdims=True)))
     self.assertAllClose(transform, true_transform, rtol=0.05, atol=10)
 
-  def test_fit_raise_if_num_clusters_larger_than_num_points_random_init(self):
-    points = np.array([[2.0, 3.0], [1.6, 8.2]], dtype=np.float32)
 
-    with self.assertRaisesOpError('less'):
-      kmeans = learn.KMeansClustering(
-          num_clusters=3,
-          use_mini_batch=self.use_mini_batch,
-          mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
-          initial_clusters=kmeans_lib.KMeansClustering.RANDOM_INIT)
-      kmeans.fit(input_fn=lambda: (constant_op.constant(points), None),
-                 steps=10)
+class KMeansTestMultiStageInit(KMeansTestBase):
 
-  def test_fit_raise_if_num_clusters_larger_than_num_points_kmeans_plus_plus(
-      self):
-    points = np.array([[2.0, 3.0], [1.6, 8.2]], dtype=np.float32)
+  def test_random(self):
+    points = np.array(
+        [[1, 2], [3, 4], [5, 6], [7, 8], [9, 0]], dtype=np.float32)
+    kmeans = kmeans_lib.KMeansClustering(
+        num_clusters=points.shape[0],
+        initial_clusters=kmeans_lib.KMeansClustering.RANDOM_INIT,
+        distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
+        use_mini_batch=True,
+        mini_batch_steps_per_iteration=100,
+        random_seed=24,
+        relative_tolerance=None)
+    kmeans.fit(
+        input_fn=self.input_fn(batch_size=1, points=points, randomize=False),
+        steps=1)
+    clusters = kmeans.clusters()
+    self.assertAllEqual(points, clusters)
 
+  def test_kmeans_plus_plus_batch_just_right(self):
+    points = np.array([[1, 2]], dtype=np.float32)
+    kmeans = kmeans_lib.KMeansClustering(
+        num_clusters=points.shape[0],
+        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
+        use_mini_batch=True,
+        mini_batch_steps_per_iteration=100,
+        random_seed=24,
+        relative_tolerance=None)
+    kmeans.fit(
+        input_fn=self.input_fn(batch_size=1, points=points, randomize=False),
+        steps=1)
+    clusters = kmeans.clusters()
+    self.assertAllEqual(points, clusters)
+
+  def test_kmeans_plus_plus_batch_too_small(self):
+    points = np.array(
+        [[1, 2], [3, 4], [5, 6], [7, 8], [9, 0]], dtype=np.float32)
+    kmeans = kmeans_lib.KMeansClustering(
+        num_clusters=points.shape[0],
+        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        distance_metric=kmeans_lib.KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
+        use_mini_batch=True,
+        mini_batch_steps_per_iteration=100,
+        random_seed=24,
+        relative_tolerance=None)
     with self.assertRaisesOpError(AssertionError):
-      kmeans = learn.KMeansClustering(
-          num_clusters=3,
-          use_mini_batch=self.use_mini_batch,
-          mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
-          initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT)
-      kmeans.fit(input_fn=lambda: (constant_op.constant(points), None),
-                 steps=10)
+      kmeans.fit(
+          input_fn=self.input_fn(batch_size=4, points=points, randomize=False),
+          steps=1)
 
 
 class MiniBatchKMeansTest(KMeansTest):
@@ -282,11 +321,11 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     self.true_centers = np.array(
         [
             normalize(
-                np.mean(
-                    normalize(self.points)[0:4, :], axis=0, keepdims=True))[0],
+                np.mean(normalize(self.points)[0:4, :], axis=0, keepdims=True))[
+                    0],
             normalize(
-                np.mean(
-                    normalize(self.points)[4:, :], axis=0, keepdims=True))[0]
+                np.mean(normalize(self.points)[4:, :], axis=0, keepdims=True))[
+                    0]
         ],
         dtype=np.float32)
     self.true_assignments = np.array([0] * 4 + [1] * 4)
@@ -323,8 +362,9 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     self.kmeans.fit(input_fn=self.input_fn(), max_steps=max_steps)
     centers = normalize(self.kmeans.clusters())
 
-    assignments = list(self.kmeans.predict_cluster_idx(
-        input_fn=self.input_fn(num_epochs=1, batch_size=self.num_points)))
+    assignments = list(
+        self.kmeans.predict_cluster_idx(input_fn=self.input_fn(
+            num_epochs=1, batch_size=self.num_points)))
     self.assertAllClose(
         centers[assignments],
         self.true_centers[self.true_assignments],
@@ -333,8 +373,8 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     centers = centers[centers[:, 0].argsort()]
     true_centers = self.true_centers[self.true_centers[:, 0].argsort()]
     self.assertAllClose(centers, true_centers, atol=0.04)
-    score = self.kmeans.score(input_fn=self.input_fn(
-        batch_size=self.num_points), steps=1)
+    score = self.kmeans.score(
+        input_fn=self.input_fn(batch_size=self.num_points), steps=1)
     self.assertAllClose(score, self.true_score, atol=1e-2)
 
   def test_predict_kmeans_plus_plus(self):
@@ -348,13 +388,11 @@ class KMeansCosineDistanceTest(KMeansTestBase):
     true_centers = np.array(
         [
             normalize(
-                np.mean(
-                    normalize(points)[0:2, :], axis=0, keepdims=True))[0],
+                np.mean(normalize(points)[0:2, :], axis=0, keepdims=True))[0],
+            normalize(
+                np.mean(normalize(points)[2:4, :], axis=0, keepdims=True))[0],
             normalize(
-                np.mean(
-                    normalize(points)[2:4, :], axis=0, keepdims=True))[0],
-            normalize(np.mean(
-                normalize(points)[4:, :], axis=0, keepdims=True))[0]
+                np.mean(normalize(points)[4:, :], axis=0, keepdims=True))[0]
         ],
         dtype=np.float32)
     true_assignments = [0] * 2 + [1] * 2 + [2] * 8
@@ -363,7 +401,7 @@ class KMeansCosineDistanceTest(KMeansTestBase):
 
     kmeans = kmeans_lib.KMeansClustering(
         3,
-        initial_clusters=kmeans_lib.KMeansClustering.KMEANS_PLUS_PLUS_INIT,
+        initial_clusters=self.initial_clusters,
         distance_metric=kmeans_lib.KMeansClustering.COSINE_DISTANCE,
         use_mini_batch=self.use_mini_batch,
         mini_batch_steps_per_iteration=self.mini_batch_steps_per_iteration,
@@ -375,9 +413,9 @@ class KMeansCosineDistanceTest(KMeansTestBase):
         sorted(centers.tolist()), sorted(true_centers.tolist()), atol=1e-2)
 
     def _input_fn():
-      return (
-          input_lib.limit_epochs(constant_op.constant(points), num_epochs=1),
-          None)
+      return (input_lib.limit_epochs(
+          constant_op.constant(points), num_epochs=1), None)
+
     assignments = list(kmeans.predict_cluster_idx(input_fn=_input_fn))
     self.assertAllClose(
         centers[assignments], true_centers[true_assignments], atol=1e-2)
@@ -489,8 +527,8 @@ class TensorflowKMeansBenchmark(KMeansBenchmark):
           random_seed=i * 42,
           relative_tolerance=1e-6,
           config=run_config.RunConfig(tf_random_seed=3))
-      tf_kmeans.fit(input_fn=lambda: (constant_op.constant(self.points), None),
-                    steps=50)
+      tf_kmeans.fit(
+          input_fn=lambda: (constant_op.constant(self.points), None), steps=50)
       _ = tf_kmeans.clusters()
       scores.append(
           tf_kmeans.score(
@@ -521,14 +559,15 @@ class SklearnKMeansBenchmark(KMeansBenchmark):
 class KMeansTestQueues(test.TestCase):
 
   def input_fn(self):
+
     def _fn():
-      queue = data_flow_ops.FIFOQueue(capacity=10,
-                                      dtypes=dtypes.float32,
-                                      shapes=[10, 3])
+      queue = data_flow_ops.FIFOQueue(
+          capacity=10, dtypes=dtypes.float32, shapes=[10, 3])
       enqueue_op = queue.enqueue(array_ops.zeros([10, 3], dtype=dtypes.float32))
-      queue_runner.add_queue_runner(queue_runner.QueueRunner(queue,
-                                                             [enqueue_op]))
+      queue_runner.add_queue_runner(
+          queue_runner.QueueRunner(queue, [enqueue_op]))
       return queue.dequeue(), None
+
     return _fn
 
   # This test makes sure that there are no deadlocks when using a QueueRunner.
diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index 3aaee5862d..9235a3ce00 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -42,6 +42,7 @@ _DEFAULT_UID_WHITE_LIST = [
     'session_config',
     'keep_checkpoint_max',
     'keep_checkpoint_every_n_hours',
+    'log_step_count_steps',
 ]
 
 
@@ -230,6 +231,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
                save_checkpoints_steps=None,
                keep_checkpoint_max=5,
                keep_checkpoint_every_n_hours=10000,
+               log_step_count_steps=100,
                evaluation_master='',
                model_dir=None,
                session_config=None):
@@ -261,6 +263,8 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
       keep_checkpoint_every_n_hours: Number of hours between each checkpoint
         to be saved. The default value of 10,000 hours effectively disables
         the feature.
+      log_step_count_steps: The frequency, in number of global steps, that the
+        global step/sec will be logged during training.
       evaluation_master: the master on which to perform evaluation.
       model_dir: directory where model parameters, graph etc are saved. If
         `None`, will use `model_dir` property in `TF_CONFIG` environment
@@ -284,6 +288,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
     self._tf_random_seed = tf_random_seed
     self._save_summary_steps = save_summary_steps
     self._save_checkpoints_secs = save_checkpoints_secs
+    self._log_step_count_steps = log_step_count_steps
     self._session_config = session_config
     if save_checkpoints_secs == RunConfig._USE_DEFAULT:
       if save_checkpoints_steps is None:
@@ -367,6 +372,10 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
   def keep_checkpoint_every_n_hours(self):
     return self._keep_checkpoint_every_n_hours
 
+  @property
+  def log_step_count_steps(self):
+    return self._log_step_count_steps
+
 
 def _count_ps(cluster_spec):
   """Counts the number of parameter servers in cluster_spec."""
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 1c6ac08e46..cd47b82d82 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -523,7 +523,12 @@ class Experiment(object):
       differences in resource control. First, the resources (e.g., memory) used
       by training will be released before evaluation (`train_and_evaluate` takes
       double resources). Second, more checkpoints will be saved as a checkpoint
-      is generated at the end of each small training iteration.
+      is generated at the end of each trainning iteration.
+
+      3. As the estimator.train starts from scratch (new graph, new states for
+      input, etc) at each iteration, it is recommended to have the
+      `train_steps_per_iteration` larger. It is also recommended to shuffle your
+      input.
 
     Args:
       continuous_eval_predicate_fn: A predicate function determining whether to
diff --git a/tensorflow/contrib/linalg/BUILD b/tensorflow/contrib/linalg/BUILD
index 9b4f36da15..72d784adb0 100644
--- a/tensorflow/contrib/linalg/BUILD
+++ b/tensorflow/contrib/linalg/BUILD
@@ -165,12 +165,16 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:common_shapes",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:tensor_util",
+        "//tensorflow/python:util",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
diff --git a/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py b/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py
index 32b7f956e4..79a5928a21 100644
--- a/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py
+++ b/tensorflow/contrib/linear_optimizer/python/sdca_estimator_test.py
@@ -22,6 +22,7 @@ import numpy as np
 
 from tensorflow.contrib.layers.python.layers import feature_column as feature_column_lib
 from tensorflow.contrib.linear_optimizer.python import sdca_estimator
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.platform import test
@@ -40,15 +41,17 @@ class SDCALogisticClassifierTest(test.TestCase):
           'weights': constant_op.constant([[1.0], [1.0]])
       }, constant_op.constant([[0], [1]])
 
-    maintenance_cost = feature_column_lib.real_valued_column('maintenance_cost')
-    sq_footage = feature_column_lib.real_valued_column('sq_footage')
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[maintenance_cost, sq_footage],
-        weight_column_name='weights')
-    classifier.fit(input_fn=input_fn, steps=100)
-    loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.05)
+    with self.test_session():
+      maintenance_cost = feature_column_lib.real_valued_column(
+          'maintenance_cost')
+      sq_footage = feature_column_lib.real_valued_column('sq_footage')
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[maintenance_cost, sq_footage],
+          weight_column_name='weights')
+      classifier.fit(input_fn=input_fn, steps=100)
+      loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.05)
 
   def testRealValuedFeatureWithHigherDimension(self):
     """Tests SDCALogisticClassifier with high-dimension real valued features."""
@@ -63,13 +66,14 @@ class SDCALogisticClassifierTest(test.TestCase):
               constant_op.constant([[500.0, 800.0], [200.0, 600.0]])
       }, constant_op.constant([[0], [1]])
 
-    dense_feature = feature_column_lib.real_valued_column(
-        'dense_feature', dimension=2)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id', feature_columns=[dense_feature])
-    classifier.fit(input_fn=input_fn, steps=100)
-    loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.05)
+    with self.test_session():
+      dense_feature = feature_column_lib.real_valued_column(
+          'dense_feature', dimension=2)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id', feature_columns=[dense_feature])
+      classifier.fit(input_fn=input_fn, steps=100)
+      loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.05)
 
   def testBucketizedFeatures(self):
     """Tests SDCALogisticClassifier with bucketized features."""
@@ -82,19 +86,21 @@ class SDCALogisticClassifierTest(test.TestCase):
           'weights': constant_op.constant([[1.0], [1.0], [1.0]])
       }, constant_op.constant([[1], [0], [1]])
 
-    price_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('price'),
-        boundaries=[500.0, 700.0])
-    sq_footage_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('sq_footage'), boundaries=[650.0])
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[price_bucket, sq_footage_bucket],
-        weight_column_name='weights',
-        l2_regularization=1.0)
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      price_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('price'),
+          boundaries=[500.0, 700.0])
+      sq_footage_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('sq_footage'),
+          boundaries=[650.0])
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[price_bucket, sq_footage_bucket],
+          weight_column_name='weights',
+          l2_regularization=1.0)
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testSparseFeatures(self):
     """Tests SDCALogisticClassifier with sparse features."""
@@ -114,16 +120,17 @@ class SDCALogisticClassifierTest(test.TestCase):
               constant_op.constant([[1.0], [1.0], [1.0]])
       }, constant_op.constant([[1], [0], [1]])
 
-    price = feature_column_lib.real_valued_column('price')
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[price, country],
-        weight_column_name='weights')
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      price = feature_column_lib.real_valued_column('price')
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[price, country],
+          weight_column_name='weights')
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testWeightedSparseFeatures(self):
     """Tests SDCALogisticClassifier with weighted sparse features."""
@@ -144,16 +151,17 @@ class SDCALogisticClassifierTest(test.TestCase):
                   dense_shape=[3, 5])
       }, constant_op.constant([[1], [0], [1]])
 
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    country_weighted_by_price = feature_column_lib.weighted_sparse_column(
-        country, 'price')
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[country_weighted_by_price])
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      country_weighted_by_price = feature_column_lib.weighted_sparse_column(
+          country, 'price')
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[country_weighted_by_price])
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testCrossedFeatures(self):
     """Tests SDCALogisticClassifier with crossed features."""
@@ -174,17 +182,18 @@ class SDCALogisticClassifierTest(test.TestCase):
                   dense_shape=[3, 1])
       }, constant_op.constant([[0], [0], [1]])
 
-    language = feature_column_lib.sparse_column_with_hash_bucket(
-        'language', hash_bucket_size=5)
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    country_language = feature_column_lib.crossed_column(
-        [language, country], hash_bucket_size=10)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id', feature_columns=[country_language])
-    classifier.fit(input_fn=input_fn, steps=10)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      language = feature_column_lib.sparse_column_with_hash_bucket(
+          'language', hash_bucket_size=5)
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      country_language = feature_column_lib.crossed_column(
+          [language, country], hash_bucket_size=10)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id', feature_columns=[country_language])
+      classifier.fit(input_fn=input_fn, steps=10)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
   def testMixedFeatures(self):
     """Tests SDCALogisticClassifier with a mix of features."""
@@ -206,25 +215,35 @@ class SDCALogisticClassifierTest(test.TestCase):
               constant_op.constant([[3.0], [1.0], [1.0]])
       }, constant_op.constant([[1], [0], [1]])
 
-    price = feature_column_lib.real_valued_column('price')
-    sq_footage_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('sq_footage'),
-        boundaries=[650.0, 800.0])
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    sq_footage_country = feature_column_lib.crossed_column(
-        [sq_footage_bucket, country], hash_bucket_size=10)
-    classifier = sdca_estimator.SDCALogisticClassifier(
-        example_id_column='example_id',
-        feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
-        weight_column_name='weights')
-    classifier.fit(input_fn=input_fn, steps=50)
-    metrics = classifier.evaluate(input_fn=input_fn, steps=1)
-    self.assertGreater(metrics['accuracy'], 0.9)
+    with self.test_session():
+      price = feature_column_lib.real_valued_column('price')
+      sq_footage_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('sq_footage'),
+          boundaries=[650.0, 800.0])
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      sq_footage_country = feature_column_lib.crossed_column(
+          [sq_footage_bucket, country], hash_bucket_size=10)
+      classifier = sdca_estimator.SDCALogisticClassifier(
+          example_id_column='example_id',
+          feature_columns=[
+              price, sq_footage_bucket, country, sq_footage_country
+          ],
+          weight_column_name='weights')
+      classifier.fit(input_fn=input_fn, steps=50)
+      metrics = classifier.evaluate(input_fn=input_fn, steps=1)
+      self.assertGreater(metrics['accuracy'], 0.9)
 
 
 class SDCALinearRegressorTest(test.TestCase):
 
+  def _single_threaded_test_session(self):
+    # TODO(andreasst): figure out why SDCALinearRegressor needs a single
+    # threaded session to pass in tsan mode but SDCALogisticClassifier does not.
+    config = config_pb2.ConfigProto(
+        inter_op_parallelism_threads=1, intra_op_parallelism_threads=1)
+    return self.test_session(config=config)
+
   def testRealValuedLinearFeatures(self):
     """Tests SDCALinearRegressor works with real valued features."""
     x = [[1.2, 2.0, -1.5], [-2.0, 3.0, -0.5], [1.0, -0.5, 4.0]]
@@ -238,18 +257,19 @@ class SDCALinearRegressorTest(test.TestCase):
           'weights': constant_op.constant([[10.0], [10.0], [10.0]])
       }, constant_op.constant(y)
 
-    x_column = feature_column_lib.real_valued_column('x', dimension=3)
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[x_column],
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.01)
-    self.assertIn('linear/x/weight', regressor.get_variable_names())
-    regressor_weights = regressor.get_variable_value('linear/x/weight')
-    self.assertAllClose(
-        [w[0] for w in weights], regressor_weights.flatten(), rtol=0.1)
+    with self._single_threaded_test_session():
+      x_column = feature_column_lib.real_valued_column('x', dimension=3)
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[x_column],
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.01)
+      self.assertIn('linear/x/weight', regressor.get_variable_names())
+      regressor_weights = regressor.get_variable_value('linear/x/weight')
+      self.assertAllClose(
+          [w[0] for w in weights], regressor_weights.flatten(), rtol=0.1)
 
   def testMixedFeaturesArbitraryWeights(self):
     """Tests SDCALinearRegressor works with a mix of features."""
@@ -271,22 +291,25 @@ class SDCALinearRegressorTest(test.TestCase):
               constant_op.constant([[3.0], [5.0], [7.0]])
       }, constant_op.constant([[1.55], [-1.25], [-3.0]])
 
-    price = feature_column_lib.real_valued_column('price')
-    sq_footage_bucket = feature_column_lib.bucketized_column(
-        feature_column_lib.real_valued_column('sq_footage'),
-        boundaries=[650.0, 800.0])
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    sq_footage_country = feature_column_lib.crossed_column(
-        [sq_footage_bucket, country], hash_bucket_size=10)
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
-        l2_regularization=1.0,
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    self.assertLess(loss, 0.05)
+    with self._single_threaded_test_session():
+      price = feature_column_lib.real_valued_column('price')
+      sq_footage_bucket = feature_column_lib.bucketized_column(
+          feature_column_lib.real_valued_column('sq_footage'),
+          boundaries=[650.0, 800.0])
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      sq_footage_country = feature_column_lib.crossed_column(
+          [sq_footage_bucket, country], hash_bucket_size=10)
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[
+              price, sq_footage_bucket, country, sq_footage_country
+          ],
+          l2_regularization=1.0,
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
     """SDCALinearRegressor works with sparse features and L1 regularization."""
@@ -306,56 +329,57 @@ class SDCALinearRegressorTest(test.TestCase):
               constant_op.constant([[10.0], [10.0], [10.0]])
       }, constant_op.constant([[1.4], [-0.8], [2.6]])
 
-    price = feature_column_lib.real_valued_column('price')
-    country = feature_column_lib.sparse_column_with_hash_bucket(
-        'country', hash_bucket_size=5)
-    # Regressor with no L1 regularization.
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[price, country],
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    no_l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    variable_names = regressor.get_variable_names()
-    self.assertIn('linear/price/weight', variable_names)
-    self.assertIn('linear/country/weights', variable_names)
-    no_l1_reg_weights = {
-        'linear/price/weight':
-            regressor.get_variable_value('linear/price/weight'),
-        'linear/country/weights':
-            regressor.get_variable_value('linear/country/weights'),
-    }
-
-    # Regressor with L1 regularization.
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[price, country],
-        l1_regularization=1.0,
-        weight_column_name='weights')
-    regressor.fit(input_fn=input_fn, steps=20)
-    l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
-    l1_reg_weights = {
-        'linear/price/weight':
-            regressor.get_variable_value('linear/price/weight'),
-        'linear/country/weights':
-            regressor.get_variable_value('linear/country/weights'),
-    }
-
-    # Unregularized loss is lower when there is no L1 regularization.
-    self.assertLess(no_l1_reg_loss, l1_reg_loss)
-    self.assertLess(no_l1_reg_loss, 0.05)
-
-    # But weights returned by the regressor with L1 regularization have smaller
-    # L1 norm.
-    l1_reg_weights_norm, no_l1_reg_weights_norm = 0.0, 0.0
-    for var_name in sorted(l1_reg_weights):
-      l1_reg_weights_norm += sum(
-          np.absolute(l1_reg_weights[var_name].flatten()))
-      no_l1_reg_weights_norm += sum(
-          np.absolute(no_l1_reg_weights[var_name].flatten()))
-      print('Var name: %s, value: %s' % (var_name,
-                                         no_l1_reg_weights[var_name].flatten()))
-    self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
+    with self._single_threaded_test_session():
+      price = feature_column_lib.real_valued_column('price')
+      country = feature_column_lib.sparse_column_with_hash_bucket(
+          'country', hash_bucket_size=5)
+      # Regressor with no L1 regularization.
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[price, country],
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      no_l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      variable_names = regressor.get_variable_names()
+      self.assertIn('linear/price/weight', variable_names)
+      self.assertIn('linear/country/weights', variable_names)
+      no_l1_reg_weights = {
+          'linear/price/weight':
+              regressor.get_variable_value('linear/price/weight'),
+          'linear/country/weights':
+              regressor.get_variable_value('linear/country/weights'),
+      }
+
+      # Regressor with L1 regularization.
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[price, country],
+          l1_regularization=1.0,
+          weight_column_name='weights')
+      regressor.fit(input_fn=input_fn, steps=20)
+      l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+      l1_reg_weights = {
+          'linear/price/weight':
+              regressor.get_variable_value('linear/price/weight'),
+          'linear/country/weights':
+              regressor.get_variable_value('linear/country/weights'),
+      }
+
+      # Unregularized loss is lower when there is no L1 regularization.
+      self.assertLess(no_l1_reg_loss, l1_reg_loss)
+      self.assertLess(no_l1_reg_loss, 0.05)
+
+      # But weights returned by the regressor with L1 regularization have
+      # smaller L1 norm.
+      l1_reg_weights_norm, no_l1_reg_weights_norm = 0.0, 0.0
+      for var_name in sorted(l1_reg_weights):
+        l1_reg_weights_norm += sum(
+            np.absolute(l1_reg_weights[var_name].flatten()))
+        no_l1_reg_weights_norm += sum(
+            np.absolute(no_l1_reg_weights[var_name].flatten()))
+        print('Var name: %s, value: %s' %
+              (var_name, no_l1_reg_weights[var_name].flatten()))
+      self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
 
   def testBiasOnly(self):
     """Tests SDCALinearRegressor has a valid bias weight."""
@@ -380,12 +404,13 @@ class SDCALinearRegressorTest(test.TestCase):
       }, constant_op.constant([[1 if i % 4 is 0 else 0]
                                for i in range(num_examples)])
 
-    place_holder = feature_column_lib.real_valued_column('place_holder')
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id', feature_columns=[place_holder])
-    regressor.fit(input_fn=input_fn, steps=100)
-    self.assertNear(
-        regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
+    with self._single_threaded_test_session():
+      place_holder = feature_column_lib.real_valued_column('place_holder')
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id', feature_columns=[place_holder])
+      regressor.fit(input_fn=input_fn, steps=100)
+      self.assertNear(
+          regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
 
   def testBiasAndOtherColumns(self):
     """SDCALinearRegressor has valid bias weight with other columns present."""
@@ -426,26 +451,27 @@ class SDCALinearRegressorTest(test.TestCase):
            for x in [1, 0, 0, 1, 1, 0, 0, 0, 1, 0] * int(half / 10) +
            [0, 1, 0, 0, 0, 0, 0, 0, 1, 0] * int(half / 10)])
 
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[
-            feature_column_lib.real_valued_column('a'),
-            feature_column_lib.real_valued_column('b')
-        ])
-
-    regressor.fit(input_fn=input_fn, steps=200)
-
-    variable_names = regressor.get_variable_names()
-    self.assertIn('linear/bias_weight', variable_names)
-    self.assertIn('linear/a/weight', variable_names)
-    self.assertIn('linear/b/weight', variable_names)
-    # TODO(b/29339026): Change the expected results to expect a centered bias.
-    self.assertNear(
-        regressor.get_variable_value('linear/bias_weight')[0], 0.2, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/a/weight')[0], 0.2, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
+    with self._single_threaded_test_session():
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[
+              feature_column_lib.real_valued_column('a'),
+              feature_column_lib.real_valued_column('b')
+          ])
+
+      regressor.fit(input_fn=input_fn, steps=200)
+
+      variable_names = regressor.get_variable_names()
+      self.assertIn('linear/bias_weight', variable_names)
+      self.assertIn('linear/a/weight', variable_names)
+      self.assertIn('linear/b/weight', variable_names)
+      # TODO(b/29339026): Change the expected results to expect a centered bias.
+      self.assertNear(
+          regressor.get_variable_value('linear/bias_weight')[0], 0.2, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/a/weight')[0], 0.2, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
 
   def testBiasAndOtherColumnsFabricatedCentered(self):
     """SDCALinearRegressor has valid bias weight when instances are centered."""
@@ -476,25 +502,26 @@ class SDCALinearRegressorTest(test.TestCase):
       }, constant_op.constant([[1 if x % 10 == 0 else 0] for x in range(half)] +
                               [[-1 if x % 10 == 0 else 0] for x in range(half)])
 
-    regressor = sdca_estimator.SDCALinearRegressor(
-        example_id_column='example_id',
-        feature_columns=[
-            feature_column_lib.real_valued_column('a'),
-            feature_column_lib.real_valued_column('b')
-        ])
-
-    regressor.fit(input_fn=input_fn, steps=100)
-
-    variable_names = regressor.get_variable_names()
-    self.assertIn('linear/bias_weight', variable_names)
-    self.assertIn('linear/a/weight', variable_names)
-    self.assertIn('linear/b/weight', variable_names)
-    self.assertNear(
-        regressor.get_variable_value('linear/bias_weight')[0], 0.0, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/a/weight')[0], 0.1, err=0.05)
-    self.assertNear(
-        regressor.get_variable_value('linear/b/weight')[0], -0.1, err=0.05)
+    with self._single_threaded_test_session():
+      regressor = sdca_estimator.SDCALinearRegressor(
+          example_id_column='example_id',
+          feature_columns=[
+              feature_column_lib.real_valued_column('a'),
+              feature_column_lib.real_valued_column('b')
+          ])
+
+      regressor.fit(input_fn=input_fn, steps=100)
+
+      variable_names = regressor.get_variable_names()
+      self.assertIn('linear/bias_weight', variable_names)
+      self.assertIn('linear/a/weight', variable_names)
+      self.assertIn('linear/b/weight', variable_names)
+      self.assertNear(
+          regressor.get_variable_value('linear/bias_weight')[0], 0.0, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/a/weight')[0], 0.1, err=0.05)
+      self.assertNear(
+          regressor.get_variable_value('linear/b/weight')[0], -0.1, err=0.05)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/lookup/BUILD b/tensorflow/contrib/lookup/BUILD
index 1090cecab5..b8455477b0 100644
--- a/tensorflow/contrib/lookup/BUILD
+++ b/tensorflow/contrib/lookup/BUILD
@@ -18,14 +18,9 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:constant_op",
-        "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:lookup_ops_gen",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:string_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:util",
     ],
@@ -42,11 +37,10 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:lookup_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
diff --git a/tensorflow/contrib/losses/BUILD b/tensorflow/contrib/losses/BUILD
index d9074e385a..f75b0aa1b3 100644
--- a/tensorflow/contrib/losses/BUILD
+++ b/tensorflow/contrib/losses/BUILD
@@ -38,12 +38,11 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
diff --git a/tensorflow/contrib/makefile/proto_text_cc_files.txt b/tensorflow/contrib/makefile/proto_text_cc_files.txt
index 67bc8f8ada..d56e388477 100644
--- a/tensorflow/contrib/makefile/proto_text_cc_files.txt
+++ b/tensorflow/contrib/makefile/proto_text_cc_files.txt
@@ -1,5 +1,6 @@
 tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc
 tensorflow/tools/proto_text/gen_proto_text_functions.cc
+tensorflow/core/framework/resource_handle.cc
 tensorflow/core/platform/default/protobuf.cc
 tensorflow/core/platform/tracing.cc
 tensorflow/core/platform/tensor_coding.cc
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 9839307e9b..4a3b3e7762 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -1,3 +1,10 @@
+tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc
+tensorflow/contrib/boosted_trees/ops/model_ops.cc
+tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
+tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
+tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
+tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
+tensorflow/contrib/boosted_trees/ops/training_ops.cc
 tensorflow/core/kernels/xent_op.cc
 tensorflow/core/kernels/where_op.cc
 tensorflow/core/kernels/variable_ops.cc
@@ -209,6 +216,7 @@ tensorflow/core/kernels/requantization_range_op.cc
 tensorflow/core/kernels/requantize.cc
 tensorflow/core/kernels/remote_fused_graph_execute_op.cc
 tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+tensorflow/core/kernels/batch_matmul_op_real.cc
 tensorflow/core/ops/training_ops.cc
 tensorflow/core/ops/string_ops.cc
 tensorflow/core/ops/state_ops.cc
diff --git a/tensorflow/contrib/makefile/tf_proto_files.txt b/tensorflow/contrib/makefile/tf_proto_files.txt
index 36d9cb74a7..a1a9aa7190 100644
--- a/tensorflow/contrib/makefile/tf_proto_files.txt
+++ b/tensorflow/contrib/makefile/tf_proto_files.txt
@@ -1,3 +1,7 @@
+tensorflow/contrib/boosted_trees/proto/learner.proto
+tensorflow/contrib/boosted_trees/proto/quantiles.proto
+tensorflow/contrib/boosted_trees/proto/split_info.proto
+tensorflow/contrib/boosted_trees/proto/tree_config.proto
 tensorflow/core/util/test_log.proto
 tensorflow/core/util/saved_tensor_slice.proto
 tensorflow/core/util/memmapped_file_system.proto
diff --git a/tensorflow/contrib/ndlstm/BUILD b/tensorflow/contrib/ndlstm/BUILD
index 73a4ad2e70..ba23c70dc0 100644
--- a/tensorflow/contrib/ndlstm/BUILD
+++ b/tensorflow/contrib/ndlstm/BUILD
@@ -33,6 +33,7 @@ py_library(
         "//tensorflow/python:platform",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:rnn",
+        "//tensorflow/python:rnn_cell",
         "//tensorflow/python:sparse_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
diff --git a/tensorflow/contrib/resampler/BUILD b/tensorflow/contrib/resampler/BUILD
new file mode 100644
index 0000000000..1b9efd1ecd
--- /dev/null
+++ b/tensorflow/contrib/resampler/BUILD
@@ -0,0 +1,92 @@
+licenses(["notice"])  # Apache 2.0 License
+
+exports_files(["LICENSE"])
+
+package(default_visibility = ["//visibility:public"])
+
+load(
+    "//tensorflow:tensorflow.bzl",
+    "tf_custom_op_library",
+    "tf_custom_op_py_library",
+    "tf_gen_op_libs",
+    "tf_gen_op_wrapper_py",
+    "tf_kernel_library",
+)
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
+
+tf_custom_op_py_library(
+    name = "resampler_py",
+    srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
+    dso = [":python/ops/_resampler_ops.so"],
+    kernels = [
+        ":resampler_ops_kernels",
+        ":resampler_ops_op_lib",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":resampler_ops",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:util",
+    ],
+)
+
+tf_kernel_library(
+    name = "resampler_ops_kernels",
+    prefix = "resampler_ops",
+    deps = [
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_resampler_ops.so",
+    srcs = [
+        "kernels/resampler_ops.cc",
+        "kernels/resampler_ops.h",
+        "ops/resampler_ops.cc",
+    ],
+    gpu_srcs = [
+        "kernels/resampler_ops_gpu.cu.cc",
+        "kernels/resampler_ops.h",
+    ],
+)
+
+tf_gen_op_libs(
+    op_lib_names = [
+        "resampler_ops",
+    ],
+)
+
+tf_gen_op_wrapper_py(
+    name = "resampler_ops",
+    deps = [":resampler_ops_op_lib"],
+)
+
+cuda_py_test(
+    name = "resampler_ops_test",
+    size = "small",
+    srcs = ["python/ops/resampler_ops_test.py"],
+    additional_deps = [
+        ":resampler_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:array_ops",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/resampler/__init__.py b/tensorflow/contrib/resampler/__init__.py
new file mode 100644
index 0000000000..3e04e5762d
--- /dev/null
+++ b/tensorflow/contrib/resampler/__init__.py
@@ -0,0 +1,26 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Ops and modules related to resampler."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.resampler.python.ops.resampler_ops import *
+from tensorflow.python.util.all_util import remove_undocumented
+
+remove_undocumented(__name__, ["resampler"])
diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops.cc b/tensorflow/contrib/resampler/kernels/resampler_ops.cc
new file mode 100644
index 0000000000..afc8bcd446
--- /dev/null
+++ b/tensorflow/contrib/resampler/kernels/resampler_ops.cc
@@ -0,0 +1,465 @@
+// Copyright 2017 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/contrib/resampler/kernels/resampler_ops.h"
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+using CPUDevice = Eigen::ThreadPoolDevice;
+using GPUDevice = Eigen::GpuDevice;
+
+namespace functor {
+
+template <typename T>
+struct Resampler2DFunctor<CPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const CPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   T* __restrict__ output,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points){
+    const int warp_batch_stride = num_sampling_points * 2;
+    const int data_batch_stride = data_height * data_width * data_channels;
+    const int output_batch_stride = num_sampling_points * data_channels;
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+
+    auto resample_batches = [&](const int start, const int limit) {
+      for (int batch_id = start; batch_id < limit; ++batch_id) {
+        // Utility lambda to access data point and set output values.
+        // The functions take care of performing the relevant pointer
+        // arithmetics abstracting away the low level details in the
+        // main loop over samples. Note that data is stored in NHWC format.
+        auto set_output = [&](const int sample_id,
+                              const int channel,
+                              const T value) {
+          output[batch_id * output_batch_stride +
+                 sample_id * data_channels +
+                 channel] = value;
+        };
+
+        auto get_data_point = [&](const int x,
+                                  const int y,
+                                  const int chan) {
+          const bool point_is_in_range =
+              (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1);
+          return point_is_in_range
+                 ? data[batch_id * data_batch_stride +
+                        data_channels * (y * data_width + x) +
+                        chan]
+                 : zero;
+        };
+
+        for (int sample_id = 0; sample_id < num_sampling_points; ++sample_id) {
+          const T x = warp[batch_id * warp_batch_stride + sample_id * 2];
+          const T y = warp[batch_id * warp_batch_stride + sample_id * 2 + 1];
+          // The interpolation function:
+          // a) implicitly pads the input data with 0s (hence the unusual checks
+          // with {x,y} > -1)
+          // b) returns 0 when sampling outside the (padded) image.
+          // The effect is that the sampled signal smoothly goes to 0 outside
+          // the original input domain, rather than presenting a jump
+          // discontinuity at the image boundaries.
+          if (x > static_cast<T>(-1.0) &&
+              y > static_cast<T>(-1.0) &&
+              x < static_cast<T>(data_width) &&
+              y < static_cast<T>(data_height)) {
+            // Precompute floor (f) and ceil (c) values for x and y.
+            const int fx = std::floor(static_cast<float>(x));
+            const int fy = std::floor(static_cast<float>(y));
+            const int cx = fx + 1;
+            const int cy = fy + 1;
+            const T dx = static_cast<T>(cx) - x;
+            const T dy = static_cast<T>(cy) - y;
+
+            for (int chan = 0; chan < data_channels; ++chan) {
+              const T img_fxfy = dx * dy * get_data_point(fx, fy, chan);
+              const T img_cxcy = (one - dx) * (one - dy) *
+                                   get_data_point(cx, cy, chan);
+              const T img_fxcy = dx * (one - dy) *
+                                   get_data_point(fx, cy, chan);
+              const T img_cxfy = (one - dx) * dy *
+                                   get_data_point(cx, fy, chan);
+              set_output(sample_id, chan,
+                         img_fxfy + img_cxcy + img_fxcy + img_cxfy);
+            }
+          } else {
+            for (int chan = 0; chan < data_channels; ++chan) {
+              set_output(sample_id, chan, zero);
+            }
+          }
+        }
+      }
+    };
+    // Rough estimate of work for each batch entry.
+    // From third_party/tensorflow/core/util/work_sharder.cc we gather that an
+    // estimate of the cost of each work unit is needed to correclty shard the
+    // workload. Shard assumes each cost unit is 1ns, minimum cost per shard
+    // being 10us.
+    const int64 cost =  static_cast<int64>(num_sampling_points) *
+        data_channels * 1000;
+    auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
+    ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers,
+                        batch_size, cost, resample_batches);
+  }
+};
+
+}  // namespace functor
+
+template <typename Device, typename T>
+class ResamplerOp : public ::tensorflow::OpKernel {
+ public:
+  explicit ResamplerOp(::tensorflow::OpKernelConstruction* context) :
+      ::tensorflow::OpKernel(context) {}
+
+  void Compute(::tensorflow::OpKernelContext* ctx) override {
+    const ::tensorflow::Tensor& data = ctx->input(0);
+    const ::tensorflow::Tensor& warp = ctx->input(1);
+
+    const ::tensorflow::TensorShape& data_shape = data.shape();
+    OP_REQUIRES(ctx, data_shape.dims() == 4,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is currently supported. The "
+                    "input data shape must be [batch_size, data_height, "
+                    "data_width, data_channels], but is: ",
+                    data_shape.DebugString()));
+    const ::tensorflow::TensorShape& warp_shape = warp.shape();
+    OP_REQUIRES(ctx,
+                ::tensorflow::TensorShapeUtils::IsMatrixOrHigher(warp_shape),
+                ::tensorflow::errors::InvalidArgument(
+                    "warp should be at least a matrix, got shape ",
+                    warp_shape.DebugString()));
+    OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims()-1) == 2,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is supported, warping "
+                    "coordinates must be 2D; warp shape last entry should be "
+                    "2, but shape vector is: ", warp_shape.DebugString()));
+    OP_REQUIRES(ctx, data_shape.dim_size(0) == warp_shape.dim_size(0),
+                ::tensorflow::errors::InvalidArgument(
+                    "Batch size of data and warp tensor must be the same, but "
+                    "input shapes are: ", data_shape.DebugString(), ", ",
+                    warp_shape.DebugString()));
+    const int batch_size = data_shape.dim_size(0);
+    const int data_height = data_shape.dim_size(1);
+    const int data_width = data_shape.dim_size(2);
+    const int data_channels = data_shape.dim_size(3);
+    ::tensorflow::TensorShape output_shape = warp.shape();
+    output_shape.set_dim(output_shape.dims() - 1, data_channels);
+    const int num_sampling_points = warp.NumElements() / batch_size / 2;
+    ::tensorflow::Tensor* output = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, output_shape, &output));
+
+    // Execute kernel only for nonempty output; otherwise Eigen crashes on GPU.
+    if (num_sampling_points > 0) {
+      functor::Resampler2DFunctor<Device, T>()(ctx,
+                                               ctx->eigen_device<Device>(),
+                                               data.flat<T>().data(),
+                                               warp.flat<T>().data(),
+                                               output->flat<T>().data(),
+                                               batch_size,
+                                               data_height,
+                                               data_width,
+                                               data_channels,
+                                               num_sampling_points);
+    }
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ResamplerOp);
+};
+
+
+#define REGISTER(TYPE)                       \
+  REGISTER_KERNEL_BUILDER(                   \
+      Name("Resampler")                      \
+          .Device(DEVICE_CPU)  \
+          .TypeConstraint<TYPE>("T"),        \
+      ResamplerOp<CPUDevice, TYPE>);
+
+TF_CALL_half(REGISTER);
+TF_CALL_float(REGISTER);
+TF_CALL_double(REGISTER);
+#undef REGISTER
+
+#if GOOGLE_CUDA
+#define REGISTER(TYPE)                                           \
+  REGISTER_KERNEL_BUILDER(Name("Resampler")                      \
+                              .Device(DEVICE_GPU)  \
+                              .TypeConstraint<TYPE>("T"),        \
+                          ResamplerOp<GPUDevice, TYPE>)
+TF_CALL_float(REGISTER);
+TF_CALL_double(REGISTER);
+#undef REGISTER
+#endif  // GOOGLE_CUDA
+
+
+namespace functor {
+
+template <typename T>
+struct ResamplerGrad2DFunctor<CPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const CPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   const T* __restrict__ grad_output,
+                   T* __restrict__ grad_data,
+                   T* __restrict__ grad_warp,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points){
+    // Set gradients to 0, because the kernel incrementally updates the
+    // tensor entries by adding partial contributions.
+    const int resampler_output_size = batch_size * num_sampling_points *
+        data_channels;
+    const int grad_warp_size = resampler_output_size / data_channels * 2;
+    const int grad_data_size = data_height * data_width * data_channels *
+        batch_size;
+    memset(grad_data, 0, sizeof(T) * grad_data_size);
+    memset(grad_warp, 0, sizeof(T) * grad_warp_size);
+
+    const auto&& data_batch_stride = data_height * data_width * data_channels;
+    const auto&& warp_batch_stride = num_sampling_points * 2;
+    const int output_batch_stride = num_sampling_points * data_channels;
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+
+    auto update_grads_for_batches = [&](const int start, const int limit) {
+      for (int batch_id = start; batch_id < limit; ++batch_id) {
+        // Utility lambdas to access data and update gradient tensors.
+        // The functions take care of performing the relevant pointer
+        // arithmetics abstracting away the low level details in the
+        // main loop over samples. Note that data is stored in NHWC format.
+        auto get_data_point = [&](const int x,
+                                  const int y,
+                                  const int chan) {
+          const bool point_is_in_range =
+            (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1);
+          return point_is_in_range
+                 ? data[batch_id * data_batch_stride +
+                        data_channels * (y * data_width + x) +
+                        chan]
+                 : zero;
+        };
+
+        auto update_grad_data = [&](const int x, const int y, const int chan,
+                                    const T value) {
+          const bool point_is_in_range =
+              (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1);
+          if (point_is_in_range){
+            grad_data[batch_id * data_batch_stride +
+                      data_channels * (y * data_width + x) +
+                      chan] += value;
+          }
+        };
+
+        auto update_grad_warp = [&](const int sample_id,
+                                    const int channel,
+                                    const T value) {
+          grad_warp[batch_id * warp_batch_stride +
+                    sample_id * 2 +
+                    channel] += value;
+        };
+
+        for (int sample_id = 0; sample_id < num_sampling_points; ++sample_id) {
+          const T x = warp[batch_id * warp_batch_stride + sample_id * 2];
+          const T y = warp[batch_id * warp_batch_stride + sample_id * 2 + 1];
+          // The interpolation function whose gradient this function implements:
+          // a) implicitly pads the input data with 0s (hence the unusual checks
+          // with {x,y} > -1)
+          // b) returns 0 when sampling outside the (padded) image.
+          // The effect is that the sampled signal smoothly goes to 0 outside
+          // the original input domain, rather than presenting a jump
+          // discontinuity at the image boundaries.
+          if (x > static_cast<T>(-1.0) &&
+              y > static_cast<T>(-1.0) &&
+              x < static_cast<T>(data_width) &&
+              y < static_cast<T>(data_height)) {
+            // Precompute floor (f) and ceil (c) values for x and y.
+            const int fx = std::floor(static_cast<float>(x));
+            const int fy = std::floor(static_cast<float>(y));
+            const int cx = fx + 1;
+            const int cy = fy + 1;
+            const T dx = static_cast<T>(cx) - x;
+            const T dy = static_cast<T>(cy) - y;
+
+            for (int chan = 0; chan < data_channels; ++chan) {
+              const T grad_output_value =
+                  grad_output[batch_id * output_batch_stride +
+                              sample_id * data_channels +
+                              chan];
+              const T img_fxfy = get_data_point(fx, fy, chan);
+              const T img_cxcy = get_data_point(cx, cy, chan);
+              const T img_fxcy = get_data_point(fx, cy, chan);
+              const T img_cxfy = get_data_point(cx, fy, chan);
+
+              // Update partial gradients wrt relevant warp field entries
+              update_grad_warp(sample_id, 0,
+                               grad_output_value *
+                                   ((one - dy) * (img_cxcy - img_fxcy) +
+                                    dy * (img_cxfy - img_fxfy)));
+
+              update_grad_warp(sample_id, 1,
+                               grad_output_value *
+                                   ((one - dx) * (img_cxcy - img_cxfy) +
+                                    dx * (img_fxcy - img_fxfy)));
+
+              // Update partial gradients wrt sampled data
+              update_grad_data(fx, fy, chan,
+                               grad_output_value * dx * dy);
+              update_grad_data(cx, cy, chan,
+                               grad_output_value * (one - dx) * (one - dy));
+              update_grad_data(fx, cy, chan,
+                               grad_output_value * dx * (one - dy));
+              update_grad_data(cx, fy, chan,
+                               grad_output_value * (one - dx) * dy);
+            }
+          }
+        }
+      }
+    };
+    // Rough estimate of work for each batch entry.
+    // From third_party/tensorflow/core/util/work_sharder.cc we gather that an
+    // estimate of the cost of each work unit is needed to correctly shard the
+    // workload. Shard assumes each cost unit is 1ns, minimum cost per shard
+    // being 10us.
+    // TODO(fviola): Check out if there is a better way of doing this.
+    auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
+    const int64 cost =  static_cast<int64>(num_sampling_points) *
+        data_channels * 1000;
+    ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers,
+                        batch_size, cost, update_grads_for_batches);
+  }
+};
+
+}  // namespace functor
+
+
+template <typename Device, typename T>
+class ResamplerGradOp : public ::tensorflow::OpKernel {
+ public:
+  explicit ResamplerGradOp(::tensorflow::OpKernelConstruction* context) :
+      ::tensorflow::OpKernel(context) {}
+
+  void Compute(::tensorflow::OpKernelContext* ctx) override {
+    const ::tensorflow::Tensor& data = ctx->input(0);
+    const ::tensorflow::Tensor& warp = ctx->input(1);
+    const ::tensorflow::Tensor& grad_output = ctx->input(2);
+
+    const ::tensorflow::TensorShape& data_shape = data.shape();
+    OP_REQUIRES(ctx, data_shape.dims() == 4,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is supported, the input data "
+                    "tensor must be a batch of 2d data; data shape should have "
+                    "4 entries corresponding to [batch_size, data_height, "
+                    "data_width, data_channels], but is: ",
+                data_shape.DebugString()));
+    const int batch_size = data_shape.dim_size(0);
+    const int data_height = data_shape.dim_size(1);
+    const int data_width = data_shape.dim_size(2);
+    const int data_channels = data_shape.dim_size(3);
+    const ::tensorflow::TensorShape& warp_shape = warp.shape();
+    OP_REQUIRES(ctx,
+                ::tensorflow::TensorShapeUtils::IsMatrixOrHigher(warp_shape),
+                ::tensorflow::errors::InvalidArgument(
+                    "warp should be at least a matrix, got shape ",
+                    warp_shape.DebugString()));
+    OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims()-1) == 2,
+                ::tensorflow::errors::Unimplemented(
+                    "Only bilinear interpolation is supported, warping "
+                    "coordinates must be 2D; warp shape last entry should be "
+                    "2, but shape vector is: ",
+                    warp_shape.DebugString()));
+    const ::tensorflow::TensorShape& grad_output_shape = grad_output.shape();
+    ::tensorflow::TensorShape resampler_output_shape = warp.shape();
+    resampler_output_shape.set_dim(resampler_output_shape.dims() - 1,
+                                   data_channels);
+    OP_REQUIRES(ctx, grad_output_shape == resampler_output_shape,
+                ::tensorflow::errors::InvalidArgument(
+                   "grad_output shape is not consistent with data and warp "
+                   "shapes; it should be ",
+                   resampler_output_shape.DebugString(), " but is ",
+                   grad_output_shape.DebugString()))
+    const int num_sampling_points = warp.NumElements() / batch_size / 2;
+    ::tensorflow::Tensor* grad_data = nullptr;
+    ::tensorflow::Tensor* grad_warp = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, data.shape(), &grad_data));
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(1, warp.shape(), &grad_warp));
+    // Execute kernel only for nonempty output; otherwise Eigen crashes on GPU.
+    if (num_sampling_points > 0) {
+      functor::ResamplerGrad2DFunctor<Device, T>()(ctx,
+                                                   ctx->eigen_device<Device>(),
+                                                   data.flat<T>().data(),
+                                                   warp.flat<T>().data(),
+                                                   grad_output.flat<T>().data(),
+                                                   grad_data->flat<T>().data(),
+                                                   grad_warp->flat<T>().data(),
+                                                   batch_size,
+                                                   data_height,
+                                                   data_width,
+                                                   data_channels,
+                                                   num_sampling_points);
+    }
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(ResamplerGradOp);
+};
+
+#define REGISTER(TYPE)                       \
+  REGISTER_KERNEL_BUILDER(                   \
+      Name("ResamplerGrad")                  \
+          .Device(DEVICE_CPU)  \
+          .TypeConstraint<TYPE>("T"),        \
+      ResamplerGradOp<CPUDevice, TYPE>);
+
+TF_CALL_half(REGISTER);
+TF_CALL_float(REGISTER);
+TF_CALL_double(REGISTER);
+#undef REGISTER
+
+#if GOOGLE_CUDA
+#define REGISTER(TYPE)                                           \
+  REGISTER_KERNEL_BUILDER(Name("ResamplerGrad")                  \
+                              .Device(DEVICE_GPU)  \
+                              .TypeConstraint<TYPE>("T"),        \
+                          ResamplerGradOp<GPUDevice, TYPE>)
+// Disable half and double precision since atomicAdds are not supported
+// TF_CALL_half(REGISTER);
+// TF_CALL_double(REGISTER);
+TF_CALL_float(REGISTER);
+
+#undef REGISTER
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops.h b/tensorflow/contrib/resampler/kernels/resampler_ops.h
new file mode 100644
index 0000000000..8258ecaf5d
--- /dev/null
+++ b/tensorflow/contrib/resampler/kernels/resampler_ops.h
@@ -0,0 +1,68 @@
+// Copyright 2017 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_RESAMPLER_KERNELS_RESAMPLER_OPS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_RESAMPLER_KERNELS_RESAMPLER_OPS_H_
+
+#if PLATFORM_WINDOWS
+#define __restrict__ __restrict
+#endif
+
+namespace tensorflow {
+class OpKernelContext;
+}
+
+namespace tensorflow {
+namespace functor {
+
+// Helper functor for the Resampler Op in 2D
+template <typename Device, typename T>
+struct Resampler2DFunctor{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const Device& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   T* __restrict__ output,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points);
+};
+
+
+// Helper functor for the Resampler Gradient Op in 2D
+template <typename Device, typename T>
+struct ResamplerGrad2DFunctor{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const Device& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   const T* __restrict__ grad_output,
+                   T* __restrict__ grad_data,
+                   T* __restrict__ grad_warp,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points);
+};
+
+
+}  // namespace functor
+}  // namespace tensorflow
+
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_RESAMPLER_KERNELS_RESAMPLER_OPS_H_
diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc b/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc
new file mode 100644
index 0000000000..636847a212
--- /dev/null
+++ b/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc
@@ -0,0 +1,310 @@
+// Copyright 2016 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "tensorflow/contrib/resampler/kernels/resampler_ops.h"
+
+#include <stdio.h>
+#include <cmath>
+
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
+namespace tensorflow {
+
+using GPUDevice = Eigen::GpuDevice;
+
+namespace {
+
+#define GET_DATA_POINT(x, y)                   \
+  data[batch_id * data_batch_stride +          \
+       data_channels * (y * data_width + x) +  \
+       chan]
+
+template <typename T>
+__global__ void Resampler2DKernel(const T* __restrict__ data,
+                                  const T* __restrict__ warp,
+                                  T* __restrict__ output,
+                                  const int batch_size,
+                                  const int data_height,
+                                  const int data_width,
+                                  const int data_channels,
+                                  const int num_sampling_points) {
+  const int output_data_size = batch_size * num_sampling_points * data_channels;
+  CUDA_1D_KERNEL_LOOP(index, output_data_size) {
+    const int out_index = index;
+
+    // Get (idxSample, channel, point) from the index.
+    // Use this formula
+    //   index = batch_id * num_sampling_points * num_chans +
+    //           sample_id * num_chans + chan_id,
+    // with sample_id = [0, ... ,num_sampling_points)
+    const int data_batch_stride = data_height * data_width * data_channels;
+    const int warp_batch_stride = num_sampling_points * 2;
+    const int output_batch_stride = num_sampling_points * data_channels;
+
+    const int batch_id = index / output_batch_stride;
+    const int index_in_batch = index % output_batch_stride;
+    const int chan = index_in_batch % data_channels;
+    const int sample_id = index_in_batch / data_channels;
+
+    // Get coords of 2D point where data will be resampled
+    const T x = warp[batch_id * warp_batch_stride + sample_id * 2];
+    const T y = warp[batch_id * warp_batch_stride + sample_id * 2 + 1];
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+    // The interpolation function:
+    // a) implicitly pads the input data with 0s (hence the unusual checks
+    // with {x,y} > -1)
+    // b) returns 0 when sampling outside the (padded) image.
+    // The effect is that the sampled signal smoothly goes to 0 outside
+    // the original input domain, rather than presenting a jump
+    // discontinuity at the image boundaries.
+    if (x > static_cast<T>(-1.0) &&
+        y > static_cast<T>(-1.0) &&
+        x < static_cast<T>(data_width) &&
+        y < static_cast<T>(data_height)) {
+      // Precompute floor (f) and ceil (c) values for x and y.
+      const int fx = std::floor(static_cast<float>(x));
+      const int fy = std::floor(static_cast<float>(y));
+      const int cx = fx + 1;
+      const int cy = fy + 1;
+      const T dx = static_cast<T>(cx) - x;
+      const T dy = static_cast<T>(cy) - y;
+
+      const T img_fxfy = (fx >= 0 && fy >= 0)
+                         ? dx * dy * GET_DATA_POINT(fx, fy)
+                         : zero;
+
+      const T img_cxcy = (cx <= data_width - 1 && cy <= data_height - 1)
+                         ? (one - dx) * (one - dy) * GET_DATA_POINT(cx, cy)
+                         : zero;
+
+      const T img_fxcy = (fx >= 0 && cy <= data_height - 1)
+                         ? dx * (one - dy) * GET_DATA_POINT(fx, cy)
+                         : zero;
+
+      const T img_cxfy = (cx <= data_width - 1 && fy >= 0)
+                         ? (one - dx) * dy * GET_DATA_POINT(cx, fy)
+                         : zero;
+
+      output[out_index] = img_fxfy + img_cxcy + img_fxcy + img_cxfy;
+    } else {
+      output[out_index] = zero;
+    }
+  }
+}
+
+}  // namespace
+
+namespace functor {
+
+template <typename T>
+struct Resampler2DFunctor<GPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const GPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   T* __restrict__ output,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points) {
+  const int output_data_size = batch_size * num_sampling_points * data_channels;
+  ::tensorflow::CudaLaunchConfig config =
+      ::tensorflow::GetCudaLaunchConfig(output_data_size, d);
+  Resampler2DKernel<T>
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          data, warp, output, batch_size, data_height, data_width,
+          data_channels, num_sampling_points);
+  }
+};
+
+// TODO(fviola): gcudacc fails at compile time with Eigen::half.
+// template struct Resampler2DFunctor<GPUDevice, Eigen::half>;
+template struct Resampler2DFunctor<GPUDevice, float>;
+template struct Resampler2DFunctor<GPUDevice, double>;
+
+}  // namespace functor
+
+namespace {
+
+#define UPDATE_GRAD_DATA_POINT(x, y, v)                  \
+  atomicAdd(grad_data + (batch_id * data_batch_stride +  \
+            data_channels * (y * data_width + x) +       \
+            chan),                                       \
+            v)
+
+
+template <typename T>
+__global__ void ResamplerGrad2DKernel(const T* __restrict__ data,
+                                      const T* __restrict__ warp,
+                                      const T* __restrict__ grad_output,
+                                      T* __restrict__ grad_data,
+                                      T* __restrict__ grad_warp,
+                                      const int batch_size,
+                                      const int data_height,
+                                      const int data_width,
+                                      const int data_channels,
+                                      const int num_sampling_points) {
+  const int resampler_output_size = batch_size * num_sampling_points *
+      data_channels;
+  CUDA_1D_KERNEL_LOOP(index, resampler_output_size) {
+    const int out_index = index;
+
+    // Get (idxSample, channel, point) from the index.
+    // Use this formula
+    //   index = batch_id * num_sampling_points * num_chans +
+    //           sample_id * num_chans + chan_id,
+    // with sample_id = [0, ... ,num_sampling_points)
+    const int data_batch_stride = data_height * data_width * data_channels;
+    const int warp_batch_stride = num_sampling_points * 2;
+    const int output_batch_stride = num_sampling_points * data_channels;
+
+    const int batch_id = index / output_batch_stride;
+    const int index_in_batch = index % output_batch_stride;
+    const int chan = index_in_batch % data_channels;
+    const int sample_id = index_in_batch / data_channels;
+
+    // Get coords of 2D point where data will be resampled
+    const int warp_id_x = batch_id * warp_batch_stride + sample_id * 2;
+    const int warp_id_y = warp_id_x + 1;
+    const T x = warp[warp_id_x];
+    const T y = warp[warp_id_y];
+    const T zero = static_cast<T>(0.0);
+    const T one = static_cast<T>(1.0);
+
+    // Get grad output
+    const T grad_output_value = grad_output[out_index];
+    // The interpolation function whose gradient this kernel implements:
+    // a) implicitly pads the input data with 0s (hence the unusual checks
+    // with {x,y} > -1)
+    // b) returns 0 when sampling outside the (padded) image.
+    // The effect is that the sampled signal smoothly goes to 0 outside
+    // the original input domain, rather than presenting a jump
+    // discontinuity at the image boundaries.
+    if (x > static_cast<T>(-1.0) &&
+        y > static_cast<T>(-1.0) &&
+        x < static_cast<T>(data_width) &&
+        y < static_cast<T>(data_height)) {
+      // Precompute floor (f) and ceil (c) values for x and y.
+      const int fx = std::floor(static_cast<float>(x));
+      const int fy = std::floor(static_cast<float>(y));
+      const int cx = fx + 1;
+      const int cy = fy + 1;
+      const T dx = static_cast<T>(cx) - x;
+      const T dy = static_cast<T>(cy) - y;
+
+      const T img_fxfy = (fx >= 0 && fy >= 0)
+                         ? GET_DATA_POINT(fx, fy)
+                         : zero;
+
+      const T img_cxcy = (cx <= data_width - 1 && cy <= data_height - 1)
+                         ? GET_DATA_POINT(cx, cy)
+                         : zero;
+
+      const T img_fxcy = (fx >= 0 && cy <= data_height - 1)
+                         ? GET_DATA_POINT(fx, cy)
+                         : zero;
+
+      const T img_cxfy = (cx <= data_width - 1 && fy >= 0)
+                         ? GET_DATA_POINT(cx, fy)
+                         : zero;
+
+      // Update partial gradients wrt relevant warp field entries
+      atomicAdd(grad_warp + warp_id_x,
+                grad_output_value * ((one - dy) * (img_cxcy - img_fxcy) +
+                                     dy * (img_cxfy - img_fxfy)));
+      atomicAdd(grad_warp + warp_id_y,
+                grad_output_value * ((one - dx) * (img_cxcy - img_cxfy) +
+                                     dx * (img_fxcy - img_fxfy)));
+
+      // Update partial gradients wrt sampled data
+      if (fx >= 0 && fy >= 0) {
+        UPDATE_GRAD_DATA_POINT(fx, fy, grad_output_value * dx * dy);
+      }
+      if (cx <= data_width - 1 && cy <= data_height - 1) {
+        UPDATE_GRAD_DATA_POINT(cx, cy,
+                               grad_output_value  * (one - dx) * (one - dy));
+      }
+      if (fx >= 0 && cy <= data_height - 1) {
+        UPDATE_GRAD_DATA_POINT(fx, cy, grad_output_value * dx * (one - dy));
+      }
+      if (cx <= data_width - 1 && fy >= 0) {
+        UPDATE_GRAD_DATA_POINT(cx, fy, grad_output_value * (one - dx) * dy);
+      }
+    }
+  }
+}
+
+#undef GET_DATA_POINT
+#undef UPDATE_GRAD_DATA_POINT
+
+}  // namespace
+
+namespace functor {
+
+template <typename T>
+struct ResamplerGrad2DFunctor<GPUDevice, T>{
+  void operator ()(::tensorflow::OpKernelContext* ctx,
+                   const GPUDevice& d,
+                   const T* __restrict__ data,
+                   const T* __restrict__ warp,
+                   const T* __restrict__ grad_output,
+                   T* __restrict__ grad_data,
+                   T* __restrict__ grad_warp,
+                   const int batch_size,
+                   const int data_height,
+                   const int data_width,
+                   const int data_channels,
+                   const int num_sampling_points) {
+  // Set gradients to 0, because the kernel incrementally updates the
+  // tensor entries by adding partial contributions.
+  const int grad_warp_size = batch_size * num_sampling_points * 2;
+  const int grad_data_size = batch_size * data_height * data_width *
+      data_channels;
+
+  ::tensorflow::CudaLaunchConfig config =
+     ::tensorflow::GetCudaLaunchConfig(grad_warp_size, d);
+  ::tensorflow::SetZero
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          grad_warp_size, grad_warp);
+
+  config = ::tensorflow::GetCudaLaunchConfig(grad_data_size, d);
+  ::tensorflow::SetZero
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          grad_data_size, grad_data);
+
+  const int resampler_output_size = batch_size * num_sampling_points *
+      data_channels;
+  config = ::tensorflow::GetCudaLaunchConfig(resampler_output_size, d);
+  ResamplerGrad2DKernel<T>
+      <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+          data, warp, grad_output, grad_data, grad_warp, batch_size,
+          data_height, data_width, data_channels, num_sampling_points);
+  }
+};
+
+template struct ResamplerGrad2DFunctor<GPUDevice, float>;
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/resampler/ops/resampler_ops.cc b/tensorflow/contrib/resampler/ops/resampler_ops.cc
new file mode 100644
index 0000000000..5ab212032e
--- /dev/null
+++ b/tensorflow/contrib/resampler/ops/resampler_ops.cc
@@ -0,0 +1,59 @@
+// Copyright 2017 The Sonnet Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using ::tensorflow::shape_inference::InferenceContext;
+using ::tensorflow::shape_inference::ShapeHandle;
+
+REGISTER_OP("Resampler")
+    .Input("data: T")
+    .Input("warp: T")
+    .Output("output: T")
+    .Attr("T: {half, float, double}")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle data;
+      ShapeHandle warp;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &data));
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &warp));
+
+      ShapeHandle output;  // will be warp[:-1] + [data[-1]]
+      TF_RETURN_IF_ERROR(c->Subshape(warp, 0, -1, &output));
+      TF_RETURN_IF_ERROR(
+          c->Concatenate(output, c->Vector(c->Dim(data, -1)), &output));
+
+      c->set_output(0, output);
+      return ::tensorflow::Status::OK();
+    })
+    .Doc(R"doc(Resampler op.)doc");
+
+REGISTER_OP("ResamplerGrad")
+    .Input("data: T")
+    .Input("warp: T")
+    .Input("grad_output: T")
+    .Output("grad_data: T")
+    .Output("grad_warp: T")
+    .Attr("T: {half, float, double}")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->input(0));
+      c->set_output(1, c->input(1));
+      return ::tensorflow::Status::OK();
+    })
+    .Doc(R"doc(Resampler Grad op.)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/resampler/python/__init__.py b/tensorflow/contrib/resampler/python/__init__.py
new file mode 100644
index 0000000000..c5ca3a623f
--- /dev/null
+++ b/tensorflow/contrib/resampler/python/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""ops module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
diff --git a/tensorflow/contrib/resampler/python/ops/resampler_ops.py b/tensorflow/contrib/resampler/python/ops/resampler_ops.py
new file mode 100644
index 0000000000..355d15f0c7
--- /dev/null
+++ b/tensorflow/contrib/resampler/python/ops/resampler_ops.py
@@ -0,0 +1,69 @@
+# pylint: disable=g-bad-file-header
+# Copyright 2017 The Sonnet Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Tensorflow op performing differentiable resampling."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.resampler.ops import gen_resampler_ops
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import resource_loader
+
+_resampler_so = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_resampler_ops.so"))
+
+
+def resampler(data, warp, name="resampler"):
+  """Resamples input data at user defined coordinates.
+
+  The resampler currently only supports bilinear interpolation of 2D data.
+
+  Args:
+    data: Tensor of shape `[batch_size, data_height, data_width,
+      data_num_channels]` containing 2D data that will be resampled.
+    warp: Tensor of minimum rank 2 containing the coordinates at which
+      resampling will be performed. Since only bilinear interpolation is
+      currently supported, the last dimension of the `warp` tensor must be 2.
+    name: Optional name of the op.
+
+  Returns:
+    Tensor of resampled values from `data`. The output tensor shape is
+    determined by the shape of the warp tensor. For example, if `data` is of
+    shape `[batch_size, data_height, data_width, data_num_channels]` and warp of
+    shape `[batch_size, dim_0, ... , dim_n, 2]` the output will be of shape
+    `[batch_size, dim_0, ... , dim_n, data_num_channels]`.
+
+  Raises:
+    ImportError: if the wrapper generated during compilation is not present when
+    the function is called.
+  """
+  with ops.name_scope(name, "resampler", [data, warp]):
+    data_tensor = ops.convert_to_tensor(data, name="data")
+    warp_tensor = ops.convert_to_tensor(warp, name="warp")
+    return gen_resampler_ops.resampler(data_tensor, warp_tensor)
+
+
+@ops.RegisterGradient("Resampler")
+def _resampler_grad(op, grad_output):
+  data, warp = op.inputs
+  grad_output_tensor = ops.convert_to_tensor(grad_output, name="grad_output")
+  return gen_resampler_ops.resampler_grad(data, warp, grad_output_tensor)
+
+
+ops.NotDifferentiable("ResamplerGrad")
diff --git a/tensorflow/contrib/resampler/python/ops/resampler_ops_test.py b/tensorflow/contrib/resampler/python/ops/resampler_ops_test.py
new file mode 100644
index 0000000000..6a4360150c
--- /dev/null
+++ b/tensorflow/contrib/resampler/python/ops/resampler_ops_test.py
@@ -0,0 +1,270 @@
+# pylint: disable=g-bad-file-header
+# Copyright 2017 The Sonnet Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Tests for contrib.resampler.python.ops.resampler_ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.contrib import resampler
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors_impl
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import test
+
+
+def _bilinearly_interpolate(data, x, y):
+  """Performs bilinenar interpolation of grid data at user defined coordinates.
+
+  This interpolation function:
+    a) implicitly pads the input data with 0s.
+    b) returns 0 when sampling outside the (padded) image.
+  The effect is that the sampled signal smoothly goes to 0 outside the original
+  input domain, rather than producing a jump discontinuity at the image
+  boundaries.
+
+  Args:
+    data: numpy array of shape `[data_height, data_width]` containing data
+      samples assumed to be defined at the corresponding pixel coordinates.
+    x: numpy array of shape `[warp_height, warp_width]` containing x coordinates
+      at which interpolation will be performed.
+    y: numpy array of shape `[warp_height, warp_width]` containing y coordinates
+      at which interpolation will be performed.
+
+  Returns:
+    Numpy array of shape `[warp_height, warp_width]` containing interpolated
+      values.
+  """
+  shape = x.shape
+  x = np.asarray(x) + 1
+  y = np.asarray(y) + 1
+  data = np.lib.pad(data, 1, "constant", constant_values=0)
+
+  x_0 = np.floor(x).astype(int)
+  x_1 = x_0 + 1
+  y_0 = np.floor(y).astype(int)
+  y_1 = y_0 + 1
+
+  x_0 = np.clip(x_0, 0, data.shape[1] - 1)
+  x_1 = np.clip(x_1, 0, data.shape[1] - 1)
+  y_0 = np.clip(y_0, 0, data.shape[0] - 1)
+  y_1 = np.clip(y_1, 0, data.shape[0] - 1)
+
+  i_a = data[y_0, x_0]
+  i_b = data[y_1, x_0]
+  i_c = data[y_0, x_1]
+  i_d = data[y_1, x_1]
+
+  w_a = (x_1 - x) * (y_1 - y)
+  w_b = (x_1 - x) * (y - y_0)
+  w_c = (x - x_0) * (y_1 - y)
+  w_d = (x - x_0) * (y - y_0)
+
+  samples = (w_a * i_a + w_b * i_b + w_c * i_c + w_d * i_d)
+  samples.reshape(shape)
+
+  return samples
+
+
+def _make_warp(batch_size, warp_height, warp_width, dtype):
+  """Creates batch of warping coordinates."""
+  x, y = np.meshgrid(np.linspace(0, warp_width - 1, warp_width),
+                     np.linspace(0, warp_height - 1, warp_height))
+  warp = np.concatenate((x.reshape([warp_height, warp_width, 1]),
+                         y.reshape([warp_height, warp_width, 1])), 2)
+  warp = np.tile(warp.reshape([1, warp_height, warp_width, 2]),
+                 [batch_size, 1, 1, 1])
+  warp += np.random.randn(*warp.shape)
+  return warp.astype(dtype)
+
+
+class ResamplerTest(test.TestCase):
+
+  def test_op_forward_pass_gpu_float32(self):
+    self._test_op_forward_pass(True, dtypes.float32, 1e-4)
+
+  def test_op_forward_pass_gpu_float64(self):
+    self._test_op_forward_pass(True, dtypes.float64, 1e-5)
+
+  def test_op_forward_pass_cpu_float16(self):
+    self._test_op_forward_pass(False, dtypes.float16, 1e-2)
+
+  def test_op_forward_pass_cpu_float32(self):
+    self._test_op_forward_pass(False, dtypes.float32, 1e-4)
+
+  def test_op_forward_pass_cpu_float64(self):
+    self._test_op_forward_pass(False, dtypes.float64, 1e-5)
+
+  def test_op_backward_pass_gpu_float32(self):
+    self._test_op_backward_pass(True, dtypes.float32, 1e-3)
+
+  def test_op_backward_pass_cpu_float16(self):
+    self._test_op_backward_pass(False, dtypes.float16, 1e-3)
+
+  def test_op_backward_pass_cpu_float32(self):
+    self._test_op_backward_pass(False, dtypes.float32, 1e-4)
+
+  def test_op_backward_pass_cpu_float64(self):
+    self._test_op_backward_pass(False, dtypes.float64, 1e-6)
+
+  def _test_op_forward_pass(self, on_gpu, dtype, tol):
+    np.random.seed(0)
+    data_width = 7
+    data_height = 9
+    data_channels = 5
+    warp_width = 4
+    warp_height = 8
+    batch_size = 10
+
+    warp = _make_warp(batch_size, warp_height, warp_width, dtype.as_numpy_dtype)
+    data_shape = (batch_size, data_height, data_width, data_channels)
+    data = np.random.rand(*data_shape).astype(dtype.as_numpy_dtype)
+
+    with self.test_session(use_gpu=on_gpu, force_gpu=False) as sess:
+      data_ph = array_ops.placeholder(dtype, shape=(None,) + data.shape[1:])
+      warp_ph = array_ops.placeholder(dtype, shape=(None,) + warp.shape[1:])
+      outputs = resampler.resampler(data=data_ph, warp=warp_ph)
+      self.assertEqual(outputs.get_shape().as_list(),
+                       [None, warp_height, warp_width, data_channels])
+      out = sess.run(outputs, feed_dict={data_ph: data, warp_ph: warp})
+
+    # Generate reference output via bilinear interpolation in numpy
+    reference_output = np.zeros_like(out)
+    for batch in xrange(batch_size):
+      for c in xrange(data_channels):
+        reference_output[batch, :, :, c] = _bilinearly_interpolate(
+            data[batch, :, :, c],
+            warp[batch, :, :, 0],
+            warp[batch, :, :, 1])
+
+    self.assertAllClose(out, reference_output, rtol=tol, atol=tol)
+
+  def _test_op_backward_pass(self, on_gpu, dtype, tol):
+    np.random.seed(13)
+    data_width = 5
+    data_height = 4
+    data_channels = 3
+    warp_width = 2
+    warp_height = 6
+    batch_size = 10
+
+    warp = _make_warp(batch_size, warp_height, warp_width, dtype.as_numpy_dtype)
+    data_shape = (batch_size, data_height, data_width, data_channels)
+    data = np.random.rand(*data_shape).astype(dtype.as_numpy_dtype)
+
+    with self.test_session(use_gpu=on_gpu, force_gpu=False):
+      data_tensor = constant_op.constant(data)
+      warp_tensor = constant_op.constant(warp)
+      output_tensor = resampler.resampler(data=data_tensor, warp=warp_tensor)
+
+      grads = test.compute_gradient([data_tensor, warp_tensor], [
+          data_tensor.get_shape().as_list(),
+          warp_tensor.get_shape().as_list()
+      ], output_tensor, output_tensor.get_shape().as_list(), [data, warp])
+
+      if not on_gpu:
+        # On CPU we perform numerical differentiation at the best available
+        # precision, and compare against that. This is necessary for test to
+        # pass for float16.
+        data_tensor_64 = constant_op.constant(data, dtype=dtypes.float64)
+        warp_tensor_64 = constant_op.constant(warp, dtype=dtypes.float64)
+        output_tensor_64 = resampler.resampler(data=data_tensor_64,
+                                               warp=warp_tensor_64)
+        grads_64 = test.compute_gradient([data_tensor_64, warp_tensor_64], [
+            data_tensor.get_shape().as_list(),
+            warp_tensor.get_shape().as_list()
+        ], output_tensor_64, output_tensor.get_shape().as_list(), [data, warp])
+
+        for g, g_64 in zip(grads, grads_64):
+          self.assertLess(np.fabs(g[0] - g_64[1]).max(), tol)
+
+      else:
+        for g in grads:
+          self.assertLess(np.fabs(g[0] - g[1]).max(), tol)
+
+  def test_op_errors(self):
+    data_width = 7
+    data_height = 9
+    data_depth = 3
+    data_channels = 5
+    warp_width = 4
+    warp_height = 8
+    batch_size = 10
+
+    # Input data shape is not defined over a 2D grid, i.e. its shape is not like
+    # (batch_size, data_height, data_width, data_channels).
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_depth,
+                    data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size, warp_height, warp_width, 2)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.UnimplementedError,
+                                   "Only bilinear interpolation is currently "
+                                   "supported."):
+        sess.run(outputs)
+
+    # Warp tensor must be at least a matrix, with shape [batch_size, 2].
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size,)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   "warp should be at least a matrix"):
+        sess.run(outputs)
+
+    # The batch size of the data and warp tensors must be the same.
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size+1, warp_height, warp_width, 2)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   "Batch size of data and warp tensor"):
+        sess.run(outputs)
+
+    # The warp tensor must contain 2D coordinates, i.e. its shape last dimension
+    # must be 2.
+    with self.test_session() as sess:
+      data_shape = (batch_size, data_height, data_width, data_channels)
+      data = np.zeros(data_shape)
+      warp_shape = (batch_size, warp_height, warp_width, 3)
+      warp = np.zeros(warp_shape)
+      outputs = resampler.resampler(constant_op.constant(data),
+                                    constant_op.constant(warp))
+
+      with self.assertRaisesRegexp(errors_impl.UnimplementedError,
+                                   "Only bilinear interpolation is supported, "
+                                   "warping"):
+        sess.run(outputs)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
index 97b9dcc905..48c2c5a724 100644
--- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py
+++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
@@ -352,8 +352,8 @@ class LSTMBlockCell(rnn_cell_impl.RNNCell):
         `_lstm_block_cell()` for details.
       use_peephole: Whether to use peephole connections or not.
 
-      When restoring from CudnnLSTM-trained checkpoints, must set the following:
-      forget_bias, clip_cell, use_peephole  = 0, False, False
+      When restoring from CudnnLSTM-trained checkpoints, must use
+      CudnnCompatibleLSTMBlockCell instead.
     """
     self._num_units = num_units
     self._forget_bias = forget_bias
diff --git a/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py b/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py
index 1cbd27a2e5..da129b68a6 100644
--- a/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py
+++ b/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py
@@ -124,6 +124,20 @@ _RNN_NAME_REPLACEMENTS = collections.OrderedDict([
      'attention_cell_wrapper/attention/kernel'),
     ('attention_cell_wrapper/attention/biases',
      'attention_cell_wrapper/attention/bias'),
+    ############################################################################
+    # contrib/legacy_seq2seq/python/ops/seq2seq.py
+    ('attention_decoder/weights',
+     'attention_decoder/kernel'),
+    ('attention_decoder/biases',
+     'attention_decoder/bias'),
+    ('attention_decoder/Attention_0/weights',
+     'attention_decoder/Attention_0/kernel'),
+    ('attention_decoder/Attention_0/biases',
+     'attention_decoder/Attention_0/bias'),
+    ('attention_decoder/AttnOutputProjection/weights',
+     'attention_decoder/AttnOutputProjection/kernel'),
+    ('attention_decoder/AttnOutputProjection/biases',
+     'attention_decoder/AttnOutputProjection/bias'),
 ])
 
 _RNN_SHARDED_NAME_REPLACEMENTS = collections.OrderedDict([
diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD
index 6ab9631d29..e86fd318bd 100644
--- a/tensorflow/contrib/saved_model/BUILD
+++ b/tensorflow/contrib/saved_model/BUILD
@@ -37,6 +37,9 @@ py_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:lib",
+        "//tensorflow/python:util",
+        "//tensorflow/python/saved_model:constants",
     ],
 )
 
@@ -64,6 +67,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":saved_model_py",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
diff --git a/tensorflow/contrib/seq2seq/__init__.py b/tensorflow/contrib/seq2seq/__init__.py
index d36d7e16de..c4abef268b 100644
--- a/tensorflow/contrib/seq2seq/__init__.py
+++ b/tensorflow/contrib/seq2seq/__init__.py
@@ -57,7 +57,13 @@ _allowed_symbols = [
     "AttentionWrapperState",
     "AttentionWrapper",
     "AttentionMechanism",
-    "tile_batch"]
+    "tile_batch",
+    "safe_cumprod",
+    "monotonic_attention",
+    "monotonic_probability_fn",
+    "BahdanauMonotonicAttention",
+    "LuongMonotonicAttention",
+]
 
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
index 99e51589c9..8401918128 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
@@ -28,9 +28,11 @@ from tensorflow.contrib.seq2seq.python.ops import decoder
 from tensorflow.contrib.seq2seq.python.ops import attention_wrapper as wrapper
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.contrib.seq2seq.python.ops import basic_decoder
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import variable_scope as vs
@@ -174,6 +176,7 @@ class AttentionWrapperTest(test.TestCase):
                                              sess_results['final_outputs'])
       final_state_info = nest.map_structure(get_result_summary,
                                             sess_results['final_state'])
+      print(name)
       print('Copy/paste:\nexpected_final_output = %s' % str(final_output_info))
       print('expected_final_state = %s' % str(final_state_info))
       nest.map_structure(self.assertAllCloseOrEqual, expected_final_output,
@@ -335,6 +338,285 @@ class AttentionWrapperTest(test.TestCase):
         attention_layer_size=None,
         name='testNotUseAttentionLayer')
 
+  def test_safe_cumprod(self):
+    # Create some random test input
+    test_input = np.random.uniform(size=(10, 20))
+
+    for axis in [0, 1]:
+      for exclusive in [True, False]:
+        with self.test_session():
+          # Compute cumprod with regular tf.cumprod
+          cumprod_output = math_ops.cumprod(
+              test_input, axis=axis, exclusive=exclusive).eval()
+          # Compute cumprod with safe_cumprod
+          safe_cumprod_output = wrapper.safe_cumprod(
+              test_input, axis=axis, exclusive=exclusive).eval()
+        for x, y in zip(cumprod_output.shape, safe_cumprod_output.shape):
+          self.assertEqual(x, y)
+        for x, y in zip(cumprod_output.flatten(),
+                        safe_cumprod_output.flatten()):
+          # Use assertAlmostEqual for the actual values due to floating point
+          self.assertAlmostEqual(x, y, places=5)
+
+  def test_monotonic_attention(self):
+    def monotonic_attention_explicit(p_choose_i, previous_attention):
+      """Explicitly compute monotonic attention distribution using numpy."""
+      # Base case for recurrence relation
+      out = [previous_attention[0]]
+      # Explicitly follow the recurrence relation
+      for j in range(1, p_choose_i.shape[0]):
+        out.append((1 - p_choose_i[j - 1])*out[j - 1] + previous_attention[j])
+      return p_choose_i*np.array(out)
+
+    # Generate a random batch of choosing probabilities for seq. len. 20
+    p_choose_i = np.random.uniform(size=(10, 20)).astype(np.float32)
+    # Generate random previous attention distributions
+    previous_attention = np.random.uniform(size=(10, 20)).astype(np.float32)
+    previous_attention /= previous_attention.sum(axis=1).reshape((-1, 1))
+
+    # Create the output to test against
+    explicit_output = np.array([
+        monotonic_attention_explicit(p, a)
+        for p, a in zip(p_choose_i, previous_attention)])
+
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      recursive_output = wrapper.monotonic_attention(
+          p_choose_i, previous_attention, 'recursive').eval()
+
+    self.assertEqual(recursive_output.ndim, explicit_output.ndim)
+    for x, y in zip(recursive_output.shape, explicit_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(recursive_output.flatten(), explicit_output.flatten()):
+      # Use assertAlmostEqual for the actual values due to floating point
+      self.assertAlmostEqual(x, y, places=5)
+
+    # Generate new p_choose_i for parallel, which is unstable when p_choose_i[n]
+    # is close to 1
+    p_choose_i = np.random.uniform(0, 0.9, size=(10, 20)).astype(np.float32)
+
+    # Create new output to test against
+    explicit_output = np.array([
+        monotonic_attention_explicit(p, a)
+        for p, a in zip(p_choose_i, previous_attention)])
+
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      parallel_output = wrapper.monotonic_attention(
+          p_choose_i, previous_attention, 'parallel').eval()
+
+    self.assertEqual(parallel_output.ndim, explicit_output.ndim)
+    for x, y in zip(parallel_output.shape, explicit_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(parallel_output.flatten(), explicit_output.flatten()):
+      # Use assertAlmostEqual for the actual values due to floating point
+      self.assertAlmostEqual(x, y, places=5)
+
+    # Now, test hard mode, where probabilities must be 0 or 1
+    p_choose_i = np.random.choice(np.array([0, 1], np.float32), (10, 20))
+    previous_attention = np.zeros((10, 20), np.float32)
+    # Randomly choose input sequence indices at each timestep
+    random_idx = np.random.randint(0, previous_attention.shape[1],
+                                   previous_attention.shape[0])
+    previous_attention[np.arange(previous_attention.shape[0]), random_idx] = 1
+
+    # Create the output to test against
+    explicit_output = np.array([
+        monotonic_attention_explicit(p, a)
+        for p, a in zip(p_choose_i, previous_attention)])
+
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      hard_output = wrapper.monotonic_attention(
+          # TensorFlow is unhappy when these are not wrapped as tf.constant
+          constant_op.constant(p_choose_i),
+          constant_op.constant(previous_attention),
+          'hard').eval()
+
+    self.assertEqual(hard_output.ndim, explicit_output.ndim)
+    for x, y in zip(hard_output.shape, explicit_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(hard_output.flatten(), explicit_output.flatten()):
+      # Use assertAlmostEqual for the actual values due to floating point
+      self.assertAlmostEqual(x, y, places=5)
+
+    # Now, test recursively computing attention distributions vs. sampling
+    def sample(p_choose_i):
+      """Generate a sequence of emit-ingest decisions from p_choose_i."""
+      output = np.zeros(p_choose_i.shape)
+      t_im1 = 0
+      for i in range(p_choose_i.shape[0]):
+        for j in range(t_im1, p_choose_i.shape[1]):
+          if np.random.uniform() <= p_choose_i[i, j]:
+            output[i, j] = 1
+            t_im1 = j
+            break
+        else:
+          t_im1 = p_choose_i.shape[1]
+      return output
+
+    # Now, the first axis is output timestep and second is input timestep
+    p_choose_i = np.random.uniform(size=(4, 5)).astype(np.float32)
+    # Generate the average of a bunch of samples
+    n_samples = 100000
+    sampled_output = np.mean(
+        [sample(p_choose_i) for _ in range(n_samples)], axis=0)
+
+    # Create initial previous_attention base case
+    recursive_output = [np.array([1] + [0]*(p_choose_i.shape[1] - 1),
+                                 np.float32)]
+    # Compute output with TensorFlow function, for both calculation types
+    with self.test_session():
+      for j in range(p_choose_i.shape[0]):
+        # Compute attention distribution for this output time step
+        recursive_output.append(wrapper.monotonic_attention(
+            # newaxis is for adding the expected batch dimension
+            p_choose_i[j][np.newaxis],
+            recursive_output[-1][np.newaxis], 'recursive').eval()[0])
+      # Stack together distributions; remove basecase
+      recursive_output = np.array(recursive_output[1:])
+
+    self.assertEqual(recursive_output.ndim, sampled_output.ndim)
+    for x, y in zip(recursive_output.shape, sampled_output.shape):
+      self.assertEqual(x, y)
+    for x, y in zip(recursive_output.flatten(), sampled_output.flatten()):
+      # Use a very forgiving threshold since we are sampling
+      self.assertAlmostEqual(x, y, places=2)
+
+  def testBahdanauMonotonicNotNormalized(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.BahdanauMonotonicAttention, sigmoid_noise=1.0,
+        sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.002122893),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.7333333333333334))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040002423),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019968653)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-5.9313523e-05),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.032228071),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.050430927)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testBahdanauMonotonicNotNormalized')
+
+  def testBahdanauMonotonicNormalized(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.BahdanauMonotonicAttention, normalize=True,
+        sigmoid_noise=1.0, sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0025896581),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.8666666666666667))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040013152),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019973689)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-0.00069823361),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.028698336),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.046009291)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testBahdanauMonotonicNormalized')
+
+  def testLuongMonotonicNotNormalized(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.LuongMonotonicAttention, sigmoid_noise=1.0,
+        sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0021257224),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.7333333333333334))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040003359),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.001996913)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-5.2024145e-05),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.032198936),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.050387777)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        attention_mechanism_depth=9,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testLuongMonotonicNotNormalized')
+
+  def testLuongMonotonicScaled(self):
+    create_attention_mechanism = functools.partial(
+        wrapper.LuongMonotonicAttention, scale=True, sigmoid_noise=1.0,
+        sigmoid_noise_seed=3)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0021257224),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.7333333333333334))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040003359),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.001996913)),
+        attention=ResultSummary(
+            shape=(5, 6), dtype=dtype('float32'), mean=-5.2024145e-05),
+        time=3,
+        alignments=ResultSummary(
+            shape=(5, 8), dtype=dtype('float32'), mean=0.032198936),
+        alignment_history=())
+    expected_final_alignment_history = ResultSummary(
+        shape=(3, 5, 8), dtype=dtype('float32'), mean=0.050387777)
+
+    self._testWithAttention(
+        create_attention_mechanism,
+        expected_final_output,
+        expected_final_state,
+        attention_mechanism_depth=9,
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testLuongMonotonicScaled')
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index fbbfb25aea..0b1692ef2e 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -19,8 +19,11 @@ from __future__ import division
 from __future__ import print_function
 
 import collections
+import functools
 import math
 
+import numpy as np
+
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -28,9 +31,12 @@ from tensorflow.python.layers import base as layers_base
 from tensorflow.python.layers import core as layers_core
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import functional_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope
@@ -44,6 +50,10 @@ __all__ = [
     "LuongAttention",
     "BahdanauAttention",
     "hardmax",
+    "safe_cumprod",
+    "monotonic_attention",
+    "BahdanauMonotonicAttention",
+    "LuongMonotonicAttention",
 ]
 
 
@@ -241,6 +251,66 @@ class _BaseAttentionMechanism(AttentionMechanism):
     return _zero_state_tensors(max_time, batch_size, dtype)
 
 
+def _luong_score(query, keys, scale):
+  """Implements Luong-style (multiplicative) scoring function.
+
+  This attention has two forms.  The first is standard Luong attention,
+  as described in:
+
+  Minh-Thang Luong, Hieu Pham, Christopher D. Manning.
+  "Effective Approaches to Attention-based Neural Machine Translation."
+  EMNLP 2015.  https://arxiv.org/abs/1508.04025
+
+  The second is the scaled form inspired partly by the normalized form of
+  Bahdanau attention.
+
+  To enable the second form, call this function with `scale=True`.
+
+  Args:
+    query: Tensor, shape `[batch_size, num_units]` to compare to keys.
+    keys: Processed memory, shape `[batch_size, max_time, num_units]`.
+    scale: Whether to apply a scale to the score function.
+
+  Returns:
+    A `[batch_size, max_time]` tensor of unnormalized score values.
+
+  Raises:
+    ValueError: If `key` and `query` depths do not match.
+  """
+  depth = query.get_shape()[-1]
+  key_units = keys.get_shape()[-1]
+  if depth != key_units:
+    raise ValueError(
+        "Incompatible or unknown inner dimensions between query and keys.  "
+        "Query (%s) has units: %s.  Keys (%s) have units: %s.  "
+        "Perhaps you need to set num_units to the the keys' dimension (%s)?"
+        % (query, depth, keys, key_units, key_units))
+  dtype = query.dtype
+
+  # Reshape from [batch_size, depth] to [batch_size, 1, depth]
+  # for matmul.
+  query = array_ops.expand_dims(query, 1)
+
+  # Inner product along the query units dimension.
+  # matmul shapes: query is [batch_size, 1, depth] and
+  #                keys is [batch_size, max_time, depth].
+  # the inner product is asked to **transpose keys' inner shape** to get a
+  # batched matmul on:
+  #   [batch_size, 1, depth] . [batch_size, depth, max_time]
+  # resulting in an output shape of:
+  #   [batch_time, 1, max_time].
+  # we then squeee out the center singleton dimension.
+  score = math_ops.matmul(query, keys, transpose_b=True)
+  score = array_ops.squeeze(score, [1])
+
+  if scale:
+    # Scalar used in weight scaling
+    g = variable_scope.get_variable(
+        "attention_g", dtype=dtype, initializer=1.)
+    score = g * score
+  return score
+
+
 class LuongAttention(_BaseAttentionMechanism):
   """Implements Luong-style (multiplicative) attention scoring.
 
@@ -317,47 +387,66 @@ class LuongAttention(_BaseAttentionMechanism):
       alignments: Tensor of dtype matching `self.values` and shape
         `[batch_size, alignments_size]` (`alignments_size` is memory's
         `max_time`).
-
-    Raises:
-      ValueError: If `key` and `query` depths do not match.
     """
-    depth = query.get_shape()[-1]
-    key_units = self.keys.get_shape()[-1]
-    if depth != key_units:
-      raise ValueError(
-          "Incompatible or unknown inner dimensions between query and keys.  "
-          "Query (%s) has units: %s.  Keys (%s) have units: %s.  "
-          "Perhaps you need to set num_units to the keys' dimension (%s)?"
-          % (query, depth, self.keys, key_units, key_units))
-    dtype = query.dtype
-
     with variable_scope.variable_scope(None, "luong_attention", [query]):
-      # Reshape from [batch_size, depth] to [batch_size, 1, depth]
-      # for matmul.
-      query = array_ops.expand_dims(query, 1)
-
-      # Inner product along the query units dimension.
-      # matmul shapes: query is [batch_size, 1, depth] and
-      #                keys is [batch_size, max_time, depth].
-      # the inner product is asked to **transpose keys' inner shape** to get a
-      # batched matmul on:
-      #   [batch_size, 1, depth] . [batch_size, depth, max_time]
-      # resulting in an output shape of:
-      #   [batch_time, 1, max_time].
-      # we then squeeze out the center singleton dimension.
-      score = math_ops.matmul(query, self.keys, transpose_b=True)
-      score = array_ops.squeeze(score, [1])
-
-      if self._scale:
-        # Scalar used in weight scaling
-        g = variable_scope.get_variable(
-            "attention_g", dtype=dtype, initializer=1.)
-        score = g * score
-
+      score = _luong_score(query, self._keys, self._scale)
     alignments = self._probability_fn(score, previous_alignments)
     return alignments
 
 
+def _bahdanau_score(processed_query, keys, normalize):
+  """Implements Bahdanau-style (additive) scoring function.
+
+  This attention has two forms.  The first is Bhandanau attention,
+  as described in:
+
+  Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
+  "Neural Machine Translation by Jointly Learning to Align and Translate."
+  ICLR 2015. https://arxiv.org/abs/1409.0473
+
+  The second is the normalized form.  This form is inspired by the
+  weight normalization article:
+
+  Tim Salimans, Diederik P. Kingma.
+  "Weight Normalization: A Simple Reparameterization to Accelerate
+   Training of Deep Neural Networks."
+  https://arxiv.org/abs/1602.07868
+
+  To enable the second form, set `normalize=True`.
+
+  Args:
+    processed_query: Tensor, shape `[batch_size, num_units]` to compare to keys.
+    keys: Processed memory, shape `[batch_size, max_time, num_units]`.
+    normalize: Whether to normalize the score function.
+
+  Returns:
+    A `[batch_size, max_time]` tensor of unnormalized score values.
+  """
+  dtype = processed_query.dtype
+  # Get the number of hidden units from the trailing dimension of keys
+  num_units = keys.shape[2].value or array_ops.shape(keys)[2]
+  # Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
+  processed_query = array_ops.expand_dims(processed_query, 1)
+  v = variable_scope.get_variable(
+      "attention_v", [num_units], dtype=dtype)
+  if normalize:
+    # Scalar used in weight normalization
+    g = variable_scope.get_variable(
+        "attention_g", dtype=dtype,
+        initializer=math.sqrt((1. / num_units)))
+    # Bias added prior to the nonlinearity
+    b = variable_scope.get_variable(
+        "attention_b", [num_units], dtype=dtype,
+        initializer=init_ops.zeros_initializer())
+    # normed_v = g * v / ||v||
+    normed_v = g * v * math_ops.rsqrt(
+        math_ops.reduce_sum(math_ops.square(v)))
+    return math_ops.reduce_sum(
+        normed_v * math_ops.tanh(keys + processed_query + b), [2])
+  else:
+    return math_ops.reduce_sum(v * math_ops.tanh(keys + processed_query), [2])
+
+
 class BahdanauAttention(_BaseAttentionMechanism):
   """Implements Bahdanau-style (additive) attention.
 
@@ -441,30 +530,377 @@ class BahdanauAttention(_BaseAttentionMechanism):
     """
     with variable_scope.variable_scope(None, "bahdanau_attention", [query]):
       processed_query = self.query_layer(query) if self.query_layer else query
-      dtype = processed_query.dtype
-      # Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
-      processed_query = array_ops.expand_dims(processed_query, 1)
-      keys = self._keys
-      v = variable_scope.get_variable(
-          "attention_v", [self._num_units], dtype=dtype)
-      if self._normalize:
-        # Scalar used in weight normalization
-        g = variable_scope.get_variable(
-            "attention_g", dtype=dtype,
-            initializer=math.sqrt((1. / self._num_units)))
-        # Bias added prior to the nonlinearity
-        b = variable_scope.get_variable(
-            "attention_b", [self._num_units], dtype=dtype,
-            initializer=init_ops.zeros_initializer())
-        # normed_v = g * v / ||v||
-        normed_v = g * v * math_ops.rsqrt(
-            math_ops.reduce_sum(math_ops.square(v)))
-        score = math_ops.reduce_sum(
-            normed_v * math_ops.tanh(keys + processed_query + b), [2])
-      else:
-        score = math_ops.reduce_sum(v * math_ops.tanh(keys + processed_query),
-                                    [2])
+      score = _bahdanau_score(processed_query, self._keys, self._normalize)
+    alignments = self._probability_fn(score, previous_alignments)
+    return alignments
+
+
+def safe_cumprod(x, *args, **kwargs):
+  """Computes cumprod of x in logspace using cumsum to avoid underflow.
+
+  The cumprod function and its gradient can result in numerical instabilities
+  when its argument has very small and/or zero values.  As long as the argument
+  is all positive, we can instead compute the cumulative product as
+  exp(cumsum(log(x))).  This function can be called identically to tf.cumprod.
+
+  Args:
+    x: Tensor to take the cumulative product of.
+    *args: Passed on to cumsum; these are identical to those in cumprod.
+    **kwargs: Passed on to cumsum; these are identical to those in cumprod.
+  Returns:
+    Cumulative product of x.
+  """
+  with ops.name_scope(None, "SafeCumprod", [x]):
+    x = ops.convert_to_tensor(x, name="x")
+    tiny = np.finfo(x.dtype.as_numpy_dtype).tiny
+    return math_ops.exp(math_ops.cumsum(
+        math_ops.log(clip_ops.clip_by_value(x, tiny, 1)), *args, **kwargs))
+
+
+def monotonic_attention(p_choose_i, previous_attention, mode):
+  """Compute monotonic attention distribution from choosing probabilities.
+
+  Monotonic attention implies that the input sequence is processed in an
+  explicitly left-to-right manner when generating the output sequence.  In
+  addition, once an input sequence element is attended to at a given output
+  timestep, elements occurring before it cannot be attended to at subsequent
+  output timesteps.  This function generates attention distributions according
+  to these assumptions.  For more information, see ``Online and Linear-Time
+  Attention by Enforcing Monotonic Alignments''.
+
+  Args:
+    p_choose_i: Probability of choosing input sequence/memory element i.  Should
+      be of shape (batch_size, input_sequence_length), and should all be in the
+      range [0, 1].
+    previous_attention: The attention distribution from the previous output
+      timestep.  Should be of shape (batch_size, input_sequence_length).  For
+      the first output timestep, preevious_attention[n] should be [1, 0, 0, ...,
+      0] for all n in [0, ... batch_size - 1].
+    mode: How to compute the attention distribution.  Must be one of
+      'recursive', 'parallel', or 'hard'.
+        * 'recursive' uses tf.scan to recursively compute the distribution.
+          This is slowest but is exact, general, and does not suffer from
+          numerical instabilities.
+        * 'parallel' uses parallelized cumulative-sum and cumulative-product
+          operations to compute a closed-form solution to the recurrence
+          relation defining the attention distribution.  This makes it more
+          efficient than 'recursive', but it requires numerical checks which
+          make the distribution non-exact.  This can be a problem in particular
+          when input_sequence_length is long and/or p_choose_i has entries very
+          close to 0 or 1.
+        * 'hard' requires that the probabilities in p_choose_i are all either 0
+          or 1, and subsequently uses a more efficient and exact solution.
+
+  Returns:
+    A tensor of shape (batch_size, input_sequence_length) representing the
+    attention distributions for each sequence in the batch.
+
+  Raises:
+    ValueError: mode is not one of 'recursive', 'parallel', 'hard'.
+  """
+  # Force things to be tensors
+  p_choose_i = ops.convert_to_tensor(p_choose_i, name="p_choose_i")
+  previous_attention = ops.convert_to_tensor(
+      previous_attention, name="previous_attention")
+  if mode == "recursive":
+    # Use .shape[0].value when it's not None, or fall back on symbolic shape
+    batch_size = p_choose_i.shape[0].value or array_ops.shape(p_choose_i)[0]
+    # Compute [1, 1 - p_choose_i[0], 1 - p_choose_i[1], ..., 1 - p_choose_i[-2]]
+    shifted_1mp_choose_i = array_ops.concat(
+        [array_ops.ones((batch_size, 1)), 1 - p_choose_i[:, :-1]], 1)
+    # Compute attention distribution recursively as
+    # q[i] = (1 - p_choose_i[i])*q[i - 1] + previous_attention[i]
+    # attention[i] = p_choose_i[i]*q[i]
+    attention = p_choose_i*array_ops.transpose(functional_ops.scan(
+        # Need to use reshape to remind TF of the shape between loop iterations
+        lambda x, yz: array_ops.reshape(yz[0]*x + yz[1], (batch_size,)),
+        # Loop variables yz[0] and yz[1]
+        [array_ops.transpose(shifted_1mp_choose_i),
+         array_ops.transpose(previous_attention)],
+        # Initial value of x is just zeros
+        array_ops.zeros((batch_size,))))
+  elif mode == "parallel":
+    # safe_cumprod computes cumprod in logspace with numeric checks
+    cumprod_1mp_choose_i = safe_cumprod(1 - p_choose_i, axis=1, exclusive=True)
+    # Compute recurrence relation solution
+    attention = p_choose_i*cumprod_1mp_choose_i*math_ops.cumsum(
+        previous_attention /
+        # Clip cumprod_1mp to avoid divide-by-zero
+        clip_ops.clip_by_value(cumprod_1mp_choose_i, 1e-10, 1.), axis=1)
+  elif mode == "hard":
+    # Remove any probabilities before the index chosen last time step
+    p_choose_i *= math_ops.cumsum(previous_attention, axis=1)
+    # Now, use exclusive cumprod to remove probabilities after the first
+    # chosen index, like so:
+    # p_choose_i = [0, 0, 0, 1, 1, 0, 1, 1]
+    # cumprod(1 - p_choose_i, exclusive=True) = [1, 1, 1, 1, 0, 0, 0, 0]
+    # Product of above: [0, 0, 0, 1, 0, 0, 0, 0]
+    attention = p_choose_i*math_ops.cumprod(
+        1 - p_choose_i, axis=1, exclusive=True)
+  else:
+    raise ValueError("mode must be 'recursive', 'parallel', or 'hard'.")
+  return attention
+
+
+def _monotonic_probability_fn(score, previous_alignments, sigmoid_noise, mode,
+                              seed=None):
+  """Attention probability function for monotonic attention.
+
+  Takes in unnormalized attention scores, adds pre-sigmoid noise to encourage
+  the model to make discrete attention decisions, passes them through a sigmoid
+  to obtain "choosing" probabilities, and then calls monotonic_attention to
+  obtain the attention distribution.  For more information, see
+
+  Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
+  "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
+  ICML 2017.  https://arxiv.org/abs/1704.00784
+
+  Args:
+    score: Unnormalized attention scores, shape `[batch_size, alignments_size]`
+    previous_alignments: Previous attention distribution, shape
+      `[batch_size, alignments_size]`
+    sigmoid_noise: Standard deviation of pre-sigmoid noise.  Setting this larger
+      than 0 will encourage the model to produce large attention scores,
+      effectively making the choosing probabilities discrete and the resulting
+      attention distribution one-hot.  It should be set to 0 at test-time, and
+      when hard attention is not desired.
+    mode: How to compute the attention distribution.  Must be one of
+      'recursive', 'parallel', or 'hard'.  See the docstring for
+      `tf.contrib.seq2seq.monotonic_attention` for more information.
+    seed: (optional) Random seed for pre-sigmoid noise.
+
+  Returns:
+    A `[batch_size, alignments_size]`-shape tensor corresponding to the
+    resulting attention distribution.
+  """
+  # Optionally add pre-sigmoid noise to the scores
+  if sigmoid_noise > 0:
+    noise = random_ops.random_normal(array_ops.shape(score), dtype=score.dtype,
+                                     seed=seed)
+    score += sigmoid_noise*noise
+  # Compute "choosing" probabilities from the attention scores
+  p_choose_i = math_ops.sigmoid(score)
+  # Convert from choosing probabilities to attention distribution
+  return monotonic_attention(p_choose_i, previous_alignments, mode)
+
+
+class _BaseMonotonicAttentionMechanism(_BaseAttentionMechanism):
+  """Base attention mechanism for monotonic attention.
+
+  Simply overrides the initial_alignments function to provide a dirac
+  distribution,which is needed in order for the monotonic attention
+  distributions to have the correct behavior.
+  """
+
+  def initial_alignments(self, batch_size, dtype):
+    """Creates the initial alignment values for the monotonic attentions.
+
+    Initializes to dirac distributions, i.e. [1, 0, 0, ...memory length..., 0]
+    for all entries in the batch.
+
+    Args:
+      batch_size: `int32` scalar, the batch_size.
+      dtype: The `dtype`.
+
+    Returns:
+      A `dtype` tensor shaped `[batch_size, alignments_size]`
+      (`alignments_size` is the values' `max_time`).
+    """
+    max_time = self._alignments_size
+    return array_ops.one_hot(
+        array_ops.zeros((batch_size,), dtype=dtypes.int32), max_time,
+        dtype=dtype)
+
+
+class BahdanauMonotonicAttention(_BaseMonotonicAttentionMechanism):
+  """Monotonic attention mechanism with Bahadanau-style energy function.
+
+  This type of attention encorces a monotonic constraint on the attention
+  distributions; that is once the model attends to a given point in the memory
+  it can't attend to any prior points at subsequence output timesteps.  It
+  achieves this by using the _monotonic_probability_fn instead of softmax to
+  construct its attention distributions.  Since the attention scores are passed
+  through a sigmoid, a learnable scalar bias parameter is applied after the
+  score function and before the sigmoid.  Otherwise, it is equivalent to
+  BahdanauAttention.  This approach is proposed in
+
+  Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
+  "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
+  ICML 2017.  https://arxiv.org/abs/1704.00784
+  """
+
+  def __init__(self,
+               num_units,
+               memory,
+               memory_sequence_length=None,
+               normalize=False,
+               score_mask_value=float("-inf"),
+               sigmoid_noise=0.,
+               sigmoid_noise_seed=None,
+               score_bias_init=0.,
+               mode="parallel",
+               name="BahdanauMonotonicAttention"):
+    """Construct the Attention mechanism.
+
+    Args:
+      num_units: The depth of the query mechanism.
+      memory: The memory to query; usually the output of an RNN encoder.  This
+        tensor should be shaped `[batch_size, max_time, ...]`.
+      memory_sequence_length (optional): Sequence lengths for the batch entries
+        in memory.  If provided, the memory tensor rows are masked with zeros
+        for values past the respective sequence lengths.
+      normalize: Python boolean.  Whether to normalize the energy term.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
+      sigmoid_noise: Standard deviation of pre-sigmoid noise.  See the docstring
+        for `_monotonic_probability_fn` for more information.
+      sigmoid_noise_seed: (optional) Random seed for pre-sigmoid noise.
+      score_bias_init: Initial value for score bias scalar.  It's recommended to
+        initialize this to a negative value when the length of the memory is
+        large.
+      mode: How to compute the attention distribution.  Must be one of
+        'recursive', 'parallel', or 'hard'.  See the docstring for
+        `tf.contrib.seq2seq.monotonic_attention` for more information.
+      name: Name to use when creating ops.
+    """
+    # Set up the monotonic probability fn with supplied parameters
+    wrapped_probability_fn = functools.partial(
+        _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode,
+        seed=sigmoid_noise_seed)
+    super(BahdanauMonotonicAttention, self).__init__(
+        query_layer=layers_core.Dense(
+            num_units, name="query_layer", use_bias=False),
+        memory_layer=layers_core.Dense(
+            num_units, name="memory_layer", use_bias=False),
+        memory=memory,
+        probability_fn=wrapped_probability_fn,
+        memory_sequence_length=memory_sequence_length,
+        score_mask_value=score_mask_value,
+        name=name)
+    self._num_units = num_units
+    self._normalize = normalize
+    self._name = name
+    self._score_bias_init = score_bias_init
+
+  def __call__(self, query, previous_alignments):
+    """Score the query based on the keys and values.
+
+    Args:
+      query: Tensor of dtype matching `self.values` and shape
+        `[batch_size, query_depth]`.
+      previous_alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]`
+        (`alignments_size` is memory's `max_time`).
+
+    Returns:
+      alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]` (`alignments_size` is memory's
+        `max_time`).
+    """
+    with variable_scope.variable_scope(
+        None, "bahdanau_monotonic_attention", [query]):
+      processed_query = self.query_layer(query) if self.query_layer else query
+      score = _bahdanau_score(processed_query, self._keys, self._normalize)
+      score_bias = variable_scope.get_variable(
+          "attention_score_bias", dtype=processed_query.dtype,
+          initializer=self._score_bias_init)
+      score += score_bias
+    alignments = self._probability_fn(score, previous_alignments)
+    return alignments
+
+
+class LuongMonotonicAttention(_BaseMonotonicAttentionMechanism):
+  """Monotonic attention mechanism with Luong-style energy function.
+
+  This type of attention encorces a monotonic constraint on the attention
+  distributions; that is once the model attends to a given point in the memory
+  it can't attend to any prior points at subsequence output timesteps.  It
+  achieves this by using the _monotonic_probability_fn instead of softmax to
+  construct its attention distributions.  Otherwise, it is equivalent to
+  LuongAttention.  This approach is proposed in
 
+  Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
+  "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
+  ICML 2017.  https://arxiv.org/abs/1704.00784
+  """
+
+  def __init__(self,
+               num_units,
+               memory,
+               memory_sequence_length=None,
+               scale=False,
+               score_mask_value=float("-inf"),
+               sigmoid_noise=0.,
+               sigmoid_noise_seed=None,
+               score_bias_init=0.,
+               mode="parallel",
+               name="LuongMonotonicAttention"):
+    """Construct the Attention mechanism.
+
+    Args:
+      num_units: The depth of the query mechanism.
+      memory: The memory to query; usually the output of an RNN encoder.  This
+        tensor should be shaped `[batch_size, max_time, ...]`.
+      memory_sequence_length (optional): Sequence lengths for the batch entries
+        in memory.  If provided, the memory tensor rows are masked with zeros
+        for values past the respective sequence lengths.
+      scale: Python boolean.  Whether to scale the energy term.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
+      sigmoid_noise: Standard deviation of pre-sigmoid noise.  See the docstring
+        for `_monotonic_probability_fn` for more information.
+      sigmoid_noise_seed: (optional) Random seed for pre-sigmoid noise.
+      score_bias_init: Initial value for score bias scalar.  It's recommended to
+        initialize this to a negative value when the length of the memory is
+        large.
+      mode: How to compute the attention distribution.  Must be one of
+        'recursive', 'parallel', or 'hard'.  See the docstring for
+        `tf.contrib.seq2seq.monotonic_attention` for more information.
+      name: Name to use when creating ops.
+    """
+    # Set up the monotonic probability fn with supplied parameters
+    wrapped_probability_fn = functools.partial(
+        _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode,
+        seed=sigmoid_noise_seed)
+    super(LuongMonotonicAttention, self).__init__(
+        query_layer=layers_core.Dense(
+            num_units, name="query_layer", use_bias=False),
+        memory_layer=layers_core.Dense(
+            num_units, name="memory_layer", use_bias=False),
+        memory=memory,
+        probability_fn=wrapped_probability_fn,
+        memory_sequence_length=memory_sequence_length,
+        score_mask_value=score_mask_value,
+        name=name)
+    self._num_units = num_units
+    self._scale = scale
+    self._score_bias_init = score_bias_init
+    self._name = name
+
+  def __call__(self, query, previous_alignments):
+    """Score the query based on the keys and values.
+
+    Args:
+      query: Tensor of dtype matching `self.values` and shape
+        `[batch_size, query_depth]`.
+      previous_alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]`
+        (`alignments_size` is memory's `max_time`).
+
+    Returns:
+      alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]` (`alignments_size` is memory's
+        `max_time`).
+    """
+    with variable_scope.variable_scope(None, "luong_monotonic_attention",
+                                       [query]):
+      score = _luong_score(query, self._keys, self._scale)
+      score_bias = variable_scope.get_variable(
+          "attention_score_bias", dtype=query.dtype,
+          initializer=self._score_bias_init)
+      score += score_bias
     alignments = self._probability_fn(score, previous_alignments)
     return alignments
 
diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
index 1d1babda16..ef1735a6c9 100644
--- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
@@ -486,7 +486,7 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
   total_probs = array_ops.expand_dims(beam_state.log_probs, 2) + step_log_probs
 
   # Calculate the continuation lengths by adding to all continuing beams.
-  vocab_size = logits.shape[-1].value
+  vocab_size = logits.shape[-1].value or array_ops.shape(logits)[-1]
   lengths_to_add = array_ops.one_hot(
       indices=array_ops.tile(
           array_ops.reshape(end_token, [1, 1]), [batch_size, beam_width]),
diff --git a/tensorflow/contrib/slim/BUILD b/tensorflow/contrib/slim/BUILD
index 427c25e07c..2d48a91f9f 100644
--- a/tensorflow/contrib/slim/BUILD
+++ b/tensorflow/contrib/slim/BUILD
@@ -15,9 +15,6 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/contrib/training:training_py",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:ops",
-        "//tensorflow/python:platform",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
     ],
@@ -38,10 +35,8 @@ py_test(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -56,17 +51,15 @@ py_library(
     deps = [
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/training:training_py",
-        "//tensorflow/python:array_ops",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:clip_ops",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:lib",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
@@ -88,11 +81,9 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -128,7 +119,6 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:ops",
         "//tensorflow/python:training",
     ],
 )
@@ -150,7 +140,6 @@ py_library(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/losses:losses_py",
         "//tensorflow/contrib/metrics:metrics_py",
-        "//tensorflow/contrib/slim/python/slim/data",
         "//tensorflow/contrib/slim/python/slim/data:data_decoder",
         "//tensorflow/contrib/slim/python/slim/data:data_provider",
         "//tensorflow/contrib/slim/python/slim/data:dataset",
@@ -167,7 +156,7 @@ py_library(
     srcs = ["python/slim/summaries.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:logging_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:summary",
@@ -183,7 +172,7 @@ py_test(
         ":summaries",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:summary",
     ],
diff --git a/tensorflow/contrib/slim/python/slim/data/BUILD b/tensorflow/contrib/slim/python/slim/data/BUILD
index 1326f4093c..4d42a11d16 100644
--- a/tensorflow/contrib/slim/python/slim/data/BUILD
+++ b/tensorflow/contrib/slim/python/slim/data/BUILD
@@ -72,12 +72,10 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:io_ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:platform",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -87,11 +85,9 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:io_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
@@ -111,9 +107,7 @@ py_test(
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:io_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
     ],
@@ -125,10 +119,8 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
     ],
@@ -141,11 +133,11 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":prefetch_queue",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -174,13 +166,13 @@ py_library(
         ":data_decoder",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:functional_ops",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
     ],
 )
 
@@ -195,11 +187,9 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:parsing_ops",
-        "//tensorflow/python:platform_test",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/contrib/slim/python/slim/nets/BUILD b/tensorflow/contrib/slim/python/slim/nets/BUILD
index 737bbbe57b..e2035ab014 100644
--- a/tensorflow/contrib/slim/python/slim/nets/BUILD
+++ b/tensorflow/contrib/slim/python/slim/nets/BUILD
@@ -243,9 +243,9 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -272,14 +272,15 @@ py_test(
     srcs = ["resnet_v2_test.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":resnet_utils",
         ":resnet_v2",
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -299,7 +300,6 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//third_party/py/numpy",
diff --git a/tensorflow/contrib/sparsemax/BUILD b/tensorflow/contrib/sparsemax/BUILD
index 7441f1429f..fcfaa2aba4 100644
--- a/tensorflow/contrib/sparsemax/BUILD
+++ b/tensorflow/contrib/sparsemax/BUILD
@@ -31,6 +31,8 @@ py_library(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/contrib/staging/BUILD b/tensorflow/contrib/staging/BUILD
index 8ffc96c346..bc4a289468 100644
--- a/tensorflow/contrib/staging/BUILD
+++ b/tensorflow/contrib/staging/BUILD
@@ -22,4 +22,5 @@ py_library(
     name = "staging",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow/python:data_flow_ops"],
 )
diff --git a/tensorflow/contrib/stat_summarizer/BUILD b/tensorflow/contrib/stat_summarizer/BUILD
index 11f65b4748..5fd02efbf6 100644
--- a/tensorflow/contrib/stat_summarizer/BUILD
+++ b/tensorflow/contrib/stat_summarizer/BUILD
@@ -13,6 +13,10 @@ py_library(
     name = "stat_summarizer_py",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:pywrap_tensorflow",
+        "//tensorflow/python:util",
+    ],
 )
 
 tf_py_test(
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 7b5f9472e7..95ee1852ca 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -28,37 +28,35 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
+# ---------------------------------- V2 ops ------------------------------------------#
 filegroup(
-    name = "custom_op_sources",
-    srcs = glob(
-        [
-            "kernels/*.cc",
-            "ops/*.cc",
-        ],
-        exclude = [
-            "kernels/*_test.cc",
-            "kernels/tree_utils.cc",
-        ],
-    ),
+    name = "v2_op_sources",
+    srcs = [
+        "kernels/best_splits_op.cc",
+        "kernels/count_extremely_random_stats_op.cc",
+        "kernels/finished_nodes_op.cc",
+        "kernels/grow_tree_op.cc",
+        "kernels/reinterpret_string_to_float_op.cc",
+        "kernels/sample_inputs_op.cc",
+        "kernels/scatter_add_ndim_op.cc",
+        "kernels/tree_predictions_op.cc",
+        "kernels/update_fertile_slots_op.cc",
+    ],
 )
 
 filegroup(
-    name = "custom_op_headers",
-    srcs = glob(
-        [
-            "kernels/*.h",
-        ],
-        exclude = [
-            "kernels/data_spec.h",
-            "kernels/tree_utils.h",
-        ],
-    ),
+    name = "v2_op_defs",
+    srcs = [
+        "ops/tensor_forest_ops.cc",
+    ],
 )
 
 cc_library(
-    name = "all_ops",
-    srcs = [":custom_op_sources"],
-    hdrs = [":custom_op_headers"],
+    name = "v2_ops",
+    srcs = [
+        ":v2_op_defs",
+        ":v2_op_sources",
+    ],
     deps = [
         ":tree_utils",
         "//tensorflow/core:framework_headers_lib",
@@ -105,16 +103,8 @@ tf_gen_op_wrapper_py(
 tf_custom_op_library(
     name = "python/ops/_tensor_forest_ops.so",
     srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
-        "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
-        "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
-        "ops/tensor_forest_ops.cc",
+        ":v2_op_defs",
+        ":v2_op_sources",
     ],
     deps = [":tree_utils"],
 )
@@ -131,7 +121,9 @@ py_library(
         ":constants",
         ":data_ops_py",
         ":eval_metrics",
+        ":model_ops_py",
         ":random_forest",
+        ":stats_ops_py",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
     ],
@@ -140,21 +132,11 @@ py_library(
 tf_kernel_library(
     name = "tensor_forest_kernels",
     srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
-        "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
-        "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
+        ":v2_op_sources",
     ],
     deps = [
         ":tree_utils",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
+        "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core/kernels:bounds_check",
     ],
 )
@@ -181,6 +163,198 @@ tf_custom_op_py_library(
     ],
 )
 
+cc_test(
+    name = "tensor_forest_ops_test",
+    size = "small",
+    srcs = [
+        "kernels/tensor_forest_ops_test.cc",
+        ":v2_op_defs",
+        ":v2_op_sources",
+    ],
+    deps = [
+        ":tree_utils",
+        "//tensorflow/core",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//third_party/eigen3",
+    ],
+)
+
+# -------------------------------------- V4 ops ------------------------------- #
+cc_library(
+    name = "tensor_forest_v4_kernels",
+    deps = [
+        ":model_ops_kernels",
+        ":stats_ops_kernels",
+    ],
+)
+
+cc_library(
+    name = "tensor_forest_v4_ops_op_lib",
+    deps = [
+        ":model_ops_op_lib",
+        ":stats_ops_op_lib",
+    ],
+)
+
+py_library(
+    name = "tensor_forest_v4_ops_py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model_ops_py",
+        ":stats_ops_py",
+    ],
+)
+
+# Model Ops.
+cc_library(
+    name = "model_ops_lib",
+    srcs = ["kernels/model_ops.cc"],
+    deps = [
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_gen_op_libs(
+    op_lib_names = ["model_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_model_ops_py",
+    out = "python/ops/gen_model_ops.py",
+    deps = [":model_ops_op_lib"],
+)
+
+tf_kernel_library(
+    name = "model_ops_kernels",
+    deps = [
+        ":model_ops_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_model_ops.so",
+    srcs = [
+        "ops/model_ops.cc",
+    ],
+    deps = [
+        ":model_ops_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "model_ops_py",
+    srcs = ["python/ops/model_ops.py"],
+    dso = ["python/ops/_model_ops.so"],
+    kernels = [
+        ":model_ops_kernels",
+        ":model_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_model_ops_py",
+        ":stats_ops_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+# Stats Ops.
+cc_library(
+    name = "stats_ops_lib",
+    srcs = ["kernels/stats_ops.cc"],
+    deps = [
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:decision-tree-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:fertile-stats-resource",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_data",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:input_target",
+        "//tensorflow/contrib/tensor_forest/kernels/v4:params",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_gen_op_libs(
+    op_lib_names = ["stats_ops"],
+)
+
+tf_gen_op_wrapper_py(
+    name = "gen_stats_ops_py",
+    out = "python/ops/gen_stats_ops.py",
+    deps = [":stats_ops_op_lib"],
+)
+
+tf_kernel_library(
+    name = "stats_ops_kernels",
+    deps = [
+        ":stats_ops_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_stats_ops.so",
+    srcs = [
+        "ops/stats_ops.cc",
+    ],
+    deps = [
+        ":stats_ops_lib",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "stats_ops_py",
+    srcs = ["python/ops/stats_ops.py"],
+    dso = ["python/ops/_stats_ops.so"],
+    kernels = [
+        ":stats_ops_kernels",
+        ":stats_ops_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gen_stats_ops_py",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
+# ---------------------------------- Common libs ------------------------ #
+cc_library(
+    name = "tree_utils",
+    srcs = ["kernels/tree_utils.cc"],
+    hdrs = [
+        "kernels/data_spec.h",
+        "kernels/tree_utils.h",
+    ],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+        "@protobuf//:protobuf_headers",
+    ],
+)
+
+# --------------------------------- Python -------------------------------- #
+
 py_library(
     name = "eval_metrics",
     srcs = ["client/eval_metrics.py"],
@@ -217,20 +391,8 @@ py_library(
         ":eval_metrics",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
-    ],
-)
-
-cc_library(
-    name = "tree_utils",
-    srcs = ["kernels/tree_utils.cc"],
-    hdrs = [
-        "kernels/data_spec.h",
-        "kernels/tree_utils.h",
-    ],
-    deps = [
-        "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        ":tensor_forest_v4_ops_py",
+        ":tensor_forest_v4_py",
     ],
 )
 
@@ -366,6 +528,21 @@ py_library(
     ],
 )
 
+py_library(
+    name = "tensor_forest_v4_py",
+    srcs = ["python/tensor_forest_v4.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tensor_forest_v4_ops_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_py",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:platform",
+    ],
+)
+
 py_test(
     name = "tensor_forest_test",
     size = "small",
@@ -380,25 +557,6 @@ py_test(
     ],
 )
 
-cc_test(
-    name = "tensor_forest_ops_test",
-    size = "small",
-    srcs = [
-        "kernels/tensor_forest_ops_test.cc",
-        ":custom_op_sources",
-    ],
-    deps = [
-        ":tree_utils",
-        "//tensorflow/core",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-        "//tensorflow/core:testlib",
-        "//third_party/eigen3",
-    ],
-)
-
 py_library(
     name = "random_forest",
     srcs = ["client/random_forest.py"],
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index ef2f0337ac..47685df4a5 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -26,21 +26,26 @@ from tensorflow.contrib.learn.python.learn.estimators import prediction_key
 
 from tensorflow.contrib.tensor_forest.client import eval_metrics
 from tensorflow.contrib.tensor_forest.python import tensor_forest
+from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
 
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import basic_session_run_hooks
-from tensorflow.python.training import monitored_session
 from tensorflow.python.training import session_run_hook
 
 
 KEYS_NAME = 'keys'
 LOSS_NAME = 'rf_training_loss'
 
+VERSION_BUILDERS = {'v2': tensor_forest.RandomForestGraphs,
+                    'v4': tensor_forest_v4.RandomForestGraphsV4}
+
 
 def _assert_float32(tensors):
   """Assert all tensors are float32.
@@ -74,8 +79,9 @@ class TensorForestRunOpAtEndHook(session_run_hook.SessionRunHook):
 class TensorForestLossHook(session_run_hook.SessionRunHook):
   """Monitor to request stop when loss stops decreasing."""
 
-  def __init__(self, early_stopping_rounds):
+  def __init__(self, early_stopping_rounds, loss_op=None):
     self.early_stopping_rounds = early_stopping_rounds
+    self.loss_op = loss_op
     self.min_loss = None
     self.last_step = -1
     # self.steps records the number of steps for which the loss has been
@@ -83,10 +89,12 @@ class TensorForestLossHook(session_run_hook.SessionRunHook):
     self.steps = 0
 
   def before_run(self, run_context):
+    loss = (self.loss_op if self.loss_op is not None else
+            run_context.session.graph.get_operation_by_name(
+                LOSS_NAME).outputs[0])
     return session_run_hook.SessionRunArgs(
         {'global_step': contrib_framework.get_global_step(),
-         'current_loss': run_context.session.graph.get_operation_by_name(
-             LOSS_NAME).outputs[0]})
+         'current_loss': loss})
 
   def after_run(self, run_context, run_values):
     current_loss = run_values.results['current_loss']
@@ -135,7 +143,6 @@ def get_model_fn(params,
                  num_trainers=1,
                  trainer_id=0,
                  report_feature_importances=False,
-                 model_dir=None,
                  local_eval=False):
   """Return a model function given a way to construct a graph builder."""
   def _model_fn(features, labels, mode):
@@ -198,15 +205,6 @@ def get_model_fn(params,
               trainer_id=trainer_id),
           state_ops.assign_add(contrib_framework.get_global_step(), 1))
       loss_deps.append(training_graph)
-      if hasattr(graph_builder, 'finalize_training'):
-        finalize_listener = EveryCheckpointPreSaveListener(
-            graph_builder.finalize_training())
-        scaffold = monitored_session.Scaffold()
-        training_hooks.append(
-            basic_session_run_hooks.CheckpointSaverHook(
-                model_dir, save_secs=600, save_steps=None,
-                scaffold=scaffold,
-                listeners=[finalize_listener]))
 
     training_loss = None
     if (mode == model_fn_lib.ModeKeys.EVAL or
@@ -220,7 +218,8 @@ def get_model_fn(params,
       features[weights_name] = weights
 
     if early_stopping_rounds:
-      training_hooks.append(TensorForestLossHook(early_stopping_rounds))
+      training_hooks.append(TensorForestLossHook(early_stopping_rounds,
+                                                 loss_op=training_loss))
 
     if report_feature_importances:
       training_hooks.append(TensorForestRunOpAtEndHook(
@@ -277,7 +276,7 @@ class TensorForestEstimator(estimator.Estimator):
                early_stopping_rounds=100,
                num_trainers=1, trainer_id=0,
                report_feature_importances=False,
-               local_eval=False):
+               local_eval=False, version=None):
     """Initializes a TensorForestEstimator instance.
 
     Args:
@@ -291,7 +290,8 @@ class TensorForestEstimator(estimator.Estimator):
         directory into an estimator.
       graph_builder_class: An `object` instance that defines how TF graphs for
         random forest training and inference are built. By default will use
-        `tensor_forest.RandomForestGraphs`.
+        `tensor_forest.RandomForestGraphs`. Can be overridden by version
+        kwarg.
       config: `RunConfig` object to configure the runtime settings.
       weights_name: A string defining feature column name representing
         weights. Will be multiplied by the loss of the example. Used to
@@ -313,10 +313,19 @@ class TensorForestEstimator(estimator.Estimator):
       local_eval: If True, don't use a device assigner for eval. This is to
         support some common setups where eval is done on a single machine, even
         though training might be distributed.
+      version: String indicating TensorForest version to use, for backward
+        compatibility. Either 'v2', 'v4', or None to let system pick.
+        Overrides grpah_builder_class.
 
     Returns:
       A `TensorForestEstimator` instance.
     """
+    if version:
+      if version not in VERSION_BUILDERS:
+        logging.error('Unknown version %s. Specify either v2 or v4', version)
+      else:
+        logging.info('Setting graph_builder_class to %s', version)
+        graph_builder_class = VERSION_BUILDERS[version]
     super(TensorForestEstimator, self).__init__(
         model_fn=get_model_fn(
             params.fill(),
@@ -328,9 +337,153 @@ class TensorForestEstimator(estimator.Estimator):
             num_trainers=num_trainers,
             trainer_id=trainer_id,
             report_feature_importances=report_feature_importances,
-            model_dir=model_dir,
             local_eval=local_eval),
         model_dir=model_dir,
         config=config,
         feature_engineering_fn=feature_engineering_fn)
 
+
+def get_combined_model_fn(model_fns):
+  """Get a combined model function given a list of other model fns.
+
+  The model function returned will call the individual model functions and
+  combine them appropriately.  For:
+
+  training ops: tf.group them.
+  loss: average them.
+  predictions: concat probabilities such that predictions[*][0-C1] are the
+    probablities for output 1 (where C1 is the number of classes in output 1),
+    predictions[*][C1-(C1+C2)] are the probabilities for output 2 (where C2
+    is the number of classes in output 2), etc.  Also stack predictions such
+    that predictions[i][j] is the class prediction for example i and output j.
+
+  This assumes that labels are 2-dimensional, with labels[i][j] being the
+  label for example i and output j, where forest j is trained using only
+  output j.
+
+  Args:
+    model_fns: A list of model functions obtained from get_model_fn.
+
+  Returns:
+    A ModelFnOps instance.
+  """
+  def _model_fn(features, labels, mode):
+    """Function that returns predictions, training loss, and training op."""
+    model_fn_ops = []
+    for i in range(len(model_fns)):
+      with variable_scope.variable_scope('label_{0}'.format(i)):
+        sliced_labels = array_ops.slice(labels, [0, i], [-1, 1])
+        model_fn_ops.append(
+            model_fns[i](features, sliced_labels, mode))
+    training_hooks = []
+    for mops in model_fn_ops:
+      training_hooks += mops.training_hooks
+    predictions = {}
+    if (mode == model_fn_lib.ModeKeys.EVAL or
+        mode == model_fn_lib.ModeKeys.INFER):
+      # Flatten the probabilities into one dimension.
+      predictions[eval_metrics.INFERENCE_PROB_NAME] = array_ops.concat(
+          [mops.predictions[eval_metrics.INFERENCE_PROB_NAME]
+           for mops in model_fn_ops], axis=1)
+      predictions[eval_metrics.INFERENCE_PRED_NAME] = array_ops.stack(
+          [mops.predictions[eval_metrics.INFERENCE_PRED_NAME]
+           for mops in model_fn_ops], axis=1)
+    loss = None
+    if (mode == model_fn_lib.ModeKeys.EVAL or
+        mode == model_fn_lib.ModeKeys.TRAIN):
+      loss = math_ops.reduce_sum(
+          array_ops.stack(
+              [mops.loss for mops in model_fn_ops])) / len(model_fn_ops)
+
+    train_op = None
+    if mode == model_fn_lib.ModeKeys.TRAIN:
+      train_op = control_flow_ops.group(
+          *[mops.train_op for mops in model_fn_ops])
+    return model_fn_lib.ModelFnOps(
+        mode=mode,
+        predictions=predictions,
+        loss=loss,
+        train_op=train_op,
+        training_hooks=training_hooks,
+        scaffold=None,
+        output_alternatives=None)
+
+  return _model_fn
+
+
+class MultiForestMultiHeadEstimator(estimator.Estimator):
+  """An estimator that can train a forest for a multi-headed problems.
+
+  This class essentially trains separate forests (each with their own
+  ForestHParams) for each output.
+
+  For multi-headed regression, a single-headed TensorForestEstimator can
+  be used to train a single model that predicts all outputs.  This class can
+  be used to train separate forests for each output.
+  """
+
+  def __init__(self, params_list, device_assigner=None, model_dir=None,
+               graph_builder_class=tensor_forest.RandomForestGraphs,
+               config=None, weights_name=None, keys_name=None,
+               feature_engineering_fn=None,
+               early_stopping_rounds=100,
+               num_trainers=1, trainer_id=0,
+               report_feature_importances=False,
+               local_eval=False):
+    """Initializes a TensorForestEstimator instance.
+
+    Args:
+      params_list: A list of ForestHParams objects for each head, given in order
+        of outputs in the label tensor to be trained on.
+      device_assigner: An `object` instance that controls how trees get
+        assigned to devices. If `None`, will use
+        `tensor_forest.RandomForestDeviceAssigner`.
+      model_dir: Directory to save model parameters, graph, etc. To continue
+        training a previously saved model, load checkpoints saved to this
+        directory into an estimator.
+      graph_builder_class: An `object` instance that defines how TF graphs for
+        random forest training and inference are built. By default will use
+        `tensor_forest.RandomForestGraphs`.
+      config: `RunConfig` object to configure the runtime settings.
+      weights_name: A string defining feature column name representing
+        weights. Will be multiplied by the loss of the example. Used to
+        downweight or boost examples during training.
+      keys_name: A string naming one of the features to strip out and
+        pass through into the inference/eval results dict.  Useful for
+        associating specific examples with their prediction.
+      feature_engineering_fn: Feature engineering function. Takes features and
+        labels which are the output of `input_fn` and returns features and
+        labels which will be fed into the model.
+      early_stopping_rounds: Allows training to terminate early if the forest is
+        no longer growing. 100 by default.  Set to a Falsy value to disable
+        the default training hook.
+      num_trainers: Number of training jobs, which will partition trees
+        among them.
+      trainer_id: Which trainer this instance is.
+      report_feature_importances: If True, print out feature importances
+        during evaluation.
+      local_eval: If True, don't use a device assigner for eval. This is to
+        support some common setups where eval is done on a single machine, even
+        though training might be distributed.
+
+    Returns:
+      A `TensorForestEstimator` instance.
+    """
+    model_fns = [get_model_fn(
+        params.fill(),
+        graph_builder_class,
+        device_assigner,
+        weights_name=weights_name,
+        keys_name=keys_name,
+        early_stopping_rounds=early_stopping_rounds,
+        num_trainers=num_trainers,
+        trainer_id=trainer_id,
+        report_feature_importances=report_feature_importances,
+        local_eval=local_eval) for params in params_list]
+
+    super(MultiForestMultiHeadEstimator, self).__init__(
+        model_fn=get_combined_model_fn(model_fns),
+        model_dir=model_dir,
+        config=config,
+        feature_engineering_fn=feature_engineering_fn)
+
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
new file mode 100644
index 0000000000..ac8ec68d80
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
@@ -0,0 +1,293 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Creates a tree  variable.
+class CreateTreeVariableOp : public OpKernel {
+ public:
+  explicit CreateTreeVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* tree_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
+                errors::InvalidArgument("Tree config must be a scalar."));
+
+    auto* result = new DecisionTreeResource();
+    if (!ParseProtoUnlimited(result->mutable_decision_tree(),
+                             tree_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false,
+                  errors::InvalidArgument("Unable to parse tree  config."));
+    }
+
+    result->MaybeInitialize();
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for serializing a model.
+class TreeSerializeOp : public OpKernel {
+ public:
+  explicit TreeSerializeOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, TensorShape(), &output_config_t));
+    output_config_t->scalar<string>()() =
+        decision_tree_resource->decision_tree().SerializeAsString();
+  }
+};
+
+// Op for deserializing a tree variable from a checkpoint.
+class TreeDeserializeOp : public OpKernel {
+ public:
+  explicit TreeDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    auto handle = HandleFromInput(context, 0);
+    OP_REQUIRES_OK(context,
+                   LookupResource(context, handle, &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    const Tensor* tree_config_t;
+    OP_REQUIRES_OK(context, context->input("tree_config", &tree_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
+                errors::InvalidArgument("Tree config must be a scalar."));
+    // Deallocate all the previous objects on the resource.
+    decision_tree_resource->Reset();
+    decision_trees::Model* config =
+        decision_tree_resource->mutable_decision_tree();
+    OP_REQUIRES(context,
+                ParseProtoUnlimited(config, tree_config_t->scalar<string>()()),
+                errors::InvalidArgument("Unable to parse tree  config."));
+    decision_tree_resource->MaybeInitialize();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for getting tree size.
+class TreeSizeOp : public OpKernel {
+ public:
+  explicit TreeSizeOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+    Tensor* output_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, TensorShape(), &output_t));
+    output_t->scalar<int32>()() =
+        decision_tree_resource->decision_tree().decision_tree().nodes_size();
+  }
+};
+
+// Op for tree inference.
+class TreePredictionsV4Op : public OpKernel {
+ public:
+  explicit TreePredictionsV4Op(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    string serialized_proto;
+    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
+    input_spec_.ParseFromString(serialized_proto);
+
+    data_set_ =
+        std::unique_ptr<TensorDataSet>(new TensorDataSet(input_spec_, 0));
+
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_data = context->input(1);
+    const Tensor& sparse_input_indices = context->input(2);
+    const Tensor& sparse_input_values = context->input(3);
+
+    data_set_->set_input_tensors(input_data, sparse_input_indices,
+                                 sparse_input_values);
+
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    Tensor* output_predictions = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(data_set_->NumItems());
+    output_shape.AddDim(param_proto_.num_outputs());
+    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape,
+                                                     &output_predictions));
+
+    auto out = output_predictions->tensor<float, 2>();
+    for (int i = 0; i < data_set_->NumItems(); ++i) {
+      const int32 leaf_id =
+          decision_tree_resource->TraverseTree(data_set_, i, nullptr);
+      const decision_trees::Leaf& leaf =
+          decision_tree_resource->get_leaf(leaf_id);
+      for (int j = 0; j < param_proto_.num_outputs(); ++j) {
+        const float count = model_op_->GetOutputValue(leaf, j);
+        out(i, j) = count;
+      }
+    }
+  }
+
+ private:
+  tensorforest::TensorForestDataSpec input_spec_;
+  std::unique_ptr<TensorDataSet> data_set_;
+  std::unique_ptr<LeafModelOperator> model_op_;
+  TensorForestParams param_proto_;
+};
+
+// Op for getting feature usage counts.
+class FeatureUsageCountsOp : public OpKernel {
+ public:
+  explicit FeatureUsageCountsOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    const auto& tree = decision_tree_resource->decision_tree();
+
+    Tensor* output_counts = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(param_proto_.num_features());
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, output_shape, &output_counts));
+
+    auto counts = output_counts->unaligned_flat<int32>();
+    counts.setZero();
+
+    for (const auto& node : tree.decision_tree().nodes()) {
+      if (node.has_custom_node_type()) {
+        LOG(WARNING) << "Can't count feature usage for custom nodes.";
+      } else if (node.has_binary_node()) {
+        const auto& bnode = node.binary_node();
+        if (bnode.has_custom_left_child_test()) {
+          decision_trees::MatchingValuesTest test;
+          if (!bnode.custom_left_child_test().UnpackTo(&test)) {
+            LOG(WARNING) << "Unknown custom child test";
+            continue;
+          }
+          int32 feat;
+          safe_strto32(test.feature_id().id().value(), &feat);
+          ++counts(feat);
+        } else {
+          const auto& test = bnode.inequality_left_child_test();
+          if (test.has_feature_id()) {
+            int32 feat;
+            safe_strto32(test.feature_id().id().value(), &feat);
+            ++counts(feat);
+          } else if (test.has_oblique()) {
+            for (const auto& featid : test.oblique().features()) {
+              int32 feat;
+              safe_strto32(featid.id().value(), &feat);
+              ++counts(feat);
+            }
+          }
+        }
+      }
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeResource);
+
+REGISTER_KERNEL_BUILDER(Name("TreeIsInitializedOp").Device(DEVICE_CPU),
+                        IsResourceInitialized<DecisionTreeResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateTreeVariable").Device(DEVICE_CPU),
+                        CreateTreeVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeSerialize").Device(DEVICE_CPU),
+                        TreeSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeDeserialize").Device(DEVICE_CPU),
+                        TreeDeserializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreeSize").Device(DEVICE_CPU), TreeSizeOp);
+
+REGISTER_KERNEL_BUILDER(Name("TreePredictionsV4").Device(DEVICE_CPU),
+                        TreePredictionsV4Op);
+
+REGISTER_KERNEL_BUILDER(Name("FeatureUsageCounts").Device(DEVICE_CPU),
+                        FeatureUsageCountsOp);
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
new file mode 100644
index 0000000000..e3d8aa89d6
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -0,0 +1,562 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include <queue>
+
+#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/gtl/map_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+using gtl::FindOrNull;
+
+// Creates a stats variable.
+class CreateFertileStatsVariableOp : public OpKernel {
+ public:
+  explicit CreateFertileStatsVariableOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor* stats_config_t;
+    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
+                errors::InvalidArgument("Stats config must be a scalar."));
+    auto* result = new FertileStatsResource(param_proto_);
+    FertileStats stats;
+    if (!ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()())) {
+      result->Unref();
+      OP_REQUIRES(context, false,
+                  errors::InvalidArgument("Unable to parse stats config."));
+    }
+
+    result->ExtractFromProto(stats);
+    result->MaybeInitialize();
+
+    // Only create one, if one does not exist already. Report status for all
+    // other exceptions.
+    auto status = CreateResource(context, HandleFromInput(context, 0), result);
+    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
+      OP_REQUIRES(context, false, status);
+    }
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for serializing a model.
+class FertileStatsSerializeOp : public OpKernel {
+ public:
+  explicit FertileStatsSerializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &fertile_stats_resource));
+    mutex_lock l(*fertile_stats_resource->get_mutex());
+    core::ScopedUnref unref_me(fertile_stats_resource);
+    Tensor* output_config_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, TensorShape(), &output_config_t));
+
+    FertileStats stats;
+    fertile_stats_resource->PackToProto(&stats);
+    output_config_t->scalar<string>()() = stats.SerializeAsString();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+// Op for deserializing a stats variable from a checkpoint.
+class FertileStatsDeserializeOp : public OpKernel {
+ public:
+  explicit FertileStatsDeserializeOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &fertile_stats_resource));
+    mutex_lock l(*fertile_stats_resource->get_mutex());
+    core::ScopedUnref unref_me(fertile_stats_resource);
+
+    const Tensor* stats_config_t;
+    OP_REQUIRES_OK(context, context->input("stats_config", &stats_config_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(stats_config_t->shape()),
+                errors::InvalidArgument("Stats config must be a scalar."));
+    // Deallocate all the previous objects on the resource.
+    fertile_stats_resource->Reset();
+    FertileStats stats;
+    OP_REQUIRES(context,
+                ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()()),
+                errors::InvalidArgument("Unable to parse stats config."));
+
+    fertile_stats_resource->ExtractFromProto(stats);
+    fertile_stats_resource->MaybeInitialize();
+  }
+
+ private:
+  TensorForestParams param_proto_;
+};
+
+void TraverseTree(const DecisionTreeResource* tree_resource,
+                  const std::unique_ptr<TensorDataSet>& data, int32 start,
+                  int32 end, std::vector<int32>* leaf_ids,
+                  std::vector<int32>* leaf_depths) {
+  for (int i = start; i < end; ++i) {
+    int32 depth;
+    const int32 leaf_id = tree_resource->TraverseTree(data, i, &depth);
+    (*leaf_ids)[i] = leaf_id;
+    (*leaf_depths)[i] = depth;
+  }
+}
+
+// Try to update a leaf's stats by acquiring its lock.  If it can't be
+// acquired, put it in a waiting queue to come back to later and try the next
+// one.  Once all leaf_ids have been visited, cycle through the waiting ids
+// until they're gone.
+void UpdateStats(FertileStatsResource* fertile_stats_resource,
+                 const std::unique_ptr<TensorDataSet>& data,
+                 const Tensor& input_labels, const Tensor& input_weights,
+                 int num_targets, const std::vector<int32>& leaf_ids,
+                 const std::vector<int32>& leaf_depths,
+                 std::unordered_map<int32, std::unique_ptr<mutex>>* locks,
+                 mutex* set_lock, int32 start, int32 end,
+                 std::unordered_set<int32>* ready_to_split) {
+  const auto labels = input_labels.unaligned_flat<float>();
+  const auto weights = input_weights.unaligned_flat<float>();
+  // Stores leaf_id, leaf_depth, example_id for examples that are waiting
+  // on another to finish.
+  std::queue<std::tuple<int32, int32, int32>> waiting;
+
+  int32 i = start;
+  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
+  while (i < end || !waiting.empty()) {
+    int32 leaf_id;
+    int32 leaf_depth;
+    int32 example_id;
+    bool was_waiting = false;
+    if (i >= end) {
+      std::tie(leaf_id, leaf_depth, example_id) = waiting.front();
+      waiting.pop();
+      was_waiting = true;
+    } else {
+      leaf_id = leaf_ids[i];
+      leaf_depth = leaf_depths[i];
+      example_id = i;
+      ++i;
+    }
+    const std::unique_ptr<mutex>& leaf_lock = (*locks)[leaf_id];
+    if (was_waiting) {
+      leaf_lock->lock();
+    } else {
+      if (!leaf_lock->try_lock()) {
+        waiting.emplace(leaf_id, leaf_depth, example_id);
+        continue;
+      }
+    }
+
+    bool is_finished;
+    fertile_stats_resource->AddExampleToStatsAndInitialize(
+        data, &target, {example_id}, leaf_id, leaf_depth, &is_finished);
+    leaf_lock->unlock();
+    if (is_finished) {
+      set_lock->lock();
+      ready_to_split->insert(leaf_id);
+      set_lock->unlock();
+    }
+  }
+}
+
+// Update leaves from start through end in the leaf_examples iterator.
+void UpdateStatsCollated(
+    FertileStatsResource* fertile_stats_resource,
+    DecisionTreeResource* tree_resource,
+    const std::unique_ptr<TensorDataSet>& data, const Tensor& input_labels,
+    const Tensor& input_weights, int num_targets,
+    const std::unordered_map<int32, std::vector<int>>& leaf_examples,
+    const std::vector<int32>& leaf_depths, mutex* set_lock, int32 start,
+    int32 end, std::unordered_set<int32>* ready_to_split) {
+  const auto labels = input_labels.unaligned_flat<float>();
+  const auto weights = input_weights.unaligned_flat<float>();
+
+  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
+  auto it = leaf_examples.begin();
+  std::advance(it, start);
+  auto end_it = leaf_examples.begin();
+  std::advance(end_it, end);
+  while (it != end_it) {
+    int32 leaf_id = it->first;
+    bool is_finished;
+    fertile_stats_resource->AddExampleToStatsAndInitialize(
+        data, &target, it->second, leaf_id, leaf_depths[it->second[0]],
+        &is_finished);
+    if (is_finished) {
+      set_lock->lock();
+      ready_to_split->insert(leaf_id);
+      set_lock->unlock();
+    }
+    ++it;
+  }
+}
+
+// Op for traversing the tree with each example, accumulating statistics, and
+// outputting node ids that are ready to split.
+class ProcessInputOp : public OpKernel {
+ public:
+  explicit ProcessInputOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_));
+
+    string serialized_proto;
+    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
+    input_spec_.ParseFromString(serialized_proto);
+
+    data_set_ = std::unique_ptr<TensorDataSet>(
+        new TensorDataSet(input_spec_, random_seed_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_data = context->input(2);
+    const Tensor& sparse_input_indices = context->input(3);
+    const Tensor& sparse_input_values = context->input(4);
+    const Tensor& input_labels = context->input(6);
+    const Tensor& input_weights = context->input(7);
+
+    data_set_->set_input_tensors(input_data, sparse_input_indices,
+                                 sparse_input_values);
+
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+    core::ScopedUnref unref_tree(tree_resource);
+
+    const int32 num_data = data_set_->NumItems();
+    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
+    int num_threads = worker_threads->num_threads;
+
+    // First find the leaf ids for each example.
+    std::vector<int32> leaf_ids(num_data);
+
+    // The depth of the leaf for example i.
+    std::vector<int32> leaf_depths(num_data);
+
+    const int64 costPerTraverse = 500;
+    auto traverse = [this, &leaf_ids, &leaf_depths, tree_resource, num_data](
+                        int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      TraverseTree(tree_resource, data_set_, static_cast<int32>(start),
+                   static_cast<int32>(end), &leaf_ids, &leaf_depths);
+    };
+    Shard(num_threads, worker_threads->workers, num_data, costPerTraverse,
+          traverse);
+
+    // Create one mutex per leaf. We need to protect access to leaf pointers,
+    // so instead of grouping examples by leaf, we spread examples out among
+    // threads to provide uniform work for each of them and protect access
+    // with mutexes.
+    std::unordered_map<int, std::unique_ptr<mutex>> locks;
+    std::unordered_map<int32, std::vector<int>> leaf_examples;
+    if (param_proto_.collate_examples()) {
+      for (int i = 0; i < num_data; ++i) {
+        leaf_examples[leaf_ids[i]].push_back(i);
+      }
+    } else {
+      for (const int32 id : leaf_ids) {
+        if (FindOrNull(locks, id) == nullptr) {
+          // TODO(gilberth): Consider using a memory pool for these.
+          locks[id] = std::unique_ptr<mutex>(new mutex);
+        }
+      }
+    }
+
+    const int32 num_leaves = leaf_examples.size();
+    const int32 label_dim =
+        input_labels.shape().dims() <= 1
+            ? 0
+            : static_cast<int>(input_labels.shape().dim_size(1));
+    const int32 num_targets =
+        param_proto_.is_regression() ? (std::max(1, label_dim)) : 1;
+
+    // Ids of leaves that can split.
+    std::unordered_set<int32> ready_to_split;
+    mutex set_lock;
+
+    // TODO(gilberth): This is a rough approximation based on measurements
+    // from a digits run on local desktop.  Heuristics might be necessary
+    // if it really matters that much.
+    const int64 costPerUpdate = 1000;
+    auto update = [this, &input_labels, &input_weights, &leaf_ids, &leaf_depths,
+                   &num_targets, fertile_stats_resource, &locks, &set_lock,
+                   &ready_to_split, num_data](int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      UpdateStats(fertile_stats_resource, data_set_, input_labels,
+                  input_weights, num_targets, leaf_ids, leaf_depths, &locks,
+                  &set_lock, static_cast<int32>(start), static_cast<int32>(end),
+                  &ready_to_split);
+    };
+
+    auto update_collated = [this, &input_labels, &input_weights, &leaf_ids,
+                            &num_targets, &leaf_depths, fertile_stats_resource,
+                            tree_resource, &leaf_examples, &set_lock,
+                            &ready_to_split,
+                            num_leaves](int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_leaves);
+      UpdateStatsCollated(
+          fertile_stats_resource, tree_resource, data_set_, input_labels,
+          input_weights, num_targets, leaf_examples, leaf_depths, &set_lock,
+          static_cast<int32>(start), static_cast<int32>(end), &ready_to_split);
+    };
+
+    if (param_proto_.collate_examples()) {
+      Shard(num_threads, worker_threads->workers, num_leaves, costPerUpdate,
+            update_collated);
+    } else {
+      Shard(num_threads, worker_threads->workers, num_data, costPerUpdate,
+            update);
+    }
+
+    Tensor* output_finished_t = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(ready_to_split.size());
+    OP_REQUIRES_OK(
+        context, context->allocate_output(0, output_shape, &output_finished_t));
+    auto output = output_finished_t->unaligned_flat<int32>();
+    std::copy(ready_to_split.begin(), ready_to_split.end(), output.data());
+  }
+
+ private:
+  int32 random_seed_;
+  tensorforest::TensorForestDataSpec input_spec_;
+  std::unique_ptr<TensorDataSet> data_set_;
+  TensorForestParams param_proto_;
+};
+
+// Op for growing finished nodes.
+class GrowTreeOp : public OpKernel {
+ public:
+  explicit GrowTreeOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+    core::ScopedUnref unref_tree(tree_resource);
+
+    const Tensor& finished_nodes = context->input(2);
+
+    const auto finished = finished_nodes.unaligned_flat<int32>();
+
+    const int32 num_nodes =
+        static_cast<int32>(finished_nodes.shape().dim_size(0));
+
+    // TODO(gilberth): distribute this work over a number of threads.
+    for (int i = 0;
+         i < num_nodes &&
+         tree_resource->decision_tree().decision_tree().nodes_size() <
+             param_proto_.max_nodes();
+         ++i) {
+      const int32 node = finished(i);
+      std::unique_ptr<SplitCandidate> best(new SplitCandidate);
+      int32 parent_depth;
+      bool found =
+          fertile_stats_resource->BestSplit(node, best.get(), &parent_depth);
+      if (found) {
+        std::vector<int32> new_children;
+        tree_resource->SplitNode(node, best.get(), &new_children);
+        fertile_stats_resource->Allocate(parent_depth, new_children);
+        fertile_stats_resource->set_leaf_stat(best->left_stats(),
+                                              new_children[0]);
+        fertile_stats_resource->set_leaf_stat(best->right_stats(),
+                                              new_children[1]);
+        // We are done with best, so it is now safe to clear node.
+        fertile_stats_resource->Clear(node);
+        CHECK(tree_resource->get_mutable_tree_node(node)->has_leaf() == false);
+      } else {  // reset
+        fertile_stats_resource->ResetSplitStats(node, parent_depth);
+      }
+    }
+  }
+
+ private:
+  tensorforest::TensorForestDataSpec input_spec_;
+  TensorForestParams param_proto_;
+};
+
+void FinalizeLeaf(const LeafStat& leaf_stats, bool is_regression,
+                  bool drop_final_class,
+                  const std::unique_ptr<LeafModelOperator>& leaf_op,
+                  decision_trees::Leaf* leaf) {
+  leaf_op->ExportModel(leaf_stats, leaf);
+
+  // TODO(thomaswc): Move the rest of this into ExportModel.
+
+  // regression models are already stored in leaf in normalized form.
+  if (is_regression) {
+    return;
+  }
+
+  float sum = leaf_stats.weight_sum();
+  if (sum <= 0.0) {
+    LOG(WARNING) << "Leaf with sum " << sum << " has stats "
+                 << leaf->ShortDebugString();
+    return;
+  }
+
+  if (leaf->has_vector()) {
+    for (int i = 0; i < leaf->vector().value_size(); i++) {
+      auto* v = leaf->mutable_vector()->mutable_value(i);
+      v->set_float_value(v->float_value() / sum);
+    }
+    if (drop_final_class) {
+      leaf->mutable_vector()->mutable_value()->RemoveLast();
+    }
+    return;
+  }
+
+  if (leaf->has_sparse_vector()) {
+    for (auto& it : *leaf->mutable_sparse_vector()->mutable_sparse_value()) {
+      it.second.set_float_value(it.second.float_value() / sum);
+    }
+    return;
+  }
+
+  LOG(FATAL) << "Unknown leaf type in " << leaf->DebugString();
+}
+
+// Op for finalizing a tree at the end of training.
+class FinalizeTreeOp : public OpKernel {
+ public:
+  explicit FinalizeTreeOp(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    DecisionTreeResource* tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &tree_resource));
+    FertileStatsResource* fertile_stats_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
+                                           &fertile_stats_resource));
+
+    mutex_lock l1(*fertile_stats_resource->get_mutex());
+    mutex_lock l2(*tree_resource->get_mutex());
+
+    core::ScopedUnref unref_me(tree_resource);
+    core::ScopedUnref unref_stats(fertile_stats_resource);
+
+    // TODO(thomaswc): Add threads
+    int num_nodes = tree_resource->decision_tree().decision_tree().nodes_size();
+    for (int i = 0; i < num_nodes; i++) {
+      auto* node = tree_resource->mutable_decision_tree()
+                       ->mutable_decision_tree()
+                       ->mutable_nodes(i);
+      if (node->has_leaf()) {
+        const auto& leaf_stats = fertile_stats_resource->leaf_stat(i);
+        FinalizeLeaf(leaf_stats, param_proto_.is_regression(),
+                     param_proto_.drop_final_class(), model_op_,
+                     node->mutable_leaf());
+      }
+    }
+  }
+
+ private:
+  std::unique_ptr<LeafModelOperator> model_op_;
+  TensorForestParams param_proto_;
+};
+
+REGISTER_RESOURCE_HANDLE_KERNEL(FertileStatsResource);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsIsInitializedOp").Device(DEVICE_CPU),
+                        IsResourceInitialized<FertileStatsResource>);
+
+REGISTER_KERNEL_BUILDER(Name("CreateFertileStatsVariable").Device(DEVICE_CPU),
+                        CreateFertileStatsVariableOp);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsSerialize").Device(DEVICE_CPU),
+                        FertileStatsSerializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("FertileStatsDeserialize").Device(DEVICE_CPU),
+                        FertileStatsDeserializeOp);
+
+REGISTER_KERNEL_BUILDER(Name("ProcessInputV4").Device(DEVICE_CPU),
+                        ProcessInputOp);
+
+REGISTER_KERNEL_BUILDER(Name("GrowTreeV4").Device(DEVICE_CPU), GrowTreeOp);
+
+REGISTER_KERNEL_BUILDER(Name("FinalizeTree").Device(DEVICE_CPU),
+                        FinalizeTreeOp);
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index 0542508a8e..06856c3c07 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -40,9 +40,7 @@ cc_library(
         ":split_collection_operators",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:framework",
         "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:lib",
     ],
 )
 
@@ -78,7 +76,6 @@ cc_library(
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
         "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
     ],
 )
 
@@ -111,7 +108,7 @@ cc_library(
         "//tensorflow/contrib/tensor_forest:tree_utils",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -153,7 +150,7 @@ cc_library(
         ":input_data",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -174,6 +171,25 @@ cc_library(
     name = "split_collection_operators",
     srcs = ["split_collection_operators.cc"],
     hdrs = ["split_collection_operators.h"],
+    deps = [
+        ":grow_stats",
+        ":input_data",
+        ":input_target",
+        ":leaf_model_operators",
+        ":params",
+        ":stat_utils",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
+        "//tensorflow/contrib/tensor_forest:tree_utils",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
+        "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
+    ],
+)
+
+cc_library(
+    name = "graph_collection_operator",
+    srcs = ["graph_collection_operator.cc"],
+    hdrs = ["graph_collection_operator.h"],
     deps = [
         ":candidate_graph_runner",
         ":grow_stats",
@@ -181,6 +197,7 @@ cc_library(
         ":input_target",
         ":leaf_model_operators",
         ":params",
+        ":split_collection_operators",
         ":stat_utils",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
new file mode 100644
index 0000000000..2c925b5dd7
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
@@ -0,0 +1,142 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h"
+
+#include <cfloat>
+
+#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+REGISTER_SPLIT_COLLECTION(GRAPH_RUNNER_COLLECTION,
+                          GraphRunnerSplitCollectionOperator);
+
+std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
+    int32 node_id, int32 depth) const {
+  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
+}
+
+int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
+                                                   int32 split_id) const {
+  return node_id * num_splits_to_consider_ + split_id;
+}
+
+bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
+                                                   SplitCandidate* best,
+                                                   int32* depth) const {
+  float min_score = FLT_MAX;
+  int best_index = -1;
+  auto* slot = stats_.at(node_id).get();
+  *depth = slot->depth();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    // TODO(gilberth): Support uselessness.
+    auto& runner = runners_[UniqueId(node_id, i)];
+    const float split_score = runner->SplitScore();
+    if (split_score < min_score) {
+      min_score = split_score;
+      best_index = i;
+    }
+  }
+
+  // This could happen if all the splits are useless.
+  if (best_index < 0) {
+    return false;
+  }
+
+  // Fill in split info and left/right stats to initialize models with.
+  *best = SplitCandidate();
+  auto& runner = runners_[UniqueId(node_id, best_index)];
+  runner->GetLeftStats(best->mutable_left_stats());
+  runner->GetRightStats(best->mutable_right_stats());
+  runner->GetSplit(best->mutable_split());
+  return true;
+}
+
+void GraphRunnerSplitCollectionOperator::AddExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    const std::vector<int>& examples, int32 node_id) const {
+  // Build input Tensors.
+  int size = examples.size();
+  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
+  auto ex_data = examples_t.flat<int32>();
+  std::copy(examples.begin(), examples.end(), ex_data.data());
+
+  const TensorInputTarget* tensor_target =
+      dynamic_cast<const TensorInputTarget*>(target);
+  CHECK_NOTNULL(tensor_target);
+
+  const Tensor& data_t = input_data->original_tensor();
+  const Tensor& target_t = tensor_target->original_tensor();
+
+  // Add to candidates.
+  auto* slot = stats_.at(node_id).get();
+  for (int i = 0; i < slot->num_splits(); ++i) {
+    auto& runner = runners_[UniqueId(node_id, i)];
+    runner->AddExample(data_t, target_t, examples_t);
+  }
+
+  // Update simple weight sums so we know when we're done.
+  for (int example : examples) {
+    slot->AddExample(input_data, target, example);
+  }
+}
+
+void GraphRunnerSplitCollectionOperator::
+    CreateAndInitializeCandidateWithExample(
+        const std::unique_ptr<TensorDataSet>& input_data, int example,
+        int32 node_id) const {
+  auto* slot = stats_.at(node_id).get();
+  int cand_num = slot->num_splits();
+  const int64 unique_id = UniqueId(node_id, cand_num);
+
+  decision_trees::BinaryNode split;
+
+  decision_trees::InequalityTest* test =
+      split.mutable_inequality_left_child_test();
+  auto* oblique = test->mutable_oblique();
+  for (int i = 0; i < features_per_node_; ++i) {
+    float bias;
+    int type;
+    // This is really just a way to select a list of random features.
+    // Also a way to warn the user that categoricals don't make sense here.
+    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
+
+    if (type == kDataFloat) {
+      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
+
+      // The comparison bias is assumed to be zero.
+      test->mutable_threshold()->set_float_value(0);
+    } else {
+      LOG(ERROR) << "Categorical features not supported with this system.";
+      return;
+    }
+  }
+
+  slot->AddSplit(split);
+
+  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
+  runners_[unique_id]->Init();
+}
+
+void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
+  SplitCollectionOperator::ClearSlot(node_id);
+  for (int i = 0; i < num_splits_to_consider_; ++i) {
+    runners_.erase(UniqueId(node_id, i));
+  }
+}
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
new file mode 100644
index 0000000000..9b18e3e969
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
@@ -0,0 +1,81 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
+
+#include <vector>
+#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
+#include "tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h"
+#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
+#include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
+
+namespace tensorflow {
+namespace tensorforest {
+
+// Holds split candidates that are trained by running any TF graph.
+class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
+ public:
+  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
+      : SplitCollectionOperator(params) {
+    if (params.num_splits_to_consider().ParamType_case() ==
+        DepthDependentParam::PARAMTYPE_NOT_SET) {
+      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
+                 << " num_splits_to_consider";
+    } else {
+      num_splits_to_consider_ =
+          params.num_splits_to_consider().constant_value();
+    }
+  }
+
+  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
+                                             int32 depth) const override;
+
+  // Updates the slot's candidates with the new example.
+  // Assumes slot has been initialized.
+  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
+                  const InputTarget* target, const std::vector<int>& examples,
+                  int32 node_id) const override;
+
+  // Create a new candidate and initialize it with the given example.
+  void CreateAndInitializeCandidateWithExample(
+      const std::unique_ptr<TensorDataSet>& input_data, int example,
+      int32 node_id) const override;
+
+  bool BestSplit(int32 node_id, SplitCandidate* best,
+                 int32* depth) const override;
+
+  void ClearSlot(int32 node_id) override;
+
+ protected:
+  int64 UniqueId(int32 node_id, int32 split_id) const;
+
+  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
+      runners_;
+  int features_per_node_;
+  string graph_dir_;
+  // Must have a constant value because of how we make unique ids right now.
+  int32 num_splits_to_consider_;
+};
+
+}  // namespace tensorforest
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_GRAPH_COLLECTION_OPERATOR_H_
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
index fe7b24d36c..226376c571 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
@@ -30,7 +30,8 @@ static const int32 LEFT_INDEX = 0;
 static const int32 RIGHT_INDEX = 1;
 
 GrowStats::GrowStats(const TensorForestParams& params, int32 depth)
-    : depth_(depth),
+    : weight_sum_(0),
+      depth_(depth),
       params_(params),
       split_after_samples_(ResolveParam(params.split_after_samples(), depth)),
       num_splits_to_consider_(
@@ -58,6 +59,8 @@ ClassificationStats::ClassificationStats(const TensorForestParams& params,
   // Early splitting params.
   if (params.finish_type().type() == SPLIT_FINISH_BASIC) {
     min_split_samples_ = split_after_samples_;
+    finish_sample_epoch_ = 1;
+    finish_check_every_ = split_after_samples_ * 2;
   } else {
     if (!params.has_dominate_fraction() || !params.has_min_split_samples()) {
       LOG(FATAL) << "dominate_fraction and min_split_samples "
@@ -98,6 +101,9 @@ ClassificationStats::ClassificationStats(const TensorForestParams& params,
       default:
         LOG(WARNING) << "Unknown pruning type";
     }
+  } else {
+    prune_check_every_ = split_after_samples_ * 2;
+    prune_sample_epoch_ = 1;
   }
 
   if (params.use_running_stats_method()) {
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
index ddf4be8799..c207c0859d 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
@@ -23,22 +23,20 @@
 namespace tensorflow {
 namespace tensorforest {
 
+std::unordered_map<int, CollectionCreator*>
+    SplitCollectionOperatorFactory::factories_;  // NOLINT
+REGISTER_SPLIT_COLLECTION(COLLECTION_BASIC, SplitCollectionOperator);
+
 std::unique_ptr<SplitCollectionOperator>
 SplitCollectionOperatorFactory::CreateSplitCollectionOperator(
     const TensorForestParams& params) {
-  switch (params.collection_type()) {
-    case COLLECTION_BASIC:
-      return std::unique_ptr<SplitCollectionOperator>(
-          new SplitCollectionOperator(params));
-
-    case GRAPH_RUNNER_COLLECTION:
-      return std::unique_ptr<SplitCollectionOperator>(
-          new GraphRunnerSplitCollectionOperator(params));
-
-    default:
-      LOG(ERROR) << "Unknown split collection operator: "
-                 << params.collection_type();
-      return nullptr;
+  auto it = factories_.find(params.collection_type());
+  if (it == factories_.end()) {
+    LOG(ERROR) << "Unknown split collection operator: "
+               << params.collection_type();
+    return nullptr;
+  } else {
+    return it->second->Create(params);
   }
 }
 
@@ -137,121 +135,5 @@ bool SplitCollectionOperator::BestSplit(int32 node_id,
   return slot->BestSplit(best);
 }
 
-// -------------------------------- GraphRunner ------------------ //
-
-std::unique_ptr<GrowStats> GraphRunnerSplitCollectionOperator::CreateGrowStats(
-    int32 node_id, int32 depth) const {
-  return std::unique_ptr<GrowStats>(new SimpleStats(params_, depth));
-}
-
-int64 GraphRunnerSplitCollectionOperator::UniqueId(int32 node_id,
-                                                   int32 split_id) const {
-  return node_id * num_splits_to_consider_ + split_id;
-}
-
-bool GraphRunnerSplitCollectionOperator::BestSplit(int32 node_id,
-                                                   SplitCandidate* best,
-                                                   int32* depth) const {
-  float min_score = FLT_MAX;
-  int best_index = -1;
-  auto* slot = stats_.at(node_id).get();
-  *depth = slot->depth();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    // TODO(gilberth): Support uselessness.
-    auto& runner = runners_[UniqueId(node_id, i)];
-    const float split_score = runner->SplitScore();
-    if (split_score < min_score) {
-      min_score = split_score;
-      best_index = i;
-    }
-  }
-
-  // This could happen if all the splits are useless.
-  if (best_index < 0) {
-    return false;
-  }
-
-  // Fill in split info and left/right stats to initialize models with.
-  *best = SplitCandidate();
-  auto& runner = runners_[UniqueId(node_id, best_index)];
-  runner->GetLeftStats(best->mutable_left_stats());
-  runner->GetRightStats(best->mutable_right_stats());
-  runner->GetSplit(best->mutable_split());
-  return true;
-}
-
-void GraphRunnerSplitCollectionOperator::AddExample(
-    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
-    const std::vector<int>& examples, int32 node_id) const {
-  // Build input Tensors.
-  int size = examples.size();
-  Tensor examples_t(tensorflow::DT_INT32, TensorShape({size}));
-  auto ex_data = examples_t.flat<int32>();
-  std::copy(examples.begin(), examples.end(), ex_data.data());
-
-  const TensorInputTarget* tensor_target =
-      dynamic_cast<const TensorInputTarget*>(target);
-  CHECK_NOTNULL(tensor_target);
-
-  const Tensor& data_t = input_data->original_tensor();
-  const Tensor& target_t = tensor_target->original_tensor();
-
-  // Add to candidates.
-  auto* slot = stats_.at(node_id).get();
-  for (int i = 0; i < slot->num_splits(); ++i) {
-    auto& runner = runners_[UniqueId(node_id, i)];
-    runner->AddExample(data_t, target_t, examples_t);
-  }
-
-  // Update simple weight sums so we know when we're done.
-  for (int example : examples) {
-    slot->AddExample(input_data, target, example);
-  }
-}
-
-void GraphRunnerSplitCollectionOperator::
-    CreateAndInitializeCandidateWithExample(
-        const std::unique_ptr<TensorDataSet>& input_data, int example,
-        int32 node_id) const {
-  auto* slot = stats_.at(node_id).get();
-  int cand_num = slot->num_splits();
-  const int64 unique_id = UniqueId(node_id, cand_num);
-
-  decision_trees::BinaryNode split;
-
-  decision_trees::InequalityTest* test =
-      split.mutable_inequality_left_child_test();
-  auto* oblique = test->mutable_oblique();
-  for (int i = 0; i < features_per_node_; ++i) {
-    float bias;
-    int type;
-    // This is really just a way to select a list of random features.
-    // Also a way to warn the user that categoricals don't make sense here.
-    input_data->RandomSample(example, oblique->add_features(), &bias, &type);
-
-    if (type == kDataFloat) {
-      test->set_type(decision_trees::InequalityTest::LESS_OR_EQUAL);
-
-      // The comparison bias is assumed to be zero.
-      test->mutable_threshold()->set_float_value(0);
-    } else {
-      LOG(ERROR) << "Categorical features not supported with this system.";
-      return;
-    }
-  }
-
-  slot->AddSplit(split);
-
-  runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
-  runners_[unique_id]->Init();
-}
-
-void GraphRunnerSplitCollectionOperator::ClearSlot(int32 node_id) {
-  SplitCollectionOperator::ClearSlot(node_id);
-  for (int i = 0; i < num_splits_to_consider_; ++i) {
-    runners_.erase(UniqueId(node_id, i));
-  }
-}
-
 }  // namespace tensorforest
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
index d0ea33612a..81d820a6b2 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
@@ -17,7 +17,6 @@
 
 #include <vector>
 #include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
-#include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_target.h"
@@ -86,60 +85,38 @@ class SplitCollectionOperator {
   std::unordered_map<int32, std::unique_ptr<GrowStats>> stats_;
 };
 
-
-class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
+class CollectionCreator {
  public:
-  explicit GraphRunnerSplitCollectionOperator(const TensorForestParams& params)
-      : SplitCollectionOperator(params) {
-    if (params.num_splits_to_consider().ParamType_case() ==
-        DepthDependentParam::PARAMTYPE_NOT_SET) {
-      LOG(FATAL) << "GRAPH_RUNNER_COLLECTION must specify a constant value for "
-                 << " num_splits_to_consider";
-    } else {
-      num_splits_to_consider_ =
-          params.num_splits_to_consider().constant_value();
-    }
-  }
-
-  std::unique_ptr<GrowStats> CreateGrowStats(int32 node_id,
-                                             int32 depth) const override;
-
-  // Updates the slot's candidates with the new example.
-  // Assumes slot has been initialized.
-  void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
-                  const InputTarget* target, const std::vector<int>& examples,
-                  int32 node_id) const override;
-
-  // Create a new candidate and initialize it with the given example.
-  void CreateAndInitializeCandidateWithExample(
-      const std::unique_ptr<TensorDataSet>& input_data, int example,
-      int32 node_id) const override;
-
-  bool BestSplit(int32 node_id, SplitCandidate* best,
-                 int32* depth) const override;
-
-  void ClearSlot(int32 node_id) override;
-
- protected:
-  int64 UniqueId(int32 node_id, int32 split_id) const;
-
-  mutable std::unordered_map<int64, std::unique_ptr<CandidateGraphRunner>>
-      runners_;
-  int features_per_node_;
-  string graph_dir_;
-  // Must have a constant value because of how we make unique ids right now.
-  int32 num_splits_to_consider_;
+  virtual std::unique_ptr<SplitCollectionOperator> Create(
+      const TensorForestParams& params) = 0;
+  virtual ~CollectionCreator() {}
 };
 
-// Creates a type of SplitCollectionOperator depending on the type passed,
-// which is SplitCollectionType in fertile_stats.proto.
-// Can create a SplitCollectionOperator itself, known as "basic".
 class SplitCollectionOperatorFactory {
  public:
   static std::unique_ptr<SplitCollectionOperator> CreateSplitCollectionOperator(
       const TensorForestParams& params);
+
+  static std::unordered_map<int, CollectionCreator*> factories_;
+};
+
+template <typename T>
+class AnyCollectionCreator : public CollectionCreator {
+ public:
+  AnyCollectionCreator(SplitCollectionType type) {
+    SplitCollectionOperatorFactory::factories_[type] = this;
+  }
+  virtual std::unique_ptr<SplitCollectionOperator> Create(
+      const TensorForestParams& params) {
+    return std::unique_ptr<SplitCollectionOperator>(new T(params));
+  }
 };
 
+#define REGISTER_SPLIT_COLLECTION(name, cls) \
+  namespace {                                \
+  AnyCollectionCreator<cls> creator(name);   \
+  }
+
 }  // namespace tensorforest
 }  // namespace tensorflow
 
diff --git a/tensorflow/contrib/tensor_forest/ops/model_ops.cc b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
new file mode 100644
index 0000000000..c9acc4a6ae
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
@@ -0,0 +1,134 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+using shape_inference::DimensionHandle;
+using shape_inference::InferenceContext;
+
+namespace tensorforest {
+
+REGISTER_RESOURCE_HANDLE_OP(DecisionTreeResource);
+
+REGISTER_OP("TreeIsInitializedOp")
+    .Input("tree_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a tree has been initialized.
+)doc");
+
+REGISTER_OP("CreateTreeVariable")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Creates a tree  model and returns a handle to it.
+
+params: A serialized TensorForestParams proto.
+tree_handle: handle to the tree resource to be created.
+tree_config: Serialized proto of the tree.
+)doc");
+
+REGISTER_OP("TreeSerialize")
+    .Input("tree_handle: resource")
+    .Output("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Serializes the tree  to a proto.
+
+tree_handle: The handle to the tree.
+tree_config: Serialized proto of the tree.
+)doc");
+
+REGISTER_OP("TreeDeserialize")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("tree_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Deserializes a serialized tree config and replaces current tree.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree .
+tree_config: Serialized proto of the .
+)doc");
+
+REGISTER_OP("TreeSize")
+    .Input("tree_handle: resource")
+    .Output("tree_size: int32")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Outputs the size of the tree, including leaves.
+
+tree_handle: The handle to the tree.
+tree_size: Size scalar.
+)doc");
+
+REGISTER_OP("TreePredictionsV4")
+    .Attr("input_spec: string")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("input_data: float")
+    .Input("sparse_input_indices: int64")
+    .Input("sparse_input_values: float")
+    .Input("sparse_input_shape: int64")
+    .Output("predictions: float")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle num_points = c->UnknownDim();
+
+      if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0) {
+        num_points = c->Dim(c->input(1), 0);
+      }
+
+      c->set_output(0, c->Matrix(num_points, c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs the predictions for the given input data.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
+   gives the j-th feature of the i-th input.
+sparse_input_indices: The indices tensor from the SparseTensor input.
+sparse_input_values: The values tensor from the SparseTensor input.
+sparse_input_shape: The shape tensor from the SparseTensor input.
+predictions: `predictions[i][j]` is the probability that input i is class j.
+)doc");
+
+REGISTER_OP("FeatureUsageCounts")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Output("feature_counts: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs the number of times each feature was used in a split.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+feature_counts: `feature_counts[i]` is the number of times feature i was used
+    in a split.
+)doc");
+
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/ops/stats_ops.cc b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
new file mode 100644
index 0000000000..9652749768
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
@@ -0,0 +1,143 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+using shape_inference::InferenceContext;
+
+namespace tensorforest {
+
+REGISTER_RESOURCE_HANDLE_OP(FertileStatsResource);
+
+REGISTER_OP("FertileStatsIsInitializedOp")
+    .Input("stats_handle: resource")
+    .Output("is_initialized: bool")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Checks whether a stats has been initialized.
+)doc");
+
+REGISTER_OP("CreateFertileStatsVariable")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Input("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Creates a stats model and returns a handle to it.
+
+params: A serialized TensorForestParams proto.
+stats_handle: handle to the stats resource to be created.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("FertileStatsSerialize")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Output("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::ScalarShape)
+    .Doc(R"doc(
+Serializes the stats to a proto.
+
+params: A serialized TensorForestParams proto.
+stats_handle: The handle to the stats.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("FertileStatsDeserialize")
+    .Attr("params: string")
+    .Input("stats_handle: resource")
+    .Input("stats_config: string")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Deserializes a serialized stats config and replaces current stats.
+
+params: A serialized TensorForestParams proto.
+stats_handle: The handle to the stats.
+stats_config: Serialized proto of the stats.
+)doc");
+
+REGISTER_OP("GrowTreeV4")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .Input("finshed_nodes: int32")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Grows the tree for finished nodes and allocates waiting nodes.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+finshed_nodes: A 1-d Tensor of finished node ids from ProcessInput.
+)doc");
+
+REGISTER_OP("ProcessInputV4")
+    .Attr("random_seed: int")
+    .Attr("input_spec: string")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .Input("input_data: float")
+    .Input("sparse_input_indices: int64")
+    .Input("sparse_input_values: float")
+    .Input("sparse_input_shape: int64")
+    .Input("input_labels: float")
+    .Input("input_weights: float")
+    .Output("finished_nodes: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->Vector(c->UnknownDim()));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Add labels to stats after traversing the tree for each example.
+
+Outputs node ids that are finished.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
+   gives the j-th feature of the i-th input.
+sparse_input_indices: The indices tensor from the SparseTensor input.
+sparse_input_values: The values tensor from the SparseTensor input.
+sparse_input_shape: The shape tensor from the SparseTensor input.
+input_labels: The training batch's labels as a 1 or 2-d tensor.
+  'input_labels[i][j]' gives the j-th label/target for the i-th input.
+input_weights: The training batch's eample weights as a 1-d tensor.
+  'input_weights[i]' gives the weight for the i-th input.
+finished_nodes: A 1-d tensor of node ids that have finished and are ready to
+  grow.
+)doc");
+
+REGISTER_OP("FinalizeTree")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("stats_handle: resource")
+    .SetShapeFn([](InferenceContext* c) { return Status::OK(); })
+    .Doc(R"doc(
+Puts the Leaf models inside the tree into their final form.
+
+If drop_final_class is true, the per-class probability prediction of the
+last class is not stored in the leaf models.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+stats_handle: The handle to the stats.
+)doc");
+}  // namespace tensorforest
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/python/__init__.py b/tensorflow/contrib/tensor_forest/python/__init__.py
index 0d41d3500d..df0ecbac76 100644
--- a/tensorflow/contrib/tensor_forest/python/__init__.py
+++ b/tensorflow/contrib/tensor_forest/python/__init__.py
@@ -20,5 +20,8 @@ from __future__ import print_function
 
 from tensorflow.contrib.tensor_forest.python import constants
 from tensorflow.contrib.tensor_forest.python import tensor_forest
+from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
+from tensorflow.contrib.tensor_forest.python.ops import model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
 from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
diff --git a/tensorflow/contrib/tensor_forest/python/ops/model_ops.py b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
new file mode 100644
index 0000000000..4c7218305b
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
@@ -0,0 +1,124 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Model ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tensor_forest.python.ops import gen_model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
+
+# pylint: disable=unused-import
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import feature_usage_counts
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_predictions_v4
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_size
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+_model_ops = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_model_ops.so"))
+
+
+ops.NotDifferentiable("TreeVariable")
+ops.NotDifferentiable("TreeSerialize")
+ops.NotDifferentiable("TreeDeserialize")
+ops.NotDifferentiable("TreeSize")
+ops.NotDifferentiable("TreePredictionsV4")
+ops.NotDifferentiable("FeatureUsageCounts")
+
+
+class TreeVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for TreeVariable."""
+
+  def __init__(self, params, tree_handle, stats_handle, create_op, name):
+    """Creates a TreeVariableSavable object.
+
+    Args:
+      params: A TensorForestParams object.
+      tree_handle: handle to the tree variable.
+      stats_handle: handle to the stats variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree variable under.
+    """
+    self.params = params
+    deps = []
+    if stats_handle is not None:
+      deps.append(stats_ops.finalize_tree(
+          tree_handle, stats_handle,
+          params=params.serialized_params_proto))
+    with ops.control_dependencies(deps):
+      tensor = gen_model_ops.tree_serialize(tree_handle)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree variable. So we just pass an empty value.
+    slice_spec = ""
+    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
+    super(TreeVariableSavable,
+          self).__init__(tree_handle, specs, name)
+    self._tree_handle = tree_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return gen_model_ops.tree_deserialize(
+          self._tree_handle,
+          restored_tensors[0],
+          params=self.params.serialized_params_proto)
+
+
+def tree_variable(params, tree_config, stats_handle, name, container=None):
+  r"""Creates a tree model and returns a handle to it.
+
+  Args:
+    params: A TensorForestParams object.
+    tree_config: A `Tensor` of type `string`. Serialized proto of the tree.
+    stats_handle: Resource handle to the stats object.
+    name: A name for the variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the tree.
+  """
+  with ops.name_scope(name, "TreeVariable") as name:
+    resource_handle = gen_model_ops.decision_tree_resource_handle_op(
+        container, name, name=name)
+
+    create_op = gen_model_ops.create_tree_variable(
+        resource_handle,
+        tree_config,
+        params=params.serialized_params_proto)
+    is_initialized_op = gen_model_ops.tree_is_initialized_op(resource_handle)
+    # Adds the variable to the savable list.
+    saveable = TreeVariableSavable(params, resource_handle, stats_handle,
+                                   create_op,
+                                   "tree_checkpoint_{0}".format(name))
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
diff --git a/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py b/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
new file mode 100644
index 0000000000..be9f2e12b7
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/ops/stats_ops.py
@@ -0,0 +1,114 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Stats ops python wrappers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.tensor_forest.python.ops import gen_stats_ops
+# pylint: disable=unused-import
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import finalize_tree
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import grow_tree_v4
+from tensorflow.contrib.tensor_forest.python.ops.gen_stats_ops import process_input_v4
+# pylint: enable=unused-import
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.training import saver
+
+
+_stats_ops = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_stats_ops.so"))
+
+
+ops.NotDifferentiable("FertileStatsVariable")
+ops.NotDifferentiable("FertileStatsSerialize")
+ops.NotDifferentiable("FertileStatsDeserialize")
+ops.NotDifferentiable("GrowTreeV4")
+ops.NotDifferentiable("ProcessInputV4")
+ops.NotDifferentiable("FinalizeTree")
+
+
+class FertileStatsVariableSavable(saver.BaseSaverBuilder.SaveableObject):
+  """SaveableObject implementation for FertileStatsVariable."""
+
+  def __init__(self, params, stats_handle, create_op, name):
+    """Creates a FertileStatsVariableSavable object.
+
+    Args:
+      params: A TensorForestParams object.
+      stats_handle: handle to the tree variable.
+      create_op: the op to initialize the variable.
+      name: the name to save the tree variable under.
+    """
+    self.params = params
+    tensor = gen_stats_ops.fertile_stats_serialize(
+        stats_handle, params=params.serialized_params_proto)
+    # slice_spec is useful for saving a slice from a variable.
+    # It's not meaningful the tree variable. So we just pass an empty value.
+    slice_spec = ""
+    specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
+    super(FertileStatsVariableSavable,
+          self).__init__(stats_handle, specs, name)
+    self._stats_handle = stats_handle
+    self._create_op = create_op
+
+  def restore(self, restored_tensors, unused_restored_shapes):
+    """Restores the associated tree from 'restored_tensors'.
+
+    Args:
+      restored_tensors: the tensors that were loaded from a checkpoint.
+      unused_restored_shapes: the shapes this object should conform to after
+        restore. Not meaningful for trees.
+
+    Returns:
+      The operation that restores the state of the tree variable.
+    """
+    with ops.control_dependencies([self._create_op]):
+      return gen_stats_ops.fertile_stats_deserialize(
+          self._stats_handle, restored_tensors[0],
+          params=self.params.serialized_params_proto)
+
+
+def fertile_stats_variable(params, stats_config, name,
+                           container=None):
+  r"""Creates a stats object and returns a handle to it.
+
+  Args:
+    params: A TensorForestParams object.
+    stats_config: A `Tensor` of type `string`. Serialized proto of the stats.
+    name: A name for the variable.
+    container: An optional `string`. Defaults to `""`.
+
+  Returns:
+    A `Tensor` of type mutable `string`. The handle to the stats.
+  """
+  with ops.name_scope(name, "FertileStatsVariable") as name:
+    resource_handle = gen_stats_ops.fertile_stats_resource_handle_op(
+        container, name, name=name)
+
+    create_op = gen_stats_ops.create_fertile_stats_variable(
+        resource_handle, stats_config,
+        params=params.serialized_params_proto)
+    is_initialized_op = gen_stats_ops.fertile_stats_is_initialized_op(
+        resource_handle)
+    # Adds the variable to the savable list.
+    saveable = FertileStatsVariableSavable(params, resource_handle, create_op,
+                                           "stats_checkpoint_{0}".format(name))
+    ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
+    resources.register_resource(resource_handle, create_op, is_initialized_op)
+    return resource_handle
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
new file mode 100644
index 0000000000..5773b5d729
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
@@ -0,0 +1,302 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Extremely random forest graph builder using TF resources handles."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+
+from google.protobuf import text_format
+
+from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto
+from tensorflow.contrib.tensor_forest.proto import tensor_forest_params_pb2 as _params_proto
+from tensorflow.contrib.tensor_forest.python import tensor_forest
+from tensorflow.contrib.tensor_forest.python.ops import model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.platform import tf_logging as logging
+
+
+# Stores tuples of (leaf model type, stats model type)
+CLASSIFICATION_LEAF_MODEL_TYPES = {
+    'all_dense': (_params_proto.MODEL_DENSE_CLASSIFICATION,
+                  _params_proto.STATS_DENSE_GINI),
+    'all_sparse': (_params_proto.MODEL_SPARSE_CLASSIFICATION,
+                   _params_proto.STATS_SPARSE_GINI),
+    'sparse_then_dense':
+        (_params_proto.MODEL_SPARSE_OR_DENSE_CLASSIFICATION,
+         _params_proto.STATS_SPARSE_THEN_DENSE_GINI),
+}
+REGRESSION_MODEL_TYPE = (
+    _params_proto.MODEL_REGRESSION,
+    _params_proto.STATS_LEAST_SQUARES_REGRESSION,
+    _params_proto.COLLECTION_BASIC)
+
+COLLECTION_TYPES = {
+    'basic': _params_proto.COLLECTION_BASIC,
+    'graph_runner': _params_proto.GRAPH_RUNNER_COLLECTION
+}
+
+FINISH_TYPES = {
+    'basic': _params_proto.SPLIT_FINISH_BASIC,
+    'hoeffding': _params_proto.SPLIT_FINISH_DOMINATE_HOEFFDING,
+    'bootstrap': _params_proto.SPLIT_FINISH_DOMINATE_BOOTSTRAP
+}
+PRUNING_TYPES = {
+    'none': _params_proto.SPLIT_PRUNE_NONE,
+    'half': _params_proto.SPLIT_PRUNE_HALF,
+    'quarter': _params_proto.SPLIT_PRUNE_QUARTER,
+    '10_percent': _params_proto.SPLIT_PRUNE_10_PERCENT,
+    'hoeffding': _params_proto.SPLIT_PRUNE_HOEFFDING,
+}
+SPLIT_TYPES = {
+    'less_or_equal': _tree_proto.InequalityTest.LESS_OR_EQUAL,
+    'less': _tree_proto.InequalityTest.LESS_THAN
+}
+
+
+def build_params_proto(params):
+  """Build a TensorForestParams proto out of the V4ForestHParams object."""
+  proto = _params_proto.TensorForestParams()
+  proto.num_trees = params.num_trees
+  proto.max_nodes = params.max_nodes
+  proto.is_regression = params.regression
+  proto.num_outputs = params.num_classes
+  proto.num_features = params.num_features
+
+  proto.leaf_type = params.v4_leaf_model_type
+  proto.stats_type = params.v4_stats_model_type
+  proto.collection_type = params.v4_split_collection_type
+  proto.pruning_type.type = params.v4_pruning_type
+  proto.finish_type.type = params.v4_finish_type
+
+  proto.inequality_test_type = params.v4_split_type
+
+  proto.drop_final_class = False
+  proto.collate_examples = params.v4_collate_examples
+  proto.checkpoint_stats = params.v4_checkpoint_stats
+  proto.use_running_stats_method = params.v4_use_running_stats_method
+
+  if params.v4_prune_every_samples:
+    text_format.Merge(params.v4_prune_every_samples,
+                      proto.pruning_type.prune_every_samples)
+  else:
+    # Pruning half-way through split_after_samples seems like a decent default,
+    # making it easy to select the number being pruned with v4_pruning_type
+    # while not paying the cost of pruning too often.  Note that this only holds
+    # if not using a depth-dependent split_after_samples.
+    if params.v4_split_after_samples:
+      logging.error(
+          'If using depth-dependent split_after_samples and also pruning, '
+          'need to set v4_prune_every_samples')
+    proto.pruning_type.prune_every_samples.constant_value = (
+        params.split_after_samples / 2)
+
+  if params.v4_finish_check_every_samples:
+    text_format.Merge(params.v4_finish_check_every_samples,
+                      proto.finish_type.check_every_steps)
+  else:
+    # Checking for finish every quarter through split_after_samples seems
+    # like a decent default. We don't want to incur the checking cost too often,
+    # but (at least for hoeffding) it's lower than the cost of pruning so
+    # we can do it a little more frequently.
+    proto.finish_type.check_every_steps.constant_value = int(
+        params.split_after_samples / 4)
+
+  if params.v4_split_after_samples:
+    text_format.Merge(params.v4_split_after_samples, proto.split_after_samples)
+  else:
+    proto.split_after_samples.constant_value = params.split_after_samples
+
+  if params.v4_num_splits_to_consider:
+    text_format.Merge(params.v4_num_splits_to_consider,
+                      proto.num_splits_to_consider)
+  else:
+    proto.num_splits_to_consider.constant_value = params.num_splits_to_consider
+
+  proto.dominate_fraction.constant_value = params.dominate_fraction
+  proto.min_split_samples.constant_value = params.split_after_samples
+
+  if params.v4_param_file:
+    with open(params.v4_param_file) as f:
+      text_format.Merge(f.read(), proto)
+
+  return proto
+
+
+class V4ForestHParams(object):
+
+  def __init__(self, hparams):
+    for k, v in six.iteritems(hparams.__dict__):
+      setattr(self, k, v)
+
+    # How to store leaf models.
+    model_name = getattr(self, 'v4_model_name', 'all_dense')
+    self.v4_leaf_model_type = (
+        REGRESSION_MODEL_TYPE[0] if self.regression else
+        CLASSIFICATION_LEAF_MODEL_TYPES[model_name][0])
+
+    # How to store stats objects.
+    self.v4_stats_model_type = (
+        REGRESSION_MODEL_TYPE[1] if self.regression else
+        CLASSIFICATION_LEAF_MODEL_TYPES[model_name][1])
+
+    split_collection_name = getattr(self, 'v4_split_collection_name',
+                                    'basic')
+    self.v4_split_collection_type = (
+        REGRESSION_MODEL_TYPE[2] if self.regression else
+        COLLECTION_TYPES[split_collection_name])
+
+    finish_name = getattr(self, 'v4_finish_name', 'basic')
+    self.v4_finish_type = (
+        _params_proto.SPLIT_FINISH_BASIC if self.regression else
+        FINISH_TYPES[finish_name])
+
+    pruning_name = getattr(self, 'v4_pruning_name', 'none')
+    self.v4_pruning_type = PRUNING_TYPES[pruning_name]
+
+    self.v4_collate_examples = getattr(self, 'v4_collate_examples', False)
+
+    self.v4_checkpoint_stats = getattr(self, 'v4_checkpoint_stats', False)
+    self.v4_use_running_stats_method = getattr(
+        self, 'v4_use_running_stats_method', False)
+
+    self.v4_param_file = getattr(self, 'v4_param_file', None)
+
+    self.v4_split_type = getattr(self, 'v4_split_type',
+                                 SPLIT_TYPES['less_or_equal'])
+
+    # Special versions of the normal parameters, that support depth-dependence
+    self.v4_num_splits_to_consider = getattr(self, 'v4_num_splits_to_consider',
+                                             None)
+    self.v4_split_after_samples = getattr(self, 'v4_split_after_samples',
+                                          None)
+    self.v4_finish_check_every_samples = getattr(
+        self, 'v4_finish_check_every_samples', None)
+    self.v4_prune_every_samples = getattr(
+        self, 'v4_prune_every_samples', None)
+
+
+class TreeTrainingVariablesV4(tensor_forest.TreeTrainingVariables):
+  """Stores tf.Variables for training a single random tree."""
+
+  def __init__(self, params, tree_num, training):
+    if (not hasattr(params, 'params_proto') or
+        not isinstance(params.params_proto,
+                       _params_proto.TensorForestParams)):
+      params.params_proto = build_params_proto(params)
+
+    params.serialized_params_proto = params.params_proto.SerializeToString()
+    self.stats = None
+    if training:
+      # TODO(gilberth): Manually shard this to be able to fit it on
+      # multiple machines.
+      self.stats = stats_ops.fertile_stats_variable(
+          params, '', self.get_tree_name('stats', tree_num))
+    self.tree = model_ops.tree_variable(
+        params, '', self.stats, self.get_tree_name('tree', tree_num))
+
+
+class RandomTreeGraphsV4(tensor_forest.RandomTreeGraphs):
+  """Builds TF graphs for random tree training and inference."""
+
+  def tree_initialization(self):
+    return control_flow_ops.no_op()
+
+  def training_graph(self, input_data,
+                     input_labels,
+                     random_seed,
+                     data_spec,
+                     sparse_features=None,
+                     input_weights=None):
+    if input_weights is None:
+      input_weights = []
+
+    sparse_indices = []
+    sparse_values = []
+    sparse_shape = []
+    if sparse_features is not None:
+      sparse_indices = sparse_features.indices
+      sparse_values = sparse_features.values
+      sparse_shape = sparse_features.dense_shape
+
+    if input_data is None:
+      input_data = []
+
+    finished_nodes = stats_ops.process_input_v4(
+        self.variables.tree,
+        self.variables.stats,
+        input_data,
+        sparse_indices,
+        sparse_values,
+        sparse_shape,
+        input_labels,
+        input_weights,
+        input_spec=data_spec.SerializeToString(),
+        random_seed=random_seed,
+        params=self.params.serialized_params_proto)
+
+    return stats_ops.grow_tree_v4(self.variables.tree, self.variables.stats,
+                                  finished_nodes,
+                                  params=self.params.serialized_params_proto)
+
+  def inference_graph(self, input_data, data_spec, sparse_features=None):
+    sparse_indices = []
+    sparse_values = []
+    sparse_shape = []
+    if sparse_features is not None:
+      sparse_indices = sparse_features.indices
+      sparse_values = sparse_features.values
+      sparse_shape = sparse_features.dense_shape
+    if input_data is None:
+      input_data = []
+
+    return model_ops.tree_predictions_v4(
+        self.variables.tree,
+        input_data,
+        sparse_indices,
+        sparse_values,
+        sparse_shape,
+        input_spec=data_spec.SerializeToString(),
+        params=self.params.serialized_params_proto)
+
+  def average_impurity(self):
+    return constant_op.constant(0)
+
+  def size(self):
+    """Constructs a TF graph for evaluating the current number of nodes.
+
+    Returns:
+      The current number of nodes in the tree.
+    """
+    return model_ops.tree_size(self.variables.tree)
+
+  def feature_usage_counts(self):
+    return model_ops.feature_usage_counts(
+        self.variables.tree, params=self.params.serialized_params_proto)
+
+
+class RandomForestGraphsV4(tensor_forest.RandomForestGraphs):
+
+  def __init__(self, params, tree_graphs=None, tree_variables_class=None,
+               **kwargs):
+    if not isinstance(params, V4ForestHParams):
+      params = V4ForestHParams(params)
+    super(RandomForestGraphsV4, self).__init__(
+        params, tree_graphs=tree_graphs or RandomTreeGraphsV4,
+        tree_variables_class=(tree_variables_class or TreeTrainingVariablesV4),
+        **kwargs)
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
index e844046811..83e3c5e35f 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
@@ -65,6 +65,7 @@ cuda_py_test(
     name = "run_metadata_test",
     srcs = ["run_metadata_test.py"],
     additional_deps = [
+        ":model_analyzer_testlib",
         "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
@@ -72,6 +73,12 @@ cuda_py_test(
         "//tensorflow/python:random_ops",
         "//tensorflow/tools/tfprof:protos_all_py",
     ],
+    tags = [
+        "manual",
+        "no_pip",
+        "noguitar",
+        "notap",
+    ],
 )
 
 # -----------------------------------------------------------------------------
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
index 091fcdef88..9eaf74d7a1 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
@@ -77,6 +77,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts.min_params = TEST_OPTIONS['min_params']
     opts.min_float_ops = TEST_OPTIONS['min_float_ops']
     opts.order_by = TEST_OPTIONS['order_by']
+    opts.step = -1
     for p in TEST_OPTIONS['account_type_regexes']:
       opts.account_type_regexes.append(p)
     for p in TEST_OPTIONS['start_name_regexes']:
@@ -157,6 +158,9 @@ class PrintModelAnalysisTest(test.TestCase):
             cpu_exec_micros: 0
             total_accelerator_exec_micros: 0
             total_cpu_exec_micros: 0
+            run_count: 0
+            total_run_count: 0
+            total_definition_count: 1
           }
           children {
             name: "DW"
@@ -213,6 +217,9 @@ class PrintModelAnalysisTest(test.TestCase):
               cpu_exec_micros: 0
               total_accelerator_exec_micros: 0
               total_cpu_exec_micros: 0
+              run_count: 0
+              total_run_count: 0
+              total_definition_count: 1
             }
             children {
               name: "DW/Initializer"
@@ -249,6 +256,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/mean"
@@ -263,6 +273,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/mul"
@@ -302,6 +315,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/shape"
@@ -316,6 +332,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 children {
                   name: "DW/Initializer/random_normal/stddev"
@@ -330,6 +349,9 @@ class PrintModelAnalysisTest(test.TestCase):
                   cpu_exec_micros: 0
                   total_accelerator_exec_micros: 0
                   total_cpu_exec_micros: 0
+                  run_count: 0
+                  total_run_count: 0
+                  total_definition_count: 1
                 }
                 float_ops: 0
                 total_float_ops: 0
@@ -362,6 +384,9 @@ class PrintModelAnalysisTest(test.TestCase):
                 cpu_exec_micros: 0
                 total_accelerator_exec_micros: 0
                 total_cpu_exec_micros: 0
+                run_count: 0
+                total_run_count: 0
+                total_definition_count: 6
               }
               float_ops: 0
               total_float_ops: 0
@@ -369,6 +394,9 @@ class PrintModelAnalysisTest(test.TestCase):
               cpu_exec_micros: 0
               total_accelerator_exec_micros: 0
               total_cpu_exec_micros: 0
+              run_count: 0
+              total_run_count: 0
+              total_definition_count: 7
             }
             children {
               name: "DW/read"
@@ -400,6 +428,9 @@ class PrintModelAnalysisTest(test.TestCase):
               cpu_exec_micros: 0
               total_accelerator_exec_micros: 0
               total_cpu_exec_micros: 0
+              run_count: 0
+              total_run_count: 0
+              total_definition_count: 1
             }
             float_ops: 0
             total_float_ops: 0
@@ -407,6 +438,9 @@ class PrintModelAnalysisTest(test.TestCase):
             cpu_exec_micros: 0
             total_accelerator_exec_micros: 0
             total_cpu_exec_micros: 0
+            run_count: 0
+            total_run_count: 0
+            total_definition_count: 10
           }
           children {
             name: "zeros"
@@ -421,13 +455,19 @@ class PrintModelAnalysisTest(test.TestCase):
             cpu_exec_micros: 0
             total_accelerator_exec_micros: 0
             total_cpu_exec_micros: 0
+            run_count: 0
+            total_run_count: 0
+            total_definition_count: 1
           }
           float_ops: 0
           total_float_ops: 0
           accelerator_exec_micros: 0
           cpu_exec_micros: 0
           total_accelerator_exec_micros: 0
-          total_cpu_exec_micros: 0""", expected_pb)
+          total_cpu_exec_micros: 0
+          run_count: 0
+          total_run_count: 0
+          total_definition_count: 13""", expected_pb)
       self.assertEqual(expected_pb, tfprof_pb)
 
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
index 40f29ae8a2..582c36e339 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/pywrap_tensorflow_print_model_analysis.i
@@ -43,7 +43,6 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
-%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index 4c5d3e0b2b..71468dde37 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -27,12 +27,13 @@ from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 # pylint: disable=g-bad-import-order
 # XXX: this depends on pywrap_tensorflow and must come later
 from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
-
+from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib
 SIZE = 1300
 
 
@@ -68,15 +69,32 @@ def _run_model():
     return tfprof_node, run_metadata
 
 
+def _run_loop_model():
+  with session.Session() as sess:
+    x = lib.BuildFullModel()
+
+    sess.run(variables.global_variables_initializer())
+    run_meta = config_pb2.RunMetadata()
+    _ = sess.run(x,
+                 options=config_pb2.RunOptions(
+                     trace_level=config_pb2.RunOptions.FULL_TRACE),
+                 run_metadata=run_meta)
+
+    tfprof_node = model_analyzer.print_model_analysis(
+        sess.graph, run_meta,
+        tfprof_options=model_analyzer.PRINT_ALL_TIMING_MEMORY)
+    return tfprof_node, run_meta
+
+
 class RunMetadataTest(test.TestCase):
 
   def testGPU(self):
     if not test.is_gpu_available(cuda_only=True):
       return
 
+    ops.reset_default_graph()
     with ops.device('/gpu:0'):
       tfprof_node, run_meta = _run_model()
-
       self.assertEqual(tfprof_node.children[0].name, 'MatMul')
       self.assertGreater(tfprof_node.children[0].exec_micros, 10)
 
@@ -94,9 +112,9 @@ class RunMetadataTest(test.TestCase):
     self.assertTrue(has_all_stream)
 
   def testCPU(self):
+    ops.reset_default_graph()
     with ops.device('/cpu:0'):
       tfprof_node, run_meta = _run_model()
-
       self.assertEqual(tfprof_node.children[0].name, 'MatMul')
       self.assertGreater(tfprof_node.children[0].exec_micros, 10)
 
@@ -107,5 +125,61 @@ class RunMetadataTest(test.TestCase):
     ret = _extract_node(run_meta, 'MatMul:MatMul')
     self.assertEqual(len(ret), 0)
 
+  def testLoopCPU(self):
+    ops.reset_default_graph()
+    with ops.device('/cpu:0'):
+      tfprof_node, run_meta = _run_loop_model()
+      # The while-loop caused a node to appear 4 times in scheduling.
+      ret = _extract_node(run_meta,
+                          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+      self.assertEqual(len(ret['/job:localhost/replica:0/task:0/cpu:0']), 4)
+
+      total_cpu_execs = 0
+      for node in ret['/job:localhost/replica:0/task:0/cpu:0']:
+        total_cpu_execs += node.op_end_rel_micros
+
+      mm_node = lib.SearchTFProfNode(
+          tfprof_node,
+          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+
+      self.assertEqual(mm_node.run_count, 4)
+      self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
+      self.assertEqual(mm_node.exec_micros, total_cpu_execs)
+
+  def testLoopGPU(self):
+    if not test.is_gpu_available():
+      return
+
+    ops.reset_default_graph()
+    with ops.device('/gpu:0'):
+      tfprof_node, run_meta = _run_loop_model()
+      # The while-loop caused a node to appear 4 times in scheduling.
+      ret = _extract_node(run_meta,
+                          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+      self.assertEqual(len(ret['/job:localhost/replica:0/task:0/gpu:0']), 4)
+
+      total_cpu_execs = 0
+      for node in ret['/job:localhost/replica:0/task:0/gpu:0']:
+        total_cpu_execs += node.op_end_rel_micros
+
+      ret = _extract_node(
+          run_meta,
+          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul:MatMul')
+      self.assertGreaterEqual(len(ret['/gpu:0/stream:all']), 4)
+
+      total_accelerator_execs = 0
+      for node in ret['/gpu:0/stream:all']:
+        total_accelerator_execs += node.op_end_rel_micros
+
+      mm_node = lib.SearchTFProfNode(
+          tfprof_node,
+          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
+
+      self.assertEqual(mm_node.run_count, 4)
+      self.assertEqual(mm_node.accelerator_exec_micros, total_accelerator_execs)
+      self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
+      self.assertEqual(mm_node.exec_micros,
+                       total_cpu_execs + total_accelerator_execs)
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index 419beac0b9..c781d2af4e 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -20,6 +20,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import six
+
 from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
 from tensorflow.contrib.tfprof.python.tools.tfprof.internal import pywrap_tensorflow_print_model_analysis_lib as print_mdl
 from tensorflow.python.framework import errors
@@ -108,49 +110,77 @@ PRINT_ALL_TIMING_MEMORY = {
     'dump_to_file': ''
 }
 
+# The following options are for 'advise' tfprof_cmd.
+# Show all advice.
+ALL_ADVICE = {
+    'ExpensiveOperationChecker': {},
+    'AcceleratorUtilizationChecker': {},
+    'JobChecker': {},  # Only available internally.
+    'OperationChecker': {},
+}
+
 # pylint: enable=bad-whitespace
 # pylint: enable=bad-continuation
 
 
-def _build_options(tfprof_options):
+def _build_options(options):
   """Build tfprof.OptionsProto.
 
   Args:
-    tfprof_options: A dictionary of options.
+    options: A dictionary of options.
   Returns:
     tfprof.OptionsProto.
   """
   opts = tfprof_options_pb2.OptionsProto()
-  opts.max_depth = tfprof_options.get('max_depth', 10)
-  opts.min_bytes = tfprof_options.get('min_bytes', 0)
-  opts.min_micros = tfprof_options.get('min_micros', 0)
-  opts.min_params = tfprof_options.get('min_params', 0)
-  opts.min_float_ops = tfprof_options.get('min_float_ops', 0)
-  opts.min_occurrence = tfprof_options.get('min_occurrence', 0)
+  opts.max_depth = options.get('max_depth', 10)
+  opts.min_bytes = options.get('min_bytes', 0)
+  opts.min_micros = options.get('min_micros', 0)
+  opts.min_params = options.get('min_params', 0)
+  opts.min_float_ops = options.get('min_float_ops', 0)
+  opts.min_occurrence = options.get('min_occurrence', 0)
 
-  opts.step = tfprof_options.get('step', -1)
+  opts.step = options.get('step', -1)
 
-  opts.order_by = tfprof_options.get('order_by', 'name')
+  opts.order_by = options.get('order_by', 'name')
 
-  for p in tfprof_options.get('account_type_regexes', []):
+  for p in options.get('account_type_regexes', []):
     opts.account_type_regexes.append(p)
-  for p in tfprof_options.get('start_name_regexes', []):
+  for p in options.get('start_name_regexes', []):
     opts.start_name_regexes.append(p)
-  for p in tfprof_options.get('trim_name_regexes', []):
+  for p in options.get('trim_name_regexes', []):
     opts.trim_name_regexes.append(p)
-  for p in tfprof_options.get('show_name_regexes', []):
+  for p in options.get('show_name_regexes', []):
     opts.show_name_regexes.append(p)
-  for p in tfprof_options.get('hide_name_regexes', []):
+  for p in options.get('hide_name_regexes', []):
     opts.hide_name_regexes.append(p)
-  opts.account_displayed_op_only = tfprof_options.get(
-      'account_displayed_op_only', False)
+  opts.account_displayed_op_only = options.get('account_displayed_op_only',
+                                               False)
 
-  for p in tfprof_options.get('select', []):
+  for p in options.get('select', []):
     opts.select.append(p)
 
-  opts.output = tfprof_options.get('output', 'stdout')
-  opts.dump_to_file = tfprof_options.get('dump_to_file', '')
+  opts.output = options.get('output', 'stdout')
+  opts.dump_to_file = options.get('dump_to_file', '')
+
+  return opts
+
+
+def _build_advisor_options(options):
+  """Build tfprof.AdvisorOptionsProto.
 
+  Args:
+    options: A dictionary of options. See ALL_ADVICE example.
+  Returns:
+    tfprof.AdvisorOptionsProto.
+  """
+  opts = tfprof_options_pb2.AdvisorOptionsProto()
+  if options is None:
+    return opts
+  for checker, checker_opts in six.iteritems(options):
+    checker_ops_pb = tfprof_options_pb2.AdvisorOptionsProto.CheckerOption()
+    for k, v in six.iteritems(checker_opts):
+      checker_ops_pb[k] = v
+    opts.checkers[checker].MergeFrom(checker_ops_pb)
   return opts
 
 
@@ -190,7 +220,7 @@ class Profiler(object):
       else:
         _ = sess.run(...)
     # Auto detect problems and generate advice.
-    profiler.advise()
+    profiler.advise(model_analyzer.ALL_ADVICE)
   """
 
   def __init__(self, graph, op_log=None):
@@ -288,9 +318,19 @@ class Profiler(object):
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
 
-  def advise(self):
-    """Automatically detect problems and generate reports."""
-    print_mdl.Advise()
+  def advise(self, options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+    """Automatically detect problems and generate reports.
+
+    Args:
+      options: A dict of options.
+    Returns:
+      A Advise proto that conains the reports from all checkers.
+    """
+    advise_pb = tfprof_output_pb2.AdviceProto()
+    opts = _build_advisor_options(options)
+    advise_pb.ParseFromString(
+        print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString()))
+    return advise_pb
 
 
 def print_model_analysis(graph,
@@ -354,3 +394,36 @@ def print_model_analysis(graph,
         None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
 
   return tfprof_node
+
+
+def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+  """Auto profile and advise.
+
+    Builds profiles and automatically check anormalies of various
+    aspects. See go/tfprof or README for examples and tutorials.
+
+  Args:
+    graph: tf.Graph.
+    run_meta: tensorflow::RunMetadata proto. Allows auto-profile
+              time and memroy.
+    tfprof_options: see ALL_ADVICE example above.
+  Returns:
+    Returns AdviceProto proto
+  """
+  # pylint: disable=protected-access
+  op_log = tfprof_logger._merge_default_with_oplog(
+      graph, None, run_meta, add_trace=True)
+  # pylint: enable=protected-access
+
+  run_meta_str = run_meta.SerializeToString() if run_meta else b''
+
+  opts = _build_advisor_options(tfprof_options)
+  ret = tfprof_output_pb2.AdviceProto()
+  ret.ParseFromString(
+      print_mdl.PrintModelAnalysis(
+          graph.as_graph_def(add_shapes=True).SerializeToString(),
+          run_meta_str,
+          op_log.SerializeToString(),
+          'advise'.encode('utf-8'),
+          opts.SerializeToString()))
+  return ret
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index 1b5041441f..fea27a82a5 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -34,7 +34,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testDumpToFile(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
+    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
     opts['output'] = 'file:outfile=' + outfile
 
@@ -52,7 +52,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testSelectEverything(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
+    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
     opts['output'] = 'file:outfile=' + outfile
     opts['account_type_regexes'] = ['.*']
@@ -77,7 +77,7 @@ class PrintModelAnalysisTest(test.TestCase):
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
         self.assertEqual(
-            'node name | # parameters | # float_ops | assigned devices | op types | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, _kTFScopeParent, )\n  Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x6x6x3|1:3x3x3x6)\n  Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 0:2x3x3x6|1:2x2x6x12)\n  DW (3x3x3x6, 162/162 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW/Assign (0/0 params, 0/0 flops, Assign, 0:3x3x3x6|1:3x3x3x6)\n    DW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      DW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:4)\n        DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:3x3x3x6)\n  DW2 (2x2x6x12, 288/288 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, )\n    DW2/Assign (0/0 params, 0/0 flops, Assign, 0:2x2x6x12|1:2x2x6x12)\n    DW2/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      DW2/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:4)\n        DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 0:2x2x6x12)\n  ScalarW (1, 1/1 params, 0/0 flops, VariableV2|_trainable_variables, )\n    ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0:1|1:1)\n    ScalarW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, )\n      ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0:1|1:1)\n        ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0:0)\n        ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, )\n        ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0:1|1:1)\n        ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, )\n        ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, )\n    ScalarW/read (0/0 params, 0/0 flops, Identity, 0:1)\n  init (0/0 params, 0/0 flops, NoOp, 0:1|1:3x3x3x6|2:2x2x6x12)\n  zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, )\n',
+            'node name | # parameters | # float_ops | assigned devices | op types | op count (run|defined) | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, _kTFScopeParent, --/7|--/35, )\n  Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x6x6x3|1:3x3x3x6)\n  Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x3x3x6|1:2x2x6x12)\n  DW (3x3x3x6, 162/162 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n    DW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:3x3x3x6|1:3x3x3x6)\n    DW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      DW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n        DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:3x3x3x6)\n  DW2 (2x2x6x12, 288/288 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n    DW2/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:2x2x6x12|1:2x2x6x12)\n    DW2/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      DW2/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n        DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:2x2x6x12)\n  ScalarW (1, 1/1 params, 0/0 flops, VariableV2|_trainable_variables, 0/0|1/10, )\n    ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:1|1:1)\n    ScalarW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:1|1:1)\n        ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:0)\n        ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:1|1:1)\n        ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    ScalarW/read (0/0 params, 0/0 flops, Identity, 0/0|1/1, 0:1)\n  init (0/0 params, 0/0 flops, NoOp, 0/0|1/1, 0:1|1:3x3x3x6|2:2x2x6x12)\n  zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, 1/1|1/1, )\n',
             f.read())
         # pylint: enable=line-too-long
 
@@ -126,7 +126,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['account_displayed_op_only'] = False
     opts['select'] = ['params', 'float_ops']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       x = lib.BuildFullModel()
 
       sess.run(variables.global_variables_initializer())
@@ -143,12 +143,12 @@ class PrintModelAnalysisTest(test.TestCase):
       with gfile.Open(outfile, 'r') as f:
         lines = f.read().split('\n')
         result = '\n'.join([l[:min(len(l), 80)] for l in lines])
-        self.assertEqual('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/54.08k flops)\n  model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (0/1.80k para\n    model_analyzer_testlib.py:35:BuildSmallModel:image = array_ops... (0/0 param\n    model_analyzer_testlib.py:39:BuildSmallModel:initializer=init_... (0/4 param\n    model_analyzer_testlib.py:43:BuildSmallModel:initializer=init_... (0/648 par\n    model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n    model_analyzer_testlib.py:48:BuildSmallModel:initializer=init_... (0/1.15k p\n    model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n  model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (0/1.04k para\n  model_analyzer_testlib.py:64:BuildFullModel:target = array_op... (0/0 params, \n  model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (0/0 params, \n  model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min... (0/0 params, \n',
+        self.assertEqual('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/91.04k flops)\n  model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (0/1.80k para\n    model_analyzer_testlib.py:35:BuildSmallModel:image = array_ops... (0/0 param\n    model_analyzer_testlib.py:39:BuildSmallModel:initializer=init_... (0/4 param\n    model_analyzer_testlib.py:43:BuildSmallModel:initializer=init_... (0/648 par\n    model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n    model_analyzer_testlib.py:48:BuildSmallModel:initializer=init_... (0/1.15k p\n    model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n  model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (0/1.04k para\n  model_analyzer_testlib.py:64:BuildFullModel:target = array_op... (0/0 params, \n  model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (0/0 params, \n  model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min... (0/0 params, \n',
                          result)
 
       self.assertLess(0, tfprof_node.total_exec_micros)
       self.assertEqual(2844, tfprof_node.total_parameters)
-      self.assertEqual(54080, tfprof_node.total_float_ops)
+      self.assertEqual(91040, tfprof_node.total_float_ops)
       self.assertEqual(5, len(tfprof_node.children))
       self.assertEqual('_TFProfRoot', tfprof_node.name)
       self.assertEqual(
@@ -176,6 +176,7 @@ class PrintModelAnalysisTest(test.TestCase):
     opts['select'] = [
         'bytes', 'params', 'float_ops', 'device'
     ]
+    opts['output'] = 'none'
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -225,7 +226,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testOpView(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
+    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
     opts['output'] = 'file:outfile=' + outfile
     opts['account_type_regexes'] = ['.*']
@@ -249,8 +250,8 @@ class PrintModelAnalysisTest(test.TestCase):
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
         self.assertEqual(
-            'nodename|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence|input',
-            f.read().replace('\t', '').replace(' ', '')[0:100])
+            'nodename|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence(run|defined)|inputshapes\n',
+            f.read().replace('\t', '').replace(' ', '')[0:120])
         # pylint: enable=line-too-long
 
       total_children = 0
@@ -276,6 +277,33 @@ class PrintModelAnalysisTest(test.TestCase):
       self.assertEqual(total_children, 15)
       self.assertGreater(input_shapes, 0)
 
+  def testAdvisor(self):
+    ops.reset_default_graph()
+
+    with session.Session() as sess:
+      x = lib.BuildFullModel()
+
+      sess.run(variables.global_variables_initializer())
+      run_meta = config_pb2.RunMetadata()
+      _ = sess.run(
+          x,
+          options=config_pb2.RunOptions(
+              trace_level=config_pb2.RunOptions.FULL_TRACE),
+          run_metadata=run_meta)
+
+      advice_pb = model_analyzer.advise(sess.graph, run_meta)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
index 5daaafd7c8..c7113b6a57 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
@@ -129,7 +129,7 @@ class ProfilerTest(test.TestCase):
     opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
     opts['account_type_regexes'] = ['.*']
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    with session.Session() as sess:
       r1, r2, r3 = lib.BuildSplitableModel()
       sess.run(variables.global_variables_initializer())
 
@@ -179,8 +179,18 @@ class ProfilerTest(test.TestCase):
       self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None)
       self.assertGreater(lib.SearchTFProfNode(pb3, 'add').exec_micros, 0)
 
-      # TODO(xpan): Better test of advisor.
-      profiler.advise()
+      advice_pb = profiler.advise(model_analyzer.ALL_ADVICE)
+      self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
+      self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
+      self.assertTrue('OperationChecker' in advice_pb.checkers)
+
+      checker = advice_pb.checkers['AcceleratorUtilizationChecker']
+      if test.is_gpu_available():
+        self.assertGreater(len(checker.reports), 0)
+      else:
+        self.assertEqual(len(checker.reports), 0)
+      checker = advice_pb.checkers['ExpensiveOperationChecker']
+      self.assertGreater(len(checker.reports), 0)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py
index 157b0fc1ac..a65a86514b 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu.py
@@ -480,8 +480,11 @@ def shard(computation,
   for (axis, all_shards, x) in zip(output_shard_axes, outputs_from_all_shards,
                                    zip(*outputs)):
     if all_shards:
-      # Concatenate all of the outputs together.
-      results.append(array_ops.concat(list(x), axis=axis))
+      # Concatenate all of the outputs together (use stack for scalars).
+      shape = x[0].shape
+      is_scalar = shape is not None and (shape.ndims == 0)
+      results.append((array_ops.stack(list(x)) if is_scalar
+                      else array_ops.concat(list(x), axis=axis)))
     else:
       # TODO(phawkins): use a smarter policy, e.g., round-robin across shards.
       results.append(x[0])
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 8503a4a545..0f15133d74 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -13,12 +13,13 @@
 # limitations under the License.
 # ===================================================================
 
-"""Tpu Estimator class."""
+"""TpuEstimator class."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import copy
 import threading
 from six.moves import queue as Queue  # pylint: disable=redefined-builtin
 
@@ -41,6 +42,9 @@ from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training
 
 
+_BATCH_SIZE_KEY = 'batch_size'
+
+
 def _tpu_job(run_config):
   # The tpu job is determined by the run_config. Right now, this method is
   # required as tpu_config is not part of the RunConfig.
@@ -143,10 +147,29 @@ class TpuInfeedSessionHook(session_run_hook.SessionRunHook):
     session.run(self._finalize_op)
 
 
+class _PerShardOutput(object):
+  """Wraps input_fn's outputs into per-shard outputs.
+
+  Used so that the wrapped model_fn can distinguish between sharded input and
+  unsharded inputs (e.g., for export_savedmodel()).
+  """
+
+  def __init__(self, output):
+    self.output = output
+
+  def as_list(self):
+    return self.output
+
+
 class TpuEstimator(estimator_lib.Estimator):
   """Estimator with TPU support.
 
-  The only difference is a wrapped model_fn is set in the constructor.
+  TpuEstimator handles many of the details of running on TPU devices, such as
+  replicating inputs and models for each core, and returning to host
+  periodically to run hooks.
+
+  Note: TpuEstimator transforms a global batch size in params to a per-shard
+        batch size when calling the input_fn.
   """
   def __init__(self,
                model_fn=None,
@@ -154,10 +177,32 @@ class TpuEstimator(estimator_lib.Estimator):
                config=None,
                params=None,
                use_tpu=True):
+    if config is None or not isinstance(config, tpu_config.RunConfig):
+      raise ValueError(
+          '`config` must be provided with type `tpu_config.RunConfig`')
+
+    if use_tpu and params is not None and _BATCH_SIZE_KEY in params:
+      if not isinstance(params[_BATCH_SIZE_KEY], int):
+        raise ValueError(
+            '`{}` in params must be an int'.format(_BATCH_SIZE_KEY))
+      params = copy.deepcopy(params)
+      # The specified batch size is the batch size for the entire computation.
+      # The input_fn is called per-shard, so we want to calculate the per-shard
+      # batch size and pass that.
+      if params[_BATCH_SIZE_KEY] % config.tpu_config.num_shards != 0:
+        raise ValueError(
+            'batch size {} must be divisible by number of shards {}'
+            .format(params[_BATCH_SIZE_KEY], config.tpu_config.num_shards))
+
     if use_tpu:
       if not isinstance(config, tpu_config.RunConfig):
         raise ValueError('`config` must be `tpu_config.RunConfig`')
-      model_function = wrapped_model_fn(model_fn, config)
+      # Verifies the model_fn signature according to Estimator framework.
+      estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
+      # We cannot store config and params in this constructor as parent
+      # constructor might change them, such as assigning a temp dir for
+      # config.model_dir.
+      model_function = wrapped_model_fn(model_fn)
     else:
       model_function = model_fn
     super(TpuEstimator, self).__init__(
@@ -165,6 +210,7 @@ class TpuEstimator(estimator_lib.Estimator):
         model_dir=model_dir,
         config=config,
         params=params)
+    self.use_tpu = use_tpu
 
   def _create_global_step(self, graph):
     """Creates a global step suitable for TPUs.
@@ -193,6 +239,96 @@ class TpuEstimator(estimator_lib.Estimator):
           collections=[ops.GraphKeys.GLOBAL_VARIABLES,
                        ops.GraphKeys.GLOBAL_STEP])
 
+  def _call_input_fn(self, input_fn, mode):
+    """Calls the input function.
+
+    Args:
+      input_fn: The input function.
+      mode: ModeKeys
+
+    Returns:
+      Either features or (features, labels) where features and labels are:
+        features - `Tensor` or dictionary of string feature name to `Tensor`.
+        labels - `Tensor` or dictionary of `Tensor` with labels.
+
+    Raises:
+      ValueError: if input_fn takes invalid arguments.
+    """
+    if not self.use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
+      return super(TpuEstimator, self)._call_input_fn(input_fn, mode)
+
+    input_fn_args = estimator_lib._fn_args(input_fn)  # pylint: disable=protected-access
+    config = self.config  # a deep copy.
+    kwargs = {}
+    if 'params' in input_fn_args:
+      kwargs['params'] = self.params  # a deep copy.
+    if 'config' in input_fn_args:
+      kwargs['config'] = config
+
+    # Now for TPU training.
+    if 'params' in kwargs and _BATCH_SIZE_KEY in kwargs['params']:
+      kwargs['params'][_BATCH_SIZE_KEY] //= config.tpu_config.num_shards
+
+    job = _tpu_job(config)
+    def placement_function(index):
+      if job is None:
+        return '/replica:0/task:0/device:CPU:0'
+      else:
+        return '/job:%s/replica:0/task:%d/device:CPU:0' % (job, index / 8)
+
+    features = []
+    labels = []
+    for i in range(config.tpu_config.num_shards):
+      with ops.device(placement_function(i)):
+        result = input_fn(**kwargs)
+        # input_fn may return either features or (features, labels)
+        if isinstance(result, tuple):
+          features.append(result[0])
+          labels.append(result[1])
+        else:
+          features.append(result)
+    if not labels or all(l is None for l in labels):
+      return _PerShardOutput(features), None
+    return _PerShardOutput(features), _PerShardOutput(labels)
+
+
+def _verify_estimator_spec(estimator_spec):
+  """Validates the estimator_spec."""
+  err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
+  if estimator_spec.training_chief_hooks:
+    raise ValueError(err_msg.format('training_chief_hooks'))
+  if estimator_spec.training_hooks:
+    raise ValueError(err_msg.format('training_hooks'))
+  return estimator_spec
+
+
+def _call_model_fn(model_fn, features, labels, mode, config, params):
+  """Calls the model_fn with required parameters."""
+  model_fn_args = estimator_lib._fn_args(model_fn)  # pylint: disable=protected-access
+  kwargs = {}
+  if 'mode' in model_fn_args:
+    kwargs['mode'] = mode
+  if 'params' in model_fn_args:
+    kwargs['params'] = params
+  if 'config' in model_fn_args:
+    kwargs['config'] = config
+  return model_fn(features=features, labels=labels, **kwargs)
+
+
+def _call_model_fn_with_tpu(model_fn, features, labels, mode, config, params):
+  """Calls user provided `model_fn` and verifies the estimator_spec."""
+  # Makes deep copy with `config` and params` in case user mutates them.
+  config = copy.deepcopy(config)
+  params = copy.deepcopy(params)
+  return _verify_estimator_spec(_call_model_fn(
+      model_fn, features, labels, mode, config, params))
+
+
+def _call_model_fn_without_tpu(
+    model_fn, features, labels, mode, config, params):
+  # Deepcopy of config and params is not required in this branch.
+  return _call_model_fn(model_fn, features, labels, mode, config, params)
+
 
 # TODO(xiejw): Improve the structure of this input_fn to infeed converion.
 # The code now looks not like Estimator style. We need to abstract many
@@ -212,69 +348,102 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
     labels: labels
 
   Returns:
-    A tuple of (dequeue_fn, and thread main function)
+    A tuple of (dequeue_fn, enqueue_fn)
   """
   infeed_names = None
-  infeed_tuple = []
-  if isinstance(features, dict):
+  sharded_inputs = []
+  if isinstance(features[0], dict):
     # We need a fixed ordering for enqueueing and dequeueing.
-    infeed_names = [name for name in features]
-    infeed_tuple.extend([features[name] for name in infeed_names])
-  else:
-    infeed_tuple.append(features)
-  # TODO(jhseu): Handle multi-head and None labels
-  infeed_tuple.append(labels)
-  # TODO(jhseu): Update when b/36470756 is settled.
+    infeed_names = [name for name in features[0]]
+
+  for shard in range(run_config.tpu_config.num_shards):
+    inputs = []
+    if infeed_names is None:
+      inputs.append(features[shard])
+    else:
+      for name in infeed_names:
+        inputs.append(features[shard][name])
+    if labels is not None:
+      inputs.append(labels[shard])
+    sharded_inputs.append(inputs)
+
   infeed_queue = tpu_feed.InfeedQueue(
-      tuple_types=[t.dtype for t in infeed_tuple],
-      tuple_shapes=[t.shape for t in infeed_tuple])
-  infeed_queue.set_number_of_shards(run_config.tpu_config.num_shards)
+      number_of_tuple_elements=len(sharded_inputs[0]))
+  infeed_queue.set_configuration_from_sharded_input_tensors(sharded_inputs)
 
   def dequeue_fn():
     """dequeue_fn is used by the train_step in TPU to retrieve the tensors."""
     values = infeed_queue.generate_dequeue_op()
+
+    expected_num_tensors = 0
+    if labels is not None:
+      expected_num_tensors += 1
+    if infeed_names is None:
+      expected_num_tensors += 1
+    else:
+      expected_num_tensors += len(infeed_names)
+    assert len(values) == expected_num_tensors
+
+    dequeue_label = None
+    if labels is not None:
+      dequeue_label = values[-1]
     if infeed_names is None:
-      return values
+      return values[0], dequeue_label
     # Restore the feature dictionary and label.
     dequeued_features = {}
-    for i in range(len(values) - 1):
+    for i in range(len(infeed_names)):
       dequeued_features[infeed_names[i]] = values[i]
-    label = values[-1]
-    return dequeued_features, label
+    return dequeued_features, dequeue_label
+
+  def tpu_ordinal_function(index):
+    """Return the TPU ordinal associated with a shard.
+
+    Required because the enqueue ops are placed on CPU.
+
+    Args:
+      index: the shard index
+
+    Returns:
+      The ordinal of the TPU device the shard's infeed should be placed on.
+    """
+    return index % 8
 
   def enqueue_fn():
     """enqueue_fn is used to add ops to the graph to send tensors."""
-    job = _tpu_job(run_config)
-    def placement_function(index):
-      if job is None:
-        return '/replica:0/task:0/device:CPU:0'
-      else:
-        return '/job:%s/replica:0/task:%d/device:CPU:0' % (job, index / 8)
-    return infeed_queue.split_inputs_and_generate_enqueue_ops(
-        infeed_tuple, placement_function=placement_function)
+    return infeed_queue.generate_enqueue_ops(
+        sharded_inputs, tpu_ordinal_function=tpu_ordinal_function)
 
   return (dequeue_fn, enqueue_fn)
 
 
-def wrapped_model_fn(model_fn, run_config):
+def wrapped_model_fn(model_fn):
   """Returns a new model_fn, which wraps the TPU support."""
 
-  # Verifies the model_fn signature according to Estimator framework.
-  estimator_lib._verify_model_fn_args(model_fn, params=None)  # pylint: disable=protected-access
-
-  def _model_fn(features, labels, mode):
+  def _model_fn(features, labels, mode, config, params=None):
     """model_fn."""
+
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
     if mode != model_fn_lib.ModeKeys.TRAIN:
-      return model_fn(features, labels, mode)
+      return _call_model_fn_without_tpu(
+          model_fn, features, labels, mode, config, params)
+
+    # Now for TPU training.
+    if params is not None and _BATCH_SIZE_KEY in params:
+      params[_BATCH_SIZE_KEY] //= config.tpu_config.num_shards
+
+    assert isinstance(features, _PerShardOutput)
+    features = features.as_list()
+    if labels is not None:
+      assert isinstance(labels, _PerShardOutput)
+      labels = labels.as_list()
 
     dequeue_fn, enqueue_fn = (
-        _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels))
+        _create_infeed_enqueue_ops_and_dequeue_fn(config, features, labels))
 
     loss = _train_on_tpu_shards(
-        run_config,
+        config,
         train_step=_convert_model_fn_to_train_step(
-            model_fn, dequeue_fn, mode, run_config))
+            model_fn, dequeue_fn, mode, config, params))
 
     # Gets the variables back from TPU nodes. This means the variables updated
     # by TPU will now be *synced* to host memory.
@@ -285,7 +454,7 @@ def wrapped_model_fn(model_fn, run_config):
     ]
 
     hooks = [
-        TpuInfeedSessionHook(run_config, enqueue_fn),
+        TpuInfeedSessionHook(config, enqueue_fn),
         training.LoggingTensorHook(
             {'loss': array_ops.identity(loss),
              'step': training.get_global_step()},
@@ -300,30 +469,9 @@ def wrapped_model_fn(model_fn, run_config):
   return _model_fn
 
 
-def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config):
-  """generates a train step based on the model_fn."""
-
-  def _call_model_fn(features, labels):
-    """Calls the model_fn with required parameters."""
-    model_fn_args = estimator_lib._model_fn_args(model_fn)  # pylint: disable=protected-access
-    kwargs = {}
-    if 'mode' in model_fn_args:
-      kwargs['mode'] = mode
-    # Uncomment the following lines once `params` is supported.
-    #   if 'params' in model_fn_args:
-    #     kwargs['params'] = params
-    if 'config' in model_fn_args:
-      kwargs['config'] = run_config
-    return model_fn(features=features, labels=labels, **kwargs)
-
-  def _verify_estimator_spec(estimator_spec):
-    """Validates the estimator_spec."""
-    err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
-    if estimator_spec.training_chief_hooks:
-      raise ValueError(err_msg.format('training_chief_hooks'))
-    if estimator_spec.training_hooks:
-      raise ValueError(err_msg.format('training_hooks'))
-    return estimator_spec
+def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config,
+                                    params):
+  """Generates a train step based on the model_fn."""
 
   def train_step(loss):
     """Training step function for use inside a while loop."""
@@ -337,7 +485,8 @@ def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config):
     # the graph construction part in the model_fn should be separated from the
     # control part (such as hooks and savers). By that the graph construction
     # could de defered on TPU chip, while the control logic can stay in host.
-    estimator_spec = _verify_estimator_spec(_call_model_fn(features, labels))
+    estimator_spec = _call_model_fn_with_tpu(
+        model_fn, features, labels, mode, run_config, params)
     loss, train_op = estimator_spec.loss, estimator_spec.train_op
     with ops.control_dependencies([train_op]):
       return array_ops.identity(loss)
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
index 668b5b8b91..c955de524c 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
@@ -448,7 +448,7 @@ class InfeedQueue(object):
             name=full_name,
             device_ordinal=tpu_ordinal)
 
-  def generate_enqueue_ops(self, sharded_inputs):
+  def generate_enqueue_ops(self, sharded_inputs, tpu_ordinal_function=None):
     """Generates the host-side Ops to enqueue the shards of a tuple.
 
     sharded_inputs is a list, one for each shard, of lists of
@@ -466,6 +466,10 @@ class InfeedQueue(object):
       sharded_inputs: a list of lists of Tensors. The length of the outer list
         determines the number of shards. Each inner list indicates the types
         and shapes of the tuples in the corresponding shard.
+      tpu_ordinal_function: if not None, a function that takes the
+        shard index as input and returns the ordinal of the TPU device
+        the shard's infeed should be placed on. tpu_ordinal_function must be
+        set if the inputs are placed on CPU devices.
 
     Returns:
       A list of host-side Ops, one for each shard, that when executed together
@@ -487,9 +491,12 @@ class InfeedQueue(object):
     if self._generated_enqueue_ops:
       raise ValueError("Can't generate two enqueue Ops from the same queue")
     self._generated_enqueue_ops = True
+    if tpu_ordinal_function is None:
+      tpu_ordinal_function = lambda index: -1
     name_prefix = "%s/enqueue" % self._name
     return [
-        self._generate_enqueue_op(shard, name_prefix, index)
+        self._generate_enqueue_op(shard, name_prefix, index,
+                                  tpu_ordinal=tpu_ordinal_function(index))
         for (shard, index) in zip(sharded_inputs, xrange(self.number_of_shards))
     ]
 
diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py
index c19a36eabc..5de177ae44 100644
--- a/tensorflow/contrib/training/python/training/hparam.py
+++ b/tensorflow/contrib/training/python/training/hparam.py
@@ -91,9 +91,9 @@ def parse_values(values, type_map):
           % (name, type_.__name__, value, values))
     if type_ == bool:
       def parse_bool(value):
-        if value == 'true':
+        if value in ['true', 'True']:
           return True
-        elif value == 'false':
+        elif value in ['false', 'False']:
           return False
         else:
           try:
diff --git a/tensorflow/contrib/training/python/training/hparam_test.py b/tensorflow/contrib/training/python/training/hparam_test.py
index 0b900e65d8..b4130d04b4 100644
--- a/tensorflow/contrib/training/python/training/hparam_test.py
+++ b/tensorflow/contrib/training/python/training/hparam_test.py
@@ -96,11 +96,11 @@ class HParamsTest(test.TestCase):
     self.assertEquals('2.3"', hparams2.c_c)
 
   def testBoolParsing(self):
-    for value in 'true', 'false', '1', '0':
+    for value in 'true', 'false', 'True', 'False', '1', '0':
       for initial in False, True:
         hparams = hparam.HParams(use_gpu=initial)
         hparams.parse('use_gpu=' + value)
-        self.assertEqual(hparams.use_gpu, value in ['true', '1'])
+        self.assertEqual(hparams.use_gpu, value in ['True', 'true', '1'])
 
         # Exports to proto.
         hparam_def = hparams.to_proto()
diff --git a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
index 1f079027ef..3e387129eb 100644
--- a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
+++ b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
@@ -16,6 +16,7 @@ limitations under the License.
 
 #include <unordered_set>
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor.pb.h"
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index a23c801bd7..24b0f702b3 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1245,6 +1245,9 @@ cc_library(
                 "platform/*.cc",
                 "platform/profile_utils/**/*.h",
                 "platform/profile_utils/**/*.cc",
+            ] + [
+                "framework/resource_handle.h",
+                "framework/resource_handle.cc",
             ],
             exclude = [
                 "**/*test*",
@@ -1436,6 +1439,7 @@ tf_cuda_library(
             "framework/fake_input.*",
             "framework/op_gen_lib.*",
             "framework/reader_base.*",
+            "framework/resource_handle.cc",
             "util/memmapped_file_system.*",
             "util/memmapped_file_system_writer.*",
             "util/version_info.cc",
diff --git a/tensorflow/core/common_runtime/device_mgr.cc b/tensorflow/core/common_runtime/device_mgr.cc
index 31f12d4833..471463fc8b 100644
--- a/tensorflow/core/common_runtime/device_mgr.cc
+++ b/tensorflow/core/common_runtime/device_mgr.cc
@@ -33,6 +33,7 @@ DeviceMgr::DeviceMgr(const std::vector<Device*>& devices)
     string full_name = d->name();
     device_map_[CopyToBackingStore(full_name)] = d;
 
+    // TODO(b/62909072): Upgrade device_map_ to a better data structure.
     DeviceNameUtils::ParsedName parsed_name = d->parsed_name();
     if (parsed_name.has_job && parsed_name.has_replica &&
         parsed_name.has_task && parsed_name.has_type && parsed_name.has_id) {
@@ -40,6 +41,11 @@ DeviceMgr::DeviceMgr(const std::vector<Device*>& devices)
           parsed_name.job, parsed_name.replica, parsed_name.task,
           parsed_name.type, parsed_name.id);
       device_map_[CopyToBackingStore(canonical_name)] = d;
+
+      string legacy_name = DeviceNameUtils::LegacyName(
+          parsed_name.job, parsed_name.replica, parsed_name.task,
+          parsed_name.type, parsed_name.id);
+      device_map_[CopyToBackingStore(legacy_name)] = d;
     }
     string lname = DeviceNameUtils::LocalName(d->name());
     device_map_[CopyToBackingStore(lname)] = d;
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index e771674a1e..4b951691fb 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -34,6 +34,7 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/log_memory.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph.h"
@@ -85,17 +86,48 @@ thread::ThreadPool* NewThreadPoolFromSessionOptions(
   return new thread::ThreadPool(options.env, "Compute", num_threads);
 }
 
-thread::ThreadPool* NewThreadPoolFromThreadPoolOptions(
+Status NewThreadPoolFromThreadPoolOptions(
     const SessionOptions& options,
-    const ThreadPoolOptionProto& thread_pool_options, int pool_number) {
+    const ThreadPoolOptionProto& thread_pool_options, int pool_number,
+    thread::ThreadPool** pool, bool* owned) {
   int32 num_threads = thread_pool_options.num_threads();
   if (num_threads == 0) {
     num_threads = NumInterOpThreadsFromSessionOptions(options);
   }
-  VLOG(1) << "Direct session inter op parallelism threads for pool "
-          << pool_number << ": " << num_threads;
-  return new thread::ThreadPool(
-      options.env, strings::StrCat("Compute", pool_number), num_threads);
+  const string& name = thread_pool_options.global_name();
+  if (name.empty()) {
+    // Session-local threadpool.
+    VLOG(1) << "Direct session inter op parallelism threads for pool "
+            << pool_number << ": " << num_threads;
+    *pool = new thread::ThreadPool(
+        options.env, strings::StrCat("Compute", pool_number), num_threads);
+    *owned = true;
+    return Status::OK();
+  }
+
+  // Global, named threadpool.
+  typedef std::pair<int32, thread::ThreadPool*> MapValue;
+  static std::map<string, MapValue>* global_pool_map =
+      new std::map<string, MapValue>;
+  static mutex* mu = new mutex();
+  mutex_lock l(*mu);
+  MapValue* mvalue = &(*global_pool_map)[name];
+  if (mvalue->second == nullptr) {
+    mvalue->first = thread_pool_options.num_threads();
+    mvalue->second = new thread::ThreadPool(
+        options.env, strings::StrCat("Compute", pool_number), num_threads);
+  } else {
+    if (mvalue->first != thread_pool_options.num_threads()) {
+      return errors::InvalidArgument(
+          "Pool ", name,
+          " configured previously with num_threads=", mvalue->first,
+          "; cannot re-configure with num_threads=",
+          thread_pool_options.num_threads());
+    }
+  }
+  *owned = false;
+  *pool = mvalue->second;
+  return Status::OK();
 }
 
 thread::ThreadPool* GlobalThreadPool(const SessionOptions& options) {
@@ -232,16 +264,18 @@ DirectSession::DirectSession(const SessionOptions& options,
   if (options_.config.session_inter_op_thread_pool_size() > 0) {
     for (int i = 0; i < options_.config.session_inter_op_thread_pool_size();
          ++i) {
-      thread_pools_.push_back(NewThreadPoolFromThreadPoolOptions(
-          options_, options_.config.session_inter_op_thread_pool(i), i));
+      thread::ThreadPool* pool = nullptr;
+      bool owned = false;
+      init_error_.Update(NewThreadPoolFromThreadPoolOptions(
+          options_, options_.config.session_inter_op_thread_pool(i), i, &pool,
+          &owned));
+      thread_pools_.emplace_back(pool, owned);
     }
-    owns_thread_pools_ = true;
   } else if (options_.config.use_per_session_threads()) {
-    thread_pools_.push_back(NewThreadPoolFromSessionOptions(options_));
-    owns_thread_pools_ = true;
+    thread_pools_.emplace_back(NewThreadPoolFromSessionOptions(options_),
+                               true /* owned */);
   } else {
-    thread_pools_.push_back(GlobalThreadPool(options));
-    owns_thread_pools_ = false;
+    thread_pools_.emplace_back(GlobalThreadPool(options), false /* owned */);
   }
   // The default value of sync_on_finish will be flipped soon and this
   // environment variable will be removed as well.
@@ -290,8 +324,8 @@ DirectSession::~DirectSession() {
     d->op_segment()->RemoveHold(session_handle_);
   }
   delete cancellation_manager_;
-  if (owns_thread_pools_) {
-    for (auto* p : thread_pools_) delete p;
+  for (const auto& p_and_owned : thread_pools_) {
+    if (p_and_owned.second) delete p_and_owned.first;
   }
 
   execution_state_.reset(nullptr);
@@ -330,6 +364,7 @@ Status DirectSession::MaybeInitializeExecutionState(
 }
 
 Status DirectSession::Create(const GraphDef& graph) {
+  TF_RETURN_IF_ERROR(init_error_);
   if (graph.node_size() > 0) {
     mutex_lock l(graph_def_lock_);
     if (graph_created_) {
@@ -423,7 +458,8 @@ Status DirectSession::Run(const RunOptions& run_options,
     return errors::InvalidArgument("Invalid inter_op_thread_pool: ",
                                    run_options.inter_op_thread_pool());
   }
-  thread::ThreadPool* pool = thread_pools_[run_options.inter_op_thread_pool()];
+  thread::ThreadPool* pool =
+      thread_pools_[run_options.inter_op_thread_pool()].first;
 
   // Check if we already have an executor for these arguments.
   ExecutorsAndKeys* executors_and_keys;
@@ -644,7 +680,7 @@ Status DirectSession::PRunSetup(const std::vector<string>& input_names,
   }
 
   // RunOptions is not available in PRunSetup, so use thread pool 0.
-  thread::ThreadPool* pool = thread_pools_[0];
+  thread::ThreadPool* pool = thread_pools_[0].first;
 
   // Check if we already have an executor for these arguments.
   ExecutorsAndKeys* executors_and_keys;
diff --git a/tensorflow/core/common_runtime/direct_session.h b/tensorflow/core/common_runtime/direct_session.h
index b14a517188..c3cc573f2e 100644
--- a/tensorflow/core/common_runtime/direct_session.h
+++ b/tensorflow/core/common_runtime/direct_session.h
@@ -266,9 +266,11 @@ class DirectSession : public Session {
   mutex graph_def_lock_;
   GraphDef graph_def_ GUARDED_BY(graph_def_lock_);
 
-  // The thread-pools to use for running ops.
-  std::vector<thread::ThreadPool*> thread_pools_;
-  bool owns_thread_pools_ = false;
+  // The thread-pools to use for running ops, with a bool indicating if the pool
+  // is owned.
+  std::vector<std::pair<thread::ThreadPool*, bool>> thread_pools_;
+
+  Status init_error_;  // Set to an error if construction failed.
 
   // If true, blocks until device has finished all queued operations in a step.
   bool sync_on_finish_ = true;
diff --git a/tensorflow/core/common_runtime/direct_session_test.cc b/tensorflow/core/common_runtime/direct_session_test.cc
index 107c84e39a..5d6b2eeeed 100644
--- a/tensorflow/core/common_runtime/direct_session_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_test.cc
@@ -882,7 +882,8 @@ class BlockingOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("BlockingOp").Device(DEVICE_CPU), BlockingOp);
 REGISTER_OP("BlockingOp").Input("x: float").Output("y: float").Doc("");
 
-static void TestSessionInterOpThreadsImpl(bool use_function_lib) {
+static void TestSessionInterOpThreadsImpl(bool use_function_lib,
+                                          bool use_global_pools) {
   FunctionDefLibrary library_graph_def;
   if (use_function_lib) {
     const string lib = R"proto(
@@ -921,24 +922,45 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib) {
   (*options.config.mutable_device_count())["GPU"] = 0;
   (*options.config.mutable_device_count())["SYCL"] = 0;
 
-  options.config.add_session_inter_op_thread_pool();
   auto* p = options.config.add_session_inter_op_thread_pool();
+  if (use_global_pools) p->set_global_name("large pool");
+  p = options.config.add_session_inter_op_thread_pool();
+  if (use_global_pools) p->set_global_name("small pool");
   p->set_num_threads(1);
   const int kLargePool = 0;
   const int kSmallPool = 1;
 
-  std::unique_ptr<Session> session(NewSession(options));
-  ASSERT_TRUE(session != nullptr);
-  TF_ASSERT_OK(session->Create(def));
+  std::vector<std::unique_ptr<Session>> sessions;
+  if (!use_global_pools) {
+    sessions.emplace_back(NewSession(options));
+    TF_ASSERT_OK(sessions.back()->Create(def));
+  }
+  mutex sessions_mu;
 
   std::atomic<int32> num_done(0);
   // Runs session to compute <node>:0 using inter_op thread pool <pool>.
-  auto add_session_run_call = [&session, &num_done](
-      thread::ThreadPool* tp, Node* node, int inter_op_pool) {
-    auto fn = [&session, inter_op_pool, node, &num_done]() {
+  auto add_session_run_call = [use_global_pools, &def, &options, &sessions,
+                               &sessions_mu,
+                               &num_done](thread::ThreadPool* tp, Node* node,
+                                          int inter_op_pool) {
+    auto fn = [use_global_pools, &def, &options, &sessions, &sessions_mu,
+               inter_op_pool, node, &num_done]() {
       RunOptions run_options;
       run_options.set_inter_op_thread_pool(inter_op_pool);
       std::vector<Tensor> outputs;
+
+      Session* session;
+      if (use_global_pools) {
+        std::unique_ptr<Session> s(NewSession(options));
+        TF_ASSERT_OK(s->Create(def));
+        session = s.get();
+
+        mutex_lock l(sessions_mu);
+        sessions.emplace_back(std::move(s));
+      } else {
+        session = sessions[0].get();
+      }
+
       Status s = session->Run(run_options, {} /* inputs */,
                               {node->name() + ":0"} /* output_names */, {},
                               &outputs, nullptr /* run_metadata */);
@@ -999,11 +1021,23 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib) {
 }
 
 TEST(DirectSessionTest, TestSessionInterOpThreads) {
-  TestSessionInterOpThreadsImpl(false /* use_function_lib */);
+  TestSessionInterOpThreadsImpl(false /* use_function_lib */,
+                                false /*use_global_pools */);
 }
 
 TEST(DirectSessionTest, TestSessionInterOpThreadsWithFunctions) {
-  TestSessionInterOpThreadsImpl(true /* use_function_lib */);
+  TestSessionInterOpThreadsImpl(true /* use_function_lib */,
+                                false /*use_global_pools */);
+}
+
+TEST(DirectSessionTest, TestSessionInterOpGlobalPools) {
+  TestSessionInterOpThreadsImpl(false /* use_function_lib */,
+                                true /*use_global_pools */);
+}
+
+TEST(DirectSessionTest, TestSessionInterOpGlobalPoolsWithFunctions) {
+  TestSessionInterOpThreadsImpl(true /* use_function_lib */,
+                                true /*use_global_pools */);
 }
 
 TEST(DirectSessionTest, TestSessionInterOpThreadsInvalidOptions) {
@@ -1023,19 +1057,42 @@ TEST(DirectSessionTest, TestSessionInterOpThreadsInvalidOptions) {
   options.config.add_session_inter_op_thread_pool();
 
   // Wrong pool number on Run call.
-  std::unique_ptr<Session> session(NewSession(options));
-  ASSERT_TRUE(session != nullptr);
-  TF_ASSERT_OK(session->Create(def));
-  for (int pool_num = -1; pool_num <= 1; pool_num += 2) {
-    RunOptions run_options;
-    run_options.set_inter_op_thread_pool(pool_num);
-    std::vector<Tensor> outputs;
-    Status s = session->Run(run_options, {} /* inputs */,
-                            {x->name() + ":0"} /* output_names */, {}, &outputs,
-                            nullptr /* run_metadata */);
-    EXPECT_EQ(strings::StrCat(
-                  "Invalid argument: Invalid inter_op_thread_pool: ", pool_num),
-              s.ToString());
+  {
+    std::unique_ptr<Session> session(NewSession(options));
+    TF_ASSERT_OK(session->Create(def));
+    for (int pool_num = -1; pool_num <= 1; pool_num += 2) {
+      RunOptions run_options;
+      run_options.set_inter_op_thread_pool(pool_num);
+      std::vector<Tensor> outputs;
+      Status s = session->Run(run_options, {} /* inputs */,
+                              {x->name() + ":0"} /* output_names */, {},
+                              &outputs, nullptr /* run_metadata */);
+      EXPECT_EQ(
+          strings::StrCat("Invalid argument: Invalid inter_op_thread_pool: ",
+                          pool_num),
+          s.ToString());
+    }
+  }
+
+  // Global name changes thread count.
+  std::vector<std::unique_ptr<Session>> sessions;
+  auto* pool_config = options.config.mutable_session_inter_op_thread_pool(0);
+  pool_config->set_num_threads(0);
+  pool_config->set_global_name("foo");
+  sessions.emplace_back(NewSession(options));
+  TF_ASSERT_OK(sessions.back()->Create(def));
+  sessions.emplace_back(NewSession(options));  // repeat creation, okay.
+  TF_ASSERT_OK(sessions.back()->Create(def));
+  for (int pass = 0; pass < 2; ++pass) {
+    for (int i = 1; i < 128; ++i) {
+      pool_config->set_num_threads(i);
+      sessions.emplace_back(NewSession(options));
+      auto status = sessions.back()->Create(def);
+      ASSERT_FALSE(status.ok()) << status;
+    }
+
+    // Clear existing sessions before second pass; error still happens.
+    sessions.clear();
   }
 }
 
diff --git a/tensorflow/core/common_runtime/executor.cc b/tensorflow/core/common_runtime/executor.cc
index 24b519fb07..ce6bbb0d65 100644
--- a/tensorflow/core/common_runtime/executor.cc
+++ b/tensorflow/core/common_runtime/executor.cc
@@ -1743,7 +1743,7 @@ Status ExecutorState::PrepareInputs(const NodeItem& item, Entry* first_input,
         if (!entry->ref->IsInitialized() && !IsInitializationOp(item.node)) {
           return AttachDef(errors::FailedPrecondition(
                                "Attempting to use uninitialized value ",
-                               item.kernel->def().input(i)),
+                               item.kernel->requested_input(i)),
                            item.kernel->def());
         }
       }
diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index b0b834b66a..99389968ee 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/memory_types.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index 7eda5c90a1..f60d3a89c6 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -391,7 +391,7 @@ void BaseGPUDevice::ComputeHelper(OpKernel* op_kernel,
 
   if (vlog_1) {
     VLOG(1) << "GpuDevice::Compute " << op_kernel->name() << " op "
-            << op_kernel->def().op() << " on GPU" << gpu_id_ << " stream["
+            << op_kernel->type_string() << " on GPU" << gpu_id_ << " stream["
             << stream_id << "]";
   }
 
@@ -465,7 +465,7 @@ void BaseGPUDevice::ComputeAsync(AsyncOpKernel* op_kernel,
   const auto stream_id = gpu_device_context->stream_id();
 
   VLOG(1) << "GpuDevice::ComputeAsync " << op_kernel->name() << " op "
-          << op_kernel->def().op() << " on GPU" << gpu_id_ << " stream["
+          << op_kernel->type_string() << " on GPU" << gpu_id_ << " stream["
           << stream_id << "]";
 
   // When TraceMe profiling is off (which is the default), the
diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index 0f6c3c1cb1..a6204b9d0d 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -294,7 +294,7 @@ Status ShapeRefiner::TryToInferTensorOutputFromInputShapes(const Edge* edge,
   }
   InferenceContext* c = it->second.get();
 
-  if (node->def().op() == "Shape") {
+  if (node->type_string() == "Shape") {
     // If input shapes to the shape op are fully defined,
     // we can infer the shape op's output tensor.
     bool fully_defined_inputs = c->FullyDefined(c->input(0));
@@ -324,7 +324,7 @@ Status ShapeRefiner::TryToInferTensorOutputFromInputShapes(const Edge* edge,
       *output = t;
       *success = true;
     }
-  } else if (node->def().op() == "Rank") {
+  } else if (node->type_string() == "Rank") {
     bool rank_known = c->RankKnown(c->input(0));
     if (rank_known) {
       int32 input_rank = c->Rank(c->input(0));
@@ -333,7 +333,7 @@ Status ShapeRefiner::TryToInferTensorOutputFromInputShapes(const Edge* edge,
       *output = t;
       *success = true;
     }
-  } else if (node->def().op() == "Size") {
+  } else if (node->type_string() == "Size") {
     bool fully_defined_inputs = c->FullyDefined(c->input(0));
     if (fully_defined_inputs) {
       int32 rank = c->Rank(c->input(0));
@@ -666,10 +666,10 @@ Status ShapeRefiner::RunShapeFn(const Node* node,
 
 bool ShapeRefiner::SameDefinedShape(InferenceContext* c, ShapeHandle s0,
                                     ShapeHandle s1) {
-  if (c->Rank(s0) != c->Rank(s1)) {
+  if (!c->RankKnown(s0)) {
+    return !c->RankKnown(s1);
+  } else if (!c->RankKnown(s1) || c->Rank(s0) != c->Rank(s1)) {
     return false;
-  } else if (!c->RankKnown(s0)) {
-    return true;
   }
 
   for (int i = 0; i < c->Rank(s0); ++i) {
diff --git a/tensorflow/core/common_runtime/simple_graph_execution_state.cc b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
index 8206a678b4..c00eb3a2fc 100644
--- a/tensorflow/core/common_runtime/simple_graph_execution_state.cc
+++ b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/simple_placer.h"
 #include "tensorflow/core/framework/graph.pb_text.h"
 #include "tensorflow/core/framework/graph_def_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/subgraph.h"
diff --git a/tensorflow/core/distributed_runtime/graph_mgr.cc b/tensorflow/core/distributed_runtime/graph_mgr.cc
index f4bf9dcd3b..69f5b7d944 100644
--- a/tensorflow/core/distributed_runtime/graph_mgr.cc
+++ b/tensorflow/core/distributed_runtime/graph_mgr.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h"
 #include "tensorflow/core/framework/cancellation.h"
 #include "tensorflow/core/framework/log_memory.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc
index c79f68a068..e98d9cecfc 100644
--- a/tensorflow/core/distributed_runtime/master_session.cc
+++ b/tensorflow/core/distributed_runtime/master_session.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/worker_interface.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/graph/graph_partition.h"
@@ -993,8 +994,7 @@ MasterSession::MasterSession(
           << " #remote " << remote_devs_->size();
 
   LOG(INFO) << "Start master session " << handle_
-            << " with config: " << std::endl
-            << session_opts_.config.DebugString();
+            << " with config: " << session_opts_.config.ShortDebugString();
 }
 
 MasterSession::~MasterSession() {
diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index c0918ef445..bd381dd10f 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -193,6 +193,7 @@ cc_library(
         ":grpc_master_service_impl",
         ":grpc_util",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core:master_proto_cc",
         "//tensorflow/core/distributed_runtime:call_options",
         "//tensorflow/core/distributed_runtime:master_interface",
@@ -210,6 +211,7 @@ cc_library(
         ":grpc_master_service_impl",
         ":grpc_util",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core:master_proto_cc",
         "//tensorflow/core/distributed_runtime:master",
         "@grpc//:grpc++_unsecure",
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_call.h b/tensorflow/core/distributed_runtime/rpc/grpc_call.h
index e85b8ccbd3..cb133737dd 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_call.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_call.h
@@ -233,6 +233,11 @@ class Call : public UntypedCall<Service> {
   RequestMessage request;
   ResponseMessage response;
 
+  const std::multimap<::grpc::string_ref, ::grpc::string_ref>& client_metadata()
+      const {
+    return ctx_.client_metadata();
+  }
+
  private:
   // Creates a completion queue tag for handling cancellation by the client.
   // NOTE: This method must be called before this call is enqueued on a
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc
index bcd2c71f84..7efc0ba6d8 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc
@@ -67,7 +67,7 @@ Status NewHostPortGrpcChannel(const string& target,
   // on connection failure, which makes our tests time out.
   args.SetInt("grpc.testing.fixed_reconnect_backoff_ms", 1000);
   *channel_pointer = ::grpc::CreateCustomChannel(
-      target, ::grpc::InsecureChannelCredentials(), args);
+      "dns:///" + target, ::grpc::InsecureChannelCredentials(), args);
   return Status::OK();
 }
 
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
index 07205bb2c2..41ee81c01d 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
@@ -40,6 +40,7 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/tracing.h"
 #include "tensorflow/core/protobuf/master.pb.h"
 
 namespace tensorflow {
@@ -172,6 +173,7 @@ class GrpcMasterService : public AsyncServiceInterface {
 
   // RPC handler for running one step in a session.
   void RunStepHandler(MasterCall<RunStepRequest, RunStepResponse>* call) {
+    auto* trace = TraceRpc("RunStep/Server", call->client_metadata());
     CallOptions* call_opts = new CallOptions;
     if (call->request.options().timeout_in_ms() > 0) {
       call_opts->SetTimeout(call->request.options().timeout_in_ms());
@@ -184,11 +186,12 @@ class GrpcMasterService : public AsyncServiceInterface {
         new NonOwnedProtoRunStepResponse(&call->response);
     call->SetCancelCallback([call_opts]() { call_opts->StartCancel(); });
     master_impl_->RunStep(call_opts, wrapped_request, wrapped_response,
-                          [call, call_opts, wrapped_request,
-                           wrapped_response](const Status& status) {
+                          [call, call_opts, wrapped_request, wrapped_response,
+                           trace](const Status& status) {
                             call->ClearCancelCallback();
                             delete call_opts;
                             delete wrapped_request;
+                            delete trace;
                             call->SendResponse(ToGrpcStatus(status));
                           });
     ENQUEUE_REQUEST(RunStep, true);
@@ -224,6 +227,18 @@ class GrpcMasterService : public AsyncServiceInterface {
   }
 #undef ENQUEUE_REQUEST
 
+  // Start tracing, including the ID attached to the RPC.
+  port::Tracing::TraceMe* TraceRpc(
+      StringPiece name,
+      const std::multimap<::grpc::string_ref, ::grpc::string_ref>& metadata) {
+    StringPiece id;
+    auto it = metadata.find(GrpcIdKey());
+    if (it != metadata.end()) {
+      id = StringPiece(it->second.data(), it->second.size());
+    }
+    return new port::Tracing::TraceMe(name, id);
+  }
+
   TF_DISALLOW_COPY_AND_ASSIGN(GrpcMasterService);
 };
 
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc
index bf72d9a7fc..c04aa44941 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc
@@ -23,6 +23,8 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/tracing.h"
 #include "tensorflow/core/protobuf/master.pb.h"
 
 namespace tensorflow {
@@ -66,6 +68,7 @@ class GrpcRemoteMaster : public MasterInterface {
   Status RunStep(CallOptions* call_options, RunStepRequestWrapper* request,
                  MutableRunStepResponseWrapper* response) override {
     ::grpc::ClientContext ctx;
+    auto trace = TraceRpc("RunStep/Client", &ctx);
     ctx.set_fail_fast(false);
     SetDeadline(&ctx, call_options->GetTimeout());
     return FromGrpcStatus(stub_->RunStep(&ctx, request->ToProto(),
@@ -99,6 +102,14 @@ class GrpcRemoteMaster : public MasterInterface {
   }
 
  private:
+  // Start tracing, attaching a unique ID to both the trace and the RPC.
+  port::Tracing::TraceMe TraceRpc(StringPiece name,
+                                  ::grpc::ClientContext* ctx) {
+    string trace_id = strings::StrCat(port::Tracing::UniqueId());
+    ctx->AddMetadata(GrpcIdKey(), trace_id);
+    return port::Tracing::TraceMe(name, trace_id);
+  }
+
   std::unique_ptr<grpc::MasterService::Stub> stub_;
 
   void SetDeadline(::grpc::ClientContext* ctx, int64 time_in_ms) {
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_util.h b/tensorflow/core/distributed_runtime/rpc/grpc_util.h
index 44473d1150..5244cd1c13 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_util.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_util.h
@@ -43,6 +43,8 @@ inline ::grpc::Status ToGrpcStatus(const ::tensorflow::Status& s) {
 
 typedef std::shared_ptr<::grpc::Channel> SharedGrpcChannelPtr;
 
+inline string GrpcIdKey() { return "tf-rpc"; }
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RPC_GRPC_UTIL_H_
diff --git a/tensorflow/core/example/example_parser_configuration.cc b/tensorflow/core/example/example_parser_configuration.cc
index e4a3f26209..485cf6da4b 100644
--- a/tensorflow/core/example/example_parser_configuration.cc
+++ b/tensorflow/core/example/example_parser_configuration.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/example/feature.pb_text.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/numeric_op.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h
index cb58896f49..1cb183d81e 100644
--- a/tensorflow/core/framework/allocator.h
+++ b/tensorflow/core/framework/allocator.h
@@ -21,7 +21,7 @@ limitations under the License.
 #include <limits>
 
 #include "tensorflow/core/framework/numeric_types.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/framework/common_shape_fns.cc b/tensorflow/core/framework/common_shape_fns.cc
index 035bceb640..dc2db3d395 100644
--- a/tensorflow/core/framework/common_shape_fns.cc
+++ b/tensorflow/core/framework/common_shape_fns.cc
@@ -922,5 +922,67 @@ Status ValidateSparseTensor(InferenceContext* c, ShapeHandle indices_shape,
   return Status::OK();
 }
 
+Status ScatterNdUpdateShape(InferenceContext* c) {
+  ShapeHandle input_shape = c->input(0);
+  ShapeHandle indices_shape;
+  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &indices_shape));
+  ShapeHandle updates_shape;
+  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(2), 1, &updates_shape));
+
+  if (c->Value(c->NumElements(input_shape)) == 0 &&
+      (c->Value(c->NumElements(indices_shape)) > 0 ||
+       c->Value(c->NumElements(updates_shape)) > 0)) {
+    return errors::InvalidArgument(
+        "Indices and updates specified for empty output shape");
+  }
+
+  if (c->RankKnown(indices_shape) && c->RankKnown(updates_shape)) {
+    const int64 num_outer_dims = c->Rank(indices_shape) - 1;
+    const DimensionHandle index_size = c->Dim(indices_shape, -1);
+
+    // We can only do more validation if the last dimension of indices
+    // is a known value.
+    if (c->ValueKnown(index_size)) {
+      const int64 ix = c->Value(index_size);
+      ShapeHandle unused;
+      ShapeHandle prefix_indices;
+      TF_RETURN_IF_ERROR(
+          c->Subshape(indices_shape, 0, num_outer_dims, &prefix_indices));
+      ShapeHandle prefix_updates;
+      TF_RETURN_IF_ERROR(
+          c->Subshape(updates_shape, 0, num_outer_dims, &prefix_updates));
+
+      Status s = c->Merge(prefix_indices, prefix_updates, &unused);
+      if (!s.ok()) {
+        return errors::InvalidArgument(
+            "The outer ", num_outer_dims,
+            " dimensions of indices.shape=", c->DebugString(indices_shape),
+            " must match the outer ", num_outer_dims,
+            " dimensions of updates.shape=", c->DebugString(updates_shape),
+            ": ", s.error_message());
+      }
+
+      ShapeHandle input_suffix;
+      TF_RETURN_IF_ERROR(c->Subshape(input_shape, ix, &input_suffix));
+      ShapeHandle suffix_updates;
+      TF_RETURN_IF_ERROR(
+          c->Subshape(updates_shape, num_outer_dims, &suffix_updates));
+      s = c->Merge(input_suffix, suffix_updates, &unused);
+      if (!s.ok()) {
+        return errors::InvalidArgument(
+            "The inner ", c->Rank(input_shape) - ix,
+            " dimensions of input.shape=", c->DebugString(input_shape),
+            " must match the inner ", c->Rank(updates_shape) - num_outer_dims,
+            " dimensions of updates.shape=", c->DebugString(updates_shape),
+            ": ", s.error_message());
+      }
+    }
+  }
+
+  c->set_output(0, input_shape);
+  return Status::OK();
+}
+
 }  // namespace shape_inference
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/common_shape_fns.h b/tensorflow/core/framework/common_shape_fns.h
index dc99e48adb..aafd9eff27 100644
--- a/tensorflow/core/framework/common_shape_fns.h
+++ b/tensorflow/core/framework/common_shape_fns.h
@@ -207,6 +207,9 @@ Status RandomShape(shape_inference::InferenceContext* c);
 Status ValidateSparseTensor(InferenceContext* c, ShapeHandle indices_shape,
                             ShapeHandle values_shape, ShapeHandle shape_shape);
 
+// Shape function for ScatterNd update/add/sub/... operations.
+Status ScatterNdUpdateShape(InferenceContext* c);
+
 }  // namespace shape_inference
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index 9026075a2f..fe6e9a6cd6 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/function.pb_text.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/graph/graph.h"
diff --git a/tensorflow/core/framework/function_testlib.cc b/tensorflow/core/framework/function_testlib.cc
index 7caddf3cb8..4ee23226da 100644
--- a/tensorflow/core/framework/function_testlib.cc
+++ b/tensorflow/core/framework/function_testlib.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/framework/function_testlib.h"
 
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/public/version.h"
diff --git a/tensorflow/core/framework/function_testlib.h b/tensorflow/core/framework/function_testlib.h
index 56b8f0aa1b..49e5b0c99d 100644
--- a/tensorflow/core/framework/function_testlib.h
+++ b/tensorflow/core/framework/function_testlib.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/types.h"
 
diff --git a/tensorflow/core/framework/graph_def_util.cc b/tensorflow/core/framework/graph_def_util.cc
index 8496774793..aeedf4b0ef 100644
--- a/tensorflow/core/framework/graph_def_util.cc
+++ b/tensorflow/core/framework/graph_def_util.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/versions.pb_text.h"
diff --git a/tensorflow/core/framework/memory_types.cc b/tensorflow/core/framework/memory_types.cc
index c1dde1504a..6a2eed94b9 100644
--- a/tensorflow/core/framework/memory_types.cc
+++ b/tensorflow/core/framework/memory_types.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/core/framework/kernel_def.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/framework/node_def_builder.h b/tensorflow/core/framework/node_def_builder.h
index c09d96bfa6..fd26d0ae64 100644
--- a/tensorflow/core/framework/node_def_builder.h
+++ b/tensorflow/core/framework/node_def_builder.h
@@ -20,6 +20,7 @@ limitations under the License.
 #include <vector>
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb.h"
diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc
index dec987e1ed..3892320b7d 100644
--- a/tensorflow/core/framework/op_kernel.cc
+++ b/tensorflow/core/framework/op_kernel.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/framework/kernel_def.pb_text.h"
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/memory_types.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/types.h"
@@ -77,7 +78,7 @@ Status MatchSignatureHelper(const DataTypeSlice expected_inputs,
 // OpKernel ------------------------------------------------------------------
 
 OpKernel::OpKernel(OpKernelConstruction* context)
-    : def_(context->def()),
+    : def_(new NodeDef(context->def())),
       input_types_(context->input_types().begin(),
                    context->input_types().end()),
       input_memory_types_(context->input_memory_types().begin(),
@@ -91,7 +92,7 @@ OpKernel::OpKernel(OpKernelConstruction* context)
       input_name_map_(context->num_inputs()),
       output_name_map_(context->num_outputs()) {
   OP_REQUIRES_OK(context,
-                 NameRangesForNode(def_, *context->op_def_, &input_name_map_,
+                 NameRangesForNode(*def_, *context->op_def_, &input_name_map_,
                                    &output_name_map_));
   OP_REQUIRES_OK(context, CheckOpDeprecation(*context->op_def_,
                                              context->graph_def_version()));
@@ -103,6 +104,11 @@ OpKernel::OpKernel(OpKernelConstruction* context)
 
 OpKernel::~OpKernel() {}
 
+const string& OpKernel::name() const { return def_->name(); }
+const string& OpKernel::type_string() const { return def_->op(); }
+const string& OpKernel::requested_device() const { return def_->device(); }
+const string& OpKernel::requested_input(int i) const { return def_->input(i); }
+
 Status OpKernel::InputRange(StringPiece input_name, int* start,
                             int* stop) const {
   const auto result = input_name_map_.find(input_name.ToString());
@@ -165,6 +171,26 @@ Tensor* PersistentTensor::AccessTensor(OpKernelContext* context) {
 
 // OpKernelConstruction ------------------------------------------------------
 
+OpKernelConstruction::OpKernelConstruction(
+    DeviceType device_type, DeviceBase* device, Allocator* allocator,
+    const NodeDef* node_def, const OpDef* op_def, FunctionLibraryRuntime* flib,
+    const DataTypeSlice& input_types, const MemoryTypeSlice& input_memory_types,
+    const DataTypeSlice& output_types,
+    const MemoryTypeSlice& output_memory_types, int graph_def_version,
+    Status* status)
+    : device_type_(std::move(device_type)),
+      device_(device),
+      allocator_(allocator),
+      def_(node_def),
+      op_def_(op_def),
+      flib_(flib),
+      input_types_(input_types),
+      input_memory_types_(input_memory_types),
+      output_types_(output_types),
+      output_memory_types_(output_memory_types),
+      graph_def_version_(graph_def_version),
+      status_(status) {}
+
 void OpKernelConstruction::SetStatus(const Status& status) {
   status_->Update(status);
 }
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index 63caa054d7..14b6d8134b 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -109,9 +109,10 @@ class OpKernel {
   virtual bool IsExpensive() { return expensive_; }
 
   // Accessors.
-  const NodeDef& def() const { return def_; }
-  const string& name() const { return def_.name(); }
-  const string& type_string() const { return def_.op(); }
+  const NodeDef& def() const { return *def_; }
+  const string& name() const;              // Same as def().name()
+  const string& type_string() const;       // Same as def().op()
+  const string& requested_device() const;  // Same as def().device()
   bool is_internal() const { return is_internal_; }
 
   int num_inputs() const { return input_types_.size(); }
@@ -120,6 +121,7 @@ class OpKernel {
   const MemoryTypeVector& input_memory_types() const {
     return input_memory_types_;
   }
+  const string& requested_input(int i) const;  // Same as def().input(i)
 
   int num_outputs() const { return output_types_.size(); }
   DataType output_type(int o) const { return output_types_[o]; }
@@ -157,7 +159,7 @@ class OpKernel {
   Status MakeShape(const Tensor& shape, TensorShape* out) const;
 
  private:
-  const NodeDef def_;
+  const std::unique_ptr<const NodeDef> def_;
   const DataTypeVector input_types_;
   const MemoryTypeVector input_memory_types_;
   const DataTypeVector output_types_;
@@ -227,19 +229,7 @@ class OpKernelConstruction {
                        const MemoryTypeSlice& input_memory_types,
                        const DataTypeSlice& output_types,
                        const MemoryTypeSlice& output_memory_types,
-                       int graph_def_version, Status* status)
-      : device_type_(std::move(device_type)),
-        device_(device),
-        allocator_(allocator),
-        def_(node_def),
-        op_def_(op_def),
-        flib_(flib),
-        input_types_(input_types),
-        input_memory_types_(input_memory_types),
-        output_types_(output_types),
-        output_memory_types_(output_memory_types),
-        graph_def_version_(graph_def_version),
-        status_(status) {}
+                       int graph_def_version, Status* status);
 
   Env* env() const { return device_->env(); }
 
diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index d79468afa1..2f7b140295 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -18,7 +18,7 @@ limitations under the License.
 // This file is used by cuda code and must remain compilable by nvcc.
 
 #include "tensorflow/core/framework/numeric_types.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/platform/types.h"
 
 // Two sets of macros:
diff --git a/tensorflow/core/framework/resource_handle.cc b/tensorflow/core/framework/resource_handle.cc
new file mode 100644
index 0000000000..39ef82765f
--- /dev/null
+++ b/tensorflow/core/framework/resource_handle.cc
@@ -0,0 +1,69 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/resource_handle.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+
+namespace tensorflow {
+
+ResourceHandle::ResourceHandle() {}
+
+ResourceHandle::ResourceHandle(const ResourceHandleProto& proto) {
+  FromProto(proto);
+}
+
+ResourceHandle::~ResourceHandle() {}
+
+void ResourceHandle::AsProto(ResourceHandleProto* proto) const {
+  proto->set_device(device());
+  proto->set_container(container());
+  proto->set_name(name());
+  proto->set_hash_code(hash_code());
+  proto->set_maybe_type_name(maybe_type_name());
+}
+
+void ResourceHandle::FromProto(const ResourceHandleProto& proto) {
+  set_device(proto.device());
+  set_container(proto.container());
+  set_name(proto.name());
+  set_hash_code(proto.hash_code());
+  set_maybe_type_name(proto.maybe_type_name());
+}
+
+string ResourceHandle::SerializeAsString() const {
+  ResourceHandleProto proto;
+  AsProto(&proto);
+  return proto.SerializeAsString();
+}
+
+bool ResourceHandle::ParseFromString(const string& s) {
+  ResourceHandleProto proto;
+  const bool status = proto.ParseFromString(s);
+  if (status) FromProto(proto);
+  return status;
+}
+
+string ResourceHandle::DebugString() const {
+  return strings::StrCat("device: ", device(), " container: ", container(),
+                         " name: ", name(), " hash_code: ", hash_code(),
+                         " maybe_type_name: ", maybe_type_name());
+}
+
+string ProtoDebugString(const ResourceHandle& handle) {
+  return handle.DebugString();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/framework/resource_handle.h b/tensorflow/core/framework/resource_handle.h
new file mode 100644
index 0000000000..06df1b9046
--- /dev/null
+++ b/tensorflow/core/framework/resource_handle.h
@@ -0,0 +1,82 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_FRAMEWORK_RESOURCE_HANDLE_H_
+#define TENSORFLOW_FRAMEWORK_RESOURCE_HANDLE_H_
+
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+class ResourceHandleProto;
+
+// Class representing a handle to a tensorflow resource. Handles are
+// not valid across executions, but can be serialized back and forth from within
+// a single run.
+//
+// This is the native C++ class equivalent of ResourceHandleProto.  They are
+// separate so that kernels do not need to depend on protos.
+class ResourceHandle {
+ public:
+  ResourceHandle();
+  ResourceHandle(const ResourceHandleProto& proto);
+  ~ResourceHandle();
+
+  // Unique name for the device containing the resource.
+  const string& device() const { return device_; }
+  void set_device(const string& device) { device_ = device; }
+
+  // Container in which this resource is placed.
+  const string& container() const { return container_; }
+  void set_container(const string& container) { container_ = container; }
+
+  // Unique name of this resource.
+  const string& name() const { return name_; }
+  void set_name(const string& name) { name_ = name; }
+
+  // Hash code for the type of the resource. Is only valid in the same device
+  // and in the same execution.
+  uint64 hash_code() const { return hash_code_; }
+  void set_hash_code(uint64 hash_code) { hash_code_ = hash_code; }
+
+  // For debug-only, the name of the type pointed to by this handle, if
+  // available.
+  const string& maybe_type_name() const { return maybe_type_name_; }
+  void set_maybe_type_name(const string& value) { maybe_type_name_ = value; }
+
+  // Conversion to and from ResourceHandleProto
+  void AsProto(ResourceHandleProto* proto) const;
+  void FromProto(const ResourceHandleProto& proto);
+
+  // Serialization via ResourceHandleProto
+  string SerializeAsString() const;
+  bool ParseFromString(const string& s);
+
+  string DebugString() const;
+
+ public:
+  string device_;
+  string container_;
+  string name_;
+  uint64 hash_code_ = 0;
+  string maybe_type_name_;
+};
+
+// For backwards compatibility for when this was a proto
+string ProtoDebugString(const ResourceHandle& handle);
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_FRAMEWORK_RESOURCE_HANDLE_H_
diff --git a/tensorflow/core/framework/resource_handle.proto b/tensorflow/core/framework/resource_handle.proto
index f9f19ca5b4..b1921337f5 100644
--- a/tensorflow/core/framework/resource_handle.proto
+++ b/tensorflow/core/framework/resource_handle.proto
@@ -2,14 +2,14 @@ syntax = "proto3";
 
 package tensorflow;
 option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandleProto";
+option java_outer_classname = "ResourceHandle";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
 
 // Protocol buffer representing a handle to a tensorflow resource. Handles are
 // not valid across executions, but can be serialized back and forth from within
 // a single run.
-message ResourceHandle {
+message ResourceHandleProto {
   // Unique name for the device containing the resource.
   string device = 1;
 
diff --git a/tensorflow/core/framework/resource_mgr.cc b/tensorflow/core/framework/resource_mgr.cc
index 4365a861e5..3018e4f655 100644
--- a/tensorflow/core/framework/resource_mgr.cc
+++ b/tensorflow/core/framework/resource_mgr.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/resource_mgr.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
diff --git a/tensorflow/core/framework/resource_mgr_test.cc b/tensorflow/core/framework/resource_mgr_test.cc
index cc7613b97d..df4d8c3591 100644
--- a/tensorflow/core/framework/resource_mgr_test.cc
+++ b/tensorflow/core/framework/resource_mgr_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/resource_mgr.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
diff --git a/tensorflow/core/framework/selective_registration.h b/tensorflow/core/framework/selective_registration.h
index af86092ac7..503947969d 100644
--- a/tensorflow/core/framework/selective_registration.h
+++ b/tensorflow/core/framework/selective_registration.h
@@ -31,8 +31,8 @@ limitations under the License.
 //    functions should be defined as valid constexpr functions, so that they are
 //    evaluated at compile time: this is needed to make symbols referenced by
 //    un-registered objects unused, and therefore allow the linker to strip them
-//    out.  See tools/print_required_ops/print_selective_registration_header.py
-//    for a tool that can be used to generate ops_to_register.h.
+//    out.  See python/tools/print_selective_registration_header.py for a tool
+//    that can be used to generate ops_to_register.h.
 //
 // ops_to_register.h should define macros for:
 //   // Ops for which this is false will not be registered.
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 2c18ddd48f..62f85d2dac 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -97,6 +97,21 @@ InferenceContext::InferenceContext(
 
 InferenceContext::~InferenceContext() {}
 
+Status InferenceContext::Run(
+    const std::function<Status(shape_inference::InferenceContext* c)>& fn) {
+  Status s = fn(this);
+  if (!s.ok()) {
+    return AttachContext(s);
+  }
+#ifndef NDEBUG
+  for (int i = 0; i < num_outputs(); ++i) {
+    DCHECK(output(i).IsSet())
+        << i << " for " << node_def_.name() << " of type " << node_def_.op();
+  }
+#endif  // NDEBUG
+  return s;
+}
+
 Status InferenceContext::set_output(StringPiece output_name,
                                     const std::vector<ShapeHandle>& shapes) {
   const auto result = output_name_map_.find(output_name.ToString());
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index 5668667659..460aefe29e 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -196,19 +196,7 @@ class InferenceContext {
   //
   // On error, additional context is provided in the error message.
   Status Run(
-      const std::function<Status(shape_inference::InferenceContext* c)>& fn) {
-    Status s = fn(this);
-    if (!s.ok()) {
-      return AttachContext(s);
-    }
-#ifndef NDEBUG
-    for (int i = 0; i < num_outputs(); ++i) {
-      DCHECK(output(i).IsSet())
-          << i << " for " << node_def_.name() << " of type " << node_def_.op();
-    }
-#endif  // NDEBUG
-    return s;
-  }
+      const std::function<Status(shape_inference::InferenceContext* c)>& fn);
 
   // Merge the stored shape of the input in position idx with <shape> according
   // to the following rules:
diff --git a/tensorflow/core/framework/shape_inference_testutil.h b/tensorflow/core/framework/shape_inference_testutil.h
index 6bd2cd4291..03c39e6dc1 100644
--- a/tensorflow/core/framework/shape_inference_testutil.h
+++ b/tensorflow/core/framework/shape_inference_testutil.h
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <vector>
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 243b30c007..cc9f11ef6a 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 
 #include "tensorflow/core/framework/log_memory.h"
+#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/framework/types.h"
@@ -270,7 +271,7 @@ struct ProtoHelper<int64> {
 
 template <>
 struct ProtoHelper<ResourceHandle> {
-  static protobuf::RepeatedPtrField<ResourceHandle>::const_iterator Begin(
+  static protobuf::RepeatedPtrField<ResourceHandleProto>::const_iterator Begin(
       const TensorProto& proto) {
     return proto.resource_handle_val().begin();
   }
@@ -278,8 +279,11 @@ struct ProtoHelper<ResourceHandle> {
     return proto.resource_handle_val().size();
   }
   static void Fill(const ResourceHandle* data, size_t n, TensorProto* proto) {
-    protobuf::RepeatedPtrField<ResourceHandle> copy(data, data + n);
-    proto->mutable_resource_handle_val()->Swap(&copy);
+    auto* handles = proto->mutable_resource_handle_val();
+    handles->Clear();
+    for (size_t i = 0; i < n; i++) {
+      data[i].AsProto(handles->Add());
+    }
   }
 };
 
diff --git a/tensorflow/core/framework/tensor.proto b/tensorflow/core/framework/tensor.proto
index 5d383bcc66..98ecef225a 100644
--- a/tensorflow/core/framework/tensor.proto
+++ b/tensorflow/core/framework/tensor.proto
@@ -71,5 +71,5 @@ message TensorProto {
   repeated double dcomplex_val = 12 [packed = true];
 
   // DT_RESOURCE
-  repeated ResourceHandle resource_handle_val = 14;
+  repeated ResourceHandleProto resource_handle_val = 14;
 };
diff --git a/tensorflow/core/framework/types.h b/tensorflow/core/framework/types.h
index f562880e7c..889056647c 100644
--- a/tensorflow/core/framework/types.h
+++ b/tensorflow/core/framework/types.h
@@ -28,7 +28,7 @@ limitations under the License.
 // clang-format on
 #include "tensorflow/core/framework/bfloat16.h"
 #include "tensorflow/core/framework/numeric_types.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
diff --git a/tensorflow/core/graph/gradients.cc b/tensorflow/core/graph/gradients.cc
index 09c3d8d567..d3e7ff781c 100644
--- a/tensorflow/core/graph/gradients.cc
+++ b/tensorflow/core/graph/gradients.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/executor.h"
 #include "tensorflow/core/common_runtime/graph_optimizer.h"
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index dcb8520cf7..e06e479264 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/graph/graph.h"
 
 #include <vector>
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -28,6 +29,21 @@ namespace tensorflow {
 
 const int Graph::kControlSlot = -1;
 
+struct NodeProperties {
+ public:
+  NodeProperties(const OpDef* op_def, const NodeDef& node_def,
+                 const DataTypeSlice inputs, const DataTypeSlice outputs)
+      : op_def(op_def),
+        node_def(node_def),
+        input_types(inputs.begin(), inputs.end()),
+        output_types(outputs.begin(), outputs.end()) {}
+
+  const OpDef* op_def;  // not owned
+  NodeDef node_def;
+  const DataTypeVector input_types;
+  const DataTypeVector output_types;
+};
+
 // Node
 
 #define REF_CLASS(key, value) \
@@ -93,26 +109,17 @@ Node::Node()
       props_(nullptr),
       assigned_device_name_index_(0) {}
 
-Node::~Node() {
-  if (props_) {
-    props_->Unref();
-  }
-}
-
-void Node::Initialize(int id, int cost_id, Properties* props) {
+void Node::Initialize(int id, int cost_id,
+                      std::shared_ptr<NodeProperties> props) {
   DCHECK_EQ(id_, -1);
   DCHECK(in_edges_.empty());
   DCHECK(out_edges_.empty());
   id_ = id;
   cost_id_ = cost_id;
 
-  // Unref the old, assign the new properties.
-  if (props_) {
-    props_->Unref();
-  }
-  props_ = props;
+  props_ = std::move(props);
   // Initialize the class_ based on the type string
-  class_ = GetNodeClassForOp(props->node_def_.op());
+  class_ = GetNodeClassForOp(props_->node_def.op());
 }
 
 void Node::Clear() {
@@ -121,15 +128,33 @@ void Node::Clear() {
   id_ = -1;
   cost_id_ = -1;
   class_ = NC_UNINITIALIZED;
+  props_.reset();
+  assigned_device_name_index_ = 0;
+}
 
-  if (props_) {
-    props_->Unref();
-    props_ = nullptr;
-  }
+const string& Node::name() const { return props_->node_def.name(); }
+const string& Node::type_string() const { return props_->node_def.op(); }
+const NodeDef& Node::def() const { return props_->node_def; }
+const OpDef& Node::op_def() const { return *props_->op_def; }
 
-  assigned_device_name_index_ = 0;
+int32 Node::num_inputs() const { return props_->input_types.size(); }
+DataType Node::input_type(int32 i) const { return props_->input_types[i]; }
+const DataTypeVector& Node::input_types() const { return props_->input_types; }
+
+int32 Node::num_outputs() const { return props_->output_types.size(); }
+DataType Node::output_type(int32 o) const { return props_->output_types[o]; }
+const DataTypeVector& Node::output_types() const {
+  return props_->output_types;
 }
 
+AttrSlice Node::attrs() const { return AttrSlice(def()); }
+
+const protobuf::RepeatedPtrField<string>& Node::requested_inputs() const {
+  return def().input();
+}
+
+const string& Node::requested_device() const { return def().device(); }
+
 gtl::iterator_range<NeighborIter> Node::out_nodes() const {
   return gtl::make_range(NeighborIter(out_edges_.begin(), false),
                          NeighborIter(out_edges_.end(), false));
@@ -141,19 +166,20 @@ gtl::iterator_range<NeighborIter> Node::in_nodes() const {
 }
 
 void Node::MaybeCopyOnWrite() {
-  // Properties may be shared between Nodes. Make a copy if so.
-  if (!props_->RefCountIsOne()) {
-    Properties* new_props =
-        new Properties(props_->op_def_, props_->node_def_, props_->input_types_,
-                       props_->output_types_);
-    props_->Unref();
-    props_ = new_props;
+  // NodeProperties may be shared between Nodes. Make a copy if so.
+  if (!props_.unique()) {
+    props_ = std::make_shared<NodeProperties>(*props_);
   }
 }
 
+AttrValue* Node::AddAttrHelper(const string& name) {
+  MaybeCopyOnWrite();
+  return &((*props_->node_def.mutable_attr())[name]);
+}
+
 void Node::ClearAttr(const string& name) {
   MaybeCopyOnWrite();
-  (*props_->node_def_.mutable_attr()).erase(name);
+  (*props_->node_def.mutable_attr()).erase(name);
 }
 
 Status Node::input_edge(int idx, const Edge** e) const {
@@ -225,17 +251,6 @@ Status Node::input_node(int idx, const Node** const_n) const {
   return Status::OK();
 }
 
-// Node::Properties
-
-Node::Properties::Properties(const OpDef* op_def, const NodeDef& node_def,
-                             const DataTypeSlice inputs,
-                             const DataTypeSlice outputs)
-    : op_def_(op_def),
-      node_def_(node_def),
-      input_types_(inputs.begin(), inputs.end()),
-      output_types_(outputs.begin(), outputs.end()) {}
-
-Node::Properties::~Properties() {}
 
 // Graph
 
@@ -300,16 +315,15 @@ Node* Graph::AddNode(const NodeDef& node_def, Status* status) {
   }
 
   Node* node = AllocateNode(
-      new Node::Properties(op_def, node_def, inputs, outputs), nullptr);
+      std::make_shared<NodeProperties>(op_def, node_def, inputs, outputs),
+      nullptr);
   return node;
 }
 
 Node* Graph::CopyNode(Node* node) {
   DCHECK(!node->IsSource());
   DCHECK(!node->IsSink());
-  Node::Properties* props = node->properties();
-  props->Ref();
-  Node* copy = AllocateNode(props, node);
+  Node* copy = AllocateNode(node->props_, node);
   copy->set_assigned_device_name(node->assigned_device_name());
 
   // Since the OpDef of a function may be owned by the Graph that owns 'node',
@@ -317,9 +331,9 @@ Node* Graph::CopyNode(Node* node) {
   // node properties with the updated OpDef.
   const OpDef* op_def;
   TF_CHECK_OK(ops_.LookUpOpDef(node->type_string(), &op_def));
-  if (op_def != props->op_def_) {
+  if (op_def != node->props_->op_def) {
     copy->MaybeCopyOnWrite();
-    copy->props_->op_def_ = op_def;
+    copy->props_->op_def = op_def;
   }
 
   return copy;
@@ -502,7 +516,8 @@ bool Graph::IsValidNode(Node* node) const {
   return nodes_[id] == node;
 }
 
-Node* Graph::AllocateNode(Node::Properties* props, const Node* cost_node) {
+Node* Graph::AllocateNode(std::shared_ptr<NodeProperties> props,
+                          const Node* cost_node) {
   Node* node = nullptr;
   if (free_nodes_.empty()) {
     node = new (arena_.Alloc(sizeof(Node))) Node;  // placement new
@@ -513,7 +528,7 @@ Node* Graph::AllocateNode(Node::Properties* props, const Node* cost_node) {
   node->graph_ = this;
   const int id = nodes_.size();
   int cost_id = cost_node ? cost_node->cost_id() : id;
-  node->Initialize(id, cost_id, props);
+  node->Initialize(id, cost_id, std::move(props));
   nodes_.push_back(node);
   ++num_nodes_;
   return node;
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 8cb270170e..e19e0b727d 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -63,14 +63,15 @@ class Node;
 
 class NeighborIter;  // Declared below
 class NodeIter;      // Declared below
+class NodeProperties;  // Defined in .cc
 
 class Node {
  public:
   string DebugString() const;
   int id() const { return id_; }
   int cost_id() const { return cost_id_; }
-  const string& name() const { return props_->node_def_.name(); }
-  const string& type_string() const { return props_->node_def_.op(); }
+  const string& name() const;
+  const string& type_string() const;
 
   // def() provides the NodeDef the user supplied, but the specifics
   // of this Node may have changed due to placement, optimization, etc.
@@ -82,21 +83,21 @@ class Node {
   //   the actual assigned device, see assigned_device_name() below;
   // * def().attr() is authoritative.
   // TODO(irving): Replace with NodeInfo.
-  const NodeDef& def() const { return props_->node_def_; }
-  const OpDef& op_def() const { return *props_->op_def_; }
+  const NodeDef& def() const;
+  const OpDef& op_def() const;
 
   // input and output types
-  int32 num_inputs() const { return props_->input_types_.size(); }
-  DataType input_type(int32 i) const { return props_->input_types_[i]; }
-  const DataTypeVector& input_types() const { return props_->input_types_; }
+  int32 num_inputs() const;
+  DataType input_type(int32 i) const;
+  const DataTypeVector& input_types() const;
 
-  int32 num_outputs() const { return props_->output_types_.size(); }
-  DataType output_type(int32 o) const { return props_->output_types_[o]; }
-  const DataTypeVector& output_types() const { return props_->output_types_; }
+  int32 num_outputs() const;
+  DataType output_type(int32 o) const;
+  const DataTypeVector& output_types() const;
 
   // The device requested by the user.  For the actual assigned device,
   // use assigned_device_name() below.
-  const string& requested_device() const { return def().device(); }
+  const string& requested_device() const;
 
   // This gives the device the runtime has assigned this node to.  If
   // you want the device the user requested, use def().device() instead.
@@ -113,12 +114,10 @@ class Node {
   void set_assigned_device_name_index(int index);
 
   // Read only access to attributes
-  AttrSlice attrs() const { return AttrSlice(def()); }
+  AttrSlice attrs() const;
 
   // Inputs requested by the NodeDef.  For the actual inputs, use in_edges.
-  const protobuf::RepeatedPtrField<string>& requested_inputs() const {
-    return def().input();
-  }
+  const protobuf::RepeatedPtrField<string>& requested_inputs() const;
 
   // Get the neighboring nodes via edges either in or out of this node.
   gtl::iterator_range<NeighborIter> in_nodes() const;
@@ -162,8 +161,7 @@ class Node {
 
   template <typename T>
   void AddAttr(const string& name, const T& val) {
-    MaybeCopyOnWrite();
-    SetAttrValue(val, &((*props_->node_def_.mutable_attr())[name]));
+    SetAttrValue(val, AddAttrHelper(name));
   }
 
   void ClearAttr(const string& name);
@@ -183,38 +181,22 @@ class Node {
  private:
   friend class Graph;
   Node();
-  ~Node();
-
-  class Properties : public core::RefCounted {
-   public:
-    Properties(const OpDef* op_def, const NodeDef& node_def,
-               const DataTypeSlice inputs, const DataTypeSlice outputs);
 
-    const OpDef* op_def_;  // not owned
-    NodeDef node_def_;
-    const DataTypeVector input_types_;
-    const DataTypeVector output_types_;
+  NodeProperties* properties() const { return props_.get(); }
 
-   private:
-    // Destructor invoked when last reference goes away via Unref()
-    virtual ~Properties();
-    TF_DISALLOW_COPY_AND_ASSIGN(Properties);
-  };
+  void Initialize(int id, int cost_id, std::shared_ptr<NodeProperties> props);
 
-  Properties* properties() const { return props_; }
-
-  // Initialize() adopts a reference to props, and so is suitable if props was
-  // just allocated or you call props->Ref() to increment the reference
-  // count for a props being held by another Node.
-  void Initialize(int id, int cost_id, Properties* props);
   // Releases memory from props_, in addition to restoring *this to its
   // uninitialized state.
   void Clear();
+
   // Make a copy of the Node's props_ if props_ is shared with
   // other nodes. This must be called before mutating properties,
   // e.g. in AddAttr.
   void MaybeCopyOnWrite();
 
+  AttrValue* AddAttrHelper(const string& name);
+
   // A set of mutually exclusive classes for different kinds of nodes,
   // class_ is initialized in the Node::Initialize routine based on the
   // node's type_string().
@@ -252,7 +234,10 @@ class Node {
   EdgeSet in_edges_;
   EdgeSet out_edges_;
 
-  Properties* props_;
+  // NOTE(skyewm): inheriting from core::RefCounted may have a slight
+  // performance benefit over using shared_ptr, at the cost of manual ref
+  // counting
+  std::shared_ptr<NodeProperties> props_;
 
   // Index within Graph::device_names_ of the name of device assigned
   // to perform this computation.
@@ -519,7 +504,10 @@ class Graph {
   // If cost_node is non-null, then cost accounting (in CostModel)
   // will be associated with that node rather than the new one being
   // created.
-  Node* AllocateNode(Node::Properties* props, const Node* cost_node);
+  //
+  // Ownership of the returned Node is not transferred to caller.
+  Node* AllocateNode(std::shared_ptr<NodeProperties> props,
+                     const Node* cost_node);
   void ReleaseNode(Node* node);
 
   // Registry of all known ops, including functions.
diff --git a/tensorflow/core/graph/graph_constructor.cc b/tensorflow/core/graph/graph_constructor.cc
index 10f110686f..38a780dfac 100644
--- a/tensorflow/core/graph/graph_constructor.cc
+++ b/tensorflow/core/graph/graph_constructor.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/versions.h"
diff --git a/tensorflow/core/graph/optimizer_cse.cc b/tensorflow/core/graph/optimizer_cse.cc
index 54cfd10cdf..47337ce8a2 100644
--- a/tensorflow/core/graph/optimizer_cse.cc
+++ b/tensorflow/core/graph/optimizer_cse.cc
@@ -42,6 +42,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/hash/hash.h"
diff --git a/tensorflow/core/graph/validate.cc b/tensorflow/core/graph/validate.cc
index bfdc5cab0d..bd905651d2 100644
--- a/tensorflow/core/graph/validate.cc
+++ b/tensorflow/core/graph/validate.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/graph/validate.h"
 
 #include "tensorflow/core/framework/graph_def_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/versions.pb.h"
diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc
index 32d9be9aee..95329f3f14 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster.cc
+++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc
@@ -60,6 +60,7 @@ Status VirtualCluster::Run(const GraphDef& graph,
   if (metadata) {
     metadata->clear_step_stats();
     metadata->clear_cost_graph();
+    metadata->clear_partition_graphs();
   }
 
   Costs node_costs;
@@ -94,7 +95,7 @@ Status VirtualCluster::Run(const GraphDef& graph,
   } while (scheduler.MarkCurrNodeExecuted(node_costs));
 
   if (metadata) {
-    scheduler.Summary(metadata->mutable_step_stats());
+    scheduler.Summary(metadata);
   }
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index b222236126..eb1fb71f72 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -620,16 +620,19 @@ Costs VirtualScheduler::Summary() const {
   return critical_path_costs;
 }
 
-Costs VirtualScheduler::Summary(StepStats* stepstats) {
-  if (stepstats != nullptr) {
+Costs VirtualScheduler::Summary(RunMetadata* metadata) {
+  if (metadata != nullptr) {
+    StepStats* stepstats = metadata->mutable_step_stats();
     for (const auto& device : device_) {
+      GraphDef* device_partition_graph =
+          metadata->mutable_partition_graphs()->Add();
       DeviceStepStats* device_stepstats = stepstats->add_dev_stats();
       device_stepstats->set_device(device.first);
       for (const auto& node_def : device.second.nodes_executed) {
         const NodeState& nodestate = node_map_.at(node_def);
         NodeExecStats* node_stats = device_stepstats->add_node_stats();
-        node_stats->set_node_name(node_def->op());
-        node_stats->set_timeline_label(node_def->name());
+        node_stats->set_timeline_label(node_def->op());
+        node_stats->set_node_name(node_def->name());
         node_stats->set_op_start_rel_micros(0);
         node_stats->set_all_start_micros(
             nodestate.time_scheduled.asMicroSeconds().count());
@@ -639,6 +642,7 @@ Costs VirtualScheduler::Summary(StepStats* stepstats) {
         node_stats->set_all_end_rel_micros(
             nodestate.time_finished.asMicroSeconds().count() -
             nodestate.time_scheduled.asMicroSeconds().count());
+        *device_partition_graph->mutable_node()->Add() = *node_def;
       }
     }
   }
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index 8b9eccbd43..d00766d9fa 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -206,9 +206,9 @@ class VirtualScheduler {
 
   // Prints out summary of execution (timing, memory usage, etc.)
   Costs Summary() const;
-  // Like the above, but writes detailed stats to stepstats.
-  // If stepstats is nullptr, then just calls and return Summary().
-  Costs Summary(StepStats* stepstats);
+  // Like the above, but writes detailed stats to RunMetadata.
+  // If metadata is nullptr, then just calls and return Summary().
+  Costs Summary(RunMetadata* metadata);
 
  protected:
   // GetDeviceStates and GetNodeStates are currently for testing purpuse only.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index a9174c7417..f6595fcbb3 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -563,8 +563,9 @@ TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
   CreateGrapplerItemWithMatmulChain();
   InitScheduler();
   auto ops_executed = RunScheduler("");
-  StepStats stepstats;
-  Costs c = scheduler_->Summary(&stepstats);
+  RunMetadata metadata;
+  Costs c = scheduler_->Summary(&metadata);
+  StepStats stepstats = metadata.step_stats();
   EXPECT_EQ(13000000, c.execution_time.asMicroSeconds().count());
 
   // Should only be 1 device!
@@ -574,11 +575,9 @@ TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
   std::map<string, std::pair<int64, int64>> start_end_times;
   for (const auto& device_step_stats : stepstats.dev_stats()) {
     for (const auto& stats : device_step_stats.node_stats()) {
-      // The node name is actually in the timeline_label.
       int64 start = stats.all_start_micros();
       int64 end = start + stats.all_end_rel_micros();
-      start_end_times[stats.timeline_label()] =
-          std::pair<int64, int64>(start, end);
+      start_end_times[stats.node_name()] = std::pair<int64, int64>(start, end);
     }
   }
 
diff --git a/tensorflow/core/grappler/grappler_item.cc b/tensorflow/core/grappler/grappler_item.cc
index 88ddd6c1b3..d27ddb17f1 100644
--- a/tensorflow/core/grappler/grappler_item.cc
+++ b/tensorflow/core/grappler/grappler_item.cc
@@ -30,6 +30,16 @@ std::vector<const NodeDef*> GrapplerItem::MainOpsFanin() const {
   return ComputeTransitiveFanin(graph, fetch);
 }
 
+std::vector<const NodeDef*> GrapplerItem::EnqueueOpsFanin() const {
+  std::vector<string> enqueue_ops;
+  for (const auto& queue_runner : queue_runners) {
+    for (const string& enqueue_op : queue_runner.enqueue_op_name()) {
+      enqueue_ops.push_back(enqueue_op);
+    }
+  }
+  return ComputeTransitiveFanin(graph, enqueue_ops);
+}
+
 std::vector<const NodeDef*> GrapplerItem::InitOpsFanin() const {
   return ComputeTransitiveFanin(graph, init_ops);
 }
diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h
index 1fb54f6093..1e7a9dfaf5 100644
--- a/tensorflow/core/grappler/grappler_item.h
+++ b/tensorflow/core/grappler/grappler_item.h
@@ -51,6 +51,8 @@ struct GrapplerItem {
 
   // Return the set of node evaluated during a regular train/inference step.
   std::vector<const NodeDef*> MainOpsFanin() const;
+  // Return the set of node run to populate the queues (if any).
+  std::vector<const NodeDef*> EnqueueOpsFanin() const;
   // Return the set nodes used by TensorFlow to initialize the graph.
   std::vector<const NodeDef*> InitOpsFanin() const;
   // Return the set of variables accessed during a regular train/inference step.
diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 969376917b..0a0e000b52 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -70,6 +70,10 @@ void InitializeTensor(DataType type, Tensor* tensor) {
 // correct optimizations.
 Status OptimizeGraph(const GraphDef& graph_def, GraphDef* output_graph_def,
                      const ItemConfig& cfg) {
+  if (!cfg.apply_optimizations && !cfg.inline_functions) {
+    return Status::OK();
+  }
+
   // Create a session option for a single GPU device.
   SessionOptions options;
 
@@ -328,7 +332,7 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
   Status optimize_status =
       OptimizeGraph(new_item->graph, &new_item->graph, cfg);
   if (!optimize_status.ok()) {
-    LOG(ERROR) << "Function optimization failed: " << optimize_status;
+    LOG(ERROR) << "Graph preprocessing failed: " << optimize_status;
     return nullptr;
   }
 
diff --git a/tensorflow/core/grappler/grappler_item_builder.h b/tensorflow/core/grappler/grappler_item_builder.h
index 7135c83801..d385a1916e 100644
--- a/tensorflow/core/grappler/grappler_item_builder.h
+++ b/tensorflow/core/grappler/grappler_item_builder.h
@@ -32,7 +32,7 @@ struct ItemConfig {
         ignore_colocation(true),
         placeholder_unknown_output_shape_dim(-1),
         apply_optimizations(false),
-        inline_functions(true) {}
+        inline_functions(false) {}
 
   // If true, ignore all user specified node placement.
   bool ignore_user_placement;
diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index 51146011b0..700a66d3a6 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -50,6 +50,11 @@ bool IsNoOp(const NodeDef& node) {
   return op == "NoOp";
 }
 
+bool IsNextIteration(const NodeDef& node) {
+  const auto& op = node.op();
+  return op == "NextIteration" || op == "RefNextIteration";
+}
+
 bool IsPlaceholder(const NodeDef& node) {
   const auto op = node.op();
   return op == "Placeholder" || op == "PlaceholderV2" ||
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index b2102c688d..cba44e905e 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -26,6 +26,7 @@ bool IsConstant(const NodeDef& node);
 bool IsDequeueOp(const NodeDef& node);
 bool IsIdentity(const NodeDef& node);
 bool IsMerge(const NodeDef& node);
+bool IsNextIteration(const NodeDef& node);
 bool IsNoOp(const NodeDef& node);
 bool IsPlaceholder(const NodeDef& node);
 bool IsRecv(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 3a705b85f7..5a7f11e149 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -197,6 +197,7 @@ cc_library(
         ":static_schedule",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:op_types",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core/grappler/costs:graph_properties",
         "//tensorflow/core/grappler/utils:topological_sort",
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index bcd0e98eb7..6958d1ddf9 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -100,8 +101,9 @@ string AsControlDependency(const NodeDef& node) {
 
 ConstantFolding::ConstantFolding() {
   ops_to_preserve_ = std::regex(
-      "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|Enter|Exit|"
-      "NextIteration");
+      "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|"
+      "Enter|RefEnter|Exit|RefExit|NextIteration|RefNextIteration|"
+      ".*Quantized.*");
 }
 
 string ConstantFolding::AddControlDependency(const string& input_name) {
@@ -267,9 +269,10 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
   // Only fold ops with a CPU implementation available.
-  if (device_types[0] != DeviceType(DEVICE_CPU)) {
+  if (device_types.empty()) {
     return false;
   }
+  DCHECK_EQ(DeviceType(DEVICE_CPU), device_types[0]);
 
   // Folding not applicable to ops with no inputs.
   if (node.input().empty()) {
@@ -370,6 +373,12 @@ Status ConstantFolding::EvaluateNode(const NodeDef& node,
 Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
                                             std::vector<NodeDef>* outputs) {
   TensorVector inputs;
+  auto inputs_cleanup = gtl::MakeCleanup([&inputs] {
+    for (const auto& input : inputs) {
+      delete input.tensor;
+    }
+  });
+
   for (const auto& input : node.input()) {
     if (IsControlInput(input)) {
       break;
@@ -382,9 +391,6 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
 
   TensorVector output_tensors;
   TF_RETURN_IF_ERROR(EvaluateNode(node, inputs, &output_tensors));
-  for (const auto& input : inputs) {
-    delete input.tensor;
-  }
   if (output_tensors.empty()) {
     Status(error::INVALID_ARGUMENT, "Expected at least one output.");
   }
@@ -441,10 +447,22 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
         // Try the next input
         continue;
       }
+
+      string const_out_name =
+          AddPrefixToNodeName(node.name(), kConstantFoldingConst);
+      string const_index_name = AddPrefixToNodeName(
+          strings::StrCat(node.name(), "_index"), kConstantFoldingConst);
+      if (node_map_->GetNode(const_out_name) ||
+          node_map_->GetNode(const_index_name)) {
+        // Intended name already exists.
+        return errors::AlreadyExists(
+            strings::StrCat(const_out_name, " or ", const_index_name,
+                            "already present in the graph"));
+      }
+
       NodeDef* const_out = output->add_node();
       *const_out = *input_node;
-      const_out->set_name(
-          AddPrefixToNodeName(node.name(), kConstantFoldingConst));
+      const_out->set_name(const_out_name);
       *const_out->add_input() = AsControlDependency(node);
       node_map_->AddNode(const_out->name(), const_out);
 
@@ -455,8 +473,7 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
       (*const_index->mutable_attr())["dtype"].set_type(DT_INT32);
       index.AsProtoTensorContent(
           (*const_index->mutable_attr())["value"].mutable_tensor());
-      const_index->set_name(AddPrefixToNodeName(
-          strings::StrCat(node.name(), "_index"), kConstantFoldingConst));
+      const_index->set_name(const_index_name);
       *const_index->add_input() = AsControlDependency(node);
       node_map_->AddNode(const_index->name(), const_index);
 
@@ -493,6 +510,12 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
       // below to preserve the overall behavior of the graph wrt dead edges.
       continue;
     }
+
+    if (node_map_->GetNode(const_node.name())) {
+      // Intended name already exists.
+      return errors::AlreadyExists(
+          strings::StrCat(const_node.name(), "already present in the graph"));
+    }
     NodeDef* added_node = output->add_node();
     *added_node = const_node;
     node_map_->AddNode(added_node->name(), added_node);
@@ -546,23 +569,25 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
 }
 
 Status ConstantFolding::FoldGraph(GraphDef* output) {
-  std::set<string> processed_nodes;
-  while (1) {
-    int previous_processed = processed_nodes.size();
+  std::unordered_set<string> processed_nodes;
+  int previously_processed = 0;
+  do {
+    previously_processed = processed_nodes.size();
     for (const auto& node : graph_.node()) {
       if (IsFoldable(node) &&
           processed_nodes.find(node.name()) == processed_nodes.end()) {
-        TF_RETURN_IF_ERROR(FoldNode(node, output));
+        Status s = FoldNode(node, output);
+        if (!s.ok()) {
+          VLOG(1) << "Failed to fold node " << node.name() << ": " << s;
+        }
         processed_nodes.insert(node.name());
       }
     }
-    int current_processed = processed_nodes.size();
-    LOG(INFO) << "Previous number of processed nodes: " << previous_processed
-              << "; Current number of processed nodes: " << current_processed;
-    if (current_processed == previous_processed) {
-      break;
-    }
-  }
+    // Try again as long as we find new constants. In most cases, this loop will
+    // only run once since the graph is already in topological order.
+    VLOG(1) << "Folded " << processed_nodes.size() - previously_processed
+            << " nodes in this pass";
+  } while (previously_processed != processed_nodes.size());
 
   // Build the graph after constant folding. Note that we keep all processed
   // nodes in the graph in case users need to fetch their values.
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 5a017cb0e8..28d663e2f7 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -240,20 +240,22 @@ class NodeProcessor {
     AttrValue attr_data_type_perm;
     attr_data_type_perm.set_type(DT_INT32);
     node->mutable_attr()->insert({"Tperm", attr_data_type_perm});
-    AttrValue attr_output_shape;
-    auto output_shape = attr_output_shape.mutable_list()->add_shape();
-    if (NHWCToNCHW) {
-      output_shape->add_dim()->set_size(input_shape.dim(0).size());
-      output_shape->add_dim()->set_size(input_shape.dim(3).size());
-      output_shape->add_dim()->set_size(input_shape.dim(1).size());
-      output_shape->add_dim()->set_size(input_shape.dim(2).size());
-    } else {
-      output_shape->add_dim()->set_size(input_shape.dim(0).size());
-      output_shape->add_dim()->set_size(input_shape.dim(2).size());
-      output_shape->add_dim()->set_size(input_shape.dim(3).size());
-      output_shape->add_dim()->set_size(input_shape.dim(1).size());
+    if (!input_shape.unknown_rank()) {
+      AttrValue attr_output_shape;
+      auto output_shape = attr_output_shape.mutable_list()->add_shape();
+      if (NHWCToNCHW) {
+        output_shape->add_dim()->set_size(input_shape.dim(0).size());
+        output_shape->add_dim()->set_size(input_shape.dim(3).size());
+        output_shape->add_dim()->set_size(input_shape.dim(1).size());
+        output_shape->add_dim()->set_size(input_shape.dim(2).size());
+      } else {
+        output_shape->add_dim()->set_size(input_shape.dim(0).size());
+        output_shape->add_dim()->set_size(input_shape.dim(2).size());
+        output_shape->add_dim()->set_size(input_shape.dim(3).size());
+        output_shape->add_dim()->set_size(input_shape.dim(1).size());
+      }
+      node->mutable_attr()->insert({"_output_shapes", attr_output_shape});
     }
-    node->mutable_attr()->insert({"_output_shapes", attr_output_shape});
   }
 
   virtual Status AddLayoutTransposeToInputs() {
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index bd8ce8c30c..16a638a7d3 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/op_types.h"
 #include "tensorflow/core/grappler/optimizers/graph_rewriter.h"
 #include "tensorflow/core/grappler/optimizers/static_schedule.h"
 #include "tensorflow/core/grappler/utils.h"
@@ -304,7 +305,11 @@ AddRecomputeControlDependencyNodes(
   for (const NodeDef* target_node : target_nodes) {
     for (const string& target_input_name_raw : target_node->input()) {
       const NodeDef* target_input = node_map.GetNode(target_input_name_raw);
-      if (recomputed_source_nodes.count(target_input) != 0 ||
+      // If this node has already had one of its inputs recomputed during this
+      // rewriting pass, we ignore that recomputed node here (it will not be in
+      // the NodeMap).
+      if (target_input == nullptr ||
+          recomputed_source_nodes.count(target_input) != 0 ||
           components.find(target_node)->second ==
               components.find(target_input)->second) {
         continue;
@@ -552,8 +557,8 @@ static const NodeDef* FindSwapTrigger(
     // Don't jump over frames, since adding a control dependency from one frame
     // to the next isn't supported. Don't go through branches, since we don't
     // know whether they'll be executed or not.
-    if (input_node->op() == "NextIteration" || input_node->op() == "Switch" ||
-        input_node->op() == "Merge") {
+    if (IsNextIteration(*input_node) || IsSwitch(*input_node) ||
+        IsMerge(*input_node)) {
       continue;
     }
     auto it2 = execution_times.find(input_node);
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index 27cad07d5b..eb7a8eb343 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -34,7 +34,7 @@ class RecomputeSubgraphTest : public ::testing::Test {};
 TEST_F(RecomputeSubgraphTest, SimpleSubgraph) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Const(s.WithOpName("a"), 1.f, {2, 3, 4});
+  Output a = ops::Variable(s.WithOpName("a"), {2, 3, 4}, DT_FLOAT);
   Output b = ops::Identity(s.WithOpName("b"), a);  // Recomputed
   Output c = ops::Identity(s.WithOpName("c"), b);
   Output d = ops::AddN(s.WithOpName("gradients/d"), {c});
@@ -69,10 +69,40 @@ TEST_F(RecomputeSubgraphTest, SimpleSubgraph) {
   EXPECT_EQ("^gradients/d", recompute_trigger->input(0));
 }
 
+TEST_F(RecomputeSubgraphTest, TwoInputSubgraphs) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+
+  Output a = ops::Variable(s.WithOpName("a"), {2, 3, 4}, DT_FLOAT);
+  Output b = ops::Variable(s.WithOpName("b"), {2, 3, 4}, DT_FLOAT);
+  Output d = ops::AddN(s.WithOpName("gradients/two_subgraph_inputs"), {a, b});
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+  EXPECT_EQ(3, item.graph.node_size());
+  NodeMap pre_transform_node_map(&item.graph);
+  (*pre_transform_node_map.GetNode("a")->mutable_attr())["_recompute_hint"]
+      .set_i(0);
+  (*pre_transform_node_map.GetNode("b")->mutable_attr())["_recompute_hint"]
+      .set_i(0);
+
+  MemoryOptimizer optimizer(RewriterConfig::MANUAL);
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+
+  TF_EXPECT_OK(status);
+  NodeMap post_transform_node_map(&output);
+  // Mostly checking that this case does not crash.
+  EXPECT_EQ(7, output.node_size());
+  EXPECT_NE(post_transform_node_map.GetNode("Recomputed/a"), nullptr);
+  EXPECT_NE(post_transform_node_map.GetNode("Recomputed/b"), nullptr);
+  EXPECT_NE(post_transform_node_map.GetNode("RecomputeTrigger/a"), nullptr);
+  EXPECT_NE(post_transform_node_map.GetNode("RecomputeTrigger/b"), nullptr);
+}
+
 TEST_F(RecomputeSubgraphTest, MultiNode) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Const(s.WithOpName("Conv"), 1.f, {2, 3, 4});
+  Output a = ops::Variable(s.WithOpName("Conv"), {2, 3, 4}, DT_FLOAT);
   Output b = ops::Identity(s.WithOpName("BN"), a);    // Recomputed
   Output c = ops::Identity(s.WithOpName("ReLU"), b);  // Recomputed
   Output d = ops::Identity(s.WithOpName("Conv1"), c);
@@ -152,7 +182,7 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
   // Build a simple graph with an op that's marked for swapping.
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
+  Output a = ops::Variable(s.WithOpName("a"), {10, 10}, DT_FLOAT);
   Output b = ops::AddN(s.WithOpName("b"), {a});
   Output c = ops::AddN(s.WithOpName("c"), {b});
   Output d = ops::AddN(s.WithOpName("d"), {c});
diff --git a/tensorflow/core/grappler/utils/BUILD b/tensorflow/core/grappler/utils/BUILD
index 8839f07bc5..fd3894553b 100644
--- a/tensorflow/core/grappler/utils/BUILD
+++ b/tensorflow/core/grappler/utils/BUILD
@@ -48,6 +48,7 @@ cc_library(
     deps = [
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:op_types",
         "//tensorflow/core/grappler:utils",
     ],
 )
diff --git a/tensorflow/core/grappler/utils/topological_sort.cc b/tensorflow/core/grappler/utils/topological_sort.cc
index fdf7fb0d3d..9c5d27f3c5 100644
--- a/tensorflow/core/grappler/utils/topological_sort.cc
+++ b/tensorflow/core/grappler/utils/topological_sort.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include <deque>
 #include <unordered_map>
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/grappler/op_types.h"
 #include "tensorflow/core/grappler/utils.h"
 
 namespace tensorflow {
@@ -35,7 +36,7 @@ void TopologicalSort(GraphDef* graph) {
     if (node.op() == "Merge") {
       ready_inputs[&node] = 0;
       for (const auto& input : node.input()) {
-        if (node_map.GetNode(input)->op() == "NextIteration") {
+        if (IsNextIteration(*node_map.GetNode(input))) {
           ready_inputs[&node]++;
         }
       }
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 046ed37d82..0b1c1085f2 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -300,6 +300,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -322,6 +323,7 @@ cc_library(
         ":typed_queue",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -413,6 +415,7 @@ cc_library(
     hdrs = ["warn_about_ints.h"],
     deps = [
         "//tensorflow/core:framework",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -1419,7 +1422,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "random_shuffle_queue_op",
     prefix = "random_shuffle_queue_op",
-    deps = DATA_FLOW_DEPS,
+    deps = DATA_FLOW_DEPS + ["//tensorflow/core:protos_all_cc"],
 )
 
 tf_kernel_library(
@@ -1520,6 +1523,7 @@ cc_library(
         ":typed_queue",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -1534,6 +1538,7 @@ cc_library(
         ":typed_queue",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -3476,8 +3481,26 @@ tf_kernel_library(
 
 tf_kernel_library(
     name = "scatter_nd_op",
-    prefix = "scatter_nd_op",
-    deps = STATE_DEPS,
+    srcs = [
+        "scatter_nd_op.cc",
+        "scatter_nd_op_cpu_impl_0.cc",
+        "scatter_nd_op_cpu_impl_1.cc",
+        "scatter_nd_op_cpu_impl_2.cc",
+        "scatter_nd_op_cpu_impl_3.cc",
+        "scatter_nd_op_cpu_impl_4.cc",
+        "scatter_nd_op_cpu_impl_5.cc",
+    ],
+    hdrs = [
+        "dense_update_ops.h",
+        "scatter_nd_op.h",
+        "scatter_nd_op_cpu_impl.h",
+    ],
+    gpu_srcs = [
+        "dense_update_ops.h",
+        "scatter_nd_op.h",
+        "scatter_nd_op_gpu.cu.cc",
+    ],
+    deps = STATE_DEPS + [":dense_update_ops"],
 )
 
 tf_kernel_library(
@@ -4107,6 +4130,7 @@ filegroup(
     srcs = [
         "argmax_op.h",
         "avgpooling_op.h",
+        "batch_matmul_op_impl.h",
         "batch_norm_op.h",
         "control_flow_ops.h",
         "conv_2d.h",
@@ -4152,6 +4176,7 @@ filegroup(
     srcs = [
         "argmax_op.cc",
         "avgpooling_op.cc",
+        "batch_matmul_op_real.cc",
         "batch_norm_op.cc",
         "bcast_ops.cc",
         "check_numerics_op.cc",
diff --git a/tensorflow/core/kernels/argmax_op.cc b/tensorflow/core/kernels/argmax_op.cc
index 4d16601b5b..49cd997fed 100644
--- a/tensorflow/core/kernels/argmax_op.cc
+++ b/tensorflow/core/kernels/argmax_op.cc
@@ -40,7 +40,7 @@ namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 
-template <typename Device, typename T, typename ArgFunctor>
+template <typename Device, typename T, typename Tout, typename ArgFunctor>
 class ArgOp : public OpKernel {
  public:
   explicit ArgOp(OpKernelConstruction* context) : OpKernel(context) {}
@@ -76,11 +76,11 @@ class ArgOp : public OpKernel {
     Tensor* output = nullptr;
     OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
 
-#define HANDLE_DIM(NDIM)                                         \
-  case NDIM:                                                     \
-    ArgFunctor::Reduce##NDIM(context->eigen_device<Device>(),    \
-                             input.tensor<T, NDIM>(), axis,      \
-                             output->tensor<int64, NDIM - 1>()); \
+#define HANDLE_DIM(NDIM)                                        \
+  case NDIM:                                                    \
+    ArgFunctor::Reduce##NDIM(context->eigen_device<Device>(),   \
+                             input.tensor<T, NDIM>(), axis,     \
+                             output->tensor<Tout, NDIM - 1>()); \
     break;
 
     switch (input_dims) {
@@ -102,31 +102,47 @@ class ArgOp : public OpKernel {
   TF_DISALLOW_COPY_AND_ASSIGN(ArgOp);
 };
 
-template <typename Device, typename T>
-class ArgMaxOp : public ArgOp<Device, T, functor::ArgMax<Device, T> > {
+template <typename Device, typename T, typename Tout>
+class ArgMaxOp
+    : public ArgOp<Device, T, Tout, functor::ArgMax<Device, T, Tout> > {
  public:
   explicit ArgMaxOp(OpKernelConstruction* context)
-      : ArgOp<Device, T, functor::ArgMax<Device, T> >(context) {}
+      : ArgOp<Device, T, Tout, functor::ArgMax<Device, T, Tout> >(context) {}
 };
 
-template <typename Device, typename T>
-class ArgMinOp : public ArgOp<Device, T, functor::ArgMin<Device, T> > {
+template <typename Device, typename T, typename Tout>
+class ArgMinOp
+    : public ArgOp<Device, T, Tout, functor::ArgMin<Device, T, Tout> > {
  public:
   explicit ArgMinOp(OpKernelConstruction* context)
-      : ArgOp<Device, T, functor::ArgMin<Device, T> >(context) {}
+      : ArgOp<Device, T, Tout, functor::ArgMin<Device, T, Tout> >(context) {}
 };
 
-#define REGISTER_ARGMAX(type)                            \
-  REGISTER_KERNEL_BUILDER(Name("ArgMax")                 \
-                              .Device(DEVICE_CPU)        \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("dimension"),  \
-                          ArgMaxOp<CPUDevice, type>);    \
-  REGISTER_KERNEL_BUILDER(Name("ArgMin")                 \
-                              .Device(DEVICE_CPU)        \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("dimension"),  \
-                          ArgMinOp<CPUDevice, type>);
+#define REGISTER_ARGMAX(type)                                       \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<CPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<CPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<CPUDevice, type, int32>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_CPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<CPUDevice, type, int32>);
 
 TF_CALL_REAL_NUMBER_TYPES(REGISTER_ARGMAX);
 
@@ -135,26 +151,33 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_ARGMAX);
 // Forward declarations of the functor specializations for GPU.
 namespace functor {
 
-#define DECLARE_GPU_SPEC(T, Dims)                                              \
-  template <>                                                                  \
-  void ArgMax<GPUDevice, T>::Reduce##Dims(                                     \
-      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,         \
-      const int32 dimension, typename TTypes<int64, Dims - 1>::Tensor output); \
-  template <>                                                                  \
-  void ArgMin<GPUDevice, T>::Reduce##Dims(                                     \
-      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,         \
-      const int32 dimension, typename TTypes<int64, Dims - 1>::Tensor output);
-
-#define DECLARE_GPU_SPECS(T) \
-  DECLARE_GPU_SPEC(T, 1);    \
-  DECLARE_GPU_SPEC(T, 2);    \
-  DECLARE_GPU_SPEC(T, 3);    \
-  DECLARE_GPU_SPEC(T, 4);    \
-  DECLARE_GPU_SPEC(T, 5);
-
-#define DECLARE_GPU_CLASS(T)                   \
-  extern template struct ArgMax<GPUDevice, T>; \
-  extern template struct ArgMin<GPUDevice, T>;
+#define DECLARE_GPU_SPEC(T, Tout, Dims)                                       \
+  template <>                                                                 \
+  void ArgMax<GPUDevice, T, Tout>::Reduce##Dims(                              \
+      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,        \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output); \
+  template <>                                                                 \
+  void ArgMin<GPUDevice, T, Tout>::Reduce##Dims(                              \
+      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,        \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output);
+
+#define DECLARE_GPU_SPECS(T)     \
+  DECLARE_GPU_SPEC(T, int64, 1); \
+  DECLARE_GPU_SPEC(T, int64, 2); \
+  DECLARE_GPU_SPEC(T, int64, 3); \
+  DECLARE_GPU_SPEC(T, int64, 4); \
+  DECLARE_GPU_SPEC(T, int64, 5); \
+  DECLARE_GPU_SPEC(T, int32, 1); \
+  DECLARE_GPU_SPEC(T, int32, 2); \
+  DECLARE_GPU_SPEC(T, int32, 3); \
+  DECLARE_GPU_SPEC(T, int32, 4); \
+  DECLARE_GPU_SPEC(T, int32, 5);
+
+#define DECLARE_GPU_CLASS(T)                          \
+  extern template struct ArgMax<GPUDevice, T, int64>; \
+  extern template struct ArgMin<GPUDevice, T, int64>; \
+  extern template struct ArgMax<GPUDevice, T, int32>; \
+  extern template struct ArgMin<GPUDevice, T, int32>;
 
 TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS);
 TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_CLASS);
@@ -165,19 +188,35 @@ TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_CLASS);
 }  // namespace functor
 
 // Registration of the GPU implementations.
-#define REGISTER_ARGMAX_GPU(type)                            \
-  REGISTER_KERNEL_BUILDER(Name("ArgMax")                     \
-                              .Device(DEVICE_GPU)            \
-                              .TypeConstraint<type>("T")     \
-                              .TypeConstraint<int32>("Tidx") \
-                              .HostMemory("dimension"),      \
-                          ArgMaxOp<GPUDevice, type>);        \
-  REGISTER_KERNEL_BUILDER(Name("ArgMin")                     \
-                              .Device(DEVICE_GPU)            \
-                              .TypeConstraint<type>("T")     \
-                              .TypeConstraint<int32>("Tidx") \
-                              .HostMemory("dimension"),      \
-                          ArgMinOp<GPUDevice, type>);
+#define REGISTER_ARGMAX_GPU(type)                                   \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<GPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int64>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<GPUDevice, type, int64>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMax")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMaxOp<GPUDevice, type, int32>);        \
+  REGISTER_KERNEL_BUILDER(Name("ArgMin")                            \
+                              .Device(DEVICE_GPU)                   \
+                              .TypeConstraint<type>("T")            \
+                              .TypeConstraint<int32>("output_type") \
+                              .TypeConstraint<int32>("Tidx")        \
+                              .HostMemory("dimension"),             \
+                          ArgMinOp<GPUDevice, type, int32>);
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_ARGMAX_GPU);
 
diff --git a/tensorflow/core/kernels/argmax_op.h b/tensorflow/core/kernels/argmax_op.h
index 850a0390bc..b8bc41e089 100644
--- a/tensorflow/core/kernels/argmax_op.h
+++ b/tensorflow/core/kernels/argmax_op.h
@@ -25,14 +25,13 @@ namespace tensorflow {
 
 namespace functor {
 
-template <typename Device, typename T>
+template <typename Device, typename T, typename Tout>
 struct ArgMax {
-#define DECLARE_COMPUTE_SPEC(Dims)                                     \
-  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                        \
-      const Device& d, typename TTypes<T, Dims>::ConstTensor input,    \
-      const int32 dimension,                                           \
-      typename TTypes<int64, Dims - 1>::Tensor output) {               \
-    output.device(d) = input.argmax(dimension).template cast<int64>(); \
+#define DECLARE_COMPUTE_SPEC(Dims)                                             \
+  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                                \
+      const Device& d, typename TTypes<T, Dims>::ConstTensor input,            \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output) { \
+    output.device(d) = input.argmax(dimension).template cast<Tout>();          \
   }
 
   DECLARE_COMPUTE_SPEC(1);
@@ -44,14 +43,13 @@ struct ArgMax {
 #undef DECLARE_COMPUTE_SPEC
 };
 
-template <typename Device, typename T>
+template <typename Device, typename T, typename Tout>
 struct ArgMin {
-#define DECLARE_COMPUTE_SPEC(Dims)                                     \
-  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                        \
-      const Device& d, typename TTypes<T, Dims>::ConstTensor input,    \
-      const int32 dimension,                                           \
-      typename TTypes<int64, Dims - 1>::Tensor output) {               \
-    output.device(d) = input.argmin(dimension).template cast<int64>(); \
+#define DECLARE_COMPUTE_SPEC(Dims)                                             \
+  EIGEN_ALWAYS_INLINE static void Reduce##Dims(                                \
+      const Device& d, typename TTypes<T, Dims>::ConstTensor input,            \
+      const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output) { \
+    output.device(d) = input.argmin(dimension).template cast<Tout>();          \
   }
 
   DECLARE_COMPUTE_SPEC(1);
diff --git a/tensorflow/core/kernels/argmax_op_gpu.cu.cc b/tensorflow/core/kernels/argmax_op_gpu.cu.cc
index 2915da4c08..52e523003b 100644
--- a/tensorflow/core/kernels/argmax_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/argmax_op_gpu.cu.cc
@@ -24,9 +24,11 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-#define DEFINE_GPU_SPEC(T)                       \
-  template struct functor::ArgMax<GPUDevice, T>; \
-  template struct functor::ArgMin<GPUDevice, T>;
+#define DEFINE_GPU_SPEC(T)                              \
+  template struct functor::ArgMax<GPUDevice, T, int64>; \
+  template struct functor::ArgMin<GPUDevice, T, int64>; \
+  template struct functor::ArgMax<GPUDevice, T, int32>; \
+  template struct functor::ArgMin<GPUDevice, T, int32>;
 
 TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPEC);
 
diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
index b2bbc5831c..2307c2de0e 100644
--- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
+++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
@@ -272,75 +272,6 @@ __global__ void SwapDimension1And2InTensor3UsingTiles(const T* input,
   }
 }
 
-// Use shared memory tiles to swap dimension-1 and dimension-2 of a 3D tensor
-// when only one of the dimension sizes is smaller than 16,
-// where dimensions are zero-based: output[i][j][k] = input[i][k][j].
-//
-// small_dim = the_smaller_dimension_size
-// large_dim = the_larger_dimension_size
-// tile_num_per_block = blockDim.x
-// kTileLength = small_dim
-//
-// Each thread block operates on a single rectangle tile, where its width is
-// kTileLength (we currently set it to 64) and its height is small_dim,
-// We set the thread block's X dimension to be tile_num_per_block, and its Y
-// and Z to be one.
-template <typename T, bool SmallDim2>
-__global__ void SwapDimension1And2InTensor3SmallDim(const T* input,
-                                                    Dimension<3> input_dims,
-                                                    T* output) {
-  // TODO(yangzihao) avoid share memory bank conflict.
-  extern __shared__ __align__(sizeof(T)) unsigned char shmem[];
-  T* shared_memory_tile = reinterpret_cast<T*>(shmem);
-
-  eigen_assert(blockDim.y == 1);
-  eigen_assert(blockDim.z == 1);
-  eigen_assert(gridDim.z == 1);
-
-  int x = threadIdx.x;
-  int tile_height = blockDim.x;
-
-  // Get tile height, width, and thread/block origin indices.
-  int small_dim = SmallDim2 ? input_dims[2] : input_dims[1];
-  int large_dim = SmallDim2 ? input_dims[1] : input_dims[2];
-  int block_origin_idx = small_dim * large_dim * blockIdx.y;
-  int block_offset = blockIdx.x * blockDim.x;
-  int thread_origin_idx =
-      block_origin_idx + (SmallDim2 ? block_offset * small_dim : block_offset) +
-      x;
-
-  if (block_offset + blockDim.x > large_dim) {
-    tile_height = large_dim - block_offset;
-  }
-
-  // Load a continous memory region to shared memory tile.
-  if (x < tile_height) {
-    for (int y = 0; y < small_dim; y++) {
-      int shmem_index = SmallDim2 ? (x + y * tile_height) : (x * small_dim + y);
-      shared_memory_tile[shmem_index] =
-          ldg(input + thread_origin_idx +
-              y * (SmallDim2 ? tile_height : large_dim));
-    }
-  }
-
-  __syncthreads();
-
-  // Get block origin index for output array.
-  int output_block_offset = block_origin_idx;
-  int output_block_idx = SmallDim2 ? block_offset : block_offset * small_dim;
-  int output_block_origin_idx = output_block_offset + output_block_idx;
-
-  // Store the tranposed memory region in shared memory to device.
-  if (x < tile_height) {
-    for (int y = 0; y < small_dim; y++) {
-      int output_idx = output_block_origin_idx + x +
-                       y * (SmallDim2 ? large_dim : tile_height);
-      int shmem_index = SmallDim2 ? (x * small_dim + y) : (x + y * tile_height);
-      output[output_idx] = shared_memory_tile[shmem_index];
-    }
-  }
-}
-
 // A Cuda custom kernel that convert input to output, given proper padding on
 // the left and the top. The padded value is zero.
 template <typename T, int NDIMS>
@@ -489,60 +420,25 @@ template <typename T>
 void RunSwapDimension1And2InTensor3(const GPUDevice& d, const T* input,
                                     const Dimension<3>& input_dims, T* output) {
   // If both dimensions are not trivial, use tiles for the actual swapping.
-  // If one dimension is trivial, use SmallDim kernel for swapping.
   // Otherwise, the trivial swapping relying on the ldg cache is more efficient.
   static const int kMinDimensionToUseTiles = 16;
   bool use_tiles = (input_dims[1] >= kMinDimensionToUseTiles &&
                     input_dims[2] >= kMinDimensionToUseTiles);
-  bool use_small_dim = ((input_dims[1] >= kMinDimensionToUseTiles &&
-                         input_dims[2] < kMinDimensionToUseTiles)) ||
-                       ((input_dims[1] < kMinDimensionToUseTiles &&
-                         input_dims[2] >= kMinDimensionToUseTiles));
-
-  static const int NumSubTiles = 8;
   if (use_tiles) {
+    // We get best performance when TileSize is the number of threads in a warp
+    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
+    // threads.
     static const int TileSize = 32;
+    static const int NumSubTiles = 8;
     Dimension<3> input_dims_in_tiles = {
         input_dims[0], (input_dims[1] + TileSize - 1) / TileSize,
         (input_dims[2] + TileSize - 1) / TileSize,
     };
     int total_tiles_count = input_dims_in_tiles[0] * input_dims_in_tiles[1] *
                             input_dims_in_tiles[2];
-    // We get best performance when TileSize is the number of threads in a warp
-    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
-    // threads.
     SwapDimension1And2InTensor3UsingTiles<T, TileSize, NumSubTiles><<<
         total_tiles_count, dim3(TileSize, NumSubTiles), 0, d.stream()>>>(
         input, input_dims, output);
-  } else if (use_small_dim) {
-    // When only one of the dimensions is smaller than kMinDimensionToUseTiles,
-    // we use one block to process a rectangle region with the size of
-    // kTileLength * small_dim. We found that when set kTileLength to 64 on
-    // TitanX Maxwell GPU, it achieves the best performance.
-    //              large_dim
-    //            +---------------...--------+
-    //            |            |        |    |
-    // small_dim  |            |  ...   |    |
-    //            |            |        |    |
-    //            +--------------...---------+
-    //            \----- ------/         \- -/
-    //                  V                  V
-    //    kTileLength(tile_height)    tile_height
-    static const int kTileLength = 64;
-    int small_dim = std::min(input_dims[2], input_dims[1]);
-    int large_dim = std::max(input_dims[2], input_dims[1]);
-    int tile_num_per_block = (large_dim + kTileLength - 1) / kTileLength;
-    if (input_dims[2] < input_dims[1]) {
-      SwapDimension1And2InTensor3SmallDim<T, true>
-          <<<dim3(tile_num_per_block, input_dims[0]), kTileLength,
-             kTileLength * small_dim * sizeof(T), d.stream()>>>(
-              input, input_dims, output);
-    } else {
-      SwapDimension1And2InTensor3SmallDim<T, false>
-          <<<dim3(tile_num_per_block, input_dims[0]), kTileLength,
-             kTileLength * small_dim * sizeof(T), d.stream()>>>(
-              input, input_dims, output);
-    }
   } else {
     int total_element_count = input_dims[0] * input_dims[1] * input_dims[2];
     CudaLaunchConfig config = GetCudaLaunchConfig(total_element_count, d);
diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc
index 33991fa1f9..ef34946d96 100644
--- a/tensorflow/core/kernels/dense_update_ops.cc
+++ b/tensorflow/core/kernels/dense_update_ops.cc
@@ -111,7 +111,7 @@ class DenseUpdateOp : public OpKernel {
     OP_REQUIRES(context, Tparams.IsInitialized(),
                 errors::FailedPrecondition("Attempting to use uninitialized "
                                            "parameters: ",
-                                           def().input(0)));
+                                           requested_input(0)));
     OP_REQUIRES(
         context, Tparams.IsSameSize(Tupdate),
         errors::InvalidArgument("Parameters and update must be the same size"));
diff --git a/tensorflow/core/kernels/fifo_queue.cc b/tensorflow/core/kernels/fifo_queue.cc
index 030cf8a49d..ea86b04762 100644
--- a/tensorflow/core/kernels/fifo_queue.cc
+++ b/tensorflow/core/kernels/fifo_queue.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include <deque>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/kernels/iterator_ops.cc b/tensorflow/core/kernels/iterator_ops.cc
index b6825b4e95..f143d39816 100644
--- a/tensorflow/core/kernels/iterator_ops.cc
+++ b/tensorflow/core/kernels/iterator_ops.cc
@@ -168,7 +168,7 @@ class OneShotIteratorOp : public AsyncOpKernel {
         thread_pool_(new thread::ThreadPool(
             ctx->env(), ThreadOptions(),
             strings::StrCat("one_shot_iterator_initialization_thread_",
-                            SanitizeThreadSuffix(def().name())),
+                            SanitizeThreadSuffix(name())),
             1 /* num_threads */, false /* low_latency_hint */))
 
   {
@@ -359,7 +359,7 @@ class IteratorGetNextOp : public AsyncOpKernel {
         thread_pool_(new thread::ThreadPool(
             ctx->env(), ThreadOptions(),
             strings::StrCat("iterator_get_next_thread_",
-                            SanitizeThreadSuffix(def().name())),
+                            SanitizeThreadSuffix(name())),
             1 /* num_threads */, false /* low_latency_hint */)) {}
 
   void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
diff --git a/tensorflow/core/kernels/ops_testutil.h b/tensorflow/core/kernels/ops_testutil.h
index 96de4094fe..2554359d13 100644
--- a/tensorflow/core/kernels/ops_testutil.h
+++ b/tensorflow/core/kernels/ops_testutil.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
diff --git a/tensorflow/core/kernels/padding_fifo_queue.cc b/tensorflow/core/kernels/padding_fifo_queue.cc
index f4626d4a5d..d0f7683f3d 100644
--- a/tensorflow/core/kernels/padding_fifo_queue.cc
+++ b/tensorflow/core/kernels/padding_fifo_queue.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <deque>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/core/kernels/priority_queue.cc b/tensorflow/core/kernels/priority_queue.cc
index 9cbd832957..4c406fc1ed 100644
--- a/tensorflow/core/kernels/priority_queue.cc
+++ b/tensorflow/core/kernels/priority_queue.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <queue>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/kernels/quantized_conv_ops.cc b/tensorflow/core/kernels/quantized_conv_ops.cc
index 56a7e161df..5658dcc069 100644
--- a/tensorflow/core/kernels/quantized_conv_ops.cc
+++ b/tensorflow/core/kernels/quantized_conv_ops.cc
@@ -211,7 +211,7 @@ class Im2ColConvFunctor {
         ++warning_count;
         LOG(WARNING)
             << "For kernel '" << context->op_kernel().name() << "' from input '"
-            << context->op_kernel().def().input(0)
+            << context->op_kernel().requested_input(0)
             << "': Zero is not representable in the quantized range used by the"
             << " input. This means QuantizedConv2d has to fall back to a slow"
             << " implementation, since the border of zero values can't be"
diff --git a/tensorflow/core/kernels/queue_base.cc b/tensorflow/core/kernels/queue_base.cc
index 07ff70a875..8a9af39e1f 100644
--- a/tensorflow/core/kernels/queue_base.cc
+++ b/tensorflow/core/kernels/queue_base.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/queue_base.h"
 
 #include <vector>
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/mutex.h"
diff --git a/tensorflow/core/kernels/random_poisson_op.cc b/tensorflow/core/kernels/random_poisson_op.cc
index 66123e47c6..b3957cbed6 100644
--- a/tensorflow/core/kernels/random_poisson_op.cc
+++ b/tensorflow/core/kernels/random_poisson_op.cc
@@ -303,10 +303,6 @@ class RandomPoissonOp : public OpKernel {
 
     const auto rate_flat = rate_t.flat<T>().data();
     const int64 num_rate = rate_t.NumElements();
-    OP_REQUIRES(
-        ctx, num_rate > 0,
-        errors::InvalidArgument(
-            "Input rate should have non-zero element count, got: ", num_rate));
     auto samples_flat = samples_t->flat<T>().data();
     random::PhiloxRandom rng = generator_.ReserveRandomOutputs(
         num_samples * num_rate, kReservedSamplesPerOutput);
diff --git a/tensorflow/core/kernels/random_shuffle_queue_op.cc b/tensorflow/core/kernels/random_shuffle_queue_op.cc
index d9efb5fe7d..30bbbd4aed 100644
--- a/tensorflow/core/kernels/random_shuffle_queue_op.cc
+++ b/tensorflow/core/kernels/random_shuffle_queue_op.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <deque>
 #include <vector>
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
diff --git a/tensorflow/core/kernels/reader_ops.cc b/tensorflow/core/kernels/reader_ops.cc
index e2eb40677b..abd16de6a1 100644
--- a/tensorflow/core/kernels/reader_ops.cc
+++ b/tensorflow/core/kernels/reader_ops.cc
@@ -50,8 +50,7 @@ class ReaderVerbAsyncOpKernel : public AsyncOpKernel {
       : AsyncOpKernel(context),
         thread_pool_(new thread::ThreadPool(
             context->env(), ThreadOptions(),
-            strings::StrCat("reader_thread_",
-                            SanitizeThreadSuffix(def().name())),
+            strings::StrCat("reader_thread_", SanitizeThreadSuffix(name())),
             1 /* num_threads */, false /* low_latency_hint */)) {}
 
   void ComputeAsync(OpKernelContext* context, DoneCallback done) override {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index aed320b6a2..dbda321016 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -157,6 +157,8 @@ string DumpCluster(const RemoteFusedGraphExecuteUtils::ClusterInfo& cluster) {
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_BORDER_INPUTS;
 /* static */ constexpr const char* const
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_BORDER_OUTPUTS;
+/* static */ constexpr const char* const
+    RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES;
 /* static */ constexpr const char* const
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_INPUT_TYPES;
 /* static */ constexpr const char* const
@@ -782,7 +784,9 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
           ++input_count;
         }
       }
-      CHECK(input_count == 0 || input_count == node->in_edges().size());
+      CHECK(input_count == 0 || input_count == node->in_edges().size())
+          << "Invalid input_count(" << input_count << ", "
+          << node->in_edges().size() << ") " << node_name;
 
       for (const Edge* out_edge : node->out_edges()) {
         const Node* dst_node = out_edge->dst();
@@ -1063,14 +1067,35 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
       remote_graph_executor_name, require_shape_type, output_graph_def);
 }
 
+/* static */ Status RemoteFusedGraphExecuteUtils::FuseRemoteGraphByOpTypes(
+    const GraphDef& input_graph_def, const std::vector<string>& inputs,
+    const std::vector<string>& outputs,
+    const string& remote_fused_graph_node_name_prefix,
+    const std::unordered_set<string>& fused_op_types,
+    const string& remote_fused_graph_executor_name,
+    const bool require_shape_type, GraphDef* output_graph_def) {
+  const std::unordered_set<string> fused_nodes_filtered_by_op_types =
+      BuildNodeMapFromOpTypes(input_graph_def, fused_op_types);
+
+  return FuseRemoteGraphByNodeNames(
+      input_graph_def, inputs, outputs, remote_fused_graph_node_name_prefix,
+      fused_nodes_filtered_by_op_types, remote_fused_graph_executor_name,
+      require_shape_type, output_graph_def);
+}
+
 /* static */ Status RemoteFusedGraphExecuteUtils::PlaceRemoteGraphArguments(
     const std::vector<string>& inputs, const std::vector<string>& outputs,
     const std::unordered_set<string>& fused_node_names,
     const std::vector<string>& border_inputs,
     const std::vector<string>& border_outputs,
+    const std::unordered_set<string>& fused_op_types,
     const string& remote_fused_graph_node_name,
     const string& remote_graph_executor_name, GraphDef* graph_def) {
   CHECK_NOTNULL(graph_def);
+
+  const std::unordered_set<string> fused_nodes_filtered_by_op_types =
+      BuildNodeMapFromOpTypes(*graph_def, fused_op_types);
+
   for (NodeDef& node_def : *graph_def->mutable_node()) {
     string attr_str;
     TensorId tid;
@@ -1095,6 +1120,12 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::FUSED_NODE);
       }
     }
+    for (const string& fused_node_name : fused_nodes_filtered_by_op_types) {
+      if (fused_node_name == node_def.name()) {
+        AppendDeliminator(&attr_str);
+        attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::FUSED_NODE);
+      }
+    }
     for (size_t i = 0; i < border_inputs.size(); ++i) {
       if (IsSameNodeName(node_def, border_inputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
@@ -1343,6 +1374,18 @@ RemoteFusedGraphExecuteUtils::FuseRemoteGraphByPlacedArguments(
   return Status::OK();
 }
 
+/* static */ std::unordered_set<string>
+RemoteFusedGraphExecuteUtils::BuildNodeMapFromOpTypes(
+    const GraphDef& graph_def, const std::unordered_set<string>& op_types) {
+  std::unordered_set<string> retval;
+  for (const NodeDef& node_def : graph_def.node()) {
+    if (op_types.count(node_def.op()) > 0) {
+      retval.emplace(node_def.name());
+    }
+  }
+  return retval;
+}
+
 /* static */ Status RemoteFusedGraphExecuteUtils::ReplaceInputNodeByPlaceHolder(
     const string& input, const DataType type, const TensorShape& shape,
     GraphDef* graph_def) {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
index 1d4423ed46..28d170d982 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
@@ -57,6 +57,8 @@ class RemoteFusedGraphExecuteUtils {
       "border_inputs";
   static constexpr const char* const TRANSFORM_ARG_BORDER_OUTPUTS =
       "border_outputs";
+  static constexpr const char* const TRANSFORM_ARG_FUSED_OP_TYPES =
+      "fused_op_types";
   static constexpr const char* const TRANSFORM_ARG_INPUT_TYPES = "input_types";
   static constexpr const char* const TRANSFORM_ARG_INPUT_SHAPES =
       "input_shapes";
@@ -230,12 +232,22 @@ class RemoteFusedGraphExecuteUtils {
       const string& remote_graph_executor_name, const bool require_shape_type,
       GraphDef* output_graph_def);
 
+  // Fuse subgraph of specified op types.
+  static Status FuseRemoteGraphByOpTypes(
+      const GraphDef& input_graph_def, const std::vector<string>& inputs,
+      const std::vector<string>& outputs,
+      const string& remote_fused_graph_node_name_prefix,
+      const std::unordered_set<string>& fused_op_types,
+      const string& remote_fused_graph_executor_name,
+      const bool require_shape_type, GraphDef* output_graph_def);
+
   // Place arguments to fuse remote graph.
   static Status PlaceRemoteGraphArguments(
       const std::vector<string>& inputs, const std::vector<string>& outputs,
       const std::unordered_set<string>& fused_node_names,
       const std::vector<string>& border_inputs,
       const std::vector<string>& border_outputs,
+      const std::unordered_set<string>& fused_op_types,
       const string& remote_fused_graph_node_name,
       const string& remote_graph_executor_name, GraphDef* graph_def);
 
@@ -258,6 +270,9 @@ class RemoteFusedGraphExecuteUtils {
   static Status CopyByteArrayToTensor(const void* src_ptr, const int src_size,
                                       Tensor* tensor);
 
+  static std::unordered_set<string> BuildNodeMapFromOpTypes(
+      const GraphDef& graph_def, const std::unordered_set<string>& op_types);
+
  private:
   static void EmplaceTensorShapeType(const string& name, const Tensor& tensor,
                                      TensorShapeMap* tensor_shape_map);
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
index b24482f2d5..059070305f 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/remote_fused_graph_execute_utils.h"
 #include "tensorflow/cc/framework/scope.h"
 #include "tensorflow/core/common_runtime/shape_refiner.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -79,6 +80,13 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
         /*require_shape_type=*/false, &result_graph_def_);
   }
 
+  Status FuseByOpTypes() {
+    return RemoteFusedGraphExecuteUtils::FuseRemoteGraphByOpTypes(
+        graph_def_, inputs_, outputs_, "remote_fused_graph_node_names",
+        subgraph_op_types_, "remote_graph_executor_name",
+        /*require_shape_type=*/false, &result_graph_def_);
+  }
+
   Status BuildAndAddTensorShape() {
     return RemoteFusedGraphExecuteUtils::BuildAndAddTensorShapes(
         input_tensors_, /*dry_run_inference=*/true, &graph_def_);
@@ -87,8 +95,9 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
   Status PlaceRemoteGraphArguments() {
     return RemoteFusedGraphExecuteUtils::PlaceRemoteGraphArguments(
         inputs_, outputs_, subgraph_node_names_, subgraph_input_names_,
-        subgraph_output_names_, "remote_fused_graph_node_names",
-        "remote_graph_executor_name", &graph_def_);
+        subgraph_output_names_, subgraph_op_types_,
+        "remote_fused_graph_node_names", "remote_graph_executor_name",
+        &graph_def_);
   }
 
   Status FuseByPlacedArguments() {
@@ -104,6 +113,15 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
                                                      input_tensors_);
   }
 
+  void ReplaceOpType(const std::unordered_set<string>& op_name,
+                     const string& new_op_type) {
+    for (NodeDef& node_def : *graph_def_.mutable_node()) {
+      if (op_name.count(node_def.name()) > 0) {
+        node_def.set_op(new_op_type);
+      }
+    }
+  }
+
  public:
   const std::vector<std::pair<string, Tensor>> input_tensors_{
       {"A", {DT_FLOAT, {1, 1, 1, 1}}}};
@@ -114,6 +132,7 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
   std::vector<string> subgraph_input_names_;
   std::vector<string> subgraph_output_names_;
   std::unordered_set<string> subgraph_node_names_;
+  std::unordered_set<string> subgraph_op_types_;
 };
 
 void SetSubgraphArguments(const std::vector<string>& input_names,
@@ -649,6 +668,32 @@ TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByNodes_ABCDEFGHIJK) {
       << SummarizeGraphDef(result_graph_def_);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByOpTypes_HIJ) {
+  subgraph_op_types_ = {"Mul"};
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+
+  TF_ASSERT_OK(FuseByOpTypes());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+  EXPECT_EQ(9, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByOpTypes_FGHIJ) {
+  subgraph_op_types_ = {"Const", "Mul"};
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+
+  TF_ASSERT_OK(FuseByOpTypes());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+  EXPECT_EQ(3, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
 TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_H) {
   subgraph_node_names_ = {"H"};
 
@@ -755,5 +800,41 @@ TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_ABCDE_K) {
       << SummarizeGraphDef(result_graph_def_);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_MUL_HIJ) {
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+  subgraph_op_types_ = {"Mul"};
+
+  TF_ASSERT_OK(PlaceRemoteGraphArguments());
+  ASSERT_TRUE(IsFuseReady());
+  TF_ASSERT_OK(BuildAndAddTensorShape());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+
+  TF_ASSERT_OK(FuseByPlacedArguments());
+
+  EXPECT_EQ(9, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_CONST_MUL_FGHIJ) {
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+  subgraph_op_types_ = {"Const", "Mul"};
+
+  TF_ASSERT_OK(PlaceRemoteGraphArguments());
+  ASSERT_TRUE(IsFuseReady());
+  TF_ASSERT_OK(BuildAndAddTensorShape());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+
+  TF_ASSERT_OK(FuseByPlacedArguments());
+
+  EXPECT_EQ(3, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
index 1a2fe2097c..11670e80c7 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
@@ -66,6 +66,7 @@ static Status ParseArguments(const TransformFuncContext& context,
                              string* input_types_str, string* input_shapes_str,
                              string* fused_nodes_str, string* border_inputs_str,
                              string* border_outputs_str,
+                             string* fused_op_types_str,
                              string* remote_fused_graph_node_name,
                              string* remote_graph_executor_name) {
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
@@ -83,6 +84,9 @@ static Status ParseArguments(const TransformFuncContext& context,
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
       RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_BORDER_OUTPUTS, "",
       border_outputs_str));
+  TF_RETURN_IF_ERROR(context.GetOneStringParameter(
+      RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES, "",
+      fused_op_types_str));
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
       RemoteFusedGraphExecuteUtils::
           TRANSFORM_ARG_REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
@@ -135,12 +139,13 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
   string fused_nodes_str;
   string border_inputs_str;
   string border_outputs_str;
+  string fused_op_types_str;
   string remote_fused_graph_node_name;
   string remote_graph_executor_name;
   TF_RETURN_IF_ERROR(ParseArguments(
       context, &input_types_str, &input_shapes_str, &fused_nodes_str,
-      &border_inputs_str, &border_outputs_str, &remote_fused_graph_node_name,
-      &remote_graph_executor_name));
+      &border_inputs_str, &border_outputs_str, &fused_op_types_str,
+      &remote_fused_graph_node_name, &remote_graph_executor_name));
 
   if (!input_types_str.empty()) {
     TF_RETURN_IF_ERROR(PlaceShapeType(inputs, outputs, input_types_str,
@@ -173,6 +178,15 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
         mutable_input_graph_def, inputs, outputs, remote_fused_graph_node_name,
         border_inputs, border_outputs, remote_graph_executor_name,
         require_shape_type, output_graph_def));
+  } else if (!fused_op_types_str.empty()) {
+    const std::vector<string> fused_op_type_vector =
+        str_util::Split(fused_op_types_str, ",");
+    const std::unordered_set<string> fused_op_types(
+        fused_op_type_vector.begin(), fused_op_type_vector.end());
+    TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::FuseRemoteGraphByOpTypes(
+        mutable_input_graph_def, inputs, outputs, remote_fused_graph_node_name,
+        fused_op_types, remote_graph_executor_name, require_shape_type,
+        output_graph_def));
   } else {
     CHECK(false) << "Fuse targets are not specified.";
   }
@@ -191,14 +205,15 @@ Status PlaceRemoteGraphArguments(const GraphDef& input_graph_def,
   string input_types_str;
   string input_shapes_str;
   string fused_nodes_str;
+  string fused_op_types_str;
   string border_inputs_str;
   string border_outputs_str;
   string remote_fused_graph_node_name;
   string remote_graph_executor_name;
   TF_RETURN_IF_ERROR(ParseArguments(
       context, &input_types_str, &input_shapes_str, &fused_nodes_str,
-      &border_inputs_str, &border_outputs_str, &remote_fused_graph_node_name,
-      &remote_graph_executor_name));
+      &border_inputs_str, &border_outputs_str, &fused_op_types_str,
+      &remote_fused_graph_node_name, &remote_graph_executor_name));
 
   if (!input_types_str.empty()) {
     TF_RETURN_IF_ERROR(PlaceShapeType(inputs, outputs, input_types_str,
@@ -213,9 +228,14 @@ Status PlaceRemoteGraphArguments(const GraphDef& input_graph_def,
       str_util::Split(border_inputs_str, ",");
   const std::vector<string> border_outputs =
       str_util::Split(border_outputs_str, ",");
+  const std::vector<string> fused_op_type_vector =
+      str_util::Split(fused_op_types_str, ",");
+  const std::unordered_set<string> fused_op_types(fused_op_type_vector.begin(),
+                                                  fused_op_type_vector.end());
+
   TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::PlaceRemoteGraphArguments(
       inputs, outputs, fused_node_names, border_inputs, border_outputs,
-      remote_fused_graph_node_name, remote_graph_executor_name,
+      fused_op_types, remote_fused_graph_node_name, remote_graph_executor_name,
       output_graph_def));
 
   return Status::OK();
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
index 143cdd7c0a..113f5c55ea 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
@@ -107,6 +107,12 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
            {border_outputs_str_}}));
     }
 
+    if (!fused_op_types_str_.empty()) {
+      context.params.insert(std::pair<string, std::vector<string>>(
+          {RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES,
+           {fused_op_types_str_}}));
+    }
+
     context.params.insert(std::pair<string, std::vector<string>>(
         {RemoteFusedGraphExecuteUtils::
              TRANSFORM_ARG_REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
@@ -129,6 +135,15 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
     input_shapes_ = "1,1,1,1";
   }
 
+  void ReplaceOpType(const std::unordered_set<string>& op_name,
+                     const string& new_op_type) {
+    for (NodeDef& node_def : *input_graph_def_.mutable_node()) {
+      if (op_name.count(node_def.name()) > 0) {
+        node_def.set_op(new_op_type);
+      }
+    }
+  }
+
   void CheckGraph(int expected_node_count, int expected_cluster_count) {
     EXPECT_EQ(expected_node_count, output_graph_def_.node_size());
 
@@ -162,6 +177,7 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
   string fused_node_names_str_;
   string border_inputs_str_;
   string border_outputs_str_;
+  string fused_op_types_str_;
 };
 
 TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
@@ -228,6 +244,22 @@ TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
   CheckGraph(7, 1);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       FuseRemoteGraphByOpTypes_HIJ) {
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Mul";
+  TF_ASSERT_OK(Fuse());
+  CheckGraph(9, 1);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       FuseRemoteGraphByOpTypes_FGHIJ) {
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Const,Mul";
+  TF_ASSERT_OK(Fuse());
+  CheckGraph(3, 1);
+}
+
 TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest, PlaceAndFuse_HIJ) {
   fused_node_names_str_ = "H,I,J";
   TF_ASSERT_OK(PlaceFuseArgs());
@@ -259,6 +291,27 @@ TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest, PlaceAndFuse_ABCDE_K) {
   CheckGraph(7, 1);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest, PlaceAndFuse_MUL_HIJ) {
+  SetInputShapeType();
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Mul";
+
+  TF_ASSERT_OK(PlaceFuseArgs());
+  TF_ASSERT_OK(FuseWithPlacedArgs());
+  CheckGraph(9, 1);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       PlaceAndFuse_CONST_MUL_FGHIJ) {
+  SetInputShapeType();
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+  fused_op_types_str_ = "Const,Mul";
+
+  TF_ASSERT_OK(PlaceFuseArgs());
+  TF_ASSERT_OK(FuseWithPlacedArgs());
+  CheckGraph(3, 1);
+}
+
 }  // namespace
 }  // namespace graph_transforms
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index fadfb1e637..db6d8c20ec 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -395,6 +395,7 @@ class ResourceScatterUpdateOp : public OpKernel {
   void Compute(OpKernelContext* c) override {
     Var* v = nullptr;
     OP_REQUIRES_OK(c, LookupResource(c, HandleFromInput(c, 0), &v));
+    core::ScopedUnref unref_v(v);
     mutex_lock ml(*v->mu());
     Tensor* params = v->tensor();
     const Tensor& indices = c->input(1);
diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc
index 48565d8cb9..5f96dfa0f3 100644
--- a/tensorflow/core/kernels/scatter_nd_op.cc
+++ b/tensorflow/core/kernels/scatter_nd_op.cc
@@ -16,12 +16,17 @@ limitations under the License.
 // See docs in ../ops/state_ops.cc.
 #define EIGEN_USE_THREADS
 
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif  // GOOGLE_CUDA
+
 #include "tensorflow/core/kernels/scatter_nd_op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/kernels/dense_update_ops.h"
 #include "tensorflow/core/kernels/fill_functor.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
@@ -29,7 +34,7 @@ limitations under the License.
 
 #ifdef TENSORFLOW_USE_SYCL
 #include "tensorflow/core/common_runtime/sycl/sycl_util.h"
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 namespace tensorflow {
 
@@ -37,7 +42,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 #ifdef TENSORFLOW_USE_SYCL
 typedef Eigen::SyclDevice SYCLDevice;
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 // Check whether updates.shape = indices.shape[:batch_dim] +
 // params_shape[slice_dim:]
@@ -91,11 +96,13 @@ static void PrepareAndValidateInputs(OpKernelContext* c,
       errors::InvalidArgument("Output must be at least 1-D, ",
                               "got shape: ", params_shape.DebugString()));
 
-  OP_REQUIRES(c,
-              params_shape.num_elements() >= 0 ||
-                  (indices.NumElements() == 0 && updates.NumElements() == 0),
-              errors::InvalidArgument(
-                  "Indices and updates specified for empty output", " shape"));
+  OP_REQUIRES(
+      c,
+      params_shape.num_elements() > 0 ||
+          (indices.NumElements() == 0 && updates.NumElements() == 0),
+      errors::InvalidArgument(
+          "Indices and updates specified for empty output.  indices shape: ",
+          indices.shape().DebugString()));
 
   OP_REQUIRES(c, updates.dim_size(0) == indices.dim_size(0),
               errors::InvalidArgument(
@@ -147,9 +154,9 @@ static void PrepareAndValidateInputs(OpKernelContext* c,
 
 template <typename Device, typename Index>
 class IndexFlattener {
-public:
-  inline typename TTypes<Index, 2>::ConstTensor
-  operator()(OpKernelContext*, const Tensor& indices) {
+ public:
+  inline typename TTypes<Index, 2>::ConstTensor operator()(
+      OpKernelContext*, const Tensor& indices) {
     return indices.flat_inner_dims<Index>();
   }
 };
@@ -157,12 +164,12 @@ public:
 #ifdef TENSORFLOW_USE_SYCL
 template <typename Index>
 class IndexFlattener<SYCLDevice, Index> {
-public:
+ public:
   IndexFlattener() { indices_host_ = nullptr; }
   ~IndexFlattener() { delete[] indices_host_; }
 
-  inline typename TTypes<Index, 2>::ConstTensor
-  operator()(OpKernelContext* c, const Tensor& indices) {
+  inline typename TTypes<Index, 2>::ConstTensor operator()(
+      OpKernelContext* c, const Tensor& indices) {
     size_t num_indices = indices.NumElements();
     indices_host_ = new Index[num_indices];
     auto device = c->eigen_sycl_device();
@@ -170,11 +177,11 @@ public:
     auto src_ptr = GetBase(&indices);
     device.memcpyDeviceToHost(indices_host_, static_cast<const Index*>(src_ptr),
                               size);
-    return typename TTypes<Index, 2>::ConstTensor(indices_host_,
-           indices.shape().AsEigenDSizes<2>());
+    return typename TTypes<Index, 2>::ConstTensor(
+        indices_host_, indices.shape().AsEigenDSizes<2>());
   }
 
-private:
+ private:
   Index* indices_host_;
 };
 #endif
@@ -213,6 +220,9 @@ class ScatterNdOp : public OpKernel {
 
     Tensor* out = nullptr;
     OP_REQUIRES_OK(c, c->allocate_output(0, shape, &out));
+
+    if (shape.num_elements() == 0) return;
+
     functor::SetZeroFunctor<Device, T> fill;
     fill(c->eigen_device<Device>(), out->flat<T>());
     auto output_matrix = out->template shaped<T, 2>(
@@ -271,12 +281,19 @@ class ScatterNdUpdateOp : public OpKernel {
     const DataType dt = DataTypeToEnum<T>::v();
     const DataType dt_ref = DataTypeToEnum<T>::ref();
     const DataType index_t = DataTypeToEnum<Index>::v();
-    OP_REQUIRES_OK(c, c->MatchSignature({dt_ref, index_t, dt}, {dt_ref}));
-    OP_REQUIRES_OK(c, c->GetAttr("use_locking", &use_exclusive_lock_));
+    if (IsRefType(c->input_type(0))) {
+      OP_REQUIRES_OK(c, c->MatchSignature({dt_ref, index_t, dt}, {dt_ref}));
+      OP_REQUIRES_OK(c, c->GetAttr("use_locking", &use_exclusive_lock_));
+    } else {
+      OP_REQUIRES_OK(c, c->MatchSignature({dt, index_t, dt}, {dt}));
+      use_exclusive_lock_ = false;
+    }
   }
 
   void Compute(OpKernelContext* c) override {
     if (use_exclusive_lock_) {
+      // If we're here, it means the input type is a ref.
+      DCHECK(IsRefType(c->input_dtype(0)));
       // Hold mutex while we apply updates
       mutex_lock l(*c->input_ref_mutex(0));
       DoCompute(c);
@@ -289,20 +306,41 @@ class ScatterNdUpdateOp : public OpKernel {
   bool use_exclusive_lock_;
 
   void DoCompute(OpKernelContext* c) {
-    Tensor params = c->mutable_input(0, use_exclusive_lock_);
     const Tensor& indices = c->input(1);
     const Tensor& updates = c->input(2);
-    const TensorShape& params_shape(params.shape());
 
     int64 slice_dim;
     Index num_updates;
     Index slice_size;
 
-    OP_REQUIRES(c, params.IsInitialized(),
-                errors::FailedPrecondition("Null ref for params"));
+    Tensor params;
+    TensorShape params_shape;
+
+    if (IsRefType(c->input_dtype(0))) {
+      params = c->mutable_input(0, use_exclusive_lock_);
+      params_shape = params.shape();
+      c->forward_ref_input_to_ref_output(0, 0);
+      OP_REQUIRES(c, params.IsInitialized(),
+                  errors::FailedPrecondition("Null ref for params"));
+    } else {
+      Tensor* params_ptr;
+      params_shape = c->input(0).shape();
+      if (!c->forward_input_to_output_with_shape(0, 0, params_shape,
+                                                 &params_ptr)) {
+        // We weren't able to forward the input to output, so just
+        // allocate a new output tensor and copy the values over.
+        OP_REQUIRES_OK(c, c->allocate_output(0, params_shape, &params_ptr));
+        params = *params_ptr;
+        functor::DenseUpdate<Device, T, ASSIGN> copy;
+        const Tensor& input_copy = c->input(0);
+        copy(c->eigen_device<Device>(), params.flat<T>(), input_copy.flat<T>());
+      }
+    }
+
     PrepareAndValidateInputs<Index>(c, params_shape, indices, updates,
                                     &slice_dim, &num_updates, &slice_size);
     if (!c->status().ok()) return;
+    if (params_shape.num_elements() == 0) return;
 
     IndexFlattener<Device, Index> index_flattener;
     auto indices_flat = index_flattener(c, indices);
@@ -310,7 +348,6 @@ class ScatterNdUpdateOp : public OpKernel {
     auto params_matrix = params.template shaped<T, 2>(
         {params_shape.num_elements() / slice_size, slice_size});
     Index bad_i = -1;
-    c->forward_ref_input_to_ref_output(0, 0);
 
     switch (slice_dim) {
 #define PARAMS_CASE(IXDIM)                                                  \
@@ -376,10 +413,12 @@ class ScatterNdUpdateOp : public OpKernel {
   REGISTER_SCATTER_ND_UPDATE_KERNEL_INDEX(type, int32, dev, name, op); \
   REGISTER_SCATTER_ND_UPDATE_KERNEL_INDEX(type, int64, dev, name, op)
 
-#define REGISTER_SCATTER_ND_ADD_SUB(type, dev)                     \
-  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdAdd",     \
-                                    scatter_nd_op::UpdateOp::ADD); \
-  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdSub",     \
+#define REGISTER_SCATTER_ND_ADD_SUB(type, dev)                            \
+  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdAdd",            \
+                                    scatter_nd_op::UpdateOp::ADD);        \
+  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdNonAliasingAdd", \
+                                    scatter_nd_op::UpdateOp::ADD);        \
+  REGISTER_SCATTER_ND_UPDATE_KERNEL(type, dev, "ScatterNdSub",            \
                                     scatter_nd_op::UpdateOp::SUB);
 // TODO(simister): Find a way to reduce amount of templated generated code
 // to reduce build size, then re-enable these additional operations.
@@ -421,9 +460,31 @@ TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_CPU);
 
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_GPU);
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_GPU);
-
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_GPU);
 
+#ifdef TENSORFLOW_USE_SYCL
+#define REGISTER_SCATTER_ND_ADD_SUB_SYCL(type) \
+  REGISTER_SCATTER_ND_ADD_SUB(type, SYCL);
+
+#define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \
+  REGISTER_SCATTER_ND_UPDATE(type, SYCL);
+
+TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL);
+TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL);
+#undef REGISTER_SCATTER_ND_ADD_SUB_SYCL
+#undef REGISTER_SCATTER_ND_UPDATE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
+
+#undef REGISTER_SCATTER_ND_ADD
+#undef REGISTER_SCATTER_ND_ADD_SUB
+#undef REGISTER_SCATTER_ND_ADD_SUB_CPU
+#undef REGISTER_SCATTER_ND_ADD_SUB_GPU
+#undef REGISTER_SCATTER_ND_UPDATE
+#undef REGISTER_SCATTER_ND_UPDATE_CPU
+#undef REGISTER_SCATTER_ND_UPDATE_GPU
+#undef REGISTER_SCATTER_ND_KERNEL
+#undef REGISTER_SCATTER_ND_KERNEL_INDEX
+
 // Forward declarations of the functor specializations for GPU.
 namespace functor {
 #define DECLARE_GPU_SPECS_INDEX_OP_IXDIM(T, Index, op, IXDIM)           \
@@ -458,31 +519,19 @@ TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DECLARE_GPU_SPECS);
 #undef DECLARE_GPU_SPECS
 #undef DECLARE_GPU_SPECS_INDEX
 #undef DECLARE_GPU_SPECS_INDEX_OP
-}  // namespace functor
-
-#endif  // GOOGLE_CUDA
 
-#ifdef TENSORFLOW_USE_SYCL
-#define REGISTER_SCATTER_ND_ADD_SUB_SYCL(type) \
-  REGISTER_SCATTER_ND_ADD_SUB(type, SYCL);
+#define REGISTER_GPU_KERNELS(type)                         \
+  template <>                                              \
+  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(   \
+      const GPUDevice& d, typename TTypes<type>::Flat lhs, \
+      typename TTypes<type>::ConstFlat rhs);               \
+  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;
 
-#define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \
-  REGISTER_SCATTER_ND_UPDATE(type, SYCL);
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
+#undef REGISTER_GPU_KERNELS
 
-TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL);
-TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL);
-#undef REGISTER_SCATTER_ND_ADD_SUB_SYCL
-#undef REGISTER_SCATTER_ND_UPDATE_SYCL
-#endif // TENSORFLOW_USE_SYCL
+}  // namespace functor
 
-#undef REGISTER_SCATTER_ND_ADD
-#undef REGISTER_SCATTER_ND_ADD_SUB
-#undef REGISTER_SCATTER_ND_ADD_SUB_CPU
-#undef REGISTER_SCATTER_ND_ADD_SUB_GPU
-#undef REGISTER_SCATTER_ND_UPDATE
-#undef REGISTER_SCATTER_ND_UPDATE_CPU
-#undef REGISTER_SCATTER_ND_UPDATE_GPU
-#undef REGISTER_SCATTER_ND_KERNEL
-#undef REGISTER_SCATTER_ND_KERNEL_INDEX
+#endif  // GOOGLE_CUDA
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/session_ops.cc b/tensorflow/core/kernels/session_ops.cc
index 27ad2fcd87..185c5b248f 100644
--- a/tensorflow/core/kernels/session_ops.cc
+++ b/tensorflow/core/kernels/session_ops.cc
@@ -43,21 +43,21 @@ class GetSessionHandleOp : public OpKernel {
   void Compute(OpKernelContext* ctx) override {
     const Tensor& val = ctx->input(0);
     int64 id = ctx->session_state()->GetNewId();
-    TensorStore::TensorAndKey tk{val, id, def().device()};
-    OP_REQUIRES_OK(ctx, ctx->tensor_store()->AddTensor(def().name(), tk));
+    TensorStore::TensorAndKey tk{val, id, requested_device()};
+    OP_REQUIRES_OK(ctx, ctx->tensor_store()->AddTensor(name(), tk));
 
     Tensor* handle = nullptr;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &handle));
     if (ctx->expected_output_dtype(0) == DT_RESOURCE) {
       ResourceHandle resource_handle = MakeResourceHandle<Tensor>(
           ctx, SessionState::kTensorHandleResourceTypeName,
-          tk.GetHandle(def().name()));
+          tk.GetHandle(name()));
       resource_handle.set_maybe_type_name(
           SessionState::kTensorHandleResourceTypeName);
       handle->scalar<ResourceHandle>()() = resource_handle;
     } else {
       // Legacy behavior in V1.
-      handle->flat<string>().setConstant(tk.GetHandle(def().name()));
+      handle->flat<string>().setConstant(tk.GetHandle(name()));
     }
   }
 
diff --git a/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc b/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc
index 3b915e419b..81eead11d1 100644
--- a/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc
+++ b/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc
@@ -74,6 +74,7 @@ class SparseFillEmptyRowsOp : public OpKernel {
 
     const int64 N = indices_t->shape().dim_size(0);
     const int64 dense_rows = dense_shape(0);
+
     Tensor* empty_row_indicator_t;
     OP_REQUIRES_OK(context, context->allocate_output("empty_row_indicator",
                                                      TensorShape({dense_rows}),
@@ -84,6 +85,29 @@ class SparseFillEmptyRowsOp : public OpKernel {
         context, context->allocate_output("reverse_index_map", TensorShape({N}),
                                           &reverse_index_map_t));
     auto reverse_index_map = reverse_index_map_t->vec<int64>();
+
+    int rank = indices_t->shape().dim_size(1);
+
+    if (dense_rows == 0) {
+      OP_REQUIRES(
+          context, N == 0,
+          errors::InvalidArgument("Received SparseTensor with dense_shape[0] = "
+                                  "0 but indices.shape[0] = ",
+                                  N));
+      Tensor* output_indices_t;
+      TensorShape output_indices_shape({0, rank});
+      OP_REQUIRES_OK(context, context->allocate_output("output_indices",
+                                                       output_indices_shape,
+                                                       &output_indices_t));
+      Tensor* output_values_t;
+      OP_REQUIRES_OK(context,
+                     context->allocate_output("output_values", TensorShape({0}),
+                                              &output_values_t));
+
+      // Exit early, nothing more to do.
+      return;
+    }
+
     Tensor scratch_t;
     OP_REQUIRES_OK(context,
                    context->allocate_temp(DT_INT64, TensorShape({dense_rows}),
@@ -115,7 +139,6 @@ class SparseFillEmptyRowsOp : public OpKernel {
       }
     }
     Tensor* output_indices_t;
-    int rank = indices_t->shape().dim_size(1);
     const int64 N_full = scratch(dense_rows - 1);
     TensorShape output_indices_shape({N_full, rank});
     OP_REQUIRES_OK(context, context->allocate_output("output_indices",
diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc
index 47eb85999e..fa518712ce 100644
--- a/tensorflow/core/kernels/strided_slice_op.cc
+++ b/tensorflow/core/kernels/strided_slice_op.cc
@@ -96,17 +96,13 @@ class StridedSliceOp : public OpKernel {
     gtl::InlinedVector<int64, 4> end;
     gtl::InlinedVector<int64, 4> strides;
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     OP_REQUIRES_OK(
         context, ValidateStridedSliceOp(
                      &context->input(1), &context->input(2), context->input(3),
-                     ShapeReadWriteFromTensorShape(&context->input(0).shape()),
-                     begin_mask, end_mask, ellipsis_mask, new_axis_mask,
-                     shrink_axis_mask, &wrapped_processing_shape,
-                     &wrapped_final_shape, &is_identity, &is_simple_slice,
-                     &slice_dim0, &begin, &end, &strides));
-
+                     context->input(0).shape(), begin_mask, end_mask,
+                     ellipsis_mask, new_axis_mask, shrink_axis_mask,
+                     &processing_shape, &final_shape, &is_identity,
+                     &is_simple_slice, &slice_dim0, &begin, &end, &strides));
     const Tensor& input = context->input(0);
 
     // Optimization #1, slice is a no-op plus reshape
@@ -218,15 +214,12 @@ class StridedSliceGradOp : public OpKernel {
       LOG(FATAL) << "shape must have type int32 or int64.";
     }
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     OP_REQUIRES_OK(
         context,
         ValidateStridedSliceOp(
             &context->input(1), &context->input(2), context->input(3),
-            ShapeReadWriteFromTensorShape(&input_shape), begin_mask, end_mask,
-            ellipsis_mask, new_axis_mask, shrink_axis_mask,
-            &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
+            input_shape, begin_mask, end_mask, ellipsis_mask, new_axis_mask,
+            shrink_axis_mask, &processing_shape, &final_shape, &is_identity,
             &is_simple_slice, &slice_dim0, &begin, &end, &strides));
 
     // Check to make sure dy is consistent with the original slice
@@ -306,15 +299,12 @@ class StridedSliceAssignOp : public OpKernel {
       old_lhs = context->mutable_input(0, true);
     }
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     OP_REQUIRES_OK(
         context,
         ValidateStridedSliceOp(
             &context->input(1), &context->input(2), context->input(3),
-            ShapeReadWriteFromTensorShape(&old_lhs.shape()), begin_mask,
-            end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask,
-            &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
+            old_lhs.shape(), begin_mask, end_mask, ellipsis_mask, new_axis_mask,
+            shrink_axis_mask, &processing_shape, &final_shape, &is_identity,
             &is_simple_slice, &slice_dim0, &begin, &end, &strides));
 
     if (processing_shape.num_elements()) {
diff --git a/tensorflow/core/kernels/strided_slice_op.h b/tensorflow/core/kernels/strided_slice_op.h
index 7893e07949..0f72c4b771 100644
--- a/tensorflow/core/kernels/strided_slice_op.h
+++ b/tensorflow/core/kernels/strided_slice_op.h
@@ -19,7 +19,7 @@ limitations under the License.
 // Functor definition for StridedSliceOp, must be compilable by nvcc.
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/platform/types.h"
 
diff --git a/tensorflow/core/kernels/strided_slice_op_test.cc b/tensorflow/core/kernels/strided_slice_op_test.cc
index ca66ccad8b..281ca0f58f 100644
--- a/tensorflow/core/kernels/strided_slice_op_test.cc
+++ b/tensorflow/core/kernels/strided_slice_op_test.cc
@@ -115,13 +115,11 @@ static void BM_ValidateStridedSliceOp(int iters) {
     const int32 new_axis_mask = 0;
     const int32 shrink_axis_mask = 0;
 
-    ShapeReadWriteFromTensorShape wrapped_processing_shape(&processing_shape);
-    ShapeReadWriteFromTensorShape wrapped_final_shape(&final_shape);
     TF_CHECK_OK(ValidateStridedSliceOp(
-        &begin, &end, strides, ShapeReadWriteFromTensorShape(&input_shape),
-        begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask,
-        &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
-        &is_simple_slice, &slice_dim0, &begin_out, &end_out, &strides_out));
+        &begin, &end, strides, input_shape, begin_mask, end_mask, ellipsis_mask,
+        new_axis_mask, shrink_axis_mask, &processing_shape, &final_shape,
+        &is_identity, &is_simple_slice, &slice_dim0, &begin_out, &end_out,
+        &strides_out));
   }
 }
 
diff --git a/tensorflow/core/kernels/training_op_helpers.cc b/tensorflow/core/kernels/training_op_helpers.cc
index 11d51188fc..01ea6877b1 100644
--- a/tensorflow/core/kernels/training_op_helpers.cc
+++ b/tensorflow/core/kernels/training_op_helpers.cc
@@ -71,6 +71,7 @@ Status GetInputTensorFromVariable(OpKernelContext* ctx, int input,
   if (ctx->input_dtype(input) == DT_RESOURCE) {
     Var* var;
     if (LookupResource(ctx, HandleFromInput(ctx, input), &var).ok()) {
+      core::ScopedUnref unref_var(var);
       if (lock_held) {
         *out = *var->tensor();
       } else {
diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc
index f6b6194f0a..e798a7e4a4 100644
--- a/tensorflow/core/kernels/training_ops.cc
+++ b/tensorflow/core/kernels/training_ops.cc
@@ -352,7 +352,7 @@ class ApplyGradientDescentOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -391,7 +391,7 @@ class ApplyGradientDescentOp < SYCLDevice, T > : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha_dev = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha_dev.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -480,7 +480,7 @@ class ApplyDelayCompensatedGradientDescentOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -575,15 +575,15 @@ class ApplyAdadeltaOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, accum_update.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& lr = ctx->input(3);
     const Tensor& rho = ctx->input(4);
@@ -711,15 +711,15 @@ class SparseApplyAdadeltaOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum_grad.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, accum_update.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum_grad.shape()),
         errors::InvalidArgument("var and accum_grad do not have the same shape",
@@ -851,7 +851,7 @@ class ApplyProximalGradientDescentOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     const Tensor& alpha = ctx->input(1);
     OP_REQUIRES(ctx, IsLegacyScalar(alpha.shape()),
                 errors::InvalidArgument("alpha is not a scalar: ",
@@ -1066,11 +1066,11 @@ class ApplyAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     const Tensor& lr = ctx->input(2);
     OP_REQUIRES(ctx, IsLegacyScalar(lr.shape()),
                 errors::InvalidArgument("lr is not a scalar: ",
@@ -1159,11 +1159,11 @@ class ApplyProximalAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1266,11 +1266,11 @@ class SparseApplyAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1400,11 +1400,11 @@ class SparseApplyProximalAdagradOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1575,15 +1575,15 @@ class ApplyAdagradDAOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, gradient_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, gradient_squared_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(gradient_accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1677,15 +1677,15 @@ class SparseApplyAdagradDAOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, gradient_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, gradient_squared_accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(gradient_accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -1874,15 +1874,15 @@ class ApplyFtrlOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, linear.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& grad = ctx->input(3);
     OP_REQUIRES(
@@ -1988,15 +1988,15 @@ class SparseApplyFtrlOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, linear.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -2196,11 +2196,11 @@ class ApplyMomentumOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     const Tensor& lr = ctx->input(2);
     OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr.shape()),
                 errors::InvalidArgument("lr is not a scalar: ",
@@ -2299,11 +2299,11 @@ class SparseApplyMomentumOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, accum.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, var.shape().IsSameSize(accum.shape()),
         errors::InvalidArgument("var and accum do not have the same shape",
@@ -2419,15 +2419,15 @@ class ApplyAdamOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, m.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, v.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& beta1_power = ctx->input(3);
     const Tensor& beta2_power = ctx->input(4);
@@ -2505,15 +2505,15 @@ class ApplyAdamOp < SYCLDevice, T> : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, m.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, v.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& beta1_power_dev = ctx->input(3);
     const Tensor& beta2_power_dev = ctx->input(4);
@@ -2679,15 +2679,15 @@ class ApplyRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& lr = ctx->input(3);
     const Tensor& rho = ctx->input(4);
@@ -2764,19 +2764,19 @@ class ApplyCenteredRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, mg.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(3)));
+            "Attempting to use uninitialized variables: ", requested_input(3)));
 
     const Tensor& lr = ctx->input(4);
     const Tensor& rho = ctx->input(5);
@@ -2922,15 +2922,15 @@ class SparseApplyRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(1)));
+            "Attempting to use uninitialized variables: ", requested_input(1)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
 
     const Tensor& lr = ctx->input(3);
     const Tensor& rho = ctx->input(4);
@@ -3054,15 +3054,15 @@ class SparseApplyCenteredRMSPropOp : public OpKernel {
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(0)));
+            "Attempting to use uninitialized variables: ", requested_input(0)));
     OP_REQUIRES(
         ctx, ms.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(2)));
+            "Attempting to use uninitialized variables: ", requested_input(2)));
     OP_REQUIRES(
         ctx, mom.IsInitialized(),
         errors::FailedPrecondition(
-            "Attempting to use uninitialized variables: ", def().input(3)));
+            "Attempting to use uninitialized variables: ", requested_input(3)));
 
     const Tensor& lr = ctx->input(4);
     const Tensor& rho = ctx->input(5);
diff --git a/tensorflow/core/kernels/unique_op.cc b/tensorflow/core/kernels/unique_op.cc
index b57e13a28c..6e51696d6f 100644
--- a/tensorflow/core/kernels/unique_op.cc
+++ b/tensorflow/core/kernels/unique_op.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include <unordered_map>
 #include <utility>
 
 #include "tensorflow/core/framework/op_kernel.h"
@@ -20,7 +21,6 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/lib/gtl/flatmap.h"
 
 namespace tensorflow {
 
@@ -50,7 +50,8 @@ class UniqueOp : public OpKernel {
                                 {0}, 1, input.shape(), &idx));
     auto idx_vec = idx->template vec<int32>();
 
-    gtl::FlatMap<T, int32> uniq(N);
+    std::unordered_map<T, int32> uniq;
+    uniq.reserve(2 * N);
     for (int64 i = 0, j = 0; i < N; ++i) {
       auto it = uniq.insert(std::make_pair(Tin(i), j));
       idx_vec(i) = it.first->second;
diff --git a/tensorflow/core/kernels/warn_about_ints.cc b/tensorflow/core/kernels/warn_about_ints.cc
index fd0a889c99..75ecdf2ae4 100644
--- a/tensorflow/core/kernels/warn_about_ints.cc
+++ b/tensorflow/core/kernels/warn_about_ints.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/kernels/warn_about_ints.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 85a6cfcac9..c2b81615bd 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -2284,21 +2284,17 @@ REGISTER_OP("StridedSlice")
       const Tensor* begin_value = c->input_tensor(1);
       const Tensor* end_value = c->input_tensor(2);
 
-      TensorShapeProto processing_shape, final_shape;
-      ShapeReadWriteFromTensorShapeProto wrapped_processing_shape(
-          &processing_shape);
-      ShapeReadWriteFromTensorShapeProto wrapped_final_shape(&final_shape);
+      PartialTensorShape processing_shape, final_shape;
       bool is_identity, is_simple_slice, slice_dim0;
       gtl::InlinedVector<int64, 4> begin, end, strides;
       TF_RETURN_IF_ERROR(ValidateStridedSliceOp(
-          begin_value, end_value, *strides_value,
-          ShapeReadWriteFromTensorShapeProto(&input_shape_proto), begin_mask,
+          begin_value, end_value, *strides_value, input_shape_proto, begin_mask,
           end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask,
-          &wrapped_processing_shape, &wrapped_final_shape, &is_identity,
-          &is_simple_slice, &slice_dim0, &begin, &end, &strides));
+          &processing_shape, &final_shape, &is_identity, &is_simple_slice,
+          &slice_dim0, &begin, &end, &strides));
 
       ShapeHandle out;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(final_shape, &out));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(final_shape, &out));
       c->set_output(0, out);
 
       return Status::OK();
@@ -4916,6 +4912,62 @@ output: A new tensor with the given shape and updates applied according
   to the indices.
 )doc");
 
+REGISTER_OP("ScatterNdNonAliasingAdd")
+    .Input("input: T")
+    .Input("indices: Tindices")
+    .Input("updates: T")
+    .Output("output: T")
+    .Attr("T: numbertype")
+    .Attr("Tindices: {int32, int64}")
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
+    .Doc(R"doc(
+Applies sparse addition to `input` using individual values or slices
+from `updates` according to indices `indices`.  The updates are non-aliasing:
+`input` is only modified in-place if no other operations will use it.
+Otherwise, a copy of `input` is made.  This operation has a gradient with
+respect to both `input` and `updates`.
+
+`input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
+
+`indices` must be integer tensor, containing indices into `input`.
+It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
+
+The innermost dimension of `indices` (with length `K`) corresponds to
+indices into elements (if `K = P`) or `(P-K)`-dimensional slices
+(if `K < P`) along the `K`th dimension of `input`.
+
+`updates` is `Tensor` of rank `Q-1+P-K` with shape:
+
+```
+[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].
+```
+
+For example, say we want to add 4 scattered elements to a rank-1 tensor to 8
+elements. In Python, that addition would look like this:
+
+    input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
+    indices = tf.constant([[4], [3], [1], [7]])
+    updates = tf.constant([9, 10, 11, 12])
+    output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
+    with tf.Session() as sess:
+      print(sess.run(output))
+
+The resulting value `output` would look like this:
+
+    [1, 13, 3, 14, 14, 6, 7, 20]
+
+See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
+slices.
+
+input: A Tensor.
+indices: A Tensor. Must be one of the following types: `int32`, `int64`.
+  A tensor of indices into `input`.
+updates: A Tensor. Must have the same type as ref. A tensor of updated values
+  to add to `input`.
+output: A `Tensor` with the same shape as `input`, containing values of `input`
+  updated with `updates`.
+)doc");
+
 REGISTER_OP("FakeQuantWithMinMaxArgs")
     .Attr("min: float = -6.0")
     .Attr("max: float = 6.0")
diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index edc21b6029..9631538227 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -1540,6 +1540,69 @@ op {
     }
   }
 }
+op {
+  name: "ArgMax"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "dimension"
+    type_attr: "Tidx"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "output_type"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tidx"
+    type: "type"
+    default_value {
+      type: DT_INT32
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "ArgMin"
   input_arg {
@@ -1590,6 +1653,69 @@ op {
     }
   }
 }
+op {
+  name: "ArgMin"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "dimension"
+    type_attr: "Tidx"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "output_type"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tidx"
+    type: "type"
+    default_value {
+      type: DT_INT32
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "AsString"
   input_arg {
@@ -5250,6 +5376,30 @@ op {
     }
   }
 }
+op {
+  name: "Cosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "CountUpTo"
   input_arg {
@@ -21810,6 +21960,57 @@ op {
     }
   }
 }
+op {
+  name: "ScatterNdNonAliasingAdd"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "updates"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "ScatterNdSub"
   input_arg {
@@ -22821,6 +23022,30 @@ op {
     }
   }
 }
+op {
+  name: "Sinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "Size"
   input_arg {
@@ -24662,6 +24887,108 @@ op {
     }
   }
 }
+op {
+  name: "SparseReduceMax"
+  input_arg {
+    name: "input_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+}
+op {
+  name: "SparseReduceMaxSparse"
+  input_arg {
+    name: "input_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+}
 op {
   name: "SparseReduceSum"
   input_arg {
diff --git a/tensorflow/core/ops/io_ops.cc b/tensorflow/core/ops/io_ops.cc
index e8379af4bb..350aefe2c8 100644
--- a/tensorflow/core/ops/io_ops.cc
+++ b/tensorflow/core/ops/io_ops.cc
@@ -109,8 +109,7 @@ REGISTER_OP("RestoreV2")
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &shape1));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &shape2));
       TF_RETURN_IF_ERROR(c->Merge(shape1, shape2, &shape0));
-      c->set_output(0, c->UnknownShape());
-      return Status::OK();
+      return UnknownShape(c);
     })
     .Doc(R"doc(
 Restores tensors from a V2 checkpoint.
diff --git a/tensorflow/core/ops/io_ops_test.cc b/tensorflow/core/ops/io_ops_test.cc
index a915cdbe12..785fb96c64 100644
--- a/tensorflow/core/ops/io_ops_test.cc
+++ b/tensorflow/core/ops/io_ops_test.cc
@@ -79,6 +79,26 @@ TEST(IoOpsTest, Restore_ShapeFn) {
   INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[];[?]");
 }
 
+TEST(IoOpsTest, RestoreV2_ShapeFn) {
+  ShapeInferenceTestOp op("RestoreV2");
+
+  TF_ASSERT_OK(NodeDefBuilder("test", op.name)
+                   .Input({"prefix", 0, DT_STRING})
+                   .Input({"tensor_names", 0, DT_STRING})
+                   .Input({"shapes_and_slices", 0, DT_STRING})
+                   .Attr("dtypes", {DT_FLOAT, DT_INT64})
+                   .Finalize(&op.node_def));
+
+  INFER_OK(op, "?;?;?", "?;?");
+  INFER_OK(op, "[];[10];[10]", "?;?");
+
+  // Input shape validation.
+  INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[?];[?];[?]");
+  INFER_ERROR("Shape must be rank 1 but is rank 2", op, "[];[?,?];[?]");
+  INFER_ERROR("Shape must be rank 1 but is rank 2", op, "[];[?];[?,?]");
+  INFER_ERROR("in both shapes must be equal", op, "[];[10];[20]");
+}
+
 TEST(IoOpsTest, RestoreSlice_ShapeFn) {
   ShapeInferenceTestOp op("RestoreSlice");
 
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 8b9dd39536..30d6987707 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -1289,33 +1289,37 @@ Status ArgOpShape(shape_inference::InferenceContext* c) {
 REGISTER_OP("ArgMax")
     .Input("input: T")
     .Input("dimension: Tidx")
-    .Output("output: int64")
+    .Output("output: output_type")
     .Attr("T: numbertype")
     .Attr("Tidx: {int32, int64} = DT_INT32")
+    .Attr("output_type: {int32, int64} = DT_INT64")
     .SetShapeFn(ArgOpShape)
     .Doc(R"doc(
 Returns the index with the largest value across dimensions of a tensor.
 
 Note that in case of ties the identity of the return value is not guaranteed.
 
-dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-  of the input Tensor to reduce across. For vectors, use dimension = 0.
+dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+  which dimension of the input Tensor to reduce across. For vectors,
+  use dimension = 0.
 )doc");
 
 REGISTER_OP("ArgMin")
     .Input("input: T")
     .Input("dimension: Tidx")
-    .Output("output: int64")
+    .Output("output: output_type")
     .Attr("T: numbertype")
     .Attr("Tidx: {int32, int64} = DT_INT32")
+    .Attr("output_type: {int32, int64} = DT_INT64")
     .SetShapeFn(ArgOpShape)
     .Doc(R"doc(
 Returns the index with the smallest value across dimensions of a tensor.
 
 Note that in case of ties the identity of the return value is not guaranteed.
 
-dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-  of the input Tensor to reduce across. For vectors, use dimension = 0.
+dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+  which dimension of the input Tensor to reduce across. For vectors,
+  use dimension = 0.
 )doc");
 
 namespace {
@@ -2271,7 +2275,12 @@ REGISTER_OP("QuantizedMul")
     .Attr("T2: quantizedtype")
     .Attr("Toutput: quantizedtype = DT_QINT32")
     .SetIsCommutative()
-    .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn)
+    .SetShapeFn([](InferenceContext* c) {
+      TF_RETURN_IF_ERROR(shape_inference::BroadcastBinaryOpShapeFn(c));
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Scalar());
+      return Status::OK();
+    })
     .Doc(R"doc(
 Returns x * y element-wise, working on quantized buffers.
 
@@ -2300,7 +2309,12 @@ REGISTER_OP("QuantizedAdd")
     .Attr("T2: quantizedtype")
     .Attr("Toutput: quantizedtype = DT_QINT32")
     .SetIsCommutative()
-    .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn)
+    .SetShapeFn([](InferenceContext* c) {
+      TF_RETURN_IF_ERROR(shape_inference::BroadcastBinaryOpShapeFn(c));
+      c->set_output(1, c->Scalar());
+      c->set_output(2, c->Scalar());
+      return Status::OK();
+    })
     .Doc(R"doc(
 Returns x + y element-wise, working on quantized buffers.
 
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 57615c3fda..998ac79ae2 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -1578,12 +1578,12 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32, 0 <= dimension < rank(input).  Describes which dimension\nof the input Tensor to reduce across. For vectors, use dimension = 0."
+    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
     name: "output"
-    type: DT_INT64
+    type_attr: "output_type"
   }
   attr {
     name: "T"
@@ -1620,6 +1620,19 @@ op {
       }
     }
   }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
   summary: "Returns the index with the largest value across dimensions of a tensor."
   description: "Note that in case of ties the identity of the return value is not guaranteed."
 }
@@ -1631,12 +1644,12 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32, 0 <= dimension < rank(input).  Describes which dimension\nof the input Tensor to reduce across. For vectors, use dimension = 0."
+    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
     name: "output"
-    type: DT_INT64
+    type_attr: "output_type"
   }
   attr {
     name: "T"
@@ -1673,6 +1686,19 @@ op {
       }
     }
   }
+  attr {
+    name: "output_type"
+    type: "type"
+    default_value {
+      type: DT_INT64
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
   summary: "Returns the index with the smallest value across dimensions of a tensor."
   description: "Note that in case of ties the identity of the return value is not guaranteed."
 }
@@ -5173,6 +5199,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -6275,7 +6302,7 @@ op {
     }
   }
   summary: "Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors."
-  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
+  description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\n```\nfor k in 0..in_channels-1\n  for q in 0..channel_multiplier-1\n    output[b, i, j, k * channel_multiplier + q] =\n      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n                        filter[di, dj, k, q]\n```\n\nMust have `strides[0] = strides[3] = 1`.  For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
 }
 op {
   name: "DepthwiseConv2dNativeBackpropFilter"
@@ -21682,6 +21709,63 @@ op {
   summary: "Applies sparse addition between `updates` and individual values or slices"
   description: "within a given variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    add = tf.scatter_nd_add(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(add)\n\nThe resulting update to ref would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
 }
+op {
+  name: "ScatterNdNonAliasingAdd"
+  input_arg {
+    name: "input"
+    description: "A Tensor."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    description: "A Tensor. Must be one of the following types: `int32`, `int64`.\nA tensor of indices into `input`."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "updates"
+    description: "A Tensor. Must have the same type as ref. A tensor of updated values\nto add to `input`."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "A `Tensor` with the same shape as `input`, containing values of `input`\nupdated with `updates`."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Applies sparse addition to `input` using individual values or slices"
+  description: "from `updates` according to indices `indices`.  The updates are non-aliasing:\n`input` is only modified in-place if no other operations will use it.\nOtherwise, a copy of `input` is made.  This operation has a gradient with\nrespect to both `input` and `updates`.\n\n`input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `input`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or `(P-K)`-dimensional slices\n(if `K < P`) along the `K`th dimension of `input`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    output = tf.scatter_nd_non_aliasing_add(input, indices, updates)\n    with tf.Session() as sess:\n      print(sess.run(output))\n\nThe resulting value `output` would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
+}
 op {
   name: "ScatterNdSub"
   input_arg {
@@ -22805,6 +22889,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -24910,6 +24995,123 @@ op {
   summary: "Multiply matrix \"a\" by matrix \"b\"."
   description: "The inputs must be two-dimensional matrices and the inner dimension of \"a\" must\nmatch the outer dimension of \"b\". This op is optimized for the case where at\nleast one of \"a\" or \"b\" is sparse. The breakeven for using this versus a dense\nmatrix multiply on one platform was 30% zero values in the sparse matrix.\n\nThe gradient computation of this operation will only take advantage of sparsity\nin the input gradient when that gradient comes from a Relu."
 }
+op {
+  name: "SparseReduceMax"
+  input_arg {
+    name: "input_indices"
+    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    description: "1-D.  Shape of the input SparseTensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    description: "1-D.  Length-`K` vector containing the reduction axes."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    description: "`R-K`-D.  The reduced Tensor."
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, retain reduced dimensions with length 1."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+  summary: "Computes the max of elements across dimensions of a SparseTensor."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`\ninstead of a sparse one.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
+}
+op {
+  name: "SparseReduceMaxSparse"
+  input_arg {
+    name: "input_indices"
+    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    description: "1-D.  Shape of the input SparseTensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    description: "1-D.  Length-`K` vector containing the reduction axes."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, retain reduced dimensions with length 1."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_UINT16
+        type: DT_HALF
+      }
+    }
+  }
+  summary: "Computes the max of elements across dimensions of a SparseTensor."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a\nSparseTensor.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
+}
 op {
   name: "SparseReduceSum"
   input_arg {
diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc
index 0890d5fc7c..35f965b6a9 100644
--- a/tensorflow/core/ops/state_ops.cc
+++ b/tensorflow/core/ops/state_ops.cc
@@ -472,63 +472,6 @@ use_locking: If True, the operation will be protected by a lock;
   otherwise the behavior is undefined, but may exhibit less contention.
 )doc");
 
-namespace {
-
-Status ScatterNdUpdateShape(InferenceContext* c) {
-  ShapeHandle ref_shape = c->input(0);
-  ShapeHandle indices_shape;
-  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &indices_shape));
-  ShapeHandle updates_shape;
-  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(2), 1, &updates_shape));
-
-  if (c->RankKnown(indices_shape) && c->RankKnown(updates_shape)) {
-    const int64 outer_dims = c->Rank(indices_shape) - 1;
-    const DimensionHandle ixdim = c->Dim(indices_shape, -1);
-
-    // We can only do more validation if the last dimension of indices
-    // is a known value.
-    if (c->ValueKnown(ixdim)) {
-      int64 ix = c->Value(ixdim);
-      ShapeHandle unused;
-      ShapeHandle prefix_indices;
-      TF_RETURN_IF_ERROR(
-          c->Subshape(indices_shape, 0, outer_dims, &prefix_indices));
-      ShapeHandle prefix_updates;
-      TF_RETURN_IF_ERROR(
-          c->Subshape(updates_shape, 0, outer_dims, &prefix_updates));
-
-      Status s = c->Merge(prefix_indices, prefix_updates, &unused);
-      if (!s.ok()) {
-        return errors::InvalidArgument(
-            "The outer ", outer_dims, " dimensions of indices.shape=",
-            c->DebugString(indices_shape), "must match the outer ", outer_dims,
-            " dimensions of updates.shape=", c->DebugString(updates_shape),
-            ": ", s.error_message());
-      }
-
-      ShapeHandle suffix_ref;
-      TF_RETURN_IF_ERROR(c->Subshape(ref_shape, ix, &suffix_ref));
-      ShapeHandle suffix_updates;
-      TF_RETURN_IF_ERROR(
-          c->Subshape(updates_shape, outer_dims, &suffix_updates));
-      s = c->Merge(suffix_ref, suffix_updates, &unused);
-      if (!s.ok()) {
-        return errors::InvalidArgument(
-            "The inner ", c->Rank(ref_shape) - ix, " dimensions of ref.shape=",
-            c->DebugString(ref_shape), "must match the inner ",
-            c->Rank(updates_shape) - outer_dims,
-            " dimensions of updates.shape=", c->DebugString(updates_shape),
-            ": ", s.error_message());
-      }
-    }
-  }
-
-  c->set_output(0, ref_shape);
-  return Status::OK();
-}
-
-}  // namespace
-
 REGISTER_OP("ScatterNdUpdate")
     .Input("ref: Ref(T)")
     .Input("indices: Tindices")
@@ -537,7 +480,7 @@ REGISTER_OP("ScatterNdUpdate")
     .Attr("T: type")
     .Attr("Tindices: {int32, int64}")
     .Attr("use_locking: bool = true")
-    .SetShapeFn(ScatterNdUpdateShape)
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
     .Doc(R"doc(
 Applies sparse `updates` to individual values or slices within a given
 variable according to `indices`.
@@ -596,7 +539,7 @@ REGISTER_OP("ScatterNdAdd")
     .Attr("T: numbertype")
     .Attr("Tindices: {int32, int64}")
     .Attr("use_locking: bool = false")
-    .SetShapeFn(ScatterNdUpdateShape)
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
     .Doc(R"doc(
 Applies sparse addition between `updates` and individual values or slices
 within a given variable according to `indices`.
@@ -653,7 +596,7 @@ REGISTER_OP("ScatterNdSub")
     .Attr("T: numbertype")
     .Attr("Tindices: {int32, int64}")
     .Attr("use_locking: bool = false")
-    .SetShapeFn(ScatterNdUpdateShape)
+    .SetShapeFn(shape_inference::ScatterNdUpdateShape)
     .Doc(R"doc(
 Applies sparse subtraction between `updates` and individual values or slices
 within a given variable according to `indices`.
@@ -713,7 +656,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 //     .Attr("T: numbertype")
 //     .Attr("Tindices: {int32, int64}")
 //     .Attr("use_locking: bool = false")
-//     .SetShapeFn(ScatterNdUpdateShape)
+//     .SetShapeFn(shape_inference::ScatterNdUpdateShape)
 //     .Doc(
 //         R"doc(Applies sparse subtraction between `updates` and individual
 //         values or slices within a given variable according to `indices`.
@@ -769,7 +712,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 //     .Attr("T: numbertype")
 //     .Attr("Tindices: {int32, int64}")
 //     .Attr("use_locking: bool = false")
-//     .SetShapeFn(ScatterNdUpdateShape)
+//     .SetShapeFn(shape_inference::ScatterNdUpdateShape)
 //     .Doc(
 //         R"doc(Applies sparse subtraction between `updates` and individual
 //         values or slices within a given variable according to `indices`.
diff --git a/tensorflow/core/platform/tensor_coding.cc b/tensorflow/core/platform/tensor_coding.cc
index 73ccd68e5d..17dc81f7e0 100644
--- a/tensorflow/core/platform/tensor_coding.cc
+++ b/tensorflow/core/platform/tensor_coding.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/platform/tensor_coding.h"
 
 #include <vector>
+#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
@@ -67,12 +68,14 @@ void CopyFromArray(string* s, const char* base, size_t bytes) {
 
 void EncodeResourceHandleList(const ResourceHandle* p, int64 n, string* out) {
   out->clear();
+  string rest;
+  ResourceHandleProto proto;
   for (int i = 0; i < n; ++i) {
-    core::PutVarint32(out, p[i].ByteSize());
-  }
-  for (int i = 0; i < n; ++i) {
-    p[i].AppendToString(out);
+    p[i].AsProto(&proto);
+    core::PutVarint32(out, proto.ByteSize());
+    proto.AppendToString(&rest);
   }
+  *out += rest;
 }
 
 bool DecodeResourceHandleList(const string& in, ResourceHandle* ps, int64 n) {
@@ -86,10 +89,12 @@ bool DecodeResourceHandleList(const string& in, ResourceHandle* ps, int64 n) {
   if (total != static_cast<int64>(reader.size())) {
     return false;
   }
+  ResourceHandleProto proto;
   for (int i = 0; i < n; ++i) {
-    if (!ps[i].ParseFromArray(reader.data(), sizes[i])) {
+    if (!proto.ParseFromArray(reader.data(), sizes[i])) {
       return false;
     }
+    ps[i].FromProto(proto);
     reader.remove_prefix(sizes[i]);
   }
   return true;
diff --git a/tensorflow/core/platform/tensor_coding.h b/tensorflow/core/platform/tensor_coding.h
index 2dd65fb97a..19f53e6374 100644
--- a/tensorflow/core/platform/tensor_coding.h
+++ b/tensorflow/core/platform/tensor_coding.h
@@ -18,7 +18,7 @@ limitations under the License.
 #define TENSORFLOW_PLATFORM_TENSOR_CODING_H_
 
 #include <string>
-#include "tensorflow/core/framework/resource_handle.pb.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/lib/core/refcount.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/platform/platform.h"
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index c57b110847..1bcabf8e1d 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -171,6 +171,23 @@ message ThreadPoolOptionProto {
   // 0 means the system picks a value based on where this option proto is used
   // (see the declaration of the specific field for more info).
   int32 num_threads = 1;
+
+  // The global name of the threadpool.
+  //
+  // If empty, then the threadpool is made and used according to the scope it's
+  // in - e.g., for a session threadpool, it is used by that session only.
+  //
+  // If non-empty, then:
+  // - a global threadpool associated with this name is looked
+  //   up or created. This allows, for example, sharing one threadpool across
+  //   many sessions (e.g., like the default behavior, if
+  //   inter_op_parallelism_threads is not configured), but still partitioning
+  //   into a large and small pool.
+  // - if the threadpool for this global_name already exists, then it is an
+  //   error if the existing pool was created using a different num_threads
+  //   value as is specified on this call.
+  // - threadpools created this way are never garbage collected.
+  string global_name = 2;
 };
 
 message RPCOptions {
@@ -218,13 +235,24 @@ message ConfigProto {
   bool use_per_session_threads = 9;
 
   // This option is experimental - it may be replaced with a different mechanism
-  // in the future. The intended use is for when some session invocations need
-  // to run in a background pool limited to a small number of threads.
+  // in the future.
   //
   // Configures session thread pools. If this is configured, then RunOptions for
   // a Run call can select the thread pool to use.
   //
-  // If a pool's num_threads is 0, then inter_op_parallelism_threads is used.
+  // The intended use is for when some session invocations need to run in a
+  // background pool limited to a small number of threads:
+  // - For example, a session may be configured to have one large pool (for
+  // regular compute) and one small pool (for periodic, low priority work);
+  // using the small pool is currently the mechanism for limiting the inter-op
+  // parallelism of the low priority work.  Note that it does not limit the
+  // parallelism of work spawned by a single op kernel implementation.
+  // - Using this setting is normally not needed in training, but may help some
+  // serving use cases.
+  // - It is also generally recommended to set the global_name field of this
+  // proto, to avoid creating multiple large pools. It is typically better to
+  // run the non-low-priority work, even across sessions, in a single large
+  // pool.
   repeated ThreadPoolOptionProto session_inter_op_thread_pool = 12;
 
   // Assignment of Nodes to Devices is recomputed every placement_period
diff --git a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
index abd72b7d77..623f7bab90 100644
--- a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
+++ b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
@@ -164,7 +164,7 @@ TEST_F(CudaLaunchConfigTest, GetCudaLaunchConfig) {
   TEST_LAUNCH_PARAMETER(8191);
   TEST_LAUNCH_PARAMETER(8192);
   TEST_LAUNCH_PARAMETER(123456);
-  TEST_LAUNCH_PARAMETER(1 << 31 - 1);  // max value of int
+  TEST_LAUNCH_PARAMETER(1 << 30);
   #undef TEST_LAUNCH_PARAMETER
 }
 
@@ -243,8 +243,8 @@ TEST_F(CudaLaunchConfigTest, GetCuda2DLaunchConfig) {
   TEST_LAUNCH_PARAMETER(8191, 1);
   TEST_LAUNCH_PARAMETER(8192, 10);
   TEST_LAUNCH_PARAMETER(123456, 12);
-  TEST_LAUNCH_PARAMETER(1, (1 << 31 - 1));
-  TEST_LAUNCH_PARAMETER((1 << 31 - 1), 1);
+  TEST_LAUNCH_PARAMETER(1, 1 << 30);
+  TEST_LAUNCH_PARAMETER(1 << 30, 1);
   #undef TEST_LAUNCH_PARAMETER
 }
 
@@ -292,9 +292,9 @@ TEST_F(CudaLaunchConfigTest, GetCuda3DLaunchConfig) {
   TEST_LAUNCH_PARAMETER(8191, 1, 1024);
   TEST_LAUNCH_PARAMETER(8192, 10, 32);
   TEST_LAUNCH_PARAMETER(123456, 12, 21);
-  TEST_LAUNCH_PARAMETER(1, 1, (1 << 31 - 1));
-  TEST_LAUNCH_PARAMETER(1, (1 << 31 - 1), 1);
-  TEST_LAUNCH_PARAMETER((1 << 31 - 1), 1, 1);
+  TEST_LAUNCH_PARAMETER(1, 1, 1 << 30);
+  TEST_LAUNCH_PARAMETER(1, 1 << 30, 1);
+  TEST_LAUNCH_PARAMETER(1 << 30, 1, 1);
   #undef TEST_LAUNCH_PARAMETER
 }
 
diff --git a/tensorflow/core/util/device_name_utils.cc b/tensorflow/core/util/device_name_utils.cc
index 64aa0ac209..1773ac00b7 100644
--- a/tensorflow/core/util/device_name_utils.cc
+++ b/tensorflow/core/util/device_name_utils.cc
@@ -97,6 +97,18 @@ string DeviceNameUtils::FullName(const string& job, int replica, int task,
                          "/device:", type, ":", id);
 }
 
+/* static */
+string DeviceNameUtils::LegacyName(const string& job, int replica, int task,
+                                   const string& type, int id) {
+  CHECK(IsJobName(job)) << job;
+  CHECK_LE(0, replica);
+  CHECK_LE(0, task);
+  CHECK(!type.empty());
+  CHECK_LE(0, id);
+  return strings::StrCat("/job:", job, "/replica:", replica, "/task:", task,
+                         "/", str_util::Lowercase(type), ":", id);
+}
+
 bool DeviceNameUtils::ParseFullName(StringPiece fullname, ParsedName* p) {
   p->Clear();
   if (fullname == "/") {
diff --git a/tensorflow/core/util/device_name_utils.h b/tensorflow/core/util/device_name_utils.h
index 4228af8b8c..1f32828bae 100644
--- a/tensorflow/core/util/device_name_utils.h
+++ b/tensorflow/core/util/device_name_utils.h
@@ -48,6 +48,9 @@ class DeviceNameUtils {
   // Returns a fully qualified device name given the parameters.
   static string FullName(const string& job, int replica, int task,
                          const string& type, int id);
+  // Returns a fully qualified device name given the parameters in legacy style.
+  static string LegacyName(const string& job, int replica, int task,
+                           const string& type, int id);
 
   struct ParsedName {
     void Clear() {
diff --git a/tensorflow/core/util/device_name_utils_test.cc b/tensorflow/core/util/device_name_utils_test.cc
index ed511629b6..e44b840967 100644
--- a/tensorflow/core/util/device_name_utils_test.cc
+++ b/tensorflow/core/util/device_name_utils_test.cc
@@ -69,6 +69,9 @@ TEST(DeviceNameUtilsTest, Basic) {
   EXPECT_EQ(DeviceNameUtils::FullName("hello", 1, 2, "CPU", 3),
             "/job:hello/replica:1/task:2/device:CPU:3");
 
+  EXPECT_EQ(DeviceNameUtils::LegacyName("hello", 1, 2, "CPU", 3),
+            "/job:hello/replica:1/task:2/cpu:3");
+
   {
     DeviceNameUtils::ParsedName p;
     EXPECT_FALSE(DeviceNameUtils::ParseFullName("foobar", &p));
diff --git a/tensorflow/core/util/equal_graph_def.cc b/tensorflow/core/util/equal_graph_def.cc
index 2db026da56..45d6a6662a 100644
--- a/tensorflow/core/util/equal_graph_def.cc
+++ b/tensorflow/core/util/equal_graph_def.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <unordered_map>
 #include <unordered_set>
 #include "tensorflow/core/framework/attr_value_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/protobuf.h"
diff --git a/tensorflow/core/util/strided_slice_op.cc b/tensorflow/core/util/strided_slice_op.cc
index dc78a5a01f..cfe9275a09 100644
--- a/tensorflow/core/util/strided_slice_op.cc
+++ b/tensorflow/core/util/strided_slice_op.cc
@@ -16,37 +16,10 @@ limitations under the License.
 #include "tensorflow/core/util/strided_slice_op.h"
 
 #include <array>
-
 #include "tensorflow/core/kernels/bounds_check.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
-
-int ShapeReadWriteFromTensorShape::dims() const { return const_shape_->dims(); }
-
-int64 ShapeReadWriteFromTensorShape::dim_size(int idx) const {
-  return const_shape_->dim_size(idx);
-}
-
-void ShapeReadWriteFromTensorShape::add_dim(int64 size) {
-  DCHECK_NE(size, -1);
-  DCHECK(shape_ != nullptr) << "add_dim can only be called on non-const shape";
-  shape_->AddDim(size);
-}
-
-int ShapeReadWriteFromTensorShapeProto::dims() const {
-  return const_shape_->dim_size();
-}
-
-int64 ShapeReadWriteFromTensorShapeProto::dim_size(int idx) const {
-  return const_shape_->dim(idx).size();
-}
-
-void ShapeReadWriteFromTensorShapeProto::add_dim(int64 size) {
-  DCHECK(shape_ != nullptr) << "add_dim can only be called on non-const shape";
-  shape_->add_dim()->set_size(size);
-}
-
 namespace {
 
 /// Constants
@@ -173,12 +146,11 @@ static Status TF_MUST_USE_RESULT BuildDenseSpec(
 
 Status ValidateStridedSliceOp(
     const Tensor* begin_tensor, const Tensor* end_tensor,
-    const Tensor& strides_tensor, const ShapeReadWriteInterface& input_shape,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
     int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
     int32 new_axis_mask, int32 shrink_axis_mask,
-    ShapeReadWriteInterface* processing_shape,
-    ShapeReadWriteInterface* final_shape, bool* is_identity,
-    bool* is_simple_slice, bool* slice_dim0,
+    PartialTensorShape* processing_shape, PartialTensorShape* final_shape,
+    bool* is_identity, bool* is_simple_slice, bool* slice_dim0,
     gtl::InlinedVector<int64, 4>* begin, gtl::InlinedVector<int64, 4>* end,
     gtl::InlinedVector<int64, 4>* strides) {
   const bool begin_is_wrong =
@@ -275,6 +247,7 @@ Status ValidateStridedSliceOp(
   *is_identity = true;
   *slice_dim0 = true;
   *is_simple_slice = true;
+  processing_shape->Clear();
   for (int i = 0; i < input_shape.dims(); ++i) {
     int64& begin_i = (*begin)[i];
     int64& end_i = (*end)[i];
@@ -285,7 +258,7 @@ Status ValidateStridedSliceOp(
     }
     bool shrink_i = (dense_spec.shrink_axis_mask & (1 << i));
     if (dim_i == -1) {
-      processing_shape->add_dim(shrink_i ? 1 : -1);
+      processing_shape->AddDim(shrink_i ? 1 : -1);
       continue;
     }
 
@@ -372,9 +345,9 @@ Status ValidateStridedSliceOp(
         size_i = interval_length / stride_i +
                  (interval_length % stride_i != 0 ? 1 : 0);
       }
-      processing_shape->add_dim(size_i);
+      processing_shape->AddDim(size_i);
     } else {
-      processing_shape->add_dim(-1);
+      processing_shape->AddDim(-1);
     }
   }
 
@@ -383,14 +356,41 @@ Status ValidateStridedSliceOp(
   // new_axis will increase dimension by 1 (with a one-size dimension)
   // slices like foo[3,...] will reduce dimension by 1.
   // This cannot be done earlier, because it depends on Step 3.
+  final_shape->Clear();
   for (auto gather_index : dense_spec.final_shape_gather_indices) {
     if (gather_index >= 0) {
-      final_shape->add_dim(processing_shape->dim_size(gather_index));
+      final_shape->AddDim(processing_shape->dim_size(gather_index));
     } else if (gather_index == kNewAxis) {
-      final_shape->add_dim(1);
+      final_shape->AddDim(1);
     }
   }
   return Status::OK();
 }
 
+Status ValidateStridedSliceOp(
+    const Tensor* begin_tensor, const Tensor* end_tensor,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
+    int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
+    int32 new_axis_mask, int32 shrink_axis_mask, TensorShape* processing_shape,
+    TensorShape* final_shape, bool* is_identity, bool* is_simple_slice,
+    bool* slice_dim0, gtl::InlinedVector<int64, 4>* begin,
+    gtl::InlinedVector<int64, 4>* end, gtl::InlinedVector<int64, 4>* strides) {
+  // Validate with PartialTensorShape output
+  PartialTensorShape partial_processing_shape, partial_final_shape;
+  TF_RETURN_IF_ERROR(ValidateStridedSliceOp(
+      begin_tensor, end_tensor, strides_tensor, input_shape, begin_mask_spec,
+      end_mask_spec, ellipsis_mask, new_axis_mask, shrink_axis_mask,
+      &partial_processing_shape, &partial_final_shape, is_identity,
+      is_simple_slice, slice_dim0, begin, end, strides));
+
+  // Verify that the output shapes are fully known
+  if (!partial_processing_shape.AsTensorShape(processing_shape) ||
+      !partial_final_shape.AsTensorShape(final_shape)) {
+    return errors::Internal("ValidateStridedSliceOp returned partial shapes ",
+                            partial_processing_shape.DebugString(), " and ",
+                            partial_final_shape.DebugString());
+  }
+  return Status::OK();
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/util/strided_slice_op.h b/tensorflow/core/util/strided_slice_op.h
index 72dc5756fd..abca98f27b 100644
--- a/tensorflow/core/util/strided_slice_op.h
+++ b/tensorflow/core/util/strided_slice_op.h
@@ -23,60 +23,6 @@ limitations under the License.
 
 namespace tensorflow {
 
-// This class and its subclasses allow ValidateStridedSliceOp to be called with
-// different implementations of partial tensors.
-class ShapeReadWriteInterface {
- public:
-  virtual ~ShapeReadWriteInterface() {}
-  virtual int dims() const = 0;
-  // Returns -1 for unknown size.
-  virtual int64 dim_size(int idx) const = 0;
-  // Passes -1 for unknown dim size.
-  virtual void add_dim(int64 size) = 0;
-};
-
-// Implementation of ShapeReadWriteInterface that modifies the given TensorShape
-// <shape> in-place. Does not support adding unknown dims in add_dim.
-class ShapeReadWriteFromTensorShape : public ShapeReadWriteInterface {
- public:
-  ShapeReadWriteFromTensorShape(TensorShape* shape)
-      : const_shape_(shape), shape_(shape) {}
-  ShapeReadWriteFromTensorShape(const TensorShape* shape)
-      : const_shape_(shape) {}
-  ~ShapeReadWriteFromTensorShape() override {}
-  int dims() const override;
-  int64 dim_size(int idx) const override;
-  void add_dim(int64 size) override;
-
- private:
-  const TensorShape* const const_shape_;
-  // same as const_shape_, or nullptr if the non-const ctr is used.
-  TensorShape* const shape_ = nullptr;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(ShapeReadWriteFromTensorShape);
-};
-
-// Implementation of ShapeReadWriteInterface that modifies the given
-// TensorShapeProto in place.
-class ShapeReadWriteFromTensorShapeProto : public ShapeReadWriteInterface {
- public:
-  ShapeReadWriteFromTensorShapeProto(TensorShapeProto* shape)
-      : const_shape_(shape), shape_(shape) {}
-  ShapeReadWriteFromTensorShapeProto(const TensorShapeProto* shape)
-      : const_shape_(shape) {}
-  ~ShapeReadWriteFromTensorShapeProto() override {}
-  int dims() const override;
-  int64 dim_size(int idx) const override;
-  void add_dim(int64 size) override;
-
- private:
-  const TensorShapeProto* const const_shape_;
-  // same as shape_, or nullptr if the non-const ctr is used.
-  TensorShapeProto* const shape_ = nullptr;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(ShapeReadWriteFromTensorShapeProto);
-};
-
 // Runs validation on the strided slice op parameters.
 //
 // Is a separate translation unit from the kernel so that:
@@ -96,15 +42,24 @@ class ShapeReadWriteFromTensorShapeProto : public ShapeReadWriteInterface {
 // performed.
 Status ValidateStridedSliceOp(
     const Tensor* begin_tensor, const Tensor* end_tensor,
-    const Tensor& strides_tensor, const ShapeReadWriteInterface& input_shape,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
     int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
     int32 new_axis_mask, int32 shrink_axis_mask,
-    ShapeReadWriteInterface* processing_shape,
-    ShapeReadWriteInterface* final_shape, bool* is_identity,
-    bool* is_simple_slice, bool* slice_dim0,
+    PartialTensorShape* processing_shape, PartialTensorShape* final_shape,
+    bool* is_identity, bool* is_simple_slice, bool* slice_dim0,
     gtl::InlinedVector<int64, 4>* begin, gtl::InlinedVector<int64, 4>* end,
     gtl::InlinedVector<int64, 4>* strides);
 
+// Same as above, but the outputs are TensorShape, not PartialTensorShape
+Status ValidateStridedSliceOp(
+    const Tensor* begin_tensor, const Tensor* end_tensor,
+    const Tensor& strides_tensor, const PartialTensorShape& input_shape,
+    int32 begin_mask_spec, int32 end_mask_spec, const int32 ellipsis_mask,
+    int32 new_axis_mask, int32 shrink_axis_mask, TensorShape* processing_shape,
+    TensorShape* final_shape, bool* is_identity, bool* is_simple_slice,
+    bool* slice_dim0, gtl::InlinedVector<int64, 4>* begin,
+    gtl::InlinedVector<int64, 4>* end, gtl::InlinedVector<int64, 4>* strides);
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_UTIL_STRIDED_SLICE_OP_H_
diff --git a/tensorflow/docs_src/api_guides/cc/guide.md b/tensorflow/docs_src/api_guides/cc/guide.md
index c5473cad97..f30bf3797e 100644
--- a/tensorflow/docs_src/api_guides/cc/guide.md
+++ b/tensorflow/docs_src/api_guides/cc/guide.md
@@ -59,7 +59,8 @@ cc_binary(
 )
 ```
 
-You should be able to build and run the example using the following command:
+You should be able to build and run the example using the following command
+(be sure to run `./configure` in your build sandbox first):
 
 ```shell
 bazel run -c opt //tensorflow/cc/example:example
diff --git a/tensorflow/docs_src/api_guides/python/test.md b/tensorflow/docs_src/api_guides/python/test.md
index 93a1d50b29..5dc88124e7 100644
--- a/tensorflow/docs_src/api_guides/python/test.md
+++ b/tensorflow/docs_src/api_guides/python/test.md
@@ -31,6 +31,9 @@ methods.  See @{tf.test.TestCase} for details.
 
 ## Utilities
 
+Note: `tf.test.mock` is an alias to the python `mock` or `unittest.mock`
+depending on the python version.
+
 *   @{tf.test.assert_equal_graph_def}
 *   @{tf.test.get_temp_dir}
 *   @{tf.test.is_built_with_cuda}
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index b257c5a1ca..d1c9cd696c 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -16,14 +16,11 @@ machine learning researchers and others who require fine levels of control over
 their models. The higher level APIs are built on top of TensorFlow Core. These
 higher level APIs are typically easier to learn and use than TensorFlow Core. In
 addition, the higher level APIs make repetitive tasks easier and more consistent
-between different users. A high-level API like tf.contrib.learn helps you manage
-data sets, estimators, training and inference. Note that a few of the high-level
-TensorFlow APIs--those whose method names contain `contrib`-- are still in
-development. It is possible that some `contrib` methods will change or become
-obsolete in subsequent TensorFlow releases.
+between different users. A high-level API like tf.estimator helps you manage
+data sets, estimators, training and inference.
 
 This guide begins with a tutorial on TensorFlow Core. Later, we
-demonstrate how to implement the same model in tf.contrib.learn. Knowing
+demonstrate how to implement the same model in tf.estimator. Knowing
 TensorFlow Core principles will give you a great mental model of how things are
 working internally when you use the more compact higher level API.
 
@@ -339,38 +336,36 @@ with random values.
 This more complicated program can still be visualized in TensorBoard
 ![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png)
 
-## `tf.contrib.learn`
+## `tf.estimator`
 
-`tf.contrib.learn` is a high-level TensorFlow library that simplifies the
+`tf.estimator` is a high-level TensorFlow library that simplifies the
 mechanics of machine learning, including the following:
 
 *   running training loops
 *   running evaluation loops
 *   managing data sets
-*   managing feeding
 
-tf.contrib.learn defines many common models.
+tf.estimator defines many common models.
 
 ### Basic usage
 
 Notice how much simpler the linear regression program becomes with
-`tf.contrib.learn`:
+`tf.estimator`:
 
 ```python
 import tensorflow as tf
 # NumPy is often used to load, manipulate and preprocess data.
 import numpy as np
 
-# Declare list of features. We only have one real-valued feature. There are many
+# Declare list of features. We only have one numeric feature. There are many
 # other types of columns that are more complicated and useful.
-features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
+feature_columns = [tf.feature_column.numeric_column("x", shape=[1])]
 
 # An estimator is the front end to invoke training (fitting) and evaluation
 # (inference). There are many predefined types like linear regression,
-# logistic regression, linear classification, logistic classification, and
-# many neural network classifiers and regressors. The following code
-# provides an estimator that does linear regression.
-estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
+# linear classification, and many neural network classifiers and regressors.
+# The following code provides an estimator that does linear regression.
+estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)
 
 # TensorFlow provides many helper methods to read and set up data sets.
 # Here we use two data sets: one for training and one for evaluation
@@ -380,51 +375,53 @@ x_train = np.array([1., 2., 3., 4.])
 y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
-input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x_train}, y_train,
-                                              batch_size=4,
-                                              num_epochs=1000)
-eval_input_fn = tf.contrib.learn.io.numpy_input_fn(
-    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000)
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
 
 # We can invoke 1000 training steps by invoking the  method and passing the
 # training data set.
-estimator.fit(input_fn=input_fn, steps=1000)
+estimator.train(input_fn=input_fn, steps=1000)
 
 # Here we evaluate how well our model did.
-train_loss = estimator.evaluate(input_fn=input_fn)
-eval_loss = estimator.evaluate(input_fn=eval_input_fn)
-print("train loss: %r"% train_loss)
-print("eval loss: %r"% eval_loss)
+train_metrics = estimator.evaluate(input_fn=train_input_fn)
+eval_metrics = estimator.evaluate(input_fn=eval_input_fn)
+print("train metrics: %r"% train_metrics)
+print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-    train loss: {'global_step': 1000, 'loss': 4.3049088e-08}
-    eval loss: {'global_step': 1000, 'loss': 0.0025487561}
+    train metrics: {'global_step': 1000, 'loss': 4.3049088e-08}
+    eval metrics: {'global_step': 1000, 'loss': 0.0025487561}
 ```
 Notice how our eval data has a higher loss, but it is still close to zero.
 That means we are learning properly.
 
 ### A custom model
 
-`tf.contrib.learn` does not lock you into its predefined models. Suppose we
+`tf.estimator` does not lock you into its predefined models. Suppose we
 wanted to create a custom model that is not built into TensorFlow. We can still
 retain the high level abstraction of data set, feeding, training, etc. of
-`tf.contrib.learn`. For illustration, we will show how to implement our own
+`tf.estimator`. For illustration, we will show how to implement our own
 equivalent model to `LinearRegressor` using our knowledge of the lower level
 TensorFlow API.
 
-To define a custom model that works with `tf.contrib.learn`, we need to use
-`tf.contrib.learn.Estimator`. `tf.contrib.learn.LinearRegressor` is actually
-a sub-class of `tf.contrib.learn.Estimator`. Instead of sub-classing
+To define a custom model that works with `tf.estimator`, we need to use
+`tf.estimator.Estimator`. `tf.estimator.LinearRegressor` is actually
+a sub-class of `tf.estimator.Estimator`. Instead of sub-classing
 `Estimator`, we simply provide `Estimator` a function `model_fn` that tells
-`tf.contrib.learn` how it can evaluate predictions, training steps, and
+`tf.estimator` how it can evaluate predictions, training steps, and
 loss. The code is as follows:
 
 ```python
 import numpy as np
 import tensorflow as tf
+
 # Declare list of features, we only have one real-valued feature
-def model(features, labels, mode):
+def model_fn(features, labels, mode):
   # Build a linear model and predict values
   W = tf.get_variable("W", [1], dtype=tf.float64)
   b = tf.get_variable("b", [1], dtype=tf.float64)
@@ -436,36 +433,42 @@ def model(features, labels, mode):
   optimizer = tf.train.GradientDescentOptimizer(0.01)
   train = tf.group(optimizer.minimize(loss),
                    tf.assign_add(global_step, 1))
-  # ModelFnOps connects subgraphs we built to the
+  # EstimatorSpec connects subgraphs we built to the
   # appropriate functionality.
-  return tf.contrib.learn.ModelFnOps(
-      mode=mode, predictions=y,
+  return tf.estimator.EstimatorSpec(
+      mode=mode,
+      predictions=y,
       loss=loss,
       train_op=train)
 
-estimator = tf.contrib.learn.Estimator(model_fn=model)
+estimator = tf.estimator.Estimator(model_fn=model_fn)
 # define our data sets
 x_train = np.array([1., 2., 3., 4.])
 y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
-input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_train}, y_train, 4, num_epochs=1000)
-eval_input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_eval}, y_eval, 4, num_epochs=1000)
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
+
 # train
-estimator.fit(input_fn=input_fn, steps=1000)
-# Here we evaluate how well our model did. 
-train_loss = estimator.evaluate(input_fn=input_fn)
-eval_loss = estimator.evaluate(input_fn=eval_input_fn)
-print("train loss: %r"% train_loss)
-print("eval loss: %r"% eval_loss)
+estimator.train(input_fn=input_fn, steps=1000)
+# Here we evaluate how well our model did.
+train_metrics = estimator.evaluate(input_fn=train_input_fn)
+eval_metrics = estimator.evaluate(input_fn=eval_input_fn)
+print("train metrics: %r"% train_metrics)
+print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-train loss: {'global_step': 1000, 'loss': 4.9380226e-11}
-eval loss: {'global_step': 1000, 'loss': 0.01010081}
+train metrics: {'global_step': 1000, 'loss': 4.9380226e-11}
+eval metrics: {'global_step': 1000, 'loss': 0.01010081}
 ```
 
-Notice how the contents of the custom `model()` function are very similar
+Notice how the contents of the custom `model_fn()` function are very similar
 to our manual model training loop from the lower level API.
 
 ## Next steps
diff --git a/tensorflow/docs_src/get_started/input_fn.md b/tensorflow/docs_src/get_started/input_fn.md
index 2ef050d9ad..422f45c586 100644
--- a/tensorflow/docs_src/get_started/input_fn.md
+++ b/tensorflow/docs_src/get_started/input_fn.md
@@ -1,4 +1,4 @@
-# Building Input Functions with tf.contrib.learn
+# Building Input Functions with tf.estimator
 
 This tutorial introduces you to creating input functions in tf.estimator.
 You'll get an overview of how to construct an `input_fn` to preprocess and feed
diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index 120260448d..9cb27c7e95 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -624,7 +624,7 @@ See also
 [`the original batch normalization paper`](https://arxiv.org/abs/1502.03167)
 for a detailed description of the algorithm.
 
-<b> Warning: Not implemented yet </b>
+<b> Warning: Not implemented on GPU backend yet. </b>
 
 Normalizes an array across batch and spatial dimensions.
 
@@ -681,7 +681,7 @@ spatial dimensions using the formulars above.
 See also
 [`ComputationBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
 
-<b> Warning: Not implemented yet </b>
+<b> Warning: Not implemented yet. </b>
 
 Normalizes an array across batch and spatial dimensions.
 
@@ -718,11 +718,11 @@ The output is a n dimensional, normalized array with the same shape as input
 See also
 [`ComputationBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
 
-<b> Warning: Not implemented yet </b>
+<b> Warning: Not implemented yet. </b>
 
 Calculates gradients of batch norm.
 
-<b> `BatchNormGrad(x, scale, mean, variance, epsilon, grad_y, feature_index)` </b>
+<b> `BatchNormGrad(operand, scale, mean, variance, grad_output, epsilon, feature_index)` </b>
 
 | Arguments       | Type                    | Semantics                        |
 | --------------  | ----------------------- | -------------------------------- |
diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 994633dad7..c369ff1737 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -42,8 +42,9 @@ then gets stuck at a low (near-chance) level:
 > Accuracy at step 4: 0.098
 
 Wondering what might have gone wrong, you suspect that certain nodes in the
-training graph generated bad numeric values such as `inf`s and `nan`s. Let's
-use tfdbg to debug this issue and pinpoint the exact graph node where this
+training graph generated bad numeric values such as `inf`s and `nan`s, because
+this is a common cause of this type of training failure.
+Let's use tfdbg to debug this issue and pinpoint the exact graph node where this
 numeric problem first surfaced.
 
 ## Wrapping TensorFlow Sessions with tfdbg
@@ -68,8 +69,8 @@ This wrapper has the same interface as Session, so enabling debugging requires
 no other changes to the code. The wrapper provides additional features,
 including:
 
-* Bringing up a CLI before and after `run()` calls, to let you control the
-execution and inspect the graph's internal state.
+* Bringing up a CLI before and after `Session.run()` calls, to let you
+control the execution and inspect the graph's internal state.
 * Allowing you to register special `filters` for tensor values, to facilitate
 the diagnosis of issues.
 
@@ -97,7 +98,7 @@ python -m tensorflow.python.debug.examples.debug_mnist --debug
 ```
 
 The debug wrapper session will prompt you when it is about to execute the first
-`run()` call, with information regarding the fetched tensor and feed
+`Session.run()` call, with information regarding the fetched tensor and feed
 dictionaries displayed on the screen.
 
 ![tfdbg run-start UI](https://www.tensorflow.org/images/tfdbg_screenshot_run_start.png)
@@ -113,8 +114,11 @@ Enter the `run` command (or just `r`) at the command prompt:
 tfdbg> run
 ```
 
-tfdbg calculates the accuracy using a test data set and then displays all dumped
-intermediate tensors from the run in the *run-end CLI*. For example:
+The `run` command causes tfdbg to execute until the end of the next
+`Session.run()` call, which calculates the model's accuracy using a test data
+set. tfdbg augments the runtime Graph to dump all intermediate tensors.
+After the run ends, tfdbg displays all the dumped tensors values in the
+*run-end CLI*. For example:
 
 ![tfdbg run-end UI: accuracy](https://www.tensorflow.org/images/tfdbg_screenshot_run_end_accuracy.png)
 
@@ -172,6 +176,11 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | **`help`** | | **Print general help information** | `help` |
 | | `help <command>` | Print help for given command. | `help lt` |
 
+Note that each time you enter a command, a new screen output
+will appear. This is somewhat analogous to web pages in a browser. You can
+navigate between these screens by clicking the `<--` and
+`-->` text arrows near the top-left corner of the CLI.
+
 ### Other Features of the tfdbg CLI
 
 In addition to the commands listed above, the tfdbg CLI provides the following
@@ -197,25 +206,27 @@ addditional features:
 
 ### Finding `nan`s and `inf`s
 
-In this first `run()` call, there happen to be no problematic numerical values.
-You can move on to the next run by using the command `run` or its shorthand `r`.
+In this first `Session.run()` call, there happen to be no problematic numerical
+values. You can move on to the next run by using the command `run` or its
+shorthand `r`.
 
-> TIP: If you enter `run` or `r` repeatedly, you will be able to move through the
-> `run()` calls in a sequential manner.
+> TIP: If you enter `run` or `r` repeatedly, you will be able to move through
+> the `Session.run()` calls in a sequential manner.
 >
-> You can also use the `-t` flag to move ahead a number of `run()` calls at a time, for example:
+> You can also use the `-t` flag to move ahead a number of `Session.run()` calls
+> at a time, for example:
 >
 > ```
 > tfdbg> run -t 10
 > ```
 
 Instead of entering `run` repeatedly and manually searching for `nan`s and
-`inf`s in the run-end UI after every `run()` call (for example, by using the `pt`
-command shown in the table above) , you can use the following
-command to let the debugger repeatedly execute `run()` calls without stopping at
-the run-start or run-end prompt, until the first `nan` or `inf` value shows up
-in the graph. This is analogous to *conditional breakpoints* in some
-procedural-language debuggers:
+`inf`s in the run-end UI after every `Session.run()` call (for example, by using
+the `pt` command shown in the table above) , you can use the following
+command to let the debugger repeatedly execute `Session.run()` calls without
+stopping at the run-start or run-end prompt, until the first `nan` or `inf`
+value shows up in the graph. This is analogous to *conditional breakpoints* in
+some procedural-language debuggers:
 
 ```none
 tfdbg> run -f has_inf_or_nan
@@ -224,8 +235,8 @@ tfdbg> run -f has_inf_or_nan
 > NOTE: The preceding command works properly because we have registered a filter
 > for `nan`s and `inf`s called `has_inf_or_nan` (as explained previously).
 > If you have registered any other filters, you can
-> let tfdbg run till any tensors pass that filter (cause the filter to return True)
-> as well, for example,
+> use "run -f" to have tfdbg run until any tensor triggers that filter (cause
+> the filter to return True).
 >
 > ```
 > # In python code:
@@ -235,10 +246,15 @@ tfdbg> run -f has_inf_or_nan
 > tfdbg> run -f my_filter
 > ```
 
+See [this API document](https://www.tensorflow.org/api_docs/python/tfdbg/DebugDumpDir#find)
+for more information on the expected signature and return value of the predicate
+`Callable` used with `add_tensor_filter()`.
+
 ![tfdbg run-end UI: infs and nans](https://www.tensorflow.org/images/tfdbg_screenshot_run_end_inf_nan.png)
 
 As the screen display indicates, the `has_inf_or_nan` filter is first passed
-during the fourth `run()` call: an [Adam optimizer](https://arxiv.org/abs/1412.6980)
+during the fourth `Session.run()` call: an
+[Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer)
 forward-backward training pass on the graph. In this run, 36 (out of the total
 95) intermediate tensors contain `nan` or `inf` values. These tensors are listed
 in chronological order, with their timestamps displayed on the left. At the top
@@ -268,7 +284,7 @@ tfdbg> /(inf|nan)
 
 Why did these infinities appear? To further debug, display more information
 about the node `cross_entropy/Log` by clicking the underlined `node_info` menu
-item on the top or entering the equivalent command:
+item on the top or entering the equivalent node_info (`ni`) command:
 
 ```none
 tfdbg> ni cross_entropy/Log
@@ -310,7 +326,7 @@ line:
 diff = y_ * tf.log(y)
 ```
 
-***tfdbg** has a feature that makes it ease to trace Tensors and ops back to
+***tfdbg** has a feature that makes it easy to trace Tensors and ops back to
 lines in Python source files. It can annotate lines of a Python file with
 the ops or Tensors created by them. To use this feature,
 simply click the underlined line numbers in the stack trace output of the
@@ -351,7 +367,7 @@ accuracy now continues to rise rather than getting stuck. Success!
 
 ## Debugging tf-learn Estimators and Experiments
 
-This section explains how to debug TensorFlow programs that use the `Estimators`
+This section explains how to debug TensorFlow programs that use the `Estimator`
 and `Experiment` APIs. Part of the convenience provided by these APIs is that
 they manage `Session`s internally. This makes the `LocalCLIDebugWrapperSession`
 described in the preceding sections inapplicable. Fortunately, you can still
@@ -363,7 +379,7 @@ Currently, `tfdbg` can debug the
 @{tf.contrib.learn.BaseEstimator.fit$`fit()`}
 @{tf.contrib.learn.BaseEstimator.evaluate$`evaluate()`}
 methods of tf-learn `Estimator`s. To debug `Estimator.fit()`,
-create a `LocalCLIDebugHook` and supply it as the `monitors` argument. For example:
+create a `LocalCLIDebugHook` and supply it in the `monitors` argument. For example:
 
 ```python
 # First, let your BUILD target depend on "//tensorflow/python/debug:debug_py"
@@ -371,9 +387,9 @@ create a `LocalCLIDebugHook` and supply it as the `monitors` argument. For examp
 #  install of open-source TensorFlow.)
 from tensorflow.python import debug as tf_debug
 
+# Create a LocalCLIDebugHook and use it as a monitor when calling fit().
 hooks = [tf_debug.LocalCLIDebugHook()]
 
-# Create a local CLI debug hook and use it as a monitor when calling fit().
 classifier.fit(x=training_set.data,
                y=training_set.target,
                steps=1000,
@@ -441,22 +457,38 @@ calls, as a function of the `fetches` and `feed_dict` and other states. See
 @{tfdbg.DumpingDebugWrapperSession.__init__$this API doc}
 for more details.
 
+## Debugging Keras Models with TFDBG
+
+To use TFDBG with [Keras](https://keras.io/), let the Keras backend use
+a TFDBG-wrapped Session object. For example, to use the CLI wrapper:
+
+``` python
+import tensorflow as tf
+from keras import backend as keras_backend
+from tensorflow.python import debug as tf_debug
+
+keras_backend.set_session(tf_debug.LocalCLIDebugWrapperSession(tf.Session()))
+
+# Define your keras model, called "model".
+model.fit(...)  # This will break into the TFDBG CLI.
+```
+
 ## Offline Debugging of Remotely-Running Sessions
 
-Oftentimes, your model is running in a remote machine or process that you don't
+Often, your model is running on a remote machine or a process that you don't
 have terminal access to. To perform model debugging in such cases, you can use
-the `offline_analyzer` binary of `tfdbg`. It operates on dumped data
-directories. This can be done to both the lower-level `Session` API and the
-higher-level `Estimator` and `Experiment` APIs.
+the `offline_analyzer` binary of `tfdbg` (described below). It operates on
+dumped data directories. This can be done to both the lower-level `Session` API
+and the higher-level `Estimator` and `Experiment` APIs.
 
-### Debugging Remotely-Running tf.Sessions
+### Debugging Remote tf.Sessions
 
 If you interact directly with the `tf.Session` API in `python`, you can
 configure the `RunOptions` proto that you call your `Session.run()` method
 with, by using the method @{tfdbg.watch_graph}.
 This will cause the intermediate tensors and runtime graphs to be dumped to a
-shared storage location of your choice when the `Session.run()` call occurs.
-For example:
+shared storage location of your choice when the `Session.run()` call occurs
+(at the cost of slower performance). For example:
 
 ```python
 from tensorflow.python import debug as tf_debug
@@ -547,7 +579,7 @@ python -m tensorflow.python.debug.cli.offline_analyzer \
        performance in a non-debugging session?_
 
 **A**: No. The debugger inserts additional special-purpose debug nodes to the
-       graph to record the values of intermediate tensors. These nodes certainly
+       graph to record the values of intermediate tensors. These nodes
        slow down the graph execution. If you are interested in profiling your
        model, check out
        [tfprof](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tfprof)
@@ -591,7 +623,7 @@ only from the main thread?_
 **A**:
 This is a common use case, in which the `Session` object is used from multiple
 threads concurrently. Typically, the child threads take care of background tasks
-such as running enqueue operations. Oftentimes, you want to debug only the main
+such as running enqueue operations. Often, you want to debug only the main
 thread (or less frequently, only one of the child threads). You can use the
 `thread_name_filter` keyword argument of `LocalCLIDebugWrapperSession` to
 achieve this type of thread-selective debugging. For example, to debug from the
@@ -640,6 +672,12 @@ There are three possible workarounds or solutions:
    tfdbg> run --tensor_dtype_filter int.*
    ```
 
+   The first command above watches only nodes whose name match the
+   regular-expression pattern `.*hidden.*`. The second command watches only
+   operations whose name match the pattern `Variable.*`. The third one watches
+   only the tensors whose dtype match the pattern `int.*` (e.g., `int32`).
+
+
 **Q**: _Why can't I select text in the tfdbg CLI?_
 
 **A**: This is because the tfdbg CLI enables mouse events in the terminal by
@@ -664,9 +702,8 @@ sess.run(b)
        tensor `b` is effectively also a constant tensor. TensorFlow's graph
        optimization folds the graph that contains `a` and `b` into a single
        node to speed up future runs of the graph, which is why `tfdbg` does
-       not generate any intermedate-tensor dumps. If `a` were a
-       @{tf.Variable}, the constant-folding would not occur and `tfdbg`
-       should show the intermeidate-tensor dumps. For example:
+       not generate any intermediate tensor dumps. However, if `a` were a
+       @{tf.Variable}, as in the following example:
 
 ``` python
 import numpy as np
@@ -678,3 +715,6 @@ sess.run(tf.global_variables_initializer())
 sess = tf_debug.LocalCLIDebugWrapperSession(sess)
 sess.run(b)
 ```
+
+the constant-folding would not occur and `tfdbg` should show the intermediate
+tensor dumps.
diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index e31d2717a6..74407ed315 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -174,8 +174,7 @@ for more information on how to use them.
 
 ## Variables
 
-See also the how-to documentation on @{$variables$variables}
-and @{$variable_scope$variable scopes}, and
+See also the how-to documentation on @{$variables$variables} and
 @{$python/state_ops$the API documentation for variables}.
 
 #### What is the lifetime of a variable?
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md
index 5ed7c09a57..6074b2e92d 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/programmers_guide/index.md
@@ -4,13 +4,11 @@ The documents in this unit dive into the details of writing TensorFlow
 code.  This section begins with the following guides, each of which
 explain a particular aspect of TensorFlow:
 
-  * @{$variables$Variables: Creation, Initialization, Saving, and Loading},
-    which details the mechanics of TensorFlow Variables.
+  * @{$variables$Variables: Creation, Initialization, Saving, Loading, and
+     Sharing}, which details the mechanics of TensorFlow Variables.
   * @{$dims_types$Tensor Ranks, Shapes, and Types}, which explains Tensor
     rank (the number of dimensions), shape (the size of each dimension),
     and datatypes.
-  * @{$variable_scope$Sharing Variables}, which explains how to share and
-    manage large sets of variables when building complex models.
   * @{$threading_and_queues$Threading and Queues}, which explains TensorFlow's
     rich queuing system.
   * @{$reading_data$Reading Data}, which documents three different mechanisms
diff --git a/tensorflow/docs_src/programmers_guide/reading_data.md b/tensorflow/docs_src/programmers_guide/reading_data.md
index 3c31d3a1a7..a7d9372053 100644
--- a/tensorflow/docs_src/programmers_guide/reading_data.md
+++ b/tensorflow/docs_src/programmers_guide/reading_data.md
@@ -476,4 +476,4 @@ This is what is done in
 
 You can have the train and eval in the same graph in the same process, and share
 their trained variables.  See
-@{$variable_scope$the shared variables tutorial}.
+@{$variables$the shared variables tutorial}.
diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/programmers_guide/tensors.md
new file mode 100644
index 0000000000..ff747f326f
--- /dev/null
+++ b/tensorflow/docs_src/programmers_guide/tensors.md
@@ -0,0 +1,330 @@
+# Tensors
+
+TensorFlow, as the name indicates, is a framework to define and run computations
+involving tensors. A **tensor** is a generalization of vectors and matrices to
+potentially higher dimensions. Internally, TensorFlow represents tensors as
+n-dimensional arrays of base datatypes.
+
+When writing a TensorFlow program, the main object you manipulate and pass
+around is the `tf.Tensor`. A `tf.Tensor` object represents a partially defined
+computation that will eventually produce a value. TensorFlow programs work by
+first building a graph of `tf.Tensor` objects, detailing how each tensor is
+computed based on the other available tensors and then by running parts of this
+graph to achieve the desired results.
+
+A `tf.Tensor` has the following properties:
+
+ * a data type (`float32`, `int32`, or `string`, for example)
+ * a shape
+
+
+Each element in the Tensor has the same data type, and the data type is always
+known. The shape (that is, the number of dimensions it has and the size of each
+dimension) might be only partially known. Most operations produce tensors of
+fully-known shapes if the shapes of their inputs are also fully known, but in
+some cases it's only possible to find the shape of a tensor at graph execution
+time.
+
+Some types of tensors are special, and these will be covered in other
+units of the Programmer's guide. The main ones are:
+
+  * `tf.Variable`
+  * `tf.Constant`
+  * `tf.Placeholder`
+  * `tf.SparseTensor`
+
+With the exception of `tf.Variable`, the value of a tensor is immutable, which
+means that in the context of a single execution tensors only have a single
+value. However, evaluating the same tensor twice can return different values;
+for example that tensor can be the result of reading data from disk, or
+generating a random number.
+
+## Rank
+
+The **rank** of a `tf.Tensor` object is its number of dimensions. Synonyms for
+rank include **order** or **degree** or **n-dimension**.
+Note that rank in TensorFlow is not the same as matrix rank in mathematics. 
+As the following table shows, each rank in TensorFlow corresponds to a 
+different mathematical entity:
+
+Rank | Math entity
+--- | ---
+0 | Scalar (magnitude only)
+1 | Vector (magnitude and direction)
+2 | Matrix (table of numbers)
+3 | 3-Tensor (cube of numbers)
+n | n-Tensor (you get the idea)
+
+
+### Rank 0 
+
+The following snippet demonstrates creating a few rank 0 variables:
+
+```python
+mammal = tf.Variable("Elephant", tf.string)
+ignition = tf.Variable(451, tf.int16)
+floating = tf.Variable(3.14159265359, tf.float64)
+its_complicated = tf.Variable((12.3, -4.85), tf.complex64)
+```
+
+Note: A string is treated as a single item in TensorFlow, not as a sequence of
+characters. It is possible to have scalar strings, vectors of strings, etc.
+
+### Rank 1
+
+To create a rank 1 `tf.Tensor` object, you can pass a list of items as the
+initial value. For example:
+
+```python
+mystr = tf.Variable(["Hello"], tf.string)
+cool_numbers  = tf.Variable([3.14159, 2.71828], tf.float32)
+first_primes = tf.Variable([2, 3, 5, 7, 11], tf.int32)
+its_very_complicated = tf.Variable([(12.3, -4.85), (7.5, -6.23)], tf.complex64)
+```
+
+
+### Higher ranks
+
+A rank 2 `tf.Tensor` object consists of at least one row and at least
+one column:
+
+```python
+mymat = tf.Variable([[7],[11]], tf.int16)
+myxor = tf.Variable([[False, True],[True, False]], tf.bool)
+linear_squares = tf.Variable([[4], [9], [16], [25]], tf.int32)
+squarish_squares = tf.Variable([ [4, 9], [16, 25] ], tf.int32)
+rank_of_squares = tf.rank(squarish_squares)
+mymatC = tf.Variable([[7],[11]], tf.int32)
+```
+
+Higher-rank Tensors, similarly, consist of an n-dimensional array. For example,
+during image processing, many tensors of rank 4 are used, with dimensions
+corresponding to example-in-batch, image width, image height, and color channel.
+
+``` python
+my_image = tf.zeros([10, 299, 299, 3])  # batch x height x width x color
+```
+
+### Getting a `tf.Tensor` object's rank
+
+To determine the rank of a `tf.Tensor` object, call the `tf.rank` method.
+For example, the following method programmatically determines the rank 
+of the `tf.Tensor` defined in the previous section:
+
+```python
+r = tf.rank(my3d)
+# After the graph runs, r will hold the value 3.
+```
+
+### Referring to `tf.Tensor` slices
+
+Since a `tf.Tensor` is an n-dimensional array of cells, to access a single cell
+in a `tf.Tensor` you need to specify n indices.
+
+For a rank 0 tensor (a scalar), no indices are necessary, since it is already a
+single number.
+
+For a rank 1 tensor (a vector), passing a single index allows you to access a
+number:
+
+```python
+my_scalar = my_vector[2]
+```
+
+Note that the index passed inside the `[]` can itself be a scalar `tf.Tensor`, if
+you want to dynamically choose an element from the vector.
+
+For tensors of rank 2 or higher, the situation is more interesting. For a
+`tf.Tensor` of rank 2, passing two numbers returns a scalar, as expected:
+
+
+```python
+my_scalar = my_matrix[1, 2]
+```
+
+
+Passing a single number, however, returns a subvector of a matrix, as follows:
+
+
+```python
+my_row_vetor = my_matrix[2]
+my_column_vector = my_matrix[:, 3]
+```
+
+The `:` notation is python slicing syntax for "leave this dimension alone". This
+is useful in higher-rank Tensors, as it allows you to access its subvectors,
+submatrices, and even other subtensors.
+
+
+## Shape
+
+The **shape** of a tensor is the number of elements in each dimension.
+TensorFlow automatically infers shapes during graph construction. These inferred
+shapes might have known or unknown rank. If the rank is known, the sizes of each
+dimension might be known or unknown.
+
+The TensorFlow documentation uses three notational conventions to describe
+tensor dimensionality: rank, shape, and dimension number. The following table
+shows how these relate to one another:
+
+Rank | Shape | Dimension number | Example
+--- | --- | --- | ---
+0 | [] | 0-D | A 0-D tensor.  A scalar.
+1 | [D0] | 1-D | A 1-D tensor with shape [5].
+2 | [D0, D1] | 2-D | A 2-D tensor with shape [3, 4].
+3 | [D0, D1, D2] | 3-D | A 3-D tensor with shape [1, 4, 3].
+n | [D0, D1, ... Dn-1] | n-D | A tensor with shape [D0, D1, ... Dn-1].
+
+Shapes can be represented via Python lists / tuples of ints, or with the
+@{tf.TensorShape}.
+
+### Getting a `tf.Tensor` object's shape
+
+There are two ways of accessing the shape of a `tf.Tensor`. While building the
+graph, it is often useful to ask what is already known about a tensor's
+shape. This can be done by reading the `shape` property of a `tf.Tensor` object.
+This method returns a `TensorShape` object, which is a convenient way of
+representing partially-specified shapes (since, when building the graph, not all
+shapes will be fully known).
+
+It is also possible to get a `tf.Tensor` that will represent the fully-defined
+shape of another `tf.Tensor` at runtime. This is done by calling the `tf.shape`
+operation. This way, you can build a graph that manipulates the shapes of
+tensors by building other tensors that depend on the dynamic shape of the input
+`tf.Tensor`.
+
+For example, here is how to make a vector of zeros with the same size as the
+number of columns in a given matrix:
+
+``` python
+zeros = tf.zeros(tf.shape(my_matrix)[1])
+```
+
+### Changing the shape of a `tf.Tensor`
+
+The **number of elements** of a tensor is the product of the sizes of all its
+shapes. The number of elements of a scalar is always `1`. Since there are often
+many different shapes that have the same number of elements, it's often
+convenient to be able to change the shape of a `tf.Tensor`, keeping its elements
+fixed. This can be done with `tf.reshape`.
+
+The following examples demonstrate how to reshape tensors:
+
+```python
+rank_three_tensor = tf.ones([3, 4, 5])
+matrix = tf.reshape(rank_three_tensor, [6, 10])  # Reshape existing content into
+                                                 # a 6x10 matrix
+matrixB = tf.reshape(matrix, [3, -1])  #  Reshape existing content into a 3x20
+                                       # matrix. -1 tells reshape to calculate
+                                       # the size of this dimension.
+matrixAlt = tf.reshape(matrixB, [4, 3, -1])  # Reshape existing content into a
+                                             #4x3x5 tensor
+
+# Note that the number of elements of the reshaped Tensors has to match the
+# original number of elements. Therefore, the following example generates an
+# error because no possible value for the last dimension will match the number
+# of elements.
+yet_another = tf.reshape(matrixAlt, [13, 2, -1])  # ERROR!
+```
+
+## Data types
+
+In addition to dimensionality, Tensors have a data type. Refer to the
+`tf.DataType` page in the programmer's guide for a full list of the data types.
+
+It is not possible to have a `tf.Tensor` with more than one data type. It is
+possible, however, to serialize arbitrary data structures as `string`s and store
+those in `tf.Tensor`s.
+
+It is possible to cast `tf.Tensor`s from one datatype to another using
+`tf.cast`:
+
+``` python
+# Cast a constant integer tensor into floating point.
+float_tensor = tf.cast(tf.constant([1, 2, 3]), dtype=tf.float32)
+```
+
+To inspect a `tf.Tensor`'s data type use the `Tensor.dtype` property.
+
+When creating a `tf.Tensor` from a python object you may optionally specify the
+datatype. If you don't, TensorFlow chooses a datatype that can represent your
+data. TensorFlow converts Python integers to `tf.int32` and python floating
+point numbers to `tf.float32`. Otherwise TensorFlow uses the same rules numpy
+uses when converting to arrays.
+
+## Evaluating Tensors
+
+Once the computation graph has been built, you can run the computation that
+produces a particular `tf.Tensor` and fetch the value assigned to it. This is
+often useful for debugging as well as being required for much of TensorFlow to
+work.
+
+The simplest way to evaluate a Tensor is using the `Tensor.eval` method. For
+example:
+
+```python
+constant = tf.constant([1, 2, 3])
+tensor = constant * constant
+print tensor.eval()
+```
+
+The `eval` method only works when a default `tf.Session` is active (see
+Graphs and Sessions for more information).
+
+`Tensor.eval` returns a numpy array with the same contents as the tensor.
+
+Sometimes it is not possible to evaluate a `tf.Tensor` with no context because
+its value might depend on dynamic information that is not available. For
+example, tensors that depend on `Placeholder`s can't be evaluated without
+providing a value for the `Placeholder`.
+
+``` python
+p = tf.placeholder(tf.float32)
+t = p + 1.0
+t.eval()  # This will fail, since the placeholder did not get a value.
+t.eval(feed_dict={p:2.0})  # This will succeed because we're feeding a value
+                           # to the placeholder.
+```
+
+Note that it is possible to feed any `tf.Tensor`, not just placeholders.
+
+Other model constructs might make evaluating a `tf.Tensor`
+complicated. TensorFlow can't directly evaluate `tf.Tensor`s defined inside
+functions or inside control flow constructs. If a `tf.Tensor` depends on a value
+from a queue, evaluating the `tf.Tensor` will only work once something has been
+enqueued; otherwise, evaluating it will hang. When working with queues, remember
+to call `tf.train.start_queue_runners` before evaluating any `tf.Tensor`s.
+
+## Printing Tensors
+
+For debugging purposes you might want to print the value of a `tf.Tensor`. While
+ @{$debugger$tfdbg} provides advanced debugging support, TensorFlow also has an
+ operation to directly print the value of a `tf.Tensor`.
+
+Note that you rarely want to use the following pattern when printing a
+`tf.Tensor`:
+
+``` python
+t = <<some tensorflow operation>>
+print t  # This will print the symbolic tensor when the graph is being built.
+         # This tensor does not have a value in this context.
+```
+
+This code prints the `tf.Tensor` object (which represents deferred computation)
+and not its value. Instead, TensorFlow provides the `tf.Print` operation, which
+returns its first tensor argument unchanged while printing the set of
+`tf.Tensor`s it is passed as the second argument.
+
+To correctly use `tf.Print` its return value must be used. See the example below
+
+``` python
+t = <<some tensorflow operation>>
+tf.Print(t, [t])  # This does nothing
+t = tf.Print(t, [t])  # Here we are using the value returned by tf.Print
+result = t + 1  # Now when result is evaluated the value of `t` will be printed.
+```
+
+When you evaluate `result` you will evaluate everything `result` depends
+upon. Since `result` depends upon `t`, and evaluating `t` has the side effect of
+printing its input (the old value of `t`), `t` gets printed.
+
diff --git a/tensorflow/docs_src/programmers_guide/variable_scope.md b/tensorflow/docs_src/programmers_guide/variable_scope.md
deleted file mode 100644
index f4d2b3f37b..0000000000
--- a/tensorflow/docs_src/programmers_guide/variable_scope.md
+++ /dev/null
@@ -1,373 +0,0 @@
-# Sharing Variables
-
-You can create, initialize, save and load single variables
-in the way described in the @{$variables$Variables HowTo}.
-But when building complex models you often need to share large sets of
-variables and you might want to initialize all of them in one place.
-This tutorial shows how this can be done using `tf.variable_scope()` and
-`tf.get_variable()`.
-
-## The Problem
-
-Imagine you create a simple model for image filters, similar to our
-@{$deep_cnn$Convolutional Neural Networks Tutorial}
-model but with only 2 convolutions (for simplicity of this example). If you use
-just `tf.Variable`, as explained in @{$variables$Variables HowTo},
-your model might look like this.
-
-```python
-def my_image_filter(input_images):
-    conv1_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv1_weights")
-    conv1_biases = tf.Variable(tf.zeros([32]), name="conv1_biases")
-    conv1 = tf.nn.conv2d(input_images, conv1_weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    relu1 = tf.nn.relu(conv1 + conv1_biases)
-
-    conv2_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv2_weights")
-    conv2_biases = tf.Variable(tf.zeros([32]), name="conv2_biases")
-    conv2 = tf.nn.conv2d(relu1, conv2_weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv2 + conv2_biases)
-```
-
-As you can easily imagine, models quickly get much more complicated than
-this one, and even here we already have 4 different variables: `conv1_weights`,
-`conv1_biases`, `conv2_weights`, and `conv2_biases`.
-
-The problem arises when you want to reuse this model. Assume you want to
-apply your image filter to 2 different images, `image1` and `image2`.
-You want both images processed by the same filter with the same parameters.
-You can call `my_image_filter()` twice, but this will create two sets
-of variables, 4 variables in each one, for a total of 8 variables.
-
-```python
-# First call creates one set of 4 variables.
-result1 = my_image_filter(image1)
-# Another set of 4 variables is created in the second call.
-result2 = my_image_filter(image2)
-```
-
-A common way to share variables is to create them in a separate piece of code
-and pass them to functions that use them.   For example by using a dictionary:
-
-```python
-variables_dict = {
-    "conv1_weights": tf.Variable(tf.random_normal([5, 5, 32, 32]),
-        name="conv1_weights")
-    "conv1_biases": tf.Variable(tf.zeros([32]), name="conv1_biases")
-    ... etc. ...
-}
-
-def my_image_filter(input_images, variables_dict):
-    conv1 = tf.nn.conv2d(input_images, variables_dict["conv1_weights"],
-        strides=[1, 1, 1, 1], padding='SAME')
-    relu1 = tf.nn.relu(conv1 + variables_dict["conv1_biases"])
-
-    conv2 = tf.nn.conv2d(relu1, variables_dict["conv2_weights"],
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv2 + variables_dict["conv2_biases"])
-
-# Both calls to my_image_filter() now use the same variables
-result1 = my_image_filter(image1, variables_dict)
-result2 = my_image_filter(image2, variables_dict)
-```
-
-While convenient, creating variables like above,
-outside of the code, breaks encapsulation:
-
-*  The code that builds the graph must document the names, types,
-   and shapes of variables to create.
-*  When the code changes, the callers may have to create more, or less,
-   or different variables.
-
-One way to address the problem is to use classes to create a model,
-where the classes take care of managing the variables they need.
-For a lighter solution, not involving classes, TensorFlow provides
-a *Variable Scope* mechanism that allows to easily share named variables
-while constructing a graph.
-
-## Variable Scope Example
-
-Variable Scope mechanism in TensorFlow consists of two main functions:
-
-* `tf.get_variable(<name>, <shape>, <initializer>)`:
-  Creates or returns a variable with a given name.
-* `tf.variable_scope(<scope_name>)`:
-  Manages namespaces for names passed to `tf.get_variable()`.
-
-The function `tf.get_variable()` is used to get or create a variable instead
-of a direct call to `tf.Variable`. It uses an *initializer* instead of passing
-the value directly, as in `tf.Variable`. An initializer is a function that
-takes the shape and provides a tensor with that shape. Here are some
-initializers available in TensorFlow:
-
-* `tf.constant_initializer(value)` initializes everything to the provided value,
-* `tf.random_uniform_initializer(a, b)` initializes uniformly from [a, b],
-* `tf.random_normal_initializer(mean, stddev)` initializes from the normal
-  distribution with the given mean and standard deviation.
-
-To see how `tf.get_variable()` solves the problem discussed
-before, let's refactor the code that created one convolution into
-a separate function, named `conv_relu`:
-
-```python
-def conv_relu(input, kernel_shape, bias_shape):
-    # Create variable named "weights".
-    weights = tf.get_variable("weights", kernel_shape,
-        initializer=tf.random_normal_initializer())
-    # Create variable named "biases".
-    biases = tf.get_variable("biases", bias_shape,
-        initializer=tf.constant_initializer(0.0))
-    conv = tf.nn.conv2d(input, weights,
-        strides=[1, 1, 1, 1], padding='SAME')
-    return tf.nn.relu(conv + biases)
-```
-
-This function uses short names `"weights"` and `"biases"`.
-We'd like to use it for both `conv1` and `conv2`, but
-the variables need to have different names.
-This is where `tf.variable_scope()` comes into play:
-it pushes a namespace for variables.
-
-```python
-def my_image_filter(input_images):
-    with tf.variable_scope("conv1"):
-        # Variables created here will be named "conv1/weights", "conv1/biases".
-        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
-    with tf.variable_scope("conv2"):
-        # Variables created here will be named "conv2/weights", "conv2/biases".
-        return conv_relu(relu1, [5, 5, 32, 32], [32])
-```
-
-Now, let's see what happens when we call `my_image_filter()` twice.
-
-```
-result1 = my_image_filter(image1)
-result2 = my_image_filter(image2)
-# Raises ValueError(... conv1/weights already exists ...)
-```
-
-As you can see, `tf.get_variable()` checks that already existing variables
-are not shared by accident. If you want to share them, you need to specify
-it by setting `reuse_variables()` as follows.
-
-```
-with tf.variable_scope("image_filters") as scope:
-    result1 = my_image_filter(image1)
-    scope.reuse_variables()
-    result2 = my_image_filter(image2)
-```
-
-This is a good way to share variables, lightweight and safe.
-
-## How Does Variable Scope Work?
-
-### Understanding `tf.get_variable()`
-
-To understand variable scope it is necessary to first
-fully understand how `tf.get_variable()` works.
-Here is how `tf.get_variable` is usually called.
-
-```python
-v = tf.get_variable(name, shape, dtype, initializer)
-```
-
-This call does one of two things depending on the scope it is called in.
-Here are the two options.
-
-* Case 1: the scope is set for creating new variables, as evidenced by
-`tf.get_variable_scope().reuse == False`.
-
-In this case, `v` will be a newly created `tf.Variable` with the provided
-shape and data type. The full name of the created variable will be set to
-the current variable scope name + the provided `name` and a check will be
-performed to ensure that no variable with this full name exists yet.
-If a variable with this full name already exists, the function will
-raise a `ValueError`. If a new variable is created, it will be
-initialized to the value `initializer(shape)`. For example:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-assert v.name == "foo/v:0"
-```
-
-* Case 2: the scope is set for reusing variables, as evidenced by
-`tf.get_variable_scope().reuse == True`.
-
-In this case, the call will search for an already existing variable with
-name equal to the current variable scope name + the provided `name`.
-If no such variable exists, a `ValueError` will be raised. If the variable
-is found, it will be returned. For example:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-with tf.variable_scope("foo", reuse=True):
-    v1 = tf.get_variable("v", [1])
-assert v1 is v
-```
-
-### Basics of `tf.variable_scope()`
-
-Knowing how `tf.get_variable()` works makes it easy to understand variable
-scope. The primary function of variable scope is to carry a name that will
-be used as prefix for variable names and a reuse-flag to distinguish the two
-cases described above. Nesting variable scopes appends their names in a way
-analogous to how directories work:
-
-```python
-with tf.variable_scope("foo"):
-    with tf.variable_scope("bar"):
-        v = tf.get_variable("v", [1])
-assert v.name == "foo/bar/v:0"
-```
-
-The current variable scope can be retrieved using `tf.get_variable_scope()`
-and the `reuse` flag of the current variable scope can be set to `True` by
-calling `tf.get_variable_scope().reuse_variables()`:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-    tf.get_variable_scope().reuse_variables()
-    v1 = tf.get_variable("v", [1])
-assert v1 is v
-```
-
-Note that you *cannot* set the `reuse` flag to `False`. The reason behind
-this is to allow to compose functions that create models. Imagine you write
-a function `my_image_filter(inputs)` as before. Someone calling the function
-in a variable scope with `reuse=True` would expect all inner variables to be
-reused as well. Allowing to force `reuse=False` inside the function would break
-this contract and make it hard to share parameters in this way.
-
-Even though you cannot set `reuse` to `False` explicitly, you can enter
-a reusing variable scope and then exit it, going back to a non-reusing one.
-This can be done using a `reuse=True` parameter when opening a variable scope.
-Note also that, for the same reason as above, the `reuse` parameter is
-inherited. So when you open a reusing variable scope, all sub-scopes will
-be reusing too.
-
-```python
-with tf.variable_scope("root"):
-    # At start, the scope is not reusing.
-    assert tf.get_variable_scope().reuse == False
-    with tf.variable_scope("foo"):
-        # Opened a sub-scope, still not reusing.
-        assert tf.get_variable_scope().reuse == False
-    with tf.variable_scope("foo", reuse=True):
-        # Explicitly opened a reusing scope.
-        assert tf.get_variable_scope().reuse == True
-        with tf.variable_scope("bar"):
-            # Now sub-scope inherits the reuse flag.
-            assert tf.get_variable_scope().reuse == True
-    # Exited the reusing scope, back to a non-reusing one.
-    assert tf.get_variable_scope().reuse == False
-```
-
-### Capturing variable scope
-
-In all examples presented above, we shared parameters only because their
-names agreed, that is, because we opened a reusing variable scope with
-exactly the same string. In more complex cases, it might be useful to pass
-a VariableScope object rather than rely on getting the names right.
-To this end, variable scopes can be captured and used instead of names
-when opening a new variable scope.
-
-```python
-with tf.variable_scope("foo") as foo_scope:
-    v = tf.get_variable("v", [1])
-with tf.variable_scope(foo_scope):
-    w = tf.get_variable("w", [1])
-with tf.variable_scope(foo_scope, reuse=True):
-    v1 = tf.get_variable("v", [1])
-    w1 = tf.get_variable("w", [1])
-assert v1 is v
-assert w1 is w
-```
-
-When opening a variable scope using a previously existing scope
-we jump out of the current variable scope prefix to an entirely
-different one. This is fully independent of where we do it.
-
-```python
-with tf.variable_scope("foo") as foo_scope:
-    assert foo_scope.name == "foo"
-with tf.variable_scope("bar"):
-    with tf.variable_scope("baz") as other_scope:
-        assert other_scope.name == "bar/baz"
-        with tf.variable_scope(foo_scope) as foo_scope2:
-            assert foo_scope2.name == "foo"  # Not changed.
-```
-
-### Initializers in variable scope
-
-Using `tf.get_variable()` allows to write functions that create or reuse
-variables and can be transparently called from outside. But what if we wanted
-to change the initializer of the created variables? Do we need to pass an extra
-argument to every function that creates variables? What about the most common
-case, when we want to set the default initializer for all variables in one
-place, on top of all functions? To help with these cases, variable scope
-can carry a default initializer. It is inherited by sub-scopes and passed
-to each `tf.get_variable()` call. But it will be overridden if another
-initializer is specified explicitly.
-
-```python
-with tf.variable_scope("foo", initializer=tf.constant_initializer(0.4)):
-    v = tf.get_variable("v", [1])
-    assert v.eval() == 0.4  # Default initializer as set above.
-    w = tf.get_variable("w", [1], initializer=tf.constant_initializer(0.3)):
-    assert w.eval() == 0.3  # Specific initializer overrides the default.
-    with tf.variable_scope("bar"):
-        v = tf.get_variable("v", [1])
-        assert v.eval() == 0.4  # Inherited default initializer.
-    with tf.variable_scope("baz", initializer=tf.constant_initializer(0.2)):
-        v = tf.get_variable("v", [1])
-        assert v.eval() == 0.2  # Changed default initializer.
-```
-
-### Names of ops in `tf.variable_scope()`
-
-We discussed how `tf.variable_scope` governs the names of variables.
-But how does it influence the names of other ops in the scope?
-It is natural that ops created inside a variable scope should also
-share that name. For this reason, when we do `with tf.variable_scope("name")`,
-this implicitly opens a `tf.name_scope("name")`. For example:
-
-```python
-with tf.variable_scope("foo"):
-    x = 1.0 + tf.get_variable("v", [1])
-assert x.op.name == "foo/add"
-```
-
-Name scopes can be opened in addition to a variable scope, and then
-they will only affect the names of the ops, but not of variables.
-
-```python
-with tf.variable_scope("foo"):
-    with tf.name_scope("bar"):
-        v = tf.get_variable("v", [1])
-        x = 1.0 + v
-assert v.name == "foo/v:0"
-assert x.op.name == "foo/bar/add"
-```
-
-When opening a variable scope using a captured object instead of a string,
-we do not alter the current name scope for ops.
-
-
-## Examples of Use
-
-Here are pointers to a few files that make use of variable scope. They can all
-be found in the [TensorFlow models repo](https://github.com/tensorflow/models).
-In particular, variable scope is heavily used for recurrent neural networks and
-sequence-to-sequence models.
-
-File | What's in it?
---- | ---
-`tutorials/image/cifar10/cifar10.py` | Model for detecting objects in images.
-`tutorials/rnn/rnn_cell.py` | Cell functions for recurrent neural networks.
-`tutorials/rnn/seq2seq.py` | Functions for building sequence-to-sequence models.
diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index e8d1e519f0..a7b7db626a 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -1,190 +1,283 @@
-# Variables: Creation, Initialization, Saving, and Loading
+# Variables: persistent state in TensorFlow
 
-When you train a model, you use @{$python/state_ops$variables}
-to hold and update parameters.  Variables are in-memory buffers containing
-tensors.  They must be explicitly initialized and can be saved to disk during
-and after training. You can later restore saved values to exercise or analyze
-the model.
+A TensorFlow **variable** is the best way to represent shared, persistent state
+manipulated by your program.
 
-This document references the following TensorFlow classes.  Follow the links to
-their reference manual for a complete description of their API:
+Variables are manipulated via the `tf.Variable` class. A `tf.Variable`
+represents a tensor whose value can be changed by running ops on it. Unlike
+`tf.Tensor` objects, a `tf.Variable` exists outside the context of a single
+`session.run` call.
 
-*  The @{tf.Variable} class.
-*  The @{tf.train.Saver} class.
+Internally, a `tf.Variable` stores a persistent tensor. Specific ops allow you
+to read and modify the values of this tensor. These modifications are visible
+across multiple `tf.Session`s, so multiple workers can see the same values for a
+`tf.Variable`.
 
+## Creating a Variable
 
-## Creation
+The best way to create a variable is to call the `tf.get_variable`
+function. This function requires you to specify the Variable's name. This name
+will be used by other replicas to access the same variable, as well as to name
+this variable's value when checkpointing and exporting models. `tf.get_variable`
+also allows you to reuse a previously created variable of the same name, making it
+easy to define models which reuse layers.
 
-When you create a @{$python/state_ops$Variable} you pass a
-`Tensor` as its initial value to the `Variable()` constructor.  TensorFlow
-provides a collection of ops that produce tensors often used for initialization
-from @{$python/constant_op$constants or random values}.
+To create a variable with `tf.get_variable`, simply provide the name and shape
 
-Note that all these ops require you to specify the shape of the tensors.  That
-shape automatically becomes the shape of the variable.  Variables generally
-have a fixed shape, but TensorFlow provides advanced mechanisms to reshape
-variables.
+``` python
+my_variable = tf.get_variable("my_variable", [1, 2, 3])
+```
 
-```python
-# Create two variables.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-biases = tf.Variable(tf.zeros([200]), name="biases")
+This creates a variable named "my_variable" which is a three-dimensional tensor
+with shape `[1, 2, 3]`. This variable will, by default, have the `dtype`
+`tf.float32` and its initial value will be randomized via
+`tf.glorot_uniform_initializer`.
+
+You may optionally specify the `dtype` and initializer to `tf.get_variable`. For
+example:
+
+``` python
+my_int_variable = tf.get_variable("my_int_variable", [1, 2, 3], dtype=tf.int32, 
+  initializer=tf.zeros_initializer)
 ```
 
-Calling `tf.Variable()` adds several ops to the graph:
+TensorFlow provides many convenient initializers. Alternatively, you may
+initialize a `tf.Variable` to have the value of a `tf.Tensor`. For example:
 
-*  A `variable` op that holds the variable value.
-*  An initializer op that sets the variable to its initial value.  This is
-   actually a `tf.assign` op.
-*  The ops for the initial value, such as the `zeros` op for the `biases`
-   variable in the example are also added to the graph.
+``` python
+other_variable = tf.get_variable("other_variable", dtype=tf.int32, 
+  initializer=tf.constant([23, 42]))
+```
 
-The value returned by `tf.Variable()` value is an instance of the Python class
-`tf.Variable`.
+Note that when the initializer is a `tf.Tensor` you should not specify the
+variable's shape, as the shape of the initializer tensor will be used.
+
+### Variable collections
+
+Because disconnected parts of a TensorFlow program might want to create
+variables, it is sometimes useful to have a single way to access all of
+them. For this reason TensorFlow provides **collections**, which are named lists
+of tensors or other objects, such as `tf.Variable` instances.
+
+By default every `tf.Variable` gets placed in the following two collections:
+ * `tf.GraphKeys.GLOBAL_VARIABLES` --- variables that can be shared across
+multiple devices,
+ * `tf.GraphKeys.TRAINABLE_VARIABLES`--- variables for which TensorFlow will
+   calculate gradients.
+ 
+If you don't want a variable to be trainable, add it to the
+`tf.GraphKeys.LOCAL_VARIABLES` collection instead. For example, the following
+snippet demonstrates how to add a variable named `my_local` to this collection:
+
+``` python
+my_local = tf.get_variable("my_local", shape=(), 
+collections=[tf.GraphKeys.LOCAL_VARIABLES])
+```
 
-### Device placement
+Alternatively, you can specify `trainable=False` as an argument to
+`tf.get_variable`:
 
-A variable can be pinned to a particular device when it is created, using a
-@{tf.device$`with tf.device(...):`} block:
+``` python
+my_non_trainable = tf.get_variable("my_non_trainable", 
+                                   shape=(), 
+                                   trainable=False)
+```
 
-```python
-# Pin a variable to CPU.
-with tf.device("/cpu:0"):
-  v = tf.Variable(...)
 
-# Pin a variable to GPU.
-with tf.device("/gpu:0"):
-  v = tf.Variable(...)
+You can also use your own collections. Any string is a valid collection name,
+and there is no need to explicitly create a collection. To add a variable (or
+any other object) to a collection after creating the variable, call
+`tf.add_to_collection`.  For example, the following code adds an existing
+variable named `my_local` to a collection named `my_collection_name`:
 
-# Pin a variable to a particular parameter server task.
-with tf.device("/job:ps/task:7"):
-  v = tf.Variable(...)
+``` python
+tf.add_to_collection("my_collection_name", my_local)
 ```
 
-**NOTE** Operations that mutate a variable, such as
-@{tf.Variable.assign} and the parameter
-update operations in a
-@{tf.train.Optimizer} *must* run on
-the same device as the variable. Incompatible device placement directives will
-be ignored when creating these operations.
+And to retrieve a list of all the variables (or other objects) you've placed in
+a collection you can use:
 
-Device placement is particularly important when running in a replicated
-setting. See
-@{tf.train.replica_device_setter}
-for details of a device function that can simplify the configuration for devices
-for a replicated model.
+``` python
+tf.get_collection("my_collection_name")
+```
 
-## Initialization
+### Device placement
 
-Variable initializers must be run explicitly before other ops in your model can
-be run.  The easiest way to do that is to add an op that runs all the variable
-initializers, and run that op before using the model.
+Just like any other TensorFlow operation, you can place variables on particular
+devices. For example, the following snippet creates a variable named `v` and
+places it on the second GPU device:
 
-You can alternatively restore variable values from a checkpoint file, see
-below.
+``` python
+with tf.device("/gpu:1"):
+  v = tf.get_variable("v", [1])
+```
 
-Use `tf.global_variables_initializer()` to add an op to run variable initializers.
-Only run that op after you have fully constructed your model and launched it in
-a session.
+It is particularly important for variables to be in the correct device in
+distributed settings. Accidentally putting variables on workers instead of
+parameter servers, for example, can severely slow down training or, in the worst
+case, let each worker blithely forge ahead with its own independent copy of each
+variable. For this reason we provide @{tf.train.replica_device_setter}, which
+can automatically place variables in parameter servers. For example:
+
+``` python
+cluster_spec = {
+    "ps": ["ps0:2222", "ps1:2222"],
+    "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
+with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
+  v = tf.get_variable("v", shape=[20, 20])  # this variable is placed 
+                                            # in the parameter server
+                                            # by the replica_device_setter
+```
 
-```python
-# Create two variables.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-biases = tf.Variable(tf.zeros([200]), name="biases")
-...
-# Add an op to initialize the variables.
-init_op = tf.global_variables_initializer()
+## Initializing variables
 
-# Later, when launching the model
-with tf.Session() as sess:
-  # Run the init operation.
-  sess.run(init_op)
-  ...
-  # Use the model
-  ...
+Before you can use a variable, it must be initialized. If you are programming in
+the low-level TensorFlow API (that is, you are explicitly creating your own
+graphs and sessions), you must explicitly initialize the variables.  Most
+high-level frameworks such as `tf.contrib.slim`, `tf.estimator.Estimator` and
+`Keras` automatically initialize variables for you before training a model.
+
+Explicit initialization is otherwise useful because it allows you not to rerun
+potentially expensive initializers when reloading a model from a checkpoint as
+well as allowing determinism when randomly-initialized variables are shared in a
+distributed setting. 
+
+To initialize all trainable variables in one go, before training starts, call
+`tf.global_variables_initializer()`. This function returns a single operation
+responsible for initializing all variables in the
+`tf.GraphKeys.GLOBAL_VARIABLES` collection. Running this operation initializes
+all variables. For example:
+
+``` python
+session.run(tf.global_variables_initializer())
+# Now all variables are initialized.
 ```
 
-### Initialization from another Variable
+If you do need to initialize variables yourself, you can run the variable's
+initializer operation. For example:
 
-You sometimes need to initialize a variable from the initial value of another
-variable.  As the op added by `tf.global_variables_initializer()` initializes all
-variables in parallel you have to be careful when this is needed.
+``` python
+session.run(my_variable.initializer)
+```
 
-To initialize a new variable from the value of another variable use the other
-variable's `initialized_value()` property.  You can use the initialized value
-directly as the initial value for the new variable, or you can use it as any
-other tensor to compute a value for the new variable.
 
+You can also ask which variables have still not been initialized. For example,
+the following code prints the names of all variables which have not yet been
+initialized:
 
-```python
-# Create a variable with a random value.
-weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
-                      name="weights")
-# Create another variable with the same value as 'weights'.
-w2 = tf.Variable(weights.initialized_value(), name="w2")
-# Create another variable with twice the value of 'weights'
-w_twice = tf.Variable(weights.initialized_value() * 2.0, name="w_twice")
+``` python
+print(session.run(tf.report_uninitialized_variables()))
+```
+
+
+Note that by default `tf.global_variables_initializer` does not specify the
+order in which variables are initialized. Therefore, if the initial value of a
+variable depends on another variable's value, it's likely that you'll get an
+error. Any time you use the value of a variable in a context in which not all
+variables are initialized (say, if you use a variable's value while initializing
+another variable), it is best to use `variable.initialized_value()` instead of
+`variable`:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+w = tf.get_variable("w", initializer=v.initialized_value() + 1)
+```
+
+## Using variables
+
+To use the value of a `tf.Variable` in a TensorFlow graph, simply treat it like
+a normal `tf.Tensor`:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+w = v + 1  # w is a tf.Tensor which is computed based on the value of v.
+           # Any time a variable is used in an expression it gets automatically
+           # converted to a tf.Tensor representing its value.
 ```
 
-### Custom Initialization
+To assign a value to a variable, use the methods `assign`, `assign_add`, and
+friends in the `tf.Variable` class. For example, here is how you can call these
+methods:
 
-The convenience function `tf.global_variables_initializer()` adds an op to
-initialize *all variables* in the model.  You can also pass an explicit list of
-variables to initialize to `tf.variables_initializer`.  See the
-@{$python/state_ops$Variables Documentation} for more options,
-including checking if variables are initialized.
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+assignment = v.assign_add(1)
+tf.global_variables_initializer().run()
+assignment.run()
+```
+
+Most TensorFlow optimizers have specialized ops that efficiently update the
+values of variables according to some gradient descent-like algorithm. See
+@{tf.train.Optimizer} for an explanation of how to use optimizers.
+
+Because variables are mutable it's sometimes useful to know what version of a
+variable's value is being used at any point in time. To force a re-read of the
+value of a variable after something has happened, you can use
+`tf.Variable.read_value`. For example:
+
+``` python
+v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
+assignment = v.assign_add(1)
+with tf.control_dependencies([assignment]):
+  w = v.read_value()  # w is guaranteed to reflect v's value after the
+                      # assign_add operation.
+```
 
 ## Saving and Restoring
 
 The easiest way to save and restore a model is to use a `tf.train.Saver` object.
 The constructor adds `save` and `restore` ops to the graph for all, or a
-specified list, of the variables in the graph.  The saver object provides
+specified list, of the variables in the graph.  The `Saver` object provides
 methods to run these ops, specifying paths for the checkpoint files to write to
 or read from.
 
-Note that to restore a model checkpoint without a graph one must first import
-the graph from the meta graph file (typical extension is `.meta`). This is
-done with @{tf.train.import_meta_graph}, which in turn returns a `Saver` from
-which one can than perform a `restore`.
+To restore a model checkpoint without a graph, you must first import the graph
+from the `MetaGraph` file (typical extension is `.meta`). Do this by calling
+@{tf.train.import_meta_graph}, which in turn returns a `Saver` from which one
+can than perform a `restore`.
 
 ### Checkpoint Files
 
-Variables are saved in binary files that, roughly, contain a map from variable
+TensorFlow saves variables in binary files that, roughly speaking, map variable
 names to tensor values.
 
 When you create a `Saver` object, you can optionally choose names for the
-variables in the checkpoint files.  By default, it uses the value of the
+variables in the checkpoint files.  By default, `Saver` uses the value of the
 @{tf.Variable.name} property for
 each variable.
 
-To understand what variables are in a checkpoint, you can use the
-[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py)
-library, and in particular, the `print_tensors_in_checkpoint_file` function.
+To inspect the variables in a checkpoint, you can use
+the
+[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library,
+particularly the `print_tensors_in_checkpoint_file` function.
 
 ### Saving Variables
 
-Create a `Saver` with `tf.train.Saver()` to manage all variables in
-the model.
+Create a `Saver` with `tf.train.Saver()` to manage all variables in the
+model. For example, the following snippet demonstrates how to call the
+`tf.train.Saver.save` method to save variables to a checkpoint file:
 
 ```python
 # Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
+v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
+v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
+
+inc_v1 = v1.assign(v1+1)
+dec_v2 = v2.assign(v2-1)
+
 # Add an op to initialize the variables.
 init_op = tf.global_variables_initializer()
 
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
-# Later, launch the model, initialize the variables, do some work, save the
+# Later, launch the model, initialize the variables, do some work, and save the
 # variables to disk.
 with tf.Session() as sess:
   sess.run(init_op)
   # Do some work with the model.
-  ..
+  inc_v1.op.run()
+  dec_v2.op.run()
   # Save the variables to disk.
   save_path = saver.save(sess, "/tmp/model.ckpt")
   print("Model saved in file: %s" % save_path)
@@ -192,14 +285,19 @@ with tf.Session() as sess:
 
 ### Restoring Variables
 
-The same `Saver` object is used to restore variables.  Note that when you
-restore variables from a file you do not have to initialize them beforehand.
+The `tf.train.Saver` object not only saves variables to checkpoint files, it
+also restores variables.  Note that when you restore variables from a file you
+do not have to initialize them beforehand. For example, the following snippet
+demonstrates how to call the `tf.train.Saver.restore` method to restore
+variables from a checkpoint file:
 
 ```python
+tf.reset_default_graph()
+
 # Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
+v1 = tf.get_variable("v1", shape=[3])
+v2 = tf.get_variable("v2", shape=[5])
+
 # Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
@@ -209,50 +307,156 @@ with tf.Session() as sess:
   # Restore variables from disk.
   saver.restore(sess, "/tmp/model.ckpt")
   print("Model restored.")
-  # Do some work with the model
-  ...
+  # Check the values of the variables
+  print("v1 : %s" % v1.eval())
+  print("v2 : %s" % v2.eval())
 ```
 
+
+
 ### Choosing which Variables to Save and Restore
 
-If you do not pass any argument to `tf.train.Saver()` the saver handles all
-variables in the graph.  Each one of them is saved under the name that was
-passed when the variable was created.
+If you do not pass any argument to `tf.train.Saver()`, the saver handles all
+variables in the graph.  Each variable is saved under the name that was passed
+when the variable was created.
 
 It is sometimes useful to explicitly specify names for variables in the
 checkpoint files.  For example, you may have trained a model with a variable
-named `"weights"` whose value you want to restore in a new variable named
+named `"weights"` whose value you want to restore into a variable named
 `"params"`.
 
 It is also sometimes useful to only save or restore a subset of the variables
-used by a model.  For example, you may have trained a neural net with 5 layers,
-and you now want to train a new model with 6 layers, restoring the parameters
-from the 5 layers of the previously trained model into the first 5 layers of
-the new model.
+used by a model.  For example, you may have trained a neural net with five
+layers, and you now want to train a new model with six layers that reuses the
+existing weights of the five trained layers. You can use the saver to restore
+the weights of just the first five layers.
+
+You can easily specify the names and variables to save or load by passing to the
+`tf.train.Saver()` constructor either a list of variables (which will be stored 
+under their own names), or a Python dictionary in which keys are the names to 
+use and values are the variables to manage. 
+
+Continuing from the save/restore examples, above:
+
+```python
+tf.reset_default_graph()
+# Create some variables.
+v1 = tf.get_variable("v1", [3], initializer = tf.zeros_initializer)
+v2 = tf.get_variable("v2", [5], initializer = tf.zeros_initializer)
 
-You can easily specify the names and variables to save by passing to the
-`tf.train.Saver()` constructor a Python dictionary: keys are the
-names to use, values are the variables to manage.
+# Add ops to save and restore only `v2` using the name "v2"
+saver = tf.train.Saver({"v2": v2})
+
+# Use the saver object normally after that.
+with tf.Session() as sess:
+  # Initialize v1 since the saver will not.
+  v1.initializer.run()
+  saver.restore(sess, "/tmp/model.ckpt")
+  
+  print("v1 : %s" % v1.eval())
+  print("v2 : %s" % v2.eval())
+
+```
 
 Notes:
 
-*  You can create as many saver objects as you want if you need to save and
+*  You can create as many `Saver` objects as you want if you need to save and
    restore different subsets of the model variables.  The same variable can be
-   listed in multiple saver objects, its value is only changed when the saver
-   `restore()` method is run.
+   listed in multiple saver objects, its value is only changed when the
+   `Saver.restore()` method is run.
+
+*  If you only restore a subset of the model variables at the start of a
+   session, you have to run an initialize op for the other variables.  See
+   @{tf.variables_initializer} for more information.
+
+
+## Sharing variables
+
+TensorFlow supports two ways of sharing variables:
 
-*  If you only restore a subset of the model variables at the start
-   of a session, you have to run an initialize op for the other variables.  See
-   @{tf.variables_initializer}
-   for more information.
+ * Explicitly passing `tf.Variable` objects around.
+ * Implicitly wrapping `tf.Variable` objects within `tf.variable_scope` objects.
+
+While code which explicitly passes variables around is very clear, it is
+sometimes convenient to write TensorFlow functions that implicitly use
+variables in their implementations. Most of the functional layers from
+`tf.layer` use this approach, as well as all `tf.metrics`, and a few other
+library utilities.
+
+Variable scopes allow you to control variable reuse when calling functions which
+implicitly create and use variables. They also allow you to name your variables
+in a hierarchical and understandable way.
+
+For example, let's say we write a function to create a convolutional / relu
+layer:
 
 ```python
-# Create some variables.
-v1 = tf.Variable(..., name="v1")
-v2 = tf.Variable(..., name="v2")
-...
-# Add ops to save and restore only 'v2' using the name "my_v2"
-saver = tf.train.Saver({"my_v2": v2})
-# Use the saver object normally after that.
-...
+def conv_relu(input, kernel_shape, bias_shape):
+    # Create variable named "weights".
+    weights = tf.get_variable("weights", kernel_shape,
+        initializer=tf.random_normal_initializer())
+    # Create variable named "biases".
+    biases = tf.get_variable("biases", bias_shape,
+        initializer=tf.constant_initializer(0.0))
+    conv = tf.nn.conv2d(input, weights,
+        strides=[1, 1, 1, 1], padding='SAME')
+    return tf.nn.relu(conv + biases)
 ```
+
+This function uses short names `weights` and `biases`, which is good for
+clarity. In a real model, however, we want many such convolutional layers, and
+calling this function repeatedly would not work:
+
+``` python
+input1 = tf.random_normal([1,10,10,32])
+input2 = tf.random_normal([1,20,20,32])
+x = conv_relu(input1, kernel_shape=[5, 5, 32, 32], bias_shape=[32])
+x = conv_relu(x, kernel_shape=[5, 5, 32, 32], bias_shape = [32])  # This fails.
+```
+
+Since the desired behavior is unclear (create new variables or reuse the
+existing ones?) TensorFlow will fail. Calling `conv_relu` in different scopes,
+however, clarifies that we want to create new variables:
+
+```python
+def my_image_filter(input_images):
+    with tf.variable_scope("conv1"):
+        # Variables created here will be named "conv1/weights", "conv1/biases".
+        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
+    with tf.variable_scope("conv2"):
+        # Variables created here will be named "conv2/weights", "conv2/biases".
+        return conv_relu(relu1, [5, 5, 32, 32], [32])
+```
+
+If you do want the variables to be shared, you have two options. First, you can
+create a scope with the same name using `reuse=True`:
+
+``` python
+with tf.variable_scope("model"):
+  output1 = my_image_filter(input1)
+with tf.variable_scope("model", reuse=True):
+  output2 = my_image_filter(input2)
+
+```
+
+You can also call `scope.reuse_variables()` to trigger a reuse:
+
+``` python
+with tf.variable_scope("model") as scope:
+  output1 = my_image_filter(input1)
+  scope.reuse_variables()
+  output2 = my_image_filter(input2)
+
+```
+
+Since depending on exact string names of scopes can feel dangerous, it's also
+possible to initialize a variable scope based on another one:
+
+``` python
+with tf.variable_scope("model") as scope:
+  output1 = my_image_filter(input1)
+with tf.variable_scope(scope, reuse=True):
+  output2 = my_image_filter(input2)
+
+```
+
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index b0617326ff..8013746eca 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -417,7 +417,7 @@ scope indicating that they should be run on the first GPU.
 All variables are pinned to the CPU and accessed via
 @{tf.get_variable}
 in order to share them in a multi-GPU version.
-See how-to on @{$variable_scope$Sharing Variables}.
+See how-to on @{$variables$Sharing Variables}.
 
 ### Launching and Training the Model on Multiple GPU cards
 
diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index 0fdfcf5d2a..37df19a8af 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -27,9 +27,6 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
-from tensorflow.contrib import learn
-from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
-
 tf.logging.set_verbosity(tf.logging.INFO)
 
 # Our application logic will be added here
@@ -122,15 +119,14 @@ Open `cnn_mnist.py` and add the following `cnn_model_fn` function, which
 conforms to the interface expected by TensorFlow's Estimator API (more on this
 later in [Create the Estimator](#create-the-estimator)). `cnn_mnist.py` takes
 MNIST feature data, labels, and
-@{tf.contrib.learn.ModeKeys$model mode} (`TRAIN`, `EVAL`,
-`INFER`) as arguments; configures the CNN; and returns predictions, loss, and a
-training operation:
+@{tf.estimator.ModeKeys$model mode} (`TRAIN`, `EVAL`, `PREDICT`) as arguments;
+configures the CNN; and returns predictions, loss, and a training operation:
 
 ```python
 def cnn_model_fn(features, labels, mode):
   """Model function for CNN."""
   # Input Layer
-  input_layer = tf.reshape(features, [-1, 28, 28, 1])
+  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
 
   # Convolutional Layer #1
   conv1 = tf.layers.conv2d(
@@ -156,39 +152,39 @@ def cnn_model_fn(features, labels, mode):
   pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
   dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
   dropout = tf.layers.dropout(
-      inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
+      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
 
   # Logits Layer
   logits = tf.layers.dense(inputs=dropout, units=10)
 
-  loss = None
-  train_op = None
+  # Generate Predictions (for PREDICT mode)
+  predicted_classes = tf.argmax(input=logits, axis=1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        "classes": predicted_classes,
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+    }
+    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
   # Calculate Loss (for both TRAIN and EVAL modes)
-  if mode != learn.ModeKeys.INFER:
-    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
-    loss = tf.losses.softmax_cross_entropy(
-        onehot_labels=onehot_labels, logits=logits)
+  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
 
   # Configure the Training Op (for TRAIN mode)
-  if mode == learn.ModeKeys.TRAIN:
-    train_op = tf.contrib.layers.optimize_loss(
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
+    train_op = optimizer.minimize(
         loss=loss,
-        global_step=tf.contrib.framework.get_global_step(),
-        learning_rate=0.001,
-        optimizer="SGD")
-
-  # Generate Predictions
-  predictions = {
-      "classes": tf.argmax(
-          input=logits, axis=1),
-      "probabilities": tf.nn.softmax(
-          logits, name="softmax_tensor")
-  }
-
-  # Return a ModelFnOps object
-  return model_fn_lib.ModelFnOps(
-      mode=mode, predictions=predictions, loss=loss, train_op=train_op)
+        global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
+
+  # Add evaluation metrics (for EVAL mode)
+  eval_metric_ops = {
+      "accuracy": tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)}
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 ```
 
 The following sections (with headings corresponding to each code block above)
@@ -222,18 +218,18 @@ To convert our input feature map (`features`) to this shape, we can perform the
 following `reshape` operation:
 
 ```python
-input_layer = tf.reshape(features, [-1, 28, 28, 1])
+input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
 ```
 
 Note that we've indicated `-1` for batch size, which specifies that this
 dimension should be dynamically computed based on the number of input values in
-`features`, holding the size of all other dimensions constant. This allows us to
-treat `batch_size` as a hyperparameter that we can tune. For example, if we feed
-examples into our model in batches of 5, `features` will contain 3,920 values
-(one value for each pixel in each image), and `input_layer` will have a shape of
-`[5, 28, 28, 1]`. Similarly, if we feed examples in batches of 100, `features`
-will contain 78,400 values, and `input_layer` will have a shape of `[100, 28,
-28, 1]`.
+`features["x"]`, holding the size of all other dimensions constant. This allows
+us to treat `batch_size` as a hyperparameter that we can tune. For example, if
+we feed examples into our model in batches of 5, `features["x"]` will contain
+3,920 values (one value for each pixel in each image), and `input_layer` will
+have a shape of `[5, 28, 28, 1]`. Similarly, if we feed examples in batches of
+100, `features["x"]` will contain 78,400 values, and `input_layer` will have a
+shape of `[100, 28, 28, 1]`.
 
 ### Convolutional Layer #1
 
@@ -386,7 +382,7 @@ to our dense layer, using the `dropout` method in `layers`:
 
 ```python
 dropout = tf.layers.dropout(
-    inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
+    inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
 ```
 
 Again, `inputs` specifies the input tensor, which is the output tensor from our
@@ -415,6 +411,55 @@ logits = tf.layers.dense(inputs=dropout, units=10)
 Our final output tensor of the CNN, `logits`, has shape
 <code>[<em>batch_size</em>, 10]</code>.
 
+### Generate Predictions {#generate_predictions}
+
+The logits layer of our model returns our predictions as raw values in a
+<code>[<em>batch_size</em>, 10]</code>-dimensional tensor. Let's convert these
+raw values into two different formats that our model function can return:
+
+*   The **predicted class** for each example: a digit from 0–9.
+*   The **probabilities** for each possible target class for each example: the
+    probability that the example is a 0, is a 1, is a 2, etc.
+
+For a given example, our predicted class is the element in the corresponding row
+of the logits tensor with the highest raw value. We can find the index of this
+element using the @{tf.argmax}
+function:
+
+```python
+tf.argmax(input=logits, axis=1)
+```
+
+The `input` argument specifies the tensor from which to extract maximum
+values—here `logits`. The `axis` argument specifies the axis of the `input`
+tensor along which to find the greatest value. Here, we want to find the largest
+value along the dimension with index of 1, which corresponds to our predictions
+(recall that our logits tensor has shape <code>[<em>batch_size</em>,
+10]</code>).
+
+We can derive probabilities from our logits layer by applying softmax activation
+using @{tf.nn.softmax}:
+
+```python
+tf.nn.softmax(logits, name="softmax_tensor")
+```
+
+> Note: We use the `name` argument to explicitly name this operation
+> `softmax_tensor`, so we can reference it later. (We'll set up logging for the
+> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook).
+
+We compile our predictions in a dict, and return an `EstimatorSpec` object:
+
+```python
+predicted_classes = tf.argmax(input=logits, axis=1)
+if mode == tf.estimator.ModeKeys.PREDICT:
+  predictions = {
+      "classes": predicted_classes,
+      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+  }
+  return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+```
+
 ### Calculate Loss {#calculating-loss}
 
 For both training and evaluation, we need to define a
@@ -426,14 +471,9 @@ as the loss metric. The following code calculates cross entropy when the model
 runs in either `TRAIN` or `EVAL` mode:
 
 ```python
-loss = None
-train_op = None
-
-# Calculate loss for both TRAIN and EVAL modes
-if mode != learn.ModeKeys.INFER:
-  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
-  loss = tf.losses.softmax_cross_entropy(
-      onehot_labels=onehot_labels, logits=logits)
+onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
+loss = tf.losses.softmax_cross_entropy(
+    onehot_labels=onehot_labels, logits=logits)
 ```
 
 Let's take a closer look at what's happening above.
@@ -474,90 +514,42 @@ predictions from our logits layer. `tf.losses.softmax_cross_entropy()` takes
 
 ```python
 loss = tf.losses.softmax_cross_entropy(
-        onehot_labels=onehot_labels, logits=logits)
+    onehot_labels=onehot_labels, logits=logits)
 ```
 
 ### Configure the Training Op
 
 In the previous section, we defined loss for our CNN as the softmax
 cross-entropy of the logits layer and our labels. Let's configure our model to
-optimize this loss value during training, using the
-@{tf.contrib.layers.optimize_loss}
-method in `tf.contrib.layers`. We'll use a learning rate of 0.001 and
+optimize this loss value during training. We'll use a learning rate of 0.001 and
 [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent)
 as the optimization algorithm:
 
 ```python
-# Configure the Training Op (for TRAIN mode)
-if mode == learn.ModeKeys.TRAIN:
-    train_op = tf.contrib.layers.optimize_loss(
-        loss=loss,
-        global_step=tf.contrib.framework.get_global_step(),
-        learning_rate=0.001,
-        optimizer="SGD")
+if mode == tf.estimator.ModeKeys.TRAIN:
+  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
+  train_op = optimizer.minimize(
+      loss=loss,
+      global_step=tf.train.get_global_step())
+  return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
 ```
 
 > Note: For a more in-depth look at configuring training ops for Estimator model
 > functions, see @{$estimators#defining-the-training-op-for-the-model$"Defining
 > the training op for the model"} in the @{$estimators$"Creating Estimations in
-> tf.contrib.learn"} tutorial.
-
-### Generate Predictions {#generate_predictions}
-
-The logits layer of our model returns our predictions as raw values in a
-<code>[<em>batch_size</em>, 10]</code>-dimensional tensor. Let's convert these
-raw values into two different formats that our model function can return:
-
-*   The **predicted class** for each example: a digit from 0–9.
-*   The **probabilities** for each possible target class for each example: the
-    probability that the example is a 0, is a 1, is a 2, etc.
-
-For a given example, our predicted class is the element in the corresponding row
-of the logits tensor with the highest raw value. We can find the index of this
-element using the @{tf.argmax}
-function:
-
-```python
-tf.argmax(input=logits, axis=1)
-```
-
-The `input` argument specifies the tensor from which to extract maximum
-values—here `logits`. The `axis` argument specifies the axis of the `input`
-tensor along which to find the greatest value. Here, we want to find the largest
-value along the dimension with index of 1, which corresponds to our predictions
-(recall that our logits tensor has shape <code>[<em>batch_size</em>,
-10]</code>).
-
-We can derive probabilities from our logits layer by applying softmax activation
-using @{tf.nn.softmax}:
-
-```python
-tf.nn.softmax(logits, name="softmax_tensor")
-```
+> tf.estimator"} tutorial.
 
-> Note: We use the `name` argument to explicitly name this operation
-> `softmax_tensor`, so we can reference it later. (We'll set up logging for the
-> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook).
-
-We compile our predictions in a dict as follows:
-
-```python
-predictions = {
-    "classes": tf.argmax(
-        input=logits, axis=1),
-    "probabilities": tf.nn.softmax(
-        logits, name="softmax_tensor")
-}
-```
+### Add evaluation metrics
 
-Finally, now that we've got our `predictions`, `loss`, and `train_op`, we can
-return them, along with our `mode` argument, in a
-@{tf.contrib.learn.ModelFnOps} object:
+To add accuracy metric in our model, we define `eval_metric_ops` dict in EVAL
+mode as follows:
 
 ```python
-# Return a ModelFnOps object
-return model_fn_lib.ModelFnOps(
-    mode=mode, predictions=predictions, loss=loss, train_op=train_op)
+eval_metric_ops = {
+    "accuracy": tf.metrics.accuracy(
+        labels=labels, predictions=predicted_classes)}
+return tf.estimator.EstimatorSpec(
+    mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 ```
 
 ## Training and Evaluating the CNN MNIST Classifier {#training_and_evaluating_the_cnn_mnist_classifier}
@@ -573,7 +565,7 @@ First, let's load our training and test data. Add a `main()` function to
 ```python
 def main(unused_argv):
   # Load training and eval data
-  mnist = learn.datasets.load_dataset("mnist")
+  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
   train_data = mnist.train.images # Returns np.array
   train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
   eval_data = mnist.test.images # Returns np.array
@@ -596,19 +588,19 @@ to `main()`:
 
 ```python
 # Create the Estimator
-mnist_classifier = learn.Estimator(
-      model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
+mnist_classifier = tf.estimator.Estimator(
+    model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
 ```
 
 The `model_fn` argument specifies the model function to use for training,
-evaluation, and inference; we pass it the `cnn_model_fn` we created in
+evaluation, and prediction; we pass it the `cnn_model_fn` we created in
 ["Building the CNN MNIST Classifier."](#building-the-cnn-mnist-classifier) The
 `model_dir` argument specifies the directory where model data (checkpoints) will
 be saved (here, we specify the temp directory `/tmp/mnist_convnet_model`, but
 feel free to change to another directory of your choice).
 
 > Note: For an in-depth walkthrough of the TensorFlow `Estimator` API, see the
-> tutorial @{$estimators$"Creating Estimators in tf.contrib.learn."}
+> tutorial @{$estimators$"Creating Estimators in tf.estimator."}
 
 ### Set Up a Logging Hook {#set_up_a_logging_hook}
 
@@ -643,65 +635,54 @@ should be logged after every 50 steps of training.
 
 ### Train the Model
 
-Now we're ready to train our model, which we can do by calling `fit()` on
-`mnist_classifier`. Add the following to `main()`:
+Now we're ready to train our model, which we can do by creating `train_input_fn`
+ans calling `train()` on `mnist_classifier`. Add the following to `main()`:
 
 ```python
 # Train the model
-mnist_classifier.fit(
-    x=train_data,
+train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": train_data},
     y=train_labels,
     batch_size=100,
+    num_epochs=None,
+    shuffle=True)
+mnist_classifier.train(
+    input_fn=train_input_fn,
     steps=20000,
-    monitors=[logging_hook])
+    hooks=[logging_hook])
 ```
 
-In the `fit` call, we pass the training feature data and labels to `x` and `y`,
-respectively. We set a `batch_size` of `100` (which means that the model will
-train on minibatches of 100 examples at each step), and `steps` of `20000`
+In the `numpy_input_fn` call, we pass the training feature data and labels to
+`x` (as a dict) and `y`, respectively. We set a `batch_size` of `100` (which
+means that the model will train on minibatches of 100 examples at each step).
+`num_epochs=None` means that the model will train until the specified number of
+steps is reached. We also set `shuffle=True` to shuffle the training data.
+In the `train` call, we set `steps=20000`
 (which means the model will train for 20,000 steps total). We pass our
-`logging_hook` to the `monitors` argument, so that it will be triggered during
+`logging_hook` to the `hooks` argument, so that it will be triggered during
 training.
 
 ### Evaluate the Model
 
 Once training is complete, we want to evaluate our model to determine its
-accuracy on the MNIST test set. To set up the accuracy metric for our model, we
-need to create a metrics dict with a @{tf.contrib.learn.MetricSpec}
-that calculates accuracy. Add the following to `main()`:
-
-```python
-# Configure the accuracy metric for evaluation
-metrics = {
-    "accuracy":
-        learn.MetricSpec(
-            metric_fn=tf.metrics.accuracy, prediction_key="classes"),
-}
-```
-
-We create our `MetricSpec`s with the following two arguments:
-
-*   `metric_fn`. The function that calculates and returns the value of our
-    metric. Here, we can use the predefined `accuracy` function in the
-    @{tf.metrics} module.
-*   `prediction_key`. The key of the tensor that contains the predictions
-    returned by the model function. Here, because we're building a
-    classification model, the prediction key is `"classes"`, which we specified
-    back in ["Generate Predictions."](#generate_predictions)
-
-Now that we've set up our `metrics` dict, we can evaluate the model. Add the
-following code, which performs evaluation and prints the results:
+accuracy on the MNIST test set. We call the `evaluate` method, which evaluates
+the metrics we specified in `eval_metric_ops` argument in the `model_fn`.
+Add the following to `main()`:
 
 ```python
 # Evaluate the model and print results
-eval_results = mnist_classifier.evaluate(
-    x=eval_data, y=eval_labels, metrics=metrics)
+eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"x": eval_data},
+    y=eval_labels,
+    num_epochs=1,
+    shuffle=False)
+eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
 print(eval_results)
 ```
 
-We pass our evaluation feature data and labels to `evaluate()` in the `x` and
-`y` arguments, respectively. The `metrics` argument takes the metrics dict we
-just defined.
+To create `eval_input_fn`, we set `num_epochs=1`, so that the model evaluates
+the metrics over one epoch of data and returns the result. We also set
+`shuffle=False` to iterate through the data sequentially.
 
 ### Run the Model
 
@@ -711,7 +692,7 @@ logic; now let's see the results. Run `cnn_mnist.py`.
 > Note: Training CNNs is quite computationally intensive. Estimated completion
 > time of `cnn_mnist.py` will vary depending on your processor, but will likely
 > be upwards of 1 hour on CPU. To train more quickly, you can decrease the
-> number of `steps` passed to `fit()`, but note that this will affect accuracy.
+> number of `steps` passed to `train()`, but note that this will affect accuracy.
 
 As the model trains, you'll see log output like the following:
 
@@ -738,7 +719,7 @@ Here, we've achieved an accuracy of 97.3% on our test data set.
 To learn more about TensorFlow Estimators and CNNs in TensorFlow, see the
 following resources:
 
-*   @{$estimators$Creating Estimators in tf.contrib.learn}. An
+*   @{$estimators$Creating Estimators in tf.estimator}. An
     introduction to the TensorFlow Estimator API, which walks through
     configuring an Estimator, writing a model function, calculating loss, and
     defining a training op.
diff --git a/tensorflow/docs_src/tutorials/linear.md b/tensorflow/docs_src/tutorials/linear.md
index 45856173fa..4201a8021b 100644
--- a/tensorflow/docs_src/tutorials/linear.md
+++ b/tensorflow/docs_src/tutorials/linear.md
@@ -1,17 +1,17 @@
 # Large-scale Linear Models with TensorFlow
 
-The tf.contrib.learn API provides (among other things) a rich set of tools for working
-with linear models in TensorFlow. This document provides an overview of those
-tools. It explains:
+The tf.estimator API provides (among other things) a rich set of tools for
+working with linear models in TensorFlow. This document provides an overview of
+those tools. It explains:
 
    * what a linear model is.
    * why you might want to use a linear model.
-   * how tf.contrib.learn makes it easy to build linear models in TensorFlow.
-   * how you can use tf.contrib.learn to combine linear models with
+   * how tf.estimator makes it easy to build linear models in TensorFlow.
+   * how you can use tf.estimator to combine linear models with
    deep learning to get the advantages of both.
 
-Read this overview to decide whether the tf.contrib.learn linear model tools might be
-useful to you. Then do the @{$wide$Linear Models tutorial} to
+Read this overview to decide whether the tf.estimator linear model tools might
+be useful to you. Then do the @{$wide$Linear Models tutorial} to
 give it a try. This overview uses code samples from the tutorial, but the
 tutorial walks through the code in greater detail.
 
@@ -51,21 +51,22 @@ Linear models:
    * provide an excellent starting point for learning about machine learning.
    * are widely used in industry.
 
-## How does tf.contrib.learn help you build linear models?
+## How does tf.estimator help you build linear models?
 
 You can build a linear model from scratch in TensorFlow without the help of a
-special API. But tf.contrib.learn provides some tools that make it easier to build
+special API. But tf.estimator provides some tools that make it easier to build
 effective large-scale linear models.
 
 ### Feature columns and transformations
 
 Much of the work of designing a linear model consists of transforming raw data
-into suitable input features. tf.contrib.learn uses the `FeatureColumn` abstraction to
+into suitable input features. Tensorflow uses the `FeatureColumn` abstraction to
 enable these transformations.
 
 A `FeatureColumn` represents a single feature in your data. A `FeatureColumn`
 may represent a quantity like 'height', or it may represent a category like
-'eye_color' where the value is drawn from a set of discrete possibilities like {'blue', 'brown', 'green'}.
+'eye_color' where the value is drawn from a set of discrete possibilities like
+{'blue', 'brown', 'green'}.
 
 In the case of both *continuous features* like 'height' and *categorical
 features* like 'eye_color', a single value in the data might get transformed
@@ -85,10 +86,10 @@ become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called
 "sparse" because they may be very long, with many zeros, when the set of
 possible values is very large (such as all English words).
 
-While you don't need to use sparse columns to use tf.contrib.learn linear models, one
-of the strengths of linear models is their ability to deal with large sparse
-vectors. Sparse features are a primary use case for the tf.contrib.learn linear model
-tools.
+While you don't need to use categorical columns to use tf.estimator linear
+models, one of the strengths of linear models is their ability to deal with
+large sparse vectors. Sparse features are a primary use case for the
+tf.estimator linear model tools.
 
 ##### Encoding sparse columns
 
@@ -96,19 +97,19 @@ tools.
 automatically, with code like this:
 
 ```python
-eye_color = tf.contrib.layers.sparse_column_with_keys(
-  column_name="eye_color", keys=["blue", "brown", "green"])
+eye_color = tf.feature_column.categorical_column_with_vocabulary_list(
+    "eye_color", vocabulary_list=["blue", "brown", "green"])
 ```
 
 where `eye_color` is the name of a column in your source data.
 
 You can also generate `FeatureColumn`s for categorical features for which you
 don't know all possible values. For this case you would use
-`sparse_column_with_hash_bucket()`, which uses a hash function to assign
+`categorical_column_with_hash_bucket()`, which uses a hash function to assign
 indices to feature values.
 
 ```python
-education = tf.contrib.layers.sparse_column_with_hash_bucket(\
+education = tf.feature_column.categorical_column_with_hash_bucket(
     "education", hash_bucket_size=1000)
 ```
 
@@ -130,12 +131,8 @@ a *feature cross*.
 The `crossed_column()` method makes it easy to set up feature crosses:
 
 ```python
-sport = tf.contrib.layers.sparse_column_with_hash_bucket(\
-    "sport", hash_bucket_size=1000)
-city = tf.contrib.layers.sparse_column_with_hash_bucket(\
-    "city", hash_bucket_size=1000)
-sport_x_city = tf.contrib.layers.crossed_column(
-    [sport, city], hash_bucket_size=int(1e4))
+sport_x_city = tf.feature_column.crossed_column(
+    ["sport", "city"], hash_bucket_size=int(1e4))
 ```
 
 #### Continuous columns
@@ -143,11 +140,11 @@ sport_x_city = tf.contrib.layers.crossed_column(
 You can specify a continuous feature like so:
 
 ```python
-age = tf.contrib.layers.real_valued_column("age")
+age = tf.feature_column.numeric_column("age")
 ```
 
 Although, as a single real number, a continuous feature can often be input
-directly into the model, tf.contrib.learn offers useful transformations for this sort
+directly into the model, Tensorflow offers useful transformations for this sort
 of column as well.
 
 ##### Bucketization
@@ -160,7 +157,7 @@ Bucketization divides the range of possible values into subranges called
 buckets:
 
 ```python
-age_buckets = tf.contrib.layers.bucketized_column(
+age_buckets = tf.feature_column.bucketized_column(
     age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
 ```
 
@@ -176,43 +173,45 @@ the data itself. You provide the data through an input function.
 The input function must return a dictionary of tensors. Each key corresponds to
 the name of a `FeatureColumn`. Each key's value is a tensor containing the
 values of that feature for all data instances. See
-@{$input_fn$Building Input Functions with tf.contrib.learn} for a
+@{$input_fn$Building Input Functions with tf.estimator} for a
 more comprehensive look at input functions, and `input_fn` in the
 [linear models tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py)
 for an example implementation of an input function.
 
-The input function is passed to the `fit()` and `evaluate()` calls that
+The input function is passed to the `train()` and `evaluate()` calls that
 initiate training and testing, as described in the next section.
 
 ### Linear estimators
 
-tf.contrib.learn's estimator classes provide a unified training and evaluation harness
+Tensorflow estimator classes provide a unified training and evaluation harness
 for regression and classification models. They take care of the details of the
 training and evaluation loops and allow the user to focus on model inputs and
 architecture.
 
 To build a linear estimator, you can use either the
-`tf.contrib.learn.LinearClassifier` estimator or the
-`tf.contrib.learn.LinearRegressor` estimator, for classification and
+`tf.estimator.LinearClassifier` estimator or the
+`tf.estimator.LinearRegressor` estimator, for classification and
 regression respectively.
 
-As with all tf.contrib.learn estimators, to run the estimator you just:
+As with all tensorflow estimators, to run the estimator you just:
 
    1. Instantiate the estimator class. For the two linear estimator classes,
    you pass a list of `FeatureColumn`s to the constructor.
-   2. Call the estimator's `fit()` method to train it.
+   2. Call the estimator's `train()` method to train it.
    3. Call the estimator's `evaluate()` method to see how it does.
 
 For example:
 
 ```python
-e = tf.contrib.learn.LinearClassifier(feature_columns=[
-  native_country, education, occupation, workclass, marital_status,
-  race, age_buckets, education_x_occupation, age_buckets_x_race_x_occupation],
-  model_dir=YOUR_MODEL_DIRECTORY)
-e.fit(input_fn=input_fn_train, steps=200)
+e = tf.estimator.LinearClassifier(
+    feature_columns=[
+        native_country, education, occupation, workclass, marital_status,
+        race, age_buckets, education_x_occupation,
+        age_buckets_x_race_x_occupation],
+    model_dir=YOUR_MODEL_DIRECTORY)
+e.train(input_fn=input_fn_train, steps=200)
 # Evaluate for one step (one pass through the test data).
-results = e.evaluate(input_fn=input_fn_test, steps=1)
+results = e.evaluate(input_fn=input_fn_test)
 
 # Print the stats for the evaluation.
 for key in sorted(results):
@@ -221,14 +220,14 @@ for key in sorted(results):
 
 ### Wide and deep learning
 
-The tf.contrib.learn API also provides an estimator class that lets you jointly train
-a linear model and a deep neural network. This novel approach combines the
+The tf.estimator API also provides an estimator class that lets you jointly
+train a linear model and a deep neural network. This novel approach combines the
 ability of linear models to "memorize" key features with the generalization
-ability of neural nets. Use `tf.contrib.learn.DNNLinearCombinedClassifier` to
+ability of neural nets. Use `tf.estimator.DNNLinearCombinedClassifier` to
 create this sort of "wide and deep" model:
 
 ```python
-e = tf.contrib.learn.DNNLinearCombinedClassifier(
+e = tf.estimator.DNNLinearCombinedClassifier(
     model_dir=YOUR_MODEL_DIR,
     linear_feature_columns=wide_columns,
     dnn_feature_columns=deep_columns,
diff --git a/tensorflow/docs_src/tutorials/recurrent.md b/tensorflow/docs_src/tutorials/recurrent.md
index 708a9620dd..346b6be06c 100644
--- a/tensorflow/docs_src/tutorials/recurrent.md
+++ b/tensorflow/docs_src/tutorials/recurrent.md
@@ -75,7 +75,9 @@ The basic pseudocode is as follows:
 words_in_dataset = tf.placeholder(tf.float32, [num_batches, batch_size, num_features])
 lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
 # Initial state of the LSTM memory.
-state = tf.zeros([batch_size, lstm.state_size])
+hidden_state = tf.zeros([batch_size, lstm.state_size])
+current_state = tf.zeros([batch_size, lstm.state_size])
+state = hidden_state, current_state
 probabilities = []
 loss = 0.0
 for current_batch_of_words in words_in_dataset:
diff --git a/tensorflow/examples/image_retraining/BUILD b/tensorflow/examples/image_retraining/BUILD
index 394c413b33..c8c136ac14 100644
--- a/tensorflow/examples/image_retraining/BUILD
+++ b/tensorflow/examples/image_retraining/BUILD
@@ -18,6 +18,7 @@ py_binary(
         "//tensorflow:tensorflow_py",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:graph_util",
         "//tensorflow/python:platform",
         "//tensorflow/python:util",
         "//third_party/py/numpy",
@@ -50,10 +51,16 @@ py_test(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":label_image",
         ":retrain",
         "//tensorflow:tensorflow_py",
         "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:graph_util",
+        "//tensorflow/python:platform",
         "//tensorflow/python:platform_test",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:util",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/examples/learn/BUILD b/tensorflow/examples/learn/BUILD
index 1606e1a947..91fc105f27 100644
--- a/tensorflow/examples/learn/BUILD
+++ b/tensorflow/examples/learn/BUILD
@@ -69,16 +69,6 @@ py_binary(
     ],
 )
 
-py_binary(
-    name = "iris_val_based_early_stopping",
-    srcs = ["iris_val_based_early_stopping.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
-    ],
-)
-
 py_binary(
     name = "iris_with_pipeline",
     srcs = ["iris_with_pipeline.py"],
@@ -201,7 +191,6 @@ sh_test(
         ":iris_custom_decay_dnn",
         ":iris_custom_model",
         ":iris_run_config",
-        ":iris_val_based_early_stopping",
         ":iris_with_pipeline",
         ":random_forest_mnist",
         ":resnet",
diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index 3166f37e78..6671d68831 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -19,7 +19,6 @@ processing (`sudo pip install pandas`).
 
 ## Techniques
 
-* [Improving Performance Using Early Stopping with Iris Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_val_based_early_stopping.py)
 * [Using skflow with Pipeline]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_with_pipeline.py)
 * [Deep Neural Network with Customized Decay Function]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
 
diff --git a/tensorflow/examples/learn/boston.py b/tensorflow/examples/learn/boston.py
index 7a7024e001..c9ce508dfd 100644
--- a/tensorflow/examples/learn/boston.py
+++ b/tensorflow/examples/learn/boston.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
 from sklearn import model_selection
@@ -39,22 +40,31 @@ def main(unused_argv):
   x_train = scaler.fit_transform(x_train)
 
   # Build 2 layer fully connected DNN with 10, 10 units respectively.
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  regressor = tf.contrib.learn.DNNRegressor(
+  feature_columns = [
+      tf.feature_column.numeric_column('x', shape=np.array(x_train).shape[1:])]
+  regressor = tf.estimator.DNNRegressor(
       feature_columns=feature_columns, hidden_units=[10, 10])
 
-  # Fit
-  regressor.fit(x_train, y_train, steps=5000, batch_size=1)
-  
-  # Transform
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={'x': x_train}, y=y_train, batch_size=1, num_epochs=None, shuffle=True)
+  regressor.train(input_fn=train_input_fn, steps=2000)
+
+  # Predict.
   x_transformed = scaler.transform(x_test)
-  
-  # Predict and score
-  y_predicted = list(regressor.predict(x_transformed, as_iterable=True))
-  score = metrics.mean_squared_error(y_predicted, y_test)
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={'x': x_transformed}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = regressor.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['predictions'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score_sklearn = metrics.mean_squared_error(y_predicted, y_test)
+  print('MSE (sklearn): {0:f}'.format(score_sklearn))
 
-  print('MSE: {0:f}'.format(score))
+  # Score with tensorflow.
+  scores = regressor.evaluate(input_fn=test_input_fn)
+  print('MSE (tensorflow): {0:f}'.format(scores['average_loss']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index 6a7bfa49b9..4c5893384a 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -49,7 +49,6 @@ test iris
 test iris_custom_decay_dnn
 test iris_custom_model
 test iris_run_config
-test iris_val_based_early_stopping
 test iris_with_pipeline
 test random_forest_mnist
 test resnet
diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py
index ec2aa9b573..33e8d45801 100644
--- a/tensorflow/examples/learn/iris.py
+++ b/tensorflow/examples/learn/iris.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
 from sklearn import model_selection
@@ -24,6 +25,9 @@ from sklearn import model_selection
 import tensorflow as tf
 
 
+X_FEATURE = 'x'  # Name of the input feature.
+
+
 def main(unused_argv):
   # Load dataset.
   iris = datasets.load_iris()
@@ -31,16 +35,31 @@ def main(unused_argv):
       iris.data, iris.target, test_size=0.2, random_state=42)
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  classifier = tf.contrib.learn.DNNClassifier(
+  feature_columns = [
+      tf.feature_column.numeric_column(
+          X_FEATURE, shape=np.array(x_train).shape[1:])]
+  classifier = tf.estimator.DNNClassifier(
       feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
 
-  # Fit and predict.
-  classifier.fit(x_train, y_train, steps=200)
-  predictions = list(classifier.predict(x_test, as_iterable=True))
-  score = metrics.accuracy_score(y_test, predictions)
-  print('Accuracy: {0:f}'.format(score))
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=200)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class_ids'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score = metrics.accuracy_score(y_test, y_predicted)
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/iris_custom_decay_dnn.py b/tensorflow/examples/learn/iris_custom_decay_dnn.py
index 31acbd30cd..072357e51c 100644
--- a/tensorflow/examples/learn/iris_custom_decay_dnn.py
+++ b/tensorflow/examples/learn/iris_custom_decay_dnn.py
@@ -17,36 +17,87 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
-from sklearn.cross_validation import train_test_split
+from sklearn import model_selection
 import tensorflow as tf
 
 
-def optimizer_exp_decay():
-  global_step = tf.contrib.framework.get_or_create_global_step()
-  learning_rate = tf.train.exponential_decay(
-      learning_rate=0.1, global_step=global_step,
-      decay_steps=100, decay_rate=0.001)
-  return tf.train.AdagradOptimizer(learning_rate=learning_rate)
+X_FEATURE = 'x'  # Name of the input feature.
+
+
+def my_model(features, labels, mode):
+  """DNN with three hidden layers."""
+  # Create three fully connected layers respectively of size 10, 20, and 10.
+  net = features[X_FEATURE]
+  for units in [10, 20, 10]:
+    net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
+
+  # Compute logits (1 per class).
+  logits = tf.layers.dense(net, 3, activation=None)
+
+  # Compute predictions.
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        'class': predicted_classes,
+        'prob': tf.nn.softmax(logits)
+    }
+    return tf.estimator.EstimatorSpec(mode, predictions=predictions)
+
+  # Convert the labels to a one-hot tensor of shape (length of features, 3) and
+  # with a on-value of 1 for each one-hot vector of length 3.
+  onehot_labels = tf.one_hot(labels, 3, 1, 0)
+  # Compute loss.
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+
+  # Create training op with exponentially decaying learning rate.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    global_step = tf.train.get_global_step()
+    learning_rate = tf.train.exponential_decay(
+        learning_rate=0.1, global_step=global_step,
+        decay_steps=100, decay_rate=0.001)
+    optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
+    train_op = optimizer.minimize(loss, global_step=global_step)
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  # Compute evaluation metrics.
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = train_test_split(
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
-                                              hidden_units=[10, 20, 10],
-                                              n_classes=3,
-                                              optimizer=optimizer_exp_decay)
-
-  classifier.fit(x_train, y_train, steps=800)
-  predictions = list(classifier.predict(x_test, as_iterable=True))
-  score = metrics.accuracy_score(y_test, predictions)
-  print('Accuracy: {0:f}'.format(score))
+  classifier = tf.estimator.Estimator(model_fn=my_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=1000)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score = metrics.accuracy_score(y_test, y_predicted)
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/iris_custom_model.py b/tensorflow/examples/learn/iris_custom_model.py
index fbc50716c9..471a99ba76 100644
--- a/tensorflow/examples/learn/iris_custom_model.py
+++ b/tensorflow/examples/learn/iris_custom_model.py
@@ -16,62 +16,85 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-from sklearn import cross_validation
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
+from sklearn import model_selection
 import tensorflow as tf
 
-layers = tf.contrib.layers
-learn = tf.contrib.learn
 
+X_FEATURE = 'x'  # Name of the input feature.
 
-def my_model(features, target):
-  """DNN with three hidden layers, and dropout of 0.1 probability."""
-  # Convert the target to a one-hot tensor of shape (length of features, 3) and
-  # with a on-value of 1 for each one-hot vector of length 3.
-  target = tf.one_hot(target, 3, 1, 0)
 
+def my_model(features, labels, mode):
+  """DNN with three hidden layers, and dropout of 0.1 probability."""
   # Create three fully connected layers respectively of size 10, 20, and 10 with
   # each layer having a dropout probability of 0.1.
-  normalizer_fn = layers.dropout
-  normalizer_params = {'keep_prob': 0.9}
-  features = layers.stack(
-      features,
-      layers.fully_connected, [10, 20, 10],
-      normalizer_fn=normalizer_fn,
-      normalizer_params=normalizer_params)
-
-  # Compute logits (1 per class) and compute loss.
-  logits = layers.fully_connected(features, 3, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-
-  # Create a tensor for training op.
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adagrad',
-      learning_rate=0.1)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  net = features[X_FEATURE]
+  for units in [10, 20, 10]:
+    net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
+    net = tf.layers.dropout(net, rate=0.1)
+
+  # Compute logits (1 per class).
+  logits = tf.layers.dense(net, 3, activation=None)
+
+  # Compute predictions.
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        'class': predicted_classes,
+        'prob': tf.nn.softmax(logits)
+    }
+    return tf.estimator.EstimatorSpec(mode, predictions=predictions)
+
+  # Convert the labels to a one-hot tensor of shape (length of features, 3) and
+  # with a on-value of 1 for each one-hot vector of length 3.
+  onehot_labels = tf.one_hot(labels, 3, 1, 0)
+  # Compute loss.
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+
+  # Create training op.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  # Compute evaluation metrics.
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = cross_validation.train_test_split(
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
-  classifier = learn.Estimator(model_fn=my_model)
-  classifier.fit(x_train, y_train, steps=1000)
+  classifier = tf.estimator.Estimator(model_fn=my_model)
 
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=1000)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/iris_run_config.py b/tensorflow/examples/learn/iris_run_config.py
index b7b8b5cd01..286c824e30 100644
--- a/tensorflow/examples/learn/iris_run_config.py
+++ b/tensorflow/examples/learn/iris_run_config.py
@@ -18,37 +18,53 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from sklearn import cross_validation
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
+from sklearn import model_selection
 import tensorflow as tf
 
 
+X_FEATURE = 'x'  # Name of the input feature.
+
+
 def main(unused_argv):
   # Load dataset.
   iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = cross_validation.train_test_split(
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
   # You can define you configurations by providing a RunConfig object to
-  # estimator to control session configurations, e.g. num_cores
-  # and gpu_memory_fraction
-  run_config = tf.contrib.learn.estimators.RunConfig(
-      num_cores=3, gpu_memory_fraction=0.6)
+  # estimator to control session configurations, e.g. tf_random_seed.
+  run_config = tf.estimator.RunConfig().replace(tf_random_seed=1)
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
-  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
-      x_train)
-  classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
-                                              hidden_units=[10, 20, 10],
-                                              n_classes=3,
-                                              config=run_config)
-
-  # Fit and predict.
-  classifier.fit(x_train, y_train, steps=200)
-  predictions = list(classifier.predict(x_test, as_iterable=True))
-  score = metrics.accuracy_score(y_test, predictions)
-  print('Accuracy: {0:f}'.format(score))
+  feature_columns = [
+      tf.feature_column.numeric_column(
+          X_FEATURE, shape=np.array(x_train).shape[1:])]
+  classifier = tf.estimator.DNNClassifier(
+      feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
+      config=run_config)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=200)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class_ids'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score = metrics.accuracy_score(y_test, y_predicted)
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/iris_val_based_early_stopping.py b/tensorflow/examples/learn/iris_val_based_early_stopping.py
deleted file mode 100644
index 991d1831d7..0000000000
--- a/tensorflow/examples/learn/iris_val_based_early_stopping.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-"""Example of DNNClassifier for Iris plant dataset, with early stopping."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import shutil
-
-from sklearn import datasets
-from sklearn import metrics
-from sklearn.cross_validation import train_test_split
-import tensorflow as tf
-
-learn = tf.contrib.learn
-
-
-def clean_folder(folder):
-  """Cleans the given folder if it exists."""
-  try:
-    shutil.rmtree(folder)
-  except OSError:
-    pass
-
-
-def main(unused_argv):
-  iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = train_test_split(
-      iris.data, iris.target, test_size=0.2, random_state=42)
-
-  x_train, x_val, y_train, y_val = train_test_split(
-      x_train, y_train, test_size=0.2, random_state=42)
-  val_monitor = learn.monitors.ValidationMonitor(
-      x_val, y_val, early_stopping_rounds=200)
-
-  model_dir = '/tmp/iris_model'
-  clean_folder(model_dir)
-
-  # classifier with early stopping on training data
-  classifier1 = learn.DNNClassifier(
-      feature_columns=learn.infer_real_valued_columns_from_input(x_train),
-      hidden_units=[10, 20, 10],
-      n_classes=3,
-      model_dir=model_dir)
-  classifier1.fit(x=x_train, y=y_train, steps=2000)
-  predictions1 = list(classifier1.predict(x_test, as_iterable=True))
-  score1 = metrics.accuracy_score(y_test, predictions1)
-
-  model_dir = '/tmp/iris_model_val'
-  clean_folder(model_dir)
-
-  # classifier with early stopping on validation data, save frequently for
-  # monitor to pick up new checkpoints.
-  classifier2 = learn.DNNClassifier(
-      feature_columns=learn.infer_real_valued_columns_from_input(x_train),
-      hidden_units=[10, 20, 10],
-      n_classes=3,
-      model_dir=model_dir,
-      config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))
-  classifier2.fit(x=x_train, y=y_train, steps=2000, monitors=[val_monitor])
-  predictions2 = list(classifier2.predict(x_test, as_iterable=True))
-  score2 = metrics.accuracy_score(y_test, predictions2)
-
-  # In many applications, the score is improved by using early stopping
-  print('score1: ', score1)
-  print('score2: ', score2)
-  print('score2 > score1: ', score2 > score1)
-
-
-if __name__ == '__main__':
-  tf.app.run()
diff --git a/tensorflow/examples/learn/text_classification.py b/tensorflow/examples/learn/text_classification.py
index 7e10014c39..21d98e9ea2 100644
--- a/tensorflow/examples/learn/text_classification.py
+++ b/tensorflow/examples/learn/text_classification.py
@@ -24,43 +24,67 @@ import numpy as np
 import pandas
 from sklearn import metrics
 import tensorflow as tf
-from tensorflow.contrib.layers.python.layers import encoders
-
-learn = tf.contrib.learn
 
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 10
 EMBEDDING_SIZE = 50
 n_words = 0
+MAX_LABEL = 15
+WORDS_FEATURE = 'words'  # Name of the input words feature.
+
+
+def estimator_spec_for_softmax_classification(
+    logits, labels, mode):
+  """Returns EstimatorSpec instance for softmax classification."""
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
+
+
+def bag_of_words_model(features, labels, mode):
+  """A bag-of-words model. Note it disregards the word order in the text."""
+  bow_column = tf.feature_column.categorical_column_with_identity(
+      WORDS_FEATURE, num_buckets=n_words)
+  bow_embedding_column = tf.feature_column.embedding_column(
+      bow_column, dimension=EMBEDDING_SIZE)
+  bow = tf.feature_column.input_layer(
+      features,
+      feature_columns=[bow_embedding_column])
+  logits = tf.layers.dense(bow, MAX_LABEL, activation=None)
 
+  return estimator_spec_for_softmax_classification(
+      logits=logits, labels=labels, mode=mode)
 
-def bag_of_words_model(features, target):
-  """A bag-of-words model. Note it disregards the word order in the text."""
-  target = tf.one_hot(target, 15, 1, 0)
-  features = encoders.bow_encoder(
-      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
-  logits = tf.contrib.layers.fully_connected(features, 15, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
-
-
-def rnn_model(features, target):
+
+def rnn_model(features, labels, mode):
   """RNN model to predict from sequence of words to a class."""
   # Convert indexes of words into embeddings.
   # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
   # maps word indexes of the sequence into [batch_size, sequence_length,
   # EMBEDDING_SIZE].
   word_vectors = tf.contrib.layers.embed_sequence(
-      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE, scope='words')
+      features[WORDS_FEATURE], vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
 
   # Split into list of embedding per word, while removing doc length dim.
   # word_list results to be a list of tensors [batch_size, EMBEDDING_SIZE].
@@ -74,29 +98,17 @@ def rnn_model(features, target):
   _, encoding = tf.contrib.rnn.static_rnn(cell, word_list, dtype=tf.float32)
 
   # Given encoding of RNN, take encoding of last step (e.g hidden size of the
-  # neural network of last step) and pass it as features for logistic
-  # regression over output classes.
-  target = tf.one_hot(target, 15, 1, 0)
-  logits = tf.contrib.layers.fully_connected(encoding, 15, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
-
-  # Create a training op.
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  # neural network of last step) and pass it as features for softmax
+  # classification over output classes.
+  logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)
+  return estimator_spec_for_softmax_classification(
+      logits=logits, labels=labels, mode=mode)
 
 
 def main(unused_argv):
   global n_words
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -104,14 +116,15 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
-  
+  vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(
+      MAX_DOCUMENT_LENGTH)
+
   x_transform_train = vocab_processor.fit_transform(x_train)
   x_transform_test = vocab_processor.transform(x_test)
-  
+
   x_train = np.array(list(x_transform_train))
   x_test = np.array(list(x_transform_test))
-  
+
   n_words = len(vocab_processor.vocabulary_)
   print('Total words: %d' % n_words)
 
@@ -119,17 +132,41 @@ def main(unused_argv):
   # Switch between rnn_model and bag_of_words_model to test different models.
   model_fn = rnn_model
   if FLAGS.bow_model:
+    # Subtract 1 because VocabularyProcessor outputs a word-id matrix where word
+    # ids start from 1 and 0 means 'no word'. But
+    # categorical_column_with_identity assumes 0-based count and uses -1 for
+    # missing word.
+    x_train -= 1
+    x_test -= 1
     model_fn = bag_of_words_model
-  classifier = learn.Estimator(model_fn=model_fn)
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  classifier = tf.estimator.Estimator(model_fn=model_fn)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_train},
+      y=y_train,
+      batch_size=len(x_train),
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/text_classification_character_cnn.py b/tensorflow/examples/learn/text_classification_character_cnn.py
index 5ad53acf9f..5f7c8e7371 100644
--- a/tensorflow/examples/learn/text_classification_character_cnn.py
+++ b/tensorflow/examples/learn/text_classification_character_cnn.py
@@ -11,8 +11,7 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-"""This is an example of using convolutional networks over characters for
-   DBpedia dataset to predict class from description of an entity.
+"""Example of using convolutional networks over characters for DBpedia dataset.
 
 This model is similar to one described in this paper:
    "Character-level Convolutional Networks for Text Classification"
@@ -34,8 +33,6 @@ import pandas
 from sklearn import metrics
 import tensorflow as tf
 
-learn = tf.contrib.learn
-
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 100
@@ -44,53 +41,73 @@ FILTER_SHAPE1 = [20, 256]
 FILTER_SHAPE2 = [20, N_FILTERS]
 POOLING_WINDOW = 4
 POOLING_STRIDE = 2
+MAX_LABEL = 15
+CHARS_FEATURE = 'chars'  # Name of the input character feature.
 
 
-def char_cnn_model(features, target):
+def char_cnn_model(features, labels, mode):
   """Character level convolutional neural network model to predict classes."""
-  target = tf.one_hot(target, 15, 1, 0)
-  byte_list = tf.reshape(
-      tf.one_hot(features, 256), [-1, MAX_DOCUMENT_LENGTH, 256, 1])
+  features_onehot = tf.one_hot(features[CHARS_FEATURE], 256)
+  input_layer = tf.reshape(
+      features_onehot, [-1, MAX_DOCUMENT_LENGTH, 256, 1])
   with tf.variable_scope('CNN_Layer1'):
     # Apply Convolution filtering on input sequence.
-    conv1 = tf.contrib.layers.convolution2d(
-        byte_list, N_FILTERS, FILTER_SHAPE1, padding='VALID')
-    # Add a ReLU for non linearity.
-    conv1 = tf.nn.relu(conv1)
+    conv1 = tf.layers.conv2d(
+        input_layer,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE1,
+        padding='VALID',
+        # Add a ReLU for non linearity.
+        activation=tf.nn.relu)
     # Max pooling across output of Convolution+Relu.
-    pool1 = tf.nn.max_pool(
+    pool1 = tf.layers.max_pooling2d(
         conv1,
-        ksize=[1, POOLING_WINDOW, 1, 1],
-        strides=[1, POOLING_STRIDE, 1, 1],
+        pool_size=POOLING_WINDOW,
+        strides=POOLING_STRIDE,
         padding='SAME')
     # Transpose matrix so that n_filters from convolution becomes width.
     pool1 = tf.transpose(pool1, [0, 1, 3, 2])
   with tf.variable_scope('CNN_Layer2'):
     # Second level of convolution filtering.
-    conv2 = tf.contrib.layers.convolution2d(
-        pool1, N_FILTERS, FILTER_SHAPE2, padding='VALID')
+    conv2 = tf.layers.conv2d(
+        pool1,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE2,
+        padding='VALID')
     # Max across each filter to get useful features for classification.
     pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])
 
   # Apply regular WX + B and classification.
-  logits = tf.contrib.layers.fully_connected(pool2, 15, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  logits = tf.layers.dense(pool2, MAX_LABEL, activation=None)
+
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data, size='large')
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -98,21 +115,43 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
+  char_processor = tf.contrib.learn.preprocessing.ByteProcessor(
+      MAX_DOCUMENT_LENGTH)
   x_train = np.array(list(char_processor.fit_transform(x_train)))
   x_test = np.array(list(char_processor.transform(x_test)))
 
+  x_train = x_train.reshape([-1, MAX_DOCUMENT_LENGTH, 1, 1])
+  x_test = x_test.reshape([-1, MAX_DOCUMENT_LENGTH, 1, 1])
+
   # Build model
-  classifier = learn.Estimator(model_fn=char_cnn_model)
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  classifier = tf.estimator.Estimator(model_fn=char_cnn_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_train},
+      y=y_train,
+      batch_size=len(x_train),
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/text_classification_character_rnn.py b/tensorflow/examples/learn/text_classification_character_rnn.py
index 1cb2cd2f88..1fc9388a1a 100644
--- a/tensorflow/examples/learn/text_classification_character_rnn.py
+++ b/tensorflow/examples/learn/text_classification_character_rnn.py
@@ -11,7 +11,7 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-"""This is an example of using recurrent neural networks over characters for DBpedia dataset to predict class from description of an entity.
+"""Example of recurrent neural networks over characters for DBpedia dataset.
 
 This model is similar to one described in this paper:
    "Character-level Convolutional Networks for Text Classification"
@@ -33,41 +33,52 @@ import pandas
 from sklearn import metrics
 import tensorflow as tf
 
-learn = tf.contrib.learn
-
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 100
 HIDDEN_SIZE = 20
+MAX_LABEL = 15
+CHARS_FEATURE = 'chars'  # Name of the input character feature.
 
 
-def char_rnn_model(features, target):
+def char_rnn_model(features, labels, mode):
   """Character level recurrent neural network model to predict classes."""
-  target = tf.one_hot(target, 15, 1, 0)
-  byte_list = tf.one_hot(features, 256, 1, 0)
-  byte_list = tf.unstack(byte_list, axis=1)
+  byte_vectors = tf.one_hot(features[CHARS_FEATURE], 256, 1., 0.)
+  byte_list = tf.unstack(byte_vectors, axis=1)
 
   cell = tf.contrib.rnn.GRUCell(HIDDEN_SIZE)
   _, encoding = tf.contrib.rnn.static_rnn(cell, byte_list, dtype=tf.float32)
 
-  logits = tf.contrib.layers.fully_connected(encoding, 15, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
-
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)
+
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -75,21 +86,40 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
+  char_processor = tf.contrib.learn.preprocessing.ByteProcessor(
+      MAX_DOCUMENT_LENGTH)
   x_train = np.array(list(char_processor.fit_transform(x_train)))
   x_test = np.array(list(char_processor.transform(x_test)))
 
   # Build model
-  classifier = learn.Estimator(model_fn=char_rnn_model)
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  classifier = tf.estimator.Estimator(model_fn=char_rnn_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_train},
+      y=y_train,
+      batch_size=len(x_train),
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={CHARS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/text_classification_cnn.py b/tensorflow/examples/learn/text_classification_cnn.py
index 468a96b58f..0ee2405c8b 100644
--- a/tensorflow/examples/learn/text_classification_cnn.py
+++ b/tensorflow/examples/learn/text_classification_cnn.py
@@ -25,8 +25,6 @@ import pandas
 from sklearn import metrics
 import tensorflow as tf
 
-learn = tf.contrib.learn
-
 FLAGS = None
 
 MAX_DOCUMENT_LENGTH = 100
@@ -38,59 +36,78 @@ FILTER_SHAPE2 = [WINDOW_SIZE, N_FILTERS]
 POOLING_WINDOW = 4
 POOLING_STRIDE = 2
 n_words = 0
+MAX_LABEL = 15
+WORDS_FEATURE = 'words'  # Name of the input words feature.
 
 
-def cnn_model(features, target):
+def cnn_model(features, labels, mode):
   """2 layer ConvNet to predict from sequence of words to a class."""
   # Convert indexes of words into embeddings.
   # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
   # maps word indexes of the sequence into [batch_size, sequence_length,
   # EMBEDDING_SIZE].
-  target = tf.one_hot(target, 15, 1, 0)
   word_vectors = tf.contrib.layers.embed_sequence(
-      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE, scope='words')
+      features[WORDS_FEATURE], vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
   word_vectors = tf.expand_dims(word_vectors, 3)
   with tf.variable_scope('CNN_Layer1'):
     # Apply Convolution filtering on input sequence.
-    conv1 = tf.contrib.layers.convolution2d(
-        word_vectors, N_FILTERS, FILTER_SHAPE1, padding='VALID')
-    # Add a RELU for non linearity.
-    conv1 = tf.nn.relu(conv1)
+    conv1 = tf.layers.conv2d(
+        word_vectors,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE1,
+        padding='VALID',
+        # Add a ReLU for non linearity.
+        activation=tf.nn.relu)
     # Max pooling across output of Convolution+Relu.
-    pool1 = tf.nn.max_pool(
+    pool1 = tf.layers.max_pooling2d(
         conv1,
-        ksize=[1, POOLING_WINDOW, 1, 1],
-        strides=[1, POOLING_STRIDE, 1, 1],
+        pool_size=POOLING_WINDOW,
+        strides=POOLING_STRIDE,
         padding='SAME')
     # Transpose matrix so that n_filters from convolution becomes width.
     pool1 = tf.transpose(pool1, [0, 1, 3, 2])
   with tf.variable_scope('CNN_Layer2'):
     # Second level of convolution filtering.
-    conv2 = tf.contrib.layers.convolution2d(
-        pool1, N_FILTERS, FILTER_SHAPE2, padding='VALID')
+    conv2 = tf.layers.conv2d(
+        pool1,
+        filters=N_FILTERS,
+        kernel_size=FILTER_SHAPE2,
+        padding='VALID')
     # Max across each filter to get useful features for classification.
     pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])
 
   # Apply regular WX + B and classification.
-  logits = tf.contrib.layers.fully_connected(pool2, 15, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adam',
-      learning_rate=0.01)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+  logits = tf.layers.dense(pool2, MAX_LABEL, activation=None)
+
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={
+            'class': predicted_classes,
+            'prob': tf.nn.softmax(logits)
+        })
+
+  onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   global n_words
   # Prepare training and testing data
-  dbpedia = learn.datasets.load_dataset(
+  dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
   x_train = pandas.DataFrame(dbpedia.train.data)[1]
   y_train = pandas.Series(dbpedia.train.target)
@@ -98,20 +115,42 @@ def main(unused_argv):
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
-  vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
+  vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(
+      MAX_DOCUMENT_LENGTH)
   x_train = np.array(list(vocab_processor.fit_transform(x_train)))
   x_test = np.array(list(vocab_processor.transform(x_test)))
   n_words = len(vocab_processor.vocabulary_)
   print('Total words: %d' % n_words)
 
   # Build model
-  classifier = learn.SKCompat(learn.Estimator(model_fn=cnn_model))
-
-  # Train and predict
-  classifier.fit(x_train, y_train, steps=100)
-  y_predicted = classifier.predict(x_test)['class']
+  classifier = tf.estimator.Estimator(model_fn=cnn_model)
+
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_train},
+      y=y_train,
+      batch_size=len(x_train),
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={WORDS_FEATURE: x_test},
+      y=y_test,
+      num_epochs=1,
+      shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
index a0c6df821a..6a3ae50f0b 100644
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ b/tensorflow/examples/learn/wide_n_deep_tutorial.py
@@ -21,21 +21,89 @@ import argparse
 import sys
 import tempfile
 
-from six.moves import urllib
-
 import pandas as pd
+from six.moves import urllib
 import tensorflow as tf
 
 
-COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
-           "marital_status", "occupation", "relationship", "race", "gender",
-           "capital_gain", "capital_loss", "hours_per_week", "native_country",
-           "income_bracket"]
-LABEL_COLUMN = "label"
-CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation",
-                       "relationship", "race", "gender", "native_country"]
-CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss",
-                      "hours_per_week"]
+CSV_COLUMNS = [
+    "age", "workclass", "fnlwgt", "education", "education_num",
+    "marital_status", "occupation", "relationship", "race", "gender",
+    "capital_gain", "capital_loss", "hours_per_week", "native_country",
+    "income_bracket"
+]
+
+gender = tf.feature_column.categorical_column_with_vocabulary_list(
+    "gender", [" Female", " Male"])
+education = tf.feature_column.categorical_column_with_vocabulary_list(
+    "education", [
+        "Bachelors", "HS-grad", "11th", "Masters", "9th",
+        "Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th",
+        "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
+        "Preschool", "12th"
+    ])
+tf.feature_column.categorical_column_with_vocabulary_list(
+    "marital_status", [
+        "Married-civ-spouse", "Divorced", "Married-spouse-absent",
+        "Never-married", "Separated", "Married-AF-spouse", "Widowed"
+    ])
+relationship = tf.feature_column.categorical_column_with_vocabulary_list(
+    "relationship", [
+        "Husband", "Not-in-family", "Wife", "Own-child", "Unmarried",
+        "Other-relative"
+    ])
+workclass = tf.feature_column.categorical_column_with_vocabulary_list(
+    "workclass", [
+        "Self-emp-not-inc", "Private", "State-gov", "Federal-gov",
+        "Local-gov", "?", "Self-emp-inc", "Without-pay", " Never-worked"
+    ])
+
+# To show an example of hashing:
+occupation = tf.feature_column.categorical_column_with_hash_bucket(
+    "occupation", hash_bucket_size=1000)
+native_country = tf.feature_column.categorical_column_with_hash_bucket(
+    "native_country", hash_bucket_size=1000)
+
+# Continuous base columns.
+age = tf.feature_column.numeric_column("age")
+education_num = tf.feature_column.numeric_column("education_num")
+capital_gain = tf.feature_column.numeric_column("capital_gain")
+capital_loss = tf.feature_column.numeric_column("capital_loss")
+hours_per_week = tf.feature_column.numeric_column("hours_per_week")
+
+# Transformations.
+age_buckets = tf.feature_column.bucketized_column(
+    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
+
+# Wide columns and deep columns.
+base_columns = [
+    gender, native_country, education, occupation, workclass, relationship,
+    age_buckets,
+]
+
+crossed_columns = [
+    tf.feature_column.crossed_column(
+        ["education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        [age_buckets, "education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        ["native_country", "occupation"], hash_bucket_size=1000)
+]
+
+deep_columns = [
+    tf.feature_column.indicator_column(workclass),
+    tf.feature_column.indicator_column(education),
+    tf.feature_column.indicator_column(gender),
+    tf.feature_column.indicator_column(relationship),
+    # To show an example of embedding
+    tf.feature_column.embedding_column(native_country, dimension=8),
+    tf.feature_column.embedding_column(occupation, dimension=8),
+    age,
+    education_num,
+    capital_gain,
+    capital_loss,
+    hours_per_week,
+]
 
 
 def maybe_download(train_data, test_data):
@@ -44,7 +112,9 @@ def maybe_download(train_data, test_data):
     train_file_name = train_data
   else:
     train_file = tempfile.NamedTemporaryFile(delete=False)
-    urllib.request.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name)  # pylint: disable=line-too-long
+    urllib.request.urlretrieve(
+        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
+        train_file.name)  # pylint: disable=line-too-long
     train_file_name = train_file.name
     train_file.close()
     print("Training data is downloaded to %s" % train_file_name)
@@ -53,138 +123,72 @@ def maybe_download(train_data, test_data):
     test_file_name = test_data
   else:
     test_file = tempfile.NamedTemporaryFile(delete=False)
-    urllib.request.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name)  # pylint: disable=line-too-long
+    urllib.request.urlretrieve(
+        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",
+        test_file.name)  # pylint: disable=line-too-long
     test_file_name = test_file.name
     test_file.close()
-    print("Test data is downloaded to %s" % test_file_name)
+    print("Test data is downloaded to %s"% test_file_name)
 
   return train_file_name, test_file_name
 
 
 def build_estimator(model_dir, model_type):
   """Build an estimator."""
-  # Sparse base columns.
-  gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender",
-                                                     keys=["female", "male"])
-  education = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "education", hash_bucket_size=1000)
-  relationship = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "relationship", hash_bucket_size=100)
-  workclass = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "workclass", hash_bucket_size=100)
-  occupation = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "occupation", hash_bucket_size=1000)
-  native_country = tf.contrib.layers.sparse_column_with_hash_bucket(
-      "native_country", hash_bucket_size=1000)
-
-  # Continuous base columns.
-  age = tf.contrib.layers.real_valued_column("age")
-  education_num = tf.contrib.layers.real_valued_column("education_num")
-  capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
-  capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
-  hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")
-
-  # Transformations.
-  age_buckets = tf.contrib.layers.bucketized_column(age,
-                                                    boundaries=[
-                                                        18, 25, 30, 35, 40, 45,
-                                                        50, 55, 60, 65
-                                                    ])
-
-  # Wide columns and deep columns.
-  wide_columns = [gender, native_country, education, occupation, workclass,
-                  relationship, age_buckets,
-                  tf.contrib.layers.crossed_column([education, occupation],
-                                                   hash_bucket_size=int(1e4)),
-                  tf.contrib.layers.crossed_column(
-                      [age_buckets, education, occupation],
-                      hash_bucket_size=int(1e6)),
-                  tf.contrib.layers.crossed_column([native_country, occupation],
-                                                   hash_bucket_size=int(1e4))]
-  deep_columns = [
-      tf.contrib.layers.embedding_column(workclass, dimension=8),
-      tf.contrib.layers.embedding_column(education, dimension=8),
-      tf.contrib.layers.embedding_column(gender, dimension=8),
-      tf.contrib.layers.embedding_column(relationship, dimension=8),
-      tf.contrib.layers.embedding_column(native_country,
-                                         dimension=8),
-      tf.contrib.layers.embedding_column(occupation, dimension=8),
-      age,
-      education_num,
-      capital_gain,
-      capital_loss,
-      hours_per_week,
-  ]
+  # Categorical base columns.
 
   if model_type == "wide":
-    m = tf.contrib.learn.LinearClassifier(model_dir=model_dir,
-                                          feature_columns=wide_columns)
+    m = tf.estimator.LinearClassifier(
+        model_dir=model_dir, feature_columns=base_columns + crossed_columns)
   elif model_type == "deep":
-    m = tf.contrib.learn.DNNClassifier(model_dir=model_dir,
-                                       feature_columns=deep_columns,
-                                       hidden_units=[100, 50])
+    m = tf.estimator.DNNClassifier(
+        model_dir=model_dir,
+        feature_columns=deep_columns,
+        hidden_units=[100, 50])
   else:
-    m = tf.contrib.learn.DNNLinearCombinedClassifier(
+    m = tf.estimator.DNNLinearCombinedClassifier(
         model_dir=model_dir,
-        linear_feature_columns=wide_columns,
+        linear_feature_columns=crossed_columns,
         dnn_feature_columns=deep_columns,
-        dnn_hidden_units=[100, 50],
-        fix_global_step_increment_bug=True)
+        dnn_hidden_units=[100, 50])
   return m
 
 
-def input_fn(df):
+def input_fn(data_file, num_epochs, shuffle):
   """Input builder function."""
-  # Creates a dictionary mapping from each continuous feature column name (k) to
-  # the values of that column stored in a constant Tensor.
-  continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS}
-  # Creates a dictionary mapping from each categorical feature column name (k)
-  # to the values of that column stored in a tf.SparseTensor.
-  categorical_cols = {
-      k: tf.SparseTensor(
-          indices=[[i, 0] for i in range(df[k].size)],
-          values=df[k].values,
-          dense_shape=[df[k].size, 1])
-      for k in CATEGORICAL_COLUMNS}
-  # Merges the two dictionaries into one.
-  feature_cols = dict(continuous_cols)
-  feature_cols.update(categorical_cols)
-  # Converts the label column into a constant Tensor.
-  label = tf.constant(df[LABEL_COLUMN].values)
-  # Returns the feature columns and the label.
-  return feature_cols, label
+  df_data = pd.read_csv(
+      tf.gfile.Open(data_file),
+      names=CSV_COLUMNS,
+      skipinitialspace=True,
+      engine="python",
+      skiprows=1)
+  # remove NaN elements
+  df_data = df_data.dropna(how="any", axis=0)
+  labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int)
+  return tf.estimator.inputs.pandas_input_fn(
+      x=df_data,
+      y=labels,
+      batch_size=100,
+      num_epochs=num_epochs,
+      shuffle=shuffle,
+      num_threads=5)
 
 
 def train_and_eval(model_dir, model_type, train_steps, train_data, test_data):
   """Train and evaluate the model."""
   train_file_name, test_file_name = maybe_download(train_data, test_data)
-  df_train = pd.read_csv(
-      tf.gfile.Open(train_file_name),
-      names=COLUMNS,
-      skipinitialspace=True,
-      engine="python")
-  df_test = pd.read_csv(
-      tf.gfile.Open(test_file_name),
-      names=COLUMNS,
-      skipinitialspace=True,
-      skiprows=1,
-      engine="python")
-
-  # remove NaN elements
-  df_train = df_train.dropna(how='any', axis=0)
-  df_test = df_test.dropna(how='any', axis=0)
-
-  df_train[LABEL_COLUMN] = (
-      df_train["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
-  df_test[LABEL_COLUMN] = (
-      df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
-
   model_dir = tempfile.mkdtemp() if not model_dir else model_dir
-  print("model directory = %s" % model_dir)
 
   m = build_estimator(model_dir, model_type)
-  m.fit(input_fn=lambda: input_fn(df_train), steps=train_steps)
-  results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)
+  # set num_epochs to None to get infinite stream of data.
+  m.train(
+      input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True),
+      steps=train_steps)
+  # set steps to None to run evaluation until all data consumed.
+  results = m.evaluate(
+      input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False),
+      steps=None)
+  print("model directory = %s" % model_dir)
   for key in sorted(results):
     print("%s: %s" % (key, results[key]))
 
@@ -215,7 +219,7 @@ if __name__ == "__main__":
   parser.add_argument(
       "--train_steps",
       type=int,
-      default=200,
+      default=2000,
       help="Number of training steps."
   )
   parser.add_argument(
diff --git a/tensorflow/examples/multibox_detector/BUILD b/tensorflow/examples/multibox_detector/BUILD
index 5d0c769007..24c7040ac1 100644
--- a/tensorflow/examples/multibox_detector/BUILD
+++ b/tensorflow/examples/multibox_detector/BUILD
@@ -21,7 +21,10 @@ cc_binary(
     linkopts = ["-lm"],
     deps = [
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
         "//tensorflow/core:tensorflow",
     ],
 )
diff --git a/tensorflow/examples/saved_model/BUILD b/tensorflow/examples/saved_model/BUILD
index 844e99dcd4..72066eabc3 100644
--- a/tensorflow/examples/saved_model/BUILD
+++ b/tensorflow/examples/saved_model/BUILD
@@ -32,5 +32,7 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/python:lib",
+        "//tensorflow/python/saved_model:main_op",
     ],
 )
diff --git a/tensorflow/examples/tutorials/estimators/BUILD b/tensorflow/examples/tutorials/estimators/BUILD
index 0ff606831c..ecbc1a431d 100644
--- a/tensorflow/examples/tutorials/estimators/BUILD
+++ b/tensorflow/examples/tutorials/estimators/BUILD
@@ -16,6 +16,7 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/examples/tutorials/layers/BUILD b/tensorflow/examples/tutorials/layers/BUILD
index ba9e1f60e1..7621beec48 100644
--- a/tensorflow/examples/tutorials/layers/BUILD
+++ b/tensorflow/examples/tutorials/layers/BUILD
@@ -16,6 +16,8 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/contrib/learn",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/examples/tutorials/layers/cnn_mnist.py b/tensorflow/examples/tutorials/layers/cnn_mnist.py
index aa92b1758a..f92277dac7 100644
--- a/tensorflow/examples/tutorials/layers/cnn_mnist.py
+++ b/tensorflow/examples/tutorials/layers/cnn_mnist.py
@@ -20,9 +20,6 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
-from tensorflow.contrib import learn
-from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
-
 tf.logging.set_verbosity(tf.logging.INFO)
 
 
@@ -31,7 +28,7 @@ def cnn_model_fn(features, labels, mode):
   # Input Layer
   # Reshape X to 4-D tensor: [batch_size, width, height, channels]
   # MNIST images are 28x28 pixels, and have one color channel
-  input_layer = tf.reshape(features, [-1, 28, 28, 1])
+  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
 
   # Convolutional Layer #1
   # Computes 32 features using a 5x5 filter with ReLU activation.
@@ -82,53 +79,53 @@ def cnn_model_fn(features, labels, mode):
 
   # Add dropout operation; 0.6 probability that element will be kept
   dropout = tf.layers.dropout(
-      inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
+      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
 
   # Logits layer
   # Input Tensor Shape: [batch_size, 1024]
   # Output Tensor Shape: [batch_size, 10]
   logits = tf.layers.dense(inputs=dropout, units=10)
 
-  loss = None
-  train_op = None
+  # Generate Predictions (for PREDICT mode)
+  predicted_classes = tf.argmax(input=logits, axis=1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        "classes": predicted_classes,
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+    }
+    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
   # Calculate Loss (for both TRAIN and EVAL modes)
-  if mode != learn.ModeKeys.INFER:
-    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
-    loss = tf.losses.softmax_cross_entropy(
-        onehot_labels=onehot_labels, logits=logits)
+  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
 
   # Configure the Training Op (for TRAIN mode)
-  if mode == learn.ModeKeys.TRAIN:
-    train_op = tf.contrib.layers.optimize_loss(
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
+    train_op = optimizer.minimize(
         loss=loss,
-        global_step=tf.contrib.framework.get_global_step(),
-        learning_rate=0.001,
-        optimizer="SGD")
-
-  # Generate Predictions
-  predictions = {
-      "classes": tf.argmax(
-          input=logits, axis=1),
-      "probabilities": tf.nn.softmax(
-          logits, name="softmax_tensor")
-  }
+        global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
 
-  # Return a ModelFnOps object
-  return model_fn_lib.ModelFnOps(
-      mode=mode, predictions=predictions, loss=loss, train_op=train_op)
+  # Add evaluation metrics (for EVAL mode)
+  eval_metric_ops = {
+      "accuracy": tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)}
+  return tf.estimator.EstimatorSpec(
+      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   # Load training and eval data
-  mnist = learn.datasets.load_dataset("mnist")
+  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
   train_data = mnist.train.images  # Returns np.array
   train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
   eval_data = mnist.test.images  # Returns np.array
   eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
 
   # Create the Estimator
-  mnist_classifier = learn.Estimator(
+  mnist_classifier = tf.estimator.Estimator(
       model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
 
   # Set up logging for predictions
@@ -138,23 +135,24 @@ def main(unused_argv):
       tensors=tensors_to_log, every_n_iter=50)
 
   # Train the model
-  mnist_classifier.fit(
-      x=train_data,
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": train_data},
       y=train_labels,
       batch_size=100,
+      num_epochs=None,
+      shuffle=True)
+  mnist_classifier.train(
+      input_fn=train_input_fn,
       steps=20000,
-      monitors=[logging_hook])
-
-  # Configure the accuracy metric for evaluation
-  metrics = {
-      "accuracy":
-          learn.MetricSpec(
-              metric_fn=tf.metrics.accuracy, prediction_key="classes"),
-  }
+      hooks=[logging_hook])
 
   # Evaluate the model and print results
-  eval_results = mnist_classifier.evaluate(
-      x=eval_data, y=eval_labels, metrics=metrics)
+  eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={"x": eval_data},
+      y=eval_labels,
+      num_epochs=1,
+      shuffle=False)
+  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
   print(eval_results)
 
 
diff --git a/tensorflow/examples/tutorials/mnist/fully_connected_feed.py b/tensorflow/examples/tutorials/mnist/fully_connected_feed.py
index be50f4529f..af89c8c77b 100644
--- a/tensorflow/examples/tutorials/mnist/fully_connected_feed.py
+++ b/tensorflow/examples/tutorials/mnist/fully_connected_feed.py
@@ -20,7 +20,7 @@ from __future__ import print_function
 
 # pylint: disable=missing-docstring
 import argparse
-import os.path
+import os
 import sys
 import time
 
@@ -257,13 +257,15 @@ if __name__ == '__main__':
   parser.add_argument(
       '--input_data_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/input_data',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/input_data'),
       help='Directory to put the input data.'
   )
   parser.add_argument(
       '--log_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/logs/fully_connected_feed',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/logs/fully_connected_feed'),
       help='Directory to put the log data.'
   )
   parser.add_argument(
diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py
index 2896eee77d..4df925c1f3 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_deep.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py
@@ -52,42 +52,50 @@ def deepnn(x):
   # Reshape to use within a convolutional neural net.
   # Last dimension is for "features" - there is only one here, since images are
   # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
-  x_image = tf.reshape(x, [-1, 28, 28, 1])
+  with tf.name_scope('reshape'):
+    x_image = tf.reshape(x, [-1, 28, 28, 1])
 
   # First convolutional layer - maps one grayscale image to 32 feature maps.
-  W_conv1 = weight_variable([5, 5, 1, 32])
-  b_conv1 = bias_variable([32])
-  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+  with tf.name_scope('conv1'):
+    W_conv1 = weight_variable([5, 5, 1, 32])
+    b_conv1 = bias_variable([32])
+    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
 
   # Pooling layer - downsamples by 2X.
-  h_pool1 = max_pool_2x2(h_conv1)
+  with tf.name_scope('pool1'):
+    h_pool1 = max_pool_2x2(h_conv1)
 
   # Second convolutional layer -- maps 32 feature maps to 64.
-  W_conv2 = weight_variable([5, 5, 32, 64])
-  b_conv2 = bias_variable([64])
-  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+  with tf.name_scope('conv2'):
+    W_conv2 = weight_variable([5, 5, 32, 64])
+    b_conv2 = bias_variable([64])
+    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
 
   # Second pooling layer.
-  h_pool2 = max_pool_2x2(h_conv2)
+  with tf.name_scope('pool2'):
+    h_pool2 = max_pool_2x2(h_conv2)
 
   # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
   # is down to 7x7x64 feature maps -- maps this to 1024 features.
-  W_fc1 = weight_variable([7 * 7 * 64, 1024])
-  b_fc1 = bias_variable([1024])
+  with tf.name_scope('fc1'):
+    W_fc1 = weight_variable([7 * 7 * 64, 1024])
+    b_fc1 = bias_variable([1024])
 
-  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
-  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
+    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
 
   # Dropout - controls the complexity of the model, prevents co-adaptation of
   # features.
-  keep_prob = tf.placeholder(tf.float32)
-  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+  with tf.name_scope('dropout'):
+    keep_prob = tf.placeholder(tf.float32)
+    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
   # Map the 1024 features to 10 classes, one for each digit
-  W_fc2 = weight_variable([1024, 10])
-  b_fc2 = bias_variable([10])
+  with tf.name_scope('fc2'):
+    W_fc2 = weight_variable([1024, 10])
+    b_fc2 = bias_variable([10])
 
-  y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
+    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
   return y_conv, keep_prob
 
 
@@ -127,11 +135,18 @@ def main(_):
   # Build the graph for the deep net
   y_conv, keep_prob = deepnn(x)
 
-  cross_entropy = tf.reduce_mean(
-      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
-  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
-  correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
-  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+  with tf.name_scope('loss'):
+    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
+                                                            logits=y_conv)
+  cross_entropy = tf.reduce_mean(cross_entropy)
+
+  with tf.name_scope('adam_optimizer'):
+    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+
+  with tf.name_scope('accuracy'):
+    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+    correct_prediction = tf.cast(correct_prediction, tf.float32)
+  accuracy = tf.reduce_mean(correct_prediction)
 
   with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index dc0d870315..c401d09df8 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -25,6 +25,7 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
+import os
 import sys
 
 import tensorflow as tf
@@ -200,12 +201,14 @@ if __name__ == '__main__':
   parser.add_argument(
       '--data_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/input_data',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/input_data'),
       help='Directory for storing input data')
   parser.add_argument(
       '--log_dir',
       type=str,
-      default='/tmp/tensorflow/mnist/logs/mnist_with_summaries',
+      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
+                           'tensorflow/mnist/logs/mnist_with_summaries'),
       help='Summaries log directory')
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/examples/tutorials/monitors/BUILD b/tensorflow/examples/tutorials/monitors/BUILD
index 6aa0b7ee47..4220e8144d 100644
--- a/tensorflow/examples/tutorials/monitors/BUILD
+++ b/tensorflow/examples/tutorials/monitors/BUILD
@@ -20,7 +20,6 @@ py_binary(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/examples/wav_to_spectrogram/BUILD b/tensorflow/examples/wav_to_spectrogram/BUILD
index 5923fa5929..dc29236b08 100644
--- a/tensorflow/examples/wav_to_spectrogram/BUILD
+++ b/tensorflow/examples/wav_to_spectrogram/BUILD
@@ -19,7 +19,10 @@ cc_library(
     hdrs = ["wav_to_spectrogram.h"],
     deps = [
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
         "//tensorflow/core:tensorflow",
     ],
 )
@@ -30,6 +33,7 @@ cc_binary(
     deps = [
         ":wav_to_spectrogram_lib",
         "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
     ],
 )
 
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 664c690e34..beede5a6d9 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -16994,6 +16994,68 @@ func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer
 	return op.Output(0)
 }
 
+// Applies sparse addition to `input` using individual values or slices
+//
+// from `updates` according to indices `indices`.  The updates are non-aliasing:
+// `input` is only modified in-place if no other operations will use it.
+// Otherwise, a copy of `input` is made.  This operation has a gradient with
+// respect to both `input` and `updates`.
+//
+// `input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
+//
+// `indices` must be integer tensor, containing indices into `input`.
+// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
+//
+// The innermost dimension of `indices` (with length `K`) corresponds to
+// indices into elements (if `K = P`) or `(P-K)`-dimensional slices
+// (if `K < P`) along the `K`th dimension of `input`.
+//
+// `updates` is `Tensor` of rank `Q-1+P-K` with shape:
+//
+// ```
+// [d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].
+// ```
+//
+// For example, say we want to add 4 scattered elements to a rank-1 tensor to 8
+// elements. In Python, that addition would look like this:
+//
+//     input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
+//     indices = tf.constant([[4], [3], [1], [7]])
+//     updates = tf.constant([9, 10, 11, 12])
+//     output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
+//     with tf.Session() as sess:
+//       print(sess.run(output))
+//
+// The resulting value `output` would look like this:
+//
+//     [1, 13, 3, 14, 14, 6, 7, 20]
+//
+// See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
+// slices.
+//
+// Arguments:
+//	input: A Tensor.
+//	indices: A Tensor. Must be one of the following types: `int32`, `int64`.
+// A tensor of indices into `input`.
+//	updates: A Tensor. Must have the same type as ref. A tensor of updated values
+// to add to `input`.
+//
+// Returns A `Tensor` with the same shape as `input`, containing values of `input`
+// updated with `updates`.
+func ScatterNdNonAliasingAdd(scope *Scope, input tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ScatterNdNonAliasingAdd",
+		Input: []tf.Input{
+			input, indices, updates,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // QuantizedReluXAttr is an optional argument to QuantizedReluX.
 type QuantizedReluXAttr func(optionalAttr)
 
@@ -20574,23 +20636,40 @@ func Requantize(scope *Scope, input tf.Output, input_min tf.Output, input_max tf
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// ArgMinAttr is an optional argument to ArgMin.
+type ArgMinAttr func(optionalAttr)
+
+// ArgMinOutputType sets the optional output_type attribute to value.
+// If not specified, defaults to DT_INT64
+func ArgMinOutputType(value tf.DataType) ArgMinAttr {
+	return func(m optionalAttr) {
+		m["output_type"] = value
+	}
+}
+
 // Returns the index with the smallest value across dimensions of a tensor.
 //
 // Note that in case of ties the identity of the return value is not guaranteed.
 //
 // Arguments:
 //
-//	dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-// of the input Tensor to reduce across. For vectors, use dimension = 0.
-func ArgMin(scope *Scope, input tf.Output, dimension tf.Output) (output tf.Output) {
+//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+// which dimension of the input Tensor to reduce across. For vectors,
+// use dimension = 0.
+func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
 		Type: "ArgMin",
 		Input: []tf.Input{
 			input, dimension,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
@@ -21848,23 +21927,40 @@ func IsFinite(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// ArgMaxAttr is an optional argument to ArgMax.
+type ArgMaxAttr func(optionalAttr)
+
+// ArgMaxOutputType sets the optional output_type attribute to value.
+// If not specified, defaults to DT_INT64
+func ArgMaxOutputType(value tf.DataType) ArgMaxAttr {
+	return func(m optionalAttr) {
+		m["output_type"] = value
+	}
+}
+
 // Returns the index with the largest value across dimensions of a tensor.
 //
 // Note that in case of ties the identity of the return value is not guaranteed.
 //
 // Arguments:
 //
-//	dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
-// of the input Tensor to reduce across. For vectors, use dimension = 0.
-func ArgMax(scope *Scope, input tf.Output, dimension tf.Output) (output tf.Output) {
+//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
+// which dimension of the input Tensor to reduce across. For vectors,
+// use dimension = 0.
+func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
 		Type: "ArgMax",
 		Input: []tf.Input{
 			input, dimension,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
diff --git a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
index d817239919..15f7b99135 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
@@ -64,9 +64,11 @@ final class NativeLibrary {
     if (resource == null) {
       throw new UnsatisfiedLinkError(
           String.format(
-              "Cannot find TensorFlow native library for OS: %s, architecture: %s. "
-                  + "See https://github.com/tensorflow/tensorflow/tree/master/java/README.md "
-                  + "for possible solutions (such as building the library from source).",
+              "Cannot find TensorFlow native library for OS: %s, architecture: %s. See "
+                  + "https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java/README.md"
+                  + " for possible solutions (such as building the library from source). Additional"
+                  + " information on attempts to find the native library can be obtained by adding"
+                  + " org.tensorflow.NativeLibrary.DEBUG=1 to the system properties of the JVM.",
               os(), architecture()));
     }
     try {
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index ad556c8e25..7f291b0b13 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -41,7 +41,6 @@ py_library(
         "//tensorflow/contrib/learn/python/learn/datasets:__pkg__",  # TODO(b/34059704): remove when fixed
         "//tensorflow/python/debug:__pkg__",  # TODO(b/34059704): remove when fixed
         "//tensorflow/python/tools:__pkg__",  # TODO(b/34059704): remove when fixed
-        "//tensorflow/tensorboard/scripts:__pkg__",  # TODO(b/34059704): remove when fixed
         "//tensorflow/tools/quantization:__pkg__",  # TODO(b/34059704): remove when fixed
     ],
     deps = [
@@ -176,6 +175,20 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "model_analyzer_lib",
+    srcs = ["grappler/model_analyzer.cc"],
+    hdrs = ["grappler/model_analyzer.h"],
+    deps = [
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler/clusters:cluster",
+        "//tensorflow/core/grappler/costs:graph_properties",
+        "//tensorflow/core/grappler/costs:utils",
+    ],
+)
+
 cc_library(
     name = "numpy_lib",
     srcs = ["lib/core/numpy.cc"],
@@ -2743,6 +2756,7 @@ tf_py_wrap_cc(
         "framework/cpp_shape_inference.i",
         "framework/python_op_gen.i",
         "grappler/cost_analyzer.i",
+        "grappler/model_analyzer.i",
         "grappler/tf_optimizer.i",
         "lib/core/py_func.i",
         "lib/core/strings.i",
@@ -2760,6 +2774,7 @@ tf_py_wrap_cc(
     ],
     deps = [
         ":cost_analyzer_lib",
+        ":model_analyzer_lib",
         ":cpp_shape_inference",
         ":kernel_registry",
         ":numpy_lib",
@@ -3040,6 +3055,7 @@ py_test(
     srcs = ["client/session_clusterspec_prop_test.py"],
     srcs_version = "PY2AND3",
     tags = [
+        "no_gpu",
         "no_pip_gpu",
     ],
     deps = [
@@ -3064,6 +3080,7 @@ py_test(
     srcs = ["client/session_list_devices_test.py"],
     srcs_version = "PY2AND3",
     tags = [
+        "no_gpu",
         "no_pip_gpu",
     ],
     deps = [
@@ -3769,26 +3786,6 @@ cuda_py_test(
     main = "ops/concat_benchmark.py",
 )
 
-cuda_py_test(
-    name = "conv2d_benchmark",
-    size = "large",
-    srcs = ["ops/conv2d_benchmark.py"],
-    additional_deps = [
-        ":client",
-        ":client_testlib",
-        ":control_flow_ops",
-        ":framework_for_generated_wrappers",
-        ":nn_ops",
-        ":platform",
-        ":platform_benchmark",
-        ":random_ops",
-        ":variables",
-        "//third_party/py/numpy",
-        "//tensorflow/core:protos_all_py",
-    ],
-    main = "ops/conv2d_benchmark.py",
-)
-
 cuda_py_test(
     name = "split_benchmark",
     srcs = ["ops/split_benchmark.py"],
@@ -3952,3 +3949,30 @@ py_test(
         "//third_party/py/numpy",
     ],
 )
+
+py_library(
+    name = "model_analyzer",
+    srcs = [
+        "grappler/model_analyzer.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [":pywrap_tensorflow_internal"],
+)
+
+py_test(
+    name = "model_analyzer_test",
+    size = "small",
+    srcs = ["grappler/model_analyzer_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":array_ops",
+        ":client_testlib",
+        ":framework_for_generated_wrappers",
+        ":math_ops",
+        ":model_analyzer",
+        ":state_ops",
+        "//tensorflow/core:protos_all_py",
+        "//third_party/py/numpy",
+    ],
+)
diff --git a/tensorflow/python/client/session_clusterspec_prop_test.py b/tensorflow/python/client/session_clusterspec_prop_test.py
index f982c6ae37..37cbf8062c 100644
--- a/tensorflow/python/client/session_clusterspec_prop_test.py
+++ b/tensorflow/python/client/session_clusterspec_prop_test.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Tests for tensorflow.python.client.session.Session's ClusterSpec Propagation.
 
 These tests exercise the ClusterSpec Propagation capabilities of distributed
@@ -111,6 +110,63 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
     output = sess.run(const)
     self.assertEqual(17, output)
 
+  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
+  def testCanonicalDeviceNames(self):
+    server1 = server_lib.Server.create_local_server()
+    server2 = server_lib.Server.create_local_server()
+    cluster_def = cluster_pb2.ClusterDef()
+    job = cluster_def.job.add()
+    job.name = 'worker'
+    job.tasks[0] = server1.target[len('grpc://'):]
+    job.tasks[1] = server2.target[len('grpc://'):]
+    config = config_pb2.ConfigProto(cluster_def=cluster_def)
+
+    with ops.Graph().as_default() as g, ops.device(
+        '/job:worker/task:1/device:CPU:0'):
+      const = constant_op.constant(17)
+    sess = session.Session(server1.target, config=config, graph=g)
+    run_options = config_pb2.RunOptions(
+        trace_level=config_pb2.RunOptions.FULL_TRACE)
+    run_metadata = config_pb2.RunMetadata()
+    output = sess.run(const, options=run_options, run_metadata=run_metadata)
+    self.assertEqual(17, output)
+    self.assertEqual(1,
+                     len([
+                         node_stats
+                         for dev_stats in run_metadata.step_stats.dev_stats
+                         for node_stats in dev_stats.node_stats
+                         if '/job:worker/replica:0/task:1/device:CPU:0' ==
+                         dev_stats.device and 'Const' == node_stats.node_name
+                     ]))
+
+  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
+  def testLegacyDeviceNames(self):
+    server1 = server_lib.Server.create_local_server()
+    server2 = server_lib.Server.create_local_server()
+    cluster_def = cluster_pb2.ClusterDef()
+    job = cluster_def.job.add()
+    job.name = 'worker'
+    job.tasks[0] = server1.target[len('grpc://'):]
+    job.tasks[1] = server2.target[len('grpc://'):]
+    config = config_pb2.ConfigProto(cluster_def=cluster_def)
+
+    with ops.Graph().as_default() as g, ops.device('/job:worker/task:1/cpu:0'):
+      const = constant_op.constant(17)
+    sess = session.Session(server1.target, config=config, graph=g)
+    run_options = config_pb2.RunOptions(
+        trace_level=config_pb2.RunOptions.FULL_TRACE)
+    run_metadata = config_pb2.RunMetadata()
+    output = sess.run(const, options=run_options, run_metadata=run_metadata)
+    self.assertEqual(17, output)
+    self.assertEqual(1,
+                     len([
+                         node_stats
+                         for dev_stats in run_metadata.step_stats.dev_stats
+                         for node_stats in dev_stats.node_stats
+                         if '/job:worker/replica:0/task:1/device:CPU:0' ==
+                         dev_stats.device and 'Const' == node_stats.node_name
+                     ]))
+
   @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationThreeServers2Graphs(self):
     """Boots 3 servers, creates 2 sessions, ensures appropriate operations.
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index ef8f25c873..103e864f4b 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -128,6 +128,17 @@ class SessionTest(test_util.TensorFlowTestCase):
       results = s.run([inp])
       self.assertAllEqual([20.0], results)
 
+    pool = config.session_inter_op_thread_pool.add()
+    pool.num_threads = 1
+    pool.global_name = 't1'
+    run_options = config_pb2.RunOptions()
+    run_options.inter_op_thread_pool = (
+        len(config.session_inter_op_thread_pool) - 1)
+    with session.Session(config=config) as s:
+      inp = constant_op.constant(30.0, name='W2')
+      results = s.run([inp], options=run_options)
+      self.assertAllEqual([30.0], results)
+
   def testErrorsReported(self):
     with session.Session() as s:
       constant_op.constant(10.0, name='W1')
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 54ae5047ca..86d7b5f770 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -1,5 +1,12 @@
 # Description:
 #   Python Client Code of the TensorFlow Debugger (tfdbg).
+#
+# Public target(s):
+#
+# ":debug_py": Public Python methods and classes of tfdbg.
+#   For API documentation, see https://www.tensorflow.org/api_docs/python/tfdbg
+#   For a user interface walkthrough, see https://www.tensorflow.org/programmers_guide/debugger
+# (Omitted internal-only public target)
 
 package(
     default_visibility = ["//tensorflow:internal"],
diff --git a/tensorflow/python/debug/README.md b/tensorflow/python/debug/README.md
new file mode 100644
index 0000000000..1c8dee2c1c
--- /dev/null
+++ b/tensorflow/python/debug/README.md
@@ -0,0 +1,49 @@
+# TensorFlow Debugger (TFDBG)
+
+[TOC]
+
+TensorFlow Debugger (TFDBG) is a specialized debugger for TensorFlow's computation
+graphs. It provides access to internal graph structures and tensor values at
+TensorFlow runtime.
+
+<!-- TODO(cais): Add release notes starting from 1.3. -->
+
+## Why TFDBG?
+
+In TensorFlow's current
+[computation-graph framework](https://www.tensorflow.org/get_started/get_started#the_computational_graph),
+almost all actual computation after graph construction happens in a single
+Python function, namely
+[tf.Session.run](https://www.tensorflow.org/api_docs/python/tf/Session#run).
+Basic Python debugging tools such as [pdb](https://docs.python.org/2/library/pdb.html)
+cannot be used to debug `Session.run`, due to the fact that TensorFlow's graph
+execution happens in the underlying C++ layer. C++ debugging tools such as
+[gdb](https://www.gnu.org/software/gdb/) are not ideal either, because of their
+inability to recognize and organize the stack frames and variables in a way
+relevant to TensorFlow's operations, tensors and other graph constructs.
+
+TFDBG addresses these limitations. Among the features provided by TFDBG, the
+following ones are designed to facilitate runtime debugging of TensorFlow
+models:
+
+* Easy access through session wrappers
+* Easy integration with common high-level APIs, such as
+  [tf-learn](https://www.tensorflow.org/get_started/tflearn) and
+  [Keras](keras)
+* Inspection of runtime tensor values and node connections
+* Conditional breaking after runs that generate tensors satisfying given
+  predicates, which makes common debugging tasks such as tracing the origin
+  of infinities and [NaNs](https://en.wikipedia.org/wiki/NaN) easier
+* Association of nodes and tensors in graphs with Python source lines
+* Profiling of models at the level of graph nodes and Python source lines.
+(Omitted internal-only feature)
+
+## How to use TFDBG?
+
+* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/programmers_guide/debugger.
+* For programmatic use of the API of TFDBG, see https://www.tensorflow.org/api_docs/python/tfdbg.
+
+## Related Publications
+
+* Cai, S., Breck E., Nielsen E., Salib M., Sculley D. (2016) TensorFlow Debugger:
+  Debugging Dataflow Graphs for Machine Learning. https://research.google.com/pubs/pub45789.html
diff --git a/tensorflow/python/debug/examples/debug_fibonacci.py b/tensorflow/python/debug/examples/debug_fibonacci.py
index 86689c7d44..704dbda357 100644
--- a/tensorflow/python/debug/examples/debug_fibonacci.py
+++ b/tensorflow/python/debug/examples/debug_fibonacci.py
@@ -53,8 +53,8 @@ def main(_):
     sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
     sess.add_tensor_filter("has_negative", has_negative)
 
-  print("Fibonacci number at position %d: %d" %
-        (FLAGS.length, int(sess.run(n1))))
+  print("Fibonacci number at position %d:\n%s" %
+        (FLAGS.length, sess.run(n1)))
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/debug/examples/examples_test.sh b/tensorflow/python/debug/examples/examples_test.sh
index 0b5401a7f2..25916f1903 100755
--- a/tensorflow/python/debug/examples/examples_test.sh
+++ b/tensorflow/python/debug/examples/examples_test.sh
@@ -56,7 +56,7 @@ fi
 
 # Override the default ui_type=curses to allow the test to pass in a tty-less
 # test environment.
-cat << EOF | ${DEBUG_FIBONACCI_BIN} --ui_type=readline
+cat << EOF | ${DEBUG_FIBONACCI_BIN} --tensor_size=2 --ui_type=readline
 run
 exit
 EOF
diff --git a/tensorflow/python/debug/wrappers/dumping_wrapper.py b/tensorflow/python/debug/wrappers/dumping_wrapper.py
index 63229a8539..7382cd5fa2 100644
--- a/tensorflow/python/debug/wrappers/dumping_wrapper.py
+++ b/tensorflow/python/debug/wrappers/dumping_wrapper.py
@@ -77,6 +77,8 @@ class DumpingDebugWrapperSession(framework.NonInteractiveDebugWrapperSession):
         raise ValueError(
             "session_root path points to a non-empty directory: %s" %
             session_root)
+    else:
+      gfile.MakeDirs(session_root)
     self._session_root = session_root
 
     self._run_counter = 0
diff --git a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py
index 5474b0e27f..d987ba84b5 100644
--- a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py
+++ b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py
@@ -88,6 +88,14 @@ class DumpingDebugWrapperSessionTest(test_util.TensorFlowTestCase):
       dumping_wrapper.DumpingDebugWrapperSession(
           session.Session(), session_root=file_path, log_usage=False)
 
+  def testConstructWrapperWithNonexistentSessionRootCreatesDirectory(self):
+    new_dir_path = os.path.join(tempfile.mkdtemp(), "new_dir")
+    dumping_wrapper.DumpingDebugWrapperSession(
+        session.Session(), session_root=new_dir_path, log_usage=False)
+    self.assertTrue(gfile.IsDirectory(new_dir_path))
+    # Cleanup.
+    gfile.DeleteRecursively(new_dir_path)
+
   def testDumpingOnASingleRunWorks(self):
     sess = dumping_wrapper.DumpingDebugWrapperSession(
         self.sess, session_root=self.session_root, log_usage=False)
diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 0cfd02466e..ca46daf306 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -373,7 +373,6 @@ py_library(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
-        "//tensorflow/python:logging_ops",
         "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:metrics",
@@ -381,7 +380,7 @@ py_library(
         "//tensorflow/python:platform",
         "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:string_ops",
-        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:summary",
         "//tensorflow/python:weights_broadcast_ops",
         "//tensorflow/python/feature_column",
         "//tensorflow/python/ops/losses",
@@ -394,7 +393,9 @@ py_test(
     size = "small",
     srcs = ["canned/head_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["no_pip"],
     deps = [
+        ":dnn_testing_utils",
         ":head",
         ":metric_keys",
         ":model_fn",
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
index 486b302ab5..2151df8423 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py
@@ -664,7 +664,8 @@ class DNNLinearCombinedTests(test.TestCase):
       self.assertTrue(
           all([name.startswith(var_name_prefix) for name in var_names]))
       # var is used to check this op called by training.
-      var = variables_lib.Variable(0., name=(var_name_prefix + '_called'))
+      with ops.name_scope(''):
+        var = variables_lib.Variable(0., name=(var_name_prefix + '_called'))
       with ops.control_dependencies([var.assign(100.)]):
         return real_optimizer.minimize(loss, global_step, var_list)
 
@@ -695,11 +696,11 @@ class DNNLinearCombinedTests(test.TestCase):
     # verifies train_op fires linear minimize op
     self.assertEqual(100.,
                      checkpoint_utils.load_variable(
-                         self._model_dir, 'binary_logistic_head/linear_called'))
+                         self._model_dir, 'linear_called'))
     # verifies train_op fires dnn minimize op
     self.assertEqual(100.,
                      checkpoint_utils.load_variable(
-                         self._model_dir, 'binary_logistic_head/dnn_called'))
+                         self._model_dir, 'dnn_called'))
 
   def test_dnn_and_linear_logits_are_added(self):
     with ops.Graph().as_default():
diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index a1c1f1be0b..bc868a493f 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -32,17 +32,16 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
-from tensorflow.python.ops import logging_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import metrics as metrics_lib
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import string_ops
-from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import weights_broadcast_ops
 from tensorflow.python.ops.losses import losses
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.summary import summary
 
 _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
 
@@ -367,10 +366,7 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head):
   def create_estimator_spec(
       self, features, mode, logits, labels=None, train_op_fn=None):
     """See `Head`."""
-    with variable_scope.variable_scope(
-        None,
-        default_name='multi_class_head',
-        values=(tuple(six.itervalues(features)) + (labels, logits))):
+    with ops.name_scope('head'):
       logits = _check_logits(logits, self.logits_dimension)
 
       # Predict.
@@ -441,17 +437,18 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head):
       # Train.
       if train_op_fn is None:
         raise ValueError('train_op_fn can not be None.')
-      logging_ops.scalar_summary(metric_keys.MetricKeys.LOSS, training_loss)
-      logging_ops.scalar_summary(
-          metric_keys.MetricKeys.LOSS_MEAN,
-          losses.compute_weighted_loss(
-              unweighted_loss, weights=weights,
-              reduction=losses.Reduction.MEAN))
-      return model_fn.EstimatorSpec(
-          mode=model_fn.ModeKeys.TRAIN,
-          predictions=predictions,
-          loss=training_loss,
-          train_op=train_op_fn(training_loss))
+    with ops.name_scope(''):
+      summary.scalar(metric_keys.MetricKeys.LOSS, training_loss)
+      summary.scalar(metric_keys.MetricKeys.LOSS_MEAN,
+                     losses.compute_weighted_loss(
+                         unweighted_loss,
+                         weights=weights,
+                         reduction=losses.Reduction.MEAN))
+    return model_fn.EstimatorSpec(
+        mode=model_fn.ModeKeys.TRAIN,
+        predictions=predictions,
+        loss=training_loss,
+        train_op=train_op_fn(training_loss))
 
 
 def _binary_logistic_head_with_sigmoid_cross_entropy_loss(
@@ -579,32 +576,31 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(_Head):
   def create_estimator_spec(
       self, features, mode, logits, labels=None, train_op_fn=None):
     """See `Head`."""
-    with variable_scope.variable_scope(
-        None, default_name='binary_logistic_head',
-        values=(tuple(six.itervalues(features)) + (labels, logits))):
-
-      # Predict.
-      pred_keys = prediction_keys.PredictionKeys
-      logits = _check_logits(logits, self.logits_dimension)
-      logistic = math_ops.sigmoid(logits, name=pred_keys.LOGISTIC)
-      two_class_logits = array_ops.concat(
-          (array_ops.zeros_like(logits), logits), 1, name='two_class_logits')
-      scores = nn.softmax(two_class_logits, name=pred_keys.PROBABILITIES)
-      class_ids = array_ops.reshape(
-          math_ops.argmax(two_class_logits, axis=1), (-1, 1), name='classes')
-      if self._label_vocabulary:
-        table = lookup_ops.index_to_string_table_from_tensor(
-            vocabulary_list=self._label_vocabulary, name='class_string_lookup')
-        classes = table.lookup(class_ids)
-      else:
-        classes = string_ops.as_string(class_ids, name='str_classes')
-      predictions = {
-          pred_keys.LOGITS: logits,
-          pred_keys.LOGISTIC: logistic,
-          pred_keys.PROBABILITIES: scores,
-          pred_keys.CLASS_IDS: class_ids,
-          pred_keys.CLASSES: classes,
-      }
+    # Predict.
+    with ops.name_scope('head'):
+      with ops.name_scope(None, 'predictions', (logits,)):
+        pred_keys = prediction_keys.PredictionKeys
+        logits = _check_logits(logits, self.logits_dimension)
+        logistic = math_ops.sigmoid(logits, name=pred_keys.LOGISTIC)
+        two_class_logits = array_ops.concat(
+            (array_ops.zeros_like(logits), logits), 1, name='two_class_logits')
+        scores = nn.softmax(two_class_logits, name=pred_keys.PROBABILITIES)
+        class_ids = array_ops.reshape(
+            math_ops.argmax(two_class_logits, axis=1), (-1, 1), name='classes')
+        if self._label_vocabulary:
+          table = lookup_ops.index_to_string_table_from_tensor(
+              vocabulary_list=self._label_vocabulary,
+              name='class_string_lookup')
+          classes = table.lookup(class_ids)
+        else:
+          classes = string_ops.as_string(class_ids, name='str_classes')
+        predictions = {
+            pred_keys.LOGITS: logits,
+            pred_keys.LOGISTIC: logistic,
+            pred_keys.PROBABILITIES: scores,
+            pred_keys.CLASS_IDS: class_ids,
+            pred_keys.CLASSES: classes,
+        }
       if mode == model_fn.ModeKeys.PREDICT:
         batch_size = array_ops.shape(logistic)[0]
         export_class_list = self._label_vocabulary
@@ -657,17 +653,18 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(_Head):
       # Train.
       if train_op_fn is None:
         raise ValueError('train_op_fn can not be None.')
-      logging_ops.scalar_summary(metric_keys.MetricKeys.LOSS, training_loss)
-      logging_ops.scalar_summary(
-          metric_keys.MetricKeys.LOSS_MEAN,
-          losses.compute_weighted_loss(
-              unweighted_loss, weights=weights,
-              reduction=losses.Reduction.MEAN))
-      return model_fn.EstimatorSpec(
-          mode=model_fn.ModeKeys.TRAIN,
-          predictions=predictions,
-          loss=training_loss,
-          train_op=train_op_fn(training_loss))
+    with ops.name_scope(''):
+      summary.scalar(metric_keys.MetricKeys.LOSS, training_loss)
+      summary.scalar(metric_keys.MetricKeys.LOSS_MEAN,
+                     losses.compute_weighted_loss(
+                         unweighted_loss,
+                         weights=weights,
+                         reduction=losses.Reduction.MEAN))
+    return model_fn.EstimatorSpec(
+        mode=model_fn.ModeKeys.TRAIN,
+        predictions=predictions,
+        loss=training_loss,
+        train_op=train_op_fn(training_loss))
 
 
 def _regression_head_with_mean_squared_error_loss(weight_column=None,
@@ -707,12 +704,8 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
   def create_estimator_spec(
       self, features, mode, logits, labels=None, train_op_fn=None):
     """See `Head`."""
-    with variable_scope.variable_scope(
-        None,
-        default_name='regression_head',
-        values=(tuple(six.itervalues(features)) + (labels, logits))):
-
-      # Predict.
+    # Predict.
+    with ops.name_scope('head'):
       logits = _check_logits(logits, self._logits_dimension)
       predictions = {prediction_keys.PredictionKeys.PREDICTIONS: logits}
       if mode == model_fn.ModeKeys.PREDICT:
@@ -744,43 +737,46 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
       # Train.
       if train_op_fn is None:
         raise ValueError('train_op_fn can not be None.')
-      logging_ops.scalar_summary(metric_keys.MetricKeys.LOSS, training_loss)
-      logging_ops.scalar_summary(
-          metric_keys.MetricKeys.LOSS_MEAN,
-          losses.compute_weighted_loss(
-              unweighted_loss, weights=weights,
-              reduction=losses.Reduction.MEAN))
-      return model_fn.EstimatorSpec(
-          mode=model_fn.ModeKeys.TRAIN,
-          predictions=predictions,
-          loss=training_loss,
-          train_op=train_op_fn(training_loss))
+    with ops.name_scope(''):
+      summary.scalar(metric_keys.MetricKeys.LOSS, training_loss)
+      summary.scalar(metric_keys.MetricKeys.LOSS_MEAN,
+                     losses.compute_weighted_loss(
+                         unweighted_loss,
+                         weights=weights,
+                         reduction=losses.Reduction.MEAN))
+    return model_fn.EstimatorSpec(
+        mode=model_fn.ModeKeys.TRAIN,
+        predictions=predictions,
+        loss=training_loss,
+        train_op=train_op_fn(training_loss))
 
 
 def _assert_range(labels, n_classes):
-  assert_less = check_ops.assert_less(
-      labels,
-      ops.convert_to_tensor(n_classes, dtype=labels.dtype),
-      message='Label IDs must < n_classes')
-  assert_greater = check_ops.assert_non_negative(
-      labels, message='Label IDs must >= 0')
-  with ops.control_dependencies((assert_less, assert_greater)):
-    return array_ops.identity(labels)
+  with ops.name_scope(None, 'assert_range', (labels,)):
+    assert_less = check_ops.assert_less(
+        labels,
+        ops.convert_to_tensor(n_classes, dtype=labels.dtype),
+        message='Label IDs must < n_classes')
+    assert_greater = check_ops.assert_non_negative(
+        labels, message='Label IDs must >= 0')
+    with ops.control_dependencies((assert_less, assert_greater)):
+      return array_ops.identity(labels)
 
 
 def _weights(features, weight_column):
   """Fetches weights from features."""
-  if weight_column is None:
-    return 1.
-  if isinstance(weight_column, six.string_types):
-    weight_column = feature_column_lib.numeric_column(key=weight_column)
-  if not isinstance(weight_column, feature_column_lib._NumericColumn):  # pylint: disable=protected-access
-    raise TypeError('Weight column must be either a string or _NumericColumn. '
-                    'Given type: {}.'.format(type(weight_column)))
-  weights = weight_column._get_dense_tensor(  # pylint: disable=protected-access
-      feature_column_lib._LazyBuilder(features))  # pylint: disable=protected-access
-  if not (weights.dtype.is_floating or weights.dtype.is_integer):
-    raise ValueError('Weight column should be castable to float. '
-                     'Given dtype: {}'.format(weights.dtype))
-  weights = _maybe_expand_dim(math_ops.to_float(weights, name='weights'))
-  return weights
+  with ops.name_scope(None, 'weights', values=features.values()):
+    if weight_column is None:
+      return 1.
+    if isinstance(weight_column, six.string_types):
+      weight_column = feature_column_lib.numeric_column(key=weight_column)
+    if not isinstance(weight_column, feature_column_lib._NumericColumn):  # pylint: disable=protected-access
+      raise TypeError('Weight column must be either a string or _NumericColumn.'
+                      ' Given type: {}.'.format(type(weight_column)))
+    weights = weight_column._get_dense_tensor(  # pylint: disable=protected-access
+        feature_column_lib._LazyBuilder(features))  # pylint: disable=protected-access
+    if not (weights.dtype.is_floating or weights.dtype.is_integer):
+      raise ValueError('Weight column should be castable to float. '
+                       'Given dtype: {}'.format(weights.dtype))
+    weights = _maybe_expand_dim(math_ops.to_float(weights, name='weights'))
+    return weights
diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py
index c6ea54f08e..5110582dd1 100644
--- a/tensorflow/python/estimator/canned/head_test.py
+++ b/tensorflow/python/estimator/canned/head_test.py
@@ -23,6 +23,7 @@ import six
 
 from tensorflow.core.framework import summary_pb2
 from tensorflow.python.estimator import model_fn
+from tensorflow.python.estimator.canned import dnn_testing_utils
 from tensorflow.python.estimator.canned import head as head_lib
 from tensorflow.python.estimator.canned import metric_keys
 from tensorflow.python.estimator.canned import prediction_keys
@@ -83,26 +84,11 @@ def _sigmoid(logits):
   return 1 / (1 + np.exp(-logits))
 
 
-# TODO(roumposg): Reuse the code from dnn_testing_utils.
-def _assert_close(expected, actual, rtol=1e-04, message='',
-                  name='assert_close'):
-  with ops.name_scope(name, 'assert_close', (expected, actual, rtol)) as scope:
-    expected = ops.convert_to_tensor(expected, name='expected')
-    actual = ops.convert_to_tensor(actual, name='actual')
-    rdiff = math_ops.abs((expected - actual) / expected, 'diff')
-    rtol = ops.convert_to_tensor(rtol, name='rtol')
-    return check_ops.assert_less(
-        rdiff,
-        rtol,
-        data=(message, 'Condition expected =~ actual did not hold element-wise:'
-              'expected = ', expected, 'actual = ', actual, 'rdiff = ', rdiff,
-              'rtol = ', rtol,),
-        summarize=expected.get_shape().num_elements(),
-        name=scope)
-
-
 class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase):
 
+  def setUp(self):
+    ops.reset_default_graph()
+
   def test_n_classes_is_none(self):
     with self.assertRaisesRegexp(ValueError, 'n_classes must be > 2'):
       head_lib._multi_class_head_with_softmax_cross_entropy_loss(
@@ -690,6 +676,9 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase):
 # TODO(ptucker): Add thresholds tests.
 class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase):
 
+  def setUp(self):
+    ops.reset_default_graph()
+
   def test_threshold_too_small(self):
     with self.assertRaisesRegexp(ValueError, r'thresholds not in \(0, 1\)'):
       head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(
@@ -1026,7 +1015,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase):
     #      = 1.2484322
     expected_loss = 1.2484322
     def _train_op_fn(loss):
-      with ops.control_dependencies((_assert_close(
+      with ops.control_dependencies((dnn_testing_utils.assert_close(
           math_ops.to_float(expected_loss), math_ops.to_float(loss)),)):
         return constant_op.constant(expected_train_result)
     spec = head.create_estimator_spec(
@@ -1258,6 +1247,9 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase):
 
 class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase):
 
+  def setUp(self):
+    ops.reset_default_graph()
+
   def test_invalid_label_dimension(self):
     with self.assertRaisesRegexp(ValueError, r'Invalid label_dimension'):
       head_lib._regression_head_with_mean_squared_error_loss(label_dimension=-1)
diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py
index 552b1bdf01..05b1e5b44a 100644
--- a/tensorflow/python/estimator/canned/linear.py
+++ b/tensorflow/python/estimator/canned/linear.py
@@ -42,8 +42,8 @@ def _get_default_optimizer(feature_columns):
   return ftrl.FtrlOptimizer(learning_rate=learning_rate)
 
 
-# TODO(b/36813849): Revisit passing params vs named arguments.
-def _linear_model_fn(features, labels, mode, params, config):
+def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
+                     partitioner, config):
   """A model_fn for linear models that use a gradient-based optimizer.
 
   Args:
@@ -51,13 +51,12 @@ def _linear_model_fn(features, labels, mode, params, config):
     labels: `Tensor` of shape `[batch_size, logits_dimension]`.
     mode: Defines whether this is training, evaluation or prediction.
       See `ModeKeys`.
-    params: A dict of hyperparameters.
-      The following hyperparameters are expected:
-      * head: A `Head` instance.
-      * feature_columns: An iterable containing all the feature columns used by
-          the model.
-      * optimizer: string, `Optimizer` object, or callable that defines the
-          optimizer to use for training. If `None`, will use a FTRL optimizer.
+    head: A `Head` instance.
+    feature_columns: An iterable containing all the feature columns used by
+      the model.
+    optimizer: string, `Optimizer` object, or callable that defines the
+      optimizer to use for training. If `None`, will use a FTRL optimizer.
+    partitioner: Partitioner for variables.
     config: `RunConfig` object to configure the runtime settings.
 
   Returns:
@@ -66,14 +65,12 @@ def _linear_model_fn(features, labels, mode, params, config):
   Raises:
     ValueError: If mode or params are invalid.
   """
-  head = params['head']
-  feature_columns = tuple(params['feature_columns'])
   optimizer = optimizers.get_optimizer_instance(
-      params.get('optimizer') or _get_default_optimizer(feature_columns),
+      optimizer or _get_default_optimizer(feature_columns),
       learning_rate=_LEARNING_RATE)
   num_ps_replicas = config.num_ps_replicas if config else 0
 
-  partitioner = params.get('partitioner') or (
+  partitioner = partitioner or (
       partitioned_variables.min_max_variable_partitioner(
           max_partitions=num_ps_replicas,
           min_slice_size=64 << 20))
@@ -210,16 +207,20 @@ class LinearClassifier(estimator.Estimator):
       head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint: disable=protected-access
           n_classes, weight_column=weight_column,
           label_vocabulary=label_vocabulary)
+    def _model_fn(features, labels, mode, config):
+      return _linear_model_fn(
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          feature_columns=tuple(feature_columns or []),
+          optimizer=optimizer,
+          partitioner=partitioner,
+          config=config)
     super(LinearClassifier, self).__init__(
-        model_fn=_linear_model_fn,
+        model_fn=_model_fn,
         model_dir=model_dir,
-        config=config,
-        params={
-            'head': head,
-            'feature_columns': feature_columns,
-            'optimizer': optimizer,
-            'partitioner': partitioner,
-        })
+        config=config)
 
 
 class LinearRegressor(estimator.Estimator):
@@ -298,21 +299,19 @@ class LinearRegressor(estimator.Estimator):
       config: `RunConfig` object to configure the runtime settings.
       partitioner: Optional. Partitioner for input layer.
     """
+    head = head_lib._regression_head_with_mean_squared_error_loss(  # pylint: disable=protected-access
+        label_dimension=label_dimension, weight_column=weight_column)
+    def _model_fn(features, labels, mode, config):
+      return _linear_model_fn(
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          feature_columns=tuple(feature_columns or []),
+          optimizer=optimizer,
+          partitioner=partitioner,
+          config=config)
     super(LinearRegressor, self).__init__(
-        model_fn=_linear_model_fn,
+        model_fn=_model_fn,
         model_dir=model_dir,
-        config=config,
-        params={
-            # pylint: disable=protected-access
-            'head':
-                head_lib._regression_head_with_mean_squared_error_loss(
-                    label_dimension=label_dimension,
-                    weight_column=weight_column),
-            # pylint: enable=protected-access
-            'feature_columns':
-                feature_columns,
-            'optimizer':
-                optimizer,
-            'partitioner':
-                partitioner,
-        })
+        config=config)
diff --git a/tensorflow/python/estimator/canned/metric_keys.py b/tensorflow/python/estimator/canned/metric_keys.py
index 91e3bf1d83..7dc4bfe5ff 100644
--- a/tensorflow/python/estimator/canned/metric_keys.py
+++ b/tensorflow/python/estimator/canned/metric_keys.py
@@ -21,12 +21,10 @@ from __future__ import print_function
 from tensorflow.python.estimator import model_fn
 
 
-# TODO(pucker): Merge with model_fn.MetricKeys once we've worked out out naming
-# conventions.
 class MetricKeys(object):
   """Metric key strings."""
-  LOSS = model_fn.MetricKeys.LOSS
-  LOSS_MEAN = model_fn.MetricKeys.AVERAGE_LOSS
+  LOSS = model_fn.LOSS_METRIC_KEY
+  LOSS_MEAN = model_fn.AVERAGE_LOSS_METRIC_KEY
 
   ACCURACY = 'accuracy'
   # This is the best the model could do by always predicting one class.
diff --git a/tensorflow/python/estimator/canned/parsing_utils.py b/tensorflow/python/estimator/canned/parsing_utils.py
index af584965bb..f153272947 100644
--- a/tensorflow/python/estimator/canned/parsing_utils.py
+++ b/tensorflow/python/estimator/canned/parsing_utils.py
@@ -38,9 +38,9 @@ def classifier_parse_example_spec(feature_columns,
   * Users need to combine parsing spec of features with labels and weights
     (if any) since they are all parsed from same tf.Example instance. This
     utility combines these specs.
-  * It is difficult to map expected label by a classifier such as DNNClassifier
-    to corresponding tf.parse_example spec. This utility encodes it by getting
-    related information from users (key, dtype).
+  * It is difficult to map expected label by a classifier such as
+    `DNNClassifier` to corresponding tf.parse_example spec. This utility encodes
+    it by getting related information from users (key, dtype).
 
   Example output of parsing spec:
 
@@ -82,7 +82,7 @@ def classifier_parse_example_spec(feature_columns,
 
 
   # Input builders
-  def input_fn_train():  # Returns a dictionary which also contains labels.
+  def input_fn_train():  # Returns a tuple of features and labels.
     features = tf.contrib.learn.read_keyed_batch_features(
         file_pattern=train_files,
         batch_size=batch_size,
@@ -115,8 +115,13 @@ def classifier_parse_example_spec(feature_columns,
       key 'clicked' it should count as negative example by setting
       `label_deafault=0`. Type of this value should be compatible with
       `label_dtype`.
-    weight_column: (Optional) A `tf.feature_column.numeric_column` represents
-      the weight used given classifier.
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example. If it is a string, it is
+      used as a key to fetch weight tensor from the `features`. If it is a
+      `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
+      then weight_column.normalizer_fn is applied on it to get weight tensor.
 
   Returns:
     A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature`
@@ -129,7 +134,6 @@ def classifier_parse_example_spec(feature_columns,
       instance.
     ValueError: If `weight_column` is not a `_NumericColumn` instance.
     ValueError: if label_key is None.
-    ValueError: if label_dtype is neither an integer nor string
   """
   parsing_spec = fc.make_parse_example_spec(feature_columns)
   if label_key in parsing_spec:
@@ -158,3 +162,138 @@ def classifier_parse_example_spec(feature_columns,
 
   parsing_spec.update(weight_column._parse_example_spec)  # pylint: disable=protected-access
   return parsing_spec
+
+
+def regressor_parse_example_spec(feature_columns,
+                                 label_key,
+                                 label_dtype=dtypes.float32,
+                                 label_default=None,
+                                 label_dimension=1,
+                                 weight_column=None):
+  """Generates parsing spec for tf.parse_example to be used with regressors.
+
+  If users keep data in tf.Example format, they need to call tf.parse_example
+  with a proper feature spec. There are two main things that this utility helps:
+
+  * Users need to combine parsing spec of features with labels and weights
+    (if any) since they are all parsed from same tf.Example instance. This
+    utility combines these specs.
+  * It is difficult to map expected label by a regressor such as `DNNRegressor`
+    to corresponding tf.parse_example spec. This utility encodes it by getting
+    related information from users (key, dtype).
+
+  Example output of parsing spec:
+
+  ```python
+  # Define features and transformations
+  feature_b = tf.feature_column.numeric_column(...)
+  feature_c_bucketized = tf.feature_column.bucketized_column(
+    tf.feature_column.numeric_column("feature_c"), ...)
+  feature_a_x_feature_c = tf.feature_column.crossed_column(
+      columns=["feature_a", feature_c_bucketized], ...)
+
+  feature_columns = [feature_b, feature_c_bucketized, feature_a_x_feature_c]
+  parsing_spec = tf.estimator.regressor_parse_example_spec(
+      feature_columns, label_key='my-label')
+
+  # For the above example, regressor_parse_example_spec would return the dict:
+  assert parsing_spec == {
+    "feature_a": parsing_ops.VarLenFeature(tf.string),
+    "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
+    "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+    "my-label" : parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+  }
+  ```
+
+  Example usage with a regressor:
+
+  ```python
+  feature_columns = # define features via tf.feature_column
+  estimator = DNNRegressor(
+      hidden_units=[256, 64, 16],
+      feature_columns=feature_columns,
+      weight_column='example-weight',
+      label_dimension=3)
+  # This label configuration tells the regressor the following:
+  # * weights are retrieved with key 'example-weight'
+  # * label is a 3 dimension tensor with float32 dtype.
+
+
+  # Input builders
+  def input_fn_train():  # Returns a tuple of features and labels.
+    features = tf.contrib.learn.read_keyed_batch_features(
+        file_pattern=train_files,
+        batch_size=batch_size,
+        # creates parsing configuration for tf.parse_example
+        features=tf.estimator.classifier_parse_example_spec(
+            feature_columns,
+            label_key='my-label',
+            label_dimension=3,
+            weight_column='example-weight'),
+        reader=tf.RecordIOReader)
+     labels = features.pop('my-label')
+     return features, labels
+
+  estimator.train(input_fn=input_fn_train)
+  ```
+
+  Args:
+    feature_columns: An iterable containing all feature columns. All items
+      should be instances of classes derived from `_FeatureColumn`.
+    label_key: A string identifying the label. It means tf.Example stores labels
+      with this key.
+    label_dtype: A `tf.dtype` identifies the type of labels. By default it is
+      `tf.float32`.
+    label_default: used as label if label_key does not exist in given
+      tf.Example. By default default_value is none, which means
+      `tf.parse_example` will error out if there is any missing label.
+    label_dimension: Number of regression targets per example. This is the
+      size of the last dimension of the labels and logits `Tensor` objects
+      (typically, these have shape `[batch_size, label_dimension]`).
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example. If it is a string, it is
+      used as a key to fetch weight tensor from the `features`. If it is a
+      `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
+      then weight_column.normalizer_fn is applied on it to get weight tensor.
+
+  Returns:
+    A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature`
+    value.
+
+  Raises:
+    ValueError: If label is used in `feature_columns`.
+    ValueError: If weight_column is used in `feature_columns`.
+    ValueError: If any of the given `feature_columns` is not a `_FeatureColumn`
+      instance.
+    ValueError: If `weight_column` is not a `_NumericColumn` instance.
+    ValueError: if label_key is None.
+  """
+  parsing_spec = fc.make_parse_example_spec(feature_columns)
+  if label_key in parsing_spec:
+    raise ValueError('label should not be used as feature. '
+                     'label_key: {}, features: {}'.format(
+                         label_key, parsing_spec.keys()))
+  parsing_spec[label_key] = parsing_ops.FixedLenFeature(
+      (label_dimension,), label_dtype, label_default)
+
+  if weight_column is None:
+    return parsing_spec
+
+  if isinstance(weight_column, six.string_types):
+    weight_column = fc.numeric_column(weight_column)
+
+  if not isinstance(weight_column, fc._NumericColumn):  # pylint: disable=protected-access
+    raise ValueError('weight_column should be an instance of '
+                     'tf.feature_column.numeric_column. '
+                     'Given type: {} value: {}'.format(
+                         type(weight_column), weight_column))
+
+  if weight_column.key in parsing_spec:
+    raise ValueError('weight_column should not be used as feature. '
+                     'weight_column: {}, features: {}'.format(
+                         weight_column.key, parsing_spec.keys()))
+
+  parsing_spec.update(weight_column._parse_example_spec)  # pylint: disable=protected-access
+  return parsing_spec
diff --git a/tensorflow/python/estimator/canned/parsing_utils_test.py b/tensorflow/python/estimator/canned/parsing_utils_test.py
index c83823e750..366bb104ca 100644
--- a/tensorflow/python/estimator/canned/parsing_utils_test.py
+++ b/tensorflow/python/estimator/canned/parsing_utils_test.py
@@ -111,5 +111,101 @@ class ClassifierParseExampleSpec(test.TestCase):
           weight_column=not_a_numeric_column)
 
 
+class RegressorParseExampleSpec(test.TestCase):
+  """Tests tf.estimator.classifier_parse_example_spec."""
+
+  def test_defaults(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')], label_key='b')
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_int64(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        label_dtype=dtypes.int64)
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.int64),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_label_default_value(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        label_default=0.)
+    expected_spec = {
+        'a':
+            parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b':
+            parsing_ops.FixedLenFeature(
+                (1,), dtype=dtypes.float32, default_value=0.),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_label_dimension(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        label_dimension=3)
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((3,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_weight_column_as_string(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        weight_column='c')
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'c': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_weight_column_as_numeric_column(self):
+    parsing_spec = parsing_utils.regressor_parse_example_spec(
+        feature_columns=[fc.numeric_column('a')],
+        label_key='b',
+        weight_column=fc.numeric_column('c'))
+    expected_spec = {
+        'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+        'c': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
+    }
+    self.assertDictEqual(expected_spec, parsing_spec)
+
+  def test_label_key_should_not_be_used_as_feature(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'label should not be used as feature'):
+      parsing_utils.regressor_parse_example_spec(
+          feature_columns=[fc.numeric_column('a')], label_key='a')
+
+  def test_weight_column_should_not_be_used_as_feature(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'weight_column should not be used as feature'):
+      parsing_utils.regressor_parse_example_spec(
+          feature_columns=[fc.numeric_column('a')],
+          label_key='b',
+          weight_column=fc.numeric_column('a'))
+
+  def test_weight_column_should_be_a_numeric_column(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'tf.feature_column.numeric_column'):
+      not_a_numeric_column = 3
+      parsing_utils.regressor_parse_example_spec(
+          feature_columns=[fc.numeric_column('a')],
+          label_key='b',
+          weight_column=not_a_numeric_column)
+
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index ab49f36e5e..c2b97c20a3 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -52,7 +52,7 @@ from tensorflow.python.util import tf_inspect
 
 
 _VALID_MODEL_FN_ARGS = set(
-    ['features', 'labels', 'mode', 'params', 'config'])
+    ['features', 'labels', 'mode', 'params', 'self', 'config'])
 
 
 class Estimator(object):
@@ -95,10 +95,10 @@ class Estimator(object):
         * Args:
 
           * `features`: This is the first item returned from the `input_fn`
-                 passed to `train`, 'evaluate`, and `predict`. This should be a
+                 passed to `train`, `evaluate`, and `predict`. This should be a
                  single `Tensor` or `dict` of same.
           * `labels`: This is the second item returned from the `input_fn`
-                 passed to `train`, 'evaluate`, and `predict`. This should be a
+                 passed to `train`, `evaluate`, and `predict`. This should be a
                  single `Tensor` or `dict` of same (for multi-head models). If
                  mode is `ModeKeys.PREDICT`, `labels=None` will be passed. If
                  the `model_fn`'s signature does not accept `mode`, the
@@ -172,7 +172,7 @@ class Estimator(object):
       raise ValueError('model_fn must be provided to Estimator.')
     _verify_model_fn_args(model_fn, params)
     self._model_fn = model_fn
-    self._params = params or {}
+    self._params = copy.deepcopy(params or {})
 
   @property
   def model_dir(self):
@@ -334,7 +334,8 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       self._create_and_assert_global_step(g)
-      features = self._get_features_from_input_fn(input_fn)
+      features = self._get_features_from_input_fn(
+          input_fn, model_fn_lib.ModeKeys.PREDICT)
       estimator_spec = self._call_model_fn(features, None,
                                            model_fn_lib.ModeKeys.PREDICT)
       predictions = self._extract_keys(estimator_spec.predictions, predict_keys)
@@ -357,7 +358,7 @@ class Estimator(object):
               }
 
   def _assert_members_are_not_overridden(self):
-    allowed_overrides = set(['_create_global_step'])
+    allowed_overrides = set(['_call_input_fn', '_create_global_step'])
     estimator_members = set([m for m in Estimator.__dict__.keys()
                              if not m.startswith('__')])
     subclass_members = set(self.__class__.__dict__.keys())
@@ -484,8 +485,8 @@ class Estimator(object):
 
       return export_dir
 
-  def _get_features_from_input_fn(self, input_fn):
-    result = input_fn()
+  def _get_features_from_input_fn(self, input_fn, mode):
+    result = self._call_input_fn(input_fn, mode)
     if not ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
       logging.warning('Input graph does not contain a QueueRunner. '
                       'That means predict yields forever. '
@@ -549,6 +550,31 @@ class Estimator(object):
     assert step.dtype.is_integer
     return step
 
+  def _call_input_fn(self, input_fn, mode):
+    """Calls the input function.
+
+    Args:
+      input_fn: The input function.
+      mode: ModeKeys
+
+    Returns:
+      Either features or (features, labels) where features and labels are:
+        features - `Tensor` or dictionary of string feature name to `Tensor`.
+        labels - `Tensor` or dictionary of `Tensor` with labels.
+
+    Raises:
+      ValueError: if input_fn takes invalid arguments.
+    """
+    del mode  # unused
+    input_fn_args = _fn_args(input_fn)
+    kwargs = {}
+    if 'params' in input_fn_args:
+      kwargs['params'] = self.params
+    if 'config' in input_fn_args:
+      kwargs['config'] = self.config
+    with ops.device('/cpu:0'):
+      return input_fn(**kwargs)
+
   def _call_model_fn(self, features, labels, mode):
     """Calls model function.
 
@@ -563,7 +589,7 @@ class Estimator(object):
     Raises:
       ValueError: if model_fn returns invalid objects.
     """
-    model_fn_args = _model_fn_args(self._model_fn)
+    model_fn_args = _fn_args(self._model_fn)
     kwargs = {}
     if 'mode' in model_fn_args:
       kwargs['mode'] = mode
@@ -584,8 +610,8 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      with ops.device('/cpu:0'):
-        features, labels = input_fn()
+      features, labels = self._call_input_fn(
+          input_fn, model_fn_lib.ModeKeys.TRAIN)
       estimator_spec = self._call_model_fn(features, labels,
                                            model_fn_lib.ModeKeys.TRAIN)
       ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
@@ -639,7 +665,8 @@ class Estimator(object):
               tuple(chief_hooks) + tuple(estimator_spec.training_chief_hooks)),
           save_checkpoint_secs=0,  # Saving is handled by a hook.
           save_summaries_steps=self._config.save_summary_steps,
-          config=self._session_config) as mon_sess:
+          config=self._session_config,
+          log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
         loss = None
         while not mon_sess.should_stop():
           _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
@@ -666,17 +693,18 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = input_fn()
+      features, labels = self._call_input_fn(
+          input_fn, model_fn_lib.ModeKeys.EVAL)
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
 
-      if model_fn_lib.MetricKeys.LOSS in estimator_spec.eval_metric_ops:
+      if model_fn_lib.LOSS_METRIC_KEY in estimator_spec.eval_metric_ops:
         raise ValueError(
             'Metric with name "%s" is not allowed, because Estimator ' % (
-                model_fn_lib.MetricKeys.LOSS) +
+                model_fn_lib.LOSS_METRIC_KEY) +
             'already defines a default metric with the same name.')
       estimator_spec.eval_metric_ops[
-          model_fn_lib.MetricKeys.LOSS] = metrics_lib.mean(estimator_spec.loss)
+          model_fn_lib.LOSS_METRIC_KEY] = metrics_lib.mean(estimator_spec.loss)
 
       update_op, eval_dict = _extract_metric_update_ops(
           estimator_spec.eval_metric_ops)
@@ -749,7 +777,7 @@ def _get_replica_device_setter(config):
     return None
 
 
-def _model_fn_args(fn):
+def _fn_args(fn):
   """Get argument names for function-like object.
 
   Args:
@@ -762,6 +790,9 @@ def _model_fn_args(fn):
     ValueError: if partial function has positionally bound arguments
   """
   _, fn = tf_decorator.unwrap(fn)
+  if hasattr(fn, '__call__') and tf_inspect.ismethod(fn.__call__):
+    # Handle callables.
+    return tuple(tf_inspect.getargspec(fn.__call__).args)
   if hasattr(fn, 'func') and hasattr(fn, 'keywords') and hasattr(fn, 'args'):
     # Handle functools.partial and similar objects.
     return tuple([
@@ -774,7 +805,7 @@ def _model_fn_args(fn):
 
 def _verify_model_fn_args(model_fn, params):
   """Verifies model fn arguments."""
-  args = set(_model_fn_args(model_fn))
+  args = set(_fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
   if 'labels' not in args:
diff --git a/tensorflow/python/estimator/estimator_lib.py b/tensorflow/python/estimator/estimator_lib.py
index 52cedad012..8e7d966564 100644
--- a/tensorflow/python/estimator/estimator_lib.py
+++ b/tensorflow/python/estimator/estimator_lib.py
@@ -18,7 +18,15 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# pylint: disable=unused-import,line-too-long
+# pylint: disable=unused-import,line-too-long,wildcard-import
+from tensorflow.python.estimator.canned.dnn import DNNClassifier
+from tensorflow.python.estimator.canned.dnn import DNNRegressor
+from tensorflow.python.estimator.canned.dnn_linear_combined import DNNLinearCombinedClassifier
+from tensorflow.python.estimator.canned.dnn_linear_combined import DNNLinearCombinedRegressor
+from tensorflow.python.estimator.canned.linear import LinearClassifier
+from tensorflow.python.estimator.canned.linear import LinearRegressor
+from tensorflow.python.estimator.canned.parsing_utils import classifier_parse_example_spec
+from tensorflow.python.estimator.canned.parsing_utils import regressor_parse_example_spec
 from tensorflow.python.estimator.estimator import Estimator
 from tensorflow.python.estimator.export import export_lib as export
 from tensorflow.python.estimator.inputs import inputs
@@ -27,9 +35,17 @@ from tensorflow.python.estimator.model_fn import ModeKeys
 from tensorflow.python.estimator.run_config import RunConfig
 
 from tensorflow.python.util.all_util import remove_undocumented
-# pylint: enable=unused-import,line-too-long
+# pylint: enable=unused-import,line-too-long,wildcard-import
 
 _allowed_symbols = [
+    'DNNClassifier',
+    'DNNRegressor',
+    'DNNLinearCombinedClassifier',
+    'DNNLinearCombinedRegressor',
+    'LinearClassifier',
+    'LinearRegressor',
+    'classifier_parse_example_spec',
+    'regressor_parse_example_spec',
     'inputs',
     'export',
     'Estimator',
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index b86afece43..77aff66ccf 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -120,6 +120,9 @@ class EstimatorInheritanceConstraintTest(test.TestCase):
       def __init__(self):
         super(_Estimator, self).__init__(model_fn=dummy_model_fn)
 
+      def _call_input_fn(self, input_fn, mode):
+        return input_fn()
+
       def _create_global_step(self, graph):
         pass
 
@@ -252,6 +255,17 @@ class EstimatorConstructorTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, 'params'):
       estimator.Estimator(model_fn=model_fn, params={'hidden_layers': 4})
 
+  def test_internal_params_is_a_deepcopy(self):
+
+    def model_fn(features, labels, params):
+      _, _, _ = features, labels, params
+
+    params = {'hidden_layers': 4}
+    est = estimator.Estimator(model_fn=model_fn, params=params)
+
+    params['hidden_layers'] = 5
+    self.assertEqual(4, est.params['hidden_layers'])
+
   def test_not_known_model_fn_args(self):
 
     def model_fn(features, labels, something):
@@ -325,6 +339,79 @@ def _make_input_fn(features, labels):
 
 class EstimatorTrainTest(test.TestCase):
 
+  def test_callable_model_fn(self):
+    expected_features = {'x': 42., 'y': 43.}
+    expected_labels = 44.
+
+    model_fn_call_count = [0]
+
+    test_self = self
+
+    class ModelFn(object):
+
+      def __call__(self, features, labels):
+        model_fn_call_count[0] += 1
+        test_self.assertItemsEqual(expected_features.keys(), features.keys())
+        return _estimator_spec(
+            expected_features, expected_labels, features, labels,
+            model_fn_lib.ModeKeys.TRAIN)
+
+    with self.assertRaisesRegexp(ValueError, 'does not include params'):
+      estimator.Estimator(model_fn=ModelFn(), params={'a': 'b'})
+    est = estimator.Estimator(model_fn=ModelFn(), config=run_config.RunConfig())
+    self.assertEqual(0, model_fn_call_count[0])
+    est.train(
+        input_fn=_make_input_fn(expected_features, expected_labels), steps=1)
+    self.assertEqual(1, model_fn_call_count[0])
+
+  def test_callable_input_fn(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    test_self = self
+
+    class InputFn(object):
+
+      def __call__(self, params, config):
+        input_fn_call_count[0] += 1
+        test_self.assertEqual(expected_params, params)
+        test_self.assertEqual(4321, config.tf_random_seed)
+        return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.train(InputFn(), steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.train(_input_fn, steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_minimal_model_fn_args(self):
     expected_features = {'x': 42., 'y': 43.}
     expected_labels = 44.
@@ -665,6 +752,29 @@ class _StepCounterHook(session_run_hook.SessionRunHook):
 
 class EstimatorEvaluateTest(test.TestCase):
 
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del params, config
+      return model_fn_global_step_incrementer(features, labels, mode)
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    est.train(dummy_input_fn, steps=1)
+    self.assertEqual(0, input_fn_call_count[0])
+    est.evaluate(_input_fn, steps=1)
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_model_fn_must_return_estimator_spec(self):
     def _model_fn(features, labels, mode):
       _, _ = features, labels
@@ -770,9 +880,9 @@ class EstimatorEvaluateTest(test.TestCase):
     est = estimator.Estimator(model_fn=_model_fn_with_incremental_loss)
     est.train(dummy_input_fn, steps=1)
     scores = est.evaluate(dummy_input_fn, steps=5)
-    self.assertIn(model_fn_lib.MetricKeys.LOSS, scores)
+    self.assertIn(model_fn_lib.LOSS_METRIC_KEY, scores)
     # Average loss will be (2 + 4 + 6 + 8 + 10)/5=6
-    self.assertAlmostEqual(6., scores[model_fn_lib.MetricKeys.LOSS])
+    self.assertAlmostEqual(6., scores[model_fn_lib.LOSS_METRIC_KEY])
 
   def test_hooks_should_be_session_run_hook(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
@@ -866,6 +976,33 @@ class EstimatorEvaluateTest(test.TestCase):
 
 class EstimatorPredictTest(test.TestCase):
 
+  def test_input_fn_args(self):
+    expected_params = {'batch_size': 10}
+    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
+    input_fn_call_count = [0]
+
+    def _model_fn(features, labels, mode, params, config):
+      del features, labels, params, config
+      return model_fn_lib.EstimatorSpec(
+          mode,
+          loss=constant_op.constant(0.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          predictions=constant_op.constant([[10.]]))
+
+    def _input_fn(params, config):
+      input_fn_call_count[0] += 1
+      self.assertEqual(expected_params, params)
+      self.assertEqual(4321, config.tf_random_seed)
+      return dummy_input_fn()
+
+    est = estimator.Estimator(model_fn=_model_fn,
+                              params=expected_params,
+                              config=expected_config)
+    est.train(dummy_input_fn, steps=1)
+    self.assertEqual(0, input_fn_call_count[0])
+    next(est.predict(_input_fn))
+    self.assertEqual(1, input_fn_call_count[0])
+
   def test_no_trained_model_in_model_dir(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
     with self.assertRaisesRegexp(ValueError,
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index 1aa2623962..2508a1a3b1 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -48,10 +48,8 @@ class ModeKeys(object):
   PREDICT = 'infer'
 
 
-class MetricKeys(object):
-  """Metric key strings."""
-  LOSS = 'loss'
-  AVERAGE_LOSS = 'average_loss'
+LOSS_METRIC_KEY = 'loss'
+AVERAGE_LOSS_METRIC_KEY = 'average_loss'
 
 
 class EstimatorSpec(
diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py
index 30ba18d07d..ac521cc791 100644
--- a/tensorflow/python/estimator/run_config.py
+++ b/tensorflow/python/estimator/run_config.py
@@ -35,6 +35,7 @@ _DEFAULT_REPLACEABLE_LIST = [
     'session_config',
     'keep_checkpoint_max',
     'keep_checkpoint_every_n_hours',
+    'log_step_count_steps'
 ]
 
 _SAVE_CKPT_ERR = (
@@ -89,6 +90,8 @@ def _validate_properties(run_config):
             message='keep_checkpoint_max should be >= 0')
   _validate('keep_checkpoint_every_n_hours', lambda keep_hours: keep_hours > 0,
             message='keep_checkpoint_every_n_hours should be > 0')
+  _validate('log_step_count_steps', lambda num_steps: num_steps > 0,
+            message='log_step_count_steps should be > 0')
 
   _validate('tf_random_seed', lambda seed: isinstance(seed, six.integer_types),
             message='tf_random_seed must be integer.')
@@ -112,6 +115,7 @@ class RunConfig(object):
     self._session_config = None
     self._keep_checkpoint_max = 5
     self._keep_checkpoint_every_n_hours = 10000
+    self._log_step_count_steps = 100
     _validate_properties(self)
 
   @property
@@ -174,6 +178,10 @@ class RunConfig(object):
   def keep_checkpoint_every_n_hours(self):
     return self._keep_checkpoint_every_n_hours
 
+  @property
+  def log_step_count_steps(self):
+    return self._log_step_count_steps
+
   @property
   def model_dir(self):
     return self._model_dir
@@ -190,6 +198,7 @@ class RunConfig(object):
       - `session_config`,
       - `keep_checkpoint_max`,
       - `keep_checkpoint_every_n_hours`,
+      - `log_step_count_steps`,
 
     In addition, either `save_checkpoints_steps` or `save_checkpoints_secs`
     can be set (should not be both).
diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index 99aedc5d4b..c75144a9c9 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -400,11 +400,14 @@ def make_parse_example_spec(feature_columns):
   ```
 
   For the above example, make_parse_example_spec would return the dict:
+
+  ```python
   {
-    "feature_a": parsing_ops.VarLenFeature(tf.string),
-    "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
-    "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+      "feature_a": parsing_ops.VarLenFeature(tf.string),
+      "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
+      "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
   }
+  ```
 
   Args:
     feature_columns: An iterable containing all feature columns. All items
@@ -594,15 +597,21 @@ def bucketized_column(source_column, boundaries):
   `[1., 2.)`, and `[2., +inf)`.
 
   For example, if the inputs are
-    `boundaries` = [0, 10, 100]
-    input tensor = [[-5, 10000]
-                    [150,   10]
-                    [5,    100]]
+
+  ```python
+  boundaries = [0, 10, 100]
+  input tensor = [[-5, 10000]
+                  [150,   10]
+                  [5,    100]]
+  ```
 
   then the output will be
-    output = [[0, 3]
-              [3, 2]
-              [1, 3]]
+
+  ```python
+  output = [[0, 3]
+            [3, 2]
+            [1, 3]]
+  ```
 
   Example:
 
@@ -621,6 +630,7 @@ def bucketized_column(source_column, boundaries):
 
   `bucketized_column` can also be crossed with another categorical column using
   `crossed_column`:
+
   ```python
   price = numeric_column('price')
   # bucketized_column converts numerical feature to a categorical one.
@@ -745,6 +755,7 @@ def categorical_column_with_vocabulary_file(
   abbreviation. All inputs with values in that file are assigned an ID 0-49,
   corresponding to its line number. All other values are hashed and assigned an
   ID 50-54.
+
   ```python
   states = categorical_column_with_vocabulary_file(
       key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
@@ -759,6 +770,7 @@ def categorical_column_with_vocabulary_file(
   other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX'
   in input, and other values missing from the file, will be assigned ID 0. All
   others are assigned the corresponding line number 1-50.
+
   ```python
   states = categorical_column_with_vocabulary_file(
       key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
@@ -769,6 +781,7 @@ def categorical_column_with_vocabulary_file(
   ```
 
   And to make an embedding with either:
+
   ```python
   columns = [embedding_column(states, 3),...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
@@ -847,6 +860,7 @@ def categorical_column_with_vocabulary_list(
   inputs are assigned `default_value` 0.
 
   Linear model:
+
   ```python
   colors = categorical_column_with_vocabulary_list(
       key='colors', vocabulary_list=('X', 'R', 'G', 'B', 'Y'), default_value=0)
@@ -856,6 +870,7 @@ def categorical_column_with_vocabulary_list(
   ```
 
   Embedding for a DNN model:
+
   ```python
   columns = [embedding_column(colors, 3),...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
@@ -926,6 +941,7 @@ def categorical_column_with_identity(key, num_buckets, default_value=None):
   literal 0 in inputs will result in the same default ID.
 
   Linear model:
+
   ```python
   video_id = categorical_column_with_identity(
       key='video_id', num_buckets=1000000, default_value=0)
@@ -935,6 +951,7 @@ def categorical_column_with_identity(key, num_buckets, default_value=None):
   ```
 
   Embedding for a DNN model:
+
   ```python
   columns = [embedding_column(video_id, 9),...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
@@ -976,8 +993,8 @@ def indicator_column(categorical_column):
   `embedding_column` if the inputs are sparse.
 
   ```python
-  name = indicator_column(categorical_column_with_vocabulary_list('name',
-      ['bob', 'george', 'wanda'])
+  name = indicator_column(categorical_column_with_vocabulary_list(
+      'name', ['bob', 'george', 'wanda'])
   columns = [name, ...]
   features = tf.parse_example(..., features=make_parse_example_spec(columns))
   dense_tensor = input_layer(features, columns)
@@ -1009,6 +1026,8 @@ def weighted_categorical_column(
   Example:
 
   Input `tf.Example` objects:
+
+  ```proto
   [
     features {
       feature {
@@ -1031,6 +1050,7 @@ def weighted_categorical_column(
       }
     }
   ]
+  ```
 
   ```python
   categorical_column = categorical_column_with_hash_bucket(
@@ -1076,22 +1096,41 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
     Hash(cartesian product of features) % `hash_bucket_size`
 
   For example, if the input features are:
-  * SparseTensor referred by first key: shape = [2, 2]
-      [0, 0]: "a"
-      [1, 0]: "b"
-      [1, 1]: "c"
 
-  * SparseTensor referred by second key: shape = [2, 1]
-      [0, 0]: "d"
-      [1, 0]: "e"
+  * SparseTensor referred by first key:
+
+    ```python
+    shape = [2, 2]
+    {
+        [0, 0]: "a"
+        [1, 0]: "b"
+        [1, 1]: "c"
+    }
+    ```
+
+  * SparseTensor referred by second key:
+
+    ```python
+    shape = [2, 1]
+    {
+        [0, 0]: "d"
+        [1, 0]: "e"
+    }
+    ```
 
   then crossed feature will look like:
-      shape = [2, 2]
+
+  ```python
+   shape = [2, 2]
+  {
       [0, 0]: Hash64("d", Hash64("a")) % hash_bucket_size
       [1, 0]: Hash64("e", Hash64("b")) % hash_bucket_size
       [1, 1]: Hash64("e", Hash64("c")) % hash_bucket_size
+  }
+  ```
 
   Here is an example to create a linear model with crosses of string features:
+
   ```python
   keywords_x_doc_terms = crossed_column(['keywords', 'doc_terms'], 50K)
   columns = [keywords_x_doc_terms, ...]
@@ -1100,6 +1139,7 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
   ```
 
   You could also use vocabulary lookup before crossing:
+
   ```python
   keywords = categorical_column_with_vocabulary_file(
       'keywords', '/path/to/vocabulary/file', vocabulary_size=1K)
@@ -1111,6 +1151,7 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
 
   If an input feature is of numeric type, you can use
   `categorical_column_with_identity`, or `bucketized_column`, as in the example:
+
   ```python
   # vertical_id is an integer categorical feature.
   vertical_id = categorical_column_with_identity('vertical_id', 10K)
@@ -1125,6 +1166,7 @@ def crossed_column(keys, hash_bucket_size, hash_key=None):
 
   To use crossed column in DNN model, you need to add it in an embedding column
   as in this example:
+
   ```python
   vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
   vertical_id_x_price_embedded = embedding_column(vertical_id_x_price, 10)
diff --git a/tensorflow/python/framework/cpp_shape_inference.cc b/tensorflow/python/framework/cpp_shape_inference.cc
index d5e58c174b..8ebdbafb85 100644
--- a/tensorflow/python/framework/cpp_shape_inference.cc
+++ b/tensorflow/python/framework/cpp_shape_inference.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/python/framework/cpp_shape_inference.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index ccd1099f80..f52127f119 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -27,6 +27,7 @@ import sys
 import threading
 
 import six
+from six.moves import xrange  # pylint: disable=redefined-builtin
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import function_pb2
 from tensorflow.core.framework import graph_pb2
@@ -1169,7 +1170,7 @@ class Operation(object):
       a._add_consumer(self)  # pylint: disable=protected-access
     if output_types is None:
       output_types = []
-    self._output_types = output_types
+    self._output_types_val = output_types
     self._outputs = [Tensor(self, i, output_type)
                      for i, output_type in enumerate(output_types)]
     if input_types is None:
@@ -1372,7 +1373,48 @@ class Operation(object):
       assigned, or an empty string if it has not been assigned to a
       device.
     """
-    return self._node_def.device
+    if _USE_C_API:
+      # TODO(iga): Remove this assert after converting to C API by default.
+      # Just being a bit paranoid here.
+      assert self._node_def.device == c_api.TF_OperationDevice(self._c_op)
+      return c_api.TF_OperationDevice(self._c_op)
+    else:
+      return self._node_def.device
+
+  @property
+  def _output_types(self):
+    """List this operation's output types.
+
+    Returns:
+      List of the types of the Tensors computed by this operation.
+      Each element in the list is an integer whose value is one of
+      the TF_DataType enums defined in c_api.h
+      The length of this list indicates the number of output endpoints
+      of the operation.
+    """
+    if _USE_C_API:
+      num_outputs = c_api.TF_OperationNumOutputs(self._c_op)
+      output_types = [c_api.TF_OperationOutputType(self._tf_output(i)) for
+                      i in xrange(num_outputs)]
+      # TODO(iga): Remove this assert after converting to C API by default.
+      # Just being a bit paranoid here.
+      assert self._output_types_val == output_types
+      # In all the tests we have output_types that are passed into
+      # Operation.__init__ are a list of ints (which is illegal according
+      # to the docstring), but input_types are instances of DType.
+      # This extra assert is to catch if we ever use DType for output_types.
+      if output_types:
+        assert isinstance(output_types[0], int)
+      return output_types
+    else:
+      return self._output_types_val
+
+  def _tf_output(self, output_idx):
+    """Create and return a new TF_Output for output_idx'th output of this op."""
+    tf_output = c_api.TF_Output()
+    tf_output.oper = self._c_op
+    tf_output.index = output_idx
+    return tf_output
 
   def _set_device(self, device):
     """Set the device of this operation.
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 00891d2d21..2d0a6cbf8a 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -39,6 +39,7 @@ from tensorflow.python.framework import test_util
 from tensorflow.python.framework import versions
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resources
 from tensorflow.python.ops import variable_scope
@@ -1063,7 +1064,7 @@ class ComparisonTest(test_util.TensorFlowTestCase):
 class ControlDependenciesTest(test_util.TensorFlowTestCase):
 
   def testBasic(self):
-    ops._USE_C_API = True
+    ops._USE_C_API = True  # pylint: disable=protected-access
     try:
       g = ops.Graph()
       with g.as_default():
@@ -1084,7 +1085,7 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase):
       # e should be dominated by c.
       self.assertEqual(e.op.control_inputs, [])
     finally:
-      ops._USE_C_API = False
+      ops._USE_C_API = False  # pylint: disable=protected-access
 
   def testBasicWithConversion(self):
     g = ops.Graph()
@@ -1772,5 +1773,54 @@ class TracebackTest(test_util.TensorFlowTestCase):
           self.assertEquals(frame, frame_with_start_line[:-1])
 
 
+class OutputTypesTest(test_util.TensorFlowTestCase):
+  """Tests Operation._output_types property.
+
+  This test should not exist as _output_types is a private property.
+  This property is used by util.copy_elements and its tests would normally
+  cover Operation._output_types. However, we can't yet run these tests in C
+  API mode because their use _set_device method. This test will be deleted
+  once we port _set_device and run the copy tests with C API on.
+  """
+  # TODO(iga): Remove this test
+
+  def setUp(self):
+    self.prev_use_c_api = ops._USE_C_API  # pylint: disable=protected-access
+    ops._USE_C_API = True  # pylint: disable=protected-access
+
+  def tearDown(self):
+    ops._USE_C_API = self.prev_use_c_api  # pylint: disable=protected-access
+
+  def testOneOutput(self):
+    g = ops.Graph()
+    with g.as_default():
+      # Using a constant because creating unregistered ops
+      # doesn't work with the C API.
+      op = constant_op.constant(12, dtype=dtypes.uint16).op
+      # pylint: disable=protected-access
+      self.assertEqual([types_pb2.DT_UINT16], op._output_types)
+      # pylint: enable=protected-access
+
+  def testTwoDifferentOutputs(self):
+    g = ops.Graph()
+    with g.as_default():
+      x = constant_op.constant([1, 1, 2, 4, 4, 4, 7, 8, 8],
+                               dtype=dtypes.double)
+      y, _ = gen_array_ops.unique(x)
+      self.assertEqual([types_pb2.DT_DOUBLE, types_pb2.DT_INT32],
+                       y.op._output_types)  # pylint: disable=protected-access
+
+  def testThreeOutputs(self):
+    g = ops.Graph()
+    with g.as_default():
+      # Using a split operationt because creating unregistered ops
+      # doesn't work with the C API.
+      a = constant_op.constant("abc", dtype=dtypes.string, shape=[5, 30])
+      split0, _, _ = array_ops.split(a, [4, 15, 11], 1)
+      # pylint: disable=protected-access
+      self.assertEqual([types_pb2.DT_STRING] * 3, split0.op._output_types)
+      # pylint: enable=protected-access
+
+
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/python/grappler/cost_analyzer.i b/tensorflow/python/grappler/cost_analyzer.i
index 6066b6131f..1f024e439d 100644
--- a/tensorflow/python/grappler/cost_analyzer.i
+++ b/tensorflow/python/grappler/cost_analyzer.i
@@ -48,7 +48,10 @@ per_node_report) {
   cfg.apply_optimizations = false;
   std::unique_ptr<tensorflow::grappler::GrapplerItem> item =
       tensorflow::grappler::GrapplerItemFromMetaGraphDef("metagraph", metagraph, cfg);
-
+  if (!item) {
+    return "Error: failed to preprocess metagraph: check your log file for errors";
+  }
+  
   // TODO(bsteiner): we should wrap the tf session instead to properly handle the case of a
   // distributed setup.
   const int timeout_s = 3600;
diff --git a/tensorflow/python/grappler/model_analyzer.cc b/tensorflow/python/grappler/model_analyzer.cc
new file mode 100644
index 0000000000..1374967ca7
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.cc
@@ -0,0 +1,75 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/python/grappler/model_analyzer.h"
+
+#include <iomanip>
+#include "tensorflow/core/grappler/costs/graph_properties.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+
+namespace tensorflow {
+namespace grappler {
+
+ModelAnalyzer::ModelAnalyzer(const GrapplerItem& item) : item_(item) {}
+
+Status ModelAnalyzer::GenerateReport(std::ostream& os) {
+  GraphProperties properties(item_);
+  TF_RETURN_IF_ERROR(properties.InferStatically());
+
+  for (const auto& node : item_.MainOpsFanin()) {
+    PrintNodeInfo(node, properties, os);
+  }
+  for (const auto& node : item_.EnqueueOpsFanin()) {
+    PrintNodeInfo(node, properties, os);
+  }
+
+  return Status::OK();
+}
+
+void ModelAnalyzer::PrintNodeInfo(const NodeDef* node,
+                                  const GraphProperties& properties,
+                                  std::ostream& os) const {
+  os << node->name() << " [" << node->op() << "]" << std::endl;
+  if (properties.HasOutputProperties(node->name())) {
+    std::vector<OpInfo::TensorProperties> props =
+        properties.GetOutputProperties(node->name());
+    for (int i = 0; i < props.size(); ++i) {
+      const OpInfo::TensorProperties& prop = props[i];
+      os << "\t"
+         << "output " << i << " (" << DataTypeString(prop.dtype())
+         << ") has shape ";
+      if (prop.shape().unknown_rank()) {
+        os << "?";
+      } else {
+        os << "[";
+        for (int i = 0; i < prop.shape().dim_size(); ++i) {
+          if (i > 0) {
+            os << ", ";
+          }
+          if (prop.shape().dim(i).size() < 0) {
+            os << "?";
+          } else {
+            os << prop.shape().dim(i).size();
+          }
+        }
+        os << "]";
+      }
+      os << std::endl;
+    }
+  }
+}
+
+}  // end namespace grappler
+}  // end namespace tensorflow
diff --git a/tensorflow/python/grappler/model_analyzer.h b/tensorflow/python/grappler/model_analyzer.h
new file mode 100644
index 0000000000..a14034103c
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.h
@@ -0,0 +1,46 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_MODEL_ANALYZER_H_
+#define TENSORFLOW_CORE_GRAPPLER_COSTS_MODEL_ANALYZER_H_
+
+#include <iostream>
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+namespace grappler {
+struct GrapplerItem;
+class GraphProperties;
+
+// Generate a report detailing how much information is known statically for most
+// operations in the model, including output data types and output shapes.
+class ModelAnalyzer {
+ public:
+  explicit ModelAnalyzer(const GrapplerItem& item);
+  Status GenerateReport(std::ostream& os);
+
+ private:
+  void PrintNodeInfo(const NodeDef* node, const GraphProperties& properties,
+                     std::ostream& os) const;
+
+  const GrapplerItem& item_;
+};
+
+}  // end namespace grappler
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_GRAPPLER_COSTS_MODEL_ANALYZER_H_
diff --git a/tensorflow/python/grappler/model_analyzer.i b/tensorflow/python/grappler/model_analyzer.i
new file mode 100644
index 0000000000..d74bd37c63
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.i
@@ -0,0 +1,62 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+%include "tensorflow/python/lib/core/strings.i"
+%include "tensorflow/python/platform/base.i"
+
+%typemap(in) const tensorflow::MetaGraphDef& (tensorflow::MetaGraphDef temp) {
+  char* c_string;
+  Py_ssize_t py_size;
+  if (PyBytes_AsStringAndSize($input, &c_string, &py_size) == -1) {
+    // Python has raised an error (likely TypeError or UnicodeEncodeError).
+    SWIG_fail;
+  }
+
+  if (!temp.ParseFromString(string(c_string, py_size))) {
+    PyErr_SetString(
+        PyExc_TypeError,
+        "The MetaGraphDef could not be parsed as a valid protocol buffer");
+    SWIG_fail;
+  }
+  $1 = &temp;
+}
+
+%{
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/grappler/grappler_item_builder.h"
+#include "tensorflow/python/grappler/model_analyzer.h"
+%}
+
+%{
+string GenerateModelReport(const tensorflow::MetaGraphDef& metagraph) {
+  tensorflow::grappler::ItemConfig cfg;
+  cfg.apply_optimizations = false;
+  std::unique_ptr<tensorflow::grappler::GrapplerItem> item =
+      tensorflow::grappler::GrapplerItemFromMetaGraphDef("metagraph", metagraph, cfg);
+  if (!item) {
+    return "Error: failed to preprocess metagraph: check your log file for errors";
+  }
+  
+  string suffix;
+  tensorflow::grappler::ModelAnalyzer analyzer(*item);
+
+  std::stringstream os;
+  analyzer.GenerateReport(os);
+  return os.str();
+}
+
+%}
+
+string GenerateModelReport(const tensorflow::MetaGraphDef& metagraph);
diff --git a/tensorflow/python/grappler/model_analyzer.py b/tensorflow/python/grappler/model_analyzer.py
new file mode 100644
index 0000000000..c852d71ad8
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer.py
@@ -0,0 +1,37 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Provides a proper python API for the symbols exported through swig."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python import pywrap_tensorflow as tf_wrap
+from tensorflow.python.framework import errors
+
+
+def GenerateModelReport(metagraph):
+  """Report what's known statically about each node in the provided metagraph.
+
+  Args:
+    metagraph: A TensorFlow MetaGraphDef.
+
+  Returns:
+    A string containing the report.
+  """
+  with errors.raise_exception_on_not_ok_status():
+    ret_from_swig = tf_wrap.GenerateModelReport(metagraph.SerializeToString())
+
+  return ret_from_swig
diff --git a/tensorflow/python/grappler/model_analyzer_test.py b/tensorflow/python/grappler/model_analyzer_test.py
new file mode 100644
index 0000000000..b59d1650f4
--- /dev/null
+++ b/tensorflow/python/grappler/model_analyzer_test.py
@@ -0,0 +1,54 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the cost analyzer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import meta_graph
+from tensorflow.python.framework import ops
+from tensorflow.python.grappler import model_analyzer
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+class PyWrapOptimizeGraphTest(test.TestCase):
+
+  def testBasic(self):
+    """Make sure arguments can be passed correctly."""
+    a = constant_op.constant([10, 11], name="a")
+    b = constant_op.constant([10], name="b")
+    c = math_ops.add(a, b, name="c")
+    d = math_ops.add_n([a, c], name="d")
+    train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
+    train_op.append(d)
+    mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
+
+    report = model_analyzer.GenerateModelReport(mg)
+
+    # Check the report headers
+    self.assertTrue(b"a [Const]" in report)
+    self.assertTrue(b"a [Const]" in report)
+    self.assertTrue(b"c [Add]" in report)
+    self.assertTrue(b"d [AddN]" in report)
+
+    # Also print the report to make it easier to debug
+    print("{}".format(report))
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index c89118d4bf..d8e069105f 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2479,6 +2479,7 @@ cuda_py_test(
         "//tensorflow/python:embedding_ops",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:partitioned_variables",
         "//tensorflow/python:platform",
diff --git a/tensorflow/python/kernel_tests/argmax_op_test.py b/tensorflow/python/kernel_tests/argmax_op_test.py
index a5352561aa..ae171dec55 100644
--- a/tensorflow/python/kernel_tests/argmax_op_test.py
+++ b/tensorflow/python/kernel_tests/argmax_op_test.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
@@ -36,6 +37,8 @@ class ArgMaxTest(test.TestCase):
       ans = method(x, dimension=dimension)
       if expected_err_re is None:
         tf_ans = ans.eval()
+        # Defaults to int64 output.
+        self.assertEqual(np.int64, tf_ans.dtype)
         self.assertAllEqual(tf_ans, expected_values)
         self.assertShapeEqual(expected_values, ans)
       else:
@@ -71,6 +74,23 @@ class ArgMaxTest(test.TestCase):
     self._testBasic(np.float32)
     self._testDim(np.float32)
 
+  def testFloatInt32Output(self):
+    x = np.asarray(100 * np.random.randn(200), dtype=np.float32)
+    expected_values = x.argmax()
+    with self.test_session(use_gpu=True):
+      ans = math_ops.argmax(x, dimension=0, output_type=dtypes.int32)
+      tf_ans = ans.eval()
+      self.assertEqual(np.int32, tf_ans.dtype)
+      # The values are equal when comparing int32 to int64 because
+      # the values don't have a range that exceeds 32-bit integers.
+      self.assertAllEqual(tf_ans, expected_values)
+    expected_values = x.argmin()
+    with self.test_session(use_gpu=True):
+      ans = math_ops.argmin(x, dimension=0, output_type=dtypes.int32)
+      tf_ans = ans.eval()
+      self.assertEqual(np.int32, tf_ans.dtype)
+      self.assertAllEqual(tf_ans, expected_values)
+
   def testDouble(self):
     self._testBasic(np.float64)
     self._testDim(np.float64)
diff --git a/tensorflow/python/kernel_tests/distributions/BUILD b/tensorflow/python/kernel_tests/distributions/BUILD
index 50a0795200..f124065e0d 100644
--- a/tensorflow/python/kernel_tests/distributions/BUILD
+++ b/tensorflow/python/kernel_tests/distributions/BUILD
@@ -29,6 +29,24 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "util_test",
+    size = "small",
+    srcs = ["util_test.py"],
+    additional_deps = [
+        "//tensorflow/python/ops/distributions",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "kullback_leibler_test",
     size = "small",
diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
index ef93c4dab0..a269d72273 100644
--- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
+++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
@@ -172,7 +172,7 @@ class BernoulliTest(test.TestCase):
       dist = bernoulli.Bernoulli(probs=p, validate_args=True)
       with self.assertRaisesOpError("must be non-negative."):
         dist.prob([1, 1, -1]).eval()
-      with self.assertRaisesOpError("is not less than or equal to 1."):
+      with self.assertRaisesOpError("Elements cannot exceed 1."):
         dist.prob([2, 0, 1]).eval()
 
   def testPmfWithP(self):
diff --git a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py
index 2f8f85866d..7922fb0606 100644
--- a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py
+++ b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py
@@ -74,10 +74,10 @@ class DirichletMultinomialTest(test.TestCase):
       dist = ds.DirichletMultinomial(n, alpha, validate_args=True)
       dist.prob([2., 3, 0]).eval()
       dist.prob([3., 0, 2]).eval()
-      with self.assertRaisesOpError("counts must be non-negative"):
+      with self.assertRaisesOpError("must be non-negative"):
         dist.prob([-1., 4, 2]).eval()
       with self.assertRaisesOpError(
-          "counts last-dimension must sum to `self.total_count`"):
+          "last-dimension must sum to `self.total_count`"):
         dist.prob([3., 3, 0]).eval()
 
   def testPmfNonIntegerCounts(self):
@@ -91,7 +91,7 @@ class DirichletMultinomialTest(test.TestCase):
       # Both equality and integer checking fail.
       placeholder = array_ops.placeholder(dtypes.float32)
       with self.assertRaisesOpError(
-          "counts cannot contain fractional components"):
+          "cannot contain fractional components"):
         dist.prob(placeholder).eval(feed_dict={placeholder: [1.0, 2.5, 1.5]})
       dist = ds.DirichletMultinomial(n, alpha, validate_args=False)
       dist.prob([1., 2., 3.]).eval()
diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
new file mode 100644
index 0000000000..07b1ba71bb
--- /dev/null
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -0,0 +1,719 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for utility functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import importlib
+
+import numpy as np
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradient_checker
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops.distributions import util as distribution_util
+import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging
+
+
+def try_import(name):  # pylint: disable=invalid-name
+  module = None
+  try:
+    module = importlib.import_module(name)
+  except ImportError as e:
+    tf_logging.warning("Could not import %s: %s" % (name, str(e)))
+  return module
+
+
+special = try_import("scipy.special")
+
+
+def _logit(x):
+  x = np.asarray(x)
+  return np.log(x) - np.log1p(-x)
+
+
+class AssertCloseTest(test.TestCase):
+
+  def testAssertCloseIntegerDtype(self):
+    x = array_ops.placeholder(dtypes.int32)
+    y = x
+    z = array_ops.placeholder(dtypes.int32)
+    feed_dict = {x: [1, 5, 10, 15, 20], z: [2, 5, 10, 15, 20]}
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
+          array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
+          array_ops.identity(y).eval(feed_dict=feed_dict)
+
+  def testAssertCloseNonIntegerDtype(self):
+    x = array_ops.placeholder(dtypes.float32)
+    y = x + 1e-8
+    z = array_ops.placeholder(dtypes.float32)
+    feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]}
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_close(x, y)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with ops.control_dependencies([distribution_util.assert_close(y, x)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(x, z)]):
+          array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
+          array_ops.identity(y).eval(feed_dict=feed_dict)
+
+  def testAssertCloseEpsilon(self):
+    x = [0., 5, 10, 15, 20]
+    # x != y
+    y = [0.1, 5, 10, 15, 20]
+    # x = z
+    z = [1e-8, 5, 10, 15, 20]
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_close(x, z)]):
+        array_ops.identity(x).eval()
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(x, y)]):
+          array_ops.identity(x).eval()
+
+      with self.assertRaisesOpError("Condition x ~= y"):
+        with ops.control_dependencies([distribution_util.assert_close(y, z)]):
+          array_ops.identity(y).eval()
+
+  def testAssertIntegerForm(self):
+    # This should only be detected as an integer.
+    x = array_ops.placeholder(dtypes.float32)
+    y = array_ops.placeholder(dtypes.float32)
+    # First component isn't less than float32.eps = 1e-7
+    z = array_ops.placeholder(dtypes.float32)
+    # This shouldn"t be detected as an integer.
+    w = array_ops.placeholder(dtypes.float32)
+    feed_dict = {x: [1., 5, 10, 15, 20], y: [1.1, 5, 10, 15, 20],
+                 z: [1.0001, 5, 10, 15, 20], w: [1e-8, 5, 10, 15, 20]}
+    with self.test_session():
+      with ops.control_dependencies([distribution_util.assert_integer_form(x)]):
+        array_ops.identity(x).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("has non-integer components"):
+        with ops.control_dependencies(
+            [distribution_util.assert_integer_form(y)]):
+          array_ops.identity(y).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("has non-integer components"):
+        with ops.control_dependencies(
+            [distribution_util.assert_integer_form(z)]):
+          array_ops.identity(z).eval(feed_dict=feed_dict)
+
+      with self.assertRaisesOpError("has non-integer components"):
+        with ops.control_dependencies(
+            [distribution_util.assert_integer_form(w)]):
+          array_ops.identity(w).eval(feed_dict=feed_dict)
+
+
+class GetLogitsAndProbsTest(test.TestCase):
+
+  def testImproperArguments(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        distribution_util.get_logits_and_probs(logits=None, probs=None)
+
+      with self.assertRaises(ValueError):
+        distribution_util.get_logits_and_probs(logits=[0.1], probs=[0.1])
+
+  def testLogits(self):
+    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
+    logits = _logit(p)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          logits=logits, validate_args=True)
+
+      self.assertAllClose(p, new_p.eval(), rtol=1e-5, atol=0.)
+      self.assertAllClose(logits, new_logits.eval(), rtol=1e-5, atol=0.)
+
+  def testLogitsMultidimensional(self):
+    p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
+    logits = np.log(p)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          logits=logits, multidimensional=True, validate_args=True)
+
+      self.assertAllClose(new_p.eval(), p)
+      self.assertAllClose(new_logits.eval(), logits)
+
+  def testProbability(self):
+    p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          probs=p, validate_args=True)
+
+      self.assertAllClose(_logit(p), new_logits.eval())
+      self.assertAllClose(p, new_p.eval())
+
+  def testProbabilityMultidimensional(self):
+    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
+
+    with self.test_session():
+      new_logits, new_p = distribution_util.get_logits_and_probs(
+          probs=p, multidimensional=True, validate_args=True)
+
+      self.assertAllClose(np.log(p), new_logits.eval())
+      self.assertAllClose(p, new_p.eval())
+
+  def testProbabilityValidateArgs(self):
+    p = [0.01, 0.2, 0.5, 0.7, .99]
+    # Component less than 0.
+    p2 = [-1, 0.2, 0.5, 0.3, .2]
+    # Component greater than 1.
+    p3 = [2, 0.2, 0.5, 0.3, .2]
+
+    with self.test_session():
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p, validate_args=True)
+      prob.eval()
+
+      with self.assertRaisesOpError("Condition x >= 0"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p2, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p2, validate_args=False)
+      prob.eval()
+
+      with self.assertRaisesOpError("probs has components greater than 1"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p3, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p3, validate_args=False)
+      prob.eval()
+
+  def testProbabilityValidateArgsMultidimensional(self):
+    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
+    # Component less than 0. Still sums to 1.
+    p2 = np.array([[-.3, 0.4, 0.9], [0.1, 0.5, 0.4]], dtype=np.float32)
+    # Component greater than 1. Does not sum to 1.
+    p3 = np.array([[1.3, 0.0, 0.0], [0.1, 0.5, 0.4]], dtype=np.float32)
+    # Does not sum to 1.
+    p4 = np.array([[1.1, 0.3, 0.4], [0.1, 0.5, 0.4]], dtype=np.float32)
+
+    with self.test_session():
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p, multidimensional=True)
+      prob.eval()
+
+      with self.assertRaisesOpError("Condition x >= 0"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p2, multidimensional=True, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p2, multidimensional=True, validate_args=False)
+      prob.eval()
+
+      with self.assertRaisesOpError(
+          "(probs has components greater than 1|probs does not sum to 1)"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p3, multidimensional=True, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p3, multidimensional=True, validate_args=False)
+      prob.eval()
+
+      with self.assertRaisesOpError("probs does not sum to 1"):
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p4, multidimensional=True, validate_args=True)
+        prob.eval()
+
+      _, prob = distribution_util.get_logits_and_probs(
+          probs=p4, multidimensional=True, validate_args=False)
+      prob.eval()
+
+  def testProbsMultidimShape(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        p = array_ops.ones([int(2**11+1)], dtype=np.float16)
+        distribution_util.get_logits_and_probs(
+            probs=p, multidimensional=True, validate_args=True)
+
+      with self.assertRaisesOpError(
+          "Number of classes exceeds `dtype` precision"):
+        p = array_ops.placeholder(dtype=dtypes.float16)
+        _, prob = distribution_util.get_logits_and_probs(
+            probs=p, multidimensional=True, validate_args=True)
+        prob.eval(feed_dict={p: np.ones([int(2**11+1)])})
+
+  def testLogitsMultidimShape(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        l = array_ops.ones([int(2**11+1)], dtype=np.float16)
+        distribution_util.get_logits_and_probs(
+            logits=l, multidimensional=True, validate_args=True)
+
+      with self.assertRaisesOpError(
+          "Number of classes exceeds `dtype` precision"):
+        l = array_ops.placeholder(dtype=dtypes.float16)
+        logit, _ = distribution_util.get_logits_and_probs(
+            logits=l, multidimensional=True, validate_args=True)
+        logit.eval(feed_dict={l: np.ones([int(2**11+1)])})
+
+
+class EmbedCheckCategoricalEventShapeTest(test.TestCase):
+
+  def testTooSmall(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        param = array_ops.ones([1], dtype=np.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+
+      with self.assertRaisesOpError(
+          "must have at least 2 events"):
+        param = array_ops.placeholder(dtype=dtypes.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+        checked_param.eval(feed_dict={param: np.ones([1])})
+
+  def testTooLarge(self):
+    with self.test_session():
+      with self.assertRaises(ValueError):
+        param = array_ops.ones([int(2**11+1)], dtype=dtypes.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+
+      with self.assertRaisesOpError(
+          "Number of classes exceeds `dtype` precision"):
+        param = array_ops.placeholder(dtype=dtypes.float16)
+        checked_param = distribution_util.embed_check_categorical_event_shape(
+            param)
+        checked_param.eval(feed_dict={param: np.ones([int(2**11+1)])})
+
+  def testUnsupportedDtype(self):
+    with self.test_session():
+      with self.assertRaises(TypeError):
+        param = array_ops.ones([int(2**11+1)], dtype=dtypes.qint16)
+        distribution_util.embed_check_categorical_event_shape(param)
+
+
+class EmbedCheckIntegerCastingClosedTest(test.TestCase):
+
+  def testCorrectlyAssertsNonnegative(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements must be non-negative"):
+        x = array_ops.placeholder(dtype=dtypes.float16)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.int16)
+        x_checked.eval(feed_dict={x: np.array([1, -1], dtype=np.float16)})
+
+  def testCorrectlyAssersIntegerForm(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements must be int16-equivalent."):
+        x = array_ops.placeholder(dtype=dtypes.float16)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.int16)
+        x_checked.eval(feed_dict={x: np.array([1, 1.5], dtype=np.float16)})
+
+  def testCorrectlyAssertsLargestPossibleInteger(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements cannot exceed 32767."):
+        x = array_ops.placeholder(dtype=dtypes.int32)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.int16)
+        x_checked.eval(feed_dict={x: np.array([1, 2**15], dtype=np.int32)})
+
+  def testCorrectlyAssertsSmallestPossibleInteger(self):
+    with self.test_session():
+      with self.assertRaisesOpError("Elements cannot be smaller than 0."):
+        x = array_ops.placeholder(dtype=dtypes.int32)
+        x_checked = distribution_util.embed_check_integer_casting_closed(
+            x, target_dtype=dtypes.uint16, assert_nonnegative=False)
+        x_checked.eval(feed_dict={x: np.array([1, -1], dtype=np.int32)})
+
+
+class LogCombinationsTest(test.TestCase):
+
+  def testLogCombinationsBinomial(self):
+    n = [2, 5, 12, 15]
+    k = [1, 2, 4, 11]
+
+    if not special:
+      return
+
+    log_combs = np.log(special.binom(n, k))
+
+    with self.test_session():
+      n = np.array(n, dtype=np.float32)
+      counts = [[1., 1], [2., 3], [4., 8], [11, 4]]
+      log_binom = distribution_util.log_combinations(n, counts)
+      self.assertEqual([4], log_binom.get_shape())
+      self.assertAllClose(log_combs, log_binom.eval())
+
+  def testLogCombinationsShape(self):
+    # Shape [2, 2]
+    n = [[2, 5], [12, 15]]
+
+    with self.test_session():
+      n = np.array(n, dtype=np.float32)
+      # Shape [2, 2, 4]
+      counts = [[[1., 1, 0, 0], [2., 2, 1, 0]], [[4., 4, 1, 3], [10, 1, 1, 4]]]
+      log_binom = distribution_util.log_combinations(n, counts)
+      self.assertEqual([2, 2], log_binom.get_shape())
+
+
+class DynamicShapeTest(test.TestCase):
+
+  def testSameDynamicShape(self):
+    with self.test_session():
+      scalar = constant_op.constant(2.0)
+      scalar1 = array_ops.placeholder(dtype=dtypes.float32)
+
+      vector = [0.3, 0.4, 0.5]
+      vector1 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
+      vector2 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
+
+      multidimensional = [[0.3, 0.4], [0.2, 0.6]]
+      multidimensional1 = array_ops.placeholder(
+          dtype=dtypes.float32, shape=[None, None])
+      multidimensional2 = array_ops.placeholder(
+          dtype=dtypes.float32, shape=[None, None])
+
+      # Scalar
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(scalar, scalar1).eval({
+              scalar1: 2.0
+          }))
+
+      # Vector
+
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(vector, vector1).eval({
+              vector1: [2.0, 3.0, 4.0]
+          }))
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(vector1, vector2).eval({
+              vector1: [2.0, 3.0, 4.0],
+              vector2: [2.0, 3.5, 6.0]
+          }))
+
+      # Multidimensional
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(
+              multidimensional, multidimensional1).eval({
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+              }))
+      self.assertTrue(
+          distribution_util.same_dynamic_shape(
+              multidimensional1, multidimensional2).eval({
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
+                  multidimensional2: [[1.0, 3.5], [6.3, 2.3]]
+              }))
+
+      # Scalar, X
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar, vector1).eval({
+              vector1: [2.0, 3.0, 4.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar1, vector1).eval({
+              scalar1: 2.0,
+              vector1: [2.0, 3.0, 4.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar, multidimensional1).eval({
+              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(scalar1, multidimensional1).eval(
+              {
+                  scalar1: 2.0,
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+              }))
+
+      # Vector, X
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector, vector1).eval({
+              vector1: [2.0, 3.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector1, vector2).eval({
+              vector1: [2.0, 3.0, 4.0],
+              vector2: [6.0]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector, multidimensional1).eval({
+              multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+          }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(vector1, multidimensional1).eval(
+              {
+                  vector1: [2.0, 3.0, 4.0],
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
+              }))
+
+      # Multidimensional, X
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(
+              multidimensional, multidimensional1).eval({
+                  multidimensional1: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
+              }))
+      self.assertFalse(
+          distribution_util.same_dynamic_shape(
+              multidimensional1, multidimensional2).eval({
+                  multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
+                  multidimensional2: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
+              }))
+
+
+class RotateTransposeTest(test.TestCase):
+
+  def _np_rotate_transpose(self, x, shift):
+    if not isinstance(x, np.ndarray):
+      x = np.array(x)
+    return np.transpose(x, np.roll(np.arange(len(x.shape)), shift))
+
+  def testRollStatic(self):
+    with self.test_session():
+      with self.assertRaisesRegexp(ValueError, "None values not supported."):
+        distribution_util.rotate_transpose(None, 1)
+      for x in (np.ones(1), np.ones((2, 1)), np.ones((3, 2, 1))):
+        for shift in np.arange(-5, 5):
+          y = distribution_util.rotate_transpose(x, shift)
+          self.assertAllEqual(self._np_rotate_transpose(x, shift), y.eval())
+          self.assertAllEqual(np.roll(x.shape, shift), y.get_shape().as_list())
+
+  def testRollDynamic(self):
+    with self.test_session() as sess:
+      x = array_ops.placeholder(dtypes.float32)
+      shift = array_ops.placeholder(dtypes.int32)
+      for x_value in (np.ones(
+          1, dtype=x.dtype.as_numpy_dtype()), np.ones(
+              (2, 1), dtype=x.dtype.as_numpy_dtype()), np.ones(
+                  (3, 2, 1), dtype=x.dtype.as_numpy_dtype())):
+        for shift_value in np.arange(-5, 5):
+          self.assertAllEqual(
+              self._np_rotate_transpose(x_value, shift_value),
+              sess.run(distribution_util.rotate_transpose(x, shift),
+                       feed_dict={x: x_value,
+                                  shift: shift_value}))
+
+
+class PickVectorTest(test.TestCase):
+
+  def testCorrectlyPicksVector(self):
+    with self.test_session():
+      x = np.arange(10, 12)
+      y = np.arange(15, 18)
+      self.assertAllEqual(x,
+                          distribution_util.pick_vector(
+                              math_ops.less(0, 5), x, y).eval())
+      self.assertAllEqual(y,
+                          distribution_util.pick_vector(
+                              math_ops.less(5, 0), x, y).eval())
+      self.assertAllEqual(x,
+                          distribution_util.pick_vector(
+                              constant_op.constant(True), x, y))  # No eval.
+      self.assertAllEqual(y,
+                          distribution_util.pick_vector(
+                              constant_op.constant(False), x, y))  # No eval.
+
+
+class FillLowerTriangularTest(test.TestCase):
+
+  def setUp(self):
+    self._rng = np.random.RandomState(42)
+
+  def _fill_lower_triangular(self, x):
+    """Numpy implementation of `fill_lower_triangular`."""
+    x = np.asarray(x)
+    d = x.shape[-1]
+    # d = n(n+1)/2 implies n is:
+    n = int(0.5 * (np.sqrt(1. + 8. * d) - 1.))
+    ids = np.tril_indices(n)
+    y = np.zeros(list(x.shape[:-1]) + [n, n], dtype=x.dtype)
+    y[..., ids[0], ids[1]] = x
+    return y
+
+  def testCorrectlyMakes1x1LowerTril(self):
+    with self.test_session():
+      x = ops.convert_to_tensor(self._rng.randn(3, 1))
+      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
+      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
+      self.assertAllEqual(expected.shape, actual.get_shape())
+      self.assertAllEqual(expected, actual.eval())
+
+  def testCorrectlyMakesNoBatchLowerTril(self):
+    with self.test_session():
+      x = ops.convert_to_tensor(self._rng.randn(10))
+      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
+      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
+      self.assertAllEqual(expected.shape, actual.get_shape())
+      self.assertAllEqual(expected, actual.eval())
+      g = gradients_impl.gradients(
+          distribution_util.fill_lower_triangular(x), x)
+      self.assertAllEqual(np.tri(4).reshape(-1), g[0].values.eval())
+
+  def testCorrectlyMakesBatchLowerTril(self):
+    with self.test_session():
+      x = ops.convert_to_tensor(self._rng.randn(2, 2, 6))
+      expected = self._fill_lower_triangular(tensor_util.constant_value(x))
+      actual = distribution_util.fill_lower_triangular(x, validate_args=True)
+      self.assertAllEqual(expected.shape, actual.get_shape())
+      self.assertAllEqual(expected, actual.eval())
+      self.assertAllEqual(
+          np.ones((2, 2, 6)),
+          gradients_impl.gradients(
+              distribution_util.fill_lower_triangular(x), x)[0].eval())
+
+
+class GenNewSeedTest(test.TestCase):
+
+  def testOnlyNoneReturnsNone(self):
+    self.assertFalse(distribution_util.gen_new_seed(0, "salt") is None)
+    self.assertTrue(distribution_util.gen_new_seed(None, "salt") is None)
+
+
+# TODO(jvdillon): Merge this test back into:
+# tensorflow/python/kernel_tests/softplus_op_test.py
+# once TF core is accepting new ops.
+class SoftplusTest(test.TestCase):
+
+  def _npSoftplus(self, np_features):
+    np_features = np.asarray(np_features)
+    zero = np.asarray(0).astype(np_features.dtype)
+    return np.logaddexp(zero, np_features)
+
+  def _testSoftplus(self, np_features, use_gpu=False):
+    np_features = np.asarray(np_features)
+    np_softplus = self._npSoftplus(np_features)
+    with self.test_session(use_gpu=use_gpu) as sess:
+      softplus = nn_ops.softplus(np_features)
+      softplus_inverse = distribution_util.softplus_inverse(softplus)
+      [tf_softplus, tf_softplus_inverse] = sess.run([
+          softplus, softplus_inverse])
+    self.assertAllCloseAccordingToType(np_softplus, tf_softplus)
+    rtol = {"float16": 0.07, "float32": 0.003, "float64": 0.002}.get(
+        str(np_features.dtype), 1e-6)
+    # This will test that we correctly computed the inverse by verifying we
+    # recovered the original input.
+    self.assertAllCloseAccordingToType(
+        np_features, tf_softplus_inverse,
+        atol=0., rtol=rtol)
+    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
+                        tf_softplus > 0)
+
+    self.assertShapeEqual(np_softplus, softplus)
+    self.assertShapeEqual(np_softplus, softplus_inverse)
+
+    self.assertAllEqual(np.ones_like(tf_softplus).astype(np.bool),
+                        np.isfinite(tf_softplus))
+    self.assertAllEqual(np.ones_like(tf_softplus_inverse).astype(np.bool),
+                        np.isfinite(tf_softplus_inverse))
+
+  def testNumbers(self):
+    for t in [np.float16, np.float32, np.float64]:
+      lower = {np.float16: -15, np.float32: -50, np.float64: -50}.get(t, -100)
+      upper = {np.float16: 50, np.float32: 50, np.float64: 50}.get(t, 100)
+      self._testSoftplus(
+          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
+              [2, -1]),
+          use_gpu=False)
+      self._testSoftplus(
+          np.array(np.linspace(lower, upper, int(1e3)).astype(t)).reshape(
+              [2, -1]),
+          use_gpu=True)
+      log_eps = np.log(np.finfo(t).eps)
+      one = t(1)
+      ten = t(10)
+      self._testSoftplus(
+          [
+              log_eps, log_eps - one, log_eps + one, log_eps - ten,
+              log_eps + ten, -log_eps, -log_eps - one, -log_eps + one,
+              -log_eps - ten, -log_eps + ten
+          ],
+          use_gpu=False)
+      self._testSoftplus(
+          [
+              log_eps, log_eps - one, log_eps + one, log_eps - ten,
+              log_eps + ten - log_eps, -log_eps - one, -log_eps + one,
+              -log_eps - ten, -log_eps + ten
+          ],
+          use_gpu=True)
+
+  def testGradient(self):
+    with self.test_session():
+      x = constant_op.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5],
+          name="x")
+      y = nn_ops.softplus(x, name="softplus")
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float32,
+          order="F")
+      err = gradient_checker.compute_gradient_error(
+          x, [2, 5], y, [2, 5], x_init_value=x_init)
+    tf_logging.vlog(2, "softplus (float) gradient err = ", err)
+    self.assertLess(err, 1e-4)
+
+  def testInverseSoftplusGradientNeverNan(self):
+    with self.test_session():
+      # Note that this range contains both zero and inf.
+      x = constant_op.constant(np.logspace(-8, 6).astype(np.float16))
+      y = distribution_util.softplus_inverse(x)
+      grads = gradients_impl.gradients(y, x)[0].eval()
+      # Equivalent to `assertAllFalse` (if it existed).
+      self.assertAllEqual(np.zeros_like(grads).astype(np.bool), np.isnan(grads))
+
+  def testInverseSoftplusGradientFinite(self):
+    with self.test_session():
+      # This range of x is all finite, and so is 1 / x.  So the
+      # gradient and its approximations should be finite as well.
+      x = constant_op.constant(np.logspace(-4.8, 4.5).astype(np.float16))
+      y = distribution_util.softplus_inverse(x)
+      grads = gradients_impl.gradients(y, x)[0].eval()
+      # Equivalent to `assertAllTrue` (if it existed).
+      self.assertAllEqual(
+          np.ones_like(grads).astype(np.bool), np.isfinite(grads))
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/kernel_tests/embedding_ops_test.py b/tensorflow/python/kernel_tests/embedding_ops_test.py
index 057da9d7af..a1f44bafcc 100644
--- a/tensorflow/python/kernel_tests/embedding_ops_test.py
+++ b/tensorflow/python/kernel_tests/embedding_ops_test.py
@@ -31,6 +31,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import gradient_checker
+from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import state_ops
@@ -77,11 +78,11 @@ class ScatterAddSubTest(test.TestCase):
     # Compute the expected 'p' using numpy operations.
     for i, ind in enumerate(indices):
       if scatter_op == state_ops.scatter_add:
-        p_init.reshape(shape[0], -1)[ind, :] += (
-            vals_init.reshape(vals_shape[0], -1)[i, :])
+        p_init.reshape(shape[0], -1)[ind, :] += (vals_init.reshape(
+            vals_shape[0], -1)[i, :])
       else:
-        p_init.reshape(shape[0], -1)[ind, :] -= (
-            vals_init.reshape(vals_shape[0], -1)[i, :])
+        p_init.reshape(shape[0], -1)[ind, :] -= (vals_init.reshape(
+            vals_shape[0], -1)[i, :])
     self.assertTrue(all((p_init == result).ravel()))
 
   def testNoRepetitions(self):
@@ -111,8 +112,7 @@ class ScatterAddSubTest(test.TestCase):
   def testWrongShape(self):
     # Indices and values mismatch.
     var = variables.Variable(
-        array_ops.zeros(
-            shape=[1024, 64, 64], dtype=dtypes.float32))
+        array_ops.zeros(shape=[1024, 64, 64], dtype=dtypes.float32))
     indices = array_ops.placeholder(dtypes.int32, shape=[32])
     values = array_ops.placeholder(dtypes.float32, shape=[33, 64, 64])
     with self.assertRaises(ValueError):
@@ -208,8 +208,8 @@ def _EmbeddingResult(params,
         else:
           partition = extras + (i - threshold) // ids_per_partition
           offset = (i - threshold) % ids_per_partition
-        val = np.copy(params[_PName(partition) + ":0"][
-            offset, :]) * weight_value
+        val = np.copy(
+            params[_PName(partition) + ":0"][offset, :]) * weight_value
       else:
         assert False
       if value_aggregation is None:
@@ -274,8 +274,7 @@ class EmbeddingLookupTest(test.TestCase):
           [embeddings], ids, max_norm=2.0)
 
       norms = math_ops.sqrt(
-          math_ops.reduce_sum(
-              embeddings * embeddings, axis=1))
+          math_ops.reduce_sum(embeddings * embeddings, axis=1))
       normalized = embeddings / array_ops.stack([norms, norms], axis=1)
       self.assertAllEqual(embedding.eval(), 2 * normalized.eval())
 
@@ -510,8 +509,7 @@ class EmbeddingLookupTest(test.TestCase):
   def testConstructionNonSharded(self):
     with ops.Graph().as_default():
       p = variables.Variable(
-          array_ops.zeros(
-              shape=[100, 100], dtype=dtypes.float32))
+          array_ops.zeros(shape=[100, 100], dtype=dtypes.float32))
       ids = constant_op.constant([0, 1, 1, 7], dtype=dtypes.int32)
       embedding_ops.embedding_lookup([p], ids)
 
@@ -521,8 +519,7 @@ class EmbeddingLookupTest(test.TestCase):
       for _ in range(2):
         p += [
             variables.Variable(
-                array_ops.zeros(
-                    shape=[100, 100], dtype=dtypes.float32))
+                array_ops.zeros(shape=[100, 100], dtype=dtypes.float32))
         ]
         ids = constant_op.constant([0, 1, 1, 17], dtype=dtypes.int32)
       embedding_ops.embedding_lookup(p, ids)
@@ -550,19 +547,22 @@ class EmbeddingLookupTest(test.TestCase):
   def testHigherRankMaxNorm(self):
     np.random.seed(8)
     with self.test_session():
-      for params_shape in (12,), (6, 3):
+      for params_shape in (12,), (6, 3), (6, 2, 3):
+        # Test embedding rank 0, 1, 2.
+        # Note: the first dimension must be a common multiple of procs below.
         params = 2 * np.ones(params_shape)
         params_norm = params / np.sqrt(
-            np.sum(params*params, tuple(range(params.ndim)[1:]), keepdims=True))
+            np.sum(
+                params * params, tuple(range(params.ndim)[1:]), keepdims=True))
         for ids_shape in (), (3), (4, 3), (2, 3, 4):
           ids = np.random.randint(
-              params.shape[0], size=np.prod(ids_shape, dtype=np.int64)).reshape(
-                  ids_shape)
+              params.shape[0], size=np.prod(ids_shape,
+                                            dtype=np.int64)).reshape(ids_shape)
           # Compare nonsharded to gather
           simple = embedding_ops.embedding_lookup(
               params, ids, max_norm=1.0).eval()
           self.assertAllEqual(simple, array_ops.gather(params_norm, ids).eval())
-          # Run a few random sharded versions
+          # Run a few different sharded versions.
           for procs in 1, 2, 3:
             stride = procs * math_ops.range(params.shape[0] // procs)
             split_params = [
@@ -572,6 +572,42 @@ class EmbeddingLookupTest(test.TestCase):
                 split_params, ids, max_norm=1.0).eval()
             self.assertAllEqual(simple, sharded)
 
+  def testTransform(self):
+    # This tests all combinations of:
+    #   - ids rank 0, 1, >1
+    #   - params sharded/unsharded
+    # It always applies max_norm.
+    np.random.seed(8)
+    l2_norm = 2.
+    with self.test_session():
+      # Param values are in [l2_norm, l2_norm+1) so it will always clip.
+      params = np.random.rand(6, 3) + l2_norm
+      params_norm = l2_norm * params / np.sqrt(
+          np.sum(params * params, axis=1, keepdims=True))
+      # Compute the norm of each embedding. This will change the embedding
+      # rank to 0.
+      params_norm = np.linalg.norm(params_norm, axis=1)
+      transform = lambda x: linalg_ops.norm(x, axis=1)
+      for ids_shape in (), (3), (4, 3), (2, 3, 4):
+        # Test ids rank 0, 1, 2, 3.
+        ids = np.random.randint(
+            params.shape[0], size=np.prod(ids_shape,
+                                          dtype=np.int64)).reshape(ids_shape)
+        # Compare nonsharded to gather.
+        simple = embedding_ops._embedding_lookup_and_transform(
+            params, ids, max_norm=l2_norm, transform_fn=transform).eval()
+        self.assertAllClose(simple, array_ops.gather(params_norm, ids).eval())
+        # Run a few different sharded versions.
+        for procs in 1, 2, 3:
+          stride = procs * math_ops.range(params.shape[0] // procs)
+          split_params = [
+              array_ops.gather(params, stride + p) for p in xrange(procs)
+          ]
+          sharded = embedding_ops._embedding_lookup_and_transform(
+              split_params, ids, max_norm=l2_norm,
+              transform_fn=transform).eval()
+          self.assertAllEqual(simple, sharded)
+
 
 class EmbeddingLookupSparseTest(test.TestCase):
 
@@ -625,8 +661,8 @@ class EmbeddingLookupSparseTest(test.TestCase):
         np.ones(np.sum(vals_per_batch_entry)), vals_per_batch_entry)
 
     for num_shards, combiner, dtype, ignore_weights in itertools.product(
-        [1, 5], ["sum", "mean", "sqrtn"], [dtypes.float32, dtypes.float64],
-        [True, False]):
+        [1, 5], ["sum", "mean", "sqrtn"], [dtypes.float32,
+                                           dtypes.float64], [True, False]):
 
       with self.test_session():
         p, params, feed_dict = _EmbeddingParams(
@@ -647,8 +683,8 @@ class EmbeddingLookupSparseTest(test.TestCase):
             grouped_ids,
             num_shards,
             vocab_size,
-            weight_vals=grouped_ignored_weights if ignore_weights else
-            grouped_weights)
+            weight_vals=grouped_ignored_weights
+            if ignore_weights else grouped_weights)
         if combiner == "mean":
           np_embedding_sum /= np.reshape(np_weight_sum, (batch_size, 1, 1))
         if combiner == "sqrtn":
@@ -660,12 +696,12 @@ class EmbeddingLookupSparseTest(test.TestCase):
     vocab_size = 12
     batch_size = 4
     param_shape = [2, 3]
-    sp_ids, sp_weights, _, _, _ = (
-        self._RandomIdsAndWeights(batch_size, vocab_size))
+    sp_ids, sp_weights, _, _, _ = (self._RandomIdsAndWeights(
+        batch_size, vocab_size))
 
     for num_shards, combiner, dtype, ignore_weights in itertools.product(
-        [1, 3], ["sum", "mean", "sqrtn"], [dtypes.float32, dtypes.float64],
-        [True, False]):
+        [1, 3], ["sum", "mean", "sqrtn"], [dtypes.float32,
+                                           dtypes.float64], [True, False]):
       with self.test_session():
         x, params, _ = _EmbeddingParams(
             num_shards, vocab_size, shape=param_shape, dtype=dtype)
@@ -705,10 +741,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testCint32Cpu(self):
     with self.test_session(use_gpu=False):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -716,10 +754,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testCint32Gpu(self):
     with self.test_session(use_gpu=True):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -727,10 +767,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testInt32Cpu(self):
     with self.test_session(use_gpu=False):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -738,10 +780,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testInt32Gpu(self):
     with self.test_session(use_gpu=True):
       indices = [
-          ops.convert_to_tensor([0, 1, 2]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([12, 23, 34]), ops.convert_to_tensor([1, 2])
+          ops.convert_to_tensor([12, 23, 34]),
+          ops.convert_to_tensor([1, 2])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [12, 23, 1, 2])
@@ -749,10 +793,12 @@ class DynamicStitchOpTest(test.TestCase):
   def testSumGradArgs(self):
     with self.test_session(use_gpu=False):
       indices = [
-          ops.convert_to_tensor([0, 1, 2, 3]), ops.convert_to_tensor([2, 3])
+          ops.convert_to_tensor([0, 1, 2, 3]),
+          ops.convert_to_tensor([2, 3])
       ]
       values = [
-          ops.convert_to_tensor([2, 3, 5, 7]), ops.convert_to_tensor([1, 1])
+          ops.convert_to_tensor([2, 3, 5, 7]),
+          ops.convert_to_tensor([1, 1])
       ]
       self.assertAllEqual(
           data_flow_ops.dynamic_stitch(indices, values).eval(), [2, 3, 1, 1])
diff --git a/tensorflow/python/kernel_tests/random_poisson_test.py b/tensorflow/python/kernel_tests/random_poisson_test.py
index 01281b7bd0..107c9bbe14 100644
--- a/tensorflow/python/kernel_tests/random_poisson_test.py
+++ b/tensorflow/python/kernel_tests/random_poisson_test.py
@@ -131,8 +131,14 @@ class RandomPoissonTest(test.TestCase):
         # be at least 1 if they are different.
         self.assertGreaterEqual(np.linalg.norm(diff.eval()), 1)
 
+  def testZeroShape(self):
+    with self.test_session():
+      rnd = random_ops.random_poisson([], [], seed=12345)
+      self.assertEqual([0], rnd.get_shape().as_list())
+      self.assertAllClose(np.array([], dtype=np.float32), rnd.eval())
+
   def testShape(self):
-    # Fully known shape.
+    # Fully known shape
     rnd = random_ops.random_poisson(2.0, [150], seed=12345)
     self.assertEqual([150], rnd.get_shape().as_list())
     rnd = random_ops.random_poisson(
diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
index 8519d19fe1..ebc5686212 100644
--- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
+++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
@@ -87,7 +87,7 @@ def _NumpyDiv(ref, indices, updates):
   return _NumpyScatterNd(ref, indices, updates, lambda p, u: p / u)
 
 
-class ScatterNdTest(test.TestCase):
+class StatefulScatterNdTest(test.TestCase):
 
   def _VariableRankTest(self,
                         np_scatter,
@@ -261,10 +261,6 @@ class ScatterNdTest(test.TestCase):
     indices = array_ops.zeros([2, 2, 2], dtypes.int32)
     updates = array_ops.zeros([2, 2, 2], dtypes.int32)
     shape = np.array([2, 2, 2])
-    self.assertAllEqual(
-        array_ops.scatter_nd(indices, updates, shape).get_shape().as_list(),
-        shape)
-
     ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
     self.assertAllEqual(
         state_ops.scatter_nd_update(ref, indices,
@@ -274,37 +270,120 @@ class ScatterNdTest(test.TestCase):
     indices = array_ops.zeros([1, 1, 2], dtypes.int32)
     updates = array_ops.zeros([1, 1], dtypes.int32)
     shape = np.array([2, 2])
-    scatter = array_ops.scatter_nd(indices, updates, shape)
-    self.assertAllEqual(scatter.get_shape().as_list(), shape)
-    expected_result = np.zeros([2, 2], dtype=np.int32)
-    with self.test_session():
-      self.assertAllEqual(expected_result, scatter.eval())
-
     ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
     scatter_update = state_ops.scatter_nd_update(ref, indices, updates)
     self.assertAllEqual(scatter_update.get_shape().as_list(), shape)
 
+    expected_result = np.zeros([2, 2], dtype=np.int32)
     with self.test_session():
       ref.initializer.run()
       self.assertAllEqual(expected_result, scatter_update.eval())
 
+  def testRank3InvalidShape1(self):
+    indices = array_ops.zeros([3, 2, 2], dtypes.int32)
+    updates = array_ops.zeros([2, 2, 2], dtypes.int32)
+    shape = np.array([2, 2, 2])
+    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
+    with self.assertRaisesWithPredicateMatch(
+        ValueError, "The outer \\d+ dimensions of indices\\.shape="):
+      state_ops.scatter_nd_update(ref, indices, updates)
+
+  def testRank3InvalidShape2(self):
+    indices = array_ops.zeros([2, 2, 1], dtypes.int32)
+    updates = array_ops.zeros([2, 2], dtypes.int32)
+    shape = np.array([2, 2, 2])
+    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
+    with self.assertRaisesWithPredicateMatch(
+        ValueError, "The inner \\d+ dimensions of input\\.shape="):
+      state_ops.scatter_nd_update(ref, indices, updates)
+
+  def testConcurrentUpdates(self):
+    num_updates = 10000
+    update_values = np.random.rand(num_updates)
+    ref = variables.Variable(np.zeros([2, 2]), dtype=dtypes.float64)
+    indices = constant_op.constant([[0, 1]] * num_updates, dtype=dtypes.int32)
+    updates = constant_op.constant(update_values, dtype=dtypes.float64)
+
+    expected_result = np.zeros([2, 2], dtype=np.float64)
+    expected_result[0, 1] = np.sum(update_values)
+
+    scatter = state_ops.scatter_nd_add(ref, indices, updates)
+    init = variables.global_variables_initializer()
+
+    with session.Session() as sess:
+      sess.run(init)
+      result = sess.run(scatter)
+      assert np.allclose(result, expected_result)
+
+  # TODO(fpmc): Re-enable this test when gpu_pip test actually runs on a GPU.
+  def _disabledTestScatterOutOfRangeGpu(self):
+    if not test.IsBuiltWithCuda():
+      return
+    # TODO(simister): Re-enable once binary size increase due to
+    # scatter_nd ops is under control.
+    # tf.scatter_nd_mul, tf.scatter_nd_div,
+    for op in (state_ops.scatter_nd_add, state_ops.scatter_nd_sub,
+               state_ops.scatter_nd_update):
+      params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
+      updates = np.array([-3, -4, -5]).astype(np.float32)
+      # With GPU, the code ignores indices that are out of range.
+      # We don't test the implementation; just test there's no failures.
+      with self.test_session(force_gpu=True):
+        ref = variables.Variable(params)
+        ref.initializer.run()
+
+        # Indices all in range, no problem.
+        indices = np.array([2, 0, 5])
+        op(ref, indices, updates).eval()
+
+        # Indicies out of range should not fail.
+        indices = np.array([-1, 0, 5])
+        op(ref, indices, updates).eval()
+        indices = np.array([2, 0, 6])
+        op(ref, indices, updates).eval()
+
+
+class ScatterNdTest(test.TestCase):
+  non_aliasing_add_test = False
+
+  def scatter_nd(self, indices, updates, shape, input_=None):
+    del input_  # input_ is not used in scatter_nd
+    return array_ops.scatter_nd(indices, updates, shape)
+
+  def testRank3ValidShape(self):
+    indices = array_ops.zeros([2, 2, 2], dtypes.int32)
+    updates = array_ops.zeros([2, 2, 2], dtypes.int32)
+    shape = np.array([2, 2, 2])
+    self.assertAllEqual(
+        self.scatter_nd(indices, updates, shape).get_shape().as_list(), shape)
+
+  def testExtraIndicesDimensions(self):
+    indices = array_ops.zeros([1, 1, 2], dtypes.int32)
+    updates = array_ops.zeros([1, 1], dtypes.int32)
+    shape = np.array([2, 2])
+    scatter = self.scatter_nd(indices, updates, shape)
+    self.assertAllEqual(scatter.get_shape().as_list(), shape)
+    expected_result = np.zeros([2, 2], dtype=np.int32)
+    with self.test_session():
+      self.assertAllEqual(expected_result, scatter.eval())
+
   def testUndefinedIndicesShape(self):
     indices = array_ops.placeholder(dtypes.int32, shape=None)
     updates = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     shape = constant_op.constant([2, 2, 2], dtypes.int32)
-    array_ops.scatter_nd(indices, updates, shape)
+    self.scatter_nd(indices, updates, shape)
 
   def testUndefinedUpdatesShape(self):
     indices = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     updates = array_ops.placeholder(dtypes.int32, shape=None)
     shape = constant_op.constant([2, 2, 2], dtypes.int32)
-    array_ops.scatter_nd(indices, updates, shape)
+    self.scatter_nd(indices, updates, shape)
 
   def testUndefinedOutputShape(self):
     indices = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     updates = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2])
     shape = array_ops.placeholder(dtypes.int32, shape=[None])
-    array_ops.scatter_nd(indices, updates, shape)
+    self.scatter_nd(indices, updates, shape)
 
   def testEmptyOutputShape1(self):
     indices = array_ops.zeros([2, 2, 2], dtypes.int32)
@@ -313,7 +392,7 @@ class ScatterNdTest(test.TestCase):
 
     with self.assertRaisesWithPredicateMatch(
         ValueError, "Indices and updates specified for empty output shape"):
-      array_ops.scatter_nd(indices, updates, shape)
+      self.scatter_nd(indices, updates, shape)
 
   def testEmptyOutputShape2(self):
     indices = array_ops.placeholder(dtypes.int32, shape=None)
@@ -321,18 +400,18 @@ class ScatterNdTest(test.TestCase):
     shape = constant_op.constant([0, 3, 2], dtypes.int32)
 
     with self.test_session():
-      array_ops.scatter_nd(indices, updates, shape).eval(feed_dict={
-          indices: np.zeros(
-              [2, 2, 2], dtype=np.int32),
-          updates: np.zeros(
-              [2, 2, 2], dtype=np.int32)
-      })
+      with self.assertRaisesOpError(
+          "Indices and updates specified for empty output"):
+        self.scatter_nd(indices, updates, shape).eval(feed_dict={
+            indices: np.zeros([2, 2, 2], dtype=np.int32),
+            updates: np.zeros([2, 2, 2], dtype=np.int32)
+        })
 
   def testEmptyOutputShape3(self):
     indices = array_ops.zeros([0], dtypes.int32)
     updates = array_ops.zeros([0], dtypes.int32)
     shape = constant_op.constant([0], dtypes.int32)
-    scatter = array_ops.scatter_nd(indices, updates, shape)
+    scatter = self.scatter_nd(indices, updates, shape)
 
     with self.test_session():
       self.assertEqual(scatter.eval().size, 0)
@@ -343,49 +422,49 @@ class ScatterNdTest(test.TestCase):
     shape = np.array([2, 2, 2])
     with self.assertRaisesWithPredicateMatch(
         ValueError, "The outer \\d+ dimensions of indices\\.shape="):
-      array_ops.scatter_nd(indices, updates, shape)
-
-    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
-    with self.assertRaisesWithPredicateMatch(
-        ValueError, "The outer \\d+ dimensions of indices\\.shape="):
-      state_ops.scatter_nd_update(ref, indices, updates)
+      self.scatter_nd(indices, updates, shape)
 
   def testRank3InvalidShape2(self):
     indices = array_ops.zeros([2, 2, 1], dtypes.int32)
     updates = array_ops.zeros([2, 2], dtypes.int32)
     shape = np.array([2, 2, 2])
     with self.assertRaisesWithPredicateMatch(
-        ValueError, "The inner \\d+ dimensions of output\\.shape="):
-      array_ops.scatter_nd(indices, updates, shape)
-
-    ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
-    with self.assertRaisesWithPredicateMatch(
-        ValueError, "The inner \\d+ dimensions of ref\\.shape="):
-      state_ops.scatter_nd_update(ref, indices, updates)
+        ValueError, "The inner \\d+ dimensions of (input|output)\\.shape="):
+      self.scatter_nd(indices, updates, shape)
 
   def testGradientsRank2ElementUpdate(self):
     indices = constant_op.constant([[0, 0], [1, 1]], dtype=dtypes.int32)
     updates = constant_op.constant([1, 4], dtype=dtypes.float64)
     shape = constant_op.constant([2, 2], dtype=dtypes.int32)
-    outputs = array_ops.scatter_nd(indices, updates, shape)
+    input_ = array_ops.zeros(shape, dtype=dtypes.float64)
+    outputs = self.scatter_nd(indices, updates, shape, input_)
 
     grad_vals = constant_op.constant([[1, 2], [3, 4]], dtype=dtypes.float64)
-    grads = gradients_impl.gradients([outputs], [updates], [grad_vals])[0]
-    expected_grads = np.array([1, 4], dtype=np.float64)
+    updates_grad, input_grad = gradients_impl.gradients(
+        [outputs], [updates, input_], [grad_vals])
+    expected_updates_grad = np.array([1, 4], dtype=np.float64)
+    expected_input_grad = np.array([[1, 2], [3, 4]], dtype=np.float64)
     with self.test_session():
-      self.assertAllEqual(expected_grads, grads.eval())
+      self.assertAllEqual(expected_updates_grad, updates_grad.eval())
+      if self.non_aliasing_add_test:
+        self.assertAllEqual(expected_input_grad, input_grad.eval())
 
   def testGradientsRank2SliceUpdate(self):
     indices = constant_op.constant([[1], [0]], dtype=dtypes.int32)
     updates = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64)
     shape = constant_op.constant([2, 2], dtype=dtypes.int32)
-    outputs = array_ops.scatter_nd(indices, updates, shape)
+    input_ = array_ops.zeros(shape, dtype=dtypes.float64)
+    outputs = self.scatter_nd(indices, updates, shape, input_)
 
     grad_vals = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64)
-    grads = gradients_impl.gradients([outputs], [updates], [grad_vals])[0]
-    expected_grads = np.array([[1, 2], [3, 4]], dtype=np.float64)
+    updates_grad, input_grad = gradients_impl.gradients(
+        [outputs], [updates, input_], [grad_vals])
+    expected_updates_grad = np.array([[1, 2], [3, 4]], dtype=np.float64)
+    expected_input_grad = np.array([[3, 4], [1, 2]], dtype=np.float64)
     with self.test_session():
-      self.assertAllEqual(expected_grads, grads.eval())
+      self.assertAllEqual(expected_updates_grad, updates_grad.eval())
+      if self.non_aliasing_add_test:
+        self.assertAllEqual(expected_input_grad, input_grad.eval())
 
   def testGradientsRank3SliceUpdate(self):
     indices = constant_op.constant(
@@ -393,67 +472,28 @@ class ScatterNdTest(test.TestCase):
     updates = constant_op.constant(
         [[[5, 7], [2, 4]], [[1, 3], [6, 8]]], dtype=dtypes.float64)
     shape = constant_op.constant([2, 2, 2], dtype=dtypes.int32)
-    outputs = array_ops.scatter_nd(indices, updates, shape)
+    input_ = array_ops.zeros(shape, dtype=dtypes.float64)
+    outputs = self.scatter_nd(indices, updates, shape, input_)
 
     grad_vals = constant_op.constant(
         [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=dtypes.float64)
-    grads = gradients_impl.gradients([outputs], [updates], [grad_vals])[0]
-    expected_grads = np.array(
+    updates_grad, input_grad = gradients_impl.gradients(
+        [outputs], [updates, input_], [grad_vals])
+    expected_updates_grad = np.array(
         [[[3, 4], [5, 6]], [[1, 2], [7, 8]]], dtype=np.float64)
+    expected_input_grad = np.array(
+        [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float64)
     with self.test_session():
-      self.assertAllEqual(expected_grads, grads.eval())
-
-  def testConcurrentUpdates(self):
-    num_updates = 10000
-    update_values = np.random.rand(num_updates)
-    ref = variables.Variable(np.zeros([2, 2]), dtype=dtypes.float64)
-    indices = constant_op.constant([[0, 1]] * num_updates, dtype=dtypes.int32)
-    updates = constant_op.constant(update_values, dtype=dtypes.float64)
-
-    expected_result = np.zeros([2, 2], dtype=np.float64)
-    expected_result[0, 1] = np.sum(update_values)
-
-    scatter = state_ops.scatter_nd_add(ref, indices, updates)
-    init = variables.global_variables_initializer()
-
-    with session.Session() as sess:
-      sess.run(init)
-      result = sess.run(scatter)
-      assert np.allclose(result, expected_result)
-
-  # TODO(fpmc): Re-enable this test when gpu_pip test actually runs on a GPU.
-  def _disabledTestScatterOutOfRangeGpu(self):
-    if not test.IsBuiltWithCuda():
-      return
-    # TODO(simister): Re-enable once binary size increase due to
-    # scatter_nd ops is under control.
-    # tf.scatter_nd_mul, tf.scatter_nd_div,
-    for op in (state_ops.scatter_nd_add, state_ops.scatter_nd_sub,
-               state_ops.scatter_nd_update):
-      params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
-      updates = np.array([-3, -4, -5]).astype(np.float32)
-      # With GPU, the code ignores indices that are out of range.
-      # We don't test the implementation; just test there's no failures.
-      with self.test_session(force_gpu=True):
-        ref = variables.Variable(params)
-        ref.initializer.run()
-
-        # Indices all in range, no problem.
-        indices = np.array([2, 0, 5])
-        op(ref, indices, updates).eval()
-
-        # Indicies out of range should not fail.
-        indices = np.array([-1, 0, 5])
-        op(ref, indices, updates).eval()
-        indices = np.array([2, 0, 6])
-        op(ref, indices, updates).eval()
+      self.assertAllEqual(expected_updates_grad, updates_grad.eval())
+      if self.non_aliasing_add_test:
+        self.assertAllEqual(expected_input_grad, input_grad.eval())
 
   def testScatterNdRepatedIndicesAdd(self):
     indices = array_ops.zeros([100000, 1], dtypes.int32)
     values = np.random.randn(100000)
     shape = [1]
     with self.test_session():
-      val = array_ops.scatter_nd(indices, values, shape).eval()
+      val = self.scatter_nd(indices, values, shape).eval()
     self.assertAllClose([np.sum(values)], val)
 
   def testSmokeScatterNdBatch2DSliceDim2(self):
@@ -461,28 +501,37 @@ class ScatterNdTest(test.TestCase):
       indices = array_ops.zeros([3, 5, 2], dtype=dtypes.int32)
       values = array_ops.zeros([3, 5, 7])
       shape = [4, 6, 7]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
 
   def testSmokeScatterNdBatch1DSliceDim2(self):
     with self.test_session():
       indices = array_ops.zeros([0, 2], dtype=dtypes.int32)
       values = array_ops.zeros([0, 7])
       shape = [4, 6, 7]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
 
   def testSmokeScatterNdBatch1DSliceDim3ShapeRank7(self):
     with self.test_session():
       indices = array_ops.zeros([1, 3], dtype=dtypes.int32)
       values = array_ops.zeros([1, 6, 7, 8, 9])
       shape = [3, 4, 5, 6, 7, 8, 9]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
 
   def testSmokeScatterNdBatch2DSliceDim3ShapeRank7(self):
     with self.test_session():
       indices = array_ops.zeros([1, 2, 3], dtype=dtypes.int32)
       values = array_ops.zeros([1, 2, 6, 7, 8, 9])
       shape = [3, 4, 5, 6, 7, 8, 9]
-      array_ops.scatter_nd(indices, values, shape).eval()
+      self.scatter_nd(indices, values, shape).eval()
+
+
+class ScatterNdNonAliasingAddTest(ScatterNdTest):
+  non_aliasing_add_test = True
+
+  def scatter_nd(self, indices, updates, shape, input_=None):
+    input_ = (input_ if input_ is not None else array_ops.zeros(
+        shape, dtype=updates.dtype))
+    return array_ops.scatter_nd_non_aliasing_add(input_, indices, updates)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index 5c6d309e6c..dcae3e0c7b 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -670,3 +670,10 @@ def _ScatterNdGrad(op, grad):
   indices = op.inputs[0]
   updates_grad = array_ops.gather_nd(grad, indices)
   return [None, updates_grad, None]
+
+
+@ops.RegisterGradient("ScatterNdNonAliasingAdd")
+def _ScatterNdNonAliasingAddGrad(op, grad):
+  indices = op.inputs[1]
+  updates_grad = array_ops.gather_nd(grad, indices)
+  return [grad, None, updates_grad]
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 478e0a9472..f2c3dae03c 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1363,18 +1363,20 @@ class ControlFlowContext(object):
       import_scope: Optional `string`. Name scope to add.
     """
     assert isinstance(values_def, control_flow_pb2.ValuesDef)
-    self._values = set(values_def.values)
+    self._values = set(
+        ops.prepend_name_scope(value, import_scope)
+        for value in values_def.values)
     g = ops.get_default_graph()
     self._external_values = {}
     for k, v in values_def.external_values.items():
+      k = ops.prepend_name_scope(k, import_scope)
       self._external_values[k] = g.as_graph_element(
           ops.prepend_name_scope(v, import_scope))
     op_names = set([op.split(":")[0]
                     for op in self._values - set(self._external_values)])
     for op in op_names:
       # pylint: disable=protected-access
-      g.as_graph_element(ops.prepend_name_scope(
-          op, import_scope))._set_control_flow_context(self)
+      g.as_graph_element(op)._set_control_flow_context(self)
       # pylint: enable=protected-access
 
   @property
@@ -1404,6 +1406,7 @@ class ControlFlowContext(object):
         [ops.strip_name_scope(v, export_scope)
          for v in sorted(self._values)])
     for k, v in self._external_values.items():
+      k = ops.strip_name_scope(k, export_scope)
       values_def.external_values[k] = ops.strip_name_scope(
           v.name, export_scope)
     return values_def
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index 4e95783e5a..07c47e05d2 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -416,6 +416,31 @@ class ContextTest(TensorFlowTestCase):
               c.to_proto(),
               control_flow_ops.WhileContext.from_proto(c.to_proto()).to_proto())
 
+  def testControlContextImportScope(self):
+    with self.test_session():
+      constant_op.constant(0, name="a")
+      constant_op.constant(2, name="test_scope/a")
+      b1 = constant_op.constant(1, name="b")
+      b2 = constant_op.constant(3, name="test_scope/b")
+
+      c = control_flow_ops.ControlFlowContext()
+      c._values = ["a", "b"]
+      c._external_values = {"a": b1}
+
+      c_with_scope = control_flow_ops.ControlFlowContext._from_proto(
+          c._to_proto(), import_scope="test_scope")
+
+      # _values and _external_values should be have scope prepended.
+      self.assertEquals(
+          c_with_scope._values, set(["test_scope/a", "test_scope/b"]))
+      self.assertEquals(
+          c_with_scope._external_values, {"test_scope/a": b2})
+
+      # Calling _to_proto() with export_scope should remove "test_scope".
+      compare.ProtoEq(
+          c._to_proto(),
+          c_with_scope._to_proto(export_scope="test_scope"))
+
 
 def _GetNestedShape(nested):
   def _GetShape(tensor):
diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py
deleted file mode 100644
index c6352b2a98..0000000000
--- a/tensorflow/python/ops/conv2d_benchmark.py
+++ /dev/null
@@ -1,141 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Benchmark for Conv2D op."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import itertools
-import time
-
-from tensorflow.python.client import session as session_lib
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import nn_ops
-from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import variables
-from tensorflow.python.platform import test
-
-
-def build_graph(device, input_shape, filter_shape, strides, padding, num_iters):
-  """builds a graph containing a sequence of conv2d operations.
-
-  Args:
-    device: String, the device to run on.
-    input_shape: Shape of the input tensor.
-    filter_shape: Shape of the filter tensor.
-    strides: A list of ints. 1-D of length 4. The stride of sliding
-             window for each dimension of input.
-    padding: A string from: "SAME", "VALID". The type of padding
-             algorithm to use.
-    num_iters: number of iterations to run conv2d.
-
-  Returns:
-    An array of tensors to run()
-  """
-  with ops.device("/%s:0" % device):
-    inp = variables.Variable(random_ops.truncated_normal(input_shape))
-    filt = variables.Variable(random_ops.truncated_normal(filter_shape))
-
-    outputs = []
-    conv2d_op = nn_ops.conv2d(inp, filt, strides, padding, data_format="NHWC")
-    outputs.append(conv2d_op)
-    for _ in range(1, num_iters):
-      with ops.control_dependencies([conv2d_op]):
-        conv2d_op = nn_ops.conv2d(
-            inp, filt, strides, padding, data_format="NHWC")
-        outputs.append(conv2d_op)
-    return control_flow_ops.group(*outputs)
-
-
-class Conv2DBenchmark(test.Benchmark):
-  """Benchmark conv2d!"""
-
-  def _run_graph(self, device, input_shape, filter_shape, strides, padding,
-                 num_iters):
-    """runs the graph and print its execution time.
-
-    Args:
-      device: String, the device to run on.
-      input_shape: Shape of the input tensor.
-      filter_shape: Shape of the filter tensor.
-      strides: A list of ints. 1-D of length 4. The stride of sliding
-               window for each dimension of input.
-      padding: A string from: "SAME", "VALID". The type of padding
-               algorithm to use.  num_iters: Number of iterations to run the
-                 benchmark.
-      num_iters: number of iterations to run conv2d.
-
-    Returns:
-      The duration of the run in seconds.
-    """
-    graph = ops.Graph()
-    with graph.as_default():
-      outputs = build_graph(device, input_shape, filter_shape, strides, padding,
-                            num_iters)
-      with session_lib.Session(graph=graph) as session:
-        variables.global_variables_initializer().run()
-        # warmup runs
-        session.run(outputs)
-
-        start_time = time.time()
-        session.run(outputs)
-        duration = (time.time() - start_time) / num_iters
-
-        print("%s inputshape:%s filtershape:%s strides:%s padding:%s "
-              "%d iters: %.8f sec" %
-              (device, str(input_shape).replace(" ", ""),
-               str(filter_shape).replace(" ", ""),
-               str(strides).replace(" ", ""), padding, num_iters, duration))
-
-    name_template = (
-        "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_"
-        "strides_{strides}_padding_{padding}")
-
-    self.report_benchmark(
-        name=name_template.format(
-            device=device,
-            inputshape=str(input_shape).replace(" ", ""),
-            filtershape=str(filter_shape).replace(" ", ""),
-            strides=str(strides).replace(" ", ""),
-            padding=padding).replace(" ", ""),
-        iters=num_iters,
-        wall_time=duration / num_iters)
-
-    return duration
-
-  def benchmark_conv2d(self):
-    print("conv2d benchmark:")
-
-    h = 1000
-    w = 1000
-    fh = 3
-    fw = 3
-    input_shapes = []
-    filter_shapes = []
-    for b, c in itertools.product([4, 16, 32], [i for i in range(3, 16)]):
-      input_shapes += [[b, h, w, c]]
-      filter_shapes += [[fh, fw, c, b]]
-    strides = [[1, 2, 2, 1]]
-    paddings = ["VALID", "SAME"]
-    for ishape, fshape in zip(input_shapes, filter_shapes):
-      for stride in strides:
-        for padding in paddings:
-          self._run_graph("gpu", ishape, fshape, stride, padding, 80)
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/python/ops/distributions/bernoulli.py b/tensorflow/python/ops/distributions/bernoulli.py
index 3281b57e83..2b981e7b19 100644
--- a/tensorflow/python/ops/distributions/bernoulli.py
+++ b/tensorflow/python/ops/distributions/bernoulli.py
@@ -22,7 +22,6 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
@@ -121,7 +120,10 @@ class Bernoulli(distribution.Distribution):
     return math_ops.cast(sample, self.dtype)
 
   def _log_prob(self, event):
-    event = self._maybe_assert_valid_sample(event)
+    if self.validate_args:
+      event = distribution_util.embed_check_integer_casting_closed(
+          event, target_dtype=dtypes.bool)
+
     # TODO(jaana): The current sigmoid_cross_entropy_with_logits has
     # inconsistent  behavior for logits = inf/-inf.
     event = math_ops.cast(event, self.logits.dtype)
@@ -162,17 +164,6 @@ class Bernoulli(distribution.Distribution):
     """Returns `1` if `prob > 0.5` and `0` otherwise."""
     return math_ops.cast(self.probs > 0.5, self.dtype)
 
-  def _maybe_assert_valid_sample(self, event, check_integer=True):
-    if not self.validate_args:
-      return event
-    event = distribution_util.embed_check_nonnegative_discrete(
-        event, check_integer=check_integer)
-    return control_flow_ops.with_dependencies([
-        check_ops.assert_less_equal(
-            event, array_ops.ones_like(event),
-            message="event is not less than or equal to 1."),
-    ], event)
-
 
 class BernoulliWithSigmoidProbs(Bernoulli):
   """Bernoulli with `probs = nn.sigmoid(logits)`."""
diff --git a/tensorflow/python/ops/distributions/categorical.py b/tensorflow/python/ops/distributions/categorical.py
index bad7e6e42f..d72f6a1fe5 100644
--- a/tensorflow/python/ops/distributions/categorical.py
+++ b/tensorflow/python/ops/distributions/categorical.py
@@ -34,28 +34,74 @@ from tensorflow.python.ops.distributions import util as distribution_util
 class Categorical(distribution.Distribution):
   """Categorical distribution.
 
-  The categorical distribution is parameterized by the log-probabilities
-  of a set of classes.
+  The Categorical distribution is parameterized by either probabilities or
+  log-probabilities of a set of `K` classes. It is defined over the integers
+  `{0, 1, ..., K}`.
+
+  The Categorical distribution is closely related to the `OneHotCategorical` and
+  `Multinomial` distributions.  The Categorical distribution can be intuited as
+  generating samples according to `argmax{ OneHotCategorical(probs) }` itself
+  being identical to `argmax{ Multinomial(probs, total_count=1) }.
+
+  #### Mathematical Details
+
+  The probability mass function (pmf) is,
+
+  ```none
+  pmf(k; pi) = prod_j pi_j**[k == j]
+  ```
+
+  #### Pitfalls
+
+  The number of classes, `K`, must not exceed:
+  - the largest integer representable by `self.dtype`, i.e.,
+    `2**(mantissa_bits+1)` (IEE754),
+  - the maximum `Tensor` index, i.e., `2**31-1`.
+
+  In other words,
+
+  ```python
+  K <= min(2**31-1, {
+    tf.float16: 2**11,
+    tf.float32: 2**24,
+    tf.float64: 2**53 }[param.dtype])
+  ```
+
+  Note: This condition is validated only when `self.validate_args = True`.
 
   #### Examples
 
-  Creates a 3-class distribution, with the 2nd class, the most likely to be
-  drawn from.
+  Creates a 3-class distribution with the 2nd class being most likely.
 
   ```python
-  p = [0.1, 0.5, 0.4]
-  dist = Categorical(probs=p)
+  dist = Categorical(probs=[0.1, 0.5, 0.4])
+  n = 1e4
+  empirical_prob = tf.cast(
+      tf.histogram_fixed_width(
+        dist.sample(int(n)),
+        [0., 2],
+        nbins=3),
+      dtype=tf.float32) / n
+  # ==> array([ 0.1005,  0.5037,  0.3958], dtype=float32)
   ```
 
-  Creates a 3-class distribution, with the 2nd class the most likely to be
-  drawn from, using logits.
+  Creates a 3-class distribution with the 2nd class being most likely.
+  Parameterized by [logits](https://en.wikipedia.org/wiki/Logit) rather than
+  probabilities.
 
   ```python
-  logits = [-50, 400, 40]
-  dist = Categorical(logits=logits)
+  dist = Categorical(logits=np.log([0.1, 0.5, 0.4])
+  n = 1e4
+  empirical_prob = tf.cast(
+      tf.histogram_fixed_width(
+        dist.sample(int(n)),
+        [0., 2],
+        nbins=3),
+      dtype=tf.float32) / n
+  # ==> array([0.1045,  0.5047, 0.3908], dtype=float32)
   ```
 
-  Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
+  Creates a 3-class distribution with the 3rd class being most likely.
   The distribution functions can be evaluated on counts.
 
   ```python
@@ -116,6 +162,10 @@ class Categorical(distribution.Distribution):
           multidimensional=True,
           name=name)
 
+      if validate_args:
+        self._logits = distribution_util.embed_check_categorical_event_shape(
+            self._logits)
+
       logits_shape_static = self._logits.get_shape().with_rank_at_least(1)
       if logits_shape_static.ndims is not None:
         self._batch_rank = ops.convert_to_tensor(
@@ -186,22 +236,25 @@ class Categorical(distribution.Distribution):
       logits_2d = self.logits
     else:
       logits_2d = array_ops.reshape(self.logits, [-1, self.event_size])
-    samples = random_ops.multinomial(logits_2d, n, seed=seed)
-    samples = math_ops.cast(samples, self.dtype)
-    ret = array_ops.reshape(
-        array_ops.transpose(samples),
+    draws = random_ops.multinomial(logits_2d, n, seed=seed)
+    draws = array_ops.reshape(
+        array_ops.transpose(draws),
         array_ops.concat([[n], self.batch_shape_tensor()], 0))
-    return ret
+    return math_ops.cast(draws, self.dtype)
 
   def _cdf(self, k):
     k = ops.convert_to_tensor(k, name="k")
+    if self.validate_args:
+      k = distribution_util.embed_check_integer_casting_closed(
+          k, target_dtype=dtypes.int32)
 
     # If there are multiple batch dimension, flatten them into one.
     batch_flattened_probs = array_ops.reshape(self._probs,
                                               [-1, self._event_size])
-    batch_flattened_k = array_ops.reshape(k, (-1,))
+    batch_flattened_k = array_ops.reshape(k, [-1])
 
     # Form a tensor to sum over.
+    # We don't need to cast k to integer since `sequence_mask` does this for us.
     mask_tensor = array_ops.sequence_mask(batch_flattened_k, self._event_size)
     to_sum_over = array_ops.where(mask_tensor,
                                   batch_flattened_probs,
@@ -211,6 +264,10 @@ class Categorical(distribution.Distribution):
 
   def _log_prob(self, k):
     k = ops.convert_to_tensor(k, name="k")
+    if self.validate_args:
+      k = distribution_util.embed_check_integer_casting_closed(
+          k, target_dtype=dtypes.int32)
+
     if self.logits.get_shape()[:-1] == k.get_shape():
       logits = self.logits
     else:
@@ -219,12 +276,18 @@ class Categorical(distribution.Distribution):
       logits_shape = array_ops.shape(logits)[:-1]
       k *= array_ops.ones(logits_shape, dtype=k.dtype)
       k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
+      if k.dtype.is_integer:
+        pass
+      elif k.dtype.is_floating:
+        # When `validate_args=True` we've already ensured int/float casting
+        # is closed.
+        return ops.cast(k, dtype=dtypes.int32)
+      else:
+        raise TypeError("`value` should have integer `dtype` or "
+                        "`self.dtype` ({})".format(self.dtype.base_dtype))
     return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
                                                             logits=logits)
 
-  def _prob(self, k):
-    return math_ops.exp(self._log_prob(k))
-
   def _entropy(self):
     return -math_ops.reduce_sum(
         nn_ops.log_softmax(self.logits) * self.probs, axis=-1)
diff --git a/tensorflow/python/ops/distributions/dirichlet_multinomial.py b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
index 61e22f4753..d792e9fe52 100644
--- a/tensorflow/python/ops/distributions/dirichlet_multinomial.py
+++ b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
@@ -36,7 +36,7 @@ __all__ = [
 
 
 _dirichlet_multinomial_sample_note = """For each batch of counts,
-`value = [n_0, ..., n_{k-1}]`, `P[value]` is the probability that after
+`value = [n_0, ..., n_{K-1}]`, `P[value]` is the probability that after
 sampling `self.total_count` draws from this Dirichlet-Multinomial distribution,
 the number of draws falling in class `j` is `n_j`. Since this definition is
 [exchangeable](https://en.wikipedia.org/wiki/Exchangeable_random_variables);
@@ -53,16 +53,16 @@ class DirichletMultinomial(distribution.Distribution):
   """Dirichlet-Multinomial compound distribution.
 
   The Dirichlet-Multinomial distribution is parameterized by a (batch of)
-  length-`k` `concentration` vectors (`k > 1`) and a `total_count` number of
+  length-`K` `concentration` vectors (`K > 1`) and a `total_count` number of
   trials, i.e., the number of trials per draw from the DirichletMultinomial. It
-  is defined over a (batch of) length-`k` vector `counts` such that
+  is defined over a (batch of) length-`K` vector `counts` such that
   `tf.reduce_sum(counts, -1) = total_count`. The Dirichlet-Multinomial is
-  identically the Beta-Binomial distribution when `k = 2`.
+  identically the Beta-Binomial distribution when `K = 2`.
 
   #### Mathematical Details
 
-  The Dirichlet-Multinomial is a distribution over `k`-class counts, i.e., a
-  length-`k` vector of non-negative integer `counts = n = [n_0, ..., n_{k-1}]`.
+  The Dirichlet-Multinomial is a distribution over `K`-class counts, i.e., a
+  length-`K` vector of non-negative integer `counts = n = [n_0, ..., n_{K-1}]`.
 
   The probability mass function (pmf) is,
 
@@ -73,7 +73,7 @@ class DirichletMultinomial(distribution.Distribution):
 
   where:
 
-  * `concentration = alpha = [alpha_0, ..., alpha_{k-1}]`, `alpha_j > 0`,
+  * `concentration = alpha = [alpha_0, ..., alpha_{K-1}]`, `alpha_j > 0`,
   * `total_count = N`, `N` a positive integer,
   * `N!` is `N` factorial, and,
   * `Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j)` is the
@@ -88,9 +88,9 @@ class DirichletMultinomial(distribution.Distribution):
   samples are generated as follows.
 
     1. Choose class probabilities:
-       `probs = [p_0,...,p_{k-1}] ~ Dir(concentration)`
+       `probs = [p_0,...,p_{K-1}] ~ Dir(concentration)`
     2. Draw integers:
-       `counts = [n_0,...,n_{k-1}] ~ Multinomial(total_count, probs)`
+       `counts = [n_0,...,n_{K-1}] ~ Multinomial(total_count, probs)`
 
   The last `concentration` dimension parametrizes a single Dirichlet-Multinomial
   distribution. When calling distribution functions (e.g., `dist.prob(counts)`),
@@ -101,6 +101,24 @@ class DirichletMultinomial(distribution.Distribution):
   Distribution parameters are automatically broadcast in all functions; see
   examples for details.
 
+  #### Pitfalls
+
+  The number of classes, `K`, must not exceed:
+  - the largest integer representable by `self.dtype`, i.e.,
+    `2**(mantissa_bits+1)` (IEE754),
+  - the maximum `Tensor` index, i.e., `2**31-1`.
+
+  In other words,
+
+  ```python
+  K <= min(2**31-1, {
+    tf.float16: 2**11,
+    tf.float32: 2**24,
+    tf.float64: 2**53 }[param.dtype])
+  ```
+
+  Note: This condition is validated only when `self.validate_args = True`.
+
   #### Examples
 
   ```python
@@ -157,8 +175,8 @@ class DirichletMultinomial(distribution.Distribution):
         Dirichlet multinomial distributions. Its components should be equal to
         integer values.
       concentration: Positive floating point tensor, whose dtype is the
-        same as `n` with shape broadcastable to `[N1,..., Nm, k]` `m >= 0`.
-        Defines this as a batch of `N1 x ... x Nm` different `k` class Dirichlet
+        same as `n` with shape broadcastable to `[N1,..., Nm, K]` `m >= 0`.
+        Defines this as a batch of `N1 x ... x Nm` different `K` class Dirichlet
         multinomial distributions.
       validate_args: Python `bool`, default `False`. When `True` distribution
         parameters are checked for validity despite possibly degrading runtime
@@ -180,9 +198,11 @@ class DirichletMultinomial(distribution.Distribution):
       #   created automatically by prepending). This forces enough explicitness.
       # * All calls involving `counts` eventually require a broadcast between
       #  `counts` and concentration.
-      self._total_count = self._maybe_assert_valid_total_count(
-          ops.convert_to_tensor(total_count, name="total_count"),
-          validate_args)
+      self._total_count = ops.convert_to_tensor(total_count, name="total_count")
+      if validate_args:
+        self._total_count = (
+            distribution_util.embed_check_nonnegative_integer_form(
+                self._total_count))
       self._concentration = self._maybe_assert_valid_concentration(
           ops.convert_to_tensor(concentration,
                                 name="concentration"),
@@ -242,7 +262,8 @@ class DirichletMultinomial(distribution.Distribution):
         seed=distribution_util.gen_new_seed(seed, salt="dirichlet_multinomial"))
     x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k), -2)
     final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
-    return array_ops.reshape(x, final_shape)
+    x = array_ops.reshape(x, final_shape)
+    return math_ops.cast(x, self.dtype)
 
   @distribution_util.AppendDocstring(_dirichlet_multinomial_sample_note)
   def _log_prob(self, counts):
@@ -302,42 +323,21 @@ class DirichletMultinomial(distribution.Distribution):
     """Checks the validity of the concentration parameter."""
     if not validate_args:
       return concentration
+    concentration = distribution_util.embed_check_categorical_event_shape(
+        concentration)
     return control_flow_ops.with_dependencies([
         check_ops.assert_positive(
             concentration,
             message="Concentration parameter must be positive."),
-        check_ops.assert_rank_at_least(
-            concentration, 1,
-            message="Concentration parameter must have >=1 dimensions."),
-        check_ops.assert_less(
-            1, array_ops.shape(concentration)[-1],
-            message="Concentration parameter must have event_size >= 2."),
     ], concentration)
 
-  def _maybe_assert_valid_total_count(self, total_count, validate_args):
-    if not validate_args:
-      return total_count
-    return control_flow_ops.with_dependencies([
-        check_ops.assert_non_negative(
-            total_count,
-            message="total_count must be non-negative."),
-        distribution_util.assert_integer_form(
-            total_count,
-            message="total_count cannot contain fractional values."),
-    ], total_count)
-
   def _maybe_assert_valid_sample(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
     if not self.validate_args:
       return counts
+    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
     return control_flow_ops.with_dependencies([
-        check_ops.assert_non_negative(
-            counts,
-            message="counts must be non-negative."),
         check_ops.assert_equal(
             self.total_count, math_ops.reduce_sum(counts, -1),
             message="counts last-dimension must sum to `self.total_count`"),
-        distribution_util.assert_integer_form(
-            counts,
-            message="counts cannot contain fractional components."),
     ], counts)
diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index 0236c31f6d..9b15d4c76e 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -52,17 +52,17 @@ class Multinomial(distribution.Distribution):
   """Multinomial distribution.
 
   This Multinomial distribution is parameterized by `probs`, a (batch of)
-  length-`k` `prob` (probability) vectors (`k > 1`) such that
+  length-`K` `prob` (probability) vectors (`K > 1`) such that
   `tf.reduce_sum(probs, -1) = 1`, and a `total_count` number of trials, i.e.,
   the number of trials per draw from the Multinomial. It is defined over a
-  (batch of) length-`k` vector `counts` such that
+  (batch of) length-`K` vector `counts` such that
   `tf.reduce_sum(counts, -1) = total_count`. The Multinomial is identically the
-  Binomial distribution when `k = 2`.
+  Binomial distribution when `K = 2`.
 
   #### Mathematical Details
 
-  The Multinomial is a distribution over `k`-class counts, i.e., a length-`k`
-  vector of non-negative integer `counts = n = [n_0, ..., n_{k-1}]`.
+  The Multinomial is a distribution over `K`-class counts, i.e., a length-`K`
+  vector of non-negative integer `counts = n = [n_0, ..., n_{K-1}]`.
 
   The probability mass function (pmf) is,
 
@@ -72,7 +72,7 @@ class Multinomial(distribution.Distribution):
   ```
 
   where:
-  * `probs = pi = [pi_0, ..., pi_{k-1}]`, `pi_j > 0`, `sum_j pi_j = 1`,
+  * `probs = pi = [pi_0, ..., pi_{K-1}]`, `pi_j > 0`, `sum_j pi_j = 1`,
   * `total_count = N`, `N` a positive integer,
   * `Z` is the normalization constant, and,
   * `N!` denotes `N` factorial.
@@ -80,6 +80,24 @@ class Multinomial(distribution.Distribution):
   Distribution parameters are automatically broadcast in all functions; see
   examples for details.
 
+  #### Pitfalls
+
+  The number of classes, `K`, must not exceed:
+  - the largest integer representable by `self.dtype`, i.e.,
+    `2**(mantissa_bits+1)` (IEE754),
+  - the maximum `Tensor` index, i.e., `2**31-1`.
+
+  In other words,
+
+  ```python
+  K <= min(2**31-1, {
+    tf.float16: 2**11,
+    tf.float32: 2**24,
+    tf.float64: 2**53 }[param.dtype])
+  ```
+
+  Note: This condition is validated only when `self.validate_args = True`.
+
   #### Examples
 
   Create a 3-class distribution, with the 3rd class is most likely to be drawn,
@@ -138,14 +156,14 @@ class Multinomial(distribution.Distribution):
         to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
         `N1 x ... x Nm` different Multinomial distributions. Its components
         should be equal to integer values.
-      logits: Floating point tensor representing the log-odds of a
-        positive event with shape broadcastable to `[N1,..., Nm, k], m >= 0`,
-        and the same dtype as `total_count`. Defines this as a batch of
-        `N1 x ... x Nm` different `k` class Multinomial distributions. Only one
-        of `logits` or `probs` should be passed in.
+      logits: Floating point tensor representing unnormalized log-probabilities
+        of a positive event with shape broadcastable to
+        `[N1,..., Nm, K]` `m >= 0`, and the same dtype as `total_count`. Defines
+        this as a batch of `N1 x ... x Nm` different `K` class Multinomial
+        distributions. Only one of `logits` or `probs` should be passed in.
       probs: Positive floating point tensor with shape broadcastable to
-        `[N1,..., Nm, k]` `m >= 0` and same dtype as `total_count`. Defines
-        this as a batch of `N1 x ... x Nm` different `k` class Multinomial
+        `[N1,..., Nm, K]` `m >= 0` and same dtype as `total_count`. Defines
+        this as a batch of `N1 x ... x Nm` different `K` class Multinomial
         distributions. `probs`'s components in the last portion of its shape
         should sum to `1`. Only one of `logits` or `probs` should be passed in.
       validate_args: Python `bool`, default `False`. When `True` distribution
@@ -160,9 +178,11 @@ class Multinomial(distribution.Distribution):
     """
     parameters = locals()
     with ops.name_scope(name, values=[total_count, logits, probs]):
-      self._total_count = self._maybe_assert_valid_total_count(
-          ops.convert_to_tensor(total_count, name="total_count"),
-          validate_args)
+      self._total_count = ops.convert_to_tensor(total_count, name="total_count")
+      if validate_args:
+        self._total_count = (
+            distribution_util.embed_check_nonnegative_integer_form(
+                self._total_count))
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits=logits,
           probs=probs,
@@ -222,25 +242,22 @@ class Multinomial(distribution.Distribution):
     k = self.event_shape_tensor()[0]
     # Flatten batch dims so logits has shape [B, k],
     # where B = reduce_prod(self.batch_shape_tensor()).
-    draws = random_ops.multinomial(
+    x = random_ops.multinomial(
         logits=array_ops.reshape(self.logits, [-1, k]),
         num_samples=n * n_draws,
         seed=seed)
-    draws = array_ops.reshape(draws, shape=[-1, n, n_draws])
-    x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k),
+    x = array_ops.reshape(x, shape=[-1, n, n_draws])
+    x = math_ops.reduce_sum(array_ops.one_hot(x, depth=k),
                             axis=-2)  # shape: [B, n, k]
     x = array_ops.transpose(x, perm=[1, 0, 2])
     final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
-    return array_ops.reshape(x, final_shape)
+    x = array_ops.reshape(x, final_shape)
+    return math_ops.cast(x, self.dtype)
 
   @distribution_util.AppendDocstring(_multinomial_sample_note)
   def _log_prob(self, counts):
     return self._log_unnormalized_prob(counts) - self._log_normalization(counts)
 
-  @distribution_util.AppendDocstring(_multinomial_sample_note)
-  def _prob(self, counts):
-    return math_ops.exp(self._log_prob(counts))
-
   def _log_unnormalized_prob(self, counts):
     counts = self._maybe_assert_valid_sample(counts)
     return math_ops.reduce_sum(counts * math_ops.log(self.probs), -1)
@@ -265,25 +282,11 @@ class Multinomial(distribution.Distribution):
         self.total_count)[..., array_ops.newaxis]
     return self._mean_val - self._mean_val * p
 
-  def _maybe_assert_valid_total_count(self, total_count, validate_args):
-    if not validate_args:
-      return total_count
-    return control_flow_ops.with_dependencies([
-        check_ops.assert_non_negative(
-            total_count,
-            message="total_count must be non-negative."),
-        distribution_util.assert_integer_form(
-            total_count,
-            message="total_count cannot contain fractional values."),
-    ], total_count)
-
   def _maybe_assert_valid_sample(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
     if not self.validate_args:
       return counts
-
-    counts = distribution_util.embed_check_nonnegative_discrete(
-        counts, check_integer=True)
+    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
     return control_flow_ops.with_dependencies([
         check_ops.assert_equal(
             self.total_count, math_ops.reduce_sum(counts, -1),
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 4135bf9b2d..c808f1fa7a 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -74,7 +74,8 @@ def assert_close(
 
 
 def assert_integer_form(
-    x, data=None, summarize=None, message=None, name="assert_integer_form"):
+    x, data=None, summarize=None, message=None,
+    int_dtype=None, name="assert_integer_form"):
   """Assert that x has integer components (or floats equal to integers).
 
   Args:
@@ -83,18 +84,30 @@ def assert_integer_form(
       error message and first few entries of `x` and `y`.
     summarize: Print this many entries of each tensor.
     message: A string to prefix to the default message.
+    int_dtype: A `tf.dtype` used to cast the float to. The default (`None`)
+      implies the smallest possible signed int will be used for casting.
     name: A name for this operation (optional).
 
   Returns:
-    Op raising `InvalidArgumentError` if round(x) != x.
+    Op raising `InvalidArgumentError` if `cast(x, int_dtype) != x`.
   """
-
-  message = message or "x has non-integer components"
-  x = ops.convert_to_tensor(x, name="x")
-  casted_x = math_ops.to_int64(x)
-  return check_ops.assert_equal(
-      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
-      data=data, summarize=summarize, message=message, name=name)
+  with ops.name_scope(name, values=[x, data]):
+    x = ops.convert_to_tensor(x, name="x")
+    if x.dtype.is_integer:
+      return control_flow_ops.no_op()
+    message = message or "{} has non-integer components".format(x.op.name)
+    if int_dtype is None:
+      try:
+        int_dtype = {
+            dtypes.float16: dtypes.int16,
+            dtypes.float32: dtypes.int32,
+            dtypes.float64: dtypes.int64,
+        }[x.dtype.base_dtype]
+      except KeyError:
+        raise TypeError("Unrecognized type {}".format(x.dtype.name))
+    return check_ops.assert_equal(
+        x, math_ops.cast(math_ops.cast(x, int_dtype), x.dtype),
+        data=data, summarize=summarize, message=message, name=name)
 
 
 def assert_symmetric(matrix):
@@ -103,14 +116,22 @@ def assert_symmetric(matrix):
       [check_ops.assert_equal(matrix, matrix_t)], matrix)
 
 
-def embed_check_nonnegative_discrete(x, check_integer=True):
+def embed_check_nonnegative_integer_form(
+    x, name="embed_check_nonnegative_integer_form"):
   """Assert x is a non-negative tensor, and optionally of integers."""
-  assertions = [check_ops.assert_non_negative(
-      x, message="x must be non-negative.")]
-  if check_integer:
-    assertions += [assert_integer_form(
-        x, message="x cannot contain fractional components.")]
-  return control_flow_ops.with_dependencies(assertions, x)
+  with ops.name_scope(name, values=[x]):
+    x = ops.convert_to_tensor(x, name="x")
+    assertions = [
+        check_ops.assert_non_negative(
+            x, message="'{}' must be non-negative.".format(x.op.name)),
+    ]
+    if not x.dtype.is_integer:
+      assertions += [
+          assert_integer_form(
+              x, message="'{}' cannot contain fractional components.".format(
+                  x.op.name)),
+      ]
+    return control_flow_ops.with_dependencies(assertions, x)
 
 
 def same_dynamic_shape(a, b):
@@ -175,16 +196,26 @@ def get_logits_and_probs(logits=None,
 
     if probs is None:
       logits = ops.convert_to_tensor(logits, name="logits")
+      if not logits.dtype.is_floating:
+        raise TypeError("logits must having floating type.")
+      # We can early return since we constructed probs and therefore know
+      # they're valid.
       if multidimensional:
+        if validate_args:
+          logits = embed_check_categorical_event_shape(logits)
         return logits, nn.softmax(logits, name="probs")
       return logits, math_ops.sigmoid(logits, name="probs")
 
     probs = ops.convert_to_tensor(probs, name="probs")
+    if not probs.dtype.is_floating:
+      raise TypeError("probs must having floating type.")
+
     if validate_args:
       with ops.name_scope("validate_probs"):
         one = constant_op.constant(1., probs.dtype)
         dependencies = [check_ops.assert_non_negative(probs)]
         if multidimensional:
+          probs = embed_check_categorical_event_shape(probs)
           dependencies += [assert_close(math_ops.reduce_sum(probs, -1), one,
                                         message="probs does not sum to 1.")]
         else:
@@ -205,6 +236,247 @@ def get_logits_and_probs(logits=None,
       return math_ops.log(probs) - math_ops.log1p(-1. * probs), probs
 
 
+def _is_known_unsigned_by_dtype(dt):
+  """Helper returning True if dtype is known to be unsigned."""
+  return {
+      dtypes.bool: True,
+      dtypes.uint8: True,
+      dtypes.uint16: True,
+  }.get(dt.base_dtype, False)
+
+
+def _is_known_signed_by_dtype(dt):
+  """Helper returning True if dtype is known to be signed."""
+  return {
+      dtypes.float16: True,
+      dtypes.float32: True,
+      dtypes.float64: True,
+      dtypes.int8: True,
+      dtypes.int16: True,
+      dtypes.int32: True,
+      dtypes.int64: True,
+  }.get(dt.base_dtype, False)
+
+
+def _is_known_dtype(dt):
+  """Helper returning True if dtype is known."""
+  return _is_known_unsigned_by_dtype(dt) or _is_known_signed_by_dtype(dt)
+
+
+def _largest_integer_by_dtype(dt):
+  """Helper returning the largest integer exactly representable by dtype."""
+  if not _is_known_dtype(dt):
+    raise TypeError("Unrecognized dtype: {}".format(dt.name))
+  if dt.is_floating:
+    return int(2**(np.finfo(dt.as_numpy_dtype).nmant + 1))
+  if dt.is_integer:
+    return np.iinfo(dt.as_numpy_dtype).max
+  if dt.base_dtype == dtypes.bool:
+    return int(1)
+  # We actually can't land here but keep the case for completeness.
+  raise TypeError("Unrecognized dtype: {}".format(dt.name))
+
+
+def _smallest_integer_by_dtype(dt):
+  """Helper returning the smallest integer exactly representable by dtype."""
+  if not _is_known_dtype(dt):
+    raise TypeError("Unrecognized dtype: {}".format(dt.name))
+  if _is_known_unsigned_by_dtype(dt):
+    return 0
+  return -1 * _largest_integer_by_dtype(dt)
+
+
+def _is_integer_like_by_dtype(dt):
+  """Helper returning True if dtype.is_interger or is `bool`."""
+  if not _is_known_dtype(dt):
+    raise TypeError("Unrecognized dtype: {}".format(dt.name))
+  return dt.is_integer or dt.base_dtype == dtypes.bool
+
+
+def embed_check_categorical_event_shape(
+    categorical_param,
+    name="embed_check_categorical_event_shape"):
+  """Embeds checks that categorical distributions don't have too many classes.
+
+  A categorical-type distribution is one which, e.g., returns the class label
+  rather than a one-hot encoding.  E.g., `Categorical(probs)`.
+
+  Since distributions output samples in the same dtype as the parameters, we
+  must ensure that casting doesn't lose precision. That is, the
+  `parameter.dtype` implies a maximum number of classes. However, since shape is
+  `int32` and categorical variables are presumed to be indexes into a `Tensor`,
+  we must also ensure that the number of classes is no larger than the largest
+  possible `int32` index, i.e., `2**31-1`.
+
+  In other words the number of classes, `K`, must satisfy the following
+  condition:
+
+  ```python
+  K <= min(
+      int(2**31 - 1),  # Largest float as an index.
+      {
+          dtypes.float16: int(2**11),   # Largest int as a float16.
+          dtypes.float32: int(2**24),
+          dtypes.float64: int(2**53),
+      }.get(categorical_param.dtype.base_dtype, 0))
+  ```
+
+  Args:
+    categorical_param: Floating-point `Tensor` representing parameters of
+      distribution over categories. The rightmost shape is presumed to be the
+      number of categories.
+    name: A name for this operation (optional).
+
+  Returns:
+    categorical_param: Input `Tensor` with appropriate assertions embedded.
+
+  Raises:
+    TypeError: if `categorical_param` has an unknown `dtype`.
+    ValueError: if we can statically identify `categorical_param` as being too
+      large (for being closed under int32/float casting).
+  """
+  with ops.name_scope(name, values=[categorical_param]):
+    x = ops.convert_to_tensor(categorical_param, name="categorical_param")
+    # The size must not exceed both of:
+    # - The largest possible int32 (since categorical values are presumed to be
+    #   indexes into a Tensor).
+    # - The largest possible integer exactly representable under the given
+    #   floating-point dtype (since we need to cast to/from).
+    #
+    # The chosen floating-point thresholds are 2**(1 + mantissa_bits).
+    # For more details, see:
+    # https://en.wikipedia.org/wiki/Floating-point_arithmetic#Internal_representation
+    x_dtype = x.dtype.base_dtype
+    max_event_size = (_largest_integer_by_dtype(x_dtype)
+                      if x_dtype.is_floating else 0)
+    if max_event_size is 0:
+      raise TypeError("Unable to validate size of unrecognized dtype "
+                      "({}).".format(x_dtype.name))
+    try:
+      x_shape_static = x.get_shape().with_rank_at_least(1)
+    except ValueError:
+      raise ValueError("A categorical-distribution parameter must have "
+                       "at least 1 dimension.")
+    if x_shape_static[-1].value is not None:
+      event_size = x_shape_static[-1].value
+      if event_size < 2:
+        raise ValueError("A categorical-distribution parameter must have at "
+                         "least 2 events.")
+      if event_size > max_event_size:
+        raise ValueError(
+            "Number of classes exceeds `dtype` precision, i.e., "
+            "{} implies shape ({}) cannot exceed {}.".format(
+                x_dtype.name, event_size, max_event_size))
+      return x
+    else:
+      event_size = array_ops.shape(x, name="x_shape")[-1]
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_rank_at_least(
+              x, 1, message=("A categorical-distribution parameter must have "
+                             "at least 1 dimension.")),
+          check_ops.assert_greater_equal(
+              array_ops.shape(x)[-1], 2,
+              message=("A categorical-distribution parameter must have at "
+                       "least 2 events.")),
+          check_ops.assert_less_equal(
+              event_size, max_event_size,
+              message="Number of classes exceeds `dtype` precision, "
+                      "i.e., {} dtype cannot exceed {} shape.".format(
+                          x_dtype.name, max_event_size)),
+      ], x)
+
+
+def embed_check_integer_casting_closed(
+    x,
+    target_dtype,
+    assert_nonnegative=True,
+    name="embed_check_casting_closed"):
+  """Ensures integers remain unaffected despite casting to/from int/float types.
+
+  Example integer-types: `uint8`, `int32`, `bool`.
+  Example floating-types: `float32`, `float64`.
+
+  The largest possible integer representable by an IEEE754 floating-point is
+  `2**(1 + mantissa_bits)` yet the largest possible integer as an int-type is
+  `2**(bits - 1) - 1`. This function ensures that a `Tensor` purporting to have
+  integer-form values can be cast to some other type without loss of precision.
+
+  The smallest representable integer is the negative of the largest
+  representable integer, except for types: `uint8`, `uint16`, `bool`. For these
+  types, the smallest representable integer is `0`.
+
+  Args:
+    x: `Tensor` representing integer-form values.
+    target_dtype: TF `dtype` under which `x` should have identical values.
+    assert_nonnegative: `bool` indicating `x` should contain nonnegative values.
+    name: A name for this operation (optional).
+
+  Returns:
+    x: Input `Tensor` with appropriate assertions embedded.
+
+  Raises:
+    TypeError: if `x` is neither integer- nor floating-type.
+    TypeError: if `target_dtype` is neither integer- nor floating-type.
+    TypeError: if neither `x` nor `target_dtype` are integer-type.
+  """
+
+  with ops.name_scope(name, values=[x]):
+    x = ops.convert_to_tensor(x, name="x")
+    if (not _is_integer_like_by_dtype(x.dtype)
+        and not x.dtype.is_floating):
+      raise TypeError("{}.dtype must be floating- or "
+                      "integer-type.".format(x.dtype.name))
+    if (not _is_integer_like_by_dtype(target_dtype)
+        and not target_dtype.is_floating):
+      raise TypeError("target_dtype ({}) must be floating- or "
+                      "integer-type.".format(target_dtype.name))
+    if (not _is_integer_like_by_dtype(x.dtype)
+        and not _is_integer_like_by_dtype(target_dtype)):
+      raise TypeError("At least one of {}.dtype ({}) and target_dtype ({}) "
+                      "must be integer-type.".format(
+                          x.op.name, x.dtype.name, target_dtype.name))
+
+    assertions = []
+    if assert_nonnegative:
+      assertions += [
+          check_ops.assert_non_negative(
+              x, message="Elements must be non-negative."),
+      ]
+
+    if x.dtype.is_floating:
+      # Being here means _is_integer_like_by_dtype(target_dtype) = True.
+      # Since this check implies the magnitude check below, we need only it.
+      assertions += [
+          assert_integer_form(
+              x, int_dtype=target_dtype,
+              message="Elements must be {}-equivalent.".format(
+                  target_dtype.name)),
+      ]
+    else:
+      if (_largest_integer_by_dtype(x.dtype)
+          > _largest_integer_by_dtype(target_dtype)):
+        # Cast may lose integer precision.
+        assertions += [
+            check_ops.assert_less_equal(
+                x, _largest_integer_by_dtype(target_dtype),
+                message=("Elements cannot exceed {}.".format(
+                    _largest_integer_by_dtype(target_dtype)))),
+        ]
+      if (not assert_nonnegative and
+          (_smallest_integer_by_dtype(x.dtype)
+           < _smallest_integer_by_dtype(target_dtype))):
+        assertions += [
+            check_ops.assert_greater_equal(
+                x, _smallest_integer_by_dtype(target_dtype),
+                message=("Elements cannot be smaller than {}.".format(
+                    _smallest_integer_by_dtype(target_dtype)))),
+        ]
+
+    if not assertions:
+      return x
+    return control_flow_ops.with_dependencies(assertions, x)
+
+
 def log_combinations(n, counts, name="log_combinations"):
   """Multinomial coefficient.
 
@@ -456,6 +728,8 @@ def fill_lower_triangular(x, validate_args=False, name="fill_lower_triangular"):
   e.g., `tril = tf.matrix_band_part(full, -1, 0)`, rather than directly
   construct a lower triangular.
 
+  Warning: This Op is intended for convenience, not efficiency.
+
   Example:
 
   ```python
@@ -563,6 +837,74 @@ def fill_lower_triangular(x, validate_args=False, name="fill_lower_triangular"):
     return y
 
 
+def tridiag(below=None, diag=None, above=None, name=None):
+  """Creates a matrix with values set above, below, and on the diagonal.
+
+  Example:
+
+  ```python
+  tridiag(below=[1., 2., 3.],
+          diag=[4., 5., 6., 7.],
+          above=[8., 9., 10.])
+  # ==> array([[  4.,   8.,   0.,   0.],
+  #            [  1.,   5.,   9.,   0.],
+  #            [  0.,   2.,   6.,  10.],
+  #            [  0.,   0.,   3.,   7.]], dtype=float32)
+  ```
+
+  Warning: This Op is intended for convenience, not efficiency.
+
+  Args:
+    below: `Tensor` of shape `[B1, ..., Bb, d-1]` corresponding to the below
+      diagonal part. `None` is logically equivalent to `below = 0`.
+    diag: `Tensor` of shape `[B1, ..., Bb, d]` corresponding to the diagonal
+      part.  `None` is logically equivalent to `diag = 0`.
+    above: `Tensor` of shape `[B1, ..., Bb, d-1]` corresponding to the above
+      diagonal part.  `None` is logically equivalent to `above = 0`.
+    name: Python `str`. The name to give this op.
+
+  Returns:
+    tridiag: `Tensor` with values set above, below and on the diagonal.
+
+  Raises:
+    ValueError: if all inputs are `None`.
+  """
+
+  def _pad(x):
+    """Prepends and appends a zero to every vector in a batch of vectors."""
+    shape = array_ops.concat([array_ops.shape(x)[:-1], [1]], axis=0)
+    z = array_ops.zeros(shape, dtype=x.dtype)
+    return array_ops.concat([z, x, z], axis=-1)
+
+  def _add(*x):
+    """Adds list of Tensors, ignoring `None`."""
+    s = None
+    for y in x:
+      if y is None:
+        continue
+      elif s is None:
+        s = y
+      else:
+        s += y
+    if s is None:
+      raise ValueError("Must specify at least one of `below`, `diag`, `above`.")
+    return s
+
+  with ops.name_scope(name, "tridiag", [below, diag, above]):
+    if below is not None:
+      below = ops.convert_to_tensor(below, name="below")
+      below = array_ops.matrix_diag(_pad(below))[..., :-1, 1:]
+    if diag is not None:
+      diag = ops.convert_to_tensor(diag, name="diag")
+      diag = array_ops.matrix_diag(diag)
+    if above is not None:
+      above = ops.convert_to_tensor(above, name="above")
+      above = array_ops.matrix_diag(_pad(above))[..., 1:, :-1]
+    # TODO(jvdillon): Consider using scatter_nd instead of creating three full
+    # matrices.
+    return _add(below, diag, above)
+
+
 # TODO(jvdillon): Merge this test back into:
 # tensorflow/python/ops/softplus_op_test.py
 # once TF core is accepting new ops.
diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py
index 4c94f9e9b5..f34d7fd389 100644
--- a/tensorflow/python/ops/embedding_ops.py
+++ b/tensorflow/python/ops/embedding_ops.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Operations for embeddings."""
 from __future__ import absolute_import
 from __future__ import division
@@ -35,65 +34,73 @@ from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 
 
-def _do_gather(params, ids, name=None):
-  """Deals with doing gather differently for resource variables."""
-  if isinstance(params, resource_variable_ops.ResourceVariable):
-    return params.sparse_read(ids, name=name)
-  return array_ops.gather(params, ids, name=name)
-
-
-def embedding_lookup(params, ids, partition_strategy="mod", name=None,
-                     validate_indices=True,  # pylint: disable=unused-argument
-                     max_norm=None):
-  """Looks up `ids` in a list of embedding tensors.
-
-  This function is used to perform parallel lookups on the list of
-  tensors in `params`.  It is a generalization of
-  @{tf.gather}, where `params` is
-  interpreted as a partitioning of a large embedding tensor.  `params` may be
-  a `PartitionedVariable` as returned by using `tf.get_variable()` with a
-  partitioner.
-
-  If `len(params) > 1`, each element `id` of `ids` is partitioned between
-  the elements of `params` according to the `partition_strategy`.
-  In all strategies, if the id space does not evenly divide the number of
-  partitions, each of the first `(max_id + 1) % len(params)` partitions will
-  be assigned one more id.
-
-  If `partition_strategy` is `"mod"`, we assign each id to partition
-  `p = id % len(params)`. For instance,
-  13 ids are split across 5 partitions as:
-  `[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
-
-  If `partition_strategy` is `"div"`, we assign ids to partitions in a
-  contiguous manner. In this case, 13 ids are split across 5 partitions as:
-  `[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`
+def _gather_and_clip(params, ids, max_norm, name=None):
+  """Helper function for _embedding_lookup_and_transform.
 
-  The results of the lookup are concatenated into a dense
-  tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
+  This function gathers embeddings from a single tensor. The gather deals with
+  resource variables specially. The embeddings are clipped to an l2-norm of
+  max_norm if provided.
 
   Args:
-    params: A single tensor representing the complete embedding tensor,
-      or a list of P tensors all of same shape except for the first dimension,
-      representing sharded embedding tensors.  Alternatively, a
-      `PartitionedVariable`, created by partitioning along dimension 0. Each
-      element must be appropriately sized for the given `partition_strategy`.
-    ids: A `Tensor` with type `int32` or `int64` containing the ids to be looked
-      up in `params`.
-    partition_strategy: A string specifying the partitioning strategy, relevant
-      if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
-      is `"mod"`.
+    params: A `Tensor` of embeddings.
+    ids: A `Tensor` indexing the embeddings to be retrieved from `params`.
+    max_norm: If provided, embedding values are l2-normalized to the value of
+      max_norm.
     name: A name for the operation (optional).
-    validate_indices: DEPRECATED. If this operation is assigned to CPU, values
-      in `indices` are always validated to be within range.  If assigned to GPU,
-      out-of-bound indices result in safe but unspecified behavior, which may
-      include raising an error.
-    max_norm: If not None, embedding values are l2-normalized to the value of
-     max_norm.
 
   Returns:
-    A `Tensor` with the same type as the tensors in `params`.
+    A `Tensor` with the same type as `params`.
+  """
+  if isinstance(params, resource_variable_ops.ResourceVariable):
+    embs = params.sparse_read(ids, name=name)
+  else:
+    embs = array_ops.gather(params, ids, name=name)
+  if max_norm is None:
+    return embs
+  static = True
+  ids_rank = ops.convert_to_tensor(ids).get_shape().ndims
+  if ids_rank is None:
+    ids_rank = array_ops.rank(ids)
+    static = False
+  embs_rank = embs.get_shape().ndims
+  if embs_rank is None:
+    embs_rank = array_ops.rank(embs)
+    static = False
+  return clip_ops.clip_by_norm(
+      embs,
+      max_norm,
+      axes=list(range(ids_rank, embs_rank))
+      if static else math_ops.range(ids_rank, embs_rank))
+
+
+def _embedding_lookup_and_transform(params,
+                                    ids,
+                                    partition_strategy="mod",
+                                    name=None,
+                                    max_norm=None,
+                                    transform_fn=None):
+  """Helper function for embedding_lookup and _compute_sampled_logits.
+
+  This function is a generalization of embedding_lookup that optionally
+  applies a caller-specified transformation to each embedding. This is
+  done through the `transform_fn` argument. If provided, the function is
+  applied to each partitioned tensor of retrieved embeddings, colocated
+  with the embeddings. This function will be called with a single `Tensor`
+  argument of the same type as the `params` tensor and should return a
+  `Tensor`. The shape of the argument will be the same as `params` except
+  for the size of the first dimension. The first dimension of the result's
+  shape must be the same size as the argument's.
 
+  Args:
+    params: See embedding_lookup.
+    ids: See embedding_lookup.
+    partition_strategy: See embedding_lookup.
+    name: See embedding_lookup.
+    max_norm: See embedding_lookup.
+    transform_fn: An optional function to apply to each retrieved embedding.
+
+  Returns:
+    See embedding_lookup for details.
   Raises:
     ValueError: If `params` is empty.
   """
@@ -104,35 +111,25 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
   if not isinstance(params, list):
     params = [params]
 
-  def maybe_normalize(x):
-    """Normalizes the embeddings in x if max_norm is not None."""
-    if max_norm is None:
-      return x
-    static = True
-    ids_rank = ops.convert_to_tensor(ids).get_shape().ndims
-    if ids_rank is None:
-      ids_rank = array_ops.rank(ids)
-      static = False
-    x_rank = x.get_shape().ndims
-    if x_rank is None:
-      x_rank = array_ops.rank(x)
-      static = False
-    return clip_ops.clip_by_norm(
-        x, max_norm,
-        axes=list(range(ids_rank, x_rank)) if static
-        else math_ops.range(ids_rank, x_rank))
-
   with ops.name_scope(name, "embedding_lookup", params + [ids]) as name:
     np = len(params)  # Number of partitions
     # Preserve the resource variable status to avoid accidental dense reads.
-    if not any(isinstance(p, resource_variable_ops.ResourceVariable)
-               for p in params):
+    if not any(
+        isinstance(p, resource_variable_ops.ResourceVariable) for p in params):
       params = ops.convert_n_to_tensor_or_indexed_slices(params, name="params")
-    if np == 1:
+    ids = ops.convert_to_tensor(ids, name="ids")
+    if np == 1 and (transform_fn is None or ids.get_shape().ndims == 1):
       with ops.colocate_with(params[0]):
-        return maybe_normalize(_do_gather(params[0], ids, name=name))
+        result = _gather_and_clip(params[0], ids, max_norm, name=name)
+        if transform_fn is not None:
+          result = transform_fn(result)
+        return result
     else:
-      ids = ops.convert_to_tensor(ids, name="ids")
+      # Flatten the ids. There are two cases where we need to do this.
+      # - There is more than one params tensor.
+      # - There is a transform_fn and ids is not statically known to be 1-D.
+      #   We must flatten in this case because transform_fn expects a flat
+      #   a flat tensor of embeddings.
       flat_ids = array_ops.reshape(ids, [-1])
       original_indices = math_ops.range(array_ops.size(flat_ids))
 
@@ -167,13 +164,12 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
             (flat_ids - extras) // ids_per_partition)
 
         # Emulate a conditional using a boolean indicator tensor
-        is_in_first_extras_partitions = math_ops.cast(
-            p_assignments < extras, flat_ids.dtype)
-        new_ids = (
-            is_in_first_extras_partitions * (
-                flat_ids % (ids_per_partition + 1)) +
-            (1 - is_in_first_extras_partitions) * (
-                (flat_ids - extras) % ids_per_partition))
+        is_in_first_extras_partitions = math_ops.cast(p_assignments < extras,
+                                                      flat_ids.dtype)
+        new_ids = (is_in_first_extras_partitions * (flat_ids %
+                                                    (ids_per_partition + 1)) +
+                   (1 - is_in_first_extras_partitions) *
+                   ((flat_ids - extras) % ids_per_partition))
       else:
         raise ValueError("Unrecognized partition strategy: " +
                          partition_strategy)
@@ -190,36 +186,117 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
       partitioned_result = []
       for p in xrange(np):
         with ops.colocate_with(params[p]):
-          partitioned_result.append(_do_gather(params[p], gather_ids[p]))
+          result = _gather_and_clip(params[p], gather_ids[p], max_norm)
+          if transform_fn is not None:
+            result = transform_fn(result)
+          partitioned_result.append(result)
       # Stitch these back together
-      ret = data_flow_ops.dynamic_stitch(pindices, partitioned_result,
-                                         name=name)
-      # Reshape to reverse the flattening of ids.
-      element_shape = params[0].get_shape()[1:]
-      for p in params[1:]:
-        element_shape = element_shape.merge_with(p.get_shape()[1:])
-      if element_shape.is_fully_defined():
-        ret = array_ops.reshape(ret,
-                                array_ops.concat(
-                                    [array_ops.shape(ids), element_shape], 0))
+      ret = data_flow_ops.dynamic_stitch(
+          pindices, partitioned_result, name=name)
+
+      # Determine the static element shape.
+      if transform_fn is None:
+        element_shape_s = params[0].get_shape()[1:]
+        for p in params[1:]:
+          element_shape_s = element_shape_s.merge_with(p.get_shape()[1:])
       else:
+        element_shape_s = ret.get_shape()[1:]
+
+      # Compute the dynamic element shape.
+      if element_shape_s.is_fully_defined():
+        element_shape_d = element_shape_s
+      elif transform_fn is None:
         # It's important that we compute params[0].shape on the right device
         # to avoid data motion.
         with ops.colocate_with(params[0]):
           params_shape = array_ops.shape(params[0])
-        ret = array_ops.reshape(ret,
-                                array_ops.concat([
-                                    array_ops.shape(ids),
-                                    array_ops.slice(params_shape, [1], [-1])
-                                ], 0))
-      # output shape = ids.shape + params[*].shape[1:]
+        element_shape_d = params_shape[1:]
+      else:
+        element_shape_d = array_ops.shape(ret)[1:]
+
+      # Reshape to reverse the flattening of ids.
+      ret = array_ops.reshape(ret,
+                              array_ops.concat(
+                                  [array_ops.shape(ids), element_shape_d], 0))
+
       # Normally the reshape is sufficient, but setting shape explicitly
-      # teaches shape inference that params[1:].get_shape() matters.
-      ret.set_shape(ids.get_shape().concatenate(element_shape))
-      return maybe_normalize(ret)
+      # teaches shape inference that params[1:].get_shape() matters
+      # (in the case that transform_fn is None).
+      ret.set_shape(ids.get_shape().concatenate(element_shape_s))
+      return ret
+
+
+def embedding_lookup(
+    params,
+    ids,
+    partition_strategy="mod",
+    name=None,
+    validate_indices=True,  # pylint: disable=unused-argument
+    max_norm=None):
+  """Looks up `ids` in a list of embedding tensors.
+
+  This function is used to perform parallel lookups on the list of
+  tensors in `params`.  It is a generalization of
+  @{tf.gather}, where `params` is
+  interpreted as a partitioning of a large embedding tensor.  `params` may be
+  a `PartitionedVariable` as returned by using `tf.get_variable()` with a
+  partitioner.
+
+  If `len(params) > 1`, each element `id` of `ids` is partitioned between
+  the elements of `params` according to the `partition_strategy`.
+  In all strategies, if the id space does not evenly divide the number of
+  partitions, each of the first `(max_id + 1) % len(params)` partitions will
+  be assigned one more id.
+
+  If `partition_strategy` is `"mod"`, we assign each id to partition
+  `p = id % len(params)`. For instance,
+  13 ids are split across 5 partitions as:
+  `[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
+
+  If `partition_strategy` is `"div"`, we assign ids to partitions in a
+  contiguous manner. In this case, 13 ids are split across 5 partitions as:
+  `[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`
+
+  The results of the lookup are concatenated into a dense
+  tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
+
+  Args:
+    params: A single tensor representing the complete embedding tensor,
+      or a list of P tensors all of same shape except for the first dimension,
+      representing sharded embedding tensors.  Alternatively, a
+      `PartitionedVariable`, created by partitioning along dimension 0. Each
+      element must be appropriately sized for the given `partition_strategy`.
+    ids: A `Tensor` with type `int32` or `int64` containing the ids to be looked
+      up in `params`.
+    partition_strategy: A string specifying the partitioning strategy, relevant
+      if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
+      is `"mod"`.
+    name: A name for the operation (optional).
+    validate_indices: DEPRECATED. If this operation is assigned to CPU, values
+      in `indices` are always validated to be within range.  If assigned to GPU,
+      out-of-bound indices result in safe but unspecified behavior, which may
+      include raising an error.
+    max_norm: If provided, embedding values are l2-normalized to the value of
+      max_norm.
 
+  Returns:
+    A `Tensor` with the same type as the tensors in `params`.
 
-def embedding_lookup_sparse(params, sp_ids, sp_weights,
+  Raises:
+    ValueError: If `params` is empty.
+  """
+  return _embedding_lookup_and_transform(
+      params=params,
+      ids=ids,
+      partition_strategy=partition_strategy,
+      name=name,
+      max_norm=max_norm,
+      transform_fn=None)
+
+
+def embedding_lookup_sparse(params,
+                            sp_ids,
+                            sp_weights,
                             partition_strategy="mod",
                             name=None,
                             combiner=None,
@@ -254,7 +331,7 @@ def embedding_lookup_sparse(params, sp_ids, sp_weights,
       "mean" is the weighted sum divided by the total weight.
       "sqrtn" is the weighted sum divided by the square root of the sum of the
       squares of the weights.
-    max_norm: If not None, each embedding is normalized to have l2 norm equal
+    max_norm: If provided, each embedding is normalized to have l2 norm equal
       to max_norm before combining.
 
   Returns:
@@ -349,8 +426,9 @@ def embedding_lookup_sparse(params, sp_ids, sp_weights,
       # Set the weight shape, since after reshaping to bcast_weights_shape,
       # the shape becomes None.
       if embeddings.get_shape().ndims is not None:
-        weights.set_shape(orig_weights_shape.concatenate(
-            [1 for _ in range(embeddings.get_shape().ndims - 1)]))
+        weights.set_shape(
+            orig_weights_shape.concatenate(
+                [1 for _ in range(embeddings.get_shape().ndims - 1)]))
 
       embeddings *= weights
 
@@ -371,14 +449,14 @@ def embedding_lookup_sparse(params, sp_ids, sp_weights,
     else:
       assert idx is not None
       if combiner == "sum":
-        embeddings = math_ops.sparse_segment_sum(embeddings, idx, segment_ids,
-                                                 name=name)
+        embeddings = math_ops.sparse_segment_sum(
+            embeddings, idx, segment_ids, name=name)
       elif combiner == "mean":
-        embeddings = math_ops.sparse_segment_mean(embeddings, idx, segment_ids,
-                                                  name=name)
+        embeddings = math_ops.sparse_segment_mean(
+            embeddings, idx, segment_ids, name=name)
       elif combiner == "sqrtn":
-        embeddings = math_ops.sparse_segment_sqrt_n(embeddings, idx,
-                                                    segment_ids, name=name)
+        embeddings = math_ops.sparse_segment_sqrt_n(
+            embeddings, idx, segment_ids, name=name)
       else:
         assert False, "Unrecognized combiner"
 
diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index c913d7a379..438e271275 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -531,7 +531,7 @@ def mean_squared_error(
     predictions = math_ops.to_float(predictions)
     labels = math_ops.to_float(labels)
     predictions.get_shape().assert_is_compatible_with(labels.get_shape())
-    losses = math_ops.square(math_ops.subtract(predictions, labels))
+    losses = math_ops.squared_difference(predictions, labels)
     return compute_weighted_loss(
         losses, weights, scope, loss_collection, reduction=reduction)
 
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 16aa5d8247..bf46bbdcc2 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -172,14 +172,18 @@ linspace = gen_math_ops.lin_space
 
 # pylint: disable=redefined-builtin
 # TODO(aselle): deprecate arg_max
-def argmax(input, axis=None, name=None, dimension=None):
+def argmax(input,
+           axis=None,
+           name=None,
+           dimension=None,
+           output_type=dtypes.int64):
   if dimension is not None:
     if axis is not None:
       raise ValueError("Cannot specify both 'axis' and 'dimension'")
     axis = dimension
   elif axis is None:
     axis = 0
-  return gen_math_ops.arg_max(input, axis, name)
+  return gen_math_ops.arg_max(input, axis, name=name, output_type=output_type)
 
 
 argmax.__doc__ = (gen_math_ops.arg_max.__doc__.replace("dimensions",
@@ -188,14 +192,18 @@ argmax.__doc__ = (gen_math_ops.arg_max.__doc__.replace("dimensions",
 
 
 # TODO(aselle:deprecate arg_min)
-def argmin(input, axis=None, name=None, dimension=None):
+def argmin(input,
+           axis=None,
+           name=None,
+           dimension=None,
+           output_type=dtypes.int64):
   if dimension is not None:
     if axis is not None:
       raise ValueError("Cannot specify both 'axis' and 'dimension'")
     axis = dimension
   elif axis is None:
     axis = 0
-  return gen_math_ops.arg_min(input, axis, name)
+  return gen_math_ops.arg_min(input, axis, name=name, output_type=output_type)
 
 
 argmin.__doc__ = (gen_math_ops.arg_min.__doc__.replace("dimensions",
diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 254a8432d3..5aebdb5cd0 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -97,9 +97,11 @@ def log_poisson_loss(targets, log_input, compute_full_loss=False, name=None):
     return result
 
 
-def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid-name
-                                      labels=None, logits=None,
-                                      name=None):
+def sigmoid_cross_entropy_with_logits(  # pylint: disable=invalid-name
+    _sentinel=None,
+    labels=None,
+    logits=None,
+    name=None):
   """Computes sigmoid cross entropy given `logits`.
 
   Measures the probability error in discrete classification tasks in which each
@@ -143,8 +145,8 @@ def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid
     ValueError: If `logits` and `labels` do not have the same shape.
   """
   # pylint: disable=protected-access
-  nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits",
-                           _sentinel, labels, logits)
+  nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits", _sentinel,
+                           labels, logits)
   # pylint: enable=protected-access
 
   with ops.name_scope(name, "logistic_loss", [logits, labels]) as name:
@@ -153,8 +155,8 @@ def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid
     try:
       labels.get_shape().merge_with(logits.get_shape())
     except ValueError:
-      raise ValueError("logits and labels must have the same shape (%s vs %s)"
-                       % (logits.get_shape(), labels.get_shape()))
+      raise ValueError("logits and labels must have the same shape (%s vs %s)" %
+                       (logits.get_shape(), labels.get_shape()))
 
     # The logistic loss formula from above is
     #   x - x * z + log(1 + exp(-x))
@@ -168,9 +170,10 @@ def sigmoid_cross_entropy_with_logits(_sentinel=None,  # pylint: disable=invalid
     cond = (logits >= zeros)
     relu_logits = array_ops.where(cond, logits, zeros)
     neg_abs_logits = array_ops.where(cond, -logits, logits)
-    return math_ops.add(relu_logits - logits * labels,
-                        math_ops.log1p(math_ops.exp(neg_abs_logits)),
-                        name=name)
+    return math_ops.add(
+        relu_logits - logits * labels,
+        math_ops.log1p(math_ops.exp(neg_abs_logits)),
+        name=name)
 
 
 def weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None):
@@ -226,8 +229,9 @@ def weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None):
     try:
       targets.get_shape().merge_with(logits.get_shape())
     except ValueError:
-      raise ValueError("logits and targets must have the same shape (%s vs %s)"
-                       % (logits.get_shape(), targets.get_shape()))
+      raise ValueError(
+          "logits and targets must have the same shape (%s vs %s)" %
+          (logits.get_shape(), targets.get_shape()))
 
     # The logistic loss formula from above is
     #   (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))
@@ -569,9 +573,10 @@ def normalize_moments(counts, mean_ss, variance_ss, shift, name=None):
     else:  # no shift.
       shifted_mean = math_ops.multiply(mean_ss, divisor, name="mean")
       mean = shifted_mean
-    variance = math_ops.subtract(math_ops.multiply(variance_ss, divisor),
-                                 math_ops.square(shifted_mean),
-                                 name="variance")
+    variance = math_ops.subtract(
+        math_ops.multiply(variance_ss, divisor),
+        math_ops.square(shifted_mean),
+        name="variance")
   return (mean, variance)
 
 
@@ -876,6 +881,70 @@ def _sum_rows(x):
   return array_ops.reshape(math_ops.matmul(x, ones), [-1])
 
 
+def _retrieve_weights(weights, inputs, labels_flat, sampled, all_ids,
+                      partition_strategy, colocate_logits):
+  """Helper function for _compute_sampled_logits.
+
+  Retrieves the true weights and the logits of the sampled weights.
+
+  Args:
+    weights: See _compute_sampled_logits.
+    inputs: See _compute_sampled_logits.
+    labels_flat: A [batch_size * num_true] tensor.
+    sampled: A [num_sampled] tensor.
+    all_ids: The concatenation of labels_flat and sampled.
+    partition_strategy: See _compute_sampled_logits.
+    colocate_logits: See _compute_sampled_logits.
+
+  Returns:
+    A pair of a [batch_size * num_true, dim] tensor of the true weights
+    and a [batch_size, num_sampled] tensor of the sampled logits.
+  """
+  if len(weights) > 1 and colocate_logits:
+    # This codepath makes two calls to lookup the embeddings, one for the true
+    # weights and one to compute the sampled logits. Because we take this
+    # codepath only if len(weights) > 1, we will only incur the double-lookup
+    # cost if we can benefit from the co-located logit computation.
+
+    true_w = embedding_ops.embedding_lookup(
+        weights, labels_flat, partition_strategy=partition_strategy)
+
+    def logit(embeddings):
+      return math_ops.matmul(embeddings, inputs, transpose_b=True)
+
+    # pylint: disable=protected-access
+    sampled_logits = embedding_ops._embedding_lookup_and_transform(
+        weights,
+        sampled,
+        partition_strategy=partition_strategy,
+        transform_fn=logit)
+    # pylint: enable=protected-access
+    sampled_logits = array_ops.transpose(sampled_logits)
+
+  else:
+    # This codepath retrieves the weights by performing a single call to
+    # embedding_lookup and slicing out the true weights and sampled weights.
+    # Because the call to embedding_lookup is shared, the sampled logit
+    # computation is done here after the slice.
+
+    # weights shape is [num_classes, dim]
+    all_w = embedding_ops.embedding_lookup(
+        weights, all_ids, partition_strategy=partition_strategy)
+
+    true_w = array_ops.slice(all_w, [0, 0],
+                             array_ops.stack(
+                                 [array_ops.shape(labels_flat)[0], -1]))
+
+    sampled_w = array_ops.slice(
+        all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
+    # inputs has shape [batch_size, dim]
+    # sampled_w has shape [num_sampled, dim]
+    # Apply X*W', which yields [batch_size, num_sampled]
+    sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)
+
+  return true_w, sampled_logits
+
+
 def _compute_sampled_logits(weights,
                             biases,
                             labels,
@@ -887,6 +956,7 @@ def _compute_sampled_logits(weights,
                             subtract_log_q=True,
                             remove_accidental_hits=False,
                             partition_strategy="mod",
+                            colocate_logits=False,
                             name=None):
   """Helper function for nce_loss and sampled_softmax_loss functions.
 
@@ -924,6 +994,9 @@ def _compute_sampled_logits(weights,
     partition_strategy: A string specifying the partitioning strategy, relevant
         if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
         Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
+    colocate_logits: A `bool`, relevant if `len(weights) > 1`, specifying
+        whether to colocate the computation of the sampled logits with the
+        weights.
     name: A name for the operation (optional).
   Returns:
     out_logits, out_labels: `Tensor` objects each with shape
@@ -965,16 +1038,21 @@ def _compute_sampled_logits(weights,
     # sampled is a [num_sampled] int tensor
     all_ids = array_ops.concat([labels_flat, sampled], 0)
 
-    # weights shape is [num_classes, dim]
-    all_w = embedding_ops.embedding_lookup(
-        weights, all_ids, partition_strategy=partition_strategy)
+    # Retrieve the true weights and the logits of the sampled weights.
+    # - true_w shape is [batch_size * num_true, dim]
+    # - sampled_logits shape is [batch_size, num_sampled]
+    true_w, sampled_logits = _retrieve_weights(
+        weights, inputs, labels_flat, sampled, all_ids, partition_strategy,
+        colocate_logits)
+
+    # Retrieve the true and sampled biases, compute the true logits, and
+    # add the biases to the true and sampled logits.
     all_b = embedding_ops.embedding_lookup(
         biases, all_ids, partition_strategy=partition_strategy)
-    # true_w shape is [batch_size * num_true, dim]
     # true_b is a [batch_size * num_true] tensor
-    true_w = array_ops.slice(
-        all_w, [0, 0], array_ops.stack([array_ops.shape(labels_flat)[0], -1]))
+    # sampled_b is a [num_sampled] float tensor
     true_b = array_ops.slice(all_b, [0], array_ops.shape(labels_flat))
+    sampled_b = array_ops.slice(all_b, array_ops.shape(labels_flat), [-1])
 
     # inputs shape is [batch_size, dim]
     # true_w shape is [batch_size * num_true, dim]
@@ -991,20 +1069,7 @@ def _compute_sampled_logits(weights,
     true_logits = array_ops.reshape(_sum_rows(dots_as_matrix), [-1, num_true])
     true_b = array_ops.reshape(true_b, [-1, num_true])
     true_logits += true_b
-
-    # Lookup weights and biases for sampled labels.
-    #   sampled_w shape is [num_sampled, dim]
-    #   sampled_b is a [num_sampled] float tensor
-    sampled_w = array_ops.slice(
-        all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
-    sampled_b = array_ops.slice(all_b, array_ops.shape(labels_flat), [-1])
-
-    # inputs has shape [batch_size, dim]
-    # sampled_w has shape [num_sampled, dim]
-    # sampled_b has shape [num_sampled]
-    # Apply X*W'+B, which yields [batch_size, num_sampled]
-    sampled_logits = math_ops.matmul(
-        inputs, sampled_w, transpose_b=True) + sampled_b
+    sampled_logits += sampled_b
 
     if remove_accidental_hits:
       acc_hits = candidate_sampling_ops.compute_accidental_hits(
@@ -1019,8 +1084,8 @@ def _compute_sampled_logits(weights,
                                         "sparse_indices")
       # Create sampled_logits_shape = [batch_size, num_sampled]
       sampled_logits_shape = array_ops.concat(
-          [array_ops.shape(labels)[:1], array_ops.expand_dims(num_sampled, 0)],
-          0)
+          [array_ops.shape(labels)[:1],
+           array_ops.expand_dims(num_sampled, 0)], 0)
       if sampled_logits.dtype != acc_weights.dtype:
         acc_weights = math_ops.cast(acc_weights, sampled_logits.dtype)
       sampled_logits += sparse_ops.sparse_to_dense(
@@ -1148,6 +1213,7 @@ def nce_loss(weights,
       subtract_log_q=True,
       remove_accidental_hits=remove_accidental_hits,
       partition_strategy=partition_strategy,
+      colocate_logits=False,
       name=name)
   sampled_losses = sigmoid_cross_entropy_with_logits(
       labels=labels, logits=logits, name="sampled_losses")
@@ -1244,8 +1310,9 @@ def sampled_softmax_loss(weights,
       subtract_log_q=True,
       remove_accidental_hits=remove_accidental_hits,
       partition_strategy=partition_strategy,
+      colocate_logits=False,
       name=name)
-  sampled_losses = nn_ops.softmax_cross_entropy_with_logits(labels=labels,
-                                                            logits=logits)
+  sampled_losses = nn_ops.softmax_cross_entropy_with_logits(
+      labels=labels, logits=logits)
   # sampled_losses is a [batch_size] tensor.
   return sampled_losses
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index 5cf8c3291c..84c5f4527d 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -136,9 +136,8 @@ class LogPoissonLossTest(test_lib.TestCase):
           z_np, x_tf, compute_full_loss=True)
       err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
                                                     x_shape)
-      err_stirling = gradient_checker.compute_gradient_error(x_tf, x_shape,
-                                                             y_tf_stirling,
-                                                             x_shape)
+      err_stirling = gradient_checker.compute_gradient_error(
+          x_tf, x_shape, y_tf_stirling, x_shape)
     eps = 1e-6
     self.assertLess(err, eps)
     self.assertLess(err_stirling, eps)
@@ -404,273 +403,362 @@ class DropoutTest(test_lib.TestCase):
 class ComputeSampledLogitsTest(test_lib.TestCase):
 
   def setUp(self):
-    self._num_classes = 5
-    self._dim = 10
-    self._batch_size = 3
-    self._num_shards = 3
+    self._eps = 1e-3
+
+  def _GenerateTestData(self, num_classes, dim, batch_size, num_true, labels,
+                        sampled, subtract_log_q):
+    """Randomly generates input/output data for a single test case.
+
+    This function returns numpy constants for use in a test case.
+
+    Args:
+      num_classes: An int. The number of embedding classes in the test case.
+      dim: An int. The dimension of the embedding.
+      batch_size: An int. The batch size.
+      num_true: An int. The number of target classes per training example.
+      labels: A list of batch_size * num_true ints. The target classes.
+      sampled: A list of indices in [0, num_classes).
+      subtract_log_q: A bool corresponding to the parameter in
+          _compute_sampled_logits().
+
+    Returns:
+      weights: Embedding weights to use as test input. It is a numpy array
+          of shape [num_classes, dim]
+      biases: Embedding biases to use as test input. It is a numpy array
+          of shape [num_classes].
+      hidden_acts: Forward activations of the network to use as test input.
+          It is a numpy array of shape [batch_size, dim].
+      sampled_vals: A tuple based on `sampled` to use as test input in the
+          format returned by a *_candidate_sampler function.
+      exp_logits: The output logits expected from _compute_sampled_logits().
+          It is a numpy array of shape [batch_size, num_true + len(sampled)].
+      exp_labels: The output labels expected from _compute_sampled_logits().
+          It is a numpy array of shape [batch_size, num_true + len(sampled)].
+    """
+    weights = np.random.randn(num_classes, dim).astype(np.float32)
+    biases = np.random.randn(num_classes).astype(np.float32)
+    hidden_acts = np.random.randn(batch_size, dim).astype(np.float32)
+
+    true_exp = np.full([batch_size, 1], fill_value=0.5, dtype=np.float32)
+    sampled_exp = np.full([len(sampled)], fill_value=0.5, dtype=np.float32)
+    sampled_vals = (sampled, true_exp, sampled_exp)
 
-  def _GenerateTestInputs(self):
-    np.random.seed(0)
-    weights = np.random.randn(self._num_classes, self._dim).astype(np.float32)
-    biases = np.random.randn(self._num_classes).astype(np.float32)
-    hidden_acts = np.random.randn(self._batch_size,
-                                  self._dim).astype(np.float32)
+    sampled_w, sampled_b = weights[sampled], biases[sampled]
+    true_w, true_b = weights[labels], biases[labels]
 
+    true_logits = np.sum(
+        hidden_acts.reshape((batch_size, 1, dim)) * true_w.reshape(
+            (batch_size, num_true, dim)),
+        axis=2)
+    true_b = true_b.reshape((batch_size, num_true))
+    true_logits += true_b
+    sampled_logits = np.dot(hidden_acts, sampled_w.T) + sampled_b
+
+    if subtract_log_q:
+      true_logits -= np.log(true_exp)
+      sampled_logits -= np.log(sampled_exp[np.newaxis, :])
+
+    exp_logits = np.concatenate([true_logits, sampled_logits], axis=1)
+    exp_labels = np.hstack((np.ones_like(true_logits) / num_true,
+                            np.zeros_like(sampled_logits)))
+
+    return weights, biases, hidden_acts, sampled_vals, exp_logits, exp_labels
+
+  def _ShardTestEmbeddings(self, weights, biases, num_shards):
+    """Shards the weights and biases returned by _GenerateTestData.
+
+    Args:
+      weights: The weights returned by _GenerateTestData.
+      biases: The biases returned by _GenerateTestData.
+      num_shards: The number of shards to create.
+
+    Returns:
+      sharded_weights: A list of size `num_shards` containing all the weights.
+      sharded_biases: A list of size `num_shards` containing all the biases.
+    """
     with ops.Graph().as_default() as g:
       sharded_weights = variable_scope.get_variable(
           "w",
-          partitioner=partitioned_variables.fixed_size_partitioner(
-              self._num_shards),
+          partitioner=partitioned_variables.fixed_size_partitioner(num_shards),
           initializer=constant_op.constant(weights))
       sharded_biases = variable_scope.get_variable(
           "b",
-          partitioner=partitioned_variables.fixed_size_partitioner(
-              self._num_shards),
+          partitioner=partitioned_variables.fixed_size_partitioner(num_shards),
           initializer=constant_op.constant(biases))
       with self.test_session(graph=g) as sess:
         variables.global_variables_initializer().run()
+        return sess.run([list(sharded_weights), list(sharded_biases)])
 
-        sharded_weights_v, sharded_biases_v = sess.run(
-            [list(sharded_weights), list(sharded_biases)])
-
-    return weights, biases, hidden_acts, sharded_weights_v, sharded_biases_v
-
-  def _ComputeSampledLogitsNP(self,
-                              true_w,
-                              true_b,
-                              sampled_w,
-                              sampled_b,
-                              hidden_acts,
-                              num_true=1,
-                              true_expected=None,
-                              sampled_expected=None):
-
-    batch_size, dim = hidden_acts.shape
-    true_logits = np.sum(hidden_acts.reshape(
-        (batch_size, 1, dim)) * true_w.reshape((batch_size, num_true, dim)),
-                         axis=2)
-    true_b = true_b.reshape((batch_size, num_true))
-    true_logits += true_b
-    sampled_logits = np.dot(hidden_acts, sampled_w.T) + sampled_b
-
-    if true_expected is not None:
-      true_logits -= np.log(true_expected)
-    if sampled_expected is not None:
-      sampled_logits -= np.log(sampled_expected[np.newaxis, :])
-
-    out_logits = np.concatenate([true_logits, sampled_logits], axis=1)
-    out_labels = np.hstack((np.ones_like(true_logits) / num_true,
-                            np.zeros_like(sampled_logits)))
-
-    return out_logits, out_labels
-
-  def _ComputeSampledLogitsTF(self,
-                              weights,
-                              biases,
-                              hidden_acts,
-                              labels,
-                              num_sampled,
-                              num_classes,
-                              num_true,
-                              sampled_vals,
-                              subtract_log_q,
-                              remove_accidental_hits,
-                              name="sampled_loss_TF"):
-    # Should be called from within a `with test_session():` block
-    if isinstance(weights, list):
-      weights_tf = [constant_op.constant(shard) for shard in weights]
-    else:
-      weights_tf = constant_op.constant(weights)
-    if isinstance(biases, list):
-      biases_tf = [constant_op.constant(shard) for shard in biases]
-    else:
-      biases_tf = constant_op.constant(biases)
-    hidden_acts_tf = constant_op.constant(
-        hidden_acts, shape=(self._batch_size, self._dim))
-    labels_tf = constant_op.constant(
-        labels, dtype=dtypes.int64, shape=(self._batch_size, num_true))
-
-    pred_logits_tf, pred_labels_tf = _compute_sampled_logits(
-        weights_tf,
-        biases_tf,
-        labels_tf,
-        hidden_acts_tf,
-        num_sampled,
-        num_classes,
-        num_true,
-        sampled_vals,
-        subtract_log_q=subtract_log_q,
-        remove_accidental_hits=remove_accidental_hits,
-        name=name,
-        partition_strategy="div")
-    return pred_logits_tf, pred_labels_tf
-
-  def testComputeSampledLogitsShapes(self):
-    # We just check that the shapes of the returned values are correct.
-    weights, biases, hidden_acts, _, _ = self._GenerateTestInputs()
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = sampled_exp = [1., 1., 1., 1.]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-
+  def testShapes(self):
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     with self.test_session() as sess:
-      for num_true_test in range(1, 5):
+      for num_true in range(1, 5):
         labels = np.random.randint(
-            low=0,
-            high=self._num_classes,
-            size=self._batch_size * num_true_test)
-        true_w, true_b = weights[labels], biases[labels]
-
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test)
-
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
-            remove_accidental_hits=True,
-            subtract_log_q=False)
-
-      logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-      self.assertEqual(logits_np.shape, logits_tf_val.shape)
-      self.assertEqual(labels_np.shape, labels_tf_val.shape)
-
-  def testComputeSampledLogitsValues(self):
-    # Here we check the actual numerics.
-    weights, biases, hidden_acts, sharded_weights, sharded_biases = (
-        self._GenerateTestInputs())
-    eps = 1e-3
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = np.empty([self._batch_size, 1], dtype=np.float32)
-    true_exp.fill(0.5)
-    sampled_exp = np.empty([num_sampled], dtype=np.float32)
-    sampled_exp.fill(0.5)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
-
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
+            subtract_log_q=False,
+            remove_accidental_hits=False,
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_basic_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertEqual(exp_logits.shape, got_logits.shape, self._eps)
+        self.assertEqual(exp_labels.shape, got_labels.shape, self._eps)
+
+  def testBasic(self):
+    """Without accidental hit removal or subtract_log_q."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     with self.test_session() as sess:
-      for num_true_test in range(1, 5):
-        # Generate test data for this run
+      for num_true in range(1, 5):
         labels = np.random.randint(
-            low=0,
-            high=self._num_classes,
-            size=self._batch_size * num_true_test)
-        true_w, true_b = weights[labels], biases[labels]
-
-        # Test 1: Without accidental hit removal or subtract_log_q
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test)
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=False,
             remove_accidental_hits=False,
-            name="sampled_loss_test1_num_true%d" % num_true_test)
-
-        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-        self.assertAllClose(logits_np, logits_tf_val, eps)
-        self.assertAllClose(labels_np, labels_tf_val, eps)
-
-        # Test 2: With accidental hit removal, no subtract_log_q
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_basic_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testAccidentalHitRemoval(self):
+    """With accidental hit removal, no subtract_log_q."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    sampled = [1, 0, 2, 3]
+    with self.test_session():
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, _,
+         _) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=sampled,
+             subtract_log_q=False)
+        logits_tensor, _ = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=len(sampled),
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=False,
             remove_accidental_hits=True,
-            name="sampled_loss_test2_num_true%d" % num_true_test)
-
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_accidental_hit_removal_num_true_%d" % num_true)
         # Test that the exponentiated logits of accidental hits are near 0.
         # First we need to find the hits in this random test run:
-        labels_reshape = labels.reshape((self._batch_size, num_true_test))
-        logits_tf_np = logits_tf.eval()
-        for row in xrange(self._batch_size):
+        labels_reshape = labels.reshape((batch_size, num_true))
+        got_logits = logits_tensor.eval()
+        for row in xrange(batch_size):
           row_labels = labels_reshape[row, :]
-          for col in xrange(num_sampled):
+          for col in xrange(len(sampled)):
             if sampled[col] in row_labels:
               # We need to add the num_true_test offset into logits_*
               self.assertNear(
-                  np.exp(logits_tf_np[row, col + num_true_test]), 0., eps)
-
-        # Test 3: With subtract_log_q, no accidental hit removal
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test,
-            true_expected=true_exp,
-            sampled_expected=sampled_exp)
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            weights,
-            biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+                  np.exp(got_logits[row, col + num_true]), 0., self._eps)
+
+  def testSubtractLogQ(self):
+    """With subtract_log_q, no accidental hit removal."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=True)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=True,
             remove_accidental_hits=False,
-            name="sampled_loss_test3_num_true%d" % num_true_test)
-
-        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-        self.assertAllClose(logits_np, logits_tf_val, eps)
-        self.assertAllClose(labels_np, labels_tf_val, eps)
-
-        # Test 4: Test 1, with sharded weights and sharded biases.
-        logits_np, labels_np = self._ComputeSampledLogitsNP(
-            true_w,
-            true_b,
-            sampled_w,
-            sampled_b,
-            hidden_acts,
-            num_true=num_true_test)
-        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
-            sharded_weights,
-            sharded_biases,
-            hidden_acts,
-            labels,
-            num_sampled,
-            self._num_classes,
-            num_true=num_true_test,
-            sampled_vals=test_sampled_vals,
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_subtract_log_q_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testSharded(self):
+    """With sharded weights and sharded biases."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        weight_shards, bias_shards = self._ShardTestEmbeddings(
+            weights, biases, num_shards=3)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=[constant_op.constant(shard) for shard in weight_shards],
+            biases=[constant_op.constant(shard) for shard in bias_shards],
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
             subtract_log_q=False,
             remove_accidental_hits=False,
-            name="sampled_loss_test1_num_true%d" % num_true_test)
-
-        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
-        self.assertAllClose(logits_np, logits_tf_val, eps)
-        self.assertAllClose(labels_np, labels_tf_val, eps)
+            partition_strategy="div",
+            colocate_logits=False,
+            name="sampled_logits_sharded_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testColocatedLogits(self):
+    """With colocated logit computation."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=constant_op.constant(weights),
+            biases=constant_op.constant(biases),
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
+            subtract_log_q=False,
+            remove_accidental_hits=False,
+            partition_strategy="div",
+            colocate_logits=True,
+            name="sampled_logits_colocated_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
+
+  def testShardedColocatedLogits(self):
+    """Sharded weights and biases and with colocated logit computation."""
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
+    with self.test_session() as sess:
+      for num_true in range(1, 5):
+        labels = np.random.randint(
+            low=0, high=num_classes, size=batch_size * num_true)
+        (weights, biases, hidden_acts, sampled_vals, exp_logits,
+         exp_labels) = self._GenerateTestData(
+             num_classes=num_classes,
+             dim=10,
+             batch_size=batch_size,
+             num_true=num_true,
+             labels=labels,
+             sampled=[1, 0, 2, 3],
+             subtract_log_q=False)
+        weight_shards, bias_shards = self._ShardTestEmbeddings(
+            weights, biases, num_shards=3)
+        logits_tensor, labels_tensor = _compute_sampled_logits(
+            weights=[constant_op.constant(shard) for shard in weight_shards],
+            biases=[constant_op.constant(shard) for shard in bias_shards],
+            labels=constant_op.constant(
+                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
+            inputs=constant_op.constant(hidden_acts),
+            num_sampled=4,
+            num_classes=num_classes,
+            num_true=num_true,
+            sampled_values=sampled_vals,
+            subtract_log_q=False,
+            remove_accidental_hits=False,
+            partition_strategy="div",
+            colocate_logits=True,
+            name="sampled_logits_sharded_colocated_num_true_%d" % num_true)
+        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
+        self.assertAllClose(exp_logits, got_logits, self._eps)
+        self.assertAllClose(exp_labels, got_labels, self._eps)
 
   def testNCELoss(self):
     # A simple test to verify the numerics.
@@ -683,62 +771,51 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
       pred = np.minimum(np.maximum(pred, eps), 1 - eps)
       return -targets * np.log(pred) - (1. - targets) * np.log(1. - pred)
 
-    weights, biases, hidden_acts, sharded_weights, sharded_biases = (
-        self._GenerateTestInputs())
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     labels = [0, 1, 2]
-    true_w, true_b = weights[labels], biases[labels]
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = np.empty([self._batch_size, 1], dtype=np.float32)
-    true_exp.fill(0.5)
-    sampled_exp = np.empty([num_sampled], dtype=np.float32)
-    sampled_exp.fill(0.5)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
+    (weights, biases, hidden_acts, sampled_vals, exp_logits,
+     exp_labels) = self._GenerateTestData(
+         num_classes=num_classes,
+         dim=10,
+         batch_size=batch_size,
+         num_true=1,
+         labels=labels,
+         sampled=[1, 0, 2, 3],
+         subtract_log_q=True)
+    exp_nce_loss = np.sum(
+        _SigmoidCrossEntropyWithLogits(exp_logits, exp_labels), 1)
 
     with self.test_session():
-      logits_np, labels_np = self._ComputeSampledLogitsNP(
-          true_w,
-          true_b,
-          sampled_w,
-          sampled_b,
-          hidden_acts,
-          true_expected=true_exp,
-          sampled_expected=sampled_exp)
-      nce_loss_np = np.sum(
-          _SigmoidCrossEntropyWithLogits(logits_np, labels_np), 1)
-
-      labels_tf = constant_op.constant(labels, shape=(self._batch_size, 1))
-      weights_tf = constant_op.constant(weights)
-      biases_tf = constant_op.constant(biases)
-      inputs_tf = constant_op.constant(hidden_acts)
-
-      nce_loss_tf = nn_impl.nce_loss(
-          weights_tf,
-          biases_tf,
-          labels_tf,
-          inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      got_nce_loss = nn_impl.nce_loss(
+          weights=constant_op.constant(weights),
+          biases=constant_op.constant(biases),
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           partition_strategy="div")
 
-      self.assertAllClose(nce_loss_np, nce_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_nce_loss, got_nce_loss.eval(), 1e-4)
 
       # Test with sharded weights and sharded biases.
-      nce_loss_tf = nn_impl.nce_loss(
-          sharded_weights,
-          sharded_biases,
-          labels_tf,
-          inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      weight_shards, bias_shards = self._ShardTestEmbeddings(
+          weights, biases, num_shards=3)
+      got_nce_loss = nn_impl.nce_loss(
+          weights=[constant_op.constant(shard) for shard in weight_shards],
+          biases=[constant_op.constant(shard) for shard in bias_shards],
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           partition_strategy="div")
 
-      self.assertAllClose(nce_loss_np, nce_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_nce_loss, got_nce_loss.eval(), 1e-4)
 
   def testSampledSoftmaxLoss(self):
     # A simple test to verify the numerics.
@@ -751,64 +828,55 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
       pred = stable_exp_logits / np.sum(stable_exp_logits, 1, keepdims=True)
       return -np.sum(targets * np.log(pred + 1.0e-20), axis=1)
 
-    weights, biases, hidden_acts, sharded_weights, sharded_biases = (
-        self._GenerateTestInputs())
+    np.random.seed(0)
+    num_classes = 5
+    batch_size = 3
     labels = [0, 1, 2]
-    true_w, true_b = weights[labels], biases[labels]
-    sampled = [1, 0, 2, 3]
-    num_sampled = len(sampled)
-    true_exp = np.full([self._batch_size, 1], fill_value=0.5, dtype=np.float32)
-    sampled_exp = np.full([num_sampled], fill_value=0.5, dtype=np.float32)
-    sampled_w, sampled_b = weights[sampled], biases[sampled]
-    test_sampled_vals = (sampled, true_exp, sampled_exp)
+    (weights, biases, hidden_acts, sampled_vals, exp_logits,
+     exp_labels) = self._GenerateTestData(
+         num_classes=num_classes,
+         dim=10,
+         batch_size=batch_size,
+         num_true=1,
+         labels=labels,
+         sampled=[1, 0, 2, 3],
+         subtract_log_q=True)
+    exp_sampled_softmax_loss = _SoftmaxCrossEntropyWithLogits(
+        exp_logits, exp_labels)
 
     with self.test_session():
-      logits_np, labels_np = self._ComputeSampledLogitsNP(
-          true_w,
-          true_b,
-          sampled_w,
-          sampled_b,
-          hidden_acts,
-          true_expected=true_exp,
-          sampled_expected=sampled_exp)
-      sampled_softmax_loss_np = _SoftmaxCrossEntropyWithLogits(logits_np,
-                                                               labels_np)
-
-      labels_tf = constant_op.constant(labels, shape=(self._batch_size, 1))
-      weights_tf = constant_op.constant(weights)
-      biases_tf = constant_op.constant(biases)
-      inputs_tf = constant_op.constant(hidden_acts)
-
-      sampled_softmax_loss_tf = nn_impl.sampled_softmax_loss(
-          weights=weights_tf,
-          biases=biases_tf,
-          labels=labels_tf,
-          inputs=inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      got_sampled_softmax_loss = nn_impl.sampled_softmax_loss(
+          weights=constant_op.constant(weights),
+          biases=constant_op.constant(biases),
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           remove_accidental_hits=False,
           partition_strategy="div")
 
-      self.assertAllClose(sampled_softmax_loss_np,
-                          sampled_softmax_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_sampled_softmax_loss,
+                          got_sampled_softmax_loss.eval(), 1e-4)
 
       # Test with sharded weights and sharded biases.
-      sampled_softmax_loss_tf = nn_impl.sampled_softmax_loss(
-          weights=sharded_weights,
-          biases=sharded_biases,
-          labels=labels_tf,
-          inputs=inputs_tf,
-          num_sampled=num_sampled,
-          num_classes=self._num_classes,
+      weight_shards, bias_shards = self._ShardTestEmbeddings(
+          weights, biases, num_shards=3)
+      got_sampled_softmax_loss = nn_impl.sampled_softmax_loss(
+          weights=[constant_op.constant(shard) for shard in weight_shards],
+          biases=[constant_op.constant(shard) for shard in bias_shards],
+          labels=constant_op.constant(labels, shape=(batch_size, 1)),
+          inputs=constant_op.constant(hidden_acts),
+          num_sampled=4,
+          num_classes=num_classes,
           num_true=1,
-          sampled_values=test_sampled_vals,
+          sampled_values=sampled_vals,
           remove_accidental_hits=False,
           partition_strategy="div")
 
-      self.assertAllClose(sampled_softmax_loss_np,
-                          sampled_softmax_loss_tf.eval(), 1e-4)
+      self.assertAllClose(exp_sampled_softmax_loss,
+                          got_sampled_softmax_loss.eval(), 1e-4)
 
 
 class CReluTest(test_lib.TestCase):
@@ -848,18 +916,16 @@ class MomentsTest(test_lib.TestCase):
       for sigma in [1.0, 0.1]:
         for keep_dims in [True, False]:
           input_values = np.random.rand(*input_shape) * sigma + mu
-          expected_mean = np.mean(input_values, axis=moments_axes,
-                                  keepdims=keep_dims)
-          expected_var = np.var(input_values, axis=moments_axes,
-                                keepdims=keep_dims)
+          expected_mean = np.mean(
+              input_values, axis=moments_axes, keepdims=keep_dims)
+          expected_var = np.var(
+              input_values, axis=moments_axes, keepdims=keep_dims)
           with ops.Graph().as_default() as g:
             with self.test_session(graph=g) as sess:
-              inputs = constant_op.constant(input_values,
-                                            shape=input_shape,
-                                            dtype=dtypes.float32)
-              mean, variance = nn_impl.moments(inputs,
-                                               moments_axes,
-                                               keep_dims=keep_dims)
+              inputs = constant_op.constant(
+                  input_values, shape=input_shape, dtype=dtypes.float32)
+              mean, variance = nn_impl.moments(
+                  inputs, moments_axes, keep_dims=keep_dims)
 
               [mean, variance] = sess.run([mean, variance])
               # Make sure that there are no NaNs
@@ -902,8 +968,8 @@ class MomentsTest(test_lib.TestCase):
     expected_var = np.var(input_values, axis=moments_axes)
 
     with self.test_session() as sess:
-      inputs = constant_op.constant(input_values, shape=input_shape,
-                                    dtype=dtypes.float32)
+      inputs = constant_op.constant(
+          input_values, shape=input_shape, dtype=dtypes.float32)
       mean, variance = nn_impl.moments(inputs, moments_axes, shift=0.0)
 
       [mean, variance] = sess.run([mean, variance])
@@ -914,5 +980,6 @@ class MomentsTest(test_lib.TestCase):
       # The variance is unstable
       self.assertGreater(np.abs(variance - expected_var), 0.1)
 
+
 if __name__ == "__main__":
   test_lib.main()
diff --git a/tensorflow/python/ops/parsing_ops.py b/tensorflow/python/ops/parsing_ops.py
index c2f9961731..0071e7d868 100644
--- a/tensorflow/python/ops/parsing_ops.py
+++ b/tensorflow/python/ops/parsing_ops.py
@@ -120,8 +120,10 @@ class SparseFeature(
       `value_key` are already sorted by their index position. If so skip
       sorting. False by default (optional).
   """
-  pass
-SparseFeature.__new__.__defaults__ = (False,)
+
+  def __new__(cls, index_key, value_key, dtype, size, already_sorted=False):
+    return super(SparseFeature, cls).__new__(
+        cls, index_key, value_key, dtype, size, already_sorted)
 
 
 class FixedLenFeature(collections.namedtuple(
@@ -137,8 +139,10 @@ class FixedLenFeature(collections.namedtuple(
     default_value: Value to be used if an example is missing this feature. It
         must be compatible with `dtype` and of the specified `shape`.
   """
-  pass
-FixedLenFeature.__new__.__defaults__ = (None,)
+
+  def __new__(cls, shape, dtype, default_value=None):
+    return super(FixedLenFeature, cls).__new__(
+        cls, shape, dtype, default_value)
 
 
 class FixedLenSequenceFeature(collections.namedtuple(
@@ -168,8 +172,10 @@ class FixedLenSequenceFeature(collections.namedtuple(
       `SequenceExample`. Defaults to "" for dtype string and 0 otherwise
       (optional).
   """
-  pass
-FixedLenSequenceFeature.__new__.__defaults__ = (False, None)
+
+  def __new__(cls, shape, dtype, allow_missing=False, default_value=None):
+    return super(FixedLenSequenceFeature, cls).__new__(
+        cls, shape, dtype, allow_missing, default_value)
 
 
 def _features_to_raw_params(features, types):
diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py
index 15613289a0..1e0bb925d4 100644
--- a/tensorflow/python/ops/random_ops.py
+++ b/tensorflow/python/ops/random_ops.py
@@ -206,7 +206,8 @@ def random_uniform(shape,
     maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
       the range of random values to generate.  Defaults to 1 if `dtype` is
       floating point.
-    dtype: The type of the output: `float32`, `float64`, `int32`, or `int64`.
+    dtype: The type of the output: 'float16`, `float32`, `float64`, `int32`,
+      or `int64`.
     seed: A Python integer. Used to create a random seed for the distribution.
       See @{tf.set_random_seed}
       for behavior.
@@ -219,6 +220,9 @@ def random_uniform(shape,
     ValueError: If `dtype` is integral and `maxval` is not specified.
   """
   dtype = dtypes.as_dtype(dtype)
+  if dtype not in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
+                   dtypes.int64):
+    raise ValueError("Invalid dtype %r" % dtype)
   if maxval is None:
     if dtype.is_integer:
       raise ValueError("Must specify maxval for integer dtype %r" % dtype)
@@ -325,7 +329,7 @@ def multinomial(logits, num_samples, seed=None, name=None):
 
   Args:
     logits: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice
-      `[i, :]` represents the log-odds for all classes.
+      `[i, :]` represents the unnormalized log-probabilities for all classes.
     num_samples: 0-D.  Number of independent samples to draw for each row slice.
     seed: A Python integer. Used to create a random seed for the distribution.
       See
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index bffd3275ee..f7854e86c0 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -354,6 +354,9 @@ class BasicLSTMCell(RNNCell):
       reuse: (optional) Python boolean describing whether to reuse variables
         in an existing scope.  If not `True`, and the existing scope already has
         the given variables, an error is raised.
+
+      When restoring from CudnnLSTM-trained checkpoints, must use
+      CudnnCompatibleLSTMCell instead.
     """
     super(BasicLSTMCell, self).__init__(_reuse=reuse)
     if not state_is_tuple:
@@ -374,7 +377,20 @@ class BasicLSTMCell(RNNCell):
     return self._num_units
 
   def call(self, inputs, state):
-    """Long short-term memory cell (LSTM)."""
+    """Long short-term memory cell (LSTM).
+
+    Args:
+      inputs: `2-D` tensor with shape `[batch_size x input_size]`.
+      state: An `LSTMStateTuple` of state tensors, each shaped
+        `[batch_size x self.state_size]`, if `state_is_tuple` has been set to
+        `True`.  Otherwise, a `Tensor` shaped
+        `[batch_size x 2 * self.state_size]`.
+
+    Returns:
+      A pair containing the new hidden state, and the new state (either a
+        `LSTMStateTuple` or a concatenated state, depending on
+        `state_is_tuple`).
+    """
     sigmoid = math_ops.sigmoid
     # Parameters of gates are concatenated into one multiply for efficiency.
     if self._state_is_tuple:
@@ -455,6 +471,9 @@ class LSTMCell(RNNCell):
       reuse: (optional) Python boolean describing whether to reuse variables
         in an existing scope.  If not `True`, and the existing scope already has
         the given variables, an error is raised.
+
+      When restoring from CudnnLSTM-trained checkpoints, must use
+      CudnnCompatibleLSTMCell instead.
     """
     super(LSTMCell, self).__init__(_reuse=reuse)
     if not state_is_tuple:
diff --git a/tensorflow/python/ops/session_ops.py b/tensorflow/python/ops/session_ops.py
index e74c52b8cf..dc4d913c93 100644
--- a/tensorflow/python/ops/session_ops.py
+++ b/tensorflow/python/ops/session_ops.py
@@ -74,7 +74,7 @@ class TensorHandle(object):
   def _get_resource_handle(self):
     """The ResourceHandle representation of this handle."""
     if not self._resource_handle:
-      self._resource_handle = resource_handle_pb2.ResourceHandle()
+      self._resource_handle = resource_handle_pb2.ResourceHandleProto()
       self._resource_handle.device = self._handle.split(";")[-1]
       self._resource_handle.container = (
           pywrap_tensorflow_internal.TENSOR_HANDLE_KEY)
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 42f02e83a6..93a9656950 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -1275,10 +1275,6 @@ def sparse_reset_shape(sp_input, new_shape=None):
   return sparse_tensor.SparseTensor(in_indices, in_values, output_shape_tensor)
 
 
-# TODO(b/37517434): Delete this variable on 20170610.
-_SPARSE_FILL_EMPTY_ROWS_FAST_PATH = False
-
-
 def sparse_fill_empty_rows(sp_input, default_value, name=None):
   """Fills empty rows in the input 2-D `SparseTensor` with a default value.
 
@@ -1327,52 +1323,19 @@ def sparse_fill_empty_rows(sp_input, default_value, name=None):
     TypeError: If `sp_input` is not a `SparseTensor`.
   """
   sp_input = _convert_to_sparse_tensor(sp_input)
-
-  # TODO(b/37517434): Delete the slow path and only use the fast path
-  # on 20170610.
   with ops.name_scope(name, "SparseFillEmptyRows", [sp_input]):
     default_value = ops.convert_to_tensor(
         default_value, dtype=sp_input.values.dtype)
-    if _SPARSE_FILL_EMPTY_ROWS_FAST_PATH:
-      (output_indices, output_values, empty_row_indicator,
-       unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows(
-           indices=sp_input.indices,
-           values=sp_input.values,
-           dense_shape=sp_input.dense_shape,
-           default_value=default_value)
-      return (sparse_tensor.SparseTensor(
-          indices=output_indices,
-          values=output_values,
-          dense_shape=sp_input.dense_shape), empty_row_indicator)
-    else:
-      num_rows = math_ops.cast(sp_input.dense_shape[0], dtypes.int32)
-      all_row_indices = math_ops.cast(math_ops.range(num_rows), dtypes.int64)
-      empty_row_indices, _ = array_ops.setdiff1d(all_row_indices,
-                                                 sp_input.indices[:, 0])
-      empty_row_indicator = sparse_to_dense(
-          empty_row_indices,
-          array_ops.expand_dims(sp_input.dense_shape[0], -1), True,
-          False)
-
-      empty_row_indices_as_column = array_ops.reshape(
-          empty_row_indices, [-1, 1])
-      additional_indices = array_ops.concat([
-          empty_row_indices_as_column,
-          array_ops.zeros_like(empty_row_indices_as_column)
-      ], 1)
-      additional_values = array_ops.fill(
-          array_ops.shape(empty_row_indices), default_value)
-
-      all_indices_unordered = array_ops.concat(
-          [sp_input.indices, additional_indices], 0)
-      all_values_unordered = array_ops.concat(
-          [sp_input.values, additional_values], 0)
-      sp_unordered_output = sparse_tensor.SparseTensor(
-          all_indices_unordered,
-          all_values_unordered, sp_input.dense_shape)
-      sp_ordered_output = sparse_reorder(sp_unordered_output)
-
-      return sp_ordered_output, empty_row_indicator
+    (output_indices, output_values, empty_row_indicator,
+     unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows(
+         indices=sp_input.indices,
+         values=sp_input.values,
+         dense_shape=sp_input.dense_shape,
+         default_value=default_value)
+    return (sparse_tensor.SparseTensor(indices=output_indices,
+                                       values=output_values,
+                                       dense_shape=sp_input.dense_shape),
+            empty_row_indicator)
 
 
 def serialize_sparse(sp_input, name=None):
diff --git a/tensorflow/python/ops/transpose_benchmark.py b/tensorflow/python/ops/transpose_benchmark.py
index cddefacf2e..6bd3fe5e5a 100644
--- a/tensorflow/python/ops/transpose_benchmark.py
+++ b/tensorflow/python/ops/transpose_benchmark.py
@@ -1,4 +1,4 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -32,7 +32,7 @@ from tensorflow.python.platform import test
 
 
 def build_graph(device, input_shape, perm, datatype, num_iters):
-  """builds a graph containing a sequence of conv2d operations.
+  """Build a graph containing a sequence of conv2d operations.
 
   Args:
     device: String, the device to run on.
@@ -50,12 +50,10 @@ def build_graph(device, input_shape, perm, datatype, num_iters):
     t = constant_op.constant(inp, shape=input_shape)
 
     outputs = []
-    transpose_op = array_ops.transpose(t, perm)
-    outputs.append(transpose_op)
-    for _ in range(1, num_iters):
-      with ops.control_dependencies([transpose_op]):
-        transpose_op = array_ops.transpose(t, perm)
-        outputs.append(transpose_op)
+    outputs.append(array_ops.transpose(t, perm))
+    for i in range(1, num_iters):
+      with ops.control_dependencies([outputs[i - 1]]):
+        outputs.append(array_ops.transpose(t, perm))
     return control_flow_ops.group(*outputs)
 
 
@@ -63,7 +61,7 @@ class TransposeBenchmark(test.Benchmark):
   """Benchmark transpose!"""
 
   def _run_graph(self, device, input_shape, perm, num_iters, datatype):
-    """runs the graph and print its execution time.
+    """Run the graph and print its execution time.
 
     Args:
       device: String, the device to run on.
@@ -84,11 +82,9 @@ class TransposeBenchmark(test.Benchmark):
         session.run(outputs)
         start_time = time.time()
         session.run(outputs)
-
         duration = (time.time() - start_time) / num_iters
-        throughput = np.prod(
-            np.array(input_shape)) * datatype().itemsize * 2 / duration / 1e9
-
+        throughput = np.prod(np.array(
+            input_shape)) * datatype().itemsize * 2 / duration / 1e9
         print("%s %s inputshape:%s perm:%s %d %.6fsec, %.4fGB/s." %
               (device, str(datatype), str(input_shape).replace(" ", ""),
                str(perm).replace(" ", ""), num_iters, duration, throughput))
@@ -112,19 +108,19 @@ class TransposeBenchmark(test.Benchmark):
 
     datatypes = [np.complex128, np.float64, np.float32, np.float16, np.int8]
 
-    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2
-    small_shapes += [[2, 100, 100, 16], [2, 16, 100, 100]] * 2
-    small_shapes += [[2, 5000, 16], [2, 16, 5000]] * 2
-    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
-    small_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
-    small_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2 + [[
+        2, 100, 100, 16
+    ], [2, 16, 100, 100]] * 2 + [[2, 5000, 16], [2, 16, 5000]] * 2
+    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
+        [0, 3, 1, 2], [0, 2, 3, 1]
+    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
-    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2
-    large_shapes += [[2, 1000, 1000, 32], [2, 1000, 1000, 64]] * 2
-    large_shapes += [[2, 1000000, 32], [2, 1000000, 64]] * 2
-    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
-    large_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
-    large_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2 + [[
+        2, 1000, 1000, 32
+    ], [2, 1000, 1000, 64]] * 2 + [[2, 1000000, 32], [2, 1000000, 64]] * 2
+    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
+        [0, 3, 1, 2], [0, 2, 3, 1]
+    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
     huge_shapes = [[2, 100, 100, 100, 128], [2, 1000, 1000, 128],
                    [2, 1000000, 128]] * 2
@@ -147,23 +143,5 @@ class TransposeBenchmark(test.Benchmark):
             for ishape, perm in zip(huge_shapes, huge_perms):
               self._run_graph("gpu", ishape, perm, num_iters, datatype)
 
-    small_dim_large_shapes = [[2, 1000000, 3], [2, 3, 1000000], [2, 1000000, 8],
-                              [2, 8, 1000000]]
-    small_dim_small_shapes = [[2, 5000, 3], [2, 3, 5000], [2, 5000, 8],
-                              [2, 8, 5000]]
-    small_dim_perms = [[0, 2, 1]] * 4
-
-    num_iters = 320
-    small_dim_large_shape_datatypes = [np.float64, np.float32, np.int8]
-    for datatype in small_dim_large_shape_datatypes:
-      for ishape, perm in zip(small_dim_large_shapes, small_dim_perms):
-        self._run_graph("gpu", ishape, perm, num_iters, datatype)
-
-    small_dim_small_shape_datatypes = [np.complex128, np.float16]
-    for datatype in small_dim_small_shape_datatypes:
-      for ishape, perm in zip(small_dim_small_shapes, small_dim_perms):
-        self._run_graph("gpu", ishape, perm, num_iters, datatype)
-
-
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index 24616aeac3..c2193a24d0 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -1068,7 +1068,7 @@ get_variable_or_local_docstring = (
 
 %sThis function prefixes the name with the current variable scope
 and performs reuse checks. See the
-@{$variable_scope$Variable Scope How To}
+@{$variables$Variable Scope How To}
 for an extensive description of how reusing works. Here is a basic example:
 
 ```python
@@ -1444,7 +1444,7 @@ def variable_scope(name_or_scope,
 
   Variable scope allows to create new variables and to share already created
   ones while providing checks to not create or share by accident. For details,
-  see the @{$variable_scope$Variable Scope How To},
+  see the @{$variables$Variable Scope How To},
   here we present only a few basic examples.
 
   Simple example of how to create a new variable:
diff --git a/tensorflow/python/platform/test.py b/tensorflow/python/platform/test.py
index a307347f60..fdaae04fb6 100644
--- a/tensorflow/python/platform/test.py
+++ b/tensorflow/python/platform/test.py
@@ -17,6 +17,9 @@
 
 See the @{$python/test} guide.
 
+Note: `tf.test.mock` is an alias to the python `mock` or `unittest.mock`
+depending on the python version.
+
 @@main
 @@TestCase
 @@test_src_dir_path
diff --git a/tensorflow/python/tensorflow.i b/tensorflow/python/tensorflow.i
index a9a0b7fffa..e877d2717d 100644
--- a/tensorflow/python/tensorflow.i
+++ b/tensorflow/python/tensorflow.i
@@ -43,3 +43,4 @@ limitations under the License.
 
 %include "tensorflow/python/grappler/tf_optimizer.i"
 %include "tensorflow/python/grappler/cost_analyzer.i"
+%include "tensorflow/python/grappler/model_analyzer.i"
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 20d520fd7b..d9304ff50a 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -1547,9 +1547,14 @@ class Saver(object):
     Args:
       sess: A `Session` to use to restore the parameters.
       save_path: Path where parameters were previously saved.
+
+    Raises:
+      ValueError: If save_path is None.
     """
     if self._is_empty:
       return
+    if save_path is None:
+      raise ValueError("Can't load save_path when it is None.")
     logging.info("Restoring parameters from %s", save_path)
     sess.run(self.saver_def.restore_op_name,
              {self.saver_def.filename_tensor_name: save_path})
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index d17b7e93a1..f351efd843 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -1951,7 +1951,8 @@ class MetaGraphTest(test.TestCase):
                                                         logits=logit)
         adam.AdamOptimizer().minimize(cost, name="optimize")
       meta_graph_def = saver_module.export_meta_graph(clear_devices=True)
-      graph_io.write_graph(meta_graph_def, "/tmp", "meta_graph.pbtxt")
+      graph_io.write_graph(meta_graph_def, self.get_temp_dir(),
+                           "meta_graph.pbtxt")
 
     with session.Session(graph=ops_lib.Graph()) as sess:
       saver_module.import_meta_graph(meta_graph_def, import_scope="new_model")
diff --git a/tensorflow/python/util/kernel_registry.cc b/tensorflow/python/util/kernel_registry.cc
index d451bbace2..7d47692f6b 100644
--- a/tensorflow/python/util/kernel_registry.cc
+++ b/tensorflow/python/util/kernel_registry.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 #include "tensorflow/python/util/kernel_registry.h"
 
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
diff --git a/tensorflow/stream_executor/lib/status_macros.h b/tensorflow/stream_executor/lib/status_macros.h
index 816132e85a..ff8f4a71c8 100644
--- a/tensorflow/stream_executor/lib/status_macros.h
+++ b/tensorflow/stream_executor/lib/status_macros.h
@@ -40,7 +40,7 @@ limitations under the License.
   if (!__name.ok()) {                                  \
     return __name.status();                            \
   }                                                    \
-  __lhs = __name.ConsumeValueOrDie();
+  __lhs = std::move(__name.ValueOrDie());
 
 // Early-returns the status if it is in error; otherwise, assigns the
 // right-hand-side expression to the left-hand-side expression.
diff --git a/tensorflow/stream_executor/lib/statusor.h b/tensorflow/stream_executor/lib/statusor.h
index e06550009a..138738ecab 100644
--- a/tensorflow/stream_executor/lib/statusor.h
+++ b/tensorflow/stream_executor/lib/statusor.h
@@ -49,7 +49,7 @@ limitations under the License.
 //
 //  StatusOr<std::unique_ptr<Foo>> result = FooFactory::MakeNewFoo(arg);
 //  if (result.ok()) {
-//    std::unique_ptr<Foo> foo = result.ConsumeValueOrDie();
+//    std::unique_ptr<Foo> foo = std::move(result.ValueOrDie());
 //    foo->DoSomethingCool();
 //  } else {
 //    LOG(ERROR) << result.status();
@@ -163,6 +163,7 @@ class StatusOr {
   // If you need to initialize a T object from the stored value,
   // ConsumeValueOrDie() may be more efficient.
   const T& ValueOrDie() const;
+  T& ValueOrDie();
 
   // Returns our current value, requires this->ok(). Use this if
   // you would otherwise want to say std::move(s.ValueOrDie()), for example
@@ -206,6 +207,12 @@ const T& StatusOr<T>::ValueOrDie() const {
   return value_;
 }
 
+template <typename T>
+T& StatusOr<T>::ValueOrDie() {
+  TF_CHECK_OK(status_);
+  return value_;
+}
+
 template <typename T>
 T StatusOr<T>::ConsumeValueOrDie() {
   TF_CHECK_OK(status_);
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index cbf8c11ef1..97193af777 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -59,6 +59,10 @@ string ToVlogString(dnn::ActivationMode mode) {
   return dnn::ActivationModeString(mode);
 }
 
+string ToVlogString(const dnn::AlgorithmConfig &algo_config) {
+  return algo_config.ToString();
+}
+
 string ToVlogString(dnn::ElementwiseOperation op) {
   return dnn::ElementwiseOperationString(op);
 }
@@ -482,7 +486,8 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(biases),
-            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output));
+            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output),
+            PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
@@ -515,7 +520,8 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(biases),
-            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output));
+            PARAM(activation_mode), PARAM(output_descriptor), PARAM(output),
+            PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
@@ -546,7 +552,7 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(output_descriptor),
-            PARAM(output));
+            PARAM(output), PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
@@ -577,7 +583,7 @@ Stream &Stream::ThenConvolveWithAlgorithm(
   VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
             PARAM(filter_descriptor), PARAM(filter_data),
             PARAM(convolution_descriptor), PARAM(output_descriptor),
-            PARAM(output));
+            PARAM(output), PARAM(algorithm_config));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index d01342827d..5b1d9a517e 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -167,7 +167,7 @@ def tf_opts_nortti_if_android():
       "-fno-rtti",
       "-DGOOGLE_PROTOBUF_NO_RTTI",
       "-DGOOGLE_PROTOBUF_NO_STATIC_INITIALIZER",
-  ]) + if_android_x86(["-msse4.1"])
+  ])
 
 
 # LINT.ThenChange(//tensorflow/contrib/android/cmake/CMakeLists.txt)
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
new file mode 100644
index 0000000000..3a6f770153
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNClassifier"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn.DNNClassifier\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Adagrad\', \'<function relu instance>\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
new file mode 100644
index 0000000000..83e53d3960
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNLinearCombinedClassifier"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn_linear_combined.DNNLinearCombinedClassifier\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'<function relu instance>\', \'None\', \'2\', \'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
new file mode 100644
index 0000000000..17f30a04fb
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNLinearCombinedRegressor"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn_linear_combined.DNNLinearCombinedRegressor\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'label_dimension\', \'weight_column\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'<function relu instance>\', \'None\', \'1\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
new file mode 100644
index 0000000000..edd68f0bb9
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.DNNRegressor"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.dnn.DNNRegressor\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Adagrad\', \'<function relu instance>\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
new file mode 100644
index 0000000000..3715dd5ec7
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.LinearClassifier"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.linear.LinearClassifier\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
new file mode 100644
index 0000000000..ccb4abf675
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
@@ -0,0 +1,38 @@
+path: "tensorflow.estimator.LinearRegressor"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.canned.linear.LinearRegressor\'>"
+  is_instance: "<class \'tensorflow.python.estimator.estimator.Estimator\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "config"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "model_dir"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "params"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "export_savedmodel"
+    argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "train"
+    argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
index d69c475a31..801260c450 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
@@ -22,6 +22,10 @@ tf_class {
     name: "keep_checkpoint_max"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "log_step_count_steps"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "master"
     mtype: "<type \'property\'>"
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt
index 0d5dc73271..07b04810b5 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt
@@ -1,5 +1,21 @@
 path: "tensorflow.estimator"
 tf_module {
+  member {
+    name: "DNNClassifier"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DNNLinearCombinedClassifier"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DNNLinearCombinedRegressor"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "DNNRegressor"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Estimator"
     mtype: "<type \'type\'>"
@@ -8,6 +24,14 @@ tf_module {
     name: "EstimatorSpec"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "LinearClassifier"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "LinearRegressor"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "ModeKeys"
     mtype: "<type \'type\'>"
@@ -24,4 +48,12 @@ tf_module {
     name: "inputs"
     mtype: "<type \'module\'>"
   }
+  member_method {
+    name: "classifier_parse_example_spec"
+    argspec: "args=[\'feature_columns\', \'label_key\', \'label_dtype\', \'label_default\', \'weight_column\'], varargs=None, keywords=None, defaults=[\"<dtype: \'int64\'>\", \'None\', \'None\'], "
+  }
+  member_method {
+    name: "regressor_parse_example_spec"
+    argspec: "args=[\'feature_columns\', \'label_key\', \'label_dtype\', \'label_default\', \'label_dimension\', \'weight_column\'], varargs=None, keywords=None, defaults=[\"<dtype: \'float32\'>\", \'None\', \'1\', \'None\'], "
+  }
 }
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index d5a19d3a16..eca017edf5 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -530,19 +530,19 @@ tf_module {
   }
   member_method {
     name: "arg_max"
-    argspec: "args=[\'input\', \'dimension\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'input\', \'dimension\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "arg_min"
-    argspec: "args=[\'input\', \'dimension\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'input\', \'dimension\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "argmax"
-    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\', \'output_type\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"<dtype: \'int64\'>\"], "
   }
   member_method {
     name: "argmin"
-    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+    argspec: "args=[\'input\', \'axis\', \'name\', \'dimension\', \'output_type\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"<dtype: \'int64\'>\"], "
   }
   member_method {
     name: "as_dtype"
diff --git a/tensorflow/tools/api/lib/BUILD b/tensorflow/tools/api/lib/BUILD
index cdfa0e7be5..2d3b838957 100644
--- a/tensorflow/tools/api/lib/BUILD
+++ b/tensorflow/tools/api/lib/BUILD
@@ -22,7 +22,8 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":api_objects_proto_py",
-        "//tensorflow/tools/common:traverse",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
diff --git a/tensorflow/tools/api/tests/BUILD b/tensorflow/tools/api/tests/BUILD
index 8421d8fce2..e9aeeb3855 100644
--- a/tensorflow/tools/api/tests/BUILD
+++ b/tensorflow/tools/api/tests/BUILD
@@ -23,11 +23,12 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:lib",
         "//tensorflow/python:platform",
         "//tensorflow/tools/api/lib:python_object_to_proto_visitor",
         "//tensorflow/tools/common:public_api",
         "//tensorflow/tools/common:traverse",
-        "@protobuf//:protobuf_python",
     ],
 )
 
diff --git a/tensorflow/tools/benchmark/benchmark_model.cc b/tensorflow/tools/benchmark/benchmark_model.cc
index dfad11adf0..81892aef96 100644
--- a/tensorflow/tools/benchmark/benchmark_model.cc
+++ b/tensorflow/tools/benchmark/benchmark_model.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/graph/algorithm.h"
diff --git a/tensorflow/tools/common/BUILD b/tensorflow/tools/common/BUILD
index f92edd0dd8..8a8667957a 100644
--- a/tensorflow/tools/common/BUILD
+++ b/tensorflow/tools/common/BUILD
@@ -15,6 +15,7 @@ py_library(
     name = "public_api",
     srcs = ["public_api.py"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow/python:util"],
 )
 
 py_test(
@@ -32,6 +33,7 @@ py_library(
     name = "traverse",
     srcs = ["traverse.py"],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow/python:util"],
 )
 
 py_test(
diff --git a/tensorflow/tools/compatibility/BUILD b/tensorflow/tools/compatibility/BUILD
index fb40cf0833..19959ea6d2 100644
--- a/tensorflow/tools/compatibility/BUILD
+++ b/tensorflow/tools/compatibility/BUILD
@@ -24,7 +24,9 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         "tf_upgrade",
-        "//tensorflow:tensorflow_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_test_lib",
+        "@six_archive//:six",
     ],
 )
 
diff --git a/tensorflow/tools/dist_test/scripts/BUILD b/tensorflow/tools/dist_test/scripts/BUILD
index c329f0bbe8..ce2fa5c743 100644
--- a/tensorflow/tools/dist_test/scripts/BUILD
+++ b/tensorflow/tools/dist_test/scripts/BUILD
@@ -17,6 +17,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":k8s_tensorflow_lib",
-        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:platform_test",
     ],
 )
diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index 587aaeb683..1b7d1a07f8 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -182,7 +182,7 @@ def add_dict_to_dict(add_from, add_to):
 
 # Exclude some libaries in contrib from the documentation altogether.
 def _get_default_private_map():
-  return {}
+  return {'tf.test': ['mock']}
 
 
 # Exclude members of some libaries.
diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD
index fa2cf15cb1..d9ec8e8e9b 100644
--- a/tensorflow/tools/graph_transforms/BUILD
+++ b/tensorflow/tools/graph_transforms/BUILD
@@ -240,7 +240,6 @@ cc_binary(
     deps = [
         ":transform_utils",
         "//tensorflow/core:core_cpu_internal",
-        "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
     ],
@@ -250,7 +249,12 @@ py_library(
     name = "transform_graph_py",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow/python:pywrap_tensorflow"],
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:pywrap_tensorflow",
+        "//tensorflow/python:util",
+    ],
 )
 
 tf_py_test(
diff --git a/tensorflow/tools/graph_transforms/rename_attribute_test.cc b/tensorflow/tools/graph_transforms/rename_attribute_test.cc
index a0a33e9fc0..31619d82ad 100644
--- a/tensorflow/tools/graph_transforms/rename_attribute_test.cc
+++ b/tensorflow/tools/graph_transforms/rename_attribute_test.cc
@@ -43,17 +43,17 @@ class RenameAttributeTest : public ::testing::Test {
     mul_node1->set_op("Mul");
     mul_node1->add_input("add_node2");
     mul_node1->add_input("add_node3");
-    AddNodeAttr<int32>("foo", 23, mul_node1);
-    AddNodeAttr<string>("bar", "something", mul_node1);
+    AddNodeAttr("foo", 23, mul_node1);
+    AddNodeAttr("bar", "something", mul_node1);
 
     NodeDef* add_node2 = graph_def.add_node();
     add_node2->set_name("add_node2");
     add_node2->set_op("Add");
     add_node2->add_input("const_node1");
     add_node2->add_input("const_node2");
-    AddNodeAttr<int32>("foo", 46, add_node2);
-    AddNodeAttr<int32>("bob", 23, add_node2);
-    AddNodeAttr<string>("bar", "something else", add_node2);
+    AddNodeAttr("foo", 46, add_node2);
+    AddNodeAttr("bob", 23, add_node2);
+    AddNodeAttr("bar", "something else", add_node2);
 
     NodeDef* add_node3 = graph_def.add_node();
     add_node3->set_name("add_node3");
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index d3952b5cec..6a6ce5b9e3 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -134,6 +134,7 @@ sh_binary(
             ":included_headers",
             ":simple_console",
             "//tensorflow:tensorflow_py",
+            "//tensorflow/contrib/boosted_trees:boosted_trees_pip",
             "//tensorflow/contrib/graph_editor:graph_editor_pip",
             "//tensorflow/contrib/keras:keras",
             "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip",
diff --git a/tensorflow/tools/quantization/BUILD b/tensorflow/tools/quantization/BUILD
index cb41185219..e99ad06a06 100644
--- a/tensorflow/tools/quantization/BUILD
+++ b/tensorflow/tools/quantization/BUILD
@@ -13,7 +13,20 @@ py_library(
     name = "quantize_graph_lib",
     srcs = ["quantize_graph.py"],
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow:tensorflow_py"],
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:graph_util",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:session",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:tensor_util",
+        "//third_party/py/numpy",
+    ],
 )
 
 py_binary(
@@ -27,18 +40,17 @@ py_binary(
         "//tensorflow/python:client",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:graph_util",
         "//tensorflow/python:platform",
+        "//tensorflow/python:tensor_util",
         "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 
 py_test(
     name = "quantize_graph_test",
     size = "small",
-    srcs = [
-        "quantize_graph_test.py",
-    ],
+    srcs = ["quantize_graph_test.py"],
     srcs_version = "PY2AND3",
     tags = ["nomsan"],  # http://b/32242946
     deps = [
@@ -48,6 +60,7 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:graph_util",
         "//tensorflow/python:platform",
         "//third_party/py/numpy",
     ],
@@ -55,12 +68,13 @@ py_test(
 
 py_binary(
     name = "graph_to_dot",
-    srcs = [
-        "graph_to_dot.py",
-    ],
+    srcs = ["graph_to_dot.py"],
     main = "graph_to_dot.py",
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow/python:platform"],
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:platform",
+    ],
 )
 
 filegroup(
diff --git a/tensorflow/tools/test/BUILD b/tensorflow/tools/test/BUILD
index 9367bcd4a3..28d651e910 100644
--- a/tensorflow/tools/test/BUILD
+++ b/tensorflow/tools/test/BUILD
@@ -22,6 +22,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:errors",
         "//tensorflow/python:platform",
@@ -46,6 +47,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":system_info_lib",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:platform",
     ],
 )
@@ -54,8 +56,10 @@ py_binary(
     name = "run_and_gather_logs",
     srcs = ["run_and_gather_logs.py"],
     srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
     deps = [
         ":run_and_gather_logs_lib",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:platform",
     ],
diff --git a/tensorflow/tools/test/performance.bzl b/tensorflow/tools/test/performance.bzl
index 2956c6dde7..64fff844a7 100644
--- a/tensorflow/tools/test/performance.bzl
+++ b/tensorflow/tools/test/performance.bzl
@@ -28,7 +28,7 @@ def tf_cc_logged_benchmark(
       name = name,
       tags = all_tags,
       size = "large",
-      srcs = ["//tensorflow/tools/test:run_and_gather_logs.py"],
+      srcs = ["//tensorflow/tools/test:run_and_gather_logs"],
       args = [
           "--name=//%s:%s" % (PACKAGE_NAME, name),
           "--test_name=" + target,
diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py
index 829333e056..77cc9f75f7 100644
--- a/tensorflow/tools/test/upload_test_benchmarks.py
+++ b/tensorflow/tools/test/upload_test_benchmarks.py
@@ -162,7 +162,7 @@ def upload_benchmark_data(client, data):
   t_val.update({
       "test": test_name,
       "start": start_time,
-      "info": unicode(test_result)
+      "info": unicode(data)
   })
   batch.append(t_val)
 
diff --git a/tensorflow/tools/tfprof/BUILD b/tensorflow/tools/tfprof/BUILD
index 57cccd8921..541df78e47 100644
--- a/tensorflow/tools/tfprof/BUILD
+++ b/tensorflow/tools/tfprof/BUILD
@@ -33,6 +33,7 @@ cc_binary(
         "//tensorflow/tools/tfprof/internal:tfprof_options",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
         "//tensorflow/tools/tfprof/internal:tfprof_utils",
+        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
         "@linenoise//:linenoise",
     ],
 )
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/tools/tfprof/README.md
index 54f3cd62f2..e86584acd1 100644
--- a/tensorflow/tools/tfprof/README.md
+++ b/tensorflow/tools/tfprof/README.md
@@ -7,7 +7,11 @@
 * Profile model performance
   * execution time, memory consumption
   * Profile multiple steps.
-* Auto detect and advise. (Experimental)
+* Auto profile and advise.
+  * accelerator utilization check
+  * expensive operation check
+  * operation configuration check
+  * distributed runtime check (Not OSS)
 
 ### Interfaces
 
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/tools/tfprof/g3doc/advise.md
index 3bce6270ff..e30add6fbf 100644
--- a/tensorflow/tools/tfprof/g3doc/advise.md
+++ b/tensorflow/tools/tfprof/g3doc/advise.md
@@ -3,6 +3,7 @@
 tfprof analyzes profiles and generates advises for common issues.
 
 ### Run Advise.
+
 ```python
 # First create a profiler. See profiler tutorials for more details.
 profiler = model_analyzer.Profiler(sess.graph)
@@ -13,8 +14,63 @@ _ = sess.run(r1,
              run_metadata=run_meta)
 profiler.add_step(1, run_meta)
 
-# Start advise.
-profiler.advise()
+# Then Start advise.
+profiler.advise(model_analyzer.ALL_ADVICE)
+
+# For one-shot API
+tf.contrib.tfprof.model_analyzer.advise(
+    sess.graph, run_meta=run_metadata)
+```
+
+```shell
+# Run advisor on CLI
+# See CLI tutorial on generating the files.
+tfprof --graph_path=graph.pbtxt \
+       --run_meta_path=run_metadata \
+       --op_log_path=tfprof_log
+
+tfprof> advise
+AcceleratorUtilizationChecker:
+device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03
+device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08
+device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04
+device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21
+
+OperationChecker:
+Found operation using NHWC data_format on GPU. Maybe NCHW is faster.
+
+ExpensiveOperationChecker:
+top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%)
+top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%)
+top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%)
+seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec
+  seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec
+    seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec
+      seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize), cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+        seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha..., cpu: 164us, accelerator: 0us, total: 164us
+      seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms
+      seq2seq_lib.py:151:sequence_loss_by_...:total_size = tf.a..., cpu: 154us, accelerator: 23us, total: 177us
+    seq2seq_lib.py:184:sampled_sequence_...:return cost / tf...., cpu: 97us, accelerator: 8us, total: 105us
+      math_ops.py:690:cast:return gen_math_o..., cpu: 62us, accelerator: 3us, total: 65us
+      math_ops.py:839:binary_op_wrapper:return func(x, y,..., cpu: 35us, accelerator: 5us, total: 40us
+  seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms
+    seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 303.76ms, accelerator: 73.54ms, total: 377.30ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 2.75ms, accelerator: 0us, total: 2.75ms
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 67us, accelerator: 0us, total: 67us
+    seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms
+        rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 292.04ms, accelerator: 73.54ms, total: 365.58ms
+        rnn.py:216:_rnn_step:time >= max_seque..., cpu: 4.07ms, accelerator: 0us, total: 4.07ms
+      core_rnn.py:178:static_rnn:min_sequence_leng..., cpu: 85us, accelerator: 0us, total: 85us
+      core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 16us, accelerator: 0us, total: 16us
+    seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms
+ ...(omitted)
+top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms
+top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms
+top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms
 ```
 
 ### Checker
@@ -25,16 +81,24 @@ area with the profile and report issues. A `Checker` is like a plugin.
 
 For example:
 
-####JobChecker (Not Available OSS)
-* Checking RecvTensor RPC latency and bandwidth.
-* Checking CPU/Memory utilization of the job.
+#### JobChecker (Not Available OSS)
+
+*   Checks RecvTensor RPC latency and bandwidth.
+*   Checks CPU/Memory utilization of the job.
 
 ####AcceleratorUtilization Checker
 * Checks what percentage of time the accelerator spends on computation.
 
-####Operation Checker
-* Check whether the operation runs with optimal options.
-* Checks if there is a better implementation to replace the current operation.
+#### OperationChecker
+
+*   Checks whether the operation runs with optimal options.
+*   Checks if there is a better implementation to replace the current operation.
+
+#### ExpensiveOperationChecker
+
+*   Checks the most expensive operation type.
+*   Checks the most expensive graph nodes.
+*   Checks the most expensive graph-building Python codes.
 
 ####Contribute Your Checker
 
diff --git a/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md b/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
index 5ad5a56513..cf99adcf02 100644
--- a/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
+++ b/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
@@ -52,10 +52,15 @@ use the definition to calculate float operations. Contributes are welcome.
 to calculate the statistics. It is suggested to pass in `-run_meta_path` if
 shape is only known during runtime. tfprof can fill in the missing shape with
 the runtime shape information from RunMetadata.
-
 Hence, it is suggested to use `-account_displayed_name_only`
 option so that you know the statistics are only for the operations printed out.
 
+* If no RunMetadata provided, tfprof count float_ops of each graph node once,
+even if it is defined in tf.while_loop. This is because tfprof doesn't know
+how many times are run statically. If RunMetadata provided, tfprof calculate
+float_ops as float_ops * run_count.
+
+
 
 ```python
 # To profile float opertions in commandline, you need to pass --graph_path
diff --git a/tensorflow/tools/tfprof/g3doc/profile_time.md b/tensorflow/tools/tfprof/g3doc/profile_time.md
index c89d7b0b03..c865e577f7 100644
--- a/tensorflow/tools/tfprof/g3doc/profile_time.md
+++ b/tensorflow/tools/tfprof/g3doc/profile_time.md
@@ -1,10 +1,42 @@
 ##Profile Time
 
+* [Times in TensorFlow and tfprof](#times-in-tensorflow-and-tfprof)
 * [Profile by Python Code](#profile-by-python-code)
 * [Profile by Operation Type](#profile-by-operation-type)
 * [Profile by Graph](#profile-by-graph)
 * [Profile by Name Scope](#profile-by-name-scope)
 
+
+###Times in TensorFlow and tfprof
+When we run a model, Tensorflow schedules and runs the nodes (operations)
+in the graph. An operation can be placed on an accelerator or on CPU.
+
+
+#### On Accelerator
+When an operation is placed on accelerator, it will first be scheduled
+by TensorFlow on CPU. Normally, it's the code in OpKernel::Compute.
+OpKernel::Compute can decide to dispatch some of the computations on the
+accelerator. While some computation (e.g. pre-processing) is still done
+in CPU. OpKernel::Compute can dispatch computation on accelerator
+and return, or it can also wait for the accelerator to finish.
+
+tfprof reports 3 execution times:
+
+  * <b>accelerator_micros</b>, which is the part of computation time spent on accelerator.
+  * <b>cpu_micros</b>, which is the part of computation time spent on cpu, including
+    any wait times that might happen if OpKernel::Compute decides to wait.
+  * <b>exec_micros</b>, which is the sum of accelerator_micros and cpu_micros.
+
+Since accelerator, such as GPU, usually runs operation asynchronously, you
+might notice an operation finishes on cpu before it starts running on
+accelerator.
+
+#### On CPU
+When an operation is placed on CPU, it will completely run on CPU. Hence,
+<b>exec_micros</b> is equal to <b>cpu_micros</b> and <b>accelerator_micros</b>
+should be 0.
+
+
 ###Profile by Python Code
 ```python
 # In code view, the time of each line of Python code is the aggregated
diff --git a/tensorflow/tools/tfprof/internal/BUILD b/tensorflow/tools/tfprof/internal/BUILD
index c47008b069..25c895af5b 100644
--- a/tensorflow/tools/tfprof/internal/BUILD
+++ b/tensorflow/tools/tfprof/internal/BUILD
@@ -41,7 +41,6 @@ cc_library(
         ":tfprof_utils",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
-        "//tensorflow/tools/tfprof:protos_all_cc",
         "@jsoncpp_git//:jsoncpp",
     ],
 )
@@ -56,6 +55,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
+        "//tensorflow/tools/tfprof:protos_all_cc",
     ],
 )
 
@@ -92,6 +92,7 @@ cc_library(
         ":tfprof_tensor",
         ":tfprof_utils",
         "//tensorflow/c:c_api",
+        "//tensorflow/c:checkpoint_reader",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
@@ -264,9 +265,7 @@ cc_library(
 
 cc_library(
     name = "print_model_analysis_hdr",
-    hdrs = [
-        "print_model_analysis.h",
-    ],
+    hdrs = ["print_model_analysis.h"],
     deps = [
         "//tensorflow/core:framework_lite",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/tools/tfprof/internal/advisor/BUILD
index 30012fa7b1..629322373a 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/tools/tfprof/internal/advisor/BUILD
@@ -14,6 +14,7 @@ cc_library(
     name = "checker",
     hdrs = ["checker.h"],
     deps = [
+        "//tensorflow/core:lib",
         "//tensorflow/tools/tfprof/internal:tfprof_stats",
     ],
 )
@@ -23,7 +24,7 @@ cc_library(
     srcs = ["internal_checker_runner_dummy.cc"],
     hdrs = ["internal_checker_runner.h"],
     deps = [
-        ":checker",
+        "//tensorflow/tools/tfprof/internal:tfprof_utils",
     ],
 )
 
@@ -43,12 +44,21 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "expensive_operation_checker",
+    hdrs = ["expensive_operation_checker.h"],
+    deps = [
+        ":checker",
+    ],
+)
+
 cc_library(
     name = "tfprof_advisor",
     hdrs = ["tfprof_advisor.h"],
     deps = [
         ":accelerator_utilization_checker",
         ":checker",
+        ":expensive_operation_checker",
         ":internal_checker_runner_dummy",
         ":operation_checker",
     ],
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
index fb7f65d7dc..074b8e57b0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
@@ -33,10 +33,11 @@ struct ExecStats {
 
 class AcceleratorUtilizationChecker : public Checker {
  public:
-  string name() override { return "AcceleratorUtilizationChecker"; }
+  string name() const override { return kCheckers[0]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -48,24 +49,21 @@ class AcceleratorUtilizationChecker : public Checker {
     return CheckInternal();
   }
 
-  std::vector<string> CheckInternal() {
+  AdviceProto::Checker CheckInternal() {
     for (const auto& s : accelerator_exec_stats_) {
       const ExecStats& stat = s.second;
       int64 total_micros = stat.end_micros - stat.start_micros;
       if (total_micros <= 0) continue;
       double utilization = 1.0 * stat.exec_micros / total_micros;
       if (utilization >= 0.5) {
-        reports_.push_back(strings::Printf("%s: device: %s utilization: %.2f",
-                                           kLevel[0], s.first.c_str(),
-                                           utilization));
+        reports_.add_reports(strings::Printf("device: %s utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization < 0.5 && utilization > 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[1],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       } else if (utilization <= 0.2) {
-        reports_.push_back(
-            strings::Printf("%s: device: %s low utilization: %.2f", kLevel[2],
-                            s.first.c_str(), utilization));
+        reports_.add_reports(strings::Printf("device: %s low utilization: %.2f",
+                                             s.first.c_str(), utilization));
       }
     }
     return reports_;
@@ -102,7 +100,7 @@ class AcceleratorUtilizationChecker : public Checker {
 
   std::map<string, ExecStats> accelerator_exec_stats_;
   std::map<string, int64> ps_placement_;
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/checker.h b/tensorflow/tools/tfprof/internal/advisor/checker.h
index b8b057be5b..3ce80cd8c4 100644
--- a/tensorflow/tools/tfprof/internal/advisor/checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/checker.h
@@ -18,27 +18,33 @@ limitations under the License.
 
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
-static const char* const kLevel[] = {
-    "NOTE",     // Good to know.
-    "SUGGEST",  // Might get better.
-    "WARN",     // Please do it for better.
+// Append only.
+static const char* const kCheckers[] = {
+    "AcceleratorUtilizationChecker", "OperationChecker",
+    "ExpensiveOperationChecker",
+    "JobChecker",  // Internal checker.
 };
 
 class Checker {
  public:
-  virtual ~Checker(){};
+  virtual ~Checker() {}
 
-  virtual string name() = 0;
+  virtual string name() const = 0;
 
-  std::vector<string> Run(const TFStats* stats) { return Check(stats); }
+  AdviceProto::Checker Run(const AdvisorOptionsProto::CheckerOption& options,
+                           const TFStats* stats) {
+    return Check(options, stats);
+  }
 
  protected:
-  // Returns a vector of string, each one being an advice.
-  virtual std::vector<string> Check(const TFStats* stats) = 0;
+  virtual AdviceProto::Checker Check(
+      const AdvisorOptionsProto::CheckerOption& options,
+      const TFStats* stats) = 0;
 };
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
new file mode 100644
index 0000000000..14e30edd87
--- /dev/null
+++ b/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
@@ -0,0 +1,138 @@
+/* Copyright 2016 The TensorFlow Authors All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// This checker checks the most expensive operations.
+#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+
+#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+
+namespace tensorflow {
+namespace tfprof {
+
+class ExpensiveOperationChecker : public Checker {
+ public:
+  string name() const override { return kCheckers[2]; }
+
+ private:
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
+    if (!stats) {
+      fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
+              name().c_str());
+      return reports_;
+    }
+    if (stats->steps().empty()) {
+      fprintf(stderr, "Missing RunMetadata info. Skip %s\n", name().c_str());
+    }
+    CheckOpView(stats);
+    CheckCodeView(stats);
+    CheckScopeView(stats);
+    return reports_;
+  }
+
+  void CheckOpView(const TFStats* stats) {
+    if (stats->steps().empty()) {
+      fprintf(stderr, "Missing run_meta for %s\n", name().c_str());
+      return;
+    }
+    Options opts(3, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"}, {},
+                 false, {"micros", "occurrence"}, "none", {});
+    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("op", opts);
+    if (root.children_size() == 0) {
+      return;
+    }
+    const TFMultiGraphNodeProto* node = &root;
+    std::vector<string> outputs;
+    for (int i = 0; i < 3 && node->children_size() > 0; ++i) {
+      node = &node->children(0);
+      outputs.push_back(strings::Printf(
+          "top %d operation type: %s, "
+          "cpu: %s, accelerator: %s, total: %s (%.2f%%)",
+          i + 1, node->name().c_str(),
+          FormatTime(node->cpu_exec_micros()).c_str(),
+          FormatTime(node->accelerator_exec_micros()).c_str(),
+          FormatTime(node->exec_micros()).c_str(),
+          100.0 * node->exec_micros() / (root.total_exec_micros() + 1e-10)));
+    }
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CheckCodeView(const TFStats* stats) {
+    if (!stats->has_code_traces()) {
+      fprintf(stderr, "Missing op_log (code traces) for %s\n", name().c_str());
+      return;
+    }
+    Options opts(100, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
+                 {}, false, {"micros"}, "none", {});
+    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("code", opts);
+    const TFMultiGraphNodeProto* node = &root;
+    // A trick here is: Usually, codes in library file are usually referenced
+    // only once, while user's own code are referenced multiple times.
+    while (node->children_size() == 1) {
+      node = &node->children(0);
+    }
+    if (node->children_size() == 0) {
+      return;
+    }
+
+    std::vector<string> outputs;
+    CodeViewHelper(node, 0, &outputs);
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CheckScopeView(const TFStats* stats) {
+    Options opts(100, 0, 100, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
+                 {}, false, {"micros"}, "none", {});
+    const TFGraphNodeProto root = stats->ShowGraphNode("scope", opts);
+    if (root.children_size() == 0) {
+      return;
+    }
+    std::vector<string> outputs;
+    const TFGraphNodeProto* node = &root;
+    for (int i = 0; i < 3 && i < root.children_size(); ++i) {
+      const TFGraphNodeProto& node = root.children(i);
+      outputs.push_back(strings::Printf(
+          "top %d graph node: %s, cpu: %s, accelerator: %s, total: %s", i + 1,
+          node.name().c_str(), FormatTime(node.cpu_exec_micros()).c_str(),
+          FormatTime(node.accelerator_exec_micros()).c_str(),
+          FormatTime(node.exec_micros()).c_str()));
+    }
+    reports_.add_reports(str_util::Join(outputs, "\n"));
+  }
+
+  void CodeViewHelper(const TFMultiGraphNodeProto* node, int depth,
+                      std::vector<string>* outputs) {
+    if (node->children_size() <= 1 || depth > 4) {
+      return;
+    }
+    for (int j = 0; j < 3 && j < node->children_size(); ++j) {
+      const TFMultiGraphNodeProto* c = &node->children(j);
+      outputs->push_back(strings::Printf(
+          "%s%s, cpu: %s, accelerator: %s, total: %s",
+          string(depth * 2, ' ').c_str(), c->name().c_str(),
+          FormatTime(c->total_cpu_exec_micros()).c_str(),
+          FormatTime(c->total_accelerator_exec_micros()).c_str(),
+          FormatTime(c->total_exec_micros()).c_str()));
+      CodeViewHelper(c, depth + 1, outputs);
+    }
+  }
+
+  AdviceProto::Checker reports_;
+};
+
+}  // namespace tfprof
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OP_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
index 1238b57f20..ed8ae571b6 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
@@ -17,13 +17,16 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
 
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
 
 class TFStats;
 
-std::map<string, std::vector<string>> RunInternalCheckers(const TFStats* stats);
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats);
 
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
index 8204d2b04e..67962c8e8b 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
@@ -17,9 +17,9 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-std::map<string, std::vector<string>> RunInternalCheckers(
-    const TFStats* stats) {
-  return std::map<string, std::vector<string>>();
+AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
+                                const TFStats* stats) {
+  return AdviceProto();
 }
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
index 2a05f9bfd0..4d0d68e3bf 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
@@ -24,10 +24,11 @@ namespace tfprof {
 
 class OperationChecker : public Checker {
  public:
-  string name() override { return "OperationChecker"; }
+  string name() const override { return kCheckers[1]; }
 
  private:
-  std::vector<string> Check(const TFStats* stats) override {
+  AdviceProto::Checker Check(const AdvisorOptionsProto::CheckerOption& options,
+                             const TFStats* stats) override {
     if (!stats) {
       fprintf(stderr, "Missing profiles (e.g. graph, run_meta). Skip %s\n",
               name().c_str());
@@ -53,22 +54,20 @@ class OperationChecker : public Checker {
       }
     }
     if (use_batch_norm && !use_fused_batch_norm) {
-      reports_.push_back(strings::Printf(
-          "%s: Maybe use faster FusedBatchNorm instead of BatchNorm",
-          kLevel[1]));
+      reports_.add_reports(
+          "Maybe use faster FusedBatchNorm instead of BatchNorm");
     }
     if (recommend_nchw) {
       // TODO(xpan): Maybe print which Op supports NCHW.
-      reports_.push_back(strings::Printf(
-          "%s: Found operation using NHWC data_format on GPU. Maybe "
-          "NCHW is faster.",
-          kLevel[1]));
+      reports_.add_reports(
+          "Found operation using NHWC data_format on GPU. Maybe "
+          "NCHW is faster.");
     }
     return reports_;
   }
 
  private:
-  std::vector<string> reports_;
+  AdviceProto::Checker reports_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
index 856f515459..d2257fb9b5 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
@@ -18,8 +18,10 @@ limitations under the License.
 
 #include "tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+#include "tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h"
 #include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
 #include "tensorflow/tools/tfprof/internal/advisor/operation_checker.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -29,23 +31,44 @@ class Advisor {
  public:
   Advisor(const TFStats* stats) : stats_(stats) {}
 
-  std::map<string, std::vector<string>> Advise() {
+  static AdvisorOptionsProto DefaultOptions() {
+    AdvisorOptionsProto options;
+    std::vector<string> checkers(
+        kCheckers, kCheckers + sizeof(kCheckers) / sizeof(*kCheckers));
+    for (const string& checker : checkers) {
+      (*options.mutable_checkers())[checker];
+    }
+    return options;
+  }
+
+  AdviceProto Advise(const AdvisorOptionsProto& options) {
     // Note: Release a checker's memory ASAP.
-    std::map<string, std::vector<string>> reports = RunInternalCheckers(stats_);
-    // TODO(xpan): Think of a way to turn off/on specific checkers.
-    AcceleratorUtilizationChecker au_checker;
-    reports[au_checker.name()] = au_checker.Run(stats_);
-    OperationChecker op_checker;
-    reports[op_checker.name()] = op_checker.Run(stats_);
-
-    for (const auto& checker_r : reports) {
-      fprintf(stdout, "%s reports:\n", checker_r.first.c_str());
-      for (const auto& r : checker_r.second) {
+    AdviceProto ret = RunInternalCheckers(options, stats_);
+
+    if (options.checkers().find(kCheckers[0]) != options.checkers().end()) {
+      AcceleratorUtilizationChecker au_checker;
+      (*ret.mutable_checkers())[kCheckers[0]].MergeFrom(
+          au_checker.Run(options.checkers().at(kCheckers[0]), stats_));
+    }
+    if (options.checkers().find(kCheckers[1]) != options.checkers().end()) {
+      OperationChecker op_checker;
+      (*ret.mutable_checkers())[kCheckers[1]].MergeFrom(
+          op_checker.Run(options.checkers().at(kCheckers[1]), stats_));
+    }
+    if (options.checkers().find(kCheckers[2]) != options.checkers().end()) {
+      ExpensiveOperationChecker expensive_op_checker;
+      (*ret.mutable_checkers())[kCheckers[2]].MergeFrom(
+          expensive_op_checker.Run(options.checkers().at(kCheckers[2]),
+                                   stats_));
+    }
+    for (const auto& checker : ret.checkers()) {
+      fprintf(stdout, "\n%s:\n", checker.first.c_str());
+      for (const string& r : checker.second.reports()) {
         fprintf(stdout, "%s\n", r.c_str());
       }
     }
     fflush(stdout);
-    return reports;
+    return ret;
   }
 
  private:
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
index b41d0770dc..3b40253954 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
+++ b/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
@@ -29,15 +29,16 @@ class TFProfAdvisorTest : public ::testing::Test {
                              nullptr, nullptr));
 
     stats_->AddNodeForTest(
-        "n1", CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 10, 2));
-    stats_->AddNodeForTest("n2", CreateNode("n2", "Conv2D", {}, 20, 2));
+        0, CreateNode("n1", "Conv2D", {{"data_format", "NHWC"}}, 0, 10, 2));
+    stats_->AddNodeForTest(0, CreateNode("n2", "Conv2D", {}, 0, 20, 2));
+    stats_->BuildAllViews();
     advisor_.reset(new Advisor(stats_.get()));
   }
 
   std::unique_ptr<TFGraphNode> CreateNode(const string& name,
                                           const string& type,
                                           std::map<string, string> attrs,
-                                          int64 start_miros,
+                                          int64 step, int64 start_miros,
                                           int64 end_rel_micros) {
     node_defs_.push_back(std::unique_ptr<NodeDef>(new NodeDef()));
     NodeDef* def = node_defs_.back().get();
@@ -52,10 +53,10 @@ class TFProfAdvisorTest : public ::testing::Test {
     NodeExecStats node_stat;
     node_stat.set_all_start_micros(start_miros);
     node_stat.set_op_end_rel_micros(end_rel_micros);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0", node_stat);
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:all",
                       node_stat);
-    node->AddStepStat(0, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
+    node->AddStepStat(step, "/job:localhost/replica:0/task:0/gpu:0:stream:0",
                       node_stat);
     return node;
   }
@@ -66,23 +67,38 @@ class TFProfAdvisorTest : public ::testing::Test {
 };
 
 TEST_F(TFProfAdvisorTest, Basics) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_TRUE(reports.find("AcceleratorUtilizationChecker") != reports.end());
-  EXPECT_TRUE(reports.find("OperationChecker") != reports.end());
+  AdvisorOptionsProto options = Advisor::DefaultOptions();
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(advice.checkers().find(kCheckers[0]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[1]) != advice.checkers().end());
+  EXPECT_TRUE(advice.checkers().find(kCheckers[2]) != advice.checkers().end());
 }
 
 TEST_F(TFProfAdvisorTest, OperationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["OperationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["OperationChecker"][0]).contains("NCHW"));
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[1]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[1]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[1]).reports(0))
+                  .contains("NCHW"));
 }
 
 TEST_F(TFProfAdvisorTest, UtilizationChecker) {
-  std::map<string, std::vector<string>> reports = advisor_->Advise();
-  EXPECT_EQ(reports["AcceleratorUtilizationChecker"].size(), 1);
-  EXPECT_TRUE(StringPiece(reports["AcceleratorUtilizationChecker"][0])
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[0]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_EQ(advice.checkers().at(kCheckers[0]).reports_size(), 1);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[0]).reports(0))
                   .contains("low utilization"));
 }
 
+TEST_F(TFProfAdvisorTest, ExpensiveOperationChecker) {
+  AdvisorOptionsProto options;
+  (*options.mutable_checkers())[kCheckers[2]];
+  AdviceProto advice = advisor_->Advise(options);
+  EXPECT_TRUE(StringPiece(advice.checkers().at(kCheckers[2]).reports(0))
+                  .contains("top 1 operation type: Conv2D"));
+}
+
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.cc b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
index 37d01db3a1..5a9c44d8e6 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.cc
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/tfprof_log.pb.h"
+#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
 #include "tensorflow/tools/tfprof/tfprof_output.pb.h"
 
 namespace tensorflow {
@@ -36,6 +37,18 @@ TFStats* tf_stat = nullptr;
 
 string RunProfile(const string& command, const string& options,
                   TFStats* tf_stats) {
+  if (command == kCmds[4]) {
+    AdvisorOptionsProto option_pb;
+    if (!option_pb.ParseFromString(options)) {
+      fprintf(stderr, "Cannot parse AdvisorOptionsProto\n");
+      return "";
+    }
+    tf_stats->BuildAllViews();
+    return Advisor(tf_stats).Advise(option_pb).SerializeAsString();
+  } else {
+    tf_stats->BuildView(command);
+  }
+
   Options opts;
   tensorflow::Status s = Options::FromProtoStr(options, &opts);
   if (!s.ok()) {
@@ -97,14 +110,14 @@ void AddStep(int64 step, const string* run_meta, const string* op_log) {
   // TODO(xpan): Better error handling.
   std::unique_ptr<RunMetadata> run_meta_ptr(new RunMetadata());
   run_meta_ptr->ParseFromString(*run_meta);
-  tf_stat->ParseRunMeta(step, std::move(run_meta_ptr));
+  tf_stat->AddRunMeta(step, std::move(run_meta_ptr));
 
   std::unique_ptr<OpLog> op_log_ptr;
   if (op_log && !op_log->empty()) {
     op_log_ptr.reset(new OpLog());
     op_log_ptr->ParseFromString(*op_log);
   }
-  tf_stat->ParseOpLog(std::move(op_log_ptr));
+  tf_stat->AddOpLog(std::move(op_log_ptr));
 }
 
 string Profile(const string* command, const string* options) {
@@ -144,7 +157,5 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
   return RunProfile(*command, *options, &tf_stats);
 }
 
-void Advise() { Advisor(tf_stat).Advise(); }
-
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.h b/tensorflow/tools/tfprof/internal/print_model_analysis.h
index 84165e542d..46db63646d 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.h
+++ b/tensorflow/tools/tfprof/internal/print_model_analysis.h
@@ -39,8 +39,6 @@ void AddStep(int64 step, const string* run_meta, const string* op_log);
 
 string Profile(const string* command, const string* options);
 
-void Advise();
-
 // Single-step Profiler.
 //
 // Interface defined for Python API swig. Calls the tfprof core API.
diff --git a/tensorflow/tools/tfprof/internal/tfprof_code.cc b/tensorflow/tools/tfprof/internal/tfprof_code.cc
index 70e341e8d4..f7b71bff31 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_code.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_code.cc
@@ -235,6 +235,17 @@ std::vector<CodeNode*> TFCode::Account(const std::vector<CodeNode*>& roots,
 
 string TFCode::FormatNode(CodeNode* node, const Options& opts, int64 indent) {
   std::vector<string> attrs;
+  if (opts.select.find(kShown[0]) != opts.select.end()) {
+    string memory = FormatMemory(node->proto().total_requested_bytes());
+    if (node->account) {
+      memory = FormatMemory(node->proto().requested_bytes()) + "/" + memory;
+    } else {
+      memory = "--/" + memory;
+    }
+    attrs.push_back(memory);
+  }
+  std::vector<string> time_attrs = FormatTimes(node, opts);
+  attrs.insert(attrs.end(), time_attrs.begin(), time_attrs.end());
 
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     string params = FormatNumber(node->proto().total_parameters()) + " params";
@@ -255,17 +266,6 @@ string TFCode::FormatNode(CodeNode* node, const Options& opts, int64 indent) {
     }
     attrs.push_back(fops);
   }
-  if (opts.select.find(kShown[0]) != opts.select.end()) {
-    string memory = FormatMemory(node->proto().total_requested_bytes());
-    if (node->account) {
-      memory = FormatMemory(node->proto().requested_bytes()) + "/" + memory;
-    } else {
-      memory = "--/" + memory;
-    }
-    attrs.push_back(memory);
-  }
-  std::vector<string> time_attrs = FormatTimes(node, opts);
-  attrs.insert(attrs.end(), time_attrs.begin(), time_attrs.end());
 
   if (opts.select.find(kShown[5]) != opts.select.end() &&
       !node->node->devices().empty()) {
@@ -275,7 +275,10 @@ string TFCode::FormatNode(CodeNode* node, const Options& opts, int64 indent) {
     std::set<string> op_types = node->node->op_types();
     attrs.push_back(str_util::Join(op_types, "|"));
   }
-
+  if (opts.select.find(kShown[7]) != opts.select.end()) {
+    // TODO(xpan): Make op count available in code view?
+    attrs.push_back(strings::Printf("%s N/A in code view", kShown[7]));
+  }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     attrs.push_back(strings::Printf("%s N/A in code view", kShown[8]));
   }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.h b/tensorflow/tools/tfprof/internal/tfprof_graph.h
index fbeae8673d..194a21f0cc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_graph.h
@@ -54,8 +54,8 @@ class TFGraph : public TFShow {
   const ShowNode* ShowInternal(const Options& opts,
                                Timeline* timeline) override;
 
-  bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                         int depth) const override {
     return true;
   }
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.cc b/tensorflow/tools/tfprof/internal/tfprof_node.cc
index 72f74902b5..54e14b1dd7 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_node.cc
@@ -70,10 +70,11 @@ void ExecStep::AddTimeStats(const string& dev, const NodeExecStats& step_stat) {
       accelerator_execs_[dev].push_back(pair);
       op_execs_[dev].push_back(pair);
     } else if (CountAsCPUTime(dev)) {
-      // TODO(xpan): A while-loop can has multiple nodes sharing the
-      // same name. They shouldn't be counted in one node.
       cpu_execs_[dev].push_back(pair);
       op_execs_[dev].push_back(pair);
+      // In while-loop, a graph node is executed multiple times under
+      // the same name.
+      run_count_ += 1;
     }
   }
 }
@@ -169,7 +170,8 @@ int64 ExecStep::accelerator_exec_micros() const {
   // Normally, an op should only be scheduled on 1 accelerator device.
   // Hence there should generally be 1 element in accelerator_execs_.
   for (const auto& execs : accelerator_execs_) {
-    // A op can fire multiple kernel runs hence multiple elements here.
+    // An op can fire multiple kernels or
+    // being scheduled multiple times in while-loop.
     for (const auto& exec : execs.second) {
       total += exec.second;
     }
@@ -179,13 +181,9 @@ int64 ExecStep::accelerator_exec_micros() const {
 
 int64 ExecStep::cpu_exec_micros() const {
   int64 total = 0;
-  // Here we use for loop just for consistent appearence with
-  // accelerator_execs.
-  // We only expect cpu_execs_ to have 1 element because an
-  // op can only be scheduled on 1 device.
+  // Normally, an op can only be scheduled on 1 device.
   for (const auto& execs : cpu_execs_) {
-    // We only expect exec to have 1 element because an op
-    // can only be schedule once.
+    // An op can be scheduled multiple times in while-loop.
     for (const auto& exec : execs.second) {
       total += exec.second;
     }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.h b/tensorflow/tools/tfprof/internal/tfprof_node.h
index 6fd65c1ee7..f4da0ab9ee 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_node.h
@@ -46,6 +46,7 @@ class ExecStep {
  public:
   ExecStep(TFGraphNode* node)
       : node(node),
+        run_count_(0),
         all_start_micros_(0),
         latest_end_micros_(0),
         mem_initiated_(false),
@@ -60,6 +61,7 @@ class ExecStep {
 
   void AddMemoryStats(const string& dev, const NodeExecStats& step_stat);
 
+  int64 run_count() const { return run_count_; }
   // The execution time of an op. If it runs on accelerator, then it's
   // accelerator_exec_micros(). Otherwise, it's CPU time.
   int64 exec_micros() const;
@@ -89,15 +91,18 @@ class ExecStep {
 
  private:
   TFGraphNode* node;
+  // Can be larger than 1 if run multiple times in loop.
+  int64 run_count_;
   // The earliest/latest time including scheduling and execution.
   int64 all_start_micros_;
   int64 latest_end_micros_;
   // device -> vector of {op_start_micros, op_exec_micros} pairs.
   // accelerator_execs: gpu:id/stream:all -> {op_start_micros, op_exec_micros}
-  // For accelerator, vector size can be larger than 1, multiple kernel fires.
+  // For accelerator, vector size can be larger than 1, multiple kernel fires
+  // or in tf.while_loop.
   std::map<string, std::vector<std::pair<int64, int64>>> accelerator_execs_;
   // cpu_execs: cpu/gpu:id -> {op_start_micros, op_exec_micros}
-  // For cpu, normally vector size is 1, that is only one run.
+  // For cpu, vector size can be larger than 1 if in tf.while_loop.
   std::map<string, std::vector<std::pair<int64, int64>>> cpu_execs_;
   // combines accelerator_execs_ and cpu_execs_.
   std::map<string, std::vector<std::pair<int64, int64>>> op_execs_;
@@ -188,6 +193,23 @@ class TFGraphNode {
     return src_output_idx_;
   }
 
+  // Number of times the graph node is executed. When step < 0, the
+  // average number of times executed across all steps.
+  int64 run_count(int64 step) const {
+    if (execs_.empty()) {
+      return 0;
+    }
+    if (step >= 0) {
+      auto exec = execs_.find(step);
+      CHECK(exec != execs_.end());
+      return exec->second.run_count();
+    }
+    int64 total_run_count = 0;
+    for (const auto& exec : execs_) {
+      total_run_count += exec.second.run_count();
+    }
+    return total_run_count / execs_.size();
+  }
   // This is overall computation time, including both cpu and accelerator.
   // Note, cpu and accelerator might or might not run in parallel.
   int64 exec_micros(int64 step) const {
@@ -318,7 +340,14 @@ class TFGraphNode {
     return exec->second.allocator_bytes_in_use();
   }
 
-  int64 float_ops() const { return float_ops_; }
+  int64 float_ops(int64 step) const {
+    // If not run, return static analysis.
+    if (execs_.empty()) {
+      return float_ops_;
+    }
+    // Otherwise, return dynamic float_ops.
+    return float_ops_ * run_count(step);
+  }
   const CodeDef& code() { return code_; }
   string canonical_device() const { return canonical_device_; }
   string host_device() const { return host_device_; }
@@ -371,6 +400,7 @@ class TFMultiGraphNode {
  public:
   TFMultiGraphNode(const string& name)
       : name_(name),
+        run_count_(0),
         exec_micros_(0),
         accelerator_exec_micros_(0),
         cpu_exec_micros_(0),
@@ -378,6 +408,7 @@ class TFMultiGraphNode {
         float_ops_(0) {}
 
   bool SnapshotNodes(int64 step, const std::vector<string>& type_regexes) {
+    run_count_ = 0;
     exec_micros_ = 0;
     accelerator_exec_micros_ = 0;
     cpu_exec_micros_ = 0;
@@ -398,12 +429,13 @@ class TFMultiGraphNode {
     for (const TFGraphNode* node : nodes) {
       op_types_.insert(node->op_types().begin(), node->op_types().end());
 
+      run_count_ += node->run_count(step);
       exec_micros_ += node->exec_micros(step);
       accelerator_exec_micros_ += node->accelerator_exec_micros(step);
       cpu_exec_micros_ += node->cpu_exec_micros(step);
 
       requested_bytes_ += node->requested_bytes(step);
-      float_ops_ += node->float_ops();
+      float_ops_ += node->float_ops(step);
       if (node->shape().size() > 0) {
         shapes_.push_back(node->shape());
       }
@@ -436,6 +468,7 @@ class TFMultiGraphNode {
 
   const string& name() const { return name_; }
 
+  int64 run_count() const { return run_count_; }
   int64 exec_micros() const { return exec_micros_; }
   int64 accelerator_exec_micros() const { return accelerator_exec_micros_; }
   int64 cpu_exec_micros() const { return cpu_exec_micros_; }
@@ -480,6 +513,7 @@ class TFMultiGraphNode {
   const string name_;
   // Snapshot based on type_regexes
   std::set<string> op_types_;
+  int64 run_count_;
   int64 exec_micros_;
   int64 accelerator_exec_micros_;
   int64 cpu_exec_micros_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc b/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
index 7b22e88e07..e9073baf0d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
@@ -31,13 +31,14 @@ void ShowNode::ReInit(int64 step) {
   if (!node->canonical_device().empty()) {
     mutable_proto()->add_devices(node->canonical_device());
   }
+  mutable_proto()->set_run_count(node->run_count(step));
   mutable_proto()->set_exec_micros(node->exec_micros(step));
   mutable_proto()->set_accelerator_exec_micros(
       node->accelerator_exec_micros(step));
   mutable_proto()->set_cpu_exec_micros(node->cpu_exec_micros(step));
 
   mutable_proto()->set_requested_bytes(node->requested_bytes(step));
-  mutable_proto()->set_float_ops(node->float_ops());
+  mutable_proto()->set_float_ops(node->float_ops(step));
 
   mutable_proto()->clear_input_shapes();
   for (const auto& inp : node->input_shapes()) {
@@ -71,6 +72,10 @@ const TFGraphNodeProto& ShowNode::proto() const { return proto_; }
 
 void ShowNode::AggregateTotalStats(ShowNode* node) {
   TFGraphNodeProto* node_pb = node->mutable_proto();
+  mutable_proto()->set_total_run_count(proto().total_run_count() +
+                                       node_pb->total_run_count());
+  mutable_proto()->set_total_definition_count(
+      proto().total_definition_count() + node_pb->total_definition_count());
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          node_pb->total_exec_micros());
   mutable_proto()->set_total_accelerator_exec_micros(
@@ -88,6 +93,10 @@ void ShowNode::AggregateTotalStats(ShowNode* node) {
 }
 
 void ShowNode::AddSelfToTotalStats() {
+  mutable_proto()->set_total_definition_count(proto().total_definition_count() +
+                                              1);
+  mutable_proto()->set_total_run_count(proto().total_run_count() +
+                                       proto().run_count());
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          proto().exec_micros());
   mutable_proto()->set_total_accelerator_exec_micros(
@@ -105,6 +114,8 @@ void ShowNode::AddSelfToTotalStats() {
 }
 
 void ShowNode::ResetTotalStats() {
+  mutable_proto()->set_total_definition_count(0);
+  mutable_proto()->set_total_run_count(0);
   mutable_proto()->set_total_exec_micros(0);
   mutable_proto()->set_total_accelerator_exec_micros(0);
   mutable_proto()->set_total_cpu_exec_micros(0);
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/tools/tfprof/internal/tfprof_op.cc
index ac702320b3..77a2593623 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.cc
@@ -126,6 +126,7 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
   }
   nodes = SortNodes(nodes, opts);
 
+  // pre keeps track of previous visited node.
   OpNode* pre = nullptr;
   std::vector<OpNode*> account_nodes;
   for (auto it = nodes.rbegin(); it != nodes.rend(); ++it) {
@@ -170,16 +171,20 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
     root_->ResetTotalStats();
     if (pre) {
       root_->AggregateTotalStats(pre);
-      root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
-      pre->mutable_proto()->clear_children();
     }
   }
+  if (pre) {
+    root_->mutable_proto()->add_children()->MergeFrom(pre->proto());
+    pre->mutable_proto()->clear_children();
+  }
 
   if (opts.output_type == kOutput[1] || opts.output_type == kOutput[2]) {
     string display_str = FormatLegend(opts);
     for (OpNode* node : show_nodes) {
       display_str += FormatNode(node, root_.get(), opts);
     }
+    // In op view, we don't show root (total). But it will still in proto.
+    // TODO(xpan): Is it the right choice?
     root_->formatted_str = display_str;
   }
   return root_.get();
@@ -201,7 +206,7 @@ int64 TFOp::SearchRoot(const std::vector<OpNode*> nodes,
   return i;
 }
 
-string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
+string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const {
   std::vector<string> attrs;
 
   if (opts.select.find(kShown[0]) != opts.select.end()) {
@@ -278,9 +283,14 @@ string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
   }
 
   if (opts.select.find(kShown[7]) != opts.select.end()) {
+    int64 total_runs = 0;
+    for (const auto& gnode : node->proto().graph_nodes()) {
+      total_runs += gnode.run_count();
+    }
     attrs.push_back(strings::Printf(
         "%10s",
-        strings::Printf("%d", node->proto().graph_nodes_size()).c_str()));
+        strings::Printf("%lld|%d", total_runs, node->proto().graph_nodes_size())
+            .c_str()));
   }
 
   string node_str = strings::Printf("%-25s%s\n", node->name().c_str(),
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.h b/tensorflow/tools/tfprof/internal/tfprof_op.h
index 5b16490363..34812f54be 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_op.h
@@ -56,15 +56,15 @@ class TFOp : public TFMultiShow {
   int64 SearchRoot(const std::vector<OpNode*> nodes,
                    const std::vector<string>& regexes);
 
-  bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                         int depth) override {
+  bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                         int depth) const override {
     if (opts.min_occurrence > node->node->graph_nodes().size()) {
       return false;
     }
     return true;
   }
 
-  string FormatNode(OpNode* node, OpNode* root, const Options& opts);
+  string FormatNode(OpNode* node, OpNode* root, const Options& opts) const;
 
   std::unique_ptr<OpNode> root_;
   std::map<string, std::unique_ptr<OpNode>> cnodes_map_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/tools/tfprof/internal/tfprof_options.h
index 6c9db24342..d39333e3fc 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_options.h
@@ -59,10 +59,10 @@ static const char* const kShown[] = {
     "cpu_micros"};
 
 static const char* const kCmds[] = {
-    "scope", "graph", "code", "op", "set", "help",
+    "scope", "graph", "code", "op", "advise", "set", "help",
 };
 
-static const char* const kOutput[] = {"timeline", "stdout", "file"};
+static const char* const kOutput[] = {"timeline", "stdout", "file", "none"};
 
 static const char* const kTimelineOpts[] = {
     "outfile",
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/tools/tfprof/internal/tfprof_show.cc
index eaab5b9608..6c1183a4d5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.cc
@@ -26,7 +26,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFGraphNodeProto& TFShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -64,7 +66,8 @@ bool TFShow::LookUpCheckPoint(const string& name,
   return true;
 }
 
-bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
+bool TFShow::ShouldShow(const ShowNode* node, const Options& opts,
+                        int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -72,6 +75,7 @@ bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
       node->proto().exec_micros() < opts.min_micros ||
       node->proto().parameters() < opts.min_params ||
       node->proto().float_ops() < opts.min_float_ops ||
+      node->proto().run_count() < opts.min_occurrence ||
       depth > opts.max_depth || !ShouldShowIfExtra(node, opts, depth)) {
     return false;
   }
@@ -96,7 +100,8 @@ bool TFShow::ShouldShow(ShowNode* node, const Options& opts, int depth) {
   return true;
 }
 
-bool TFShow::ShouldTrim(ShowNode* node, const std::vector<string>& regexes) {
+bool TFShow::ShouldTrim(const ShowNode* node,
+                        const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -121,7 +126,7 @@ bool TFShow::ReAccount(ShowNode* node, const Options& opts) {
   return false;
 }
 
-string TFShow::FormatNode(ShowNode* node, const Options& opts) {
+string TFShow::FormatNode(ShowNode* node, const Options& opts) const {
   std::vector<string> info;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     const string shape = FormatShapes(node->node->shape());
@@ -177,6 +182,21 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) {
     const std::set<string>& op_types = node->node->op_types();
     info.push_back(str_util::Join(op_types, "|"));
   }
+  if (opts.select.find(kShown[7]) != opts.select.end()) {
+    string run = FormatNumber(node->proto().total_run_count());
+    if (node->account) {
+      run = FormatNumber(node->proto().run_count()) + "/" + run;
+    } else {
+      run = "--/" + run;
+    }
+    string definition = FormatNumber(node->proto().total_definition_count());
+    if (node->account) {
+      definition = "1/" + definition;
+    } else {
+      definition = "--/" + definition;
+    }
+    info.push_back(run + "|" + definition);
+  }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     std::vector<string> shape_vec;
     for (const auto& s : node->node->input_shapes()) {
@@ -194,7 +214,7 @@ string TFShow::FormatNode(ShowNode* node, const Options& opts) {
                          str_util::Join(info, ", ").c_str());
 }
 
-string TFShow::FormatLegend(const Options& opts) {
+string TFShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[2]) != opts.select.end()) {
     legends.push_back("# parameters");
@@ -224,6 +244,9 @@ string TFShow::FormatLegend(const Options& opts) {
   if (opts.select.find(kShown[6]) != opts.select.end()) {
     legends.push_back("op types");
   }
+  if (opts.select.find(kShown[7]) != opts.select.end()) {
+    legends.push_back("op count (run|defined)");
+  }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     legends.push_back("input shapes");
   }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/tools/tfprof/internal/tfprof_show.h
index 2c61b4fd73..95513e086f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show.h
@@ -54,20 +54,21 @@ class TFShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowNode* node, const Options& opts, int depth) const;
 
-  bool ShouldTrim(ShowNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowNode* node, const Options& opts);
 
-  string FormatNode(ShowNode* node, const Options& opts);
+  string FormatNode(ShowNode* node, const Options& opts) const;
 
-  string FormatLegend(const Options& opts);
+  string FormatLegend(const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
index 97f204d25b..84d542714a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
@@ -28,7 +28,9 @@ namespace tensorflow {
 namespace tfprof {
 
 const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
-  if (opts.output_type == kOutput[0]) {
+  if (opts.output_type == kOutput[3]) {
+    return ShowInternal(opts, nullptr)->proto();
+  } else if (opts.output_type == kOutput[0]) {
     Timeline timeline(opts.step, opts.output_options.at(kTimelineOpts[0]));
     return ShowInternal(opts, &timeline)->proto();
   } else if (opts.output_type == kOutput[2]) {
@@ -48,8 +50,8 @@ const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
   }
 }
 
-bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
-                            int depth) {
+bool TFMultiShow::ShouldShow(const ShowMultiNode* node, const Options& opts,
+                             int depth) const {
   // Always show kTFProfRoot.
   if (node->name() == kTFProfRoot) return true;
 
@@ -88,8 +90,8 @@ bool TFMultiShow::ShouldShow(ShowMultiNode* node, const Options& opts,
   return true;
 }
 
-bool TFMultiShow::ShouldTrim(ShowMultiNode* node,
-                            const std::vector<string>& regexes) {
+bool TFMultiShow::ShouldTrim(const ShowMultiNode* node,
+                             const std::vector<string>& regexes) const {
   for (const string& regex : regexes) {
     if (RE2::FullMatch(node->name(), regex)) {
       return true;
@@ -102,7 +104,7 @@ bool TFMultiShow::ReAccount(ShowMultiNode* node, const Options& opts) {
   return node->ReInit(opts.step, opts.account_type_regexes);
 }
 
-string TFMultiShow::FormatLegend(const Options& opts) {
+string TFMultiShow::FormatLegend(const Options& opts) const {
   std::vector<string> legends;
   if (opts.select.find(kShown[0]) != opts.select.end()) {
     legends.push_back("output bytes");
@@ -133,7 +135,7 @@ string TFMultiShow::FormatLegend(const Options& opts) {
     legends.push_back("op types");
   }
   if (opts.select.find(kShown[7]) != opts.select.end()) {
-    legends.push_back("op occurrence");
+    legends.push_back("op occurrence (run|defined)");
   }
   if (opts.select.find(kShown[8]) != opts.select.end()) {
     legends.push_back("input shapes");
@@ -142,9 +144,10 @@ string TFMultiShow::FormatLegend(const Options& opts) {
                          str_util::Join(legends, " | ").c_str());
 }
 
-string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
-  std::map<string, int> input_shapes_str;
-  std::map<string, int> input_time_str;
+string TFMultiShow::FormatInputShapes(
+    const TFMultiGraphNodeProto& proto) const {
+  // input_shape string -> (static defined count, run count, run_micros)
+  std::map<string, std::tuple<int64, int64, int64>> input_shapes_attr;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
     const TFGraphNodeProto& gnode = proto.graph_nodes(i);
     // Convert and sort by input_idx.
@@ -164,33 +167,42 @@ string TFMultiShow::FormatInputShapes(const TFMultiGraphNodeProto& proto) {
     }
     string shape_type_str = strings::Printf(
         "input_type: %s", str_util::Join(input_vec, ",\t").c_str());
-    input_shapes_str[shape_type_str] += 1;
-    input_time_str[shape_type_str] += gnode.exec_micros();
+    auto t = input_shapes_attr.find(shape_type_str);
+    if (t == input_shapes_attr.end()) {
+      input_shapes_attr.insert(
+          std::make_pair(shape_type_str, std::make_tuple(0, 0, 0)));
+      t = input_shapes_attr.find(shape_type_str);
+    }
+    input_shapes_attr[shape_type_str] = std::make_tuple(
+        std::get<0>(t->second) + 1, std::get<1>(t->second) + gnode.run_count(),
+        std::get<2>(t->second) + gnode.exec_micros());
   }
-  if (input_shapes_str.empty()) {
+  if (input_shapes_attr.empty()) {
     return "";
   }
 
-  std::vector<std::pair<string, int>> shape_count_vec(input_shapes_str.begin(),
-                                                      input_shapes_str.end());
-  std::sort(shape_count_vec.begin(), shape_count_vec.end(),
-            [](const std::pair<const string, int>& a,
-               const std::pair<const string, int>& b) {
-              return a.second > b.second;
-            });
+  std::vector<std::pair<string, std::tuple<int64, int64, int64>>>
+      shape_count_vec(input_shapes_attr.begin(), input_shapes_attr.end());
+  std::sort(
+      shape_count_vec.begin(), shape_count_vec.end(),
+      [](const std::pair<const string, std::tuple<int64, int64, int64>>& a,
+         const std::pair<const string, std::tuple<int64, int64, int64>>& b) {
+        return std::get<1>(a.second) > std::get<1>(b.second);
+      });
 
   std::vector<string> input_types;
   input_types.reserve(shape_count_vec.size());
   for (const auto& s : shape_count_vec) {
-    input_types.push_back(
-        strings::Printf("%s\t(*%d)\texec_time: %s", s.first.c_str(), s.second,
-                        FormatTime(input_time_str[s.first]).c_str()));
+    std::tuple<int64, int64, int64> t = s.second;
+    input_types.push_back(strings::Printf(
+        "%s\t(run*%lld|defined*%lld)\texec_time: %s", s.first.c_str(),
+        std::get<1>(t), std::get<0>(t), FormatTime(std::get<2>(t)).c_str()));
   }
   return str_util::Join(input_types, "\n");
 }
 
 std::vector<string> TFMultiShow::FormatTimes(const ShowMultiNode* node,
-                                             const Options& opts) {
+                                             const Options& opts) const {
   std::vector<string> attrs;
   if (opts.select.find(kShown[1]) != opts.select.end()) {
     attrs.push_back(FormatTotalExecTime(node, opts));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
index e6faf1231d..ce309816a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
@@ -55,21 +55,23 @@ class TFMultiShow {
                         std::unique_ptr<TFProfTensor>* tensor);
 
   // Overridden by subclass if extra requirements need to be met.
-  virtual bool ShouldShowIfExtra(ShowMultiNode* node, const Options& opts,
-                                 int depth) {
+  virtual bool ShouldShowIfExtra(const ShowMultiNode* node, const Options& opts,
+                                 int depth) const {
     return true;
   }
 
-  bool ShouldShow(ShowMultiNode* node, const Options& opts, int depth);
+  bool ShouldShow(const ShowMultiNode* node, const Options& opts,
+                  int depth) const;
 
-  bool ShouldTrim(ShowMultiNode* node, const std::vector<string>& regexes);
+  bool ShouldTrim(const ShowMultiNode* node,
+                  const std::vector<string>& regexes) const;
 
   bool ReAccount(ShowMultiNode* node, const Options& opts);
 
-  string FormatLegend(const Options& opts);
-  string FormatInputShapes(const TFMultiGraphNodeProto& proto);
+  string FormatLegend(const Options& opts) const;
+  string FormatInputShapes(const TFMultiGraphNodeProto& proto) const;
   std::vector<string> FormatTimes(const ShowMultiNode* node,
-                                  const Options& opts);
+                                  const Options& opts) const;
 
   template <typename T>
   std::vector<T*> SortNodes(const std::vector<T*>& nodes, const Options& opts) {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
index 478e269f87..46b6ebc09a 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
@@ -65,6 +65,7 @@ class TFProfShowTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
@@ -165,15 +166,16 @@ TEST_F(TFProfShowTest, DumpOpMode) {
   TF_CHECK_OK(ReadFileToString(Env::Default(), dump_file, &dump_str));
   EXPECT_EQ(
       "nodename|outputbytes|totalexecutiontime|acceleratorexecutiontime|"
-      "cpuexecutiontime|#parameters|#float_ops|opoccurrence|"
+      "cpuexecutiontime|#parameters|#float_ops|opoccurrence(run|defined)|"
       "inputshapes\nVariableV21.48KB(100.00%,17.10%),5us(100.00%,5.15%),0us(0."
       "00%,0.00%),5us(100.00%,5.15%),370params(100.00%,100.00%),0float_ops(100."
-      "00%,0.00%),4\n\ninput_type:\t(*4)\texec_time:5us\n\nAssign0B(0.00%,0.00%"
-      "),0us(94.85%,0.00%),0us(0.00%,0.00%),0us(94.85%,0.00%),0params(0.00%,0."
-      "00%),0float_ops(100.00%,0.00%),8\n\ninput_type:0:unknown,\t1:unknown\t(*"
-      "8)\texec_time:0us\n\nConst1.54KB(58.87%,17.74%),1us(80.41%,1.03%),0us(0."
-      "00%,0.00%),1us(80.41%,1.03%),0params(0.00%,0.00%),0float_ops(98.49%,0."
-      "00%),24\n\ninput_type:\t(*24)\texec_time:1us\n\n",
+      "00%,0.00%),4|4\n\ninput_type:\t(run*4|defined*4)\texec_time:"
+      "5us\n\nAssign0B(0.00%,0.00%),0us(94.85%,0.00%),0us(0.00%,0.00%),0us(94."
+      "85%,0.00%),0params(0.00%,0.00%),0float_ops(100.00%,0.00%),0|8\n\ninput_"
+      "type:0:unknown,\t1:unknown\t(run*0|defined*8)\texec_time:0us\n\nConst1."
+      "54KB(58.87%,17.74%),1us(80.41%,1.03%),0us(0.00%,0.00%),1us(80.41%,1.03%)"
+      ",0params(0.00%,0.00%),0float_ops(98.49%,0.00%),1|24\n\ninput_type:\t("
+      "run*1|defined*24)\texec_time:1us\n\n",
       StringReplace(dump_str, " ", ""));
 }
 }  // namespace tfprof
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.cc b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
index f5b8dad4e2..64da7ae7cf 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.cc
@@ -29,16 +29,17 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
                  std::unique_ptr<RunMetadata> run_meta,
                  std::unique_ptr<OpLog> op_log,
                  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader)
-    : graph_(std::move(graph)),
+    : has_code_traces_(false),
+      graph_(std::move(graph)),
       ckpt_reader_(std::move(ckpt_reader)) {
   CHECK(graph_) << "Must at least have GraphDef";
 
   printf("Parsing Inputs...\n");
   ParseGraph();
   if (run_meta && run_meta->has_step_stats()) {
-    ParseRunMeta(0, std::move(run_meta));
+    AddRunMeta(0, std::move(run_meta));
   }
-  ParseOpLog(std::move(op_log));
+  AddOpLog(std::move(op_log));
 
   if (ckpt_reader_) {
     for (const auto& v : ckpt_reader_->GetVariableToShapeMap()) {
@@ -48,27 +49,48 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
       }
     }
   }
+}
 
-  printf("Preparing Views...\n");
-  scope_view_ = std::unique_ptr<TFScope>(new TFScope(ckpt_reader_.get()));
-  graph_view_ = std::unique_ptr<TFGraph>(new TFGraph(ckpt_reader_.get()));
-  code_view_ = std::unique_ptr<TFCode>(new TFCode());
-  op_view_ = std::unique_ptr<TFOp>(new TFOp());
+void TFStats::BuildView(const string& cmd) {
+  if (cmd == kCmds[0] && !scope_view_) {
+    scope_view_.reset(new TFScope(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      scope_view_->AddNode(it->second.get());
+    }
+    scope_view_->Build();
+  }
+  if (cmd == kCmds[1] && !graph_view_) {
+    graph_view_.reset(new TFGraph(ckpt_reader_.get()));
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      graph_view_->AddNode(it->second.get());
+    }
+    graph_view_->Build();
+  }
+  if (cmd == kCmds[2] && !code_view_) {
+    code_view_.reset(new TFCode());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      code_view_->AddNode(it->second.get());
+    }
+    code_view_->Build();
+  }
+  if (cmd == kCmds[3] && !op_view_) {
+    op_view_.reset(new TFOp());
+    for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
+      op_view_->AddNode(it->second.get());
+    }
+    op_view_->Build();
+  }
+}
 
-  for (auto it = nodes_map_.begin(); it != nodes_map_.end(); it++) {
-    scope_view_->AddNode(it->second.get());
-    graph_view_->AddNode(it->second.get());
-    code_view_->AddNode(it->second.get());
-    op_view_->AddNode(it->second.get());
-  }
-  scope_view_->Build();
-  graph_view_->Build();
-  code_view_->Build();
-  op_view_->Build();
+void TFStats::BuildAllViews() {
+  std::vector<string> cmds_str(kCmds, kCmds + sizeof(kCmds) / sizeof(*kCmds));
+  for (const string& cmd : cmds_str) {
+    BuildView(cmd);
+  }
 }
 
 const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
-                                               const Options& opts) {
+                                               const Options& opts) const {
   if (!Validate(opts)) {
     return empty_graph_node_;
   }
@@ -82,8 +104,8 @@ const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
   }
 }
 
-const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(const string& cmd,
-                                                         const Options& opts) {
+const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(
+    const string& cmd, const Options& opts) const {
   if (!Validate(opts)) {
     return empty_multi_graph_node_;
   }
@@ -130,7 +152,7 @@ void TFStats::ParseGraph() {
   }
 }
 
-void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
+void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
   if (!op_log) {
     return;
   }
@@ -144,12 +166,13 @@ void TFStats::ParseOpLog(std::unique_ptr<OpLog> op_log) {
       node->second->AddFloatOps(entry.float_ops());
     }
     if (entry.has_code_def()) {
+      has_code_traces_ = true;
       node->second->AddCode(entry.code_def());
     }
   }
 }
 
-void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
+void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   if (!run_meta || !run_meta->has_step_stats()) {
     fprintf(stderr, "Invalid RunMetadata for step %lld\n", step);
     return;
@@ -176,7 +199,7 @@ void TFStats::ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
   }
 }
 
-bool TFStats::Validate(const Options& opts) {
+bool TFStats::Validate(const Options& opts) const {
   if (opts.step >= 0 && steps_.find(opts.step) == steps_.end()) {
     fprintf(stderr, "Options -step=%lld not found\n", opts.step);
     return false;
@@ -184,9 +207,9 @@ bool TFStats::Validate(const Options& opts) {
   return true;
 }
 
-void TFStats::AddNodeForTest(const string& name,
-                             std::unique_ptr<TFGraphNode> node) {
-  nodes_map_[name] = std::move(node);
+void TFStats::AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node) {
+  steps_.insert(step);
+  nodes_map_[node->name()] = std::move(node);
 }
 }  // namespace tfprof
 }  // namespace tensorflow
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.h b/tensorflow/tools/tfprof/internal/tfprof_stats.h
index dfb190e703..b26d274f80 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.h
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats.h
@@ -59,28 +59,38 @@ class TFStats {
   const std::map<string, std::unique_ptr<TFGraphNode>>& nodes() const {
     return nodes_map_;
   }
+  const std::set<int64>& steps() const { return steps_; }
+  bool has_code_traces() const { return has_code_traces_; }
 
+  void BuildView(const string& cmd);
+  void BuildAllViews();
+
+  // Note: Must first BuildView(view_foo) before ShowXXX(view_foo) methods.
+  //
   // Organize the TensorFlow model as different types of views, and generate
   // outputs for profiling.
-  const TFGraphNodeProto& ShowGraphNode(const string& cmd, const Options& opts);
+  // TODO(xpan): Should it return reference here?
+  const TFGraphNodeProto& ShowGraphNode(const string& cmd,
+                                        const Options& opts) const;
   const TFMultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
-                                                  const Options& opts);
+                                                  const Options& opts) const;
 
   // Add a step of run time meta data.
-  void ParseRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
+  void AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
   // Add tfprof operation meta data, such as customized op type, float_ops,
   // and code traces.
-  void ParseOpLog(std::unique_ptr<OpLog> op_log);
+  void AddOpLog(std::unique_ptr<OpLog> op_log);
 
   // For test purpose only.
-  void AddNodeForTest(const string& name, std::unique_ptr<TFGraphNode> node);
+  void AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node);
 
  private:
-  bool Validate(const Options& opts);
+  bool Validate(const Options& opts) const;
 
   void ParseGraph();
 
   std::set<int64> steps_;
+  bool has_code_traces_;
   std::unique_ptr<GraphDef> graph_;
   std::unique_ptr<TFScope> scope_view_;
   std::unique_ptr<TFGraph> graph_view_;
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
index 948ce49df4..00d5d8cdb3 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
@@ -66,6 +66,7 @@ class TFProfStatsTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
@@ -91,17 +92,20 @@ TEST_F(TFProfStatsTest, CustomOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d/kernel\"\n    exec_micros: 1\n    requested_bytes: 540\n    "
       "parameters: 135\n    total_exec_micros: 1\n    total_requested_bytes: "
       "540\n    total_parameters: 135\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "2\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 2\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 3\n  total_requested_bytes: "
       "920\n  total_parameters: 230\n  children {\n    name: "
       "\"conv2d_1/bias\"\n    exec_micros: 1\n    requested_bytes: 20\n    "
@@ -110,19 +114,23 @@ TEST_F(TFProfStatsTest, CustomOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d_1/kernel\"\n    exec_micros: 2\n    requested_bytes: 900\n    "
       "parameters: 225\n    total_exec_micros: 2\n    total_requested_bytes: "
       "900\n    total_parameters: 225\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 2\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 2\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 2\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 3\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
       "3\n}\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 5\n",
+      "0\ntotal_cpu_exec_micros: 5\nrun_count: 0\ntotal_run_count: "
+      "4\ntotal_definition_count: 6\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -147,17 +155,20 @@ TEST_F(TFProfStatsTest, CheckPointOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d/kernel\"\n    exec_micros: 1\n    requested_bytes: 540\n    "
       "parameters: 135\n    total_exec_micros: 1\n    total_requested_bytes: "
       "540\n    total_parameters: 135\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "2\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 2\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 3\n  total_requested_bytes: "
       "920\n  total_parameters: 230\n  children {\n    name: "
       "\"conv2d_1/bias\"\n    exec_micros: 1\n    requested_bytes: 20\n    "
@@ -166,19 +177,23 @@ TEST_F(TFProfStatsTest, CheckPointOpType) {
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 1\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 1\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 1\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d_1/kernel\"\n    exec_micros: 2\n    requested_bytes: 900\n    "
       "parameters: 225\n    total_exec_micros: 2\n    total_requested_bytes: "
       "900\n    total_parameters: 225\n    devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n    float_ops: 0\n    "
       "total_float_ops: 0\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 2\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 2\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 2\n    run_count: 1\n    total_run_count: 1\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 3\n  "
+      "run_count: 0\n  total_run_count: 2\n  total_definition_count: "
       "3\n}\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 5\n",
+      "0\ntotal_cpu_exec_micros: 5\nrun_count: 0\ntotal_run_count: "
+      "4\ntotal_definition_count: 6\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -196,7 +211,8 @@ TEST_F(TFProfStatsTest, TestGraph) {
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
       "8656\ntotal_parameters: 370\nfloat_ops: 0\ntotal_float_ops: "
       "34360\naccelerator_exec_micros: 0\ncpu_exec_micros: "
-      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 97\n",
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: "
+      "97\nrun_count: 0\ntotal_run_count: 13\ntotal_definition_count: 60\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -218,7 +234,8 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       "unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    value "
       "{\n      unknown_rank: true\n    }\n  }\n  accelerator_exec_micros: 0\n "
       " cpu_exec_micros: 12\n  total_accelerator_exec_micros: 0\n  "
-      "total_cpu_exec_micros: 12\n}\nchildren {\n  name: "
+      "total_cpu_exec_micros: 12\n  run_count: 1\n  total_run_count: 1\n  "
+      "total_definition_count: 1\n}\nchildren {\n  name: "
       "\"conv2d/convolution\"\n  exec_micros: 60\n  requested_bytes: 1440\n  "
       "total_exec_micros: 60\n  total_requested_bytes: 1440\n  "
       "total_parameters: 0\n  devices: "
@@ -227,8 +244,9 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       " unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    "
       "value {\n      unknown_rank: true\n    }\n  }\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 60\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "60\n}\nchildren {\n  name: \"conv2d_2/BiasAdd\"\n  exec_micros: 2\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 60\n  "
+      "run_count: 1\n  total_run_count: 1\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_2/BiasAdd\"\n  exec_micros: 2\n  "
       "requested_bytes: 640\n  total_exec_micros: 2\n  total_requested_bytes: "
       "640\n  total_parameters: 0\n  devices: "
       "\"/job:localhost/replica:0/task:0/cpu:0\"\n  float_ops: 160\n  "
@@ -236,7 +254,8 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       "unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    value "
       "{\n      unknown_rank: true\n    }\n  }\n  accelerator_exec_micros: 0\n "
       " cpu_exec_micros: 2\n  total_accelerator_exec_micros: 0\n  "
-      "total_cpu_exec_micros: 2\n}\nchildren {\n  name: "
+      "total_cpu_exec_micros: 2\n  run_count: 1\n  total_run_count: 1\n  "
+      "total_definition_count: 1\n}\nchildren {\n  name: "
       "\"conv2d_2/convolution\"\n  exec_micros: 13\n  requested_bytes: 640\n  "
       "total_exec_micros: 13\n  total_requested_bytes: 640\n  "
       "total_parameters: 0\n  devices: "
@@ -245,10 +264,12 @@ TEST_F(TFProfStatsTest, TestFloatOps) {
       " unknown_rank: true\n    }\n  }\n  input_shapes {\n    key: 1\n    "
       "value {\n      unknown_rank: true\n    }\n  }\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 13\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "13\n}\nfloat_ops: 0\ntotal_float_ops: 34360\naccelerator_exec_micros: "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 13\n  "
+      "run_count: 1\n  total_run_count: 1\n  total_definition_count: "
+      "3\n}\nfloat_ops: 0\ntotal_float_ops: 34360\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 97\n",
+      "0\ntotal_cpu_exec_micros: 97\nrun_count: 0\ntotal_run_count: "
+      "13\ntotal_definition_count: 68\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -266,7 +287,8 @@ TEST_F(TFProfStatsTest, TestAccountShownNameOnly) {
       "0\ntotal_exec_micros: 0\ntotal_requested_bytes: 0\ntotal_parameters: "
       "0\nfloat_ops: 0\ntotal_float_ops: 0\naccelerator_exec_micros: "
       "0\ncpu_exec_micros: 0\ntotal_accelerator_exec_micros: "
-      "0\ntotal_cpu_exec_micros: 0\n",
+      "0\ntotal_cpu_exec_micros: 0\nrun_count: 0\ntotal_run_count: "
+      "0\ntotal_definition_count: 1\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
@@ -283,7 +305,8 @@ TEST_F(TFProfStatsTest, TestShowTensorValue) {
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
       "8656\ntotal_parameters: 370\nfloat_ops: 0\ntotal_float_ops: "
       "34360\naccelerator_exec_micros: 0\ncpu_exec_micros: "
-      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 97\n",
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: "
+      "97\nrun_count: 0\ntotal_run_count: 13\ntotal_definition_count: 68\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
index 698738c23c..0482657ca5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
@@ -50,6 +50,7 @@ class TFProfTensorTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 std::move(op_log_pb), std::move(ckpt_reader)));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
@@ -75,7 +76,8 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0\n      value_double: 0\n      value_double: 0\n      "
       "value_double: 0\n    }\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 0\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 0\n    run_count: 0\n    total_run_count: 0\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d/kernel\"\n    exec_micros: 0\n    requested_bytes: 0\n    "
       "parameters: 135\n    total_exec_micros: 0\n    total_requested_bytes: "
       "0\n    total_parameters: 135\n    float_ops: 0\n    total_float_ops: "
@@ -148,10 +150,12 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0.146625\n      value_double: 0.167754\n      "
       "value_double: 0.249554\n    }\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 0\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
+      "total_cpu_exec_micros: 0\n    run_count: 0\n    total_run_count: 0\n    "
+      "total_definition_count: 1\n  }\n  float_ops: 0\n  total_float_ops: 0\n  "
       "accelerator_exec_micros: 0\n  cpu_exec_micros: 0\n  "
-      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: "
-      "0\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
+      "total_accelerator_exec_micros: 0\n  total_cpu_exec_micros: 0\n  "
+      "run_count: 0\n  total_run_count: 0\n  total_definition_count: "
+      "3\n}\nchildren {\n  name: \"conv2d_1\"\n  exec_micros: 0\n  "
       "requested_bytes: 0\n  total_exec_micros: 0\n  total_requested_bytes: "
       "0\n  total_parameters: 230\n  children {\n    name: \"conv2d_1/bias\"\n "
       "   exec_micros: 0\n    requested_bytes: 0\n    parameters: 5\n    "
@@ -161,7 +165,8 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: 0\n      value_double: 0\n      value_double: 0\n      "
       "value_double: 0\n    }\n    accelerator_exec_micros: 0\n    "
       "cpu_exec_micros: 0\n    total_accelerator_exec_micros: 0\n    "
-      "total_cpu_exec_micros: 0\n  }\n  children {\n    name: "
+      "total_cpu_exec_micros: 0\n    run_count: 0\n    total_run_count: 0\n    "
+      "total_definition_count: 1\n  }\n  children {\n    name: "
       "\"conv2d_1/kernel\"\n    exec_micros: 0\n    requested_bytes: 0\n    "
       "parameters: 225\n    total_exec_micros: 0\n    total_requested_bytes: "
       "0\n    total_parameters: 225\n    float_ops: 0\n    total_float_ops: "
@@ -279,12 +284,15 @@ TEST_F(TFProfTensorTest, Basics) {
       "value_double: -0.123842\n      value_double: 0.0858642\n      "
       "value_double: 0.23111\n      value_double: 0.0491742\n    }\n    "
       "accelerator_exec_micros: 0\n    cpu_exec_micros: 0\n    "
-      "total_accelerator_exec_micros: 0\n    total_cpu_exec_micros: 0\n  }\n  "
-      "float_ops: 0\n  total_float_ops: 0\n  accelerator_exec_micros: 0\n  "
-      "cpu_exec_micros: 0\n  total_accelerator_exec_micros: 0\n  "
-      "total_cpu_exec_micros: 0\n}\nfloat_ops: 0\ntotal_float_ops: "
+      "total_accelerator_exec_micros: 0\n    total_cpu_exec_micros: 0\n    "
+      "run_count: 0\n    total_run_count: 0\n    total_definition_count: 1\n  "
+      "}\n  float_ops: 0\n  total_float_ops: 0\n  accelerator_exec_micros: 0\n "
+      " cpu_exec_micros: 0\n  total_accelerator_exec_micros: 0\n  "
+      "total_cpu_exec_micros: 0\n  run_count: 0\n  total_run_count: 0\n  "
+      "total_definition_count: 3\n}\nfloat_ops: 0\ntotal_float_ops: "
       "0\naccelerator_exec_micros: 0\ncpu_exec_micros: "
-      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: 0\n",
+      "0\ntotal_accelerator_exec_micros: 0\ntotal_cpu_exec_micros: "
+      "0\nrun_count: 0\ntotal_run_count: 0\ntotal_definition_count: 6\n",
       &expected));
   EXPECT_EQ(expected.DebugString(), root.DebugString());
 }
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
index 0e9bb9658c..cad31050a9 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
@@ -52,6 +52,7 @@ class TFProfTimelineTest : public ::testing::Test {
 
     tf_stats_.reset(new TFStats(std::move(graph_pb), std::move(run_meta_pb),
                                 nullptr, nullptr));
+    tf_stats_->BuildAllViews();
   }
 
   std::unique_ptr<TFStats> tf_stats_;
diff --git a/tensorflow/tools/tfprof/tfprof_log.proto b/tensorflow/tools/tfprof/tfprof_log.proto
index 5c47142e0a..0e0fb74aae 100644
--- a/tensorflow/tools/tfprof/tfprof_log.proto
+++ b/tensorflow/tools/tfprof/tfprof_log.proto
@@ -1,4 +1,4 @@
-syntax = "proto2";
+syntax = "proto3";
 
 package tensorflow.tfprof;
 
@@ -6,27 +6,27 @@ package tensorflow.tfprof;
 message CodeDef {
   repeated Trace traces = 1;
   message Trace {
-    optional string file = 1;
-    optional int32 lineno = 2;
-    optional string function = 3;
-    optional string line = 4;
+    string file = 1;
+    int32 lineno = 2;
+    string function = 3;
+    string line = 4;
   }
 }
 
 message OpLogEntry {
   // op name.
-  optional string name = 1;
+  string name = 1;
   // float_ops is filled by tfprof Python API when called. It requires the
   // op has RegisterStatistics defined. Currently, Conv2D, MatMul, etc, are
   // implemented.
-  optional int64 float_ops = 2;
+  int64 float_ops = 2;
   // User can define extra op type information for an op. This allows the user
   // to select a group of ops precisely using op_type as a key.
   repeated string types = 3;
   // Used to support tfprof "code" view.
-  optional CodeDef code_def = 4;
+  CodeDef code_def = 4;
 }
 
 message OpLog {
   repeated OpLogEntry log_entries = 1;
-}
\ No newline at end of file
+}
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/tools/tfprof/tfprof_main.cc
index 7a4e7e85ff..5e70c093cc 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/tools/tfprof/tfprof_main.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/util/command_line_flags.h"
+#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_options.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
 #include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
@@ -161,12 +162,13 @@ int Run(int argc, char** argv) {
         "Profiling everything!\n");
     return 0;
   } else if (argc > 1) {
-    if (string(argv[1]) == kCmds[5]) {
+    if (string(argv[1]) == kCmds[6]) {
       PrintHelp();
       return 0;
     }
     if (string(argv[1]) == kCmds[0] || string(argv[1]) == kCmds[1] ||
-        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3]) {
+        string(argv[1]) == kCmds[2] || string(argv[1]) == kCmds[3] ||
+        string(argv[1]) == kCmds[4]) {
       cmd = argv[1];
     }
   }
@@ -216,7 +218,13 @@ int Run(int argc, char** argv) {
               run_meta_files[i].c_str(), s.ToString().c_str());
       return 1;
     }
-    tf_stat.ParseRunMeta(i, std::move(run_meta));
+    tf_stat.AddRunMeta(i, std::move(run_meta));
+  }
+
+  if (cmd == kCmds[4]) {
+    tf_stat.BuildAllViews();
+    Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
+    return 0;
   }
 
   Options opts(FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros,
@@ -227,9 +235,11 @@ int Run(int argc, char** argv) {
                output_type, output_options);
 
   if (cmd == kCmds[2] || cmd == kCmds[3]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowMultiGraphNode(cmd, opts);
     return 0;
   } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+    tf_stat.BuildView(cmd);
     tf_stat.ShowGraphNode(cmd, opts);
     return 0;
   }
@@ -254,14 +264,19 @@ int Run(int argc, char** argv) {
       fprintf(stderr, "E: %s\n", s.ToString().c_str());
       continue;
     }
-    if (cmd == kCmds[4]) {
+    if (cmd == kCmds[5]) {
       opts = new_opts;
-    } else if (cmd == kCmds[5]) {
+    } else if (cmd == kCmds[6]) {
       PrintHelp();
     } else if (cmd == kCmds[2] || cmd == kCmds[3]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowMultiGraphNode(cmd, new_opts);
     } else if (cmd == kCmds[0] || cmd == kCmds[1]) {
+      tf_stat.BuildView(cmd);
       tf_stat.ShowGraphNode(cmd, new_opts);
+    } else if (cmd == kCmds[4]) {
+      tf_stat.BuildAllViews();
+      Advisor(&tf_stat).Advise(Advisor::DefaultOptions());
     }
   }
   return 0;
diff --git a/tensorflow/tools/tfprof/tfprof_options.proto b/tensorflow/tools/tfprof/tfprof_options.proto
index 27eafb1ca9..5882833039 100644
--- a/tensorflow/tools/tfprof/tfprof_options.proto
+++ b/tensorflow/tools/tfprof/tfprof_options.proto
@@ -1,26 +1,34 @@
-syntax = "proto2";
+syntax = "proto3";
 
 package tensorflow.tfprof;
 
 // Refers to tfprof_options.h/cc for documentation.
 // Only used to pass tfprof options from Python to C++.
 message OptionsProto {
-  optional int64 max_depth = 1;
-  optional int64 min_bytes = 2;
-  optional int64 min_micros = 3;
-  optional int64 min_params = 4;
-  optional int64 min_float_ops = 5;
-  optional int64 min_occurrence = 17;
-  optional int64 step = 18 [default = -1];
+  int64 max_depth = 1;
+  int64 min_bytes = 2;
+  int64 min_micros = 3;
+  int64 min_params = 4;
+  int64 min_float_ops = 5;
+  int64 min_occurrence = 17;
+  int64 step = 18;
 
-  optional string order_by = 7;
+  string order_by = 7;
   repeated string account_type_regexes = 8;
   repeated string start_name_regexes = 9;
   repeated string trim_name_regexes = 10;
   repeated string show_name_regexes = 11;
   repeated string hide_name_regexes = 12;
-  optional bool account_displayed_op_only = 13;
+  bool account_displayed_op_only = 13;
   repeated string select = 14;
-  optional string output = 15;
-  optional string dump_to_file = 16;
+  string output = 15;
+  string dump_to_file = 16;
+}
+
+message AdvisorOptionsProto {
+  // checker name -> a dict of key-value options.
+  map<string, CheckerOption> checkers = 1;
+  message CheckerOption {
+    map<string, string> options = 1;
+  }
 }
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/tools/tfprof/tfprof_output.proto
index 1ea956152c..abf141f1e7 100644
--- a/tensorflow/tools/tfprof/tfprof_output.proto
+++ b/tensorflow/tools/tfprof/tfprof_output.proto
@@ -1,4 +1,4 @@
-syntax = "proto2";
+syntax = "proto3";
 
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/types.proto";
@@ -6,7 +6,7 @@ import "tensorflow/core/framework/types.proto";
 package tensorflow.tfprof;
 
 message TFProfTensorProto {
-  optional DataType dtype = 1;
+  DataType dtype = 1;
   // Flatten tensor in row-major.
   // Only one of the following array is set.
   repeated double value_double = 2;
@@ -17,34 +17,40 @@ message TFProfTensorProto {
 // A node in TensorFlow graph. Used by scope/graph view.
 message TFGraphNodeProto {
   // op name.
-  optional string name = 1;
+  string name = 1;
   // tensor value restored from checkpoint.
-  optional TFProfTensorProto tensor_value = 15;
+  TFProfTensorProto tensor_value = 15;
   // op execution time.
-  optional int64 exec_micros = 2;
-  optional int64 accelerator_exec_micros = 17;
-  optional int64 cpu_exec_micros = 18;
+  // A node can be defined once but run multiple times in tf.while_loop.
+  // the times sum up all different runs.
+  int64 run_count = 21;
+  int64 exec_micros = 2;
+  int64 accelerator_exec_micros = 17;
+  int64 cpu_exec_micros = 18;
 
   // Total requested bytes by the op.
-  optional int64 requested_bytes = 3;
+  int64 requested_bytes = 3;
   // Number of parameters if available.
-  optional int64 parameters = 4;
+  int64 parameters = 4;
   // Number of float operations.
-  optional int64 float_ops = 13;
+  int64 float_ops = 13;
   // Device the op is assigned to.
   // Since an op can fire multiple kernel calls, there can be multiple devices.
   repeated string devices = 10;
 
-  // The following are the aggregated stats from all accounted children and
-  // the node itself. The actual children depend on the data structure used
-  // (scope, graph).
-  optional int64 total_exec_micros = 6;
-  optional int64 total_accelerator_exec_micros = 19;
-  optional int64 total_cpu_exec_micros = 20;
+  // The following are the aggregated stats from all *accounted* children and
+  // the node itself. The actual children depend on the data structure used.
+  // In graph view, children are inputs recursively.
+  // In scope view, children are nodes under the name scope.
+  int64 total_definition_count = 23;
+  int64 total_run_count = 22;
+  int64 total_exec_micros = 6;
+  int64 total_accelerator_exec_micros = 19;
+  int64 total_cpu_exec_micros = 20;
 
-  optional int64 total_requested_bytes = 7;
-  optional int64 total_parameters = 8;
-  optional int64 total_float_ops = 14;
+  int64 total_requested_bytes = 7;
+  int64 total_parameters = 8;
+  int64 total_float_ops = 14;
 
   // shape information, if available.
   // TODO(xpan): Why is this repeated?
@@ -66,33 +72,41 @@ message TFGraphNodeProto {
 //            of the op (e.g. MatMul, Conv2D).
 message TFMultiGraphNodeProto {
   // Name of the node.
-  optional string name = 1;
+  string name = 1;
 
   // code execution time.
-  optional int64 exec_micros = 2;
-  optional int64 accelerator_exec_micros = 12;
-  optional int64 cpu_exec_micros = 13;
+  int64 exec_micros = 2;
+  int64 accelerator_exec_micros = 12;
+  int64 cpu_exec_micros = 13;
 
   // Total requested bytes by the code.
-  optional int64 requested_bytes = 3;
+  int64 requested_bytes = 3;
   // Number of parameters if available.
-  optional int64 parameters = 4;
+  int64 parameters = 4;
   // Number of float operations.
-  optional int64 float_ops = 5;
+  int64 float_ops = 5;
 
   // The following are the aggregated stats from descendants.
   // The actual descendants depend on the data structure used.
-  optional int64 total_exec_micros = 6;
-  optional int64 total_accelerator_exec_micros = 14;
-  optional int64 total_cpu_exec_micros = 15;
+  int64 total_exec_micros = 6;
+  int64 total_accelerator_exec_micros = 14;
+  int64 total_cpu_exec_micros = 15;
 
-  optional int64 total_requested_bytes = 7;
-  optional int64 total_parameters = 8;
-  optional int64 total_float_ops = 9;
+  int64 total_requested_bytes = 7;
+  int64 total_parameters = 8;
+  int64 total_float_ops = 9;
 
   // TensorFlow graph nodes contained by the TFMultiGraphNodeProto.
   repeated TFGraphNodeProto graph_nodes = 10;
   // Descendants of the node. The actual descendants depend on the data
   // structure used.
   repeated TFMultiGraphNodeProto children = 11;
-}
\ No newline at end of file
+}
+
+message AdviceProto {
+  // checker name -> a list of reports from the checker.
+  map<string, Checker> checkers = 1;
+  message Checker {
+    repeated string reports = 2;
+  }
+}
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 68bfefcf3c..a5172187f7 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -484,11 +484,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   temp_workaround_http_archive(
       name = "llvm",
       urls = [
-          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/e156d99231a7735d06a97b5b83de70bf4ce4f034.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/e156d99231a7735d06a97b5b83de70bf4ce4f034.tar.gz",
+          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/5d353d4750143a13e53abf721d521684ba6a5165.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/5d353d4750143a13e53abf721d521684ba6a5165.tar.gz",
       ],
-      sha256 = "72e34e2411a06d4200a2688ee83832805fbef23a12ea481f31c2b8866fde007a",
-      strip_prefix = "llvm-e156d99231a7735d06a97b5b83de70bf4ce4f034",
+      sha256 = "148e96f1cfb0b880e0831a287f44d48251eabc20e24589e6fa4db5916c0c8153",
+      strip_prefix = "llvm-5d353d4750143a13e53abf721d521684ba6a5165",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
       repository = tf_repo_name,
   )
diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD
index 32266997a7..678626b374 100644
--- a/third_party/llvm/llvm.BUILD
+++ b/third_party/llvm/llvm.BUILD
@@ -453,6 +453,7 @@ llvm_target_list = [
             "include/llvm/IR/Intrinsics*.td",
             "include/llvm/TableGen/*.td",
             "include/llvm/Target/*.td",
+            "include/llvm/Target/GlobalISel/*.td",
         ]),
     )
     for target in llvm_target_list
@@ -1067,6 +1068,8 @@ cc_library(
         "include/llvm/BinaryFormat/*.h",
         "include/llvm/BinaryFormat/*.def",
         "include/llvm/BinaryFormat/*.inc",
+        "include/llvm/BinaryFormat/ELFRelocs/*.def",
+        "include/llvm/BinaryFormat/WasmRelocs/*.def",
     ]),
     deps = [
         ":config",
@@ -1194,6 +1197,7 @@ cc_library(
         "include/llvm/DebugInfo/CodeView/*.inc",
     ]),
     deps = [
+        ":binary_format",
         ":config",
         ":debug_info_msf",
         ":support",
@@ -1426,6 +1430,7 @@ cc_library(
         "include/llvm/MC/*.inc",
     ]),
     deps = [
+        ":binary_format",
         ":config",
         ":debug_info_code_view",
         ":support",
@@ -1921,6 +1926,8 @@ cc_library(
         "lib/Support/Unix/*.h",
         "include/llvm-c/*.h",
         "include/llvm/CodeGen/MachineValueType.h",
+        "include/llvm/BinaryFormat/COFF.h",
+        "include/llvm/BinaryFormat/MachO.h",
         "lib/Support/*.h",
     ]),
     hdrs = glob([
@@ -1931,6 +1938,7 @@ cc_library(
         "include/llvm/Support/ELFRelocs/*.def",
         "include/llvm/Support/WasmRelocs/*.def",
     ]) + [
+        "include/llvm/BinaryFormat/MachO.def",
         "include/llvm/Support/DataTypes.h",
         "include/llvm/ExecutionEngine/ObjectMemoryBuffer.h",
     ],
diff --git a/third_party/toolchains/cpus/BUILD b/third_party/toolchains/cpus/BUILD
index c33dcd4e11..45ec9f8c87 100644
--- a/third_party/toolchains/cpus/BUILD
+++ b/third_party/toolchains/cpus/BUILD
@@ -2,6 +2,8 @@
 # execution service
 # DO NOT EDIT: automatically generated BUILD file
 
+licenses(["restricted"])
+
 package(default_visibility = ["//visibility:public"])
 
 cc_library(
diff --git a/third_party/toolchains/gpus/cuda/cuda/cuda_config.h b/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
index dddee32193..f6662274cc 100644
--- a/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
+++ b/third_party/toolchains/gpus/cuda/cuda/cuda_config.h
@@ -14,7 +14,14 @@ limitations under the License.
 ==============================================================================*/
 
 // DO NOT EDIT: automatically generated file
-#ifndef THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
-#define THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
+#ifndef CUDA_CUDA_CONFIG_H_
+#define CUDA_CUDA_CONFIG_H_
 
-#endif  // THIRD_PARTY_TENSORFLOW_OPENSOURCE_ONLY_TOOLCHAINS_GPUS_CUDA_CUDA_CONFIG_H_
+#define TF_CUDA_CAPABILITIES CudaVersion("3.0")
+
+#define TF_CUDA_VERSION "8.0"
+#define TF_CUDNN_VERSION "5"
+
+#define TF_CUDA_TOOLKIT_PATH "/usr/local/cuda-8.0"
+
+#endif  // CUDA_CUDA_CONFIG_H_
-- 
GitLab


From a4f18c32ceb24d243f2c084317cef843b99e7d4f Mon Sep 17 00:00:00 2001
From: Alex Lattas <a-lattas@users.noreply.github.com>
Date: Wed, 28 Jun 2017 21:09:02 +0300
Subject: [PATCH 0555/1375] Add is_closed() method to Queue (#10175)

* add QueueBase.is_closed(). Fixes #7355

* Make closed() const

* Remove unecessary documentation

* Make closed() const pure virtual method

* Rename closed to is_closed

* Change all Queue to queue in documentation

* Update API to include Queue_Base.is_closed

* Recall change on goldens

* Update API goldens for Queuebase is_closed
---
 tensorflow/core/framework/queue_interface.h   |  3 +++
 tensorflow/core/kernels/barrier_ops.cc        |  2 +-
 tensorflow/core/kernels/queue_base.h          |  2 +-
 tensorflow/core/kernels/queue_ops.cc          | 21 +++++++++++++++
 tensorflow/core/ops/data_flow_ops.cc          | 26 +++++++++++++++++++
 tensorflow/python/ops/data_flow_ops.py        | 19 ++++++++++++++
 .../golden/tensorflow.-f-i-f-o-queue.pbtxt    |  4 +++
 .../tensorflow.-padding-f-i-f-o-queue.pbtxt   |  4 +++
 .../golden/tensorflow.-priority-queue.pbtxt   |  4 +++
 .../api/golden/tensorflow.-queue-base.pbtxt   |  4 +++
 .../tensorflow.-random-shuffle-queue.pbtxt    |  4 +++
 11 files changed, 91 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/framework/queue_interface.h b/tensorflow/core/framework/queue_interface.h
index baddf0bbfa..4aeaab3d9b 100644
--- a/tensorflow/core/framework/queue_interface.h
+++ b/tensorflow/core/framework/queue_interface.h
@@ -77,6 +77,9 @@ class QueueInterface : public ResourceBase {
   virtual void Close(OpKernelContext* ctx, bool cancel_pending_enqueues,
                      DoneCallback callback) = 0;
 
+  // Returns true if a given queue is closed and false if it is open.
+  virtual bool is_closed() const = 0;
+
   // Assuming *this represents a shared queue, verify that it matches
   // another instantiation indicated by node_def.
   virtual Status MatchesNodeDef(const NodeDef& node_def) = 0;
diff --git a/tensorflow/core/kernels/barrier_ops.cc b/tensorflow/core/kernels/barrier_ops.cc
index 83633a1dd9..3b880a9635 100644
--- a/tensorflow/core/kernels/barrier_ops.cc
+++ b/tensorflow/core/kernels/barrier_ops.cc
@@ -413,7 +413,7 @@ class Barrier : public ResourceBase {
     }
     queue_closed_ = true;
     if (cancel_pending_enqueues) queue_cancelled_ = true;
-    if (!ready_queue_->closed()) {
+    if (!ready_queue_->is_closed()) {
       ready_queue_->Close(ctx, cancel_pending_enqueues, callback);
     }
   }
diff --git a/tensorflow/core/kernels/queue_base.h b/tensorflow/core/kernels/queue_base.h
index 0a0e51a7c3..216e743457 100644
--- a/tensorflow/core/kernels/queue_base.h
+++ b/tensorflow/core/kernels/queue_base.h
@@ -69,7 +69,7 @@ class QueueBase : public QueueInterface {
 
   int32 capacity() const { return capacity_; }
 
-  bool closed() {
+  bool is_closed() const{
     mutex_lock lock(mu_);
     return closed_;
   }
diff --git a/tensorflow/core/kernels/queue_ops.cc b/tensorflow/core/kernels/queue_ops.cc
index f2ac09c4e6..d51dc4ecb0 100644
--- a/tensorflow/core/kernels/queue_ops.cc
+++ b/tensorflow/core/kernels/queue_ops.cc
@@ -425,6 +425,27 @@ class QueueSizeOp : public QueueOpKernel {
 REGISTER_KERNEL_BUILDER(Name("QueueSize").Device(DEVICE_CPU), QueueSizeOp);
 REGISTER_KERNEL_BUILDER(Name("QueueSizeV2").Device(DEVICE_CPU), QueueSizeOp);
 
+class QueueIsClosedOp : public QueueOpKernel {
+ public:
+  explicit QueueIsClosedOp(OpKernelConstruction* context)
+     : QueueOpKernel(context) {}
+ 
+ protected:
+  void ComputeAsync(OpKernelContext* ctx, QueueInterface* queue,
+                    DoneCallback callback) override {
+    Tensor* Tqueue_is_closed = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &Tqueue_is_closed));
+    Tqueue_is_closed->flat<bool>().setConstant(queue->is_closed());
+    callback();
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(QueueIsClosedOp);
+};
+
+REGISTER_KERNEL_BUILDER(Name("QueueIsClosed").Device(DEVICE_CPU), QueueIsClosedOp);
+REGISTER_KERNEL_BUILDER(Name("QueueIsClosedV2").Device(DEVICE_CPU), QueueIsClosedOp);
+
 class FakeQueueOp : public OpKernel {
  public:
   explicit FakeQueueOp(OpKernelConstruction* context) : OpKernel(context) {
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index 4bf3d24324..24577393cb 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -881,6 +881,32 @@ cancel_pending_enqueues: If true, all pending enqueue requests that are
   blocked on the given queue will be canceled.
 )doc");
 
+REGISTER_OP("QueueIsClosed")
+    .Input("handle: Ref(string)")
+    .Output("is_closed: bool")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Returns true if queue is closed.
+
+This operation returns true if the queue is closed and false if the queue
+is open.
+
+handle: The handle to a queue.
+)doc");
+
+REGISTER_OP("QueueIsClosedV2")
+    .Input("handle: resource")
+    .Output("is_closed: bool")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Returns true if queue is closed.
+
+This operation returns true if the queue is closed and false if the queue
+is open.
+
+handle: The handle to a queue.
+)doc");
+
 REGISTER_OP("QueueSize")
     .Input("handle: Ref(string)")
     .Output("size: int32")
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 1dc5eb2f90..3ff93b61c5 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -537,6 +537,25 @@ class QueueBase(object):
           self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues,
           name=name)
 
+  def is_closed(self, name=None):
+    """ Returns true if queue is closed.
+
+    This operation returns true if the queue is closed and false if the queue
+    is open.
+
+    Args:
+      name: A name for the operation (optional).
+	
+    Returns:
+      True if the queue is closed and false if the queue is open.
+    """
+    if name is None:
+      name = "%s_Is_Closed" % self._name
+    if self._queue_ref.dtype == _dtypes.resource:
+      return gen_data_flow_ops.queue_is_closed_v2(self._queue_ref,name=name)
+    else:
+      return gen_data_flow_ops.queue_is_closed_(self._queue_ref,name=name)
+
   def size(self, name=None):
     """Compute the number of elements in this queue.
 
diff --git a/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt
index 72cc532447..a095616c00 100644
--- a/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt
index 1bfe723ce7..8fed133561 100644
--- a/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt
index dbe25f3a5b..ebb017e81b 100644
--- a/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt b/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt
index 9263d73a51..761f90989f 100644
--- a/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt
@@ -54,6 +54,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt
index ec783ffe5a..f3ca841393 100644
--- a/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-- 
GitLab


From b112ae95082321416dcde17991c1a13b397ec421 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Wed, 28 Jun 2017 11:09:34 -0700
Subject: [PATCH 0556/1375] Add support of Sparse Tensor Slice (#9540)

* Add support of Sparse Tensor Slice

This fix tries to address the issue raised in 1588
to support slice of sparse tensors.

This fix fixes 1588.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Add backtick

* Fix build errors caused by rebase

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

Update golden API for compatibility

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/core/kernels/BUILD                 |   7 +
 tensorflow/core/kernels/sparse_slice_op.cc    | 104 ++++++++
 tensorflow/core/ops/sparse_ops.cc             |  66 ++++-
 tensorflow/core/util/sparse/sparse_tensor.h   |  90 ++++++-
 .../core/util/sparse/sparse_tensor_test.cc    |  46 +++-
 tensorflow/python/kernel_tests/BUILD          |  12 +
 .../kernel_tests/sparse_slice_op_test.py      | 251 ++++++++++++++++++
 tensorflow/python/ops/sparse_ops.py           |  45 ++++
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   4 +
 9 files changed, 618 insertions(+), 7 deletions(-)
 create mode 100644 tensorflow/core/kernels/sparse_slice_op.cc
 create mode 100644 tensorflow/python/kernel_tests/sparse_slice_op_test.py

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 0b1c1085f2..8d2748ed8b 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -3216,6 +3216,7 @@ cc_library(
         ":sparse_reduce_op",
         ":sparse_reorder_op",
         ":sparse_reshape_op",
+        ":sparse_slice_op",
         ":sparse_softmax",
         ":sparse_sparse_binary_op_shared",
         ":sparse_split_op",
@@ -3298,6 +3299,12 @@ tf_kernel_library(
     deps = SPARSE_DEPS,
 )
 
+tf_kernel_library(
+    name = "sparse_slice_op",
+    prefix = "sparse_slice_op",
+    deps = SPARSE_DEPS,
+)
+
 tf_kernel_library(
     name = "sparse_softmax",
     prefix = "sparse_softmax",
diff --git a/tensorflow/core/kernels/sparse_slice_op.cc b/tensorflow/core/kernels/sparse_slice_op.cc
new file mode 100644
index 0000000000..10dc208ab6
--- /dev/null
+++ b/tensorflow/core/kernels/sparse_slice_op.cc
@@ -0,0 +1,104 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include <vector>
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/util/sparse/sparse_tensor.h"
+
+namespace tensorflow {
+
+template <typename T>
+class SparseSliceOp : public OpKernel {
+ public:
+  explicit SparseSliceOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_indices = context->input(0);
+    const Tensor& input_values = context->input(1);
+    const Tensor& input_shape = context->input(2);
+    const Tensor& input_start = context->input(3);
+    const Tensor& input_size = context->input(4);
+
+    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices.shape()),
+                errors::InvalidArgument(
+                    "Input indices should be a matrix but received shape ",
+                    input_indices.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_values.shape()),
+                errors::InvalidArgument(
+                    "Input values should be a vector but received shape ",
+                    input_indices.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape.shape()),
+                errors::InvalidArgument(
+                    "Input shape should be a vector but received shape ",
+                    input_shape.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_start.shape()),
+                errors::InvalidArgument(
+                    "Input start should be a vector but received shape ",
+                    input_start.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_size.shape()),
+                errors::InvalidArgument(
+                    "Input size should be a vector but received shape ",
+                    input_size.shape().DebugString()));
+
+    const int input_dims = input_shape.NumElements();
+    OP_REQUIRES(context, input_dims == input_start.NumElements(),
+                errors::InvalidArgument(
+                    "Expected start to be a vector of length ", input_dims,
+                    " but got length ", input_start.NumElements()));
+
+    OP_REQUIRES(context, input_dims == input_size.NumElements(),
+                errors::InvalidArgument(
+                    "Expected size to be a vector of length ", input_dims,
+                    " but got length ", input_size.NumElements()));
+
+    sparse::SparseTensor sparse_tensor(input_indices, input_values,
+                                       TensorShape(input_shape.vec<int64>()));
+
+    const gtl::ArraySlice<int64> start(input_start.flat<int64>().data(),
+                                       input_dims);
+    const gtl::ArraySlice<int64> size(input_size.flat<int64>().data(),
+                                      input_dims);
+
+    const sparse::SparseTensor output =
+        sparse::SparseTensor::Slice<T>(sparse_tensor, start, size);
+
+    context->set_output(0, output.indices());
+    context->set_output(1, output.values());
+
+    const TensorShape output_shape(output.shape());
+
+    Tensor* shape = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(2, {output_shape.dims()}, &shape));
+    for (int dim = 0; dim < output_shape.dims(); ++dim) {
+      shape->vec<int64>()(dim) = output_shape.dim_size(dim);
+    }
+  }
+
+ private:
+};
+
+#define REGISTER_KERNELS(type)                                          \
+  REGISTER_KERNEL_BUILDER(                                              \
+      Name("SparseSlice").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      SparseSliceOp<type>)
+
+TF_CALL_ALL_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 6aca2c3b01..646c379586 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -597,6 +597,60 @@ output_shape: A list of 1-D tensors represents the shape of the output sparse
   tensors.
 )doc");
 
+REGISTER_OP("SparseSlice")
+    .Input("indices: int64")
+    .Input("values: T")
+    .Input("shape: int64")
+    .Input("start: int64")
+    .Input("size: int64")
+    .Output("output_indices: int64")
+    .Output("output_values: T")
+    .Output("output_shape: int64")
+    .Attr("T: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input_shape = c->input(2);
+      ShapeHandle output_indices =
+          c->Matrix(InferenceContext::kUnknownDim, c->NumElements(input_shape));
+      ShapeHandle output_values = c->Vector(InferenceContext::kUnknownDim);
+      ShapeHandle output_shape = input_shape;
+
+      c->set_output(0, output_indices);
+      c->set_output(1, output_values);
+      c->set_output(2, output_shape);
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Slice a `SparseTensor` based on the `start` and `size`.
+
+For example, if the input is
+
+    input_tensor = shape = [2, 7]
+    [    a   d e  ]
+    [b c          ]
+
+Graphically the output tensors are:
+
+    sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
+    [    a  ]
+    [b c    ]
+
+    sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
+    [ d e  ]
+    [      ]
+
+indices: 2-D tensor represents the indices of the sparse tensor.
+values: 1-D tensor represents the values of the sparse tensor.
+shape: 1-D. tensor represents the shape of the sparse tensor.
+start: 1-D. tensor represents the start of the slice.
+size: 1-D. tensor represents the size of the slice.
+output indices: A list of 1-D tensors represents the indices of the output
+sparse tensors.
+output_values: A list of 1-D tensors represents the values of the output sparse
+  tensors.
+output_shape: A list of 1-D tensors represents the shape of the output sparse
+  tensors.
+)doc");
+
 REGISTER_OP("SparseReorder")
     .Input("input_indices: int64")
     .Input("input_values: T")
@@ -862,7 +916,9 @@ keep_dims: If true, retain reduced dimensions with length 1.
         return Status::OK();                                     \
       })
 
-REGISTER_OP("SparseDenseCwiseMul").SPARSE_DENSE_CWISE_SIGNATURE().Doc(R"doc(
+REGISTER_OP("SparseDenseCwiseMul")
+    .SPARSE_DENSE_CWISE_SIGNATURE()
+    .Doc(R"doc(
 Component-wise multiplies a SparseTensor by a dense Tensor.
 
 The output locations corresponding to the implicitly zero elements in the sparse
@@ -880,7 +936,9 @@ dense: `R`-D.  The dense Tensor operand.
 output: 1-D.  The `N` values that are operated on.
 )doc");
 
-REGISTER_OP("SparseDenseCwiseDiv").SPARSE_DENSE_CWISE_SIGNATURE().Doc(R"doc(
+REGISTER_OP("SparseDenseCwiseDiv")
+    .SPARSE_DENSE_CWISE_SIGNATURE()
+    .Doc(R"doc(
 Component-wise divides a SparseTensor by a dense Tensor.
 
 *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
@@ -894,7 +952,9 @@ dense: `R`-D.  The dense Tensor operand.
 output: 1-D.  The `N` values that are operated on.
 )doc");
 
-REGISTER_OP("SparseDenseCwiseAdd").SPARSE_DENSE_CWISE_SIGNATURE().Doc(R"doc(
+REGISTER_OP("SparseDenseCwiseAdd")
+    .SPARSE_DENSE_CWISE_SIGNATURE()
+    .Doc(R"doc(
 Adds up a SparseTensor and a dense Tensor, using these special rules:
 
 (1) Broadcasts the dense side to have the same shape as the sparse side, if
diff --git a/tensorflow/core/util/sparse/sparse_tensor.h b/tensorflow/core/util/sparse/sparse_tensor.h
index d8e5d90142..0ea74c38b1 100644
--- a/tensorflow/core/util/sparse/sparse_tensor.h
+++ b/tensorflow/core/util/sparse/sparse_tensor.h
@@ -20,7 +20,6 @@ limitations under the License.
 #include <numeric>
 #include <vector>
 
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/types.h"
@@ -32,6 +31,7 @@ limitations under the License.
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/util/sparse/dim_comparator.h"
 #include "tensorflow/core/util/sparse/group_iterator.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 
 namespace tensorflow {
 namespace sparse {
@@ -59,8 +59,8 @@ class SparseTensor {
         shape_(shape.begin(), shape.end()),
         order_(order.begin(), order.end()),
         dims_(GetDimsFromIx(ix)) {
-    CHECK_EQ(ix.dtype(), DT_INT64)
-        << "indices must be type int64 but got: " << ix.dtype();
+    CHECK_EQ(ix.dtype(), DT_INT64) << "indices must be type int64 but got: "
+                                   << ix.dtype();
     CHECK(TensorShapeUtils::IsVector(vals.shape()))
         << "vals must be a vec, but got: " << vals.shape().DebugString();
     CHECK_EQ(ix.shape().dim_size(0), vals.shape().dim_size(0))
@@ -155,6 +155,15 @@ class SparseTensor {
                                          const int split_dim,
                                          const int num_split);
 
+  // Slice() will slice the input SparseTensor into a SparseTensor based on
+  // specified start and size. Both start and size are 1-D array with each
+  // element of the array representing one dimension. The start is the start
+  // index at each dimension and the size is the size at each dimension.
+  template <typename T>
+  static SparseTensor Slice(const SparseTensor& tensor,
+                            const gtl::ArraySlice<int64>& start,
+                            const gtl::ArraySlice<int64>& size);
+
   // Picks out the dimensions according to `dim_indices`.
   std::vector<int64> PickDims(gtl::ArraySlice<int64> dim_indices) const {
     std::vector<int64> res(dim_indices.size());
@@ -541,6 +550,81 @@ std::vector<SparseTensor> SparseTensor::Split(const SparseTensor& input_tensor,
   return output_tensors;
 }
 
+template <typename T>
+SparseTensor SparseTensor::Slice(const SparseTensor& input_tensor,
+                                 const gtl::ArraySlice<int64>& start,
+                                 const gtl::ArraySlice<int64>& size) {
+  TensorShape output_shape(input_tensor.shape());
+
+  const int dims = input_tensor.dims();
+  for (int dim = 0; dim < dims; dim++) {
+    int64 dim_size = start[dim] + size[dim] < output_shape.dim_size(dim)
+                         ? size[dim]
+                         : output_shape.dim_size(dim) - start[dim];
+    output_shape.set_dim(dim, dim_size);
+  }
+
+  auto input_indices_t = input_tensor.indices().matrix<int64>();
+  auto input_values_t = input_tensor.values().vec<T>();
+
+  // Find the number of indices that fall inside start and size.
+  int count = 0;
+  for (int i = 0; i < input_tensor.indices().dim_size(0); i++) {
+    // The following will check to see if an input is within the
+    // range specified by start and size.
+    // The for loop below iterates through all dimensions. In case
+    // the index falls outside of the start and size at any dimension,
+    // it will be considered as a "no hit" (hit = false). In this
+    // case, it will not be counted as the index that fall inside
+    // the range specified by start and size.
+    bool hit = true;
+    for (int dim = 0; dim < dims; dim++) {
+      if (!(start[dim] <= input_indices_t(i, dim) &&
+            input_indices_t(i, dim) < start[dim] + size[dim])) {
+        hit = false;
+        break;
+      }
+    }
+    if (!hit) {
+      continue;
+    }
+    count++;
+  }
+
+  Tensor output_values(DataTypeToEnum<T>::v(), TensorShape({count}));
+  Tensor output_indices(DT_INT64, TensorShape({count, dims}));
+
+  auto output_values_t = output_values.vec<T>();
+  auto output_indices_t = output_indices.matrix<int64>();
+
+  // Obtain the output indices that fall inside start and size.
+  int index = 0;
+  for (int i = 0; i < input_tensor.indices().dim_size(0) && index < count;
+       i++) {
+    // The logic here is similiar as the above except that the above
+    // only count the number of indices while here we actually generate
+    // the output.
+    bool hit = true;
+    for (int dim = 0; dim < dims; dim++) {
+      if (!(start[dim] <= input_indices_t(i, dim) &&
+            input_indices_t(i, dim) < start[dim] + size[dim])) {
+        hit = false;
+        break;
+      }
+    }
+    if (!hit) {
+      continue;
+    }
+    output_values_t(index) = input_values_t(i);
+    for (int dim = 0; dim < dims; dim++) {
+      output_indices_t(index, dim) = input_indices_t(i, dim) - start[dim];
+    }
+    index++;
+  }
+
+  return SparseTensor(output_indices, output_values, output_shape);
+}
+
 }  // namespace sparse
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/util/sparse/sparse_tensor_test.cc b/tensorflow/core/util/sparse/sparse_tensor_test.cc
index 5edd6cb1d8..efdd97fd3d 100644
--- a/tensorflow/core/util/sparse/sparse_tensor_test.cc
+++ b/tensorflow/core/util/sparse/sparse_tensor_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include <string>
 #include <vector>
 
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -26,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/test_benchmark.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 
 namespace tensorflow {
 namespace sparse {
@@ -612,6 +612,50 @@ TEST(SparseTensorTest, Split) {
   EXPECT_EQ(st_list[1].indices().matrix<int64>()(0, 1), 0);
 }
 
+TEST(SparseTensorTest, Slice) {
+  const int N = 4;
+  const int DIM = 2;
+
+  Tensor ids(DT_INT64, TensorShape({N, DIM}));
+  Tensor vals(DT_INT64, TensorShape({N}));
+
+  ids.matrix<int64>()(0, 0) = 0;
+  ids.matrix<int64>()(0, 1) = 0;
+  ids.matrix<int64>()(1, 0) = 1;
+  ids.matrix<int64>()(1, 1) = 1;
+  ids.matrix<int64>()(2, 0) = 1;
+  ids.matrix<int64>()(2, 1) = 2;
+  ids.matrix<int64>()(3, 0) = 3;
+  ids.matrix<int64>()(3, 1) = 0;
+
+  vals.vec<int64>()(0) = 1;
+  vals.vec<int64>()(1) = 2;
+  vals.vec<int64>()(2) = 3;
+  vals.vec<int64>()(3) = 4;
+
+  SparseTensor st(ids, vals, TensorShape({4, 3}));
+
+  std::vector<int64> start(2, 0);
+  std::vector<int64> size(2);
+  size[0] = 2;
+  size[1] = 3;
+
+  SparseTensor slice = SparseTensor::Slice<int64>(st, start, size);
+
+  EXPECT_EQ(TensorShape(slice.shape()), TensorShape({2, 3}));
+  EXPECT_EQ(slice.values().NumElements(), 3);
+  EXPECT_EQ(slice.values().vec<int64>()(0), 1);
+  EXPECT_EQ(slice.values().vec<int64>()(1), 2);
+  EXPECT_EQ(slice.values().vec<int64>()(2), 3);
+  EXPECT_EQ(slice.indices().NumElements(), 6);
+  EXPECT_EQ(slice.indices().matrix<int64>()(0, 0), 0);
+  EXPECT_EQ(slice.indices().matrix<int64>()(0, 1), 0);
+  EXPECT_EQ(slice.indices().matrix<int64>()(1, 0), 1);
+  EXPECT_EQ(slice.indices().matrix<int64>()(1, 1), 1);
+  EXPECT_EQ(slice.indices().matrix<int64>()(2, 0), 1);
+  EXPECT_EQ(slice.indices().matrix<int64>()(2, 1), 2);
+}
+
 TEST(SparseTensorTest, Dim0SparseTensorToDenseTensor) {
   Tensor ix(DT_INT64, TensorShape({1, 0}));
   Tensor vals(DT_INT32, TensorShape({1}));
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index d8e069105f..f2bfb4ae64 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -727,6 +727,18 @@ tf_py_test(
     ],
 )
 
+tf_py_test(
+    name = "sparse_slice_op_test",
+    size = "small",
+    srcs = ["sparse_slice_op_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:sparse_ops",
+    ],
+)
+
 tf_py_test(
     name = "sparse_to_dense_op_py_test",
     size = "small",
diff --git a/tensorflow/python/kernel_tests/sparse_slice_op_test.py b/tensorflow/python/kernel_tests/sparse_slice_op_test.py
new file mode 100644
index 0000000000..762e400447
--- /dev/null
+++ b/tensorflow/python/kernel_tests/sparse_slice_op_test.py
@@ -0,0 +1,251 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for SparseReorder."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import sparse_ops
+from tensorflow.python.platform import test
+
+
+class SparseSliceOpTest(test.TestCase):
+
+  def _SparseTensor_4x6(self):
+    # [0 |  |2 |  |4 |5 ]
+    # [  |11|  |13|14|  ]
+    # [20|  |  |23|  |25]
+    # [30|  |32|33|  |35]
+    ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4],
+                    [2, 0], [2, 3], [2, 5], [3, 0], [3, 2], [3, 3],
+                    [3, 5]]).astype(np.int64)
+    val = np.array(
+        [0, 2, 4, 5, 11, 13, 14, 20, 23, 25, 30, 32, 33, 35]).astype(np.int64)
+    shape = np.array([4, 6]).astype(np.int64)
+    return sparse_tensor.SparseTensor(ind, val, shape)
+
+  def _SparseTensor_5x7(self):
+    # [0 |  |2 |  |4 |5 |  ]
+    # [  |11|  |13|14|  |16]
+    # [20|  |  |23|  |25|  ]
+    # [30|  |32|33|  |35|  ]
+    # [  |41|  |  |44|  |46]
+    ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4],
+                    [1, 6], [2, 0], [2, 3], [2, 5], [3, 0], [3, 2], [3, 3],
+                    [3, 5], [4, 1], [4, 4], [4, 6]]).astype(np.int64)
+    val = np.array(
+        [0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25, 30, 32, 33, 35, 41, 44,
+         46]).astype(np.int64)
+    shape = np.array([5, 7]).astype(np.int64)
+    return sparse_tensor.SparseTensor(ind, val, shape)
+
+  def _SparseTensorValue_3x4x2(self):
+    #  slice(:,:, 0)
+    #  ['a0'|    |'b0'|    ]
+    #  [    |'c0'|    |'d0']
+    #  [    |    |'e0'|    ]
+    #  slice(:,:, 1)
+    #  ['a1'|    |'b1'|    ]
+    #  [    |'c1'|    |'d1']
+    #  [    |    |'e1'|    ]
+    ind = np.array([[0, 0, 0], [0, 0, 1], [0, 2, 0], [0, 2, 1], [1, 1, 0],
+                    [1, 1, 1], [1, 3, 0], [1, 3, 1], [2, 2, 0],
+                    [2, 2, 1]]).astype(np.int64)
+    val = np.array(['a0', 'a1', 'b0', 'b1', 'c0', 'c1', 'd0', 'd1', 'e0', 'e1'])
+    shape = np.array([3, 4, 2]).astype(np.int64)
+    return sparse_tensor.SparseTensorValue(ind, val, shape)
+
+  def _SparseTensor_3x4x2(self):
+    return sparse_tensor.SparseTensor.from_value(self._SparseTensorValue_3x4x2(
+    ))
+
+  def testSliceMatrixRows(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [2, 6])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [2, 0], [3, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(), [[0, 0], [0, 2], [0, 4],
+                                                      [0, 5], [1, 1], [1, 3],
+                                                      [1, 4]])
+      self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5, 11, 13, 14])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [2, 6])
+      self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 0], [0, 3], [0, 5],
+                                                      [1, 0], [1, 2], [1, 3],
+                                                      [1, 5]])
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [20, 23, 25, 30, 32, 33, 35])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 6])
+
+  def testSliceMatrixUnevenCols(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_5x7()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [5, 3])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 3], [5, 2])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 5], [5, 2])
+
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [0, 2], [1, 1], [2, 0], [3, 0], [3, 2],
+                           [4, 1]])
+      self.assertAllEqual(sp_tensor0.values.eval(),
+                          [0, 2, 11, 20, 30, 32, 41])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [5, 3])
+      self.assertAllEqual(sp_tensor1.indices.eval(),
+                          [[0, 1], [1, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [4, 13, 14, 23, 33, 44])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor2.indices.eval(),
+                          [[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [5, 16, 25, 35, 46])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [5, 2])
+
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [5, 2])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 2], [5, 2])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 2])
+      sp_tensor3 = sparse_ops.sparse_slice(sp_input, [0, 6], [5, 2])
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
+      self.assertAllEqual(sp_tensor0.values.eval(), [0, 11, 20, 30, 41])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor1.indices.eval(),
+                          [[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
+      self.assertAllEqual(sp_tensor1.values.eval(), [2, 13, 23, 32, 33])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor2.indices.eval(),
+                          [[0, 0], [0, 1], [1, 0], [2, 1], [3, 1], [4, 0]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [4, 5, 14, 25, 35, 44])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor3.indices.eval(), [[1, 0], [4, 0]])
+      self.assertAllEqual(sp_tensor3.values.eval(), [16, 46])
+      self.assertAllEqual(sp_tensor3.dense_shape.eval(), [5, 1])
+
+  def testSliceMatrixUnevenRows(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_5x7()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [3, 7])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [3, 0], [3, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
+                           [1, 4], [1, 6], [2, 0], [2, 3], [2, 5]])
+      self.assertAllEqual(sp_tensor0.values.eval(),
+                          [0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [3, 7])
+      self.assertAllEqual(sp_tensor1.indices.eval(),
+                          [[0, 0], [0, 2], [0, 3], [0, 5], [1, 1], [1, 4],
+                           [1, 6]])
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [30, 32, 33, 35, 41, 44, 46])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 7])
+
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [2, 7])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [2, 0], [2, 7])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [4, 0], [2, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
+                           [1, 4], [1, 6]])
+      self.assertAllEqual(sp_tensor0.values.eval(),
+                          [0, 2, 4, 5, 11, 13, 14, 16])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [2, 7])
+
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [20, 23, 25, 30, 32, 33, 35])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 7])
+      self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 1], [0, 4],
+                                                           [0, 6]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [41, 44, 46])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [1, 7])
+    return
+
+  def testSliceAllRows(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [1, 6])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [1, 0], [1, 6])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [2, 0], [1, 7])
+      sp_tensor3 = sparse_ops.sparse_slice(sp_input, [3, 0], [2, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(), [[0, 0], [0, 2], [0, 4],
+                                                         [0, 5]])
+      self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [1, 6])
+      self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 1], [0, 3], [0,
+                                                                          4]])
+      self.assertAllEqual(sp_tensor1.values.eval(), [11, 13, 14])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [1, 6])
+      self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 0], [0, 3], [0,
+                                                                          5]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [20, 23, 25])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [1, 6])
+      self.assertAllEqual(sp_tensor3.indices.eval(), [[0, 0], [0, 2], [0, 3],
+                                                         [0, 5]])
+      self.assertAllEqual(sp_tensor3.values.eval(), [30, 32, 33, 35])
+      self.assertAllEqual(sp_tensor3.dense_shape.eval(), [1, 6])
+
+  def testSliceColumns(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sparse_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [4, 2])
+      sparse_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 2], [5, 2])
+      sparse_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 3])
+
+      self.assertAllEqual(sparse_tensor0.indices.eval(), [[0, 0], [1, 1],
+                                                             [2, 0], [3, 0]])
+      self.assertAllEqual(sparse_tensor0.values.eval(), [0, 11, 20, 30])
+      self.assertAllEqual(sparse_tensor0.dense_shape.eval(), [4, 2])
+      self.assertAllEqual(sparse_tensor1.indices.eval(),
+                          [[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
+      self.assertAllEqual(sparse_tensor1.values.eval(), [2, 13, 23, 32, 33])
+      self.assertAllEqual(sparse_tensor1.dense_shape.eval(), [4, 2])
+      self.assertAllEqual(sparse_tensor2.indices.eval(),
+                          [[0, 0], [0, 1], [1, 0], [2, 1], [3, 1]])
+      self.assertAllEqual(sparse_tensor2.values.eval(), [4, 5, 14, 25, 35])
+      self.assertAllEqual(sparse_tensor2.dense_shape.eval(), [4, 2])
+
+  def testSliceAllColumns(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sparse_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [4, 1])
+      sparse_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 1], [4, 1])
+      sparse_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 2], [4, 1])
+      sparse_tensor3 = sparse_ops.sparse_slice(sp_input, [0, 3], [4, 1])
+      sparse_tensor4 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 1])
+      sparse_tensor5 = sparse_ops.sparse_slice(sp_input, [0, 5], [6, 3])
+      self.assertAllEqual(sparse_tensor0.indices.eval(), [[0, 0], [2, 0],
+                                                             [3, 0]])
+      self.assertAllEqual(sparse_tensor0.values.eval(), [0, 20, 30])
+      self.assertAllEqual(sparse_tensor0.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor1.indices.eval(), [[1, 0]])
+      self.assertAllEqual(sparse_tensor1.values.eval(), [11])
+      self.assertAllEqual(sparse_tensor1.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor2.indices.eval(), [[0, 0], [3, 0]])
+      self.assertAllEqual(sparse_tensor2.values.eval(), [2, 32])
+      self.assertAllEqual(sparse_tensor2.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor3.indices.eval(), [[1, 0], [2, 0],
+                                                             [3, 0]])
+      self.assertAllEqual(sparse_tensor3.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor3.values.eval(), [13, 23, 33])
+      self.assertAllEqual(sparse_tensor4.indices.eval(), [[0, 0], [1, 0]])
+      self.assertAllEqual(sparse_tensor4.values.eval(), [4, 14])
+      self.assertAllEqual(sparse_tensor4.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor5.indices.eval(), [[0, 0], [2, 0],
+                                                             [3, 0]])
+      self.assertAllEqual(sparse_tensor5.values.eval(), [5, 25, 35])
+      self.assertAllEqual(sparse_tensor5.dense_shape.eval(), [4, 1])
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 93a9656950..d05fabce86 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -25,6 +25,7 @@
 @@sparse_concat
 @@sparse_reorder
 @@sparse_reshape
+@@sparse_slice
 @@sparse_split
 @@sparse_retain
 @@sparse_reset_shape
@@ -657,6 +658,50 @@ def sparse_split(keyword_required=KeywordRequired(),
   return sparse_tensors
 
 
+def sparse_slice(sp_input, start, size, name=None):
+  """Slice a `SparseTensor` based on the `start` and `size.
+
+  For example, if the input is
+
+      input_tensor = shape = [2, 7]
+      [    a   d e  ]
+      [b c          ]
+
+  Graphically the output tensors are:
+
+      sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
+      [    a  ]
+      [b c    ]
+
+      sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
+      [ d e  ]
+      [      ]
+
+  Args:
+    sp_input: The `SparseTensor` to split.
+    start: 1-D. tensor represents the start of the slice.
+    size: 1-D. tensor represents the size of the slice.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `SparseTensor` objects resulting from splicing.
+
+  Raises:
+    TypeError: If `sp_input` is not a `SparseTensor`.
+  """
+  sp_input = _convert_to_sparse_tensor(sp_input)
+  start = ops.convert_to_tensor(start, dtypes.int64)
+  size = ops.convert_to_tensor(size, dtypes.int64)
+
+  with ops.name_scope(name, "SparseSlice", [sp_input]) as name:
+    output_indices, output_values, output_shape = gen_sparse_ops.sparse_slice(
+        sp_input.indices, sp_input.values, sp_input.dense_shape, start, size, name=name)
+
+    return sparse_tensor.SparseTensor(
+        output_indices,
+        output_values,
+        output_shape)
+
 def sparse_to_dense(sparse_indices,
                     output_shape,
                     sparse_values,
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index eca017edf5..4a179bae84 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -1748,6 +1748,10 @@ tf_module {
     name: "sparse_segment_sum"
     argspec: "args=[\'data\', \'indices\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "sparse_slice"
+    argspec: "args=[\'sp_input\', \'start\', \'size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "sparse_softmax"
     argspec: "args=[\'sp_input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-- 
GitLab


From 9e569794987e7f38cb5720e438de228b20be1e2f Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Wed, 28 Jun 2017 11:10:02 -0700
Subject: [PATCH 0557/1375] Fix broken link in programmers_guide/embedding.md
 (#11105)

This fix fixes broken link in programmers_guide/embedding.md:

`tensorflow/tensorboard -> tensorflow/contrib/tensorboard`

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/docs_src/programmers_guide/embedding.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/embedding.md b/tensorflow/docs_src/programmers_guide/embedding.md
index 975850349f..91beb36f96 100644
--- a/tensorflow/docs_src/programmers_guide/embedding.md
+++ b/tensorflow/docs_src/programmers_guide/embedding.md
@@ -84,7 +84,7 @@ labels/images to the data points. You can do this by generating a
 [metadata file](#metadata) containing the labels for each point and configuring
 the projector either by using our Python API, or manually constructing and
 saving a
-<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/plugins/projector/projector_config.proto)</code>
+<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto)</code>
 in the same directory as your checkpoint file.
 
 ### Setup
@@ -113,7 +113,7 @@ saver.save(session, os.path.join(LOG_DIR, "model.ckpt"), step)
 
 If you have any metadata (labels, images) associated with your embedding, you
 can tell TensorBoard about it either by directly storing a
-<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/plugins/projector/projector_config.proto)</code>
+<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto)</code>
 in the <code>LOG_DIR</code>, or use our python API.
 
 For instance, the following <code>projector_config.ptxt</code> associates the
-- 
GitLab


From c1184705b1f5d2662f02b13700062a3a3d828e19 Mon Sep 17 00:00:00 2001
From: Tapan Prakash <tapanprakasht@gmail.com>
Date: Wed, 28 Jun 2017 23:42:44 +0530
Subject: [PATCH 0558/1375] Fix #10823 moving vlog to higher level (#11083)

* Fix #10823 moving vlog to higher level

* Put everything under VLOG(3)

Otherwise, under VLOG(1/2), the "header" All Register Ops message would have been orphaned.
---
 tensorflow/core/framework/op.cc | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/framework/op.cc b/tensorflow/core/framework/op.cc
index fe333dc9ff..4f5a1f80a0 100644
--- a/tensorflow/core/framework/op.cc
+++ b/tensorflow/core/framework/op.cc
@@ -77,9 +77,10 @@ Status OpRegistry::LookUp(const string& op_type_name,
     if (first_unregistered) {
       OpList op_list;
       Export(true, &op_list);
-      VLOG(1) << "All registered Ops:";
-      for (const auto& op : op_list.op()) {
-        VLOG(1) << SummarizeOpDef(op);
+      if (VLOG_IS_ON(3)) {
+         LOG(INFO) << "All registered Ops:";
+         for (const auto& op : op_list.op())
+            LOG(INFO) << SummarizeOpDef(op);
       }
       first_unregistered = false;
     }
-- 
GitLab


From 07827d9f14b644a042ed1e94d140d3bbb9110313 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 11:13:35 -0700
Subject: [PATCH 0559/1375] Refine build configuration for reduce window tests.

PiperOrigin-RevId: 160429376
---
 tensorflow/compiler/xla/tests/BUILD | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 2fd0680afa..f5a72a9bcc 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -874,16 +874,14 @@ xla_test(
     ],
 )
 
-filegroup(
-    name = "reduce_window_test_srcs",
-    testonly = 1,
-    srcs = ["reduce_window_test.cc"],
-)
-
+# Note that the backend-specific macros (e.g. DISABLED_ON_CPU) would not work here, because the
+# library does not get recompiled for each backend.
 cc_library(
-    name = "reduce_window_test_deps",
+    name = "reduce_window_test_library",
     testonly = 1,
+    srcs = ["reduce_window_test.cc"],
     deps = [
+        ":test_macros_header",
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:array4d",
@@ -905,9 +903,9 @@ cc_library(
 xla_test(
     name = "reduce_window_test",
     timeout = "long",
-    srcs = ["reduce_window_test_srcs"],
+    srcs = [],
     deps = [
-        ":reduce_window_test_deps",
+        ":reduce_window_test_library",
     ],
 )
 
-- 
GitLab


From 6485741bceb5c4dab105cd330a65ba9d364787ac Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Jun 2017 11:47:37 -0700
Subject: [PATCH 0560/1375] When GPU is configured, selection of config to set
 depends on value of TF_CUDA_CLANG (--config=cuda / --config=cuda_clang)

PiperOrigin-RevId: 160433337
---
 configure | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/configure b/configure
index 1531b4d40a..4bcf11e0a9 100755
--- a/configure
+++ b/configure
@@ -714,8 +714,13 @@ if is_windows; then
   write_to_bazelrc "test --config=win-cuda"
 else
   # If CUDA is enabled, always use GPU during build and test.
-  write_to_bazelrc "build --config=cuda"
-  write_to_bazelrc "test --config=cuda"
+  if [ "$TF_CUDA_CLANG" == "1" ]; then
+    write_to_bazelrc "build --config=cuda_clang"
+    write_to_bazelrc "test --config=cuda_clang"
+  else
+    write_to_bazelrc "build --config=cuda"
+    write_to_bazelrc "test --config=cuda"
+  fi
 fi
 
 # end of if "$TF_NEED_CUDA" == "1"
-- 
GitLab


From eaa571b56fda945351b08586cf5eeaa50863f536 Mon Sep 17 00:00:00 2001
From: Yan Chen <yanchen036@gmail.com>
Date: Thu, 29 Jun 2017 02:52:09 +0800
Subject: [PATCH 0561/1375] extract common functions (#11117)

---
 tensorflow/core/kernels/ops_util.h            | 14 +++++++++++
 tensorflow/core/kernels/tile_functor.h        | 15 -----------
 tensorflow/core/kernels/tile_functor_cpu.cc   |  1 +
 .../core/kernels/tile_functor_gpu.cu.cc       |  1 +
 tensorflow/core/kernels/transpose_functor.h   | 24 ++++++++----------
 .../core/kernels/transpose_functor_cpu.cc     | 21 +++-------------
 .../core/kernels/transpose_functor_gpu.cu.cc  | 25 +++++--------------
 7 files changed, 36 insertions(+), 65 deletions(-)

diff --git a/tensorflow/core/kernels/ops_util.h b/tensorflow/core/kernels/ops_util.h
index 68a9c37406..d3d1b56c9d 100644
--- a/tensorflow/core/kernels/ops_util.h
+++ b/tensorflow/core/kernels/ops_util.h
@@ -84,6 +84,20 @@ bool IsDim0SliceAligned(const TensorShape& s, int64 start, int64 end_or_size) {
 // Returns <suffix> sanitized to have only [a-zA-Z0-9-_].
 string SanitizeThreadSuffix(string suffix);
 
+// Helper to compute 'strides' given a tensor 'shape'. I.e.,
+// strides[i] = prod(shape.dim_size[(i+1):])
+template <typename T>
+gtl::InlinedVector<T, 8> ComputeStride(const TensorShape& shape) {
+  const int ndims = shape.dims();
+  gtl::InlinedVector<T, 8> strides(ndims);
+  T stride = 1;
+  for (int i = ndims - 1; i >= 0; --i) {
+    strides[i] = stride;
+    stride *= static_cast<T>(shape.dim_size(i));
+  }
+  return strides;
+}
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_KERNELS_OPS_UTIL_H_
diff --git a/tensorflow/core/kernels/tile_functor.h b/tensorflow/core/kernels/tile_functor.h
index d80898b24f..28af2dace3 100644
--- a/tensorflow/core/kernels/tile_functor.h
+++ b/tensorflow/core/kernels/tile_functor.h
@@ -25,21 +25,6 @@ namespace tensorflow {
 
 namespace internal {
 
-// Helper to compute 'strides' given a tensor 'shape'. I.e.,
-// strides[i] = prod(shape.dim_size[(i+1):])
-template <typename Index>
-gtl::InlinedVector<Index, 8> ComputeStride(const TensorShape& shape) {
-  const int ndims = shape.dims();
-  gtl::InlinedVector<Index, 8> strides(ndims);
-  Index stride = 1;
-  for (int i = ndims - 1; i >= 0; --i) {
-    strides[i] = stride;
-    stride *= static_cast<Index>(shape.dim_size(i));
-  }
-  return strides;
-}
-
-
 // Device-specific naive implementation for tile.
 template <typename Device, typename T>
 void TileSimple(const Device& d, Tensor* out, const Tensor& in);
diff --git a/tensorflow/core/kernels/tile_functor_cpu.cc b/tensorflow/core/kernels/tile_functor_cpu.cc
index 3af00350b9..c5418fa142 100644
--- a/tensorflow/core/kernels/tile_functor_cpu.cc
+++ b/tensorflow/core/kernels/tile_functor_cpu.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/tile_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/tile_functor_gpu.cu.cc b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
index 08aca8c731..1c61c3030a 100644
--- a/tensorflow/core/kernels/tile_functor_gpu.cu.cc
+++ b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
@@ -19,6 +19,7 @@ limitations under the License.
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/kernels/tile_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/util/cuda_kernel_helper.h"
 #include "tensorflow/core/framework/register_types.h"
 
diff --git a/tensorflow/core/kernels/transpose_functor.h b/tensorflow/core/kernels/transpose_functor.h
index f1ab770eeb..498030fdfe 100644
--- a/tensorflow/core/kernels/transpose_functor.h
+++ b/tensorflow/core/kernels/transpose_functor.h
@@ -38,18 +38,6 @@ namespace internal {
 typedef gtl::InlinedVector<int64, 8> TransposeDimsVec;
 typedef gtl::InlinedVector<int32, 8> TransposePermsVec;
 
-// Helper to compute 'strides' given a tensor 'shape'. I.e.,
-// strides[i] = prod(shape.dim_size[(i+1):])
-template <typename Index>
-void ComputeStride(const TensorShape& shape, Index* strides) {
-  const int ndims = shape.dims();
-  Index stride = 1;
-  for (int i = ndims - 1; i >= 0; --i) {
-    strides[i] = stride;
-    stride *= static_cast<Index>(shape.dim_size(i));
-  }
-}
-
 // Helper function that takes a tensor shape, a permutation, combines the
 // neighboring shapes if their indices in the permutation are consecutive.
 // The function outputs the combined shape and new permutation.
@@ -130,7 +118,17 @@ void TransposeSimple(const Device& d, const Tensor& in,
 // Uses Eigen to transpose.
 template <typename Device, typename T, int NDIMS>
 void TransposeUsingEigen(const Device& d, const Tensor& in,
-                         const gtl::ArraySlice<int32> perm, Tensor* out);
+                         const gtl::ArraySlice<int32> perm, Tensor* out) {
+  Eigen::array<int, NDIMS> p;
+  for (int i = 0; i < NDIMS; ++i) p[i] = perm[i];
+  auto x = typename TTypes<T, NDIMS>::ConstTensor(
+      reinterpret_cast<const T*>(in.tensor_data().data()),
+      in.shape().AsEigenDSizes<NDIMS>());
+  auto y = typename TTypes<T, NDIMS>::Tensor(
+      reinterpret_cast<T*>(const_cast<char*>(out->tensor_data().data())),
+      out->shape().AsEigenDSizes<NDIMS>());
+  y.device(d) = x.shuffle(p);
+}
 
 
 #ifdef TENSORFLOW_USE_SYCL
diff --git a/tensorflow/core/kernels/transpose_functor_cpu.cc b/tensorflow/core/kernels/transpose_functor_cpu.cc
index 248c11976e..3a975a52c1 100644
--- a/tensorflow/core/kernels/transpose_functor_cpu.cc
+++ b/tensorflow/core/kernels/transpose_functor_cpu.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #define EIGEN_USE_THREADS
 
 #include "tensorflow/core/kernels/transpose_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 
 namespace tensorflow {
 namespace internal {
@@ -24,10 +25,8 @@ template <typename Device, typename T>
 void TransposeSimple(const Device& d, const Tensor& in,
                      const gtl::ArraySlice<int32> perm, Tensor* out) {
   const int ndims = in.dims();
-  gtl::InlinedVector<int64, 8> in_strides(ndims);
-  ComputeStride(in.shape(), in_strides.data());
-  gtl::InlinedVector<int64, 8> out_strides(ndims);
-  ComputeStride(out->shape(), out_strides.data());
+  gtl::InlinedVector<int64, 8> in_strides = ComputeStride<int64>(in.shape());
+  gtl::InlinedVector<int64, 8> out_strides = ComputeStride<int64>(out->shape());
   const int64 nelem = in.NumElements();
   const T* p = reinterpret_cast<const T*>(in.tensor_data().data());
   T* q = reinterpret_cast<T*>(const_cast<char*>((out->tensor_data().data())));
@@ -45,20 +44,6 @@ void TransposeSimple(const Device& d, const Tensor& in,
   }
 }
 
-template <typename Device, typename T, int NDIMS>
-void TransposeUsingEigen(const Device& d, const Tensor& in,
-                         const gtl::ArraySlice<int32> perm, Tensor* out) {
-  Eigen::array<int, NDIMS> p;
-  for (int i = 0; i < NDIMS; ++i) p[i] = perm[i];
-  auto x = typename TTypes<T, NDIMS>::ConstTensor(
-      reinterpret_cast<const T*>(in.tensor_data().data()),
-      in.shape().AsEigenDSizes<NDIMS>());
-  auto y = typename TTypes<T, NDIMS>::Tensor(
-      reinterpret_cast<T*>(const_cast<char*>(out->tensor_data().data())),
-      out->shape().AsEigenDSizes<NDIMS>());
-  y.device(d) = x.shuffle(p);
-}
-
 }  // end namespace internal
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
diff --git a/tensorflow/core/kernels/transpose_functor_gpu.cu.cc b/tensorflow/core/kernels/transpose_functor_gpu.cu.cc
index bc72bfb2fd..a118cc80c9 100644
--- a/tensorflow/core/kernels/transpose_functor_gpu.cu.cc
+++ b/tensorflow/core/kernels/transpose_functor_gpu.cu.cc
@@ -19,6 +19,7 @@ limitations under the License.
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/kernels/transpose_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/util/cuda_kernel_helper.h"
 
 // TODO(yangzihao): Remove the dependency of conv_2d.h once we move all
@@ -53,13 +54,13 @@ void TransposeSimple(const Device& d, const Tensor& in,
   CHECK_LT(nelem, kint32max) << "Tensor too large to transpose on GPU";
   // Pack strides and permutation into one buffer.
   const int32 ndims = in.dims();
-  gtl::InlinedVector<int32, 16> host_buf(ndims * 3);
-  // Input strides.
-  ComputeStride(in.shape(), &host_buf[0]);
-  // Output strides.
-  ComputeStride(out->shape(), &host_buf[ndims]);
+  gtl::InlinedVector<int32, 24> host_buf(ndims * 3);
+  gtl::InlinedVector<int32, 8> in_strides = ComputeStride<int32>(in.shape());
+  gtl::InlinedVector<int32, 8> out_strides = ComputeStride<int32>(out->shape());
   // Dimension permutation.
   for (int i = 0; i < ndims; ++i) {
+    host_buf[i] = in_strides[i];
+    host_buf[ndims + i] = out_strides[i];
     host_buf[ndims * 2 + i] = perm[i];
   }
   // Copies the input strides, output strides and permutation to the device.
@@ -79,20 +80,6 @@ void TransposeSimple(const Device& d, const Tensor& in,
   d.deallocate(dev_buf);
 }
 
-template <typename Device, typename T, int NDIMS>
-void TransposeUsingEigen(const Device& d, const Tensor& in,
-                         const gtl::ArraySlice<int32> perm, Tensor* out) {
-  Eigen::array<int, NDIMS> p;
-  for (int i = 0; i < NDIMS; ++i) p[i] = perm[i];
-  auto x = typename TTypes<T, NDIMS>::ConstTensor(
-      reinterpret_cast<const T*>(in.tensor_data().data()),
-      in.shape().AsEigenDSizes<NDIMS>());
-  auto y = typename TTypes<T, NDIMS>::Tensor(
-      reinterpret_cast<T*>(const_cast<char*>(out->tensor_data().data())),
-      out->shape().AsEigenDSizes<NDIMS>());
-  y.device(d) = x.shuffle(p);
-}
-
 // TransposeUsingTile tries to reduce the dimension of the input tensor to 3 and
 // then call special kernels to swap either dimension 1 and dimension 2 or
 // dimension 0 and dimension 2. It returns true if the operation is success,
-- 
GitLab


From 1c42cc48227dab508dae3827f04ec9ef7523bdf4 Mon Sep 17 00:00:00 2001
From: Brian Williammee <bwill@google.com>
Date: Wed, 28 Jun 2017 12:00:26 -0700
Subject: [PATCH 0562/1375] Make import_pb_to_tensorboard executable

Add main, FLAGS, and BUILD rules.

PiperOrigin-RevId: 160434643
---
 tensorflow/python/tools/BUILD                 | 14 +++++++++++
 .../python/tools/import_pb_to_tensorboard.py  | 24 +++++++++++++++++++
 2 files changed, 38 insertions(+)

diff --git a/tensorflow/python/tools/BUILD b/tensorflow/python/tools/BUILD
index 1780d34b39..47b09f67b4 100644
--- a/tensorflow/python/tools/BUILD
+++ b/tensorflow/python/tools/BUILD
@@ -44,6 +44,20 @@ py_binary(
     ],
 )
 
+py_binary(
+    name = "import_pb_to_tensorboard",
+    srcs = ["import_pb_to_tensorboard.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:client",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:summary",
+    ],
+)
+
 py_test(
     name = "freeze_graph_test",
     size = "small",
diff --git a/tensorflow/python/tools/import_pb_to_tensorboard.py b/tensorflow/python/tools/import_pb_to_tensorboard.py
index 2bb055e978..2cab409cfb 100644
--- a/tensorflow/python/tools/import_pb_to_tensorboard.py
+++ b/tensorflow/python/tools/import_pb_to_tensorboard.py
@@ -18,10 +18,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import argparse
+import sys
+
 from tensorflow.core.framework import graph_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import importer
 from tensorflow.python.framework import ops
+from tensorflow.python.platform import app
 from tensorflow.python.platform import gfile
 from tensorflow.python.summary import summary
 
@@ -48,3 +52,23 @@ def import_to_tensorboard(model_dir, log_dir):
     pb_visual_writer.add_graph(sess.graph)
     print("Model Imported. Visualize by running: "
           "> tensorboard --logdir={}".format(log_dir))
+
+
+def main(unused_args):
+  import_to_tensorboard(FLAGS.model_dir, FLAGS.log_dir)
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  parser.register("type", "bool", lambda v: v.lower() == "true")
+  parser.add_argument(
+      "--model_dir",
+      type=str,
+      default="",
+      help="The location of the protobuf (\'pb\') model to visualize.")
+  parser.add_argument(
+      "--log_dir",
+      type=str,
+      default="",
+      help="The location for the Tensorboard log to begin visualization from.")
+  FLAGS, unparsed = parser.parse_known_args()
+  app.run(main=main, argv=[sys.argv[0]] + unparsed)
-- 
GitLab


From 9a4a736ae45a1cc88db472354c5129853ec9fb50 Mon Sep 17 00:00:00 2001
From: Mike Brodie <mikebbrodie@gmail.com>
Date: Wed, 28 Jun 2017 13:08:50 -0600
Subject: [PATCH 0563/1375] minor fix to slim README (#11119)

---
 tensorflow/contrib/slim/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index 4bde661698..c0aa6d445a 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -775,7 +775,7 @@ images, labels = LoadTestData(...)
 predictions = MyModel(images)
 
 mae_value_op, mae_update_op = slim.metrics.streaming_mean_absolute_error(predictions, labels)
-mre_value_op, mre_update_op = slim.metrics.streaming_mean_relative_error(predictions, labels, labels)
+mre_value_op, mre_update_op = slim.metrics.streaming_mean_relative_error(predictions, labels)
 pl_value_op, pl_update_op = slim.metrics.percentage_less(mean_relative_errors, 0.3)
 ```
 
-- 
GitLab


From 59d37536717f08dd6e462ed785ee0e912ff344f5 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 28 Jun 2017 12:17:56 -0700
Subject: [PATCH 0564/1375] Delete unecessary reshape ops

PiperOrigin-RevId: 160436466
---
 .../core/grappler/costs/graph_properties.cc   |  16 +--
 .../core/grappler/costs/graph_properties.h    |   6 +-
 tensorflow/core/grappler/op_types.cc          |   2 +
 tensorflow/core/grappler/op_types.h           |   1 +
 .../grappler/optimizers/constant_folding.cc   |  97 +++++++++++++++--
 .../grappler/optimizers/constant_folding.h    |   8 +-
 .../optimizers/constant_folding_test.cc       | 102 ++++++++++++++++--
 7 files changed, 207 insertions(+), 25 deletions(-)

diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 0b92237dc7..7361968cea 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -432,26 +432,30 @@ Status GraphProperties::InferFromCostGraph(const CostGraphDef& cost_graph) {
   return Status::OK();
 }
 
+bool GraphProperties::HasInputProperties(const string& name) const {
+  return input_properties_.find(name) != input_properties_.end();
+}
+
 bool GraphProperties::HasOutputProperties(const string& name) const {
   return output_properties_.find(name) != output_properties_.end();
 }
 
-std::vector<OpInfo::TensorProperties> GraphProperties::GetInputProperties(
-    const string& node_name) const {
+const std::vector<OpInfo::TensorProperties>&
+GraphProperties::GetInputProperties(const string& node_name) const {
   auto it = input_properties_.find(node_name);
   if (it != input_properties_.end()) {
     return it->second;
   }
-  return std::vector<OpInfo::TensorProperties>();
+  return missing_properties_;
 }
 
-std::vector<OpInfo::TensorProperties> GraphProperties::GetOutputProperties(
-    const string& node_name) const {
+const std::vector<OpInfo::TensorProperties>&
+GraphProperties::GetOutputProperties(const string& node_name) const {
   auto it = output_properties_.find(node_name);
   if (it != output_properties_.end()) {
     return it->second;
   }
-  return std::vector<OpInfo::TensorProperties>();
+  return missing_properties_;
 }
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/costs/graph_properties.h b/tensorflow/core/grappler/costs/graph_properties.h
index 954aeb3905..d4dc7161bf 100644
--- a/tensorflow/core/grappler/costs/graph_properties.h
+++ b/tensorflow/core/grappler/costs/graph_properties.h
@@ -39,10 +39,11 @@ class GraphProperties {
   Status InferDynamically(Cluster* cluster);
   Status InferFromCostGraph(const CostGraphDef& cost_graph);
 
+  bool HasInputProperties(const string& name) const;
   bool HasOutputProperties(const string& name) const;
-  std::vector<OpInfo::TensorProperties> GetInputProperties(
+  const std::vector<OpInfo::TensorProperties>& GetInputProperties(
       const string& node_name) const;
-  std::vector<OpInfo::TensorProperties> GetOutputProperties(
+  const std::vector<OpInfo::TensorProperties>& GetOutputProperties(
       const string& node_name) const;
 
  private:
@@ -50,6 +51,7 @@ class GraphProperties {
   GrapplerItem item_;
   std::map<string, std::vector<OpInfo::TensorProperties>> input_properties_;
   std::map<string, std::vector<OpInfo::TensorProperties>> output_properties_;
+  const std::vector<OpInfo::TensorProperties> missing_properties_;
 
   // Merges shapes <shapes_and_types>, determined from an EnqueueV2 node, into
   // <*queue_shapes_and_types>.
diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index 700a66d3a6..d2dd784b09 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -72,6 +72,8 @@ bool IsReduction(const NodeDef& node) {
          op == "Mean" || op == "Any" || op == "All";
 }
 
+bool IsReshape(const NodeDef& node) { return (node.op() == "Reshape"); }
+
 bool IsSend(const NodeDef& node) {
   const auto op = node.op();
   return op == "_Send";
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index cba44e905e..603095b44d 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -31,6 +31,7 @@ bool IsNoOp(const NodeDef& node);
 bool IsPlaceholder(const NodeDef& node);
 bool IsRecv(const NodeDef& node);
 bool IsReduction(const NodeDef& node);
+bool IsReshape(const NodeDef& node);
 bool IsSend(const NodeDef& node);
 bool IsSwitch(const NodeDef& node);
 bool IsTranspose(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 2b6d9859b3..1322ceb016 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -145,9 +145,8 @@ string ConstantFolding::AddControlDependency(const string& input_name) {
   }
 }
 
-Status ConstantFolding::MaterializeShapes(const GrapplerItem& item) {
-  GraphProperties properties(item);
-  TF_RETURN_IF_ERROR(properties.InferStatically());
+Status ConstantFolding::MaterializeShapes(const GrapplerItem& item,
+                                          const GraphProperties& properties) {
   // We may add some nodes to the graph to encode control dependencies: there is
   // no need to process these, so only iterate over the nodes of the input
   // graph.
@@ -622,7 +621,65 @@ bool ConstantFolding::IsSimplifiableReduction(const NodeDef& node) const {
   return false;
 }
 
-Status ConstantFolding::SimplifyGraph(GraphDef* output) {
+bool ConstantFolding::IsSimplifiableReshape(
+    const NodeDef& node, const GraphProperties& properties) const {
+  if (!IsReshape(node)) {
+    return false;
+  }
+  CHECK_LE(2, node.input_size());
+  const NodeDef* new_shape = node_map_->GetNode(node.input(1));
+  if (!IsConstant(*new_shape)) {
+    return false;
+  }
+  TensorVector outputs;
+  auto outputs_cleanup = gtl::MakeCleanup([&outputs] {
+    for (const auto& output : outputs) {
+      delete output.tensor;
+    }
+  });
+
+  Status s = EvaluateNode(*new_shape, TensorVector(), &outputs);
+  if (!s.ok()) {
+    return false;
+  }
+  CHECK_EQ(1, outputs.size());
+
+  const std::vector<OpInfo::TensorProperties>& props =
+      properties.GetInputProperties(node.name());
+  if (props.empty()) {
+    return false;
+  }
+  const OpInfo::TensorProperties& prop = props[0];
+  if (prop.dtype() == DT_INVALID) {
+    return false;
+  }
+  const PartialTensorShape shape(prop.shape());
+  if (!shape.IsFullyDefined()) {
+    return false;
+  }
+
+  PartialTensorShape new_dims;
+  if (outputs[0]->dtype() == DT_INT32) {
+    std::vector<int32> shp;
+    for (int i = 0; i < outputs[0]->NumElements(); ++i) {
+      int32 dim = outputs[0]->flat<int32>()(i);
+      shp.push_back(dim);
+    }
+    TF_CHECK_OK(TensorShapeUtils::MakeShape(shp, &new_dims));
+  } else {
+    std::vector<int64> shp;
+    for (int i = 0; i < outputs[0]->NumElements(); ++i) {
+      int64 dim = outputs[0]->flat<int64>()(i);
+      shp.push_back(dim);
+    }
+    TF_CHECK_OK(TensorShapeUtils::MakeShape(shp, &new_dims));
+  }
+
+  return shape.IsCompatibleWith(new_dims);
+}
+
+Status ConstantFolding::SimplifyGraph(GraphDef* output,
+                                      const GraphProperties& properties) {
   for (auto& node : *output->mutable_node()) {
     if (IsSimplifiableReduction(node)) {
       // Replace the reduction node with an identity node, that can be further
@@ -643,9 +700,23 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output) {
       }
       node.mutable_input()->RemoveLast();
       for (const auto& input : reductions_indices->input()) {
-        if (IsControlInput(input)) {
-          *node.add_input() = input;
-        }
+        DCHECK(IsControlInput(input));
+        *node.add_input() = input;
+      }
+    }
+    if (IsSimplifiableReshape(node, properties)) {
+      const NodeDef* new_shape = node_map_->GetNode(node.input(1));
+      DataType output_type = node.attr().at("T").type();
+      node.set_op("Identity");
+      node.clear_attr();
+      (*node.mutable_attr())["T"].set_type(output_type);
+      if (node.input_size() > 2) {
+        node.mutable_input()->SwapElements(1, node.input_size() - 1);
+      }
+      node.mutable_input()->RemoveLast();
+      for (const auto& input : new_shape->input()) {
+        DCHECK(IsControlInput(input));
+        *node.add_input() = input;
       }
     }
   }
@@ -665,9 +736,17 @@ Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
   }
   device_.reset(new DeviceSimple());
   *output = GraphDef();
-  TF_RETURN_IF_ERROR(MaterializeShapes(item));
+
+  GraphProperties properties(item);
+  Status s = properties.InferStatically();
+  if (!s.ok()) {
+    VLOG(1) << "Failed to infer graph shapes: " << s;
+  } else {
+    TF_RETURN_IF_ERROR(MaterializeShapes(item, properties));
+  }
+
   TF_RETURN_IF_ERROR(FoldGraph(output));
-  TF_RETURN_IF_ERROR(SimplifyGraph(output));
+  TF_RETURN_IF_ERROR(SimplifyGraph(output, properties));
   LOG(INFO) << "Optimized graph size: " << output->node_size();
 
   *output->mutable_library() = item.graph.library();
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index 36c9ad2c64..1c20233345 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include <regex>
 #include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
 #include "tensorflow/core/grappler/utils.h"
 
@@ -45,7 +46,8 @@ class ConstantFolding : public GraphOptimizer {
 
  private:
   string AddControlDependency(const string& input_name);
-  Status MaterializeShapes(const GrapplerItem& item);
+  Status MaterializeShapes(const GrapplerItem& item,
+                           const GraphProperties& properties);
 
   bool IsFoldable(const NodeDef& node) const;
 
@@ -63,7 +65,9 @@ class ConstantFolding : public GraphOptimizer {
   Status FoldGraph(GraphDef* output);
 
   bool IsSimplifiableReduction(const NodeDef& node) const;
-  Status SimplifyGraph(GraphDef* output);
+  bool IsSimplifiableReshape(const NodeDef& node,
+                             const GraphProperties& properties) const;
+  Status SimplifyGraph(GraphDef* output, const GraphProperties& properties);
 
   std::unique_ptr<DeviceBase> device_;
   GraphDef graph_;
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index cb29ccc341..2d1a25bd79 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -149,18 +149,16 @@ TEST_F(ConstantFoldingTest, ControlDependencies) {
   Output dflt = ops::Const(scope.WithOpName("dflt"), 3.14f, {1});
   Output p1 = ops::PlaceholderWithDefault(scope.WithOpName("p1"), dflt, {1});
   Output p2 = ops::PlaceholderWithDefault(scope.WithOpName("p2"), dflt, {1});
-  Output c = ops::Const(scope.WithOpName("c"), 10, {3});
+  Output c =
+      ops::Const(scope.WithOpName("c").WithControlDependencies(p1), 10, {3});
   Output i1 = ops::Identity(scope.WithOpName("i1"), {c});
-  Output i2 = ops::Identity(scope.WithOpName("i2"), {i1});
+  Output i2 =
+      ops::Identity(scope.WithOpName("i2").WithControlDependencies(p2), {i1});
   Output i3 = ops::Identity(scope.WithOpName("e"), {i2});
 
   GrapplerItem item;
   item.fetch.push_back("i3");
   TF_CHECK_OK(scope.ToGraphDef(&item.graph));
-  ASSERT_EQ("c", item.graph.node(3).name());
-  (*item.graph.mutable_node(3)->add_input()) = "^p1";
-  ASSERT_EQ("i2", item.graph.node(5).name());
-  (*item.graph.mutable_node(5)->add_input()) = "^p2";
 
   ConstantFolding fold;
   GraphDef output;
@@ -515,6 +513,98 @@ TEST_F(ConstantFoldingTest, NoOpReduction) {
   EXPECT_TRUE(found);
 }
 
+TEST_F(ConstantFoldingTest, NoOpReshape) {
+  // Build a simple graph with a reshape that can be reduced to the identity.
+  tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
+
+  // A reshape than can be optimized
+  Output d1 = ops::Const(scope.WithOpName("d1"), 3.14f, {17});
+  Output v1 = ops::PlaceholderWithDefault(scope.WithOpName("v1"), d1, {17});
+  Output c1 =
+      ops::Const(scope.WithOpName("c1").WithControlDependencies(v1), 17, {1});
+  Output i1 = ops::Identity(scope.WithOpName("i1"), c1);
+  Output r1 =
+      ops::Reshape(scope.WithOpName("r1").WithControlDependencies(d1), v1, i1);
+  Output s1 = ops::Square(scope.WithOpName("s1"), r1);
+
+  // A multi dimensional reshape than can be optimized
+  Output d3 = ops::Const(scope.WithOpName("d3"), 3.14f, {5, 5, 5});
+  Output v3 =
+      ops::PlaceholderWithDefault(scope.WithOpName("v3"), d3, {5, 5, 5});
+  Output c3 =
+      ops::Const(scope.WithOpName("c3").WithControlDependencies(v3), 5, {3});
+  Output i3 = ops::Identity(scope.WithOpName("i3"), c3);
+  Output r3 = ops::Reshape(scope.WithOpName("r3"), v3, i3);
+  Output s3 = ops::Square(scope.WithOpName("s3"), r3);
+
+  // A multi dimensional partially defined reshape than can be optimized
+  Output d4 = ops::Const(scope.WithOpName("d4"), 3.14f, {5, 5, 5});
+  Output v4 =
+      ops::PlaceholderWithDefault(scope.WithOpName("v4"), d4, {5, 5, 5});
+  Output c4 = ops::Const(scope.WithOpName("c4").WithControlDependencies(v4),
+                         {5, -1, 5}, {3});
+  Output i4 = ops::Identity(scope.WithOpName("i4"), c4);
+  Output r4 = ops::Reshape(scope.WithOpName("r4"), v4, i4);
+  Output s4 = ops::Square(scope.WithOpName("s4"), r4);
+
+  // A reshape that can't be optimized
+  Output d2 = ops::Const(scope.WithOpName("d2"), 2.7f, {17, 1});
+  Output v2 = ops::PlaceholderWithDefault(scope.WithOpName("v2"), d2, {17, 1});
+  Output c2 =
+      ops::Const(scope.WithOpName("c2").WithControlDependencies(v2), 17, {1});
+  Output r2 = ops::Reshape(scope.WithOpName("r2"), v2, c2);
+  Output s2 = ops::Square(scope.WithOpName("s2"), r2);
+
+  GrapplerItem item;
+  item.fetch = {"s1", "s2", "s3", "s4"};
+  TF_CHECK_OK(scope.ToGraphDef(&item.graph));
+
+  ConstantFolding fold;
+  GraphDef output;
+  Status status = fold.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  auto expected = EvaluateNodes(item.graph, item.fetch);
+  auto optimized = EvaluateNodes(output, item.fetch);
+  ASSERT_EQ(4, expected.size());
+  ASSERT_EQ(4, optimized.size());
+  test::ExpectTensorEqual<float>(expected[0], optimized[0]);
+  test::ExpectTensorEqual<float>(expected[1], optimized[1]);
+  test::ExpectTensorEqual<float>(expected[2], optimized[2]);
+  test::ExpectTensorEqual<float>(expected[3], optimized[3]);
+
+  int found = 0;
+  for (const auto& node : output.node()) {
+    if (node.name() == "r1") {
+      ++found;
+      EXPECT_EQ("Identity", node.op());
+      ASSERT_EQ(3, node.input_size());
+      EXPECT_EQ("v1", node.input(0));
+      EXPECT_EQ("^d1", node.input(1));
+      EXPECT_EQ("^v1", node.input(2));
+    } else if (node.name() == "r3") {
+      ++found;
+      EXPECT_EQ("Identity", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("v3", node.input(0));
+      EXPECT_EQ("^v3", node.input(1));
+    } else if (node.name() == "r4") {
+      ++found;
+      EXPECT_EQ("Identity", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("v4", node.input(0));
+      EXPECT_EQ("^v4", node.input(1));
+    } else if (node.name() == "r2") {
+      ++found;
+      EXPECT_EQ("Reshape", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("v2", node.input(0));
+      EXPECT_EQ("c2", node.input(1));
+    }
+  }
+  EXPECT_EQ(4, found);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From 175661d6869e83471d8a4b8b0f3998470249654b Mon Sep 17 00:00:00 2001
From: Jayeol Chun <chunjy92@gmail.com>
Date: Wed, 28 Jun 2017 15:39:36 -0400
Subject: [PATCH 0565/1375] fix link in README.md

correctly link parameterized_docker_build.sh to its actual location, instead of README.md
---
 tensorflow/tools/docker/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index 6d5a9bdc4c..3780bde2be 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -55,7 +55,7 @@ for additional containers, such as release candidates or nightly builds.
 ## Rebuilding the containers
 
 Building TensorFlow Docker containers should be done through the
-[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/README.md)
+[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/parameterized_docker_build.sh)
 script. The raw Dockerfiles should not be used directly as they contain strings
 to be replaced by the script during the build.
 
-- 
GitLab


From 90675330aaa468a0498db1f235396eb2da0e3b57 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Wed, 28 Jun 2017 12:58:41 -0700
Subject: [PATCH 0566/1375] [XLA] Minor changes to base test classes: - Change
 codegen_test_base and hlo_test_base to disable constant folding. - Add
 convenience method ExecuteAsync to ClientLibraryTestBase.

PiperOrigin-RevId: 160440787
---
 tensorflow/compiler/xla/tests/client_library_test_base.cc | 6 ++++++
 tensorflow/compiler/xla/tests/client_library_test_base.h  | 3 +++
 tensorflow/compiler/xla/tests/codegen_test_base.cc        | 3 +++
 tensorflow/compiler/xla/tests/hlo_test_base.cc            | 8 +++++++-
 4 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index 4e2e0c7776..1f7a0ec9e7 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -69,6 +69,12 @@ StatusOr<std::unique_ptr<GlobalData>> ClientLibraryTestBase::Execute(
   return client_->Execute(computation, arguments, &execution_options_);
 }
 
+StatusOr<ExecutionHandle> ClientLibraryTestBase::ExecuteAsync(
+    const Computation& computation,
+    tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
+  return client_->ExecuteAsync(computation, arguments, &execution_options_);
+}
+
 StatusOr<std::unique_ptr<Literal>> ClientLibraryTestBase::ExecuteAndTransfer(
     const Computation& computation,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments,
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index 763ff09965..f665bb4514 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -72,6 +72,9 @@ class ClientLibraryTestBase : public ::testing::Test {
   StatusOr<std::unique_ptr<GlobalData>> Execute(
       ComputationBuilder* builder,
       tensorflow::gtl::ArraySlice<GlobalData*> arguments);
+  StatusOr<ExecutionHandle> ExecuteAsync(
+      const Computation& computation,
+      tensorflow::gtl::ArraySlice<GlobalData*> arguments);
   StatusOr<std::unique_ptr<Literal>> ExecuteAndTransfer(
       ComputationBuilder* builder,
       tensorflow::gtl::ArraySlice<GlobalData*> arguments,
diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.cc b/tensorflow/compiler/xla/tests/codegen_test_base.cc
index 8d597eb72c..90767c4a17 100644
--- a/tensorflow/compiler/xla/tests/codegen_test_base.cc
+++ b/tensorflow/compiler/xla/tests/codegen_test_base.cc
@@ -39,7 +39,10 @@ std::unique_ptr<HloModule> CodegenTestBase::CreateNewModuleWithEmbeddedIr(
   auto debug_options = legacy_flags::GetDebugOptionsFromFlags();
   debug_options.set_xla_embed_ir_in_executable(true);
   debug_options.set_xla_gpu_ftz(ftz);
+  // TODO(b/38354253): Change tests to use Parameters instead of Constants.
+  debug_options.add_xla_disable_hlo_passes("constant_folding");
   config.set_debug_options(debug_options);
+
   return MakeUnique<HloModule>(TestName(), VersionedComputationHandle(),
                                config);
 }
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index f8452e37f8..8149e2b7cc 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -67,7 +67,13 @@ HloTestBase::~HloTestBase() {
 /* static */
 std::unique_ptr<HloModule> HloTestBase::CreateNewModule() {
   HloModuleConfig config;
-  config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags());
+
+  auto debug_options = legacy_flags::GetDebugOptionsFromFlags();
+  // TODO(b/38354253): Change tests to use Parameters instead of Constants.
+  debug_options.add_xla_disable_hlo_passes("constant_folding");
+
+  config.set_debug_options(debug_options);
+
   return MakeUnique<HloModule>(TestName(), VersionedComputationHandle(),
                                config);
 }
-- 
GitLab


From 7ab72bf2205b1775607932b6ccbcd7099368705e Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 28 Jun 2017 13:45:06 -0700
Subject: [PATCH 0567/1375] [XLA] Move remaining hlo graph dumper flags into
 debug_options.

Also pipe debug_options through the code in hlo_graph_dumper, since the number
of individual parameters was growing too large.

PiperOrigin-RevId: 160446088
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ----
 .../xla/legacy_flags/debug_options_flags.cc   | 11 +++
 .../legacy_flags/hlo_graph_dumper_flags.cc    | 67 -------------------
 .../xla/legacy_flags/hlo_graph_dumper_flags.h | 50 --------------
 tensorflow/compiler/xla/service/BUILD         |  2 +-
 .../compiler/xla/service/graphviz_example.cc  |  9 ++-
 .../compiler/xla/service/hlo_graph_dumper.cc  | 28 ++++----
 .../compiler/xla/service/hlo_graph_dumper.h   |  6 +-
 .../hlo_subcomputation_unification_test.cc    | 18 +++--
 .../compiler/xla/service/layout_assignment.cc |  6 +-
 tensorflow/compiler/xla/tools/BUILD           |  1 -
 .../dumped_computation_to_tf_graphdef.cc      |  5 --
 tensorflow/compiler/xla/xla.proto             | 42 +++++++-----
 15 files changed, 70 insertions(+), 190 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 7dfed901fc..20057c1ae4 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -129,7 +129,6 @@ cc_library(
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/compiler/xla/legacy_flags:util_flags",
         "//tensorflow/compiler/xla/service:compiler",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 72cb676b82..1298ac1ae5 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
@@ -133,7 +132,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
-  xla::legacy_flags::AppendHloGraphDumperFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::legacy_flags::AppendServiceFlags(&flag_list);
   xla::legacy_flags::AppendUtilFlags(&flag_list);
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 33dc94fe00..9444cc1061 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -90,18 +90,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "hlo_graph_dumper_flags",
-    srcs = ["hlo_graph_dumper_flags.cc"],
-    hdrs = ["hlo_graph_dumper_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "service_flags",
     srcs = ["service_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index f87cf7083e..43634792ce 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -27,6 +27,8 @@ struct DebugOptionsFlags {
   string xla_generate_hlo_graph;
   bool xla_hlo_graph_addresses;
   bool xla_hlo_graph_layout;
+  string xla_hlo_graph_path;
+  bool xla_hlo_dump_as_graphdef;
   string xla_log_hlo_text;
   string xla_generate_hlo_text_to;
 
@@ -61,6 +63,8 @@ void AllocateFlags() {
   flag_values->xla_generate_hlo_graph = "";
   flag_values->xla_hlo_graph_addresses = false;
   flag_values->xla_hlo_graph_layout = false;
+  flag_values->xla_hlo_graph_path = "/tmp/";
+  flag_values->xla_hlo_dump_as_graphdef = false;
   flag_values->xla_log_hlo_text = "";
   flag_values->xla_generate_hlo_text_to = "";
   flag_values->xla_disable_hlo_passes = "";
@@ -90,6 +94,12 @@ void AllocateFlags() {
            "xla_hlo_graph_layout", &flag_values->xla_hlo_graph_layout,
            "With xla_generate_hlo_graph, show layout of HLO ops in "
            "graph dump."),
+       tensorflow::Flag(
+           "xla_hlo_graph_path", &flag_values->xla_hlo_graph_path,
+           "With xla_generate_hlo_graph, dump the graphs into this path."),
+       tensorflow::Flag("xla_hlo_dump_as_graphdef",
+                        &flag_values->xla_hlo_dump_as_graphdef,
+                        "Dump HLO graphs as TensorFlow GraphDefs."),
        tensorflow::Flag(
            "xla_log_hlo_text", &flag_values->xla_log_hlo_text,
            "HLO modules matching this regex will be dumped to LOG(INFO). "),
@@ -166,6 +176,7 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
   options.set_xla_generate_hlo_graph(flag_values->xla_generate_hlo_graph);
   options.set_xla_hlo_graph_addresses(flag_values->xla_hlo_graph_addresses);
   options.set_xla_hlo_graph_layout(flag_values->xla_hlo_graph_layout);
+  options.set_xla_hlo_graph_path(flag_values->xla_hlo_graph_path);
   options.set_xla_log_hlo_text(flag_values->xla_log_hlo_text);
   options.set_xla_generate_hlo_text_to(flag_values->xla_generate_hlo_text_to);
 
diff --git a/tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.cc b/tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.cc
deleted file mode 100644
index ba43a59195..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.cc
+++ /dev/null
@@ -1,67 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's hlo_graph_dumper module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static HloGraphDumperFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new HloGraphDumperFlags;
-  flags->xla_hlo_dump_graph_path = "/tmp/";
-  flags->xla_hlo_dump_as_graphdef = false;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_hlo_dump_graph_path",
-                       &flags->xla_hlo_dump_graph_path,
-                       "Path to write dumped HLO graphs to"),
-      tensorflow::Flag("xla_hlo_dump_as_graphdef",
-                       &flags->xla_hlo_dump_as_graphdef,
-                       "Dumps HLO graphs as tensorflow GraphDefs"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's hlo_graph_dumper
-// module.
-void AppendHloGraphDumperFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the HloGraphDumperFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-HloGraphDumperFlags* GetHloGraphDumperFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h b/tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h
deleted file mode 100644
index d0b4d092ff..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_GRAPH_DUMPER_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_GRAPH_DUMPER_FLAGS_H_
-
-// Legacy flags for XLA's hlo_graph_dumper module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's hlo_graph_dumper
-// module.
-void AppendHloGraphDumperFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's hlo_graph_dumper module.
-typedef struct {
-  string xla_hlo_dump_graph_path;  // Path to write dumped HLO graphs to
-  // If set, dumps HLO graphs as tensorflow GraphDef; otherwise, dumps HLO
-  // graphs as DOT graph.
-  bool xla_hlo_dump_as_graphdef;
-} HloGraphDumperFlags;
-
-// Return a pointer to the HloGraphDumperFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-HloGraphDumperFlags* GetHloGraphDumperFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_HLO_GRAPH_DUMPER_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 0f1bb363b5..ec12a8acf0 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1803,7 +1803,7 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:window_util",
-        "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
+        "//tensorflow/compiler/xla:xla_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:regexp_internal",
     ],
diff --git a/tensorflow/compiler/xla/service/graphviz_example.cc b/tensorflow/compiler/xla/service/graphviz_example.cc
index d194b3a310..049e8d80d8 100644
--- a/tensorflow/compiler/xla/service/graphviz_example.cc
+++ b/tensorflow/compiler/xla/service/graphviz_example.cc
@@ -156,10 +156,9 @@ int main(int argc, char** argv) {
 
   auto module = xla::MakeBigGraph();
 
-  printf("Graph URL: %s\n",
-         xla::hlo_graph_dumper::DumpGraph(
-             *module->entry_computation(), "Example computation",
-             /*show_addresses=*/false, /*show_layouts=*/false)
-             .c_str());
+  printf("Graph URL: %s\n", xla::hlo_graph_dumper::DumpGraph(
+                                *module->entry_computation(),
+                                "Example computation", xla::DebugOptions())
+                                .c_str());
   return 0;
 }
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 69166a8d13..7bb0ca2329 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include <string>
 
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_tfgraph_builder.h"
@@ -518,10 +517,9 @@ namespace {
 
 class FileGraphRenderer : public GraphRendererInterface {
  public:
-  string RenderGraph(const string& graph, GraphKind graph_kind) override {
+  string RenderGraph(const string& graph, GraphKind graph_kind,
+                     const DebugOptions& debug_options) override {
     static std::atomic<int> output_num(0);
-    legacy_flags::HloGraphDumperFlags* flags =
-        legacy_flags::GetHloGraphDumperFlags();
     string file_extension;
     switch (graph_kind) {
       case DOT_GRAPH:
@@ -532,7 +530,7 @@ class FileGraphRenderer : public GraphRendererInterface {
         break;
     }
     string path =
-        JoinPath(flags->xla_hlo_dump_graph_path,
+        JoinPath(debug_options.xla_hlo_graph_path(),
                  StrCat("hlo_graph_", output_num++, ".XXXXXX", file_extension));
     auto status = Status::OK();
     int fd = mkstemps(&path[0], file_extension.length());
@@ -558,13 +556,11 @@ XLA_REGISTER_GRAPH_RENDERER(FileGraphRenderer, 0);
 }  // namespace
 
 string DumpGraph(const HloComputation& computation, const string& label,
-                 bool show_addresses, bool show_layouts,
+                 const DebugOptions& debug_options,
                  const HloExecutionProfile* hlo_execution_profile) {
   string graph;
   string graph_url;
-  legacy_flags::HloGraphDumperFlags* flags =
-      legacy_flags::GetHloGraphDumperFlags();
-  if (flags->xla_hlo_dump_as_graphdef) {
+  if (debug_options.xla_hlo_dump_as_graphdef()) {
     HloTfGraphBuilder builder;
     TF_CHECK_OK(builder.AddComputation(computation));
     CHECK(tensorflow::protobuf::TextFormat::PrintToString(builder.GetGraphDef(),
@@ -573,12 +569,13 @@ string DumpGraph(const HloComputation& computation, const string& label,
     // renderers support rendering GraphDefs. Always dump GraphDefs to files
     // for now.
     graph_url = FileGraphRenderer().RenderGraph(
-        graph, GraphRendererInterface::TF_GRAPHDEF);
+        graph, GraphRendererInterface::TF_GRAPHDEF, debug_options);
   } else {
-    graph = ComputationToDotGraph(computation, label, show_addresses,
-                                  show_layouts, hlo_execution_profile);
+    graph = ComputationToDotGraph(
+        computation, label, debug_options.xla_hlo_graph_addresses(),
+        debug_options.xla_hlo_graph_layout(), hlo_execution_profile);
     graph_url = GetGraphRenderer()->RenderGraph(
-        graph, GraphRendererInterface::DOT_GRAPH);
+        graph, GraphRendererInterface::DOT_GRAPH, debug_options);
   }
   LOG(INFO) << "computation " << computation.name() << " [" << label
             << "]: " << graph_url;
@@ -605,9 +602,8 @@ string MaybeDumpHloModule(const HloModule& module, const string& label,
   if (!debug_options.xla_generate_hlo_graph().empty() &&
       RE2::PartialMatch(module.name(),
                         debug_options.xla_generate_hlo_graph())) {
-    graph_url = DumpGraph(*module.entry_computation(), label,
-                          debug_options.xla_hlo_graph_addresses(),
-                          debug_options.xla_hlo_graph_layout(), profile);
+    graph_url =
+        DumpGraph(*module.entry_computation(), label, debug_options, profile);
   }
   if (!debug_options.xla_log_hlo_text().empty() &&
       RE2::PartialMatch(module.name(), debug_options.xla_log_hlo_text())) {
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.h b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
index eb65cf2d32..bc404a7a37 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.h
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla.pb.h"
 
 namespace xla {
 namespace hlo_graph_dumper {
@@ -38,7 +39,8 @@ class GraphRendererInterface {
 
   // Renders a DOT graph, returning a description of the rendered output
   // (e.g., a URL)
-  virtual string RenderGraph(const string& graph, GraphKind graph_kind) = 0;
+  virtual string RenderGraph(const string& graph, GraphKind graph_kind,
+                             const DebugOptions& debug_options) = 0;
 };
 
 // Dump the given HLO module if a dump is requested in its debug options. Based
@@ -52,7 +54,7 @@ string MaybeDumpHloModule(const HloModule& module, const string& label,
 // graph (e.g., a URL) based on the renderer. The "best" renderer in the
 // registry is used.
 string DumpGraph(const HloComputation& computation, const string& label,
-                 bool show_addresses, bool show_layouts,
+                 const DebugOptions& debug_options,
                  const HloExecutionProfile* hlo_execution_profile = nullptr);
 
 // Dumps the HloModule::ToString() as a file into the provided directory path
diff --git a/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc b/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc
index c98856b192..e3d287d4c9 100644
--- a/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc
@@ -89,12 +89,14 @@ TEST_F(HloSubcomputationUnificationTest, UnifyIdentities) {
   EXPECT_NE(x->to_apply(), y->to_apply());
   if (VLOG_IS_ON(1)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
-                                "before unification", false, false, nullptr);
+                                "before unification",
+                                module->config().debug_options());
   }
   EXPECT_TRUE(HloSubcomputationUnification().Run(module.get()).ValueOrDie());
   if (VLOG_IS_ON(1)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
-                                "after unification", false, false, nullptr);
+                                "after unification",
+                                module->config().debug_options());
   }
   EXPECT_EQ(2, module->computations().size());
   EXPECT_EQ(x->to_apply(), y->to_apply());
@@ -126,12 +128,14 @@ TEST_F(HloSubcomputationUnificationTest, UnifyAdditions) {
   EXPECT_NE(x->to_apply(), y->to_apply());
   if (VLOG_IS_ON(1)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
-                                "before unification", false, false, nullptr);
+                                "before unification",
+                                module->config().debug_options());
   }
   EXPECT_TRUE(HloSubcomputationUnification().Run(module.get()).ValueOrDie());
   if (VLOG_IS_ON(1)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
-                                "after unification", false, false, nullptr);
+                                "after unification",
+                                module->config().debug_options());
   }
   EXPECT_EQ(2, module->computations().size());
   EXPECT_EQ(x->to_apply(), y->to_apply());
@@ -164,12 +168,14 @@ TEST_F(HloSubcomputationUnificationTest, DifferentParameterShapes) {
   EXPECT_NE(x->to_apply(), y->to_apply());
   if (VLOG_IS_ON(1)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
-                                "before unification", false, false, nullptr);
+                                "before unification",
+                                module->config().debug_options());
   }
   EXPECT_FALSE(HloSubcomputationUnification().Run(module.get()).ValueOrDie());
   if (VLOG_IS_ON(1)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
-                                "after unification", false, false, nullptr);
+                                "after unification",
+                                module->config().debug_options());
   }
   EXPECT_EQ(3, module->computations().size());
   EXPECT_NE(x->to_apply(), y->to_apply());
diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index ff1493230b..aafface0b9 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -1346,8 +1346,7 @@ StatusOr<bool> LayoutAssignment::Run(HloModule* module) {
   if (VLOG_IS_ON(10)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
                                 "before layout assignment",
-                                /*show_addresses=*/false,
-                                /*show_layouts=*/true);
+                                module->config().debug_options());
   }
 
   // Assign layouts to computations in an order such that a callee computation
@@ -1373,8 +1372,7 @@ StatusOr<bool> LayoutAssignment::Run(HloModule* module) {
   if (VLOG_IS_ON(10)) {
     hlo_graph_dumper::DumpGraph(*module->entry_computation(),
                                 "after layout assignment",
-                                /*show_addresses=*/false,
-                                /*show_layouts=*/true);
+                                module->config().debug_options());
   }
 
   // All layouts are reset then reassigned by this pass.
diff --git a/tensorflow/compiler/xla/tools/BUILD b/tensorflow/compiler/xla/tools/BUILD
index 8ba76ddc1b..4bbe0ba0dd 100644
--- a/tensorflow/compiler/xla/tools/BUILD
+++ b/tensorflow/compiler/xla/tools/BUILD
@@ -188,7 +188,6 @@ cc_binary(
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:hlo_graph_dumper_flags",
         "//tensorflow/compiler/xla/service",
         "//tensorflow/compiler/xla/service:hlo_graph_dumper",
         "//tensorflow/compiler/xla/service:session_proto",
diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
index ebaea0511c..a8879f1a29 100644
--- a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
+++ b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
@@ -31,7 +31,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/hlo_graph_dumper_flags.h"
 #include "tensorflow/compiler/xla/service/service.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -74,10 +73,6 @@ int main(int argc, char** argv) {
 
   tensorflow::port::InitMain(argv[0], &argc, &argv);
 
-  xla::legacy_flags::HloGraphDumperFlags* dumper_flags =
-      xla::legacy_flags::GetHloGraphDumperFlags();
-  dumper_flags->xla_hlo_dump_as_graphdef = true;
-
   tensorflow::gtl::ArraySlice<char*> args(argv, argc);
   args.pop_front();  // Pop off the binary name, argv[0]
   xla::tools::RealMain(args);
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 8db0c26da3..9ac89d1b1c 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -29,25 +29,34 @@ message DebugOptions {
   string xla_generate_hlo_graph = 1;
 
   // Show addresses of HLO ops in graph dump.
-  bool xla_hlo_graph_addresses = 21;
+  bool xla_hlo_graph_addresses = 2;
 
   // Show layout of HLO ops in graph dump.
-  bool xla_hlo_graph_layout = 22;
+  bool xla_hlo_graph_layout = 3;
+
+  // Path to dump HLO graphs to.
+  string xla_hlo_graph_path = 4;
+
+  // Dump HLO graphs as TensorFlow GraphDefs.
+  bool xla_hlo_dump_as_graphdef = 5;
 
   // HLO modules matching this regex will be dumped to LOG(INFO). Set to ".*" to
   // dump *all* HLO modules.
-  string xla_log_hlo_text = 23;
+  string xla_log_hlo_text = 6;
 
   // Dump all HLO modules as text into the provided directory path.
-  string xla_generate_hlo_text_to = 24;
+  string xla_generate_hlo_text_to = 7;
+
+  // Dump compilation artifacts as JSON into this directory.
+  string xla_dump_debug_json_to = 8;
 
   // List of HLO passes to disable. These names must exactly match the pass
   // names as specified by the HloPassInterface::name() method.
-  repeated string xla_disable_hlo_passes = 2;
+  repeated string xla_disable_hlo_passes = 30;
 
   // Numerical optimization level for the XLA compiler backend; the specific
   // interpretation of this value is left to the backends.
-  int32 xla_backend_optimization_level = 3;
+  int32 xla_backend_optimization_level = 31;
 
   // When true, "unsafe" mathematical optimizations are enabled. These
   // transformations include but are not limited to:
@@ -56,38 +65,35 @@ message DebugOptions {
   //    function, or transforming x/y into x * (1/y)).
   //  - Assuming that operations never produce or consume NaN or +/- Inf.
   //  - Assuming that +0 and -0 are indistinguishable.
-  bool xla_enable_fast_math = 4;
+  bool xla_enable_fast_math = 32;
 
   // Embed the compiler IR as a string in the executable.
-  bool xla_embed_ir_in_executable = 5;
+  bool xla_embed_ir_in_executable = 33;
 
   // Dump the compiler IR into this file/path.
-  string xla_dump_ir_to = 6;
-
-  // Dump compilation artifacts as JSON into this directory.
-  string xla_dump_debug_json_to = 7;
+  string xla_dump_ir_to = 34;
 
   // When generating calls to Eigen in the CPU backend, use multi-threaded Eigen
   // mode.
-  bool xla_cpu_multi_thread_eigen = 8;
+  bool xla_cpu_multi_thread_eigen = 60;
 
   // Path to directory with cuda/ptx tools and libraries.
-  string xla_gpu_cuda_data_dir = 9;
+  string xla_gpu_cuda_data_dir = 61;
 
   // Enable flush-to-zero semantics in the GPU backend.
-  bool xla_gpu_ftz = 10;
+  bool xla_gpu_ftz = 62;
 
   // If true, in LLVM-based backends, emit !alias.scope metadata in
   // generated IR.
-  bool xla_llvm_enable_alias_scope_metadata = 11;
+  bool xla_llvm_enable_alias_scope_metadata = 63;
 
   // If true, in LLVM-based backends, emit !noalias metadata in the
   // generated IR.
-  bool xla_llvm_enable_noalias_metadata = 12;
+  bool xla_llvm_enable_noalias_metadata = 64;
 
   // If true, in LLVM-based backends, emit !invariant.load metadata in
   // the generated IR.
-  bool xla_llvm_enable_invariant_load_metadata = 13;
+  bool xla_llvm_enable_invariant_load_metadata = 65;
 
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
-- 
GitLab


From 1cb8405d8065903e7a7151614ec05bf4debeba64 Mon Sep 17 00:00:00 2001
From: Rockford Wei <rocky@perfect.org>
Date: Wed, 28 Jun 2017 19:46:49 -0400
Subject: [PATCH 0568/1375] Adding Swift Language Bindings. (#11074)

https://github.com/PerfectlySoft/Perfect-TensorFlow
Supports Swift 3.1/4.0 on both macOS / Ubuntu Linux, with full documentation and example.
---
 tensorflow/docs_src/community/welcome.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md
index 4c8c4e1a97..0f85e8f1c0 100644
--- a/tensorflow/docs_src/community/welcome.md
+++ b/tensorflow/docs_src/community/welcome.md
@@ -25,6 +25,7 @@ The TensorFlow community has created many great projects around TensorFlow, incl
 * [Bitfusion's` GPU-enabled AWS EC2 TensorFlow AMI](https://github.com/bitfusionio/amis/tree/master/awsmrkt-bfboost-ubuntu14-cuda75-tensorflow) ([Launch AMI](https://aws.amazon.com/marketplace/pp/B01EYKBEQ0))
 * [Rust language bindings](https://github.com/google/tensorflow-rust)
 * [Operator Vectorization Library](https://github.com/opveclib/opveclib)
+* [Swift language bindings](https://github.com/PerfectlySoft/Perfect-TensorFlow)
 
 ## TensorFlow Communities Around the World
 
-- 
GitLab


From 314008aceb061675eb30daf7e0cae303ebfe8723 Mon Sep 17 00:00:00 2001
From: Liangliang He <lliang.he@gmail.com>
Date: Thu, 29 Jun 2017 08:42:50 +0800
Subject: [PATCH 0569/1375] Allow variable sized tensor for RDMA transport
 (#10700)

---
 tensorflow/contrib/verbs/rdma.cc | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/verbs/rdma.cc b/tensorflow/contrib/verbs/rdma.cc
index 6f3a616fe8..445cbe290a 100644
--- a/tensorflow/contrib/verbs/rdma.cc
+++ b/tensorflow/contrib/verbs/rdma.cc
@@ -761,8 +761,8 @@ void RdmaTensorBuffer::SendNextItem() {
           (buffer_size > size_ && local_status_ == idle &&
            remote_status_ == idle)) {
         if ((local_status_ != none) && (buffer_size > size_)) {
-          CHECK(rm.data_type_ == DT_STRING)
-              << "Only string tensor allows to change size";
+          VLOG(2) << "Extend RDMA buffer from " << size_ << " to "
+                  << buffer_size;
         }
         CreateCPUBuffer(buffer_size, false);
         mu_.unlock();
@@ -782,11 +782,13 @@ void RdmaTensorBuffer::SendNextItem() {
         // local/remote_status_ won't be set back to idle
         // unitl Write() is successful
         mu_.unlock();
-        CHECK((buffer_size == size_ && rm.data_type_ != DT_STRING) ||
-              (buffer_size <= size_ && rm.data_type_ == DT_STRING))
-            << "tensor and buffer size do not agree!"
-            << " buffer_size = " << size_
-            << " requested tensor size = " << buffer_size << in.DebugString();
+        if (!((buffer_size == size_ && rm.data_type_ != DT_STRING) ||
+              (buffer_size <= size_ && rm.data_type_ == DT_STRING))) {
+          VLOG(2) << "Tensor and buffer size do not agree,"
+                  << " buffer_size = " << size_
+                  << " requested tensor size = "
+                  << buffer_size << in.DebugString();
+        }
         uint32_t imm_data = LookupBufferIndex(key);
         rm.type_ = RDMA_MESSAGE_TENSOR_WRITE;
         string message = RdmaMessage::CreateMessage(rm);
-- 
GitLab


From 0df000162c698a4247faa5dc34ce25cb029eac45 Mon Sep 17 00:00:00 2001
From: Marcel Hlopko <marcel@hlopko.com>
Date: Thu, 29 Jun 2017 03:07:49 +0200
Subject: [PATCH 0570/1375] Remove implicit iteration from
 third_party/toolchains/cpu/CROSSTOOL (#11111)

* Remove implicit iteration from third_party/toolchains/cpu/CROSSTOOL

Bazel will stop supporting implicit iteration soon, and will require explicit
'iterate_over' message. This cl updates the only affected Tensorflow crosstool.

* Remove incorrect double iteration from cpus crosstool
---
 third_party/toolchains/cpus/CROSSTOOL | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/third_party/toolchains/cpus/CROSSTOOL b/third_party/toolchains/cpus/CROSSTOOL
index 0e2d0119af..0fff8191e0 100644
--- a/third_party/toolchains/cpus/CROSSTOOL
+++ b/third_party/toolchains/cpus/CROSSTOOL
@@ -374,12 +374,15 @@ toolchain {
       action: 'c++-header-preprocessing'
       action: 'c++-module-compile'
       flag_group {
+        iterate_over: 'quote_include_paths'
         flag: '/I%{quote_include_paths}'
       }
       flag_group {
+        iterate_over: 'include_paths'
         flag: '/I%{include_paths}'
       }
       flag_group {
+        iterate_over: 'system_include_paths'
         flag: '/I%{system_include_paths}'
       }
     }
@@ -630,6 +633,7 @@ toolchain {
       action: 'c++-link-dynamic-library'
       expand_if_all_available: 'linkstamp_paths'
       flag_group {
+        iterate_over: 'linkstamp_paths'
         flag: '%{linkstamp_paths}'
       }
     }
@@ -671,6 +675,7 @@ toolchain {
       action: 'c++-link-pic-static-library'
       action: 'c++-link-alwayslink-pic-static-library'
       flag_group {
+        iterate_over: 'libopts'
         flag: '%{libopts}'
       }
     }
@@ -770,6 +775,7 @@ toolchain {
       action: 'c++-link-executable'
       action: 'c++-link-dynamic-library'
       flag_group {
+        iterate_over: 'legacy_link_flags'
         flag: '%{legacy_link_flags}'
       }
     }
-- 
GitLab


From 4bba002fb572f45f58dfcf2d0674cb452b4ae9de Mon Sep 17 00:00:00 2001
From: Tiago Morais Morgado <eremitatm88@gmail.com>
Date: Thu, 29 Jun 2017 02:08:00 +0100
Subject: [PATCH 0571/1375] fixing a single typo (#11108)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index abbead98a7..41dffec069 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
 code.  TensorFlow also includes TensorBoard, a data visualization toolkit.
 
 TensorFlow was originally developed by researchers and engineers
-working on the Google Brain team within Google's Machine Intelligence research
+working on the Google Brain team within Google's Machine Intelligence Research
 organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
-- 
GitLab


From 79e65acb81f750ffa88b366c566646d48d16c574 Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Thu, 29 Jun 2017 03:04:16 +0100
Subject: [PATCH 0572/1375] [OpenCL] Cleans cwise ops (#10320)

* [OpenCL] Cleans cwise ops

* [OpenCL] Reduces noise in the pull request

* [OpenCL] Uncomment SqrtGrad registration for SYCL

* [OpenCL] Registers RsqrtGrad (#94)

* [OpenCL] Registers RsqrtGrad

* fixed typo
---
 tensorflow/core/kernels/cwise_op_abs.cc       | 20 +++++-----
 tensorflow/core/kernels/cwise_op_acos.cc      | 16 ++------
 tensorflow/core/kernels/cwise_op_add_1.cc     | 31 ++++++----------
 tensorflow/core/kernels/cwise_op_asin.cc      | 16 ++------
 tensorflow/core/kernels/cwise_op_atan.cc      | 16 ++------
 tensorflow/core/kernels/cwise_op_ceil.cc      | 16 ++------
 tensorflow/core/kernels/cwise_op_cos.cc       | 16 ++------
 tensorflow/core/kernels/cwise_op_div.cc       | 37 ++++++-------------
 .../core/kernels/cwise_op_equal_to_1.cc       |  4 +-
 tensorflow/core/kernels/cwise_op_exp.cc       | 16 ++------
 tensorflow/core/kernels/cwise_op_expm1.cc     |  2 +-
 tensorflow/core/kernels/cwise_op_floor.cc     | 15 ++------
 tensorflow/core/kernels/cwise_op_greater.cc   |  6 +--
 .../core/kernels/cwise_op_greater_equal.cc    |  2 +-
 tensorflow/core/kernels/cwise_op_isfinite.cc  | 16 ++------
 tensorflow/core/kernels/cwise_op_isinf.cc     | 16 ++------
 tensorflow/core/kernels/cwise_op_isnan.cc     | 16 ++------
 tensorflow/core/kernels/cwise_op_less.cc      |  1 -
 .../core/kernels/cwise_op_less_equal.cc       |  5 +--
 tensorflow/core/kernels/cwise_op_log.cc       | 16 ++------
 tensorflow/core/kernels/cwise_op_log1p.cc     | 16 ++------
 tensorflow/core/kernels/cwise_op_maximum.cc   |  7 +---
 tensorflow/core/kernels/cwise_op_minimum.cc   |  3 +-
 tensorflow/core/kernels/cwise_op_mul_1.cc     | 28 +++++---------
 tensorflow/core/kernels/cwise_op_neg.cc       | 17 +--------
 tensorflow/core/kernels/cwise_op_pow.cc       | 15 ++------
 tensorflow/core/kernels/cwise_op_round.cc     |  3 --
 tensorflow/core/kernels/cwise_op_rsqrt.cc     | 19 ++++------
 tensorflow/core/kernels/cwise_op_select.cc    |  2 +
 tensorflow/core/kernels/cwise_op_sign.cc      |  5 +--
 tensorflow/core/kernels/cwise_op_sin.cc       | 16 ++------
 tensorflow/core/kernels/cwise_op_sqrt.cc      | 20 ++++------
 tensorflow/core/kernels/cwise_op_square.cc    | 22 +++++------
 tensorflow/core/kernels/cwise_op_sub.cc       | 31 +++++-----------
 tensorflow/core/kernels/cwise_op_tan.cc       | 16 ++------
 tensorflow/core/kernels/cwise_op_tanh.cc      | 16 ++------
 36 files changed, 152 insertions(+), 367 deletions(-)

diff --git a/tensorflow/core/kernels/cwise_op_abs.cc b/tensorflow/core/kernels/cwise_op_abs.cc
index 8cf1eac41e..5fd38d9dc2 100644
--- a/tensorflow/core/kernels/cwise_op_abs.cc
+++ b/tensorflow/core/kernels/cwise_op_abs.cc
@@ -22,17 +22,6 @@ REGISTER5(UnaryOp, CPU, "Abs", functor::abs, float, Eigen::half, double, int32,
 REGISTER2(UnaryOp, CPU, "ComplexAbs", functor::abs, complex64, complex128);
 #endif
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Abs")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::abs<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER4(UnaryOp, GPU, "Abs", functor::abs, float, Eigen::half, double, int64);
 REGISTER2(UnaryOp, GPU, "ComplexAbs", functor::abs, complex64, complex128);
@@ -48,4 +37,13 @@ REGISTER_KERNEL_BUILDER(Name("Abs")
                         UnaryOp<CPUDevice, functor::abs<int32>>);
 #endif
 
+#if TENSORFLOW_USE_SYCL
+REGISTER3(UnaryOp, SYCL, "Abs", functor::abs, float, double, int64);
+REGISTER_KERNEL_BUILDER(Name("Abs")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .TypeConstraint<int32>("T"),
+                        UnaryOp<CPUDevice, functor::abs<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_acos.cc b/tensorflow/core/kernels/cwise_op_acos.cc
index 65801da3c7..12cc6c8bdd 100644
--- a/tensorflow/core/kernels/cwise_op_acos.cc
+++ b/tensorflow/core/kernels/cwise_op_acos.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Acos", functor::acos, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Acos")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::acos<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Acos", functor::acos, float, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Acos", functor::acos, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_add_1.cc b/tensorflow/core/kernels/cwise_op_add_1.cc
index f6e9b59cf8..acf1f2ad49 100644
--- a/tensorflow/core/kernels/cwise_op_add_1.cc
+++ b/tensorflow/core/kernels/cwise_op_add_1.cc
@@ -19,26 +19,6 @@ namespace tensorflow {
 REGISTER5(BinaryOp, CPU, "Add", functor::add, float, Eigen::half, double, int32,
           int64);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Add")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::add<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-
-REGISTER_KERNEL_BUILDER(Name("Add")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::add<int32>>);
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(BinaryOp, GPU, "Add", functor::add, float, Eigen::half, double);
 
@@ -54,4 +34,15 @@ REGISTER_KERNEL_BUILDER(Name("Add")
                         BinaryOp<CPUDevice, functor::add<int32>>);
 #endif
 
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(BinaryOp, SYCL, "Add", functor::add, float, double);
+REGISTER_KERNEL_BUILDER(Name("Add")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::add<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_asin.cc b/tensorflow/core/kernels/cwise_op_asin.cc
index c9ebfe759b..c28e27d95a 100644
--- a/tensorflow/core/kernels/cwise_op_asin.cc
+++ b/tensorflow/core/kernels/cwise_op_asin.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Asin", functor::asin, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Asin")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::asin<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Asin", functor::asin, float, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Asin", functor::asin, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_atan.cc b/tensorflow/core/kernels/cwise_op_atan.cc
index 72645b303f..7d73de4810 100644
--- a/tensorflow/core/kernels/cwise_op_atan.cc
+++ b/tensorflow/core/kernels/cwise_op_atan.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Atan", functor::atan, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Atan")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::atan<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Atan", functor::atan, float, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Atan", functor::atan, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_ceil.cc b/tensorflow/core/kernels/cwise_op_ceil.cc
index c74e10576d..0111e9d5fd 100644
--- a/tensorflow/core/kernels/cwise_op_ceil.cc
+++ b/tensorflow/core/kernels/cwise_op_ceil.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "Ceil", functor::ceil, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Ceil")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::ceil<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Ceil", functor::ceil, float, Eigen::half, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Ceil", functor::ceil, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_cos.cc b/tensorflow/core/kernels/cwise_op_cos.cc
index 634c90adc6..d4b3b0e393 100644
--- a/tensorflow/core/kernels/cwise_op_cos.cc
+++ b/tensorflow/core/kernels/cwise_op_cos.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Cos", functor::cos, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Cos")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::cos<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Cos", functor::cos, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Cos", functor::cos, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_div.cc b/tensorflow/core/kernels/cwise_op_div.cc
index 1e2300832f..d44c1bf473 100644
--- a/tensorflow/core/kernels/cwise_op_div.cc
+++ b/tensorflow/core/kernels/cwise_op_div.cc
@@ -24,32 +24,6 @@ REGISTER5(BinaryOp, CPU, "TruncateDiv", functor::safe_div, uint8, uint16, int16,
           int32, int64);
 REGISTER5(BinaryOp, CPU, "RealDiv", functor::div, float, Eigen::half, double,
           complex64, complex128);
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Div")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::div<TYPE>>);  \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("RealDiv")                             \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::div<TYPE>>);
-REGISTER_SYCL_KERNEL(float)
-REGISTER_SYCL_KERNEL(double)
-#undef REGISTER_SYCL_KERNEL
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
-REGISTER_KERNEL_BUILDER(Name("Div")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::safe_div<int32>>);
-#endif // TENSORFLOW_USE_SYCL
 #if GOOGLE_CUDA
 REGISTER9(BinaryOp, GPU, "Div", functor::div, float, Eigen::half, double, uint8,
           uint16, int16, int64, complex64, complex128);
@@ -70,4 +44,15 @@ REGISTER_KERNEL_BUILDER(Name("Div")
                         BinaryOp<CPUDevice, functor::safe_div<int32>>);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(BinaryOp, SYCL, "Div", functor::div, float, double);
+REGISTER2(BinaryOp, SYCL, "RealDiv", functor::div, float, double);
+REGISTER_KERNEL_BUILDER(Name("Div")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::safe_div<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_equal_to_1.cc b/tensorflow/core/kernels/cwise_op_equal_to_1.cc
index 7049305deb..ea10ebe9a0 100644
--- a/tensorflow/core/kernels/cwise_op_equal_to_1.cc
+++ b/tensorflow/core/kernels/cwise_op_equal_to_1.cc
@@ -47,8 +47,8 @@ REGISTER_KERNEL_BUILDER(Name("Equal")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER2(BinaryOp, SYCL, "Equal", functor::equal_to, float, double);
-
+REGISTER5(BinaryOp, SYCL, "Equal", functor::equal_to, float, double, uint8,
+          int8, int16);
 REGISTER_KERNEL_BUILDER(Name("Equal")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_exp.cc b/tensorflow/core/kernels/cwise_op_exp.cc
index 2e3a60cf79..9d4d654427 100644
--- a/tensorflow/core/kernels/cwise_op_exp.cc
+++ b/tensorflow/core/kernels/cwise_op_exp.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Exp", functor::exp, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Exp")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::exp<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Exp", functor::exp, float, Eigen::half, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Exp", functor::exp, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_expm1.cc b/tensorflow/core/kernels/cwise_op_expm1.cc
index 5573c2bcc2..4f72308006 100644
--- a/tensorflow/core/kernels/cwise_op_expm1.cc
+++ b/tensorflow/core/kernels/cwise_op_expm1.cc
@@ -22,6 +22,6 @@ REGISTER5(UnaryOp, CPU, "Expm1", functor::expm1, float, Eigen::half, double,
 REGISTER3(UnaryOp, GPU, "Expm1", functor::expm1, float, Eigen::half, double);
 #endif
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(UnaryOp, SYCL, "Expm1", functor::expm1, float);
+REGISTER2(UnaryOp, SYCL, "Expm1", functor::expm1, float, double);
 #endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_floor.cc b/tensorflow/core/kernels/cwise_op_floor.cc
index 59e32d7f6f..5a142b9ce9 100644
--- a/tensorflow/core/kernels/cwise_op_floor.cc
+++ b/tensorflow/core/kernels/cwise_op_floor.cc
@@ -18,19 +18,10 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "Floor", functor::floor, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Floor")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::floor<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Floor", functor::floor, float, Eigen::half, double);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Floor", functor::floor, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_greater.cc b/tensorflow/core/kernels/cwise_op_greater.cc
index 6b5a806aa2..ba89899fb3 100644
--- a/tensorflow/core/kernels/cwise_op_greater.cc
+++ b/tensorflow/core/kernels/cwise_op_greater.cc
@@ -34,11 +34,8 @@ REGISTER_KERNEL_BUILDER(Name("Greater")
                         BinaryOp<CPUDevice, functor::greater<int32>>);
 #endif
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "Greater", functor::greater, float);
+REGISTER2(BinaryOp, SYCL, "Greater", functor::greater, float, double);
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Greater")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
@@ -47,5 +44,4 @@ REGISTER_KERNEL_BUILDER(Name("Greater")
                             .TypeConstraint<int32>("T"),
                         BinaryOp<CPUDevice, functor::greater<int32>>);
 #endif // TENSORFLOW_USE_SYCL
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_greater_equal.cc b/tensorflow/core/kernels/cwise_op_greater_equal.cc
index ac21528256..8f0c483aec 100644
--- a/tensorflow/core/kernels/cwise_op_greater_equal.cc
+++ b/tensorflow/core/kernels/cwise_op_greater_equal.cc
@@ -35,7 +35,7 @@ REGISTER_KERNEL_BUILDER(Name("GreaterEqual")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float);
+REGISTER2(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float, double);
 
 REGISTER_KERNEL_BUILDER(Name("GreaterEqual")
                             .Device(DEVICE_SYCL)
diff --git a/tensorflow/core/kernels/cwise_op_isfinite.cc b/tensorflow/core/kernels/cwise_op_isfinite.cc
index 0faeffa95c..53ec1c1c63 100644
--- a/tensorflow/core/kernels/cwise_op_isfinite.cc
+++ b/tensorflow/core/kernels/cwise_op_isfinite.cc
@@ -19,20 +19,12 @@ namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "IsFinite", functor::isfinite, float, Eigen::half,
           double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("IsFinite")                            \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::isfinite<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "IsFinite", functor::isfinite, float, Eigen::half,
           double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "IsFinite", functor::isfinite, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_isinf.cc b/tensorflow/core/kernels/cwise_op_isinf.cc
index df63006b3f..4b34744304 100644
--- a/tensorflow/core/kernels/cwise_op_isinf.cc
+++ b/tensorflow/core/kernels/cwise_op_isinf.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "IsInf", functor::isinf, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("IsInf")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::isinf<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "IsInf", functor::isinf, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "IsInf", functor::isinf, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_isnan.cc b/tensorflow/core/kernels/cwise_op_isnan.cc
index e1cf7a8637..ad2dd3f722 100644
--- a/tensorflow/core/kernels/cwise_op_isnan.cc
+++ b/tensorflow/core/kernels/cwise_op_isnan.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "IsNan", functor::isnan, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("IsNan")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::isnan<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "IsNan", functor::isnan, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "IsNan", functor::isnan, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_less.cc b/tensorflow/core/kernels/cwise_op_less.cc
index a38f1024a9..136c3666df 100644
--- a/tensorflow/core/kernels/cwise_op_less.cc
+++ b/tensorflow/core/kernels/cwise_op_less.cc
@@ -35,7 +35,6 @@ REGISTER_KERNEL_BUILDER(Name("Less")
 #endif
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER3(BinaryOp, SYCL, "Less", functor::less, float, double, int64);
-
 REGISTER_KERNEL_BUILDER(Name("Less")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_less_equal.cc b/tensorflow/core/kernels/cwise_op_less_equal.cc
index 3a2cc2ae0e..97a2508d12 100644
--- a/tensorflow/core/kernels/cwise_op_less_equal.cc
+++ b/tensorflow/core/kernels/cwise_op_less_equal.cc
@@ -35,8 +35,8 @@ REGISTER_KERNEL_BUILDER(Name("LessEqual")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "LessEqual", functor::less_equal, float);
-
+REGISTER6(BinaryOp, SYCL, "LessEqual", functor::less_equal, float, double,
+          int64, uint8, int8, int16);
 REGISTER_KERNEL_BUILDER(Name("LessEqual")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
@@ -45,5 +45,4 @@ REGISTER_KERNEL_BUILDER(Name("LessEqual")
                             .TypeConstraint<int32>("T"),
                         BinaryOp<CPUDevice, functor::less_equal<int32>>);
 #endif // TENSORFLOW_USE_SYCL
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_log.cc b/tensorflow/core/kernels/cwise_op_log.cc
index 5e74e778c7..7fdfdff0e3 100644
--- a/tensorflow/core/kernels/cwise_op_log.cc
+++ b/tensorflow/core/kernels/cwise_op_log.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Log", functor::log, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Log")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::log<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Log", functor::log, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Log", functor::log, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_log1p.cc b/tensorflow/core/kernels/cwise_op_log1p.cc
index edb821318e..25ad7b24bb 100644
--- a/tensorflow/core/kernels/cwise_op_log1p.cc
+++ b/tensorflow/core/kernels/cwise_op_log1p.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Log1p", functor::log1p, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Log1p")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::log1p<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Log1p", functor::log1p, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Log1p", functor::log1p, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_maximum.cc b/tensorflow/core/kernels/cwise_op_maximum.cc
index 7311f25ec0..87d54e380b 100644
--- a/tensorflow/core/kernels/cwise_op_maximum.cc
+++ b/tensorflow/core/kernels/cwise_op_maximum.cc
@@ -35,11 +35,7 @@ REGISTER_KERNEL_BUILDER(Name("Maximum")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "Maximum", functor::maximum, float);
-
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
+REGISTER3(BinaryOp, SYCL, "Maximum", functor::maximum, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Maximum")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
@@ -48,5 +44,4 @@ REGISTER_KERNEL_BUILDER(Name("Maximum")
                             .TypeConstraint<int32>("T"),
                         BinaryOp<CPUDevice, functor::maximum<int32>>);
 #endif // TENSORFLOW_USE_SYCL
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_minimum.cc b/tensorflow/core/kernels/cwise_op_minimum.cc
index 99e5a76620..442171193b 100644
--- a/tensorflow/core/kernels/cwise_op_minimum.cc
+++ b/tensorflow/core/kernels/cwise_op_minimum.cc
@@ -35,8 +35,7 @@ REGISTER_KERNEL_BUILDER(Name("Minimum")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "Minimum", functor::minimum, float);
-
+REGISTER3(BinaryOp, SYCL, "Minimum", functor::minimum, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Minimum")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_mul_1.cc b/tensorflow/core/kernels/cwise_op_mul_1.cc
index a3cdfa5f84..023eb07ca3 100644
--- a/tensorflow/core/kernels/cwise_op_mul_1.cc
+++ b/tensorflow/core/kernels/cwise_op_mul_1.cc
@@ -26,24 +26,6 @@ REGISTER5(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double,
 REGISTER(BinaryOp, CPU, "Mul", functor::mul, int32);
 #endif  // __ANDROID_TYPES_SLIM__
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Mul")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::mul<TYPE>>);
-REGISTER_SYCL_KERNEL(float)
-REGISTER_SYCL_KERNEL(double)
-#undef REGISTER_SYCL_KERNEL
-REGISTER_KERNEL_BUILDER(Name("Mul")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::mul<int32>>);
-#endif // TENSORFLOW_USE_SYCL
 #if GOOGLE_CUDA
 REGISTER4(BinaryOp, GPU, "Mul", functor::mul, float, Eigen::half, double,
            uint8);
@@ -59,4 +41,14 @@ REGISTER_KERNEL_BUILDER(Name("Mul")
                         BinaryOp<CPUDevice, functor::mul<int32>>);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(BinaryOp, SYCL, "Mul", functor::mul, float, double, uint8);
+REGISTER_KERNEL_BUILDER(Name("Mul")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::mul<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_neg.cc b/tensorflow/core/kernels/cwise_op_neg.cc
index eb7e3764d9..536891b548 100644
--- a/tensorflow/core/kernels/cwise_op_neg.cc
+++ b/tensorflow/core/kernels/cwise_op_neg.cc
@@ -19,27 +19,14 @@ namespace tensorflow {
 REGISTER7(UnaryOp, CPU, "Neg", functor::neg, float, Eigen::half, double, int32,
           complex64, int64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Neg")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::neg<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(UnaryOp, SYCL, "Neg", functor::neg, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Neg")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
                             .HostMemory("y")
                             .TypeConstraint<int32>("T"),
                         UnaryOp<CPUDevice, functor::neg<int32>>);
-
-#undef REGISTER_SYCL_KERNEL
 #endif // TENSORFLOW_USE_SYCL
 
 #if GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_pow.cc b/tensorflow/core/kernels/cwise_op_pow.cc
index f1780168e4..5fb0735ac1 100644
--- a/tensorflow/core/kernels/cwise_op_pow.cc
+++ b/tensorflow/core/kernels/cwise_op_pow.cc
@@ -19,20 +19,11 @@ namespace tensorflow {
 REGISTER7(BinaryOp, CPU, "Pow", functor::pow, float, Eigen::half, double, int32,
           int64, complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Pow")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::pow<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER4(BinaryOp, GPU, "Pow", functor::pow, float, Eigen::half, double,
           int64);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(BinaryOp, SYCL, "Pow", functor::pow, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_round.cc b/tensorflow/core/kernels/cwise_op_round.cc
index e192f89782..163814aac4 100644
--- a/tensorflow/core/kernels/cwise_op_round.cc
+++ b/tensorflow/core/kernels/cwise_op_round.cc
@@ -21,9 +21,6 @@ REGISTER5(UnaryOp, CPU, "Round", functor::round, Eigen::half, float, double,
 
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER2(UnaryOp, SYCL, "Round", functor::round, float, double);
-namespace functor {
-DEFINE_UNARY2(round, float, double);
-}  // namespace functor
 #endif
 
 #if GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_rsqrt.cc b/tensorflow/core/kernels/cwise_op_rsqrt.cc
index f23725f48e..a434538fbf 100644
--- a/tensorflow/core/kernels/cwise_op_rsqrt.cc
+++ b/tensorflow/core/kernels/cwise_op_rsqrt.cc
@@ -19,21 +19,12 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Rsqrt", functor::rsqrt, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Rsqrt")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::rsqrt<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Rsqrt", functor::rsqrt, float, Eigen::half, double);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Rsqrt", functor::rsqrt, float, double);
+#endif  // TENSORFLOW_USE_SYCL
 
 REGISTER5(SimpleBinaryOp, CPU, "RsqrtGrad", functor::rsqrt_grad, float,
           Eigen::half, double, complex64, complex128);
@@ -41,4 +32,8 @@ REGISTER5(SimpleBinaryOp, CPU, "RsqrtGrad", functor::rsqrt_grad, float,
 REGISTER3(SimpleBinaryOp, GPU, "RsqrtGrad", functor::rsqrt_grad, float,
           Eigen::half, double);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(SimpleBinaryOp, SYCL, "RsqrtGrad", functor::rsqrt_grad, float,
+          double);
+#endif  //  TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_select.cc b/tensorflow/core/kernels/cwise_op_select.cc
index 709628da13..3dd9de8d89 100644
--- a/tensorflow/core/kernels/cwise_op_select.cc
+++ b/tensorflow/core/kernels/cwise_op_select.cc
@@ -181,7 +181,9 @@ REGISTER_SELECT_GPU(complex128);
       SelectOp<SYCLDevice, type>);
 
 REGISTER_SELECT_SYCL(float);
+REGISTER_SELECT_SYCL(double);
 REGISTER_SELECT_SYCL(int32);
+REGISTER_SELECT_SYCL(int64);
 #undef REGISTER_SELECT_SYCL
 #endif // TENSORFLOW_USE_SYCL
 
diff --git a/tensorflow/core/kernels/cwise_op_sign.cc b/tensorflow/core/kernels/cwise_op_sign.cc
index dedd414db5..a4084d5ad1 100644
--- a/tensorflow/core/kernels/cwise_op_sign.cc
+++ b/tensorflow/core/kernels/cwise_op_sign.cc
@@ -34,10 +34,7 @@ REGISTER_KERNEL_BUILDER(Name("Sign")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(UnaryOp, SYCL, "Sign", functor::sign, float);
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
+REGISTER3(UnaryOp, SYCL, "Sign", functor::sign, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Sign")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_sin.cc b/tensorflow/core/kernels/cwise_op_sin.cc
index ab54c61b56..b91ff1ac30 100644
--- a/tensorflow/core/kernels/cwise_op_sin.cc
+++ b/tensorflow/core/kernels/cwise_op_sin.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Sin", functor::sin, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Sin")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::sin<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Sin", functor::sin, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Sin", functor::sin, float, double);
+#endif // TENSORFLOW_USE_SYC
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_sqrt.cc b/tensorflow/core/kernels/cwise_op_sqrt.cc
index 55acf648db..00efbb00f1 100644
--- a/tensorflow/core/kernels/cwise_op_sqrt.cc
+++ b/tensorflow/core/kernels/cwise_op_sqrt.cc
@@ -19,26 +19,22 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Sqrt", functor::sqrt, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Sqrt")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::sqrt<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Sqrt", functor::sqrt, float, Eigen::half, double);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Sqrt", functor::sqrt, float, double);
+#endif // TENSORFLOW_USE_SYCL
+
 REGISTER5(SimpleBinaryOp, CPU, "SqrtGrad", functor::sqrt_grad, float,
           Eigen::half, double, complex64, complex128);
 #if GOOGLE_CUDA
 REGISTER3(SimpleBinaryOp, GPU, "SqrtGrad", functor::sqrt_grad, float,
           Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(SimpleBinaryOp, SYCL, "SqrtGrad", functor::sqrt_grad, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_square.cc b/tensorflow/core/kernels/cwise_op_square.cc
index afcacfec1c..07a4b0b084 100644
--- a/tensorflow/core/kernels/cwise_op_square.cc
+++ b/tensorflow/core/kernels/cwise_op_square.cc
@@ -19,18 +19,6 @@ namespace tensorflow {
 REGISTER7(UnaryOp, CPU, "Square", functor::square, float, Eigen::half, double,
           int32, int64, complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Square")                              \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::square<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER4(UnaryOp, GPU, "Square", functor::square, float, Eigen::half, double,
           int64);
@@ -45,4 +33,14 @@ REGISTER_KERNEL_BUILDER(Name("Square")
                             .TypeConstraint<int32>("T"),
                         UnaryOp<CPUDevice, functor::square<int32>>);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(UnaryOp, SYCL, "Square", functor::square, float, double, int64);
+REGISTER_KERNEL_BUILDER(Name("Square")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .TypeConstraint<int32>("T"),
+                        UnaryOp<CPUDevice, functor::square<int32>>);
+#endif // TENSORFLOW_USE_SYC
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_sub.cc b/tensorflow/core/kernels/cwise_op_sub.cc
index eab1e2a09c..eb173c7040 100644
--- a/tensorflow/core/kernels/cwise_op_sub.cc
+++ b/tensorflow/core/kernels/cwise_op_sub.cc
@@ -24,28 +24,7 @@ REGISTER7(BinaryOp, CPU, "Sub", functor::sub, float, Eigen::half, double, int32,
 // int32 version of this op is needed, so explicitly include it.
 REGISTER(BinaryOp, CPU, "Sub", functor::sub, int32);
 #endif  // __ANDROID_TYPES_SLIM__
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Sub")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::sub<TYPE>>);
-  REGISTER_SYCL_KERNEL(float);
-  REGISTER_SYCL_KERNEL(double);
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
-REGISTER_KERNEL_BUILDER(Name("Sub")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::sub<int32>>);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
 #if GOOGLE_CUDA
 REGISTER6(BinaryOp, GPU, "Sub", functor::sub, float, Eigen::half, double, int64,
           complex64, complex128);
@@ -62,4 +41,14 @@ REGISTER_KERNEL_BUILDER(Name("Sub")
                         BinaryOp<CPUDevice, functor::sub<int32>>);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(BinaryOp, SYCL, "Sub", functor::sub, float, double, int64);
+REGISTER_KERNEL_BUILDER(Name("Sub")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::sub<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_tan.cc b/tensorflow/core/kernels/cwise_op_tan.cc
index 9c850c9420..7891b1183d 100644
--- a/tensorflow/core/kernels/cwise_op_tan.cc
+++ b/tensorflow/core/kernels/cwise_op_tan.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Tan", functor::tan, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Tan")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::tan<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Tan", functor::tan, float, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Tan", functor::tan, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_tanh.cc b/tensorflow/core/kernels/cwise_op_tanh.cc
index 1dbc13061b..8b3900892c 100644
--- a/tensorflow/core/kernels/cwise_op_tanh.cc
+++ b/tensorflow/core/kernels/cwise_op_tanh.cc
@@ -20,22 +20,14 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Tanh", functor::tanh, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Tanh")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::tanh<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Tanh", functor::tanh, float, Eigen::half, double);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Tanh", functor::tanh, float, double);
+#endif // TENSORFLOW_USE_SYCL
+
 REGISTER5(SimpleBinaryOp, CPU, "TanhGrad", functor::tanh_grad, float,
           Eigen::half, double, complex64, complex128);
 #if GOOGLE_CUDA
-- 
GitLab


From e6a45475735ee8a31c7d6c8e28e9164cda7d1853 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 28 Jun 2017 15:32:11 -0700
Subject: [PATCH 0573/1375] [XLA] Move the flag from user_computation_flags
 into debug_options_flags

This requires some plumbing in user_computation to pipe the debug options
through a few layers.

PiperOrigin-RevId: 160459822
---
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ----
 .../xla/legacy_flags/debug_options_flags.cc   |  9 +++
 .../legacy_flags/user_computation_flags.cc    | 64 -------------------
 .../xla/legacy_flags/user_computation_flags.h | 48 --------------
 tensorflow/compiler/xla/service/BUILD         |  4 +-
 .../xla/service/computation_tracker.cc        |  1 +
 .../compiler/xla/service/user_computation.cc  | 32 ++++++----
 .../compiler/xla/service/user_computation.h   |  3 +-
 .../xla/service/user_computation_test.cc      | 37 ++++++-----
 tensorflow/compiler/xla/tests/BUILD           |  4 --
 .../xla/tests/array_elementwise_ops_test.cc   |  2 -
 .../xla/tests/broadcast_simple_test.cc        |  2 -
 .../compiler/xla/tests/deep_graph_test.cc     |  2 -
 .../xla/tests/reduce_precision_test.cc        |  2 -
 tensorflow/compiler/xla/xla.proto             |  4 ++
 15 files changed, 57 insertions(+), 169 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/user_computation_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/user_computation_flags.h

diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 9444cc1061..27b1d0c3bb 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -125,18 +125,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "user_computation_flags",
-    srcs = ["user_computation_flags.cc"],
-    hdrs = ["user_computation_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 # -----------------------------------------------------------------------------
 
 filegroup(
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 43634792ce..3d5ea4d32a 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -41,6 +41,7 @@ struct DebugOptionsFlags {
   bool xla_embed_ir_in_executable;
   string xla_dump_ir_to;
   string xla_dump_debug_json_to;
+  bool xla_eliminate_hlo_implicit_broadcast;
 
   bool xla_cpu_multi_thread_eigen;
 
@@ -76,6 +77,7 @@ void AllocateFlags() {
   flag_values->xla_embed_ir_in_executable = false;
   flag_values->xla_dump_ir_to = "";
   flag_values->xla_dump_debug_json_to = "";
+  flag_values->xla_eliminate_hlo_implicit_broadcast = false;
   flag_values->xla_cpu_multi_thread_eigen = true;
   flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
   flag_values->xla_gpu_ftz = false;
@@ -137,6 +139,11 @@ void AllocateFlags() {
                         "Embed the compiler IR as a string in the executable."),
        tensorflow::Flag("xla_dump_ir_to", &flag_values->xla_dump_ir_to,
                         "Dump the compiler IR into this file/path."),
+       tensorflow::Flag("xla_eliminate_hlo_implicit_broadcast",
+                        &flag_values->xla_eliminate_hlo_implicit_broadcast,
+                        "Eliminate implicit broadcasts when lowering user "
+                        "computations to HLO instructions; use explicit "
+                        "broadcast instead."),
        tensorflow::Flag("xla_cpu_multi_thread_eigen",
                         &flag_values->xla_cpu_multi_thread_eigen,
                         "When generating calls to Eigen in the CPU backend, "
@@ -192,6 +199,8 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
   options.set_xla_embed_ir_in_executable(
       flag_values->xla_embed_ir_in_executable);
   options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
+  options.set_xla_eliminate_hlo_implicit_broadcast(
+      flag_values->xla_eliminate_hlo_implicit_broadcast);
   options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
   options.set_xla_cpu_multi_thread_eigen(
       flag_values->xla_cpu_multi_thread_eigen);
diff --git a/tensorflow/compiler/xla/legacy_flags/user_computation_flags.cc b/tensorflow/compiler/xla/legacy_flags/user_computation_flags.cc
deleted file mode 100644
index a9597d0cd8..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/user_computation_flags.cc
+++ /dev/null
@@ -1,64 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static UserComputationFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new UserComputationFlags;
-  flags->xla_eliminate_hlo_implicit_broadcast = false;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_eliminate_hlo_implicit_broadcast",
-                       &flags->xla_eliminate_hlo_implicit_broadcast,
-                       "Eliminate implicit broadcast on when lowering user "
-                       "computation to HLO instructions, use explicit "
-                       "broadcast instead."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's hlo_pass_pipeline
-// module.
-void AppendUserComputationFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the UserComputationFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-UserComputationFlags* GetUserComputationFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/user_computation_flags.h b/tensorflow/compiler/xla/legacy_flags/user_computation_flags.h
deleted file mode 100644
index f5222c927c..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/user_computation_flags.h
+++ /dev/null
@@ -1,48 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef THIRD_PARTY_TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_USER_COMPUTATION_FLAGS_H_
-#define THIRD_PARTY_TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_USER_COMPUTATION_FLAGS_H_
-
-// Legacy flags for XLA's user_computation module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flags definitions associated with XLA's user_computation
-// module.
-void AppendUserComputationFlags(std::vector<tensorflow::Flag>* flag_list);
-
-typedef struct {
-  // Eliminate implicit broadcast on when lowering user computation to HLO
-  // instructions, use explicit broadcast instead.
-  bool xla_eliminate_hlo_implicit_broadcast;
-} UserComputationFlags;
-
-// Return a pointer to the UserComputationFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-UserComputationFlags* GetUserComputationFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // THIRD_PARTY_TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_USER_COMPUTATION_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index ec12a8acf0..2a74fd1961 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -288,7 +288,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
+        "//tensorflow/compiler/xla:xla_proto",
         "//tensorflow/core:lib",
     ],
 )
@@ -306,7 +306,7 @@ cc_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/core:test",
     ],
diff --git a/tensorflow/compiler/xla/service/computation_tracker.cc b/tensorflow/compiler/xla/service/computation_tracker.cc
index 9aa32a1fb7..70e25eebdb 100644
--- a/tensorflow/compiler/xla/service/computation_tracker.cc
+++ b/tensorflow/compiler/xla/service/computation_tracker.cc
@@ -216,6 +216,7 @@ StatusOr<std::unique_ptr<HloModule>> ComputationTracker::BuildHloModule(
     TF_ASSIGN_OR_RETURN(
         std::unique_ptr<HloComputation> hlo_computation,
         computation->BuildHloComputation(versioned_handle.version, resolver,
+                                         config.debug_options(),
                                          include_unreachable_instructions));
 
     // Add the newly created computation to VersionedHandle-to-HloComputation
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 92b8c7bb21..90a24fb44d 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -1931,26 +1930,31 @@ class ComputationLowerer {
       const SessionComputation& session_computation,
       VersionedComputationHandle::Version version,
       UserComputation::HloComputationResolver hlo_resolver,
+      const DebugOptions& debug_options,
       bool include_unreachable_instructions) {
     ComputationLowerer lowerer(computation_name, session_computation, version,
-                               std::move(hlo_resolver));
-    return lowerer.Lower(include_unreachable_instructions);
+                               std::move(hlo_resolver), debug_options,
+                               include_unreachable_instructions);
+    return lowerer.Lower();
   }
 
  private:
   ComputationLowerer(const string& computation_name,
                      const SessionComputation& session_computation,
                      VersionedComputationHandle::Version version,
-                     UserComputation::HloComputationResolver hlo_resolver)
+                     UserComputation::HloComputationResolver hlo_resolver,
+                     const DebugOptions& debug_options,
+                     bool include_unreachable_instructions)
       : hlo_builder_(computation_name),
         session_computation_(session_computation),
         version_(version),
-        hlo_resolver_(std::move(hlo_resolver)) {}
+        hlo_resolver_(std::move(hlo_resolver)),
+        debug_options_(debug_options),
+        include_unreachable_instructions_(include_unreachable_instructions) {}
 
   // Build an HLO computation from the SessionComputation at the given
   // version.
-  StatusOr<std::unique_ptr<HloComputation>> Lower(
-      bool include_unreachable_instructions);
+  StatusOr<std::unique_ptr<HloComputation>> Lower();
 
  private:
   // Traverses the computation 'root' using a DFS, calling 'visit' in postorder.
@@ -1980,6 +1984,8 @@ class ComputationLowerer {
   const SessionComputation& session_computation_;
   const VersionedComputationHandle::Version version_;
   const UserComputation::HloComputationResolver hlo_resolver_;
+  const DebugOptions& debug_options_;
+  const bool include_unreachable_instructions_;
 };
 
 // Calls 'apply' on each operand of 'request'.
@@ -2273,8 +2279,7 @@ void ComputationLowerer::TraversePostorder(
   }
 }
 
-StatusOr<std::unique_ptr<HloComputation>> ComputationLowerer::Lower(
-    bool include_unreachable_instructions) {
+StatusOr<std::unique_ptr<HloComputation>> ComputationLowerer::Lower() {
   // Map from ComputationDataHandle to HLO instruction. Serves as a record of
   // which operations have been visited as well as a cache for looking up
   // ComputationDataHandles as HloInstructions.
@@ -2290,7 +2295,7 @@ StatusOr<std::unique_ptr<HloComputation>> ComputationLowerer::Lower(
   HloInstruction* hlo_root =
       instructions.at(root_request->output_handle().handle());
 
-  if (include_unreachable_instructions) {
+  if (include_unreachable_instructions_) {
     // Iterate through all computation data handles, and visit any unvisited
     // operations.
     for (int64 request_num = 1; request_num <= version_; ++request_num) {
@@ -2785,8 +2790,7 @@ void ComputationLowerer::Visit(
         lhs = (lhs == operand_to_broadcast) ? broadcasted_operand : lhs;
         rhs = (rhs == operand_to_broadcast) ? broadcasted_operand : rhs;
       }
-      if (legacy_flags::GetUserComputationFlags()
-              ->xla_eliminate_hlo_implicit_broadcast) {
+      if (debug_options_.xla_eliminate_hlo_implicit_broadcast()) {
         if (!ShapeUtil::SameDimensions(request.output_shape(), lhs->shape())) {
           // lhs side is being implicitly broadcast. Change to explicit.
           lhs =
@@ -2845,7 +2849,7 @@ void ComputationLowerer::Visit(
 
 StatusOr<std::unique_ptr<HloComputation>> UserComputation::BuildHloComputation(
     VersionedComputationHandle::Version version,
-    HloComputationResolver hlo_resolver,
+    HloComputationResolver hlo_resolver, const DebugOptions& debug_options,
     bool include_unreachable_instructions) const {
   tensorflow::mutex_lock lock(mutex_);
 
@@ -2857,7 +2861,7 @@ StatusOr<std::unique_ptr<HloComputation>> UserComputation::BuildHloComputation(
       std::unique_ptr<HloComputation> hlo_computation,
       ComputationLowerer::Lower(
           tensorflow::strings::StrCat(name(), ".v", version),
-          session_computation_, version, std::move(hlo_resolver),
+          session_computation_, version, std::move(hlo_resolver), debug_options,
           include_unreachable_instructions));
 
   XLA_VLOG_LINES(2, hlo_computation->ToString());
diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h
index 9bb7bf491a..3cc3bd0918 100644
--- a/tensorflow/compiler/xla/service/user_computation.h
+++ b/tensorflow/compiler/xla/service/user_computation.h
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/versioned_computation_handle.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla.pb.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -264,7 +265,7 @@ class UserComputation {
       std::function<HloComputation*(const VersionedComputationHandle& handle)>;
   StatusOr<std::unique_ptr<HloComputation>> BuildHloComputation(
       VersionedComputationHandle::Version version,
-      HloComputationResolver hlo_resolver,
+      HloComputationResolver hlo_resolver, const DebugOptions& debug_options,
       bool include_unreachable_instructions = true) const;
 
   // Return a vector containing the embedded computations used by this
diff --git a/tensorflow/compiler/xla/service/user_computation_test.cc b/tensorflow/compiler/xla/service/user_computation_test.cc
index 41bb641f43..0d50810dc4 100644
--- a/tensorflow/compiler/xla/service/user_computation_test.cc
+++ b/tensorflow/compiler/xla/service/user_computation_test.cc
@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/user_computation.h"
 
-#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_matchers.h"
@@ -92,7 +92,8 @@ TEST_F(UserComputationTest, SimpleComputation) {
     // Build the HLO computation.
     TF_ASSIGN_OR_ASSERT_OK(
         std::unique_ptr<HloComputation> hlo_computation,
-        computation.BuildHloComputation(latest_version.version, hlo_resolver));
+        computation.BuildHloComputation(latest_version.version, hlo_resolver,
+                                        DebugOptions()));
     // There should be one HloInstruction per UserComputation operation.
     EXPECT_EQ(3, hlo_computation->instruction_count());
     // The root of the instruction should be the parameter instruction (not the
@@ -117,9 +118,10 @@ TEST_F(UserComputationTest, SimpleComputation) {
 
     // There should be two instructions, one for the constant and one for the
     // parameter. The outfeed instruction should not be included.
-    TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<HloComputation> hlo_computation,
-                           computation.BuildHloComputation(
-                               version_at_param.version, hlo_resolver));
+    TF_ASSIGN_OR_ASSERT_OK(
+        std::unique_ptr<HloComputation> hlo_computation,
+        computation.BuildHloComputation(version_at_param.version, hlo_resolver,
+                                        DebugOptions()));
     EXPECT_EQ(2, hlo_computation->instruction_count());
     EXPECT_THAT(hlo_computation->root_instruction(), op::Parameter());
   }
@@ -130,10 +132,11 @@ TEST_F(UserComputationTest, SimpleComputation) {
         computation.GetVersionedHandle();
 
     // Build the HLO computation.
-    TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<HloComputation> hlo_computation,
-                           computation.BuildHloComputation(
-                               latest_version.version, hlo_resolver,
-                               /*include_unreachable_instructions=*/false));
+    TF_ASSIGN_OR_ASSERT_OK(
+        std::unique_ptr<HloComputation> hlo_computation,
+        computation.BuildHloComputation(
+            latest_version.version, hlo_resolver, DebugOptions(),
+            /*include_unreachable_instructions=*/false));
     // There is only one reachable instruction, the parameter.
     EXPECT_EQ(1, hlo_computation->instruction_count());
     // The root of the instruction should be the parameter instruction (not the
@@ -145,8 +148,8 @@ TEST_F(UserComputationTest, SimpleComputation) {
 }
 
 TEST_F(UserComputationTest, EliminateScalarBroadcast) {
-  if (!legacy_flags::GetUserComputationFlags()
-           ->xla_eliminate_hlo_implicit_broadcast) {
+  if (!legacy_flags::GetDebugOptionsFromFlags()
+           .xla_eliminate_hlo_implicit_broadcast()) {
     return;
   }
 
@@ -184,7 +187,8 @@ TEST_F(UserComputationTest, EliminateScalarBroadcast) {
   // Build the HLO computation.
   TF_ASSIGN_OR_ASSERT_OK(
       std::unique_ptr<HloComputation> hlo_computation,
-      computation.BuildHloComputation(latest_version.version, hlo_resolver));
+      computation.BuildHloComputation(latest_version.version, hlo_resolver,
+                                      DebugOptions()));
   // The binary operation has implicit scalar broadcast, should be converted
   // to an explicit broadcast intruction and a binary instruction.
   EXPECT_EQ(4, hlo_computation->instruction_count());
@@ -196,8 +200,8 @@ TEST_F(UserComputationTest, EliminateScalarBroadcast) {
 }
 
 TEST_F(UserComputationTest, EliminateDegenerateBroadcastAfterIndimBroadcast) {
-  if (!legacy_flags::GetUserComputationFlags()
-           ->xla_eliminate_hlo_implicit_broadcast) {
+  if (!legacy_flags::GetDebugOptionsFromFlags()
+           .xla_eliminate_hlo_implicit_broadcast()) {
     return;
   }
 
@@ -240,7 +244,8 @@ TEST_F(UserComputationTest, EliminateDegenerateBroadcastAfterIndimBroadcast) {
   // Build the HLO computation.
   TF_ASSIGN_OR_ASSERT_OK(
       std::unique_ptr<HloComputation> hlo_computation,
-      computation.BuildHloComputation(latest_version.version, hlo_resolver));
+      computation.BuildHloComputation(latest_version.version, hlo_resolver,
+                                      DebugOptions()));
 
   // The binary operation has in-dim broadcast and degenerate broadcast, should
   // first do the in-dim broadcast then convert the degnerate broadcast into a
@@ -266,7 +271,7 @@ TEST_F(UserComputationTest, EliminateDegenerateBroadcastAfterIndimBroadcast) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index f5a72a9bcc..cb050f6f4d 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -478,7 +478,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -500,7 +499,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
@@ -1015,7 +1013,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
     ],
@@ -1468,7 +1465,6 @@ xla_test(
     srcs = ["deep_graph_test.cc"],
     deps = [
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
     ],
 )
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 024988743c..198a997799 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -27,7 +27,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
@@ -1870,7 +1869,6 @@ INSTANTIATE_TEST_CASE_P(ArrayElementwiseOpTestParamCount,
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
index aab2c74634..2a57835ca9 100644
--- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
@@ -699,7 +698,6 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/deep_graph_test.cc b/tensorflow/compiler/xla/tests/deep_graph_test.cc
index 7a5601ada3..60953a7421 100644
--- a/tensorflow/compiler/xla/tests/deep_graph_test.cc
+++ b/tensorflow/compiler/xla/tests/deep_graph_test.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 
 namespace xla {
@@ -42,7 +41,6 @@ TEST_F(ClientLibraryTestBase, DeepGraph) {
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
index 3c87fffadb..a66c9b4487 100644
--- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/user_computation_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
@@ -247,7 +246,6 @@ INSTANTIATE_TEST_CASE_P(ReducePrecisionTest, ReducePrecisionTest,
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendUserComputationFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_result) {
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 9ac89d1b1c..15fa1255ae 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -73,6 +73,10 @@ message DebugOptions {
   // Dump the compiler IR into this file/path.
   string xla_dump_ir_to = 34;
 
+  // Eliminate implicit broadcasts when lowering user computations to HLO
+  // instructions; use explicit broadcast instead.
+  bool xla_eliminate_hlo_implicit_broadcast = 35;
+
   // When generating calls to Eigen in the CPU backend, use multi-threaded Eigen
   // mode.
   bool xla_cpu_multi_thread_eigen = 60;
-- 
GitLab


From 481a4b4c8c773196943aa04cb19a3ccbeb8c8c38 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Wed, 28 Jun 2017 19:59:49 -0700
Subject: [PATCH 0574/1375] Update sample_distorted_bounding_box to use Tensor
 Input for min_object_covered (#10840)

* Update sample_distorted_bounding_box to use Tensor Input for min_object_covered

This fix tries to address the issue raised in #10715 where it was not possible
to use dynamic values for min_object_covered in sample_distorted_bounding_box.

This fix created an Op of SampleDistortedBoundingBoxV2 so that min_object_covered
takes input. SampleDistortedBoundingBox remains the same (min_object_covered as attr).

Additional test cases have been added.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Update API golden to pass api_compatibility_test

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 .../sample_distorted_bounding_box_op.cc       |  53 ++++++---
 tensorflow/core/ops/image_ops.cc              | 104 ++++++++++++++++--
 tensorflow/python/ops/hidden_ops.txt          |   2 +
 tensorflow/python/ops/image_ops_impl.py       | 102 +++++++++++++++++
 tensorflow/python/ops/image_ops_test.py       |  49 ++++++++-
 5 files changed, 284 insertions(+), 26 deletions(-)

diff --git a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
index 4dae5da635..44a817a5c7 100644
--- a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
+++ b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
@@ -192,18 +192,20 @@ bool GenerateRandomCrop(int original_width, int original_height,
 }  // namespace
 
 template <typename T>
-class SampleDistortedBoundingBoxOp : public OpKernel {
+class SampleDistortedBoundingBoxV2Op : public OpKernel {
  public:
-  explicit SampleDistortedBoundingBoxOp(OpKernelConstruction* context)
+  explicit SampleDistortedBoundingBoxV2Op(OpKernelConstruction* context)
       : OpKernel(context) {
     OP_REQUIRES_OK(context, generator_.Init(context));
 
-    OP_REQUIRES_OK(
-        context, context->GetAttr("min_object_covered", &min_object_covered_));
-    OP_REQUIRES(
-        context, min_object_covered_ >= 0,
-        errors::InvalidArgument("Min object covered must be non-negative: ",
-                                min_object_covered_));
+    if (context->num_inputs() == 2) {
+      OP_REQUIRES_OK(context, context->GetAttr("min_object_covered",
+                                               &min_object_covered_));
+      OP_REQUIRES(
+          context, min_object_covered_ >= 0,
+          errors::InvalidArgument("Min object covered must be non-negative: ",
+                                  min_object_covered_));
+    }
 
     OP_REQUIRES_OK(context, context->GetAttr("use_image_if_no_bounding_boxes",
                                              &use_image_if_no_bounding_boxes_));
@@ -275,6 +277,25 @@ class SampleDistortedBoundingBoxOp : public OpKernel {
                     "bounding boxes must have shape [4] or [*, 4], got ",
                     input_boxes.shape().DebugString()));
 
+    float min_object_covered_val = 0.0;
+    if (context->num_inputs() == 3) {
+      const Tensor& min_object_covered = context->input(2);
+
+      OP_REQUIRES(
+          context, TensorShapeUtils::IsScalar(min_object_covered.shape()),
+          errors::InvalidArgument("min_object_covered must be 0-D, got shape ",
+                                  min_object_covered.shape().DebugString()));
+
+      min_object_covered_val = min_object_covered.scalar<float>()();
+
+      OP_REQUIRES(
+          context, min_object_covered_val >= 0,
+          errors::InvalidArgument("Min object covered must be non-negative: ",
+                                  min_object_covered_val));
+    } else {
+      min_object_covered_val = min_object_covered_;
+    }
+
     std::vector<Rectangle> bounding_boxes;
     if (input_boxes.NumElements() > 0) {
       TTypes<float>::ConstMatrix boxes = input_boxes.flat_inner_dims<float>();
@@ -325,7 +346,7 @@ class SampleDistortedBoundingBoxOp : public OpKernel {
 
       if (GenerateRandomCrop(width, height, min_sample_area, max_sample_area,
                              sample_aspect_ratio, &random, &crop_rect)) {
-        if (SatisfiesOverlapConstraints(crop_rect, min_object_covered_,
+        if (SatisfiesOverlapConstraints(crop_rect, min_object_covered_val,
                                         bounding_boxes)) {
           sample_generated = true;
           break;
@@ -399,11 +420,15 @@ class SampleDistortedBoundingBoxOp : public OpKernel {
   bool use_image_if_no_bounding_boxes_;
 };
 
-#define REGISTER_KERNELS(type)                               \
-  REGISTER_KERNEL_BUILDER(                                   \
-      Name("SampleDistortedBoundingBox").Device(DEVICE_CPU)   \
-           .TypeConstraint<type>("T"),                       \
-      SampleDistortedBoundingBoxOp<type>)
+#define REGISTER_KERNELS(type)                                  \
+  REGISTER_KERNEL_BUILDER(Name("SampleDistortedBoundingBox")    \
+                              .Device(DEVICE_CPU)               \
+                              .TypeConstraint<type>("T"),       \
+                          SampleDistortedBoundingBoxV2Op<type>) \
+  REGISTER_KERNEL_BUILDER(Name("SampleDistortedBoundingBoxV2")  \
+                              .Device(DEVICE_CPU)               \
+                              .TypeConstraint<type>("T"),       \
+                          SampleDistortedBoundingBoxV2Op<type>)
 
 TF_CALL_INTEGRAL_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index 68f4863026..0f8563a2da 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -82,9 +82,8 @@ Status DecodeImageShapeFn(InferenceContext* c) {
     channels_dim = c->MakeDim(channels);
   }
 
-  c->set_output(0,
-                c->MakeShape({InferenceContext::kUnknownDim,
-                              InferenceContext::kUnknownDim, channels_dim}));
+  c->set_output(0, c->MakeShape({InferenceContext::kUnknownDim,
+                                 InferenceContext::kUnknownDim, channels_dim}));
   return Status::OK();
 }
 
@@ -628,10 +627,9 @@ REGISTER_OP("DecodeGif")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
-      c->set_output(0,
-                    c->MakeShape({InferenceContext::kUnknownDim,
-                                  InferenceContext::kUnknownDim,
-                                  InferenceContext::kUnknownDim, 3}));
+      c->set_output(0, c->MakeShape({InferenceContext::kUnknownDim,
+                                     InferenceContext::kUnknownDim,
+                                     InferenceContext::kUnknownDim, 3}));
       return Status::OK();
     })
     .Doc(R"doc(
@@ -813,6 +811,98 @@ use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes supplied.
   raise an error.
 )doc");
 
+REGISTER_OP("SampleDistortedBoundingBoxV2")
+  .Input("image_size: T")
+  .Input("bounding_boxes: float")
+  .Input("min_object_covered: float")
+  .Output("begin: T")
+  .Output("size: T")
+  .Output("bboxes: float")
+  .Attr("T: {uint8, int8, int16, int32, int64}")
+  .Attr("seed: int = 0")
+  .Attr("seed2: int = 0")
+  .Attr("aspect_ratio_range: list(float) = [0.75, 1.33]")
+  .Attr("area_range: list(float) = [0.05, 1.0]")
+  .Attr("max_attempts: int = 100")
+  .Attr("use_image_if_no_bounding_boxes: bool = false")
+  .SetIsStateful()
+  .SetShapeFn([](InferenceContext* c) {
+    c->set_output(0, c->Vector(3));
+    c->set_output(1, c->Vector(3));
+    c->set_output(2, c->MakeShape({1, 1, 4}));
+    return Status::OK();
+  })
+  .Doc(R"doc(
+Generate a single randomly distorted bounding box for an image.
+
+Bounding box annotations are often supplied in addition to ground-truth labels
+in image recognition or object localization tasks. A common technique for
+training such a system is to randomly distort an image while preserving
+its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+localization of an object, i.e. bounding box, given an `image_size`,
+`bounding_boxes` and a series of constraints.
+
+The output of this Op is a single bounding box that may be used to crop the
+original image. The output is returned as 3 tensors: `begin`, `size` and
+`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
+what the bounding box looks like.
+
+Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+height of the underlying image.
+
+For example,
+
+```python
+    # Generate a single distorted bounding box.
+    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+        tf.shape(image),
+        bounding_boxes=bounding_boxes)
+
+    # Draw the bounding box in an image summary.
+    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+                                                  bbox_for_draw)
+    tf.image_summary('images_with_box', image_with_box)
+
+    # Employ the bounding box to distort the image.
+    distorted_image = tf.slice(image, begin, size)
+```
+
+Note that if no bounding box information is available, setting
+`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+false and no bounding boxes are supplied, an error is raised.
+
+image_size: 1-D, containing `[height, width, channels]`.
+bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
+  associated with the image.
+min_object_covered: The cropped area of the image must contain at least this
+  fraction of any bounding box supplied. The value of this parameter should be
+  non-negative. In the case of 0, the cropped area does not need to overlap
+  any of the bounding boxes supplied.
+begin: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+  `tf.slice`.
+size: 1-D, containing `[target_height, target_width, -1]`. Provide as input to
+  `tf.slice`.
+bboxes: 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+  Provide as input to `tf.image.draw_bounding_boxes`.
+seed: If either `seed` or `seed2` are set to non-zero, the random number
+  generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
+  seed.
+seed2: A second seed to avoid seed collision.
+aspect_ratio_range: The cropped area of the image must have an aspect ratio =
+  width / height within this range.
+area_range: The cropped area of the image must contain a fraction of the
+  supplied image within in this range.
+max_attempts: Number of attempts at generating a cropped region of the image
+  of the specified constraints. After `max_attempts` failures, return the entire
+  image.
+use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes supplied.
+  If true, assume an implicit bounding box covering the whole input. If false,
+  raise an error.
+)doc");
+
 // --------------------------------------------------------------------------
 
 // glimpse = extract_glimpse(input, size, offsets) extract the glimpse
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index 9aef6bffde..bd6e03652d 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -157,6 +157,8 @@ ResizeBilinearGrad
 ResizeNearestNeighborGrad
 AdjustContrastv2
 ScaleImageGrad
+SampleDistortedBoundingBox
+SampleDistortedBoundingBoxV2
 
 # io_ops
 FixedLengthRecordReader
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 65a1399c5b..2be92fac63 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -47,6 +47,7 @@ ops.NotDifferentiable('RGBToHSV')
 ops.NotDifferentiable('HSVToRGB')
 ops.NotDifferentiable('DrawBoundingBoxes')
 ops.NotDifferentiable('SampleDistortedBoundingBox')
+ops.NotDifferentiable('SampleDistortedBoundingBoxV2')
 # TODO(bsteiner): Implement the gradient function for extract_glimpse
 # TODO(b/31222613): This op may be differentiable, and there may be
 # latent bugs here.
@@ -1472,3 +1473,104 @@ def total_variation(images, name=None):
                math_ops.reduce_sum(math_ops.abs(pixel_dif2), axis=sum_axis))
 
   return tot_var
+
+
+def sample_distorted_bounding_box(image_size, bounding_boxes, seed=None,
+                                  seed2=None, min_object_covered=None,
+                                  aspect_ratio_range=None, area_range=None,
+                                  max_attempts=None,
+                                  use_image_if_no_bounding_boxes=None,
+                                  name=None):
+  """Generate a single randomly distorted bounding box for an image.
+
+  Bounding box annotations are often supplied in addition to ground-truth labels
+  in image recognition or object localization tasks. A common technique for
+  training such a system is to randomly distort an image while preserving
+  its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+  localization of an object, i.e. bounding box, given an `image_size`,
+  `bounding_boxes` and a series of constraints.
+
+  The output of this Op is a single bounding box that may be used to crop the
+  original image. The output is returned as 3 tensors: `begin`, `size` and
+  `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+  image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
+  what the bounding box looks like.
+
+  Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+  bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+  height of the underlying image.
+
+  For example,
+
+  ```python
+      # Generate a single distorted bounding box.
+      begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+          tf.shape(image),
+          bounding_boxes=bounding_boxes)
+
+      # Draw the bounding box in an image summary.
+      image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+                                                    bbox_for_draw)
+      tf.image_summary('images_with_box', image_with_box)
+
+      # Employ the bounding box to distort the image.
+      distorted_image = tf.slice(image, begin, size)
+  ```
+
+  Note that if no bounding box information is available, setting
+  `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+  bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+  false and no bounding boxes are supplied, an error is raised.
+
+  Args:
+    image_size: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
+      1-D, containing `[height, width, channels]`.
+    bounding_boxes: A `Tensor` of type `float32`.
+      3-D with shape `[batch, N, 4]` describing the N bounding boxes
+      associated with the image.
+    seed: An optional `int`. Defaults to `0`.
+      If either `seed` or `seed2` are set to non-zero, the random number
+      generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
+      seed.
+    seed2: An optional `int`. Defaults to `0`.
+      A second seed to avoid seed collision.
+    min_object_covered: An optional `float`. Defaults to `0.1`.
+      The cropped area of the image must contain at least this
+      fraction of any bounding box supplied. The value of this parameter should be
+      non-negative. In the case of 0, the cropped area does not need to overlap
+      any of the bounding boxes supplied.
+    aspect_ratio_range: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
+      The cropped area of the image must have an aspect ratio =
+      width / height within this range.
+    area_range: An optional list of `floats`. Defaults to `[0.05, 1]`.
+      The cropped area of the image must contain a fraction of the
+      supplied image within in this range.
+    max_attempts: An optional `int`. Defaults to `100`.
+      Number of attempts at generating a cropped region of the image
+      of the specified constraints. After `max_attempts` failures, return the entire
+      image.
+    use_image_if_no_bounding_boxes: An optional `bool`. Defaults to `False`.
+      Controls behavior if no bounding boxes supplied.
+      If true, assume an implicit bounding box covering the whole input. If false,
+      raise an error.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (begin, size, bboxes).
+
+    begin: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+      `tf.slice`.
+    size: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
+      `tf.slice`.
+    bboxes: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+      Provide as input to `tf.image.draw_bounding_boxes`.
+  """
+  with ops.name_scope(name, 'sample_distorted_bounding_box'):
+    # TODO (yongtang): Need to switch to v2 after 3 weeks.
+    return gen_image_ops._sample_distorted_bounding_box(image_size,
+                bounding_boxes, seed=seed,
+                seed2=seed2, min_object_covered=min_object_covered,
+                aspect_ratio_range=aspect_ratio_range, area_range=area_range,
+                max_attempts=max_attempts,
+                use_image_if_no_bounding_boxes=use_image_if_no_bounding_boxes,
+                name=name)
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py
index 5588d18ef1..5564194ea1 100644
--- a/tensorflow/python/ops/image_ops_test.py
+++ b/tensorflow/python/ops/image_ops_test.py
@@ -1501,16 +1501,41 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
           image_size_np, shape=image_size_np.shape)
       bounding_box_tf = constant_op.constant(
           bounding_box_np, dtype=dtypes.float32, shape=bounding_box_np.shape)
-      begin, size, _ = image_ops.sample_distorted_bounding_box(
+
+      # sample_distorted_bounding_box and sample_distorted_bounding_box_v2
+      for op in [image_ops.sample_distorted_bounding_box,
+                 gen_image_ops._sample_distorted_bounding_box_v2]:
+        begin, size, _ = op(
+            image_size=image_size_tf,
+            bounding_boxes=bounding_box_tf,
+            min_object_covered=min_object_covered,
+            aspect_ratio_range=aspect_ratio_range,
+            area_range=area_range)
+        y = array_ops.strided_slice(image_tf, begin, begin + size)
+
+        for _ in xrange(num_iter):
+          y_tf = y.eval()
+          crop_height = y_tf.shape[0]
+          crop_width = y_tf.shape[1]
+          aspect_ratio = float(crop_width) / float(crop_height)
+          area = float(crop_width * crop_height)
+
+          aspect_ratios.append(aspect_ratio)
+          area_ratios.append(area / original_area)
+          fraction_object_covered.append(float(np.sum(y_tf)) / bounding_box_area)
+
+      # sample_distorted_bounding_box_v2
+      min_object_covered_placeholder = array_ops.placeholder(dtypes.float32)
+      begin, size, _ = gen_image_ops._sample_distorted_bounding_box_v2(
           image_size=image_size_tf,
           bounding_boxes=bounding_box_tf,
-          min_object_covered=min_object_covered,
+          min_object_covered=min_object_covered_placeholder,
           aspect_ratio_range=aspect_ratio_range,
           area_range=area_range)
       y = array_ops.strided_slice(image_tf, begin, begin + size)
 
       for _ in xrange(num_iter):
-        y_tf = y.eval()
+        y_tf = y.eval(feed_dict={min_object_covered_placeholder: min_object_covered})
         crop_height = y_tf.shape[0]
         crop_width = y_tf.shape[1]
         aspect_ratio = float(crop_width) / float(crop_height)
@@ -1605,10 +1630,24 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
           [0.0, 0.0, 1.0, 1.0],
           shape=[4],
           dtype=dtypes.float32,)
-      begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box(
+      for op in [image_ops.sample_distorted_bounding_box,
+                 gen_image_ops._sample_distorted_bounding_box_v2]:
+        begin, end, bbox_for_drawing = op(
+            image_size=image_size,
+            bounding_boxes=bounding_box,
+            min_object_covered=0.1,
+            aspect_ratio_range=(0.75, 1.33),
+            area_range=(0.05, 1.0))
+
+        # Test that the shapes are correct.
+        self.assertAllEqual([3], begin.get_shape().as_list())
+        self.assertAllEqual([3], end.get_shape().as_list())
+        self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list())
+
+      begin, end, bbox_for_drawing = op(
           image_size=image_size,
           bounding_boxes=bounding_box,
-          min_object_covered=0.1,
+          min_object_covered=array_ops.placeholder(dtypes.float32),
           aspect_ratio_range=(0.75, 1.33),
           area_range=(0.05, 1.0))
 
-- 
GitLab


From 7d3572c615901bb867af66f7142fca4b8fdef1f0 Mon Sep 17 00:00:00 2001
From: myPrecious <Moriadry@users.noreply.github.com>
Date: Thu, 29 Jun 2017 11:00:12 +0800
Subject: [PATCH 0575/1375] [Java] Add support for list(string) attributes in
 OperationBuilder (#11107)

* Feature: implement setAttrStringList method in Operation Builder with java api

* make sure methodss more explicit

* fix
---
 .../java/org/tensorflow/OperationBuilder.java | 20 ++++++++++++--
 .../src/main/native/operation_builder_jni.cc  | 27 +++++++++++++++++++
 .../src/main/native/operation_builder_jni.h   |  8 ++++++
 3 files changed, 53 insertions(+), 2 deletions(-)

diff --git a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
index 8f7559d39e..849dd36ffb 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
@@ -256,6 +256,21 @@ public final class OperationBuilder {
     return this;
   }
 
+  public OperationBuilder setAttr(String name,  String[] value) {
+    Charset utf8 = Charset.forName("UTF-8");
+    Object[] objects = new Object[value.length];
+    for (int i = 0; i < value.length; ++i) {
+      objects[i] = value[i].getBytes(utf8);
+    }
+    Graph.Reference r = graph.ref();
+    try {
+      setAttrStringList(unsafeNativeHandle, name, objects);
+    } finally {
+      r.close();
+    }
+    return this;
+  }
+
   private long unsafeNativeHandle;
   private Graph graph;
 
@@ -275,8 +290,6 @@ public final class OperationBuilder {
   // Java library types. Roughly, setAttrFoo calls the TensorFlow C library function: TF_SetAttrFoo.
   //
   // TODO(ashankar):
-  // - setAttrStringList: Which would take in an array of byte[] (java Strings will need to be UTF-8
-  //   encoded?)
   // - setAttrShapeList: Which would take in a long[][]
 
   private static native void setAttrString(long handle, String name, byte[] value);
@@ -302,4 +315,7 @@ public final class OperationBuilder {
   private static native void setAttrTensorList(long handle, String name, long[] tensorHandle);
 
   private static native void setAttrShape(long handle, String name, long[] shape, int numDims);
+
+  private static native void setAttrStringList(long handle, String name, Object[] value);
+
 }
diff --git a/tensorflow/java/src/main/native/operation_builder_jni.cc b/tensorflow/java/src/main/native/operation_builder_jni.cc
index a7696182c7..37f01a943a 100644
--- a/tensorflow/java/src/main/native/operation_builder_jni.cc
+++ b/tensorflow/java/src/main/native/operation_builder_jni.cc
@@ -257,3 +257,30 @@ JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrShape(
   TF_SetAttrShape(d, cname, cvalue.get(), static_cast<int>(num_dims));
   env->ReleaseStringUTFChars(name, cname);
 }
+
+JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrStringList(
+        JNIEnv* env, jclass object, jlong handle, jstring name, jobjectArray values) {
+  TF_OperationDescription* d = requireHandle(env, handle);
+  if (d == nullptr) return;
+  const char* cname = env->GetStringUTFChars(name, nullptr);
+  int num_values = env->GetArrayLength(values);
+  static_assert(sizeof(jbyte) == 1,
+                "Require Java byte to be represented as a single byte");
+  std::unique_ptr<jbyteArray[]> jarrays(new jbyteArray[num_values]);
+  std::unique_ptr<jbyte*[]> jvalues(new jbyte*[num_values]);
+  std::unique_ptr<void*[]> cvalues(new void*[num_values]);
+  std::unique_ptr<size_t[]> lengths(new size_t[num_values]);
+
+  for (int i = 0; i < num_values; ++i) {
+    jbyteArray v = static_cast<jbyteArray>(env->GetObjectArrayElement(values, i));
+    jarrays[i] = v;
+    jvalues[i] = env->GetByteArrayElements(v, nullptr);
+    cvalues[i] = jvalues[i];
+    lengths[i] = static_cast<size_t>(env->GetArrayLength(v));
+  }
+  TF_SetAttrStringList(d, cname, cvalues.get(), lengths.get(), num_values);
+  for (int i = 0; i < num_values; ++i) {
+    env->ReleaseByteArrayElements(jarrays[i], jvalues[i], JNI_ABORT);
+  }
+  env->ReleaseStringUTFChars(name, cname);
+}
diff --git a/tensorflow/java/src/main/native/operation_builder_jni.h b/tensorflow/java/src/main/native/operation_builder_jni.h
index 9b64c32820..2e72bd68da 100644
--- a/tensorflow/java/src/main/native/operation_builder_jni.h
+++ b/tensorflow/java/src/main/native/operation_builder_jni.h
@@ -169,6 +169,14 @@ JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrTensorList(
 JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrShape(
     JNIEnv *, jclass, jlong, jstring, jlongArray, jint);
 
+/*
+ * Class:     org_tensorflow_OperationBuilder
+ * Method:    setAttrStringList
+ * Signature: (JLjava/lang/String;[L)V
+ */
+JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrStringList(
+    JNIEnv *, jclass, jlong, jstring, jobjectArray);
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif  // __cplusplus
-- 
GitLab


From cf18c6d384a96a53b448bd51a90c117af0ed7c96 Mon Sep 17 00:00:00 2001
From: Lakshay Garg <lakshayg@outlook.in>
Date: Thu, 29 Jun 2017 08:30:52 +0530
Subject: [PATCH 0576/1375] Implements inverse hyperbolic operations (fix for
 #7531) (#10598)

* Implemented asinh op

* Implemented acosh op

* Fixed test functions for acosh and asinh

* Implemented atanh op

* Fixed test cases for atanh
---
 tensorflow/cc/gradients/math_grad.cc          | 40 +++++++++
 tensorflow/cc/gradients/math_grad_test.cc     | 82 +++++++++++++++++++
 tensorflow/core/kernels/cwise_op_acosh.cc     | 38 +++++++++
 tensorflow/core/kernels/cwise_op_asinh.cc     | 38 +++++++++
 tensorflow/core/kernels/cwise_op_atanh.cc     | 38 +++++++++
 .../core/kernels/cwise_op_gpu_acosh.cu.cc     | 27 ++++++
 .../core/kernels/cwise_op_gpu_asinh.cu.cc     | 27 ++++++
 .../core/kernels/cwise_op_gpu_atanh.cu.cc     | 27 ++++++
 tensorflow/core/kernels/cwise_ops.h           | 42 ++++++++++
 tensorflow/core/ops/math_grad.cc              | 36 ++++++++
 tensorflow/core/ops/math_grad_test.cc         | 38 +++++++++
 tensorflow/core/ops/math_ops.cc               | 12 +++
 tensorflow/core/ops/ops.pbtxt                 | 72 ++++++++++++++++
 .../docs_src/api_guides/python/math_ops.md    |  3 +
 tensorflow/go/op/wrappers.go                  | 45 ++++++++++
 .../python/kernel_tests/basic_gpu_test.py     |  4 +
 .../python/kernel_tests/cwise_ops_test.py     | 15 ++++
 tensorflow/python/ops/math_grad.py            | 30 +++++++
 tensorflow/python/ops/math_ops.py             |  7 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  | 12 +++
 20 files changed, 631 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/core/kernels/cwise_op_acosh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_asinh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_atanh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc

diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc
index 71d9a8ed7b..0b9b665b1e 100644
--- a/tensorflow/cc/gradients/math_grad.cc
+++ b/tensorflow/cc/gradients/math_grad.cc
@@ -203,6 +203,46 @@ Status TanhGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Tanh", TanhGrad);
 
+Status AsinhGrad(const Scope& scope, const Operation& op,
+                 const std::vector<Output>& grad_inputs,
+                 std::vector<Output>* grad_outputs) {
+  // y = asinh(x)
+  // dy/dx = 1 / cosh(y)
+  auto dydx = Reciprocal(scope, Cosh(scope, op.output(0)));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Asinh", AsinhGrad);
+
+Status AcoshGrad(const Scope& scope, const Operation& op,
+                 const std::vector<Output>& grad_inputs,
+                 std::vector<Output>* grad_outputs) {
+  // y = acosh(x)
+  // dy/dx = 1 / sinh(y)
+  auto dydx = Reciprocal(scope, Sinh(scope, op.output(0)));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Acosh", AcoshGrad);
+
+Status AtanhGrad(const Scope& scope, const Operation& op,
+                 const std::vector<Output>& grad_inputs,
+                 std::vector<Output>* grad_outputs) {
+  // y = atanh(x)
+  // dy/dx = 1 / (1 - x^2)
+  auto one = Cast(scope, Const(scope, 1.0), op.input(0).type());
+  auto dydx = Reciprocal(scope, Sub(scope, one, Square(scope, op.input(0))));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Atanh", AtanhGrad);
+
 Status SigmoidGrad(const Scope& scope, const Operation& op,
                    const std::vector<Output>& grad_inputs,
                    std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc
index 1653b04378..48b3ddbe90 100644
--- a/tensorflow/cc/gradients/math_grad_test.cc
+++ b/tensorflow/cc/gradients/math_grad_test.cc
@@ -48,6 +48,9 @@ class CWiseUnaryGradTest : public ::testing::Test {
     SINH,
     COSH,
     TANH,
+    ASINH,
+    ACOSH,
+    ATANH,
     SIGMOID,
     SIGN,
     SIN,
@@ -122,6 +125,15 @@ class CWiseUnaryGradTest : public ::testing::Test {
       case TANH:
         y = Tanh(scope_, x);
         break;
+      case ASINH:
+        y = Asinh(scope_, x);
+        break;
+      case ACOSH:
+        y = Acosh(scope_, x);
+        break;
+      case ATANH:
+        y = Atanh(scope_, x);
+        break;
       case SIGMOID:
         y = Sigmoid(scope_, x);
         break;
@@ -413,6 +425,76 @@ TEST_F(CWiseUnaryGradTest, Tanh_Complex) {
   TestCWiseGrad<complex64>(TANH, x_fn, dy_fn, dx_fn);
 }
 
+TEST_F(CWiseUnaryGradTest, Asinh) {
+  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    auto y = std::asinh(x);
+    return dy / std::cosh(y);
+  };
+  TestCWiseGrad<float>(ASINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Asinh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    auto y = std::asinh(x);
+    return dy / conjugate(std::cosh(y));
+  };
+  TestCWiseGrad<complex64>(ASINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Acosh) {
+  auto x_fn = [this](const int i) { return RV({1, 2, 3, 4, 5, 6, 7}); };
+  auto dy_fn = [this](const float x) { return x + RV({8, 9, 10, 11, 12, 13, 14}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    auto y = std::acosh(x);
+    return dy / std::sinh(y);
+  };
+  TestCWiseGrad<float>(ACOSH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Acosh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 1}, {2, 1}, {1, 4}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{2, 2}, {3, 3}, {1, 4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    auto y = std::acosh(x);
+    return dy / conjugate(std::sinh(y));
+  };
+  TestCWiseGrad<complex64>(ACOSH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Atanh) {
+  auto x_fn = [this](const int i) { return RV({0, -0.5, 0.5, -0.1, 0.1}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    return dy * (1. / (1. - x * x));
+  };
+  TestCWiseGrad<float>(ATANH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Atanh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{0.1, 0}, {0, 0.1}, {0.2, -0.1}, {0.1, 0.2}, {0.3, 0.4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    return dy / conjugate(one_ - x * x);
+  };
+  TestCWiseGrad<complex64>(ATANH, x_fn, dy_fn, dx_fn);
+}
+
 TEST_F(CWiseUnaryGradTest, Sigmoid) {
   auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
   auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
diff --git a/tensorflow/core/kernels/cwise_op_acosh.cc b/tensorflow/core/kernels/cwise_op_acosh.cc
new file mode 100644
index 0000000000..c3fec350d7
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_acosh.cc
@@ -0,0 +1,38 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+#include "tensorflow/core/kernels/cwise_ops_gradients.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Acosh", functor::acosh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Acosh")                               \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::acosh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYC
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Acosh", functor::acosh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_asinh.cc b/tensorflow/core/kernels/cwise_op_asinh.cc
new file mode 100644
index 0000000000..e0644323c0
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_asinh.cc
@@ -0,0 +1,38 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+#include "tensorflow/core/kernels/cwise_ops_gradients.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Asinh", functor::asinh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Asinh")                               \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::asinh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYC
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Asinh", functor::asinh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_atanh.cc b/tensorflow/core/kernels/cwise_op_atanh.cc
new file mode 100644
index 0000000000..058f5140c5
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_atanh.cc
@@ -0,0 +1,38 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+#include "tensorflow/core/kernels/cwise_ops_gradients.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Atanh", functor::atanh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Atanh")                               \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::atanh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYC
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Atanh", functor::atanh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc
new file mode 100644
index 0000000000..a29c9a374d
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc
@@ -0,0 +1,27 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+#include "tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(acosh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc
new file mode 100644
index 0000000000..c78f09e5e9
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc
@@ -0,0 +1,27 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+#include "tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(asinh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc
new file mode 100644
index 0000000000..895dcbff02
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc
@@ -0,0 +1,27 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+#include "tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(atanh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h
index c11d6cfabb..e198fda473 100644
--- a/tensorflow/core/kernels/cwise_ops.h
+++ b/tensorflow/core/kernels/cwise_ops.h
@@ -43,6 +43,39 @@ struct functor_traits<scalar_fmod2_op<T>> {
   };
 };
 
+template<typename T> struct scalar_asinh_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
+    return std::asinh(a);
+  }
+};
+template<typename T>
+struct functor_traits<scalar_asinh_op<T>> {
+  enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
+};
+
+template<typename T> struct scalar_acosh_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_acosh_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
+    return std::acosh(a);
+  }
+};
+template<typename T>
+struct functor_traits<scalar_acosh_op<T>> {
+  enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
+};
+
+template<typename T> struct scalar_atanh_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_atanh_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
+    return std::atanh(a);
+  }
+};
+template<typename T>
+struct functor_traits<scalar_atanh_op<T>> {
+  enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
+};
+
 // TODO(rmlarsen): This is a workaround for upstream change
 // https://bitbucket.org/eigen/eigen/commits/f339468d04d0f87caeb6cab9aef568627e9f6ea9
 // that renamed scalar_binary_pow_op to scalar_pow_op and deleted the unary
@@ -492,6 +525,15 @@ struct cosh : base<T, Eigen::internal::scalar_cosh_op<T> > {};
 template <typename T>
 struct tanh : base<T, Eigen::internal::scalar_tanh_op<T> > {};
 
+template <typename T>
+struct asinh : base<T, Eigen::internal::scalar_asinh_op<T> > {};
+
+template <typename T>
+struct acosh : base<T, Eigen::internal::scalar_acosh_op<T> > {};
+
+template <typename T>
+struct atanh : base<T, Eigen::internal::scalar_atanh_op<T> > {};
+
 template <typename T>
 struct lgamma : base<T, Eigen::internal::scalar_lgamma_op<T> > {};
 
diff --git a/tensorflow/core/ops/math_grad.cc b/tensorflow/core/ops/math_grad.cc
index 9a58a31757..5e082ce8f5 100644
--- a/tensorflow/core/ops/math_grad.cc
+++ b/tensorflow/core/ops/math_grad.cc
@@ -189,6 +189,42 @@ Status TanhGrad(const AttrSlice& attrs, FunctionDef* g) {
 }
 REGISTER_OP_GRADIENT("Tanh", TanhGrad);
 
+Status AsinhGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"y"}, "Asinh", {"x"}},
+      {{"cosh"}, "Cosh", {"y"}},
+      {{"dx"}, "Mul", {"dy", "cosh"}},  // dy * cosh(y)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Asinh", AsinhGrad);
+
+Status AcoshGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"y"}, "Acosh", {"x"}},
+      {{"sinh"}, "Sinh", {"y"}},
+      {{"dx"}, "Mul", {"dy", "sinh"}},  // dy * sinh(y)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Acosh", AcoshGrad);
+
+Status AtanhGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+    {{"x2"}, "Square", {"x"}},
+    FDH::Const("const", 1.0f),
+    {{"one"}, "Cast", {"const"}, {{"SrcT", DT_FLOAT}, {"DstT", "$T"}}},
+    {{"a"}, "Sub", {"one", "x2"}}, // 1 - x^2
+    {{"inv"}, "Reciprocal", {"a"}},
+    {{"dx"}, "Mul", {"dy", "inv"}}
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Atanh", AtanhGrad);
+
 Status SigmoidGrad(const AttrSlice& attrs, FunctionDef* g) {
   // clang-format off
   return GradForUnaryCwise(g, {
diff --git a/tensorflow/core/ops/math_grad_test.cc b/tensorflow/core/ops/math_grad_test.cc
index aa9706a328..1393bffb91 100644
--- a/tensorflow/core/ops/math_grad_test.cc
+++ b/tensorflow/core/ops/math_grad_test.cc
@@ -528,6 +528,44 @@ TEST_F(MathGradTest, Tanh) {
   test::ExpectClose(ans, dx);
 }
 
+TEST_F(MathGradTest, Asinh) {
+  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) {
+    auto y = std::asinh(x);
+    return std::cosh(y);
+  };
+  auto dx = test::AsTensor<float>(
+      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Asinh", x);
+  test::ExpectClose(ans, dx);
+}
+
+TEST_F(MathGradTest, Acosh) {
+  auto x = test::AsTensor<float>({6.f, 5.f, 4.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) {
+    auto y = std::acosh(x);
+    return std::sinh(y);
+  };
+  auto dx = test::AsTensor<float>(
+      {g(6.f), g(5.f), g(4.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Acosh", x);
+  test::ExpectClose(ans, dx);
+}
+
+TEST_F(MathGradTest, Atanh) {
+  auto x = test::AsTensor<float>({-0.3f, -0.2f, -0.1f, 0.1f, 0.2f, 0.3f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) {
+    return 1.f / (1.f - x * x);
+  };
+  auto dx = test::AsTensor<float>(
+      {g(-0.3f), g(-0.2f), g(-0.1f), g(0.1f), g(0.2f), g(0.3f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Atanh", x);
+  test::ExpectClose(ans, dx);
+}
+
 TEST_F(MathGradTest, Sigmoid) {
   auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
                                  TensorShape({2, 3}));
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 30d6987707..89818bc210 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -305,6 +305,18 @@ REGISTER_OP("Tanh").UNARY_COMPLEX().Doc(R"doc(
 Computes hyperbolic tangent of `x` element-wise.
 )doc");
 
+REGISTER_OP("Asinh").UNARY_COMPLEX().Doc(R"doc(
+Computes inverse hyperbolic sine of x element-wise.
+)doc");
+
+REGISTER_OP("Acosh").UNARY_COMPLEX().Doc(R"doc(
+Computes inverse hyperbolic cosine of x element-wise.
+)doc");
+
+REGISTER_OP("Atanh").UNARY_COMPLEX().Doc(R"doc(
+Computes inverse hyperbolic tangent of x element-wise.
+)doc");
+
 REGISTER_OP("TanhGrad").UNARY_GRADIENT_COMPLEX().Doc(R"doc(
 Computes the gradient for the tanh of `x` wrt its input.
 
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 998ac79ae2..1e04b9bdd9 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -190,6 +190,30 @@ op {
   }
   summary: "Computes acos of x element-wise."
 }
+op {
+  name: "Acosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes acosh of x element-wise."
+}
 op {
   name: "Add"
   input_arg {
@@ -1797,6 +1821,30 @@ op {
   }
   summary: "Computes asin of x element-wise."
 }
+op {
+  name: "Asinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes asinh of x element-wise."
+}
 op {
   name: "Assert"
   input_arg {
@@ -2028,6 +2076,30 @@ op {
   summary: "Computes arctangent of `y/x` element-wise, respecting signs of the arguments."
   description: "This is the angle \\( \\theta \\in [-\\pi, \\pi] \\) such that\n\\[ x = r \\cos(\\theta) \\]\nand\n\\[ y = r \\sin(\\theta) \\]\nwhere \\(r = \\sqrt(x^2 + y^2) \\)."
 }
+op {
+  name: "Atanh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes atanh of x element-wise."
+}
 op {
   name: "AudioSpectrogram"
   input_arg {
diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md
index 3d9f203297..b3c7a0c010 100644
--- a/tensorflow/docs_src/api_guides/python/math_ops.md
+++ b/tensorflow/docs_src/api_guides/python/math_ops.md
@@ -61,6 +61,9 @@ mathematical functions to your graph.
 *   @{tf.atan}
 *   @{tf.cosh}
 *   @{tf.sinh}
+*   @{tf.asinh}
+*   @{tf.acosh}
+*   @{tf.atanh}
 *   @{tf.lgamma}
 *   @{tf.digamma}
 *   @{tf.erf}
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index beede5a6d9..b977077e76 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14333,6 +14333,51 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// Computes inverse hyperbolic sine of x element-wise.
+func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Asinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes inverse hyperbolic cosine of x element-wise.
+func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Acosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes inverse hyperbolic tangent of x element-wise.
+func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
+  if scope.Err() != nil {
+    return
+  }
+  opspec := tf.OpSpec{
+    Type: "Atanh",
+    Input: []tf.Input{
+      x,
+    },
+  }
+  op := scope.AddOperation(opspec)
+  return op.Output(0)
+}
+
 // AvgPool3DGradAttr is an optional argument to AvgPool3DGrad.
 type AvgPool3DGradAttr func(optionalAttr)
 
diff --git a/tensorflow/python/kernel_tests/basic_gpu_test.py b/tensorflow/python/kernel_tests/basic_gpu_test.py
index b5b17ff80a..155aad8bd9 100644
--- a/tensorflow/python/kernel_tests/basic_gpu_test.py
+++ b/tensorflow/python/kernel_tests/basic_gpu_test.py
@@ -107,9 +107,12 @@ class MathBuiltinUnaryTest(test.TestCase):
 
   def _testDtype(self, dtype, use_gpu):
     data = (np.arange(-3, 3) / 4.).reshape([1, 3, 2]).astype(dtype)
+    data_gt_1 = data + 2 # for x > 1
     self._compare(data, np.abs, math_ops.abs, use_gpu)
     self._compare(data, np.arccos, math_ops.acos, use_gpu)
     self._compare(data, np.arcsin, math_ops.asin, use_gpu)
+    self._compare(data, np.arcsinh, math_ops.asinh, use_gpu)
+    self._compare(data_gt_1, np.arccosh, math_ops.acosh, use_gpu)
     self._compare(data, np.arctan, math_ops.atan, use_gpu)
     self._compare(data, np.ceil, math_ops.ceil, use_gpu)
     self._compare(data, np.cos, math_ops.cos, use_gpu)
@@ -126,6 +129,7 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.square, math_ops.square, use_gpu)
     self._compare(data, np.tan, math_ops.tan, use_gpu)
     self._compare(data, np.tanh, math_ops.tanh, use_gpu)
+    self._compare(data, np.arctanh, math_ops.atanh, use_gpu)
 
   def testTypes(self):
     for dtype in [np.float32]:
diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py
index b47139e6b8..3bc4394780 100644
--- a/tensorflow/python/kernel_tests/cwise_ops_test.py
+++ b/tensorflow/python/kernel_tests/cwise_ops_test.py
@@ -184,6 +184,7 @@ class UnaryOpTest(test.TestCase):
 
   def testFloatBasic(self):
     x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32)
+    w = x - x.min() + 1.01 # all greater than 1
     y = (x + .5).astype(np.float32)  # no zero
     z = (x + 15.5).astype(np.float32)  # all positive
     k = np.arange(-0.90, 0.90, 0.25).astype(np.float32)  # between -1 and 1
@@ -203,6 +204,9 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.sinh, math_ops.sinh)
     self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
+    self._compareBoth(x, np.arcsinh, math_ops.asinh)
+    self._compareBoth(w, np.arccosh, math_ops.acosh)
+    self._compareBoth(k, np.arctanh, math_ops.atanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, self._log_sigmoid, math_ops.log_sigmoid)
     self._compareBoth(y, np.sign, math_ops.sign)
@@ -248,6 +252,7 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.log, math_ops.log)
     self._compareBoth(x, np.log1p, math_ops.log1p)
     self._compareBoth(x, np.sinh, math_ops.sinh)
+    self._compareBoth(x, np.arcsinh, math_ops.asinh)
     self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
@@ -273,6 +278,7 @@ class UnaryOpTest(test.TestCase):
 
   def testDoubleBasic(self):
     x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64)
+    w = x - x.min() + 1.01 # all greater than 1
     y = (x + .5).astype(np.float64)  # no zero
     z = (x + 15.5).astype(np.float64)  # all positive
     k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype(
@@ -292,6 +298,9 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.sinh, math_ops.sinh)
     self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
+    self._compareBoth(x, np.arcsinh, math_ops.asinh)
+    self._compareBoth(w, np.arccosh, math_ops.acosh)
+    self._compareBoth(k, np.arctanh, math_ops.atanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(y, np.sign, math_ops.sign)
     self._compareBoth(x, np.sin, math_ops.sin)
@@ -398,6 +407,9 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.sinh, math_ops.sinh)
     self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
+    self._compareCpu(x, np.arcsinh, math_ops.asinh)
+    self._compareCpu(x, np.arccosh, math_ops.acosh)
+    self._compareCpu(x, np.arctanh, math_ops.atanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
     self._compareCpu(x, np.cos, math_ops.cos)
@@ -434,6 +446,9 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.sinh, math_ops.sinh)
     self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
+    self._compareCpu(x, np.arcsinh, math_ops.asinh)
+    self._compareCpu(x, np.arccosh, math_ops.acosh)
+    self._compareCpu(x, np.arctanh, math_ops.atanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
     self._compareCpu(x, np.cos, math_ops.cos)
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index fa65052a89..b8266a527d 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -399,6 +399,36 @@ def _TanhGrad(op, grad):
     return gen_math_ops._tanh_grad(y, grad)
 
 
+@ops.RegisterGradient("Asinh")
+def _AsinhGrad(op, grad):
+  """Returns grad * 1/cosh(y)."""
+  y = op.outputs[0]
+  with ops.control_dependencies([grad.op]):
+    y = math_ops.conj(y)
+    return grad / math_ops.cosh(y)
+
+
+@ops.RegisterGradient("Acosh")
+def _AcoshGrad(op, grad):
+  """Returns grad * 1/sinh(y)."""
+  y = op.outputs[0]
+  with ops.control_dependencies([grad.op]):
+    y = math_ops.conj(y)
+    return grad / math_ops.sinh(y)
+
+
+@ops.RegisterGradient("Atanh")
+def _AtanhGrad(op, grad):
+  """Returns grad * 1/ (1 - x^2)."""
+  x = op.inputs[0]
+  with ops.control_dependencies([grad.op]):
+    x = math_ops.conj(x)
+    x2 = math_ops.square(x)
+    one = constant_op.constant(1, dtype=grad.dtype)
+    inv = math_ops.reciprocal(math_ops.subtract(one, x2))
+    return grad * inv
+
+
 @ops.RegisterGradient("TanhGrad")
 def _TanhGradGrad(op, grad):
   with ops.control_dependencies([grad.op]):
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index bf46bbdcc2..7edd1c47d2 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -47,6 +47,9 @@ See the @{$python/math_ops} guide.
 @@log1p
 @@sinh
 @@cosh
+@@asinh
+@@acosh
+@@atanh
 @@ceil
 @@floor
 @@maximum
@@ -1691,8 +1694,8 @@ def matmul(a,
            name=None):
   """Multiplies matrix `a` by matrix `b`, producing `a` * `b`.
 
-  The inputs must, following any transpositions, be tensors of rank >= 2 
-  where the inner 2 dimensions specify valid matrix multiplication arguments, 
+  The inputs must, following any transpositions, be tensors of rank >= 2
+  where the inner 2 dimensions specify valid matrix multiplication arguments,
   and any further outer dimensions match.
 
   Both matrices must be of the same type. The supported types are:
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 4a179bae84..430e6b3076 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -508,6 +508,10 @@ tf_module {
     name: "acos"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "acosh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "add"
     argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -556,6 +560,10 @@ tf_module {
     name: "asin"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "asinh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "assert_equal"
     argspec: "args=[\'x\', \'y\', \'data\', \'summarize\', \'message\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
@@ -648,6 +656,10 @@ tf_module {
     name: "atan2"
     argspec: "args=[\'y\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "atanh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "batch_to_space"
     argspec: "args=[\'input\', \'crops\', \'block_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-- 
GitLab


From 181816fe27684585bface6e2260a0ff1c890e3e9 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Wed, 28 Jun 2017 21:50:44 -0700
Subject: [PATCH 0577/1375] Speed up TuplePointsToAnalysis.

This analysis is one of the most expensive parts of the HLO optimization
pipeline.

 - Avoid one or two unnecessary hashtable lookups in
   PopulateDefinedBuffersAndAliases.

 - Add a mode to ShapeTree wherein we avoid copying Shapes.

 - Use templated functors rather than std::function in ShapeTree's
   iterators, thus avoiding the overhead of std::function.

PiperOrigin-RevId: 160487485
---
 .../xla/service/tuple_points_to_analysis.cc   |  11 +-
 .../xla/service/tuple_points_to_analysis.h    |   5 +-
 tensorflow/compiler/xla/shape_tree.h          | 151 +++++++++++++-----
 tensorflow/compiler/xla/shape_tree_test.cc    |  26 +++
 4 files changed, 142 insertions(+), 51 deletions(-)

diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
index 07b4dd4966..9ab0e75bb9 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
@@ -166,9 +166,6 @@ Status TuplePointsToAnalysis::PopulateDefinedBuffersAndAliases(
             const ShapeIndex& index,
             const std::vector<const LogicalBuffer*>& pointed_to_buffers) {
           for (const LogicalBuffer* buffer : pointed_to_buffers) {
-            if (buffer_aliases_.count(buffer) == 0) {
-              buffer_aliases_.insert({buffer, std::vector<BufferAlias>()});
-            }
             buffer_aliases_[buffer].emplace_back(instruction.get(), index);
           }
         });
@@ -337,9 +334,11 @@ const PointsToSet& TuplePointsToAnalysis::GetPointsToSet(
 
 PointsToSet& TuplePointsToAnalysis::CreateEmptyPointsToSet(
     const HloInstruction* instruction) {
-  CHECK_EQ(0, points_to_.count(instruction));
-  points_to_[instruction] = MakeUnique<PointsToSet>(instruction->shape());
-  return *FindOrDie(points_to_, instruction);
+  auto set = MakeUnique<PointsToSet>(&instruction->shape());
+  auto res = points_to_.emplace(instruction, std::move(set));
+  CHECK(res.second) << "instruction should not have been present in the map.";
+  // Return *set using the iterator returned by emplace.
+  return *res.first->second;
 }
 
 bool TuplePointsToAnalysis::InstructionDefinesBufferAtIndex(
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
index a05b4c8ebc..be821d5154 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
@@ -48,7 +48,10 @@ namespace xla {
 // the corresponding buffer.
 class PointsToSet : public ShapeTree<std::vector<const LogicalBuffer*>> {
  public:
-  explicit PointsToSet(const Shape& shape)
+  // Construct our ShapeTree with a pointer rather than a reference to a Shape
+  // because this is very hot code, and copying (and then destroying) all these
+  // Shapes is slow.
+  explicit PointsToSet(const Shape* shape)
       : ShapeTree<std::vector<const LogicalBuffer*>>(shape),
         tuple_sources_(shape) {}
 
diff --git a/tensorflow/compiler/xla/shape_tree.h b/tensorflow/compiler/xla/shape_tree.h
index cc456df4fc..81cdbf5117 100644
--- a/tensorflow/compiler/xla/shape_tree.h
+++ b/tensorflow/compiler/xla/shape_tree.h
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/lib/gtl/optional.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -81,19 +82,56 @@ struct ShapeTreeNode {
 // Like the Shape data structure, this is a tree and tuple elements cannot be
 // duplicated. That is, every distinct ShapeIndex in the Shape has a unique T
 // object.
+//
+// Normally a ShapeTree owns its Shape, but for efficiency reasons, sometimes
+// it's helpful not to copy a Shape just to make a ShapeTree.  In these cases,
+// you can pass a Shape* instead of a Shape& to the ShapeTree constructor.  It's
+// then up to you to ensure that the pointed-to Shape doesn't die or mutate
+// before its ShapeTree goes away.
 template <typename T>
 class ShapeTree {
  public:
   // Default constructor creates a tree with a nil shape (i.e. an empty tuple).
   ShapeTree() : ShapeTree(ShapeUtil::MakeNil()) {}
+
   // Create ShapeTree with the given shape, and default-constructed T values for
   // all nodes.
-  explicit ShapeTree(const Shape& shape);
+  //
+  // The version that takes a pointer may be cheaper because it doesn't require
+  // any Shape copies, but then it's up to you to ensure that the pointer stays
+  // alive longer than this ShapeTree.
+  explicit ShapeTree(Shape shape);
+  explicit ShapeTree(const Shape* shape);
+
   // Create ShapeTree with the given shape, and init_value for all nodes.
-  ShapeTree(const Shape& shape, const T& init_value);
+  ShapeTree(Shape shape, const T& init_value);
+  ShapeTree(const Shape* shape, const T& init_value);
+
+  ShapeTree(const ShapeTree& other)
+      : root_(other.root_), shape_storage_(other.shape_storage_) {
+    // Fix up internal pointer if necessary.
+    if (shape_storage_) {
+      CHECK_EQ(other.shape_, &*other.shape_storage_);
+      shape_ = &*shape_storage_;
+    } else {
+      shape_ = other.shape_;
+    }
+  }
 
-  ShapeTree(const ShapeTree& other) = default;
-  ShapeTree& operator=(const ShapeTree& other) = default;
+  ShapeTree& operator=(const ShapeTree& other) {
+    root_ = other.root_;
+    shape_storage_ = other.shape_storage_;
+
+    // Fix up internal pointer if necessary.
+    if (shape_storage_) {
+      CHECK_EQ(other.shape_, &*other.shape_storage_);
+      shape_ = &*shape_storage_;
+    } else {
+      shape_ = other.shape_;
+    }
+
+    return *this;
+  }
 
   // Returns the data element associated with the array in the shape at the
   // given index (see ShapeUtil::GetSubshape for how indexes are defined).
@@ -101,7 +139,7 @@ class ShapeTree {
   T* mutable_element(const ShapeIndex& index);
 
   // Return the shape represented with this ShapeTree.
-  const Shape& shape() const { return shape_; }
+  const Shape& shape() const { return *shape_; }
 
   // Returns true if the node at the given index is a leaf node (an array
   // shape).
@@ -112,27 +150,27 @@ class ShapeTree {
   // Recursively traverses the shape and calls the given function at each
   // element. The function has the following arguments:
   //
+  //   Fn :    A callable of type void(const ShapeIndex& index, const T& data)
+  //           (or compatible).
   //   index : the index of the element in the shape. See ShapeUtil::GetSubshape
   //           for definition of index.
   //   data : The data value at this elemnt.
-  using VisitorFunction =
-      std::function<void(const ShapeIndex& /*index*/, const T& /*data*/)>;
-  void ForEachElement(const VisitorFunction& func) const;
-
-  using MutableVisitorFunction =
-      std::function<void(const ShapeIndex& /*index*/, T* /*data*/)>;
-  void ForEachMutableElement(const MutableVisitorFunction& func);
+  template <typename Fn>
+  void ForEachElement(const Fn& func) const;
 
-  // Variants of ForEach(Mutable)Element which propagate a Status value from the
-  // visitor.
-  using StatusVisitorFunction =
-      std::function<Status(const ShapeIndex& /*index*/, const T& /*data*/)>;
-  Status ForEachElementWithStatus(const StatusVisitorFunction& func) const;
+  // Like ForEachElement, but the callable has type
+  //
+  //   void (const ShapeIndex& index, T* data).
+  //
+  template <typename Fn>
+  void ForEachMutableElement(const Fn& func);
 
-  using MutableStatusVisitorFunction =
-      std::function<Status(const ShapeIndex& /*index*/, T* /*data*/)>;
-  Status ForEachMutableElementWithStatus(
-      const MutableStatusVisitorFunction& func);
+  // Like ForEach(Mutable)Element, but the callable returns a Status instead of
+  // void.  The first non-OK return value is returned by the ForEach* function.
+  template <typename Fn>
+  Status ForEachElementWithStatus(const Fn& func) const;
+  template <typename Fn>
+  Status ForEachMutableElementWithStatus(const Fn& func);
 
   // Copy the subtree of values from 'other' rooted at ShapeIndex
   // 'source_base_index' into the subtree of value in this ShapeTree rooted at
@@ -161,10 +199,12 @@ class ShapeTree {
   // Helpers for traversing the shape via ForEachElement. The helpers
   // recursively traverse the subtree rooted at "index" (defined as in
   // ShapeUtil::GetSubshape).
-  static Status ForEachHelper(const StatusVisitorFunction& func,
-                              const Node& node, ShapeIndex* index);
-  static Status ForEachMutableHelper(const MutableStatusVisitorFunction& func,
-                                     Node* node, ShapeIndex* index);
+  template <typename Fn>
+  static Status ForEachHelper(const Fn& func, const Node& node,
+                              ShapeIndex* index);
+  template <typename Fn>
+  static Status ForEachMutableHelper(const Fn& func, Node* node,
+                                     ShapeIndex* index);
 
   // Return the tree node at the given index.
   Node* Lookup(const ShapeIndex& index);
@@ -173,8 +213,13 @@ class ShapeTree {
   // The root node, which contains all other nodes.
   Node root_;
 
-  // The XLA shape mirrored in this ShapeTree.
-  Shape shape_;
+  // If we own our Shape, this field contains it, and shape_ is a pointer into
+  // here.  Otherwise if we don't own our shape, this is nullopt.
+  tensorflow::gtl::optional<Shape> shape_storage_;
+
+  // The XLA shape mirrored in this ShapeTree.  This is either a pointer into
+  // shape_storage_ or the Shape pointer passed to our constructor.
+  const Shape* shape_;
 };
 
 template <typename T>
@@ -200,20 +245,34 @@ void ShapeTree<T>::InitChildren(const Shape& shape, Node* node) {
 }
 
 template <typename T>
-ShapeTree<T>::ShapeTree(const Shape& shape) : root_(), shape_(shape) {
+ShapeTree<T>::ShapeTree(Shape shape)
+    : root_(), shape_storage_(std::move(shape)), shape_(&*shape_storage_) {
   // The shape_ field is just used to hold the structure of the shape.
   // It should not be relied upon to store layout information.
-  LayoutUtil::ClearLayout(&shape_);
-  InitChildren(shape_, &root_);
+  LayoutUtil::ClearLayout(&*shape_storage_);
+  InitChildren(*shape_, &root_);
 }
 
 template <typename T>
-ShapeTree<T>::ShapeTree(const Shape& shape, const T& init_value)
-    : root_(init_value), shape_(shape) {
+ShapeTree<T>::ShapeTree(const Shape* shape) : root_(), shape_(shape) {
+  InitChildren(*shape_, &root_);
+}
+
+template <typename T>
+ShapeTree<T>::ShapeTree(Shape shape, const T& init_value)
+    : root_(init_value),
+      shape_storage_(std::move(shape)),
+      shape_(&*shape_storage_) {
   // The shape_ field is just used to hold the structure of the shape.
   // It should not be relied upon to store layout information.
-  LayoutUtil::ClearLayout(&shape_);
-  InitChildren(shape_, init_value, &root_);
+  LayoutUtil::ClearLayout(&*shape_storage_);
+  InitChildren(*shape_, init_value, &root_);
+}
+
+template <typename T>
+ShapeTree<T>::ShapeTree(const Shape* shape, const T& init_value)
+    : root_(init_value), shape_(shape) {
+  InitChildren(*shape_, init_value, &root_);
 }
 
 template <typename T>
@@ -245,8 +304,9 @@ const internal::ShapeTreeNode<T>* ShapeTree<T>::Lookup(
 
 /* static */
 template <typename T>
-Status ShapeTree<T>::ForEachHelper(const StatusVisitorFunction& func,
-                                   const Node& node, ShapeIndex* index) {
+template <typename Fn>
+Status ShapeTree<T>::ForEachHelper(const Fn& func, const Node& node,
+                                   ShapeIndex* index) {
   TF_RETURN_IF_ERROR(func(*index, node.data));
   for (int64 i = 0; i < node.children.size(); ++i) {
     index->push_back(i);
@@ -258,8 +318,9 @@ Status ShapeTree<T>::ForEachHelper(const StatusVisitorFunction& func,
 
 /* static */
 template <typename T>
-Status ShapeTree<T>::ForEachMutableHelper(
-    const MutableStatusVisitorFunction& func, Node* node, ShapeIndex* index) {
+template <typename Fn>
+Status ShapeTree<T>::ForEachMutableHelper(const Fn& func, Node* node,
+                                          ShapeIndex* index) {
   TF_RETURN_IF_ERROR(func(*index, &node->data));
   for (int64 i = 0; i < node->children.size(); ++i) {
     index->push_back(i);
@@ -271,21 +332,22 @@ Status ShapeTree<T>::ForEachMutableHelper(
 }
 
 template <typename T>
-Status ShapeTree<T>::ForEachElementWithStatus(
-    const StatusVisitorFunction& func) const {
+template <typename Fn>
+Status ShapeTree<T>::ForEachElementWithStatus(const Fn& func) const {
   ShapeIndex index;
   return ForEachHelper(func, root_, &index);
 }
 
 template <typename T>
-Status ShapeTree<T>::ForEachMutableElementWithStatus(
-    const MutableStatusVisitorFunction& func) {
+template <typename Fn>
+Status ShapeTree<T>::ForEachMutableElementWithStatus(const Fn& func) {
   ShapeIndex index;
   return ForEachMutableHelper(func, &root_, &index);
 }
 
 template <typename T>
-void ShapeTree<T>::ForEachElement(const VisitorFunction& func) const {
+template <typename Fn>
+void ShapeTree<T>::ForEachElement(const Fn& func) const {
   ShapeIndex index;
   return ForEachHelper(
              [&func](const ShapeIndex& index, const T& data) {
@@ -297,7 +359,8 @@ void ShapeTree<T>::ForEachElement(const VisitorFunction& func) const {
 }
 
 template <typename T>
-void ShapeTree<T>::ForEachMutableElement(const MutableVisitorFunction& func) {
+template <typename Fn>
+void ShapeTree<T>::ForEachMutableElement(const Fn& func) {
   ShapeIndex index;
   return ForEachMutableHelper(
              [&func](const ShapeIndex& index, T* data) {
diff --git a/tensorflow/compiler/xla/shape_tree_test.cc b/tensorflow/compiler/xla/shape_tree_test.cc
index afc3a2b2a3..3a5db1b3a6 100644
--- a/tensorflow/compiler/xla/shape_tree_test.cc
+++ b/tensorflow/compiler/xla/shape_tree_test.cc
@@ -365,5 +365,31 @@ TEST_F(ShapeTreeTest, OperatorEquals) {
   }
 }
 
+TEST_F(ShapeTreeTest, ConstructWithPointerToShape) {
+  // Construct a ShapeTree using a pointer to a shape, rather than a reference
+  // to a shape.  This constructor is an optimization to let us avoid
+  // constructing and destroying temporary shapes when we have many ShapeTrees.
+  ShapeTree<int> t(&nested_tuple_shape_, 42);
+  int num_nodes = 0;
+  t.ForEachElement([&num_nodes](const ShapeIndex& /*index*/, int data) {
+    EXPECT_EQ(42, data);
+    ++num_nodes;
+  });
+  EXPECT_EQ(10, num_nodes);
+}
+
+TEST_F(ShapeTreeTest, CopyWithPointerToShape) {
+  ShapeTree<int> source(&nested_tuple_shape_, 0);
+  ShapeTree<int> dest(source);
+  EXPECT_EQ(&dest.shape(), &nested_tuple_shape_);
+}
+
+TEST_F(ShapeTreeTest, CopyAssignWithPointerToShape) {
+  ShapeTree<int> source(&nested_tuple_shape_, 0);
+  ShapeTree<int> dest;
+  dest = source;
+  EXPECT_EQ(&dest.shape(), &nested_tuple_shape_);
+}
+
 }  // namespace
 }  // namespace xla
-- 
GitLab


From f714831735d75d8e3ceefc23c5da8c975bf6699c Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 28 Jun 2017 22:06:56 -0700
Subject: [PATCH 0578/1375] Preserve node placement when folding constants

PiperOrigin-RevId: 160488521
---
 tensorflow/core/grappler/optimizers/constant_folding.cc      | 5 +++++
 tensorflow/core/grappler/optimizers/constant_folding_test.cc | 4 +++-
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 1322ceb016..0ed73bfae9 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -137,6 +137,8 @@ string ConstantFolding::AddControlDependency(const string& input_name) {
     NodeDef* added_node = graph_.add_node();
     added_node->set_name(ctrl_dep_name);
     added_node->set_op("Identity");
+    added_node->set_device(node->device());
+
     (*added_node->mutable_attr())["T"].set_type(output_type);
     *added_node->add_input() = input_name;
     node_map_->AddNode(added_node->name(), added_node);
@@ -462,6 +464,7 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
       NodeDef* const_out = output->add_node();
       *const_out = *input_node;
       const_out->set_name(const_out_name);
+      const_out->set_device(node.device());
       *const_out->add_input() = AsControlDependency(node);
       node_map_->AddNode(const_out->name(), const_out);
 
@@ -473,6 +476,7 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
       index.AsProtoTensorContent(
           (*const_index->mutable_attr())["value"].mutable_tensor());
       const_index->set_name(const_index_name);
+      const_index->set_device(node.device());
       *const_index->add_input() = AsControlDependency(node);
       node_map_->AddNode(const_index->name(), const_index);
 
@@ -517,6 +521,7 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
     }
     NodeDef* added_node = output->add_node();
     *added_node = const_node;
+    added_node->set_device(node.device());
     node_map_->AddNode(added_node->name(), added_node);
 
     for (const auto& input : node.input()) {
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 2d1a25bd79..e2150f9e0f 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -50,7 +50,7 @@ TEST_F(ConstantFoldingTest, SimpleFolding) {
 
   Output a = ops::Const(s.WithOpName("a"), 1.0f, {1});
   Output b = ops::Const(s.WithOpName("b"), 2.0f, {1});
-  Output c = ops::AddN(s.WithOpName("c"), {a, b});
+  Output c = ops::AddN(s.WithOpName("c").WithDevice("/CPU:0"), {a, b});
   Output d = ops::AddN(s.WithOpName("d"), {b, c});
 
   GrapplerItem item;
@@ -67,6 +67,7 @@ TEST_F(ConstantFoldingTest, SimpleFolding) {
   const NodeDef& new_c = output.node(0);
   EXPECT_EQ("ConstantFolding/c", new_c.name());
   EXPECT_EQ("Const", new_c.op());
+  EXPECT_EQ("/CPU:0", new_c.device());
 
   const NodeDef& new_a = output.node(1);
   EXPECT_EQ("a", new_a.name());
@@ -80,6 +81,7 @@ TEST_F(ConstantFoldingTest, SimpleFolding) {
   const NodeDef& new_d = output.node(4);
   EXPECT_EQ("d", new_d.name());
   EXPECT_EQ("ConstantFolding/c", new_d.input(1));
+  EXPECT_EQ("", new_d.device());
 
   std::vector<string> fetch = {"a", "b", "c", "d"};
   auto tensors_expected = EvaluateNodes(item.graph, fetch);
-- 
GitLab


From b3fd7075a8e9d908774118cca4f2b04377248883 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 28 Jun 2017 22:07:31 -0700
Subject: [PATCH 0579/1375] Properly handle partially known placeholder shapes

PiperOrigin-RevId: 160488554
---
 tensorflow/core/grappler/grappler_item_builder.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 0a0e000b52..607f2c286b 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -187,7 +187,7 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
           shape_proto.add_dim()->set_size(
               cfg.placeholder_unknown_output_shape_dim);
         } else {
-          dims.push_back(dim_proto.size());
+          dims.push_back(std::max<int32>(1, dim_proto.size()));
           shape_proto.add_dim()->set_size(dim_proto.size());
         }
       }
-- 
GitLab


From 0a797c2825d450ad6692911f99c3fe4699a3dc49 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 05:38:42 -0700
Subject: [PATCH 0580/1375] Adds QuantizeAndDequantizeV3 op, which allows using
 a tensor for the number of quantization bits.

PiperOrigin-RevId: 160515361
---
 tensorflow/cc/gradients/array_grad.cc         |  11 +
 .../kernels/quantize_and_dequantize_op.cc     |  69 +++++++
 .../quantize_and_dequantize_op_test.cc        | 188 ++++++++++++++++++
 tensorflow/core/ops/array_ops.cc              |  25 +++
 tensorflow/python/ops/array_grad.py           |   6 +
 5 files changed, 299 insertions(+)

diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc
index e69db10580..48185db3cb 100644
--- a/tensorflow/cc/gradients/array_grad.cc
+++ b/tensorflow/cc/gradients/array_grad.cc
@@ -100,6 +100,17 @@ Status QuantizeAndDequantizeV2Grad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("QuantizeAndDequantizeV2", QuantizeAndDequantizeV2Grad);
 
+Status QuantizeAndDequantizeV3Grad(const Scope& scope, const Operation& op,
+                                   const std::vector<Output>& grad_inputs,
+                                   std::vector<Output>* grad_outputs) {
+  grad_outputs->push_back(Identity(scope, grad_inputs[0]));
+  grad_outputs->push_back(NoGradient());
+  grad_outputs->push_back(NoGradient());
+  grad_outputs->push_back(NoGradient());
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("QuantizeAndDequantizeV3", QuantizeAndDequantizeV3Grad);
+
 Status SplitGrad(const Scope& scope, const Operation& op,
                  const std::vector<Output>& grad_inputs,
                  std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/core/kernels/quantize_and_dequantize_op.cc b/tensorflow/core/kernels/quantize_and_dequantize_op.cc
index 4dd5e24e5a..dadc15b69e 100644
--- a/tensorflow/core/kernels/quantize_and_dequantize_op.cc
+++ b/tensorflow/core/kernels/quantize_and_dequantize_op.cc
@@ -85,6 +85,64 @@ class QuantizeAndDequantizeV2Op : public OpKernel {
   bool range_given_;
 };
 
+// Simulate quantization precision loss in a float tensor by:
+// 1. Quantize the tensor to fixed point numbers, which should match the target
+//    quantization method when it is used in inference.
+// 2. Dequantize it back to floating point numbers for the following ops, most
+//    likely matmul.
+// Almost identical to QuantizeAndDequantizeV2Op, except that num_bits is a
+// tensor.
+template <typename Device, typename T>
+class QuantizeAndDequantizeV3Op : public OpKernel {
+ public:
+  explicit QuantizeAndDequantizeV3Op(OpKernelConstruction* ctx)
+      : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("signed_input", &signed_input_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("range_given", &range_given_));
+  }
+
+  void Compute(OpKernelContext* ctx) override {
+    const Tensor& input = ctx->input(0);
+
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input.shape(), &output));
+
+    Tensor num_bits_tensor;
+    num_bits_tensor = ctx->input(3);
+    int num_bits_val = num_bits_tensor.scalar<int32>()();
+
+    OP_REQUIRES(
+        ctx, num_bits_val > 0 && num_bits_val < (signed_input_ ? 62 : 63),
+        errors::InvalidArgument("num_bits is out of range: ", num_bits_val,
+                                " with signed_input_ ", signed_input_));
+
+    Tensor input_min_tensor;
+    Tensor input_max_tensor;
+    if (range_given_) {
+      input_min_tensor = ctx->input(1);
+      input_max_tensor = ctx->input(2);
+      auto min_val = input_min_tensor.scalar<T>()();
+      auto max_val = input_max_tensor.scalar<T>()();
+      OP_REQUIRES(ctx, min_val <= max_val,
+                  errors::InvalidArgument("Invalid range: input_min ", min_val,
+                                          " > input_max ", max_val));
+    } else {
+      OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                             TensorShape(), &input_min_tensor));
+      OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                             TensorShape(), &input_max_tensor));
+    }
+
+    functor::QuantizeAndDequantizeOneScaleFunctor<Device, T> f;
+    f(ctx->eigen_device<Device>(), input.flat<T>(), signed_input_, num_bits_val,
+      range_given_, &input_min_tensor, &input_max_tensor, output->flat<T>());
+  }
+
+ private:
+  bool signed_input_;
+  bool range_given_;
+};
+
 // DEPRECATED: Use QuantizeAndDequantizeV2Op.
 template <typename Device, typename T>
 class QuantizeAndDequantizeOp : public OpKernel {
@@ -153,6 +211,10 @@ struct QuantizeAndDequantizeOneScaleFunctor<CPUDevice, T> {
                               .Device(DEVICE_CPU)                              \
                               .TypeConstraint<T>("T"),                         \
                           QuantizeAndDequantizeV2Op<CPUDevice, T>);            \
+  REGISTER_KERNEL_BUILDER(Name("QuantizeAndDequantizeV3")                      \
+                              .Device(DEVICE_CPU)                              \
+                              .TypeConstraint<T>("T"),                         \
+                          QuantizeAndDequantizeV3Op<CPUDevice, T>);            \
   REGISTER_KERNEL_BUILDER(                                                     \
       Name("QuantizeAndDequantize").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
       QuantizeAndDequantizeOp<CPUDevice, T>);
@@ -168,6 +230,13 @@ TF_CALL_double(REGISTER_CPU_KERNEL);
                               .HostMemory("input_min")                         \
                               .TypeConstraint<T>("T"),                         \
                           QuantizeAndDequantizeV2Op<GPUDevice, T>);            \
+  REGISTER_KERNEL_BUILDER(Name("QuantizeAndDequantizeV3")                      \
+                              .Device(DEVICE_GPU)                              \
+                              .HostMemory("input_max")                         \
+                              .HostMemory("input_min")                         \
+                              .HostMemory("num_bits")                          \
+                              .TypeConstraint<T>("T"),                         \
+                          QuantizeAndDequantizeV3Op<GPUDevice, T>);            \
   REGISTER_KERNEL_BUILDER(                                                     \
       Name("QuantizeAndDequantize").Device(DEVICE_GPU).TypeConstraint<T>("T"), \
       QuantizeAndDequantizeOp<GPUDevice, T>);
diff --git a/tensorflow/core/kernels/quantize_and_dequantize_op_test.cc b/tensorflow/core/kernels/quantize_and_dequantize_op_test.cc
index ccd0d7203a..7c55958cc2 100644
--- a/tensorflow/core/kernels/quantize_and_dequantize_op_test.cc
+++ b/tensorflow/core/kernels/quantize_and_dequantize_op_test.cc
@@ -62,6 +62,32 @@ TEST_F(QuantizeAndDequantizeTest, Convert_scalar_tensor) {
   EXPECT_EQ(inputs_[2]->scalar<float>()(), 0.0);
 }
 
+TEST_F(QuantizeAndDequantizeTest, Convert_scalar_tensor_V3) {
+  TF_ASSERT_OK(
+      NodeDefBuilder("quantize_and_dequantize_op", "QuantizeAndDequantizeV3")
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_INT32))
+          .Attr("signed_input", true)
+          .Attr("range_given", false)
+          .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  AddInputFromArray<float>(TensorShape({1}), {-3.5});
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Min
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Max
+  AddInputFromArray<int32>(TensorShape({}), {8});    // num_bits
+
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({1}));
+  test::FillValues<float>(&expected, {-3.5});
+  test::ExpectTensorEqual<float>(expected, *GetOutput(0));
+
+  // Ensure that the inputs haven't been changed.
+  EXPECT_EQ(inputs_[1]->scalar<float>()(), 0.0);
+  EXPECT_EQ(inputs_[2]->scalar<float>()(), 0.0);
+}
+
 // Convert a 1D tensor with signed 8 bits.
 TEST_F(QuantizeAndDequantizeTest, Convert_1D_tensor_with_int8) {
   TF_ASSERT_OK(
@@ -92,6 +118,37 @@ TEST_F(QuantizeAndDequantizeTest, Convert_1D_tensor_with_int8) {
   EXPECT_EQ(inputs_[2]->scalar<float>()(), 0.0);
 }
 
+// Convert a 1D tensor with signed 8 bits.
+TEST_F(QuantizeAndDequantizeTest, Convert_1D_tensor_with_int8_V3) {
+  TF_ASSERT_OK(
+      NodeDefBuilder("quantize_and_dequantize_op", "QuantizeAndDequantizeV3")
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_INT32))
+          .Attr("signed_input", true)
+          .Attr("range_given", false)
+          .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  AddInputFromArray<float>(TensorShape({6}), {-1, -0.5, 0, 0.3, 0.8, 0.555});
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Min
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Max
+  AddInputFromArray<int32>(TensorShape({}), {8});    // num_bits
+
+  // With int8, the tensor is quantized to {-127, -63, 0, 38, 102, 70}.
+  // Scale is: 1/127
+  // Then it is dequantized to {-1, -63.0/127, 0, 38.0/127, 102.0/127, 70.0/127}
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({6}));
+  test::FillValues<float>(
+      &expected, {-1, -63.0 / 127, 0, 38.0 / 127, 102.0 / 127, 70.0 / 127});
+  test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
+
+  // Ensure that the inputs haven't been changed.
+  EXPECT_EQ(inputs_[1]->scalar<float>()(), 0.0);
+  EXPECT_EQ(inputs_[2]->scalar<float>()(), 0.0);
+}
+
 // Convert a 1D tensor with signed 4 bits.
 TEST_F(QuantizeAndDequantizeTest, Convert_1D_tensor_with_int4) {
   TF_ASSERT_OK(
@@ -121,6 +178,36 @@ TEST_F(QuantizeAndDequantizeTest, Convert_1D_tensor_with_int4) {
   EXPECT_EQ(inputs_[2]->scalar<float>()(), 0.0);
 }
 
+// Convert a 1D tensor with signed 4 bits.
+TEST_F(QuantizeAndDequantizeTest, Convert_1D_tensor_with_int4_V3) {
+  TF_ASSERT_OK(
+      NodeDefBuilder("quantize_and_dequantize_op", "QuantizeAndDequantizeV3")
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_INT32))
+          .Attr("signed_input", true)
+          .Attr("range_given", false)
+          .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  AddInputFromArray<float>(TensorShape({6}), {-1, -0.5, 0, 0.3, 0.8, 0.555});
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Min
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Max
+  AddInputFromArray<int32>(TensorShape({}), {4});    // num_bits
+
+  // With int4, the tensor is quantized to {-7, -3, 0, 2, 6, 4}.
+  // Scale is: 1/7
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({6}));
+  test::FillValues<float>(&expected,
+                          {-1, -3.0 / 7, 0, 2.0 / 7, 6.0 / 7, 4.0 / 7});
+  test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
+
+  // Ensure that the inputs haven't been changed.
+  EXPECT_EQ(inputs_[1]->scalar<float>()(), 0.0);
+  EXPECT_EQ(inputs_[2]->scalar<float>()(), 0.0);
+}
+
 // Convert a 2D tensor with signed 8 bits with given range.
 TEST_F(QuantizeAndDequantizeTest, Convert_2D_tensor_with_int8_range_given) {
   TF_ASSERT_OK(
@@ -150,6 +237,36 @@ TEST_F(QuantizeAndDequantizeTest, Convert_2D_tensor_with_int8_range_given) {
   test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
 }
 
+// Convert a 2D tensor with signed 8 bits with given range.
+TEST_F(QuantizeAndDequantizeTest, Convert_2D_tensor_with_int8_range_given_V3) {
+  TF_ASSERT_OK(
+      NodeDefBuilder("quantize_and_dequantize_op", "QuantizeAndDequantizeV3")
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_INT32))
+          .Attr("signed_input", true)
+          .Attr("range_given", true)
+          .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  // Note that the last two values are saturated.
+  AddInputFromArray<float>(TensorShape({2, 4}),
+                           {-0.8, -0.5, 0, 0.3, 0.8, 0.555, -2, 33});
+  AddInputFromArray<float>(TensorShape({}), {-1.0});  // Min
+  AddInputFromArray<float>(TensorShape({}), {1.0});   // Max
+  AddInputFromArray<int32>(TensorShape({}), {8});     // num_bits
+
+  // Note that the range is given as [-1, 1].
+  // With int8, the tensor is quantized to {-102, -63, 0, 38, 102, 70, -127,
+  // 127}.
+  // Scale is: 1/127
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 4}));
+  test::FillValues<float>(&expected, {-102.0 / 127, -63.0 / 127, 0, 38.0 / 127,
+                                      102.0 / 127, 70.0 / 127, -1, 1});
+  test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
+}
+
 // Convert a 4D tensor with unsigned 8 bits with given range.
 TEST_F(QuantizeAndDequantizeTest, Convert_4D_tensor_with_uint8_range_given) {
   TF_ASSERT_OK(
@@ -175,6 +292,32 @@ TEST_F(QuantizeAndDequantizeTest, Convert_4D_tensor_with_uint8_range_given) {
   test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
 }
 
+// Convert a 4D tensor with unsigned 8 bits with given range.
+TEST_F(QuantizeAndDequantizeTest, Convert_4D_tensor_with_uint8_range_given_V3) {
+  TF_ASSERT_OK(
+      NodeDefBuilder("quantize_and_dequantize_op", "QuantizeAndDequantizeV3")
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_INT32))
+          .Attr("signed_input", false)
+          .Attr("range_given", true)
+          .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  AddInputFromArray<float>(TensorShape({2, 2, 1, 1}), {-0.5, 0, 0.3, 0.8});
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Min
+  AddInputFromArray<float>(TensorShape({}), {1.0});  // Max
+  AddInputFromArray<int32>(TensorShape({}), {8});    // num_bits
+
+  // Note that the range is given as [0, 1].
+  // With int8, the tensor is quantized to {0, 0, 77, 204}
+  // Scale is: 1/255
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 1, 1}));
+  test::FillValues<float>(&expected, {0, 0, 77.0 / 255, 204.0 / 255});
+  test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
+}
+
 // Convert a tensor with all 0.
 TEST_F(QuantizeAndDequantizeTest, Convert_tensor_with_all_0) {
   TF_ASSERT_OK(
@@ -197,6 +340,29 @@ TEST_F(QuantizeAndDequantizeTest, Convert_tensor_with_all_0) {
   test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
 }
 
+// Convert a tensor with all 0.
+TEST_F(QuantizeAndDequantizeTest, Convert_tensor_with_all_0_V3) {
+  TF_ASSERT_OK(
+      NodeDefBuilder("quantize_and_dequantize_op", "QuantizeAndDequantizeV3")
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_INT32))
+          .Attr("signed_input", false)
+          .Attr("range_given", false)
+          .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  AddInputFromArray<float>(TensorShape({2, 2, 1, 1}), {0, 0, 0, 0});
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Min
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Max
+  AddInputFromArray<int32>(TensorShape({}), {8});    // num_bits
+
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 1, 1}));
+  test::FillValues<float>(&expected, {0, 0, 0, 0});
+  test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-5);
+}
+
 // Range is invalid
 TEST_F(QuantizeAndDequantizeTest, Invalid_range_given) {
   TF_ASSERT_OK(
@@ -218,6 +384,28 @@ TEST_F(QuantizeAndDequantizeTest, Invalid_range_given) {
       << s;
 }
 
+// Range is invalid
+TEST_F(QuantizeAndDequantizeTest, Invalid_range_given_V3) {
+  TF_ASSERT_OK(
+      NodeDefBuilder("quantize_and_dequantize_Op", "QuantizeAndDequantizeV3")
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_FLOAT))
+          .Input(FakeInput(DT_INT32))
+          .Attr("range_given", true)
+          .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  AddInputFromArray<float>(TensorShape({2, 2, 1, 1}), {-0.5, 0, 0.3, 0.8});
+  AddInputFromArray<float>(TensorShape({}), {1.0});  // Min
+  AddInputFromArray<float>(TensorShape({}), {0.0});  // Max
+  AddInputFromArray<int32>(TensorShape({}), {8});    // num_bits
+
+  Status s = RunOpKernel();
+  EXPECT_TRUE(StringPiece(s.ToString())
+                  .contains("Invalid range: input_min 1 > input_max 0"))
+      << s;
+}
+
 #define BM_SIMPLE_QUAN_DEQUAN(DEVICE)                           \
   static void BM_SIMPLE_QUAN_DEQUAN_##DEVICE(int iters) {       \
     auto root = Scope::NewRootScope().ExitOnError();            \
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index c2b81615bd..557443f229 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -4431,6 +4431,7 @@ REGISTER_OP("QuantizeAndDequantize")
 Use QuantizeAndDequantizeV2 instead.
 )doc");
 
+// TODO(suharshs): Deprecate QuantizeAndDequantizeV2.
 REGISTER_OP("QuantizeAndDequantizeV2")
     .Input("input: T")
     .Input("input_min: T")
@@ -4510,6 +4511,30 @@ input_max: If range_given, this is the max of the range, otherwise this input
            will be ignored.
 )doc");
 
+REGISTER_OP("QuantizeAndDequantizeV3")
+    .Input("input: T")
+    .Input("input_min: T")
+    .Input("input_max: T")
+    .Input("num_bits: int32")
+    .Attr("signed_input: bool = true")
+    .Attr("range_given: bool = true")
+    .Output("output: T")
+    .Attr("T: {float, double}")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));
+      c->set_output(0, c->input(0));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Quantizes then dequantizes a tensor.
+
+This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a
+tensor, so its value can change during training.
+)doc");
+
 REGISTER_OP("QuantizeV2")
     .Input("input: float")
     .Input("min_range: float")
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index dcae3e0c7b..51b3b81500 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -586,6 +586,12 @@ def _QuantizeAndDequantizeV2Grad(_, grad):
   return [grad, None, None]
 
 
+@ops.RegisterGradient("QuantizeAndDequantizeV3")
+def _QuantizeAndDequantizeV3Grad(_, grad):
+  # Only propagate the gradient for the unquantized input.
+  return [grad, None, None, None]
+
+
 @ops.RegisterGradient("ExtractImagePatches")
 def _ExtractImagePatchesGrad(op, grad):
 
-- 
GitLab


From 2345d24d3bb35df0f70090bb956eb91cb8aa6bb9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 05:42:25 -0700
Subject: [PATCH 0581/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 160515573
---
 tensorflow/go/op/wrappers.go | 42 ++++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 785717c9ba..8d5cd1d93d 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -7195,6 +7195,48 @@ func Equal(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
+// QuantizeAndDequantizeV3Attr is an optional argument to QuantizeAndDequantizeV3.
+type QuantizeAndDequantizeV3Attr func(optionalAttr)
+
+// QuantizeAndDequantizeV3SignedInput sets the optional signed_input attribute to value.
+// If not specified, defaults to true
+func QuantizeAndDequantizeV3SignedInput(value bool) QuantizeAndDequantizeV3Attr {
+	return func(m optionalAttr) {
+		m["signed_input"] = value
+	}
+}
+
+// QuantizeAndDequantizeV3RangeGiven sets the optional range_given attribute to value.
+// If not specified, defaults to true
+func QuantizeAndDequantizeV3RangeGiven(value bool) QuantizeAndDequantizeV3Attr {
+	return func(m optionalAttr) {
+		m["range_given"] = value
+	}
+}
+
+// Quantizes then dequantizes a tensor.
+//
+// This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a
+// tensor, so its value can change during training.
+func QuantizeAndDequantizeV3(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, num_bits tf.Output, optional ...QuantizeAndDequantizeV3Attr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "QuantizeAndDequantizeV3",
+		Input: []tf.Input{
+			input, input_min, input_max, num_bits,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // AvgPool3DAttr is an optional argument to AvgPool3D.
 type AvgPool3DAttr func(optionalAttr)
 
-- 
GitLab


From f3d5b5c0aad4e91f84800e953b8ed6c7d450d8f9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 06:20:29 -0700
Subject: [PATCH 0582/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 160518040
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 47 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 49 +++++++++++++++++++
 2 files changed, 96 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 9631538227..e230385c2e 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -16263,6 +16263,53 @@ op {
     }
   }
 }
+op {
+  name: "QuantizeAndDequantizeV3"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_min"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_max"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "num_bits"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "signed_input"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+  attr {
+    name: "range_given"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+}
 op {
   name: "QuantizeDownAndShrinkRange"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 416d18a0ec..5e0a53b3b0 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -15498,6 +15498,55 @@ op {
   summary: "Quantizes then dequantizes a tensor."
   description: "This op simulates the precision loss from the quantized forward pass by:\n1. Quantizing the tensor to fixed point numbers, which should match the target\n   quantization method when it is used in inference.\n2. Dequantizing it back to floating point numbers for the following ops, most\n   likely matmul.\n\nThere are different ways to quantize. This version does not use the full range\nof the output type, choosing to elide the lowest possible value for symmetry\n(e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit\nquantization), so that 0.0 maps to 0.\n\nTo perform this op, we first find the range of values in our tensor. The range\nwe use is always centered on 0, so we find m such that\n\n1. m = max(abs(input_min), abs(input_max)) if range_given is true,\n2. m = max(abs(min_elem(input)), abs(max_elem(input))) otherwise.\n\nOur input tensor range is then [-m, m].\n\nNext, we choose our fixed-point quantization buckets, [min_fixed, max_fixed].\nIf signed_input is true, this is\n\n  [min_fixed, max_fixed ] =\n      [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1].\n\nOtherwise, if signed_input is false, the fixed-point range is\n\n  [min_fixed, max_fixed] = [0, (1 << num_bits) - 1].\n\nFrom this we compute our scaling factor, s:\n\n  s = (max_fixed - min_fixed) / (2 * m).\n\nNow we can quantize and dequantize the elements of our tensor.  An element e\nis transformed into e\':\n\n  e\' = (e * s).round_to_nearest() / s.\n\nNote that we have a different number of buckets in the signed vs. unsigned\ncases.  For example, if num_bits == 8, we get 254 buckets in the signed case\nvs. 255 in the unsigned case.\n\nFor example, suppose num_bits = 8 and m = 1.  Then\n\n  [min_fixed, max_fixed] = [-127, 127], and\n  s = (127 + 127) / 2 = 127.\n\nGiven the vector {-1, -0.5, 0, 0.3}, this is quantized to\n{-127, -63, 0, 38}, and dequantized to {-1, -63.0/127, 0, 38.0/127}."
 }
+op {
+  name: "QuantizeAndDequantizeV3"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_min"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_max"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "num_bits"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "signed_input"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+  attr {
+    name: "range_given"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  summary: "Quantizes then dequantizes a tensor."
+  description: "This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a\ntensor, so its value can change during training."
+}
 op {
   name: "QuantizeDownAndShrinkRange"
   input_arg {
-- 
GitLab


From 56d54aa44b81bf9c51eec2ffa0a460e597aced16 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 29 Jun 2017 06:53:47 -0700
Subject: [PATCH 0583/1375] tfdbg: Fix wrong URL in README.md

PiperOrigin-RevId: 160520466
---
 tensorflow/python/debug/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/debug/README.md b/tensorflow/python/debug/README.md
index 1c8dee2c1c..b26411cd15 100644
--- a/tensorflow/python/debug/README.md
+++ b/tensorflow/python/debug/README.md
@@ -29,7 +29,7 @@ models:
 * Easy access through session wrappers
 * Easy integration with common high-level APIs, such as
   [tf-learn](https://www.tensorflow.org/get_started/tflearn) and
-  [Keras](keras)
+  [Keras](https://keras.io/)
 * Inspection of runtime tensor values and node connections
 * Conditional breaking after runs that generate tensors satisfying given
   predicates, which makes common debugging tasks such as tracing the origin
-- 
GitLab


From 0c9b8974b49c147942af5888e73e325d42574db8 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 29 Jun 2017 08:10:45 -0700
Subject: [PATCH 0584/1375] Fix shape error when a Placeholder operator with a
 partial shape is executed and not fed. The PlaceholderOp constructor was
 attempting to convert its shape attribute to a TensorShape, which requires
 that the shape is fully defined. This failed with a misleading error message
 for a partial shape. Instead, use a PartialTensorShape.

PiperOrigin-RevId: 160526882
---
 tensorflow/core/kernels/constant_op.h              |  2 +-
 tensorflow/python/kernel_tests/constant_op_test.py | 10 ++++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/constant_op.h b/tensorflow/core/kernels/constant_op.h
index 8531a7a4b4..b98153e347 100644
--- a/tensorflow/core/kernels/constant_op.h
+++ b/tensorflow/core/kernels/constant_op.h
@@ -56,7 +56,7 @@ class PlaceholderOp : public OpKernel {
   void Compute(OpKernelContext* ctx) override;
 
  private:
-  TensorShape expected_shape_;
+  PartialTensorShape expected_shape_;
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/python/kernel_tests/constant_op_test.py b/tensorflow/python/kernel_tests/constant_op_test.py
index 40c6a9e614..0cc6745de3 100644
--- a/tensorflow/python/kernel_tests/constant_op_test.py
+++ b/tensorflow/python/kernel_tests/constant_op_test.py
@@ -684,6 +684,16 @@ class PlaceholderTest(test.TestCase):
           ValueError, lambda e: "Cannot feed value of shape" in str(e)):
         p_identity.eval(feed_dict={p: feed_array[:5, :2]})
 
+  def testPartialShapeWhenNotFed(self):
+    with self.test_session():
+      p = array_ops.placeholder(dtypes_lib.float32, shape=[None, 3], name="p")
+      p_identity = array_ops.identity(p)
+
+      # Should trigger an operator error, not a shape error.
+      with self.assertRaisesOpError(
+          "must feed a value for placeholder tensor 'p' with dtype float"):
+        p_identity.eval()
+
   def testControlDependency(self):
     with self.test_session():
       p = array_ops.placeholder(dtypes_lib.int32, shape=[], name="p")
-- 
GitLab


From 6a50643cb51bc869d8f4bba0fd17d112164e8f37 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 29 Jun 2017 08:26:25 -0700
Subject: [PATCH 0585/1375] Allow the pruner to prune fully placed graphs

PiperOrigin-RevId: 160528361
---
 .../core/grappler/optimizers/graph_rewriter.cc   |  4 +++-
 .../core/grappler/optimizers/model_pruner.cc     |  4 ----
 .../grappler/optimizers/model_pruner_test.cc     | 16 +++-------------
 3 files changed, 6 insertions(+), 18 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.cc b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
index d1ab5a1d9b..3a3cfdf8cb 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.cc
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
@@ -51,7 +51,9 @@ void GraphRewriter::ForwardInputs(
     auto itr = nodes_.find(input_node_name);
     CHECK(itr != nodes_.end());
     const NodeDef* input_node = itr->second;
-    if (nodes_to_delete.find(input_node) != nodes_to_delete.end()) {
+    if ((input_node->device().empty() || original_node.device().empty() ||
+         input_node->device() == original_node.device()) &&
+        nodes_to_delete.find(input_node) != nodes_to_delete.end()) {
       ForwardInputs(*input_node, nodes_to_delete, new_node);
     } else {
       *new_node->add_input() = input;
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index 34fb5b92c7..ebc18a43b2 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -48,10 +48,6 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     if (nodes_to_preserve.find(node.name()) != nodes_to_preserve.end()) {
       continue;
     }
-    // Don't remove nodes that are explicitly placed.
-    if (!node.device().empty()) {
-      continue;
-    }
     // Don't remove nodes that drive control dependencies.
     // Don't remove nodes that are driven by control dependencies either since
     // we can't ensure (yet) that we won't increase the number of control
diff --git a/tensorflow/core/grappler/optimizers/model_pruner_test.cc b/tensorflow/core/grappler/optimizers/model_pruner_test.cc
index 67954d2914..3118f3a360 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner_test.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner_test.cc
@@ -94,17 +94,13 @@ TEST_F(ModelPrunerTest, IdentityPruning) {
 
   Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
   Output b = ops::AddN(s.WithOpName("b"), {a});
-  Output c = ops::Identity(s.WithOpName("c"), b);
-  Output d = ops::Identity(s.WithOpName("d"), c);
+  Output c = ops::Identity(s.WithOpName("c").WithDevice("CPU:0"), b);
+  Output d = ops::Identity(s.WithOpName("d").WithDevice("GPU:0"), c);
   Output e = ops::AddN(s.WithOpName("e"), {d});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
-  // Force the placement of c. This should ensure it is preserved.
-  EXPECT_EQ("c", item.graph.node(2).name());
-  item.graph.mutable_node(2)->set_device("CPU");
-
   ModelPruner pruner;
   GraphDef output;
   Status status = pruner.Optimize(nullptr, item, &output);
@@ -136,17 +132,11 @@ TEST_F(ModelPrunerTest, PruningSkipsCtrlDependencies) {
   Output b = ops::AddN(s.WithOpName("b"), {a});
   Output c = ops::Identity(s.WithOpName("c"), b);
   Output d = ops::Identity(s.WithOpName("d"), c);
-  Output e = ops::AddN(s.WithOpName("e"), {d});
+  Output e = ops::AddN(s.WithOpName("e").WithControlDependencies(c), {d});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
-  // Add a control dependency between c and e. This should ensure c is
-  // preserved.
-  EXPECT_EQ("c", item.graph.node(2).name());
-  EXPECT_EQ("e", item.graph.node(4).name());
-  *item.graph.mutable_node(4)->add_input() = "^c";
-
   ModelPruner pruner;
   GraphDef output;
   Status status = pruner.Optimize(nullptr, item, &output);
-- 
GitLab


From 7eba207ed3046fbbe7e9b628fba30ae7498697d0 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 29 Jun 2017 09:33:38 -0700
Subject: [PATCH 0586/1375] [TF:XLA] Change XlaCompiler's
 resolve_compile_time_constants option to a per-compilation option.

Don't resolve compile time constants in while loops since we must preserve the loop signature.

PiperOrigin-RevId: 160535393
---
 .../compiler/tf2xla/kernels/while_op.cc       |  5 +++-
 tensorflow/compiler/tf2xla/xla_compiler.cc    |  2 +-
 tensorflow/compiler/tf2xla/xla_compiler.h     | 12 +++++-----
 .../compiler/tf2xla/xla_compiler_test.cc      | 23 ++++++++-----------
 4 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc
index 0caa9c5f37..2f85902981 100644
--- a/tensorflow/compiler/tf2xla/kernels/while_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc
@@ -113,12 +113,14 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
   // present as loop body outputs; the signature of the loop's input and
   // output must match. We ensure this by asking the compiler to include the
   // current values of all resources, even if they haven't been updated by the
-  // computation.
+  // computation. We must also ask the compiler to keep compile-time constant
+  // outputs as part of the generated computation, for the same reason.
   // TODO(phawkins): consider adding loop-invariant inputs to XLA's While()
   // operator.
   XlaCompiler::CompileOptions body_options;
   body_options.use_tuple_arg = use_tuple_arg;
   body_options.return_updated_values_for_all_resources = true;
+  body_options.resolve_compile_time_constants = false;
   XlaCompiler::CompilationResult body;
   OP_REQUIRES_OK(ctx, compiler->CompileFunction(body_options, body_name_attr_,
                                                 arguments, &body));
@@ -161,6 +163,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
 
   XlaCompiler::CompileOptions cond_options;
   cond_options.use_tuple_arg = use_tuple_arg;
+  cond_options.resolve_compile_time_constants = false;
   XlaCompiler::CompilationResult cond;
   OP_REQUIRES_OK(ctx, compiler->CompileFunction(cond_options, cond_name_attr_,
                                                 arguments, &cond));
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index 50b384997a..02e7c138d2 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -423,7 +423,7 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
   xla::ComputationBuilder builder(client(), name);
   XlaContext* context =
       new XlaContext(this, &builder, options_.allow_cpu_custom_calls,
-                     options_.resolve_compile_time_constants);
+                     options.resolve_compile_time_constants);
   core::ScopedUnref context_unref(context);
 
   result->tuple_arg = options.use_tuple_arg;
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h
index 58e42c3474..197e45617c 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.h
+++ b/tensorflow/compiler/tf2xla/xla_compiler.h
@@ -209,12 +209,6 @@ class XlaCompiler {
     // stored in device memory.
     bool local_executable_has_hybrid_result = false;
 
-    // If 'resolve_compile_time_constants' is true, then outputs of a
-    // computation that are known to be compile-time constants will be returned
-    // as Tensors at compile-time, rather than as run-time outputs of the
-    // computation.
-    bool resolve_compile_time_constants = true;
-
     // If not nullptr, populate_resource_manager is called with the
     // compilation device's resource manager when the compilation
     // device is created, and can be used to create metadata objects
@@ -238,6 +232,12 @@ class XlaCompiler {
     // modified by the computation. Used when compiling loop bodies to ensure
     // the input and output signatures match.
     bool return_updated_values_for_all_resources = false;
+
+    // If 'resolve_compile_time_constants' is true, then outputs of a
+    // computation that are known to be compile-time constants will be returned
+    // as Tensors at compile-time, rather than as run-time outputs of the
+    // computation.
+    bool resolve_compile_time_constants = true;
   };
 
   // Compiles a Tensorflow function `fn_name_attrs` into an XLA computation.
diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
index 427b14534f..97cd951000 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
@@ -203,19 +203,19 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
   args[0].type = DT_INT32;
   args[0].shape = TensorShape({2});
 
+  XlaCompiler::Options options = DefaultOptions();
+  XlaCompiler compiler(options);
   {
     // Compiles the graph, with resolve_compile_time_constants enabled.
-    XlaCompiler::Options options = DefaultOptions();
-    options.resolve_compile_time_constants = true;
-    XlaCompiler compiler(options);
 
     std::unique_ptr<Graph> graph_copy(new Graph(OpRegistry::Global()));
     CopyGraph(*graph, graph_copy.get());
 
+    XlaCompiler::CompileOptions compile_options;
+    compile_options.resolve_compile_time_constants = true;
     XlaCompiler::CompilationResult result;
-    TF_ASSERT_OK(compiler.CompileGraph(XlaCompiler::CompileOptions(),
-                                       "constants", std::move(graph_copy), args,
-                                       &result));
+    TF_ASSERT_OK(compiler.CompileGraph(compile_options, "constants",
+                                       std::move(graph_copy), args, &result));
 
     ASSERT_EQ(2, result.outputs.size());
     EXPECT_TRUE(result.outputs[0].is_constant);
@@ -242,17 +242,14 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
 
   {
     // Compiles the graph, with resolve_compile_time_constants disabled.
-    XlaCompiler::Options options = DefaultOptions();
-    options.resolve_compile_time_constants = false;
-    XlaCompiler compiler(options);
-
     std::unique_ptr<Graph> graph_copy(new Graph(OpRegistry::Global()));
     CopyGraph(*graph, graph_copy.get());
 
+    XlaCompiler::CompileOptions compile_options;
+    compile_options.resolve_compile_time_constants = false;
     XlaCompiler::CompilationResult result;
-    TF_ASSERT_OK(compiler.CompileGraph(XlaCompiler::CompileOptions(),
-                                       "constants", std::move(graph_copy), args,
-                                       &result));
+    TF_ASSERT_OK(compiler.CompileGraph(compile_options, "constants",
+                                       std::move(graph_copy), args, &result));
 
     ASSERT_EQ(2, result.outputs.size());
     EXPECT_FALSE(result.outputs[0].is_constant);
-- 
GitLab


From 7049f8c87291fc43d93e7a4fa72dbb192f79c815 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Fri, 30 Jun 2017 01:42:13 +0900
Subject: [PATCH 0587/1375] Fix typos (#11129)

---
 .../compiler/xla/service/cpu/cpu_parallelization_preparation.h  | 2 +-
 tensorflow/compiler/xla/service/cpu/shape_partition.cc          | 2 +-
 .../handlers/sparse-quantized-feature-column-handler.cc         | 2 +-
 .../boosted_trees/python/kernel_tests/prediction_ops_test.py    | 2 +-
 tensorflow/contrib/learn/python/learn/experiment.py             | 2 +-
 tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h        | 2 +-
 tensorflow/python/util/deprecation_test.py                      | 2 +-
 7 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
index 70e3492934..d53fc46150 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
@@ -63,7 +63,7 @@ class ParallelizationPreparation : public HloPassInterface {
 
   // Outlines 'instruction' from entry computation, if it had
   // been assigned parallel tasks in an earlier pass through the computation.
-  // Returns true if 'instruction' was succesfully outlined, false otherwise.
+  // Returns true if 'instruction' was successfully outlined, false otherwise.
   bool OutlineParallelizableInstruction(HloInstruction* instruction);
 
   // Returns true if 'instruction' can be outlined into the same sub-computation
diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition.cc b/tensorflow/compiler/xla/service/cpu/shape_partition.cc
index e27ff13edd..61b408b8c2 100644
--- a/tensorflow/compiler/xla/service/cpu/shape_partition.cc
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition.cc
@@ -20,7 +20,7 @@ namespace cpu {
 
 std::vector<int64> ShapePartitionAssigner::Run(int64 target_partition_count) {
   // Gather outer-most dims where dim_size >= 'target_partition_count'.
-  // Note: always leave inner-dim static for vectorization/optimzations.
+  // Note: always leave inner-dim static for vectorization/optimizations.
   std::vector<int64> outer_dims;
   int64 outer_dim_size = 1;
   // TODO(b/27458679) Consider reserving enough minor dimensions (based on
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
index 76da16ab93..c7f49a4699 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
@@ -124,7 +124,7 @@ void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
 
     // Determine if we need a backward pass by checking if the residual gradient
     // after forward aggregation is almost the same as the aggregated gradient.
-    // for the current root. This helps avoid unecessary computation as well
+    // for the current root. This helps avoid unnecessary computation as well
     // as consistency due to floating point precision.
     if (!right_gradient_stats.IsAlmostZero()) {
       // Backward pass with left default direction.
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
index aa665d95d7..e5624da965 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
@@ -1149,7 +1149,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
       adjusted_tree_ensemble_config = (
           tree_config_pb2.DecisionTreeEnsembleConfig())
       # When we say to average over more trees than possible, it is averaging
-      # accross all trees.
+      # across all trees.
       total_num = 100
       for i in range(0, total_num):
         tree = tree_ensemble_config.trees.add()
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index cd47b82d82..0d2f84da9e 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -523,7 +523,7 @@ class Experiment(object):
       differences in resource control. First, the resources (e.g., memory) used
       by training will be released before evaluation (`train_and_evaluate` takes
       double resources). Second, more checkpoints will be saved as a checkpoint
-      is generated at the end of each trainning iteration.
+      is generated at the end of each training iteration.
 
       3. As the estimator.train starts from scratch (new graph, new states for
       input, etc) at each iteration, it is recommended to have the
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
index 8d32b4961b..f9c91d8ff0 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
@@ -104,7 +104,7 @@ class GrowStats {
 
   const TensorForestParams& params_;
 
-  // We cache these beacuse they're used often.
+  // We cache these because they're used often.
   const int split_after_samples_;
   const int num_splits_to_consider_;
 
diff --git a/tensorflow/python/util/deprecation_test.py b/tensorflow/python/util/deprecation_test.py
index e2d9a594a3..16d246c4df 100644
--- a/tensorflow/python/util/deprecation_test.py
+++ b/tensorflow/python/util/deprecation_test.py
@@ -608,7 +608,7 @@ class DeprecatedArgsTest(test.TestCase):
     self._assert_subset(set(["after " + date, instructions, "d2"]),
                         set(args2[1:]))
 
-    # Assert calls with the deprecated arguments dont log warnings if
+    # Assert calls with the deprecated arguments don't log warnings if
     # the value matches the 'ok_val'.
     mock_warning.reset_mock()
     self.assertEqual(3, _fn(1, None, 2, d2="my_ok_val"))
-- 
GitLab


From d0360669091554fbafe532bc33768e572b0f01f8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 10:04:16 -0700
Subject: [PATCH 0588/1375] Updates remaining examples in examples/learn.

PiperOrigin-RevId: 160538962
---
 .../examples/learn/hdf5_classification.py     |  41 +++-
 tensorflow/examples/learn/mnist.py            | 131 +++++++-----
 tensorflow/examples/learn/multiple_gpu.py     | 107 ++++++----
 tensorflow/examples/learn/resnet.py           | 197 ++++++++++--------
 4 files changed, 277 insertions(+), 199 deletions(-)

diff --git a/tensorflow/examples/learn/hdf5_classification.py b/tensorflow/examples/learn/hdf5_classification.py
index db37500246..3a46bbcf41 100644
--- a/tensorflow/examples/learn/hdf5_classification.py
+++ b/tensorflow/examples/learn/hdf5_classification.py
@@ -11,25 +11,27 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-"""Example of DNNClassifier for Iris plant dataset, h5 format."""
+"""Example of DNNClassifier for Iris plant dataset, hdf5 format."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-from sklearn import cross_validation
+from sklearn import datasets
 from sklearn import metrics
+from sklearn import model_selection
 import tensorflow as tf
 import h5py  # pylint: disable=g-bad-import-order
 
-learn = tf.contrib.learn
+
+X_FEATURE = 'x'  # Name of the input feature.
 
 
 def main(unused_argv):
   # Load dataset.
-  iris = learn.datasets.load_dataset('iris')
-  x_train, x_test, y_train, y_test = cross_validation.train_test_split(
+  iris = datasets.load_iris()
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
   # Note that we are saving and load iris data as h5 format as a simple
@@ -48,14 +50,31 @@ def main(unused_argv):
   y_test = np.array(h5f['y_test'])
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
-  feature_columns = learn.infer_real_valued_columns_from_input(x_train)
-  classifier = learn.DNNClassifier(
+  feature_columns = [
+      tf.feature_column.numeric_column(
+          X_FEATURE, shape=np.array(x_train).shape[1:])]
+  classifier = tf.estimator.DNNClassifier(
       feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
 
-  # Fit and predict.
-  classifier.fit(x_train, y_train, steps=200)
-  score = metrics.accuracy_score(y_test, classifier.predict(x_test))
-  print('Accuracy: {0:f}'.format(score))
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=200)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class_ids'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
+  score = metrics.accuracy_score(y_test, y_predicted)
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/mnist.py b/tensorflow/examples/learn/mnist.py
index 15cf4b91dd..5344526b52 100644
--- a/tensorflow/examples/learn/mnist.py
+++ b/tensorflow/examples/learn/mnist.py
@@ -22,89 +22,110 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-from sklearn import metrics
 import tensorflow as tf
 
-layers = tf.contrib.layers
-learn = tf.contrib.learn
 
+N_DIGITS = 10  # Number of digits.
+X_FEATURE = 'x'  # Name of the input feature.
 
-def max_pool_2x2(tensor_in):
-  return tf.nn.max_pool(
-      tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 
-
-def conv_model(feature, target, mode):
+def conv_model(features, labels, mode):
   """2-layer convolution model."""
-  # Convert the target to a one-hot tensor of shape (batch_size, 10) and
-  # with a on-value of 1 for each one-hot vector of length 10.
-  target = tf.one_hot(tf.cast(target, tf.int32), 10, 1, 0)
-
   # Reshape feature to 4d tensor with 2nd and 3rd dimensions being
   # image width and height final dimension being the number of color channels.
-  feature = tf.reshape(feature, [-1, 28, 28, 1])
+  feature = tf.reshape(features[X_FEATURE], [-1, 28, 28, 1])
 
   # First conv layer will compute 32 features for each 5x5 patch
   with tf.variable_scope('conv_layer1'):
-    h_conv1 = layers.convolution2d(
-        feature, 32, kernel_size=[5, 5], activation_fn=tf.nn.relu)
-    h_pool1 = max_pool_2x2(h_conv1)
+    h_conv1 = tf.layers.conv2d(
+        feature,
+        filters=32,
+        kernel_size=[5, 5],
+        padding='same',
+        activation=tf.nn.relu)
+    h_pool1 = tf.layers.max_pooling2d(
+        h_conv1, pool_size=2, strides=2, padding='same')
 
   # Second conv layer will compute 64 features for each 5x5 patch.
   with tf.variable_scope('conv_layer2'):
-    h_conv2 = layers.convolution2d(
-        h_pool1, 64, kernel_size=[5, 5], activation_fn=tf.nn.relu)
-    h_pool2 = max_pool_2x2(h_conv2)
+    h_conv2 = tf.layers.conv2d(
+        h_pool1,
+        filters=64,
+        kernel_size=[5, 5],
+        padding='same',
+        activation=tf.nn.relu)
+    h_pool2 = tf.layers.max_pooling2d(
+        h_conv2, pool_size=2, strides=2, padding='same')
     # reshape tensor into a batch of vectors
     h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
 
   # Densely connected layer with 1024 neurons.
-  h_fc1 = layers.dropout(
-      layers.fully_connected(
-          h_pool2_flat, 1024, activation_fn=tf.nn.relu),
-      keep_prob=0.5,
-      is_training=mode == tf.contrib.learn.ModeKeys.TRAIN)
+  h_fc1 = tf.layers.dense(h_pool2_flat, 1024, activation=tf.nn.relu)
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    h_fc1 = tf.layers.dropout(h_fc1, rate=0.5)
 
   # Compute logits (1 per class) and compute loss.
-  logits = layers.fully_connected(h_fc1, 10, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-
-  # Create a tensor for training op.
-  train_op = layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='SGD',
-      learning_rate=0.001)
-
-  return tf.argmax(logits, 1), loss, train_op
+  logits = tf.layers.dense(h_fc1, N_DIGITS, activation=None)
+
+  # Compute predictions.
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        'class': predicted_classes,
+        'prob': tf.nn.softmax(logits)
+    }
+    return tf.estimator.EstimatorSpec(mode, predictions=predictions)
+
+  # Compute loss.
+  onehot_labels = tf.one_hot(tf.cast(labels, tf.int32), N_DIGITS, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+
+  # Create training op.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  # Compute evaluation metrics.
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_args):
   ### Download and load MNIST dataset.
-  mnist = learn.datasets.load_dataset('mnist')
+  mnist = tf.contrib.learn.datasets.DATASETS['mnist']('/tmp/mnist')
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: mnist.train.images},
+      y=mnist.train.labels.astype(np.int32),
+      batch_size=100,
+      num_epochs=None,
+      shuffle=True)
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: mnist.train.images},
+      y=mnist.train.labels.astype(np.int32),
+      num_epochs=1,
+      shuffle=False)
 
   ### Linear classifier.
-  feature_columns = learn.infer_real_valued_columns_from_input(
-      mnist.train.images)
-  classifier = learn.LinearClassifier(
-      feature_columns=feature_columns, n_classes=10)
-  classifier.fit(mnist.train.images,
-                 mnist.train.labels.astype(np.int32),
-                 batch_size=100,
-                 steps=1000)
-  score = metrics.accuracy_score(mnist.test.labels,
-                                 list(classifier.predict(mnist.test.images)))
-  print('Accuracy: {0:f}'.format(score))
+  feature_columns = [
+      tf.feature_column.numeric_column(
+          X_FEATURE, shape=mnist.train.images.shape[1:])]
+  classifier = tf.estimator.LinearClassifier(
+      feature_columns=feature_columns, n_classes=N_DIGITS)
+  classifier.train(input_fn=train_input_fn, steps=200)
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (LinearClassifier): {0:f}'.format(scores['accuracy']))
 
   ### Convolutional network
-  classifier = learn.Estimator(model_fn=conv_model)
-  classifier.fit(mnist.train.images,
-                 mnist.train.labels,
-                 batch_size=100,
-                 steps=20000)
-  score = metrics.accuracy_score(mnist.test.labels,
-                                 list(classifier.predict(mnist.test.images)))
-  print('Accuracy: {0:f}'.format(score))
+  classifier = tf.estimator.Estimator(model_fn=conv_model)
+  classifier.train(input_fn=train_input_fn, steps=200)
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (conv_model): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/multiple_gpu.py b/tensorflow/examples/learn/multiple_gpu.py
index df58906b39..c7364d1f72 100644
--- a/tensorflow/examples/learn/multiple_gpu.py
+++ b/tensorflow/examples/learn/multiple_gpu.py
@@ -20,75 +20,100 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from sklearn import cross_validation
+import numpy as np
 from sklearn import datasets
 from sklearn import metrics
+from sklearn import model_selection
 import tensorflow as tf
 
-layers = tf.contrib.layers
-learn = tf.contrib.learn
 
+X_FEATURE = 'x'  # Name of the input feature.
 
-def my_model(features, target):
+
+def my_model(features, labels, mode):
   """DNN with three hidden layers, and dropout of 0.1 probability.
 
   Note: If you want to run this example with multiple GPUs, Cuda Toolkit 7.0 and
   CUDNN 6.5 V2 from NVIDIA need to be installed beforehand.
 
   Args:
-    features: `Tensor` of input features.
-    target: `Tensor` of targets.
+    features: Dict of input `Tensor`.
+    labels: Label `Tensor`.
+    mode: One of `ModeKeys`.
 
   Returns:
-    Tuple of predictions, loss and training op.
+    `EstimatorSpec`.
   """
-  # Convert the target to a one-hot tensor of shape (length of features, 3) and
-  # with a on-value of 1 for each one-hot vector of length 3.
-  target = tf.one_hot(target, 3, 1, 0)
-
   # Create three fully connected layers respectively of size 10, 20, and 10 with
   # each layer having a dropout probability of 0.1.
-  normalizer_fn = layers.dropout
-  normalizer_params = {'keep_prob': 0.5}
+  net = features[X_FEATURE]
   with tf.device('/gpu:1'):
-    features = layers.stack(
-        features,
-        layers.fully_connected, [10, 20, 10],
-        normalizer_fn=normalizer_fn,
-        normalizer_params=normalizer_params)
+    for units in [10, 20, 10]:
+      net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
+      net = tf.layers.dropout(net, rate=0.1)
 
   with tf.device('/gpu:2'):
-    # Compute logits (1 per class) and compute loss.
-    logits = layers.fully_connected(features, 3, activation_fn=None)
-    loss = tf.losses.softmax_cross_entropy(target, logits)
-
-    # Create a tensor for training op.
-    train_op = tf.contrib.layers.optimize_loss(
-        loss,
-        tf.contrib.framework.get_global_step(),
-        optimizer='Adagrad',
-        learning_rate=0.1)
-
-  return ({
-      'class': tf.argmax(logits, 1),
-      'prob': tf.nn.softmax(logits)
-  }, loss, train_op)
+    # Compute logits (1 per class).
+    logits = tf.layers.dense(net, 3, activation=None)
+
+    # Compute predictions.
+    predicted_classes = tf.argmax(logits, 1)
+    if mode == tf.estimator.ModeKeys.PREDICT:
+      predictions = {
+          'class': predicted_classes,
+          'prob': tf.nn.softmax(logits)
+      }
+      return tf.estimator.EstimatorSpec(mode, predictions=predictions)
+
+    # Convert the labels to a one-hot tensor of shape (length of features, 3)
+    # and with a on-value of 1 for each one-hot vector of length 3.
+    onehot_labels = tf.one_hot(labels, 3, 1, 0)
+    # Compute loss.
+    loss = tf.losses.softmax_cross_entropy(
+        onehot_labels=onehot_labels, logits=logits)
+
+    # Create training op.
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
+      train_op = optimizer.minimize(
+          loss, global_step=tf.train.get_global_step())
+      return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+    # Compute evaluation metrics.
+    eval_metric_ops = {
+        'accuracy': tf.metrics.accuracy(
+            labels=labels, predictions=predicted_classes)
+    }
+    return tf.estimator.EstimatorSpec(
+        mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
 
 def main(unused_argv):
   iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = cross_validation.train_test_split(
+  x_train, x_test, y_train, y_test = model_selection.train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42)
 
-  classifier = learn.Estimator(model_fn=my_model)
-  classifier.fit(x_train, y_train, steps=1000)
+  classifier = tf.estimator.Estimator(model_fn=my_model)
 
-  y_predicted = [
-      p['class'] for p in classifier.predict(
-          x_test, as_iterable=True)
-  ]
+  # Train.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Predict.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
+  predictions = classifier.predict(input_fn=test_input_fn)
+  y_predicted = np.array(list(p['class'] for p in predictions))
+  y_predicted = y_predicted.reshape(np.array(y_test).shape)
+
+  # Score with sklearn.
   score = metrics.accuracy_score(y_test, y_predicted)
-  print('Accuracy: {0:f}'.format(score))
+  print('Accuracy (sklearn): {0:f}'.format(score))
+
+  # Score with tensorflow.
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/learn/resnet.py b/tensorflow/examples/learn/resnet.py
index 881905fde8..33a09bb6e0 100755
--- a/tensorflow/examples/learn/resnet.py
+++ b/tensorflow/examples/learn/resnet.py
@@ -25,31 +25,17 @@ from __future__ import print_function
 
 from collections import namedtuple
 from math import sqrt
-import os
 
+import numpy as np
 import tensorflow as tf
 
-batch_norm = tf.contrib.layers.batch_norm
-convolution2d = tf.contrib.layers.convolution2d
 
+N_DIGITS = 10  # Number of digits.
+X_FEATURE = 'x'  # Name of the input feature.
 
-def res_net(x, y, activation=tf.nn.relu):
-  """Builds a residual network.
 
-  Note that if the input tensor is 2D, it must be square in order to be
-  converted to a 4D tensor.
-
-  Borrowed structure from:
-  github.com/pkmital/tensorflow_tutorials/blob/master/10_residual_network.py
-
-  Args:
-    x: Input of the network
-    y: Output of the network
-    activation: Activation function to apply after each convolution
-
-  Returns:
-    Predictions and loss tensors.
-  """
+def res_net_model(features, labels, mode):
+  """Builds a residual network."""
 
   # Configurations for each bottleneck group.
   BottleneckGroup = namedtuple('BottleneckGroup',
@@ -59,6 +45,7 @@ def res_net(x, y, activation=tf.nn.relu):
       BottleneckGroup(3, 512, 128), BottleneckGroup(3, 1024, 256)
   ]
 
+  x = features[X_FEATURE]
   input_shape = x.get_shape().as_list()
 
   # Reshape the input into the right shape if it's 2D tensor
@@ -68,15 +55,24 @@ def res_net(x, y, activation=tf.nn.relu):
 
   # First convolution expands to 64 channels
   with tf.variable_scope('conv_layer1'):
-    net = convolution2d(
-        x, 64, 7, normalizer_fn=batch_norm, activation_fn=activation)
+    net = tf.layers.conv2d(
+        x,
+        filters=64,
+        kernel_size=7,
+        activation=tf.nn.relu)
+    net = tf.layers.batch_normalization(net)
 
   # Max pool
-  net = tf.nn.max_pool(net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
+  net = tf.layers.max_pooling2d(
+      net, pool_size=3, strides=2, padding='same')
 
   # First chain of resnets
   with tf.variable_scope('conv_layer2'):
-    net = convolution2d(net, groups[0].num_filters, 1, padding='VALID')
+    net = tf.layers.conv2d(
+        net,
+        filters=groups[0].num_filters,
+        kernel_size=1,
+        padding='valid')
 
   # Create the bottleneck groups, each of which contains `num_blocks`
   # bottleneck groups.
@@ -86,33 +82,33 @@ def res_net(x, y, activation=tf.nn.relu):
 
       # 1x1 convolution responsible for reducing dimension
       with tf.variable_scope(name + '/conv_in'):
-        conv = convolution2d(
+        conv = tf.layers.conv2d(
             net,
-            group.bottleneck_size,
-            1,
-            padding='VALID',
-            activation_fn=activation,
-            normalizer_fn=batch_norm)
+            filters=group.num_filters,
+            kernel_size=1,
+            padding='valid',
+            activation=tf.nn.relu)
+        conv = tf.layers.batch_normalization(conv)
 
       with tf.variable_scope(name + '/conv_bottleneck'):
-        conv = convolution2d(
+        conv = tf.layers.conv2d(
             conv,
-            group.bottleneck_size,
-            3,
-            padding='SAME',
-            activation_fn=activation,
-            normalizer_fn=batch_norm)
+            filters=group.bottleneck_size,
+            kernel_size=3,
+            padding='same',
+            activation=tf.nn.relu)
+        conv = tf.layers.batch_normalization(conv)
 
       # 1x1 convolution responsible for restoring dimension
       with tf.variable_scope(name + '/conv_out'):
         input_dim = net.get_shape()[-1].value
-        conv = convolution2d(
+        conv = tf.layers.conv2d(
             conv,
-            input_dim,
-            1,
-            padding='VALID',
-            activation_fn=activation,
-            normalizer_fn=batch_norm)
+            filters=input_dim,
+            kernel_size=1,
+            padding='valid',
+            activation=tf.nn.relu)
+        conv = tf.layers.batch_normalization(conv)
 
       # shortcut connections that turn the network into its counterpart
       # residual function (identity shortcut)
@@ -122,13 +118,13 @@ def res_net(x, y, activation=tf.nn.relu):
       # upscale to the next group size
       next_group = groups[group_i + 1]
       with tf.variable_scope('block_%d/conv_upscale' % group_i):
-        net = convolution2d(
+        net = tf.layers.conv2d(
             net,
-            next_group.num_filters,
-            1,
-            activation_fn=None,
-            biases_initializer=None,
-            padding='SAME')
+            filters=next_group.num_filters,
+            kernel_size=1,
+            padding='same',
+            activation=None,
+            bias_initializer=None)
     except IndexError:
       pass
 
@@ -142,48 +138,65 @@ def res_net(x, y, activation=tf.nn.relu):
   net_shape = net.get_shape().as_list()
   net = tf.reshape(net, [-1, net_shape[1] * net_shape[2] * net_shape[3]])
 
-  target = tf.one_hot(y, depth=10, dtype=tf.float32)
-  logits = tf.contrib.layers.fully_connected(net, 10, activation_fn=None)
-  loss = tf.losses.softmax_cross_entropy(target, logits)
-  return tf.nn.softmax(logits), loss
-
-
-def res_net_model(x, y):
-  prediction, loss = res_net(x, y)
-  predicted = tf.argmax(prediction, 1)
-  accuracy = tf.equal(predicted, tf.cast(y, tf.int64))
-  predictions = {'prob': prediction, 'class': predicted, 'accuracy': accuracy}
-  train_op = tf.contrib.layers.optimize_loss(
-      loss,
-      tf.contrib.framework.get_global_step(),
-      optimizer='Adagrad',
-      learning_rate=0.001)
-  return predictions, loss, train_op
-
-
-# Download and load MNIST data.
-mnist = tf.contrib.learn.datasets.load_dataset('mnist')
-
-# Create a new resnet classifier.
-classifier = tf.contrib.learn.Estimator(model_fn=res_net_model)
-
-tf.logging.set_verbosity(tf.logging.INFO)  # Show training logs. (avoid silence)
-
-# Train model and save summaries into logdir.
-classifier.fit(mnist.train.images,
-               mnist.train.labels,
-               batch_size=100,
-               steps=1000)
-
-# Calculate accuracy.
-result = classifier.evaluate(
-    x=mnist.test.images,
-    y=mnist.test.labels,
-    metrics={
-        'accuracy':
-            tf.contrib.learn.MetricSpec(
-                metric_fn=tf.contrib.metrics.streaming_accuracy,
-                prediction_key='accuracy'),
-    })
-score = result['accuracy']
-print('Accuracy: {0:f}'.format(score))
+  # Compute logits (1 per class) and compute loss.
+  logits = tf.layers.dense(net, N_DIGITS, activation=None)
+
+  # Compute predictions.
+  predicted_classes = tf.argmax(logits, 1)
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    predictions = {
+        'class': predicted_classes,
+        'prob': tf.nn.softmax(logits)
+    }
+    return tf.estimator.EstimatorSpec(mode, predictions=predictions)
+
+  # Compute loss.
+  onehot_labels = tf.one_hot(tf.cast(labels, tf.int32), N_DIGITS, 1, 0)
+  loss = tf.losses.softmax_cross_entropy(
+      onehot_labels=onehot_labels, logits=logits)
+
+  # Create training op.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    optimizer = tf.train.AdagradOptimizer(learning_rate=0.01)
+    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+  # Compute evaluation metrics.
+  eval_metric_ops = {
+      'accuracy': tf.metrics.accuracy(
+          labels=labels, predictions=predicted_classes)
+  }
+  return tf.estimator.EstimatorSpec(
+      mode, loss=loss, eval_metric_ops=eval_metric_ops)
+
+
+def main(unused_args):
+  # Download and load MNIST data.
+  mnist = tf.contrib.learn.datasets.DATASETS['mnist']('/tmp/mnist')
+
+  # Create a new resnet classifier.
+  classifier = tf.estimator.Estimator(model_fn=res_net_model)
+
+  tf.logging.set_verbosity(tf.logging.INFO)  # Show training logs.
+
+  # Train model and save summaries into logdir.
+  train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: mnist.train.images},
+      y=mnist.train.labels.astype(np.int32),
+      batch_size=100,
+      num_epochs=None,
+      shuffle=True)
+  classifier.train(input_fn=train_input_fn, steps=100)
+
+  # Calculate accuracy.
+  test_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x={X_FEATURE: mnist.train.images},
+      y=mnist.train.labels.astype(np.int32),
+      num_epochs=1,
+      shuffle=False)
+  scores = classifier.evaluate(input_fn=test_input_fn)
+  print('Accuracy: {0:f}'.format(scores['accuracy']))
+
+
+if __name__ == '__main__':
+  tf.app.run()
-- 
GitLab


From c1087b3a0b851b62a027201c0c41c0bd4e44e303 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 29 Jun 2017 10:28:43 -0700
Subject: [PATCH 0589/1375] Preserve feed nodes when pruning models Also
 preserve invalid inputs and avoid asserting on bogus graphs

PiperOrigin-RevId: 160542417
---
 tensorflow/core/grappler/optimizers/graph_rewriter.cc | 5 ++++-
 tensorflow/core/grappler/optimizers/model_pruner.cc   | 3 +++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.cc b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
index 3a3cfdf8cb..c8b100abae 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.cc
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
@@ -49,7 +49,10 @@ void GraphRewriter::ForwardInputs(
   for (const auto& input : original_node.input()) {
     string input_node_name = NodeName(input);
     auto itr = nodes_.find(input_node_name);
-    CHECK(itr != nodes_.end());
+    if (itr == nodes_.end()) {
+      // Invalid input, preserve it as is.
+      *new_node->add_input() = input;
+    }
     const NodeDef* input_node = itr->second;
     if ((input_node->device().empty() || original_node.device().empty() ||
          input_node->device() == original_node.device()) &&
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index ebc18a43b2..caf521d979 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -33,6 +33,9 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
   for (const auto& node : item.fetch) {
     nodes_to_preserve.insert(NodeName(node));
   }
+  for (const auto& feed : item.feed) {
+    nodes_to_preserve.insert(NodeName(feed.first));
+  }
   for (const auto& node : item.init_ops) {
     nodes_to_preserve.insert(NodeName(node));
   }
-- 
GitLab


From 8849a5214b3427abb52255198b80b6da1942f8e8 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Thu, 29 Jun 2017 11:08:59 -0700
Subject: [PATCH 0590/1375] Remove duplicate BUILD rule from merge result

---
 tensorflow/compiler/xla/tests/BUILD | 22 ----------------------
 1 file changed, 22 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index b9a4f3e7e0..c7578115cd 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -505,28 +505,6 @@ xla_test(
     ],
 )
 
-xla_test(
-    name = "reduce_precision_test",
-    srcs = ["reduce_precision_test.cc"],
-    deps = [
-        "//tensorflow/compiler/xla:array2d",
-        "//tensorflow/compiler/xla:literal_util",
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla:statusor",
-        "//tensorflow/compiler/xla:test",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
-        "//tensorflow/compiler/xla/client:global_data",
-        "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:user_computation_flags",
-        "//tensorflow/compiler/xla/tests:client_library_test_base",
-        "//tensorflow/compiler/xla/tests:literal_test_util",
-        "//tensorflow/core:lib",
-    ],
-)
-
 xla_test(
     name = "dot_operation_test",
     srcs = ["dot_operation_test.cc"],
-- 
GitLab


From 9b11f458196f6f0528c9974238497a6c8b6da547 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 29 Jun 2017 11:10:56 -0700
Subject: [PATCH 0591/1375] [tf.contrib.data] Fix the handling of dict-typed
 elements in functions.

Previously, we were treating a `dict` as a sequence, which led to
incorrect behavior like passing all of the dict's keys rather than
values as the arguments to a map or filter function. This change
changes the behavior so that the dict is passed as a single argument
to these functions. It additionally fixes the ported version of
`nest.flatten_up_to()` so that `Dataset.padded_batch()` works with
dict-typed elements.

Fixes #11016.

PiperOrigin-RevId: 160548475
---
 .../python/kernel_tests/bucketing_test.py     | 24 ++++++++++++-------
 .../kernel_tests/filter_dataset_op_test.py    | 17 +++++++++++++
 .../kernel_tests/flat_map_dataset_op_test.py  | 17 +++++++++++++
 .../kernel_tests/map_dataset_op_test.py       | 16 +++++++++++++
 .../contrib/data/python/ops/dataset_ops.py    | 13 ++++++----
 tensorflow/contrib/data/python/util/nest.py   |  4 +++-
 .../contrib/data/python/util/nest_test.py     |  8 +++++++
 7 files changed, 86 insertions(+), 13 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py
index 20d66d7f23..71df1ee0a5 100644
--- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py
@@ -31,7 +31,7 @@ from tensorflow.python.ops import string_ops
 from tensorflow.python.platform import test
 
 
-class BucketingTest(test.TestCase):
+class GroupByWindowTest(test.TestCase):
 
   def testSimple(self):
     components = np.random.randint(100, size=(200,)).astype(np.int64)
@@ -257,16 +257,24 @@ class BucketTest(test.TestCase):
 
   def testEvenOddBucketsFilterOutAllOdd(self):
     def _map_fn(v):
-      return (v, array_ops.fill([v], v),
-              array_ops.fill([3], string_ops.as_string(v)))
+      return {"x": v,
+              "y": array_ops.fill([v], v),
+              "z": array_ops.fill([3], string_ops.as_string(v))}
+
+    def _dynamic_pad_fn(bucket, window, _):
+      return dataset_ops.Dataset.zip(
+          (dataset_ops.Dataset.from_tensors(bucket), window.padded_batch(
+              32, {"x": tensor_shape.TensorShape([]),
+                   "y": tensor_shape.TensorShape([None]),
+                   "z": tensor_shape.TensorShape([3])})))
 
     input_dataset = (
         dataset_ops.Dataset.from_tensor_slices(math_ops.range(128)).map(_map_fn)
-        .filter(lambda x, y, z: math_ops.equal(x % 2, 0)))
+        .filter(lambda d: math_ops.equal(d["x"] % 2, 0)))
 
     bucketed_dataset = input_dataset.group_by_window(
-        lambda x, y, z: math_ops.cast(x % 2, dtypes.int64),
-        lambda k, bucket: self._dynamicPad(k, bucket, 32), 32)
+        lambda d: math_ops.cast(d["x"] % 2, dtypes.int64),
+        lambda k, bucket: _dynamic_pad_fn(k, bucket, 32), 32)
 
     iterator = dataset_ops.Iterator.from_dataset(bucketed_dataset)
     init_op = iterator.initializer
@@ -283,9 +291,9 @@ class BucketTest(test.TestCase):
       self.assertAllEqual(0, which_bucket0)
       self.assertAllEqual(0, which_bucket1)
       self.assertAllEqual(
-          np.arange(0, 64, 2, dtype=np.int64), bucketed_values_even0[0])
+          np.arange(0, 64, 2, dtype=np.int64), bucketed_values_even0["x"])
       self.assertAllEqual(
-          np.arange(64, 128, 2, dtype=np.int64), bucketed_values_even1[0])
+          np.arange(64, 128, 2, dtype=np.int64), bucketed_values_even1["x"])
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py
index 19be94e174..e6d50dc154 100644
--- a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py
@@ -83,6 +83,23 @@ class FilterDatasetTest(test.TestCase):
       self.assertEqual(1, sess.run(get_next))
       self.assertEqual(3, sess.run(get_next))
 
+  def testFilterDict(self):
+    iterator = (dataset_ops.Dataset.range(10)
+                .map(lambda x: {"foo": x * 2, "bar": x ** 2})
+                .filter(lambda d: math_ops.equal(d["bar"] % 2, 0))
+                .map(lambda d: d["foo"] + d["bar"])
+                .make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for i in range(10):
+        if (i ** 2) % 2 == 0:
+          self.assertEqual(i * 2 + i ** 2, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py
index 3c9c714bde..ace0dd3668 100644
--- a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py
@@ -101,6 +101,23 @@ class FlatMapDatasetTest(test.TestCase):
         with self.assertRaises(errors.OutOfRangeError):
           sess = random.choice([sess1, sess2])
           sess.run(get_next)
+
+  def testMapDict(self):
+    iterator = (dataset_ops.Dataset.range(10)
+                .map(lambda x: {"foo": x * 2, "bar": x ** 2})
+                .flat_map(lambda d: dataset_ops.Dataset.from_tensors(d["foo"])
+                          .repeat(d["bar"]))
+                .make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for i in range(10):
+        for _ in range(i ** 2):
+          self.assertEqual(i * 2, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
   # pylint: enable=g-long-lambda
 
 
diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
index b5956ac49c..2c07248c54 100644
--- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
@@ -324,5 +324,21 @@ class MapDatasetTest(test.TestCase):
       # Randomness is repeatable given same seed
       self.assertAllClose(random_values, random_values_2)
 
+  def testMapDict(self):
+    iterator = (dataset_ops.Dataset.range(10)
+                .map(lambda x: {"foo": x * 2, "bar": x ** 2})
+                .map(lambda d: d["foo"] + d["bar"])
+                .make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for i in range(10):
+        self.assertEqual(i * 2 + i ** 2, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index 29f1209a58..a689bfc901 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -1357,6 +1357,11 @@ class DenseToSparseBatchDataset(Dataset):
     return (dtypes.int64, self._input_dataset.output_types, dtypes.int64)
 
 
+def _should_unpack_args(args):
+  """Returns `True` if `args` should be `*args` when passed to a callable."""
+  return nest.is_sequence(args) and not isinstance(args, dict)
+
+
 class _ResourceDataset(Dataset):
   """A Dataset wrapper for a tf.resource-typed function argument."""
 
@@ -1394,7 +1399,7 @@ class GroupByWindowDataset(Dataset):
       for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
         arg.set_shape(shape)
       nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
-      if nest.is_sequence(nested_args):
+      if _should_unpack_args(nested_args):
         ret = key_func(*nested_args)
       else:
         ret = key_func(nested_args)
@@ -1483,7 +1488,7 @@ class MapDataset(Dataset):
 
       nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
 
-      if nest.is_sequence(nested_args):
+      if _should_unpack_args(nested_args):
         ret = map_func(*nested_args)
       else:
         ret = map_func(nested_args)
@@ -1559,7 +1564,7 @@ class FlatMapDataset(Dataset):
 
       nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
 
-      if nest.is_sequence(nested_args):
+      if _should_unpack_args(nested_args):
         dataset = map_func(*nested_args)
       else:
         dataset = map_func(nested_args)
@@ -1609,7 +1614,7 @@ class FilterDataset(Dataset):
 
       nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
 
-      if nest.is_sequence(nested_args):
+      if _should_unpack_args(nested_args):
         ret = predicate(*nested_args)
       else:
         ret = predicate(nested_args)
diff --git a/tensorflow/contrib/data/python/util/nest.py b/tensorflow/contrib/data/python/util/nest.py
index 91c8416d5a..a29c3c562b 100644
--- a/tensorflow/contrib/data/python/util/nest.py
+++ b/tensorflow/contrib/data/python/util/nest.py
@@ -286,7 +286,8 @@ def map_structure(func, *structure, **check_types_dict):
 def _yield_flat_up_to(shallow_tree, input_tree):
   """Yields elements `input_tree` partially flattened up to `shallow_tree`."""
   if is_sequence(shallow_tree):
-    for shallow_branch, input_branch in zip(shallow_tree, input_tree):
+    for shallow_branch, input_branch in zip(_elements_of(shallow_tree),
+                                            _elements_of(input_tree)):
       for input_leaf in _yield_flat_up_to(shallow_branch, input_branch):
         yield input_leaf
   else:
@@ -495,6 +496,7 @@ def map_structure_up_to(shallow_tree, func, *inputs):
   # then repack based on the structure of the first input.
   all_flattened_up_to = [flatten_up_to(shallow_tree, input_tree)
                          for input_tree in inputs]
+
   results = [func(*tensors) for tensors in zip(*all_flattened_up_to)]
   return pack_sequence_as(structure=shallow_tree, flat_sequence=results)
 
diff --git a/tensorflow/contrib/data/python/util/nest_test.py b/tensorflow/contrib/data/python/util/nest_test.py
index 7852e4f861..5132881afb 100644
--- a/tensorflow/contrib/data/python/util/nest_test.py
+++ b/tensorflow/contrib/data/python/util/nest_test.py
@@ -287,6 +287,14 @@ class NestTest(test.TestCase):
     flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
     self.assertEqual(flattened_shallow_tree, list(shallow_tree))
 
+    # Using dict.
+    input_tree = {"a": ((2, 2), (3, 3)), "b": ((4, 9), (5, 5))}
+    shallow_tree = {"a": (True, True), "b": (False, True)}
+    flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
+    flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
+    self.assertEqual(flattened_input_tree, [(2, 2), (3, 3), (4, 9), (5, 5)])
+    self.assertEqual(flattened_shallow_tree, [True, True, False, True])
+
   def testMapStructureUpTo(self):
     ab_tuple = collections.namedtuple("ab_tuple", "a, b")
     op_tuple = collections.namedtuple("op_tuple", "add, mul")
-- 
GitLab


From 7baf12b174467cf5dded8a2703a9ca3c9615006e Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Thu, 29 Jun 2017 11:23:47 -0700
Subject: [PATCH 0592/1375] Fix manual merge error.

---
 tensorflow/compiler/xla/xla.proto | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 7ede4cf9d1..15fa1255ae 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -49,7 +49,6 @@ message DebugOptions {
 
   // Dump compilation artifacts as JSON into this directory.
   string xla_dump_debug_json_to = 8;
->>>>>>> d0360669091554fbafe532bc33768e572b0f01f8
 
   // List of HLO passes to disable. These names must exactly match the pass
   // names as specified by the HloPassInterface::name() method.
-- 
GitLab


From 5bc5c556841d2825961c9a36157dbe881cd8b786 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Thu, 29 Jun 2017 11:39:57 -0700
Subject: [PATCH 0593/1375] Remove duplicate member from bad merge.

---
 tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 12ff9080c2..3d5ea4d32a 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -45,8 +45,6 @@ struct DebugOptionsFlags {
 
   bool xla_cpu_multi_thread_eigen;
 
-  bool xla_cpu_multi_thread_eigen;
-
   string xla_gpu_cuda_data_dir;
   bool xla_gpu_ftz;
 
-- 
GitLab


From e85d3df92deb9d717befdf173966a2913ac2aea0 Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Thu, 29 Jun 2017 11:44:13 -0700
Subject: [PATCH 0594/1375] Prepare to remove a bunch of proto.h includes from
 tensorflow/core headers

The goal is to make kernels mostly independent of proto headers, which will let
us lock down our .so imports.  This CL does not remove any actual headers, but
changes a bunch of files so that header removal is possible in a followup CL.
It also marks the headers that will be removed with

    // TODO(b/62899350): Remove

RELNOTES: n/a
PiperOrigin-RevId: 160552878
---
 tensorflow/c/BUILD                            |  1 +
 tensorflow/c/c_api.cc                         | 11 +++
 tensorflow/c/c_api_internal.h                 |  9 +--
 tensorflow/c/c_api_test.cc                    |  1 +
 tensorflow/cc/BUILD                           |  2 +
 tensorflow/cc/framework/cc_op_gen.cc          |  4 +
 tensorflow/cc/framework/gradients_test.cc     |  1 +
 tensorflow/cc/framework/scope.cc              |  2 +-
 tensorflow/compiler/aot/compile.cc            |  1 +
 tensorflow/compiler/jit/BUILD                 |  1 +
 tensorflow/compiler/jit/xla_device.cc         |  2 +
 .../compiler/tf2xla/kernels/const_op.cc       |  1 +
 tensorflow/compiler/tf2xla/test_util.h        |  1 +
 tensorflow/compiler/tf2xla/xla_op_registry.cc |  1 +
 ...single_image_random_dot_stereograms_ops.cc | 14 ++--
 .../kernels/sparse_feature_cross_kernel.cc    |  1 +
 tensorflow/contrib/tpu/ops/infeed_ops.cc      |  8 +-
 .../convert_graphdef_memmapped_format_lib.cc  |  2 +
 tensorflow/core/BUILD                         |  8 +-
 .../core/common_runtime/direct_session.cc     |  1 +
 tensorflow/core/common_runtime/function.cc    |  1 +
 .../core/common_runtime/function_test.cc      |  1 +
 .../core/common_runtime/gpu/gpu_util.cc       |  1 +
 .../core/common_runtime/graph_runner.cc       |  1 +
 .../kernel_benchmark_testlib.cc               |  1 +
 .../core/common_runtime/shape_refiner.cc      |  5 ++
 .../core/common_runtime/shape_refiner.h       |  4 +
 .../simple_graph_execution_state.cc           |  1 +
 .../common_runtime/step_stats_collector.cc    |  2 +
 tensorflow/core/debug/debug_io_utils.cc       |  1 +
 tensorflow/core/distributed_runtime/BUILD     |  2 +
 .../core/distributed_runtime/executor_test.cc |  1 +
 .../core/distributed_runtime/graph_mgr.cc     |  1 +
 .../distributed_runtime/master_session.cc     |  2 +
 tensorflow/core/distributed_runtime/rpc/BUILD |  1 +
 .../rpc/grpc_tensor_coding.cc                 |  2 +
 .../core/distributed_runtime/tensor_coding.cc |  2 +
 .../distributed_runtime/tensor_coding_test.cc |  1 +
 .../example/example_parser_configuration.cc   |  2 +
 tensorflow/core/framework/allocator.cc        |  8 ++
 tensorflow/core/framework/attr_value_util.cc  |  3 +
 tensorflow/core/framework/attr_value_util.h   | 10 ++-
 .../core/framework/attr_value_util_test.cc    |  1 +
 .../core/framework/common_shape_fns_test.cc   | 14 +---
 tensorflow/core/framework/device_base.cc      |  4 +
 tensorflow/core/framework/device_base.h       | 13 ++--
 tensorflow/core/framework/fake_input.cc       |  1 +
 tensorflow/core/framework/function.cc         |  1 +
 tensorflow/core/framework/function.h          |  4 +-
 tensorflow/core/framework/graph_def_util.cc   |  2 +
 tensorflow/core/framework/graph_def_util.h    |  6 +-
 .../core/framework/kernel_def_builder.cc      | 13 +++-
 .../core/framework/kernel_def_builder.h       | 16 ++--
 tensorflow/core/framework/memory_types.h      |  4 +-
 tensorflow/core/framework/node_def_builder.cc | 71 ++++++++++++++++--
 tensorflow/core/framework/node_def_builder.h  | 75 +++++++++----------
 tensorflow/core/framework/node_def_util.cc    |  1 +
 tensorflow/core/framework/node_def_util.h     |  5 +-
 tensorflow/core/framework/op.h                |  2 +-
 tensorflow/core/framework/op_def_builder.cc   |  1 +
 tensorflow/core/framework/op_def_util.cc      |  1 +
 tensorflow/core/framework/op_gen_lib.cc       |  9 ++-
 tensorflow/core/framework/op_gen_lib.h        | 13 +++-
 tensorflow/core/framework/op_kernel.cc        |  1 +
 tensorflow/core/framework/op_kernel.h         | 16 ++--
 tensorflow/core/framework/op_kernel_test.cc   |  2 +
 .../framework/partial_tensor_shape_test.cc    |  1 +
 tensorflow/core/framework/reader_base.cc      |  1 +
 tensorflow/core/framework/reader_base.h       |  4 +-
 tensorflow/core/framework/resource_mgr.cc     |  1 +
 tensorflow/core/framework/resource_mgr.h      |  3 +-
 .../core/framework/resource_mgr_test.cc       |  1 +
 tensorflow/core/framework/shape_inference.cc  | 53 +++++++++++++
 tensorflow/core/framework/shape_inference.h   | 22 +++++-
 .../core/framework/shape_inference_test.cc    | 19 ++---
 .../core/framework/shape_inference_testutil.h |  2 -
 tensorflow/core/framework/tensor.cc           |  2 +
 tensorflow/core/framework/tensor.h            | 13 +++-
 tensorflow/core/framework/tensor_reference.h  |  1 -
 tensorflow/core/framework/tensor_shape.cc     |  1 +
 tensorflow/core/framework/tensor_shape.h      |  3 +-
 .../core/framework/tensor_shape_test.cc       |  1 +
 tensorflow/core/framework/tensor_test.cc      |  1 +
 tensorflow/core/framework/versions.cc         |  1 +
 tensorflow/core/framework/versions.h          |  4 +-
 tensorflow/core/graph/costmodel.cc            |  2 +
 tensorflow/core/graph/graph.cc                | 13 +++-
 tensorflow/core/graph/graph.h                 | 12 +--
 tensorflow/core/graph/graph_constructor.cc    |  2 +
 .../core/graph/graph_constructor_test.cc      |  1 +
 .../core/graph/graph_def_builder_test.cc      |  1 +
 tensorflow/core/graph/graph_partition.cc      |  2 +
 tensorflow/core/graph/graph_partition_test.cc |  1 +
 tensorflow/core/graph/node_builder.cc         |  1 +
 .../grappler/clusters/single_machine_test.cc  |  1 +
 .../core/grappler/clusters/virtual_cluster.cc |  1 +
 .../grappler/clusters/virtual_cluster_test.cc |  1 +
 tensorflow/core/grappler/costs/BUILD          |  4 +
 .../costs/analytical_cost_estimator.cc        |  1 +
 .../core/grappler/costs/graph_properties.cc   |  2 +-
 .../grappler/costs/graph_properties_test.cc   |  1 +
 .../grappler/costs/op_level_cost_estimator.cc |  2 +
 tensorflow/core/grappler/costs/utils.cc       |  1 +
 .../core/grappler/costs/virtual_scheduler.cc  |  3 +
 .../grappler/costs/virtual_scheduler_test.cc  |  2 +
 .../core/grappler/grappler_item_builder.cc    |  2 +
 .../core/grappler/optimizers/auto_parallel.cc |  1 +
 .../grappler/optimizers/constant_folding.cc   |  1 +
 .../grappler/optimizers/layout_optimizer.cc   |  2 +
 .../grappler/optimizers/memory_optimizer.cc   |  1 +
 tensorflow/core/kernels/BUILD                 |  2 +-
 tensorflow/core/kernels/decode_image_op.cc    |  1 +
 tensorflow/core/kernels/gather_nd_op.cc       |  1 +
 .../kernels/hexagon/graph_transfer_utils.cc   |  1 +
 .../core/kernels/hexagon/graph_transferer.cc  |  2 +-
 .../hexagon/hexagon_graph_execution_test.cc   |  1 +
 tensorflow/core/kernels/identity_reader_op.cc |  2 +
 .../core/kernels/reduction_ops_common.cc      |  2 +
 .../remote_fused_graph_execute_utils.cc       | 13 ++--
 .../remote_fused_graph_execute_utils_test.cc  |  4 +-
 tensorflow/core/kernels/scatter_nd_op.cc      |  1 +
 .../core/kernels/serialize_sparse_op.cc       |  1 +
 tensorflow/core/kernels/sparse_cross_op.cc    |  1 +
 tensorflow/core/kernels/unique_op_test.cc     |  1 +
 .../core/kernels/whole_file_read_ops.cc       |  2 +
 tensorflow/core/ops/array_ops.cc              | 46 +++++-------
 tensorflow/core/ops/array_ops_test.cc         |  2 +
 tensorflow/core/ops/math_ops_test.cc          |  1 +
 tensorflow/core/ops/parsing_ops.cc            | 14 ++--
 tensorflow/core/ops/parsing_ops_test.cc       | 17 ++---
 tensorflow/core/ops/resource_variable_ops.cc  |  4 +-
 tensorflow/core/ops/state_ops.cc              | 16 ++--
 tensorflow/core/ops/state_ops_test.cc         | 21 ++----
 tensorflow/core/util/equal_graph_def.cc       |  2 +
 tensorflow/core/util/equal_graph_def.h        |  5 +-
 .../core/util/memmapped_file_system_test.cc   |  1 +
 tensorflow/core/util/padding.h                |  4 +-
 tensorflow/core/util/stat_summarizer.cc       | 10 +++
 tensorflow/core/util/stat_summarizer.h        |  8 +-
 .../core/util/tensor_bundle/tensor_bundle.cc  |  1 +
 .../core/util/tensor_slice_writer_test.cc     |  1 +
 .../adding_an_op/zero_out_op_kernel_2.cc      |  1 +
 tensorflow/python/BUILD                       |  1 +
 .../python/framework/cpp_shape_inference.cc   |  1 +
 tensorflow/python/grappler/model_analyzer.cc  |  1 +
 tensorflow/python/lib/core/py_func.cc         |  1 +
 .../fold_old_batch_norms_test.cc              |  1 +
 .../strip_unused_nodes_test.cc                |  1 +
 .../graph_transforms/summarize_graph_main.cc  |  2 +
 .../tools/graph_transforms/transform_utils.h  |  2 +
 150 files changed, 555 insertions(+), 240 deletions(-)

diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD
index 3ab4e8efcd..9267ef77ef 100644
--- a/tensorflow/c/BUILD
+++ b/tensorflow/c/BUILD
@@ -62,6 +62,7 @@ tf_cuda_library(
             "//tensorflow/cc:scope_internal",
             "//tensorflow/core:core_cpu",
             "//tensorflow/core:framework",
+            "//tensorflow/core:protos_all_cc",
             "//tensorflow/core:lib",
         ],
     }),
diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index c320fe94a1..fb06389af2 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -28,12 +28,15 @@ limitations under the License.
 #endif
 #include "tensorflow/c/c_api_internal.h"
 #include "tensorflow/core/common_runtime/shape_refiner.h"
+#include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/node_builder.h"
@@ -1587,6 +1590,14 @@ void TF_OperationToNodeDef(TF_Operation* oper, TF_Buffer* output_node_def,
 
 // TF_Graph functions ---------------------------------------------------------
 
+TF_Graph::TF_Graph()
+    : graph(tensorflow::OpRegistry::Global()),
+      refiner(graph.versions().producer(), graph.op_registry()),
+      num_sessions(0),
+      delete_requested(false),
+      parent(nullptr),
+      parent_inputs(nullptr) {}
+
 TF_Graph* TF_NewGraph() { return new TF_Graph; }
 
 void TF_DeleteGraph(TF_Graph* g) {
diff --git a/tensorflow/c/c_api_internal.h b/tensorflow/c/c_api_internal.h
index f2773ae20f..7e987a65f7 100644
--- a/tensorflow/c/c_api_internal.h
+++ b/tensorflow/c/c_api_internal.h
@@ -56,13 +56,8 @@ struct TF_Library {
 };
 
 struct TF_Graph {
-  TF_Graph()
-      : graph(tensorflow::OpRegistry::Global()),
-        refiner(graph.versions().producer(), graph.op_registry()),
-        num_sessions(0),
-        delete_requested(false),
-        parent(nullptr),
-        parent_inputs(nullptr) {}
+  TF_Graph();
+
   tensorflow::mutex mu;
   tensorflow::Graph graph GUARDED_BY(mu);
 
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 04540bd793..35d6e295c2 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/graph/tensor_id.h"
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index 9801add1da..b461a475c1 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -45,6 +45,7 @@ tf_cc_test(
         "//tensorflow/core:all_kernels",
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
@@ -432,6 +433,7 @@ cc_library_with_android_deps(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:op_gen_lib",
+        "//tensorflow/core:op_gen_overrides_proto_cc",
         "//tensorflow/core:proto_text",
         "//tensorflow/core:protos_all_cc",
     ],
diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc
index 71aa986f91..80dd272f6f 100644
--- a/tensorflow/cc/framework/cc_op_gen.cc
+++ b/tensorflow/cc/framework/cc_op_gen.cc
@@ -18,8 +18,12 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/cc/framework/cc_op_gen.h"
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/op_gen_lib.h"
+#include "tensorflow/core/framework/op_gen_overrides.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.pb_text.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
diff --git a/tensorflow/cc/framework/gradients_test.cc b/tensorflow/cc/framework/gradients_test.cc
index 6a24982581..2aad978480 100644
--- a/tensorflow/cc/framework/gradients_test.cc
+++ b/tensorflow/cc/framework/gradients_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include "tensorflow/cc/framework/grad_op_registry.h"
 #include "tensorflow/cc/framework/testutil.h"
 #include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
diff --git a/tensorflow/cc/framework/scope.cc b/tensorflow/cc/framework/scope.cc
index 32c0822de6..1948dd4e46 100644
--- a/tensorflow/cc/framework/scope.cc
+++ b/tensorflow/cc/framework/scope.cc
@@ -136,7 +136,7 @@ Scope::Impl::Impl(const std::shared_ptr<Graph>& graph,
 Scope Scope::NewRootScope() {
   Graph* graph = new Graph(OpRegistry::Global());
   ShapeRefiner* refiner =
-      new ShapeRefiner(graph->versions().producer(), graph->op_registry());
+      new ShapeRefiner(graph->versions(), graph->op_registry());
   return Scope(new Impl(graph, new Status, new Impl::NameMap, refiner));
 }
 
diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc
index 317baa8971..59ff14600b 100644
--- a/tensorflow/compiler/aot/compile.cc
+++ b/tensorflow/compiler/aot/compile.cc
@@ -40,6 +40,7 @@ limitations under the License.
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 7ebd842218..8b2d0b7659 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -139,6 +139,7 @@ cc_library(
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:stream_executor_no_cuda",
         "//tensorflow/core:tensorflow_opensource",
         "//tensorflow/core/kernels:constant_op",
diff --git a/tensorflow/compiler/jit/xla_device.cc b/tensorflow/compiler/jit/xla_device.cc
index 5e336c5287..615e2230f4 100644
--- a/tensorflow/compiler/jit/xla_device.cc
+++ b/tensorflow/compiler/jit/xla_device.cc
@@ -31,9 +31,11 @@ limitations under the License.
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/kernel_def.pb.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/lib/core/notification.h"
diff --git a/tensorflow/compiler/tf2xla/kernels/const_op.cc b/tensorflow/compiler/tf2xla/kernels/const_op.cc
index ad676e7a2b..9833323d85 100644
--- a/tensorflow/compiler/tf2xla/kernels/const_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/const_op.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 
 namespace tensorflow {
 namespace {
diff --git a/tensorflow/compiler/tf2xla/test_util.h b/tensorflow/compiler/tf2xla/test_util.h
index 362558bcfc..e6e4ae92ed 100644
--- a/tensorflow/compiler/tf2xla/test_util.h
+++ b/tensorflow/compiler/tf2xla/test_util.h
@@ -23,6 +23,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/lib/core/status.h"
 
diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc
index 20adf300ec..d059c7a23e 100644
--- a/tensorflow/compiler/tf2xla/xla_op_registry.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/common_runtime/local_device.h"
 #include "tensorflow/core/framework/device_base.h"
+#include "tensorflow/core/framework/kernel_def.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/platform/mem.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
diff --git a/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc b/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc
index 6efcc29654..9f0bf37aed 100755
--- a/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc
+++ b/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc
@@ -54,8 +54,8 @@ class SingleImageRandomDotStereogramsOp : public OpKernel {
   float normalize_min;
   float border_level;
   int number_colors;
-  ::tensorflow::TensorShapeProto output_image_shape;
-  ::tensorflow::TensorShapeProto output_data_window;
+  ::tensorflow::PartialTensorShape output_image_shape;
+  ::tensorflow::PartialTensorShape output_data_window;
 
   uint8 Cblack = 0;
   uint8 Cwhite = 255;
@@ -109,15 +109,15 @@ class SingleImageRandomDotStereogramsOp : public OpKernel {
     input_Yvalue =
         input_tensor.shape().dim_size(0);  // Y value is the number of rows
 
-    output_Ximage = output_image_shape.dim(0).size();
-    output_Yimage = output_image_shape.dim(1).size();
-    output_Cimage = output_image_shape.dim(2).size();
+    output_Ximage = output_image_shape.dim_size(0);
+    output_Yimage = output_image_shape.dim_size(1);
+    output_Cimage = output_image_shape.dim_size(2);
 
     if (number_colors > 256)  // Go to full color image
       output_Cimage = 3;
 
-    int data_Xwindow = output_data_window.dim(0).size();
-    int data_Ywindow = output_data_window.dim(1).size();
+    int data_Xwindow = output_data_window.dim_size(0);
+    int data_Ywindow = output_data_window.dim_size(1);
 
     int deltaX_border_image = output_Ximage - data_Xwindow;
     int deltaY_border_image = output_Yimage - data_Ywindow;
diff --git a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
index 72df272af8..badf9d486a 100644
--- a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
+++ b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/fingerprint.h"
 #include "tensorflow/core/util/work_sharder.h"
 
diff --git a/tensorflow/contrib/tpu/ops/infeed_ops.cc b/tensorflow/contrib/tpu/ops/infeed_ops.cc
index be4d4f9649..c12e83137a 100644
--- a/tensorflow/contrib/tpu/ops/infeed_ops.cc
+++ b/tensorflow/contrib/tpu/ops/infeed_ops.cc
@@ -29,10 +29,8 @@ REGISTER_OP("InfeedDequeue")
     .SetShapeFn([](InferenceContext* c) {
       PartialTensorShape shape;
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
-      TensorShapeProto shape_proto;
-      shape.AsProto(&shape_proto);
       ShapeHandle out;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shape, &out));
       c->set_output(0, out);
       return Status::OK();
     })
@@ -87,10 +85,8 @@ REGISTER_OP("InfeedDequeueTuple")
       std::vector<PartialTensorShape> shapes;
       TF_RETURN_IF_ERROR(c->GetAttr("shapes", &shapes));
       for (int i = 0; i < shapes.size(); ++i) {
-        TensorShapeProto shape_proto;
-        shapes[i].AsProto(&shape_proto);
         ShapeHandle out;
-        TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
+        TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shapes[i], &out));
         c->set_output(i, out);
       }
       return Status::OK();
diff --git a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
index 3e387129eb..2992a61ea8 100644
--- a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
+++ b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc
@@ -15,11 +15,13 @@ limitations under the License.
 #include "tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.h"
 
 #include <unordered_set>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/kernels/immutable_constant_op.h"
 #include "tensorflow/core/platform/env.h"
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 24b0f702b3..d4097f4032 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -500,7 +500,6 @@ cc_library(
 # Generates library per group of ops.
 tf_gen_op_libs(
     op_lib_names = [
-        "array_ops",
         "bitwise_ops",
         "candidate_sampling_ops",
         "control_flow_ops",
@@ -534,6 +533,13 @@ tf_gen_op_libs(
     ],
 )
 
+tf_gen_op_libs(
+    op_lib_names = [
+        "array_ops",
+    ],
+    deps = [":protos_all_cc"],
+)
+
 tf_gen_op_libs(
     op_lib_names = [
         "audio_ops",
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index 4b951691fb..7bc48aba27 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -36,6 +36,7 @@ limitations under the License.
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index 99389968ee..e3cc97c946 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/gradients.h"
 #include "tensorflow/core/graph/graph_constructor.h"
diff --git a/tensorflow/core/common_runtime/function_test.cc b/tensorflow/core/common_runtime/function_test.cc
index dec6ca996a..b00bb453b1 100644
--- a/tensorflow/core/common_runtime/function_test.cc
+++ b/tensorflow/core/common_runtime/function_test.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/lib/core/notification.h"
 #include "tensorflow/core/lib/core/status.h"
diff --git a/tensorflow/core/common_runtime/gpu/gpu_util.cc b/tensorflow/core/common_runtime/gpu/gpu_util.cc
index ae9e5aeaa3..b69c1ae8fe 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_util.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_util.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/common_runtime/gpu_device_context.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_reference.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/core/common_runtime/graph_runner.cc b/tensorflow/core/common_runtime/graph_runner.cc
index 74b2252c7c..2ce1e8b483 100644
--- a/tensorflow/core/common_runtime/graph_runner.cc
+++ b/tensorflow/core/common_runtime/graph_runner.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor_util.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/node_builder.h"
diff --git a/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc b/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc
index 4a5b88d5fd..420dfe338e 100644
--- a/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc
+++ b/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/op_segment.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/lib/core/notification.h"
diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index a6204b9d0d..e61ea9d84c 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/kernels/bounds_check.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
@@ -39,6 +40,10 @@ ShapeRefiner::ShapeRefiner(int graph_def_version,
       ops_registry_(ops),
       graph_runner_(Env::Default()) {}
 
+ShapeRefiner::ShapeRefiner(const VersionDef& versions,
+                           const OpRegistryInterface* ops)
+    : ShapeRefiner(versions.producer(), ops) {}
+
 ShapeRefiner::~ShapeRefiner() {
   // The lifetime of the tensors are bound to the GraphRunner, so the tensors
   // should be deleted before it.
diff --git a/tensorflow/core/common_runtime/shape_refiner.h b/tensorflow/core/common_runtime/shape_refiner.h
index 1af7835392..21e58381a5 100644
--- a/tensorflow/core/common_runtime/shape_refiner.h
+++ b/tensorflow/core/common_runtime/shape_refiner.h
@@ -36,6 +36,10 @@ class GraphProperties;
 class ShapeRefiner {
  public:
   ShapeRefiner(int graph_def_version, const OpRegistryInterface* ops);
+
+  // Same as ShapeRefiner(versions.producer(), ops)
+  ShapeRefiner(const VersionDef& versions, const OpRegistryInterface* ops);
+
   ~ShapeRefiner();
 
   // Performs validation of 'node' and runs 'node's shape function,
diff --git a/tensorflow/core/common_runtime/simple_graph_execution_state.cc b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
index c00eb3a2fc..358550f641 100644
--- a/tensorflow/core/common_runtime/simple_graph_execution_state.cc
+++ b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb_text.h"
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/subgraph.h"
diff --git a/tensorflow/core/common_runtime/step_stats_collector.cc b/tensorflow/core/common_runtime/step_stats_collector.cc
index 9b43385d6f..d410a164ea 100644
--- a/tensorflow/core/common_runtime/step_stats_collector.cc
+++ b/tensorflow/core/common_runtime/step_stats_collector.cc
@@ -15,7 +15,9 @@ limitations under the License.
 
 #include "tensorflow/core/common_runtime/step_stats_collector.h"
 #include "tensorflow/core/common_runtime/costmodel_manager.h"
+#include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/tensor_description.pb.h"
 #include "tensorflow/core/graph/costmodel.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/strings/scanner.h"
diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index 69fc367789..875a4763f8 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #endif
 
 #include "tensorflow/core/debug/debugger_event_metadata.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/summary.pb.h"
 #include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/io/path.h"
diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD
index f59e5f4dc2..3bea72c4a3 100644
--- a/tensorflow/core/distributed_runtime/BUILD
+++ b/tensorflow/core/distributed_runtime/BUILD
@@ -153,6 +153,7 @@ cc_library(
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:worker_proto_cc",
     ],
 )
@@ -205,6 +206,7 @@ cc_test(
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
diff --git a/tensorflow/core/distributed_runtime/executor_test.cc b/tensorflow/core/distributed_runtime/executor_test.cc
index 17843ff6b0..1a4980a61b 100644
--- a/tensorflow/core/distributed_runtime/executor_test.cc
+++ b/tensorflow/core/distributed_runtime/executor_test.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/rendezvous.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/random/simple_philox.h"
diff --git a/tensorflow/core/distributed_runtime/graph_mgr.cc b/tensorflow/core/distributed_runtime/graph_mgr.cc
index 69f5b7d944..205843d342 100644
--- a/tensorflow/core/distributed_runtime/graph_mgr.cc
+++ b/tensorflow/core/distributed_runtime/graph_mgr.cc
@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/graph_partition.h"
diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc
index e98d9cecfc..2b6e0c5268 100644
--- a/tensorflow/core/distributed_runtime/master_session.cc
+++ b/tensorflow/core/distributed_runtime/master_session.cc
@@ -26,11 +26,13 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/scheduler.h"
 #include "tensorflow/core/distributed_runtime/worker_cache.h"
 #include "tensorflow/core/distributed_runtime/worker_interface.h"
+#include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_description.pb.h"
 #include "tensorflow/core/graph/graph_partition.h"
 #include "tensorflow/core/graph/tensor_id.h"
 #include "tensorflow/core/lib/core/blocking_counter.h"
diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index bd381dd10f..c7349f0dd7 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -108,6 +108,7 @@ cc_library(
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:worker_proto_cc",
         "@grpc//:grpc++_unsecure",
     ],
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
index 90e311a493..0dd6b5c89e 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
@@ -18,7 +18,9 @@ limitations under the License.
 #include "grpc++/support/slice.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_reference.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/io/proto_encode_helper.h"
 #include "tensorflow/core/platform/env.h"
diff --git a/tensorflow/core/distributed_runtime/tensor_coding.cc b/tensorflow/core/distributed_runtime/tensor_coding.cc
index f98bd17ab9..94d54a2b16 100644
--- a/tensorflow/core/distributed_runtime/tensor_coding.cc
+++ b/tensorflow/core/distributed_runtime/tensor_coding.cc
@@ -17,6 +17,8 @@ limitations under the License.
 
 #include "google/protobuf/any.pb.h"
 #include "tensorflow/core/common_runtime/device.h"
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/distributed_runtime/tensor_coding_test.cc b/tensorflow/core/distributed_runtime/tensor_coding_test.cc
index 540b76ada6..52a057bdb2 100644
--- a/tensorflow/core/distributed_runtime/tensor_coding_test.cc
+++ b/tensorflow/core/distributed_runtime/tensor_coding_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/distributed_runtime/tensor_coding.h"
 
+#include "tensorflow/core/framework/device_attributes.pb.h"
 #include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
diff --git a/tensorflow/core/example/example_parser_configuration.cc b/tensorflow/core/example/example_parser_configuration.cc
index 485cf6da4b..5660465c51 100644
--- a/tensorflow/core/example/example_parser_configuration.cc
+++ b/tensorflow/core/example/example_parser_configuration.cc
@@ -17,9 +17,11 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/example/feature.pb_text.h"
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/numeric_op.h"
 #include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
diff --git a/tensorflow/core/framework/allocator.cc b/tensorflow/core/framework/allocator.cc
index 943dcab362..e7092f549b 100644
--- a/tensorflow/core/framework/allocator.cc
+++ b/tensorflow/core/framework/allocator.cc
@@ -48,6 +48,14 @@ constexpr size_t Allocator::kAllocatorAlignment;
 
 Allocator::~Allocator() {}
 
+void RunResourceCtor(ResourceHandle* p, size_t n) {
+  for (size_t i = 0; i < n; ++p, ++i) new (p) ResourceHandle();
+}
+
+void RunResourceDtor(ResourceHandle* p, size_t n) {
+  for (size_t i = 0; i < n; ++p, ++i) p->~ResourceHandle();
+}
+
 // If true, cpu allocator collects more stats.
 static bool cpu_allocator_collect_stats = false;
 // If true, cpu allocator collects full stats.
diff --git a/tensorflow/core/framework/attr_value_util.cc b/tensorflow/core/framework/attr_value_util.cc
index b18ce3decc..9fdb3da6a0 100644
--- a/tensorflow/core/framework/attr_value_util.cc
+++ b/tensorflow/core/framework/attr_value_util.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <vector>
 #include "tensorflow/core/framework/attr_value.pb_text.h"
 #include "tensorflow/core/framework/tensor.pb_text.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/types.pb_text.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -287,6 +288,8 @@ bool ParseAttrValue(StringPiece type, StringPiece text, AttrValue* out) {
   return ProtoParseFromString(to_parse, out);
 }
 
+void SetAttrValue(const AttrValue& value, AttrValue* out) { *out = value; }
+
 #define DEFINE_SET_ATTR_VALUE_ONE(ARG_TYPE, FIELD) \
   void SetAttrValue(ARG_TYPE value, AttrValue* out) { out->set_##FIELD(value); }
 
diff --git a/tensorflow/core/framework/attr_value_util.h b/tensorflow/core/framework/attr_value_util.h
index 0e25cec4ab..08cc3b7158 100644
--- a/tensorflow/core/framework/attr_value_util.h
+++ b/tensorflow/core/framework/attr_value_util.h
@@ -18,7 +18,7 @@ limitations under the License.
 
 #include <string>
 #include <vector>
-#include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/attr_value.pb.h"  // TODO(62899350): Remove
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
@@ -29,6 +29,10 @@ limitations under the License.
 
 namespace tensorflow {
 
+// Forward declare protos so their symbols can be removed from .so exports
+class AttrValue;
+class NameAttrList;
+
 // A human-readable rendering of attr_value, that is more concise than a
 // text-format proto.
 string SummarizeAttrValue(const AttrValue& attr_value);
@@ -80,9 +84,7 @@ void SetAttrValue(gtl::ArraySlice<Tensor> value, AttrValue* out);
 void SetAttrValue(gtl::ArraySlice<TensorProto> value, AttrValue* out);
 void SetAttrValue(gtl::ArraySlice<NameAttrList> value, AttrValue* out);
 
-inline void SetAttrValue(const AttrValue& value, AttrValue* out) {
-  *out = value;
-}
+void SetAttrValue(const AttrValue& value, AttrValue* out);
 
 // Returns true if a and b have the same value.
 // NOTE: May return false negatives for tensor values.
diff --git a/tensorflow/core/framework/attr_value_util_test.cc b/tensorflow/core/framework/attr_value_util_test.cc
index c14ea9b322..5d30d327ae 100644
--- a/tensorflow/core/framework/attr_value_util_test.cc
+++ b/tensorflow/core/framework/attr_value_util_test.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/framework/attr_value_util.h"
 
 #include <vector>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
 
diff --git a/tensorflow/core/framework/common_shape_fns_test.cc b/tensorflow/core/framework/common_shape_fns_test.cc
index d14e1dfee0..99d3ef3af1 100644
--- a/tensorflow/core/framework/common_shape_fns_test.cc
+++ b/tensorflow/core/framework/common_shape_fns_test.cc
@@ -26,19 +26,11 @@ namespace shape_inference {
 
 namespace {
 
-TensorShapeProto S(std::initializer_list<int64> dims) {
-  PartialTensorShape shape(dims);
-  TensorShapeProto ret;
-  shape.AsProto(&ret);
-  return ret;
+PartialTensorShape S(std::initializer_list<int64> dims) {
+  return PartialTensorShape(dims);
 }
 
-TensorShapeProto Unknown() {
-  PartialTensorShape shape;
-  TensorShapeProto ret;
-  shape.AsProto(&ret);
-  return ret;
-}
+PartialTensorShape Unknown() { return PartialTensorShape(); }
 
 OpDef MakeOpDef(int num_inputs, int num_outputs) {
   OpRegistrationData op_reg_data;
diff --git a/tensorflow/core/framework/device_base.cc b/tensorflow/core/framework/device_base.cc
index ea0ed3ccbb..f5bc24aafe 100644
--- a/tensorflow/core/framework/device_base.cc
+++ b/tensorflow/core/framework/device_base.cc
@@ -19,4 +19,8 @@ namespace tensorflow {
 
 DeviceBase::~DeviceBase() {}
 
+const DeviceAttributes& DeviceBase::attributes() const {
+  LOG(FATAL) << "Device does not implement attributes()";
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/device_base.h b/tensorflow/core/framework/device_base.h
index 27fe28fe60..e1eb387d88 100644
--- a/tensorflow/core/framework/device_base.h
+++ b/tensorflow/core/framework/device_base.h
@@ -19,9 +19,9 @@ limitations under the License.
 #include <memory>
 #include <unordered_map>
 
-#include "tensorflow/core/framework/device_attributes.pb.h"
+#include "tensorflow/core/framework/device_attributes.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/refcount.h"
 #include "tensorflow/core/lib/core/status.h"
@@ -44,10 +44,12 @@ class Stream;
 namespace tensorflow {
 
 class Device;
+class DeviceAttributes;
 class Env;
 class EventMgr;
 class OpKernelContext;
 class ResourceMgr;
+class TensorProto;
 
 namespace thread {
 class ThreadPool;
@@ -194,11 +196,8 @@ class DeviceBase {
                                      DeviceContext* /*dc*/,
                                      Allocator* /*allocator*/) {}
 
-  virtual const DeviceAttributes& attributes() const {
-    LOG(FATAL) << "Device does not implement attributes()";
-    static DeviceAttributes dummy;
-    return dummy;
-  }
+  // Unimplemented by default
+  virtual const DeviceAttributes& attributes() const;
 
   // Materializes the given TensorProto into 'tensor' stored in Device
   // memory.  Most devices will want to override this.
diff --git a/tensorflow/core/framework/fake_input.cc b/tensorflow/core/framework/fake_input.cc
index 7a21dd5066..ad301a8aa4 100644
--- a/tensorflow/core/framework/fake_input.cc
+++ b/tensorflow/core/framework/fake_input.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/framework/fake_input.h"
 
 #include <vector>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/op_def_util.h"
diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index fe6e9a6cd6..9d43aab5a5 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/function.pb_text.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h
index 6c2da84790..b8d5b8797a 100644
--- a/tensorflow/core/framework/function.h
+++ b/tensorflow/core/framework/function.h
@@ -17,9 +17,10 @@ limitations under the License.
 #define TENSORFLOW_FRAMEWORK_FUNCTION_H_
 
 #include <vector>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/function.pb.h"
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/selective_registration.h"
@@ -33,6 +34,7 @@ limitations under the License.
 namespace tensorflow {
 
 class CancellationManager;
+class GraphDef;
 class OpKernel;
 class ResourceMgr;
 class ScopedStepContainer;
diff --git a/tensorflow/core/framework/graph_def_util.cc b/tensorflow/core/framework/graph_def_util.cc
index aeedf4b0ef..bd018b7243 100644
--- a/tensorflow/core/framework/graph_def_util.cc
+++ b/tensorflow/core/framework/graph_def_util.cc
@@ -20,7 +20,9 @@ limitations under the License.
 #include <unordered_set>
 #include <vector>
 
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
diff --git a/tensorflow/core/framework/graph_def_util.h b/tensorflow/core/framework/graph_def_util.h
index 950737c39a..838c9fd4ce 100644
--- a/tensorflow/core/framework/graph_def_util.h
+++ b/tensorflow/core/framework/graph_def_util.h
@@ -17,13 +17,15 @@ limitations under the License.
 #define TENSORFLOW_FRAMEWORK_GRAPH_DEF_UTIL_H_
 
 #include <set>
-
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
 
+// Forward declare proto so that it's symbols can be removed from .so exports
+class GraphDef;
+
 // Produce a human-readable version of a GraphDef that is more concise
 // than a text-format proto.
 string SummarizeGraphDef(const GraphDef& graph_def);
diff --git a/tensorflow/core/framework/kernel_def_builder.cc b/tensorflow/core/framework/kernel_def_builder.cc
index 6366ac5beb..eb86f18ff0 100644
--- a/tensorflow/core/framework/kernel_def_builder.cc
+++ b/tensorflow/core/framework/kernel_def_builder.cc
@@ -13,9 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include "tensorflow/core/framework/kernel_def_builder.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/kernel_def.pb_text.h"
-#include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/kernel_def.pb.h"
 
 namespace tensorflow {
 
@@ -24,6 +25,10 @@ KernelDefBuilder::KernelDefBuilder(const char* op_name) {
   kernel_def_->set_op(op_name);
 }
 
+KernelDefBuilder::~KernelDefBuilder() {
+  DCHECK(kernel_def_ == nullptr) << "Did not call Build()";
+}
+
 KernelDefBuilder& KernelDefBuilder::Device(const char* device_type) {
   kernel_def_->set_device_type(device_type);
   return *this;
@@ -61,4 +66,10 @@ KernelDefBuilder& KernelDefBuilder::Label(const char* label) {
   return *this;
 }
 
+const KernelDef* KernelDefBuilder::Build() {
+  KernelDef* r = kernel_def_;
+  kernel_def_ = nullptr;
+  return r;
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/kernel_def_builder.h b/tensorflow/core/framework/kernel_def_builder.h
index 84657f8dbf..27f768c72f 100644
--- a/tensorflow/core/framework/kernel_def_builder.h
+++ b/tensorflow/core/framework/kernel_def_builder.h
@@ -16,7 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_FRAMEWORK_KERNEL_DEF_BUILDER_H_
 #define TENSORFLOW_FRAMEWORK_KERNEL_DEF_BUILDER_H_
 
-#include "tensorflow/core/framework/kernel_def.pb.h"
+#include "tensorflow/core/framework/kernel_def.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/macros.h"
@@ -24,16 +24,16 @@ limitations under the License.
 
 namespace tensorflow {
 
+// Forward declare proto so that kernels don't need to depend on it
+class KernelDef;
+
 // Builder class passed to the REGISTER_KERNEL_BUILDER() macro.
 class KernelDefBuilder {
  public:
   // Starts with just the name field set.
   // Caller MUST call Build() and take ownership of the result.
   explicit KernelDefBuilder(const char* op_name);
-
-  ~KernelDefBuilder() {
-    DCHECK(kernel_def_ == nullptr) << "Did not call Build()";
-  }
+  ~KernelDefBuilder();
 
   // Required: specify the type of device this kernel supports.
   // Returns *this.
@@ -68,11 +68,7 @@ class KernelDefBuilder {
   // Returns a pointer to a KernelDef with fields set based on the
   // above calls to this instance.
   // Caller takes ownership of the result.
-  const KernelDef* Build() {
-    KernelDef* r = kernel_def_;
-    kernel_def_ = nullptr;
-    return r;
-  }
+  const KernelDef* Build();
 
  private:
   KernelDef* kernel_def_;
diff --git a/tensorflow/core/framework/memory_types.h b/tensorflow/core/framework/memory_types.h
index e35e22f590..a82aea9f07 100644
--- a/tensorflow/core/framework/memory_types.h
+++ b/tensorflow/core/framework/memory_types.h
@@ -16,12 +16,14 @@ limitations under the License.
 #ifndef TENSORFLOW_FRAMEWORK_MEMORY_TYPES_H_
 #define TENSORFLOW_FRAMEWORK_MEMORY_TYPES_H_
 
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/types.h"
 
 namespace tensorflow {
 
+class NodeDef;
+
 // Returns into *{input,output}_memory_types the memory type of each
 // {input,output} tensor.
 //
diff --git a/tensorflow/core/framework/node_def_builder.cc b/tensorflow/core/framework/node_def_builder.cc
index 9385d1266a..f9cf6ce873 100644
--- a/tensorflow/core/framework/node_def_builder.cc
+++ b/tensorflow/core/framework/node_def_builder.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def_builder.h"
 
 #include <vector>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -83,6 +84,25 @@ NodeDefBuilder& NodeDefBuilder::Input(FakeInputFunctor fake_input) {
   return *this;
 }
 
+NodeDefBuilder& NodeDefBuilder::Input(StringPiece src_node, int src_index,
+                                      DataType dt) {
+  const OpDef::ArgDef* arg = NextArgDef();
+  if (arg != nullptr) SingleInput(arg, src_node, src_index, dt);
+  return *this;
+}
+
+NodeDefBuilder& NodeDefBuilder::Input(const NodeOut& src) {
+  Input(src.node, src.index, src.data_type);
+  return *this;
+}
+
+// For inputs that take a list of tensors.
+NodeDefBuilder& NodeDefBuilder::Input(gtl::ArraySlice<NodeOut> src_list) {
+  const OpDef::ArgDef* arg = NextArgDef();
+  if (arg != nullptr) ListInput(arg, src_list);
+  return *this;
+}
+
 void NodeDefBuilder::SingleInput(const OpDef::ArgDef* input_arg,
                                  StringPiece src_node, int src_index,
                                  DataType dt) {
@@ -228,14 +248,51 @@ Status NodeDefBuilder::Finalize(NodeDef* node_def) const {
   }
 }
 
-void NodeDefBuilder::CheckInconsistency(StringPiece attr_name,
-                                        const AttrValue& found,
-                                        const AttrValue& attr_value) {
-  if (!AreAttrValuesEqual(found, attr_value)) {
-    errors_.push_back(strings::StrCat(
-        "Inconsistent values for attr '", attr_name, "' ",
-        SummarizeAttrValue(found), " vs. ", SummarizeAttrValue(attr_value)));
+NodeDefBuilder& NodeDefBuilder::Attr(StringPiece name, const AttrValue& value) {
+  if (const AttrValue* found = AttrSlice(node_def_).Find(name)) {
+    if (!AreAttrValuesEqual(*found, value)) {
+      errors_.push_back(strings::StrCat("Inconsistent values for attr '", name,
+                                        "' ", SummarizeAttrValue(*found),
+                                        " vs. ", SummarizeAttrValue(value)));
+    }
+  } else {
+    AddNodeAttr(name, value, &node_def_);
   }
+  return *this;
 }
 
+#define ATTR(T)                                                     \
+  NodeDefBuilder& NodeDefBuilder::Attr(StringPiece name, T value) { \
+    AttrValue attr_value;                                           \
+    SetAttrValue(value, &attr_value);                               \
+    return Attr(name, attr_value);                                  \
+  }
+ATTR(StringPiece)
+ATTR(const char*)
+ATTR(int32)
+ATTR(int64)
+ATTR(float)
+ATTR(double)
+ATTR(bool)
+ATTR(DataType)
+ATTR(const PartialTensorShape&)
+ATTR(const Tensor&)
+ATTR(const TensorProto&)
+ATTR(const NameAttrList&)
+ATTR(gtl::ArraySlice<StringPiece>)
+ATTR(gtl::ArraySlice<const char*>)
+ATTR(gtl::ArraySlice<string>)
+ATTR(gtl::ArraySlice<int32>)
+ATTR(gtl::ArraySlice<int64>)
+ATTR(gtl::ArraySlice<float>)
+ATTR(gtl::ArraySlice<bool>)
+ATTR(const std::vector<bool>&)
+ATTR(gtl::ArraySlice<DataType>)
+ATTR(gtl::ArraySlice<TensorShape>)
+ATTR(gtl::ArraySlice<PartialTensorShape>)
+ATTR(gtl::ArraySlice<TensorShapeProto>)
+ATTR(gtl::ArraySlice<Tensor>)
+ATTR(gtl::ArraySlice<NameAttrList>)
+#undef ATTR
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/node_def_builder.h b/tensorflow/core/framework/node_def_builder.h
index fd26d0ae64..d7f1d36540 100644
--- a/tensorflow/core/framework/node_def_builder.h
+++ b/tensorflow/core/framework/node_def_builder.h
@@ -19,7 +19,7 @@ limitations under the License.
 #include <functional>
 #include <vector>
 #include "tensorflow/core/framework/attr_value_util.h"
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
@@ -72,22 +72,11 @@ class NodeDefBuilder {
   // *and in the same order as the input_args appear in the OpDef.*
 
   // For inputs that take a single tensor.
-  NodeDefBuilder& Input(StringPiece src_node, int src_index, DataType dt) {
-    const OpDef::ArgDef* arg = NextArgDef();
-    if (arg != nullptr) SingleInput(arg, src_node, src_index, dt);
-    return *this;
-  }
-  NodeDefBuilder& Input(const NodeOut& src) {
-    Input(src.node, src.index, src.data_type);
-    return *this;
-  }
+  NodeDefBuilder& Input(StringPiece src_node, int src_index, DataType dt);
+  NodeDefBuilder& Input(const NodeOut& src);
 
   // For inputs that take a list of tensors.
-  NodeDefBuilder& Input(gtl::ArraySlice<NodeOut> src_list) {
-    const OpDef::ArgDef* arg = NextArgDef();
-    if (arg != nullptr) ListInput(arg, src_list);
-    return *this;
-  }
+  NodeDefBuilder& Input(gtl::ArraySlice<NodeOut> src_list);
 
   // To create inputs in tests, see fake_input.h.
   NodeDefBuilder& Input(FakeInputFunctor fake_input);
@@ -100,13 +89,39 @@ class NodeDefBuilder {
 
   // Sets the attr, if not already set.  If already set with a different
   // value, an error will be returned from Finalize().
+  NodeDefBuilder& Attr(StringPiece name, const AttrValue& value);
+  NodeDefBuilder& Attr(StringPiece name, StringPiece value);
+  NodeDefBuilder& Attr(StringPiece name, const char* value);
+  NodeDefBuilder& Attr(StringPiece name, int32 value);
+  NodeDefBuilder& Attr(StringPiece name, int64 value);
+  NodeDefBuilder& Attr(StringPiece name, float value);
+  NodeDefBuilder& Attr(StringPiece name, double value);
+  NodeDefBuilder& Attr(StringPiece name, bool value);
+  NodeDefBuilder& Attr(StringPiece name, DataType value);
+  NodeDefBuilder& Attr(StringPiece name, const PartialTensorShape& value);
+  NodeDefBuilder& Attr(StringPiece name, const Tensor& value);
+  NodeDefBuilder& Attr(StringPiece name, const TensorProto& value);
+  NodeDefBuilder& Attr(StringPiece name, const NameAttrList& value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<StringPiece> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<const char*> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<string> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<int32> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<int64> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<float> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<bool> value);
+  NodeDefBuilder& Attr(StringPiece name, const std::vector<bool>& value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<DataType> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<TensorShape> value);
+  NodeDefBuilder& Attr(StringPiece name,
+                       gtl::ArraySlice<PartialTensorShape> value);
+  NodeDefBuilder& Attr(StringPiece name,
+                       gtl::ArraySlice<TensorShapeProto> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<Tensor> value);
+  NodeDefBuilder& Attr(StringPiece name, gtl::ArraySlice<NameAttrList> value);
+
   template <class T>
-  NodeDefBuilder& Attr(StringPiece attr_name, T&& value);
-  // Note: overload needed to allow {...} expressions for value.
-  template <class T>
-  NodeDefBuilder& Attr(StringPiece attr_name, std::initializer_list<T> value) {
-    Attr<std::initializer_list<T>>(attr_name, std::move(value));
-    return *this;
+  NodeDefBuilder& Attr(StringPiece name, std::initializer_list<T> value) {
+    return Attr(name, gtl::ArraySlice<T>(value));
   }
 
   // Finish building the NodeDef, returning any errors or setting
@@ -152,9 +167,6 @@ class NodeDefBuilder {
     return input_arg->is_ref() ? MakeRefType(dt) : dt;
   }
 
-  void CheckInconsistency(StringPiece attr_name, const AttrValue& found,
-                          const AttrValue& attr_value);
-
   const OpDef* op_def_;
   NodeDef node_def_;
   int inputs_specified_;
@@ -162,21 +174,6 @@ class NodeDefBuilder {
   std::vector<string> errors_;
 };
 
-// IMPLEMENTATION -------------------------------------------------------------
-
-template <class T>
-NodeDefBuilder& NodeDefBuilder::Attr(StringPiece attr_name, T&& value) {
-  const AttrValue* found = AttrSlice(node_def_).Find(attr_name);
-  if (found == nullptr) {
-    AddNodeAttr(attr_name, std::forward<T>(value), &node_def_);
-  } else {
-    AttrValue attr_value;
-    SetAttrValue(std::forward<T>(value), &attr_value);
-    CheckInconsistency(attr_name, *found, attr_value);
-  }
-  return *this;
-}
-
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_FRAMEWORK_NODE_DEF_BUILDER_H_
diff --git a/tensorflow/core/framework/node_def_util.cc b/tensorflow/core/framework/node_def_util.cc
index 79feb20d53..b98a6033d0 100644
--- a/tensorflow/core/framework/node_def_util.cc
+++ b/tensorflow/core/framework/node_def_util.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op_def.pb_text.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/tensor.pb_text.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
diff --git a/tensorflow/core/framework/node_def_util.h b/tensorflow/core/framework/node_def_util.h
index 5d4864db66..a829243a75 100644
--- a/tensorflow/core/framework/node_def_util.h
+++ b/tensorflow/core/framework/node_def_util.h
@@ -21,7 +21,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/attr_value_util.h"
-#include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/op_def.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/platform/protobuf.h"
@@ -30,8 +30,9 @@ namespace tensorflow {
 
 class Node;
 
-// We forward declare NodeDef so that kernels don't need to depend on protos
+// We forward declare protos so that kernels don't need to depend on them
 class NodeDef;
+class OpDef;
 
 // Name of the attribute used to encode node colocation constraints.
 //
diff --git a/tensorflow/core/framework/op.h b/tensorflow/core/framework/op.h
index a4dd06de45..1c63a6f4c0 100644
--- a/tensorflow/core/framework/op.h
+++ b/tensorflow/core/framework/op.h
@@ -20,7 +20,7 @@ limitations under the License.
 #include <unordered_map>
 
 #include <vector>
-#include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/op_def.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/op_def_builder.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/selective_registration.h"
diff --git a/tensorflow/core/framework/op_def_builder.cc b/tensorflow/core/framework/op_def_builder.cc
index 58a30a87a8..62b504691b 100644
--- a/tensorflow/core/framework/op_def_builder.cc
+++ b/tensorflow/core/framework/op_def_builder.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <limits>
 #include <vector>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/framework/op_def_util.cc b/tensorflow/core/framework/op_def_util.cc
index c36e6dd653..2f25b6e18f 100644
--- a/tensorflow/core/framework/op_def_util.cc
+++ b/tensorflow/core/framework/op_def_util.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <set>
 #include <unordered_map>
 #include <unordered_set>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/op_def.pb_text.h"
 #include "tensorflow/core/framework/types.h"
diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc
index da623ae5b2..517120ecab 100644
--- a/tensorflow/core/framework/op_gen_lib.cc
+++ b/tensorflow/core/framework/op_gen_lib.cc
@@ -17,6 +17,8 @@ limitations under the License.
 
 #include <vector>
 #include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/op_gen_overrides.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -71,6 +73,9 @@ bool ConsumeEquals(StringPiece* description) {
   return false;
 }
 
+OpGenOverrideMap::OpGenOverrideMap() {}
+OpGenOverrideMap::~OpGenOverrideMap() {}
+
 Status OpGenOverrideMap::LoadFileList(Env* env, const string& filenames) {
   std::vector<string> v = str_util::Split(filenames, ",");
   for (const string& f : v) {
@@ -86,7 +91,7 @@ Status OpGenOverrideMap::LoadFile(Env* env, const string& filename) {
   OpGenOverrides all;
   protobuf::TextFormat::ParseFromString(contents, &all);
   for (const auto& one : all.op()) {
-    map_[one.name()] = one;
+    map_[one.name()].reset(new OpGenOverride(one));
   }
   return Status::OK();
 }
@@ -142,7 +147,7 @@ const OpGenOverride* OpGenOverrideMap::ApplyOverride(OpDef* op_def) const {
   // Look up
   const auto iter = map_.find(op_def->name());
   if (iter == map_.end()) return nullptr;
-  const OpGenOverride& proto = iter->second;
+  const OpGenOverride& proto = *iter->second;
 
   // Apply overrides from `proto`.
   if (!proto.rename_to().empty()) {
diff --git a/tensorflow/core/framework/op_gen_lib.h b/tensorflow/core/framework/op_gen_lib.h
index e92dc8d924..a74b651bb6 100644
--- a/tensorflow/core/framework/op_gen_lib.h
+++ b/tensorflow/core/framework/op_gen_lib.h
@@ -18,14 +18,18 @@ limitations under the License.
 
 #include <string>
 #include <unordered_map>
-#include "tensorflow/core/framework/op_def.pb.h"
-#include "tensorflow/core/framework/op_gen_overrides.pb.h"
+#include "tensorflow/core/framework/op_def.pb.h"  // TODO(b/62899350): Remove
+#include "tensorflow/core/framework/op_gen_overrides.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/platform/env.h"
 
 namespace tensorflow {
 
+// Forward declare protos so their symbols can be removed from .so exports
+class OpDef;
+class OpGenOverride;
+
 inline string Spaces(int n) { return string(n, ' '); }
 
 // Wrap prefix + str to be at most width characters, indenting every line
@@ -43,6 +47,9 @@ bool ConsumeEquals(StringPiece* description);
 // look up the specific override for any given op.
 class OpGenOverrideMap {
  public:
+  OpGenOverrideMap();
+  ~OpGenOverrideMap();
+
   // `filenames` is a comma-separated list of file names.  If an op
   // is mentioned in more than one file, the last one takes priority.
   Status LoadFileList(Env* env, const string& filenames);
@@ -61,7 +68,7 @@ class OpGenOverrideMap {
   const OpGenOverride* ApplyOverride(OpDef* op_def) const;
 
  private:
-  std::unordered_map<string, OpGenOverride> map_;
+  std::unordered_map<string, std::unique_ptr<OpGenOverride>> map_;
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc
index 3892320b7d..067d30d21a 100644
--- a/tensorflow/core/framework/op_kernel.cc
+++ b/tensorflow/core/framework/op_kernel.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/attr_value_util.h"
+#include "tensorflow/core/framework/device_attributes.pb.h"
 #include "tensorflow/core/framework/graph.pb_text.h"
 #include "tensorflow/core/framework/kernel_def.pb_text.h"
 #include "tensorflow/core/framework/log_memory.h"
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index fd5c8fdbb8..1b716c5a5a 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -24,19 +24,19 @@ limitations under the License.
 #include "tensorflow/core/framework/cancellation.h"
 #include "tensorflow/core/framework/control_flow.h"
 #include "tensorflow/core/framework/device_base.h"
-#include "tensorflow/core/framework/function.h"
-#include "tensorflow/core/framework/graph.pb.h"
-#include "tensorflow/core/framework/kernel_def.pb.h"
+#include "tensorflow/core/framework/function.h"  // TODO(b/62899350): Remove
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
+#include "tensorflow/core/framework/kernel_def.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/kernel_def_builder.h"
 #include "tensorflow/core/framework/node_def_util.h"
-#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/rendezvous.h"
 #include "tensorflow/core/framework/selective_registration.h"
 #include "tensorflow/core/framework/session_state.h"
-#include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/step_stats.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
-#include "tensorflow/core/framework/tensor_shape.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/tracking_allocator.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/types.pb.h"
@@ -65,9 +65,13 @@ class TensorSliceReaderCacheWrapper;
 }  // namespace checkpoint
 
 class AsyncOpKernel;
+class FunctionCallFrame;
+class FunctionLibraryRuntime;
 class OpKernelConstruction;  // declared below
 class OpKernelContext;       // declared below
+class OpRegistryInterface;
 class ResourceMgr;
+class ScopedStepContainer;
 
 class OpKernel {
  public:
diff --git a/tensorflow/core/framework/op_kernel_test.cc b/tensorflow/core/framework/op_kernel_test.cc
index f87b717844..47523358be 100644
--- a/tensorflow/core/framework/op_kernel_test.cc
+++ b/tensorflow/core/framework/op_kernel_test.cc
@@ -19,10 +19,12 @@ limitations under the License.
 #include <utility>
 #include <vector>
 #include "tensorflow/core/framework/allocator.h"
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/fake_input.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
diff --git a/tensorflow/core/framework/partial_tensor_shape_test.cc b/tensorflow/core/framework/partial_tensor_shape_test.cc
index f8ebd99bf8..54ae019f9b 100644
--- a/tensorflow/core/framework/partial_tensor_shape_test.cc
+++ b/tensorflow/core/framework/partial_tensor_shape_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
diff --git a/tensorflow/core/framework/reader_base.cc b/tensorflow/core/framework/reader_base.cc
index ebed957d99..b8c771a0a1 100644
--- a/tensorflow/core/framework/reader_base.cc
+++ b/tensorflow/core/framework/reader_base.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/reader_base.h"
 
+#include "tensorflow/core/framework/reader_base.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/core/framework/reader_base.h b/tensorflow/core/framework/reader_base.h
index 0528841814..2b5052e959 100644
--- a/tensorflow/core/framework/reader_base.h
+++ b/tensorflow/core/framework/reader_base.h
@@ -19,12 +19,14 @@ limitations under the License.
 #include <memory>
 #include <string>
 #include "tensorflow/core/framework/queue_interface.h"
-#include "tensorflow/core/framework/reader_base.pb.h"
+#include "tensorflow/core/framework/reader_base.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/reader_interface.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
 namespace tensorflow {
 
+class ReaderBaseState;
+
 // Default implementation of ReaderInterface.
 class ReaderBase : public ReaderInterface {
  public:
diff --git a/tensorflow/core/framework/resource_mgr.cc b/tensorflow/core/framework/resource_mgr.cc
index 3018e4f655..bc3ba914e0 100644
--- a/tensorflow/core/framework/resource_mgr.cc
+++ b/tensorflow/core/framework/resource_mgr.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/resource_mgr.h"
 
+#include "tensorflow/core/framework/device_attributes.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/core/framework/resource_mgr.h b/tensorflow/core/framework/resource_mgr.h
index 0e1a5a82d3..c92c0a36ba 100644
--- a/tensorflow/core/framework/resource_mgr.h
+++ b/tensorflow/core/framework/resource_mgr.h
@@ -22,8 +22,9 @@ limitations under the License.
 #include <unordered_map>
 
 #include "tensorflow/core/framework/common_shape_fns.h"
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
diff --git a/tensorflow/core/framework/resource_mgr_test.cc b/tensorflow/core/framework/resource_mgr_test.cc
index df4d8c3591..07272e2374 100644
--- a/tensorflow/core/framework/resource_mgr_test.cc
+++ b/tensorflow/core/framework/resource_mgr_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/resource_mgr.h"
 
+#include "tensorflow/core/framework/device_attributes.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 62f85d2dac..6947f68002 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -16,6 +16,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/node_def.pb_text.h"
 #include "tensorflow/core/framework/partial_tensor_shape.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/kernels/bounds_check.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/numbers.h"
@@ -79,6 +80,58 @@ InferenceContext::InferenceContext(
   PostInputInit(std::move(handle_data));
 }
 
+// Same as above, but with PartialTensorShape instead of TensorShapeProto
+InferenceContext::InferenceContext(
+    int graph_def_version, const NodeDef* node_def, const OpDef& op_def,
+    const std::vector<PartialTensorShape>& input_shapes,
+    const std::vector<const Tensor*>& input_tensors,
+    const std::vector<PartialTensorShape>& input_tensors_as_shapes,
+    const std::vector<
+        std::unique_ptr<std::vector<std::pair<PartialTensorShape, DataType>>>>&
+        input_handle_shapes_and_types)
+    : graph_def_version_(graph_def_version),
+      node_def_(*CHECK_NOTNULL(node_def)) {
+  std::vector<ShapeHandle> input_tensors_as_shape_handles;
+  for (const PartialTensorShape& p : input_tensors_as_shapes) {
+    ShapeHandle shape;
+    construction_status_.Update(MakeShapeFromPartialTensorShape(p, &shape));
+    if (!construction_status_.ok()) {
+      return;
+    }
+    input_tensors_as_shape_handles.push_back(shape);
+  }
+  PreInputInit(op_def, input_tensors, input_tensors_as_shape_handles);
+  if (!construction_status_.ok()) return;
+  for (const PartialTensorShape& p : input_shapes) {
+    ShapeHandle shape;
+    construction_status_.Update(MakeShapeFromPartialTensorShape(p, &shape));
+    if (!construction_status_.ok()) {
+      return;
+    }
+    inputs_.push_back(shape);
+  }
+  std::vector<std::unique_ptr<std::vector<ShapeAndType>>> handle_data(
+      input_shapes.size());
+  for (int i = 0; i < input_handle_shapes_and_types.size(); ++i) {
+    const auto& v = input_handle_shapes_and_types[i];
+    if (v == nullptr) {
+      continue;
+    }
+    handle_data[i].reset(new std::vector<ShapeAndType>(v->size()));
+    auto& new_v = *handle_data[i];
+    for (int j = 0; j < v->size(); ++j) {
+      const auto& p = (*v)[j];
+      construction_status_.Update(
+          MakeShapeFromPartialTensorShape(p.first, &new_v[j].shape));
+      if (!construction_status_.ok()) {
+        return;
+      }
+      new_v[j].dtype = p.second;
+    }
+  }
+  PostInputInit(std::move(handle_data));
+}
+
 InferenceContext::InferenceContext(
     int graph_def_version, const NodeDef* node_def, const OpDef& op_def,
     const std::vector<ShapeHandle>& input_shapes,
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index 460aefe29e..716cec5c4a 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -17,7 +17,7 @@ limitations under the License.
 
 #include <vector>
 
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -189,6 +189,26 @@ class InferenceContext {
           std::unique_ptr<std::vector<std::pair<TensorShapeProto, DataType>>>>&
           input_handle_shapes_and_types);
 
+  // <input_tensors> is NULL-padded to be the same size as <input_shapes>.
+  //
+  // Elements of <input_tensors_as_shapes> are used for when a shape
+  // function makes a call to MakeShapeFromShapeTensor; in particular, when
+  // the input_tensors[i] is nullptr but the shape represented by it is
+  // partially known from analysis of the graph. <input_tensors_as_shapes>
+  // can have fewer elements than <input_shapes>. Values of
+  // <input_tensors_as_shapes> do not need to outlive the context.
+  //
+  // REQUIRES: <node_def> is not NULL, and must outlive the
+  // InferenceContext.
+  InferenceContext(
+      int graph_def_version, const NodeDef* node_def, const OpDef& op_def,
+      const std::vector<PartialTensorShape>& input_shapes,
+      const std::vector<const Tensor*>& input_tensors,
+      const std::vector<PartialTensorShape>& input_tensors_as_shapes,
+      const std::vector<std::unique_ptr<
+          std::vector<std::pair<PartialTensorShape, DataType>>>>&
+          input_handle_shapes_and_types);
+
   ~InferenceContext();
 
   // Runs the shape inference function 'fn' with 'this' as the
diff --git a/tensorflow/core/framework/shape_inference_test.cc b/tensorflow/core/framework/shape_inference_test.cc
index 66cfbf8747..57d8dc9353 100644
--- a/tensorflow/core/framework/shape_inference_test.cc
+++ b/tensorflow/core/framework/shape_inference_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include "tensorflow/core/framework/fake_input.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/op_def_builder.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -36,19 +37,11 @@ OpDef MakeOpDefWithLists() {
   return op_reg_data.op_def;
 }
 
-TensorShapeProto S(std::initializer_list<int64> dims) {
-  PartialTensorShape shape(dims);
-  TensorShapeProto ret;
-  shape.AsProto(&ret);
-  return ret;
+PartialTensorShape S(std::initializer_list<int64> dims) {
+  return PartialTensorShape(dims);
 }
 
-TensorShapeProto Unknown() {
-  PartialTensorShape shape;
-  TensorShapeProto ret;
-  shape.AsProto(&ret);
-  return ret;
-}
+PartialTensorShape Unknown() { return PartialTensorShape(); }
 
 }  // namespace
 
@@ -1537,7 +1530,7 @@ void ShapeInferenceTest::TestMergeHandles(bool input_not_output) {
                      {});
   auto make_shape = [&c](std::initializer_list<int64> dim_sizes) {
     ShapeHandle s;
-    TF_CHECK_OK(c.MakeShapeFromShapeProto(S(dim_sizes), &s));
+    TF_CHECK_OK(c.MakeShapeFromPartialTensorShape(S(dim_sizes), &s));
     return s;
   };
   auto get_shapes_and_types_from_context = [&](int idx) {
@@ -1648,7 +1641,7 @@ void ShapeInferenceTest::TestRelaxHandles(bool input_not_output) {
                      {});
   auto make_shape = [&c](std::initializer_list<int64> dim_sizes) {
     ShapeHandle s;
-    TF_CHECK_OK(c.MakeShapeFromShapeProto(S(dim_sizes), &s));
+    TF_CHECK_OK(c.MakeShapeFromPartialTensorShape(S(dim_sizes), &s));
     return s;
   };
   auto get_shapes_and_types_from_context = [&](int idx) {
diff --git a/tensorflow/core/framework/shape_inference_testutil.h b/tensorflow/core/framework/shape_inference_testutil.h
index 03c39e6dc1..fbfd24538b 100644
--- a/tensorflow/core/framework/shape_inference_testutil.h
+++ b/tensorflow/core/framework/shape_inference_testutil.h
@@ -16,7 +16,6 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_CORE_FRAMEWORK_SHAPE_INFERENCE_TESTUTIL_H_
 
 #include <vector>
-#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/lib/core/status.h"
@@ -28,7 +27,6 @@ limitations under the License.
 
 namespace tensorflow {
 
-class NodeDef;
 class Tensor;
 
 struct ShapeInferenceTestOp {
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index cc9f11ef6a..980703f190 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -29,9 +29,11 @@ limitations under the License.
 
 #include "tensorflow/core/framework/tensor.h"
 
+#include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_description.pb.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/coding.h"
diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h
index a164fe61b5..a8f9d21511 100644
--- a/tensorflow/core/framework/tensor.h
+++ b/tensorflow/core/framework/tensor.h
@@ -17,10 +17,10 @@ limitations under the License.
 #define TENSORFLOW_CORE_FRAMEWORK_TENSOR_H_
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/core/framework/allocation_description.pb.h"
+#include "tensorflow/core/framework/allocation_description.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/allocator.h"
-#include "tensorflow/core/framework/tensor.pb.h"
-#include "tensorflow/core/framework/tensor_description.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"  // TODO(b/62899350): Remove
+#include "tensorflow/core/framework/tensor_description.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/types.h"
@@ -35,8 +35,13 @@ limitations under the License.
 
 namespace tensorflow {
 
-class TensorBuffer;  // Forward declaration.
+// Forward declarations.  In particular, we forward declare protos so that their
+// symbols can be removed from .so exports.
+class AllocationDescription;
+class TensorBuffer;
 class TensorCApi;
+class TensorDescription;
+class TensorProto;
 
 /// @ingroup core
 /// Represents an n-dimensional array of values.
diff --git a/tensorflow/core/framework/tensor_reference.h b/tensorflow/core/framework/tensor_reference.h
index 186820785d..37e588d4f1 100644
--- a/tensorflow/core/framework/tensor_reference.h
+++ b/tensorflow/core/framework/tensor_reference.h
@@ -16,7 +16,6 @@ limitations under the License.
 #ifndef TENSORFLOW_FRAMEWORK_TENSOR_REFERENCE_H_
 #define TENSORFLOW_FRAMEWORK_TENSOR_REFERENCE_H_
 
-#include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 
diff --git a/tensorflow/core/framework/tensor_shape.cc b/tensorflow/core/framework/tensor_shape.cc
index 1284214952..14d9cea20e 100644
--- a/tensorflow/core/framework/tensor_shape.cc
+++ b/tensorflow/core/framework/tensor_shape.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/tensor_shape.h"
 
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/kernels/bounds_check.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
diff --git a/tensorflow/core/framework/tensor_shape.h b/tensorflow/core/framework/tensor_shape.h
index b201607461..e56c3d7b93 100644
--- a/tensorflow/core/framework/tensor_shape.h
+++ b/tensorflow/core/framework/tensor_shape.h
@@ -19,7 +19,7 @@ limitations under the License.
 #include <string>
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/core/framework/tensor_shape.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
@@ -35,6 +35,7 @@ namespace tensorflow {
 template <class Shape>
 class TensorShapeIter;
 class TensorShape;
+class TensorShapeProto;
 class PartialTensorShape;
 // END_SKIP_DOXYGEN
 
diff --git a/tensorflow/core/framework/tensor_shape_test.cc b/tensorflow/core/framework/tensor_shape_test.cc
index d6fe9a1511..51a7b14fed 100644
--- a/tensorflow/core/framework/tensor_shape_test.cc
+++ b/tensorflow/core/framework/tensor_shape_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/tensor_shape.h"
 
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/random/simple_philox.h"
 #include "tensorflow/core/lib/strings/str_util.h"
diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc
index 369f64e9e2..6c9c803af6 100644
--- a/tensorflow/core/framework/tensor_test.cc
+++ b/tensorflow/core/framework/tensor_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/tensor.h"
 
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/strings/strcat.h"
diff --git a/tensorflow/core/framework/versions.cc b/tensorflow/core/framework/versions.cc
index 58937556d9..3ff0723cee 100644
--- a/tensorflow/core/framework/versions.cc
+++ b/tensorflow/core/framework/versions.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/framework/versions.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/public/version.h"
 
diff --git a/tensorflow/core/framework/versions.h b/tensorflow/core/framework/versions.h
index 01429b26a6..e8f07f9016 100644
--- a/tensorflow/core/framework/versions.h
+++ b/tensorflow/core/framework/versions.h
@@ -16,11 +16,13 @@ limitations under the License.
 #ifndef TENSORFLOW_FRAMEWORK_VERSIONS_H_
 #define TENSORFLOW_FRAMEWORK_VERSIONS_H_
 
-#include "tensorflow/core/framework/versions.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
 
+class VersionDef;
+
 // Check whether data with the given versions is compatible with the given
 // consumer and min producer.  upper_name and lower_name are used to form
 // error messages upon failure.  Example usage:
diff --git a/tensorflow/core/graph/costmodel.cc b/tensorflow/core/graph/costmodel.cc
index f798af85e1..3ed32068ae 100644
--- a/tensorflow/core/graph/costmodel.cc
+++ b/tensorflow/core/graph/costmodel.cc
@@ -16,8 +16,10 @@ limitations under the License.
 #include "tensorflow/core/graph/costmodel.h"
 
 #include <vector>
+#include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/tensor_description.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/platform/logging.h"
 
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index e06e479264..9469e3f98f 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -16,9 +16,11 @@ limitations under the License.
 #include "tensorflow/core/graph/graph.h"
 
 #include <vector>
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -255,9 +257,11 @@ Status Node::input_node(int idx, const Node** const_n) const {
 // Graph
 
 Graph::Graph(const OpRegistryInterface* ops)
-    : ops_(ops, FunctionDefLibrary()), arena_(8 << 10 /* 8kB */) {
-  versions_.set_producer(TF_GRAPH_DEF_VERSION);
-  versions_.set_min_consumer(TF_GRAPH_DEF_VERSION_MIN_CONSUMER);
+    : ops_(ops, FunctionDefLibrary()),
+      versions_(new VersionDef),
+      arena_(8 << 10 /* 8kB */) {
+  versions_->set_producer(TF_GRAPH_DEF_VERSION);
+  versions_->set_min_consumer(TF_GRAPH_DEF_VERSION_MIN_CONSUMER);
 
   // Initialize the name interning table for assigned_device_name.
   device_names_.push_back("");
@@ -301,6 +305,9 @@ Graph::~Graph() {
   // destroy them.
 }
 
+const VersionDef& Graph::versions() const { return *versions_; }
+void Graph::set_versions(const VersionDef& versions) { *versions_ = versions; }
+
 Node* Graph::AddNode(const NodeDef& node_def, Status* status) {
   const OpDef* op_def;
   status->Update(ops_.LookUpOpDef(node_def.op(), &op_def));
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index e19e0b727d..565f430455 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -41,10 +41,10 @@ limitations under the License.
 #include <string>
 #include <vector>
 #include "tensorflow/core/framework/function.h"
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/framework/versions.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/graph/edgeset.h"
 #include "tensorflow/core/lib/core/arena.h"
 #include "tensorflow/core/lib/core/refcount.h"
@@ -59,7 +59,9 @@ namespace tensorflow {
 class Edge;
 class EdgeSetTest;
 class Graph;
+class GraphDef;
 class Node;
+class VersionDef;
 
 class NeighborIter;  // Declared below
 class NodeIter;      // Declared below
@@ -370,8 +372,8 @@ class Graph {
   static const int kControlSlot;
 
   // The GraphDef version range of this graph (see graph.proto).
-  const VersionDef& versions() const { return versions_; }
-  void set_versions(const VersionDef& versions) { versions_ = versions; }
+  const VersionDef& versions() const;
+  void set_versions(const VersionDef& versions);
 
   // Adds a new node to this graph, and returns it. Infers the Op and
   // input/output types for the node. *this owns the returned instance.
@@ -514,7 +516,7 @@ class Graph {
   FunctionLibraryDefinition ops_;
 
   // GraphDef versions
-  VersionDef versions_;
+  const std::unique_ptr<VersionDef> versions_;
 
   // Allocator which will give us good locality.
   core::Arena arena_;
diff --git a/tensorflow/core/graph/graph_constructor.cc b/tensorflow/core/graph/graph_constructor.cc
index a2929d0210..582c8727c6 100644
--- a/tensorflow/core/graph/graph_constructor.cc
+++ b/tensorflow/core/graph/graph_constructor.cc
@@ -27,8 +27,10 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/versions.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/tensor_id.h"
diff --git a/tensorflow/core/graph/graph_constructor_test.cc b/tensorflow/core/graph/graph_constructor_test.cc
index b8d1879fa0..f222b9b5f1 100644
--- a/tensorflow/core/graph/graph_constructor_test.cc
+++ b/tensorflow/core/graph/graph_constructor_test.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/node_builder.h"
 #include "tensorflow/core/kernels/ops_util.h"
diff --git a/tensorflow/core/graph/graph_def_builder_test.cc b/tensorflow/core/graph/graph_def_builder_test.cc
index 867eca0c41..e85de71ef7 100644
--- a/tensorflow/core/graph/graph_def_builder_test.cc
+++ b/tensorflow/core/graph/graph_def_builder_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/graph/graph_def_builder.h"
 
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index f8c6895dfa..f452299a83 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -22,7 +22,9 @@ limitations under the License.
 
 #include "tensorflow/core/framework/memory_types.h"
 #include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/control_flow.h"
 #include "tensorflow/core/graph/costmodel.h"
diff --git a/tensorflow/core/graph/graph_partition_test.cc b/tensorflow/core/graph/graph_partition_test.cc
index ca49ea0ac4..cb9e4b7973 100644
--- a/tensorflow/core/graph/graph_partition_test.cc
+++ b/tensorflow/core/graph/graph_partition_test.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/sendrecv_ops.h"
 #include "tensorflow/core/framework/function_testlib.h"
 #include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/graph_def_builder.h"
diff --git a/tensorflow/core/graph/node_builder.cc b/tensorflow/core/graph/node_builder.cc
index 500ac129e8..138952dcb3 100644
--- a/tensorflow/core/graph/node_builder.cc
+++ b/tensorflow/core/graph/node_builder.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <vector>
 #include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/clusters/single_machine_test.cc b/tensorflow/core/grappler/clusters/single_machine_test.cc
index 84e796c960..b73b084793 100644
--- a/tensorflow/core/grappler/clusters/single_machine_test.cc
+++ b/tensorflow/core/grappler/clusters/single_machine_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
 #include "tensorflow/core/grappler/utils.h"
diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc
index 95329f3f14..e717f6e761 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster.cc
+++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/clusters/virtual_cluster.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/grappler/costs/op_level_cost_estimator.h"
 #include "tensorflow/core/grappler/costs/virtual_scheduler.h"
diff --git a/tensorflow/core/grappler/clusters/virtual_cluster_test.cc b/tensorflow/core/grappler/clusters/virtual_cluster_test.cc
index 6f25e7b0d4..ec21f5f426 100644
--- a/tensorflow/core/grappler/clusters/virtual_cluster_test.cc
+++ b/tensorflow/core/grappler/clusters/virtual_cluster_test.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/clusters/virtual_cluster.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
 #include "tensorflow/core/platform/test.h"
diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD
index 96d37c9a97..37623f8997 100644
--- a/tensorflow/core/grappler/costs/BUILD
+++ b/tensorflow/core/grappler/costs/BUILD
@@ -60,6 +60,7 @@ cc_test(
         "//tensorflow/cc:scope",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib_proto_parsing",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -196,6 +197,7 @@ cc_test(
         ":virtual_placer",
         ":virtual_scheduler",
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:tensorflow",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -232,6 +234,7 @@ cc_library(
         ":cost_estimator",
         ":op_performance_data_cc",
         "//tensorflow/core:framework",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler/clusters:utils",
         "//third_party/eigen3",
     ],
@@ -265,6 +268,7 @@ cc_library(
         ":virtual_scheduler",
         "//tensorflow/core:core_cpu_base",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
     ],
 )
diff --git a/tensorflow/core/grappler/costs/analytical_cost_estimator.cc b/tensorflow/core/grappler/costs/analytical_cost_estimator.cc
index e530f66415..569efaf96d 100644
--- a/tensorflow/core/grappler/costs/analytical_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/analytical_cost_estimator.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <limits>
 #include <unordered_map>
 
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/graph/types.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/costs/op_performance_data.pb.h"
diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 7361968cea..cd673a64a0 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -179,7 +179,7 @@ Status GraphProperties::RelaxEnqueueShapesAndMergeTypes(
 
 Status GraphProperties::InferStatically() {
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   shape_refiner.set_require_shape_inference_fns(false);
   ImportGraphDefOptions options;
   Status s = ImportGraphDef(options, item_.graph, &graph, &shape_refiner);
diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc
index 29b6adef5e..109f973956 100644
--- a/tensorflow/core/grappler/costs/graph_properties_test.cc
+++ b/tensorflow/core/grappler/costs/graph_properties_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include "tensorflow/cc/framework/scope.h"
 #include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/grappler/clusters/single_machine.h"
 #include "tensorflow/core/grappler/grappler_item.h"
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index ba6686e7df..7d3298ded4 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -16,7 +16,9 @@ limitations under the License.
 #include "tensorflow/core/grappler/costs/op_level_cost_estimator.h"
 
 #include "third_party/eigen3/Eigen/Core"
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/clusters/utils.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/grappler/costs/utils.cc b/tensorflow/core/grappler/costs/utils.cc
index 2fbd54d759..4135d9b331 100644
--- a/tensorflow/core/grappler/costs/utils.cc
+++ b/tensorflow/core/grappler/costs/utils.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/graph/graph.h"
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 3be4f917bf..11650cb6a2 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -19,6 +19,9 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_description.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/clusters/utils.h"
 #include "tensorflow/core/grappler/costs/utils.h"
 #include "tensorflow/core/grappler/op_types.h"
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index dc54b8d0d1..9743db33db 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -15,6 +15,8 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/costs/virtual_scheduler.h"
 #include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/framework/tensor_description.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/clusters/virtual_cluster.h"
 #include "tensorflow/core/grappler/costs/virtual_placer.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 607f2c286b..86775306ff 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -28,8 +28,10 @@ limitations under the License.
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/framework/variable.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/grappler/inputs/utils.h"
 #include "tensorflow/core/grappler/op_types.h"
diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.cc b/tensorflow/core/grappler/optimizers/auto_parallel.cc
index d46b849ad4..3f58a2abea 100644
--- a/tensorflow/core/grappler/optimizers/auto_parallel.cc
+++ b/tensorflow/core/grappler/optimizers/auto_parallel.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/grappler/clusters/cluster.h"
 #include "tensorflow/core/grappler/devices.h"
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 0ed73bfae9..63159dc3aa 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/versions.pb.h"
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 28d663e2f7..ddf8a5acd2 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -17,6 +17,8 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/clusters/cluster.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/devices.h"
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 16a638a7d3..462cfb928f 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -23,6 +23,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 0b1c1085f2..f0dc3312c2 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -3337,7 +3337,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "serialize_sparse_op",
     prefix = "serialize_sparse_op",
-    deps = SPARSE_DEPS,
+    deps = SPARSE_DEPS + ["//tensorflow/core:protos_all_cc"],
 )
 
 tf_kernel_library(
diff --git a/tensorflow/core/kernels/decode_image_op.cc b/tensorflow/core/kernels/decode_image_op.cc
index 76f8c22543..f5a74048af 100644
--- a/tensorflow/core/kernels/decode_image_op.cc
+++ b/tensorflow/core/kernels/decode_image_op.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/lib/gif/gif_io.h"
 #include "tensorflow/core/lib/jpeg/jpeg_mem.h"
 #include "tensorflow/core/lib/png/png_io.h"
+#include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/kernels/gather_nd_op.cc b/tensorflow/core/kernels/gather_nd_op.cc
index 73f30cdae3..9526f1119b 100644
--- a/tensorflow/core/kernels/gather_nd_op.cc
+++ b/tensorflow/core/kernels/gather_nd_op.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/mem.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc b/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc
index 04697c3b15..8538ebc5c6 100644
--- a/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include "tensorflow/cc/framework/scope.h"
 #include "tensorflow/cc/ops/const_op.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/graph/node_builder.h"
 #include "tensorflow/core/platform/logging.h"
 namespace tensorflow {
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.cc b/tensorflow/core/kernels/hexagon/graph_transferer.cc
index a82ae61ad9..65677cc3de 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transferer.cc
@@ -87,7 +87,7 @@ Status GraphTransferer::LoadGraphFromProto(
     const std::vector<string>& output_node_names,
     const bool shape_inference_for_unknown_shape) {
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   Status status = ImportGraphDef({}, graph_def, &graph, &shape_refiner);
   if (!status.ok()) {
     return status;
diff --git a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
index cb9091e29f..130109b813 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
@@ -26,6 +26,7 @@ adb push /tmp/imagenet_comp_graph_label_strings.txt /data/local/tmp
 
 #include <memory>
 
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/kernels/hexagon/graph_transfer_utils.h"
 #include "tensorflow/core/kernels/hexagon/graph_transferer.h"
diff --git a/tensorflow/core/kernels/identity_reader_op.cc b/tensorflow/core/kernels/identity_reader_op.cc
index ddd012b910..6e5714b313 100644
--- a/tensorflow/core/kernels/identity_reader_op.cc
+++ b/tensorflow/core/kernels/identity_reader_op.cc
@@ -17,8 +17,10 @@ limitations under the License.
 
 #include <memory>
 #include "tensorflow/core/framework/reader_base.h"
+#include "tensorflow/core/framework/reader_base.pb.h"
 #include "tensorflow/core/framework/reader_op_kernel.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/protobuf.h"
 
diff --git a/tensorflow/core/kernels/reduction_ops_common.cc b/tensorflow/core/kernels/reduction_ops_common.cc
index 0cb63abf2a..5eba4288ac 100644
--- a/tensorflow/core/kernels/reduction_ops_common.cc
+++ b/tensorflow/core/kernels/reduction_ops_common.cc
@@ -15,6 +15,8 @@ limitations under the License.
 
 #include "tensorflow/core/kernels/reduction_ops_common.h"
 
+#include "tensorflow/core/lib/strings/str_util.h"
+
 namespace tensorflow {
 
 TensorShape ReductionHelper::out_reshape() const {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index 890ff15c48..ed088bfba7 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -21,6 +21,8 @@ limitations under the License.
 
 #include "tensorflow/core/common_runtime/shape_refiner.h"
 #include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/node_builder.h"
 #include "tensorflow/core/public/session.h"
@@ -580,8 +582,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteGraphInputsAndOutputsFromProto(
   } else {
     ImportGraphDefOptions opts;
     Graph graph(OpRegistry::Global());
-    ShapeRefiner shape_refiner(graph.versions().producer(),
-                               graph.op_registry());
+    ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
     TF_RETURN_IF_ERROR(
         ImportGraphDef(opts, *graph_def, &graph, &shape_refiner));
     TF_RETURN_IF_ERROR(PropagateShapeInference(*graph_def, input_tensors,
@@ -724,7 +725,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
     const std::unordered_set<string>& node_names, const GraphDef& graph_def,
     std::vector<ClusterInfo>* cluster_infos) {
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   TF_RETURN_IF_ERROR(ImportGraphDef({}, graph_def, &graph, &shape_refiner));
   std::unordered_set<string> remaining_nodes = node_names;
 
@@ -829,7 +830,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
       BuildNodeSetFromNodeNamesAndPorts(std::get<1>(cluster));
 
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   TF_RETURN_IF_ERROR(ImportGraphDef({}, graph_def, &graph, &shape_refiner));
 
   for (Node* node : graph.nodes()) {
@@ -883,7 +884,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
     const std::vector<string>& border_outputs, const GraphDef& graph_def,
     ClusterInfo* cluster) {
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   TF_RETURN_IF_ERROR(ImportGraphDef({}, graph_def, &graph, &shape_refiner));
 
   std::unordered_set<const Node*> visited;
@@ -955,7 +956,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
       BuildClusterSubgraphDef(cluster, input_graph_def, &subgraph_def));
 
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   TF_RETURN_IF_ERROR(
       ImportGraphDef({}, input_graph_def, &graph, &shape_refiner));
 
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
index 059070305f..e7a2da4c53 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
@@ -308,7 +308,7 @@ TEST(RemoteFusedGraphExecuteUtils, PropagateAndBuildTensorShapeMap) {
       NAME_A, NODE_A_VAL, NAME_B, NODE_B_VAL, NAME_A_PLUS_B, &def));
   ImportGraphDefOptions opts;
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   Status status = ImportGraphDef(opts, def, &graph, &shape_refiner);
   ASSERT_TRUE(RemoteFusedGraphExecuteUtils::PropagateShapeInference(
                   def, inputs, &graph, &shape_refiner)
@@ -427,7 +427,7 @@ TEST(RemoteFusedGraphExecuteUtils, BuildRemoteFusedGraphExecuteOpNode) {
       NAME_A, NODE_A_VAL, NAME_B, NODE_B_VAL, NAME_A_PLUS_B, &def));
 
   Graph graph(OpRegistry::Global());
-  ShapeRefiner shape_refiner(graph.versions().producer(), graph.op_registry());
+  ShapeRefiner shape_refiner(graph.versions(), graph.op_registry());
   TF_ASSERT_OK(ImportGraphDef({}, def, &graph, &shape_refiner));
 
   Node* node;
diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc
index 49fcf878d5..1428546d52 100644
--- a/tensorflow/core/kernels/scatter_nd_op.cc
+++ b/tensorflow/core/kernels/scatter_nd_op.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/bounds_check.h"
 #include "tensorflow/core/kernels/dense_update_ops.h"
 #include "tensorflow/core/kernels/fill_functor.h"
+#include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/util/util.h"
diff --git a/tensorflow/core/kernels/serialize_sparse_op.cc b/tensorflow/core/kernels/serialize_sparse_op.cc
index 67234e2a40..2c7ad5bab0 100644
--- a/tensorflow/core/kernels/serialize_sparse_op.cc
+++ b/tensorflow/core/kernels/serialize_sparse_op.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_util.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
diff --git a/tensorflow/core/kernels/sparse_cross_op.cc b/tensorflow/core/kernels/sparse_cross_op.cc
index c7bf250fad..07d935d55f 100644
--- a/tensorflow/core/kernels/sparse_cross_op.cc
+++ b/tensorflow/core/kernels/sparse_cross_op.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/fingerprint.h"
 #include "tensorflow/core/util/work_sharder.h"
 
diff --git a/tensorflow/core/kernels/unique_op_test.cc b/tensorflow/core/kernels/unique_op_test.cc
index 0dc9066273..176280b7a1 100644
--- a/tensorflow/core/kernels/unique_op_test.cc
+++ b/tensorflow/core/kernels/unique_op_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/graph/node_builder.h"
diff --git a/tensorflow/core/kernels/whole_file_read_ops.cc b/tensorflow/core/kernels/whole_file_read_ops.cc
index 8f42bb2832..d7ef4bc4c4 100644
--- a/tensorflow/core/kernels/whole_file_read_ops.cc
+++ b/tensorflow/core/kernels/whole_file_read_ops.cc
@@ -18,11 +18,13 @@ limitations under the License.
 #include <memory>
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/reader_base.h"
+#include "tensorflow/core/framework/reader_base.pb.h"
 #include "tensorflow/core/framework/reader_op_kernel.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/io/buffered_inputstream.h"
 #include "tensorflow/core/lib/io/random_inputstream.h"
+#include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/protobuf.h"
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 557443f229..1b4d5436c0 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/util/mirror_pad_mode.h"
 #include "tensorflow/core/util/padding.h"
 #include "tensorflow/core/util/strided_slice_op.h"
@@ -172,11 +173,11 @@ REGISTER_OP("ParallelConcat")
     .Attr("shape: shape")
     .SetShapeFn([](InferenceContext* c) {
       // Validate that the shape attr is correct.
-      TensorShapeProto passed_shape_proto;
-      TF_RETURN_IF_ERROR(c->GetAttr("shape", &passed_shape_proto));
+      PartialTensorShape shape;
+      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
       ShapeHandle passed_shape;
       TF_RETURN_IF_ERROR(
-          c->MakeShapeFromShapeProto(passed_shape_proto, &passed_shape));
+          c->MakeShapeFromPartialTensorShape(shape, &passed_shape));
       if (!c->FullyDefined(passed_shape)) {
         return errors::InvalidArgument("shape attr must be fully defined.");
       }
@@ -637,11 +638,9 @@ REGISTER_OP("ImmutableConst")
     .SetShapeFn([](InferenceContext* c) {
       TensorShape shape_from_attr;
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_from_attr));
-      TensorShapeProto shape_proto;
-      shape_from_attr.AsProto(&shape_proto);
       ShapeHandle output_shape;
       TF_RETURN_IF_ERROR(
-          c->MakeShapeFromShapeProto(shape_proto, &output_shape));
+          c->MakeShapeFromPartialTensorShape(shape_from_attr, &output_shape));
       c->set_output(0, output_shape);
       return Status::OK();
     })
@@ -1306,11 +1305,11 @@ REGISTER_OP("_ParallelConcatStart")
     .Attr("dtype: type")
     .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
-      TensorShapeProto shape_proto;
-      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_proto));
+      PartialTensorShape shape;
+      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
       ShapeHandle output_shape;
       TF_RETURN_IF_ERROR(
-          c->MakeShapeFromShapeProto(shape_proto, &output_shape));
+          c->MakeShapeFromPartialTensorShape(shape, &output_shape));
       c->set_output(0, output_shape);
       return Status::OK();
     })
@@ -2266,11 +2265,10 @@ REGISTER_OP("StridedSlice")
         return Status::OK();
       }
 
-      TensorShapeProto input_shape_proto;
+      PartialTensorShape input_shape({});
       for (int i = 0; i < c->Rank(input); ++i) {
         auto dim = c->Dim(input, i);
-        input_shape_proto.add_dim()->set_size(c->ValueKnown(dim) ? c->Value(dim)
-                                                                 : -1);
+        input_shape.AddDim(c->ValueKnown(dim) ? c->Value(dim) : -1);
       }
 
       int32 begin_mask, end_mask, ellipsis_mask, new_axis_mask,
@@ -2288,7 +2286,7 @@ REGISTER_OP("StridedSlice")
       bool is_identity, is_simple_slice, slice_dim0;
       gtl::InlinedVector<int64, 4> begin, end, strides;
       TF_RETURN_IF_ERROR(ValidateStridedSliceOp(
-          begin_value, end_value, *strides_value, input_shape_proto, begin_mask,
+          begin_value, end_value, *strides_value, input_shape, begin_mask,
           end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask,
           &processing_shape, &final_shape, &is_identity, &is_simple_slice,
           &slice_dim0, &begin, &end, &strides));
@@ -2866,10 +2864,8 @@ REGISTER_OP("Placeholder")
         return shape_inference::UnknownShape(c);
       }
 
-      TensorShapeProto shape_proto;
-      shape.AsProto(&shape_proto);
       ShapeHandle out;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shape, &out));
       c->set_output(0, out);
       return Status::OK();
     })
@@ -2894,10 +2890,10 @@ REGISTER_OP("PlaceholderV2")
     .Attr("dtype: type")
     .Attr("shape: shape")
     .SetShapeFn([](InferenceContext* c) {
-      TensorShapeProto shape;
+      PartialTensorShape shape;
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
       ShapeHandle output;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape, &output));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shape, &output));
       c->set_output(0, output);
       return Status::OK();
     })
@@ -2925,10 +2921,8 @@ REGISTER_OP("PlaceholderWithDefault")
       ShapeHandle input = c->input(0);
       PartialTensorShape shape;
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
-      TensorShapeProto shape_proto;
-      shape.AsProto(&shape_proto);
       ShapeHandle out;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shape, &out));
 
       // We merge for compatibility checking, but return the output,
       // since output_shape may be less precise than input_shape.
@@ -3899,9 +3893,8 @@ REGISTER_OP("SpaceToDepth")
       TF_RETURN_IF_ERROR(c->Multiply(c->Dim(input, 3), block_size * block_size,
                                      &output_depth));
 
-      c->set_output(0,
-                    c->MakeShape({c->Dim(input, 0), output_height, output_width,
-                                  output_depth}));
+      c->set_output(0, c->MakeShape({c->Dim(input, 0), output_height,
+                                     output_width, output_depth}));
       return Status::OK();
     })
     .Doc(R"doc(
@@ -4005,9 +3998,8 @@ REGISTER_OP("DepthToSpace")
       TF_RETURN_IF_ERROR(c->Divide(c->Dim(input, 3), block_size * block_size,
                                    true /* evenly_divisible */, &output_depth));
 
-      c->set_output(0,
-                    c->MakeShape({c->Dim(input, 0), output_height, output_width,
-                                  output_depth}));
+      c->set_output(0, c->MakeShape({c->Dim(input, 0), output_height,
+                                     output_width, output_depth}));
       return Status::OK();
     })
     .Doc(R"doc(
diff --git a/tensorflow/core/ops/array_ops_test.cc b/tensorflow/core/ops/array_ops_test.cc
index b1d334e454..dc5d46e6fa 100644
--- a/tensorflow/core/ops/array_ops_test.cc
+++ b/tensorflow/core/ops/array_ops_test.cc
@@ -18,6 +18,8 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/framework/shape_inference_testutil.h"
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
diff --git a/tensorflow/core/ops/math_ops_test.cc b/tensorflow/core/ops/math_ops_test.cc
index c10e667f56..28f9969de5 100644
--- a/tensorflow/core/ops/math_ops_test.cc
+++ b/tensorflow/core/ops/math_ops_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference_testutil.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
diff --git a/tensorflow/core/ops/parsing_ops.cc b/tensorflow/core/ops/parsing_ops.cc
index 22f87f5fdf..2e605fdffc 100644
--- a/tensorflow/core/ops/parsing_ops.cc
+++ b/tensorflow/core/ops/parsing_ops.cc
@@ -85,11 +85,10 @@ REGISTER_OP("ParseExample")
       }
 
       // Output dense_shapes.
-      TensorShapeProto shape_proto;
       for (int i = 0; i < attrs.num_dense; ++i) {
-        attrs.dense_shapes[i].AsProto(&shape_proto);
         ShapeHandle dense;
-        TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &dense));
+        TF_RETURN_IF_ERROR(
+            c->MakeShapeFromPartialTensorShape(attrs.dense_shapes[i], &dense));
         TF_RETURN_IF_ERROR(c->Concatenate(input, dense, &dense));
         c->set_output(output_idx++, dense);
       }
@@ -196,11 +195,10 @@ REGISTER_OP("ParseSingleSequenceExample")
       }
 
       // Output context_dense_shapes.
-      TensorShapeProto shape_proto;
       for (int i = 0; i < attrs.num_context_dense; ++i) {
-        attrs.context_dense_shapes[i].AsProto(&shape_proto);
         ShapeHandle s;
-        TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &s));
+        TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(
+            attrs.context_dense_shapes[i], &s));
         c->set_output(output_idx++, s);
       }
 
@@ -218,9 +216,9 @@ REGISTER_OP("ParseSingleSequenceExample")
 
       // Output feature_list_dense_shapes.
       for (int i = 0; i < attrs.num_feature_list_dense; ++i) {
-        attrs.feature_list_dense_shapes[i].AsProto(&shape_proto);
         ShapeHandle s;
-        TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &s));
+        TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(
+            attrs.feature_list_dense_shapes[i], &s));
         TF_RETURN_IF_ERROR(
             c->Concatenate(c->Vector(InferenceContext::kUnknownDim), s, &s));
         c->set_output(output_idx++, s);
diff --git a/tensorflow/core/ops/parsing_ops_test.cc b/tensorflow/core/ops/parsing_ops_test.cc
index 5c29e21d00..c6e521e33e 100644
--- a/tensorflow/core/ops/parsing_ops_test.cc
+++ b/tensorflow/core/ops/parsing_ops_test.cc
@@ -59,25 +59,24 @@ TEST(ParsingOpsTest, DecodeCSV_ShapeFn) {
   INFER_ERROR("Shape of a default must be", op, "?;[2];?");
 }
 
-static std::vector<TensorShapeProto> MakeDenseShapes(int size,
-                                                     bool add_extra_shape,
-                                                     int unknown_outer_dims) {
-  std::vector<TensorShapeProto> shapes(size);
+static std::vector<PartialTensorShape> MakeDenseShapes(int size,
+                                                       bool add_extra_shape,
+                                                       int unknown_outer_dims) {
+  std::vector<PartialTensorShape> shapes(size);
   for (int i = 0; i < size; ++i) {
     // Make shapes be the sequence [?,1]; [?,1,2], [?,1,2,3]...
     // where the number of prefixed ? depends on unknown_outer_dims.
     if (i == 0) {
+      shapes[i].Clear();
       for (int d = 0; d < unknown_outer_dims; ++d) {
-        shapes[i].add_dim()->set_size(-1);
+        shapes[i].AddDim(-1);
       }
     } else {
       shapes[i] = shapes[i - 1];
     }
-    shapes[i].add_dim()->set_size(i + 1);
-  }
-  if (add_extra_shape) {
-    shapes.resize(shapes.size() + 1);
+    shapes[i].AddDim(i + 1);
   }
+  if (add_extra_shape) shapes.push_back(PartialTensorShape({}));
   return shapes;
 }
 
diff --git a/tensorflow/core/ops/resource_variable_ops.cc b/tensorflow/core/ops/resource_variable_ops.cc
index 3b48559b1f..034946f17a 100644
--- a/tensorflow/core/ops/resource_variable_ops.cc
+++ b/tensorflow/core/ops/resource_variable_ops.cc
@@ -68,10 +68,10 @@ REGISTER_OP("VarHandleOp")
       c->set_output(0, c->Scalar());
       DataType t;
       TF_RETURN_IF_ERROR(c->GetAttr("dtype", &t));
-      TensorShapeProto p;
+      PartialTensorShape p;
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &p));
       ShapeHandle s;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(p, &s));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(p, &s));
       c->set_output_handle_shapes_and_types(0,
                                             std::vector<ShapeAndType>{{s, t}});
 
diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc
index 35f965b6a9..1c9ae90a26 100644
--- a/tensorflow/core/ops/state_ops.cc
+++ b/tensorflow/core/ops/state_ops.cc
@@ -30,11 +30,11 @@ REGISTER_OP("VariableV2")
     .Attr("shared_name: string = ''")
     .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
-      TensorShapeProto shape_proto;
-      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_proto));
+      PartialTensorShape shape;
+      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
       ShapeHandle output_shape;
       TF_RETURN_IF_ERROR(
-          c->MakeShapeFromShapeProto(shape_proto, &output_shape));
+          c->MakeShapeFromPartialTensorShape(shape, &output_shape));
       c->set_output(0, output_shape);
       return Status::OK();
     })
@@ -72,10 +72,8 @@ REGISTER_OP("Variable")
         return shape_inference::UnknownShape(c);
       }
 
-      TensorShapeProto shape_proto;
-      shape.AsProto(&shape_proto);
       ShapeHandle out;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shape, &out));
       c->set_output(0, out);
       return Status::OK();
     })
@@ -103,10 +101,10 @@ REGISTER_OP("TemporaryVariable")
     .Attr("var_name: string = ''")
     .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
-      TensorShapeProto shape_proto;
-      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_proto));
+      PartialTensorShape shape;
+      TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
       ShapeHandle output;
-      TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &output));
+      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(shape, &output));
       c->set_output(0, output);
       return Status::OK();
     })
diff --git a/tensorflow/core/ops/state_ops_test.cc b/tensorflow/core/ops/state_ops_test.cc
index bcc1c92493..6d05dd0b96 100644
--- a/tensorflow/core/ops/state_ops_test.cc
+++ b/tensorflow/core/ops/state_ops_test.cc
@@ -63,49 +63,41 @@ TEST(StateOpsTest, ScatterUpdate_ShapeFn) {
 TEST(StateOpsTest, TemporaryVariable_ShapeFn) {
   ShapeInferenceTestOp op("TemporaryVariable");
   TensorShape shape({1, 2, 3});
-  TensorShapeProto shape_proto;
-  shape.AsProto(&shape_proto);
   TF_ASSERT_OK(NodeDefBuilder("test", "TemporaryVariable")
-                   .Attr("shape", shape_proto)
+                   .Attr("shape", shape)
                    .Finalize(&op.node_def));
   INFER_OK(op, "", "[1,2,3]");
 }
 
 TEST(StateOpsTest, Variable_ShapeFn) {
   ShapeInferenceTestOp op("Variable");
-  TensorShapeProto shape_proto;
 
   // Unknown rank.
-  PartialTensorShape().AsProto(&shape_proto);
   TF_ASSERT_OK(NodeDefBuilder("test", "Variable")
-                   .Attr("shape", shape_proto)
+                   .Attr("shape", PartialTensorShape())
                    .Finalize(&op.node_def));
   INFER_OK(op, "", "?");
 
   // For historical reasons an empty TensorShapeProto can be either an unknown
   // rank or a scalar, so the shape function conservatively says "unknown"
-  shape_proto.Clear();
   TF_ASSERT_OK(NodeDefBuilder("test", "Variable")
-                   .Attr("shape", shape_proto)
+                   .Attr("shape", TensorShape({}))
                    .Finalize(&op.node_def));
   INFER_OK(op, "", "?");
 
   // Specified shape.
-  TensorShape({1, 2, 3}).AsProto(&shape_proto);
   TF_ASSERT_OK(NodeDefBuilder("test", "Variable")
-                   .Attr("shape", shape_proto)
+                   .Attr("shape", TensorShape({1, 2, 3}))
                    .Finalize(&op.node_def));
   INFER_OK(op, "", "[1,2,3]");
 }
 
 TEST(StateOpsTest, VariableV2_ShapeFn) {
   ShapeInferenceTestOp op("VariableV2");
-  TensorShapeProto shape_proto;
 
   // Unknown rank.
-  shape_proto.set_unknown_rank(true);
   TF_ASSERT_OK(NodeDefBuilder("test", "VariableV2")
-                   .Attr("shape", shape_proto)
+                   .Attr("shape", PartialTensorShape())
                    .Finalize(&op.node_def));
   INFER_OK(op, "", "?");
 
@@ -116,9 +108,8 @@ TEST(StateOpsTest, VariableV2_ShapeFn) {
   INFER_OK(op, "", "[]");
 
   // Specified shape.
-  TensorShape({1, 2, 3}).AsProto(&shape_proto);
   TF_ASSERT_OK(NodeDefBuilder("test", "VariableV2")
-                   .Attr("shape", shape_proto)
+                   .Attr("shape", TensorShape({1, 2, 3}))
                    .Finalize(&op.node_def));
   INFER_OK(op, "", "[1,2,3]");
 }
diff --git a/tensorflow/core/util/equal_graph_def.cc b/tensorflow/core/util/equal_graph_def.cc
index 45d6a6662a..919a46bfb8 100644
--- a/tensorflow/core/util/equal_graph_def.cc
+++ b/tensorflow/core/util/equal_graph_def.cc
@@ -17,7 +17,9 @@ limitations under the License.
 
 #include <unordered_map>
 #include <unordered_set>
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
diff --git a/tensorflow/core/util/equal_graph_def.h b/tensorflow/core/util/equal_graph_def.h
index 1ce6181c2e..14f5bdfda4 100644
--- a/tensorflow/core/util/equal_graph_def.h
+++ b/tensorflow/core/util/equal_graph_def.h
@@ -16,13 +16,16 @@ limitations under the License.
 #ifndef TENSORFLOW_GRAPH_EQUAL_GRAPH_DEF_H_
 #define TENSORFLOW_GRAPH_EQUAL_GRAPH_DEF_H_
 
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
 
+class GraphDef;
+class NodeDef;
+
 struct EqualGraphDefOptions {
   // Should internal attributes (attribute names that start with '_') be
   // ignored?
diff --git a/tensorflow/core/util/memmapped_file_system_test.cc b/tensorflow/core/util/memmapped_file_system_test.cc
index 1d01c6b083..24ce5ebafc 100644
--- a/tensorflow/core/util/memmapped_file_system_test.cc
+++ b/tensorflow/core/util/memmapped_file_system_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 #include "tensorflow/core/util/memmapped_file_system.h"
 
 #include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/graph/graph_def_builder.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/io/path.h"
diff --git a/tensorflow/core/util/padding.h b/tensorflow/core/util/padding.h
index 6f56d9c25a..2e6003226c 100644
--- a/tensorflow/core/util/padding.h
+++ b/tensorflow/core/util/padding.h
@@ -21,11 +21,13 @@ limitations under the License.
 
 #include <string>
 
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"  // TODO(b/62899350): Remove
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
 
+class NodeDef;
+
 // Padding: the padding we apply to the input tensor along the rows and columns
 // dimensions. This is usually used to make sure that the spatial dimensions do
 // not shrink when we progress with convolutions. Two types of padding are
diff --git a/tensorflow/core/util/stat_summarizer.cc b/tensorflow/core/util/stat_summarizer.cc
index fa59f73581..8447028e38 100644
--- a/tensorflow/core/util/stat_summarizer.cc
+++ b/tensorflow/core/util/stat_summarizer.cc
@@ -22,6 +22,8 @@ limitations under the License.
 #include <string>
 
 #include "tensorflow/core/framework/step_stats.pb.h"
+#include "tensorflow/core/framework/tensor_description.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/logging.h"
@@ -35,6 +37,14 @@ StatSummarizer::StatSummarizer(const StatSummarizerOptions& options)
 StatSummarizer::StatSummarizer(const tensorflow::GraphDef& tensorflow_graph)
     : StatSummarizer(StatSummarizerOptions()) {}
 
+StatSummarizer::~StatSummarizer() {}
+
+void StatSummarizer::Reset() {
+  run_total_us_.Reset();
+  memory_.Reset();
+  details_.clear();
+}
+
 void StatSummarizer::Validate(const Detail* detail,
                               const NodeExecStats& ns) const {
   if (detail->outputs.size() != ns.output_size()) {
diff --git a/tensorflow/core/util/stat_summarizer.h b/tensorflow/core/util/stat_summarizer.h
index 6111e276ea..f7b63e8686 100644
--- a/tensorflow/core/util/stat_summarizer.h
+++ b/tensorflow/core/util/stat_summarizer.h
@@ -154,6 +154,8 @@ class StatSummarizer {
   // GraphDef is not needed by the StatSummarizer.
   explicit StatSummarizer(const tensorflow::GraphDef& tensorflow_graph);
 
+  ~StatSummarizer();
+
   // Adds another run's StepStats output to the aggregate counts.
   void ProcessStepStats(const StepStats& step_stats);
 
@@ -181,11 +183,7 @@ class StatSummarizer {
                                SortingMetric sorting_metric,
                                int num_stats) const;
 
-  void Reset() {
-    run_total_us_.Reset();
-    memory_.Reset();
-    details_.clear();
-  }
+  void Reset();
 
   // Returns number of runs.
   int num_runs() const { return run_total_us_.count(); }
diff --git a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
index 301eae2c4d..41dc9f8a78 100644
--- a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
+++ b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/types.pb_text.h"
 #include "tensorflow/core/framework/versions.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
diff --git a/tensorflow/core/util/tensor_slice_writer_test.cc b/tensorflow/core/util/tensor_slice_writer_test.cc
index be636c04c4..d935eba2e5 100644
--- a/tensorflow/core/util/tensor_slice_writer_test.cc
+++ b/tensorflow/core/util/tensor_slice_writer_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <array>
 
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
diff --git a/tensorflow/examples/adding_an_op/zero_out_op_kernel_2.cc b/tensorflow/examples/adding_an_op/zero_out_op_kernel_2.cc
index 04c34c5968..4a04e5c3c9 100644
--- a/tensorflow/examples/adding_an_op/zero_out_op_kernel_2.cc
+++ b/tensorflow/examples/adding_an_op/zero_out_op_kernel_2.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/shape_inference.h"
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 0c04d36aff..b761391f91 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -233,6 +233,7 @@ cc_library(
         ":numpy_lib",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:script_ops_op_lib",
         "//third_party/py/numpy:headers",
         "//util/python:python_headers",
diff --git a/tensorflow/python/framework/cpp_shape_inference.cc b/tensorflow/python/framework/cpp_shape_inference.cc
index 8ebdbafb85..34f68b4fae 100644
--- a/tensorflow/python/framework/cpp_shape_inference.cc
+++ b/tensorflow/python/framework/cpp_shape_inference.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/python/framework/cpp_shape_inference.pb.h"
diff --git a/tensorflow/python/grappler/model_analyzer.cc b/tensorflow/python/grappler/model_analyzer.cc
index 1374967ca7..4ec7620bce 100644
--- a/tensorflow/python/grappler/model_analyzer.cc
+++ b/tensorflow/python/grappler/model_analyzer.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/python/grappler/model_analyzer.h"
 
 #include <iomanip>
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 
diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc
index c48296eccb..a1618d5349 100644
--- a/tensorflow/python/lib/core/py_func.cc
+++ b/tensorflow/python/lib/core/py_func.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <array>
 
 #include "numpy/arrayobject.h"
+#include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/threadpool.h"
diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc
index 1c4958d83c..8123b0a0c6 100644
--- a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc
+++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/sendrecv_ops.h"
 #include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/test_benchmark.h"
diff --git a/tensorflow/tools/graph_transforms/strip_unused_nodes_test.cc b/tensorflow/tools/graph_transforms/strip_unused_nodes_test.cc
index 4eb074998f..c0107014e2 100644
--- a/tensorflow/tools/graph_transforms/strip_unused_nodes_test.cc
+++ b/tensorflow/tools/graph_transforms/strip_unused_nodes_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/nn_ops.h"
 #include "tensorflow/cc/ops/sendrecv_ops.h"
 #include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
diff --git a/tensorflow/tools/graph_transforms/summarize_graph_main.cc b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
index 91670f54d4..e79e7ba121 100644
--- a/tensorflow/tools/graph_transforms/summarize_graph_main.cc
+++ b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
@@ -23,8 +23,10 @@ limitations under the License.
 // bazel-bin/tensorflow/tools/graph_transforms/summarize_graph \
 // --in_graph=my_graph.pb
 
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/init_main.h"
diff --git a/tensorflow/tools/graph_transforms/transform_utils.h b/tensorflow/tools/graph_transforms/transform_utils.h
index 6ed549a958..2db0a24267 100644
--- a/tensorflow/tools/graph_transforms/transform_utils.h
+++ b/tensorflow/tools/graph_transforms/transform_utils.h
@@ -20,10 +20,12 @@ limitations under the License.
 #include <unordered_set>
 #include <vector>
 
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
-- 
GitLab


From 030054d6ef50d5ba4b1efdcc8309c187b31b07a1 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Thu, 29 Jun 2017 12:19:02 -0700
Subject: [PATCH 0595/1375] Load py_test in
 tensorflow/contrib/boosted_trees/BUILD to fix pip test visibility failures.

(Bad manual merge)
---
 tensorflow/contrib/boosted_trees/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 7c8721e8f4..4419b92d7f 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -7,6 +7,7 @@ package(default_visibility = [
     "//visibility:public",
 ])
 
+load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
-- 
GitLab


From 2bf202b6566a91e75ba69e4aeb5c6122e6d7b64e Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Thu, 29 Jun 2017 12:40:33 -0700
Subject: [PATCH 0596/1375] Manually remove duplicate versions of Cosh and
 Sinh, presumably from a faulty auto merge.

---
 tensorflow/go/op/wrappers.go | 30 ------------------------------
 1 file changed, 30 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index ac2949cb07..e504d22a0e 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14388,36 +14388,6 @@ func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
-// Computes hyperbolic sine of x element-wise.
-func Sinh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Sinh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes hyperbolic cosine of x element-wise.
-func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Cosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes hyperbolic tangent of `x` element-wise.
 func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From 4c15e948e0e4a17329b2363a5f8f3d4b4178ef7b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 12:55:08 -0700
Subject: [PATCH 0597/1375] Implement BatchNorm HLOs on PARALLEL_CPU.

Perhaps surprisingly, implementing
HloInstruction::CloneWithNewOperands was sufficient to pass the
checked in test cases.
PiperOrigin-RevId: 160561399
---
 .../compiler/xla/service/hlo_instruction.cc   |  6 ++++-
 .../xla/tests/batch_normalization_test.cc     | 25 ++++++++-----------
 2 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 379d14adc6..c7708968a2 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -878,8 +878,12 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CloneFusionWithNewOperands(shape, new_operands);
     case HloOpcode::kParameter:
       return CreateParameter(parameter_number_, shape, parameter_name_);
-    // Unsupported ops for cloning.
     case HloOpcode::kBatchNormTraining:
+      CHECK_EQ(new_operands.size(), 3);
+      return CreateBatchNormTraining(shape, new_operands[0], new_operands[1],
+                                     new_operands[2], epsilon(),
+                                     feature_index());
+    // Unsupported ops for cloning.
     case HloOpcode::kRecv:
     case HloOpcode::kSend:
     case HloOpcode::kUpdate:
diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index 4afeb68b5f..98bf2063c2 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -211,9 +211,8 @@ class BatchNormTest : public ClientLibraryTestBase,
                       public ::testing::WithParamInterface<BatchNormTestParam> {
 };
 
-// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
-XLA_TEST_P(BatchNormTest,
-           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(RandomizedTests))) {
+// TODO(b/62764704): Implement on GPU. Disabled on 2017-06-20.
+XLA_TEST_P(BatchNormTest, DISABLED_ON_GPU(RandomizedTests)) {
   float epsilon = 0.001;
   ComputationBuilder builder(client_, TestName());
   const std::vector<int64>& bounds = GetParam().bounds;
@@ -329,9 +328,8 @@ INSTANTIATE_TEST_CASE_P(
                       // is correct after relayout.
                       BatchNormTestParam{{1, 2, 3, 4}, 0, 100, 100}));
 
-// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
-XLA_TEST_F(BatchNormTest,
-           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(BasicTraining))) {
+// TODO(b/62764704): Implement on GPU. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(BasicTraining)) {
   const int kFeatureIndex = 3;
   ComputationBuilder builder(client_, TestName());
 
@@ -355,9 +353,8 @@ XLA_TEST_F(BatchNormTest,
   ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
 }
 
-// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
-XLA_TEST_F(BatchNormTest,
-           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(BasicTrainingOnSublane))) {
+// TODO(b/62764704): Implement on GPU. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(BasicTrainingOnSublane)) {
   const int kFeatureIndex = 2;
   ComputationBuilder builder(client_, TestName());
 
@@ -381,9 +378,8 @@ XLA_TEST_F(BatchNormTest,
   ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
 }
 
-// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
-XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(
-                              TrainingWithFeatureOnLowDimension))) {
+// TODO(b/62764704): Implement on GPU. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(TrainingWithFeatureOnLowDimension)) {
   // Use 0 dimension as feature, tests layout analyzer.
   const int kFeatureIndex = 0;
   ComputationBuilder builder(client_, TestName());
@@ -415,9 +411,8 @@ XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(
                          ErrorSpec(0.1));
 }
 
-// TODO(b/62764704): Implement on GPU and CPU_PARALLEL. Disabled on 2017-06-20.
-XLA_TEST_F(BatchNormTest,
-           DISABLED_ON_GPU(DISABLED_ON_CPU_PARALLEL(LargeEpsilonTest))) {
+// TODO(b/62764704): Implement on GPU. Disabled on 2017-06-20.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(LargeEpsilonTest)) {
   // Test the correctness of choosing a large epsilon value.
   const int kFeatureIndex = 2;
   ComputationBuilder builder(client_, TestName());
-- 
GitLab


From 9e52c3c23d7d232013ebe22918c8a15634e88dc5 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Thu, 29 Jun 2017 13:11:27 -0700
Subject: [PATCH 0598/1375] Reverts the unnecessary check due to the github
 merge.

PiperOrigin-RevId: 160563344
---
 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 0f15133d74..168726a6b3 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -171,6 +171,7 @@ class TpuEstimator(estimator_lib.Estimator):
   Note: TpuEstimator transforms a global batch size in params to a per-shard
         batch size when calling the input_fn.
   """
+
   def __init__(self,
                model_fn=None,
                model_dir=None,
@@ -195,8 +196,6 @@ class TpuEstimator(estimator_lib.Estimator):
             .format(params[_BATCH_SIZE_KEY], config.tpu_config.num_shards))
 
     if use_tpu:
-      if not isinstance(config, tpu_config.RunConfig):
-        raise ValueError('`config` must be `tpu_config.RunConfig`')
       # Verifies the model_fn signature according to Estimator framework.
       estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
       # We cannot store config and params in this constructor as parent
@@ -205,6 +204,7 @@ class TpuEstimator(estimator_lib.Estimator):
       model_function = wrapped_model_fn(model_fn)
     else:
       model_function = model_fn
+
     super(TpuEstimator, self).__init__(
         model_fn=model_function,
         model_dir=model_dir,
-- 
GitLab


From 8be70f540bf57688c31eb807ff0d50e2186eb02f Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 29 Jun 2017 13:58:34 -0700
Subject: [PATCH 0599/1375] Avoid taking a reference on a temporary return
 value

PiperOrigin-RevId: 160569305
---
 tensorflow/core/common_runtime/direct_session.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index 7bc48aba27..db7c5109c8 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -1333,7 +1333,7 @@ Status DirectSession::CreateGraphs(
 
   // Check for valid partitions.
   for (const auto& partition : partitions) {
-    const string& local_partition_name =
+    const string local_partition_name =
         DeviceNameUtils::LocalName(partition.first);
     if (std::count(device_names.begin(), device_names.end(),
                    local_partition_name) == 0) {
-- 
GitLab


From 62c1104e766c15c71f1fa53145503899eb470d62 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 14:03:26 -0700
Subject: [PATCH 0600/1375] Renable a subset of test cases in
 run_metadata_test.py

The flaky test case is due to RunMetadata randomly missing
gpu kernel execution time, the reason of which is still being
investigated. Enable the test to avoid other behaviors got
broken as well.

PiperOrigin-RevId: 160570056
---
 .../contrib/tfprof/python/tools/tfprof/internal/BUILD       | 3 ---
 .../python/tools/tfprof/internal/run_metadata_test.py       | 6 ++++++
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
index ca60e504ed..a498574a95 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
@@ -60,10 +60,7 @@ cuda_py_test(
         "//tensorflow/tools/tfprof:protos_all_py",
     ],
     tags = [
-        "manual",
         "no_pip",
-        "noguitar",
-        "notap",
     ],
 )
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
index a472ade362..8f3351999f 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
@@ -146,6 +146,10 @@ class RunMetadataTest(test.TestCase):
       self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
       self.assertEqual(mm_node.exec_micros, total_cpu_execs)
 
+  # pylint: disable=pointless-string-statement
+  """
+  TODO(xpan): This test is flaky because RunMetadata returned from TensorFlow
+  is random. Still being investigated.
   def testLoopGPU(self):
     if not test.is_gpu_available():
       return
@@ -180,6 +184,8 @@ class RunMetadataTest(test.TestCase):
       self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
       self.assertEqual(mm_node.exec_micros,
                        total_cpu_execs + total_accelerator_execs)
+  """
+
 
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From f6c4e6bca7e69c29193ca2c92349cb430a34a111 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Thu, 29 Jun 2017 14:22:21 -0700
Subject: [PATCH 0601/1375] Properly merge reduce_window_test targets.

---
 tensorflow/compiler/xla/tests/BUILD | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index c7578115cd..2ff1eea1a5 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -878,11 +878,6 @@ cc_library(
     name = "reduce_window_test_library",
     testonly = 1,
     srcs = ["reduce_window_test.cc"],
-)
-
-cc_library(
-    name = "reduce_window_test_deps",
-    testonly = 1,
     deps = [
         ":test_macros_header",
         "//tensorflow/compiler/xla:array2d",
-- 
GitLab


From a9b1a56aee08fc7b501c42f394b41cab98d61ffa Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Thu, 29 Jun 2017 14:29:56 -0700
Subject: [PATCH 0602/1375] Propagate nomac tag again.

---
 tensorflow/contrib/boosted_trees/BUILD | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 4419b92d7f..c1ce8bfa2e 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -73,6 +73,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/ensemble_optimizer_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
     deps = [
         ":ensemble_optimizer_ops_py",
         ":model_ops_py",
@@ -91,6 +92,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/model_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
     deps = [
         ":ensemble_optimizer_ops_py",
         ":model_ops_py",
@@ -111,6 +113,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/prediction_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
     deps = [
         ":model_ops_py",
         ":prediction_ops_py",
@@ -129,6 +132,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/quantile_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
     deps = [
         ":quantile_ops_py",
         "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
@@ -159,6 +163,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/stats_accumulator_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
     deps = [
         ":stats_accumulator_ops_py",
         "//tensorflow/python:framework_test_lib",
@@ -174,6 +179,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/training_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63097632
     deps = [
         ":model_ops_py",
         ":training_ops_py",
-- 
GitLab


From 2d7c62e2c16b673fa2e60cecd45cf9a7bed8f01c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 14:48:36 -0700
Subject: [PATCH 0603/1375] Multiclass in batch (full hessian)

PiperOrigin-RevId: 160576592
---
 tensorflow/contrib/boosted_trees/BUILD        |   1 +
 .../kernels/split_handler_ops.cc              |  21 ++-
 .../boosted_trees/ops/split_handler_ops.cc    |   8 +-
 .../ops/stats_accumulator_ops.cc              |   8 +-
 .../kernel_tests/split_handler_ops_test.py    | 148 +++++++++++++++++-
 .../python/ops/batch_ops_utils.py             |  15 +-
 .../python/ops/stats_accumulator_ops.py       |   2 +-
 7 files changed, 179 insertions(+), 24 deletions(-)

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 7c8721e8f4..90348d0b22 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -146,6 +146,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":split_handler_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
index dcc263d810..a2bf374cf5 100644
--- a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
@@ -37,6 +37,7 @@ namespace tensorflow {
 using boosted_trees::learner::SplitInfo;
 using boosted_trees::learner::stochastic::GradientStats;
 using boosted_trees::learner::stochastic::NodeStats;
+using boosted_trees::learner::LearnerConfig_MultiClassStrategy;
 
 class BaseBuildSplitOp : public OpKernel {
  public:
@@ -53,16 +54,24 @@ class BaseBuildSplitOp : public OpKernel {
                                              &tree_complexity_regularization_));
     OP_REQUIRES_OK(context,
                    context->GetAttr("min_node_weight", &min_node_weight_));
+
+    int strategy;
+    OP_REQUIRES_OK(context, context->GetAttr("multiclass_strategy", &strategy));
+    OP_REQUIRES(
+        context,
+        boosted_trees::learner::LearnerConfig_MultiClassStrategy_IsValid(
+            strategy),
+        errors::InvalidArgument("Wrong multiclass strategy passed."));
+    multiclass_strategy_ = LearnerConfig_MultiClassStrategy(strategy);
   }
 
   NodeStats ComputeNodeStats(const GradientStats& grad_stats) {
-    return NodeStats(
-        l1_regularization_, l2_regularization_, min_node_weight_,
-        boosted_trees::learner::LearnerConfig_MultiClassStrategy_TREE_PER_CLASS,
-        grad_stats);
+    return NodeStats(l1_regularization_, l2_regularization_, min_node_weight_,
+                     multiclass_strategy_, grad_stats);
   }
 
  protected:
+  LearnerConfig_MultiClassStrategy multiclass_strategy_;
   int32 feature_column_group_id_;
   float l1_regularization_;
   float l2_regularization_;
@@ -159,7 +168,7 @@ class BuildDenseInequalitySplitsOp : public BaseBuildSplitOp {
       int32 best_bucket_idx = 0;
       NodeStats best_right_node_stats(0);
       NodeStats best_left_node_stats(0);
-      GradientStats left_gradient_stats(0, 0);
+      GradientStats left_gradient_stats;
       for (int64 bucket_idx = start_index; bucket_idx < end_index;
            ++bucket_idx) {
         GradientStats g(*gradients_t, *hessians_t, bucket_idx);
@@ -301,7 +310,7 @@ class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
       int32 best_bucket_idx = 0;
       NodeStats best_right_node_stats(0);
       NodeStats best_left_node_stats(0);
-      GradientStats left_gradient_stats(0, 0);
+      GradientStats left_gradient_stats;
       bool default_right = false;
       for (int64 bucket_idx = start_index + 1; bucket_idx < end_index;
            ++bucket_idx) {
diff --git a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
index d32507127c..95ec738796 100644
--- a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
+++ b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
@@ -28,6 +28,7 @@ REGISTER_OP("BuildDenseInequalitySplits")
     .Attr("l2_regularization: float")
     .Attr("tree_complexity_regularization: float")
     .Attr("min_node_weight: float")
+    .Attr("multiclass_strategy: int")
     .Input("num_minibatches: int64")
     .Input("partition_ids: int32")
     .Input("bucket_ids: int64")
@@ -57,7 +58,7 @@ REGISTER_OP("BuildDenseInequalitySplits")
       TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
                                   c->Dim(hessians_shape, 0), &unused_dim));
       ShapeHandle bucket_boundaries_shape;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &bucket_boundaries_shape));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 1, &bucket_boundaries_shape));
       c->set_output(0, c->Vector(c->UnknownDim()));
       c->set_output(1, c->Vector(c->UnknownDim()));
       c->set_output(2, c->Vector(c->UnknownDim()));
@@ -87,6 +88,7 @@ REGISTER_OP("BuildSparseInequalitySplits")
     .Attr("l2_regularization: float")
     .Attr("tree_complexity_regularization: float")
     .Attr("min_node_weight: float")
+    .Attr("multiclass_strategy: int")
     .Input("num_minibatches: int64")
     .Input("partition_ids: int32")
     .Input("bucket_ids: int64")
@@ -116,7 +118,7 @@ REGISTER_OP("BuildSparseInequalitySplits")
       TF_RETURN_IF_ERROR(c->Merge(c->Dim(partition_ids_shape, 0),
                                   c->Dim(hessians_shape, 0), &unused_dim));
       ShapeHandle bucket_boundaries_shape;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 1, &bucket_boundaries_shape));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 1, &bucket_boundaries_shape));
       c->set_output(0, c->Vector(c->UnknownDim()));
       c->set_output(1, c->Vector(c->UnknownDim()));
       c->set_output(2, c->Vector(c->UnknownDim()));
@@ -146,6 +148,7 @@ REGISTER_OP("BuildCategoricalEqualitySplits")
     .Attr("l2_regularization: float")
     .Attr("tree_complexity_regularization: float")
     .Attr("min_node_weight: float")
+    .Attr("multiclass_strategy: int")
     .Input("num_minibatches: int64")
     .Input("partition_ids: int32")
     .Input("feature_ids: int64")
@@ -195,3 +198,4 @@ split_infos: A rank 1 tensor of serialized protos which contains the
 )doc");
 
 }  // namespace tensorflow
+   // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
index 9fa5ec9a83..b986899be2 100644
--- a/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
+++ b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc
@@ -339,8 +339,8 @@ REGISTER_OP("StatsAccumulatorTensorFlush")
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_input));
       // num_updates
       c->set_output(0, c->Scalar());
-      c->set_output(1, c->Matrix(c->UnknownDim(), 1));
-      c->set_output(2, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(1, c->Vector(c->UnknownDim()));
+      c->set_output(2, c->Vector(c->UnknownDim()));
       c->set_output(3, c->UnknownShape());
       c->set_output(4, c->UnknownShape());
       return Status::OK();
@@ -425,8 +425,8 @@ REGISTER_OP("StatsAccumulatorTensorSerialize")
       c->set_output(0, c->Scalar());
       // num_updates
       c->set_output(1, c->Scalar());
-      c->set_output(2, c->Matrix(c->UnknownDim(), 1));
-      c->set_output(3, c->Matrix(c->UnknownDim(), 1));
+      c->set_output(2, c->Vector(c->UnknownDim()));
+      c->set_output(3, c->Vector(c->UnknownDim()));
       c->set_output(4, c->UnknownShape());
       c->set_output(5, c->UnknownShape());
       return Status::OK();
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
index 3ab02db89a..aa504d1742 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
 from tensorflow.contrib.boosted_trees.proto import split_info_pb2
 from tensorflow.contrib.boosted_trees.python.ops import split_handler_ops
 from tensorflow.python.framework import dtypes
@@ -53,7 +54,8 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               l2_regularization=1,
               tree_complexity_regularization=0,
               min_node_weight=0,
-              feature_column_group_id=0))
+              feature_column_group_id=0,
+              multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = sess.run([partitions, gains, splits])
     self.assertAllEqual([0, 1], partitions)
 
@@ -102,6 +104,45 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
     self.assertEqual(0, split_node.feature_column)
     self.assertAllClose(0.52, split_node.threshold, 0.00001)
 
+  def testMakeMulticlassDenseSplit(self):
+    """Tests split handler op."""
+    with self.test_session() as sess:
+      partition_ids = array_ops.constant([0, 0, 1], dtype=dtypes.int32)
+      bucket_ids = array_ops.constant([0, 1, 1], dtype=dtypes.int64)
+      gradients = array_ops.constant([[2.4, 3.0], [-0.6, 0.1], [8.0, 1.0]])
+      hessians = array_ops.constant([[[0.4, 1], [1, 1]], [[0.38, 1], [1, 1]],
+                                     [[0.26, 1], [1, 1]]])
+      bucket_boundaries = [0.3, 0.52]
+      partitions, gains, splits = (
+          split_handler_ops.build_dense_inequality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              bucket_ids=bucket_ids,
+              gradients=gradients,
+              hessians=hessians,
+              bucket_boundaries=bucket_boundaries,
+              l1_regularization=0,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    self.assertAllEqual([0, 1], partitions)
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+    self.assertEqual(0, split_node.feature_column)
+    self.assertAllClose(0.3, split_node.threshold, 1e-6)
+
   def testMakeDenseSplitEmptyInputs(self):
     """Tests empty inputs op."""
     with self.test_session() as sess:
@@ -122,7 +163,8 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               l2_regularization=1,
               tree_complexity_regularization=0,
               min_node_weight=0,
-              feature_column_group_id=0))
+              feature_column_group_id=0,
+              multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = sess.run([partitions, gains, splits])
     # .assertEmpty doesn't exist on ubuntu-contrib
     self.assertEqual(0, len(partitions))
@@ -157,7 +199,8 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               tree_complexity_regularization=0,
               min_node_weight=0,
               feature_column_group_id=0,
-              bias_feature_id=-1))
+              bias_feature_id=-1,
+              multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = (sess.run([partitions, gains, splits]))
     self.assertAllEqual([0, 1], partitions)
     # Check the split on partition 0.
@@ -209,6 +252,53 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
 
     self.assertAllClose(0.52, split_node.split.threshold)
 
+  def testMakeMulticlassSparseSplit(self):
+    """Tests split handler op."""
+    with self.test_session() as sess:
+      partition_ids = array_ops.constant([0, 0, 0, 1, 1], dtype=dtypes.int32)
+    bucket_ids = array_ops.constant([-1, 0, 1, -1, 1], dtype=dtypes.int64)
+    gradients = array_ops.constant([[1.8, 3.5], [2.4, 1.0], [0.4, 4.0],
+                                    [8.0, 3.1], [8.0, 0.8]])
+
+    hessian_0 = [[0.78, 1], [12, 1]]
+    hessian_1 = [[0.4, 1], [1, 1]]
+    hessian_2 = [[0.24, 1], [1, 1]]
+    hessian_3 = [[0.26, 1], [1, 1]]
+    hessian_4 = [[0.26, 1], [1, 1]]
+
+    hessians = array_ops.constant(
+        [hessian_0, hessian_1, hessian_2, hessian_3, hessian_4])
+    bucket_boundaries = array_ops.constant([0.3, 0.52])
+    partitions, gains, splits = (
+        split_handler_ops.build_sparse_inequality_splits(
+            num_minibatches=2,
+            partition_ids=partition_ids,
+            bucket_ids=bucket_ids,
+            gradients=gradients,
+            hessians=hessians,
+            bucket_boundaries=bucket_boundaries,
+            l1_regularization=0,
+            l2_regularization=2,
+            tree_complexity_regularization=0,
+            min_node_weight=0,
+            feature_column_group_id=0,
+            bias_feature_id=-1,
+            multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN))
+    partitions, gains, splits = (sess.run([partitions, gains, splits]))
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_right
+
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+
+    self.assertEqual(0, split_node.split.feature_column)
+    self.assertAllClose(0.52, split_node.split.threshold)
+
   def testMakeCategoricalEqualitySplit(self):
     """Tests split handler op for categorical equality split."""
     with self.test_session() as sess:
@@ -235,7 +325,8 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               tree_complexity_regularization=0,
               min_node_weight=0,
               feature_column_group_id=0,
-              bias_feature_id=-1))
+              bias_feature_id=-1,
+              multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = sess.run([partitions, gains, splits])
     self.assertAllEqual([0, 1], partitions)
 
@@ -300,6 +391,52 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
 
     self.assertEqual(1, split_node.feature_id)
 
+  def testMakeMulticlassCategoricalEqualitySplit(self):
+    """Tests split handler op for categorical equality split in multiclass."""
+    with self.test_session() as sess:
+      gradients = array_ops.constant([[1.8, 3.5], [2.4, 1.0], [0.4, 4.0],
+                                      [9.0, 3.1], [3.0, 0.8]])
+
+      hessian_0 = [[0.78, 1], [12, 1]]
+      hessian_1 = [[0.4, 1], [1, 1]]
+      hessian_2 = [[0.24, 1], [1, 1]]
+      hessian_3 = [[0.16, 2], [-1, 1]]
+      hessian_4 = [[0.6, 1], [2, 1]]
+
+      hessians = array_ops.constant(
+          [hessian_0, hessian_1, hessian_2, hessian_3, hessian_4])
+      partition_ids = [0, 0, 0, 1, 1]
+      feature_ids = array_ops.constant([-1, 1, 2, -1, 1], dtype=dtypes.int64)
+      partitions, gains, splits = (
+          split_handler_ops.build_categorical_equality_splits(
+              num_minibatches=2,
+              partition_ids=partition_ids,
+              feature_ids=feature_ids,
+              gradients=gradients,
+              hessians=hessians,
+              l1_regularization=0.1,
+              l2_regularization=1,
+              tree_complexity_regularization=0,
+              min_node_weight=0,
+              feature_column_group_id=0,
+              bias_feature_id=-1,
+              multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN))
+      partitions, gains, splits = sess.run([partitions, gains, splits])
+    self.assertAllEqual([0, 1], partitions)
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+
+    self.assertEqual(0, split_node.feature_column)
+    self.assertEqual(1, split_node.feature_id)
+
   def testMakeCategoricalEqualitySplitEmptyInput(self):
     with self.test_session() as sess:
       gradients = []
@@ -318,7 +455,8 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               tree_complexity_regularization=0,
               min_node_weight=0,
               feature_column_group_id=0,
-              bias_feature_id=-1))
+              bias_feature_id=-1,
+              multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = (sess.run([partitions, gains, splits]))
     self.assertEqual(0, len(partitions))
     self.assertEqual(0, len(gains))
diff --git a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
index e9e6a5e6ed..23168bf493 100644
--- a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
+++ b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py
@@ -68,11 +68,12 @@ def _move_tensors(tensors, device):
         return array_ops.unstack(values)
     else:
       with ops.device(tensors[0].device):
-        sizes = array_ops.stack([array_ops.size(tensor) for tensor in tensors])
+        sizes = array_ops.stack(
+            [array_ops.shape(tensor)[0] for tensor in tensors])
         values = array_ops.concat(tensors, axis=0)
       with ops.device(device):
         sizes = array_ops.unstack(sizes)
-        return list(array_ops.split(values, sizes))
+        return list(array_ops.split(values, sizes, axis=0))
 
 
 def _scheduled_stamp_resource_op_runner(batch, stamp):
@@ -90,8 +91,10 @@ def _scheduled_stamp_resource_op_runner(batch, stamp):
       grouped_args[key].append(op.args[key])
     resource_handles.append(op.resource_handle)
   # Move all the inputs to the op device in one RPC.
-  grouped_args = {k: _move_tensors(v, resource_handles[0].device)
-                  for k, v in grouped_args.items()}
+  grouped_args = {
+      k: _move_tensors(v, resource_handles[0].device)
+      for k, v in grouped_args.items()
+  }
   with ops.device(resource_handles[0].device):
     return batch[0].op(resource_handles, stamp, **grouped_args)
 
@@ -110,8 +113,8 @@ def run_handler_scheduled_ops(per_handler_ops, stamp, worker_device):
     results = batch[0].batch_runner_fn()(batch, stamp)
     # If the result is a tuple, move each entry in the tuple in one RPC.
     if isinstance(results, tuple):
-      results = tuple(_move_tensors(result, worker_device) for result in
-                      results)
+      results = tuple(
+          _move_tensors(result, worker_device) for result in results)
       # Once all the results are on the worker, create individual tuple for
       # each scheduled op request.
       for i in range(len(batch)):
diff --git a/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
index 9e6ef1a270..ea6c4275f1 100644
--- a/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
+++ b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
@@ -78,7 +78,7 @@ class StatsAccumulator(saver.BaseSaverBuilder.SaveableObject):
             self._resource_handle, stamp_token, gradient_shape.as_list(),
             hessian_shape.as_list())
         is_initialized_op = (
-            gen_stats_accumulator_ops.stats_accumulator_scalar_is_initialized(
+            gen_stats_accumulator_ops.stats_accumulator_tensor_is_initialized(
                 self._resource_handle))
 
     self._create_op = create_op
-- 
GitLab


From 4aa7c4d2330ce110b5be348144ee67143841272c Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Thu, 29 Jun 2017 15:18:14 -0700
Subject: [PATCH 0604/1375] [XLA] Move the flag from buffer_assignment_flags to
 debug_options_flags

PiperOrigin-RevId: 160580780
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 11 ----
 .../legacy_flags/buffer_assignment_flags.cc   | 63 -------------------
 .../legacy_flags/buffer_assignment_flags.h    | 46 --------------
 .../xla/legacy_flags/debug_options_flags.cc   |  7 +++
 tensorflow/compiler/xla/service/BUILD         |  1 -
 .../compiler/xla/service/buffer_assignment.cc | 20 +++---
 .../compiler/xla/service/buffer_assignment.h  |  3 +-
 tensorflow/compiler/xla/xla.proto             |  3 +
 10 files changed, 21 insertions(+), 136 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 20057c1ae4..12179b7637 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -127,7 +127,6 @@ cc_library(
     deps = [
         ":tfcompile_lib",
         ":tfcompile_proto",
-        "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/compiler/xla/legacy_flags:util_flags",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 1298ac1ae5..12825344d5 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/flags.h"
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
@@ -131,7 +130,6 @@ int main(int argc, char** argv) {
 
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
-  xla::legacy_flags::AppendBufferAssignmentFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::legacy_flags::AppendServiceFlags(&flag_list);
   xla::legacy_flags::AppendUtilFlags(&flag_list);
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 27b1d0c3bb..1fdb6d59cf 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -102,17 +102,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "buffer_assignment_flags",
-    srcs = ["buffer_assignment_flags.cc"],
-    hdrs = ["buffer_assignment_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "backend_flags",
     srcs = ["backend_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.cc b/tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.cc
deleted file mode 100644
index 71873f73af..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's buffer_assignment module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static BufferAssignmentFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new BufferAssignmentFlags;
-  flags->xla_enable_buffer_reuse = true;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_enable_buffer_reuse",
-                       &flags->xla_enable_buffer_reuse,
-                       "Enable reuse of buffers."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's buffer_assignment
-// module.
-void AppendBufferAssignmentFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the BufferAssignmentFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-BufferAssignmentFlags* GetBufferAssignmentFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h b/tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h
deleted file mode 100644
index 5f098c2663..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_BUFFER_ASSIGNMENT_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_BUFFER_ASSIGNMENT_FLAGS_H_
-
-// Legacy flags for XLA's buffer_assignment module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's buffer_assignment
-// module.
-void AppendBufferAssignmentFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's buffer_assignment module.
-typedef struct {
-  bool xla_enable_buffer_reuse;  // Enable reuse of buffers.
-} BufferAssignmentFlags;
-
-// Return a pointer to the BufferAssignmentFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-BufferAssignmentFlags* GetBufferAssignmentFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_BUFFER_ASSIGNMENT_FLAGS_H_
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 3d5ea4d32a..e9b85ef7f5 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -42,6 +42,7 @@ struct DebugOptionsFlags {
   string xla_dump_ir_to;
   string xla_dump_debug_json_to;
   bool xla_eliminate_hlo_implicit_broadcast;
+  bool xla_enable_buffer_reuse;
 
   bool xla_cpu_multi_thread_eigen;
 
@@ -78,6 +79,7 @@ void AllocateFlags() {
   flag_values->xla_dump_ir_to = "";
   flag_values->xla_dump_debug_json_to = "";
   flag_values->xla_eliminate_hlo_implicit_broadcast = false;
+  flag_values->xla_enable_buffer_reuse = true;
   flag_values->xla_cpu_multi_thread_eigen = true;
   flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
   flag_values->xla_gpu_ftz = false;
@@ -144,6 +146,9 @@ void AllocateFlags() {
                         "Eliminate implicit broadcasts when lowering user "
                         "computations to HLO instructions; use explicit "
                         "broadcast instead."),
+       tensorflow::Flag("xla_enable_buffer_reuse",
+                        &flag_values->xla_enable_buffer_reuse,
+                        "Enable reuse of buffers between HLO operations."),
        tensorflow::Flag("xla_cpu_multi_thread_eigen",
                         &flag_values->xla_cpu_multi_thread_eigen,
                         "When generating calls to Eigen in the CPU backend, "
@@ -201,7 +206,9 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
   options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
   options.set_xla_eliminate_hlo_implicit_broadcast(
       flag_values->xla_eliminate_hlo_implicit_broadcast);
+  options.set_xla_enable_buffer_reuse(flag_values->xla_enable_buffer_reuse);
   options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
+
   options.set_xla_cpu_multi_thread_eigen(
       flag_values->xla_cpu_multi_thread_eigen);
   options.set_xla_gpu_cuda_data_dir(flag_values->xla_gpu_cuda_data_dir);
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 2a74fd1961..1e7c36758c 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -724,7 +724,6 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:buffer_assignment_flags",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
     ],
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index 78db842f0b..2f83fd82ec 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -22,7 +22,6 @@ limitations under the License.
 #include <ostream>
 #include <utility>
 
-#include "tensorflow/compiler/xla/legacy_flags/buffer_assignment_flags.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/heap_simulator.h"
@@ -670,7 +669,8 @@ bool BufferAssigner::MaybeAssignBuffer(BufferAllocation* allocation,
 }
 
 Status BufferAssigner::AssignBuffersForComputation(
-    const HloComputation* computation, bool is_thread_local,
+    const HloComputation* computation, const DebugOptions& debug_options,
+    bool is_thread_local,
     const FlatSet<const LogicalBuffer*>& colocated_buffers,
     const FlatSet<BufferAllocation::Index>& colocated_allocations,
     FlatMap<const HloComputation*, FlatSet<const LogicalBuffer*>>*
@@ -794,9 +794,7 @@ Status BufferAssigner::AssignBuffersForComputation(
       continue;
     }
 
-    legacy_flags::BufferAssignmentFlags* flags =
-        legacy_flags::GetBufferAssignmentFlags();
-    if (!flags->xla_enable_buffer_reuse || is_thread_local ||
+    if (!debug_options.xla_enable_buffer_reuse() || is_thread_local ||
         instruction->opcode() == HloOpcode::kCustomCall) {
       // Custom call operations never have reusable buffers. Also we do not
       // reuse thread-local buffers for now, because they are dynamically
@@ -1364,9 +1362,9 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
       buffers_to_assign_sequentially;
   for (auto* computation : global_computations) {
     TF_RETURN_IF_ERROR(AssignBuffersForComputation(
-        computation, /*is_thread_local=*/false, colocated_buffers,
-        colocated_allocations, &buffers_to_assign_sequentially,
-        assignment.get()));
+        computation, module->config().debug_options(),
+        /*is_thread_local=*/false, colocated_buffers, colocated_allocations,
+        &buffers_to_assign_sequentially, assignment.get()));
   }
   // Assign buffers with sequential ordering, if any. If all global computations
   // are sequential, we can run heap simuation on the whole module, which
@@ -1382,9 +1380,9 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
   for (auto* computation : thread_local_computations) {
     TF_RET_CHECK(computation != module->entry_computation());
     TF_RETURN_IF_ERROR(AssignBuffersForComputation(
-        computation, /*is_thread_local=*/true, colocated_buffers,
-        colocated_allocations, /*buffers_to_assign_sequentially=*/nullptr,
-        assignment.get()));
+        computation, module->config().debug_options(),
+        /*is_thread_local=*/true, colocated_buffers, colocated_allocations,
+        /*buffers_to_assign_sequentially=*/nullptr, assignment.get()));
   }
 
   // Mark all buffers which may be live out of the entry computation as
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h
index f02ad58dfa..c95ced7a36 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.h
+++ b/tensorflow/compiler/xla/service/buffer_assignment.h
@@ -450,7 +450,8 @@ class BufferAssigner {
   // true, then all assigned buffers have the is_thread_local flag set to
   // true.
   Status AssignBuffersForComputation(
-      const HloComputation* computation, bool is_thread_local,
+      const HloComputation* computation, const DebugOptions& debug_options,
+      bool is_thread_local,
       const tensorflow::gtl::FlatSet<const LogicalBuffer*>& colocated_buffers,
       const tensorflow::gtl::FlatSet<BufferAllocation::Index>&
           colocated_allocations,
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 15fa1255ae..43dac085d4 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -99,6 +99,9 @@ message DebugOptions {
   // the generated IR.
   bool xla_llvm_enable_invariant_load_metadata = 65;
 
+  // Enable reuse of buffers between HLO operations.
+  bool xla_enable_buffer_reuse = 66;
+
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
   map<string, string> xla_backend_extra_options = 500;
-- 
GitLab


From fa867b0e3fe5f6c36405a7019ec6b8b0e001bed5 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Thu, 29 Jun 2017 22:32:25 +0000
Subject: [PATCH 0605/1375] Fix sparse_placeholder error when int is passed in
 shape argument

This fix tries to address the issue raised in #6749 where and error
```
ValueError: Tensor conversion requested dtype int64 for Tensor with dtype int32: 'Tensor("Const:0", shape=(2,), dtype=int32)'
```
is thrown out in case int is passed in the shape argument.

This fix fixes the issue and adds test cases for it.

This fix fixes #6749.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/python/kernel_tests/sparse_ops_test.py | 11 +++++++++++
 tensorflow/python/ops/array_ops.py                |  2 +-
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 14eb2cba68..681b636435 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -909,5 +909,16 @@ class SparseTransposeTest(test.TestCase):
           self.assertAllEqual(dn_trans, expected_trans)
 
 
+class SparsePlaceholderTest(test.TestCase):
+
+  def testPlaceholder(self):
+    with self.test_session(use_gpu=False):
+      foo = array_ops.sparse_placeholder(dtypes.float32, shape=(10, 47))
+      self.assertAllEqual([10, 47], foo.get_shape())
+
+      foo = array_ops.sparse_placeholder(dtypes.float32, shape=(None, 47))
+      self.assertAllEqual(None, foo.get_shape())
+
+
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 8db5efb447..65335dc2a6 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1548,7 +1548,7 @@ def _normalize_sparse_shape(shape, name):
     for el in shape:
       if el is None:
         return None
-  return ops.convert_to_tensor(shape, name=name)
+  return ops.convert_to_tensor(shape, dtype=dtypes.int64, name=name)
 
 
 def sparse_placeholder(dtype, shape=None, name=None):
-- 
GitLab


From 8280e0ae9083a65b23608b34723f07e028a56dc8 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 29 Jun 2017 15:33:13 -0700
Subject: [PATCH 0606/1375] GPU-enabled WhereOp using CUB.

* Import CUB.
* Add GPU-enabled async WhereOp.
* Added benchmarks.
* Added support for bool ResourceVariables on GPU.

Benchmark results on machine with single K40 tesla GPU:

Where on bool matrix shape [m x n] with p percentage values true below.
For small-medium sizes, running WhereOp on GPU is ~4-2x slower.  For
realistic large problem sizes, it's 2-5x faster.  This timing ignores
the time spent copying a tensor from GPU -> CPU and back from CPU -> GPU
when the WhereOp is between GPU computations (so the performance impact
should actually be better).

Benchmark: m_10_n_10_p_0.01_use_gpu_False        wall_time: 9.01e-05 s   Throughput: 0.00129 GB/s
Benchmark: m_10_n_10_p_0.01_use_gpu_True         wall_time: 0.000187 s   Throughput: 0.000621 GB/s
Benchmark: m_10_n_10_p_0.5_use_gpu_False         wall_time: 9.3e-05 s    Throughput: 0.00968 GB/s
Benchmark: m_10_n_10_p_0.5_use_gpu_True          wall_time: 0.000252 s   Throughput: 0.00357 GB/s
Benchmark: m_10_n_10_p_0.99_use_gpu_False        wall_time: 0.000152 s   Throughput: 0.0111 GB/s
Benchmark: m_10_n_10_p_0.99_use_gpu_True         wall_time: 0.000245 s   Throughput: 0.00687 GB/s
Benchmark: m_10_n_100_p_0.01_use_gpu_False       wall_time: 9.3e-05 s    Throughput: 0.0125 GB/s
Benchmark: m_10_n_100_p_0.01_use_gpu_True        wall_time: 0.000253 s   Throughput: 0.00458 GB/s
Benchmark: m_10_n_100_p_0.5_use_gpu_False        wall_time: 9.8e-05 s    Throughput: 0.0918 GB/s
Benchmark: m_10_n_100_p_0.5_use_gpu_True         wall_time: 0.00026 s    Throughput: 0.0346 GB/s
Benchmark: m_10_n_100_p_0.99_use_gpu_False       wall_time: 0.000104 s   Throughput: 0.162 GB/s
Benchmark: m_10_n_100_p_0.99_use_gpu_True        wall_time: 0.000288 s   Throughput: 0.0586 GB/s
Benchmark: m_10_n_1000_p_0.01_use_gpu_False      wall_time: 0.000105 s   Throughput: 0.111 GB/s
Benchmark: m_10_n_1000_p_0.01_use_gpu_True       wall_time: 0.000283 s   Throughput: 0.041 GB/s
Benchmark: m_10_n_1000_p_0.5_use_gpu_False       wall_time: 0.000185 s   Throughput: 0.486 GB/s
Benchmark: m_10_n_1000_p_0.5_use_gpu_True        wall_time: 0.000335 s   Throughput: 0.269 GB/s
Benchmark: m_10_n_1000_p_0.99_use_gpu_False      wall_time: 0.000203 s   Throughput: 0.83 GB/s
Benchmark: m_10_n_1000_p_0.99_use_gpu_True       wall_time: 0.000346 s   Throughput: 0.486 GB/s
Benchmark: m_10_n_10000_p_0.01_use_gpu_False     wall_time: 0.00019 s    Throughput: 0.609 GB/s
Benchmark: m_10_n_10000_p_0.01_use_gpu_True      wall_time: 0.00028 s    Throughput: 0.414 GB/s
Benchmark: m_10_n_10000_p_0.5_use_gpu_False      wall_time: 0.00117 s    Throughput: 0.771 GB/s
Benchmark: m_10_n_10000_p_0.5_use_gpu_True       wall_time: 0.000426 s   Throughput: 2.11 GB/s
Benchmark: m_10_n_10000_p_0.99_use_gpu_False     wall_time: 0.0014 s     Throughput: 1.2 GB/s
Benchmark: m_10_n_10000_p_0.99_use_gpu_True      wall_time: 0.000482 s   Throughput: 3.5 GB/s
Benchmark: m_10_n_100000_p_0.01_use_gpu_False    wall_time: 0.00129 s    Throughput: 0.899 GB/s
Benchmark: m_10_n_100000_p_0.01_use_gpu_True     wall_time: 0.000336 s   Throughput: 3.45 GB/s
Benchmark: m_10_n_100000_p_0.5_use_gpu_False     wall_time: 0.0102 s     Throughput: 0.885 GB/s
Benchmark: m_10_n_100000_p_0.5_use_gpu_True      wall_time: 0.00136 s    Throughput: 6.6 GB/s
Benchmark: m_10_n_100000_p_0.99_use_gpu_False    wall_time: 0.0116 s     Throughput: 1.45 GB/s
Benchmark: m_10_n_100000_p_0.99_use_gpu_True     wall_time: 0.00233 s    Throughput: 7.23 GB/s
Benchmark: m_10_n_1000000_p_0.01_use_gpu_False   wall_time: 0.0111 s     Throughput: 1.04 GB/s
Benchmark: m_10_n_1000000_p_0.01_use_gpu_True    wall_time: 0.00109 s    Throughput: 10.6 GB/s
Benchmark: m_10_n_1000000_p_0.5_use_gpu_False    wall_time: 0.0895 s     Throughput: 1.01 GB/s
Benchmark: m_10_n_1000000_p_0.5_use_gpu_True     wall_time: 0.0103 s     Throughput: 8.7 GB/s
Benchmark: m_10_n_1000000_p_0.99_use_gpu_False   wall_time: 0.107 s      Throughput: 1.58 GB/s
Benchmark: m_10_n_1000000_p_0.99_use_gpu_True    wall_time: 0.0201 s     Throughput: 8.39 GB/s
PiperOrigin-RevId: 160582709
---
 tensorflow/contrib/cmake/CMakeLists.txt       |   3 +
 tensorflow/contrib/cmake/external/cub.cmake   |  29 ++
 .../contrib/cmake/patches/cub/CMakeLists.txt  |   3 +
 tensorflow/core/framework/register_types.h    |   5 +-
 tensorflow/core/kernels/BUILD                 |   5 +-
 tensorflow/core/kernels/dense_update_ops.cc   |   2 +-
 .../core/kernels/dense_update_ops_gpu.cu.cc   |   8 +-
 .../core/kernels/resource_variable_ops.cc     |   4 +-
 tensorflow/core/kernels/where_op.cc           | 259 ++++++++++++++++--
 tensorflow/core/kernels/where_op.h            |  61 ++---
 tensorflow/core/kernels/where_op_gpu.cu.cc    | 253 +++++++++++++++++
 tensorflow/python/kernel_tests/BUILD          |   2 +-
 .../python/kernel_tests/where_op_test.py      |  59 +++-
 tensorflow/tools/lib_package/BUILD            |   2 +
 tensorflow/tools/pip_package/BUILD            |   1 +
 tensorflow/workspace.bzl                      |  16 ++
 third_party/cub.BUILD                         |  26 ++
 17 files changed, 665 insertions(+), 73 deletions(-)
 create mode 100644 tensorflow/contrib/cmake/external/cub.cmake
 create mode 100644 tensorflow/contrib/cmake/patches/cub/CMakeLists.txt
 create mode 100644 tensorflow/core/kernels/where_op_gpu.cu.cc
 create mode 100644 third_party/cub.BUILD

diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index fe66f8c49a..655e0b29a0 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -122,6 +122,7 @@ include(fft2d)
 include(highwayhash)
 include(protobuf)
 include(re2)
+include(cub)
 if (tensorflow_BUILD_CC_TESTS)
   include(googletest)
 endif()
@@ -151,6 +152,7 @@ set(tensorflow_EXTERNAL_DEPENDENCIES
     protobuf
     eigen
     gemmlowp
+    cub
     fft2d
     re2
 )
@@ -170,6 +172,7 @@ include_directories(
     ${jsoncpp_INCLUDE_DIR}
     ${farmhash_INCLUDE_DIR}
     ${highwayhash_INCLUDE_DIR}
+    ${cub_INCLUDE_DIR}
     ${PROTOBUF_INCLUDE_DIRS}
     ${re2_INCLUDE_DIR}
 )
diff --git a/tensorflow/contrib/cmake/external/cub.cmake b/tensorflow/contrib/cmake/external/cub.cmake
new file mode 100644
index 0000000000..54329dbd70
--- /dev/null
+++ b/tensorflow/contrib/cmake/external/cub.cmake
@@ -0,0 +1,29 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+include (ExternalProject)
+
+set(cub_URL https://github.com/NVlabs/cub/archive/1.6.4.zip)
+set(cub_HASH SHA256=966d0c4f41e2bdc81aebf9ccfbf0baffaac5a74f00b826b06f4dee79b2bb8cee)
+set(cub_BUILD ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
+set(cub_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
+
+ExternalProject_Add(cub
+    PREFIX cub
+    URL ${cub_URL}
+    URL_HASH ${cub_HASH}
+    DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
+    BUILD_IN_SOURCE 1
+    PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/cub/CMakeLists.txt ${cub_BUILD}
+    INSTALL_COMMAND "")
diff --git a/tensorflow/contrib/cmake/patches/cub/CMakeLists.txt b/tensorflow/contrib/cmake/patches/cub/CMakeLists.txt
new file mode 100644
index 0000000000..36890f0ce6
--- /dev/null
+++ b/tensorflow/contrib/cmake/patches/cub/CMakeLists.txt
@@ -0,0 +1,3 @@
+cmake_minimum_required(VERSION 2.8.3)
+
+project(cub)
diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index 2f7b140295..b62fe647e2 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -167,10 +167,13 @@ limitations under the License.
 // Call "m" on POD and string types.
 #define TF_CALL_POD_STRING_TYPES(m) TF_CALL_POD_TYPES(m) TF_CALL_string(m)
 
-// Call "m" on all types supported on GPU.
+// Call "m" on all number types supported on GPU.
 #define TF_CALL_GPU_NUMBER_TYPES(m) \
   TF_CALL_half(m) TF_CALL_float(m) TF_CALL_double(m)
 
+// Call "m" on all types supported on GPU.
+#define TF_CALL_GPU_ALL_TYPES(m) TF_CALL_GPU_NUMBER_TYPES(m) TF_CALL_bool(m)
+
 #define TF_CALL_GPU_NUMBER_TYPES_NO_HALF(m) TF_CALL_float(m) TF_CALL_double(m)
 
 // Call "m" on all quantized types.
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index f0dc3312c2..568e9b717d 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -763,7 +763,10 @@ tf_kernel_library(
 tf_kernel_library(
     name = "where_op",
     prefix = "where_op",
-    deps = ARRAY_DEPS,
+    deps = if_cuda([
+        ":cuda_solvers",
+        "@cub_archive//:cub",
+    ]) + ARRAY_DEPS,
 )
 
 tf_cc_test(
diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc
index ef34946d96..af16c55a5d 100644
--- a/tensorflow/core/kernels/dense_update_ops.cc
+++ b/tensorflow/core/kernels/dense_update_ops.cc
@@ -154,7 +154,7 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
       Name("Assign").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
       AssignOpT<GPUDevice, type>);
 
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
+TF_CALL_GPU_ALL_TYPES(REGISTER_GPU_KERNELS);
 #undef REGISTER_GPU_KERNELS
 #endif  // GOOGLE_CUDA
 
diff --git a/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc b/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
index 0f61506afe..cf2d493061 100644
--- a/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
+++ b/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
@@ -27,11 +27,15 @@ typedef Eigen::GpuDevice GPUDevice;
 
 #define DEFINE_GPU_KERNELS(T)                              \
   template struct functor::DenseUpdate<GPUDevice, T, ADD>; \
-  template struct functor::DenseUpdate<GPUDevice, T, SUB>; \
-  template struct functor::DenseUpdate<GPUDevice, T, ASSIGN>;
+  template struct functor::DenseUpdate<GPUDevice, T, SUB>;
 TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_KERNELS);
 #undef DEFINE_GPU_KERNELS
 
+#define DEFINE_GPU_KERNELS(T) \
+  template struct functor::DenseUpdate<GPUDevice, T, ASSIGN>;
+TF_CALL_GPU_ALL_TYPES(DEFINE_GPU_KERNELS);
+#undef DEFINE_GPU_KERNELS
+
 }  // end namespace tensorflow
 
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index db6d8c20ec..e108a59275 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -94,7 +94,7 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
                               .HostMemory("resource"),         \
                           ReadVariableOp<GPUDevice, type>);
 
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
+TF_CALL_GPU_ALL_TYPES(REGISTER_GPU_KERNELS);
 #undef REGISTER_GPU_KERNELS
 #endif  // GOOGLE_CUDA
 
@@ -230,7 +230,7 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
                               .HostMemory("resource"),         \
                           AssignVariableOp<GPUDevice, type>);
 
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
+TF_CALL_GPU_ALL_TYPES(REGISTER_GPU_KERNELS);
 #undef REGISTER_GPU_KERNELS
 #endif  // GOOGLE_CUDA
 
diff --git a/tensorflow/core/kernels/where_op.cc b/tensorflow/core/kernels/where_op.cc
index e56a498845..6fdcb331cc 100644
--- a/tensorflow/core/kernels/where_op.cc
+++ b/tensorflow/core/kernels/where_op.cc
@@ -17,9 +17,14 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif  // GOOGLE_CUDA
+
 #include "tensorflow/core/kernels/where_op.h"
 
 #include <memory>
+#include <numeric>
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
@@ -27,43 +32,113 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/kernels/bounds_check.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/types.h"
 
+#if GOOGLE_CUDA
+#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h"
+#include "tensorflow/core/kernels/cuda_solvers.h"
+#endif  // GOOGLE_CUDA
+
 namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 
-template <typename Device>
-class WhereOp : public OpKernel {
+namespace functor {
+
+template <>
+struct NumTrue<CPUDevice, int64> {
+  static Status Compute(OpKernelContext* ctx, const CPUDevice& d,
+                        TTypes<bool>::ConstFlat input,
+                        TTypes<int64>::Scalar num_true) {
+    *num_true.data() =
+        std::accumulate(input.data(), input.data() + input.size(), 0);
+    return Status::OK();
+  }
+};
+
+template <int DIMS, typename TIndex>
+struct Where<CPUDevice, DIMS, TIndex> {
+  EIGEN_ALWAYS_INLINE static void WriteIndexRowMajor(
+      typename TTypes<int64>::Matrix output,
+      const typename Eigen::DSizes<TIndex, DIMS>& strides, TIndex true_n,
+      TIndex index) {
+    for (int i = 0; i < DIMS; ++i) {
+      output(true_n, i) = index / strides[i];
+      index -= output(true_n, i) * strides[i];
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE static Status Compute(
+      OpKernelContext* ctx, const CPUDevice& d,
+      typename TTypes<bool, DIMS>::ConstTensor input,
+      typename TTypes<int64>::Matrix output, TIndex* found_true) {
+    Eigen::DSizes<Eigen::DenseIndex, DIMS> dims = input.dimensions();
+    Eigen::DSizes<TIndex, DIMS> strides;
+
+    EIGEN_STATIC_ASSERT((static_cast<int>(decltype(input)::Layout) ==
+                         static_cast<int>(Eigen::RowMajor)),
+                        INTERNAL_ERROR_INPUT_SHOULD_BE_ROWMAJOR);
+
+    strides[DIMS - 1] = 1;
+    for (int i = DIMS - 2; i >= 0; --i) {
+      strides[i] = strides[i + 1] * dims[i + 1];
+    }
+
+    Eigen::DenseIndex output_size = output.dimension(0);
+    for (Eigen::DenseIndex n = 0; n < input.size(); ++n) {
+      if (input.data()[n]) {
+        if (FastBoundsCheck(*found_true, output_size)) {
+          WriteIndexRowMajor(output, strides, *found_true, n);
+        }
+        ++*found_true;
+      }
+    }
+    return Status::OK();
+  }
+};
+
+}  // namespace functor
+
+class WhereCPUOp : public OpKernel {
  public:
-  explicit WhereOp(OpKernelConstruction* context) : OpKernel(context) {}
+  explicit WhereCPUOp(OpKernelConstruction* context) : OpKernel(context) {}
 
   void Compute(OpKernelContext* context) override {
     const Tensor& input = context->input(0);
 
     const int input_dims = input.dims();
+
     Tensor num_true;
     OP_REQUIRES_OK(
         context, context->allocate_temp(DT_INT64, TensorShape({}), &num_true));
     auto num_true_t = num_true.scalar<int64>();
 
-    functor::NumTrue<Device>::Compute(context->eigen_device<Device>(),
-                                      input.flat<bool>(), num_true_t);
+    Status s = functor::NumTrue<CPUDevice, int64>::Compute(
+        context, context->eigen_device<CPUDevice>(), input.flat<bool>(),
+        num_true_t);
+    OP_REQUIRES_OK(context, s);
     TensorShape output_shape({num_true_t(), input_dims});
     Tensor* output = nullptr;
     OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
 
-#define HANDLE_DIM(NDIM)                                             \
-  case NDIM:                                                         \
-    found_true = functor::Where<Device, NDIM>::Compute(              \
-        context->eigen_device<Device>(), input.tensor<bool, NDIM>(), \
-        output->matrix<int64>());                                    \
-    break;
-
+    // TODO(ebrevdo): Replace single-threaded copy with a
+    // multithreaded block copy by getting block counts above instead
+    // of a global NumTrue, then having each block filled in in
+    // separate threads below.
     int64 found_true = 0;
+
+#define HANDLE_DIM(NDIM)                                                   \
+  case NDIM: {                                                             \
+    Status s = functor::Where<CPUDevice, NDIM, int64>::Compute(            \
+        context, context->eigen_device<CPUDevice>(),                       \
+        input.tensor<bool, NDIM>(), output->matrix<int64>(), &found_true); \
+    OP_REQUIRES_OK(context, s);                                            \
+  } break;
+
     switch (input_dims) {
       HANDLE_DIM(1);
       HANDLE_DIM(2);
@@ -79,7 +154,7 @@ class WhereOp : public OpKernel {
 #undef HANDLE_DIM
 
     OP_REQUIRES(
-        context, num_true_t() == found_true,
+        context, found_true == num_true_t(),
         errors::InvalidArgument(
             "WhereOp: Race condition between counting the number of true "
             "elements and writing them.  When counting, saw ",
@@ -88,12 +163,162 @@ class WhereOp : public OpKernel {
   }
 
  private:
-  TF_DISALLOW_COPY_AND_ASSIGN(WhereOp);
+  TF_DISALLOW_COPY_AND_ASSIGN(WhereCPUOp);
+};
+
+REGISTER_KERNEL_BUILDER(Name("Where").Device(DEVICE_CPU), WhereCPUOp);
+
+#if GOOGLE_CUDA
+
+namespace functor {
+
+#define DECLARE_GPU_NUMTRUE(Tindex)                                            \
+  template <>                                                                  \
+  Status NumTrue<GPUDevice, Tindex>::Compute(                                  \
+      OpKernelContext* ctx, const GPUDevice& d, TTypes<bool>::ConstFlat input, \
+      TTypes<Tindex>::Scalar num_true);                                        \
+  extern template struct NumTrue<GPUDevice, Tindex>
+
+DECLARE_GPU_NUMTRUE(int32);
+DECLARE_GPU_NUMTRUE(int64);
+#undef DECLARE_GPU_NUMTRUE
+
+#define DECLARE_GPU_WHERE_INDEX(Dims, Tindex)                     \
+  template <>                                                     \
+  Status Where<GPUDevice, Dims, Tindex>::Compute(                 \
+      OpKernelContext* ctx, const GPUDevice& d,                   \
+      typename TTypes<bool, Dims>::ConstTensor input,             \
+      typename TTypes<int64>::Matrix output, Tindex* found_true); \
+  extern template struct Where<GPUDevice, Dims, Tindex>;
+#define DECLARE_GPU_WHERE(Dims)         \
+  DECLARE_GPU_WHERE_INDEX(Dims, int32); \
+  DECLARE_GPU_WHERE_INDEX(Dims, int64);
+
+DECLARE_GPU_WHERE(1);
+DECLARE_GPU_WHERE(2);
+DECLARE_GPU_WHERE(3);
+DECLARE_GPU_WHERE(4);
+DECLARE_GPU_WHERE(5);
+#undef DECLARE_GPU_WHERE
+#undef DECLARE_GPU_WHERE_INDEX
+
+}  // namespace functor
+
+class WhereGPUOp : public AsyncOpKernel {
+ public:
+  explicit WhereGPUOp(OpKernelConstruction* context) : AsyncOpKernel(context) {}
+
+  void ComputeAsync(OpKernelContext* context, DoneCallback done) override {
+    const Tensor& input = context->input(0);
+    const int input_dims = input.dims();
+
+    if (input.NumElements() < std::numeric_limits<int32>::max()) {
+      ComputeAsyncType<int32>(input, input_dims, context, done);
+    } else {
+      ComputeAsyncType<int64>(input, input_dims, context, done);
+    }
+  }
+
+  template <typename Tindex>
+  void ComputeAsyncType(const Tensor& input, const int input_dims,
+                        OpKernelContext* context, DoneCallback done) {
+    // Step 0: alloc nnz
+    // Step 1: call nnz kernel
+    // Step 2: copy nnz to host
+    // Step 3: call create_output
+    // Step 4: call where kernel
+    Tensor num_true;
+    OP_REQUIRES_OK_ASYNC(context,
+                         context->allocate_temp(DataTypeToEnum<Tindex>::v(),
+                                                TensorShape({}), &num_true),
+                         done);
+
+    auto num_true_t = num_true.scalar<Tindex>();
+
+    perftools::gputools::DeviceMemoryBase num_true_ptr(
+        static_cast<void*>(num_true_t.data()));
+    // Push kernel to stream to get number of true elements.
+    const GPUDevice& d = context->eigen_device<GPUDevice>();
+    Status s = functor::NumTrue<GPUDevice, Tindex>::Compute(
+        context, d, input.flat<bool>(), num_true_t);
+    OP_REQUIRES_OK_ASYNC(context, s, done);
+
+    // Copy num_true to host;
+    ScratchSpace<Tindex> num_true_host(context, 1, /* on_host */ true);
+
+    auto stream = context->op_device_context()->stream();
+    OP_REQUIRES_ASYNC(
+        context,
+        stream
+            ->ThenMemcpy(num_true_host.mutable_data(), num_true_ptr,
+                         sizeof(Tindex))
+            .ok(),
+        errors::Internal("WhereOp: failed to copy num_true from device"), done);
+
+    auto create_and_check_output = [context, &d, &input, input_dims,
+                                    num_true_host, done]() {
+
+      Tindex num_true = *num_true_host.data();
+
+      // TODO(ebrevdo): Properly copy back found_true value to CPU for
+      // validation checking.  Currently Where<GPUDevice>::Compute()
+      // does not perform this copy back to CPU.
+      Tindex found_true = -1;
+
+      // Step 1: Allocate the output and perform the selection/copy.
+      Tensor* output;
+      OP_REQUIRES_OK_ASYNC(context,
+                           context->allocate_output(
+                               0, TensorShape({num_true, input_dims}), &output),
+                           done);
+
+#define HANDLE_DIM(NDIM)                                                 \
+  case NDIM: {                                                           \
+    Status s = functor::Where<GPUDevice, NDIM, Tindex>::Compute(         \
+        context, d, input.tensor<bool, NDIM>(), output->matrix<int64>(), \
+        &found_true);                                                    \
+    OP_REQUIRES_OK_ASYNC(context, s, done);                              \
+  } break;
+
+      switch (input_dims) {
+        HANDLE_DIM(1);
+        HANDLE_DIM(2);
+        HANDLE_DIM(3);
+        HANDLE_DIM(4);
+        HANDLE_DIM(5);
+
+        default:
+          OP_REQUIRES_ASYNC(
+              context, false,
+              errors::InvalidArgument("WhereOp: Unhandled input dimensions: ",
+                                      input_dims),
+              done);
+      }
+#undef HANDLE_DIM
+
+      // TODO(ebrevdo): Fix the copy back to host.
+
+      // OP_REQUIRES_ASYNC(
+      //     context, found_true == num_true,
+      //     errors::InvalidArgument(
+      //         "WhereOp: Race condition between counting the number of true "
+      //         "elements and writing them.  When counting, saw ",
+      //         num_true, " elements; but when writing their indices, saw ",
+      //         found_true, " elements."),
+      //     done);
+
+      done();
+    };
+    context->device()->tensorflow_gpu_device_info()->event_mgr->ThenExecute(
+        stream, create_and_check_output);
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(WhereGPUOp);
 };
 
-#define REGISTER_WHERE() \
-  REGISTER_KERNEL_BUILDER(Name("Where").Device(DEVICE_CPU), WhereOp<CPUDevice>);
+REGISTER_KERNEL_BUILDER(Name("Where").Device(DEVICE_GPU), WhereGPUOp);
 
-REGISTER_WHERE();
+#endif  // GOOGLE_CUDA
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/where_op.h b/tensorflow/core/kernels/where_op.h
index aa27123714..e040325e3d 100644
--- a/tensorflow/core/kernels/where_op.h
+++ b/tensorflow/core/kernels/where_op.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_KERNELS_WHERE_OP_H_
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/types.h"
@@ -25,55 +26,25 @@ namespace tensorflow {
 
 namespace functor {
 
-template <typename Device>
+template <typename Device, typename TIndex>
 struct NumTrue {
-  EIGEN_ALWAYS_INLINE static void Compute(
-      const Device& d, typename TTypes<bool>::ConstFlat input,
-      TTypes<int64>::Scalar num_true) {
-    num_true.device(d) = input.template cast<int64>().sum();
-  }
+  EIGEN_ALWAYS_INLINE static Status Compute(
+      OpKernelContext* ctx, const Device& d, TTypes<bool>::ConstFlat input,
+      typename TTypes<TIndex>::Scalar num_true);
 };
 
-template <typename Device, int NDIM>
+template <typename Device, int NDIM, typename TIndex>
 struct Where {
-  EIGEN_ALWAYS_INLINE static int64 Compute(
-      const Device& d, typename TTypes<bool, NDIM>::ConstTensor input,
-      typename TTypes<int64>::Matrix output) {
-    Eigen::DenseIndex true_n = 0;
-    Eigen::DSizes<Eigen::DenseIndex, NDIM> dims = input.dimensions();
-    Eigen::DSizes<Eigen::DenseIndex, NDIM> strides;
-
-    // Calculate strides for RowMajor order.
-    EIGEN_STATIC_ASSERT((static_cast<int>(decltype(input)::Layout) ==
-                         static_cast<int>(Eigen::RowMajor)),
-                        INTERNAL_ERROR_INPUT_SHOULD_BE_ROWMAJOR);
-
-    strides[NDIM - 1] = 1;
-    for (int i = NDIM - 2; i >= 0; --i) {
-      strides[i] = strides[i + 1] * dims[i + 1];
-    }
-
-    Eigen::DenseIndex output_size = output.dimension(0);
-    for (Eigen::DenseIndex n = 0; n < input.size(); ++n) {
-      if (input.data()[n]) {
-        if (TF_PREDICT_TRUE(true_n < output_size)) {
-          WriteIndexRowMajor(output, strides, true_n, n);
-        }
-        ++true_n;
-      }
-    }
-    return true_n;
-  }
-
-  EIGEN_ALWAYS_INLINE static void WriteIndexRowMajor(
-      typename TTypes<int64>::Matrix output,
-      const Eigen::DSizes<Eigen::DenseIndex, NDIM>& strides,
-      Eigen::DenseIndex true_n, Eigen::DenseIndex index) {
-    for (int i = 0; i < NDIM; ++i) {
-      output(true_n, i) = index / strides[i];
-      index %= strides[i];
-    }
-  }
+  // Copies indices of true values in input into output.  The pointer
+  // found_true should sit on the host.  Compute should copy the
+  // number of true elements found into it.  At the end, if
+  //   *found_true != output.dimension(0),
+  // then the input may have changed between the initial counting of
+  // the true values and the call to Where.
+  EIGEN_ALWAYS_INLINE static Status Compute(
+      OpKernelContext* ctx, const Device& d,
+      typename TTypes<bool, NDIM>::ConstTensor input,
+      typename TTypes<int64>::Matrix output, TIndex* found_true);
 };
 
 }  // namespace functor
diff --git a/tensorflow/core/kernels/where_op_gpu.cu.cc b/tensorflow/core/kernels/where_op_gpu.cu.cc
new file mode 100644
index 0000000000..09e8be58fa
--- /dev/null
+++ b/tensorflow/core/kernels/where_op_gpu.cu.cc
@@ -0,0 +1,253 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "external/cub_archive/cub/device/device_reduce.cuh"
+#include "external/cub_archive/cub/device/device_select.cuh"
+#include "external/cub_archive/cub/iterator/counting_input_iterator.cuh"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/kernels/where_op.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
+namespace tensorflow {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace functor {
+
+template <int NDIM, typename TIndex>
+__global__ void PropagateWhereIndicesKernel(
+    const TIndex output_rows, const typename Eigen::array<TIndex, NDIM> strides,
+    int64* output) {
+  // TODO(ebrevdo): Use a multi-dimensional loop, increasing the
+  // dimensions of individual indices manually, instead of relying on
+  // a scalar loop variable and using integer division.
+  CUDA_1D_KERNEL_LOOP(i, output_rows) {
+    TIndex index_value = ldg(output + NDIM * i);
+#pragma unroll
+    for (int c = 0; c < NDIM; ++c) {
+      *(output + NDIM * i + c) = index_value / strides[c];
+      index_value %= strides[c];
+    }
+  }
+}
+
+template <typename TIndex>
+struct NumTrue<GPUDevice, TIndex> {
+  EIGEN_ALWAYS_INLINE static Status Compute(
+      OpKernelContext* ctx, const GPUDevice& d, TTypes<bool>::ConstFlat input,
+      typename TTypes<TIndex>::Scalar num_true) {
+    std::size_t temp_storage_bytes = 0;
+
+    const bool* input_data = input.data();
+    TIndex* num_true_data = num_true.data();
+
+    auto first_success =
+        cub::DeviceReduce::Sum(/*temp_storage*/ nullptr, temp_storage_bytes,
+                               /*d_in*/ input_data,
+                               /*d_out*/ num_true_data,
+                               /*num_items*/ input.size(),
+                               /*stream*/ d.stream());
+
+    if (first_success != cudaSuccess) {
+      return errors::Internal(
+          "WhereOp: Could not launch cub::DeviceReduce::Sum to calculate "
+          "temp_storage_bytes, status: ",
+          cudaGetErrorString(first_success));
+    }
+
+    Tensor temp_storage;
+    TF_RETURN_IF_ERROR(ctx->allocate_temp(
+        DT_INT8, TensorShape({static_cast<int64>(temp_storage_bytes)}),
+        &temp_storage));
+
+    auto second_success = cub::DeviceReduce::Sum(
+        /*temp_storage*/ temp_storage.flat<int8>().data(), temp_storage_bytes,
+        /*d_in*/ input_data,
+        /*d_out*/ num_true_data,
+        /*num_items*/ input.size(),
+        /*stream*/ d.stream());
+
+    if (second_success != cudaSuccess) {
+      return errors::Internal(
+          "WhereOp: Could not launch cub::DeviceReduce::Sum to count "
+          "number of true indices, status: ",
+          cudaGetErrorString(second_success));
+    }
+
+    return Status::OK();
+  }
+};
+
+template struct NumTrue<GPUDevice, int32>;
+template struct NumTrue<GPUDevice, int64>;
+
+template <int NDIM>
+class WhereOutputIterator {
+ public:
+  // Required iterator traits
+  typedef WhereOutputIterator self_type;
+  typedef std::ptrdiff_t difference_type;
+  typedef void value_type;
+  typedef void pointer;
+  typedef int64& reference;
+
+#if (THRUST_VERSION >= 100700)
+  // Use Thrust's iterator categories so we can use these iterators in Thrust
+  // 1.7 (or newer) methods
+  typedef typename thrust::detail::iterator_facade_category<
+      thrust::device_system_tag, thrust::random_access_traversal_tag,
+      value_type,
+      reference>::type iterator_category;  ///< The iterator category
+#else
+  typedef std::random_access_iterator_tag
+      iterator_category;  ///< The iterator category
+#endif  // THRUST_VERSION
+
+  WhereOutputIterator(int64* ptr, const Eigen::DenseIndex max_row)
+      : ptr_(ptr), max_row_(max_row) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int64& operator[](int n) const {
+    // If the selection mechanism finds too many true values (because
+    // the input tensor changed between allocation of output and now),
+    // we may accidentally try to write past the allowable memory.  If
+    // valid is false, then we don't do this.  Instead, we'll read off
+    // the number of items found in Flagged()'s d_num_selected_out at
+    // the end and confirm that it matches the number of rows of output.
+    const bool valid = FastBoundsCheck(n, max_row_);
+    return *(ptr_ + (valid ? (NDIM * n) : 0));
+  }
+
+ private:
+  int64* ptr_;
+  const Eigen::DenseIndex max_row_;
+};
+
+template <typename TIndex, int NDIM>
+Eigen::array<TIndex, NDIM> CalculateStrides(
+    typename TTypes<bool, NDIM>::ConstTensor input) {
+  const Eigen::DSizes<Eigen::DenseIndex, NDIM> dims = input.dimensions();
+  Eigen::array<TIndex, NDIM> strides;
+  EIGEN_STATIC_ASSERT((static_cast<int>(decltype(input)::Layout) ==
+                       static_cast<int>(Eigen::RowMajor)),
+                      INTERNAL_ERROR_INPUT_SHOULD_BE_ROWMAJOR);
+  strides[NDIM - 1] = 1;
+  for (int i = NDIM - 2; i >= 0; --i) {
+    strides[i] = strides[i + 1] * dims[i + 1];
+  }
+  return strides;
+}
+
+template <int NDIM, typename Tindex>
+struct Where<GPUDevice, NDIM, Tindex> {
+  EIGEN_ALWAYS_INLINE static Status Compute(
+      OpKernelContext* ctx, const GPUDevice& d,
+      typename TTypes<bool, NDIM>::ConstTensor input,
+      typename TTypes<int64>::Matrix output, Tindex* found_true_host) {
+    if (output.dimension(0) == 0) {
+      // Nothing to do.
+      return Status::OK();
+    }
+
+    std::size_t temp_storage_bytes = 0;
+
+    cub::CountingInputIterator<Tindex> select_counter(0);
+
+    Tensor found_true_t;
+    TF_RETURN_IF_ERROR(ctx->allocate_temp(DataTypeToEnum<Tindex>::v(),
+                                          TensorShape({}), &found_true_t));
+    Tindex* found_true_device = found_true_t.scalar<Tindex>().data();
+
+    WhereOutputIterator<NDIM> output_iterator(
+        output.data(),
+        /* max_row */ output.dimension(0));
+
+    auto first_success =
+        cub::DeviceSelect::Flagged(/*temp_storage*/ nullptr, temp_storage_bytes,
+                                   /*d_in*/ select_counter,
+                                   /*d_flags*/ input.data(),
+                                   /*d_out*/ output_iterator,
+                                   /*d_num_selected_out*/ found_true_device,
+                                   /*num_items*/ input.size(),
+                                   /*stream*/ d.stream());
+    if (first_success != cudaSuccess) {
+      return errors::Internal(
+          "WhereOp: Could not launch cub::DeviceSelect::Flagged to calculate "
+          "temp_storage_bytes, status: ",
+          cudaGetErrorString(first_success));
+    }
+
+    Tensor temp_storage;
+    TF_RETURN_IF_ERROR(ctx->allocate_temp(
+        DT_INT8, TensorShape({static_cast<int64>(temp_storage_bytes)}),
+        &temp_storage));
+
+    auto second_success = cub::DeviceSelect::Flagged(
+        /*temp_storage*/ temp_storage.flat<int8>().data(), temp_storage_bytes,
+        /*d_in*/ select_counter,
+        /*d_flags*/ input.data(),
+        /*d_out*/ output_iterator,
+        /*d_num_selected_out*/ found_true_device,
+        /*num_items*/ input.size(),
+        /*stream*/ d.stream());
+
+    if (second_success != cudaSuccess) {
+      return errors::Internal(
+          "WhereOp: Could not launch cub::DeviceSelect::Flagged to copy "
+          "indices out, status: ",
+          cudaGetErrorString(second_success));
+    }
+
+    // TODO(ebrevdo): Find a way to synchronously copy back data from
+    // found_true_device to *found_true_host.
+
+    const Eigen::array<Tindex, NDIM> strides =
+        CalculateStrides<Tindex, NDIM>(input);
+    const Tindex output_rows = output.dimension(0);
+    CudaLaunchConfig config = GetCudaLaunchConfig(output_rows, d);
+    PropagateWhereIndicesKernel<NDIM, Tindex>
+        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+            output_rows, strides, output.data());
+
+    return Status::OK();
+  }
+};
+
+#define DECLARE_GPU_SPEC_INDEX(Dims, Tindex) \
+  template struct Where<GPUDevice, Dims, Tindex>
+#define DECLARE_GPU_SPEC(Dims)         \
+  DECLARE_GPU_SPEC_INDEX(Dims, int32); \
+  DECLARE_GPU_SPEC_INDEX(Dims, int64)
+
+DECLARE_GPU_SPEC(1);
+DECLARE_GPU_SPEC(2);
+DECLARE_GPU_SPEC(3);
+DECLARE_GPU_SPEC(4);
+DECLARE_GPU_SPEC(5);
+
+#undef DECLARE_GPU_SPEC
+#undef DECLARE_GPU_SPEC_INDEX
+
+}  // namespace functor
+
+}  // namespace tensorflow
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index d8e069105f..64d0b8fa52 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -900,7 +900,7 @@ tf_py_test(
     ],
 )
 
-tf_py_test(
+cuda_py_test(
     name = "where_op_test",
     size = "small",
     srcs = ["where_op_test.py"],
diff --git a/tensorflow/python/kernel_tests/where_op_test.py b/tensorflow/python/kernel_tests/where_op_test.py
index b47159ae7f..a428d26996 100644
--- a/tensorflow/python/kernel_tests/where_op_test.py
+++ b/tensorflow/python/kernel_tests/where_op_test.py
@@ -18,17 +18,25 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import itertools
+import sys
+
 import numpy as np
 
+from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import test
 
 
 class WhereOpTest(test.TestCase):
 
   def _testWhere(self, x, truth, expected_err_re=None):
-    with self.test_session():
+    with self.test_session(use_gpu=True):
       ans = array_ops.where(x)
       self.assertEqual([None, x.ndim], ans.get_shape().as_list())
       if expected_err_re is None:
@@ -39,12 +47,30 @@ class WhereOpTest(test.TestCase):
           ans.eval()
 
   def testWrongNumbers(self):
-    with self.test_session():
+    with self.test_session(use_gpu=True):
       with self.assertRaises(ValueError):
         array_ops.where([False, True], [1, 2], None)
       with self.assertRaises(ValueError):
         array_ops.where([False, True], None, [1, 2])
 
+  def testBasicVec(self):
+    x = np.asarray([True, False])
+    truth = np.asarray([[0]], dtype=np.int64)
+    self._testWhere(x, truth)
+
+    x = np.asarray([False, True, False])
+    truth = np.asarray([[1]], dtype=np.int64)
+    self._testWhere(x, truth)
+
+    x = np.asarray([False, False, True, False, True])
+    truth = np.asarray([[2], [4]], dtype=np.int64)
+    self._testWhere(x, truth)
+
+  def testRandomVec(self):
+    x = np.random.rand(1000000) > 0.5
+    truth = np.vstack([np.where(x)[0].astype(np.int64)]).T
+    self._testWhere(x, truth)
+
   def testBasicMat(self):
     x = np.asarray([[True, False], [True, False]])
 
@@ -67,10 +93,37 @@ class WhereOpTest(test.TestCase):
   def testThreeArgument(self):
     x = np.array([[-2, 3, -1], [1, -3, -3]])
     np_val = np.where(x > 0, x * x, -x)
-    with self.test_session():
+    with self.test_session(use_gpu=True):
       tf_val = array_ops.where(constant_op.constant(x) > 0, x * x, -x).eval()
     self.assertAllEqual(tf_val, np_val)
 
 
+class WhereBenchmark(test.Benchmark):
+
+  def benchmarkWhereCPU(self):
+    for (m, n, p, use_gpu) in itertools.product(
+        [10],
+        [10, 100, 1000, 10000, 100000, 1000000],
+        [0.01, 0.5, 0.99],
+        [False, True]):
+      name = "m_%d_n_%d_p_%g_use_gpu_%s" % (m, n, p, use_gpu)
+      device = "/%s:0" % ("gpu" if use_gpu else "cpu")
+      with ops.Graph().as_default():
+        with ops.device(device):
+          x = random_ops.random_uniform((m, n), dtype=dtypes.float32) <= p
+          v = resource_variable_ops.ResourceVariable(x)
+          op = array_ops.where(v)
+        with session.Session() as sess:
+          v.initializer.run()
+          r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
+          gb_processed_input = m * n / 1.0e9
+          # approximate size of output: m*n*p int64s for each axis.
+          gb_processed_output = 2 * 8 * m * n * p / 1.0e9
+          gb_processed = gb_processed_input + gb_processed_output
+          throughput = gb_processed / r["wall_time"]
+          print("Benchmark: %s \t wall_time: %0.03g s \t "
+                "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
+          sys.stdout.flush()
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD
index 51ba3b7a0b..9da5d5cb5b 100644
--- a/tensorflow/tools/lib_package/BUILD
+++ b/tensorflow/tools/lib_package/BUILD
@@ -87,6 +87,7 @@ genrule(
         "//third_party/fft2d:LICENSE",
         "@boringssl//:LICENSE",
         "@com_googlesource_code_re2//:LICENSE",
+        "@cub_archive//:LICENSE.TXT",
         "@curl//:COPYING",
         "@eigen_archive//:COPYING.MPL2",
         "@farmhash_archive//:COPYING",
@@ -117,6 +118,7 @@ genrule(
         "//third_party/fft2d:LICENSE",
         "@boringssl//:LICENSE",
         "@com_googlesource_code_re2//:LICENSE",
+        "@cub_archive//:LICENSE.TXT",
         "@curl//:COPYING",
         "@eigen_archive//:COPYING.MPL2",
         "@farmhash_archive//:COPYING",
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 6a6ce5b9e3..0bd7cda1c5 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -95,6 +95,7 @@ filegroup(
         "//third_party/hadoop:LICENSE.txt",
         "@boringssl//:LICENSE",
         "@com_googlesource_code_re2//:LICENSE",
+        "@cub_archive//:LICENSE.TXT",
         "@curl//:COPYING",
         "@eigen_archive//:COPYING.MPL2",
         "@farmhash_archive//:COPYING",
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index a5172187f7..a6c5117e22 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -643,3 +643,19 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       strip_prefix = "pprof-c0fb62ec88c411cc91194465e54db2632845b650",
       build_file = str(Label("//third_party:pprof.BUILD")),
   )
+
+  native.new_http_archive(
+      name = "cub_archive",
+      urls = [
+          "http://mirror.bazel.build/github.com/NVlabs/cub/archive/1.6.4.zip",
+          "https://github.com/NVlabs/cub/archive/1.6.4.zip",
+      ],
+      sha256 = "966d0c4f41e2bdc81aebf9ccfbf0baffaac5a74f00b826b06f4dee79b2bb8cee",
+      strip_prefix = "cub-1.6.4",
+      build_file = str(Label("//third_party:cub.BUILD")),
+  )
+
+  native.bind(
+      name = "cub",
+      actual = "@cub_archive//:cub",
+  )
diff --git a/third_party/cub.BUILD b/third_party/cub.BUILD
new file mode 100644
index 0000000000..29159c9dad
--- /dev/null
+++ b/third_party/cub.BUILD
@@ -0,0 +1,26 @@
+# Description: CUB library which is a set of primitives for GPU programming.
+
+package(
+    default_visibility = ["//visibility:public"],
+)
+
+licenses(["notice"])  # BSD
+
+exports_files(["LICENSE.TXT"])
+
+load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts", "if_cuda")
+
+filegroup(
+    name = "cub_header_files",
+    srcs = glob([
+        "cub/**",
+    ]),
+)
+
+cc_library(
+    name = "cub",
+    hdrs = if_cuda([":cub_header_files"]),
+    deps = [
+        "@local_config_cuda//cuda:cuda_headers",
+    ],
+)
-- 
GitLab


From 953a6db9b6c3473f40e6ba2db207c62ef0b19097 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 29 Jun 2017 16:14:52 -0700
Subject: [PATCH 0607/1375] Improve the accuracy of the cost estimates for the
 size, shape, and rank ops.

PiperOrigin-RevId: 160587845
---
 .../grappler/costs/op_level_cost_estimator.cc | 20 ++++++++++++++++++-
 .../grappler/costs/op_level_cost_estimator.h  |  1 +
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index 7d3298ded4..7f4cc95f31 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -37,6 +37,9 @@ constexpr char kRecv[] = "_Recv";
 constexpr char kBatchMatMul[] = "BatchMatMul";
 constexpr char kVariable[] = "Variable";
 constexpr char kVariableV2[] = "VariableV2";
+constexpr char kRank[] = "Rank";
+constexpr char kShape[] = "Shape";
+constexpr char kSize[] = "Size";
 
 namespace {
 
@@ -157,7 +160,10 @@ OpLevelCostEstimator::OpLevelCostEstimator() {
       {kRecv, wrap(&OpLevelCostEstimator::PredictNoOp)},
       {kVariable, wrap(&OpLevelCostEstimator::PredictNoOp)},
       {kVariableV2, wrap(&OpLevelCostEstimator::PredictNoOp)},
-      {kBatchMatMul, wrap(&OpLevelCostEstimator::PredictBatchMatMul)}};
+      {kBatchMatMul, wrap(&OpLevelCostEstimator::PredictBatchMatMul)},
+      {kRank, wrap(&OpLevelCostEstimator::PredictMetadata)},
+      {kShape, wrap(&OpLevelCostEstimator::PredictMetadata)},
+      {kSize, wrap(&OpLevelCostEstimator::PredictMetadata)}};
 
   elementwise_ops_ = {
       // Unary ops alphabetically sorted
@@ -846,5 +852,17 @@ Costs OpLevelCostEstimator::PredictBatchMatMul(
   return costs;
 }
 
+Costs OpLevelCostEstimator::PredictMetadata(const OpInfo& op_features) const {
+  Costs costs;
+  costs.max_memory = CalculateOutputSize(op_features, &costs.inaccurate);
+  // Metadata operations are so cheap we assume they take the minimum amount of
+  // time we can represent (1 ns).
+  costs.execution_time = 1;
+  costs.compute_time = 1;
+  costs.memory_time = 0;
+
+  return costs;
+}
+
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.h b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
index 28d49a7703..59ced70ba6 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.h
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
@@ -129,6 +129,7 @@ class OpLevelCostEstimator {
   Costs PredictMatMul(const OpInfo& op_features) const;
   Costs PredictNoOp(const OpInfo& op_features) const;
   Costs PredictBatchMatMul(const OpInfo& op_features) const;
+  Costs PredictMetadata(const OpInfo& op_features) const;
 
   // Utility function for safe division. Returns 0
   // if rhs is 0 or negative.
-- 
GitLab


From 034236aa27ae5e40a6fe619d43c94fb01f529601 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 16:20:28 -0700
Subject: [PATCH 0608/1375] Optimize FusedBatchNorm for inference.

PiperOrigin-RevId: 160588466
---
 .../tools/optimize_for_inference_lib.py       | 66 ++++++++++-----
 .../tools/optimize_for_inference_test.py      | 44 ++++++++++
 .../graph_transforms/fold_old_batch_norms.cc  | 37 ++++++---
 .../fold_old_batch_norms_test.cc              | 81 +++++++++++++++++++
 4 files changed, 198 insertions(+), 30 deletions(-)

diff --git a/tensorflow/python/tools/optimize_for_inference_lib.py b/tensorflow/python/tools/optimize_for_inference_lib.py
index 3e80f1ecd7..c2687bf557 100644
--- a/tensorflow/python/tools/optimize_for_inference_lib.py
+++ b/tensorflow/python/tools/optimize_for_inference_lib.py
@@ -67,6 +67,25 @@ flags = flags_lib
 FLAGS = flags.FLAGS
 
 
+# Support folding two types of batch norm ops:
+# BatchNormWithGlobalNormalization and FusedBatchNorm.  The two types only
+# differ in input order and attribute names, so we've collected their
+# differences up front.
+INPUT_ORDER = {
+    # Order of inputs for BatchNormWithGlobalNormalization.
+    "BatchNormWithGlobalNormalization": [
+        "conv_op", "mean_op", "var_op", "beta_op", "gamma_op"
+    ],
+    # Order of inputs for FusedBatchNorm.
+    "FusedBatchNorm": ["conv_op", "gamma_op", "beta_op", "mean_op", "var_op"]
+}
+# Name of the attribute epsilon value is stored in.
+EPSILON_ATTR = {
+    "BatchNormWithGlobalNormalization": "variance_epsilon",
+    "FusedBatchNorm": "epsilon"
+}
+
+
 def optimize_for_inference(input_graph_def, input_node_names, output_node_names,
                            placeholder_type_enum):
   """Applies a series of inference optimizations on the input graph.
@@ -85,10 +104,9 @@ def optimize_for_inference(input_graph_def, input_node_names, output_node_names,
   """
   ensure_graph_is_valid(input_graph_def)
   optimized_graph_def = input_graph_def
-  optimized_graph_def = strip_unused_lib.strip_unused(optimized_graph_def,
-                                                      input_node_names,
-                                                      output_node_names,
-                                                      placeholder_type_enum)
+  optimized_graph_def = strip_unused_lib.strip_unused(
+      optimized_graph_def, input_node_names, output_node_names,
+      placeholder_type_enum)
   optimized_graph_def = graph_util.remove_training_nodes(
       optimized_graph_def, output_node_names)
   optimized_graph_def = fold_batch_norms(optimized_graph_def)
@@ -173,6 +191,13 @@ def values_from_const(node_def):
   return tensor_value
 
 
+# Whether to scale by gamma after normalization.
+def scale_after_normalization(node):
+  if node.op == "BatchNormWithGlobalNormalization":
+    return node.attr["scale_after_normalization"].b
+  return True
+
+
 def fold_batch_norms(input_graph_def):
   """Removes batch normalization ops by folding them into convolutions.
 
@@ -195,7 +220,6 @@ def fold_batch_norms(input_graph_def):
   Raises:
     ValueError: If the graph is badly formed with duplicate node names.
   """
-
   input_node_map = {}
   for node in input_graph_def.node:
     if node.name not in input_node_map.keys():
@@ -206,13 +230,14 @@ def fold_batch_norms(input_graph_def):
   nodes_to_skip = {}
   new_ops = []
   for node in input_graph_def.node:
-    if node.op != "BatchNormWithGlobalNormalization":
+    if node.op not in ("BatchNormWithGlobalNormalization", "FusedBatchNorm"):
       continue
 
-    conv_op = node_from_map(input_node_map, node.input[0])
+    conv_op = node_from_map(input_node_map,
+                            node.input[INPUT_ORDER[node.op].index("conv_op")])
     if conv_op.op != "Conv2D":
-      tf_logging.warning("Didn't find expected Conv2D input to '%s'" %
-                         node.name)
+      tf_logging.warning(
+          "Didn't find expected Conv2D input to '%s'" % node.name)
       continue
 
     weights_op = node_from_map(input_node_map, conv_op.input[1])
@@ -224,7 +249,8 @@ def fold_batch_norms(input_graph_def):
     weights = values_from_const(weights_op)
     channel_count = weights.shape[3]
 
-    mean_op = node_from_map(input_node_map, node.input[1])
+    mean_op = node_from_map(input_node_map,
+                            node.input[INPUT_ORDER[node.op].index("mean_op")])
     if mean_op.op != "Const":
       tf_logging.warning("Didn't find expected mean Constant input to '%s',"
                          " found %s instead. Maybe because freeze_graph wasn't"
@@ -237,7 +263,8 @@ def fold_batch_norms(input_graph_def):
                              (channel_count,)), node.name))
       continue
 
-    var_op = node_from_map(input_node_map, node.input[2])
+    var_op = node_from_map(input_node_map,
+                           node.input[INPUT_ORDER[node.op].index("var_op")])
     if var_op.op != "Const":
       tf_logging.warning("Didn't find expected var Constant input to '%s',"
                          " found %s instead. Maybe because freeze_graph wasn't"
@@ -250,7 +277,8 @@ def fold_batch_norms(input_graph_def):
                              (channel_count,)), node.name))
       continue
 
-    beta_op = node_from_map(input_node_map, node.input[3])
+    beta_op = node_from_map(input_node_map,
+                            node.input[INPUT_ORDER[node.op].index("beta_op")])
     if beta_op.op != "Const":
       tf_logging.warning("Didn't find expected beta Constant input to '%s',"
                          " found %s instead. Maybe because freeze_graph wasn't"
@@ -263,7 +291,8 @@ def fold_batch_norms(input_graph_def):
                              (channel_count,)), node.name))
       continue
 
-    gamma_op = node_from_map(input_node_map, node.input[4])
+    gamma_op = node_from_map(input_node_map,
+                             node.input[INPUT_ORDER[node.op].index("gamma_op")])
     if gamma_op.op != "Const":
       tf_logging.warning("Didn't find expected gamma Constant input to '%s',"
                          " found %s instead. Maybe because freeze_graph wasn't"
@@ -276,8 +305,7 @@ def fold_batch_norms(input_graph_def):
                              (channel_count,)), node.name))
       continue
 
-    variance_epsilon_value = node.attr["variance_epsilon"].f
-    scale_after_normalization = node.attr["scale_after_normalization"].b
+    variance_epsilon_value = node.attr[EPSILON_ATTR[node.op]].f
     nodes_to_skip[node.name] = True
     nodes_to_skip[weights_op.name] = True
     nodes_to_skip[mean_op.name] = True
@@ -286,7 +314,7 @@ def fold_batch_norms(input_graph_def):
     nodes_to_skip[gamma_op.name] = True
     nodes_to_skip[conv_op.name] = True
 
-    if scale_after_normalization:
+    if scale_after_normalization(node):
       scale_value = (
           (1.0 / np.vectorize(math.sqrt)(var_value + variance_epsilon_value)) *
           gamma_value)
@@ -346,6 +374,8 @@ def fuse_resize_and_conv(input_graph_def, output_node_names):
 
   Args:
     input_graph_def: A GraphDef containing a model.
+    output_node_names: A list of names of the nodes that produce the final
+      results.
 
   Returns:
     Modified graph with resize and pad ops merged.
@@ -428,8 +458,8 @@ def fuse_resize_and_conv(input_graph_def, output_node_names):
           resize_op.input[0], resize_op.input[1], mirror_paddings_name,
           conv_op.input[1]
       ])
-      fused_conv_op.attr["resize_align_corners"].CopyFrom(resize_op.attr[
-          "align_corners"])
+      fused_conv_op.attr["resize_align_corners"].CopyFrom(
+          resize_op.attr["align_corners"])
     else:
       fused_conv_op.input.extend(
           [mirror_pad_op.input[0], mirror_paddings_name, conv_op.input[1]])
diff --git a/tensorflow/python/tools/optimize_for_inference_test.py b/tensorflow/python/tools/optimize_for_inference_test.py
index 7428fa76b8..e547d0e292 100644
--- a/tensorflow/python/tools/optimize_for_inference_test.py
+++ b/tensorflow/python/tools/optimize_for_inference_test.py
@@ -170,6 +170,50 @@ class OptimizeForInferenceTest(test.TestCase):
     for node in optimized_graph_def.node:
       self.assertNotEqual("BatchNormWithGlobalNormalization", node.op)
 
+  def testFoldFusedBatchNorms(self):
+    with self.test_session() as sess:
+      inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
+      input_op = constant_op.constant(
+          np.array(inputs), shape=[1, 1, 6, 2], dtype=dtypes.float32)
+      weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
+      weights_op = constant_op.constant(
+          np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32)
+      conv_op = nn_ops.conv2d(
+          input_op, weights_op, [1, 1, 1, 1], padding="SAME", name="conv_op")
+      mean_op = constant_op.constant(
+          np.array([10, 20]), shape=[2], dtype=dtypes.float32)
+      variance_op = constant_op.constant(
+          np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32)
+      beta_op = constant_op.constant(
+          np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32)
+      gamma_op = constant_op.constant(
+          np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32)
+      ops.get_default_graph().graph_def_versions.producer = 9
+      gen_nn_ops._fused_batch_norm(
+          conv_op,
+          gamma_op,
+          beta_op,
+          mean_op,
+          variance_op,
+          0.00001,
+          is_training=False,
+          name="output")
+      original_graph_def = sess.graph_def
+      original_result = sess.run(["output:0"])
+    optimized_graph_def = optimize_for_inference_lib.fold_batch_norms(
+        original_graph_def)
+
+    with self.test_session() as sess:
+      _ = importer.import_graph_def(
+          optimized_graph_def, input_map={}, name="optimized")
+      optimized_result = sess.run(["optimized/output:0"])
+
+    self.assertAllClose(
+        original_result, optimized_result, rtol=1e-04, atol=1e-06)
+
+    for node in optimized_graph_def.node:
+      self.assertNotEqual("FusedBatchNorm", node.op)
+
   def testFuseResizePadAndConv(self):
     with self.test_session() as sess:
       inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
index 066727614c..0978c336b4 100644
--- a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
+++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
@@ -54,7 +54,7 @@ Status FoldOldBatchNorms(const GraphDef& input_graph_def,
     GraphDef replaced_graph_def;
     TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes(
         current_graph_def,  // clang-format off
-      {"BatchNormWithGlobalNormalization",    // batch_norm_node
+      {"BatchNormWithGlobalNormalization|FusedBatchNorm",    // batch_norm_node
         {
           {"Conv2D",                          // conv_node
             {
@@ -74,19 +74,33 @@ Status FoldOldBatchNorms(const GraphDef& input_graph_def,
                             std::vector<NodeDef>* new_nodes) {
           // Find all the nodes we expect in the subgraph.
           const NodeDef& batch_norm_node = match.node;
-          CHECK_EQ("BatchNormWithGlobalNormalization", batch_norm_node.op());
+          // BatchNormWithGlobalNormalization and FusedBatchNorm ops only differ
+          // by input order and attribute names.
+          CHECK(batch_norm_node.op() == "BatchNormWithGlobalNormalization" ||
+                batch_norm_node.op() == "FusedBatchNorm");
+          const bool is_fused = batch_norm_node.op() == "FusedBatchNorm";
+          const int mean_idx = is_fused ? 3 : 1;
+          const int var_idx = is_fused ? 4 : 2;
+          const int beta_idx = is_fused ? 2 : 3;
+          const int gamma_idx = is_fused ? 1 : 4;
+          const string epsilon_attr = is_fused ? "epsilon" : "variance_epsilon";
+          // FusedBatchNorm always scales after normalization.
+          const bool scale_after_normalization =
+              is_fused ||
+              batch_norm_node.attr().at("scale_after_normalization").b();
+
           const NodeDef& conv_node = match.inputs[0].node;
           CHECK_EQ("Conv2D", conv_node.op());
           const NodeDef& input_node = match.inputs[0].inputs[0].node;
           const NodeDef& weights_node = match.inputs[0].inputs[1].node;
           CHECK_EQ("Const", weights_node.op());
-          const NodeDef& mean_node = match.inputs[1].node;
+          const NodeDef& mean_node = match.inputs[mean_idx].node;
           CHECK_EQ("Const", mean_node.op());
-          const NodeDef& variance_node = match.inputs[2].node;
+          const NodeDef& variance_node = match.inputs[var_idx].node;
           CHECK_EQ("Const", variance_node.op());
-          const NodeDef& beta_node = match.inputs[3].node;
+          const NodeDef& beta_node = match.inputs[beta_idx].node;
           CHECK_EQ("Const", beta_node.op());
-          const NodeDef& gamma_node = match.inputs[4].node;
+          const NodeDef& gamma_node = match.inputs[gamma_idx].node;
           CHECK_EQ("Const", gamma_node.op());
 
           // We have a set of vectors that we want to combine into a vector of
@@ -98,9 +112,7 @@ Status FoldOldBatchNorms(const GraphDef& input_graph_def,
           Tensor beta = GetNodeTensorAttr(beta_node, "value");
           Tensor gamma = GetNodeTensorAttr(gamma_node, "value");
           const float variance_epsilon =
-              batch_norm_node.attr().at("variance_epsilon").f();
-          const bool scale_after_normalization =
-              batch_norm_node.attr().at("scale_after_normalization").b();
+              batch_norm_node.attr().at(epsilon_attr).f();
 
           // Make sure all the inputs really are vectors, with as many entries
           // as there are columns in the weights.
@@ -119,16 +131,17 @@ Status FoldOldBatchNorms(const GraphDef& input_graph_def,
               scale_values[i] =
                   (1.0f / sqrtf(variance.flat<float>()(i) + variance_epsilon)) *
                   gamma.flat<float>()(i);
-              offset_values[i] = 0.0f;
             }
           } else {
             for (int i = 0; i < weights_cols; ++i) {
               scale_values[i] =
                   (1.0f / sqrtf(variance.flat<float>()(i) + variance_epsilon));
-              offset_values[i] = (-mean.flat<float>()(i) * scale_values[i]) +
-                                 beta.flat<float>()(i);
             }
           }
+          for (int i = 0; i < weights_cols; ++i) {
+            offset_values[i] = (-mean.flat<float>()(i) * scale_values[i]) +
+                               beta.flat<float>()(i);
+          }
 
           // Multiply the original weights by the scale vector.
           auto weights_matrix = weights.flat_inner_dims<float>();
diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc
index 8123b0a0c6..3be9110b47 100644
--- a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc
+++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc
@@ -119,11 +119,92 @@ class FoldOldBatchNormsTest : public ::testing::Test {
       EXPECT_NE("BatchNormWithGlobalNormalization", node.op());
     }
   }
+
+  void TestFoldFusedBatchNorms() {
+    auto root = tensorflow::Scope::NewRootScope();
+    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+
+    Tensor input_data(DT_FLOAT, TensorShape({1, 1, 6, 2}));
+    test::FillValues<float>(
+        &input_data, {1.0f, 4.0f, 2.0f, 5.0f, 3.0f, 6.0f, -1.0f, -4.0f, -2.0f,
+                      -5.0f, -3.0f, -6.0f});
+    Output input_op =
+        Const(root.WithOpName("input_op"), Input::Initializer(input_data));
+
+    Tensor weights_data(DT_FLOAT, TensorShape({1, 2, 2, 2}));
+    test::FillValues<float>(&weights_data,
+                            {1.0f, 2.0f, 3.0f, 4.0f, 0.1f, 0.2f, 0.3f, 0.4f});
+    Output weights_op =
+        Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
+
+    Output conv_op = Conv2D(root.WithOpName("conv_op"), input_op, weights_op,
+                            {1, 1, 1, 1}, "VALID");
+
+    Tensor mean_data(DT_FLOAT, TensorShape({2}));
+    test::FillValues<float>(&mean_data, {10.0f, 20.0f});
+    Output mean_op =
+        Const(root.WithOpName("mean_op"), Input::Initializer(mean_data));
+
+    Tensor variance_data(DT_FLOAT, TensorShape({2}));
+    test::FillValues<float>(&variance_data, {0.25f, 0.5f});
+    Output variance_op = Const(root.WithOpName("variance_op"),
+                               Input::Initializer(variance_data));
+
+    Tensor beta_data(DT_FLOAT, TensorShape({2}));
+    test::FillValues<float>(&beta_data, {0.1f, 0.6f});
+    Output beta_op =
+        Const(root.WithOpName("beta_op"), Input::Initializer(beta_data));
+
+    Tensor gamma_data(DT_FLOAT, TensorShape({2}));
+    test::FillValues<float>(&gamma_data, {1.0f, 2.0f});
+    Output gamma_op =
+        Const(root.WithOpName("gamma_op"), Input::Initializer(gamma_data));
+
+    GraphDef original_graph_def;
+    TF_ASSERT_OK(root.ToGraphDef(&original_graph_def));
+
+    NodeDef batch_norm_node;
+    batch_norm_node.set_op("FusedBatchNorm");
+    batch_norm_node.set_name("output");
+    AddNodeInput("conv_op", &batch_norm_node);
+    AddNodeInput("gamma_op", &batch_norm_node);
+    AddNodeInput("beta_op", &batch_norm_node);
+    AddNodeInput("mean_op", &batch_norm_node);
+    AddNodeInput("variance_op", &batch_norm_node);
+    SetNodeAttr("T", DT_FLOAT, &batch_norm_node);
+    SetNodeAttr("epsilon", 0.00001f, &batch_norm_node);
+    SetNodeAttr("is_training", false, &batch_norm_node);
+    *(original_graph_def.mutable_node()->Add()) = batch_norm_node;
+
+    std::unique_ptr<Session> original_session(NewSession(SessionOptions()));
+    TF_ASSERT_OK(original_session->Create(original_graph_def));
+    std::vector<Tensor> original_outputs;
+    TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs));
+
+    GraphDef fused_graph_def;
+    TF_ASSERT_OK(FoldOldBatchNorms(original_graph_def, {{}, {"output"}},
+                                   &fused_graph_def));
+
+    std::unique_ptr<Session> fused_session(NewSession(SessionOptions()));
+    TF_ASSERT_OK(fused_session->Create(fused_graph_def));
+    std::vector<Tensor> fused_outputs;
+    TF_ASSERT_OK(fused_session->Run({}, {"output"}, {}, &fused_outputs));
+
+    test::ExpectTensorNear<float>(original_outputs[0], fused_outputs[0], 1e-5);
+
+    for (const NodeDef& node : fused_graph_def.node()) {
+      EXPECT_NE("FusedBatchNorm", node.op());
+    }
+  }
 };
 
 TEST_F(FoldOldBatchNormsTest, TestFoldOldBatchNorms) {
   TestFoldOldBatchNorms();
 }
 
+TEST_F(FoldOldBatchNormsTest, TestFoldFusedBatchNorms) {
+  TestFoldFusedBatchNorms();
+}
+
 }  // namespace graph_transforms
 }  // namespace tensorflow
-- 
GitLab


From 4eb71a7f1aa01639a57b67413e7225fc26512ead Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 29 Jun 2017 16:30:42 -0700
Subject: [PATCH 0609/1375] Don't crash when converting ill formed graphs to
 graph defs: this can happen with legacy fed inputs whose 2 inputs may remain
 unconnected.

PiperOrigin-RevId: 160589677
---
 tensorflow/core/graph/graph.cc | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 9469e3f98f..7a5e76fdd0 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -502,7 +502,11 @@ void Graph::ToGraphDefSubRange(GraphDef* graph_def, int from_node_id) const {
     for (size_t i = 0; i < inputs.size(); ++i) {
       const Edge* edge = inputs[i];
       if (edge == nullptr) {
-        node_def->add_input(node->requested_inputs()[i]);
+        if (i < node->requested_inputs().size()) {
+          node_def->add_input(node->requested_inputs()[i]);
+        } else {
+          node_def->add_input("");
+        }
       } else {
         const Node* src = edge->src();
         if (!src->IsOp()) continue;
-- 
GitLab


From 38faead386d9d19b9af10150ac9d0cddd7b788e8 Mon Sep 17 00:00:00 2001
From: Mark Heffernan <meheff@google.com>
Date: Thu, 29 Jun 2017 16:51:03 -0700
Subject: [PATCH 0610/1375] [XLA] Move HLO reachability into its own file and
 make update-able. As part of the CL, change the underlying representation in
 the reachability map to BitVectors which allows efficient update by OR'ing
 the vectors together.

PiperOrigin-RevId: 160591849
---
 tensorflow/compiler/xla/service/BUILD         |  32 ++++
 tensorflow/compiler/xla/service/gpu/BUILD     |   2 +
 .../compiler/xla/service/gpu/hlo_schedule.cc  |  43 +++---
 .../xla/service/gpu/stream_assignment.cc      |   7 +-
 .../compiler/xla/service/hlo_computation.cc   |  96 +++++-------
 .../compiler/xla/service/hlo_computation.h    |  44 ++----
 .../xla/service/hlo_computation_test.cc       |  24 +++
 .../compiler/xla/service/hlo_instruction.cc   |   2 +-
 .../compiler/xla/service/hlo_ordering.h       |   2 +-
 .../compiler/xla/service/hlo_reachability.cc  |  61 ++++++++
 .../compiler/xla/service/hlo_reachability.h   | 138 ++++++++++++++++++
 .../xla/service/hlo_reachability_test.cc      |  86 +++++++++++
 .../xla/service/instruction_fusion.cc         |   5 +-
 .../compiler/xla/service/instruction_fusion.h |   7 +-
 14 files changed, 421 insertions(+), 128 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/hlo_reachability.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_reachability.h
 create mode 100644 tensorflow/compiler/xla/service/hlo_reachability_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 1e7c36758c..08fd10172c 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -141,6 +141,7 @@ cc_library(
     deps = [
         ":hlo_module_config",
         ":hlo_proto",
+        ":hlo_reachability",
         ":name_uniquer",
         ":versioned_computation_handle",
         "//tensorflow/compiler/xla:literal_util",
@@ -158,6 +159,37 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "hlo_reachability",
+    srcs = [
+        "hlo_reachability.cc",
+    ],
+    hdrs = [
+        "hlo_reachability.h",
+    ],
+    deps = [
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
+cc_test(
+    name = "hlo_reachability_test",
+    srcs = ["hlo_reachability_test.cc"],
+    deps = [
+        ":hlo",
+        ":hlo_reachability",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:test_helpers",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/core:test_main",
+    ],
+)
+
 cc_library(
     name = "hlo_matchers",
     testonly = 1,
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index 6b7ab7a864..6809099695 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -70,6 +70,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/legacy_flags:stream_assignment_flags",
         "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:hlo_reachability",
         "//tensorflow/core:lib",
     ],
 )
@@ -499,6 +500,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_ordering",
+        "//tensorflow/compiler/xla/service:hlo_reachability",
         "//tensorflow/compiler/xla/service:hlo_scheduling",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
index f964ffb748..c61e47a93c 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/gpu/hlo_schedule.h"
 
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_reachability.h"
 #include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/types.h"
 
@@ -67,41 +68,39 @@ GpuHloOrdering::GpuHloOrdering(
   // GpuExecutable adds cross-stream dependency edges to ensure each instruction
   // waits for its operands before executing.
   //
-  // The predecessor map is built incrementally, in reverse thunk launch
-  // order. We record the most-recently seen instructions per stream in
-  // 'earliest_instruction_per_stream'. This lets us quickly determine the
+  // The predecessor map is built incrementally, in thunk launch order. We
+  // record the most-recently seen instructions per stream in
+  // 'last_instruction_per_stream'. This lets us quickly determine the
   // same-stream predecessors of each instruction.
 
   // Compute the set of all instructions we will want to set reachability on.
-  auto predecessor_map = MakeUnique<HloComputation::ReachabilityMap>(
+  auto predecessor_map = MakeUnique<HloReachabilityMap>(
       module->entry_computation()->MakeInstructionPostOrder());
 
   // The most recently visited instruction per stream.
-  std::vector<const HloInstruction*> earliest_instruction_per_stream(
+  std::vector<const HloInstruction*> last_instruction_per_stream(
       stream_assignment.StreamCount(), nullptr);
 
-  for (auto it = thunk_launch_order.rbegin(); it != thunk_launch_order.rend();
-       ++it) {
-    const HloInstruction* hlo = *it;
+  for (const HloInstruction* hlo : thunk_launch_order) {
     predecessor_map->SetReachable(hlo, hlo);
     if (stream_assignment.HasStreamAssigned(*hlo)) {
+      // Gather all instruction which are immediate predecessors of 'hlo' in the
+      // reachability graph.
+      std::vector<const HloInstruction*> immediate_preds;
+      immediate_preds.insert(immediate_preds.end(), hlo->operands().begin(),
+                             hlo->operands().end());
+      immediate_preds.insert(immediate_preds.end(),
+                             hlo->control_predecessors().begin(),
+                             hlo->control_predecessors().end());
+
       // All ops already queued on the same instruction stream, and their
-      // transitive predecessors, are predecessors. Since the relation is
-      // transitive, we just set the transitive closure of the previous op.
+      // transitive predecessors, are predecessors.
       const int stream_no = stream_assignment.StreamNumberForHlo(*hlo);
-      if (earliest_instruction_per_stream[stream_no] != nullptr) {
-        // Because we are iterating in reverse order, 'hlo' precedes the
-        // last visited instruction on this stream.
-        predecessor_map->SetReachableAndTransitiveClosure(
-            hlo, earliest_instruction_per_stream[stream_no]);
-      }
-      for (const HloInstruction* operand : hlo->operands()) {
-        predecessor_map->SetReachableAndTransitiveClosure(operand, hlo);
-      }
-      for (const HloInstruction* pred : hlo->control_predecessors()) {
-        predecessor_map->SetReachableAndTransitiveClosure(pred, hlo);
+      if (last_instruction_per_stream[stream_no] != nullptr) {
+        immediate_preds.push_back(last_instruction_per_stream[stream_no]);
       }
-      earliest_instruction_per_stream[stream_no] = hlo;
+      predecessor_map->SetReachabilityToUnion(immediate_preds, hlo);
+      last_instruction_per_stream[stream_no] = hlo;
     } else {
       // Only parameters and constants don't have an assigned stream, since they
       // don't require a thunk. These ops don't have any predecessors.
diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
index 02da005f6f..a304decc49 100644
--- a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
+++ b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_reachability.h"
 
 namespace xla {
 namespace gpu {
@@ -47,7 +48,7 @@ namespace {
 // Returns whether the two HLOs can run concurrently, i.e., neither is a
 // transitive consumer of the other.
 bool CanRunConcurrently(const HloInstruction& a, const HloInstruction& b,
-                        const HloComputation::ReachabilityMap& reachability) {
+                        const HloReachabilityMap& reachability) {
   return !reachability.IsConnected(&a, &b);
 }
 
@@ -57,7 +58,7 @@ bool CanRunConcurrently(const HloInstruction& a, const HloInstruction& b,
 // are topologically before `hlo`.
 int ComputeStreamToAssign(
     const HloInstruction& hlo, const StreamAssignment& stream_assignment,
-    const HloComputation::ReachabilityMap& reachability,
+    const HloReachabilityMap& reachability,
     const std::vector<const HloInstruction*>& seen_gemms) {
   if (hlo.opcode() == HloOpcode::kParameter ||
       hlo.opcode() == HloOpcode::kConstant) {
@@ -114,7 +115,7 @@ int ComputeStreamToAssign(
 std::unique_ptr<StreamAssignment> AssignStreams(const HloModule& module) {
   auto stream_assignment = MakeUnique<StreamAssignment>();
   const HloComputation& computation = *module.entry_computation();
-  std::unique_ptr<HloComputation::ReachabilityMap> reachability =
+  std::unique_ptr<HloReachabilityMap> reachability =
       computation.ComputeReachability();
   std::vector<const HloInstruction*> seen_gemms;
   for (const auto* hlo : computation.MakeInstructionPostOrder()) {
diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index 4b33947371..fb6dd75046 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -537,72 +537,46 @@ Status HloComputation::ReplaceInstruction(HloInstruction* old_instruction,
   return RemoveInstructionAndUnusedOperands(old_instruction);
 }
 
-HloComputation::ReachabilityMap::ReachabilityMap(
-    const std::list<HloInstruction*>& all_instructions) {
-  const int n = all_instructions.size();
-  int next_id = 0;
-  for (const auto* hlo : all_instructions) {
-    ids_[hlo] = next_id;
-    next_id++;
-  }
-  DCHECK_EQ(n, ids_.size());  // instructions should be unique
-  matrix_.Reset(n * n);
-}
-
-void HloComputation::ReachabilityMap::SetReachable(const HloInstruction* a,
-                                                   const HloInstruction* b) {
-  const int id_a = FindOrDie(ids_, a);
-  const int id_b = FindOrDie(ids_, b);
-  matrix_.set(id_a * ids_.size() + id_b);
-}
-
-bool HloComputation::ReachabilityMap::IsReachable(
-    const HloInstruction* a, const HloInstruction* b) const {
-  const int id_a = FindOrDie(ids_, a);
-  const int id_b = FindOrDie(ids_, b);
-  return matrix_.get(id_a * ids_.size() + id_b);
-}
-
-bool HloComputation::ReachabilityMap::IsConnected(
-    const HloInstruction* a, const HloInstruction* b) const {
-  const int id_a = FindOrDie(ids_, a);
-  const int id_b = FindOrDie(ids_, b);
-  return matrix_.get(id_a * ids_.size() + id_b) ||
-         matrix_.get(id_b * ids_.size() + id_a);
-}
-
-void HloComputation::ReachabilityMap::SetReachableAndTransitiveClosure(
-    const HloInstruction* a, const HloInstruction* b) {
-  const int id_a = FindOrDie(ids_, a);
-  const int id_b = FindOrDie(ids_, b);
-  const int n = ids_.size();
-  matrix_.set(id_a * n + id_b);
-
-  // Copy transitive set for b into entries for a
-  for (int i = 0; i < n; i++) {
-    if (matrix_.get(id_b * n + i)) {
-      matrix_.set(id_a * n + i);
-    }
+std::unique_ptr<HloReachabilityMap> HloComputation::ComputeReachability()
+    const {
+  const std::list<HloInstruction*> all = MakeInstructionPostOrder();
+  auto result = MakeUnique<HloReachabilityMap>(all);
+
+  std::vector<HloInstruction*> inputs;
+  for (const HloInstruction* hlo : all) {
+    inputs.assign(hlo->operands().begin(), hlo->operands().end());
+    inputs.insert(inputs.end(), hlo->control_predecessors().begin(),
+                  hlo->control_predecessors().end());
+    result->SetReachabilityToUnion(inputs, hlo);
   }
+  return result;
 }
 
-std::unique_ptr<HloComputation::ReachabilityMap>
-HloComputation::ComputeReachability() const {
-  const std::list<HloInstruction*> all = MakeInstructionPostOrder();
-  auto result = MakeUnique<HloComputation::ReachabilityMap>(all);
-
-  // Fill in the dependency bit matrix. Iterate in reverse topological order.
-  for (auto it = all.rbegin(); it != all.rend(); ++it) {
-    const HloInstruction* hlo = *it;
-    result->SetReachable(hlo, hlo);
-    for (const HloInstruction* operand : hlo->operands()) {
-      result->SetReachableAndTransitiveClosure(operand, hlo);
-    }
-    for (const HloInstruction* pred : hlo->control_predecessors()) {
-      result->SetReachableAndTransitiveClosure(pred, hlo);
+void HloComputation::UpdateReachabilityThroughInstruction(
+    const HloInstruction* instruction, HloReachabilityMap* reachability_map) {
+  std::queue<const HloInstruction*> worklist;
+  worklist.push(instruction);
+
+  std::vector<HloInstruction*> inputs;
+
+  while (!worklist.empty()) {
+    const HloInstruction* item = worklist.front();
+    worklist.pop();
+
+    inputs.assign(item->operands().begin(), item->operands().end());
+    inputs.insert(inputs.end(), item->control_predecessors().begin(),
+                  item->control_predecessors().end());
+
+    if (reachability_map->SetReachabilityToUnion(inputs, item)) {
+      // Add immediate successors to worklist.
+      for (const HloInstruction* user : item->users()) {
+        worklist.push(user);
+      }
+      for (const HloInstruction* succ : item->control_successors()) {
+        worklist.push(succ);
+      }
     }
   }
-  return result;
 }
 
 std::vector<HloInstruction*> HloComputation::CollectUnreachableRoots() const {
diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h
index 27d672d42b..cf6df3c94f 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.h
+++ b/tensorflow/compiler/xla/service/hlo_computation.h
@@ -29,11 +29,11 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_reachability.h"
 #include "tensorflow/compiler/xla/service/name_uniquer.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/bitmap.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
@@ -154,13 +154,17 @@ class HloComputation {
   std::list<HloInstruction*> MakeInstructionPostOrder() const;
 
   // Computes and returns the reachability between HLO instructions in the
-  // computation. The returned ReachabilityMap is constructed such that
-  // ReachabilityMap::IsReachable(a, b) returns true iff there exists a directed
-  // path (from producer to consumer) from 'a' to 'b'. Both data dependencies
-  // (operands) and control dependencies are considered for
+  // computation. The returned HloReachabilityMap is constructed such that
+  // HloReachabilityMap::IsReachable(a, b) returns true iff there exists a
+  // directed path (from producer to consumer) from 'a' to 'b'. Both data
+  // dependencies (operands) and control dependencies are considered for
   // reachability. Trivially an instruction is reachable from itself.
-  class ReachabilityMap;
-  std::unique_ptr<ReachabilityMap> ComputeReachability() const;
+  std::unique_ptr<HloReachabilityMap> ComputeReachability() const;
+
+  // Updates the given reachabilty map after the immediate predecessor set
+  // (operands and control predecessors) of 'instruction' has changed.
+  void UpdateReachabilityThroughInstruction(
+      const HloInstruction* instruction, HloReachabilityMap* reachability_map);
 
   int64 instruction_count() const { return instructions_.size(); }
 
@@ -313,32 +317,6 @@ class HloComputation {
   TF_DISALLOW_COPY_AND_ASSIGN(HloComputation);
 };
 
-class HloComputation::ReachabilityMap {
- public:
-  // Sets up an empty reachable matrix for the full set of
-  // instructions specified in "all_instructions"
-  explicit ReachabilityMap(const std::list<HloInstruction*>& all_instructions);
-  // Sets entry so that IsReachable(a, b) will return true
-  void SetReachable(const HloInstruction* a, const HloInstruction* b);
-
-  // Sets IsReachable(a_inst, b_inst) as well as IsReachable(a_inst, trans)
-  // for all "trans" s.t. "IsReachable(b_inst, trans)" is true
-  void SetReachableAndTransitiveClosure(const HloInstruction* a_inst,
-                                        const HloInstruction* b_inst);
-
-  // Returns true if "b" is reachable from "a"
-  bool IsReachable(const HloInstruction* a, const HloInstruction* b) const;
-
-  // Returns true if "b" is reachable from "a" or "a" is reachable from "b"
-  bool IsConnected(const HloInstruction* a, const HloInstruction* b) const;
-
- private:
-  // dense id assignment from HloInstruction* to number
-  tensorflow::gtl::FlatMap<const HloInstruction*, int> ids_;
-  // matrix_(a,b) is true iff b is reachable from a
-  tensorflow::core::Bitmap matrix_;
-};
-
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_COMPUTATION_H_
diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc
index c912e97f42..4a4a855669 100644
--- a/tensorflow/compiler/xla/service/hlo_computation_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc
@@ -425,6 +425,30 @@ TEST_F(HloComputationTest, Reachability) {
   EXPECT_TRUE(reachability->IsConnected(copy, constant1));
   EXPECT_FALSE(reachability->IsConnected(negate, add));
   EXPECT_FALSE(reachability->IsConnected(add, negate));
+
+  // Remove the control dependency then update and verify the reachability map
+  ASSERT_IS_OK(add->RemoveControlDependencyTo(exp));
+  computation->UpdateReachabilityThroughInstruction(exp, reachability.get());
+
+  EXPECT_TRUE(reachability->IsReachable(constant1, constant1));
+  EXPECT_FALSE(reachability->IsReachable(constant1, constant2));
+  EXPECT_TRUE(reachability->IsReachable(constant1, add));
+  EXPECT_FALSE(reachability->IsReachable(constant1, negate));
+  EXPECT_FALSE(reachability->IsReachable(constant1, exp));
+  EXPECT_TRUE(reachability->IsReachable(constant1, mul));
+  EXPECT_FALSE(reachability->IsReachable(constant1, copy));
+
+  // Change a use within the graph then update and verify the reachability map
+  ASSERT_IS_OK(constant2->ReplaceUseWith(negate, constant1));
+  computation->UpdateReachabilityThroughInstruction(negate, reachability.get());
+
+  EXPECT_FALSE(reachability->IsReachable(constant2, constant1));
+  EXPECT_TRUE(reachability->IsReachable(constant2, constant2));
+  EXPECT_TRUE(reachability->IsReachable(constant2, add));
+  EXPECT_FALSE(reachability->IsReachable(constant2, negate));
+  EXPECT_FALSE(reachability->IsReachable(constant2, exp));
+  EXPECT_TRUE(reachability->IsReachable(constant2, mul));
+  EXPECT_FALSE(reachability->IsReachable(constant2, copy));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index c7708968a2..6284bbbeb3 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1081,7 +1081,7 @@ Status HloInstruction::RemoveControlDependencyTo(HloInstruction* instruction) {
   auto pred_it = std::find(instruction->control_predecessors_.begin(),
                            instruction->control_predecessors_.end(), this);
   TF_RET_CHECK(pred_it != instruction->control_predecessors_.end());
-  instruction->control_predecessors_.erase(succ_it);
+  instruction->control_predecessors_.erase(pred_it);
 
   return Status::OK();
 }
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.h b/tensorflow/compiler/xla/service/hlo_ordering.h
index b4cd78dd2e..130431f280 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.h
+++ b/tensorflow/compiler/xla/service/hlo_ordering.h
@@ -93,7 +93,7 @@ class PredecessorHloOrdering : public HloOrdering {
   //
   // Subclasses should fill this in to define the desired ordering.
   tensorflow::gtl::FlatMap<const HloComputation*,
-                           std::unique_ptr<HloComputation::ReachabilityMap>>
+                           std::unique_ptr<HloReachabilityMap>>
       predecessors_;
 };
 
diff --git a/tensorflow/compiler/xla/service/hlo_reachability.cc b/tensorflow/compiler/xla/service/hlo_reachability.cc
new file mode 100644
index 0000000000..fb7ecbdc2a
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_reachability.cc
@@ -0,0 +1,61 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_reachability.h"
+
+namespace xla {
+
+HloReachabilityMap::HloReachabilityMap(
+    const std::list<HloInstruction*>& instructions)
+    : size_(instructions.size()) {
+  bit_vectors_.reserve(size_);
+  for (const HloInstruction* hlo : instructions) {
+    indices_[hlo] = bit_vectors_.size();
+    bit_vectors_.emplace_back(size_);
+  }
+  CHECK_EQ(size_, indices_.size());  // instructions should be unique
+}
+
+bool HloReachabilityMap::SetReachabilityToUnion(
+    tensorflow::gtl::ArraySlice<const HloInstruction*> inputs,
+    const HloInstruction* instruction) {
+  BitVector& bit_vector = GetBitVector(instruction);
+  tmp_bit_vector_ = bit_vector;
+
+  bit_vector.SetToZero();
+  bit_vector.Set(GetIndex(instruction));
+  for (const HloInstruction* input : inputs) {
+    bit_vector.OrWith(GetBitVector(input));
+  }
+
+  return bit_vector != tmp_bit_vector_;
+}
+
+void HloReachabilityMap::SetReachable(const HloInstruction* a,
+                                      const HloInstruction* b) {
+  GetBitVector(b).Set(GetIndex(a));
+}
+
+bool HloReachabilityMap::IsReachable(const HloInstruction* a,
+                                     const HloInstruction* b) const {
+  return GetBitVector(b).Get(GetIndex(a));
+}
+
+bool HloReachabilityMap::IsConnected(const HloInstruction* a,
+                                     const HloInstruction* b) const {
+  return IsReachable(a, b) || IsReachable(b, a);
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_reachability.h b/tensorflow/compiler/xla/service/hlo_reachability.h
new file mode 100644
index 0000000000..d7bdac9c86
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_reachability.h
@@ -0,0 +1,138 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_REACHABILITY_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_REACHABILITY_H_
+
+#include <list>
+#include <vector>
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+
+class HloInstruction;
+
+// A class for computing and representing reachability between HloInstructions.
+class HloReachabilityMap {
+ public:
+  // Sets up an empty reachable matrix for the full set of instructions
+  // specified in 'instructions'.
+  explicit HloReachabilityMap(const std::list<HloInstruction*>& instructions);
+
+  // Set the reachability set of 'instruction' to the union of the reachability
+  // sets of 'inputs'. Upon return, IsReachable(x, instruction) where
+  // 'x' is not 'instruction' will return true iff IsReachable(x, input) is true
+  // for some 'input' in 'inputs'. Also sets 'instruction' to be reachable from
+  // itself. Returns whether the reachability set of 'instruction' changed.
+  bool SetReachabilityToUnion(
+      tensorflow::gtl::ArraySlice<const HloInstruction*> inputs,
+      const HloInstruction* instruction);
+
+  // Sets entry so that IsReachable(a, b) will return true
+  void SetReachable(const HloInstruction* a, const HloInstruction* b);
+
+  // Returns true if "b" is reachable from "a"
+  bool IsReachable(const HloInstruction* a, const HloInstruction* b) const;
+
+  // Returns true if "b" is reachable from "a" or "a" is reachable from "b"
+  bool IsConnected(const HloInstruction* a, const HloInstruction* b) const;
+
+ private:
+  // A bit-vector implementation specialized for this use case which provides a
+  // fast bitwise OR operation not available in tensorflow::gtl::BitMap.
+  class BitVector {
+   public:
+    BitVector() = default;
+    BitVector(size_t size)
+        : size_(size), vector_((size + kBits - 1) / kBits, 0) {}
+
+    // Return the bit at the given index.
+    bool Get(size_t index) const {
+      DCHECK(index >= 0 && index < size_);
+      return vector_[index / kBits] & (1ull << (index % kBits));
+    }
+
+    // Set the bit at the given index.
+    void Set(size_t index) {
+      DCHECK(index >= 0 && index < size_);
+      vector_[index / kBits] |= 1ull << (index % kBits);
+    }
+
+    // Set this bitvector to the Logical OR of this bitvector and 'other'.
+    void OrWith(const BitVector& other) {
+      for (size_t i = 0; i < vector_.size(); ++i) {
+        vector_[i] |= other.vector_[i];
+      }
+    }
+
+    // Set the bitvector to all zeros.
+    void SetToZero() { std::fill(vector_.begin(), vector_.end(), 0); }
+
+    bool operator==(const BitVector& other) const {
+      return vector_ == other.vector_;
+    }
+    bool operator!=(const BitVector& other) const {
+      return vector_ != other.vector_;
+    }
+
+   private:
+    using Word = uint64;
+    static const size_t kBits = 64;
+
+    // Number of bits in the bitvector.
+    size_t size_;
+
+    std::vector<Word> vector_;
+  };
+
+  // Return the bitvector storing the reachability-to of the given instruction.
+  const BitVector& GetBitVector(const HloInstruction* instruction) const {
+    return bit_vectors_[GetIndex(instruction)];
+  }
+  BitVector& GetBitVector(const HloInstruction* instruction) {
+    return bit_vectors_[GetIndex(instruction)];
+  }
+
+  // Return the index of the given instruction. The value is used to index into
+  // the vector of BitVectors and the BitVectors themselves.
+  int GetIndex(const HloInstruction* instruction) const {
+    return FindOrDie(indices_, instruction);
+  }
+
+  // The number of instructions in the reachability map.
+  const size_t size_;
+
+  // Dense assignment from HloInstruction* to number. These numbers index
+  // into the bit_vectors_ vector and into the bits within a BitVector.
+  tensorflow::gtl::FlatMap<const HloInstruction*, int> indices_;
+
+  // Bitvectors holding the reachability to each instruction. The bit vector for
+  // instruction X includes ones for each instruction which X is reachable from.
+  std::vector<BitVector> bit_vectors_;
+
+  // A temporary used by SetReachabilityToUnion to avoid an allocation with each
+  // call to the method.
+  BitVector tmp_bit_vector_;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_REACHABILITY_H_
diff --git a/tensorflow/compiler/xla/service/hlo_reachability_test.cc b/tensorflow/compiler/xla/service/hlo_reachability_test.cc
new file mode 100644
index 0000000000..657a9ee83d
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_reachability_test.cc
@@ -0,0 +1,86 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_reachability.h"
+
+#include <set>
+
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/test_helpers.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+
+namespace xla {
+
+namespace {
+
+class HloReachabilityTest : public HloTestBase {};
+
+TEST_F(HloReachabilityTest, Reachability) {
+  // Construct and test a reachability graph of the following form:
+  /*
+       a
+      / \
+     b   c
+      \ / \
+       d   e
+  */
+  auto builder = HloComputation::Builder(TestName());
+  auto a = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
+  auto b = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
+  auto c = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
+  auto d = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
+  auto e = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(0.0f)));
+  builder.Build();
+
+  HloReachabilityMap reachability({a, b, c, d, e});
+  reachability.SetReachable(a, a);
+  EXPECT_TRUE(reachability.SetReachabilityToUnion({a}, b));
+  EXPECT_TRUE(reachability.SetReachabilityToUnion({a}, c));
+  EXPECT_TRUE(reachability.SetReachabilityToUnion({b, c}, d));
+  EXPECT_TRUE(reachability.SetReachabilityToUnion({c}, e));
+
+  EXPECT_TRUE(reachability.IsReachable(a, a));
+  EXPECT_TRUE(reachability.IsReachable(a, b));
+  EXPECT_TRUE(reachability.IsReachable(a, c));
+  EXPECT_TRUE(reachability.IsReachable(a, d));
+  EXPECT_TRUE(reachability.IsReachable(a, e));
+
+  EXPECT_FALSE(reachability.IsReachable(b, a));
+  EXPECT_TRUE(reachability.IsReachable(b, b));
+  EXPECT_FALSE(reachability.IsReachable(b, c));
+  EXPECT_TRUE(reachability.IsReachable(b, d));
+  EXPECT_FALSE(reachability.IsReachable(b, e));
+
+  EXPECT_FALSE(reachability.IsReachable(e, a));
+  EXPECT_FALSE(reachability.IsReachable(e, b));
+  EXPECT_FALSE(reachability.IsReachable(e, c));
+  EXPECT_FALSE(reachability.IsReachable(e, d));
+  EXPECT_TRUE(reachability.IsReachable(e, e));
+
+  // Recomputing the same reachability for a previously computed instruction
+  // should return false (no change).
+  EXPECT_FALSE(reachability.SetReachabilityToUnion({a}, b));
+  EXPECT_FALSE(reachability.SetReachabilityToUnion({b, c}, d));
+}
+
+}  // namespace
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 121d163c9f..2a66943f9b 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -145,9 +145,8 @@ bool EffectivelyUnary(HloInstruction* hlo) {
 }  // namespace
 
 bool InstructionFusion::CanFuseOnAllPaths(
-    const HloComputation::ReachabilityMap& reachability_map,
-    HloInstruction* producer, HloInstruction* consumer,
-    DoNotFuseSet* do_not_fuse) {
+    const HloReachabilityMap& reachability_map, HloInstruction* producer,
+    HloInstruction* consumer, DoNotFuseSet* do_not_fuse) {
   auto could_fuse_on_all_paths = [&] {
     // First check to see if we have already marked this producer as infeasible
     // to fuse into consumer.
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.h b/tensorflow/compiler/xla/service/instruction_fusion.h
index 06f03f63af..f6f37bb79b 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.h
+++ b/tensorflow/compiler/xla/service/instruction_fusion.h
@@ -77,10 +77,9 @@ class InstructionFusion : public HloPassInterface {
 
   // Whether or not we can fuse consumer into original_producer on all paths
   // from the producer to the consumer where nodes are HLOs and edges are uses.
-  bool CanFuseOnAllPaths(
-      const HloComputation::ReachabilityMap& reachability_map,
-      HloInstruction* producer, HloInstruction* consumer,
-      DoNotFuseSet* do_not_fuse);
+  bool CanFuseOnAllPaths(const HloReachabilityMap& reachability_map,
+                         HloInstruction* producer, HloInstruction* consumer,
+                         DoNotFuseSet* do_not_fuse);
 
   // Used to determine if an HLO is expensive. Expensive operations will not be
   // duplicated.
-- 
GitLab


From cd15cb8206d19dde403e9e9c2293fa9dea7ec9ee Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 16:57:04 -0700
Subject: [PATCH 0611/1375] Remove unused internal facility for loss functions
 to colocate logit computations with the weights.

PiperOrigin-RevId: 160592547
---
 tensorflow/python/ops/nn_impl.py | 91 ++++++--------------------------
 tensorflow/python/ops/nn_test.py | 81 ----------------------------
 2 files changed, 16 insertions(+), 156 deletions(-)

diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 5aebdb5cd0..7bc99ac725 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -881,70 +881,6 @@ def _sum_rows(x):
   return array_ops.reshape(math_ops.matmul(x, ones), [-1])
 
 
-def _retrieve_weights(weights, inputs, labels_flat, sampled, all_ids,
-                      partition_strategy, colocate_logits):
-  """Helper function for _compute_sampled_logits.
-
-  Retrieves the true weights and the logits of the sampled weights.
-
-  Args:
-    weights: See _compute_sampled_logits.
-    inputs: See _compute_sampled_logits.
-    labels_flat: A [batch_size * num_true] tensor.
-    sampled: A [num_sampled] tensor.
-    all_ids: The concatenation of labels_flat and sampled.
-    partition_strategy: See _compute_sampled_logits.
-    colocate_logits: See _compute_sampled_logits.
-
-  Returns:
-    A pair of a [batch_size * num_true, dim] tensor of the true weights
-    and a [batch_size, num_sampled] tensor of the sampled logits.
-  """
-  if len(weights) > 1 and colocate_logits:
-    # This codepath makes two calls to lookup the embeddings, one for the true
-    # weights and one to compute the sampled logits. Because we take this
-    # codepath only if len(weights) > 1, we will only incur the double-lookup
-    # cost if we can benefit from the co-located logit computation.
-
-    true_w = embedding_ops.embedding_lookup(
-        weights, labels_flat, partition_strategy=partition_strategy)
-
-    def logit(embeddings):
-      return math_ops.matmul(embeddings, inputs, transpose_b=True)
-
-    # pylint: disable=protected-access
-    sampled_logits = embedding_ops._embedding_lookup_and_transform(
-        weights,
-        sampled,
-        partition_strategy=partition_strategy,
-        transform_fn=logit)
-    # pylint: enable=protected-access
-    sampled_logits = array_ops.transpose(sampled_logits)
-
-  else:
-    # This codepath retrieves the weights by performing a single call to
-    # embedding_lookup and slicing out the true weights and sampled weights.
-    # Because the call to embedding_lookup is shared, the sampled logit
-    # computation is done here after the slice.
-
-    # weights shape is [num_classes, dim]
-    all_w = embedding_ops.embedding_lookup(
-        weights, all_ids, partition_strategy=partition_strategy)
-
-    true_w = array_ops.slice(all_w, [0, 0],
-                             array_ops.stack(
-                                 [array_ops.shape(labels_flat)[0], -1]))
-
-    sampled_w = array_ops.slice(
-        all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
-    # inputs has shape [batch_size, dim]
-    # sampled_w has shape [num_sampled, dim]
-    # Apply X*W', which yields [batch_size, num_sampled]
-    sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)
-
-  return true_w, sampled_logits
-
-
 def _compute_sampled_logits(weights,
                             biases,
                             labels,
@@ -956,7 +892,6 @@ def _compute_sampled_logits(weights,
                             subtract_log_q=True,
                             remove_accidental_hits=False,
                             partition_strategy="mod",
-                            colocate_logits=False,
                             name=None):
   """Helper function for nce_loss and sampled_softmax_loss functions.
 
@@ -994,9 +929,6 @@ def _compute_sampled_logits(weights,
     partition_strategy: A string specifying the partitioning strategy, relevant
         if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
         Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
-    colocate_logits: A `bool`, relevant if `len(weights) > 1`, specifying
-        whether to colocate the computation of the sampled logits with the
-        weights.
     name: A name for the operation (optional).
   Returns:
     out_logits, out_labels: `Tensor` objects each with shape
@@ -1039,11 +971,22 @@ def _compute_sampled_logits(weights,
     all_ids = array_ops.concat([labels_flat, sampled], 0)
 
     # Retrieve the true weights and the logits of the sampled weights.
-    # - true_w shape is [batch_size * num_true, dim]
-    # - sampled_logits shape is [batch_size, num_sampled]
-    true_w, sampled_logits = _retrieve_weights(
-        weights, inputs, labels_flat, sampled, all_ids, partition_strategy,
-        colocate_logits)
+
+    # weights shape is [num_classes, dim]
+    all_w = embedding_ops.embedding_lookup(
+        weights, all_ids, partition_strategy=partition_strategy)
+
+    # true_w shape is [batch_size * num_true, dim]
+    true_w = array_ops.slice(all_w, [0, 0],
+                             array_ops.stack(
+                                 [array_ops.shape(labels_flat)[0], -1]))
+
+    sampled_w = array_ops.slice(
+        all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
+    # inputs has shape [batch_size, dim]
+    # sampled_w has shape [num_sampled, dim]
+    # Apply X*W', which yields [batch_size, num_sampled]
+    sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)
 
     # Retrieve the true and sampled biases, compute the true logits, and
     # add the biases to the true and sampled logits.
@@ -1213,7 +1156,6 @@ def nce_loss(weights,
       subtract_log_q=True,
       remove_accidental_hits=remove_accidental_hits,
       partition_strategy=partition_strategy,
-      colocate_logits=False,
       name=name)
   sampled_losses = sigmoid_cross_entropy_with_logits(
       labels=labels, logits=logits, name="sampled_losses")
@@ -1310,7 +1252,6 @@ def sampled_softmax_loss(weights,
       subtract_log_q=True,
       remove_accidental_hits=remove_accidental_hits,
       partition_strategy=partition_strategy,
-      colocate_logits=False,
       name=name)
   sampled_losses = nn_ops.softmax_cross_entropy_with_logits(
       labels=labels, logits=logits)
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index 84c5f4527d..f3592941d1 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -519,7 +519,6 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
             subtract_log_q=False,
             remove_accidental_hits=False,
             partition_strategy="div",
-            colocate_logits=False,
             name="sampled_logits_basic_num_true_%d" % num_true)
         got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
         self.assertEqual(exp_logits.shape, got_logits.shape, self._eps)
@@ -556,7 +555,6 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
             subtract_log_q=False,
             remove_accidental_hits=False,
             partition_strategy="div",
-            colocate_logits=False,
             name="sampled_logits_basic_num_true_%d" % num_true)
         got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
         self.assertAllClose(exp_logits, got_logits, self._eps)
@@ -594,7 +592,6 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
             subtract_log_q=False,
             remove_accidental_hits=True,
             partition_strategy="div",
-            colocate_logits=False,
             name="sampled_logits_accidental_hit_removal_num_true_%d" % num_true)
         # Test that the exponentiated logits of accidental hits are near 0.
         # First we need to find the hits in this random test run:
@@ -639,7 +636,6 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
             subtract_log_q=True,
             remove_accidental_hits=False,
             partition_strategy="div",
-            colocate_logits=False,
             name="sampled_logits_subtract_log_q_num_true_%d" % num_true)
         got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
         self.assertAllClose(exp_logits, got_logits, self._eps)
@@ -678,88 +674,11 @@ class ComputeSampledLogitsTest(test_lib.TestCase):
             subtract_log_q=False,
             remove_accidental_hits=False,
             partition_strategy="div",
-            colocate_logits=False,
             name="sampled_logits_sharded_num_true_%d" % num_true)
         got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
         self.assertAllClose(exp_logits, got_logits, self._eps)
         self.assertAllClose(exp_labels, got_labels, self._eps)
 
-  def testColocatedLogits(self):
-    """With colocated logit computation."""
-    np.random.seed(0)
-    num_classes = 5
-    batch_size = 3
-    with self.test_session() as sess:
-      for num_true in range(1, 5):
-        labels = np.random.randint(
-            low=0, high=num_classes, size=batch_size * num_true)
-        (weights, biases, hidden_acts, sampled_vals, exp_logits,
-         exp_labels) = self._GenerateTestData(
-             num_classes=num_classes,
-             dim=10,
-             batch_size=batch_size,
-             num_true=num_true,
-             labels=labels,
-             sampled=[1, 0, 2, 3],
-             subtract_log_q=False)
-        logits_tensor, labels_tensor = _compute_sampled_logits(
-            weights=constant_op.constant(weights),
-            biases=constant_op.constant(biases),
-            labels=constant_op.constant(
-                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
-            inputs=constant_op.constant(hidden_acts),
-            num_sampled=4,
-            num_classes=num_classes,
-            num_true=num_true,
-            sampled_values=sampled_vals,
-            subtract_log_q=False,
-            remove_accidental_hits=False,
-            partition_strategy="div",
-            colocate_logits=True,
-            name="sampled_logits_colocated_num_true_%d" % num_true)
-        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
-        self.assertAllClose(exp_logits, got_logits, self._eps)
-        self.assertAllClose(exp_labels, got_labels, self._eps)
-
-  def testShardedColocatedLogits(self):
-    """Sharded weights and biases and with colocated logit computation."""
-    np.random.seed(0)
-    num_classes = 5
-    batch_size = 3
-    with self.test_session() as sess:
-      for num_true in range(1, 5):
-        labels = np.random.randint(
-            low=0, high=num_classes, size=batch_size * num_true)
-        (weights, biases, hidden_acts, sampled_vals, exp_logits,
-         exp_labels) = self._GenerateTestData(
-             num_classes=num_classes,
-             dim=10,
-             batch_size=batch_size,
-             num_true=num_true,
-             labels=labels,
-             sampled=[1, 0, 2, 3],
-             subtract_log_q=False)
-        weight_shards, bias_shards = self._ShardTestEmbeddings(
-            weights, biases, num_shards=3)
-        logits_tensor, labels_tensor = _compute_sampled_logits(
-            weights=[constant_op.constant(shard) for shard in weight_shards],
-            biases=[constant_op.constant(shard) for shard in bias_shards],
-            labels=constant_op.constant(
-                labels, dtype=dtypes.int64, shape=(batch_size, num_true)),
-            inputs=constant_op.constant(hidden_acts),
-            num_sampled=4,
-            num_classes=num_classes,
-            num_true=num_true,
-            sampled_values=sampled_vals,
-            subtract_log_q=False,
-            remove_accidental_hits=False,
-            partition_strategy="div",
-            colocate_logits=True,
-            name="sampled_logits_sharded_colocated_num_true_%d" % num_true)
-        got_logits, got_labels = sess.run([logits_tensor, labels_tensor])
-        self.assertAllClose(exp_logits, got_logits, self._eps)
-        self.assertAllClose(exp_labels, got_labels, self._eps)
-
   def testNCELoss(self):
     # A simple test to verify the numerics.
 
-- 
GitLab


From 7ccfbdffde54d11bc8fad4b49fde53cf2b550434 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 17:14:39 -0700
Subject: [PATCH 0612/1375] Clean up python configuration: - Remove
 experimental attributes (local_checks, python_include, numpy_include,
 remote_config_repo). Use of experimental remote repo can only be enabled now
 via env variable (preferred to change to workspace file) - Fix hardcoded
 fallback value for python bin. New fallback is to use 'which python' and fail
 if python not set in path (solves tensorflow/tensorflow#9436 / works around
 bazelbuild/bazel#3057). - Fixes minor issues with template: adds spaces to
 srcs in genrule, remove extra space after genrules. - Some dead code clean up
 (use of empty configuration)

PiperOrigin-RevId: 160594421
---
 third_party/py/BUILD.tpl            |   2 -
 third_party/py/python_configure.bzl | 117 +++++++++++-----------------
 2 files changed, 45 insertions(+), 74 deletions(-)

diff --git a/third_party/py/BUILD.tpl b/third_party/py/BUILD.tpl
index cdfe9c9b45..2f3503e793 100644
--- a/third_party/py/BUILD.tpl
+++ b/third_party/py/BUILD.tpl
@@ -31,7 +31,5 @@ config_setting(
 )
 
 %{PYTHON_INCLUDE_GENRULE}
-
 %{PYTHON_IMPORT_LIB_GENRULE}
-
 %{NUMPY_INCLUDE_GENRULE}
diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl
index 31c4d2b98a..bbc07905fc 100644
--- a/third_party/py/python_configure.bzl
+++ b/third_party/py/python_configure.bzl
@@ -9,9 +9,7 @@
   * `PYTHON_LIB_PATH`: Location of python libraries.
 """
 
-_NUMPY_INCLUDE_PATH = "NUMPY_INCLUDE_PATH"
 _PYTHON_BIN_PATH = "PYTHON_BIN_PATH"
-_PYTHON_INCLUDE_PATH = "PYTHON_INCLUDE_PATH"
 _PYTHON_LIB_PATH = "PYTHON_LIB_PATH"
 _TF_PYTHON_CONFIG_REPO = "TF_PYTHON_CONFIG_REPO"
 
@@ -121,7 +119,7 @@ def _genrule(src_dir, genrule_name, command, outs):
       '    cmd = """\n' +
       command +
       '\n   """,\n' +
-      ')\n\n'
+      ')\n'
   )
 
 
@@ -157,12 +155,27 @@ def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name,
       # On Windows, symlink is not supported, so we just copy all the files.
       cmd = 'cp -f' if _is_windows(repository_ctx) else 'ln -s'
       command.append(cmd + ' "%s" "%s"' % (src_files[i] , dest))
-      outs.append('      "' + dest_dir + dest_files[i] + '",')
+      outs.append('        "' + dest_dir + dest_files[i] + '",')
   genrule = _genrule(src_dir, genrule_name, " && ".join(command),
                      "\n".join(outs))
   return genrule
 
 
+def _get_python_bin(repository_ctx):
+  """Gets the python bin path."""
+  python_bin = _get_env_var(repository_ctx, _PYTHON_BIN_PATH,
+                            None, False)
+  if python_bin != None:
+    return python_bin
+  python_bin_path = repository_ctx.which("python")
+  if python_bin_path != None:
+    return str(python_bin_path)
+  path = _get_env_var(repository_ctx, "PATH")
+  _python_configure_fail("Cannot find python in PATH, please make sure " +
+      "python is installed and add its directory in PATH, or set the " +
+      "environment variable PYTHON_BIN_PATH.\nPATH=%s" % (path))
+
+
 def _get_python_lib(repository_ctx, python_bin):
   """Gets the python lib path."""
   print_lib = ("<<END\n" +
@@ -252,63 +265,32 @@ def _get_numpy_include(repository_ctx, python_bin):
 
 def _create_local_python_repository(repository_ctx):
   """Creates the repository containing files set up to build with Python."""
-  python_include = None
-  numpy_include = None
-  empty_config = False
-  # If local checks were requested, the python and numpy include will be auto
-  # detected on the host config (using _PYTHON_BIN_PATH).
-  python_bin = _get_env_var(repository_ctx, _PYTHON_BIN_PATH,
-                            "/usr/bin/python")
-  if repository_ctx.attr.local_checks:
-    # TODO(nlopezgi): The default argument here is a workaround until
-    #                 bazelbuild/bazel#3057 is resolved.
-    _check_python_bin(repository_ctx, python_bin)
-    python_lib = _get_env_var(repository_ctx, _PYTHON_LIB_PATH,
+  python_bin = _get_python_bin(repository_ctx)
+  _check_python_bin(repository_ctx, python_bin)
+  python_lib = _get_env_var(repository_ctx, _PYTHON_LIB_PATH,
                               _get_python_lib(repository_ctx, python_bin))
-    _check_python_lib(repository_ctx, python_lib)
-    python_include = _get_python_include(repository_ctx, python_bin)
-    numpy_include = _get_numpy_include(repository_ctx, python_bin) + '/numpy'
-  else:
-    # Otherwise, we assume user provides all paths (via ENV or attrs)
-    python_include = _get_env_var(repository_ctx, _PYTHON_INCLUDE_PATH,
-                                  repository_ctx.attr.python_include)
-    numpy_include = _get_env_var(repository_ctx, _NUMPY_INCLUDE_PATH,
-                                 repository_ctx.attr.numpy_include) + '/numpy'
-  if empty_config:
-    _tpl(repository_ctx, "BUILD", {
-        "%{PYTHON_INCLUDE_GENRULE}": ('filegroup(\n' +
-                                      '    name = "python_include",\n' +
-                                      '    srcs = [],\n' +
-                                      ')\n'),
-        "%{PYTHON_IMPORT_LIB_GENRULE}": ('filegroup(\n' +
-                                      '    name = "python_import_lib",\n' +
-                                      '    srcs = [],\n' +
-                                      ')\n'),
-        "%{NUMPY_INCLUDE_GENRULE}": ('filegroup(\n' +
-                                      '    name = "numpy_include",\n' +
-                                      '    srcs = [],\n' +
-                                      ')\n'),
-    })
-  else:
-    python_include_rule = _symlink_genrule_for_dir(
-        repository_ctx, python_include, 'python_include', 'python_include')
-    python_import_lib_genrule = ""
-    # To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib
-    # See https://docs.python.org/3/extending/windows.html
-    if _is_windows(repository_ctx):
-      python_include = _norm_path(python_include)
-      python_import_lib_name = _get_python_import_lib_name(repository_ctx, python_bin)
-      python_import_lib_src = python_include.rsplit('/', 1)[0] + "/libs/" + python_import_lib_name
-      python_import_lib_genrule = _symlink_genrule_for_dir(
-        repository_ctx, None, '', 'python_import_lib',
-        [python_import_lib_src], [python_import_lib_name])
-    numpy_include_rule = _symlink_genrule_for_dir(
-        repository_ctx, numpy_include, 'numpy_include/numpy', 'numpy_include')
-    _tpl(repository_ctx, "BUILD", {
-        "%{PYTHON_INCLUDE_GENRULE}": python_include_rule,
-        "%{PYTHON_IMPORT_LIB_GENRULE}": python_import_lib_genrule,
-        "%{NUMPY_INCLUDE_GENRULE}": numpy_include_rule,
-    })
+  _check_python_lib(repository_ctx, python_lib)
+  python_include = _get_python_include(repository_ctx, python_bin)
+  numpy_include = _get_numpy_include(repository_ctx, python_bin) + '/numpy'
+  python_include_rule = _symlink_genrule_for_dir(
+      repository_ctx, python_include, 'python_include', 'python_include')
+  python_import_lib_genrule = ""
+  # To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib
+  # See https://docs.python.org/3/extending/windows.html
+  if _is_windows(repository_ctx):
+    python_include = _norm_path(python_include)
+    python_import_lib_name = _get_python_import_lib_name(repository_ctx, python_bin)
+    python_import_lib_src = python_include.rsplit('/', 1)[0] + "/libs/" + python_import_lib_name
+    python_import_lib_genrule = _symlink_genrule_for_dir(
+      repository_ctx, None, '', 'python_import_lib',
+      [python_import_lib_src], [python_import_lib_name])
+  numpy_include_rule = _symlink_genrule_for_dir(
+      repository_ctx, numpy_include, 'numpy_include/numpy', 'numpy_include')
+  _tpl(repository_ctx, "BUILD", {
+      "%{PYTHON_INCLUDE_GENRULE}": python_include_rule,
+      "%{PYTHON_IMPORT_LIB_GENRULE}": python_import_lib_genrule,
+      "%{NUMPY_INCLUDE_GENRULE}": numpy_include_rule,
+  })
 
 
 def _create_remote_python_repository(repository_ctx, remote_config_repo):
@@ -321,27 +303,18 @@ def _create_remote_python_repository(repository_ctx, remote_config_repo):
 
 def _python_autoconf_impl(repository_ctx):
   """Implementation of the python_autoconf repository rule."""
-  remote_config_repo = _get_env_var(repository_ctx, _TF_PYTHON_CONFIG_REPO,
-      repository_ctx.attr.remote_config_repo, False)
-  if remote_config_repo != "":
-    _create_remote_python_repository(repository_ctx, remote_config_repo)
+  if _TF_PYTHON_CONFIG_REPO in repository_ctx.os.environ:
+      _create_remote_python_repository(repository_ctx,
+          repository_ctx.os.environ[_TF_PYTHON_CONFIG_REPO])
   else:
     _create_local_python_repository(repository_ctx)
 
 
 python_configure = repository_rule(
     implementation = _python_autoconf_impl,
-    attrs = {
-        "local_checks": attr.bool(mandatory = False, default = True),
-        "python_include": attr.string(mandatory = False),
-        "numpy_include": attr.string(mandatory = False),
-        "remote_config_repo": attr.string(mandatory = False, default =""),
-    },
     environ = [
         _PYTHON_BIN_PATH,
-        _PYTHON_INCLUDE_PATH,
         _PYTHON_LIB_PATH,
-        _NUMPY_INCLUDE_PATH,
         _TF_PYTHON_CONFIG_REPO,
     ],
 )
-- 
GitLab


From 1cf351583fc810f8270f8a6c185b3aa585f9b3b0 Mon Sep 17 00:00:00 2001
From: Dhruv <printdhruv@yahoo.com>
Date: Thu, 29 Jun 2017 17:19:49 -0700
Subject: [PATCH 0613/1375] Printdhruv patch 2 (#11150)

* Variable rename

'bools_' renamed to 'bools_test'

* Revert "Variable rename"

* Update deep_cnn.md

"an evaluation of a model "
instead of
"evaluation on a model"

* Update for ISSUE_TEMPLATE.md

Python version

* Update ISSUE_TEMPLATE.md

Inclusion of python
---
 ISSUE_TEMPLATE.md                         | 1 +
 tensorflow/docs_src/tutorials/deep_cnn.md | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 4f1666c2e7..5bf13ee152 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -17,6 +17,7 @@ If you open a GitHub issue, here is our policy:
 - **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
 - **TensorFlow installed from (source or binary)**:
 - **TensorFlow version (use command below)**:
+- **Python version**: 
 - **Bazel version (if compiling from source)**:
 - **CUDA/cuDNN version**:
 - **GPU model and memory**:
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index 8013746eca..a9e9dda12b 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -178,7 +178,7 @@ the network architecture to return normalized predictions using
 @{tf.nn.softmax}.
 
 The `inputs()` and `inference()` functions provide all the components
-necessary to perform evaluation on a model. We now shift our focus towards
+necessary to perform an evaluation of a model. We now shift our focus towards
 building operations for training a model.
 
 > **EXERCISE:** The model architecture in `inference()` differs slightly from
-- 
GitLab


From ef7d78ff3d80c1e6e4332c75c4b89d177e861932 Mon Sep 17 00:00:00 2001
From: Kongsea <kongsea@gmail.com>
Date: Thu, 29 Jun 2017 19:20:18 -0500
Subject: [PATCH 0614/1375] Fix typos (#11125)

---
 tensorflow/python/layers/convolutional.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index 54c775b37d..1bf29e718e 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -1501,8 +1501,9 @@ def conv3d_transpose(inputs,
     data_format: A string, one of `channels_last` (default) or `channels_first`.
       The ordering of the dimensions in the inputs.
       `channels_last` corresponds to inputs with shape
-      `(batch, height, width, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, height, width)`.
+      `(batch, depth, height, width, channels)` while `channels_first`
+      corresponds to inputs with shape
+      `(batch, channels, depth, height, width)`.
     activation: Activation function. Set it to None to maintain a
       linear activation.
     use_bias: Boolean, whether the layer uses a bias.
-- 
GitLab


From 1c27162649359e5fabade5f806ef851401ec2f1e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 17:20:56 -0700
Subject: [PATCH 0615/1375] Clean up cuda configuration: - Remove experimental
 attributes (remote_config_repo). Use of experimental remote repo can only be
 enabled now via env variable (preferred to change to workspace file) - Fixes
 minor issues with template: adds spaces to srcs in genrule, remove extra
 space after genrules. - Fix ordering of attributes in build template

PiperOrigin-RevId: 160595014
---
 third_party/gpus/cuda/BUILD.tpl     |  4 ++--
 third_party/gpus/cuda_configure.bzl | 16 +++-------------
 2 files changed, 5 insertions(+), 15 deletions(-)

diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index f7610dd7a9..87c8678714 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -94,8 +94,8 @@ cc_library(
     srcs = ["lib/%{cusolver_lib}"],
     data = ["lib/%{cusolver_lib}"],
     includes = ["include"],
-    linkstatic = 1,
     linkopts = ["-lgomp"],
+    linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
@@ -129,9 +129,9 @@ cc_library(
 cc_library(
     name = "cuda",
     deps = [
+        ":cublas",
         ":cuda_headers",
         ":cudart",
-        ":cublas",
         ":cudnn",
         ":cufft",
         ":curand",
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 1e29f23d90..cc445eb0ae 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -830,7 +830,7 @@ def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name,
       # On Windows, symlink is not supported, so we just copy all the files.
       cmd = 'cp -f' if _is_windows(repository_ctx) else 'ln -s'
       command.append(cmd + ' "%s" "%s"' % (src_files[i] , dest))
-      outs.append('      "' + dest_dir + dest_files[i] + '",')
+      outs.append('        "' + dest_dir + dest_files[i] + '",')
   genrule = _genrule(src_dir, genrule_name, " && ".join(command),
                      "\n".join(outs))
   return genrule
@@ -851,7 +851,7 @@ def _genrule(src_dir, genrule_name, command, outs):
       '    cmd = """\n' +
       command +
       '\n   """,\n' +
-      ')\n\n'
+      ')\n'
   )
 
 
@@ -1027,18 +1027,12 @@ def _cuda_autoconf_impl(repository_ctx):
     if _TF_CUDA_CONFIG_REPO in repository_ctx.os.environ:
       _create_remote_cuda_repository(repository_ctx,
           repository_ctx.os.environ[_TF_CUDA_CONFIG_REPO])
-    elif repository_ctx.attr.remote_config_repo != "":
-      _create_remote_cuda_repository(repository_ctx,
-          repository_ctx.attr.remote_config_repo)
     else:
       _create_local_cuda_repository(repository_ctx)
 
 
 cuda_configure = repository_rule(
     implementation = _cuda_autoconf_impl,
-    attrs = {
-        "remote_config_repo": attr.string(mandatory = False, default =""),
-    },
     environ = [
         _GCC_HOST_COMPILER_PATH,
         "TF_NEED_CUDA",
@@ -1056,13 +1050,9 @@ cuda_configure = repository_rule(
 Add the following to your WORKSPACE FILE:
 
 ```python
-cuda_configure(
-  name = "local_config_cuda"
-  remote_config_repo = "@remote_cuda_config_tf//"
-)
+cuda_configure(name = "local_config_cuda")
 ```
 
 Args:
   name: A unique name for this workspace rule.
-  remote_config_repo: Location of a pre-generated config (optional).
 """
-- 
GitLab


From 4404efb7fecbb4a68f273c5a66705e20dc90ba93 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 18:01:59 -0700
Subject: [PATCH 0616/1375] Fix comment on squared hellinger / triangular
 divergences.

PiperOrigin-RevId: 160598523
---
 .../contrib/bayesflow/python/ops/csiszar_divergence_impl.py   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 09389e5d38..e6e82f3b6f 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -389,7 +389,7 @@ def pearson(logu, name=None):
 
 
 def squared_hellinger(logu, name=None):
-  """The Amari-alpha Csiszar-function in log-space.
+  """The Squared-Hellinger Csiszar-function in log-space.
 
   A Csiszar-function is a member of,
 
@@ -424,7 +424,7 @@ def squared_hellinger(logu, name=None):
 
 
 def triangular(logu, name=None):
-  """The Amari-alpha Csiszar-function in log-space.
+  """The Triangular Csiszar-function in log-space.
 
   A Csiszar-function is a member of,
 
-- 
GitLab


From 36a224aef07d3aa7c0b3ab351100a53fb75cfb86 Mon Sep 17 00:00:00 2001
From: Baptiste Arnaud <baptiste.arnaud95@gmail.com>
Date: Fri, 30 Jun 2017 03:48:53 +0200
Subject: [PATCH 0617/1375] Update welcome.md (#11133)

---
 tensorflow/docs_src/community/welcome.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md
index 0f85e8f1c0..f4de7f3a70 100644
--- a/tensorflow/docs_src/community/welcome.md
+++ b/tensorflow/docs_src/community/welcome.md
@@ -26,6 +26,7 @@ The TensorFlow community has created many great projects around TensorFlow, incl
 * [Rust language bindings](https://github.com/google/tensorflow-rust)
 * [Operator Vectorization Library](https://github.com/opveclib/opveclib)
 * [Swift language bindings](https://github.com/PerfectlySoft/Perfect-TensorFlow)
+* [Sublime Tensorflow - A plugin for Sublime Text](https://github.com/baptisteArnaud/Sublime-Tensorflow)
 
 ## TensorFlow Communities Around the World
 
-- 
GitLab


From 3bb862fa61ae5bda865e43588448a2ff657dd947 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Fri, 30 Jun 2017 10:49:23 +0900
Subject: [PATCH 0618/1375] Make consistent author information format (#11128)

---
 tensorflow/docs_src/tutorials/seq2seq.md | 2 +-
 tensorflow/python/training/adam.py       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md
index dd2ca8d524..84c8a9c9f3 100644
--- a/tensorflow/docs_src/tutorials/seq2seq.md
+++ b/tensorflow/docs_src/tutorials/seq2seq.md
@@ -140,7 +140,7 @@ When training models with large output vocabularies, i.e., when
 tensors. Instead, it is better to return smaller output tensors, which will
 later be projected onto a large output tensor using `output_projection`.
 This allows to use our seq2seq models with a sampled softmax loss, as described
-in [Jean et. al., 2014](http://arxiv.org/abs/1412.2007)
+in [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
 ([pdf](http://arxiv.org/pdf/1412.2007.pdf)).
 
 In addition to `basic_rnn_seq2seq` and `embedding_rnn_seq2seq` there are a few
diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py
index 459c735ea3..796402425a 100644
--- a/tensorflow/python/training/adam.py
+++ b/tensorflow/python/training/adam.py
@@ -31,7 +31,7 @@ from tensorflow.python.training import training_ops
 class AdamOptimizer(optimizer.Optimizer):
   """Optimizer that implements the Adam algorithm.
 
-  See [Kingma et. al., 2014](http://arxiv.org/abs/1412.6980)
+  See [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
   ([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
   """
 
-- 
GitLab


From 62041cb51a30826153ac0f20f63cf48e32d96c6b Mon Sep 17 00:00:00 2001
From: Kongsea <kongsea@gmail.com>
Date: Fri, 30 Jun 2017 10:19:20 +0800
Subject: [PATCH 0619/1375] Fix an error

Fix an error or the condition will never be true.
---
 tensorflow/contrib/keras/python/keras/testing_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/keras/python/keras/testing_utils.py b/tensorflow/contrib/keras/python/keras/testing_utils.py
index bf6f661adf..2f51ace945 100644
--- a/tensorflow/contrib/keras/python/keras/testing_utils.py
+++ b/tensorflow/contrib/keras/python/keras/testing_utils.py
@@ -78,7 +78,7 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
       if e is None:
         input_data_shape[i] = np.random.randint(1, 4)
     input_data = 10 * np.random.random(input_data_shape)
-    if input_dtype[:4] == 'float':
+    if input_dtype[:5] == 'float':
       input_data -= 0.5
     input_data = input_data.astype(input_dtype)
   elif input_shape is None:
-- 
GitLab


From 2e68836850b3cea3a67bba3ab8232804727a1b22 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Fri, 30 Jun 2017 04:47:30 +0200
Subject: [PATCH 0620/1375] Make TensorFlow build with wrapper-free MSVC
 CROSSTOOL (#11114)

* Make TensorFlow build with wrapper-free MSVC CROSSTOOL

* Revert protobuf version to 3.3.1
---
 tensorflow/core/kernels/BUILD                 |  5 ++--
 .../core/platform/default/build_config.bzl    |  5 ++--
 tensorflow/core/util/tensor_bundle/BUILD      |  3 ++-
 tensorflow/python/BUILD                       |  4 ++--
 tensorflow/tensorflow.bzl                     | 12 ++++++----
 tensorflow/tools/tfprof/internal/BUILD        |  5 ++--
 third_party/lmdb.BUILD                        |  4 ++--
 third_party/snappy.BUILD                      | 24 +++++++++++++++----
 third_party/zlib.BUILD                        | 24 +++++++++++++++----
 9 files changed, 63 insertions(+), 23 deletions(-)

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 8d2748ed8b..8c6f1f8964 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -32,6 +32,7 @@ load(
     "tf_kernel_library",
     "tf_mkl_kernel_library",
     "cc_header_only_library",
+    "if_not_windows",
 )
 load("@local_config_sycl//sycl:build_defs.bzl", "if_sycl")
 load("//tensorflow:tensorflow.bzl", "tf_cuda_cc_test")
@@ -231,7 +232,7 @@ cc_library(
     name = "ops_util",
     srcs = ["ops_util.cc"],
     hdrs = ["ops_util.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -344,7 +345,7 @@ cc_library(
     name = "save_restore_tensor",
     srcs = ["save_restore_tensor.cc"],
     hdrs = ["save_restore_tensor.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         ":bounds_check",
         "//tensorflow/core:framework",
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 94f255663e..f9c876cc21 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -3,6 +3,7 @@
 load("@protobuf//:protobuf.bzl", "cc_proto_library")
 load("@protobuf//:protobuf.bzl", "py_proto_library")
 load("//tensorflow:tensorflow.bzl", "if_not_mobile")
+load("//tensorflow:tensorflow.bzl", "if_not_windows")
 
 # Appends a suffix to a list of deps.
 def tf_deps(deps, suffix):
@@ -45,11 +46,11 @@ def tf_proto_library_cc(name, srcs = [], has_services = None,
       srcs = srcs,
       deps = tf_deps(protodeps, "_cc") + ["@protobuf//:cc_wkt_protos"],
       cc_libs = cc_libs + ["@protobuf//:protobuf"],
-      copts = [
+      copts = if_not_windows([
           "-Wno-unknown-warning-option",
           "-Wno-unused-but-set-variable",
           "-Wno-sign-compare",
-      ],
+      ]),
       protoc = "@protobuf//:protoc",
       default_runtime = "@protobuf//:protobuf",
       use_grpc_plugin = use_grpc_plugin,
diff --git a/tensorflow/core/util/tensor_bundle/BUILD b/tensorflow/core/util/tensor_bundle/BUILD
index ca92713a62..1045cf84b4 100644
--- a/tensorflow/core/util/tensor_bundle/BUILD
+++ b/tensorflow/core/util/tensor_bundle/BUILD
@@ -11,6 +11,7 @@ licenses(["notice"])  # Apache 2.0
 load(
     "//tensorflow:tensorflow.bzl",
     "cc_header_only_library",
+    "if_not_windows",
     "tf_copts",
 )
 
@@ -34,7 +35,7 @@ cc_library(
     name = "tensor_bundle",
     srcs = ["tensor_bundle.cc"],
     hdrs = ["tensor_bundle.h"],
-    copts = tf_copts() + ["-Wno-sign-compare"],
+    copts = tf_copts() + if_not_windows(["-Wno-sign-compare"]),
     deps = [
         ":naming",
         "//tensorflow/core:core_cpu_internal",
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 0c04d36aff..84fc4bc334 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -266,7 +266,7 @@ cc_library(
 cc_binary(
     name = "framework/test_file_system.so",
     srcs = ["framework/test_file_system.cc"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     linkopts = select({
         "//conditions:default": [
             "-lm",
@@ -2688,7 +2688,7 @@ cc_library(
     name = "cpp_shape_inference",
     srcs = ["framework/cpp_shape_inference.cc"],
     hdrs = ["framework/cpp_shape_inference.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     visibility = ["//visibility:public"],
     deps = [
         ":cpp_shape_inference_proto_cc",
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 5b1d9a517e..83a9313e50 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -138,16 +138,20 @@ WIN_COPTS = [
     "/DTF_COMPILE_LIBRARY",
     "/DEIGEN_HAS_C99_MATH",
     "/DTENSORFLOW_USE_EIGEN_THREADPOOL",
+    "/DEIGEN_AVOID_STL_ARRAY",
+    "/Iexternal/gemmlowp",
+    "/wd4018", # -Wno-sign-compare
+    "/U_HAS_EXCEPTIONS", "/D_HAS_EXCEPTIONS=1", "/EHsc", # -fno-exceptions
 ]
 
 # LINT.IfChange
 def tf_copts():
-  return ([
+  return (if_not_windows([
       "-DEIGEN_AVOID_STL_ARRAY",
       "-Iexternal/gemmlowp",
       "-Wno-sign-compare",
       "-fno-exceptions",
-  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
+  ]) + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
       ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
@@ -1021,9 +1025,9 @@ def tf_py_wrap_cc(name,
   native.cc_binary(
       name=cc_library_name,
       srcs=[module_name + ".cc"],
-      copts=(copts + [
+      copts=(copts + if_not_windows([
           "-Wno-self-assign", "-Wno-sign-compare", "-Wno-write-strings"
-      ] + tf_extension_copts()),
+      ]) + tf_extension_copts()),
       linkopts=tf_extension_linkopts() + extra_linkopts,
       linkstatic=1,
       linkshared=1,
diff --git a/tensorflow/tools/tfprof/internal/BUILD b/tensorflow/tools/tfprof/internal/BUILD
index 25c895af5b..c51c71867f 100644
--- a/tensorflow/tools/tfprof/internal/BUILD
+++ b/tensorflow/tools/tfprof/internal/BUILD
@@ -9,6 +9,7 @@ package(
 licenses(["notice"])  # Apache 2.0
 
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
+load("//tensorflow:tensorflow.bzl", "if_not_windows")
 
 cc_library(
     name = "tfprof_stats",
@@ -243,7 +244,7 @@ cc_library(
     name = "tfprof_utils",
     srcs = ["tfprof_utils.cc"],
     hdrs = ["tfprof_utils.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         ":tfprof_options",
         "//tensorflow/core:lib",
@@ -315,7 +316,7 @@ cc_library(
     name = "tfprof_tensor",
     srcs = ["tfprof_tensor.cc"],
     hdrs = ["tfprof_tensor.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
diff --git a/third_party/lmdb.BUILD b/third_party/lmdb.BUILD
index 7c6e3dc3f0..61228bfd43 100644
--- a/third_party/lmdb.BUILD
+++ b/third_party/lmdb.BUILD
@@ -19,8 +19,8 @@ cc_library(
         "-w",
     ],
     linkopts = select({
-        ":windows": ["-Wl,advapi32.lib"],  # InitializeSecurityDescriptor, SetSecurityDescriptorDacl
-        ":windows_msvc": ["-Wl,advapi32.lib"],
+        ":windows": ["-DEFAULTLIB:advapi32.lib"], # InitializeSecurityDescriptor, SetSecurityDescriptorDacl
+        ":windows_msvc": ["-DEFAULTLIB:advapi32.lib"],
         "//conditions:default": ["-lpthread"],
     }),
     visibility = ["//visibility:public"],
diff --git a/third_party/snappy.BUILD b/third_party/snappy.BUILD
index 120028dc52..ae40fa7446 100644
--- a/third_party/snappy.BUILD
+++ b/third_party/snappy.BUILD
@@ -4,6 +4,18 @@ licenses(["notice"])  # BSD 3-Clause
 
 exports_files(["COPYING"])
 
+config_setting(
+    name = "windows",
+    values = {"cpu": "x64_windows"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "windows_msvc",
+    values = {"cpu": "x64_windows_msvc"},
+    visibility = ["//visibility:public"],
+)
+
 cc_library(
     name = "snappy",
     srcs = [
@@ -19,10 +31,14 @@ cc_library(
         "snappy-stubs-public.h",
     ],
     hdrs = ["snappy.h"],
-    copts = [
-        "-Wno-shift-negative-value",
-        "-Wno-implicit-function-declaration",
-    ],
+    copts = select({
+        ":windows": [],
+        ":windows_msvc": [],
+        "//conditions:default": [
+          "-Wno-shift-negative-value",
+          "-Wno-implicit-function-declaration",
+        ],
+    }),
 )
 
 genrule(
diff --git a/third_party/zlib.BUILD b/third_party/zlib.BUILD
index 279e6395b0..9e9817b860 100644
--- a/third_party/zlib.BUILD
+++ b/third_party/zlib.BUILD
@@ -2,6 +2,18 @@ package(default_visibility = ["//visibility:public"])
 
 licenses(["notice"])  # BSD/MIT-like license (for zlib)
 
+config_setting(
+    name = "windows",
+    values = {"cpu": "x64_windows"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "windows_msvc",
+    values = {"cpu": "x64_windows_msvc"},
+    visibility = ["//visibility:public"],
+)
+
 cc_library(
     name = "zlib",
     srcs = [
@@ -32,9 +44,13 @@ cc_library(
         "zutil.h",
     ],
     hdrs = ["zlib.h"],
-    copts = [
-        "-Wno-shift-negative-value",
-        "-Wno-implicit-function-declaration",
-    ],
+    copts = select({
+        ":windows": [],
+        ":windows_msvc": [],
+        "//conditions:default": [
+          "-Wno-shift-negative-value",
+          "-Wno-implicit-function-declaration",
+        ],
+    }),
     includes = ["."],
 )
-- 
GitLab


From f8fc078a4ec357abbe5cb8c6bc4b4c2f546e31e6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 22:19:13 -0700
Subject: [PATCH 0621/1375] CPU Infeed: Tuple support GPU Infeed: Cleanups and
 fix TransferBufferToInfeed

PiperOrigin-RevId: 160612256
---
 .../xla/service/cpu/infeed_manager.cc         |  6 +-
 .../compiler/xla/service/cpu/infeed_manager.h | 11 +--
 .../xla/service/cpu/infeed_manager_test.cc    | 10 +--
 .../compiler/xla/service/cpu/ir_emitter.cc    | 63 +++++++++++---
 .../compiler/xla/service/cpu/ir_emitter.h     |  3 +
 .../xla/service/cpu_transfer_manager.cc       | 81 ++++++++++++++----
 .../xla/service/cpu_transfer_manager.h        |  7 ++
 .../xla/service/gpu/infeed_manager.cc         |  4 +-
 .../compiler/xla/service/gpu/infeed_manager.h |  4 +-
 .../xla/service/gpu_transfer_manager.cc       | 83 ++++++++++---------
 .../xla/service/gpu_transfer_manager.h        | 13 +--
 11 files changed, 196 insertions(+), 89 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager.cc b/tensorflow/compiler/xla/service/cpu/infeed_manager.cc
index 14c882a06e..2ce27d22c7 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu/infeed_manager.cc
@@ -34,10 +34,12 @@ void InfeedManager::Reset() {
   enqueued_buffer_.clear();
 }
 
-void InfeedManager::EnqueueBuffer(InfeedBuffer* buffer) {
+void InfeedManager::EnqueueBuffers(const std::vector<InfeedBuffer*>& buffers) {
   tensorflow::mutex_lock l(mu_);
   bool was_empty = enqueued_buffer_.empty();
-  enqueued_buffer_.push_back(buffer);
+  for (InfeedBuffer* b : buffers) {
+    enqueued_buffer_.push_back(b);
+  }
   if (was_empty) {
     // This has the potential to suffer from the notified thread
     // immediately trying and failing to acquire mu_, but seems
diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager.h b/tensorflow/compiler/xla/service/cpu/infeed_manager.h
index 77472746e6..756582ef78 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager.h
+++ b/tensorflow/compiler/xla/service/cpu/infeed_manager.h
@@ -52,11 +52,12 @@ class InfeedManager {
   // condition is to call Reset when no computation is taking place.
   void Reset();
 
-  // Adds buffer to the infeed queue. buffer->Done will be called when
-  // the buffer will no longer be accessed by the InfeedManager,
-  // either as a result of a call to Reset or because the runtime has
-  // dequeued and used the buffer.
-  void EnqueueBuffer(InfeedBuffer* buffer);
+  // Adds a set of buffers to the infeed queue
+  // atomically. buffer->Done will be called when the buffer will no
+  // longer be accessed by the InfeedManager, either as a result of a
+  // call to Reset or because the runtime has dequeued and used the
+  // buffer.
+  void EnqueueBuffers(const std::vector<InfeedBuffer*>& buffers);
 
   // Blocks until the infeed queue is non-empty, then returns the
   // buffer at the head of the queue. Sets the current buffer to be
diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager_test.cc b/tensorflow/compiler/xla/service/cpu/infeed_manager_test.cc
index c65d821660..a59fa35fdb 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/infeed_manager_test.cc
@@ -57,8 +57,8 @@ TEST_F(InfeedManagerTest, SingleThreadedSequential) {
 
   cpu::runtime::InfeedManager* infeed = cpu::runtime::GetInfeedManager();
 
-  infeed->EnqueueBuffer(a);
-  infeed->EnqueueBuffer(b);
+  infeed->EnqueueBuffers({a});
+  infeed->EnqueueBuffers({b});
   ProcessNextBuffer(a->length());
   ProcessNextBuffer(b->length());
 }
@@ -69,9 +69,9 @@ TEST_F(InfeedManagerTest, SingleThreadedInterleaved) {
 
   cpu::runtime::InfeedManager* infeed = cpu::runtime::GetInfeedManager();
 
-  infeed->EnqueueBuffer(a);
+  infeed->EnqueueBuffers({a});
   ProcessNextBuffer(a->length());
-  infeed->EnqueueBuffer(b);
+  infeed->EnqueueBuffers({b});
   ProcessNextBuffer(b->length());
 }
 
@@ -92,7 +92,7 @@ TEST_F(InfeedManagerTest, MultiThreaded) {
       }
     }
     TestInfeedBuffer* a = new TestInfeedBuffer(length);
-    infeed->EnqueueBuffer(a);
+    infeed->EnqueueBuffers({a});
   });
 
   ProcessNextBuffer(length);
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 86d5b884d4..10676a1e93 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -374,6 +374,57 @@ Status IrEmitter::HandleSelect(HloInstruction* select, HloInstruction* pred,
 Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
   VLOG(2) << "HandleInfeed: " << infeed->ToString();
 
+  const Shape& shape = infeed->shape();
+
+  TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                      EmitTargetAddressForOp(infeed));
+
+  if (ShapeUtil::IsTuple(shape)) {
+    TF_RET_CHECK(!ShapeUtil::IsNestedTuple(shape));
+
+    // For a tuple, we first copy each of the internal elements to
+    // their corresponding target locations. We then construct the
+    // tuple outer buffer containing pointers to the internal
+    // elements.
+    std::vector<llvm::Value*> tuple_element_addresses;
+    for (int64 i = 0; i < shape.tuple_shapes_size(); ++i) {
+      TF_ASSIGN_OR_RETURN(BufferAllocation::Slice buffer,
+                          assignment_.GetUniqueSlice(infeed, {i}));
+
+      const Shape& tuple_element_shape =
+          ShapeUtil::GetTupleElementShape(shape, i);
+
+      // Only the outer tuple buffer's target address is obtained from
+      // EmitTargetAddressForOp to handle the case when Infeed is the
+      // root instruction. Target addresses for internal elements can
+      // be obtained from EmitTempBufferPointer.
+      llvm::Value* tuple_element_address =
+          EmitTempBufferPointer(buffer, tuple_element_shape);
+
+      TF_RETURN_IF_ERROR(EmitInfeedTransfer(ByteSizeOf(tuple_element_shape),
+                                            tuple_element_address));
+
+      tuple_element_addresses.push_back(tuple_element_address);
+    }
+
+    llvm_ir::EmitTuple(llvm_ir::IrArray(target_address, shape),
+                       tuple_element_addresses, &ir_builder_);
+  } else {
+    TF_RETURN_IF_ERROR(EmitInfeedTransfer(ByteSizeOf(shape), target_address));
+  }
+
+  emitted_value_[infeed] = target_address;
+
+  return Status::OK();
+}
+
+Status IrEmitter::EmitInfeedTransfer(int64 length,
+                                     llvm::Value* target_address) {
+  if (length > std::numeric_limits<int32>::max()) {
+    return InvalidArgument("infeed buffer length %lld is too large", length);
+  }
+  int32 length_32 = static_cast<int32>(length);
+
   // The signature of the acquire infeed buffer function is:
   //
   //   (void*)(int32 length);
@@ -400,26 +451,14 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
           runtime::kReleaseInfeedBufferAfterDequeueSymbolName, release_type));
   release_func->setCallingConv(llvm::CallingConv::C);
 
-  const Shape& shape = infeed->shape();
-  int64 length = ByteSizeOf(shape);
-  if (length > std::numeric_limits<int32>::max()) {
-    return InvalidArgument("infeed buffer length %lld is too large", length);
-  }
-  int32 length_32 = static_cast<int32>(length);
-
   llvm::Value* acquired_pointer =
       ir_builder_.CreateCall(acquire_func, {ir_builder_.getInt32(length_32)});
 
-  TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
-                      EmitTargetAddressForOp(infeed));
-
   ir_builder_.CreateMemCpy(target_address, acquired_pointer, length_32, 1);
 
   ir_builder_.CreateCall(release_func,
                          {ir_builder_.getInt32(length_32), acquired_pointer});
 
-  emitted_value_[infeed] = target_address;
-
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index a1b7bd9e6d..123797b5e9 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -423,6 +423,9 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   // Returns the number of bytes within the shape.
   int64 ByteSizeOf(const Shape& shape) const;
 
+  // Emit IR to transfer an infeed buffer to the target address.
+  Status EmitInfeedTransfer(int64 length, llvm::Value* target_address);
+
   const HloModuleConfig& hlo_module_config_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(IrEmitter);
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
index 1d553cab1a..262ba83f3d 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
@@ -21,7 +21,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
-#include "tensorflow/compiler/xla/service/cpu/infeed_manager.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -29,6 +28,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
 
@@ -66,40 +66,87 @@ CpuTransferManager::CpuTransferManager()
 Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
                                                    const Literal& literal) {
   const Shape& shape = literal.shape();
-  VLOG(2) << "transferring literal shape to infeed: "
+  VLOG(2) << "Transferring literal to infeed with shape: "
           << ShapeUtil::HumanString(shape);
 
-  // TODO(b/31381668) handle tuples.
-  if (ShapeUtil::IsTuple(shape)) {
-    return Unimplemented("Infeed with a tuple shape is not supported: %s",
-                         ShapeUtil::HumanString(literal.shape()).c_str());
+  if (!ShapeUtil::IsTuple(shape)) {
+    int64 size = GetByteSizeRequirement(shape);
+    return TransferBufferToInfeed(executor, size, literal.InternalData());
   }
 
-  int64 size = GetByteSizeRequirement(shape);
-  if (size > std::numeric_limits<int32>::max()) {
-    return Unimplemented("Infeed shape is too large: %s needs %lld bytes",
-                         ShapeUtil::HumanString(literal.shape()).c_str(), size);
+  if (ShapeUtil::IsNestedTuple(shape)) {
+    return Unimplemented(
+        "Infeed with a nested tuple shape is not supported: %s",
+        ShapeUtil::HumanString(literal.shape()).c_str());
+  }
+
+  // For a tuple, we transfer each of its elements to the device and
+  // enqueue the resulting destination device addresses with the
+  // infeed manager.
+  std::vector<cpu::runtime::InfeedBuffer*> buffers;
+  buffers.reserve(literal.tuple_literals_size());
+  auto cleanup = tensorflow::gtl::MakeCleanup([buffers]() {
+    for (cpu::runtime::InfeedBuffer* b : buffers) {
+      b->Done();
+    }
+  });
+
+  for (const auto& tuple_element : literal.tuple_literals()) {
+    const Shape& tuple_element_shape = tuple_element.shape();
+    int64 tuple_element_size = GetByteSizeRequirement(tuple_element_shape);
+    TF_ASSIGN_OR_RETURN(
+        cpu::runtime::InfeedBuffer * buffer,
+        TransferBufferToInfeedInternal(executor, tuple_element_size,
+                                       tuple_element.InternalData()));
+    buffers.push_back(buffer);
   }
 
-  return TransferBufferToInfeed(executor, size, literal.InternalData());
+  cpu::runtime::InfeedManager* infeed_manager =
+      cpu::runtime::GetInfeedManager();
+  infeed_manager->EnqueueBuffers(buffers);
+
+  cleanup.release();
+  return Status::OK();
 }
 
 Status CpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor,
                                                   int64 size,
                                                   const void* source) {
-  int32 size_32 = static_cast<int32>(size);
-  CpuInfeedBuffer* queued_buffer = new CpuInfeedBuffer(size_32);
-  TF_RETURN_IF_ERROR(TransferBufferToDevice(executor, /*size=*/size,
-                                            /*source=*/source,
-                                            queued_buffer->device_memory()));
+  TF_ASSIGN_OR_RETURN(cpu::runtime::InfeedBuffer * buffer,
+                      TransferBufferToInfeedInternal(executor, size, source));
 
   cpu::runtime::InfeedManager* infeed_manager =
       cpu::runtime::GetInfeedManager();
-  infeed_manager->EnqueueBuffer(queued_buffer);
+  infeed_manager->EnqueueBuffers({buffer});
 
   return Status::OK();
 }
 
+StatusOr<cpu::runtime::InfeedBuffer*>
+CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor,
+                                                   int64 size,
+                                                   const void* source) {
+  if (size > std::numeric_limits<int32>::max()) {
+    return InvalidArgument("Infeed shape is too large: needs %lld bytes", size);
+  }
+
+  if (size == 0) {
+    return InvalidArgument("Infeed shape needs 0 bytes");
+  }
+
+  int32 size_32 = static_cast<int32>(size);
+  CpuInfeedBuffer* queued_buffer = new CpuInfeedBuffer(size_32);
+  Status s =
+      TransferBufferToDevice(executor, /*size=*/size,
+                             /*source=*/source, queued_buffer->device_memory());
+
+  if (!s.ok()) {
+    queued_buffer->Done();
+    return s;
+  }
+  return queued_buffer;
+}
+
 }  // namespace xla
 
 static std::unique_ptr<xla::TransferManager> CreateCpuTransferManager() {
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.h b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
index 5d10b62a17..96ffb94d71 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include <vector>
 
+#include "tensorflow/compiler/xla/service/cpu/infeed_manager.h"
 #include "tensorflow/compiler/xla/service/generic_transfer_manager.h"
 #include "tensorflow/compiler/xla/service/transfer_manager.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -41,6 +42,12 @@ class CpuTransferManager : public GenericTransferManager {
                                 int64 size, const void* source) override;
 
  private:
+  // Transfers infeed data to device. InfeedBuffer->Done() must be
+  // called to clean up the memory allocated for InfeedBuffer.
+  StatusOr<cpu::runtime::InfeedBuffer*> TransferBufferToInfeedInternal(
+      perftools::gputools::StreamExecutor* executor, int64 size,
+      const void* source);
+
   TF_DISALLOW_COPY_AND_ASSIGN(CpuTransferManager);
 };
 
diff --git a/tensorflow/compiler/xla/service/gpu/infeed_manager.cc b/tensorflow/compiler/xla/service/gpu/infeed_manager.cc
index 8b948d89f5..ee5b447c9c 100644
--- a/tensorflow/compiler/xla/service/gpu/infeed_manager.cc
+++ b/tensorflow/compiler/xla/service/gpu/infeed_manager.cc
@@ -35,7 +35,7 @@ void InfeedManager::Reset() {
   enqueued_buffer_.clear();
 }
 
-void InfeedManager::EnqueueBuffers(std::vector<InfeedBuffer*> buffers) {
+void InfeedManager::EnqueueBuffers(const std::vector<InfeedBuffer*>& buffers) {
   tensorflow::mutex_lock l(mu_);
   bool was_empty = enqueued_buffer_.empty();
   for (gpu::InfeedBuffer* b : buffers) {
@@ -61,7 +61,7 @@ InfeedBuffer* InfeedManager::BlockingDequeueBuffer() {
   return current_buffer;
 }
 
-void InfeedManager::ReleaseBuffers(std::vector<InfeedBuffer*> buffers) {
+void InfeedManager::ReleaseBuffers(const std::vector<InfeedBuffer*>& buffers) {
   {
     tensorflow::mutex_lock l(mu_);
     for (gpu::InfeedBuffer* b : buffers) {
diff --git a/tensorflow/compiler/xla/service/gpu/infeed_manager.h b/tensorflow/compiler/xla/service/gpu/infeed_manager.h
index 23fbc5bd08..73d5a5ce35 100644
--- a/tensorflow/compiler/xla/service/gpu/infeed_manager.h
+++ b/tensorflow/compiler/xla/service/gpu/infeed_manager.h
@@ -86,7 +86,7 @@ class InfeedManager {
   // will be called when the buffer will no longer be accessed by the
   // InfeedManager, either as a result of a call to Reset or because the
   // runtime has dequeued and used the buffer.
-  void EnqueueBuffers(std::vector<InfeedBuffer*> buffers);
+  void EnqueueBuffers(const std::vector<InfeedBuffer*>& buffers);
 
   // Blocks until the infeed queue is non-empty, then returns the
   // buffer at the head of the queue. Adds the current buffer to the
@@ -94,7 +94,7 @@ class InfeedManager {
   InfeedBuffer* BlockingDequeueBuffer();
 
   // Releases a set of buffers from the to-be released set.
-  void ReleaseBuffers(std::vector<InfeedBuffer*> buffers);
+  void ReleaseBuffers(const std::vector<InfeedBuffer*>& buffers);
 
   // Returns a cached stream associated with an executor. Allocates a
   // new stream on the first invocation. On subsequent invocations, if
diff --git a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
index cd1b182b22..74f0bdb7db 100644
--- a/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/gpu_transfer_manager.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
 
@@ -46,27 +47,52 @@ Status GpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
   VLOG(2) << "Transferring literal to infeed with shape: "
           << ShapeUtil::HumanString(shape);
 
-  std::vector<gpu::InfeedBuffer*> buffers;
+  if (!ShapeUtil::IsTuple(shape)) {
+    int64 size = GetByteSizeRequirement(shape);
+    return TransferBufferToInfeed(executor, size, literal.InternalData());
+  }
 
-  if (ShapeUtil::IsTuple(shape)) {
-    if (ShapeUtil::IsNestedTuple(shape)) {
-      return Unimplemented(
-          "Infeed with a nested tuple shape is not supported: %s",
-          ShapeUtil::HumanString(literal.shape()).c_str());
-    }
+  if (ShapeUtil::IsNestedTuple(shape)) {
+    return Unimplemented(
+        "Infeed with a nested tuple shape is not supported: %s",
+        ShapeUtil::HumanString(literal.shape()).c_str());
+  }
 
-    for (const auto& tuple_element : literal.tuple_literals()) {
-      TF_ASSIGN_OR_RETURN(
-          gpu::InfeedBuffer * buffer,
-          TransferLiteralToInfeedInternal(executor, tuple_element));
-      buffers.push_back(buffer);
+  // For a tuple, we transfer each of its elements to the device and
+  // enqueue the resulting destination device addresses with the
+  // infeed manager.
+  std::vector<gpu::InfeedBuffer*> buffers;
+  buffers.reserve(literal.tuple_literals_size());
+  auto cleanup = tensorflow::gtl::MakeCleanup([buffers]() {
+    for (gpu::InfeedBuffer* b : buffers) {
+      b->Done();
     }
-  } else {
-    TF_ASSIGN_OR_RETURN(gpu::InfeedBuffer * buffer,
-                        TransferLiteralToInfeedInternal(executor, literal));
+  });
+
+  for (const auto& tuple_element : literal.tuple_literals()) {
+    const Shape& tuple_element_shape = tuple_element.shape();
+    int64 tuple_element_size = GetByteSizeRequirement(tuple_element_shape);
+    TF_ASSIGN_OR_RETURN(
+        gpu::InfeedBuffer * buffer,
+        TransferBufferToInfeedInternal(executor, tuple_element_size,
+                                       tuple_element.InternalData()));
     buffers.push_back(buffer);
   }
 
+  cleanup.release();
+  return EnqueueBuffersToInfeed(executor, buffers);
+}
+
+Status GpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor,
+                                                  int64 size,
+                                                  const void* source) {
+  TF_ASSIGN_OR_RETURN(gpu::InfeedBuffer * buffer,
+                      TransferBufferToInfeedInternal(executor, size, source));
+  return EnqueueBuffersToInfeed(executor, {buffer});
+}
+
+Status GpuTransferManager::EnqueueBuffersToInfeed(
+    se::StreamExecutor* executor, std::vector<gpu::InfeedBuffer*> buffers) {
   gpu::InfeedManager* infeed_manager = gpu::GetOrCreateInfeedManager();
   se::Stream* stream = infeed_manager->GetStream(executor);
 
@@ -89,35 +115,16 @@ Status GpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
   return Status::OK();
 }
 
-Status GpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor,
-                                                  int64 size,
-                                                  const void* source) {
-  return TransferBufferToInfeedInternal(executor, size, source).status();
-}
-
-StatusOr<gpu::InfeedBuffer*>
-GpuTransferManager::TransferLiteralToInfeedInternal(
-    se::StreamExecutor* executor, const Literal& literal) {
-  const Shape& shape = literal.shape();
-  CHECK(!ShapeUtil::IsTuple(shape));
-
-  int64 size = GetByteSizeRequirement(shape);
-
+StatusOr<gpu::InfeedBuffer*> GpuTransferManager::TransferBufferToInfeedInternal(
+    se::StreamExecutor* executor, int64 size, const void* source) {
   if (size > std::numeric_limits<int32>::max()) {
-    return Unimplemented("Infeed shape is too large: %s needs %lld bytes",
-                         ShapeUtil::HumanString(literal.shape()).c_str(), size);
+    return InvalidArgument("Infeed shape is too large: needs %lld bytes", size);
   }
 
   if (size == 0) {
-    return Unimplemented("Infeed shape %s needs 0 bytes",
-                         ShapeUtil::HumanString(literal.shape()).c_str());
+    return InvalidArgument("Infeed shape needs 0 bytes");
   }
 
-  return TransferBufferToInfeedInternal(executor, size, literal.InternalData());
-}
-
-StatusOr<gpu::InfeedBuffer*> GpuTransferManager::TransferBufferToInfeedInternal(
-    se::StreamExecutor* executor, int64 size, const void* source) {
   gpu::InfeedManager* infeed_manager = gpu::GetOrCreateInfeedManager();
   se::Stream* stream = infeed_manager->GetStream(executor);
   if (stream == nullptr) {
diff --git a/tensorflow/compiler/xla/service/gpu_transfer_manager.h b/tensorflow/compiler/xla/service/gpu_transfer_manager.h
index 4fc6c911a4..9aa369c668 100644
--- a/tensorflow/compiler/xla/service/gpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/gpu_transfer_manager.h
@@ -42,16 +42,17 @@ class GpuTransferManager : public GenericTransferManager {
                                 int64 size, const void* source) override;
 
  private:
-  // Internal helper function for TransferLiteralToInfeed(). Input
-  // literal cannot be a tuple.
-  StatusOr<gpu::InfeedBuffer*> TransferLiteralToInfeedInternal(
-      perftools::gputools::StreamExecutor* executor, const Literal& literal);
-
-  // Internal helper function for TransferLiteralToInfeed().
+  // Initiates the infeed data transfers. InfeedBuffer->Done() must be
+  // called to clean up the memory allocated for InfeedBuffer.
   StatusOr<gpu::InfeedBuffer*> TransferBufferToInfeedInternal(
       perftools::gputools::StreamExecutor* executor, int64 size,
       const void* source);
 
+  // Enqueues infeed data buffers with the infeed manager after their
+  // transfer completes.
+  Status EnqueueBuffersToInfeed(perftools::gputools::StreamExecutor* executor,
+                                std::vector<gpu::InfeedBuffer*> buffers);
+
   TF_DISALLOW_COPY_AND_ASSIGN(GpuTransferManager);
 };
 
-- 
GitLab


From fae5687ed0220d5856dc25fc8911045a23329149 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 29 Jun 2017 23:07:07 -0700
Subject: [PATCH 0622/1375] Improve consistency in python unittest assert
 methods: order (expected, actual).

PiperOrigin-RevId: 160614541
---
 tensorflow/python/framework/test_util.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index f49a682dbe..9b260d874b 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -627,7 +627,7 @@ class TensorFlowTestCase(googletest.TestCase):
       print("not close dif = ", np.abs(x - y))
       print("not close tol = ", atol + rtol * np.abs(y))
       print("dtype = %s, shape = %s" % (a.dtype, a.shape))
-      np.testing.assert_allclose(a, b, rtol=rtol, atol=atol, err_msg=msg)
+      np.testing.assert_allclose(b, a, rtol=rtol, atol=atol, err_msg=msg)
 
   def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6):
     """Asserts that two numpy arrays, or dicts of same, have near values.
@@ -635,10 +635,10 @@ class TensorFlowTestCase(googletest.TestCase):
     This does not support nested dicts.
 
     Args:
-      a: A numpy ndarray (or anything can be converted to one), or dict of same.
-        Must be a dict iff `b` is a dict.
-      b: A numpy ndarray (or anything can be converted to one), or dict of same.
-        Must be a dict iff `a` is a dict.
+      a: The expected numpy ndarray (or anything can be converted to one), or
+        dict of same. Must be a dict iff `b` is a dict.
+      b: The actual numpy ndarray (or anything can be converted to one), or
+        dict of same. Must be a dict iff `a` is a dict.
       rtol: relative tolerance.
       atol: absolute tolerance.
 
@@ -674,8 +674,8 @@ class TensorFlowTestCase(googletest.TestCase):
     one of the arguments is of type float16.
 
     Args:
-      a: a numpy ndarray or anything can be converted to one.
-      b: a numpy ndarray or anything can be converted to one.
+      a: the expected numpy ndarray or anything can be converted to one.
+      b: the actual numpy ndarray or anything can be converted to one.
       rtol: relative tolerance.
       atol: absolute tolerance.
       float_rtol: relative tolerance for float32.
@@ -699,8 +699,8 @@ class TensorFlowTestCase(googletest.TestCase):
     """Asserts that two numpy arrays have the same values.
 
     Args:
-      a: a numpy ndarray or anything can be converted to one.
-      b: a numpy ndarray or anything can be converted to one.
+      a: the expected numpy ndarray or anything can be converted to one.
+      b: the actual numpy ndarray or anything can be converted to one.
     """
     a = self._GetNdArray(a)
     b = self._GetNdArray(b)
@@ -722,7 +722,7 @@ class TensorFlowTestCase(googletest.TestCase):
         x, y = a, b
       print("not equal lhs = ", x)
       print("not equal rhs = ", y)
-      np.testing.assert_array_equal(a, b)
+      np.testing.assert_array_equal(b, a)
 
   # pylint: disable=g-doc-return-or-yield
   @contextlib.contextmanager
-- 
GitLab


From 0bce3ad96382de0506256b6cbc5dd29a8691bdbf Mon Sep 17 00:00:00 2001
From: Blake Hechtman <blakehechtman@google.com>
Date: Fri, 30 Jun 2017 09:45:07 -0700
Subject: [PATCH 0623/1375] [XLA] Various algebraic simplifications involving
 division and exponenentials

PiperOrigin-RevId: 160655974
---
 .../xla/service/algebraic_simplifier.cc       | 103 +++++++-
 .../xla/service/algebraic_simplifier_test.cc  | 232 +++++++++++++++++
 .../xla/tests/array_elementwise_ops_test.cc   | 240 +++++++++++++++++-
 3 files changed, 570 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 5709ac3067..cbe47e5128 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -444,6 +444,72 @@ Status AlgebraicSimplifierVisitor::HandleDivide(HloInstruction* divide,
                                             subtract));
   }
 
+  // A/exp(B) => A*exp(-B)
+  if (rhs->opcode() == HloOpcode::kExp) {
+    VLOG(10) << "transform [A/exp(B) => A*exp(-B)]: " << divide->ToString();
+    HloInstruction* negate =
+        computation_->AddInstruction(HloInstruction::CreateUnary(
+            divide->shape(), HloOpcode::kNegate, rhs->mutable_operand(0)));
+    HloInstruction* new_exp = computation_->AddInstruction(
+        HloInstruction::CreateUnary(divide->shape(), HloOpcode::kExp, negate));
+    return ReplaceWithNewInstruction(
+        divide, HloInstruction::CreateBinary(
+                    divide->shape(), HloOpcode::kMultiply, lhs, new_exp));
+  }
+
+  // A/pow(B,C) => A*pow(B,-C)
+  if (rhs->opcode() == HloOpcode::kPower) {
+    VLOG(10) << "transform [A/pow(B,C) => A*pow(B,-C)]: " << divide->ToString();
+    HloInstruction* negate =
+        computation_->AddInstruction(HloInstruction::CreateUnary(
+            divide->shape(), HloOpcode::kNegate, rhs->mutable_operand(1)));
+    HloInstruction* new_power = computation_->AddInstruction(
+        HloInstruction::CreateBinary(divide->shape(), HloOpcode::kPower,
+                                     rhs->mutable_operand(0), negate));
+    return ReplaceWithNewInstruction(
+        divide, HloInstruction::CreateBinary(
+                    divide->shape(), HloOpcode::kMultiply, lhs, new_power));
+  }
+
+  // Simplifying integral division would produce unexpected results.
+  if (ShapeUtil::ElementIsIntegral(divide->shape())) {
+    return Status::OK();
+  }
+
+  // (A / B) / (C / D)  =>  (A / B)*(D / C) => (A * D) / (B * C)
+  if (lhs->opcode() == HloOpcode::kDivide &&
+      rhs->opcode() == HloOpcode::kDivide) {
+    auto a_times_d = computation_->AddInstruction(HloInstruction::CreateBinary(
+        divide->shape(), HloOpcode::kMultiply, lhs->mutable_operand(0),
+        rhs->mutable_operand(1)));
+    auto b_times_c = computation_->AddInstruction(HloInstruction::CreateBinary(
+        divide->shape(), HloOpcode::kMultiply, lhs->mutable_operand(1),
+        rhs->mutable_operand(0)));
+    return ReplaceWithNewInstruction(
+        divide, HloInstruction::CreateBinary(
+                    divide->shape(), HloOpcode::kDivide, a_times_d, b_times_c));
+  }
+
+  // (A / B) / C => A / (B * C)
+  if (lhs->opcode() == HloOpcode::kDivide) {
+    auto b_times_c = computation_->AddInstruction(HloInstruction::CreateBinary(
+        divide->shape(), HloOpcode::kMultiply, lhs->mutable_operand(1), rhs));
+    return ReplaceWithNewInstruction(
+        divide,
+        HloInstruction::CreateBinary(divide->shape(), HloOpcode::kDivide,
+                                     lhs->mutable_operand(0), b_times_c));
+  }
+
+  // A / (B / C) => (A*C) / B
+  if (rhs->opcode() == HloOpcode::kDivide) {
+    auto a_times_c = computation_->AddInstruction(HloInstruction::CreateBinary(
+        divide->shape(), HloOpcode::kMultiply, lhs, rhs->mutable_operand(1)));
+    return ReplaceWithNewInstruction(
+        divide,
+        HloInstruction::CreateBinary(divide->shape(), HloOpcode::kDivide,
+                                     a_times_c, rhs->mutable_operand(0)));
+  }
+
   return Status::OK();
 }
 
@@ -591,6 +657,16 @@ Status AlgebraicSimplifierVisitor::HandleMultiply(HloInstruction* multiply,
   if (IsAll(lhs, 1) && ReplaceInstructionIfSameShape(multiply, rhs)) {
     return Status::OK();
   }
+
+  // exp(A) * exp(B) => exp(A+B)
+  if (lhs->opcode() == HloOpcode::kExp && rhs->opcode() == HloOpcode::kExp) {
+    auto add = computation_->AddInstruction(HloInstruction::CreateBinary(
+        multiply->shape(), HloOpcode::kAdd, lhs->mutable_operand(0),
+        rhs->mutable_operand(0)));
+    return ReplaceWithNewInstruction(
+        multiply,
+        HloInstruction::CreateUnary(multiply->shape(), HloOpcode::kExp, add));
+  }
   return Status::OK();
 }
 
@@ -602,6 +678,17 @@ Status AlgebraicSimplifierVisitor::HandleLog(HloInstruction* log,
       ReplaceInstructionIfSameShape(log, operand->mutable_operand(0))) {
     return Status::OK();
   }
+
+  // ln(pow(A,B)) => B*ln(A)
+  if (operand->opcode() == HloOpcode::kPower) {
+    auto new_log = computation_->AddInstruction(HloInstruction::CreateUnary(
+        log->shape(), HloOpcode::kLog, operand->mutable_operand(0)));
+    return ReplaceWithNewInstruction(
+        log,
+        HloInstruction::CreateBinary(log->shape(), HloOpcode::kMultiply,
+                                     new_log, operand->mutable_operand(1)));
+  }
+
   return Status::OK();
 }
 
@@ -873,10 +960,10 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) {
     }
 
     // Verify that the slice shape matches the pad shape.
-    TF_ASSIGN_OR_RETURN(Shape inferred_slice_shape,
-                        ShapeInference::InferSliceShape(
-                            nonzero_pad_shape, start_indices, end_indices,
-                            strides));
+    TF_ASSIGN_OR_RETURN(
+        Shape inferred_slice_shape,
+        ShapeInference::InferSliceShape(nonzero_pad_shape, start_indices,
+                                        end_indices, strides));
     TF_RET_CHECK(ShapeUtil::Compatible(inferred_slice_shape, pad->shape()));
 
     std::unique_ptr<HloInstruction> slice = HloInstruction::CreateSlice(
@@ -909,6 +996,14 @@ Status AlgebraicSimplifierVisitor::HandlePower(HloInstruction* power,
     return Status::OK();
   }
 
+  // pow(exp(A),B) => exp(A*B)
+  if (lhs->opcode() == HloOpcode::kExp) {
+    auto a_times_b = computation_->AddInstruction(HloInstruction::CreateBinary(
+        power->shape(), HloOpcode::kMultiply, lhs->operands()[0], rhs));
+    return ReplaceWithNewInstruction(
+        power, HloInstruction::CreateUnary(power->shape(), HloOpcode::kExp,
+                                           a_times_b));
+  }
   VLOG(10) << "trying transform [pow(A, 2) => A*A]: " << power->ToString();
   if (IsAll(rhs, 2)) {
     return ReplaceWithNewInstruction(
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index 7e52c8fb0c..95230b040f 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -138,6 +138,155 @@ TEST_F(AlgebraicSimplifierTest, SubZero) {
   EXPECT_EQ(root, param0);
 }
 
+// Test that (A/B)/C is simplified to A/(B*C).
+TEST_F(AlgebraicSimplifierTest, LhsDivOfDiv) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* param2 = builder.AddInstruction(
+      HloInstruction::CreateParameter(2, r0f32, "param2"));
+  HloInstruction* div = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, param1));
+  builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, div, param2));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Divide(op::Divide(param0, param1), param2));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Divide(param0, op::Multiply(param1, param2)));
+}
+
+// Test that A/(B/C) is simplified to (A*C)/B.
+TEST_F(AlgebraicSimplifierTest, RhsDivOfDiv) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* param2 = builder.AddInstruction(
+      HloInstruction::CreateParameter(2, r0f32, "param2"));
+  HloInstruction* div = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param1, param2));
+  builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, div));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Divide(param0, op::Divide(param1, param2)));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Divide(op::Multiply(param0, param2), param1));
+}
+
+// Test that (A/B)/(C/D) is simplified to (A*D)/(B*C).
+TEST_F(AlgebraicSimplifierTest, DivOfDivAndDiv) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* param2 = builder.AddInstruction(
+      HloInstruction::CreateParameter(2, r0f32, "param2"));
+  HloInstruction* param3 = builder.AddInstruction(
+      HloInstruction::CreateParameter(3, r0f32, "param3"));
+  HloInstruction* div0 = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, param1));
+  HloInstruction* div1 = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param2, param3));
+  builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, div0, div1));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(
+      computation->root_instruction(),
+      op::Divide(op::Divide(param0, param1), op::Divide(param2, param3)));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(
+      computation->root_instruction(),
+      op::Divide(op::Multiply(param0, param3), op::Multiply(param1, param2)));
+}
+
+// Test that A/exp(B) is simplified to A*exp(-B).
+TEST_F(AlgebraicSimplifierTest, DivOfExp) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32, HloOpcode::kExp, param1));
+  builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, exp));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Divide(param0, op::Exp(param1)));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Multiply(param0, op::Exp(op::Negate(param1))));
+}
+
+// Test that A/pow(B,C) is simplified to A*pow(B,-C).
+TEST_F(AlgebraicSimplifierTest, DivOfPower) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* param2 = builder.AddInstruction(
+      HloInstruction::CreateParameter(2, r0f32, "param2"));
+  HloInstruction* power = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param1, param2));
+  builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, power));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Divide(param0, op::Power(param1, param2)));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Multiply(param0, op::Power(param1, op::Negate(param2))));
+}
+
 // Test that A/1 is simplified to A for a scalar.
 TEST_F(AlgebraicSimplifierTest, DivOneScalar) {
   Shape r0f32 = ShapeUtil::MakeShape(F32, {});
@@ -239,6 +388,89 @@ TEST_F(AlgebraicSimplifierTest, ExpDiv) {
               op::Exp(op::Subtract(param0, param1)));
 }
 
+// Test that exp(A)*exp(B) is simplified to exp(A+B)
+TEST_F(AlgebraicSimplifierTest, ExpMul) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* exp0 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32, HloOpcode::kExp, param0));
+  HloInstruction* exp1 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32, HloOpcode::kExp, param1));
+  builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kMultiply, exp0, exp1));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Multiply(op::Exp(param0), op::Exp(param1)));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Exp(op::Add(param0, param1)));
+}
+
+// Test that pow(exp(A), B) is simplified to exp(A*B)
+TEST_F(AlgebraicSimplifierTest, PowExp) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* exp0 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32, HloOpcode::kExp, param0));
+  builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, exp0, param1));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Power(op::Exp(param0), param1));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Exp(op::Multiply(param0, param1)));
+}
+
+// Test that ln(pow(A, B)) is simplified to ln(A)*B
+TEST_F(AlgebraicSimplifierTest, LnPow) {
+  Shape r0f32 = ShapeUtil::MakeShape(F32, {});
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, r0f32, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, r0f32, "param1"));
+  HloInstruction* pow = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, param1));
+  builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32, HloOpcode::kLog, pow));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Log(op::Power(param0, param1)));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Multiply(op::Log(param0), param1));
+}
+
 // Test that ln(exp(A)) is simplified to A
 TEST_F(AlgebraicSimplifierTest, LnExp) {
   Shape r0f32 = ShapeUtil::MakeShape(F32, {});
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 198a997799..fa6c97bcb8 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -43,7 +43,7 @@ namespace {
 
 class ArrayElementwiseOpTest : public ClientLibraryTestBase {
  public:
-  ErrorSpec error_spec_{0.0001};
+  ErrorSpec error_spec_{0.0001, 0.0001};
 };
 
 class ArrayElementwiseOpTestParamCount
@@ -824,6 +824,244 @@ TEST_F(ArrayElementwiseOpTest, PowSpecialF32) {
   ComputeAndCompareR1<float>(&b, expected, {param_data.get()}, error_spec_);
 }
 
+TEST_F(ArrayElementwiseOpTest, PowOfExpF32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.0f, 5.7f};
+  std::vector<float> values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  b.Pow(b.Exp(param0), param1);
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = std::pow(std::exp(values0[i]), values1[i]);
+  }
+
+  ComputeAndCompareR1<float>(&b, expected, {data0.get(), data1.get()},
+                             error_spec_);
+}
+
+TEST_F(ArrayElementwiseOpTest, LogOfPowerF32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, 4.0f, 0.5f, 5.7f};
+  std::vector<float> values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  b.Log(b.Pow(param0, param1));
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = std::log(std::pow(values0[i], values1[i]));
+  }
+
+  ComputeAndCompareR1<float>(&b, expected, {data0.get(), data1.get()},
+                             error_spec_);
+}
+
+TEST_F(ArrayElementwiseOpTest, MulOfExpF32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.0f, 5.7f};
+  std::vector<float> values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  b.Mul(b.Exp(param0), b.Exp(param1));
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = std::exp(values0[i]) * std::exp(values1[i]);
+  }
+
+  ComputeAndCompareR1<float>(&b, expected, {data0.get(), data1.get()},
+                             error_spec_);
+}
+
+TEST_F(ArrayElementwiseOpTest, DivOfExpF32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.0f, 5.7f};
+  std::vector<float> values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  b.Div(param0, b.Exp(param1));
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = values0[i] / std::exp(values1[i]);
+  }
+
+  ComputeAndCompareR1<float>(&b, expected, {data0.get(), data1.get()},
+                             error_spec_);
+}
+
+TEST_F(ArrayElementwiseOpTest, Div3_lhs_F32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.45f, 5.7f};
+  std::vector<float> values1 = {0.1f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
+  std::vector<float> values2 = {0.1f, 1.1f, 6.9f, 12.5f, -15.0f, -0.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal2 = Literal::CreateR1<float>(values2);
+  std::unique_ptr<GlobalData> data2 =
+      client_->TransferToServer(*literal2).ConsumeValueOrDie();
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  auto param2 = b.Parameter(2, literal2->shape(), "param2");
+  b.Div(b.Div(param0, param1), param2);
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = (values0[i] / values1[i]) / values2[i];
+  }
+
+  ComputeAndCompareR1<float>(
+      &b, expected, {data0.get(), data1.get(), data2.get()}, error_spec_);
+}
+
+TEST_F(ArrayElementwiseOpTest, Div3_rhs_F32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.45f, 5.7f};
+  std::vector<float> values1 = {0.1f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
+  std::vector<float> values2 = {0.1f, 1.1f, 6.9f, 12.5f, -15.0f, -0.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal2 = Literal::CreateR1<float>(values2);
+  std::unique_ptr<GlobalData> data2 =
+      client_->TransferToServer(*literal2).ConsumeValueOrDie();
+
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  auto param2 = b.Parameter(2, literal2->shape(), "param2");
+  b.Div(param0, b.Div(param1, param2));
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = values0[i] / (values1[i] / values2[i]);
+  }
+
+  ComputeAndCompareR1<float>(
+      &b, expected, {data0.get(), data1.get(), data2.get()}, error_spec_);
+}
+
+TEST_F(ArrayElementwiseOpTest, DivOfPowerF32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.45f, 5.7f};
+  std::vector<float> values1 = {0.1f, 1.0f, 2.0f, 0.5f, 1.0f, 0.5f};
+  std::vector<float> values2 = {0.1f, 1.1f, 6.9f, 9.5f, -11.0f, -0.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal2 = Literal::CreateR1<float>(values2);
+  std::unique_ptr<GlobalData> data2 =
+      client_->TransferToServer(*literal2).ConsumeValueOrDie();
+
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  auto param2 = b.Parameter(2, literal2->shape(), "param2");
+  b.Div(param0, b.Pow(param1, param2));
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = values0[i] / std::pow(values1[i], values2[i]);
+  }
+
+  ComputeAndCompareR1<float>(
+      &b, expected, {data0.get(), data1.get(), data2.get()}, error_spec_);
+}
+
+TEST_F(ArrayElementwiseOpTest, Div4F32) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<float> values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.45f, 5.7f};
+  std::vector<float> values1 = {0.1f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f};
+  std::vector<float> values2 = {0.1f, 1.1f, 6.9f, 12.5f, -15.0f, -0.5f};
+  std::vector<float> values3 = {2.1f, 3.1f, 9.9f, -4.5f, -11.0f, -21.5f};
+
+  std::unique_ptr<Literal> literal0 = Literal::CreateR1<float>(values0);
+  std::unique_ptr<GlobalData> data0 =
+      client_->TransferToServer(*literal0).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal1 = Literal::CreateR1<float>(values1);
+  std::unique_ptr<GlobalData> data1 =
+      client_->TransferToServer(*literal1).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal2 = Literal::CreateR1<float>(values2);
+  std::unique_ptr<GlobalData> data2 =
+      client_->TransferToServer(*literal2).ConsumeValueOrDie();
+
+  std::unique_ptr<Literal> literal3 = Literal::CreateR1<float>(values3);
+  std::unique_ptr<GlobalData> data3 =
+      client_->TransferToServer(*literal3).ConsumeValueOrDie();
+
+  auto param0 = b.Parameter(0, literal0->shape(), "param0");
+  auto param1 = b.Parameter(1, literal1->shape(), "param1");
+  auto param2 = b.Parameter(2, literal2->shape(), "param2");
+  auto param3 = b.Parameter(3, literal3->shape(), "param2");
+  b.Div(b.Div(param0, param1), b.Div(param2, param3));
+
+  std::vector<float> expected(values0.size());
+  for (int64 i = 0; i < values0.size(); ++i) {
+    expected[i] = (values0[i] / values1[i]) / (values2[i] / values3[i]);
+  }
+
+  ComputeAndCompareR1<float>(
+      &b, expected, {data0.get(), data1.get(), data2.get(), data3.get()},
+      error_spec_);
+}
+
 TEST_P(ArrayElementwiseOpTestParamCount, SquareManyValues) {
   const int count = GetParam();
   ComputationBuilder builder(client_, TestName());
-- 
GitLab


From 88a0722c74d6e5f9d0766fb54c641923569c3ba0 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Fri, 30 Jun 2017 10:09:37 -0700
Subject: [PATCH 0624/1375] [tf.contrib.data] Add `Dataset.ignore_errors()`
 transformation.

This transformation acts as the identity function on an input dataset,
except that any errors that arise when computing an input element are
silently dropped. It may be useful when an input pipeline contains ops
that perform I/O and/or parse unclean data.

PiperOrigin-RevId: 160658678
---
 .../kernel_tests/map_dataset_op_test.py       | 77 +++++++++++++++-
 .../contrib/data/python/ops/dataset_ops.py    | 45 ++++++++++
 tensorflow/core/kernels/BUILD                 | 13 +++
 .../core/kernels/ignore_errors_dataset_op.cc  | 90 +++++++++++++++++++
 tensorflow/core/ops/dataset_ops.cc            | 10 +++
 5 files changed, 234 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/core/kernels/ignore_errors_dataset_op.cc

diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
index 2c07248c54..ceba00602c 100644
--- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
@@ -17,6 +17,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
+
 import numpy as np
 
 from tensorflow.contrib.data.python.ops import dataset_ops
@@ -25,12 +27,14 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import io_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import test
+from tensorflow.python.util import compat
 
 
 class MapDatasetTest(test.TestCase):
@@ -203,7 +207,8 @@ class MapDatasetTest(test.TestCase):
     components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32)
 
     dataset = (dataset_ops.Dataset.from_tensor_slices(components)
-               .map(lambda x: array_ops.check_numerics(x, "message")))
+               .map(lambda x: array_ops.check_numerics(x, "message"),
+                    num_threads=2, output_buffer_size=2))
     iterator = dataset.make_initializable_iterator()
     init_op = iterator.initializer
     get_next = iterator.get_next()
@@ -219,6 +224,76 @@ class MapDatasetTest(test.TestCase):
       with self.assertRaises(errors.OutOfRangeError):
         sess.run(get_next)
 
+  def testMapIgnoreError(self):
+    components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32)
+
+    dataset = (dataset_ops.Dataset.from_tensor_slices(components)
+               .map(lambda x: array_ops.check_numerics(x, "message"))
+               .ignore_errors())
+    iterator = dataset.make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for x in [1., 2., 3., 5.]:
+        self.assertEqual(x, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+  def testParallelMapIgnoreError(self):
+    components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32)
+
+    dataset = (dataset_ops.Dataset.from_tensor_slices(components)
+               .map(lambda x: array_ops.check_numerics(x, "message"),
+                    num_threads=2, output_buffer_size=2)
+               .ignore_errors())
+    iterator = dataset.make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for x in [1., 2., 3., 5.]:
+        self.assertEqual(x, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+  def testReadFileIgnoreError(self):
+    def write_string_to_file(value, filename):
+      with open(filename, "w") as f:
+        f.write(value)
+    filenames = [os.path.join(self.get_temp_dir(), "file_%d.txt" % i)
+                 for i in range(5)]
+    for filename in filenames:
+      write_string_to_file(filename, filename)
+
+    dataset = (dataset_ops.Dataset.from_tensor_slices(filenames)
+               .map(io_ops.read_file, num_threads=2, output_buffer_size=2)
+               .ignore_errors())
+    iterator = dataset.make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      # All of the files are present.
+      sess.run(init_op)
+      for filename in filenames:
+        self.assertEqual(compat.as_bytes(filename), sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+      # Delete one of the files.
+      os.remove(filenames[0])
+
+      # Attempting to read filenames[0] will fail, but ignore_errors()
+      # will catch the error.
+      sess.run(init_op)
+      for filename in filenames[1:]:
+        self.assertEqual(compat.as_bytes(filename), sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
   def testCaptureHashTable(self):
     # NOTE(mrry): We must use the V2 variants of `HashTable`
     # etc. because these produce a `tf.resource`-typed output that is
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index a689bfc901..fde85d8783 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -723,6 +723,28 @@ class Dataset(object):
     """
     return SkipDataset(self, count)
 
+  def ignore_errors(self):
+    """Creates a `Dataset` from this one and silently ignores any errors.
+
+    Use this transformation to produce a dataset that contains the same elements
+    as the input, but silently drops any elements that caused an error. For
+    example:
+
+    ```python
+    dataset = tf.contrib.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
+
+    # Computing `tf.check_numerics(1. / 0.)` will raise an InvalidArgumentError.
+    dataset = dataset.map(lambda x: tf.check_numerics(1. / x, "error"))
+
+    # Using `ignore_errors()` will drop the element that causes an error.
+    dataset = dataset.ignore_errors()  # ==> { 1., 0.5, 0.2 }
+    ```
+
+    Returns:
+      A `Dataset`.
+    """
+    return IgnoreErrorsDataset(self)
+
   def batch(self, batch_size):
     """Combines consecutive elements of this dataset into batches.
 
@@ -1204,6 +1226,29 @@ class SkipDataset(Dataset):
     return self._input_dataset.output_types
 
 
+class IgnoreErrorsDataset(Dataset):
+  """A `Dataset` that silently ignores errors when computing its input."""
+
+  def __init__(self, input_dataset):
+    """See `Dataset.ignore_errors()` for details."""
+    super(IgnoreErrorsDataset, self).__init__()
+    self._input_dataset = input_dataset
+
+  def make_dataset_resource(self):
+    return gen_dataset_ops.ignore_errors_dataset(
+        self._input_dataset.make_dataset_resource(),
+        output_shapes=nest.flatten(self.output_shapes),
+        output_types=nest.flatten(self.output_types))
+
+  @property
+  def output_shapes(self):
+    return self._input_dataset.output_shapes
+
+  @property
+  def output_types(self):
+    return self._input_dataset.output_types
+
+
 class BatchDataset(Dataset):
   """A `Dataset` that batches contiguous elements from its input."""
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 568e9b717d..04f7bd0d47 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -5435,6 +5435,18 @@ tf_kernel_library(
     ],
 )
 
+tf_kernel_library(
+    name = "ignore_errors_dataset_op",
+    srcs = ["ignore_errors_dataset_op.cc"],
+    deps = [
+        ":dataset",
+        "//tensorflow/core:dataset_ops_op_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
 tf_kernel_library(
     name = "range_dataset_op",
     srcs = ["range_dataset_op.cc"],
@@ -5555,6 +5567,7 @@ tf_kernel_library(
         ":filter_dataset_op",
         ":flat_map_dataset_op",
         ":group_by_window_dataset_op",
+        ":ignore_errors_dataset_op",
         ":iterator_ops",
         ":map_dataset_op",
         ":padded_batch_dataset_op",
diff --git a/tensorflow/core/kernels/ignore_errors_dataset_op.cc b/tensorflow/core/kernels/ignore_errors_dataset_op.cc
new file mode 100644
index 0000000000..83d5bb605c
--- /dev/null
+++ b/tensorflow/core/kernels/ignore_errors_dataset_op.cc
@@ -0,0 +1,90 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/kernels/dataset.h"
+
+#include "tensorflow/core/framework/partial_tensor_shape.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/random/random.h"
+
+namespace tensorflow {
+
+namespace {
+
+// See documentation in ../ops/dataset_ops.cc for a high-level
+// description of the following op.
+
+class IgnoreErrorsDatasetOp : public UnaryDatasetOpKernel {
+ public:
+  explicit IgnoreErrorsDatasetOp(OpKernelConstruction* ctx)
+      : UnaryDatasetOpKernel(ctx) {}
+
+  void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
+                   DatasetBase** output) override {
+    *output = new Dataset(input);
+  }
+
+ private:
+  class Dataset : public DatasetBase {
+   public:
+    explicit Dataset(const DatasetBase* input) : input_(input) {
+      input_->Ref();
+    }
+
+    ~Dataset() override { input_->Unref(); }
+
+    std::unique_ptr<IteratorBase> MakeIterator() const override {
+      return std::unique_ptr<IteratorBase>(new Iterator(this));
+    }
+
+    const DataTypeVector& output_dtypes() const override {
+      return input_->output_dtypes();
+    }
+    const std::vector<PartialTensorShape>& output_shapes() const override {
+      return input_->output_shapes();
+    }
+
+    string DebugString() override { return "IgnoreErrorsDatasetOp::Dataset"; }
+
+   private:
+    class Iterator : public DatasetIterator<Dataset> {
+     public:
+      explicit Iterator(const Dataset* dataset)
+          : DatasetIterator<Dataset>(dataset),
+            input_impl_(dataset->input_->MakeIterator()) {}
+
+      Status GetNext(IteratorContext* ctx, std::vector<Tensor>* out_tensors,
+                     bool* end_of_sequence) override {
+        Status s = input_impl_->GetNext(ctx, out_tensors, end_of_sequence);
+        while (!s.ok()) {
+          out_tensors->clear();
+          s = input_impl_->GetNext(ctx, out_tensors, end_of_sequence);
+        }
+        return Status::OK();
+      }
+
+     private:
+      const std::unique_ptr<IteratorBase> input_impl_;
+    };
+
+    const DatasetBase* const input_;
+  };
+};
+
+REGISTER_KERNEL_BUILDER(Name("IgnoreErrorsDataset").Device(DEVICE_CPU),
+                        IgnoreErrorsDatasetOp);
+
+}  // namespace
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index 4fb554ea16..8b87429705 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -119,6 +119,16 @@ count: A scalar representing the number of elements from the `input_dataset`
   that should be skipped.  If count is -1, skips everything.
 )doc");
 
+REGISTER_OP("IgnoreErrorsDataset")
+    .Input("input_dataset: resource")
+    .Output("handle: resource")
+    .Attr("output_types: list(type) >= 1")
+    .Attr("output_shapes: list(shape) >= 1")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Creates a dataset that contains the elements of `input_dataset` ignoring errors.
+)doc");
+
 REGISTER_OP("MapDataset")
     .Input("input_dataset: resource")
     .Input("other_arguments: Targuments")
-- 
GitLab


From 8423338563b3d65bd0cbc7922add197beb781ef2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 10:12:20 -0700
Subject: [PATCH 0625/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 160659014
---
 tensorflow/go/op/wrappers.go | 67 ++++++++++++++++++++++--------------
 1 file changed, 42 insertions(+), 25 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 8d5cd1d93d..c16c6c8765 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -4408,31 +4408,6 @@ func MatrixDiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
 	return op.Output(0)
 }
 
-// Concatenates tensors along one dimension.
-//
-// Arguments:
-//	values: List of `N` Tensors to concatenate. Their ranks and types must match,
-// and their sizes must match in all dimensions except `concat_dim`.
-//	axis: 0-D.  The dimension along which to concatenate.  Must be in the
-// range [-rank(values), rank(values)).
-//
-// Returns A `Tensor` with the concatenation of values stacked along the
-// `concat_dim` dimension.  This tensor's shape matches that of `values` except
-// in `concat_dim` where it has the sum of the sizes.
-func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "ConcatV2",
-		Input: []tf.Input{
-			tf.OutputList(values), axis,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2.
 type QueueDequeueUpToV2Attr func(optionalAttr)
 
@@ -5656,6 +5631,48 @@ func TensorArrayConcatV2(scope *Scope, handle tf.Output, flow_in tf.Output, dtyp
 	return op.Output(0), op.Output(1)
 }
 
+// Concatenates tensors along one dimension.
+//
+// Arguments:
+//	values: List of `N` Tensors to concatenate. Their ranks and types must match,
+// and their sizes must match in all dimensions except `concat_dim`.
+//	axis: 0-D.  The dimension along which to concatenate.  Must be in the
+// range [-rank(values), rank(values)).
+//
+// Returns A `Tensor` with the concatenation of values stacked along the
+// `concat_dim` dimension.  This tensor's shape matches that of `values` except
+// in `concat_dim` where it has the sum of the sizes.
+func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ConcatV2",
+		Input: []tf.Input{
+			tf.OutputList(values), axis,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Creates a dataset that contains the elements of `input_dataset` ignoring errors.
+func IgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+	opspec := tf.OpSpec{
+		Type: "IgnoreErrorsDataset",
+		Input: []tf.Input{
+			input_dataset,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Creates a dataset that splits a SparseTensor into elements row-wise.
 func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (handle tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From 547a6cb9863c1287f52dc3368f88d047f2c315df Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 10:13:33 -0700
Subject: [PATCH 0626/1375] Add shape accessor to HloValue, and validation that
 shapes match.

PiperOrigin-RevId: 160659135
---
 tensorflow/compiler/xla/service/hlo_buffer.h |  6 ++--
 tensorflow/compiler/xla/service/hlo_value.cc | 31 ++++++++++++++------
 tensorflow/compiler/xla/service/hlo_value.h  | 11 +++++--
 3 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_buffer.h b/tensorflow/compiler/xla/service/hlo_buffer.h
index 238d29a6d6..e38499d210 100644
--- a/tensorflow/compiler/xla/service/hlo_buffer.h
+++ b/tensorflow/compiler/xla/service/hlo_buffer.h
@@ -74,10 +74,10 @@ class HloBuffer {
  public:
   using Id = int64;
 
-  HloBuffer(int64 id) : id_(id) {}
+  HloBuffer(Id id) : id_(id) {}
 
   // Return the unique identifier for this HloBuffer.
-  int64 id() const { return id_; }
+  Id id() const { return id_; }
 
   // Add a value to the set of values held by this buffer. Also adds the
   // HloLocations of the value to the locations vector of the buffer. If the
@@ -101,7 +101,7 @@ class HloBuffer {
   // Unique identifier for this HloBuffer.
   const Id id_;
 
-  // The set of values contained in the this buffer.
+  // The set of values contained in this buffer.
   std::vector<HloValue::Id> value_ids_;
 
   // The set of locations where this buffer is used.
diff --git a/tensorflow/compiler/xla/service/hlo_value.cc b/tensorflow/compiler/xla/service/hlo_value.cc
index 55e6e77c0f..2ce796307b 100644
--- a/tensorflow/compiler/xla/service/hlo_value.cc
+++ b/tensorflow/compiler/xla/service/hlo_value.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_value.h"
 
 #include <algorithm>
+#include <utility>
 
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
@@ -37,6 +38,10 @@ namespace xla {
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
+const Shape& HloLocation::shape() const {
+  return ShapeUtil::GetSubshape(instruction->shape(), index);
+}
+
 string HloLocation::ToString() const {
   string index_str =
       ShapeUtil::IsTuple(instruction->shape()) ? (" " + index.ToString()) : "";
@@ -148,25 +153,33 @@ bool MayUseOperandValue(int64 operand_number, const ShapeIndex& index,
 
 void HloValue::AddLocation(HloInstruction* instruction,
                            const ShapeIndex& index) {
-  // The given location should not already exist in locations_.
+  HloLocation new_location{instruction, index};
+
+  // The new location must not already exist in locations_.
   for (const HloLocation& location : locations_) {
-    DCHECK(!(location.instruction == instruction && location.index == index));
+    DCHECK_NE(location, new_location);
+  }
+  // The shape of the new location must match existing locations.
+  if (!locations_.empty()) {
+    CHECK(
+        ShapeUtil::Compatible(locations_.front().shape(), new_location.shape()))
+        << "front: " << locations_.front() << " new: " << new_location;
   }
 
-  locations_.push_back(HloLocation{instruction, index});
+  locations_.push_back(std::move(new_location));
 
-  //  Update uses.
+  // Update uses.
   for (HloInstruction* user : instruction->users()) {
     for (int64 operand_number : user->OperandIndices(instruction)) {
       if (MayUseOperandValue(operand_number, index, user)) {
+        HloUse new_use{user, operand_number, index};
+
+        // The new use must not already exist in uses_.
         for (const HloUse& use : uses_) {
-          // Verify that this use does not already exist.
-          DCHECK(!(use.instruction == user &&
-                   use.operand_number == operand_number &&
-                   use.operand_index == index));
+          DCHECK_NE(use, new_use);
         }
 
-        uses_.push_back(HloUse{user, operand_number, index});
+        uses_.push_back(std::move(new_use));
       }
     }
   }
diff --git a/tensorflow/compiler/xla/service/hlo_value.h b/tensorflow/compiler/xla/service/hlo_value.h
index 866ce4501b..b0caf24a21 100644
--- a/tensorflow/compiler/xla/service/hlo_value.h
+++ b/tensorflow/compiler/xla/service/hlo_value.h
@@ -22,7 +22,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/shape_tree.h"
-#include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
@@ -37,6 +36,9 @@ struct HloLocation {
   HloInstruction* instruction;
   ShapeIndex index;
 
+  // Returns the shape at this location.
+  const Shape& shape() const;
+
   string ToString() const;
 
   bool operator==(const HloLocation& other) const {
@@ -99,8 +101,8 @@ class HloValue {
   // is_phi is true, then this value is a phi value, for example, at the
   // parameter of a while body computation. Phi values are only used in the SSA
   // dataflow analysis (HloDataflowAnalysis::ssa_form_ is true).
-  HloValue(HloValue::Id id, HloInstruction* instruction,
-           const ShapeIndex& index, bool is_phi = false);
+  HloValue(Id id, HloInstruction* instruction, const ShapeIndex& index,
+           bool is_phi = false);
 
   // Return a unique identifier for this HloValue. This value is used for stable
   // sorting and iteration
@@ -121,6 +123,9 @@ class HloValue {
   // its defining instruction.
   const ShapeIndex& defining_index() const { return defining_location().index; }
 
+  // Return the shape of this HloValue.
+  const Shape& shape() const { return defining_location().shape(); }
+
   // Add or remove a location at which the HloValue appears. The definition
   // location can not be removed. The uses of the HloValue are updated.
   void AddLocation(HloInstruction* instruction, const ShapeIndex& index);
-- 
GitLab


From e5ff9d2b059e5abd343359ea0388fa7eaf56317b Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Fri, 30 Jun 2017 19:23:47 +0200
Subject: [PATCH 0627/1375] Add missing source files declaration for Bazel
 build (#11136)

Also deleted an extra line
---
 tensorflow/core/BUILD | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 24b0f702b3..c333e923fa 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1216,6 +1216,8 @@ LIB_INTERNAL_WINDOWS_DEPS = glob(
         "platform/*.cc",
         "platform/profile_utils/**/*.h",
         "platform/profile_utils/**/*.cc",
+        "framework/resource_handle.h",
+        "framework/resource_handle.cc",
     ],
     exclude = [
         "**/*test*",
@@ -1245,7 +1247,6 @@ cc_library(
                 "platform/*.cc",
                 "platform/profile_utils/**/*.h",
                 "platform/profile_utils/**/*.cc",
-            ] + [
                 "framework/resource_handle.h",
                 "framework/resource_handle.cc",
             ],
-- 
GitLab


From 0034029ac66bb60f272fa1aae05eca5dd9d210d1 Mon Sep 17 00:00:00 2001
From: Tayo Oguntebi <tayo@google.com>
Date: Fri, 30 Jun 2017 10:19:21 -0700
Subject: [PATCH 0628/1375] Refactor reduce window tests via parameterization.

Add an R1 reduction to the reference util.

PiperOrigin-RevId: 160659719
---
 tensorflow/compiler/xla/reference_util.cc     |  73 ++-
 tensorflow/compiler/xla/reference_util.h      |  15 +-
 .../compiler/xla/tests/reduce_window_test.cc  | 422 +++++++-----------
 3 files changed, 223 insertions(+), 287 deletions(-)

diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index ac4fec4b70..138e360c29 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -135,6 +135,49 @@ ReferenceUtil::SeparableConvArray4D(const Array4D<float>& input,
   return tensorflow::MathUtil::CeilOfRatio(unpadded_width, stride);
 }
 
+/* static  */ std::unique_ptr<std::vector<float>>
+ReferenceUtil::ReduceWindow1DGeneric(
+    const tensorflow::gtl::ArraySlice<float>& operand, float init,
+    const std::function<float(float, float)>& reduce_func,
+    const tensorflow::gtl::ArraySlice<int64>& window,
+    const tensorflow::gtl::ArraySlice<int64>& stride, Padding padding) {
+  std::vector<int64> dim_lengths{static_cast<int64>(operand.size())};
+  auto padding_both = xla::MakePadding(dim_lengths, window, stride, padding);
+
+  std::vector<int64> window_counts(window.size(), 0);
+  std::vector<int64> pad_low(window.size(), 0);
+  for (int64 i = 0; i < window.size(); ++i) {
+    window_counts[i] =
+        WindowCount(dim_lengths[i], window[i], stride[i], padding);
+    pad_low[i] = padding_both[i].first;
+  }
+  auto result = MakeUnique<std::vector<float>>(window_counts[0]);
+
+  // Do a full 1D reduce window.
+  for (int64 i0 = 0; i0 < window_counts[0]; ++i0) {
+    int64 i0_base = i0 * stride[0] - pad_low[0];
+
+    float val = init;
+    for (int64 i0_win = 0; i0_win < window[0]; ++i0_win) {
+      if (i0_base + i0_win >= 0 && i0_base + i0_win < dim_lengths[0]) {
+        val = reduce_func(val, operand[i0_base + i0_win]);
+      }
+    }
+    (*result)[i0] = val;
+  }
+  return result;
+}
+
+/* static  */ std::unique_ptr<std::vector<float>>
+ReferenceUtil::ReduceWindow1DAdd(
+    const tensorflow::gtl::ArraySlice<float>& operand, float init,
+    const tensorflow::gtl::ArraySlice<int64>& window,
+    const tensorflow::gtl::ArraySlice<int64>& stride, Padding padding) {
+  const auto add_reduce = [](float arg1, float arg2) { return arg1 + arg2; };
+  return ReduceWindow1DGeneric(operand, init, add_reduce, window, stride,
+                               padding);
+}
+
 /* static  */ std::unique_ptr<Array2D<float>> ReferenceUtil::ReduceWindow2DAdd(
     const Array2D<float>& operand, float init,
     const tensorflow::gtl::ArraySlice<int64>& window,
@@ -453,21 +496,21 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
   // Lambda to access the rhs operand at the given 4D index.  height_over_dky
   // should be equal to height / dky, and width_over_dkx should be equal to
   // width / dkx.  (This is an optimization to avoid doing divisions.)
-  const auto rhs_element = [&](
-      int64 kernel_output_feature, int64 kernel_input_feature, int64 height,
-      int64 width, int64 height_over_dky, int64 width_over_dkx) {
-    DCHECK_EQ(height % dky, 0);
-    DCHECK_EQ(width % dkx, 0);
-    DCHECK_EQ(height / dky, height_over_dky);
-    DCHECK_EQ(width / dkx, width_over_dkx);
-
-    std::array<int64, 4> index;
-    index[dnums.kernel_output_feature_dimension()] = kernel_output_feature;
-    index[dnums.kernel_input_feature_dimension()] = kernel_input_feature;
-    index[dnums.kernel_spatial_dimensions(0)] = height_over_dky;
-    index[dnums.kernel_spatial_dimensions(1)] = width_over_dkx;
-    return rhs(index[0], index[1], index[2], index[3]);
-  };
+  const auto rhs_element =
+      [&](int64 kernel_output_feature, int64 kernel_input_feature, int64 height,
+          int64 width, int64 height_over_dky, int64 width_over_dkx) {
+        DCHECK_EQ(height % dky, 0);
+        DCHECK_EQ(width % dkx, 0);
+        DCHECK_EQ(height / dky, height_over_dky);
+        DCHECK_EQ(width / dkx, width_over_dkx);
+
+        std::array<int64, 4> index;
+        index[dnums.kernel_output_feature_dimension()] = kernel_output_feature;
+        index[dnums.kernel_input_feature_dimension()] = kernel_input_feature;
+        index[dnums.kernel_spatial_dimensions(0)] = height_over_dky;
+        index[dnums.kernel_spatial_dimensions(1)] = width_over_dkx;
+        return rhs(index[0], index[1], index[2], index[3]);
+      };
 
   // Lambda to access the result data at the given 4D index.
   const auto result_element = [&](int64 batch, int64 kernel_output_feature,
diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index a60a648b8d..1d326aff5f 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -149,19 +149,26 @@ class ReferenceUtil {
   static int64 WindowCount(int64 unpadded_width, int64 window_len, int64 stride,
                            Padding padding);
 
-  // Performs a 2D window reduction with Add as the function to apply.
+  // Windowed reductions with Add as the function to apply.
+  static std::unique_ptr<std::vector<float>> ReduceWindow1DAdd(
+      const tensorflow::gtl::ArraySlice<float>& operand, float init,
+      const tensorflow::gtl::ArraySlice<int64>& window,
+      const tensorflow::gtl::ArraySlice<int64>& stride, Padding padding);
   static std::unique_ptr<Array2D<float>> ReduceWindow2DAdd(
       const Array2D<float>& operand, float init,
       const tensorflow::gtl::ArraySlice<int64>& window,
       const tensorflow::gtl::ArraySlice<int64>& stride, Padding padding);
-
-  // Performs a 4D window reduction with Add as the function to apply.
   static std::unique_ptr<Array4D<float>> ReduceWindow4DAdd(
       const Array4D<float>& operand, float init,
       const tensorflow::gtl::ArraySlice<int64>& window,
       const tensorflow::gtl::ArraySlice<int64>& stride, Padding padding);
 
-  // Performs a 4D window reduction with a generic reduce function.
+  // Windowed reductions with a generic reduce function.
+  static std::unique_ptr<std::vector<float>> ReduceWindow1DGeneric(
+      const tensorflow::gtl::ArraySlice<float>& operand, float init,
+      const std::function<float(float, float)>& reduce_func,
+      const tensorflow::gtl::ArraySlice<int64>& window,
+      const tensorflow::gtl::ArraySlice<int64>& stride, Padding padding);
   static std::unique_ptr<Array4D<float>> ReduceWindow4DGeneric(
       const Array4D<float>& operand, float init,
       const std::function<float(float, float)>& reduce_func,
diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index a3cb04c685..4b2fa683d8 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -74,6 +74,12 @@ class ReduceWindowTest : public ClientLibraryTestBase {
   ComputationBuilder builder_;
 };
 
+TEST_F(ReduceWindowTest, DISABLED_ON_CPU(DISABLED_ON_GPU(Min3In5Stride2))) {
+  const auto input = builder_.ConstantR1<float>({10000, 1000, 100, 10, 1});
+  ReduceWindowMin(input, {3}, {2}, Padding::kValid);
+  ComputeAndCompareR1<float>(&builder_, {100, 1}, {}, ErrorSpec(0.0001));
+}
+
 XLA_TEST_F(ReduceWindowTest, ZeroElementSmall) {
   Array4D<float> input_array(1, 0, 2, 1);
 
@@ -203,202 +209,6 @@ TEST_F(ReduceWindowTest, DISABLED_AmongMajor2DimsMediumSizeLargePadding) {
   ComputeAndCompareR4<float>(&builder_, *result, {}, ErrorSpec(1e-3, 1e-3));
 }
 
-// TODO(b/31809540): Implement minor dim reduction to reduce num of reshapes.
-TEST_F(ReduceWindowTest, ReduceR4AmongXYMinorSmall) {
-  Array4D<float> input_array(2, 2, 4, 16);
-
-  Array2D<float> yx({{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f,
-                      11.f, 12.f, 13.f, 14.f, 15.f},
-                     {16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f,
-                      25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f},
-                     {32.f, 33.f, 34.f, 35.f, 36.f, 37.f, 38.f, 39.f, 40.f,
-                      41.f, 42.f, 43.f, 44.f, 45.f, 46.f, 47.f},
-                     {48.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f,
-                      57.f, 58.f, 59.f, 60.f, 61.f, 62.f, 63.f}});
-  input_array.FillWithYX(yx);
-
-  int win_len = 2;
-  int win_stride = 2;
-  const auto input = builder_.ConstantR4FromArray4D<float>(input_array);
-  Padding padding = Padding::kValid;
-  ReduceWindowAdd(input, {1, 1, win_len, win_len},
-                  {1, 1, win_stride, win_stride}, padding);
-
-  auto res = ReferenceUtil::ReduceWindow4DAdd(
-      input_array, 0.0f, {1, 1, win_len, win_len},
-      {1, 1, win_stride, win_stride}, padding);
-  ComputeAndCompareR4<float>(&builder_, *res, {}, ErrorSpec(1e-3, 1e-3));
-}
-
-// TODO(b/31809540): Implement minor dim reduction to reduce num of reshapes.
-TEST_F(ReduceWindowTest, ReduceR4AmongXYMinorSmallOverlapped) {
-  constexpr int64 p = 2;
-  constexpr int64 z = 2;
-  constexpr int64 y = 4;
-  constexpr int64 x = 16;
-  Array4D<float> input_array(p, z, y, x);
-
-  Array2D<float> yx({{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f,
-                      11.f, 12.f, 13.f, 14.f, 15.f},
-                     {16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f,
-                      25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f},
-                     {32.f, 33.f, 34.f, 35.f, 36.f, 37.f, 38.f, 39.f, 40.f,
-                      41.f, 42.f, 43.f, 44.f, 45.f, 46.f, 47.f},
-                     {48.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f,
-                      57.f, 58.f, 59.f, 60.f, 61.f, 62.f, 63.f}});
-  input_array.FillWithYX(yx);
-
-  int win_len = 4;
-  int win_stride = 2;
-  const auto input = builder_.ConstantR4FromArray4D<float>(input_array);
-  ReduceWindowAdd(input, {1, 1, win_len, win_len},
-                  {1, 1, win_stride, win_stride}, Padding::kValid);
-
-  // Expected result
-  Array2D<float> yx_result({{408.f, 440.f, 472.f, 504.f, 536.f, 568.f, 600.f}});
-  Array4D<float> expected(p, z, 1, 7);
-  expected.FillWithYX(yx_result);
-  ComputeAndCompareR4<float>(&builder_, expected, {}, ErrorSpec(1e-3, 1e-3));
-}
-
-TEST_F(ReduceWindowTest, MaxTrivial) {
-  const auto input = builder_.ConstantR1<float>({42});
-  ReduceWindowMax(input, {1}, {1}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {42}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add3In3) {
-  const auto input = builder_.ConstantR1<float>({20, 100, 3});
-  ReduceWindowAdd(input, {3}, {1}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {123}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add4In16Stride4) {
-  const auto input = builder_.ConstantR1<float>(
-      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
-  ReduceWindowAdd(input, {4}, {4}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {10, 26, 42, 58}, {},
-                             ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, DISABLED_ON_CPU(DISABLED_ON_GPU(Min3In5Stride2))) {
-  const auto input = builder_.ConstantR1<float>({10000, 1000, 100, 10, 1});
-  ReduceWindowMin(input, {3}, {2}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {100, 1}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Max3In3) {
-  const auto input = builder_.ConstantR1<float>({20, 100, 3});
-  ReduceWindowMax(input, {3}, {1}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {100}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add2In3) {
-  const auto input = builder_.ConstantR1<float>({100, 10, 1});
-  ReduceWindowAdd(input, {2}, {1}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {110, 11}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add3In5Stride2) {
-  const auto input = builder_.ConstantR1<float>({10000, 1000, 100, 10, 1});
-  ReduceWindowAdd(input, {3}, {2}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {11100, 111}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Max4In16Stride4) {
-  const auto input = builder_.ConstantR1<float>(
-      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
-  ReduceWindowMax(input, {4}, {4}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {4, 8, 12, 16}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Max4In16Stride3) {
-  const auto input = builder_.ConstantR1<float>(
-      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
-  ReduceWindowMax(input, {4}, {3}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {4, 7, 10, 13, 16}, {},
-                             ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Max4In16Stride8) {
-  const auto input = builder_.ConstantR1<float>(
-      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
-  ReduceWindowMax(input, {4}, {8}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {4, 12}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Max3In5Stride2) {
-  const auto input = builder_.ConstantR1<float>({10000, 1000, 100, 10, 1});
-  ReduceWindowMax(input, {3}, {2}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {10000, 100}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Max3In5Stride1) {
-  const auto input = builder_.ConstantR1<float>({10000, 1000, 100, 10, 101});
-  ReduceWindowMax(input, {3}, {1}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {10000, 1000, 101}, {},
-                             ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add3In4Stride2) {
-  const auto input = builder_.ConstantR1<float>({1000, 100, 10, 1});
-  ReduceWindowAdd(input, {3}, {2}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {1110}, {}, ErrorSpec(0.0001));
-}
-
-XLA_TEST_F(ReduceWindowTest, Add2In3SamePad) {
-  const auto input = builder_.ConstantR1<float>({100, 10, 1});
-  ReduceWindowAdd(input, {2}, {1}, Padding::kSame);
-  ComputeAndCompareR1<float>(&builder_, {110, 11, 1}, {}, ErrorSpec(0.0001));
-}
-
-XLA_TEST_F(ReduceWindowTest, Add3In3SamePad) {
-  const auto input = builder_.ConstantR1<float>({100, 10, 1});
-  ReduceWindowAdd(input, {3}, {1}, Padding::kSame);
-  ComputeAndCompareR1<float>(&builder_, {110, 111, 11}, {}, ErrorSpec(0.0001));
-}
-
-XLA_TEST_F(ReduceWindowTest, Add3In3Stride3SamePad) {
-  const auto input = builder_.ConstantR1<float>({100, 10, 1});
-  ReduceWindowAdd(input, {3}, {2}, Padding::kSame);
-  ComputeAndCompareR1<float>(&builder_, {110, 11}, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add2x2In2x2Overlapped) {
-  Array2D<float> input_array({{1.2f, -2.5f, 0.9f, 1.0f},
-                              {3.7f, 0.2f, -1.0f, -0.2f},
-                              {-0.4f, 2.7f, 1.1f, 2.2f},
-                              {0.6f, 1.7f, 1.4f, -0.2f}});
-  auto input = builder_.ConstantR2FromArray2D<float>(input_array);
-  ReduceWindowAdd(input, {2, 2}, {1, 1}, Padding::kValid);
-  Array2D<float> expected(
-      {{2.6f, -2.4f, 0.7f}, {6.2f, 3.0f, 2.1f}, {4.6f, 6.9f, 4.5f}});
-  ComputeAndCompareR2<float>(&builder_, expected, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add2x2In2x2Disjoint) {
-  Array2D<float> input_array({{1.2f, -2.5f, 0.9f, 1.0f},
-                              {3.7f, 0.2f, -1.0f, -0.2f},
-                              {-0.4f, 2.7f, 1.1f, 2.2f},
-                              {0.6f, 1.7f, 1.4f, -0.2f}});
-  auto input = builder_.ConstantR2FromArray2D<float>(input_array);
-  ReduceWindowAdd(input, {2, 2}, {2, 2}, Padding::kValid);
-  Array2D<float> expected({
-      {2.6f, 0.7f}, {4.6f, 4.5f},
-  });
-  ComputeAndCompareR2<float>(&builder_, expected, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, Add1x2In2x2Same) {
-  Array2D<float> input_array({{1.0f, 2.0f}, {3.0f, 4.0f}});
-  auto input = builder_.ConstantR2FromArray2D<float>(input_array);
-  ReduceWindowAdd(input, {1, 2}, {1, 1}, Padding::kSame);
-  Array2D<float> expected({
-      {3.0f, 2.0f}, {7.0f, 4.0f},
-  });
-  ComputeAndCompareR2<float>(&builder_, expected, {}, ErrorSpec(0.0001));
-}
-
 XLA_TEST_F(ReduceWindowTest, Add1x1x2In2x1x2) {
   Array3D<float> input_array(2, 1, 2);
   input_array(0, 0, 0) = 1000;
@@ -599,35 +409,6 @@ TEST_F(ReduceWindowTest, AmongMajor2DimsMultipleMinor) {
   ComputeAndCompareR4<float>(&builder_, *result, {}, ErrorSpec(1e-3, 1e-3));
 }
 
-TEST_F(ReduceWindowTest, R1_128x2_OverlappingWindows) {
-  std::vector<float> input_vector(128 * 2);
-  std::iota(std::begin(input_vector), std::end(input_vector), 0);
-  const auto input = builder_.ConstantR1<float>(input_vector);
-  ReduceWindowAdd(input, {30}, {27}, Padding::kValid);
-  ComputeAndCompareR1<float>(
-      &builder_, {435, 1245, 2055, 2865, 3675, 4485, 5295, 6105, 6915}, {},
-      ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, R1_128x1_OverlappingWindows) {
-  std::vector<float> input_vector(128 * 1);
-  std::iota(std::begin(input_vector), std::end(input_vector), 0);
-  const auto input = builder_.ConstantR1<float>(input_vector);
-  ReduceWindowAdd(input, {32}, {24}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {496, 1264, 2032, 2800, 3568}, {},
-                             ErrorSpec(0.0001));
-}
-
-TEST_F(ReduceWindowTest, R1_128x17_NoOverlap) {
-  std::vector<float> input_vector(128 * 17, 1);
-  const auto input = builder_.ConstantR1<float>(input_vector);
-  ReduceWindowAdd(input, {7}, {64}, Padding::kValid);
-  ComputeAndCompareR1<float>(
-      &builder_, {7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
-                  7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7},
-      {}, ErrorSpec(0.0001));
-}
-
 XLA_TEST_F(ReduceWindowTest, Add24In1152_NoOverlap) {
   std::vector<float> input_vector(128 * 9, 1);
   const auto input = builder_.ConstantR1<float>(input_vector);
@@ -636,15 +417,6 @@ XLA_TEST_F(ReduceWindowTest, Add24In1152_NoOverlap) {
                              {}, ErrorSpec(0.0001));
 }
 
-TEST_F(ReduceWindowTest, Add24In256_NoOverlap) {
-  std::vector<float> input_vector(128 * 2);  // {0, 1, 2, .. len-1}
-  std::iota(std::begin(input_vector), std::end(input_vector), 0);
-  const auto input = builder_.ConstantR1<float>(input_vector);
-  ReduceWindowAdd(input, {32}, {56}, Padding::kValid);
-  ComputeAndCompareR1<float>(&builder_, {496, 2288, 4080, 5872, 7664}, {},
-                             ErrorSpec(0.0001));
-}
-
 XLA_TEST_F(ReduceWindowTest, Add128In128Stride128) {
   const auto input = builder_.ConstantR1<float>(
       {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
@@ -659,32 +431,6 @@ XLA_TEST_F(ReduceWindowTest, Add128In128Stride128) {
   ComputeAndCompareR1<float>(&builder_, {1088}, {}, ErrorSpec(0.0001));
 }
 
-// Regression test for a bug that appeared in Inception (b/34784899).
-TEST_F(ReduceWindowTest, R2ReduceWindowInception) {
-  Array2D<float> input_array(28, 28);
-  input_array.FillRandom(2.0f);
-  std::unique_ptr<Literal> input_literal =
-      Literal::CreateR2FromArray2DWithLayout(input_array,
-                                             LayoutUtil::MakeLayout({0, 1}));
-  ComputationDataHandle input =
-      builder_.Parameter(0, input_literal->shape(), "operand");
-
-  LOG(INFO) << "Input array: " << input_array.ToString();
-
-  int win_len = 3;
-  int stride = 1;
-  Padding padding = Padding::kSame;
-  ReduceWindowAdd(input, {win_len, win_len}, {stride, stride}, padding);
-
-  auto res = ReferenceUtil::ReduceWindow2DAdd(
-      input_array, 0.0f, {win_len, win_len}, {stride, stride}, padding);
-
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
-                         client_->TransferToServer(*input_literal));
-  ComputeAndCompareR2<float>(&builder_, *res, {input_data.get()},
-                             ErrorSpec(1e-3, 1e-3));
-}
-
 // Regression test for a bug that appeared in Inception (b/34784899).
 TEST_F(ReduceWindowTest, R2ReduceWindowInceptionFromBroadcast) {
   Array2D<float> input_array(14, 14, 1.0f);
@@ -906,6 +652,22 @@ const R4ReduceWindowTestData kR4ReduceWindowTestValues[] = {
                            /*pad_low=*/{0, 0, 0, 0},
                            /*pad_high=*/{0, 0, 0, 0},
                            /*reducer=*/kAdd},
+
+    // With minor dims reduction and non-overlapped stride.
+    R4ReduceWindowTestData{/*base_bounds=*/{2, 2, 4, 16},
+                           /*window_bounds=*/{1, 1, 2, 2},
+                           /*strides=*/{1, 1, 2, 2},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
+
+    // With minor dims reduction and overlapped stride.
+    R4ReduceWindowTestData{/*base_bounds=*/{2, 2, 4, 16},
+                           /*window_bounds=*/{1, 1, 4, 4},
+                           /*strides=*/{1, 1, 2, 2},
+                           /*pad_low=*/{0, 0, 0, 0},
+                           /*pad_high=*/{0, 0, 0, 0},
+                           /*reducer=*/kAdd},
 };
 
 INSTANTIATE_TEST_CASE_P(R4ReduceWindowTestInstantiation, R4ReduceWindowTest,
@@ -948,28 +710,39 @@ struct R2ReduceWindowTestData {
 } kR2TestCases[] = {
     {/*base_bounds=*/{4, 18}, /*window_bounds=*/{2, 4},
      /*strides=*/{1, 2}, /*layout=*/{0, 1},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
     {/*base_bounds=*/{2, 5}, /*window_bounds=*/{2, 4},
      /*strides=*/{1, 1}, /*layout=*/{0, 1},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
     {/*base_bounds=*/{1, 3}, /*window_bounds=*/{2, 3},
      /*strides=*/{1, 1}, /*layout=*/{0, 1},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
     {/*base_bounds=*/{3, 129}, /*window_bounds=*/{1, 100},
      /*strides=*/{2, 99}, /*layout=*/{0, 1},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
     {/*base_bounds=*/{6, 152}, /*window_bounds=*/{2, 25},
      /*strides=*/{5, 4}, /*layout=*/{0, 1},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
     {/*base_bounds=*/{6, 4}, /*window_bounds=*/{4, 2},
      /*strides=*/{3, 3}, /*layout=*/{0, 1},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
     {/*base_bounds=*/{5, 147}, /*window_bounds=*/{1, 36},
      /*strides=*/{4, 5}, /*layout=*/{1, 0},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
     {/*base_bounds=*/{4, 153}, /*window_bounds=*/{2, 93},
      /*strides=*/{1, 1}, /*layout=*/{1, 0},
-     /*padding=*/Padding::kSame, /*reducer*/ Reducer::kAdd},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
+    // Regression test for a bug that appeared in Inception (b/34784899).
+    {/*base_bounds=*/{28, 28}, /*window_bounds=*/{3, 3},
+     /*strides=*/{1, 1}, /*layout=*/{1, 0},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
+    // Regression test for a bug that appeared in Inception (b/34784899).
+    {/*base_bounds=*/{4, 32}, /*window_bounds=*/{2, 2},
+     /*strides=*/{2, 2}, /*layout=*/{1, 0},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+    {/*base_bounds=*/{4, 4}, /*window_bounds=*/{2, 2},
+     /*strides=*/{1, 1}, /*layout=*/{1, 0},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
 };
 
 string R2ReduceWindowTestDataToString(
@@ -1021,6 +794,119 @@ INSTANTIATE_TEST_CASE_P(R2ReduceWindowTestInstantiation, R2ReduceWindowTest,
                         ::testing::ValuesIn(kR2TestCases),
                         R2ReduceWindowTestDataToString);
 
+struct R1ReduceWindowTestData {
+  int64 base_bounds[1];
+  int64 window_bounds[1];
+  int64 strides[1];
+  Padding padding;
+  Reducer reducer;
+} kR1TestCases[] = {
+    {/*base_bounds=*/{1}, /*window_bounds=*/{1},
+     /*strides=*/{1},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{3}, /*window_bounds=*/{3},
+     /*strides=*/{1},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{3}, /*window_bounds=*/{2},
+     /*strides=*/{1},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{5}, /*window_bounds=*/{1},
+     /*strides=*/{1},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kMax},
+
+    {/*base_bounds=*/{16}, /*window_bounds=*/{4},
+     /*strides=*/{4},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kMax},
+
+    {/*base_bounds=*/{16}, /*window_bounds=*/{4},
+     /*strides=*/{3},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{128 * 2}, /*window_bounds=*/{30},
+     /*strides=*/{27},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{128 * 17}, /*window_bounds=*/{7},
+     /*strides=*/{64},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{128 * 2}, /*window_bounds=*/{32},
+     /*strides=*/{56},
+     /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{3}, /*window_bounds=*/{2},
+     /*strides=*/{1},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{5}, /*window_bounds=*/{3},
+     /*strides=*/{2},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
+
+    {/*base_bounds=*/{16}, /*window_bounds=*/{4},
+     /*strides=*/{3},
+     /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd},
+};
+
+string R1ReduceWindowTestDataToString(
+    const ::testing::TestParamInfo<R1ReduceWindowTestData>& data) {
+  string str = tensorflow::strings::StrCat(
+      "base_bounds_",
+      tensorflow::str_util::Join(data.param.base_bounds, "x"),  //
+      "__window_bounds_",
+      tensorflow::str_util::Join(data.param.window_bounds, "x"),              //
+      "__strides_", tensorflow::str_util::Join(data.param.strides, "x"),      //
+      "__padding_", data.param.padding == Padding::kSame ? "same" : "valid",  //
+      "__reducer_", data.param.reducer == kAdd ? "add" : "max");
+  return str;
+}
+
+class R1ReduceWindowTest
+    : public ClientLibraryTestBase,
+      public ::testing::WithParamInterface<R1ReduceWindowTestData> {};
+
+TEST_P(R1ReduceWindowTest, DoIt) {
+  ComputationBuilder b(client_, TestName());
+  const auto& param = GetParam();
+  CHECK(param.reducer == kAdd || param.reducer == kMax);
+
+  const float kInitValue = 0.0f;
+  std::vector<float> input_vector(param.base_bounds[0]);
+  std::iota(std::begin(input_vector), std::end(input_vector), 0);
+  std::unique_ptr<Literal> input_literal =
+      Literal::CreateR1(tensorflow::gtl::ArraySlice<float>(input_vector));
+  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
+                         client_->TransferToServer(*input_literal));
+
+  auto computation = param.reducer == kAdd
+                         ? CreateScalarAddComputation(F32, &b)
+                         : CreateScalarMaxComputation(F32, &b);
+  b.ReduceWindow(/*operand=*/
+                 b.Parameter(0, input_literal->shape(), "p0"),
+                 /*init_value=*/b.ConstantR0<float>(kInitValue),
+                 /*computation=*/computation,
+                 /*window_dimensions=*/param.window_bounds,
+                 /*window_strides=*/param.strides, /*padding=*/param.padding);
+
+  auto reduce_func = param.reducer == kAdd
+                         ? +[](float a, float b) { return a + b; }
+                         : +[](float a, float b) { return std::max(a, b); };
+  auto expected = ReferenceUtil::ReduceWindow1DGeneric(
+      /*operand=*/tensorflow::gtl::ArraySlice<float>(input_vector),
+      /*init=*/kInitValue,
+      /*reduce_func=*/reduce_func,
+      /*window=*/param.window_bounds,
+      /*stride=*/param.strides, /*padding=*/param.padding);
+
+  ComputeAndCompareR1<float>(&b, tensorflow::gtl::ArraySlice<float>(*expected),
+                             {input_arg.get()}, ErrorSpec(1e-3, 1e-3));
+}
+
+INSTANTIATE_TEST_CASE_P(R1ReduceWindowTestInstantiation, R1ReduceWindowTest,
+                        ::testing::ValuesIn(kR1TestCases),
+                        R1ReduceWindowTestDataToString);
 }  // namespace
 }  // namespace xla
 
-- 
GitLab


From e9f357b16b04ff17599b89a90498d64bc10c3c91 Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Fri, 30 Jun 2017 10:26:31 -0700
Subject: [PATCH 0629/1375] Merging 1.2.1 back into master.

---
 README.md                                     | 14 ++++-----
 RELEASE.md                                    |  6 ++++
 tensorflow/core/public/version.h              |  2 +-
 tensorflow/docs_src/install/install_c.md      |  2 +-
 tensorflow/docs_src/install/install_go.md     |  2 +-
 tensorflow/docs_src/install/install_java.md   | 18 ++++++------
 tensorflow/docs_src/install/install_linux.md  | 29 ++++++++++++-------
 tensorflow/docs_src/install/install_mac.md    | 15 ++++++----
 .../docs_src/install/install_sources.md       |  4 +--
 .../docs_src/install/install_windows.md       |  4 +--
 tensorflow/tools/pip_package/setup.py         |  2 +-
 11 files changed, 59 insertions(+), 39 deletions(-)

diff --git a/README.md b/README.md
index 41dffec069..90d50f6768 100644
--- a/README.md
+++ b/README.md
@@ -34,13 +34,13 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
diff --git a/RELEASE.md b/RELEASE.md
index 9875838d7e..0cd4eef5d6 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,3 +1,9 @@
+# Release 1.2.1
+
+## Bug Fixes and Other Changes
+* Updating markdown version required to >= 2.6.8.
+* Support tensors as dropout rates again, by removing the min(max(..))
+
 # Release 1.2.0
 
 ## Major Features and Improvements
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 0e5611e359..c1b4fe1c5c 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -20,7 +20,7 @@ limitations under the License.
 
 #define TF_MAJOR_VERSION 1
 #define TF_MINOR_VERSION 2
-#define TF_PATCH_VERSION 0
+#define TF_PATCH_VERSION 1
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 81aa6e3f76..ae877c8293 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.1.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 3f9096b822..6fb8f7eb55 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.1.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 40ed9e1826..a8e6356f9d 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0</version>
+  <version>1.2.1</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0</version>
+                 <version>1.2.1</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.1.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.1.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.1.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.1.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.1.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.1.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 99f27d7b85..82299bab56 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -171,8 +171,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
-     <pre>(tensorflow)$ <b>pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
+     <pre>(tensorflow)$ <b>pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -276,8 +276,13 @@ take the following steps:
      the following command:
 
      <pre>
+<<<<<<< HEAD
      $ <b>sudo pip install --upgrade \
      https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
+=======
+     $ <b>sudo pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b>
+>>>>>>> google/r1.2
      </pre>
 
      If this step fails, see
@@ -464,7 +469,11 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
+<<<<<<< HEAD
      https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
+=======
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b></pre>
+>>>>>>> google/r1.2
 
 
 <a name="ValidateYourInstallation"></a>
@@ -632,14 +641,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -651,14 +660,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -670,14 +679,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -689,14 +698,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 8ff0fb872f..7d136cdc59 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -108,8 +108,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
+     <pre> $ <b>pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -229,8 +229,13 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
+<<<<<<< HEAD
      <pre> $ <b>sudo pip install --upgrade \
      https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
+=======
+     <pre> $ <b>sudo pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b> </pre>
+>>>>>>> google/r1.2
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +344,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +517,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +525,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index a082c3ce78..37ef3816af 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -342,10 +342,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0 on Linux:
+for TensorFlow 1.2.1 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.1-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 8282afaab4..221e6d4ff1 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -115,12 +115,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 54ba8064e8..39499e1775 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0'
+_VERSION = '1.2.1'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
-- 
GitLab


From 86bb48c21e9aff2a7180af48a7dfd7b404875d4a Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Fri, 30 Jun 2017 10:34:08 -0700
Subject: [PATCH 0630/1375] Forgot about a couple of conflicts.

---
 tensorflow/docs_src/install/install_linux.md | 9 ---------
 tensorflow/docs_src/install/install_mac.md   | 5 -----
 2 files changed, 14 deletions(-)

diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 82299bab56..c371712f57 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -276,13 +276,8 @@ take the following steps:
      the following command:
 
      <pre>
-<<<<<<< HEAD
-     $ <b>sudo pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
-=======
      $ <b>sudo pip3 install --upgrade \
      https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b>
->>>>>>> google/r1.2
      </pre>
 
      If this step fails, see
@@ -469,11 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-<<<<<<< HEAD
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
-=======
      https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b></pre>
->>>>>>> google/r1.2
 
 
 <a name="ValidateYourInstallation"></a>
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 7d136cdc59..c8a9c49559 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -229,13 +229,8 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-<<<<<<< HEAD
-     <pre> $ <b>sudo pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
-=======
      <pre> $ <b>sudo pip3 install --upgrade \
      https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b> </pre>
->>>>>>> google/r1.2
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
-- 
GitLab


From d75c8e62bb07c41b299ba89d8ee6ce01f65f7c7c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 10:50:39 -0700
Subject: [PATCH 0631/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 160663472
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 24 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 25 +++++++++++++++++++
 2 files changed, 49 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index e230385c2e..33461bbfdc 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -10336,6 +10336,30 @@ op {
     }
   }
 }
+op {
+  name: "IgnoreErrorsDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  is_stateful: true
+}
 op {
   name: "Imag"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 5e0a53b3b0..41469c4029 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -9674,6 +9674,31 @@ op {
   summary: "Compute the upper regularized incomplete Gamma function `Q(a, x)`."
   description: "The upper regularized incomplete Gamma function is defined as:\n\n\\\\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\\\)\n\nwhere\n\n\\\\(Gamma(a, x) = int_{x}^{\\infty} t^{a-1} exp(-t) dt\\\\)\n\nis the upper incomplete Gama function.\n\nNote, above `P(a, x)` (`Igamma`) is the lower regularized complete\nGamma function."
 }
+op {
+  name: "IgnoreErrorsDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  summary: "Creates a dataset that contains the elements of `input_dataset` ignoring errors."
+  is_stateful: true
+}
 op {
   name: "Imag"
   input_arg {
-- 
GitLab


From 8584e7216c4038b34533003ccdb106880091d339 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 11:06:26 -0700
Subject: [PATCH 0632/1375] [XLA] Support different alignment requirements
 depending on buffer color.

PiperOrigin-RevId: 160665742
---
 .../compiler/xla/service/buffer_assignment.cc | 32 ++++++++------
 .../compiler/xla/service/buffer_assignment.h  | 34 +++++++--------
 .../xla/service/buffer_assignment_test.cc     | 42 +++++++++++--------
 .../compiler/xla/service/cpu/cpu_compiler.cc  |  7 ++--
 .../compiler/xla/service/gpu/gpu_compiler.cc  |  4 +-
 .../compiler/xla/service/logical_buffer.h     |  2 +-
 6 files changed, 69 insertions(+), 52 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index 2f83fd82ec..d00749247e 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -327,8 +327,9 @@ void BufferAssignment::CombineTempAllocations() {
       // Each temp allocation is placed end-to-end, accounting for alignment.
       // The offset of each buffer in the combined allocation is computed from
       // the base offset of the allocation.
+      int64 alignment = color_alignment_(color);
       const int64 base =
-          RoundUpToNearest(combined_allocation->size(), alignment_);
+          RoundUpToNearest(combined_allocation->size(), alignment);
       combined_allocation->set_size(base + temp_allocation.size());
       for (const auto& buffer_offset_size : temp_allocation.assigned_buffers_) {
         const LogicalBuffer* buffer = buffer_offset_size.first;
@@ -582,12 +583,13 @@ Status GatherComputationsByAllocationType(
 /* static */
 StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::Run(
     const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
-    LogicalBuffer::SizeFunction buffer_size, int64 alignment,
+    LogicalBuffer::SizeFunction buffer_size,
+    LogicalBuffer::AlignmentFunction color_alignment,
     bool allow_input_output_aliasing, TuplePointsToAnalysis::Colorer colorer) {
-  BufferAssigner assigner(alignment, allow_input_output_aliasing,
-                          std::move(colorer));
+  BufferAssigner assigner(allow_input_output_aliasing, std::move(colorer));
   return assigner.CreateAssignment(module, std::move(hlo_ordering),
-                                   std::move(buffer_size));
+                                   std::move(buffer_size),
+                                   std::move(color_alignment));
 }
 
 bool BufferAssigner::MaybeAssignBuffer(BufferAllocation* allocation,
@@ -944,11 +946,13 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering(
     }
     auto color_map = SplitBuffersByColor(all_buffers_to_assign);
     for (auto& single_colored_set : color_map) {
-      VLOG(2) << "Simulating heap for color " << single_colored_set.first;
+      auto color = single_colored_set.first;
+      VLOG(2) << "Simulating heap for color " << color;
+      int64 alignment = assignment->color_alignment_(color);
       TF_ASSIGN_OR_RETURN(
           const HeapSimulator::Result result,
           HeapSimulator::Run(MakeUnique<DecreasingSizeRunsHeap>(
-                                 MakeUnique<LazyBestFitHeap>(alignment_)),
+                                 MakeUnique<LazyBestFitHeap>(alignment)),
                              assignment->module(), module_sequence,
                              assignment->points_to_analysis(),
                              assignment->buffer_size_,
@@ -969,11 +973,13 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering(
       CHECK(instruction_sequence != nullptr) << computation->name();
       auto color_map = SplitBuffersByColor(buffers_to_assign);
       for (auto& single_colored_set : color_map) {
-        VLOG(2) << "Simulating heap for color " << single_colored_set.first;
+        auto color = single_colored_set.first;
+        VLOG(2) << "Simulating heap for color " << color;
+        int64 alignment = assignment->color_alignment_(color);
         TF_ASSIGN_OR_RETURN(
             const HeapSimulator::Result result,
             HeapSimulator::Run(MakeUnique<DecreasingSizeRunsHeap>(
-                                   MakeUnique<LazyBestFitHeap>(alignment_)),
+                                   MakeUnique<LazyBestFitHeap>(alignment)),
                                *computation, *instruction_sequence,
                                assignment->points_to_analysis(),
                                assignment->buffer_size_,
@@ -1322,7 +1328,8 @@ void BufferAssigner::AssignColocatedBufferSets(
 
 StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
     const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
-    LogicalBuffer::SizeFunction buffer_size) {
+    LogicalBuffer::SizeFunction buffer_size,
+    LogicalBuffer::AlignmentFunction color_alignment) {
   TF_ASSIGN_OR_RETURN(std::unique_ptr<BufferLiveness> liveness,
                       BufferLiveness::Run(module, std::move(hlo_ordering)));
 
@@ -1332,8 +1339,9 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
   XLA_VLOG_LINES(3, liveness->points_to_analysis().ToString());
 
   // Can't use MakeUnique because BufferAssignment constructor is private.
-  std::unique_ptr<BufferAssignment> assignment(new BufferAssignment(
-      module, std::move(liveness), alignment_, std::move(buffer_size)));
+  std::unique_ptr<BufferAssignment> assignment(
+      new BufferAssignment(module, std::move(liveness), std::move(buffer_size),
+                           std::move(color_alignment)));
 
   // Assign buffers with the tightest constraints first (colocated buffer sets).
   // Once b/32491382 enables module-level liveness analysis, we may be able
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h
index c95ced7a36..e0b49505d2 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.h
+++ b/tensorflow/compiler/xla/service/buffer_assignment.h
@@ -355,12 +355,12 @@ class BufferAssignment {
 
   explicit BufferAssignment(const HloModule* module,
                             std::unique_ptr<BufferLiveness> liveness,
-                            int64 alignment,
-                            LogicalBuffer::SizeFunction buffer_size)
+                            LogicalBuffer::SizeFunction buffer_size,
+                            LogicalBuffer::AlignmentFunction color_alignment)
       : module_(module),
         liveness_(std::move(liveness)),
-        alignment_(alignment),
-        buffer_size_(std::move(buffer_size)) {}
+        buffer_size_(std::move(buffer_size)),
+        color_alignment_(std::move(color_alignment)) {}
 
   // Creates and returns a new BufferAllocation, with no assigned
   // LogicalBuffers. Ownership is maintained internally.
@@ -406,11 +406,13 @@ class BufferAssignment {
 
   const HloModule* module_;
   const std::unique_ptr<BufferLiveness> liveness_;
-  const int64 alignment_;
 
   // Function which returns the buffer size for a given logical buffer (shape).
   LogicalBuffer::SizeFunction buffer_size_;
 
+  // Function which returns the alignment for a given logical buffer color.
+  LogicalBuffer::AlignmentFunction color_alignment_;
+
   Stats stats_;
   std::vector<HeapSimulatorTrace> heap_simulator_traces_;
 
@@ -421,29 +423,30 @@ class BufferAssignment {
 class BufferAssigner {
  public:
   // Build and return a BufferAssignment for the given module. The given
-  // HloOrdering is used to determine buffer liveness. buffer_size is a function
-  // which returns the size of a LogicalBuffer. Alignment is the minimum
-  // alignment of any buffer. allow_input_output_aliasing specifies whether
-  // input buffer are allowed to be reused as outbut buffers by the client code.
+  // HloOrdering is used to determine buffer liveness. buffer_size and
+  // color_alignment are functions which returns the size and alignment of a
+  // LogicalBuffer.  allow_input_output_aliasing specifies whether input buffer
+  // are allowed to be reused as outbut buffers by the client code.
   static StatusOr<std::unique_ptr<BufferAssignment>> Run(
       const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
-      LogicalBuffer::SizeFunction buffer_size, int64 alignment,
+      LogicalBuffer::SizeFunction buffer_size,
+      LogicalBuffer::AlignmentFunction color_alignment,
       bool allow_input_output_aliasing = false,
       TuplePointsToAnalysis::Colorer colorer =
           TuplePointsToAnalysis::DefaultColorer());
 
  private:
-  BufferAssigner(int64 alignment, bool allow_input_output_aliasing,
+  BufferAssigner(bool allow_input_output_aliasing,
                  TuplePointsToAnalysis::Colorer colorer)
-      : alignment_(alignment),
-        allow_input_output_aliasing_(allow_input_output_aliasing),
+      : allow_input_output_aliasing_(allow_input_output_aliasing),
         colorer_(colorer) {}
   virtual ~BufferAssigner() = default;
 
   // Create a buffer assignment.
   StatusOr<std::unique_ptr<BufferAssignment>> CreateAssignment(
       const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
-      LogicalBuffer::SizeFunction buffer_size);
+      LogicalBuffer::SizeFunction buffer_size,
+      LogicalBuffer::AlignmentFunction color_alignment);
 
   // Assigns buffers to the instructions in the given computation. "assignment"
   // is modified to reflect the new buffer assignments. If is_thread_local is
@@ -530,9 +533,6 @@ class BufferAssigner {
   SplitBuffersByColor(
       const tensorflow::gtl::FlatSet<const LogicalBuffer*>& buffers);
 
-  // Minimum alignment of any buffer.
-  int64 alignment_;
-
   // If true, buffer assignments assumes that input parameter buffers and output
   // buffers can be shared if their sizes match.
   bool allow_input_output_aliasing_;
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index abc3b49522..21ba083dd9 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -86,17 +86,19 @@ class BufferAssignmentTest : public HloTestBase {
                                                         int64 alignment = 1) {
     return BufferAssigner::Run(
                module, MakeUnique<DependencyHloOrdering>(module),
-               backend_->compiler()->BufferSizeBytesFunction(), alignment)
+               backend_->compiler()->BufferSizeBytesFunction(),
+               [alignment](LogicalBuffer::Color) { return alignment; })
         .ConsumeValueOrDie();
   }
 
   std::unique_ptr<BufferAssignment> RunColoredBufferAssignment(
       HloModule* module, TuplePointsToAnalysis::Colorer colorer,
       int64 alignment = 1) {
-    return BufferAssigner::Run(module,
-                               MakeUnique<DependencyHloOrdering>(module),
-                               backend_->compiler()->BufferSizeBytesFunction(),
-                               alignment, false, std::move(colorer))
+    return BufferAssigner::Run(
+               module, MakeUnique<DependencyHloOrdering>(module),
+               backend_->compiler()->BufferSizeBytesFunction(),
+               [alignment](LogicalBuffer::Color) { return alignment; }, false,
+               std::move(colorer))
         .ConsumeValueOrDie();
   }
 
@@ -1009,9 +1011,10 @@ TEST_F(BufferAssignmentTest, TupleParameterAsOutput) {
   // Test a computation that returns a tuple parameter.
   auto builder = HloComputation::Builder(TestName());
   auto tuple_param = builder.AddInstruction(HloInstruction::CreateParameter(
-      0, ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(PRED, {1, 2, 3, 4}),
-                                    ShapeUtil::MakeShape(F32, {}),
-                                    ShapeUtil::MakeShape(S32, {42})}),
+      0,
+      ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(PRED, {1, 2, 3, 4}),
+                                 ShapeUtil::MakeShape(F32, {}),
+                                 ShapeUtil::MakeShape(S32, {42})}),
       "param0"));
 
   auto module = CreateNewModule();
@@ -1040,10 +1043,11 @@ TEST_F(BufferAssignmentTest, ElementOfNestedTupleParameterAsOutput) {
   // parameter.
   auto builder = HloComputation::Builder(TestName());
   auto tuple_param = builder.AddInstruction(HloInstruction::CreateParameter(
-      0, ShapeUtil::MakeTupleShape(
-             {ShapeUtil::MakeShape(PRED, {1, 2, 3, 4}),
-              ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(S32, {42}),
-                                         ShapeUtil::MakeShape(S32, {101})})}),
+      0,
+      ShapeUtil::MakeTupleShape(
+          {ShapeUtil::MakeShape(PRED, {1, 2, 3, 4}),
+           ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(S32, {42}),
+                                      ShapeUtil::MakeShape(S32, {101})})}),
       "param0"));
   auto tuple_element =
       builder.AddInstruction(HloInstruction::CreateGetTupleElement(
@@ -1411,7 +1415,8 @@ class WhileBufferAssignmentTest : public HloTestBase {
         CreateMemoryMinimizingSequence(*module, ByteSizeOf).ConsumeValueOrDie();
     return BufferAssigner::Run(
                module, MakeUnique<SequentialHloOrdering>(module, sequence),
-               ByteSizeOf, alignment)
+               ByteSizeOf,
+               [alignment](LogicalBuffer::Color) { return alignment; })
         .ConsumeValueOrDie();
   }
 
@@ -1666,11 +1671,12 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) {
   sequence[module->entry_computation()] =
       std::move(sequence_for_buffer_assigment);
 
-  auto assignment = BufferAssigner::Run(module.get(),
-                                        MakeUnique<SequentialHloOrdering>(
-                                            module.get(), sequence),
-                                        ByteSizeOf, 1)
-                        .ConsumeValueOrDie();
+  auto assignment =
+      BufferAssigner::Run(
+          module.get(),
+          MakeUnique<SequentialHloOrdering>(module.get(), sequence), ByteSizeOf,
+          [](LogicalBuffer::Color) { return 1; })
+          .ConsumeValueOrDie();
 
   EXPECT_TRUE(BuffersDistinct({while0}, {while1}, *assignment));
 }
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 18cdb25176..45da0cea33 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -317,6 +317,7 @@ namespace {
 
 // Align buffers to 16-byte boundaries.
 constexpr int64 kMemoryAlignment = 16;
+auto memory_alignment = [](LogicalBuffer::Color) { return kMemoryAlignment; };
 
 llvm::TargetOptions CompilerTargetOptions(
     const HloModuleConfig& module_config) {
@@ -415,7 +416,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
         std::unique_ptr<BufferAssignment> assignment,
         BufferAssigner::Run(module.get(),
                             MakeUnique<DependencyHloOrdering>(module.get()),
-                            BufferSizeBytesFunction(), kMemoryAlignment));
+                            BufferSizeBytesFunction(), memory_alignment));
 
     if (!dump_debug_json_to.empty()) {
       HloProto proto = MakeHloProto(*module, *assignment);
@@ -515,7 +516,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
         BufferAssigner::Run(
             module.get(),
             MakeUnique<SequentialHloOrdering>(module.get(), module_sequence),
-            BufferSizeBytesFunction(), kMemoryAlignment));
+            BufferSizeBytesFunction(), memory_alignment));
 
     if (!dump_debug_json_to.empty()) {
       HloProto proto = MakeHloProto(*module, *assignment);
@@ -681,7 +682,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
         std::unique_ptr<BufferAssignment> assignment,
         BufferAssigner::Run(
             module, MakeUnique<SequentialHloOrdering>(module, module_sequence),
-            BufferSizeBytesFunction(), kMemoryAlignment));
+            BufferSizeBytesFunction(), memory_alignment));
 
     const string dump_debug_json_to =
         module->config().debug_options().xla_dump_debug_json_to();
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index ffed9c7e04..47fba36822 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -264,7 +264,9 @@ StatusOr<std::unique_ptr<Executable>> GpuCompiler::Compile(
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr<BufferAssignment> buffer_assignment,
       BufferAssigner::Run(module.get(), hlo_schedule->ConsumeHloOrdering(),
-                          BufferSizeBytesFunction(), kMemoryAlignment));
+                          BufferSizeBytesFunction(), [](LogicalBuffer::Color) {
+                            return kMemoryAlignment;
+                          }));
 
   const string dump_debug_json_to =
       module->config().debug_options().xla_dump_debug_json_to();
diff --git a/tensorflow/compiler/xla/service/logical_buffer.h b/tensorflow/compiler/xla/service/logical_buffer.h
index 7aaa29b098..a9f6688612 100644
--- a/tensorflow/compiler/xla/service/logical_buffer.h
+++ b/tensorflow/compiler/xla/service/logical_buffer.h
@@ -95,7 +95,7 @@ class LogicalBuffer {
 
   // Functions which return the size and alignment of a logical buffer in bytes.
   using SizeFunction = std::function<int64(const LogicalBuffer&)>;
-  using AlignmentFunction = std::function<int64(const LogicalBuffer&)>;
+  using AlignmentFunction = std::function<int64(LogicalBuffer::Color)>;
 
   LogicalBuffer(HloInstruction* instruction, const ShapeIndex& index, Id id)
       : instruction_(instruction),
-- 
GitLab


From 7d7aa7ce21343d568c332b82770b071b61dfb14d Mon Sep 17 00:00:00 2001
From: Nayana Thorat <nayana.thorat@gmail.com>
Date: Sat, 1 Jul 2017 00:05:24 +0530
Subject: [PATCH 0633/1375] Adding support for s390x for boringssl  (#11170)

* Add patch to boringssl code to support s390x

* Add patch to boringssl code to support s390x
---
 tensorflow/workspace.bzl                        |  5 ++++-
 third_party/boringssl/BUILD                     |  0
 third_party/boringssl/add_boringssl_s390x.patch | 14 ++++++++++++++
 3 files changed, 18 insertions(+), 1 deletion(-)
 create mode 100644 third_party/boringssl/BUILD
 create mode 100644 third_party/boringssl/add_boringssl_s390x.patch

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index a5172187f7..9632b9f73f 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -520,7 +520,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       actual = "@jsoncpp_git//:jsoncpp",
   )
 
-  native.http_archive(
+  patched_http_archive(
       name = "boringssl",
       urls = [
           "http://mirror.bazel.build/github.com/google/boringssl/archive/bbcaa15b0647816b9a1a9b9e0d209cd6712f0105.tar.gz",
@@ -528,6 +528,9 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       ],
       sha256 = "025264d6e9a7ad371f2f66d17a28b6627de0c9592dc2eb54afd062f68f1f9aa3",
       strip_prefix = "boringssl-bbcaa15b0647816b9a1a9b9e0d209cd6712f0105",
+
+      # Add patch to boringssl code to support s390x
+      patch_file = str(Label("//third_party/boringssl:add_boringssl_s390x.patch")),
   )
 
   native.new_http_archive(
diff --git a/third_party/boringssl/BUILD b/third_party/boringssl/BUILD
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/third_party/boringssl/add_boringssl_s390x.patch b/third_party/boringssl/add_boringssl_s390x.patch
new file mode 100644
index 0000000000..7bdc796cdf
--- /dev/null
+++ b/third_party/boringssl/add_boringssl_s390x.patch
@@ -0,0 +1,14 @@
+diff --git a/src/include/openssl/base.h b/src/include/openssl/base.h
+index 7a3adfb..88012ad 100644
+--- a/src/include/openssl/base.h
++++ b/src/include/openssl/base.h
+@@ -94,6 +94,8 @@ extern "C" {
+ #elif defined(__pnacl__)
+ #define OPENSSL_32_BIT
+ #define OPENSSL_PNACL
++#elif defined(__s390x__)
++#define OPENSSL_64_BIT
+ #else
+ #error "Unknown target CPU"
+ #endif
+
-- 
GitLab


From 85eff0c5ea479424fe6237ac9d35e34ac7789792 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Fri, 30 Jun 2017 12:00:15 -0700
Subject: [PATCH 0634/1375] Add required include vector for Mac build.

---
 tensorflow/compiler/xla/service/cpu/infeed_manager.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager.h b/tensorflow/compiler/xla/service/cpu/infeed_manager.h
index 756582ef78..e965988453 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager.h
+++ b/tensorflow/compiler/xla/service/cpu/infeed_manager.h
@@ -21,6 +21,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_INFEED_MANAGER_H_
 
 #include <deque>
+#include <vector>
 
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/platform/mutex.h"
-- 
GitLab


From 2a318e74663bdf166852a8295a61d55284d0e500 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 30 Jun 2017 12:32:11 -0700
Subject: [PATCH 0635/1375] Made sure that grappler is aware of feed and fetch
 nodes

PiperOrigin-RevId: 160675704
---
 .../simple_graph_execution_state.cc           | 151 +++++++++++-------
 .../simple_graph_execution_state.h            |   3 +
 .../grappler/optimizers/layout_optimizer.cc   |  60 ++++---
 3 files changed, 137 insertions(+), 77 deletions(-)

diff --git a/tensorflow/core/common_runtime/simple_graph_execution_state.cc b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
index 358550f641..5a69d2440a 100644
--- a/tensorflow/core/common_runtime/simple_graph_execution_state.cc
+++ b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/subgraph.h"
+#include "tensorflow/core/graph/tensor_id.h"
 #include "tensorflow/core/graph/validate.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
@@ -238,60 +239,6 @@ Status SimpleGraphExecutionState::InitBaseGraph(
     const BuildGraphOptions& options) {
   const GraphDef* graph_def = &original_graph_def_;
 
-#ifndef IS_MOBILE_PLATFORM
-  GraphDef optimized_graph;
-
-  const RewriterConfig& rewrite_options =
-      session_options_->config.graph_options().rewrite_options();
-
-  if (grappler::MetaOptimizerEnabled(rewrite_options)) {
-    // Adding this functionality in steps. The first step is to make sure
-    // we don't break dependencies. The second step will be to turn the
-    // functionality on by default.
-    grappler::GrapplerItem item;
-    item.id = "tf_graph";
-    item.graph = original_graph_def_;
-
-    item.fetch = options.fetch_endpoints;
-    item.fetch.insert(item.fetch.end(), options.target_nodes.begin(),
-                      options.target_nodes.end());
-
-    Status s;
-    if (!options.feed_endpoints.empty()) {
-      std::unordered_set<string> feeds(options.feed_endpoints.begin(),
-                                       options.feed_endpoints.end());
-      for (const NodeDef& node : original_graph_def_.node()) {
-        if (feeds.find(node.name()) == feeds.end()) {
-          continue;
-        }
-        if (node.attr().count("dtype") == 0 ||
-            node.attr().count("shape") == 0) {
-          s = errors::InvalidArgument("Missing node shape or type");
-          break;
-        }
-        TensorShape shape(node.attr().at("shape").shape());
-        DataType type = node.attr().at("dtype").type();
-        Tensor fake_input(type, shape);
-        item.feed.emplace_back(node.name(), fake_input);
-      }
-    }
-
-    if (s.ok()) {
-      std::unordered_map<string, DeviceProperties> device_map;
-      for (const auto& device : device_set_->devices()) {
-        device_map[device->name()] =
-            grappler::GetDeviceInfo(device->parsed_name());
-      }
-      grappler::VirtualCluster cluster(device_map);
-      s = grappler::RunMetaOptimizer(item, rewrite_options, &cluster,
-                                     &optimized_graph);
-    }
-    if (s.ok()) {
-      graph_def = &optimized_graph;
-    }
-  }
-#endif  // IS_MOBILE_PLATFORM
-
   std::unique_ptr<Graph> new_graph(new Graph(OpRegistry::Global()));
   GraphConstructorOptions opts;
   TF_RETURN_IF_ERROR(ConvertGraphDefToGraph(opts, *graph_def, new_graph.get()));
@@ -341,6 +288,92 @@ Status SimpleGraphExecutionState::InitBaseGraph(
   return Status::OK();
 }
 
+Status SimpleGraphExecutionState::OptimizeGraph(
+    const BuildGraphOptions& options, std::unique_ptr<Graph>* optimized_graph) {
+#ifndef IS_MOBILE_PLATFORM
+  const RewriterConfig& rewrite_options =
+      session_options_->config.graph_options().rewrite_options();
+
+  if (grappler::MetaOptimizerEnabled(rewrite_options)) {
+    // Adding this functionality in steps. The first step is to make sure
+    // we don't break dependencies. The second step will be to turn the
+    // functionality on by default.
+    grappler::GrapplerItem item;
+    item.id = "tf_graph";
+    graph_->ToGraphDef(&item.graph);
+
+    item.fetch = options.fetch_endpoints;
+    item.fetch.insert(item.fetch.end(), options.target_nodes.begin(),
+                      options.target_nodes.end());
+
+    if (!options.feed_endpoints.empty()) {
+      std::unordered_set<string> feeds;
+      for (const string& feed : options.feed_endpoints) {
+        TensorId id = ParseTensorName(feed);
+        if (id.second != 0) {
+          return errors::InvalidArgument("Unsupported feed: ", feed);
+        }
+        feeds.insert(id.first.ToString());
+      }
+      for (const NodeDef& node : original_graph_def_.node()) {
+        if (feeds.find(node.name()) == feeds.end()) {
+          continue;
+        }
+        if (node.attr().count("dtype") == 0 ||
+            node.attr().count("shape") == 0) {
+          return errors::InvalidArgument("Missing node shape or type");
+        }
+        TensorShapeProto shape_proto(node.attr().at("shape").shape());
+        // If the shape of the placeholder value is only partially known, we're
+        // free to use any dimension we want to feed the placeholder. We choose
+        // 1 to minimize the memory impact. Note that this only matters if an
+        // optimizer choose to run the graph to build its cost model, which
+        // doesn't happen (yet)
+        if (shape_proto.unknown_rank()) {
+          shape_proto.set_unknown_rank(false);
+        }
+        for (auto& dim : *shape_proto.mutable_dim()) {
+          if (dim.size() < 0) {
+            dim.set_size(1);
+          }
+        }
+        TensorShape shape(shape_proto);
+        DataType type = node.attr().at("dtype").type();
+        Tensor fake_input(type, shape);
+        item.feed.emplace_back(node.name(), fake_input);
+      }
+    }
+
+    std::unordered_map<string, DeviceProperties> device_map;
+    for (const auto& device : device_set_->devices()) {
+      device_map[device->name()] =
+          grappler::GetDeviceInfo(device->parsed_name());
+    }
+    grappler::VirtualCluster cluster(device_map);
+    GraphDef new_graph;
+    TF_RETURN_IF_ERROR(grappler::RunMetaOptimizer(item, rewrite_options,
+                                                  &cluster, &new_graph));
+    GraphConstructorOptions opts;
+    opts.allow_internal_ops = true;
+    optimized_graph->reset(new Graph(OpRegistry::Global()));
+    TF_RETURN_IF_ERROR(
+        ConvertGraphDefToGraph(opts, new_graph, optimized_graph->get()));
+    // The graph conversion sets the requested device names but not the assigned
+    // device names. However, since at this point the graph is placed TF expects
+    // an assigned device name for every node. Therefore we copy the requested
+    // device into the assigned device field.
+    for (Node* node : optimized_graph->get()->nodes()) {
+      node->set_assigned_device_name(node->requested_device());
+    }
+    return Status::OK();
+  } else {
+    return errors::InvalidArgument("Meta Optimizer disabled");
+  }
+#else
+  return errors::InvalidArgument("Mobile platforms not supported");
+#endif  // IS_MOBILE_PLATFORM
+}
+
 Status SimpleGraphExecutionState::BuildGraph(
     const BuildGraphOptions& options, std::unique_ptr<SimpleClientGraph>* out) {
   VLOG(1) << "BuildGraph";
@@ -350,8 +383,14 @@ Status SimpleGraphExecutionState::BuildGraph(
     return errors::Internal(
         "Attempted to prune a graph that has not been fully initialized.");
   }
-  std::unique_ptr<Graph> ng(new Graph(flib_def_.get()));
-  CopyGraph(*graph_, ng.get());
+
+  std::unique_ptr<Graph> ng;
+  Status s = OptimizeGraph(options, &ng);
+  if (!s.ok()) {
+    // Simply copy the original graph if we couldn't optimize it.
+    ng.reset(new Graph(flib_def_.get()));
+    CopyGraph(*graph_, ng.get());
+  }
 
   subgraph::RewriteGraphMetadata rewrite_metadata;
   if (session_options_ == nullptr ||
diff --git a/tensorflow/core/common_runtime/simple_graph_execution_state.h b/tensorflow/core/common_runtime/simple_graph_execution_state.h
index 00b5509fd7..c7f34a42d6 100644
--- a/tensorflow/core/common_runtime/simple_graph_execution_state.h
+++ b/tensorflow/core/common_runtime/simple_graph_execution_state.h
@@ -186,6 +186,9 @@ class SimpleGraphExecutionState {
   void SaveStatefulNodes(Graph* graph);
   void RestoreStatefulNodes(Graph* graph);
 
+  Status OptimizeGraph(const BuildGraphOptions& options,
+                       std::unique_ptr<Graph>* optimized_graph);
+
   GraphDef original_graph_def_;            // Immutable after ctor.
   const DeviceSet* device_set_;            // Not owned
   const SessionOptions* session_options_;  // Not owned
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index ddf8a5acd2..c6f3ec3950 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -227,9 +227,10 @@ class NodeProcessor {
     return input_pos;
   }
 
-  void AddNodeTranspose(const string& node_name, const string& input_name,
-                        DataType data_type, const TensorShapeProto& input_shape,
-                        bool NHWCToNCHW) {
+  NodeDef* AddNodeTranspose(const string& node_name, const string& input_name,
+                            DataType data_type,
+                            const TensorShapeProto& input_shape,
+                            bool NHWCToNCHW) {
     NodeDef* node = graph_->add_node();
     node_map_->AddNode(node_name, node);
     node->set_name(node_name);
@@ -258,6 +259,7 @@ class NodeProcessor {
       }
       node->mutable_attr()->insert({"_output_shapes", attr_output_shape});
     }
+    return node;
   }
 
   virtual Status AddLayoutTransposeToInputs() {
@@ -270,10 +272,11 @@ class NodeProcessor {
       int output_pos = NodePosition(node_->input(pos));
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "T"));
       TF_RETURN_IF_ERROR(HasAttribute(*input_node, "_output_shapes"));
-      AddNodeTranspose(
+      NodeDef* transpose = AddNodeTranspose(
           node_name, node_->input(pos), node_->attr().at("T").type(),
           input_node->attr().at("_output_shapes").list().shape(output_pos),
           true);
+      transpose->set_device(node_->device());
       node_map_->UpdateOutput(node_->input(pos), node_->name(), node_name);
       node_map_->AddOutput(node_name, node_->name());
       *node_->mutable_input(pos) = node_name;
@@ -309,9 +312,10 @@ class NodeProcessor {
       }
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "T"));
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "_output_shapes"));
-      AddNodeTranspose(node_name, node_->name(), node_->attr().at("T").type(),
-                       node_->attr().at("_output_shapes").list().shape(0),
-                       false);
+      NodeDef* transpose = AddNodeTranspose(
+          node_name, node_->name(), node_->attr().at("T").type(),
+          node_->attr().at("_output_shapes").list().shape(0), false);
+      transpose->set_device(node_->device());
       *it = node_name;
       node_map_->UpdateOutput(node_->name(), output->name(), node_name);
       node_map_->AddOutput(node_name, output->name());
@@ -594,7 +598,7 @@ class BinaryOpProcessor : public AgnosticNodeProcessor {
 
   bool Is4DOperateWithVector() const { return Is4DOperateWithND(1); }
 
-  void AddNodeShapeConst(const string& name, int num_channels) {
+  NodeDef* AddNodeShapeConst(const string& name, int num_channels) {
     NodeDef* node = graph_->add_node();
     node_map_->AddNode(name, node);
     node->set_name(name);
@@ -611,10 +615,12 @@ class BinaryOpProcessor : public AgnosticNodeProcessor {
     }
     tensor.AsProtoTensorContent(attr_tensor.mutable_tensor());
     node->mutable_attr()->insert({"value", attr_tensor});
+    return node;
   }
 
-  void AddNodeReshape(const string& node_name, const string& input_name,
-                      const string& shape_const_node_name, DataType data_type) {
+  NodeDef* AddNodeReshape(const string& node_name, const string& input_name,
+                          const string& shape_const_node_name,
+                          DataType data_type) {
     NodeDef* node = graph_->add_node();
     node_map_->AddNode(node_name, node);
     node->set_name(node_name);
@@ -629,6 +635,7 @@ class BinaryOpProcessor : public AgnosticNodeProcessor {
     AttrValue attr_type_params;
     attr_type_params.set_type(data_type);
     node->mutable_attr()->insert({"T", attr_type_params});
+    return node;
   }
 
   Status CustomizedProcessing() override {
@@ -642,10 +649,13 @@ class BinaryOpProcessor : public AgnosticNodeProcessor {
       TF_RETURN_IF_ERROR(HasAttribute(*input_node, "_output_shapes"));
       int vector_size =
           input_node->attr().at("_output_shapes").list().shape(0).dim(0).size();
-      AddNodeShapeConst(shape_const_node_name, vector_size);
+      NodeDef* shp = AddNodeShapeConst(shape_const_node_name, vector_size);
+      shp->set_device("/job:localhost/replica:0/task:0/cpu:0");
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "T"));
-      AddNodeReshape(reshape_node_name, node_->input(1), shape_const_node_name,
-                     node_->attr().at("T").type());
+      NodeDef* reshape =
+          AddNodeReshape(reshape_node_name, node_->input(1),
+                         shape_const_node_name, node_->attr().at("T").type());
+      reshape->set_device(node_->device());
       node_map_->AddOutput(shape_const_node_name, reshape_node_name);
       node_map_->UpdateOutput(node_->input(1), node_->name(),
                               reshape_node_name);
@@ -950,8 +960,8 @@ class DataLayoutOptimizer {
   }
 
  private:
-  void AddNodePermConst(const string& name,
-                        const std::vector<int>& permutation) {
+  NodeDef* AddNodePermConst(const string& name,
+                            const std::vector<int>& permutation) {
     NodeDef* node = graph_->add_node();
     node_map_.AddNode(name, node);
     node->set_name(name);
@@ -966,9 +976,10 @@ class DataLayoutOptimizer {
     }
     tensor.AsProtoTensorContent(attr_tensor.mutable_tensor());
     node->mutable_attr()->insert({"value", attr_tensor});
+    return node;
   }
 
-  void AddNodeConcatConst() {
+  NodeDef* AddNodeConcatConst() {
     NodeDef* node = graph_->add_node();
     node_map_.AddNode(kConcatConst, node);
     node->set_name(kConcatConst);
@@ -981,9 +992,10 @@ class DataLayoutOptimizer {
     tensor.scalar<int>()() = 1;
     tensor.AsProtoTensorContent(attr_tensor.mutable_tensor());
     node->mutable_attr()->insert({"value", attr_tensor});
+    return node;
   }
 
-  void AddNodeReductionConst() {
+  NodeDef* AddNodeReductionConst() {
     NodeDef* node = graph_->add_node();
     node_map_.AddNode(kReductionConst, node);
     node->set_name(kReductionConst);
@@ -1000,6 +1012,7 @@ class DataLayoutOptimizer {
     }
     tensor.AsProtoTensorContent(attr_tensor.mutable_tensor());
     node->mutable_attr()->insert({"value", attr_tensor});
+    return node;
   }
 
   // Expand all nodes which is in NHWC, but supports NCHW or is layout agnostic.
@@ -1044,10 +1057,14 @@ class DataLayoutOptimizer {
     // only needs to be performed if at least one node in the previous pass is
     // expanded.
     if (graph_->node_size() > node_size_original) {
-      AddNodePermConst(kPermNHWCToNCHW, {0, 3, 1, 2});
-      AddNodePermConst(kPermNCHWToNHWC, {0, 2, 3, 1});
-      AddNodeConcatConst();
-      AddNodeReductionConst();
+      NodeDef* n = AddNodePermConst(kPermNHWCToNCHW, {0, 3, 1, 2});
+      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
+      n = AddNodePermConst(kPermNCHWToNHWC, {0, 2, 3, 1});
+      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
+      n = AddNodeConcatConst();
+      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
+      n = AddNodeReductionConst();
+      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       std::set<string> ops_format_agnostic = GetOpsFormatAgnostic();
       for (int i = 0; i < graph_->node_size(); i++) {
         if (ops_format_agnostic.find(graph_->node(i).op()) !=
@@ -1203,6 +1220,7 @@ Status LayoutOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
   if (!status.ok()) {
     *output = item.graph;
   }
+
   return status;
 }
 
-- 
GitLab


From b8237a4583f08f3164b5213f27aaf7c1add0c9a5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 12:46:21 -0700
Subject: [PATCH 0636/1375] Add the support of emitting the fusion node with
 multiple outputs.

PiperOrigin-RevId: 160677124
---
 .../compiler/xla/service/cpu/ir_emitter.cc    |  55 +++--
 .../compiler/xla/service/cpu/ir_emitter.h     |   3 +-
 .../compiler/xla/service/gpu/fusion_merger.cc |  25 ++-
 .../xla/service/gpu/hlo_to_ir_bindings.cc     |  73 ++++---
 .../xla/service/gpu/hlo_to_ir_bindings.h      |  20 +-
 .../compiler/xla/service/gpu/ir_emitter.h     |   5 +-
 .../xla/service/gpu/ir_emitter_unnested.cc    |  35 +++-
 .../xla/service/gpu/parallel_loop_emitter.cc  |   7 +
 .../xla/service/gpu/parallel_loop_emitter.h   |   6 +
 tensorflow/compiler/xla/service/llvm_ir/BUILD |   1 +
 .../xla/service/llvm_ir/fused_ir_emitter.cc   |  21 ++
 .../xla/service/llvm_ir/fused_ir_emitter.h    |   5 +
 .../xla/service/llvm_ir/loop_emitter.cc       |  36 +++-
 .../xla/service/llvm_ir/loop_emitter.h        |   4 +
 tensorflow/compiler/xla/tests/BUILD           |  25 +++
 .../xla/tests/multioutput_fusion_test.cc      | 191 ++++++++++++++++++
 16 files changed, 455 insertions(+), 57 deletions(-)
 create mode 100644 tensorflow/compiler/xla/tests/multioutput_fusion_test.cc

diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 10676a1e93..cc27db8993 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -2035,9 +2035,9 @@ llvm::Value* IrEmitter::EmitArrayFunctionCall(
 }
 
 StatusOr<llvm::Value*> IrEmitter::EmitTargetAddressForOp(
-    const HloInstruction* op) {
-  const Shape& target_shape = op->shape();
-  if (op == op->parent()->root_instruction()) {
+    const HloInstruction* op, const ShapeIndex& shape_index) {
+  const Shape& target_shape = ShapeUtil::GetSubshape(op->shape(), shape_index);
+  if (op == op->parent()->root_instruction() && shape_index.empty()) {
     // For the root node, we write directly to the output buffer of the
     // function.
     llvm::Argument* retval = GetResultArgument();
@@ -2069,19 +2069,46 @@ Status IrEmitter::EmitTargetElementLoop(
   TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
                       EmitTargetAddressForOp(target_op));
   VLOG(2) << "  target address: " << llvm_ir::DumpToString(*target_address);
-  llvm_ir::IrArray target_array(target_address, target_shape);
-  AddAliasingInformationToIrArray(*target_op, &target_array);
-
-  if (num_dynamic_loop_bounds_ > 0 &&
-      target_op == target_op->parent()->root_instruction()) {
-    // Emit parallel loop for root instruction if dynamic outer-dimension loop
-    // bounds were specified.
-    TF_RETURN_IF_ERROR(EmitParallelTargetElementLoop(
-        target_shape, element_generator, &target_array));
-  } else {
+
+  if (target_op->IsMultiOutputFusion()) {
+    // For multiple outputs fusion, we need to emit each operand and the root.
+    TF_RET_CHECK(num_dynamic_loop_bounds_ == 0);
+    std::vector<llvm_ir::IrArray> output_arrays;
+    for (int64 i = 0; i < ShapeUtil::TupleElementCount(target_shape); ++i) {
+      TF_ASSIGN_OR_RETURN(BufferAllocation::Slice slice,
+                          assignment_.GetUniqueSlice(target_op, {i}));
+      const Shape& element_shape = ShapeUtil::GetSubshape(target_shape, {i});
+      llvm::Value* op_target_address =
+          EmitTempBufferPointer(slice, element_shape);
+      output_arrays.push_back(
+          llvm_ir::IrArray(op_target_address, element_shape));
+    }
     TF_RETURN_IF_ERROR(
-        llvm_ir::LoopEmitter(element_generator, target_array, &ir_builder_)
+        llvm_ir::LoopEmitter(element_generator, output_arrays, &ir_builder_)
             .EmitLoop());
+
+    std::vector<llvm::Value*> tuple_operand_ptrs;
+    for (int64 i = 0; i < output_arrays.size(); ++i) {
+      tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer());
+    }
+    llvm_ir::EmitTuple(llvm_ir::IrArray(target_address, target_shape),
+                       tuple_operand_ptrs, &ir_builder_);
+
+  } else {
+    llvm_ir::IrArray target_array(target_address, target_shape);
+    AddAliasingInformationToIrArray(*target_op, &target_array);
+
+    if (num_dynamic_loop_bounds_ > 0 &&
+        target_op == target_op->parent()->root_instruction()) {
+      // Emit parallel loop for root instruction if dynamic outer-dimension loop
+      // bounds were specified.
+      TF_RETURN_IF_ERROR(EmitParallelTargetElementLoop(
+          target_shape, element_generator, &target_array));
+    } else {
+      TF_RETURN_IF_ERROR(
+          llvm_ir::LoopEmitter(element_generator, target_array, &ir_builder_)
+              .EmitLoop());
+    }
   }
 
   emitted_value_[target_op] = target_address;
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 123797b5e9..ff1ce7218b 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -286,7 +286,8 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   // Emit IR to compute the target address of the buffer for the given op.
   // The returned Value is a pointer to a IR type that represents the op's
   // element type.
-  StatusOr<llvm::Value*> EmitTargetAddressForOp(const HloInstruction* op);
+  StatusOr<llvm::Value*> EmitTargetAddressForOp(
+      const HloInstruction* op, const ShapeIndex& shape_index = {});
 
   // Structurizes "array_elements" into an MD array that represents "shape".
   // This is a recursive function, and "dimension_index" indicates the index of
diff --git a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc
index afb78b8300..e698646d18 100644
--- a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc
+++ b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc
@@ -98,7 +98,13 @@ double CalculateFlopsToBytesRatio(HloInstruction* fusion) {
   // Calculate total bytes transferred in/out.
   double bytes = CalculateBytesReadByFusionInstruction(fusion);
   // Add bytes written to root instructions buffer.
-  bytes += ShapeUtil::ByteSizeOf(fusion->fused_expression_root()->shape());
+  if (fusion->IsMultiOutputFusion()) {
+    for (auto& operand : fusion->fused_expression_root()->operands()) {
+      bytes += ShapeUtil::ByteSizeOf(operand->shape());
+    }
+  } else {
+    bytes += ShapeUtil::ByteSizeOf(fusion->fused_expression_root()->shape());
+  }
   // Calculate flops for all fused instructions. Use a null shape size function
   // because we don't care about bytes accessed by the ops.
   HloCostAnalysis analysis([](const Shape& shape) { return 0; });
@@ -112,8 +118,15 @@ double CalculateFlopsToBytesRatio(HloInstruction* fusion) {
 double GetCurrentBytesTransferred(HloInstruction* fusion) {
   CHECK_EQ(HloOpcode::kFusion, fusion->opcode());
   const double bytes_read = CalculateBytesReadByFusionInstruction(fusion);
-  const double bytes_written =
-      ShapeUtil::ByteSizeOf(fusion->fused_expression_root()->shape());
+  double bytes_written = 0;
+  if (fusion->IsMultiOutputFusion()) {
+    for (auto& operand : fusion->fused_expression_root()->operands()) {
+      bytes_written += ShapeUtil::ByteSizeOf(operand->shape());
+    }
+  } else {
+    bytes_written =
+        ShapeUtil::ByteSizeOf(fusion->fused_expression_root()->shape());
+  }
   // Current bytes transferred (ignoring non 'fusion' user operands) is bytes
   // read and written by 'fusion', plus reads of size 'bytes_written' for each
   // user.
@@ -198,6 +211,12 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) {
     ++num_fail_not_loop_fusion_;
     return Status::OK();
   }
+
+  // Skip multiple output fusion. It's not yet supported.
+  if (fusion->IsMultiOutputFusion()) {
+    ++num_fail_not_loop_fusion_;
+    return Status::OK();
+  }
   // Skip 'fusion' instruction if we cannot merge into all of its users.
   // Merging into all users enables the removal of 'fusion' from the
   // computation.
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc
index 1a61eec353..a04214930d 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc
@@ -86,23 +86,35 @@ void HloToIrBindings::EmitBasePointersForHlos(
       continue;
     }
 
-    // A non-IO HLO with a buffer is bound to
-    // (1) an alloca if it is thread-local, or
-    // (2) an internal pointer in temp_buffer_base according to its offset.
-    const BufferAllocation::Slice slice =
-        buffer_assignment_->GetUniqueTopLevelSlice(non_io_hlo)
-            .ConsumeValueOrDie();
-    if (slice.allocation()->is_thread_local()) {
-      llvm::Type* pointee_type =
-          llvm_ir::ShapeToIrType(non_io_hlo->shape(), ir_builder_);
-      BindHloToIrValue(*non_io_hlo, ir_builder_->CreateAlloca(pointee_type));
-    } else {
-      const int64 offset = slice.offset();
-      CHECK_NE(nullptr, temp_buffer_base_);
-      BindHloToIrValue(*non_io_hlo,
-                       ir_builder_->CreateInBoundsGEP(
-                           temp_buffer_base_, ir_builder_->getInt64(offset)));
-    }
+    ShapeUtil::ForEachSubshape(
+        non_io_hlo->shape(),
+        [&](const Shape& /*subshape*/, const ShapeIndex& index) {
+          // A non-IO HLO with a buffer is bound to
+          // (1) an alloca if it is thread-local, or
+          // (2) an internal pointer in temp_buffer_base according to its
+          // offset.
+          auto slice_result =
+              buffer_assignment_->GetUniqueSlice(non_io_hlo, index);
+          if (!slice_result.ok()) {
+            return;
+          }
+          const BufferAllocation::Slice slice =
+              slice_result.ConsumeValueOrDie();
+          if (slice.allocation()->is_thread_local()) {
+            llvm::Type* pointee_type =
+                llvm_ir::ShapeToIrType(non_io_hlo->shape(), ir_builder_);
+            BindHloToIrValue(*non_io_hlo,
+                             ir_builder_->CreateAlloca(pointee_type), index);
+          } else {
+            const int64 offset = slice.offset();
+            CHECK_NE(nullptr, temp_buffer_base_);
+            BindHloToIrValue(
+                *non_io_hlo,
+                ir_builder_->CreateInBoundsGEP(temp_buffer_base_,
+                                               ir_builder_->getInt64(offset)),
+                index);
+          }
+        });
   }
 }
 
@@ -112,7 +124,7 @@ llvm::Value* HloToIrBindings::EmitGetTupleElement(const HloInstruction* gte,
   if (gte->operand(0)->opcode() != HloOpcode::kGetTupleElement) {
     return llvm_ir::EmitGetTupleElement(
         gte->shape(), gte->tuple_index(), /*alignment=*/1,
-        GetTypedIrValue(*gte->operand(0), base_ptr), ir_builder_);
+        GetTypedIrValue(*gte->operand(0), {}, base_ptr), ir_builder_);
   }
   return llvm_ir::EmitGetTupleElement(
       gte->shape(), gte->tuple_index(), /*alignment=*/1,
@@ -120,8 +132,10 @@ llvm::Value* HloToIrBindings::EmitGetTupleElement(const HloInstruction* gte,
 }
 
 llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo,
+                                              const ShapeIndex& shape_index,
                                               llvm::Value* ir_value) {
-  llvm::Type* pointee_type = llvm_ir::ShapeToIrType(hlo.shape(), ir_builder_);
+  llvm::Type* pointee_type = llvm_ir::ShapeToIrType(
+      ShapeUtil::GetSubshape(hlo.shape(), shape_index), ir_builder_);
   llvm::Type* dest_type = pointee_type->getPointerTo();
 
   llvm::Value* typed_ir_value;
@@ -139,13 +153,24 @@ llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo,
 }
 
 void HloToIrBindings::BindHloToIrValue(const HloInstruction& hlo,
-                                       llvm::Value* ir_value) {
+                                       llvm::Value* ir_value,
+                                       const ShapeIndex& shape_index) {
   VLOG(2) << "Binding " << hlo.ToString();
-  InsertOrDie(&base_ptrs_, &hlo, GetTypedIrValue(hlo, ir_value));
+
+  const Shape& hlo_shape = hlo.shape();
+  llvm::Value* typed_ir_value = GetTypedIrValue(hlo, shape_index, ir_value);
+
+  if (!BoundToIrValue(hlo)) {
+    // Set the root of ShapeTree first before assigning the element ir value.
+    InsertOrDie(&base_ptrs_, &hlo, ShapeTree<llvm::Value*>(hlo_shape, nullptr));
+  }
+  *(base_ptrs_[&hlo].mutable_element(shape_index)) = typed_ir_value;
 }
 
-llvm_ir::IrArray HloToIrBindings::GetIrArray(const HloInstruction& hlo) {
-  llvm_ir::IrArray ir_array(GetBasePointer(hlo), hlo.shape());
+llvm_ir::IrArray HloToIrBindings::GetIrArray(const HloInstruction& hlo,
+                                             const ShapeIndex& shape_index) {
+  llvm_ir::IrArray ir_array(GetBasePointer(hlo, shape_index),
+                            ShapeUtil::GetSubshape(hlo.shape(), shape_index));
   alias_analysis_.AddAliasingInformationToIrArray(hlo, &ir_array);
   return ir_array;
 }
@@ -154,7 +179,7 @@ void HloToIrBindings::UnbindAllLocalIrValues() {
   std::vector<const HloInstruction*> hlos_to_unbind;
   for (auto& key_value : base_ptrs_) {
     if (!llvm::isa<llvm::GlobalVariable>(
-            key_value.second->stripPointerCasts())) {
+            (key_value.second.element({}))->stripPointerCasts())) {
       hlos_to_unbind.push_back(key_value.first);
     }
   }
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h
index 5be2150801..2c59886e9a 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h
+++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h
@@ -48,7 +48,8 @@ class HloToIrBindings {
       tensorflow::gtl::ArraySlice<const HloInstruction*> non_io_hlos);
 
   // Rebinds the given HLO to the LLVM IR value that represent its address.
-  void BindHloToIrValue(const HloInstruction& hlo, llvm::Value* ir_value);
+  void BindHloToIrValue(const HloInstruction& hlo, llvm::Value* ir_value,
+                        const ShapeIndex& shape_index = {});
 
   // Unbinds all IR values that's defined in an LLVM function, e.g., function
   // arguments and stack variables. Global variables will be kept in bindings_.
@@ -64,15 +65,18 @@ class HloToIrBindings {
 
   llvm::Value* GetTempBufferBase() const { return temp_buffer_base_; }
 
-  // A helper method that returns the base pointer of the IrArray for "inst".
-  llvm::Value* GetBasePointer(const HloInstruction& hlo) const {
+  // A helper method that returns the base pointer of the IrArray containing the
+  // output of "inst".at the given ShapeIndex.
+  llvm::Value* GetBasePointer(const HloInstruction& hlo,
+                              const ShapeIndex& shape_index = {}) const {
     auto it = base_ptrs_.find(&hlo);
     CHECK(it != base_ptrs_.end());
-    return it->second;
+    return it->second.element(shape_index);
   }
 
   // Return the underlying IrArray of the output of the given instruction.
-  llvm_ir::IrArray GetIrArray(const HloInstruction& hlo);
+  llvm_ir::IrArray GetIrArray(const HloInstruction& hlo,
+                              const ShapeIndex& shape_index = {});
 
  private:
   // Emits IR to resolve (possibly) recursive GetTupleElement instructions.
@@ -81,6 +85,7 @@ class HloToIrBindings {
 
   // Returns an llvm typed ir representation of 'ir_value' based on 'hlo' shape.
   llvm::Value* GetTypedIrValue(const HloInstruction& hlo,
+                               const ShapeIndex& shape_index,
                                llvm::Value* ir_value);
 
   const BufferAssignment* buffer_assignment_;
@@ -90,7 +95,10 @@ class HloToIrBindings {
   llvm::IRBuilder<>* ir_builder_;
 
   // Stores the underlying llvm::IrArray for each HloInstruction.
-  std::unordered_map<const HloInstruction*, llvm::Value*> base_ptrs_;
+  // For an instruction that generates multiple outputs, the root will be a
+  // tuple shape. The IrArray for each element output is stored in the subnode
+  // in the ShapeTree.
+  std::unordered_map<const HloInstruction*, ShapeTree<llvm::Value*>> base_ptrs_;
 
   // The address of the memory block that contains all temporary buffers.
   llvm::Value* temp_buffer_base_;
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h
index de72ac738e..718e27101e 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h
@@ -118,8 +118,9 @@ class IrEmitter : public DfsHloVisitorWithDefault {
                      IrEmitterContext* ir_emitter_context, bool is_nested);
 
   // A convenient helper for calling HloToIrBindings::GetIrArray.
-  llvm_ir::IrArray GetIrArray(const HloInstruction& inst) {
-    return bindings_.GetIrArray(inst);
+  llvm_ir::IrArray GetIrArray(const HloInstruction& inst,
+                              const ShapeIndex& shape_index = {}) {
+    return bindings_.GetIrArray(inst, shape_index);
   }
   // A convenient helper for calling HloToIrBindings::GetBasePointer.
   llvm::Value* GetBasePointer(const HloInstruction& inst) const {
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
index ea71d92417..484de36967 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
@@ -1886,15 +1886,38 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildForThunk(
 Status IrEmitterUnnested::EmitTargetElementLoopInThunk(
     const HloInstruction& hlo,
     const llvm_ir::ElementGenerator& element_generator, KernelThunk* thunk) {
+  const Shape& element_shape = hlo.IsMultiOutputFusion()
+                                   ? ShapeUtil::GetSubshape(hlo.shape(), {0})
+                                   : hlo.shape();
   LaunchDimensions launch_dimensions = CalculateLaunchDimensions(
-      hlo.shape(), ir_emitter_context_->device_description());
+      element_shape, ir_emitter_context_->device_description());
   UpdateLaunchDimensions(launch_dimensions, thunk,
                          ir_emitter_context_->llvm_module());
-  // Otherwise, emit a parallel loop that computes the partition that each
-  // thread is in charge of.
-  return ParallelLoopEmitter(element_generator, GetIrArray(hlo),
-                             launch_dimensions, &ir_builder_)
-      .EmitLoop();
+  if (!hlo.IsMultiOutputFusion()) {
+    return ParallelLoopEmitter(element_generator, GetIrArray(hlo),
+                               launch_dimensions, &ir_builder_)
+        .EmitLoop();
+  }
+
+  // For multiple outputs fusion, we need to emit each operand and the root.
+  std::vector<llvm_ir::IrArray> output_arrays;
+  for (int64 i = 0; i < ShapeUtil::TupleElementCount(hlo.shape()); ++i) {
+    output_arrays.push_back(GetIrArray(hlo, {i}));
+  }
+  TF_RETURN_IF_ERROR(ParallelLoopEmitter(element_generator, output_arrays,
+                                         launch_dimensions, &ir_builder_)
+                         .EmitLoop());
+
+  std::vector<llvm::Value*> tuple_operand_ptrs;
+  for (int64 i = 0; i < output_arrays.size(); ++i) {
+    tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer());
+  }
+  ir_builder_.SetInsertPoint(ir_builder_.GetInsertBlock()->getTerminator());
+  //  const HloInstruction* root = hlo.fused_expression_root();
+  llvm_ir::EmitTuple(
+      GetIrArray(*hlo.fused_expression_root()->fusion_instruction()),
+      tuple_operand_ptrs, &ir_builder_);
+  return Status::OK();
 }
 
 Status IrEmitterUnnested::EmitTargetElementLoop(
diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc
index 65610b0995..d5543d296b 100644
--- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc
+++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc
@@ -36,6 +36,13 @@ ParallelLoopEmitter::ParallelLoopEmitter(
     : LoopEmitter(body_emitter, shape, ir_builder),
       launch_dimensions_(launch_dimensions) {}
 
+ParallelLoopEmitter::ParallelLoopEmitter(
+    const llvm_ir::ElementGenerator& target_element_generator,
+    tensorflow::gtl::ArraySlice<llvm_ir::IrArray> target_arrays,
+    const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder)
+    : LoopEmitter(target_element_generator, target_arrays, ir_builder),
+      launch_dimensions_(launch_dimensions) {}
+
 ParallelLoopEmitter::ParallelLoopEmitter(
     const llvm_ir::ElementGenerator& target_element_generator,
     const llvm_ir::IrArray& target_array,
diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h
index 73ca28cd84..d324a50698 100644
--- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h
+++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h
@@ -41,6 +41,12 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter {
                       const llvm_ir::IrArray& target_array,
                       const LaunchDimensions& launch_dimensions,
                       llvm::IRBuilder<>* ir_builder);
+
+  ParallelLoopEmitter(
+      const llvm_ir::ElementGenerator& target_element_generator,
+      tensorflow::gtl::ArraySlice<llvm_ir::IrArray> target_arrays,
+      const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder);
+
   ParallelLoopEmitter(const ParallelLoopEmitter&) = delete;
   ParallelLoopEmitter& operator=(const ParallelLoopEmitter&) = delete;
   ~ParallelLoopEmitter() override = default;
diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD
index c5b3f317b2..61945bd128 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/BUILD
+++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD
@@ -91,6 +91,7 @@ cc_library(
     deps = [
         ":ir_array",
         ":llvm_loop",
+        ":ops",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc
index b259d34870..26e73a6ec3 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc
@@ -128,6 +128,27 @@ Status FusedIrEmitter::HandleParameter(HloInstruction* parameter) {
   return Status::OK();
 }
 
+Status FusedIrEmitter::HandleTuple(
+    HloInstruction* tuple,
+    tensorflow::gtl::ArraySlice<HloInstruction*> operands) {
+  std::vector<llvm::Type*> operand_elemental_ir_types;
+  for (HloInstruction* operand : operands) {
+    operand_elemental_ir_types.push_back(llvm_ir::PrimitiveTypeToIrType(
+        operand->shape().element_type(), ir_builder_));
+  }
+  generators_[tuple] =
+      [=](const IrArray::Index& index) -> StatusOr<llvm::Value*> {
+    llvm::Value* ret = llvm::UndefValue::get(llvm::StructType::get(
+        ir_builder_->getContext(), operand_elemental_ir_types));
+    for (size_t i = 0; i < ShapeUtil::TupleElementCount(tuple->shape()); ++i) {
+      TF_ASSIGN_OR_RETURN(llvm::Value * val_i, generators_[operands[i]](index));
+      ret = ir_builder_->CreateInsertValue(ret, val_i, i);
+    }
+    return ret;
+  };
+  return Status::OK();
+}
+
 Status FusedIrEmitter::FinishVisit(HloInstruction* root) {
   fused_root_ = root;
   return tensorflow::Status::OK();
diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h
index 79007b7099..1cd8d11946 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h
@@ -54,6 +54,11 @@ class FusedIrEmitter : public DfsHloVisitorWithDefault {
 
   Status HandleParameter(HloInstruction* parameter) override;
 
+  // Emits the ir value for each element in the tuple.
+  Status HandleTuple(
+      HloInstruction* tuple,
+      tensorflow::gtl::ArraySlice<HloInstruction*> operands) override;
+
   Status FinishVisit(HloInstruction* root) override;
 
   // Returns the generator function for the root of the fused computation.
diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc
index 9a128b2aa6..8839ec582d 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h"
+#include "tensorflow/compiler/xla/service/llvm_ir/ops.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -51,8 +52,41 @@ LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator,
       shape_(target_array.GetShape()),
       ir_builder_(ir_builder) {}
 
+LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator,
+                         tensorflow::gtl::ArraySlice<IrArray> target_arrays,
+                         llvm::IRBuilder<>* ir_builder)
+    : body_emitter_([=](const llvm_ir::IrArray::Index array_index)
+                        -> ::tensorflow::Status {
+        // Convert target_element_generator to a BodyEmitter.
+        TF_ASSIGN_OR_RETURN(llvm::Value * target_element,
+                            target_element_generator(array_index));
+        if (target_arrays.size() == 1) {
+          target_arrays[0].EmitWriteArrayElement(array_index, target_element,
+                                                 ir_builder);
+          return tensorflow::Status::OK();
+        }
+
+        for (int64 i = 0; i < target_arrays.size(); ++i) {
+          target_arrays[i].EmitWriteArrayElement(
+              array_index, ir_builder_->CreateExtractValue(target_element, i),
+              ir_builder);
+        }
+        return tensorflow::Status::OK();
+      }),
+      ir_builder_(ir_builder) {
+  if (target_arrays.size() > 1) {
+    // The sanity check for multiple outputs.
+    shape_ = target_arrays[0].GetShape();
+    for (int64 i = 1; i < target_arrays.size(); ++i) {
+      const Shape& element_shape = target_arrays[i].GetShape();
+      CHECK(ShapeUtil::SameDimensions(shape_, element_shape));
+    }
+  } else {
+    shape_ = target_arrays[0].GetShape();
+  }
+}
+
 IrArray::Index LoopEmitter::EmitIndexAndSetExitBasicBlock() {
-  CHECK(!ShapeUtil::IsTuple(shape_));
   if (ShapeUtil::IsScalar(shape_)) {
     // No loop needed, so set exit_bb_ to nullptr.
     exit_bb_ = nullptr;
diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h
index 08171e9e9d..ab6b702c44 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h
@@ -47,6 +47,10 @@ class LoopEmitter {
   // element of the given target array.
   LoopEmitter(const ElementGenerator& target_element_generator,
               const IrArray& target_array, llvm::IRBuilder<>* ir_builder);
+  // Same as previous method except emits multiple targets in an array.
+  LoopEmitter(const ElementGenerator& target_element_generator,
+              tensorflow::gtl::ArraySlice<IrArray> target_arrays,
+              llvm::IRBuilder<>* ir_builder);
   LoopEmitter(const LoopEmitter&) = delete;
   LoopEmitter& operator=(const LoopEmitter&) = delete;
   virtual ~LoopEmitter() = default;
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index cb050f6f4d..7a899d6e23 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1368,6 +1368,31 @@ xla_test(
     ],
 )
 
+xla_test(
+    name = "multioutput_fusion_test",
+    srcs = ["multioutput_fusion_test.cc"],
+    deps = [
+        "//tensorflow/compiler/xla:array2d",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/client:client_library",
+        "//tensorflow/compiler/xla/client:computation",
+        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
+        "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:platform_util",
+        "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/compiler/xla/tests:literal_test_util",
+        "//tensorflow/compiler/xla/tests:test_utils",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+    ],
+)
+
 cc_test(
     name = "local_client_aot_test",
     srcs = [
diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
new file mode 100644
index 0000000000..ba9b16d6b6
--- /dev/null
+++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
@@ -0,0 +1,191 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <math.h>
+#include <algorithm>
+#include <memory>
+#include <new>
+#include <utility>
+
+#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/primitive_util.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/tests/literal_test_util.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/compiler/xla/tests/test_utils.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/test_benchmark.h"
+#include "tensorflow/core/platform/types.h"
+
+using tensorflow::gtl::ArraySlice;
+
+namespace xla {
+namespace {
+
+class MultiOutputFusionTest : public HloTestBase {
+ public:
+  ErrorSpec error_spec_{0.0001, 1e-2};
+
+ protected:
+  MultiOutputFusionTest() {}
+  void RunTest2D(bool manual_fusion, int64 size) {
+    auto builder = HloComputation::Builder(TestName());
+    auto hlo_module = CreateNewModule();
+
+    const Shape elem_shape0 = ShapeUtil::MakeShape(F32, {});
+    const Shape elem_shape2 = ShapeUtil::MakeShape(F32, {size, size});
+
+    auto const0 = builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR0<float>(8.0f)));
+    auto param0 = builder.AddInstruction(
+        HloInstruction::CreateParameter(0, elem_shape0, "0"));
+
+    auto add1 = builder.AddInstruction(HloInstruction::CreateBinary(
+        elem_shape0, HloOpcode::kAdd, param0, const0));
+
+    HloInstruction* broadcast = builder.AddInstruction(
+        HloInstruction::CreateBroadcast(elem_shape2, add1, {0, 1}));
+
+    auto param1 = builder.AddInstruction(
+        HloInstruction::CreateParameter(1, elem_shape2, "1"));
+
+    HloInstruction* add2 = builder.AddInstruction(HloInstruction::CreateBinary(
+        elem_shape2, HloOpcode::kAdd, broadcast, param1));
+    HloInstruction* sub = builder.AddInstruction(HloInstruction::CreateBinary(
+        elem_shape2, HloOpcode::kSubtract, param1, broadcast));
+    HloInstruction* dot = builder.AddInstruction(
+        HloInstruction::CreateBinary(elem_shape2, HloOpcode::kDot, sub, add2));
+    auto computation = hlo_module->AddEntryComputation(builder.Build(dot));
+
+    if (manual_fusion) {
+      auto tuple = computation->AddInstruction(HloInstruction::CreateTuple(
+          ArraySlice<HloInstruction*>({sub, add2}, 0, 2)));
+      computation->AddInstruction(
+          HloInstruction::CreateGetTupleElement(elem_shape2, tuple, 0));
+      computation->AddInstruction(
+          HloInstruction::CreateGetTupleElement(elem_shape2, tuple, 1));
+      CHECK_NE(
+          computation->CreateFusionInstruction(
+              {tuple, sub, add2, broadcast}, HloInstruction::FusionKind::kLoop),
+          nullptr);
+    }
+
+    Literal input;
+    input.PopulateWithValue<float>(2.5f, {size, size});
+    auto p1 = TransferToDevice(input);
+    auto p0 = TransferToDevice(*Literal::CreateR0<float>(-9.0f));
+
+    Literal expect;
+    expect.PopulateWithValue<float>(size * 1.5f * 3.5f, {size, size});
+    auto actual = ExecuteAndTransfer(std::move(hlo_module), {p0, p1});
+    LiteralTestUtil::ExpectNear(expect, *actual, error_spec_);
+  }
+
+  void RunTest1D(bool manual_fusion, int size) {
+    auto builder = HloComputation::Builder(TestName());
+    auto hlo_module = CreateNewModule();
+
+    const Shape elem_shape_F32 = ShapeUtil::MakeShape(F32, {size});
+    const Shape elem_shape_U8 = ShapeUtil::MakeShape(F64, {size});
+    auto param0 = builder.AddInstruction(
+        HloInstruction::CreateParameter(0, elem_shape_F32, "0"));
+    auto param1 = builder.AddInstruction(
+        HloInstruction::CreateParameter(1, elem_shape_U8, "1"));
+
+    HloInstruction* param0_U8 = builder.AddInstruction(
+        HloInstruction::CreateConvert(elem_shape_U8, param0));
+    HloInstruction* param1_F32 = builder.AddInstruction(
+        HloInstruction::CreateConvert(elem_shape_F32, param1));
+    HloInstruction* add = builder.AddInstruction(HloInstruction::CreateBinary(
+        elem_shape_F32, HloOpcode::kAdd, param0, param1_F32));
+    HloInstruction* sub_U8 =
+        builder.AddInstruction(HloInstruction::CreateBinary(
+            elem_shape_U8, HloOpcode::kSubtract, param0_U8, param1));
+    HloInstruction* sub = builder.AddInstruction(
+        HloInstruction::CreateConvert(elem_shape_F32, sub_U8));
+
+    HloInstruction* reshape =
+        builder.AddInstruction(HloInstruction::CreateReshape(
+            ShapeUtil::MakeShape(F32, {size, 1}), add));
+    HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary(
+        ShapeUtil::MakeShape(F32, {}), HloOpcode::kDot, sub, reshape));
+    auto computation = hlo_module->AddEntryComputation(builder.Build(dot));
+
+    if (manual_fusion) {
+      auto tuple = computation->AddInstruction(HloInstruction::CreateTuple(
+          ArraySlice<HloInstruction*>({sub_U8, add}, 0, 2)));
+
+      computation->AddInstruction(
+          HloInstruction::CreateGetTupleElement(elem_shape_U8, tuple, 0));
+      computation->AddInstruction(
+          HloInstruction::CreateGetTupleElement(elem_shape_F32, tuple, 1));
+      CHECK_NE(computation->CreateFusionInstruction(
+                   {tuple, sub_U8, add, param0_U8, param1_F32},
+                   HloInstruction::FusionKind::kLoop),
+               nullptr);
+    }
+
+    Literal input0, input1;
+    input0.PopulateWithValue<float>(2.5f, {size});
+    input1.PopulateWithValue<double>(1, {size});
+    auto p0 = TransferToDevice(input0);
+    auto p1 = TransferToDevice(input1);
+
+    Literal expect = *Literal::CreateR0<float>(size * 1.5f * 3.5f);
+    auto actual = ExecuteAndTransfer(std::move(hlo_module), {p0, p1});
+    LiteralTestUtil::ExpectNear(expect, *actual, error_spec_);
+  }
+};
+
+XLA_TEST_F(MultiOutputFusionTest, 2DNofusion) { RunTest2D(false, 5); }
+XLA_TEST_F(MultiOutputFusionTest, 2DFusion) { RunTest2D(true, 5); }
+XLA_TEST_F(MultiOutputFusionTest, 2DFusionSize129) { RunTest2D(true, 129); }
+XLA_TEST_F(MultiOutputFusionTest, DiffentTypesNoFusion) { RunTest1D(false, 8); }
+XLA_TEST_F(MultiOutputFusionTest, DiffentTypesFusion) { RunTest1D(true, 8); }
+
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
+  testing::InitGoogleTest(&argc, argv);
+  if (argc > 1) {
+    LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
+    return 2;
+  }
+
+  return RUN_ALL_TESTS();
+}
-- 
GitLab


From 679bb02274de245c3ce00d2229081010c656dc3c Mon Sep 17 00:00:00 2001
From: Kevin van der Burgt <mail@kevinvdburgt.com>
Date: Fri, 30 Jun 2017 22:54:13 +0200
Subject: [PATCH 0637/1375] Fixed 404 page in the docs (install_c.md) (#11173)

When following the current link to `c_api.h` the link will results in a 404 page, see: https://github.com/tensorflow/tensorflow/tree/master/c/c_api.h
---
 tensorflow/docs_src/install/install_c.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index ae877c8293..e971a03d36 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -1,7 +1,7 @@
 # Installing TensorFlow for C
 
 TensorFlow provides a C API defined in
-[`c_api.h`](https://github.com/tensorflow/tensorflow/tree/master/c/c_api.h),
+[`c_api.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h),
 which is suitable for
 [building bindings for other languages](https://www.tensorflow.org/extend/language_bindings).
 The API leans towards simplicity and uniformity rather than convenience.
-- 
GitLab


From e766804ba7c711ff785b7e14311b56d0d3c9b487 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 14:11:23 -0700
Subject: [PATCH 0638/1375] Updates wide and wide_n_deep tutorials.

PiperOrigin-RevId: 160686911
---
 tensorflow/docs_src/tutorials/wide.md         | 259 ++++++++++--------
 .../docs_src/tutorials/wide_and_deep.md       | 242 +++++++++-------
 .../examples/learn/wide_n_deep_tutorial.py    |  12 +-
 3 files changed, 296 insertions(+), 217 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index 24c866eee5..609769f89c 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -1,12 +1,13 @@
 # TensorFlow Linear Model Tutorial
 
-In this tutorial, we will use the tf.contrib.learn API in TensorFlow to solve a binary
-classification problem: Given census data about a person such as age, gender,
-education and occupation (the features), we will try to predict whether or not
-the person earns more than 50,000 dollars a year (the target label). We will
-train a **logistic regression** model, and given an individual's information our
-model will output a number between 0 and 1, which can be interpreted as the
-probability that the individual has an annual income of over 50,000 dollars.
+In this tutorial, we will use the tf.estimator API in TensorFlow to solve a
+binary classification problem: Given census data about a person such as age,
+gender, education and occupation (the features), we will try to predict whether
+or not the person earns more than 50,000 dollars a year (the target label). We
+will train a **logistic regression** model, and given an individual's
+information our model will output a number between 0 and 1, which can be
+interpreted as the probability that the individual has an annual income of over
+50,000 dollars.
 
 ## Setup
 
@@ -16,7 +17,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.estimator doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
@@ -65,12 +66,13 @@ Once the CSV files are downloaded, let's read them into
 
 ```python
 import pandas as pd
-COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
-           "marital_status", "occupation", "relationship", "race", "gender",
-           "capital_gain", "capital_loss", "hours_per_week", "native_country",
-           "income_bracket"]
-df_train = pd.read_csv(train_file.name, names=COLUMNS, skipinitialspace=True)
-df_test = pd.read_csv(test_file.name, names=COLUMNS, skipinitialspace=True, skiprows=1)
+CSV_COLUMNS = [
+    "age", "workclass", "fnlwgt", "education", "education_num",
+    "marital_status", "occupation", "relationship", "race", "gender",
+    "capital_gain", "capital_loss", "hours_per_week", "native_country",
+    "income_bracket"]
+df_train = pd.read_csv(train_file.name, names=CSV_COLUMNS, skipinitialspace=True)
+df_test = pd.read_csv(test_file.name, names=CSV_COLUMNS, skipinitialspace=True, skiprows=1)
 ```
 
 Since the task is a binary classification problem, we'll construct a label
@@ -78,9 +80,8 @@ column named "label" whose value is 1 if the income is over 50K, and 0
 otherwise.
 
 ```python
-LABEL_COLUMN = "label"
-df_train[LABEL_COLUMN] = (df_train["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
-df_test[LABEL_COLUMN] = (df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
+train_labels = (df_train["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
+test_labels = (df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
 ```
 
 Next, let's take a look at the dataframe and see which columns we can use to
@@ -95,12 +96,6 @@ and continuous columns:
     a continuous range. For example, the capital gain of a person (e.g. $14,084)
     is a continuous column.
 
-```python
-CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation",
-                       "relationship", "race", "gender", "native_country"]
-CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss", "hours_per_week"]
-```
-
 Here's a list of columns available in the Census Income dataset:
 
 | Column Name    | Type        | Description                       | {.sortable}
@@ -136,13 +131,11 @@ Here's a list of columns available in the Census Income dataset:
 
 ## Converting Data into Tensors
 
-When building a tf.contrib.learn model, the input data is specified by means of an Input
-Builder function. This builder function will not be called until it is later
-passed to tf.contrib.learn methods such as `fit` and `evaluate`. The purpose of this
-function is to construct the input data, which is represented in the form of
-@{tf.Tensor}s
-or
-@{tf.SparseTensor}s.
+When building a tf.estimator model, the input data is specified by means of an
+Input Builder function. This builder function will not be called until it is
+later passed to tf.estimator.Estimator methods such as `train` and `evaluate`.
+The purpose of this function is to construct the input data, which is
+represented in the form of @{tf.Tensor}s or @{tf.SparseTensor}s.
 In more detail, the Input Builder function returns the following as a pair:
 
 1.  `feature_cols`: A dict from feature column names to `Tensors` or
@@ -150,52 +143,43 @@ In more detail, the Input Builder function returns the following as a pair:
 2.  `label`: A `Tensor` containing the label column.
 
 The keys of the `feature_cols` will be used to construct columns in the
-next section. Because we want to call the `fit` and `evaluate` methods with
-different data, we define two different input builder functions,
-`train_input_fn` and `test_input_fn` which are identical except that they pass
-different data to `input_fn`. Note that `input_fn` will be called while
+next section. Because we want to call the `train` and `evaluate` methods with
+different data, we define a method that returns an input function based on the
+given data. Note that the returned input function will be called while
 constructing the TensorFlow graph, not while running the graph. What it is
 returning is a representation of the input data as the fundamental unit of
 TensorFlow computations, a `Tensor` (or `SparseTensor`).
 
-Our model represents the input data as *constant* tensors, meaning that the
-tensor represents a constant value, in this case the values of a particular
-column of `df_train` or `df_test`. This is the simplest way to pass data into
-TensorFlow. Another more advanced way to represent input data would be to
-construct an @{$python/io_ops#inputs-and-readers$Inputs And Readers}
-that represents a file or other data source, and iterates through the file as
-TensorFlow runs the graph. Each continuous column in the train or test dataframe
+We use the `tf.estimator.inputs.pandas_input_fn` method to create an input
+function from pandas dataframes.
+Each continuous column in the train or test dataframe
 will be converted into a `Tensor`, which in general is a good format to
 represent dense data. For categorical data, we must represent the data as a
 `SparseTensor`. This data format is good for representing sparse data.
+Another more advanced way to represent input data would be to
+construct an @{$python/io_ops#inputs-and-readers$Inputs And Readers}
+that represents a file or other data source, and iterates through the file as
+TensorFlow runs the graph.
 
 ```python
-import tensorflow as tf
-
-def input_fn(df):
-  # Creates a dictionary mapping from each continuous feature column name (k) to
-  # the values of that column stored in a constant Tensor.
-  continuous_cols = {k: tf.constant(df[k].values)
-                     for k in CONTINUOUS_COLUMNS}
-  # Creates a dictionary mapping from each categorical feature column name (k)
-  # to the values of that column stored in a tf.SparseTensor.
-  categorical_cols = {k: tf.SparseTensor(
-      indices=[[i, 0] for i in range(df[k].size)],
-      values=df[k].values,
-      dense_shape=[df[k].size, 1])
-                      for k in CATEGORICAL_COLUMNS}
-  # Merges the two dictionaries into one.
-  feature_cols = dict(continuous_cols.items() + categorical_cols.items())
-  # Converts the label column into a constant Tensor.
-  label = tf.constant(df[LABEL_COLUMN].values)
-  # Returns the feature columns and the label.
-  return feature_cols, label
-
-def train_input_fn():
-  return input_fn(df_train)
-
-def eval_input_fn():
-  return input_fn(df_test)
+def input_fn(data_file, num_epochs, shuffle):
+  """Input builder function."""
+  df_data = pd.read_csv(
+      tf.gfile.Open(data_file),
+      names=CSV_COLUMNS,
+      skipinitialspace=True,
+      engine="python",
+      skiprows=1)
+  # remove NaN elements
+  df_data = df_data.dropna(how="any", axis=0)
+  labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int)
+  return tf.estimator.inputs.pandas_input_fn(
+      x=df_data,
+      y=labels,
+      batch_size=100,
+      num_epochs=num_epochs,
+      shuffle=shuffle,
+      num_threads=5)
 ```
 
 ## Selecting and Engineering Features for the Model
@@ -211,37 +195,38 @@ to predict the target label.
 ### Base Categorical Feature Columns
 
 To define a feature column for a categorical feature, we can create a
-`SparseColumn` using the tf.contrib.learn API. If you know the set of all possible
-feature values of a column and there are only a few of them, you can use
-`sparse_column_with_keys`. Each key in the list will get assigned an
-auto-incremental ID starting from 0. For example, for the `gender` column we can
-assign the feature string "Female" to an integer ID of 0 and "Male" to 1 by
-doing:
+`CategoricalColumn` using the tf.feature_column API. If you know the set of all
+possible feature values of a column and there are only a few of them, you can
+use `categorical_column_with_vocabulary_list`. Each key in the list will get
+assigned an auto-incremental ID starting from 0. For example, for the `gender`
+column we can assign the feature string "Female" to an integer ID of 0 and
+"Male" to 1 by doing:
 
 ```python
-gender = tf.contrib.layers.sparse_column_with_keys(
-  column_name="gender", keys=["Female", "Male"])
+gender = tf.feature_column.categorical_column_with_vocabulary_list(
+    "gender", ["Female", "Male"])
 ```
 
 What if we don't know the set of possible values in advance? Not a problem. We
-can use `sparse_column_with_hash_bucket` instead:
+can use `categorical_column_with_hash_bucket` instead:
 
 ```python
-education = tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size=1000)
+occupation = tf.feature_column.categorical_column_with_hash_bucket(
+    "occupation", hash_bucket_size=1000)
 ```
 
-What will happen is that each possible value in the feature column `education`
+What will happen is that each possible value in the feature column `occupation`
 will be hashed to an integer ID as we encounter them in training. See an example
 illustration below:
 
 ID  | Feature
 --- | -------------
 ... |
-9   | `"Bachelors"`
+9   | `"Machine-op-inspct"`
 ... |
-103 | `"Doctorate"`
+103 | `"Farming-fishing"`
 ... |
-375 | `"Masters"`
+375 | `"Protective-serv"`
 ... |
 
 No matter which way we choose to define a `SparseColumn`, each feature string
@@ -255,25 +240,43 @@ learned through the model training process we'll go through later.
 We'll do the similar trick to define the other categorical features:
 
 ```python
-race = tf.contrib.layers.sparse_column_with_hash_bucket("race", hash_bucket_size=100)
-marital_status = tf.contrib.layers.sparse_column_with_hash_bucket("marital_status", hash_bucket_size=100)
-relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size=100)
-workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100)
-occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000)
-native_country = tf.contrib.layers.sparse_column_with_hash_bucket("native_country", hash_bucket_size=1000)
+education = tf.feature_column.categorical_column_with_vocabulary_list(
+    "education", [
+        "Bachelors", "HS-grad", "11th", "Masters", "9th",
+        "Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th",
+        "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
+        "Preschool", "12th"
+    ])
+marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
+    "marital_status", [
+        "Married-civ-spouse", "Divorced", "Married-spouse-absent",
+        "Never-married", "Separated", "Married-AF-spouse", "Widowed"
+    ])
+relationship = tf.feature_column.categorical_column_with_vocabulary_list(
+    "relationship", [
+        "Husband", "Not-in-family", "Wife", "Own-child", "Unmarried",
+        "Other-relative"
+    ])
+workclass = tf.feature_column.categorical_column_with_vocabulary_list(
+    "workclass", [
+        "Self-emp-not-inc", "Private", "State-gov", "Federal-gov",
+        "Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked"
+    ])
+native_country = tf.feature_column.categorical_column_with_hash_bucket(
+    "native_country", hash_bucket_size=1000)
 ```
 
 ### Base Continuous Feature Columns
 
-Similarly, we can define a `RealValuedColumn` for each continuous feature column
+Similarly, we can define a `NumericColumn` for each continuous feature column
 that we want to use in the model:
 
 ```python
-age = tf.contrib.layers.real_valued_column("age")
-education_num = tf.contrib.layers.real_valued_column("education_num")
-capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
-capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
-hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")
+age = tf.feature_column.numeric_column("age")
+education_num = tf.feature_column.numeric_column("education_num")
+capital_gain = tf.feature_column.numeric_column("capital_gain")
+capital_loss = tf.feature_column.numeric_column("capital_loss")
+hours_per_week = tf.feature_column.numeric_column("hours_per_week")
 ```
 
 ### Making Continuous Features Categorical through Bucketization
@@ -297,7 +300,8 @@ ID (as a categorical feature) depending on which bucket that value falls into.
 So, we can define a `bucketized_column` over `age` as:
 
 ```python
-age_buckets = tf.contrib.layers.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
+age_buckets = tf.feature_column.bucketized_column(
+    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
 ```
 
 where the `boundaries` is a list of bucket boundaries. In this case, there are
@@ -317,7 +321,8 @@ differences between different feature combinations, we can add **crossed feature
 columns** to the model.
 
 ```python
-education_x_occupation = tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4))
+education_x_occupation = tf.feature_column.crossed_column(
+    ["education", "occupation"], hash_bucket_size=1000)
 ```
 
 We can also create a `CrossedColumn` over more than two columns. Each
@@ -326,8 +331,8 @@ constituent column can be either a base feature column that is categorical
 or even another `CrossColumn`. Here's an example:
 
 ```python
-age_buckets_x_education_x_occupation = tf.contrib.layers.crossed_column(
-  [age_buckets, education, occupation], hash_bucket_size=int(1e6))
+age_buckets_x_education_x_occupation = tf.feature_column.crossed_column(
+    [age_buckets, "education", "occupation"], hash_bucket_size=1000)
 ```
 
 ## Defining The Logistic Regression Model
@@ -337,8 +342,8 @@ ready to put them all together and build a Logistic Regression model. In the
 previous section we've seen several types of base and derived feature columns,
 including:
 
-*   `SparseColumn`
-*   `RealValuedColumn`
+*   `CategoricalColumn`
+*   `NumericColumn`
 *   `BucketizedColumn`
 *   `CrossedColumn`
 
@@ -346,11 +351,22 @@ All of these are subclasses of the abstract `FeatureColumn` class, and can be
 added to the `feature_columns` field of a model:
 
 ```python
+base_columns = [
+    gender, native_country, education, occupation, workclass, relationship,
+    age_buckets,
+]
+crossed_columns = [
+    tf.feature_column.crossed_column(
+        ["education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        [age_buckets, "education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        ["native_country", "occupation"], hash_bucket_size=1000)
+]
+
 model_dir = tempfile.mkdtemp()
-m = tf.contrib.learn.LinearClassifier(feature_columns=[
-  gender, native_country, education, occupation, workclass, marital_status, race,
-  age_buckets, education_x_occupation, age_buckets_x_education_x_occupation],
-  model_dir=model_dir)
+m = tf.estimator.LinearClassifier(
+    model_dir=model_dir, feature_columns=base_columns + crossed_columns)
 ```
 
 The model also automatically learns a bias term, which controls the prediction
@@ -361,19 +377,26 @@ in `model_dir`.
 ## Training and Evaluating Our Model
 
 After adding all the features to the model, now let's look at how to actually
-train the model. Training a model is just a one-liner using the tf.contrib.learn API:
+train the model. Training a model is just a one-liner using the tf.estimator
+API:
 
 ```python
-m.fit(input_fn=train_input_fn, steps=200)
+# set num_epochs to None to get infinite stream of data.
+m.train(
+    input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True),
+    steps=train_steps)
 ```
 
 After the model is trained, we can evaluate how good our model is at predicting
 the labels of the holdout data:
 
 ```python
-results = m.evaluate(input_fn=eval_input_fn, steps=1)
+results = m.evaluate(
+    input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False),
+    steps=None)
+print("model directory = %s" % model_dir)
 for key in sorted(results):
-    print("%s: %s" % (key, results[key]))
+  print("%s: %s" % (key, results[key]))
 ```
 
 The first line of the output should be something like `accuracy: 0.83557522`,
@@ -398,14 +421,13 @@ In the Linear Model library, you can add L1 and L2 regularizations to the model
 as:
 
 ```
-m = tf.contrib.learn.LinearClassifier(feature_columns=[
-  gender, native_country, education, occupation, workclass, marital_status, race,
-  age_buckets, education_x_occupation, age_buckets_x_education_x_occupation],
-  optimizer=tf.train.FtrlOptimizer(
-    learning_rate=0.1,
-    l1_regularization_strength=1.0,
-    l2_regularization_strength=1.0),
-  model_dir=model_dir)
+m = tf.estimator.LinearClassifier(
+    model_dir=model_dir, feature_columns=base_columns + crossed_columns,
+    optimizer=tf.train.FtrlOptimizer(
+      learning_rate=0.1,
+      l1_regularization_strength=1.0,
+      l2_regularization_strength=1.0),
+    model_dir=model_dir)
 ```
 
 One important difference between L1 and L2 regularization is that L1
@@ -465,6 +487,7 @@ value would be high.
 
 ## Learn Deeper
 
-If you're interested in learning more, check out our @{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to combine
-the strengths of linear models and deep neural networks by jointly training them
-using the tf.contrib.learn API.
+If you're interested in learning more, check out our
+@{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to
+combine the strengths of linear models and deep neural networks by jointly
+training them using the tf.estimator API.
diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/wide_and_deep.md
index 0978005d6c..64b3fa7dbf 100644
--- a/tensorflow/docs_src/tutorials/wide_and_deep.md
+++ b/tensorflow/docs_src/tutorials/wide_and_deep.md
@@ -9,13 +9,13 @@ great for training deep neural networks too, and you might be thinking which one
 you should choose—Well, why not both? Would it be possible to combine the
 strengths of both in one model?
 
-In this tutorial, we'll introduce how to use the tf.contrib.learn API to jointly train a
-wide linear model and a deep feed-forward neural network. This approach combines
-the strengths of memorization and generalization. It's useful for generic
-large-scale regression and classification problems with sparse input features
-(e.g., categorical features with a large number of possible feature values). If
-you're interested in learning more about how Wide & Deep Learning works, please
-check out our [research paper](http://arxiv.org/abs/1606.07792).
+In this tutorial, we'll introduce how to use the tf.estimator API to jointly
+train a wide linear model and a deep feed-forward neural network. This approach
+combines the strengths of memorization and generalization. It's useful for
+generic large-scale regression and classification problems with sparse input
+features (e.g., categorical features with a large number of possible feature
+values). If you're interested in learning more about how Wide & Deep Learning
+works, please check out our [research paper](http://arxiv.org/abs/1606.07792).
 
 ![Wide & Deep Spectrum of Models](https://www.tensorflow.org/images/wide_n_deep.svg "Wide & Deep")
 
@@ -23,7 +23,7 @@ The figure above shows a comparison of a wide model (logistic regression with
 sparse features and transformations), a deep model (feed-forward neural network
 with an embedding layer and several hidden layers), and a Wide & Deep model
 (joint training of both). At a high level, there are only 3 steps to configure a
-wide, deep, or Wide & Deep model using the tf.contrib.learn API:
+wide, deep, or Wide & Deep model using the tf.estimator API:
 
 1.  Select features for the wide part: Choose the sparse base columns and
     crossed columns you want to use.
@@ -42,7 +42,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.estimator doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
@@ -78,23 +78,47 @@ part and the deep part of the model.
 ```python
 import tensorflow as tf
 
-# Categorical base columns.
-gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender", keys=["Female", "Male"])
-race = tf.contrib.layers.sparse_column_with_keys(column_name="race", keys=[
-  "Amer-Indian-Eskimo", "Asian-Pac-Islander", "Black", "Other", "White"])
-education = tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size=1000)
-relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size=100)
-workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100)
-occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000)
-native_country = tf.contrib.layers.sparse_column_with_hash_bucket("native_country", hash_bucket_size=1000)
+gender = tf.feature_column.categorical_column_with_vocabulary_list(
+    "gender", ["Female", "Male"])
+education = tf.feature_column.categorical_column_with_vocabulary_list(
+    "education", [
+        "Bachelors", "HS-grad", "11th", "Masters", "9th",
+        "Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th",
+        "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
+        "Preschool", "12th"
+    ])
+tf.feature_column.categorical_column_with_vocabulary_list(
+    "marital_status", [
+        "Married-civ-spouse", "Divorced", "Married-spouse-absent",
+        "Never-married", "Separated", "Married-AF-spouse", "Widowed"
+    ])
+relationship = tf.feature_column.categorical_column_with_vocabulary_list(
+    "relationship", [
+        "Husband", "Not-in-family", "Wife", "Own-child", "Unmarried",
+        "Other-relative"
+    ])
+workclass = tf.feature_column.categorical_column_with_vocabulary_list(
+    "workclass", [
+        "Self-emp-not-inc", "Private", "State-gov", "Federal-gov",
+        "Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked"
+    ])
+
+# To show an example of hashing:
+occupation = tf.feature_column.categorical_column_with_hash_bucket(
+    "occupation", hash_bucket_size=1000)
+native_country = tf.feature_column.categorical_column_with_hash_bucket(
+    "native_country", hash_bucket_size=1000)
 
 # Continuous base columns.
-age = tf.contrib.layers.real_valued_column("age")
-age_buckets = tf.contrib.layers.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
-education_num = tf.contrib.layers.real_valued_column("education_num")
-capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
-capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
-hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")
+age = tf.feature_column.numeric_column("age")
+education_num = tf.feature_column.numeric_column("education_num")
+capital_gain = tf.feature_column.numeric_column("capital_gain")
+capital_loss = tf.feature_column.numeric_column("capital_loss")
+hours_per_week = tf.feature_column.numeric_column("hours_per_week")
+
+# Transformations.
+age_buckets = tf.feature_column.bucketized_column(
+    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
 ```
 
 ## The Wide Model: Linear Model with Crossed Feature Columns
@@ -103,11 +127,19 @@ The wide model is a linear model with a wide set of sparse and crossed feature
 columns:
 
 ```python
-wide_columns = [
-  gender, native_country, education, occupation, workclass, relationship, age_buckets,
-  tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4)),
-  tf.contrib.layers.crossed_column([native_country, occupation], hash_bucket_size=int(1e4)),
-  tf.contrib.layers.crossed_column([age_buckets, education, occupation], hash_bucket_size=int(1e6))]
+base_columns = [
+    gender, native_country, education, occupation, workclass, relationship,
+    age_buckets,
+]
+
+crossed_columns = [
+    tf.feature_column.crossed_column(
+        ["education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        [age_buckets, "education", "occupation"], hash_bucket_size=1000),
+    tf.feature_column.crossed_column(
+        ["native_country", "occupation"], hash_bucket_size=1000)
+]
 ```
 
 Wide models with crossed feature columns can memorize sparse interactions
@@ -130,27 +162,42 @@ the TensorFlow tutorial on
 [Vector Representations of Words](https://www.tensorflow.org/versions/r0.9/tutorials/word2vec/index.html),
 or [Word Embedding](https://en.wikipedia.org/wiki/Word_embedding) on Wikipedia.
 
+Another way to represent categorical columns to feed into a neural network is
+via a multi-hot representation. This is often appropriate for categorical
+columns with only a few possible values. E.g. for the gender column, `"Male"`
+can be represented as `[1, 0]` and `"Female"` as `[0, 1]`. This is a fixed
+representation, whereas embeddings are more flexible and calculated at training
+time.
+
 We'll configure the embeddings for the categorical columns using
-`embedding_column`, and concatenate them with the continuous columns:
+`embedding_column`, and concatenate them with the continuous columns.
+We also use `indicator_column` to create multi-hot representation of some
+categorical columns.
 
 ```python
 deep_columns = [
-  tf.contrib.layers.embedding_column(workclass, dimension=8),
-  tf.contrib.layers.embedding_column(education, dimension=8),
-  tf.contrib.layers.embedding_column(gender, dimension=8),
-  tf.contrib.layers.embedding_column(relationship, dimension=8),
-  tf.contrib.layers.embedding_column(native_country, dimension=8),
-  tf.contrib.layers.embedding_column(occupation, dimension=8),
-  age, education_num, capital_gain, capital_loss, hours_per_week]
+    tf.feature_column.indicator_column(workclass),
+    tf.feature_column.indicator_column(education),
+    tf.feature_column.indicator_column(gender),
+    tf.feature_column.indicator_column(relationship),
+    # To show an example of embedding
+    tf.feature_column.embedding_column(native_country, dimension=8),
+    tf.feature_column.embedding_column(occupation, dimension=8),
+    age,
+    education_num,
+    capital_gain,
+    capital_loss,
+    hours_per_week,
+]
 ```
 
 The higher the `dimension` of the embedding is, the more degrees of freedom the
 model will have to learn the representations of the features. For simplicity, we
 set the dimension to 8 for all feature columns here. Empirically, a more
 informed decision for the number of dimensions is to start with a value on the
-order of \\(\log_2(n)\\) or \\(k\sqrt[4]n\\), where \\(n\\) is the number of unique
-features in a feature column and \\(k\\) is a small constant (usually smaller than
-10).
+order of \\(\log_2(n)\\) or \\(k\sqrt[4]n\\), where \\(n\\) is the number of
+unique features in a feature column and \\(k\\) is a small constant (usually
+smaller than 10).
 
 Through dense embeddings, deep models can generalize better and make predictions
 on feature pairs that were previously unseen in the training data. However, it
@@ -176,9 +223,9 @@ handled for you under the hood, so you simply need to create a
 ```python
 import tempfile
 model_dir = tempfile.mkdtemp()
-m = tf.contrib.learn.DNNLinearCombinedClassifier(
+m = tf.estimator.DNNLinearCombinedClassifier(
     model_dir=model_dir,
-    linear_feature_columns=wide_columns,
+    linear_feature_columns=crossed_columns,
     dnn_feature_columns=deep_columns,
     dnn_hidden_units=[100, 50])
 ```
@@ -194,62 +241,73 @@ import pandas as pd
 import urllib
 
 # Define the column names for the data sets.
-COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
-  "marital_status", "occupation", "relationship", "race", "gender",
-  "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket"]
-LABEL_COLUMN = 'label'
-CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation",
-                       "relationship", "race", "gender", "native_country"]
-CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss",
-                      "hours_per_week"]
-
-# Download the training and test data to temporary files.
-# Alternatively, you can download them yourself and change train_file and
-# test_file to your own paths.
-train_file = tempfile.NamedTemporaryFile()
-test_file = tempfile.NamedTemporaryFile()
-urllib.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.data", train_file.name)
-urllib.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.test", test_file.name)
-
-# Read the training and test data sets into Pandas dataframe.
-df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
-df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1)
-df_train[LABEL_COLUMN] = (df_train['income_bracket'].apply(lambda x: '>50K' in x)).astype(int)
-df_test[LABEL_COLUMN] = (df_test['income_bracket'].apply(lambda x: '>50K' in x)).astype(int)
-
-def input_fn(df):
-  # Creates a dictionary mapping from each continuous feature column name (k) to
-  # the values of that column stored in a constant Tensor.
-  continuous_cols = {k: tf.constant(df[k].values)
-                     for k in CONTINUOUS_COLUMNS}
-  # Creates a dictionary mapping from each categorical feature column name (k)
-  # to the values of that column stored in a tf.SparseTensor.
-  categorical_cols = {k: tf.SparseTensor(
-      indices=[[i, 0] for i in range(df[k].size)],
-      values=df[k].values,
-      dense_shape=[df[k].size, 1])
-                      for k in CATEGORICAL_COLUMNS}
-  # Merges the two dictionaries into one.
-  feature_cols = dict(continuous_cols.items() + categorical_cols.items())
-  # Converts the label column into a constant Tensor.
-  label = tf.constant(df[LABEL_COLUMN].values)
-  # Returns the feature columns and the label.
-  return feature_cols, label
-
-def train_input_fn():
-  return input_fn(df_train)
-
-def eval_input_fn():
-  return input_fn(df_test)
+CSV_COLUMNS = [
+    "age", "workclass", "fnlwgt", "education", "education_num",
+    "marital_status", "occupation", "relationship", "race", "gender",
+    "capital_gain", "capital_loss", "hours_per_week", "native_country",
+    "income_bracket"
+]
+
+def maybe_download(train_data, test_data):
+  """Maybe downloads training data and returns train and test file names."""
+  if train_data:
+    train_file_name = train_data
+  else:
+    train_file = tempfile.NamedTemporaryFile(delete=False)
+    urllib.request.urlretrieve(
+        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
+        train_file.name)  # pylint: disable=line-too-long
+    train_file_name = train_file.name
+    train_file.close()
+    print("Training data is downloaded to %s" % train_file_name)
+
+  if test_data:
+    test_file_name = test_data
+  else:
+    test_file = tempfile.NamedTemporaryFile(delete=False)
+    urllib.request.urlretrieve(
+        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",
+        test_file.name)  # pylint: disable=line-too-long
+    test_file_name = test_file.name
+    test_file.close()
+    print("Test data is downloaded to %s"% test_file_name)
+
+  return train_file_name, test_file_name
+
+def input_fn(data_file, num_epochs, shuffle):
+  """Input builder function."""
+  df_data = pd.read_csv(
+      tf.gfile.Open(data_file),
+      names=CSV_COLUMNS,
+      skipinitialspace=True,
+      engine="python",
+      skiprows=1)
+  # remove NaN elements
+  df_data = df_data.dropna(how="any", axis=0)
+  labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int)
+  return tf.estimator.inputs.pandas_input_fn(
+      x=df_data,
+      y=labels,
+      batch_size=100,
+      num_epochs=num_epochs,
+      shuffle=shuffle,
+      num_threads=5)
 ```
 
 After reading in the data, you can train and evaluate the model:
 
 ```python
-m.fit(input_fn=train_input_fn, steps=200)
-results = m.evaluate(input_fn=eval_input_fn, steps=1)
+# set num_epochs to None to get infinite stream of data.
+m.train(
+    input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True),
+    steps=train_steps)
+# set steps to None to run evaluation until all data consumed.
+results = m.evaluate(
+    input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False),
+    steps=None)
+print("model directory = %s" % model_dir)
 for key in sorted(results):
-    print("%s: %s" % (key, results[key]))
+  print("%s: %s" % (key, results[key]))
 ```
 
 The first line of the output should be something like `accuracy: 0.84429705`. We
diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
index 6a3ae50f0b..48c207bed1 100644
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ b/tensorflow/examples/learn/wide_n_deep_tutorial.py
@@ -34,7 +34,7 @@ CSV_COLUMNS = [
 ]
 
 gender = tf.feature_column.categorical_column_with_vocabulary_list(
-    "gender", [" Female", " Male"])
+    "gender", ["Female", "Male"])
 education = tf.feature_column.categorical_column_with_vocabulary_list(
     "education", [
         "Bachelors", "HS-grad", "11th", "Masters", "9th",
@@ -42,7 +42,7 @@ education = tf.feature_column.categorical_column_with_vocabulary_list(
         "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
         "Preschool", "12th"
     ])
-tf.feature_column.categorical_column_with_vocabulary_list(
+marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
     "marital_status", [
         "Married-civ-spouse", "Divorced", "Married-spouse-absent",
         "Never-married", "Separated", "Married-AF-spouse", "Widowed"
@@ -55,7 +55,7 @@ relationship = tf.feature_column.categorical_column_with_vocabulary_list(
 workclass = tf.feature_column.categorical_column_with_vocabulary_list(
     "workclass", [
         "Self-emp-not-inc", "Private", "State-gov", "Federal-gov",
-        "Local-gov", "?", "Self-emp-inc", "Without-pay", " Never-worked"
+        "Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked"
     ])
 
 # To show an example of hashing:
@@ -77,8 +77,8 @@ age_buckets = tf.feature_column.bucketized_column(
 
 # Wide columns and deep columns.
 base_columns = [
-    gender, native_country, education, occupation, workclass, relationship,
-    age_buckets,
+    gender, education, marital_status, relationship, workclass, occupation,
+    native_country, age_buckets,
 ]
 
 crossed_columns = [
@@ -135,8 +135,6 @@ def maybe_download(train_data, test_data):
 
 def build_estimator(model_dir, model_type):
   """Build an estimator."""
-  # Categorical base columns.
-
   if model_type == "wide":
     m = tf.estimator.LinearClassifier(
         model_dir=model_dir, feature_columns=base_columns + crossed_columns)
-- 
GitLab


From 04af4f9d9bf15f7420ffde2ed81ed3602ed8242a Mon Sep 17 00:00:00 2001
From: Alexey Surkov <surkov@google.com>
Date: Fri, 30 Jun 2017 14:12:10 -0700
Subject: [PATCH 0639/1375] Always set CURLOPT_READDATA when
 CURLOPT_READFUNCTION is set.

The code relied on the assumption that libcurl wouldn't call the read callback
if the header specified Content-Length: 0, which is not true on MacOS and
overall is a wrong assumption to make.

PiperOrigin-RevId: 160687029
---
 .../core/platform/cloud/http_request.cc       |  6 +++++
 tensorflow/core/platform/cloud/http_request.h |  1 -
 .../core/platform/cloud/http_request_test.cc  | 25 +++++++++++++++++--
 3 files changed, 29 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/platform/cloud/http_request.cc b/tensorflow/core/platform/cloud/http_request.cc
index 2d0141e50e..829fcf1e8b 100644
--- a/tensorflow/core/platform/cloud/http_request.cc
+++ b/tensorflow/core/platform/cloud/http_request.cc
@@ -252,6 +252,8 @@ Status HttpRequest::SetPutFromFile(const string& body_filepath, size_t offset) {
   libcurl_->curl_easy_setopt(curl_, CURLOPT_PUT, 1);
   libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
                              reinterpret_cast<void*>(put_body_));
+  // Using the default CURLOPT_READFUNCTION, which is doing an fread() on the
+  // FILE * userdata set with CURLOPT_READDATA.
   return Status::OK();
 }
 
@@ -263,6 +265,8 @@ Status HttpRequest::SetPutEmptyBody() {
   libcurl_->curl_easy_setopt(curl_, CURLOPT_PUT, 1);
   curl_headers_ =
       libcurl_->curl_slist_append(curl_headers_, "Content-Length: 0");
+  libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
+                             reinterpret_cast<void*>(this));
   libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
                              &HttpRequest::ReadCallback);
   return Status::OK();
@@ -292,6 +296,8 @@ Status HttpRequest::SetPostEmptyBody() {
   libcurl_->curl_easy_setopt(curl_, CURLOPT_POST, 1);
   curl_headers_ =
       libcurl_->curl_slist_append(curl_headers_, "Content-Length: 0");
+  libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
+                             reinterpret_cast<void*>(this));
   libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
                              &HttpRequest::ReadCallback);
   return Status::OK();
diff --git a/tensorflow/core/platform/cloud/http_request.h b/tensorflow/core/platform/cloud/http_request.h
index afcbb9f35c..584e525657 100644
--- a/tensorflow/core/platform/cloud/http_request.h
+++ b/tensorflow/core/platform/cloud/http_request.h
@@ -143,7 +143,6 @@ class HttpRequest {
   size_t post_body_read_ = 0;
 
   std::vector<char>* response_buffer_ = nullptr;
-  size_t response_buffer_size_ = 0;
   CURL* curl_ = nullptr;
   curl_slist* curl_headers_ = nullptr;
 
diff --git a/tensorflow/core/platform/cloud/http_request_test.cc b/tensorflow/core/platform/cloud/http_request_test.cc
index 6d66dfdee1..dfca7a6164 100644
--- a/tensorflow/core/platform/cloud/http_request_test.cc
+++ b/tensorflow/core/platform/cloud/http_request_test.cc
@@ -138,7 +138,7 @@ class FakeLibCurl : public LibCurl {
     return CURLE_OK;
   }
   CURLcode curl_easy_perform(CURL* curl) override {
-    if (read_data_) {
+    if (is_post_ || is_put_) {
       char buffer[3];
       int bytes_read;
       posted_content_ = "";
@@ -148,7 +148,7 @@ class FakeLibCurl : public LibCurl {
             strings::StrCat(posted_content_, StringPiece(buffer, bytes_read));
       } while (bytes_read > 0);
     }
-    if (write_data_) {
+    if (write_data_ || write_callback_) {
       write_callback_(response_content_.c_str(), 1, response_content_.size(),
                       write_data_);
     }
@@ -437,6 +437,27 @@ TEST(HttpRequestTest, PutRequest_WithBody_FromFile_NonZeroOffset) {
   std::remove(content_filename.c_str());
 }
 
+TEST(HttpRequestTest, PutRequest_WithoutBody) {
+  FakeLibCurl libcurl("", 200);
+  HttpRequest http_request(&libcurl);
+  TF_EXPECT_OK(http_request.Init());
+
+  TF_EXPECT_OK(http_request.SetUri("http://www.testuri.com"));
+  TF_EXPECT_OK(http_request.AddAuthBearerHeader("fake-bearer"));
+  TF_EXPECT_OK(http_request.SetPutEmptyBody());
+  TF_EXPECT_OK(http_request.Send());
+
+  // Check interactions with libcurl.
+  EXPECT_TRUE(libcurl.is_initialized_);
+  EXPECT_EQ("http://www.testuri.com", libcurl.url_);
+  EXPECT_EQ("", libcurl.custom_request_);
+  EXPECT_EQ(2, libcurl.headers_->size());
+  EXPECT_EQ("Authorization: Bearer fake-bearer", (*libcurl.headers_)[0]);
+  EXPECT_EQ("Content-Length: 0", (*libcurl.headers_)[1]);
+  EXPECT_TRUE(libcurl.is_put_);
+  EXPECT_EQ("", libcurl.posted_content_);
+}
+
 TEST(HttpRequestTest, PostRequest_WithBody_FromMemory) {
   FakeLibCurl libcurl("", 200);
   HttpRequest http_request(&libcurl);
-- 
GitLab


From e2c2cc7e023801c2d405d9f92b5d6f1e97f06779 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 14:20:35 -0700
Subject: [PATCH 0640/1375] Add support for num_oov_buckets to categorical
 columns with in-memory vocab

PiperOrigin-RevId: 160688152
---
 .../python/feature_column/feature_column.py   | 61 ++++++++++++----
 .../feature_column/feature_column_test.py     | 69 +++++++++++++++++--
 .../golden/tensorflow.feature_column.pbtxt    |  2 +-
 3 files changed, 110 insertions(+), 22 deletions(-)

diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index c75144a9c9..37da89c484 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -812,7 +812,8 @@ def categorical_column_with_vocabulary_file(
   Raises:
     ValueError: `vocabulary_file` is missing.
     ValueError: `vocabulary_size` is missing or < 1.
-    ValueError: `num_oov_buckets` is not a non-negative integer.
+    ValueError: `num_oov_buckets` is a negative integer.
+    ValueError: `num_oov_buckets` and `default_value` are both specified.
     ValueError: `dtype` is neither string nor integer.
   """
   if not vocabulary_file:
@@ -839,27 +840,39 @@ def categorical_column_with_vocabulary_file(
 
 
 def categorical_column_with_vocabulary_list(
-    key, vocabulary_list, dtype=None, default_value=-1):
+    key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0):
   """A `_CategoricalColumn` with in-memory vocabulary.
 
-  Logic for feature f is:
-  id = vocabulary_list.index_of(f) if f in vocabulary_list else default_value
-
   Use this when your inputs are in string or integer format, and you have an
   in-memory vocabulary mapping each value to an integer ID. By default,
-  out-of-vocabulary values are ignored. Use `default_value` to specify how to
-  include out-of-vocabulary values.
+  out-of-vocabulary values are ignored. Use either (but not both) of
+  `num_oov_buckets` and `default_value` to specify how to include
+  out-of-vocabulary values.
 
   For input dictionary `features`, `features[key]` is either `Tensor` or
   `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
   and `''` for string. Note that these values are independent of the
   `default_value` argument.
 
-  In the following examples, each input in `vocabulary_list` is assigned an ID
-  0-4 corresponding to its index (e.g., input 'B' produces output 2). All other
+  Example with `num_oov_buckets`:
+  In the following example, each input in `vocabulary_list` is assigned an ID
+  0-3 corresponding to its index (e.g., input 'B' produces output 2). All other
+  inputs are hashed and assigned an ID 4-5.
+
+  ```python
+  colors = categorical_column_with_vocabulary_list(
+      key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
+      num_oov_buckets=2)
+  columns = [colors, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction, _, _ = linear_model(features, columns)
+  ```
+
+  Example with `default_value`:
+  In the following example, each input in `vocabulary_list` is assigned an ID
+  0-4 corresponding to its index (e.g., input 'B' produces output 3). All other
   inputs are assigned `default_value` 0.
 
-  Linear model:
 
   ```python
   colors = categorical_column_with_vocabulary_list(
@@ -869,7 +882,7 @@ def categorical_column_with_vocabulary_list(
   linear_prediction, _, _ = linear_model(features, columns)
   ```
 
-  Embedding for a DNN model:
+  And to make an embedding with either:
 
   ```python
   columns = [embedding_column(colors, 3),...]
@@ -886,13 +899,22 @@ def categorical_column_with_vocabulary_list(
       Must be castable to `dtype`.
     dtype: The type of features. Only string and integer types are supported.
       If `None`, it will be inferred from `vocabulary_list`.
-    default_value: The value to use for values not in `vocabulary_list`.
+    default_value: The integer ID value to return for out-of-vocabulary feature
+      values, defaults to `-1`. This can not be specified with a positive
+      `num_oov_buckets`.
+    num_oov_buckets: Non-negative integer, the number of out-of-vocabulary
+      buckets. All out-of-vocabulary inputs will be assigned IDs in the range
+      `[len(vocabulary_list), len(vocabulary_list)+num_oov_buckets)` based on a
+      hash of the input value. A positive `num_oov_buckets` can not be specified
+      with `default_value`.
 
   Returns:
     A `_CategoricalColumn` with in-memory vocabulary.
 
   Raises:
     ValueError: if `vocabulary_list` is empty, or contains duplicate keys.
+    ValueError: `num_oov_buckets` is a negative integer.
+    ValueError: `num_oov_buckets` and `default_value` are both specified.
     ValueError: if `dtype` is not integer or string.
   """
   if (vocabulary_list is None) or (len(vocabulary_list) < 1):
@@ -904,6 +926,14 @@ def categorical_column_with_vocabulary_list(
         'Duplicate keys in vocabulary_list {}, column_name: {}'.format(
             vocabulary_list, key))
   vocabulary_dtype = dtypes.as_dtype(np.array(vocabulary_list).dtype)
+  if num_oov_buckets:
+    if default_value != -1:
+      raise ValueError(
+          'Can\'t specify both num_oov_buckets and default_value in {}.'.format(
+              key))
+    if num_oov_buckets < 0:
+      raise ValueError('Invalid num_oov_buckets {} in {}.'.format(
+          num_oov_buckets, key))
   _assert_string_or_int(
       vocabulary_dtype, prefix='column_name: {} vocabulary'.format(key))
   if dtype is None:
@@ -916,7 +946,7 @@ def categorical_column_with_vocabulary_list(
 
   return _VocabularyListCategoricalColumn(
       key=key, vocabulary_list=tuple(vocabulary_list), dtype=dtype,
-      default_value=default_value)
+      default_value=default_value, num_oov_buckets=num_oov_buckets)
 
 
 def categorical_column_with_identity(key, num_buckets, default_value=None):
@@ -2032,7 +2062,7 @@ class _VocabularyFileCategoricalColumn(
 class _VocabularyListCategoricalColumn(
     _CategoricalColumn,
     collections.namedtuple('_VocabularyListCategoricalColumn', (
-        'key', 'vocabulary_list', 'dtype', 'default_value'
+        'key', 'vocabulary_list', 'dtype', 'default_value', 'num_oov_buckets'
     ))):
   """See `categorical_column_with_vocabulary_list`."""
 
@@ -2066,13 +2096,14 @@ class _VocabularyListCategoricalColumn(
     return lookup_ops.index_table_from_tensor(
         vocabulary_list=tuple(self.vocabulary_list),
         default_value=self.default_value,
+        num_oov_buckets=self.num_oov_buckets,
         dtype=key_dtype,
         name='{}_lookup'.format(self.key)).lookup(input_tensor)
 
   @property
   def _num_buckets(self):
     """Returns number of buckets in this sparse feature."""
-    return len(self.vocabulary_list)
+    return len(self.vocabulary_list) + self.num_oov_buckets
 
   def _get_sparse_tensors(
       self, inputs, weight_collections=None, trainable=None):
diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py
index cfa2a0f7d4..b14ec73ba2 100644
--- a/tensorflow/python/feature_column/feature_column_test.py
+++ b/tensorflow/python/feature_column/feature_column_test.py
@@ -2535,6 +2535,21 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
       fc.categorical_column_with_vocabulary_list(
           key='aaa', vocabulary_list=(12, 24, 12))
 
+  def test_invalid_num_oov_buckets(self):
+    with self.assertRaisesRegexp(ValueError, 'Invalid num_oov_buckets'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=(12, 24, 36),
+          num_oov_buckets=-1)
+
+  def test_invalid_buckets_and_default_value(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'both num_oov_buckets and default_value'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa',
+          vocabulary_list=(12, 24, 36),
+          num_oov_buckets=100,
+          default_value=2)
+
   def test_invalid_input_dtype_int32(self):
     column = fc.categorical_column_with_vocabulary_list(
         key='aaa',
@@ -2693,6 +2708,26 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
               dense_shape=inputs.dense_shape),
           id_weight_pair.id_tensor.eval())
 
+  def test_get_sparse_tensors_with_oov_buckets(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'),
+        num_oov_buckets=100)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (1, 2)),
+        values=('marlo', 'skywalker', 'omar', 'heisenberg'),
+        dense_shape=(2, 3))
+    id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 33, 0, 62), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
   def test_get_sparse_tensors_int32(self):
     column = fc.categorical_column_with_vocabulary_list(
         key='aaa',
@@ -2736,11 +2771,33 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
               dense_shape=(3, 3)),
           id_weight_pair.id_tensor.eval())
 
+  def test_get_sparse_tensors_int32_with_oov_buckets(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
+        dtype=dtypes.int32,
+        num_oov_buckets=100)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+        values=(11, 100, 30, 22),
+        dense_shape=(3, 3))
+    id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 60, 0, 4), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
   def test_linear_model(self):
     wire_column = fc.categorical_column_with_vocabulary_list(
         key='aaa',
-        vocabulary_list=('omar', 'stringer', 'marlo'))
-    self.assertEqual(3, wire_column._num_buckets)
+        vocabulary_list=('omar', 'stringer', 'marlo'),
+        num_oov_buckets=1)
+    self.assertEqual(4, wire_column._num_buckets)
     with ops.Graph().as_default():
       predictions = fc.linear_model({
           wire_column.name: sparse_tensor.SparseTensorValue(
@@ -2752,12 +2809,12 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
       wire_var = get_linear_model_column_var(wire_column)
       with _initialized_session():
         self.assertAllClose((0.,), bias.eval())
-        self.assertAllClose(((0.,), (0.,), (0.,)), wire_var.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), wire_var.eval())
         self.assertAllClose(((0.,), (0.,)), predictions.eval())
-        wire_var.assign(((1.,), (2.,), (3.,))).eval()
+        wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
         # 'marlo' -> 2: wire_var[2] = 3
-        # 'skywalker' -> None, 'omar' -> 0: wire_var[0] = 1
-        self.assertAllClose(((3.,), (1.,)), predictions.eval())
+        # 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
+        self.assertAllClose(((3.,), (5.,)), predictions.eval())
 
 
 class IdentityCategoricalColumnTest(test.TestCase):
diff --git a/tensorflow/tools/api/golden/tensorflow.feature_column.pbtxt b/tensorflow/tools/api/golden/tensorflow.feature_column.pbtxt
index 4c633a850f..2a57a845cd 100644
--- a/tensorflow/tools/api/golden/tensorflow.feature_column.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.feature_column.pbtxt
@@ -18,7 +18,7 @@ tf_module {
   }
   member_method {
     name: "categorical_column_with_vocabulary_list"
-    argspec: "args=[\'key\', \'vocabulary_list\', \'dtype\', \'default_value\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], "
+    argspec: "args=[\'key\', \'vocabulary_list\', \'dtype\', \'default_value\', \'num_oov_buckets\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\', \'0\'], "
   }
   member_method {
     name: "crossed_column"
-- 
GitLab


From afca393e1a77af5230a763b4872c92daf993c09a Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 30 Jun 2017 14:26:46 -0700
Subject: [PATCH 0641/1375] Disable graph rewrites in tests that assert a given
 graph structure. This will make it possible to optimize these graphs in the
 future without having to update the test with every improvement.

PiperOrigin-RevId: 160688938
---
 .../tools/tfprof/model_analyzer_test.py       |  7 ++++++-
 .../python/debug/cli/stepper_cli_test.py      |  8 +++++++-
 tensorflow/python/debug/lib/stepper_test.py   | 20 ++++++++++++++++---
 3 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index 9f95074022..32a6d5fdb2 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 
 import os
 from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import variables
@@ -61,7 +62,11 @@ class PrintModelAnalysisTest(test.TestCase):
         'input_shapes'
     ]
 
-    with session.Session() as sess, ops.device('/cpu:0'):
+    rewriter_config = rewriter_config_pb2.RewriterConfig(
+        disable_model_pruning=True)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+    config = config_pb2.ConfigProto(graph_options=graph_options)
+    with session.Session(config=config) as sess, ops.device('/cpu:0'):
       x = lib.BuildSmallModel()
 
       sess.run(variables.global_variables_initializer())
diff --git a/tensorflow/python/debug/cli/stepper_cli_test.py b/tensorflow/python/debug/cli/stepper_cli_test.py
index 06e1228b95..ee8cabca0d 100644
--- a/tensorflow/python/debug/cli/stepper_cli_test.py
+++ b/tensorflow/python/debug/cli/stepper_cli_test.py
@@ -22,6 +22,8 @@ import re
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.debug.cli import stepper_cli
 from tensorflow.python.debug.lib import stepper
@@ -143,7 +145,11 @@ class NodeStepperSimpleGraphTest(test_util.TensorFlowTestCase):
     self.opt = gradient_descent.GradientDescentOptimizer(0.1).minimize(
         self.e, name="opt")
 
-    self.sess = session.Session()
+    rewriter_config = rewriter_config_pb2.RewriterConfig(
+        disable_model_pruning=True)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+    config = config_pb2.ConfigProto(graph_options=graph_options)
+    self.sess = session.Session(config=config)
 
     self.sess.run(self.a.initializer)
     self.sess.run(self.b.initializer)
diff --git a/tensorflow/python/debug/lib/stepper_test.py b/tensorflow/python/debug/lib/stepper_test.py
index 78e7b3b5eb..686fb45238 100644
--- a/tensorflow/python/debug/lib/stepper_test.py
+++ b/tensorflow/python/debug/lib/stepper_test.py
@@ -17,6 +17,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.debug.lib.stepper import NodeStepper
 from tensorflow.python.framework import constant_op
@@ -52,7 +54,11 @@ class StepperTest(test_util.TensorFlowTestCase):
 
     self.z = math_ops.multiply(self.x, self.y, name="z")  # Should be -4.0.
 
-    self.sess = session.Session()
+    rewriter_config = rewriter_config_pb2.RewriterConfig(
+        disable_model_pruning=True)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+    config = config_pb2.ConfigProto(graph_options=graph_options)
+    self.sess = session.Session(config=config)
     self.sess.run(variables.global_variables_initializer())
 
   def tearDown(self):
@@ -581,7 +587,11 @@ class StepperAssignAddTest(test_util.TensorFlowTestCase):
                                        1.0,
                                        name="v_add_plus_one")
 
-    self.sess = session.Session()
+    rewriter_config = rewriter_config_pb2.RewriterConfig(
+        disable_model_pruning=True)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+    config = config_pb2.ConfigProto(graph_options=graph_options)
+    self.sess = session.Session(config=config)
     self.sess.run(self.v.initializer)
 
   def tearDown(self):
@@ -708,7 +718,11 @@ class StepperBackwardRunTest(test_util.TensorFlowTestCase):
     gradient_descent.GradientDescentOptimizer(0.01).minimize(
         self.f, name="optim")
 
-    self.sess = session.Session()
+    rewriter_config = rewriter_config_pb2.RewriterConfig(
+        disable_model_pruning=True)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+    config = config_pb2.ConfigProto(graph_options=graph_options)
+    self.sess = session.Session(config=config)
     self.sess.run(variables.global_variables_initializer())
 
   def tearDown(self):
-- 
GitLab


From a5ea13c5b55e5ad822772351b7c7006f3f2a5beb Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Fri, 30 Jun 2017 14:39:52 -0700
Subject: [PATCH 0642/1375] Disable single_machine_test on GPU

PiperOrigin-RevId: 160690573
---
 tensorflow/core/grappler/clusters/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index 3cdddeb38f..667023845c 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -110,6 +110,7 @@ cc_test(
     name = "single_machine_test",
     srcs = ["single_machine_test.cc"],
     args = ["--heap_check=local"],  # The GPU tracer leaks memory
+    tags = ["no_gpu"],
     deps = [
         ":single_machine",
         "//tensorflow/cc:cc_ops",
-- 
GitLab


From 4cf4f524cb93ad0c1d6c35e9ccec62cf9015b217 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Fri, 30 Jun 2017 14:45:06 -0700
Subject: [PATCH 0643/1375] Expose publicly 3 missing initializers.

PiperOrigin-RevId: 160691254
---
 tensorflow/python/ops/state_ops.py             |  5 ++++-
 tensorflow/tools/api/golden/tensorflow.pbtxt   | 12 ++++++++++++
 ...nsorflow.variance_scaling_initializer.pbtxt | 18 ++++++++++++++++++
 3 files changed, 34 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt

diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py
index dbc637975d..d84949814a 100644
--- a/tensorflow/python/ops/state_ops.py
+++ b/tensorflow/python/ops/state_ops.py
@@ -46,10 +46,13 @@
 @@random_normal_initializer
 @@truncated_normal_initializer
 @@random_uniform_initializer
-@@uniform_unit_scaling_initializer
+@@glorot_uniform_initializer
+@@glorot_normal_initializer
 @@zeros_initializer
 @@ones_initializer
 @@orthogonal_initializer
+@@variance_scaling_initializer
+@@uniform_unit_scaling_initializer
 @@fixed_size_partitioner
 @@variable_axis_size_partitioner
 @@min_max_variable_partitioner
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index eca017edf5..d2ec2193c5 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -476,6 +476,10 @@ tf_module {
     name: "user_ops"
     mtype: "<type \'module\'>"
   }
+  member {
+    name: "variance_scaling_initializer"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "zeros_initializer"
     mtype: "<type \'type\'>"
@@ -1032,6 +1036,14 @@ tf_module {
     name: "global_variables_initializer"
     argspec: "args=[], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "glorot_normal_initializer"
+    argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"<dtype: \'float32\'>\"], "
+  }
+  member_method {
+    name: "glorot_uniform_initializer"
+    argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"<dtype: \'float32\'>\"], "
+  }
   member_method {
     name: "gradients"
     argspec: "args=[\'ys\', \'xs\', \'grad_ys\', \'name\', \'colocate_gradients_with_ops\', \'gate_gradients\', \'aggregation_method\'], varargs=None, keywords=None, defaults=[\'None\', \'gradients\', \'False\', \'False\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt b/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt
new file mode 100644
index 0000000000..a58398d645
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt
@@ -0,0 +1,18 @@
+path: "tensorflow.variance_scaling_initializer"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.init_ops.VarianceScaling\'>"
+  is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'normal\', \'None\', \"<dtype: \'float32\'>\"], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}
-- 
GitLab


From b6674ce782c7eb5ce4c03af167a33957dfa1b3f7 Mon Sep 17 00:00:00 2001
From: Kiril Gorovoy <kgorovoy@google.com>
Date: Fri, 30 Jun 2017 14:48:27 -0700
Subject: [PATCH 0644/1375] Install grpcio in ci_build.

PiperOrigin-RevId: 160691698
---
 tensorflow/tools/ci_build/install/install_pip_packages.sh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index 7fcd235e62..44fc21df94 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -85,3 +85,7 @@ pip2 install mock
 
 pip2 install portpicker
 pip3 install portpicker
+
+# TensorFlow Serving integration tests require the following:
+pip2 install grpcio
+pip3 install grpcio
-- 
GitLab


From 08c7323af36e71e29b39a0a01241adb43f63e903 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Fri, 30 Jun 2017 15:21:31 -0700
Subject: [PATCH 0645/1375] [TF:XLA] Add Dot, Conv and Pad support to
 HloEvaluator. They are also by default enabled for constant folding.

PiperOrigin-RevId: 160695489
---
 tensorflow/compiler/xla/service/BUILD         |   5 +
 .../compiler/xla/service/hlo_evaluator.cc     | 253 +++++++
 .../compiler/xla/service/hlo_evaluator.h      |   3 +-
 .../xla/service/hlo_evaluator_test.cc         | 671 +++++++++++++++++-
 4 files changed, 920 insertions(+), 12 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 08fd10172c..d6a1bdc2ae 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -88,6 +88,7 @@ cc_library(
     deps = [
         ":hlo",
         ":hlo_query",
+        ":shape_inference",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status",
@@ -95,6 +96,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
     ],
@@ -108,12 +110,15 @@ cc_test(
         ":hlo",
         ":hlo_evaluator",
         "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index f0512c0eaf..72b215217f 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -31,11 +31,13 @@ limitations under the License.
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/service/hlo_query.h"
+#include "tensorflow/compiler/xla/service/shape_inference.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/compiler/xla/window_util.h"
 #include "tensorflow/core/lib/core/bitmap.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
@@ -400,6 +402,257 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   };
 
+  Status HandleConvolution(HloInstruction* conv, HloInstruction* lhs,
+                           HloInstruction* rhs, const Window& window) override {
+    CHECK(ShapeUtil::IsArray(lhs->shape()));
+    CHECK(ShapeUtil::IsArray(rhs->shape()));
+    CHECK(ShapeUtil::SameElementType(lhs->shape(), rhs->shape()));
+    CHECK(ShapeUtil::SameElementType(lhs->shape(), conv->shape()));
+    TF_CHECK_OK(ShapeUtil::ValidateShape(lhs->shape()));
+    TF_CHECK_OK(ShapeUtil::ValidateShape(rhs->shape()));
+
+    const auto& dnums = conv->convolution_dimension_numbers();
+    const int64 num_spatial_dims = dnums.spatial_dimensions_size();
+    CHECK_EQ(num_spatial_dims, dnums.kernel_spatial_dimensions_size());
+    CHECK_GE(num_spatial_dims, 1);
+    CHECK_EQ(window.dimensions_size(), num_spatial_dims);
+
+    CHECK_EQ(num_spatial_dims + 2, ShapeUtil::Rank(lhs->shape()));
+    CHECK_EQ(num_spatial_dims + 2, ShapeUtil::Rank(rhs->shape()));
+
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferConvolveShape(
+                            lhs->shape(), rhs->shape(), window, dnums));
+    CHECK(ShapeUtil::Compatible(conv->shape(), inferred_return_shape))
+        << "return shape set to: " << ShapeUtil::HumanString(conv->shape())
+        << " but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+
+    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
+    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
+
+    const auto lhs_rank = ShapeUtil::Rank(lhs->shape());
+    const auto rhs_rank = ShapeUtil::Rank(rhs->shape());
+
+    // Dimension number applicable for both input (lhs), and output.
+    const int64 batch_dim = dnums.batch_dimension();
+    const int64 z_dim = dnums.feature_dimension();
+    // Dimension number applicable for kernel (rhs).
+    const int64 kernel_input_z_dim = dnums.kernel_input_feature_dimension();
+    const int64 kernel_output_z_dim = dnums.kernel_output_feature_dimension();
+
+    const int64 z_size = ShapeUtil::GetDimension(lhs->shape(), z_dim);
+
+    std::vector<int64> window_dimension_sizes;
+    for (auto i : dnums.kernel_spatial_dimensions()) {
+      window_dimension_sizes.push_back(
+          ShapeUtil::GetDimension(rhs->shape(), i));
+    }
+
+    const Shape& window_shape = ShapeUtil::MakeShape(
+        rhs->shape().element_type(), window_dimension_sizes);
+
+    auto result = Literal::CreateFromShape(conv->shape());
+    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
+        [&](tensorflow::gtl::ArraySlice<int64> out_index) {
+          ReturnT result_val = static_cast<ReturnT>(0);
+
+          std::vector<int64> lhs_index(lhs_rank, 0);
+          std::vector<int64> rhs_index(rhs_rank, 0);
+
+          lhs_index[batch_dim] = out_index[batch_dim];
+          rhs_index[kernel_output_z_dim] = out_index[z_dim];
+
+          std::vector<int64> rhs_spatial_index(
+              dnums.kernel_spatial_dimensions_size(), 0);
+
+          // Convolve input feature with kernel.
+          do {
+            for (int64 iz = 0; iz < z_size; ++iz) {
+              lhs_index[z_dim] = iz;
+              rhs_index[kernel_input_z_dim] = iz;
+
+              // Find corresponding spatial dimension index for input (lhs).
+              for (int64 ki = 0; ki < rhs_spatial_index.size(); ++ki) {
+                // Spatial dimension number for input (lhs) and output.
+                const int64 spatial_dim = dnums.spatial_dimensions(ki);
+
+                // Calculate lhs (input) index without taking base dilation into
+                // account.
+                const int64 undilated_index =
+                    out_index[spatial_dim] * window.dimensions(ki).stride() -
+                    window.dimensions(ki).padding_low() +
+                    rhs_spatial_index[ki] *
+                        window.dimensions(ki).window_dilation();
+                // Skip if the lhs (input) index is to be dilated.
+                if (undilated_index % window.dimensions(ki).base_dilation() !=
+                    0) {
+                  goto cnt;
+                }
+
+                // Calculate the actual lhs (input) index after dilation.
+                lhs_index[spatial_dim] =
+                    undilated_index / window.dimensions(ki).base_dilation();
+
+                // Skip if input index is not in bound.
+                if (!(lhs_index[spatial_dim] >= 0 &&
+                      lhs_index[spatial_dim] <
+                          lhs->shape().dimensions(spatial_dim))) {
+                  goto cnt;
+                }
+
+                rhs_index[dnums.kernel_spatial_dimensions(ki)] =
+                    rhs_spatial_index[ki];
+              }
+
+              result_val += lhs_literal.Get<ReturnT>(lhs_index) *
+                            rhs_literal.Get<ReturnT>(rhs_index);
+            }
+          cnt:;
+          } while (IndexUtil::BumpIndices(window_shape, &rhs_spatial_index));
+
+          return result_val;
+        }));
+
+    parent_->evaluated_[conv] = std::move(result);
+    return Status::OK();
+  };
+
+  Status HandleDot(HloInstruction* dot, HloInstruction* lhs,
+                   HloInstruction* rhs) override {
+    CHECK(ShapeUtil::IsArray(dot->shape()));
+    CHECK(ShapeUtil::IsArray(lhs->shape()));
+    CHECK(ShapeUtil::IsArray(rhs->shape()));
+
+    // Dot only supports operands of rank 1 and 2.
+    const auto dot_rank = ShapeUtil::Rank(dot->shape());
+    const auto lhs_rank = ShapeUtil::Rank(lhs->shape());
+    const auto rhs_rank = ShapeUtil::Rank(rhs->shape());
+    CHECK(lhs_rank > 0 && lhs_rank <= 2);
+    CHECK(rhs_rank > 0 && rhs_rank <= 2);
+    CHECK_EQ(dot_rank, lhs_rank + rhs_rank - 2);
+
+    CHECK(ShapeUtil::SameElementType(lhs->shape(), rhs->shape()));
+    CHECK(ShapeUtil::SameElementType(lhs->shape(), dot->shape()));
+
+    // Check contracted dimensions are the same.
+    //
+    // Determine the index of the contracted dimensions for input tensors.
+    // dimensions -1 of lhs and dimension 0 of rhs are contracted.
+    const int64 lhs_contracted_dimension =
+        ShapeUtil::GetDimensionNumber(lhs->shape(), -1);
+    const int64 rhs_contracted_dimension = 0;
+    CHECK_EQ(lhs->shape().dimensions(lhs_contracted_dimension),
+             rhs->shape().dimensions(rhs_contracted_dimension))
+        << "lhs contracted dimension: "
+        << lhs->shape().dimensions(lhs_contracted_dimension)
+        << " rhs contracted dimension: "
+        << rhs->shape().dimensions(rhs_contracted_dimension);
+    const int64 contracted_dimension_size =
+        lhs->shape().dimensions(lhs_contracted_dimension);
+
+    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
+    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
+
+    auto result = Literal::CreateFromShape(dot->shape());
+    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
+        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+          ReturnT result_val = static_cast<ReturnT>(0);
+
+          std::vector<int64> lhs_index(lhs_rank, 0);
+          std::vector<int64> rhs_index(rhs_rank, 0);
+          // Set index for non-contracted dimension for lhs and rhs.
+          if (lhs_rank > 1) {
+            lhs_index[0] = multi_index[0];
+          }
+          if (rhs_rank > 1) {
+            rhs_index[1] = multi_index[multi_index.size() - 1];
+          }
+
+          // Accumulates resulting product along the contracted dimension.
+          for (int64 i = 0; i < contracted_dimension_size; ++i) {
+            lhs_index[lhs_contracted_dimension] = i;
+            rhs_index[rhs_contracted_dimension] = i;
+
+            result_val += lhs_literal.Get<ReturnT>(lhs_index) *
+                          rhs_literal.Get<ReturnT>(rhs_index);
+          }
+
+          return result_val;
+        }));
+
+    parent_->evaluated_[dot] = std::move(result);
+    return Status::OK();
+  };
+
+  Status HandlePad(HloInstruction* pad) override {
+    CHECK(!ShapeUtil::IsTuple(pad->operand(0)->shape()));
+    // Padding value must be scalar.
+    CHECK(ShapeUtil::IsScalar(pad->operand(1)->shape()));
+    CHECK_EQ(ShapeUtil::Rank(pad->operand(0)->shape()),
+             pad->padding_config().dimensions_size());
+
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferPadShape(
+                            /*operand_shape=*/pad->operand(0)->shape(),
+                            /*padding_value_shape=*/pad->operand(1)->shape(),
+                            /*padding_config=*/pad->padding_config()));
+    CHECK(ShapeUtil::Compatible(pad->shape(), inferred_return_shape))
+        << "return shape is set to: " << ShapeUtil::HumanString(pad->shape())
+        << "but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+
+    // Create new HLO of padded shape with padding value.
+    ReturnT scalar =
+        parent_->GetEvaluatedLiteralFor(pad->operand(1)).Get<ReturnT>({});
+    auto result = Literal::CreateFromShape(pad->shape());
+    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
+        [&scalar](tensorflow::gtl::ArraySlice<int64> multi_index) {
+          return scalar;
+        }));
+
+    auto evaluated_operand = parent_->GetEvaluatedLiteralFor(pad->operand(0));
+
+    std::vector<int64> input_index(ShapeUtil::Rank(evaluated_operand.shape()),
+                                   0);
+    std::vector<int64> target_index(ShapeUtil::Rank(result->shape()), 0);
+
+    // Loop through each element of the operand, assign them to the
+    // corresponding index of the resulting padded literal.
+    const PaddingConfig& pad_config = pad->padding_config();
+
+    auto func = [&](const std::vector<int64>& input_index) {
+      for (auto i = 0; i < input_index.size(); ++i) {
+        // Interior padding occurs logically before edge padding, so in the case
+        // of negative edge padding elements are removed from the
+        // interior-padded operand.
+        target_index[i] =
+            pad_config.dimensions(i).edge_padding_low() +
+            input_index[i] * (pad_config.dimensions(i).interior_padding() + 1);
+
+        // Account for negative low and high padding: skip assignment if the
+        // any target index is out of range.
+        if (!(target_index[i] >= 0 &&
+              target_index[i] < pad->shape().dimensions(i))) {
+          return true;
+        }
+      }
+      result->Set<ReturnT>(target_index,
+                           evaluated_operand.Get<ReturnT>(input_index));
+      return true;
+    };
+
+    std::vector<int64> zero_base(evaluated_operand.shape().dimensions_size(),
+                                 0);
+    std::vector<int64> step(evaluated_operand.shape().dimensions_size(), 1);
+
+    ShapeUtil::ForEachIndex(evaluated_operand.shape(), zero_base,
+                            evaluated_operand.shape().dimensions(), step, func);
+
+    parent_->evaluated_[pad] = std::move(result);
+    return Status::OK();
+  };
+
   Status Preprocess(HloInstruction* hlo) override {
     VLOG(2) << hlo->ToString();
     return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h
index 91fd56f54c..976a2325ea 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.h
@@ -92,7 +92,8 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
     return hlo->Visit(typed_visitors_.at(hlo->shape().element_type()).get());
   }
 
-  // Operations that are type-agnostic.
+  // Operations that are type-agnostic or always return a specific type, such as
+  // HandleIsFinite where boolean is always returned.
   //
   Status HandleParameter(HloInstruction* parameter) override;
 
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index a11a5abc03..626bd3b02b 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -14,12 +14,16 @@ limitations under the License.
 ==============================================================================*/
 #include "tensorflow/compiler/xla/service/hlo_evaluator.h"
 
+#include <initializer_list>
 #include <memory>
 #include <string>
+#include <tuple>
 #include <utility>
 #include <vector>
 
+#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -29,6 +33,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -61,7 +66,7 @@ TEST_F(HloEvaluatorTest, DoesClamp) {
 
   auto expected = Literal::CreateR2<float>({{0, 4}, {2, 4}});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs select
@@ -83,7 +88,7 @@ TEST_F(HloEvaluatorTest, DoesSelect) {
 
   auto expected = Literal::CreateR2<float>({{2, 5}, {0, 4}});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
@@ -103,7 +108,7 @@ TEST_F(HloEvaluatorTest, DoesAdd) {
 
   auto expected = Literal::CreateR2<int64>({{3, 4}, {-96, 8}});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
@@ -123,7 +128,7 @@ TEST_F(HloEvaluatorTest, DoesDivide) {
 
   auto expected = Literal::CreateR2<int64>({{0, 0}, {-25, 1}});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 
   auto lhs_f64 = Literal::CreateR2<double>({{1.0, 0.0}, {-100.0, 4.0}});
   auto rhs_f64 = Literal::CreateR2<double>({{2.2, 4.0}, {4.0, 4.0}});
@@ -138,7 +143,7 @@ TEST_F(HloEvaluatorTest, DoesDivide) {
 
   expected = Literal::CreateR2<double>({{0.45454545454545453, 0}, {-25, 1}});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
@@ -155,7 +160,7 @@ TEST_F(HloEvaluatorTest, DoesAbs) {
 
   auto expected = Literal::CreateR2<int64>({{1, 20}, {100, 4}});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 
   // For R0 literal.
   const Shape& r0 = ShapeUtil::MakeShape(F32, {});
@@ -165,7 +170,7 @@ TEST_F(HloEvaluatorTest, DoesAbs) {
   result = evaluator_->Evaluate(instruction.get()).ConsumeValueOrDie();
   expected = Literal::CreateR0<float>(1.0f);
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 
   // For R1 literal with dimension of size 0.
   Shape empty_r1 = ShapeUtil::MakeShape(F32, {0});
@@ -177,7 +182,7 @@ TEST_F(HloEvaluatorTest, DoesAbs) {
   result = evaluator_->Evaluate(instruction.get()).ConsumeValueOrDie();
   expected = Literal::CreateR1<float>({});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 }  // namespace
 
 // Verifies that HloEvaluator evaluates a HLO Computation with non-parameter nor
@@ -206,7 +211,7 @@ TEST_F(HloEvaluatorTest, DoesTraverseInstructions) {
 
   auto expected = Literal::CreateR2<int64>({{4, -16}, {-196, 12}});
 
-  EXPECT_TRUE(result->Equal(*expected));
+  LiteralTestUtil::ExpectEqual(*expected, *result);
 }
 
 // Verifies Reshape operation is correctly evaluated.
@@ -271,7 +276,6 @@ TEST_F(HloEvaluatorTest, ConvertWithSameLayout) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
 
-  EXPECT_TRUE(ShapeUtil::Equal(result->shape(), expected->shape()));
   LiteralTestUtil::ExpectEqual(*result, *expected);
 }
 
@@ -293,9 +297,654 @@ TEST_F(HloEvaluatorTest, ConvertWithDifferentLayout) {
   std::unique_ptr<Literal> result =
       evaluator_->Evaluate(builder.Build().get(), {}).ConsumeValueOrDie();
 
-  EXPECT_TRUE(ShapeUtil::Equal(result->shape(), expected->shape()));
   LiteralTestUtil::ExpectEqual(*result, *expected);
 }
 
+PaddingConfig CreatePaddingConfig(
+    std::initializer_list<std::array<int64, 3>> padding_dimensions) {
+  PaddingConfig padding_config;
+
+  for (auto& paddings_per_dim : padding_dimensions) {
+    auto dimension = padding_config.add_dimensions();
+    dimension->set_edge_padding_low(paddings_per_dim[0]);
+    dimension->set_edge_padding_high(paddings_per_dim[1]);
+    dimension->set_interior_padding(paddings_per_dim[2]);
+  }
+  return padding_config;
+}
+
+TEST_F(HloEvaluatorTest, Pad2DIntegerArrayWithZeroDimension) {
+  auto operand = Literal::CreateR2<int32>({{}, {}});
+  auto operand_instruction = HloInstruction::CreateConstant(std::move(operand));
+
+  constexpr int32 kPadValue = 10;
+  auto pad_value = Literal::CreateR0<int32>(kPadValue);
+  auto padding_value_instruction =
+      HloInstruction::CreateConstant(std::move(pad_value));
+
+  auto padding_config = CreatePaddingConfig({{{1, 0, 2}}, {{0, 2, 1}}});
+  Shape shape = ShapeUtil::MakeShape(S32, {5, 2});
+  auto pad_instruction = HloInstruction::CreatePad(
+      shape, operand_instruction.get(), padding_value_instruction.get(),
+      padding_config);
+
+  auto result = evaluator_->Evaluate(pad_instruction.get()).ConsumeValueOrDie();
+
+  auto expected = Literal::CreateR2<int32>(
+      {{10, 10}, {10, 10}, {10, 10}, {10, 10}, {10, 10}});
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, Pad4DFloatArrayWithInteriorPadding) {
+  HloComputation::Builder b(TestName());
+
+  Array4D<float> input_array(3, 2, 1, 1, {1, 2, 3, 4, 5, 6});
+  auto input = Literal::CreateR4FromArray4D<float>(input_array);
+  HloInstruction* input_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(input)));
+  constexpr float kPadValue = 1.5;
+  auto pad_value = Literal::CreateR0<float>(kPadValue);
+  HloInstruction* pad_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(pad_value)));
+
+  Shape shape = ShapeUtil::MakeShape(F32, {8, 5, 1, 1});
+  auto r4_padding_on_dim0_dim1 =
+      CreatePaddingConfig({{{1, 0, 2}}, {{0, 2, 1}}, {{0, 0, 0}}, {{0, 0, 0}}});
+  b.AddInstruction(HloInstruction::CreatePad(
+      shape, input_instruction, pad_instruction, r4_padding_on_dim0_dim1));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  auto expected_array = MakeUnique<Array4D<float>>(8, 5, 1, 1);
+  expected_array->Fill(kPadValue);
+  (*expected_array)(1, 0, 0, 0) = 1.0f;
+  (*expected_array)(1, 2, 0, 0) = 2.0f;
+  (*expected_array)(4, 0, 0, 0) = 3.0f;
+  (*expected_array)(4, 2, 0, 0) = 4.0f;
+  (*expected_array)(7, 0, 0, 0) = 5.0f;
+  (*expected_array)(7, 2, 0, 0) = 6.0f;
+
+  auto expected = Literal::CreateR4FromArray4D<float>(*expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, NegativePadding2D) {
+  HloComputation::Builder b(TestName());
+
+  // input_array:
+  // f32[4,3] {
+  //  { 1, 2, 3 },
+  //  { 5, 6, 7 },
+  //  { 9, 10, 11 },
+  //  { 13, 14, 15 },
+  // }
+  auto input_array = MakeUnique<Array2D<float>>(4, 3);
+  input_array->FillUnique(1.0f);
+  auto input = Literal::CreateR2FromArray2D<float>(*input_array);
+  HloInstruction* input_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(input)));
+
+  auto pad_value_instruction = b.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.718f)));
+
+  auto r2_padding_on_dim0_dim1 =
+      CreatePaddingConfig({{{-1, -2, 0}}, {{-2, 4, 0}}});
+  Shape shape = ShapeUtil::MakeShape(F32, {1, 5});
+  b.AddInstruction(HloInstruction::CreatePad(shape, input_instruction,
+                                             pad_value_instruction,
+                                             r2_padding_on_dim0_dim1));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  // f32[1,5] { 7.0, 2.718, 2.718, 2.718, 2.718 }
+  auto expected_array = MakeUnique<Array2D<float>>(1, 5);
+  (*expected_array)(0, 0) = 7.0f;
+  (*expected_array)(0, 1) = 2.718f;
+  (*expected_array)(0, 2) = 2.718f;
+  (*expected_array)(0, 3) = 2.718f;
+  (*expected_array)(0, 4) = 2.718f;
+  auto expected = Literal::CreateR2FromArray2D<float>(*expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, NegativeAndInteriorPadding2D) {
+  HloComputation::Builder b(TestName());
+
+  // f32[4,3] {
+  //  { 1, 2, 3 },
+  //  { 5, 6, 7 },
+  //  { 9, 10, 11 },
+  //  { 13, 14, 15 },
+  // }
+  auto input_array = MakeUnique<Array2D<float>>(4, 3);
+  input_array->FillUnique(1.0f);
+  auto input = Literal::CreateR2FromArray2D<float>(*input_array);
+  HloInstruction* input_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(input)));
+
+  auto pad_value_instruction = b.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.718f)));
+
+  PaddingConfig padding_config = MakeNoPaddingConfig(2);
+
+  // Negative padding that results in zero dimensions.
+  auto r2_padding_on_dim0_dim1 =
+      CreatePaddingConfig({{{-2, -5, 1}}, {{-2, 4, 2}}});
+
+  Shape shape = ShapeUtil::MakeShape(F32, {0, 9});
+  b.AddInstruction(HloInstruction::CreatePad(shape, input_instruction,
+                                             pad_value_instruction,
+                                             r2_padding_on_dim0_dim1));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  auto expected_array = MakeUnique<Array2D<float>>(0, 9);
+  auto expected = Literal::CreateR2FromArray2D<float>(*expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, DotRank2AndRank1) {
+  HloComputation::Builder b(TestName());
+
+  // lhs:
+  // f32[4,1] {
+  //  { 1 },
+  //  { 2 },
+  //  { 3 },
+  //  { 4 },
+  // }
+  auto lhs_array = MakeUnique<Array2D<float>>(4, 1);
+  lhs_array->FillUnique(1.0f);
+  auto lhs_literal = Literal::CreateR2FromArray2D<float>(*lhs_array);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  // rhs:
+  // f32[2] { 1, 2 },
+  auto rhs_literal = Literal::CreateR2<float>({{1, 2}});
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Shape shape = ShapeUtil::MakeShape(F32, {4, 2});
+  b.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot, lhs_instruction, rhs_instruction));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  // clang-format off
+  auto expected_array = Array2D<float>({
+      {1.f, 2.f},
+      {2.f, 4.f},
+      {3.f, 6.f},
+      {4.f, 8.f},
+  });
+  // clang-format on
+  auto expected = Literal::CreateR2FromArray2D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, DotRank1AndRank2) {
+  HloComputation::Builder b(TestName());
+
+  // lhs:
+  // f32[3]
+  //  { 1, 2, 3 },
+  auto lhs_literal = Literal::CreateR1<float>({1, 2, 3});
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  // rhs:
+  // f32[3,2] {
+  //  { 1, 2 },
+  //  { 3, 4 },
+  //  { 5, 6 },
+  // }
+  auto rhs_array = MakeUnique<Array2D<float>>(3, 2);
+  rhs_array->FillUnique(1.0f);
+  auto rhs_literal = Literal::CreateR2FromArray2D<float>(*rhs_array);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Shape shape = ShapeUtil::MakeShape(F32, {2});
+  b.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot, lhs_instruction, rhs_instruction));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  auto expected = Literal::CreateR1<float>({22.f, 28.f});
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, DotRank2AndRank2) {
+  HloComputation::Builder b(TestName());
+
+  // lhs:
+  // f32[4,3] {
+  //  { 1, 2, 3 },
+  //  { 5, 6, 7 },
+  //  { 9, 10, 11 },
+  //  { 13, 14, 15 },
+  // }
+  auto lhs_array = MakeUnique<Array2D<float>>(4, 3);
+  lhs_array->FillUnique(1.0f);
+  auto lhs_literal = Literal::CreateR2FromArray2D<float>(*lhs_array);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  // rhs:
+  // f32[3,2] {
+  //  { 1, 2 },
+  //  { 3, 4 },
+  //  { 5, 6 },
+  // }
+  auto rhs_array = MakeUnique<Array2D<float>>(3, 2);
+  rhs_array->FillUnique(1.0f);
+  auto rhs_literal = Literal::CreateR2FromArray2D<float>(*rhs_array);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Shape shape = ShapeUtil::MakeShape(F32, {4, 2});
+  b.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot, lhs_instruction, rhs_instruction));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  auto expected_array = Array2D<float>({
+      {22.f, 28.f}, {58.f, 76.f}, {94.f, 124.f}, {130.f, 172.f},
+  });
+  auto expected = Literal::CreateR2FromArray2D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, SimpleConv1D) {
+  HloComputation::Builder b(TestName());
+
+  Array3D<float> lhs_array = {{{1, 2, 3}}};
+  auto lhs_literal = Literal::CreateR3FromArray3D<float>(lhs_array);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  Array3D<float> rhs_array = {{{3.f, 4.f}}};
+  auto rhs_literal = Literal::CreateR3FromArray3D<float>(rhs_array);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Window window;
+  WindowDimension dim;
+  dim.set_size(2);
+  dim.set_stride(1);
+  dim.set_padding_low(0);
+  dim.set_padding_high(1);
+  dim.set_window_dilation(1);
+  dim.set_base_dilation(1);
+  *window.add_dimensions() = dim;
+
+  ConvolutionDimensionNumbers dnums;
+  dnums.set_batch_dimension(0);
+  dnums.set_feature_dimension(1);
+  dnums.add_spatial_dimensions(2);
+
+  dnums.set_kernel_output_feature_dimension(0);
+  dnums.set_kernel_input_feature_dimension(1);
+  dnums.add_kernel_spatial_dimensions(2);
+
+  const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 3});
+  b.AddInstruction(HloInstruction::CreateConvolve(
+      shape, lhs_instruction, rhs_instruction, window, dnums));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  Array3D<float> expected_array = {{{11.f, 18.f, 9.f}}};
+  auto expected = Literal::CreateR3FromArray3D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, Simple4x4Conv2DWith2x2Kernel) {
+  HloComputation::Builder b(TestName());
+
+  Array4D<float> lhs_array(1, 1, 4, 4);
+  // clang-format off
+  lhs_array.FillWithYX(Array2D<float>({
+    {1,  2,  3,  4 },
+    {5,  6,  7,  8 },
+    {9,  10, 11, 12},
+    {13, 14, 15, 16},
+  }));
+  // clang-format on
+  auto lhs_literal = Literal::CreateR4FromArray4D<float>(lhs_array);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  Array4D<float> rhs_array(1, 1, 2, 2);
+  // clang-format off
+  rhs_array.FillWithYX(Array2D<float>({
+    {5, 6},
+    {7, 8},
+  }));
+  // clang-format on
+  auto rhs_literal = Literal::CreateR4FromArray4D<float>(rhs_array);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Window window;
+  WindowDimension dim;
+  dim.set_size(2);
+  dim.set_stride(1);
+  dim.set_padding_low(0);
+  dim.set_padding_high(1);
+  dim.set_window_dilation(1);
+  dim.set_base_dilation(1);
+  *window.add_dimensions() = dim;
+  *window.add_dimensions() = dim;
+
+  ConvolutionDimensionNumbers dnums =
+      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+
+  const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 4, 4});
+  b.AddInstruction(HloInstruction::CreateConvolve(
+      shape, lhs_instruction, rhs_instruction, window, dnums));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  Array4D<float> expected_array(1, 1, 4, 4);
+  // clang-format off
+  expected_array.FillWithYX(Array2D<float>({
+    {100, 126, 152,  76},
+    {204, 230, 256, 124},
+    {308, 334, 360, 172},
+    {149, 160, 171,  80},
+  }));
+  // clang-format on
+  auto expected = Literal::CreateR4FromArray4D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, Conv2DGeneralDimensions) {
+  HloComputation::Builder b(TestName());
+
+  // clang-format off
+  // Input dimensions: [feature=2, height=3, batch=1, width=4]
+  Array4D<float> input({
+    {{{1, 2, 3, 4}},
+     {{5, 6, 7, 8}},
+     {{9, 10, 11, 12}}},
+    {{{13, 14, 15, 16}},
+     {{17, 18, 19, 20}},
+     {{21, 22, 23, 24}}}
+  });
+  // Weight dimensions:
+  // [kernel_output_feature=1, width=3, kernel_input_feature=2, height=3]
+  Array4D<float> weight({{
+    {{1, 7, 13},
+     {4, 10, 16}},
+    {{2, 8, 14},
+     {5, 11, 17}},
+    {{3, 9, 15},
+     {6, 12, 18}}
+  }});
+  // clang-format on
+
+  auto lhs_literal = Literal::CreateR4FromArray4D<float>(input);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  auto rhs_literal = Literal::CreateR4FromArray4D<float>(weight);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Window window;
+  WindowDimension dim;
+  dim.set_size(3);
+  dim.set_stride(1);
+  dim.set_padding_low(0);
+  dim.set_padding_high(0);
+  dim.set_window_dilation(1);
+  dim.set_base_dilation(1);
+  *window.add_dimensions() = dim;
+  *window.add_dimensions() = dim;
+
+  ConvolutionDimensionNumbers dnums;
+  dnums.set_batch_dimension(2);
+  dnums.set_feature_dimension(0);
+  dnums.add_spatial_dimensions(1);
+  dnums.add_spatial_dimensions(3);
+
+  dnums.set_kernel_output_feature_dimension(0);
+  dnums.set_kernel_input_feature_dimension(2);
+  dnums.add_kernel_spatial_dimensions(3);
+  dnums.add_kernel_spatial_dimensions(1);
+
+  const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 1, 2});
+  b.AddInstruction(HloInstruction::CreateConvolve(
+      shape, lhs_instruction, rhs_instruction, window, dnums));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  // clang-format off
+  // Result dimensions: [feature=1, height=1, batch=1, width=2]
+  Array4D<float> expected_array({{{{2514, 2685}}}});
+  // clang-format on
+  auto expected = Literal::CreateR4FromArray4D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, DilatedBaseConv2DWithHighPadding) {
+  HloComputation::Builder b(TestName());
+
+  Array4D<float> lhs_array(1, 1, 4, 4);
+  // clang-format off
+  lhs_array.FillWithYX(Array2D<float>({
+    {1,  2,  3,  4 },
+    {5,  6,  7,  8 },
+    {9,  10, 11, 12},
+    {13, 14, 15, 16},
+  }));
+  // clang-format on
+  auto lhs_literal = Literal::CreateR4FromArray4D<float>(lhs_array);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  Array4D<float> rhs_array(1, 1, 2, 2);
+  // clang-format off
+  rhs_array.FillWithYX(Array2D<float>({
+    {5, 6},
+    {7, 8},
+  }));
+  // clang-format on
+  auto rhs_literal = Literal::CreateR4FromArray4D<float>(rhs_array);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Window window;
+  WindowDimension dim;
+  dim.set_size(2);
+  dim.set_stride(1);
+  dim.set_padding_low(0);
+  dim.set_padding_high(1);
+  dim.set_window_dilation(1);
+  dim.set_base_dilation(2);
+  *window.add_dimensions() = dim;
+  *window.add_dimensions() = dim;
+
+  ConvolutionDimensionNumbers dnums =
+      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+
+  const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 7, 7});
+  b.AddInstruction(HloInstruction::CreateConvolve(
+      shape, lhs_instruction, rhs_instruction, window, dnums));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  Array4D<float> expected_array(1, 1, 7, 7);
+  expected_array.FillWithYX(Array2D<float>({
+      {5, 12, 10, 18, 15, 24, 20},
+      {35, 48, 42, 56, 49, 64, 56},
+      {25, 36, 30, 42, 35, 48, 40},
+      {63, 80, 70, 88, 77, 96, 84},
+      {45, 60, 50, 66, 55, 72, 60},
+      {91, 112, 98, 120, 105, 128, 112},
+      {65, 84, 70, 90, 75, 96, 80},
+  }));
+  auto expected = Literal::CreateR4FromArray4D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest, DilatedBaseConv2DWithLowAndHighPadding) {
+  HloComputation::Builder b(TestName());
+
+  Array4D<float> lhs_array(1, 1, 4, 4);
+  // clang-format off
+  lhs_array.FillWithYX(Array2D<float>({
+    {1,  2,  3,  4 },
+    {5,  6,  7,  8 },
+    {9,  10, 11, 12},
+    {13, 14, 15, 16},
+  }));
+  // clang-format on
+  auto lhs_literal = Literal::CreateR4FromArray4D<float>(lhs_array);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  Array4D<float> rhs_array(1, 1, 2, 2);
+  // clang-format off
+  rhs_array.FillWithYX(Array2D<float>({
+    {5, 6},
+    {7, 8},
+  }));
+  // clang-format on
+  auto rhs_literal = Literal::CreateR4FromArray4D<float>(rhs_array);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Window window;
+  WindowDimension dim;
+  dim.set_size(2);
+  dim.set_stride(1);
+  dim.set_padding_low(1);
+  dim.set_padding_high(1);
+  dim.set_window_dilation(1);
+  dim.set_base_dilation(2);
+  *window.add_dimensions() = dim;
+  *window.add_dimensions() = dim;
+
+  ConvolutionDimensionNumbers dnums =
+      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+
+  const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 8, 8});
+  b.AddInstruction(HloInstruction::CreateConvolve(
+      shape, lhs_instruction, rhs_instruction, window, dnums));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  Array4D<float> expected_array(1, 1, 8, 8);
+  expected_array.FillWithYX(Array2D<float>({
+      {8, 7, 16, 14, 24, 21, 32, 28},
+      {6, 5, 12, 10, 18, 15, 24, 20},
+      {40, 35, 48, 42, 56, 49, 64, 56},
+      {30, 25, 36, 30, 42, 35, 48, 40},
+      {72, 63, 80, 70, 88, 77, 96, 84},
+      {54, 45, 60, 50, 66, 55, 72, 60},
+      {104, 91, 112, 98, 120, 105, 128, 112},
+      {78, 65, 84, 70, 90, 75, 96, 80},
+  }));
+  auto expected = Literal::CreateR4FromArray4D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
+TEST_F(HloEvaluatorTest,
+       DilatedWindowAndBaseConv2DWithDifferentLowAndHighPaddingAndStrides) {
+  HloComputation::Builder b(TestName());
+
+  Array4D<float> lhs_array(1, 1, 4, 4);
+  // clang-format off
+  lhs_array.FillWithYX(Array2D<float>({
+    {1,  2,  3,  4 },
+    {5,  6,  7,  8 },
+    {9,  10, 11, 12},
+    {13, 14, 15, 16},
+  }));
+  // clang-format on
+  auto lhs_literal = Literal::CreateR4FromArray4D<float>(lhs_array);
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+
+  Array4D<float> rhs_array(1, 1, 2, 3);
+  // clang-format off
+  rhs_array.FillWithYX(Array2D<float>({
+    {5, 6, 7},
+    {8, 9, 10},
+  }));
+  // clang-format on
+  auto rhs_literal = Literal::CreateR4FromArray4D<float>(rhs_array);
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  Window window;
+  WindowDimension dim;
+  dim.set_size(2);
+  dim.set_stride(1);
+  dim.set_padding_low(2);
+  dim.set_padding_high(2);
+  dim.set_window_dilation(2);
+  dim.set_base_dilation(2);
+  *window.add_dimensions() = dim;
+  dim.set_size(3);
+  dim.set_stride(3);
+  dim.set_padding_low(2);
+  dim.set_padding_high(-1);
+  dim.set_window_dilation(1);
+  dim.set_base_dilation(3);
+  *window.add_dimensions() = dim;
+
+  ConvolutionDimensionNumbers dnums =
+      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+
+  const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 9, 3});
+  b.AddInstruction(HloInstruction::CreateConvolve(
+      shape, lhs_instruction, rhs_instruction, window, dnums));
+
+  std::unique_ptr<Literal> result =
+      evaluator_->Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  Array4D<float> expected_array(1, 1, 9, 3);
+  expected_array.FillWithYX(Array2D<float>({
+      {10, 20, 30},
+      {0, 0, 0},
+      {57, 74, 91},
+      {0, 0, 0},
+      {125, 142, 159},
+      {0, 0, 0},
+      {193, 210, 227},
+      {0, 0, 0},
+      {91, 98, 105},
+  }));
+  auto expected = Literal::CreateR4FromArray4D<float>(expected_array);
+
+  LiteralTestUtil::ExpectEqual(*expected, *result);
+}
+
 }  // namespace
 }  // namespace xla
-- 
GitLab


From 9c887941d52fe9a3e5d7da3efb30639e89228f0c Mon Sep 17 00:00:00 2001
From: Andrew Selle <aselle@google.com>
Date: Fri, 30 Jun 2017 15:32:36 -0700
Subject: [PATCH 0646/1375] Fix tf.extract_image_patches documentation.

Fixes #5770

PiperOrigin-RevId: 160696796
---
 tensorflow/core/ops/array_ops.cc | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 1b4d5436c0..f60134ac01 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -4179,7 +4179,8 @@ Extract `patches` from `images` and put them in the "depth" output dimension.
 images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`.
 patches: 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *
   ksize_cols * depth]` containing image patches with size
-  `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension.
+  `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. Note
+  `out_rows` and `out_cols` are the dimensions of the output patches.
 ksizes: The size of the sliding window for each dimension of `images`.
 strides: 1-D of length 4. How far the centers of two consecutive patches are in
   the images. Must be: `[1, stride_rows, stride_cols, 1]`.
@@ -4187,7 +4188,8 @@ rates: 1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the
   input stride, specifying how far two consecutive patch samples are in the
   input. Equivalent to extracting patches with
   `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by
-  subsampling them spatially by a factor of `rates`.
+  subsampling them spatially by a factor of `rates`. This is equivalent to
+  `rate` in dilated (a.k.a. Atrous) convolutions.
 padding: The type of padding algorithm to use.
 
 We specify the size-related attributes as:
-- 
GitLab


From 2289a65626a67c119ce1f6ec0b8162b3bfe0efe7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 15:38:59 -0700
Subject: [PATCH 0647/1375] Expanding the comments so that users know that
 logits shape and spatial squeezing depend on input image size.

PiperOrigin-RevId: 160697474
---
 .../contrib/slim/python/slim/nets/inception_v2.py   | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v2.py b/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
index 46062600e6..e44cb770ba 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
@@ -495,7 +495,9 @@ def inception_v2(inputs,
   Constructs an Inception v2 network for classification as described in
   http://arxiv.org/abs/1502.03167.
 
-  The default image size used to train this network is 224x224.
+  The recommended image size used to train this network is 224x224. For image
+  sizes that differ substantially, it is recommended to use inception_v2_base()
+  and connect custom final layers to the output.
 
   Args:
     inputs: a tensor of shape [batch_size, height, width, channels].
@@ -510,8 +512,12 @@ def inception_v2(inputs,
       usage will be to set this value in (0, 1) to reduce the number of
       parameters or computation cost of the model.
     prediction_fn: a function to get predictions out of logits.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+        Note that input image sizes other than 224x224 might lead to different
+        spatial dimensions, and hence cannot be squeezed. In this event,
+        it is best to set spatial_squeeze as False, and perform a reduce_mean
+        over the resulting spatial dimensions with sizes exceeding 1.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.
     scope: Optional variable_scope.
@@ -523,8 +529,7 @@ def inception_v2(inputs,
       activation.
 
   Raises:
-    ValueError: if final_endpoint is not set to one of the predefined values,
-                or depth_multiplier <= 0
+    ValueError: if depth_multiplier <= 0.
   """
   if depth_multiplier <= 0:
     raise ValueError('depth_multiplier is not greater than zero.')
-- 
GitLab


From 741be9c4d7ea9f283322d6a2070c8f33451fad56 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 16:05:21 -0700
Subject: [PATCH 0648/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 160700202
---
 tensorflow/core/ops/ops.pbtxt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 41469c4029..971f6f82f8 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -7511,7 +7511,7 @@ op {
   }
   output_arg {
     name: "patches"
-    description: "4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *\nksize_cols * depth]` containing image patches with size\n`ksize_rows x ksize_cols x depth` vectorized in the \"depth\" dimension."
+    description: "4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *\nksize_cols * depth]` containing image patches with size\n`ksize_rows x ksize_cols x depth` vectorized in the \"depth\" dimension. Note\n`out_rows` and `out_cols` are the dimensions of the output patches."
     type_attr: "T"
   }
   attr {
@@ -7531,7 +7531,7 @@ op {
   attr {
     name: "rates"
     type: "list(int)"
-    description: "1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the\ninput stride, specifying how far two consecutive patch samples are in the\ninput. Equivalent to extracting patches with\n`patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by\nsubsampling them spatially by a factor of `rates`."
+    description: "1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the\ninput stride, specifying how far two consecutive patch samples are in the\ninput. Equivalent to extracting patches with\n`patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by\nsubsampling them spatially by a factor of `rates`. This is equivalent to\n`rate` in dilated (a.k.a. Atrous) convolutions."
     has_minimum: true
     minimum: 4
   }
-- 
GitLab


From 48f91b50abe5e7d8915f556332130809ea447be7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 16:11:43 -0700
Subject: [PATCH 0649/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 160700827
---
 tensorflow/go/op/wrappers.go | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index c16c6c8765..ce3bd0260f 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -595,7 +595,8 @@ func Bitcast(scope *Scope, input tf.Output, type_ tf.DataType) (output tf.Output
 // input stride, specifying how far two consecutive patch samples are in the
 // input. Equivalent to extracting patches with
 // `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by
-// subsampling them spatially by a factor of `rates`.
+// subsampling them spatially by a factor of `rates`. This is equivalent to
+// `rate` in dilated (a.k.a. Atrous) convolutions.
 //	padding: The type of padding algorithm to use.
 //
 // We specify the size-related attributes as:
@@ -608,7 +609,8 @@ func Bitcast(scope *Scope, input tf.Output, type_ tf.DataType) (output tf.Output
 //
 // Returns 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *
 // ksize_cols * depth]` containing image patches with size
-// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension.
+// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. Note
+// `out_rows` and `out_cols` are the dimensions of the output patches.
 func ExtractImagePatches(scope *Scope, images tf.Output, ksizes []int64, strides []int64, rates []int64, padding string) (patches tf.Output) {
 	if scope.Err() != nil {
 		return
-- 
GitLab


From e4f4c0677def89b3233d8f9404dc7476a481ee19 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 18:18:18 -0700
Subject: [PATCH 0650/1375] Internal change.

PiperOrigin-RevId: 160711225
---
 tensorflow/contrib/opt/BUILD              | 1 +
 tensorflow/core/distributed_runtime/BUILD | 6 +++++-
 tensorflow/python/BUILD                   | 4 ++++
 3 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 0953a6d001..36a738a47a 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -100,6 +100,7 @@ py_test(
     srcs_version = "PY2AND3",
     tags = [
         "manual",  # Flaky: b/29892493
+        "notap",  # data race due to b/62910646
     ],
     deps = [
         ":opt_py",
diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD
index 3bea72c4a3..89a5f433bd 100644
--- a/tensorflow/core/distributed_runtime/BUILD
+++ b/tensorflow/core/distributed_runtime/BUILD
@@ -451,7 +451,11 @@ tf_cuda_cc_test(
     size = "small",
     srcs = ["remote_device_test.cc"],
     linkstatic = tf_kernel_tests_linkstatic(),
-    tags = tf_cuda_tests_tags() + ["manual"],
+    tags = tf_cuda_tests_tags() + [
+        "manual",
+        "notap",  # Memory leak due to b/62910646
+        "noguitar",  # Memory leak due to b/62910646
+    ],
     deps = [
         ":master",
         ":remote_device",
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index b761391f91..75798b79ee 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2996,6 +2996,9 @@ tf_py_test(
         ":training",
         ":variables",
     ],
+    tags = [
+        "notsan",  # data race due to b/62910646
+    ],
 )
 
 py_library(
@@ -3084,6 +3087,7 @@ py_test(
     tags = [
         "no_gpu",
         "no_pip_gpu",
+        "notsan",  # data race due to b/62910646
     ],
     deps = [
         ":client",
-- 
GitLab


From d34eb19d7307e6e225336632303b870edbf4c568 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 30 Jun 2017 18:59:57 -0700
Subject: [PATCH 0651/1375] tfdbg CLI: replace accordion pattern in the
 print_source output with INIT_SCROLL_POS_KEY

PiperOrigin-RevId: 160712855
---
 tensorflow/python/debug/cli/analyzer_cli.py   | 41 +++++++------------
 .../python/debug/cli/analyzer_cli_test.py     | 12 +-----
 2 files changed, 17 insertions(+), 36 deletions(-)

diff --git a/tensorflow/python/debug/cli/analyzer_cli.py b/tensorflow/python/debug/cli/analyzer_cli.py
index e863c2ddb9..d8f57ad19a 100644
--- a/tensorflow/python/debug/cli/analyzer_cli.py
+++ b/tensorflow/python/debug/cli/analyzer_cli.py
@@ -999,11 +999,10 @@ class DebugAnalyzer(object):
 
   def _reconstruct_print_source_command(self,
                                         parsed,
-                                        line_begin_decrease=0,
+                                        line_begin,
                                         max_elements_per_line_increase=0):
     return "ps %s %s -b %d -m %d" % (
-        parsed.source_file_path, "-t" if parsed.tensors else "",
-        max(parsed.line_begin - line_begin_decrease, 1),
+        parsed.source_file_path, "-t" if parsed.tensors else "", line_begin,
         parsed.max_elements_per_line + max_elements_per_line_increase)
 
   def print_source(self, args, screen_info=None):
@@ -1015,38 +1014,26 @@ class DebugAnalyzer(object):
     source_annotation = source_utils.annotate_source(
         self._debug_dump,
         parsed.source_file_path,
-        do_dumped_tensors=parsed.tensors,
-        min_line=parsed.line_begin)
+        do_dumped_tensors=parsed.tensors)
 
     source_lines, line_num_width = source_utils.load_source(
         parsed.source_file_path)
 
     labeled_source_lines = []
-    if parsed.line_begin > 1:
-      omitted_info_line = RL(
-          "(... Omitted %d source lines ...) " % (parsed.line_begin - 1),
-          "bold")
-      omitted_info_line += RL(
-          "+5",
-          debugger_cli_common.MenuItem(
-              None,
-              self._reconstruct_print_source_command(
-                  parsed, line_begin_decrease=5)))
-      labeled_source_lines.append(omitted_info_line)
-
-    for i, line in enumerate(source_lines[parsed.line_begin - 1:]):
-      annotated_line = RL("L%d" % (i + parsed.line_begin),
-                          cli_shared.COLOR_YELLOW)
+    actual_initial_scroll_target = 0
+    for i, line in enumerate(source_lines):
+      annotated_line = RL("L%d" % (i + 1), cli_shared.COLOR_YELLOW)
       annotated_line += " " * (line_num_width - len(annotated_line))
       annotated_line += line
       labeled_source_lines.append(annotated_line)
 
-      if i + parsed.line_begin in source_annotation:
-        sorted_elements = sorted(source_annotation[i + parsed.line_begin])
+      if i + 1 == parsed.line_begin:
+        actual_initial_scroll_target = len(labeled_source_lines) - 1
+
+      if i + 1 in source_annotation:
+        sorted_elements = sorted(source_annotation[i + 1])
         for k, element in enumerate(sorted_elements):
           if k >= parsed.max_elements_per_line:
-            # TODO(cais): Replace this accordion pattern with the easier-to-use
-            # INIT_SCROLL_POS_KEY.
             omitted_info_line = RL("    (... Omitted %d of %d %s ...) " % (
                 len(sorted_elements) - parsed.max_elements_per_line,
                 len(sorted_elements),
@@ -1056,7 +1043,7 @@ class DebugAnalyzer(object):
                 debugger_cli_common.MenuItem(
                     None,
                     self._reconstruct_print_source_command(
-                        parsed, max_elements_per_line_increase=5)))
+                        parsed, i + 1, max_elements_per_line_increase=5)))
             labeled_source_lines.append(omitted_info_line)
             break
 
@@ -1071,7 +1058,9 @@ class DebugAnalyzer(object):
           labeled_source_lines.append(label)
 
     output = debugger_cli_common.rich_text_lines_from_rich_line_list(
-        labeled_source_lines)
+        labeled_source_lines,
+        annotations={debugger_cli_common.INIT_SCROLL_POS_KEY:
+                     actual_initial_scroll_target})
     _add_main_menu(output, node_name=None)
     return output
 
diff --git a/tensorflow/python/debug/cli/analyzer_cli_test.py b/tensorflow/python/debug/cli/analyzer_cli_test.py
index ce224fff20..6fba9ed0b4 100644
--- a/tensorflow/python/debug/cli/analyzer_cli_test.py
+++ b/tensorflow/python/debug/cli/analyzer_cli_test.py
@@ -1241,16 +1241,8 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase):
         [self._curr_file_path, "-b", "3"],
         screen_info={"cols": 80})
 
-    self.assertIn("Omitted 2 source lines", out.lines[0])
-    self.assertTrue(out.lines[0].endswith("+5"))
-    expand_lines_command = out.font_attr_segs[0][-1][2].content
-    self.assertStartsWith(expand_lines_command,
-                          "ps %s " % self._curr_file_path)
-    self.assertIn("-b 1", expand_lines_command)
-
-    self.assertIsNone(self._findSourceLine(out, 1))
-    self.assertIsNone(self._findSourceLine(out, 2))
-    self.assertIsNotNone(self._findSourceLine(out, 3))
+    self.assertEqual(
+        2, out.annotations[debugger_cli_common.INIT_SCROLL_POS_KEY])
 
     index = self._findSourceLine(out, self._u_line_number)
     self.assertEqual(
-- 
GitLab


From c805dc197f8a7ef9ca43e1140a713f0905d87dc3 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Fri, 30 Jun 2017 19:37:20 -0700
Subject: [PATCH 0652/1375] [TF:XLA] Fix open source build.

PiperOrigin-RevId: 160714466
---
 tensorflow/compiler/xla/service/hlo_evaluator.cc | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 72b215217f..a0c5cbe916 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -646,8 +646,9 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
                                  0);
     std::vector<int64> step(evaluated_operand.shape().dimensions_size(), 1);
 
-    ShapeUtil::ForEachIndex(evaluated_operand.shape(), zero_base,
-                            evaluated_operand.shape().dimensions(), step, func);
+    ShapeUtil::ForEachIndex(
+        evaluated_operand.shape(), zero_base,
+        AsInt64Slice(evaluated_operand.shape().dimensions()), step, func);
 
     parent_->evaluated_[pad] = std::move(result);
     return Status::OK();
-- 
GitLab


From a6b0a90f23a969687dbb624761dfe88516cbbad1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 30 Jun 2017 20:17:59 -0700
Subject: [PATCH 0653/1375] Specify cluster topology properties.

PiperOrigin-RevId: 160715763
---
 tensorflow/core/protobuf/device_properties.proto | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/tensorflow/core/protobuf/device_properties.proto b/tensorflow/core/protobuf/device_properties.proto
index 8ef7864990..9b1497c710 100644
--- a/tensorflow/core/protobuf/device_properties.proto
+++ b/tensorflow/core/protobuf/device_properties.proto
@@ -49,11 +49,3 @@ message DeviceProperties {
   // Memory bandwidth in KB/s
   int64 bandwidth = 13;
 }
-
-message DeviceMap {
-  message NamedDevice {
-    string name = 1;
-    DeviceProperties device = 2;
-  };
-  repeated NamedDevice name_and_device = 1;
-}
\ No newline at end of file
-- 
GitLab


From 8051221686e2942f32740355deea84fd8e39322c Mon Sep 17 00:00:00 2001
From: Lakshay Garg <lakshay.garg.1996@gmail.com>
Date: Sat, 1 Jul 2017 11:50:00 +0530
Subject: [PATCH 0654/1375] fix typo: SYC -> SYCL

---
 tensorflow/core/kernels/cwise_op_acosh.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/cwise_op_acosh.cc b/tensorflow/core/kernels/cwise_op_acosh.cc
index c3fec350d7..7bdd8d22a3 100644
--- a/tensorflow/core/kernels/cwise_op_acosh.cc
+++ b/tensorflow/core/kernels/cwise_op_acosh.cc
@@ -30,7 +30,7 @@ REGISTER4(UnaryOp, CPU, "Acosh", functor::acosh, float, double,
 REGISTER_SYCL_KERNEL(float);
 REGISTER_SYCL_KERNEL(double);
 #undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
+#endif // TENSORFLOW_USE_SYCL
 
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Acosh", functor::acosh, float, double);
-- 
GitLab


From 9999dd321a3b8ec8f0c6d8a3aae3682c45c57976 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 1 Jul 2017 04:27:07 -0700
Subject: [PATCH 0655/1375] Replace sync_replicas_optimizer_test's
 `create_local_cluster` with the standard implementation.

PiperOrigin-RevId: 160731226
---
 .../training/sync_replicas_optimizer_test.py  | 28 +------------------
 1 file changed, 1 insertion(+), 27 deletions(-)

diff --git a/tensorflow/python/training/sync_replicas_optimizer_test.py b/tensorflow/python/training/sync_replicas_optimizer_test.py
index 15f938df8c..85e8a8a4bb 100644
--- a/tensorflow/python/training/sync_replicas_optimizer_test.py
+++ b/tensorflow/python/training/sync_replicas_optimizer_test.py
@@ -20,41 +20,15 @@ from __future__ import print_function
 
 import time
 
-import portpicker
-
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
+from tensorflow.python.framework.test_util import create_local_cluster
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.training import gradient_descent
-from tensorflow.python.training import server_lib
 from tensorflow.python.training import training
 
 
-def create_local_cluster(num_workers, num_ps, protocol="grpc"):
-  """Create local GRPC servers and return them."""
-  worker_ports = [portpicker.pick_unused_port() for _ in range(num_workers)]
-  ps_ports = [portpicker.pick_unused_port() for _ in range(num_ps)]
-  cluster_dict = {
-      "worker": ["localhost:%s" % port for port in worker_ports],
-      "ps": ["localhost:%s" % port for port in ps_ports]
-  }
-  cs = server_lib.ClusterSpec(cluster_dict)
-
-  workers = [
-      server_lib.Server(
-          cs, job_name="worker", protocol=protocol, task_index=ix, start=True)
-      for ix in range(num_workers)
-  ]
-  ps_servers = [
-      server_lib.Server(
-          cs, job_name="ps", protocol=protocol, task_index=ix, start=True)
-      for ix in range(num_ps)
-  ]
-
-  return workers, ps_servers
-
-
 # Creates the workers and return their sessions, graphs, train_ops.
 def get_workers(num_workers, replicas_to_aggregate, workers):
   sessions = []
-- 
GitLab


From 818b768cdabbb5137e7d522a34c9754872fd50b7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 1 Jul 2017 08:23:11 -0700
Subject: [PATCH 0656/1375] Minor cleanup: remove unused constructions

PiperOrigin-RevId: 160736783
---
 .../compiler/jit/encapsulate_subgraphs_pass.cc  |  1 -
 tensorflow/compiler/plugin/executor/compiler.cc | 17 ++++++-----------
 .../compiler/plugin/executor/executable.cc      | 15 ++++++---------
 tensorflow/compiler/plugin/executor/executor.cc | 14 ++++----------
 .../xla/service/hlo_rematerialization_test.cc   |  4 ++--
 .../kernels/ensemble_optimizer_ops.cc           |  1 -
 .../contrib/boosted_trees/kernels/model_ops.cc  |  1 -
 .../contrib/tensor_forest/kernels/model_ops.cc  |  1 -
 .../contrib/tensor_forest/kernels/stats_ops.cc  |  1 -
 tensorflow/core/ops/state_ops.cc                |  1 -
 tensorflow/python/framework/test_ops.cc         |  1 -
 11 files changed, 18 insertions(+), 39 deletions(-)

diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc
index 14d8f2ab35..a1ddad3e9b 100644
--- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc
+++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc
@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/dump_graph.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/optimization_registry.h"
-#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/node_def_builder.h"
diff --git a/tensorflow/compiler/plugin/executor/compiler.cc b/tensorflow/compiler/plugin/executor/compiler.cc
index 3a84f08c00..72fe7ba451 100644
--- a/tensorflow/compiler/plugin/executor/compiler.cc
+++ b/tensorflow/compiler/plugin/executor/compiler.cc
@@ -18,7 +18,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/plugin/executor/compiler.h"
 #include "tensorflow/compiler/plugin/executor/executable.h"
-
 #include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
 #include "tensorflow/compiler/xla/service/flatten_call_graph.h"
 #include "tensorflow/compiler/xla/service/hlo_constant_folding.h"
@@ -30,18 +29,15 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/inliner.h"
 #include "tensorflow/compiler/xla/service/reshape_mover.h"
 #include "tensorflow/compiler/xla/status_macros.h"
-
+#include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/stream_executor/lib/initialize.h"
 #include "tensorflow/stream_executor/lib/strcat.h"
 
-#include "tensorflow/core/lib/core/errors.h"
+namespace xla {
+namespace executorplugin {
 
 namespace se = ::perftools::gputools;
 namespace sep = ::perftools::gputools::executorplugin;
-namespace port = ::perftools::gputools::port;
-
-namespace xla {
-namespace executorplugin {
 
 /*
  * Run optimization passes on the module.  The graph is transformed by
@@ -111,12 +107,11 @@ ExecutorCompiler::ShapeSizeBytesFunction() const {
   return ExecutorExecutable::ShapeSizeBytes;
 }
 
-
-}  // namespace executorplugin
-}  // namespace xla
-
 REGISTER_MODULE_INITIALIZER(executor_compiler, {
   xla::Compiler::RegisterCompilerFactory(sep::kExecutorPlatformId, []() {
     return xla::MakeUnique<xla::executorplugin::ExecutorCompiler>();
   });
 });
+
+}  // namespace executorplugin
+}  // namespace xla
diff --git a/tensorflow/compiler/plugin/executor/executable.cc b/tensorflow/compiler/plugin/executor/executable.cc
index 79eea9af3f..4673a90e0a 100644
--- a/tensorflow/compiler/plugin/executor/executable.cc
+++ b/tensorflow/compiler/plugin/executor/executable.cc
@@ -15,18 +15,16 @@ limitations under the License.
 
 #include "tensorflow/compiler/plugin/executor/executable.h"
 #include "tensorflow/compiler/plugin/executor/executor.h"
-
-#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
-
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 
-namespace se = ::perftools::gputools;
-namespace sep = ::perftools::gputools::executorplugin;
-
 namespace xla {
 namespace executorplugin {
 
+namespace se = ::perftools::gputools;
+namespace sep = ::perftools::gputools::executorplugin;
+
 ExecutorExecutable::ExecutorExecutable(std::unique_ptr<HloModule> hlo_module)
     : Executable(std::move(hlo_module), ShapeSizeBytes) {}
 
@@ -90,15 +88,14 @@ StatusOr<se::DeviceMemoryBase> ExecutorExecutable::ExecuteOnStream(
     arg_literals_ptrs.push_back(arg_literals.back().get());
 
     // Copy in the data from the stream_executor buffers
-    void* buffer = arg_literals.back().get()->MutableInternalData();
+    void* buffer = arg_literals.back()->MutableInternalData();
     memcpy(buffer, arguments[p].opaque(),
            ShapeUtil::ByteSizeOf(param->shape()));
   }
 
   // Execute the graph using the evaluator
   HloEvaluator evaluator;
-  std::unique_ptr<Literal> output;
-  TF_ASSIGN_OR_RETURN(output,
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<Literal> output,
                       evaluator.Evaluate(computation, arg_literals_ptrs));
 
   // Copy the result into the return buffer
diff --git a/tensorflow/compiler/plugin/executor/executor.cc b/tensorflow/compiler/plugin/executor/executor.cc
index e72c2711f7..908b996bc9 100644
--- a/tensorflow/compiler/plugin/executor/executor.cc
+++ b/tensorflow/compiler/plugin/executor/executor.cc
@@ -14,14 +14,12 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/plugin/executor/executor.h"
-#include "tensorflow/compiler/plugin/executor/platform_id.h"
-
-#include "tensorflow/compiler/xla/status_macros.h"
 
 #include <stdlib.h>
 #include <string.h>
 
-namespace se = ::perftools::gputools;
+#include "tensorflow/compiler/plugin/executor/platform_id.h"
+#include "tensorflow/compiler/xla/status_macros.h"
 
 namespace perftools {
 namespace gputools {
@@ -37,10 +35,7 @@ ExecutorExecutor::ExecutorExecutor(const PluginConfig &plugin_config)
 
 ExecutorExecutor::~ExecutorExecutor() {}
 
-void *ExecutorExecutor::Allocate(uint64 size) {
-  void *buf = new char[size];
-  return buf;
-}
+void *ExecutorExecutor::Allocate(uint64 size) { return new char[size]; }
 
 void *ExecutorExecutor::AllocateSubBuffer(DeviceMemoryBase *parent,
                                          uint64 offset_bytes,
@@ -126,8 +121,7 @@ DeviceDescription *ExecutorExecutor::PopulateDeviceDescription() const {
   builder.set_device_memory_size(static_cast<uint64>(4) * 1024 * 1024 * 1024);
   builder.set_clock_rate_ghz(static_cast<float>(CLOCKS_PER_SEC) / 1e9);
 
-  auto built = builder.Build();
-  return built.release();
+  return builder.Build().release();
 }
 
 }  // namespace executorplugin
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
index 1a861cd16b..3a935dcf96 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
@@ -357,7 +357,7 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}, /*slices=*/{1}));
+        /*limit_indices=*/{1024}, /*strides=*/{1}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
@@ -473,7 +473,7 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) {
         /*dimension=*/0));
     builder.AddInstruction(HloInstruction::CreateSlice(
         vec1024_shape_, concat, /*start_indices=*/{0},
-        /*limit_indices=*/{1024}, /*slices=*/{1}));
+        /*limit_indices=*/{1024}, /*strides=*/{1}));
     subcomputation = module->AddEmbeddedComputation(builder.Build());
   }
 
diff --git a/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
index a6b7a050c8..5cde229010 100644
--- a/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc
@@ -19,7 +19,6 @@
 #include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
 #include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
 #include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
index 6f553df6d7..42112c586a 100644
--- a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
@@ -18,7 +18,6 @@
 #include "tensorflow/contrib/boosted_trees/proto/tree_config.pb.h"
 #include "tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h"
 #include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
index ac8ec68d80..5b265a869d 100644
--- a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
@@ -19,7 +19,6 @@
 #include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
 #include "tensorflow/contrib/tensor_forest/proto/tensor_forest_params.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
index e3d8aa89d6..c80802e97b 100644
--- a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -22,7 +22,6 @@
 #include "tensorflow/contrib/tensor_forest/kernels/v4/params.h"
 #include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc
index 1c9ae90a26..aaab790d02 100644
--- a/tensorflow/core/ops/state_ops.cc
+++ b/tensorflow/core/ops/state_ops.cc
@@ -18,7 +18,6 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
 using shape_inference::ShapeHandle;
 
diff --git a/tensorflow/python/framework/test_ops.cc b/tensorflow/python/framework/test_ops.cc
index 275f9bec25..d22b5b3e25 100644
--- a/tensorflow/python/framework/test_ops.cc
+++ b/tensorflow/python/framework/test_ops.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/resource_handle.pb.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/public/version.h"
-- 
GitLab


From dbb6cb0554eeb0915623c16937638faade9bc939 Mon Sep 17 00:00:00 2001
From: Rohan Jain <rohanj@google.com>
Date: Sat, 1 Jul 2017 08:26:01 -0700
Subject: [PATCH 0657/1375] Adding options to pass in a ConfigProto to
 create_local_cluster. This allows for having multiple devices per worker,
 among other things.

PiperOrigin-RevId: 160736824
---
 tensorflow/python/framework/test_util.py          | 12 +++++++++---
 tensorflow/tools/api/golden/tensorflow.test.pbtxt |  2 +-
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 9b260d874b..71328a7f88 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -813,7 +813,8 @@ class TensorFlowTestCase(googletest.TestCase):
     # pylint: enable=invalid-name
 
 
-def create_local_cluster(num_workers, num_ps, protocol="grpc"):
+def create_local_cluster(num_workers, num_ps, protocol="grpc",
+                         worker_config=None, ps_config=None):
   """Create and start local servers and return the associated `Server` objects.
 
   Example:
@@ -839,6 +840,9 @@ def create_local_cluster(num_workers, num_ps, protocol="grpc"):
     num_ps: Number of PS servers to start.
     protocol: Communication protocol.  Allowed values are documented in
       the documentation of `tf.train.Server`.
+    worker_config: (optional) ConfigProto to initialize workers. Can be used
+      to instantiate multiple devices etc.
+    ps_config: (optional) ConfigProto to initialize PS servers.
 
   Returns:
     A tuple `(worker_servers, ps_servers)`.  `worker_servers` is a list
@@ -860,12 +864,14 @@ def create_local_cluster(num_workers, num_ps, protocol="grpc"):
 
   workers = [
       server_lib.Server(
-          cs, job_name="worker", protocol=protocol, task_index=ix, start=True)
+          cs, job_name="worker", protocol=protocol, task_index=ix,
+          config=worker_config, start=True)
       for ix in range(num_workers)
   ]
   ps_servers = [
       server_lib.Server(
-          cs, job_name="ps", protocol=protocol, task_index=ix, start=True)
+          cs, job_name="ps", protocol=protocol, task_index=ix,
+          config=ps_config, start=True)
       for ix in range(num_ps)
   ]
 
diff --git a/tensorflow/tools/api/golden/tensorflow.test.pbtxt b/tensorflow/tools/api/golden/tensorflow.test.pbtxt
index 2a88f26ed0..6237207821 100644
--- a/tensorflow/tools/api/golden/tensorflow.test.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.test.pbtxt
@@ -30,7 +30,7 @@ tf_module {
   }
   member_method {
     name: "create_local_cluster"
-    argspec: "args=[\'num_workers\', \'num_ps\', \'protocol\'], varargs=None, keywords=None, defaults=[\'grpc\'], "
+    argspec: "args=[\'num_workers\', \'num_ps\', \'protocol\', \'worker_config\', \'ps_config\'], varargs=None, keywords=None, defaults=[\'grpc\', \'None\', \'None\'], "
   }
   member_method {
     name: "get_temp_dir"
-- 
GitLab


From 5467b44c6144c5cc97a85f4ff0bdc1d8c0c5fb31 Mon Sep 17 00:00:00 2001
From: davidpham87 <davidpham87@users.noreply.github.com>
Date: Sat, 1 Jul 2017 19:12:14 +0200
Subject: [PATCH 0658/1375] fix_typo of map_structure doc (#11206)

---
 tensorflow/python/util/nest.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py
index 01b3dfacd1..e0740251c5 100644
--- a/tensorflow/python/util/nest.py
+++ b/tensorflow/python/util/nest.py
@@ -272,9 +272,9 @@ def map_structure(func, *structure, **check_types_dict):
   and the return value will contain the results in the same structure.
 
   Args:
-    func: A callable that acceps as many arguments are there are structures.
+    func: A callable that accepts as many arguments as there are structures.
     *structure: scalar, or tuple or list of constructed scalars and/or other
-      tuples/lists, or scalars.  Note: numpy arrays are considered scalars.
+      tuples/lists, or scalars.  Note: numpy arrays are considered  as scalars.
     **check_types_dict: only valid keyword argument is `check_types`. If set to
       `True` (default) the types of iterables within the  structures have to be
       same (e.g. `map_structure(func, [1], (1,))` raises a `TypeError`
-- 
GitLab


From 3db38f15877457c8c2a5c92d66afc0df29fe1f66 Mon Sep 17 00:00:00 2001
From: Chase Roberts <keeper6928@gmail.com>
Date: Sat, 1 Jul 2017 14:04:52 -0400
Subject: [PATCH 0659/1375] Added assertion error in reset_default_graph()
 (#11158)

* Added assertion error in reset_default_graph()

* Added missing argument
---
 tensorflow/python/framework/ops.py      | 9 +++++++++
 tensorflow/python/framework/ops_test.py | 6 ++++++
 2 files changed, 15 insertions(+)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index f52127f119..8a2ddf253d 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -3873,6 +3873,9 @@ class _DefaultStack(threading.local):
   def reset(self):
     self.stack = []
 
+  def is_cleared(self):
+    return self.stack == []
+
   @property
   def enforce_nesting(self):
     return self._enforce_nesting
@@ -4078,7 +4081,13 @@ def reset_default_graph():
   a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
   behavior. Using any previously created `tf.Operation` or `tf.Tensor` objects
   after calling this function will result in undefined behavior.
+  Raises:
+    AssertionError: If this function is called within a nested graph.
   """
+  if not _default_graph_stack.is_cleared():
+    raise AssertionError("Do not use tf.reset_default_graph() to clear "
+                         "nested graphs. If you need a cleared graph, "
+                         "exit the nesting and create a new graph.")
   _default_graph_stack.reset()
 
 
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 2d0a6cbf8a..01841e6306 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -1315,6 +1315,12 @@ class GraphTest(test_util.TensorFlowTestCase):
   def _AssertDefault(self, expected):
     self.assertIs(expected, ops.get_default_graph())
 
+  def testResetDefaultGraphNesting(self):
+    g0 = ops.Graph()
+    with self.assertRaises(AssertionError):
+      with g0.as_default():
+        ops.reset_default_graph()
+
   def testGraphContextManager(self):
     g0 = ops.Graph()
     with g0.as_default() as g1:
-- 
GitLab


From 15f827963ae2dd2fe1828cc46505b6e0963e2688 Mon Sep 17 00:00:00 2001
From: Wei Wu <lazyparser@gmail.com>
Date: Sun, 2 Jul 2017 02:05:16 +0800
Subject: [PATCH 0660/1375] doc: update the conv1 shape. (#11075)

Update the conv1 shape from [5, 5, 32, 32] to [5, 5, 1, 32]. (1 means gray image.)
---
 tensorflow/docs_src/programmers_guide/variables.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index a7b7db626a..0ccd2c4899 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -410,7 +410,7 @@ calling this function repeatedly would not work:
 ``` python
 input1 = tf.random_normal([1,10,10,32])
 input2 = tf.random_normal([1,20,20,32])
-x = conv_relu(input1, kernel_shape=[5, 5, 32, 32], bias_shape=[32])
+x = conv_relu(input1, kernel_shape=[5, 5, 1, 32], bias_shape=[32])
 x = conv_relu(x, kernel_shape=[5, 5, 32, 32], bias_shape = [32])  # This fails.
 ```
 
@@ -422,7 +422,7 @@ however, clarifies that we want to create new variables:
 def my_image_filter(input_images):
     with tf.variable_scope("conv1"):
         # Variables created here will be named "conv1/weights", "conv1/biases".
-        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
+        relu1 = conv_relu(input_images, [5, 5, 1, 32], [32])
     with tf.variable_scope("conv2"):
         # Variables created here will be named "conv2/weights", "conv2/biases".
         return conv_relu(relu1, [5, 5, 32, 32], [32])
-- 
GitLab


From a204938b931ccf9b8ea6bfed7c9e7c8fceac4798 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Sun, 2 Jul 2017 00:21:09 +0000
Subject: [PATCH 0661/1375] Fix broken link for reader_base.proto

This fix fixes broken links for reader_base.proto in version_compat.md and version_semantics.md:
`tensorflow/core/kernels/reader_base.proto -> tensorflow/core/framework/reader_base.proto`

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/docs_src/programmers_guide/version_compat.md    | 2 +-
 tensorflow/docs_src/programmers_guide/version_semantics.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/programmers_guide/version_compat.md
index 13c410fada..db6d596acf 100644
--- a/tensorflow/docs_src/programmers_guide/version_compat.md
+++ b/tensorflow/docs_src/programmers_guide/version_compat.md
@@ -54,7 +54,7 @@ patch versions.  The public APIs consist of
     * [`event`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/event.proto)
     * [`graph`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto)
     * [`op_def`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto)
-    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/reader_base.proto)
+    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/reader_base.proto)
     * [`summary`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto)
     * [`tensor`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto)
     * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto)
diff --git a/tensorflow/docs_src/programmers_guide/version_semantics.md b/tensorflow/docs_src/programmers_guide/version_semantics.md
index cee3b105de..14d7be64a8 100644
--- a/tensorflow/docs_src/programmers_guide/version_semantics.md
+++ b/tensorflow/docs_src/programmers_guide/version_semantics.md
@@ -43,7 +43,7 @@ patch versions.  The public APIs consist of
   [`event`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/event.proto),
   [`graph`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto),
   [`op_def`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto),
-  [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/reader_base.proto),
+  [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/reader_base.proto),
   [`summary`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto),
   [`tensor`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto),
   [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto),
-- 
GitLab


From 46952dd163954be40c4649894abcab587a63ecfe Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Sat, 1 Jul 2017 20:11:22 -0700
Subject: [PATCH 0662/1375] Internal-only changes

PiperOrigin-RevId: 160754438
---
 third_party/boringssl/add_boringssl_s390x.patch | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 third_party/boringssl/add_boringssl_s390x.patch

diff --git a/third_party/boringssl/add_boringssl_s390x.patch b/third_party/boringssl/add_boringssl_s390x.patch
new file mode 100644
index 0000000000..0b41a4aa96
--- /dev/null
+++ b/third_party/boringssl/add_boringssl_s390x.patch
@@ -0,0 +1,13 @@
+diff --git a/src/include/openssl/base.h b/src/include/openssl/base.h
+index 7a3adfb..88012ad 100644
+--- a/src/include/openssl/base.h
++++ b/src/include/openssl/base.h
+@@ -94,6 +94,8 @@ extern "C" {
+ #elif defined(__pnacl__)
+ #define OPENSSL_32_BIT
+ #define OPENSSL_PNACL
++#elif defined(__s390x__)
++#define OPENSSL_64_BIT
+ #else
+ #error "Unknown target CPU"
+ #endif
-- 
GitLab


From fad6cc605436451dad498e54c9718b4d3d096f89 Mon Sep 17 00:00:00 2001
From: John Lawson <john@codeplay.com>
Date: Sun, 2 Jul 2017 06:32:33 +0100
Subject: [PATCH 0663/1375] [OpenCL] Fixes device name comparison stage_op_test
  (#10760)

* [OpenCL] Fixes device name comparison stage_op_test

Changes the fixed '/device:GPU:0' expected device name string to the
name returned by 'test/gpu_device_name()', as the device name could be
'/device:SYCL:0' or '/device:GPU:0'.

* [OpenCL] Fixes testing of CUDA device name

The CUDA device name reported by test_util is in the form '/gpu:0',
while the device name used in the nodes looks like '/device:GPU:0'. When
running on CUDA we need to change the expected device name accordingly,
however this isn't a problem on SYCL.
---
 tensorflow/python/kernel_tests/stage_op_test.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py
index 1a6a869e3d..4073fc6b79 100644
--- a/tensorflow/python/kernel_tests/stage_op_test.py
+++ b/tensorflow/python/kernel_tests/stage_op_test.py
@@ -102,8 +102,8 @@ class StageTest(test.TestCase):
       with ops.device(gpu_dev):
         stager = data_flow_ops.StagingArea([dtypes.float32])
         y = stager.put([v])
-        self.assertEqual(y.device, '/device:GPU:0' if gpu_dev
-                                                   else gpu_dev)
+        expected_name = gpu_dev if 'gpu' not in gpu_dev else '/device:GPU:0'
+        self.assertEqual(y.device, expected_name)
       with ops.device('/cpu:0'):
         x = stager.get()
         self.assertEqual(x.device, '/device:CPU:0')
-- 
GitLab


From 744120fd8a0c3be0a90cca5a971459894c90b859 Mon Sep 17 00:00:00 2001
From: gunan <gunan@google.com>
Date: Sun, 2 Jul 2017 14:04:56 -0700
Subject: [PATCH 0664/1375] Disable flaky cwise_ops_test due to broken acosh on
 windows. (#11213)

---
 tensorflow/contrib/cmake/tf_tests.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 7151549276..8a49d70d75 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -169,7 +169,8 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     # requires scipy
     "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/preprocessing/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py"
-    # flaky test
+    # flaky tests
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/cwise_ops_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
   )
   if (WIN32)
-- 
GitLab


From aed7d9e9bae911d49fab4792182168e3602d2f3e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sun, 2 Jul 2017 14:45:47 -0700
Subject: [PATCH 0665/1375] Update deprecated ByteSize to ByteSizeLong

PiperOrigin-RevId: 160784174
---
 tensorflow/c/c_api.cc                                  |  2 +-
 .../cloud/kernels/bigquery_table_accessor_test.cc      |  6 +++---
 .../core/distributed_runtime/rpc/grpc_session.cc       | 10 ++++------
 .../core/distributed_runtime/rpc/grpc_tensor_coding.cc |  2 +-
 tensorflow/core/util/tensor_slice_writer_test.cc       |  4 ++--
 5 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index fb06389af2..f620e248c1 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -166,7 +166,7 @@ Status MessageToBuffer(const tensorflow::protobuf::Message& in,
   if (out->data != nullptr) {
     return InvalidArgument("Passing non-empty TF_Buffer is invalid.");
   }
-  const auto proto_size = in.ByteSize();
+  const auto proto_size = in.ByteSizeLong();
   void* buf = tensorflow::port::Malloc(proto_size);
   in.SerializeToArray(buf, proto_size);
   out->data = buf;
diff --git a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc
index 4851d485cc..b31b882fa1 100644
--- a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc
+++ b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc
@@ -28,7 +28,7 @@ constexpr char kTestProject[] = "test-project";
 constexpr char kTestDataset[] = "test-dataset";
 constexpr char kTestTable[] = "test-table";
 
-static bool HasSubstr(const string& base, const string& substr) {
+bool HasSubstr(const string& base, const string& substr) {
   bool ok = StringPiece(base).contains(substr);
   EXPECT_TRUE(ok) << base << ", expected substring " << substr;
   return ok;
@@ -42,8 +42,8 @@ class FakeAuthProvider : public AuthProvider {
   }
 };
 
-static string DeterministicSerialization(const tensorflow::Example& example) {
-  const int size = example.ByteSize();
+string DeterministicSerialization(const tensorflow::Example& example) {
+  const std::size_t size = example.ByteSizeLong();
   string result(size, '\0');
   ::tensorflow::protobuf::io::ArrayOutputStream array_stream(
       gtl::string_as_array(&result), size);
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_session.cc b/tensorflow/core/distributed_runtime/rpc/grpc_session.cc
index fbde7aa924..c97fb30c94 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_session.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_session.cc
@@ -31,16 +31,14 @@ limitations under the License.
 
 namespace tensorflow {
 
+const char* const kSchemePrefix = "grpc://";
+const size_t kSchemePrefixLength = strlen(kSchemePrefix);
+
 GrpcSession::GrpcSession(const SessionOptions& options)
     : options_(options), current_graph_version_(-1) {}
 
 GrpcSession::~GrpcSession() {}
 
-namespace {
-const char* kSchemePrefix = "grpc://";
-const size_t kSchemePrefixLength = strlen(kSchemePrefix);
-}  // namespace
-
 /* static */
 Status GrpcSession::Create(const SessionOptions& options,
                            std::unique_ptr<GrpcSession>* out_session) {
@@ -76,7 +74,7 @@ void ReEncodeConsts(GraphDef* gdef) {
         }
       }
       if (proto != nullptr && proto->tensor_content().empty() &&
-          proto->ByteSize() > 64) {
+          proto->ByteSizeLong() > 64) {
         // If the constant is encoded with repeated proto fields and
         // it is moderate large, we re-encode it in tensor_content as
         // a Cord. This is mildly helpful for reducing the peak memory
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
index 0dd6b5c89e..df226a92a5 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
@@ -37,7 +37,7 @@ static void unref_tensorbuffer(void* raw) {
 
 void EncodeRecvTensorResponseToByteBuffer(const RecvTensorResponse& proto,
                                           ::grpc::ByteBuffer* result) {
-  size_t len = proto.ByteSize();
+  size_t len = proto.ByteSizeLong();
   gpr_slice s = gpr_slice_malloc(len);
   proto.SerializeWithCachedSizesToArray(
       reinterpret_cast<uint8*>(GPR_SLICE_START_PTR(s)));
diff --git a/tensorflow/core/util/tensor_slice_writer_test.cc b/tensorflow/core/util/tensor_slice_writer_test.cc
index d935eba2e5..ff5bfd65ae 100644
--- a/tensorflow/core/util/tensor_slice_writer_test.cc
+++ b/tensorflow/core/util/tensor_slice_writer_test.cc
@@ -275,13 +275,13 @@ size_t BytesPerElementHelper(DT value) {
   std::fill(lo_data.begin(), lo_data.end(), value);
   TF_EXPECT_OK(
       TensorSliceWriter::SaveData(lo_data.data(), lo_data.size(), &ss));
-  int lo_byte_size = ss.ByteSize();
+  size_t lo_byte_size = ss.ByteSizeLong();
 
   std::array<DT, 1001> hi_data;
   std::fill(hi_data.begin(), hi_data.end(), value);
   TF_EXPECT_OK(
       TensorSliceWriter::SaveData(hi_data.data(), hi_data.size(), &ss));
-  int hi_byte_size = ss.ByteSize();
+  size_t hi_byte_size = ss.ByteSizeLong();
 
   return (hi_byte_size - lo_byte_size) / (hi_data.size() - lo_data.size());
 }
-- 
GitLab


From 49e4e55665c47885d2f90608169962c56852fe7f Mon Sep 17 00:00:00 2001
From: Pankaj Gupta <beckman16@gmail.com>
Date: Sun, 2 Jul 2017 19:07:21 -0400
Subject: [PATCH 0666/1375] Add 'wget' to be installed in docker image (#11215)

### System information
- **Have I written custom code (as opposed to using a stock example script provided in TensorFlow)**: Using scripts provided with tensorflow to download and build imagenet from scratch.
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Ubuntu 16.04
- **TensorFlow installed from (source or binary)**: binary
- **TensorFlow version (use command below)**: ('v1.2.0-5-g435cdfc', '1.2.1')
- **Python version**: 2.7.12
- **Bazel version (if compiling from source)**: NA
- **CUDA/cuDNN version**: 8.0
- **GPU model and memory**: Nvidia GRID M6-8Q(8GB)
- **Exact command to reproduce**: bazel-bin/inception/download_and_preprocess_imagenet "${DATA_DIR}"

### Describe the problem
I am trying to train inception v3 net for imagenet dataset using instructions at-
[](https://github.com/tensorflow/models/tree/master/slim)
[](https://github.com/tensorflow/models/blob/master/inception/README.md#getting-started)

After setting the download path using following command -
```
# location of where to place the ImageNet data
DATA_DIR=$HOME/imagenet-data
```
I ran the bazel command to build preprocessing script-
```
# build the preprocessing script.
cd tensorflow-models/inception
bazel build //inception:download_and_preprocess_imagenet
```
And then run it-
```
# run it
bazel-bin/inception/download_and_preprocess_imagenet "${DATA_DIR}"
```
Running above command fails saying it cannot find `wget` -

> root@docker_container:/data/workspace/models/inception# bazel-bin/inception/download_and_preprocess_imagenet "${DATA_DIR}"
> In order to download the imagenet data, you have to create an account with
> image-net.org. This will get you a username and an access key. You can set the
> IMAGENET_USERNAME and IMAGENET_ACCESS_KEY environment variables, or you can
> enter the credentials here.
> Username: <my username>
> Access key: <my password>
> Saving downloaded files to /data/imagenet-data/raw-data/
> Downloading bounding box annotations.
> bazel-bin/inception/download_and_preprocess_imagenet.runfiles/inception/inception/data/download_imagenet.sh: line 58: wget: command not found
> bazel-bin/inception/download_and_preprocess_imagenet.runfiles/inception/inception/data/download_imagenet.sh: line 64: wget: command not found

I have reported this as bug #11214 as well
---
 tensorflow/tools/docker/Dockerfile.devel-gpu | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index d0a038a9db..e10565a064 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -19,6 +19,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         zlib1g-dev \
         openjdk-8-jdk \
         openjdk-8-jre-headless \
+        wget \
         && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
-- 
GitLab


From eee76155b5f02911bfdf94792efbcee5395c8cc9 Mon Sep 17 00:00:00 2001
From: myPrecious <Moriadry@users.noreply.github.com>
Date: Mon, 3 Jul 2017 07:09:04 +0800
Subject: [PATCH 0667/1375] Updating Java doc formatting issues (#11225)

* javadoc issues formated

* And more misspellings
---
 .../java/src/main/java/org/tensorflow/Input.java      |  2 +-
 .../java/src/main/java/org/tensorflow/Operation.java  |  2 +-
 .../main/java/org/tensorflow/OperationBuilder.java    | 11 ++++++++++-
 .../java/src/main/java/org/tensorflow/Session.java    |  2 +-
 .../java/src/main/java/org/tensorflow/Shape.java      |  2 +-
 5 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
index dff3a45463..8e6685ee0f 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Input.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Input.java
@@ -42,7 +42,7 @@ public interface Input {
    * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
    * used to obtain a symbolic handle that represents the computation of the input.
    *
-   * @see {@link OperationBuilder#addInput(Output)}.
+   * @see OperationBuilder#addInput(Output)
    */
   Output asOutput();
 }
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index 200350f7ae..d584085282 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -139,7 +139,7 @@ public final class Operation {
    *
    * @param name identifier of the list of tensors (of which there may be many) inputs to this
    *     operation.
-   * @returns the size of the list of Tensors produced by this named input.
+   * @return the size of the list of Tensors produced by this named input.
    * @throws IllegalArgumentException if this operation has no input with the provided name.
    */
   public int inputListLength(final String name) {
diff --git a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
index 849dd36ffb..15077ce439 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
@@ -63,6 +63,16 @@ public final class OperationBuilder {
     }
   }
 
+
+  /**
+   * Returns the builder to create an operation.
+   *
+   * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
+   * used to add a input to a {@link OperationBuilder}.
+   *
+   * @param input {@link Output} supposed to be the input of the OperationBuilder.
+   * @return the OperationBuilder instance for chaining.
+   */
   public OperationBuilder addInput(Output input) {
     Graph.Reference r = graph.ref();
     try {
@@ -288,7 +298,6 @@ public final class OperationBuilder {
 
   // The names of all the setAttr* family functions below correspond to the C library types, not the
   // Java library types. Roughly, setAttrFoo calls the TensorFlow C library function: TF_SetAttrFoo.
-  //
   // TODO(ashankar):
   // - setAttrShapeList: Which would take in a long[][]
 
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Session.java b/tensorflow/java/src/main/java/org/tensorflow/Session.java
index f73cded4e3..83a300a560 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Session.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Session.java
@@ -113,7 +113,7 @@ public final class Session implements AutoCloseable {
    *
    * <p>A Runner runs the necessary graph fragments to execute every {@link Operation} required to
    * evaluate the {@link Tensor}s to fetch. The {@link #feed(String,int,Tensor)} call allows callers
-   * to override the value of {@link Tensor}s in the graph by substituing the provided {@link
+   * to override the value of {@link Tensor}s in the graph by substituting the provided {@link
    * Tensor}s for the outputs of the operations provided to {@link #feed(String,int,Tensor)}.
    */
   public final class Runner {
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Shape.java b/tensorflow/java/src/main/java/org/tensorflow/Shape.java
index 90d6cf7b85..9aa92be111 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Shape.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Shape.java
@@ -77,7 +77,7 @@ public final class Shape {
     return shape[i];
   }
 
-  /** Succint description of the shape meant for debugging. */
+  /** Succinct description of the shape meant for debugging. */
   @Override
   public String toString() {
     if (shape == null) {
-- 
GitLab


From afa03e2fd91a2d4a7dc960c5ec4ee448c808d026 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Mon, 3 Jul 2017 09:34:38 +0900
Subject: [PATCH 0668/1375] Fix typos (#11166)

---
 tensorflow/compiler/aot/BUILD                              | 2 +-
 tensorflow/contrib/makefile/create_ios_frameworks.sh       | 2 +-
 tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py | 2 +-
 tensorflow/core/util/memmapped_file_system_test.cc         | 2 +-
 tensorflow/python/framework/tensor_shape.py                | 2 +-
 tensorflow/python/kernel_tests/pooling_ops_test.py         | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 12179b7637..42d6de34c0 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -156,7 +156,7 @@ tf_library(
 )
 
 # A test of tf_library that includes a graph with an unknown op, but where
-# the compilation works because the the unknown op is not needed for the fetches.
+# the compilation works because the unknown op is not needed for the fetches.
 tf_library(
     name = "test_graph_tfunknownop",
     testonly = 1,
diff --git a/tensorflow/contrib/makefile/create_ios_frameworks.sh b/tensorflow/contrib/makefile/create_ios_frameworks.sh
index 2ad095b397..2bbde6aa88 100644
--- a/tensorflow/contrib/makefile/create_ios_frameworks.sh
+++ b/tensorflow/contrib/makefile/create_ios_frameworks.sh
@@ -79,7 +79,7 @@ cd $SCRIPT_DIR/gen/proto
 tar cf $FW_DIR_TFCORE_HDRS/tmp.tar tensorflow
 cd $FW_DIR_TFCORE_HDRS
 tar xf tmp.tar
-# Dont include the auto downloaded/generated to build this library
+# Don't include the auto downloaded/generated to build this library
 rm -rf tensorflow/contrib/makefile
 rm -f tmp.tar
 
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index 0b1692ef2e..cd65a54b83 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -283,7 +283,7 @@ def _luong_score(query, keys, scale):
     raise ValueError(
         "Incompatible or unknown inner dimensions between query and keys.  "
         "Query (%s) has units: %s.  Keys (%s) have units: %s.  "
-        "Perhaps you need to set num_units to the the keys' dimension (%s)?"
+        "Perhaps you need to set num_units to the keys' dimension (%s)?"
         % (query, depth, keys, key_units, key_units))
   dtype = query.dtype
 
diff --git a/tensorflow/core/util/memmapped_file_system_test.cc b/tensorflow/core/util/memmapped_file_system_test.cc
index 24ce5ebafc..a5f24b08b3 100644
--- a/tensorflow/core/util/memmapped_file_system_test.cc
+++ b/tensorflow/core/util/memmapped_file_system_test.cc
@@ -110,7 +110,7 @@ TEST(MemmappedFileSystemTest, SimpleTest) {
             memmapped_env.FileExists("bla-bla-bla").code());
 }
 
-TEST(MemmappedFileSystemTest, NotInitalized) {
+TEST(MemmappedFileSystemTest, NotInitialized) {
   MemmappedEnv memmapped_env(Env::Default());
   std::unique_ptr<ReadOnlyMemoryRegion> memory_region;
   EXPECT_EQ(
diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py
index 3aedbfef0d..4725dcef84 100644
--- a/tensorflow/python/framework/tensor_shape.py
+++ b/tensorflow/python/framework/tensor_shape.py
@@ -365,7 +365,7 @@ class Dimension(object):
 def as_dimension(value):
   """Converts the given value to a Dimension.
 
-  A Dimenson input will be returned unmodified.
+  A Dimension input will be returned unmodified.
   An input of `None` will be converted to an unknown Dimension.
   An integer input will be converted to a Dimension with that value.
 
diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py
index 1b6c8bef98..f5fb7e4e03 100644
--- a/tensorflow/python/kernel_tests/pooling_ops_test.py
+++ b/tensorflow/python/kernel_tests/pooling_ops_test.py
@@ -521,7 +521,7 @@ class PoolingTest(test.TestCase):
               padding="SAME").eval()
 
   # The following are tests that verify that the CPU and GPU implementations
-  # produce the same resuts.
+  # produce the same results.
   def _CompareMaxPoolingFwd(self, input_shape, ksize, strides, padding):
     for dtype in np.float64, np.float32, np.float16:
       tensor_input = np.random.rand(*input_shape).astype(dtype)
-- 
GitLab


From 88633a8ebbd0fada4ae73f11410939a3f0d818d8 Mon Sep 17 00:00:00 2001
From: Andrei Costinescu <AndreiCostinescu@users.noreply.github.com>
Date: Mon, 3 Jul 2017 16:36:53 +0200
Subject: [PATCH 0669/1375] Update gradients_impl.py (#11230)

* Update gradients_impl.py

Corrected documentation of hessians() function.

* Update gradients_impl.py
---
 tensorflow/python/ops/gradients_impl.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index 52ce451238..e073dbc640 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -917,7 +917,7 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False,
     aggregation_method: See `gradients()` documentation for details.
 
   Returns:
-    A list of Hessian matrices of `sum(y)` for each `x` in `xs`.
+    A list of Hessian matrices of `sum(ys)` for each `x` in `xs`.
 
   Raises:
     LookupError: if one of the operations between `xs` and `ys` does not
-- 
GitLab


From 9b65e81a948e4fb79161338427d764b2463ecb17 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Mon, 3 Jul 2017 11:10:14 -0700
Subject: [PATCH 0670/1375] Disable some boosted_trees python tests on Mac

PiperOrigin-RevId: 160839622
---
 tensorflow/contrib/boosted_trees/BUILD | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 90348d0b22..4f21744836 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -72,6 +72,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/ensemble_optimizer_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63258195
     deps = [
         ":ensemble_optimizer_ops_py",
         ":model_ops_py",
@@ -90,6 +91,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/model_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63258195
     deps = [
         ":ensemble_optimizer_ops_py",
         ":model_ops_py",
@@ -110,6 +112,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/prediction_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63258195
     deps = [
         ":model_ops_py",
         ":prediction_ops_py",
@@ -128,6 +131,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/quantile_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63258195
     deps = [
         ":quantile_ops_py",
         "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
@@ -159,6 +163,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/stats_accumulator_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63258195
     deps = [
         ":stats_accumulator_ops_py",
         "//tensorflow/python:framework_test_lib",
@@ -174,6 +179,7 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/training_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["nomac"],  # b/63258195
     deps = [
         ":model_ops_py",
         ":training_ops_py",
-- 
GitLab


From 5142213e88b8c04e02c5f940e2840b959ff9b142 Mon Sep 17 00:00:00 2001
From: Guenther Schmuelling <guschmue@microsoft.com>
Date: Mon, 3 Jul 2017 14:07:26 -0700
Subject: [PATCH 0671/1375] install cub under external/cub_archive to fix
 windows gpu build (#11246)

---
 tensorflow/contrib/cmake/external/cub.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/external/cub.cmake b/tensorflow/contrib/cmake/external/cub.cmake
index 54329dbd70..1119f0ccba 100644
--- a/tensorflow/contrib/cmake/external/cub.cmake
+++ b/tensorflow/contrib/cmake/external/cub.cmake
@@ -18,6 +18,7 @@ set(cub_URL https://github.com/NVlabs/cub/archive/1.6.4.zip)
 set(cub_HASH SHA256=966d0c4f41e2bdc81aebf9ccfbf0baffaac5a74f00b826b06f4dee79b2bb8cee)
 set(cub_BUILD ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
 set(cub_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
+set(cub_ARCHIVE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/cub_archive)
 
 ExternalProject_Add(cub
     PREFIX cub
@@ -26,4 +27,4 @@ ExternalProject_Add(cub
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
     PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/cub/CMakeLists.txt ${cub_BUILD}
-    INSTALL_COMMAND "")
+    INSTALL_COMMAND  ${CMAKE_COMMAND} -E copy_directory  ${cub_INCLUDE_DIR}/cub ${cub_ARCHIVE_DIR}/cub)
-- 
GitLab


From 31b439dca7b8fcab600daac8923963967734dced Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Tue, 4 Jul 2017 04:23:48 +0100
Subject: [PATCH 0672/1375] [OpenCL] Fixes transpose operation (#11176)

---
 .../core/kernels/transpose_functor_cpu.cc     | 30 ++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/transpose_functor_cpu.cc b/tensorflow/core/kernels/transpose_functor_cpu.cc
index 3a975a52c1..a004cb2293 100644
--- a/tensorflow/core/kernels/transpose_functor_cpu.cc
+++ b/tensorflow/core/kernels/transpose_functor_cpu.cc
@@ -167,7 +167,35 @@ template <typename T>
 struct Transpose<SYCLDevice, T> {
   static void run(const SYCLDevice& d, const Tensor& in,
                   const gtl::ArraySlice<int32> perm, Tensor* out) {
-    // Should add a specialized implementation for SYCLDevice here.
+    switch (in.dims()) {
+      case 1:
+        internal::TransposeUsingEigen<SYCLDevice, T, 1>(d, in, perm, out);
+        break;
+      case 2:
+        internal::TransposeUsingEigen<SYCLDevice, T, 2>(d, in, perm, out);
+        break;
+      case 3:
+        internal::TransposeUsingEigen<SYCLDevice, T, 3>(d, in, perm, out);
+        break;
+      case 4:
+        internal::TransposeUsingEigen<SYCLDevice, T, 4>(d, in, perm, out);
+        break;
+      case 5:
+        internal::TransposeUsingEigen<SYCLDevice, T, 5>(d, in, perm, out);
+        break;
+      case 6:
+        internal::TransposeUsingEigen<SYCLDevice, T, 6>(d, in, perm, out);
+        break;
+      case 7:
+        internal::TransposeUsingEigen<SYCLDevice, T, 7>(d, in, perm, out);
+        break;
+      case 8:
+        internal::TransposeUsingEigen<SYCLDevice, T, 8>(d, in, perm, out);
+        break;
+      default:
+        LOG(FATAL) << "Unsupported TransposeUsingEigen for: " << in.dims();
+        break;
+    }
   }
 };
 
-- 
GitLab


From 1e48740b8793feda7cc67db3ba26299ba868b867 Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Tue, 4 Jul 2017 04:31:08 +0100
Subject: [PATCH 0673/1375] [OpenCL] Fixes warning caused by half type (#11177)

* [OpenCL] Fixes warning caused by half type

  - half requires opencl extension that might not be avaiable on all platforms
  - extends cast operation to cover int8, int16, uint8, uint16

* Fixed typo

* Fixes formatting
---
 tensorflow/core/kernels/cast_op_impl.h        | 40 ++++++++++++-------
 tensorflow/core/kernels/cast_op_impl_bool.cc  |  5 +--
 .../core/kernels/cast_op_impl_double.cc       |  5 +--
 tensorflow/core/kernels/cast_op_impl_float.cc |  5 +--
 tensorflow/core/kernels/cast_op_impl_int16.cc |  9 +++++
 tensorflow/core/kernels/cast_op_impl_int32.cc |  4 +-
 tensorflow/core/kernels/cast_op_impl_int64.cc |  4 +-
 tensorflow/core/kernels/cast_op_impl_int8.cc  |  9 +++++
 .../core/kernels/cast_op_impl_uint16.cc       |  9 +++++
 tensorflow/core/kernels/cast_op_impl_uint8.cc |  9 +++++
 10 files changed, 71 insertions(+), 28 deletions(-)

diff --git a/tensorflow/core/kernels/cast_op_impl.h b/tensorflow/core/kernels/cast_op_impl.h
index 1ee0796ac1..6309e4a4dc 100644
--- a/tensorflow/core/kernels/cast_op_impl.h
+++ b/tensorflow/core/kernels/cast_op_impl.h
@@ -45,20 +45,23 @@ struct CastFunctor<Eigen::SyclDevice, O, I> {
 
 }  // namespace functor
 
-#define CURRY_TYPES3(FN, arg0, arg1)   \
-  FN(arg0, arg1, bool);                \
-  FN(arg0, arg1, uint8);               \
-  FN(arg0, arg1, int8);                \
-  FN(arg0, arg1, uint16);              \
-  FN(arg0, arg1, int16);               \
-  FN(arg0, arg1, int32);               \
-  FN(arg0, arg1, int64);               \
-  FN(arg0, arg1, Eigen::half);         \
-  FN(arg0, arg1, float);               \
-  FN(arg0, arg1, double);              \
-  FN(arg0, arg1, std::complex<float>); \
+#define CURRY_TYPES3_NO_HALF(FN, arg0, arg1)   \
+  FN(arg0, arg1, bool);                        \
+  FN(arg0, arg1, uint8);                       \
+  FN(arg0, arg1, int8);                        \
+  FN(arg0, arg1, uint16);                      \
+  FN(arg0, arg1, int16);                       \
+  FN(arg0, arg1, int32);                       \
+  FN(arg0, arg1, int64);                       \
+  FN(arg0, arg1, float);                       \
+  FN(arg0, arg1, double);                      \
+  FN(arg0, arg1, std::complex<float>);         \
   FN(arg0, arg1, std::complex<double>)
 
+#define CURRY_TYPES3(FN, arg0, arg1)           \
+  CURRY_TYPES3_NO_HALF(FN, arg0, arg1)         \
+  FN(arg0, arg1, Eigen::half);
+
 #define CAST_CASE(DEVICE, IN, OUT)                                         \
   if (DataTypeToEnum<OUT>::value == dst_dtype) {                           \
     return [](OpKernelContext* ctx, const Tensor& inp, Tensor* out) {      \
@@ -154,6 +157,15 @@ GetGpuCastFromBfloat(DataType dst_dtype);
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromBool(DataType dst_dtype);
 
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint8(DataType dst_dtype);
+
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint16(DataType dst_dtype);
+
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromInt16(DataType dst_dtype);
+
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromInt32(DataType dst_dtype);
 
@@ -165,10 +177,8 @@ GetSyclCastFromFloat(DataType dst_dtype);
 
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromDouble(DataType dst_dtype);
-
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_CAST_OP_IMPL_H_
-
diff --git a/tensorflow/core/kernels/cast_op_impl_bool.cc b/tensorflow/core/kernels/cast_op_impl_bool.cc
index a13f163009..5cd63f2458 100644
--- a/tensorflow/core/kernels/cast_op_impl_bool.cc
+++ b/tensorflow/core/kernels/cast_op_impl_bool.cc
@@ -38,10 +38,9 @@ GetGpuCastFromBool(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromBool(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, bool);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, bool);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
-
diff --git a/tensorflow/core/kernels/cast_op_impl_double.cc b/tensorflow/core/kernels/cast_op_impl_double.cc
index fdc8d51158..1203f066a2 100644
--- a/tensorflow/core/kernels/cast_op_impl_double.cc
+++ b/tensorflow/core/kernels/cast_op_impl_double.cc
@@ -38,10 +38,9 @@ GetGpuCastFromDouble(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromDouble(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, double);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, double);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYC
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
-
diff --git a/tensorflow/core/kernels/cast_op_impl_float.cc b/tensorflow/core/kernels/cast_op_impl_float.cc
index 1241dcd8f2..2ff9af21f2 100644
--- a/tensorflow/core/kernels/cast_op_impl_float.cc
+++ b/tensorflow/core/kernels/cast_op_impl_float.cc
@@ -53,10 +53,9 @@ GetGpuCastFromFloat(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromFloat(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, float);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, float);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
-
diff --git a/tensorflow/core/kernels/cast_op_impl_int16.cc b/tensorflow/core/kernels/cast_op_impl_int16.cc
index 3c2d6185e3..f12d852e95 100644
--- a/tensorflow/core/kernels/cast_op_impl_int16.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int16.cc
@@ -34,4 +34,13 @@ GetGpuCastFromInt16(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromInt16(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int16);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_int32.cc b/tensorflow/core/kernels/cast_op_impl_int32.cc
index 69ed760455..2a4b27a12d 100644
--- a/tensorflow/core/kernels/cast_op_impl_int32.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int32.cc
@@ -38,9 +38,9 @@ GetGpuCastFromInt32(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromInt32(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, int32);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int32);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_int64.cc b/tensorflow/core/kernels/cast_op_impl_int64.cc
index 7a8363ca39..065defabbb 100644
--- a/tensorflow/core/kernels/cast_op_impl_int64.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int64.cc
@@ -38,9 +38,9 @@ GetGpuCastFromInt64(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromInt64(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, int64);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int64);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_int8.cc b/tensorflow/core/kernels/cast_op_impl_int8.cc
index 62971fa95c..8d678c4733 100644
--- a/tensorflow/core/kernels/cast_op_impl_int8.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int8.cc
@@ -34,4 +34,13 @@ GetGpuCastFromInt8(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromInt8(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int8);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_uint16.cc b/tensorflow/core/kernels/cast_op_impl_uint16.cc
index 529d9758f0..c917aaf7bd 100644
--- a/tensorflow/core/kernels/cast_op_impl_uint16.cc
+++ b/tensorflow/core/kernels/cast_op_impl_uint16.cc
@@ -34,4 +34,13 @@ GetGpuCastFromUint16(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint16(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint16);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_uint8.cc b/tensorflow/core/kernels/cast_op_impl_uint8.cc
index 1a5025b054..377c8ca953 100644
--- a/tensorflow/core/kernels/cast_op_impl_uint8.cc
+++ b/tensorflow/core/kernels/cast_op_impl_uint8.cc
@@ -34,4 +34,13 @@ GetGpuCastFromUint8(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint8(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint8);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
-- 
GitLab


From e7ef23f89afb54c87d641eaa4b0967769ff085a5 Mon Sep 17 00:00:00 2001
From: Melody Guan <melodyguan@google.com>
Date: Mon, 3 Jul 2017 20:31:22 -0700
Subject: [PATCH 0674/1375] Internal change

PiperOrigin-RevId: 160858706
---
 tensorflow/python/ops/nn_ops.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index e4eaeff67a..44f98d64cd 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -544,7 +544,9 @@ def convolution(input, filter,  # pylint: disable=redefined-builtin
                          x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1],
                          q]
   ```
-  where `padded_input` is obtained by zero padding the input using an effective
+  where b is the index into the batch, k is the output channel number, q is the
+  input channel number, and z is the N-D spatial offset within the filter. Here,
+  `padded_input` is obtained by zero padding the input using an effective
   spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and
   output striding `strides` as described in the
   @{tf.nn.convolution$comment here}.
-- 
GitLab


From fc87d9110c44896427ef8c0bc08a96ef59f0e995 Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Tue, 4 Jul 2017 07:05:02 +0100
Subject: [PATCH 0675/1375] Add -Wno-c++11-narrowing to ComputeCpp device
 compiler flags to avoid build errors on 32-bit targets. (#109) (#11244)

---
 third_party/sycl/crosstool/computecpp.tpl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/third_party/sycl/crosstool/computecpp.tpl b/third_party/sycl/crosstool/computecpp.tpl
index 8b0a7a7d4a..c699eabb6f 100755
--- a/third_party/sycl/crosstool/computecpp.tpl
+++ b/third_party/sycl/crosstool/computecpp.tpl
@@ -73,7 +73,7 @@ def main():
   bc_out = filename + '.sycl'
 
   # strip asan for the device
-  computecpp_device_compiler_flags = ['-sycl-compress-name', '-Wno-unused-variable',
+  computecpp_device_compiler_flags = ['-sycl-compress-name', '-Wno-unused-variable', '-Wno-c++11-narrowing',
                                       '-I', COMPUTECPP_INCLUDE, '-isystem', COMPUTECPP_INCLUDE,
                                       '-std=c++11', '-sycl', '-emit-llvm', '-no-serial-memop',
                                       '-Xclang', '-cl-denorms-are-zero', '-Xclang', '-cl-fp32-correctly-rounded-divide-sqrt']
-- 
GitLab


From 03f44a7baa049c1298b7b48cdda596fb0b9c471a Mon Sep 17 00:00:00 2001
From: James Qin <jamesqin@google.com>
Date: Tue, 4 Jul 2017 00:07:01 -0700
Subject: [PATCH 0676/1375] (unidirectional)Cudnn compatible GRU Cell

PiperOrigin-RevId: 160867044
---
 .../python/kernel_tests/cudnn_rnn_ops_test.py | 163 +++----
 .../cudnn_rnn/python/ops/cudnn_rnn_ops.py     | 419 ++++++++++++++----
 2 files changed, 422 insertions(+), 160 deletions(-)

diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
index f41fdc382b..5a48b535ff 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import itertools
 import os
 import unittest
 import numpy as np
@@ -33,7 +34,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import rnn
+from tensorflow.python.ops import rnn as rnn_lib
 from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
@@ -44,6 +45,31 @@ from tensorflow.python.training import gradient_descent
 from tensorflow.python.training import saver as saver_lib
 
 
+def _create_cudnn_compatible_canonical_rnn(cudnn_model,
+                                           inputs,
+                                           use_block_cell,
+                                           scope="rnn"):
+  model = cudnn_model.rnn_mode
+  if model not in (cudnn_rnn_ops.CUDNN_LSTM, cudnn_rnn_ops.CUDNN_GRU):
+    raise ValueError("%s is not supported!" % model)
+  if model == cudnn_rnn_ops.CUDNN_GRU and use_block_cell:
+    raise ValueError("gru is not supported when using block cell!")
+
+  num_units = cudnn_model.num_units
+  num_layers = cudnn_model.num_layers
+  # To reuse cuDNN-trained models, must use cudnn compatible rnn cells.
+  if use_block_cell:
+    single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMBlockCell(num_units)
+  else:
+    if model == cudnn_rnn_ops.CUDNN_LSTM:
+      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMCell(num_units)
+    else:
+      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleGRUCell(num_units)
+  cell = rnn_cell_impl.MultiRNNCell([single_cell() for _ in range(num_layers)])
+  return rnn_lib.dynamic_rnn(
+      cell, inputs, dtype=dtypes.float32, time_major=True, scope=scope)
+
+
 class CudnnRNNTest(TensorFlowTestCase):
 
   def _CreateModel(self,
@@ -53,16 +79,16 @@ class CudnnRNNTest(TensorFlowTestCase):
                    input_size,
                    input_mode="linear_input",
                    dropout=0.):
-    if rnn_mode == "lstm":
+    if rnn_mode == cudnn_rnn_ops.CUDNN_LSTM:
       model = cudnn_rnn_ops.CudnnLSTM(
           num_layers, num_units, input_size, dropout=dropout)
-    elif rnn_mode == "gru":
+    elif rnn_mode == cudnn_rnn_ops.CUDNN_GRU:
       model = cudnn_rnn_ops.CudnnGRU(
           num_layers, num_units, input_size, dropout=dropout)
-    elif rnn_mode == "rnn_tanh":
+    elif rnn_mode == cudnn_rnn_ops.CUDNN_RNN_TANH:
       model = cudnn_rnn_ops.CudnnRNNTanh(
           num_layers, num_units, input_size, dropout=dropout)
-    elif rnn_mode == "rnn_relu":
+    elif rnn_mode == cudnn_rnn_ops.CUDNN_RNN_RELU:
       model = cudnn_rnn_ops.CudnnRNNRelu(
           num_layers, num_units, input_size, dropout=dropout)
     else:
@@ -102,30 +128,6 @@ class CudnnRNNTest(TensorFlowTestCase):
       params_v_restored = sess.run(params)
       self.assertAllEqual(params_v, params_v_restored)
 
-  def _create_equivalent_canonical_rnn(self,
-                                       cudnn_model,
-                                       inputs,
-                                       use_block_cell,
-                                       scope="rnn"):
-    if cudnn_model.rnn_mode is not "lstm":
-      raise ValueError("%s is not supported!" % cudnn_model.rnn_mode)
-
-    num_units = cudnn_model.num_units
-    num_layers = cudnn_model.num_layers
-
-    # To reuse cuDNN-trained models, must use CudnnCompatibleLSTM...Cells.
-    if use_block_cell:
-      # pylint: disable=g-long-lambda
-      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMBlockCell(
-          num_units)
-      # pylint: enable=g-long-lambda
-    else:
-      single_cell = lambda: cudnn_rnn_ops.CudnnCompatibleLSTMCell(num_units)
-    cell = rnn_cell_impl.MultiRNNCell(
-        [single_cell() for _ in range(num_layers)])
-    return rnn.dynamic_rnn(
-        cell, inputs, dtype=dtypes.float32, time_major=True, scope=scope)
-
   def _build_forward_cudnn_model(self,
                                  rnn_mode,
                                  num_layers,
@@ -140,7 +142,7 @@ class CudnnRNNTest(TensorFlowTestCase):
     # Set zero init input states
     input_h = constant_op.constant(
         np.zeros([num_layers, batch_size, num_units]), dtype=dtypes.float32)
-    has_input_c = (rnn_mode == "lstm")
+    has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
     if has_input_c:
       input_c = constant_op.constant(
           np.zeros([num_layers, batch_size, num_units]), dtype=dtypes.float32)
@@ -167,7 +169,7 @@ class CudnnRNNTest(TensorFlowTestCase):
 
   @unittest.skipUnless(test.is_built_with_cuda(),
                        "Test only applicable when running on GPUs")
-  def testCudnnCompatibleLSTMCells(self):
+  def testCudnnCompatibleRnnCells(self):
     configs = [
         {
             "num_layers": 1,
@@ -175,7 +177,6 @@ class CudnnRNNTest(TensorFlowTestCase):
             "num_units": 4,
             "input_size": 5,
             "batch_size": 6,
-            "rnn_mode": "lstm"
         },
         {
             "num_layers": 2,
@@ -183,7 +184,6 @@ class CudnnRNNTest(TensorFlowTestCase):
             "num_units": 4,
             "input_size": 8,
             "batch_size": 16,
-            "rnn_mode": "lstm"
         },
         {
             "num_layers": 2,
@@ -191,7 +191,6 @@ class CudnnRNNTest(TensorFlowTestCase):
             "num_units": 4,
             "input_size": 5,
             "batch_size": 6,
-            "rnn_mode": "lstm"
         },
         {
             "num_layers": 1,
@@ -199,30 +198,24 @@ class CudnnRNNTest(TensorFlowTestCase):
             "num_units": 2,
             "input_size": 4,
             "batch_size": 1,
-            "rnn_mode": "lstm"
         },
     ]
-    for cfg in configs:
-      self._testCudnnCompatibleLSTMCells(
-          cfg["num_layers"],
-          cfg["seq_length"],
-          cfg["num_units"],
-          cfg["input_size"],
-          cfg["batch_size"],
-          cfg["rnn_mode"],
-          use_block_cell=False)
-      self._testCudnnCompatibleLSTMCells(
-          cfg["num_layers"],
-          cfg["seq_length"],
-          cfg["num_units"],
-          cfg["input_size"],
-          cfg["batch_size"],
-          cfg["rnn_mode"],
-          use_block_cell=True)
-
-  def _testCudnnCompatibleLSTMCells(
-      self, num_layers, seq_length, num_units, input_size, batch_size, rnn_mode,
-      use_block_cell):
+    for rnn, cfg, use_block_cell in itertools.product(
+        (cudnn_rnn_ops.CUDNN_LSTM,), configs, (True, False,)):
+      self._testCudnnCompatibleRnnCells(cfg["num_layers"], cfg["seq_length"],
+                                        cfg["num_units"], cfg["input_size"],
+                                        cfg["batch_size"], rnn, use_block_cell)
+    # TODO(jamesqin): Add CudnnCompatibleGRUBlockCell.
+    for rnn, cfg, use_block_cell in itertools.product(
+        (cudnn_rnn_ops.CUDNN_GRU,), configs, (False,)):
+      self._testCudnnCompatibleRnnCells(cfg["num_layers"], cfg["seq_length"],
+                                        cfg["num_units"], cfg["input_size"],
+                                        cfg["batch_size"], rnn, use_block_cell)
+
+  def _testCudnnCompatibleRnnCells(self, num_layers, seq_length, num_units,
+                                   input_size, batch_size, rnn_mode,
+                                   use_block_cell):
+    has_state_c = rnn_mode == cudnn_rnn_ops.CUDNN_LSTM
     np.random.seed(0)
     # Train graph
     with ops.Graph().as_default():
@@ -280,15 +273,15 @@ class CudnnRNNTest(TensorFlowTestCase):
         self.assertAllEqual(cudnn_params_v, restored_cudnn_params_v)
 
         # Cudnn inference
-        (cudnn_output, cudnn_output_h, cudnn_output_c) = sess.run(
+        cudnn_output = sess.run(
             cudnn_output_tuple, feed_dict={cudnn_inputs: inference_input})
 
-    # LSTMBlockCell inference graph
+    # Canonical RNN inference graph
     with ops.Graph().as_default():
       random_seed.set_random_seed(299)
       cell_inputs = array_ops.placeholder(
           dtypes.float32, shape=[seq_length, batch_size, input_size])
-      (output, states) = self._create_equivalent_canonical_rnn(
+      (output, states) = _create_cudnn_compatible_canonical_rnn(
           cudnn_model, cell_inputs, use_block_cell)
       saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
 
@@ -301,15 +294,19 @@ class CudnnRNNTest(TensorFlowTestCase):
             [output, states], feed_dict={cell_inputs: inference_input})
 
         # output across timestamps are packed into one tensor.
-        self.assertAllClose(cudnn_output, output_v, atol=1e-6, rtol=1e-6)
+        self.assertAllClose(cudnn_output[0], output_v, atol=1e-6, rtol=1e-6)
 
         for i in range(num_layers):
-          # output_h
-          self.assertAllClose(
-              cudnn_output_h[i, :], states_v[i].h, atol=1e-6, rtol=1e-6)
-          # output_c
-          self.assertAllClose(
-              cudnn_output_c[i, :], states_v[i].c, atol=1e-6, rtol=1e-6)
+          if has_state_c:
+            # output_h
+            self.assertAllClose(
+                cudnn_output[1][i, :], states_v[i].h, atol=1e-6, rtol=1e-6)
+            # output_c
+            self.assertAllClose(
+                cudnn_output[2][i, :], states_v[i].c, atol=1e-6, rtol=1e-6)
+          else:
+            self.assertAllClose(
+                cudnn_output[1][i, :], states_v[i], atol=1e-6, rtol=1e-6)
 
   def _testSaveRestoreOutput(self, rnn_mode):
     num_layers = 2
@@ -326,7 +323,7 @@ class CudnnRNNTest(TensorFlowTestCase):
     save_path = os.path.join(self.get_temp_dir(), "save-restore-output-test")
     saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
 
-    has_input_c = (rnn_mode == "lstm")
+    has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
     input_data = array_ops.ones([seq_length, batch_size, input_size])
     input_h = array_ops.ones([num_layers * dir_count, batch_size, num_units])
     if has_input_c:
@@ -359,7 +356,10 @@ class CudnnRNNTest(TensorFlowTestCase):
   @unittest.skipUnless(test.is_built_with_cuda(),
                        "Test only applicable when running on GPUs")
   def testSaveRestore(self):
-    rnn_modes = ["lstm", "gru", "rnn_tanh", "rnn_relu"]
+    rnn_modes = [
+        cudnn_rnn_ops.CUDNN_LSTM, cudnn_rnn_ops.CUDNN_GRU,
+        cudnn_rnn_ops.CUDNN_RNN_TANH, cudnn_rnn_ops.CUDNN_RNN_RELU
+    ]
     for rnn_mode in rnn_modes:
       self._testSaveRestoreVariable(rnn_mode)
       self._testSaveRestoreOutput(rnn_mode)
@@ -369,8 +369,8 @@ class CudnnRNNTest(TensorFlowTestCase):
                         num_units,
                         input_size,
                         input_mode="auto_select",
-                        direction="unidirection"):
-    if direction != "unidirection":
+                        direction=cudnn_rnn_ops.CUDNN_RNN_UNIDIRECTION):
+    if direction != cudnn_rnn_ops.CUDNN_RNN_UNIDIRECTION:
       # TODO(zhengxq): support bidirection in parameter size estimate.
       raise ValueError("Only unidirection in parameter size estimate")
     first_layer_weights = 4 * num_units * (num_units + input_size)
@@ -380,7 +380,8 @@ class CudnnRNNTest(TensorFlowTestCase):
 
   def _testOneLSTMParamsSize(self, num_layers, num_units, input_size):
     min_params_size = self._MinLSTMParamSize(num_layers, num_units, input_size)
-    model = self._CreateModel("lstm", num_layers, num_units, input_size)
+    model = self._CreateModel(cudnn_rnn_ops.CUDNN_LSTM, num_layers, num_units,
+                              input_size)
     params_size = model.params_size()
     with self.test_session(use_gpu=True) as sess:
       params_size_v = sess.run(params_size)
@@ -412,7 +413,7 @@ class CudnnRNNTest(TensorFlowTestCase):
         input_size,
         input_mode="auto_select",
         dropout=dropout)
-    has_input_c = (rnn_mode == "lstm")
+    has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
     params_size_t = model.params_size()
     input_data = array_ops.ones([seq_length, batch_size, input_size])
     input_h = array_ops.ones([num_layers * dir_count, batch_size, num_units])
@@ -454,7 +455,7 @@ class CudnnRNNTest(TensorFlowTestCase):
     # demonstrative of the dropout-invariant nature of CudnnRnn.)
     test_configs = [
         {
-            "rnn_mode": "lstm",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_LSTM,
             "dropout": [0., 0.5, 1.],
             "expected": 231833.22,
             "tolerance": 1e-2,
@@ -468,7 +469,7 @@ class CudnnRNNTest(TensorFlowTestCase):
             },
         },
         {
-            "rnn_mode": "gru",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_GRU,
             "dropout": [0., 0.5, 1.],
             "expected": 56000,
             "tolerance": 1e-2,
@@ -482,7 +483,7 @@ class CudnnRNNTest(TensorFlowTestCase):
             },
         },
         {
-            "rnn_mode": "rnn_tanh",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_TANH,
             "dropout": [0., 0.5, 1.],
             "expected": 56000,
             "tolerance": 1e-2,
@@ -496,7 +497,7 @@ class CudnnRNNTest(TensorFlowTestCase):
             },
         },
         {
-            "rnn_mode": "rnn_relu",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_RELU,
             "dropout": [0., 0.5, 1.],
             "expected": 130688,
             "tolerance": 1e-2,
@@ -530,7 +531,7 @@ class CudnnRNNTest(TensorFlowTestCase):
     # make sure the drop patterns across the two runs are the same.
     old_env_state = os.environ.get("TF_CUDNN_RESET_RND_GEN_STATE", str(False))
     os.environ["TF_CUDNN_RESET_RND_GEN_STATE"] = str(True)
-    has_input_c = (rnn_mode == "lstm")
+    has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
     random_seed.set_random_seed(1234)
     model = self._CreateModel(
         rnn_mode, num_layers, num_units, input_size, dropout=dropout)
@@ -587,7 +588,7 @@ class CudnnRNNTest(TensorFlowTestCase):
   def testSimpleTraining(self):
     test_configs = [
         {
-            "rnn_mode": "lstm",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_LSTM,
             "dropout": [0., 0.5, 1.],
             "tolerance": 1e-2,
             "shape": {
@@ -600,7 +601,7 @@ class CudnnRNNTest(TensorFlowTestCase):
             },
         },
         {
-            "rnn_mode": "gru",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_GRU,
             "dropout": [0., 0.5, 1.],
             "tolerance": 4e-3,
             "shape": {
@@ -613,7 +614,7 @@ class CudnnRNNTest(TensorFlowTestCase):
             },
         },
         {
-            "rnn_mode": "rnn_tanh",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_TANH,
             "dropout": [0., 0.5, 1.],
             "tolerance": 5e-3,
             "shape": {
@@ -626,7 +627,7 @@ class CudnnRNNTest(TensorFlowTestCase):
             },
         },
         {
-            "rnn_mode": "rnn_relu",
+            "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_RELU,
             "dropout": [0., 0.5, 1.],
             "tolerance": 4e-1,
             "shape": {
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index 25b3bde837..81ebc821a3 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -26,14 +26,30 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.training import saver
 
 _cudnn_rnn_ops_so = loader.load_op_library(
     resource_loader.get_path_to_datafile("_cudnn_rnn_ops.so"))
 
+_flatten_transpose = lambda t: array_ops.reshape(array_ops.transpose(t), [-1])
+# pylint: disable=g-long-lambda
+_transpose_reshape = lambda t, shape: array_ops.transpose(
+    array_ops.reshape(t, shape))
+# pylint: enable=g-long-lambda
+
+CUDNN_RNN_UNIDIRECTION = "unidirectional"
+CUDNN_RNN_BIDIRECTION = "bidirectional"
+CUDNN_LSTM = "lstm"
+CUDNN_GRU = "gru"
+CUDNN_RNN_RELU = "rnn_relu"
+CUDNN_RNN_TANH = "rnn_tanh"
+
 
 # TODO(yaozhang): make sure we only save the canonical version of params and
 # don't save the platform-specific version to avoid potential race
@@ -118,11 +134,20 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
     super(RNNParamsSaveable, self).__init__(None, specs, name)
 
   def restore(self, restored_tensors, restored_shapes):
-    if (self._cudnn_rnn.direction == "unidirectional" and
-        self._cudnn_rnn.rnn_mode == "lstm"):
-      assert len(restored_tensors) % 4 == 0
+    if (self._cudnn_rnn.direction == CUDNN_RNN_UNIDIRECTION and
+        self._cudnn_rnn.rnn_mode == CUDNN_LSTM):
+      if len(restored_tensors) % 4 != 0:
+        raise ValueError(
+            "Invalid count of restored_tensors, expecting a multiple of 4.")
       weights = restored_tensors[:len(restored_tensors) // 4]
       biases = restored_tensors[len(restored_tensors) // 4:]
+    elif (self._cudnn_rnn.direction == CUDNN_RNN_UNIDIRECTION and
+          self._cudnn_rnn.rnn_mode == CUDNN_GRU):
+      if len(restored_tensors) % 8 != 0:
+        raise ValueError(
+            "Invalid count of restored_tensors, expecting a multiple of 8.")
+      weights = restored_tensors[:len(restored_tensors) // 8 * 3]
+      biases = restored_tensors[len(restored_tensors) // 8 * 3:]
     else:
       weights = restored_tensors[:len(restored_tensors) // 2]
       biases = restored_tensors[len(restored_tensors) // 2:]
@@ -141,39 +166,66 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
                                                 array[base_idx + 1])
 
   def _transform_canonical(self, weights, biases):
-    if (self._cudnn_rnn.direction != "unidirectional" or
-        self._cudnn_rnn.rnn_mode != "lstm"):
+    if self._cudnn_rnn.direction != CUDNN_RNN_UNIDIRECTION:
       return weights, biases
-    return self._transform_lstm_canonical(weights, biases)
+    elif self._cudnn_rnn.rnn_mode == CUDNN_LSTM:
+      return self._transform_lstm_canonical(weights, biases)
+    elif self._cudnn_rnn.rnn_mode == CUDNN_GRU:
+      return self._transform_gru_canonical(weights, biases)
+    return weights, biases
 
   def _transformed_canonical_names(self, weights, biases):
-    """Return canonical names for transformed weight and bias tensors."""
-    if (self._cudnn_rnn.direction != "unidirectional" or
-        self._cudnn_rnn.rnn_mode != "lstm"):
+    """Returns canonical names for transformed weight and bias tensors."""
+    if self._cudnn_rnn.direction != CUDNN_RNN_UNIDIRECTION:
       assert len(weights) == len(biases)
       return ([w.name for w in weights], [b.name for b in biases])
-    else:
-      w_names, b_names = [], []
+    elif self._cudnn_rnn.rnn_mode == CUDNN_LSTM:
       assert len(weights) * 3 == len(biases)
-      num_layers = self._cudnn_rnn.num_layers
-      # TODO(jamesqin): get rid of multi_rnn_cell when num_layers is 1
-      for i in range(num_layers):
-        # One transformed weight tensor each layer.
-        w_names.append(
-            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/kernel" % i)
-        # Three transformed bias tensors each layer:
-        # the 1st is for CudnnCompatbleLSTM(Block)Cell restore; the latter two
-        # sum up to the 1st, and are used for cuDNN restore.
-        b_names.append(
-            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/bias" % i)
-        b_names.extend([
-            "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell/bias_cudnn_%d" %
-            (i, j) for j in range(2)
-        ])
-      return w_names, b_names
+      return self._transformed_lstm_canonical_names()
+    elif self._cudnn_rnn.rnn_mode == CUDNN_GRU:
+      assert len(weights) * 5 == len(biases) * 3
+      return self._transformed_gru_canonical_names()
+    assert len(weights) == len(biases)
+    return ([w.name for w in weights], [b.name for b in biases])
+
+  def _transformed_lstm_canonical_names(self):
+    w_names, b_names = [], []
+    num_layers = self._cudnn_rnn.num_layers
+    # TODO(jamesqin): get rid of multi_rnn_cell when num_layers is 1
+    for i in range(num_layers):
+      # One transformed weight tensor each layer.
+      prefix = "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell" % i
+      w_names.append(prefix + "/kernel")
+      # Three transformed bias tensors each layer:
+      # the 1st is for CudnnCompatbleLSTM(Block)Cell restore; the latter two
+      # sum up to the 1st, and are used for cuDNN restore.
+      b_names.append(prefix + "/bias")
+      b_names.extend([prefix + "/bias_cudnn_%d" % j for j in range(2)])
+    return w_names, b_names
+
+  def _transformed_gru_canonical_names(self):
+    w_names, b_names = [], []
+    num_layers = self._cudnn_rnn.num_layers
+    # TODO(jamesqin): get rid of multi_rnn_cell when num_layers is 1
+    for i in range(num_layers):
+      prefix = "multi_rnn_cell/cell_%d/cudnn_compatible_gru_cell" % i
+      # 2 transformed weight tensor each layer.
+      w_names.append(prefix + "/gates/kernel")
+      w_names.append(prefix + "/candidate/input_projection/kernel")
+      w_names.append(prefix + "/candidate/hidden_projection/kernel")
+      # 5 transformed bias tensors each layer:
+      b_names.append(prefix + "/gates/bias")
+      b_names.append(prefix + "/candidate/input_projection/bias")
+      b_names.append(prefix + "/candidate/hidden_projection/bias")
+      b_names.extend([
+          "multi_rnn_cell/cell_%d/cudnn_compatible_gru_cell/bias_cudnn %d" % (i,
+                                                                              j)
+          for j in range(2)
+      ])
+    return w_names, b_names
 
   def _transform_lstm_canonical(self, weights, biases):
-    """Create transformed lstm canonical params.
+    """Create transformed canonical params.
 
     Produce properly-shaped monolithic weight and bias tensors to share between
     cuDNN and cudnn_compatible non-platform specific LSTM cells.
@@ -234,12 +286,86 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       transformed_biases.extend([b_in + b_rec, b_in, b_rec])
     return transformed_weights, transformed_biases
 
-  def _untransform_canonical(self, transformed_weights, transformed_biases):
-    if (self._cudnn_rnn.direction != "unidirectional" or
-        self._cudnn_rnn.rnn_mode != "lstm"):
-      return transformed_weights, transformed_biases
-    return self._untransform_lstm_canonical(transformed_weights,
-                                            transformed_biases)
+  def _transform_gru_canonical(self, weights, biases):
+    """Creates transformed gru canonical params.
+
+    Produce properly-formatted weight and bias tensors to share between
+    cuDNN and cudnn_compatible non-platform specific GRU cells.
+    Args:
+      weights: a list of Tensors recovered from cuDNN params_to_canonical.
+      biases: a list of Tensors recovered from cuDNN params_to_canonical.
+    Returns:
+      Two lists of tensors, one for weight and bias each.
+      weight list: 3 tensors each layer. One for reset and update gates, the
+        other two for candidate gate.
+      bias list: 5 tensors each layer. The 1st for reset_and_update gate,
+        the next 2 in line for candidate gate. The last 2 are original
+        tensors for reset_and_update gates stitched together, retained since
+        cuDNN biases are not restorable from the transformed version.
+    """
+    transformed_weights, transformed_biases = [], []
+    for i in range(self._cudnn_rnn.num_layers):
+      base_idx = i * 6
+      num_units = self._cudnn_rnn.num_units
+      input_size = self._cudnn_rnn.input_size if i == 0 else num_units
+      # cuDNN tensor shapes per time_step:
+      # input.shape:         [batch_size, input_size],
+      # input_weights.shape: [num_units, input_size] (first layer)
+      #                      [num_units, num_units]  (other layers)
+      # state_weights.shape: [num_units, num_units]
+      # biases.shape:        [num_units]
+      #
+      # cuDNN compatible GRU cell:
+      # reset and update gate:
+      #  [x, h_prev] * weights + biases
+      # new memory gate (same as cuDNN):
+      #  x * W_h + B_wh + r \dot (h * R_h + B_rh)
+      #
+      # Therefore for each layer, it expects:
+      # reset and update gate:
+      # weight.shape: [input_size + num_units, 2 * num_units] (first_layer)
+      #               [num_units + num_units, 2 * num_units]  (other layers)
+      # bias.shape:   [4 * num_units]
+      # new memory gate: same weights and biases as cuDNN GRU.
+
+      stitched_w = []
+      # Stitch together weights for reset and update gate.
+      for j in range(2):
+        stitched_w.append(
+            array_ops.concat(
+                [
+                    array_ops.reshape(weights[base_idx + j],
+                                      [num_units, input_size]),
+                    array_ops.reshape(weights[base_idx + j + 3],
+                                      [num_units, num_units])
+                ],
+                axis=1))
+      transformed_weights.append(
+          array_ops.transpose(array_ops.concat(stitched_w[:2], axis=0)))
+      # weights for new memory gate are kept separate.
+      transformed_weights.append(
+          _transpose_reshape(weights[base_idx + 2], [num_units, input_size]))
+      transformed_weights.append(
+          _transpose_reshape(weights[base_idx + 5], [num_units, num_units]))
+
+      # Bias for reset and update gates.
+      b_r = array_ops.concat(biases[base_idx:base_idx + 2], axis=0)
+      b_u = array_ops.concat(biases[base_idx + 3:base_idx + 5], axis=0)
+      # Biases for new memory gate.
+      b_c = biases[base_idx + 2]
+      b_h = biases[base_idx + 5]
+
+      transformed_biases.extend([b_r + b_u, b_c, b_h, b_r, b_u])
+    return transformed_weights, transformed_biases
+
+  def _untransform_canonical(self, weights, biases):
+    if self._cudnn_rnn.direction != CUDNN_RNN_UNIDIRECTION:
+      return weights, biases
+    elif self._cudnn_rnn.rnn_mode == CUDNN_LSTM:
+      return self._untransform_lstm_canonical(weights, biases)
+    elif self._cudnn_rnn.rnn_mode == CUDNN_GRU:
+      return self._untransform_gru_canonical(weights, biases)
+    return weights, biases
 
   def _untransform_lstm_canonical(self, transformed_weights,
                                   transformed_biases):
@@ -291,6 +417,62 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       biases.extend(br_list)
     return weights, biases
 
+  def _untransform_gru_canonical(self, transformed_weights, transformed_biases):
+    """The reverse procedure of _fuse_gru_canonical().
+
+    Args:
+      transformed_weights: a list of tensors, 3 for each layer. The 1st for
+        reset and update gates; the 2nd and 3rd for the new memory gate.
+      transformed_biases: 5 tensors each layer. The first for reset_and_update
+        gate; the next two in line for candidate gate. The last 2 are original
+        tensors for reset_and_update gates, retained since cuDNN biases are not
+        restorable from the fused version.
+
+    Returns:
+      Two lists of tensors for weights and biases respectively.
+      There are 6 tensors per weight and per bias for each layer:
+      tensor 0-2 are applied to the input from the previous layer and
+      tensor 3-5 to the recurrent input. Tensor 0 and 3 are for the reset gate;
+      tensor 1 and 4 the update gate; tensor 2 and 5 the new memory gate.
+    """
+    weights, biases = [], []
+    assert 5 * len(transformed_weights) == len(transformed_biases) * 3
+    for i in range(len(transformed_weights) // 3):
+      base_idx = 3 * i
+      num_units = self._cudnn_rnn.num_units
+      input_size = self._cudnn_rnn.input_size if i == 0 else num_units
+      # reset and update gate weights applied on layer inputs.
+      w_i = array_ops.slice(transformed_weights[base_idx], [0, 0],
+                            [input_size, 2 * num_units])
+      # reset and update gate weights applied on recurrent inputs.
+      w_r = array_ops.slice(transformed_weights[base_idx], [input_size, 0],
+                            [num_units, 2 * num_units])
+      wi_list = array_ops.split(w_i, 2, axis=1)
+      wr_list = array_ops.split(w_r, 2, axis=1)
+
+      wi_list = [_flatten_transpose(w) for w in wi_list]
+      wr_list = [_flatten_transpose(w) for w in wr_list]
+
+      # candidate gate weights
+      ih, hh = [
+          _flatten_transpose(w)
+          for w in transformed_weights[base_idx + 1:base_idx + 3]
+      ]
+      weights.extend(wi_list)
+      weights.append(ih)
+      weights.extend(wr_list)
+      weights.append(hh)
+
+      base_idx = 5 * i
+      # Recover biases for reset and update gates.
+      bi_list = array_ops.split(transformed_biases[base_idx + 3], 2, axis=0)
+      br_list = array_ops.split(transformed_biases[base_idx + 4], 2, axis=0)
+      biases.extend(bi_list)
+      biases.append(transformed_biases[base_idx + 1])
+      biases.extend(br_list)
+      biases.append(transformed_biases[base_idx + 2])
+    return weights, biases
+
 
 _cudnn_rnn_common_doc_string = """
   Cudnn RNN has an opaque parameter buffer that can be used for inference and
@@ -337,7 +519,7 @@ class _CudnnRNN(object):
                num_units,
                input_size,
                input_mode="linear_input",
-               direction="unidirectional",
+               direction=CUDNN_RNN_UNIDIRECTION,
                dropout=0.,
                seed=0):
     """Creates a CudnnRNN model from model spec.
@@ -432,7 +614,7 @@ class _CudnnRNN(object):
       output_h: the final state for h.
       output_c: the final state for c. This is only relevant for LSTM.
     """
-    if self._rnn_mode != "lstm":
+    if self._rnn_mode != CUDNN_LSTM:
       # For model that doesn't take input_c, replace with a dummy tensor.
       input_c = array_ops.constant([], dtype=dtypes.float32)
     output, output_h, output_c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
@@ -508,7 +690,7 @@ class CudnnLSTM(_CudnnRNN):
                num_units,
                input_size,
                input_mode="linear_input",
-               direction="unidirectional",
+               direction=CUDNN_RNN_UNIDIRECTION,
                dropout=0.,
                seed=0):
     """Creates a Cudnn LSTM model from model spec.
@@ -530,7 +712,7 @@ class CudnnLSTM(_CudnnRNN):
       seed: the seed used for initializing dropout.
     """
     super(CudnnLSTM, self).__init__(
-        "lstm",
+        CUDNN_LSTM,
         num_layers,
         num_units,
         input_size,
@@ -571,7 +753,7 @@ class _CudnnRNNNoInputC(_CudnnRNN):
                num_units,
                input_size,
                input_mode="linear_input",
-               direction="unidirectional",
+               direction=CUDNN_RNN_UNIDIRECTION,
                dropout=0.,
                seed=0):
     """Creates a Cudnn RNN model from model without hidden-state C.
@@ -591,8 +773,14 @@ class _CudnnRNNNoInputC(_CudnnRNN):
           'unidirectional' or 'bidirectional'
       dropout: whether to enable dropout. With it is 0, dropout is disabled.
       seed: the seed used for initializing dropout.
+
+    Raises:
+      ValueError: if direction is not 'unidirectional' or 'bidirectional'.
     """
 
+    if direction not in (CUDNN_RNN_UNIDIRECTION, CUDNN_RNN_BIDIRECTION):
+      raise ValueError("Invalid direction: %s", direction)
+
     super(_CudnnRNNNoInputC, self).__init__(
         self._rnn_mode,
         num_layers,
@@ -626,7 +814,7 @@ class _CudnnRNNNoInputC(_CudnnRNN):
 class CudnnGRU(_CudnnRNNNoInputC):
   """Cudnn implementation of the GRU model."""
   __doc__ += _cudnn_rnn_common_doc_string
-  _rnn_mode = "gru"
+  _rnn_mode = CUDNN_GRU
   # 3 sets of weight and bias parameters for the recurrent input, and 3 for the
   # previous layer input.
   _NUM_PARAMS_PER_LAYER = 6
@@ -635,7 +823,7 @@ class CudnnGRU(_CudnnRNNNoInputC):
 class CudnnRNNTanh(_CudnnRNNNoInputC):
   """Cudnn implementation of the RNN-tanh model."""
   __doc__ += _cudnn_rnn_common_doc_string
-  _rnn_mode = "rnn_tanh"
+  _rnn_mode = CUDNN_RNN_TANH
   # 1 set of weight and bias parameters for the recurrent input, and 1 for the
   # previous layer input.
   _NUM_PARAMS_PER_LAYER = 2
@@ -644,12 +832,122 @@ class CudnnRNNTanh(_CudnnRNNNoInputC):
 class CudnnRNNRelu(_CudnnRNNNoInputC):
   """Cudnn implementation of the RNN-relu model."""
   __doc__ += _cudnn_rnn_common_doc_string
-  _rnn_mode = "rnn_relu"
+  _rnn_mode = CUDNN_RNN_RELU
   # 1 set of weight and bias parameters for the recurrent input, and 1 for the
   # previous layer input.
   _NUM_PARAMS_PER_LAYER = 2
 
 
+class CudnnCompatibleLSTMBlockCell(lstm_ops.LSTMBlockCell):
+  """Cudnn Compatible LSTMBlockCell.
+
+  A simple wrapper around @{tf.contrib.rnn.LSTMBlockCell} to use along with
+  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
+  this cell seamlessly. It is the more performant than
+  @{tf.contrib.cudnn_rnn.CudnnCompatibleLSTMCell}, the same way
+  @{tf.contrib.rnn.LSTMBlockCell} can be more performant than
+  @{tf.nn.rnn_cell.LSTMCell}.
+  """
+
+  def __init__(self, num_units):
+    super(CudnnCompatibleLSTMBlockCell, self).__init__(
+        num_units, forget_bias=0, clip_cell=False, use_peephole=False)
+    self._names.update({"scope": "cudnn_compatible_lstm_cell"})
+
+
+class CudnnCompatibleLSTMCell(rnn_cell_impl.LSTMCell):
+  """Cudnn Compatible LSTMCell.
+
+  A simple wrapper around @{tf.nn.rnn_cell.LSTMCell} to use along with
+  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
+  former seamlessly.
+  """
+
+  def __init__(self, num_units, reuse=None):
+    super(CudnnCompatibleLSTMCell, self).__init__(
+        num_units,
+        use_peepholes=False,
+        cell_clip=None,
+        num_proj=None,
+        proj_clip=None,
+        state_is_tuple=True,
+        activation=None,
+        reuse=reuse,
+        forget_bias=0)
+
+
+class CudnnCompatibleGRUCell(rnn_cell_impl.GRUCell):
+  """Cudnn Compatible GRUCell.
+
+  A GRU impl akin to @{tf.nn.rnn_cell.GRUCell} to use along with
+  @{tf.contrib.cudnn_rnn.CudnnGRU}. The latter's params can be used by the
+  it seamlessly.
+
+  It differs from non-cudnn-compatible GRUs in how the new memory gate is
+  calculated. Nvidia picks this variant based on GRU author's[1] suggestion and
+  the fact it has no accuracy impact[2].
+  [1] https://arxiv.org/abs/1406.1078
+  [2] http://svail.github.io/diff_graphs/
+
+  cuDNN compatible GRU (from cuDNN library user guide):
+  ```python
+  r_t = sigma(x_t * W_r + h_t-1 * R_h + b_Wr + b_Rr)  # reset gate
+  i_t = sigma(x_t * W_i + h_t-1 * R_i + b_Wi + b_Ru)  # update gate
+  h'_t = tanh(x_t * W_h + r_t .* (h_t-1 * R_h + b_Rh) + b_Wh)  # new memory gate
+  h_t = (1 - i_t) .* h'_t + i_t .* h_t-1
+  ```
+
+  Other GRU (see @{tf.nn.rnn_cell.GRUCell} and @{tf.contrib.rnn.GRUBlockCell}):
+  ```python
+  h'_t = tanh(x_t * W_h + (r_t .* h_t-1) * R_h + b_Wh)  # new memory gate
+  ```
+
+  Note: in addition to the extra bias term b_Rh,
+  ```python
+  r .* (h * R) != (r .* h) * R
+  ```
+
+  TODO(jamesqin): change the impl to mirror the canonical version, since cuDNN
+  will do the same after v7.1.
+  """
+
+  def __init__(self, num_units, reuse=None, kernel_initializer=None):
+    super(CudnnCompatibleGRUCell, self).__init__(
+        num_units,
+        activation=None,
+        reuse=reuse,
+        kernel_initializer=kernel_initializer)
+
+  def call(self, inputs, state):
+    """Gated recurrent unit (GRU) with nunits cells."""
+    with vs.variable_scope("gates"):  # Reset gate and update gate.
+      # We start with bias of 1.0 to not reset and not update.
+      bias_ones = self._bias_initializer
+      if self._bias_initializer is None:
+        dtype = inputs.dtype
+        bias_ones = init_ops.constant_initializer(1.0, dtype=dtype)
+      # pylint: disable=protected-access
+      value = math_ops.sigmoid(
+          rnn_cell_impl._linear([inputs, state], 2 * self._num_units, True,
+                                bias_ones, self._kernel_initializer))
+      r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
+      # pylint: enable=protected-access
+    with vs.variable_scope("candidate"):
+      # pylint: disable=protected-access
+      with vs.variable_scope("input_projection"):
+        hi = rnn_cell_impl._linear(inputs, self._num_units, True,
+                                   self._bias_initializer,
+                                   self._kernel_initializer)
+      with vs.variable_scope("hidden_projection"):
+        hh = r * (rnn_cell_impl._linear(state, self._num_units, True,
+                                        self._bias_initializer,
+                                        self._kernel_initializer))
+      # pylint: enable=protected-access
+      c = self._activation(hi + hh)
+    new_h = u * state + (1 - u) * c
+    return new_h, new_h
+
+
 @ops.RegisterGradient("CudnnRNN")
 def _cudnn_rnn_backward(op, *grad):
   if not op.get_attr("is_training"):
@@ -680,40 +978,3 @@ ops.RegisterShape("CudnnRNNParamsToCanonical")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNNCanonicalToParams")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNN")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("CudnnRNNBackprop")(common_shapes.call_cpp_shape_fn)
-
-
-class CudnnCompatibleLSTMCell(rnn_cell_impl.LSTMCell):
-  """Cudnn Compatible LSTMCell.
-
-  A simple wrapper around @{tf.nn.rnn_cell.LSTMCell} to use along with
-  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
-  former seamlessly.
-  """
-
-  def __init__(self, num_units, reuse=None):
-    super(CudnnCompatibleLSTMCell, self).__init__(
-        num_units,
-        use_peepholes=False,
-        cell_clip=None,
-        num_proj=None,
-        proj_clip=None,
-        state_is_tuple=True,
-        activation=None,
-        reuse=reuse,
-        forget_bias=0)
-
-
-class CudnnCompatibleLSTMBlockCell(lstm_ops.LSTMBlockCell):
-  """Cudnn Compatible LSTMBlockCell.
-
-  A simple wrapper around @{tf.contrib.rnn.LSTMBlockCell} to use along with
-  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by the
-  former seamlessly. It is the more performant than
-  ${tf.contrib.cudnn_rnn.CudnnCompatibleLSTMCell}, the same way
-  @{tf.contrib.rnn.LSTMBlockCell} is than @{tf.nn.rnn_cell.LSTMCell}.
-  """
-
-  def __init__(self, num_units):
-    super(CudnnCompatibleLSTMBlockCell, self).__init__(
-        num_units, forget_bias=0, clip_cell=False, use_peephole=False)
-    self._names.update({"scope": "cudnn_compatible_lstm_cell"})
-- 
GitLab


From f9f078e6f344f16653c5e5822e22e64cc5f6ed60 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 4 Jul 2017 03:21:37 -0700
Subject: [PATCH 0677/1375] Make HloInstruction names unique with an HloModule.

This will allow most uses of HloInstruction::FullyQualifiedName() to
be replaced with HloInstruction::Name().

PiperOrigin-RevId: 160879036
---
 tensorflow/compiler/xla/service/BUILD              |  5 ++++-
 tensorflow/compiler/xla/service/hlo_computation.cc |  9 +++++----
 tensorflow/compiler/xla/service/hlo_module.cc      | 12 +++++-------
 tensorflow/compiler/xla/service/hlo_module.h       | 11 ++++++++---
 tensorflow/compiler/xla/service/hlo_verifier.cc    | 13 +++++++++++++
 5 files changed, 35 insertions(+), 15 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index d6a1bdc2ae..f6ed998ed3 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1520,7 +1520,10 @@ cc_library(
     name = "hlo_verifier",
     srcs = ["hlo_verifier.cc"],
     hdrs = ["hlo_verifier.h"],
-    deps = [":hlo_pass"],
+    deps = [
+        ":hlo_pass",
+        "//tensorflow/core:lib",
+    ],
 )
 
 cc_library(
diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index fb6dd75046..fef6cfc95a 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -66,8 +67,7 @@ HloComputation::HloComputation(
     HloInstruction* root_instruction, bool is_fusion_computation)
     : name_(name),
       root_instruction_(root_instruction),
-      is_fusion_computation_(is_fusion_computation),
-      instruction_name_uniquer_(/*separator=*/".") {
+      is_fusion_computation_(is_fusion_computation) {
   param_instructions_.resize(parameter_count, nullptr);
   bool root_found = false;
   for (auto& instruction : *instructions) {
@@ -94,8 +94,9 @@ HloInstruction* HloComputation::AddInstruction(
 
 HloInstruction* HloComputation::AddInstructionInternal(
     std::unique_ptr<HloInstruction> instruction) {
-  // Generate a unique name for the instruction.
-  instruction->UniquifyName(&instruction_name_uniquer_);
+  if (parent() != nullptr) {
+    instruction->UniquifyName(&parent()->instruction_name_uniquer());
+  }
   Reparent(instruction.get());
   HloInstruction* pinst = instruction.get();
   instruction_iterators_[pinst] =
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index 4ed0af6033..da6f1d77ec 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -37,19 +37,17 @@ HloModule::HloModule(const string& name,
                      const HloModuleConfig& config)
     : name_(name),
       config_(config),
-      entry_computation_(nullptr),
       has_entry_computation_handle_(true),
-      entry_computation_handle_(entry_computation_handle),
-      computation_name_uniquer_(/*separator=*/".") {}
+      entry_computation_handle_(entry_computation_handle) {}
 
-HloModule::HloModule(const string& name)
-    : name_(name),
-      entry_computation_(nullptr),
-      computation_name_uniquer_(/*separator=*/".") {}
+HloModule::HloModule(const string& name) : name_(name) {}
 
 HloComputation* HloModule::AddComputationInternal(
     std::unique_ptr<HloComputation> computation) {
   computation->UniquifyName(&computation_name_uniquer_);
+  for (auto& instruction : computation->instructions()) {
+    instruction->UniquifyName(&instruction_name_uniquer_);
+  }
   computation->set_parent(this);
   computations_.push_back(std::move(computation));
   return computations_.back().get();
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index d089493c70..ae8ec02fbd 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -124,13 +124,16 @@ class HloModule {
     return computation_name_uniquer_.GetUniqueName(prefix);
   }
 
+  // Returns the NameUniquer for uniquing instruction names in this module.
+  NameUniquer& instruction_name_uniquer() { return instruction_name_uniquer_; }
+
  private:
   HloComputation* AddComputationInternal(
       std::unique_ptr<HloComputation> computation);
 
   const string name_;
   HloModuleConfig config_;
-  HloComputation* entry_computation_;
+  HloComputation* entry_computation_ = nullptr;
   std::vector<std::unique_ptr<HloComputation>> computations_;
 
   // Random number generator engine to use when generating random numbers per
@@ -144,8 +147,10 @@ class HloModule {
   bool has_entry_computation_handle_ = false;
   VersionedComputationHandle entry_computation_handle_;
 
-  // Unique name generator for computation names, which are unique per module.
-  NameUniquer computation_name_uniquer_;
+  // Unique name generator for computation and instruction names, which are
+  // unique per module.
+  NameUniquer computation_name_uniquer_{/*separator=*/"."};
+  NameUniquer instruction_name_uniquer_{/*separator=*/"."};
 };
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc
index de6081e57e..01fba49bc5 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.cc
+++ b/tensorflow/compiler/xla/service/hlo_verifier.cc
@@ -14,10 +14,13 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/hlo_verifier.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
 
 namespace xla {
 
 StatusOr<bool> HloVerifier::Run(HloModule* module) {
+  tensorflow::gtl::FlatMap<string, const HloInstruction*> instructions;
+
   for (auto& computation : module->computations()) {
     for (const auto& instruction : computation->instructions()) {
       TF_RET_CHECK(instruction->parent() == computation.get());
@@ -30,6 +33,16 @@ StatusOr<bool> HloVerifier::Run(HloModule* module) {
               << " computation: " << computation.get();
         }
       }
+
+      auto previous = instructions.find(instruction->name());
+      TF_RET_CHECK(previous == instructions.end())
+          << "HLO has name that is not unique within module:\n"
+          << instruction->ToString()
+          << " in computation: " << computation->name()
+          << "\nPrevious HLO with same name:\n"
+          << previous->second->ToString()
+          << " in computation: " << previous->second->parent()->name();
+      instructions[instruction->name()] = instruction.get();
     }
   }
 
-- 
GitLab


From 1f2a3b9c7452deda1a21725962869f6c408e8131 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carl=20Thom=C3=A9?= <carlthome@gmail.com>
Date: Tue, 4 Jul 2017 14:39:13 +0200
Subject: [PATCH 0678/1375] Spelling

---
 tensorflow/compiler/tf2xla/functionalize_control_flow.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
index 623c52120f..c8451eb7bb 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
@@ -380,7 +380,7 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame,
         }
       }
       if (arg.exit == nullptr) {
-        return errors::InvalidArgument("Mising Exit successor to ",
+        return errors::InvalidArgument("Missing Exit successor to ",
                                        arg.switch_node->name());
       }
     }
-- 
GitLab


From a7032f21d72dd051f09b94733ed890dcd7ceaac8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 4 Jul 2017 08:01:20 -0700
Subject: [PATCH 0679/1375] Clarify rules for names of HloInstructions and
 HloOpcodes.

- The name of an HloInstruction may not contain "::", as this is used as a
  separator in fully qualified names.
- The name of an HloOpcode may not contain ':', as this is used as a separator
  in extended opcode strings.

PiperOrigin-RevId: 160894413
---
 .../xla/client/computation_builder.cc         |  5 ++++
 .../compiler/xla/client/computation_builder.h |  2 +-
 .../compiler/xla/service/hlo_instruction.cc   |  3 +++
 .../compiler/xla/service/hlo_instruction.h    | 26 ++++++++++++-------
 tensorflow/compiler/xla/service/hlo_opcode.cc |  7 +++++
 tensorflow/compiler/xla/xla_data.proto        |  1 +
 6 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index dcc313707b..9b717e1fb1 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -192,6 +192,11 @@ ComputationDataHandle ComputationBuilder::Parameter(int64 parameter_number,
   if (!first_error_.ok() || !PrepareComputation().ok()) {
     return ComputationDataHandle();
   }
+  if (name.find("::") != string::npos) {
+    NoteError(InvalidArgument("Parameter name (\"%s\") may not contain \"::\"",
+                              name.c_str()));
+    return ComputationDataHandle();
+  }
 
   ParameterRequest request;
   *request.mutable_shape() = shape;
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index b411346459..7beecf9fe4 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -87,7 +87,7 @@ class ComputationBuilder {
   }
 
   // Enqueues a "retrieve parameter value" instruction for a parameter that was
-  // passed to the computation.
+  // passed to the computation. The parameter name may not contain "::".
   ComputationDataHandle Parameter(int64 parameter_number, const Shape& shape,
                                   const string& name);
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 6284bbbeb3..8616c7ec51 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -51,6 +51,7 @@ using ::tensorflow::strings::StrCat;
 
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateParameter(
     int64 parameter_number, const Shape& shape, const string& name) {
+  DCHECK(name.find("::") == string::npos);
   auto instruction =
       WrapUnique(new HloInstruction(HloOpcode::kParameter, shape));
   instruction->parameter_number_ = parameter_number;
@@ -898,6 +899,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
 HloInstruction::~HloInstruction() {}
 
 std::unique_ptr<HloInstruction> HloInstruction::Clone(const string& suffix) {
+  DCHECK(suffix.find("::") == string::npos);
   std::unique_ptr<HloInstruction> clone =
       CloneWithNewOperands(shape_, operands_);
   if (suffix.empty()) {
@@ -2426,6 +2428,7 @@ HloModule* HloInstruction::GetModule() const {
 
 void HloInstruction::UniquifyName(NameUniquer* name_uniquer) {
   name_ = name_uniquer->GetUniqueName(name_);
+  DCHECK(name_.find("::") == string::npos);
 }
 
 void HloInstruction::set_outer_dimension_partitions(
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index bf50542cc0..5b0e20685a 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -69,7 +69,8 @@ class HloInstruction {
   };
 
   ~HloInstruction();
-  // Creates a parameter-retrieving instruction.
+  // Creates a parameter-retrieving instruction. The parameter name may not
+  // contain "::" (the separator used in fully qualified names).
   static std::unique_ptr<HloInstruction> CreateParameter(int64 parameter_number,
                                                          const Shape& shape,
                                                          const string& name);
@@ -447,8 +448,10 @@ class HloInstruction {
     return parameter_name_;
   }
 
+  // Sets the parameter name. The name may not contain "::".
   void set_parameter_name(const string& str) {
     CHECK_EQ(HloOpcode::kParameter, opcode_);
+    DCHECK(str.find("::") == string::npos);
     parameter_name_ = str;
   }
 
@@ -525,9 +528,12 @@ class HloInstruction {
   // or "elementwise".
   string ToCategory() const;
 
-  // Returns the string concatenation of parent name and this instructions
-  // name. This name is guaranteed to be unique among all instructions in the
-  // HloModule.
+  // Returns the string concatenation of:
+  // - Parent name
+  // - If this is a fused instruction: The fusion instruction's name
+  // - This instruction's name
+  // The components are separated using "::". The fully qualified name is
+  // guaranteed to be unique among all instructions in the HloModule.
   string FullyQualifiedName() const;
 
   // Returns a logging instruction, if the output of this instruction is logged.
@@ -737,7 +743,8 @@ class HloInstruction {
   // Clones the HLO instruction. The clone will have the same opcode, shape, and
   // operands. After creation the clone has no uses. "this" (the instruction
   // cloned from) is not changed. Suffix is the string to append to the name of
-  // the instruction to form the name of the cloned instruction.
+  // the instruction to form the name of the cloned instruction; it may not
+  // contain "::".
   std::unique_ptr<HloInstruction> Clone(const string& suffix = "clone");
 
   // Clones the HLO instruction as above but with new shape and operands.
@@ -795,9 +802,9 @@ class HloInstruction {
   std::tuple<bool, std::vector<int64>, std::vector<int64>>
   ReshapeMerelyInsertsOrDeletes1SizedDimensions() const;
 
-  // Returns the opcode string for this instruction. Compared with
-  // HloOpcodeString method, this wrapper dumps additional information
-  // such as fusion kind.
+  // Returns the opcode string for this instruction. This is the result from
+  // HloOpcodeString plus, for fusion nodes, the fusion kind, separated by a
+  // ':'.
   string ExtendedOpcodeStr() const;
 
   // Returns a string identifier for this instruction. If no string identifier
@@ -806,7 +813,8 @@ class HloInstruction {
   const string& name() const { return name_; }
 
   // Use the given NameUniquer to select a unique name for the instruction based
-  // on the instruction's existing name.
+  // on the instruction's existing name. The NameUniquer may not introduce the
+  // substring "::" into the name.
   void UniquifyName(NameUniquer* name_uniquer);
 
   // Sets the debug metadata for this instruction.
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.cc b/tensorflow/compiler/xla/service/hlo_opcode.cc
index 4d68d0d088..53a93f3dac 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.cc
+++ b/tensorflow/compiler/xla/service/hlo_opcode.cc
@@ -19,6 +19,13 @@ limitations under the License.
 namespace xla {
 
 string HloOpcodeString(HloOpcode opcode) {
+  // Note: Do not use ':' in opcode strings. It is used as a special character
+  // in these places:
+  // - In extended opcode strings (HloInstruction::ExtendedOpcodeString()), to
+  //   separate the opcode from the fusion kind
+  // - In fully qualified names (HloInstruction::FullyQualifiedName()), to
+  //   separate the qualifiers (name of the computation and potentially the
+  //   fusion instruction) from the name
   switch (opcode) {
     case HloOpcode::kAbs:
       return "abs";
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 86c72b3449..724832a9f1 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -552,6 +552,7 @@ message TransposeRequest {
 message ParameterRequest {
   Shape shape = 2;
   int64 parameter = 3;
+  // Parameter name. May not contain "::".
   string name = 4;
 }
 
-- 
GitLab


From 9ec810573bd3666e36f960923a8aff7cb72df1dc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 4 Jul 2017 08:04:25 -0700
Subject: [PATCH 0680/1375] Symlink files for cuda configuration one level
 below the BUILD file (i.e., one level under local_config_cuda/cuda).
 END_PUBLIC

BEGIN_PUBLIC
Automated g4 rollback of changelist 159643278

PiperOrigin-RevId: 160894669
---
 third_party/gpus/cuda/BUILD.tpl     | 82 +++++++++++++++++++----------
 third_party/gpus/cuda_configure.bzl | 50 ++++++++++--------
 2 files changed, 84 insertions(+), 48 deletions(-)

diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index 87c8678714..51d9e4e994 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -40,20 +40,23 @@ config_setting(
 cc_library(
     name = "cuda_headers",
     hdrs = [
-        "cuda_config.h",
+        "cuda/cuda_config.h",
         %{cuda_headers}
     ],
     includes = [
         ".",
-        "include",
+        "cuda/include",
     ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cudart_static",
-    srcs = ["lib/%{cudart_static_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cudart_static_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkopts = select({
         ":freebsd": [],
         "//conditions:default": ["-ldl"],
@@ -66,34 +69,46 @@ cc_library(
 
 cc_library(
     name = "cuda_driver",
-    srcs = ["lib/%{cuda_driver_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cuda_driver_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cudart",
-    srcs = ["lib/%{cudart_lib}"],
-    data = ["lib/%{cudart_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cudart_lib}"],
+    data = ["cuda/lib/%{cudart_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cublas",
-    srcs = ["lib/%{cublas_lib}"],
-    data = ["lib/%{cublas_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cublas_lib}"],
+    data = ["cuda/lib/%{cublas_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cusolver",
-    srcs = ["lib/%{cusolver_lib}"],
-    data = ["lib/%{cusolver_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cusolver_lib}"],
+    data = ["cuda/lib/%{cusolver_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkopts = ["-lgomp"],
     linkstatic = 1,
     visibility = ["//visibility:public"],
@@ -101,27 +116,36 @@ cc_library(
 
 cc_library(
     name = "cudnn",
-    srcs = ["lib/%{cudnn_lib}"],
-    data = ["lib/%{cudnn_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cudnn_lib}"],
+    data = ["cuda/lib/%{cudnn_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cufft",
-    srcs = ["lib/%{cufft_lib}"],
-    data = ["lib/%{cufft_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{cufft_lib}"],
+    data = ["cuda/lib/%{cufft_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "curand",
-    srcs = ["lib/%{curand_lib}"],
-    data = ["lib/%{curand_lib}"],
-    includes = ["include"],
+    srcs = ["cuda/lib/%{curand_lib}"],
+    data = ["cuda/lib/%{curand_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     linkstatic = 1,
     visibility = ["//visibility:public"],
 )
@@ -142,19 +166,23 @@ cc_library(
 cc_library(
     name = "cupti_headers",
     hdrs = [
-        "cuda_config.h",
+        "cuda/cuda_config.h",
         ":cuda-extras",
     ],
     includes = [
         ".",
-        "extras/CUPTI/include/",
+        "cuda/extras/CUPTI/include/",
     ],
     visibility = ["//visibility:public"],
 )
 
 cc_library(
     name = "cupti_dsos",
-    data = ["lib/%{cupti_lib}"],
+    data = ["cuda/lib/%{cupti_lib}"],
+    includes = [
+        ".",
+        "cuda/include",
+    ],
     visibility = ["//visibility:public"],
 )
 
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index cc445eb0ae..4dd3169d41 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -740,19 +740,19 @@ def _create_dummy_repository(repository_ctx):
 
   # Create dummy files for the CUDA toolkit since they are still required by
   # tensorflow/core/platform/default/build_config:cuda.
-  repository_ctx.file("cuda/include/cuda.h", "")
-  repository_ctx.file("cuda/include/cublas.h", "")
-  repository_ctx.file("cuda/include/cudnn.h", "")
-  repository_ctx.file("cuda/extras/CUPTI/include/cupti.h", "")
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cuda", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cudart", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cudart_static", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cublas", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cusolver", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cudnn", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("curand", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cufft", cpu_value))
-  repository_ctx.file("cuda/lib/%s" % _lib_name("cupti", cpu_value))
+  repository_ctx.file("cuda/cuda/include/cuda.h", "")
+  repository_ctx.file("cuda/cuda/include/cublas.h", "")
+  repository_ctx.file("cuda/cuda/include/cudnn.h", "")
+  repository_ctx.file("cuda/cuda/extras/CUPTI/include/cupti.h", "")
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cuda", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudart", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudart_static", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cublas", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cusolver", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudnn", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("curand", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cufft", cpu_value))
+  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cupti", cpu_value))
 
   # Set up cuda_config.h, which is used by
   # tensorflow/stream_executor/dso_loader.cc.
@@ -764,7 +764,7 @@ def _create_dummy_repository(repository_ctx):
                "CudaVersion(\"%s\")" % c
                for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES]),
            "%{cuda_toolkit_path}": _DEFAULT_CUDA_TOOLKIT_PATH,
-       })
+       }, "cuda/cuda/cuda_config.h")
 
   # If cuda_configure is not configured to build with GPU support, and the user
   # attempts to build with --config=cuda, add a dummy build rule to intercept
@@ -821,6 +821,13 @@ def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name,
     dest_files = files.replace(src_dir, '').splitlines()
     src_files = files.splitlines()
   command = []
+  if not _is_windows(repository_ctx):
+    # We clear folders that might have been generated previously to avoid
+    # undesired inclusions
+    command.append('if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi')
+    command.append('if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi')
+    command.append('if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi')
+    command.append('if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi')
   outs = []
   for i in range(len(dest_files)):
     if dest_files[i] != "":
@@ -906,19 +913,19 @@ def _create_local_cuda_repository(repository_ctx):
   cuda_toolkit_path = cuda_config.cuda_toolkit_path
   cuda_include_path = cuda_toolkit_path + "/include"
   genrules = [_symlink_genrule_for_dir(repository_ctx,
-      cuda_include_path, "include", "cuda-include")]
+      cuda_include_path, "cuda/include", "cuda-include")]
   genrules.append(_symlink_genrule_for_dir(repository_ctx,
-      cuda_toolkit_path + "/nvvm", "nvvm", "cuda-nvvm"))
+      cuda_toolkit_path + "/nvvm", "cuda/nvvm", "cuda-nvvm"))
   genrules.append(_symlink_genrule_for_dir(repository_ctx,
       cuda_toolkit_path + "/extras/CUPTI/include",
-      "extras/CUPTI/include", "cuda-extras"))
+      "cuda/extras/CUPTI/include", "cuda-extras"))
 
   cuda_libs = _find_libs(repository_ctx, cuda_config)
   cuda_lib_src = []
   cuda_lib_dest = []
   for lib in cuda_libs.values():
     cuda_lib_src.append(lib.path)
-    cuda_lib_dest.append("lib/" + lib.file_name)
+    cuda_lib_dest.append("cuda/lib/" + lib.file_name)
   genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "", "cuda-lib",
                                        cuda_lib_src, cuda_lib_dest))
 
@@ -927,8 +934,9 @@ def _create_local_cuda_repository(repository_ctx):
   included_files = _read_dir(repository_ctx, cuda_include_path).replace(
       cuda_include_path, '').splitlines()
   if '/cudnn.h' not in included_files:
-    genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "include/",
-        "cudnn-include", [cudnn_header_dir + "/cudnn.h"], ["cudnn.h"]))
+    genrules.append(_symlink_genrule_for_dir(repository_ctx, None,
+        "cuda/include/", "cudnn-include", [cudnn_header_dir + "/cudnn.h"],
+        ["cudnn.h"]))
   else:
     genrules.append(
             'filegroup(\n' +
@@ -1000,7 +1008,7 @@ def _create_local_cuda_repository(repository_ctx):
                ["CudaVersion(\"%s\")" % c
                 for c in cuda_config.compute_capabilities]),
                "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path,
-       })
+       }, "cuda/cuda/cuda_config.h")
 
 def _create_remote_cuda_repository(repository_ctx, remote_config_repo):
   """Creates pointers to a remotely configured repo set up to build with CUDA."""
-- 
GitLab


From ad20e176e0c5fb36f142b739c823a00cb89225c3 Mon Sep 17 00:00:00 2001
From: Daniel Rasmussen <daniel.rasmussen@appliedbrainresearch.com>
Date: Tue, 4 Jul 2017 11:45:16 -0400
Subject: [PATCH 0681/1375] Fix memory leak when using `tf.layers`

Uses `weakref` so that PER_GRAPH_LAYER_NAME_UIDS doesn't prevent Graphs from being garbage collected.

Fixes #11273
---
 tensorflow/contrib/keras/python/keras/backend.py | 4 ++++
 tensorflow/python/layers/base.py                 | 6 ++++--
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index 324f510301..0bde586257 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -21,6 +21,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import json
 import os
 
@@ -263,6 +264,9 @@ def get_uid(prefix=''):
   ```
   """
   graph = ops.get_default_graph()
+  if graph not in tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS:
+    tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS[graph] = collections.defaultdict(
+      int)
   layer_name_uids = tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS[graph]
   layer_name_uids[prefix] += 1
   return layer_name_uids[prefix]
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index a37308f702..18f38837e4 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -27,6 +27,7 @@ import collections
 import copy
 import functools
 import re
+import weakref
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import numpy as np
@@ -671,8 +672,7 @@ def _object_list_uid(object_list):
 # A global dictionary mapping graph objects to an index of counters used
 # for various layer names in each graph.
 # Allows to give unique autogenerated names to layers, in a graph-specific way.
-PER_GRAPH_LAYER_NAME_UIDS = collections.defaultdict(
-    lambda: collections.defaultdict(int))
+PER_GRAPH_LAYER_NAME_UIDS = weakref.WeakKeyDictionary()
 
 
 def _unique_layer_name(name):
@@ -694,6 +694,8 @@ def _unique_layer_name(name):
   ```
   """
   graph = ops.get_default_graph()
+  if graph not in PER_GRAPH_LAYER_NAME_UIDS:
+    PER_GRAPH_LAYER_NAME_UIDS[graph] = collections.defaultdict(int)
   layer_name_uids = PER_GRAPH_LAYER_NAME_UIDS[graph]
   layer_name_uids[name] += 1
   return name + '_' + str(layer_name_uids[name])
-- 
GitLab


From 11ec8b7cfdec0fd498182d0ad8f550b4a8ddaf13 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 4 Jul 2017 09:24:41 -0700
Subject: [PATCH 0682/1375] Update uses of RTDyldObjectLinkingLayer after
 upstream interface change r307058.

PiperOrigin-RevId: 160899333
---
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index 7920042bae..8f567b4f8c 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -155,6 +155,8 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
                                 /*MAttrs=*/DetectMachineAttributes()))),
       disassembler_(*target_machine_),
       data_layout_(target_machine_->createDataLayout()),
+      object_layer_(
+          [] { return std::make_shared<llvm::SectionMemoryManager>(); }),
       compile_layer_(object_layer_,
                      CompilerFunctor(target_machine_.get(), &disassembler_,
                                      opt_level, GetAvailableIntrinsics(),
@@ -166,9 +168,8 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
 
 SimpleOrcJIT::ModuleHandleT SimpleOrcJIT::AddModule(
     std::unique_ptr<llvm::Module> module) {
-  auto handle = compile_layer_.addModule(
-      std::move(module), MakeUnique<llvm::SectionMemoryManager>(),
-      MakeUnique<SimpleResolver>());
+  auto handle =
+      compile_layer_.addModule(std::move(module), MakeUnique<SimpleResolver>());
   module_handles_.push_back(handle);
   return handle;
 }
-- 
GitLab


From af23ae65db2585f4a18d0bc5f21f15e94805aa4f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 4 Jul 2017 10:19:42 -0700
Subject: [PATCH 0683/1375] Migrating tfprof python API to
 tensorflow/python/profiler Migrating tfprof c++ to tensorflow/core/profiler

API changes:
New tf.profiler namespace.

Within tf.profiler namespace:
tf.profiler.advise  # One-shot advise function
tf.profiler.profile  # One-shot profile function
tf.profiler.Profiler  # Multi-step profile/advise class
tf.profiler.write_op_log  # Write profile for offline analysis

PiperOrigin-RevId: 160901831
---
 tensorflow/BUILD                              |  10 +-
 tensorflow/contrib/cmake/CMakeLists.txt       |   1 +
 .../contrib/cmake/tf_core_profiler.cmake      |  36 +++++++
 tensorflow/contrib/cmake/tf_python.cmake      |  19 ++--
 tensorflow/contrib/cmake/tf_tests.cmake       |  24 ++---
 tensorflow/contrib/cmake/tf_tools.cmake       |  20 ----
 tensorflow/contrib/tfprof/BUILD               |  19 +++-
 tensorflow/contrib/tfprof/README.md           |  46 ++++-----
 tensorflow/contrib/tfprof/__init__.py         |   5 +-
 tensorflow/contrib/tfprof/model_analyzer.py   |  47 +++++++++
 tensorflow/contrib/tfprof/tfprof_logger.py    |  24 +++++
 tensorflow/core/BUILD                         |   2 +-
 .../{tools/tfprof => core/profiler}/BUILD     |   8 +-
 .../{tools/tfprof => core/profiler}/README.md |   0
 .../tfprof => core/profiler}/g3doc/advise.md  |   6 +-
 .../profiler}/g3doc/code_timeline.png         | Bin
 .../profiler}/g3doc/command_line.md           |  24 ++---
 .../profiler}/g3doc/graph_timeline.png        | Bin
 .../tfprof => core/profiler}/g3doc/options.md |   0
 .../profiler}/g3doc/profile_memory.md         |   0
 .../g3doc/profile_model_architecture.md       |  10 +-
 .../profiler}/g3doc/profile_time.md           |   2 +-
 .../profiler}/g3doc/python_api.md             |  24 ++---
 .../profiler}/g3doc/scope_timeline.png        | Bin
 .../tfprof => core/profiler}/internal/BUILD   |  34 +++----
 .../profiler}/internal/advisor/BUILD          |   6 +-
 .../advisor/accelerator_utilization_checker.h |   8 +-
 .../profiler}/internal/advisor/checker.h      |  10 +-
 .../advisor/expensive_operation_checker.h     |   8 +-
 .../advisor/internal_checker_runner.h         |  12 +--
 .../advisor/internal_checker_runner_dummy.cc  |   2 +-
 .../internal/advisor/operation_checker.h      |   8 +-
 .../internal/advisor/tfprof_advisor.h         |  18 ++--
 .../internal/advisor/tfprof_advisor_test.cc   |   2 +-
 .../internal/print_model_analysis.cc          |  14 +--
 .../profiler}/internal/print_model_analysis.h |   6 +-
 .../testdata/ckpt.data-00000-of-00001         | Bin
 .../profiler}/internal/testdata/ckpt.index    | Bin
 .../profiler}/internal/testdata/ckpt.meta     | Bin
 .../profiler}/internal/testdata/graph.pbtxt   |   0
 .../profiler}/internal/testdata/run_meta      | Bin
 .../profiler}/internal/testdata/tfprof_log    |   0
 .../profiler}/internal/tfprof_code.cc         |   4 +-
 .../profiler}/internal/tfprof_code.h          |  20 ++--
 .../profiler}/internal/tfprof_constants.h     |   6 +-
 .../profiler}/internal/tfprof_graph.cc        |   6 +-
 .../profiler}/internal/tfprof_graph.h         |  16 +--
 .../profiler}/internal/tfprof_node.cc         |   4 +-
 .../profiler}/internal/tfprof_node.h          |  10 +-
 .../profiler}/internal/tfprof_node_show.cc    |   2 +-
 .../profiler}/internal/tfprof_node_show.h     |  16 +--
 .../profiler}/internal/tfprof_op.cc           |   6 +-
 .../profiler}/internal/tfprof_op.h            |  16 +--
 .../profiler}/internal/tfprof_options.cc      |   4 +-
 .../profiler}/internal/tfprof_options.h       |   6 +-
 .../profiler}/internal/tfprof_scope.cc        |   6 +-
 .../profiler}/internal/tfprof_scope.h         |  16 +--
 .../profiler}/internal/tfprof_show.cc         |   2 +-
 .../profiler}/internal/tfprof_show.h          |  22 ++--
 .../profiler}/internal/tfprof_show_multi.cc   |   4 +-
 .../profiler}/internal/tfprof_show_multi.h    |  24 ++---
 .../profiler}/internal/tfprof_show_test.cc    |  20 ++--
 .../profiler}/internal/tfprof_stats.cc        |   4 +-
 .../profiler}/internal/tfprof_stats.h         |  26 ++---
 .../profiler}/internal/tfprof_stats_test.cc   |  20 ++--
 .../profiler}/internal/tfprof_tensor.cc       |   2 +-
 .../profiler}/internal/tfprof_tensor.h        |   8 +-
 .../profiler}/internal/tfprof_tensor_test.cc  |  14 +--
 .../profiler}/internal/tfprof_timeline.cc     |   4 +-
 .../profiler}/internal/tfprof_timeline.h      |   8 +-
 .../internal/tfprof_timeline_test.cc          |  16 +--
 .../profiler}/internal/tfprof_utils.cc        |   2 +-
 .../profiler}/internal/tfprof_utils.h         |   8 +-
 .../tfprof => core/profiler}/tfprof_log.proto |   0
 .../tfprof => core/profiler}/tfprof_main.cc   |  10 +-
 .../profiler}/tfprof_options.proto            |   0
 .../profiler}/tfprof_output.proto             |   0
 .../docs_src/programmers_guide/debugger.md    |   2 +-
 tensorflow/python/BUILD                       |   3 +-
 tensorflow/python/__init__.py                 |   4 +-
 .../tools/tfprof => python/profiler}/BUILD    |  23 +++--
 .../tfprof => python/profiler}/__init__.py    |   0
 .../tfprof => python/profiler}/internal/BUILD |  13 +--
 .../internal/model_analyzer_testlib.py        |   0
 .../internal/print_model_analysis_test.py     |   4 +-
 .../profiler}/internal/run_metadata_test.py   |  12 +--
 .../profiler}/model_analyzer.py               |  94 ++++++++++--------
 .../profiler}/model_analyzer_test.py          |  30 +++---
 .../profiler}/pprof_profiler.py               |   0
 .../profiler}/pprof_profiler_test.py          |   2 +-
 tensorflow/python/profiler/profiler.py        |  32 ++++++
 .../profiler}/profiler_test.py                |  30 +++---
 .../profiler}/tfprof_logger.py                |   2 +-
 .../profiler}/tfprof_logger_test.py           |   8 +-
 tensorflow/python/util/tfprof.i               |   4 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   4 +
 .../tensorflow.profiler.-profiler.pbtxt       |  33 ++++++
 .../api/golden/tensorflow.profiler.pbtxt      |  19 ++++
 98 files changed, 659 insertions(+), 447 deletions(-)
 create mode 100644 tensorflow/contrib/cmake/tf_core_profiler.cmake
 create mode 100644 tensorflow/contrib/tfprof/model_analyzer.py
 create mode 100644 tensorflow/contrib/tfprof/tfprof_logger.py
 rename tensorflow/{tools/tfprof => core/profiler}/BUILD (82%)
 rename tensorflow/{tools/tfprof => core/profiler}/README.md (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/advise.md (97%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/code_timeline.png (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/command_line.md (94%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/graph_timeline.png (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/options.md (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/profile_memory.md (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/profile_model_architecture.md (91%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/profile_time.md (99%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/python_api.md (87%)
 rename tensorflow/{tools/tfprof => core/profiler}/g3doc/scope_timeline.png (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/BUILD (90%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/BUILD (90%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/accelerator_utilization_checker.h (91%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/checker.h (80%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/expensive_operation_checker.h (93%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/internal_checker_runner.h (67%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/internal_checker_runner_dummy.cc (92%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/operation_checker.h (88%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/tfprof_advisor.h (78%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/advisor/tfprof_advisor_test.cc (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/print_model_analysis.cc (92%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/print_model_analysis.h (87%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/testdata/ckpt.data-00000-of-00001 (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/testdata/ckpt.index (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/testdata/ckpt.meta (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/testdata/graph.pbtxt (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/testdata/run_meta (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/testdata/tfprof_log (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_code.cc (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_code.h (79%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_constants.h (86%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_graph.cc (97%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_graph.h (84%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_node.cc (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_node.h (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_node_show.cc (99%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_node_show.h (85%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_op.cc (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_op.h (80%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_options.cc (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_options.h (95%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_scope.cc (97%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_scope.h (82%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_show.cc (99%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_show.h (86%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_show_multi.cc (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_show_multi.h (84%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_show_test.cc (93%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_stats.cc (98%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_stats.h (81%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_stats_test.cc (96%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_tensor.cc (97%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_tensor.h (94%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_tensor_test.cc (97%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_timeline.cc (99%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_timeline.h (95%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_timeline_test.cc (86%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_utils.cc (99%)
 rename tensorflow/{tools/tfprof => core/profiler}/internal/tfprof_utils.h (88%)
 rename tensorflow/{tools/tfprof => core/profiler}/tfprof_log.proto (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/tfprof_main.cc (97%)
 rename tensorflow/{tools/tfprof => core/profiler}/tfprof_options.proto (100%)
 rename tensorflow/{tools/tfprof => core/profiler}/tfprof_output.proto (100%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/BUILD (84%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/__init__.py (100%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/internal/BUILD (84%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/internal/model_analyzer_testlib.py (100%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/internal/print_model_analysis_test.py (99%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/internal/run_metadata_test.py (94%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/model_analyzer.py (82%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/model_analyzer_test.py (94%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/pprof_profiler.py (100%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/pprof_profiler_test.py (98%)
 create mode 100644 tensorflow/python/profiler/profiler.py
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/profiler_test.py (87%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/tfprof_logger.py (99%)
 rename tensorflow/{contrib/tfprof/python/tools/tfprof => python/profiler}/tfprof_logger_test.py (91%)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-profiler.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.pbtxt

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 6e76db02de..6a7affe82c 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -305,7 +305,7 @@ filegroup(
         "//tensorflow/contrib/tensorboard:all_files",
         "//tensorflow/contrib/testing:all_files",
         "//tensorflow/contrib/text:all_files",
-        "//tensorflow/contrib/tfprof/python/tools/tfprof:all_files",
+        "//tensorflow/contrib/tfprof:all_files",
         "//tensorflow/contrib/tpu:all_files",
         "//tensorflow/contrib/training:all_files",
         "//tensorflow/contrib/util:all_files",
@@ -328,6 +328,9 @@ filegroup(
         "//tensorflow/core/platform/cloud:all_files",
         "//tensorflow/core/platform/default/build_config:all_files",
         "//tensorflow/core/platform/hadoop:all_files",
+        "//tensorflow/core/profiler:all_files",
+        "//tensorflow/core/profiler/internal:all_files",
+        "//tensorflow/core/profiler/internal/advisor:all_files",
         "//tensorflow/core/util/ctc:all_files",
         "//tensorflow/core/util/tensor_bundle:all_files",
         "//tensorflow/examples/android:all_files",
@@ -352,6 +355,8 @@ filegroup(
         "//tensorflow/python/kernel_tests:all_files",
         "//tensorflow/python/kernel_tests/distributions:all_files",
         "//tensorflow/python/ops/distributions:all_files",
+        "//tensorflow/python/profiler:all_files",
+        "//tensorflow/python/profiler/internal:all_files",
         "//tensorflow/python/saved_model:all_files",
         "//tensorflow/python/tools:all_files",
         "//tensorflow/tools/api/golden:all_files",
@@ -367,9 +372,6 @@ filegroup(
         "//tensorflow/tools/proto_text:all_files",
         "//tensorflow/tools/quantization:all_files",
         "//tensorflow/tools/test:all_files",
-        "//tensorflow/tools/tfprof:all_files",
-        "//tensorflow/tools/tfprof/internal:all_files",
-        "//tensorflow/tools/tfprof/internal/advisor:all_files",
         "//tensorflow/user_ops:all_files",
         "//third_party/hadoop:all_files",
         "//third_party/sycl:all_files",
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index 655e0b29a0..83c82c75be 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -278,6 +278,7 @@ endif()
 include(tf_cc_ops.cmake)
 include(tf_c.cmake)
 include(tf_grappler.cmake)
+include(tf_core_profiler.cmake)
 if(tensorflow_BUILD_CC_EXAMPLE)
   include(tf_tutorials.cmake)
   include(tf_label_image_example.cmake)
diff --git a/tensorflow/contrib/cmake/tf_core_profiler.cmake b/tensorflow/contrib/cmake/tf_core_profiler.cmake
new file mode 100644
index 0000000000..61ed6a1e14
--- /dev/null
+++ b/tensorflow/contrib/cmake/tf_core_profiler.cmake
@@ -0,0 +1,36 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+########################################################
+# tf_core_profiler library
+########################################################
+file(GLOB_RECURSE tf_core_profiler_srcs
+    "${tensorflow_source_dir}/tensorflow/core/profiler/*.proto"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/*.h"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/advisor/*.h"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/advisor/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/platform/regexp.h"
+)
+
+file(GLOB_RECURSE tf_core_profiler_exclude_srcs
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/*test.cc"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/advisor/*test.cc"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/print_model_analysis.cc"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/print_model_analysis.h"
+)
+list(REMOVE_ITEM tf_core_profiler_srcs ${tf_core_profiler_exclude_srcs})
+
+add_library(tf_core_profiler OBJECT ${tf_core_profiler_srcs})
+add_dependencies(tf_core_profiler tf_core_lib)
\ No newline at end of file
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 14b11dbc0e..15c65e8443 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -122,13 +122,13 @@ endfunction()
 
 file(GLOB_RECURSE tf_protos_python_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/core/*.proto"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/*.proto"
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/decision_trees/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/*.proto"
 )
 RELATIVE_PROTOBUF_GENERATE_PYTHON(
     ${tensorflow_source_dir} PYTHON_PROTO_GENFILES ${tf_protos_python_srcs}
@@ -138,11 +138,11 @@ RELATIVE_PROTOBUF_GENERATE_PYTHON(
 # can cause benign-but-failing-on-Windows-due-to-file-locking conflicts
 # when two rules attempt to generate the same file.
 file(GLOB_RECURSE tf_python_protos_cc_srcs RELATIVE ${tensorflow_source_dir}
+    "${tensorflow_source_dir}/tensorflow/core/profiler/*.proto"
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/*.proto"
 )
 RELATIVE_PROTOBUF_GENERATE_CPP(PROTO_SRCS PROTO_HDRS
     ${tensorflow_source_dir} ${tf_python_protos_cc_srcs}
@@ -227,6 +227,8 @@ add_python_module("tensorflow/python/ops/losses")
 add_python_module("tensorflow/python/platform")
 add_python_module("tensorflow/python/platform/default")
 add_python_module("tensorflow/python/platform/summary")
+add_python_module("tensorflow/python/profiler/")
+add_python_module("tensorflow/python/profiler/internal")
 add_python_module("tensorflow/python/saved_model")
 add_python_module("tensorflow/python/summary")
 add_python_module("tensorflow/python/summary/writer")
@@ -525,10 +527,7 @@ add_python_module("tensorflow/contrib/text/kernels")
 add_python_module("tensorflow/contrib/text/ops")
 add_python_module("tensorflow/contrib/text/python")
 add_python_module("tensorflow/contrib/text/python/ops")
-add_python_module("tensorflow/contrib/tfprof/python")
-add_python_module("tensorflow/contrib/tfprof/python/tools")
-add_python_module("tensorflow/contrib/tfprof/python/tools/tfprof")
-add_python_module("tensorflow/contrib/tfprof/python/tools/tfprof/internal")
+add_python_module("tensorflow/contrib/tfprof")
 add_python_module("tensorflow/contrib/tpu")
 add_python_module("tensorflow/contrib/tpu/ops")
 add_python_module("tensorflow/contrib/tpu/python")
@@ -714,6 +713,8 @@ add_custom_command(
       VERBATIM )
 
 set (pywrap_tensorflow_internal_src
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/print_model_analysis.h"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/print_model_analysis.cc"
     "${tensorflow_source_dir}/tensorflow/python/client/tf_session_helper.h"
     "${tensorflow_source_dir}/tensorflow/python/client/tf_session_helper.cc"
     "${tensorflow_source_dir}/tensorflow/python/framework/cpp_shape_inference.h"
@@ -735,8 +736,6 @@ set (pywrap_tensorflow_internal_src
     "${tensorflow_source_dir}/tensorflow/cc/framework/ops.cc"
     "${tensorflow_source_dir}/tensorflow/cc/framework/scope.cc"
     "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.h"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.cc"
 )
 
 if(WIN32)
@@ -755,12 +754,12 @@ if(WIN32)
         $<TARGET_OBJECTS:tf_core_lib>
         $<TARGET_OBJECTS:tf_core_cpu>
         $<TARGET_OBJECTS:tf_core_framework>
+        $<TARGET_OBJECTS:tf_core_profiler>
         $<TARGET_OBJECTS:tf_cc>
         $<TARGET_OBJECTS:tf_cc_ops>
         $<TARGET_OBJECTS:tf_core_ops>
         $<TARGET_OBJECTS:tf_core_direct_session>
         $<TARGET_OBJECTS:tf_grappler>
-        $<TARGET_OBJECTS:tf_tools_tfprof>
         $<TARGET_OBJECTS:tf_tools_transform_graph_lib>
         $<$<BOOL:${tensorflow_ENABLE_GRPC_SUPPORT}>:$<TARGET_OBJECTS:tf_core_distributed_runtime>>
         $<TARGET_OBJECTS:tf_core_kernels>
@@ -803,12 +802,12 @@ add_library(pywrap_tensorflow_internal SHARED
     $<TARGET_OBJECTS:tf_core_lib>
     $<TARGET_OBJECTS:tf_core_cpu>
     $<TARGET_OBJECTS:tf_core_framework>
+    $<TARGET_OBJECTS:tf_core_profiler>
     $<TARGET_OBJECTS:tf_cc>
     $<TARGET_OBJECTS:tf_cc_ops>
     $<TARGET_OBJECTS:tf_core_ops>
     $<TARGET_OBJECTS:tf_core_direct_session>
     $<TARGET_OBJECTS:tf_grappler>
-    $<TARGET_OBJECTS:tf_tools_tfprof>
     $<TARGET_OBJECTS:tf_tools_transform_graph_lib>
     $<$<BOOL:${tensorflow_ENABLE_GRPC_SUPPORT}>:$<TARGET_OBJECTS:tf_core_distributed_runtime>>
     $<TARGET_OBJECTS:tf_core_kernels>
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 7151549276..0d49ecd0fd 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -141,6 +141,8 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/python/debug/lib/*_test.py"
     "${tensorflow_source_dir}/tensorflow/python/debug/wrappers/*_test.py"
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/*.py"
+    "${tensorflow_source_dir}/tensorflow/python/profiler/*_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/profiler/internal/*_test.py"
     "${tensorflow_source_dir}/tensorflow/python/saved_model/*_test.py"
     "${tensorflow_source_dir}/tensorflow/python/training/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/data/*_test.py"
@@ -150,8 +152,6 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     # NOTE: tensor_forest tests in tensor_forest/hybrid/... still don't pass.
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/*_test.py"
-    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/*_test.py"
-    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/*_test.py"
   )
 
   # exclude the ones we don't want
@@ -165,12 +165,12 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/__init__.py"
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/benchmark_test.py"
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/resource_variable_ops_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/profiler/pprof_profiler_test.py"
+    # flaky test
+    "${tensorflow_source_dir}/tensorflow/python/profiler/internal/run_metadata_test.py"
     "${tensorflow_source_dir}/tensorflow/python/saved_model/saved_model_test.py"
     # requires scipy
     "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/preprocessing/*_test.py"
-    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py"
-    # flaky test
-    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
@@ -377,9 +377,9 @@ if (tensorflow_BUILD_CC_TESTS)
     ${tf_cc_saved_model_test_srcs}
   )
 
-  file(GLOB tf_tools_tfprof_test_srcs
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*_test.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*_test.cc"
+  file(GLOB tf_core_profiler_test_srcs
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/*_test.cc"
+    "${tensorflow_source_dir}/tensorflow/core/profiler/internal/advisor/*_test.cc"
   )
 
   set(tf_test_lib tf_test_lib)
@@ -427,13 +427,13 @@ if (tensorflow_BUILD_CC_TESTS)
     LIBS ${tf_test_libs}
   )
 
-  file(GLOB_RECURSE tf_tools_tfprof_test_data
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/testdata/*"
+  file(GLOB_RECURSE tf_core_profiler_test_data
+    "${tensorflow_source_dir}/tensorflow/core/profiler/testdata/*"
   )
 
   AddTests(
-    SOURCES ${tf_tools_tfprof_test_srcs}
-    DATA ${tf_tools_tfprof_test_data}
+    SOURCES ${tf_core_profiler_test_srcs}
+    DATA ${tf_core_profiler_test_data}
     OBJECTS ${tf_obj_test}
     LIBS ${tf_test_libs}
   )
diff --git a/tensorflow/contrib/cmake/tf_tools.cmake b/tensorflow/contrib/cmake/tf_tools.cmake
index 6bc973d641..6ef9598963 100644
--- a/tensorflow/contrib/cmake/tf_tools.cmake
+++ b/tensorflow/contrib/cmake/tf_tools.cmake
@@ -147,23 +147,3 @@ target_link_libraries(${benchmark_model} PUBLIC
   ${tf_core_gpu_kernels_lib}
   ${tensorflow_EXTERNAL_LIBRARIES}
 )
-
-file(GLOB_RECURSE tf_tools_tfprof_srcs
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/*.proto"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*.h"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*.h"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*.cc"
-    "${tensorflow_source_dir}/tensorflow/core/platform/regexp.h"
-)
-
-file(GLOB_RECURSE tf_tools_tfprof_exclude_srcs
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/*test.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/advisor/*test.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/tfprof/internal/print_model_analysis.h"
-)
-list(REMOVE_ITEM tf_tools_tfprof_srcs ${tf_tools_tfprof_exclude_srcs})
-
-add_library(tf_tools_tfprof OBJECT ${tf_tools_tfprof_srcs})
-add_dependencies(tf_tools_tfprof tf_core_lib)
diff --git a/tensorflow/contrib/tfprof/BUILD b/tensorflow/contrib/tfprof/BUILD
index 944d767e21..9b9215234e 100644
--- a/tensorflow/contrib/tfprof/BUILD
+++ b/tensorflow/contrib/tfprof/BUILD
@@ -8,12 +8,25 @@ py_library(
     name = "tfprof",
     srcs = [
         "__init__.py",
+        "model_analyzer.py",
+        "tfprof_logger.py",
     ],
     srcs_version = "PY2AND3",
     visibility = ["//tensorflow:__subpackages__"],
     deps = [
-        "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer",
-        "//tensorflow/contrib/tfprof/python/tools/tfprof:tfprof_logger",
-        "//tensorflow/python:util",
+        "//tensorflow/python/profiler:model_analyzer",
+        "//tensorflow/python/profiler:tfprof_logger",
     ],
 )
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index 4fa1ccea69..eefd88793e 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -1,26 +1,24 @@
 # tfprof: TensorFlow Profiler and Beyond
 
-# Full Document in tensorflow/tools/tfprof/README.md
-
-Author: Xin Pan (xpan@google.com, github: panyx0718), Jon Shlens, Yao Zhang
-
-Consultants: Jon Shlens, Pete Warden
-
-###Major Features
-
-1.  Measure model parameters, float operations, tensor shapes.
-2.  Profile op execution times, requested memory size and device placement.
-3.  Inspect checkpoint tensors' shapes and their values.
-4.  Selectively group, filter, account and order ops.
-
-####tfprof supports 3 views to organize TensorFlow model profiles
-
-    *  code view: Stats are associated your Python codes and organized as call stacks.
-    *  scope view: Stats are organized as name scope hierarchies.
-    *  graph view: Stats are organized as Tensorflow Op graph.
-
-####For each view, there are 3 ways to display outputs:
-
-    *  stdout: Results are written to stdout.
-    *  timeline: Visualized in chrome browser as time series.
-    *  file: Results are dumped to file.
+<h1>Please use `tf.profiler.xxx` instead of `tf.contrib.tfprof.xxx`</h1>
+<h1>Full Document in tensorflow/core/profiler/README.md<h1>
+
+###Features
+
+* Profile model architectures
+  * parameters, tensor shapes, float operations, device placement, etc.
+* Profile model performance
+  * execution time, memory consumption
+  * Profile multiple steps.
+* Auto profile and advise.
+  * accelerator utilization check
+  * expensive operation check
+  * operation configuration check
+  * distributed runtime check (Not OSS)
+
+###Interfaces
+
+* Python API
+* Command Line
+* Visualization
+* C++ API (Not public, contact us if needed.)
diff --git a/tensorflow/contrib/tfprof/__init__.py b/tensorflow/contrib/tfprof/__init__.py
index f3952f6cb5..7a023e5d67 100644
--- a/tensorflow/contrib/tfprof/__init__.py
+++ b/tensorflow/contrib/tfprof/__init__.py
@@ -17,5 +17,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
-from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
+# pylint: disable=unused-import
+from tensorflow.contrib.tfprof import model_analyzer
+from tensorflow.contrib.tfprof import tfprof_logger
diff --git a/tensorflow/contrib/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/model_analyzer.py
new file mode 100644
index 0000000000..04b5063218
--- /dev/null
+++ b/tensorflow/contrib/tfprof/model_analyzer.py
@@ -0,0 +1,47 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Model Analyzer.
+
+Analyze model, including shape, params, time, memory, structure, etc.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+# Import the names here for existing users.
+# pylint: disable=unused-import
+from tensorflow.python.profiler.model_analyzer import advise as _advise
+from tensorflow.python.profiler.model_analyzer import ALL_ADVICE
+from tensorflow.python.profiler.model_analyzer import FLOAT_OPS_OPTIONS
+from tensorflow.python.profiler.model_analyzer import PRINT_ALL_TIMING_MEMORY
+from tensorflow.python.profiler.model_analyzer import profile as _profile
+from tensorflow.python.profiler.model_analyzer import Profiler
+from tensorflow.python.profiler.model_analyzer import TRAINABLE_VARS_PARAMS_STAT_OPTIONS
+
+_DEFAULT_PROFILE_OPTIONS = 0
+_DEFAULT_ADVISE_OPTIONS = 0
+
+
+def advise(graph, run_meta=None, tfprof_options=_DEFAULT_ADVISE_OPTIONS):
+  return _advise(graph, run_meta, tfprof_options)
+
+
+def print_model_analysis(graph,
+                         run_meta=None,
+                         op_log=None,
+                         tfprof_cmd='scope',
+                         tfprof_options=_DEFAULT_PROFILE_OPTIONS):
+  return _profile(graph, run_meta, op_log, tfprof_cmd, tfprof_options)
diff --git a/tensorflow/contrib/tfprof/tfprof_logger.py b/tensorflow/contrib/tfprof/tfprof_logger.py
new file mode 100644
index 0000000000..9588bd2985
--- /dev/null
+++ b/tensorflow/contrib/tfprof/tfprof_logger.py
@@ -0,0 +1,24 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Logging tensorflow::tfprof::OpLog.
+
+OpLog is used to add extra model information for offline analysis by tfprof.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import
+from tensorflow.python.profiler.tfprof_logger import write_op_log
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index d4097f4032..1d9bd665c0 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1716,7 +1716,7 @@ cc_library(
     ],
     visibility = [
         "//tensorflow/compiler:__subpackages__",
-        "//tensorflow/tools/tfprof:__subpackages__",
+        "//tensorflow/core/profiler:__subpackages__",
     ],
     deps = [":lib_internal"],
 )
diff --git a/tensorflow/tools/tfprof/BUILD b/tensorflow/core/profiler/BUILD
similarity index 82%
rename from tensorflow/tools/tfprof/BUILD
rename to tensorflow/core/profiler/BUILD
index 541df78e47..5c75eb9a83 100644
--- a/tensorflow/tools/tfprof/BUILD
+++ b/tensorflow/core/profiler/BUILD
@@ -30,10 +30,10 @@ cc_binary(
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
-        "//tensorflow/tools/tfprof/internal:tfprof_options",
-        "//tensorflow/tools/tfprof/internal:tfprof_stats",
-        "//tensorflow/tools/tfprof/internal:tfprof_utils",
-        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
+        "//tensorflow/core/profiler/internal:tfprof_options",
+        "//tensorflow/core/profiler/internal:tfprof_stats",
+        "//tensorflow/core/profiler/internal:tfprof_utils",
+        "//tensorflow/core/profiler/internal/advisor:tfprof_advisor",
         "@linenoise//:linenoise",
     ],
 )
diff --git a/tensorflow/tools/tfprof/README.md b/tensorflow/core/profiler/README.md
similarity index 100%
rename from tensorflow/tools/tfprof/README.md
rename to tensorflow/core/profiler/README.md
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/core/profiler/g3doc/advise.md
similarity index 97%
rename from tensorflow/tools/tfprof/g3doc/advise.md
rename to tensorflow/core/profiler/g3doc/advise.md
index e30add6fbf..6973c6a84c 100644
--- a/tensorflow/tools/tfprof/g3doc/advise.md
+++ b/tensorflow/core/profiler/g3doc/advise.md
@@ -6,7 +6,7 @@ tfprof analyzes profiles and generates advises for common issues.
 
 ```python
 # First create a profiler. See profiler tutorials for more details.
-profiler = model_analyzer.Profiler(sess.graph)
+profiler = tf.profiler.Profiler(sess.graph)
 run_meta = config_pb2.RunMetadata()
 _ = sess.run(r1,
              options=config_pb2.RunOptions(
@@ -15,10 +15,10 @@ _ = sess.run(r1,
 profiler.add_step(1, run_meta)
 
 # Then Start advise.
-profiler.advise(model_analyzer.ALL_ADVICE)
+profiler.advise(tf.contrib.tfprof.model_analyzer.ALL_ADVICE)
 
 # For one-shot API
-tf.contrib.tfprof.model_analyzer.advise(
+tf.profiler.advise(
     sess.graph, run_meta=run_metadata)
 ```
 
diff --git a/tensorflow/tools/tfprof/g3doc/code_timeline.png b/tensorflow/core/profiler/g3doc/code_timeline.png
similarity index 100%
rename from tensorflow/tools/tfprof/g3doc/code_timeline.png
rename to tensorflow/core/profiler/g3doc/code_timeline.png
diff --git a/tensorflow/tools/tfprof/g3doc/command_line.md b/tensorflow/core/profiler/g3doc/command_line.md
similarity index 94%
rename from tensorflow/tools/tfprof/g3doc/command_line.md
rename to tensorflow/core/profiler/g3doc/command_line.md
index 9f0de72e07..06ebe8e949 100644
--- a/tensorflow/tools/tfprof/g3doc/command_line.md
+++ b/tensorflow/core/profiler/g3doc/command_line.md
@@ -43,7 +43,7 @@ type for -account_type_regexes option.
 The following code snippet writes a OpLog file.
 
 ```python
-tf.contrib.tfprof.tfprof_logger.write_op_log(graph, log_dir, op_log=None)
+tf.profiler.write_op_log(graph, log_dir, op_log=None)
 ```
 
 <b>--checkpoint_path:</b> TensorFlow checkpoint (optional).
@@ -56,10 +56,10 @@ It defines _checkpoint_variable op type. It also provides checkpointed tensors'
 
 ```shell
 # Build the tool.
-bazel build --config opt tensorflow/tools/tfprof/...
+bazel build --config opt tensorflow/core/profiler/...
 
 # Help information, including detail 'option' instructions.
-bazel-bin/tensorflow/tools/tfprof/tfprof help
+bazel-bin/tensorflow/core/profiler/tfprof help
 ```
 
 #### Start `tfprof` Interactive Mode
@@ -72,17 +72,17 @@ bazel-bin/tensorflow/tools/tfprof/tfprof help
 # --checkpoint_path contains the model checkpoint data.
 #
 # Only includes model architecture, parameters and shapes.
-bazel-bin/tensorflow/tools/tfprof/tfprof \
+bazel-bin/tensorflow/core/profiler/tfprof \
     --graph_path=graph.pbtxt
 #
 # Additionally profile ops memory and timing.
-bazel-bin/tensorflow/tools/tfprof/tfprof \
+bazel-bin/tensorflow/core/profiler/tfprof \
     --graph_path=graph.pbtxt \
     --run_meta_path=run_meta \
 #
 # tfprof_log is used to define customized op types, float ops and code traces.
 # Use tfprof_logger.write_op_log() to create tfprof_log.
-bazel-bin/tensorflow/tools/tfprof/tfprof \
+bazel-bin/tensorflow/core/profiler/tfprof \
     --graph_path=graph.pbtxt \
     --run_meta_path=run_meta \
     --op_log_path=tfprof_log \
@@ -90,7 +90,7 @@ bazel-bin/tensorflow/tools/tfprof/tfprof \
 # Additionally profile checkpoint statistics and values.
 # Use '-account_type_regexes _checkpoint_variables' to select
 # checkpoint tensors.
-bazel-bin/tensorflow/tools/tfprof/tfprof \
+bazel-bin/tensorflow/core/profiler/tfprof \
     --graph_path=graph.pbtxt \
     --run_meta_path=run_meta \
     --op_log_path=tfprof_log \
@@ -101,7 +101,7 @@ bazel-bin/tensorflow/tools/tfprof/tfprof \
 
 ```python
 # Runs tfprof in one-shot.
-bazel-bin/tensorflow/tools/tfprof/tfprof scope \
+bazel-bin/tensorflow/core/profiler/tfprof scope \
     --graph_path=graph.pbtxt \
     --max_depth=3
 ```
@@ -243,7 +243,7 @@ In tfprof, 'device' is an op_type. For example, if op1 and op2 are placed on
 gpu:0. They share an operation type.
 
 ```shell
-bazel-bin/tensorflow/tools/tfprof/tfprof \
+bazel-bin/tensorflow/core/profiler/tfprof \
   --graph_path=/tmp/graph.pbtxt  \
   --run_meta_path=/tmp/run_meta
 
@@ -273,11 +273,11 @@ entry.types.append('pool_logit')
 Second, call write_op_log to write the OpLog proto.
 
 ```python
-tf.contrib.tfprof.tfprof_logger.write_op_log(
+tf.profiler.write_op_log(
     sess.graph, /tmp/my_op_log_dir, op_log)
 
 # Get run-time shape information in order to fill shapes and get flops.
-tf.contrib.tfprof.tfprof_logger.write_op_log(
+tf.profiler.write_op_log(
     sess.graph, /tmp/my_op_log_dir, op_log, run_meta)
 ```
 
@@ -291,7 +291,7 @@ _TFProfRoot (--/650 params)
   pool_logit/biases (10, 10/10 params)
 ```
 
-Note that `tf.contrib.tfprof.tfprof_logger.write_op_log(...)` automatically
+Note that `tf.profiler.write_op_log(...)` automatically
 assigns all `Variables` inside `tf.trainable_variables()` a customized
 operation type: `_trainable_variables`.
 
diff --git a/tensorflow/tools/tfprof/g3doc/graph_timeline.png b/tensorflow/core/profiler/g3doc/graph_timeline.png
similarity index 100%
rename from tensorflow/tools/tfprof/g3doc/graph_timeline.png
rename to tensorflow/core/profiler/g3doc/graph_timeline.png
diff --git a/tensorflow/tools/tfprof/g3doc/options.md b/tensorflow/core/profiler/g3doc/options.md
similarity index 100%
rename from tensorflow/tools/tfprof/g3doc/options.md
rename to tensorflow/core/profiler/g3doc/options.md
diff --git a/tensorflow/tools/tfprof/g3doc/profile_memory.md b/tensorflow/core/profiler/g3doc/profile_memory.md
similarity index 100%
rename from tensorflow/tools/tfprof/g3doc/profile_memory.md
rename to tensorflow/core/profiler/g3doc/profile_memory.md
diff --git a/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md b/tensorflow/core/profiler/g3doc/profile_model_architecture.md
similarity index 91%
rename from tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
rename to tensorflow/core/profiler/g3doc/profile_model_architecture.md
index cf99adcf02..6d49cdcb75 100644
--- a/tensorflow/tools/tfprof/g3doc/profile_model_architecture.md
+++ b/tensorflow/core/profiler/g3doc/profile_model_architecture.md
@@ -30,11 +30,11 @@ _TFProfRoot (--/930.58k params)
 
 # The Python API profiles tf.trainable_variables() instead of VariableV2.
 #
-# By default, it's printed to stdout. User can update tfprof_options['output']
+# By default, it's printed to stdout. User can update options['output']
 # to write to file. The result is always returned as a proto buffer.
-param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis(
+param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    tfprof_options=tf.contrib.tfprof.model_analyzer.
+    options=tf.contrib.tfprof.model_analyzer.
         TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
 sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
 ```
@@ -86,7 +86,7 @@ MatMul                   491.52k float_ops (0.00%, 0.00%)
 BiasAdd                  1.28k float_ops (0.00%, 0.00%)
 
 # You can also do that in Python API.
-tf.contrib.tfprof.model_analyzer.print_model_analysis(
+tf.profiler.profile(
     tf.get_default_graph(),
-    tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
+    options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
 ```
diff --git a/tensorflow/tools/tfprof/g3doc/profile_time.md b/tensorflow/core/profiler/g3doc/profile_time.md
similarity index 99%
rename from tensorflow/tools/tfprof/g3doc/profile_time.md
rename to tensorflow/core/profiler/g3doc/profile_time.md
index c865e577f7..db555b3617 100644
--- a/tensorflow/tools/tfprof/g3doc/profile_time.md
+++ b/tensorflow/core/profiler/g3doc/profile_time.md
@@ -102,7 +102,7 @@ opts['account_displayed_op_only'] = False
 opts['select'] = ['micros']
 
 tfprof_node = model_analyzer.print_model_analysis(
-    sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts)
+    sess.graph, run_meta, cmd='code', options=opts)
 ```
 
 You can generate some visualization in code view:
diff --git a/tensorflow/tools/tfprof/g3doc/python_api.md b/tensorflow/core/profiler/g3doc/python_api.md
similarity index 87%
rename from tensorflow/tools/tfprof/g3doc/python_api.md
rename to tensorflow/core/profiler/g3doc/python_api.md
index 581e66baa2..5dda419711 100644
--- a/tensorflow/tools/tfprof/g3doc/python_api.md
+++ b/tensorflow/core/profiler/g3doc/python_api.md
@@ -11,18 +11,18 @@
 ### Parameters and Shapes.
 ```python
 # Print trainable variable parameter statistics to stdout.
-param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis(
+param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    tfprof_options=tf.contrib.tfprof.model_analyzer.
+    options=tf.contrib.tfprof.model_analyzer.
         TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
 
 # Use code view to associate statistics with Python codes.
 opts = tf.contrib.tfprof.model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
 opts['show_name_regexes'] = ['.*my_code1.py.*', '.*my_code2.py.*']
-param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis(
+param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    tfprof_cmd='code'
-    tfprof_options=opts)
+    cmd='code'
+    options=opts)
 
 # param_stats can be tensorflow.tfprof.TFGraphNodeProto or
 # tensorflow.tfprof.TFMultiGraphNodeProto, depending on the view.
@@ -36,9 +36,9 @@ sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
 ``` python
 # Print to stdout an analysis of the number of floating point operations in the
 # model broken down by individual operations.
-tf.contrib.tfprof.model_analyzer.print_model_analysis(
+tf.profiler.profile(
     tf.get_default_graph(),
-    tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
+    options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
 ```
 
 ### Time and Memory
@@ -68,18 +68,18 @@ information of the model.
 # broken down by python codes.
 opts = tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
 opts['show_name_regexes'] = ['.*my_code.py.*']
-tf.contrib.tfprof.model_analyzer.print_model_analysis(
+tf.profiler.profile(
     tf.get_default_graph(),
     run_meta=run_metadata,
-    tfprof_cmd='code',
-    tfprof_options=opts)
+    cmd='code',
+    options=opts)
 
 # Print to stdout an analysis of the memory usage and the timing information
 # broken down by operations.
-tf.contrib.tfprof.model_analyzer.print_model_analysis(
+tf.profiler.profile(
     tf.get_default_graph(),
     run_meta=run_metadata,
-    tfprof_options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
+    options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
 ```
 
 ### Visualize
diff --git a/tensorflow/tools/tfprof/g3doc/scope_timeline.png b/tensorflow/core/profiler/g3doc/scope_timeline.png
similarity index 100%
rename from tensorflow/tools/tfprof/g3doc/scope_timeline.png
rename to tensorflow/core/profiler/g3doc/scope_timeline.png
diff --git a/tensorflow/tools/tfprof/internal/BUILD b/tensorflow/core/profiler/internal/BUILD
similarity index 90%
rename from tensorflow/tools/tfprof/internal/BUILD
rename to tensorflow/core/profiler/internal/BUILD
index 25c895af5b..abb4534f77 100644
--- a/tensorflow/tools/tfprof/internal/BUILD
+++ b/tensorflow/core/profiler/internal/BUILD
@@ -28,7 +28,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -55,7 +55,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -76,7 +76,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -97,7 +97,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -118,7 +118,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -137,7 +137,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -152,7 +152,7 @@ cc_library(
         ":tfprof_utils",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -172,7 +172,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -194,7 +194,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:regexp_internal",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -217,7 +217,7 @@ tf_cc_test(
         ":tfprof_tf_testlib",
         ":tfprof_utils",
         "//tensorflow/c:checkpoint_reader",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -235,7 +235,7 @@ tf_cc_test(
         ":tfprof_stats",
         ":tfprof_tf_testlib",
         ":tfprof_utils",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -259,7 +259,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core:lib",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -282,8 +282,8 @@ cc_library(
         "//tensorflow/c:checkpoint_reader",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
-        "//tensorflow/tools/tfprof:protos_all_cc",
-        "//tensorflow/tools/tfprof/internal/advisor:tfprof_advisor",
+        "//tensorflow/core/profiler:protos_all_cc",
+        "//tensorflow/core/profiler/internal/advisor:tfprof_advisor",
     ],
     alwayslink = 1,
 )
@@ -307,7 +307,7 @@ tf_cc_test(
         ":tfprof_tf_testlib",
         ":tfprof_utils",
         "//tensorflow/c:checkpoint_reader",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -319,7 +319,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
@@ -339,7 +339,7 @@ tf_cc_test(
         ":tfprof_tf_testlib",
         ":tfprof_utils",
         "//tensorflow/c:checkpoint_reader",
-        "//tensorflow/tools/tfprof:protos_all_cc",
+        "//tensorflow/core/profiler:protos_all_cc",
     ],
 )
 
diff --git a/tensorflow/tools/tfprof/internal/advisor/BUILD b/tensorflow/core/profiler/internal/advisor/BUILD
similarity index 90%
rename from tensorflow/tools/tfprof/internal/advisor/BUILD
rename to tensorflow/core/profiler/internal/advisor/BUILD
index 629322373a..e50145bf46 100644
--- a/tensorflow/tools/tfprof/internal/advisor/BUILD
+++ b/tensorflow/core/profiler/internal/advisor/BUILD
@@ -15,7 +15,7 @@ cc_library(
     hdrs = ["checker.h"],
     deps = [
         "//tensorflow/core:lib",
-        "//tensorflow/tools/tfprof/internal:tfprof_stats",
+        "//tensorflow/core/profiler/internal:tfprof_stats",
     ],
 )
 
@@ -24,7 +24,7 @@ cc_library(
     srcs = ["internal_checker_runner_dummy.cc"],
     hdrs = ["internal_checker_runner.h"],
     deps = [
-        "//tensorflow/tools/tfprof/internal:tfprof_utils",
+        "//tensorflow/core/profiler/internal:tfprof_utils",
     ],
 )
 
@@ -69,7 +69,7 @@ tf_cc_test(
     srcs = ["tfprof_advisor_test.cc"],
     deps = [
         ":tfprof_advisor",
-        "//tensorflow/tools/tfprof/internal:tfprof_tf_testlib",
+        "//tensorflow/core/profiler/internal:tfprof_tf_testlib",
     ],
 )
 
diff --git a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h b/tensorflow/core/profiler/internal/advisor/accelerator_utilization_checker.h
similarity index 91%
rename from tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
rename to tensorflow/core/profiler/internal/advisor/accelerator_utilization_checker.h
index 074b8e57b0..c6544fe0b0 100644
--- a/tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h
+++ b/tensorflow/core/profiler/internal/advisor/accelerator_utilization_checker.h
@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 // This checker checks the accelerator's utilization.
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_ACCELERATOR_UTILIZATION_CHECKER_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_ACCELERATOR_UTILIZATION_CHECKER_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_ACCELERATOR_UTILIZATION_CHECKER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_ACCELERATOR_UTILIZATION_CHECKER_H_
 
-#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+#include "tensorflow/core/profiler/internal/advisor/checker.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -106,4 +106,4 @@ class AcceleratorUtilizationChecker : public Checker {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_ACCELERATOR_UTILIZATION_CHECKER_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_ACCELERATOR_UTILIZATION_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/checker.h b/tensorflow/core/profiler/internal/advisor/checker.h
similarity index 80%
rename from tensorflow/tools/tfprof/internal/advisor/checker.h
rename to tensorflow/core/profiler/internal/advisor/checker.h
index 3ce80cd8c4..4b5ebcf9e8 100644
--- a/tensorflow/tools/tfprof/internal/advisor/checker.h
+++ b/tensorflow/core/profiler/internal/advisor/checker.h
@@ -13,12 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_CHECKER_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_CHECKER_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_CHECKER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_CHECKER_H_
 
 #include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -49,4 +49,4 @@ class Checker {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_CHECKER_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h b/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
similarity index 93%
rename from tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
rename to tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
index 14e30edd87..bc94346d2d 100644
--- a/tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h
+++ b/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 // This checker checks the most expensive operations.
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_EXPENSIVE_OPERATION_CHECKER_H_
 
-#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+#include "tensorflow/core/profiler/internal/advisor/checker.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -135,4 +135,4 @@ class ExpensiveOperationChecker : public Checker {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_EXPENSIVE_OP_CHECKER_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_EXPENSIVE_OP_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h b/tensorflow/core/profiler/internal/advisor/internal_checker_runner.h
similarity index 67%
rename from tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
rename to tensorflow/core/profiler/internal/advisor/internal_checker_runner.h
index ed8ae571b6..ec52741b19 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h
+++ b/tensorflow/core/profiler/internal/advisor/internal_checker_runner.h
@@ -13,12 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
 
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_options.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -31,4 +31,4 @@ AdviceProto RunInternalCheckers(const AdvisorOptionsProto& options,
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_INTERNAL_CHECKER_RUNNER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc b/tensorflow/core/profiler/internal/advisor/internal_checker_runner_dummy.cc
similarity index 92%
rename from tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
rename to tensorflow/core/profiler/internal/advisor/internal_checker_runner_dummy.cc
index 67962c8e8b..436d943cdd 100644
--- a/tensorflow/tools/tfprof/internal/advisor/internal_checker_runner_dummy.cc
+++ b/tensorflow/core/profiler/internal/advisor/internal_checker_runner_dummy.cc
@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-#include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
+#include "tensorflow/core/profiler/internal/advisor/internal_checker_runner.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h b/tensorflow/core/profiler/internal/advisor/operation_checker.h
similarity index 88%
rename from tensorflow/tools/tfprof/internal/advisor/operation_checker.h
rename to tensorflow/core/profiler/internal/advisor/operation_checker.h
index 4d0d68e3bf..08f6d3c389 100644
--- a/tensorflow/tools/tfprof/internal/advisor/operation_checker.h
+++ b/tensorflow/core/profiler/internal/advisor/operation_checker.h
@@ -14,10 +14,10 @@ limitations under the License.
 ==============================================================================*/
 // This checker checks common wrong configurations of operations.
 //
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_OPERATION_CHECKER_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_OPERATION_CHECKER_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_OPERATION_CHECKER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_OPERATION_CHECKER_H_
 
-#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
+#include "tensorflow/core/profiler/internal/advisor/checker.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -73,4 +73,4 @@ class OperationChecker : public Checker {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_OPERATION_CHECKER_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_OPERATION_CHECKER_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h b/tensorflow/core/profiler/internal/advisor/tfprof_advisor.h
similarity index 78%
rename from tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
rename to tensorflow/core/profiler/internal/advisor/tfprof_advisor.h
index d2257fb9b5..42bd6d5438 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h
+++ b/tensorflow/core/profiler/internal/advisor/tfprof_advisor.h
@@ -13,15 +13,15 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_TFPROF_ADVICE_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_TFPROF_ADVICE_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_TFPROF_ADVICE_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_TFPROF_ADVICE_H_
 
-#include "tensorflow/tools/tfprof/internal/advisor/accelerator_utilization_checker.h"
-#include "tensorflow/tools/tfprof/internal/advisor/checker.h"
-#include "tensorflow/tools/tfprof/internal/advisor/expensive_operation_checker.h"
-#include "tensorflow/tools/tfprof/internal/advisor/internal_checker_runner.h"
-#include "tensorflow/tools/tfprof/internal/advisor/operation_checker.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
+#include "tensorflow/core/profiler/internal/advisor/accelerator_utilization_checker.h"
+#include "tensorflow/core/profiler/internal/advisor/checker.h"
+#include "tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h"
+#include "tensorflow/core/profiler/internal/advisor/internal_checker_runner.h"
+#include "tensorflow/core/profiler/internal/advisor/operation_checker.h"
+#include "tensorflow/core/profiler/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -78,4 +78,4 @@ class Advisor {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_ADVISOR_TFPROF_ADVICE_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_ADVISOR_TFPROF_ADVICE_H_
diff --git a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc b/tensorflow/core/profiler/internal/advisor/tfprof_advisor_test.cc
similarity index 98%
rename from tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
rename to tensorflow/core/profiler/internal/advisor/tfprof_advisor_test.cc
index 3b40253954..096c1d915c 100644
--- a/tensorflow/tools/tfprof/internal/advisor/tfprof_advisor_test.cc
+++ b/tensorflow/core/profiler/internal/advisor/tfprof_advisor_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
+#include "tensorflow/core/profiler/internal/advisor/tfprof_advisor.h"
 
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/platform/env.h"
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.cc b/tensorflow/core/profiler/internal/print_model_analysis.cc
similarity index 92%
rename from tensorflow/tools/tfprof/internal/print_model_analysis.cc
rename to tensorflow/core/profiler/internal/print_model_analysis.cc
index 5a9c44d8e6..a912f59837 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.cc
+++ b/tensorflow/core/profiler/internal/print_model_analysis.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
+#include "tensorflow/core/profiler/internal/print_model_analysis.h"
 
 #include <stdio.h>
 #include <memory>
@@ -23,12 +23,12 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/advisor/tfprof_advisor.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/tfprof_options.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/print_model_analysis.h b/tensorflow/core/profiler/internal/print_model_analysis.h
similarity index 87%
rename from tensorflow/tools/tfprof/internal/print_model_analysis.h
rename to tensorflow/core/profiler/internal/print_model_analysis.h
index 46db63646d..de8216d54b 100644
--- a/tensorflow/tools/tfprof/internal/print_model_analysis.h
+++ b/tensorflow/core/profiler/internal/print_model_analysis.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_PRINT_MODEL_ANALYSIS_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_PRINT_MODEL_ANALYSIS_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_PRINT_MODEL_ANALYSIS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_PRINT_MODEL_ANALYSIS_H_
 
 #include <string>
 
@@ -53,4 +53,4 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_PRINT_MODEL_ANALYSIS_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_PRINT_MODEL_ANALYSIS_H_
diff --git a/tensorflow/tools/tfprof/internal/testdata/ckpt.data-00000-of-00001 b/tensorflow/core/profiler/internal/testdata/ckpt.data-00000-of-00001
similarity index 100%
rename from tensorflow/tools/tfprof/internal/testdata/ckpt.data-00000-of-00001
rename to tensorflow/core/profiler/internal/testdata/ckpt.data-00000-of-00001
diff --git a/tensorflow/tools/tfprof/internal/testdata/ckpt.index b/tensorflow/core/profiler/internal/testdata/ckpt.index
similarity index 100%
rename from tensorflow/tools/tfprof/internal/testdata/ckpt.index
rename to tensorflow/core/profiler/internal/testdata/ckpt.index
diff --git a/tensorflow/tools/tfprof/internal/testdata/ckpt.meta b/tensorflow/core/profiler/internal/testdata/ckpt.meta
similarity index 100%
rename from tensorflow/tools/tfprof/internal/testdata/ckpt.meta
rename to tensorflow/core/profiler/internal/testdata/ckpt.meta
diff --git a/tensorflow/tools/tfprof/internal/testdata/graph.pbtxt b/tensorflow/core/profiler/internal/testdata/graph.pbtxt
similarity index 100%
rename from tensorflow/tools/tfprof/internal/testdata/graph.pbtxt
rename to tensorflow/core/profiler/internal/testdata/graph.pbtxt
diff --git a/tensorflow/tools/tfprof/internal/testdata/run_meta b/tensorflow/core/profiler/internal/testdata/run_meta
similarity index 100%
rename from tensorflow/tools/tfprof/internal/testdata/run_meta
rename to tensorflow/core/profiler/internal/testdata/run_meta
diff --git a/tensorflow/tools/tfprof/internal/testdata/tfprof_log b/tensorflow/core/profiler/internal/testdata/tfprof_log
similarity index 100%
rename from tensorflow/tools/tfprof/internal/testdata/tfprof_log
rename to tensorflow/core/profiler/internal/testdata/tfprof_log
diff --git a/tensorflow/tools/tfprof/internal/tfprof_code.cc b/tensorflow/core/profiler/internal/tfprof_code.cc
similarity index 98%
rename from tensorflow/tools/tfprof/internal/tfprof_code.cc
rename to tensorflow/core/profiler/internal/tfprof_code.cc
index 4a345dd17d..564890a6fb 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_code.cc
+++ b/tensorflow/core/profiler/internal/tfprof_code.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_code.h"
+#include "tensorflow/core/profiler/internal/tfprof_code.h"
 
 #include <stdio.h>
 #include <utility>
@@ -25,7 +25,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/regexp.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_code.h b/tensorflow/core/profiler/internal/tfprof_code.h
similarity index 79%
rename from tensorflow/tools/tfprof/internal/tfprof_code.h
rename to tensorflow/core/profiler/internal/tfprof_code.h
index 613f626739..cc5a4fe6ba 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_code.h
+++ b/tensorflow/core/profiler/internal/tfprof_code.h
@@ -16,8 +16,8 @@ limitations under the License.
 // Build a tree structure based on the TensorFlow model's python code stacks.
 // Stats are aggregated from descendants from ancestors.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_CODE_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_CODE_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_CODE_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_CODE_H_
 
 #include <map>
 #include <memory>
@@ -27,13 +27,13 @@ limitations under the License.
 #include "tensorflow/c/checkpoint_reader.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_show_multi.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_timeline.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_show_multi.h"
+#include "tensorflow/core/profiler/internal/tfprof_timeline.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -77,4 +77,4 @@ class TFCode : public TFMultiShow {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_CODE_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_CODE_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_constants.h b/tensorflow/core/profiler/internal/tfprof_constants.h
similarity index 86%
rename from tensorflow/tools/tfprof/internal/tfprof_constants.h
rename to tensorflow/core/profiler/internal/tfprof_constants.h
index e495128728..6a4eaaa890 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_constants.h
+++ b/tensorflow/core/profiler/internal/tfprof_constants.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_CONSTANTS_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_CONSTANTS_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_CONSTANTS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_CONSTANTS_H_
 
 namespace tensorflow {
 namespace tfprof {
@@ -34,4 +34,4 @@ static const char* const kCkptVarType = "_checkpoint_variables";
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_CONSTANTS_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_CONSTANTS_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.cc b/tensorflow/core/profiler/internal/tfprof_graph.cc
similarity index 97%
rename from tensorflow/tools/tfprof/internal/tfprof_graph.cc
rename to tensorflow/core/profiler/internal/tfprof_graph.cc
index 4c562ae840..9b7063553c 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.cc
+++ b/tensorflow/core/profiler/internal/tfprof_graph.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_graph.h"
+#include "tensorflow/core/profiler/internal/tfprof_graph.h"
 
 #include <stdio.h>
 #include <utility>
@@ -21,8 +21,8 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/regexp.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_tensor.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_tensor.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_graph.h b/tensorflow/core/profiler/internal/tfprof_graph.h
similarity index 84%
rename from tensorflow/tools/tfprof/internal/tfprof_graph.h
rename to tensorflow/core/profiler/internal/tfprof_graph.h
index 194a21f0cc..422b67aa62 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_graph.h
+++ b/tensorflow/core/profiler/internal/tfprof_graph.h
@@ -16,8 +16,8 @@ limitations under the License.
 // Build a graph structure based on op inputs/outputs. The graph is a directed
 // acyclic graph pointing *from outputs to inputs*.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_GRAPH_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_GRAPH_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_GRAPH_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_GRAPH_H_
 
 #include <deque>
 #include <map>
@@ -29,11 +29,11 @@ limitations under the License.
 #include "tensorflow/c/checkpoint_reader.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_show.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -86,4 +86,4 @@ class TFGraph : public TFShow {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_GRAPH_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_GRAPH_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.cc b/tensorflow/core/profiler/internal/tfprof_node.cc
similarity index 98%
rename from tensorflow/tools/tfprof/internal/tfprof_node.cc
rename to tensorflow/core/profiler/internal/tfprof_node.cc
index 54e14b1dd7..732576d29c 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.cc
+++ b/tensorflow/core/profiler/internal/tfprof_node.cc
@@ -13,9 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
 
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node.h b/tensorflow/core/profiler/internal/tfprof_node.h
similarity index 98%
rename from tensorflow/tools/tfprof/internal/tfprof_node.h
rename to tensorflow/core/profiler/internal/tfprof_node.h
index f4da0ab9ee..9142ad51c5 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node.h
+++ b/tensorflow/core/profiler/internal/tfprof_node.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_NODE_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_NODE_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_NODE_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_NODE_H_
 
 #include <map>
 #include <set>
@@ -31,8 +31,8 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/regexp.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -533,4 +533,4 @@ bool IsPlacedOnAccelerator(const string& device);
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_NODE_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_NODE_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc b/tensorflow/core/profiler/internal/tfprof_node_show.cc
similarity index 99%
rename from tensorflow/tools/tfprof/internal/tfprof_node_show.cc
rename to tensorflow/core/profiler/internal/tfprof_node_show.cc
index e9073baf0d..8dc88166d0 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node_show.cc
+++ b/tensorflow/core/profiler/internal/tfprof_node_show.cc
@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-#include "tensorflow/tools/tfprof/internal/tfprof_node_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_node_show.h"
 
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
diff --git a/tensorflow/tools/tfprof/internal/tfprof_node_show.h b/tensorflow/core/profiler/internal/tfprof_node_show.h
similarity index 85%
rename from tensorflow/tools/tfprof/internal/tfprof_node_show.h
rename to tensorflow/core/profiler/internal/tfprof_node_show.h
index a1091f4d66..97d22a29d6 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_node_show.h
+++ b/tensorflow/core/profiler/internal/tfprof_node_show.h
@@ -21,8 +21,8 @@ limitations under the License.
 // ScopeNode and GraphNode each maps to one TFGraphNode.
 // CodeNode and OpNode each maps to one TFMultiGraphNode.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_NODE_SHOW_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_NODE_SHOW_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_NODE_SHOW_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_NODE_SHOW_H_
 
 #include <algorithm>
 #include <string>
@@ -30,11 +30,11 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -127,4 +127,4 @@ class OpNode : public ShowMultiNode {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_NODE_SHOW_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_NODE_SHOW_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.cc b/tensorflow/core/profiler/internal/tfprof_op.cc
similarity index 98%
rename from tensorflow/tools/tfprof/internal/tfprof_op.cc
rename to tensorflow/core/profiler/internal/tfprof_op.cc
index 77a2593623..46a81da80f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.cc
+++ b/tensorflow/core/profiler/internal/tfprof_op.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_op.h"
+#include "tensorflow/core/profiler/internal/tfprof_op.h"
 
 #include <stdio.h>
 #include <utility>
@@ -21,8 +21,8 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/regexp.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_tensor.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_tensor.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_op.h b/tensorflow/core/profiler/internal/tfprof_op.h
similarity index 80%
rename from tensorflow/tools/tfprof/internal/tfprof_op.h
rename to tensorflow/core/profiler/internal/tfprof_op.h
index 34812f54be..9e20f5c3f4 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_op.h
+++ b/tensorflow/core/profiler/internal/tfprof_op.h
@@ -15,8 +15,8 @@ limitations under the License.
 
 // Build a flat structure of ops.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_OP_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_OP_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_OP_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_OP_H_
 
 #include <deque>
 #include <map>
@@ -28,11 +28,11 @@ limitations under the License.
 #include "tensorflow/c/checkpoint_reader.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_show_multi.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_show_multi.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -74,4 +74,4 @@ class TFOp : public TFMultiShow {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_OP_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_OP_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.cc b/tensorflow/core/profiler/internal/tfprof_options.cc
similarity index 98%
rename from tensorflow/tools/tfprof/internal/tfprof_options.cc
rename to tensorflow/core/profiler/internal/tfprof_options.cc
index 83c275756b..53e401dd60 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.cc
+++ b/tensorflow/core/profiler/internal/tfprof_options.cc
@@ -13,13 +13,13 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
 
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/tools/tfprof/tfprof_options.pb.h"
+#include "tensorflow/core/profiler/tfprof_options.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_options.h b/tensorflow/core/profiler/internal/tfprof_options.h
similarity index 95%
rename from tensorflow/tools/tfprof/internal/tfprof_options.h
rename to tensorflow/core/profiler/internal/tfprof_options.h
index d39333e3fc..6d0c213b3d 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_options.h
+++ b/tensorflow/core/profiler/internal/tfprof_options.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_OPTIONS_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_OPTIONS_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_OPTIONS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_OPTIONS_H_
 
 #include <set>
 #include <string>
@@ -151,4 +151,4 @@ tensorflow::Status ParseOutput(const string& output_opt, string* output_type,
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_OPTIONS_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_OPTIONS_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_scope.cc b/tensorflow/core/profiler/internal/tfprof_scope.cc
similarity index 97%
rename from tensorflow/tools/tfprof/internal/tfprof_scope.cc
rename to tensorflow/core/profiler/internal/tfprof_scope.cc
index 2b9e56e451..68becefad6 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_scope.cc
+++ b/tensorflow/core/profiler/internal/tfprof_scope.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_scope.h"
+#include "tensorflow/core/profiler/internal/tfprof_scope.h"
 
 #include <stdio.h>
 #include <utility>
@@ -22,8 +22,8 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/regexp.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_tensor.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_tensor.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_scope.h b/tensorflow/core/profiler/internal/tfprof_scope.h
similarity index 82%
rename from tensorflow/tools/tfprof/internal/tfprof_scope.h
rename to tensorflow/core/profiler/internal/tfprof_scope.h
index ca5cabe457..3c87caa92b 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_scope.h
+++ b/tensorflow/core/profiler/internal/tfprof_scope.h
@@ -17,8 +17,8 @@ limitations under the License.
 // For example, 'name1/name2' is a child of 'name1'.
 // Stats are aggregated from descendants from ancestors.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SCOPE_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SCOPE_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SCOPE_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SCOPE_H_
 
 #include <map>
 #include <memory>
@@ -28,11 +28,11 @@ limitations under the License.
 #include "tensorflow/c/checkpoint_reader.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_show.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -74,4 +74,4 @@ class TFScope : public TFShow {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SCOPE_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SCOPE_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.cc b/tensorflow/core/profiler/internal/tfprof_show.cc
similarity index 99%
rename from tensorflow/tools/tfprof/internal/tfprof_show.cc
rename to tensorflow/core/profiler/internal/tfprof_show.cc
index 6c1183a4d5..aae7517d6f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.cc
+++ b/tensorflow/core/profiler/internal/tfprof_show.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_show.h"
 
 #include <memory>
 #include <set>
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show.h b/tensorflow/core/profiler/internal/tfprof_show.h
similarity index 86%
rename from tensorflow/tools/tfprof/internal/tfprof_show.h
rename to tensorflow/core/profiler/internal/tfprof_show.h
index 95513e086f..4c97fad1af 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show.h
+++ b/tensorflow/core/profiler/internal/tfprof_show.h
@@ -15,8 +15,8 @@ limitations under the License.
 
 // Parent class and utilities for tfprof_graph and tfprof_scope.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SHOW_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SHOW_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SHOW_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SHOW_H_
 
 #include <algorithm>
 #include <string>
@@ -26,14 +26,14 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node_show.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_tensor.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_timeline.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_node_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_tensor.h"
+#include "tensorflow/core/profiler/internal/tfprof_timeline.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -135,4 +135,4 @@ string FormatAcceleratorExecTime(const T* node, const Options& opts) {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SHOW_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SHOW_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc b/tensorflow/core/profiler/internal/tfprof_show_multi.cc
similarity index 98%
rename from tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
rename to tensorflow/core/profiler/internal/tfprof_show_multi.cc
index 84d542714a..651b754386 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.cc
+++ b/tensorflow/core/profiler/internal/tfprof_show_multi.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_show_multi.h"
+#include "tensorflow/core/profiler/internal/tfprof_show_multi.h"
 
 #include <memory>
 #include <set>
@@ -22,7 +22,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/regexp.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_scope.h"
+#include "tensorflow/core/profiler/internal/tfprof_scope.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h b/tensorflow/core/profiler/internal/tfprof_show_multi.h
similarity index 84%
rename from tensorflow/tools/tfprof/internal/tfprof_show_multi.h
rename to tensorflow/core/profiler/internal/tfprof_show_multi.h
index ce309816a9..e89ee4f95b 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_multi.h
+++ b/tensorflow/core/profiler/internal/tfprof_show_multi.h
@@ -15,8 +15,8 @@ limitations under the License.
 
 // Parent class and utilities for tfprof_code.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SHOW_MULTI_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SHOW_MULTI_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SHOW_MULTI_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SHOW_MULTI_H_
 
 #include <algorithm>
 #include <string>
@@ -26,15 +26,15 @@ limitations under the License.
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node_show.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_show.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_tensor.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_timeline.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_node_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_tensor.h"
+#include "tensorflow/core/profiler/internal/tfprof_timeline.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -117,4 +117,4 @@ class TFMultiShow {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_SHOW_MULTI_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_SHOW_MULTI_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc b/tensorflow/core/profiler/internal/tfprof_show_test.cc
similarity index 93%
rename from tensorflow/tools/tfprof/internal/tfprof_show_test.cc
rename to tensorflow/core/profiler/internal/tfprof_show_test.cc
index 46b6ebc09a..c3e98f8ea2 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_show_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_show_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
 
 #include <utility>
 
@@ -23,11 +23,11 @@ limitations under the License.
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -36,7 +36,7 @@ class TFProfShowTest : public ::testing::Test {
   TFProfShowTest() {
     string graph_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/graph.pbtxt");
+                     "core/profiler/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
@@ -45,18 +45,18 @@ class TFProfShowTest : public ::testing::Test {
         new tensorflow::RunMetadata());
     string run_meta_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/run_meta");
+                     "core/profiler/internal/testdata/run_meta");
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
     std::unique_ptr<OpLog> op_log_pb(new OpLog());
     string op_log_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/tfprof_log");
+                     "core/profiler/internal/testdata/tfprof_log");
     TF_CHECK_OK(ReadBinaryProto(Env::Default(), op_log_path, op_log_pb.get()));
 
     string ckpt_path = io::JoinPath(testing::TensorFlowSrcRoot(),
-                                    "tools/tfprof/internal/testdata/ckpt");
+                                    "core/profiler/internal/testdata/ckpt");
     TF_Status* status = TF_NewStatus();
     std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader(
         new checkpoint::CheckpointReader(ckpt_path, status));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.cc b/tensorflow/core/profiler/internal/tfprof_stats.cc
similarity index 98%
rename from tensorflow/tools/tfprof/internal/tfprof_stats.cc
rename to tensorflow/core/profiler/internal/tfprof_stats.cc
index 64da7ae7cf..175cdb5eba 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.cc
+++ b/tensorflow/core/profiler/internal/tfprof_stats.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
 
 #include <stdio.h>
 #include <utility>
@@ -21,7 +21,7 @@ limitations under the License.
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/regexp.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_timeline.h"
+#include "tensorflow/core/profiler/internal/tfprof_timeline.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats.h b/tensorflow/core/profiler/internal/tfprof_stats.h
similarity index 81%
rename from tensorflow/tools/tfprof/internal/tfprof_stats.h
rename to tensorflow/core/profiler/internal/tfprof_stats.h
index b26d274f80..fd2628fd8b 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats.h
+++ b/tensorflow/core/profiler/internal/tfprof_stats.h
@@ -20,8 +20,8 @@ limitations under the License.
 // 3. Accept command and options to selectively aggregate stats for analysis
 //    and print out the results.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_STATS_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_STATS_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_STATS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_STATS_H_
 
 #include <map>
 #include <memory>
@@ -35,16 +35,16 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_code.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_graph.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_op.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_scope.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_show.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_code.h"
+#include "tensorflow/core/profiler/internal/tfprof_graph.h"
+#include "tensorflow/core/profiler/internal/tfprof_node.h"
+#include "tensorflow/core/profiler/internal/tfprof_op.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_scope.h"
+#include "tensorflow/core/profiler/internal/tfprof_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -107,4 +107,4 @@ class TFStats {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_STATS_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_STATS_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc b/tensorflow/core/profiler/internal/tfprof_stats_test.cc
similarity index 96%
rename from tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
rename to tensorflow/core/profiler/internal/tfprof_stats_test.cc
index 00d5d8cdb3..4f7db87a00 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_stats_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_stats_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
 
 #include <utility>
 
@@ -24,11 +24,11 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -37,7 +37,7 @@ class TFProfStatsTest : public ::testing::Test {
   TFProfStatsTest() {
     string graph_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/graph.pbtxt");
+                     "core/profiler/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
@@ -46,18 +46,18 @@ class TFProfStatsTest : public ::testing::Test {
         new tensorflow::RunMetadata());
     string run_meta_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/run_meta");
+                     "core/profiler/internal/testdata/run_meta");
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
     std::unique_ptr<OpLog> op_log_pb(new OpLog());
     string op_log_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/tfprof_log");
+                     "core/profiler/internal/testdata/tfprof_log");
     TF_CHECK_OK(ReadBinaryProto(Env::Default(), op_log_path, op_log_pb.get()));
 
     string ckpt_path = io::JoinPath(testing::TensorFlowSrcRoot(),
-                                    "tools/tfprof/internal/testdata/ckpt");
+                                    "core/profiler/internal/testdata/ckpt");
     TF_Status* status = TF_NewStatus();
     std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader(
         new checkpoint::CheckpointReader(ckpt_path, status));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor.cc b/tensorflow/core/profiler/internal/tfprof_tensor.cc
similarity index 97%
rename from tensorflow/tools/tfprof/internal/tfprof_tensor.cc
rename to tensorflow/core/profiler/internal/tfprof_tensor.cc
index 297258fee1..d8ec086315 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor.cc
+++ b/tensorflow/core/profiler/internal/tfprof_tensor.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_tensor.h"
+#include "tensorflow/core/profiler/internal/tfprof_tensor.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor.h b/tensorflow/core/profiler/internal/tfprof_tensor.h
similarity index 94%
rename from tensorflow/tools/tfprof/internal/tfprof_tensor.h
rename to tensorflow/core/profiler/internal/tfprof_tensor.h
index 5804837ffb..d6c4ae1311 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor.h
+++ b/tensorflow/core/profiler/internal/tfprof_tensor.h
@@ -19,8 +19,8 @@ limitations under the License.
 //    is not supported by TensorFlow CheckPointReader library, though it is
 //    supported in current code.
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_TENSOR_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_TENSOR_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_TENSOR_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_TENSOR_H_
 
 #include <typeinfo>
 
@@ -28,7 +28,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -118,4 +118,4 @@ class TFProfTensor {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_TENSOR_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_TENSOR_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc b/tensorflow/core/profiler/internal/tfprof_tensor_test.cc
similarity index 97%
rename from tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
rename to tensorflow/core/profiler/internal/tfprof_tensor_test.cc
index 0482657ca5..782f1a6117 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_tensor_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_tensor_test.cc
@@ -19,11 +19,11 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -32,7 +32,7 @@ class TFProfTensorTest : public ::testing::Test {
   TFProfTensorTest() {
     string graph_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/graph.pbtxt");
+                     "core/profiler/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
@@ -41,7 +41,7 @@ class TFProfTensorTest : public ::testing::Test {
     std::unique_ptr<OpLog> op_log_pb;
 
     string ckpt_path = io::JoinPath(testing::TensorFlowSrcRoot(),
-                                    "tools/tfprof/internal/testdata/ckpt");
+                                    "core/profiler/internal/testdata/ckpt");
     TF_Status* status = TF_NewStatus();
     std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader(
         new checkpoint::CheckpointReader(ckpt_path, status));
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline.cc b/tensorflow/core/profiler/internal/tfprof_timeline.cc
similarity index 99%
rename from tensorflow/tools/tfprof/internal/tfprof_timeline.cc
rename to tensorflow/core/profiler/internal/tfprof_timeline.cc
index c835da8162..ce0d8b7c7b 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline.cc
+++ b/tensorflow/core/profiler/internal/tfprof_timeline.cc
@@ -13,13 +13,13 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_timeline.h"
+#include "tensorflow/core/profiler/internal/tfprof_timeline.h"
 
 #include <utility>
 
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline.h b/tensorflow/core/profiler/internal/tfprof_timeline.h
similarity index 95%
rename from tensorflow/tools/tfprof/internal/tfprof_timeline.h
rename to tensorflow/core/profiler/internal/tfprof_timeline.h
index 0bba67066f..6c62d1046f 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline.h
+++ b/tensorflow/core/profiler/internal/tfprof_timeline.h
@@ -13,15 +13,15 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_TIMELINE_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_TIMELINE_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_TIMELINE_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_TIMELINE_H_
 
 #include "include/json/json.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_node_show.h"
+#include "tensorflow/core/profiler/internal/tfprof_node_show.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -191,4 +191,4 @@ class Timeline {
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_TIMELINE_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_TIMELINE_H_
diff --git a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc b/tensorflow/core/profiler/internal/tfprof_timeline_test.cc
similarity index 86%
rename from tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
rename to tensorflow/core/profiler/internal/tfprof_timeline_test.cc
index cad31050a9..6842f262c6 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_timeline_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_timeline_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
 
 #include <utility>
 
@@ -24,11 +24,11 @@ limitations under the License.
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_constants.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
-#include "tensorflow/tools/tfprof/tfprof_output.pb.h"
+#include "tensorflow/core/profiler/internal/tfprof_constants.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/tfprof_output.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -37,7 +37,7 @@ class TFProfTimelineTest : public ::testing::Test {
   TFProfTimelineTest() {
     string graph_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/graph.pbtxt");
+                     "core/profiler/internal/testdata/graph.pbtxt");
     std::unique_ptr<tensorflow::GraphDef> graph_pb(new tensorflow::GraphDef());
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
@@ -46,7 +46,7 @@ class TFProfTimelineTest : public ::testing::Test {
         new tensorflow::RunMetadata());
     string run_meta_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
-                     "tools/tfprof/internal/testdata/run_meta");
+                     "core/profiler/internal/testdata/run_meta");
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
diff --git a/tensorflow/tools/tfprof/internal/tfprof_utils.cc b/tensorflow/core/profiler/internal/tfprof_utils.cc
similarity index 99%
rename from tensorflow/tools/tfprof/internal/tfprof_utils.cc
rename to tensorflow/core/profiler/internal/tfprof_utils.cc
index 8f208b75d3..0f0a6d0b36 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_utils.cc
+++ b/tensorflow/core/profiler/internal/tfprof_utils.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
 
 #include <stdio.h>
 #include <algorithm>
diff --git a/tensorflow/tools/tfprof/internal/tfprof_utils.h b/tensorflow/core/profiler/internal/tfprof_utils.h
similarity index 88%
rename from tensorflow/tools/tfprof/internal/tfprof_utils.h
rename to tensorflow/core/profiler/internal/tfprof_utils.h
index 6130325bab..3407517ce0 100644
--- a/tensorflow/tools/tfprof/internal/tfprof_utils.h
+++ b/tensorflow/core/profiler/internal/tfprof_utils.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_UTILS_H_
-#define THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_UTILS_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_UTILS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_UTILS_H_
 
 #include <string>
 #include <vector>
@@ -23,7 +23,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/protobuf/config.pb.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
 
 namespace tensorflow {
 namespace tfprof {
@@ -69,4 +69,4 @@ void PrintHelp();
 }  // namespace tfprof
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_TFPROF_INTERNAL_TFPROF_UTILS_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PROFILER_INTERNAL_TFPROF_UTILS_H_
diff --git a/tensorflow/tools/tfprof/tfprof_log.proto b/tensorflow/core/profiler/tfprof_log.proto
similarity index 100%
rename from tensorflow/tools/tfprof/tfprof_log.proto
rename to tensorflow/core/profiler/tfprof_log.proto
diff --git a/tensorflow/tools/tfprof/tfprof_main.cc b/tensorflow/core/profiler/tfprof_main.cc
similarity index 97%
rename from tensorflow/tools/tfprof/tfprof_main.cc
rename to tensorflow/core/profiler/tfprof_main.cc
index 5e70c093cc..871db346af 100644
--- a/tensorflow/tools/tfprof/tfprof_main.cc
+++ b/tensorflow/core/profiler/tfprof_main.cc
@@ -33,11 +33,11 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/util/command_line_flags.h"
-#include "tensorflow/tools/tfprof/internal/advisor/tfprof_advisor.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
-#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
-#include "tensorflow/tools/tfprof/tfprof_log.pb.h"
+#include "tensorflow/core/profiler/internal/advisor/tfprof_advisor.h"
+#include "tensorflow/core/profiler/internal/tfprof_options.h"
+#include "tensorflow/core/profiler/internal/tfprof_stats.h"
+#include "tensorflow/core/profiler/internal/tfprof_utils.h"
+#include "tensorflow/core/profiler/tfprof_log.pb.h"
 
 namespace tensorflow {
 namespace tfprof {
diff --git a/tensorflow/tools/tfprof/tfprof_options.proto b/tensorflow/core/profiler/tfprof_options.proto
similarity index 100%
rename from tensorflow/tools/tfprof/tfprof_options.proto
rename to tensorflow/core/profiler/tfprof_options.proto
diff --git a/tensorflow/tools/tfprof/tfprof_output.proto b/tensorflow/core/profiler/tfprof_output.proto
similarity index 100%
rename from tensorflow/tools/tfprof/tfprof_output.proto
rename to tensorflow/core/profiler/tfprof_output.proto
diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 710f19b424..177ffaa646 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -587,7 +587,7 @@ python -m tensorflow.python.debug.cli.offline_analyzer \
        graph to record the values of intermediate tensors. These nodes
        slow down the graph execution. If you are interested in profiling your
        model, check out
-       [tfprof](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tfprof)
+       [tfprof](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler)
        and other profiling tools for TensorFlow.
 
 **Q**: _How do I link tfdbg against my `Session` in Bazel? Why do I see an
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 75798b79ee..55983eb3bb 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -87,6 +87,7 @@ py_library(
         "//tensorflow/python/feature_column:feature_column_py",
         "//tensorflow/python/ops/losses",
         "//tensorflow/python/ops/distributions",
+        "//tensorflow/python/profiler",
         "//tensorflow/python/saved_model",
     ] + if_not_windows([
         "//tensorflow/contrib:contrib_py",
@@ -2798,8 +2799,8 @@ tf_py_wrap_cc(
         "//tensorflow/core:reader_base",
         "//tensorflow/core/debug",
         "//tensorflow/core/distributed_runtime:server_lib",
+        "//tensorflow/core/profiler/internal:print_model_analysis",
         "//tensorflow/tools/graph_transforms:transform_graph_lib",
-        "//tensorflow/tools/tfprof/internal:print_model_analysis",
         "//util/python:python_headers",
     ] + (tf_additional_lib_deps() +
          tf_additional_plugin_deps() +
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index db2c5b1fd9..a2c86cbb6c 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -84,6 +84,7 @@ from tensorflow.python.ops import nn
 from tensorflow.python.ops import sets
 from tensorflow.python.ops import spectral_ops as spectral
 from tensorflow.python.ops.losses import losses
+from tensorflow.python.profiler import profiler
 from tensorflow.python.user_ops import user_ops
 from tensorflow.python.util import compat
 from tensorflow.python.saved_model import saved_model
@@ -235,6 +236,7 @@ _allowed_symbols.extend([
     'train',
     'user_ops',
     'layers',
+    'profiler',
 ])
 
 # Variables framework.versions:
@@ -251,7 +253,7 @@ remove_undocumented(__name__, _allowed_symbols, [
     control_flow_ops, confusion_matrix_m, functional_ops, histogram_ops, io_ops,
     losses, math_ops, metrics, nn, resource_loader, sets, script_ops,
     session_ops, sparse_ops, state_ops, string_ops, summary, tensor_array_ops,
-    train, layers
+    train, layers, profiler
 ])
 
 # Special dunders that we choose to export:
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD b/tensorflow/python/profiler/BUILD
similarity index 84%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
rename to tensorflow/python/profiler/BUILD
index b7c79edfca..3122c0c5f4 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
+++ b/tensorflow/python/profiler/BUILD
@@ -6,14 +6,26 @@ load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_py_test")
 
+py_library(
+    name = "profiler",
+    srcs = ["profiler.py"],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":model_analyzer",
+        ":tfprof_logger",
+        "//tensorflow/python:util",
+    ],
+)
+
 py_library(
     name = "model_analyzer",
     srcs = ["model_analyzer.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":tfprof_logger",
+        "//tensorflow/core/profiler:protos_all_py",
         "//tensorflow/python:pywrap_tensorflow",
-        "//tensorflow/tools/tfprof:protos_all_py",
     ],
 )
 
@@ -22,7 +34,7 @@ cuda_py_test(
     srcs = ["model_analyzer_test.py"],
     additional_deps = [
         ":model_analyzer",
-        "//tensorflow/contrib/tfprof/python/tools/tfprof/internal:model_analyzer_testlib",
+        "//tensorflow/python/profiler/internal:model_analyzer_testlib",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
@@ -37,11 +49,11 @@ cuda_py_test(
     srcs = ["profiler_test.py"],
     additional_deps = [
         ":model_analyzer",
-        "//tensorflow/contrib/tfprof/python/tools/tfprof/internal:model_analyzer_testlib",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "//tensorflow/python/profiler/internal:model_analyzer_testlib",
         "//tensorflow/python:variables",
     ],
     tags = ["no_pip"],
@@ -52,9 +64,9 @@ py_library(
     srcs = ["tfprof_logger.py"],
     srcs_version = "PY2AND3",
     deps = [
+        "//tensorflow/core/profiler:protos_all_py",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
-        "//tensorflow/tools/tfprof:protos_all_py",
         "@six_archive//:six",
     ],
 )
@@ -65,14 +77,13 @@ tf_py_test(
     srcs = ["tfprof_logger_test.py"],
     additional_deps = [
         ":tfprof_logger",
-        "//tensorflow/contrib/copy_graph:copy_graph_py",
         "//tensorflow/core:protos_all_py",
+        "//tensorflow/core/profiler:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
-        "//tensorflow/tools/tfprof:protos_all_py",
     ],
 )
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/__init__.py b/tensorflow/python/profiler/__init__.py
similarity index 100%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/__init__.py
rename to tensorflow/python/profiler/__init__.py
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/python/profiler/internal/BUILD
similarity index 84%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
rename to tensorflow/python/profiler/internal/BUILD
index a498574a95..5a6e53121c 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD
+++ b/tensorflow/python/profiler/internal/BUILD
@@ -1,4 +1,4 @@
-package(default_visibility = ["//tensorflow/contrib/tfprof/python/tools/tfprof:__subpackages__"])
+package(default_visibility = ["//tensorflow/python/profiler:__subpackages__"])
 
 licenses(["notice"])  # Apache 2.0
 
@@ -12,19 +12,20 @@ py_library(
     srcs = ["model_analyzer_testlib.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/rnn:rnn_py",
-        "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_grad",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:rnn",
+        "//tensorflow/python:tensor_array_grad",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/profiler:model_analyzer",
     ],
 )
 
@@ -33,6 +34,7 @@ py_test(
     srcs = ["print_model_analysis_test.py"],
     srcs_version = "PY2AND3",
     deps = [
+        "//tensorflow/core/profiler:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
@@ -43,7 +45,6 @@ py_test(
         "//tensorflow/python:platform_test",
         "//tensorflow/python:pywrap_tensorflow",
         "//tensorflow/python:variable_scope",
-        "//tensorflow/tools/tfprof:protos_all_py",
     ],
 )
 
@@ -52,12 +53,12 @@ cuda_py_test(
     srcs = ["run_metadata_test.py"],
     additional_deps = [
         ":model_analyzer_testlib",
-        "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer",
+        "//tensorflow/core/profiler:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python/profiler:model_analyzer",
         "//tensorflow/python:random_ops",
-        "//tensorflow/tools/tfprof:protos_all_py",
     ],
     tags = [
         "no_pip",
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/model_analyzer_testlib.py b/tensorflow/python/profiler/internal/model_analyzer_testlib.py
similarity index 100%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/internal/model_analyzer_testlib.py
rename to tensorflow/python/profiler/internal/model_analyzer_testlib.py
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py b/tensorflow/python/profiler/internal/print_model_analysis_test.py
similarity index 99%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
rename to tensorflow/python/profiler/internal/print_model_analysis_test.py
index 7ded5e890f..54854e8164 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py
+++ b/tensorflow/python/profiler/internal/print_model_analysis_test.py
@@ -20,6 +20,8 @@ from __future__ import print_function
 
 from google.protobuf import text_format
 
+from tensorflow.core.profiler import tfprof_options_pb2
+from tensorflow.core.profiler import tfprof_output_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -28,8 +30,6 @@ from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import test
-from tensorflow.tools.tfprof import tfprof_options_pb2
-from tensorflow.tools.tfprof import tfprof_output_pb2
 
 # pylint: disable=g-bad-import-order
 # XXX: this depends on pywrap_tensorflow and must come later
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/python/profiler/internal/run_metadata_test.py
similarity index 94%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
rename to tensorflow/python/profiler/internal/run_metadata_test.py
index 8f3351999f..a619aa6ebd 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py
+++ b/tensorflow/python/profiler/internal/run_metadata_test.py
@@ -32,8 +32,8 @@ from tensorflow.python.platform import test
 
 # pylint: disable=g-bad-import-order
 # XXX: this depends on pywrap_tensorflow and must come later
-from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
-from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib
+from tensorflow.python.profiler import model_analyzer
+from tensorflow.python.profiler.internal import model_analyzer_testlib as lib
 SIZE = 1300
 
 
@@ -61,10 +61,10 @@ def _run_model():
                  options=config_pb2.RunOptions(
                      trace_level=config_pb2.RunOptions.FULL_TRACE),
                  run_metadata=run_metadata)
-    tfprof_node = model_analyzer.print_model_analysis(
+    tfprof_node = model_analyzer.profile(
         sess.graph,
         run_meta=run_metadata,
-        tfprof_options=opts)
+        options=opts)
 
     return tfprof_node, run_metadata
 
@@ -80,9 +80,9 @@ def _run_loop_model():
                      trace_level=config_pb2.RunOptions.FULL_TRACE),
                  run_metadata=run_meta)
 
-    tfprof_node = model_analyzer.print_model_analysis(
+    tfprof_node = model_analyzer.profile(
         sess.graph, run_meta,
-        tfprof_options=model_analyzer.PRINT_ALL_TIMING_MEMORY)
+        options=model_analyzer.PRINT_ALL_TIMING_MEMORY)
     return tfprof_node, run_meta
 
 
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/python/profiler/model_analyzer.py
similarity index 82%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
rename to tensorflow/python/profiler/model_analyzer.py
index 5b6111efdf..2e336f017e 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/python/profiler/model_analyzer.py
@@ -22,15 +22,18 @@ from __future__ import print_function
 
 import six
 
-from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
+from tensorflow.core.profiler import tfprof_options_pb2
+from tensorflow.core.profiler import tfprof_output_pb2
 from tensorflow.python import pywrap_tensorflow as print_mdl
 from tensorflow.python.framework import errors
-from tensorflow.tools.tfprof import tfprof_options_pb2
-from tensorflow.tools.tfprof import tfprof_output_pb2
+from tensorflow.python.profiler import tfprof_logger
+
+_DEFAULT_PROFILE_OPTIONS = 0
+_DEFAULT_ADVISE_OPTIONS = 0
 
 # pylint: disable=bad-whitespace
 # pylint: disable=bad-continuation
-# 2 example tfprof_options for print_model_analysis API.
+# options examples for profiling API.
 #
 # Show the parameter statistics of trainable variables.
 TRAINABLE_VARS_PARAMS_STAT_OPTIONS = {
@@ -110,7 +113,7 @@ PRINT_ALL_TIMING_MEMORY = {
     'dump_to_file': ''
 }
 
-# The following options are for 'advise' tfprof_cmd.
+# The following options are for 'advise' cmd.
 # Show all advice.
 ALL_ADVICE = {
     'ExpensiveOperationChecker': {},
@@ -187,7 +190,7 @@ def _build_advisor_options(options):
 class Profiler(object):
   """TensorFlow multi-step profiler.
 
-  See go/tfprof or README for details.
+  https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
 
   Typical use case:
     # Currently we are only allowed to create 1 profiler per process.
@@ -260,13 +263,15 @@ class Profiler(object):
     print_mdl.AddStep(
         step, run_meta.SerializeToString(), op_log.SerializeToString())
 
-  def profile_python_codes(self, options):
+  def profile_python(self, options):
     """Profile the statistics of the Python codes.
 
-      Hint: set options['show_name_regexes'] = ['.*my_code.py.*']
+      By default, it shows the call stack from root. To avoid
+      redundant output, you may use options to filter as below
+        options['show_name_regexes'] = ['.*my_code.py.*']
 
     Args:
-      options: A dict of profiler options.
+      options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
       a TFMultiGraphNodeProto that records the results.
     """
@@ -280,7 +285,7 @@ class Profiler(object):
     """Profile the statistics of the Operation types (e.g. MatMul, Conv2D).
 
     Args:
-      options: A dict of profiler options.
+      options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
       a TFMultiGraphNodeProto that records the results.
     """
@@ -294,7 +299,7 @@ class Profiler(object):
     """Profile the statistics of graph nodes, organized by name scope.
 
     Args:
-      options: A dict of profiler options.
+      options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
       a TFGraphNodeProto that records the results.
     """
@@ -308,7 +313,7 @@ class Profiler(object):
     """Profile the statistics of graph nodes, organized by dataflow graph.
 
     Args:
-      options: A dict of profiler options.
+      options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
       a TFGraphNodeProto that records the results.
     """
@@ -318,11 +323,11 @@ class Profiler(object):
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
 
-  def advise(self, options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+  def advise(self, options):
     """Automatically detect problems and generate reports.
 
     Args:
-      options: A dict of options.
+      options: A dict of options. See ALL_ADVICE example above.
     Returns:
       A Advise proto that conains the reports from all checkers.
     """
@@ -333,16 +338,14 @@ class Profiler(object):
     return advise_pb
 
 
-def print_model_analysis(graph,
-                         run_meta=None,
-                         op_log=None,
-                         tfprof_cmd='scope',
-                         tfprof_options=TRAINABLE_VARS_PARAMS_STAT_OPTIONS):
+def profile(graph,
+            run_meta=None,
+            op_log=None,
+            cmd='scope',
+            options=_DEFAULT_PROFILE_OPTIONS):
   """Print model statistics.
 
-    See go/tfprof or README for examples and tutorials.
-    Run tfprof tool for help:
-    'bazel run third_party/tensorflow/tools/tfprof help'
+    https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
 
   Args:
     graph: tf.Graph.
@@ -351,65 +354,72 @@ def print_model_analysis(graph,
               'micros' and 'bytes'.
     op_log: tensorflow::tfprof::OpLog proto. users can use this proto to
             group together ops and use a op_type to select the group.
-    tfprof_cmd: string. Either 'op', 'scope', 'graph', 'code'.
-                'op' view organize outputs using operation type. (e.g. MatMul)
-                'scope' view organize outputs using graph node name scope.
-                'graph' view organize outputs using graph node inputs/outputs.
-                'code' view organize outputs using Python call stack.
-    tfprof_options: See 'tfprof help' for details.
+    cmd: string. Either 'op', 'scope', 'graph', 'code'.
+         'op' view organize outputs using operation type. (e.g. MatMul)
+         'scope' view organize outputs using graph node name scope.
+         'graph' view organize outputs using graph node inputs/outputs.
+         'code' view organize outputs using Python call stack.
+    options: A dict of options. See core/profiler/g3doc/options.md.
   Returns:
-    If tfprof_cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
-    If tfprof_cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto.
-    Side effect: stdout/file/timeline.json depending on tfprof_options['output']
+    If cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
+    If cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto.
+    Side effect: stdout/file/timeline.json depending on options['output']
   """
+  if options == _DEFAULT_PROFILE_OPTIONS:
+    options = TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
+
   # pylint: disable=protected-access
   op_log = tfprof_logger._merge_default_with_oplog(
-      graph, op_log, run_meta, add_trace=tfprof_cmd == 'code')
+      graph, op_log, run_meta, add_trace=cmd == 'code')
   # pylint: enable=protected-access
 
-  opts = _build_options(tfprof_options)
+  opts = _build_options(options)
 
   run_meta_str = run_meta.SerializeToString() if run_meta else b''
 
-  if tfprof_cmd == 'code' or tfprof_cmd == 'op':
+  if cmd == 'code' or cmd == 'op':
     tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.PrintModelAnalysis(
             graph.as_graph_def(add_shapes=True).SerializeToString(),
             run_meta_str,
             op_log.SerializeToString(),
-            tfprof_cmd.encode('utf-8'),
+            cmd.encode('utf-8'),
             opts.SerializeToString()))
-  elif tfprof_cmd == 'graph' or tfprof_cmd == 'scope':
+  elif cmd == 'graph' or cmd == 'scope':
     tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.PrintModelAnalysis(
             graph.as_graph_def(add_shapes=True).SerializeToString(),
             run_meta_str,
             op_log.SerializeToString(),
-            tfprof_cmd.encode('utf-8'),
+            cmd.encode('utf-8'),
             opts.SerializeToString()))
   else:
     raise errors.InvalidArgumentError(
-        None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
+        None, None, 'unknown cmd: %s\n' % cmd)
 
   return tfprof_node
 
 
-def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=dangerous-default-value
+def advise(graph, run_meta=None, options=_DEFAULT_ADVISE_OPTIONS):
   """Auto profile and advise.
 
     Builds profiles and automatically check anormalies of various
-    aspects. See go/tfprof or README for examples and tutorials.
+    aspects. For more details:
+    https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
 
   Args:
     graph: tf.Graph.
     run_meta: tensorflow::RunMetadata proto. Allows auto-profile
               time and memroy.
-    tfprof_options: see ALL_ADVICE example above.
+    options: see ALL_ADVICE example above.
   Returns:
     Returns AdviceProto proto
   """
+  if options == _DEFAULT_ADVISE_OPTIONS:
+    options = ALL_ADVICE.copy()
+
   # pylint: disable=protected-access
   op_log = tfprof_logger._merge_default_with_oplog(
       graph, None, run_meta, add_trace=True)
@@ -417,7 +427,7 @@ def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE):  # pylint: disable=
 
   run_meta_str = run_meta.SerializeToString() if run_meta else b''
 
-  opts = _build_advisor_options(tfprof_options)
+  opts = _build_advisor_options(options)
   ret = tfprof_output_pb2.AdviceProto()
   ret.ParseFromString(
       print_mdl.PrintModelAnalysis(
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py
similarity index 94%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
rename to tensorflow/python/profiler/model_analyzer_test.py
index 32a6d5fdb2..b506352d08 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/python/profiler/model_analyzer_test.py
@@ -27,8 +27,8 @@ from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 
 # XXX: this depends on pywrap_tensorflow and must come later
-from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
-from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib
+from tensorflow.python.profiler import model_analyzer
+from tensorflow.python.profiler.internal import model_analyzer_testlib as lib
 
 
 class PrintModelAnalysisTest(test.TestCase):
@@ -41,7 +41,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
     with session.Session() as sess:
       _ = lib.BuildSmallModel()
-      model_analyzer.print_model_analysis(sess.graph, tfprof_options=opts)
+      model_analyzer.profile(sess.graph, options=opts)
 
       with gfile.Open(outfile, 'r') as f:
         self.assertEqual(u'node name | # parameters\n'
@@ -76,8 +76,8 @@ class PrintModelAnalysisTest(test.TestCase):
                        trace_level=config_pb2.RunOptions.FULL_TRACE),
                    run_metadata=run_meta)
 
-      model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_options=opts)
+      model_analyzer.profile(
+          sess.graph, run_meta, options=opts)
 
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
@@ -111,8 +111,8 @@ class PrintModelAnalysisTest(test.TestCase):
                        trace_level=config_pb2.RunOptions.FULL_TRACE),
                    run_metadata=run_meta)
 
-      model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts)
+      model_analyzer.profile(
+          sess.graph, run_meta, cmd='code', options=opts)
 
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
@@ -141,8 +141,8 @@ class PrintModelAnalysisTest(test.TestCase):
                        trace_level=config_pb2.RunOptions.FULL_TRACE),
                    run_metadata=run_meta)
 
-      tfprof_node = model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts)
+      tfprof_node = model_analyzer.profile(
+          sess.graph, run_meta, cmd='code', options=opts)
 
       # pylint: disable=line-too-long
       with gfile.Open(outfile, 'r') as f:
@@ -193,8 +193,8 @@ class PrintModelAnalysisTest(test.TestCase):
                        trace_level=config_pb2.RunOptions.FULL_TRACE),
                    run_metadata=run_meta)
 
-      tfprof_node = model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts)
+      tfprof_node = model_analyzer.profile(
+          sess.graph, run_meta, cmd='code', options=opts)
 
       leaf = tfprof_node
       while leaf.children:
@@ -222,8 +222,8 @@ class PrintModelAnalysisTest(test.TestCase):
               trace_level=config_pb2.RunOptions.FULL_TRACE),
           run_metadata=run_meta)
 
-      _ = model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='graph', tfprof_options=opts)
+      _ = model_analyzer.profile(
+          sess.graph, run_meta, cmd='graph', options=opts)
 
       with gfile.Open(outfile, 'r') as f:
         # Test that a json file is created.
@@ -254,8 +254,8 @@ class PrintModelAnalysisTest(test.TestCase):
                        trace_level=config_pb2.RunOptions.FULL_TRACE),
                    run_metadata=run_meta)
 
-      tfprof_node = model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='op', tfprof_options=opts)
+      tfprof_node = model_analyzer.profile(
+          sess.graph, run_meta, cmd='op', options=opts)
 
       with gfile.Open(outfile, 'r') as f:
         # pylint: disable=line-too-long
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler.py b/tensorflow/python/profiler/pprof_profiler.py
similarity index 100%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler.py
rename to tensorflow/python/profiler/pprof_profiler.py
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py b/tensorflow/python/profiler/pprof_profiler_test.py
similarity index 98%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py
rename to tensorflow/python/profiler/pprof_profiler_test.py
index 6487adf992..c2469f012d 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py
+++ b/tensorflow/python/profiler/pprof_profiler_test.py
@@ -21,13 +21,13 @@ from __future__ import print_function
 import gzip
 
 from proto import profile_pb2
-from tensorflow.contrib.tfprof.python.tools.tfprof import pprof_profiler
 from tensorflow.core.framework import step_stats_pb2
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
+from tensorflow.python.profiler import pprof_profiler
 
 
 class PprofProfilerTest(test.TestCase):
diff --git a/tensorflow/python/profiler/profiler.py b/tensorflow/python/profiler/profiler.py
new file mode 100644
index 0000000000..19b253ab2f
--- /dev/null
+++ b/tensorflow/python/profiler/profiler.py
@@ -0,0 +1,32 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""tfprof is a tool that profile various aspect of TensorFlow model.
+
+@@Profiler
+@@profile
+@@advise
+@@write_op_log
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.python.profiler.model_analyzer import *
+from tensorflow.python.profiler.tfprof_logger import *
+from tensorflow.python.util.all_util import remove_undocumented
+
+
+remove_undocumented(__name__, [])
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/python/profiler/profiler_test.py
similarity index 87%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
rename to tensorflow/python/profiler/profiler_test.py
index dd25340564..35e7c2f12d 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py
+++ b/tensorflow/python/profiler/profiler_test.py
@@ -26,8 +26,8 @@ from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 
 # pylint: disable=g-bad-import-order
-from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
-from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib
+from tensorflow.python.profiler import model_analyzer
+from tensorflow.python.profiler.internal import model_analyzer_testlib as lib
 
 
 class ProfilerTest(test.TestCase):
@@ -51,8 +51,8 @@ class ProfilerTest(test.TestCase):
     with gfile.Open(outfile, 'r') as f:
       profiler_str = f.read()
 
-    model_analyzer.print_model_analysis(
-        sess.graph, tfprof_cmd='scope', tfprof_options=opts)
+    model_analyzer.profile(
+        sess.graph, cmd='scope', options=opts)
     with gfile.Open(outfile, 'r') as f:
       pma_str = f.read()
     self.assertEqual(pma_str, profiler_str)
@@ -69,18 +69,18 @@ class ProfilerTest(test.TestCase):
     with gfile.Open(outfile, 'r') as f:
       profiler_str = f.read()
 
-    model_analyzer.print_model_analysis(
-        sess.graph, tfprof_cmd='graph', run_meta=run_meta, tfprof_options=opts)
+    model_analyzer.profile(
+        sess.graph, cmd='graph', run_meta=run_meta, options=opts)
     with gfile.Open(outfile, 'r') as f:
       pma_str = f.read()
     self.assertEqual(pma_str, profiler_str)
 
-    profiler.profile_python_codes(opts)
+    profiler.profile_python(opts)
     with gfile.Open(outfile, 'r') as f:
       profiler_str = f.read()
 
-    model_analyzer.print_model_analysis(
-        sess.graph, tfprof_cmd='code', run_meta=run_meta, tfprof_options=opts)
+    model_analyzer.profile(
+        sess.graph, cmd='code', run_meta=run_meta, options=opts)
     with gfile.Open(outfile, 'r') as f:
       pma_str = f.read()
     self.assertEqual(pma_str, profiler_str)
@@ -89,14 +89,14 @@ class ProfilerTest(test.TestCase):
     with gfile.Open(outfile, 'r') as f:
       profiler_str = f.read()
 
-    model_analyzer.print_model_analysis(
-        sess.graph, tfprof_cmd='op', run_meta=run_meta, tfprof_options=opts)
+    model_analyzer.profile(
+        sess.graph, cmd='op', run_meta=run_meta, options=opts)
     with gfile.Open(outfile, 'r') as f:
       pma_str = f.read()
     self.assertEqual(pma_str, profiler_str)
 
-    model_analyzer.print_model_analysis(
-        sess.graph, tfprof_cmd='scope', run_meta=run_meta, tfprof_options=opts)
+    model_analyzer.profile(
+        sess.graph, cmd='scope', run_meta=run_meta, options=opts)
     with gfile.Open(outfile, 'r') as f:
       pma_str = f.read()
     self.assertNotEqual(pma_str, profiler_str)
@@ -107,8 +107,8 @@ class ProfilerTest(test.TestCase):
     with gfile.Open(outfile, 'r') as f:
       profiler_str = f.read()
 
-    model_analyzer.print_model_analysis(
-        sess.graph, tfprof_cmd='scope', run_meta=run_meta, tfprof_options=opts2)
+    model_analyzer.profile(
+        sess.graph, cmd='scope', run_meta=run_meta, options=opts2)
     with gfile.Open(outfile, 'r') as f:
       pma_str = f.read()
     self.assertEqual(pma_str, profiler_str)
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py b/tensorflow/python/profiler/tfprof_logger.py
similarity index 99%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py
rename to tensorflow/python/profiler/tfprof_logger.py
index 52febef26c..10bb6cdce5 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py
+++ b/tensorflow/python/profiler/tfprof_logger.py
@@ -24,10 +24,10 @@ import os
 import sys
 
 import six
+from tensorflow.core.profiler import tfprof_log_pb2
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.platform import gfile
-from tensorflow.tools.tfprof import tfprof_log_pb2
 
 TRAINABLE_VARIABLES = '_trainable_variables'
 REGISTERED_FLOP_STATS = 'flops'
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger_test.py b/tensorflow/python/profiler/tfprof_logger_test.py
similarity index 91%
rename from tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger_test.py
rename to tensorflow/python/profiler/tfprof_logger_test.py
index 87dfdc0fc1..141144f987 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger_test.py
+++ b/tensorflow/python/profiler/tfprof_logger_test.py
@@ -17,13 +17,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.copy_graph.python.util import copy_elements
-from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
@@ -42,6 +37,8 @@ class TFProfLoggerTest(test.TestCase):
     b = constant_op.constant([[1, 2], [3, 4]])
     return math_ops.matmul(a, b)
 
+  # pylint: disable=pointless-string-statement
+  """# TODO(xpan): This this out of core so it doesn't depend on contrib.
   def testFillMissingShape(self):
     a, b, y = self._BuildSmallPlaceholderlModel()
     run_options = config_pb2.RunOptions(
@@ -76,6 +73,7 @@ class TFProfLoggerTest(test.TestCase):
     # run_metadata has special name for MatMul, hence failed to fill shape.
     tfprof_logger._fill_missing_graph_shape(graph2, run_metadata)
     self.assertEquals('<unknown>', str(y2.get_shape()))
+  """
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/util/tfprof.i b/tensorflow/python/util/tfprof.i
index 11bcd7db40..3949fec4e6 100644
--- a/tensorflow/python/util/tfprof.i
+++ b/tensorflow/python/util/tfprof.i
@@ -17,8 +17,8 @@ limitations under the License.
 %include "tensorflow/python/platform/base.i"
 
 %{
-#include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/profiler/internal/print_model_analysis.h"
 
 using tensorflow::int64;
 %}
@@ -49,6 +49,6 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::Profile;
 %unignore tensorflow::tfprof::Advise;
 
-%include "tensorflow/tools/tfprof/internal/print_model_analysis.h"
+%include "tensorflow/core/profiler/internal/print_model_analysis.h"
 
 %unignoreall
\ No newline at end of file
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index d2ec2193c5..1857589417 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -380,6 +380,10 @@ tf_module {
     name: "orthogonal_initializer"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "profiler"
+    mtype: "<type \'module\'>"
+  }
   member {
     name: "python_io"
     mtype: "<type \'module\'>"
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-profiler.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-profiler.pbtxt
new file mode 100644
index 0000000000..0fb363aca4
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-profiler.pbtxt
@@ -0,0 +1,33 @@
+path: "tensorflow.profiler.Profiler"
+tf_class {
+  is_instance: "<class \'tensorflow.python.profiler.model_analyzer.Profiler\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'graph\', \'op_log\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_step"
+    argspec: "args=[\'self\', \'step\', \'run_meta\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "advise"
+    argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "profile_graph"
+    argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "profile_name_scope"
+    argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "profile_operations"
+    argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "profile_python"
+    argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt
new file mode 100644
index 0000000000..b1a97f2e10
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt
@@ -0,0 +1,19 @@
+path: "tensorflow.profiler"
+tf_module {
+  member {
+    name: "Profiler"
+    mtype: "<type \'type\'>"
+  }
+  member_method {
+    name: "advise"
+    argspec: "args=[\'graph\', \'run_meta\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'0\'], "
+  }
+  member_method {
+    name: "profile"
+    argspec: "args=[\'graph\', \'run_meta\', \'op_log\', \'cmd\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'scope\', \'0\'], "
+  }
+  member_method {
+    name: "write_op_log"
+    argspec: "args=[\'graph\', \'log_dir\', \'op_log\', \'run_meta\', \'add_trace\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\'], "
+  }
+}
-- 
GitLab


From 9a0525ad2f642e661ce44be894e5b826cb3869eb Mon Sep 17 00:00:00 2001
From: Dustin Tran <dustinviettran@gmail.com>
Date: Tue, 4 Jul 2017 11:15:44 -0700
Subject: [PATCH 0684/1375] Add Edward and GPflow to community page (#11260)

* add Edward to community page

* add GPflow to community page
---
 tensorflow/docs_src/community/welcome.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md
index f4de7f3a70..194649a304 100644
--- a/tensorflow/docs_src/community/welcome.md
+++ b/tensorflow/docs_src/community/welcome.md
@@ -27,6 +27,8 @@ The TensorFlow community has created many great projects around TensorFlow, incl
 * [Operator Vectorization Library](https://github.com/opveclib/opveclib)
 * [Swift language bindings](https://github.com/PerfectlySoft/Perfect-TensorFlow)
 * [Sublime Tensorflow - A plugin for Sublime Text](https://github.com/baptisteArnaud/Sublime-Tensorflow)
+* [Edward - A library for probabilistic modeling, inference, and criticism](http://edwardlib.org) ([Github](https://github.com/blei-lab/edward), [Forum](https://discourse.edwardlib.org))
+* [GPflow - Gaussian processes in TensorFlow](https://github.com/GPflow/GPflow)
 
 ## TensorFlow Communities Around the World
 
-- 
GitLab


From 43a819e1386195f7010f8ca9c74ed96ab81913d8 Mon Sep 17 00:00:00 2001
From: horance <horance-liu@users.noreply.github.com>
Date: Wed, 5 Jul 2017 02:16:07 +0800
Subject: [PATCH 0685/1375] remove some warning (#11242)

---
 tensorflow/contrib/image/kernels/image_ops.cc | 12 ++++++------
 tensorflow/core/graph/graph.cc                |  2 +-
 tensorflow/core/kernels/queue_base.h          |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/image/kernels/image_ops.cc b/tensorflow/contrib/image/kernels/image_ops.cc
index 8a97f07732..6adf837ca0 100644
--- a/tensorflow/contrib/image/kernels/image_ops.cc
+++ b/tensorflow/contrib/image/kernels/image_ops.cc
@@ -32,11 +32,11 @@ namespace functor {
 // Explicit instantiation of the CPU functor.
 typedef Eigen::ThreadPoolDevice CPUDevice;
 
-template class FillProjectiveTransform<CPUDevice, uint8>;
-template class FillProjectiveTransform<CPUDevice, int32>;
-template class FillProjectiveTransform<CPUDevice, int64>;
-template class FillProjectiveTransform<CPUDevice, float>;
-template class FillProjectiveTransform<CPUDevice, double>;
+template struct FillProjectiveTransform<CPUDevice, uint8>;
+template struct FillProjectiveTransform<CPUDevice, int32>;
+template struct FillProjectiveTransform<CPUDevice, int64>;
+template struct FillProjectiveTransform<CPUDevice, float>;
+template struct FillProjectiveTransform<CPUDevice, double>;
 
 }  // end namespace functor
 
@@ -116,7 +116,7 @@ namespace functor {
   void FillProjectiveTransform<GPUDevice, TYPE>::operator()(                \
       const GPUDevice& device, OutputType* output, const InputType& images, \
       const TransformsType& transform) const;                               \
-  extern template class FillProjectiveTransform<GPUDevice, TYPE>
+  extern template struct FillProjectiveTransform<GPUDevice, TYPE>
 
 TF_CALL_uint8(DECLARE_FUNCTOR);
 TF_CALL_int32(DECLARE_FUNCTOR);
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 7a5e76fdd0..c2e225363d 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -31,7 +31,7 @@ namespace tensorflow {
 
 const int Graph::kControlSlot = -1;
 
-struct NodeProperties {
+class NodeProperties {
  public:
   NodeProperties(const OpDef* op_def, const NodeDef& node_def,
                  const DataTypeSlice inputs, const DataTypeSlice outputs)
diff --git a/tensorflow/core/kernels/queue_base.h b/tensorflow/core/kernels/queue_base.h
index 216e743457..c101fb3579 100644
--- a/tensorflow/core/kernels/queue_base.h
+++ b/tensorflow/core/kernels/queue_base.h
@@ -69,7 +69,7 @@ class QueueBase : public QueueInterface {
 
   int32 capacity() const { return capacity_; }
 
-  bool is_closed() const{
+  bool is_closed() const override {
     mutex_lock lock(mu_);
     return closed_;
   }
-- 
GitLab


From dfdcd0b45063cb8d7b51bcfe890e16143ea2e2d1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Defferrard?= <michael.defferrard@epfl.ch>
Date: Fri, 30 Jun 2017 13:47:39 +0000
Subject: [PATCH 0686/1375] Only use weakref.finalize from backports in Python
 < 3.4

---
 tensorflow/python/util/tf_should_use.py | 9 +++++++--
 tensorflow/tools/pip_package/setup.py   | 5 ++++-
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/util/tf_should_use.py b/tensorflow/python/util/tf_should_use.py
index 05c99856d2..491b78f1dd 100644
--- a/tensorflow/python/util/tf_should_use.py
+++ b/tensorflow/python/util/tf_should_use.py
@@ -25,7 +25,12 @@ import types
 
 import six  # pylint: disable=unused-import
 
-from backports import weakref  # pylint: disable=g-bad-import-order
+# pylint: disable=g-bad-import-order
+try:
+  from weakref import finalize
+except ImportError:
+  from backports.weakref import finalize
+# pylint: enable=g-bad-import-order
 
 from tensorflow.python.platform import tf_logging
 from tensorflow.python.util import tf_decorator
@@ -107,7 +112,7 @@ def _add_should_use_warning(x, fatal_error=False):
       # garbage collected.  Can't add self as the args because the
       # loop will break garbage collection.  We keep track of
       # ourselves via python ids.
-      weakref.finalize(self, _deleted, self._tf_ref_id, fatal_error)
+      finalize(self, _deleted, self._tf_ref_id, fatal_error)
 
     # Not sure why this pylint warning is being used; this is not an
     # old class form.
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 54ba8064e8..5d8cce2add 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -35,7 +35,6 @@ REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
     'six >= 1.10.0',
     'protobuf >= 3.2.0',
-    'backports.weakref == 1.0rc1',
     'tensorflow-tensorboard',
 ]
 
@@ -54,6 +53,10 @@ else:
   # mock comes with unittest.mock for python3, need to install for python2
   REQUIRED_PACKAGES.append('mock >= 2.0.0')
 
+# weakref.finalize was introduced in Python 3.4
+if sys.version_info < (3, 4):
+  REQUIRED_PACKAGES.append('backports.weakref >= 1.0rc1')
+
 # pylint: disable=line-too-long
 CONSOLE_SCRIPTS = [
     'saved_model_cli = tensorflow.python.tools.saved_model_cli:main',
-- 
GitLab


From dd9549709072a489254a72d190d9e36632d58d95 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Wed, 5 Jul 2017 07:37:35 -0700
Subject: [PATCH 0687/1375] [XLA] Check for the correct number of strides in
 shape inference. Add backward compatibility for serialized computations
 without strides.

PiperOrigin-RevId: 160956181
---
 .../compiler/xla/service/hlo_instruction.cc   |  6 +++++
 .../compiler/xla/service/shape_inference.cc   | 27 ++++++++++---------
 2 files changed, 20 insertions(+), 13 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 8616c7ec51..4a33e0ff1d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -314,6 +314,12 @@ HloInstruction::CreateCrossReplicaSum(const Shape& shape,
   instruction->slice_starts_.assign(start_indices.begin(), start_indices.end());
   instruction->slice_limits_.assign(limit_indices.begin(), limit_indices.end());
   instruction->slice_strides_.assign(strides.begin(), strides.end());
+  // For backward compatibility with old serialized computations: if there are
+  // no strides, assume all strides are 1.
+  // TODO(b/63317920): remove this code.
+  if (instruction->slice_strides_.empty()) {
+    instruction->slice_strides_ = std::vector<int64>(start_indices.size(), 1LL);
+  }
   return instruction;
 }
 
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 5e4df9ddd6..f02df232d8 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -1149,6 +1149,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
                            starts.size(), limits.size());
   }
 
+  if (starts.size() != strides.size()) {
+    return InvalidArgument("slice start and strides sizes differ: %zu vs %zu",
+                           starts.size(), strides.size());
+  }
+
   if (starts.size() != ShapeUtil::Rank(arg)) {
     return InvalidArgument(
         "slice index count does not match argument rank: %zu vs %lld",
@@ -1164,9 +1169,6 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       return InvalidArgument("negative start index to slice: %lld",
                              start_index);
     }
-    if (stride == 0) {
-      return InvalidArgument("Zero stride");
-    }
     if (limit_index > arg.dimensions(dimension)) {
       return InvalidArgument(
           "limit index (%lld) must be less than or equal to dimension "
@@ -1177,17 +1179,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
                                            start_index);
     VLOG(2) << tensorflow::strings::Printf("limits[%lld] = %lld", dimension,
                                            limit_index);
-    if (stride > 0) {
-      if (start_index > limit_index) {
-        return InvalidArgument(
-            "limit index (%lld) must be greater or equal to "
-            "start index (%lld) in slice with positive stride",
-            limit_index, start_index);
-      }
-      sizes.push_back((limit_index - start_index + stride - 1) / stride);
-    } else {
-      return InvalidArgument("Negative strides not supported");
+    if (start_index > limit_index) {
+      return InvalidArgument(
+          "limit index (%lld) must be greater or equal to "
+          "start index (%lld) in slice with positive stride",
+          limit_index, start_index);
+    }
+    if (stride <= 0) {
+      return InvalidArgument("stride (%lld) must be positive", stride);
     }
+    sizes.push_back((limit_index - start_index + stride - 1) / stride);
   }
 
   return ShapeUtil::MakeShape(arg.element_type(), sizes);
-- 
GitLab


From 129b39e916cf6e3076f8a03b1d6277f5f7ecd166 Mon Sep 17 00:00:00 2001
From: Julian Viereck <julian.viereck@gmail.com>
Date: Wed, 5 Jul 2017 17:20:41 +0200
Subject: [PATCH 0688/1375] Fix #11152: Adding import for pythons threading
 library (#11226)

---
 tensorflow/docs_src/programmers_guide/threading_and_queues.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/docs_src/programmers_guide/threading_and_queues.md b/tensorflow/docs_src/programmers_guide/threading_and_queues.md
index 7d3edb788e..b5cf43e229 100644
--- a/tensorflow/docs_src/programmers_guide/threading_and_queues.md
+++ b/tensorflow/docs_src/programmers_guide/threading_and_queues.md
@@ -79,6 +79,9 @@ Any thread can decide that the computation should stop.  It only has to call
 return `True`.
 
 ```python
+# Using Python's threading library.
+import threading
+
 # Thread body: loop until the coordinator indicates a stop was requested.
 # If some condition becomes true, ask the coordinator to stop.
 def MyLoop(coord):
-- 
GitLab


From 94d52acdc0087d5829f220c4d46eea67e0d30305 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 08:42:04 -0700
Subject: [PATCH 0689/1375] Eliminate HloInstruction::FullyQualifiedName().

Now that HloInstruction names are unique within an HloModule, we can replace
all uses of FullyQualifiedName() with simply name().

PiperOrigin-RevId: 160961583
---
 tensorflow/compiler/xla/service/hlo_alias_analysis.cc  |  4 ++--
 .../compiler/xla/service/hlo_dataflow_analysis.cc      |  2 +-
 tensorflow/compiler/xla/service/hlo_instruction.cc     | 10 +---------
 tensorflow/compiler/xla/service/hlo_instruction.h      |  8 --------
 tensorflow/compiler/xla/service/hlo_value.cc           |  9 ++++-----
 tensorflow/compiler/xla/service/logical_buffer.cc      |  2 +-
 .../compiler/xla/service/tuple_points_to_analysis.cc   |  6 +++---
 7 files changed, 12 insertions(+), 29 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
index 3c5f0e19f1..d03093f89f 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
@@ -141,7 +141,7 @@ void HloAliasAnalysis::FlattenInstructionBufferSets(
   VLOG(4) << "Flattening buffer sets of instructions: "
           << Join(instructions, ", ",
                   [this](string* out, const HloInstruction* instruction) {
-                    StrAppend(out, instruction->FullyQualifiedName());
+                    StrAppend(out, instruction->name());
                   });
   if (instructions.size() < 2) {
     return;
@@ -183,7 +183,7 @@ string HloAliasAnalysis::ToString() const {
        module_->computations()) {
     for (const std::unique_ptr<HloInstruction>& instruction :
          computation->instructions()) {
-      StrAppend(&out, "    ", instruction->FullyQualifiedName(), ":\n");
+      StrAppend(&out, "    ", instruction->name(), ":\n");
       auto buffer_str = [this](const HloBuffer& buffer) {
         return StrCat(
             "Buffer ", buffer.id(), ", values: ",
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
index 8f372c3420..91592c1902 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
@@ -99,7 +99,7 @@ string HloDataflowAnalysis::ToString() const {
        module_->computations()) {
     for (const std::unique_ptr<HloInstruction>& instruction :
          computation->instructions()) {
-      StrAppend(&out, "    ", instruction->FullyQualifiedName(), ":\n");
+      StrAppend(&out, "    ", instruction->name(), ":\n");
       if (ShapeUtil::IsTuple(instruction->shape())) {
         GetInstructionValueSet(instruction.get())
             .ForEachElement([this, &instruction, &out](
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 4a33e0ff1d..aa259456ab 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1623,7 +1623,7 @@ HloInstructionProto HloInstruction::ToProto() const {
     case HloOpcode::kFusion: {
       HloComputationProto* proto_fused_computation =
           proto.mutable_fused_instructions_computation();
-      proto_fused_computation->set_name(FullyQualifiedName());
+      proto_fused_computation->set_name(name());
 
       // Fill in fused instructions. Note that fused_instructions() returns in
       // reverse post-order (i.e. root first), so we reverse to get post-order.
@@ -1699,14 +1699,6 @@ string HloInstruction::ToCategory() const {
   return HloOpcodeString(opcode());
 }
 
-string HloInstruction::FullyQualifiedName() const {
-  if (IsFused()) {
-    return StrCat(fusion_instruction()->parent()->name(),
-                  "::", fusion_instruction()->name(), "::", name_);
-  }
-  return StrCat(parent_->name(), "::", name_);
-}
-
 HloInstruction* HloInstruction::tracing() const { return trace_instruction_; }
 
 void HloInstruction::set_tracing(HloInstruction* trace_instruction) {
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 5b0e20685a..65e4e08a4c 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -528,14 +528,6 @@ class HloInstruction {
   // or "elementwise".
   string ToCategory() const;
 
-  // Returns the string concatenation of:
-  // - Parent name
-  // - If this is a fused instruction: The fusion instruction's name
-  // - This instruction's name
-  // The components are separated using "::". The fully qualified name is
-  // guaranteed to be unique among all instructions in the HloModule.
-  string FullyQualifiedName() const;
-
   // Returns a logging instruction, if the output of this instruction is logged.
   //
   // Postcondition: retval == nullptr || retval->opcode() == HloOpcode::kTrace
diff --git a/tensorflow/compiler/xla/service/hlo_value.cc b/tensorflow/compiler/xla/service/hlo_value.cc
index 2ce796307b..a5046a712e 100644
--- a/tensorflow/compiler/xla/service/hlo_value.cc
+++ b/tensorflow/compiler/xla/service/hlo_value.cc
@@ -45,7 +45,7 @@ const Shape& HloLocation::shape() const {
 string HloLocation::ToString() const {
   string index_str =
       ShapeUtil::IsTuple(instruction->shape()) ? (" " + index.ToString()) : "";
-  return StrCat(instruction->FullyQualifiedName(), index_str);
+  return StrCat(instruction->name(), index_str);
 }
 
 std::ostream& operator<<(std::ostream& out, const HloLocation& location) {
@@ -58,8 +58,7 @@ string HloUse::ToString() const {
       ShapeUtil::IsTuple(instruction->operand(operand_number)->shape())
           ? (" " + operand_index.ToString())
           : "";
-  return StrCat(instruction->FullyQualifiedName(), ", operand ", operand_number,
-                index_str);
+  return StrCat(instruction->name(), ", operand ", operand_number, index_str);
 }
 
 std::ostream& operator<<(std::ostream& out, const HloUse& use) {
@@ -90,8 +89,8 @@ string HloValue::ToShortString() const {
   string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
                          ? defining_index().ToString()
                          : "";
-  return StrCat(is_phi_ ? "PHI " : "",
-                defining_instruction()->FullyQualifiedName(), index_str);
+  return StrCat(is_phi_ ? "PHI " : "", defining_instruction()->name(),
+                index_str);
 }
 
 string HloValue::ToString(int indent) const {
diff --git a/tensorflow/compiler/xla/service/logical_buffer.cc b/tensorflow/compiler/xla/service/logical_buffer.cc
index d24a592f46..3e843b2029 100644
--- a/tensorflow/compiler/xla/service/logical_buffer.cc
+++ b/tensorflow/compiler/xla/service/logical_buffer.cc
@@ -27,7 +27,7 @@ limitations under the License.
 namespace xla {
 
 string LogicalBuffer::ToString() const {
-  return tensorflow::strings::StrCat(instruction_->FullyQualifiedName(), "[",
+  return tensorflow::strings::StrCat(instruction_->name(), "[",
                                      tensorflow::str_util::Join(index_, ","),
                                      "](#", id_, " @", color_.value(), ")");
 }
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
index 9ab0e75bb9..182e99cf1c 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
@@ -33,9 +33,9 @@ limitations under the License.
 namespace xla {
 
 string BufferAlias::ToString() const {
-  return tensorflow::strings::StrCat(
-      "BufferAlias(", instruction_->FullyQualifiedName(), "[",
-      tensorflow::str_util::Join(index_, ","), "])");
+  return tensorflow::strings::StrCat("BufferAlias(", instruction_->name(), "[",
+                                     tensorflow::str_util::Join(index_, ","),
+                                     "])");
 }
 
 std::ostream& operator<<(std::ostream& out, const BufferAlias& buffer_alias) {
-- 
GitLab


From 46b3fc62de853c3fff658b3b6d9ef472bac0c16a Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Thu, 6 Jul 2017 00:59:11 +0900
Subject: [PATCH 0690/1375] Fix typos (#11288)

---
 tensorflow/compiler/xla/service/cpu/shape_partition.h      | 2 +-
 tensorflow/contrib/layers/python/layers/embedding_ops.py   | 2 +-
 tensorflow/contrib/tensor_forest/client/random_forest.py   | 2 +-
 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py         | 2 +-
 tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h  | 2 +-
 tensorflow/python/estimator/canned/linear_testing_utils.py | 2 +-
 tensorflow/python/ops/sets_impl.py                         | 2 +-
 7 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition.h b/tensorflow/compiler/xla/service/cpu/shape_partition.h
index bdbcb874c1..7a2d00421c 100644
--- a/tensorflow/compiler/xla/service/cpu/shape_partition.h
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition.h
@@ -38,7 +38,7 @@ namespace cpu {
 //
 //     [0, 1), [1, 2), [2, 3), [3, 4), [4, 5) [5, 8)
 //
-//   Note that the last parition has residule because the dimension size is
+//   Note that the last partition has residule because the dimension size is
 //   not a multiple of the partition count.
 //
 //
diff --git a/tensorflow/contrib/layers/python/layers/embedding_ops.py b/tensorflow/contrib/layers/python/layers/embedding_ops.py
index f8f4122d1d..b62e3050cd 100644
--- a/tensorflow/contrib/layers/python/layers/embedding_ops.py
+++ b/tensorflow/contrib/layers/python/layers/embedding_ops.py
@@ -871,7 +871,7 @@ def _embedding_lookup_with_distributed_aggregation(params,
           p_segment_ids = array_ops.gather(segment_ids, pindices[p])
           # Number the p_segment_ids to meet segment_sum's requirements. Note
           # that unique_p_segment_ids contains unique segment ids of this
-          # partiton and these ids' order is unchanged.
+          # partition and these ids' order is unchanged.
           unique_p_segment_ids, unique_p_segment_idx = array_ops.unique(
               p_segment_ids)
           partitioned_segment_ids.append(unique_p_segment_ids)
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index 47685df4a5..1221165baa 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -315,7 +315,7 @@ class TensorForestEstimator(estimator.Estimator):
         though training might be distributed.
       version: String indicating TensorForest version to use, for backward
         compatibility. Either 'v2', 'v4', or None to let system pick.
-        Overrides grpah_builder_class.
+        Overrides graph_builder_class.
 
     Returns:
       A `TensorForestEstimator` instance.
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 168726a6b3..833b406fb9 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -480,7 +480,7 @@ def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config,
 
     # TODO(xiejw): how to do we support hook and savers in the original
     # model_fn. Realistically, the original
-    # model_fn will be excuted on TPU chips in a replica way. The hooks
+    # model_fn will be executed on TPU chips in a replica way. The hooks
     # returned by the model_fn cannot be supported at all. If we have to,
     # the graph construction part in the model_fn should be separated from the
     # control part (such as hooks and savers). By that the graph construction
diff --git a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
index bd1120e1df..d0689428dd 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
+++ b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 namespace tensorflow {
 
-// HexagonOpsDefinitions provides ops definitons supported in hexagon library
+// HexagonOpsDefinitions provides ops definitions supported in hexagon library
 // TODO(satok): add a functionality to call functions in hexagon library
 class HexagonOpsDefinitions final : public IGraphTransferOpsDefinitions {
  public:
diff --git a/tensorflow/python/estimator/canned/linear_testing_utils.py b/tensorflow/python/estimator/canned/linear_testing_utils.py
index 965ac8cbdd..c9bde91f9b 100644
--- a/tensorflow/python/estimator/canned/linear_testing_utils.py
+++ b/tensorflow/python/estimator/canned/linear_testing_utils.py
@@ -124,7 +124,7 @@ def sigmoid(x):
 
 
 class CheckPartitionerVarHook(session_run_hook.SessionRunHook):
-  """A `SessionRunHook` to check a paritioned variable."""
+  """A `SessionRunHook` to check a partitioned variable."""
 
   def __init__(self, test_case, var_name, var_dim, partitions):
     self._test_case = test_case
diff --git a/tensorflow/python/ops/sets_impl.py b/tensorflow/python/ops/sets_impl.py
index f3b52636d4..e623c4f2ee 100644
--- a/tensorflow/python/ops/sets_impl.py
+++ b/tensorflow/python/ops/sets_impl.py
@@ -99,7 +99,7 @@ def _set_operation(a, b, set_operation, validate_indices=True):
     b: `Tensor` or `SparseTensor` of the same type as `a`. Must be
         `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
         sorted in row-major order.
-    set_operation: String indicating set operaiton. See
+    set_operation: String indicating set operation. See
         SetOperationOp::SetOperationFromContext for valid values.
     validate_indices: Whether to validate the order and range of sparse indices
        in `a` and `b`.
-- 
GitLab


From 3a64879a86e46908ad90a387efe56ad32be61e94 Mon Sep 17 00:00:00 2001
From: Guenther Schmuelling <guschmue@microsoft.com>
Date: Wed, 5 Jul 2017 08:59:35 -0700
Subject: [PATCH 0691/1375] fix beam search for windows and enable seq2seq unit
 tests (#11298)

---
 tensorflow/contrib/cmake/tf_core_kernels.cmake | 4 +++-
 tensorflow/contrib/cmake/tf_tests.cmake        | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 6b8c130490..57df33c13e 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -126,13 +126,15 @@ if(WIN32)
       "${tensorflow_source_dir}/tensorflow/core/kernels/*quantiz*.h"
       "${tensorflow_source_dir}/tensorflow/core/kernels/*quantiz*.cc"
       "${tensorflow_source_dir}/tensorflow/core/kernels/neon/*"
-      # no in tensorflow.dll - comes from .so
+      # not in core - those are loaded dynamically as dll
       "${tensorflow_source_dir}/tensorflow/contrib/resampler/kernels/resampler_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/blas_gemm.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/lstm_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/lstm_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc"
       # temporarily disable nccl (nccl itself needs to be ported to windows first)
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_manager.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_ops.cc"
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 8a49d70d75..59a8e2ea02 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -146,6 +146,7 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/contrib/data/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/factorization/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/integration_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/python/kernel_tests/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/stateless/python/kernel_tests/*_test.py"
     # NOTE: tensor_forest tests in tensor_forest/hybrid/... still don't pass.
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/*_test.py"
-- 
GitLab


From 19220809123803af8fdcbbe628ee9f80fb4521c8 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Wed, 5 Jul 2017 10:10:03 -0700
Subject: [PATCH 0692/1375] [TF:XLA] Refactor XLAShapeToTensorShape so it
 returns an error Status if passed an XLA tuple shape, rather than
 CHECK-failing.

PiperOrigin-RevId: 160971216
---
 .../tf2xla/kernels/dynamic_stitch_op.cc       | 11 ++++++----
 .../tf2xla/kernels/tensor_array_ops.cc        |  7 +++++--
 .../compiler/tf2xla/kernels/while_op.cc       |  3 ++-
 tensorflow/compiler/tf2xla/literal_util.cc    |  3 ++-
 tensorflow/compiler/tf2xla/shape_util.cc      | 14 +++++++++----
 tensorflow/compiler/tf2xla/shape_util.h       |  6 ++++--
 tensorflow/compiler/tf2xla/xla_compiler.cc    | 21 +++++++++++--------
 tensorflow/compiler/tf2xla/xla_op_kernel.cc   | 12 ++++++-----
 8 files changed, 49 insertions(+), 28 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
index 0330e34c98..dde7898015 100644
--- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc
@@ -63,11 +63,14 @@ class DynamicStitchOp : public XlaOpKernel {
     std::vector<xla::Literal> indices(indices_input.size());
 
     const TensorShape& data0_shape = data_shapes[0];
-    const TensorShape indices0_shape =
-        XLAShapeToTensorShape(indices_input[0].shape());
+    TensorShape indices0_shape;
+    OP_REQUIRES_OK(
+        ctx, XLAShapeToTensorShape(indices_input[0].shape(), &indices0_shape));
     for (int input_num = 0; input_num < indices_input.size(); input_num++) {
-      const TensorShape indices_shape =
-          XLAShapeToTensorShape(indices_input[input_num].shape());
+      TensorShape indices_shape;
+      OP_REQUIRES_OK(ctx,
+                     XLAShapeToTensorShape(indices_input[input_num].shape(),
+                                           &indices_shape));
       const TensorShape& data_shape = data_shapes[input_num];
       OP_REQUIRES(ctx, TensorShapeUtils::StartsWith(data_shape, indices_shape),
                   errors::InvalidArgument(
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index 9367c1ef22..d720496f74 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -74,7 +74,9 @@ Status MaybeInitializeTensorArray(xla::ComputationBuilder* builder,
     if (!shape_or_status.ok()) {
       return shape_or_status.status();
     }
-    TensorShape shape = XLAShapeToTensorShape(*shape_or_status.ValueOrDie());
+    TensorShape shape;
+    TF_RETURN_IF_ERROR(
+        XLAShapeToTensorShape(*shape_or_status.ValueOrDie(), &shape));
     if (ta_shape != shape) {
       return errors::InvalidArgument(
           "Mismatched TensorArray sizes: ", ta_shape.DebugString(), " vs ",
@@ -115,7 +117,8 @@ Status GetTensorArrayShape(const XlaResource* resource,
   if (!shape_or_status.ok()) {
     return shape_or_status.status();
   }
-  *shape = XLAShapeToTensorShape(*shape_or_status.ValueOrDie());
+  TF_RETURN_IF_ERROR(
+      XLAShapeToTensorShape(*shape_or_status.ValueOrDie(), shape));
   if (shape->dims() < 1) {
     return errors::InvalidArgument("TensorArray rank must be >= 1");
   }
diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc
index 2f85902981..37c611d147 100644
--- a/tensorflow/compiler/tf2xla/kernels/while_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc
@@ -63,7 +63,8 @@ Status MakeXlaCompilerArgumentsFromInputs(
       if (arg.initialized) {
         auto shape = ctx->builder()->GetShape(resource->value);
         TF_RETURN_IF_ERROR(shape.status());
-        arg.shape = XLAShapeToTensorShape(*shape.ValueOrDie());
+        TF_RETURN_IF_ERROR(
+            XLAShapeToTensorShape(*shape.ValueOrDie(), &arg.shape));
       } else {
         *has_uninitialized_vars = true;
       }
diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc
index e166e8a9b0..576cd9bf9a 100644
--- a/tensorflow/compiler/tf2xla/literal_util.cc
+++ b/tensorflow/compiler/tf2xla/literal_util.cc
@@ -51,7 +51,8 @@ Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type,
         " to tensor of type ", DataTypeString(target_type));
   }
 
-  TensorShape shape = XLAShapeToTensorShape(literal.shape());
+  TensorShape shape;
+  TF_RETURN_IF_ERROR(XLAShapeToTensorShape(literal.shape(), &shape));
   *host_tensor = Tensor(target_type, shape);
   size_t total_bytes = host_tensor->TotalBytes();
   if (total_bytes > 0) {
diff --git a/tensorflow/compiler/tf2xla/shape_util.cc b/tensorflow/compiler/tf2xla/shape_util.cc
index f5ecb51a5b..9d1992205b 100644
--- a/tensorflow/compiler/tf2xla/shape_util.cc
+++ b/tensorflow/compiler/tf2xla/shape_util.cc
@@ -24,12 +24,18 @@ limitations under the License.
 namespace tensorflow {
 
 // Convert an XLA Shape into the equivalent TensorFlow shape.
-TensorShape XLAShapeToTensorShape(const xla::Shape& shape) {
-  TensorShape tensor_shape;
+Status XLAShapeToTensorShape(const xla::Shape& shape,
+                             TensorShape* tensor_shape) {
+  if (xla::ShapeUtil::IsTuple(shape)) {
+    return errors::InvalidArgument("XLA shape ",
+                                   xla::ShapeUtil::HumanString(shape),
+                                   " cannot be converted to a TensorShape");
+  }
+  *tensor_shape = TensorShape();
   for (int i = 0; i < xla::ShapeUtil::Rank(shape); ++i) {
-    tensor_shape.AddDim(shape.dimensions(i));
+    tensor_shape->AddDim(shape.dimensions(i));
   }
-  return tensor_shape;
+  return Status::OK();
 }
 
 // Convert a TensorShape into the equivalent XLA Shape proto.
diff --git a/tensorflow/compiler/tf2xla/shape_util.h b/tensorflow/compiler/tf2xla/shape_util.h
index 516dd636a9..58240b9c96 100644
--- a/tensorflow/compiler/tf2xla/shape_util.h
+++ b/tensorflow/compiler/tf2xla/shape_util.h
@@ -24,8 +24,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-// Convert an XLA Shape into the equivalent TensorFlow shape.
-TensorShape XLAShapeToTensorShape(const xla::Shape& shape);
+// Convert an XLA Shape into the equivalent TensorFlow shape. May fail since
+// not all XLA shapes can be represented as TensorShapes.
+Status XLAShapeToTensorShape(const xla::Shape& shape,
+                             TensorShape* tensor_shape);
 
 // Convert a TensorShape into the equivalent XLA Shape proto. Unlike Tensorflow,
 // XLA shapes include the type. Not all `dtype` values can be represented by
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index 02e7c138d2..4bbc4384c3 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -508,11 +508,13 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
       OutputDescription& output = result->outputs[i];
       output.is_constant = false;
       if (num_computation_outputs > 1) {
-        output.shape =
-            XLAShapeToTensorShape(xla::ShapeUtil::GetTupleElementShape(
-                result->xla_output_shape, computation_output));
+        TF_RETURN_IF_ERROR(XLAShapeToTensorShape(
+            xla::ShapeUtil::GetTupleElementShape(result->xla_output_shape,
+                                                 computation_output),
+            &output.shape));
       } else {
-        output.shape = XLAShapeToTensorShape(result->xla_output_shape);
+        TF_RETURN_IF_ERROR(
+            XLAShapeToTensorShape(result->xla_output_shape, &output.shape));
       }
       ++computation_output;
     }
@@ -521,13 +523,14 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
   for (std::vector<ResourceUpdate>::size_type i = 0;
        i < result->resource_updates.size(); ++i) {
     if (num_computation_outputs > 1) {
-      result->resource_updates[i].shape =
-          XLAShapeToTensorShape(xla::ShapeUtil::GetTupleElementShape(
-              result->xla_output_shape, computation_output));
+      TF_RETURN_IF_ERROR(XLAShapeToTensorShape(
+          xla::ShapeUtil::GetTupleElementShape(result->xla_output_shape,
+                                               computation_output),
+          &result->resource_updates[i].shape));
     } else {
       CHECK_EQ(0, computation_output);
-      result->resource_updates[i].shape =
-          XLAShapeToTensorShape(result->xla_output_shape);
+      TF_RETURN_IF_ERROR(XLAShapeToTensorShape(
+          result->xla_output_shape, &result->resource_updates[i].shape));
     }
     ++computation_output;
   }
diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
index dd46702223..c5a68e05d9 100644
--- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc
@@ -279,7 +279,8 @@ Status XlaOpKernelContext::GetVariableTypeAndShape(int index, DataType* type,
   if (!shape_or_status.ok()) {
     return shape_or_status.status();
   }
-  *shape = XLAShapeToTensorShape(*shape_or_status.ValueOrDie());
+  TF_RETURN_IF_ERROR(
+      XLAShapeToTensorShape(*shape_or_status.ValueOrDie(), shape));
   return Status::OK();
 }
 
@@ -296,10 +297,11 @@ void XlaOpKernelContext::SetOutput(int index,
   // The step's default allocator is the dummy XlaCompilationAllocator which
   // simply allocates a metadata buffer to hold the expression to which it
   // corresponds.
-  OP_REQUIRES_OK(
-      context_,
-      context_->allocate_output(
-          index, XLAShapeToTensorShape(*shape.ValueOrDie()), &output));
+  TensorShape tensor_shape;
+  OP_REQUIRES_OK(context_,
+                 XLAShapeToTensorShape(*shape.ValueOrDie(), &tensor_shape));
+  OP_REQUIRES_OK(context_,
+                 context_->allocate_output(index, tensor_shape, &output));
 
   // The expression is stored in the tensor's data buffer. Fill in the
   // fields now.
-- 
GitLab


From 8ddd4687e40edc01971349aa3ae7ccca7cd70030 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 10:15:37 -0700
Subject: [PATCH 0693/1375] Fix multioutput fusion test case.

PiperOrigin-RevId: 160971944
---
 .../compiler/xla/tests/multioutput_fusion_test.cc  | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
index ba9b16d6b6..b34e1d7db2 100644
--- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
@@ -86,10 +86,13 @@ class MultiOutputFusionTest : public HloTestBase {
     if (manual_fusion) {
       auto tuple = computation->AddInstruction(HloInstruction::CreateTuple(
           ArraySlice<HloInstruction*>({sub, add2}, 0, 2)));
-      computation->AddInstruction(
+      auto gte0 = computation->AddInstruction(
           HloInstruction::CreateGetTupleElement(elem_shape2, tuple, 0));
-      computation->AddInstruction(
+      auto gte1 = computation->AddInstruction(
           HloInstruction::CreateGetTupleElement(elem_shape2, tuple, 1));
+      TF_CHECK_OK(dot->ReplaceOperandWith(0, gte0));
+      TF_CHECK_OK(dot->ReplaceOperandWith(1, gte1));
+
       CHECK_NE(
           computation->CreateFusionInstruction(
               {tuple, sub, add2, broadcast}, HloInstruction::FusionKind::kLoop),
@@ -141,10 +144,13 @@ class MultiOutputFusionTest : public HloTestBase {
       auto tuple = computation->AddInstruction(HloInstruction::CreateTuple(
           ArraySlice<HloInstruction*>({sub_U8, add}, 0, 2)));
 
-      computation->AddInstruction(
+      auto gte0 = computation->AddInstruction(
           HloInstruction::CreateGetTupleElement(elem_shape_U8, tuple, 0));
-      computation->AddInstruction(
+      auto gte1 = computation->AddInstruction(
           HloInstruction::CreateGetTupleElement(elem_shape_F32, tuple, 1));
+      TF_CHECK_OK(sub->ReplaceOperandWith(0, gte0));
+      TF_CHECK_OK(reshape->ReplaceOperandWith(0, gte1));
+
       CHECK_NE(computation->CreateFusionInstruction(
                    {tuple, sub_U8, add, param0_U8, param1_F32},
                    HloInstruction::FusionKind::kLoop),
-- 
GitLab


From d8cd3775215d09acb8ef997fe7045f1bc592f2d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 10:17:24 -0700
Subject: [PATCH 0694/1375] Add support for flattening dicts in tensorflow nest
 utilities.

PiperOrigin-RevId: 160972188
---
 tensorflow/python/util/nest.py      | 115 +++++++++++++++++++++-------
 tensorflow/python/util/nest_test.py |  73 ++++++++++++++++++
 2 files changed, 161 insertions(+), 27 deletions(-)

diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py
index 01b3dfacd1..8d77f8c845 100644
--- a/tensorflow/python/util/nest.py
+++ b/tensorflow/python/util/nest.py
@@ -15,11 +15,13 @@
 
 """## Functions for working with arbitrarily nested sequences of elements.
 
-This module is used to perform any operations on nested structures. A nested
-structure is a Python sequence that contains non-sequence elements or other
-sequences. The utilities here assume (and do not check) that the nested
-structures form a 'tree', i.e. no references in the structure of the input of
-these functions should be recursive.
+This module can perform operations on nested structures. A nested structure is a
+Python sequence, tuple (including `namedtuple`), or dict that can contain
+further sequences, tuples, and dicts.
+
+The utilities here assume (and do not check) that the nested structures form a
+'tree', i.e., no references in the structure of the input of these functions
+should be recursive.
 
 Example structures: `((3, 4), 5, (6, 7, (9, 10), 8))`, `(np.array(0),
   (np.array([3, 4]), tf.constant([3, 4])))`
@@ -33,6 +35,7 @@ import collections as _collections
 
 import six as _six
 
+from tensorflow.python.platform import tf_logging as _tf_logging
 from tensorflow.python.util.all_util import remove_undocumented
 
 
@@ -40,16 +43,23 @@ def _sequence_like(instance, args):
   """Converts the sequence `args` to the same type as `instance`.
 
   Args:
-    instance: an instance of `tuple`, `list`, or a `namedtuple` class.
+    instance: an instance of `tuple`, `list`, `namedtuple`, `dict`, or
+        `collections.NamedDict`.
     args: elements to be converted to a sequence.
 
   Returns:
     `args` with the type of `instance`.
   """
-  if (isinstance(instance, tuple) and
-      hasattr(instance, "_fields") and
-      isinstance(instance._fields, _collections.Sequence) and
-      all(isinstance(f, _six.string_types) for f in instance._fields)):
+  if isinstance(instance, dict):
+    # For dictionaries with their values extracted, we always order the values
+    # by sorting the keys first (see note below). This code allows recreating
+    # e.g., `OrderedDict`s with their original key ordering.
+    result = dict(zip(sorted(_six.iterkeys(instance)), args))
+    return type(instance)((key, result[key]) for key in _six.iterkeys(instance))
+  elif (isinstance(instance, tuple) and
+        hasattr(instance, "_fields") and
+        isinstance(instance._fields, _collections.Sequence) and
+        all(isinstance(f, _six.string_types) for f in instance._fields)):
     # This is a namedtuple
     return type(instance)(*args)
   else:
@@ -57,8 +67,21 @@ def _sequence_like(instance, args):
     return type(instance)(args)
 
 
+def _yield_value(iterable):
+  if isinstance(iterable, dict):
+    # Iterate through dictionaries in a deterministic order. Note: we
+    # intentionally ignore the order in an `OrderedDict` because of the
+    # potential to introduce bugs if the user mixes ordered and plain dicts with
+    # the same keys. (This is based on experience.)
+    for key in sorted(_six.iterkeys(iterable)):
+      yield iterable[key]
+  else:
+    for value in iterable:
+      yield value
+
+
 def _yield_flat_nest(nest):
-  for n in nest:
+  for n in _yield_value(nest):
     if is_sequence(n):
       for ni in _yield_flat_nest(n):
         yield ni
@@ -66,6 +89,17 @@ def _yield_flat_nest(nest):
       yield n
 
 
+# Used by `_warn_once` to remember which warning messages have been given.
+_ALREADY_WARNED = {}
+
+
+def _warn_once(message):
+  """Logs a warning message, once per unique string."""
+  if message not in _ALREADY_WARNED:
+    _ALREADY_WARNED[message] = True
+    _tf_logging.warning(message)
+
+
 def is_sequence(seq):
   """Returns a true if its input is a collections.Sequence (except strings).
 
@@ -73,8 +107,14 @@ def is_sequence(seq):
     seq: an input sequence.
 
   Returns:
-    True if the sequence is a not a string and is a collections.Sequence.
+    True if the sequence is a not a string and is a collections.Sequence or a
+    dict.
   """
+  if isinstance(seq, dict):
+    return True
+  if isinstance(seq, set):
+    _warn_once("Sets are not currently considered sequences, but this may "
+               "change in the future, so consider avoiding using them.")
   return (isinstance(seq, _collections.Sequence)
           and not isinstance(seq, _six.string_types))
 
@@ -82,36 +122,55 @@ def is_sequence(seq):
 def flatten(nest):
   """Returns a flat sequence from a given nested structure.
 
-  If `nest` is not a sequence, this returns a single-element list: `[nest]`.
+  If `nest` is not a sequence, tuple, or dict, then returns a single-element
+  list: `[nest]`.
 
   Args:
-    nest: an arbitrarily nested structure or a scalar object.
-      Note, numpy arrays are considered scalars.
+    nest: an arbitrarily nested structure or a scalar object. Note, numpy
+        arrays are considered scalars.
 
   Returns:
     A Python list, the flattened version of the input.
   """
-  return list(_yield_flat_nest(nest)) if is_sequence(nest) else [nest]
+  if is_sequence(nest):
+    return list(_yield_flat_nest(nest))
+  else:
+    return [nest]
 
 
 def _recursive_assert_same_structure(nest1, nest2, check_types):
+  """Helper function for `assert_same_structure`."""
   is_sequence_nest1 = is_sequence(nest1)
   if is_sequence_nest1 != is_sequence(nest2):
     raise ValueError(
         "The two structures don't have the same nested structure. "
         "First structure: %s, second structure: %s." % (nest1, nest2))
 
-  if is_sequence_nest1:
+  if not is_sequence_nest1:
+    return  # finished checking
+
+  if check_types:
     type_nest1 = type(nest1)
     type_nest2 = type(nest2)
-    if check_types and type_nest1 != type_nest2:
+    if type_nest1 != type_nest2:
       raise TypeError(
           "The two structures don't have the same sequence type. First "
           "structure has type %s, while second structure has type %s."
           % (type_nest1, type_nest2))
 
-    for n1, n2 in zip(nest1, nest2):
-      _recursive_assert_same_structure(n1, n2, check_types)
+    if isinstance(nest1, dict):
+      keys1 = set(_six.iterkeys(nest1))
+      keys2 = set(_six.iterkeys(nest2))
+      if keys1 != keys2:
+        raise ValueError(
+            "The two dictionaries don't have the same set of keys. First "
+            "structure has keys {}, while second structure has keys {}."
+            .format(keys1, keys2))
+
+  nest1_as_sequence = [n for n in _yield_value(nest1)]
+  nest2_as_sequence = [n for n in _yield_value(nest2)]
+  for n1, n2 in zip(nest1_as_sequence, nest2_as_sequence):
+    _recursive_assert_same_structure(n1, n2, check_types)
 
 
 def assert_same_structure(nest1, nest2, check_types=True):
@@ -121,8 +180,9 @@ def assert_same_structure(nest1, nest2, check_types=True):
     nest1: an arbitrarily nested structure.
     nest2: an arbitrarily nested structure.
     check_types: if `True` (default) types of sequences are checked as
-      well. If set to `False`, for example a list and a tuple of objects will
-      look same if they have the same size.
+        well, incuding the keys of dictionaries. If set to `False`, for example
+        a list and a tuple of objects will look the same if they have the same
+        size.
 
   Raises:
     ValueError: If the two structures do not have the same number of elements or
@@ -196,10 +256,10 @@ def flatten_dict_items(dictionary):
 
 
 def _packed_nest_with_indices(structure, flat, index):
-  """Helper function for pack_nest_as.
+  """Helper function for pack_sequence_as.
 
   Args:
-    structure: Substructure (tuple of elements and/or tuples) to mimic
+    structure: Substructure (list / tuple / dict) to mimic.
     flat: Flattened values to output substructure for.
     index: Index at which to start reading from flat.
 
@@ -215,7 +275,7 @@ def _packed_nest_with_indices(structure, flat, index):
       (assuming indexing starts from `index`).
   """
   packed = []
-  for s in structure:
+  for s in _yield_value(structure):
     if is_sequence(s):
       new_index, child = _packed_nest_with_indices(s, flat, index)
       packed.append(_sequence_like(s, child))
@@ -233,8 +293,9 @@ def pack_sequence_as(structure, flat_sequence):
   in this case the return value is `flat_sequence[0]`.
 
   Args:
-    structure: tuple or list constructed of scalars and/or other tuples/lists,
-      or a scalar.  Note: numpy arrays are considered scalars.
+    structure: Nested structure, whose structure is given by nested lists,
+        tuples, and dicts. Note: numpy arrays and strings are considered
+        scalars.
     flat_sequence: flat sequence to pack.
 
   Returns:
diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py
index 8a17d990da..6b787602ed 100644
--- a/tensorflow/python/util/nest_test.py
+++ b/tensorflow/python/util/nest_test.py
@@ -65,11 +65,80 @@ class NestTest(test.TestCase):
     with self.assertRaises(ValueError):
       nest.pack_sequence_as([5, 6, [7, 8]], ["a", "b", "c"])
 
+  def testFlattenAndPack_withDicts(self):
+    # A nice messy mix of tuples, lists, dicts, and `OrderedDict`s.
+    named_tuple = collections.namedtuple("A", ("b", "c"))
+    mess = [
+        "z",
+        named_tuple(3, 4),
+        {
+            "c": [
+                1,
+                collections.OrderedDict([
+                    ("b", 3),
+                    ("a", 2),
+                ]),
+            ],
+            "b": 5
+        },
+        17
+    ]
+
+    flattened = nest.flatten(mess)
+    self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 17])
+
+    structure_of_mess = [
+        14,
+        named_tuple("a", True),
+        {
+            "c": [
+                0,
+                collections.OrderedDict([
+                    ("b", 9),
+                    ("a", 8),
+                ]),
+            ],
+            "b": 3
+        },
+        "hi everybody",
+    ]
+
+    unflattened = nest.pack_sequence_as(structure_of_mess, flattened)
+    self.assertEqual(unflattened, mess)
+
+    # Check also that the OrderedDict was created, with the correct key order.
+    unflattened_ordered_dict = unflattened[2]["c"][1]
+    self.assertIsInstance(unflattened_ordered_dict, collections.OrderedDict)
+    self.assertEqual(unflattened_ordered_dict.keys(), ["b", "a"])
+
+  def testFlatten_numpyIsNotFlattened(self):
+    structure = np.array([1, 2, 3])
+    flattened = nest.flatten(structure)
+    self.assertEqual(len(flattened), 1)
+
+  def testFlatten_stringIsNotFlattened(self):
+    structure = "lots of letters"
+    flattened = nest.flatten(structure)
+    self.assertEqual(len(flattened), 1)
+
+  def testPackSequenceAs_notIterableError(self):
+    with self.assertRaisesRegexp(TypeError,
+                                 "flat_sequence must be a sequence"):
+      nest.pack_sequence_as("hi", "bye")
+
+  def testPackSequenceAs_wrongLengthsError(self):
+    with self.assertRaisesRegexp(
+        ValueError,
+        "Structure had 2 elements, but flat_sequence had 3 elements."):
+      nest.pack_sequence_as(["hello", "world"],
+                            ["and", "goodbye", "again"])
+
   def testIsSequence(self):
     self.assertFalse(nest.is_sequence("1234"))
     self.assertTrue(nest.is_sequence([1, 3, [4, 5]]))
     self.assertTrue(nest.is_sequence(((7, 8), (5, 6))))
     self.assertTrue(nest.is_sequence([]))
+    self.assertTrue(nest.is_sequence({"a": 1, "b": 2}))
     self.assertFalse(nest.is_sequence(set([1, 2])))
     ones = array_ops.ones([2, 3])
     self.assertFalse(nest.is_sequence(ones))
@@ -146,6 +215,10 @@ class NestTest(test.TestCase):
     nest.assert_same_structure(structure1, structure2, check_types=False)
     nest.assert_same_structure(structure1, structure1_list, check_types=False)
 
+    with self.assertRaisesRegexp(ValueError,
+                                 "don't have the same set of keys"):
+      nest.assert_same_structure({"a": 1}, {"b": 1})
+
   def testMapStructure(self):
     structure1 = (((1, 2), 3), 4, (5, 6))
     structure2 = (((7, 8), 9), 10, (11, 12))
-- 
GitLab


From 472cd28c5c35b9a31bc78750e61317eabb05d2b1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 10:19:17 -0700
Subject: [PATCH 0695/1375] Better error handling when given input key is not
 valid for saved_model_cli.

PiperOrigin-RevId: 160972434
---
 tensorflow/python/tools/saved_model_cli.py      | 10 ++++++++++
 tensorflow/python/tools/saved_model_cli_test.py | 10 ++++++++++
 2 files changed, 20 insertions(+)

diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py
index e1be305505..9075a707a2 100644
--- a/tensorflow/python/tools/saved_model_cli.py
+++ b/tensorflow/python/tools/saved_model_cli.py
@@ -255,6 +255,7 @@ def run_saved_model_with_feed_dict(saved_model_dir, tag_set, signature_def_key,
         SavedModel.
 
   Raises:
+    ValueError: When any of the input tensor keys is not valid.
     RuntimeError: An error when output file already exists and overwrite is not
     enabled.
   """
@@ -265,6 +266,15 @@ def run_saved_model_with_feed_dict(saved_model_dir, tag_set, signature_def_key,
   # uses tensor name.
   inputs_tensor_info = _get_inputs_tensor_info_from_meta_graph_def(
       meta_graph_def, signature_def_key)
+
+  # Check if input tensor keys are valid.
+  for input_key_name in input_tensor_key_feed_dict.keys():
+    if input_key_name not in inputs_tensor_info.keys():
+      raise ValueError(
+          '"%s" is not a valid input key. Please choose from %s, or use '
+          '--show option.' %
+          (input_key_name, '"' + '", "'.join(inputs_tensor_info.keys()) + '"'))
+
   inputs_feed_dict = {
       inputs_tensor_info[key].name: tensor
       for key, tensor in input_tensor_key_feed_dict.items()
diff --git a/tensorflow/python/tools/saved_model_cli_test.py b/tensorflow/python/tools/saved_model_cli_test.py
index 8f79c888eb..a55cf168b2 100644
--- a/tensorflow/python/tools/saved_model_cli_test.py
+++ b/tensorflow/python/tools/saved_model_cli_test.py
@@ -391,6 +391,16 @@ Method name is: tensorflow/serving/predict"""
     y_expected = np.array([[2.5], [3.0]])
     self.assertAllClose(y_expected, y_actual)
 
+  def testRunCommandInvalidInputKeyError(self):
+    self.parser = saved_model_cli.create_parser()
+    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
+    args = self.parser.parse_args([
+        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
+        'regress_x2_to_y3', '--input_exprs', 'x2=np.ones((3,1))'
+    ])
+    with self.assertRaises(ValueError):
+      saved_model_cli.run(args)
+
   def testRunCommandOutputFileExistError(self):
     self.parser = saved_model_cli.create_parser()
     base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
-- 
GitLab


From 06b22d5d9f4b820734941e056910f698fe3e2405 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 10:53:08 -0700
Subject: [PATCH 0696/1375] Add a block cache to the GCS filesystem, with block
 size defaulted to the readahead buffer size.  Additionally make the block
 size and block count configurable via env variables.

PiperOrigin-RevId: 160977285
---
 tensorflow/core/platform/cloud/BUILD          |  20 ++
 .../core/platform/cloud/file_block_cache.cc   |  97 +++++++
 .../core/platform/cloud/file_block_cache.h    | 101 +++++++
 .../platform/cloud/file_block_cache_test.cc   | 236 ++++++++++++++++
 .../core/platform/cloud/gcs_file_system.cc    | 108 ++++----
 .../core/platform/cloud/gcs_file_system.h     |  18 +-
 .../platform/cloud/gcs_file_system_test.cc    | 258 +++++++++++-------
 7 files changed, 674 insertions(+), 164 deletions(-)
 create mode 100644 tensorflow/core/platform/cloud/file_block_cache.cc
 create mode 100644 tensorflow/core/platform/cloud/file_block_cache.h
 create mode 100644 tensorflow/core/platform/cloud/file_block_cache_test.cc

diff --git a/tensorflow/core/platform/cloud/BUILD b/tensorflow/core/platform/cloud/BUILD
index 4df88b8d37..183a447012 100644
--- a/tensorflow/core/platform/cloud/BUILD
+++ b/tensorflow/core/platform/cloud/BUILD
@@ -24,6 +24,14 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
+cc_library(
+    name = "file_block_cache",
+    srcs = ["file_block_cache.cc"],
+    hdrs = ["file_block_cache.h"],
+    visibility = ["//tensorflow:__subpackages__"],
+    deps = ["//tensorflow/core:lib"],
+)
+
 cc_library(
     name = "gcs_file_system",
     srcs = [
@@ -35,6 +43,7 @@ cc_library(
     linkstatic = 1,  # Needed since alwayslink is broken in bazel b/27630669
     visibility = ["//visibility:public"],
     deps = [
+        ":file_block_cache",
         ":google_auth_provider",
         ":http_request",
         ":retrying_file_system",
@@ -155,6 +164,17 @@ cc_library(
     ],
 )
 
+tf_cc_test(
+    name = "file_block_cache_test",
+    size = "small",
+    srcs = ["file_block_cache_test.cc"],
+    deps = [
+        ":file_block_cache",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
 tf_cc_test(
     name = "gcs_file_system_test",
     size = "small",
diff --git a/tensorflow/core/platform/cloud/file_block_cache.cc b/tensorflow/core/platform/cloud/file_block_cache.cc
new file mode 100644
index 0000000000..b794614cd7
--- /dev/null
+++ b/tensorflow/core/platform/cloud/file_block_cache.cc
@@ -0,0 +1,97 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/platform/cloud/file_block_cache.h"
+#include <cstring>
+#include <memory>
+#include "tensorflow/core/platform/env.h"
+
+namespace tensorflow {
+
+Status FileBlockCache::Read(uint64 offset, size_t n, std::vector<char>* out) {
+  out->clear();
+  if (n == 0) {
+    return Status::OK();
+  }
+  if (block_size_ == 0 || block_count_ == 0) {
+    // The cache is effectively disabled, so we pass the read through to the
+    // fetcher without breaking it up into blocks.
+    return block_fetcher_(offset, n, out);
+  }
+  // Calculate the block-aligned start and end of the read.
+  uint64 start = block_size_ * (offset / block_size_);
+  uint64 finish = block_size_ * ((offset + n) / block_size_);
+  if (finish < offset + n) {
+    finish += block_size_;
+  }
+  // Now iterate through the blocks, reading them one at a time. Reads are
+  // locked so that only one block_fetcher call is active at any given time.
+  mutex_lock lock(mu_);
+  for (uint64 pos = start; pos < finish; pos += block_size_) {
+    auto entry = block_map_.find(pos);
+    if (entry == block_map_.end()) {
+      // We need to fetch the block from the remote filesystem. Trim the LRU
+      // cache if needed - we do this up front in order to avoid any period of
+      // time during which the cache size exceeds its desired limit. The
+      // tradeoff is that if the fetcher fails, the cache may evict a block
+      // prematurely.
+      while (lru_list_.size() >= block_count_) {
+        block_map_.erase(lru_list_.back());
+        lru_list_.pop_back();
+      }
+      std::unique_ptr<Block> block(new Block);
+      TF_RETURN_IF_ERROR(block_fetcher_(pos, block_size_, &block->data));
+      // Sanity check to detect interrupted reads leading to partial blocks: a
+      // partial block must have a higher key than the highest existing key in
+      // the block map.
+      if (block->data.size() < block_size_ && !block_map_.empty() &&
+          pos < block_map_.rbegin()->first) {
+        // We expected to read a full block at this position.
+        return errors::FailedPrecondition("File contents are inconsistent");
+      }
+      entry = block_map_.emplace(std::make_pair(pos, std::move(block))).first;
+    } else {
+      // Cache hit. Remove the block from the LRU list at its prior location.
+      lru_list_.erase(entry->second->lru_iterator);
+    }
+    // Push the block to the front of the LRU list.
+    lru_list_.push_front(pos);
+    entry->second->lru_iterator = lru_list_.begin();
+    // Copy the relevant portion of the block into the result buffer.
+    const auto& data = entry->second->data;
+    if (offset >= pos + data.size()) {
+      // The requested offset is at or beyond the end of the file. This can
+      // happen if `offset` is not block-aligned, and the read returns the last
+      // block in the file, which does not extend all the way out to `offset`.
+      return errors::OutOfRange("EOF at offset ", offset);
+    }
+    auto begin = data.begin();
+    if (offset > pos) {
+      // The block begins before the slice we're reading.
+      begin += offset - pos;
+    }
+    auto end = data.end();
+    if (pos + data.size() > offset + n) {
+      // The block extends past the end of the slice we're reading.
+      end -= (pos + data.size()) - (offset + n);
+    }
+    if (begin < end) {
+      out->insert(out->end(), begin, end);
+    }
+  }
+  return Status::OK();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/file_block_cache.h b/tensorflow/core/platform/cloud/file_block_cache.h
new file mode 100644
index 0000000000..442b1b0319
--- /dev/null
+++ b/tensorflow/core/platform/cloud/file_block_cache.h
@@ -0,0 +1,101 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_PLATFORM_CLOUD_FILE_BLOCK_CACHE_H_
+#define TENSORFLOW_CORE_PLATFORM_CLOUD_FILE_BLOCK_CACHE_H_
+
+#include <functional>
+#include <list>
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+/// \brief An LRU block cache of file contents.
+///
+/// This class should be used by read-only random access files on a remote
+/// filesystem (e.g. GCS).
+class FileBlockCache {
+ public:
+  /// The callback executed when a block is not found in the cache, and needs to
+  /// be fetched from the backing filesystem. This callback is provided when the
+  /// cache is constructed. The returned Status should be OK as long as the
+  /// read from the remote filesystem succeeded (similar to the semantics of the
+  /// read(2) system call).
+  typedef std::function<Status(uint64, size_t, std::vector<char>*)>
+      BlockFetcher;
+
+  FileBlockCache(uint64 block_size, uint32 block_count,
+                 BlockFetcher block_fetcher)
+      : block_size_(block_size),
+        block_count_(block_count),
+        block_fetcher_(block_fetcher) {}
+
+  /// Read `n` bytes starting at `offset` into `out`. This method will return:
+  ///
+  /// 1) The error from the remote filesystem, if the read from the remote
+  ///    filesystem failed.
+  /// 2) PRECONDITION_FAILED if the read from the remote filesystem succeeded,
+  ///    but the read returned a partial block, and the LRU cache contained a
+  ///    block at a higher offset (indicating that the partial block should have
+  ///    been a full block).
+  /// 3) OUT_OF_RANGE if the read from the remote filesystem succeeded, but
+  ///    the file contents do not extend past `offset` and thus nothing was
+  ///    placed in `out`.
+  /// 4) OK otherwise (i.e. the read succeeded, and at least one byte was placed
+  ///    in `out`).
+  Status Read(uint64 offset, size_t n, std::vector<char>* out);
+
+ private:
+  /// The size of the blocks stored in the LRU cache, as well as the size of the
+  /// reads from the underlying filesystem.
+  const uint64 block_size_;
+  /// The maximum number of blocks allowed in the LRU cache.
+  const uint32 block_count_;
+  /// The callback to read a block from the underlying filesystem.
+  const BlockFetcher block_fetcher_;
+
+  /// \brief A block of a file.
+  ///
+  /// A file block consists of the block data and the block's current position
+  /// in the LRU cache.
+  struct Block {
+    /// The block data.
+    std::vector<char> data;
+    /// A list iterator pointing to the block's position in the LRU list.
+    std::list<uint64>::iterator lru_iterator;
+  };
+
+  /// Guards access to the block map and LRU list.
+  mutex mu_;
+
+  /// The block map (map from offset in the file to Block object).
+  std::map<uint64, std::unique_ptr<Block>> block_map_ GUARDED_BY(mu_);
+
+  /// The LRU list of offsets in the file. The front of the list is the position
+  /// of the most recently accessed block.
+  std::list<uint64> lru_list_ GUARDED_BY(mu_);
+};
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_PLATFORM_CLOUD_FILE_BLOCK_CACHE_H_
diff --git a/tensorflow/core/platform/cloud/file_block_cache_test.cc b/tensorflow/core/platform/cloud/file_block_cache_test.cc
new file mode 100644
index 0000000000..4f62e4ee6a
--- /dev/null
+++ b/tensorflow/core/platform/cloud/file_block_cache_test.cc
@@ -0,0 +1,236 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/platform/cloud/file_block_cache.h"
+#include <cstring>
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace {
+
+TEST(FileBlockCacheTest, PassThrough) {
+  const uint64 want_offset = 42;
+  const size_t want_n = 1024;
+  int calls = 0;
+  auto fetcher = [&calls, want_offset, want_n](uint64 got_offset, size_t got_n,
+                                               std::vector<char>* out) {
+    EXPECT_EQ(got_offset, want_offset);
+    EXPECT_EQ(got_n, want_n);
+    calls++;
+    out->resize(got_n, 'x');
+    return Status::OK();
+  };
+  // If block_size, block_count, or both are zero, the cache is a pass-through.
+  FileBlockCache cache1(1, 0, fetcher);
+  FileBlockCache cache2(0, 1, fetcher);
+  FileBlockCache cache3(0, 0, fetcher);
+  std::vector<char> out;
+  TF_EXPECT_OK(cache1.Read(want_offset, want_n, &out));
+  EXPECT_EQ(calls, 1);
+  TF_EXPECT_OK(cache2.Read(want_offset, want_n, &out));
+  EXPECT_EQ(calls, 2);
+  TF_EXPECT_OK(cache3.Read(want_offset, want_n, &out));
+  EXPECT_EQ(calls, 3);
+}
+
+TEST(FileBlockCacheTest, BlockAlignment) {
+  // Initialize a 256-byte buffer.  This is the file underlying the reads we'll
+  // do in this test.
+  const size_t size = 256;
+  std::vector<char> buf;
+  for (int i = 0; i < size; i++) {
+    buf.push_back(i);
+  }
+  // The fetcher just fetches slices of the buffer.
+  auto fetcher = [&buf](uint64 offset, size_t n, std::vector<char>* out) {
+    if (offset < buf.size()) {
+      if (offset + n > buf.size()) {
+        out->insert(out->end(), buf.begin() + offset, buf.end());
+      } else {
+        out->insert(out->end(), buf.begin() + offset, buf.begin() + offset + n);
+      }
+    }
+    return Status::OK();
+  };
+  for (uint64_t block_size = 2; block_size <= 4; block_size++) {
+    // Make a cache of N-byte block size (1 block) and verify that reads of
+    // varying offsets and lengths return correct data.
+    FileBlockCache cache(block_size, 1, fetcher);
+    for (uint64_t offset = 0; offset < 10; offset++) {
+      for (size_t n = block_size - 2; n <= block_size + 2; n++) {
+        std::vector<char> got;
+        TF_EXPECT_OK(cache.Read(offset, n, &got));
+        // Verify the size of the read.
+        if (offset + n <= size) {
+          // Expect a full read.
+          EXPECT_EQ(got.size(), n) << "block size = " << block_size
+                                   << ", offset = " << offset << ", n = " << n;
+        } else {
+          // Expect a partial read.
+          EXPECT_EQ(got.size(), size - offset)
+              << "block size = " << block_size << ", offset = " << offset
+              << ", n = " << n;
+        }
+        // Verify the contents of the read.
+        std::vector<char>::const_iterator begin = buf.begin() + offset;
+        std::vector<char>::const_iterator end =
+            offset + n > buf.size() ? buf.end() : begin + n;
+        std::vector<char> want(begin, end);
+        EXPECT_EQ(got, want) << "block size = " << block_size
+                             << ", offset = " << offset << ", n = " << n;
+      }
+    }
+  }
+}
+
+TEST(FileBlockCacheTest, CacheHits) {
+  const uint64 block_size = 16;
+  std::set<uint64_t> calls;
+  auto fetcher = [&calls, block_size](uint64 offset, size_t n,
+                                      std::vector<char>* out) {
+    EXPECT_EQ(n, block_size);
+    EXPECT_EQ(offset % block_size, 0);
+    EXPECT_EQ(calls.find(offset), calls.end()) << "at offset " << offset;
+    calls.insert(offset);
+    out->resize(n, 'x');
+    return Status::OK();
+  };
+  const uint32 block_count = 256;
+  FileBlockCache cache(block_size, block_count, fetcher);
+  std::vector<char> out;
+  // The cache has space for `block_count` blocks. The loop with i = 0 should
+  // fill the cache, and the loop with i = 1 should be all cache hits. The
+  // fetcher checks that it is called once and only once for each offset (to
+  // fetch the corresponding block).
+  for (int i = 0; i < 2; i++) {
+    for (int j = 0; j < block_count; j++) {
+      TF_EXPECT_OK(cache.Read(block_size * j, block_size, &out));
+    }
+  }
+}
+
+TEST(FileBlockCacheTest, OutOfRange) {
+  // Tests reads of a 24-byte file with block size 16.
+  const uint64 block_size = 16;
+  const uint64 file_size = 24;
+  bool first_block = false;
+  bool second_block = false;
+  auto fetcher = [block_size, file_size, &first_block, &second_block](
+                     uint64 offset, size_t n, std::vector<char>* out) {
+    EXPECT_EQ(n, block_size);
+    EXPECT_EQ(offset % block_size, 0);
+    if (offset == 0) {
+      // The first block (16 bytes) of the file.
+      out->resize(n, 'x');
+      first_block = true;
+    } else if (offset == block_size) {
+      // The second block (8 bytes) of the file.
+      out->resize(file_size - block_size, 'x');
+      second_block = true;
+    }
+    return Status::OK();
+  };
+  FileBlockCache cache(block_size, 1, fetcher);
+  std::vector<char> out;
+  // Reading the first 16 bytes should be fine.
+  TF_EXPECT_OK(cache.Read(0, block_size, &out));
+  EXPECT_TRUE(first_block);
+  EXPECT_EQ(out.size(), block_size);
+  // Reading at offset file_size + 4 will read the second block (since the read
+  // at file_size + 4 = 28 will be aligned to an offset of 16) but will return
+  // OutOfRange because the offset is past the end of the 24-byte file.
+  Status status = cache.Read(file_size + 4, 4, &out);
+  EXPECT_EQ(status.code(), error::OUT_OF_RANGE);
+  EXPECT_TRUE(second_block);
+  EXPECT_EQ(out.size(), 0);
+  // Reading the second full block will return 8 bytes, from a cache hit.
+  second_block = false;
+  TF_EXPECT_OK(cache.Read(block_size, block_size, &out));
+  EXPECT_FALSE(second_block);
+  EXPECT_EQ(out.size(), file_size - block_size);
+}
+
+TEST(FileBlockCacheTest, Inconsistent) {
+  // Tests the detection of interrupted reads leading to partially filled blocks
+  // where we expected complete blocks.
+  const uint64 block_size = 16;
+  // This fetcher returns OK but only fills in one byte for any offset.
+  auto fetcher = [block_size](uint64 offset, size_t n, std::vector<char>* out) {
+    EXPECT_EQ(n, block_size);
+    EXPECT_EQ(offset % block_size, 0);
+    out->resize(1, 'x');
+    return Status::OK();
+  };
+  FileBlockCache cache(block_size, 2, fetcher);
+  std::vector<char> out;
+  // Read the second block; this should yield an OK status and a single byte.
+  TF_EXPECT_OK(cache.Read(block_size, block_size, &out));
+  EXPECT_EQ(out.size(), 1);
+  // Now read the first block; this should yield FAILED_PRECONDITION because we
+  // had already cached a partial block at a later position.
+  Status status = cache.Read(0, block_size, &out);
+  EXPECT_EQ(status.code(), error::FAILED_PRECONDITION);
+}
+
+TEST(FileBlockCacheTest, LRU) {
+  const uint64 block_size = 16;
+  std::list<uint64_t> calls;
+  auto fetcher = [&calls, block_size](uint64 offset, size_t n,
+                                      std::vector<char>* out) {
+    EXPECT_EQ(n, block_size);
+    EXPECT_FALSE(calls.empty()) << "at offset = " << offset;
+    if (!calls.empty()) {
+      EXPECT_EQ(offset, calls.front());
+      calls.pop_front();
+    }
+    out->resize(n, 'x');
+    return Status::OK();
+  };
+  const uint32 block_count = 2;
+  FileBlockCache cache(block_size, block_count, fetcher);
+  std::vector<char> out;
+  // Read blocks from the cache, and verify the LRU behavior based on the
+  // fetcher calls that the cache makes.
+  calls.push_back(0);
+  // Cache miss - drains an element from `calls`.
+  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  // Cache hit - does not drain an element from `calls`.
+  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  calls.push_back(block_size);
+  // Cache miss followed by cache hit.
+  TF_EXPECT_OK(cache.Read(block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read(block_size, 1, &out));
+  calls.push_back(2 * block_size);
+  // Cache miss followed by cache hit.  Causes eviction of LRU element.
+  TF_EXPECT_OK(cache.Read(2 * block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read(2 * block_size, 1, &out));
+  // LRU element was at offset 0.  Cache miss.
+  calls.push_back(0);
+  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  // Element at 2 * block_size is still in cache, and this read should update
+  // its position in the LRU list so it doesn't get evicted by the next read.
+  TF_EXPECT_OK(cache.Read(2 * block_size, 1, &out));
+  // Element at block_size was evicted.  Reading this element will also cause
+  // the LRU element (at 0) to be evicted.
+  calls.push_back(block_size);
+  TF_EXPECT_OK(cache.Read(block_size, 1, &out));
+  // Element at 0 was evicted again.
+  calls.push_back(0);
+  TF_EXPECT_OK(cache.Read(0, 1, &out));
+}
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 97e4c207d8..9f00735304 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/cloud/file_block_cache.h"
 #include "tensorflow/core/platform/cloud/google_auth_provider.h"
 #include "tensorflow/core/platform/cloud/retrying_utils.h"
 #include "tensorflow/core/platform/cloud/time_util.h"
@@ -53,8 +54,14 @@ constexpr uint64 kUploadRetryDelayMicros = 1000000L;
 // The HTTP response code "308 Resume Incomplete".
 constexpr uint64 HTTP_CODE_RESUME_INCOMPLETE = 308;
 // The environment variable that overrides the size of the readahead buffer.
+// DEPRECATED. Use GCS_BLOCK_SIZE_MB instead.
 constexpr char kReadaheadBufferSize[] = "GCS_READAHEAD_BUFFER_SIZE_BYTES";
-
+// The environment variable that overrides the block size for aligned reads from
+// GCS. Specified in MB (e.g. "16" = 16 x 1024 x 1024 = 16777216 bytes).
+constexpr char kBlockSize[] = "GCS_READ_CACHE_BLOCK_SIZE_MB";
+// The environment variable that overrides the block count in the LRU cache of
+// blocks read from GCS.
+constexpr char kBlockCount[] = "GCS_READ_CACHE_BLOCK_COUNT";
 // The file statistics returned by Stat() for directories.
 const FileStatistics DIRECTORY_STAT(0, 0, true);
 
@@ -212,49 +219,25 @@ class GcsRandomAccessFile : public RandomAccessFile {
   GcsRandomAccessFile(const string& bucket, const string& object,
                       AuthProvider* auth_provider,
                       HttpRequest::Factory* http_request_factory,
-                      size_t read_ahead_bytes)
+                      size_t block_size, uint32 block_count)
       : bucket_(bucket),
         object_(object),
         auth_provider_(auth_provider),
         http_request_factory_(http_request_factory),
-        read_ahead_bytes_(read_ahead_bytes) {}
+        file_block_cache_(
+            block_size, block_count,
+            [this](uint64 offset, size_t n, std::vector<char>* out) {
+              return LoadBufferFromGCS(offset, n, out);
+            }) {}
 
-  /// The implementation of reads with a read-ahead buffer. Thread-safe.
+  /// The implementation of reads with an LRU block cache. Thread safe.
   Status Read(uint64 offset, size_t n, StringPiece* result,
               char* scratch) const override {
-    mutex_lock lock(mu_);
-    const bool range_start_included = offset >= buffer_start_offset_;
-    const bool range_end_included =
-        offset + n <= buffer_start_offset_ + buffer_.size();
-    if (range_start_included && range_end_included) {
-      // The requested range can be filled from the buffer.
-      const size_t offset_in_buffer =
-          std::min<uint64>(offset - buffer_start_offset_, buffer_.size());
-      const auto copy_size = std::min(n, buffer_.size() - offset_in_buffer);
-      std::copy(buffer_.begin() + offset_in_buffer,
-                buffer_.begin() + offset_in_buffer + copy_size, scratch);
-      *result = StringPiece(scratch, copy_size);
-    } else {
-      // Update the buffer content based on the new requested range.
-      const size_t desired_buffer_size = n + read_ahead_bytes_;
-      if (n > buffer_.capacity() ||
-          desired_buffer_size > 2 * buffer_.capacity()) {
-        // Re-allocate only if buffer capacity increased significantly.
-        buffer_.reserve(desired_buffer_size);
-      }
-
-      // Shift the offset and clear the buffer so that the state stays
-      // consistent if loading from GCS fails.
-      buffer_start_offset_ = offset;
-      buffer_.clear();
-
-      TF_RETURN_IF_ERROR(LoadBufferFromGCS());
-
-      // Set the results.
-      std::memcpy(scratch, buffer_.data(), std::min(buffer_.size(), n));
-      *result = StringPiece(scratch, std::min(buffer_.size(), n));
-    }
-
+    result->clear();
+    std::vector<char> out;
+    TF_RETURN_IF_ERROR(file_block_cache_.Read(offset, n, &out));
+    std::memcpy(scratch, out.data(), std::min(out.size(), n));
+    *result = StringPiece(scratch, std::min(out.size(), n));
     if (result->size() < n) {
       // This is not an error per se. The RandomAccessFile interface expects
       // that Read returns OutOfRange if fewer bytes were read than requested.
@@ -266,9 +249,8 @@ class GcsRandomAccessFile : public RandomAccessFile {
   }
 
  private:
-  /// A helper function to actually read the data from GCS. This function loads
-  /// buffer_ from GCS based on its current capacity.
-  Status LoadBufferFromGCS() const EXCLUSIVE_LOCKS_REQUIRED(mu_) {
+  /// A helper function to actually read the data from GCS.
+  Status LoadBufferFromGCS(uint64_t offset, size_t n, std::vector<char>* out) {
     string auth_token;
     TF_RETURN_IF_ERROR(AuthProvider::GetToken(auth_provider_, &auth_token));
 
@@ -278,9 +260,8 @@ class GcsRandomAccessFile : public RandomAccessFile {
         request->SetUri(strings::StrCat("https://", kStorageHost, "/", bucket_,
                                         "/", request->EscapeString(object_))));
     TF_RETURN_IF_ERROR(request->AddAuthBearerHeader(auth_token));
-    TF_RETURN_IF_ERROR(request->SetRange(
-        buffer_start_offset_, buffer_start_offset_ + buffer_.capacity() - 1));
-    TF_RETURN_IF_ERROR(request->SetResultBuffer(&buffer_));
+    TF_RETURN_IF_ERROR(request->SetRange(offset, offset + n - 1));
+    TF_RETURN_IF_ERROR(request->SetResultBuffer(out));
     TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when reading gs://",
                                     bucket_, "/", object_);
     return Status::OK();
@@ -290,14 +271,7 @@ class GcsRandomAccessFile : public RandomAccessFile {
   string object_;
   AuthProvider* auth_provider_;
   HttpRequest::Factory* http_request_factory_;
-  const size_t read_ahead_bytes_;
-
-  // The buffer-related members need to be mutable, because they are modified
-  // by the const Read() method.
-  mutable mutex mu_;
-  mutable std::vector<char> buffer_ GUARDED_BY(mu_);
-  // The original file offset of the first byte in the buffer.
-  mutable size_t buffer_start_offset_ GUARDED_BY(mu_) = 0;
+  mutable FileBlockCache file_block_cache_;
 };
 
 /// \brief GCS-based implementation of a writeable file.
@@ -589,11 +563,28 @@ GcsFileSystem::GcsFileSystem()
     : auth_provider_(new GoogleAuthProvider()),
       http_request_factory_(new HttpRequest::Factory()) {
   // Apply the sys env override for the readahead buffer size if it's provided.
-  const char* readahead_buffer_size = std::getenv(kReadaheadBufferSize);
-  if (readahead_buffer_size) {
+  const char* readahead_buffer_size_env = std::getenv(kReadaheadBufferSize);
+  if (readahead_buffer_size_env) {
+    uint64 value;
+    if (strings::safe_strtou64(readahead_buffer_size_env, &value)) {
+      block_size_ = value;
+    }
+  }
+  // Apply the override for the block size if provided. This takes precedence
+  // over the readahead buffer size.
+  const char* block_size_env = std::getenv(kBlockSize);
+  if (block_size_env) {
     uint64 value;
-    if (strings::safe_strtou64(readahead_buffer_size, &value)) {
-      read_ahead_bytes_ = value;
+    if (strings::safe_strtou64(block_size_env, &value)) {
+      block_size_ = value * 1024 * 1024;
+    }
+  }
+  // Apply the override for the block count if provided.
+  const char* block_count_env = std::getenv(kBlockCount);
+  if (block_count_env) {
+    uint32 value;
+    if (strings::safe_strtou32(block_count_env, &value)) {
+      block_count_ = value;
     }
   }
 }
@@ -601,10 +592,11 @@ GcsFileSystem::GcsFileSystem()
 GcsFileSystem::GcsFileSystem(
     std::unique_ptr<AuthProvider> auth_provider,
     std::unique_ptr<HttpRequest::Factory> http_request_factory,
-    size_t read_ahead_bytes, int64 initial_retry_delay_usec)
+    size_t block_size, uint32 block_count, int64 initial_retry_delay_usec)
     : auth_provider_(std::move(auth_provider)),
       http_request_factory_(std::move(http_request_factory)),
-      read_ahead_bytes_(read_ahead_bytes),
+      block_size_(block_size),
+      block_count_(block_count),
       initial_retry_delay_usec_(initial_retry_delay_usec) {}
 
 Status GcsFileSystem::NewRandomAccessFile(
@@ -613,7 +605,7 @@ Status GcsFileSystem::NewRandomAccessFile(
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, false, &bucket, &object));
   result->reset(new GcsRandomAccessFile(bucket, object, auth_provider_.get(),
                                         http_request_factory_.get(),
-                                        read_ahead_bytes_));
+                                        block_size_, block_count_));
   return Status::OK();
 }
 
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.h b/tensorflow/core/platform/cloud/gcs_file_system.h
index 18d2de482b..ef8899892e 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.h
+++ b/tensorflow/core/platform/cloud/gcs_file_system.h
@@ -35,7 +35,8 @@ class GcsFileSystem : public FileSystem {
   GcsFileSystem();
   GcsFileSystem(std::unique_ptr<AuthProvider> auth_provider,
                 std::unique_ptr<HttpRequest::Factory> http_request_factory,
-                size_t read_ahead_bytes, int64 initial_retry_delay_usec);
+                size_t block_size, uint32 block_count,
+                int64 initial_retry_delay_usec);
 
   Status NewRandomAccessFile(
       const string& filename,
@@ -74,7 +75,8 @@ class GcsFileSystem : public FileSystem {
 
   Status DeleteRecursively(const string& dirname, int64* undeleted_files,
                            int64* undeleted_dirs) override;
-  size_t get_readahead_buffer_size() const { return read_ahead_bytes_; }
+  size_t block_size() const { return block_size_; }
+  uint32 block_count() const { return block_count_; }
 
  private:
   /// \brief Checks if the bucket exists. Returns OK if the check succeeded.
@@ -111,11 +113,15 @@ class GcsFileSystem : public FileSystem {
   std::unique_ptr<AuthProvider> auth_provider_;
   std::unique_ptr<HttpRequest::Factory> http_request_factory_;
 
-  // The number of bytes to read ahead for buffering purposes in the
-  // RandomAccessFile implementation. Defaults to 256Mb.
-  size_t read_ahead_bytes_ = 256 * 1024 * 1024;
+  /// The block size for block-aligned reads in the RandomAccessFile
+  /// implementation. Defaults to 256Mb.
+  size_t block_size_ = 256 * 1024 * 1024;
 
-  // The initial delay for exponential backoffs when retrying failed calls.
+  /// The block count for the LRU cache of blocks in the RandomAccessFile
+  /// implementation.  Defaults to 1.
+  uint32 block_count_ = 1;
+
+  /// The initial delay for exponential backoffs when retrying failed calls.
   const int64 initial_retry_delay_usec_ = 1000000L;
 
   TF_DISALLOW_COPY_AND_ASSIGN(GcsFileSystem);
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index c3a8678fbc..defedfdb93 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -30,7 +30,7 @@ class FakeAuthProvider : public AuthProvider {
   }
 };
 
-TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead) {
+TEST(GcsFileSystemTest, NewRandomAccessFile_NoBlockCache) {
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
            "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
@@ -45,7 +45,8 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
@@ -64,7 +65,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead) {
   EXPECT_EQ("6789", result);
 }
 
-TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead_differentN) {
+TEST(GcsFileSystemTest, NewRandomAccessFile_NoBlockCache_differentN) {
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
            "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
@@ -79,7 +80,8 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead_differentN) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
@@ -101,7 +103,9 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead_differentN) {
   EXPECT_EQ("3456789", result);
 }
 
-TEST(GcsFileSystemTest, NewRandomAccessFile_WithReadAhead) {
+TEST(GcsFileSystemTest, NewRandomAccessFile_WithBlockCache) {
+  // Our underlying file in this test is a 15 byte file with contents
+  // "0123456789abcde".
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
            "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
@@ -111,32 +115,23 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithReadAhead) {
        new FakeHttpRequest(
            "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
            "Auth Token: fake_token\n"
-           "Range: 6-14\n",
-           "6789abcde"),
+           "Range: 9-17\n",
+           "9abcde"),
        new FakeHttpRequest(
            "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
            "Auth Token: fake_token\n"
-           "Range: 6-20\n",
-           "6789abcd"),
-       new FakeHttpRequest(
-           "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
-           "Auth Token: fake_token\n"
-           "Range: 7-21\n",
-           "789abcdef"),
-       new FakeHttpRequest(
-           "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
-           "Auth Token: fake_token\n"
-           "Range: 20-34\n",
+           "Range: 18-26\n",
            ""),
        new FakeHttpRequest(
            "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
            "Auth Token: fake_token\n"
-           "Range: 0-14\n",
-           "01234567")});
+           "Range: 0-8\n",
+           "012345678")});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   5 /* read ahead bytes */, 0 /* initial retry delay */);
+                   9 /* block size */, 2 /* block count */,
+                   0 /* initial retry delay */);
 
   char scratch[100];
   StringPiece result;
@@ -147,48 +142,42 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithReadAhead) {
     TF_EXPECT_OK(
         fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
 
-    // Read the first chunk. The buffer will be updated with 4 + 5 = 9 bytes.
+    // Read the first chunk. The cache will be populated with the first block of
+    // 9 bytes.
     scratch[5] = 'x';
     TF_EXPECT_OK(file->Read(0, 4, &result, scratch));
     EXPECT_EQ("0123", result);
     EXPECT_EQ(scratch[5], 'x');  // Make sure we only copied 4 bytes.
 
-    // The second chunk will be fully loaded from the buffer, no requests are
+    // The second chunk will be fully loaded from the cache, no requests are
     // made.
     TF_EXPECT_OK(file->Read(4, 4, &result, scratch));
     EXPECT_EQ("4567", result);
 
-    // The chunk is only partially buffered -- the request will be made to
-    // reload the buffer. 9 bytes will be requested (same as initial buffer
-    // size).
+    // The chunk is only partially cached -- the request will be made to fetch
+    // the next block. 9 bytes will be requested, starting at offset 9.
     TF_EXPECT_OK(file->Read(6, 5, &result, scratch));
     EXPECT_EQ("6789a", result);
 
-    // The range can only be partially satisfied. An attempt to fill the buffer
-    // with 10 + 5 = 15 bytes will be made (buffer is resized for this request).
+    // The range can only be partially satisfied, as the second block contains
+    // only 6 bytes for a total of 9 + 6 = 15 bytes in the file.
     EXPECT_EQ(errors::Code::OUT_OF_RANGE,
               file->Read(6, 10, &result, scratch).code());
-    EXPECT_EQ("6789abcd", result);
-
-    // The range cannot be satisfied, and the requested offset lies within the
-    // buffer, but the end of the range is outside of the buffer.
-    // A new request will be made to read 10 + 5 = 15 bytes.
-    EXPECT_EQ(errors::Code::OUT_OF_RANGE,
-              file->Read(7, 10, &result, scratch).code());
-    EXPECT_EQ("789abcdef", result);
+    EXPECT_EQ("6789abcde", result);
 
-    // The range cannot be satisfied, and the requested offset is greater than
-    // the
-    // buffered range. A new request will be made to read 10 + 5 = 15 bytes.
+    // The range cannot be satisfied, and the requested offset is past the end
+    // of the cache. A new request will be made to read 9 bytes starting at
+    // offset 18. This request will return an empty response, and there will not
+    // be another request.
     EXPECT_EQ(errors::Code::OUT_OF_RANGE,
               file->Read(20, 10, &result, scratch).code());
     EXPECT_TRUE(result.empty());
 
+    // The beginning of the file has been evicted from the LRU cache.  This will
+    // result in another request. The buffer size is still 15.
     TF_EXPECT_OK(file->Read(0, 4, &result, scratch));
   }
 
-  // The beginning of the file is not in the buffer. This call will result
-  // in another request. The buffer size is still 15.
   EXPECT_EQ("0123", result);
 }
 
@@ -197,7 +186,8 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   5 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* read ahead bytes */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -221,7 +211,8 @@ TEST(GcsFileSystemTest, NewWritableFile) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -275,7 +266,8 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceeds) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -307,7 +299,8 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceedsOnGetStatus) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -364,7 +357,8 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadAllAttemptsFail) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 2 /* max upload attempts */);
+                   0 /* block size */, 0 /* block count */,
+                   2 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -411,7 +405,8 @@ TEST(GcsFileSystemTest, NewWritableFile_UploadReturns410) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -436,7 +431,8 @@ TEST(GcsFileSystemTest, NewWritableFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   5 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -465,7 +461,8 @@ TEST(GcsFileSystemTest, NewAppendableFile) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewAppendableFile("gs://bucket/path/appendable.txt", &file));
@@ -479,7 +476,8 @@ TEST(GcsFileSystemTest, NewAppendableFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   5 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -505,7 +503,8 @@ TEST(GcsFileSystemTest, NewReadOnlyMemoryRegionFromFile) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<ReadOnlyMemoryRegion> region;
   TF_EXPECT_OK(fs.NewReadOnlyMemoryRegionFromFile(
@@ -520,7 +519,8 @@ TEST(GcsFileSystemTest, NewReadOnlyMemoryRegionFromFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   5 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<ReadOnlyMemoryRegion> region;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -537,7 +537,8 @@ TEST(GcsFileSystemTest, FileExists_YesAsObject) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket/path/file1.txt"));
 }
@@ -559,7 +560,8 @@ TEST(GcsFileSystemTest, FileExists_YesAsFolder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket/path/subfolder"));
 }
@@ -577,7 +579,8 @@ TEST(GcsFileSystemTest, FileExists_YesAsBucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket1"));
   TF_EXPECT_OK(fs.FileExists("gs://bucket1/"));
@@ -599,7 +602,8 @@ TEST(GcsFileSystemTest, FileExists_NotAsObjectOrFolder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(errors::Code::NOT_FOUND,
             fs.FileExists("gs://bucket/path/file1.txt").code());
@@ -618,7 +622,8 @@ TEST(GcsFileSystemTest, FileExists_NotAsBucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
             fs.FileExists("gs://bucket2/").code());
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -635,7 +640,8 @@ TEST(GcsFileSystemTest, GetChildren_NoItems) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -656,7 +662,8 @@ TEST(GcsFileSystemTest, GetChildren_ThreeFiles) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -678,7 +685,8 @@ TEST(GcsFileSystemTest, GetChildren_SelfDirectoryMarker) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -699,7 +707,8 @@ TEST(GcsFileSystemTest, GetChildren_ThreeFiles_NoSlash) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path", &children));
@@ -717,7 +726,8 @@ TEST(GcsFileSystemTest, GetChildren_Root) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket-a-b-c", &children));
@@ -735,7 +745,8 @@ TEST(GcsFileSystemTest, GetChildren_Empty) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -768,7 +779,8 @@ TEST(GcsFileSystemTest, GetChildren_Pagination) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path", &children));
@@ -788,7 +800,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_NoWildcard) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(
@@ -809,7 +822,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_BucketAndWildcard) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/*/*", &result));
@@ -831,7 +845,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_FolderAndWildcard_Matches) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*/file2.txt", &result));
@@ -850,7 +865,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_SelfDirectoryMarker) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*", &result));
@@ -869,7 +885,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_FolderAndWildcard_NoMatches) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*/file3.txt", &result));
@@ -881,7 +898,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_OnlyWildcard) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -898,7 +916,8 @@ TEST(GcsFileSystemTest, DeleteFile) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteFile("gs://bucket/path/file1.txt"));
 }
@@ -908,7 +927,8 @@ TEST(GcsFileSystemTest, DeleteFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   5 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
             fs.DeleteFile("gs://bucket/").code());
@@ -923,7 +943,8 @@ TEST(GcsFileSystemTest, DeleteDir_Empty) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket/path/"));
 }
@@ -944,7 +965,8 @@ TEST(GcsFileSystemTest, DeleteDir_OnlyDirMarkerLeft) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket/path/"));
 }
@@ -957,7 +979,8 @@ TEST(GcsFileSystemTest, DeleteDir_BucketOnly) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket"));
 }
@@ -972,7 +995,8 @@ TEST(GcsFileSystemTest, DeleteDir_NonEmpty) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::FAILED_PRECONDITION,
             fs.DeleteDir("gs://bucket/path/").code());
@@ -988,7 +1012,8 @@ TEST(GcsFileSystemTest, GetFileSize) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   uint64 size;
   TF_EXPECT_OK(fs.GetFileSize("gs://bucket/file.txt", &size));
@@ -1000,7 +1025,8 @@ TEST(GcsFileSystemTest, GetFileSize_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   5 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   uint64 size;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -1072,7 +1098,8 @@ TEST(GcsFileSystemTest, RenameFile_Folder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.RenameFile("gs://bucket/path1", "gs://bucket/path2/"));
 }
@@ -1110,7 +1137,8 @@ TEST(GcsFileSystemTest, RenameFile_Object) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(
       fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
@@ -1157,7 +1185,8 @@ TEST(GcsFileSystemTest, RenameFile_Object_DeletionRetried) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(
       fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
@@ -1190,7 +1219,8 @@ TEST(GcsFileSystemTest, RenameFile_Object_Incomplete) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(
       errors::Code::UNIMPLEMENTED,
@@ -1208,7 +1238,8 @@ TEST(GcsFileSystemTest, Stat_Object) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/file.txt", &stat));
@@ -1234,7 +1265,8 @@ TEST(GcsFileSystemTest, Stat_Folder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/subfolder", &stat));
@@ -1259,7 +1291,8 @@ TEST(GcsFileSystemTest, Stat_ObjectOrFolderNotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   EXPECT_EQ(error::Code::NOT_FOUND, fs.Stat("gs://bucket/path", &stat).code());
@@ -1273,7 +1306,8 @@ TEST(GcsFileSystemTest, Stat_Bucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/", &stat));
@@ -1290,7 +1324,8 @@ TEST(GcsFileSystemTest, Stat_BucketNotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   EXPECT_EQ(error::Code::NOT_FOUND, fs.Stat("gs://bucket/", &stat).code());
@@ -1312,7 +1347,8 @@ TEST(GcsFileSystemTest, IsDirectory_NotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::NOT_FOUND,
             fs.IsDirectory("gs://bucket/file.txt").code());
@@ -1335,7 +1371,8 @@ TEST(GcsFileSystemTest, IsDirectory_NotDirectoryButObject) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::FAILED_PRECONDITION,
             fs.IsDirectory("gs://bucket/file.txt").code());
@@ -1358,7 +1395,8 @@ TEST(GcsFileSystemTest, IsDirectory_Yes) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/subfolder"));
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/subfolder/"));
@@ -1377,7 +1415,8 @@ TEST(GcsFileSystemTest, IsDirectory_Bucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket"));
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/"));
@@ -1391,7 +1430,8 @@ TEST(GcsFileSystemTest, IsDirectory_BucketNotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::NOT_FOUND, fs.IsDirectory("gs://bucket/").code());
 }
@@ -1423,7 +1463,8 @@ TEST(GcsFileSystemTest, CreateDir_Folder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/subpath"));
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/subpath/"));
@@ -1442,7 +1483,8 @@ TEST(GcsFileSystemTest, CreateDir_Bucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/"));
   TF_EXPECT_OK(fs.CreateDir("gs://bucket"));
@@ -1501,7 +1543,8 @@ TEST(GcsFileSystemTest, DeleteRecursively_Ok) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   TF_EXPECT_OK(fs.DeleteRecursively("gs://bucket/path", &undeleted_files,
@@ -1579,7 +1622,8 @@ TEST(GcsFileSystemTest, DeleteRecursively_DeletionErrors) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   TF_EXPECT_OK(fs.DeleteRecursively("gs://bucket/path", &undeleted_files,
@@ -1606,7 +1650,8 @@ TEST(GcsFileSystemTest, DeleteRecursively_NotAFolder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* block count */,
+                   0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   EXPECT_EQ(error::Code::NOT_FOUND,
@@ -1617,13 +1662,26 @@ TEST(GcsFileSystemTest, DeleteRecursively_NotAFolder) {
   EXPECT_EQ(1, undeleted_dirs);
 }
 
-TEST(GcsFileSystemTest, OverrideReadaheadBufferSize) {
+TEST(GcsFileSystemTest, OverrideCacheParameters) {
+  // Verify defaults are propagated correctly.
   GcsFileSystem fs1;
-  EXPECT_EQ(256 * 1024 * 1024, fs1.get_readahead_buffer_size());
+  EXPECT_EQ(256 * 1024 * 1024, fs1.block_size());
+  EXPECT_EQ(1, fs1.block_count());
 
+  // Verify legacy readahead buffer override sets block size.
   setenv("GCS_READAHEAD_BUFFER_SIZE_BYTES", "123456789", 1);
   GcsFileSystem fs2;
-  EXPECT_EQ(123456789L, fs2.get_readahead_buffer_size());
+  EXPECT_EQ(123456789L, fs2.block_size());
+
+  // Verify block size override.
+  setenv("GCS_READ_CACHE_BLOCK_SIZE_MB", "1", 1);
+  GcsFileSystem fs3;
+  EXPECT_EQ(1048576L, fs3.block_size());
+
+  // Verify block count override.
+  setenv("GCS_READ_CACHE_BLOCK_COUNT", "16", 1);
+  GcsFileSystem fs4;
+  EXPECT_EQ(16, fs4.block_count());
 }
 
 }  // namespace
-- 
GitLab


From 61741db7b38135de931276a96709301c65ec4e12 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 11:15:50 -0700
Subject: [PATCH 0697/1375] Add shape function tests for TensorForest v4.

PiperOrigin-RevId: 160980783
---
 tensorflow/contrib/tensor_forest/BUILD        | 36 ++++++++++
 .../tensor_forest/kernels/model_ops_test.cc   | 72 +++++++++++++++++++
 .../tensor_forest/kernels/stats_ops_test.cc   | 56 +++++++++++++++
 3 files changed, 164 insertions(+)
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc
 create mode 100644 tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc

diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 95ee1852ca..1115f39b2a 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -273,6 +273,24 @@ tf_custom_op_py_library(
     ],
 )
 
+cc_test(
+    name = "model_ops_test",
+    size = "small",
+    srcs = [
+        "kernels/model_ops_test.cc",
+        "ops/model_ops.cc",
+    ],
+    deps = [
+        ":model_ops_lib",
+        "//tensorflow/core",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//third_party/eigen3",
+    ],
+)
+
 # Stats Ops.
 cc_library(
     name = "stats_ops_lib",
@@ -338,6 +356,24 @@ tf_custom_op_py_library(
     ],
 )
 
+cc_test(
+    name = "stats_ops_test",
+    size = "small",
+    srcs = [
+        "kernels/stats_ops_test.cc",
+        "ops/stats_ops.cc",
+    ],
+    deps = [
+        ":stats_ops_lib",
+        "//tensorflow/core",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//third_party/eigen3",
+    ],
+)
+
 # ---------------------------------- Common libs ------------------------ #
 cc_library(
     name = "tree_utils",
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc
new file mode 100644
index 0000000000..cece61a54c
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc
@@ -0,0 +1,72 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference_testutil.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+
+TEST(ModelOpsTest, CreateTreeVariable_ShapeFn) {
+  ShapeInferenceTestOp op("CreateTreeVariable");
+  INFER_OK(op, "[1];[1]", "");
+}
+
+TEST(ModelOpsTest, TreeSerialize_ShapeFn) {
+  ShapeInferenceTestOp op("TreeSerialize");
+  INFER_OK(op, "[1]", "[]");
+}
+
+TEST(ModelOpsTest, TreeDeserialize_ShapeFn) {
+  ShapeInferenceTestOp op("TreeDeserialize");
+  INFER_OK(op, "[1];[1]", "");
+}
+
+TEST(ModelOpsTest, TreeSize_ShapeFn) {
+  ShapeInferenceTestOp op("TreeSize");
+  INFER_OK(op, "[1]", "[]");
+}
+
+TEST(ModelOpsTest, TreePredictionsV4_ShapeFn) {
+  ShapeInferenceTestOp op("TreePredictionsV4");
+  TF_ASSERT_OK(NodeDefBuilder("test", "TreePredictionsV4")
+                   .Input("a", 0, DT_RESOURCE)
+                   .Input("b", 1, DT_FLOAT)
+                   .Input("c", 2, DT_INT64)
+                   .Input("d", 3, DT_FLOAT)
+                   .Input("e", 5, DT_INT64)
+                   .Attr("input_spec", "")
+                   .Attr("params", "")
+                   .Finalize(&op.node_def));
+
+  // num_points = 2, sparse shape not known
+  INFER_OK(op, "?;[2,3];?;?;?", "[d1_0,?]");
+
+  // num_points = 2, sparse and dense shape rank known and > 1
+  INFER_OK(op, "?;[2,3];?;?;[10,11]", "[d1_0,?]");
+
+  // num_points = 2, sparse shape rank known and > 1
+  INFER_OK(op, "?;?;?;?;[10,11]", "[?,?]");
+}
+
+TEST(ModelOpsTest, FeatureUsageCounts_ShapeFn) {
+  ShapeInferenceTestOp op("FeatureUsageCounts");
+  INFER_OK(op, "[1]", "[?]");
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc
new file mode 100644
index 0000000000..e5b86b0520
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc
@@ -0,0 +1,56 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// =============================================================================
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference_testutil.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+
+TEST(StatsOpsTest, CreateFertileStatsVariable_ShapeFn) {
+  ShapeInferenceTestOp op("CreateFertileStatsVariable");
+  INFER_OK(op, "[1];[1]", "");
+}
+
+TEST(StatsOpsTest, FertileStatsSerialize_ShapeFn) {
+  ShapeInferenceTestOp op("FertileStatsSerialize");
+  INFER_OK(op, "[1]", "[]");
+}
+
+TEST(StatsOpsTest, FertileStatsDeserialize_ShapeFn) {
+  ShapeInferenceTestOp op("FertileStatsDeserialize");
+  INFER_OK(op, "[1];[1]", "");
+}
+
+TEST(StatsOpsTest, GrowTreeV4_ShapeFn) {
+  ShapeInferenceTestOp op("GrowTreeV4");
+  INFER_OK(op, "[1];[1];?", "");
+}
+
+TEST(StatsOpsTest, ProcessInputV4_ShapeFn) {
+  ShapeInferenceTestOp op("ProcessInputV4");
+  INFER_OK(op, "[1];[1];?;?;?;?;?;?", "[?]");
+}
+
+TEST(StatsOpsTest, FinalizeTree_ShapeFn) {
+  ShapeInferenceTestOp op("FinalizeTree");
+  INFER_OK(op, "[1];[1]", "");
+}
+
+}  // namespace tensorflow
-- 
GitLab


From f37c2b939bdb0a51e18cc155d585367f55a3cc2a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 11:16:42 -0700
Subject: [PATCH 0698/1375] Expanding test input pattern for benchmark of
 UniqueOp to more heavily exercise the handling of input data
 collisions/insertion.

PiperOrigin-RevId: 160980888
---
 tensorflow/core/kernels/unique_op_test.cc | 99 ++++++++++++++++++++---
 1 file changed, 90 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/kernels/unique_op_test.cc b/tensorflow/core/kernels/unique_op_test.cc
index 176280b7a1..0273f08090 100644
--- a/tensorflow/core/kernels/unique_op_test.cc
+++ b/tensorflow/core/kernels/unique_op_test.cc
@@ -35,12 +35,39 @@ namespace {
 
 const int kMaxStrLen = 40;
 
-static void BM_Unique_INT32(int iters, int dim) {
+TensorProto GetRandomInt32TensorProto(int dim, int max_int) {
+  TensorProto tensor_proto;
+  tensor_proto.set_dtype(DT_INT32);
+  tensor_proto.mutable_tensor_shape()->add_dim()->set_size(dim);
+  tensor_proto.mutable_tensor_shape()->set_unknown_rank(false);
+  for (int i = 0; i < dim; ++i) {
+    const int int_val = std::rand() % max_int;
+    tensor_proto.add_int_val(int_val);
+  }
+  return tensor_proto;
+}
+
+TensorProto GetRandomInt32TensorProtoWithRepeat(int dim, int repeat,
+                                                int max_int) {
+  TensorProto tensor_proto;
+  tensor_proto.set_dtype(DT_INT32);
+  tensor_proto.mutable_tensor_shape()->add_dim()->set_size(dim);
+  tensor_proto.mutable_tensor_shape()->set_unknown_rank(false);
+  for (int i = 0; i < dim; ++i) {
+    const int int_val = std::rand() % max_int;
+    for (int j = 0; j < repeat; ++j) {
+      tensor_proto.add_int_val(int_val);
+    }
+  }
+  return tensor_proto;
+}
+
+static void BM_Unique_INT32(int iters, int dim, int max_int) {
   testing::StopTiming();
   Graph* g = new Graph(OpRegistry::Global());
 
   Tensor input(DT_INT32, TensorShape({dim}));
-  input.flat<int32>().setRandom();
+  CHECK(input.FromProto(GetRandomInt32TensorProto(dim, max_int)));
 
   Node* node;
   TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Unique")
@@ -54,6 +81,27 @@ static void BM_Unique_INT32(int iters, int dim) {
   test::Benchmark("cpu", g).Run(iters);
 }
 
+static void BM_Unique_INT32_Repeat(int iters, int dim, int max_int) {
+  testing::StopTiming();
+  Graph* g = new Graph(OpRegistry::Global());
+
+  Tensor input(DT_INT32, TensorShape({dim * 200}));
+  CHECK(
+      input.FromProto(GetRandomInt32TensorProtoWithRepeat(dim, 200, max_int)));
+
+  Node* node;
+  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Unique")
+                  .Input(test::graph::Constant(g, input))
+                  .Attr("T", DT_INT32)
+                  .Finalize(g, &node));
+
+  testing::BytesProcessed(static_cast<int64>(iters) * dim * 200 *
+                          sizeof(int32));
+  testing::UseRealTime();
+  testing::StartTiming();
+  test::Benchmark("cpu", g).Run(iters);
+}
+
 TensorProto GetRandomStringsTensorProto(int dim, int max_str_len) {
   TensorProto tensor_proto;
   tensor_proto.set_dtype(DT_STRING);
@@ -91,13 +139,46 @@ static void BM_Unique_STRING(int iters, int dim) {
 }
 
 BENCHMARK(BM_Unique_INT32)
-    ->Arg(32)
-    ->Arg(256)
-    ->Arg(1024)
-    ->Arg(4 * 1024)
-    ->Arg(16 * 1024)
-    ->Arg(64 * 1024)
-    ->Arg(256 * 1024);
+    ->ArgPair(32, 1024 * 1024)
+    ->ArgPair(256, 1024 * 1024)
+    ->ArgPair(1024, 1024 * 1024)
+    ->ArgPair(4 * 1024, 1024 * 1024)
+    ->ArgPair(16 * 1024, 1024 * 1024)
+    ->ArgPair(64 * 1024, 1024 * 1024)
+    ->ArgPair(1024 * 1024, 1024 * 1024)
+    ->ArgPair(4 * 1024 * 1024, 1024 * 1024)
+    ->ArgPair(32, 64 * 1024 * 1024)
+    ->ArgPair(256, 64 * 1024 * 1024)
+    ->ArgPair(1024, 64 * 1024 * 1024)
+    ->ArgPair(4 * 1024, 64 * 1024 * 1024)
+    ->ArgPair(16 * 1024, 64 * 1024 * 1024)
+    ->ArgPair(64 * 1024, 64 * 1024 * 1024)
+    ->ArgPair(1024 * 1024, 64 * 1024 * 1024)
+    ->ArgPair(4 * 1024 * 1024, 64 * 1024 * 1024);
+
+BENCHMARK(BM_Unique_INT32_Repeat)
+    ->ArgPair(32, 1024 * 1024)
+    ->ArgPair(256, 1024 * 1024)
+    ->ArgPair(1024, 1024 * 1024)
+    ->ArgPair(4 * 1024, 1024 * 1024)
+    ->ArgPair(16 * 1024, 1024 * 1024)
+    ->ArgPair(64 * 1024, 1024 * 1024)
+    ->ArgPair(1024 * 1024, 1024 * 1024)
+    ->ArgPair(4 * 1024 * 1024, 1024 * 1024)
+    ->ArgPair(32, 32 * 1024 * 1024)
+    ->ArgPair(256, 32 * 1024 * 1024)
+    ->ArgPair(1024, 32 * 1024 * 1024)
+    ->ArgPair(4 * 1024, 32 * 1024 * 1024)
+    ->ArgPair(16 * 1024, 32 * 1024 * 1024)
+    ->ArgPair(64 * 1024, 32 * 1024 * 1024)
+    ->ArgPair(1024 * 1024, 32 * 1024 * 1024)
+    ->ArgPair(32, 64 * 1024 * 1024)
+    ->ArgPair(256, 64 * 1024 * 1024)
+    ->ArgPair(1024, 64 * 1024 * 1024)
+    ->ArgPair(4 * 1024, 64 * 1024 * 1024)
+    ->ArgPair(16 * 1024, 64 * 1024 * 1024)
+    ->ArgPair(64 * 1024, 64 * 1024 * 1024)
+    ->ArgPair(1024 * 1024, 64 * 1024 * 1024);
 
 BENCHMARK(BM_Unique_STRING)
     ->Arg(32)
-- 
GitLab


From 103e739895d75d16490627cc052f513d01d4c3f8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 11:23:22 -0700
Subject: [PATCH 0699/1375] Internal change to BUILD file.

PiperOrigin-RevId: 160981881
---
 tensorflow/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 6a7affe82c..3f7fba86cf 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -178,7 +178,10 @@ config_setting(
 
 package_group(
     name = "internal",
-    packages = ["//tensorflow/..."],
+    packages = [
+        "//learning/protonn/llgtm/...",
+        "//tensorflow/...",
+    ],
 )
 
 filegroup(
-- 
GitLab


From e4351ac3f8d4c4134dbd90ec9f8f03fdef31c20a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 12:17:45 -0700
Subject: [PATCH 0700/1375] Repair Python3 test for support for flattening
 dicts in tensorflow nest utilities.

PiperOrigin-RevId: 160989335
---
 tensorflow/python/util/nest_test.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py
index 6b787602ed..cb17dfc7eb 100644
--- a/tensorflow/python/util/nest_test.py
+++ b/tensorflow/python/util/nest_test.py
@@ -109,7 +109,7 @@ class NestTest(test.TestCase):
     # Check also that the OrderedDict was created, with the correct key order.
     unflattened_ordered_dict = unflattened[2]["c"][1]
     self.assertIsInstance(unflattened_ordered_dict, collections.OrderedDict)
-    self.assertEqual(unflattened_ordered_dict.keys(), ["b", "a"])
+    self.assertEqual(list(unflattened_ordered_dict.keys()), ["b", "a"])
 
   def testFlatten_numpyIsNotFlattened(self):
     structure = np.array([1, 2, 3])
-- 
GitLab


From 055500bbcea60513c0160d213a10a7055f079312 Mon Sep 17 00:00:00 2001
From: Pete Warden <petewarden@google.com>
Date: Wed, 5 Jul 2017 13:12:07 -0700
Subject: [PATCH 0701/1375] Added Mobilenet support to TensorFlow for Poets
 training script.

PiperOrigin-RevId: 160995543
---
 .../docs_src/tutorials/image_retraining.md    |  44 ++
 .../examples/image_retraining/retrain.py      | 480 ++++++++++++------
 .../examples/image_retraining/retrain_test.py |  29 +-
 3 files changed, 401 insertions(+), 152 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md
index 85e6ec76dc..898db0e73c 100644
--- a/tensorflow/docs_src/tutorials/image_retraining.md
+++ b/tensorflow/docs_src/tutorials/image_retraining.md
@@ -170,6 +170,10 @@ above
 [`label_image` script](https://www.tensorflow.org/code/tensorflow/examples/image_retraining/label_image.py)
 is a reasonable starting point.
 
+If you find the default Inception v3 model is too large or slow for your
+application, take a look at the [Other Model Architectures section](/tutorials/image_retraining#other_model_architectures)
+below for options to speed up and slim down your network.
+
 ## Training on Your Own Categories
 
 If you've managed to get the script working on the flower example images, you
@@ -328,3 +332,43 @@ errors in the input data set, such as mislabeled, low-quality, or ambiguous
 images. However, one should generally avoid point-fixing individual errors in
 the test set, since they are likely to merely reflect more general problems in
 the (much larger) training set.
+
+## Other Model Architectures
+
+By default the script uses a pretrained version of the Inception v3 model
+architecture. This is a good place to start because it provides high accuracy
+results, but if you intend to deploy your model on mobile devices or other 
+resource-constrained environments you may want to trade off a little accuracy
+for much smaller file sizes or faster speeds. To help with that, the
+[retrain.py script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py)
+supports 32 different variations on the [Mobilenet architecture](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html).
+
+These are a little less precise than Inception v3, but can result in far
+smaller file sizes (down to less than a megabyte) and can be many times faster
+to run. To train with one of these models, pass in the `--architecture` flag,
+for example:
+
+```
+python tensorflow/examples/image_retraining/retrain.py \
+    --image_dir ~/flower_photos --architecture mobilenet_0.25_128_quantized
+```
+
+This will create a 941KB model file in `/tmp/output_graph.pb`, with 25% of the
+parameters of the full Mobilenet, taking 128x128 sized input images, and with
+its weights quantized down to eight bits on disk. You can choose '1.0', '0.75',
+'0.50', or '0.25' to control the number of weight parameters, and so the file
+size (and to some extent the speed), '224', '192', '160', or '128' for the input
+image size, with smaller sizes giving faster speeds, and an optional
+'_quantized' at the end to indicate whether the file should contain 8-bit or
+32-bit float weights.
+
+The speed and size advantages come at a loss to accuracy of course, but for many
+purposes this isn't critical. They can also be somewhat offset with improved
+training data. For example, training with distortions allows me to get above 80%
+accuracy on the flower data set even with the 0.25/128/quantized graph above.
+
+If you're going to be using the Mobilenet models in label_image or your own
+programs, you'll need to feed in an image of the specified size converted to a
+float range into the 'input' tensor. Typically 24-bit images are in the range
+[0,255], and you must convert them to the [-1,1] float range expected by the
+model with the formula  `(image - 128.)/128.`.
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 44a3097d80..ac39639af1 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -12,18 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Simple transfer learning with an Inception v3 architecture model.
+r"""Simple transfer learning with Inception v3 or Mobilenet models.
 
 With support for TensorBoard.
 
-This example shows how to take a Inception v3 architecture model trained on
+This example shows how to take a Inception v3 or Mobilenet model trained on
 ImageNet images, and train a new top layer that can recognize other classes of
 images.
 
-The top layer receives as input a 2048-dimensional vector for each image. We
-train a softmax layer on top of this representation. Assuming the softmax layer
-contains N labels, this corresponds to learning N + 2048*N model parameters
-corresponding to the learned biases and weights.
+The top layer receives as input a 2048-dimensional vector (1001-dimensional for
+Mobilenet) for each image. We train a softmax layer on top of this
+representation. Assuming the softmax layer contains N labels, this corresponds
+to learning N + 2048*N (or 1001*N)  model parameters corresponding to the
+learned biases and weights.
 
 Here's an example, which assumes you have a folder containing class-named
 subfolders, each full of images for each label. The example folder flower_photos
@@ -62,6 +63,23 @@ in.
 This produces a new model file that can be loaded and run by any TensorFlow
 program, for example the label_image sample code.
 
+By default this script will use the high accuracy, but comparatively large and
+slow Inception v3 model architecture. It's recommended that you start with this
+to validate that you have gathered good training data, but if you want to deploy
+on resource-limited platforms, you can try the `--architecture` flag with a
+Mobilenet model. For example:
+
+```bash
+python tensorflow/examples/image_retraining/retrain.py \
+    --image_dir ~/flower_photos --architecture mobilenet_1.0_224
+```
+
+There are 32 different Mobilenet models to choose from, with a variety of file
+size and latency options. The first number can be '1.0', '0.75', '0.50', or
+'0.25' to control the size, and the second controls the input image size, either
+'224', '192', '160', or '128', with smaller sizes running faster. See
+https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
+for more information on Mobilenet.
 
 To use with TensorBoard:
 
@@ -82,7 +100,6 @@ import hashlib
 import os.path
 import random
 import re
-import struct
 import sys
 import tarfile
 
@@ -101,16 +118,6 @@ FLAGS = None
 # we're using for Inception v3. These include things like tensor names and their
 # sizes. If you want to adapt this script to work with another model, you will
 # need to update these to reflect the values in the network you're using.
-# pylint: disable=line-too-long
-DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
-# pylint: enable=line-too-long
-BOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'
-BOTTLENECK_TENSOR_SIZE = 2048
-MODEL_INPUT_WIDTH = 299
-MODEL_INPUT_HEIGHT = 299
-MODEL_INPUT_DEPTH = 3
-JPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'
-RESIZED_INPUT_TENSOR_NAME = 'ResizeBilinear:0'
 MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M
 
 
@@ -131,7 +138,7 @@ def create_image_lists(image_dir, testing_percentage, validation_percentage):
     into training, testing, and validation sets within each label.
   """
   if not gfile.Exists(image_dir):
-    print("Image directory '" + image_dir + "' not found.")
+    tf.logging.error("Image directory '" + image_dir + "' not found.")
     return None
   result = {}
   sub_dirs = [x[0] for x in gfile.Walk(image_dir)]
@@ -146,18 +153,20 @@ def create_image_lists(image_dir, testing_percentage, validation_percentage):
     dir_name = os.path.basename(sub_dir)
     if dir_name == image_dir:
       continue
-    print("Looking for images in '" + dir_name + "'")
+    tf.logging.info("Looking for images in '" + dir_name + "'")
     for extension in extensions:
       file_glob = os.path.join(image_dir, dir_name, '*.' + extension)
       file_list.extend(gfile.Glob(file_glob))
     if not file_list:
-      print('No files found')
+      tf.logging.warning('No files found')
       continue
     if len(file_list) < 20:
-      print('WARNING: Folder has less than 20 images, which may cause issues.')
+      tf.logging.warning(
+          'WARNING: Folder has less than 20 images, which may cause issues.')
     elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:
-      print('WARNING: Folder {} has more than {} images. Some images will '
-            'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS))
+      tf.logging.warning(
+          'WARNING: Folder {} has more than {} images. Some images will '
+          'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS))
     label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())
     training_images = []
     testing_images = []
@@ -230,7 +239,7 @@ def get_image_path(image_lists, label_name, index, image_dir, category):
 
 
 def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
-                        category):
+                        category, architecture):
   """"Returns a path to a bottleneck file for a label at the given index.
 
   Args:
@@ -241,35 +250,42 @@ def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
     bottleneck_dir: Folder string holding cached files of bottleneck values.
     category: Name string of set to pull images from - training, testing, or
     validation.
+    architecture: The name of the model architecture.
 
   Returns:
     File system path string to an image that meets the requested parameters.
   """
   return get_image_path(image_lists, label_name, index, bottleneck_dir,
-                        category) + '.txt'
+                        category) + '_' + architecture + '.txt'
 
 
-def create_inception_graph():
+def create_model_graph(model_info):
   """"Creates a graph from saved GraphDef file and returns a Graph object.
 
+  Args:
+    model_info: Dictionary containing information about the model architecture.
+
   Returns:
     Graph holding the trained Inception network, and various tensors we'll be
     manipulating.
   """
   with tf.Graph().as_default() as graph:
-    model_filename = os.path.join(
-        FLAGS.model_dir, 'classify_image_graph_def.pb')
-    with gfile.FastGFile(model_filename, 'rb') as f:
+    model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name'])
+    with gfile.FastGFile(model_path, 'rb') as f:
       graph_def = tf.GraphDef()
       graph_def.ParseFromString(f.read())
-      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
-          tf.import_graph_def(graph_def, name='', return_elements=[
-              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
-              RESIZED_INPUT_TENSOR_NAME]))
-  return graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor
+      bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
+          graph_def,
+          name='',
+          return_elements=[
+              model_info['bottleneck_tensor_name'],
+              model_info['resized_input_tensor_name'],
+          ]))
+  return graph, bottleneck_tensor, resized_input_tensor
 
 
 def run_bottleneck_on_image(sess, image_data, image_data_tensor,
+                            decoded_image_tensor, resized_input_tensor,
                             bottleneck_tensor):
   """Runs inference on an image to extract the 'bottleneck' summary layer.
 
@@ -277,28 +293,36 @@ def run_bottleneck_on_image(sess, image_data, image_data_tensor,
     sess: Current active TensorFlow Session.
     image_data: String of raw JPEG data.
     image_data_tensor: Input data layer in the graph.
+    decoded_image_tensor: Output of initial image resizing and  preprocessing.
+    resized_input_tensor: The input node of the recognition graph.
     bottleneck_tensor: Layer before the final softmax.
 
   Returns:
     Numpy array of bottleneck values.
   """
-  bottleneck_values = sess.run(
-      bottleneck_tensor,
-      {image_data_tensor: image_data})
+  # First decode the JPEG image, resize it, and rescale the pixel values.
+  resized_input_values = sess.run(decoded_image_tensor,
+                                  {image_data_tensor: image_data})
+  # Then run it through the recognition network.
+  bottleneck_values = sess.run(bottleneck_tensor,
+                               {resized_input_tensor: resized_input_values})
   bottleneck_values = np.squeeze(bottleneck_values)
   return bottleneck_values
 
 
-def maybe_download_and_extract():
+def maybe_download_and_extract(data_url):
   """Download and extract model tar file.
 
   If the pretrained model we're using doesn't already exist, this function
   downloads it from the TensorFlow.org website and unpacks it into a directory.
+
+  Args:
+    data_url: Web location of the tar file containing the pretrained model.
   """
   dest_directory = FLAGS.model_dir
   if not os.path.exists(dest_directory):
     os.makedirs(dest_directory)
-  filename = DATA_URL.split('/')[-1]
+  filename = data_url.split('/')[-1]
   filepath = os.path.join(dest_directory, filename)
   if not os.path.exists(filepath):
 
@@ -308,12 +332,11 @@ def maybe_download_and_extract():
                         float(count * block_size) / float(total_size) * 100.0))
       sys.stdout.flush()
 
-    filepath, _ = urllib.request.urlretrieve(DATA_URL,
-                                             filepath,
-                                             _progress)
+    filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress)
     print()
     statinfo = os.stat(filepath)
-    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
+    tf.logging.info('Successfully downloaded', filename, statinfo.st_size,
+                    'bytes.')
   tarfile.open(filepath, 'r:gz').extractall(dest_directory)
 
 
@@ -327,43 +350,15 @@ def ensure_dir_exists(dir_name):
     os.makedirs(dir_name)
 
 
-def write_list_of_floats_to_file(list_of_floats, file_path):
-  """Writes a given list of floats to a binary file.
-
-  Args:
-    list_of_floats: List of floats we want to write to a file.
-    file_path: Path to a file where list of floats will be stored.
-
-  """
-
-  s = struct.pack('d' * BOTTLENECK_TENSOR_SIZE, *list_of_floats)
-  with open(file_path, 'wb') as f:
-    f.write(s)
-
-
-def read_list_of_floats_from_file(file_path):
-  """Reads list of floats from a given file.
-
-  Args:
-    file_path: Path to a file where list of floats was stored.
-  Returns:
-    Array of bottleneck values (list of floats).
-
-  """
-
-  with open(file_path, 'rb') as f:
-    s = struct.unpack('d' * BOTTLENECK_TENSOR_SIZE, f.read())
-    return list(s)
-
-
 bottleneck_path_2_bottleneck_values = {}
 
 
 def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                            image_dir, category, sess, jpeg_data_tensor,
+                           decoded_image_tensor, resized_input_tensor,
                            bottleneck_tensor):
   """Create a single bottleneck file."""
-  print('Creating bottleneck at ' + bottleneck_path)
+  tf.logging.info('Creating bottleneck at ' + bottleneck_path)
   image_path = get_image_path(image_lists, label_name, index,
                               image_dir, category)
   if not gfile.Exists(image_path):
@@ -371,10 +366,11 @@ def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
   image_data = gfile.FastGFile(image_path, 'rb').read()
   try:
     bottleneck_values = run_bottleneck_on_image(
-        sess, image_data, jpeg_data_tensor, bottleneck_tensor)
-  except:
-    raise RuntimeError('Error during processing file %s' % image_path)
-
+        sess, image_data, jpeg_data_tensor, decoded_image_tensor,
+        resized_input_tensor, bottleneck_tensor)
+  except Exception as e:
+    raise RuntimeError('Error during processing file %s (%s)' % (image_path,
+                                                                 str(e)))
   bottleneck_string = ','.join(str(x) for x in bottleneck_values)
   with open(bottleneck_path, 'w') as bottleneck_file:
     bottleneck_file.write(bottleneck_string)
@@ -382,7 +378,8 @@ def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
 
 def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
                              category, bottleneck_dir, jpeg_data_tensor,
-                             bottleneck_tensor):
+                             decoded_image_tensor, resized_input_tensor,
+                             bottleneck_tensor, architecture):
   """Retrieves or calculates bottleneck values for an image.
 
   If a cached version of the bottleneck data exists on-disk, return that,
@@ -400,7 +397,10 @@ def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
     or validation.
     bottleneck_dir: Folder string holding cached files of bottleneck values.
     jpeg_data_tensor: The tensor to feed loaded jpeg data into.
+    decoded_image_tensor: The output of decoding and resizing the image.
+    resized_input_tensor: The input node of the recognition graph.
     bottleneck_tensor: The output tensor for the bottleneck values.
+    architecture: The name of the model architecture.
 
   Returns:
     Numpy array of values produced by the bottleneck layer for the image.
@@ -410,10 +410,11 @@ def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
   sub_dir_path = os.path.join(bottleneck_dir, sub_dir)
   ensure_dir_exists(sub_dir_path)
   bottleneck_path = get_bottleneck_path(image_lists, label_name, index,
-                                        bottleneck_dir, category)
+                                        bottleneck_dir, category, architecture)
   if not os.path.exists(bottleneck_path):
     create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                            image_dir, category, sess, jpeg_data_tensor,
+                           decoded_image_tensor, resized_input_tensor,
                            bottleneck_tensor)
   with open(bottleneck_path, 'r') as bottleneck_file:
     bottleneck_string = bottleneck_file.read()
@@ -421,11 +422,12 @@ def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
   try:
     bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
   except ValueError:
-    print('Invalid float found, recreating bottleneck')
+    tf.logging.warning('Invalid float found, recreating bottleneck')
     did_hit_error = True
   if did_hit_error:
     create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                            image_dir, category, sess, jpeg_data_tensor,
+                           decoded_image_tensor, resized_input_tensor,
                            bottleneck_tensor)
     with open(bottleneck_path, 'r') as bottleneck_file:
       bottleneck_string = bottleneck_file.read()
@@ -436,7 +438,8 @@ def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
 
 
 def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
-                      jpeg_data_tensor, bottleneck_tensor):
+                      jpeg_data_tensor, decoded_image_tensor,
+                      resized_input_tensor, bottleneck_tensor, architecture):
   """Ensures all the training, testing, and validation bottlenecks are cached.
 
   Because we're likely to read the same image multiple times (if there are no
@@ -453,7 +456,10 @@ def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
     images.
     bottleneck_dir: Folder string holding cached files of bottleneck values.
     jpeg_data_tensor: Input tensor for jpeg data from file.
+    decoded_image_tensor: The output of decoding and resizing the image.
+    resized_input_tensor: The input node of the recognition graph.
     bottleneck_tensor: The penultimate output layer of the graph.
+    architecture: The name of the model architecture.
 
   Returns:
     Nothing.
@@ -464,18 +470,21 @@ def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
     for category in ['training', 'testing', 'validation']:
       category_list = label_lists[category]
       for index, unused_base_name in enumerate(category_list):
-        get_or_create_bottleneck(sess, image_lists, label_name, index,
-                                 image_dir, category, bottleneck_dir,
-                                 jpeg_data_tensor, bottleneck_tensor)
+        get_or_create_bottleneck(
+            sess, image_lists, label_name, index, image_dir, category,
+            bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
+            resized_input_tensor, bottleneck_tensor, architecture)
 
         how_many_bottlenecks += 1
         if how_many_bottlenecks % 100 == 0:
-          print(str(how_many_bottlenecks) + ' bottleneck files created.')
+          tf.logging.info(
+              str(how_many_bottlenecks) + ' bottleneck files created.')
 
 
 def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
                                   bottleneck_dir, image_dir, jpeg_data_tensor,
-                                  bottleneck_tensor):
+                                  decoded_image_tensor, resized_input_tensor,
+                                  bottleneck_tensor, architecture):
   """Retrieves bottleneck values for cached images.
 
   If no distortions are being applied, this function can retrieve the cached
@@ -493,7 +502,10 @@ def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
     image_dir: Root folder string of the subfolders containing the training
     images.
     jpeg_data_tensor: The layer to feed jpeg image data into.
+    decoded_image_tensor: The output of decoding and resizing the image.
+    resized_input_tensor: The input node of the recognition graph.
     bottleneck_tensor: The bottleneck output layer of the CNN graph.
+    architecture: The name of the model architecture.
 
   Returns:
     List of bottleneck arrays, their corresponding ground truths, and the
@@ -511,10 +523,10 @@ def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
       image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
       image_name = get_image_path(image_lists, label_name, image_index,
                                   image_dir, category)
-      bottleneck = get_or_create_bottleneck(sess, image_lists, label_name,
-                                            image_index, image_dir, category,
-                                            bottleneck_dir, jpeg_data_tensor,
-                                            bottleneck_tensor)
+      bottleneck = get_or_create_bottleneck(
+          sess, image_lists, label_name, image_index, image_dir, category,
+          bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
+          resized_input_tensor, bottleneck_tensor, architecture)
       ground_truth = np.zeros(class_count, dtype=np.float32)
       ground_truth[label_index] = 1.0
       bottlenecks.append(bottleneck)
@@ -527,10 +539,10 @@ def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
           image_lists[label_name][category]):
         image_name = get_image_path(image_lists, label_name, image_index,
                                     image_dir, category)
-        bottleneck = get_or_create_bottleneck(sess, image_lists, label_name,
-                                              image_index, image_dir, category,
-                                              bottleneck_dir, jpeg_data_tensor,
-                                              bottleneck_tensor)
+        bottleneck = get_or_create_bottleneck(
+            sess, image_lists, label_name, image_index, image_dir, category,
+            bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
+            resized_input_tensor, bottleneck_tensor, architecture)
         ground_truth = np.zeros(class_count, dtype=np.float32)
         ground_truth[label_index] = 1.0
         bottlenecks.append(bottleneck)
@@ -583,12 +595,12 @@ def get_random_distorted_bottlenecks(
     # might be optimized in other implementations.
     distorted_image_data = sess.run(distorted_image,
                                     {input_jpeg_tensor: jpeg_data})
-    bottleneck = run_bottleneck_on_image(sess, distorted_image_data,
-                                         resized_input_tensor,
-                                         bottleneck_tensor)
+    bottleneck_values = sess.run(bottleneck_tensor,
+                                 {resized_input_tensor: distorted_image_data})
+    bottleneck_values = np.squeeze(bottleneck_values)
     ground_truth = np.zeros(class_count, dtype=np.float32)
     ground_truth[label_index] = 1.0
-    bottlenecks.append(bottleneck)
+    bottlenecks.append(bottleneck_values)
     ground_truths.append(ground_truth)
   return bottlenecks, ground_truths
 
@@ -612,7 +624,8 @@ def should_distort_images(flip_left_right, random_crop, random_scale,
 
 
 def add_input_distortions(flip_left_right, random_crop, random_scale,
-                          random_brightness):
+                          random_brightness, input_width, input_height,
+                          input_depth, input_mean, input_std):
   """Creates the operations to apply the specified distortions.
 
   During training it can help to improve the results if we run the images
@@ -660,13 +673,18 @@ def add_input_distortions(flip_left_right, random_crop, random_scale,
     random_scale: Integer percentage of how much to vary the scale by.
     random_brightness: Integer range to randomly multiply the pixel values by.
     graph.
+    input_width: Horizontal size of expected input image to model.
+    input_height: Vertical size of expected input image to model.
+    input_depth: How many channels the expected input image should have.
+    input_mean: Pixel value that should be zero in the image for the graph.
+    input_std: How much to divide the pixel values by before recognition.
 
   Returns:
     The jpeg input layer and the distorted result tensor.
   """
 
   jpeg_data = tf.placeholder(tf.string, name='DistortJPGInput')
-  decoded_image = tf.image.decode_jpeg(jpeg_data, channels=MODEL_INPUT_DEPTH)
+  decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
   decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)
   decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
   margin_scale = 1.0 + (random_crop / 100.0)
@@ -676,16 +694,15 @@ def add_input_distortions(flip_left_right, random_crop, random_scale,
                                          minval=1.0,
                                          maxval=resize_scale)
   scale_value = tf.multiply(margin_scale_value, resize_scale_value)
-  precrop_width = tf.multiply(scale_value, MODEL_INPUT_WIDTH)
-  precrop_height = tf.multiply(scale_value, MODEL_INPUT_HEIGHT)
+  precrop_width = tf.multiply(scale_value, input_width)
+  precrop_height = tf.multiply(scale_value, input_height)
   precrop_shape = tf.stack([precrop_height, precrop_width])
   precrop_shape_as_int = tf.cast(precrop_shape, dtype=tf.int32)
   precropped_image = tf.image.resize_bilinear(decoded_image_4d,
                                               precrop_shape_as_int)
   precropped_image_3d = tf.squeeze(precropped_image, squeeze_dims=[0])
   cropped_image = tf.random_crop(precropped_image_3d,
-                                 [MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH,
-                                  MODEL_INPUT_DEPTH])
+                                 [input_height, input_width, input_depth])
   if flip_left_right:
     flipped_image = tf.image.random_flip_left_right(cropped_image)
   else:
@@ -696,7 +713,9 @@ def add_input_distortions(flip_left_right, random_crop, random_scale,
                                        minval=brightness_min,
                                        maxval=brightness_max)
   brightened_image = tf.multiply(flipped_image, brightness_value)
-  distort_result = tf.expand_dims(brightened_image, 0, name='DistortResult')
+  offset_image = tf.subtract(brightened_image, input_mean)
+  mul_image = tf.multiply(offset_image, 1.0 / input_std)
+  distort_result = tf.expand_dims(mul_image, 0, name='DistortResult')
   return jpeg_data, distort_result
 
 
@@ -713,7 +732,8 @@ def variable_summaries(var):
     tf.summary.histogram('histogram', var)
 
 
-def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
+def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
+                           bottleneck_tensor_size):
   """Adds a new softmax and fully-connected layer for training.
 
   We need to retrain the top layer to identify our new classes, so this function
@@ -728,6 +748,7 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
     recognize.
     final_tensor_name: Name string for the new final node that produces results.
     bottleneck_tensor: The output of the main CNN graph.
+    bottleneck_tensor_size: How many entries in the bottleneck vector.
 
   Returns:
     The tensors for the training and cross entropy results, and tensors for the
@@ -735,7 +756,8 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
   """
   with tf.name_scope('input'):
     bottleneck_input = tf.placeholder_with_default(
-        bottleneck_tensor, shape=[None, BOTTLENECK_TENSOR_SIZE],
+        bottleneck_tensor,
+        shape=[None, bottleneck_tensor_size],
         name='BottleneckInputPlaceholder')
 
     ground_truth_input = tf.placeholder(tf.float32,
@@ -747,8 +769,8 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
   layer_name = 'final_training_ops'
   with tf.name_scope(layer_name):
     with tf.name_scope('weights'):
-      initial_value = tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, class_count],
-                                          stddev=0.001)
+      initial_value = tf.truncated_normal(
+          [bottleneck_tensor_size, class_count], stddev=0.001)
 
       layer_weights = tf.Variable(initial_value, name='final_weights')
 
@@ -802,7 +824,7 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
 
 def save_graph_to_file(sess, graph, graph_file_name):
   output_graph_def = graph_util.convert_variables_to_constants(
-    sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
+      sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
   with gfile.FastGFile(graph_file_name, 'wb') as f:
     f.write(output_graph_def.SerializeToString())
   return
@@ -818,25 +840,160 @@ def prepare_file_system():
   return
 
 
+def create_model_info(architecture):
+  """Given the name of a model architecture, returns information about it.
+
+  There are different base image recognition pretrained models that can be
+  retrained using transfer learning, and this function translates from the name
+  of a model to the attributes that are needed to download and train with it.
+
+  Args:
+    architecture: Name of a model architecture.
+
+  Returns:
+    Dictionary of information about the model, or None if the name isn't
+    recognized
+
+  Raises:
+    ValueError: If architecture name is unknown.
+  """
+  architecture = architecture.lower()
+  if architecture == 'inception_v3':
+    # pylint: disable=line-too-long
+    data_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+    # pylint: enable=line-too-long
+    bottleneck_tensor_name = 'pool_3/_reshape:0'
+    bottleneck_tensor_size = 2048
+    input_width = 299
+    input_height = 299
+    input_depth = 3
+    resized_input_tensor_name = 'Mul:0'
+    model_file_name = 'classify_image_graph_def.pb'
+    input_mean = 128
+    input_std = 128
+  elif architecture.startswith('mobilenet_'):
+    parts = architecture.split('_')
+    if len(parts) != 3 and len(parts) != 4:
+      tf.logging.error("Couldn't understand architecture name '%s'",
+                       architecture)
+      return None
+    version_string = parts[1]
+    if (version_string != '1.0' and version_string != '0.75' and
+        version_string != '0.50' and version_string != '0.25'):
+      tf.logging.error(
+          """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25',
+  but found '%s' for architecture '%s'""",
+          version_string, architecture)
+      return None
+    size_string = parts[2]
+    if (size_string != '224' and size_string != '192' and
+        size_string != '160' and size_string != '128'):
+      tf.logging.error(
+          """The Mobilenet input size should be '224', '192', '160', or '128',
+ but found '%s' for architecture '%s'""",
+          size_string, architecture)
+      return None
+    if len(parts) == 3:
+      is_quantized = False
+    else:
+      if parts[3] != 'quantized':
+        tf.logging.error(
+            "Couldn't understand architecture suffix '%s' for '%s'", parts[3],
+            architecture)
+        return None
+      is_quantized = True
+    data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
+    data_url += version_string + '_' + size_string + '_frozen.tgz'
+    bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
+    bottleneck_tensor_size = 1001
+    input_width = int(size_string)
+    input_height = int(size_string)
+    input_depth = 3
+    resized_input_tensor_name = 'input:0'
+    if is_quantized:
+      model_base_name = 'quantized_graph.pb'
+    else:
+      model_base_name = 'frozen_graph.pb'
+    model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string
+    model_file_name = os.path.join(model_dir_name, model_base_name)
+    input_mean = 127.5
+    input_std = 127.5
+  else:
+    tf.logging.error("Couldn't understand architecture name '%s'", architecture)
+    raise ValueError('Unknown architecture', architecture)
+
+  return {
+      'data_url': data_url,
+      'bottleneck_tensor_name': bottleneck_tensor_name,
+      'bottleneck_tensor_size': bottleneck_tensor_size,
+      'input_width': input_width,
+      'input_height': input_height,
+      'input_depth': input_depth,
+      'resized_input_tensor_name': resized_input_tensor_name,
+      'model_file_name': model_file_name,
+      'input_mean': input_mean,
+      'input_std': input_std,
+  }
+
+
+def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
+                      input_std):
+  """Adds operations that perform JPEG decoding and resizing to the graph..
+
+  Args:
+    input_width: Desired width of the image fed into the recognizer graph.
+    input_height: Desired width of the image fed into the recognizer graph.
+    input_depth: Desired channels of the image fed into the recognizer graph.
+    input_mean: Pixel value that should be zero in the image for the graph.
+    input_std: How much to divide the pixel values by before recognition.
+
+  Returns:
+    Tensors for the node to feed JPEG data into, and the output of the
+      preprocessing steps.
+  """
+  jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput')
+  decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
+  decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)
+  decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
+  resize_shape = tf.stack([input_height, input_width])
+  resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32)
+  resized_image = tf.image.resize_bilinear(decoded_image_4d,
+                                           resize_shape_as_int)
+  offset_image = tf.subtract(resized_image, input_mean)
+  mul_image = tf.multiply(offset_image, 1.0 / input_std)
+  return jpeg_data, mul_image
+
+
 def main(_):
+  # Needed to make sure the logging output is visible.
+  # See https://github.com/tensorflow/tensorflow/issues/3047
+  tf.logging.set_verbosity(tf.logging.INFO)
+
   # Prepare necessary directories  that can be used during training
   prepare_file_system()
 
+  # Gather information about the model architecture we'll be using.
+  model_info = create_model_info(FLAGS.architecture)
+  if not model_info:
+    tf.logging.error('Did not recognize architecture flag')
+    return -1
+
   # Set up the pre-trained graph.
-  maybe_download_and_extract()
-  graph, bottleneck_tensor, jpeg_data_tensor, resized_image_tensor = (
-      create_inception_graph())
+  maybe_download_and_extract(model_info['data_url'])
+  graph, bottleneck_tensor, resized_image_tensor = (
+      create_model_graph(model_info))
 
   # Look at the folder structure, and create lists of all the images.
   image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage,
                                    FLAGS.validation_percentage)
   class_count = len(image_lists.keys())
   if class_count == 0:
-    print('No valid folders of images found at ' + FLAGS.image_dir)
+    tf.logging.error('No valid folders of images found at ' + FLAGS.image_dir)
     return -1
   if class_count == 1:
-    print('Only one valid folder of images found at ' + FLAGS.image_dir +
-          ' - multiple classes are needed for classification.')
+    tf.logging.error('Only one valid folder of images found at ' +
+                     FLAGS.image_dir +
+                     ' - multiple classes are needed for classification.')
     return -1
 
   # See if the command-line flags mean we're applying any distortions.
@@ -845,25 +1002,33 @@ def main(_):
       FLAGS.random_brightness)
 
   with tf.Session(graph=graph) as sess:
+    # Set up the image decoding sub-graph.
+    jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding(
+        model_info['input_width'], model_info['input_height'],
+        model_info['input_depth'], model_info['input_mean'],
+        model_info['input_std'])
 
     if do_distort_images:
       # We will be applying distortions, so setup the operations we'll need.
       (distorted_jpeg_data_tensor,
        distorted_image_tensor) = add_input_distortions(
-           FLAGS.flip_left_right, FLAGS.random_crop,
-           FLAGS.random_scale, FLAGS.random_brightness)
+           FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
+           FLAGS.random_brightness, model_info['input_width'],
+           model_info['input_height'], model_info['input_depth'],
+           model_info['input_mean'], model_info['input_std'])
     else:
       # We'll make sure we've calculated the 'bottleneck' image summaries and
       # cached them on disk.
       cache_bottlenecks(sess, image_lists, FLAGS.image_dir,
                         FLAGS.bottleneck_dir, jpeg_data_tensor,
-                        bottleneck_tensor)
+                        decoded_image_tensor, resized_image_tensor,
+                        bottleneck_tensor, FLAGS.architecture)
 
     # Add the new layer that we'll be training.
     (train_step, cross_entropy, bottleneck_input, ground_truth_input,
-     final_tensor) = add_final_training_ops(len(image_lists.keys()),
-                                            FLAGS.final_tensor_name,
-                                            bottleneck_tensor)
+     final_tensor) = add_final_training_ops(
+         len(image_lists.keys()), FLAGS.final_tensor_name, bottleneck_tensor,
+         model_info['bottleneck_tensor_size'])
 
     # Create the operations we need to evaluate the accuracy of our new layer.
     evaluation_step, prediction = add_evaluation_step(
@@ -896,10 +1061,10 @@ def main(_):
          train_ground_truth, _) = get_random_cached_bottlenecks(
              sess, image_lists, FLAGS.train_batch_size, 'training',
              FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
-             bottleneck_tensor)
+             decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
+             FLAGS.architecture)
       # Feed the bottlenecks and ground truth into the graph, and run a training
       # step. Capture training summaries for TensorBoard with the `merged` op.
-
       train_summary, _ = sess.run(
           [merged, train_step],
           feed_dict={bottleneck_input: train_bottlenecks,
@@ -913,15 +1078,16 @@ def main(_):
             [evaluation_step, cross_entropy],
             feed_dict={bottleneck_input: train_bottlenecks,
                        ground_truth_input: train_ground_truth})
-        print('%s: Step %d: Train accuracy = %.1f%%' % (datetime.now(), i,
-                                                        train_accuracy * 100))
-        print('%s: Step %d: Cross entropy = %f' % (datetime.now(), i,
-                                                   cross_entropy_value))
+        tf.logging.info('%s: Step %d: Train accuracy = %.1f%%' %
+                        (datetime.now(), i, train_accuracy * 100))
+        tf.logging.info('%s: Step %d: Cross entropy = %f' %
+                        (datetime.now(), i, cross_entropy_value))
         validation_bottlenecks, validation_ground_truth, _ = (
             get_random_cached_bottlenecks(
                 sess, image_lists, FLAGS.validation_batch_size, 'validation',
                 FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
-                bottleneck_tensor))
+                decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
+                FLAGS.architecture))
         # Run a validation step and capture training summaries for TensorBoard
         # with the `merged` op.
         validation_summary, validation_accuracy = sess.run(
@@ -929,38 +1095,43 @@ def main(_):
             feed_dict={bottleneck_input: validation_bottlenecks,
                        ground_truth_input: validation_ground_truth})
         validation_writer.add_summary(validation_summary, i)
-        print('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' %
-              (datetime.now(), i, validation_accuracy * 100,
-               len(validation_bottlenecks)))
+        tf.logging.info('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' %
+                        (datetime.now(), i, validation_accuracy * 100,
+                         len(validation_bottlenecks)))
 
       # Store intermediate results
       intermediate_frequency = FLAGS.intermediate_store_frequency
 
-      if intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0:
-        intermediate_file_name = FLAGS.intermediate_output_graphs_dir + 'intermediate_' + str(i) + '.pb'
-        print('Save intermediate result to : ' + intermediate_file_name)
+      if (intermediate_frequency > 0 and (i % intermediate_frequency == 0)
+          and i > 0):
+        intermediate_file_name = (FLAGS.intermediate_output_graphs_dir +
+                                  'intermediate_' + str(i) + '.pb')
+        tf.logging.info('Save intermediate result to : ' +
+                        intermediate_file_name)
         save_graph_to_file(sess, graph, intermediate_file_name)
-                
+
     # We've completed all our training, so run a final test evaluation on
     # some new images we haven't used before.
     test_bottlenecks, test_ground_truth, test_filenames = (
-        get_random_cached_bottlenecks(sess, image_lists, FLAGS.test_batch_size,
-                                      'testing', FLAGS.bottleneck_dir,
-                                      FLAGS.image_dir, jpeg_data_tensor,
-                                      bottleneck_tensor))
+        get_random_cached_bottlenecks(
+            sess, image_lists, FLAGS.test_batch_size, 'testing',
+            FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
+            decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
+            FLAGS.architecture))
     test_accuracy, predictions = sess.run(
         [evaluation_step, prediction],
         feed_dict={bottleneck_input: test_bottlenecks,
                    ground_truth_input: test_ground_truth})
-    print('Final test accuracy = %.1f%% (N=%d)' % (
-        test_accuracy * 100, len(test_bottlenecks)))
+    tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
+                    (test_accuracy * 100, len(test_bottlenecks)))
 
     if FLAGS.print_misclassified_test_images:
-      print('=== MISCLASSIFIED TEST IMAGES ===')
+      tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
       for i, test_filename in enumerate(test_filenames):
         if predictions[i] != test_ground_truth[i].argmax():
-          print('%70s  %s' % (test_filename,
-                              list(image_lists.keys())[predictions[i]]))
+          tf.logging.info('%70s  %s' %
+                          (test_filename,
+                           list(image_lists.keys())[predictions[i]]))
 
     # Write out the trained graph and labels with the weights stored as
     # constants.
@@ -993,7 +1164,10 @@ if __name__ == '__main__':
       '--intermediate_store_frequency',
       type=int,
       default=0,
-      help='How many steps to store intermediate graph. If "0" then will not store.'
+      help="""\
+         How many steps to store intermediate graph. If "0" then will not
+         store.\
+      """
   )
   parser.add_argument(
       '--output_labels',
@@ -1134,5 +1308,19 @@ if __name__ == '__main__':
       input pixels up or down by.\
       """
   )
+  parser.add_argument(
+      '--architecture',
+      type=str,
+      default='inception_v3',
+      help="""\
+      Which model architecture to use. 'inception_v3' is the most accurate, but
+      also the slowest. For faster or smaller models, chose a MobileNet with the
+      form 'mobilenet_<parameter size>_<input_size>[_quantized]'. For example,
+      'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224
+      pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much
+      less accurate, but smaller and faster network that's 920 KB on disk and
+      takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
+      for more information on Mobilenet.\
+      """)
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py
index 8af5cc7114..467c15d0de 100644
--- a/tensorflow/examples/image_retraining/retrain_test.py
+++ b/tensorflow/examples/image_retraining/retrain_test.py
@@ -48,10 +48,10 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
 
   def testGetBottleneckPath(self):
     image_lists = self.dummyImageLists()
-    self.assertEqual('bottleneck_dir/somedir/image_five.jpg.txt',
+    self.assertEqual('bottleneck_dir/somedir/image_five.jpg_imagenet_v3.txt',
                      retrain.get_bottleneck_path(
                          image_lists, 'label_one', 0, 'bottleneck_dir',
-                         'validation'))
+                         'validation', 'imagenet_v3'))
 
   def testShouldDistortImage(self):
     self.assertEqual(False, retrain.should_distort_images(False, 0, 0, 0))
@@ -63,7 +63,7 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
   def testAddInputDistortions(self):
     with tf.Graph().as_default():
       with tf.Session() as sess:
-        retrain.add_input_distortions(True, 10, 10, 10)
+        retrain.add_input_distortions(True, 10, 10, 10, 299, 299, 3, 128, 128)
         self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortJPGInput:0'))
         self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0'))
 
@@ -72,9 +72,9 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
     with tf.Graph().as_default():
       with tf.Session() as sess:
         bottleneck = tf.placeholder(
-            tf.float32, [1, retrain.BOTTLENECK_TENSOR_SIZE],
-            name=retrain.BOTTLENECK_TENSOR_NAME.split(':')[0])
-        retrain.add_final_training_ops(5, 'final', bottleneck)
+            tf.float32, [1, 1024],
+            name='bottleneck')
+        retrain.add_final_training_ops(5, 'final', bottleneck, 1024)
         self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
 
   def testAddEvaluationStep(self):
@@ -113,5 +113,22 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
       result = label_image.run_graph(image, labels, jpeg, 'final:0', 3)
       self.assertEqual(result, 0)
 
+  def testAddJpegDecoding(self):
+    with tf.Graph().as_default():
+      jpeg_data, mul_image = retrain.add_jpeg_decoding(10, 10, 3, 0, 255)
+      self.assertIsNotNone(jpeg_data)
+      self.assertIsNotNone(mul_image)
+
+  def testCreateModelInfo(self):
+    did_raise_value_error = False
+    try:
+      retrain.create_model_info('no_such_model_name')
+    except ValueError:
+      did_raise_value_error = True
+    self.assertTrue(did_raise_value_error)
+    model_info = retrain.create_model_info('inception_v3')
+    self.assertIsNotNone(model_info)
+    self.assertEqual(299, model_info['input_width'])
+
 if __name__ == '__main__':
   tf.test.main()
-- 
GitLab


From d13fd822890ba03564f8f5142fae03cd8c66dee5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 13:22:42 -0700
Subject: [PATCH 0702/1375] Adds option to pass default head key through
 `make_parsing_export_strategy`

`make_parsing_export_strategy` is used commonly, including in TFX60.
This makes migrating to a multi_head model difficult, since it
requires passing a default_output_alternative_key.

PiperOrigin-RevId: 160996852
---
 .../learn/utils/saved_model_export_utils.py   | 26 ++++++++++++++++---
 1 file changed, 23 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
index 3f0f309253..1b9a25ae41 100644
--- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
@@ -417,7 +417,11 @@ def make_export_strategy(serving_input_fn,
   return export_strategy.ExportStrategy('Servo', export_fn)
 
 
-def make_parsing_export_strategy(feature_columns, exports_to_keep=5):
+def make_parsing_export_strategy(feature_columns,
+                                 default_output_alternative_key=None,
+                                 assets_extra=None,
+                                 as_text=False,
+                                 exports_to_keep=5):
   """Create an ExportStrategy for use with Experiment, using `FeatureColumn`s.
 
   Creates a SavedModel export that expects to be fed with a single string
@@ -427,6 +431,18 @@ def make_parsing_export_strategy(feature_columns, exports_to_keep=5):
   Args:
     feature_columns: An iterable of `FeatureColumn`s representing the features
       that must be provided at serving time (excluding labels!).
+    default_output_alternative_key: the name of the head to serve when an
+      incoming serving request does not explicitly request a specific head.
+      Must be `None` if the estimator inherits from ${tf.estimator.Estimator}
+      or for single-headed models.
+    assets_extra: A dict specifying how to populate the assets.extra directory
+      within the exported SavedModel.  Each key should give the destination
+      path (including the filename) relative to the assets.extra directory.
+      The corresponding value gives the full path of the source file to be
+      copied.  For example, the simple case of copying a single file without
+      renaming it is specified as
+      `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
+    as_text: whether to write the SavedModel proto in text format.
     exports_to_keep: Number of exports to keep.  Older exports will be
       garbage-collected.  Defaults to 5.  Set to None to disable garbage
       collection.
@@ -436,5 +452,9 @@ def make_parsing_export_strategy(feature_columns, exports_to_keep=5):
   """
   feature_spec = feature_column.create_feature_spec_for_parsing(feature_columns)
   serving_input_fn = input_fn_utils.build_parsing_serving_input_fn(feature_spec)
-  return make_export_strategy(serving_input_fn, exports_to_keep=exports_to_keep)
-
+  return make_export_strategy(
+      serving_input_fn,
+      default_output_alternative_key=default_output_alternative_key,
+      assets_extra=assets_extra,
+      as_text=as_text,
+      exports_to_keep=exports_to_keep)
-- 
GitLab


From 70804d820bc5fbbb6c70a573aa4fae555c110155 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 5 Jul 2017 13:32:08 -0700
Subject: [PATCH 0703/1375] Disable graph rewrites in tests that assert a given
 graph structure (round 2). This will make it possible to optimize these
 graphs in the future without having to update the test with every
 improvement.

PiperOrigin-RevId: 160997959
---
 .../python/debug/cli/analyzer_cli_test.py     | 16 +++++++++---
 .../debug/lib/session_debug_file_test.py      |  9 ++++++-
 .../python/debug/lib/session_debug_testlib.py | 26 ++++++++++++-------
 .../python/debug/wrappers/framework_test.py   | 10 ++++++-
 4 files changed, 46 insertions(+), 15 deletions(-)

diff --git a/tensorflow/python/debug/cli/analyzer_cli_test.py b/tensorflow/python/debug/cli/analyzer_cli_test.py
index 6fba9ed0b4..8302a4b3cb 100644
--- a/tensorflow/python/debug/cli/analyzer_cli_test.py
+++ b/tensorflow/python/debug/cli/analyzer_cli_test.py
@@ -25,6 +25,7 @@ import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
 from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.debug.cli import analyzer_cli
 from tensorflow.python.debug.cli import cli_shared
@@ -43,6 +44,13 @@ from tensorflow.python.platform import test
 from tensorflow.python.util import tf_inspect
 
 
+def no_rewrite_session_config():
+  rewriter_config = rewriter_config_pb2.RewriterConfig(
+      disable_model_pruning=True)
+  graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+  return config_pb2.ConfigProto(graph_options=graph_options)
+
+
 def line_number_above():
   return tf_inspect.stack()[1][2] - 1
 
@@ -506,7 +514,7 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase):
     cls._curr_file_path = os.path.abspath(
         tf_inspect.getfile(tf_inspect.currentframe()))
 
-    cls._sess = session.Session()
+    cls._sess = session.Session(config=no_rewrite_session_config())
     with cls._sess as sess:
       u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
       v_init_val = np.array([[2.0], [-1.0]])
@@ -1382,7 +1390,7 @@ class AnalyzerCLIPrintLargeTensorTest(test_util.TensorFlowTestCase):
   def setUpClass(cls):
     cls._dump_root = tempfile.mkdtemp()
 
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       # 2400 elements should exceed the default threshold (2000).
       x = constant_op.constant(np.zeros([300, 8]), name="large_tensors/x")
 
@@ -1459,7 +1467,7 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase):
     else:
       cls._main_device = "/job:localhost/replica:0/task:0/cpu:0"
 
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       x_init_val = np.array([5.0, 3.0])
       x_init = constant_op.constant(x_init_val, shape=[2])
       x = variables.Variable(x_init, name="control_deps/x")
@@ -1799,7 +1807,7 @@ class AnalyzerCLIWhileLoopTest(test_util.TensorFlowTestCase):
   def setUpClass(cls):
     cls._dump_root = tempfile.mkdtemp()
 
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       loop_var = constant_op.constant(0, name="while_loop_test/loop_var")
       cond = lambda loop_var: math_ops.less(loop_var, 10)
       body = lambda loop_var: math_ops.add(loop_var, 1)
diff --git a/tensorflow/python/debug/lib/session_debug_file_test.py b/tensorflow/python/debug/lib/session_debug_file_test.py
index bb3e4ead35..48f31771db 100644
--- a/tensorflow/python/debug/lib/session_debug_file_test.py
+++ b/tensorflow/python/debug/lib/session_debug_file_test.py
@@ -22,6 +22,7 @@ import shutil
 import tempfile
 
 from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.debug.lib import debug_data
 from tensorflow.python.debug.lib import debug_utils
@@ -35,6 +36,12 @@ from tensorflow.python.platform import googletest
 
 class SessionDebugTest(session_debug_testlib.SessionDebugTestBase):
 
+  def _no_rewrite_session_config(self):
+    rewriter_config = rewriter_config_pb2.RewriterConfig(
+        disable_model_pruning=True)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+    return config_pb2.ConfigProto(graph_options=graph_options)
+
   def _debug_urls(self, run_number=None):
     return ["file://%s" % self._debug_dump_dir(run_number=run_number)]
 
@@ -47,7 +54,7 @@ class SessionDebugTest(session_debug_testlib.SessionDebugTestBase):
   def testAllowsDifferentWatchesOnDifferentRuns(self):
     """Test watching different tensors on different runs of the same graph."""
 
-    with session.Session() as sess:
+    with session.Session(config=self._no_rewrite_session_config()) as sess:
       u_init_val = [[5.0, 3.0], [-1.0, 0.0]]
       v_init_val = [[2.0], [-1.0]]
 
diff --git a/tensorflow/python/debug/lib/session_debug_testlib.py b/tensorflow/python/debug/lib/session_debug_testlib.py
index 67f5e9d29e..3facf306e2 100644
--- a/tensorflow/python/debug/lib/session_debug_testlib.py
+++ b/tensorflow/python/debug/lib/session_debug_testlib.py
@@ -29,6 +29,7 @@ import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
 from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.core.util import event_pb2
 from tensorflow.python.client import session
 from tensorflow.python.debug.lib import debug_data
@@ -53,6 +54,13 @@ from tensorflow.python.platform import test
 from tensorflow.python.training import gradient_descent
 
 
+def no_rewrite_session_config():
+  rewriter_config = rewriter_config_pb2.RewriterConfig(
+      disable_model_pruning=True)
+  graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+  return config_pb2.ConfigProto(graph_options=graph_options)
+
+
 class _RNNCellForTest(rnn_cell_impl.RNNCell):
   """RNN cell for testing."""
 
@@ -160,7 +168,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
         validate=validate)
 
   def _generate_dump_from_simple_addition_graph(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
       v_init_val = np.array([[2.0], [-1.0]])
 
@@ -304,7 +312,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
       results.dump.node_op_type("foo_bar")
 
   def testDumpStringTensorsWorks(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       str1_init_val = np.array(b"abc")
       str2_init_val = np.array(b"def")
 
@@ -419,7 +427,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
       self.assertEqual(s_init_val, sess.run(s))
 
   def testDebugWhileLoopGeneratesMultipleDumps(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       num_iter = 10
 
       # "u" is the Variable being updated in the loop.
@@ -659,7 +667,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
       self.assertEqual(x_name, first_bad_datum[0].node_name)
 
   def _session_run_for_graph_structure_lookup(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       u_name = "testDumpGraphStructureLookup/u"
       v_name = "testDumpGraphStructureLookup/v"
       w_name = "testDumpGraphStructureLookup/w"
@@ -798,7 +806,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
     self.assertTrue(dump.loaded_partition_graphs())
 
   def testGraphPathFindingOnControlEdgesWorks(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       v1 = variables.Variable(1.0, name="v1")
       v2 = variables.Variable(2.0, name="v2")
       v3 = variables.Variable(3.0, name="v3")
@@ -814,7 +822,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
       self.assertIsNone(dump.find_some_path("v1", "c", include_control=False))
 
   def testGraphPathFindingReverseRefEdgeWorks(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       v = variables.Variable(10.0, name="v")
       delta = variables.Variable(1.0, name="delta")
       inc_v = state_ops.assign_add(v, delta, name="inc_v")
@@ -1164,7 +1172,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
       self.assertAllClose(np.array([[-3.0, 0.0]]), x_dumps[0].get_tensor())
 
   def testDebugNumericSummaryOnInitializedTensorGivesCorrectResult(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       a = variables.Variable(
           [
               np.nan, np.nan, 0.0, 0.0, 0.0, -1.0, -3.0, 3.0, 7.0, -np.inf,
@@ -1252,7 +1260,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
       self.assertIn("m:0:DebugNumericSummary", dump.debug_watch_keys("m"))
 
   def testDebugNumericSummaryInvalidAttributesStringAreCaught(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       a = variables.Variable(10.0, name="a")
       b = variables.Variable(0.0, name="b")
       c = variables.Variable(0.0, name="c")
@@ -1300,7 +1308,7 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
         sess.run(y, options=run_options, run_metadata=run_metadata)
 
   def testDebugNumericSummaryMuteOnHealthyMutesOnlyHealthyTensorDumps(self):
-    with session.Session() as sess:
+    with session.Session(config=no_rewrite_session_config()) as sess:
       a = variables.Variable(10.0, name="a")
       b = variables.Variable(0.0, name="b")
       c = variables.Variable(0.0, name="c")
diff --git a/tensorflow/python/debug/wrappers/framework_test.py b/tensorflow/python/debug/wrappers/framework_test.py
index 2b2289d6a8..e5efbb12f3 100644
--- a/tensorflow/python/debug/wrappers/framework_test.py
+++ b/tensorflow/python/debug/wrappers/framework_test.py
@@ -24,6 +24,8 @@ import threading
 
 import numpy as np
 
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.debug.lib import debug_data
 from tensorflow.python.debug.wrappers import framework
@@ -139,6 +141,12 @@ class TestDebugWrapperSessionBadAction(framework.BaseDebugWrapperSession):
 
 class DebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
+  def _no_rewrite_session_config(self):
+    rewriter_config = rewriter_config_pb2.RewriterConfig(
+        disable_model_pruning=True)
+    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+    return config_pb2.ConfigProto(graph_options=graph_options)
+
   def setUp(self):
     self._observer = {
         "sess_init_count": 0,
@@ -153,7 +161,7 @@ class DebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
     self._dump_root = tempfile.mkdtemp()
 
-    self._sess = session.Session()
+    self._sess = session.Session(config=self._no_rewrite_session_config())
 
     self._a_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
     self._b_init_val = np.array([[2.0], [-1.0]])
-- 
GitLab


From eccd162119675d0bf5bc6f8e6a93dcda7ab6db4a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 14:14:54 -0700
Subject: [PATCH 0704/1375] Fix moments calculation to never result in negative
 variance and avoid doing extra work when shift = None.  With the current
 calculation shift is ignored.

PiperOrigin-RevId: 161003939
---
 tensorflow/python/ops/nn_impl.py | 34 ++++++++++++--------------------
 tensorflow/python/ops/nn_test.py | 22 ---------------------
 2 files changed, 13 insertions(+), 43 deletions(-)

diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 7bc99ac725..98ede2031b 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -580,15 +580,16 @@ def normalize_moments(counts, mean_ss, variance_ss, shift, name=None):
   return (mean, variance)
 
 
-def moments(x, axes, shift=None, name=None, keep_dims=False):
+def moments(x, axes,
+            shift=None,  # pylint: disable=unused-argument
+            name=None, keep_dims=False):
   """Calculate the mean and variance of `x`.
 
   The mean and variance are calculated by aggregating the contents of `x`
   across `axes`.  If `x` is 1-D and `axes = [0]` this is just the mean
   and variance of a vector.
 
-  Note: for numerical stability, when shift=None, the true mean
-  would be computed and used as shift.
+  Note: shift is currently not used, the true mean is computed and used.
 
   When using these moments for batch normalization (see
   `tf.nn.batch_normalization`):
@@ -601,35 +602,26 @@ def moments(x, axes, shift=None, name=None, keep_dims=False):
     x: A `Tensor`.
     axes: Array of ints.  Axes along which to compute mean and
       variance.
-    shift: A `Tensor` containing the value by which to shift the data for
-      numerical stability, or `None` in which case the true mean of the data is
-      used as shift. A shift close to the true mean provides the most
-      numerically stable results.
+    shift: Not used in the current implementation
     name: Name used to scope the operations that compute the moments.
     keep_dims: produce moments with the same dimensionality as the input.
 
   Returns:
     Two `Tensor` objects: `mean` and `variance`.
   """
-  with ops.name_scope(name, "moments", [x, axes, shift]):
+  with ops.name_scope(name, "moments", [x, axes]):
     # The dynamic range of fp16 is too limited to support the collection of
     # sufficient statistics. As a workaround we simply perform the operations
     # on 32-bit floats before converting the mean and variance back to fp16
     y = math_ops.cast(x, dtypes.float32) if x.dtype == dtypes.float16 else x
-    if shift is None:
-      # Compute true mean while keeping the dims for proper broadcasting.
-      shift = array_ops.stop_gradient(
-          math_ops.reduce_mean(y, axes, keep_dims=True))
-    else:
-      shift = math_ops.cast(shift, y.dtype)
-    shifted_mean = math_ops.reduce_mean(
-        math_ops.subtract(y, shift), axes, keep_dims=True, name="shifted_mean")
-    variance = math_ops.subtract(
-        math_ops.reduce_mean(
-            math_ops.squared_difference(y, shift), axes, keep_dims=True),
-        math_ops.square(shifted_mean),
+    # Compute true mean while keeping the dims for proper broadcasting.
+    mean = math_ops.reduce_mean(y, axes, keep_dims=True, name="mean")
+    # sample variance, not unbiased variance
+    variance = math_ops.reduce_mean(
+        math_ops.squared_difference(y, array_ops.stop_gradient(mean)),
+        axes,
+        keep_dims=True,
         name="variance")
-    mean = math_ops.add(shifted_mean, shift, name="mean")
     if not keep_dims:
       mean = array_ops.squeeze(mean, axes)
       variance = array_ops.squeeze(variance, axes)
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index f3592941d1..87f6f92a8a 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -877,28 +877,6 @@ class MomentsTest(test_lib.TestCase):
   def testOutput4DInput123(self):
     self.doOutputTest((10, 10, 10, 30), (1, 2, 3))
 
-  def testUnstableOutputShiftNone(self):
-    input_shape = (10, 300)
-    moments_axes = (0, 1)
-    mu, sigma = 1e3, 0.1
-    tol = 1e-3
-    input_values = np.random.rand(*input_shape) * sigma + mu
-    expected_mean = np.mean(input_values, axis=moments_axes)
-    expected_var = np.var(input_values, axis=moments_axes)
-
-    with self.test_session() as sess:
-      inputs = constant_op.constant(
-          input_values, shape=input_shape, dtype=dtypes.float32)
-      mean, variance = nn_impl.moments(inputs, moments_axes, shift=0.0)
-
-      [mean, variance] = sess.run([mean, variance])
-      # Make sure that there are no NaNs
-      self.assertFalse(np.isnan(mean).any())
-      self.assertFalse(np.isnan(variance).any())
-      self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol)
-      # The variance is unstable
-      self.assertGreater(np.abs(variance - expected_var), 0.1)
-
 
 if __name__ == "__main__":
   test_lib.main()
-- 
GitLab


From 1857e187c98b4863669b62a469acc1251e1c1f04 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 14:47:12 -0700
Subject: [PATCH 0705/1375] Add some utility functions for supporting an
 alternate pbtxt format that supports multi-line text without uncomfortable
 escaping.  So:

  description: "A `SparseTensor` ... `sparse_indices`,\n`sparse_values`, and `sparse_shape`, where\n\n```sparse_indices.shape[1] == sparse_shape.shape[0] == R```\n\nAn `N`-minibatch ..."

would become:

  description: <<END
A `SparseTensor` ... `sparse_indices`,
`sparse_values`, and `sparse_shape`, where

```sparse_indices.shape[1] == sparse_shape.shape[0] == R```

An `N`-minibatch ...
END

PiperOrigin-RevId: 161008382
---
 tensorflow/BUILD                             |   1 +
 tensorflow/core/BUILD                        |  11 ++
 tensorflow/core/framework/op_gen_lib.cc      | 172 +++++++++++++++++++
 tensorflow/core/framework/op_gen_lib.h       |   5 +
 tensorflow/core/framework/op_gen_lib_test.cc | 131 ++++++++++++++
 tensorflow/tools/mlpbtxt/BUILD               |  44 +++++
 tensorflow/tools/mlpbtxt/frommlpbtxt.cc      |  70 ++++++++
 tensorflow/tools/mlpbtxt/tomlpbtxt.cc        |  81 +++++++++
 8 files changed, 515 insertions(+)
 create mode 100644 tensorflow/core/framework/op_gen_lib_test.cc
 create mode 100644 tensorflow/tools/mlpbtxt/BUILD
 create mode 100644 tensorflow/tools/mlpbtxt/frommlpbtxt.cc
 create mode 100644 tensorflow/tools/mlpbtxt/tomlpbtxt.cc

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 3f7fba86cf..70365ec999 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -372,6 +372,7 @@ filegroup(
         "//tensorflow/tools/docker/notebooks:all_files",
         "//tensorflow/tools/docs:all_files",
         "//tensorflow/tools/git:all_files",
+        "//tensorflow/tools/mlpbtxt:all_files",
         "//tensorflow/tools/proto_text:all_files",
         "//tensorflow/tools/quantization:all_files",
         "//tensorflow/tools/test:all_files",
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 1d9bd665c0..74d21a3934 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -2109,6 +2109,17 @@ cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "framework_op_gen_lib_test",
+    size = "small",
+    srcs = ["framework/op_gen_lib_test.cc"],
+    deps = [
+        ":op_gen_lib",
+        ":test",
+        ":test_main",
+    ],
+)
+
 tf_cc_test(
     name = "quantize_training_test",
     srcs = ["graph/quantize_training_test.cc"],
diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc
index 517120ecab..cedfd6fc9c 100644
--- a/tensorflow/core/framework/op_gen_lib.cc
+++ b/tensorflow/core/framework/op_gen_lib.cc
@@ -73,6 +73,178 @@ bool ConsumeEquals(StringPiece* description) {
   return false;
 }
 
+// Split `*orig` into two pieces at the first occurence of `split_ch`.
+// Returns whether `split_ch` was found. Afterwards, `*before_split`
+// contains the maximum prefix of the input `*orig` that doesn't
+// contain `split_ch`, and `*orig` contains everything after the
+// first `split_ch`.
+static bool SplitAt(char split_ch, StringPiece* orig,
+                    StringPiece* before_split) {
+  auto pos = orig->find(split_ch);
+  if (pos == StringPiece::npos) {
+    *before_split = *orig;
+    orig->clear();
+    return false;
+  } else {
+    *before_split = orig->substr(0, pos);
+    orig->remove_prefix(pos + 1);
+    return true;
+  }
+}
+
+// Does this line start with "<spaces><field>:" where "<field>" is
+// in multi_line_fields? Sets *colon_pos to the position of the colon.
+static bool StartsWithFieldName(StringPiece line,
+                                const std::vector<string>& multi_line_fields) {
+  StringPiece up_to_colon;
+  if (!SplitAt(':', &line, &up_to_colon)) return false;
+  while (up_to_colon.Consume(" "))
+    ;  // Remove leading spaces.
+  for (const auto& field : multi_line_fields) {
+    if (up_to_colon == field) {
+      return true;
+    }
+  }
+  return false;
+}
+
+static bool ConvertLine(StringPiece line,
+                        const std::vector<string>& multi_line_fields,
+                        string* ml) {
+  // Is this a field we should convert?
+  if (!StartsWithFieldName(line, multi_line_fields)) {
+    return false;
+  }
+  // Has a matching field name, so look for "..." after the colon.
+  StringPiece up_to_colon;
+  StringPiece after_colon = line;
+  SplitAt(':', &after_colon, &up_to_colon);
+  while (after_colon.Consume(" "))
+    ;  // Remove leading spaces.
+  if (!after_colon.Consume("\"")) {
+    // We only convert string fields, so don't convert this line.
+    return false;
+  }
+  auto last_quote = after_colon.rfind('\"');
+  if (last_quote == StringPiece::npos) {
+    // Error: we don't see the expected matching quote, abort the conversion.
+    return false;
+  }
+  StringPiece escaped = after_colon.substr(0, last_quote);
+  StringPiece suffix = after_colon.substr(last_quote + 1);
+  // We've now parsed line into '<up_to_colon>: "<escaped>"<suffix>'
+
+  string unescaped;
+  if (!str_util::CUnescape(escaped, &unescaped, nullptr)) {
+    // Error unescaping, abort the conversion.
+    return false;
+  }
+  // No more errors possible at this point.
+
+  // Find a string to mark the end that isn't in unescaped.
+  string end = "END";
+  for (int s = 0; unescaped.find(end) != string::npos; ++s) {
+    end = strings::StrCat("END", s);
+  }
+
+  // Actually start writing the converted output.
+  strings::StrAppend(ml, up_to_colon, ": <<", end, "\n", unescaped, "\n", end);
+  if (!suffix.empty()) {
+    // Output suffix, in case there was a trailing comment in the source.
+    strings::StrAppend(ml, suffix);
+  }
+  strings::StrAppend(ml, "\n");
+  return true;
+}
+
+string PBTxtToMultiline(StringPiece pbtxt,
+                        const std::vector<string>& multi_line_fields) {
+  string ml;
+  // Probably big enough, since the input and output are about the
+  // same size, but just a guess.
+  ml.reserve(pbtxt.size() * (17. / 16));
+  StringPiece line;
+  while (!pbtxt.empty()) {
+    // Split pbtxt into its first line and everything after.
+    SplitAt('\n', &pbtxt, &line);
+    // Convert line or output it unchanged
+    if (!ConvertLine(line, multi_line_fields, &ml)) {
+      strings::StrAppend(&ml, line, "\n");
+    }
+  }
+  return ml;
+}
+
+// Given a single line of text `line` with first : at `colon`, determine if
+// there is an "<<END" expression after the colon and if so return true and set
+// `*end` to everything after the "<<".
+static bool FindMultiline(StringPiece line, size_t colon, string* end) {
+  if (colon == StringPiece::npos) return false;
+  line.remove_prefix(colon + 1);
+  while (line.Consume(" ")) {
+  }
+  if (line.Consume("<<")) {
+    *end = line.ToString();
+    return true;
+  }
+  return false;
+}
+
+string PBTxtFromMultiline(StringPiece multiline_pbtxt) {
+  string pbtxt;
+  // Probably big enough, since the input and output are about the
+  // same size, but just a guess.
+  pbtxt.reserve(multiline_pbtxt.size() * (33. / 32));
+  StringPiece line;
+  while (!multiline_pbtxt.empty()) {
+    // Split multiline_pbtxt into its first line and everything after.
+    if (!SplitAt('\n', &multiline_pbtxt, &line)) {
+      strings::StrAppend(&pbtxt, line);
+      break;
+    }
+
+    string end;
+    auto colon = line.find(':');
+    if (!FindMultiline(line, colon, &end)) {
+      // Normal case: not a multi-line string, just output the line as-is.
+      strings::StrAppend(&pbtxt, line, "\n");
+      continue;
+    }
+
+    // Multi-line case:
+    //     something: <<END
+    // xx
+    // yy
+    // END
+    // Should be converted to:
+    //     something: "xx\nyy"
+
+    // Output everything up to the colon ("    something:").
+    strings::StrAppend(&pbtxt, line.substr(0, colon + 1));
+
+    // Add every line to unescaped until we see the "END" string.
+    string unescaped;
+    bool first = true;
+    string suffix;
+    while (!multiline_pbtxt.empty()) {
+      SplitAt('\n', &multiline_pbtxt, &line);
+      if (line.Consume(end)) break;
+      if (first) {
+        first = false;
+      } else {
+        unescaped.push_back('\n');
+      }
+      strings::StrAppend(&unescaped, line);
+      line.clear();
+    }
+
+    // Escape what we extracted and then output it in quotes.
+    strings::StrAppend(&pbtxt, " \"", str_util::CEscape(unescaped), "\"", line,
+                       "\n");
+  }
+  return pbtxt;
+}
+
 OpGenOverrideMap::OpGenOverrideMap() {}
 OpGenOverrideMap::~OpGenOverrideMap() {}
 
diff --git a/tensorflow/core/framework/op_gen_lib.h b/tensorflow/core/framework/op_gen_lib.h
index a74b651bb6..70c140ca54 100644
--- a/tensorflow/core/framework/op_gen_lib.h
+++ b/tensorflow/core/framework/op_gen_lib.h
@@ -43,6 +43,11 @@ string WordWrap(StringPiece prefix, StringPiece str, int width);
 // returns false.
 bool ConsumeEquals(StringPiece* description);
 
+// Convert text-serialized protobufs to/from multiline format.
+string PBTxtToMultiline(StringPiece pbtxt,
+                        const std::vector<string>& multi_line_fields);
+string PBTxtFromMultiline(StringPiece multiline_pbtxt);
+
 // Takes a list of files with OpGenOverrides text protos, and allows you to
 // look up the specific override for any given op.
 class OpGenOverrideMap {
diff --git a/tensorflow/core/framework/op_gen_lib_test.cc b/tensorflow/core/framework/op_gen_lib_test.cc
new file mode 100644
index 0000000000..cc1d117f38
--- /dev/null
+++ b/tensorflow/core/framework/op_gen_lib_test.cc
@@ -0,0 +1,131 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op_gen_lib.h"
+
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace {
+
+TEST(OpGenLibTest, MultilinePBTxt) {
+  // Non-multiline pbtxt
+  const string pbtxt = R"(foo: "abc"
+foo: ""
+foo: "\n\n"
+foo: "abc\nEND"
+  foo: "ghi\njkl\n"
+bar: "quotes:\""
+)";
+
+  // Field "foo" converted to multiline but not "bar".
+  const string ml_foo = R"(foo: <<END
+abc
+END
+foo: <<END
+
+END
+foo: <<END
+
+
+
+END
+foo: <<END0
+abc
+END
+END0
+  foo: <<END
+ghi
+jkl
+
+END
+bar: "quotes:\""
+)";
+
+  // Both fields "foo" and "bar" converted to multiline.
+  const string ml_foo_bar = R"(foo: <<END
+abc
+END
+foo: <<END
+
+END
+foo: <<END
+
+
+
+END
+foo: <<END0
+abc
+END
+END0
+  foo: <<END
+ghi
+jkl
+
+END
+bar: <<END
+quotes:"
+END
+)";
+
+  // ToMultiline
+  EXPECT_EQ(ml_foo, PBTxtToMultiline(pbtxt, {"foo"}));
+  EXPECT_EQ(pbtxt, PBTxtToMultiline(pbtxt, {"baz"}));
+  EXPECT_EQ(ml_foo_bar, PBTxtToMultiline(pbtxt, {"foo", "bar"}));
+
+  // FromMultiline
+  EXPECT_EQ(pbtxt, PBTxtFromMultiline(pbtxt));
+  EXPECT_EQ(pbtxt, PBTxtFromMultiline(ml_foo));
+  EXPECT_EQ(pbtxt, PBTxtFromMultiline(ml_foo_bar));
+}
+
+TEST(OpGenLibTest, PBTxtToMultilineErrorCases) {
+  // Everything correct.
+  EXPECT_EQ("f: <<END\n7\nEND\n", PBTxtToMultiline("f: \"7\"\n", {"f"}));
+
+  // In general, if there is a problem parsing in PBTxtToMultiline, it leaves
+  // the line alone.
+
+  // No colon
+  EXPECT_EQ("f \"7\"\n", PBTxtToMultiline("f \"7\"\n", {"f"}));
+  // Only converts strings.
+  EXPECT_EQ("f: 7\n", PBTxtToMultiline("f: 7\n", {"f"}));
+  // No quote after colon.
+  EXPECT_EQ("f: 7\"\n", PBTxtToMultiline("f: 7\"\n", {"f"}));
+  // Only one quote
+  EXPECT_EQ("f: \"7\n", PBTxtToMultiline("f: \"7\n", {"f"}));
+  // Illegal escaping
+  EXPECT_EQ("f: \"7\\\"\n", PBTxtToMultiline("f: \"7\\\"\n", {"f"}));
+}
+
+TEST(OpGenLibTest, PBTxtToMultilineComments) {
+  const string pbtxt = R"(f: "bar"  # Comment 1
+    f: "\n"  # Comment 2
+)";
+  const string ml = R"(f: <<END
+bar
+END  # Comment 1
+    f: <<END
+
+
+END  # Comment 2
+)";
+
+  EXPECT_EQ(ml, PBTxtToMultiline(pbtxt, {"f"}));
+  EXPECT_EQ(pbtxt, PBTxtFromMultiline(ml));
+}
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/tools/mlpbtxt/BUILD b/tensorflow/tools/mlpbtxt/BUILD
new file mode 100644
index 0000000000..fc63e9a0b7
--- /dev/null
+++ b/tensorflow/tools/mlpbtxt/BUILD
@@ -0,0 +1,44 @@
+# Description:
+# This package provides binaries that convert between multi-line and standard
+# pbtxt (text-serialization of protocol message) files.
+
+package(default_visibility = ["//visibility:private"])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files([
+    "LICENSE",
+    "placeholder.txt",
+])
+
+cc_binary(
+    name = "tomlpbtxt",
+    srcs = ["tomlpbtxt.cc"],
+    deps = [
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:op_gen_lib",
+    ],
+)
+
+cc_binary(
+    name = "frommlpbtxt",
+    srcs = ["frommlpbtxt.cc"],
+    deps = [
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:op_gen_lib",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/tools/mlpbtxt/frommlpbtxt.cc b/tensorflow/tools/mlpbtxt/frommlpbtxt.cc
new file mode 100644
index 0000000000..643924b318
--- /dev/null
+++ b/tensorflow/tools/mlpbtxt/frommlpbtxt.cc
@@ -0,0 +1,70 @@
+/* Copyright 2017 The TensorFlow Authors All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <stdio.h>
+
+#include "tensorflow/core/framework/op_gen_lib.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/init_main.h"
+#include "tensorflow/core/util/command_line_flags.h"
+
+namespace tensorflow {
+namespace {
+
+int Run(int argc, char** argv) {
+  string FLAGS_in = "";
+  string FLAGS_out = "";
+
+  std::vector<Flag> flag_list = {
+      Flag("in", &FLAGS_in, "Input multi-line proto text (.mlpbtxt) file name"),
+      Flag("out", &FLAGS_out, "Output proto text (.pbtxt) file name")};
+
+  // Parse the command-line.
+  const string usage = Flags::Usage(argv[0], flag_list);
+  const bool parse_ok = Flags::Parse(&argc, argv, flag_list);
+  if (argc != 1 || !parse_ok) {
+    printf("%s", usage.c_str());
+    return 2;
+  }
+
+  port::InitMain(argv[0], &argc, &argv);
+
+  // Read the input file --in.
+  string in_contents;
+  Status s = ReadFileToString(Env::Default(), FLAGS_in, &in_contents);
+  if (!s.ok()) {
+    printf("Error reading file %s: %s\n", FLAGS_in.c_str(),
+           s.ToString().c_str());
+    return 1;
+  }
+
+  // Write the output file --out.
+  const string out_contents = PBTxtFromMultiline(in_contents);
+  s = WriteStringToFile(Env::Default(), FLAGS_out, out_contents);
+  if (!s.ok()) {
+    printf("Error writing file %s: %s\n", FLAGS_out.c_str(),
+           s.ToString().c_str());
+    return 1;
+  }
+
+  return 0;
+}
+
+}  // namespace
+}  // namespace tensorflow
+
+int main(int argc, char** argv) { return tensorflow::Run(argc, argv); }
diff --git a/tensorflow/tools/mlpbtxt/tomlpbtxt.cc b/tensorflow/tools/mlpbtxt/tomlpbtxt.cc
new file mode 100644
index 0000000000..469be49ed3
--- /dev/null
+++ b/tensorflow/tools/mlpbtxt/tomlpbtxt.cc
@@ -0,0 +1,81 @@
+/* Copyright 2017 The TensorFlow Authors All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <stdio.h>
+
+#include "tensorflow/core/framework/op_gen_lib.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/init_main.h"
+#include "tensorflow/core/util/command_line_flags.h"
+
+namespace tensorflow {
+namespace {
+
+int Run(int argc, char** argv) {
+  string FLAGS_in = "";
+  string FLAGS_out = "";
+  string FLAGS_fields = "description";
+
+  std::vector<Flag> flag_list = {
+      Flag("in", &FLAGS_in, "Input proto text (.pbtxt) file name"),
+      Flag("out", &FLAGS_out,
+           "Output multi-line proto text (.mlpbtxt) file name"),
+      Flag("fields", &FLAGS_fields, "Comma-separated list of field names")};
+
+  // Parse the command-line.
+  const string usage = Flags::Usage(argv[0], flag_list);
+  const bool parse_ok = Flags::Parse(&argc, argv, flag_list);
+  if (argc != 1 || !parse_ok) {
+    printf("%s", usage.c_str());
+    return 2;
+  }
+
+  // Parse the --fields option.
+  std::vector<string> fields =
+      str_util::Split(FLAGS_fields, ',', str_util::SkipEmpty());
+  if (fields.empty()) {
+    printf("--fields must be non-empty.\n%s", usage.c_str());
+    return 2;
+  }
+
+  port::InitMain(argv[0], &argc, &argv);
+
+  // Read the input file --in.
+  string in_contents;
+  Status s = ReadFileToString(Env::Default(), FLAGS_in, &in_contents);
+  if (!s.ok()) {
+    printf("Error reading file %s: %s\n", FLAGS_in.c_str(),
+           s.ToString().c_str());
+    return 1;
+  }
+
+  // Write the output file --out.
+  const string out_contents = PBTxtToMultiline(in_contents, fields);
+  s = WriteStringToFile(Env::Default(), FLAGS_out, out_contents);
+  if (!s.ok()) {
+    printf("Error writing file %s: %s\n", FLAGS_out.c_str(),
+           s.ToString().c_str());
+    return 1;
+  }
+
+  return 0;
+}
+
+}  // namespace
+}  // namespace tensorflow
+
+int main(int argc, char** argv) { return tensorflow::Run(argc, argv); }
-- 
GitLab


From 1b7d13181843631f59c515a00a73c21711ec5802 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Wed, 5 Jul 2017 15:31:54 -0700
Subject: [PATCH 0706/1375] Initial commit of time series models

PiperOrigin-RevId: 161014271
---
 tensorflow/BUILD                              |    4 +
 tensorflow/contrib/BUILD                      |    1 +
 tensorflow/contrib/__init__.py                |    1 +
 tensorflow/contrib/cmake/tf_python.cmake      |    6 +
 tensorflow/contrib/timeseries/BUILD           |   35 +
 tensorflow/contrib/timeseries/README.md       |   25 +
 tensorflow/contrib/timeseries/__init__.py     |   42 +
 tensorflow/contrib/timeseries/examples/BUILD  |  105 ++
 .../contrib/timeseries/examples/__init__.py   |    0
 .../timeseries/examples/data/changepoints.csv |  201 +++
 .../examples/data/multivariate_level.csv      | 1000 ++++++++++++++
 .../timeseries/examples/data/period_trend.csv |  500 +++++++
 .../timeseries/examples/known_anomaly.py      |  150 ++
 .../timeseries/examples/known_anomaly_test.py |   49 +
 .../timeseries/examples/multivariate.py       |  117 ++
 .../timeseries/examples/multivariate_test.py  |   36 +
 .../contrib/timeseries/examples/predict.py    |  132 ++
 .../timeseries/examples/predict_test.py       |   62 +
 .../contrib/timeseries/python/__init__.py     |   23 +
 .../timeseries/python/timeseries/BUILD        |  337 +++++
 .../timeseries/python/timeseries/__init__.py  |   28 +
 .../timeseries/python/timeseries/ar_model.py  |  706 ++++++++++
 .../python/timeseries/ar_model_test.py        |  337 +++++
 .../python/timeseries/estimators.py           |  379 ++++++
 .../python/timeseries/estimators_test.py      |  149 ++
 .../python/timeseries/feature_keys.py         |   74 +
 .../python/timeseries/input_pipeline.py       |  832 ++++++++++++
 .../python/timeseries/input_pipeline_test.py  |  319 +++++
 .../python/timeseries/math_utils.py           |  951 +++++++++++++
 .../python/timeseries/math_utils_test.py      |  344 +++++
 .../timeseries/python/timeseries/model.py     |  737 ++++++++++
 .../python/timeseries/model_utils.py          |  417 ++++++
 .../python/timeseries/model_utils_test.py     |  275 ++++
 .../python/timeseries/saved_model_utils.py    |  167 +++
 .../python/timeseries/state_management.py     |  264 ++++
 .../timeseries/state_management_test.py       |  313 +++++
 .../timeseries/state_space_models/BUILD       |  293 ++++
 .../timeseries/state_space_models/__init__.py |    0
 .../filtering_postprocessor.py                |  263 ++++
 .../filtering_postprocessor_test.py           |   71 +
 .../g3doc/periodic_multires_derivation.md     |  279 ++++
 .../state_space_models/kalman_filter.py       |  341 +++++
 .../state_space_models/kalman_filter_test.py  |  425 ++++++
 .../state_space_models/level_trend.py         |  145 ++
 .../state_space_models/level_trend_test.py    |   53 +
 .../timeseries/state_space_models/periodic.py |  535 ++++++++
 .../state_space_models/periodic_test.py       |   81 ++
 .../state_space_models/state_space_model.py   | 1207 +++++++++++++++++
 .../state_space_model_test.py                 |  758 +++++++++++
 .../state_space_models/structural_ensemble.py |  266 ++++
 .../structural_ensemble_test.py               |  152 +++
 .../state_space_models/test_utils.py          |  108 ++
 .../timeseries/state_space_models/varma.py    |  197 +++
 .../state_space_models/varma_test.py          |   94 ++
 .../python/timeseries/test_utils.py           |  282 ++++
 .../tools/pip_package/pip_smoke_test.py       |    6 +
 56 files changed, 14674 insertions(+)
 create mode 100644 tensorflow/contrib/timeseries/BUILD
 create mode 100644 tensorflow/contrib/timeseries/README.md
 create mode 100644 tensorflow/contrib/timeseries/__init__.py
 create mode 100644 tensorflow/contrib/timeseries/examples/BUILD
 create mode 100644 tensorflow/contrib/timeseries/examples/__init__.py
 create mode 100644 tensorflow/contrib/timeseries/examples/data/changepoints.csv
 create mode 100644 tensorflow/contrib/timeseries/examples/data/multivariate_level.csv
 create mode 100644 tensorflow/contrib/timeseries/examples/data/period_trend.csv
 create mode 100644 tensorflow/contrib/timeseries/examples/known_anomaly.py
 create mode 100644 tensorflow/contrib/timeseries/examples/known_anomaly_test.py
 create mode 100644 tensorflow/contrib/timeseries/examples/multivariate.py
 create mode 100644 tensorflow/contrib/timeseries/examples/multivariate_test.py
 create mode 100644 tensorflow/contrib/timeseries/examples/predict.py
 create mode 100644 tensorflow/contrib/timeseries/examples/predict_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/__init__.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/BUILD
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/__init__.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/ar_model.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/estimators.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/feature_keys.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/math_utils.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/model.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/model_utils.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/model_utils_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/saved_model_utils.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_management.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_management_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/__init__.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/test_utils.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py
 create mode 100644 tensorflow/contrib/timeseries/python/timeseries/test_utils.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 70365ec999..779dd1e8ed 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -309,6 +309,10 @@ filegroup(
         "//tensorflow/contrib/testing:all_files",
         "//tensorflow/contrib/text:all_files",
         "//tensorflow/contrib/tfprof:all_files",
+        "//tensorflow/contrib/timeseries:all_files",
+        "//tensorflow/contrib/timeseries/examples:all_files",
+        "//tensorflow/contrib/timeseries/python/timeseries:all_files",
+        "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:all_files",
         "//tensorflow/contrib/tpu:all_files",
         "//tensorflow/contrib/training:all_files",
         "//tensorflow/contrib/util:all_files",
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index f6729a666a..7ada956124 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -71,6 +71,7 @@ py_library(
         "//tensorflow/contrib/testing:testing_py",
         "//tensorflow/contrib/text:text_py",
         "//tensorflow/contrib/tfprof",
+        "//tensorflow/contrib/timeseries",
         "//tensorflow/contrib/tpu:tpu_estimator",
         "//tensorflow/contrib/tpu:tpu_helper_library",
         "//tensorflow/contrib/tpu:tpu_py",
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index 01cbfd51c3..f70b810ec6 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -66,6 +66,7 @@ from tensorflow.contrib import tensor_forest
 from tensorflow.contrib import tensorboard
 from tensorflow.contrib import testing
 from tensorflow.contrib import tfprof
+from tensorflow.contrib import timeseries
 from tensorflow.contrib import tpu
 from tensorflow.contrib import training
 from tensorflow.contrib import util
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 15c65e8443..c9666a776c 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -528,6 +528,12 @@ add_python_module("tensorflow/contrib/text/ops")
 add_python_module("tensorflow/contrib/text/python")
 add_python_module("tensorflow/contrib/text/python/ops")
 add_python_module("tensorflow/contrib/tfprof")
+add_python_module("tensorflow/contrib/timeseries")
+add_python_module("tensorflow/contrib/timeseries/examples")
+add_python_module("tensorflow/contrib/timeseries/examples/data")
+add_python_module("tensorflow/contrib/timeseries/python")
+add_python_module("tensorflow/contrib/timeseries/python/timeseries")
+add_python_module("tensorflow/contrib/timeseries/python/timeseries/state_space_models")
 add_python_module("tensorflow/contrib/tpu")
 add_python_module("tensorflow/contrib/tpu/ops")
 add_python_module("tensorflow/contrib/tpu/python")
diff --git a/tensorflow/contrib/timeseries/BUILD b/tensorflow/contrib/timeseries/BUILD
new file mode 100644
index 0000000000..a64624bb0f
--- /dev/null
+++ b/tensorflow/contrib/timeseries/BUILD
@@ -0,0 +1,35 @@
+package(default_visibility = [
+    "//tensorflow:internal",
+])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+py_library(
+    name = "timeseries",
+    srcs = [
+        "__init__.py",
+        "python/__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/timeseries/python/timeseries:estimators",
+        "//tensorflow/contrib/timeseries/python/timeseries:feature_keys",
+        "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
+        "//tensorflow/contrib/timeseries/python/timeseries:py_init",
+        "//tensorflow/contrib/timeseries/python/timeseries:saved_model_utils",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/timeseries/README.md b/tensorflow/contrib/timeseries/README.md
new file mode 100644
index 0000000000..ddc77ca54b
--- /dev/null
+++ b/tensorflow/contrib/timeseries/README.md
@@ -0,0 +1,25 @@
+# TensorFlow Time Series
+
+TensorFlow Time Series (TFTS) is a collection of ready-to-use classic models
+(state space, autoregressive), and flexible infrastructure for building
+high-performance time series models whatever the architecture. It includes tools
+for chunking and batching a series, and for saving model state across chunks,
+making use of parallel computation even when training sequential models on long
+series (using truncated backpropagation).
+
+To get started, take a look at the `examples/` directory, which includes:
+
+ - Making probabilistic forecasts
+ - Using exogenous features to train on data with known anomalies/changepoints
+ - Learning correlations between series (multivariate forecasting/anomaly
+   detection)
+
+TFTS includes many other modeling tools, including non-linear autoregression
+(see the `hidden_layer_sizes` argument to `ARRegressor` in `estimators.py`) and
+a collection of components for linear state space modeling (level, trend,
+period, vector autoregression, moving averages; see the
+`StructuralEnsembleRegressor` in `estimators.py`). Both model classes support
+heuristics for ignoring un-labeled anomalies in training data. Trained models
+can be exported for inference/serving in
+[SavedModel format](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md)
+(see `examples/multivariate.py`).
diff --git a/tensorflow/contrib/timeseries/__init__.py b/tensorflow/contrib/timeseries/__init__.py
new file mode 100644
index 0000000000..3cd9366f1f
--- /dev/null
+++ b/tensorflow/contrib/timeseries/__init__.py
@@ -0,0 +1,42 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A time series library in TensorFlow (TFTS).
+
+@@StructuralEnsembleRegressor
+@@ARRegressor
+
+@@ARModel
+
+@@CSVReader
+@@RandomWindowInputFn
+@@WholeDatasetInputFn
+@@predict_continuation_input_fn
+
+@@TrainEvalFeatures
+@@FilteringResults
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.timeseries.python.timeseries import *
+# pylint: enable=wildcard-import
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+remove_undocumented(module_name=__name__,
+                    allowed_exception_list=['saved_model_utils'])
diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD
new file mode 100644
index 0000000000..de85c0d5ad
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/BUILD
@@ -0,0 +1,105 @@
+load("//tensorflow:tensorflow.bzl", "py_test")
+
+package(
+    default_visibility = [
+        "//tensorflow:internal",
+    ],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+py_binary(
+    name = "predict",
+    srcs = [
+        "predict.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "predict_test",
+    srcs = [
+        "predict_test.py",
+    ],
+    data = [
+        "data/period_trend.csv",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":predict",
+    ],
+)
+
+py_binary(
+    name = "known_anomaly",
+    srcs = [
+        "known_anomaly.py",
+    ],
+    data = [
+        "data/changepoints.csv",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "known_anomaly_test",
+    srcs = [
+        "known_anomaly_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":known_anomaly",
+    ],
+)
+
+py_binary(
+    name = "multivariate",
+    srcs = [
+        "multivariate.py",
+    ],
+    data = [
+        "data/multivariate_level.csv",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//tensorflow/python:platform",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "multivariate_test",
+    srcs = [
+        "multivariate_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":multivariate",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/timeseries/examples/__init__.py b/tensorflow/contrib/timeseries/examples/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tensorflow/contrib/timeseries/examples/data/changepoints.csv b/tensorflow/contrib/timeseries/examples/data/changepoints.csv
new file mode 100644
index 0000000000..377091675f
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/data/changepoints.csv
@@ -0,0 +1,201 @@
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diff --git a/tensorflow/contrib/timeseries/examples/data/multivariate_level.csv b/tensorflow/contrib/timeseries/examples/data/multivariate_level.csv
new file mode 100644
index 0000000000..17a9d4f879
--- /dev/null
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diff --git a/tensorflow/contrib/timeseries/examples/data/period_trend.csv b/tensorflow/contrib/timeseries/examples/data/period_trend.csv
new file mode 100644
index 0000000000..39c1952339
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/data/period_trend.csv
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diff --git a/tensorflow/contrib/timeseries/examples/known_anomaly.py b/tensorflow/contrib/timeseries/examples/known_anomaly.py
new file mode 100644
index 0000000000..7659dd308a
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/known_anomaly.py
@@ -0,0 +1,150 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Example of using an exogenous feature to ignore a known anomaly."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import csv
+from os import path
+
+import numpy as np
+import tensorflow as tf
+
+
+try:
+  import matplotlib  # pylint: disable=g-import-not-at-top
+  matplotlib.use("TkAgg")  # Need Tk for interactive plots.
+  from matplotlib import pyplot  # pylint: disable=g-import-not-at-top
+  HAS_MATPLOTLIB = True
+except ImportError:
+  # Plotting requires matplotlib, but the unit test running this code may
+  # execute in an environment without it (i.e. matplotlib is not a build
+  # dependency). We'd still like to test the TensorFlow-dependent parts of this
+  # example, namely train_and_predict.
+  HAS_MATPLOTLIB = False
+
+_MODULE_PATH = path.dirname(__file__)
+_DATA_FILE = path.join(_MODULE_PATH, "data/changepoints.csv")
+
+
+def train_and_evaluate_exogenous(csv_file_name=_DATA_FILE, train_steps=300):
+  """Training, evaluating, and predicting on a series with changepoints."""
+
+  # Indicate the format of our exogenous feature, in this case a string
+  # representing a boolean value.
+  string_feature = tf.contrib.layers.sparse_column_with_keys(
+      column_name="is_changepoint", keys=["no", "yes"])
+  # Specify the way this feature is presented to the model, here using a one-hot
+  # encoding.
+  one_hot_feature = tf.contrib.layers.one_hot_column(
+      sparse_id_column=string_feature)
+
+  estimator = tf.contrib.timeseries.StructuralEnsembleRegressor(
+      periodicities=12,
+      # Extract a smooth period by constraining the number of latent values
+      # being cycled between.
+      cycle_num_latent_values=3,
+      num_features=1,
+      exogenous_feature_columns=[one_hot_feature],
+      # Make exogenous updates sparse by setting an update condition. This in
+      # effect allows missing exogenous features: if the condition evaluates to
+      # False, no update is performed. Otherwise we sometimes end up with
+      # "leaky" updates which add unnecessary uncertainty to the model even when
+      # there is no changepoint.
+      exogenous_update_condition=
+      lambda times, features: tf.equal(features["is_changepoint"], "yes"))
+  reader = tf.contrib.timeseries.CSVReader(
+      csv_file_name,
+      # Indicate the format of our CSV file. First we have two standard columns,
+      # one for times and one for values. The third column is a custom exogenous
+      # feature indicating whether each timestep is a changepoint. The
+      # changepoint feature name must match the string_feature column name
+      # above.
+      column_names=(tf.contrib.timeseries.TrainEvalFeatures.TIMES,
+                    tf.contrib.timeseries.TrainEvalFeatures.VALUES,
+                    "is_changepoint"),
+      # Indicate dtypes for our features.
+      column_dtypes=(tf.int64, tf.float32, tf.string),
+      # This CSV has a header line; here we just ignore it.
+      skip_header_lines=1)
+  train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
+      # Use truncated backpropagation with a window size of 64, batching
+      # together 4 of these windows (random offsets) per training step. Training
+      # with exogenous features often requires somewhat larger windows.
+      reader, batch_size=4, window_size=64)
+  estimator.train(input_fn=train_input_fn, steps=train_steps)
+  evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
+  evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)
+  # Create an input_fn for prediction, with a simulated changepoint. Since all
+  # of the anomalies in the training data are explained by the exogenous
+  # feature, we should get relatively confident predictions before the indicated
+  # changepoint (since we are telling the model that no changepoint exists at
+  # those times) and relatively uncertain predictions after.
+  (predictions,) = tuple(estimator.predict(
+      input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
+          evaluation, steps=100,
+          exogenous_features={
+              "is_changepoint": [["no"] * 49 + ["yes"] + ["no"] * 50]})))
+  times = evaluation["times"][0]
+  observed = evaluation["observed"][0, :, 0]
+  mean = np.squeeze(np.concatenate(
+      [evaluation["mean"][0], predictions["mean"]], axis=0))
+  variance = np.squeeze(np.concatenate(
+      [evaluation["covariance"][0], predictions["covariance"]], axis=0))
+  all_times = np.concatenate([times, predictions["times"]], axis=0)
+  upper_limit = mean + np.sqrt(variance)
+  lower_limit = mean - np.sqrt(variance)
+  # Indicate the locations of the changepoints for plotting vertical lines.
+  anomaly_locations = []
+  with open(csv_file_name, "r") as csv_file:
+    csv_reader = csv.DictReader(csv_file)
+    for row in csv_reader:
+      if row["is_changepoint"] == "yes":
+        anomaly_locations.append(int(row["time"]))
+  anomaly_locations.append(predictions["times"][49])
+  return (times, observed, all_times, mean, upper_limit, lower_limit,
+          anomaly_locations)
+
+
+def make_plot(name, training_times, observed, all_times, mean,
+              upper_limit, lower_limit, anomaly_locations):
+  """Plot the time series and anomalies in a new figure."""
+  pyplot.figure()
+  pyplot.plot(training_times, observed, "b", label="training series")
+  pyplot.plot(all_times, mean, "r", label="forecast")
+  pyplot.axvline(anomaly_locations[0], linestyle="dotted", label="changepoints")
+  for anomaly_location in anomaly_locations[1:]:
+    pyplot.axvline(anomaly_location, linestyle="dotted")
+  pyplot.fill_between(all_times, lower_limit, upper_limit, color="grey",
+                      alpha="0.2")
+  pyplot.axvline(training_times[-1], color="k", linestyle="--")
+  pyplot.xlabel("time")
+  pyplot.ylabel("observations")
+  pyplot.legend(loc=0)
+  pyplot.title(name)
+
+
+def main(unused_argv):
+  if not HAS_MATPLOTLIB:
+    raise ImportError(
+        "Please install matplotlib to generate a plot from this example.")
+  make_plot("Ignoring a known anomaly", *train_and_evaluate_exogenous())
+  pyplot.show()
+
+
+if __name__ == "__main__":
+  tf.app.run(main=main)
diff --git a/tensorflow/contrib/timeseries/examples/known_anomaly_test.py b/tensorflow/contrib/timeseries/examples/known_anomaly_test.py
new file mode 100644
index 0000000000..c3e307cad8
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/known_anomaly_test.py
@@ -0,0 +1,49 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests that the TensorFlow parts of the known anomaly example run."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.examples import known_anomaly
+
+from tensorflow.python.platform import test
+
+
+class KnownAnaomalyExampleTest(test.TestCase):
+
+  def test_shapes_and_variance_structural(self):
+    (times, observed, all_times, mean, upper_limit, lower_limit,
+     anomaly_locations) = known_anomaly.train_and_evaluate_exogenous(
+         train_steps=50)
+    self.assertAllEqual(
+        anomaly_locations,
+        [25, 50, 75, 100, 125, 150, 175, 249])
+    self.assertAllEqual([200], times.shape)
+    self.assertAllEqual([200], observed.shape)
+    self.assertAllEqual([300], all_times.shape)
+    self.assertAllEqual([300], mean.shape)
+    self.assertAllEqual([300], upper_limit.shape)
+    self.assertAllEqual([300], lower_limit.shape)
+    # Check that initial predictions are relatively confident.
+    self.assertLess(upper_limit[210] - lower_limit[210],
+                    3.0 * (upper_limit[200] - lower_limit[200]))
+    # Check that post-changepoint predictions are less confident
+    self.assertGreater(upper_limit[290] - lower_limit[290],
+                       3.0 * (upper_limit[240] - lower_limit[240]))
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/examples/multivariate.py b/tensorflow/contrib/timeseries/examples/multivariate.py
new file mode 100644
index 0000000000..ed799542fd
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/multivariate.py
@@ -0,0 +1,117 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A multivariate TFTS example.
+
+Fits a multivariate model, exports it, and visualizes the learned correlations
+by iteratively predicting and sampling from the predictions.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from os import path
+import tempfile
+
+import numpy
+import tensorflow as tf
+
+try:
+  import matplotlib  # pylint: disable=g-import-not-at-top
+  matplotlib.use("TkAgg")  # Need Tk for interactive plots.
+  from matplotlib import pyplot  # pylint: disable=g-import-not-at-top
+  HAS_MATPLOTLIB = True
+except ImportError:
+  # Plotting requires matplotlib, but the unit test running this code may
+  # execute in an environment without it (i.e. matplotlib is not a build
+  # dependency). We'd still like to test the TensorFlow-dependent parts of this
+  # example, namely train_and_predict.
+  HAS_MATPLOTLIB = False
+
+_MODULE_PATH = path.dirname(__file__)
+_DATA_FILE = path.join(_MODULE_PATH, "data/multivariate_level.csv")
+
+
+def multivariate_train_and_sample(
+    csv_file_name=_DATA_FILE, export_directory=None, training_steps=500):
+  """Trains, evaluates, and exports a multivariate model."""
+  estimator = tf.contrib.timeseries.StructuralEnsembleRegressor(
+      periodicities=[], num_features=5)
+  reader = tf.contrib.timeseries.CSVReader(
+      csv_file_name,
+      column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
+                    + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5))
+  train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
+      # Larger window sizes generally produce a better covariance matrix.
+      reader, batch_size=4, window_size=64)
+  estimator.train(input_fn=train_input_fn, steps=training_steps)
+  evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
+  current_state = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)
+  values = [current_state["observed"]]
+  times = [current_state[tf.contrib.timeseries.FilteringResults.TIMES]]
+  # Export the model so we can do iterative prediction and filtering without
+  # reloading model checkpoints.
+  if export_directory is None:
+    export_directory = tempfile.mkdtemp()
+  input_receiver_fn = estimator.build_raw_serving_input_receiver_fn()
+  export_location = estimator.export_savedmodel(
+      export_directory, input_receiver_fn)
+  with tf.Graph().as_default():
+    numpy.random.seed(1)  # Make the example a bit more deterministic
+    with tf.Session() as session:
+      signatures = tf.saved_model.loader.load(
+          session, [tf.saved_model.tag_constants.SERVING], export_location)
+      for _ in range(100):
+        current_prediction = (
+            tf.contrib.timeseries.saved_model_utils.predict_continuation(
+                continue_from=current_state, signatures=signatures,
+                session=session, steps=1))
+        next_sample = numpy.random.multivariate_normal(
+            # Squeeze out the batch and series length dimensions (both 1).
+            mean=numpy.squeeze(current_prediction["mean"], axis=[0, 1]),
+            cov=numpy.squeeze(current_prediction["covariance"], axis=[0, 1]))
+        # Update model state so that future predictions are conditional on the
+        # value we just sampled.
+        filtering_features = {
+            tf.contrib.timeseries.TrainEvalFeatures.TIMES: current_prediction[
+                tf.contrib.timeseries.FilteringResults.TIMES],
+            tf.contrib.timeseries.TrainEvalFeatures.VALUES: next_sample[
+                None, None, :]}
+        current_state = (
+            tf.contrib.timeseries.saved_model_utils.filter_continuation(
+                continue_from=current_state,
+                session=session,
+                signatures=signatures,
+                features=filtering_features))
+        values.append(next_sample[None, None, :])
+        times.append(current_state["times"])
+  all_observations = numpy.squeeze(numpy.concatenate(values, axis=1), axis=0)
+  all_times = numpy.squeeze(numpy.concatenate(times, axis=1), axis=0)
+  return all_times, all_observations
+
+
+def main(unused_argv):
+  if not HAS_MATPLOTLIB:
+    raise ImportError(
+        "Please install matplotlib to generate a plot from this example.")
+  all_times, all_observations = multivariate_train_and_sample()
+  # Show where sampling starts on the plot
+  pyplot.axvline(1000, linestyle="dotted")
+  pyplot.plot(all_times, all_observations)
+  pyplot.show()
+
+
+if __name__ == "__main__":
+  tf.app.run(main=main)
diff --git a/tensorflow/contrib/timeseries/examples/multivariate_test.py b/tensorflow/contrib/timeseries/examples/multivariate_test.py
new file mode 100644
index 0000000000..3b8ee0606e
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/multivariate_test.py
@@ -0,0 +1,36 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests that the TensorFlow parts of the multivariate example run."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.examples import multivariate
+
+from tensorflow.python.platform import test
+
+
+class MultivariateExampleTest(test.TestCase):
+
+  def test_shapes_structural(self):
+    times, values = multivariate.multivariate_train_and_sample(
+        export_directory=self.get_temp_dir(), training_steps=5)
+    self.assertAllEqual([1100], times.shape)
+    self.assertAllEqual([1100, 5], values.shape)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/examples/predict.py b/tensorflow/contrib/timeseries/examples/predict.py
new file mode 100644
index 0000000000..8147d40caa
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/predict.py
@@ -0,0 +1,132 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""An example of training and predicting with a TFTS estimator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+import numpy as np
+import tensorflow as tf
+
+
+try:
+  import matplotlib  # pylint: disable=g-import-not-at-top
+  matplotlib.use("TkAgg")  # Need Tk for interactive plots.
+  from matplotlib import pyplot  # pylint: disable=g-import-not-at-top
+  HAS_MATPLOTLIB = True
+except ImportError:
+  # Plotting requires matplotlib, but the unit test running this code may
+  # execute in an environment without it (i.e. matplotlib is not a build
+  # dependency). We'd still like to test the TensorFlow-dependent parts of this
+  # example, namely train_and_predict.
+  HAS_MATPLOTLIB = False
+
+FLAGS = None
+
+
+def structural_ensemble_train_and_predict(csv_file_name):
+  # Cycle between 5 latent values over a period of 100. This leads to a very
+  # smooth periodic component (and a small model), which is a good fit for our
+  # example data. Modeling high-frequency periodic variations will require a
+  # higher cycle_num_latent_values.
+  structural = tf.contrib.timeseries.StructuralEnsembleRegressor(
+      periodicities=100, num_features=1, cycle_num_latent_values=5)
+  return train_and_predict(structural, csv_file_name, training_steps=150)
+
+
+def ar_train_and_predict(csv_file_name):
+  # An autoregressive model, with periodicity handled as a time-based
+  # regression. Note that this requires windows of size 16 (input_window_size +
+  # output_window_size) for training.
+  ar = tf.contrib.timeseries.ARRegressor(
+      periodicities=100, input_window_size=10, output_window_size=6,
+      num_features=1,
+      # Use the (default) normal likelihood loss to adaptively fit the
+      # variance. SQUARED_LOSS overestimates variance when there are trends in
+      # the series.
+      loss=tf.contrib.timeseries.ARModel.NORMAL_LIKELIHOOD_LOSS)
+  return train_and_predict(ar, csv_file_name, training_steps=600)
+
+
+def train_and_predict(estimator, csv_file_name, training_steps):
+  """A simple example of training and predicting."""
+  # Read data in the default "time,value" CSV format with no header
+  reader = tf.contrib.timeseries.CSVReader(csv_file_name)
+  # Set up windowing and batching for training
+  train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
+      reader, batch_size=16, window_size=16)
+  # Fit model parameters to data
+  estimator.train(input_fn=train_input_fn, steps=training_steps)
+  # Evaluate on the full dataset sequentially, collecting in-sample predictions
+  # for a qualitative evaluation. Note that this loads the whole dataset into
+  # memory. For quantitative evaluation, use RandomWindowChunker.
+  evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
+  evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)
+  # Predict starting after the evaluation
+  (predictions,) = tuple(estimator.predict(
+      input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
+          evaluation, steps=200)))
+  times = evaluation["times"][0]
+  observed = evaluation["observed"][0, :, 0]
+  mean = np.squeeze(np.concatenate(
+      [evaluation["mean"][0], predictions["mean"]], axis=0))
+  variance = np.squeeze(np.concatenate(
+      [evaluation["covariance"][0], predictions["covariance"]], axis=0))
+  all_times = np.concatenate([times, predictions["times"]], axis=0)
+  upper_limit = mean + np.sqrt(variance)
+  lower_limit = mean - np.sqrt(variance)
+  return times, observed, all_times, mean, upper_limit, lower_limit
+
+
+def make_plot(name, training_times, observed, all_times, mean,
+              upper_limit, lower_limit):
+  """Plot a time series in a new figure."""
+  pyplot.figure()
+  pyplot.plot(training_times, observed, "b", label="training series")
+  pyplot.plot(all_times, mean, "r", label="forecast")
+  pyplot.plot(all_times, upper_limit, "g", label="forecast upper bound")
+  pyplot.plot(all_times, lower_limit, "g", label="forecast lower bound")
+  pyplot.fill_between(all_times, lower_limit, upper_limit, color="grey",
+                      alpha="0.2")
+  pyplot.axvline(training_times[-1], color="k", linestyle="--")
+  pyplot.xlabel("time")
+  pyplot.ylabel("observations")
+  pyplot.legend(loc=0)
+  pyplot.title(name)
+
+
+def main(unused_argv):
+  if not HAS_MATPLOTLIB:
+    raise ImportError(
+        "Please install matplotlib to generate a plot from this example.")
+  make_plot("Structural ensemble",
+            *structural_ensemble_train_and_predict(FLAGS.input_filename))
+  make_plot("AR", *ar_train_and_predict(FLAGS.input_filename))
+  pyplot.show()
+
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+      "--input_filename",
+      type=str,
+      required=True,
+      help="Input csv file.")
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/contrib/timeseries/examples/predict_test.py b/tensorflow/contrib/timeseries/examples/predict_test.py
new file mode 100644
index 0000000000..678fd71cd8
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/predict_test.py
@@ -0,0 +1,62 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests that the TensorFlow parts of the prediction example run."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from os import path
+
+from tensorflow.contrib.timeseries.examples import predict
+
+from tensorflow.python.platform import test
+
+
+_MODULE_PATH = path.dirname(__file__)
+_DATA_FILE = path.join(_MODULE_PATH, "data/period_trend.csv")
+
+
+class PeriodTrendExampleTest(test.TestCase):
+
+  def test_shapes_and_variance_structural(self):
+    (times, observed, all_times, mean, upper_limit, lower_limit
+    ) = predict.structural_ensemble_train_and_predict(_DATA_FILE)
+    # Just check that plotting will probably be OK. We can't actually run the
+    # plotting code since we don't want to pull in matplotlib as a dependency
+    # for this test.
+    self.assertAllEqual([500], times.shape)
+    self.assertAllEqual([500], observed.shape)
+    self.assertAllEqual([700], all_times.shape)
+    self.assertAllEqual([700], mean.shape)
+    self.assertAllEqual([700], upper_limit.shape)
+    self.assertAllEqual([700], lower_limit.shape)
+    # Check that variance hasn't blown up too much. This is a relatively good
+    # indication that training was successful.
+    self.assertLess(upper_limit[-1] - lower_limit[-1],
+                    1.5 * (upper_limit[0] - lower_limit[0]))
+
+  def test_ar(self):
+    (times, observed, all_times, mean,
+     upper_limit, lower_limit) = predict.ar_train_and_predict(_DATA_FILE)
+    self.assertAllEqual(times.shape, observed.shape)
+    self.assertAllEqual(all_times.shape, mean.shape)
+    self.assertAllEqual(all_times.shape, upper_limit.shape)
+    self.assertAllEqual(all_times.shape, lower_limit.shape)
+    self.assertLess((upper_limit - lower_limit).mean(), 4.)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/__init__.py b/tensorflow/contrib/timeseries/python/__init__.py
new file mode 100644
index 0000000000..5072feab59
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/__init__.py
@@ -0,0 +1,23 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A time series library in TensorFlow (TFTS)."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.timeseries.python.timeseries import *
+# pylint: enable=wildcard-import
diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
new file mode 100644
index 0000000000..103b9ea541
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -0,0 +1,337 @@
+load("//tensorflow:tensorflow.bzl", "py_test")
+
+package(
+    default_visibility = [
+        "//tensorflow:internal",
+    ],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+py_library(
+    name = "py_init",
+    srcs = [
+        "__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "feature_keys",
+    srcs = [
+        "feature_keys.py",
+    ],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "saved_model_utils",
+    srcs = [
+        "saved_model_utils.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python/saved_model",
+    ],
+)
+
+py_library(
+    name = "model",
+    srcs = [
+        "model.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":feature_keys",
+        ":math_utils",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:tensor_array_ops",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_library(
+    name = "estimators",
+    srcs = [
+        "estimators.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":ar_model",
+        ":model_utils",
+        ":state_management",
+        "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:filtering_postprocessor",
+        "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:structural_ensemble",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python/estimator:estimator_py",
+    ],
+)
+
+py_test(
+    name = "estimators_test",
+    timeout = "long",
+    srcs = [
+        "estimators_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":estimators",
+        ":feature_keys",
+        ":input_pipeline",
+        ":saved_model_utils",
+        ":state_management",
+        "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:structural_ensemble",
+        "//tensorflow/python:platform",
+    ],
+)
+
+py_library(
+    name = "model_utils",
+    srcs = [
+        "model_utils.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":feature_keys",
+        "//tensorflow/contrib/framework:framework_py",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python/estimator:estimator_py",
+    ],
+)
+
+py_test(
+    name = "model_utils_test",
+    srcs = [
+        "model_utils_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model_utils",
+        ":state_management",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
+py_library(
+    name = "state_management",
+    srcs = [
+        "state_management.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":math_utils",
+        ":model",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "state_management_test",
+    srcs = [
+        "state_management_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":input_pipeline",
+        ":model",
+        ":state_management",
+        ":test_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_library(
+    name = "input_pipeline",
+    srcs = [
+        "input_pipeline.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":feature_keys",
+        ":model_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:io_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:tensor_array_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python/estimator:estimator_py",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "input_pipeline_test",
+    srcs = [
+        "input_pipeline_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":input_pipeline",
+        ":test_utils",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_library(
+    name = "test_utils",
+    srcs = [
+        "test_utils.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":estimators",
+        ":input_pipeline",
+        ":state_management",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/estimator:estimator_py",
+    ],
+)
+
+py_library(
+    name = "ar_model",
+    srcs = [
+        "ar_model.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":math_utils",
+        ":model",
+        ":model_utils",
+        "//tensorflow/contrib/distributions:distributions_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:tensor_array_ops",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python/estimator:estimator_py",
+    ],
+)
+
+py_test(
+    name = "ar_model_test",
+    srcs = [
+        "ar_model_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":ar_model",
+        ":estimators",
+        ":input_pipeline",
+        ":model_utils",
+        ":test_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:variables",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_library(
+    name = "math_utils",
+    srcs = [
+        "math_utils.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":input_pipeline",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:functional_ops",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:linalg_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:variable_scope",
+    ],
+)
+
+py_test(
+    name = "math_utils_test",
+    srcs = [
+        "math_utils_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":math_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:variables",
+        "//third_party/py/numpy",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/__init__.py b/tensorflow/contrib/timeseries/python/timeseries/__init__.py
new file mode 100644
index 0000000000..c683dad71d
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A time series library in TensorFlow (TFTS)."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries import saved_model_utils
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.timeseries.python.timeseries.ar_model import *
+from tensorflow.contrib.timeseries.python.timeseries.estimators import *
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import *
+from tensorflow.contrib.timeseries.python.timeseries.input_pipeline import *
+# pylint: enable=wildcard-import
diff --git a/tensorflow/contrib/timeseries/python/timeseries/ar_model.py b/tensorflow/contrib/timeseries/python/timeseries/ar_model.py
new file mode 100644
index 0000000000..7452dc7dc3
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/ar_model.py
@@ -0,0 +1,706 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Auto-Regressive models for time series data."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib import distributions
+
+from tensorflow.contrib.timeseries.python.timeseries import model
+from tensorflow.contrib.timeseries.python.timeseries import model_utils
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import tensor_array_ops
+from tensorflow.python.ops import variable_scope
+
+
+class ARModel(model.TimeSeriesModel):
+  """Auto-regressive model, both linear and non-linear.
+
+  Features to the model include time and values of input_window_size timesteps,
+  and times for output_window_size timesteps. These are passed through zero or
+  more hidden layers, and then fed to a loss function (e.g. squared loss).
+
+  Note that this class can also be used to regress against time only by setting
+  the input_window_size to zero.
+  """
+  SQUARED_LOSS = "squared_loss"
+  NORMAL_LIKELIHOOD_LOSS = "normal_likelihood_loss"
+
+  def __init__(self,
+               periodicities,
+               input_window_size,
+               output_window_size,
+               num_features,
+               num_time_buckets=10,
+               loss=NORMAL_LIKELIHOOD_LOSS,
+               hidden_layer_sizes=None):
+    """Constructs an auto-regressive model.
+
+    Args:
+      periodicities: periodicities of the input data, in the same units as the
+        time feature. Note this can be a single value or a list of values for
+        multiple periodicities.
+      input_window_size: Number of past time steps of data to look at when doing
+        the regression.
+      output_window_size: Number of future time steps to predict. Note that
+        setting it to > 1 empiricaly seems to give a better fit.
+      num_features: number of input features per time step.
+      num_time_buckets: Number of buckets into which to divide (time %
+        periodicity) for generating time based features.
+      loss: Loss function to use for training. Currently supported values are
+        SQUARED_LOSS and NORMAL_LIKELIHOOD_LOSS. Note that for
+        NORMAL_LIKELIHOOD_LOSS, we train the covariance term as well. For
+        SQUARED_LOSS, the evaluation loss is reported based on un-scaled
+        observations and predictions, while the training loss is computed on
+        normalized data (if input statistics are available).
+      hidden_layer_sizes: list of sizes of hidden layers.
+    """
+    self.input_window_size = input_window_size
+    self.output_window_size = output_window_size
+    if hidden_layer_sizes is None:
+      hidden_layer_sizes = []
+    self.hidden_layer_sizes = hidden_layer_sizes
+    self.window_size = self.input_window_size + self.output_window_size
+    self.loss = loss
+    self.stats_means = None
+    self.stats_sigmas = None
+    super(ARModel, self).__init__(
+        num_features=num_features)
+    assert num_time_buckets > 0
+    self._buckets = int(num_time_buckets)
+    if periodicities is None or not periodicities:
+      periodicities = []
+    elif (not isinstance(periodicities, list) and
+          not isinstance(periodicities, tuple)):
+      periodicities = [periodicities]
+    self._periods = [int(p) for p in periodicities]
+    for p in self._periods:
+      assert p > 0
+    assert len(self._periods) or self.input_window_size
+    assert output_window_size > 0
+
+  def scale_data(self, data):
+    """Scale data according to stats."""
+    if self._input_statistics is not None:
+      return (data - self.stats_means) / self.stats_sigmas
+    else:
+      return data
+
+  def scale_back_data(self, data):
+    if self._input_statistics is not None:
+      return (data * self.stats_sigmas) + self.stats_means
+    else:
+      return data
+
+  def scale_back_variance(self, var):
+    if self._input_statistics is not None:
+      return var * self.stats_sigmas * self.stats_sigmas
+    else:
+      return var
+
+  def initialize_graph(self, input_statistics=None):
+    super(ARModel, self).initialize_graph(input_statistics=input_statistics)
+    if self._input_statistics:
+      self.stats_means, variances = (
+          self._input_statistics.overall_feature_moments)
+      self.stats_sigmas = math_ops.sqrt(variances)
+
+  def get_start_state(self):
+    # State which matches the format we'll return later. Typically this will not
+    # be used by the model directly, but the shapes and dtypes should match so
+    # that the serving input_receiver_fn gets placeholder shapes correct.
+    return (array_ops.zeros([self.input_window_size], dtype=dtypes.int64),
+            array_ops.zeros(
+                [self.input_window_size, self.num_features], dtype=self.dtype))
+
+  # TODO(allenl,agarwal): Support sampling for AR.
+  def random_model_parameters(self, seed=None):
+    pass
+
+  def generate(self, number_of_series, series_length,
+               model_parameters=None, seed=None):
+    pass
+
+  def _predicted_covariance_op(self, activations, num_values):
+    activation, activation_size = activations[-1]
+    if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
+      log_sigma_square = model_utils.fully_connected(
+          activation,
+          activation_size,
+          self.output_window_size * num_values,
+          name="log_sigma_square",
+          activation=None)
+      predicted_covariance = gen_math_ops.exp(log_sigma_square)
+      predicted_covariance = array_ops.reshape(
+          predicted_covariance, [-1, self.output_window_size, num_values])
+    else:
+      shape = array_ops.stack([
+          array_ops.shape(activation)[0],
+          constant_op.constant(self.output_window_size),
+          constant_op.constant(num_values)
+      ])
+      predicted_covariance = array_ops.ones(shape=shape, dtype=activation.dtype)
+    return predicted_covariance
+
+  def _predicted_mean_op(self, activations):
+    activation, activation_size = activations[-1]
+    predicted_mean = model_utils.fully_connected(
+        activation,
+        activation_size,
+        self.output_window_size * self.num_features,
+        name="predicted_mean",
+        activation=None)
+    return array_ops.reshape(predicted_mean,
+                             [-1, self.output_window_size, self.num_features])
+
+  def _create_hidden_stack(self, activation, activation_size):
+    activations = []
+    for layer_number, layer_size in enumerate(self.hidden_layer_sizes):
+      # TODO(agarwal): Migrate to fully_connected in tf slim
+      activation = model_utils.fully_connected(
+          activation, activation_size, layer_size,
+          name="layer_{}".format(layer_number))
+      activation_size = layer_size
+      activations.append((activation, activation_size))
+    return activations
+
+  def prediction_ops(self, times, values):
+    """Compute model predictions given input data.
+
+    Args:
+      times: A [batch size, self.window_size] integer Tensor, the first
+          self.input_window_size times in each part of the batch indicating
+          input features, and the last self.output_window_size times indicating
+          prediction times.
+      values: A [batch size, self.input_window_size, self.num_features] Tensor
+          with input features.
+    Returns:
+      Tuple (predicted_mean, predicted_covariance), where each element is a
+      Tensor with shape [batch size, self.output_window_size,
+      self.num_features].
+    """
+    times.get_shape().assert_is_compatible_with([None, self.window_size])
+    activations = []
+    if self.input_window_size:
+      values.get_shape().assert_is_compatible_with(
+          [None, self.input_window_size, self.num_features])
+    # Create input features.
+    if self._periods:
+      _, time_features = self._compute_time_features(times)
+      activation_size = self.window_size * self._buckets * len(self._periods)
+      activation = array_ops.reshape(time_features, [-1, activation_size])
+    else:
+      activation_size = 0
+      activation = None
+
+    if self.input_window_size:
+      inp = array_ops.slice(values, [0, 0, 0], [-1, self.input_window_size, -1])
+      inp_size = self.input_window_size * self.num_features
+      inp = array_ops.reshape(inp, [-1, inp_size])
+      if activation is not None:
+        activation = array_ops.concat([inp, activation], 1)
+      else:
+        activation = inp
+      activation_size += inp_size
+    assert activation_size
+    activations.append((activation, activation_size))
+    # Create hidden layers.
+    activations += self._create_hidden_stack(activation, activation_size)
+    # Create mean and convariance ops.
+    predicted_mean = self._predicted_mean_op(activations)
+    predicted_covariance = self._predicted_covariance_op(activations,
+                                                         self.num_features)
+    return {"activations": activations,
+            "mean": predicted_mean,
+            "covariance": predicted_covariance}
+
+  def loss_op(self, targets, prediction_ops):
+    """Create loss_op."""
+    prediction = prediction_ops["mean"]
+    if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
+      covariance = prediction_ops["covariance"]
+      sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5))
+      normal = distributions.Normal(loc=targets, scale=sigma)
+      loss_op = -math_ops.reduce_sum(normal.log_prob(prediction))
+    else:
+      assert self.loss == ARModel.SQUARED_LOSS, self.loss
+      loss_op = math_ops.reduce_sum(math_ops.square(prediction - targets))
+    loss_op /= math_ops.cast(
+        math_ops.reduce_prod(array_ops.shape(targets)), loss_op.dtype)
+    return loss_op
+
+  # TODO(allenl, agarwal): Consider better ways of warm-starting predictions.
+  def predict(self, features):
+    """Computes predictions multiple steps into the future.
+
+    Args:
+      features: A dictionary with the following key/value pairs:
+        PredictionFeatures.TIMES: A [batch size, predict window size]
+          integer Tensor of times, after the window of data indicated by
+          `STATE_TUPLE`, to make predictions for.
+        PredictionFeatures.STATE_TUPLE: A tuple of (times, values), times with
+          shape [batch size, self.input_window_size], values with shape [batch
+          size, self.input_window_size, self.num_features] representing a
+          segment of the time series before `TIMES`. This data is used
+          to start of the autoregressive computation. This should have data for
+          at least self.input_window_size timesteps.
+    Returns:
+      A dictionary with keys, "mean", "covariance". The
+      values are Tensors of shape [batch_size, predict window size,
+      num_features] and correspond to the values passed in `TIMES`.
+    """
+    predict_times = math_ops.cast(
+        ops.convert_to_tensor(features[PredictionFeatures.TIMES]), dtypes.int32)
+    batch_size = array_ops.shape(predict_times)[0]
+    num_predict_values = array_ops.shape(predict_times)[1]
+    prediction_iterations = ((num_predict_values + self.output_window_size - 1)
+                             // self.output_window_size)
+    # Pad predict_times so as to have exact multiple of self.output_window_size
+    # values per example.
+    padding_size = (prediction_iterations * self.output_window_size -
+                    num_predict_values)
+    padding = array_ops.zeros([batch_size, padding_size], predict_times.dtype)
+    predict_times = control_flow_ops.cond(
+        padding_size > 0, lambda: array_ops.concat([predict_times, padding], 1),
+        lambda: predict_times)
+    state = features[PredictionFeatures.STATE_TUPLE]
+    (state_times, state_values) = state
+    state_times = math_ops.cast(
+        ops.convert_to_tensor(state_times), dtypes.int32)
+    state_values = ops.convert_to_tensor(state_values, dtype=self.dtype)
+
+    initial_input_times = predict_times[:, :self.output_window_size]
+    if self.input_window_size > 0:
+      initial_input_times = array_ops.concat(
+          [state_times[:, -self.input_window_size:], initial_input_times], 1)
+      values_size = array_ops.shape(state_values)[1]
+      times_size = array_ops.shape(state_times)[1]
+      with ops.control_dependencies([
+          check_ops.assert_greater_equal(values_size, self.input_window_size),
+          check_ops.assert_equal(values_size, times_size)
+      ]):
+        initial_input_values = state_values[:, -self.input_window_size:, :]
+    else:
+      initial_input_values = 0
+
+    # Iterate over the predict_times, predicting self.output_window_size values
+    # in each iteration.
+    def _while_condition(iteration_number, *unused_args):
+      return math_ops.less(iteration_number, prediction_iterations)
+
+    def _while_body(iteration_number, input_times, input_values,
+                    mean_ta, covariance_ta):
+      """Predict self.output_window_size values."""
+      prediction_ops = self.prediction_ops(input_times, input_values)
+      predicted_mean = prediction_ops["mean"]
+      predicted_covariance = prediction_ops["covariance"]
+      offset = self.output_window_size * gen_math_ops.minimum(
+          iteration_number + 1, prediction_iterations - 1)
+      if self.input_window_size > 0:
+        if self.output_window_size < self.input_window_size:
+          new_input_values = array_ops.concat(
+              [input_values[:, self.output_window_size:, :], predicted_mean], 1)
+          new_input_times = array_ops.concat([
+              input_times[:, self.output_window_size:],
+              predict_times[:, offset:offset + self.output_window_size]
+          ], 1)
+        else:
+          new_input_values = predicted_mean[:, -self.input_window_size:, :]
+          new_input_times = predict_times[
+              :,
+              offset - self.input_window_size:offset + self.output_window_size]
+      else:
+        new_input_values = input_values
+        new_input_times = predict_times[:,
+                                        offset:offset + self.output_window_size]
+      new_input_times.set_shape(initial_input_times.get_shape())
+      new_mean_ta = mean_ta.write(iteration_number, predicted_mean)
+      if isinstance(covariance_ta, tensor_array_ops.TensorArray):
+        new_covariance_ta = covariance_ta.write(iteration_number,
+                                                predicted_covariance)
+      else:
+        new_covariance_ta = covariance_ta
+      return (iteration_number + 1,
+              new_input_times,
+              new_input_values,
+              new_mean_ta,
+              new_covariance_ta)
+
+    # Note that control_flow_ops.while_loop doesn't seem happy with None. Hence
+    # using 0 for cases where we don't want to predict covariance.
+    covariance_ta_init = (tensor_array_ops.TensorArray(
+        dtype=self.dtype, size=prediction_iterations)
+                          if self.loss != ARModel.SQUARED_LOSS else 0.)
+    mean_ta_init = tensor_array_ops.TensorArray(
+        dtype=self.dtype, size=prediction_iterations)
+    _, _, _, mean_ta, covariance_ta = control_flow_ops.while_loop(
+        _while_condition, _while_body, [
+            0, initial_input_times, initial_input_values, mean_ta_init,
+            covariance_ta_init
+        ])
+
+    def _parse_ta(values_ta):
+      """Helper function to parse the returned TensorArrays."""
+
+      if not isinstance(values_ta, tensor_array_ops.TensorArray):
+        return None
+      predictions_length = prediction_iterations * self.output_window_size
+      # Shape [prediction_iterations, batch_size, self.output_window_size,
+      #        self.num_features]
+      values_packed = values_ta.stack()
+      # Transpose to move batch dimension outside.
+      output_values = array_ops.reshape(
+          array_ops.transpose(values_packed, [1, 0, 2, 3]),
+          array_ops.stack([batch_size, predictions_length, -1]))
+      # Clip to desired size
+      return output_values[:, :num_predict_values, :]
+
+    predicted_mean = _parse_ta(mean_ta)
+    predicted_covariance = _parse_ta(covariance_ta)
+    if predicted_covariance is None:
+      predicted_covariance = array_ops.ones_like(predicted_mean)
+
+    # Transform and scale the mean and covariance appropriately.
+    predicted_mean = self.scale_back_data(predicted_mean)
+    predicted_covariance = self.scale_back_variance(predicted_covariance)
+
+    return {"mean": predicted_mean,
+            "covariance": predicted_covariance}
+
+  def _process_window(self, features, mode):
+    """Compute model outputs on a single window of data."""
+    # TODO(agarwal): Use exogenous features
+    times = math_ops.cast(features[TrainEvalFeatures.TIMES], dtypes.int64)
+    values = math_ops.cast(features[TrainEvalFeatures.VALUES], dtype=self.dtype)
+    original_values = values
+
+    # Extra shape checking for the window size (above that in
+    # model_utils.make_model_fn).
+    expected_times_shape = [None, self.window_size]
+    if not times.get_shape().is_compatible_with(expected_times_shape):
+      raise ValueError(
+          ("ARModel with input_window_size={input_window_size} "
+           "and output_window_size={output_window_size} expects "
+           "feature '{times_feature}' to have shape (batch_size, "
+           "{window_size}) (for any batch_size), but got shape {times_shape}. "
+           "If you are using RandomWindowInputFn, set "
+           "window_size={window_size} or adjust the input_window_size and "
+           "output_window_size arguments to ARModel.").format(
+               input_window_size=self.input_window_size,
+               output_window_size=self.output_window_size,
+               times_feature=TrainEvalFeatures.TIMES,
+               window_size=self.window_size,
+               times_shape=times.get_shape()))
+    values = self.scale_data(values)
+    if self.input_window_size > 0:
+      input_values = values[:, :self.input_window_size, :]
+    else:
+      input_values = None
+    prediction_ops = self.prediction_ops(times, input_values)
+    prediction = prediction_ops["mean"]
+    covariance = prediction_ops["covariance"]
+    targets = array_ops.slice(values, [0, self.input_window_size, 0],
+                              [-1, -1, -1])
+    targets.get_shape().assert_is_compatible_with(prediction.get_shape())
+    if (mode == estimator_lib.ModeKeys.EVAL
+        and self.loss == ARModel.SQUARED_LOSS):
+      # Report an evaluation loss which matches the expected
+      #  (observed - predicted) ** 2.
+      # Note that this affects only evaluation; the training loss is unaffected.
+      loss = self.loss_op(
+          self.scale_back_data(targets),
+          {"mean": self.scale_back_data(prediction_ops["mean"])})
+    else:
+      loss = self.loss_op(targets, prediction_ops)
+
+    # Scale back the prediction.
+    prediction = self.scale_back_data(prediction)
+    covariance = self.scale_back_variance(covariance)
+
+    return model.ModelOutputs(
+        loss=loss,
+        end_state=(times[:, -self.input_window_size:],
+                   values[:, -self.input_window_size:, :]),
+        predictions={"mean": prediction, "covariance": covariance,
+                     "observed": original_values[:, -self.output_window_size:]},
+        prediction_times=times[:, -self.output_window_size:])
+
+  def get_batch_loss(self, features, mode, state):
+    """Computes predictions and a loss.
+
+    Args:
+      features: A dictionary (such as is produced by a chunker) with the
+        following key/value pairs (shapes are given as required for training):
+          TrainEvalFeatures.TIMES: A [batch size, self.window_size] integer
+            Tensor with times for each observation. To train on longer
+            sequences, the data should first be chunked.
+          TrainEvalFeatures.VALUES: A [batch size, self.window_size,
+            self.num_features] Tensor with values for each observation.
+        When evaluating, `TIMES` and `VALUES` must have a window size of at
+        least self.window_size, but it may be longer, in which case the last
+        window_size - self.input_window_size times (or fewer if this is not
+        divisible by self.output_window_size) will be evaluated on with
+        non-overlapping output windows (and will have associated
+        predictions). This is primarily to support qualitative
+        evaluation/plotting, and is not a recommended way to compute evaluation
+        losses (since there is no overlap in the output windows, which for
+        window-based models is an undesirable bias).
+      mode: The tf.estimator.ModeKeys mode to use (TRAIN or EVAL).
+      state: Unused
+    Returns:
+      A model.ModelOutputs object.
+    Raises:
+      ValueError: If `mode` is not TRAIN or EVAL, or if static shape information
+      is incorrect.
+    """
+    features = {feature_name: ops.convert_to_tensor(feature_value)
+                for feature_name, feature_value in features.items()}
+    if mode == estimator_lib.ModeKeys.TRAIN:
+      # For training, we require the window size to be self.window_size as
+      # iterating sequentially on larger windows could introduce a bias.
+      return self._process_window(features, mode=mode)
+    elif mode == estimator_lib.ModeKeys.EVAL:
+      # For evaluation, we allow the user to pass in a larger window, in which
+      # case we try to cover as much of the window as possible without
+      # overlap. Quantitative evaluation is more efficient/correct with fixed
+      # windows matching self.window_size (as with training), but this looping
+      # allows easy plotting of "in-sample" predictions.
+      times = features[TrainEvalFeatures.TIMES]
+      times.get_shape().assert_has_rank(2)
+      static_window_size = times.get_shape()[1].value
+      if (static_window_size is not None
+          and static_window_size < self.window_size):
+        raise ValueError(
+            ("ARModel requires a window of at least input_window_size + "
+             "output_window_size to evaluate on (input_window_size={}, "
+             "output_window_size={}, and got shape {} for feature '{}' (batch "
+             "size, window size)).").format(
+                 self.input_window_size, self.output_window_size,
+                 times.get_shape(), TrainEvalFeatures.TIMES))
+      num_iterations = ((array_ops.shape(times)[1] -  self.input_window_size)
+                        // self.output_window_size)
+      output_size = num_iterations * self.output_window_size
+      # Rather than dealing with overlapping windows of output, discard a bit at
+      # the beginning if output windows don't cover evenly.
+      crop_length = output_size + self.input_window_size
+      features = {feature_name: feature_value[:, -crop_length:]
+                  for feature_name, feature_value in features.items()}
+      # Note that, unlike the ARModel's predict() while_loop and the
+      # SequentialTimeSeriesModel while_loop, each iteration here can run in
+      # parallel, since we are not feeding predictions or state from previous
+      # iterations.
+      def _while_condition(iteration_number, loss_ta, mean_ta, covariance_ta):
+        del loss_ta, mean_ta, covariance_ta  # unused
+        return iteration_number < num_iterations
+
+      def _while_body(iteration_number, loss_ta, mean_ta, covariance_ta):
+        """Perform a processing step on a single window of data."""
+        base_offset = iteration_number * self.output_window_size
+        model_outputs = self._process_window(
+            features={
+                feature_name:
+                feature_value[:, base_offset:base_offset + self.window_size]
+                for feature_name, feature_value in features.items()},
+            mode=mode)
+        # This code needs to be updated if new predictions are added in
+        # self._process_window
+        assert len(model_outputs.predictions) == 3
+        assert "mean" in model_outputs.predictions
+        assert "covariance" in model_outputs.predictions
+        assert "observed" in model_outputs.predictions
+        return (iteration_number + 1,
+                loss_ta.write(
+                    iteration_number, model_outputs.loss),
+                mean_ta.write(
+                    iteration_number, model_outputs.predictions["mean"]),
+                covariance_ta.write(
+                    iteration_number, model_outputs.predictions["covariance"]))
+      _, loss_ta, mean_ta, covariance_ta = control_flow_ops.while_loop(
+          _while_condition, _while_body,
+          [0,
+           tensor_array_ops.TensorArray(dtype=self.dtype, size=num_iterations),
+           tensor_array_ops.TensorArray(dtype=self.dtype, size=num_iterations),
+           tensor_array_ops.TensorArray(dtype=self.dtype, size=num_iterations)])
+      values = math_ops.cast(features[TrainEvalFeatures.VALUES],
+                             dtype=self.dtype)
+      batch_size = array_ops.shape(times)[0]
+      prediction_shape = [batch_size, self.output_window_size * num_iterations,
+                          self.num_features]
+      previous_state_times, previous_state_values = state
+      # Make sure returned state always has windows of self.input_window_size,
+      # even if we were passed fewer than self.input_window_size points this
+      # time.
+      if self.input_window_size > 0:
+        new_state_times = array_ops.concat(
+            [previous_state_times,
+             math_ops.cast(times, dtype=dtypes.int64)],
+            axis=1)[:, -self.input_window_size:]
+        new_state_times.set_shape((None, self.input_window_size))
+        new_state_values = array_ops.concat(
+            [previous_state_values,
+             self.scale_data(values)], axis=1)[:, -self.input_window_size:, :]
+        new_state_values.set_shape((None, self.input_window_size,
+                                    self.num_features))
+      else:
+        # There is no state to keep, and the strided slices above do not handle
+        # input_window_size=0.
+        new_state_times = previous_state_times
+        new_state_values = previous_state_values
+      return model.ModelOutputs(
+          loss=math_ops.reduce_mean(loss_ta.stack(), axis=0),
+          end_state=(new_state_times, new_state_values),
+          predictions={
+              "mean": array_ops.reshape(
+                  array_ops.transpose(mean_ta.stack(), [1, 0, 2, 3]),
+                  prediction_shape),
+              "covariance": array_ops.reshape(
+                  array_ops.transpose(covariance_ta.stack(), [1, 0, 2, 3]),
+                  prediction_shape),
+              "observed": values[:, -output_size:]},
+          prediction_times=times[:, -output_size:])
+    else:
+      raise ValueError(
+          "Unknown mode '{}' passed to get_batch_loss.".format(mode))
+
+  def _compute_time_features(self, time):
+    """Compute some features on the time value."""
+    batch_size = array_ops.shape(time)[0]
+    num_periods = len(self._periods)
+    # Reshape to 3D.
+    periods = constant_op.constant(
+        self._periods, shape=[1, 1, num_periods, 1], dtype=time.dtype)
+    time = array_ops.reshape(time, [batch_size, -1, 1, 1])
+    window_offset = time / self._periods
+    # Cast to appropriate type and scale to [0, 1) range
+    mod = (math_ops.cast(time % periods, self.dtype) * self._buckets /
+           math_ops.cast(periods, self.dtype))
+    # Bucketize based on some fixed width intervals. For a value t and interval
+    # [a, b), we return (t - a) if a <= t < b, else 0.
+    intervals = array_ops.reshape(
+        math_ops.range(self._buckets, dtype=self.dtype),
+        [1, 1, 1, self._buckets])
+    mod = nn_ops.relu(mod - intervals)
+    mod = array_ops.where(mod < 1.0, mod, array_ops.zeros_like(mod))
+    return window_offset, mod
+
+
+class AnomalyMixtureARModel(ARModel):
+  """Model data as a mixture of normal and anomaly distributions.
+
+  Note that this model works by changing the loss function to reduce the penalty
+  when predicting an anomalous target. However the predictions are still based
+  on anomalous input features, and this may affect the quality of fit. One
+  possible solution is to downweight/filter anomalous inputs, but that requires
+  more sequential processing instead of completely random windows.
+  """
+
+  GAUSSIAN_ANOMALY = "gaussian"
+  CAUCHY_ANOMALY = "cauchy"
+
+  def __init__(self,
+               periodicities,
+               anomaly_prior_probability,
+               input_window_size,
+               output_window_size,
+               num_features,
+               anomaly_distribution=GAUSSIAN_ANOMALY,
+               num_time_buckets=10,
+               hidden_layer_sizes=None):
+    assert (anomaly_prior_probability < 1.0 and
+            anomaly_prior_probability > 0.0)
+    self._anomaly_prior_probability = anomaly_prior_probability
+    assert anomaly_distribution in [
+        AnomalyMixtureARModel.GAUSSIAN_ANOMALY,
+        AnomalyMixtureARModel.CAUCHY_ANOMALY]
+    self._anomaly_distribution = anomaly_distribution
+    super(AnomalyMixtureARModel, self).__init__(
+        periodicities=periodicities,
+        num_features=num_features,
+        num_time_buckets=num_time_buckets,
+        input_window_size=input_window_size,
+        output_window_size=output_window_size,
+        loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
+        hidden_layer_sizes=hidden_layer_sizes)
+
+  def _create_anomaly_ops(self, times, values, prediction_ops_dict):
+    anomaly_log_param = variable_scope.get_variable(
+        "anomaly_log_param",
+        shape=[],
+        dtype=self.dtype,
+        initializer=init_ops.zeros_initializer())
+    # Anomaly param is the variance for Gaussian and scale for Cauchy
+    # distribution.
+    prediction_ops_dict["anomaly_params"] = gen_math_ops.exp(anomaly_log_param)
+
+  def prediction_ops(self, times, values):
+    prediction_ops_dict = super(AnomalyMixtureARModel, self).prediction_ops(
+        times, values)
+    self._create_anomaly_ops(times, values, prediction_ops_dict)
+    return prediction_ops_dict
+
+  def _anomaly_log_prob(self, targets, prediction_ops):
+    prediction = prediction_ops["mean"]
+    if self._anomaly_distribution == AnomalyMixtureARModel.GAUSSIAN_ANOMALY:
+      anomaly_variance = prediction_ops["anomaly_params"]
+      anomaly_sigma = math_ops.sqrt(
+          gen_math_ops.maximum(anomaly_variance, 1e-5))
+      normal = distributions.Normal(loc=targets, scale=anomaly_sigma)
+      log_prob = normal.log_prob(prediction)
+    else:
+      assert self._anomaly_distribution == AnomalyMixtureARModel.CAUCHY_ANOMALY
+      anomaly_scale = prediction_ops["anomaly_params"]
+      cauchy = distributions.StudentT(
+          df=array_ops.ones([], dtype=anomaly_scale.dtype),
+          loc=targets,
+          scale=anomaly_scale)
+      log_prob = cauchy.log_prob(prediction)
+    return log_prob
+
+  def loss_op(self, targets, prediction_ops):
+    """Create loss_op."""
+    prediction = prediction_ops["mean"]
+    covariance = prediction_ops["covariance"]
+    # Normal data log probability.
+    sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5))
+    normal1 = distributions.Normal(loc=targets, scale=sigma)
+    log_prob1 = normal1.log_prob(prediction)
+    log_prob1 += math_ops.log(1 - self._anomaly_prior_probability)
+    # Anomaly log probability.
+    log_prob2 = self._anomaly_log_prob(targets, prediction_ops)
+    log_prob2 += math_ops.log(self._anomaly_prior_probability)
+    # We need to compute log(exp(log_prob1) + exp(log_prob2). For numerical
+    # stability, we rewrite the expression as below.
+    p1 = gen_math_ops.minimum(log_prob1, log_prob2)
+    p2 = gen_math_ops.maximum(log_prob1, log_prob2)
+    mixed_log_prob = p2 + math_ops.log(1 + gen_math_ops.exp(p1 - p2))
+    loss_op = -math_ops.reduce_sum(mixed_log_prob)
+    loss_op /= math_ops.cast(
+        math_ops.reduce_prod(array_ops.shape(targets)), self.dtype)
+    return loss_op
diff --git a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
new file mode 100644
index 0000000000..3b1a01ed44
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
@@ -0,0 +1,337 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ar_model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import test_utils
+from tensorflow.contrib.timeseries.python.timeseries.ar_model import AnomalyMixtureARModel
+from tensorflow.contrib.timeseries.python.timeseries.ar_model import ARModel
+from tensorflow.contrib.timeseries.python.timeseries.estimators import ARRegressor
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+
+from tensorflow.python.client import session
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import coordinator as coordinator_lib
+from tensorflow.python.training import queue_runner_impl
+from tensorflow.python.training import training
+
+
+class ARModelTest(test.TestCase):
+
+  def create_data(self,
+                  noise_stddev,
+                  anomaly_prob,
+                  multiple_periods=False,
+                  anomaly_stddev_scale=20):
+    self.period = 25
+    num_samples = 200
+    time = 1 + 3 * np.arange(num_samples).astype(np.int64)
+    time_offset = (2 * np.pi * (time % self.period).astype(np.float) /
+                   self.period).reshape([-1, 1])
+    if multiple_periods:
+      period2 = 55
+      self.period = [self.period, period2]
+      time_offset2 = ((time % period2).astype(np.float) / period2).reshape(
+          [-1, 1])
+      data1 = np.sin(time_offset / 2.0) ** 2 * (1 + time_offset2)
+    else:
+      data1 = np.sin(2 * time_offset) + np.cos(3 * time_offset)
+    data1 += noise_stddev / 4. * np.random.randn(num_samples, 1)
+    data2 = (np.sin(3 * time_offset) + np.cos(5 * time_offset) +
+             noise_stddev / 3. * np.random.randn(num_samples, 1))
+    # Add some anomalies to data1
+    if anomaly_prob > 0.:
+      num_anomalies = anomaly_prob * num_samples
+      anomaly_values = (anomaly_stddev_scale * noise_stddev / 4 *
+                        np.random.randn(num_anomalies))
+      indices = np.random.randint(0, num_samples, num_anomalies)
+      for index, val in zip(indices, anomaly_values):
+        data1[index] += val
+
+    data = np.concatenate((4 * data1, 3 * data2), axis=1)
+    split = int(num_samples * 0.8)
+    train_data = {TrainEvalFeatures.TIMES: time[0:split],
+                  TrainEvalFeatures.VALUES: data[0:split]}
+    test_data = {TrainEvalFeatures.TIMES: time[split:],
+                 TrainEvalFeatures.VALUES: data[split:]}
+    return (train_data, test_data)
+
+  # Note that most models will require many more steps to fully converge. We
+  # have used a small number of steps here to keep the running time small.
+  def train_helper(self, input_window_size, loss,
+                   max_loss=None, train_steps=200,
+                   anomaly_prob=0.01,
+                   anomaly_distribution=None,
+                   multiple_periods=False):
+    np.random.seed(3)
+    data_noise_stddev = 0.2
+    if max_loss is None:
+      if loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
+        max_loss = 1.0
+      else:
+        max_loss = 0.05 / (data_noise_stddev ** 2)
+    train_data, test_data = self.create_data(
+        noise_stddev=data_noise_stddev,
+        anomaly_prob=anomaly_prob,
+        multiple_periods=multiple_periods)
+    output_window_size = 10
+    window_size = input_window_size + output_window_size
+
+    class _RunConfig(estimator_lib.RunConfig):
+
+      @property
+      def tf_random_seed(self):
+        return 3
+
+    estimator = ARRegressor(
+        periodicities=self.period,
+        anomaly_prior_probability=0.01 if anomaly_distribution else None,
+        anomaly_distribution=anomaly_distribution,
+        num_features=2,
+        output_window_size=output_window_size,
+        num_time_buckets=20,
+        input_window_size=input_window_size,
+        hidden_layer_sizes=[16],
+        loss=loss,
+        config=_RunConfig())
+    train_input_fn = input_pipeline.RandomWindowInputFn(
+        time_series_reader=input_pipeline.NumpyReader(train_data),
+        window_size=window_size,
+        batch_size=64,
+        num_threads=1,
+        shuffle_seed=2)
+    test_input_fn = test_utils.AllWindowInputFn(
+        time_series_reader=input_pipeline.NumpyReader(test_data),
+        window_size=window_size)
+
+    # Test training
+    estimator.train(
+        input_fn=train_input_fn,
+        steps=train_steps)
+    test_evaluation = estimator.evaluate(input_fn=test_input_fn, steps=1)
+    test_loss = test_evaluation["loss"]
+    logging.info("Final test loss: %f", test_loss)
+    self.assertLess(test_loss, max_loss)
+    if loss == ARModel.SQUARED_LOSS:
+      # Test that the evaluation loss is reported without input scaling.
+      self.assertAllClose(
+          test_loss,
+          np.mean((test_evaluation["mean"] - test_evaluation["observed"]) ** 2))
+
+    # Test predict
+    train_data_times = train_data[TrainEvalFeatures.TIMES]
+    train_data_values = train_data[TrainEvalFeatures.VALUES]
+    test_data_times = test_data[TrainEvalFeatures.TIMES]
+    test_data_values = test_data[TrainEvalFeatures.VALUES]
+    predict_times = np.expand_dims(np.concatenate(
+        [train_data_times[input_window_size:], test_data_times]), 0)
+    predict_true_values = np.expand_dims(np.concatenate(
+        [train_data_values[input_window_size:], test_data_values]), 0)
+    state_times = np.expand_dims(train_data_times[:input_window_size], 0)
+    state_values = np.expand_dims(
+        train_data_values[:input_window_size, :], 0)
+
+    def prediction_input_fn():
+      return ({
+          PredictionFeatures.TIMES: training.limit_epochs(
+              predict_times, num_epochs=1),
+          PredictionFeatures.STATE_TUPLE: (state_times, state_values)
+      }, {})
+    (predictions,) = tuple(estimator.predict(input_fn=prediction_input_fn))
+    predicted_mean = predictions["mean"][:, 0]
+    true_values = predict_true_values[0, :, 0]
+
+    if loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
+      variances = predictions["covariance"][:, 0]
+      standard_deviations = np.sqrt(variances)
+      # Note that we may get tighter bounds with more training steps.
+      errors = np.abs(predicted_mean - true_values) > 4 * standard_deviations
+      fraction_errors = np.mean(errors)
+      logging.info("Fraction errors: %f", fraction_errors)
+
+  def test_time_regression_squared(self):
+    self.train_helper(input_window_size=0,
+                      train_steps=350,
+                      loss=ARModel.SQUARED_LOSS)
+
+  def test_autoregression_squared(self):
+    self.train_helper(input_window_size=15,
+                      loss=ARModel.SQUARED_LOSS)
+
+  def test_autoregression_short_input_window(self):
+    self.train_helper(input_window_size=8,
+                      loss=ARModel.SQUARED_LOSS)
+
+  def test_autoregression_normal(self):
+    self.train_helper(input_window_size=10,
+                      loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
+                      train_steps=300,
+                      max_loss=1.5,
+                      anomaly_distribution=None)
+
+  def test_autoregression_normal_multiple_periods(self):
+    self.train_helper(input_window_size=10,
+                      loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
+                      max_loss=2.0,
+                      multiple_periods=True,
+                      anomaly_distribution=None)
+
+  def test_autoregression_normal_anomalies_normal(self):
+    self.train_helper(
+        input_window_size=10,
+        loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
+        anomaly_distribution=AnomalyMixtureARModel.GAUSSIAN_ANOMALY)
+
+  def test_autoregression_normal_anomalies_cauchy(self):
+    self.train_helper(
+        input_window_size=10,
+        max_loss=1.5,
+        loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
+        anomaly_distribution=AnomalyMixtureARModel.CAUCHY_ANOMALY)
+
+  def test_wrong_window_size(self):
+    estimator = ARRegressor(
+        periodicities=10, num_features=1,
+        input_window_size=10, output_window_size=6)
+    def _bad_window_size_input_fn():
+      return ({TrainEvalFeatures.TIMES: [[1]],
+               TrainEvalFeatures.VALUES: [[[1.]]]},
+              None)
+    def _good_data():
+      return ({TrainEvalFeatures.TIMES: np.arange(16)[None, :],
+               TrainEvalFeatures.VALUES: array_ops.reshape(
+                   np.arange(16), [1, 16, 1])},
+              None)
+    with self.assertRaisesRegexp(ValueError, "set window_size=16"):
+      estimator.train(input_fn=_bad_window_size_input_fn, steps=1)
+    # Get a checkpoint for evaluation
+    estimator.train(input_fn=_good_data, steps=1)
+    with self.assertRaisesRegexp(ValueError, "requires a window of at least"):
+      estimator.evaluate(input_fn=_bad_window_size_input_fn, steps=1)
+
+  def test_predictions_direct(self):
+    g = ops.Graph()
+    with g.as_default():
+      model = ARModel(periodicities=2,
+                      num_features=1,
+                      num_time_buckets=10,
+                      input_window_size=2,
+                      output_window_size=2,
+                      hidden_layer_sizes=[40, 10])
+      with session.Session():
+        predicted_values = model.predict({
+            PredictionFeatures.TIMES: [[4, 6, 10]],
+            PredictionFeatures.STATE_TUPLE: ([[1, 2]], [[[1.], [2.]]])
+        })
+        variables.global_variables_initializer().run()
+        self.assertAllEqual(predicted_values["mean"].eval().shape,
+                            [1, 3, 1])
+
+  def test_long_eval(self):
+    g = ops.Graph()
+    with g.as_default():
+      model = ARModel(periodicities=2,
+                      num_features=1,
+                      num_time_buckets=10,
+                      input_window_size=2,
+                      output_window_size=1)
+      raw_features = {
+          TrainEvalFeatures.TIMES: [[1, 3, 5, 7, 11]],
+          TrainEvalFeatures.VALUES: [[[1.], [2.], [3.], [4.], [5.]]]}
+      chunked_features, _ = test_utils.AllWindowInputFn(
+          time_series_reader=input_pipeline.NumpyReader(raw_features),
+          window_size=3)()
+      model.initialize_graph()
+      with variable_scope.variable_scope("armodel") as scope:
+        raw_evaluation = model.define_loss(
+            raw_features, mode=estimator_lib.ModeKeys.EVAL)
+      with variable_scope.variable_scope(scope, reuse=True):
+        chunked_evaluation = model.define_loss(
+            chunked_features, mode=estimator_lib.ModeKeys.EVAL)
+      with session.Session() as sess:
+        coordinator = coordinator_lib.Coordinator()
+        queue_runner_impl.start_queue_runners(sess, coord=coordinator)
+        variables.global_variables_initializer().run()
+        raw_evaluation_evaled, chunked_evaluation_evaled = sess.run(
+            [raw_evaluation, chunked_evaluation])
+        self.assertAllEqual(chunked_evaluation_evaled.loss,
+                            raw_evaluation_evaled.loss)
+        last_chunk_evaluation_state = [
+            state[-1, None] for state in
+            chunked_evaluation_evaled.end_state]
+        for last_chunk_state_member, raw_state_member in zip(
+            last_chunk_evaluation_state, raw_evaluation_evaled.end_state):
+          self.assertAllEqual(last_chunk_state_member, raw_state_member)
+        self.assertAllEqual([[5, 7, 11]],
+                            raw_evaluation_evaled.prediction_times)
+        for feature_name in raw_evaluation.predictions:
+          self.assertAllEqual(
+              [1, 3, 1],  # batch, window, num_features. The window size has 2
+                          # cut off for the first input_window.
+              raw_evaluation_evaled.predictions[feature_name].shape)
+          self.assertAllEqual(
+              np.reshape(chunked_evaluation_evaled.predictions[feature_name],
+                         [-1]),
+              np.reshape(raw_evaluation_evaled.predictions[feature_name],
+                         [-1]))
+        coordinator.request_stop()
+        coordinator.join()
+
+  def test_long_eval_discard_indivisible(self):
+    g = ops.Graph()
+    with g.as_default():
+      model = ARModel(periodicities=2,
+                      num_features=1,
+                      num_time_buckets=10,
+                      input_window_size=2,
+                      output_window_size=2)
+      raw_features = {
+          TrainEvalFeatures.TIMES: [[1, 3, 5, 7, 11]],
+          TrainEvalFeatures.VALUES: [[[1.], [2.], [3.], [4.], [5.]]]}
+      model.initialize_graph()
+      raw_evaluation = model.define_loss(
+          raw_features, mode=estimator_lib.ModeKeys.EVAL)
+      with session.Session() as sess:
+        coordinator = coordinator_lib.Coordinator()
+        queue_runner_impl.start_queue_runners(sess, coord=coordinator)
+        variables.global_variables_initializer().run()
+        raw_evaluation_evaled = sess.run(raw_evaluation)
+        self.assertAllEqual([[7, 11]],
+                            raw_evaluation_evaled.prediction_times)
+        for feature_name in raw_evaluation.predictions:
+          self.assertAllEqual(
+              [1, 2, 1],  # batch, window, num_features. The window has two cut
+                          # off for the first input window and one discarded so
+                          # that the remainder is divisible into output windows.
+              raw_evaluation_evaled.predictions[feature_name].shape)
+        coordinator.request_stop()
+        coordinator.join()
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
new file mode 100644
index 0000000000..50c7779243
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
@@ -0,0 +1,379 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Estimators for time series models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries import ar_model
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries import model_utils
+from tensorflow.contrib.timeseries.python.timeseries import state_management
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import structural_ensemble
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models.filtering_postprocessor import StateInterpolatingAnomalyDetector
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.estimator.export import export_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.training import training as train
+
+
+class _TimeSeriesRegressor(estimator_lib.Estimator):
+  """An Estimator to fit and evaluate a time series model."""
+
+  def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
+               config=None):
+    """Initialize the Estimator.
+
+    Args:
+      model: The time series model to wrap (inheriting from TimeSeriesModel).
+      state_manager: The state manager to use, or (by default)
+          PassthroughStateManager if none is needed.
+      optimizer: The optimization algorithm to use when training, inheriting
+          from tf.train.Optimizer. Defaults to Adam with step size 0.02.
+      model_dir: See `Estimator`.
+      config: See `Estimator`.
+    """
+    input_statistics_generator = math_utils.InputStatisticsFromMiniBatch(
+        dtype=model.dtype, num_features=model.num_features)
+    if state_manager is None:
+      state_manager = state_management.PassthroughStateManager()
+    if optimizer is None:
+      optimizer = train.AdamOptimizer(0.02)
+    self._model = model
+    model_fn = model_utils.make_model_fn(
+        model, state_manager, optimizer,
+        input_statistics_generator=input_statistics_generator)
+    super(_TimeSeriesRegressor, self).__init__(
+        model_fn=model_fn,
+        model_dir=model_dir,
+        config=config)
+
+  # TODO(allenl): A parsing input receiver function, which takes a serialized
+  # tf.Example containing all features (times, values, any exogenous features)
+  # and serialized model state (possibly also as a tf.Example).
+  def build_raw_serving_input_receiver_fn(self,
+                                          exogenous_features=None,
+                                          default_batch_size=None,
+                                          default_series_length=None):
+    """Build an input_receiver_fn for export_savedmodel which accepts arrays.
+
+    Args:
+      exogenous_features: A dictionary mapping feature keys to exogenous
+        features (either Numpy arrays or Tensors). Used to determine the shapes
+        of placeholders for these features.
+      default_batch_size: If specified, must be a scalar integer. Sets the batch
+        size in the static shape information of all feature Tensors, which means
+        only this batch size will be accepted by the exported model. If None
+        (default), static shape information for batch sizes is omitted.
+      default_series_length: If specified, must be a scalar integer. Sets the
+        series length in the static shape information of all feature Tensors,
+        which means only this series length will be accepted by the exported
+        model. If None (default), static shape information for series length is
+        omitted.
+    Returns:
+      An input_receiver_fn which may be passed to the Estimator's
+      export_savedmodel.
+    """
+    if exogenous_features is None:
+      exogenous_features = {}
+
+    def _serving_input_receiver_fn():
+      """A receiver function to be passed to export_savedmodel."""
+      placeholders = {}
+      placeholders[feature_keys.TrainEvalFeatures.TIMES] = (
+          array_ops.placeholder(
+              name=feature_keys.TrainEvalFeatures.TIMES,
+              dtype=dtypes.int64,
+              shape=[default_batch_size, default_series_length]))
+      # Values are only necessary when filtering. For prediction the default
+      # value will be ignored.
+      placeholders[feature_keys.TrainEvalFeatures.VALUES] = (
+          array_ops.placeholder_with_default(
+              name=feature_keys.TrainEvalFeatures.VALUES,
+              input=array_ops.zeros(
+                  shape=[
+                      default_batch_size
+                      if default_batch_size else 0, default_series_length
+                      if default_series_length else 0, self._model.num_features
+                  ],
+                  dtype=self._model.dtype),
+              shape=(default_batch_size, default_series_length,
+                     self._model.num_features)))
+      for feature_key, feature_value in exogenous_features.items():
+        value_tensor = ops.convert_to_tensor(feature_value)
+        value_tensor.get_shape().with_rank_at_least(2)
+        feature_shape = value_tensor.get_shape().as_list()
+        feature_shape[0] = default_batch_size
+        feature_shape[1] = default_series_length
+        placeholders[feature_key] = array_ops.placeholder(
+            dtype=value_tensor.dtype, name=feature_key, shape=feature_shape)
+      # Models may not know the shape of their state without creating some
+      # variables/ops. Avoid polluting the default graph by making a new one. We
+      # use only static metadata from the returned Tensors.
+      with ops.Graph().as_default():
+        self._model.initialize_graph()
+        model_start_state = self._model.get_start_state()
+      for prefixed_state_name, state_tensor in model_utils.state_to_dictionary(
+          model_start_state).items():
+        state_shape_with_batch = tensor_shape.TensorShape(
+            (default_batch_size,)).concatenate(state_tensor.get_shape())
+        placeholders[prefixed_state_name] = array_ops.placeholder(
+            name=prefixed_state_name,
+            shape=state_shape_with_batch,
+            dtype=state_tensor.dtype)
+      return export_lib.ServingInputReceiver(placeholders, placeholders)
+
+    return _serving_input_receiver_fn
+
+
+class ARRegressor(_TimeSeriesRegressor):
+  """An Estimator for an (optionally non-linear) autoregressive model.
+
+  ARRegressor is a window-based model, inputting fixed windows of length
+  `input_window_size` and outputting fixed windows of length
+  `output_window_size`. These two parameters must add up to the window_size
+  passed to the `Chunker` used to create an `input_fn` for training or
+  evaluation. `RandomWindowInputFn` is suggested for both training and
+  evaluation, although it may be seeded for deterministic evaluation.
+  """
+
+  def __init__(
+      self, periodicities, input_window_size, output_window_size,
+      num_features, num_time_buckets=10,
+      loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS, hidden_layer_sizes=None,
+      anomaly_prior_probability=None, anomaly_distribution=None,
+      optimizer=None, model_dir=None, config=None):
+    """Initialize the Estimator.
+
+    Args:
+      periodicities: periodicities of the input data, in the same units as the
+        time feature. Note this can be a single value or a list of values for
+        multiple periodicities.
+      input_window_size: Number of past time steps of data to look at when doing
+        the regression.
+      output_window_size: Number of future time steps to predict. Note that
+        setting it to > 1 empirically seems to give a better fit.
+      num_features: The dimensionality of the time series (one for univariate,
+          more than one for multivariate).
+      num_time_buckets: Number of buckets into which to divide (time %
+        periodicity) for generating time based features.
+      loss: Loss function to use for training. Currently supported values are
+        SQUARED_LOSS and NORMAL_LIKELIHOOD_LOSS. Note that for
+        NORMAL_LIKELIHOOD_LOSS, we train the covariance term as well. For
+        SQUARED_LOSS, the evaluation loss is reported based on un-scaled
+        observations and predictions, while the training loss is computed on
+        normalized data.
+      hidden_layer_sizes: list of sizes of hidden layers.
+      anomaly_prior_probability: If specified, constructs a mixture model under
+        which anomalies (modeled with `anomaly_distribution`) have this prior
+        probability. See `AnomalyMixtureARModel`.
+      anomaly_distribution: May not be specified unless
+        anomaly_prior_probability is specified and is not None. Controls the
+        distribution of anomalies under the mixture model. Currently either
+        `ar_model.AnomalyMixtureARModel.GAUSSIAN_ANOMALY` or
+        `ar_model.AnomalyMixtureARModel.CAUCHY_ANOMALY`. See
+        `AnomalyMixtureARModel`. Defaults to `GAUSSIAN_ANOMALY`.
+      optimizer: The optimization algorithm to use when training, inheriting
+          from tf.train.Optimizer. Defaults to Adagrad with step size 0.1.
+      model_dir: See `Estimator`.
+      config: See `Estimator`.
+    Raises:
+      ValueError: For invalid combinations of arguments.
+    """
+    if optimizer is None:
+      optimizer = train.AdagradOptimizer(0.1)
+    if anomaly_prior_probability is None and anomaly_distribution is not None:
+      raise ValueError("anomaly_prior_probability is required if "
+                       "anomaly_distribution is specified.")
+    if anomaly_prior_probability is None:
+      if anomaly_distribution is None:
+        anomaly_distribution = ar_model.AnomalyMixtureARModel.GAUSSIAN_ANOMALY
+      model = ar_model.ARModel(
+          periodicities=periodicities, num_features=num_features,
+          num_time_buckets=num_time_buckets,
+          input_window_size=input_window_size,
+          output_window_size=output_window_size, loss=loss,
+          hidden_layer_sizes=hidden_layer_sizes)
+    else:
+      if loss != ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS:
+        raise ValueError(
+            "AnomalyMixtureARModel only supports "
+            "ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS for its loss argument.")
+      model = ar_model.AnomalyMixtureARModel(
+          periodicities=periodicities,
+          input_window_size=input_window_size,
+          output_window_size=output_window_size,
+          num_features=num_features,
+          num_time_buckets=num_time_buckets,
+          hidden_layer_sizes=hidden_layer_sizes,
+          anomaly_prior_probability=anomaly_prior_probability,
+          anomaly_distribution=anomaly_distribution)
+    state_manager = state_management.FilteringOnlyStateManager()
+    super(ARRegressor, self).__init__(
+        model=model,
+        state_manager=state_manager,
+        optimizer=optimizer,
+        model_dir=model_dir,
+        config=config)
+
+
+class StateSpaceRegressor(_TimeSeriesRegressor):
+  """An Estimator for general state space models."""
+
+  def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
+               config=None):
+    """See _TimeSeriesRegressor. Uses the ChainingStateManager by default."""
+    if not isinstance(model, state_space_model.StateSpaceModel):
+      raise ValueError(
+          "StateSpaceRegressor only supports state space models (children of "
+          "StateSpaceModel) in its `model` argument, got {}.".format(model))
+    if state_manager is None:
+      state_manager = state_management.ChainingStateManager()
+    super(StateSpaceRegressor, self).__init__(
+        model=model,
+        state_manager=state_manager,
+        optimizer=optimizer,
+        model_dir=model_dir,
+        config=config)
+
+
+class StructuralEnsembleRegressor(StateSpaceRegressor):
+  """An Estimator for structural time series models.
+
+  "Structural" refers to the fact that this model explicitly accounts for
+  structure in the data, such as periodicity and trends.
+
+  `StructuralEnsembleRegressor` is a state space model. It contains components
+  for modeling level, local linear trends, periodicity, and mean-reverting
+  transients via a moving average component. Multivariate series are fit with
+  full covariance matrices for observation and latent state transition noise,
+  each feature of the multivariate series having its own latent components.
+
+  Note that unlike `ARRegressor`, `StructuralEnsembleRegressor` is sequential,
+  and so accepts variable window sizes with the same model.
+
+  For training, `RandomWindowInputFn` is recommended as an `input_fn`. Model
+  state is managed through `ChainingStateManager`: since state space models are
+  inherently sequential, we save state from previous iterations to get
+  approximate/eventual consistency while achieving good performance through
+  batched computation.
+
+  For evaluation, either pass a significant chunk of the series in a single
+  window (e.g. set `window_size` to the whole series with
+  `WholeDatasetInputFn`), or use enough random evaluation iterations to cover
+  several passes through the whole dataset. Either method will ensure that stale
+  saved state has been flushed.
+  """
+
+  def __init__(self,
+               periodicities,
+               num_features,
+               cycle_num_latent_values=11,
+               moving_average_order=4,
+               autoregressive_order=0,
+               exogenous_feature_columns=None,
+               exogenous_update_condition=None,
+               dtype=dtypes.float64,
+               anomaly_prior_probability=None,
+               optimizer=None,
+               model_dir=None,
+               config=None):
+    """Initialize the Estimator.
+
+    Args:
+      periodicities: The expected periodicity of the data (for example 24 if
+          feeding hourly data with a daily periodicity, or 60 * 24 if feeding
+          minute-level data with daily periodicity). Either a scalar or a
+          list. This parameter can be any real value, and does not control the
+          size of the model. However, increasing this without increasing
+          `num_values_per_cycle` will lead to smoother periodic behavior, as the
+          same number of distinct values will be cycled through over a longer
+          period of time.
+      num_features: The dimensionality of the time series (one for univariate,
+          more than one for multivariate).
+      cycle_num_latent_values: Along with `moving_average_order` and
+          `num_features`, controls the latent state size of the model. Square
+          matrices of size `num_features * (moving_average_order +
+          cycle_num_latent_values + 3)` are created and multiplied, so larger
+          values may be slow. The trade-off is with resolution: cycling between
+          a smaller number of latent values means that only smoother functions
+          can be modeled.
+      moving_average_order: Controls model size (along with
+          `cycle_num_latent_values` and `autoregressive_order`) and the number
+          of steps before transient deviations revert to the mean defined by the
+          period and level/trend components.
+      autoregressive_order: Each contribution from this component is a linear
+          combination of this many previous contributions. Also helps to
+          determine the model size. Learning autoregressive coefficients
+          typically requires more steps and a smaller step size than other
+          components.
+      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
+          objects (for example tf.contrib.layers.embedding_column) corresponding
+          to exogenous features which provide extra information to the model but
+          are not part of the series to be predicted. Passed to
+          tf.contrib.layers.input_from_feature_columns.
+      exogenous_update_condition: A function taking two Tensor arguments,
+          `times` (shape [batch size]) and `features` (a dictionary mapping
+          exogenous feature keys to Tensors with shapes [batch size, ...]), and
+          returning a boolean Tensor with shape [batch size] indicating whether
+          state should be updated using exogenous features for each part of the
+          batch. Where it is False, no exogenous update is performed. If None
+          (default), exogenous updates are always performed. Useful for avoiding
+          "leaky" frequent exogenous updates when sparse updates are
+          desired. Called only during graph construction. See the "known
+          anomaly" example for example usage.
+      dtype: The floating point data type to compute with. float32 may be
+        faster, but can be problematic for larger models and longer time series.
+      anomaly_prior_probability: If not None, the model attempts to
+          automatically detect and ignore anomalies during training. This
+          parameter then controls the prior probability of an anomaly. Values
+          closer to 0 mean that points will be discarded less frequently. The
+          default value (None) means that anomalies are not discarded, which may
+          be slightly faster.
+      optimizer: The optimization algorithm to use when training, inheriting
+          from tf.train.Optimizer. Defaults to Adam with step size 0.02.
+      model_dir: See `Estimator`.
+      config: See `Estimator`.
+    """
+    if anomaly_prior_probability is not None:
+      filtering_postprocessor = StateInterpolatingAnomalyDetector(
+          anomaly_prior_probability=anomaly_prior_probability)
+    else:
+      filtering_postprocessor = None
+    state_space_model_configuration = (
+        state_space_model.StateSpaceModelConfiguration(
+            num_features=num_features,
+            dtype=dtype,
+            filtering_postprocessor=filtering_postprocessor,
+            exogenous_feature_columns=exogenous_feature_columns,
+            exogenous_update_condition=exogenous_update_condition))
+    model = structural_ensemble.MultiResolutionStructuralEnsemble(
+        cycle_num_latent_values=cycle_num_latent_values,
+        moving_average_order=moving_average_order,
+        autoregressive_order=autoregressive_order,
+        periodicities=periodicities,
+        configuration=state_space_model_configuration)
+    super(StructuralEnsembleRegressor, self).__init__(
+        model=model,
+        optimizer=optimizer,
+        model_dir=model_dir,
+        config=config)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
new file mode 100644
index 0000000000..f4304f2560
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
@@ -0,0 +1,149 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tempfile
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries import ar_model
+from tensorflow.contrib.timeseries.python.timeseries import estimators
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import saved_model_utils
+
+from tensorflow.python.client import session
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import test
+from tensorflow.python.saved_model import loader
+from tensorflow.python.saved_model import tag_constants
+
+
+class _SeedRunConfig(estimator_lib.RunConfig):
+
+  @property
+  def tf_random_seed(self):
+    return 3
+
+
+class TimeSeriesRegressorTest(test.TestCase):
+
+  def _fit_restore_fit_test_template(self, estimator_fn, dtype):
+    """Tests restoring previously fit models."""
+    model_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
+    first_estimator = estimator_fn(model_dir)
+    times = numpy.arange(20, dtype=numpy.int64)
+    values = numpy.arange(20, dtype=dtype.as_numpy_dtype)
+    features = {
+        feature_keys.TrainEvalFeatures.TIMES: times,
+        feature_keys.TrainEvalFeatures.VALUES: values
+    }
+    train_input_fn = input_pipeline.RandomWindowInputFn(
+        input_pipeline.NumpyReader(features), shuffle_seed=2, num_threads=1,
+        batch_size=16, window_size=16)
+    eval_input_fn = input_pipeline.RandomWindowInputFn(
+        input_pipeline.NumpyReader(features), shuffle_seed=3, num_threads=1,
+        batch_size=16, window_size=16)
+    first_estimator.train(input_fn=train_input_fn, steps=5)
+    first_loss_before_fit = first_estimator.evaluate(
+        input_fn=eval_input_fn, steps=1)["loss"]
+    first_estimator.train(input_fn=train_input_fn, steps=50)
+    first_loss_after_fit = first_estimator.evaluate(
+        input_fn=eval_input_fn, steps=1)["loss"]
+    self.assertLess(first_loss_after_fit, first_loss_before_fit)
+    second_estimator = estimator_fn(model_dir)
+    second_estimator.train(input_fn=train_input_fn, steps=2)
+    whole_dataset_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(features))
+    whole_dataset_evaluation = second_estimator.evaluate(
+        input_fn=whole_dataset_input_fn, steps=1)
+    predict_input_fn = input_pipeline.predict_continuation_input_fn(
+        evaluation=whole_dataset_evaluation,
+        steps=10)
+    # Also tests that limit_epochs in predict_continuation_input_fn prevents
+    # infinite iteration
+    (estimator_predictions,
+    ) = list(second_estimator.predict(input_fn=predict_input_fn))
+    self.assertAllEqual([10, 1], estimator_predictions["mean"].shape)
+    input_receiver_fn = first_estimator.build_raw_serving_input_receiver_fn()
+    export_location = first_estimator.export_savedmodel(self.get_temp_dir(),
+                                                        input_receiver_fn)
+    with ops.Graph().as_default():
+      with session.Session() as sess:
+        signatures = loader.load(sess, [tag_constants.SERVING], export_location)
+        # Test that prediction and filtering can continue from evaluation output
+        saved_prediction = saved_model_utils.predict_continuation(
+            continue_from=whole_dataset_evaluation,
+            steps=10,
+            signatures=signatures,
+            session=sess)
+        # Saved model predictions should be the same as Estimator predictions
+        # starting from the same evaluation.
+        for prediction_key, prediction_value in estimator_predictions.items():
+          self.assertAllClose(prediction_value,
+                              numpy.squeeze(
+                                  saved_prediction[prediction_key], axis=0))
+        first_filtering = saved_model_utils.filter_continuation(
+            continue_from=whole_dataset_evaluation,
+            features={
+                feature_keys.FilteringFeatures.TIMES: times[None, -1] + 2,
+                feature_keys.FilteringFeatures.VALUES: values[None, -1] + 2.
+            },
+            signatures=signatures,
+            session=sess)
+        # Test that prediction and filtering can continue from filtering output
+        second_saved_prediction = saved_model_utils.predict_continuation(
+            continue_from=first_filtering,
+            steps=1,
+            signatures=signatures,
+            session=sess)
+        self.assertEqual(
+            times[-1] + 3,
+            numpy.squeeze(
+                second_saved_prediction[feature_keys.PredictionResults.TIMES]))
+        saved_model_utils.filter_continuation(
+            continue_from=first_filtering,
+            features={
+                feature_keys.FilteringFeatures.TIMES: times[-1] + 3,
+                feature_keys.FilteringFeatures.VALUES: values[-1] + 3.
+            },
+            signatures=signatures,
+            session=sess)
+
+  def test_fit_restore_fit_ar_regressor(self):
+    def _estimator_fn(model_dir):
+      return estimators.ARRegressor(
+          periodicities=10, input_window_size=10, output_window_size=6,
+          num_features=1, model_dir=model_dir, config=_SeedRunConfig(),
+          # This test is flaky with normal likelihood loss (could add more
+          # training iterations instead).
+          loss=ar_model.ARModel.SQUARED_LOSS)
+    self._fit_restore_fit_test_template(_estimator_fn, dtype=dtypes.float32)
+
+  def test_fit_restore_fit_structural_ensemble_regressor(self):
+    dtype = dtypes.float32
+    def _estimator_fn(model_dir):
+      return estimators.StructuralEnsembleRegressor(
+          num_features=1, periodicities=10, model_dir=model_dir, dtype=dtype,
+          config=_SeedRunConfig())
+    self._fit_restore_fit_test_template(_estimator_fn, dtype=dtype)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/feature_keys.py b/tensorflow/contrib/timeseries/python/timeseries/feature_keys.py
new file mode 100644
index 0000000000..970b9aa8ac
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/feature_keys.py
@@ -0,0 +1,74 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Commonly used special feature names for time series models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.saved_model import signature_constants
+
+
+class State(object):
+  """Key formats for accepting/returning state."""
+  # The model-dependent state to start from, as a single tuple.
+  STATE_TUPLE = "start_tuple"
+  # Same meaning as STATE_TUPLE, but prefixes keys representing flattened model
+  # state rather than mapping to a nested tuple containing model state,
+  # primarily for use with export_savedmodel.
+  STATE_PREFIX = "model_state"
+
+
+class Times(object):
+  """Key formats for accepting/returning times."""
+  # An increasing vector of integers.
+  TIMES = "times"
+
+
+class Values(object):
+  """Key formats for accepting/returning values."""
+  # Floating point, with one or more values corresponding to each time in TIMES.
+  VALUES = "values"
+
+
+class TrainEvalFeatures(Times, Values):
+  """Feature names used during training and evaluation."""
+  pass
+
+
+class PredictionFeatures(Times, State):
+  """Feature names used during prediction."""
+  pass
+
+
+class FilteringFeatures(Times, Values, State):
+  """Special feature names for filtering."""
+  pass
+
+
+class PredictionResults(Times):
+  """Keys returned when predicting (not comprehensive)."""
+  pass
+
+
+class FilteringResults(Times, State):
+  """Keys returned from evaluation/filtering."""
+  pass
+
+
+class SavedModelLabels(object):
+  """Names of signatures exported with export_savedmodel."""
+  PREDICT = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+  FILTER = "filter"
diff --git a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py
new file mode 100644
index 0000000000..d4ee590366
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py
@@ -0,0 +1,832 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines ways of splicing and re-arranging time series.
+
+This file provides methods for reading, parsing, and re-arranging a time
+series. The main departure from standard TensorFlow input pipelines is a focus
+on "chunking" a time series, i.e. slicing it into small contiguous windows which
+are then batched together for training, a form of truncated
+backpropagation. This typically provides a significant speedup compared to
+looping over the whole series sequentially, by exploiting data parallelism and
+by reducing redundant contributions to gradients (due to redundant information
+in the series itself).
+
+A series, consisting of times (an increasing vector of integers) and values (one
+or more floating point values for each time) along with any exogenous features,
+is stored either in memory or on disk in various formats (e.g. "one record per
+timestep" on disk, or as a dictionary of Numpy arrays in memory). The location
+and format is specified by configuring a `TimeSeriesReader` object
+(e.g. `NumpyReader`, `CSVReader`), which reads the data into the TensorFlow
+graph. A `TimeSeriesInputFn` object (typically `RandomWindowInputFn`) then
+performs windowing and batching.
+
+Time series are passed through this pipeline as dictionaries mapping feature
+names to their values. For training and evaluation, these require at minimum
+`TrainEvalFeatures.TIMES` (scalar integers, one per timestep) and
+`TrainEvalFeatures.VALUES` (may be either univariate or multivariate). Exogenous
+features may have any shape, but are likewise associated with a timestep. Times
+themselves need not be contiguous or regular (although smaller/fewer gaps are
+generally better), but each timestep must have all `VALUES` and any exogenous
+features (i.e. times may be missing, but given that a time is specified, every
+other feature must also be specified for that step; some models may support
+making exogenous updates conditional).
+
+The expected use case of a `TimeSeriesInputFn` is that it is first configured
+(for example setting a batch or window size) and passed a reader (a
+`TimeSeriesReader` object). The `TimeSeriesInputFn` can then be passed as the
+input_fn of an Estimator.
+
+For example, `RandomWindowInputFn` is useful for creating batches of random
+chunks of a series for training:
+
+```
+  # Read data in the default "time,value" CSV format with no header
+  reader = input_pipeline.CSVReader(csv_file_name)
+  # Set up windowing and batching for training
+  train_input_fn = input_pipeline.RandomWindowInputFn(
+      reader, batch_size=16, window_size=16)
+  # Fit model parameters to data
+  estimator.train(input_fn=train_input_fn, steps=150)
+```
+
+`RandomWindowInputFn` is the primary tool for training and quantitative
+evaluation of time series. `WholeDatasetInputFn`, which reads a whole series
+into memory, is useful for qualitative evaluation and preparing to make
+predictions with `predict_continuation_input_fn`.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import model_utils
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import io_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import tensor_array_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.training import input as input_lib
+from tensorflow.python.training import training
+from tensorflow.python.util import nest
+
+
+def predict_continuation_input_fn(
+    evaluation, steps=None, times=None, exogenous_features=None):
+  """An Estimator input_fn for running predict() after evaluate().
+
+  If the call to evaluate() we are making predictions based on had a batch_size
+  greater than one, predictions will start after each of these windows
+  (i.e. will have the same batch dimension).
+
+  Args:
+    evaluation: The dictionary returned by `Estimator.evaluate`, with keys
+      FilteringResults.STATE_TUPLE and FilteringResults.TIMES.
+    steps: The number of steps to predict (scalar), starting after the
+      evaluation. If `times` is specified, `steps` must not be; one is required.
+    times: A [batch_size x window_size] array of integers (not a Tensor)
+      indicating times to make predictions for. These times must be after the
+      corresponding evaluation. If `steps` is specified, `times` must not be;
+      one is required. If the batch dimension is omitted, it is assumed to be 1.
+    exogenous_features: Optional dictionary. If specified, indicates exogenous
+      features for the model to use while making the predictions. Values must
+      have shape [batch_size x window_size x ...], where `batch_size` matches
+      the batch dimension used when creating `evaluation`, and `window_size` is
+      either the `steps` argument or the `window_size` of the `times` argument
+      (depending on which was specified).
+  Returns:
+    An `input_fn` suitable for passing to the `predict` function of a time
+    series `Estimator`.
+  Raises:
+    ValueError: If `times` or `steps` are misspecified.
+  """
+  if exogenous_features is None:
+    exogenous_features = {}
+  predict_times = model_utils.canonicalize_times_or_steps_from_output(
+      times=times, steps=steps, previous_model_output=evaluation)
+  features = {
+      feature_keys.PredictionFeatures.STATE_TUPLE:
+          evaluation[feature_keys.FilteringResults.STATE_TUPLE],
+      feature_keys.PredictionFeatures.TIMES:
+          predict_times
+  }
+  features.update(exogenous_features)
+  def _predict_input_fn():
+    """An input_fn for predict()."""
+    # Prevents infinite iteration with a constant output in an Estimator's
+    # predict().
+    limited_features = {}
+    for key, values in features.items():
+      limited_values = nest.map_structure(
+          lambda value: training.limit_epochs(value, num_epochs=1), values)
+      limited_features[key] = limited_values
+    return (limited_features, None)
+  return _predict_input_fn
+
+
+class TimeSeriesReader(object):
+  """Reads from and parses a data source for a `TimeSeriesInputFn`.
+
+  This class provides methods that read a few records (`read`) or the full data
+  set at once (`read_full`), and returns them as dictionaries mapping feature
+  names to feature Tensors. Please see note at the top of the file for the
+  structure of these dictionaries. The output is generally chunked by a
+  `TimeSeriesInputFn` before being passed to the model.
+  """
+
+  def check_dataset_size(self, minimum_dataset_size):
+    """When possible, raises an error if the dataset is too small.
+
+    This method allows TimeSeriesReaders to raise informative error messages if
+    the user has selected a window size in their TimeSeriesInputFn which is
+    larger than the dataset size. However, many TimeSeriesReaders will not have
+    access to a dataset size, in which case they do not need to override this
+    method.
+
+    Args:
+      minimum_dataset_size: The minimum number of records which should be
+        contained in the dataset. Readers should attempt to raise an error when
+        possible if an epoch of data contains fewer records.
+    """
+    pass
+
+  @abc.abstractmethod
+  def read(self):
+    """Parses one or more records into a feature dictionary.
+
+    This method is expected to be called by a `TimeSeriesInputFn` object, and is
+    not for use with models directly.
+
+    A `TimeSeriesReader` object reads multiple records at a single time for
+    efficiency; the size of these batches is an implementation detail internal
+    to the input pipeline. These records should generally be sequential,
+    although some out-of-order records due to file wraparounds are expected and
+    must be handled by callers.
+
+    Returns:
+      A dictionary mapping feature names to `Tensor` values, each with an
+      arbitrary batch dimension (for efficiency) as their first dimension.
+    """
+    pass
+
+  @abc.abstractmethod
+  def read_full(self):
+    """Return the full dataset.
+
+    Largely for interactive use/plotting (or evaluation on small
+    datasets). Generally not very efficient. Not recommended for training.
+
+    Returns:
+      Same return type as `read`, but with the full dataset rather than an
+      arbitrary chunk of it. A dictionary mapping feature names to `Tensor`
+      values, where the size of the first dimension of each `Tensor` is the
+      number of samples in the entire dataset. These `Tensor`s should be
+      constant across graph invocations, assuming that the underlying data
+      remains constant. Current implementations re-read data on each graph
+      invocation, although this may change in the future.
+    """
+    pass
+
+
+class NumpyReader(TimeSeriesReader):
+  """A time series parser for feeding Numpy arrays to a `TimeSeriesInputFn`.
+
+  Avoids embedding data in the graph as constants.
+  """
+
+  def __init__(self, data, read_num_records_hint=4096):
+    """Numpy array input for a `TimeSeriesInputFn`.
+
+    Args:
+      data: A dictionary mapping feature names to Numpy arrays, with two
+        possible shapes (requires keys `TrainEvalFeatures.TIMES` and
+        `TrainEvalFeatures.VALUES`):
+          Univariate; `TIMES` and `VALUES` are both vectors of shape [series
+            length]
+          Multivariate; `TIMES` is a vector of shape [series length], `VALUES`
+            has shape [series length x number of features].
+        In any case, `VALUES` and any exogenous features must have their shapes
+        prefixed by the shape of the value corresponding to the `TIMES` key.
+      read_num_records_hint: The maximum number of samples to read at one time,
+        for efficiency.
+    """
+    self._features = _canonicalize_numpy_data(
+        data, require_single_batch=True)
+    self._read_num_records_hint = read_num_records_hint
+
+  def check_dataset_size(self, minimum_dataset_size):
+    """Raise an error if the dataset is too small."""
+    dataset_size = self._features[feature_keys.TrainEvalFeatures.TIMES].shape[1]
+    if dataset_size < minimum_dataset_size:
+      raise ValueError(
+          ("A TimeSeriesInputFn is configured to create windows of size {}, "
+           "but only {} records were available in the dataset. Either decrease "
+           "the window size or provide more records.").format(
+               minimum_dataset_size, dataset_size))
+
+  def read(self):
+    """Returns a large chunk of the Numpy arrays for later re-chunking."""
+    # Remove the batch dimension from all features
+    features = {key: numpy.squeeze(value, axis=0)
+                for key, value in self._features.items()}
+    return estimator_lib.inputs.numpy_input_fn(
+        x=features,
+        # The first dimensions of features are the series length, since we have
+        # removed the batch dimension above. We now pull out
+        # self._read_num_records_hint steps of this single time series to pass
+        # to the TimeSeriesInputFn.
+        batch_size=self._read_num_records_hint,
+        num_epochs=None,
+        shuffle=False)()
+
+  def read_full(self):
+    """Returns `Tensor` versions of the full Numpy arrays."""
+    features = estimator_lib.inputs.numpy_input_fn(
+        x=self._features,
+        batch_size=1,
+        num_epochs=None,
+        queue_capacity=2,  # Each queue element is a full copy of the dataset
+        shuffle=False)()
+    # TimeSeriesInputFn expect just a batch dimension
+    return {feature_name: array_ops.squeeze(feature_value, axis=0)
+            for feature_name, feature_value in features.items()}
+
+
+class ReaderBaseTimeSeriesParser(TimeSeriesReader):
+  """Base for time series readers which wrap a `tf.ReaderBase`."""
+
+  def __init__(self, filenames, read_num_records_hint=4096):
+    """Configure the time series reader.
+
+    Args:
+      filenames: A string or list of strings indicating files to read records
+        from.
+      read_num_records_hint: When not reading a full dataset, indicates the
+        number of records to transfer in a single chunk (for efficiency). The
+        actual number transferred at one time may vary.
+    """
+    self._filenames = filenames
+    self._read_num_records_hint = read_num_records_hint
+
+  @abc.abstractmethod
+  def _get_reader(self):
+    """Get an instance of the tf.ReaderBase associated with this class."""
+    pass
+
+  @abc.abstractmethod
+  def _process_records(self, lines):
+    """Given string items, return a processed dictionary of Tensors.
+
+    Args:
+      lines: A 1-dimensional string Tensor, each representing a record to parse
+        (source dependent, e.g. a line of a file, or a serialized protocol
+        buffer).
+
+    Returns:
+      A dictionary mapping feature names to their values. The batch dimensions
+      should match the length of `lines`.
+    """
+    pass
+
+  def _get_filename_queue(self, epoch_limit):
+    """Constructs a filename queue with an epoch limit.
+
+    `epoch_limit` is intended as an error checking fallback to prevent a reader
+    from infinitely looping in its requests for more work items if none are
+    available in any file. It should be set high enough that it is never reached
+    assuming at least one record exists in some file.
+
+    Args:
+      epoch_limit: The maximum number of times to read through the complete list
+        of files before throwing an OutOfRangeError.
+    Returns:
+      A tuple of (filename_queue, epoch_limiter):
+        filename_queue: A FIFOQueue with filename work items.
+        epoch_limiter: The local variable used for epoch limitation. This should
+          be set to zero before a reader is passed `filename_queue` in order to
+          reset the epoch limiter's state.
+    """
+    epoch_limiter = variable_scope.variable(
+        initial_value=constant_op.constant(0, dtype=dtypes.int64),
+        name="epoch_limiter",
+        trainable=False,
+        collections=[ops.GraphKeys.LOCAL_VARIABLES])
+    filenames_tensor = array_ops.reshape(
+        ops.convert_to_tensor(self._filenames), [-1])
+    # We can't rely on epoch_limiter being initialized, since queue runners are
+    # started before local variables are initialized. Instead, we ignore epoch
+    # limits before variable initialization. This means that prior to variable
+    # initialization, a QueueRunner may cause a reader to enter an un-checked
+    # infinite loop. However, as soon as local variables are initialized, we
+    # will start incrementing and checking epoch_limiter, which will interrupt
+    # any in-progress loops.
+    conditional_count_up_to = control_flow_ops.cond(
+        state_ops.is_variable_initialized(epoch_limiter),
+        lambda: epoch_limiter.count_up_to(epoch_limit),
+        lambda: constant_op.constant(0, dtype=dtypes.int64))
+    with ops.control_dependencies([conditional_count_up_to]):
+      filenames_tensor = array_ops.identity(filenames_tensor)
+    filename_queue = input_lib.string_input_producer(
+        filenames_tensor, shuffle=False, capacity=1)
+    return filename_queue, epoch_limiter
+
+  def read(self):
+    """Reads a chunk of data from the `tf.ReaderBase` for later re-chunking."""
+    # Assuming there is at least one item to be read among all of the files in
+    # self._filenames, we will not need to go through more than
+    # self._read_num_records_hint epochs to get a batch of
+    # self._read_num_records_hint records. Setting this limit and resetting it
+    # before each reader.read_up_to call prevents infinite looping when there
+    # are no records available in any of the files.
+    filename_queue, epoch_limiter = self._get_filename_queue(
+        epoch_limit=self._read_num_records_hint)
+    reader = self._get_reader()
+    epoch_reset_op = state_ops.assign(epoch_limiter, 0)
+    with ops.control_dependencies([epoch_reset_op]):
+      _, records = reader.read_up_to(
+          filename_queue, self._read_num_records_hint)
+    return self._process_records(records)
+
+  def read_full(self):
+    """Reads a full epoch of data into memory."""
+    reader = self._get_reader()
+    # Set a hard limit of 2 epochs through self._filenames. If there are any
+    # records available, we should only end up reading the first record in the
+    # second epoch before exiting the while loop and subsequently resetting the
+    # epoch limit. If there are no records available in any of the files, this
+    # hard limit prevents the reader.read_up_to call from looping infinitely.
+    filename_queue, epoch_limiter = self._get_filename_queue(epoch_limit=2)
+    epoch_reset_op = state_ops.assign(epoch_limiter, 0)
+    with ops.control_dependencies([epoch_reset_op]):
+      first_key, first_value = reader.read_up_to(filename_queue, 1)
+    # Read until we get a duplicate key (one epoch)
+    def _while_condition(
+        current_key, current_value, current_index, collected_records):
+      del current_value, current_index, collected_records  # unused
+      return math_ops.not_equal(array_ops.squeeze(current_key, axis=0),
+                                array_ops.squeeze(first_key, axis=0))
+
+    def _while_body(
+        current_key, current_value, current_index, collected_records):
+      del current_key  # unused
+      new_key, new_value = reader.read_up_to(filename_queue, 1)
+      new_key.set_shape([1])
+      new_value.set_shape([1])
+      return (new_key,
+              new_value,
+              current_index + 1,
+              collected_records.write(current_index, current_value))
+    _, _, _, records_ta = control_flow_ops.while_loop(
+        _while_condition,
+        _while_body,
+        [constant_op.constant([""]), first_value,
+         0,  # current_index starting value
+         tensor_array_ops.TensorArray(  # collected_records
+             dtype=dtypes.string, size=0, dynamic_size=True)])
+    records = records_ta.concat()
+    # Reset the reader when we're done so that subsequent requests for data get
+    # the dataset in the proper order.
+    with ops.control_dependencies([records]):
+      reader_reset_op = reader.reset()
+    with ops.control_dependencies([reader_reset_op]):
+      records = array_ops.identity(records)
+    return self._process_records(records)
+
+
+class CSVReader(ReaderBaseTimeSeriesParser):
+  """Reads from a collection of CSV-formatted files."""
+
+  def __init__(self,
+               filenames,
+               column_names=(feature_keys.TrainEvalFeatures.TIMES,
+                             feature_keys.TrainEvalFeatures.VALUES),
+               column_dtypes=None,
+               skip_header_lines=None,
+               read_num_records_hint=4096):
+    """CSV-parsing reader for a `TimeSeriesInputFn`.
+
+    Args:
+      filenames: A filename or list of filenames to read the time series
+          from. Each line must have columns corresponding to `column_names`.
+      column_names: A list indicating names for each
+          feature. `TrainEvalFeatures.TIMES` and `TrainEvalFeatures.VALUES` are
+          required; `VALUES` may be repeated to indicate a multivariate series.
+      column_dtypes: If provided, must be a list with the same length as
+          `column_names`, indicating dtypes for each column. Defaults to
+          `tf.int64` for `TrainEvalFeatures.TIMES` and `tf.float32` for
+          everything else.
+      skip_header_lines: Passed on to `tf.TextLineReader`; skips this number of
+          lines at the beginning of each file.
+      read_num_records_hint: When not reading a full dataset, indicates the
+          number of records to parse/transfer in a single chunk (for
+          efficiency). The actual number transferred at one time may be more or
+          less.
+    Raises:
+      ValueError: If required column names are not specified, or if lengths do
+        not match.
+    """
+    if feature_keys.TrainEvalFeatures.TIMES not in column_names:
+      raise ValueError("'{}' is a required column.".format(
+          feature_keys.TrainEvalFeatures.TIMES))
+    if feature_keys.TrainEvalFeatures.VALUES not in column_names:
+      raise ValueError("'{}' is a required column.".format(
+          feature_keys.TrainEvalFeatures.VALUES))
+    if column_dtypes is not None and len(column_dtypes) != len(column_names):
+      raise ValueError(
+          ("If specified, the length of column_dtypes must match the length of "
+           "column_names (got column_dtypes={} and column_names={}).").format(
+               column_dtypes, column_names))
+    if sum(1 for column_name in column_names
+           if column_name == feature_keys.TrainEvalFeatures.TIMES) != 1:
+      raise ValueError(
+          "Got more than one times column ('{}'), but exactly "
+          "one is required.".format(feature_keys.TrainEvalFeatures.TIMES))
+    self._column_names = column_names
+    self._column_dtypes = column_dtypes
+    self._skip_header_lines = skip_header_lines
+    super(CSVReader, self).__init__(
+        filenames=filenames, read_num_records_hint=read_num_records_hint)
+
+  def _get_reader(self):
+    return io_ops.TextLineReader(skip_header_lines=self._skip_header_lines)
+
+  def _process_records(self, lines):
+    """Parse `lines` as CSV records."""
+    if self._column_dtypes is None:
+      default_values = [(array_ops.zeros([], dtypes.int64),)
+                        if column_name == feature_keys.TrainEvalFeatures.TIMES
+                        else () for column_name in self._column_names]
+    else:
+      default_values = [(array_ops.zeros([], dtype),)
+                        for dtype in self._column_dtypes]
+    columns = parsing_ops.decode_csv(lines, default_values)
+    features_lists = {}
+    for column_name, value in zip(self._column_names, columns):
+      features_lists.setdefault(column_name, []).append(value)
+    features = {}
+    for column_name, values in features_lists.items():
+      if (len(values) == 1 and
+          column_name != feature_keys.TrainEvalFeatures.VALUES):
+        features[column_name] = values[0]
+      else:
+        features[column_name] = array_ops.stack(values, axis=1)
+    return features
+
+
+class TimeSeriesInputFn(object):
+  """Base for classes which create batches of windows from a time series."""
+
+  @abc.abstractmethod
+  def create_batch(self):
+    """Creates chunked Tensors from times, values, and other features.
+
+    Suitable for use as the input_fn argument of a tf.estimator.Estimator's
+    fit() or evaluate() method.
+
+    Returns:
+      A tuple of (features, targets):
+        features: A dictionary with `TrainEvalFeatures.TIMES` and
+          `TrainEvalFeatures.VALUES` as keys, `TIMES` having an associated value
+          with shape [batch size x window length], `VALUES` with shape [batch
+          size x window length x number of features]. Any other features will
+          also have shapes prefixed with [batch size x window length].
+        targets: Not used, but must have a value for compatibility with the
+          Estimator API. That value should be None.
+    """
+    pass
+
+  def __call__(self):
+    # Allow a TimeSeriesInputFn to be used as an input function directly
+    return self.create_batch()
+
+
+class WholeDatasetInputFn(TimeSeriesInputFn):
+  """Supports passing a full time series to a model for evaluation/inference.
+
+  Note that this `TimeSeriesInputFn` is not designed for high throughput, and
+  should not be used for training. It allows for sequential evaluation on a full
+  dataset (with sequential in-sample predictions), which then feeds naturally
+  into `predict_continuation_input_fn` for making out-of-sample
+  predictions. While this is useful for plotting and interactive use,
+  `RandomWindowInputFn` is better suited to training and quantitative
+  evaluation.
+  """
+  # TODO(allenl): A SequentialWindowInputFn for getting model end state without
+  # loading the whole dataset into memory (or for quantitative evaluation of
+  # sequential models). Note that an Estimator using such a TimeSeriesInputFn
+  # won't return in-sample predictions for the whole dataset, which means it
+  # won't be terribly useful for interactive use/plotting (unless the user
+  # passes in concat metrics). Also need to be careful about state saving for
+  # sequential models, particularly the gaps between chunks.
+
+  def __init__(self, time_series_reader):
+    """Initialize the `TimeSeriesInputFn`.
+
+    Args:
+      time_series_reader: A TimeSeriesReader object.
+    """
+    self._reader = time_series_reader
+    super(WholeDatasetInputFn, self).__init__()
+
+  def create_batch(self):
+    """A suitable `input_fn` for an `Estimator`'s `evaluate()`.
+
+    Returns:
+      A dictionary mapping feature names to `Tensors`, each shape
+      prefixed by [1, data set size] (i.e. a batch size of 1).
+    """
+    features = self._reader.read_full()
+    # Add a batch dimension of one to each feature.
+    return ({feature_name: feature_value[None, ...]
+             for feature_name, feature_value in features.items()},
+            None)
+
+
+class RandomWindowInputFn(TimeSeriesInputFn):
+  """Wraps a `TimeSeriesReader` to create random batches of windows.
+
+  Tensors are first collected into sequential windows (in a windowing queue
+  created by `tf.train.batch`, based on the order returned from
+  `time_series_reader`), then these windows are randomly batched (in a
+  `RandomShuffleQueue`), the Tensors returned by `create_batch` having shapes
+  prefixed by [`batch_size`, `window_size`].
+
+  This `TimeSeriesInputFn` is useful for both training and quantitative
+  evaluation (but be sure to run several epochs for sequential models such as
+  `StructuralEnsembleRegressor` to completely flush stale state left over from
+  training). For qualitative evaluation or when preparing for predictions, use
+  `WholeDatasetInputFn`.
+  """
+
+  def __init__(
+      self, time_series_reader, window_size, batch_size,
+      queue_capacity_multiplier=1000, shuffle_min_after_dequeue_multiplier=2,
+      discard_out_of_order=True, discard_consecutive_batches_limit=1000,
+      jitter=True, num_threads=2, shuffle_seed=None):
+    """Configure the RandomWindowInputFn.
+
+    Args:
+      time_series_reader: A TimeSeriesReader object.
+      window_size: The number of examples to keep together sequentially. This
+        controls the length of truncated backpropagation: smaller values mean
+        less sequential computation, which can lead to faster training, but
+        create a coarser approximation to the gradient (which would ideally be
+        computed by a forward pass over the entire sequence in order).
+      batch_size: The number of windows to place together in a batch. Larger
+        values will lead to more stable gradients during training.
+      queue_capacity_multiplier: The capacity for the queues used to create
+        batches, specified as a multiple of `batch_size` (for
+        RandomShuffleQueue) and `batch_size * window_size` (for the
+        FIFOQueue). Controls the maximum number of windows stored. Should be
+        greater than `shuffle_min_after_dequeue_multiplier`.
+      shuffle_min_after_dequeue_multiplier: The minimum number of windows in the
+        RandomShuffleQueue after a dequeue, which controls the amount of entropy
+        introduced during batching. Specified as a multiple of `batch_size`.
+      discard_out_of_order: If True, windows of data which have times which
+        decrease (a higher time followed by a lower time) are discarded. If
+        False, the window and associated features are instead sorted so that
+        times are non-decreasing. Discarding is typically faster, as models do
+        not have to deal with artificial gaps in the data. However, discarding
+        does create a bias where the beginnings and endings of files are
+        under-sampled.
+      discard_consecutive_batches_limit: Raise an OutOfRangeError if more than
+        this number of batches are discarded without a single non-discarded
+        window (prevents infinite looping when the dataset is too small).
+      jitter: If True, randomly discards examples between some windows in order
+        to avoid deterministic chunking patterns. This is important for models
+        like AR which may otherwise overfit a fixed chunking.
+      num_threads: Use this number of threads for queues. Setting a value of 1
+        removes one source of non-determinism (and in combination with
+        shuffle_seed should provide deterministic windowing).
+      shuffle_seed: A seed for window shuffling. The default value of None
+        provides random behavior. With `shuffle_seed` set and
+        `num_threads=1`, provides deterministic behavior.
+    """
+    self._reader = time_series_reader
+    self._window_size = window_size
+    self._reader.check_dataset_size(minimum_dataset_size=self._window_size)
+    self._batch_size = batch_size
+    self._queue_capacity_multiplier = queue_capacity_multiplier
+    self._shuffle_min_after_dequeue_multiplier = (
+        shuffle_min_after_dequeue_multiplier)
+    self._discard_out_of_order = discard_out_of_order
+    self._discard_limit = discard_consecutive_batches_limit
+    self._jitter = jitter
+    if num_threads is None:
+      self._num_threads = self._batch_size
+    else:
+      self._num_threads = num_threads
+    self._shuffle_seed = shuffle_seed
+    super(RandomWindowInputFn, self).__init__()
+
+  def create_batch(self):
+    """Create queues to window and batch time series data.
+
+    Returns:
+      A dictionary of Tensors corresponding to the output of `self._reader`
+      (from the `time_series_reader` constructor argument), each with shapes
+      prefixed by [`batch_size`, `window_size`].
+    """
+    features = self._reader.read()
+    if self._jitter:
+      # TODO(agarwal, allenl): Figure out if more jitter is needed here.
+      jitter = random_ops.random_uniform(shape=[], maxval=2, dtype=dtypes.int32)
+    else:
+      jitter = 0
+    # To keep things efficient, we pass from the windowing batcher to the
+    # batch-of-windows batcher in batches. This avoids the need for huge numbers
+    # of threads, but does mean that jitter is only applied occasionally.
+    # TODO(allenl): Experiment with different internal passing sizes.
+    internal_passing_size = self._batch_size
+    features_windowed = input_lib.batch(
+        features,
+        batch_size=self._window_size * internal_passing_size + jitter,
+        enqueue_many=True,
+        capacity=(self._queue_capacity_multiplier
+                  * internal_passing_size * self._window_size),
+        num_threads=self._num_threads)
+    raw_features_windowed = features_windowed
+    if self._jitter:
+      features_windowed = {
+          key: value[jitter:]
+          for key, value in features_windowed.items()}
+    features_windowed = {
+        key: array_ops.reshape(
+            value,
+            array_ops.concat(
+                [[internal_passing_size, self._window_size],
+                 array_ops.shape(value)[1:]],
+                axis=0))
+        for key, value in features_windowed.items()}
+    batch_and_window_shape = tensor_shape.TensorShape(
+        [internal_passing_size, self._window_size])
+    for key in features_windowed.keys():
+      features_windowed[key].set_shape(
+          batch_and_window_shape.concatenate(
+              raw_features_windowed[key].get_shape()[1:]))
+    # When switching files, we may end up with windows where the time is not
+    # decreasing, even if times within each file are sorted (and even if those
+    # files are visited in order, when looping back around to the beginning of
+    # the first file). This is hard for models to deal with, so we either
+    # discard such examples, creating a bias where the beginning and end of the
+    # series is under-sampled, or we sort the window, creating large gaps.
+    times = features_windowed[feature_keys.TrainEvalFeatures.TIMES]
+    if self._discard_out_of_order:
+      non_decreasing = math_ops.reduce_all(
+          times[:, 1:] >= times[:, :-1], axis=1)
+      # Ensure that no more than self._discard_limit complete batches are
+      # discarded contiguously (resetting the count when we find a single clean
+      # window). This prevents infinite looping when the dataset is smaller than
+      # the window size.
+      # TODO(allenl): Figure out a way to return informative errors from
+      # count_up_to.
+      discarded_windows_limiter = variable_scope.variable(
+          initial_value=constant_op.constant(0, dtype=dtypes.int64),
+          name="discarded_windows_limiter",
+          trainable=False,
+          collections=[ops.GraphKeys.LOCAL_VARIABLES])
+      def _initialized_limit_check():
+        return control_flow_ops.cond(
+            math_ops.reduce_any(non_decreasing),
+            lambda: state_ops.assign(discarded_windows_limiter, 0),
+            lambda: discarded_windows_limiter.count_up_to(self._discard_limit))
+      discard_limit_op = control_flow_ops.cond(
+          state_ops.is_variable_initialized(discarded_windows_limiter),
+          _initialized_limit_check,
+          lambda: constant_op.constant(0, dtype=dtypes.int64))
+      with ops.control_dependencies([discard_limit_op]):
+        non_decreasing = array_ops.identity(non_decreasing)
+    else:
+      _, indices_descending = nn.top_k(
+          times, k=array_ops.shape(times)[-1], sorted=True)
+      indices = array_ops.reverse(indices_descending, axis=[0])
+      features_windowed = {
+          key: array_ops.gather(params=value, indices=indices)
+          for key, value in features_windowed.items()
+      }
+      non_decreasing = True
+    features_batched = input_lib.maybe_shuffle_batch(
+        features_windowed,
+        num_threads=self._num_threads,
+        seed=self._shuffle_seed,
+        batch_size=self._batch_size,
+        capacity=self._queue_capacity_multiplier * self._batch_size,
+        min_after_dequeue=(self._shuffle_min_after_dequeue_multiplier *
+                           self._batch_size),
+        keep_input=non_decreasing,
+        enqueue_many=True)
+    return (features_batched, None)
+
+
+def _canonicalize_numpy_data(data, require_single_batch):
+  """Do basic checking and reshaping for Numpy data.
+
+  Args:
+    data: A dictionary mapping keys to Numpy arrays, with several possible
+      shapes (requires keys `TrainEvalFeatures.TIMES` and
+      `TrainEvalFeatures.VALUES`):
+        Single example; `TIMES` is a scalar and `VALUES` is either a scalar or a
+          vector of length [number of features].
+        Sequence; `TIMES` is a vector of shape [series length], `VALUES` either
+          has shape [series length] (univariate) or [series length x number of
+          features] (multivariate).
+        Batch of sequences; `TIMES` is a vector of shape [batch size x series
+          length], `VALUES` has shape [batch size x series length] or [batch
+          size x series length x number of features].
+      In any case, `VALUES` and any exogenous features must have their shapes
+      prefixed by the shape of the value corresponding to the `TIMES` key.
+    require_single_batch: If True, raises an error if the provided data has a
+      batch dimension > 1.
+  Returns:
+    A dictionary with features normalized to have shapes prefixed with [batch
+    size x series length]. The sizes of dimensions which were omitted in the
+    inputs are 1.
+  Raises:
+    ValueError: If dimensions are incorrect or do not match, or required
+      features are missing.
+  """
+  features = {key: numpy.array(value) for key, value in data.items()}
+  if (feature_keys.TrainEvalFeatures.TIMES not in features or
+      feature_keys.TrainEvalFeatures.VALUES not in features):
+    raise ValueError("{} and {} are required features.".format(
+        feature_keys.TrainEvalFeatures.TIMES,
+        feature_keys.TrainEvalFeatures.VALUES))
+  times = features[feature_keys.TrainEvalFeatures.TIMES]
+  for key, value in features.items():
+    if value.shape[:len(times.shape)] != times.shape:
+      raise ValueError(
+          ("All features must have their shapes prefixed by the shape of the"
+           " times feature. Got shape {} for feature '{}', but shape {} for"
+           " '{}'").format(value.shape, key, times.shape,
+                           feature_keys.TrainEvalFeatures.TIMES))
+  if not times.shape:  # a single example
+    if not features[feature_keys.TrainEvalFeatures.VALUES].shape:  # univariate
+      # Add a feature dimension (with one feature)
+      features[feature_keys.TrainEvalFeatures.VALUES] = features[
+          feature_keys.TrainEvalFeatures.VALUES][..., None]
+    elif len(features[feature_keys.TrainEvalFeatures.VALUES].shape) > 1:
+      raise ValueError(
+          ("Got an unexpected number of dimensions for the '{}' feature."
+           " Was expecting at most 1 dimension"
+           " ([number of features]) since '{}' does not "
+           "have a batch or time dimension, but got shape {}").format(
+               feature_keys.TrainEvalFeatures.VALUES,
+               feature_keys.TrainEvalFeatures.TIMES,
+               features[feature_keys.TrainEvalFeatures.VALUES].shape))
+    # Add trivial batch and time dimensions for every feature
+    features = {key: value[None, None, ...] for key, value in features.items()}
+  if len(times.shape) == 1:  # shape [series length]
+    if len(features[feature_keys.TrainEvalFeatures.VALUES]
+           .shape) == 1:  # shape [series length]
+      # Add a feature dimension (with one feature)
+      features[feature_keys.TrainEvalFeatures.VALUES] = features[
+          feature_keys.TrainEvalFeatures.VALUES][..., None]
+    elif len(features[feature_keys.TrainEvalFeatures.VALUES].shape) > 2:
+      raise ValueError(
+          ("Got an unexpected number of dimensions for the '{}' feature."
+           " Was expecting at most 2 dimensions"
+           " ([series length, number of features]) since '{}' does not "
+           "have a batch dimension, but got shape {}").format(
+               feature_keys.TrainEvalFeatures.VALUES,
+               feature_keys.TrainEvalFeatures.TIMES,
+               features[feature_keys.TrainEvalFeatures.VALUES].shape))
+    # Add trivial batch dimensions for every feature
+    features = {key: value[None, ...] for key, value in features.items()}
+  elif len(features[feature_keys.TrainEvalFeatures.TIMES]
+           .shape) != 2:  # shape [batch size, series length]
+    raise ValueError(
+        ("Got an unexpected number of dimensions for times. Was expecting at "
+         "most two ([batch size, series length]), but got shape {}.").format(
+             times.shape))
+  if require_single_batch:
+    # We don't expect input to be already batched; batching is done later
+    if features[feature_keys.TrainEvalFeatures.TIMES].shape[0] != 1:
+      raise ValueError("Got batch input, was expecting unbatched input.")
+  return features
diff --git a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py
new file mode 100644
index 0000000000..ed78a835a4
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py
@@ -0,0 +1,319 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the time series input pipeline."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import csv
+import tempfile
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import test_utils
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+
+from tensorflow.python.framework import errors
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.training import coordinator as coordinator_lib
+from tensorflow.python.training import queue_runner_impl
+
+
+def _make_csv_temp_file(to_write, test_tmpdir):
+  _, data_file = tempfile.mkstemp(dir=test_tmpdir)
+  with open(data_file, "w") as f:
+    csvwriter = csv.writer(f)
+    for record in to_write:
+      csvwriter.writerow(record)
+  return data_file
+
+
+def _make_csv_time_series(num_features, num_samples, test_tmpdir):
+  filename = _make_csv_temp_file(
+      [[i] + [float(i) * 2. + feature_number
+              for feature_number in range(num_features)]
+       for i in range(num_samples)],
+      test_tmpdir=test_tmpdir)
+  return filename
+
+
+def _make_numpy_time_series(num_features, num_samples):
+  times = numpy.arange(num_samples)
+  values = times[:, None] * 2. + numpy.arange(num_features)[None, :]
+  return {TrainEvalFeatures.TIMES: times,
+          TrainEvalFeatures.VALUES: values}
+
+
+class RandomWindowInputFnTests(test.TestCase):
+
+  def _random_window_input_fn_test_template(
+      self, time_series_reader, window_size, batch_size, num_features,
+      discard_out_of_order=False):
+    input_fn = input_pipeline.RandomWindowInputFn(
+        time_series_reader=time_series_reader,
+        window_size=window_size, batch_size=batch_size)
+    result, _ = input_fn()
+    init_op = variables.local_variables_initializer()
+    with self.test_session() as session:
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      session.run(init_op)
+      features = session.run(result)
+      coordinator.request_stop()
+      coordinator.join()
+    self.assertAllEqual([batch_size, window_size],
+                        features[TrainEvalFeatures.TIMES].shape)
+    for window_position in range(window_size - 1):
+      for batch_position in range(batch_size):
+        # Checks that all times are contiguous
+        self.assertEqual(
+            features[TrainEvalFeatures.TIMES][batch_position,
+                                              window_position + 1],
+            features[TrainEvalFeatures.TIMES][batch_position,
+                                              window_position] + 1)
+    self.assertAllEqual([batch_size, window_size, num_features],
+                        features[TrainEvalFeatures.VALUES].shape)
+    self.assertEqual("int64", features[TrainEvalFeatures.TIMES].dtype)
+    for feature_number in range(num_features):
+      self.assertAllEqual(
+          features[TrainEvalFeatures.TIMES] * 2. + feature_number,
+          features[TrainEvalFeatures.VALUES][:, :, feature_number])
+    return features
+
+  def _test_out_of_order(self, time_series_reader, discard_out_of_order):
+    self._random_window_input_fn_test_template(
+        time_series_reader=time_series_reader,
+        num_features=1, window_size=2, batch_size=5,
+        discard_out_of_order=discard_out_of_order)
+
+  def test_csv_sort_out_of_order(self):
+    filename = _make_csv_time_series(num_features=1, num_samples=50,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader([filename])
+    self._test_out_of_order(time_series_reader, discard_out_of_order=False)
+
+  def test_numpy_sort_out_of_order(self):
+    data = _make_numpy_time_series(num_features=1, num_samples=50)
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._test_out_of_order(time_series_reader, discard_out_of_order=False)
+
+  def test_csv_discard_out_of_order(self):
+    filename = _make_csv_time_series(num_features=1, num_samples=50,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader([filename])
+    self._test_out_of_order(time_series_reader, discard_out_of_order=True)
+
+  def test_csv_discard_out_of_order_window_equal(self):
+    filename = _make_csv_time_series(num_features=1, num_samples=3,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader([filename])
+    self._random_window_input_fn_test_template(
+        time_series_reader=time_series_reader,
+        num_features=1, window_size=3, batch_size=5,
+        discard_out_of_order=True)
+
+  def test_csv_discard_out_of_order_window_too_large(self):
+    filename = _make_csv_time_series(num_features=1, num_samples=2,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader([filename])
+    with self.assertRaises(errors.OutOfRangeError):
+      self._random_window_input_fn_test_template(
+          time_series_reader=time_series_reader,
+          num_features=1, window_size=3, batch_size=5,
+          discard_out_of_order=True)
+
+  def test_csv_no_data(self):
+    filename = _make_csv_time_series(num_features=1, num_samples=0,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader([filename])
+    with self.assertRaises(errors.OutOfRangeError):
+      self._test_out_of_order(time_series_reader, discard_out_of_order=True)
+
+  def test_numpy_discard_out_of_order(self):
+    data = _make_numpy_time_series(num_features=1, num_samples=50)
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._test_out_of_order(time_series_reader, discard_out_of_order=True)
+
+  def test_numpy_discard_out_of_order_window_equal(self):
+    data = _make_numpy_time_series(num_features=1, num_samples=3)
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._random_window_input_fn_test_template(
+        time_series_reader=time_series_reader,
+        num_features=1, window_size=3, batch_size=5,
+        discard_out_of_order=True)
+
+  def test_numpy_discard_out_of_order_window_too_large(self):
+    data = _make_numpy_time_series(num_features=1, num_samples=2)
+    time_series_reader = input_pipeline.NumpyReader(data)
+    with self.assertRaisesRegexp(ValueError, "only 2 records were available"):
+      self._random_window_input_fn_test_template(
+          time_series_reader=time_series_reader,
+          num_features=1, window_size=3, batch_size=5,
+          discard_out_of_order=True)
+
+  def _test_multivariate(self, time_series_reader, num_features):
+    self._random_window_input_fn_test_template(
+        time_series_reader=time_series_reader,
+        num_features=num_features,
+        window_size=2,
+        batch_size=5)
+
+  def test_csv_multivariate(self):
+    filename = _make_csv_time_series(num_features=2, num_samples=50,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader(
+        [filename],
+        column_names=(TrainEvalFeatures.TIMES, TrainEvalFeatures.VALUES,
+                      TrainEvalFeatures.VALUES))
+    self._test_multivariate(time_series_reader=time_series_reader,
+                            num_features=2)
+
+  def test_numpy_multivariate(self):
+    data = _make_numpy_time_series(num_features=3, num_samples=50)
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._test_multivariate(time_series_reader, num_features=3)
+
+  def test_numpy_withbatch(self):
+    data_nobatch = _make_numpy_time_series(num_features=4, num_samples=100)
+    data = {feature_name: feature_value[None]
+            for feature_name, feature_value in data_nobatch.items()}
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._random_window_input_fn_test_template(
+        time_series_reader=time_series_reader,
+        num_features=4,
+        window_size=3,
+        batch_size=5)
+
+  def test_numpy_nobatch_nofeatures(self):
+    data = _make_numpy_time_series(num_features=1, num_samples=100)
+    data[TrainEvalFeatures.VALUES] = data[TrainEvalFeatures.VALUES][:, 0]
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._random_window_input_fn_test_template(
+        time_series_reader=time_series_reader,
+        num_features=1,
+        window_size=16,
+        batch_size=16)
+
+
+class WholeDatasetInputFnTests(test.TestCase):
+
+  def _whole_dataset_input_fn_test_template(
+      self, time_series_reader, num_features, num_samples):
+    result, _ = input_pipeline.WholeDatasetInputFn(time_series_reader)()
+    with self.test_session() as session:
+      session.run(variables.local_variables_initializer())
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      features = session.run(result)
+      coordinator.request_stop()
+      coordinator.join()
+    self.assertEqual("int64", features[TrainEvalFeatures.TIMES].dtype)
+    self.assertAllEqual(numpy.arange(num_samples, dtype=numpy.int64)[None, :],
+                        features[TrainEvalFeatures.TIMES])
+    for feature_number in range(num_features):
+      self.assertAllEqual(
+          features[TrainEvalFeatures.TIMES] * 2. + feature_number,
+          features[TrainEvalFeatures.VALUES][:, :, feature_number])
+
+  def test_csv(self):
+    filename = _make_csv_time_series(num_features=3, num_samples=50,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader(
+        [filename],
+        column_names=(TrainEvalFeatures.TIMES, TrainEvalFeatures.VALUES,
+                      TrainEvalFeatures.VALUES, TrainEvalFeatures.VALUES))
+    self._whole_dataset_input_fn_test_template(
+        time_series_reader=time_series_reader, num_features=3, num_samples=50)
+
+  def test_csv_no_data(self):
+    filename = _make_csv_time_series(num_features=1, num_samples=0,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader([filename])
+    with self.assertRaises(errors.OutOfRangeError):
+      self._whole_dataset_input_fn_test_template(
+          time_series_reader=time_series_reader, num_features=1, num_samples=50)
+
+  def test_numpy(self):
+    data = _make_numpy_time_series(num_features=4, num_samples=100)
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._whole_dataset_input_fn_test_template(
+        time_series_reader=time_series_reader, num_features=4, num_samples=100)
+
+  def test_numpy_withbatch(self):
+    data_nobatch = _make_numpy_time_series(num_features=4, num_samples=100)
+    data = {feature_name: feature_value[None]
+            for feature_name, feature_value in data_nobatch.items()}
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._whole_dataset_input_fn_test_template(
+        time_series_reader=time_series_reader, num_features=4, num_samples=100)
+
+  def test_numpy_nobatch_nofeatures(self):
+    data = _make_numpy_time_series(num_features=1, num_samples=100)
+    data[TrainEvalFeatures.VALUES] = data[TrainEvalFeatures.VALUES][:, 0]
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._whole_dataset_input_fn_test_template(
+        time_series_reader=time_series_reader, num_features=1, num_samples=100)
+
+
+class AllWindowInputFnTests(test.TestCase):
+
+  def _all_window_input_fn_test_template(
+      self, time_series_reader, num_samples, window_size,
+      original_numpy_features=None):
+    input_fn = test_utils.AllWindowInputFn(
+        time_series_reader=time_series_reader,
+        window_size=window_size)
+    features, _ = input_fn()
+    init_op = variables.local_variables_initializer()
+    with self.test_session() as session:
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      session.run(init_op)
+      chunked_times, chunked_values = session.run(
+          [features[TrainEvalFeatures.TIMES],
+           features[TrainEvalFeatures.VALUES]])
+      coordinator.request_stop()
+      coordinator.join()
+    self.assertAllEqual([num_samples - window_size + 1, window_size],
+                        chunked_times.shape)
+    if original_numpy_features is not None:
+      original_times = original_numpy_features[TrainEvalFeatures.TIMES]
+      original_values = original_numpy_features[TrainEvalFeatures.VALUES]
+      self.assertAllEqual(original_times, numpy.unique(chunked_times))
+      self.assertAllEqual(original_values[chunked_times],
+                          chunked_values)
+
+  def test_csv(self):
+    filename = _make_csv_time_series(num_features=1, num_samples=50,
+                                     test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.CSVReader(
+        [filename],
+        column_names=(TrainEvalFeatures.TIMES, TrainEvalFeatures.VALUES))
+    self._all_window_input_fn_test_template(
+        time_series_reader=time_series_reader, num_samples=50, window_size=10)
+
+  def test_numpy(self):
+    data = _make_numpy_time_series(num_features=2, num_samples=31)
+    time_series_reader = input_pipeline.NumpyReader(data)
+    self._all_window_input_fn_test_template(
+        time_series_reader=time_series_reader, original_numpy_features=data,
+        num_samples=31, window_size=5)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/math_utils.py b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
new file mode 100644
index 0000000000..b7832a10cd
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
@@ -0,0 +1,951 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Miscellaneous utilities used by time series models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import math
+
+from tensorflow.contrib import lookup
+from tensorflow.contrib.layers.python.layers import layers
+
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import functional_ops
+from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.util import nest
+
+
+def clip_covariance(
+    covariance_matrix, maximum_variance_ratio, minimum_variance):
+  """Enforce constraints on a covariance matrix to improve numerical stability.
+
+  Args:
+    covariance_matrix: A [..., N, N] batch of covariance matrices.
+    maximum_variance_ratio: The maximum allowed ratio of two diagonal
+      entries. Any entries lower than the maximum entry divided by this ratio
+      will be set to that value.
+    minimum_variance: A floor for diagonal entries in the returned matrix.
+  Returns:
+    A new covariance matrix with the requested constraints enforced. If the
+    input was positive definite, the output will be too.
+  """
+  # TODO(allenl): Smarter scaling here so that correlations are preserved when
+  # fiddling with diagonal elements.
+  diagonal = array_ops.matrix_diag_part(covariance_matrix)
+  maximum = math_ops.reduce_max(diagonal, axis=-1, keep_dims=True)
+  new_diagonal = gen_math_ops.maximum(
+      diagonal, maximum / maximum_variance_ratio)
+  return array_ops.matrix_set_diag(
+      covariance_matrix, math_ops.maximum(new_diagonal, minimum_variance))
+
+
+def block_diagonal(matrices, dtype=dtypes.float32, name="block_diagonal"):
+  r"""Constructs block-diagonal matrices from a list of batched 2D tensors.
+
+  Args:
+    matrices: A list of Tensors with shape [..., N_i, M_i] (i.e. a list of
+      matrices with the same batch dimension).
+    dtype: Data type to use. The Tensors in `matrices` must match this dtype.
+    name: A name for the returned op.
+  Returns:
+    A matrix with the input matrices stacked along its main diagonal, having
+    shape [..., \sum_i N_i, \sum_i M_i].
+  """
+  matrices = [ops.convert_to_tensor(matrix, dtype=dtype) for matrix in matrices]
+  blocked_rows = tensor_shape.Dimension(0)
+  blocked_cols = tensor_shape.Dimension(0)
+  batch_shape = tensor_shape.TensorShape(None)
+  for matrix in matrices:
+    full_matrix_shape = matrix.get_shape().with_rank_at_least(2)
+    batch_shape = batch_shape.merge_with(full_matrix_shape[:-2])
+    blocked_rows += full_matrix_shape[-2]
+    blocked_cols += full_matrix_shape[-1]
+  ret_columns_list = []
+  for matrix in matrices:
+    matrix_shape = array_ops.shape(matrix)
+    ret_columns_list.append(matrix_shape[-1])
+  ret_columns = math_ops.add_n(ret_columns_list)
+  row_blocks = []
+  current_column = 0
+  for matrix in matrices:
+    matrix_shape = array_ops.shape(matrix)
+    row_before_length = current_column
+    current_column += matrix_shape[-1]
+    row_after_length = ret_columns - current_column
+    row_blocks.append(
+        array_ops.pad(
+            tensor=matrix,
+            paddings=array_ops.concat(
+                [
+                    array_ops.zeros(
+                        [array_ops.rank(matrix) - 1, 2], dtype=dtypes.int32), [(
+                            row_before_length, row_after_length)]
+                ],
+                axis=0)))
+  blocked = array_ops.concat(row_blocks, -2, name=name)
+  blocked.set_shape(batch_shape.concatenate((blocked_rows, blocked_cols)))
+  return blocked
+
+
+def power_sums_tensor(array_size, power_matrix, multiplier):
+  r"""Computes \sum_{i=0}^{N-1} A^i B (A^i)^T for N=0..(array_size + 1).
+
+  Args:
+    array_size: The number of non-trivial sums to pre-compute.
+    power_matrix: The "A" matrix above.
+    multiplier: The "B" matrix above
+  Returns:
+    A Tensor with S[N] = \sum_{i=0}^{N-1} A^i B (A^i)^T
+      S[0] is the zero matrix
+      S[1] is B
+      S[2] is A B A^T + B
+      ...and so on
+  """
+  array_size = math_ops.cast(array_size, dtypes.int32)
+  power_matrix = ops.convert_to_tensor(power_matrix)
+  identity_like_power_matrix = linalg_ops.eye(
+      array_ops.shape(power_matrix)[0], dtype=power_matrix.dtype)
+  identity_like_power_matrix.set_shape(
+      ops.convert_to_tensor(power_matrix).get_shape())
+  transition_powers = functional_ops.scan(
+      lambda previous_power, _: math_ops.matmul(previous_power, power_matrix),
+      math_ops.range(array_size - 1),
+      initializer=identity_like_power_matrix)
+  summed = math_ops.cumsum(
+      array_ops.concat([
+          array_ops.expand_dims(multiplier, 0), math_ops.matmul(
+              batch_times_matrix(transition_powers, multiplier),
+              transition_powers,
+              adjoint_b=True)
+      ], 0))
+  return array_ops.concat(
+      [array_ops.expand_dims(array_ops.zeros_like(multiplier), 0), summed], 0)
+
+
+def matrix_to_powers(matrix, powers):
+  """Raise a single matrix to multiple powers."""
+  matrix_tiled = array_ops.tile(
+      array_ops.expand_dims(matrix, 0), [array_ops.size(powers), 1, 1])
+  return batch_matrix_pow(matrix_tiled, powers)
+
+
+def batch_matrix_pow(matrices, powers):
+  """Compute powers of matrices, e.g. A^3 = matmul(matmul(A, A), A).
+
+  Uses exponentiation by squaring, with O(log(p)) matrix multiplications to
+  compute A^p.
+
+  Args:
+    matrices: [batch size x N x N]
+    powers: Which integer power to raise each matrix to [batch size]
+  Returns:
+    The matrices raised to their respective powers, same dimensions as the
+    "matrices" argument.
+  """
+
+  def terminate_when_all_zero(current_argument, residual_powers, accumulator):
+    del current_argument, accumulator  # not used for condition
+    do_exit = math_ops.reduce_any(
+        math_ops.greater(residual_powers, array_ops.ones_like(residual_powers)))
+    return do_exit
+
+  def do_iteration(current_argument, residual_powers, accumulator):
+    """Compute one step of iterative exponentiation by squaring.
+
+    The recursive form is:
+      power(A, p) = { power(matmul(A, A), p / 2) for even p
+                    { matmul(A, power(matmul(A, A), (p - 1) / 2)) for odd p
+      power(A, 0) = I
+
+    The power(A, 0) = I case is handeled by starting with accumulator set to the
+    identity matrix; matrices with zero residual powers are passed through
+    unchanged.
+
+    Args:
+      current_argument: On this step, what is the first argument (A^2..^2) to
+          the (unrolled) recursive function? [batch size x N x N]
+      residual_powers: On this step, what is the second argument (residual p)?
+          [batch_size]
+      accumulator: Accumulates the exterior multiplications from the odd
+          powers (initially the identity matrix). [batch_size x N x N]
+    Returns:
+      Updated versions of each argument for one step of the unrolled
+      computation. Does not change parts of the batch which have a residual
+      power of zero.
+    """
+    is_even = math_ops.equal(residual_powers % 2,
+                             array_ops.zeros(
+                                 array_ops.shape(residual_powers),
+                                 dtype=dtypes.int32))
+    new_accumulator = array_ops.where(is_even, accumulator,
+                                      math_ops.matmul(accumulator,
+                                                      current_argument))
+    new_argument = math_ops.matmul(current_argument, current_argument)
+    do_update = math_ops.greater(residual_powers, 1)
+    new_residual_powers = residual_powers - residual_powers % 2
+    new_residual_powers //= 2
+    # Stop updating if we've reached our base case; some batch elements may
+    # finish sooner than others
+    accumulator = array_ops.where(do_update, new_accumulator, accumulator)
+    current_argument = array_ops.where(do_update, new_argument,
+                                       current_argument)
+    residual_powers = array_ops.where(do_update, new_residual_powers,
+                                      residual_powers)
+    return (current_argument, residual_powers, accumulator)
+
+  matrices = ops.convert_to_tensor(matrices)
+  powers = math_ops.cast(powers, dtype=dtypes.int32)
+  ident = array_ops.expand_dims(
+      array_ops.diag(
+          array_ops.ones([array_ops.shape(matrices)[1]], dtype=matrices.dtype)),
+      0)
+  ident_tiled = array_ops.tile(ident, [array_ops.shape(matrices)[0], 1, 1])
+  (final_argument,
+   final_residual_power, final_accumulator) = control_flow_ops.while_loop(
+       terminate_when_all_zero, do_iteration, [matrices, powers, ident_tiled])
+  return array_ops.where(
+      math_ops.equal(final_residual_power,
+                     array_ops.zeros_like(
+                         final_residual_power, dtype=dtypes.int32)),
+      ident_tiled, math_ops.matmul(final_argument, final_accumulator))
+
+
+# TODO(allenl): would be useful if this was built into batch_matmul
+def batch_times_matrix(batch, matrix, adj_x=False, adj_y=False):
+  """Multiply a batch of matrices by a single matrix.
+
+  Functionally equivalent to:
+  tf.matmul(batch, array_ops.tile(gen_math_ops.expand_dims(matrix, 0),
+                                 [array_ops.shape(batch)[0], 1, 1]),
+                  adjoint_a=adj_x, adjoint_b=adj_y)
+
+  Args:
+    batch: [batch_size x N x M] after optional transpose
+    matrix: [M x P] after optional transpose
+    adj_x: If true, transpose the second two dimensions of "batch" before
+        multiplying.
+    adj_y: If true, transpose "matrix" before multiplying.
+  Returns:
+    [batch_size x N x P]
+  """
+  batch = ops.convert_to_tensor(batch)
+  matrix = ops.convert_to_tensor(matrix)
+  assert batch.get_shape().ndims == 3
+  assert matrix.get_shape().ndims == 2
+  if adj_x:
+    batch = array_ops.transpose(batch, [0, 2, 1])
+  batch_dimension = batch.get_shape()[0].value
+  first_dimension = batch.get_shape()[1].value
+  tensor_batch_shape = array_ops.shape(batch)
+  if batch_dimension is None:
+    batch_dimension = tensor_batch_shape[0]
+  if first_dimension is None:
+    first_dimension = tensor_batch_shape[1]
+  matrix_first_dimension, matrix_second_dimension = matrix.get_shape().as_list()
+  batch_reshaped = array_ops.reshape(batch, [-1, tensor_batch_shape[2]])
+  if adj_y:
+    if matrix_first_dimension is None:
+      matrix_first_dimension = array_ops.shape(matrix)[0]
+    result_shape = [batch_dimension, first_dimension, matrix_first_dimension]
+  else:
+    if matrix_second_dimension is None:
+      matrix_second_dimension = array_ops.shape(matrix)[1]
+    result_shape = [batch_dimension, first_dimension, matrix_second_dimension]
+  return array_ops.reshape(
+      math_ops.matmul(batch_reshaped, matrix, adjoint_b=adj_y), result_shape)
+
+
+def matrix_times_batch(matrix, batch, adj_x=False, adj_y=False):
+  """Like batch_times_matrix, but with the multiplication order swapped."""
+  return array_ops.transpose(
+      batch_times_matrix(
+          batch=batch, matrix=matrix, adj_x=not adj_y, adj_y=not adj_x),
+      [0, 2, 1])
+
+
+def make_toeplitz_matrix(inputs, name=None):
+  """Make a symmetric Toeplitz matrix from input array of values.
+
+  Args:
+    inputs: a 3-D tensor of shape [num_blocks, block_size, block_size].
+    name: the name of the operation.
+
+  Returns:
+    a symmetric Toeplitz matrix of shape
+      [num_blocks*block_size, num_blocks*block_size].
+  """
+  num_blocks = array_ops.shape(inputs)[0]
+  block_size = array_ops.shape(inputs)[1]
+  output_size = block_size * num_blocks
+  lags = array_ops.reshape(math_ops.range(num_blocks), shape=[1, -1])
+  indices = math_ops.abs(lags - array_ops.transpose(lags))
+  output = array_ops.gather(inputs, indices)
+  output = array_ops.reshape(
+      array_ops.transpose(output, [0, 2, 1, 3]), [output_size, output_size])
+  return array_ops.identity(output, name=name)
+
+
+# TODO(allenl): Investigate alternative parameterizations.
+def sign_magnitude_positive_definite(
+    raw, off_diagonal_scale=0., overall_scale=0.):
+  """Constructs a positive definite matrix from an unconstrained input matrix.
+
+  We want to keep the whole matrix on a log scale, but also allow off-diagonal
+  elements to be negative, so the sign of off-diagonal elements is modeled
+  separately from their magnitude (using the lower and upper triangles
+  respectively). Specifically:
+
+  for i < j, we have:
+    output_cholesky[i, j] = raw[j, i] / (abs(raw[j, i]) + 1) *
+        exp((off_diagonal_scale + overall_scale + raw[i, j]) / 2)
+
+  output_cholesky[i, i] = exp((raw[i, i] + overall_scale) / 2)
+
+  output = output_cholesky^T * output_cholesky
+
+  where raw, off_diagonal_scale, and overall_scale are
+  un-constrained real-valued variables. The resulting values are stable
+  around zero due to the exponential (and the softsign keeps the function
+  smooth).
+
+  Args:
+    raw: A [..., M, M] Tensor.
+    off_diagonal_scale: A scalar or [...] shaped Tensor controlling the relative
+        scale of off-diagonal values in the output matrix.
+    overall_scale: A scalar or [...] shaped Tensor controlling the overall scale
+        of the output matrix.
+  Returns:
+    The `output` matrix described above, a [..., M, M] positive definite matrix.
+
+  """
+  raw = ops.convert_to_tensor(raw)
+  diagonal = array_ops.matrix_diag_part(raw)
+  def _right_pad_with_ones(tensor, target_rank):
+    # Allow broadcasting even if overall_scale and off_diagonal_scale have batch
+    # dimensions
+    tensor = ops.convert_to_tensor(tensor, dtype=raw.dtype.base_dtype)
+    return array_ops.reshape(tensor,
+                             array_ops.concat(
+                                 [
+                                     array_ops.shape(tensor), array_ops.ones(
+                                         [target_rank - array_ops.rank(tensor)],
+                                         dtype=target_rank.dtype)
+                                 ],
+                                 axis=0))
+  # We divide the log values by 2 to compensate for the squaring that happens
+  # when transforming Cholesky factors into positive definite matrices.
+  sign_magnitude = (gen_math_ops.exp(
+      (raw + _right_pad_with_ones(off_diagonal_scale, array_ops.rank(raw)) +
+       _right_pad_with_ones(overall_scale, array_ops.rank(raw))) / 2.) *
+                    nn.softsign(array_ops.matrix_transpose(raw)))
+  sign_magnitude.set_shape(raw.get_shape())
+  cholesky_factor = array_ops.matrix_set_diag(
+      input=array_ops.matrix_band_part(sign_magnitude, 0, -1),
+      diagonal=gen_math_ops.exp((diagonal + _right_pad_with_ones(
+          overall_scale, array_ops.rank(diagonal))) / 2.))
+  return math_ops.matmul(cholesky_factor, cholesky_factor, transpose_a=True)
+
+
+def transform_to_covariance_matrices(input_vectors, matrix_size):
+  """Construct covariance matrices via transformations from input_vectors.
+
+  Args:
+    input_vectors: A [batch size x input size] batch of vectors to transform.
+    matrix_size: An integer indicating one dimension of the (square) output
+        matrix.
+  Returns:
+    A [batch size x matrix_size x matrix_size] batch of covariance matrices.
+  """
+  combined_values = layers.fully_connected(
+      input_vectors, matrix_size**2 + 2, activation_fn=None)
+  return sign_magnitude_positive_definite(
+      raw=array_ops.reshape(combined_values[..., :-2],
+                            array_ops.concat([
+                                array_ops.shape(combined_values)[:-1],
+                                [matrix_size, matrix_size]
+                            ], 0)),
+      off_diagonal_scale=combined_values[..., -2],
+      overall_scale=combined_values[..., -1])
+
+
+def variable_covariance_matrix(
+    size, name, dtype, initial_diagonal_values=None,
+    initial_overall_scale_log=0.):
+  """Construct a Variable-parameterized positive definite matrix.
+
+  Useful for parameterizing covariance matrices.
+
+  Args:
+    size: The size of the main diagonal, the returned matrix having shape [size
+        x size].
+    name: The name to use when defining variables and ops.
+    dtype: The floating point data type to use.
+    initial_diagonal_values: A Tensor with shape [size] with initial values for
+        the diagonal values of the returned matrix. Must be positive.
+    initial_overall_scale_log: Initial value of the bias term for every element
+        of the matrix in log space.
+  Returns:
+    A Variable-parameterized covariance matrix with shape [size x size].
+  """
+  raw_values = variable_scope.get_variable(
+      name + "_pre_transform",
+      dtype=dtype,
+      shape=[size, size],
+      initializer=init_ops.zeros_initializer())
+  if initial_diagonal_values is not None:
+    raw_values += array_ops.matrix_diag(math_ops.log(initial_diagonal_values))
+  return array_ops.identity(
+      sign_magnitude_positive_definite(
+          raw=raw_values,
+          off_diagonal_scale=variable_scope.get_variable(
+              name + "_off_diagonal_scale",
+              dtype=dtype,
+              initializer=constant_op.constant(-5., dtype=dtype)),
+          overall_scale=ops.convert_to_tensor(
+              initial_overall_scale_log, dtype=dtype) +
+          variable_scope.get_variable(
+              name + "_overall_scale",
+              dtype=dtype,
+              shape=[],
+              initializer=init_ops.zeros_initializer())),
+      name=name)
+
+
+def batch_start_time(times):
+  return times[:, 0]
+
+
+def batch_end_time(times):
+  return times[:, -1]
+
+
+def log_noninformative_covariance_prior(covariance):
+  """Compute a relatively uninformative prior for noise parameters.
+
+  Helpful for avoiding noise over-estimation, where noise otherwise decreases
+  very slowly during optimization.
+
+  See:
+    Villegas, C. On the A Priori Distribution of the Covariance Matrix.
+    Ann. Math. Statist. 40 (1969), no. 3, 1098--1099.
+
+  Args:
+    covariance: A covariance matrix.
+  Returns:
+    For a [p x p] matrix:
+      log(det(covariance)^(-(p + 1) / 2))
+  """
+  # Avoid zero/negative determinants due to numerical errors
+  covariance += array_ops.diag(1e-8 * array_ops.ones(
+      shape=[array_ops.shape(covariance)[0]], dtype=covariance.dtype))
+  power = -(math_ops.cast(array_ops.shape(covariance)[0] + 1,
+                          covariance.dtype) / 2.)
+  return power * math_ops.log(linalg_ops.matrix_determinant(covariance))
+
+
+def entropy_matched_cauchy_scale(covariance):
+  """Approximates a similar Cauchy distribution given a covariance matrix.
+
+  Since Cauchy distributions do not have moments, entropy matching provides one
+  way to set a Cauchy's scale parameter in a way that provides a similar
+  distribution. The effect is dividing the standard deviation of an independent
+  Gaussian by a constant very near 3.
+
+  To set the scale of the Cauchy distribution, we first select the diagonals of
+  `covariance`. Since this ignores cross terms, it overestimates the entropy of
+  the Gaussian. For each of these variances, we solve for the Cauchy scale
+  parameter which gives the same entropy as the Gaussian with that
+  variance. This means setting the (univariate) Gaussian entropy
+      0.5 * ln(2 * variance * pi * e)
+  equal to the Cauchy entropy
+      ln(4 * pi * scale)
+  Solving, we get scale = sqrt(variance * (e / (8 pi))).
+
+  Args:
+    covariance: A [batch size x N x N] batch of covariance matrices to produce
+        Cauchy scales for.
+  Returns:
+    A [batch size x N] set of Cauchy scale parameters for each part of the batch
+    and each dimension of the input Gaussians.
+  """
+  return math_ops.sqrt(math.e / (8. * math.pi) *
+                       array_ops.matrix_diag_part(covariance))
+
+
+class TensorValuedMutableDenseHashTable(lookup.MutableDenseHashTable):
+  """A version of MutableDenseHashTable which stores arbitrary Tensor shapes.
+
+  Since MutableDenseHashTable only allows vectors right now, simply adds reshape
+  ops on both ends.
+  """
+
+  def __init__(self, key_dtype, value_dtype, default_value, *args, **kwargs):
+    self._non_vector_value_shape = array_ops.shape(default_value)
+    super(TensorValuedMutableDenseHashTable, self).__init__(
+        key_dtype=key_dtype,
+        value_dtype=value_dtype,
+        default_value=array_ops.reshape(default_value, [-1]),
+        *args,
+        **kwargs)
+
+  def insert(self, keys, values, name=None):
+    keys = ops.convert_to_tensor(keys, dtype=self._key_dtype)
+    keys_flat = array_ops.reshape(keys, [-1])
+    return super(TensorValuedMutableDenseHashTable, self).insert(
+        keys=keys_flat,
+        # Each key has one corresponding value, so the shape of the tensor of
+        # values for every key is key_shape + value_shape
+        values=array_ops.reshape(values, [array_ops.shape(keys_flat)[0], -1]),
+        name=name)
+
+  def lookup(self, keys, name=None):
+    keys_flat = array_ops.reshape(
+        ops.convert_to_tensor(keys, dtype=self._key_dtype), [-1])
+    return array_ops.reshape(
+        super(TensorValuedMutableDenseHashTable, self).lookup(
+            keys=keys_flat, name=name),
+        array_ops.concat([array_ops.shape(keys), self._non_vector_value_shape],
+                         0))
+
+
+class TupleOfTensorsLookup(lookup.LookupInterface):
+  """A LookupInterface with nested tuples of Tensors as values.
+
+  Creates one MutableDenseHashTable per value Tensor, which has some unnecessary
+  overhead.
+  """
+
+  def __init__(
+      self, key_dtype, default_values, empty_key, name, checkpoint=True):
+    default_values_flat = nest.flatten(default_values)
+    self._hash_tables = nest.pack_sequence_as(
+        default_values,
+        [TensorValuedMutableDenseHashTable(
+            key_dtype=key_dtype,
+            value_dtype=default_value.dtype.base_dtype,
+            default_value=default_value,
+            empty_key=empty_key,
+            name=name + "_{}".format(table_number),
+            checkpoint=checkpoint)
+         for table_number, default_value
+         in enumerate(default_values_flat)])
+    self._name = name
+
+  def lookup(self, keys):
+    return nest.pack_sequence_as(
+        self._hash_tables,
+        [hash_table.lookup(keys)
+         for hash_table in nest.flatten(self._hash_tables)])
+
+  def insert(self, keys, values):
+    nest.assert_same_structure(self._hash_tables, values)
+    # Avoid race conditions by requiring that all inputs are computed before any
+    # inserts happen (an issue if one key's update relies on another's value).
+    values_flat = [array_ops.identity(value) for value in nest.flatten(values)]
+    with ops.control_dependencies(values_flat):
+      insert_ops = [hash_table.insert(keys, value)
+                    for hash_table, value
+                    in zip(nest.flatten(self._hash_tables),
+                           values_flat)]
+    return control_flow_ops.group(*insert_ops)
+
+  def check_table_dtypes(self, key_dtype, value_dtype):
+    # dtype checking is done in the objects in self._hash_tables
+    pass
+
+
+def replicate_state(start_state, batch_size):
+  """Create batch versions of state.
+
+  Takes a list of Tensors, adds a batch dimension, and replicates
+  batch_size times across that batch dimension. Used to replicate the
+  non-batch state returned by get_start_state in define_loss.
+
+  Args:
+    start_state: Model-defined state to replicate.
+    batch_size: Batch dimension for data.
+  Returns:
+    Replicated versions of the state.
+  """
+  flattened_state = nest.flatten(start_state)
+  replicated_state = [
+      array_ops.tile(
+          array_ops.expand_dims(state_nonbatch, 0),
+          array_ops.concat([[batch_size], array_ops.ones(
+              [array_ops.rank(state_nonbatch)], dtype=dtypes.int32)], 0))
+      for state_nonbatch in flattened_state
+  ]
+  return nest.pack_sequence_as(start_state, replicated_state)
+
+
+Moments = collections.namedtuple("Moments", ["mean", "variance"])
+
+
+# Currently all of these statistics are computed incrementally (i.e. are updated
+# every time a new mini-batch of training data is presented) when this object is
+# created in InputStatisticsFromMiniBatch.
+InputStatistics = collections.namedtuple(
+    "InputStatistics",
+    ["series_start_moments",  # The mean and variance of each feature in a chunk
+                              # (with a size configured in the statistics
+                              # object) at the start of the series. A tuple of
+                              # (mean, variance), each with shape [number of
+                              # features], floating point. One use is in state
+                              # space models, to keep priors calibrated even as
+                              # earlier parts of the series are presented. If
+                              # this object was created by
+                              # InputStatisticsFromMiniBatch, these moments are
+                              # computed based on the earliest chunk of data
+                              # presented so far. However, there is a race
+                              # condition in the update, so these may reflect
+                              # statistics later in the series, but should
+                              # eventually reflect statistics in a chunk at the
+                              # series start.
+     "overall_feature_moments",  # The mean and variance of each feature over
+                                 # the entire series. A tuple of (mean,
+                                 # variance), each with shape [number of
+                                 # features]. If this object was created by
+                                 # InputStatisticsFromMiniBatch, these moments
+                                 # are estimates based on the data seen so far.
+     "start_time",  # The first (lowest) time in the series, a scalar
+                    # integer. If this object was created by
+                    # InputStatisticsFromMiniBatch, this is the lowest time seen
+                    # so far rather than the lowest time that will ever be seen
+                    # (guaranteed to be at least as low as the lowest time
+                    # presented in the current minibatch).
+     "total_observation_count",  # Count of data points, a scalar integer. If
+                                 # this object was created by
+                                 # InputStatisticsFromMiniBatch, this is an
+                                 # estimate of the total number of observations
+                                 # in the whole dataset computed based on the
+                                 # density of the series and the minimum and
+                                 # maximum times seen.
+    ])
+
+
+# TODO(allenl): It would be nice to do something with full series statistics
+# when the user provides that.
+class InputStatisticsFromMiniBatch(object):
+  """Generate statistics from mini-batch input."""
+
+  def __init__(self, num_features, dtype, starting_variance_window_size=16):
+    """Configure the input statistics object.
+
+    Args:
+      num_features: Number of features for the time series
+      dtype: The floating point data type to use.
+      starting_variance_window_size: The number of datapoints to use when
+          computing the mean and variance at the start of the series.
+    """
+    self._starting_variance_window_size = starting_variance_window_size
+    self._num_features = num_features
+    self._dtype = dtype
+
+  def initialize_graph(self, features, update_statistics=True):
+    """Create any ops needed to provide input statistics.
+
+    Should be called before statistics are requested.
+
+    Args:
+      features: A dictionary, the output of a `TimeSeriesInputFn` (with keys
+          TrainEvalFeatures.TIMES and TrainEvalFeatures.VALUES).
+      update_statistics: Whether `features` should be used to update adaptive
+          statistics. Typically True for training and false for evaluation.
+    Returns:
+      An InputStatistics object composed of Variables, which will be updated
+      based on mini-batches of data if requested.
+    """
+    if (TrainEvalFeatures.TIMES in features
+        and TrainEvalFeatures.VALUES in features):
+      times = features[TrainEvalFeatures.TIMES]
+      values = features[TrainEvalFeatures.VALUES]
+    else:
+      # times and values may not be available, for example during prediction. We
+      # still need to retreive our variables so that they can be read from, even
+      # if we're not going to update them.
+      times = None
+      values = None
+    # Create/retreive variables representing input statistics, initialized
+    # without data to avoid deadlocking if variables are initialized before
+    # queue runners are started.
+    with variable_scope.variable_scope("input_statistics"):
+      statistics = self._create_variable_statistics_object()
+    with variable_scope.variable_scope("input_statistics_auxiliary"):
+      # Secondary statistics, necessary for the incremental computation of the
+      # primary statistics (e.g. counts and sums for computing a mean
+      # incrementally).
+      auxiliary_variables = self._AdaptiveInputAuxiliaryStatistics(
+          num_features=self._num_features, dtype=self._dtype)
+    if update_statistics and times is not None and values is not None:
+      # If we have times and values from mini-batch input, create update ops to
+      # take the new data into account.
+      assign_op = self._update_statistics_from_mini_batch(
+          statistics, auxiliary_variables, times, values)
+      with ops.control_dependencies([assign_op]):
+        stat_variables = nest.pack_sequence_as(statistics, [
+            array_ops.identity(tensor) for tensor in nest.flatten(statistics)
+        ])
+        # Since start time updates have a race condition, ensure that the
+        # reported start time is at least as low as the lowest time in this
+        # mini-batch. The start time should converge on the correct value
+        # eventually even with the race condition, but for example state space
+        # models have an assertion which could fail without this
+        # post-processing.
+        return stat_variables._replace(start_time=gen_math_ops.minimum(
+            stat_variables.start_time, math_ops.reduce_min(times)))
+    else:
+      return statistics
+
+  class _AdaptiveInputAuxiliaryStatistics(collections.namedtuple(
+      "_AdaptiveInputAuxiliaryStatistics",
+      ["max_time_seen",  # The maximum time seen (best effort if updated from
+                         # multiple workers; see notes about race condition
+                         # below).
+       "chunk_count",  # The number of chunks seen.
+       "inter_observation_duration_sum",  # The sum across chunks of their "time
+                                          # density" (number of times per
+                                          # example).
+       "example_count",  # The number of examples seen (each example has a
+                         # single time associated with it and one or more
+                         # real-valued features).
+       "overall_feature_sum",  # The sum of values for each feature. Shape
+                               # [number of features].
+       "overall_feature_sum_of_squares",  # The sum of squared values for each
+                                          # feature. Shape [number of features]
+      ])):
+    """Extra statistics used to incrementally update InputStatistics."""
+
+    def __new__(cls, num_features, dtype):
+      return super(
+          InputStatisticsFromMiniBatch  # pylint: disable=protected-access
+          ._AdaptiveInputAuxiliaryStatistics,
+          cls).__new__(
+              cls,
+              max_time_seen=variable_scope.get_variable(
+                  name="max_time_seen",
+                  initializer=dtypes.int64.min,
+                  dtype=dtypes.int64,
+                  trainable=False),
+              chunk_count=variable_scope.get_variable(
+                  name="chunk_count",
+                  initializer=init_ops.zeros_initializer(),
+                  shape=[],
+                  dtype=dtypes.int64,
+                  trainable=False),
+              inter_observation_duration_sum=variable_scope.get_variable(
+                  name="inter_observation_duration_sum",
+                  initializer=init_ops.zeros_initializer(),
+                  shape=[],
+                  dtype=dtype,
+                  trainable=False),
+              example_count=variable_scope.get_variable(
+                  name="example_count",
+                  shape=[],
+                  dtype=dtypes.int64,
+                  trainable=False),
+              overall_feature_sum=variable_scope.get_variable(
+                  name="overall_feature_sum",
+                  shape=[num_features],
+                  dtype=dtype,
+                  initializer=init_ops.zeros_initializer(),
+                  trainable=False),
+              overall_feature_sum_of_squares=variable_scope.get_variable(
+                  name="overall_feature_sum_of_squares",
+                  shape=[num_features],
+                  dtype=dtype,
+                  initializer=init_ops.zeros_initializer(),
+                  trainable=False))
+
+  def _update_statistics_from_mini_batch(
+      self, statistics, auxiliary_variables, times, values):
+    """Given mini-batch input, update `statistics` and `auxiliary_variables`."""
+    values = math_ops.cast(values, self._dtype)
+    # The density (measured in times per observation) that we see in each part
+    # of the mini-batch.
+    batch_inter_observation_duration = (math_ops.cast(
+        math_ops.reduce_max(times, axis=1) - math_ops.reduce_min(times, axis=1),
+        self._dtype) / math_ops.cast(
+            array_ops.shape(times)[1] - 1, self._dtype))
+    # Co-locate updates with their variables to minimize race conditions when
+    # updating statistics.
+    with ops.colocate_with(auxiliary_variables.max_time_seen):
+      # There is a race condition if this value is being updated from multiple
+      # workers. However, it should eventually reach the correct value if the
+      # last chunk is presented enough times.
+      max_time_seen_assign = state_ops.assign(
+          auxiliary_variables.max_time_seen,
+          gen_math_ops.maximum(auxiliary_variables.max_time_seen,
+                               math_ops.reduce_max(times)))
+    with ops.colocate_with(auxiliary_variables.chunk_count):
+      chunk_count_assign = state_ops.assign_add(auxiliary_variables.chunk_count,
+                                                array_ops.shape(
+                                                    times,
+                                                    out_type=dtypes.int64)[0])
+    with ops.colocate_with(auxiliary_variables.inter_observation_duration_sum):
+      inter_observation_duration_assign = state_ops.assign_add(
+          auxiliary_variables.inter_observation_duration_sum,
+          math_ops.reduce_sum(batch_inter_observation_duration))
+    with ops.colocate_with(auxiliary_variables.example_count):
+      example_count_assign = state_ops.assign_add(
+          auxiliary_variables.example_count,
+          array_ops.size(times, out_type=dtypes.int64))
+    # Note: These mean/variance updates assume that all points are equally
+    # likely, which is not true if _chunks_ are sampled uniformly from the space
+    # of all possible contiguous chunks, since points at the start and end of
+    # the series are then members of fewer chunks. For series which are much
+    # longer than the chunk size (the usual/expected case), this effect becomes
+    # irrelevant.
+    with ops.colocate_with(auxiliary_variables.overall_feature_sum):
+      overall_feature_sum_assign = state_ops.assign_add(
+          auxiliary_variables.overall_feature_sum,
+          math_ops.reduce_sum(values, axis=[0, 1]))
+    with ops.colocate_with(auxiliary_variables.overall_feature_sum_of_squares):
+      overall_feature_sum_of_squares_assign = state_ops.assign_add(
+          auxiliary_variables.overall_feature_sum_of_squares,
+          math_ops.reduce_sum(values**2, axis=[0, 1]))
+    per_chunk_aux_updates = control_flow_ops.group(
+        max_time_seen_assign, chunk_count_assign,
+        inter_observation_duration_assign, example_count_assign,
+        overall_feature_sum_assign, overall_feature_sum_of_squares_assign)
+    with ops.control_dependencies([per_chunk_aux_updates]):
+      example_count_float = math_ops.cast(auxiliary_variables.example_count,
+                                          self._dtype)
+      new_feature_mean = (auxiliary_variables.overall_feature_sum /
+                          example_count_float)
+      overall_feature_mean_update = state_ops.assign(
+          statistics.overall_feature_moments.mean, new_feature_mean)
+      overall_feature_var_update = state_ops.assign(
+          statistics.overall_feature_moments.variance,
+          # De-biased n / (n - 1) variance correction
+          example_count_float / (example_count_float - 1.) *
+          (auxiliary_variables.overall_feature_sum_of_squares /
+           example_count_float - new_feature_mean**2))
+      # TODO(b/35675805): Remove this cast
+      min_time_batch = math_ops.cast(math_ops.argmin(times[:, 0]), dtypes.int32)
+      def series_start_updates():
+        # If this is the lowest-time chunk that we have seen so far, update
+        # series start moments to reflect that. Note that these statistics are
+        # "best effort", as there are race conditions in the update (however,
+        # they should eventually converge if the start of the series is
+        # presented enough times).
+        mean, variance = nn.moments(
+            values[min_time_batch, :self._starting_variance_window_size],
+            axes=[0])
+        return control_flow_ops.group(
+            state_ops.assign(statistics.series_start_moments.mean, mean),
+            state_ops.assign(statistics.series_start_moments.variance,
+                             variance))
+      with ops.colocate_with(statistics.start_time):
+        series_start_update = control_flow_ops.cond(
+            # Update moments whenever we even match the lowest time seen so far,
+            # to ensure that series start statistics are eventually updated to
+            # their correct values, despite race conditions (i.e. eventually
+            # statistics.start_time will reflect the global lowest time, and
+            # given that we will eventually update the series start moments to
+            # their correct values).
+            math_ops.less_equal(times[min_time_batch, 0],
+                                statistics.start_time),
+            series_start_updates,
+            control_flow_ops.no_op)
+        with ops.control_dependencies([series_start_update]):
+          # There is a race condition if this update is performed in parallel on
+          # multiple workers. Since models may be sensitive to being presented
+          # with times before the putative start time, the value of this
+          # variable is post-processed above to guarantee that each worker is
+          # presented with a start time which is at least as low as the lowest
+          # time in its current mini-batch.
+          start_time_update = state_ops.assign(statistics.start_time,
+                                               gen_math_ops.minimum(
+                                                   statistics.start_time,
+                                                   math_ops.reduce_min(times)))
+      inter_observation_duration_estimate = (
+          auxiliary_variables.inter_observation_duration_sum / math_ops.cast(
+              auxiliary_variables.chunk_count, self._dtype))
+      # Estimate the total number of observations as:
+      #   (end time - start time + 1) * average intra-chunk time density
+      total_observation_count_update = state_ops.assign(
+          statistics.total_observation_count,
+          math_ops.cast(
+              gen_math_ops.round(
+                  math_ops.cast(auxiliary_variables.max_time_seen -
+                                statistics.start_time + 1, self._dtype) /
+                  inter_observation_duration_estimate), dtypes.int64))
+      per_chunk_stat_updates = control_flow_ops.group(
+          overall_feature_mean_update, overall_feature_var_update,
+          series_start_update, start_time_update,
+          total_observation_count_update)
+    return per_chunk_stat_updates
+
+  def _create_variable_statistics_object(self):
+    """Creates non-trainable variables representing input statistics."""
+    series_start_moments = Moments(
+        mean=variable_scope.get_variable(
+            name="series_start_mean",
+            shape=[self._num_features],
+            dtype=self._dtype,
+            initializer=init_ops.zeros_initializer(),
+            trainable=False),
+        variance=variable_scope.get_variable(
+            name="series_start_variance",
+            shape=[self._num_features],
+            dtype=self._dtype,
+            initializer=init_ops.ones_initializer(),
+            trainable=False))
+    overall_feature_moments = Moments(
+        mean=variable_scope.get_variable(
+            name="overall_feature_mean",
+            shape=[self._num_features],
+            dtype=self._dtype,
+            initializer=init_ops.zeros_initializer(),
+            trainable=False),
+        variance=variable_scope.get_variable(
+            name="overall_feature_var",
+            shape=[self._num_features],
+            dtype=self._dtype,
+            initializer=init_ops.ones_initializer(),
+            trainable=False))
+    start_time = variable_scope.get_variable(
+        name="start_time",
+        dtype=dtypes.int64,
+        initializer=init_ops.zeros_initializer(),
+        shape=[],
+        trainable=False)
+    total_observation_count = variable_scope.get_variable(
+        name="total_observation_count",
+        shape=[],
+        dtype=dtypes.int64,
+        initializer=init_ops.ones_initializer(),
+        trainable=False)
+    return InputStatistics(
+        series_start_moments=series_start_moments,
+        overall_feature_moments=overall_feature_moments,
+        start_time=start_time,
+        total_observation_count=total_observation_count)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py b/tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py
new file mode 100644
index 0000000000..b9f8620fd8
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py
@@ -0,0 +1,344 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for math_utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.training import coordinator as coordinator_lib
+from tensorflow.python.training import queue_runner_impl
+
+
+class MathUtilsTest(test.TestCase):
+
+  def setUp(self):
+    numpy.random.seed(10)
+
+  def test_power_sums_tensor(self):
+    transition = numpy.random.normal(size=[4, 4]).astype(numpy.float32)
+    addition = numpy.random.normal(size=[4, 4]).astype(numpy.float32)
+    array_size = 2
+    result = []
+    transition_power = numpy.identity(4)
+    running_sum = numpy.zeros([4, 4], dtype=numpy.float32)
+    for _ in range(array_size + 1):
+      result.append(running_sum)
+      current_contribution = numpy.dot(numpy.dot(transition_power, addition),
+                                       transition_power.T)
+      # pylint: disable=g-no-augmented-assignment
+      # += has different semantics here; want to make a copy
+      running_sum = running_sum + current_contribution
+      # pylint: enable=g-no-augmented-assignment
+      transition_power = numpy.dot(transition, transition_power)
+    with self.test_session():
+      self.assertAllClose(result,
+                          math_utils.power_sums_tensor(
+                              array_size, transition, addition).eval())
+
+  def test_matrix_to_powers(self):
+    matrix = numpy.random.normal(size=[4, 4]).astype(numpy.float32)
+    powers = numpy.random.randint(low=0, high=10, size=20)
+    result = []
+    for i in range(powers.shape[0]):
+      result.append(numpy.linalg.matrix_power(matrix, powers[i]))
+    with self.test_session():
+      self.assertAllClose(result,
+                          math_utils.matrix_to_powers(matrix, powers).eval(),
+                          rtol=1e-5,
+                          atol=1e-5)
+
+  def test_batch_matrix_pow(self):
+    batch = numpy.random.normal(size=[15, 4, 4]).astype(numpy.float32)
+    powers = numpy.random.randint(low=0, high=10, size=batch.shape[0])
+    result = []
+    for i in range(batch.shape[0]):
+      result.append(numpy.linalg.matrix_power(batch[i], powers[i]))
+    with self.test_session():
+      # TODO(allenl): Numerical errors seem to be creeping in. Maybe it can be
+      # made slightly more stable?
+      self.assertAllClose(result,
+                          math_utils.batch_matrix_pow(batch, powers).eval(),
+                          rtol=1e-5,
+                          atol=1e-5)
+
+  def test_batch_times_matrix(self):
+    left = numpy.random.normal(size=[5, 3, 2]).astype(numpy.float32)
+    left_transpose = numpy.transpose(left, [0, 2, 1])
+    right = numpy.random.normal(size=[2, 3]).astype(numpy.float32)
+    expected_result = numpy.dot(left, right)
+    with self.test_session():
+      self.assertAllClose(expected_result,
+                          math_utils.batch_times_matrix(
+                              left, right).eval())
+      self.assertAllClose(expected_result,
+                          math_utils.batch_times_matrix(
+                              left_transpose, right,
+                              adj_x=True).eval())
+      self.assertAllClose(expected_result,
+                          math_utils.batch_times_matrix(
+                              left, right.T,
+                              adj_y=True).eval())
+      self.assertAllClose(expected_result,
+                          math_utils.batch_times_matrix(
+                              left_transpose, right.T,
+                              adj_x=True, adj_y=True).eval())
+
+  def test_matrix_times_batch(self):
+    left = numpy.random.normal(size=[5, 7]).astype(numpy.float32)
+    right = numpy.random.normal(size=[3, 7, 9]).astype(numpy.float32)
+    right_transpose = numpy.transpose(right, [0, 2, 1])
+    expected_result = numpy.transpose(numpy.dot(right_transpose, left.T),
+                                      [0, 2, 1])
+    with self.test_session():
+      self.assertAllClose(expected_result,
+                          math_utils.matrix_times_batch(
+                              left, right).eval())
+      self.assertAllClose(expected_result,
+                          math_utils.matrix_times_batch(
+                              left.T, right,
+                              adj_x=True).eval())
+      self.assertAllClose(expected_result,
+                          math_utils.matrix_times_batch(
+                              left, right_transpose,
+                              adj_y=True).eval())
+      self.assertAllClose(expected_result,
+                          math_utils.matrix_times_batch(
+                              left.T, right_transpose,
+                              adj_x=True, adj_y=True).eval())
+
+  def test_make_diagonal_undefined_shapes(self):
+    with self.test_session():
+      completely_undefined = array_ops.placeholder(dtype=dtypes.float32)
+      partly_undefined = array_ops.placeholder(
+          shape=[None, None], dtype=dtypes.float32)
+      blocked = math_utils.block_diagonal([completely_undefined,
+                                           [[2.]],
+                                           partly_undefined])
+      self.assertEqual([None, None],
+                       blocked.get_shape().as_list())
+      self.assertAllEqual(
+          [[1., 0., 0., 0.],
+           [0., 2., 0., 0.],
+           [0., 0., 3., 4.],
+           [0., 0., 5., 6.]],
+          blocked.eval(feed_dict={
+              completely_undefined: [[1.]],
+              partly_undefined: [[3., 4.],
+                                 [5., 6.]]}))
+
+  def test_make_diagonal_mostly_defined_shapes(self):
+    with self.test_session():
+      mostly_defined = array_ops.placeholder(
+          shape=[None, 2], dtype=dtypes.float32)
+      blocked = math_utils.block_diagonal([[[2.]],
+                                           mostly_defined,
+                                           [[7.]]])
+      self.assertEqual([None, 4],
+                       blocked.get_shape().as_list())
+      self.assertAllEqual(
+          [[2., 0., 0., 0.],
+           [0., 3., 4., 0.],
+           [0., 5., 6., 0.],
+           [0., 0., 0., 7.]],
+          blocked.eval(feed_dict={
+              mostly_defined: [[3., 4.],
+                               [5., 6.]]}))
+
+
+class TestMakeToeplitzMatrix(test.TestCase):
+
+  def test_make_toeplitz_matrix_1(self):
+    inputs = numpy.array([[[1.]], [[2.]], [[3.]]])
+    output_expected = numpy.array([[1., 2, 3], [2, 1, 2], [3, 2, 1]])
+    self._test_make_toeplitz_matrix(inputs, output_expected)
+
+  def test_make_toeplitz_matrix_2(self):
+    inputs = numpy.array(
+        [[[1, 2.], [3, 4]], [[5, 6], [7, 8]], [[8, 9], [10, 11]]])
+
+    output_expected = numpy.array(
+        [[1., 2., 5., 6, 8, 9],
+         [3, 4, 7, 8, 10, 11],
+         [5, 6, 1, 2, 5, 6],
+         [7, 8, 3, 4, 7, 8],
+         [8, 9, 5, 6, 1, 2],
+         [10, 11, 7, 8, 3, 4]])
+    self._test_make_toeplitz_matrix(inputs, output_expected)
+
+  def _test_make_toeplitz_matrix(self, inputs, output_expected):
+    output_tf = math_utils.make_toeplitz_matrix(inputs)
+    with self.test_session() as sess:
+      output_tf_np = sess.run(output_tf)
+    self.assertAllClose(output_tf_np, output_expected)
+
+
+class TestMakeCovarianceMatrix(test.TestCase):
+
+  def test_zero_size_matrix(self):
+    raw = numpy.zeros([0, 0])
+    with self.test_session():
+      constructed = math_utils.sign_magnitude_positive_definite(raw=raw).eval()
+    self.assertEqual((0, 0), constructed.shape)
+
+  def test_sign_magnitude_positive_definite(self):
+    for dtype in [dtypes.float32, dtypes.float64]:
+      with self.test_session():
+        matrix_tensor = math_utils.sign_magnitude_positive_definite(
+            raw=constant_op.constant([[-1., -2.], [3., 4.]], dtype=dtype),
+            off_diagonal_scale=constant_op.constant(-1., dtype=dtype),
+            overall_scale=constant_op.constant(1., dtype=dtype))
+        matrix_evaled = matrix_tensor.eval()
+        self.assertAllClose(matrix_evaled, matrix_evaled.T)
+        self.assertTrue(numpy.all(numpy.linalg.eigvals(matrix_evaled) > 0))
+
+
+class TestLookupTable(test.TestCase):
+
+  def test_tuple_of_tensors_lookup(self):
+    hash_table = math_utils.TupleOfTensorsLookup(
+        key_dtype=dtypes.int64,
+        default_values=[[
+            array_ops.ones([3, 2], dtype=dtypes.float32), array_ops.zeros(
+                [5], dtype=dtypes.float64)
+        ], array_ops.ones([7, 7], dtype=dtypes.int64)],
+        empty_key=-1,
+        name="test_lookup")
+    def stack_tensor(base_tensor):
+      return array_ops.stack([base_tensor + 1, base_tensor + 2])
+    with self.test_session() as session:
+      ((float_output, double_output), int_output) = session.run(
+          hash_table.lookup([2, 1, 0]))
+      def expected_output_before_insert(base_tensor):
+        return [base_tensor,
+                base_tensor,
+                base_tensor]
+      self.assertAllClose(
+          expected_output_before_insert(numpy.ones([3, 2])),
+          float_output)
+      self.assertAllClose(
+          expected_output_before_insert(numpy.zeros([5])),
+          double_output)
+      self.assertAllEqual(
+          expected_output_before_insert(numpy.ones([7, 7], dtype=numpy.int64)),
+          int_output)
+      hash_table.insert(
+          keys=[1, 2],
+          values=[[
+              stack_tensor(array_ops.ones([3, 2], dtype=dtypes.float32)),
+              stack_tensor(array_ops.zeros([5], dtype=dtypes.float64))
+          ], stack_tensor(array_ops.ones([7, 7], dtype=dtypes.int64))]).run()
+      ((float_output, double_output), int_output) = session.run(
+          hash_table.lookup([2, 1, 0]))
+      def expected_output_after_insert(base_tensor):
+        return [base_tensor + 2,
+                base_tensor + 1,
+                base_tensor]
+      self.assertAllClose(
+          expected_output_after_insert(numpy.ones([3, 2])),
+          float_output)
+      self.assertAllClose(
+          expected_output_after_insert(numpy.zeros([5])),
+          double_output)
+      self.assertAllEqual(
+          expected_output_after_insert(numpy.ones([7, 7], dtype=numpy.int64)),
+          int_output)
+
+
+class InputStatisticsTests(test.TestCase):
+
+  def _input_statistics_test_template(
+      self, stat_object, num_features, dtype, give_full_data,
+      warmup_iterations=0, rtol=1e-6, data_length=500, chunk_size=4):
+    graph = ops.Graph()
+    with graph.as_default():
+      numpy_dtype = dtype.as_numpy_dtype
+      values = (
+          (numpy.arange(data_length, dtype=numpy_dtype)[..., None]
+           + numpy.arange(num_features, dtype=numpy_dtype)[None, ...])[None])
+      times = 2 * (numpy.arange(data_length)[None]) - 3
+      if give_full_data:
+        stat_object.set_data((times, values))
+      features = {TrainEvalFeatures.TIMES: times,
+                  TrainEvalFeatures.VALUES: values}
+      input_fn = input_pipeline.RandomWindowInputFn(
+          batch_size=16, window_size=chunk_size,
+          time_series_reader=input_pipeline.NumpyReader(features))
+      statistics = stat_object.initialize_graph(
+          features=input_fn()[0])
+      with self.test_session(graph=graph) as session:
+        variables.global_variables_initializer().run()
+        coordinator = coordinator_lib.Coordinator()
+        queue_runner_impl.start_queue_runners(session, coord=coordinator)
+        for _ in range(warmup_iterations):
+          # A control dependency should ensure that, for queue-based statistics,
+          # a use of any statistic is preceded by an update of all adaptive
+          # statistics.
+          statistics.total_observation_count.eval()
+        self.assertAllClose(
+            range(num_features) + numpy.mean(numpy.arange(chunk_size))[None],
+            statistics.series_start_moments.mean.eval(),
+            rtol=rtol)
+        self.assertAllClose(
+            numpy.tile(numpy.var(numpy.arange(chunk_size))[None],
+                       [num_features]),
+            statistics.series_start_moments.variance.eval(),
+            rtol=rtol)
+        self.assertAllClose(
+            numpy.mean(values[0], axis=0),
+            statistics.overall_feature_moments.mean.eval(),
+            rtol=rtol)
+        self.assertAllClose(
+            numpy.var(values[0], axis=0),
+            statistics.overall_feature_moments.variance.eval(),
+            rtol=rtol)
+        self.assertAllClose(
+            -3,
+            statistics.start_time.eval(),
+            rtol=rtol)
+        self.assertAllClose(
+            data_length,
+            statistics.total_observation_count.eval(),
+            rtol=rtol)
+        coordinator.request_stop()
+        coordinator.join()
+
+  def test_queue(self):
+    for dtype in [dtypes.float32, dtypes.float64]:
+      for num_features in [1, 2, 3]:
+        self._input_statistics_test_template(
+            math_utils.InputStatisticsFromMiniBatch(
+                num_features=num_features, dtype=dtype),
+            num_features=num_features,
+            dtype=dtype,
+            give_full_data=False,
+            warmup_iterations=1000,
+            rtol=0.1)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py
new file mode 100644
index 0000000000..016db33e69
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/model.py
@@ -0,0 +1,737 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Base class for time series models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import collections
+
+from tensorflow.contrib import layers
+
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import tensor_array_ops
+from tensorflow.python.ops import variable_scope
+
+from tensorflow.python.util import nest
+
+
+ModelOutputs = collections.namedtuple(  # pylint: disable=invalid-name
+    typename="ModelOutputs",
+    field_names=[
+        "loss",  # The scalar value to be minimized during training.
+        "end_state",  # A nested tuple specifying the model's state after
+                      # running on the specified data
+        "predictions",  # A dictionary of predictions, each with shape prefixed
+                        # by the shape of `prediction_times`.
+        "prediction_times"  # A [batch size x window size] integer Tensor
+                            # indicating times for which values in `predictions`
+                            # were computed.
+    ])
+
+
+class TimeSeriesModel(object):
+  """Base class for creating generative time series models."""
+
+  __metaclass__ = abc.ABCMeta
+
+  def __init__(self,
+               num_features,
+               exogenous_feature_columns=None,
+               dtype=dtypes.float32):
+    """Constructor for generative models.
+
+    Args:
+      num_features: Number of features for the time series
+      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
+          objects (for example tf.contrib.layers.embedding_column) corresponding
+          to exogenous features which provide extra information to the model but
+          are not part of the series to be predicted. Passed to
+          tf.contrib.layers.input_from_feature_columns.
+      dtype: The floating point datatype to use.
+    """
+    if exogenous_feature_columns:
+      self._exogenous_feature_columns = exogenous_feature_columns
+    else:
+      self._exogenous_feature_columns = []
+    self.num_features = num_features
+    self.dtype = dtype
+    self._input_statistics = None
+    self._graph_initialized = False
+
+  # TODO(allenl): Move more of the generic machinery for generating and
+  # predicting into TimeSeriesModel, and possibly share it between generate()
+  # and predict()
+  @abc.abstractmethod
+  def generate(self, number_of_series, series_length,
+               model_parameters=None, seed=None):
+    """Sample synthetic data from model parameters, with optional substitutions.
+
+    Returns `number_of_series` possible sequences of future values, sampled from
+    the generative model with each conditioned on the previous. Samples are
+    based on trained parameters, except for those parameters explicitly
+    overridden in `model_parameters`.
+
+    For distributions over future observations, see predict().
+
+    Args:
+      number_of_series: Number of time series to create.
+      series_length: Length of each time series.
+      model_parameters: A dictionary mapping model parameters to values, which
+          replace trained parameters when generating data.
+      seed: If specified, return deterministic time series according to this
+          value.
+    Returns:
+      A dictionary with keys TrainEvalFeatures.TIMES (mapping to an array with
+      shape [number_of_series, series_length]) and TrainEvalFeatures.VALUES
+      (mapping to an array with shape [number_of_series, series_length,
+      num_features]).
+    """
+    pass
+
+  def initialize_graph(self, input_statistics=None):
+    """Define ops for the model, not depending on any previously defined ops.
+
+    Args:
+      input_statistics: A math_utils.InputStatistics object containing input
+          statistics. If None, data-independent defaults are used, which may
+          result in longer or unstable training.
+    """
+    self._graph_initialized = True
+    self._input_statistics = input_statistics
+
+  def _check_graph_initialized(self):
+    if not self._graph_initialized:
+      raise ValueError(
+          "TimeSeriesModels require initialize_graph() to be called before "
+          "use. This defines variables and ops in the default graph, and "
+          "allows Tensor-valued input statistics to be specified.")
+
+  def define_loss(self, features, mode):
+    """Default loss definition with state replicated across a batch.
+
+    Time series passed to this model have a batch dimension, and each series in
+    a batch can be operated on in parallel. This loss definition assumes that
+    each element of the batch represents an independent sample conditioned on
+    the same initial state (i.e. it is simply replicated across the batch). A
+    batch size of one provides sequential operations on a single time series.
+
+    More complex processing may operate instead on get_start_state() and
+    get_batch_loss() directly.
+
+    Args:
+      features: A dictionary (such as is produced by a chunker) with at minimum
+        the following key/value pairs (others corresponding to the
+        `exogenous_feature_columns` argument to `__init__` may be included
+        representing exogenous regressors):
+        TrainEvalFeatures.TIMES: A [batch size x window size] integer Tensor
+            with times for each observation. If there is no artificial chunking,
+            the window size is simply the length of the time series.
+        TrainEvalFeatures.VALUES: A [batch size x window size x num features]
+            Tensor with values for each observation.
+      mode: The tf.estimator.ModeKeys mode to use (TRAIN, EVAL). For INFER,
+        see predict().
+    Returns:
+      A ModelOutputs object.
+    """
+    self._check_graph_initialized()
+    start_state = math_utils.replicate_state(
+        start_state=self.get_start_state(),
+        batch_size=array_ops.shape(features[TrainEvalFeatures.TIMES])[0])
+    return self.get_batch_loss(features=features, mode=mode, state=start_state)
+
+  # TODO(vitalyk,allenl): Better documentation surrounding options for chunking,
+  # references to papers, etc.
+  @abc.abstractmethod
+  def get_start_state(self):
+    """Returns a tuple of state for the start of the time series.
+
+    For example, a mean and covariance. State should not have a batch
+    dimension, and will often be TensorFlow Variables to be learned along with
+    the rest of the model parameters.
+    """
+    pass
+
+  @abc.abstractmethod
+  def get_batch_loss(self, features, mode, state):
+    """Return predictions, losses, and end state for a time series.
+
+    Args:
+      features: A dictionary with times, values, and (optionally) exogenous
+          regressors. See `define_loss`.
+      mode: The tf.estimator.ModeKeys mode to use (TRAIN, EVAL, INFER).
+      state: Model-dependent state, each with size [batch size x ...]. The
+          number and type will typically be fixed by the model (for example a
+          mean and variance).
+    Returns:
+      A ModelOutputs object.
+    """
+    pass
+
+  @abc.abstractmethod
+  def predict(self, features):
+    """Returns predictions of future observations given an initial state.
+
+    Computes distributions for future observations. For sampled draws from the
+    model where each is conditioned on the previous, see generate().
+
+    Args:
+      features: A dictionary with at minimum the following key/value pairs
+        (others corresponding to the `exogenous_feature_columns` argument to
+        `__init__` may be included representing exogenous regressors):
+        PredictionFeatures.TIMES: A [batch size x window size] Tensor with
+          times to make predictions for. Times must be increasing within each
+          part of the batch, and must be greater than the last time `state` was
+          updated.
+        PredictionFeatures.STATE_TUPLE: Model-dependent state, each with size
+          [batch size x ...]. The number and type will typically be fixed by the
+          model (for example a mean and variance). Typically these will be the
+          end state returned by get_batch_loss, predicting beyond that data.
+    Returns:
+      A dictionary with model-dependent predictions corresponding to the
+      requested times. Keys indicate the type of prediction, and values have
+      shape [batch size x window size x ...]. For example state space models
+      return a "predicted_mean" and "predicted_covariance".
+    """
+    pass
+
+  def _process_exogenous_features(self, times, features):
+    """Create a single vector from exogenous features.
+
+    Args:
+      times: A [batch size, window size] vector of times for this batch,
+          primarily used to check the shape information of exogenous features.
+      features: A dictionary of exogenous features corresponding to the columns
+          in self._exogenous_feature_columns. Each value should have a shape
+          prefixed by [batch size, window size].
+    Returns:
+      A Tensor with shape [batch size, window size, exogenous dimension], where
+      the size of the exogenous dimension depends on the exogenous feature
+      columns passed to the model's constructor.
+    Raises:
+      ValueError: If an exogenous feature has an unknown rank.
+    """
+    if self._exogenous_feature_columns:
+      exogenous_features_single_batch_dimension = {}
+      for name, tensor in features.items():
+        if tensor.get_shape().ndims is None:
+          # input_from_feature_columns does not support completely unknown
+          # feature shapes, so we save on a bit of logic and provide a better
+          # error message by checking that here.
+          raise ValueError(
+              ("Features with unknown rank are not supported. Got shape {} for "
+               "feature {}.").format(tensor.get_shape(), name))
+        tensor_shape_dynamic = array_ops.shape(tensor)
+        tensor = array_ops.reshape(
+            tensor,
+            array_ops.concat([[tensor_shape_dynamic[0]
+                               * tensor_shape_dynamic[1]],
+                              tensor_shape_dynamic[2:]], axis=0))
+        # Avoid shape warnings when embedding "scalar" exogenous features (those
+        # with only batch and window dimensions); input_from_feature_columns
+        # expects input ranks to match the embedded rank.
+        if tensor.get_shape().ndims == 1:
+          exogenous_features_single_batch_dimension[name] = tensor[:, None]
+        else:
+          exogenous_features_single_batch_dimension[name] = tensor
+      embedded_exogenous_features_single_batch_dimension = (
+          layers.input_from_feature_columns(
+              columns_to_tensors=exogenous_features_single_batch_dimension,
+              feature_columns=self._exogenous_feature_columns,
+              trainable=True))
+      exogenous_regressors = array_ops.reshape(
+          embedded_exogenous_features_single_batch_dimension,
+          array_ops.concat(
+              [
+                  array_ops.shape(times), array_ops.shape(
+                      embedded_exogenous_features_single_batch_dimension)[1:]
+              ],
+              axis=0))
+      exogenous_regressors.set_shape(times.get_shape().concatenate(
+          embedded_exogenous_features_single_batch_dimension.get_shape()[1:]))
+      exogenous_regressors = math_ops.cast(
+          exogenous_regressors, dtype=self.dtype)
+    else:
+      # Not having any exogenous features is a special case so that models can
+      # avoid superfluous updates, which may not be free of side effects due to
+      # bias terms in transformations.
+      exogenous_regressors = None
+    return exogenous_regressors
+
+
+# TODO(allenl): Add a superclass of SequentialTimeSeriesModel which fuses
+# filtering/prediction/exogenous into one step, and move looping constructs to
+# that class.
+class SequentialTimeSeriesModel(TimeSeriesModel):
+  """Base class for recurrent generative models.
+
+  Models implementing this interface have three main functions, corresponding to
+  abstract methods:
+    _filtering_step: Updates state based on observations and computes a loss.
+    _prediction_step: Predicts a batch of observations and new model state.
+    _imputation_step: Updates model state across a gap.
+    _exogenous_input_step: Updates state to account for exogenous regressors.
+
+  Models may also specify a _window_initializer to prepare for a window of data.
+
+  See StateSpaceModel for a concrete example of a model implementing this
+  interface.
+
+  """
+
+  def __init__(self,
+               train_output_names,
+               predict_output_names,
+               num_features,
+               dtype=dtypes.float32,
+               exogenous_feature_columns=None,
+               exogenous_update_condition=None,
+               static_unrolling_window_size_threshold=None):
+    """Initialize a SequentialTimeSeriesModel.
+
+    Args:
+      train_output_names: A list of products/predictions returned from
+          _filtering_step.
+      predict_output_names: A list of products/predictions returned from
+          _prediction_step.
+      num_features: Number of features for the time series
+      dtype: The floating point datatype to use.
+      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
+          objects. See `TimeSeriesModel`.
+      exogenous_update_condition: A function taking two Tensor arguments `times`
+          (shape [batch size]) and `features` (a dictionary mapping exogenous
+          feature keys to Tensors with shapes [batch size, ...]) and returning a
+          boolean Tensor with shape [batch size] indicating whether state should
+          be updated using exogenous features for each part of the batch. Where
+          it is False, no exogenous update is performed. If None (default),
+          exogenous updates are always performed. Useful for avoiding "leaky"
+          frequent exogenous updates when sparse updates are desired. Called
+          only during graph construction.
+      static_unrolling_window_size_threshold: Controls whether a `tf.while_loop`
+          is used when looping over a window of data. If
+          `static_unrolling_window_size_threshold` is None, a `tf.while_loop` is
+          always used. Otherwise it must be an integer, and the graph is
+          replicated for each step taken whenever the window size is less than
+          or equal to this value (if the window size is available in the static
+          shape information of the TrainEvalFeatures.TIMES feature). Static
+          unrolling generally decreases the per-step time for small window/batch
+          sizes, but increases graph construction time.
+    """
+    super(SequentialTimeSeriesModel, self).__init__(
+        num_features=num_features, dtype=dtype,
+        exogenous_feature_columns=exogenous_feature_columns)
+    self._exogenous_update_condition = exogenous_update_condition
+    self._train_output_names = train_output_names
+    self._predict_output_names = predict_output_names
+    self._static_unrolling_window_size_threshold = (
+        static_unrolling_window_size_threshold)
+
+  @abc.abstractmethod
+  def _filtering_step(self, current_times, current_values, state, predictions):
+    """Compute a single-step loss for a batch of data.
+
+    Args:
+      current_times: A [batch size] Tensor of times for each observation.
+      current_values: A [batch size] Tensor of values for each observaiton.
+      state: Model state, updated to current_times.
+      predictions: The outputs of _prediction_step
+    Returns:
+      A tuple of (updated state, outputs):
+        updated state: Model state taking current_values into account.
+        outputs: A dictionary of Tensors with keys corresponding to
+            self._train_output_names, plus a special "loss" key. The value
+            corresponding to "loss" is minimized during training. Other outputs
+            may include one-step-ahead predictions, for example a predicted
+            location and scale.
+    """
+    pass
+
+  @abc.abstractmethod
+  def _prediction_step(self, current_times, state):
+    """Compute a batch of single-step predictions.
+
+    Args:
+      current_times: A [batch size] Tensor of times for each observation.
+      state: Model state, imputed to one step before current_times.
+    Returns:
+      A tuple of (updated state, outputs):
+        updated state: Model state updated to current_times.
+        outputs: A dictionary of Tensors with keys corresponding to
+            self._predict_output_names.
+    """
+    pass
+
+  @abc.abstractmethod
+  def _imputation_step(self, current_times, state):
+    """Update model state across missing values.
+
+    Called to prepare model state for _filtering_step and _prediction_step.
+
+    Args:
+      current_times: A [batch size] Tensor; state will be imputed up to, but not
+          including, these timesteps.
+      state: The pre-imputation model state, Tensors with shape [batch size x
+          ...].
+    Returns:
+      Updated/imputed model state, corresponding to `state`.
+    """
+    pass
+
+  @abc.abstractmethod
+  def _exogenous_input_step(
+      self, current_times, current_exogenous_regressors, state):
+    """Update state to account for exogenous regressors.
+
+    Args:
+      current_times: A [batch size] Tensor of times for the exogenous values
+          being input.
+      current_exogenous_regressors: A [batch size x exogenous input dimension]
+          Tensor of exogenous values for each part of the batch.
+      state: Model state, a possibly nested list of Tensors, each with shape
+          [batch size x ...].
+    Returns:
+      Updated model state, structure and shapes matching the `state` argument.
+    """
+    pass
+
+  # TODO(allenl): Move regularization to a separate object (optional and
+  # configurable)
+  def _loss_additions(self, times, values, mode):
+    """Additions to per-observation normalized loss, e.g. regularization.
+
+    Args:
+      times: A [batch size x window size] Tensor with times for each
+          observation.
+      values: A [batch size x window size x num features] Tensor with values for
+          each observation.
+      mode: The tf.estimator.ModeKeys mode to use (TRAIN, EVAL, INFER).
+    Returns:
+      A scalar value to add to the per-observation normalized loss.
+    """
+    del times, values, mode
+    return 0.
+
+  def _window_initializer(self, times, state):
+    """Prepare for training or prediction on a window of data.
+
+    Args:
+      times: A [batch size x window size] Tensor with times for each
+          observation.
+      state: Model-dependent state, each with size [batch size x ...]. The
+          number and type will typically be fixed by the model (for example a
+          mean and variance).
+    Returns:
+      Nothing
+    """
+    pass
+
+  def get_batch_loss(self, features, mode, state):
+    """Calls self._filtering_step. See TimeSeriesModel.get_batch_loss."""
+    per_observation_loss, state, outputs = self.per_step_batch_loss(
+        features, mode, state)
+    # per_step_batch_loss returns [batch size, window size, ...] state, whereas
+    # get_batch_loss is expected to return [batch size, ...] state for the last
+    # element of a window
+    state = nest.pack_sequence_as(
+        state,
+        [state_element[:, -1] for state_element in nest.flatten(state)])
+    outputs["observed"] = features[TrainEvalFeatures.VALUES]
+    return ModelOutputs(
+        loss=per_observation_loss,
+        end_state=state,
+        predictions=outputs,
+        prediction_times=features[TrainEvalFeatures.TIMES])
+
+  def _apply_exogenous_update(
+      self, current_times, step_number, state, raw_features,
+      embedded_exogenous_regressors):
+    """Performs a conditional state update based on exogenous features."""
+    if embedded_exogenous_regressors is None:
+      return state
+    else:
+      current_exogenous_regressors = embedded_exogenous_regressors[
+          :, step_number, :]
+      exogenous_updated_state = self._exogenous_input_step(
+          current_times=current_times,
+          current_exogenous_regressors=current_exogenous_regressors,
+          state=state)
+      if self._exogenous_update_condition is not None:
+        current_raw_exogenous_features = {
+            key: value[:, step_number] for key, value in raw_features.items()
+            if key not in [PredictionFeatures.STATE_TUPLE,
+                           TrainEvalFeatures.TIMES,
+                           TrainEvalFeatures.VALUES]}
+        conditionally_updated_state_flat = []
+        for updated_state_element, original_state_element in zip(
+            nest.flatten(exogenous_updated_state),
+            nest.flatten(state)):
+          conditionally_updated_state_flat.append(
+              array_ops.where(
+                  self._exogenous_update_condition(
+                      times=current_times,
+                      features=current_raw_exogenous_features),
+                  updated_state_element,
+                  original_state_element))
+        return nest.pack_sequence_as(state, conditionally_updated_state_flat)
+      else:
+        return exogenous_updated_state
+
+  def per_step_batch_loss(self, features, mode, state):
+    """Computes predictions, losses, and intermediate model states.
+
+    Args:
+      features: A dictionary with times, values, and (optionally) exogenous
+          regressors. See `define_loss`.
+      mode: The tf.estimator.ModeKeys mode to use (TRAIN, EVAL, INFER).
+      state: Model-dependent state, each with size [batch size x ...]. The
+          number and type will typically be fixed by the model (for example a
+          mean and variance).
+    Returns:
+      A tuple of (loss, filtered_states, predictions)
+        loss: Average loss values across the batch.
+        filtered_states: For each Tensor in `state` with shape [batch size x
+            ...], `filtered_states` has a Tensor with shape [batch size x window
+            size x ...] with filtered state for each part of the batch and
+            window.
+        predictions: A dictionary with model-dependent one-step-ahead (or
+            at-least-one-step-ahead with missing values) predictions, with keys
+            indicating the type of prediction and values having shape [batch
+            size x window size x ...]. For example state space models provide
+            "mean", "covariance", and "log_likelihood".
+
+    """
+    self._check_graph_initialized()
+    times = math_ops.cast(features[TrainEvalFeatures.TIMES], dtype=dtypes.int64)
+    values = math_ops.cast(features[TrainEvalFeatures.VALUES], dtype=self.dtype)
+    exogenous_regressors = self._process_exogenous_features(
+        times=times,
+        features={key: value for key, value in features.items()
+                  if key not in [TrainEvalFeatures.TIMES,
+                                 TrainEvalFeatures.VALUES]})
+    def _batch_loss_filtering_step(step_number, current_times, state):
+      """Make a prediction and update it based on data."""
+      current_values = values[:, step_number, :]
+      state = self._apply_exogenous_update(
+          step_number=step_number, current_times=current_times, state=state,
+          raw_features=features,
+          embedded_exogenous_regressors=exogenous_regressors)
+      predicted_state, predictions = self._prediction_step(
+          current_times=current_times,
+          state=state)
+      filtered_state, outputs = self._filtering_step(
+          current_times=current_times,
+          current_values=current_values,
+          state=predicted_state,
+          predictions=predictions)
+      return filtered_state, outputs
+    state, outputs = self._state_update_loop(
+        times=times, state=state, state_update_fn=_batch_loss_filtering_step,
+        outputs=["loss"] + self._train_output_names)
+    outputs["loss"].set_shape(times.get_shape())
+    loss_sum = math_ops.reduce_sum(outputs["loss"])
+    per_observation_loss = (loss_sum / math_ops.cast(
+        math_ops.reduce_prod(array_ops.shape(times)), dtype=self.dtype))
+    per_observation_loss += self._loss_additions(times, values, mode)
+    # Since we have window-level additions to the loss, its per-step value is
+    # misleading, so we avoid returning it.
+    del outputs["loss"]
+    return per_observation_loss, state, outputs
+
+  def predict(self, features):
+    """Calls self._prediction_step in a loop. See TimeSeriesModel.predict."""
+    predict_times = ops.convert_to_tensor(features[PredictionFeatures.TIMES],
+                                          dtypes.int64)
+    start_state = features[PredictionFeatures.STATE_TUPLE]
+    exogenous_regressors = self._process_exogenous_features(
+        times=predict_times,
+        features={
+            key: value
+            for key, value in features.items()
+            if key not in
+            [PredictionFeatures.TIMES, PredictionFeatures.STATE_TUPLE]
+        })
+    def _call_prediction_step(step_number, current_times, state):
+      state = self._apply_exogenous_update(
+          step_number=step_number, current_times=current_times, state=state,
+          raw_features=features,
+          embedded_exogenous_regressors=exogenous_regressors)
+      state, outputs = self._prediction_step(
+          current_times=current_times, state=state)
+      return state, outputs
+    _, predictions = self._state_update_loop(
+        times=predict_times, state=start_state,
+        state_update_fn=_call_prediction_step,
+        outputs=self._predict_output_names)
+    return predictions
+
+  class _FakeTensorArray(object):
+    """An interface for Python lists that is similar to TensorArray.
+
+    Used for easy switching between static and dynamic looping.
+    """
+
+    def __init__(self):
+      self.values = []
+
+    def write(self, unused_position, value):
+      del unused_position
+      self.values.append(value)
+      return self
+
+  def _state_update_loop(self, times, state, state_update_fn, outputs):
+    """Iterates over `times`, calling `state_update_fn` to collect outputs.
+
+    Args:
+      times: A [batch size x window size] Tensor of integers to iterate over.
+      state: A list of model-specific state Tensors, each with shape [batch size
+          x ...].
+      state_update_fn: A callback taking the following arguments
+            step_number; A scalar integer Tensor indicating the current position
+              in the window.
+            current_times; A [batch size] vector of Integers indicating times
+              for each part of the batch.
+            state; Current model state.
+          It returns a tuple of (updated state, output_values), output_values
+          being a dictionary of Tensors with keys corresponding to `outputs`.
+      outputs: A list of strings indicating values which will be saved while
+          iterating. Must match the keys of the dictionary returned by
+          state_update_fn.
+    Returns:
+      A tuple of (state, output_dict)
+      state: The final model state.
+      output_dict: A dictionary of outputs corresponding to those specified in
+        `outputs` and computed in state_update_fn.
+    """
+    times = ops.convert_to_tensor(times, dtype=dtypes.int64)
+    window_static_shape = times.get_shape()[1].value
+    if self._static_unrolling_window_size_threshold is None:
+      static_unroll = False
+    else:
+      # The user has specified a threshold for static loop unrolling.
+      if window_static_shape is None:
+        # We don't have static shape information for the window size, so dynamic
+        # looping is our only option.
+        static_unroll = False
+      elif window_static_shape <= self._static_unrolling_window_size_threshold:
+        # The threshold is satisfied; unroll statically
+        static_unroll = True
+      else:
+        # A threshold was set but not satisfied
+        static_unroll = False
+
+    self._window_initializer(times, state)
+
+    def _run_condition(step_number, *unused):
+      del unused  # not part of while loop run condition
+      return math_ops.less(step_number, window_size)
+
+    def _state_update_step(
+        step_number, state, state_accumulators, output_accumulators,
+        reuse=False):
+      """Impute, then take one state_update_fn step, accumulating outputs."""
+      with variable_scope.variable_scope("state_update_step", reuse=reuse):
+        current_times = times[:, step_number]
+        state = self._imputation_step(current_times=current_times, state=state)
+        output_accumulators_dict = {
+            accumulator_key: accumulator
+            for accumulator_key, accumulator
+            in zip(outputs, output_accumulators)}
+        step_state, output_values = state_update_fn(
+            step_number=step_number,
+            current_times=current_times,
+            state=state)
+        assert set(output_values.keys()) == set(outputs)
+        new_output_accumulators = []
+        for output_key in outputs:
+          accumulator = output_accumulators_dict[output_key]
+          output_value = output_values[output_key]
+          new_output_accumulators.append(
+              accumulator.write(step_number, output_value))
+        flat_step_state = nest.flatten(step_state)
+        assert len(state_accumulators) == len(flat_step_state)
+        new_state_accumulators = []
+        new_state_flat = []
+        for step_state_value, state_accumulator, original_state in zip(
+            flat_step_state, state_accumulators, nest.flatten(state)):
+          # Make sure the static shape information is complete so while_loop
+          # does not complain about shape information changing.
+          step_state_value.set_shape(original_state.get_shape())
+          new_state_flat.append(step_state_value)
+          new_state_accumulators.append(state_accumulator.write(
+              step_number, step_state_value))
+        step_state = nest.pack_sequence_as(state, new_state_flat)
+        return (step_number + 1, step_state,
+                new_state_accumulators, new_output_accumulators)
+
+    window_size = array_ops.shape(times)[1]
+
+    def _window_size_tensor_array(dtype):
+      if static_unroll:
+        return self._FakeTensorArray()
+      else:
+        return tensor_array_ops.TensorArray(
+            dtype=dtype, size=window_size, dynamic_size=False)
+
+    initial_loop_arguments = [
+        array_ops.zeros([], dtypes.int32),
+        state,
+        [_window_size_tensor_array(element.dtype)
+         for element in nest.flatten(state)],
+        [_window_size_tensor_array(self.dtype) for _ in outputs]]
+    if static_unroll:
+      arguments = initial_loop_arguments
+      for step_number in range(times.get_shape()[1].value):
+        arguments = _state_update_step(
+            array_ops.constant(step_number, dtypes.int32), *arguments[1:],
+            reuse=(step_number > 0))  # Variable sharing between steps
+    else:
+      arguments = control_flow_ops.while_loop(
+          cond=_run_condition,
+          body=_state_update_step,
+          loop_vars=initial_loop_arguments)
+    (_, _, state_loop_result, outputs_loop_result) = arguments
+
+    def _stack_and_transpose(tensor_array):
+      """Stack and re-order the dimensions of a TensorArray."""
+      if static_unroll:
+        return array_ops.stack(tensor_array.values, axis=1)
+      else:
+        # TensorArrays from while_loop stack with window size as the first
+        # dimension, so this function swaps it and the batch dimension to
+        # maintain the [batch x window size x ...] convention used elsewhere.
+        stacked = tensor_array.stack()
+        return array_ops.transpose(
+            stacked,
+            perm=array_ops.concat([[1, 0], math_ops.range(
+                2, array_ops.rank(stacked))], 0))
+
+    outputs_dict = {output_key: _stack_and_transpose(output)
+                    for output_key, output
+                    in zip(outputs, outputs_loop_result)}
+    full_state = nest.pack_sequence_as(
+        state,
+        [_stack_and_transpose(state_element)
+         for state_element in state_loop_result])
+    return full_state, outputs_dict
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model_utils.py b/tensorflow/contrib/timeseries/python/timeseries/model_utils.py
new file mode 100644
index 0000000000..addcdb0575
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/model_utils.py
@@ -0,0 +1,417 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Helper functions for training and constructing time series Models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+
+import numpy
+
+from tensorflow.contrib.framework.python.ops import variables
+from tensorflow.contrib.layers.python.layers import optimizers
+
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.estimator.export import export_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.util import nest
+
+
+def _check_feature_shapes_compatible_with(
+    features, compatible_with_name, compatible_with_value, ignore=None):
+  """Checks all features are compatible with the given time-like feature."""
+  if ignore is None:
+    ignore = set()
+  for name, value in features.items():
+    if name in ignore:
+      continue
+    feature_shape = value.get_shape()
+    if feature_shape.ndims is None:
+      continue
+    if feature_shape.ndims < 2:
+      raise ValueError(
+          ("Features must have shape (batch dimension, window size, ...) "
+           "(got rank {} for feature '{}')").format(
+               feature_shape.ndims, name))
+    if not feature_shape[:2].is_compatible_with(
+        compatible_with_value.get_shape()):
+      raise ValueError(
+          ("Features must have shape (batch dimension, window size, ...) "
+           "where batch dimension and window size match the "
+           "'{times_feature}' feature (got shape {feature_shape} for "
+           "feature '{feature_name}' but shape {times_shape} for feature "
+           "'{times_feature}')").format(
+               times_feature=compatible_with_name,
+               feature_shape=feature_shape,
+               feature_name=name,
+               times_shape=compatible_with_value.get_shape()))
+
+
+def _check_predict_features(features):
+  """Raises errors if features are not suitable for prediction."""
+  if feature_keys.PredictionFeatures.TIMES not in features:
+    raise ValueError("Expected a '{}' feature for prediction.".format(
+        feature_keys.PredictionFeatures.TIMES))
+  if feature_keys.PredictionFeatures.STATE_TUPLE not in features:
+    raise ValueError("Expected a '{}' feature for prediction.".format(
+        feature_keys.PredictionFeatures.STATE_TUPLE))
+  times_feature = features[feature_keys.PredictionFeatures.TIMES]
+  if not times_feature.get_shape().is_compatible_with([None, None]):
+    raise ValueError(
+        ("Expected shape (batch dimension, window size) for feature '{}' "
+         "(got shape {})").format(feature_keys.PredictionFeatures.TIMES,
+                                  times_feature.get_shape()))
+  _check_feature_shapes_compatible_with(
+      features=features,
+      compatible_with_name=feature_keys.PredictionFeatures.TIMES,
+      compatible_with_value=times_feature,
+      ignore=set([
+          feature_keys.PredictionFeatures.STATE_TUPLE  # Model-dependent shapes
+      ]))
+
+
+def _check_train_eval_features(features, model):
+  """Raise errors if features are not suitable for training/evaluation."""
+  if feature_keys.TrainEvalFeatures.TIMES not in features:
+    raise ValueError("Expected a '{}' feature for training/evaluation.".format(
+        feature_keys.TrainEvalFeatures.TIMES))
+  if feature_keys.TrainEvalFeatures.VALUES not in features:
+    raise ValueError("Expected a '{}' feature for training/evaluation.".format(
+        feature_keys.TrainEvalFeatures.VALUES))
+  times_feature = features[feature_keys.TrainEvalFeatures.TIMES]
+  if not times_feature.get_shape().is_compatible_with([None, None]):
+    raise ValueError(
+        ("Expected shape (batch dimension, window size) for feature '{}' "
+         "(got shape {})").format(feature_keys.TrainEvalFeatures.TIMES,
+                                  times_feature.get_shape()))
+  values_feature = features[feature_keys.TrainEvalFeatures.VALUES]
+  if not values_feature.get_shape().is_compatible_with(
+      [None, None, model.num_features]):
+    raise ValueError(
+        ("Expected shape (batch dimension, window size, {num_features}) "
+         "for feature '{feature_name}', since the model was configured "
+         "with num_features={num_features} (got shape {got_shape})").format(
+             num_features=model.num_features,
+             feature_name=feature_keys.TrainEvalFeatures.VALUES,
+             got_shape=times_feature.get_shape()))
+  _check_feature_shapes_compatible_with(
+      features=features,
+      compatible_with_name=feature_keys.TrainEvalFeatures.TIMES,
+      compatible_with_value=times_feature,
+      ignore=set([
+          feature_keys.State.STATE_TUPLE  # Model-dependent shapes
+      ]))
+
+
+def _identity_metric_single(name, input_tensor):
+  """A metric which takes on its last updated value.
+
+  This keeps evaluation metrics in sync with one another, since update ops are
+  run separately from their result Tensors. Simply returning (input_tensor,
+  no_op) as a metric with a value but no update means that a metric will come
+  from a different batch of data than metrics which cache values in a Variable
+  (e.g. the default loss metric).
+
+  Args:
+    name: A name for the metric.
+    input_tensor: Any Tensor.
+  Returns:
+    A tuple of (value, update_op).
+  """
+  metric_variable = variable_scope.variable(
+      name="{}_identity_metric".format(name),
+      initial_value=array_ops.zeros([], dtype=input_tensor.dtype),
+      collections=[ops.GraphKeys.LOCAL_VARIABLES],
+      validate_shape=False)
+  update_op = state_ops.assign(metric_variable, input_tensor,
+                               validate_shape=False)
+  # This shape will be correct once the first update runs (but may be
+  # incomplete, so is not helpful for initializing the variable).
+  metric_variable.set_shape(input_tensor.get_shape())
+  return (metric_variable.value(), update_op)
+
+
+def _identity_metric_nested(name, input_tensors):
+  """Create identity metrics for a nested tuple of Tensors."""
+  update_ops = []
+  value_tensors = []
+  for tensor_number, tensor in enumerate(nest.flatten(input_tensors)):
+    value_tensor, update_op = _identity_metric_single(
+        name="{}_{}".format(name, tensor_number),
+        input_tensor=tensor)
+    update_ops.append(update_op)
+    value_tensors.append(value_tensor)
+  return (nest.pack_sequence_as(input_tensors, value_tensors),
+          control_flow_ops.group(*update_ops))
+
+
+def state_to_dictionary(state_tuple):
+  """Flatten model state into a dictionary with string keys."""
+  flattened = {}
+  for state_number, state_value in enumerate(nest.flatten(state_tuple)):
+    prefixed_state_name = "{}_{:02d}".format(feature_keys.State.STATE_PREFIX,
+                                             state_number)
+    flattened[prefixed_state_name] = state_value
+  return flattened
+
+
+def make_model_fn(
+    model, state_manager, optimizer, input_statistics_generator=None):
+  """Returns a model function suitable for use with a tf.estimator.
+
+  Args:
+    model: The object (inheriting from Model) to create a function for.
+    state_manager: A state manager to wrap the model with (or
+        PassthroughStateManager if no state needs to be managed).
+    optimizer: An instance of `tf.train.Optimizer` to use for training.
+    input_statistics_generator: An InputStatisticsFromMiniBatch object from
+        math_utils.py, used for collecting statistics about input data during
+        training.
+  Returns:
+    The model function, suitable for passing to a tf.estimator.Estimator.
+  """
+
+  def _convert_feature_to_tensor(name, value):
+    """Casts features to the correct dtype based on their name."""
+    if name in [
+        feature_keys.TrainEvalFeatures.TIMES,
+        feature_keys.PredictionFeatures.TIMES
+    ]:
+      return math_ops.cast(value, dtypes.int64)
+    if name == feature_keys.TrainEvalFeatures.VALUES:
+      return math_ops.cast(value, model.dtype)
+    if name == feature_keys.PredictionFeatures.STATE_TUPLE:
+      return value  # Correct dtypes are model-dependent
+    return ops.convert_to_tensor(value)
+
+  def _gather_state(features):
+    """Returns `features` with state packed, indicates if packing was done."""
+    prefixed_state_re = re.compile(r"^" + feature_keys.State.STATE_PREFIX +
+                                   r"_(\d+)$")
+    numbered_state = []
+    for key, tensor in features.items():
+      search_result = prefixed_state_re.search(key)
+      if search_result:
+        numbered_state.append((int(search_result.group(1)), key, tensor))
+    if not numbered_state:
+      return features, False
+    features = features.copy()
+    for _, key, _ in numbered_state:
+      del features[key]
+    numbered_state.sort(key=lambda number, *_: number)
+    features[feature_keys.State.STATE_TUPLE] = nest.pack_sequence_as(
+        structure=model.get_start_state(),
+        flat_sequence=[tensor for _, _, tensor in numbered_state])
+    return features, True
+
+  def _train(features):
+    """Add training ops to the graph."""
+    with variable_scope.variable_scope("model"):
+      model_outputs = state_manager.define_loss(model, features,
+                                                estimator_lib.ModeKeys.TRAIN)
+    train_op = optimizers.optimize_loss(
+        model_outputs.loss,
+        global_step=variables.get_global_step(),
+        optimizer=optimizer,
+        # Learning rate is set in the Optimizer object
+        learning_rate=None)
+    return estimator_lib.EstimatorSpec(
+        loss=model_outputs.loss,
+        mode=estimator_lib.ModeKeys.TRAIN,
+        train_op=train_op)
+
+  def _evaluate(features):
+    """Add ops for evaluation (aka filtering) to the graph."""
+    with variable_scope.variable_scope("model"):
+      model_outputs = state_manager.define_loss(model, features,
+                                                estimator_lib.ModeKeys.EVAL)
+    metrics = {}
+    # Just output in-sample predictions for the last chunk seen
+    for prediction_key, prediction_value in model_outputs.predictions.items():
+      metrics[prediction_key] = _identity_metric_single(prediction_key,
+                                                        prediction_value)
+    metrics[feature_keys.FilteringResults.TIMES] = _identity_metric_single(
+        feature_keys.FilteringResults.TIMES, model_outputs.prediction_times)
+    metrics[feature_keys.FilteringResults.STATE_TUPLE] = (
+        _identity_metric_nested(feature_keys.FilteringResults.STATE_TUPLE,
+                                model_outputs.end_state))
+    return estimator_lib.EstimatorSpec(
+        loss=model_outputs.loss,
+        mode=estimator_lib.ModeKeys.EVAL,
+        eval_metric_ops=metrics,
+        predictions={})
+
+  def _predict(features):
+    """Add ops for prediction to the graph."""
+    with variable_scope.variable_scope("model"):
+      prediction = model.predict(features=features)
+    prediction[feature_keys.PredictionResults.TIMES] = features[
+        feature_keys.PredictionFeatures.TIMES]
+    return estimator_lib.EstimatorSpec(
+        predictions=prediction, mode=estimator_lib.ModeKeys.PREDICT)
+
+  def _serving(features):
+    with variable_scope.variable_scope("model"):
+      prediction_outputs = model.predict(features=features)
+    with variable_scope.variable_scope("model", reuse=True):
+      filtering_outputs = state_manager.define_loss(model, features,
+                                                    estimator_lib.ModeKeys.EVAL)
+    return estimator_lib.EstimatorSpec(
+        mode=estimator_lib.ModeKeys.PREDICT,
+        export_outputs={
+            feature_keys.SavedModelLabels.PREDICT:
+                export_lib.PredictOutput(prediction_outputs),
+            feature_keys.SavedModelLabels.FILTER:
+                export_lib.PredictOutput(
+                    state_to_dictionary(filtering_outputs.end_state))
+        },
+        # Likely unused, but it is necessary to return `predictions` to satisfy
+        # the Estimator's error checking.
+        predictions={})
+
+  def _model_fn(features, labels, mode):
+    """Given a time series in `features`, define a loss for `mode`.
+
+    Args:
+      features: A dictionary, the output of a chunker (typically with keys
+          feature_keys.TrainEvalFeatures.TIMES and
+          feature_keys.TrainEvalFeatures.VALUES).
+      labels: Not used; included for compatibility with tf.learn.
+      mode: The tf.estimator.ModeKeys mode to use (TRAIN, EVAL, INFER).
+    Returns:
+      A tuple of predictions, a loss Tensor, and a train op.
+    Raises:
+      ValueError: If the model makes predictions which do not have static shape
+          information.
+    """
+    if labels:
+      raise ValueError("The model received a `labels` dictionary, which is not"
+                       " supported. Pass '{}' and '{}' as features.".format(
+                           feature_keys.TrainEvalFeatures.TIMES,
+                           feature_keys.TrainEvalFeatures.VALUES))
+    del labels
+    features = {name: _convert_feature_to_tensor(name=name, value=value)
+                for name, value in features.items()}
+    if input_statistics_generator is not None:
+      input_statistics = input_statistics_generator.initialize_graph(
+          features, update_statistics=(mode == estimator_lib.ModeKeys.TRAIN))
+    else:
+      input_statistics = None
+    model.initialize_graph(input_statistics=input_statistics)
+    # _gather_state requires the model to have its graph initialized (so it has
+    # access to the structure of the model's state)
+    features, passed_flat_state = _gather_state(features)
+    if (mode == estimator_lib.ModeKeys.TRAIN
+        or mode == estimator_lib.ModeKeys.EVAL):
+      _check_train_eval_features(features, model)
+    elif mode == estimator_lib.ModeKeys.PREDICT:
+      _check_predict_features(features)
+    else:
+      raise ValueError("Unknown mode '{}' passed to model_fn.".format(mode))
+    state_manager.initialize_graph(
+        model=model, input_statistics=input_statistics)
+    if mode == estimator_lib.ModeKeys.TRAIN:
+      return _train(features)
+    elif mode == estimator_lib.ModeKeys.EVAL:
+      return _evaluate(features)
+    elif mode == estimator_lib.ModeKeys.PREDICT and not passed_flat_state:
+      return _predict(features)
+    elif mode == estimator_lib.ModeKeys.PREDICT and passed_flat_state:
+      # The mode is PREDICT, but we're actually in export_savedmodel for
+      # serving. We want to return two graphs: one for filtering (state + data
+      # -> state) and one for predicting (state -> prediction).
+      return _serving(features)
+  return _model_fn
+
+
+# TODO(agarwal): Remove and replace with functionality from tf.slim
+def fully_connected(inp,
+                    inp_size,
+                    layer_size,
+                    name,
+                    activation=nn_ops.relu,
+                    dtype=dtypes.float32):
+  """Helper method to create a fully connected hidden layer."""
+  wt = variable_scope.get_variable(
+      name="{}_weight".format(name), shape=[inp_size, layer_size], dtype=dtype)
+  bias = variable_scope.get_variable(
+      name="{}_bias".format(name),
+      shape=[layer_size],
+      initializer=init_ops.zeros_initializer())
+  output = nn_ops.xw_plus_b(inp, wt, bias)
+  if activation is not None:
+    assert callable(activation)
+    output = activation(output)
+  return output
+
+
+def parameter_switch(parameter_overrides):
+  """Create a function which chooses between overridden and model parameters.
+
+  Args:
+    parameter_overrides: A dictionary with explicit overrides of model
+        parameters, mapping from Tensors to their overridden values.
+  Returns:
+    A function which takes a Tensor and returns the override if it is specified,
+        or otherwise the evaluated value (given current Variable values).
+  """
+  def get_passed_or_trained_value(parameter):
+    return ops.convert_to_tensor(
+        parameter_overrides.get(parameter, parameter)).eval()
+  return get_passed_or_trained_value
+
+
+def canonicalize_times_or_steps_from_output(times, steps,
+                                            previous_model_output):
+  """Canonicalizes either relative or absolute times, with error checking."""
+  if steps is not None and times is not None:
+    raise ValueError("Only one of `steps` and `times` may be specified.")
+  if steps is None and times is None:
+    raise ValueError("One of `steps` and `times` must be specified.")
+  if times is not None:
+    times = numpy.array(times)
+    if len(times.shape) != 2:
+      times = times[None, ...]
+    if (previous_model_output[feature_keys.FilteringResults.TIMES].shape[0] !=
+        times.shape[0]):
+      raise ValueError(
+          ("`times` must have a batch dimension matching"
+           " the previous model output (got a batch dimension of {} for `times`"
+           " and {} for the previous model output).").format(
+               times.shape[0], previous_model_output[
+                   feature_keys.FilteringResults.TIMES].shape[0]))
+    if not (previous_model_output[feature_keys.FilteringResults.TIMES][:, -1] <
+            times[:, 0]).all():
+      raise ValueError("Prediction times must be after the corresponding "
+                       "previous model output.")
+  if steps is not None:
+    predict_times = (
+        previous_model_output[feature_keys.FilteringResults.TIMES][:, -1:] + 1 +
+        numpy.arange(steps)[None, ...])
+  else:
+    predict_times = times
+  return predict_times
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model_utils_test.py b/tensorflow/contrib/timeseries/python/timeseries/model_utils_test.py
new file mode 100644
index 0000000000..2998689554
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/model_utils_test.py
@@ -0,0 +1,275 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for model_utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import model
+from tensorflow.contrib.timeseries.python.timeseries import model_utils
+from tensorflow.contrib.timeseries.python.timeseries import state_management
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import metrics
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.training import coordinator as coordinator_lib
+from tensorflow.python.training import queue_runner_impl
+from tensorflow.python.training import training as train
+
+
+class ModelUtilsTest(test.TestCase):
+
+  def test_parameter_switching(self):
+    parameter = array_ops.constant(5)
+    overridden_parameter = array_ops.constant(3)
+    with self.test_session():
+      getter = model_utils.parameter_switch({overridden_parameter: 4})
+      self.assertEqual(5, getter(parameter))
+      self.assertEqual(4, getter(overridden_parameter))
+
+  def test_labels_provided_error(self):
+    model_fn = _stub_model_fn()
+    for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL,
+                 estimator_lib.ModeKeys.PREDICT]:
+      with self.assertRaisesRegexp(ValueError, "labels"):
+        model_fn(features={}, labels={"a": "b"}, mode=mode)
+
+  def test_unknown_mode(self):
+    model_fn = _stub_model_fn()
+    with self.assertRaisesRegexp(ValueError, "Unknown mode 'Not a mode'"):
+      model_fn(features={}, labels={}, mode="Not a mode")
+
+
+class _TickerModel(object):
+  num_features = 1
+  dtype = dtypes.float32
+
+  def initialize_graph(self, input_statistics):
+    pass
+
+  def define_loss(self, features, mode):
+    del mode  # unused
+    return model.ModelOutputs(
+        loss=features["ticker"],
+        end_state=(features["ticker"], features["ticker"]),
+        prediction_times=array_ops.zeros(()),
+        predictions={"ticker": features["ticker"]})
+
+
+class EvaluationMetricsTests(test.TestCase):
+
+  def test_metrics_consistent(self):
+    # Tests that the identity metrics used to report in-sample predictions match
+    # the behavior of standard metrics.
+    g = ops.Graph()
+    with g.as_default():
+      features = {
+          feature_keys.TrainEvalFeatures.TIMES:
+              array_ops.zeros((1, 1)),
+          feature_keys.TrainEvalFeatures.VALUES:
+              array_ops.zeros((1, 1, 1)),
+          "ticker":
+              array_ops.reshape(
+                  math_ops.cast(
+                      variables.Variable(
+                          name="ticker",
+                          initial_value=0,
+                          dtype=dtypes.int64,
+                          collections=[ops.GraphKeys.LOCAL_VARIABLES])
+                      .count_up_to(10),
+                      dtype=dtypes.float32), (1, 1, 1))
+      }
+      model_fn = model_utils.make_model_fn(
+          model=_TickerModel(),
+          state_manager=state_management.PassthroughStateManager(),
+          optimizer=train.GradientDescentOptimizer(0.001))
+      outputs = model_fn(
+          features=features, labels=None, mode=estimator_lib.ModeKeys.EVAL)
+      metric_update_ops = [
+          metric[1] for metric in outputs.eval_metric_ops.values()]
+      loss_mean, loss_update = metrics.mean(outputs.loss)
+      metric_update_ops.append(loss_update)
+      with self.test_session() as sess:
+        coordinator = coordinator_lib.Coordinator()
+        queue_runner_impl.start_queue_runners(sess, coord=coordinator)
+        variables.local_variables_initializer().run()
+        sess.run(metric_update_ops)
+        loss_evaled, metric_evaled, nested_metric_evaled = sess.run(
+            (loss_mean, outputs.eval_metric_ops["ticker"][0],
+             outputs.eval_metric_ops[feature_keys.FilteringResults.STATE_TUPLE][
+                 0][0]))
+        # The custom model_utils metrics for in-sample predictions should be in
+        # sync with the Estimator's mean metric for model loss.
+        self.assertAllClose(0., loss_evaled)
+        self.assertAllClose((((0.,),),), metric_evaled)
+        self.assertAllClose((((0.,),),), nested_metric_evaled)
+        coordinator.request_stop()
+        coordinator.join()
+
+
+class _StubModel(object):
+  num_features = 3
+  dtype = dtypes.float64
+
+  def initialize_graph(self, input_statistics):
+    del input_statistics  # unused
+
+
+def _stub_model_fn():
+  return model_utils.make_model_fn(
+      model=_StubModel(),
+      state_manager=state_management.PassthroughStateManager(),
+      optimizer=train.AdamOptimizer(0.001))
+
+
+class TrainEvalFeatureCheckingTests(test.TestCase):
+
+  def test_no_time_feature(self):
+    model_fn = _stub_model_fn()
+    for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
+      with self.assertRaisesRegexp(ValueError, "Expected a '{}' feature".format(
+          feature_keys.TrainEvalFeatures.TIMES)):
+        model_fn(
+            features={feature_keys.TrainEvalFeatures.VALUES: [[[1.]]]},
+            labels=None,
+            mode=mode)
+
+  def test_no_value_feature(self):
+    model_fn = _stub_model_fn()
+    for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
+      with self.assertRaisesRegexp(ValueError, "Expected a '{}' feature".format(
+          feature_keys.TrainEvalFeatures.VALUES)):
+        model_fn(
+            features={feature_keys.TrainEvalFeatures.TIMES: [[1]]},
+            labels=None,
+            mode=mode)
+
+  def test_bad_time_rank(self):
+    model_fn = _stub_model_fn()
+    for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
+      with self.assertRaisesRegexp(ValueError,
+                                   "Expected shape.*for feature '{}'".format(
+                                       feature_keys.TrainEvalFeatures.TIMES)):
+        model_fn(
+            features={
+                feature_keys.TrainEvalFeatures.TIMES: [[[1]]],
+                feature_keys.TrainEvalFeatures.VALUES: [[[1.]]]
+            },
+            labels=None,
+            mode=mode)
+
+  def test_bad_value_rank(self):
+    model_fn = _stub_model_fn()
+    for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
+      with self.assertRaisesRegexp(ValueError,
+                                   "Expected shape.*for feature '{}'".format(
+                                       feature_keys.TrainEvalFeatures.VALUES)):
+        model_fn(
+            features={
+                feature_keys.TrainEvalFeatures.TIMES: [[1]],
+                feature_keys.TrainEvalFeatures.VALUES: [[1.]]
+            },
+            labels=None,
+            mode=mode)
+
+  def test_bad_value_num_features(self):
+    model_fn = _stub_model_fn()
+    for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
+      with self.assertRaisesRegexp(
+          ValueError, "Expected shape.*, 3.*for feature '{}'".format(
+              feature_keys.TrainEvalFeatures.VALUES)):
+        model_fn(
+            features={
+                feature_keys.TrainEvalFeatures.TIMES: [[1]],
+                feature_keys.TrainEvalFeatures.VALUES: [[[1.]]]
+            },
+            labels=None,
+            mode=mode)
+
+  def test_bad_exogenous_shape(self):
+    model_fn = _stub_model_fn()
+    for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
+      with self.assertRaisesRegexp(
+          ValueError,
+          "Features must have shape.*for feature 'exogenous'"):
+        model_fn(
+            features={
+                feature_keys.TrainEvalFeatures.TIMES: [[1]],
+                feature_keys.TrainEvalFeatures.VALUES: [[[1., 2., 3.]]],
+                "exogenous": [[1], [2]]
+            },
+            labels=None,
+            mode=mode)
+
+
+class PredictFeatureCheckingTests(test.TestCase):
+
+  def test_no_time_feature(self):
+    model_fn = _stub_model_fn()
+    with self.assertRaisesRegexp(ValueError, "Expected a '{}' feature".format(
+        feature_keys.PredictionFeatures.TIMES)):
+      model_fn(
+          features={
+              feature_keys.PredictionFeatures.STATE_TUPLE: ([[[1.]]], 1.)
+          },
+          labels=None,
+          mode=estimator_lib.ModeKeys.PREDICT)
+
+  def test_no_start_state_feature(self):
+    model_fn = _stub_model_fn()
+    with self.assertRaisesRegexp(ValueError, "Expected a '{}' feature".format(
+        feature_keys.PredictionFeatures.STATE_TUPLE)):
+      model_fn(
+          features={feature_keys.PredictionFeatures.TIMES: [[1]]},
+          labels=None,
+          mode=estimator_lib.ModeKeys.PREDICT)
+
+  def test_bad_time_rank(self):
+    model_fn = _stub_model_fn()
+    with self.assertRaisesRegexp(ValueError,
+                                 "Expected shape.*for feature '{}'".format(
+                                     feature_keys.PredictionFeatures.TIMES)):
+      model_fn(
+          features={
+              feature_keys.PredictionFeatures.TIMES: 1,
+              feature_keys.PredictionFeatures.STATE_TUPLE: (1, (2, 3.))
+          },
+          labels=None,
+          mode=estimator_lib.ModeKeys.PREDICT)
+
+  def test_bad_exogenous_shape(self):
+    model_fn = _stub_model_fn()
+    with self.assertRaisesRegexp(
+        ValueError,
+        "Features must have shape.*for feature 'exogenous'"):
+      model_fn(
+          features={
+              feature_keys.PredictionFeatures.TIMES: [[1]],
+              feature_keys.PredictionFeatures.STATE_TUPLE: (1, (2, 3.)),
+              "exogenous": 1.
+          },
+          labels=None,
+          mode=estimator_lib.ModeKeys.PREDICT)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/saved_model_utils.py b/tensorflow/contrib/timeseries/python/timeseries/saved_model_utils.py
new file mode 100644
index 0000000000..16e29f5e68
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/saved_model_utils.py
@@ -0,0 +1,167 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Convenience functions for working with time series saved_models.
+
+@@predict_continuation
+@@filter_continuation
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys as _feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline as _input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import model_utils as _model_utils
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+
+def _colate_features_to_feeds_and_fetches(continue_from, signature, features,
+                                          graph):
+  """Uses a saved model signature to construct feed and fetch dictionaries."""
+  if _feature_keys.FilteringResults.STATE_TUPLE in continue_from:
+    # We're continuing from an evaluation, so we need to unpack/flatten state.
+    state_values = _model_utils.state_to_dictionary(
+        continue_from[_feature_keys.FilteringResults.STATE_TUPLE])
+  else:
+    state_values = continue_from
+  input_feed_tensors_by_name = {
+      input_key: graph.as_graph_element(input_value.name)
+      for input_key, input_value in signature.inputs.items()
+  }
+  output_tensors_by_name = {
+      output_key: graph.as_graph_element(output_value.name)
+      for output_key, output_value in signature.outputs.items()
+  }
+  feed_dict = {}
+  for state_key, state_value in state_values.items():
+    feed_dict[input_feed_tensors_by_name[state_key]] = state_value
+  for feature_key, feature_value in features.items():
+    feed_dict[input_feed_tensors_by_name[feature_key]] = feature_value
+  return output_tensors_by_name, feed_dict
+
+
+def predict_continuation(continue_from,
+                         signatures,
+                         session,
+                         steps=None,
+                         times=None,
+                         exogenous_features=None):
+  """Perform prediction using an exported saved model.
+
+  Analogous to _input_pipeline.predict_continuation_input_fn, but operates on a
+  saved model rather than feeding into Estimator's predict method.
+
+  Args:
+    continue_from: A dictionary containing the results of either an Estimator's
+      evaluate method or filter_continuation. Used to determine the model
+      state to make predictions starting from.
+    signatures: The `MetaGraphDef` protocol buffer returned from
+      `tf.saved_model.loader.load`. Used to determine the names of Tensors to
+      feed and fetch. Must be from the same model as `continue_from`.
+    session: The session to use. The session's graph must be the one into which
+      `tf.saved_model.loader.load` loaded the model.
+    steps: The number of steps to predict (scalar), starting after the
+      evaluation or filtering. If `times` is specified, `steps` must not be; one
+      is required.
+    times: A [batch_size x window_size] array of integers (not a Tensor)
+      indicating times to make predictions for. These times must be after the
+      corresponding evaluation or filtering. If `steps` is specified, `times`
+      must not be; one is required. If the batch dimension is omitted, it is
+      assumed to be 1.
+    exogenous_features: Optional dictionary. If specified, indicates exogenous
+      features for the model to use while making the predictions. Values must
+      have shape [batch_size x window_size x ...], where `batch_size` matches
+      the batch dimension used when creating `continue_from`, and `window_size`
+      is either the `steps` argument or the `window_size` of the `times`
+      argument (depending on which was specified).
+  Returns:
+    A dictionary with model-specific predictions (typically having keys "mean"
+    and "covariance") and a feature_keys.PredictionResults.TIMES key indicating
+    the times for which the predictions were computed.
+  Raises:
+    ValueError: If `times` or `steps` are misspecified.
+  """
+  if exogenous_features is None:
+    exogenous_features = {}
+  predict_times = _model_utils.canonicalize_times_or_steps_from_output(
+      times=times, steps=steps, previous_model_output=continue_from)
+  features = {_feature_keys.PredictionFeatures.TIMES: predict_times}
+  features.update(exogenous_features)
+  predict_signature = signatures.signature_def[
+      _feature_keys.SavedModelLabels.PREDICT]
+  output_tensors_by_name, feed_dict = _colate_features_to_feeds_and_fetches(
+      continue_from=continue_from,
+      signature=predict_signature,
+      features=features,
+      graph=session.graph)
+  output = session.run(output_tensors_by_name, feed_dict=feed_dict)
+  output[_feature_keys.PredictionResults.TIMES] = features[
+      _feature_keys.PredictionFeatures.TIMES]
+  return output
+
+
+def filter_continuation(continue_from, signatures, session, features):
+  """Perform filtering using an exported saved model.
+
+  Filtering refers to updating model state based on new observations.
+  Predictions based on the returned model state will be conditioned on these
+  observations.
+
+  Args:
+    continue_from: A dictionary containing the results of either an Estimator's
+      evaluate method or a previous filter_continuation. Used to determine the
+      model state to start filtering from.
+    signatures: The `MetaGraphDef` protocol buffer returned from
+      `tf.saved_model.loader.load`. Used to determine the names of Tensors to
+      feed and fetch. Must be from the same model as `continue_from`.
+    session: The session to use. The session's graph must be the one into which
+      `tf.saved_model.loader.load` loaded the model.
+    features: A dictionary mapping keys to Numpy arrays, with several possible
+      shapes (requires keys `FilteringFeatures.TIMES` and
+      `FilteringFeatures.VALUES`):
+        Single example; `TIMES` is a scalar and `VALUES` is either a scalar or a
+          vector of length [number of features].
+        Sequence; `TIMES` is a vector of shape [series length], `VALUES` either
+          has shape [series length] (univariate) or [series length x number of
+          features] (multivariate).
+        Batch of sequences; `TIMES` is a vector of shape [batch size x series
+          length], `VALUES` has shape [batch size x series length] or [batch
+          size x series length x number of features].
+      In any case, `VALUES` and any exogenous features must have their shapes
+      prefixed by the shape of the value corresponding to the `TIMES` key.
+  Returns:
+    A dictionary containing model state updated to account for the observations
+    in `features`.
+  """
+  filter_signature = signatures.signature_def[
+      _feature_keys.SavedModelLabels.FILTER]
+  features = _input_pipeline._canonicalize_numpy_data(  # pylint: disable=protected-access
+      data=features,
+      require_single_batch=False)
+  output_tensors_by_name, feed_dict = _colate_features_to_feeds_and_fetches(
+      continue_from=continue_from,
+      signature=filter_signature,
+      features=features,
+      graph=session.graph)
+  output = session.run(output_tensors_by_name, feed_dict=feed_dict)
+  # Make it easier to chain filter -> predict by keeping track of the current
+  # time.
+  output[_feature_keys.FilteringResults.TIMES] = features[
+      _feature_keys.FilteringFeatures.TIMES]
+  return output
+
+remove_undocumented(module_name=__name__)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_management.py b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
new file mode 100644
index 0000000000..ba51a82f4c
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
@@ -0,0 +1,264 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Classes for wrapping a model to operate on different data shapes."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries.model import ModelOutputs
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.util import nest
+
+
+class PassthroughStateManager(object):
+  """A minimal wrapper for models which do not need state management."""
+
+  def __init__(self):
+    self._input_statistics = None
+    self._graph_initialized = False
+
+  def initialize_graph(self, model, input_statistics=None):
+    """Adds required operations to the graph."""
+    del model  # unused
+    self._graph_initialized = True
+    self._input_statistics = input_statistics
+
+  def define_loss(self, model, features, mode):
+    """Wrap "model" with StateManager-specific operations.
+
+    Args:
+      model: The model (inheriting from TimeSeriesModel) to manage state for.
+      features: A dictionary with the following key/value pairs:
+        feature_keys.TrainEvalFeatures.TIMES: A [batch size x window size]
+            Tensor with times for each observation.
+        feature_keys.TrainEvalFeatures.VALUES: A [batch size x window size x num
+            features] Tensor with values for each observation.
+      mode: The tf.estimator.ModeKeys mode to use (TRAIN or EVAL).
+    Returns:
+      A ModelOutputs object.
+    Raises:
+      ValueError: If start state was specified.
+    """
+    if feature_keys.State.STATE_TUPLE in features:
+      raise ValueError(
+          "Overriding start state is not supported for this model.")
+    return model.define_loss(features, mode)
+
+
+class _OverridableStateManager(PassthroughStateManager):
+  """Base class for state managers which support overriding model state."""
+
+  @abc.abstractmethod
+  def _define_loss_with_saved_state(self, model, features, mode):
+    pass
+
+  def define_loss(self, model, features, mode):
+    """Switches between explicit start state and managed state."""
+    if feature_keys.FilteringFeatures.STATE_TUPLE in features:
+      # Explicit start state has been provided, so we should use that.
+      if mode == estimator_lib.ModeKeys.TRAIN:
+        raise ValueError(
+            ("Overriding saved state for training is not supported (but a value"
+             "for feature {} was specified)."
+            ).format(feature_keys.FilteringFeatures.STATE_TUPLE))
+      start_state = features[feature_keys.FilteringFeatures.STATE_TUPLE]
+      del features[feature_keys.FilteringFeatures.STATE_TUPLE]
+      return model.get_batch_loss(
+          features=features, mode=mode, state=start_state)
+    else:
+      # No explicit start state; use managed state.
+      return self._define_loss_with_saved_state(
+          model=model, features=features, mode=mode)
+
+
+class FilteringOnlyStateManager(_OverridableStateManager):
+  """State manager for models which use state only for filtering.
+
+  Window-based models (ARModel) do not require state to be fed during training
+  (instead requiring a specific window size). Rather than requiring a minimum
+  window size for filtering, these models maintain this window in their state,
+  and so need state to be fed.
+  """
+
+  def _define_loss_with_saved_state(self, model, features, mode):
+    return model.define_loss(features, mode)
+
+
+class ChainingStateManager(_OverridableStateManager):
+  """Maintains state across a batch for SequentialTimeSeriesModel subclasses.
+
+  The batch dimension is treated as indexing sequential chunks of the same
+  timeseries. End state from each chunk is fed as start state to the next chunk
+  during the next timestep. This is an approximation to full-batch training for
+  sequential models, but is typically much faster while still accurately
+  recovering parameters. The speedup comes from reduced scheduling overhead of
+  TensorFlow ops, since each operation can do much more work.
+  """
+
+  def __init__(self, state_saving_interval=20, checkpoint_state=False):
+    """Initialize the state manager.
+
+    Args:
+      state_saving_interval: This state manager saves intermediate model state
+          every `state_saving_interval` times. Larger values save memory, and
+          checkpoint size if `checkpoint_state` is enabled, but models
+          will need to impute across artificial gaps of up to this size
+          (i.e. gaps not appearing in the original data). This imputation may
+          affect training. Set state_saving_interval to 1 to avoid any
+          artificial imputation.
+      checkpoint_state: If True, saved intermediate model state will be
+          written to checkpoints. Checkpoints will then scale with dataset
+          size. If False, state will be freshly imputed from the beginning of a
+          series each time the model is restored, which means it may take a few
+          iterations for state to warm up.
+    """
+    super(ChainingStateManager, self).__init__()
+    self._checkpoint_state = checkpoint_state
+    self._state_saving_interval = state_saving_interval
+    self._start_state = None
+    self._cached_states = None
+
+  def initialize_graph(self, model, input_statistics=None):
+    """Adds required operations to the graph."""
+    super(ChainingStateManager, self).initialize_graph(
+        model=model, input_statistics=input_statistics)
+    self._start_state = model.get_start_state()
+    self._cached_states = math_utils.TupleOfTensorsLookup(
+        key_dtype=dtypes.int64,
+        default_values=self._start_state,
+        empty_key=-1,
+        name="cached_states",
+        checkpoint=self._checkpoint_state)
+
+  def _define_loss_with_saved_state(self, model, features, mode):
+    """Feeds end state from one training iteration into the next.
+
+    Args:
+      model: The model to wrap. Compatible with children of TimeSeriesModel.
+      features: Dictionary with Tensor values defining the data to be
+        processed. The expected key/value pairs are at minimum:
+          feature_keys.TrainEvalFeatures.TIMES: A [number of chunks x window
+            size] Tensor with times for each observation, the result of chunking
+            a single longer time series.
+          feature_keys.TrainEvalFeatures.VALUES: A [number of chunks x window
+            size x num features] Tensor with values for each observation,
+            corresponding to times.
+      mode: The tf.estimator.ModeKeys mode to use. For EVAL and INFER, no
+          batching is performed, which may be slow. This is to avoid giving
+          cached and almost certainly stale values.
+    Returns:
+      A ModelOutputs object.
+    Raises:
+      ValueError: If initialize_graph has not been called.
+    """
+    if not self._graph_initialized:
+      raise ValueError("ChainingStateManager requires initialize_graph() to be "
+                       "called before use.")
+    (loss_op, end_state, batch_predictions) = self._update_cached_states(
+        model=model,
+        features=features,
+        mode=mode)
+    # Add a batch dimension so state can be used directly (e.g. for predictions)
+    # without the user manually reshaping it.
+    last_end_state_flat = [end_state_value[-1][None]
+                           for end_state_value in nest.flatten(end_state)]
+    batch_predictions["observed"] = features[
+        feature_keys.TrainEvalFeatures.VALUES]
+    return ModelOutputs(
+        loss=loss_op,
+        end_state=nest.pack_sequence_as(end_state, last_end_state_flat),
+        predictions=batch_predictions,
+        prediction_times=features[feature_keys.TrainEvalFeatures.TIMES])
+
+  def _get_chunk_number(self, time):
+    return time // self._state_saving_interval
+
+  def _get_cached_states(self, times):
+    """Retreive cached states for a batch of times."""
+    read_chunk_numbers = self._get_chunk_number(times)
+    looked_up_state = list(self._cached_states.lookup(
+        math_ops.cast(read_chunk_numbers, dtypes.int64)))
+    looked_up_state = tuple(looked_up_state)
+    # We need to special-case the first chunk in a series to explicitly rely on
+    # the model's starting state so that gradients flow back to it. Otherwise it
+    # would affect only initialization, and would not be read from or updated
+    # during training. Not doing this also isolates that part of the graph,
+    # leading to errors on model reload if there are trainable variables
+    # affecting a model's start state.
+    if self._input_statistics is not None:
+      start_time = self._input_statistics.start_time
+    else:
+      start_time = 0
+    set_to_start_state = math_ops.equal(read_chunk_numbers,
+                                        self._get_chunk_number(start_time))
+    new_states = []
+    for start_state_value, cache_variable in zip(
+        nest.flatten(
+            math_utils.replicate_state(self._start_state,
+                                       array_ops.shape(times)[0])),
+        nest.flatten(looked_up_state)):
+
+      new_states.append(
+          array_ops.where(set_to_start_state, start_state_value,
+                          cache_variable))
+    looked_up_state = nest.pack_sequence_as(looked_up_state, new_states)
+    return looked_up_state
+
+  def _update_cached_states(self, model, features, mode):
+    """Read, process, and write chunks to the cache."""
+    times = features[feature_keys.TrainEvalFeatures.TIMES]
+    looked_up_state = self._get_cached_states(times[:, 0])
+    (model_loss, intermediate_states,
+     batch_predictions) = model.per_step_batch_loss(
+         features=features,
+         mode=mode,
+         state=looked_up_state)
+    # We need to at least write to the bucket after the one we read from.
+    min_chunk_numbers = self._get_chunk_number(times) + 1
+    # We write to the bucket that would have been read had the window started at
+    # the next sample (except for the last sample in the window, which gets
+    # written to the next bucket). This assumes fixed missing times (i.e. if we
+    # were presented with times [10, 50] we will never see times [30, 50]).
+    #
+    # TODO(allenl): Retreive the highest time less than the current time rather
+    # than relying on fixed bucketing.
+    write_chunk_numbers = math_ops.maximum(
+        self._get_chunk_number(array_ops.concat(
+            [times[:, 1:], times[:, -1:] + 1], axis=1)),
+        min_chunk_numbers)
+    # Write once for every computed state; this may mean that we write multiple
+    # times to the same cell, but later writes will take precedence.
+    save_ops = [
+        self._cached_states.insert(
+            keys=write_chunk_numbers,
+            values=intermediate_states)]
+    end_state = nest.pack_sequence_as(
+        intermediate_states,
+        [state_element[:, -1]
+         for state_element in nest.flatten(intermediate_states)])
+    with ops.control_dependencies(save_ops):
+      # Make sure end states get saved at each iteration
+      loss_op = array_ops.identity(model_loss)
+    return loss_op, end_state, batch_predictions
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_management_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_management_test.py
new file mode 100644
index 0000000000..d5dce30fda
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_management_test.py
@@ -0,0 +1,313 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for state management."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries import model
+from tensorflow.contrib.timeseries.python.timeseries import state_management
+from tensorflow.contrib.timeseries.python.timeseries import test_utils
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.training import coordinator as coordinator_lib
+from tensorflow.python.training import queue_runner_impl
+from tensorflow.python.training import training as train
+from tensorflow.python.util import nest
+
+
+class StubTimeSeriesModel(model.TimeSeriesModel):
+
+  def __init__(self, correct_offset=False):
+    self._correct_offset = correct_offset
+    super(StubTimeSeriesModel, self).__init__(1)
+
+  def initialize_graph(self, input_statistics=None):
+    super(StubTimeSeriesModel, self).initialize_graph(
+        input_statistics=input_statistics)
+    self.prior_var = variable_scope.get_variable(
+        "prior", [], initializer=init_ops.constant_initializer(0.))
+
+  def generate(self, *args):
+    pass
+
+  def predict(self, *args):
+    pass
+
+  def get_start_state(self):
+    return (array_ops.zeros([], dtype=dtypes.int64), self.prior_var)
+
+  def get_batch_loss(self, features, mode, state):
+    raise NotImplementedError("This stub only supports managed state.")
+
+  def per_step_batch_loss(self, features, mode, state):
+    times = features[feature_keys.TrainEvalFeatures.TIMES]
+    values = features[feature_keys.TrainEvalFeatures.VALUES]
+    (priors_from_time, prior) = state
+    time_corrected_priors = prior + math_ops.cast(
+        math_utils.batch_start_time(times) - priors_from_time, dtypes.float32)
+    posterior = time_corrected_priors[:, None] + math_ops.cast(
+        times - math_utils.batch_start_time(times)[:, None], dtypes.float32)
+    batch_end_values = array_ops.squeeze(
+        array_ops.slice(values, [0, array_ops.shape(times)[1] - 1, 0],
+                        [-1, 1, -1]),
+        squeeze_dims=[1, 2])
+    # A pretty odd but easy to think about loss: L1 loss on the batch end
+    # values.
+    loss = math_ops.reduce_sum(
+        math_ops.abs(
+            array_ops.reshape(posterior[:, -1], [-1]) - batch_end_values))
+    if self._correct_offset:
+      posterior += batch_end_values[0] - posterior[0, -1]
+    posteriors = (times, posterior)
+    return loss, posteriors, {"dummy_predictions": array_ops.zeros_like(values)}
+
+
+class ChainingStateManagerTest(test.TestCase):
+
+  def _make_test_data(self, length, cut_start, cut_end, offset, step=1):
+    times_full = step * numpy.arange(length, dtype=numpy.int64)
+    values_full = offset + step * numpy.arange(length, dtype=numpy.float32)
+    if cut_start is not None:
+      times = numpy.concatenate((times_full[:cut_start],
+                                 times_full[cut_end:]))
+      values = numpy.concatenate((values_full[:cut_start],
+                                  values_full[cut_end:]))
+    else:
+      times = times_full
+      values = values_full
+    return {
+        feature_keys.TrainEvalFeatures.TIMES: times,
+        feature_keys.TrainEvalFeatures.VALUES: values
+    }
+
+  def _test_initialization(self, warmup_iterations, batch_size):
+    stub_model = StubTimeSeriesModel()
+    data = self._make_test_data(length=20, cut_start=None, cut_end=None,
+                                offset=0.)
+    if batch_size == -1:
+      input_fn = test_utils.AllWindowInputFn(
+          input_pipeline.NumpyReader(data), window_size=10)
+    else:
+      input_fn = input_pipeline.RandomWindowInputFn(
+          input_pipeline.NumpyReader(data),
+          window_size=10,
+          batch_size=batch_size)
+    chainer = state_management.ChainingStateManager(
+        state_saving_interval=1)
+    features, _ = input_fn()
+    stub_model.initialize_graph()
+    chainer.initialize_graph(model=stub_model)
+    model_outputs = chainer.define_loss(
+        model=stub_model, features=features, mode=estimator_lib.ModeKeys.TRAIN)
+    with self.test_session() as session:
+      variables.global_variables_initializer().run()
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      for _ in range(warmup_iterations):
+        # Warm up saved state
+        model_outputs.loss.eval()
+      outputs = model_outputs.loss.eval()
+      coordinator.request_stop()
+      coordinator.join()
+      return outputs
+
+  def test_zero_initializations(self):
+    # Even with no initialization, we are imputing values up to each chunk,
+    # which in this case gives exact values.
+    self.assertEqual(0., self._test_initialization(
+        warmup_iterations=0, batch_size=-1))
+
+  def test_one_initializations(self):
+    # Further initialization should still be correct, if redundant
+    self.assertEqual(0., self._test_initialization(
+        warmup_iterations=1, batch_size=-1))
+
+  def test_stochastic_batch(self):
+    # It shouldn't matter whether we're using a full deterministic batch or a
+    # smaller stochastic batch.
+    self.assertEqual(0., self._test_initialization(
+        warmup_iterations=1, batch_size=5))
+
+  def _test_pass_to_next(self, read_offset, step, correct_offset):
+    stub_model = StubTimeSeriesModel(correct_offset=correct_offset)
+    data = self._make_test_data(
+        length=100 + read_offset, cut_start=None, cut_end=None, offset=100.,
+        step=step)
+    init_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(
+            {k: v[:-read_offset] for k, v in data.items()}))
+    result_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(
+            {k: v[read_offset:] for k, v in data.items()}))
+
+    chainer = state_management.ChainingStateManager(
+        state_saving_interval=1)
+    stub_model.initialize_graph()
+    chainer.initialize_graph(model=stub_model)
+    init_model_outputs = chainer.define_loss(
+        model=stub_model, features=init_input_fn()[0],
+        mode=estimator_lib.ModeKeys.TRAIN)
+    result_model_outputs = chainer.define_loss(
+        model=stub_model, features=result_input_fn()[0],
+        mode=estimator_lib.ModeKeys.TRAIN)
+    with self.test_session() as session:
+      variables.global_variables_initializer().run()
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      init_model_outputs.loss.eval()
+      returned_loss = result_model_outputs.loss.eval()
+      coordinator.request_stop()
+      coordinator.join()
+      return returned_loss
+
+  def test_pass_to_next_step_one_no_correction(self):
+    self.assertEqual(100., self._test_pass_to_next(
+        read_offset=1, step=1, correct_offset=False))
+
+  def test_pass_to_next_step_one_with_correction(self):
+    self.assertEqual(0., self._test_pass_to_next(
+        read_offset=1, step=1, correct_offset=True))
+
+  def test_pass_to_next_step_three_with_correction(self):
+    self.assertEqual(0., self._test_pass_to_next(
+        read_offset=1, step=3, correct_offset=True))
+
+  def test_large_read_offset(self):
+    self.assertEqual(0., self._test_pass_to_next(
+        read_offset=50, step=20, correct_offset=True))
+
+  def test_past_init_offset(self):
+    self.assertEqual(100., self._test_pass_to_next(
+        read_offset=100, step=20, correct_offset=True))
+
+  def _test_missing_values(self, cut_start, cut_end, offset):
+    stub_model = StubTimeSeriesModel()
+    data = self._make_test_data(
+        length=100, cut_start=cut_start, cut_end=cut_end, offset=offset)
+    input_fn = test_utils.AllWindowInputFn(
+        input_pipeline.NumpyReader(data), window_size=10)
+    chainer = state_management.ChainingStateManager(
+        state_saving_interval=1)
+    features, _ = input_fn()
+    stub_model.initialize_graph()
+    chainer.initialize_graph(model=stub_model)
+    model_outputs = chainer.define_loss(
+        model=stub_model, features=features, mode=estimator_lib.ModeKeys.TRAIN)
+    with self.test_session() as session:
+      variables.global_variables_initializer().run()
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      for _ in range(10):
+        model_outputs.loss.eval()
+      returned_loss = model_outputs.loss.eval()
+      coordinator.request_stop()
+      coordinator.join()
+      return returned_loss
+
+  def test_missing_values_ten(self):
+    # Each posterior should be off by 10 from the offset in the values. 90
+    # values with a chunk size of 10 means 90 - 10 + 1 possible chunks.
+    self.assertEqual((90 - 10 + 1) * 10, self._test_missing_values(
+        cut_start=20, cut_end=30, offset=10.))
+
+  def test_missing_values_five(self):
+    self.assertEqual((95 - 10 + 1) * 10, self._test_missing_values(
+        cut_start=15, cut_end=20, offset=10.))
+
+
+class _StateOverrideModel(model.TimeSeriesModel):
+
+  def __init__(self):
+    super(_StateOverrideModel, self).__init__(num_features=1)
+
+  def generate(self, *args):
+    pass
+
+  def predict(self, *args):
+    pass
+
+  def get_start_state(self):
+    return (constant_op.constant([20, 30, 40], dtype=dtypes.int64),
+            (constant_op.constant(-10, dtype=dtypes.int64),
+             constant_op.constant([30., 50.], dtype=dtypes.float64)))
+
+  def get_batch_loss(self, features, mode, state):
+    per_observation_loss, state, outputs = self.per_step_batch_loss(
+        features, mode, state)
+    state = nest.map_structure(lambda element: element[:, -1], state)
+    outputs["observed"] = features[feature_keys.TrainEvalFeatures.VALUES]
+    return model.ModelOutputs(
+        loss=per_observation_loss,
+        end_state=state,
+        predictions=outputs,
+        prediction_times=features[feature_keys.TrainEvalFeatures.TIMES])
+
+  def per_step_batch_loss(self, features, mode, state):
+    return (
+        constant_op.constant(1.),
+        # Assumes only one step: this is the per-step loss.
+        nest.map_structure(
+            lambda element: ops.convert_to_tensor(element)[:, None], state),
+        {
+            "dummy_predictions":
+                array_ops.zeros_like(
+                    features[feature_keys.TrainEvalFeatures.VALUES])
+        })
+
+
+class _StateOverrideTest(test.TestCase):
+
+  def test_state_override(self):
+    test_start_state = (numpy.array([[2, 3, 4]]), (numpy.array([2]),
+                                                   numpy.array([[3., 5.]])))
+    data = {
+        feature_keys.FilteringFeatures.TIMES: numpy.arange(5),
+        feature_keys.FilteringFeatures.VALUES: numpy.zeros(shape=[5, 3])
+    }
+    features, _ = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(data))()
+    features[feature_keys.FilteringFeatures.STATE_TUPLE] = test_start_state
+    stub_model = _StateOverrideModel()
+    chainer = state_management.ChainingStateManager()
+    stub_model.initialize_graph()
+    chainer.initialize_graph(model=stub_model)
+    model_outputs = chainer.define_loss(
+        model=stub_model, features=features, mode=estimator_lib.ModeKeys.EVAL)
+    with train.MonitoredSession() as session:
+      end_state = session.run(model_outputs.end_state)
+    nest.assert_same_structure(test_start_state, end_state)
+    for expected, received in zip(
+        nest.flatten(test_start_state), nest.flatten(end_state)):
+      self.assertAllEqual(expected, received)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
new file mode 100644
index 0000000000..a1afa30e3b
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -0,0 +1,293 @@
+# State space components and ensembles
+load("//tensorflow:tensorflow.bzl", "tf_py_test")
+
+package(
+    default_visibility = [
+        "//tensorflow:internal",
+    ],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+py_library(
+    name = "state_space_model",
+    srcs = [
+        "state_space_model.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":kalman_filter",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/contrib/timeseries/python/timeseries:feature_keys",
+        "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
+        "//tensorflow/contrib/timeseries/python/timeseries:model",
+        "//tensorflow/contrib/timeseries/python/timeseries:model_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:linalg_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:variable_scope",
+        "//third_party/py/numpy",
+    ],
+)
+
+tf_py_test(
+    name = "state_space_model_test",
+    srcs = [
+        "state_space_model_test.py",
+    ],
+    additional_deps = [
+        ":kalman_filter",
+        ":state_space_model",
+        "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
+        "//tensorflow/contrib/timeseries/python/timeseries:estimators",
+        "//tensorflow/contrib/timeseries/python/timeseries:saved_model_utils",
+        "//tensorflow/contrib/timeseries/python/timeseries:state_management",
+        "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+    ],
+)
+
+py_library(
+    name = "kalman_filter",
+    srcs = [
+        "kalman_filter.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/distributions:distributions_py",
+        "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:linalg_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:numerics",
+    ],
+)
+
+tf_py_test(
+    name = "kalman_filter_test",
+    srcs = [
+        "kalman_filter_test.py",
+    ],
+    additional_deps = [
+        ":kalman_filter",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:math_ops",
+    ],
+)
+
+py_library(
+    name = "level_trend",
+    srcs = [
+        "level_trend.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":state_space_model",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:linalg_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:variable_scope",
+    ],
+)
+
+tf_py_test(
+    name = "level_trend_test",
+    srcs = [
+        "level_trend_test.py",
+    ],
+    additional_deps = [
+        ":level_trend",
+        ":test_utils",
+        ":state_space_model",
+        "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+    ],
+)
+
+py_library(
+    name = "periodic",
+    srcs = [
+        "periodic.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":state_space_model",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+    ],
+)
+
+tf_py_test(
+    name = "periodic_test",
+    srcs = [
+        "periodic_test.py",
+    ],
+    additional_deps = [
+        ":periodic",
+        ":test_utils",
+        ":state_space_model",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+    ],
+)
+
+py_library(
+    name = "structural_ensemble",
+    srcs = [
+        "structural_ensemble.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":level_trend",
+        ":periodic",
+        ":state_space_model",
+        ":varma",
+        "//tensorflow/python:variable_scope",
+    ],
+)
+
+tf_py_test(
+    name = "structural_ensemble_test",
+    srcs = [
+        "structural_ensemble_test.py",
+    ],
+    additional_deps = [
+        ":structural_ensemble",
+        ":state_space_model",
+        "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
+        "//tensorflow/contrib/timeseries/python/timeseries:estimators",
+        "//third_party/py/numpy",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+    ],
+)
+
+py_library(
+    name = "varma",
+    srcs = [
+        "varma.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":state_space_model",
+        "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:functional_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:variable_scope",
+    ],
+)
+
+tf_py_test(
+    name = "varma_test",
+    srcs = [
+        "varma_test.py",
+    ],
+    additional_deps = [
+        ":varma",
+        "//tensorflow/contrib/timeseries/python/timeseries:feature_keys",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/estimator:estimator_py",
+    ],
+)
+
+py_library(
+    name = "filtering_postprocessor",
+    srcs = [
+        "filtering_postprocessor.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/distributions:distributions_py",
+        "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+    ],
+)
+
+tf_py_test(
+    name = "filtering_postprocessor_test",
+    srcs = [
+        "filtering_postprocessor_test.py",
+    ],
+    additional_deps = [
+        ":filtering_postprocessor",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+    ],
+)
+
+py_library(
+    name = "test_utils",
+    srcs = [
+        "test_utils.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
+        "//tensorflow/contrib/timeseries/python/timeseries:model_utils",
+        "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_ops",
+        "//third_party/py/numpy",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/__init__.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
new file mode 100644
index 0000000000..163819ae2f
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
@@ -0,0 +1,263 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Filtering postprocessors for SequentialTimeSeriesModels."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+
+from tensorflow.contrib import distributions
+
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.util import nest
+
+
+class FilteringStepPostprocessor(object):
+  """Base class for processors that are applied after each filter step."""
+
+  __metaclass__ = abc.ABCMeta
+
+  @abc.abstractmethod
+  def process_filtering_step(self, current_times, current_values,
+                             predicted_state, filtered_state, outputs):
+    """Extends/modifies a filtering step, altering state and loss.
+
+    Args:
+      current_times: A [batch size] integer Tensor of times.
+      current_values: A [batch size x num features] Tensor of values filtering
+          is being performed on.
+      predicted_state: A (possibly nested) list of Tensors indicating model
+          state which does not take `current_times` and `current_values` into
+          account.
+      filtered_state: Same structure as predicted_state, but updated to take
+          `current_times` and `current_values` into account.
+      outputs: A dictionary of outputs produced by model filtering
+          (SequentialTimeSeriesModel._process_filtering_step).
+    Returns: A tuple of (new_state, updated_outputs);
+      new_state: Updated state with the same structure as `filtered_state` and
+          `predicted_state`.
+      updated_outputs: The `outputs` dictionary, updated with any new outputs
+          from this filtering postprocessor.
+    """
+    pass
+
+  @abc.abstractproperty
+  def output_names(self):
+    return []
+
+
+def cauchy_alternative_to_gaussian(current_times, current_values, outputs):
+  """A Cauchy anomaly distribution, centered at a Gaussian prediction.
+
+  Performs an entropy-matching approximation of the scale parameters of
+  independent Cauchy distributions given the covariance matrix of a multivariate
+  Gaussian in outputs["covariance"], and centers the Cauchy distributions at
+  outputs["mean"]. This requires that the model that we are creating an
+  alternative/anomaly distribution for produces a mean and covariance.
+
+  Args:
+    current_times: A [batch size] Tensor of times, unused.
+    current_values: A [batch size x num features] Tensor of values to evaluate
+        the anomaly distribution at.
+    outputs: A dictionary of Tensors with keys "mean" and "covariance"
+        describing the Gaussian to construct an anomaly distribution from. The
+        value corresponding to "mean" has shape [batch size x num features], and
+        the value corresponding to "covariance" has shape [batch size x num
+        features x num features].
+  Returns:
+    A [batch size] Tensor of log likelihoods; the anomaly log PDF evaluated at
+    `current_values`.
+  """
+  del current_times  # unused
+  cauchy_scale = math_utils.entropy_matched_cauchy_scale(outputs["covariance"])
+  individual_log_pdfs = distributions.StudentT(
+      df=array_ops.ones([], dtype=current_values.dtype),
+      loc=outputs["mean"],
+      scale=cauchy_scale).log_prob(current_values)
+  return math_ops.reduce_sum(individual_log_pdfs, axis=1)
+
+
+def _interpolate_state_linear(first_state, second_state, first_responsibility):
+  """Interpolate between two model states linearly."""
+  interpolated_state_flat = []
+  for first_state_tensor, second_state_tensor in zip(
+      nest.flatten(first_state), nest.flatten(second_state)):
+    assert first_state_tensor.dtype == second_state_tensor.dtype
+    if first_state_tensor.dtype.is_floating:
+      # Pad the responsibility shape with ones up to the state's rank so that it
+      # broadcasts
+      first_responsibility_padded = array_ops.reshape(
+          tensor=first_responsibility,
+          shape=array_ops.concat([
+              array_ops.shape(first_responsibility), array_ops.ones(
+                  [array_ops.rank(first_state_tensor) - 1], dtype=dtypes.int32)
+          ], 0))
+      interpolated_state = (
+          first_responsibility_padded * first_state_tensor
+          + (1. - first_responsibility_padded) * second_state_tensor)
+      interpolated_state.set_shape(first_state_tensor.get_shape())
+      interpolated_state_flat.append(interpolated_state)
+    else:
+      # Integer dtypes are probably representing times, and don't need
+      # interpolation. Make sure they're identical to be sure.
+      with ops.control_dependencies(
+          [check_ops.assert_equal(first_state_tensor, second_state_tensor)]):
+        interpolated_state_flat.append(array_ops.identity(first_state_tensor))
+  return nest.pack_sequence_as(first_state, interpolated_state_flat)
+
+
+class StateInterpolatingAnomalyDetector(FilteringStepPostprocessor):
+  """An anomaly detector which guards model state against outliers.
+
+  Smoothly interpolates between a model's predicted and inferred states, based
+  on the posterior probability of an anomaly, p(anomaly | data). This is useful
+  if anomalies would otherwise lead to model state which is hard to recover
+  from (Gaussian state space models suffer from this, for example).
+
+  Relies on (1) an alternative distribution, typically with heavier tails than
+  the model's normal predictions, and (2) a prior probability of an anomaly. The
+  prior probability acts as a penalty, discouraging the system from marking too
+  many points as anomalies. The alternative distribution indicates the
+  probability of a datapoint given that it is an anomaly, and is a heavy-tailed
+  distribution (Cauchy) centered around the model's predictions by default.
+
+  Specifically, we have:
+
+    p(anomaly | data) = p(data | anomaly) * anomaly_prior_probability
+        / (p(data | not anomaly) * (1 - anomaly_prior_probability)
+           + p(data | anomaly) * anomaly_prior_probability)
+
+  This is simply Bayes' theorem, where p(data | anomaly) is the
+  alternative/anomaly distribution, p(data | not anomaly) is the model's
+  predicted distribution, and anomaly_prior_probability is the prior probability
+  of an anomaly occuring (user-specified, defaulting to 1%).
+
+  Rather than computing p(anomaly | data) directly, we use the odds ratio:
+
+    odds_ratio = p(data | anomaly) * anomaly_prior_probability
+        / (p(data | not anomaly) * (1 - anomaly_prior_probability))
+
+  This has the same information as p(anomaly | data):
+
+    odds_ratio = p(anomaly | data) / p(not anomaly | data)
+
+  A "responsibility" score is computed for the model based on the log odds
+  ratio, and state interpolated based on this responsibility:
+
+    model_responsibility = 1 / (1 + exp(-responsibility_scaling
+                                        * ln(odds_ratio)))
+    model_state = filtered_model_state * model_responsibility
+                  + predicted_model_state * (1 - model_responsibility)
+    loss = model_responsibility
+             * ln(p(data | not anomaly) * (1 - anomaly_prior_probability))
+           + (1 - model_responsibility)
+             * ln(p(data | anomaly) * anomaly_prior_probability)
+
+  """
+
+  output_names = ["anomaly_score"]
+
+  def __init__(self,
+               anomaly_log_likelihood=cauchy_alternative_to_gaussian,
+               anomaly_prior_probability=0.01,
+               responsibility_scaling=1.0):
+    """Configure the anomaly detector.
+
+    Args:
+      anomaly_log_likelihood: A function taking `current_times`,
+          `current_values`, and `outputs` (same as the corresponding arguments
+          to process_filtering_step) and returning a [batch size] Tensor of log
+          likelihoods under an anomaly distribution.
+      anomaly_prior_probability: A scalar value, between 0 and 1, indicating the
+          prior probability of a particular example being an anomaly.
+      responsibility_scaling: A positive scalar controlling how fast
+          interpolation transitions between not-anomaly and anomaly; lower
+          values (closer to 0) create a smoother/slower transition.
+    """
+    self._anomaly_log_likelihood = anomaly_log_likelihood
+    self._responsibility_scaling = responsibility_scaling
+    self._anomaly_prior_probability = anomaly_prior_probability
+
+  def process_filtering_step(self, current_times, current_values,
+                             predicted_state, filtered_state, outputs):
+    """Fall back on `predicted_state` for anomalies.
+
+    Args:
+      current_times: A [batch size] integer Tensor of times.
+      current_values: A [batch size x num features] Tensor of values filtering
+          is being performed on.
+      predicted_state: A (possibly nested) list of Tensors indicating model
+          state which does not take `current_times` and `current_values` into
+          account.
+      filtered_state: Same structure as predicted_state, but updated to take
+          `current_times` and `current_values` into account.
+      outputs: A dictionary of outputs produced by model filtering. Must
+          include `log_likelihood`, a [batch size] Tensor indicating the log
+          likelihood of the observations under the model's predictions.
+    Returns:
+      A tuple of (new_state, updated_outputs);
+        new_state: Updated state with the same structure as `filtered_state` and
+            `predicted_state`; predicted_state for anomalies and filtered_state
+            otherwise (per batch element).
+        updated_outputs: The `outputs` dictionary, updated with a new "loss"
+            (the interpolated negative log likelihoods under the model and
+            anomaly distributions) and "anomaly_score" (the log odds ratio of
+            each part of the batch being an anomaly).
+    """
+    anomaly_log_likelihood = self._anomaly_log_likelihood(
+        current_times=current_times,
+        current_values=current_values,
+        outputs=outputs)
+    anomaly_prior_probability = ops.convert_to_tensor(
+        self._anomaly_prior_probability, dtype=current_values.dtype)
+    # p(data | anomaly) * p(anomaly)
+    data_and_anomaly_log_probability = (
+        anomaly_log_likelihood + math_ops.log(anomaly_prior_probability))
+    # p(data | no anomaly) * p(no anomaly)
+    data_and_no_anomaly_log_probability = (
+        outputs["log_likelihood"] + math_ops.log(1. - anomaly_prior_probability)
+    )
+    # A log odds ratio is slightly nicer here than computing p(anomaly | data),
+    # since it is centered around zero
+    anomaly_log_odds_ratio = (
+        data_and_anomaly_log_probability
+        - data_and_no_anomaly_log_probability)
+    model_responsibility = math_ops.sigmoid(-self._responsibility_scaling *
+                                            anomaly_log_odds_ratio)
+    # Do a linear interpolation between predicted and inferred model state
+    # based on the model's "responsibility". If we knew for sure whether
+    # this was an anomaly or not (binary responsibility), this would be the
+    # correct thing to do, but given that we don't it's just a
+    # (differentiable) heuristic.
+    interpolated_state = _interpolate_state_linear(
+        first_state=filtered_state,
+        second_state=predicted_state,
+        first_responsibility=model_responsibility)
+    # TODO(allenl): Try different responsibility scalings and interpolation
+    # methods (e.g. average in probability space rather than log space).
+    interpolated_log_likelihood = (
+        model_responsibility * data_and_no_anomaly_log_probability
+        + (1. - model_responsibility) * data_and_anomaly_log_probability)
+    outputs["loss"] = -interpolated_log_likelihood
+    outputs["anomaly_score"] = anomaly_log_odds_ratio
+    return (interpolated_state, outputs)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py
new file mode 100644
index 0000000000..53d7340e85
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py
@@ -0,0 +1,71 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for filtering postprocessors."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import filtering_postprocessor
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.platform import test
+
+
+class FilteringStepPostprocessorTest(test.TestCase):
+
+  def test_gaussian_alternative(self):
+    for float_dtype in [dtypes.float32, dtypes.float64]:
+      detector = filtering_postprocessor.StateInterpolatingAnomalyDetector(
+          anomaly_log_likelihood=(filtering_postprocessor
+                                  .cauchy_alternative_to_gaussian),
+          responsibility_scaling=10.)
+      predicted_state = [
+          constant_op.constant(
+              [[40.], [20.]], dtype=float_dtype), constant_op.constant(
+                  [3., 6.], dtype=float_dtype), constant_op.constant([-1, -2])
+      ]
+      filtered_state = [
+          constant_op.constant(
+              [[80.], [180.]], dtype=float_dtype), constant_op.constant(
+                  [1., 2.], dtype=float_dtype), constant_op.constant([-1, -2])
+      ]
+      interpolated_state, updated_outputs = detector.process_filtering_step(
+          current_times=constant_op.constant([1, 2]),
+          current_values=constant_op.constant([[0.], [1.]], dtype=float_dtype),
+          predicted_state=predicted_state,
+          filtered_state=filtered_state,
+          outputs={
+              "mean":
+                  constant_op.constant([[0.1], [10.]], dtype=float_dtype),
+              "covariance":
+                  constant_op.constant([[[1.0]], [[1.0]]], dtype=float_dtype),
+              "log_likelihood":
+                  constant_op.constant([-1., -40.], dtype=float_dtype)
+          })
+      # The first batch element is not anomalous, and so should use the inferred
+      # state. The second is anomalous, and should use the predicted state.
+      expected_state = [[[80.], [20.]],
+                        [1., 6.],
+                        [-1, -2]]
+      with self.test_session():
+        for interpolated, expected in zip(interpolated_state, expected_state):
+          self.assertAllClose(expected, interpolated.eval())
+        self.assertGreater(0., updated_outputs["anomaly_score"][0].eval())
+        self.assertLess(0., updated_outputs["anomaly_score"][1].eval())
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md
new file mode 100644
index 0000000000..b174bb6af3
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md
@@ -0,0 +1,279 @@
+# Derivations for multi-resolution cycle transition matrix powers
+
+This document contains derivations for the special-cased matrix-to-powers and
+power sums used in `state_space_models/periodic.py`'s `ResolutionCycleModel` as
+part of TensorFlow Time Series (TFTS).
+
+## Setting and notation
+
+Let $$M$$ be the number of latent values being cycled through
+(`num_latent_values` in the code).
+
+The `ResolutionCycleModel` transition matrix is based on roots of a matrix which
+cycles through $$M$$ (odd) values and constrains their sum to be
+zero (which when included in a state space model means that the expected sum
+over a complete period is zero). Call this $$M - 1$$ x
+$$M - 1$$ matrix $$C$$ (`cycle_matrix`):
+
+$$ {\boldsymbol C}_{i, j} = \begin{cases} -1 & i = 0\\ 1 & j = i - 1\\ 0 &
+\text{otherwise}\end{cases} $$
+
+`ResolutionCycleModel` takes roots of this matrix using the following
+parameterization:
+
+$$ {\boldsymbol C}^p = \text{cycle_eigenvectors} *
+\text{diag}(\text{cycle_eigenvalues})^{p} * \text{cycle_eigenvectors}^{-1} $$
+
+Where:
+
+$$\text{cycle_eigenvectors}_{i, j} = w_{\lfloor j / 2 \rfloor + 1}^{i (-1)^{j +
+1}} - w_{\lfloor j / 2 \rfloor + 1}^{(i + 1) (-1)^{j + 1}}$$
+
+$$(\text{cycle_eigenvectors}^{-1})_{i, j} = \frac{1}{M}
+\sum_{k=0}^j w_{\lfloor i / 2 \rfloor + 1}^{k (-1)^i}$$
+
+$$\text{cycle_eigenvalues}_{j} = w_{\lfloor j / 2 \rfloor + 1}^{(-1)^j}$$
+
+Where $$w_j$$ is a root of unity:
+
+$$w_j = e^{\frac{2 \pi j \sqrt{-1}}{M}}$$
+
+In Sympy (useful for checking expressions when $$M$$ is small),
+this looks like:
+
+```python
+import sympy
+def root_of_unity(nth, number, to_power=1):
+    return sympy.exp(2 * sympy.pi * number * sympy.I * to_power / nth)
+matsize = 4
+def eigvec_mat_fn(i, j):
+    number = j // 2 + 1
+    powersign = (j % 2) * 2 - 1
+    return (root_of_unity(matsize + 1, number=number,
+                          to_power=powersign * i)
+            - root_of_unity(matsize + 1, number=number,
+                            to_power=powersign * (i + 1)))
+def eigvec_inverse_mat_fn(row, column):
+    number = row // 2 + 1
+    powersign = ((row + 1) % 2) * 2 - 1
+    runningsum = 0
+    for j in range(column + 1):
+        runningsum += root_of_unity(
+          matsize + 1, number, to_power=j * powersign) / (matsize + 1)
+    return runningsum
+def make_eigval_mat_fn(to_power=1):
+    def eigval_mat_fn(i, j):
+        if i == j:
+            number = j // 2 + 1
+            powersign = ((j + 1) % 2) * 2 - 1
+            return root_of_unity(matsize + 1, number=number, 
+                                 to_power=powersign*to_power)
+        else:
+            return 0
+    return eigval_mat_fn
+eigval_power = sympy.Rational(1, 1)
+eigvecs = sympy.Matrix(matsize, matsize, eigvec_mat_fn)
+eigvals = sympy.Matrix(matsize, matsize, make_eigval_mat_fn(eigval_power))
+eigvecs_inv = sympy.Matrix(matsize, matsize, eigvec_inverse_mat_fn)
+print (eigvecs * eigvals * eigvecs_inv).evalf()
+```
+
+## Proof that these are eigenvectors/eigenvalues of `cycle_matrix`
+
+We want to show that:
+
+$${\boldsymbol C} * \text{cycle_eigenvectors}_{\bullet, j} =
+\text{cycle_eigenvalues}_j * \text{cycle_eigenvectors}_{\bullet, j} $$
+
+Where $$\text{cycle_eigenvectors}_{\bullet, j}$$ is a column vector containing
+the $$j^\text{th}$$ eigenvector.
+
+We have telescoping sum in the first entry:
+
+$$({\boldsymbol C} * \text{cycle_eigenvectors}_{\bullet, j})_i =
+\begin{cases} -\sum_{k=0}^{M - 2}
+\text{cycle_eigenvectors}_{k, j} & i = 0\\ \text{cycle_eigenvectors}_{i - 1, j}
+& \text{otherwise} \end{cases}$$
+
+$$ = \begin{cases} w_{\lfloor j / 2 \rfloor + 1}^{(M -
+1)(-1)^{j + 1}} - w_{\lfloor j / 2 \rfloor + 1}^{0(-1)^{j + 1}} & i = 0\\
+\text{cycle_eigenvectors}_{i - 1, j} & \text{otherwise} \end{cases}$$
+
+$$ = \begin{cases} w_{\lfloor j / 2 \rfloor + 1}^{(-1)^{j}} \left (w_{\lfloor j
+/ 2 \rfloor + 1}^{M(-1)^{j + 1}} - w_{\lfloor j / 2
+\rfloor + 1}^{(-1)^{j + 1}} \right ) & i = 0\\ \text{cycle_eigenvectors}_{i - 1,
+j} & \text{otherwise} \end{cases}$$
+
+$$ = \begin{cases} w_{\lfloor j / 2 \rfloor + 1}^{(-1)^{j}} \left (w_{\lfloor j
+/ 2 \rfloor + 1}^{0(-1)^{j + 1}} - w_{\lfloor j / 2 \rfloor + 1}^{(-1)^{j + 1}}
+\right ) & i = 0\\ \text{cycle_eigenvectors}_{i - 1, j} & \text{otherwise}
+\end{cases}$$
+
+$$ = \begin{cases} w_{\lfloor j / 2 \rfloor + 1}^{(-1)^{j}}
+\text{cycle_eigenvectors}_{0, j} ) & i = 0\\ \text{cycle_eigenvectors}_{i - 1,
+j} & \text{otherwise} \end{cases}$$
+
+The remaining cases follow from the fact that:
+
+$$w_{\lfloor j / 2 \rfloor + 1}^{(-1)^{j}} \text{cycle_eigenvectors}_{i, j} =
+\text{cycle_eigenvectors}_{i - 1, j}$$
+
+$$w_{\lfloor j / 2 \rfloor + 1}^{(-1)^{j}} \left( w_{\lfloor j / 2 \rfloor +
+1}^{i (-1)^{j + 1}} - w_{\lfloor j / 2 \rfloor + 1}^{(i + 1) (-1)^{j + 1}}
+\right) = w_{\lfloor j / 2 \rfloor + 1}^{(i - 1) (-1)^{j + 1}} - w_{\lfloor j /
+2 \rfloor + 1}^{i (-1)^{j + 1}}$$
+
+## Proof of eigenvector inverse matrix
+
+We want to show that (for the expressions above):
+
+$$ I = \text{cycle_eigenvectors} * \text{cycle_eigenvectors}^{-1} $$
+
+Multiplying it out, we have:
+
+$$(\text{cycle_eigenvectors} * \text{cycle_eigenvectors}^{-1})_{i, j} =
+\sum_{k=0}^{M - 2} \text{cycle_eigenvectors}_{i, k}
+(\text{cycle_eigenvectors}^{-1})_{k, j} $$
+
+$$ = \frac{1}{M} \sum_{k=0}^{M -
+2} \left[ \left( w_{\lfloor k / 2 \rfloor + 1}^{i (-1)^{k + 1}} - w_{\lfloor k /
+2 \rfloor + 1}^{(i + 1) (-1)^{k + 1}} \right) \sum_{l=0}^j w_{\lfloor k / 2
+\rfloor + 1}^{l (-1)^{k}} \right]$$
+
+$$ = \frac{1}{M} \sum_{k=0}^{M -
+2} \sum_{l=0}^j \left[ \left( w_{\lfloor k / 2 \rfloor + 1}^{i (-1)^{k + 1}} -
+w_{\lfloor k / 2 \rfloor + 1}^{(i + 1) (-1)^{k + 1}} \right) w_{\lfloor k / 2
+\rfloor + 1}^{l (-1)^{k}} \right]$$
+
+$$ = \frac{1}{M} \sum_{k=0}^{M -
+2} \sum_{l=0}^j \left[ w_{\lfloor k / 2 \rfloor + 1}^{(i - l) (-1)^{k + 1}} -
+w_{\lfloor k / 2 \rfloor + 1}^{(i - l + 1) (-1)^{k + 1}} \right]$$
+
+Using telescoping:
+
+$$ = \frac{1}{M} \sum_{k=0}^{M -
+2} \left[ w_{\lfloor k / 2 \rfloor + 1}^{(i - j) (-1)^{k + 1}} - w_{\lfloor k /
+2 \rfloor + 1}^{(i + 1) (-1)^{k + 1}} \right]$$
+
+Since $$e^{-ix} = \text{conj}(e^{ix})$$, the imaginary components cancel out of
+the sum:
+
+$$ = \frac{2}{M} \sum_{k=0}^{(M -
+1) / 2 - 1} \text{Real}\left[ w_{k + 1}^{i - j} - w_{k + 1}^{i + 1} \right]$$
+
+$$ = \frac{2}{M} \sum_{k=0}^{(M -
+1) / 2 - 1} \left[ \text{cos}\left(\frac{2 \pi (i - j) (k +
+1)}{M}\right) - \text{cos}\left(\frac{2 \pi (i + 1) (k +
+1)}{M}\right) \right]$$
+
+Using Lagrange's identity $$\sum_{n=1}^N \text{cos}(n \theta) = -\frac{1}{2} +
+\frac{\text{sin}\left(\left(N + \frac{1}{2}\right) \theta\right)}{2
+\text{sin}\left(\frac{\theta}{2}\right)}$$:
+
+$$ = \frac{2}{M} \left(
+\frac{\text{sin}\left(\left(\left(\frac{M -
+1}{2}\right) + \frac{1}{2}\right) \frac{2 \pi (i -
+j)}{M}\right)}{2 \text{sin}\left(\frac{\pi (i -
+j)}{M}\right)} \\-
+\frac{\text{sin}\left(\left(\left(\frac{M -
+1}{2}\right) + \frac{1}{2}\right) \frac{2 \pi (i +
+1)}{M}\right)}{2 \text{sin}\left(\frac{\pi (i +
+1)}{M}\right)} \right)$$
+
+$$ = \frac{1}{M} \left(\frac{\text{sin}(\pi (i -
+j))}{\text{sin}\left(\frac{\pi (i - j)}{M}\right)} -
+\frac{\text{sin}(\pi (i + 1))}{\text{sin}\left(\frac{\pi (i +
+1)}{M}\right)} \right)$$
+
+The second term will always be zero, since $$i + 1$$ is at most
+$$M - 1$$ ($$i$$ ranges from $$0$$ to
+$$M - 2$$). The first term is also zero unless $$i = j$$
+($$\text{sin}(x)$$ is zero at integer multiples of $$\pi$$). Taking a limit when
+$$i = j$$, the expression evaluates to $$1$$ (L'Hospital's rule gives a ratio of
+cosines, both one, along with a ratio of the arguments from the chain rule).
+
+## Simplification of expression for matrix to a power
+
+Having established that the eigenvalues, eigenvectors, and the inverse
+eigenvector matrix are all correct, we can now use them to derive an expression
+for the matrix raised to a power:
+
+$$ {\boldsymbol C}^p = \text{cycle_eigenvectors} *
+\text{diag}(\text{cycle_eigenvalues})^p * \text{cycle_eigenvectors}^{-1} $$
+
+$$({\boldsymbol C}^p)_{i, j} = \sum_{k=0}^{M - 2}
+\text{cycle_eigenvectors}_{i, k} * \text{cycle_eigenvalues}_k^p *
+(\text{cycle_eigenvectors}^{-1})_{k, j}$$
+
+$$ = \frac{1}{M} \sum_{k=0}^{M -
+2} \left[\left( w_{\lfloor k / 2 \rfloor + 1}^{(p - i) (-1)^{k + 1}} -
+w_{\lfloor k / 2 \rfloor + 1}^{(p - i - 1) (-1)^{k + 1}} \right) \sum_{l=0}^j
+w_{\lfloor k / 2 \rfloor + 1}^{l (-1)^{k}} \right]$$
+
+Following the same logic as for the inverse matrix proof, this leads to
+($$cos(x)$$ being an even function):
+
+$$ = \frac{2}{M} \sum_{k=0}^{(M -
+1) / 2 - 1} \left[ \text{cos}\left(\frac{2 \pi (p - i + j)
+k}{M}\right) - \text{cos}\left(\frac{2 \pi (p - i - 1)
+k)}{M}\right) \right]$$
+
+Applying Lagrange's identity and simplifying, we get:
+
+$$ = \frac{1}{M} \left(\frac{\text{sin}(\pi (p - i +
+j))}{\text{sin}\left(\frac{\pi (p - i + j)}{M}\right)} -
+\frac{\text{sin}(\pi (p - i - 1))}{\text{sin}\left(\frac{\pi (p - i -
+1)}{M}\right)} \right)$$
+
+As a special/limiting case, we get the integer powers of the original matrix
+($$I(b)$$ is 1 if $$b$$ is true and zero otherwise):
+
+$$ ({\boldsymbol C}^p)_{i, j} = \frac{1}{M} \left(
+M * I(p - i + j \equiv 0\pmod {M})
+\\- M * I(p - i - 1 \equiv 0\pmod
+{M}) \right)$$
+
+## Simplification of expression for pre- and post-multiplication of noise matrix to a power
+
+Raising the transition matrix to a power allows us to transform the inferred
+mean state across time (imputing), but performing imputation on the covariance
+of the state estimate requires adding the noise covariance at each step (and
+then transforming by pre- and post-multiplying by the transition
+matrix). However, this noise covariance has a very special form, having only one
+non-zero element in the upper left hand corner.
+
+$$\text{noise_covariance}_{i, j} = \begin{cases} \text{noise_scalar} & i = j =
+0\\ 0 & \text{otherwise}\end{cases} $$
+
+This makes it easy to compute an expression for $${\boldsymbol C}^p *
+\text{noise_covariance} * ({\boldsymbol C}^T)^p$$:
+
+$$\left ({\boldsymbol C}^p * \text{noise_covariance} \right)_{i, j}
+= ({\boldsymbol C}^p)_{i, 0} * \text{noise_scalar} * I(j = 0)$$
+
+$$\left ({\boldsymbol C}^p * \text{noise_covariance} *
+({\boldsymbol C}^T \right)^p)_{i, j} = ({\boldsymbol C}^p)_{i, 0} *
+\text{noise_scalar} * ({\boldsymbol C}^p)_{j, 0}$$
+
+$$ = \frac{1}{M^2} \left(\frac{\text{sin}(\pi (p -
+i))}{\text{sin}\left(\frac{\pi (p - i)}{M}\right)} -
+\frac{\text{sin}(\pi (p - i - 1))}{\text{sin}\left(\frac{\pi (p - i -
+1)}{M}\right)} \right) \\ \left(\frac{\text{sin}(\pi (p -
+j))}{\text{sin}\left(\frac{\pi (p - j)}{M}\right)} -
+\frac{\text{sin}(\pi (p - j - 1))}{\text{sin}\left(\frac{\pi (p - j -
+1)}{M}\right)} \right)$$
+
+This (and the transition-matrix-to-a-power expression above) can be simplified
+slightly using the fact that $$\text{sin}((x + i)\pi) = (-1)^i \text{sin}(x
+\pi)$$ for integers $$i$$.
+
+## Open questions
+
+- It would be nice to have an expression for the elements of $$f(\lambda, N) =
+  \sum_{k=0}^N {\boldsymbol C}^{\lambda k} * \text{noise_covariance} *
+  ({\boldsymbol C}^T)^{\lambda k}$$, especially for $$0 < \lambda < 1$$.
+- It seems that when $$\lambda =
+  \frac{M}{\text{periodicity}}$$, then $$f(\lambda,
+  \text{periodicity}) = \frac{\text{periodicity}}{M} f(1,
+  M)$$, but I'm not exactly sure why (i.e. have not
+  proven it).
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py
new file mode 100644
index 0000000000..1fcd3e391b
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py
@@ -0,0 +1,341 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implements Kalman filtering for linear state space models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib import distributions
+
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import numerics
+
+
+# TODO(allenl): support for always-factored covariance matrices
+class KalmanFilter(object):
+  """Inference on linear state models.
+
+  The model for observations in a given state is:
+    observation(t) = observation_model * state(t)
+        + Gaussian(0, observation_noise_covariance)
+
+  State updates take the following form:
+    state(t) = state_transition * state(t-1)
+        + state_noise_transform * Gaussian(0, state_transition_noise_covariance)
+
+  This is a real-valued analog to hidden Markov models, with linear transitions
+  and a Gaussian noise model. Given initial conditions, noise, and state
+  transition, Kalman filtering recursively estimates states and observations,
+  along with their associated uncertainty. When fed observations, future state
+  and uncertainty estimates are conditioned on those observations (in a Bayesian
+  sense).
+
+  Typically some "given"s mentioned above (noises) will be unknown, and so
+  optimizing the Kalman filter's probabilistic predictions with respect to these
+  parameters is a good approach. The state transition and observation models are
+  usually known a priori as a modeling decision.
+
+  """
+
+  def __init__(self, dtype=dtypes.float32,
+               simplified_posterior_covariance_computation=False):
+    """Initialize the Kalman filter.
+
+    Args:
+      dtype: The data type to use for floating point tensors.
+      simplified_posterior_covariance_computation: If True, uses an algebraic
+        simplification of the Kalman filtering posterior covariance update,
+        which is slightly faster at the cost of numerical stability. The
+        simplified update is often stable when using double precision on small
+        models or with fixed transition matrices.
+    """
+    self._simplified_posterior_covariance_computation = (
+        simplified_posterior_covariance_computation)
+    self.dtype = dtype
+
+  def do_filter(
+      self, estimated_state, estimated_state_covariance,
+      predicted_observation, predicted_observation_covariance,
+      observation, observation_model, observation_noise):
+    """Convenience function for scoring predictions.
+
+    Scores a prediction against an observation, and computes the updated
+    posterior over states.
+
+    Shapes given below for arguments are for single-model Kalman filtering
+    (e.g. KalmanFilter). For ensembles, prior_state and prior_state_var are
+    same-length tuples of values corresponding to each model.
+
+    Args:
+      estimated_state: A prior mean over states [batch size x state dimension]
+      estimated_state_covariance: Covariance of state prior [batch size x D x
+          D], with D depending on the Kalman filter implementation (typically
+          the state dimension).
+      predicted_observation: A prediction for the observed value, such as that
+          returned by observed_from_state. A [batch size x num features] Tensor.
+      predicted_observation_covariance: A covariance matrix corresponding to
+          `predicted_observation`, a [batch size x num features x num features]
+          Tensor.
+      observation: The observed value corresponding to the predictions
+          given [batch size x observation dimension]
+      observation_model: The [batch size x observation dimension x model state
+          dimension] Tensor indicating how a particular state is mapped to
+          (pre-noise) observations for each part of the batch.
+      observation_noise: A [batch size x observation dimension x observation
+          dimension] Tensor or [observation dimension x observation dimension]
+          Tensor with covariance matrices to use for each part of the batch (a
+          two-dimensional input will be broadcast).
+    Returns:
+      posterior_state, posterior_state_var: Posterior mean and
+          covariance, updated versions of prior_state and
+          prior_state_var.
+      log_prediction_prob: Log probability of the observations under
+          the priors, suitable for optimization (should be maximized).
+
+    """
+    symmetrized_observation_covariance = 0.5 * (
+        predicted_observation_covariance + array_ops.matrix_transpose(
+            predicted_observation_covariance))
+    instability_message = (
+        "This may occur due to numerically unstable filtering when there is "
+        "a large difference in posterior variances, or when inferences are "
+        "near-deterministic. Considering tuning the "
+        "'filtering_maximum_posterior_variance_ratio' or "
+        "'filtering_minimum_posterior_variance' parameters in your "
+        "StateSpaceModelConfiguration, or tuning the transition matrix.")
+    symmetrized_observation_covariance = numerics.verify_tensor_all_finite(
+        symmetrized_observation_covariance,
+        "Predicted observation covariance was not finite. {}".format(
+            instability_message))
+    diag = array_ops.matrix_diag_part(symmetrized_observation_covariance)
+    min_diag = math_ops.reduce_min(diag)
+    non_negative_assert = control_flow_ops.Assert(
+        min_diag >= 0.,
+        [("The predicted observation covariance "
+          "has a negative diagonal entry. {}").format(instability_message),
+         min_diag])
+    with ops.control_dependencies([non_negative_assert]):
+      observation_covariance_cholesky = linalg_ops.cholesky(
+          symmetrized_observation_covariance)
+    log_prediction_prob = distributions.MultivariateNormalTriL(
+        predicted_observation, observation_covariance_cholesky).log_prob(
+            observation)
+    (posterior_state,
+     posterior_state_var) = self.posterior_from_prior_state(
+         prior_state=estimated_state,
+         prior_state_var=estimated_state_covariance,
+         observation=observation,
+         observation_model=observation_model,
+         predicted_observations=(predicted_observation,
+                                 predicted_observation_covariance),
+         observation_noise=observation_noise)
+    return (posterior_state, posterior_state_var, log_prediction_prob)
+
+  def predict_state_mean(self, prior_state, transition_matrices):
+    """Compute state transitions.
+
+    Args:
+      prior_state: Current estimated state mean [batch_size x state_dimension]
+      transition_matrices: A [batch size, state dimension, state dimension]
+        batch of matrices (dtype matching the `dtype` argument to the
+        constructor) with the transition matrix raised to the power of the
+        number of steps to be taken (not element-wise; use
+        math_utils.matrix_to_powers if there is no efficient special case) if
+        more than one step is desired.
+    Returns:
+      State mean advanced based on `transition_matrices` (dimensions matching
+      first argument).
+    """
+    advanced_state = array_ops.squeeze(
+        math_ops.matmul(
+            transition_matrices,
+            prior_state[..., None]),
+        squeeze_dims=[-1])
+    return advanced_state
+
+  def predict_state_var(
+      self, prior_state_var, transition_matrices, transition_noise_sums):
+    r"""Compute variance for state transitions.
+
+    Computes a noise estimate corresponding to the value returned by
+    predict_state_mean.
+
+    Args:
+      prior_state_var: Covariance matrix specifying uncertainty of current state
+          estimate [batch size x state dimension x state dimension]
+      transition_matrices: A [batch size, state dimension, state dimension]
+        batch of matrices (dtype matching the `dtype` argument to the
+        constructor) with the transition matrix raised to the power of the
+        number of steps to be taken (not element-wise; use
+        math_utils.matrix_to_powers if there is no efficient special case).
+      transition_noise_sums: A [batch size, state dimension, state dimension]
+        Tensor (dtype matching the `dtype` argument to the constructor) with:
+
+          \sum_{i=0}^{num_steps - 1} (
+             state_transition_to_powers_fn(i)
+             * state_transition_noise_covariance
+             * state_transition_to_powers_fn(i)^T
+          )
+
+        for the number of steps to be taken in each part of the batch (this
+        should match `transition_matrices`). Use math_utils.power_sums_tensor
+        with `tf.gather` if there is no efficient special case.
+    Returns:
+      State variance advanced based on `transition_matrices` and
+      `transition_noise_sums` (dimensions matching first argument).
+    """
+    prior_variance_transitioned = math_ops.matmul(
+        math_ops.matmul(transition_matrices, prior_state_var),
+        transition_matrices,
+        adjoint_b=True)
+    return prior_variance_transitioned + transition_noise_sums
+
+  def posterior_from_prior_state(self, prior_state, prior_state_var,
+                                 observation, observation_model,
+                                 predicted_observations,
+                                 observation_noise):
+    """Compute a posterior over states given an observation.
+
+    Args:
+      prior_state: Prior state mean [batch size x state dimension]
+      prior_state_var: Prior state covariance [batch size x state dimension x
+          state dimension]
+      observation: The observed value corresponding to the predictions given
+          [batch size x observation dimension]
+      observation_model: The [batch size x observation dimension x model state
+          dimension] Tensor indicating how a particular state is mapped to
+          (pre-noise) observations for each part of the batch.
+      predicted_observations: An (observation mean, observation variance) tuple
+          computed based on the current state, usually the output of
+          observed_from_state.
+      observation_noise: A [batch size x observation dimension x observation
+          dimension] or [observation dimension x observation dimension] Tensor
+          with covariance matrices to use for each part of the batch (a
+          two-dimensional input will be broadcast).
+    Returns:
+      Posterior mean and covariance (dimensions matching the first two
+      arguments).
+
+    """
+    observed_mean, observed_var = predicted_observations
+    residual = observation - observed_mean
+    # TODO(allenl): Can more of this be done using matrix_solve_ls?
+    kalman_solve_rhs = math_ops.matmul(
+        observation_model, prior_state_var, adjoint_b=True)
+    # This matrix_solve adjoint doesn't make a difference symbolically (since
+    # observed_var is a covariance matrix, and should be symmetric), but
+    # filtering on multivariate series is unstable without it. See
+    # test_multivariate_symmetric_covariance_float64 in kalman_filter_test.py
+    # for an example of the instability (fails with adjoint=False).
+    kalman_gain_transposed = linalg_ops.matrix_solve(
+        matrix=observed_var, rhs=kalman_solve_rhs, adjoint=True)
+    posterior_state = prior_state + array_ops.squeeze(
+        math_ops.matmul(
+            kalman_gain_transposed,
+            array_ops.expand_dims(residual, -1),
+            adjoint_a=True),
+        squeeze_dims=[-1])
+    gain_obs = math_ops.matmul(
+        kalman_gain_transposed, observation_model, adjoint_a=True)
+    identity_extradim = linalg_ops.eye(
+        array_ops.shape(gain_obs)[1], dtype=gain_obs.dtype)[None]
+    identity_minus_factor = identity_extradim - gain_obs
+    if self._simplified_posterior_covariance_computation:
+      # posterior covariance =
+      #   (I - kalman_gain * observation_model) * prior_state_var
+      posterior_state_var = math_ops.matmul(identity_minus_factor,
+                                            prior_state_var)
+    else:
+      observation_noise = ops.convert_to_tensor(observation_noise)
+      # A Joseph form update, which provides better numeric stability than the
+      # simplified optimal Kalman gain update, at the cost of a few extra
+      # operations. Joseph form updates are valid for any gain (not just the
+      # optimal Kalman gain), and so are more forgiving of numerical errors in
+      # computing the optimal Kalman gain.
+      #
+      # posterior covariance =
+      #   (I - kalman_gain * observation_model) * prior_state_var
+      #     * (I - kalman_gain * observation_model)^T
+      #   + kalman_gain * observation_noise * kalman_gain^T
+      left_multiplied_state_var = math_ops.matmul(identity_minus_factor,
+                                                  prior_state_var)
+      multiplied_state_var = math_ops.matmul(
+          identity_minus_factor, left_multiplied_state_var, adjoint_b=True)
+      def _batch_observation_noise_update():
+        return (multiplied_state_var + math_ops.matmul(
+            math_ops.matmul(
+                kalman_gain_transposed, observation_noise, adjoint_a=True),
+            kalman_gain_transposed))
+      def _matrix_observation_noise_update():
+        return (multiplied_state_var + math_ops.matmul(
+            math_utils.batch_times_matrix(
+                kalman_gain_transposed, observation_noise, adj_x=True),
+            kalman_gain_transposed))
+      if observation_noise.get_shape().ndims is None:
+        posterior_state_var = control_flow_ops.cond(
+            math_ops.equal(array_ops.rank(observation_noise), 2),
+            _matrix_observation_noise_update, _batch_observation_noise_update)
+      else:
+        # If static shape information exists, it gets checked in each cond()
+        # branch, so we need a special case to avoid graph-build-time
+        # exceptions.
+        if observation_noise.get_shape().ndims == 2:
+          posterior_state_var = _matrix_observation_noise_update()
+        else:
+          posterior_state_var = _batch_observation_noise_update()
+    return posterior_state, posterior_state_var
+
+  def observed_from_state(self, state_mean, state_var, observation_model,
+                          observation_noise):
+    """Compute an observation distribution given a state distribution.
+
+    Args:
+      state_mean: State mean vector [batch size x state dimension]
+      state_var: State covariance [batch size x state dimension x state
+          dimension]
+      observation_model: The [batch size x observation dimension x model state
+          dimension] Tensor indicating how a particular state is mapped to
+          (pre-noise) observations for each part of the batch.
+      observation_noise: A [batch size x observation dimension x observation
+          dimension] Tensor with covariance matrices to use for each part of the
+          batch. To remove observation noise, pass a Tensor of zeros (or simply
+          0, which will broadcast).
+    Returns:
+      observed_mean: Observation mean vector [batch size x observation
+          dimension]
+      observed_var: Observation covariance [batch size x observation dimension x
+          observation dimension]
+
+    """
+    observed_mean = array_ops.squeeze(
+        math_ops.matmul(
+            array_ops.expand_dims(state_mean, 1),
+            observation_model,
+            adjoint_b=True),
+        squeeze_dims=[1])
+    observed_var = math_ops.matmul(
+        math_ops.matmul(observation_model, state_var),
+        observation_model,
+        adjoint_b=True)
+    observed_var += observation_noise
+    return observed_mean, observed_var
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py
new file mode 100644
index 0000000000..57f29f3c7f
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py
@@ -0,0 +1,425 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Kalman filtering."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import kalman_filter
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+# Two-dimensional state model with "slope" and "level" components.
+STATE_TRANSITION = [
+    [1., 1.],  # Add slope to level
+    [0., 1.]   # Maintain slope
+]
+# Independent noise for each component
+STATE_TRANSITION_NOISE = [[0.1, 0.0], [0.0, 0.2]]
+OBSERVATION_MODEL = [[[0.5, 0.0], [0.0, 1.0]]]
+OBSERVATION_NOISE = [[0.0001, 0.], [0., 0.0002]]
+STATE_NOISE_TRANSFORM = [[1.0, 0.0], [0.0, 1.0]]
+
+
+def _powers_and_sums_from_transition_matrix(
+    state_transition, state_transition_noise_covariance,
+    state_noise_transform, max_gap=1):
+  def _transition_matrix_powers(powers):
+    return math_utils.matrix_to_powers(state_transition, powers)
+  def _power_sums(num_steps):
+    power_sums_tensor = math_utils.power_sums_tensor(
+        max_gap + 1, state_transition,
+        math_ops.matmul(state_noise_transform,
+                        math_ops.matmul(
+                            state_transition_noise_covariance,
+                            state_noise_transform,
+                            adjoint_b=True)))
+    return array_ops.gather(power_sums_tensor, indices=num_steps)
+  return (_transition_matrix_powers, _power_sums)
+
+
+class MultivariateTests(test.TestCase):
+
+  def _multivariate_symmetric_covariance_test_template(
+      self, dtype, simplified_posterior_variance_computation):
+    """Check that errors aren't building up asymmetries in covariances."""
+    kf = kalman_filter.KalmanFilter(dtype=dtype)
+    observation_noise_covariance = constant_op.constant(
+        [[1., 0.5], [0.5, 1.]], dtype=dtype)
+    observation_model = constant_op.constant(
+        [[[1., 0., 0., 0.], [0., 0., 1., 0.]]], dtype=dtype)
+    state = array_ops.placeholder(shape=[1, 4], dtype=dtype)
+    state_var = array_ops.placeholder(shape=[1, 4, 4], dtype=dtype)
+    observation = array_ops.placeholder(shape=[1, 2], dtype=dtype)
+    transition_fn, power_sum_fn = _powers_and_sums_from_transition_matrix(
+        state_transition=constant_op.constant(
+            [[1., 1., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 1.],
+             [0., 0., 0., 1.]],
+            dtype=dtype),
+        state_noise_transform=linalg_ops.eye(4, dtype=dtype),
+        state_transition_noise_covariance=constant_op.constant(
+            [[1., 0., 0.5, 0.], [0., 1., 0., 0.5], [0.5, 0., 1., 0.],
+             [0., 0.5, 0., 1.]],
+            dtype=dtype))
+    pred_state = kf.predict_state_mean(
+        prior_state=state, transition_matrices=transition_fn([1]))
+    pred_state_var = kf.predict_state_var(
+        prior_state_var=state_var, transition_matrices=transition_fn([1]),
+        transition_noise_sums=power_sum_fn([1]))
+    observed_mean, observed_var = kf.observed_from_state(
+        state_mean=pred_state, state_var=pred_state_var,
+        observation_model=observation_model,
+        observation_noise=observation_noise_covariance)
+    post_state, post_state_var = kf.posterior_from_prior_state(
+        prior_state=pred_state, prior_state_var=pred_state_var,
+        observation=observation,
+        observation_model=observation_model,
+        predicted_observations=(observed_mean, observed_var),
+        observation_noise=observation_noise_covariance)
+    with self.test_session() as session:
+      evaled_state = numpy.array([[1., 1., 1., 1.]])
+      evaled_state_var = numpy.eye(4)[None]
+      for i in range(500):
+        evaled_state, evaled_state_var, evaled_observed_var = session.run(
+            [post_state, post_state_var, observed_var],
+            feed_dict={state: evaled_state,
+                       state_var: evaled_state_var,
+                       observation: [[float(i), float(i)]]})
+        self.assertAllClose(evaled_observed_var[0],
+                            evaled_observed_var[0].T)
+        self.assertAllClose(evaled_state_var[0],
+                            evaled_state_var[0].T)
+
+  def test_multivariate_symmetric_covariance_float32(self):
+    self._multivariate_symmetric_covariance_test_template(
+        dtypes.float32, simplified_posterior_variance_computation=False)
+
+  def test_multivariate_symmetric_covariance_float64(self):
+    self._multivariate_symmetric_covariance_test_template(
+        dtypes.float64, simplified_posterior_variance_computation=True)
+
+
+class KalmanFilterNonBatchTest(test.TestCase):
+  """Single-batch KalmanFilter tests."""
+
+  def setUp(self):
+    """The basic model defined above, with unit batches."""
+    self.kalman_filter = kalman_filter.KalmanFilter()
+    self.transition_fn, self.power_sum_fn = (
+        _powers_and_sums_from_transition_matrix(
+            state_transition=STATE_TRANSITION,
+            state_transition_noise_covariance=STATE_TRANSITION_NOISE,
+            state_noise_transform=STATE_NOISE_TRANSFORM,
+            max_gap=5))
+
+  def test_observed_from_state(self):
+    """Compare observation mean and noise to hand-computed values."""
+    with self.test_session():
+      state = constant_op.constant([[2., 1.]])
+      state_var = constant_op.constant([[[4., 0.], [0., 3.]]])
+      observed_mean, observed_var = self.kalman_filter.observed_from_state(
+          state, state_var,
+          observation_model=OBSERVATION_MODEL,
+          observation_noise=OBSERVATION_NOISE)
+      observed_mean_override, observed_var_override = (
+          self.kalman_filter.observed_from_state(
+              state, state_var,
+              observation_model=OBSERVATION_MODEL,
+              observation_noise=100 * constant_op.constant(
+                  OBSERVATION_NOISE)[None]))
+      self.assertAllClose(numpy.array([[1., 1.]]),
+                          observed_mean.eval())
+      self.assertAllClose(numpy.array([[1., 1.]]),
+                          observed_mean_override.eval())
+      self.assertAllClose(numpy.array([[[1.0001, 0.], [0., 3.0002]]]),
+                          observed_var.eval())
+      self.assertAllClose(numpy.array([[[1.01, 0.], [0., 3.02]]]),
+                          observed_var_override.eval())
+
+  def _posterior_from_prior_state_test_template(
+      self, state, state_var, observation, observation_model, observation_noise,
+      expected_state, expected_state_var):
+    """Test that repeated observations converge to the expected value."""
+    predicted_observations = self.kalman_filter.observed_from_state(
+        state, state_var, observation_model,
+        observation_noise=observation_noise)
+    state_update, state_var_update = (
+        self.kalman_filter.posterior_from_prior_state(
+            state, state_var, observation,
+            observation_model=observation_model,
+            predicted_observations=predicted_observations,
+            observation_noise=observation_noise))
+    with self.test_session() as session:
+      evaled_state, evaled_state_var = session.run([state, state_var])
+      for _ in range(300):
+        evaled_state, evaled_state_var = session.run(
+            [state_update, state_var_update],
+            feed_dict={state: evaled_state, state_var: evaled_state_var})
+    self.assertAllClose(expected_state,
+                        evaled_state,
+                        atol=1e-5)
+    self.assertAllClose(
+        expected_state_var,
+        evaled_state_var,
+        atol=1e-5)
+
+  def test_posterior_from_prior_state_univariate(self):
+    self._posterior_from_prior_state_test_template(
+        state=constant_op.constant([[0.3]]),
+        state_var=constant_op.constant([[[1.]]]),
+        observation=constant_op.constant([[1.]]),
+        observation_model=[[[2.]]],
+        observation_noise=[[[0.01]]],
+        expected_state=numpy.array([[0.5]]),
+        expected_state_var=[[[0.]]])
+
+  def test_posterior_from_prior_state_univariate_unit_noise(self):
+    self._posterior_from_prior_state_test_template(
+        state=constant_op.constant([[0.3]]),
+        state_var=constant_op.constant([[[1e10]]]),
+        observation=constant_op.constant([[1.]]),
+        observation_model=[[[2.]]],
+        observation_noise=[[[1.0]]],
+        expected_state=numpy.array([[0.5]]),
+        expected_state_var=[[[1. / (300. * 2. ** 2)]]])
+
+  def test_posterior_from_prior_state_multivariate_2d(self):
+    self._posterior_from_prior_state_test_template(
+        state=constant_op.constant([[1.9, 1.]]),
+        state_var=constant_op.constant([[[1., 0.], [0., 2.]]]),
+        observation=constant_op.constant([[1., 1.]]),
+        observation_model=OBSERVATION_MODEL,
+        observation_noise=OBSERVATION_NOISE,
+        expected_state=numpy.array([[2., 1.]]),
+        expected_state_var=[[[0., 0.], [0., 0.]]])
+
+  def test_posterior_from_prior_state_multivariate_3d(self):
+    self._posterior_from_prior_state_test_template(
+        state=constant_op.constant([[1.9, 1., 5.]]),
+        state_var=constant_op.constant(
+            [[[200., 0., 1.], [0., 2000., 0.], [1., 0., 40000.]]]),
+        observation=constant_op.constant([[1., 1., 3.]]),
+        observation_model=constant_op.constant(
+            [[[0.5, 0., 0.],
+              [0., 10., 0.],
+              [0., 0., 100.]]]),
+        observation_noise=linalg_ops.eye(3) / 10000.,
+        expected_state=numpy.array([[2., .1, .03]]),
+        expected_state_var=numpy.zeros([1, 3, 3]))
+
+  def test_predict_state_mean(self):
+    """Compare state mean transitions with simple hand-computed values."""
+    with self.test_session():
+      state = constant_op.constant([[4., 2.]])
+      state = self.kalman_filter.predict_state_mean(
+          state, self.transition_fn([1]))
+      for _ in range(2):
+        state = self.kalman_filter.predict_state_mean(
+            state, self.transition_fn([1]))
+      self.assertAllClose(
+          numpy.array([[2. * 3. + 4.,  # Slope * time + base
+                        2.]]),
+          state.eval())
+
+  def test_predict_state_var(self):
+    """Compare a variance transition with simple hand-computed values."""
+    with self.test_session():
+      state_var = constant_op.constant([[[1., 0.], [0., 2.]]])
+      state_var = self.kalman_filter.predict_state_var(
+          state_var, self.transition_fn([1]), self.power_sum_fn([1]))
+      self.assertAllClose(
+          numpy.array([[[3.1, 2.0], [2.0, 2.2]]]),
+          state_var.eval())
+
+  def test_do_filter(self):
+    """Tests do_filter.
+
+    Tests that correct values have high probability and incorrect values
+    have low probability when there is low uncertainty.
+    """
+    with self.test_session():
+      state = constant_op.constant([[4., 2.]])
+      state_var = constant_op.constant([[[0.0001, 0.], [0., 0.0001]]])
+      observation = constant_op.constant([[
+          .5 * (
+              4.  # Base
+              + 2.),  # State transition
+          2.
+      ]])
+      estimated_state = self.kalman_filter.predict_state_mean(
+          state, self.transition_fn([1]))
+      estimated_state_covariance = self.kalman_filter.predict_state_var(
+          state_var, self.transition_fn([1]), self.power_sum_fn([1]))
+      (predicted_observation,
+       predicted_observation_covariance) = (
+           self.kalman_filter.observed_from_state(
+               estimated_state, estimated_state_covariance,
+               observation_model=OBSERVATION_MODEL,
+               observation_noise=OBSERVATION_NOISE))
+      (_, _, first_log_prob) = self.kalman_filter.do_filter(
+          estimated_state=estimated_state,
+          estimated_state_covariance=estimated_state_covariance,
+          predicted_observation=predicted_observation,
+          predicted_observation_covariance=predicted_observation_covariance,
+          observation=observation,
+          observation_model=OBSERVATION_MODEL,
+          observation_noise=OBSERVATION_NOISE)
+      self.assertGreater(first_log_prob.eval()[0], numpy.log(0.99))
+
+  def test_predict_n_ahead_mean(self):
+    with self.test_session():
+      original_state = constant_op.constant([[4., 2.]])
+      n = 5
+      iterative_state = original_state
+      for i in range(n):
+        self.assertAllClose(
+            iterative_state.eval(),
+            self.kalman_filter.predict_state_mean(
+                original_state,
+                self.transition_fn([i])).eval())
+        iterative_state = self.kalman_filter.predict_state_mean(
+            iterative_state,
+            self.transition_fn([1]))
+
+  def test_predict_n_ahead_var(self):
+    with self.test_session():
+      original_var = constant_op.constant([[[2., 3.], [4., 5.]]])
+      n = 5
+      iterative_var = original_var
+      for i in range(n):
+        self.assertAllClose(
+            iterative_var.eval(),
+            self.kalman_filter.predict_state_var(
+                original_var,
+                self.transition_fn([i]),
+                self.power_sum_fn([i])).eval())
+        iterative_var = self.kalman_filter.predict_state_var(
+            iterative_var,
+            self.transition_fn([1]),
+            self.power_sum_fn([1]))
+
+
+class KalmanFilterBatchTest(test.TestCase):
+  """KalmanFilter tests with more than one element batches."""
+
+  def test_do_filter_batch(self):
+    """Tests do_filter, in batch mode.
+
+    Tests that correct values have high probability and incorrect values
+    have low probability when there is low uncertainty.
+    """
+    with self.test_session():
+      state = constant_op.constant([[4., 2.], [5., 3.], [6., 4.]])
+      state_var = constant_op.constant(3 * [[[0.0001, 0.], [0., 0.0001]]])
+      observation = constant_op.constant([
+          [
+              .5 * (
+                  4.  # Base
+                  + 2.),  # State transition
+              2.
+          ],
+          [
+              .5 * (
+                  5.  # Base
+                  + 3.),  # State transition
+              3.
+          ],
+          [3.14, 2.71]
+      ])  # Low probability observation
+      kf = kalman_filter.KalmanFilter()
+      transition_fn, power_sum_fn = _powers_and_sums_from_transition_matrix(
+          state_transition=STATE_TRANSITION,
+          state_transition_noise_covariance=STATE_TRANSITION_NOISE,
+          state_noise_transform=STATE_NOISE_TRANSFORM,
+          max_gap=2)
+      estimated_state = kf.predict_state_mean(state, transition_fn(3*[1]))
+      estimated_state_covariance = kf.predict_state_var(
+          state_var, transition_fn(3*[1]), power_sum_fn(3*[1]))
+      observation_model = array_ops.tile(OBSERVATION_MODEL, [3, 1, 1])
+      (predicted_observation,
+       predicted_observation_covariance) = (
+           kf.observed_from_state(
+               estimated_state, estimated_state_covariance,
+               observation_model=observation_model,
+               observation_noise=OBSERVATION_NOISE))
+      (state, state_var, log_prob) = kf.do_filter(
+          estimated_state=estimated_state,
+          estimated_state_covariance=estimated_state_covariance,
+          predicted_observation=predicted_observation,
+          predicted_observation_covariance=predicted_observation_covariance,
+          observation=observation,
+          observation_model=observation_model,
+          observation_noise=OBSERVATION_NOISE)
+      first_log_prob, second_log_prob, third_log_prob = log_prob.eval()
+      self.assertGreater(first_log_prob.sum(), numpy.log(0.99))
+      self.assertGreater(second_log_prob.sum(), numpy.log(0.99))
+      self.assertLess(third_log_prob.sum(), numpy.log(0.01))
+
+  def test_predict_n_ahead_mean(self):
+    with self.test_session():
+      kf = kalman_filter.KalmanFilter()
+      transition_fn, _ = _powers_and_sums_from_transition_matrix(
+          state_transition=STATE_TRANSITION,
+          state_transition_noise_covariance=STATE_TRANSITION_NOISE,
+          state_noise_transform=STATE_NOISE_TRANSFORM,
+          max_gap=2)
+      original_state = constant_op.constant([[4., 2.], [3., 1.], [6., 2.]])
+      state0 = original_state
+      state1 = kf.predict_state_mean(state0, transition_fn(3 * [1]))
+      state2 = kf.predict_state_mean(state1, transition_fn(3 * [1]))
+      batch_eval = kf.predict_state_mean(
+          original_state, transition_fn([1, 0, 2])).eval()
+      self.assertAllClose(state0.eval()[1], batch_eval[1])
+      self.assertAllClose(state1.eval()[0], batch_eval[0])
+      self.assertAllClose(state2.eval()[2], batch_eval[2])
+
+  def test_predict_n_ahead_var(self):
+    with self.test_session():
+      kf = kalman_filter.KalmanFilter()
+      transition_fn, power_sum_fn = _powers_and_sums_from_transition_matrix(
+          state_transition=STATE_TRANSITION,
+          state_transition_noise_covariance=STATE_TRANSITION_NOISE,
+          state_noise_transform=STATE_NOISE_TRANSFORM,
+          max_gap=2)
+      base_var = 2.0 * numpy.identity(2) + numpy.ones([2, 2])
+      original_var = constant_op.constant(
+          numpy.array(
+              [base_var, 2.0 * base_var, 3.0 * base_var], dtype=numpy.float32))
+      var0 = original_var
+      var1 = kf.predict_state_var(
+          var0, transition_fn(3 * [1]), power_sum_fn(3 * [1]))
+      var2 = kf.predict_state_var(
+          var1, transition_fn(3 * [1]), power_sum_fn(3 * [1]))
+      batch_eval = kf.predict_state_var(
+          original_var,
+          transition_fn([1, 0, 2]),
+          power_sum_fn([1, 0, 2])).eval()
+      self.assertAllClose(var0.eval()[1], batch_eval[1])
+      self.assertAllClose(var1.eval()[0], batch_eval[0])
+      self.assertAllClose(var2.eval()[2], batch_eval[2])
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend.py
new file mode 100644
index 0000000000..b9d3f55c39
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend.py
@@ -0,0 +1,145 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implements a state space model with level and local linear trends."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+
+
+class AdderStateSpaceModel(state_space_model.StateSpaceModel):
+  """A state space model component with level and slope.
+
+  At each timestep, level <- level + slope. Level is observed, slope is not.
+  """
+
+  def __init__(
+      self,
+      use_level_noise=True,
+      configuration=state_space_model.StateSpaceModelConfiguration()):
+    """Configure the model.
+
+    Args:
+      use_level_noise: Whether to model the time series as having level noise.
+      configuration: A StateSpaceModelConfiguration object.
+    """
+    self.use_level_noise = use_level_noise
+    super(AdderStateSpaceModel, self).__init__(
+        configuration=configuration)
+
+  def get_prior_mean(self):
+    """If un-chunked data is available, set initial level to the first value."""
+    with variable_scope.variable_scope(self._variable_scope):
+      if self._input_statistics is not None:
+        # TODO(allenl): Better support for multivariate series here.
+        initial_value = array_ops.stack([
+            math_ops.reduce_mean(
+                self._input_statistics.series_start_moments.mean), 0.
+        ])
+        return initial_value + variable_scope.get_variable(
+            name="prior_state_mean",
+            shape=initial_value.get_shape(),
+            initializer=init_ops.zeros_initializer(),
+            dtype=self.dtype,
+            trainable=self._configuration.trainable_start_state)
+      else:
+        return super(AdderStateSpaceModel, self).get_prior_mean()
+
+  def transition_to_powers(self, powers):
+    """Computes powers of the adder transition matrix efficiently.
+
+    Args:
+      powers: An integer Tensor, shape [...], with powers to raise the
+        transition matrix to.
+    Returns:
+      A floating point Tensor with shape [..., 2, 2] containing:
+        transition^power = [[1., power],
+                            [0., 1.]]
+    """
+    paddings = array_ops.concat(
+        [
+            array_ops.zeros([array_ops.rank(powers), 2], dtype=dtypes.int32),
+            [(0, 1), (1, 0)]
+        ],
+        axis=0)
+    powers_padded = array_ops.pad(powers[..., None, None], paddings=paddings)
+    identity_matrices = linalg_ops.eye(
+        num_rows=2, batch_shape=array_ops.shape(powers), dtype=self.dtype)
+    return identity_matrices + math_ops.cast(powers_padded, self.dtype)
+
+  def transition_power_noise_accumulator(self, num_steps):
+    """Computes power sums in closed form."""
+    def _pack_and_reshape(*values):
+      return array_ops.reshape(
+          array_ops.stack(axis=1, values=values),
+          array_ops.concat(values=[array_ops.shape(num_steps), [2, 2]], axis=0))
+
+    num_steps = math_ops.cast(num_steps, self.dtype)
+    noise_transitions = num_steps - 1
+    noise_transform = ops.convert_to_tensor(self.get_noise_transform(),
+                                            self.dtype)
+    noise_covariance_transformed = math_ops.matmul(
+        math_ops.matmul(noise_transform,
+                        self.state_transition_noise_covariance),
+        noise_transform,
+        adjoint_b=True)
+    # Un-packing the transformed noise as:
+    # [[a b]
+    #  [c d]]
+    a, b, c, d = array_ops.unstack(
+        array_ops.reshape(noise_covariance_transformed, [-1, 4]), axis=1)
+    sum_of_first_n = noise_transitions * (noise_transitions + 1) / 2
+    sum_of_first_n_squares = sum_of_first_n * (2 * noise_transitions + 1) / 3
+    return _pack_and_reshape(
+        num_steps * a + sum_of_first_n * (b + c) + sum_of_first_n_squares * d,
+        num_steps * b + sum_of_first_n * d,
+        num_steps * c + sum_of_first_n * d,
+        num_steps * d)
+
+  def get_state_transition(self):
+    return [[1., 1.],  # Add slope to level
+            [0., 1.]]  # Maintain slope
+
+  def get_noise_transform(self):
+    if self.use_level_noise:
+      return [[1., 0.],
+              [0., 1.]]
+    else:
+      return [[0.],
+              [1.]]
+
+  def get_observation_model(self, times):
+    """Observe level but not slope.
+
+    See StateSpaceModel.get_observation_model.
+
+    Args:
+      times: Unused. See the parent class for details.
+    Returns:
+      A static, univariate observation model for later broadcasting.
+    """
+    del times  # Does not rely on times. Uses broadcasting from the parent.
+    return constant_op.constant([1., 0.], dtype=self.dtype)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend_test.py
new file mode 100644
index 0000000000..1911082086
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend_test.py
@@ -0,0 +1,53 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for level and trend state space model components."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import level_trend
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import test_utils
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.platform import test
+
+
+class SpecialCaseTests(test.TestCase):
+
+  def test_adder_transition_to_powers(self):
+    num_steps = 3
+    dtype = dtypes.float64
+    adder = level_trend.AdderStateSpaceModel(
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            dtype=dtype))
+    test_utils.transition_power_test_template(
+        test_case=self, model=adder, num_steps=num_steps)
+
+  def test_adder_noise_accumulator(self):
+    num_steps = 3
+    dtype = dtypes.float64
+    use_level_noise = True
+    adder = level_trend.AdderStateSpaceModel(
+        use_level_noise=use_level_noise,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            dtype=dtype))
+    test_utils.noise_accumulator_test_template(
+        test_case=self, model=adder, num_steps=num_steps)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
new file mode 100644
index 0000000000..d0770b86fe
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
@@ -0,0 +1,535 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""State space components for modeling seasonality."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import math_ops
+
+
+class CycleStateSpaceModel(state_space_model.StateSpaceModel):
+  """A state space model component which cycles between values.
+
+  Stores N values using N - 1 latent values, the Nth being the negative sum of
+  those explicitly stored. At any given timestep one of these values is
+  observed. Noise is assumed to affect only one of the transitions.
+  """
+
+  def __init__(
+      self,
+      periodicity,
+      configuration=state_space_model.StateSpaceModelConfiguration()):
+    self._periodicity = periodicity
+    super(CycleStateSpaceModel, self).__init__(configuration=configuration)
+
+  def get_state_transition(self):
+    return self.transition_to_powers(array_ops.ones([], dtype=dtypes.int32))
+
+  def get_noise_transform(self):
+    # transition_power_noise_accumulator makes assumptions about this
+    # transformation. If the noise transform is modified or overriden,
+    # transition_power_noise_accumulator must be modified as well (or discarded,
+    # as it is simply an optimization).
+    return array_ops.pad(
+        array_ops.ones([1], dtype=self.dtype),
+        paddings=[(0, self._periodicity - 2)])[..., None]
+
+  def transition_to_powers(self, powers):
+    """Computes powers of the cycle transition matrix efficiently.
+
+    Args:
+      powers: An integer Tensor, shape [...], with powers to raise the
+        transition matrix to.
+    Returns:
+      A floating point Tensor with shape [..., self._periodicity - 1,
+      self._periodicity - 1] containing:
+        (transition^power)_{i, j} = {
+           1  if (i - j) % self._periodicity == power % self._periodicity
+          -1  if (i + 1) % self._periodicity == power % self._periodicity
+           0  otherwise}
+    """
+    powers %= self._periodicity
+    range_shape_padded = array_ops.reshape(
+        math_ops.range(self._periodicity - 1, dtype=powers.dtype),
+        array_ops.concat(
+            [
+                array_ops.ones([array_ops.rank(powers)], dtype=dtypes.int32),
+                [self._periodicity - 1]
+            ],
+            axis=0))
+    is_row_negative = math_ops.equal(range_shape_padded + 1, powers[..., None])
+    row_indicator_shape = array_ops.shape(is_row_negative)
+    negative_row_indicator = array_ops.where(is_row_negative, -array_ops.ones(
+        shape=row_indicator_shape, dtype=self.dtype),
+                                             array_ops.zeros(
+                                                 row_indicator_shape,
+                                                 dtype=self.dtype))
+    coord_diff = (range_shape_padded[..., None]
+                  - range_shape_padded[..., None, :])
+    is_one = math_ops.equal(coord_diff % self._periodicity,
+                            powers[..., None, None])
+    positive_ones = array_ops.where(is_one,
+                                    array_ops.ones(
+                                        array_ops.shape(is_one),
+                                        dtype=self.dtype),
+                                    array_ops.zeros(
+                                        array_ops.shape(is_one),
+                                        dtype=self.dtype))
+    return math_ops.cast(positive_ones + negative_row_indicator[..., None],
+                         self.dtype)
+
+  def transition_power_noise_accumulator(
+      self, num_steps, noise_addition_coefficient=1):
+    r"""Sum the transitioned covariance matrix over a number of steps.
+
+    Assumes that state_transition_noise_covariance is a matrix with a single
+    non-zero value in the upper left.
+
+    Args:
+      num_steps: A [...] shape integer Tensor with numbers of steps to compute
+        power sums for.
+      noise_addition_coefficient: A multiplier for the state transition noise
+        covariance (used in ResolutionCycleModel to compute multiples of full
+        period sums).
+    Returns:
+      The computed power sum, with shape [..., state dimension, state
+      dimension] containing:
+
+        [\sum_{p=0}^{num_steps - 1} (
+           state_transition^p
+           * state_transition_noise_covariance
+           * (state_transition^p)^T)]_{i, j} = {
+          -contribution_{j + 1}                   if j == i - 1
+          contribution_{j + 1} + contribution{j}  if j == i
+          -contribution_{j}                       if j == i + 1
+          0                                        otherwise
+        }
+
+        contribution_k = noise_scalar
+          * ((num_steps + self._periodicity - 1 - (k % self._periodicity))
+             // self._periodicity)
+
+      Where contribution_k is the sum of noise_scalar additions to component k
+      of the periodicity.
+    """
+    noise_addition_scalar = array_ops.squeeze(
+        self.state_transition_noise_covariance, axis=[-1, -2])
+    period_range_reshaped = array_ops.reshape(
+        math_ops.range(self._periodicity, dtype=num_steps.dtype),
+        array_ops.concat(
+            [
+                array_ops.ones([array_ops.rank(num_steps)], dtype=dtypes.int32),
+                [self._periodicity]
+            ],
+            axis=0))
+    reversed_remaining_steps = ((period_range_reshaped
+                                 - (num_steps[..., None] - 1))
+                                % self._periodicity)
+    period_additions_reversed = (ops.convert_to_tensor(
+        noise_addition_coefficient,
+        self.dtype)[..., None] * noise_addition_scalar * math_ops.cast(
+            (num_steps[..., None] + reversed_remaining_steps) //
+            self._periodicity,
+            dtype=self.dtype))
+    period_additions_diag = array_ops.matrix_diag(period_additions_reversed)
+    upper_band = array_ops.concat(
+        [
+            array_ops.zeros_like(period_additions_diag[..., :-1, 0:1]),
+            -period_additions_diag[..., :-1, 0:-2]
+        ],
+        axis=-1)
+    lower_band = array_ops.concat(
+        [
+            array_ops.zeros_like(period_additions_diag[..., 0:1, :-1]),
+            -period_additions_diag[..., 0:-2, :-1]
+        ],
+        axis=-2)
+    period_additions_rotated = array_ops.concat(
+        [
+            period_additions_reversed[..., -1:],
+            period_additions_reversed[..., :-2]
+        ],
+        axis=-1)
+    diagonal = array_ops.matrix_diag(period_additions_reversed[..., :-1] +
+                                     period_additions_rotated)
+    return diagonal + lower_band + upper_band
+
+  def get_observation_model(self, times):
+    """Observe only the first of the rotating latent values.
+
+    See StateSpaceModel.get_observation_model.
+    Args:
+      times: Unused. See the parent class for details.
+    Returns:
+      A static, univariate observation model for later broadcasting.
+    """
+    del times  # Does not rely on times. Uses broadcasting from the parent.
+    return array_ops.concat(
+        values=[
+            array_ops.ones([1], dtype=self.dtype), array_ops.zeros(
+                [self._periodicity - 2], dtype=self.dtype)
+        ],
+        axis=0)
+
+
+class ResolutionCycleModel(CycleStateSpaceModel):
+  """A version of CycleStateSpaceModel with variable resolution.
+
+  Cycles between "num_latent_values" latent values over a period of
+  "periodicity", smoothly interpolating. Simply raises the transition matrix
+  from CycleStateSpaceModel to the power (num_latent_values / periodicity).
+
+  Specifically, ResolutionCycleModel uses the following eigendecomposition of
+  the CycleStateSpaceModel matrix (there are several parameterizations, others
+  leading to roots of the matrix with complex values):
+
+    eigenvectors_{i, j}
+        = root_of_unity(floor(j / 2) + 1, i * (-1)^(j + 1))
+          - root_of_unity(floor(j / 2) + 1, (i + 1) * (-1)^(j + 1))
+    eigenvalues_j = root_of_unity(floor(j / 2) + 1, (-1)^j)
+    root_of_unity(root_number, to_power)
+        = exp(to_power * 2 * pi * sqrt(-1) * root_number
+              / num_latent_values)
+
+  The transition matrix for ResolutionCycleModel is then:
+
+    eigenvectors
+    * diag(eigenvalues^(num_latent_values / periodicity))
+    * eigenvectors^-1
+
+  Since the eigenvalues are paired with their conjugates (conj(e^(sqrt(-1)*x)) =
+  e^(-sqrt(-1)*x)), the resulting matrix has real components (this is why only
+  odd numbers of latent values are supported, since the size of the matrix is
+  one less than the number of latent values and there must be an even number of
+  eigenvalues to pair them off).
+
+  See ./g3doc/periodic_multires_derivation.md for details.
+  """
+
+  def __init__(
+      self,
+      num_latent_values,
+      periodicity,
+      near_integer_threshold=1e-8,
+      configuration=state_space_model.StateSpaceModelConfiguration()):
+    """Initialize the ResolutionCycleModel.
+
+    Args:
+      num_latent_values: Controls the representational power and memory usage of
+        the model. The transition matrix has shape [num_latent_values - 1,
+        num_latent_values - 1]. Must be an odd integer (see class docstring for
+        why).
+      periodicity: The number of steps for cyclic behavior. May be a Tensor, and
+        need not be an integer (although integer values greater than
+        num_latent_values have more efficient special cases).
+      near_integer_threshold: When avoiding singularities, controls how close a
+        number should be to that singularity before the special case takes over.
+      configuration: A StateSpaceModelConfiguration object.
+
+    Raises:
+      ValueError: If num_latent_values is not odd.
+    """
+    if num_latent_values % 2 != 1:
+      raise ValueError("Only odd numbers of latent values are supported.")
+    self._num_latent_values = num_latent_values
+    self._true_periodicity = periodicity
+    self._near_integer_threshold = near_integer_threshold
+    super(ResolutionCycleModel, self).__init__(
+        periodicity=num_latent_values,
+        configuration=configuration)
+
+  def _close_to_integer(self, value):
+    value = math_ops.cast(value, self.dtype)
+    return math_ops.less(
+        math_ops.abs(value - gen_math_ops.round(value)),
+        self._near_integer_threshold)
+
+  def transition_to_powers(self, powers):
+    """Computes TransitionMatrix^power efficiently.
+
+    For an n x n transition matrix we have:
+
+      (TransitionMatrix**power)_{i, j) = (-1) ** i * sin(pi * power) / (n + 1)
+          * ((-1) ** j / sin(pi / (n + 1) * (power - i + j))
+             + 1 / sin(pi / (n + 1) * (power - i - 1)))
+
+    The sin(pi * power) term is zero whenever "power" is an integer. However,
+    the 1 / sin(x) terms (cosecants) occasionally (when their arguments are
+    multiples of pi) cancel out this value. The limit as the argument approaches
+    an integer value gives the "correct" result, but computing these separately
+    gives 0 * inf = NaN. Instead, there is a special case for near-integer
+    values.
+
+    Args:
+      powers: A floating point Tensor of powers to raise the transition matrix
+        to.
+    Returns:
+      A [..., self._num_latent_values - 1, self._num_latent_values - 1] floating
+        point Tensor with the transition matrix raised to each power in
+        `powers`.
+
+    """
+    num_latent_values_float = math_ops.cast(self._num_latent_values, self.dtype)
+    latent_values_per_period = (num_latent_values_float / math_ops.cast(
+        self._true_periodicity, dtype=self.dtype))
+    original_matrix_powers = (math_ops.cast(powers, self.dtype) *
+                              latent_values_per_period)
+    global_coeff = (math_ops.sin(original_matrix_powers * numpy.pi) /
+                    num_latent_values_float)[..., None, None]
+    matrix_dimension_range = array_ops.reshape(
+        math_ops.range(self._num_latent_values - 1),
+        array_ops.concat(
+            [
+                array_ops.ones(
+                    [array_ops.rank(original_matrix_powers)],
+                    dtype=dtypes.int32), [self._num_latent_values - 1]
+            ],
+            axis=0))
+    matrix_dimension_range_float = math_ops.cast(matrix_dimension_range,
+                                                 self.dtype)
+    alternating = math_ops.cast(1 - 2 * (matrix_dimension_range % 2),
+                                self.dtype)
+    row_addend = 1. / math_ops.sin(numpy.pi / num_latent_values_float * (
+        original_matrix_powers[..., None] - matrix_dimension_range_float - 1))
+    column_minus_row = (matrix_dimension_range_float[..., None, :]
+                        - matrix_dimension_range_float[..., None])
+    full_matrix_addend = (alternating[..., None, :] / math_ops.sin(
+        numpy.pi / num_latent_values_float *
+        (original_matrix_powers[..., None, None] + column_minus_row)))
+    continuous_construction = global_coeff * alternating[..., None] * (
+        row_addend[..., None] + full_matrix_addend)
+    # For integer powers, the above formula is only correct in the limit,
+    # yielding NaNs as written. We defer to the super-class in such cases, which
+    # computes integer powers exactly.
+    return array_ops.where(
+        self._close_to_integer(original_matrix_powers),
+        super(ResolutionCycleModel, self).transition_to_powers(
+            math_ops.cast(
+                gen_math_ops.round(original_matrix_powers), dtypes.int64)),
+        continuous_construction)
+
+  def transition_power_noise_accumulator(self, num_steps):
+    """Sum the transitioned covariance matrix over a number of steps.
+
+    Args:
+      num_steps: An integer Tensor of any shape [...] indicating the number of
+        steps to compute for each part of the batch.
+
+    Returns:
+      A [..., self._num_latent_values - 1, self._num_latent_values - 1] floating
+      point Tensor corresponding to each requested number of steps, containing:
+
+          sum_{i=1}^{steps} transition^i * noise_covariance
+              * (transition^i)^T
+    """
+
+    def _whole_periods_folded():
+      """A more efficient special casing for integer periods.
+
+      We knock off full periods, leaving at most self._true_periodicity steps to
+      compute.
+
+      Returns:
+        A tuple of (remaining_whole_steps, current_accumulation):
+          remaining_whole_steps: An integer Tensor with the same shape as the
+            `num_steps` argument to `transition_power_noise_accumulator`,
+            indicating the reduced number of steps which must be computed
+            sequentially and added to `current_accumulation`.
+          current_accumulation: A [..., self._num_latent_values - 1,
+            self._num_latent_values - 1] floating point Tensor corresponding to
+            the accumulations for steps which were computed in this function.
+      """
+      original_transition_noise_addition_coefficient = (math_ops.cast(
+          self._true_periodicity, self.dtype) / math_ops.cast(
+              self._num_latent_values, self.dtype))
+      full_period_accumulation = super(
+          ResolutionCycleModel, self).transition_power_noise_accumulator(
+              noise_addition_coefficient=
+              original_transition_noise_addition_coefficient,
+              num_steps=ops.convert_to_tensor(
+                  self._num_latent_values, dtype=num_steps.dtype))
+      periodicity_integer = math_ops.cast(self._true_periodicity,
+                                          num_steps.dtype)
+      full_periods = math_ops.cast(num_steps // periodicity_integer, self.dtype)
+      current_accumulation = full_periods[..., None, None] * array_ops.reshape(
+          full_period_accumulation,
+          array_ops.concat(
+              [
+                  array_ops.ones(
+                      [array_ops.rank(full_periods)], dtype=dtypes.int32),
+                  array_ops.shape(full_period_accumulation)
+              ],
+              axis=0))
+      remaining_whole_steps = num_steps % periodicity_integer
+      return remaining_whole_steps, current_accumulation
+    def _no_whole_period_computation():
+      """A less efficient special casing for real valued periods.
+
+      This special casing is still preferable to computing using sequential
+      matrix multiplies (parallelizable, more numerically stable), but is linear
+      in the number of steps.
+
+      Returns:
+        Same shapes and types as `_whole_periods_folded`, but no folding is done
+        in this function.
+      """
+      current_accumulation = array_ops.zeros(
+          array_ops.concat(
+              [
+                  array_ops.shape(num_steps),
+                  [self._num_latent_values - 1, self._num_latent_values - 1]
+              ],
+              axis=0),
+          dtype=self.dtype)
+      remaining_whole_steps = num_steps
+      return remaining_whole_steps, current_accumulation
+    # Decide whether it's feasible to compute whole periods in closed form,
+    # taking advantage of the fact that a sum over self._true_periodicity steps
+    # in our transition matrix is proportional to a sum over
+    # self._num_latent_values steps in the unmodified matrix (because each
+    # latent value gets the same treatment). This is possible for integer
+    # self._true_periodicity, since we stay aligned to integer steps. For real
+    # valued self._true_periodicity, or when the cyclic behavior is a higher
+    # resolution than 1 per step, taking whole periods leads to misalignment
+    # with integer steps, which would be difficult to recover from.
+    remaining_whole_steps, current_accumulation = control_flow_ops.cond(
+        self._whole_period_folding(), _whole_periods_folded,
+        _no_whole_period_computation)
+    steps_to_compute = math_ops.reduce_max(remaining_whole_steps)
+    remaining_step_noise_additions = self._power_sum_array(steps_to_compute)
+    noise_addition_scalar = array_ops.squeeze(
+        self.state_transition_noise_covariance, axis=[-1, -2])
+    return current_accumulation + noise_addition_scalar * array_ops.gather(
+        remaining_step_noise_additions, indices=remaining_whole_steps)
+
+  def _whole_period_folding(self):
+    """Decides whether computing a whole period maintains alignment."""
+    return math_ops.logical_and(
+        self._close_to_integer(self._true_periodicity),
+        math_ops.greater_equal(self._true_periodicity, self._num_latent_values))
+
+  def _power_sum_array(self, max_remaining_steps):
+    r"""Computes \sum_{i=0}^{N-1} A^i B (A^i)^T for N=0..max_remaining_steps.
+
+    A is the transition matrix and B is the noise covariance.
+
+    This is more efficient in practice than math_utils.power_sums_tensor, since
+    each A^i B (A^i)^T term has a closed-form expression not depending on i - 1.
+    Thus vectorization can replace explicit looping.
+
+    Uses a cumulative sum on the following expression:
+
+      (transition^p * transition_covariance * (transition^p)^T)_{i, j}
+        = (-1)^(i + j) * sin^2(pi * p) / num_latent_values^2
+          * (1/sin(pi / num_latent_values * (p - i))
+             + 1/sin(pi / num_latent_values * (p - i - 1)))
+          * (1/sin(pi / num_latent_values * (p - j))
+             + 1/sin(pi / num_latent_values * (p - j - 1)))
+
+    The expression being derived from the eigenvectors and eigenvalues given in
+    the class docstring (and as with CycleStateSpaceModel taking advantage of
+    the sparsity of the transition covariance).
+
+    Args:
+      max_remaining_steps: A scalar integer Tensor indicating the number of
+        non-trivial values to compute.
+    Returns:
+      A [max_remaining_steps + 1, self._num_latent_values - 1,
+      self._num_latent_values - 1] floating point Tensor S with cumulative power
+      sums.
+
+      S[N] = \sum_{i=0}^{N-1} A^i B (A^i)^T
+        S[0] is the zero matrix
+        S[1] is B
+        S[2] is A B A^T + B
+
+    """
+    num_latent_values_float = math_ops.cast(self._num_latent_values, self.dtype)
+    latent_values_per_period = (num_latent_values_float / math_ops.cast(
+        self._true_periodicity, dtype=self.dtype))
+    original_matrix_powers = (math_ops.cast(
+        math_ops.range(max_remaining_steps),
+        self.dtype) * latent_values_per_period)
+    matrix_dimension_range = math_ops.range(
+        self._num_latent_values - 1)[None, ...]
+    matrix_dimension_range_float = math_ops.cast(matrix_dimension_range,
+                                                 self.dtype)
+    def _cosecant_with_freq(coefficient):
+      return 1. / math_ops.sin(numpy.pi / num_latent_values_float * coefficient)
+    power_minus_index = (original_matrix_powers[..., None]
+                         - matrix_dimension_range_float)
+    mesh_values = (_cosecant_with_freq(power_minus_index)
+                   + _cosecant_with_freq(power_minus_index - 1.))
+    meshed = mesh_values[..., None, :] * mesh_values[..., None]
+    full_matrix_alternating = math_ops.cast(1 - 2 * (
+        (matrix_dimension_range[..., None, :] +
+         matrix_dimension_range[..., None]) % 2), self.dtype)
+    def _sine_discontinuity(value):
+      """A special case for dealing with discontinuities.
+
+      Decides whether `value`  is close to an integer, and if so computes:
+
+        lim x->n |sin(x * pi)| / sin(x * pi) = sign(sin(n * pi))
+                                             = cos(n * pi)
+
+      Args:
+        value: The floating point Tensor value which may lead to a
+            discontinuity.
+      Returns:
+        A tuple of (is_discontinuous, sign):
+          is_discontinuous: A boolean Tensor of the same shape as `value`,
+              indicating whether it is near an integer.
+          sign: A floating point Tensor indicating the sign of the discontinuity
+            (being near 1 or -1 when `is_discontinuous` is True), of the same
+            shape and type as `value`.
+      """
+      normalized = value / num_latent_values_float
+      is_discontinuous = self._close_to_integer(normalized)
+      sign = math_ops.cos(normalized * numpy.pi)
+      return is_discontinuous, sign
+    index_discontinuous, index_sign = _sine_discontinuity(
+        original_matrix_powers[..., None]
+        - matrix_dimension_range_float)
+    index_minus_discontinuous, index_minus_sign = _sine_discontinuity(
+        original_matrix_powers[..., None]
+        - matrix_dimension_range_float
+        - 1)
+    ones_mask_vector = math_ops.logical_or(index_discontinuous,
+                                           index_minus_discontinuous)
+    ones_sign_vector = array_ops.where(index_discontinuous, index_sign,
+                                       index_minus_sign)
+    ones_mask = math_ops.logical_and(ones_mask_vector[..., None],
+                                     ones_mask_vector[..., None, :])
+    zeros_mask = self._close_to_integer(original_matrix_powers)
+    zeroed = array_ops.where(zeros_mask, array_ops.zeros_like(meshed), meshed)
+    global_coefficient = (math_ops.sin(numpy.pi * original_matrix_powers) /
+                          num_latent_values_float)
+    masked_meshed = array_ops.where(
+        ones_mask, ones_sign_vector[..., None] * ones_sign_vector[..., None, :],
+        zeroed * global_coefficient[..., None, None]**2)
+    powers_above_zero = full_matrix_alternating * masked_meshed
+    return array_ops.pad(
+        math_ops.cumsum(powers_above_zero), [(1, 0), (0, 0), (0, 0)])
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic_test.py
new file mode 100644
index 0000000000..9bd68e8ad2
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic_test.py
@@ -0,0 +1,81 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for periodic state space model components."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import periodic
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import test_utils
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.platform import test
+
+
+class SpecialCaseTests(test.TestCase):
+
+  def test_cycle_transition_to_powers(self):
+    num_steps = 3
+    dtype = dtypes.float64
+    periodicity = 3
+    cycle = periodic.CycleStateSpaceModel(
+        periodicity=periodicity,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            dtype=dtype))
+    test_utils.transition_power_test_template(
+        test_case=self, model=cycle, num_steps=num_steps)
+
+  def test_resolution_cycle_transition_to_powers(self):
+    num_steps = 3
+    dtype = dtypes.float64
+    latent_values = 3
+    periodicity = latent_values - 1
+    cycle = periodic.ResolutionCycleModel(
+        num_latent_values=latent_values,
+        periodicity=periodicity,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            dtype=dtype))
+    test_utils.transition_power_test_template(
+        test_case=self, model=cycle, num_steps=num_steps)
+
+  def test_cycle_noise_accumulator(self):
+    num_steps = 3
+    dtype = dtypes.float64
+    periodicity = 3
+    cycle = periodic.CycleStateSpaceModel(
+        periodicity=periodicity,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            dtype=dtype))
+    test_utils.noise_accumulator_test_template(
+        test_case=self, model=cycle, num_steps=num_steps)
+
+  def test_resolution_cycle_noise_accumulator(self):
+    num_steps = 3
+    dtype = dtypes.float64
+    latent_values = 3
+    periodicity = latent_values + 0.1
+    cycle = periodic.ResolutionCycleModel(
+        num_latent_values=latent_values,
+        periodicity=periodicity,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            dtype=dtype))
+    test_utils.noise_accumulator_test_template(
+        test_case=self, model=cycle, num_steps=num_steps)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
new file mode 100644
index 0000000000..3193d5607c
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
@@ -0,0 +1,1207 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Abstract base for state space models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import collections
+
+import numpy
+
+from tensorflow.contrib.layers.python.layers import layers
+
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries import model
+from tensorflow.contrib.timeseries.python.timeseries import model_utils
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import kalman_filter
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+
+
+class StateSpaceModelConfiguration(
+    collections.namedtuple(
+        typename="StateSpaceModelConfiguration",
+        field_names=[
+            "num_features", "use_observation_noise", "dtype",
+            "covariance_prior_fn", "bayesian_prior_weighting",
+            "filtering_postprocessor", "trainable_start_state",
+            "exogenous_noise_increases", "exogenous_noise_decreases",
+            "exogenous_feature_columns", "exogenous_update_condition",
+            "filtering_maximum_posterior_variance_ratio",
+            "filtering_minimum_posterior_variance",
+            "transition_covariance_initial_log_scale_bias",
+            "static_unrolling_window_size_threshold"])):
+  """Configuration options for StateSpaceModels."""
+
+  def __new__(
+      cls,
+      num_features=1,
+      use_observation_noise=True,
+      dtype=dtypes.float32,
+      covariance_prior_fn=math_utils.log_noninformative_covariance_prior,
+      bayesian_prior_weighting=True,
+      filtering_postprocessor=None,
+      trainable_start_state=False,
+      exogenous_noise_increases=True,
+      exogenous_noise_decreases=False,
+      exogenous_feature_columns=None,
+      exogenous_update_condition=None,
+      filtering_maximum_posterior_variance_ratio=1e6,
+      filtering_minimum_posterior_variance=0.,
+      transition_covariance_initial_log_scale_bias=-5.,
+      static_unrolling_window_size_threshold=None):
+    """Configuration options for StateSpaceModels.
+
+    Args:
+      num_features: Output dimension for model
+      use_observation_noise: If true, observations are modeled as noisy
+        functions of the current state. If false, observations are a
+        deterministic function of the current state. Only applicable to the
+        top-level model in an ensemble. Consider also changing the
+        transition_covariance_initial_log_scale_bias when disabling observation
+        noise, as its default setting assumes that observation noise is part of
+        the model.
+      dtype: The float dtype to use when defining the model.
+      covariance_prior_fn: A function mapping from a covariance matrix to a
+          scalar value (e.g. log likelihood) which can be summed across
+          matrices. Defaults to an independent Jeffreys prior on the diagonal
+          elements (regularizing as log(1. / variance)). To use a flat prior
+          (i.e. no regularization), set to `lambda _: 0.`.  Defaults to
+          relatively uninformative priors on state transition and observation
+          noise, which have the effect of encouraging low-noise solutions which
+          provide confident predictions when possible. Without regularization,
+          transition noise tends to remain high, and multi-step predictions are
+          under-confident.
+      bayesian_prior_weighting: If True, weights the prior (covariance_prior_fn)
+          based on an estimate of the full dataset size. If False, weights it
+          based on the mini-batch window size, which (while statistically
+          improper) can lead to more desirable low-noise solutions in cases
+          where the full dataset is large enough to overwhelm the prior.
+      filtering_postprocessor: A FilteringStepPostprocessor object to use,
+          useful for ignoring anomalies in training data.
+      trainable_start_state: If True, start state may depend on trainable
+          Variables. If False, it will not.
+      exogenous_noise_increases: If True, exogenous regressors can add to model
+          state, increasing uncertainty. If both this parameter and
+          exogenous_noise_decreases are False, exogenous regressors are ignored.
+      exogenous_noise_decreases: If True, exogenous regressors can "set" model
+          state, decreasing uncertainty. If both this parameter and
+          exogenous_noise_increases are False, exogenous regressors are ignored.
+      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
+          objects (for example tf.contrib.layers.embedding_column) corresponding
+          to exogenous features which provide extra information to the model but
+          are not part of the series to be predicted. Passed to
+          tf.contrib.layers.input_from_feature_columns.
+      exogenous_update_condition: A function taking two Tensor arguments `times`
+          (shape [batch size]) and `features` (a dictionary mapping exogenous
+          feature keys to Tensors with shapes [batch size, ...]) and returning a
+          boolean Tensor with shape [batch size] indicating whether state should
+          be updated using exogenous features for each part of the batch. Where
+          it is False, no exogenous update is performed. If None (default),
+          exogenous updates are always performed. Useful for avoiding "leaky"
+          frequent exogenous updates when sparse updates are desired. Called
+          only during graph construction.
+      filtering_maximum_posterior_variance_ratio: The maximum allowed ratio of
+          two diagonal entries in a state covariance matrix just prior to
+          filtering. Lower values mean that filtering will be more numerically
+          stable, at the cost of artificially increasing estimated uncertainty
+          in some cases. This parameter can be important when learning a
+          transition matrix.
+      filtering_minimum_posterior_variance: The minimum diagonal value in a
+          state covariance matrix just prior to filtering, preventing numerical
+          instability due to deterministic beliefs (sometimes an issue when
+          learning transition matrices). This value should be set several orders
+          of magnitude below any expected minimum state uncertainty.
+      transition_covariance_initial_log_scale_bias: Controls the initial
+          tradeoff between the transition noise covariance matrix and the
+          observation noise covariance matrix, on a log scale (the elements of
+          the transition noise covariance matrix are proportional to `e^{X +
+          transition_covariance_initial_log_scale_bias}` where `X` is learned
+          and may depend on input statistics, observation noise covariance is
+          proportional to `e^{Y -
+          transition_covariance_initial_log_scale_bias}`). For models *with*
+          observation noise, -5 is a reasonable value. Models which do not use
+          observation noise, and are not part of an ensemble which does use
+          observation noise, should have this set to 0 or more to avoid
+          numerical issues due to filtering with too little noise.
+      static_unrolling_window_size_threshold: Only relevant for the top-level
+          StateSpaceModel in an ensemble; enables switching between static and
+          dynamic looping (if not None, default, meaning that no static
+          unrolling is performed) based on the window size (windows with this
+          size and smaller will have their graphs unrolled statically). See the
+          SequentialTimeSeriesModel constructor for details.
+    Returns:
+      A StateSpaceModelConfiguration object.
+    """
+    if exogenous_feature_columns is None:
+      exogenous_feature_columns = []
+    return super(StateSpaceModelConfiguration, cls).__new__(
+        cls, num_features, use_observation_noise, dtype,
+        covariance_prior_fn, bayesian_prior_weighting,
+        filtering_postprocessor, trainable_start_state,
+        exogenous_noise_increases, exogenous_noise_decreases,
+        exogenous_feature_columns, exogenous_update_condition,
+        filtering_maximum_posterior_variance_ratio,
+        filtering_minimum_posterior_variance,
+        transition_covariance_initial_log_scale_bias,
+        static_unrolling_window_size_threshold)
+
+
+class StateSpaceModel(model.SequentialTimeSeriesModel):
+  """Base class for linear state space models.
+
+  Sub-classes can specify the model to be learned by overriding
+  get_state_transition, get_noise_transform, and get_observation_model.
+
+  See kalman_filter.py for a detailed description of the class of models covered
+  by StateSpaceModel.
+
+  Briefly, state space models are defined by a state transition equation:
+
+  state[t] = StateTransition * state[t-1] + NoiseTransform * StateNoise[t]
+             + ExogenousNoiseIncreasing[t]
+  StateNoise[t] ~ Gaussian(0, StateNoiseCovariance)
+  ExogenousNoiseIncreasing[t] ~ Gaussian(ExogenousNoiseIncreasingMean[t],
+                                         ExogenousNoiseIncreasingCovariance[t])
+
+  And an observation model:
+
+  observation[t] = ObservationModel * state[t] + ObservationNoise[t]
+  ObservationNoise[t] ~ Gaussian(0, ObservationNoiseCovariance)
+
+  Additionally, exogenous regressors can act as observations, decreasing
+  uncertainty:
+
+  ExogenousNoiseDecreasingObservation[t] ~ Gaussian(
+      ExogenousNoiseDecreasingMean[t], ExogenousNoiseDecreasingCovariance[t])
+
+  Attributes:
+    kalman_filter: If initialize_graph has been called, the initialized
+        KalmanFilter to use for inference. None otherwise.
+    prior_state_mean: If initialize_graph has been called, a
+        Variable-parameterized Tensor with shape [state dimension];
+        the initial prior mean for one or more time series. None otherwise.
+    prior_state_var: If initialize_graph has been called, a
+        Variable-parameterized Tensor with shape [state dimension x state
+        dimension]; the initial prior covariance. None otherwise.
+    state_transition_noise_covariance: If initialize_graph has been called, a
+        Variable-parameterized Tensor with shape [state noise dimension x state
+        noise dimension] indicating the amount of noise added at each
+        transition.
+  """
+
+  def __init__(self, configuration):
+    """Initialize a state space model.
+
+    Args:
+      configuration: A StateSpaceModelConfiguration object.
+    """
+    self._configuration = configuration
+    if configuration.filtering_postprocessor is not None:
+      filtering_postprocessor_names = (
+          configuration.filtering_postprocessor.output_names)
+    else:
+      filtering_postprocessor_names = []
+    super(StateSpaceModel, self).__init__(
+        train_output_names=(["mean", "covariance", "log_likelihood"]
+                            + filtering_postprocessor_names),
+        predict_output_names=["mean", "covariance"],
+        num_features=configuration.num_features,
+        dtype=configuration.dtype,
+        exogenous_feature_columns=configuration.exogenous_feature_columns,
+        exogenous_update_condition=configuration.exogenous_update_condition,
+        static_unrolling_window_size_threshold=
+        configuration.static_unrolling_window_size_threshold)
+    self._kalman_filter = None
+    self.prior_state_mean = None
+    self.prior_state_var = None
+    self.state_transition_noise_covariance = None
+    self._total_observation_count = None
+    self._observation_noise_covariance = None
+    # Capture the current variable scope and use it to define all model
+    # variables. Especially useful for ensembles, where variables may be defined
+    # for every component model in one function call, which would otherwise
+    # prevent the user from separating variables from different models into
+    # different scopes.
+    self._variable_scope = variable_scope.get_variable_scope()
+
+  def transition_power_noise_accumulator(self, num_steps):
+    r"""Sum a transitioned covariance matrix over a number of steps.
+
+    Computes
+
+      \sum_{i=0}^{num_steps - 1} (
+        state_transition^i
+        * state_transition_noise_covariance
+        * (state_transition^i)^T)
+
+    If special cases are available, overriding this function can lead to more
+    efficient inferences.
+
+    Args:
+      num_steps: A [...] shape integer Tensor with numbers of steps to compute
+        power sums for.
+    Returns:
+      The computed power sum, with shape [..., state dimension, state
+      dimension].
+    """
+    # TODO(allenl): This general case should use cumsum if transition_to_powers
+    # can be computed in constant time (important for correlated ensembles,
+    # where transition_power_noise_accumulator special cases cannot be
+    # aggregated from member models).
+    noise_transform = ops.convert_to_tensor(self.get_noise_transform(),
+                                            self.dtype)
+    noise_transformed = math_ops.matmul(
+        math_ops.matmul(noise_transform,
+                        self.state_transition_noise_covariance),
+        noise_transform,
+        transpose_b=True)
+    noise_additions = math_utils.power_sums_tensor(
+        math_ops.reduce_max(num_steps) + 1,
+        ops.convert_to_tensor(self.get_state_transition(), dtype=self.dtype),
+        noise_transformed)
+    return array_ops.gather(noise_additions, indices=num_steps)
+
+  def transition_to_powers(self, powers):
+    """Raise the transition matrix to a batch of powers.
+
+    Computes state_transition^powers. If special cases are available, overriding
+    this function can lead to more efficient inferences.
+
+    Args:
+      powers: A [...] shape integer Tensor with powers to raise the transition
+        matrix to.
+    Returns:
+      The computed matrix powers, with shape [..., state dimension, state
+      dimension].
+    """
+    return math_utils.matrix_to_powers(
+        ops.convert_to_tensor(self.get_state_transition(), dtype=self.dtype),
+        powers)
+
+  def _window_initializer(self, times, state):
+    """Prepare to impute across the gaps in a window."""
+    _, _, priors_from_time = state
+    times = ops.convert_to_tensor(times)
+    priors_from_time = ops.convert_to_tensor(priors_from_time)
+    with ops.control_dependencies([
+        control_flow_ops.Assert(
+            math_ops.reduce_all(priors_from_time <= times[:, 0]),
+            [priors_from_time, times[:, 0]],
+            summarize=100)
+    ]):
+      times = array_ops.identity(times)
+    intra_batch_gaps = array_ops.reshape(times[:, 1:] - times[:, :-1], [-1])
+    starting_gaps = times[:, 0] - priors_from_time
+    # Pre-define transition matrices raised to powers (and their sums) for every
+    # gap in this window. This avoids duplicate computation (for example many
+    # steps will use the transition matrix raised to the first power) and
+    # batches the computation rather than doing it inside the per-step loop.
+    unique_gaps, _ = array_ops.unique(
+        array_ops.concat([intra_batch_gaps, starting_gaps], axis=0))
+    self._window_power_sums = self.transition_power_noise_accumulator(
+        unique_gaps)
+    self._window_transition_powers = self.transition_to_powers(unique_gaps)
+    self._window_gap_sizes = unique_gaps
+
+  def _lookup_window_caches(self, caches, indices):
+    _, window_power_ids = array_ops.unique(
+        array_ops.concat(
+            [
+                self._window_gap_sizes, math_ops.cast(
+                    indices, self._window_gap_sizes.dtype)
+            ],
+            axis=0))
+    all_gathered_indices = []
+    for cache in caches:
+      gathered_indices = array_ops.gather(
+          cache, window_power_ids[-array_ops.shape(indices)[0]:])
+      gathered_indices.set_shape(indices.get_shape().concatenate(
+          gathered_indices.get_shape()[-2:]))
+      all_gathered_indices.append(gathered_indices)
+    return all_gathered_indices
+
+  def _cached_transition_powers_and_sums(self, num_steps):
+    return self._lookup_window_caches(
+        caches=[self._window_transition_powers, self._window_power_sums],
+        indices=num_steps)
+
+  def _imputation_step(self, current_times, state):
+    """Add state transition noise to catch `state` up to `current_times`.
+
+    State space models are inherently sequential, so we need to "predict
+    through" any missing time steps to catch up each element of the batch to its
+    next observation/prediction time.
+
+    Args:
+      current_times: A [batch size] Tensor of times to impute up to, not
+          inclusive.
+      state: A tuple of (mean, covariance, previous_times) having shapes
+          mean; [batch size x state dimension]
+          covariance; [batch size x state dimension x state dimension]
+          previous_times; [batch size]
+    Returns:
+      Imputed model state corresponding to the `state` argument.
+    """
+    estimated_state, estimated_state_var, previous_times = state
+    catchup_times = current_times - previous_times
+    non_negative_assertion = control_flow_ops.Assert(
+        math_ops.reduce_all(catchup_times >= 0), [
+            "Negative imputation interval", catchup_times, current_times,
+            previous_times
+        ],
+        summarize=100)
+    with ops.control_dependencies([non_negative_assertion]):
+      transition_matrices, transition_noise_sums = (  # pylint: disable=unbalanced-tuple-unpacking
+          self._cached_transition_powers_and_sums(catchup_times))
+      estimated_state = self._kalman_filter.predict_state_mean(
+          estimated_state, transition_matrices)
+      estimated_state_var = self._kalman_filter.predict_state_var(
+          estimated_state_var, transition_matrices, transition_noise_sums)
+    return (estimated_state, estimated_state_var,
+            previous_times + catchup_times)
+
+  def _filtering_step(self, current_times, current_values, state, predictions):
+    """Compute posteriors and accumulate one-step-ahead predictions.
+
+    Args:
+      current_times: A [batch size] Tensor for times for each observation.
+      current_values: A [batch size] Tensor of values for each observaiton.
+      state: A tuple of (mean, covariance, previous_times) having shapes
+          mean; [batch size x state dimension]
+          covariance; [batch size x state dimension x state dimension]
+          previous_times; [batch size]
+      predictions: A dictionary containing mean and covariance Tensors, the
+          output of _prediction_step.
+    Returns:
+      A tuple of (posteriors, outputs):
+        posteriors: Model state updated to take `current_values` into account.
+        outputs: The `predictions` dictionary updated to include "loss" and
+            "log_likelihood" entries (loss simply being negative log
+            likelihood).
+    """
+    estimated_state, estimated_state_covariance, previous_times = state
+    observation_model = self.get_broadcasted_observation_model(current_times)
+    imputed_to_current_step_assert = control_flow_ops.Assert(
+        math_ops.reduce_all(math_ops.equal(current_times, previous_times)),
+        ["Attempted to perform filtering without imputation/prediction"])
+    with ops.control_dependencies([imputed_to_current_step_assert]):
+      estimated_state_covariance = math_utils.clip_covariance(
+          estimated_state_covariance,
+          self._configuration.filtering_maximum_posterior_variance_ratio,
+          self._configuration.filtering_minimum_posterior_variance)
+      (filtered_state, filtered_state_covariance,
+       log_prob) = self._kalman_filter.do_filter(
+           estimated_state=estimated_state,
+           estimated_state_covariance=estimated_state_covariance,
+           predicted_observation=predictions["mean"],
+           predicted_observation_covariance=predictions["covariance"],
+           observation=current_values,
+           observation_model=observation_model,
+           observation_noise=self._observation_noise_covariance)
+    filtered_state = (filtered_state, filtered_state_covariance, current_times)
+    log_prob.set_shape(current_times.get_shape())
+    predictions["loss"] = -log_prob
+    predictions["log_likelihood"] = log_prob
+    if self._configuration.filtering_postprocessor is not None:
+      return self._configuration.filtering_postprocessor.process_filtering_step(
+          current_times=current_times,
+          current_values=current_values,
+          predicted_state=state,
+          filtered_state=filtered_state,
+          outputs=predictions)
+    return (filtered_state, predictions)
+
+  def _prediction_step(self, current_times, state):
+    """Make a prediction based on `state`.
+
+    Computes predictions based on the current `state`, checking that it has
+    already been updated (in `_imputation_step`) to `current_times`.
+
+    Args:
+      current_times: A [batch size] Tensor for times to make predictions for.
+      state: A tuple of (mean, covariance, previous_times) having shapes
+          mean; [batch size x state dimension]
+          covariance; [batch size x state dimension x state dimension]
+          previous_times; [batch size]
+    Returns:
+      A tuple of (updated state, predictions):
+        updated state: Model state with added transition noise.
+        predictions: A dictionary with "mean" and "covariance", having shapes
+            "mean": [batch size x num features]
+            "covariance: [batch size x num features x num features]
+    """
+    estimated_state, estimated_state_var, previous_times = state
+    advanced_to_current_assert = control_flow_ops.Assert(
+        math_ops.reduce_all(math_ops.equal(current_times, previous_times)),
+        ["Attempted to predict without imputation"])
+    with ops.control_dependencies([advanced_to_current_assert]):
+      observation_model = self.get_broadcasted_observation_model(current_times)
+      predicted_obs, predicted_obs_var = (
+          self._kalman_filter.observed_from_state(
+              state_mean=estimated_state,
+              state_var=estimated_state_var,
+              observation_model=observation_model,
+              observation_noise=self._observation_noise_covariance))
+      predicted_obs_var.set_shape(
+          ops.convert_to_tensor(current_times).get_shape()
+          .concatenate([self.num_features, self.num_features]))
+    predicted_obs.set_shape(current_times.get_shape().concatenate(
+        (self.num_features,)))
+    predicted_obs_var.set_shape(current_times.get_shape().concatenate(
+        (self.num_features, self.num_features)))
+    predictions = {
+        "mean": predicted_obs,
+        "covariance": predicted_obs_var}
+    state = (estimated_state, estimated_state_var, current_times)
+    return (state, predictions)
+
+  def _exogenous_noise_decreasing(self, current_times, exogenous_values, state):
+    """Update state with exogenous regressors, decreasing uncertainty.
+
+    Constructs a mean and covariance based on transformations of
+    `exogenous_values`, then performs Bayesian inference on the constructed
+    observation. This has the effect of lowering uncertainty.
+
+    This update refines or overrides previous inferences, useful for modeling
+    exogenous inputs which "set" state, e.g. we dumped boiling water on the
+    thermometer so we're pretty sure it's 100 degrees C.
+
+    Args:
+      current_times: A [batch size] Tensor of times for the exogenous values
+          being input.
+      exogenous_values: A [batch size x exogenous input dimension] Tensor of
+          exogenous values for each part of the batch.
+      state: A tuple of (mean, covariance, previous_times) having shapes
+          mean; [batch size x state dimension]
+          covariance; [batch size x state dimension x state dimension]
+          previous_times; [batch size]
+    Returns:
+      Updated state taking the exogenous regressors into account (with lower
+      uncertainty than the input state).
+
+    """
+    estimated_state, estimated_state_covariance, previous_times = state
+    state_transition = ops.convert_to_tensor(
+        self.get_state_transition(), dtype=self.dtype)
+    state_dimension = state_transition.get_shape()[0].value
+    # Learning the observation model would be redundant since we transform
+    # `exogenous_values` to the state space via a linear transformation anyway.
+    observation_model = linalg_ops.eye(
+        state_dimension,
+        batch_shape=array_ops.shape(exogenous_values)[:-1],
+        dtype=self.dtype)
+    with variable_scope.variable_scope("exogenous_noise_decreasing_covariance"):
+      observation_noise = math_utils.transform_to_covariance_matrices(
+          exogenous_values, state_dimension)
+    with variable_scope.variable_scope(
+        "exogenous_noise_decreasing_observation"):
+      observation = layers.fully_connected(
+          exogenous_values, state_dimension, activation_fn=None)
+    # Pretend that we are making an observation with an observation model equal
+    # to the identity matrix (i.e. a direct observation of the latent state),
+    # with learned observation noise.
+    posterior_state, posterior_state_var = (
+        self._kalman_filter.posterior_from_prior_state(
+            prior_state=estimated_state,
+            prior_state_var=estimated_state_covariance,
+            observation=observation,
+            observation_model=observation_model,
+            predicted_observations=(
+                estimated_state,
+                # The predicted noise covariance is noise due to current state
+                # uncertainty plus noise learned based on the exogenous
+                # observation (a somewhat trivial call to
+                # self._kalman_filter.observed_from_state has been omitted).
+                observation_noise + estimated_state_covariance),
+            observation_noise=observation_noise))
+    return (posterior_state, posterior_state_var, previous_times)
+
+  def _exogenous_noise_increasing(self, current_times, exogenous_values, state):
+    """Update state with exogenous regressors, increasing uncertainty.
+
+    Adds to the state mean a linear transformation of `exogenous_values`, and
+    increases uncertainty by constructing a covariance matrix based on
+    `exogenous_values` and adding it to the state covariance.
+
+    This update is useful for modeling changes relative to current state,
+    e.g. the furnace turned on so the temperature will be increasing at an
+    additional 1 degree per minute with some uncertainty, this uncertainty being
+    added to our current uncertainty in the per-minute change in temperature.
+
+    Args:
+      current_times: A [batch size] Tensor of times for the exogenous values
+          being input.
+      exogenous_values: A [batch size x exogenous input dimension] Tensor of
+          exogenous values for each part of the batch.
+      state: A tuple of (mean, covariance, previous_times) having shapes
+          mean; [batch size x state dimension]
+          covariance; [batch size x state dimension x state dimension]
+          previous_times; [batch size]
+    Returns:
+      Updated state taking the exogenous regressors into account (with higher
+      uncertainty than the input state).
+
+    """
+    start_mean, start_covariance, previous_times = state
+    with variable_scope.variable_scope("exogenous_noise_increasing_mean"):
+      mean_addition = layers.fully_connected(
+          exogenous_values, start_mean.get_shape()[1].value, activation_fn=None)
+    state_dimension = start_covariance.get_shape()[1].value
+    with variable_scope.variable_scope("exogenous_noise_increasing_covariance"):
+      covariance_addition = (
+          math_utils.transform_to_covariance_matrices(
+              exogenous_values, state_dimension))
+    return (start_mean + mean_addition,
+            start_covariance + covariance_addition,
+            previous_times)
+
+  def _exogenous_input_step(
+      self, current_times, current_exogenous_regressors, state):
+    """Update state with exogenous regressors.
+
+    Allows both increases and decreases in uncertainty.
+
+    Args:
+      current_times: A [batch size] Tensor of times for the exogenous values
+          being input.
+      current_exogenous_regressors: A [batch size x exogenous input dimension]
+          Tensor of exogenous values for each part of the batch.
+      state: A tuple of (mean, covariance, previous_times) having shapes
+          mean; [batch size x state dimension]
+          covariance; [batch size x state dimension x state dimension]
+          previous_times; [batch size]
+    Returns:
+      Updated state taking the exogenous regressors into account.
+    """
+    if self._configuration.exogenous_noise_decreases:
+      state = self._exogenous_noise_decreasing(
+          current_times, current_exogenous_regressors, state)
+    if self._configuration.exogenous_noise_increases:
+      state = self._exogenous_noise_increasing(
+          current_times, current_exogenous_regressors, state)
+    return state
+
+  def _loss_additions(self, times, values, mode):
+    """Add regularization during training."""
+    if mode == estimator_lib.ModeKeys.TRAIN:
+      if (self._input_statistics is not None
+          and self._configuration.bayesian_prior_weighting):
+        normalization = 1. / math_ops.cast(
+            self._input_statistics.total_observation_count, self.dtype)
+      else:
+        # If there is no total observation count recorded, or if we are not
+        # doing a Bayesian prior weighting, assumes/pretends that the full
+        # dataset size is the window size.
+        normalization = 1. / math_ops.cast(
+            array_ops.shape(times)[1], self.dtype)
+      transition_contribution = ops.convert_to_tensor(
+          self._configuration.covariance_prior_fn(
+              self.state_transition_noise_covariance),
+          dtype=self.dtype)
+      if (self._configuration.use_observation_noise
+          and self._observation_noise_covariance is not None):
+        observation_contribution = ops.convert_to_tensor(
+            self._configuration.covariance_prior_fn(
+                self._observation_noise_covariance),
+            dtype=self.dtype)
+        regularization_sum = transition_contribution + observation_contribution
+      else:
+        regularization_sum = transition_contribution
+      return -normalization * regularization_sum
+    else:
+      return array_ops.zeros([], dtype=self.dtype)
+
+  def _variable_observation_transition_tradeoff_log(self):
+    """Define a variable to trade off observation and transition noise."""
+    return variable_scope.get_variable(
+        name="observation_transition_tradeoff_log_scale",
+        initializer=constant_op.constant(
+            -self._configuration.transition_covariance_initial_log_scale_bias,
+            dtype=self.dtype),
+        dtype=self.dtype)
+
+  def _define_parameters(self, observation_transition_tradeoff_log=None):
+    """Define extra model-specific parameters.
+
+    Models should wrap any variables defined here in the model's variable scope.
+
+    Args:
+      observation_transition_tradeoff_log: An ensemble-global parameter
+        controlling the tradeoff between observation noise and transition
+        noise. If its value is not None, component transition noise should scale
+        with e^-observation_transition_tradeoff_log.
+    """
+    with variable_scope.variable_scope(self._variable_scope):
+      # A scalar which allows the optimizer to quickly shift from observation
+      # noise to transition noise (this value is subtracted from log transition
+      # noise and added to log observation noise).
+      if observation_transition_tradeoff_log is None:
+        self._observation_transition_tradeoff_log_scale = (
+            self._variable_observation_transition_tradeoff_log())
+      else:
+        self._observation_transition_tradeoff_log_scale = (
+            observation_transition_tradeoff_log)
+      self.state_transition_noise_covariance = (
+          self.get_state_transition_noise_covariance())
+
+  def _set_input_statistics(self, input_statistics=None):
+    super(StateSpaceModel, self).initialize_graph(
+        input_statistics=input_statistics)
+
+  def initialize_graph(self, input_statistics=None):
+    """Define variables and ops relevant to the top-level model in an ensemble.
+
+    For generic model parameters, _define_parameters() is called recursively on
+    all members of an ensemble.
+
+    Args:
+      input_statistics: A math_utils.InputStatistics object containing input
+          statistics. If None, data-independent defaults are used, which may
+          result in longer or unstable training.
+    """
+    self._set_input_statistics(input_statistics=input_statistics)
+    self._define_parameters()
+    with variable_scope.variable_scope(self._variable_scope):
+      self._observation_noise_covariance = ops.convert_to_tensor(
+          self.get_observation_noise_covariance(), dtype=self.dtype)
+    self._kalman_filter = kalman_filter.KalmanFilter(dtype=self.dtype)
+    (self.prior_state_mean,
+     self.prior_state_var) = self._make_priors()
+
+  def _make_priors(self):
+    """Creates and returns model priors."""
+    prior_state_covariance = self.get_prior_covariance()
+    prior_state_mean = self.get_prior_mean()
+    return (prior_state_mean, prior_state_covariance)
+
+  def get_prior_covariance(self):
+    """Constructs a variable prior covariance with data-based initialization.
+
+    Models should wrap any variables defined here in the model's variable scope.
+
+    Returns:
+      A two-dimensional [state dimension, state dimension] floating point Tensor
+      with a (positive definite) prior state covariance matrix.
+    """
+    with variable_scope.variable_scope(self._variable_scope):
+      state_dimension = ops.convert_to_tensor(
+          self.get_state_transition()).get_shape()[0].value
+      if self._configuration.trainable_start_state:
+        base_covariance = math_utils.variable_covariance_matrix(
+            state_dimension, "prior_state_var",
+            dtype=self.dtype)
+      else:
+        return linalg_ops.eye(state_dimension, dtype=self.dtype)
+      if self._input_statistics is not None:
+        # Make sure initial latent value uncertainty is at least on the same
+        # scale as noise in the data.
+        covariance_multiplier = math_ops.reduce_max(
+            self._input_statistics.series_start_moments.variance)
+        return base_covariance * gen_math_ops.maximum(
+            covariance_multiplier, 1.0)
+      else:
+        return base_covariance
+
+  def get_prior_mean(self):
+    """Constructs a Variable-parameterized prior mean.
+
+    Models should wrap any variables defined here in the model's variable scope.
+
+    Returns:
+      A one-dimensional floating point Tensor with shape [state dimension]
+      indicating the prior mean.
+    """
+    with variable_scope.variable_scope(self._variable_scope):
+      state_transition = ops.convert_to_tensor(
+          self.get_state_transition(), dtype=self.dtype)
+      state_dimension = state_transition.get_shape()[0].value
+      return variable_scope.get_variable(
+          name="prior_state_mean",
+          shape=[state_dimension],
+          dtype=self.dtype,
+          trainable=self._configuration.trainable_start_state)
+
+  # TODO(allenl): It would be nice if the generation were done with TensorFlow
+  # ops, and if the model parameters were somehow set instead of being passed
+  # around in a dictionary. Maybe unconditional generation should be through a
+  # special set of initializers?
+  def random_model_parameters(self, seed=None):
+    if self.num_features != 1:
+      raise NotImplementedError("Generation for multivariate state space models"
+                                " is not currently implemented.")
+    if seed:
+      numpy.random.seed(seed)
+    state_dimension, noise_dimension = ops.convert_to_tensor(
+        self.get_noise_transform()).get_shape().as_list()
+    transition_var = 1.0 / numpy.random.gamma(shape=10., scale=10.,
+                                              size=[noise_dimension])
+    initial_state = numpy.random.normal(size=[state_dimension])
+    params_dict = {}
+    if self.prior_state_mean is not None:
+      params_dict[self.prior_state_mean] = initial_state
+    if self.state_transition_noise_covariance is not None:
+      params_dict[self.state_transition_noise_covariance] = numpy.diag(
+          transition_var)
+    if self.prior_state_var is not None:
+      params_dict[self.prior_state_var] = numpy.zeros(
+          [state_dimension, state_dimension])
+    if self._configuration.use_observation_noise:
+      observation_var = 1.0 / numpy.random.gamma(shape=4, scale=4)
+      params_dict[self._observation_noise_covariance] = [[observation_var]]
+    return params_dict
+
+  def generate(self, number_of_series, series_length,
+               model_parameters=None, seed=None, add_observation_noise=None):
+    if seed is not None:
+      numpy.random.seed(seed)
+    if self.num_features != 1:
+      raise NotImplementedError("Generation for multivariate state space models"
+                                " is not currently implemented.")
+    if add_observation_noise is None:
+      add_observation_noise = self._configuration.use_observation_noise
+    if model_parameters is None:
+      model_parameters = {}
+    transitions = ops.convert_to_tensor(
+        self.get_state_transition(), dtype=self.dtype).eval(
+            feed_dict=model_parameters)
+    noise_transform = ops.convert_to_tensor(self.get_noise_transform()).eval(
+        feed_dict=model_parameters)
+
+    noise_dimension = noise_transform.shape[1]
+    get_passed_or_trained_value = model_utils.parameter_switch(model_parameters)
+    transition_var = numpy.diag(get_passed_or_trained_value(
+        self.state_transition_noise_covariance))
+    transition_std = numpy.sqrt(transition_var)
+    if add_observation_noise:
+      observation_var = get_passed_or_trained_value(
+          self._observation_noise_covariance)[0][0]
+      observation_std = numpy.sqrt(observation_var)
+    initial_state = get_passed_or_trained_value(self.prior_state_mean)
+    current_state = numpy.tile(numpy.expand_dims(initial_state, 0),
+                               [number_of_series, 1])
+    observations = numpy.zeros([number_of_series, series_length])
+    observation_models = self.get_broadcasted_observation_model(
+        times=math_ops.range(series_length)).eval(feed_dict=model_parameters)
+    for timestep, observation_model in enumerate(observation_models):
+      current_state = numpy.dot(current_state, transitions.T)
+      current_state += numpy.dot(
+          numpy.random.normal(
+              loc=numpy.zeros([number_of_series, noise_dimension]),
+              scale=numpy.tile(numpy.expand_dims(transition_std, 0),
+                               [number_of_series, 1])),
+          noise_transform.T)
+      observation_mean = numpy.dot(current_state, observation_model[0].T)
+      if add_observation_noise:
+        observations[:, timestep] = numpy.random.normal(loc=observation_mean,
+                                                        scale=observation_std)
+      else:
+        observations[:, timestep] = observation_mean
+    observations = numpy.expand_dims(observations, -1)
+    times = numpy.tile(
+        numpy.expand_dims(numpy.arange(observations.shape[1]), 0),
+        [observations.shape[0], 1])
+    return {TrainEvalFeatures.TIMES: times,
+            TrainEvalFeatures.VALUES: observations}
+
+  @abc.abstractmethod
+  def get_state_transition(self):
+    """Specifies the state transition model to use.
+
+    Returns:
+      A [state dimension x state dimension] Tensor specifying how states
+      transition from one timestep to the next.
+    """
+    pass
+
+  @abc.abstractmethod
+  def get_noise_transform(self):
+    """Specifies the noise transition model to use.
+
+    Returns:
+      A [state dimension x state noise dimension] Tensor specifying how noise
+      (generated with shape [state noise dimension]) affects the model's state.
+    """
+    pass
+
+  @abc.abstractmethod
+  def get_observation_model(self, times):
+    """Specifies the observation model to use.
+
+    Args:
+      times: A [batch dimension] int32 Tensor with times for each part of the
+          batch, on which the observation model can depend.
+    Returns:
+      This function, when overridden, has three possible return values:
+        - A [state dimension] Tensor with a static, univariate observation
+          model.
+        - A [self.num_features x state dimension] static, multivariate model.
+        - A [batch dimension x self.num_features x state dimension] observation
+          model, which may depend on `times`.
+      See get_broadcasted_observation_model for details of the broadcasting.
+    """
+    pass
+
+  def get_broadcasted_observation_model(self, times):
+    """Broadcast this model's observation model if necessary.
+
+    The model can define a univariate observation model which will be broadcast
+    over both self.num_features and the batch dimension of `times`.
+
+    The model can define a multi-variate observation model which does not depend
+    on `times`, and it will be broadcast over the batch dimension of `times`.
+
+    Finally, the model can define a multi-variate observation model with a batch
+    dimension, which will not be broadcast.
+
+    Args:
+      times: A [batch dimension] int32 Tensor with times for each part of the
+          batch, on which the observation model can depend.
+    Returns:
+      A [batch dimension x self.num_features x state dimension] Tensor
+      specifying the observation model to use for each time in `times` and each
+      feature.
+    """
+    unbroadcasted_model = ops.convert_to_tensor(
+        self.get_observation_model(times), dtype=self.dtype)
+    unbroadcasted_shape = (unbroadcasted_model.get_shape()
+                           .with_rank_at_least(1).with_rank_at_most(3))
+    if unbroadcasted_shape.ndims is None:
+      # Pass through fully undefined shapes, but make sure they're rank 3 at
+      # graph eval time
+      assert_op = control_flow_ops.Assert(
+          math_ops.equal(array_ops.rank(unbroadcasted_model), 3),
+          [array_ops.shape(unbroadcasted_model)])
+      with ops.control_dependencies([assert_op]):
+        return array_ops.identity(unbroadcasted_model)
+    if unbroadcasted_shape.ndims == 1:
+      # Unbroadcasted shape [state dimension]
+      broadcasted_model = array_ops.tile(
+          array_ops.reshape(tensor=unbroadcasted_model, shape=[1, 1, -1]),
+          [array_ops.shape(times)[0], self.num_features, 1])
+    elif unbroadcasted_shape.ndims == 2:
+      # Unbroadcasted shape [num features x state dimension]
+      broadcasted_model = array_ops.tile(
+          array_ops.expand_dims(unbroadcasted_model, dim=0),
+          [array_ops.shape(times)[0], 1, 1])
+    elif unbroadcasted_shape.ndims == 3:
+      broadcasted_model = unbroadcasted_model
+    broadcasted_model.get_shape().assert_has_rank(3)
+    return broadcasted_model
+
+  def get_state_transition_noise_covariance(
+      self, minimum_initial_variance=1e-5):
+    state_noise_transform = ops.convert_to_tensor(
+        self.get_noise_transform(), dtype=self.dtype)
+    state_noise_dimension = state_noise_transform.get_shape()[1].value
+    if self._input_statistics is not None:
+      feature_variance = self._input_statistics.series_start_moments.variance
+      initial_transition_noise_scale = math_ops.log(
+          gen_math_ops.maximum(
+              math_ops.reduce_mean(feature_variance) / math_ops.cast(
+                  self._input_statistics.total_observation_count, self.dtype),
+              minimum_initial_variance))
+    else:
+      initial_transition_noise_scale = 0.
+    # Generally high transition noise is undesirable; we want to set it quite
+    # low to start so that we don't need too much training to get to good
+    # solutions (i.e. with confident predictions into the future if possible),
+    # but not so low that training can't yield a high transition noise if the
+    # data demands it.
+    initial_transition_noise_scale -= (
+        self._observation_transition_tradeoff_log_scale)
+    return math_utils.variable_covariance_matrix(
+        state_noise_dimension, "state_transition_noise",
+        dtype=self.dtype,
+        initial_overall_scale_log=initial_transition_noise_scale)
+
+  def get_observation_noise_covariance(self, minimum_initial_variance=1e-5):
+    if self._configuration.use_observation_noise:
+      if self._input_statistics is not None:
+        # Get variance across the first few values in each batch for each
+        # feature, for an initial observation noise (over-)estimate.
+        feature_variance = self._input_statistics.series_start_moments.variance
+      else:
+        feature_variance = None
+      if feature_variance is not None:
+        feature_variance = gen_math_ops.maximum(feature_variance,
+                                                minimum_initial_variance)
+      return math_utils.variable_covariance_matrix(
+          size=self.num_features,
+          dtype=self.dtype,
+          name="observation_noise_covariance",
+          initial_diagonal_values=feature_variance,
+          initial_overall_scale_log=(
+              self._observation_transition_tradeoff_log_scale))
+    else:
+      return array_ops.zeros(
+          shape=[self.num_features, self.num_features],
+          name="observation_noise_covariance",
+          dtype=self.dtype)
+
+  def get_start_state(self):
+    """Defines and returns a non-batched prior state and covariance."""
+    # TODO(allenl,vitalyk): Add an option for non-Gaussian priors once extended
+    # Kalman filtering is implemented (ideally any Distribution object).
+    if self._input_statistics is not None:
+      start_time = self._input_statistics.start_time
+    else:
+      start_time = array_ops.zeros([], dtype=dtypes.int64)
+    return (self.prior_state_mean,
+            self.prior_state_var,
+            start_time - 1)
+
+  def get_features_for_timesteps(self, timesteps):
+    """Get features for a batch of timesteps. Default to no features."""
+    return array_ops.zeros([array_ops.shape(timesteps)[0], 0], dtype=self.dtype)
+
+
+class StateSpaceEnsemble(StateSpaceModel):
+  """Base class for combinations of state space models."""
+
+  def __init__(self, ensemble_members, configuration):
+    """Initialize the ensemble by specifying its members.
+
+    Args:
+      ensemble_members: A list of StateSpaceModel objects which will be included
+          in this ensemble.
+      configuration: A StateSpaceModelConfiguration object.
+    """
+    self._ensemble_members = ensemble_members
+    super(StateSpaceEnsemble, self).__init__(configuration=configuration)
+
+  def _set_input_statistics(self, input_statistics):
+    super(StateSpaceEnsemble, self)._set_input_statistics(input_statistics)
+    for member in self._ensemble_members:
+      member._set_input_statistics(input_statistics)  # pylint: disable=protected-access
+
+  def _loss_additions(self, times, values, mode):
+    # Allow sub-models to regularize
+    return (super(StateSpaceEnsemble, self)._loss_additions(
+        times, values, mode) + math_ops.add_n([
+            member._loss_additions(times, values, mode)  # pylint: disable=protected-access
+            for member in self._ensemble_members
+        ]))
+
+  def _compute_blocked(self, member_fn, name):
+    with variable_scope.variable_scope(self._variable_scope):
+      return math_utils.block_diagonal(
+          [member_fn(member)
+           for member in self._ensemble_members],
+          dtype=self.dtype,
+          name=name)
+
+  def transition_to_powers(self, powers):
+    return self._compute_blocked(
+        member_fn=lambda member: member.transition_to_powers(powers),
+        name="ensemble_transition_to_powers")
+
+  def _define_parameters(self, observation_transition_tradeoff_log=None):
+    with variable_scope.variable_scope(self._variable_scope):
+      if observation_transition_tradeoff_log is None:
+        # Define the tradeoff parameter between observation and transition noise
+        # once for the whole ensemble, and pass it down to members.
+        observation_transition_tradeoff_log = (
+            self._variable_observation_transition_tradeoff_log())
+      for member in self._ensemble_members:
+        member._define_parameters(observation_transition_tradeoff_log=(  # pylint: disable=protected-access
+            observation_transition_tradeoff_log))
+      super(StateSpaceEnsemble, self)._define_parameters(
+          observation_transition_tradeoff_log
+          =observation_transition_tradeoff_log)
+
+  def random_model_parameters(self, seed=None):
+    param_union = {}
+    for i, member in enumerate(self._ensemble_members):
+      member_params = member.random_model_parameters(
+          seed=seed + i if seed else None)
+      param_union.update(member_params)
+    param_union.update(
+        super(StateSpaceEnsemble, self).random_model_parameters(seed=seed))
+    return param_union
+
+  def get_prior_mean(self):
+    return array_ops.concat(
+        values=[member.get_prior_mean() for member in self._ensemble_members],
+        axis=0,
+        name="ensemble_prior_state_mean")
+
+  def get_state_transition(self):
+    return self._compute_blocked(
+        member_fn=
+        lambda member: member.get_state_transition(),
+        name="ensemble_state_transition")
+
+  def get_noise_transform(self):
+    return self._compute_blocked(
+        member_fn=
+        lambda member: member.get_noise_transform(),
+        name="ensemble_noise_transform")
+
+  def get_observation_model(self, times):
+    raise NotImplementedError("No un-broadcasted observation model defined for"
+                              " ensembles.")
+
+  def get_broadcasted_observation_model(self, times):
+    """Computes a combined observation model based on member models.
+
+    The effect is that predicted observations from each model are summed.
+
+    Args:
+      times: A [batch dimension] int32 Tensor with times for each part of the
+          batch, on which member observation models can depend.
+    Returns:
+      A [batch dimension x num features x combined state dimension] Tensor with
+      the combined observation model.
+    """
+    member_observation_models = [
+        ops.convert_to_tensor(
+            member.get_broadcasted_observation_model(times), dtype=self.dtype)
+        for member in self._ensemble_members
+    ]
+    return array_ops.concat(values=member_observation_models, axis=2)
+
+
+class StateSpaceIndependentEnsemble(StateSpaceEnsemble):
+  """Implements ensembles of independent state space models.
+
+  Useful for fitting multiple independent state space models together while
+  keeping their specifications decoupled. The "ensemble" is simply a state space
+  model with the observation models of its members concatenated, and the
+  transition matrices and noise transforms stacked in block-diagonal
+  matrices. This means that the dimensionality of the ensemble's state is the
+  sum of those of its components, which can lead to slow and memory-intensive
+  training and inference as the posterior (shape [state dimension x state
+  dimension]) gets large.
+
+  Each individual model j's state at time t is defined by:
+
+  state[t, j] = StateTransition[j] * state[t-1, j]
+      + NoiseTransform[j] * StateNoise[t, j]
+  StateNoise[t, j] ~ Gaussian(0, StateNoiseCovariance[j])
+
+  and the ensemble observation model is:
+
+  observation[t] = Sum { ObservationModel[j] * state[t, j] }
+      + ObservationNoise[t]
+  ObservationNoise[t] ~ Gaussian(0, ObservationNoiseCovariance)
+  """
+
+  def transition_power_noise_accumulator(self, num_steps):
+    return self._compute_blocked(
+        member_fn=lambda m: m.transition_power_noise_accumulator(num_steps),
+        name="ensemble_power_noise_accumulator")
+
+  def get_prior_covariance(self):
+    """Construct the ensemble prior covariance based on component models."""
+    return self._compute_blocked(
+        member_fn=
+        lambda member: member.get_prior_covariance(),
+        name="ensemble_prior_state_covariance")
+
+  def get_state_transition_noise_covariance(self):
+    """Construct the ensemble transition noise covariance from components."""
+    return self._compute_blocked(
+        member_fn=
+        lambda member: member.state_transition_noise_covariance,
+        name="ensemble_state_transition_noise")
+
+
+# TODO(allenl): It would be nice to have replicated feature models which are
+# identical batched together to reduce the graph size.
+# TODO(allenl): Support for sharing M independent models across N features, with
+# N > M.
+# TODO(allenl): Stack component prior covariances while allowing cross-model
+# correlations to be learned (currently a full covariance prior is learned, but
+# custom component model covariances are not used).
+class StateSpaceCorrelatedFeaturesEnsemble(StateSpaceEnsemble):
+  """An correlated ensemble where each model represents a feature.
+
+  Unlike `StateSpaceIndependentEnsemble`, a full state transition noise
+  covariance matrix is learned for this ensemble; the models are not assumed to
+  be independent. Rather than concatenating observation models (i.e. summing the
+  contributions of each model to each feature),
+  StateSpaceCorrelatedFeaturesEnsemble stacks observation models diagonally,
+  meaning that each model corresponds to one feature of the series.
+
+  Behaves like (and is) a single state space model where:
+
+  StateTransition = Diag(StateTransition[j] for models j)
+  ObservationModel = Diag(ObservationModel[j] for models j)
+
+  Note that each ObservationModel[j] is a [1 x S_j] matrix (S_j being the state
+  dimension of model j), i.e. a univariate model. The combined model is
+  multivariate, the number of features of the series being equal to the number
+  of component models in the ensemble.
+  """
+
+  def __init__(self, ensemble_members, configuration):
+    """Specify the ensemble's configuration and component models.
+
+    Args:
+      ensemble_members: A list of `StateSpaceModel` objects, with length equal
+        to `configuration.num_features`. Each of these models, which must be
+        univariate, corresponds to a single feature of the time series.
+      configuration: A StateSpaceModelConfiguration object.
+    Raises:
+      ValueError: If the length of `ensemble_members` does not equal the number
+        of features in the series, or any component is not univariate.
+    """
+    if len(ensemble_members) != configuration.num_features:
+      raise ValueError(
+          "The number of members in a StateSpaceCorrelatedFeaturesEnsemble "
+          "must equal the number of features in the time series.")
+    for member in ensemble_members:
+      if member.num_features != 1:
+        raise ValueError(
+            "StateSpaceCorrelatedFeaturesEnsemble components must be "
+            "univariate.")
+    super(StateSpaceCorrelatedFeaturesEnsemble, self).__init__(
+        ensemble_members=ensemble_members, configuration=configuration)
+
+  def transition_power_noise_accumulator(self, num_steps):
+    """Use a noise accumulator special case when possible."""
+    if len(self._ensemble_members) == 1:
+      # If this is a univariate series, we should use the special casing built
+      # into the single component model.
+      return self._ensemble_members[0].transition_power_noise_accumulator(
+          num_steps=num_steps)
+    # If we have multiple features, and therefore multiple models, we have
+    # introduced correlations which make noise accumulation more
+    # complicated. Here we fall back to the general case, since we can't just
+    # aggregate member special cases.
+    return super(StateSpaceCorrelatedFeaturesEnsemble,
+                 self).transition_power_noise_accumulator(num_steps=num_steps)
+
+  def get_broadcasted_observation_model(self, times):
+    """Stack observation models diagonally."""
+    def _member_observation_model(member):
+      return ops.convert_to_tensor(
+          member.get_broadcasted_observation_model(times), dtype=self.dtype)
+    return self._compute_blocked(member_fn=_member_observation_model,
+                                 name="feature_ensemble_observation_model")
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py
new file mode 100644
index 0000000000..7c8f81ec51
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py
@@ -0,0 +1,758 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for state space model infrastructure."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+
+import numpy
+
+from tensorflow.contrib import layers
+
+from tensorflow.contrib.timeseries.python.timeseries import estimators
+from tensorflow.contrib.timeseries.python.timeseries import feature_keys
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries import saved_model_utils
+from tensorflow.contrib.timeseries.python.timeseries import state_management
+from tensorflow.contrib.timeseries.python.timeseries import test_utils
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import random_seed
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.saved_model import loader
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.training import coordinator as coordinator_lib
+from tensorflow.python.training import gradient_descent
+from tensorflow.python.training import queue_runner_impl
+
+
+class RandomStateSpaceModel(state_space_model.StateSpaceModel):
+
+  def __init__(self,
+               state_dimension,
+               state_noise_dimension,
+               configuration=state_space_model.StateSpaceModelConfiguration()):
+    self.transition = numpy.random.normal(
+        size=[state_dimension, state_dimension]).astype(
+            configuration.dtype.as_numpy_dtype)
+    self.noise_transform = numpy.random.normal(
+        size=(state_dimension, state_noise_dimension)).astype(
+            configuration.dtype.as_numpy_dtype)
+    # Test batch broadcasting
+    self.observation_model = numpy.random.normal(
+        size=(configuration.num_features, state_dimension)).astype(
+            configuration.dtype.as_numpy_dtype)
+    super(RandomStateSpaceModel, self).__init__(
+        configuration=configuration._replace(
+            covariance_prior_fn=lambda _: 0.))
+
+  def get_state_transition(self):
+    return self.transition
+
+  def get_noise_transform(self):
+    return self.noise_transform
+
+  def get_observation_model(self, times):
+    return self.observation_model
+
+
+class ConstructionTests(test.TestCase):
+
+  def test_initialize_graph_error(self):
+    with self.assertRaisesRegexp(ValueError, "initialize_graph"):
+      model = RandomStateSpaceModel(2, 2)
+      outputs = model.define_loss(
+          features={
+              feature_keys.TrainEvalFeatures.TIMES:
+                  constant_op.constant([[1, 2]]),
+              feature_keys.TrainEvalFeatures.VALUES:
+                  constant_op.constant([[[1.], [2.]]])
+          },
+          mode=estimator_lib.ModeKeys.TRAIN)
+      initializer = variables.global_variables_initializer()
+      with self.test_session() as sess:
+        sess.run([initializer])
+        outputs.loss.eval()
+
+  def test_initialize_graph_state_manager_error(self):
+    with self.assertRaisesRegexp(ValueError, "initialize_graph"):
+      model = RandomStateSpaceModel(2, 2)
+      state_manager = state_management.ChainingStateManager()
+      outputs = state_manager.define_loss(
+          model=model,
+          features={
+              feature_keys.TrainEvalFeatures.TIMES:
+                  constant_op.constant([[1, 2]]),
+              feature_keys.TrainEvalFeatures.VALUES:
+                  constant_op.constant([[[1.], [2.]]])
+          },
+          mode=estimator_lib.ModeKeys.TRAIN)
+      initializer = variables.global_variables_initializer()
+      with self.test_session() as sess:
+        sess.run([initializer])
+        outputs.loss.eval()
+
+
+class GapTests(test.TestCase):
+
+  def _gap_test_template(self, times, values):
+    random_model = RandomStateSpaceModel(
+        state_dimension=1, state_noise_dimension=1,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            num_features=1))
+    random_model.initialize_graph()
+    input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader({
+            feature_keys.TrainEvalFeatures.TIMES: times,
+            feature_keys.TrainEvalFeatures.VALUES: values
+        }))
+    features, _ = input_fn()
+    times = features[feature_keys.TrainEvalFeatures.TIMES]
+    values = features[feature_keys.TrainEvalFeatures.VALUES]
+    model_outputs = random_model.get_batch_loss(
+        features={
+            feature_keys.TrainEvalFeatures.TIMES: times,
+            feature_keys.TrainEvalFeatures.VALUES: values
+        },
+        mode=None,
+        state=math_utils.replicate_state(
+            start_state=random_model.get_start_state(),
+            batch_size=array_ops.shape(times)[0]))
+    with self.test_session() as session:
+      variables.global_variables_initializer().run()
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      model_outputs.loss.eval()
+      coordinator.request_stop()
+      coordinator.join()
+
+  def test_start_gap(self):
+    self._gap_test_template(times=[20, 21, 22], values=numpy.arange(3))
+
+  def test_mid_gap(self):
+    self._gap_test_template(times=[2, 60, 61], values=numpy.arange(3))
+
+  def test_end_gap(self):
+    self._gap_test_template(times=[2, 3, 73], values=numpy.arange(3))
+
+  def test_all_gaps(self):
+    self._gap_test_template(times=[2, 4, 8, 16, 32, 64, 128],
+                            values=numpy.arange(7))
+
+
+class StateSpaceEquivalenceTests(test.TestCase):
+
+  def test_savedmodel_state_override(self):
+    random_model = RandomStateSpaceModel(
+        state_dimension=5,
+        state_noise_dimension=4,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            exogenous_feature_columns=[layers.real_valued_column("exogenous")],
+            dtype=dtypes.float64, num_features=1))
+    estimator = estimators.StateSpaceRegressor(
+        model=random_model,
+        optimizer=gradient_descent.GradientDescentOptimizer(0.1))
+    combined_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader({
+            feature_keys.FilteringFeatures.TIMES: [1, 2, 3, 4],
+            feature_keys.FilteringFeatures.VALUES: [1., 2., 3., 4.],
+            "exogenous": [-1., -2., -3., -4.]
+        }))
+    estimator.train(combined_input_fn, steps=1)
+    export_location = estimator.export_savedmodel(
+        self.get_temp_dir(),
+        estimator.build_raw_serving_input_receiver_fn(
+            exogenous_features={
+                "exogenous": numpy.zeros((0, 0), dtype=numpy.float32)}))
+    with ops.Graph().as_default() as graph:
+      random_model.initialize_graph()
+      with self.test_session(graph=graph) as session:
+        variables.global_variables_initializer().run()
+        evaled_start_state = session.run(random_model.get_start_state())
+    evaled_start_state = [
+        state_element[None, ...] for state_element in evaled_start_state]
+    with ops.Graph().as_default() as graph:
+      with self.test_session(graph=graph) as session:
+        signatures = loader.load(
+            session, [tag_constants.SERVING], export_location)
+        first_split_filtering = saved_model_utils.filter_continuation(
+            continue_from={
+                feature_keys.FilteringResults.STATE_TUPLE: evaled_start_state},
+            signatures=signatures,
+            session=session,
+            features={
+                feature_keys.FilteringFeatures.TIMES: [1, 2],
+                feature_keys.FilteringFeatures.VALUES: [1., 2.],
+                "exogenous": [-1., -2.]})
+        second_split_filtering = saved_model_utils.filter_continuation(
+            continue_from=first_split_filtering,
+            signatures=signatures,
+            session=session,
+            features={
+                feature_keys.FilteringFeatures.TIMES: [3, 4],
+                feature_keys.FilteringFeatures.VALUES: [3., 4.],
+                "exogenous": [-3., -4.]
+            })
+        combined_filtering = saved_model_utils.filter_continuation(
+            continue_from={
+                feature_keys.FilteringResults.STATE_TUPLE: evaled_start_state},
+            signatures=signatures,
+            session=session,
+            features={
+                feature_keys.FilteringFeatures.TIMES: [1, 2, 3, 4],
+                feature_keys.FilteringFeatures.VALUES: [1., 2., 3., 4.],
+                "exogenous": [-1., -2., -3., -4.]
+            })
+        split_predict = saved_model_utils.predict_continuation(
+            continue_from=second_split_filtering,
+            signatures=signatures,
+            session=session,
+            steps=1,
+            exogenous_features={
+                "exogenous": [[-5.]]})
+        combined_predict = saved_model_utils.predict_continuation(
+            continue_from=combined_filtering,
+            signatures=signatures,
+            session=session,
+            steps=1,
+            exogenous_features={
+                "exogenous": [[-5.]]})
+    for state_key, combined_state_value in combined_filtering.items():
+      if state_key == feature_keys.FilteringResults.TIMES:
+        continue
+      self.assertAllClose(
+          combined_state_value, second_split_filtering[state_key])
+    for prediction_key, combined_value in combined_predict.items():
+      self.assertAllClose(combined_value, split_predict[prediction_key])
+
+  def _equivalent_to_single_model_test_template(self, model_generator):
+    with self.test_session() as session:
+      random_model = RandomStateSpaceModel(
+          state_dimension=5,
+          state_noise_dimension=4,
+          configuration=state_space_model.StateSpaceModelConfiguration(
+              dtype=dtypes.float64, num_features=1))
+      random_model.initialize_graph()
+      series_length = 10
+      model_data = random_model.generate(
+          number_of_series=1, series_length=series_length,
+          model_parameters=random_model.random_model_parameters())
+      input_fn = input_pipeline.WholeDatasetInputFn(
+          input_pipeline.NumpyReader(model_data))
+      features, _ = input_fn()
+      model_outputs = random_model.get_batch_loss(
+          features=features,
+          mode=None,
+          state=math_utils.replicate_state(
+              start_state=random_model.get_start_state(),
+              batch_size=array_ops.shape(
+                  features[feature_keys.TrainEvalFeatures.TIMES])[0]))
+      variables.global_variables_initializer().run()
+      compare_outputs_evaled_fn = model_generator(
+          random_model, model_data)
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      compare_outputs_evaled = compare_outputs_evaled_fn(session)
+      model_outputs_evaled = session.run(
+          (model_outputs.end_state, model_outputs.predictions))
+      coordinator.request_stop()
+      coordinator.join()
+      model_posteriors, model_predictions = model_outputs_evaled
+      (_, compare_posteriors,
+       compare_predictions) = compare_outputs_evaled
+      (model_posterior_mean, model_posterior_var,
+       model_from_time) = model_posteriors
+      (compare_posterior_mean, compare_posterior_var,
+       compare_from_time) = compare_posteriors
+      self.assertAllClose(model_posterior_mean, compare_posterior_mean[0])
+      self.assertAllClose(model_posterior_var, compare_posterior_var[0])
+      self.assertAllClose(model_from_time, compare_from_time)
+      self.assertEqual(sorted(model_predictions.keys()),
+                       sorted(compare_predictions.keys()))
+      for prediction_name in model_predictions:
+        if prediction_name == "loss":
+          # Chunking means that losses will be different; skip testing them.
+          continue
+        # Compare the last chunk to their corresponding un-chunked model
+        # predictions
+        last_prediction_chunk = compare_predictions[prediction_name][-1]
+        comparison_values = last_prediction_chunk.shape[0]
+        model_prediction = (
+            model_predictions[prediction_name][0, -comparison_values:])
+        self.assertAllClose(model_prediction,
+                            last_prediction_chunk)
+
+  def _model_equivalent_to_chained_model_test_template(self, chunk_size):
+    def chained_model_outputs(original_model, data):
+      input_fn = test_utils.AllWindowInputFn(
+          input_pipeline.NumpyReader(data), window_size=chunk_size)
+      state_manager = state_management.ChainingStateManager(
+          state_saving_interval=1)
+      features, _ = input_fn()
+      state_manager.initialize_graph(original_model)
+      model_outputs = state_manager.define_loss(
+          model=original_model,
+          features=features,
+          mode=estimator_lib.ModeKeys.TRAIN)
+      def _eval_outputs(session):
+        for _ in range(50):
+          # Warm up saved state
+          model_outputs.loss.eval()
+        (posterior_mean, posterior_var,
+         priors_from_time) = model_outputs.end_state
+        posteriors = ((posterior_mean,), (posterior_var,), priors_from_time)
+        outputs = (model_outputs.loss, posteriors,
+                   model_outputs.predictions)
+        chunked_outputs_evaled = session.run(outputs)
+        return chunked_outputs_evaled
+      return _eval_outputs
+    self._equivalent_to_single_model_test_template(chained_model_outputs)
+
+  def test_model_equivalent_to_chained_model_chunk_size_one(self):
+    numpy.random.seed(2)
+    random_seed.set_random_seed(3)
+    self._model_equivalent_to_chained_model_test_template(1)
+
+  def test_model_equivalent_to_chained_model_chunk_size_five(self):
+    numpy.random.seed(4)
+    random_seed.set_random_seed(5)
+    self._model_equivalent_to_chained_model_test_template(5)
+
+
+class PredictionTests(test.TestCase):
+
+  def _check_predictions(
+      self, predicted_mean, predicted_covariance, window_size):
+    self.assertAllEqual(predicted_covariance.shape,
+                        [1,   # batch
+                         window_size,
+                         1,   # num features
+                         1])  # num features
+    self.assertAllEqual(predicted_mean.shape,
+                        [1,   # batch
+                         window_size,
+                         1])  # num features
+    for position in range(window_size - 2):
+      self.assertGreater(predicted_covariance[0, position + 2, 0, 0],
+                         predicted_covariance[0, position, 0, 0])
+
+  def test_predictions_direct(self):
+    dtype = dtypes.float64
+    with variable_scope.variable_scope(dtype.name):
+      random_model = RandomStateSpaceModel(
+          state_dimension=5, state_noise_dimension=4,
+          configuration=state_space_model.StateSpaceModelConfiguration(
+              dtype=dtype, num_features=1))
+      random_model.initialize_graph()
+      prediction_dict = random_model.predict(features={
+          feature_keys.PredictionFeatures.TIMES: [[1, 3, 5, 6]],
+          feature_keys.PredictionFeatures.STATE_TUPLE:
+              math_utils.replicate_state(
+                  start_state=random_model.get_start_state(), batch_size=1)
+      })
+      with self.test_session():
+        variables.global_variables_initializer().run()
+        predicted_mean = prediction_dict["mean"].eval()
+        predicted_covariance = prediction_dict["covariance"].eval()
+      self._check_predictions(predicted_mean, predicted_covariance,
+                              window_size=4)
+
+  def test_predictions_after_loss(self):
+    dtype = dtypes.float32
+    with variable_scope.variable_scope(dtype.name):
+      random_model = RandomStateSpaceModel(
+          state_dimension=5, state_noise_dimension=4,
+          configuration=state_space_model.StateSpaceModelConfiguration(
+              dtype=dtype, num_features=1))
+      features = {
+          feature_keys.TrainEvalFeatures.TIMES: [[1, 2, 3, 4]],
+          feature_keys.TrainEvalFeatures.VALUES:
+              array_ops.ones([1, 4, 1], dtype=dtype)
+      }
+      passthrough = state_management.PassthroughStateManager()
+      random_model.initialize_graph()
+      passthrough.initialize_graph(random_model)
+      model_outputs = passthrough.define_loss(
+          model=random_model,
+          features=features,
+          mode=estimator_lib.ModeKeys.EVAL)
+      predictions = random_model.predict({
+          feature_keys.PredictionFeatures.TIMES: [[5, 7, 8]],
+          feature_keys.PredictionFeatures.STATE_TUPLE: model_outputs.end_state
+      })
+      with self.test_session():
+        variables.global_variables_initializer().run()
+        predicted_mean = predictions["mean"].eval()
+        predicted_covariance = predictions["covariance"].eval()
+      self._check_predictions(predicted_mean, predicted_covariance,
+                              window_size=3)
+
+
+class ExogenousTests(test.TestCase):
+
+  def test_noise_increasing(self):
+    for dtype in [dtypes.float32, dtypes.float64]:
+      with variable_scope.variable_scope(dtype.name):
+        random_model = RandomStateSpaceModel(
+            state_dimension=5, state_noise_dimension=4,
+            configuration=state_space_model.StateSpaceModelConfiguration(
+                dtype=dtype, num_features=1))
+        original_covariance = array_ops.diag(array_ops.ones(shape=[5]))
+        _, new_covariance, _ = random_model._exogenous_noise_increasing(
+            current_times=[[1]],
+            exogenous_values=[[5.]],
+            state=[
+                array_ops.ones(shape=[1, 5]), original_covariance[None], [0]
+            ])
+        with self.test_session() as session:
+          variables.global_variables_initializer().run()
+          evaled_new_covariance, evaled_original_covariance = session.run(
+              [new_covariance[0], original_covariance])
+          new_variances = numpy.diag(evaled_new_covariance)
+          original_variances = numpy.diag(evaled_original_covariance)
+          for i in range(5):
+            self.assertGreater(new_variances[i], original_variances[i])
+
+  def test_noise_decreasing(self):
+    for dtype in [dtypes.float32, dtypes.float64]:
+      with variable_scope.variable_scope(dtype.name):
+        random_model = RandomStateSpaceModel(
+            state_dimension=5, state_noise_dimension=4,
+            configuration=state_space_model.StateSpaceModelConfiguration(
+                dtype=dtype, num_features=1))
+        random_model.initialize_graph()
+        original_covariance = array_ops.diag(
+            array_ops.ones(shape=[5], dtype=dtype))
+        _, new_covariance, _ = random_model._exogenous_noise_decreasing(
+            current_times=[[1]],
+            exogenous_values=constant_op.constant([[-2.]], dtype=dtype),
+            state=[
+                -array_ops.ones(shape=[1, 5], dtype=dtype),
+                original_covariance[None], [0]
+            ])
+        with self.test_session() as session:
+          variables.global_variables_initializer().run()
+          evaled_new_covariance, evaled_original_covariance = session.run(
+              [new_covariance[0], original_covariance])
+          new_variances = numpy.diag(evaled_new_covariance)
+          original_variances = numpy.diag(evaled_original_covariance)
+          for i in range(5):
+            self.assertLess(new_variances[i], original_variances[i])
+
+
+class StubStateSpaceModel(state_space_model.StateSpaceModel):
+
+  def __init__(self,
+               transition,
+               state_noise_dimension,
+               configuration=state_space_model.StateSpaceModelConfiguration()):
+    self.transition = transition
+    self.noise_transform = numpy.random.normal(
+        size=(transition.shape[0], state_noise_dimension)).astype(numpy.float32)
+    # Test feature + batch broadcasting
+    self.observation_model = numpy.random.normal(
+        size=(transition.shape[0])).astype(numpy.float32)
+    super(StubStateSpaceModel, self).__init__(
+        configuration=configuration)
+
+  def get_state_transition(self):
+    return self.transition
+
+  def get_noise_transform(self):
+    return self.noise_transform
+
+  def get_observation_model(self, times):
+    return self.observation_model
+
+
+GeneratedModel = collections.namedtuple(
+    "GeneratedModel", ["model", "data", "true_parameters"])
+
+
+class PosteriorTests(test.TestCase):
+
+  def _get_cycle_transition(self, period):
+    cycle_transition = numpy.zeros([period - 1, period - 1],
+                                   dtype=numpy.float32)
+    cycle_transition[0, :] = -1
+    cycle_transition[1:, :-1] = numpy.identity(period - 2)
+    return cycle_transition
+
+  _adder_transition = numpy.array([[1, 1],
+                                   [0, 1]], dtype=numpy.float32)
+
+  def _get_single_model(self):
+    numpy.random.seed(8)
+    stub_model = StubStateSpaceModel(
+        transition=self._get_cycle_transition(5), state_noise_dimension=0)
+    series_length = 1000
+    stub_model.initialize_graph()
+    true_params = stub_model.random_model_parameters()
+    data = stub_model.generate(
+        number_of_series=1, series_length=series_length,
+        model_parameters=true_params)
+    return GeneratedModel(
+        model=stub_model, data=data, true_parameters=true_params)
+
+  def test_exact_posterior_recovery_no_transition_noise(self):
+    with self.test_session() as session:
+      stub_model, data, true_params = self._get_single_model()
+      input_fn = input_pipeline.WholeDatasetInputFn(
+          input_pipeline.NumpyReader(data))
+      features, _ = input_fn()
+      model_outputs = stub_model.get_batch_loss(
+          features=features,
+          mode=None,
+          state=math_utils.replicate_state(
+              start_state=stub_model.get_start_state(),
+              batch_size=array_ops.shape(
+                  features[feature_keys.TrainEvalFeatures.TIMES])[0]))
+      variables.global_variables_initializer().run()
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      posterior_mean, posterior_var, posterior_times = session.run(
+          # Feed the true model parameters so that this test doesn't depend on
+          # the generated parameters being close to the variable initializations
+          # (an alternative would be training steps to fit the noise values,
+          # which would be slow).
+          model_outputs.end_state, feed_dict=true_params)
+      coordinator.request_stop()
+      coordinator.join()
+
+      self.assertAllClose(numpy.zeros([1, 4, 4]), posterior_var,
+                          atol=1e-2)
+      self.assertAllClose(
+          numpy.dot(
+              numpy.linalg.matrix_power(
+                  stub_model.transition,
+                  data[feature_keys.TrainEvalFeatures.TIMES].shape[1]),
+              true_params[stub_model.prior_state_mean]),
+          posterior_mean[0],
+          rtol=1e-1)
+      self.assertAllClose(
+          math_utils.batch_end_time(
+              features[feature_keys.TrainEvalFeatures.TIMES]).eval(),
+          posterior_times)
+
+  def test_chained_exact_posterior_recovery_no_transition_noise(self):
+    with self.test_session() as session:
+      stub_model, data, true_params = self._get_single_model()
+      chunk_size = 10
+      input_fn = test_utils.AllWindowInputFn(
+          input_pipeline.NumpyReader(data), window_size=chunk_size)
+      features, _ = input_fn()
+      state_manager = state_management.ChainingStateManager(
+          state_saving_interval=1)
+      state_manager.initialize_graph(stub_model)
+      model_outputs = state_manager.define_loss(
+          model=stub_model,
+          features=features,
+          mode=estimator_lib.ModeKeys.TRAIN)
+      variables.global_variables_initializer().run()
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(session, coord=coordinator)
+      for _ in range(
+          data[feature_keys.TrainEvalFeatures.TIMES].shape[1] // chunk_size):
+        model_outputs.loss.eval()
+      posterior_mean, posterior_var, posterior_times = session.run(
+          model_outputs.end_state, feed_dict=true_params)
+      coordinator.request_stop()
+      coordinator.join()
+      self.assertAllClose(numpy.zeros([1, 4, 4]), posterior_var,
+                          atol=1e-2)
+      self.assertAllClose(
+          numpy.dot(
+              numpy.linalg.matrix_power(
+                  stub_model.transition,
+                  data[feature_keys.TrainEvalFeatures.TIMES].shape[1]),
+              true_params[stub_model.prior_state_mean]),
+          posterior_mean[0],
+          rtol=1e-1)
+      self.assertAllClose(data[feature_keys.TrainEvalFeatures.TIMES][:, -1],
+                          posterior_times)
+
+
+class TimeDependentStateSpaceModel(state_space_model.StateSpaceModel):
+  """A mostly trivial model which predicts values = times + 1."""
+
+  def __init__(self, static_unrolling_window_size_threshold=None):
+    super(TimeDependentStateSpaceModel, self).__init__(
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            use_observation_noise=False,
+            static_unrolling_window_size_threshold=
+            static_unrolling_window_size_threshold))
+
+  def get_state_transition(self):
+    return array_ops.ones(shape=[1, 1])
+
+  def get_noise_transform(self):
+    return array_ops.ones(shape=[1, 1])
+
+  def get_observation_model(self, times):
+    return array_ops.reshape(
+        tensor=math_ops.cast(times + 1, dtypes.float32), shape=[-1, 1, 1])
+
+  def make_priors(self):
+    return (ops.convert_to_tensor([1.]), ops.convert_to_tensor([[0.]]))
+
+
+class UnknownShapeModel(TimeDependentStateSpaceModel):
+
+  def get_observation_model(self, times):
+    parent_model = super(UnknownShapeModel, self).get_observation_model(times)
+    parent_model._shape = tensor_shape.unknown_shape()
+    assert parent_model.get_shape().ndims is None
+    return parent_model
+
+
+class TimeDependentTests(test.TestCase):
+
+  def _time_dependency_test_template(self, model_type):
+    """Test that a time-dependent observation model influences predictions."""
+    model = model_type()
+    estimator = estimators.StateSpaceRegressor(
+        model=model, optimizer=gradient_descent.GradientDescentOptimizer(0.1))
+    values = numpy.reshape([1., 2., 3., 4.],
+                           newshape=[1, 4, 1])
+    input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader({
+            feature_keys.TrainEvalFeatures.TIMES: [[0, 1, 2, 3]],
+            feature_keys.TrainEvalFeatures.VALUES: values
+        }))
+    estimator.train(input_fn=input_fn, max_steps=1)
+    predicted_values = estimator.evaluate(input_fn=input_fn, steps=1)["mean"]
+    # Throw out the first value so we don't test the prior
+    self.assertAllEqual(values[1:], predicted_values[1:])
+
+  def test_undefined_shape_time_dependency(self):
+    self._time_dependency_test_template(UnknownShapeModel)
+
+  def test_loop_unrolling(self):
+    """Tests running/restoring from a checkpoint with static unrolling."""
+    model = TimeDependentStateSpaceModel(
+        # Unroll during training, but not evaluation
+        static_unrolling_window_size_threshold=2)
+    estimator = estimators.StateSpaceRegressor(model=model)
+    times = numpy.arange(100)
+    values = numpy.arange(100)
+    dataset = {
+        feature_keys.TrainEvalFeatures.TIMES: times,
+        feature_keys.TrainEvalFeatures.VALUES: values
+    }
+    train_input_fn = input_pipeline.RandomWindowInputFn(
+        input_pipeline.NumpyReader(dataset), batch_size=16, window_size=2)
+    eval_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(dataset))
+    estimator.train(input_fn=train_input_fn, max_steps=1)
+    estimator.evaluate(input_fn=eval_input_fn, steps=1)
+
+
+class LevelOnlyModel(state_space_model.StateSpaceModel):
+
+  def get_state_transition(self):
+    return linalg_ops.eye(1, dtype=self.dtype)
+
+  def get_noise_transform(self):
+    return linalg_ops.eye(1, dtype=self.dtype)
+
+  def get_observation_model(self, times):
+    return [1]
+
+
+class MultivariateLevelModel(
+    state_space_model.StateSpaceCorrelatedFeaturesEnsemble):
+
+  def __init__(self, configuration):
+    univariate_component_configuration = configuration._replace(
+        num_features=1)
+    components = []
+    for feature in range(configuration.num_features):
+      with variable_scope.variable_scope("feature{}".format(feature)):
+        components.append(
+            LevelOnlyModel(configuration=univariate_component_configuration))
+    super(MultivariateLevelModel, self).__init__(
+        ensemble_members=components, configuration=configuration)
+
+
+class MultivariateTests(test.TestCase):
+
+  def test_multivariate(self):
+    dtype = dtypes.float32
+    num_features = 3
+    covariance = numpy.eye(num_features)
+    # A single off-diagonal has a non-zero value in the true transition
+    # noise covariance.
+    covariance[-1, 0] = 1.
+    covariance[0, -1] = 1.
+    dataset_size = 100
+    values = numpy.cumsum(
+        numpy.random.multivariate_normal(
+            mean=numpy.zeros(num_features),
+            cov=covariance,
+            size=dataset_size),
+        axis=0)
+    times = numpy.arange(dataset_size)
+    model = MultivariateLevelModel(
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            num_features=num_features,
+            dtype=dtype,
+            use_observation_noise=False,
+            transition_covariance_initial_log_scale_bias=5.))
+    estimator = estimators.StateSpaceRegressor(
+        model=model, optimizer=gradient_descent.GradientDescentOptimizer(0.1))
+    data = {
+        feature_keys.TrainEvalFeatures.TIMES: times,
+        feature_keys.TrainEvalFeatures.VALUES: values
+    }
+    train_input_fn = input_pipeline.RandomWindowInputFn(
+        input_pipeline.NumpyReader(data), batch_size=16, window_size=16)
+    estimator.train(input_fn=train_input_fn, steps=1)
+    for component in model._ensemble_members:
+      # Check that input statistics propagated to component models
+      self.assertTrue(component._input_statistics)
+
+  def test_ensemble_observation_noise(self):
+    model = MultivariateLevelModel(
+        configuration=state_space_model.StateSpaceModelConfiguration())
+    model.initialize_graph()
+    outputs = model.define_loss(
+        features={
+            feature_keys.TrainEvalFeatures.TIMES:
+                constant_op.constant([[1, 2]]),
+            feature_keys.TrainEvalFeatures.VALUES:
+                constant_op.constant([[[1.], [2.]]])
+        },
+        mode=estimator_lib.ModeKeys.TRAIN)
+    initializer = variables.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run([initializer])
+      outputs.loss.eval()
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
new file mode 100644
index 0000000000..26ab726591
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
@@ -0,0 +1,266 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implements a time series model with seasonality, trends, and transients."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import level_trend
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import periodic
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import varma
+
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.util import nest
+
+
+def _replicate_level_trend_models(multivariate_configuration,
+                                  univariate_configuration):
+  """Helper function to construct a multivariate level/trend component."""
+  with variable_scope.variable_scope("adder"):
+    # Construct a level and trend model for each feature, with correlated
+    # transition noise.
+    adder_features = []
+    for feature in range(multivariate_configuration.num_features):
+      with variable_scope.variable_scope("feature{}".format(feature)):
+        adder_features.append(level_trend.AdderStateSpaceModel(
+            configuration=univariate_configuration))
+    adder_part = state_space_model.StateSpaceCorrelatedFeaturesEnsemble(
+        ensemble_members=adder_features,
+        configuration=multivariate_configuration)
+  return adder_part
+
+
+class StructuralEnsemble(state_space_model.StateSpaceIndependentEnsemble):
+  r"""A structural state space time series model.
+
+  In the spirit of:
+
+  Scott, Steven L., and Hal R. Varian. "Predicting the present with bayesian
+    structural time series." International Journal of Mathematical Modelling and
+    Numerical Optimisation 5.1-2 (2014): 4-23.
+
+  Without the spike-and-slab prior, and with point estimates of parameters
+  instead of sampling.
+
+  The model includes level, trend, seasonality, and a transient moving average.
+
+  An observation at time t is drawn according to:
+    observation_t = level_t + seasonality_t + moving_average_t
+        + observation_noise_t
+    level_t = level_{t-1} + trend_{t-1} + level_noise_t
+    trend_t = trend_{t-1} + trend_noise_t
+    seasonality_t = -\sum_{n=1}^{num_seasons-1} seasonality_{t-n} +
+        seasonality_noise_t
+    moving_average_t = transient_t
+        + \sum_{j=1}^{moving_average_order} ma_coefs_j * transient_{t - j}
+
+  `observation_noise`, `level_noise`, `trend noise`, `seasonality_noise`, and
+  `transient` are (typically scalar) Gaussian random variables whose variance is
+  learned from data, and that variance is not time dependant in this
+  implementation. Level noise is optional due to its similarity with observation
+  noise in some cases. Seasonality is enforced by constraining a full cycle of
+  seasonal variables to have zero expectation, allowing seasonality to adapt
+  over time. The moving average coefficients `ma_coefs` are learned.
+
+  When presented with a multivariate series (more than one "feature", here
+  referring to endogenous features of the series), the model is replicated
+  across these features (one copy per feature of each periodic component, and
+  one level/trend model per feature), and correlations in transition noise are
+  learned between these replicated components (see
+  StateSpaceCorrelatedFeaturesEnsemble). This is in addition to the learned
+  correlations in observation noise between features. While this is often the
+  most expressive thing to do with multiple features, it does mean that the
+  model grows quite quickly, creating and computing with square matrices with
+  each dimension equal to num_features * (sum(periodicities) +
+  moving_average_order + 3), meaning that some operations are approximately
+  cubic in this value.
+  """
+  # TODO(allenl): Implement partial model replication/sharing for multivariate
+  # series (to save time/memory when the series presented can be modeled as a
+  # smaller number of underlying series). Likely just a modification of the
+  # observation model so that each feature of the series is a learned linear
+  # combination of the replicated models.
+
+  def __init__(self,
+               periodicities,
+               moving_average_order,
+               autoregressive_order,
+               use_level_noise=True,
+               configuration=state_space_model.StateSpaceModelConfiguration()):
+    """Initialize the Basic Structural Time Series model.
+
+    Args:
+      periodicities: Number of time steps for cyclic behavior. May be a list, in
+          which case one periodic component is created for each element.
+      moving_average_order: The number of moving average coefficients to use,
+          which also defines the number of steps after which transient
+          deviations revert to the mean defined by periodic and level/trend
+          components.
+      autoregressive_order: The number of steps back for autoregression.
+      use_level_noise: Whether to model the time series as having level
+          noise. See level_noise in the model description above.
+      configuration: A StateSpaceModelConfiguration object.
+    """
+    component_model_configuration = configuration._replace(
+        use_observation_noise=False)
+    univariate_component_model_configuration = (
+        component_model_configuration._replace(
+            num_features=1))
+
+    adder_part = _replicate_level_trend_models(
+        multivariate_configuration=component_model_configuration,
+        univariate_configuration=univariate_component_model_configuration)
+    with variable_scope.variable_scope("varma"):
+      varma_part = varma.VARMA(
+          autoregressive_order=autoregressive_order,
+          moving_average_order=moving_average_order,
+          configuration=component_model_configuration)
+
+    cycle_parts = []
+    periodicity_list = nest.flatten(periodicities)
+    for cycle_number, cycle_periodicity in enumerate(periodicity_list):
+      # For each specified periodicity, construct models for each feature with
+      # correlated noise.
+      with variable_scope.variable_scope("cycle{}".format(cycle_number)):
+        cycle_features = []
+        for feature in range(configuration.num_features):
+          with variable_scope.variable_scope("feature{}".format(feature)):
+            cycle_features.append(periodic.CycleStateSpaceModel(
+                periodicity=cycle_periodicity,
+                configuration=univariate_component_model_configuration))
+        cycle_parts.append(
+            state_space_model.StateSpaceCorrelatedFeaturesEnsemble(
+                ensemble_members=cycle_features,
+                configuration=component_model_configuration))
+
+    super(StructuralEnsemble, self).__init__(
+        ensemble_members=[adder_part, varma_part] + cycle_parts,
+        configuration=configuration)
+
+
+# TODO(allenl): Implement a multi-resolution moving average component to
+# decouple model size from the length of transient deviations.
+class MultiResolutionStructuralEnsemble(
+    state_space_model.StateSpaceIndependentEnsemble):
+  """A structural ensemble modeling arbitrary periods with a fixed model size.
+
+  See periodic.ResolutionCycleModel, which allows a fixed number of latent
+  values to cycle at multiple/variable resolutions, for more details on the
+  difference between MultiResolutionStructuralEnsemble and
+  StructuralEnsemble. With `cycle_num_latent_values` (controlling model size)
+  equal to `periodicities` (controlling the time over which these values
+  complete a full cycle), the models are
+  equivalent. MultiResolutionStructuralEnsemble allows `periodicities` to vary
+  while the model size remains fixed. Note that high `periodicities` without a
+  correspondingly high `cycle_num_latent_values` means that the modeled series
+  must have a relatively smooth periodic component.
+
+  Multiple features are handled the same way as in StructuralEnsemble (one
+  replication per feature, with correlations learned between the replicated
+  models). This strategy produces a very flexible model, but means that series
+  with many features may be slow to train.
+
+  Model size (the state dimension) is:
+    num_features * (sum(cycle_num_latent_values)
+      + max(moving_average_order + 1, autoregressive_order) + 2)
+  """
+
+  def __init__(self,
+               cycle_num_latent_values,
+               moving_average_order,
+               autoregressive_order,
+               periodicities,
+               use_level_noise=True,
+               configuration=state_space_model.StateSpaceModelConfiguration()):
+    """Initialize the multi-resolution structural ensemble.
+
+    Args:
+      cycle_num_latent_values: Controls the model size and the number of latent
+          values cycled between (but not the periods over which they cycle).
+          Reducing this parameter can save significant amounts of memory, but
+          the tradeoff is with resolution: cycling between a smaller number of
+          latent values means that only smoother functions can be modeled. For
+          multivariate series, may either be a scalar integer (in which case it
+          is applied to all periodic components) or a list with length matching
+          `periodicities`.
+      moving_average_order: The number of moving average coefficients to use,
+          which also defines the number of steps after which transient
+          deviations revert to the mean defined by periodic and level/trend
+          components. Adds to model size.
+      autoregressive_order: The number of steps back for
+          autoregression. Learning autoregressive coefficients typically
+          requires more steps and a smaller step size than other components.
+      periodicities: Same meaning as for StructuralEnsemble: number of steps for
+          cyclic behavior. Floating point and Tensor values are supported. May
+          be a list of values, in which case one component is created for each
+          periodicity. If `periodicities` is a list while
+          `cycle_num_latent_values` is a scalar, its value is broadcast to each
+          periodic component. Otherwise they should be lists of the same length,
+          in which case they are paired.
+      use_level_noise: See StructuralEnsemble.
+      configuration: A StateSpaceModelConfiguration object.
+    Raises:
+      ValueError: If `cycle_num_latent_values` is neither a scalar nor agrees in
+          size with `periodicities`.
+    """
+    component_model_configuration = configuration._replace(
+        use_observation_noise=False)
+    univariate_component_model_configuration = (
+        component_model_configuration._replace(
+            num_features=1))
+
+    adder_part = _replicate_level_trend_models(
+        multivariate_configuration=component_model_configuration,
+        univariate_configuration=univariate_component_model_configuration)
+    with variable_scope.variable_scope("varma"):
+      varma_part = varma.VARMA(
+          autoregressive_order=autoregressive_order,
+          moving_average_order=moving_average_order,
+          configuration=component_model_configuration)
+
+    cycle_parts = []
+    if periodicities is None:
+      periodicities = []
+    periodicity_list = nest.flatten(periodicities)
+    latent_values_list = nest.flatten(cycle_num_latent_values)
+    if len(periodicity_list) != len(latent_values_list):
+      if len(latent_values_list) != 1:
+        raise ValueError(
+            ("`cycle_num_latent_values` must either be a list with the same "
+             "size as `periodicity` or a scalar. Received length {} "
+             "`cycle_num_latent_values`, while `periodicities` has length {}.")
+            .format(len(latent_values_list), len(periodicity_list)))
+      latent_values_list *= len(periodicity_list)
+    for cycle_number, (cycle_periodicity, num_latent_values) in enumerate(
+        zip(periodicity_list, latent_values_list)):
+      with variable_scope.variable_scope("cycle{}".format(cycle_number)):
+        cycle_features = []
+        for feature in range(configuration.num_features):
+          with variable_scope.variable_scope("feature{}".format(feature)):
+            cycle_features.append(
+                periodic.ResolutionCycleModel(
+                    num_latent_values=num_latent_values,
+                    periodicity=cycle_periodicity,
+                    configuration=univariate_component_model_configuration))
+        cycle_parts.append(
+            state_space_model.StateSpaceCorrelatedFeaturesEnsemble(
+                ensemble_members=cycle_features,
+                configuration=component_model_configuration))
+
+    super(MultiResolutionStructuralEnsemble, self).__init__(
+        ensemble_members=[adder_part, varma_part] + cycle_parts,
+        configuration=configuration)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble_test.py
new file mode 100644
index 0000000000..a5f6a5341e
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble_test.py
@@ -0,0 +1,152 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the structural state space ensembles."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy
+
+from tensorflow.contrib import layers
+from tensorflow.contrib.layers.python.layers import feature_column
+
+from tensorflow.contrib.timeseries.python.timeseries import estimators
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import structural_ensemble
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.platform import test
+
+
+class StructuralEnsembleEstimatorTests(test.TestCase):
+
+  def simple_data(self, sample_every, dtype, period, num_samples, num_features):
+    time = sample_every * numpy.arange(num_samples)
+    noise = numpy.random.normal(
+        scale=0.01, size=[num_samples, num_features])
+    values = noise + numpy.sin(
+        numpy.arange(num_features)[None, ...]
+        + time[..., None] / float(period) * 2.0 * numpy.pi).astype(
+            dtype.as_numpy_dtype)
+    return {TrainEvalFeatures.TIMES: numpy.reshape(time, [1, -1]),
+            TrainEvalFeatures.VALUES: numpy.reshape(
+                values, [1, -1, num_features])}
+
+  def dry_run_train_helper(
+      self, sample_every, period, num_samples, model_type, model_args,
+      num_features=1):
+    numpy.random.seed(1)
+    dtype = dtypes.float32
+    features = self.simple_data(
+        sample_every, dtype=dtype, period=period, num_samples=num_samples,
+        num_features=num_features)
+    model = model_type(
+        configuration=(
+            state_space_model.StateSpaceModelConfiguration(
+                num_features=num_features,
+                dtype=dtype,
+                covariance_prior_fn=lambda _: 0.)),
+        **model_args)
+
+    class _RunConfig(estimator_lib.RunConfig):
+
+      @property
+      def tf_random_seed(self):
+        return 4
+
+    estimator = estimators.StateSpaceRegressor(model, config=_RunConfig())
+    train_input_fn = input_pipeline.RandomWindowInputFn(
+        input_pipeline.NumpyReader(features), num_threads=1, shuffle_seed=1,
+        batch_size=16, window_size=16)
+    eval_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(features))
+    estimator.train(input_fn=train_input_fn, max_steps=1)
+    first_evaluation = estimator.evaluate(input_fn=eval_input_fn, steps=1)
+    estimator.train(input_fn=train_input_fn, max_steps=3)
+    second_evaluation = estimator.evaluate(input_fn=eval_input_fn, steps=1)
+    self.assertLess(second_evaluation["loss"], first_evaluation["loss"])
+
+  def test_structural_multivariate(self):
+    self.dry_run_train_helper(
+        sample_every=3,
+        period=5,
+        num_samples=100,
+        num_features=3,
+        model_type=structural_ensemble.StructuralEnsemble,
+        model_args={
+            "periodicities": 2,
+            "moving_average_order": 2,
+            "autoregressive_order": 1
+        })
+
+  def test_exogenous_input(self):
+    """Test that no errors are raised when using exogenous features."""
+    dtype = dtypes.float64
+    times = [1, 2, 3, 4, 5, 6]
+    values = [[0.01], [5.10], [5.21], [0.30], [5.41], [0.50]]
+    feature_a = [["off"], ["on"], ["on"], ["off"], ["on"], ["off"]]
+    sparse_column_a = feature_column.sparse_column_with_keys(
+        column_name="feature_a", keys=["on", "off"])
+    one_hot_a = layers.one_hot_column(sparse_id_column=sparse_column_a)
+    regressor = estimators.StructuralEnsembleRegressor(
+        periodicities=[],
+        num_features=1,
+        moving_average_order=0,
+        exogenous_feature_columns=[one_hot_a],
+        dtype=dtype)
+    features = {TrainEvalFeatures.TIMES: times,
+                TrainEvalFeatures.VALUES: values,
+                "feature_a": feature_a}
+    train_input_fn = input_pipeline.RandomWindowInputFn(
+        input_pipeline.NumpyReader(features),
+        window_size=6, batch_size=1)
+    regressor.train(input_fn=train_input_fn, steps=1)
+    eval_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(features))
+    evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
+    predict_input_fn = input_pipeline.predict_continuation_input_fn(
+        evaluation, times=[[7, 8, 9]],
+        exogenous_features={"feature_a": [[["on"], ["off"], ["on"]]]})
+    regressor.predict(input_fn=predict_input_fn)
+
+  def test_no_periodicity(self):
+    """Test that no errors are raised when periodicites is None."""
+    dtype = dtypes.float64
+    times = [1, 2, 3, 4, 5, 6]
+    values = [[0.01], [5.10], [5.21], [0.30], [5.41], [0.50]]
+    regressor = estimators.StructuralEnsembleRegressor(
+        periodicities=None,
+        num_features=1,
+        moving_average_order=0,
+        dtype=dtype)
+    features = {TrainEvalFeatures.TIMES: times,
+                TrainEvalFeatures.VALUES: values}
+    train_input_fn = input_pipeline.RandomWindowInputFn(
+        input_pipeline.NumpyReader(features),
+        window_size=6, batch_size=1)
+    regressor.train(input_fn=train_input_fn, steps=1)
+    eval_input_fn = input_pipeline.WholeDatasetInputFn(
+        input_pipeline.NumpyReader(features))
+    evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
+    predict_input_fn = input_pipeline.predict_continuation_input_fn(
+        evaluation, times=[[7, 8, 9]])
+    regressor.predict(input_fn=predict_input_fn)
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/test_utils.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/test_utils.py
new file mode 100644
index 0000000000..5f127700d9
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/test_utils.py
@@ -0,0 +1,108 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for testing state space models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy
+
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+
+
+def transition_power_test_template(test_case, model, num_steps):
+  """Tests the transition_to_powers function of a state space model."""
+  transition_matrix = ops.convert_to_tensor(
+      model.get_state_transition(), dtype=model.dtype)
+  step_number = array_ops.placeholder(shape=[], dtype=dtypes.int64)
+  state_dimension = transition_matrix.get_shape()[0].value
+  previous_matrix = array_ops.placeholder(
+      shape=[state_dimension, state_dimension], dtype=transition_matrix.dtype)
+  true_single_step_update = math_ops.matmul(previous_matrix,
+                                            transition_matrix)
+  model_output_tensor = model.transition_to_powers(powers=array_ops.stack(
+      [step_number, step_number]))
+  with test_case.test_session():
+    starting_matrix = linalg_ops.eye(
+        state_dimension, batch_shape=array_ops.shape(num_steps)).eval()
+    evaled_current_matrix = starting_matrix
+    for iteration_number in range(num_steps):
+      model_output = model_output_tensor.eval(
+          feed_dict={step_number: iteration_number})
+      test_case.assertAllClose(
+          evaled_current_matrix,
+          model_output[0],
+          rtol=1e-8 if evaled_current_matrix.dtype == numpy.float64 else 1e-4)
+      evaled_current_matrix = true_single_step_update.eval(
+          feed_dict={previous_matrix: evaled_current_matrix})
+
+
+def noise_accumulator_test_template(test_case, model, num_steps):
+  """Tests `model`'s transition_power_noise_accumulator."""
+  transition_matrix = ops.convert_to_tensor(
+      model.get_state_transition(), dtype=model.dtype)
+  noise_transform = ops.convert_to_tensor(
+      model.get_noise_transform(), dtype=model.dtype)
+  state_dimension = transition_matrix.get_shape()[0].value
+  state_noise_dimension = noise_transform.get_shape()[1].value
+  gen_noise_addition = math_utils.sign_magnitude_positive_definite(
+      raw=random_ops.random_normal(
+          shape=[state_noise_dimension, state_noise_dimension],
+          dtype=model.dtype))
+  gen_starting_noise = math_utils.sign_magnitude_positive_definite(
+      random_ops.random_normal(
+          shape=[state_dimension, state_dimension], dtype=model.dtype))
+  starting_noise = array_ops.placeholder(
+      shape=[state_dimension, state_dimension], dtype=model.dtype)
+  step_number = array_ops.placeholder(shape=[], dtype=dtypes.int64)
+  starting_transitioned = math_ops.matmul(
+      math_ops.matmul(transition_matrix, starting_noise),
+      transition_matrix,
+      adjoint_b=True)
+  with test_case.test_session():
+    evaled_starting_noise = gen_starting_noise.eval()
+    current_starting_noise_transitioned = evaled_starting_noise
+    current_noise = evaled_starting_noise
+    evaled_noise_addition = gen_noise_addition.eval()
+    evaled_noise_addition_transformed = math_ops.matmul(
+        math_ops.matmul(noise_transform, evaled_noise_addition),
+        noise_transform,
+        adjoint_b=True).eval()
+    model.state_transition_noise_covariance = evaled_noise_addition
+    model._window_initializer(  # pylint: disable=protected-access
+        times=math_ops.range(num_steps + 1)[..., None], state=(None, None, 0))
+    model_update = model.transition_power_noise_accumulator(
+        num_steps=step_number)
+    for iteration_number in range(num_steps):
+      model_new_noise = model_update.eval(
+          feed_dict={step_number: iteration_number})
+      test_case.assertAllClose(
+          current_noise,
+          model_new_noise + current_starting_noise_transitioned,
+          rtol=1e-8 if current_noise.dtype == numpy.float64 else 1e-3)
+      current_starting_noise_transitioned = starting_transitioned.eval(
+          feed_dict={starting_noise: current_starting_noise_transitioned})
+      current_noise = (
+          starting_transitioned.eval(
+              feed_dict={starting_noise: current_noise})
+          + evaled_noise_addition_transformed)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma.py
new file mode 100644
index 0000000000..110ba9738f
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma.py
@@ -0,0 +1,197 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Multivariate autoregressive model (vector autoregression).
+
+Implements the following model (num_blocks = max(ar_order, ma_order + 1)):
+
+  y(t, 1) = \sum_{i=1}^{ar_order} ar_coefs[i] * y(t - 1, i)
+  y(t, i) = y(t - 1, i - 1) + ma_coefs[i - 1] * e(t) for 1 < i < num_blocks
+  y(t, num_blocks) = y(t - 1, num_blocks - 1) + e(t)
+
+Where e(t) are Gaussian with zero mean and learned covariance.
+
+Each element of ar_coefs and ma_coefs is a [num_features x num_features]
+matrix. Each y(t, i) is a vector of length num_features. Indices in the above
+equations are one-based. Initial conditions y(0, i) come from prior state (which
+may either be learned or left as a constant with high prior covariance).
+
+If ar_order > ma_order, the observation model is:
+  y(t, 1) + observation_noise(t)
+
+If ma_order >= ar_order, it is (to observe the moving average component):
+  y(t, 1) + y(t, num_blocks) + observation_noise(t)
+
+Where observation_noise(t) are Gaussian with zero mean and learned covariance.
+
+This implementation uses a formulation which puts all of the autoregressive
+coefficients in the transition equation for the observed component, which
+enables learning using truncated backpropagation. Noise is not applied directly
+to the observed component (with the exception of standard observation noise),
+which further aids learning of the autoregressive coefficients when VARMA is in
+an ensemble with other models (in which case having an observation noise term is
+usually unavoidable).
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+
+
+class VARMA(state_space_model.StateSpaceModel):
+  """A VARMA model implementation as a special case of the state space model."""
+
+  def __init__(self,
+               autoregressive_order,
+               moving_average_order,
+               configuration=state_space_model.StateSpaceModelConfiguration()):
+    """Construct a VARMA model.
+
+    The size of the latent state for this model is:
+      num_features * max(autoregressive_order, moving_average_order + 1)
+    Square matrices of this size are constructed and multiplied.
+
+    Args:
+      autoregressive_order: The maximum autoregressive lag.
+      moving_average_order: The maximum moving average lag, after which
+        transient deviations are expected to return to their long-term mean.
+      configuration: A StateSpaceModelConfiguration object.
+    """
+    self.ar_order = autoregressive_order
+    self.ma_order = moving_average_order
+    self.state_num_blocks = max(autoregressive_order, moving_average_order + 1)
+    super(VARMA, self).__init__(configuration=configuration)
+    self.state_dimension = self.state_num_blocks * self.num_features
+
+  def _define_parameters(self, observation_transition_tradeoff_log=None):
+    with variable_scope.variable_scope(self._variable_scope):
+      # TODO(allenl): Evaluate parameter transformations for AR/MA coefficients
+      # which improve interpretability/stability.
+      self.ar_coefs = variable_scope.get_variable(
+          name="ar_coefs",
+          shape=[self.num_features, self.num_features, self.ar_order],
+          dtype=self.dtype,
+          initializer=init_ops.zeros_initializer())
+      self.ma_coefs = variable_scope.get_variable(
+          name="ma_coefs",
+          initializer=array_ops.tile(
+              linalg_ops.eye(self.num_features, dtype=self.dtype)[None, :, :],
+              [self.ma_order, 1, 1]),
+          dtype=self.dtype)
+    super(VARMA, self)._define_parameters(
+        observation_transition_tradeoff_log=observation_transition_tradeoff_log)
+
+  def get_state_transition(self):
+    """Construct state transition matrix from VARMA parameters.
+
+    Returns:
+      the state transition matrix. It has shape
+        [self.state_dimendion, self.state_dimension].
+    """
+    # Pad any unused AR blocks with zeros. The extra state is necessary if
+    # ma_order >= ar_order.
+    ar_coefs_padded = array_ops.reshape(
+        array_ops.pad(self.ar_coefs,
+                      [[0, 0], [0, 0],
+                       [0, self.state_num_blocks - self.ar_order]]),
+        [self.num_features, self.state_dimension])
+    shift_matrix = array_ops.pad(
+        linalg_ops.eye(
+            (self.state_num_blocks - 1) * self.num_features, dtype=self.dtype),
+        [[0, 0], [0, self.num_features]])
+    return array_ops.concat([ar_coefs_padded, shift_matrix], axis=0)
+
+  def get_noise_transform(self):
+    """Construct state noise transform matrix from VARMA parameters.
+
+    Returns:
+      the state noise transform matrix. It has shape
+        [self.state_dimendion, self.num_features].
+    """
+    # Noise is broadcast, through the moving average coefficients, to
+    # un-observed parts of the latent state.
+    ma_coefs_padded = array_ops.reshape(
+        array_ops.pad(self.ma_coefs,
+                      [[self.state_num_blocks - 1 - self.ma_order, 0], [0, 0],
+                       [0, 0]]),
+        [(self.state_num_blocks - 1) * self.num_features, self.num_features],
+        name="noise_transform")
+    # Deterministically apply noise to the oldest component.
+    return array_ops.concat(
+        [ma_coefs_padded,
+         linalg_ops.eye(self.num_features, dtype=self.dtype)],
+        axis=0)
+
+  def get_observation_model(self, times):
+    """Construct observation model matrix from VARMA parameters.
+
+    Args:
+      times: A [batch size] vector indicating the times observation models are
+          requested for. Unused.
+    Returns:
+      the observation model matrix. It has shape
+        [self.num_features, self.state_dimension].
+    """
+    del times  # StateSpaceModel will broadcast along the batch dimension
+    if self.ar_order > self.ma_order or self.state_num_blocks < 2:
+      return array_ops.pad(
+          linalg_ops.eye(self.num_features, dtype=self.dtype),
+          [[0, 0], [0, self.num_features * (self.state_num_blocks - 1)]],
+          name="observation_model")
+    else:
+      # Add a second observed component which "catches" the accumulated moving
+      # average errors as they reach the end of the state. If ar_order >
+      # ma_order, this is unnecessary, since accumulated errors cycle naturally.
+      return array_ops.concat(
+          [
+              array_ops.pad(
+                  linalg_ops.eye(self.num_features, dtype=self.dtype),
+                  [[0, 0], [0,
+                            self.num_features * (self.state_num_blocks - 2)]]),
+              linalg_ops.eye(self.num_features, dtype=self.dtype)
+          ],
+          axis=1,
+          name="observation_model")
+
+  def get_state_transition_noise_covariance(
+      self, minimum_initial_variance=1e-5):
+    # Most state space models use only an explicit observation noise term to
+    # model deviations from expectations, and so a low initial transition noise
+    # parameter is helpful there. Since deviations from expectations are also
+    # modeled as transition noise in VARMA, we set its initial value based on a
+    # slight over-estimate empirical observation noise.
+    if self._input_statistics is not None:
+      feature_variance = self._input_statistics.series_start_moments.variance
+      initial_transition_noise_scale = math_ops.log(
+          math_ops.maximum(
+              math_ops.reduce_mean(feature_variance), minimum_initial_variance))
+    else:
+      initial_transition_noise_scale = 0.
+    state_noise_transform = ops.convert_to_tensor(
+        self.get_noise_transform(), dtype=self.dtype)
+    state_noise_dimension = state_noise_transform.get_shape()[1].value
+    return math_utils.variable_covariance_matrix(
+        state_noise_dimension, "state_transition_noise",
+        dtype=self.dtype,
+        initial_overall_scale_log=initial_transition_noise_scale)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py
new file mode 100644
index 0000000000..84885d5c9a
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py
@@ -0,0 +1,94 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for VARMA.
+
+Tests VARMA model building and utility functions.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import state_space_model
+from tensorflow.contrib.timeseries.python.timeseries.state_space_models import varma
+
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+class MakeModelTest(test.TestCase):
+
+  def test_ar_smaller(self):
+    model = varma.VARMA(
+        autoregressive_order=0,
+        moving_average_order=3)
+    model.initialize_graph()
+    outputs = model.define_loss(
+        features={
+            TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]),
+            TrainEvalFeatures.VALUES: constant_op.constant([[[1.], [2.]]])
+        },
+        mode=estimator_lib.ModeKeys.TRAIN)
+    initializer = variables.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run([initializer])
+      outputs.loss.eval()
+
+  def test_ma_smaller(self):
+    model = varma.VARMA(
+        autoregressive_order=6,
+        moving_average_order=3,
+        configuration=state_space_model.StateSpaceModelConfiguration(
+            num_features=7))
+    model.initialize_graph()
+    outputs = model.define_loss(
+        features={
+            TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]),
+            TrainEvalFeatures.VALUES: constant_op.constant(
+                [[[1.] * 7, [2.] * 7]])
+        },
+        mode=estimator_lib.ModeKeys.TRAIN)
+    initializer = variables.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run([initializer])
+      outputs.loss.eval()
+
+  def test_make_ensemble_no_errors(self):
+    with variable_scope.variable_scope("model_one"):
+      model_one = varma.VARMA(10, 5)
+    with variable_scope.variable_scope("model_two"):
+      model_two = varma.VARMA(0, 3)
+    configuration = state_space_model.StateSpaceModelConfiguration()
+    ensemble = state_space_model.StateSpaceIndependentEnsemble(
+        ensemble_members=[model_one, model_two],
+        configuration=configuration)
+    ensemble.initialize_graph()
+    outputs = ensemble.define_loss(
+        features={
+            TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]),
+            TrainEvalFeatures.VALUES: constant_op.constant([[[1.], [2.]]])},
+        mode=estimator_lib.ModeKeys.TRAIN)
+    initializer = variables.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run([initializer])
+      outputs.loss.eval()
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/test_utils.py b/tensorflow/contrib/timeseries/python/timeseries/test_utils.py
new file mode 100644
index 0000000000..72afae14dc
--- /dev/null
+++ b/tensorflow/contrib/timeseries/python/timeseries/test_utils.py
@@ -0,0 +1,282 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for testing time series models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.python.timeseries import estimators
+from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
+from tensorflow.contrib.timeseries.python.timeseries import state_management
+from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
+
+from tensorflow.python.client import session
+from tensorflow.python.estimator import estimator_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import random_seed
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import adam
+from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.training import coordinator as coordinator_lib
+from tensorflow.python.training import queue_runner_impl
+from tensorflow.python.util import nest
+
+
+class AllWindowInputFn(input_pipeline.TimeSeriesInputFn):
+  """Returns all contiguous windows of data from a full dataset.
+
+  In contrast to WholeDatasetInputFn, which does basic shape checking but
+  maintains the flat sequencing of data, this `TimeSeriesInputFn` creates
+  batches of windows. However, unlike `RandomWindowInputFn` these windows are
+  deterministic, starting at every possible offset (i.e. batches of size
+  series_length - window_size + 1 are produced).
+  """
+
+  def __init__(self, time_series_reader, window_size):
+    """Initialize the input_pipeline.
+
+    Args:
+      time_series_reader: A `input_pipeline.TimeSeriesReader` object.
+      window_size: The size of contiguous windows of data to produce.
+    """
+    self._window_size = window_size
+    self._reader = time_series_reader
+    super(AllWindowInputFn, self).__init__()
+
+  def create_batch(self):
+    features = self._reader.read_full()
+    times = features[TrainEvalFeatures.TIMES]
+    num_windows = array_ops.shape(times)[0] - self._window_size + 1
+    indices = array_ops.reshape(math_ops.range(num_windows), [num_windows, 1])
+    # indices contains the starting point for each window. We now extend these
+    # indices to include the elements inside the windows as well by doing a
+    # broadcast addition.
+    increments = array_ops.reshape(math_ops.range(self._window_size), [1, -1])
+    all_indices = array_ops.reshape(indices + increments, [-1])
+    # Select the appropriate elements in the batch and reshape the output to 3D.
+    features = {
+        key: array_ops.reshape(
+            array_ops.gather(value, all_indices),
+            array_ops.concat(
+                [[num_windows, self._window_size], array_ops.shape(value)[1:]],
+                axis=0))
+        for key, value in features.items()
+    }
+    return (features, None)
+
+
+class _SavingTensorHook(basic_session_run_hooks.LoggingTensorHook):
+  """A hook to save Tensors during training."""
+
+  def __init__(self, tensors, every_n_iter=None, every_n_secs=None):
+    self.tensor_values = {}
+    super(_SavingTensorHook, self).__init__(
+        tensors=tensors, every_n_iter=every_n_iter,
+        every_n_secs=every_n_secs)
+
+  def after_run(self, run_context, run_values):
+    del run_context
+    if self._should_trigger:
+      for tag in self._current_tensors.keys():
+        self.tensor_values[tag] = run_values.results[tag]
+      self._timer.update_last_triggered_step(self._iter_count)
+    self._iter_count += 1
+
+
+def _train_on_generated_data(
+    generate_fn, generative_model, train_iterations, seed,
+    learning_rate=0.1, ignore_params_fn=lambda _: (),
+    derived_param_test_fn=lambda _: (),
+    train_input_fn_type=input_pipeline.WholeDatasetInputFn,
+    train_state_manager=state_management.PassthroughStateManager()):
+  """The training portion of parameter recovery tests."""
+  random_seed.set_random_seed(seed)
+  generate_graph = ops.Graph()
+  with generate_graph.as_default():
+    with session.Session(graph=generate_graph):
+      generative_model.initialize_graph()
+      time_series_reader, true_parameters = generate_fn(generative_model)
+      true_parameters = {
+          tensor.name: value for tensor, value in true_parameters.items()}
+  eval_input_fn = input_pipeline.WholeDatasetInputFn(time_series_reader)
+  eval_state_manager = state_management.PassthroughStateManager()
+  true_parameter_eval_graph = ops.Graph()
+  with true_parameter_eval_graph.as_default():
+    generative_model.initialize_graph()
+    ignore_params = ignore_params_fn(generative_model)
+    feature_dict, _ = eval_input_fn()
+    eval_state_manager.initialize_graph(generative_model)
+    feature_dict[TrainEvalFeatures.VALUES] = math_ops.cast(
+        feature_dict[TrainEvalFeatures.VALUES], generative_model.dtype)
+    model_outputs = eval_state_manager.define_loss(
+        model=generative_model,
+        features=feature_dict,
+        mode=estimator_lib.ModeKeys.EVAL)
+    with session.Session(graph=true_parameter_eval_graph) as sess:
+      variables.global_variables_initializer().run()
+      coordinator = coordinator_lib.Coordinator()
+      queue_runner_impl.start_queue_runners(sess, coord=coordinator)
+      true_param_loss = model_outputs.loss.eval(feed_dict=true_parameters)
+      true_transformed_params = {
+          param: param.eval(feed_dict=true_parameters)
+          for param in derived_param_test_fn(generative_model)}
+      coordinator.request_stop()
+      coordinator.join()
+
+  saving_hook = _SavingTensorHook(
+      tensors=true_parameters.keys(),
+      every_n_iter=train_iterations - 1)
+
+  class _RunConfig(estimator_lib.RunConfig):
+
+    @property
+    def tf_random_seed(self):
+      return seed
+
+  estimator = estimators._TimeSeriesRegressor(  # pylint: disable=protected-access
+      model=generative_model,
+      config=_RunConfig(),
+      state_manager=train_state_manager,
+      optimizer=adam.AdamOptimizer(learning_rate))
+  train_input_fn = train_input_fn_type(time_series_reader=time_series_reader)
+  trained_loss = (estimator.train(
+      input_fn=train_input_fn,
+      max_steps=train_iterations,
+      hooks=[saving_hook]).evaluate(
+          input_fn=eval_input_fn, steps=1))["loss"]
+  logging.info("Final trained loss: %f", trained_loss)
+  logging.info("True parameter loss: %f", true_param_loss)
+  return (ignore_params, true_parameters, true_transformed_params,
+          trained_loss, true_param_loss, saving_hook,
+          true_parameter_eval_graph)
+
+
+def test_parameter_recovery(
+    generate_fn, generative_model, train_iterations, test_case, seed,
+    learning_rate=0.1, rtol=0.2, atol=0.1, train_loss_tolerance_coeff=0.99,
+    ignore_params_fn=lambda _: (),
+    derived_param_test_fn=lambda _: (),
+    train_input_fn_type=input_pipeline.WholeDatasetInputFn,
+    train_state_manager=state_management.PassthroughStateManager()):
+  """Test that a generative model fits generated data.
+
+  Args:
+    generate_fn: A function taking a model and returning a `TimeSeriesReader`
+        object and dictionary mapping parameters to their
+        values. model.initialize_graph() will have been called on the model
+        before it is passed to this function.
+    generative_model: A timeseries.model.TimeSeriesModel instance to test.
+    train_iterations: Number of training steps.
+    test_case: A tf.test.TestCase to run assertions on.
+    seed: Same as for TimeSeriesModel.unconditional_generate().
+    learning_rate: Step size for optimization.
+    rtol: Relative tolerance for tests.
+    atol: Absolute tolerance for tests.
+    train_loss_tolerance_coeff: Trained loss times this value must be less
+        than the loss evaluated using the generated parameters.
+    ignore_params_fn: Function mapping from a Model to a list of parameters
+        which are not tested for accurate recovery.
+    derived_param_test_fn: Function returning a list of derived parameters
+        (Tensors) which are checked for accurate recovery (comparing the value
+        evaluated with trained parameters to the value under the true
+        parameters).
+
+        As an example, for VARMA, in addition to checking AR and MA parameters,
+        this function can be used to also check lagged covariance. See
+        varma_ssm.py for details.
+    train_input_fn_type: The `TimeSeriesInputFn` type to use when training
+        (likely `WholeDatasetInputFn` or `RandomWindowInputFn`). If None, use
+        `WholeDatasetInputFn`.
+    train_state_manager: The state manager to use when training (likely
+        `PassthroughStateManager` or `ChainingStateManager`). If None, use
+        `PassthroughStateManager`.
+  """
+  (ignore_params, true_parameters, true_transformed_params,
+   trained_loss, true_param_loss, saving_hook, true_parameter_eval_graph
+  ) = _train_on_generated_data(
+      generate_fn=generate_fn, generative_model=generative_model,
+      train_iterations=train_iterations, seed=seed, learning_rate=learning_rate,
+      ignore_params_fn=ignore_params_fn,
+      derived_param_test_fn=derived_param_test_fn,
+      train_input_fn_type=train_input_fn_type,
+      train_state_manager=train_state_manager)
+  trained_parameter_substitutions = {}
+  for param in true_parameters.keys():
+    evaled_value = saving_hook.tensor_values[param]
+    trained_parameter_substitutions[param] = evaled_value
+    true_value = true_parameters[param]
+    logging.info("True %s: %s, learned: %s",
+                 param, true_value, evaled_value)
+  with session.Session(graph=true_parameter_eval_graph):
+    for transformed_param, true_value in true_transformed_params.items():
+      trained_value = transformed_param.eval(
+          feed_dict=trained_parameter_substitutions)
+      logging.info("True %s [transformed parameter]: %s, learned: %s",
+                   transformed_param, true_value, trained_value)
+      test_case.assertAllClose(true_value, trained_value,
+                               rtol=rtol, atol=atol)
+
+  if ignore_params is None:
+    ignore_params = []
+  else:
+    ignore_params = nest.flatten(ignore_params)
+  ignore_params = [tensor.name for tensor in ignore_params]
+  if trained_loss > 0:
+    test_case.assertLess(trained_loss * train_loss_tolerance_coeff,
+                         true_param_loss)
+  else:
+    test_case.assertLess(trained_loss / train_loss_tolerance_coeff,
+                         true_param_loss)
+  for param in true_parameters.keys():
+    if param in ignore_params:
+      continue
+    evaled_value = saving_hook.tensor_values[param]
+    true_value = true_parameters[param]
+    test_case.assertAllClose(true_value, evaled_value,
+                             rtol=rtol, atol=atol)
+
+
+def parameter_recovery_dry_run(
+    generate_fn, generative_model, seed,
+    learning_rate=0.1,
+    train_input_fn_type=input_pipeline.WholeDatasetInputFn,
+    train_state_manager=state_management.PassthroughStateManager()):
+  """Test that a generative model can train on generated data.
+
+  Args:
+    generate_fn: A function taking a model and returning a
+        `input_pipeline.TimeSeriesReader` object and a dictionary mapping
+        parameters to their values. model.initialize_graph() will have been
+        called on the model before it is passed to this function.
+    generative_model: A timeseries.model.TimeSeriesModel instance to test.
+    seed: Same as for TimeSeriesModel.unconditional_generate().
+    learning_rate: Step size for optimization.
+    train_input_fn_type: The type of `TimeSeriesInputFn` to use when training
+        (likely `WholeDatasetInputFn` or `RandomWindowInputFn`). If None, use
+        `WholeDatasetInputFn`.
+    train_state_manager: The state manager to use when training (likely
+        `PassthroughStateManager` or `ChainingStateManager`). If None, use
+        `PassthroughStateManager`.
+  """
+  _train_on_generated_data(
+      generate_fn=generate_fn, generative_model=generative_model,
+      seed=seed, learning_rate=learning_rate,
+      train_input_fn_type=train_input_fn_type,
+      train_state_manager=train_state_manager,
+      train_iterations=2)
diff --git a/tensorflow/tools/pip_package/pip_smoke_test.py b/tensorflow/tools/pip_package/pip_smoke_test.py
index dec08157c2..83909d83ae 100644
--- a/tensorflow/tools/pip_package/pip_smoke_test.py
+++ b/tensorflow/tools/pip_package/pip_smoke_test.py
@@ -60,6 +60,12 @@ BLACKLIST = [
     "//tensorflow/contrib/framework:checkpoint_ops_testdata",
     "//tensorflow/contrib/bayesflow:reinforce_simple_example",
     "//tensorflow/contrib/bayesflow:examples/reinforce_simple/reinforce_simple_example.py",  # pylint:disable=line-too-long
+    "//tensorflow/contrib/timeseries/examples:predict",
+    "//tensorflow/contrib/timeseries/examples:multivariate",
+    "//tensorflow/contrib/timeseries/examples:known_anomaly",
+    "//tensorflow/contrib/timeseries/examples:data/period_trend.csv",  # pylint:disable=line-too-long
+    "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
+    "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:test_utils",  # pylint:disable=line-too-long
 ]
 
 
-- 
GitLab


From 5b82daa9948316a9df7cc196034712981e43e5e5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 15:50:50 -0700
Subject: [PATCH 0707/1375] Add support for the shrinkage-type L2 to
 FtrlOptimizer in addition to the online L2 it already supports.

PiperOrigin-RevId: 161016648
---
 tensorflow/compiler/tests/ftrl_test.py        |  33 ++
 .../compiler/tf2xla/kernels/training_ops.cc   | 222 ++++++-----
 tensorflow/core/kernels/training_ops.cc       | 357 +++++++++++++-----
 tensorflow/core/kernels/training_ops.h        |  13 +
 tensorflow/core/ops/training_ops.cc           | 172 +++++++++
 tensorflow/python/training/ftrl.py            | 186 ++++++---
 tensorflow/python/training/ftrl_test.py       |  37 ++
 .../tensorflow.train.-ftrl-optimizer.pbtxt    |   2 +-
 8 files changed, 780 insertions(+), 242 deletions(-)

diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py
index a75a5cd2cf..7918276849 100644
--- a/tensorflow/compiler/tests/ftrl_test.py
+++ b/tensorflow/compiler/tests/ftrl_test.py
@@ -218,6 +218,39 @@ class FtrlOptimizerTest(XLATestCase):
         self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval())
         self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval())
 
+  def testFtrlWithL1_L2_L2Shrinkage(self):
+    """Test the new FTRL op with support for l2 shrinkage.
+
+    The addition of this parameter which places a constant pressure on weights
+    towards the origin causes the gradient descent trajectory to differ. The
+    weights will tend to have smaller magnitudes with this parameter set.
+    """
+    for dtype in self.float_types:
+      with self.test_session(), self.test_scope():
+        var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
+        var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
+        grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
+        grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
+        opt = ftrl.FtrlOptimizer(
+            3.0,
+            initial_accumulator_value=0.1,
+            l1_regularization_strength=0.001,
+            l2_regularization_strength=2.0,
+            l2_shrinkage_regularization_strength=0.1)
+        ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+        variables.global_variables_initializer().run()
+        # Fetch params to validate initial values
+        self.assertAllClose([1.0, 2.0], var0.eval())
+        self.assertAllClose([4.0, 3.0], var1.eval())
+
+        # Run 10 steps FTRL
+        for _ in range(10):
+          ftrl_update.run()
+
+        # Validate updated params
+        self.assertAllClose(np.array([-0.21931979, -0.40642974]), var0.eval())
+        self.assertAllClose(np.array([-0.0282721, -0.07188385]), var1.eval())
+
   # When variables are initialized with Zero, FTRL-Proximal has two properties:
   # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
   # with GradientDescent.
diff --git a/tensorflow/compiler/tf2xla/kernels/training_ops.cc b/tensorflow/compiler/tf2xla/kernels/training_ops.cc
index e9ac1ee91b..42104b951a 100644
--- a/tensorflow/compiler/tf2xla/kernels/training_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/training_ops.cc
@@ -364,112 +364,160 @@ class ResourceApplyRMSProp : public XlaOpKernel {
 };
 REGISTER_XLA_OP(Name("ResourceApplyRMSProp"), ResourceApplyRMSProp);
 
-class ResourceApplyFtrl : public XlaOpKernel {
- public:
-  explicit ResourceApplyFtrl(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
-    OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_));
+void CompileFtrl(XlaOpKernelContext* ctx, DataType dtype,
+                 bool has_l2_shrinkage) {
+  xla::ComputationBuilder* b = ctx->builder();
+
+  DataType var_type, accum_type, linear_type;
+  TensorShape var_shape, accum_shape, linear_shape;
+  OP_REQUIRES_OK(ctx, ctx->GetVariableTypeAndShape(0, &var_type, &var_shape));
+  OP_REQUIRES_OK(ctx,
+                 ctx->GetVariableTypeAndShape(1, &accum_type, &accum_shape));
+  OP_REQUIRES_OK(ctx,
+                 ctx->GetVariableTypeAndShape(2, &linear_type, &linear_shape));
+
+  OP_REQUIRES(
+      ctx, dtype == var_type && dtype == accum_type && dtype == linear_type,
+      errors::InvalidArgument(
+          "Types of variable arguments to ResourceApplyFtrlV2 must match: ",
+          DataTypeString(dtype), " vs. ", DataTypeString(var_type), " and ",
+          DataTypeString(accum_type), " and ", DataTypeString(linear_type)));
+
+  OP_REQUIRES(ctx, var_shape.IsSameSize(accum_shape),
+              errors::InvalidArgument(
+                  "var and accum do not have the same shape",
+                  var_shape.DebugString(), " ", accum_shape.DebugString()));
+
+  OP_REQUIRES(ctx, var_shape.IsSameSize(linear_shape),
+              errors::InvalidArgument(
+                  "var and linear do not have the same shape",
+                  var_shape.DebugString(), " ", linear_shape.DebugString()));
+
+  TensorShape grad_shape = ctx->InputShape(3);
+  TensorShape lr_shape = ctx->InputShape(4);
+  TensorShape l1_shape = ctx->InputShape(5);
+  TensorShape l2_shape = ctx->InputShape(6);
+  TensorShape l2_shrinkage_shape;
+  TensorShape lr_power_shape;
+  if (has_l2_shrinkage) {
+    l2_shrinkage_shape = ctx->InputShape(7);
+    lr_power_shape = ctx->InputShape(8);
+  } else {
+    lr_power_shape = ctx->InputShape(7);
   }
 
-  void Compile(XlaOpKernelContext* ctx) override {
-    xla::ComputationBuilder* b = ctx->builder();
+  OP_REQUIRES(ctx, var_shape.IsSameSize(grad_shape),
+              errors::InvalidArgument("var and grad do not have the same shape",
+                                      var_shape.DebugString(), " ",
+                                      grad_shape.DebugString()));
 
-    DataType var_type, accum_type, linear_type;
-    TensorShape var_shape, accum_shape, linear_shape;
-    OP_REQUIRES_OK(ctx, ctx->GetVariableTypeAndShape(0, &var_type, &var_shape));
-    OP_REQUIRES_OK(ctx,
-                   ctx->GetVariableTypeAndShape(1, &accum_type, &accum_shape));
-    OP_REQUIRES_OK(
-        ctx, ctx->GetVariableTypeAndShape(2, &linear_type, &linear_shape));
+  OP_REQUIRES(
+      ctx, TensorShapeUtils::IsScalar(lr_shape),
+      errors::InvalidArgument("lr is not a scalar: ", lr_shape.DebugString()));
 
-    OP_REQUIRES(
-        ctx,
-        dtype_ == var_type && dtype_ == accum_type && dtype_ == linear_type,
-        errors::InvalidArgument(
-            "Types of variable arguments to ResourceApplyFtrl must match: ",
-            DataTypeString(dtype_), " vs. ", DataTypeString(var_type), " and ",
-            DataTypeString(accum_type), " and ", DataTypeString(linear_type)));
+  OP_REQUIRES(
+      ctx, TensorShapeUtils::IsScalar(l1_shape),
+      errors::InvalidArgument("l1 is not a scalar: ", l1_shape.DebugString()));
 
-    OP_REQUIRES(ctx, var_shape.IsSameSize(accum_shape),
-                errors::InvalidArgument(
-                    "var and accum do not have the same shape",
-                    var_shape.DebugString(), " ", accum_shape.DebugString()));
-
-    OP_REQUIRES(ctx, var_shape.IsSameSize(linear_shape),
-                errors::InvalidArgument(
-                    "var and linear do not have the same shape",
-                    var_shape.DebugString(), " ", linear_shape.DebugString()));
+  OP_REQUIRES(
+      ctx, TensorShapeUtils::IsScalar(l2_shape),
+      errors::InvalidArgument("l2 is not a scalar: ", l2_shape.DebugString()));
 
-    TensorShape grad_shape = ctx->InputShape(3);
-    TensorShape lr_shape = ctx->InputShape(4);
-    TensorShape l1_shape = ctx->InputShape(5);
-    TensorShape l2_shape = ctx->InputShape(6);
-    TensorShape lr_power_shape = ctx->InputShape(7);
+  if (has_l2_shrinkage) {
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(l2_shrinkage_shape),
+                errors::InvalidArgument("l2_shrinkage is not a scalar: ",
+                                        l2_shrinkage_shape.DebugString()));
+  }
 
-    OP_REQUIRES(ctx, var_shape.IsSameSize(grad_shape),
-                errors::InvalidArgument(
-                    "var and grad do not have the same shape",
-                    var_shape.DebugString(), " ", grad_shape.DebugString()));
+  OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_power_shape),
+              errors::InvalidArgument("lr_power is not a scalar: ",
+                                      lr_power_shape.DebugString()));
+
+  xla::ComputationDataHandle var, accum, linear;
+  OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, &var));
+  OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, &accum));
+  OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, &linear));
+  xla::ComputationDataHandle grad = ctx->Input(3);
+  xla::ComputationDataHandle lr = ctx->Input(4);
+  xla::ComputationDataHandle l1 = ctx->Input(5);
+  xla::ComputationDataHandle l2 = ctx->Input(6);
+  xla::ComputationDataHandle l2_shrinkage;
+  xla::ComputationDataHandle lr_power;
+  if (has_l2_shrinkage) {
+    l2_shrinkage = ctx->Input(7);
+    lr_power = ctx->Input(8);
+  } else {
+    lr_power = ctx->Input(7);
+  }
 
-    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_shape),
-                errors::InvalidArgument("lr is not a scalar: ",
-                                        lr_shape.DebugString()));
+  // grad_to_use = grad + 2 * l2_shrinkage * var
+  // new_accum = accum + grad_to_use * grad_to_use
+  // linear += grad_to_use -
+  //     (new_accum^(-lr_power) - accum^(-lr_power)) / lr * var
+  // quadratic = (new_accum^(-lr_power) / lr) + 2 * l2
+  // var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+  // accum = new_accum
+
+  xla::ComputationDataHandle zero_broadcast = b->Broadcast(
+      XlaHelpers::FloatLiteral(b, dtype, 0.0), var_shape.dim_sizes());
+  xla::ComputationDataHandle two = XlaHelpers::FloatLiteral(b, dtype, 2.0);
+  xla::ComputationDataHandle grad_to_use;
+  if (has_l2_shrinkage) {
+    grad_to_use = b->Add(grad, b->Mul(two, b->Mul(l2_shrinkage, var)));
+  } else {
+    grad_to_use = grad;
+  }
 
-    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(l1_shape),
-                errors::InvalidArgument("l1 is not a scalar: ",
-                                        l1_shape.DebugString()));
+  xla::ComputationDataHandle new_accum =
+      b->Add(accum, b->Pow(grad_to_use, two));
+  xla::ComputationDataHandle new_accum_lr_pow =
+      b->Pow(new_accum, b->Neg(lr_power));
+  xla::ComputationDataHandle accum_lr_pow = b->Pow(accum, b->Neg(lr_power));
+  linear = b->Add(
+      linear,
+      b->Sub(grad_to_use,
+             b->Mul(b->Div(b->Sub(new_accum_lr_pow, accum_lr_pow), lr), var)));
+  xla::ComputationDataHandle quadratic =
+      b->Add(b->Div(new_accum_lr_pow, lr), b->Mul(two, l2));
+  xla::ComputationDataHandle pre_shrink =
+      b->Div(b->Sub(b->Mul(l1, b->Sign(linear)), linear), quadratic);
+  var = b->Select(b->Gt(b->Abs(linear), l1), pre_shrink, zero_broadcast);
+  accum = new_accum;
+
+  OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype, var));
+  OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, dtype, accum));
+  OP_REQUIRES_OK(ctx, ctx->AssignVariable(2, dtype, linear));
+}
 
-    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(l2_shape),
-                errors::InvalidArgument("l2 is not a scalar: ",
-                                        l2_shape.DebugString()));
+class ResourceApplyFtrl : public XlaOpKernel {
+ public:
+  explicit ResourceApplyFtrl(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_));
+  }
 
-    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_power_shape),
-                errors::InvalidArgument("lr_power is not a scalar: ",
-                                        lr_power_shape.DebugString()));
+  void Compile(XlaOpKernelContext* ctx) override {
+    CompileFtrl(ctx, dtype_, /*has_l2_shrinkage=*/false);
+  }
 
-    xla::ComputationDataHandle var, accum, linear;
-    OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, &var));
-    OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, &accum));
-    OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, &linear));
-    xla::ComputationDataHandle grad = ctx->Input(3);
-    xla::ComputationDataHandle lr = ctx->Input(4);
-    xla::ComputationDataHandle l1 = ctx->Input(5);
-    xla::ComputationDataHandle l2 = ctx->Input(6);
-    xla::ComputationDataHandle lr_power = ctx->Input(7);
-
-    // new_accum = accum + grad * grad
-    // linear += grad - (new_accum^(-lr_power) - accum^(-lr_power)) / lr * var
-    // quadratic = (new_accum^(-lr_power) / lr) + 2 * l2
-    // var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
-    // accum = new_accum
-
-    xla::ComputationDataHandle zero_broadcast = b->Broadcast(
-        XlaHelpers::FloatLiteral(b, dtype_, 0.0), var_shape.dim_sizes());
-    xla::ComputationDataHandle two = XlaHelpers::FloatLiteral(b, dtype_, 2.0);
+ private:
+  DataType dtype_;
+};
+REGISTER_XLA_OP(Name("ResourceApplyFtrl"), ResourceApplyFtrl);
 
-    xla::ComputationDataHandle new_accum = b->Add(accum, b->Pow(grad, two));
-    xla::ComputationDataHandle new_accum_lr_pow =
-        b->Pow(new_accum, b->Neg(lr_power));
-    xla::ComputationDataHandle accum_lr_pow = b->Pow(accum, b->Neg(lr_power));
-    linear = b->Add(
-        linear,
-        b->Sub(grad, b->Mul(b->Div(b->Sub(new_accum_lr_pow, accum_lr_pow), lr),
-                            var)));
-    xla::ComputationDataHandle quadratic =
-        b->Add(b->Div(new_accum_lr_pow, lr), b->Mul(two, l2));
-    xla::ComputationDataHandle pre_shrink =
-        b->Div(b->Sub(b->Mul(l1, b->Sign(linear)), linear), quadratic);
-    var = b->Select(b->Gt(b->Abs(linear), l1), pre_shrink, zero_broadcast);
-    accum = new_accum;
+class ResourceApplyFtrlV2 : public XlaOpKernel {
+ public:
+  explicit ResourceApplyFtrlV2(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_));
+  }
 
-    OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, var));
-    OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, dtype_, accum));
-    OP_REQUIRES_OK(ctx, ctx->AssignVariable(2, dtype_, linear));
+  void Compile(XlaOpKernelContext* ctx) override {
+    CompileFtrl(ctx, dtype_, /*has_l2_shrinkage=*/true);
   }
 
  private:
   DataType dtype_;
 };
-REGISTER_XLA_OP(Name("ResourceApplyFtrl"), ResourceApplyFtrl);
+REGISTER_XLA_OP(Name("ResourceApplyFtrlV2"), ResourceApplyFtrlV2);
 
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc
index e798a7e4a4..79654938d2 100644
--- a/tensorflow/core/kernels/training_ops.cc
+++ b/tensorflow/core/kernels/training_ops.cc
@@ -25,7 +25,7 @@ limitations under the License.
 
 #ifdef TENSORFLOW_USE_SYCL
 #include "tensorflow/core/common_runtime/sycl/sycl_util.h"
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 namespace tensorflow {
 
@@ -40,7 +40,7 @@ inline T sgn(const T x) {
   T one(1);
   return (x == zero ? zero : (x < zero ? -one : one));
 }
-}
+}  // namespace
 
 namespace functor {
 template <typename T>
@@ -55,8 +55,8 @@ struct ApplyGradientDescent<CPUDevice, T> {
 #ifdef TENSORFLOW_USE_SYCL
 template <typename T>
 struct ApplyGradientDescentSYCL {
-  void operator()(const SYCLDevice& d, typename TTypes<T>::Flat var,
-                  T lr, typename TTypes<T>::ConstFlat grad) {
+  void operator()(const SYCLDevice& d, typename TTypes<T>::Flat var, T lr,
+                  typename TTypes<T>::ConstFlat grad) {
     var.device(d) -= grad * lr;
   }
 };
@@ -196,6 +196,47 @@ struct ApplyProximalAdagrad<CPUDevice, T> {
   }
 };
 
+template <typename T>
+struct ApplyFtrlV2<CPUDevice, T> {
+  void operator()(const CPUDevice& d, typename TTypes<T>::Flat var,
+                  typename TTypes<T>::Flat accum,
+                  typename TTypes<T>::Flat linear,
+                  typename TTypes<T>::ConstFlat grad,
+                  typename TTypes<T>::ConstScalar lr,
+                  typename TTypes<T>::ConstScalar l1,
+                  typename TTypes<T>::ConstScalar l2,
+                  typename TTypes<T>::ConstScalar l2_shrinkage,
+                  typename TTypes<T>::ConstScalar lr_power) {
+    auto grad_with_shrinkage = grad + static_cast<T>(2) * l2_shrinkage() * var;
+    auto new_accum = accum + grad_with_shrinkage.square();
+    // special case for which lr_power=-0.5.
+    if (lr_power() == static_cast<T>(-0.5)) {
+      linear.device(d) +=
+          grad_with_shrinkage - (new_accum.sqrt() - accum.sqrt()) / lr() * var;
+    } else {
+      linear.device(d) +=
+          grad_with_shrinkage -
+          (new_accum.pow(-lr_power()) - accum.pow(-lr_power())) / lr() * var;
+    }
+    auto x = (linear.constant(l1()) * linear.sign() - linear);
+    if (lr_power() == static_cast<T>(-0.5)) {
+      auto y = new_accum.sqrt() / new_accum.constant(lr()) +
+               linear.constant(static_cast<T>(2) * l2());
+      auto pre_shrink = x / y;
+      var.device(d) = (linear.abs() > linear.constant(l1()))
+                          .select(pre_shrink, var.constant(static_cast<T>(0)));
+
+    } else {
+      auto y = new_accum.pow(-lr_power()) / new_accum.constant(lr()) +
+               linear.constant(static_cast<T>(2) * l2());
+      auto pre_shrink = x / y;
+      var.device(d) = (linear.abs() > linear.constant(l1()))
+                          .select(pre_shrink, var.constant(static_cast<T>(0)));
+    }
+    accum.device(d) += grad_with_shrinkage.square();
+  }
+};
+
 template <typename T>
 struct ApplyFtrl<CPUDevice, T> {
   void operator()(const CPUDevice& d, typename TTypes<T>::Flat var,
@@ -284,16 +325,16 @@ template <typename T>
 struct ApplyAdamSYCL {
   void operator()(const SYCLDevice& d, typename TTypes<T>::Flat var,
                   typename TTypes<T>::Flat m, typename TTypes<T>::Flat v,
-                  T beta1_power, T beta2_power, T lr, T beta1, T beta2, T epsilon,
-                  typename TTypes<T>::ConstFlat grad) {
-    const T alpha = lr * Eigen::numext::sqrt(T(1) - beta2_power) /
-                    (T(1) - beta1_power);
+                  T beta1_power, T beta2_power, T lr, T beta1, T beta2,
+                  T epsilon, typename TTypes<T>::ConstFlat grad) {
+    const T alpha =
+        lr * Eigen::numext::sqrt(T(1) - beta2_power) / (T(1) - beta1_power);
     m.device(d) += (grad - m) * (T(1) - beta1);
     v.device(d) += (grad.square() - v) * (T(1) - beta2);
     var.device(d) -= (m * alpha) / (v.sqrt() + epsilon);
   }
 };
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 template <typename T>
 struct ApplyAdam<CPUDevice, T> : ApplyAdamNonCuda<CPUDevice, T> {};
@@ -334,7 +375,6 @@ struct ApplyCenteredRMSProp<CPUDevice, T> {
 
 }  // namespace functor
 
-
 template <typename Device, typename T>
 class ApplyGradientDescentOp : public OpKernel {
  public:
@@ -377,16 +417,18 @@ class ApplyGradientDescentOp : public OpKernel {
 
 #ifdef TENSORFLOW_USE_SYCL
 template <typename T>
-class ApplyGradientDescentOp < SYCLDevice, T > : public OpKernel {
+class ApplyGradientDescentOp<SYCLDevice, T> : public OpKernel {
  public:
   explicit ApplyGradientDescentOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("use_locking", &use_exclusive_lock_));
   }
 
   void Compute(OpKernelContext* ctx) override {
-    auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0});
+    auto locks =
+        MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0});
     Tensor var;
-    OP_REQUIRES_OK(ctx, GetInputTensorFromVariable(ctx, 0, use_exclusive_lock_, &var));
+    OP_REQUIRES_OK(
+        ctx, GetInputTensorFromVariable(ctx, 0, use_exclusive_lock_, &var));
 
     OP_REQUIRES(
         ctx, var.IsInitialized(),
@@ -407,10 +449,10 @@ class ApplyGradientDescentOp < SYCLDevice, T > : public OpKernel {
     auto size = sizeof(T);
     T alpha = T(0);
     auto src_ptr = GetBase(&alpha_dev);
-    device.memcpyDeviceToHost(&alpha, static_cast<const T *>(src_ptr), size);
+    device.memcpyDeviceToHost(&alpha, static_cast<const T*>(src_ptr), size);
 
-    functor::ApplyGradientDescentSYCL<T>()(device, var.flat<T>(),
-        alpha, delta.flat<T>());
+    functor::ApplyGradientDescentSYCL<T>()(device, var.flat<T>(), alpha,
+                                           delta.flat<T>());
 
     MaybeForwardRefInputToRefOutput(ctx, 0, 0);
   }
@@ -418,7 +460,7 @@ class ApplyGradientDescentOp < SYCLDevice, T > : public OpKernel {
  private:
   bool use_exclusive_lock_;
 };
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 #define REGISTER_KERNELS(D, T)                                                \
   REGISTER_KERNEL_BUILDER(                                                    \
@@ -461,7 +503,7 @@ REGISTER_KERNELS(GPU, double);
 TF_CALL_float(REGISTER_SYCL_KERNELS);
 TF_CALL_double(REGISTER_SYCL_KERNELS);
 #undef REGISTER_SYCL_KERNELS
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 #undef REGISTER_CPU_KERNELS
 #undef REGISTER_KERNELS
@@ -469,14 +511,17 @@ TF_CALL_double(REGISTER_SYCL_KERNELS);
 template <typename Device, typename T>
 class ApplyDelayCompensatedGradientDescentOp : public OpKernel {
  public:
-  explicit ApplyDelayCompensatedGradientDescentOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
+  explicit ApplyDelayCompensatedGradientDescentOp(OpKernelConstruction* ctx)
+      : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("use_locking", &use_exclusive_lock_));
   }
 
   void Compute(OpKernelContext* ctx) override {
-    auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 4});
+    auto locks =
+        MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 4});
     Tensor var;
-    OP_REQUIRES_OK(ctx, GetInputTensorFromVariable(ctx, 0, use_exclusive_lock_, &var));
+    OP_REQUIRES_OK(
+        ctx, GetInputTensorFromVariable(ctx, 0, use_exclusive_lock_, &var));
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
@@ -496,7 +541,8 @@ class ApplyDelayCompensatedGradientDescentOp : public OpKernel {
                 errors::InvalidArgument("lambda is not a scalar: ",
                                         lambda.shape().DebugString()));
     Tensor shadow;
-    OP_REQUIRES_OK(ctx, GetInputTensorFromVariable(ctx, 4, use_exclusive_lock_, &shadow));
+    OP_REQUIRES_OK(
+        ctx, GetInputTensorFromVariable(ctx, 4, use_exclusive_lock_, &shadow));
     OP_REQUIRES(
         ctx, shadow.shape().IsSameSize(var.shape()),
         errors::InvalidArgument("shadow and var do not have the same shape",
@@ -506,8 +552,7 @@ class ApplyDelayCompensatedGradientDescentOp : public OpKernel {
     const Device& device = ctx->template eigen_device<Device>();
     functor::ApplyDelayCompensatedGradientDescent<Device, T>()(
         device, var.flat<T>(), alpha.scalar<T>(), delta.flat<T>(),
-        lambda.scalar<T>(), shadow.flat<T>()
-    );
+        lambda.scalar<T>(), shadow.flat<T>());
 
     MaybeForwardRefInputToRefOutput(ctx, 0, 0);
   }
@@ -516,13 +561,13 @@ class ApplyDelayCompensatedGradientDescentOp : public OpKernel {
   bool use_exclusive_lock_;
 };
 
-#define REGISTER_KERNELS(D, T)                                 \
-  REGISTER_KERNEL_BUILDER(                                     \
-      Name("ApplyDelayCompensatedGradientDescent")             \
-          .Device(DEVICE_##D)                                  \
-          .HostMemory("var")                                   \
-          .HostMemory("shadow")                                \
-          .TypeConstraint<T>("T"),                             \
+#define REGISTER_KERNELS(D, T)                     \
+  REGISTER_KERNEL_BUILDER(                         \
+      Name("ApplyDelayCompensatedGradientDescent") \
+          .Device(DEVICE_##D)                      \
+          .HostMemory("var")                       \
+          .HostMemory("shadow")                    \
+          .TypeConstraint<T>("T"),                 \
       ApplyDelayCompensatedGradientDescentOp<D##Device, T>);
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
@@ -1851,7 +1896,7 @@ REGISTER_KERNELS(double, int32);
 REGISTER_KERNELS(double, int64);
 #undef REGISTER_KERNELS
 
-template <typename Device, typename T>
+template <typename Device, typename T, bool has_l2_shrinkage>
 class ApplyFtrlOp : public OpKernel {
  public:
   explicit ApplyFtrlOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
@@ -1921,7 +1966,8 @@ class ApplyFtrlOp : public OpKernel {
                 errors::InvalidArgument("l2 regularization strength is not a "
                                         "non-negative scalar: ",
                                         l2.shape().DebugString()));
-    const Tensor& lr_power = ctx->input(7);
+    const int lr_power_index = has_l2_shrinkage ? 8 : 7;
+    const Tensor& lr_power = ctx->input(lr_power_index);
     OP_REQUIRES(ctx,
                 TensorShapeUtils::IsScalar(lr_power.shape()) &&
                     lr_power.scalar<T>()() <= static_cast<T>(0),
@@ -1930,10 +1976,25 @@ class ApplyFtrlOp : public OpKernel {
                                         lr_power.shape().DebugString()));
 
     const Device& device = ctx->template eigen_device<Device>();
-    functor::ApplyFtrl<Device, T>()(device, var.flat<T>(), accum.flat<T>(),
-                                    linear.flat<T>(), grad.flat<T>(),
-                                    lr.scalar<T>(), l1.scalar<T>(),
-                                    l2.scalar<T>(), lr_power.scalar<T>());
+    if (has_l2_shrinkage) {
+      const Tensor& l2_shrinkage = ctx->input(7);
+      OP_REQUIRES(
+          ctx,
+          TensorShapeUtils::IsScalar(l2_shrinkage.shape()) &&
+              l2_shrinkage.scalar<T>()() >= static_cast<T>(0),
+          errors::InvalidArgument("l2 shrinkage regularization strength "
+                                  "is not a non-negative scalar: ",
+                                  l2_shrinkage.shape().DebugString()));
+      functor::ApplyFtrlV2<Device, T>()(
+          device, var.flat<T>(), accum.flat<T>(), linear.flat<T>(),
+          grad.flat<T>(), lr.scalar<T>(), l1.scalar<T>(), l2.scalar<T>(),
+          l2_shrinkage.scalar<T>(), lr_power.scalar<T>());
+    } else {
+      functor::ApplyFtrl<Device, T>()(device, var.flat<T>(), accum.flat<T>(),
+                                      linear.flat<T>(), grad.flat<T>(),
+                                      lr.scalar<T>(), l1.scalar<T>(),
+                                      l2.scalar<T>(), lr_power.scalar<T>());
+    }
 
     MaybeForwardRefInputToRefOutput(ctx, 0, 0);
   }
@@ -1948,14 +2009,36 @@ using GPUDevice = Eigen::GpuDevice;
 #define REGISTER_KERNELS(D, T)                                     \
   REGISTER_KERNEL_BUILDER(                                         \
       Name("ApplyFtrl").Device(DEVICE_##D).TypeConstraint<T>("T"), \
-      ApplyFtrlOp<D##Device, T>);                                  \
-  REGISTER_KERNEL_BUILDER(Name("ResourceApplyFtrl")                \
-                              .HostMemory("var")                   \
-                              .HostMemory("accum")                 \
-                              .HostMemory("linear")                \
-                              .Device(DEVICE_##D)                  \
-                              .TypeConstraint<T>("T"),             \
-                          ApplyFtrlOp<D##Device, T>);
+      ApplyFtrlOp<D##Device, T, /*has_l2_shrinkage=*/false>);      \
+  REGISTER_KERNEL_BUILDER(                                         \
+      Name("ResourceApplyFtrl")                                    \
+          .HostMemory("var")                                       \
+          .HostMemory("accum")                                     \
+          .HostMemory("linear")                                    \
+          .Device(DEVICE_##D)                                      \
+          .TypeConstraint<T>("T"),                                 \
+      ApplyFtrlOp<D##Device, T, /*has_l2_shrinkage=*/false>);
+#define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
+
+TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_float(REGISTER_CPU_KERNELS);
+TF_CALL_double(REGISTER_CPU_KERNELS);
+
+#undef REGISTER_CPU_KERNELS
+#undef REGISTER_KERNELS
+
+#define REGISTER_KERNELS(D, T)                                       \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("ApplyFtrlV2").Device(DEVICE_##D).TypeConstraint<T>("T"), \
+      ApplyFtrlOp<D##Device, T, /*has_l2_shrinkage=*/true>);         \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("ResourceApplyFtrlV2")                                    \
+          .HostMemory("var")                                         \
+          .HostMemory("accum")                                       \
+          .HostMemory("linear")                                      \
+          .Device(DEVICE_##D)                                        \
+          .TypeConstraint<T>("T"),                                   \
+      ApplyFtrlOp<D##Device, T, /*has_l2_shrinkage=*/true>);
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
@@ -1966,7 +2049,7 @@ TF_CALL_double(REGISTER_CPU_KERNELS);
 #undef REGISTER_KERNELS
 
 // Note, this op works on cpu only.
-template <typename Device, typename T, typename Tindex>
+template <typename Device, typename T, typename Tindex, bool has_l2_shrinkage>
 class SparseApplyFtrlOp : public OpKernel {
  public:
   explicit SparseApplyFtrlOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
@@ -2036,14 +2119,14 @@ class SparseApplyFtrlOp : public OpKernel {
                 errors::InvalidArgument("l2 regularization strength is not a "
                                         "non-negative scalar: ",
                                         l2.shape().DebugString()));
-    const Tensor& lr_power = ctx->input(8);
+    const int lr_power_index = has_l2_shrinkage ? 9 : 8;
+    const Tensor& lr_power = ctx->input(lr_power_index);
     OP_REQUIRES(ctx,
                 TensorShapeUtils::IsScalar(lr_power.shape()) &&
                     lr_power.scalar<T>()() <= static_cast<T>(0),
                 errors::InvalidArgument("lr_power is not a "
                                         "non-positive scalar: ",
                                         lr_power.shape().DebugString()));
-
     int64 inner_dim = 1;
     for (int d = 1; d < var.dims(); d++) {
       OP_REQUIRES(ctx, var.dim_size(d) == grad.dim_size(d),
@@ -2061,6 +2144,18 @@ class SparseApplyFtrlOp : public OpKernel {
                 errors::InvalidArgument(
                     "Inner dimension should be greater than zero."));
 
+    const Tensor* l2_shrinkage;
+    if (has_l2_shrinkage) {
+      l2_shrinkage = &ctx->input(8);
+      OP_REQUIRES(
+          ctx,
+          TensorShapeUtils::IsScalar(l2_shrinkage->shape()) &&
+              l2_shrinkage->scalar<T>()() >= static_cast<T>(0),
+          errors::InvalidArgument("l2 shrinkage regularization strength "
+                                  "is not a non-negative scalar: ",
+                                  l2_shrinkage->shape().DebugString()));
+    }
+
     if (N > 0) {
       if (inner_dim > 1) {
         const Tindex first_dim_size = var.dim_size(0);
@@ -2072,6 +2167,10 @@ class SparseApplyFtrlOp : public OpKernel {
         T lr_scalar = lr.scalar<T>()();
         T l1_scalar = l1.scalar<T>()();
         T l2_scalar = l2.scalar<T>()();
+        T l2_shrinkage_scalar;
+        if (has_l2_shrinkage) {
+          l2_shrinkage_scalar = l2_shrinkage->scalar<T>()();
+        }
         T lr_power_scalar = lr_power.scalar<T>()();
 
         for (Tindex i = 0; i < N; i++) {
@@ -2085,41 +2184,56 @@ class SparseApplyFtrlOp : public OpKernel {
           auto grad = grad_flat.template chip<0>(i);
           auto var = var_flat.template chip<0>(index);
 
-          auto new_accum = accum + grad.square();
-          if (lr_power_scalar == static_cast<T>(-0.5)) {
-            linear +=
-                grad - (new_accum.sqrt() - accum.sqrt()) / lr_scalar * var;
-          } else {
-            linear += grad -
-                      (new_accum.pow(-lr_power_scalar) -
-                       accum.pow(-lr_power_scalar)) /
-                          lr_scalar * var;
-          }
-          auto x = (linear.constant(l1_scalar) * linear.sign() - linear);
-          if (lr_power_scalar == static_cast<T>(-0.5)) {
-            auto y = new_accum.sqrt() / new_accum.constant(lr_scalar) +
-                     linear.constant(static_cast<T>(2) * l2_scalar);
-            var = x / y;
+// Use a macro to implement the computation here due to the templating of the
+// eigen tensor library.
+#define COMPUTE_FTRL(grad_to_use)                                              \
+  auto new_accum = accum + grad_to_use.square();                               \
+  if (lr_power_scalar == static_cast<T>(-0.5)) {                               \
+    linear +=                                                                  \
+        grad_to_use - (new_accum.sqrt() - accum.sqrt()) / lr_scalar * var;     \
+  } else {                                                                     \
+    linear += grad_to_use - (new_accum.pow(-lr_power_scalar) -                 \
+                             accum.pow(-lr_power_scalar)) /                    \
+                                lr_scalar * var;                               \
+  }                                                                            \
+  auto x = (linear.constant(l1_scalar) * linear.sign() - linear);              \
+  if (lr_power_scalar == static_cast<T>(-0.5)) {                               \
+    auto y = new_accum.sqrt() / new_accum.constant(lr_scalar) +                \
+             linear.constant(static_cast<T>(2) * l2_scalar);                   \
+    var = x / y;                                                               \
+  } else {                                                                     \
+    auto y = new_accum.pow(-lr_power_scalar) / new_accum.constant(lr_scalar) + \
+             linear.constant(static_cast<T>(2) * l2_scalar);                   \
+    var = x / y;                                                               \
+  }                                                                            \
+  var = (linear.abs() > linear.constant(l1_scalar))                            \
+            .select(var, var.constant(static_cast<T>(0)));                     \
+  accum += grad_to_use.square();
+
+          if (has_l2_shrinkage) {
+            auto grad_with_shrinkage =
+                grad + static_cast<T>(2) * l2_shrinkage_scalar * var;
+            COMPUTE_FTRL(grad_with_shrinkage);
           } else {
-            auto y = new_accum.pow(-lr_power_scalar) /
-                         new_accum.constant(lr_scalar) +
-                     linear.constant(static_cast<T>(2) * l2_scalar);
-            var = x / y;
+            COMPUTE_FTRL(grad);
           }
-          var = (linear.abs() > linear.constant(l1_scalar))
-                    .select(var, var.constant(static_cast<T>(0)));
-          accum += grad.square();
         }
+#undef COMPUTE_FTRL
       } else {
+        T lr_scalar = lr.scalar<T>()();
+        T l1_scalar = l1.scalar<T>()();
+        T l2_scalar = l2.scalar<T>()();
+        T lr_power_scalar = lr_power.scalar<T>()();
+        T l2_shrinkage_scalar;
+        if (has_l2_shrinkage) {
+          l2_shrinkage_scalar = l2_shrinkage->scalar<T>()();
+        }
+
         auto indices_vec = indices.vec<Tindex>();
         auto var_flat = var.flat<T>();
         auto accum_flat = accum.flat<T>();
         auto linear_flat = linear.flat<T>();
         auto grad_flat = grad.flat<T>();
-        T lr_scalar = lr.scalar<T>()();
-        T l1_scalar = l1.scalar<T>()();
-        T l2_scalar = l2.scalar<T>()();
-        T lr_power_scalar = lr_power.scalar<T>()();
         const Tindex first_dim_size = accum_flat.size();
 
         for (Tindex i = 0; i < N; i++) {
@@ -2131,7 +2245,13 @@ class SparseApplyFtrlOp : public OpKernel {
           T& a = accum_flat(index);
           T& l = linear_flat(index);
           T& v = var_flat(index);
-          const T& g = grad_flat(i);
+          T g;
+          if (has_l2_shrinkage) {
+            g = grad_flat(i) +
+                (static_cast<T>(2) * l2_shrinkage_scalar * var_flat(i));
+          } else {
+            g = grad_flat(i);
+          }
 
           T updated_a = a + g * g;
           using Eigen::numext::pow;
@@ -2153,17 +2273,43 @@ class SparseApplyFtrlOp : public OpKernel {
   bool use_exclusive_lock_;
 };
 
-#define REGISTER_KERNELS(T, Tindices)                                 \
-  REGISTER_KERNEL_BUILDER(Name("SparseApplyFtrl")                     \
-                              .Device(DEVICE_CPU)                     \
-                              .TypeConstraint<T>("T")                 \
-                              .TypeConstraint<Tindices>("Tindices"),  \
-                          SparseApplyFtrlOp<CPUDevice, T, Tindices>); \
-  REGISTER_KERNEL_BUILDER(Name("ResourceSparseApplyFtrl")             \
-                              .Device(DEVICE_CPU)                     \
-                              .TypeConstraint<T>("T")                 \
-                              .TypeConstraint<Tindices>("Tindices"),  \
-                          SparseApplyFtrlOp<CPUDevice, T, Tindices>);
+#define REGISTER_KERNELS(T, Tindices)                                         \
+  REGISTER_KERNEL_BUILDER(                                                    \
+      Name("SparseApplyFtrl")                                                 \
+          .Device(DEVICE_CPU)                                                 \
+          .TypeConstraint<T>("T")                                             \
+          .TypeConstraint<Tindices>("Tindices"),                              \
+      SparseApplyFtrlOp<CPUDevice, T, Tindices, /*has_l2_shrinkage=*/false>); \
+  REGISTER_KERNEL_BUILDER(                                                    \
+      Name("ResourceSparseApplyFtrl")                                         \
+          .Device(DEVICE_CPU)                                                 \
+          .TypeConstraint<T>("T")                                             \
+          .TypeConstraint<Tindices>("Tindices"),                              \
+      SparseApplyFtrlOp<CPUDevice, T, Tindices, /*has_l2_shrinkage=*/false>);
+#define REGISTER_CPU_KERNELS(T) \
+  REGISTER_KERNELS(T, int32);   \
+  REGISTER_KERNELS(T, int64);
+
+TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_float(REGISTER_CPU_KERNELS);
+TF_CALL_double(REGISTER_CPU_KERNELS);
+
+#undef REGISTER_CPU_KERNELS
+#undef REGISTER_KERNELS
+
+#define REGISTER_KERNELS(T, Tindices)                                        \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("SparseApplyFtrlV2")                                              \
+          .Device(DEVICE_CPU)                                                \
+          .TypeConstraint<T>("T")                                            \
+          .TypeConstraint<Tindices>("Tindices"),                             \
+      SparseApplyFtrlOp<CPUDevice, T, Tindices, /*has_l2_shrinkage=*/true>); \
+  REGISTER_KERNEL_BUILDER(                                                   \
+      Name("ResourceSparseApplyFtrlV2")                                      \
+          .Device(DEVICE_CPU)                                                \
+          .TypeConstraint<T>("T")                                            \
+          .TypeConstraint<Tindices>("Tindices"),                             \
+      SparseApplyFtrlOp<CPUDevice, T, Tindices, /*has_l2_shrinkage=*/true>);
 #define REGISTER_CPU_KERNELS(T) \
   REGISTER_KERNELS(T, int32);   \
   REGISTER_KERNELS(T, int64);
@@ -2487,21 +2633,25 @@ class ApplyAdamOp : public OpKernel {
 
 #ifdef TENSORFLOW_USE_SYCL
 template <typename T>
-class ApplyAdamOp < SYCLDevice, T> : public OpKernel {
+class ApplyAdamOp<SYCLDevice, T> : public OpKernel {
  public:
   explicit ApplyAdamOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("use_locking", &use_exclusive_lock_));
   }
 
   void Compute(OpKernelContext* ctx) override {
-    auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1, 2});
+    auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_,
+                                                      {0, 1, 2});
 
     Tensor var;
-    OP_REQUIRES_OK(ctx, GetInputTensorFromVariable(ctx, 0, use_exclusive_lock_, &var));
+    OP_REQUIRES_OK(
+        ctx, GetInputTensorFromVariable(ctx, 0, use_exclusive_lock_, &var));
     Tensor m;
-    OP_REQUIRES_OK(ctx, GetInputTensorFromVariable(ctx, 1, use_exclusive_lock_, &m));
+    OP_REQUIRES_OK(ctx,
+                   GetInputTensorFromVariable(ctx, 1, use_exclusive_lock_, &m));
     Tensor v;
-    OP_REQUIRES_OK(ctx, GetInputTensorFromVariable(ctx, 2, use_exclusive_lock_, &v));
+    OP_REQUIRES_OK(ctx,
+                   GetInputTensorFromVariable(ctx, 2, use_exclusive_lock_, &v));
     OP_REQUIRES(
         ctx, var.IsInitialized(),
         errors::FailedPrecondition(
@@ -2532,23 +2682,24 @@ class ApplyAdamOp < SYCLDevice, T> : public OpKernel {
     auto device = ctx->eigen_sycl_device();
     auto size = sizeof(T);
     auto src_ptr = GetBase(&beta1_power_dev);
-    device.memcpyDeviceToHost(&beta1_power, static_cast<const T *>(src_ptr), size);
+    device.memcpyDeviceToHost(&beta1_power, static_cast<const T*>(src_ptr),
+                              size);
 
     src_ptr = GetBase(&beta2_power_dev);
-    device.memcpyDeviceToHost(&beta2_power, static_cast<const T *>(src_ptr), size);
+    device.memcpyDeviceToHost(&beta2_power, static_cast<const T*>(src_ptr),
+                              size);
 
     src_ptr = GetBase(&lr_dev);
-    device.memcpyDeviceToHost(&lr, static_cast<const T *>(src_ptr), size);
+    device.memcpyDeviceToHost(&lr, static_cast<const T*>(src_ptr), size);
 
     src_ptr = GetBase(&beta1_dev);
-    device.memcpyDeviceToHost(&beta1, static_cast<const T *>(src_ptr), size);
+    device.memcpyDeviceToHost(&beta1, static_cast<const T*>(src_ptr), size);
 
     src_ptr = GetBase(&beta2_dev);
-    device.memcpyDeviceToHost(&beta2, static_cast<const T *>(src_ptr), size);
+    device.memcpyDeviceToHost(&beta2, static_cast<const T*>(src_ptr), size);
 
     src_ptr = GetBase(&epsilon_dev);
-    device.memcpyDeviceToHost(&epsilon, static_cast<const T *>(src_ptr), size);
-
+    device.memcpyDeviceToHost(&epsilon, static_cast<const T*>(src_ptr), size);
 
     OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(beta1_power_dev.shape()),
                 errors::InvalidArgument("beta1_power is not a scalar: ",
@@ -2585,11 +2736,9 @@ class ApplyAdamOp < SYCLDevice, T> : public OpKernel {
                                 var.shape().DebugString(), " ",
                                 grad.shape().DebugString()));
 
-    functor::ApplyAdamSYCL<T>()(device, var.flat<T>(), m.flat<T>(),
-                                    v.flat<T>(), beta1_power,
-                                    beta2_power, lr,
-                                    beta1, beta2,
-                                    epsilon, grad.flat<T>());
+    functor::ApplyAdamSYCL<T>()(device, var.flat<T>(), m.flat<T>(), v.flat<T>(),
+                                beta1_power, beta2_power, lr, beta1, beta2,
+                                epsilon, grad.flat<T>());
 
     MaybeForwardRefInputToRefOutput(ctx, 0, 0);
   }
@@ -2597,7 +2746,7 @@ class ApplyAdamOp < SYCLDevice, T> : public OpKernel {
  private:
   bool use_exclusive_lock_;
 };
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 using CPUDevice = Eigen::ThreadPoolDevice;
 using GPUDevice = Eigen::GpuDevice;
diff --git a/tensorflow/core/kernels/training_ops.h b/tensorflow/core/kernels/training_ops.h
index 0a3c5d361e..8b0cde19aa 100644
--- a/tensorflow/core/kernels/training_ops.h
+++ b/tensorflow/core/kernels/training_ops.h
@@ -113,6 +113,19 @@ struct ApplyFtrl {
                   typename TTypes<T>::ConstScalar lr_power);
 };
 
+template <typename Device, typename T>
+struct ApplyFtrlV2 {
+  void operator()(const Device& d, typename TTypes<T>::Flat var,
+                  typename TTypes<T>::Flat accum,
+                  typename TTypes<T>::Flat linear,
+                  typename TTypes<T>::ConstFlat grad,
+                  typename TTypes<T>::ConstScalar lr,
+                  typename TTypes<T>::ConstScalar l1,
+                  typename TTypes<T>::ConstScalar l2,
+                  typename TTypes<T>::ConstScalar l2_shrinkage,
+                  typename TTypes<T>::ConstScalar lr_power);
+};
+
 template <typename Device, typename T>
 struct ApplyMomentum {
   void operator()(const Device& d, typename TTypes<T>::Flat var,
diff --git a/tensorflow/core/ops/training_ops.cc b/tensorflow/core/ops/training_ops.cc
index 5bb93daea2..f77817fbfc 100644
--- a/tensorflow/core/ops/training_ops.cc
+++ b/tensorflow/core/ops/training_ops.cc
@@ -983,6 +983,178 @@ use_locking: If `True`, updating of the var and accum tensors will be protected
   contention.
 )doc");
 
+REGISTER_OP("ApplyFtrlV2")
+    .Input("var: Ref(T)")
+    .Input("accum: Ref(T)")
+    .Input("linear: Ref(T)")
+    .Input("grad: T")
+    .Input("lr: T")
+    .Input("l1: T")
+    .Input("l2: T")
+    .Input("l2_shrinkage: T")
+    .Input("lr_power: T")
+    .Output("out: Ref(T)")
+    .Attr("T: numbertype")
+    .Attr("use_locking: bool = false")
+    .SetShapeFn([](InferenceContext* c) {
+      return ApplyFtrlShapeFn(c, false /* sparse */);
+    })
+    .Doc(R"doc(
+Update '*var' according to the Ftrl-proximal scheme.
+
+grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+linear += grad_with_shrinkage +
+    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+accum = accum_new
+
+var: Should be from a Variable().
+accum: Should be from a Variable().
+linear: Should be from a Variable().
+grad: The gradient.
+lr: Scaling factor. Must be a scalar.
+l1: L1 regulariation. Must be a scalar.
+l2: online L2 regulariation. Must be a scalar.
+l2: L2 shrinkage regulariation. Must be a scalar.
+lr_power: Scaling factor. Must be a scalar.
+out: Same as "var".
+use_locking: If `True`, updating of the var and accum tensors will be protected
+  by a lock; otherwise the behavior is undefined, but may exhibit less
+  contention.
+)doc");
+
+REGISTER_OP("SparseApplyFtrlV2")
+    .Input("var: Ref(T)")
+    .Input("accum: Ref(T)")
+    .Input("linear: Ref(T)")
+    .Input("grad: T")
+    .Input("indices: Tindices")
+    .Input("lr: T")
+    .Input("l1: T")
+    .Input("l2: T")
+    .Input("l2_shrinkage: T")
+    .Input("lr_power: T")
+    .Output("out: Ref(T)")
+    .Attr("T: numbertype")
+    .Attr("Tindices: {int32, int64}")
+    .Attr("use_locking: bool = false")
+    .SetShapeFn([](InferenceContext* c) {
+      return ApplyFtrlShapeFn(c, true /* sparse */);
+    })
+    .Doc(R"doc(
+Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+
+That is for rows we have grad for, we update var, accum and linear as follows:
+grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+linear += grad_with_shrinkage +
+    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+accum = accum_new
+
+var: Should be from a Variable().
+accum: Should be from a Variable().
+linear: Should be from a Variable().
+grad: The gradient.
+indices: A vector of indices into the first dimension of var and accum.
+lr: Scaling factor. Must be a scalar.
+l1: L1 regularization. Must be a scalar.
+l2: onine L2 regularization. Must be a scalar.
+l2: L2 shrinkage regulariation. Must be a scalar.
+lr_power: Scaling factor. Must be a scalar.
+out: Same as "var".
+use_locking: If `True`, updating of the var and accum tensors will be protected
+  by a lock; otherwise the behavior is undefined, but may exhibit less
+  contention.
+)doc");
+
+REGISTER_OP("ResourceApplyFtrlV2")
+    .Input("var: resource")
+    .Input("accum: resource")
+    .Input("linear: resource")
+    .Input("grad: T")
+    .Input("lr: T")
+    .Input("l1: T")
+    .Input("l2: T")
+    .Input("l2_shrinkage: T")
+    .Input("lr_power: T")
+    .Attr("T: numbertype")
+    .Attr("use_locking: bool = false")
+    .SetShapeFn([](InferenceContext* c) {
+      return ApplyFtrlShapeFn(c, false /* sparse */);
+    })
+    .Doc(R"doc(
+Update '*var' according to the Ftrl-proximal scheme.
+
+grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+linear += grad_with_shrinkage +
+    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+accum = accum_new
+
+var: Should be from a Variable().
+accum: Should be from a Variable().
+linear: Should be from a Variable().
+grad: The gradient.
+lr: Scaling factor. Must be a scalar.
+l1: L1 regulariation. Must be a scalar.
+l2: onine L2 regularization. Must be a scalar.
+l2: L2 shrinkage regulariation. Must be a scalar.
+lr_power: Scaling factor. Must be a scalar.
+use_locking: If `True`, updating of the var and accum tensors will be protected
+  by a lock; otherwise the behavior is undefined, but may exhibit less
+  contention.
+)doc");
+
+REGISTER_OP("ResourceSparseApplyFtrlV2")
+    .Input("var: resource")
+    .Input("accum: resource")
+    .Input("linear: resource")
+    .Input("grad: T")
+    .Input("indices: Tindices")
+    .Input("lr: T")
+    .Input("l1: T")
+    .Input("l2: T")
+    .Input("l2_shrinkage: T")
+    .Input("lr_power: T")
+    .Attr("T: numbertype")
+    .Attr("Tindices: {int32, int64}")
+    .Attr("use_locking: bool = false")
+    .SetShapeFn([](InferenceContext* c) {
+      return ApplyFtrlShapeFn(c, true /* sparse */);
+    })
+    .Doc(R"doc(
+Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+
+That is for rows we have grad for, we update var, accum and linear as follows:
+grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+linear += grad_with_shrinkage +
+    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+accum = accum_new
+
+var: Should be from a Variable().
+accum: Should be from a Variable().
+linear: Should be from a Variable().
+grad: The gradient.
+indices: A vector of indices into the first dimension of var and accum.
+lr: Scaling factor. Must be a scalar.
+l1: L1 regularization. Must be a scalar.
+l2: onine L2 regularization. Must be a scalar.
+l2: L2 shrinkage regulariation. Must be a scalar.
+lr_power: Scaling factor. Must be a scalar.
+use_locking: If `True`, updating of the var and accum tensors will be protected
+  by a lock; otherwise the behavior is undefined, but may exhibit less
+  contention.
+)doc");
+
 static Status ApplyMomentumShapeFn(InferenceContext* c, bool sparse) {
   ShapeHandle unused;
   ShapeHandle s = ShapeOrHandleShape(c, 0);                       // var
diff --git a/tensorflow/python/training/ftrl.py b/tensorflow/python/training/ftrl.py
index 618f3baf08..81679cd724 100644
--- a/tensorflow/python/training/ftrl.py
+++ b/tensorflow/python/training/ftrl.py
@@ -29,6 +29,9 @@ class FtrlOptimizer(optimizer.Optimizer):
 
   See this [paper](
   https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
+  This version has support for both online L2 (the L2 penalty given in the paper
+  above) and shrinkage-type L2 (which is the addition of an L2 penalty to the
+  loss function).
   """
 
   def __init__(self,
@@ -40,8 +43,9 @@ class FtrlOptimizer(optimizer.Optimizer):
                use_locking=False,
                name="Ftrl",
                accum_name=None,
-               linear_name=None):
-    """Construct a new FTRL optimizer.
+               linear_name=None,
+               l2_shrinkage_regularization_strength=0.0):
+    r"""Construct a new FTRL optimizer.
 
     Args:
       learning_rate: A float value or a constant float `Tensor`.
@@ -59,6 +63,13 @@ class FtrlOptimizer(optimizer.Optimizer):
         accumulator.  If not present, defaults to name.
       linear_name: The suffix for the variable that keeps the linear gradient
         accumulator.  If not present, defaults to name + "_1".
+      l2_shrinkage_regularization_strength: A float value, must be greater than
+        or equal to zero. This differs from L2 above in that the L2 above is a
+        stabilization penalty, whereas this L2 shrinkage is a magnitude penalty.
+        Specifically, the FTRL formulation can be written as:
+        w_{t+1} = argmin_w(\hat{g}_{1:t}w + L1*||w||_1 + L2*||w||_2), where
+        \hat{g} = g + (2*L2_shrinkage*w), and g is the gradient of the loss
+        function w.r.t. the weights w.
 
     Raises:
       ValueError: If one of the arguments is invalid.
@@ -79,16 +90,23 @@ class FtrlOptimizer(optimizer.Optimizer):
       raise ValueError(
           "l2_regularization_strength %f needs to be positive or zero" %
           l2_regularization_strength)
+    if l2_shrinkage_regularization_strength < 0.0:
+      raise ValueError(
+          "l2_shrinkage_regularization_strength %f needs to be positive"
+          " or zero" % l2_shrinkage_regularization_strength)
 
     self._learning_rate = learning_rate
     self._learning_rate_power = learning_rate_power
     self._initial_accumulator_value = initial_accumulator_value
     self._l1_regularization_strength = l1_regularization_strength
     self._l2_regularization_strength = l2_regularization_strength
+    self._l2_shrinkage_regularization_strength = (
+        l2_shrinkage_regularization_strength)
     self._learning_rate_tensor = None
     self._learning_rate_power_tensor = None
     self._l1_regularization_strength_tensor = None
     self._l2_regularization_strength_tensor = None
+    self._l2_shrinkage_regularization_strength_tensor = None
     self._accum_name = accum_name
     self._linear_name = linear_name
 
@@ -108,69 +126,137 @@ class FtrlOptimizer(optimizer.Optimizer):
         self._l1_regularization_strength, name="l1_regularization_strength")
     self._l2_regularization_strength_tensor = ops.convert_to_tensor(
         self._l2_regularization_strength, name="l2_regularization_strength")
+    self._l2_shrinkage_regularization_strength_tensor = ops.convert_to_tensor(
+        self._l2_shrinkage_regularization_strength,
+        name="l2_shrinkage_regularization_strength")
     self._learning_rate_power_tensor = ops.convert_to_tensor(
         self._learning_rate_power, name="learning_rate_power")
 
   def _apply_dense(self, grad, var):
     accum = self.get_slot(var, "accum")
     linear = self.get_slot(var, "linear")
-    return training_ops.apply_ftrl(
-        var,
-        accum,
-        linear,
-        grad,
-        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
-        math_ops.cast(self._l1_regularization_strength_tensor,
-                      var.dtype.base_dtype),
-        math_ops.cast(self._l2_regularization_strength_tensor,
-                      var.dtype.base_dtype),
-        math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
-        use_locking=self._use_locking)
+    if self._l2_shrinkage_regularization_strength <= 0.0:
+      return training_ops.apply_ftrl(
+          var,
+          accum,
+          linear,
+          grad,
+          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
+          use_locking=self._use_locking)
+    else:
+      return training_ops.apply_ftrl_v2(
+          var,
+          accum,
+          linear,
+          grad,
+          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
+          use_locking=self._use_locking)
 
   def _resource_apply_dense(self, grad, var):
     accum = self.get_slot(var, "accum")
     linear = self.get_slot(var, "linear")
-    return training_ops.resource_apply_ftrl(
-        var.handle,
-        accum.handle,
-        linear.handle,
-        grad,
-        math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
-        math_ops.cast(self._l1_regularization_strength_tensor,
-                      grad.dtype.base_dtype),
-        math_ops.cast(self._l2_regularization_strength_tensor,
-                      grad.dtype.base_dtype),
-        math_ops.cast(self._learning_rate_power_tensor, grad.dtype.base_dtype),
-        use_locking=self._use_locking)
+    if self._l2_shrinkage_regularization_strength <= 0.0:
+      return training_ops.resource_apply_ftrl(
+          var.handle,
+          accum.handle,
+          linear.handle,
+          grad,
+          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
+          use_locking=self._use_locking)
+    else:
+      return training_ops.resource_apply_ftrl_v2(
+          var.handle,
+          accum.handle,
+          linear.handle,
+          grad,
+          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
+          use_locking=self._use_locking)
 
   def _apply_sparse(self, grad, var):
     accum = self.get_slot(var, "accum")
     linear = self.get_slot(var, "linear")
-    return training_ops.sparse_apply_ftrl(
-        var,
-        accum,
-        linear,
-        grad.values,
-        grad.indices,
-        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
-        math_ops.cast(self._l1_regularization_strength_tensor,
-                      var.dtype.base_dtype),
-        math_ops.cast(self._l2_regularization_strength_tensor,
-                      var.dtype.base_dtype),
-        math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
-        use_locking=self._use_locking)
+    if self._l2_shrinkage_regularization_strength <= 0.0:
+      return training_ops.sparse_apply_ftrl(
+          var,
+          accum,
+          linear,
+          grad.values,
+          grad.indices,
+          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
+          use_locking=self._use_locking)
+    else:
+      return training_ops.sparse_apply_ftrl_v2(
+          var,
+          accum,
+          linear,
+          grad.values,
+          grad.indices,
+          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor,
+                        var.dtype.base_dtype),
+          math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
+                        grad.dtype.base_dtype),
+          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
+          use_locking=self._use_locking)
 
   def _resource_apply_sparse(self, grad, var, indices):
     accum = self.get_slot(var, "accum")
     linear = self.get_slot(var, "linear")
-    return training_ops.resource_sparse_apply_ftrl(
-        var.handle,
-        accum.handle,
-        linear.handle,
-        grad,
-        indices,
-        math_ops.cast(self._learning_rate_tensor, grad.dtype),
-        math_ops.cast(self._l1_regularization_strength_tensor, grad.dtype),
-        math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
-        math_ops.cast(self._learning_rate_power_tensor, grad.dtype),
-        use_locking=self._use_locking)
+    if self._l2_shrinkage_regularization_strength <= 0.0:
+      return training_ops.resource_sparse_apply_ftrl(
+          var.handle,
+          accum.handle,
+          linear.handle,
+          grad,
+          indices,
+          math_ops.cast(self._learning_rate_tensor, grad.dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor, grad.dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
+          math_ops.cast(self._learning_rate_power_tensor, grad.dtype),
+          use_locking=self._use_locking)
+    else:
+      return training_ops.resource_sparse_apply_ftrl_v2(
+          var.handle,
+          accum.handle,
+          linear.handle,
+          grad,
+          indices,
+          math_ops.cast(self._learning_rate_tensor, grad.dtype),
+          math_ops.cast(self._l1_regularization_strength_tensor, grad.dtype),
+          math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
+          math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
+                        grad.dtype),
+          math_ops.cast(self._learning_rate_power_tensor, grad.dtype),
+          use_locking=self._use_locking)
+
diff --git a/tensorflow/python/training/ftrl_test.py b/tensorflow/python/training/ftrl_test.py
index f4cf17f6f0..775bdb3f60 100644
--- a/tensorflow/python/training/ftrl_test.py
+++ b/tensorflow/python/training/ftrl_test.py
@@ -179,6 +179,43 @@ class FtrlOptimizerTest(test.TestCase):
         self.assertAllCloseAccordingToType(
             np.array([-0.02406147, -0.04830509]), v1_val)
 
+  def testFtrlWithL1_L2_L2Shrinkage(self):
+    """Test the new FTRL op with support for l2 shrinkage.
+
+    The addition of this parameter which places a constant pressure on weights
+    towards the origin causes the gradient descent trajectory to differ. The
+    weights will tend to have smaller magnitudes with this parameter set.
+    """
+    for dtype in [dtypes.half, dtypes.float32]:
+      with self.test_session() as sess:
+        var0 = variables.Variable([1.0, 2.0], dtype=dtype)
+        var1 = variables.Variable([4.0, 3.0], dtype=dtype)
+        grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
+        grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
+
+        opt = ftrl.FtrlOptimizer(
+            3.0,
+            initial_accumulator_value=0.1,
+            l1_regularization_strength=0.001,
+            l2_regularization_strength=2.0,
+            l2_shrinkage_regularization_strength=0.1)
+        update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+        variables.global_variables_initializer().run()
+
+        v0_val, v1_val = sess.run([var0, var1])
+        self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
+        self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
+
+        # Run 10 steps FTRL
+        for _ in range(10):
+          update.run()
+
+        v0_val, v1_val = sess.run([var0, var1])
+        self.assertAllCloseAccordingToType(
+            np.array([-0.22078767, -0.41378114]), v0_val)
+        self.assertAllCloseAccordingToType(
+            np.array([-0.02919818, -0.07343706]), v1_val)
+
   def applyOptimizer(self, opt, dtype, steps=5, is_sparse=False):
     if is_sparse:
       var0 = variables.Variable([[0.0], [0.0]], dtype=dtype)
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
index 2dc11df57b..5cff6087ef 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
@@ -17,7 +17,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'learning_rate\', \'learning_rate_power\', \'initial_accumulator_value\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\', \'accum_name\', \'linear_name\'], varargs=None, keywords=None, defaults=[\'-0.5\', \'0.1\', \'0.0\', \'0.0\', \'False\', \'Ftrl\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'learning_rate\', \'learning_rate_power\', \'initial_accumulator_value\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\', \'accum_name\', \'linear_name\', \'l2_shrinkage_regularization_strength\'], varargs=None, keywords=None, defaults=[\'-0.5\', \'0.1\', \'0.0\', \'0.0\', \'False\', \'Ftrl\', \'None\', \'None\', \'0.0\'], "
   }
   member_method {
     name: "apply_gradients"
-- 
GitLab


From 5b065f41186c1c9893fb8f9f5a3fde527051e7b3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 16:06:58 -0700
Subject: [PATCH 0708/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161018659
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 318 +++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 366 ++++++++++++++++++
 2 files changed, 684 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 33461bbfdc..4da4f6bbb4 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -1136,6 +1136,82 @@ op {
     }
   }
 }
+op {
+  name: "ApplyFtrlV2"
+  input_arg {
+    name: "var"
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "accum"
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "linear"
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "grad"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "out"
+    type_attr: "T"
+    is_ref: true
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+}
 op {
   name: "ApplyGradientDescent"
   input_arg {
@@ -19999,6 +20075,75 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "ResourceApplyFtrlV2"
+  input_arg {
+    name: "var"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "accum"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "linear"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "grad"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "ResourceApplyGradientDescent"
   input_arg {
@@ -20658,6 +20803,89 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "ResourceSparseApplyFtrlV2"
+  input_arg {
+    name: "var"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "accum"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "linear"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "grad"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "lr"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "ResourceSparseApplyMomentum"
   input_arg {
@@ -24191,6 +24419,96 @@ op {
     }
   }
 }
+op {
+  name: "SparseApplyFtrlV2"
+  input_arg {
+    name: "var"
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "accum"
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "linear"
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "grad"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "lr"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "out"
+    type_attr: "T"
+    is_ref: true
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+}
 op {
   name: "SparseApplyMomentum"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 971f6f82f8..46fbbda223 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -1169,6 +1169,94 @@ op {
   summary: "Update \'*var\' according to the Ftrl-proximal scheme."
   description: "accum_new = accum + grad * grad\nlinear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
 }
+op {
+  name: "ApplyFtrlV2"
+  input_arg {
+    name: "var"
+    description: "Should be from a Variable()."
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "accum"
+    description: "Should be from a Variable()."
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "linear"
+    description: "Should be from a Variable()."
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "grad"
+    description: "The gradient."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    description: "L1 regulariation. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    description: "L2 shrinkage regulariation. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "out"
+    description: "Same as \"var\"."
+    type_attr: "T"
+    is_ref: true
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If `True`, updating of the var and accum tensors will be protected\nby a lock; otherwise the behavior is undefined, but may exhibit less\ncontention."
+  }
+  summary: "Update \'*var\' according to the Ftrl-proximal scheme."
+  description: "grad_with_shrinkage = grad + 2 * l2_shrinkage * var\naccum_new = accum + grad_with_shrinkage * grad_with_shrinkage\nlinear += grad_with_shrinkage +\n    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
+}
 op {
   name: "ApplyGradientDescent"
   input_arg {
@@ -19730,6 +19818,86 @@ op {
   description: "accum_new = accum + grad * grad\nlinear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
   is_stateful: true
 }
+op {
+  name: "ResourceApplyFtrlV2"
+  input_arg {
+    name: "var"
+    description: "Should be from a Variable()."
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "accum"
+    description: "Should be from a Variable()."
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "linear"
+    description: "Should be from a Variable()."
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "grad"
+    description: "The gradient."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    description: "L1 regulariation. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    description: "L2 shrinkage regulariation. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If `True`, updating of the var and accum tensors will be protected\nby a lock; otherwise the behavior is undefined, but may exhibit less\ncontention."
+  }
+  summary: "Update \'*var\' according to the Ftrl-proximal scheme."
+  description: "grad_with_shrinkage = grad + 2 * l2_shrinkage * var\naccum_new = accum + grad_with_shrinkage * grad_with_shrinkage\nlinear += grad_with_shrinkage +\n    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
+  is_stateful: true
+}
 op {
   name: "ResourceApplyGradientDescent"
   input_arg {
@@ -20482,6 +20650,101 @@ op {
   description: "That is for rows we have grad for, we update var, accum and linear as follows:\naccum_new = accum + grad * grad\nlinear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
   is_stateful: true
 }
+op {
+  name: "ResourceSparseApplyFtrlV2"
+  input_arg {
+    name: "var"
+    description: "Should be from a Variable()."
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "accum"
+    description: "Should be from a Variable()."
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "linear"
+    description: "Should be from a Variable()."
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "grad"
+    description: "The gradient."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    description: "A vector of indices into the first dimension of var and accum."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "lr"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    description: "L1 regularization. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    description: "L2 shrinkage regulariation. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If `True`, updating of the var and accum tensors will be protected\nby a lock; otherwise the behavior is undefined, but may exhibit less\ncontention."
+  }
+  summary: "Update relevant entries in \'*var\' according to the Ftrl-proximal scheme."
+  description: "That is for rows we have grad for, we update var, accum and linear as follows:\ngrad_with_shrinkage = grad + 2 * l2_shrinkage * var\naccum_new = accum + grad_with_shrinkage * grad_with_shrinkage\nlinear += grad_with_shrinkage +\n    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
+  is_stateful: true
+}
 op {
   name: "ResourceSparseApplyMomentum"
   input_arg {
@@ -24192,6 +24455,109 @@ op {
   summary: "Update relevant entries in \'*var\' according to the Ftrl-proximal scheme."
   description: "That is for rows we have grad for, we update var, accum and linear as follows:\naccum_new = accum + grad * grad\nlinear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
 }
+op {
+  name: "SparseApplyFtrlV2"
+  input_arg {
+    name: "var"
+    description: "Should be from a Variable()."
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "accum"
+    description: "Should be from a Variable()."
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "linear"
+    description: "Should be from a Variable()."
+    type_attr: "T"
+    is_ref: true
+  }
+  input_arg {
+    name: "grad"
+    description: "The gradient."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    description: "A vector of indices into the first dimension of var and accum."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "lr"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l1"
+    description: "L1 regularization. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2"
+    description: "L2 shrinkage regulariation. Must be a scalar."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "l2_shrinkage"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "lr_power"
+    description: "Scaling factor. Must be a scalar."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "out"
+    description: "Same as \"var\"."
+    type_attr: "T"
+    is_ref: true
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "use_locking"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If `True`, updating of the var and accum tensors will be protected\nby a lock; otherwise the behavior is undefined, but may exhibit less\ncontention."
+  }
+  summary: "Update relevant entries in \'*var\' according to the Ftrl-proximal scheme."
+  description: "That is for rows we have grad for, we update var, accum and linear as follows:\ngrad_with_shrinkage = grad + 2 * l2_shrinkage * var\naccum_new = accum + grad_with_shrinkage * grad_with_shrinkage\nlinear += grad_with_shrinkage +\n    (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var\nquadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2\nvar = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0\naccum = accum_new"
+}
 op {
   name: "SparseApplyMomentum"
   input_arg {
-- 
GitLab


From cbcaee5c0115b8c16ddffd4387c6ffa114a49726 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 16:13:01 -0700
Subject: [PATCH 0709/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161019366
---
 tensorflow/go/op/wrappers.go | 5430 +++++++++++++++++-----------------
 1 file changed, 2771 insertions(+), 2659 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index ce3bd0260f..a2a9856497 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -2296,24 +2296,6 @@ func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (aud
 	return op.Output(0), op.Output(1)
 }
 
-// Elementwise computes the bitwise XOR of `x` and `y`.
-//
-// The result will have those bits set, that are different in `x` and `y`. The
-// computation is performed on the underlying representations of `x` and `y`.
-func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "BitwiseXor",
-		Input: []tf.Input{
-			x, y,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Elementwise computes the bitwise AND of `x` and `y`.
 //
 // The result will have those bits set, that are set in both `x` and `y`. The
@@ -6462,39 +6444,6 @@ func MatchingFiles(scope *Scope, pattern tf.Output) (filenames tf.Output) {
 	return op.Output(0)
 }
 
-// Shuffle dimensions of x according to a permutation.
-//
-// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
-//   `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
-func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Transpose",
-		Input: []tf.Input{
-			x, perm,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Reads and outputs the entire contents of the input filename.
-func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "ReadFile",
-		Input: []tf.Input{
-			filename,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Store the input tensor in the state of the current session.
 //
 // Arguments:
@@ -6615,92 +6564,38 @@ func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output)
 	return op.Output(0), op.Output(1)
 }
 
-// Computes softplus gradients for a softplus operation.
-//
-// Arguments:
-//	gradients: The backpropagated gradients to the corresponding softplus operation.
-//	features: The features passed as input to the corresponding softplus operation.
-//
-// Returns The gradients: `gradients / (1 + exp(-features))`.
-func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SoftplusGrad",
-		Input: []tf.Input{
-			gradients, features,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes softplus: `log(exp(features) + 1)`.
-func Softplus(scope *Scope, features tf.Output) (activations tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Softplus",
-		Input: []tf.Input{
-			features,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// BatchMatMulAttr is an optional argument to BatchMatMul.
-type BatchMatMulAttr func(optionalAttr)
+// IdentityReaderV2Attr is an optional argument to IdentityReaderV2.
+type IdentityReaderV2Attr func(optionalAttr)
 
-// BatchMatMulAdjX sets the optional adj_x attribute to value.
+// IdentityReaderV2Container sets the optional container attribute to value.
 //
-// value: If `True`, adjoint the slices of `x`. Defaults to `False`.
-// If not specified, defaults to false
-func BatchMatMulAdjX(value bool) BatchMatMulAttr {
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func IdentityReaderV2Container(value string) IdentityReaderV2Attr {
 	return func(m optionalAttr) {
-		m["adj_x"] = value
+		m["container"] = value
 	}
 }
 
-// BatchMatMulAdjY sets the optional adj_y attribute to value.
+// IdentityReaderV2SharedName sets the optional shared_name attribute to value.
 //
-// value: If `True`, adjoint the slices of `y`. Defaults to `False`.
-// If not specified, defaults to false
-func BatchMatMulAdjY(value bool) BatchMatMulAttr {
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr {
 	return func(m optionalAttr) {
-		m["adj_y"] = value
+		m["shared_name"] = value
 	}
 }
 
-// Multiplies slices of two tensors in batches.
-//
-// Multiplies all slices of `Tensor` `x` and `y` (each slice can be
-// viewed as an element of a batch), and arranges the individual results
-// in a single output tensor of the same batch size. Each of the
-// individual slices can optionally be adjointed (to adjoint a matrix
-// means to transpose and conjugate it) before multiplication by setting
-// the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
-//
-// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`
-// and `[..., r_y, c_y]`.
-//
-// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where:
-//
-//     r_o = c_x if adj_x else r_x
-//     c_o = r_y if adj_y else c_y
-//
-// It is computed as:
-//
-//     output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
+// A Reader that outputs the queued work as both the key and value.
 //
-// Arguments:
-//	x: 2-D or higher with shape `[..., r_x, c_x]`.
-//	y: 2-D or higher with shape `[..., r_y, c_y]`.
+// To use, enqueue strings in a Queue.  ReaderRead will take the front
+// work string and output (work, work).
 //
-// Returns 3-D or higher with shape `[..., r_o, c_o]`
-func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) {
+// Returns The handle to reference the Reader.
+func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -6709,10 +6604,8 @@ func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMul
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "BatchMatMul",
-		Input: []tf.Input{
-			x, y,
-		},
+		Type: "IdentityReaderV2",
+
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
@@ -9432,6 +9325,61 @@ func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional
 	return op.Output(0)
 }
 
+// ResourceApplyFtrlV2Attr is an optional argument to ResourceApplyFtrlV2.
+type ResourceApplyFtrlV2Attr func(optionalAttr)
+
+// ResourceApplyFtrlV2UseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update '*var' according to the Ftrl-proximal scheme.
+//
+// grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+// linear += grad_with_shrinkage +
+//     (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+// accum = accum_new
+//
+// Arguments:
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	linear: Should be from a Variable().
+//	grad: The gradient.
+//	lr: Scaling factor. Must be a scalar.
+//	l1: L1 regulariation. Must be a scalar.
+//	l2: L2 shrinkage regulariation. Must be a scalar.
+//
+//	lr_power: Scaling factor. Must be a scalar.
+//
+// Returns the created operation.
+func ResourceApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlV2Attr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResourceApplyFtrlV2",
+		Input: []tf.Input{
+			var_, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
 // SkipgramAttr is an optional argument to Skipgram.
 type SkipgramAttr func(optionalAttr)
 
@@ -9662,6 +9610,27 @@ func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf
 	return op.Output(0)
 }
 
+// Computes fingerprints of the input strings.
+//
+// Arguments:
+//	input: vector of strings to compute fingerprints on.
+//
+// Returns a (N,2) shaped matrix where N is the number of elements in the input
+// vector. Each row contains the low and high parts of the fingerprint.
+func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SdcaFprint",
+		Input: []tf.Input{
+			input,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // SparseMatMulAttr is an optional argument to SparseMatMul.
 type SparseMatMulAttr func(optionalAttr)
 
@@ -11120,74 +11089,26 @@ func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// IdentityReaderV2Attr is an optional argument to IdentityReaderV2.
-type IdentityReaderV2Attr func(optionalAttr)
-
-// IdentityReaderV2Container sets the optional container attribute to value.
+// Performs a padding as a preprocess during a convolution.
 //
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func IdentityReaderV2Container(value string) IdentityReaderV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// IdentityReaderV2SharedName sets the optional shared_name attribute to value.
+// Similar to FusedResizeAndPadConv2d, this op allows for an optimized
+// implementation where the spatial padding transformation stage is fused with the
+// im2col lookup, but in this case without the bilinear filtering required for
+// resizing. Fusing the padding prevents the need to write out the intermediate
+// results as whole tensors, reducing memory pressure, and we can get some latency
+// gains by merging the transformation calculations.
+// The data_format attribute for Conv2D isn't supported by this op, and 'NHWC'
+// order is used instead.
+// Internally this op uses a single per-graph scratch buffer, which means that it
+// will block if multiple versions are being run in parallel. This is because this
+// operator is primarily an optimization to minimize memory usage.
 //
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// A Reader that outputs the queued work as both the key and value.
-//
-// To use, enqueue strings in a Queue.  ReaderRead will take the front
-// work string and output (work, work).
-//
-// Returns The handle to reference the Reader.
-func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "IdentityReaderV2",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Performs a padding as a preprocess during a convolution.
-//
-// Similar to FusedResizeAndPadConv2d, this op allows for an optimized
-// implementation where the spatial padding transformation stage is fused with the
-// im2col lookup, but in this case without the bilinear filtering required for
-// resizing. Fusing the padding prevents the need to write out the intermediate
-// results as whole tensors, reducing memory pressure, and we can get some latency
-// gains by merging the transformation calculations.
-// The data_format attribute for Conv2D isn't supported by this op, and 'NHWC'
-// order is used instead.
-// Internally this op uses a single per-graph scratch buffer, which means that it
-// will block if multiple versions are being run in parallel. This is because this
-// operator is primarily an optimization to minimize memory usage.
-//
-// Arguments:
-//	input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
-//	paddings: A two-column matrix specifying the padding sizes. The number of
-// rows must be the same as the rank of `input`.
-//	filter: 4-D with shape
-// `[filter_height, filter_width, in_channels, out_channels]`.
+// Arguments:
+//	input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
+//	paddings: A two-column matrix specifying the padding sizes. The number of
+// rows must be the same as the rank of `input`.
+//	filter: 4-D with shape
+// `[filter_height, filter_width, in_channels, out_channels]`.
 //
 //	strides: 1-D of length 4.  The stride of the sliding window for each dimension
 // of `input`. Must be in the same order as the dimension specified with format.
@@ -11323,84 +11244,103 @@ func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.D
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Batch normalization.
+// Encode audio data using the WAV file format.
 //
-// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization()
+// This operation will generate a string suitable to be saved out to create a .wav
+// audio file. It will be encoded in the 16-bit PCM format. It takes in float
+// values in the range -1.0f to 1.0f, and any outside that value will be clamped to
+// that range.
 //
-// This op is deprecated. Prefer `tf.nn.batch_normalization`.
+// `audio` is a 2-D float Tensor of shape `[length, channels]`.
+// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100).
 //
 // Arguments:
-//	t: A 4D input Tensor.
-//	m: A 1D mean Tensor with size matching the last dimension of t.
-// This is the first output from tf.nn.moments,
-// or a saved moving average thereof.
-//	v: A 1D variance Tensor with size matching the last dimension of t.
-// This is the second output from tf.nn.moments,
-// or a saved moving average thereof.
-//	beta: A 1D beta Tensor with size matching the last dimension of t.
-// An offset to be added to the normalized tensor.
-//	gamma: A 1D gamma Tensor with size matching the last dimension of t.
-// If "scale_after_normalization" is true, this tensor will be multiplied
-// with the normalized tensor.
-//	variance_epsilon: A small float number to avoid dividing by 0.
-//	scale_after_normalization: A bool indicating whether the resulted tensor
-// needs to be multiplied with gamma.
-func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) {
+//	audio: 2-D with shape `[length, channels]`.
+//	sample_rate: Scalar containing the sample frequency.
+//
+// Returns 0-D. WAV-encoded file contents.
+func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
 	opspec := tf.OpSpec{
-		Type: "BatchNormWithGlobalNormalization",
+		Type: "EncodeWav",
 		Input: []tf.Input{
-			t, m, v, beta, gamma,
+			audio, sample_rate,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Generate a glob pattern matching all sharded file names.
-func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) {
+// The gradient operator for the SparseAdd op.
+//
+// The SparseAdd op calculates A + B, where A, B, and the sum are all represented
+// as `SparseTensor` objects.  This op takes in the upstream gradient w.r.t.
+// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
+// values of A and B.
+//
+// Arguments:
+//	backprop_val_grad: 1-D with shape `[nnz(sum)]`.  The gradient with respect to
+// the non-empty values of the sum.
+//	a_indices: 2-D.  The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
+//	b_indices: 2-D.  The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
+//	sum_indices: 2-D.  The `indices` of the sum `SparseTensor`, size
+// `[nnz(sum), ndims]`.
+//
+// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the
+// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the
+// non-empty values of B.
+func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "ShardedFilespec",
+		Type: "SparseAddGrad",
 		Input: []tf.Input{
-			basename, num_shards,
+			backprop_val_grad, a_indices, b_indices, sum_indices,
 		},
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return op.Output(0), op.Output(1)
 }
 
-// Saves the input tensors to disk.
+// Batch normalization.
 //
-// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
-// is written to `filename` with name `tensor_names[i]`.
+// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization()
 //
-// See also `SaveSlices`.
+// This op is deprecated. Prefer `tf.nn.batch_normalization`.
 //
 // Arguments:
-//	filename: Must have a single element. The name of the file to which we write
-// the tensor.
-//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
-//	data: `N` tensors to save.
-//
-// Returns the created operation.
-func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) {
+//	t: A 4D input Tensor.
+//	m: A 1D mean Tensor with size matching the last dimension of t.
+// This is the first output from tf.nn.moments,
+// or a saved moving average thereof.
+//	v: A 1D variance Tensor with size matching the last dimension of t.
+// This is the second output from tf.nn.moments,
+// or a saved moving average thereof.
+//	beta: A 1D beta Tensor with size matching the last dimension of t.
+// An offset to be added to the normalized tensor.
+//	gamma: A 1D gamma Tensor with size matching the last dimension of t.
+// If "scale_after_normalization" is true, this tensor will be multiplied
+// with the normalized tensor.
+//	variance_epsilon: A small float number to avoid dividing by 0.
+//	scale_after_normalization: A bool indicating whether the resulted tensor
+// needs to be multiplied with gamma.
+func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
 	opspec := tf.OpSpec{
-		Type: "Save",
+		Type: "BatchNormWithGlobalNormalization",
 		Input: []tf.Input{
-			filename, tensor_names, tf.OutputList(data),
+			t, m, v, beta, gamma,
 		},
+		Attrs: attrs,
 	}
-	return scope.AddOperation(opspec)
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
 // L2 Loss.
@@ -11716,224 +11656,173 @@ func MergeSummary(scope *Scope, inputs []tf.Output) (summary tf.Output) {
 	return op.Output(0)
 }
 
-// Encode audio data using the WAV file format.
-//
-// This operation will generate a string suitable to be saved out to create a .wav
-// audio file. It will be encoded in the 16-bit PCM format. It takes in float
-// values in the range -1.0f to 1.0f, and any outside that value will be clamped to
-// that range.
-//
-// `audio` is a 2-D float Tensor of shape `[length, channels]`.
-// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100).
-//
-// Arguments:
-//	audio: 2-D with shape `[length, channels]`.
-//	sample_rate: Scalar containing the sample frequency.
-//
-// Returns 0-D. WAV-encoded file contents.
-func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "EncodeWav",
-		Input: []tf.Input{
-			audio, sample_rate,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// The gradient operator for the SparseAdd op.
-//
-// The SparseAdd op calculates A + B, where A, B, and the sum are all represented
-// as `SparseTensor` objects.  This op takes in the upstream gradient w.r.t.
-// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
-// values of A and B.
-//
-// Arguments:
-//	backprop_val_grad: 1-D with shape `[nnz(sum)]`.  The gradient with respect to
-// the non-empty values of the sum.
-//	a_indices: 2-D.  The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
-//	b_indices: 2-D.  The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
-//	sum_indices: 2-D.  The `indices` of the sum `SparseTensor`, size
-// `[nnz(sum), ndims]`.
+// Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
 //
-// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the
-// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the
-// non-empty values of B.
-func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SparseAddGrad",
-		Input: []tf.Input{
-			backprop_val_grad, a_indices, b_indices, sum_indices,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1)
-}
-
-// Converts each string in the input Tensor to its hash mod by a number of buckets.
+// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead.
 //
-// The hash function is deterministic on the content of the string within the
-// process. The hash function is a keyed hash function, where attribute `key`
-// defines the key of the hash function. `key` is an array of 2 elements.
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices, with the same constraints as the single matrix
+// SelfAdjointEig.
 //
-// A strong hash is important when inputs may be malicious, e.g. URLs with
-// additional components. Adversaries could try to make their inputs hash to the
-// same bucket for a denial-of-service attack or to skew the results. A strong
-// hash prevents this by making it difficult, if not infeasible, to compute inputs
-// that hash to the same bucket. This comes at a cost of roughly 4x higher compute
-// time than `tf.string_to_hash_bucket_fast`.
+// The result is a [..., M+1, M] matrix with [..., 0,:] containing the
+// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
 //
 // Arguments:
-//	input: The strings to assign a hash bucket.
-//	num_buckets: The number of buckets.
-//	key: The key for the keyed hash function passed as a list of two uint64
-// elements.
+//	input: Shape is `[..., M, M]`.
 //
-// Returns A Tensor of the same shape as the input `string_tensor`.
-func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) {
+// Returns Shape is `[..., M+1, M]`.
+func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key}
 	opspec := tf.OpSpec{
-		Type: "StringToHashBucketStrong",
+		Type: "SelfAdjointEig",
 		Input: []tf.Input{
 			input,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// DestroyResourceOpAttr is an optional argument to DestroyResourceOp.
-type DestroyResourceOpAttr func(optionalAttr)
+// QuantizeV2Attr is an optional argument to QuantizeV2.
+type QuantizeV2Attr func(optionalAttr)
 
-// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value.
-//
-// value: whether to ignore the error when the resource
-// doesn't exist.
-// If not specified, defaults to true
-func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr {
+// QuantizeV2Mode sets the optional mode attribute to value.
+// If not specified, defaults to "MIN_COMBINED"
+func QuantizeV2Mode(value string) QuantizeV2Attr {
 	return func(m optionalAttr) {
-		m["ignore_lookup_error"] = value
+		m["mode"] = value
 	}
 }
 
-// Deletes the resource specified by the handle.
+// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
 //
-// All subsequent operations using the resource will result in a NotFound
-// error status.
+// [min_range, max_range] are scalar floats that specify the range for
+// the 'input' data. The 'mode' attribute controls exactly which calculations are
+// used to convert the float values to their quantized equivalents.
 //
-// Arguments:
-//	resource: handle to the resource to delete.
+// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
 //
-// Returns the created operation.
-func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "DestroyResourceOp",
-		Input: []tf.Input{
-			resource,
-		},
-		Attrs: attrs,
-	}
-	return scope.AddOperation(opspec)
-}
-
-// Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
+// ```
+// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
+// if T == qint8, out[i] -= (range(T) + 1) / 2.0
+// ```
+// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
 //
-// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead.
+// *MIN_COMBINED Mode Example*
 //
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices, with the same constraints as the single matrix
-// SelfAdjointEig.
+// Assume the input is type float and has a possible range of [0.0, 6.0] and the
+// output type is quint8 ([0, 255]). The min_range and max_range values should be
+// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each
+// value of the input by 255/6 and cast to quint8.
 //
-// The result is a [..., M+1, M] matrix with [..., 0,:] containing the
-// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
+// If the output type was qint8 ([-128, 127]), the operation will additionally
+// subtract each value by 128 prior to casting, so that the range of values aligns
+// with the range of qint8.
+//
+// If the mode is 'MIN_FIRST', then this approach is used:
+//
+// ```
+// number_of_steps = 1 << (# of bits in T)
+// range_adjust = number_of_steps / (number_of_steps - 1)
+// range = (range_max - range_min) * range_adjust
+// range_scale = number_of_steps / range
+// quantized = round(input * range_scale) - round(range_min * range_scale) +
+//   numeric_limits<T>::min()
+// quantized = max(quantized, numeric_limits<T>::min())
+// quantized = min(quantized, numeric_limits<T>::max())
+// ```
+//
+// The biggest difference between this and MIN_COMBINED is that the minimum range
+// is rounded first, before it's subtracted from the rounded value. With
+// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
+// and dequantizing will introduce a larger and larger error.
+//
+// One thing to watch out for is that the operator may choose to adjust the
+// requested minimum and maximum values slightly during the quantization process,
+// so you should always use the output ports as the range for further calculations.
+// For example, if the requested minimum and maximum values are close to equal,
+// they will be separated by a small epsilon value to prevent ill-formed quantized
+// buffers from being created. Otherwise, you can end up with buffers where all the
+// quantized values map to the same float value, which causes problems for
+// operations that have to perform further calculations on them.
 //
 // Arguments:
-//	input: Shape is `[..., M, M]`.
 //
-// Returns Shape is `[..., M+1, M]`.
-func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) {
+//	min_range: The minimum scalar value possibly produced for the input.
+//	max_range: The maximum scalar value possibly produced for the input.
+//
+//
+// Returns The quantized data produced from the float input.The actual minimum scalar value used for the output.The actual maximum scalar value used for the output.
+func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"T": T}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "SelfAdjointEig",
+		Type: "QuantizeV2",
 		Input: []tf.Input{
-			input,
+			input, min_range, max_range,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad.
-type FractionalAvgPoolGradAttr func(optionalAttr)
+// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter.
+type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr)
 
-// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value.
-//
-// value: When set to True, it means when pooling, the values at the boundary
-// of adjacent pooling cells are used by both cells. For example:
-//
-// `index  0  1  2  3  4`
-//
-// `value  20 5  16 3  7`
+// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value.
 //
-// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
-// The result would be [41/3, 26/3] for fractional avg pooling.
-// If not specified, defaults to false
-func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+//     [batch, height, width, channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+//     [batch, channels, height, width].
+// If not specified, defaults to "NHWC"
+func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr {
 	return func(m optionalAttr) {
-		m["overlapping"] = value
+		m["data_format"] = value
 	}
 }
 
-// Computes gradient of the FractionalAvgPool function.
-//
-// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
-// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
-// out_backprop to those indices that form the same pooling cell. Therefore, we
-// just need to know the shape of original input tensor, instead of the whole
-// tensor.
+// Computes the gradients of depthwise convolution with respect to the filter.
 //
 // Arguments:
-//	orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool`
-//	out_backprop: 4-D with shape `[batch, height, width, channels]`.  Gradients
-// w.r.t. the output of `fractional_avg_pool`.
-//	row_pooling_sequence: row pooling sequence, form pooling region with
-// col_pooling_sequence.
-//	col_pooling_sequence: column pooling sequence, form pooling region with
-// row_pooling sequence.
+//	input: 4-D with shape based on `data_format`.  For example, if
+// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height,
+// in_width, in_channels]` tensor.
+//	filter_sizes: An integer vector representing the tensor shape of `filter`,
+// where `filter` is a 4-D
+// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor.
+//	out_backprop: 4-D with shape  based on `data_format`.
+// For example, if `data_format` is 'NHWC' then
+// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
+// Gradients w.r.t. the output of the convolution.
+//	strides: The stride of the sliding window for each dimension of the input
+// of the convolution.
+//	padding: The type of padding algorithm to use.
 //
-// Returns 4-D.  Gradients w.r.t. the input of `fractional_avg_pool`.
-func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) {
+// Returns 4-D with shape
+// `[filter_height, filter_width, in_channels, out_channels]`.  Gradient w.r.t.
+// the `filter` input of the convolution.
+func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
+	attrs := map[string]interface{}{"strides": strides, "padding": padding}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "FractionalAvgPoolGrad",
+		Type: "DepthwiseConv2dNativeBackpropFilter",
 		Input: []tf.Input{
-			orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence,
+			input, filter_sizes, out_backprop,
 		},
 		Attrs: attrs,
 	}
@@ -11941,153 +11830,140 @@ func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_
 	return op.Output(0)
 }
 
-// Returns which elements of x are Inf.
-//
-// @compatibility(numpy)
-// Equivalent to np.isinf
-// @end_compatibility
-func IsInf(scope *Scope, x tf.Output) (y tf.Output) {
+// Generate a glob pattern matching all sharded file names.
+func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "IsInf",
+		Type: "ShardedFilespec",
 		Input: []tf.Input{
-			x,
+			basename, num_shards,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl.
-type ResourceSparseApplyFtrlAttr func(optionalAttr)
-
-// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value.
+// Saves the input tensors to disk.
 //
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr {
-	return func(m optionalAttr) {
-		m["use_locking"] = value
-	}
-}
-
-// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
+// is written to `filename` with name `tensor_names[i]`.
 //
-// That is for rows we have grad for, we update var, accum and linear as follows:
-// accum_new = accum + grad * grad
-// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
-// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
-// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
-// accum = accum_new
+// See also `SaveSlices`.
 //
 // Arguments:
-//	var_: Should be from a Variable().
-//	accum: Should be from a Variable().
-//	linear: Should be from a Variable().
-//	grad: The gradient.
-//	indices: A vector of indices into the first dimension of var and accum.
-//	lr: Scaling factor. Must be a scalar.
-//	l1: L1 regularization. Must be a scalar.
-//	l2: L2 regularization. Must be a scalar.
-//	lr_power: Scaling factor. Must be a scalar.
+//	filename: Must have a single element. The name of the file to which we write
+// the tensor.
+//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
+//	data: `N` tensors to save.
 //
 // Returns the created operation.
-func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) {
+func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "ResourceSparseApplyFtrl",
+		Type: "Save",
 		Input: []tf.Input{
-			var_, accum, linear, grad, indices, lr, l1, l2, lr_power,
+			filename, tensor_names, tf.OutputList(data),
 		},
-		Attrs: attrs,
 	}
 	return scope.AddOperation(opspec)
 }
 
-// Reorders a SparseTensor into the canonical, row-major ordering.
-//
-// Note that by convention, all sparse ops preserve the canonical ordering along
-// increasing dimension number. The only time ordering can be violated is during
-// manual manipulation of the indices and values vectors to add entries.
-//
-// Reordering does not affect the shape of the SparseTensor.
-//
-// If the tensor has rank `R` and `N` non-empty values, `input_indices` has
-// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`.
-//
-// Arguments:
-//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
-//	input_shape: 1-D.  Shape of the input SparseTensor.
+// Shuffle dimensions of x according to a permutation.
 //
-// Returns 2-D.  `N x R` matrix with the same indices as input_indices, but
-// in canonical row-major ordering.1-D.  `N` non-empty values corresponding to `output_indices`.
-func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
+// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
+//   `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
+func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "SparseReorder",
+		Type: "Transpose",
 		Input: []tf.Input{
-			input_indices, input_values, input_shape,
+			x, perm,
 		},
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1)
+	return op.Output(0)
 }
 
-// PackAttr is an optional argument to Pack.
-type PackAttr func(optionalAttr)
-
-// PackAxis sets the optional axis attribute to value.
-//
-// value: Dimension along which to pack.  Negative values wrap around, so the
-// valid range is `[-(R+1), R+1)`.
-// If not specified, defaults to 0
-func PackAxis(value int64) PackAttr {
-	return func(m optionalAttr) {
-		m["axis"] = value
+// Reads and outputs the entire contents of the input filename.
+func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ReadFile",
+		Input: []tf.Input{
+			filename,
+		},
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
+// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap.
+type AddManySparseToTensorsMapAttr func(optionalAttr)
+
+// AddManySparseToTensorsMapContainer sets the optional container attribute to value.
 //
-// Packs the `N` tensors in `values` into a tensor with rank one higher than each
-// tensor in `values`, by packing them along the `axis` dimension.
-// Given a list of tensors of shape `(A, B, C)`;
+// value: The container name for the `SparseTensorsMap` created by this op.
+// If not specified, defaults to ""
+func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value.
 //
-// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
-// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
-// Etc.
+// value: The shared name for the `SparseTensorsMap` created by this op.
+// If blank, the new Operation's unique name is used.
+// If not specified, defaults to ""
+func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
 //
-// For example:
+// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`,
+// `sparse_values`, and `sparse_shape`, where
 //
-// ```
-// # 'x' is [1, 4]
-// # 'y' is [2, 5]
-// # 'z' is [3, 6]
-// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
-// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
-// ```
+// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R```
 //
-// This is the opposite of `unpack`.
+// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor`
+// having a first `sparse_indices` column taking values between `[0, N)`, where
+// the minibatch size `N == sparse_shape[0]`.
+//
+// The input `SparseTensor` must have rank `R` greater than 1, and the first
+// dimension is treated as the minibatch dimension.  Elements of the `SparseTensor`
+// must be sorted in increasing order of this first dimension.  The stored
+// `SparseTensor` objects pointed to by each row of the output `sparse_handles`
+// will have rank `R-1`.
+//
+// The `SparseTensor` values can then be read out as part of a minibatch by passing
+// the given keys as vector elements to `TakeManySparseFromTensorsMap`.  To ensure
+// the correct `SparseTensorsMap` is accessed, ensure that the same
+// `container` and `shared_name` are passed to that Op.  If no `shared_name`
+// is provided here, instead use the *name* of the Operation created by calling
+// `AddManySparseToTensorsMap` as the `shared_name` passed to
+// `TakeManySparseFromTensorsMap`.  Ensure the Operations are colocated.
 //
 // Arguments:
-//	values: Must be of same shape and type.
+//	sparse_indices: 2-D.  The `indices` of the minibatch `SparseTensor`.
+// `sparse_indices[:, 0]` must be ordered values in `[0, N)`.
+//	sparse_values: 1-D.  The `values` of the minibatch `SparseTensor`.
+//	sparse_shape: 1-D.  The `shape` of the minibatch `SparseTensor`.
+// The minibatch size `N == sparse_shape[0]`.
 //
-// Returns The packed tensor.
-func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) {
+// Returns 1-D.  The handles of the `SparseTensor` now stored in the
+// `SparseTensorsMap`.  Shape: `[N]`.
+func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -12096,9 +11972,9 @@ func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Out
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "Pack",
+		Type: "AddManySparseToTensorsMap",
 		Input: []tf.Input{
-			tf.OutputList(values),
+			sparse_indices, sparse_values, sparse_shape,
 		},
 		Attrs: attrs,
 	}
@@ -12106,41 +11982,65 @@ func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Out
 	return op.Output(0)
 }
 
-// Deprecated. Use TensorArraySplitV3
-func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) {
+// Creates a dataset that emits the outputs of `input_dataset` `count` times.
+//
+// Arguments:
+//
+//	count: A scalar representing the number of times that `input_dataset` should
+// be repeated. A value of `-1` indicates that it should be repeated infinitely.
+//
+//
+func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
 	opspec := tf.OpSpec{
-		Type: "TensorArraySplitV2",
+		Type: "RepeatDataset",
 		Input: []tf.Input{
-			handle, value, lengths, flow_in,
+			input_dataset, count,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// QuantizedReluAttr is an optional argument to QuantizedRelu.
-type QuantizedReluAttr func(optionalAttr)
+// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse.
+type SparseReduceMaxSparseAttr func(optionalAttr)
 
-// QuantizedReluOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_QUINT8
-func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr {
+// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr {
 	return func(m optionalAttr) {
-		m["out_type"] = value
+		m["keep_dims"] = value
 	}
 }
 
-// Computes Quantized Rectified Linear: `max(features, 0)`
+// Computes the max of elements across dimensions of a SparseTensor.
 //
-// Arguments:
+// This Op takes a SparseTensor and is the sparse counterpart to
+// `tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
+// SparseTensor.
 //
-//	min_features: The float value that the lowest quantized value represents.
-//	max_features: The float value that the highest quantized value represents.
+// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+// with length 1.
 //
-// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
-func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
+// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+// with a single element is returned.  Additionally, the axes can be negative,
+// which are interpreted according to the indexing rules in Python.
+//
+// Arguments:
+//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+//	input_shape: 1-D.  Shape of the input SparseTensor.
+//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -12149,9 +12049,9 @@ func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "QuantizedRelu",
+		Type: "SparseReduceMaxSparse",
 		Input: []tf.Input{
-			features, min_features, max_features,
+			input_indices, input_values, input_shape, reduction_axes,
 		},
 		Attrs: attrs,
 	}
@@ -12159,47 +12059,363 @@ func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Computes rectified linear gradients for a Relu operation.
+// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA.
+type ResourceApplyAdagradDAAttr func(optionalAttr)
+
+// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
+//
+// value: If True, updating of the var and accum tensors will be protected by
+// a lock; otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update '*var' according to the proximal adagrad scheme.
 //
 // Arguments:
-//	gradients: The backpropagated gradients to the corresponding Relu operation.
-//	features: The features passed as input to the corresponding Relu operation, OR
-// the outputs of that operation (both work equivalently).
+//	var_: Should be from a Variable().
+//	gradient_accumulator: Should be from a Variable().
+//	gradient_squared_accumulator: Should be from a Variable().
+//	grad: The gradient.
+//	lr: Scaling factor. Must be a scalar.
+//	l1: L1 regularization. Must be a scalar.
+//	l2: L2 regularization. Must be a scalar.
+//	global_step: Training step number. Must be a scalar.
 //
-// Returns `gradients * (features > 0)`.
-func ReluGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+// Returns the created operation.
+func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "ReluGrad",
+		Type: "ResourceApplyAdagradDA",
 		Input: []tf.Input{
-			gradients, features,
+			var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad.
+type FractionalMaxPoolGradAttr func(optionalAttr)
+
+// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value.
+//
+// value: When set to True, it means when pooling, the values at the boundary
+// of adjacent pooling cells are used by both cells. For example:
+//
+// `index  0  1  2  3  4`
+//
+// `value  20 5  16 3  7`
+//
+// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+// The result would be [20, 16] for fractional max pooling.
+// If not specified, defaults to false
+func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr {
+	return func(m optionalAttr) {
+		m["overlapping"] = value
+	}
+}
+
+// Computes gradient of the FractionalMaxPool function.
+//
+// Arguments:
+//	orig_input: Original input for `fractional_max_pool`
+//	orig_output: Original output for `fractional_max_pool`
+//	out_backprop: 4-D with shape `[batch, height, width, channels]`.  Gradients
+// w.r.t. the output of `fractional_max_pool`.
+//	row_pooling_sequence: row pooling sequence, form pooling region with
+// col_pooling_sequence.
+//	col_pooling_sequence: column pooling sequence, form pooling region with
+// row_pooling sequence.
+//
+// Returns 4-D.  Gradients w.r.t. the input of `fractional_max_pool`.
+func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "FractionalMaxPoolGrad",
+		Input: []tf.Input{
+			orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes the gradient for the inverse of `x` wrt its input.
+// Returns which elements of x are Inf.
 //
-// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
-// is the corresponding input gradient.
-func ReciprocalGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// @compatibility(numpy)
+// Equivalent to np.isinf
+// @end_compatibility
+func IsInf(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "ReciprocalGrad",
+		Type: "IsInf",
 		Input: []tf.Input{
-			x, y,
+			x,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Reverses specific dimensions of a tensor.
+// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl.
+type ResourceSparseApplyFtrlAttr func(optionalAttr)
+
+// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+//
+// That is for rows we have grad for, we update var, accum and linear as follows:
+// accum_new = accum + grad * grad
+// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+// accum = accum_new
+//
+// Arguments:
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	linear: Should be from a Variable().
+//	grad: The gradient.
+//	indices: A vector of indices into the first dimension of var and accum.
+//	lr: Scaling factor. Must be a scalar.
+//	l1: L1 regularization. Must be a scalar.
+//	l2: L2 regularization. Must be a scalar.
+//	lr_power: Scaling factor. Must be a scalar.
+//
+// Returns the created operation.
+func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResourceSparseApplyFtrl",
+		Input: []tf.Input{
+			var_, accum, linear, grad, indices, lr, l1, l2, lr_power,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// Reorders a SparseTensor into the canonical, row-major ordering.
+//
+// Note that by convention, all sparse ops preserve the canonical ordering along
+// increasing dimension number. The only time ordering can be violated is during
+// manual manipulation of the indices and values vectors to add entries.
+//
+// Reordering does not affect the shape of the SparseTensor.
+//
+// If the tensor has rank `R` and `N` non-empty values, `input_indices` has
+// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`.
+//
+// Arguments:
+//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+//	input_shape: 1-D.  Shape of the input SparseTensor.
+//
+// Returns 2-D.  `N x R` matrix with the same indices as input_indices, but
+// in canonical row-major ordering.1-D.  `N` non-empty values corresponding to `output_indices`.
+func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseReorder",
+		Input: []tf.Input{
+			input_indices, input_values, input_shape,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1)
+}
+
+// PackAttr is an optional argument to Pack.
+type PackAttr func(optionalAttr)
+
+// PackAxis sets the optional axis attribute to value.
+//
+// value: Dimension along which to pack.  Negative values wrap around, so the
+// valid range is `[-(R+1), R+1)`.
+// If not specified, defaults to 0
+func PackAxis(value int64) PackAttr {
+	return func(m optionalAttr) {
+		m["axis"] = value
+	}
+}
+
+// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
+//
+// Packs the `N` tensors in `values` into a tensor with rank one higher than each
+// tensor in `values`, by packing them along the `axis` dimension.
+// Given a list of tensors of shape `(A, B, C)`;
+//
+// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
+// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
+// Etc.
+//
+// For example:
+//
+// ```
+// # 'x' is [1, 4]
+// # 'y' is [2, 5]
+// # 'z' is [3, 6]
+// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
+// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
+// ```
+//
+// This is the opposite of `unpack`.
+//
+// Arguments:
+//	values: Must be of same shape and type.
+//
+// Returns The packed tensor.
+func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Pack",
+		Input: []tf.Input{
+			tf.OutputList(values),
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Deprecated. Use TensorArraySplitV3
+func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "TensorArraySplitV2",
+		Input: []tf.Input{
+			handle, value, lengths, flow_in,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// QuantizedReluAttr is an optional argument to QuantizedRelu.
+type QuantizedReluAttr func(optionalAttr)
+
+// QuantizedReluOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_QUINT8
+func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr {
+	return func(m optionalAttr) {
+		m["out_type"] = value
+	}
+}
+
+// Computes Quantized Rectified Linear: `max(features, 0)`
+//
+// Arguments:
+//
+//	min_features: The float value that the lowest quantized value represents.
+//	max_features: The float value that the highest quantized value represents.
+//
+// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
+func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "QuantizedRelu",
+		Input: []tf.Input{
+			features, min_features, max_features,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
+}
+
+// Computes rectified linear gradients for a Relu operation.
+//
+// Arguments:
+//	gradients: The backpropagated gradients to the corresponding Relu operation.
+//	features: The features passed as input to the corresponding Relu operation, OR
+// the outputs of that operation (both work equivalently).
+//
+// Returns `gradients * (features > 0)`.
+func ReluGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ReluGrad",
+		Input: []tf.Input{
+			gradients, features,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes the gradient for the inverse of `x` wrt its input.
+//
+// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
+// is the corresponding input gradient.
+func ReciprocalGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ReciprocalGrad",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Reverses specific dimensions of a tensor.
 //
 // NOTE `tf.reverse` has now changed behavior in preparation for 1.0.
 // `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0.
@@ -13099,68 +13315,43 @@ func TensorSummary(scope *Scope, tensor tf.Output, optional ...TensorSummaryAttr
 	return op.Output(0)
 }
 
-// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA.
-type ResourceApplyAdagradDAAttr func(optionalAttr)
-
-// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
+// Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor.
 //
-// value: If True, updating of the var and accum tensors will be protected by
-// a lock; otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr {
-	return func(m optionalAttr) {
-		m["use_locking"] = value
-	}
-}
-
-// Update '*var' according to the proximal adagrad scheme.
+// This operation folds the padded areas of `input` by `MirrorPad` according to the
+// `paddings` you specify. `paddings` must be the same as `paddings` argument
+// given to the corresponding `MirrorPad` op.
 //
-// Arguments:
-//	var_: Should be from a Variable().
-//	gradient_accumulator: Should be from a Variable().
-//	gradient_squared_accumulator: Should be from a Variable().
-//	grad: The gradient.
-//	lr: Scaling factor. Must be a scalar.
-//	l1: L1 regularization. Must be a scalar.
-//	l2: L2 regularization. Must be a scalar.
-//	global_step: Training step number. Must be a scalar.
+// The folded size of each dimension D of the output is:
 //
-// Returns the created operation.
-func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "ResourceApplyAdagradDA",
-		Input: []tf.Input{
-			var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step,
-		},
-		Attrs: attrs,
-	}
-	return scope.AddOperation(opspec)
-}
-
-// Creates a dataset that emits the outputs of `input_dataset` `count` times.
+// `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)`
 //
-// Arguments:
+// For example:
 //
-//	count: A scalar representing the number of times that `input_dataset` should
-// be repeated. A value of `-1` indicates that it should be repeated infinitely.
+// ```
+// # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
+// # 'paddings' is [[0, 1]], [0, 1]].
+// # 'mode' is SYMMETRIC.
+// # rank of 't' is 2.
+// pad(t, paddings) ==> [[ 1,  5]
+//                       [11, 28]]
+// ```
 //
+// Arguments:
+//	input: The input tensor to be folded.
+//	paddings: A two-column matrix specifying the padding sizes. The number of
+// rows must be the same as the rank of `input`.
+//	mode: The mode used in the `MirrorPad` op.
 //
-func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+// Returns The folded tensor.
+func MirrorPadGrad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+	attrs := map[string]interface{}{"mode": mode}
 	opspec := tf.OpSpec{
-		Type: "RepeatDataset",
+		Type: "MirrorPadGrad",
 		Input: []tf.Input{
-			input_dataset, count,
+			input, paddings,
 		},
 		Attrs: attrs,
 	}
@@ -13168,959 +13359,983 @@ func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, outpu
 	return op.Output(0)
 }
 
-// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse.
-type SparseReduceMaxSparseAttr func(optionalAttr)
-
-// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value.
+// Computes the inverse permutation of a tensor.
 //
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr {
-	return func(m optionalAttr) {
-		m["keep_dims"] = value
-	}
-}
-
-// Computes the max of elements across dimensions of a SparseTensor.
+// This operation computes the inverse of an index permutation. It takes a 1-D
+// integer tensor `x`, which represents the indices of a zero-based array, and
+// swaps each value with its index position. In other words, for an output tensor
+// `y` and an input tensor `x`, this operation computes the following:
 //
-// This Op takes a SparseTensor and is the sparse counterpart to
-// `tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
-// SparseTensor.
+// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
 //
-// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-// with length 1.
+// The values must include 0. There can be no duplicate values or negative values.
 //
-// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-// with a single element is returned.  Additionally, the axes can be negative,
-// which are interpreted according to the indexing rules in Python.
+// For example:
+//
+// ```
+// # tensor `x` is [3, 4, 0, 2, 1]
+// invert_permutation(x) ==> [2, 4, 3, 0, 1]
+// ```
 //
 // Arguments:
-//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
-//	input_shape: 1-D.  Shape of the input SparseTensor.
-//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
-func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
+//	x: 1-D.
+//
+// Returns 1-D.
+func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "SparseReduceMaxSparse",
+		Type: "InvertPermutation",
 		Input: []tf.Input{
-			input_indices, input_values, input_shape, reduction_axes,
+			x,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
+	return op.Output(0)
 }
 
-// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad.
-type FractionalMaxPoolGradAttr func(optionalAttr)
-
-// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value.
+// Reverses specific dimensions of a tensor.
 //
-// value: When set to True, it means when pooling, the values at the boundary
-// of adjacent pooling cells are used by both cells. For example:
+// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
+// of `tensor`, this operation reverses each dimension i of `tensor` where
+// `dims[i]` is `True`.
 //
-// `index  0  1  2  3  4`
+// `tensor` can have up to 8 dimensions. The number of dimensions
+// of `tensor` must equal the number of elements in `dims`. In other words:
 //
-// `value  20 5  16 3  7`
+// `rank(tensor) = size(dims)`
 //
-// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
-// The result would be [20, 16] for fractional max pooling.
-// If not specified, defaults to false
-func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr {
-	return func(m optionalAttr) {
-		m["overlapping"] = value
-	}
-}
-
-// Computes gradient of the FractionalMaxPool function.
+// For example:
+//
+// ```
+// # tensor 't' is [[[[ 0,  1,  2,  3],
+// #                  [ 4,  5,  6,  7],
+// #                  [ 8,  9, 10, 11]],
+// #                 [[12, 13, 14, 15],
+// #                  [16, 17, 18, 19],
+// #                  [20, 21, 22, 23]]]]
+// # tensor 't' shape is [1, 2, 3, 4]
+//
+// # 'dims' is [False, False, False, True]
+// reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
+//                         [ 7,  6,  5,  4],
+//                         [ 11, 10, 9, 8]],
+//                        [[15, 14, 13, 12],
+//                         [19, 18, 17, 16],
+//                         [23, 22, 21, 20]]]]
+//
+// # 'dims' is [False, True, False, False]
+// reverse(t, dims) ==> [[[[12, 13, 14, 15],
+//                         [16, 17, 18, 19],
+//                         [20, 21, 22, 23]
+//                        [[ 0,  1,  2,  3],
+//                         [ 4,  5,  6,  7],
+//                         [ 8,  9, 10, 11]]]]
+//
+// # 'dims' is [False, False, True, False]
+// reverse(t, dims) ==> [[[[8, 9, 10, 11],
+//                         [4, 5, 6, 7],
+//                         [0, 1, 2, 3]]
+//                        [[20, 21, 22, 23],
+//                         [16, 17, 18, 19],
+//                         [12, 13, 14, 15]]]]
+// ```
 //
 // Arguments:
-//	orig_input: Original input for `fractional_max_pool`
-//	orig_output: Original output for `fractional_max_pool`
-//	out_backprop: 4-D with shape `[batch, height, width, channels]`.  Gradients
-// w.r.t. the output of `fractional_max_pool`.
-//	row_pooling_sequence: row pooling sequence, form pooling region with
-// col_pooling_sequence.
-//	col_pooling_sequence: column pooling sequence, form pooling region with
-// row_pooling sequence.
+//	tensor: Up to 8-D.
+//	dims: 1-D. The dimensions to reverse.
 //
-// Returns 4-D.  Gradients w.r.t. the input of `fractional_max_pool`.
-func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) {
+// Returns The same shape as `tensor`.
+func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "FractionalMaxPoolGrad",
+		Type: "Reverse",
 		Input: []tf.Input{
-			orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence,
+			tensor, dims,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Creates a dataset that batches `batch_size` elements from `input_dataset`.
+// Fills empty rows in the input 2-D `SparseTensor` with a default value.
 //
-// Arguments:
+// The input `SparseTensor` is represented via the tuple of inputs
+// (`indices`, `values`, `dense_shape`).  The output `SparseTensor` has the
+// same `dense_shape` but with indices `output_indices` and values
+// `output_values`.
 //
-//	batch_size: A scalar representing the number of elements to accumulate in a
-// batch.
+// This op inserts a single entry for every row that doesn't have any values.
+// The index is created as `[row, 0, ..., 0]` and the inserted value
+// is `default_value`.
+//
+// For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
+//
+//     [0, 1]: a
+//     [0, 3]: b
+//     [2, 0]: c
+//     [3, 1]: d
 //
+// Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
 //
-func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+//     [0, 1]: a
+//     [0, 3]: b
+//     [1, 0]: default_value
+//     [2, 0]: c
+//     [3, 1]: d
+//     [4, 0]: default_value
+//
+// The output `SparseTensor` will be in row-major order and will have the
+// same shape as the input.
+//
+// This op also returns an indicator vector shaped `[dense_shape[0]]` such that
+//
+//     empty_row_indicator[i] = True iff row i was an empty row.
+//
+// And a reverse index map vector shaped `[indices.shape[0]]` that is used during
+// backpropagation,
+//
+//     reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :]
+//
+// Arguments:
+//	indices: 2-D. the indices of the sparse tensor.
+//	values: 1-D. the values of the sparse tensor.
+//	dense_shape: 1-D. the shape of the sparse tensor.
+//	default_value: 0-D. default value to insert into location `[row, 0, ..., 0]`
+//   for rows missing from the input sparse tensor.
+// output indices: 2-D. the indices of the filled sparse tensor.
+//
+// Returns 1-D. the values of the filled sparse tensor.1-D. whether the dense row was missing in the
+// input sparse tensor.1-D. a map from the input indices to the output indices.
+func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, default_value tf.Output) (output_indices tf.Output, output_values tf.Output, empty_row_indicator tf.Output, reverse_index_map tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
 	opspec := tf.OpSpec{
-		Type: "BatchDataset",
+		Type: "SparseFillEmptyRows",
 		Input: []tf.Input{
-			input_dataset, batch_size,
+			indices, values, dense_shape, default_value,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
 }
 
-// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp.
-type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr)
+// Conv2DAttr is an optional argument to Conv2D.
+type Conv2DAttr func(optionalAttr)
 
-// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
+// Conv2DUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value.
+// If not specified, defaults to true
+func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr {
+	return func(m optionalAttr) {
+		m["use_cudnn_on_gpu"] = value
+	}
+}
+
+// Conv2DDataFormat sets the optional data_format attribute to value.
 //
-// value: If `True`, updating of the var, mg, ms, and mom tensors is
-// protected by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr {
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+//     [batch, height, width, channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+//     [batch, channels, height, width].
+// If not specified, defaults to "NHWC"
+func Conv2DDataFormat(value string) Conv2DAttr {
 	return func(m optionalAttr) {
-		m["use_locking"] = value
+		m["data_format"] = value
 	}
 }
 
-// Update '*var' according to the centered RMSProp algorithm.
+// Computes a 2-D convolution given 4-D `input` and `filter` tensors.
 //
-// The centered RMSProp algorithm uses an estimate of the centered second moment
-// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
-// uses the (uncentered) second moment. This often helps with training, but is
-// slightly more expensive in terms of computation and memory.
+// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
+// and a filter / kernel tensor of shape
+// `[filter_height, filter_width, in_channels, out_channels]`, this op
+// performs the following:
 //
-// Note that in dense implementation of this algorithm, mg, ms, and mom will
-// update even if the grad is zero, but in this sparse implementation, mg, ms,
-// and mom will not update in iterations during which the grad is zero.
+// 1. Flattens the filter to a 2-D matrix with shape
+//    `[filter_height * filter_width * in_channels, output_channels]`.
+// 2. Extracts image patches from the input tensor to form a *virtual*
+//    tensor of shape `[batch, out_height, out_width,
+//    filter_height * filter_width * in_channels]`.
+// 3. For each patch, right-multiplies the filter matrix and the image patch
+//    vector.
 //
-// mean_square = decay * mean_square + (1-decay) * gradient ** 2
-// mean_grad = decay * mean_grad + (1-decay) * gradient
-// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+// In detail, with the default NHWC format,
 //
-// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
-// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
-// var <- var - mom
+//     output[b, i, j, k] =
+//         sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
+//                         filter[di, dj, q, k]
 //
-// Arguments:
-//	var_: Should be from a Variable().
-//	mg: Should be from a Variable().
-//	ms: Should be from a Variable().
-//	mom: Should be from a Variable().
-//	lr: Scaling factor. Must be a scalar.
-//	rho: Decay rate. Must be a scalar.
+// Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
+// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
 //
-//	epsilon: Ridge term. Must be a scalar.
-//	grad: The gradient.
-//	indices: A vector of indices into the first dimension of var, ms and mom.
+// Arguments:
+//	input: A 4-D tensor. The dimension order is interpreted according to the value
+// of `data_format`, see below for details.
+//	filter: A 4-D tensor of shape
+// `[filter_height, filter_width, in_channels, out_channels]`
+//	strides: 1-D tensor of length 4.  The stride of the sliding window for each
+// dimension of `input`. The dimension order is determined by the value of
+//   `data_format`, see below for details.
+//	padding: The type of padding algorithm to use.
 //
-// Returns the created operation.
-func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) {
+// Returns A 4-D tensor. The dimension order is determined by the value of
+// `data_format`, see below for details.
+func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
+	attrs := map[string]interface{}{"strides": strides, "padding": padding}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResourceSparseApplyCenteredRMSProp",
+		Type: "Conv2D",
 		Input: []tf.Input{
-			var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices,
+			input, filter,
 		},
 		Attrs: attrs,
 	}
-	return scope.AddOperation(opspec)
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// Flips all bits elementwise.
+// StringJoinAttr is an optional argument to StringJoin.
+type StringJoinAttr func(optionalAttr)
+
+// StringJoinSeparator sets the optional separator attribute to value.
 //
-// The result will have exactly those bits set, that are not set in `x`. The
-// computation is performed on the underlying representation of x.
-func Invert(scope *Scope, x tf.Output) (y tf.Output) {
+// value: string, an optional join separator.
+// If not specified, defaults to ""
+func StringJoinSeparator(value string) StringJoinAttr {
+	return func(m optionalAttr) {
+		m["separator"] = value
+	}
+}
+
+// Joins the strings in the given list of string tensors into one tensor;
+//
+// with the given separator (default is an empty separator).
+//
+// Arguments:
+//	inputs: A list of string tensors.  The tensors must all have the same shape,
+// or be scalars.  Scalars may be mixed in; these will be broadcast to the shape
+// of non-scalar inputs.
+func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "Invert",
+		Type: "StringJoin",
 		Input: []tf.Input{
-			x,
+			tf.OutputList(inputs),
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes the mean along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Computes a tensor such that
-// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
-// over `j` such that `segment_ids[j] == i` and `N` is the total number of
-// values summed.
-//
-// If the mean is empty for a given segment ID `i`, `output[i] = 0`.
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
-// </div>
+// Transforms a vector of brain.Example protos (as strings) into typed tensors.
 //
 // Arguments:
-//
-//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension.  Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+//	serialized: A vector containing a batch of binary serialized Example protos.
+//	names: A vector containing the names of the serialized protos.
+// May contain, for example, table key (descriptive) names for the
+// corresponding serialized protos.  These are purely useful for debugging
+// purposes, and the presence of values here has no effect on the output.
+// May also be an empty vector if no names are available.
+// If non-empty, this vector must be the same length as "serialized".
+//	sparse_keys: A list of Nsparse string Tensors (scalars).
+// The keys expected in the Examples' features associated with sparse values.
+//	dense_keys: A list of Ndense string Tensors (scalars).
+// The keys expected in the Examples' features associated with dense values.
+//	dense_defaults: A list of Ndense Tensors (some may be empty).
+// dense_defaults[j] provides default values
+// when the example's feature_map lacks dense_key[j].  If an empty Tensor is
+// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
+// The input type is inferred from dense_defaults[j], even when it's empty.
+// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
+// then the shape of dense_defaults[j] must match that of dense_shapes[j].
+// If dense_shapes[j] has an undefined major dimension (variable strides dense
+// feature), dense_defaults[j] must contain a single element:
+// the padding element.
+//	sparse_types: A list of Nsparse types; the data types of data in each Feature
+// given in sparse_keys.
+// Currently the ParseExample supports DT_FLOAT (FloatList),
+// DT_INT64 (Int64List), and DT_STRING (BytesList).
+//	dense_shapes: A list of Ndense shapes; the shapes of data in each Feature
+// given in dense_keys.
+// The number of elements in the Feature corresponding to dense_key[j]
+// must always equal dense_shapes[j].NumEntries().
+// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output
+// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
+// The dense outputs are just the inputs row-stacked by batch.
+// This works for dense_shapes[j] = (-1, D1, ..., DN).  In this case
+// the shape of the output Tensor dense_values[j] will be
+// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks
+// of elements of length D1 * .... * DN, across all minibatch entries
+// in the input.  Any minibatch entry with less than M blocks of elements of
+// length D1 * ... * DN will be padded with the corresponding default_value
+// scalar element along the second dimension.
+func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes}
 	opspec := tf.OpSpec{
-		Type: "SegmentMean",
+		Type: "ParseExample",
 		Input: []tf.Input{
-			data, segment_ids,
+			serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults),
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// CumprodAttr is an optional argument to Cumprod.
-type CumprodAttr func(optionalAttr)
-
-// CumprodExclusive sets the optional exclusive attribute to value.
-// If not specified, defaults to false
-func CumprodExclusive(value bool) CumprodAttr {
-	return func(m optionalAttr) {
-		m["exclusive"] = value
+	if scope.Err() != nil {
+		return
 	}
-}
-
-// CumprodReverse sets the optional reverse attribute to value.
-// If not specified, defaults to false
-func CumprodReverse(value bool) CumprodAttr {
-	return func(m optionalAttr) {
-		m["reverse"] = value
+	var idx int
+	var err error
+	if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
+		scope.UpdateErr("ParseExample", err)
+		return
+	}
+	if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
+		scope.UpdateErr("ParseExample", err)
+		return
+	}
+	if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
+		scope.UpdateErr("ParseExample", err)
+		return
+	}
+	if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
+		scope.UpdateErr("ParseExample", err)
+		return
 	}
+	return sparse_indices, sparse_values, sparse_shapes, dense_values
 }
 
-// Compute the cumulative product of the tensor `x` along `axis`.
-//
-// By default, this op performs an inclusive cumprod, which means that the first
-// element of the input is identical to the first element of the output:
-//
-// ```python
-// tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
-// ```
-//
-// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
-// performed instead:
+// Compute the pairwise cross product.
 //
-// ```python
-// tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
-// ```
+// `a` and `b` must be the same shape; they can either be simple 3-element vectors,
+// or any shape where the innermost dimension is 3. In the latter case, each pair
+// of corresponding 3-element vectors is cross-multiplied independently.
 //
-// By setting the `reverse` kwarg to `True`, the cumprod is performed in the
-// opposite direction:
+// Arguments:
+//	a: A tensor containing 3-element vectors.
+//	b: Another tensor, of same type and shape as `a`.
 //
-// ```python
-// tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
-// ```
-//
-// This is more efficient than using separate `tf.reverse` ops.
-//
-// The `reverse` and `exclusive` kwargs can also be combined:
-//
-// ```python
-// tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
-// ```
-func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
+// Returns Pairwise cross product of the vectors in `a` and `b`.
+func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "Cumprod",
+		Type: "Cross",
 		Input: []tf.Input{
-			x, axis,
+			a, b,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// OrderedMapPeekAttr is an optional argument to OrderedMapPeek.
-type OrderedMapPeekAttr func(optionalAttr)
-
-// OrderedMapPeekCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
+// Inverse 2D real-valued fast Fourier transform.
 //
-// REQUIRES: value >= 0
-func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr {
-	return func(m optionalAttr) {
-		m["capacity"] = value
-	}
-}
-
-// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
+// Computes the inverse 2-dimensional discrete Fourier transform of a real-valued
+// signal over the inner-most 2 dimensions of `input`.
 //
-// REQUIRES: value >= 0
-func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr {
-	return func(m optionalAttr) {
-		m["memory_limit"] = value
-	}
-}
-
-// OrderedMapPeekContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func OrderedMapPeekContainer(value string) OrderedMapPeekAttr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// OrderedMapPeekSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// Op peeks at the values at the specified key.  If the
+// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`:
+// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
+// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
+// from the size of the inner-most 2 dimensions of `input`. If the FFT length used
+// to compute `input` is odd, it should be provided since it cannot be inferred
+// properly.
 //
-// underlying container does not contain this key
-// this op will block until it does.   This Op is optimized for
-// performance.
-func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) {
+// Along each axis `IRFFT2D` is computed on, if `fft_length` (or
+// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
+// corresponding dimension of `input`, the dimension is cropped. If it is larger,
+// the dimension is padded with zeros.
+//
+// Arguments:
+//	input: A complex64 tensor.
+//	fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
+//
+// Returns A float32 tensor of the same rank as `input`. The inner-most 2
+//   dimensions of `input` are replaced with the `fft_length` samples of their
+//   inverse 2D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.irfft2
+// @end_compatibility
+func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"dtypes": dtypes}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "OrderedMapPeek",
+		Type: "IRFFT2D",
 		Input: []tf.Input{
-			key, indices,
+			input, fft_length,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
-	}
-	var idx int
-	var err error
-	if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
-		scope.UpdateErr("OrderedMapPeek", err)
-		return
-	}
-	return values
+	return op.Output(0)
 }
 
-// Adds two `SparseTensor` objects to produce another `SparseTensor`.
-//
-// The input `SparseTensor` objects' indices are assumed ordered in standard
-// lexicographic order.  If this is not the case, before this step run
-// `SparseReorder` to restore index ordering.
-//
-// By default, if two values sum to zero at some index, the output `SparseTensor`
-// would still include that particular location in its index, storing a zero in the
-// corresponding value slot.  To override this, callers can specify `thresh`,
-// indicating that if the sum has a magnitude strictly smaller than `thresh`, its
-// corresponding value and index would then not be included.  In particular,
-// `thresh == 0` (default) means everything is kept and actual thresholding happens
-// only for a positive value.
+// Returns element-wise remainder of division. This emulates C semantics in that
 //
-// In the following shapes, `nnz` is the count after taking `thresh` into account.
+// the result here is consistent with a truncating divide. E.g. `truncate(x / y) *
+// y + truncate_mod(x, y) = x`.
 //
-// Arguments:
-//	a_indices: 2-D.  The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
-//	a_values: 1-D.  The `values` of the first `SparseTensor`, size `[nnz]` Vector.
-//	a_shape: 1-D.  The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
-//	b_indices: 2-D.  The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
-//	b_values: 1-D.  The `values` of the second `SparseTensor`, size `[nnz]` Vector.
-//	b_shape: 1-D.  The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
-//	thresh: 0-D.  The magnitude threshold that determines if an output value/index
-// pair takes space.
-func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) {
+// *NOTE*: `TruncateMod` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func TruncateMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "SparseAdd",
+		Type: "TruncateMod",
 		Input: []tf.Input{
-			a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh,
+			x, y,
 		},
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
+	return op.Output(0)
 }
 
-// Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor.
-//
-// This operation folds the padded areas of `input` by `MirrorPad` according to the
-// `paddings` you specify. `paddings` must be the same as `paddings` argument
-// given to the corresponding `MirrorPad` op.
-//
-// The folded size of each dimension D of the output is:
-//
-// `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)`
+// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
+type ResourceApplyAdagradAttr func(optionalAttr)
+
+// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value.
 //
-// For example:
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update '*var' according to the adagrad scheme.
 //
-// ```
-// # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
-// # 'paddings' is [[0, 1]], [0, 1]].
-// # 'mode' is SYMMETRIC.
-// # rank of 't' is 2.
-// pad(t, paddings) ==> [[ 1,  5]
-//                       [11, 28]]
-// ```
+// accum += grad * grad
+// var -= lr * grad * (1 / sqrt(accum))
 //
 // Arguments:
-//	input: The input tensor to be folded.
-//	paddings: A two-column matrix specifying the padding sizes. The number of
-// rows must be the same as the rank of `input`.
-//	mode: The mode used in the `MirrorPad` op.
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	lr: Scaling factor. Must be a scalar.
+//	grad: The gradient.
 //
-// Returns The folded tensor.
-func MirrorPadGrad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) {
+// Returns the created operation.
+func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"mode": mode}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "MirrorPadGrad",
+		Type: "ResourceApplyAdagrad",
 		Input: []tf.Input{
-			input, paddings,
+			var_, accum, lr, grad,
 		},
 		Attrs: attrs,
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return scope.AddOperation(opspec)
 }
 
-// Computes the inverse permutation of a tensor.
-//
-// This operation computes the inverse of an index permutation. It takes a 1-D
-// integer tensor `x`, which represents the indices of a zero-based array, and
-// swaps each value with its index position. In other words, for an output tensor
-// `y` and an input tensor `x`, this operation computes the following:
+// SparseReduceSumAttr is an optional argument to SparseReduceSum.
+type SparseReduceSumAttr func(optionalAttr)
+
+// SparseReduceSumKeepDims sets the optional keep_dims attribute to value.
 //
-// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr {
+	return func(m optionalAttr) {
+		m["keep_dims"] = value
+	}
+}
+
+// Computes the sum of elements across dimensions of a SparseTensor.
 //
-// The values must include 0. There can be no duplicate values or negative values.
+// This Op takes a SparseTensor and is the sparse counterpart to
+// `tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`
+// instead of a sparse one.
 //
-// For example:
+// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+// with length 1.
 //
-// ```
-// # tensor `x` is [3, 4, 0, 2, 1]
-// invert_permutation(x) ==> [2, 4, 3, 0, 1]
-// ```
+// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+// with a single element is returned.  Additionally, the axes can be negative,
+// which are interpreted according to the indexing rules in Python.
 //
 // Arguments:
-//	x: 1-D.
+//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+//	input_shape: 1-D.  Shape of the input SparseTensor.
+//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
 //
-// Returns 1-D.
-func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) {
+// Returns `R-K`-D.  The reduced Tensor.
+func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "InvertPermutation",
+		Type: "SparseReduceSum",
 		Input: []tf.Input{
-			x,
+			input_indices, input_values, input_shape, reduction_axes,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Reverses specific dimensions of a tensor.
-//
-// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
-// of `tensor`, this operation reverses each dimension i of `tensor` where
-// `dims[i]` is `True`.
-//
-// `tensor` can have up to 8 dimensions. The number of dimensions
-// of `tensor` must equal the number of elements in `dims`. In other words:
-//
-// `rank(tensor) = size(dims)`
-//
-// For example:
-//
-// ```
-// # tensor 't' is [[[[ 0,  1,  2,  3],
-// #                  [ 4,  5,  6,  7],
-// #                  [ 8,  9, 10, 11]],
-// #                 [[12, 13, 14, 15],
-// #                  [16, 17, 18, 19],
-// #                  [20, 21, 22, 23]]]]
-// # tensor 't' shape is [1, 2, 3, 4]
-//
-// # 'dims' is [False, False, False, True]
-// reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
-//                         [ 7,  6,  5,  4],
-//                         [ 11, 10, 9, 8]],
-//                        [[15, 14, 13, 12],
-//                         [19, 18, 17, 16],
-//                         [23, 22, 21, 20]]]]
-//
-// # 'dims' is [False, True, False, False]
-// reverse(t, dims) ==> [[[[12, 13, 14, 15],
-//                         [16, 17, 18, 19],
-//                         [20, 21, 22, 23]
-//                        [[ 0,  1,  2,  3],
-//                         [ 4,  5,  6,  7],
-//                         [ 8,  9, 10, 11]]]]
+// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad.
+type MaxPool3DGradAttr func(optionalAttr)
+
+// MaxPool3DGradDataFormat sets the optional data_format attribute to value.
 //
-// # 'dims' is [False, False, True, False]
-// reverse(t, dims) ==> [[[[8, 9, 10, 11],
-//                         [4, 5, 6, 7],
-//                         [0, 1, 2, 3]]
-//                        [[20, 21, 22, 23],
-//                         [16, 17, 18, 19],
-//                         [12, 13, 14, 15]]]]
-// ```
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+//     [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+//     [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr {
+	return func(m optionalAttr) {
+		m["data_format"] = value
+	}
+}
+
+// Computes gradients of max pooling function.
 //
 // Arguments:
-//	tensor: Up to 8-D.
-//	dims: 1-D. The dimensions to reverse.
-//
-// Returns The same shape as `tensor`.
-func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) {
+//	orig_input: The original input tensor.
+//	orig_output: The original output tensor.
+//	grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
+//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+//	strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+//	padding: The type of padding algorithm to use.
+func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "Reverse",
+		Type: "MaxPool3DGrad",
 		Input: []tf.Input{
-			tensor, dims,
+			orig_input, orig_output, grad,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Fills empty rows in the input 2-D `SparseTensor` with a default value.
-//
-// The input `SparseTensor` is represented via the tuple of inputs
-// (`indices`, `values`, `dense_shape`).  The output `SparseTensor` has the
-// same `dense_shape` but with indices `output_indices` and values
-// `output_values`.
-//
-// This op inserts a single entry for every row that doesn't have any values.
-// The index is created as `[row, 0, ..., 0]` and the inserted value
-// is `default_value`.
-//
-// For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
-//
-//     [0, 1]: a
-//     [0, 3]: b
-//     [2, 0]: c
-//     [3, 1]: d
-//
-// Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
-//
-//     [0, 1]: a
-//     [0, 3]: b
-//     [1, 0]: default_value
-//     [2, 0]: c
-//     [3, 1]: d
-//     [4, 0]: default_value
+// ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler.
+type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr)
+
+// ThreadUnsafeUnigramCandidateSamplerSeed sets the optional seed attribute to value.
 //
-// The output `SparseTensor` will be in row-major order and will have the
-// same shape as the input.
+// value: If either seed or seed2 are set to be non-zero, the random number
+// generator is seeded by the given seed.  Otherwise, it is seeded by a
+// random seed.
+// If not specified, defaults to 0
+func ThreadUnsafeUnigramCandidateSamplerSeed(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
+	return func(m optionalAttr) {
+		m["seed"] = value
+	}
+}
+
+// ThreadUnsafeUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value.
 //
-// This op also returns an indicator vector shaped `[dense_shape[0]]` such that
+// value: An second seed to avoid seed collision.
+// If not specified, defaults to 0
+func ThreadUnsafeUnigramCandidateSamplerSeed2(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
+	return func(m optionalAttr) {
+		m["seed2"] = value
+	}
+}
+
+// Generates labels for candidate sampling with a learned unigram distribution.
 //
-//     empty_row_indicator[i] = True iff row i was an empty row.
+// See explanations of candidate sampling and the data formats at
+// go/candidate-sampling.
 //
-// And a reverse index map vector shaped `[indices.shape[0]]` that is used during
-// backpropagation,
+// For each batch, this op picks a single set of sampled candidate labels.
 //
-//     reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :]
+// The advantages of sampling candidates per-batch are simplicity and the
+// possibility of efficient dense matrix multiplication. The disadvantage is that
+// the sampled candidates must be chosen independently of the context and of the
+// true labels.
 //
 // Arguments:
-//	indices: 2-D. the indices of the sparse tensor.
-//	values: 1-D. the values of the sparse tensor.
-//	dense_shape: 1-D. the shape of the sparse tensor.
-//	default_value: 0-D. default value to insert into location `[row, 0, ..., 0]`
-//   for rows missing from the input sparse tensor.
-// output indices: 2-D. the indices of the filled sparse tensor.
+//	true_classes: A batch_size * num_true matrix, in which each row contains the
+// IDs of the num_true target_classes in the corresponding original label.
+//	num_true: Number of true labels per context.
+//	num_sampled: Number of candidates to randomly sample.
+//	unique: If unique is true, we sample with rejection, so that all sampled
+// candidates in a batch are unique. This requires some approximation to
+// estimate the post-rejection sampling probabilities.
+//	range_max: The sampler will sample integers from the interval [0, range_max).
 //
-// Returns 1-D. the values of the filled sparse tensor.1-D. whether the dense row was missing in the
-// input sparse tensor.1-D. a map from the input indices to the output indices.
-func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, default_value tf.Output) (output_indices tf.Output, output_values tf.Output, empty_row_indicator tf.Output, reverse_index_map tf.Output) {
+// Returns A vector of length num_sampled, in which each element is
+// the ID of a sampled candidate.A batch_size * num_true matrix, representing
+// the number of times each candidate is expected to occur in a batch
+// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
+// candidate representing the number of times the candidate is expected
+// to occur in a batch of sampled candidates.  If unique=true, then this is a
+// probability.
+func ThreadUnsafeUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...ThreadUnsafeUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "SparseFillEmptyRows",
+		Type: "ThreadUnsafeUnigramCandidateSampler",
 		Input: []tf.Input{
-			indices, values, dense_shape, default_value,
+			true_classes,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
+	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Conv2DAttr is an optional argument to Conv2D.
-type Conv2DAttr func(optionalAttr)
-
-// Conv2DUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value.
-// If not specified, defaults to true
-func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr {
-	return func(m optionalAttr) {
-		m["use_cudnn_on_gpu"] = value
-	}
-}
+// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad.
+type ResourceSparseApplyProximalAdagradAttr func(optionalAttr)
 
-// Conv2DDataFormat sets the optional data_format attribute to value.
+// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
 //
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-//     [batch, height, width, channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-//     [batch, channels, height, width].
-// If not specified, defaults to "NHWC"
-func Conv2DDataFormat(value string) Conv2DAttr {
+// value: If True, updating of the var and accum tensors will be protected by
+// a lock; otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr {
 	return func(m optionalAttr) {
-		m["data_format"] = value
+		m["use_locking"] = value
 	}
 }
 
-// Computes a 2-D convolution given 4-D `input` and `filter` tensors.
-//
-// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
-// and a filter / kernel tensor of shape
-// `[filter_height, filter_width, in_channels, out_channels]`, this op
-// performs the following:
-//
-// 1. Flattens the filter to a 2-D matrix with shape
-//    `[filter_height * filter_width * in_channels, output_channels]`.
-// 2. Extracts image patches from the input tensor to form a *virtual*
-//    tensor of shape `[batch, out_height, out_width,
-//    filter_height * filter_width * in_channels]`.
-// 3. For each patch, right-multiplies the filter matrix and the image patch
-//    vector.
-//
-// In detail, with the default NHWC format,
-//
-//     output[b, i, j, k] =
-//         sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
-//                         filter[di, dj, q, k]
+// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
 //
-// Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
-// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
+// That is for rows we have grad for, we update var and accum as follows:
+// accum += grad * grad
+// prox_v = var
+// prox_v -= lr * grad * (1 / sqrt(accum))
+// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
 //
 // Arguments:
-//	input: A 4-D tensor. The dimension order is interpreted according to the value
-// of `data_format`, see below for details.
-//	filter: A 4-D tensor of shape
-// `[filter_height, filter_width, in_channels, out_channels]`
-//	strides: 1-D tensor of length 4.  The stride of the sliding window for each
-// dimension of `input`. The dimension order is determined by the value of
-//   `data_format`, see below for details.
-//	padding: The type of padding algorithm to use.
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	lr: Learning rate. Must be a scalar.
+//	l1: L1 regularization. Must be a scalar.
+//	l2: L2 regularization. Must be a scalar.
+//	grad: The gradient.
+//	indices: A vector of indices into the first dimension of var and accum.
 //
-// Returns A 4-D tensor. The dimension order is determined by the value of
-// `data_format`, see below for details.
-func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) {
+// Returns the created operation.
+func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"strides": strides, "padding": padding}
+	attrs := map[string]interface{}{}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "Conv2D",
+		Type: "ResourceSparseApplyProximalAdagrad",
+		Input: []tf.Input{
+			var_, accum, lr, l1, l2, grad, indices,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// Store the input tensor in the state of the current session.
+//
+// Arguments:
+//	value: The tensor to be stored.
+//
+// Returns The handle for the tensor stored in the session state, represented
+// as a string.
+func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "GetSessionHandle",
 		Input: []tf.Input{
-			input, filter,
+			value,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// StringJoinAttr is an optional argument to StringJoin.
-type StringJoinAttr func(optionalAttr)
-
-// StringJoinSeparator sets the optional separator attribute to value.
-//
-// value: string, an optional join separator.
-// If not specified, defaults to ""
-func StringJoinSeparator(value string) StringJoinAttr {
-	return func(m optionalAttr) {
-		m["separator"] = value
-	}
-}
-
-// Joins the strings in the given list of string tensors into one tensor;
+// Decode web-safe base64-encoded strings.
 //
-// with the given separator (default is an empty separator).
+// Input may or may not have padding at the end. See EncodeBase64 for padding.
+// Web-safe means that input must use - and _ instead of + and /.
 //
 // Arguments:
-//	inputs: A list of string tensors.  The tensors must all have the same shape,
-// or be scalars.  Scalars may be mixed in; these will be broadcast to the shape
-// of non-scalar inputs.
-func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) {
+//	input: Base64 strings to decode.
+//
+// Returns Decoded strings.
+func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "StringJoin",
+		Type: "DecodeBase64",
 		Input: []tf.Input{
-			tf.OutputList(inputs),
+			input,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Transforms a vector of brain.Example protos (as strings) into typed tensors.
-//
-// Arguments:
-//	serialized: A vector containing a batch of binary serialized Example protos.
-//	names: A vector containing the names of the serialized protos.
-// May contain, for example, table key (descriptive) names for the
-// corresponding serialized protos.  These are purely useful for debugging
-// purposes, and the presence of values here has no effect on the output.
-// May also be an empty vector if no names are available.
-// If non-empty, this vector must be the same length as "serialized".
-//	sparse_keys: A list of Nsparse string Tensors (scalars).
-// The keys expected in the Examples' features associated with sparse values.
-//	dense_keys: A list of Ndense string Tensors (scalars).
-// The keys expected in the Examples' features associated with dense values.
-//	dense_defaults: A list of Ndense Tensors (some may be empty).
-// dense_defaults[j] provides default values
-// when the example's feature_map lacks dense_key[j].  If an empty Tensor is
-// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
-// The input type is inferred from dense_defaults[j], even when it's empty.
-// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
-// then the shape of dense_defaults[j] must match that of dense_shapes[j].
-// If dense_shapes[j] has an undefined major dimension (variable strides dense
-// feature), dense_defaults[j] must contain a single element:
-// the padding element.
-//	sparse_types: A list of Nsparse types; the data types of data in each Feature
-// given in sparse_keys.
-// Currently the ParseExample supports DT_FLOAT (FloatList),
-// DT_INT64 (Int64List), and DT_STRING (BytesList).
-//	dense_shapes: A list of Ndense shapes; the shapes of data in each Feature
-// given in dense_keys.
-// The number of elements in the Feature corresponding to dense_key[j]
-// must always equal dense_shapes[j].NumEntries().
-// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output
-// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
-// The dense outputs are just the inputs row-stacked by batch.
-// This works for dense_shapes[j] = (-1, D1, ..., DN).  In this case
-// the shape of the output Tensor dense_values[j] will be
-// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks
-// of elements of length D1 * .... * DN, across all minibatch entries
-// in the input.  Any minibatch entry with less than M blocks of elements of
-// length D1 * ... * DN will be padded with the corresponding default_value
-// scalar element along the second dimension.
-func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
+// Computes hyperbolic tangent of `x` element-wise.
+func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes}
 	opspec := tf.OpSpec{
-		Type: "ParseExample",
+		Type: "Tanh",
 		Input: []tf.Input{
-			serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults),
+			x,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
+	return op.Output(0)
+}
+
+// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
+type TextLineReaderV2Attr func(optionalAttr)
+
+// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
+//
+// value: Number of lines to skip from the beginning of every file.
+// If not specified, defaults to 0
+func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["skip_header_lines"] = value
 	}
-	var idx int
-	var err error
-	if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
-		scope.UpdateErr("ParseExample", err)
-		return
+}
+
+// TextLineReaderV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["container"] = value
 	}
-	if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
-		scope.UpdateErr("ParseExample", err)
-		return
+}
+
+// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
 	}
-	if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
-		scope.UpdateErr("ParseExample", err)
+}
+
+// A Reader that outputs the lines of a file delimited by '\n'.
+//
+// Returns The handle to reference the Reader.
+func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
+	if scope.Err() != nil {
 		return
 	}
-	if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
-		scope.UpdateErr("ParseExample", err)
-		return
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
 	}
-	return sparse_indices, sparse_values, sparse_shapes, dense_values
+	opspec := tf.OpSpec{
+		Type: "TextLineReaderV2",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// Compute the pairwise cross product.
+// Component-wise multiplies a SparseTensor by a dense Tensor.
 //
-// `a` and `b` must be the same shape; they can either be simple 3-element vectors,
-// or any shape where the innermost dimension is 3. In the latter case, each pair
-// of corresponding 3-element vectors is cross-multiplied independently.
+// The output locations corresponding to the implicitly zero elements in the sparse
+// tensor will be zero (i.e., will not take up storage space), regardless of the
+// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).
+//
+// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
+// the other direction.
 //
 // Arguments:
-//	a: A tensor containing 3-element vectors.
-//	b: Another tensor, of same type and shape as `a`.
+//	sp_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+// SparseTensor, possibly not in canonical ordering.
+//	sp_values: 1-D.  `N` non-empty values corresponding to `sp_indices`.
+//	sp_shape: 1-D.  Shape of the input SparseTensor.
+//	dense: `R`-D.  The dense Tensor operand.
 //
-// Returns Pairwise cross product of the vectors in `a` and `b`.
-func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) {
+// Returns 1-D.  The `N` values that are operated on.
+func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Cross",
+		Type: "SparseDenseCwiseMul",
 		Input: []tf.Input{
-			a, b,
+			sp_indices, sp_values, sp_shape, dense,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Inverse 2D real-valued fast Fourier transform.
-//
-// Computes the inverse 2-dimensional discrete Fourier transform of a real-valued
-// signal over the inner-most 2 dimensions of `input`.
-//
-// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`:
-// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
-// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
-// from the size of the inner-most 2 dimensions of `input`. If the FFT length used
-// to compute `input` is odd, it should be provided since it cannot be inferred
-// properly.
-//
-// Along each axis `IRFFT2D` is computed on, if `fft_length` (or
-// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
-// corresponding dimension of `input`, the dimension is cropped. If it is larger,
-// the dimension is padded with zeros.
-//
-// Arguments:
-//	input: A complex64 tensor.
-//	fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
-//
-// Returns A float32 tensor of the same rank as `input`. The inner-most 2
-//   dimensions of `input` are replaced with the `fft_length` samples of their
-//   inverse 2D Fourier transform.
-//
-// @compatibility(numpy)
-// Equivalent to np.fft.irfft2
-// @end_compatibility
-func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+// Creates a dataset that emits `components` as a tuple of tensors once.
+func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"output_shapes": output_shapes}
 	opspec := tf.OpSpec{
-		Type: "IRFFT2D",
+		Type: "TensorDataset",
 		Input: []tf.Input{
-			input, fft_length,
+			tf.OutputList(components),
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Returns element-wise remainder of division. This emulates C semantics in that
+// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression.
+type NonMaxSuppressionAttr func(optionalAttr)
+
+// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value.
 //
-// the result here is consistent with a truncating divide. E.g. `truncate(x / y) *
-// y + truncate_mod(x, y) = x`.
+// value: A float representing the threshold for deciding whether boxes
+// overlap too much with respect to IOU.
+// If not specified, defaults to 0.5
+func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr {
+	return func(m optionalAttr) {
+		m["iou_threshold"] = value
+	}
+}
+
+// Greedily selects a subset of bounding boxes in descending order of score,
+//
+// pruning away boxes that have high intersection-over-union (IOU) overlap
+// with previously selected boxes.  Bounding boxes are supplied as
+// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+// diagonal pair of box corners and the coordinates can be provided as normalized
+// (i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
+// is agnostic to where the origin is in the coordinate system.  Note that this
+// algorithm is invariant to orthogonal transformations and translations
+// of the coordinate system; thus translating or reflections of the coordinate
+// system result in the same boxes being selected by the algorithm.
+// The output of this operation is a set of integers indexing into the input
+// collection of bounding boxes representing the selected boxes.  The bounding
+// box coordinates corresponding to the selected indices can then be obtained
+// using the `tf.gather operation`.  For example:
+//   selected_indices = tf.image.non_max_suppression(
+//       boxes, scores, max_output_size, iou_threshold)
+//   selected_boxes = tf.gather(boxes, selected_indices)
+//
+// Arguments:
+//	boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
+//	scores: A 1-D float tensor of shape `[num_boxes]` representing a single
+// score corresponding to each box (each row of boxes).
+//	max_output_size: A scalar integer tensor representing the maximum number of
+// boxes to be selected by non max suppression.
 //
-// *NOTE*: `TruncateMod` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func TruncateMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Returns A 1-D integer tensor of shape `[M]` representing the selected
+// indices from the boxes tensor, where `M <= max_output_size`.
+func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "TruncateMod",
+		Type: "NonMaxSuppression",
 		Input: []tf.Input{
-			x, y,
+			boxes, scores, max_output_size,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
-type ResourceApplyAdagradAttr func(optionalAttr)
+// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta.
+type ResourceApplyAdadeltaAttr func(optionalAttr)
 
-// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value.
+// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value.
 //
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
+// value: If True, updating of the var, accum and update_accum tensors will be protected by
+// a lock; otherwise the behavior is undefined, but may exhibit less contention.
 // If not specified, defaults to false
-func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr {
+func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr {
 	return func(m optionalAttr) {
 		m["use_locking"] = value
 	}
 }
 
-// Update '*var' according to the adagrad scheme.
+// Update '*var' according to the adadelta scheme.
 //
-// accum += grad * grad
-// var -= lr * grad * (1 / sqrt(accum))
+// accum = rho() * accum + (1 - rho()) * grad.square();
+// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
+// update_accum = rho() * update_accum + (1 - rho()) * update.square();
+// var -= update;
 //
 // Arguments:
 //	var_: Should be from a Variable().
 //	accum: Should be from a Variable().
+//	accum_update: Should be from a Variable().
 //	lr: Scaling factor. Must be a scalar.
+//	rho: Decay factor. Must be a scalar.
+//	epsilon: Constant factor. Must be a scalar.
 //	grad: The gradient.
 //
 // Returns the created operation.
-func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) {
+func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
@@ -14129,223 +14344,197 @@ func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.O
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResourceApplyAdagrad",
+		Type: "ResourceApplyAdadelta",
 		Input: []tf.Input{
-			var_, accum, lr, grad,
+			var_, accum, accum_update, lr, rho, epsilon, grad,
 		},
 		Attrs: attrs,
 	}
 	return scope.AddOperation(opspec)
 }
 
-// SparseReduceSumAttr is an optional argument to SparseReduceSum.
-type SparseReduceSumAttr func(optionalAttr)
+// StageSizeAttr is an optional argument to StageSize.
+type StageSizeAttr func(optionalAttr)
 
-// SparseReduceSumKeepDims sets the optional keep_dims attribute to value.
+// StageSizeCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
 //
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr {
+// REQUIRES: value >= 0
+func StageSizeCapacity(value int64) StageSizeAttr {
 	return func(m optionalAttr) {
-		m["keep_dims"] = value
+		m["capacity"] = value
 	}
 }
 
-// Computes the sum of elements across dimensions of a SparseTensor.
-//
-// This Op takes a SparseTensor and is the sparse counterpart to
-// `tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`
-// instead of a sparse one.
-//
-// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-// with length 1.
-//
-// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-// with a single element is returned.  Additionally, the axes can be negative,
-// which are interpreted according to the indexing rules in Python.
-//
-// Arguments:
-//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
-//	input_shape: 1-D.  Shape of the input SparseTensor.
-//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+// StageSizeMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
 //
-// Returns `R-K`-D.  The reduced Tensor.
-func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) {
+// REQUIRES: value >= 0
+func StageSizeMemoryLimit(value int64) StageSizeAttr {
+	return func(m optionalAttr) {
+		m["memory_limit"] = value
+	}
+}
+
+// StageSizeContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func StageSizeContainer(value string) StageSizeAttr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// StageSizeSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func StageSizeSharedName(value string) StageSizeAttr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// Op returns the number of elements in the underlying container.
+func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
+	attrs := map[string]interface{}{"dtypes": dtypes}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "SparseReduceSum",
-		Input: []tf.Input{
-			input_indices, input_values, input_shape, reduction_axes,
-		},
+		Type: "StageSize",
+
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad.
-type MaxPool3DGradAttr func(optionalAttr)
+// ApplyDelayCompensatedGradientDescentAttr is an optional argument to ApplyDelayCompensatedGradientDescent.
+type ApplyDelayCompensatedGradientDescentAttr func(optionalAttr)
 
-// MaxPool3DGradDataFormat sets the optional data_format attribute to value.
+// ApplyDelayCompensatedGradientDescentUseLocking sets the optional use_locking attribute to value.
 //
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-//     [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-//     [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr {
+// value: If `True`, the subtraction will be protected by a lock;
+// otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ApplyDelayCompensatedGradientDescentUseLocking(value bool) ApplyDelayCompensatedGradientDescentAttr {
 	return func(m optionalAttr) {
-		m["data_format"] = value
+		m["use_locking"] = value
 	}
 }
 
-// Computes gradients of max pooling function.
+// var -= alpha * (delta + lambda * delta * (var - shadow))
+//
+// Update '*shadow' by changing it to the new value of 'var'
 //
 // Arguments:
-//	orig_input: The original input tensor.
-//	orig_output: The original output tensor.
-//	grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
-//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-//	strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-//	padding: The type of padding algorithm to use.
-func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) {
+//	var_: Should be from a Variable().
+//	alpha: Scaling factor. Must be a scalar.
+//	delta: The change.
+//	lambda: The variance parameter.
+//	shadow: Same as "var".
+//
+// Returns the created operation.
+func ApplyDelayCompensatedGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, lambda tf.Output, shadow tf.Output, optional ...ApplyDelayCompensatedGradientDescentAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+	attrs := map[string]interface{}{}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "MaxPool3DGrad",
+		Type: "ApplyDelayCompensatedGradientDescent",
 		Input: []tf.Input{
-			orig_input, orig_output, grad,
+			var_, alpha, delta, lambda, shadow,
 		},
 		Attrs: attrs,
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return scope.AddOperation(opspec)
 }
 
-// ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler.
-type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr)
-
-// ThreadUnsafeUnigramCandidateSamplerSeed sets the optional seed attribute to value.
-//
-// value: If either seed or seed2 are set to be non-zero, the random number
-// generator is seeded by the given seed.  Otherwise, it is seeded by a
-// random seed.
-// If not specified, defaults to 0
-func ThreadUnsafeUnigramCandidateSamplerSeed(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
-	return func(m optionalAttr) {
-		m["seed"] = value
-	}
-}
+// SizeAttr is an optional argument to Size.
+type SizeAttr func(optionalAttr)
 
-// ThreadUnsafeUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value.
-//
-// value: An second seed to avoid seed collision.
-// If not specified, defaults to 0
-func ThreadUnsafeUnigramCandidateSamplerSeed2(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
+// SizeOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_INT32
+func SizeOutType(value tf.DataType) SizeAttr {
 	return func(m optionalAttr) {
-		m["seed2"] = value
+		m["out_type"] = value
 	}
 }
 
-// Generates labels for candidate sampling with a learned unigram distribution.
-//
-// See explanations of candidate sampling and the data formats at
-// go/candidate-sampling.
-//
-// For each batch, this op picks a single set of sampled candidate labels.
+// Returns the size of a tensor.
 //
-// The advantages of sampling candidates per-batch are simplicity and the
-// possibility of efficient dense matrix multiplication. The disadvantage is that
-// the sampled candidates must be chosen independently of the context and of the
-// true labels.
+// This operation returns an integer representing the number of elements in
+// `input`.
 //
-// Arguments:
-//	true_classes: A batch_size * num_true matrix, in which each row contains the
-// IDs of the num_true target_classes in the corresponding original label.
-//	num_true: Number of true labels per context.
-//	num_sampled: Number of candidates to randomly sample.
-//	unique: If unique is true, we sample with rejection, so that all sampled
-// candidates in a batch are unique. This requires some approximation to
-// estimate the post-rejection sampling probabilities.
-//	range_max: The sampler will sample integers from the interval [0, range_max).
+// For example:
 //
-// Returns A vector of length num_sampled, in which each element is
-// the ID of a sampled candidate.A batch_size * num_true matrix, representing
-// the number of times each candidate is expected to occur in a batch
-// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
-// candidate representing the number of times the candidate is expected
-// to occur in a batch of sampled candidates.  If unique=true, then this is a
-// probability.
-func ThreadUnsafeUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...ThreadUnsafeUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
+// ```
+// # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
+// size(t) ==> 12
+// ```
+func Size(scope *Scope, input tf.Output, optional ...SizeAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
+	attrs := map[string]interface{}{}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ThreadUnsafeUnigramCandidateSampler",
+		Type: "Size",
 		Input: []tf.Input{
-			true_classes,
+			input,
 		},
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
+	return op.Output(0)
 }
 
-// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad.
-type ResourceSparseApplyProximalAdagradAttr func(optionalAttr)
+// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp.
+type ResourceApplyRMSPropAttr func(optionalAttr)
 
-// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
+// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value.
 //
-// value: If True, updating of the var and accum tensors will be protected by
-// a lock; otherwise the behavior is undefined, but may exhibit less contention.
+// value: If `True`, updating of the var, ms, and mom tensors is protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
 // If not specified, defaults to false
-func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr {
+func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr {
 	return func(m optionalAttr) {
 		m["use_locking"] = value
 	}
 }
 
-// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
+// Update '*var' according to the RMSProp algorithm.
 //
-// That is for rows we have grad for, we update var and accum as follows:
-// accum += grad * grad
-// prox_v = var
-// prox_v -= lr * grad * (1 / sqrt(accum))
-// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
+// Note that in dense implementation of this algorithm, ms and mom will
+// update even if the grad is zero, but in this sparse implementation, ms
+// and mom will not update in iterations during which the grad is zero.
+//
+// mean_square = decay * mean_square + (1-decay) * gradient ** 2
+// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+//
+// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+// var <- var - mom
 //
 // Arguments:
 //	var_: Should be from a Variable().
-//	accum: Should be from a Variable().
-//	lr: Learning rate. Must be a scalar.
-//	l1: L1 regularization. Must be a scalar.
-//	l2: L2 regularization. Must be a scalar.
+//	ms: Should be from a Variable().
+//	mom: Should be from a Variable().
+//	lr: Scaling factor. Must be a scalar.
+//	rho: Decay rate. Must be a scalar.
+//
+//	epsilon: Ridge term. Must be a scalar.
 //	grad: The gradient.
-//	indices: A vector of indices into the first dimension of var and accum.
 //
 // Returns the created operation.
-func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) {
+func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
@@ -14354,115 +14543,145 @@ func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.O
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResourceSparseApplyProximalAdagrad",
+		Type: "ResourceApplyRMSProp",
 		Input: []tf.Input{
-			var_, accum, lr, l1, l2, grad, indices,
+			var_, ms, mom, lr, rho, momentum, epsilon, grad,
 		},
 		Attrs: attrs,
 	}
 	return scope.AddOperation(opspec)
 }
 
-// Store the input tensor in the state of the current session.
-//
-// Arguments:
-//	value: The tensor to be stored.
+// Elementwise computes the bitwise XOR of `x` and `y`.
 //
-// Returns The handle for the tensor stored in the session state, represented
-// as a string.
-func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) {
+// The result will have those bits set, that are different in `x` and `y`. The
+// computation is performed on the underlying representations of `x` and `y`.
+func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "GetSessionHandle",
+		Type: "BitwiseXor",
 		Input: []tf.Input{
-			value,
+			x, y,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Decode web-safe base64-encoded strings.
+// Compute the polygamma function \\(\psi^{(n)}(x)\\).
 //
-// Input may or may not have padding at the end. See EncodeBase64 for padding.
-// Web-safe means that input must use - and _ instead of + and /.
+// The polygamma function is defined as:
 //
-// Arguments:
-//	input: Base64 strings to decode.
 //
-// Returns Decoded strings.
-func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
+// \\(\psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)\\)
+//
+// where \\(\psi(x)\\) is the digamma function.
+func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "DecodeBase64",
+		Type: "Polygamma",
 		Input: []tf.Input{
-			input,
+			a, x,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes hyperbolic tangent of `x` element-wise.
-func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
+//
+// The `input` tensor has shape `[batch, in_height, in_width, depth]` and the
+// `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each
+// input channel is processed independently of the others with its own structuring
+// function. The `output` tensor has shape
+// `[batch, out_height, out_width, depth]`. The spatial dimensions of the output
+// tensor depend on the `padding` algorithm. We currently only support the default
+// "NHWC" `data_format`.
+//
+// In detail, the grayscale morphological 2-D dilation is the max-sum correlation
+// (for consistency with `conv2d`, we use unmirrored filters):
+//
+//     output[b, y, x, c] =
+//        max_{dy, dx} input[b,
+//                           strides[1] * y + rates[1] * dy,
+//                           strides[2] * x + rates[2] * dx,
+//                           c] +
+//                     filter[dy, dx, c]
+//
+// Max-pooling is a special case when the filter has size equal to the pooling
+// kernel size and contains all zeros.
+//
+// Note on duality: The dilation of `input` by the `filter` is equal to the
+// negation of the erosion of `-input` by the reflected `filter`.
+//
+// Arguments:
+//	input: 4-D with shape `[batch, in_height, in_width, depth]`.
+//	filter: 3-D with shape `[filter_height, filter_width, depth]`.
+//	strides: The stride of the sliding window for each dimension of the input
+// tensor. Must be: `[1, stride_height, stride_width, 1]`.
+//	rates: The input stride for atrous morphological dilation. Must be:
+// `[1, rate_height, rate_width, 1]`.
+//	padding: The type of padding algorithm to use.
+//
+// Returns 4-D with shape `[batch, out_height, out_width, depth]`.
+func Dilation2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, rates []int64, padding string) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
 	opspec := tf.OpSpec{
-		Type: "Tanh",
+		Type: "Dilation2D",
 		Input: []tf.Input{
-			x,
+			input, filter,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad.
-type AvgPool3DGradAttr func(optionalAttr)
+// EncodeBase64Attr is an optional argument to EncodeBase64.
+type EncodeBase64Attr func(optionalAttr)
 
-// AvgPool3DGradDataFormat sets the optional data_format attribute to value.
+// EncodeBase64Pad sets the optional pad attribute to value.
 //
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-//     [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-//     [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr {
+// value: Bool whether padding is applied at the ends.
+// If not specified, defaults to false
+func EncodeBase64Pad(value bool) EncodeBase64Attr {
 	return func(m optionalAttr) {
-		m["data_format"] = value
+		m["pad"] = value
 	}
 }
 
-// Computes gradients of average pooling function.
+// Encode strings into web-safe base64 format.
+//
+// Refer to the following article for more information on base64 format:
+// en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the
+// end so that the encoded has length multiple of 4. See Padding section of the
+// link above.
+//
+// Web-safe means that the encoder uses - and _ instead of + and /.
 //
 // Arguments:
-//	orig_input_shape: The original input dimensions.
-//	grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
-//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-//	strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-//	padding: The type of padding algorithm to use.
+//	input: Strings to be encoded.
 //
-// Returns The backprop for input.
-func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) {
+// Returns Input strings encoded in base64.
+func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+	attrs := map[string]interface{}{}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "AvgPool3DGrad",
+		Type: "EncodeBase64",
 		Input: []tf.Input{
-			orig_input_shape, grad,
+			input,
 		},
 		Attrs: attrs,
 	}
@@ -14470,45 +14689,44 @@ func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksi
 	return op.Output(0)
 }
 
-// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
-type TextLineReaderV2Attr func(optionalAttr)
-
-// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
-//
-// value: Number of lines to skip from the beginning of every file.
-// If not specified, defaults to 0
-func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["skip_header_lines"] = value
+// Output a fact about factorials.
+func Fact(scope *Scope) (fact tf.Output) {
+	if scope.Err() != nil {
+		return
 	}
-}
-
-// TextLineReaderV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
+	opspec := tf.OpSpec{
+		Type: "Fact",
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
+// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform.
+type StatelessRandomUniformAttr func(optionalAttr)
+
+// StatelessRandomUniformDtype sets the optional dtype attribute to value.
 //
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
+// value: The type of the output.
+// If not specified, defaults to DT_FLOAT
+func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr {
 	return func(m optionalAttr) {
-		m["shared_name"] = value
+		m["dtype"] = value
 	}
 }
 
-// A Reader that outputs the lines of a file delimited by '\n'.
+// Outputs deterministic pseudorandom random values from a uniform distribution.
 //
-// Returns The handle to reference the Reader.
-func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
+// The generated values follow a uniform distribution in the range `[0, 1)`. The
+// lower bound 0 is included in the range, while the upper bound 1 is excluded.
+//
+// The outputs are a deterministic function of `shape` and `seed`.
+//
+// Arguments:
+//	shape: The shape of the output tensor.
+//	seed: 2 seeds (shape [2]).
+//
+// Returns Random values with specified shape.
+func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -14517,149 +14735,147 @@ func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_ha
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "TextLineReaderV2",
-
+		Type: "StatelessRandomUniform",
+		Input: []tf.Input{
+			shape, seed,
+		},
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox.
-type SampleDistortedBoundingBoxAttr func(optionalAttr)
-
-// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value.
-//
-// value: If either `seed` or `seed2` are set to non-zero, the random number
-// generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
-// seed.
-// If not specified, defaults to 0
-func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr {
-	return func(m optionalAttr) {
-		m["seed"] = value
-	}
-}
-
-// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value.
-//
-// value: A second seed to avoid seed collision.
-// If not specified, defaults to 0
-func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr {
-	return func(m optionalAttr) {
-		m["seed2"] = value
-	}
-}
+// PrintAttr is an optional argument to Print.
+type PrintAttr func(optionalAttr)
 
-// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value.
+// PrintMessage sets the optional message attribute to value.
 //
-// value: The cropped area of the image must contain at least this
-// fraction of any bounding box supplied. The value of this parameter should be
-// non-negative. In the case of 0, the cropped area does not need to overlap
-// any of the bounding boxes supplied.
-// If not specified, defaults to 0.1
-func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr {
+// value: A string, prefix of the error message.
+// If not specified, defaults to ""
+func PrintMessage(value string) PrintAttr {
 	return func(m optionalAttr) {
-		m["min_object_covered"] = value
+		m["message"] = value
 	}
 }
 
-// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value.
+// PrintFirstN sets the optional first_n attribute to value.
 //
-// value: The cropped area of the image must have an aspect ratio =
-// width / height within this range.
-// If not specified, defaults to <f:0.75 f:1.33 >
-func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
+// value: Only log `first_n` number of times. -1 disables logging.
+// If not specified, defaults to -1
+func PrintFirstN(value int64) PrintAttr {
 	return func(m optionalAttr) {
-		m["aspect_ratio_range"] = value
+		m["first_n"] = value
 	}
 }
 
-// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value.
+// PrintSummarize sets the optional summarize attribute to value.
 //
-// value: The cropped area of the image must contain a fraction of the
-// supplied image within in this range.
-// If not specified, defaults to <f:0.05 f:1 >
-func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
+// value: Only print this many entries of each tensor.
+// If not specified, defaults to 3
+func PrintSummarize(value int64) PrintAttr {
 	return func(m optionalAttr) {
-		m["area_range"] = value
+		m["summarize"] = value
 	}
 }
 
-// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value.
+// Prints a list of tensors.
 //
-// value: Number of attempts at generating a cropped region of the image
-// of the specified constraints. After `max_attempts` failures, return the entire
-// image.
-// If not specified, defaults to 100
-func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr {
-	return func(m optionalAttr) {
-		m["max_attempts"] = value
+// Passes `input` through to `output` and prints `data` when evaluating.
+//
+// Arguments:
+//	input: The tensor passed to `output`
+//	data: A list of tensors to print out when op is evaluated.
+//
+// Returns = The unmodified `input` tensor
+func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Print",
+		Input: []tf.Input{
+			input, tf.OutputList(data),
+		},
+		Attrs: attrs,
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
+// ResizeAreaAttr is an optional argument to ResizeArea.
+type ResizeAreaAttr func(optionalAttr)
+
+// ResizeAreaAlignCorners sets the optional align_corners attribute to value.
 //
-// value: Controls behavior if no bounding boxes supplied.
-// If true, assume an implicit bounding box covering the whole input. If false,
-// raise an error.
+// value: If true, rescale input by (new_height - 1) / (height - 1), which
+// exactly aligns the 4 corners of images and resized images. If false, rescale
+// by new_height / height. Treat similarly the width dimension.
 // If not specified, defaults to false
-func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr {
+func ResizeAreaAlignCorners(value bool) ResizeAreaAttr {
 	return func(m optionalAttr) {
-		m["use_image_if_no_bounding_boxes"] = value
+		m["align_corners"] = value
 	}
 }
 
-// Generate a single randomly distorted bounding box for an image.
-//
-// Bounding box annotations are often supplied in addition to ground-truth labels
-// in image recognition or object localization tasks. A common technique for
-// training such a system is to randomly distort an image while preserving
-// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
-// localization of an object, i.e. bounding box, given an `image_size`,
-// `bounding_boxes` and a series of constraints.
+// Resize `images` to `size` using area interpolation.
 //
-// The output of this Op is a single bounding box that may be used to crop the
-// original image. The output is returned as 3 tensors: `begin`, `size` and
-// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
-// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
-// what the bounding box looks like.
+// Input images can be of different types but output images are always float.
 //
-// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
-// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
-// height of the underlying image.
+// Arguments:
+//	images: 4-D with shape `[batch, height, width, channels]`.
+//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
+// new size for the images.
 //
-// For example,
+// Returns 4-D with shape
+// `[batch, new_height, new_width, channels]`.
+func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResizeArea",
+		Input: []tf.Input{
+			images, size,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// RealAttr is an optional argument to Real.
+type RealAttr func(optionalAttr)
+
+// RealTout sets the optional Tout attribute to value.
+// If not specified, defaults to DT_FLOAT
+func RealTout(value tf.DataType) RealAttr {
+	return func(m optionalAttr) {
+		m["Tout"] = value
+	}
+}
+
+// Returns the real part of a complex number.
 //
-// ```python
-//     # Generate a single distorted bounding box.
-//     begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
-//         tf.shape(image),
-//         bounding_boxes=bounding_boxes)
+// Given a tensor `input` of complex numbers, this operation returns a tensor of
+// type `float` that is the real part of each element in `input`. All elements in
+// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real
+//  part returned by this operation and *b* is the imaginary part.
 //
-//     # Draw the bounding box in an image summary.
-//     image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
-//                                                   bbox_for_draw)
-//     tf.image_summary('images_with_box', image_with_box)
+// For example:
 //
-//     # Employ the bounding box to distort the image.
-//     distorted_image = tf.slice(image, begin, size)
 // ```
-//
-// Note that if no bounding box information is available, setting
-// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
-// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
-// false and no bounding boxes are supplied, an error is raised.
-//
-// Arguments:
-//	image_size: 1-D, containing `[height, width, channels]`.
-//	bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
-// associated with the image.
-//
-// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
-// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to
-// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box.
-// Provide as input to `tf.image.draw_bounding_boxes`.
-func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) {
+// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+// tf.real(input) ==> [-2.25, 3.25]
+// ```
+func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -14668,328 +14884,309 @@ func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_box
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "SampleDistortedBoundingBox",
+		Type: "Real",
 		Input: []tf.Input{
-			image_size, bounding_boxes,
+			input,
 		},
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
+	return op.Output(0)
 }
 
-// Returns the truth value of (x > y) element-wise.
-//
-// *NOTE*: `Greater` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Creates a dataset that zips together `input_datasets`.
+func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
 	opspec := tf.OpSpec{
-		Type: "Greater",
+		Type: "ZipDataset",
 		Input: []tf.Input{
-			x, y,
+			tf.OutputList(input_datasets),
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp.
-type ResourceSparseApplyRMSPropAttr func(optionalAttr)
+// MutableDenseHashTableV2Attr is an optional argument to MutableDenseHashTableV2.
+type MutableDenseHashTableV2Attr func(optionalAttr)
 
-// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value.
+// MutableDenseHashTableV2Container sets the optional container attribute to value.
 //
-// value: If `True`, updating of the var, ms, and mom tensors is protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
+// value: If non-empty, this table is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func MutableDenseHashTableV2Container(value string) MutableDenseHashTableV2Attr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// MutableDenseHashTableV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this table is shared under the given name across
+// multiple sessions.
+// If not specified, defaults to ""
+func MutableDenseHashTableV2SharedName(value string) MutableDenseHashTableV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// MutableDenseHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
 // If not specified, defaults to false
-func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr {
+func MutableDenseHashTableV2UseNodeNameSharing(value bool) MutableDenseHashTableV2Attr {
 	return func(m optionalAttr) {
-		m["use_locking"] = value
+		m["use_node_name_sharing"] = value
 	}
 }
 
-// Update '*var' according to the RMSProp algorithm.
+// MutableDenseHashTableV2ValueShape sets the optional value_shape attribute to value.
 //
-// Note that in dense implementation of this algorithm, ms and mom will
-// update even if the grad is zero, but in this sparse implementation, ms
-// and mom will not update in iterations during which the grad is zero.
+// value: The shape of each value.
+// If not specified, defaults to <>
+func MutableDenseHashTableV2ValueShape(value tf.Shape) MutableDenseHashTableV2Attr {
+	return func(m optionalAttr) {
+		m["value_shape"] = value
+	}
+}
+
+// MutableDenseHashTableV2InitialNumBuckets sets the optional initial_num_buckets attribute to value.
 //
-// mean_square = decay * mean_square + (1-decay) * gradient ** 2
-// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+// value: The initial number of hash table buckets. Must be a power
+// to 2.
+// If not specified, defaults to 131072
+func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTableV2Attr {
+	return func(m optionalAttr) {
+		m["initial_num_buckets"] = value
+	}
+}
+
+// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value.
 //
-// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
-// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
-// var <- var - mom
+// value: The maximum ratio between number of entries and number of
+// buckets before growing the table. Must be between 0 and 1.
+// If not specified, defaults to 0.8
+func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr {
+	return func(m optionalAttr) {
+		m["max_load_factor"] = value
+	}
+}
+
+// Creates an empty hash table that uses tensors as the backing store.
+//
+// It uses "open addressing" with quadratic reprobing to resolve
+// collisions.
 //
-// Arguments:
-//	var_: Should be from a Variable().
-//	ms: Should be from a Variable().
-//	mom: Should be from a Variable().
-//	lr: Scaling factor. Must be a scalar.
-//	rho: Decay rate. Must be a scalar.
+// This op creates a mutable hash table, specifying the type of its keys and
+// values. Each value must be a scalar. Data can be inserted into the table using
+// the insert operations. It does not support the initialization operation.
 //
-//	epsilon: Ridge term. Must be a scalar.
-//	grad: The gradient.
-//	indices: A vector of indices into the first dimension of var, ms and mom.
+// Arguments:
+//	empty_key: The key used to represent empty key buckets internally. Must not
+// be used in insert or lookup operations.
+//	value_dtype: Type of the table values.
 //
-// Returns the created operation.
-func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) {
+// Returns Handle to a table.
+func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
+	attrs := map[string]interface{}{"value_dtype": value_dtype}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResourceSparseApplyRMSProp",
+		Type: "MutableDenseHashTableV2",
 		Input: []tf.Input{
-			var_, ms, mom, lr, rho, momentum, epsilon, grad, indices,
+			empty_key,
 		},
 		Attrs: attrs,
 	}
-	return scope.AddOperation(opspec)
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// QuantizeV2Attr is an optional argument to QuantizeV2.
-type QuantizeV2Attr func(optionalAttr)
+// LRNAttr is an optional argument to LRN.
+type LRNAttr func(optionalAttr)
 
-// QuantizeV2Mode sets the optional mode attribute to value.
-// If not specified, defaults to "MIN_COMBINED"
-func QuantizeV2Mode(value string) QuantizeV2Attr {
+// LRNDepthRadius sets the optional depth_radius attribute to value.
+//
+// value: 0-D.  Half-width of the 1-D normalization window.
+// If not specified, defaults to 5
+func LRNDepthRadius(value int64) LRNAttr {
 	return func(m optionalAttr) {
-		m["mode"] = value
+		m["depth_radius"] = value
 	}
 }
 
-// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
-//
-// [min_range, max_range] are scalar floats that specify the range for
-// the 'input' data. The 'mode' attribute controls exactly which calculations are
-// used to convert the float values to their quantized equivalents.
-//
-// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
-//
-// ```
-// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
-// if T == qint8, out[i] -= (range(T) + 1) / 2.0
-// ```
-// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
-//
-// *MIN_COMBINED Mode Example*
+// LRNBias sets the optional bias attribute to value.
 //
-// Assume the input is type float and has a possible range of [0.0, 6.0] and the
-// output type is quint8 ([0, 255]). The min_range and max_range values should be
-// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each
-// value of the input by 255/6 and cast to quint8.
+// value: An offset (usually positive to avoid dividing by 0).
+// If not specified, defaults to 1
+func LRNBias(value float32) LRNAttr {
+	return func(m optionalAttr) {
+		m["bias"] = value
+	}
+}
+
+// LRNAlpha sets the optional alpha attribute to value.
 //
-// If the output type was qint8 ([-128, 127]), the operation will additionally
-// subtract each value by 128 prior to casting, so that the range of values aligns
-// with the range of qint8.
+// value: A scale factor, usually positive.
+// If not specified, defaults to 1
+func LRNAlpha(value float32) LRNAttr {
+	return func(m optionalAttr) {
+		m["alpha"] = value
+	}
+}
+
+// LRNBeta sets the optional beta attribute to value.
 //
-// If the mode is 'MIN_FIRST', then this approach is used:
+// value: An exponent.
+// If not specified, defaults to 0.5
+func LRNBeta(value float32) LRNAttr {
+	return func(m optionalAttr) {
+		m["beta"] = value
+	}
+}
+
+// Local Response Normalization.
 //
-// ```
-// number_of_steps = 1 << (# of bits in T)
-// range_adjust = number_of_steps / (number_of_steps - 1)
-// range = (range_max - range_min) * range_adjust
-// range_scale = number_of_steps / range
-// quantized = round(input * range_scale) - round(range_min * range_scale) +
-//   numeric_limits<T>::min()
-// quantized = max(quantized, numeric_limits<T>::min())
-// quantized = min(quantized, numeric_limits<T>::max())
-// ```
+// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
+// dimension), and each vector is normalized independently.  Within a given vector,
+// each component is divided by the weighted, squared sum of inputs within
+// `depth_radius`.  In detail,
 //
-// The biggest difference between this and MIN_COMBINED is that the minimum range
-// is rounded first, before it's subtracted from the rounded value. With
-// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
-// and dequantizing will introduce a larger and larger error.
+//     sqr_sum[a, b, c, d] =
+//         sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
+//     output = input / (bias + alpha * sqr_sum) ** beta
 //
-// One thing to watch out for is that the operator may choose to adjust the
-// requested minimum and maximum values slightly during the quantization process,
-// so you should always use the output ports as the range for further calculations.
-// For example, if the requested minimum and maximum values are close to equal,
-// they will be separated by a small epsilon value to prevent ill-formed quantized
-// buffers from being created. Otherwise, you can end up with buffers where all the
-// quantized values map to the same float value, which causes problems for
-// operations that have to perform further calculations on them.
+// For details, see [Krizhevsky et al., ImageNet classification with deep
+// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
 //
 // Arguments:
-//
-//	min_range: The minimum scalar value possibly produced for the input.
-//	max_range: The maximum scalar value possibly produced for the input.
-//
-//
-// Returns The quantized data produced from the float input.The actual minimum scalar value used for the output.The actual maximum scalar value used for the output.
-func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) {
+//	input: 4-D.
+func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"T": T}
+	attrs := map[string]interface{}{}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "QuantizeV2",
+		Type: "LRN",
 		Input: []tf.Input{
-			input, min_range, max_range,
+			input,
 		},
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
+	return op.Output(0)
 }
 
-// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter.
-type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr)
+// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad.
+type ResourceSparseApplyAdagradAttr func(optionalAttr)
 
-// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value.
+// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value.
 //
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-//     [batch, height, width, channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-//     [batch, channels, height, width].
-// If not specified, defaults to "NHWC"
-func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr {
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr {
 	return func(m optionalAttr) {
-		m["data_format"] = value
+		m["use_locking"] = value
 	}
 }
 
-// Computes the gradients of depthwise convolution with respect to the filter.
+// Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
+//
+// That is for rows we have grad for, we update var and accum as follows:
+// accum += grad * grad
+// var -= lr * grad * (1 / sqrt(accum))
 //
 // Arguments:
-//	input: 4-D with shape based on `data_format`.  For example, if
-// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height,
-// in_width, in_channels]` tensor.
-//	filter_sizes: An integer vector representing the tensor shape of `filter`,
-// where `filter` is a 4-D
-// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor.
-//	out_backprop: 4-D with shape  based on `data_format`.
-// For example, if `data_format` is 'NHWC' then
-// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
-// Gradients w.r.t. the output of the convolution.
-//	strides: The stride of the sliding window for each dimension of the input
-// of the convolution.
-//	padding: The type of padding algorithm to use.
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	lr: Learning rate. Must be a scalar.
+//	grad: The gradient.
+//	indices: A vector of indices into the first dimension of var and accum.
 //
-// Returns 4-D with shape
-// `[filter_height, filter_width, in_channels, out_channels]`.  Gradient w.r.t.
-// the `filter` input of the convolution.
-func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) {
+// Returns the created operation.
+func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"strides": strides, "padding": padding}
+	attrs := map[string]interface{}{}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "DepthwiseConv2dNativeBackpropFilter",
+		Type: "ResourceSparseApplyAdagrad",
 		Input: []tf.Input{
-			input, filter_sizes, out_backprop,
+			var_, accum, lr, grad, indices,
 		},
 		Attrs: attrs,
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return scope.AddOperation(opspec)
 }
 
-// Component-wise multiplies a SparseTensor by a dense Tensor.
-//
-// The output locations corresponding to the implicitly zero elements in the sparse
-// tensor will be zero (i.e., will not take up storage space), regardless of the
-// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).
-//
-// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
-// the other direction.
-//
-// Arguments:
-//	sp_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-// SparseTensor, possibly not in canonical ordering.
-//	sp_values: 1-D.  `N` non-empty values corresponding to `sp_indices`.
-//	sp_shape: 1-D.  Shape of the input SparseTensor.
-//	dense: `R`-D.  The dense Tensor operand.
+// LRNGradAttr is an optional argument to LRNGrad.
+type LRNGradAttr func(optionalAttr)
+
+// LRNGradDepthRadius sets the optional depth_radius attribute to value.
 //
-// Returns 1-D.  The `N` values that are operated on.
-func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SparseDenseCwiseMul",
-		Input: []tf.Input{
-			sp_indices, sp_values, sp_shape, dense,
-		},
+// value: A depth radius.
+// If not specified, defaults to 5
+func LRNGradDepthRadius(value int64) LRNGradAttr {
+	return func(m optionalAttr) {
+		m["depth_radius"] = value
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
 }
 
-// Creates a dataset that emits `components` as a tuple of tensors once.
-func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"output_shapes": output_shapes}
-	opspec := tf.OpSpec{
-		Type: "TensorDataset",
-		Input: []tf.Input{
-			tf.OutputList(components),
-		},
-		Attrs: attrs,
+// LRNGradBias sets the optional bias attribute to value.
+//
+// value: An offset (usually > 0 to avoid dividing by 0).
+// If not specified, defaults to 1
+func LRNGradBias(value float32) LRNGradAttr {
+	return func(m optionalAttr) {
+		m["bias"] = value
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
 }
 
-// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression.
-type NonMaxSuppressionAttr func(optionalAttr)
+// LRNGradAlpha sets the optional alpha attribute to value.
+//
+// value: A scale factor, usually positive.
+// If not specified, defaults to 1
+func LRNGradAlpha(value float32) LRNGradAttr {
+	return func(m optionalAttr) {
+		m["alpha"] = value
+	}
+}
 
-// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value.
+// LRNGradBeta sets the optional beta attribute to value.
 //
-// value: A float representing the threshold for deciding whether boxes
-// overlap too much with respect to IOU.
+// value: An exponent.
 // If not specified, defaults to 0.5
-func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr {
+func LRNGradBeta(value float32) LRNGradAttr {
 	return func(m optionalAttr) {
-		m["iou_threshold"] = value
+		m["beta"] = value
 	}
 }
 
-// Greedily selects a subset of bounding boxes in descending order of score,
-//
-// pruning away boxes that have high intersection-over-union (IOU) overlap
-// with previously selected boxes.  Bounding boxes are supplied as
-// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
-// diagonal pair of box corners and the coordinates can be provided as normalized
-// (i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
-// is agnostic to where the origin is in the coordinate system.  Note that this
-// algorithm is invariant to orthogonal transformations and translations
-// of the coordinate system; thus translating or reflections of the coordinate
-// system result in the same boxes being selected by the algorithm.
-// The output of this operation is a set of integers indexing into the input
-// collection of bounding boxes representing the selected boxes.  The bounding
-// box coordinates corresponding to the selected indices can then be obtained
-// using the `tf.gather operation`.  For example:
-//   selected_indices = tf.image.non_max_suppression(
-//       boxes, scores, max_output_size, iou_threshold)
-//   selected_boxes = tf.gather(boxes, selected_indices)
+// Gradients for Local Response Normalization.
 //
 // Arguments:
-//	boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
-//	scores: A 1-D float tensor of shape `[num_boxes]` representing a single
-// score corresponding to each box (each row of boxes).
-//	max_output_size: A scalar integer tensor representing the maximum number of
-// boxes to be selected by non max suppression.
+//	input_grads: 4-D with shape `[batch, height, width, channels]`.
+//	input_image: 4-D with shape `[batch, height, width, channels]`.
+//	output_image: 4-D with shape `[batch, height, width, channels]`.
 //
-// Returns A 1-D integer tensor of shape `[M]` representing the selected
-// indices from the boxes tensor, where `M <= max_output_size`.
-func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) {
+// Returns The gradients for LRN.
+func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -14998,9 +15195,9 @@ func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_outp
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "NonMaxSuppression",
+		Type: "LRNGrad",
 		Input: []tf.Input{
-			boxes, scores, max_output_size,
+			input_grads, input_image, output_image,
 		},
 		Attrs: attrs,
 	}
@@ -15008,38 +15205,26 @@ func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_outp
 	return op.Output(0)
 }
 
-// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta.
-type ResourceApplyAdadeltaAttr func(optionalAttr)
+// StringToNumberAttr is an optional argument to StringToNumber.
+type StringToNumberAttr func(optionalAttr)
 
-// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value.
+// StringToNumberOutType sets the optional out_type attribute to value.
 //
-// value: If True, updating of the var, accum and update_accum tensors will be protected by
-// a lock; otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr {
+// value: The numeric type to interpret each string in `string_tensor` as.
+// If not specified, defaults to DT_FLOAT
+func StringToNumberOutType(value tf.DataType) StringToNumberAttr {
 	return func(m optionalAttr) {
-		m["use_locking"] = value
+		m["out_type"] = value
 	}
 }
 
-// Update '*var' according to the adadelta scheme.
-//
-// accum = rho() * accum + (1 - rho()) * grad.square();
-// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
-// update_accum = rho() * update_accum + (1 - rho()) * update.square();
-// var -= update;
+// Converts each string in the input Tensor to the specified numeric type.
 //
-// Arguments:
-//	var_: Should be from a Variable().
-//	accum: Should be from a Variable().
-//	accum_update: Should be from a Variable().
-//	lr: Scaling factor. Must be a scalar.
-//	rho: Decay factor. Must be a scalar.
-//	epsilon: Constant factor. Must be a scalar.
-//	grad: The gradient.
+// (Note that int32 overflow results in an error while float overflow
+// results in a rounded value.)
 //
-// Returns the created operation.
-func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) {
+// Returns A Tensor of the same shape as the input `string_tensor`.
+func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -15048,99 +15233,166 @@ func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResourceApplyAdadelta",
+		Type: "StringToNumber",
 		Input: []tf.Input{
-			var_, accum, accum_update, lr, rho, epsilon, grad,
+			string_tensor,
 		},
 		Attrs: attrs,
 	}
-	return scope.AddOperation(opspec)
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// StageSizeAttr is an optional argument to StageSize.
-type StageSizeAttr func(optionalAttr)
+// Returns the truth value of NOT x element-wise.
+func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "LogicalNot",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
 
-// StageSizeCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
+// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox.
+type SampleDistortedBoundingBoxAttr func(optionalAttr)
+
+// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value.
 //
-// REQUIRES: value >= 0
-func StageSizeCapacity(value int64) StageSizeAttr {
+// value: If either `seed` or `seed2` are set to non-zero, the random number
+// generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
+// seed.
+// If not specified, defaults to 0
+func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr {
 	return func(m optionalAttr) {
-		m["capacity"] = value
+		m["seed"] = value
 	}
 }
 
-// StageSizeMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
+// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value.
 //
-// REQUIRES: value >= 0
-func StageSizeMemoryLimit(value int64) StageSizeAttr {
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr {
 	return func(m optionalAttr) {
-		m["memory_limit"] = value
+		m["seed2"] = value
 	}
 }
 
-// StageSizeContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func StageSizeContainer(value string) StageSizeAttr {
+// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value.
+//
+// value: The cropped area of the image must contain at least this
+// fraction of any bounding box supplied. The value of this parameter should be
+// non-negative. In the case of 0, the cropped area does not need to overlap
+// any of the bounding boxes supplied.
+// If not specified, defaults to 0.1
+func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr {
 	return func(m optionalAttr) {
-		m["container"] = value
+		m["min_object_covered"] = value
 	}
 }
 
-// StageSizeSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func StageSizeSharedName(value string) StageSizeAttr {
+// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value.
+//
+// value: The cropped area of the image must have an aspect ratio =
+// width / height within this range.
+// If not specified, defaults to <f:0.75 f:1.33 >
+func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
 	return func(m optionalAttr) {
-		m["shared_name"] = value
+		m["aspect_ratio_range"] = value
 	}
 }
 
-// Op returns the number of elements in the underlying container.
-func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"dtypes": dtypes}
-	for _, a := range optional {
-		a(attrs)
+// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value.
+//
+// value: The cropped area of the image must contain a fraction of the
+// supplied image within in this range.
+// If not specified, defaults to <f:0.05 f:1 >
+func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
+	return func(m optionalAttr) {
+		m["area_range"] = value
 	}
-	opspec := tf.OpSpec{
-		Type: "StageSize",
+}
 
-		Attrs: attrs,
+// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value.
+//
+// value: Number of attempts at generating a cropped region of the image
+// of the specified constraints. After `max_attempts` failures, return the entire
+// image.
+// If not specified, defaults to 100
+func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr {
+	return func(m optionalAttr) {
+		m["max_attempts"] = value
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
 }
 
-// ApplyDelayCompensatedGradientDescentAttr is an optional argument to ApplyDelayCompensatedGradientDescent.
-type ApplyDelayCompensatedGradientDescentAttr func(optionalAttr)
-
-// ApplyDelayCompensatedGradientDescentUseLocking sets the optional use_locking attribute to value.
+// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
 //
-// value: If `True`, the subtraction will be protected by a lock;
-// otherwise the behavior is undefined, but may exhibit less contention.
+// value: Controls behavior if no bounding boxes supplied.
+// If true, assume an implicit bounding box covering the whole input. If false,
+// raise an error.
 // If not specified, defaults to false
-func ApplyDelayCompensatedGradientDescentUseLocking(value bool) ApplyDelayCompensatedGradientDescentAttr {
+func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr {
 	return func(m optionalAttr) {
-		m["use_locking"] = value
+		m["use_image_if_no_bounding_boxes"] = value
 	}
 }
 
-// var -= alpha * (delta + lambda * delta * (var - shadow))
+// Generate a single randomly distorted bounding box for an image.
 //
-// Update '*shadow' by changing it to the new value of 'var'
+// Bounding box annotations are often supplied in addition to ground-truth labels
+// in image recognition or object localization tasks. A common technique for
+// training such a system is to randomly distort an image while preserving
+// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+// localization of an object, i.e. bounding box, given an `image_size`,
+// `bounding_boxes` and a series of constraints.
 //
-// Arguments:
-//	var_: Should be from a Variable().
-//	alpha: Scaling factor. Must be a scalar.
-//	delta: The change.
-//	lambda: The variance parameter.
-//	shadow: Same as "var".
+// The output of this Op is a single bounding box that may be used to crop the
+// original image. The output is returned as 3 tensors: `begin`, `size` and
+// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
+// what the bounding box looks like.
+//
+// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+// height of the underlying image.
+//
+// For example,
+//
+// ```python
+//     # Generate a single distorted bounding box.
+//     begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+//         tf.shape(image),
+//         bounding_boxes=bounding_boxes)
+//
+//     # Draw the bounding box in an image summary.
+//     image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+//                                                   bbox_for_draw)
+//     tf.image_summary('images_with_box', image_with_box)
+//
+//     # Employ the bounding box to distort the image.
+//     distorted_image = tf.slice(image, begin, size)
+// ```
+//
+// Note that if no bounding box information is available, setting
+// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+// false and no bounding boxes are supplied, an error is raised.
 //
-// Returns the created operation.
-func ApplyDelayCompensatedGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, lambda tf.Output, shadow tf.Output, optional ...ApplyDelayCompensatedGradientDescentAttr) (o *tf.Operation) {
+// Arguments:
+//	image_size: 1-D, containing `[height, width, channels]`.
+//	bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
+// associated with the image.
+//
+// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to
+// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+// Provide as input to `tf.image.draw_bounding_boxes`.
+func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -15149,66 +15401,44 @@ func ApplyDelayCompensatedGradientDescent(scope *Scope, var_ tf.Output, alpha tf
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ApplyDelayCompensatedGradientDescent",
+		Type: "SampleDistortedBoundingBox",
 		Input: []tf.Input{
-			var_, alpha, delta, lambda, shadow,
+			image_size, bounding_boxes,
 		},
 		Attrs: attrs,
 	}
-	return scope.AddOperation(opspec)
-}
-
-// SizeAttr is an optional argument to Size.
-type SizeAttr func(optionalAttr)
-
-// SizeOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_INT32
-func SizeOutType(value tf.DataType) SizeAttr {
-	return func(m optionalAttr) {
-		m["out_type"] = value
-	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Returns the size of a tensor.
-//
-// This operation returns an integer representing the number of elements in
-// `input`.
-//
-// For example:
+// Returns the truth value of (x > y) element-wise.
 //
-// ```
-// # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
-// size(t) ==> 12
-// ```
-func Size(scope *Scope, input tf.Output, optional ...SizeAttr) (output tf.Output) {
+// *NOTE*: `Greater` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "Size",
+		Type: "Greater",
 		Input: []tf.Input{
-			input,
+			x, y,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp.
-type ResourceApplyRMSPropAttr func(optionalAttr)
+// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp.
+type ResourceSparseApplyRMSPropAttr func(optionalAttr)
 
-// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value.
+// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value.
 //
 // value: If `True`, updating of the var, ms, and mom tensors is protected
 // by a lock; otherwise the behavior is undefined, but may exhibit less
 // contention.
 // If not specified, defaults to false
-func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr {
+func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr {
 	return func(m optionalAttr) {
 		m["use_locking"] = value
 	}
@@ -15236,9 +15466,10 @@ func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr {
 //
 //	epsilon: Ridge term. Must be a scalar.
 //	grad: The gradient.
+//	indices: A vector of indices into the first dimension of var, ms and mom.
 //
 // Returns the created operation.
-func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) {
+func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
@@ -15247,230 +15478,56 @@ func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Out
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResourceApplyRMSProp",
+		Type: "ResourceSparseApplyRMSProp",
 		Input: []tf.Input{
-			var_, ms, mom, lr, rho, momentum, epsilon, grad,
+			var_, ms, mom, lr, rho, momentum, epsilon, grad, indices,
 		},
 		Attrs: attrs,
 	}
 	return scope.AddOperation(opspec)
 }
 
-// Output a fact about factorials.
-func Fact(scope *Scope) (fact tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Fact",
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform.
-type StatelessRandomUniformAttr func(optionalAttr)
-
-// StatelessRandomUniformDtype sets the optional dtype attribute to value.
-//
-// value: The type of the output.
-// If not specified, defaults to DT_FLOAT
-func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr {
-	return func(m optionalAttr) {
-		m["dtype"] = value
-	}
-}
-
-// Outputs deterministic pseudorandom random values from a uniform distribution.
-//
-// The generated values follow a uniform distribution in the range `[0, 1)`. The
-// lower bound 0 is included in the range, while the upper bound 1 is excluded.
-//
-// The outputs are a deterministic function of `shape` and `seed`.
-//
-// Arguments:
-//	shape: The shape of the output tensor.
-//	seed: 2 seeds (shape [2]).
-//
-// Returns Random values with specified shape.
-func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "StatelessRandomUniform",
-		Input: []tf.Input{
-			shape, seed,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// PrintAttr is an optional argument to Print.
-type PrintAttr func(optionalAttr)
-
-// PrintMessage sets the optional message attribute to value.
-//
-// value: A string, prefix of the error message.
-// If not specified, defaults to ""
-func PrintMessage(value string) PrintAttr {
-	return func(m optionalAttr) {
-		m["message"] = value
-	}
-}
-
-// PrintFirstN sets the optional first_n attribute to value.
-//
-// value: Only log `first_n` number of times. -1 disables logging.
-// If not specified, defaults to -1
-func PrintFirstN(value int64) PrintAttr {
-	return func(m optionalAttr) {
-		m["first_n"] = value
-	}
-}
-
-// PrintSummarize sets the optional summarize attribute to value.
-//
-// value: Only print this many entries of each tensor.
-// If not specified, defaults to 3
-func PrintSummarize(value int64) PrintAttr {
-	return func(m optionalAttr) {
-		m["summarize"] = value
-	}
-}
-
-// Prints a list of tensors.
-//
-// Passes `input` through to `output` and prints `data` when evaluating.
-//
-// Arguments:
-//	input: The tensor passed to `output`
-//	data: A list of tensors to print out when op is evaluated.
-//
-// Returns = The unmodified `input` tensor
-func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "Print",
-		Input: []tf.Input{
-			input, tf.OutputList(data),
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// ResizeAreaAttr is an optional argument to ResizeArea.
-type ResizeAreaAttr func(optionalAttr)
-
-// ResizeAreaAlignCorners sets the optional align_corners attribute to value.
-//
-// value: If true, rescale input by (new_height - 1) / (height - 1), which
-// exactly aligns the 4 corners of images and resized images. If false, rescale
-// by new_height / height. Treat similarly the width dimension.
-// If not specified, defaults to false
-func ResizeAreaAlignCorners(value bool) ResizeAreaAttr {
-	return func(m optionalAttr) {
-		m["align_corners"] = value
-	}
-}
+// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad.
+type AvgPool3DGradAttr func(optionalAttr)
 
-// Resize `images` to `size` using area interpolation.
-//
-// Input images can be of different types but output images are always float.
-//
-// Arguments:
-//	images: 4-D with shape `[batch, height, width, channels]`.
-//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
-// new size for the images.
+// AvgPool3DGradDataFormat sets the optional data_format attribute to value.
 //
-// Returns 4-D with shape
-// `[batch, new_height, new_width, channels]`.
-func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "ResizeArea",
-		Input: []tf.Input{
-			images, size,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// RealAttr is an optional argument to Real.
-type RealAttr func(optionalAttr)
-
-// RealTout sets the optional Tout attribute to value.
-// If not specified, defaults to DT_FLOAT
-func RealTout(value tf.DataType) RealAttr {
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+//     [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+//     [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr {
 	return func(m optionalAttr) {
-		m["Tout"] = value
+		m["data_format"] = value
 	}
 }
 
-// Returns the real part of a complex number.
-//
-// Given a tensor `input` of complex numbers, this operation returns a tensor of
-// type `float` that is the real part of each element in `input`. All elements in
-// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real
-//  part returned by this operation and *b* is the imaginary part.
+// Computes gradients of average pooling function.
 //
-// For example:
+// Arguments:
+//	orig_input_shape: The original input dimensions.
+//	grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
+//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+//	strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+//	padding: The type of padding algorithm to use.
 //
-// ```
-// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-// tf.real(input) ==> [-2.25, 3.25]
-// ```
-func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) {
+// Returns The backprop for input.
+func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
+	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "Real",
-		Input: []tf.Input{
-			input,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Creates a dataset that zips together `input_datasets`.
-func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
-	opspec := tf.OpSpec{
-		Type: "ZipDataset",
+		Type: "AvgPool3DGrad",
 		Input: []tf.Input{
-			tf.OutputList(input_datasets),
+			orig_input_shape, grad,
 		},
 		Attrs: attrs,
 	}
@@ -15478,176 +15535,134 @@ func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.Data
 	return op.Output(0)
 }
 
-// MutableDenseHashTableV2Attr is an optional argument to MutableDenseHashTableV2.
-type MutableDenseHashTableV2Attr func(optionalAttr)
-
-// MutableDenseHashTableV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this table is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func MutableDenseHashTableV2Container(value string) MutableDenseHashTableV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
+// OrderedMapPeekAttr is an optional argument to OrderedMapPeek.
+type OrderedMapPeekAttr func(optionalAttr)
 
-// MutableDenseHashTableV2SharedName sets the optional shared_name attribute to value.
+// OrderedMapPeekCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
 //
-// value: If non-empty, this table is shared under the given name across
-// multiple sessions.
-// If not specified, defaults to ""
-func MutableDenseHashTableV2SharedName(value string) MutableDenseHashTableV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// MutableDenseHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
-// If not specified, defaults to false
-func MutableDenseHashTableV2UseNodeNameSharing(value bool) MutableDenseHashTableV2Attr {
+// REQUIRES: value >= 0
+func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr {
 	return func(m optionalAttr) {
-		m["use_node_name_sharing"] = value
+		m["capacity"] = value
 	}
 }
 
-// MutableDenseHashTableV2ValueShape sets the optional value_shape attribute to value.
+// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
 //
-// value: The shape of each value.
-// If not specified, defaults to <>
-func MutableDenseHashTableV2ValueShape(value tf.Shape) MutableDenseHashTableV2Attr {
+// REQUIRES: value >= 0
+func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr {
 	return func(m optionalAttr) {
-		m["value_shape"] = value
+		m["memory_limit"] = value
 	}
 }
 
-// MutableDenseHashTableV2InitialNumBuckets sets the optional initial_num_buckets attribute to value.
-//
-// value: The initial number of hash table buckets. Must be a power
-// to 2.
-// If not specified, defaults to 131072
-func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTableV2Attr {
+// OrderedMapPeekContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func OrderedMapPeekContainer(value string) OrderedMapPeekAttr {
 	return func(m optionalAttr) {
-		m["initial_num_buckets"] = value
+		m["container"] = value
 	}
 }
 
-// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value.
-//
-// value: The maximum ratio between number of entries and number of
-// buckets before growing the table. Must be between 0 and 1.
-// If not specified, defaults to 0.8
-func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr {
+// OrderedMapPeekSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr {
 	return func(m optionalAttr) {
-		m["max_load_factor"] = value
+		m["shared_name"] = value
 	}
 }
 
-// Creates an empty hash table that uses tensors as the backing store.
-//
-// It uses "open addressing" with quadratic reprobing to resolve
-// collisions.
-//
-// This op creates a mutable hash table, specifying the type of its keys and
-// values. Each value must be a scalar. Data can be inserted into the table using
-// the insert operations. It does not support the initialization operation.
-//
-// Arguments:
-//	empty_key: The key used to represent empty key buckets internally. Must not
-// be used in insert or lookup operations.
-//	value_dtype: Type of the table values.
+// Op peeks at the values at the specified key.  If the
 //
-// Returns Handle to a table.
-func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) {
+// underlying container does not contain this key
+// this op will block until it does.   This Op is optimized for
+// performance.
+func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"value_dtype": value_dtype}
+	attrs := map[string]interface{}{"dtypes": dtypes}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "MutableDenseHashTableV2",
+		Type: "OrderedMapPeek",
 		Input: []tf.Input{
-			empty_key,
+			key, indices,
 		},
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// LRNAttr is an optional argument to LRN.
-type LRNAttr func(optionalAttr)
-
-// LRNDepthRadius sets the optional depth_radius attribute to value.
-//
-// value: 0-D.  Half-width of the 1-D normalization window.
-// If not specified, defaults to 5
-func LRNDepthRadius(value int64) LRNAttr {
-	return func(m optionalAttr) {
-		m["depth_radius"] = value
+	if scope.Err() != nil {
+		return
 	}
-}
-
-// LRNBias sets the optional bias attribute to value.
-//
-// value: An offset (usually positive to avoid dividing by 0).
-// If not specified, defaults to 1
-func LRNBias(value float32) LRNAttr {
-	return func(m optionalAttr) {
-		m["bias"] = value
+	var idx int
+	var err error
+	if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
+		scope.UpdateErr("OrderedMapPeek", err)
+		return
 	}
+	return values
 }
 
-// LRNAlpha sets the optional alpha attribute to value.
+// Adds two `SparseTensor` objects to produce another `SparseTensor`.
 //
-// value: A scale factor, usually positive.
-// If not specified, defaults to 1
-func LRNAlpha(value float32) LRNAttr {
-	return func(m optionalAttr) {
-		m["alpha"] = value
-	}
-}
-
-// LRNBeta sets the optional beta attribute to value.
+// The input `SparseTensor` objects' indices are assumed ordered in standard
+// lexicographic order.  If this is not the case, before this step run
+// `SparseReorder` to restore index ordering.
 //
-// value: An exponent.
-// If not specified, defaults to 0.5
-func LRNBeta(value float32) LRNAttr {
-	return func(m optionalAttr) {
-		m["beta"] = value
+// By default, if two values sum to zero at some index, the output `SparseTensor`
+// would still include that particular location in its index, storing a zero in the
+// corresponding value slot.  To override this, callers can specify `thresh`,
+// indicating that if the sum has a magnitude strictly smaller than `thresh`, its
+// corresponding value and index would then not be included.  In particular,
+// `thresh == 0` (default) means everything is kept and actual thresholding happens
+// only for a positive value.
+//
+// In the following shapes, `nnz` is the count after taking `thresh` into account.
+//
+// Arguments:
+//	a_indices: 2-D.  The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
+//	a_values: 1-D.  The `values` of the first `SparseTensor`, size `[nnz]` Vector.
+//	a_shape: 1-D.  The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
+//	b_indices: 2-D.  The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
+//	b_values: 1-D.  The `values` of the second `SparseTensor`, size `[nnz]` Vector.
+//	b_shape: 1-D.  The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
+//	thresh: 0-D.  The magnitude threshold that determines if an output value/index
+// pair takes space.
+func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseAdd",
+		Input: []tf.Input{
+			a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh,
+		},
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Local Response Normalization.
+// Creates a dataset that batches `batch_size` elements from `input_dataset`.
 //
-// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
-// dimension), and each vector is normalized independently.  Within a given vector,
-// each component is divided by the weighted, squared sum of inputs within
-// `depth_radius`.  In detail,
+// Arguments:
 //
-//     sqr_sum[a, b, c, d] =
-//         sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
-//     output = input / (bias + alpha * sqr_sum) ** beta
+//	batch_size: A scalar representing the number of elements to accumulate in a
+// batch.
 //
-// For details, see [Krizhevsky et al., ImageNet classification with deep
-// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
 //
-// Arguments:
-//	input: 4-D.
-func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) {
+func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
+	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
 	opspec := tf.OpSpec{
-		Type: "LRN",
+		Type: "BatchDataset",
 		Input: []tf.Input{
-			input,
+			input_dataset, batch_size,
 		},
 		Attrs: attrs,
 	}
@@ -15655,36 +15670,54 @@ func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output)
 	return op.Output(0)
 }
 
-// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad.
-type ResourceSparseApplyAdagradAttr func(optionalAttr)
+// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp.
+type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr)
 
-// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value.
+// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
 //
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
+// value: If `True`, updating of the var, mg, ms, and mom tensors is
+// protected by a lock; otherwise the behavior is undefined, but may exhibit less
 // contention.
 // If not specified, defaults to false
-func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr {
+func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr {
 	return func(m optionalAttr) {
 		m["use_locking"] = value
 	}
 }
 
-// Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
+// Update '*var' according to the centered RMSProp algorithm.
+//
+// The centered RMSProp algorithm uses an estimate of the centered second moment
+// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+// uses the (uncentered) second moment. This often helps with training, but is
+// slightly more expensive in terms of computation and memory.
 //
-// That is for rows we have grad for, we update var and accum as follows:
-// accum += grad * grad
-// var -= lr * grad * (1 / sqrt(accum))
+// Note that in dense implementation of this algorithm, mg, ms, and mom will
+// update even if the grad is zero, but in this sparse implementation, mg, ms,
+// and mom will not update in iterations during which the grad is zero.
+//
+// mean_square = decay * mean_square + (1-decay) * gradient ** 2
+// mean_grad = decay * mean_grad + (1-decay) * gradient
+// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+//
+// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+// var <- var - mom
 //
 // Arguments:
 //	var_: Should be from a Variable().
-//	accum: Should be from a Variable().
-//	lr: Learning rate. Must be a scalar.
+//	mg: Should be from a Variable().
+//	ms: Should be from a Variable().
+//	mom: Should be from a Variable().
+//	lr: Scaling factor. Must be a scalar.
+//	rho: Decay rate. Must be a scalar.
+//
+//	epsilon: Ridge term. Must be a scalar.
 //	grad: The gradient.
-//	indices: A vector of indices into the first dimension of var and accum.
+//	indices: A vector of indices into the first dimension of var, ms and mom.
 //
 // Returns the created operation.
-func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) {
+func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
@@ -15693,105 +15726,120 @@ func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, l
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResourceSparseApplyAdagrad",
+		Type: "ResourceSparseApplyCenteredRMSProp",
 		Input: []tf.Input{
-			var_, accum, lr, grad, indices,
+			var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices,
 		},
 		Attrs: attrs,
 	}
 	return scope.AddOperation(opspec)
 }
 
-// LRNGradAttr is an optional argument to LRNGrad.
-type LRNGradAttr func(optionalAttr)
-
-// LRNGradDepthRadius sets the optional depth_radius attribute to value.
+// Flips all bits elementwise.
 //
-// value: A depth radius.
-// If not specified, defaults to 5
-func LRNGradDepthRadius(value int64) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["depth_radius"] = value
+// The result will have exactly those bits set, that are not set in `x`. The
+// computation is performed on the underlying representation of x.
+func Invert(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
 	}
-}
-
-// LRNGradBias sets the optional bias attribute to value.
-//
-// value: An offset (usually > 0 to avoid dividing by 0).
-// If not specified, defaults to 1
-func LRNGradBias(value float32) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["bias"] = value
+	opspec := tf.OpSpec{
+		Type: "Invert",
+		Input: []tf.Input{
+			x,
+		},
 	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// LRNGradAlpha sets the optional alpha attribute to value.
+// Computes the mean along segments of a tensor.
 //
-// value: A scale factor, usually positive.
-// If not specified, defaults to 1
-func LRNGradAlpha(value float32) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["alpha"] = value
-	}
-}
-
-// LRNGradBeta sets the optional beta attribute to value.
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
 //
-// value: An exponent.
-// If not specified, defaults to 0.5
-func LRNGradBeta(value float32) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["beta"] = value
-	}
-}
-
-// Gradients for Local Response Normalization.
+// Computes a tensor such that
+// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
+// over `j` such that `segment_ids[j] == i` and `N` is the total number of
+// values summed.
+//
+// If the mean is empty for a given segment ID `i`, `output[i] = 0`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
+// </div>
 //
 // Arguments:
-//	input_grads: 4-D with shape `[batch, height, width, channels]`.
-//	input_image: 4-D with shape `[batch, height, width, channels]`.
-//	output_image: 4-D with shape `[batch, height, width, channels]`.
 //
-// Returns The gradients for LRN.
-func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
+//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension.  Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "LRNGrad",
+		Type: "SegmentMean",
 		Input: []tf.Input{
-			input_grads, input_image, output_image,
+			data, segment_ids,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// StringToNumberAttr is an optional argument to StringToNumber.
-type StringToNumberAttr func(optionalAttr)
+// CumprodAttr is an optional argument to Cumprod.
+type CumprodAttr func(optionalAttr)
 
-// StringToNumberOutType sets the optional out_type attribute to value.
-//
-// value: The numeric type to interpret each string in `string_tensor` as.
-// If not specified, defaults to DT_FLOAT
-func StringToNumberOutType(value tf.DataType) StringToNumberAttr {
+// CumprodExclusive sets the optional exclusive attribute to value.
+// If not specified, defaults to false
+func CumprodExclusive(value bool) CumprodAttr {
 	return func(m optionalAttr) {
-		m["out_type"] = value
+		m["exclusive"] = value
 	}
 }
 
-// Converts each string in the input Tensor to the specified numeric type.
+// CumprodReverse sets the optional reverse attribute to value.
+// If not specified, defaults to false
+func CumprodReverse(value bool) CumprodAttr {
+	return func(m optionalAttr) {
+		m["reverse"] = value
+	}
+}
+
+// Compute the cumulative product of the tensor `x` along `axis`.
 //
-// (Note that int32 overflow results in an error while float overflow
-// results in a rounded value.)
+// By default, this op performs an inclusive cumprod, which means that the first
+// element of the input is identical to the first element of the output:
 //
-// Returns A Tensor of the same shape as the input `string_tensor`.
-func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) {
+// ```python
+// tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
+// ```
+//
+// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
+// performed instead:
+//
+// ```python
+// tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
+// ```
+//
+// By setting the `reverse` kwarg to `True`, the cumprod is performed in the
+// opposite direction:
+//
+// ```python
+// tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
+// ```
+//
+// This is more efficient than using separate `tf.reverse` ops.
+//
+// The `reverse` and `exclusive` kwargs can also be combined:
+//
+// ```python
+// tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
+// ```
+func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -15800,9 +15848,9 @@ func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToN
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "StringToNumber",
+		Type: "Cumprod",
 		Input: []tf.Input{
-			string_tensor,
+			x, axis,
 		},
 		Attrs: attrs,
 	}
@@ -15810,91 +15858,76 @@ func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToN
 	return op.Output(0)
 }
 
-// Returns the truth value of NOT x element-wise.
-func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "LogicalNot",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
+// DestroyResourceOpAttr is an optional argument to DestroyResourceOp.
+type DestroyResourceOpAttr func(optionalAttr)
 
-// Creates a dataset that contains `count` elements from the `input_dataset`.
-//
-// Arguments:
-//
-//	count: A scalar representing the number of elements from the `input_dataset`
-// that should be taken. A value of `-1` indicates that all of `input_dataset`
-// is taken.
-//
+// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value.
 //
-func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
-	opspec := tf.OpSpec{
-		Type: "TakeDataset",
-		Input: []tf.Input{
-			input_dataset, count,
-		},
-		Attrs: attrs,
+// value: whether to ignore the error when the resource
+// doesn't exist.
+// If not specified, defaults to true
+func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr {
+	return func(m optionalAttr) {
+		m["ignore_lookup_error"] = value
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
 }
 
-// Computes gradients for the exponential linear (Elu) operation.
+// Deletes the resource specified by the handle.
+//
+// All subsequent operations using the resource will result in a NotFound
+// error status.
 //
 // Arguments:
-//	gradients: The backpropagated gradients to the corresponding Elu operation.
-//	outputs: The outputs of the corresponding Elu operation.
+//	resource: handle to the resource to delete.
 //
-// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0,
-// `gradients` otherwise.
-func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) {
+// Returns the created operation.
+func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "EluGrad",
+		Type: "DestroyResourceOp",
 		Input: []tf.Input{
-			gradients, outputs,
+			resource,
 		},
+		Attrs: attrs,
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return scope.AddOperation(opspec)
 }
 
 // Converts each string in the input Tensor to its hash mod by a number of buckets.
 //
 // The hash function is deterministic on the content of the string within the
-// process.
+// process. The hash function is a keyed hash function, where attribute `key`
+// defines the key of the hash function. `key` is an array of 2 elements.
 //
-// Note that the hash function may change from time to time.
-// This functionality will be deprecated and it's recommended to use
-// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`.
+// A strong hash is important when inputs may be malicious, e.g. URLs with
+// additional components. Adversaries could try to make their inputs hash to the
+// same bucket for a denial-of-service attack or to skew the results. A strong
+// hash prevents this by making it difficult, if not infeasible, to compute inputs
+// that hash to the same bucket. This comes at a cost of roughly 4x higher compute
+// time than `tf.string_to_hash_bucket_fast`.
 //
 // Arguments:
-//
+//	input: The strings to assign a hash bucket.
 //	num_buckets: The number of buckets.
+//	key: The key for the keyed hash function passed as a list of two uint64
+// elements.
 //
 // Returns A Tensor of the same shape as the input `string_tensor`.
-func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) {
+func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"num_buckets": num_buckets}
+	attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key}
 	opspec := tf.OpSpec{
-		Type: "StringToHashBucket",
+		Type: "StringToHashBucketStrong",
 		Input: []tf.Input{
-			string_tensor,
+			input,
 		},
 		Attrs: attrs,
 	}
@@ -16252,164 +16285,149 @@ func AsString(scope *Scope, input tf.Output, optional ...AsStringAttr) (output t
 	return op.Output(0)
 }
 
-// GatherAttr is an optional argument to Gather.
-type GatherAttr func(optionalAttr)
-
-// GatherValidateIndices sets the optional validate_indices attribute to value.
-// If not specified, defaults to true
-func GatherValidateIndices(value bool) GatherAttr {
-	return func(m optionalAttr) {
-		m["validate_indices"] = value
-	}
-}
-
-// Gather slices from `params` according to `indices`.
-//
-// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
-// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
+// Split elements of `input` based on `delimiter` into a `SparseTensor`.
 //
-// ```python
-//     # Scalar indices
-//     output[:, ..., :] = params[indices, :, ... :]
+// Let N be the size of source (typically N will be the batch size). Split each
+// element of `input` based on `delimiter` and return a `SparseTensor`
+// containing the splitted tokens. Empty tokens are ignored.
 //
-//     # Vector indices
-//     output[i, :, ..., :] = params[indices[i], :, ... :]
+// `delimiter` can be empty, or a string of split characters. If `delimiter` is an
+//  empty string, each element of `input` is split into individual single-byte
+//  character strings, including splitting of UTF-8 multibyte sequences. Otherwise
+//  every character of `delimiter` is a potential split point.
 //
-//     # Higher rank indices
-//     output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
-// ```
+// For example:
+//   N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output
+//   will be
 //
-// If `indices` is a permutation and `len(indices) == params.shape[0]` then
-// this operation will permute `params` accordingly.
+//   indices = [0, 0;
+//              0, 1;
+//              1, 0;
+//              1, 1;
+//              1, 2]
+//   shape = [2, 3]
+//   values = ['hello', 'world', 'a', 'b', 'c']
 //
-// `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in
-// `indices` are always validated to be within range. If assigned to GPU,
-// out-of-bound indices result in safe but unspecified behavior, which may include
-// raising an error.
+// Arguments:
+//	input: 1-D. Strings to split.
+//	delimiter: 0-D. Delimiter characters (bytes), or empty string.
 //
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
-// </div>
-func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) {
+// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse
+// tensor, where the first value is N and the second value is the maximum number
+// of tokens in a single input entry.
+func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output) (indices tf.Output, values tf.Output, shape tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "Gather",
+		Type: "StringSplit",
 		Input: []tf.Input{
-			params, indices,
+			input, delimiter,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Compute the polygamma function \\(\psi^{(n)}(x)\\).
+// Inverse 3D real-valued fast Fourier transform.
 //
-// The polygamma function is defined as:
+// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued
+// signal over the inner-most 3 dimensions of `input`.
 //
+// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`:
+// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
+// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
+// from the size of the inner-most 3 dimensions of `input`. If the FFT length used
+// to compute `input` is odd, it should be provided since it cannot be inferred
+// properly.
 //
-// \\(\psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)\\)
+// Along each axis `IRFFT3D` is computed on, if `fft_length` (or
+// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
+// corresponding dimension of `input`, the dimension is cropped. If it is larger,
+// the dimension is padded with zeros.
 //
-// where \\(\psi(x)\\) is the digamma function.
-func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
+// Arguments:
+//	input: A complex64 tensor.
+//	fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
+//
+// Returns A float32 tensor of the same rank as `input`. The inner-most 3
+//   dimensions of `input` are replaced with the `fft_length` samples of their
+//   inverse 3D real Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.irfftn with 3 dimensions.
+// @end_compatibility
+func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Polygamma",
+		Type: "IRFFT3D",
 		Input: []tf.Input{
-			a, x,
+			input, fft_length,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
-//
-// The `input` tensor has shape `[batch, in_height, in_width, depth]` and the
-// `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each
-// input channel is processed independently of the others with its own structuring
-// function. The `output` tensor has shape
-// `[batch, out_height, out_width, depth]`. The spatial dimensions of the output
-// tensor depend on the `padding` algorithm. We currently only support the default
-// "NHWC" `data_format`.
-//
-// In detail, the grayscale morphological 2-D dilation is the max-sum correlation
-// (for consistency with `conv2d`, we use unmirrored filters):
-//
-//     output[b, y, x, c] =
-//        max_{dy, dx} input[b,
-//                           strides[1] * y + rates[1] * dy,
-//                           strides[2] * x + rates[2] * dx,
-//                           c] +
-//                     filter[dy, dx, c]
-//
-// Max-pooling is a special case when the filter has size equal to the pooling
-// kernel size and contains all zeros.
-//
-// Note on duality: The dilation of `input` by the `filter` is equal to the
-// negation of the erosion of `-input` by the reflected `filter`.
-//
-// Arguments:
-//	input: 4-D with shape `[batch, in_height, in_width, depth]`.
-//	filter: 3-D with shape `[filter_height, filter_width, depth]`.
-//	strides: The stride of the sliding window for each dimension of the input
-// tensor. Must be: `[1, stride_height, stride_width, 1]`.
-//	rates: The input stride for atrous morphological dilation. Must be:
-// `[1, rate_height, rate_width, 1]`.
-//	padding: The type of padding algorithm to use.
+// Returns the truth value of (x != y) element-wise.
 //
-// Returns 4-D with shape `[batch, out_height, out_width, depth]`.
-func Dilation2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, rates []int64, padding string) (output tf.Output) {
+// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
 	opspec := tf.OpSpec{
-		Type: "Dilation2D",
+		Type: "NotEqual",
 		Input: []tf.Input{
-			input, filter,
+			x, y,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// EncodeBase64Attr is an optional argument to EncodeBase64.
-type EncodeBase64Attr func(optionalAttr)
+// GatherAttr is an optional argument to Gather.
+type GatherAttr func(optionalAttr)
 
-// EncodeBase64Pad sets the optional pad attribute to value.
-//
-// value: Bool whether padding is applied at the ends.
-// If not specified, defaults to false
-func EncodeBase64Pad(value bool) EncodeBase64Attr {
+// GatherValidateIndices sets the optional validate_indices attribute to value.
+// If not specified, defaults to true
+func GatherValidateIndices(value bool) GatherAttr {
 	return func(m optionalAttr) {
-		m["pad"] = value
+		m["validate_indices"] = value
 	}
 }
 
-// Encode strings into web-safe base64 format.
+// Gather slices from `params` according to `indices`.
 //
-// Refer to the following article for more information on base64 format:
-// en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the
-// end so that the encoded has length multiple of 4. See Padding section of the
-// link above.
+// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
+// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
 //
-// Web-safe means that the encoder uses - and _ instead of + and /.
+// ```python
+//     # Scalar indices
+//     output[:, ..., :] = params[indices, :, ... :]
 //
-// Arguments:
-//	input: Strings to be encoded.
+//     # Vector indices
+//     output[i, :, ..., :] = params[indices[i], :, ... :]
 //
-// Returns Input strings encoded in base64.
-func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) (output tf.Output) {
+//     # Higher rank indices
+//     output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
+// ```
+//
+// If `indices` is a permutation and `len(indices) == params.shape[0]` then
+// this operation will permute `params` accordingly.
+//
+// `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in
+// `indices` are always validated to be within range. If assigned to GPU,
+// out-of-bound indices result in safe but unspecified behavior, which may include
+// raising an error.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
+// </div>
+func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -16418,9 +16436,9 @@ func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) (
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "EncodeBase64",
+		Type: "Gather",
 		Input: []tf.Input{
-			input,
+			params, indices,
 		},
 		Attrs: attrs,
 	}
@@ -16763,6 +16781,63 @@ func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value
 	return op.Output(0)
 }
 
+// ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2.
+type ResourceSparseApplyFtrlV2Attr func(optionalAttr)
+
+// ResourceSparseApplyFtrlV2UseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2Attr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+//
+// That is for rows we have grad for, we update var, accum and linear as follows:
+// grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+// linear += grad_with_shrinkage +
+//     (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+// accum = accum_new
+//
+// Arguments:
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	linear: Should be from a Variable().
+//	grad: The gradient.
+//	indices: A vector of indices into the first dimension of var and accum.
+//	lr: Scaling factor. Must be a scalar.
+//	l1: L1 regularization. Must be a scalar.
+//	l2: L2 shrinkage regulariation. Must be a scalar.
+//
+//	lr_power: Scaling factor. Must be a scalar.
+//
+// Returns the created operation.
+func ResourceSparseApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlV2Attr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResourceSparseApplyFtrlV2",
+		Input: []tf.Input{
+			var_, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
 // Restore a reader to a previously saved state.
 //
 // Not all Readers support being restored, so this can produce an
@@ -18014,83 +18089,6 @@ func LookupTableExportV2(scope *Scope, table_handle tf.Output, Tkeys tf.DataType
 	return op.Output(0), op.Output(1)
 }
 
-// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap.
-type AddManySparseToTensorsMapAttr func(optionalAttr)
-
-// AddManySparseToTensorsMapContainer sets the optional container attribute to value.
-//
-// value: The container name for the `SparseTensorsMap` created by this op.
-// If not specified, defaults to ""
-func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value.
-//
-// value: The shared name for the `SparseTensorsMap` created by this op.
-// If blank, the new Operation's unique name is used.
-// If not specified, defaults to ""
-func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
-//
-// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`,
-// `sparse_values`, and `sparse_shape`, where
-//
-// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R```
-//
-// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor`
-// having a first `sparse_indices` column taking values between `[0, N)`, where
-// the minibatch size `N == sparse_shape[0]`.
-//
-// The input `SparseTensor` must have rank `R` greater than 1, and the first
-// dimension is treated as the minibatch dimension.  Elements of the `SparseTensor`
-// must be sorted in increasing order of this first dimension.  The stored
-// `SparseTensor` objects pointed to by each row of the output `sparse_handles`
-// will have rank `R-1`.
-//
-// The `SparseTensor` values can then be read out as part of a minibatch by passing
-// the given keys as vector elements to `TakeManySparseFromTensorsMap`.  To ensure
-// the correct `SparseTensorsMap` is accessed, ensure that the same
-// `container` and `shared_name` are passed to that Op.  If no `shared_name`
-// is provided here, instead use the *name* of the Operation created by calling
-// `AddManySparseToTensorsMap` as the `shared_name` passed to
-// `TakeManySparseFromTensorsMap`.  Ensure the Operations are colocated.
-//
-// Arguments:
-//	sparse_indices: 2-D.  The `indices` of the minibatch `SparseTensor`.
-// `sparse_indices[:, 0]` must be ordered values in `[0, N)`.
-//	sparse_values: 1-D.  The `values` of the minibatch `SparseTensor`.
-//	sparse_shape: 1-D.  The `shape` of the minibatch `SparseTensor`.
-// The minibatch size `N == sparse_shape[0]`.
-//
-// Returns 1-D.  The handles of the `SparseTensor` now stored in the
-// `SparseTensorsMap`.  Shape: `[N]`.
-func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "AddManySparseToTensorsMap",
-		Input: []tf.Input{
-			sparse_indices, sparse_values, sparse_shape,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Converts each string in the input Tensor to its hash mod by a number of buckets.
 //
 // The hash function is deterministic on the content of the string within the
@@ -18439,200 +18437,314 @@ func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output
 	return op.Output(0)
 }
 
-// Computes fingerprints of the input strings.
+// Creates a dataset that emits the lines of one or more text files.
 //
 // Arguments:
-//	input: vector of strings to compute fingerprints on.
+//	filenames: A scalar or a vector containing the name(s) of the file(s) to be
+// read.
+func TextLineDataset(scope *Scope, filenames tf.Output) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "TextLineDataset",
+		Input: []tf.Input{
+			filenames,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Returns the number of records this Reader has produced.
 //
-// Returns a (N,2) shaped matrix where N is the number of elements in the input
-// vector. Each row contains the low and high parts of the fingerprint.
-func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) {
+// This is the same as the number of ReaderRead executions that have
+// succeeded.
+//
+// Arguments:
+//	reader_handle: Handle to a Reader.
+func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "SdcaFprint",
+		Type: "ReaderNumRecordsProducedV2",
 		Input: []tf.Input{
-			input,
+			reader_handle,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Creates a dataset that emits the lines of one or more text files.
+// Computes exponential of x - 1 element-wise.
+//
+// I.e., \\(y = (\exp x) - 1\\).
+func Expm1(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Expm1",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Returns x - y element-wise.
+//
+// *NOTE*: `Sub` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Sub",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Creates a dataset that contains `count` elements from the `input_dataset`.
 //
 // Arguments:
-//	filenames: A scalar or a vector containing the name(s) of the file(s) to be
-// read.
-func TextLineDataset(scope *Scope, filenames tf.Output) (handle tf.Output) {
+//
+//	count: A scalar representing the number of elements from the `input_dataset`
+// that should be taken. A value of `-1` indicates that all of `input_dataset`
+// is taken.
+//
+//
+func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
 	opspec := tf.OpSpec{
-		Type: "TextLineDataset",
+		Type: "TakeDataset",
 		Input: []tf.Input{
-			filenames,
+			input_dataset, count,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Returns the number of records this Reader has produced.
+// Converts each string in the input Tensor to its hash mod by a number of buckets.
 //
-// This is the same as the number of ReaderRead executions that have
-// succeeded.
+// The hash function is deterministic on the content of the string within the
+// process.
+//
+// Note that the hash function may change from time to time.
+// This functionality will be deprecated and it's recommended to use
+// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`.
 //
 // Arguments:
-//	reader_handle: Handle to a Reader.
-func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) {
+//
+//	num_buckets: The number of buckets.
+//
+// Returns A Tensor of the same shape as the input `string_tensor`.
+func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"num_buckets": num_buckets}
+	opspec := tf.OpSpec{
+		Type: "StringToHashBucket",
+		Input: []tf.Input{
+			string_tensor,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes gradients for the exponential linear (Elu) operation.
+//
+// Arguments:
+//	gradients: The backpropagated gradients to the corresponding Elu operation.
+//	outputs: The outputs of the corresponding Elu operation.
+//
+// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0,
+// `gradients` otherwise.
+func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "EluGrad",
+		Input: []tf.Input{
+			gradients, outputs,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad.
+type FractionalAvgPoolGradAttr func(optionalAttr)
+
+// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value.
+//
+// value: When set to True, it means when pooling, the values at the boundary
+// of adjacent pooling cells are used by both cells. For example:
+//
+// `index  0  1  2  3  4`
+//
+// `value  20 5  16 3  7`
+//
+// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+// The result would be [41/3, 26/3] for fractional avg pooling.
+// If not specified, defaults to false
+func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr {
+	return func(m optionalAttr) {
+		m["overlapping"] = value
+	}
+}
+
+// Computes gradient of the FractionalAvgPool function.
+//
+// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
+// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
+// out_backprop to those indices that form the same pooling cell. Therefore, we
+// just need to know the shape of original input tensor, instead of the whole
+// tensor.
+//
+// Arguments:
+//	orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool`
+//	out_backprop: 4-D with shape `[batch, height, width, channels]`.  Gradients
+// w.r.t. the output of `fractional_avg_pool`.
+//	row_pooling_sequence: row pooling sequence, form pooling region with
+// col_pooling_sequence.
+//	col_pooling_sequence: column pooling sequence, form pooling region with
+// row_pooling sequence.
+//
+// Returns 4-D.  Gradients w.r.t. the input of `fractional_avg_pool`.
+func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "ReaderNumRecordsProducedV2",
+		Type: "FractionalAvgPoolGrad",
 		Input: []tf.Input{
-			reader_handle,
+			orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes exponential of x - 1 element-wise.
-//
-// I.e., \\(y = (\exp x) - 1\\).
-func Expm1(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes softplus: `log(exp(features) + 1)`.
+func Softplus(scope *Scope, features tf.Output) (activations tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Expm1",
+		Type: "Softplus",
 		Input: []tf.Input{
-			x,
+			features,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Returns x - y element-wise.
+// BatchMatMulAttr is an optional argument to BatchMatMul.
+type BatchMatMulAttr func(optionalAttr)
+
+// BatchMatMulAdjX sets the optional adj_x attribute to value.
 //
-// *NOTE*: `Sub` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Sub",
-		Input: []tf.Input{
-			x, y,
-		},
+// value: If `True`, adjoint the slices of `x`. Defaults to `False`.
+// If not specified, defaults to false
+func BatchMatMulAdjX(value bool) BatchMatMulAttr {
+	return func(m optionalAttr) {
+		m["adj_x"] = value
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
 }
 
-// Split elements of `input` based on `delimiter` into a `SparseTensor`.
-//
-// Let N be the size of source (typically N will be the batch size). Split each
-// element of `input` based on `delimiter` and return a `SparseTensor`
-// containing the splitted tokens. Empty tokens are ignored.
-//
-// `delimiter` can be empty, or a string of split characters. If `delimiter` is an
-//  empty string, each element of `input` is split into individual single-byte
-//  character strings, including splitting of UTF-8 multibyte sequences. Otherwise
-//  every character of `delimiter` is a potential split point.
-//
-// For example:
-//   N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output
-//   will be
-//
-//   indices = [0, 0;
-//              0, 1;
-//              1, 0;
-//              1, 1;
-//              1, 2]
-//   shape = [2, 3]
-//   values = ['hello', 'world', 'a', 'b', 'c']
-//
-// Arguments:
-//	input: 1-D. Strings to split.
-//	delimiter: 0-D. Delimiter characters (bytes), or empty string.
+// BatchMatMulAdjY sets the optional adj_y attribute to value.
 //
-// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse
-// tensor, where the first value is N and the second value is the maximum number
-// of tokens in a single input entry.
-func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output) (indices tf.Output, values tf.Output, shape tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "StringSplit",
-		Input: []tf.Input{
-			input, delimiter,
-		},
+// value: If `True`, adjoint the slices of `y`. Defaults to `False`.
+// If not specified, defaults to false
+func BatchMatMulAdjY(value bool) BatchMatMulAttr {
+	return func(m optionalAttr) {
+		m["adj_y"] = value
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Inverse 3D real-valued fast Fourier transform.
+// Multiplies slices of two tensors in batches.
 //
-// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued
-// signal over the inner-most 3 dimensions of `input`.
+// Multiplies all slices of `Tensor` `x` and `y` (each slice can be
+// viewed as an element of a batch), and arranges the individual results
+// in a single output tensor of the same batch size. Each of the
+// individual slices can optionally be adjointed (to adjoint a matrix
+// means to transpose and conjugate it) before multiplication by setting
+// the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
 //
-// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`:
-// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
-// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
-// from the size of the inner-most 3 dimensions of `input`. If the FFT length used
-// to compute `input` is odd, it should be provided since it cannot be inferred
-// properly.
+// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`
+// and `[..., r_y, c_y]`.
 //
-// Along each axis `IRFFT3D` is computed on, if `fft_length` (or
-// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
-// corresponding dimension of `input`, the dimension is cropped. If it is larger,
-// the dimension is padded with zeros.
+// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where:
 //
-// Arguments:
-//	input: A complex64 tensor.
-//	fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
+//     r_o = c_x if adj_x else r_x
+//     c_o = r_y if adj_y else c_y
 //
-// Returns A float32 tensor of the same rank as `input`. The inner-most 3
-//   dimensions of `input` are replaced with the `fft_length` samples of their
-//   inverse 3D real Fourier transform.
+// It is computed as:
 //
-// @compatibility(numpy)
-// Equivalent to np.irfftn with 3 dimensions.
-// @end_compatibility
-func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
+//     output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
+//
+// Arguments:
+//	x: 2-D or higher with shape `[..., r_x, c_x]`.
+//	y: 2-D or higher with shape `[..., r_y, c_y]`.
+//
+// Returns 3-D or higher with shape `[..., r_o, c_o]`
+func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "IRFFT3D",
+		Type: "BatchMatMul",
 		Input: []tf.Input{
-			input, fft_length,
+			x, y,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Returns the truth value of (x != y) element-wise.
+// Computes softplus gradients for a softplus operation.
 //
-// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+// Arguments:
+//	gradients: The backpropagated gradients to the corresponding softplus operation.
+//	features: The features passed as input to the corresponding softplus operation.
+//
+// Returns The gradients: `gradients / (1 + exp(-features))`.
+func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "NotEqual",
+		Type: "SoftplusGrad",
 		Input: []tf.Input{
-			x, y,
+			gradients, features,
 		},
 	}
 	op := scope.AddOperation(opspec)
@@ -19587,6 +19699,55 @@ func QuantizedBiasAdd(scope *Scope, input tf.Output, bias tf.Output, min_input t
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// QuantizedConv2DAttr is an optional argument to QuantizedConv2D.
+type QuantizedConv2DAttr func(optionalAttr)
+
+// QuantizedConv2DOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_QINT32
+func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
+	return func(m optionalAttr) {
+		m["out_type"] = value
+	}
+}
+
+// Computes a 2D convolution given quantized 4D input and filter tensors.
+//
+// The inputs are quantized tensors where the lowest value represents the real
+// number of the associated minimum, and the highest represents the maximum.
+// This means that you can only interpret the quantized output in the same way, by
+// taking the returned minimum and maximum values into account.
+//
+// Arguments:
+//
+//	filter: filter's input_depth dimension must match input's depth dimensions.
+//	min_input: The float value that the lowest quantized input value represents.
+//	max_input: The float value that the highest quantized input value represents.
+//	min_filter: The float value that the lowest quantized filter value represents.
+//	max_filter: The float value that the highest quantized filter value represents.
+//	strides: The stride of the sliding window for each dimension of the input
+// tensor.
+//	padding: The type of padding algorithm to use.
+//
+// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
+func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"strides": strides, "padding": padding}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "QuantizedConv2D",
+		Input: []tf.Input{
+			input, filter, min_input, max_input, min_filter, max_filter,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
+}
+
 // Quantized Batch normalization.
 //
 // This op is deprecated and will be removed in the future. Prefer
@@ -24318,52 +24479,3 @@ func WholeFileReaderV2(scope *Scope, optional ...WholeFileReaderV2Attr) (reader_
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
-
-// QuantizedConv2DAttr is an optional argument to QuantizedConv2D.
-type QuantizedConv2DAttr func(optionalAttr)
-
-// QuantizedConv2DOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_QINT32
-func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
-	return func(m optionalAttr) {
-		m["out_type"] = value
-	}
-}
-
-// Computes a 2D convolution given quantized 4D input and filter tensors.
-//
-// The inputs are quantized tensors where the lowest value represents the real
-// number of the associated minimum, and the highest represents the maximum.
-// This means that you can only interpret the quantized output in the same way, by
-// taking the returned minimum and maximum values into account.
-//
-// Arguments:
-//
-//	filter: filter's input_depth dimension must match input's depth dimensions.
-//	min_input: The float value that the lowest quantized input value represents.
-//	max_input: The float value that the highest quantized input value represents.
-//	min_filter: The float value that the lowest quantized filter value represents.
-//	max_filter: The float value that the highest quantized filter value represents.
-//	strides: The stride of the sliding window for each dimension of the input
-// tensor.
-//	padding: The type of padding algorithm to use.
-//
-// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
-func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"strides": strides, "padding": padding}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "QuantizedConv2D",
-		Input: []tf.Input{
-			input, filter, min_input, max_input, min_filter, max_filter,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
-}
-- 
GitLab


From dd951bb50d454f026083097b6d6122dae696259c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 5 Jul 2017 16:56:41 -0700
Subject: [PATCH 0710/1375] This cl is a more efficient reimplementation of non
 max suppression making it significantly faster in practice by reducing the
 number of unnecessary IOU computations.

PiperOrigin-RevId: 161023977
---
 .../core/kernels/non_max_suppression_op.cc    | 52 ++++++++++---------
 .../kernels/non_max_suppression_op_test.cc    |  8 +--
 2 files changed, 31 insertions(+), 29 deletions(-)

diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index dc95f67ff0..977d1dda64 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -65,9 +65,11 @@ static inline void DecreasingArgSort(const std::vector<float>& values,
       [&values](const int i, const int j) { return values[i] > values[j]; });
 }
 
-// Compute intersection-over-union overlap between boxes i and j.
-static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
-                               int i, int j) {
+// Return true if intersection-over-union overlap between boxes i and j
+// is greater than iou_threshold.
+static inline float IOUGreaterThanThreshold(
+    typename TTypes<float, 2>::ConstTensor boxes, int i, int j,
+    float iou_threshold) {
   const float ymin_i = std::min<float>(boxes(i, 0), boxes(i, 2));
   const float xmin_i = std::min<float>(boxes(i, 1), boxes(i, 3));
   const float ymax_i = std::max<float>(boxes(i, 0), boxes(i, 2));
@@ -76,7 +78,6 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
   const float xmin_j = std::min<float>(boxes(j, 1), boxes(j, 3));
   const float ymax_j = std::max<float>(boxes(j, 0), boxes(j, 2));
   const float xmax_j = std::max<float>(boxes(j, 1), boxes(j, 3));
-
   const float area_i = (ymax_i - ymin_i) * (xmax_i - xmin_i);
   const float area_j = (ymax_j - ymin_j) * (xmax_j - xmin_j);
   if (area_i <= 0 || area_j <= 0) return 0.0;
@@ -87,7 +88,8 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
   const float intersection_area =
       std::max<float>(intersection_ymax - intersection_ymin, 0.0) *
       std::max<float>(intersection_xmax - intersection_xmin, 0.0);
-  return intersection_area / (area_i + area_j - intersection_area);
+  const float iou = intersection_area / (area_i + area_j - intersection_area);
+  return iou > iou_threshold;
 }
 
 void DoNonMaxSuppressionOp(OpKernelContext* context,
@@ -97,7 +99,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context,
                            const float iou_threshold) {
   OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
       errors::InvalidArgument("iou_threshold must be in [0, 1]"));
-  
+
   int num_boxes = 0;
   ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
   if (!context->status().ok()) {
@@ -114,26 +116,24 @@ void DoNonMaxSuppressionOp(OpKernelContext* context,
   std::vector<int> sorted_indices;
   DecreasingArgSort(scores_data, &sorted_indices);
 
-  std::vector<bool> active(num_boxes, true);
   std::vector<int> selected;
-  int num_active = active.size();
+  std::vector<int> selected_indices(output_size, 0);
+  int num_selected = 0;
   for (int i = 0; i < num_boxes; ++i) {
-    if (num_active == 0 || selected.size() >= output_size) break;
-    if (active[i]) {
-      selected.push_back(sorted_indices[i]);
-    } else {
-      continue;
-    }
-    for (int j = i + 1; j < num_boxes; ++j) {
-      if (active[j]) {
-        float iou =
-          ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
-        if (iou > iou_threshold) {
-          active[j] = false;
-          num_active--;
-        }
+    if (selected.size() >= output_size) break;
+    bool should_select = true;
+    for (int j = 0; j < num_selected; ++j) {
+      if (IOUGreaterThanThreshold(boxes_data, sorted_indices[i],
+                                  sorted_indices[selected_indices[j]],
+                                  iou_threshold)) {
+        should_select = false;
+        break;
       }
     }
+    if (should_select) {
+      selected.push_back(sorted_indices[i]);
+      selected_indices[num_selected++] = i;
+    }
   }
 
   // Allocate output tensor
@@ -145,7 +145,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context,
   std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
 }
 
-} // namespace
+}  // namespace
 
 template <typename Device>
 class NonMaxSuppressionOp : public OpKernel {
@@ -167,7 +167,8 @@ class NonMaxSuppressionOp : public OpKernel {
         errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                 max_output_size.shape().DebugString()));
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
+                          iou_threshold_);
   }
 
  private:
@@ -201,7 +202,8 @@ class NonMaxSuppressionV2Op : public OpKernel {
 
     const float iou_threshold_val = iou_threshold.scalar<float>()();
 
-    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_val);
+    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
+                          iou_threshold_val);
   }
 };
 
diff --git a/tensorflow/core/kernels/non_max_suppression_op_test.cc b/tensorflow/core/kernels/non_max_suppression_op_test.cc
index e0e8c87f95..fdbcf05b89 100644
--- a/tensorflow/core/kernels/non_max_suppression_op_test.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op_test.cc
@@ -143,8 +143,8 @@ TEST_F(NonMaxSuppressionOpTest, TestInconsistentBoxAndScoreShapes) {
 
   ASSERT_FALSE(s.ok());
   EXPECT_TRUE(
-              StringPiece(s.ToString()).contains("scores has incompatible shape"))
-    << s;
+      StringPiece(s.ToString()).contains("scores has incompatible shape"))
+      << s;
 }
 
 TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
@@ -156,8 +156,8 @@ TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
 
   ASSERT_FALSE(s.ok());
   EXPECT_TRUE(
-              StringPiece(s.ToString()).contains("iou_threshold must be in [0, 1]"))
-    << s;
+      StringPiece(s.ToString()).contains("iou_threshold must be in [0, 1]"))
+      << s;
 }
 
 TEST_F(NonMaxSuppressionOpTest, TestEmptyInput) {
-- 
GitLab


From 3f099e7d7f1c8b8d3f4121357fc7c40391f9eafe Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Wed, 5 Jul 2017 17:15:30 -0700
Subject: [PATCH 0711/1375] tfdbg: send large Tensor Event protos through gRPC
 stream in chunks

Also fixes flakiness in session_debug_grpc_test

PiperOrigin-RevId: 161025848
---
 tensorflow/core/debug/debug_io_utils.cc       | 200 +++++++++++++++---
 tensorflow/core/debug/debug_io_utils.h        |  58 +++--
 .../core/debug/debugger_event_metadata.proto  |   2 +
 tensorflow/python/debug/lib/debug_data.py     |   9 +-
 .../python/debug/lib/session_debug_testlib.py |   4 +-
 5 files changed, 218 insertions(+), 55 deletions(-)

diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index 875a4763f8..4e185b37a8 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/debug/debugger_event_metadata.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/summary.pb.h"
+#include "tensorflow/core/lib/core/bits.h"
 #include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -45,27 +46,36 @@ namespace tensorflow {
 
 namespace {
 
-// Encapsulate the tensor value inside a Summary proto, and then inside an
-// Event proto.
-Event WrapTensorAsEvent(const DebugNodeKey& debug_node_key,
-                        const Tensor& tensor, const uint64 wall_time_us) {
+// Creates an Event proto representing a chunk of a Tensor. This method only
+// populates the field of the Event proto that represent the envelope
+// informaion (e.g., timestmap, device_name, num_chunks, chunk_index, dtype,
+// shape). It does not set the value.tensor field, which should be set by the
+// caller separately.
+Event PrepareChunkEventProto(const DebugNodeKey& debug_node_key,
+                             const uint64 wall_time_us, const size_t num_chunks,
+                             const size_t chunk_index,
+                             const DataType& tensor_dtype,
+                             const TensorShapeProto& tensor_shape) {
   Event event;
   event.set_wall_time(static_cast<double>(wall_time_us));
-  Summary::Value* summ_val = event.mutable_summary()->add_value();
+  Summary::Value* value = event.mutable_summary()->add_value();
 
   // Create the debug node_name in the Summary proto.
   // For example, if tensor_name = "foo/node_a:0", and the debug_op is
   // "DebugIdentity", the debug node_name in the Summary proto will be
   // "foo/node_a:0:DebugIdentity".
-  summ_val->set_node_name(debug_node_key.debug_node_name);
+  value->set_node_name(debug_node_key.debug_node_name);
 
-  // Tag by the node name. This allows TensorBoard to quickly fetch data per op.
-  summ_val->set_tag(debug_node_key.node_name);
+  // Tag by the node name. This allows TensorBoard to quickly fetch data
+  // per op.
+  value->set_tag(debug_node_key.node_name);
 
   // Store data within debugger metadata to be stored for each event.
   third_party::tensorflow::core::debug::DebuggerEventMetadata metadata;
   metadata.set_device(debug_node_key.device_name);
   metadata.set_output_slot(debug_node_key.output_slot);
+  metadata.set_num_chunks(num_chunks);
+  metadata.set_chunk_index(chunk_index);
 
   // Encode the data in JSON.
   string json_output;
@@ -76,8 +86,8 @@ Event WrapTensorAsEvent(const DebugNodeKey& debug_node_key,
   if (status.ok()) {
     // Store summary metadata. Set the plugin to use this data as "debugger".
     SummaryMetadata::PluginData* plugin_data =
-        summ_val->mutable_metadata()->add_plugin_data();
-    plugin_data->set_plugin_name("debugger");
+        value->mutable_metadata()->add_plugin_data();
+    plugin_data->set_plugin_name(DebugIO::kDebuggerPluginName);
     plugin_data->set_content(json_output);
   } else {
     LOG(WARNING) << "Failed to convert DebuggerEventMetadata proto to JSON. "
@@ -85,19 +95,129 @@ Event WrapTensorAsEvent(const DebugNodeKey& debug_node_key,
                  << ".";
   }
 
+  value->mutable_tensor()->set_dtype(tensor_dtype);
+  *value->mutable_tensor()->mutable_tensor_shape() = tensor_shape;
+
+  return event;
+}
+
+// Translates the length of a string to number of bytes when the string is
+// encoded as bytes in protobuf. Note that this makes a conservative estimate
+// (i.e., an estimate that is usually too large, but never too small under the
+// gRPC message size limit) of the Varint-encoded length, to workaround the lack
+// of a portable length function.
+const size_t StringValMaxBytesInProto(const string& str) {
+#if defined(PLATFORM_GOOGLE)
+  return str.size() + DebugGrpcIO::kGrpcMaxVarintLengthSize;
+#else
+  return str.size();
+#endif
+}
+
+// Breaks a string Tensor (represented as a TensorProto) as a vector of Event
+// protos.
+Status WrapStringTensorAsEvents(const DebugNodeKey& debug_node_key,
+                                const uint64 wall_time_us,
+                                const size_t chunk_size_limit,
+                                TensorProto* tensor_proto,
+                                std::vector<Event>* events) {
+  const protobuf::RepeatedPtrField<string>& strs = tensor_proto->string_val();
+  const size_t num_strs = strs.size();
+  const size_t chunk_size_ub = chunk_size_limit > 0
+                                   ? chunk_size_limit
+                                   : std::numeric_limits<size_t>::max();
+
+  // E.g., if cutoffs is {j, k, l}, the chunks will have index ranges:
+  //   [0:a), [a:b), [c:<end>].
+  std::vector<size_t> cutoffs;
+  size_t chunk_size = 0;
+  for (size_t i = 0; i < num_strs; ++i) {
+    // Take into account the extra bytes in proto buffer.
+    if (StringValMaxBytesInProto(strs[i]) > chunk_size_ub) {
+      return errors::FailedPrecondition(
+          "string value at index ", i, " from debug node ",
+          debug_node_key.debug_node_name,
+          " does not fit gRPC message size limit (", chunk_size_ub, ")");
+    }
+    if (chunk_size + StringValMaxBytesInProto(strs[i]) > chunk_size_ub) {
+      cutoffs.push_back(i);
+      chunk_size = 0;
+    }
+    chunk_size += StringValMaxBytesInProto(strs[i]);
+  }
+  cutoffs.push_back(num_strs);
+  const size_t num_chunks = cutoffs.size();
+
+  for (size_t i = 0; i < num_chunks; ++i) {
+    Event event = PrepareChunkEventProto(debug_node_key, wall_time_us,
+                                         num_chunks, i, tensor_proto->dtype(),
+                                         tensor_proto->tensor_shape());
+    Summary::Value* value = event.mutable_summary()->mutable_value(0);
+
+    if (cutoffs.size() == 1) {
+      value->mutable_tensor()->mutable_string_val()->Swap(
+          tensor_proto->mutable_string_val());
+    } else {
+      const size_t begin = (i == 0) ? 0 : cutoffs[i - 1];
+      const size_t end = cutoffs[i];
+      for (size_t j = begin; j < end; ++j) {
+        value->mutable_tensor()->add_string_val(strs[j]);
+      }
+    }
+
+    events->push_back(std::move(event));
+  }
+
+  return Status::OK();
+}
+
+// Encapsulates the tensor value inside a vector of Event protos. Large tensors
+// are broken up to multiple protos to fit the chunk_size_limit. In each Event
+// proto the field summary.tensor carries the content of the tensor.
+// If chunk_size_limit <= 0, the tensor will not be broken into chunks, i.e., a
+// length-1 vector will be returned, regardless of the size of the tensor.
+Status WrapTensorAsEvents(const DebugNodeKey& debug_node_key,
+                          const Tensor& tensor, const uint64 wall_time_us,
+                          const size_t chunk_size_limit,
+                          std::vector<Event>* events) {
+  TensorProto tensor_proto;
   if (tensor.dtype() == DT_STRING) {
-    // Treat DT_STRING specially, so that tensor_util.MakeNdarray can convert
-    // the TensorProto to string-type numpy array. MakeNdarray does not work
-    // with strings encoded by AsProtoTensorContent() in tensor_content.
-    tensor.AsProtoField(summ_val->mutable_tensor());
+    // Treat DT_STRING specially, so that tensor_util.MakeNdarray in Python can
+    // convert the TensorProto to string-type numpy array. MakeNdarray does not
+    // work with strings encoded by AsProtoTensorContent() in tensor_content.
+    tensor.AsProtoField(&tensor_proto);
+
+    TF_RETURN_IF_ERROR(WrapStringTensorAsEvents(
+        debug_node_key, wall_time_us, chunk_size_limit, &tensor_proto, events));
   } else {
-    tensor.AsProtoTensorContent(summ_val->mutable_tensor());
+    tensor.AsProtoTensorContent(&tensor_proto);
+
+    const size_t total_length = tensor_proto.tensor_content().size();
+    const size_t chunk_size_ub =
+        chunk_size_limit > 0 ? chunk_size_limit : total_length;
+    const size_t num_chunks =
+        (total_length == 0)
+            ? 1
+            : (total_length + chunk_size_ub - 1) / chunk_size_ub;
+    for (size_t i = 0; i < num_chunks; ++i) {
+      const size_t pos = i * chunk_size_ub;
+      const size_t len =
+          (i == num_chunks - 1) ? (total_length - pos) : chunk_size_ub;
+      Event event = PrepareChunkEventProto(debug_node_key, wall_time_us,
+                                           num_chunks, i, tensor_proto.dtype(),
+                                           tensor_proto.tensor_shape());
+      event.mutable_summary()
+          ->mutable_value(0)
+          ->mutable_tensor()
+          ->set_tensor_content(tensor_proto.tensor_content().substr(pos, len));
+      events->push_back(std::move(event));
+    }
   }
 
-  return event;
+  return Status::OK();
 }
 
-// Append an underscore and a timestamp to a file path. If the path already
+// Appends an underscore and a timestamp to a file path. If the path already
 // exists on the file system, append a hyphen and a 1-up index. Consecutive
 // values of the index will be tried until the first unused one is found.
 // TOCTOU race condition is not of concern here due to the fact that tfdbg
@@ -115,23 +235,28 @@ string AppendTimestampToFilePath(const string& in, const uint64 timestamp) {
 }
 
 #if defined(PLATFORM_GOOGLE)
+// Publishes encoded GraphDef through a gRPC debugger stream, in chunks,
+// conforming to the gRPC message size limit.
 Status PublishEncodedGraphDefInChunks(const string& encoded_graph_def,
                                       const string& device_name,
                                       const int64 wall_time,
                                       const string& debug_url) {
-  static const size_t kChunkSizeLimitBytes = 4000 * 1024;
   const uint64 hash = ::tensorflow::Hash64(encoded_graph_def);
   const size_t total_length = encoded_graph_def.size();
-  const size_t num_chunks = static_cast<size_t>(
-      std::ceil(static_cast<float>(total_length) / kChunkSizeLimitBytes));
+  const size_t num_chunks =
+      static_cast<size_t>(std::ceil(static_cast<float>(total_length) /
+                                    DebugGrpcIO::kGrpcMessageSizeLimitBytes));
   for (size_t i = 0; i < num_chunks; ++i) {
-    const size_t pos = i * kChunkSizeLimitBytes;
-    const size_t len =
-        (i == num_chunks - 1) ? (total_length - pos) : kChunkSizeLimitBytes;
+    const size_t pos = i * DebugGrpcIO::kGrpcMessageSizeLimitBytes;
+    const size_t len = (i == num_chunks - 1)
+                           ? (total_length - pos)
+                           : DebugGrpcIO::kGrpcMessageSizeLimitBytes;
     Event event;
     event.set_wall_time(static_cast<double>(wall_time));
     // Prefix the chunk with
     //   <hash64>,<device_name>,<wall_time>|<index>|<num_chunks>|.
+    // TODO(cais): Use DebuggerEventMetadata to store device_name, num_chunks
+    // and chunk_index, instead.
     event.set_graph_def(strings::StrCat(hash, ",", device_name, ",", wall_time,
                                         "|", i, "|", num_chunks, "|",
                                         encoded_graph_def.substr(pos, len)));
@@ -147,6 +272,9 @@ Status PublishEncodedGraphDefInChunks(const string& encoded_graph_def,
 
 }  // namespace
 
+// static
+const char* const DebugIO::kDebuggerPluginName = "debugger";
+
 // static
 const char* const DebugIO::kMetadataFilePrefix = "_tfdbg_";
 
@@ -215,6 +343,7 @@ const char* const DebugIO::kFileURLScheme = "file://";
 // static
 const char* const DebugIO::kGrpcURLScheme = "grpc://";
 
+// Publishes debug metadata to a set of debug URLs.
 // static
 Status DebugIO::PublishDebugMetadata(
     const int64 global_step, const int64 session_run_index,
@@ -532,9 +661,11 @@ Status DebugFileIO::DumpTensorToEventFile(const DebugNodeKey& debug_node_key,
                                           const Tensor& tensor,
                                           const uint64 wall_time_us,
                                           const string& file_path) {
-  return DumpEventProtoToFile(
-      WrapTensorAsEvent(debug_node_key, tensor, wall_time_us),
-      io::Dirname(file_path).ToString(), io::Basename(file_path).ToString());
+  std::vector<Event> events;
+  TF_RETURN_IF_ERROR(
+      WrapTensorAsEvents(debug_node_key, tensor, wall_time_us, 0, &events));
+  return DumpEventProtoToFile(events[0], io::Dirname(file_path).ToString(),
+                              io::Basename(file_path).ToString());
 }
 
 // static
@@ -641,6 +772,12 @@ mutex DebugGrpcIO::streams_mu;
 int64 DebugGrpcIO::channel_connection_timeout_micros = 900 * 1000 * 1000;
 // TODO(cais): Make this configurable?
 
+// static
+const size_t DebugGrpcIO::kGrpcMessageSizeLimitBytes = 4000 * 1024;
+
+// static
+const size_t DebugGrpcIO::kGrpcMaxVarintLengthSize = 6;
+
 // static
 std::unordered_map<string, std::shared_ptr<DebugGrpcChannel>>*
 DebugGrpcIO::GetStreamChannels() {
@@ -658,9 +795,14 @@ Status DebugGrpcIO::SendTensorThroughGrpcStream(
   if (gated && !IsGateOpen(debug_node_key.debug_node_name, grpc_stream_url)) {
     return Status::OK();
   } else {
-    return SendEventProtoThroughGrpcStream(
-        WrapTensorAsEvent(debug_node_key, tensor, wall_time_us),
-        grpc_stream_url);
+    std::vector<Event> events;
+    TF_RETURN_IF_ERROR(WrapTensorAsEvents(debug_node_key, tensor, wall_time_us,
+                                          kGrpcMessageSizeLimitBytes, &events));
+    for (const Event& event : events) {
+      TF_RETURN_IF_ERROR(
+          SendEventProtoThroughGrpcStream(event, grpc_stream_url));
+    }
+    return Status::OK();
   }
 }
 
diff --git a/tensorflow/core/debug/debug_io_utils.h b/tensorflow/core/debug/debug_io_utils.h
index 4caa4b5e04..98effed425 100644
--- a/tensorflow/core/debug/debug_io_utils.h
+++ b/tensorflow/core/debug/debug_io_utils.h
@@ -44,6 +44,9 @@ struct DebugNodeKey {
   DebugNodeKey(const string& device_name, const string& node_name,
                const int32 output_slot, const string& debug_op);
 
+  // Converts a device name string to a device path string.
+  // E.g., /job:localhost/replica:0/task:0/cpu:0 will be converted to
+  //   ,job_localhost,replica_0,task_0,cpu_0.
   static const string DeviceNameToDevicePath(const string& device_name);
 
   const string device_name;
@@ -56,6 +59,17 @@ struct DebugNodeKey {
 
 class DebugIO {
  public:
+  static const char* const kDebuggerPluginName;
+
+  static const char* const kMetadataFilePrefix;
+  static const char* const kCoreMetadataTag;
+  static const char* const kDeviceTag;
+  static const char* const kGraphTag;
+  static const char* const kHashTag;
+
+  static const char* const kFileURLScheme;
+  static const char* const kGrpcURLScheme;
+
   static Status PublishDebugMetadata(
       const int64 global_step, const int64 session_run_index,
       const int64 executor_step_index, const std::vector<string>& input_names,
@@ -63,7 +77,7 @@ class DebugIO {
       const std::vector<string>& target_nodes,
       const std::unordered_set<string>& debug_urls);
 
-  // Publish a tensor to a debug target URL.
+  // Publishes a tensor to a debug target URL.
   //
   // Args:
   //   debug_node_key: A DebugNodeKey identifying the debug node.
@@ -84,7 +98,7 @@ class DebugIO {
                                    const uint64 wall_time_us,
                                    const gtl::ArraySlice<string>& debug_urls);
 
-  // Publish a graph to a set of debug URLs.
+  // Publishes a graph to a set of debug URLs.
   //
   // Args:
   //   graph: The graph to be published.
@@ -92,7 +106,7 @@ class DebugIO {
   static Status PublishGraph(const Graph& graph, const string& device_name,
                              const std::unordered_set<string>& debug_urls);
 
-  // Determine whether a copy node needs to perform deep-copy of input tensor.
+  // Determines whether a copy node needs to perform deep-copy of input tensor.
   //
   // The input arguments contain sufficient information about the attached
   // downstream debug ops for this method to determine whether all the said
@@ -109,7 +123,7 @@ class DebugIO {
   static bool IsCopyNodeGateOpen(
       const std::vector<DebugWatchAndURLSpec>& specs);
 
-  // Determine whether a debug node needs to proceed given the current gRPC
+  // Determines whether a debug node needs to proceed given the current gRPC
   // gating status.
   //
   // Args:
@@ -122,7 +136,7 @@ class DebugIO {
   static bool IsDebugNodeGateOpen(const string& watch_key,
                                   const std::vector<string>& debug_urls);
 
-  // Determine whether debug information should be sent through a grpc://
+  // Determines whether debug information should be sent through a grpc://
   // debug URL given the current gRPC gating status.
   //
   // Args:
@@ -138,21 +152,12 @@ class DebugIO {
                                  const string& debug_url);
 
   static Status CloseDebugURL(const string& debug_url);
-
-  static const char* const kMetadataFilePrefix;
-  static const char* const kCoreMetadataTag;
-  static const char* const kDeviceTag;
-  static const char* const kGraphTag;
-  static const char* const kHashTag;
-
-  static const char* const kFileURLScheme;
-  static const char* const kGrpcURLScheme;
 };
 
 // Helper class for debug ops.
 class DebugFileIO {
  public:
-  // Encapsulate the Tensor in an Event protobuf and write it to a directory.
+  // Encapsulates the Tensor in an Event protobuf and write it to a directory.
   // The actual path of the dump file will be a contactenation of
   // dump_root_dir, tensor_name, along with the wall_time.
   //
@@ -189,12 +194,18 @@ class DebugFileIO {
                                 const DebugNodeKey& debug_node_key,
                                 const uint64 wall_time_us);
 
+  // Dumps an Event proto to a file.
+  //
+  // Args:
+  //   event_prot: The Event proto to be dumped.
+  //   dir_name: Directory path.
+  //   file_name: Base file name.
   static Status DumpEventProtoToFile(const Event& event_proto,
                                      const string& dir_name,
                                      const string& file_name);
 
  private:
-  // Encapsulate the Tensor in an Event protobuf and write it to file.
+  // Encapsulates the Tensor in an Event protobuf and write it to file.
   static Status DumpTensorToEventFile(const DebugNodeKey& debug_node_key,
                                       const Tensor& tensor,
                                       const uint64 wall_time_us,
@@ -267,20 +278,23 @@ class DebugGrpcChannel {
 
 class DebugGrpcIO {
  public:
-  // Send a tensor through a debug gRPC stream.
+  static const size_t kGrpcMessageSizeLimitBytes;
+  static const size_t kGrpcMaxVarintLengthSize;
+
+  // Sends a tensor through a debug gRPC stream.
   static Status SendTensorThroughGrpcStream(const DebugNodeKey& debug_node_key,
                                             const Tensor& tensor,
                                             const uint64 wall_time_us,
                                             const string& grpc_stream_url,
                                             const bool gated);
 
-  // Send an Event proto through a debug gRPC stream.
+  // Sends an Event proto through a debug gRPC stream.
   // Thread-safety: Safe with respect to other calls to the same method and
   // calls to CloseGrpcStream().
   static Status SendEventProtoThroughGrpcStream(const Event& event_proto,
                                                 const string& grpc_stream_url);
 
-  // Check whether a debug watch key is allowed to send data to a given grpc://
+  // Checks whether a debug watch key is allowed to send data to a given grpc://
   // debug URL given the current gating status.
   //
   // Args:
@@ -293,16 +307,16 @@ class DebugGrpcIO {
   //     proceed.
   static bool IsGateOpen(const string& watch_key, const string& grpc_debug_url);
 
-  // Close a gRPC stream to the given address, if it exists.
+  // Closes a gRPC stream to the given address, if it exists.
   // Thread-safety: Safe with respect to other calls to the same method and
   // calls to SendTensorThroughGrpcStream().
   static Status CloseGrpcStream(const string& grpc_stream_url);
 
-  // Enable a debug watch key at a grpc:// debug URL.
+  // Enables a debug watch key at a grpc:// debug URL.
   static void EnableWatchKey(const string& grpc_debug_url,
                              const string& watch_key);
 
-  // Disable a debug watch key at a grpc:// debug URL.
+  // Disables a debug watch key at a grpc:// debug URL.
   static void DisableWatchKey(const string& grpc_debug_url,
                               const string& watch_key);
 
diff --git a/tensorflow/core/debug/debugger_event_metadata.proto b/tensorflow/core/debug/debugger_event_metadata.proto
index 44ef305f5a..8bdedb1a50 100644
--- a/tensorflow/core/debug/debugger_event_metadata.proto
+++ b/tensorflow/core/debug/debugger_event_metadata.proto
@@ -6,4 +6,6 @@ package third_party.tensorflow.core.debug;
 message DebuggerEventMetadata {
   string device = 1;
   int32 output_slot = 2;
+  int32 num_chunks = 3;
+  int32 chunk_index = 4;
 };
diff --git a/tensorflow/python/debug/lib/debug_data.py b/tensorflow/python/debug/lib/debug_data.py
index 24214c0ddd..2da386bb0f 100644
--- a/tensorflow/python/debug/lib/debug_data.py
+++ b/tensorflow/python/debug/lib/debug_data.py
@@ -117,8 +117,13 @@ def load_tensor_from_event(event):
   """
 
   tensor_proto = event.summary.value[0].tensor
-  if tensor_proto.tensor_content or tensor_proto.string_val:
-    # Initialized tensor.
+  shape = tensor_util.TensorShapeProtoToList(tensor_proto.tensor_shape)
+  num_elements = 1
+  for shape_dim in shape:
+    num_elements *= shape_dim
+
+  if tensor_proto.tensor_content or tensor_proto.string_val or not num_elements:
+    # Initialized tensor or empty tensor.
     if tensor_proto.dtype == types_pb2.DT_RESOURCE:
       tensor_value = InconvertibleTensorProto(tensor_proto)
     else:
diff --git a/tensorflow/python/debug/lib/session_debug_testlib.py b/tensorflow/python/debug/lib/session_debug_testlib.py
index 3facf306e2..1b4053ff3b 100644
--- a/tensorflow/python/debug/lib/session_debug_testlib.py
+++ b/tensorflow/python/debug/lib/session_debug_testlib.py
@@ -393,9 +393,9 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
 
       # Add debug tensor watch for u.
       debug_utils.add_debug_tensor_watch(
-          run_options, "%s" % u_name, 0, debug_urls=debug_urls)
+          run_options, u_name, 0, debug_urls=debug_urls)
       debug_utils.add_debug_tensor_watch(
-          run_options, "%s" % s_name, 0, debug_urls=debug_urls)
+          run_options, s_name, 0, debug_urls=debug_urls)
 
       run_metadata = config_pb2.RunMetadata()
 
-- 
GitLab


From 64b628bfbef9bf8be983aef35aa59bc35d703be6 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 5 Jul 2017 17:22:10 -0700
Subject: [PATCH 0712/1375] fix link to SessionRunHook

PiperOrigin-RevId: 161026529
---
 tensorflow/docs_src/get_started/estimator.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/get_started/estimator.md b/tensorflow/docs_src/get_started/estimator.md
index a2c2a1ece5..a55454f8af 100644
--- a/tensorflow/docs_src/get_started/estimator.md
+++ b/tensorflow/docs_src/get_started/estimator.md
@@ -314,7 +314,7 @@ classifier.train(input_fn=train_input_fn, steps=1000)
 ```
 
 However, if you're looking to track the model while it trains, you'll likely
-want to instead use a TensorFlow @{tf.SessionRunHook$`SessionRunHook`}
+want to instead use a TensorFlow @{tf.train.SessionRunHook$`SessionRunHook`}
 to perform logging operations. See the tutorial
 @{$monitors$Logging and Monitoring Basics with tf.estimator}
 for more on this topic.
-- 
GitLab


From ee4259a271f56a32f046cb840215f1370acaf186 Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Wed, 5 Jul 2017 18:00:00 -0700
Subject: [PATCH 0713/1375] Fixes to the getting started documentation (#11154)

* Update get_started.md

-Streamlined python code
-Corrected outputs for example python code
-Corrected nonworking python example code

* Update get_started.md

Minor grammare fix

* Update beginner.md

Made explicit https calls

* Update get_started.md

Fixed something that might have failed the macOS test

* Keep the `dtypes`

This way it stays in sync with the upper part of the file.
---
 .../docs_src/get_started/get_started.md       | 23 ++++++++++---------
 .../docs_src/get_started/mnist/beginners.md   | 10 ++++----
 2 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index d1c9cd696c..17d07cc582 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -295,7 +295,6 @@ next section.
 The completed trainable linear regression model is shown here:
 
 ```python
-import numpy as np
 import tensorflow as tf
 
 # Model parameters
@@ -305,14 +304,16 @@ b = tf.Variable([-.3], dtype=tf.float32)
 x = tf.placeholder(tf.float32)
 linear_model = W * x + b
 y = tf.placeholder(tf.float32)
+
 # loss
 loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
 # optimizer
 optimizer = tf.train.GradientDescentOptimizer(0.01)
 train = optimizer.minimize(loss)
+
 # training data
-x_train = [1,2,3,4]
-y_train = [0,-1,-2,-3]
+x_train = [1, 2, 3, 4]
+y_train = [0, -1, -2, -3]
 # training loop
 init = tf.global_variables_initializer()
 sess = tf.Session()
@@ -329,9 +330,9 @@ When run, it produces
 W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11
 ```
 
-Notice that the loss is a very small number (close to zero). If you run this
-program your loss will not be exactly the same, because the model is initialized
-with random values.
+Notice that the loss is a very small number (very close to zero). If you run this
+program, your loss may not be the exact same because the model is initialized
+with pseudorandom values.
 
 This more complicated program can still be visualized in TensorBoard
 ![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png)
@@ -394,8 +395,8 @@ print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-    train metrics: {'global_step': 1000, 'loss': 4.3049088e-08}
-    eval metrics: {'global_step': 1000, 'loss': 0.0025487561}
+train metrics: {'loss': 1.2712867e-09, 'global_step': 1000}
+eval metrics: {'loss': 0.0025279333, 'global_step': 1000}
 ```
 Notice how our eval data has a higher loss, but it is still close to zero.
 That means we are learning properly.
@@ -425,7 +426,7 @@ def model_fn(features, labels, mode):
   # Build a linear model and predict values
   W = tf.get_variable("W", [1], dtype=tf.float64)
   b = tf.get_variable("b", [1], dtype=tf.float64)
-  y = W*features['x'] + b
+  y = W * features['x'] + b
   # Loss sub-graph
   loss = tf.reduce_sum(tf.square(y - labels))
   # Training sub-graph
@@ -464,8 +465,8 @@ print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-train metrics: {'global_step': 1000, 'loss': 4.9380226e-11}
-eval metrics: {'global_step': 1000, 'loss': 0.01010081}
+train metrics: {'loss': 1.227995e-11, 'global_step': 1000}
+eval metrics: {'loss': 0.01010036, 'global_step': 1000}
 ```
 
 Notice how the contents of the custom `model_fn()` function are very similar
diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 4858b3cf92..193dd41b2a 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -95,7 +95,7 @@ We can flatten this array into a vector of 28x28 = 784 numbers. It doesn't
 matter how we flatten the array, as long as we're consistent between images.
 From this perspective, the MNIST images are just a bunch of points in a
 784-dimensional vector space, with a
-[very rich structure](http://colah.github.io/posts/2014-10-Visualizing-MNIST/)
+[very rich structure](https://colah.github.io/posts/2014-10-Visualizing-MNIST/)
 (warning: computationally intensive visualizations).
 
 Flattening the data throws away information about the 2D structure of the image.
@@ -231,7 +231,7 @@ Now let's turn that into something that TensorFlow can use.
 
 
 To do efficient numerical computing in Python, we typically use libraries like
-[NumPy](http://www.numpy.org/) that do expensive operations such as matrix
+[NumPy](http://www.numpy.org) that do expensive operations such as matrix
 multiplication outside Python, using highly efficient code implemented in
 another language.  Unfortunately, there can still be a lot of overhead from
 switching back to Python every operation. This overhead is especially bad if you
@@ -324,7 +324,7 @@ distribution (the one-hot vector with the digit labels).  In some rough sense, t
 cross-entropy is measuring how inefficient our predictions are for describing
 the truth. Going into more detail about cross-entropy is beyond the scope of
 this tutorial, but it's well worth
-[understanding](http://colah.github.io/posts/2015-09-Visual-Information/).
+[understanding](https://colah.github.io/posts/2015-09-Visual-Information).
 
 To implement cross-entropy we need to first add a new placeholder to input the
 correct answers:
@@ -356,7 +356,7 @@ instead.
 Now that we know what we want our model to do, it's very easy to have TensorFlow
 train it to do so.  Because TensorFlow knows the entire graph of your
 computations, it can automatically use the
-[backpropagation algorithm](http://colah.github.io/posts/2015-08-Backprop/) to
+[backpropagation algorithm](https://colah.github.io/posts/2015-08-Backprop) to
 efficiently determine how your variables affect the loss you ask it to
 minimize. Then it can apply your choice of optimization algorithm to modify the
 variables and reduce the loss.
@@ -447,7 +447,7 @@ Is that good? Well, not really. In fact, it's pretty bad. This is because we're
 using a very simple model. With some small changes, we can get to 97%. The best
 models can get to over 99.7% accuracy! (For more information, have a look at
 this
-[list of results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html).)
+[list of results](https://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results).)
 
 What matters is that we learned from this model. Still, if you're feeling a bit
 down about these results, check out
-- 
GitLab


From 41bc76d28b8b301c546cc5624abd37fd8b97b64c Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Wed, 5 Jul 2017 18:17:18 -0700
Subject: [PATCH 0714/1375] Allow callables from Session.make_callable() to
 take RunOptions and RunMetadata

tfdbg: support Session.make_callable()

except invoke_stepper, which is left unimplemented in this CL.

To this end, a new kwarg `accept_options` is added to Session.make_callable(). The kwarg is a boolean that defaults to False. If set to True, it will return a callable that accepts the kwargs `options` and `run_metadata` that have the same semantics as `tf.Sessoin.run`.

Fixes #11017

PiperOrigin-RevId: 161031380
---
 tensorflow/python/client/session.py           | 64 +++++++++---
 tensorflow/python/client/session_test.py      | 44 +++++++++
 tensorflow/python/debug/cli/cli_shared.py     | 38 ++++---
 tensorflow/python/debug/wrappers/framework.py | 98 +++++++++++++++----
 .../python/debug/wrappers/framework_test.py   | 24 +++++
 .../debug/wrappers/local_cli_wrapper.py       | 31 ++++--
 .../debug/wrappers/local_cli_wrapper_test.py  | 48 +++++++++
 .../tensorflow.-interactive-session.pbtxt     |  2 +-
 .../api/golden/tensorflow.-session.pbtxt      |  2 +-
 9 files changed, 299 insertions(+), 52 deletions(-)

diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index 849f3cc5e1..d8c1489a5e 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import functools
 import re
 import threading
 
@@ -61,6 +62,7 @@ class SessionInterface(object):
     """Continues the execution with additional feeds and fetches."""
     raise NotImplementedError('partial_run')
 
+
 def _get_indexed_slices_value_from_fetches(fetched_vals):
   return ops.IndexedSlicesValue(fetched_vals[0], fetched_vals[1],
                                 fetched_vals[2]
@@ -884,12 +886,9 @@ class BaseSession(SessionInterface):
       ValueError: If `fetches` or `feed_dict` keys are invalid or refer to a
         `Tensor` that doesn't exist.
     """
-    run_metadata_ptr = tf_session.TF_NewBuffer()
-    if options:
-      options_ptr = tf_session.TF_NewBufferFromString(
-          compat.as_bytes(options.SerializeToString()))
-    else:
-      options_ptr = None
+    options_ptr = tf_session.TF_NewBufferFromString(
+        compat.as_bytes(options.SerializeToString())) if options else None
+    run_metadata_ptr = tf_session.TF_NewBuffer() if run_metadata else None
 
     try:
       result = self._run(None, fetches, feed_dict, options_ptr,
@@ -898,7 +897,8 @@ class BaseSession(SessionInterface):
         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
         run_metadata.ParseFromString(compat.as_bytes(proto_data))
     finally:
-      tf_session.TF_DeleteBuffer(run_metadata_ptr)
+      if run_metadata_ptr:
+        tf_session.TF_DeleteBuffer(run_metadata_ptr)
       if options:
         tf_session.TF_DeleteBuffer(options_ptr)
     return result
@@ -1126,7 +1126,10 @@ class BaseSession(SessionInterface):
       results = []
     return fetch_handler.build_results(self, results)
 
-  def make_callable(self, fetches, feed_list=None):
+  def make_callable(self,
+                    fetches,
+                    feed_list=None,
+                    accept_options=False):
     """Returns a Python callable that runs a particular step.
 
     The returned callable will take `len(feed_list)` arguments whose types
@@ -1146,6 +1149,12 @@ class BaseSession(SessionInterface):
         for details of the allowable fetch types.
       feed_list: (Optional.) A list of `feed_dict` keys. See
         @{tf.Session.run} for details of the allowable feed key types.
+      accept_options: (Optional.) Iff `True`, the returned `Callable` will be
+        able to accept @{tf.RunOptions} and @{tf.RunMetadata} as optional
+        keyword arguments `options` and `run_metadata`, respectively, with
+        the same syntax and semantics as @{tf.Session.run}, which is useful
+        for certain use cases (profiling and debugging) but will result in
+        measurable slowdown of the `Callable`'s performance. Default: `False`.
 
     Returns:
       A function that when called will execute the step defined by
@@ -1165,10 +1174,10 @@ class BaseSession(SessionInterface):
       # TODO(mrry): Refactor the feed handling logic from
       # `Session._run()` so that we can convert the feeds to a list of
       # strings here.
-      def _generic_run(*feed_args):
+      def _generic_run(*feed_args, **kwargs):
         feed_dict = {feed: feed_val
                      for feed, feed_val in zip(feed_list, feed_args)}
-        return self.run(fetches, feed_dict=feed_dict)
+        return self.run(fetches, feed_dict=feed_dict, **kwargs)
       return _generic_run
 
     # Ensure any changes to the graph are reflected in the runtime.
@@ -1182,7 +1191,40 @@ class BaseSession(SessionInterface):
     fetch_list_as_strings = _name_list(fetch_handler.fetches())
     target_list_as_strings = _name_list(fetch_handler.targets())
 
-    if isinstance(fetches, ops.Operation):
+    def _callable_template_with_options_and_metadata(
+        fetch_list_as_strings,
+        target_list_as_strings,
+        fetch_handler,
+        options=None,
+        run_metadata=None):
+      """Template callable that accepts RunOptions and RunMetadata."""
+      options_ptr = tf_session.TF_NewBufferFromString(
+          compat.as_bytes(options.SerializeToString())) if options else None
+      run_metadata_ptr = tf_session.TF_NewBuffer() if run_metadata else None
+      try:
+        with errors.raise_exception_on_not_ok_status() as status:
+          results = tf_session.TF_Run(
+              self._session, options_ptr, {}, fetch_list_as_strings,
+              target_list_as_strings, status, run_metadata_ptr)
+          if fetch_handler:
+            results = fetch_handler.build_results(self, results)
+          else:
+            results = results[0] if results else None
+        if run_metadata:
+          proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
+          run_metadata.ParseFromString(compat.as_bytes(proto_data))
+      finally:
+        if run_metadata_ptr:
+          tf_session.TF_DeleteBuffer(run_metadata_ptr)
+        if options:
+          tf_session.TF_DeleteBuffer(options_ptr)
+      return results
+
+    if accept_options:
+      return functools.partial(
+          _callable_template_with_options_and_metadata, fetch_list_as_strings,
+          target_list_as_strings, fetch_handler)
+    elif isinstance(fetches, ops.Operation):
       # Special case for fetching a single operation, because the
       # function will have no return value.
       assert not fetch_list_as_strings
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index 103e864f4b..571b9a4668 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -1230,6 +1230,50 @@ class SessionTest(test_util.TensorFlowTestCase):
           self.assertAllEqual(np_array, out_v)
           self.assertAllEqual(np_array, feed_v)
 
+  @test_util.disable_c_api  # session.make_callable() doesn't work with C API
+  def testMakeCallableOnTensorWithRunOptions(self):
+    with session.Session() as sess:
+      a = constant_op.constant(42.0)
+      tensor_runner = sess.make_callable(a, accept_options=True)
+      run_options = config_pb2.RunOptions(
+          trace_level=config_pb2.RunOptions.FULL_TRACE)
+      run_metadata = config_pb2.RunMetadata()
+      self.assertEqual(0, len(run_metadata.step_stats.dev_stats))
+      res = tensor_runner(options=run_options, run_metadata=run_metadata)
+      self.assertEqual(42.0, res)
+      self.assertGreater(len(run_metadata.step_stats.dev_stats), 0)
+
+  @test_util.disable_c_api  # session.make_callable() doesn't work with C API
+  def testMakeCallableOnOperationWithRunOptions(self):
+    with session.Session() as sess:
+      a = variables.Variable(42.0)
+      b = state_ops.assign_add(a, 1.0)
+      sess.run(a.initializer)
+      tensor_runner = sess.make_callable(b.op, accept_options=True)
+      run_options = config_pb2.RunOptions(
+          trace_level=config_pb2.RunOptions.FULL_TRACE)
+      run_metadata = config_pb2.RunMetadata()
+      self.assertEqual(0, len(run_metadata.step_stats.dev_stats))
+      tensor_runner(options=run_options, run_metadata=run_metadata)
+      self.assertEqual(43.0, sess.run(a))
+      self.assertGreater(len(run_metadata.step_stats.dev_stats), 0)
+
+  @test_util.disable_c_api  # session.make_callable() doesn't work with C API
+  def testMakeCallableWithFeedListAndRunOptions(self):
+    with session.Session() as sess:
+      ph = array_ops.placeholder(dtypes.float32)
+      a = math_ops.add(ph, 1.0)
+      tensor_runner = sess.make_callable(
+          a, feed_list=[ph.name], accept_options=True)
+      run_options = config_pb2.RunOptions(
+          trace_level=config_pb2.RunOptions.FULL_TRACE)
+      run_metadata = config_pb2.RunMetadata()
+      self.assertEqual(0, len(run_metadata.step_stats.dev_stats))
+      self.assertAllClose(
+          42.0,
+          tensor_runner(41.0, options=run_options, run_metadata=run_metadata))
+      self.assertGreater(len(run_metadata.step_stats.dev_stats), 0)
+
   def testFeedError(self):
     with session.Session() as sess:
       feed_t = array_ops.placeholder(dtype=dtypes.float32)
diff --git a/tensorflow/python/debug/cli/cli_shared.py b/tensorflow/python/debug/cli/cli_shared.py
index 9164e18bcf..abd2e84fde 100644
--- a/tensorflow/python/debug/cli/cli_shared.py
+++ b/tensorflow/python/debug/cli/cli_shared.py
@@ -286,10 +286,14 @@ def get_tfdbg_logo():
   return debugger_cli_common.RichTextLines(lines)
 
 
+_HORIZONTAL_BAR = "======================================"
+
+
 def get_run_start_intro(run_call_count,
                         fetches,
                         feed_dict,
-                        tensor_filters):
+                        tensor_filters,
+                        is_callable_runner=False):
   """Generate formatted intro for run-start UI.
 
   Args:
@@ -300,6 +304,8 @@ def get_run_start_intro(run_call_count,
       for more details.
     tensor_filters: (dict) A dict from tensor-filter name to tensor-filter
       callable.
+    is_callable_runner: (bool) whether a runner returned by
+        Session.make_callable is being run.
 
   Returns:
     (RichTextLines) Formatted intro message about the `Session.run()` call.
@@ -317,16 +323,19 @@ def get_run_start_intro(run_call_count,
       else:
         feed_dict_lines.append(feed_key.name)
 
-  intro_lines = [
-      "======================================",
-      "Session.run() call #%d:" % run_call_count,
-      "", "Fetch(es):"
-  ]
-  intro_lines.extend(["  " + line for line in fetch_lines])
-  intro_lines.extend(["", "Feed dict(s):"])
-  intro_lines.extend(["  " + line for line in feed_dict_lines])
+  intro_lines = [_HORIZONTAL_BAR]
+  if is_callable_runner:
+    intro_lines.append("Running a runner returned by Session.make_callabe()")
+  else:
+    intro_lines.extend([
+        "Session.run() call #%d:" % run_call_count,
+        "", "Fetch(es):"
+    ])
+    intro_lines.extend(["  " + line for line in fetch_lines])
+    intro_lines.extend(["", "Feed dict(s):"])
+    intro_lines.extend(["  " + line for line in feed_dict_lines])
   intro_lines.extend([
-      "======================================", "",
+      _HORIZONTAL_BAR, "",
       "Select one of the following commands to proceed ---->"
   ])
 
@@ -392,7 +401,10 @@ def get_run_start_intro(run_call_count,
   return out
 
 
-def get_run_short_description(run_call_count, fetches, feed_dict):
+def get_run_short_description(run_call_count,
+                              fetches,
+                              feed_dict,
+                              is_callable_runner=False):
   """Get a short description of the run() call.
 
   Args:
@@ -401,11 +413,15 @@ def get_run_short_description(run_call_count, fetches, feed_dict):
       for more details.
     feed_dict: Feeds to the `Session.run()` call. See doc of `Session.run()`
       for more details.
+    is_callable_runner: (bool) whether a runner returned by
+        Session.make_callable is being run.
 
   Returns:
     (str) A short description of the run() call, including information about
       the fetche(s) and feed(s).
   """
+  if is_callable_runner:
+    return "runner from make_callable()"
 
   description = "run #%d: " % run_call_count
 
diff --git a/tensorflow/python/debug/wrappers/framework.py b/tensorflow/python/debug/wrappers/framework.py
index 2c239038e4..622950e51d 100644
--- a/tensorflow/python/debug/wrappers/framework.py
+++ b/tensorflow/python/debug/wrappers/framework.py
@@ -193,7 +193,7 @@ class OnRunStartRequest(object):
   """
 
   def __init__(self, fetches, feed_dict, run_options, run_metadata,
-               run_call_count):
+               run_call_count, is_callable_runner=False):
     """Constructor of `OnRunStartRequest`.
 
     Args:
@@ -205,12 +205,15 @@ class OnRunStartRequest(object):
         run() method of a non-wrapped TensorFlow session.
       run_call_count: 1-based count of how many run calls (including this one)
         has been invoked.
+      is_callable_runner: (bool) whether a runner returned by
+        Session.make_callable is being run.
     """
     self.fetches = fetches
     self.feed_dict = feed_dict
     self.run_options = run_options
     self.run_metadata = run_metadata
     self.run_call_count = run_call_count
+    self.is_callable_runner = is_callable_runner
 
 
 class OnRunStartAction(object):
@@ -394,7 +397,13 @@ class BaseDebugWrapperSession(session.SessionInterface):
   def as_default(self):
     return ops.default_session(self)
 
-  def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
+  def run(self,
+          fetches,
+          feed_dict=None,
+          options=None,
+          run_metadata=None,
+          callable_runner=None,
+          callable_runner_args=None):
     """Wrapper around Session.run() that inserts tensor watch options.
 
     Args:
@@ -402,25 +411,39 @@ class BaseDebugWrapperSession(session.SessionInterface):
       feed_dict: Same as the `feed_dict` arg to regular `Session.run()`.
       options: Same as the `options` arg to regular `Session.run()`.
       run_metadata: Same as the `run_metadata` arg to regular `Session.run()`.
+      callable_runner: A `callable` returned by `Session.make_callable()`.
+        If not `None`, `fetches` and `feed_dict` must both be `None`.
+      callable_runner_args: An optional list of arguments to `callable_runner`.
 
     Returns:
       Simply forwards the output of the wrapped `Session.run()` call.
 
     Raises:
-      ValueError: On invalid `OnRunStartAction` value.
+      ValueError: On invalid `OnRunStartAction` value. Or if `callable_runner`
+        is not `None` and either or both of `fetches` and `feed_dict` is `None`.
     """
+    if not callable_runner:
+      self._run_call_count += 1
+    else:
+      if fetches or feed_dict:
+        raise ValueError(
+            "callable_runner and fetches/feed_dict are mutually exclusive, but "
+            "are used simultaneously.")
 
-    self._run_call_count += 1
     if self._is_disabled_thread():
-      return self._sess.run(fetches,
-                            feed_dict=feed_dict,
-                            options=options,
-                            run_metadata=run_metadata)
+      if callable_runner:
+        return callable_runner(*callable_runner_args)
+      else:
+        return self._sess.run(fetches,
+                              feed_dict=feed_dict,
+                              options=options,
+                              run_metadata=run_metadata)
 
     # Invoke on-run-start callback and obtain response.
     run_start_resp = self.on_run_start(
         OnRunStartRequest(fetches, feed_dict, options, run_metadata,
-                          self._run_call_count))
+                          self._run_call_count,
+                          is_callable_runner=bool(callable_runner)))
     _check_type(run_start_resp, OnRunStartResponse)
 
     if run_start_resp.action == OnRunStartAction.DEBUG_RUN:
@@ -443,10 +466,15 @@ class BaseDebugWrapperSession(session.SessionInterface):
       # runtime errors.
       tf_error = None
       try:
-        retvals = self._sess.run(fetches,
-                                 feed_dict=feed_dict,
-                                 options=decorated_run_options,
-                                 run_metadata=run_metadata)
+        if callable_runner:
+          retvals = callable_runner(*callable_runner_args,
+                                    options=decorated_run_options,
+                                    run_metadata=run_metadata)
+        else:
+          retvals = self._sess.run(fetches,
+                                   feed_dict=feed_dict,
+                                   options=decorated_run_options,
+                                   run_metadata=run_metadata)
       except errors.OpError as op_error:
         tf_error = op_error
         retvals = op_error
@@ -461,17 +489,26 @@ class BaseDebugWrapperSession(session.SessionInterface):
       decorated_run_options = options or config_pb2.RunOptions()
       run_metadata = run_metadata or config_pb2.RunMetadata()
       self._decorate_run_options_for_profile(decorated_run_options)
-      retvals = self._sess.run(fetches,
-                               feed_dict=feed_dict,
-                               options=decorated_run_options,
-                               run_metadata=run_metadata)
+      if callable_runner:
+        retvals = callable_runner(*callable_runner_args,
+                                  options=decorated_run_options,
+                                  run_metadata=run_metadata)
+      else:
+        retvals = self._sess.run(fetches,
+                                 feed_dict=feed_dict,
+                                 options=decorated_run_options,
+                                 run_metadata=run_metadata)
       run_end_req = OnRunEndRequest(
           run_start_resp.action,
           run_metadata=run_metadata,
           client_graph_def=self._sess.graph.as_graph_def())
-
     elif (run_start_resp.action == OnRunStartAction.NON_DEBUG_RUN or
           run_start_resp.action == OnRunStartAction.INVOKE_STEPPER):
+      if callable_runner:
+        raise NotImplementedError(
+            "Stepper mode is not implemented for callables created by "
+            "Session.make_callable().")
+
       if run_start_resp.action == OnRunStartAction.INVOKE_STEPPER:
         with stepper.NodeStepper(
             self._sess, fetches, feed_dict) as node_stepper:
@@ -512,6 +549,28 @@ class BaseDebugWrapperSession(session.SessionInterface):
     raise NotImplementedError(
         "partial_run is not implemented for debug-wrapper sessions.")
 
+  def list_devices(self, *args, **kwargs):
+    return self._sess.list_devices(*args, **kwargs)
+
+  def reset(self, *args, **kwargs):
+    return self._sess.reset(*args, **kwargs)
+
+  def make_callable(self,
+                    fetches,
+                    feed_list=None,
+                    accept_options=False):
+    runner = self._sess.make_callable(
+        fetches, feed_list=feed_list, accept_options=True)
+    def wrapped_runner(*runner_args, **kwargs):
+      return self.run(None,
+                      feed_dict=None,
+                      options=kwargs.get("options", None),
+                      run_metadata=kwargs.get("run_metadata", None),
+                      callable_runner=runner,
+                      callable_runner_args=runner_args)
+
+    return wrapped_runner
+
   def _decorate_run_options_for_debug(
       self,
       run_options,
@@ -618,6 +677,9 @@ class BaseDebugWrapperSession(session.SessionInterface):
   def __exit__(self, exec_type, exec_value, exec_tb):
     self._sess.__exit__(exec_type, exec_value, exec_tb)
 
+  def __del__(self):
+    self._sess.__del__()
+
   def close(self):
     self._sess.close()
 
diff --git a/tensorflow/python/debug/wrappers/framework_test.py b/tensorflow/python/debug/wrappers/framework_test.py
index e5efbb12f3..3991708b57 100644
--- a/tensorflow/python/debug/wrappers/framework_test.py
+++ b/tensorflow/python/debug/wrappers/framework_test.py
@@ -40,6 +40,7 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops  # pylint: disable=unused-import
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import googletest
+from tensorflow.python.util import tf_inspect
 
 
 class TestDebugWrapperSession(framework.BaseDebugWrapperSession):
@@ -395,5 +396,28 @@ class DebugWrapperSessionTest(test_util.TensorFlowTestCase):
         [datum.node_name for datum in dump.dumped_tensor_data])
 
 
+def _is_public_method_name(method_name):
+  return (method_name.startswith("__") and method_name.endswith("__")
+          or not method_name.startswith("_"))
+
+
+class SessionWrapperPublicMethodParityTest(test_util.TensorFlowTestCase):
+
+  def testWrapperHasAllPublicMethodsOfSession(self):
+    session_public_methods = [
+        method_tuple[0] for method_tuple in
+        tf_inspect.getmembers(session.Session, predicate=tf_inspect.ismethod)
+        if _is_public_method_name(method_tuple[0])]
+    wrapper_public_methods = [
+        method_tuple[0] for method_tuple in
+        tf_inspect.getmembers(
+            framework.BaseDebugWrapperSession, predicate=tf_inspect.ismethod)
+        if _is_public_method_name(method_tuple[0])]
+    missing_public_methods = [
+        method for method in session_public_methods
+        if method not in wrapper_public_methods]
+    self.assertFalse(missing_public_methods)
+
+
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper.py b/tensorflow/python/debug/wrappers/local_cli_wrapper.py
index fe822df6ce..9c409168a6 100644
--- a/tensorflow/python/debug/wrappers/local_cli_wrapper.py
+++ b/tensorflow/python/debug/wrappers/local_cli_wrapper.py
@@ -218,8 +218,9 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
       An instance of `OnRunStartResponse`.
     """
     self._is_run_start = True
-    self._update_run_calls_state(request.run_call_count, request.fetches,
-                                 request.feed_dict)
+    self._update_run_calls_state(
+        request.run_call_count, request.fetches, request.feed_dict,
+        is_callable_runner=request.is_callable_runner)
 
     if self._active_tensor_filter:
       # If we are running till a filter passes, we just need to keep running
@@ -532,7 +533,11 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
 
     return ["file://" + self._dump_root]
 
-  def _update_run_calls_state(self, run_call_count, fetches, feed_dict):
+  def _update_run_calls_state(self,
+                              run_call_count,
+                              fetches,
+                              feed_dict,
+                              is_callable_runner=False):
     """Update the internal state with regard to run() call history.
 
     Args:
@@ -542,18 +547,24 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
         call.
       feed_dict: None of a dict. This is the feed_dict argument to the run()
         call.
+      is_callable_runner: (bool) whether a runner returned by
+        Session.make_callable is being run.
     """
 
     self._run_call_count = run_call_count
-    self._run_description = cli_shared.get_run_short_description(run_call_count,
-                                                                 fetches,
-                                                                 feed_dict)
+    self._run_description = cli_shared.get_run_short_description(
+        run_call_count,
+        fetches,
+        feed_dict,
+        is_callable_runner=is_callable_runner)
     self._run_through_times -= 1
 
-    self._run_info = cli_shared.get_run_start_intro(run_call_count,
-                                                    fetches,
-                                                    feed_dict,
-                                                    self._tensor_filters)
+    self._run_info = cli_shared.get_run_start_intro(
+        run_call_count,
+        fetches,
+        feed_dict,
+        self._tensor_filters,
+        is_callable_runner=is_callable_runner)
 
   def invoke_node_stepper(self,
                           node_stepper,
diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py
index f8e32eca25..3a2d0fc422 100644
--- a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py
+++ b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py
@@ -21,6 +21,7 @@ import os
 import shutil
 import tempfile
 
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.debug.cli import cli_shared
 from tensorflow.python.debug.cli import debugger_cli_common
@@ -316,6 +317,53 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual(2, len(wrapped_sess.observers["debug_dumps"]))
     self.assertEqual([None, None], wrapped_sess.observers["tf_errors"])
 
+  def testDebuggingMakeCallableTensorRunnerWorks(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [[], []], self.sess, dump_root=self._tmp_dir)
+    v = variables.Variable(42)
+    tensor_runner = wrapped_sess.make_callable(v)
+    self.sess.run(v.initializer)
+
+    self.assertAllClose(42, tensor_runner())
+    self.assertEqual(1, len(wrapped_sess.observers["debug_dumps"]))
+
+  def testDebuggingMakeCallableTensorRunnerWithCustomRunOptionsWorks(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [[], []], self.sess, dump_root=self._tmp_dir)
+    a = constant_op.constant(42)
+    tensor_runner = wrapped_sess.make_callable(a)
+
+    run_options = config_pb2.RunOptions(
+        trace_level=config_pb2.RunOptions.FULL_TRACE)
+    run_metadata = config_pb2.RunMetadata()
+    self.assertAllClose(
+        42, tensor_runner(options=run_options, run_metadata=run_metadata))
+    self.assertEqual(1, len(wrapped_sess.observers["debug_dumps"]))
+    self.assertGreater(len(run_metadata.step_stats.dev_stats), 0)
+
+  def testDebuggingMakeCallableOperationRunnerWorks(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [[], []], self.sess, dump_root=self._tmp_dir)
+    v = variables.Variable(10.0)
+    inc_v = state_ops.assign_add(v, 1.0)
+    op_runner = wrapped_sess.make_callable(inc_v.op)
+    self.sess.run(v.initializer)
+
+    op_runner()
+    self.assertEqual(1, len(wrapped_sess.observers["debug_dumps"]))
+    self.assertEqual(11.0, self.sess.run(v))
+
+  def testDebuggingMakeCallableRunnerWithFeedListWorks(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [[], []], self.sess, dump_root=self._tmp_dir)
+    ph1 = array_ops.placeholder(dtypes.float32)
+    ph2 = array_ops.placeholder(dtypes.float32)
+    a = math_ops.add(ph1, ph2)
+    tensor_runner = wrapped_sess.make_callable(a, feed_list=[ph1, ph2])
+
+    self.assertAllClose(42.0, tensor_runner(41.0, 1.0))
+    self.assertEqual(1, len(wrapped_sess.observers["debug_dumps"]))
+
   def testRuntimeErrorShouldBeCaught(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
         [[], []], self.sess, dump_root=self._tmp_dir)
diff --git a/tensorflow/tools/api/golden/tensorflow.-interactive-session.pbtxt b/tensorflow/tools/api/golden/tensorflow.-interactive-session.pbtxt
index f5b0bae58d..0a3b81bf82 100644
--- a/tensorflow/tools/api/golden/tensorflow.-interactive-session.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-interactive-session.pbtxt
@@ -34,7 +34,7 @@ tf_class {
   }
   member_method {
     name: "make_callable"
-    argspec: "args=[\'self\', \'fetches\', \'feed_list\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'fetches\', \'feed_list\', \'accept_options\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], "
   }
   member_method {
     name: "partial_run"
diff --git a/tensorflow/tools/api/golden/tensorflow.-session.pbtxt b/tensorflow/tools/api/golden/tensorflow.-session.pbtxt
index 173cd1963e..1d6b037f9c 100644
--- a/tensorflow/tools/api/golden/tensorflow.-session.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-session.pbtxt
@@ -34,7 +34,7 @@ tf_class {
   }
   member_method {
     name: "make_callable"
-    argspec: "args=[\'self\', \'fetches\', \'feed_list\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'fetches\', \'feed_list\', \'accept_options\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], "
   }
   member_method {
     name: "partial_run"
-- 
GitLab


From f0e8a7259a30f3086ee0b2506d2be0392e5e0209 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Wed, 5 Jul 2017 19:39:58 -0700
Subject: [PATCH 0715/1375] Disable tests flaky in open-source builds due to
 port conflicts

PiperOrigin-RevId: 161035950
---
 tensorflow/core/debug/BUILD                            | 5 ++++-
 tensorflow/core/distributed_runtime/rpc/BUILD          | 2 +-
 tensorflow/python/BUILD                                | 4 ++++
 tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh     | 2 +-
 tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh    | 2 +-
 tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh | 2 +-
 tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh    | 2 +-
 tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh     | 2 +-
 tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh    | 2 +-
 tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh   | 2 +-
 10 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/debug/BUILD b/tensorflow/core/debug/BUILD
index 30824a9072..dd26177d25 100644
--- a/tensorflow/core/debug/BUILD
+++ b/tensorflow/core/debug/BUILD
@@ -231,7 +231,10 @@ tf_cc_test(
     size = "medium",
     srcs = ["grpc_session_debug_test.cc"],
     linkstatic = tf_kernel_tests_linkstatic(),
-    tags = ["nomac"],  # b/38276817
+    tags = [
+        "no_oss",  # b/62956105: port conflicts.
+        "nomac",  # b/38276817
+    ],
     deps = [
         ":debug_grpc_testlib",
         ":debug_io_utils",
diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index c7349f0dd7..606dfaf2a2 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -447,7 +447,7 @@ tf_cuda_cc_test(
     size = "medium",
     srcs = ["grpc_session_test.cc"],
     linkstatic = tf_kernel_tests_linkstatic(),
-    tags = tf_cuda_tests_tags() + ["manual"],
+    tags = tf_cuda_tests_tags() + ["no_oss"],  # b/62956105: port conflicts.
     deps = [
         ":grpc_channel",
         ":grpc_server_lib",
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 55983eb3bb..74133a5dd5 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2983,6 +2983,9 @@ cuda_py_test(
         ":variables",
         "//third_party/py/numpy",
     ],
+    tags = [
+        "no_oss",  # b/62956105: port conflicts.
+    ],
 )
 
 tf_py_test(
@@ -2998,6 +3001,7 @@ tf_py_test(
         ":variables",
     ],
     tags = [
+        "no_oss",  # b/62956105: port conflicts.
         "notsan",  # data race due to b/62910646
     ],
 )
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
index 467e4ab7e5..118e85fee0 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
@@ -33,7 +33,7 @@ export PYTHON_BIN_PATH=`which python`
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --test_tag_filters=-gpu,-benchmark-test --test_lang_filters=cc -k \
+bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test --test_lang_filters=cc -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 --build_tests_only \
     --test_output=errors -- \
     //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
index e2bbc0e8c0..4a30c73417 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python2`
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --test_tag_filters=-gpu,-benchmark-test --test_lang_filters=py -k \
+bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test --test_lang_filters=py -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 --build_tests_only \
     --test_output=errors -- \
     //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
index a03cab0cca..d224878643 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --test_tag_filters=-gpu,-benchmark-test -k \
+bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 \
     --test_output=errors -- \
     //tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
index 32de5cea20..39c241fb53 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --test_tag_filters=-gpu,-benchmark-test --test_lang_filters=py -k \
+bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test --test_lang_filters=py -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 --build_tests_only \
     --test_output=errors -- \
     //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
index 6acc262138..4d7b4741d6 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
@@ -35,7 +35,7 @@ export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --config=cuda --test_tag_filters=-no_gpu,-benchmark-test -k \
+bazel test --config=cuda --test_tag_filters=-no_oss,-no_gpu,-benchmark-test -k \
     --test_lang_filters=cc --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 \
     --build_tests_only --test_output=errors --local_test_jobs=8 \
     --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute -- \
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
index e73fe046c9..c0bcec4a17 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
@@ -35,7 +35,7 @@ export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --config=cuda --test_tag_filters=-no_gpu,-benchmark-test -k \
+bazel test --config=cuda --test_tag_filters=-no_oss,-no_gpu,-benchmark-test -k \
     --test_lang_filters=py --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 \
     --build_tests_only --test_output=errors --local_test_jobs=8 \
     --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute -- \
diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
index 6574fd144a..0b8c73993f 100755
--- a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
+++ b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
@@ -32,7 +32,7 @@ export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=$(which python2)
 yes "" | ./configure
 which bazel
-bazel test --test_tag_filters=-gpu,-benchmark-test,-nomac \
+bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \
     --test_timeout 300,450,1200,3600 \
     --test_size_filters=small,medium \
     --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \
-- 
GitLab


From 66604b0355b32961f9a532792be2e008cc22221f Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 5 Jul 2017 21:31:39 -0700
Subject: [PATCH 0716/1375] Allow repeated specification of a node to be
 fetched in Session::Run implementations (DirectSession, GrpcSession). My
 understanding is that the spec of Session::Run (as defined by comments in
 tensorflow/core/public/session.h) does not disallow repeated output node
 names.

Added tests fail with uninitialized or empty Tensors without the
corresponding changes to the .cc files.

This fixes #11132.

PiperOrigin-RevId: 161041177
---
 .../core/common_runtime/direct_session.cc     | 29 ++++++++++++++++---
 .../common_runtime/direct_session_test.cc     | 28 ++++++++++++++++++
 .../distributed_runtime/rpc/grpc_session.cc   | 23 +++++++++++----
 .../rpc/grpc_session_test.cc                  | 27 +++++++++++++++++
 4 files changed, 98 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index db7c5109c8..f6eac2d698 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -619,12 +619,33 @@ Status DirectSession::Run(const RunOptions& run_options,
     } else if (!s.ok()) {
       return s;
     }
+    const bool unique_outputs =
+        output_names.size() == executors_and_keys->output_name_to_index.size();
+    // first_indices[i] = j implies that j is the smallest value for which
+    // output_names[i] == output_names[j].
+    std::vector<int> first_indices;
+    if (!unique_outputs) {
+      first_indices.resize(output_names.size());
+      for (int i = 0; i < output_names.size(); ++i) {
+        for (int j = 0; j <= i; ++j) {
+          if (output_names[i] == output_names[j]) {
+            first_indices[i] = j;
+            break;
+          }
+        }
+      }
+    }
     outputs->clear();
     outputs->reserve(sorted_outputs.size());
-    for (const string& output_name : output_names) {
-      outputs->emplace_back(
-          std::move(sorted_outputs[executors_and_keys
-                                       ->output_name_to_index[output_name]]));
+    for (int i = 0; i < output_names.size(); ++i) {
+      const string& output_name = output_names[i];
+      if (first_indices.empty() || first_indices[i] == i) {
+        outputs->emplace_back(
+            std::move(sorted_outputs[executors_and_keys
+                                         ->output_name_to_index[output_name]]));
+      } else {
+        outputs->push_back((*outputs)[first_indices[i]]);
+      }
     }
   }
 
diff --git a/tensorflow/core/common_runtime/direct_session_test.cc b/tensorflow/core/common_runtime/direct_session_test.cc
index 5d6b2eeeed..8010a58f6c 100644
--- a/tensorflow/core/common_runtime/direct_session_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_test.cc
@@ -406,6 +406,34 @@ TEST(DirectSessionTest, MultipleFeedTest) {
   EXPECT_TRUE(StringPiece(s.error_message()).contains("fed more than once"));
 }
 
+TEST(DirectSessionTest, FetchMultipleTimes) {
+  Graph g(OpRegistry::Global());
+  Tensor seven_tensor(DT_INT32, TensorShape());
+  seven_tensor.flat<int32>()(0) = 7;
+  Node* seven_node = test::graph::Constant(&g, seven_tensor);
+
+  GraphDef def;
+  test::graph::ToGraphDef(&g, &def);
+
+  auto session = CreateSession();
+  ASSERT_TRUE(session != nullptr);
+  TF_ASSERT_OK(session->Create(def));
+
+  const std::vector<std::pair<string, Tensor>> inputs;
+  std::vector<Tensor> outputs;
+
+  auto seven = seven_node->name();
+  Status s = session->Run(inputs, {seven, seven}, {}, &outputs);
+  TF_ASSERT_OK(s);
+
+  EXPECT_EQ(2, outputs.size());
+  for (int i = 0; i < outputs.size(); ++i) {
+    const Tensor& t = outputs[i];
+    ASSERT_TRUE(t.IsInitialized()) << i;
+    EXPECT_EQ(7, t.flat<int32>()(0)) << i;
+  }
+}
+
 REGISTER_OP("Darth")
     .Input("x: float")
     .Output("y: float")
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_session.cc b/tensorflow/core/distributed_runtime/rpc/grpc_session.cc
index c97fb30c94..9a08335c1c 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_session.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_session.cc
@@ -190,12 +190,14 @@ Status GrpcSession::RunHelper(
     req->add_feed(it.first, it.second);
   }
 
-  // Build an index from fetch tensor name to offset.
+  // Build an index from fetch tensor name to first index in
+  // output_tensor_names.
   std::unordered_map<string, int> output_name_to_offset;
-  for (const string& output_name : output_tensor_names) {
-    req->add_fetch(output_name);
-    output_name_to_offset.insert(
-        std::make_pair(output_name, output_name_to_offset.size()));
+  for (int i = 0; i < output_tensor_names.size(); ++i) {
+    const string& name = output_tensor_names[i];
+    if (output_name_to_offset.insert(std::make_pair(name, i)).second) {
+      req->add_fetch(name);
+    }
   }
   for (const string& target : target_node_names) {
     req->add_target(target);
@@ -221,6 +223,17 @@ Status GrpcSession::RunHelper(
     TF_RETURN_IF_ERROR(resp->TensorValue(i, &output));
     (*outputs)[fetch_it->second] = output;
   }
+  // In the unlikely event that output_tensor_names contains duplicates, fill in
+  // the duplicate values.
+  if (output_name_to_offset.size() != output_tensor_names.size()) {
+    for (int i = 0; i < output_tensor_names.size(); ++i) {
+      const string& name = output_tensor_names[i];
+      int offset = output_name_to_offset[name];
+      if (offset != i) {
+        (*outputs)[i] = (*outputs)[offset];
+      }
+    }
+  }
 
   if (run_metadata) {
     run_metadata->Swap(resp->mutable_metadata());
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc b/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc
index 405b2939eb..b673f200cc 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc
@@ -183,6 +183,33 @@ TEST(GrpcSessionTest, NonLocalWithFilters) {
   }
 }
 
+TEST(GrpcSessionTest, FetchMultipleTimes) {
+  GraphDef graph;
+  string node_names[3];
+  CreateGraphDef(&graph, node_names);
+
+  std::unique_ptr<test::TestCluster> cluster;
+  TF_CHECK_OK(test::TestCluster::MakeTestCluster(Devices(1, 0), 2, &cluster));
+
+  std::unique_ptr<Session> session(
+      NewRemote(Options(cluster->targets()[0], 1)));
+  ASSERT_TRUE(session != nullptr);
+
+  TF_CHECK_OK(session->Create(graph));
+  const std::vector<std::pair<string, Tensor>> inputs;
+  std::vector<Tensor> outputs;
+
+  const string node = node_names[2] + ":0";
+  TF_CHECK_OK(session->Run(inputs, {node, node}, {}, &outputs));
+  EXPECT_EQ(2, outputs.size());
+  for (int i = 0; i < outputs.size(); ++i) {
+    const Tensor& t = outputs[i];
+    ASSERT_TRUE(t.IsInitialized()) << i;
+    ASSERT_EQ(4.0, t.flat<float>()(0)) << i;
+  }
+  TF_CHECK_OK(session->Close());
+}
+
 // A = [3 2; -1 0]; x = rand(2, 1); We want to compute the largest
 // eigenvalue for A, which is 2.0. Iteratively, we do
 //   repeat x = y / y.norm(); y = A * x; end
-- 
GitLab


From f9d3e8e29e1e8bcb2cc254b5af2c523776a41713 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 01:02:51 -0700
Subject: [PATCH 0717/1375] Update llvm revision to r307064. Need this because
 of an Orc api change r307058. Add a rule to generate an empty VCSRevision.h
 file to accomodate for r300160.

PiperOrigin-RevId: 161051888
---
 tensorflow/workspace.bzl    |  8 ++++----
 third_party/llvm/llvm.BUILD | 12 +++++++++++-
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index a6c5117e22..a6b062ea22 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -484,11 +484,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   temp_workaround_http_archive(
       name = "llvm",
       urls = [
-          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/5d353d4750143a13e53abf721d521684ba6a5165.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/5d353d4750143a13e53abf721d521684ba6a5165.tar.gz",
+          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/9e886358ff35a13de549b4adf49b52f933b9ec37.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/9e886358ff35a13de549b4adf49b52f933b9ec37.tar.gz",
       ],
-      sha256 = "148e96f1cfb0b880e0831a287f44d48251eabc20e24589e6fa4db5916c0c8153",
-      strip_prefix = "llvm-5d353d4750143a13e53abf721d521684ba6a5165",
+      sha256 = "5a56369e906e5af2d4baf5a92317a3db085800e848def7114aba176c80432ea0",
+      strip_prefix = "llvm-9e886358ff35a13de549b4adf49b52f933b9ec37",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
       repository = tf_repo_name,
   )
diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD
index 678626b374..3b13b297f8 100644
--- a/third_party/llvm/llvm.BUILD
+++ b/third_party/llvm/llvm.BUILD
@@ -257,6 +257,16 @@ cc_library(
     includes = ["include"],
 )
 
+# A creator of an empty file include/llvm/Support/VCSRevision.h.
+# This is usually populated by the upstream build infrastructure, but in this
+# case we leave it blank. See upstream revision r300160.
+genrule(
+    name = "vcs_revision_gen",
+    srcs = [],
+    outs = ["include/llvm/Support/VCSRevision.h"],
+    cmd = "echo '' > \"$@\"",
+)
+
 # Rules that apply the LLVM tblgen tool.
 gentbl(
     name = "intrinsics_gen",
@@ -1172,7 +1182,7 @@ cc_library(
         "include/llvm/IR/*.def",
         "include/llvm/IR/*.inc",
         "include/llvm/*.h",
-    ]),
+    ]) + ["include/llvm/Support/VCSRevision.h"],
     deps = [
         ":attributes_compat_gen",
         ":attributes_gen",
-- 
GitLab


From 8dd5441b18b63e12d7b049e1e5eabb946c9c4d0a Mon Sep 17 00:00:00 2001
From: yorkie <yorkiefixer@gmail.com>
Date: Thu, 6 Jul 2017 17:33:15 +0800
Subject: [PATCH 0718/1375] c: fix a possible segmentfault

---
 tensorflow/c/c_api_test.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 35d6e295c2..736f56837c 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -912,11 +912,13 @@ class CSession {
     for (TF_Operation* o : outputs) {
       outputs_.emplace_back(TF_Output{o, 0});
     }
+    output_values_.resize(outputs_.size());
   }
 
   void SetOutputs(const std::vector<TF_Output>& outputs) {
     ResetOutputValues();
     outputs_ = outputs;
+    output_values_.resize(outputs_.size());
   }
 
   void SetTargets(std::initializer_list<TF_Operation*> targets) {
-- 
GitLab


From 3ef0e4eba6013d96f761bece3c2f724ad701c2d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 03:31:42 -0700
Subject: [PATCH 0719/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 161061704
---
 tensorflow/contrib/batching/BUILD          |  9 +++++----
 tensorflow/contrib/batching/util/BUILD     |  1 +
 tensorflow/contrib/boosted_trees/lib/BUILD |  4 ----
 tensorflow/contrib/deprecated/BUILD        | 10 +++++-----
 tensorflow/contrib/opt/BUILD               | 12 +++++++-----
 5 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/tensorflow/contrib/batching/BUILD b/tensorflow/contrib/batching/BUILD
index acace9b3ba..3cf8c318eb 100644
--- a/tensorflow/contrib/batching/BUILD
+++ b/tensorflow/contrib/batching/BUILD
@@ -32,6 +32,7 @@ cc_test(
     deps = [
         ":batch_scheduler",
         "//tensorflow/core:lib",
+        "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
 )
@@ -52,6 +53,7 @@ cc_library(
     deps = [
         ":batch_scheduler",
         ":shared_batch_scheduler_hdrs",
+        "//tensorflow/contrib/batching/util:periodic_function_dynamic",
         "//tensorflow/core:lib",
     ],
     alwayslink = 1,
@@ -187,12 +189,11 @@ py_test(
     ],
     deps = [
         ":batch_py",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:gradients",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:script_ops",
-        "//tensorflow/python:state_ops",
-        "//tensorflow/python:variables",
     ],
 )
 
diff --git a/tensorflow/contrib/batching/util/BUILD b/tensorflow/contrib/batching/util/BUILD
index 55dd898aa2..21dd26722a 100644
--- a/tensorflow/contrib/batching/util/BUILD
+++ b/tensorflow/contrib/batching/util/BUILD
@@ -46,6 +46,7 @@ cc_test(
     deps = [
         ":periodic_function",
         "//tensorflow/contrib/batching/test_util:fake_clock_env",
+        "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
 )
diff --git a/tensorflow/contrib/boosted_trees/lib/BUILD b/tensorflow/contrib/boosted_trees/lib/BUILD
index 9e90ff399e..6de0b4515a 100644
--- a/tensorflow/contrib/boosted_trees/lib/BUILD
+++ b/tensorflow/contrib/boosted_trees/lib/BUILD
@@ -49,7 +49,6 @@ cc_library(
     deps = [
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
         "//tensorflow/core:framework_headers_lib",
-        "//tensorflow/core:protos_all_cc",
         "//third_party/eigen3",
     ],
 )
@@ -60,7 +59,6 @@ cc_test(
     srcs = ["utils/sparse_column_iterable_test.cc"],
     deps = [
         ":utils",
-        "//tensorflow/core:lib",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -73,7 +71,6 @@ cc_test(
     srcs = ["utils/examples_iterable_test.cc"],
     deps = [
         ":utils",
-        "//tensorflow/core:lib",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -99,7 +96,6 @@ cc_test(
     srcs = ["utils/dropout_utils_test.cc"],
     deps = [
         ":utils",
-        "//tensorflow/contrib/boosted_trees/proto:learner_proto_cc",
         "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_cc",
         "//tensorflow/core:lib",
         "//tensorflow/core:tensor_testutil",
diff --git a/tensorflow/contrib/deprecated/BUILD b/tensorflow/contrib/deprecated/BUILD
index adcef730a8..f48c6341e3 100644
--- a/tensorflow/contrib/deprecated/BUILD
+++ b/tensorflow/contrib/deprecated/BUILD
@@ -11,11 +11,12 @@ load("//tensorflow:tensorflow.bzl", "py_test")
 
 py_library(
     name = "deprecated_py",
-    srcs = [
-        "__init__.py",
-    ],
+    srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow/python:logging_ops"],
+    deps = [
+        "//tensorflow/python:logging_ops",
+        "//tensorflow/python:util",
+    ],
 )
 
 py_test(
@@ -24,7 +25,6 @@ py_test(
     srcs = ["summaries_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":deprecated_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 36a738a47a..f0f2ae5711 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -29,11 +29,16 @@ py_library(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:gradients",
+        "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:state_ops",
+        "//tensorflow/python:summary",
         "//tensorflow/python:training",
+        "//tensorflow/python:util",
+        "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//third_party/py/numpy",
         "@six_archive//:six",
     ],
 )
@@ -45,15 +50,11 @@ py_test(
     tags = ["manual"],
     deps = [
         ":opt_py",
-        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:extra_py_tests_deps",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:random_ops",
         "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
     ],
 )
 
@@ -89,7 +90,6 @@ py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
-        "//third_party/py/numpy",
         "@six_archive//:six",
     ],
 )
@@ -139,11 +139,13 @@ py_test(
     deps = [
         ":opt_py",
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
-- 
GitLab


From e294dcb258bf795cc142db1ee0fe348344cf1f59 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 03:45:43 -0700
Subject: [PATCH 0720/1375] Remove unused BUILD dependencies

PiperOrigin-RevId: 161062659
---
 tensorflow/compiler/xla/service/BUILD | 55 +++++++--------------------
 tensorflow/core/BUILD                 |  1 -
 tensorflow/python/BUILD               |  1 -
 3 files changed, 13 insertions(+), 44 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index f6ed998ed3..9cf8b83d5b 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -24,9 +24,7 @@ xla_proto_library(
 xla_proto_library(
     name = "hlo_proto",
     srcs = ["hlo.proto"],
-    deps = [
-        "//tensorflow/compiler/xla:xla_data_proto",
-    ],
+    deps = ["//tensorflow/compiler/xla:xla_data_proto"],
 )
 
 # Filegroup used to collect source files for dependency checking.
@@ -166,15 +164,9 @@ cc_library(
 
 cc_library(
     name = "hlo_reachability",
-    srcs = [
-        "hlo_reachability.cc",
-    ],
-    hdrs = [
-        "hlo_reachability.h",
-    ],
+    srcs = ["hlo_reachability.cc"],
+    hdrs = ["hlo_reachability.h"],
     deps = [
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
@@ -550,7 +542,6 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/core:lib",
-        "//tensorflow/core:regexp_internal",
         "//tensorflow/core:stream_executor_no_cuda",
         "//tensorflow/stream_executor",
     ],
@@ -1136,12 +1127,8 @@ cc_test(
 
 cc_library(
     name = "hlo_cost_analysis",
-    srcs = [
-        "hlo_cost_analysis.cc",
-    ],
-    hdrs = [
-        "hlo_cost_analysis.h",
-    ],
+    srcs = ["hlo_cost_analysis.cc"],
+    hdrs = ["hlo_cost_analysis.h"],
     deps = [
         ":hlo",
         "//tensorflow/compiler/xla:shape_util",
@@ -1243,12 +1230,8 @@ cc_test(
 
 cc_library(
     name = "logical_buffer",
-    srcs = [
-        "logical_buffer.cc",
-    ],
-    hdrs = [
-        "logical_buffer.h",
-    ],
+    srcs = ["logical_buffer.cc"],
+    hdrs = ["logical_buffer.h"],
     deps = [
         ":hlo",
         ":hlo_proto",
@@ -1278,12 +1261,8 @@ cc_library(
 
 cc_library(
     name = "hlo_dataflow_analysis",
-    srcs = [
-        "hlo_dataflow_analysis.cc",
-    ],
-    hdrs = [
-        "hlo_dataflow_analysis.h",
-    ],
+    srcs = ["hlo_dataflow_analysis.cc"],
+    hdrs = ["hlo_dataflow_analysis.h"],
     deps = [
         ":call_graph",
         ":hlo",
@@ -1379,12 +1358,8 @@ cc_test(
 
 cc_library(
     name = "tuple_points_to_analysis",
-    srcs = [
-        "tuple_points_to_analysis.cc",
-    ],
-    hdrs = [
-        "tuple_points_to_analysis.h",
-    ],
+    srcs = ["tuple_points_to_analysis.cc"],
+    hdrs = ["tuple_points_to_analysis.h"],
     deps = [
         ":hlo",
         ":logical_buffer",
@@ -1421,12 +1396,8 @@ cc_test(
 
 cc_library(
     name = "compilation_cache",
-    srcs = [
-        "compilation_cache.cc",
-    ],
-    hdrs = [
-        "compilation_cache.h",
-    ],
+    srcs = ["compilation_cache.cc"],
+    hdrs = ["compilation_cache.h"],
     deps = [
         ":executable",
         ":hlo_module_config",
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 74d21a3934..a969a39edc 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -825,7 +825,6 @@ cc_library(
         ":test",
         "//tensorflow/core/kernels:constant_op",
         "//tensorflow/core/kernels:ops_util",
-        "//tensorflow/core/platform/default/build_config:gtest",
     ],
 )
 
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 74133a5dd5..40667dc92d 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -184,7 +184,6 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
-        "//tensorflow/core/grappler/clusters:cluster",
         "//tensorflow/core/grappler/costs:graph_properties",
         "//tensorflow/core/grappler/costs:utils",
     ],
-- 
GitLab


From bc45f6930e49432aa29ca2ba53dc23e7d338b57d Mon Sep 17 00:00:00 2001
From: John Lawson <john@codeplay.com>
Date: Thu, 6 Jul 2017 11:58:46 +0100
Subject: [PATCH 0721/1375] [OpenCL] Fixes SYCL registration for MapStageOp
 (#117)

* [OpenCL] Fixes SYCL registration for MapStageOp

As the MapStageOp is actually run on the host, we need to ensure that
both the 'key' and 'indices' tensors are in host memory. This now
mirrors what CUDA is doing.

* [OpenCL] Fixes device name comparison map_stage_op_test

Changes the expected device name string, as the device name could be
'/device:SYCL:0' or '/device:GPU:0'.

The CUDA device name reported by test_util is in the form '/gpu:0',
while the device name used in the nodes looks like '/device:GPU:0'. When
running on CUDA we need to change the expected device name accordingly,
however this isn't a problem on SYCL.
---
 tensorflow/core/kernels/map_stage_op.cc           | 15 ++++++++++-----
 .../python/kernel_tests/map_stage_op_test.py      |  4 ++--
 2 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/kernels/map_stage_op.cc b/tensorflow/core/kernels/map_stage_op.cc
index 46eaf3d9e7..0168b57d35 100644
--- a/tensorflow/core/kernels/map_stage_op.cc
+++ b/tensorflow/core/kernels/map_stage_op.cc
@@ -550,12 +550,17 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapStage")
 #endif // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER_KERNEL_BUILDER(Name("MapStage").HostMemory("key").Device(DEVICE_SYCL),
+REGISTER_KERNEL_BUILDER(Name("MapStage")
+                            .HostMemory("key")
+                            .HostMemory("indices")
+                            .Device(DEVICE_SYCL),
                         MapStageOp<false>);
-REGISTER_KERNEL_BUILDER(
-    Name("OrderedMapStage").HostMemory("key").Device(DEVICE_SYCL),
-    MapStageOp<true>);
-#endif // TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(Name("OrderedMapStage")
+                            .HostMemory("key")
+                            .HostMemory("indices")
+                            .Device(DEVICE_SYCL),
+                        MapStageOp<true>);
+#endif  // TENSORFLOW_USE_SYCL
 
 template <bool Ordered>
 class MapUnstageOp : public OpKernel {
diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py
index 4ceb24862f..64f8f4bb6d 100644
--- a/tensorflow/python/kernel_tests/map_stage_op_test.py
+++ b/tensorflow/python/kernel_tests/map_stage_op_test.py
@@ -108,8 +108,8 @@ class MapStageTest(test.TestCase):
       with ops.device(gpu_dev):
         stager = data_flow_ops.MapStagingArea([dtypes.float32])
         y = stager.put(1, [v], [0])
-        self.assertEqual(y.device, '/device:GPU:0' if gpu_dev
-                                                   else gpu_dev)
+        expected_name = gpu_dev if 'gpu' not in gpu_dev else '/device:GPU:0'
+        self.assertEqual(y.device, expected_name)
       with ops.device('/cpu:0'):
         _, x = stager.get(1)
         y = stager.peek(1)
-- 
GitLab


From 875d85a113db182777c8b51d8a0589b3d6b52e00 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 04:03:27 -0700
Subject: [PATCH 0722/1375] Fix formatting of codeblocks in supervisor
 documentation

PiperOrigin-RevId: 161063715
---
 tensorflow/python/training/supervisor.py | 48 ++++++++++++------------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/tensorflow/python/training/supervisor.py b/tensorflow/python/training/supervisor.py
index 230ed1db68..cfdd03dc15 100644
--- a/tensorflow/python/training/supervisor.py
+++ b/tensorflow/python/training/supervisor.py
@@ -147,14 +147,14 @@ class Supervisor(object):
   Example: Start a thread to print losses.  We want this thread to run
   every 60 seconds, so we launch it with `sv.loop()`.
 
-    ```python
-    ...
-    sv = Supervisor(logdir='/tmp/mydir')
-    with sv.managed_session(FLAGS.master) as sess:
-      sv.loop(60, print_loss, (sess, ))
-      while not sv.should_stop():
-        sess.run(my_train_op)
-    ```
+  ```python
+  ...
+  sv = Supervisor(logdir='/tmp/mydir')
+  with sv.managed_session(FLAGS.master) as sess:
+    sv.loop(60, print_loss, (sess, ))
+    while not sv.should_stop():
+      sess.run(my_train_op)
+  ```
 
   ##### Launching fewer services
 
@@ -166,22 +166,22 @@ class Supervisor(object):
 
   Example: Create summaries manually every 100 steps in the chief.
 
-    ```python
-    # Create a Supervisor with no automatic summaries.
-    sv = Supervisor(logdir='/tmp/mydir', is_chief=is_chief, summary_op=None)
-    # As summary_op was None, managed_session() does not start the
-    # summary thread.
-    with sv.managed_session(FLAGS.master) as sess:
-      for step in xrange(1000000):
-        if sv.should_stop():
-          break
-        if is_chief and step % 100 == 0:
-          # Create the summary every 100 chief steps.
-          sv.summary_computed(sess, sess.run(my_summary_op))
-        else:
-          # Train normally
-          sess.run(my_train_op)
-    ```
+  ```python
+  # Create a Supervisor with no automatic summaries.
+  sv = Supervisor(logdir='/tmp/mydir', is_chief=is_chief, summary_op=None)
+  # As summary_op was None, managed_session() does not start the
+  # summary thread.
+  with sv.managed_session(FLAGS.master) as sess:
+    for step in xrange(1000000):
+      if sv.should_stop():
+        break
+      if is_chief and step % 100 == 0:
+        # Create the summary every 100 chief steps.
+        sv.summary_computed(sess, sess.run(my_summary_op))
+      else:
+        # Train normally
+        sess.run(my_train_op)
+  ```
 
   ##### Custom model initialization
 
-- 
GitLab


From 4d1c87f1a28d3b43010f486aeea9b537764aa191 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Thu, 6 Jul 2017 04:53:14 -0700
Subject: [PATCH 0723/1375] Upgrade the python doc generator's
 `ReferenceResolver` to handle errors consistently.

Report all errors, and the name of the file they occurred in.

The errors are not serialized with the reference resolver.

PiperOrigin-RevId: 161066611
---
 .../api_guides/python/contrib.seq2seq.md      |  8 +-
 tensorflow/tools/docs/generate_lib.py         | 11 ++-
 tensorflow/tools/docs/parser.py               | 74 ++++++++++++++-----
 3 files changed, 67 insertions(+), 26 deletions(-)

diff --git a/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md b/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md
index 2522e50c26..b56a4884b4 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md
@@ -40,7 +40,7 @@ depth.
 
 ### Attention Wrappers
 
-The basic attention wrapper is @{tf.contrib.seq2seq.DynamicAttentionWrapper}.
+The basic attention wrapper is @{tf.contrib.seq2seq.AttentionWrapper}.
 This wrapper accepts an `RNNCell` instance, an instance of `AttentionMechanism`,
 and an attention depth parameter (`attention_size`); as well as several
 optional arguments that allow one to customize intermediate calculations.
@@ -54,7 +54,7 @@ score = attention_mechanism(cell_output)
 alignments = softmax(score)
 context = matmul(alignments, attention_mechanism.values)
 attention = tf.layers.Dense(attention_size)(concat([cell_output, context], 1))
-next_state = DynamicAttentionWrapperState(
+next_state = AttentionWrapperState(
   cell_state=next_cell_state,
   attention=attention)
 output = attention
@@ -68,14 +68,14 @@ be replaced with alternative options when calculating `alignments` from the
 `score`.  Finally, the outputs returned by the wrapper can be configured to
 be the value `cell_output` instead of `attention`.
 
-The benefit of using a `DynamicAttentionWrapper` is that it plays nicely with
+The benefit of using a `AttentionWrapper` is that it plays nicely with
 other wrappers and the dynamic decoder described below.  For example, one can
 write:
 
 ```python
 cell = tf.contrib.rnn.DeviceWrapper(LSTMCell(512), "/gpu:0")
 attention_mechanism = tf.contrib.seq2seq.LuongAttention(512, encoder_outputs)
-attn_cell = tf.contrib.seq2seq.DynamicAttentionWrapper(
+attn_cell = tf.contrib.seq2seq.AttentionWrapper(
   cell, attention_mechanism, attention_size=256)
 attn_cell = tf.contrib.rnn.DeviceWrapper(attn_cell, "/gpu:1")
 top_cell = tf.contrib.rnn.DeviceWrapper(LSTMCell(512), "/gpu:1")
diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index 3f6f4383ac..23c1ea9d60 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -91,6 +91,7 @@ def write_docs(output_dir, parser_config, yaml_toc):
 
   # Parse and write Markdown pages, resolving cross-links (@{symbol}).
   for full_name, py_object in six.iteritems(parser_config.index):
+    parser_config.reference_resolver.current_doc_full_name = full_name
 
     if full_name in parser_config.duplicate_of:
       continue
@@ -391,6 +392,9 @@ def _other_docs(src_dir, output_dir, reference_resolver):
         print('Skipping excluded file %s...' % base_name)
         continue
       full_in_path = os.path.join(dirpath, base_name)
+
+      reference_resolver.current_doc_full_name = full_in_path
+
       suffix = os.path.relpath(path=full_in_path, start=src_dir)
       full_out_path = os.path.join(output_dir, suffix)
       if not base_name.endswith('.md'):
@@ -508,7 +512,6 @@ class DocGenerator(object):
     write_docs(output_dir, parser_config, yaml_toc=self.yaml_toc)
     _other_docs(flags.src_dir, flags.output_dir, reference_resolver)
 
-    if parser.all_errors:
-      print('Errors during processing:\n  ' + '\n  '.join(parser.all_errors))
-      return 1
-    return 0
+    parser_config.reference_resolver.log_errors()
+
+    return parser_config.reference_resolver.num_errors()
diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py
index 7ae1d2abd9..1bf8a2979c 100644
--- a/tensorflow/tools/docs/parser.py
+++ b/tensorflow/tools/docs/parser.py
@@ -24,6 +24,7 @@ import functools
 import json
 import os
 import re
+import sys
 
 import codegen
 import six
@@ -35,13 +36,36 @@ from tensorflow.python.util import tf_inspect
 # A regular expression capturing a python indentifier.
 IDENTIFIER_RE = '[a-zA-Z_][a-zA-Z0-9_]*'
 
-# Log of all reported errors
-all_errors = []
 
+class _Errors(object):
+  """A collection of errors."""
 
-def log_error(s):
-  all_errors.append(s)
-  print('ERROR:', s)
+  def __init__(self):
+    self._errors = []
+
+  def log_all(self):
+    """Log all the collected errors to the standard error."""
+    template = 'ERROR:\n    output file name: %s\n    %s\n\n'
+
+    for full_name, message in self._errors:
+      print(template % (full_name, message), file=sys.stderr)
+
+  def append(self, full_name, message):
+    """Add an error to the collection.
+
+    Args:
+      full_name: The path to the file in which the error occured.
+      message: The message to display with the error.
+    """
+    self._errors.append((full_name, message))
+
+  def __len__(self):
+    return len(self._errors)
+
+  def __eq__(self, other):
+    if not isinstance(other, _Errors):
+      return False
+    return self._errors == other._errors  # pylint: disable=protected-access
 
 
 def documentation_path(full_name):
@@ -107,6 +131,18 @@ class ReferenceResolver(object):
     self._all_names = set(is_class.keys())
     self._py_module_names = py_module_names
 
+    self.current_doc_full_name = None
+    self._errors = _Errors()
+
+  def add_error(self, message):
+    self._errors.append(self.current_doc_full_name, message)
+
+  def log_errors(self):
+    self._errors.log_all()
+
+  def num_errors(self):
+    return len(self._errors)
+
   @classmethod
   def from_visitor(cls, visitor, doc_index, **kwargs):
     """A factory function for building a ReferenceResolver from a visitor.
@@ -153,7 +189,8 @@ class ReferenceResolver(object):
     for key, value in self.__dict__.items():
       # Drop these two fields. `_doc_index` is not serializable. `_all_names` is
       # generated by the constructor.
-      if key in ('_doc_index', '_all_names'):
+      if key in ('_doc_index', '_all_names',
+                 '_errors', 'current_doc_full_name'):
         continue
 
       # Strip off any leading underscores on field names as these are not
@@ -186,10 +223,10 @@ class ReferenceResolver(object):
     Returns:
       `string`, with "@{symbol}" references replaced by Markdown links.
     """
-    return re.sub(SYMBOL_REFERENCE_RE,
-                  lambda match: self._one_ref(match.group(1),  # pylint: disable=g-long-lambda
-                                              relative_path_to_root),
-                  string)
+    def one_ref(match):
+      return self._one_ref(match, relative_path_to_root)
+
+    return re.sub(SYMBOL_REFERENCE_RE, one_ref, string)
 
   def python_link(self, link_text, ref_full_name, relative_path_to_root,
                   code_ref=True):
@@ -250,9 +287,8 @@ class ReferenceResolver(object):
 
     # Check whether this link exists
     if master_name not in self._all_names:
-      # TODO(josh11b): Make error reporting more uniform.
-      print('ERROR: Cannot make link to %s (original: %s): Not in index.' %
-            (master_name, ref_full_name))
+      message = 'Cannot make link to "%s": Not in index.' % master_name
+      self.add_error(message)
       return 'BROKEN_LINK'
 
     # If this is a member of a class, link to the class page with an anchor.
@@ -270,8 +306,10 @@ class ReferenceResolver(object):
 
     return os.path.join(relative_path_to_root, ref_path)
 
-  def _one_ref(self, string, relative_path_to_root):
+  def _one_ref(self, match, relative_path_to_root):
     """Return a link for a single "@{symbol}" reference."""
+    string = match.group(1)
+
     # Look for link text after $.
     dollar = string.rfind('$')
     if dollar > 0:  # Ignore $ in first character
@@ -303,8 +341,8 @@ class ReferenceResolver(object):
                                 code_ref=not manual_link_text)
 
     # Error!
-    log_error('Did not understand "@{%s}"' % string)
-    return 'ERROR:%s' % string
+    self.add_error('Did not understand "%s"' % match.group(0))
+    return 'BROKEN_LINK'
 
   def _doc_link(self, string, link_text, manual_link_text,
                 relative_path_to_root):
@@ -330,7 +368,7 @@ class ReferenceResolver(object):
   def _doc_missing(self, string, unused_hash_tag, link_text,
                    unused_manual_link_text, unused_relative_path_to_root):
     """Generate an error for unrecognized @{$...} references."""
-    log_error('Handle doc reference "@{$%s}"' % string)
+    self.add_error('Unknown Document "%s"' % string)
     return link_text
 
   def _cc_link(self, string, link_text, unused_manual_link_text,
@@ -348,7 +386,7 @@ class ReferenceResolver(object):
     elif string == 'tensorflow::ops::Const':
       ret = 'namespace/tensorflow/ops.md#const'
     else:
-      log_error('Handle C++ reference "@{%s}"' % string)
+      self.add_error('C++ reference not understood: "%s"' % string)
       return 'TODO_C++:%s' % string
     # relative_path_to_root gets you to api_docs/python, we go from there
     # to api_docs/cc, and then add ret.
-- 
GitLab


From 14c5bd7e654ce50f8d1dfbbd87499a0b2cb52b64 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 06:01:14 -0700
Subject: [PATCH 0724/1375] Allow "::" in HloInstruction names again.

FullyQualifiedName() used "::" as a separator, so it was important that "::" should not be used in HloInstruction names. Now that FullyQualifiedName() no longer exist, we can remove this restriction.

PiperOrigin-RevId: 161070812
---
 tensorflow/compiler/xla/client/computation_builder.cc |  5 -----
 tensorflow/compiler/xla/client/computation_builder.h  |  2 +-
 tensorflow/compiler/xla/service/hlo_instruction.cc    |  3 ---
 tensorflow/compiler/xla/service/hlo_instruction.h     | 11 +++--------
 tensorflow/compiler/xla/xla_data.proto                |  1 -
 5 files changed, 4 insertions(+), 18 deletions(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 9b717e1fb1..dcc313707b 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -192,11 +192,6 @@ ComputationDataHandle ComputationBuilder::Parameter(int64 parameter_number,
   if (!first_error_.ok() || !PrepareComputation().ok()) {
     return ComputationDataHandle();
   }
-  if (name.find("::") != string::npos) {
-    NoteError(InvalidArgument("Parameter name (\"%s\") may not contain \"::\"",
-                              name.c_str()));
-    return ComputationDataHandle();
-  }
 
   ParameterRequest request;
   *request.mutable_shape() = shape;
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 7beecf9fe4..b411346459 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -87,7 +87,7 @@ class ComputationBuilder {
   }
 
   // Enqueues a "retrieve parameter value" instruction for a parameter that was
-  // passed to the computation. The parameter name may not contain "::".
+  // passed to the computation.
   ComputationDataHandle Parameter(int64 parameter_number, const Shape& shape,
                                   const string& name);
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index aa259456ab..5c79605a77 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -51,7 +51,6 @@ using ::tensorflow::strings::StrCat;
 
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateParameter(
     int64 parameter_number, const Shape& shape, const string& name) {
-  DCHECK(name.find("::") == string::npos);
   auto instruction =
       WrapUnique(new HloInstruction(HloOpcode::kParameter, shape));
   instruction->parameter_number_ = parameter_number;
@@ -905,7 +904,6 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
 HloInstruction::~HloInstruction() {}
 
 std::unique_ptr<HloInstruction> HloInstruction::Clone(const string& suffix) {
-  DCHECK(suffix.find("::") == string::npos);
   std::unique_ptr<HloInstruction> clone =
       CloneWithNewOperands(shape_, operands_);
   if (suffix.empty()) {
@@ -2426,7 +2424,6 @@ HloModule* HloInstruction::GetModule() const {
 
 void HloInstruction::UniquifyName(NameUniquer* name_uniquer) {
   name_ = name_uniquer->GetUniqueName(name_);
-  DCHECK(name_.find("::") == string::npos);
 }
 
 void HloInstruction::set_outer_dimension_partitions(
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 65e4e08a4c..44eacc450c 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -69,8 +69,7 @@ class HloInstruction {
   };
 
   ~HloInstruction();
-  // Creates a parameter-retrieving instruction. The parameter name may not
-  // contain "::" (the separator used in fully qualified names).
+  // Creates a parameter-retrieving instruction.
   static std::unique_ptr<HloInstruction> CreateParameter(int64 parameter_number,
                                                          const Shape& shape,
                                                          const string& name);
@@ -448,10 +447,8 @@ class HloInstruction {
     return parameter_name_;
   }
 
-  // Sets the parameter name. The name may not contain "::".
   void set_parameter_name(const string& str) {
     CHECK_EQ(HloOpcode::kParameter, opcode_);
-    DCHECK(str.find("::") == string::npos);
     parameter_name_ = str;
   }
 
@@ -735,8 +732,7 @@ class HloInstruction {
   // Clones the HLO instruction. The clone will have the same opcode, shape, and
   // operands. After creation the clone has no uses. "this" (the instruction
   // cloned from) is not changed. Suffix is the string to append to the name of
-  // the instruction to form the name of the cloned instruction; it may not
-  // contain "::".
+  // the instruction to form the name of the cloned instruction.
   std::unique_ptr<HloInstruction> Clone(const string& suffix = "clone");
 
   // Clones the HLO instruction as above but with new shape and operands.
@@ -805,8 +801,7 @@ class HloInstruction {
   const string& name() const { return name_; }
 
   // Use the given NameUniquer to select a unique name for the instruction based
-  // on the instruction's existing name. The NameUniquer may not introduce the
-  // substring "::" into the name.
+  // on the instruction's existing name.
   void UniquifyName(NameUniquer* name_uniquer);
 
   // Sets the debug metadata for this instruction.
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 724832a9f1..86c72b3449 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -552,7 +552,6 @@ message TransposeRequest {
 message ParameterRequest {
   Shape shape = 2;
   int64 parameter = 3;
-  // Parameter name. May not contain "::".
   string name = 4;
 }
 
-- 
GitLab


From 2cddb44cd2f24341f5d85791b4db6f265bca0883 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 6 Jul 2017 06:10:13 -0700
Subject: [PATCH 0725/1375] [TF:XLA] Add support for PRED -> number casts to
 ConvertElementType. Remove special case from the TF Cast XLA lowering.

PiperOrigin-RevId: 161071519
---
 tensorflow/compiler/tf2xla/kernels/cast_op.cc  | 11 -----------
 .../xla/service/elemental_ir_emitter.cc        |  5 +++--
 tensorflow/compiler/xla/tests/convert_test.cc  | 18 ++++++++++++++++++
 3 files changed, 21 insertions(+), 13 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/cast_op.cc b/tensorflow/compiler/tf2xla/kernels/cast_op.cc
index 124e33d793..2331520230 100644
--- a/tensorflow/compiler/tf2xla/kernels/cast_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/cast_op.cc
@@ -38,17 +38,6 @@ class CastOp : public XlaOpKernel {
 
     if (src_dtype_ == dst_dtype_) {
       output = input;
-    } else if (src_dtype_ == DT_BOOL) {
-      // XLA's ConvertElementType doesn't support casting to/from
-      // bools. So we need to handle those cases separately.
-      // Builds the equivalent of (input ? 1 : 0)
-      xla::ComputationBuilder l(builder->client(), "PredCast");
-      xla::ComputationDataHandle x =
-          l.Parameter(0, xla::ShapeUtil::MakeShape(src_type_, {}), "x");
-      l.Select(x, XlaHelpers::One(&l, dst_dtype_),
-               XlaHelpers::Zero(&l, dst_dtype_));
-      xla::Computation computation = l.Build().ConsumeValueOrDie();
-      output = builder->Map({input}, computation);
     } else if (dst_dtype_ == DT_BOOL) {
       output = builder->Ne(input, XlaHelpers::Zero(builder, src_dtype_));
     } else {
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index db0a8b36cd..0a45e52811 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -63,7 +63,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerUnaryOp(
     case HloOpcode::kConvert: {
       PrimitiveType from_type = op->operand(0)->shape().element_type();
       PrimitiveType to_type = op->shape().element_type();
-      CHECK(primitive_util::IsIntegralType(from_type));
+      CHECK(primitive_util::IsIntegralType(from_type) || from_type == PRED);
       if (from_type == to_type) {
         return operand_value;
       }
@@ -78,7 +78,8 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerUnaryOp(
               operand_value,
               llvm_ir::PrimitiveTypeToIrType(to_type, ir_builder_));
         }
-        if (primitive_util::IsUnsignedIntegralType(from_type)) {
+        if (primitive_util::IsUnsignedIntegralType(from_type) ||
+            from_type == PRED) {
           return ir_builder_->CreateUIToFP(
               operand_value,
               llvm_ir::PrimitiveTypeToIrType(to_type, ir_builder_));
diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc
index f9652ed8cf..2d181938de 100644
--- a/tensorflow/compiler/xla/tests/convert_test.cc
+++ b/tensorflow/compiler/xla/tests/convert_test.cc
@@ -69,6 +69,24 @@ TEST_F(ConvertTest, ConvertR1S32ToR1F32) {
   ComputeAndCompareR1<float>(&builder, expected, {}, ErrorSpec(0.0001));
 }
 
+TEST_F(ConvertTest, ConvertR1PREDToR1S32) {
+  ComputationBuilder builder(client_, TestName());
+  auto a = builder.ConstantR1<bool>({true, false, true});
+  builder.ConvertElementType(a, S32);
+
+  std::vector<int32> expected = {1, 0, 1};
+  ComputeAndCompareR1<int32>(&builder, expected, {});
+}
+
+TEST_F(ConvertTest, ConvertR1PREDToR1F32) {
+  ComputationBuilder builder(client_, TestName());
+  auto a = builder.ConstantR1<bool>({true, false, true});
+  builder.ConvertElementType(a, F32);
+
+  std::vector<float> expected = {1., 0., 1.};
+  ComputeAndCompareR1<float>(&builder, expected, {});
+}
+
 XLA_TEST_F(ConvertTest, ConvertR1S0S32ToR1S0F32) {
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR1<int32>({});
-- 
GitLab


From 3a5807486bb7c6ab553b653eceaf1a4cb9d684f3 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir <ispir@google.com>
Date: Thu, 6 Jul 2017 06:46:49 -0700
Subject: [PATCH 0726/1375] Made labels truly optional in Estimator.

PiperOrigin-RevId: 161073970
---
 tensorflow/python/estimator/estimator.py      | 24 ++++--
 tensorflow/python/estimator/estimator_test.py | 85 +++++++++++++------
 2 files changed, 77 insertions(+), 32 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index c2b97c20a3..bc11a6c84f 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -495,6 +495,15 @@ class Estimator(object):
       return result[0]
     return result
 
+  def _get_features_and_labels_from_input_fn(self, input_fn, mode):
+    result = self._call_input_fn(input_fn, mode)
+    if isinstance(result, (list, tuple)):
+      if len(result) != 2:
+        raise ValueError(
+            'input_fn should return (feautures, labels) as a len 2 tuple.')
+      return result
+    return result, None
+
   def _extract_batch_length(self, preds_evaluated):
     """Extracts batch length of predictions."""
     batch_length = None
@@ -591,14 +600,19 @@ class Estimator(object):
     """
     model_fn_args = _fn_args(self._model_fn)
     kwargs = {}
+    if 'labels' in model_fn_args:
+      kwargs['labels'] = labels
+    else:
+      if labels is not None:
+        raise ValueError(
+            'model_fn does not take labels, but input_fn returns labels.')
     if 'mode' in model_fn_args:
       kwargs['mode'] = mode
     if 'params' in model_fn_args:
       kwargs['params'] = self.params
     if 'config' in model_fn_args:
       kwargs['config'] = self.config
-    model_fn_results = self._model_fn(
-        features=features, labels=labels, **kwargs)
+    model_fn_results = self._model_fn(features=features, **kwargs)
 
     if not isinstance(model_fn_results, model_fn_lib.EstimatorSpec):
       raise ValueError('model_fn should return an EstimatorSpec.')
@@ -610,7 +624,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(
+      features, labels = self._get_features_and_labels_from_input_fn(
           input_fn, model_fn_lib.ModeKeys.TRAIN)
       estimator_spec = self._call_model_fn(features, labels,
                                            model_fn_lib.ModeKeys.TRAIN)
@@ -693,7 +707,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(
+      features, labels = self._get_features_and_labels_from_input_fn(
           input_fn, model_fn_lib.ModeKeys.EVAL)
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
@@ -808,8 +822,6 @@ def _verify_model_fn_args(model_fn, params):
   args = set(_fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
-  if 'labels' not in args:
-    raise ValueError('model_fn (%s) must include labels argument.' % model_fn)
   if params is not None and 'params' not in args:
     raise ValueError('model_fn (%s) does not include params argument, '
                      'but params (%s) is passed to Estimator.' % (model_fn,
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 77aff66ccf..d0666e4262 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -238,13 +238,12 @@ class EstimatorConstructorTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, 'features'):
       estimator.Estimator(model_fn=model_fn)
 
-  def test_model_fn_args_must_include_labels(self):
+  def test_model_fn_args_labels_is_optional(self):
 
-    def model_fn(features, y):
-      _, _ = features, y
+    def model_fn(features):
+      _ = features
 
-    with self.assertRaisesRegexp(ValueError, 'labels'):
-      estimator.Estimator(model_fn=model_fn)
+    estimator.Estimator(model_fn=model_fn)
 
   def test_if_params_provided_then_model_fn_should_accept_it(self):
 
@@ -309,16 +308,21 @@ def model_fn_global_step_incrementer(features, labels, mode):
       train_op=state_ops.assign_add(global_step, 1))
 
 
-def _estimator_spec(
-    expected_features, expected_labels, actual_features, actual_labels, mode):
-  assert_ops = tuple([
+def assert_features_op(expected_features, actual_features):
+  return [
       check_ops.assert_equal(
           expected_features[k], actual_features[k], name='assert_%s' % k)
       for k in expected_features
-  ] + [
-      check_ops.assert_equal(
-          expected_labels, actual_labels, name='assert_labels')
-  ])
+  ]
+
+
+def _estimator_spec(
+    expected_features, expected_labels, actual_features, actual_labels, mode):
+  assert_ops = tuple(
+      assert_features_op(expected_features, actual_features) + [
+          check_ops.assert_equal(
+              expected_labels, actual_labels, name='assert_labels')
+      ])
   global_step = training.get_global_step()
   with ops.control_dependencies(assert_ops):
     return model_fn_lib.EstimatorSpec(
@@ -413,28 +417,57 @@ class EstimatorTrainTest(test.TestCase):
     self.assertEqual(1, input_fn_call_count[0])
 
   def test_minimal_model_fn_args(self):
-    expected_features = {'x': 42., 'y': 43.}
-    expected_labels = 44.
+    expected_features = {'x': 4, 'y': 5}
 
-    # TODO(ptucker): We have to roll our own mock since Estimator._get_arguments
-    # doesn't work with mock fns.
-    model_fn_call_count = [0]
+    def _input_fn():
+      return expected_features
 
-    def _model_fn(features, labels):
+    model_fn_call_count = [0]
+    def _model_fn(features):
       model_fn_call_count[0] += 1
       self.assertItemsEqual(expected_features.keys(), features.keys())
-      return _estimator_spec(
-          expected_features, expected_labels, features, labels,
-          model_fn_lib.ModeKeys.TRAIN)
+      with ops.control_dependencies(
+          assert_features_op(expected_features, features)):
+        return model_fn_lib.EstimatorSpec(
+            mode=None,
+            predictions=constant_op.constant(0.),
+            loss=constant_op.constant(0.),
+            train_op=state_ops.assign_add(training.get_global_step(), 1))
 
-    with self.assertRaisesRegexp(ValueError, 'does not include params'):
-      estimator.Estimator(model_fn=_model_fn, params={'a': 'b'})
-    est = estimator.Estimator(model_fn=_model_fn, config=run_config.RunConfig())
+    est = estimator.Estimator(model_fn=_model_fn)
     self.assertEqual(0, model_fn_call_count[0])
-    est.train(
-        input_fn=_make_input_fn(expected_features, expected_labels), steps=1)
+    est.train(input_fn=_input_fn, steps=1)
     self.assertEqual(1, model_fn_call_count[0])
 
+  def test_labels_should_be_none_if_model_fn_does_not_use_labels(self):
+
+    def _input_fn_with_labels():
+      return {'x': 4, 'y': 5}, [4]
+
+    def _model_fn(features):
+      _ = features
+      return model_fn_lib.EstimatorSpec(
+          mode=None,
+          predictions=constant_op.constant(0.),
+          loss=constant_op.constant(0.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1))
+
+    est = estimator.Estimator(model_fn=_model_fn)
+    with self.assertRaisesRegexp(ValueError, 'model_fn does not take labels'):
+      est.train(input_fn=_input_fn_with_labels, steps=1)
+
+  def test_input_fn_len_should_be_2_if_tuple_or_list(self):
+
+    def _input_fn():
+      return 4, 5, 6
+
+    def _model_fn(features):
+      _ = features
+
+    est = estimator.Estimator(model_fn=_model_fn)
+    with self.assertRaisesRegexp(ValueError, 'len 2 tuple'):
+      est.train(input_fn=_input_fn, steps=1)
+
   def test_all_model_fn_args(self):
     expected_features = {'x': 42., 'y': 43.}
     expected_labels = 44.
-- 
GitLab


From 179689eab844874d90be1cdb019046322823ef45 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 6 Jul 2017 07:07:13 -0700
Subject: [PATCH 0727/1375] [TF:XLA] Move debug graph dump code in XlaCompiler
 class to start of CompileGraph function.

PiperOrigin-RevId: 161075627
---
 tensorflow/compiler/tf2xla/xla_compiler.cc | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index 4bbc4384c3..c1af731986 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -131,9 +131,11 @@ Status XlaCompiler::CompileFunction(
   std::unique_ptr<Graph> graph(new Graph(options_.flib_def));
   CopyGraph(*fbody->graph, graph.get());
 
-  if (VLOG_IS_ON(1)) {
-    dump_graph::DumpGraphToFile(
-        strings::StrCat("xla_compile_function_input_", function_id), *graph);
+  if (VLOG_IS_ON(2)) {
+    VLOG(2) << "XlaCompiler::CompileFunction: "
+            << dump_graph::DumpGraphToFile(
+                   strings::StrCat("xla_compile_function_", function_id),
+                   *graph);
   }
 
   // Optimize the graph before running the compiler.
@@ -145,12 +147,6 @@ Status XlaCompiler::CompileFunction(
   optimizer.Optimize(flib_runtime_.get(), flib_runtime_->env(),
                      /*device=*/nullptr, &graph);
 
-  if (VLOG_IS_ON(1)) {
-    dump_graph::DumpGraphToFile(
-        strings::StrCat("xla_compile_function_optimized_", function_id),
-        *graph);
-  }
-
   VLOG(1) << "====================================================";
   TF_RETURN_IF_ERROR(
       CompileGraph(options, function_id, std::move(graph), args, result));
@@ -413,6 +409,12 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
                                  CompilationResult* result) {
   VLOG(1) << "Executing graph symbolically to populate ComputationBuilder.";
 
+  if (VLOG_IS_ON(2)) {
+    VLOG(2) << "XlaCompiler::CompileGraph: "
+            << dump_graph::DumpGraphToFile(
+                   strings::StrCat("xla_compile_graph_", name), *graph);
+  }
+
   // Report the error here if initialization failed.
   TF_RETURN_IF_ERROR(initialization_status_);
 
-- 
GitLab


From f3a391e7adadfce686505a19bb8da262d86769cb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 07:25:28 -0700
Subject: [PATCH 0728/1375] Update documentation for tf.gather_nd and fix links
 to tf.scatter_nd.

PiperOrigin-RevId: 161077059
---
 tensorflow/core/ops/array_ops.cc | 25 +++++++++++++++++--------
 tensorflow/core/ops/state_ops.cc | 10 +++++-----
 2 files changed, 22 insertions(+), 13 deletions(-)

diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index f60134ac01..7dd3ad31a0 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1433,15 +1433,25 @@ REGISTER_OP("GatherNd")
       return Status::OK();
     })
     .Doc(R"doc(
-Gather values or slices from `params` according to `indices`.
+Gather slices from `params` into a Tensor with shape specified by `indices`.
 
-`indices` is an integer tensor containing indices into `params`.  The last
-dimension of `indices` can be at most the rank of `params`:
+`indices` is an K-dimensional integer tensor, best thought of as a
+(K-1)-dimensional tensor of indices into `params`, where each element defines a
+slice of `params`:
+
+    output[i_0, ..., i_{K-2}] = params[indices[i0, ..., i_{K-2}]]
+
+Whereas in @{tf.gather} `indices` defines slices into the first
+dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the
+first `N` dimensions of `params`, where `N = indices.shape[-1]`.
+
+The last dimension of `indices` can be at most the rank of
+`params`:
 
     indices.shape[-1] <= params.rank
 
 The last dimension of `indices` corresponds to elements
-(if `indices.shape[-1] = params.rank`) or slices
+(if `indices.shape[-1] == params.rank`) or slices
 (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`
 of `params`.  The output tensor has shape
 
@@ -4854,8 +4864,8 @@ Scatter `updates` into a new (initially zero) tensor according to `indices`.
 
 Creates a new tensor by applying sparse `updates` to individual
 values or slices within a zero tensor of the given `shape` according to
-indices.  This operator is the inverse of the [tf.gather_nd](#gather_nd)
-operator which extracts values or slices from a given tensor.
+indices.  This operator is the inverse of the @{tf.gather_nd} operator which
+extracts values or slices from a given tensor.
 
 **WARNING**: The order in which updates are applied is nondeterministic, so the
 output will be nondeterministic if `indices` contains duplicates.
@@ -4975,8 +4985,7 @@ The resulting value `output` would look like this:
 
     [1, 13, 3, 14, 14, 6, 7, 20]
 
-See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
-slices.
+See @{tf.scatter_nd} for more details about how to make updates to slices.
 
 input: A Tensor.
 indices: A Tensor. Must be one of the following types: `int32`, `int64`.
diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc
index aaab790d02..7cf5dfcca8 100644
--- a/tensorflow/core/ops/state_ops.cc
+++ b/tensorflow/core/ops/state_ops.cc
@@ -513,7 +513,7 @@ The resulting update to ref would look like this:
 
     [1, 11, 3, 10, 9, 6, 7, 12]
 
-See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
+See @{tf.scatter_nd} for more details about how to make updates to
 slices.
 
 ref: A mutable Tensor. Should be from a Variable node.
@@ -570,7 +570,7 @@ The resulting update to ref would look like this:
 
     [1, 13, 3, 14, 14, 6, 7, 20]
 
-See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
+See @{tf.scatter_nd} for more details about how to make updates to
 slices.
 
 ref: A mutable Tensor. Should be from a Variable node.
@@ -627,7 +627,7 @@ The resulting update to ref would look like this:
 
     [1, -9, 3, -6, -4, 6, 7, -4]
 
-See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
+See @{tf.scatter_nd} for more details about how to make updates to
 slices.
 
 ref: A mutable Tensor. Should be from a Variable node.
@@ -687,7 +687,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 
 //     [1, 22, 3, 40, 45, 6, 7, 96]
 
-// See [tf.scatter_nd](#scatter_nd) for more details about how to make updates
+// See @{tf.scatter_nd} for more details about how to make updates
 // to slices.
 
 // ref: A mutable Tensor. Should be from a Variable node.
@@ -743,7 +743,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 
 //     [10, 5, 30, 13, 25, 60, 70, 16]
 
-// See [tf.scatter_nd](#scatter_nd) for more details about how to make updates
+// See @{tf.scatter_nd} for more details about how to make updates
 // to slices.
 
 // ref: A mutable Tensor. Should be from a Variable node.
-- 
GitLab


From c7ddd7b1ddb944442d39b02930bd71ea27d0161a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 07:25:47 -0700
Subject: [PATCH 0729/1375] Fix regexp so that it matches the tensor name. Fix
 typo.

PiperOrigin-RevId: 161077098
---
 tensorflow/docs_src/programmers_guide/debugger.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 177ffaa646..2dbccb6363 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -138,7 +138,7 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | Command            | Syntax or Option | Explanation  | Example                   |
 |:-------------------|:---------------- |:------------ |:------------------------- |
 | **`lt`** | | **List dumped tensors.** | `lt` |
-| | `-n <name_pattern>` | List dumped tensors with names matching given regular-expression pattern. | `lt -n softmax.*` |
+| | `-n <name_pattern>` | List dumped tensors with names matching given regular-expression pattern. | `lt -n Softmax.*` |
 | | `-t <op_pattern>` | List dumped tensors with op types matching given regular-expression pattern. | `lt -t MatMul` |
 | | `s <sort_key>` | Sort the output by given `sort_key`, whose possible values are `timestamp` (default), `dump_size`, `op_type` and `tensor_name`. | `lt -s dump_size` |
 | | `-r` | Sort in reverse order. | `lt -r -s dump_size` |
@@ -164,7 +164,7 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | | `-c` | Include recipients via control edges. | `lo -c -r hidden/Relu:0` |
 | **`ls`** | | **List Python source files involved in node creation.** | |
 | | `-p <path_pattern>` | Limit output to source files matching given regular-expression path pattern. | `ls -p .*debug_mnist.*` |
-| | `-n` | Limit output to node names matching given regular-expression pattern. | `ls -n softmax.*` |
+| | `-n` | Limit output to node names matching given regular-expression pattern. | `ls -n Softmax.*` |
 | **`ps`** | | **Print Python source file.** | |
 | | `ps <file_path>` | Print given Python source file source.py, with the lines annotated with the nodes created at each of them (if any). | `ps /path/to/source.py` |
 | | `-t` | Perform annotation with respect to Tensors, instead of the default, nodes. | `ps -t /path/to/source.py` |
@@ -174,7 +174,7 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | | `-n` | Execute through the next `Session.run` without debugging, and drop to CLI right before the run after that. | `run -n` |
 | | `-t <T>` | Execute `Session.run` `T - 1` times without debugging, followed by a run with debugging. Then drop to CLI right after the debugged run. | `run -t 10` |
 | | `-f <filter_name>` | Continue executing `Session.run` until any intermediate tensor triggers the specified Tensor filter (causes the filter to return `True`). | `run -f has_inf_or_nan` |
-| | `--node_name_filter <pattern>` | Execute the next `Session.run`, watching only nodes with names matching the given regular-expression pattern. | `run --node_name_filter softmax.*` |
+| | `--node_name_filter <pattern>` | Execute the next `Session.run`, watching only nodes with names matching the given regular-expression pattern. | `run --node_name_filter Softmax.*` |
 | | `--op_type_filter <pattern>` | Execute the next `Session.run`, watching only nodes with op types matching the given regular-expression pattern. | `run --op_type_filter Variable.*` |
 | | `--tensor_dtype_filter <pattern>` | Execute the next `Session.run`, dumping only Tensors with data types (`dtype`s) matching the given regular-expression pattern. | `run --tensor_dtype_filter int.*` |
 | **`ri`** | | **Display information about the run the current run, including fetches and feeds.** | `ri` |
@@ -543,7 +543,7 @@ The `watch_fn` argument accepts a `Callable` that allows you to configure what
 
 ### C++ and other languages
 
-If you model code is written in C++ or other languages, you can also
+If your model code is written in C++ or other languages, you can also
 modify the `debug_options` field of `RunOptions` to generate debug dumps that
 can be inspected offline. See
 [the proto definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/debug.proto)
-- 
GitLab


From 01126f4d141a67b9caf63419fe2c57cdaf19c22a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 07:25:54 -0700
Subject: [PATCH 0730/1375] streaming_covariance and
 streaming_pearson_correlation are modified to return nan when they have seen
 less or equal to 1 unit of weight. This addresses an issue where a first
 singleton batch would sometimes result in plotting a 0.0 for cov/correlation
 where the correct value is nan until we have >1 sample.

Along the way, also taking advantage of our upgraded numpy to use fweights= with np.cov and get rid of the _reweight workaround we had in place.

PiperOrigin-RevId: 161077110
---
 .../contrib/metrics/python/ops/metric_ops.py  |  20 ++--
 .../metrics/python/ops/metric_ops_test.py     | 102 +++++++++++++-----
 2 files changed, 89 insertions(+), 33 deletions(-)

diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py
index 6c773d9a7f..feb2b36d5e 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py
@@ -1972,9 +1972,17 @@ def streaming_covariance(predictions,
                       (prev_count * batch_count / update_count))
     update_comoment = state_ops.assign_add(comoment, delta_comoment)
 
-    covariance = _safe_div(comoment, count - 1, 'covariance')
+    covariance = array_ops.where(
+        math_ops.less_equal(count, 1.),
+        float('nan'),
+        math_ops.truediv(comoment, count - 1),
+        name='covariance')
     with ops.control_dependencies([update_comoment]):
-      update_op = _safe_div(comoment, count - 1, 'update_op')
+      update_op = array_ops.where(
+          math_ops.less_equal(count, 1.),
+          float('nan'),
+          math_ops.truediv(comoment, count - 1),
+          name='update_op')
 
   if metrics_collections:
     ops.add_to_collections(metrics_collections, covariance)
@@ -2051,16 +2059,16 @@ def streaming_pearson_correlation(predictions,
     var_labels, update_var_labels = streaming_covariance(
         labels, labels, weights=weights, name='variance_labels')
 
-    pearson_r = _safe_div(
+    pearson_r = math_ops.truediv(
         cov,
         math_ops.multiply(math_ops.sqrt(var_predictions),
                           math_ops.sqrt(var_labels)),
-        'pearson_r')
-    update_op = _safe_div(
+        name='pearson_r')
+    update_op = math_ops.truediv(
         update_cov,
         math_ops.multiply(math_ops.sqrt(update_var_predictions),
                           math_ops.sqrt(update_var_labels)),
-        'update_op')
+        name='update_op')
 
   if metrics_collections:
     ops.add_to_collections(metrics_collections, pearson_r)
diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
index 6496cecfbd..00cde08bff 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py
@@ -4183,11 +4183,6 @@ class StreamingRootMeanSquaredErrorTest(test.TestCase):
       self.assertAlmostEqual(math.sqrt(13), rmse.eval(), 5)
 
 
-def _reweight(predictions, labels, weights):
-  return (np.concatenate([[p] * int(w) for p, w in zip(predictions, weights)]),
-          np.concatenate([[l] * int(w) for l, w in zip(labels, weights)]))
-
-
 class StreamingCovarianceTest(test.TestCase):
 
   def setUp(self):
@@ -4277,8 +4272,9 @@ class StreamingCovarianceTest(test.TestCase):
       cov, update_op = metrics.streaming_covariance(
           predictions, labels, weights=weights)
 
-      p, l = _reweight([2, 4, 6, 8], [1, 3, 2, 7], [0, 1, 3, 1])
-      expected_cov = np.cov(p, l)[0, 1]
+      expected_cov = np.cov([2, 4, 6, 8],
+                            [1, 3, 2, 7],
+                            fweights=[0, 1, 3, 1])[0, 1]
       sess.run(variables.local_variables_initializer())
       self.assertAlmostEqual(expected_cov, sess.run(update_op))
       self.assertAlmostEqual(expected_cov, cov.eval())
@@ -4297,14 +4293,17 @@ class StreamingCovarianceTest(test.TestCase):
       cov, update_op = metrics.streaming_covariance(predictions_t, labels_t)
 
       sess.run(variables.local_variables_initializer())
-      prev_expected_cov = 0.
+      prev_expected_cov = NAN
       for i in range(n // stride):
         feed_dict = {
             predictions_t: predictions[stride * i:stride * (i + 1)],
             labels_t: labels[stride * i:stride * (i + 1)]
         }
-        self.assertAlmostEqual(
-            prev_expected_cov, sess.run(cov, feed_dict=feed_dict), 5)
+        self.assertEqual(np.isnan(prev_expected_cov),
+                         np.isnan(sess.run(cov, feed_dict=feed_dict)))
+        if not np.isnan(prev_expected_cov):
+          self.assertAlmostEqual(
+              prev_expected_cov, sess.run(cov, feed_dict=feed_dict), 5)
         expected_cov = np.cov(predictions[:stride * (i + 1)],
                               labels[:stride * (i + 1)])[0, 1]
         self.assertAlmostEqual(
@@ -4331,18 +4330,21 @@ class StreamingCovarianceTest(test.TestCase):
           predictions_t, labels_t, weights=weights_t)
 
       sess.run(variables.local_variables_initializer())
-      prev_expected_cov = 0.
+      prev_expected_cov = NAN
       for i in range(n // stride):
         feed_dict = {
             predictions_t: predictions[stride * i:stride * (i + 1)],
             labels_t: labels[stride * i:stride * (i + 1)],
             weights_t: weights[stride * i:stride * (i + 1)]
         }
-        self.assertAlmostEqual(
-            prev_expected_cov, sess.run(cov, feed_dict=feed_dict), 5)
-        p, l = _reweight(predictions[:stride * (i + 1)],
-                         labels[:stride * (i + 1)], weights[:stride * (i + 1)])
-        expected_cov = np.cov(p, l)[0, 1]
+        self.assertEqual(np.isnan(prev_expected_cov),
+                         np.isnan(sess.run(cov, feed_dict=feed_dict)))
+        if not np.isnan(prev_expected_cov):
+          self.assertAlmostEqual(
+              prev_expected_cov, sess.run(cov, feed_dict=feed_dict), 5)
+        expected_cov = np.cov(predictions[:stride * (i + 1)],
+                              labels[:stride * (i + 1)],
+                              fweights=weights[:stride * (i + 1)])[0, 1]
         self.assertAlmostEqual(
             expected_cov, sess.run(update_op, feed_dict=feed_dict), 5)
         self.assertAlmostEqual(
@@ -4453,8 +4455,7 @@ class StreamingPearsonRTest(test.TestCase):
       pearson_r, update_op = metrics.streaming_pearson_correlation(
           predictions_t, labels_t, weights=weights_t)
 
-      p, l = _reweight(predictions, labels, weights)
-      cmat = np.cov(p, l)
+      cmat = np.cov(predictions, labels, fweights=weights)
       expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1])
       sess.run(variables.local_variables_initializer())
       self.assertAlmostEqual(expected_r, sess.run(update_op))
@@ -4475,14 +4476,17 @@ class StreamingPearsonRTest(test.TestCase):
           predictions_t, labels_t)
 
       sess.run(variables.local_variables_initializer())
-      prev_expected_r = 0.
+      prev_expected_r = NAN
       for i in range(n // stride):
         feed_dict = {
             predictions_t: predictions[stride * i:stride * (i + 1)],
             labels_t: labels[stride * i:stride * (i + 1)]
         }
-        self.assertAlmostEqual(
-            prev_expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5)
+        self.assertEqual(np.isnan(prev_expected_r),
+                         np.isnan(sess.run(pearson_r, feed_dict=feed_dict)))
+        if not np.isnan(prev_expected_r):
+          self.assertAlmostEqual(
+              prev_expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5)
         expected_r = np.corrcoef(predictions[:stride * (i + 1)],
                                  labels[:stride * (i + 1)])[0, 1]
         self.assertAlmostEqual(
@@ -4509,18 +4513,21 @@ class StreamingPearsonRTest(test.TestCase):
           predictions_t, labels_t, weights=weights_t)
 
       sess.run(variables.local_variables_initializer())
-      prev_expected_r = 0.
+      prev_expected_r = NAN
       for i in range(n // stride):
         feed_dict = {
             predictions_t: predictions[stride * i:stride * (i + 1)],
             labels_t: labels[stride * i:stride * (i + 1)],
             weights_t: weights[stride * i:stride * (i + 1)]
         }
-        self.assertAlmostEqual(
-            prev_expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5)
-        p, l = _reweight(predictions[:stride * (i + 1)],
-                         labels[:stride * (i + 1)], weights[:stride * (i + 1)])
-        cmat = np.cov(p, l)
+        self.assertEqual(np.isnan(prev_expected_r),
+                         np.isnan(sess.run(pearson_r, feed_dict=feed_dict)))
+        if not np.isnan(prev_expected_r):
+          self.assertAlmostEqual(
+              prev_expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5)
+        cmat = np.cov(predictions[:stride * (i + 1)],
+                      labels[:stride * (i + 1)],
+                      fweights=weights[:stride * (i + 1)])
         expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1])
         self.assertAlmostEqual(
             expected_r, sess.run(update_op, feed_dict=feed_dict), 5)
@@ -4528,6 +4535,47 @@ class StreamingPearsonRTest(test.TestCase):
             expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5)
         prev_expected_r = expected_r
 
+  def testMultiUpdateWithErrorAndSingletonBatches(self):
+    with self.test_session() as sess:
+      np.random.seed(123)
+      n = 100
+      predictions = np.random.randn(n)
+      labels = 0.5 * predictions + np.random.randn(n)
+      stride = 10
+      weights = (np.arange(n).reshape(n//stride, stride) % stride == 0)
+      for row in weights:
+        np.random.shuffle(row)
+      # Now, weights is one-hot by row - one item per batch has non-zero weight.
+      weights = weights.reshape((n,))
+
+      predictions_t = array_ops.placeholder(dtypes_lib.float32, [stride])
+      labels_t = array_ops.placeholder(dtypes_lib.float32, [stride])
+      weights_t = array_ops.placeholder(dtypes_lib.float32, [stride])
+
+      pearson_r, update_op = metrics.streaming_pearson_correlation(
+          predictions_t, labels_t, weights=weights_t)
+
+      sess.run(variables.local_variables_initializer())
+      for i in range(n // stride):
+        feed_dict = {
+            predictions_t: predictions[stride * i:stride * (i + 1)],
+            labels_t: labels[stride * i:stride * (i + 1)],
+            weights_t: weights[stride * i:stride * (i + 1)]
+        }
+        cmat = np.cov(predictions[:stride * (i + 1)],
+                      labels[:stride * (i + 1)],
+                      fweights=weights[:stride * (i + 1)])
+        expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1])
+        actual_r = sess.run(update_op, feed_dict=feed_dict)
+        self.assertEqual(np.isnan(expected_r), np.isnan(actual_r))
+        self.assertEqual(np.isnan(expected_r),
+                         np.isnan(sess.run(pearson_r, feed_dict=feed_dict)))
+        if not np.isnan(expected_r):
+          self.assertAlmostEqual(
+              expected_r, actual_r, 5)
+          self.assertAlmostEqual(
+              expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5)
+
 
 class StreamingMeanCosineDistanceTest(test.TestCase):
 
-- 
GitLab


From 14101a8ba173b44179b2a9317781f140eb61b0a1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 07:28:16 -0700
Subject: [PATCH 0731/1375] Fix TensorForest for 32-bit platforms.

PiperOrigin-RevId: 161077247
---
 .../tensor_forest/kernels/stats_ops.cc        | 44 ++++++++-----------
 .../kernels/v4/grow_stats_test.cc             | 12 ++---
 .../tensor_forest/kernels/v4/input_target.h   | 18 ++++----
 .../kernels/v4/leaf_model_operators_test.cc   |  2 +-
 .../tensor_forest/kernels/v4/test_utils.h     |  5 ++-
 5 files changed, 38 insertions(+), 43 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
index c80802e97b..1fd85fd81f 100644
--- a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -159,20 +159,17 @@ void TraverseTree(const DecisionTreeResource* tree_resource,
 // until they're gone.
 void UpdateStats(FertileStatsResource* fertile_stats_resource,
                  const std::unique_ptr<TensorDataSet>& data,
-                 const Tensor& input_labels, const Tensor& input_weights,
-                 int num_targets, const std::vector<int32>& leaf_ids,
+                 const TensorInputTarget& target, int num_targets,
+                 const std::vector<int32>& leaf_ids,
                  const std::vector<int32>& leaf_depths,
                  std::unordered_map<int32, std::unique_ptr<mutex>>* locks,
                  mutex* set_lock, int32 start, int32 end,
                  std::unordered_set<int32>* ready_to_split) {
-  const auto labels = input_labels.unaligned_flat<float>();
-  const auto weights = input_weights.unaligned_flat<float>();
   // Stores leaf_id, leaf_depth, example_id for examples that are waiting
   // on another to finish.
   std::queue<std::tuple<int32, int32, int32>> waiting;
 
   int32 i = start;
-  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
   while (i < end || !waiting.empty()) {
     int32 leaf_id;
     int32 leaf_depth;
@@ -214,15 +211,11 @@ void UpdateStats(FertileStatsResource* fertile_stats_resource,
 void UpdateStatsCollated(
     FertileStatsResource* fertile_stats_resource,
     DecisionTreeResource* tree_resource,
-    const std::unique_ptr<TensorDataSet>& data, const Tensor& input_labels,
-    const Tensor& input_weights, int num_targets,
+    const std::unique_ptr<TensorDataSet>& data, const TensorInputTarget& target,
+    int num_targets,
     const std::unordered_map<int32, std::vector<int>>& leaf_examples,
     const std::vector<int32>& leaf_depths, mutex* set_lock, int32 start,
     int32 end, std::unordered_set<int32>* ready_to_split) {
-  const auto labels = input_labels.unaligned_flat<float>();
-  const auto weights = input_weights.unaligned_flat<float>();
-
-  TensorInputTarget target(&labels, &weights, input_labels, num_targets);
   auto it = leaf_examples.begin();
   std::advance(it, start);
   auto end_it = leaf_examples.begin();
@@ -335,32 +328,33 @@ class ProcessInputOp : public OpKernel {
     std::unordered_set<int32> ready_to_split;
     mutex set_lock;
 
+    TensorInputTarget target(input_labels, input_weights, num_targets);
+
     // TODO(gilberth): This is a rough approximation based on measurements
     // from a digits run on local desktop.  Heuristics might be necessary
     // if it really matters that much.
     const int64 costPerUpdate = 1000;
-    auto update = [this, &input_labels, &input_weights, &leaf_ids, &leaf_depths,
-                   &num_targets, fertile_stats_resource, &locks, &set_lock,
-                   &ready_to_split, num_data](int64 start, int64 end) {
+    auto update = [this, &target, &leaf_ids, &leaf_depths, &num_targets,
+                   fertile_stats_resource, &locks, &set_lock, &ready_to_split,
+                   num_data](int64 start, int64 end) {
       CHECK(start <= end);
       CHECK(end <= num_data);
-      UpdateStats(fertile_stats_resource, data_set_, input_labels,
-                  input_weights, num_targets, leaf_ids, leaf_depths, &locks,
-                  &set_lock, static_cast<int32>(start), static_cast<int32>(end),
+      UpdateStats(fertile_stats_resource, data_set_, target, num_targets,
+                  leaf_ids, leaf_depths, &locks, &set_lock,
+                  static_cast<int32>(start), static_cast<int32>(end),
                   &ready_to_split);
     };
 
-    auto update_collated = [this, &input_labels, &input_weights, &leaf_ids,
-                            &num_targets, &leaf_depths, fertile_stats_resource,
-                            tree_resource, &leaf_examples, &set_lock,
-                            &ready_to_split,
+    auto update_collated = [this, &target, &leaf_ids, &num_targets,
+                            &leaf_depths, fertile_stats_resource, tree_resource,
+                            &leaf_examples, &set_lock, &ready_to_split,
                             num_leaves](int64 start, int64 end) {
       CHECK(start <= end);
       CHECK(end <= num_leaves);
-      UpdateStatsCollated(
-          fertile_stats_resource, tree_resource, data_set_, input_labels,
-          input_weights, num_targets, leaf_examples, leaf_depths, &set_lock,
-          static_cast<int32>(start), static_cast<int32>(end), &ready_to_split);
+      UpdateStatsCollated(fertile_stats_resource, tree_resource, data_set_,
+                          target, num_targets, leaf_examples, leaf_depths,
+                          &set_lock, static_cast<int32>(start),
+                          static_cast<int32>(end), &ready_to_split);
     };
 
     if (param_proto_.collate_examples()) {
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
index 8d51456e30..7cd16d222a 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
@@ -76,7 +76,7 @@ TEST(GrowStatsDenseClassificationTest, Basic) {
   std::vector<float> labels = {1, 0, 1};
   std::vector<float> weights = {2.3, 20.3, 1.1};
   std::unique_ptr<TestableInputTarget> target(
-      new TestableInputTarget(&labels, &weights, 1));
+      new TestableInputTarget(labels, weights, 1));
 
   RunBatch(stat.get(), target.get());
   CHECK(stat->IsFinished());
@@ -127,7 +127,7 @@ TEST(GrowStatsDenseClassificationTest, BasicRunningStats) {
   std::vector<float> labels = {1, 0, 1};
   std::vector<float> weights = {2.3, 20.3, 1.1};
   std::unique_ptr<TestableInputTarget> target(
-      new TestableInputTarget(&labels, &weights, 1));
+      new TestableInputTarget(labels, weights, 1));
 
   RunBatch(stat.get(), target.get());
   CHECK(stat->IsFinished());
@@ -185,7 +185,7 @@ TEST(GrowStatsDenseClassificationTest, TestFinishEarly) {
   std::vector<float> labels = {1, 0, 1};
   std::vector<float> weights = {1, 1, 1};
   std::unique_ptr<TestableInputTarget> target(
-      new TestableInputTarget(&labels, &weights, 1));
+      new TestableInputTarget(labels, weights, 1));
   std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
       new tensorflow::tensorforest::TestableDataSet(
           {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, 2));
@@ -235,7 +235,7 @@ TEST(GrowStatsDenseClassificationTest, TestCheckPruneHoeffding) {
   // sends them both to the left.
   std::vector<float> labels = {0, 1};
   std::vector<float> weights = {1, 1};
-  TestableInputTarget target(&labels, &weights, 1);
+  TestableInputTarget target(labels, weights, 1);
   std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
       new tensorflow::tensorforest::TestableDataSet(
           {-1.0, -1.0, 1.0, -1.0}, 2));
@@ -306,7 +306,7 @@ TEST(GrowStatsLeastSquaresRegressionTest, Basic) {
 
   std::vector<float> labels = {2.3, 5.6, 1.1};
   std::unique_ptr<TestableInputTarget> target(
-      new TestableInputTarget(&labels, {}, 1));
+      new TestableInputTarget(labels, {}, 1));
   std::vector<int> branches = {1, 0, 1, 1, 0, 0};
 
   RunBatch(stat.get(), target.get());
@@ -340,7 +340,7 @@ TEST(GrowStatsSparseClassificationTest, Basic) {
   std::vector<float> labels = {100, 1000, 1};
   std::vector<float> weights = {2.3, 20.3, 1.1};
   std::unique_ptr<TestableInputTarget> target(
-      new TestableInputTarget(&labels, &weights, 1));
+      new TestableInputTarget(labels, weights, 1));
   std::vector<int> branches = {1, 0, 1, 1, 0, 0};
 
   RunBatch(stat.get(), target.get());
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
index 97b2314f0f..03dc332c8a 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
@@ -20,9 +20,7 @@
 namespace tensorflow {
 namespace tensorforest {
 
-typedef Eigen::TensorMap<
-    Eigen::Tensor<const float, 1, 1, long>, 0>  // NOLINT(runtime/int)
-    SingleDimStorageType;
+typedef TTypes<float, 1>::ConstTensor SingleDimStorageType;
 
 // Base class for classes that hold labels and weights. Mostly for testing
 // purposes, because it's inconvenient to construct nasty Eigen::things.
@@ -41,11 +39,12 @@ class InputTarget {
 template <typename T>
 class StoredInputTarget : public InputTarget {
  protected:
+  // Takes ownership of t and w with a std::unique_ptr.
   StoredInputTarget(const T* t, const T* w, int num_targets)
       : target_(t), weight_(w), num_targets_(num_targets) {}
 
-  const T* target_;
-  const T* weight_;
+  const std::unique_ptr<const T> target_;
+  const std::unique_ptr<const T> weight_;
   int num_targets_;
 };
 
@@ -54,10 +53,11 @@ class StoredInputTarget : public InputTarget {
 // outputs will correctly index the flattened data.
 class TensorInputTarget : public StoredInputTarget<SingleDimStorageType> {
  public:
-  TensorInputTarget(const SingleDimStorageType* t,
-                    const SingleDimStorageType* w, const Tensor& tensor,
-                    int num_targets)
-      : StoredInputTarget(t, w, num_targets), original_tensor_(tensor) {}
+  TensorInputTarget(const Tensor& target, const Tensor& weight, int num_targets)
+      : StoredInputTarget(new SingleDimStorageType(target.tensor<float, 1>()),
+                          new SingleDimStorageType(weight.tensor<float, 1>()),
+                          num_targets),
+        original_tensor_(target) {}
 
   int32 GetTargetAsClassIndex(int example_index,
                               int target_index) const override {
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
index 037e4e244c..35268d15d3 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
@@ -77,7 +77,7 @@ void TestClassificationNormalUse(const std::unique_ptr<LeafModelOperator>& op) {
   std::vector<float> labels = {1, 0, 1};
   std::vector<float> weights = {2.3, 20.3, 1.1};
   std::unique_ptr<TestableInputTarget> target(
-      new TestableInputTarget(&labels, &weights, 1));
+      new TestableInputTarget(labels, weights, 1));
 
   // Update and check value.
   op->UpdateModel(leaf.get(), target.get(), 0);
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h b/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h
index 4ac23ceb3e..b6e543b96f 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h
@@ -22,9 +22,10 @@ namespace tensorforest {
 
 class TestableInputTarget : public StoredInputTarget<std::vector<float>> {
  public:
-  TestableInputTarget(const std::vector<float>* t, const std::vector<float>* w,
+  TestableInputTarget(const std::vector<float>& t, const std::vector<float>& w,
                       int num_t)
-      : StoredInputTarget(t, w, num_t) {}
+      : StoredInputTarget(new std::vector<float>(t), new std::vector<float>(w),
+                          num_t) {}
 
   int NumItems() const {
     return target_->size();
-- 
GitLab


From 4f02051d033bc32615d7f585ae7f88732c4614b5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 07:49:46 -0700
Subject: [PATCH 0732/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161078995
---
 tensorflow/core/ops/ops.pbtxt | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 46fbbda223..4461244062 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -9189,8 +9189,8 @@ op {
       }
     }
   }
-  summary: "Gather values or slices from `params` according to `indices`."
-  description: "`indices` is an integer tensor containing indices into `params`.  The last\ndimension of `indices` can be at most the rank of `params`:\n\n    indices.shape[-1] <= params.rank\n\nThe last dimension of `indices` corresponds to elements\n(if `indices.shape[-1] = params.rank`) or slices\n(if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`\nof `params`.  The output tensor has shape\n\n    indices.shape[:-1] + params.shape[indices.shape[-1]:]\n\nSome examples below.\n\nSimple indexing into a matrix:\n\n```python\n    indices = [[0, 0], [1, 1]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [\'a\', \'d\']\n```\n\nSlice indexing into a matrix:\n\n```python\n    indices = [[1], [0]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[\'c\', \'d\'], [\'a\', \'b\']]\n```\n\nIndexing into a 3-tensor:\n\n```python\n    indices = [[1]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n\n\n    indices = [[0, 1], [1, 0]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[\'c0\', \'d0\'], [\'a1\', \'b1\']]\n\n\n    indices = [[0, 0, 1], [1, 0, 1]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [\'b0\', \'b1\']\n```\n\nBatched indexing into a matrix:\n\n```python\n    indices = [[[0, 0]], [[0, 1]]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[\'a\'], [\'b\']]\n```\n\nBatched slice indexing into a matrix:\n\n```python\n    indices = [[[1]], [[0]]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[[\'c\', \'d\']], [[\'a\', \'b\']]]\n```\n\nBatched indexing into a 3-tensor:\n\n```python\n    indices = [[[1]], [[0]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[[\'a1\', \'b1\'], [\'c1\', \'d1\']]],\n              [[[\'a0\', \'b0\'], [\'c0\', \'d0\']]]]\n\n    indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[\'c0\', \'d0\'], [\'a1\', \'b1\']],\n              [[\'a0\', \'b0\'], [\'c1\', \'d1\']]]\n\n\n    indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[\'b0\', \'b1\'], [\'d0\', \'c1\']]\n```"
+  summary: "Gather slices from `params` into a Tensor with shape specified by `indices`."
+  description: "`indices` is an K-dimensional integer tensor, best thought of as a\n(K-1)-dimensional tensor of indices into `params`, where each element defines a\nslice of `params`:\n\n    output[i_0, ..., i_{K-2}] = params[indices[i0, ..., i_{K-2}]]\n\nWhereas in @{tf.gather} `indices` defines slices into the first\ndimension of `params`, in `tf.gather_nd`, `indices` defines slices into the\nfirst `N` dimensions of `params`, where `N = indices.shape[-1]`.\n\nThe last dimension of `indices` can be at most the rank of\n`params`:\n\n    indices.shape[-1] <= params.rank\n\nThe last dimension of `indices` corresponds to elements\n(if `indices.shape[-1] == params.rank`) or slices\n(if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`\nof `params`.  The output tensor has shape\n\n    indices.shape[:-1] + params.shape[indices.shape[-1]:]\n\nSome examples below.\n\nSimple indexing into a matrix:\n\n```python\n    indices = [[0, 0], [1, 1]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [\'a\', \'d\']\n```\n\nSlice indexing into a matrix:\n\n```python\n    indices = [[1], [0]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[\'c\', \'d\'], [\'a\', \'b\']]\n```\n\nIndexing into a 3-tensor:\n\n```python\n    indices = [[1]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n\n\n    indices = [[0, 1], [1, 0]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[\'c0\', \'d0\'], [\'a1\', \'b1\']]\n\n\n    indices = [[0, 0, 1], [1, 0, 1]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [\'b0\', \'b1\']\n```\n\nBatched indexing into a matrix:\n\n```python\n    indices = [[[0, 0]], [[0, 1]]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[\'a\'], [\'b\']]\n```\n\nBatched slice indexing into a matrix:\n\n```python\n    indices = [[[1]], [[0]]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[[\'c\', \'d\']], [[\'a\', \'b\']]]\n```\n\nBatched indexing into a 3-tensor:\n\n```python\n    indices = [[[1]], [[0]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[[\'a1\', \'b1\'], [\'c1\', \'d1\']]],\n              [[[\'a0\', \'b0\'], [\'c0\', \'d0\']]]]\n\n    indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[\'c0\', \'d0\'], [\'a1\', \'b1\']],\n              [[\'a0\', \'b0\'], [\'c1\', \'d1\']]]\n\n\n    indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[\'b0\', \'b1\'], [\'d0\', \'c1\']]\n```"
 }
 op {
   name: "GetSessionHandle"
@@ -21977,7 +21977,7 @@ op {
     }
   }
   summary: "Scatter `updates` into a new (initially zero) tensor according to `indices`."
-  description: "Creates a new tensor by applying sparse `updates` to individual\nvalues or slices within a zero tensor of the given `shape` according to\nindices.  This operator is the inverse of the [tf.gather_nd](#gather_nd)\noperator which extracts values or slices from a given tensor.\n\n**WARNING**: The order in which updates are applied is nondeterministic, so the\noutput will be nondeterministic if `indices` contains duplicates.\n\n`indices` is an integer tensor containing indices into a new tensor of shape\n`shape`.  The last dimension of `indices` can be at most the rank of `shape`:\n\n    indices.shape[-1] <= shape.rank\n\nThe last dimension of `indices` corresponds to indices into elements\n(if `indices.shape[-1] = shape.rank`) or slices\n(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of\n`shape`.  `updates` is a tensor with shape\n\n    indices.shape[:-1] + shape[indices.shape[-1]:]\n\nThe simplest form of scatter is to insert individual elements in a tensor by\nindex. For example, say we want to insert 4 scattered elements in a rank-1\ntensor with 8 elements.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/ScatterNd1.png\" alt>\n</div>\n\nIn Python, this scatter operation would look like this:\n\n```python\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    shape = tf.constant([8])\n    scatter = tf.scatter_nd(indices, updates, shape)\n    with tf.Session() as sess:\n      print(sess.run(scatter))\n```\n\nThe resulting tensor would look like this:\n\n    [0, 11, 0, 10, 9, 0, 0, 12]\n\nWe can also, insert entire slices of a higher rank tensor all at once. For\nexample, if we wanted to insert two slices in the first dimension of a\nrank-3 tensor with two matrices of new values.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/ScatterNd2.png\" alt>\n</div>\n\nIn Python, this scatter operation would look like this:\n\n```python\n    indices = tf.constant([[0], [2]])\n    updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],\n                            [7, 7, 7, 7], [8, 8, 8, 8]],\n                           [[5, 5, 5, 5], [6, 6, 6, 6],\n                            [7, 7, 7, 7], [8, 8, 8, 8]]])\n    shape = tf.constant([4, 4, 4])\n    scatter = tf.scatter_nd(indices, updates, shape)\n    with tf.Session() as sess:\n      print(sess.run(scatter))\n```\n\nThe resulting tensor would look like this:\n\n    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],\n     [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],\n     [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],\n     [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]"
+  description: "Creates a new tensor by applying sparse `updates` to individual\nvalues or slices within a zero tensor of the given `shape` according to\nindices.  This operator is the inverse of the @{tf.gather_nd} operator which\nextracts values or slices from a given tensor.\n\n**WARNING**: The order in which updates are applied is nondeterministic, so the\noutput will be nondeterministic if `indices` contains duplicates.\n\n`indices` is an integer tensor containing indices into a new tensor of shape\n`shape`.  The last dimension of `indices` can be at most the rank of `shape`:\n\n    indices.shape[-1] <= shape.rank\n\nThe last dimension of `indices` corresponds to indices into elements\n(if `indices.shape[-1] = shape.rank`) or slices\n(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of\n`shape`.  `updates` is a tensor with shape\n\n    indices.shape[:-1] + shape[indices.shape[-1]:]\n\nThe simplest form of scatter is to insert individual elements in a tensor by\nindex. For example, say we want to insert 4 scattered elements in a rank-1\ntensor with 8 elements.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/ScatterNd1.png\" alt>\n</div>\n\nIn Python, this scatter operation would look like this:\n\n```python\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    shape = tf.constant([8])\n    scatter = tf.scatter_nd(indices, updates, shape)\n    with tf.Session() as sess:\n      print(sess.run(scatter))\n```\n\nThe resulting tensor would look like this:\n\n    [0, 11, 0, 10, 9, 0, 0, 12]\n\nWe can also, insert entire slices of a higher rank tensor all at once. For\nexample, if we wanted to insert two slices in the first dimension of a\nrank-3 tensor with two matrices of new values.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/ScatterNd2.png\" alt>\n</div>\n\nIn Python, this scatter operation would look like this:\n\n```python\n    indices = tf.constant([[0], [2]])\n    updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],\n                            [7, 7, 7, 7], [8, 8, 8, 8]],\n                           [[5, 5, 5, 5], [6, 6, 6, 6],\n                            [7, 7, 7, 7], [8, 8, 8, 8]]])\n    shape = tf.constant([4, 4, 4])\n    scatter = tf.scatter_nd(indices, updates, shape)\n    with tf.Session() as sess:\n      print(sess.run(scatter))\n```\n\nThe resulting tensor would look like this:\n\n    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],\n     [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],\n     [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],\n     [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]"
 }
 op {
   name: "ScatterNdAdd"
@@ -22044,7 +22044,7 @@ op {
     description: "An optional bool. Defaults to True. If True, the assignment will\nbe protected by a lock; otherwise the behavior is undefined,\nbut may exhibit less contention."
   }
   summary: "Applies sparse addition between `updates` and individual values or slices"
-  description: "within a given variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    add = tf.scatter_nd_add(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(add)\n\nThe resulting update to ref would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
+  description: "within a given variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    add = tf.scatter_nd_add(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(add)\n\nThe resulting update to ref would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee @{tf.scatter_nd} for more details about how to make updates to\nslices."
 }
 op {
   name: "ScatterNdNonAliasingAdd"
@@ -22101,7 +22101,7 @@ op {
     }
   }
   summary: "Applies sparse addition to `input` using individual values or slices"
-  description: "from `updates` according to indices `indices`.  The updates are non-aliasing:\n`input` is only modified in-place if no other operations will use it.\nOtherwise, a copy of `input` is made.  This operation has a gradient with\nrespect to both `input` and `updates`.\n\n`input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `input`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or `(P-K)`-dimensional slices\n(if `K < P`) along the `K`th dimension of `input`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    output = tf.scatter_nd_non_aliasing_add(input, indices, updates)\n    with tf.Session() as sess:\n      print(sess.run(output))\n\nThe resulting value `output` would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
+  description: "from `updates` according to indices `indices`.  The updates are non-aliasing:\n`input` is only modified in-place if no other operations will use it.\nOtherwise, a copy of `input` is made.  This operation has a gradient with\nrespect to both `input` and `updates`.\n\n`input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `input`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or `(P-K)`-dimensional slices\n(if `K < P`) along the `K`th dimension of `input`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].\n```\n\nFor example, say we want to add 4 scattered elements to a rank-1 tensor to 8\nelements. In Python, that addition would look like this:\n\n    input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    output = tf.scatter_nd_non_aliasing_add(input, indices, updates)\n    with tf.Session() as sess:\n      print(sess.run(output))\n\nThe resulting value `output` would look like this:\n\n    [1, 13, 3, 14, 14, 6, 7, 20]\n\nSee @{tf.scatter_nd} for more details about how to make updates to slices."
 }
 op {
   name: "ScatterNdSub"
@@ -22168,7 +22168,7 @@ op {
     description: "An optional bool. Defaults to True. If True, the assignment will\nbe protected by a lock; otherwise the behavior is undefined,\nbut may exhibit less contention."
   }
   summary: "Applies sparse subtraction between `updates` and individual values or slices"
-  description: "within a given variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to subtract 4 scattered elements from a rank-1 tensor\nwith 8 elements. In Python, that subtraction would look like this:\n\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    sub = tf.scatter_nd_sub(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(sub)\n\nThe resulting update to ref would look like this:\n\n    [1, -9, 3, -6, -4, 6, 7, -4]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
+  description: "within a given variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to subtract 4 scattered elements from a rank-1 tensor\nwith 8 elements. In Python, that subtraction would look like this:\n\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1], [7]])\n    updates = tf.constant([9, 10, 11, 12])\n    sub = tf.scatter_nd_sub(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(sub)\n\nThe resulting update to ref would look like this:\n\n    [1, -9, 3, -6, -4, 6, 7, -4]\n\nSee @{tf.scatter_nd} for more details about how to make updates to\nslices."
 }
 op {
   name: "ScatterNdUpdate"
@@ -22217,7 +22217,7 @@ op {
     description: "An optional bool. Defaults to True. If True, the assignment will\nbe protected by a lock; otherwise the behavior is undefined,\nbut may exhibit less contention."
   }
   summary: "Applies sparse `updates` to individual values or slices within a given"
-  description: "variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to update 4 scattered elements to a rank-1 tensor to\n8 elements. In Python, that update would look like this:\n\n```python\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1] ,[7]])\n    updates = tf.constant([9, 10, 11, 12])\n    update = tf.scatter_nd_update(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(update)\n```\n\nThe resulting update to ref would look like this:\n\n    [1, 11, 3, 10, 9, 6, 7, 12]\n\nSee [tf.scatter_nd](#scatter_nd) for more details about how to make updates to\nslices."
+  description: "variable according to `indices`.\n\n`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.\n\n`indices` must be integer tensor, containing indices into `ref`.\nIt must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.\n\nThe innermost dimension of `indices` (with length `K`) corresponds to\nindices into elements (if `K = P`) or slices (if `K < P`) along the `K`th\ndimension of `ref`.\n\n`updates` is `Tensor` of rank `Q-1+P-K` with shape:\n\n```\n[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].\n```\n\nFor example, say we want to update 4 scattered elements to a rank-1 tensor to\n8 elements. In Python, that update would look like this:\n\n```python\n    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])\n    indices = tf.constant([[4], [3], [1] ,[7]])\n    updates = tf.constant([9, 10, 11, 12])\n    update = tf.scatter_nd_update(ref, indices, updates)\n    with tf.Session() as sess:\n      print sess.run(update)\n```\n\nThe resulting update to ref would look like this:\n\n    [1, 11, 3, 10, 9, 6, 7, 12]\n\nSee @{tf.scatter_nd} for more details about how to make updates to\nslices."
 }
 op {
   name: "ScatterSub"
-- 
GitLab


From 7a7b207eec5c62e57046bfeac00298197bbddb8c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 07:55:22 -0700
Subject: [PATCH 0733/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161079466
---
 tensorflow/go/op/wrappers.go | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index a2a9856497..759484bfa3 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -1637,15 +1637,25 @@ func Identity(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
-// Gather values or slices from `params` according to `indices`.
+// Gather slices from `params` into a Tensor with shape specified by `indices`.
 //
-// `indices` is an integer tensor containing indices into `params`.  The last
-// dimension of `indices` can be at most the rank of `params`:
+// `indices` is an K-dimensional integer tensor, best thought of as a
+// (K-1)-dimensional tensor of indices into `params`, where each element defines a
+// slice of `params`:
+//
+//     output[i_0, ..., i_{K-2}] = params[indices[i0, ..., i_{K-2}]]
+//
+// Whereas in @{tf.gather} `indices` defines slices into the first
+// dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the
+// first `N` dimensions of `params`, where `N = indices.shape[-1]`.
+//
+// The last dimension of `indices` can be at most the rank of
+// `params`:
 //
 //     indices.shape[-1] <= params.rank
 //
 // The last dimension of `indices` corresponds to elements
-// (if `indices.shape[-1] = params.rank`) or slices
+// (if `indices.shape[-1] == params.rank`) or slices
 // (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`
 // of `params`.  The output tensor has shape
 //
@@ -2775,8 +2785,8 @@ func SpaceToDepth(scope *Scope, input tf.Output, block_size int64) (output tf.Ou
 //
 // Creates a new tensor by applying sparse `updates` to individual
 // values or slices within a zero tensor of the given `shape` according to
-// indices.  This operator is the inverse of the [tf.gather_nd](#gather_nd)
-// operator which extracts values or slices from a given tensor.
+// indices.  This operator is the inverse of the @{tf.gather_nd} operator which
+// extracts values or slices from a given tensor.
 //
 // **WARNING**: The order in which updates are applied is nondeterministic, so the
 // output will be nondeterministic if `indices` contains duplicates.
@@ -17148,8 +17158,7 @@ func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer
 //
 //     [1, 13, 3, 14, 14, 6, 7, 20]
 //
-// See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to
-// slices.
+// See @{tf.scatter_nd} for more details about how to make updates to slices.
 //
 // Arguments:
 //	input: A Tensor.
-- 
GitLab


From 32de8d33177d6f46c7ecd06363bd492194341731 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Thu, 6 Jul 2017 08:31:38 -0700
Subject: [PATCH 0734/1375] [XLA] Propagate debug_options in
 ComputationStatsRequest

And fix graph dumping tools to add graph dumping options automatically.

PiperOrigin-RevId: 161082718
---
 tensorflow/compiler/xla/client/BUILD                     | 1 +
 tensorflow/compiler/xla/client/client.cc                 | 9 +++++++--
 tensorflow/compiler/xla/client/client.h                  | 2 +-
 .../compiler/xla/legacy_flags/debug_options_flags.cc     | 1 +
 tensorflow/compiler/xla/service/service.cc               | 4 +++-
 .../compiler/xla/tools/dumped_computation_to_graphviz.cc | 6 +++++-
 .../xla/tools/dumped_computation_to_tf_graphdef.cc       | 6 +++++-
 tensorflow/compiler/xla/xla.proto                        | 1 +
 8 files changed, 24 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD
index f50dc934f2..289004575d 100644
--- a/tensorflow/compiler/xla/client/BUILD
+++ b/tensorflow/compiler/xla/client/BUILD
@@ -70,6 +70,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:session_proto",
         "//tensorflow/core:lib",
     ],
diff --git a/tensorflow/compiler/xla/client/client.cc b/tensorflow/compiler/xla/client/client.cc
index 454d0fbd96..555e3df427 100644
--- a/tensorflow/compiler/xla/client/client.cc
+++ b/tensorflow/compiler/xla/client/client.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <string>
 #include <utility>
 
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -376,9 +377,10 @@ StatusOr<std::vector<std::unique_ptr<GlobalData>>> Client::DeconstructTuple(
 }
 
 StatusOr<ComputationStats> Client::GetComputationStats(
-    const Computation& computation) const {
+    const Computation& computation, const DebugOptions& debug_options) const {
   ComputationStatsRequest request;
   *request.mutable_computation() = computation.handle();
+  *request.mutable_debug_options() = debug_options;
   ComputationStatsResponse response;
 
   VLOG(1) << "making computation stats request";
@@ -427,7 +429,10 @@ StatusOr<Shape> Client::GetShape(const GlobalData& data) {
 
 StatusOr<string> Client::ExecutionStatsAsString(
     const Computation& computation, const ExecutionProfile& profile) {
-  TF_ASSIGN_OR_RETURN(auto computation_stats, GetComputationStats(computation));
+  TF_ASSIGN_OR_RETURN(
+      auto computation_stats,
+      GetComputationStats(computation,
+                          legacy_flags::GetDebugOptionsFromFlags()));
   int64 total_flops =
       computation_stats.flop_count() + computation_stats.transcendental_count();
   if (profile.compute_time_ns() > 0) {
diff --git a/tensorflow/compiler/xla/client/client.h b/tensorflow/compiler/xla/client/client.h
index 797835160f..69d3642911 100644
--- a/tensorflow/compiler/xla/client/client.h
+++ b/tensorflow/compiler/xla/client/client.h
@@ -150,7 +150,7 @@ class Client {
 
   // Retrieves the statistics of the given computation.
   StatusOr<ComputationStats> GetComputationStats(
-      const Computation& computation) const;
+      const Computation& computation, const DebugOptions& debug_options) const;
 
   // Returns the Shape of the given array specified by 'data'. The shape
   // includes the Layout of the array as it is stored on the service.
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index e9b85ef7f5..4a6b0b581c 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -189,6 +189,7 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
   options.set_xla_hlo_graph_addresses(flag_values->xla_hlo_graph_addresses);
   options.set_xla_hlo_graph_layout(flag_values->xla_hlo_graph_layout);
   options.set_xla_hlo_graph_path(flag_values->xla_hlo_graph_path);
+  options.set_xla_hlo_dump_as_graphdef(flag_values->xla_hlo_dump_as_graphdef);
   options.set_xla_log_hlo_text(flag_values->xla_log_hlo_text);
   options.set_xla_generate_hlo_text_to(flag_values->xla_generate_hlo_text_to);
 
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index daed56b3eb..44e0fabe77 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -1173,9 +1173,11 @@ tensorflow::Status Service::GetComputationStats(
   VersionedComputationHandle versioned_handle =
       user_computation->GetVersionedHandle();
 
+  HloModuleConfig config;
+  config.set_debug_options(arg->debug_options());
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr<HloModule> module,
-      computation_tracker_.BuildHloModule(versioned_handle, HloModuleConfig()));
+      computation_tracker_.BuildHloModule(versioned_handle, config));
 
   hlo_graph_dumper::MaybeDumpHloModule(*module,
                                        "computation statistics subject");
diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
index 100e19e6d8..7861c3a9b7 100644
--- a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
+++ b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc
@@ -53,8 +53,12 @@ void RealMain(tensorflow::gtl::ArraySlice<char*> args) {
     TF_CHECK_OK(
         tensorflow::ReadBinaryProto(tensorflow::Env::Default(), arg, &module));
     Computation computation = client->LoadSnapshot(module).ConsumeValueOrDie();
+    DebugOptions debug_options = legacy_flags::GetDebugOptionsFromFlags();
+    debug_options.set_xla_generate_hlo_graph(".*");
+    debug_options.set_xla_hlo_graph_layout(true);
     ComputationStats stats =
-        client->GetComputationStats(computation).ConsumeValueOrDie();
+        client->GetComputationStats(computation, debug_options)
+            .ConsumeValueOrDie();
     fprintf(stdout, ">>> %s :: %s\n", arg, stats.DebugString().c_str());
   }
 }
diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
index a8879f1a29..51f90b07c6 100644
--- a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
+++ b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc
@@ -52,8 +52,12 @@ void RealMain(tensorflow::gtl::ArraySlice<char*> args) {
     TF_CHECK_OK(
         tensorflow::ReadBinaryProto(tensorflow::Env::Default(), arg, &module));
     Computation computation = client->LoadSnapshot(module).ConsumeValueOrDie();
+    DebugOptions debug_options = legacy_flags::GetDebugOptionsFromFlags();
+    debug_options.set_xla_generate_hlo_graph(".*");
+    debug_options.set_xla_hlo_dump_as_graphdef(true);
     ComputationStats stats =
-        client->GetComputationStats(computation).ConsumeValueOrDie();
+        client->GetComputationStats(computation, debug_options)
+            .ConsumeValueOrDie();
     fprintf(stdout, ">>> %s :: %s\n", arg, stats.DebugString().c_str());
   }
 }
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 43dac085d4..c4badfaede 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -204,6 +204,7 @@ message ResetDeviceResponse {
 
 message ComputationStatsRequest {
   ComputationHandle computation = 1;
+  DebugOptions debug_options = 2;
 }
 
 message ComputationStatsResponse {
-- 
GitLab


From 13e443c1dab563d1c150aa62ed48de4cb33e28d7 Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Thu, 6 Jul 2017 08:55:43 -0700
Subject: [PATCH 0735/1375] Turn off winograd-nonfused by default again. For
 better performance please set environment variable to enable it:
 os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'

PiperOrigin-RevId: 161085176
---
 tensorflow/stream_executor/cuda/cuda_dnn.cc | 24 +++++++--------------
 1 file changed, 8 insertions(+), 16 deletions(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index 062e3e59ca..af8a1826a9 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -1863,8 +1863,8 @@ bool CudnnSupport::DoConvolveImpl(
 
   if (algorithm_config.algorithm() == dnn::kDefaultAlgorithm) {
     // With the default algorithm, use Cudnn's heuristics.
-    auto get_algorithm = [&](bool specify_limit)
-        SHARED_LOCKS_REQUIRED(dnn_handle_mutex_) {
+    auto get_algorithm =
+        [&](bool specify_limit) SHARED_LOCKS_REQUIRED(dnn_handle_mutex_) {
           cudnnConvolutionFwdPreference_t preference =
               specify_limit ? CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT
                             : CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
@@ -1892,7 +1892,6 @@ bool CudnnSupport::DoConvolveImpl(
         };
 
     algo = get_algorithm(/*specify_limit=*/scratch_allocator != nullptr);
-
     if (scratch_allocator != nullptr) {
       size_t size_in_bytes;
       status = wrap::cudnnGetConvolutionForwardWorkspaceSize(
@@ -1917,7 +1916,6 @@ bool CudnnSupport::DoConvolveImpl(
   } else {
     // An algorithm has been specified.
     algo = ToConvForwardAlgo(algorithm_config.algorithm());
-
     size_t size_in_bytes;
     status = wrap::cudnnGetConvolutionForwardWorkspaceSize(
         parent_, ToHandle(dnn_handle_), /*srcDesc=*/input_nd.handle(),
@@ -1954,7 +1952,6 @@ bool CudnnSupport::DoConvolveImpl(
       }
     }
   }
-
   const bool has_biases = (biases != nullptr);
   const bool supported_activation_mode =
       (activation_mode == dnn::ActivationMode::kRelu6 ||
@@ -2059,8 +2056,9 @@ bool CudnnSupport::DoConvolveImpl(
 // env-var "TF_ENABLE_WINOGRAD_NONFUSED=0".
 // https://github.com/tensorflow/tensorflow/pull/4901
 // TODO(yangzihao): winograd_nonfused bug will only be fixed in cuDNNv7, for
-// cuDNN with smaller versions, we have added code to avoid using winograd
-// nonfused for certain input parameter set.
+// cuDNN with smaller version, we are setting the default flag to false due to
+// b/62635189. Need to root cause this and figure out a workaround or file a bug
+// against NVIDIA.
 template <bool DefaultFlag>
 class WinogradNonfused {
  public:
@@ -2102,7 +2100,7 @@ bool CudnnSupport::GetConvolveAlgorithms(
       // clang-format on
   });
 #if CUDNN_VERSION >= 5100
-  if (WinogradNonfused<true>::IsEnabled() && with_winograd_nonfused) {
+  if (WinogradNonfused<false>::IsEnabled() && with_winograd_nonfused) {
     out_algorithms->push_back(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED);
   }
 #endif
@@ -2124,7 +2122,7 @@ bool CudnnSupport::GetConvolveBackwardDataAlgorithms(
       // clang-format on
   });
 #if CUDNN_VERSION >= 5100
-  if (WinogradNonfused<true>::IsEnabled() && with_winograd_nonfused) {
+  if (WinogradNonfused<false>::IsEnabled() && with_winograd_nonfused) {
     out_algorithms->push_back(
         CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED);
   }
@@ -2144,13 +2142,7 @@ bool CudnnSupport::GetConvolveBackwardFilterAlgorithms(
       // clang-format on
   });
 #if CUDNN_VERSION >= 5100
-#if CUDNN_VERSION >= 5110
-  static constexpr bool kDefaultFlagWinogradNonfused = true;
-#else
-  static constexpr bool kDefaultFlagWinogradNonfused = false;
-#endif
-  if (WinogradNonfused<kDefaultFlagWinogradNonfused>::IsEnabled() &&
-      with_winograd_nonfused) {
+  if (WinogradNonfused<false>::IsEnabled() && with_winograd_nonfused) {
     out_algorithms->push_back(
         // Based on cudnn.h, the following is not implemented.
         // CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD,
-- 
GitLab


From c0f475b03eeca19b217527f7946da837e8438e27 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 6 Jul 2017 08:56:32 -0700
Subject: [PATCH 0736/1375] [XLA] Add support for tuple constants via a rewrite
 that replaces constant tuples with tuples of non-tuple constants.

Fix crash in HloInstruction::ToString() for tuple constants.

PiperOrigin-RevId: 161085252
---
 .../xla/service/algebraic_simplifier.cc       | 29 +++++++++++++++++++
 .../xla/service/algebraic_simplifier_test.cc  | 20 +++++++++++++
 .../compiler/xla/service/hlo_instruction.cc   |  4 +--
 tensorflow/compiler/xla/tests/tuple_test.cc   | 19 ++++++++++++
 4 files changed, 70 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index cbe47e5128..bc6a879b8a 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -126,6 +126,9 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault {
       HloInstruction* concatenate,
       tensorflow::gtl::ArraySlice<HloInstruction*> operands) override;
 
+  Status HandleConstant(HloInstruction* constant,
+                        const Literal& literal) override;
+
   Status HandleCopy(HloInstruction* copy) override;
 
   Status HandleConvert(HloInstruction* convert) override;
@@ -411,6 +414,32 @@ Status AlgebraicSimplifierVisitor::HandleConcatenate(
   return Status::OK();
 }
 
+static HloInstruction* BuildTupleConstant(HloComputation* computation,
+                                          const Literal& literal) {
+  if (ShapeUtil::IsTuple(literal.shape())) {
+    std::vector<HloInstruction*> elems;
+    elems.reserve(ShapeUtil::TupleElementCount(literal.shape()));
+    for (const Literal& child : literal.tuple_literals()) {
+      elems.push_back(BuildTupleConstant(computation, child));
+    }
+    return computation->AddInstruction(HloInstruction::CreateTuple(elems));
+  } else {
+    return computation->AddInstruction(
+        HloInstruction::CreateConstant(MakeUnique<Literal>(literal)));
+  }
+}
+
+Status AlgebraicSimplifierVisitor::HandleConstant(HloInstruction* constant,
+                                                  const Literal& literal) {
+  // Tuple constants aren't directly supported by any backend. Expand them into
+  // explicit Tuple instructions.
+  if (ShapeUtil::IsTuple(constant->shape())) {
+    return ReplaceInstruction(constant,
+                              BuildTupleConstant(computation_, literal));
+  }
+  return Status::OK();
+}
+
 Status AlgebraicSimplifierVisitor::HandleSubtract(HloInstruction* sub,
                                                   HloInstruction* lhs,
                                                   HloInstruction* rhs) {
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index 95230b040f..aceef6cd5d 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -1958,6 +1958,26 @@ TEST_F(AlgebraicSimplifierTest, IteratorInvalidation) {
   ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
 }
 
+// Test that a constant with tuple shape becomes a tuple of constants.
+TEST_F(AlgebraicSimplifierTest, ConstantTupleBecomesTupleOfConstants) {
+  HloComputation::Builder builder(TestName());
+  const float constant_scalar = 7.3f;
+  std::initializer_list<float> constant_vector = {1.1f, 2.0f, 3.3f};
+  std::unique_ptr<Literal> value =
+      Literal::MakeTuple({Literal::CreateR0<float>(constant_scalar).get(),
+                          Literal::CreateR1<float>(constant_vector).get()});
+  builder.AddInstruction(HloInstruction::CreateConstant(std::move(value)));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+  EXPECT_THAT(computation->root_instruction(),
+              op::Tuple(op::Constant(), op::Constant()));
+}
+
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 5c79605a77..f0f97c80f7 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1494,7 +1494,7 @@ string HloInstruction::ToString(bool compact_operands,
   string operands;
   if (opcode() == HloOpcode::kConstant) {
     // For constants, show the actual value in place of an empty operand list.
-    if (ShapeUtil::ElementsIn(shape()) <= 10) {
+    if (!ShapeUtil::IsTuple(shape()) && ShapeUtil::ElementsIn(shape()) <= 10) {
       // Literal::ToString emits multidimensional arrays over multiple
       // lines. Compact this into one line by stripping out white space.
       string tmp = literal().ToString();
@@ -1511,7 +1511,7 @@ string HloInstruction::ToString(bool compact_operands,
         first = false;
       }
     } else {
-      // Do not show large constants.
+      // Do not show large constants or tuples.
       operands = "{...}";
     }
   } else if (opcode() == HloOpcode::kParameter) {
diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc
index be3cdbca09..4a1c3fe962 100644
--- a/tensorflow/compiler/xla/tests/tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/tuple_test.cc
@@ -39,6 +39,25 @@ class TupleTest : public ClientLibraryTestBase {
   ErrorSpec error_spec_{0.0001};
 };
 
+// Tests a tuple-shaped constant.
+XLA_TEST_F(TupleTest, TupleConstant) {
+  ComputationBuilder builder(client_, TestName());
+
+  const float constant_scalar = 7.3f;
+  std::initializer_list<float> constant_vector = {1.1f, 2.0f, 3.3f};
+  std::initializer_list<std::initializer_list<float>> constant_matrix = {
+      {1.1f, 2.2f, 3.5f},  // row 0
+      {4.8f, 5.0f, 6.7f},  // row 1
+  };
+  auto value =
+      Literal::MakeTuple({Literal::CreateR0<float>(constant_scalar).get(),
+                          Literal::CreateR1<float>(constant_vector).get(),
+                          Literal::CreateR2<float>(constant_matrix).get()});
+
+  auto result = builder.ConstantLiteral(*value);
+  ComputeAndCompareTuple(&builder, *value, {}, error_spec_);
+}
+
 // Tests the creation of tuple data.
 XLA_TEST_F(TupleTest, TupleCreate) {
   ComputationBuilder builder(client_, TestName());
-- 
GitLab


From 4c3df76624101214f56470d7f775d1d1cbd98a20 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 08:57:33 -0700
Subject: [PATCH 0737/1375] During send_recv scheduling, ensure start_time for
 nodes is always increasing in dependency order, to avoid introducing loops
 when scheduling.

PiperOrigin-RevId: 161085351
---
 tensorflow/core/graph/graph_partition.cc      | 144 ++++++++++---
 tensorflow/core/graph/graph_partition_test.cc | 191 +++++++++++++++++-
 2 files changed, 310 insertions(+), 25 deletions(-)

diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index f452299a83..ab6fa62f34 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -16,7 +16,9 @@ limitations under the License.
 #include "tensorflow/core/graph/graph_partition.h"
 
 #include <deque>
+#include <queue>
 #include <unordered_map>
+#include <unordered_set>
 #include <utility>
 #include <vector>
 
@@ -30,6 +32,7 @@ limitations under the License.
 #include "tensorflow/core/graph/costmodel.h"
 #include "tensorflow/core/graph/graph_def_builder.h"
 #include "tensorflow/core/graph/node_builder.h"
+#include "tensorflow/core/graph/tensor_id.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/platform/logging.h"
@@ -39,6 +42,15 @@ namespace tensorflow {
 
 namespace {
 
+inline bool IsMerge(const NodeDef& node_def) {
+  return node_def.op() == "Merge" || node_def.op() == "RefMerge";
+}
+
+inline bool IsNextIteration(const NodeDef& node_def) {
+  return node_def.op() == "NextIteration" ||
+         node_def.op() == "RefNextIteration";
+}
+
 struct DupRecvKey {
   int src_node_id;           // Edge's src node id
   int src_output_slot;       // Edge's src node output slot
@@ -723,7 +735,106 @@ Status AddControlFlow(const PartitionOptions& opts, Graph* g,
   return Status::OK();
 }
 
-}  // end namespace
+struct PriorityTopoSortNode {
+  PriorityTopoSortNode(const NodeDef* n, int64 st) : node(n), start_time(st) {}
+
+  const NodeDef* node;
+  int64 start_time;
+};
+
+struct PriorityTopoSortNodeGreater {
+  bool operator()(const PriorityTopoSortNode& left,
+                  const PriorityTopoSortNode& right) {
+    return left.start_time > right.start_time;
+  }
+};
+
+}  // namespace
+
+// Returns in <nodes> the nodes that should participate in epoch-based recv
+// scheduling, along with their times; <nodes> is ordered by increasing
+// start_time. Returns in <node_to_start_time_out> the timing for all nodes,
+// even those not in <nodes>.
+//
+// Comparing to sorting on the node's start time only, this also processes the
+// nodes in dependency order, and updates start times to ensure a node's
+// start_time > the start time for all dependencies.
+//
+// Note that graph_partition_test.cc accesses this function for testing, even
+// though it's not declared in the header.
+Status TopologicalSortNodesWithTimePriority(
+    const GraphDef* gdef, std::vector<std::pair<const NodeDef*, int64>>* nodes,
+    std::unordered_map<const NodeDef*, int64>* node_to_start_time_out) {
+  // Queue of nodes to process; lowest start time is returned first.
+  std::priority_queue<PriorityTopoSortNode, std::vector<PriorityTopoSortNode>,
+                      PriorityTopoSortNodeGreater>
+      q;
+  std::unordered_map<const NodeDef*, int64> node_to_start_time;
+  auto enqueue = [&q, &node_to_start_time](const NodeDef* node) {
+    const int64 start_time = node_to_start_time[node];
+    q.emplace(node, start_time);
+  };
+
+  // Build initial structures, initial contents of queue.
+  std::unordered_map<string, std::vector<const NodeDef*>> node_to_output_nodes;
+  std::unordered_map<const NodeDef*, int> inputs_needed;
+  for (int n = 0; n < gdef->node_size(); ++n) {
+    const NodeDef* ndef = &gdef->node(n);
+    for (int i = 0; i < ndef->input_size(); ++i) {
+      node_to_output_nodes[ParseTensorName(ndef->input(i)).first.ToString()]
+          .push_back(ndef);
+    }
+    int64 start_time;
+    TF_RETURN_IF_ERROR(GetNodeAttr(*ndef, "_start_time", &start_time));
+    node_to_start_time[ndef] = start_time;
+    inputs_needed[ndef] = ndef->input_size();
+    if (ndef->input_size() == 0) {
+      enqueue(ndef);
+    }
+  }
+
+  // Determine which merge nodes are parts of loops; these
+  // need to happen in the traversal after all non-NextIteration inputs
+  // are run.
+  for (int n = 0; n < gdef->node_size(); ++n) {
+    const NodeDef* ndef = &gdef->node(n);
+    if (IsNextIteration(*ndef)) {
+      for (const NodeDef* n : node_to_output_nodes[ndef->name()]) {
+        if (IsMerge(*n)) {
+          // n is a merge that is part of a loop structure.
+          // It doesn't need to wait for this NextIteration loop
+          // when doing the traversal.
+          --inputs_needed[n];
+        }
+      }
+    }
+  }
+
+  // Traverse.
+  std::vector<std::pair<const NodeDef*, int64>> start_times;
+  start_times.reserve(gdef->node_size());
+  while (!q.empty()) {
+    PriorityTopoSortNode cur = q.top();
+    q.pop();
+
+    start_times.emplace_back(cur.node, cur.start_time);
+
+    for (const NodeDef* n : node_to_output_nodes[cur.node->name()]) {
+      auto& output_start_time = node_to_start_time[n];
+      if (output_start_time <= cur.start_time) {
+        output_start_time = cur.start_time + 1;
+      }
+      if (--inputs_needed[n] == 0) {
+        enqueue(n);
+      }
+    }
+  }
+
+  // Done.
+  nodes->swap(start_times);
+  node_to_start_time_out->swap(node_to_start_time);
+  return Status::OK();
+}
 
 Status AddControlEdges(const PartitionOptions& opts,
                        std::unordered_map<string, GraphDef>* partitions) {
@@ -732,27 +843,16 @@ Status AddControlEdges(const PartitionOptions& opts,
   const int num_epochs = 100;
   const int prefetch = 6;
 
-  typedef std::pair<const NodeDef*, int64> NodeStartTime;
   for (auto& part : *partitions) {
     GraphDef* gdef = &part.second;
-
-    std::vector<NodeStartTime> start_times;
-    start_times.resize(gdef->node_size());
-    for (int n = 0; n < gdef->node_size(); ++n) {
-      const NodeDef& ndef = gdef->node(n);
-      int64 start_time;
-      status = GetNodeAttr(ndef, "_start_time", &start_time);
-      if (!status.ok()) {
-        return status;
-      }
-      start_times[n] = std::make_pair(&ndef, start_time);
+    std::vector<std::pair<const NodeDef*, int64>> start_times;
+    std::unordered_map<const NodeDef*, int64> node_to_start_time;
+    status = TopologicalSortNodesWithTimePriority(gdef, &start_times,
+                                                  &node_to_start_time);
+    if (!status.ok()) {
+      return status;
     }
 
-    // Sort the nodes based on their start times.
-    std::sort(
-        start_times.begin(), start_times.end(),
-        [](NodeStartTime x, NodeStartTime y) { return x.second < y.second; });
-
     // Add a dummy node for every epoch, and add a control edge from the
     // "last" node in the preceding epoch to the dummy node.
     string device_name = gdef->node(0).device();
@@ -784,12 +884,8 @@ Status AddControlEdges(const PartitionOptions& opts,
     for (int n = 0; n < gdef->node_size(); ++n) {
       NodeDef* ndef = gdef->mutable_node(n);
       if (ndef->op() == "_Recv") {
-        int64 start_time;
-        status = GetNodeAttr(*ndef, "_start_time", &start_time);
-        if (!status.ok()) {
-          return status;
-        }
-        int recv_epoch = start_time / resolution;
+        const int64 start_time = node_to_start_time[ndef];
+        const int recv_epoch = start_time / resolution;
         if (recv_epoch >= prefetch) {
           NodeDef* dummy = dummys[recv_epoch - prefetch];
           AddInput(ndef, dummy->name(), Graph::kControlSlot);
diff --git a/tensorflow/core/graph/graph_partition_test.cc b/tensorflow/core/graph/graph_partition_test.cc
index cb9e4b7973..c54b9592fb 100644
--- a/tensorflow/core/graph/graph_partition_test.cc
+++ b/tensorflow/core/graph/graph_partition_test.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/const_op.h"
 #include "tensorflow/cc/ops/control_flow_ops.h"
 #include "tensorflow/cc/ops/control_flow_ops_internal.h"
+#include "tensorflow/cc/ops/math_ops.h"
 #include "tensorflow/cc/ops/random_ops.h"
 #include "tensorflow/cc/ops/sendrecv_ops.h"
 #include "tensorflow/core/framework/function_testlib.h"
@@ -39,6 +40,14 @@ limitations under the License.
 #include "tensorflow/core/util/equal_graph_def.h"
 
 namespace tensorflow {
+
+using strings::StrCat;
+
+// from graph_partition.cc
+extern Status TopologicalSortNodesWithTimePriority(
+    const GraphDef* gdef, std::vector<std::pair<const NodeDef*, int64>>* nodes,
+    std::unordered_map<const NodeDef*, int64>* node_to_start_time_out);
+
 namespace {
 
 const char gpu_device[] = "/job:a/replica:0/task:0/gpu:0";
@@ -52,7 +61,7 @@ string DeviceName(const Node* node) {
   } else {
     const string cpu_prefix = "/job:a/replica:0/task:0/cpu:";
     int index = first - 'A';
-    return strings::StrCat(cpu_prefix, index);
+    return StrCat(cpu_prefix, index);
   }
 }
 
@@ -436,5 +445,185 @@ TEST_F(GraphPartitionTest, Functions) {
   ExpectFunctions(partitions_[b].library(), {"XTimesTwo", "XTimesFour"});
 }
 
+TEST(TopologicalSortNodesWithTimePriorityTest, NoDependencies) {
+  // Create placeholders, shuffle them so the order in the graph is not strictly
+  // increasing.
+  Scope root = Scope::NewRootScope().ExitOnError();
+  std::vector<int> indexes;
+  for (int i = 0; i < 20; ++i) {
+    indexes.push_back((i + 2001) % 20);
+  }
+  std::vector<ops::Placeholder> placeholders;
+  for (int i : indexes) {
+    placeholders.emplace_back(root.WithOpName(StrCat("p", i)), DT_FLOAT);
+    placeholders.back().node()->AddAttr("_start_time", i + 1);
+  }
+
+  GraphDef gdef;
+  TF_EXPECT_OK(root.ToGraphDef(&gdef));
+
+  std::vector<std::pair<const NodeDef*, int64>> nodes;
+  std::unordered_map<const NodeDef*, int64> node_to_start_time;
+  TF_CHECK_OK(
+      TopologicalSortNodesWithTimePriority(&gdef, &nodes, &node_to_start_time));
+  ASSERT_EQ(nodes.size(), 20);
+  for (int i = 0; i < nodes.size(); ++i) {
+    EXPECT_EQ(StrCat("p", i), nodes[i].first->name());
+    EXPECT_EQ(i + 1, nodes[i].second);
+  }
+}
+
+TEST(TopologicalSortNodesWithTimePriority, Dependencies) {
+  // Create placeholders, shuffle them so the order in the graph is not strictly
+  // increasing.
+  Scope root = Scope::NewRootScope().ExitOnError();
+  std::vector<int> indexes;
+  std::vector<ops::Placeholder> placeholders_in_order;
+  const int num_leaves = 20;
+  for (int i = 0; i < num_leaves; ++i) {
+    indexes.push_back((i + 2001) % num_leaves);
+    placeholders_in_order.emplace_back(root.WithOpName(StrCat("p", i)),
+                                       DT_FLOAT);
+    placeholders_in_order.back().node()->AddAttr("_start_time", i + 1);
+  }
+  std::vector<ops::Placeholder> placeholders;
+  for (int i : indexes) {
+    placeholders.push_back(placeholders_in_order[i]);
+  }
+
+  // Create ops that depend on the placeholders. We give start times to these
+  // that are in descending order (e.g., the op that depends on the first
+  // placeholder runs last).
+  std::vector<ops::Square> squares;
+  for (int i : indexes) {
+    squares.emplace_back(root.WithOpName(StrCat("s", i)), placeholders[i]);
+    squares.back().node()->AddAttr("_start_time", 50 - (i + 1));
+  }
+
+  // Create addn to sum all squares.
+  std::vector<Input> inputs;
+  for (const auto& s : squares) inputs.push_back(s);
+  ops::AddN addn = ops::AddN(root.WithOpName("addn"),
+                             tensorflow::gtl::ArraySlice<Input>(inputs));
+  // Start times is actually listed earlier than the nodes it depends on.
+  // But because of dependency ordering, it is last in the list.
+  addn.node()->AddAttr("_start_time", 1);
+
+  GraphDef gdef;
+  TF_EXPECT_OK(root.ToGraphDef(&gdef));
+
+  std::vector<std::pair<const NodeDef*, int64>> nodes;
+  std::unordered_map<const NodeDef*, int64> node_to_start_time;
+  TF_CHECK_OK(
+      TopologicalSortNodesWithTimePriority(&gdef, &nodes, &node_to_start_time));
+  ASSERT_EQ(1 + squares.size() + placeholders.size(), nodes.size());
+  for (int i = 0; i < placeholders.size(); ++i) {
+    const NodeDef* node = nodes[i].first;
+    EXPECT_EQ(StrCat("p", i), node->name());
+    EXPECT_EQ(i + 1, nodes[i].second);
+    EXPECT_EQ(i + 1, node_to_start_time[node]);
+  }
+  for (int i = 0; i < squares.size(); ++i) {
+    int node_index = placeholders.size() + i;
+    int square_index = num_leaves - 1 - i;
+    const NodeDef* node = nodes[node_index].first;
+    EXPECT_EQ(StrCat("s", square_index), node->name());
+    EXPECT_EQ(50 - (square_index + 1), nodes[node_index].second);
+    EXPECT_EQ(50 - (square_index + 1), node_to_start_time[node]);
+  }
+  EXPECT_EQ("addn", nodes.back().first->name());
+  EXPECT_EQ(50, nodes.back().second);
+  EXPECT_EQ(50, node_to_start_time[nodes.back().first]);
+}
+
+TEST(TopologicalSortNodesWithTimePriority, WhileLoop) {
+  using namespace ::tensorflow::ops;            // NOLINT(build/namespaces)
+  using namespace ::tensorflow::ops::internal;  // NOLINT(build/namespaces)
+
+  // Create placeholders.
+  Scope root = Scope::NewRootScope().ExitOnError();
+  std::vector<int> indexes;
+  std::vector<Placeholder> placeholders_in_order;
+  const int num_leaves = 20;
+  for (int i = 0; i < num_leaves; ++i) {
+    indexes.push_back((i + 2001) % num_leaves);
+    placeholders_in_order.emplace_back(root.WithOpName(StrCat("p", i)),
+                                       DT_FLOAT);
+    placeholders_in_order.back().node()->AddAttr("_start_time", i + 1);
+  }
+  std::vector<Placeholder> placeholders;
+  for (int i : indexes) {
+    placeholders.push_back(placeholders_in_order[i]);
+  }
+
+  // Add a while loop above each placeholder.
+  std::vector<Exit> while_exits;
+  const int nodes_per_loop = 8;
+  for (int i : indexes) {
+    Scope scope = root.NewSubScope(StrCat("while", i));
+    auto dummy = Placeholder(scope, DT_FLOAT);
+
+    Enter enter(scope, placeholders[i], StrCat("frame", i));
+    Merge merge(scope, std::initializer_list<Input>{enter, dummy});
+    auto cv = Const(scope.WithControlDependencies({merge.output}), false);
+    LoopCond loop_cond(scope, cv);
+    Switch switch_node(scope, merge.output, loop_cond);
+    Identity identity(scope, switch_node.output_true);
+    NextIteration next_iteration(scope, identity);
+    while_exits.emplace_back(scope.WithOpName("exit"),
+                             switch_node.output_false);
+
+    // Complete loop by removing dummy node and attaching NextIteration to
+    // that input of the merge node.
+    scope.graph()->RemoveNode(dummy.node());
+    scope.graph()->AddEdge(next_iteration.node(), 0, merge.output.node(), 1);
+
+    int base_start_time = i * 10 + 100;
+    for (auto op : std::vector<Output>{enter, merge.output, cv, loop_cond,
+                                       switch_node.output_false, identity,
+                                       next_iteration, while_exits.back()}) {
+      op.node()->AddAttr("_start_time", base_start_time++);
+    }
+  }
+
+  // Create ops that depend on the loop exits.
+  std::vector<Square> squares;
+  for (int i : indexes) {
+    squares.emplace_back(root.WithOpName(StrCat("s", i)), while_exits[i]);
+    squares.back().node()->AddAttr("_start_time", 500 - (i + 1));
+  }
+
+  GraphDef gdef;
+  TF_EXPECT_OK(root.ToGraphDef(&gdef));
+
+  // Run the sort. The while loop nodes do not appear in the output <nodes>.
+  std::vector<std::pair<const NodeDef*, int64>> nodes;
+  std::unordered_map<const NodeDef*, int64> node_to_start_time;
+  TF_CHECK_OK(
+      TopologicalSortNodesWithTimePriority(&gdef, &nodes, &node_to_start_time));
+  ASSERT_LT(while_exits.size() + squares.size() + placeholders.size(),
+            nodes.size());
+  int node_index = 0;
+  for (int i = 0; i < placeholders.size(); ++i, ++node_index) {
+    const NodeDef* node = nodes[i].first;
+    EXPECT_EQ(StrCat("p", i), node->name());
+    EXPECT_EQ(i + 1, nodes[i].second);
+    EXPECT_EQ(i + 1, node_to_start_time[node]);
+  }
+  for (int i = 0; i < while_exits.size(); ++i, node_index += nodes_per_loop) {
+    const NodeDef* node = nodes[node_index].first;
+    EXPECT_EQ(StrCat("while", i, "/Enter"), node->name());
+    EXPECT_EQ(100 + i * 10, nodes[node_index].second);
+    EXPECT_EQ(100 + i * 10, node_to_start_time[node]);
+  }
+  for (int i = 0; i < squares.size(); ++i, ++node_index) {
+    int square_index = num_leaves - 1 - i;
+    const NodeDef* node = nodes[node_index].first;
+    EXPECT_EQ(StrCat("s", square_index), node->name());
+    EXPECT_EQ(500 - (square_index + 1), nodes[node_index].second);
+    EXPECT_EQ(500 - (square_index + 1), node_to_start_time[node]);
+  }
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From de2a2df5310880607017b461b40a43f418de0ba6 Mon Sep 17 00:00:00 2001
From: Igor Saprykin <isaprykin@google.com>
Date: Thu, 6 Jul 2017 09:10:15 -0700
Subject: [PATCH 0738/1375] Fix the broken image in the tf/extend/estimators
 tutorial.

PiperOrigin-RevId: 161086959
---
 tensorflow/docs_src/extend/estimators.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index 500beb49a2..a18da1708d 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -77,8 +77,8 @@ for abalone:
 
 The label to predict is number of rings, as a proxy for abalone age.
 
-![Abalone shell](https://www.tensorflow.org/abalone_shell.jpg) **[“Abalone
-shell”](https://www.flickr.com/photos/thenickster/16641048623/) (by [Nicki Dugan
+![Abalone shell](https://www.tensorflow.org/images/abalone_shell.jpg)
+**[“Abalone shell”](https://www.flickr.com/photos/thenickster/16641048623/) (by [Nicki Dugan
 Pogue](https://www.flickr.com/photos/thenickster/), CC BY-SA 2.0)**
 
 ## Setup
-- 
GitLab


From 60cfb47452c48743ea7567d62730fa2c23b498e8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 09:15:31 -0700
Subject: [PATCH 0739/1375] Allowing the PIP_TEST_ROOT variable to be set.

PiperOrigin-RevId: 161087696
---
 tensorflow/tools/ci_build/builds/pip.sh | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index 85c712d3c6..7cb93c1774 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -23,7 +23,7 @@
 #
 # When executing the Python unit tests, the script obeys the shell
 # variables: TF_BUILD_BAZEL_CLEAN, TF_BUILD_INSTALL_EXTRA_PIP_PACKAGES,
-# NO_TEST_ON_INSTALL
+# NO_TEST_ON_INSTALL, PIP_TEST_ROOT
 #
 # TF_BUILD_BAZEL_CLEAN, if set to any non-empty and non-0 value, directs the
 # script to perform bazel clean prior to main build and test steps.
@@ -41,6 +41,9 @@
 # If NO_TEST_TFDBG_BINARIES has any non-empty and non-0 value, the testing of
 # TensorFlow Debugger (tfdbg) binaries and examples will be skipped.
 #
+# If PIP_TEST_ROOT has a non-empty and a non-0 value, the whl files will be
+# placed in that directory.
+#
 # Any flags not listed in the usage above will be passed directly to Bazel.
 #
 # If the --test_tutorials flag is set, it will cause the script to run the
@@ -162,7 +165,10 @@ echo "Python binary path to be used in PIP install: ${PYTHON_BIN_PATH} "\
 "(Major.Minor version: ${PY_MAJOR_MINOR_VER})"
 
 # Build PIP Wheel file
-PIP_TEST_ROOT="pip_test"
+# Set default pip file folder unless specified by env variable
+if [ -z "$PIP_TEST_ROOT" ]; then
+  PIP_TEST_ROOT="pip_test"
+fi
 PIP_WHL_DIR="${PIP_TEST_ROOT}/whl"
 PIP_WHL_DIR=$(realpath ${PIP_WHL_DIR})  # Get absolute path
 rm -rf ${PIP_WHL_DIR} && mkdir -p ${PIP_WHL_DIR}
-- 
GitLab


From 5fc69181f192c5265dfb8a38b802ffad953c7577 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 09:17:39 -0700
Subject: [PATCH 0740/1375] Make the pruning of the graph in ahead-of-time
 compilation also prune out dependencies of fed tensors.

Remove special handling of control dependencies from the introduced
placeholders.  For a compilation going to XLA, those control dependencies would
not really be doing anything anyway (and the current code was only handling
Placeholder, not PlaceholderV2).

PiperOrigin-RevId: 161087978
---
 tensorflow/compiler/aot/BUILD                 |  25 ++++-
 tensorflow/compiler/aot/compile.cc            |  91 ++++++++-------
 .../compiler/aot/test_graph_tfunknownop.pbtxt |  54 +++------
 .../aot/test_graph_tfunknownop2.config.pbtxt  |  25 +++++
 .../aot/test_graph_tfunknownop3.config.pbtxt  |  26 +++++
 tensorflow/compiler/aot/tfcompile.proto       |   7 ++
 tensorflow/compiler/aot/tfcompile_util.cc     | 106 +++++++++++++++++-
 tensorflow/compiler/aot/tfcompile_util.h      |  15 +++
 8 files changed, 260 insertions(+), 89 deletions(-)
 create mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
 create mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 12179b7637..b12b5318ec 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -156,7 +156,7 @@ tf_library(
 )
 
 # A test of tf_library that includes a graph with an unknown op, but where
-# the compilation works because the the unknown op is not needed for the fetches.
+# the compilation works because the unknown op is not needed for the fetches.
 tf_library(
     name = "test_graph_tfunknownop",
     testonly = 1,
@@ -166,6 +166,29 @@ tf_library(
     tags = ["manual"],
 )
 
+# A test of tf_library that includes a graph with an unknown op, but where
+# the compilation works because the op between the unknown op and the
+# fetches is a feed.
+tf_library(
+    name = "test_graph_tfunknownop2",
+    testonly = 1,
+    config = "test_graph_tfunknownop2.config.pbtxt",
+    cpp_class = "UnknownOpAddComp",
+    graph = "test_graph_tfunknownop.pbtxt",
+    tags = ["manual"],
+)
+
+# A test of tf_library that includes a graph with an unknown op, but where
+# the compilation works because the unknown op is fed.
+tf_library(
+    name = "test_graph_tfunknownop3",
+    testonly = 1,
+    config = "test_graph_tfunknownop3.config.pbtxt",
+    cpp_class = "UnknownOpAddComp",
+    graph = "test_graph_tfunknownop.pbtxt",
+    tags = ["manual"],
+)
+
 # Utility library for benchmark binaries, used by the *_benchmark rules that are
 # added by the tfcompile bazel macro.
 cc_library(
diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc
index 59ff14600b..51d08eaa01 100644
--- a/tensorflow/compiler/aot/compile.cc
+++ b/tensorflow/compiler/aot/compile.cc
@@ -78,66 +78,51 @@ Status DumpGraph(const MainFlags& flags, const string& name,
   return WriteTextProto(Env::Default(), file, graph_def);
 }
 
-string TensorIdToString(const TensorId& id) {
-  return strings::StrCat(id.node_name(), ":", id.output_index());
-}
-
 typedef std::unordered_map<string, Node*> NodeMap;
 
 // Each feed id identifies the positional output of some node, which may consist
-// of multiple edges.  For each feed node, replaces all matching edges so that
-// they point from a new _Arg node instead.
+// of multiple edges. AddPlaceholdersForFeeds has already replaced each fed
+// tensor with a placeholder.  For each feed tensor, replaces all edges so they
+// point from a new _Arg node instead.
 Status AddArgNodes(Graph* graph, const NodeMap& node_map,
-                   const protobuf::RepeatedPtrField<Feed>& feeds) {
+                   const protobuf::RepeatedPtrField<Feed>& feeds,
+                   const std::unordered_map<string, string>& feed_remapping) {
   for (int arg_index = 0; arg_index < feeds.size(); ++arg_index) {
     const Feed& feed = feeds[arg_index];
-    const TensorId& id = feed.id();
-    auto it = node_map.find(id.node_name());
-    if (it == node_map.end()) {
-      return errors::NotFound("Can't find feed id: ", TensorIdToString(id));
-    }
-    const Node* feed_node = it->second;
-    if (id.output_index() >= feed_node->num_outputs()) {
-      return errors::InvalidArgument("Invalid feed id: ", TensorIdToString(id),
-                                     ", output index should be < ",
-                                     feed_node->num_outputs());
-    }
-    // TODO(toddw): Invoke shape inference on the graph and add a "_shape" attr
-    // if we can determine it.  That way the graph will be initialized with
-    // whatever shapes we can infer, while the user can still explicitly specify
-    // or override them.
+    // All feeds have been replaced by placeholders.
+    const int output_index = 0;
+
+    const auto remap_it = feed_remapping.find(TensorIdToString(feed.id()));
+    auto node_it = node_map.find(remap_it->second);
+    const Node* feed_node = node_it->second;
+
+    // TODO(toddw): Invoke shape inference in AddPlaceholdersForFeeds and add a
+    // "_shape" attr if we can determine it.  That way the graph will be
+    // initialized with whatever shapes we can infer, while the user can still
+    // explicitly specify or override them.
     Node* arg_node = nullptr;
     TF_RETURN_IF_ERROR(
         NodeBuilder(strings::StrCat("_arg_", arg_index), kArgOp)
-            .Attr("T", BaseType(feed_node->output_type(id.output_index())))
+            .Attr("T", BaseType(feed_node->output_type(output_index)))
             .Attr("index", arg_index)
-            .Attr(kFeedIdAttr, TensorIdToString(id))
+            .Attr(kFeedIdAttr, TensorIdToString(feed.id()))
             .Attr(kShapeAttr, TensorShape(feed.shape()))
             .Attr(kDebugNameAttr, feed.name())
             .Finalize(graph, &arg_node));
+
     // Collects out-edges from the feed node that have a matching edge index;
-    // these will be replaced with edges from the arg node instead.  Also
-    // replaces all control edges from Placeholder feed nodes; similar code
-    // exists in subgraph::RewriteGraphForExecution.
-    // TODO(toddw): Why only replace control edges from Placeholder?
+    // these will be replaced with edges from the arg node instead.
     //
     // We must collect the edges first and process them in a second pass, since
     // removing the edge from the graph invalidates feed_node->out_edges.
     std::vector<const Edge*> feed_edges;
     for (const Edge* edge : feed_node->out_edges()) {
-      if (edge->src_output() == id.output_index() ||
-          (edge->src_output() == Graph::kControlSlot &&
-           feed_node->type_string() == "Placeholder")) {
+      if (edge->src_output() == output_index) {
         feed_edges.push_back(edge);
       }
     }
     for (const Edge* edge : feed_edges) {
-      if (edge->src_output() == id.output_index()) {
-        graph->AddEdge(arg_node, 0, edge->dst(), edge->dst_input());
-      } else {
-        CHECK_EQ(edge->src_output(), Graph::kControlSlot);
-        graph->AddControlEdge(arg_node, edge->dst());
-      }
+      graph->AddEdge(arg_node, 0, edge->dst(), edge->dst_input());
       graph->RemoveEdge(edge);
     }
   }
@@ -179,13 +164,16 @@ Status AddRetvalNodes(Graph* graph, const NodeMap& node_map,
 // fetch ids respectively), and rewrites the edges so that inputs flow from _Arg
 // nodes, and outputs flow to _Retval nodes.  This allows the symbolic graph
 // execution to know the input and output args for the generated function.
-Status RewriteAndPruneGraph(Graph* graph, const Config& config,
-                            const MainFlags& flags) {
+Status RewriteAndPruneGraph(
+    Graph* graph, const Config& config,
+    const std::unordered_map<string, string>& feed_remapping,
+    const MainFlags& flags) {
   NodeMap node_map;
   for (Node* n : graph->nodes()) {
     node_map[n->name()] = n;
   }
-  TF_RETURN_IF_ERROR(AddArgNodes(graph, node_map, config.feed()));
+  TF_RETURN_IF_ERROR(
+      AddArgNodes(graph, node_map, config.feed(), feed_remapping));
   std::unordered_set<const Node*> retval_nodes;
   TF_RETURN_IF_ERROR(
       AddRetvalNodes(graph, node_map, config.fetch(), &retval_nodes));
@@ -383,17 +371,28 @@ Status InitGraph(const GraphDef& graph_def, const Config& config,
   FunctionLibraryDefinition flib_def(OpRegistry::Global(), graph_def.library());
   std::unique_ptr<Graph> g(new Graph(flib_def));
 
-  GraphDef copy_def;
+  // Replace references to fed tensors with references to newly added
+  // placeholders.
+  GraphDef first_copy_def = graph_def;
+
+  // Maps from name:port of a feed to the name:port of the placeholder to use.
+  std::unordered_map<string, string> feed_remapping;
+  TF_RETURN_IF_ERROR(AddPlaceholdersForFeeds(config, g->op_registry(),
+                                             &feed_remapping, &first_copy_def));
 
   // Prune the GraphDef first so that unknown ops that we aren't compiling get
   // filtered out.
-  TF_RETURN_IF_ERROR(PruneGraphDefInto(config, graph_def, &copy_def));
+  GraphDef second_copy_def;
+  TF_RETURN_IF_ERROR(
+      PruneGraphDefInto(config, first_copy_def, &second_copy_def));
+
+  TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(
+      &second_copy_def, *g->op_registry(), 0 /*node_offset*/));
 
-  TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&copy_def, *g->op_registry(),
-                                               0 /*node_offset*/));
+  TF_RETURN_IF_ERROR(ConvertGraphDefToGraph(GraphConstructorOptions(),
+                                            second_copy_def, g.get()));
   TF_RETURN_IF_ERROR(
-      ConvertGraphDefToGraph(GraphConstructorOptions(), copy_def, g.get()));
-  TF_RETURN_IF_ERROR(RewriteAndPruneGraph(g.get(), config, flags));
+      RewriteAndPruneGraph(g.get(), config, feed_remapping, flags));
   *graph = std::move(g);
   return Status::OK();
 }
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
index 212ffbb5ff..48b881bb94 100644
--- a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
@@ -6,21 +6,12 @@ node {
     value {
       tensor {
         dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
+        tensor_shape { dim { size: 1 } }
         int_val: 1
       }
     }
   }
-  attr {
-    key  : "dtype"
-    value {
-      type       : DT_INT32
-    }
-  }
+  attr { key  : "dtype" value { type: DT_INT32 } }
 }
 node {
   name  : "y_const"
@@ -30,56 +21,37 @@ node {
     value {
       tensor {
         dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
+        tensor_shape { dim { size: 1 } }
         int_val: 2
       }
     }
   }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
+  attr { key: "dtype" value { type: DT_INT32 } }
 }
 node {
   name  : "x_y_sum"
   op    : "Add"
   input : "x_const"
   input : "y_const"
-  attr {
-    key  : "T"
-    value {
-      type: DT_INT32
-    }
-  }
+  attr { key  : "T" value { type: DT_INT32 } }
 }
 node {
   name  : "z"
   op    : "SomeUnknownOp"
   input : "x_const"
-  attr {
-    key  : "T"
-    value {
-      type: DT_INT32
-    }
-  }
+}
+node {
+  name  : "z_identity"
+  op    : "Identity"
+  input : "z:1"
+  attr { key  : "T" value { type: DT_INT32 } }
 }
 node {
   name  : "x_z_sum"
   op    : "Add"
   input : "x_const"
-  input : "z"
-  attr {
-    key  : "T"
-    value {
-      type: DT_INT32
-    }
-  }
+  input : "z_identity"
+  attr { key  : "T" value { type: DT_INT32 } }
 }
 versions {
   producer: 15
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
new file mode 100644
index 0000000000..7370ed370d
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
@@ -0,0 +1,25 @@
+# Text form of tensorflow.tfcompile.Config proto.
+feed {
+  id { node_name: "x_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "y_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "z_identity"}
+  shape {
+    dim { size: 1 }
+  }
+}
+fetch {
+  id { node_name: "x_y_sum" }
+}
+fetch {
+  id { node_name: "x_z_sum" }
+}
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt
new file mode 100644
index 0000000000..b2d7d54574
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt
@@ -0,0 +1,26 @@
+# Text form of tensorflow.tfcompile.Config proto.
+feed {
+  id { node_name: "x_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "y_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "z" output_index: 1}
+  shape {
+    dim { size: 1 }
+  }
+  type: DT_INT32
+}
+fetch {
+  id { node_name: "x_y_sum" }
+}
+fetch {
+  id { node_name: "x_z_sum" }
+}
diff --git a/tensorflow/compiler/aot/tfcompile.proto b/tensorflow/compiler/aot/tfcompile.proto
index be3f504350..cd83840d89 100644
--- a/tensorflow/compiler/aot/tfcompile.proto
+++ b/tensorflow/compiler/aot/tfcompile.proto
@@ -7,6 +7,7 @@ option java_multiple_files = true;
 option java_package = "org.tensorflow.tfcompile";
 
 import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
 
 // TensorId identifies a tensor in a TensorFlow graph, by specifying the output
 // index of a particular node in the graph.  If the output of the named node
@@ -23,6 +24,12 @@ message Feed {
   TensorId id = 1;
   TensorShapeProto shape = 2;
   string name = 3;  // Optional name for generated code.
+
+  // Optional data type. This is not normally required, as the graph itself
+  // contains this information. However, if the node being fed is an op that
+  // is not linked into the tfcompile binary, then the type cannot be inferred
+  // from the node; in this case, the type should be set here.
+  DataType type = 4;
 };
 
 // Fetch represents a single fetch tensor in the graph, which corresponds to an
diff --git a/tensorflow/compiler/aot/tfcompile_util.cc b/tensorflow/compiler/aot/tfcompile_util.cc
index 8774a02128..4476baae63 100644
--- a/tensorflow/compiler/aot/tfcompile_util.cc
+++ b/tensorflow/compiler/aot/tfcompile_util.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 
 namespace tensorflow {
 namespace tfcompile {
@@ -119,17 +120,105 @@ Status ValidateConfig(const Config& config) {
   return Status::OK();
 }
 
+Status AddPlaceholdersForFeeds(
+    const Config& config, const OpRegistryInterface* op_registry,
+    std::unordered_map<string, string>* feed_remapping, GraphDef* graph_def) {
+  struct PlaceholderInfo {
+    const Feed* feed = nullptr;  // point to Feed in <config>.
+    string placeholder_name;
+    DataType data_type = DT_INVALID;
+  };
+
+  // Put each fed tensor into a map by name:port. A map is used for determinism
+  // when creating placeholders (genrules want deterministic output).
+  std::map<string, PlaceholderInfo> placeholder_info;
+  for (int i = 0; i < config.feed_size(); ++i) {
+    const Feed* feed = &config.feed(i);
+    const string name_port = TensorIdToString(feed->id());
+    auto& info = placeholder_info[name_port];
+    info.feed = feed;
+    info.placeholder_name = strings::StrCat(
+        "aot_feed_", feed->id().output_index(), "/", feed->id().node_name());
+    (*feed_remapping)[name_port] = info.placeholder_name;
+  }
+
+  // Verify node exists and determine data type.
+  std::unordered_map<string, const NodeDef*> name_to_node;
+  for (int i = 0; i < graph_def->node_size(); ++i) {
+    name_to_node[graph_def->node(i).name()] = &graph_def->node(i);
+  }
+  for (auto it = placeholder_info.begin(); it != placeholder_info.end(); ++it) {
+    PlaceholderInfo& info = it->second;
+    const TensorId& feed_id = info.feed->id();
+
+    // Find the existing node and determine data type.
+    auto node_it = name_to_node.find(feed_id.node_name());
+    if (node_it == name_to_node.end()) {
+      return errors::NotFound("Can't find feed node: ",
+                              TensorIdToString(feed_id));
+    }
+    const NodeDef* existing = node_it->second;
+
+    if (info.feed->type() != DT_INVALID) {
+      info.data_type = info.feed->type();
+    } else {
+      // Build the node in order to infer its type.
+      Graph g(op_registry);
+      Status status;
+      Node* feed_node = g.AddNode(*existing, &status);
+      TF_RETURN_IF_ERROR(status);
+      info.data_type =
+          BaseType(feed_node->output_type(info.feed->id().output_index()));
+    }
+  }
+
+  // Create placeholders. Note that we could avoid creating a placeholder for
+  // feeds which are already placeholders, but we omit that to avoid more cases
+  // in this code.
+  for (auto it = placeholder_info.begin(); it != placeholder_info.end(); ++it) {
+    const PlaceholderInfo& info = it->second;
+    NodeDef* d = graph_def->add_node();
+    d->set_name(info.placeholder_name);
+    d->set_op("PlaceholderV2");
+    auto& attr_map = *d->mutable_attr();
+    attr_map["dtype"].set_type(info.data_type);
+    *attr_map["shape"].mutable_shape() = info.feed->shape();
+  }
+
+  // Rewrite references to the fed tensors to refer to the placeholder.
+  for (int i = 0; i < graph_def->node_size(); ++i) {
+    NodeDef* node_def = graph_def->mutable_node(i);
+    for (int j = 0; j < node_def->input_size(); ++j) {
+      auto id = ParseTensorName(node_def->input(j));
+      auto it = placeholder_info.find(id.ToString());
+      if (it != placeholder_info.end()) {
+        node_def->set_input(j, it->second.placeholder_name);
+      }
+    }
+  }
+
+  return Status::OK();
+}
+
 Status PruneGraphDefInto(const Config& config, const GraphDef& in,
                          GraphDef* out) {
   *out = in;
   out->clear_node();
 
+  // Tensors needed for feeding.
+  std::set<std::pair<string, int>> feed_tensors;
+  for (const auto& feed_config : config.feed()) {
+    feed_tensors.insert(std::make_pair(feed_config.id().node_name(),
+                                       feed_config.id().output_index()));
+  }
+
   // Maps node name to reachability.
   std::unordered_map<string, std::pair<bool, const NodeDef*>> node_by_name;
   for (const NodeDef& node : in.node()) {
     node_by_name[node.name()] = std::pair<bool, const NodeDef*>(false, &node);
   }
 
+  // Traverse.
   std::queue<string> name_queue;
   for (int i = 0; i < config.fetch_size(); ++i) {
     name_queue.push(config.fetch(i).id().node_name());
@@ -149,8 +238,19 @@ Status PruneGraphDefInto(const Config& config, const GraphDef& in,
     }
     map_entry.first = true;
 
+    // Push input nodes of the currently visited node to name_queue.
     for (const string& in_edge : map_entry.second->input()) {
-      name_queue.push(ParseTensorName(in_edge).first.ToString());
+      auto id = ParseTensorName(in_edge);
+      const string node_name = id.first.ToString();
+      if (feed_tensors.find(std::make_pair(node_name, id.second)) ==
+          feed_tensors.end()) {
+        name_queue.push(node_name);
+      } else {
+        // The input tensor is from an edge that is being fed. Therefore,
+        // we skip recursing down that edge, to avoid requiring nodes that
+        // may not be needed (note that the input node may still be added
+        // to name_queue later if one of its output edges is not being fed).
+      }
     }
   }
 
@@ -165,5 +265,9 @@ Status PruneGraphDefInto(const Config& config, const GraphDef& in,
   return Status::OK();
 }
 
+string TensorIdToString(const TensorId& id) {
+  return strings::StrCat(id.node_name(), ":", id.output_index());
+}
+
 }  // namespace tfcompile
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/aot/tfcompile_util.h b/tensorflow/compiler/aot/tfcompile_util.h
index 84060c0761..365f7b0e7b 100644
--- a/tensorflow/compiler/aot/tfcompile_util.h
+++ b/tensorflow/compiler/aot/tfcompile_util.h
@@ -16,8 +16,11 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 #define TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 
+#include <unordered_map>
+
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
@@ -31,11 +34,23 @@ Status ValidateCppIdent(StringPiece ident, StringPiece msg);
 // ValidateConfig returns OK iff config is valid.
 Status ValidateConfig(const Config& config);
 
+// Modifies <graph_def> to include placeholders for each fed tensor, and
+// update references to the fed tensors to refer to the placeholders.
+// The existing nodes referenced by the feeds are not removed or modified
+// (except where their input edges are modified by the replacement of other
+// feeds).
+Status AddPlaceholdersForFeeds(
+    const Config& config, const OpRegistryInterface* op_registry,
+    std::unordered_map<string, string>* feed_remapping, GraphDef* graph_def);
+
 // Returns in <out> a copy of <in>, pruned to only include fetches from
 // <config>.
 Status PruneGraphDefInto(const Config& config, const GraphDef& in,
                          GraphDef* out);
 
+// Returns node:port for the given <id>.
+string TensorIdToString(const TensorId& id);
+
 }  // namespace tfcompile
 }  // namespace tensorflow
 
-- 
GitLab


From a75496f74cad8c6bd25ee4c6c17d3f52199b2fe8 Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Thu, 6 Jul 2017 09:27:19 -0700
Subject: [PATCH 0741/1375] BUGFIX:  Affine bijector didn't work in 64bit
 because some constants needed to be cast to correct dtype.

PiperOrigin-RevId: 161089149
---
 .../kernel_tests/bijectors/affine_test.py     | 33 ++++++++++++++++---
 .../python/ops/bijectors/affine_impl.py       | 12 +++++--
 2 files changed, 37 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py
index e8fd6aa2f7..b8a7d6c0ce 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py
@@ -111,28 +111,51 @@ class AffineBijectorTest(test.TestCase):
         self.assertAllClose(-np.log(2.),
                             run(bijector.inverse_log_det_jacobian, x))
 
-  def testOneBatchScalarViaIdentity(self):
+  def testOneBatchScalarViaIdentityIn64BitUserProvidesShiftOnly(self):
     with self.test_session() as sess:
 
       def static_run(fun, x):
         return fun(x).eval()
 
       def dynamic_run(fun, x_value):
-        x_value = np.array(x_value)
-        x = array_ops.placeholder(dtypes.float32, name="x")
+        x_value = np.array(x_value).astype(np.float64)
+        x = array_ops.placeholder(dtypes.float64, name="x")
         return sess.run(fun(x), feed_dict={x: x_value})
 
       for run in (static_run, dynamic_run):
-        mu = [1.]
+        mu = np.float64([1.])
         # One batch, scalar.
         # Corresponds to scale = 1.
         bijector = Affine(shift=mu, event_ndims=0)
         self.assertEqual(0, bijector.event_ndims.eval())  # "is scalar"
-        x = [1.]  # One sample from one batches.
+        x = np.float64([1.])  # One sample from one batches.
         self.assertAllClose([2.], run(bijector.forward, x))
         self.assertAllClose([0.], run(bijector.inverse, x))
         self.assertAllClose(0., run(bijector.inverse_log_det_jacobian, x))
 
+  def testOneBatchScalarViaIdentityIn64BitUserProvidesMultiplierOnly(self):
+    with self.test_session() as sess:
+
+      def static_run(fun, x):
+        return fun(x).eval()
+
+      def dynamic_run(fun, x_value):
+        x_value = np.array(x_value).astype(np.float64)
+        x = array_ops.placeholder(dtypes.float64, name="x")
+        return sess.run(fun(x), feed_dict={x: x_value})
+
+      for run in (static_run, dynamic_run):
+        multiplier = np.float64([2.])
+        # One batch, scalar.
+        # Corresponds to scale = 2, shift = 0.
+        bijector = Affine(scale_identity_multiplier=multiplier, event_ndims=0)
+        self.assertEqual(0, bijector.event_ndims.eval())  # "is scalar"
+        x = np.float64([1.])  # One sample from one batches.
+        self.assertAllClose([2.], run(bijector.forward, x))
+        self.assertAllClose([0.5], run(bijector.inverse, x))
+        self.assertAllClose([np.log(0.5)],
+                            run(bijector.inverse_log_det_jacobian, x))
+
   def testOneBatchScalarViaDiag(self):
     with self.test_session() as sess:
 
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
index 42865ed404..67d248b977 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
@@ -323,8 +323,6 @@ class Affine(bijector.Bijector):
                                          scale_perturb_factor is None)
     # When no args are specified, pretend the scale matrix is the identity
     # matrix.
-    if self._is_only_identity_multiplier and scale_identity_multiplier is None:
-      scale_identity_multiplier = 1.
     with self._name_scope("init", values=[
         shift, scale_identity_multiplier, scale_diag, scale_tril,
         scale_perturb_diag, scale_perturb_factor, event_ndims]):
@@ -336,6 +334,13 @@ class Affine(bijector.Bijector):
         event_ndims = control_flow_ops.with_dependencies(
             [is_less_than_two], event_ndims)
       self._shift = _as_tensor(shift, "shift")
+      if (self._is_only_identity_multiplier
+          and scale_identity_multiplier is None):
+        if self._shift is not None:
+          scale_identity_multiplier = ops.convert_to_tensor(
+              1., dtype=self._shift.dtype)
+        else:
+          scale_identity_multiplier = 1.
       # self._create_scale_operator returns an OperatorPD in all cases except if
       # self._is_only_identity_multiplier; in which case it returns a scalar
       # Tensor.
@@ -563,8 +568,9 @@ class Affine(bijector.Bijector):
       # TODO(jvdillon): We don't pad in this case and instead let the fldj be
       # applied via broadcast.
       d = math_ops.cast(array_ops.shape(x)[-1], dtype=self._scale.dtype)
+      one = ops.convert_to_tensor(1., self._scale.dtype)
       return math_ops.log(math_ops.abs(self._scale)) * array_ops.where(
-          math_ops.equal(self._shaper.event_ndims, 0), 1., d)
+          math_ops.equal(self._shaper.event_ndims, 0), one, d)
     fldj = self._scale.sqrt_log_abs_det()
     # We need to squeeze off the padded dimension.
     start = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
-- 
GitLab


From 9cf44446550b6d2c3141074013509875649b0fd5 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 6 Jul 2017 09:49:25 -0700
Subject: [PATCH 0742/1375] Bugfixes for GPU WhereOp.

1. Set the cuda context properly within ComputeAsync.
   Also set the cuda context properly in the WhereOp GPU callback.
2. Ensure report_uninitialized_variables runs on CPU.  This avoids intermediate
   copying of data to GPU after getting the variables' state and before
   returning it.

PiperOrigin-RevId: 161092040
---
 .../core/common_runtime/gpu/gpu_device.cc     |  2 ++
 tensorflow/core/kernels/where_op.cc           |  7 +++++
 tensorflow/core/kernels/where_op_gpu.cu.cc    |  4 +--
 tensorflow/python/ops/resources.py            | 26 ++++++++--------
 tensorflow/python/ops/variables.py            | 30 +++++++++++--------
 5 files changed, 42 insertions(+), 27 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index f60d3a89c6..991f81c5fd 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -462,6 +462,7 @@ void BaseGPUDevice::ComputeAsync(AsyncOpKernel* op_kernel,
     gpu_device_context =
         static_cast<GPUDeviceContext*>(context->op_device_context());
   }
+  gpu::Stream* stream = gpu_device_context->stream();
   const auto stream_id = gpu_device_context->stream_id();
 
   VLOG(1) << "GpuDevice::ComputeAsync " << op_kernel->name() << " op "
@@ -472,6 +473,7 @@ void BaseGPUDevice::ComputeAsync(AsyncOpKernel* op_kernel,
   // following TraceMe constructor is simply a conditional test of
   // false value. Measurements show that its overhead is negligible.
   port::Tracing::TraceMe activity(op_kernel->name(), op_kernel->type_string());
+  gpu::cuda::ScopedActivateExecutorContext scoped_activation{stream->parent()};
   op_kernel->ComputeAsync(context, done);
 }
 
diff --git a/tensorflow/core/kernels/where_op.cc b/tensorflow/core/kernels/where_op.cc
index 6fdcb331cc..59b474e41c 100644
--- a/tensorflow/core/kernels/where_op.cc
+++ b/tensorflow/core/kernels/where_op.cc
@@ -40,6 +40,9 @@ limitations under the License.
 #if GOOGLE_CUDA
 #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h"
 #include "tensorflow/core/kernels/cuda_solvers.h"
+#include "tensorflow/core/platform/cuda.h"
+
+using ::perftools::gputools::cuda::ScopedActivateExecutorContext;
 #endif  // GOOGLE_CUDA
 
 namespace tensorflow {
@@ -257,6 +260,10 @@ class WhereGPUOp : public AsyncOpKernel {
 
     auto create_and_check_output = [context, &d, &input, input_dims,
                                     num_true_host, done]() {
+      // Ensure that within the callback, the proper GPU settings are
+      // configured.
+      auto stream = context->op_device_context()->stream();
+      ScopedActivateExecutorContext scoped_activation{stream->parent()};
 
       Tindex num_true = *num_true_host.data();
 
diff --git a/tensorflow/core/kernels/where_op_gpu.cu.cc b/tensorflow/core/kernels/where_op_gpu.cu.cc
index 09e8be58fa..ca147cc21f 100644
--- a/tensorflow/core/kernels/where_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/where_op_gpu.cu.cc
@@ -90,8 +90,8 @@ struct NumTrue<GPUDevice, TIndex> {
     if (second_success != cudaSuccess) {
       return errors::Internal(
           "WhereOp: Could not launch cub::DeviceReduce::Sum to count "
-          "number of true indices, status: ",
-          cudaGetErrorString(second_success));
+          "number of true indices.  temp_storage_bytes: ",
+          temp_storage_bytes, ", status: ", cudaGetErrorString(second_success));
     }
 
     return Status::OK();
diff --git a/tensorflow/python/ops/resources.py b/tensorflow/python/ops/resources.py
index 57ba0084e8..db6740643c 100644
--- a/tensorflow/python/ops/resources.py
+++ b/tensorflow/python/ops/resources.py
@@ -85,18 +85,20 @@ def report_uninitialized_resources(resource_list=None,
   if resource_list is None:
     resource_list = shared_resources() + local_resources()
   with ops.name_scope(name):
-    if not resource_list:
-      # Return an empty tensor so we only need to check for returned tensor
-      # size being 0 as an indication of model ready.
-      return array_ops.constant([], dtype=dtypes.string)
-    # Get a 1-D boolean tensor listing whether each resource is initialized.
-    variables_mask = math_ops.logical_not(
-        array_ops.stack([r.is_initialized for r in resource_list]))
-    # Get a 1-D string tensor containing all the resource names.
-    variable_names_tensor = array_ops.constant(
-        [s.handle.name for s in resource_list])
-    # Return a 1-D tensor containing all the names of uninitialized resources.
-    return array_ops.boolean_mask(variable_names_tensor, variables_mask)
+    # Run all operations on CPU
+    with ops.device("/cpu:0"):
+      if not resource_list:
+        # Return an empty tensor so we only need to check for returned tensor
+        # size being 0 as an indication of model ready.
+        return array_ops.constant([], dtype=dtypes.string)
+      # Get a 1-D boolean tensor listing whether each resource is initialized.
+      variables_mask = math_ops.logical_not(
+          array_ops.stack([r.is_initialized for r in resource_list]))
+      # Get a 1-D string tensor containing all the resource names.
+      variable_names_tensor = array_ops.constant(
+          [s.handle.name for s in resource_list])
+      # Return a 1-D tensor containing all the names of uninitialized resources.
+      return array_ops.boolean_mask(variable_names_tensor, variables_mask)
 
 
 @tf_should_use.should_use_result
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 93d2838f6a..190c823314 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -1310,19 +1310,23 @@ def report_uninitialized_variables(var_list=None,
         if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
           var_list.append(op.outputs[0])
   with ops.name_scope(name):
-    if not var_list:
-      # Return an empty tensor so we only need to check for returned tensor
-      # size being 0 as an indication of model ready.
-      return array_ops.constant([], dtype=dtypes.string)
-    else:
-      # Get a 1-D boolean tensor listing whether each variable is initialized.
-      variables_mask = math_ops.logical_not(
-          array_ops.stack(
-              [state_ops.is_variable_initialized(v) for v in var_list]))
-      # Get a 1-D string tensor containing all the variable names.
-      variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
-      # Return a 1-D tensor containing all the names of uninitialized variables.
-      return array_ops.boolean_mask(variable_names_tensor, variables_mask)
+    # Run all operations on CPU
+    with ops.device("/cpu:0"):
+      if not var_list:
+        # Return an empty tensor so we only need to check for returned tensor
+        # size being 0 as an indication of model ready.
+        return array_ops.constant([], dtype=dtypes.string)
+      else:
+        # Get a 1-D boolean tensor listing whether each variable is initialized.
+        variables_mask = math_ops.logical_not(
+            array_ops.stack(
+                [state_ops.is_variable_initialized(v) for v in var_list]))
+        # Get a 1-D string tensor containing all the variable names.
+        variable_names_tensor = array_ops.constant(
+            [s.op.name for s in var_list])
+        # Return a 1-D tensor containing all the names of
+        # uninitialized variables.
+        return array_ops.boolean_mask(variable_names_tensor, variables_mask)
 
 # pylint: disable=protected-access
 Variable._OverloadAllOperators()
-- 
GitLab


From 4de7361c43adc65c442359104a17c449a02eee7a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 10:01:25 -0700
Subject: [PATCH 0743/1375] Fix some internal tests.

PiperOrigin-RevId: 161093488
---
 .../contrib/tensor_forest/kernels/v4/input_target.h      | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
index 03dc332c8a..0309ec1de9 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h
@@ -20,7 +20,7 @@
 namespace tensorflow {
 namespace tensorforest {
 
-typedef TTypes<float, 1>::ConstTensor SingleDimStorageType;
+typedef TTypes<float, 1>::UnalignedConstTensor SingleDimStorageType;
 
 // Base class for classes that hold labels and weights. Mostly for testing
 // purposes, because it's inconvenient to construct nasty Eigen::things.
@@ -54,9 +54,10 @@ class StoredInputTarget : public InputTarget {
 class TensorInputTarget : public StoredInputTarget<SingleDimStorageType> {
  public:
   TensorInputTarget(const Tensor& target, const Tensor& weight, int num_targets)
-      : StoredInputTarget(new SingleDimStorageType(target.tensor<float, 1>()),
-                          new SingleDimStorageType(weight.tensor<float, 1>()),
-                          num_targets),
+      : StoredInputTarget(
+            new SingleDimStorageType(target.unaligned_flat<float>()),
+            new SingleDimStorageType(weight.unaligned_flat<float>()),
+            num_targets),
         original_tensor_(target) {}
 
   int32 GetTargetAsClassIndex(int example_index,
-- 
GitLab


From d1567d35e78b8e614ee05b90071773f24b251b96 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Thu, 6 Jul 2017 10:08:38 -0700
Subject: [PATCH 0744/1375] Never recompute Tensors which are feeds in
 Grappler's memory optimizer.

I don't think anyone has run into this yet, but it would lead to incredibly hard
to debug issues (sometimes incorrect gradients with a correct forward pass).

PiperOrigin-RevId: 161094335
---
 .../grappler/optimizers/memory_optimizer.cc   | 20 +++++++++-----
 .../optimizers/memory_optimizer_test.cc       | 27 +++++++++++++++++++
 2 files changed, 41 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 462cfb928f..23479d84b0 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -417,7 +417,7 @@ void RecomputeSubgraph(
 }
 
 void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
-                                GraphDef* graph) {
+                                GraphDef* graph, const GrapplerItem& item) {
   // The topological numberings and NodeMap will be stale as soon as we start
   // modifying the graph in RecomputeSubgraph. However, RecomputeSubgraph only
   // looks up nodes which were in the original graph, and preserves the graph
@@ -428,6 +428,12 @@ void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
   TopologicalSort(graph);
   NodeMap node_map(graph);
   std::vector<RecomputedSubGraph> recomputed_subgraphs;
+  // Do not recompute nodes which are fed, since the recomputed node would not
+  // take on the fed value (i.e. gradients would be incorrect).
+  std::unordered_set<string> feeds;
+  for (const auto& feed : item.feed) {
+    feeds.insert(NodeName(feed.first));
+  }
   if (optimization_level == RewriterConfig::HEURISTICS) {
     // TODO(allenl): Handle ResNet-like architectures better. Right now all of
     // the cheap forward ops get grouped into a single subgraph which must
@@ -436,15 +442,17 @@ void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
     std::unordered_set<string> cheap_to_recompute_ops =
         GetCheapToRecomputeOps();
     recomputed_subgraphs = GetOpGroupsToRecompute(
-        graph, node_map, [&cheap_to_recompute_ops](const NodeDef& node) {
-          return !IsTargetOp(node) &&
+        graph, node_map,
+        [&cheap_to_recompute_ops, &feeds](const NodeDef& node) {
+          return !IsTargetOp(node) && feeds.count(node.name()) == 0 &&
                  (cheap_to_recompute_ops.count(node.op()) > 0 ||
                   node.attr().count(kRecomputeHint) > 0);
         });
   } else {  // optimization_level == RewriterConfig::MANUAL
     recomputed_subgraphs =
-        GetOpGroupsToRecompute(graph, node_map, [](const NodeDef& node) {
-          return !IsTargetOp(node) && node.attr().count(kRecomputeHint) > 0;
+        GetOpGroupsToRecompute(graph, node_map, [&feeds](const NodeDef& node) {
+          return !IsTargetOp(node) && feeds.count(node.name()) == 0 &&
+                 node.attr().count(kRecomputeHint) > 0;
         });
   }
   if (!recomputed_subgraphs.empty()) {
@@ -588,7 +596,7 @@ Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
                                  GraphDef* optimized_graph) {
   *optimized_graph = item.graph;
 
-  RecomputationRewritingPass(optimization_level_, optimized_graph);
+  RecomputationRewritingPass(optimization_level_, optimized_graph, item);
 
   // Figure out what needs to be swapped;
   std::unordered_map<NodeDef*, SwapInfo> nodes_to_swap;
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index eb7a8eb343..0d5d302f4a 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -69,6 +69,33 @@ TEST_F(RecomputeSubgraphTest, SimpleSubgraph) {
   EXPECT_EQ("^gradients/d", recompute_trigger->input(0));
 }
 
+TEST_F(RecomputeSubgraphTest, NoFeedsRecomputed) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+
+  Output a = ops::Variable(s.WithOpName("a"), {2, 3, 4}, DT_FLOAT);
+  Output b = ops::Identity(s.WithOpName("b"), a);  // Would be recomputed, but
+                                                   // for being fed
+  Output c = ops::Identity(s.WithOpName("c"), b);
+  Output d = ops::AddN(s.WithOpName("gradients/d"), {c});
+  Output e = ops::AddN(s.WithOpName("gradients/e"), {d, b});
+  Output f = ops::AddN(s.WithOpName("gradients/f"), {e, a});
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+  item.feed.emplace_back("b", Tensor());
+  EXPECT_EQ(6, item.graph.node_size());
+  NodeMap pre_transform_node_map(&item.graph);
+  (*pre_transform_node_map.GetNode("b")->mutable_attr())["_recompute_hint"]
+      .set_i(0);
+
+  MemoryOptimizer optimizer(RewriterConfig::MANUAL);
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+
+  TF_EXPECT_OK(status);
+  EXPECT_EQ(6, output.node_size());
+}
+
 TEST_F(RecomputeSubgraphTest, TwoInputSubgraphs) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-- 
GitLab


From 583497f167d2811eacaa390938c2a059139c0057 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 6 Jul 2017 10:29:43 -0700
Subject: [PATCH 0745/1375] Fix ongoing breakages in pip builds

1. CL/161014271 added timeseries to tensorflow, but did not add the files to the TensorFlow pip package, resulting in Jenkins test errors: e.g., http://ci.tensorflow.org/job/tensorflow-cl-cpu-python3-pip/9269/console

2. Fix numpy issues related to using float as size args, which is illegal in some recent versions of numpy.

3. The tensorflow/contrib/boosted_trees py_tests are not working in the pip (specifically bazel_pip) builds. Disable them in pip builds for now after filing b/63386583.

PiperOrigin-RevId: 161097246
---
 tensorflow/contrib/boosted_trees/BUILD        | 33 +++++++++++++++----
 tensorflow/contrib/timeseries/BUILD           | 12 +++++++
 .../python/timeseries/ar_model_test.py        |  2 +-
 tensorflow/tools/pip_package/BUILD            |  9 ++---
 4 files changed, 45 insertions(+), 11 deletions(-)

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 4f21744836..5f726cc18e 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -72,7 +72,10 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/ensemble_optimizer_ops_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["nomac"],  # b/63258195
+    tags = [
+        "no_pip",  # b/63386583
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":ensemble_optimizer_ops_py",
         ":model_ops_py",
@@ -91,7 +94,10 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/model_ops_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["nomac"],  # b/63258195
+    tags = [
+        "no_pip",  # b/63386583
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":ensemble_optimizer_ops_py",
         ":model_ops_py",
@@ -112,7 +118,10 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/prediction_ops_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["nomac"],  # b/63258195
+    tags = [
+        "no_pip",  # b/63386583
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":model_ops_py",
         ":prediction_ops_py",
@@ -131,7 +140,10 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/quantile_ops_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["nomac"],  # b/63258195
+    tags = [
+        "no_pip",  # b/63386583
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":quantile_ops_py",
         "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
@@ -148,6 +160,9 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/split_handler_ops_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip",  # b/63386583
+    ],
     deps = [
         ":split_handler_ops_py",
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
@@ -163,7 +178,10 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/stats_accumulator_ops_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["nomac"],  # b/63258195
+    tags = [
+        "no_pip",  # b/63386583
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":stats_accumulator_ops_py",
         "//tensorflow/python:framework_test_lib",
@@ -179,7 +197,10 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/training_ops_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["nomac"],  # b/63258195
+    tags = [
+        "no_pip",  # b/63386583
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":model_ops_py",
         ":training_ops_py",
diff --git a/tensorflow/contrib/timeseries/BUILD b/tensorflow/contrib/timeseries/BUILD
index a64624bb0f..40aadb02a5 100644
--- a/tensorflow/contrib/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/BUILD
@@ -22,6 +22,18 @@ py_library(
     ],
 )
 
+py_library(
+    name = "timeseries_pip",
+    deps = [
+        ":timeseries",
+        "//tensorflow/contrib/timeseries/examples:known_anomaly",
+        "//tensorflow/contrib/timeseries/examples:multivariate",
+        "//tensorflow/contrib/timeseries/examples:predict",
+        "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
+        "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:test_utils",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
index 3b1a01ed44..f5f86ae1ad 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
@@ -66,7 +66,7 @@ class ARModelTest(test.TestCase):
              noise_stddev / 3. * np.random.randn(num_samples, 1))
     # Add some anomalies to data1
     if anomaly_prob > 0.:
-      num_anomalies = anomaly_prob * num_samples
+      num_anomalies = int(anomaly_prob * num_samples)
       anomaly_values = (anomaly_stddev_scale * noise_stddev / 4 *
                         np.random.randn(num_anomalies))
       indices = np.random.randint(0, num_samples, num_anomalies)
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 0bd7cda1c5..78a652ccae 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -141,18 +141,19 @@ sh_binary(
             "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip",
             "//tensorflow/contrib/ndlstm:ndlstm",
             "//tensorflow/contrib/nn:nn_py",
+            "//tensorflow/contrib/predictor:predictor_pip",
             "//tensorflow/contrib/session_bundle:session_bundle_pip",
             "//tensorflow/contrib/signal:signal_py",
             "//tensorflow/contrib/slim:slim",
             "//tensorflow/contrib/slim/python/slim/data:data_pip",
             "//tensorflow/contrib/slim/python/slim/nets:nets_pip",
-            "//tensorflow/contrib/tpu:tpu_estimator",
-            "//tensorflow/contrib/tpu:tpu_helper_library",
-            "//tensorflow/contrib/tpu:tpu_py",
             "//tensorflow/contrib/specs:specs",
             "//tensorflow/contrib/tensor_forest:init_py",
             "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip",
-            "//tensorflow/contrib/predictor:predictor_pip",
+            "//tensorflow/contrib/timeseries:timeseries_pip",
+            "//tensorflow/contrib/tpu:tpu_estimator",
+            "//tensorflow/contrib/tpu:tpu_helper_library",
+            "//tensorflow/contrib/tpu:tpu_py",
             "//tensorflow/examples/tutorials/mnist:package",
             "//tensorflow/python:distributed_framework_test_lib",
             "//tensorflow/python:meta_graph_testdata",
-- 
GitLab


From ad0ba9e6ea5c137897ef0940cc3a71ddd5201a0d Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Thu, 6 Jul 2017 10:30:16 -0700
Subject: [PATCH 0746/1375] [XLA] Create default ExecutionOptions in
 Client::Execute* APIs when applicable.

Clients that call the XLA Client API directly (not through TF) and do not pass
execution_options explicitly will be bitten by bad default values of flags
without this change.

PiperOrigin-RevId: 161097315
---
 tensorflow/compiler/xla/client/BUILD     |  1 +
 tensorflow/compiler/xla/client/client.cc | 10 ++++++++--
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD
index 289004575d..a998b91c89 100644
--- a/tensorflow/compiler/xla/client/BUILD
+++ b/tensorflow/compiler/xla/client/BUILD
@@ -62,6 +62,7 @@ cc_library(
     deps = [
         ":computation",
         ":global_data",
+        "//tensorflow/compiler/xla:execution_options_util",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:service_interface",
         "//tensorflow/compiler/xla:status_macros",
diff --git a/tensorflow/compiler/xla/client/client.cc b/tensorflow/compiler/xla/client/client.cc
index 555e3df427..1799bbd348 100644
--- a/tensorflow/compiler/xla/client/client.cc
+++ b/tensorflow/compiler/xla/client/client.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <string>
 #include <utility>
 
+#include "tensorflow/compiler/xla/execution_options_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
@@ -198,7 +199,10 @@ StatusOr<std::unique_ptr<GlobalData>> Client::Execute(
     ExecutionProfile* execution_profile) {
   ExecuteRequest request;
   *request.mutable_computation() = computation.handle();
-  if (execution_options != nullptr) {
+
+  if (execution_options == nullptr) {
+    *request.mutable_execution_options() = CreateDefaultExecutionOptions();
+  } else {
     *request.mutable_execution_options() = *execution_options;
   }
   for (GlobalData* argument : arguments) {
@@ -299,7 +303,9 @@ StatusOr<ExecutionHandle> Client::ExecuteAsync(
   for (GlobalData* argument : arguments) {
     *request.add_arguments() = argument->handle();
   }
-  if (execution_options != nullptr) {
+  if (execution_options == nullptr) {
+    *request.mutable_execution_options() = CreateDefaultExecutionOptions();
+  } else {
     *request.mutable_execution_options() = *execution_options;
   }
 
-- 
GitLab


From 2d50faa5d577ba454641fd360c1d85477187f08e Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Thu, 6 Jul 2017 11:05:53 -0700
Subject: [PATCH 0747/1375] Adds train_batch_size into TPUEstimator.

PiperOrigin-RevId: 161102679
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 111 +++++++++++++-----
 1 file changed, 84 insertions(+), 27 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 168726a6b3..b9da8dc35a 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -43,6 +43,7 @@ from tensorflow.python.training import training
 
 
 _BATCH_SIZE_KEY = 'batch_size'
+_RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY]
 
 
 def _tpu_job(run_config):
@@ -51,6 +52,11 @@ def _tpu_job(run_config):
   return None if run_config.master in ['', 'local'] else 'tpu_worker'
 
 
+def _per_shard_batch_size(global_batch_size, run_config):
+  """Returns the batch size for each shard."""
+  return global_batch_size // run_config.tpu_config.num_shards
+
+
 class _SIGNAL(object):
   """Signal used to control the input thread of infeed."""
   NEXT_BATCH = 1
@@ -168,8 +174,11 @@ class TpuEstimator(estimator_lib.Estimator):
   replicating inputs and models for each core, and returning to host
   periodically to run hooks.
 
-  Note: TpuEstimator transforms a global batch size in params to a per-shard
-        batch size when calling the input_fn.
+  Note: For training (evaluate and predict support on TPU are not yet
+  implemented), TpuEstimator transforms a global batch size in params to a
+  per-shard batch size when calling the `input_fn` and `model_fn`. Users should
+  specify `train_batch_size` in constructor, and then get the batch size for
+  each shard in `input_fn` and `model_fn` by `params['batch_size']`.
   """
 
   def __init__(self,
@@ -177,23 +186,59 @@ class TpuEstimator(estimator_lib.Estimator):
                model_dir=None,
                config=None,
                params=None,
-               use_tpu=True):
+               use_tpu=True,
+               train_batch_size=None):
+    """Constructs an `TpuEstimator` instance.
+
+    Args:
+      model_fn: Model function as required by `Estimator`. For training, the
+        returned `EstimatorSpec` cannot have hooks as it is not supported in
+        `TPUEstimator`.
+      model_dir: Directory to save model parameters, graph and etc. This can
+        also be used to load checkpoints from the directory into a estimator to
+        continue training a previously saved model. If `None`, the model_dir in
+        `config` will be used if set. If both are set, they must be same. If
+        both are `None`, a temporary directory will be used.
+      config: An `tpu_config.RunConfig` configuration object. Cannot be `None`.
+      params: An optional `dict` of hyper parameters that will be passed into
+        `input_fn` and `model_fn`.  Keys are names of parameters, values are
+        basic python types. There are reserved keys for `TPUEstimator`,
+        including 'batch_size'.
+      use_tpu: A bool indicating whether TPU support is enabled. Currently, only
+        applied to training. Evaluate and predict still happen on CPU.
+      train_batch_size: An int representing the global training batch size.
+        TpuEstimator transforms this global batch size to a per-shard batch
+        size, as params['batch_size'], when calling `input_fn` and `model_fn`.
+        Cannot be `None` if `use_tpu` is `True`. Must be divisible by
+        `config.tpu_config.num_shards`.
+
+    Raises:
+      ValueError: `params` has reserved keys already.
+    """
     if config is None or not isinstance(config, tpu_config.RunConfig):
       raise ValueError(
           '`config` must be provided with type `tpu_config.RunConfig`')
 
-    if use_tpu and params is not None and _BATCH_SIZE_KEY in params:
-      if not isinstance(params[_BATCH_SIZE_KEY], int):
-        raise ValueError(
-            '`{}` in params must be an int'.format(_BATCH_SIZE_KEY))
-      params = copy.deepcopy(params)
+    if params is not None and any(k in params for k in _RESERVED_PARAMS_KEYS):
+      raise ValueError(
+          '{} are reserved keys but existed in params {}.'.format(
+              _RESERVED_PARAMS_KEYS, params))
+
+    if use_tpu:
+      if train_batch_size is None:
+        raise ValueError('`train_batch_size` cannot be `None`')
+      if not isinstance(train_batch_size, int):
+        raise ValueError('`train_batch_size` must be an int')
+      if train_batch_size < 1:
+        raise ValueError('`train_batch_size` must be positive')
+
       # The specified batch size is the batch size for the entire computation.
-      # The input_fn is called per-shard, so we want to calculate the per-shard
-      # batch size and pass that.
-      if params[_BATCH_SIZE_KEY] % config.tpu_config.num_shards != 0:
+      # The input_fn and model_fn are called per-shard, so we want to calculate
+      # the per-shard batch size and pass that.
+      if train_batch_size % config.tpu_config.num_shards != 0:
         raise ValueError(
             'batch size {} must be divisible by number of shards {}'
-            .format(params[_BATCH_SIZE_KEY], config.tpu_config.num_shards))
+            .format(train_batch_size, config.tpu_config.num_shards))
 
     if use_tpu:
       # Verifies the model_fn signature according to Estimator framework.
@@ -201,7 +246,7 @@ class TpuEstimator(estimator_lib.Estimator):
       # We cannot store config and params in this constructor as parent
       # constructor might change them, such as assigning a temp dir for
       # config.model_dir.
-      model_function = wrapped_model_fn(model_fn)
+      model_function = wrapped_model_fn(model_fn, train_batch_size)
     else:
       model_function = model_fn
 
@@ -210,7 +255,8 @@ class TpuEstimator(estimator_lib.Estimator):
         model_dir=model_dir,
         config=config,
         params=params)
-    self.use_tpu = use_tpu
+    self._use_tpu = use_tpu
+    self._train_batch_size = train_batch_size
 
   def _create_global_step(self, graph):
     """Creates a global step suitable for TPUs.
@@ -252,9 +298,9 @@ class TpuEstimator(estimator_lib.Estimator):
         labels - `Tensor` or dictionary of `Tensor` with labels.
 
     Raises:
-      ValueError: if input_fn takes invalid arguments.
+      ValueError: if input_fn takes invalid arguments or does not have `params`.
     """
-    if not self.use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
+    if not self._use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
       return super(TpuEstimator, self)._call_input_fn(input_fn, mode)
 
     input_fn_args = estimator_lib._fn_args(input_fn)  # pylint: disable=protected-access
@@ -262,12 +308,16 @@ class TpuEstimator(estimator_lib.Estimator):
     kwargs = {}
     if 'params' in input_fn_args:
       kwargs['params'] = self.params  # a deep copy.
+    else:
+      raise ValueError('input_fn ({}) does not include params argument, '
+                       'required by TPUEstimator to pass batch size as '
+                       'params["batch_size"]'.format(input_fn))
     if 'config' in input_fn_args:
       kwargs['config'] = config
 
     # Now for TPU training.
-    if 'params' in kwargs and _BATCH_SIZE_KEY in kwargs['params']:
-      kwargs['params'][_BATCH_SIZE_KEY] //= config.tpu_config.num_shards
+    per_shard_batch_size = _per_shard_batch_size(self._train_batch_size, config)
+    kwargs['params'][_BATCH_SIZE_KEY] = per_shard_batch_size
 
     job = _tpu_job(config)
     def placement_function(index):
@@ -287,8 +337,10 @@ class TpuEstimator(estimator_lib.Estimator):
           labels.append(result[1])
         else:
           features.append(result)
+
     if not labels or all(l is None for l in labels):
       return _PerShardOutput(features), None
+
     return _PerShardOutput(features), _PerShardOutput(labels)
 
 
@@ -302,16 +354,22 @@ def _verify_estimator_spec(estimator_spec):
   return estimator_spec
 
 
-def _call_model_fn(model_fn, features, labels, mode, config, params):
+def _call_model_fn(model_fn, features, labels, mode, config, params,
+                   require_params=False):
   """Calls the model_fn with required parameters."""
   model_fn_args = estimator_lib._fn_args(model_fn)  # pylint: disable=protected-access
   kwargs = {}
   if 'mode' in model_fn_args:
     kwargs['mode'] = mode
-  if 'params' in model_fn_args:
-    kwargs['params'] = params
   if 'config' in model_fn_args:
     kwargs['config'] = config
+  if 'params' in model_fn_args:
+    kwargs['params'] = params
+  elif require_params:
+    raise ValueError(
+        'model_fn ({}) does not include params argument, '
+        'required by TPUEstimator to pass batch size as '
+        'params[\'batch_size\']'.format(model_fn))
   return model_fn(features=features, labels=labels, **kwargs)
 
 
@@ -321,7 +379,7 @@ def _call_model_fn_with_tpu(model_fn, features, labels, mode, config, params):
   config = copy.deepcopy(config)
   params = copy.deepcopy(params)
   return _verify_estimator_spec(_call_model_fn(
-      model_fn, features, labels, mode, config, params))
+      model_fn, features, labels, mode, config, params, require_params=True))
 
 
 def _call_model_fn_without_tpu(
@@ -416,10 +474,10 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
   return (dequeue_fn, enqueue_fn)
 
 
-def wrapped_model_fn(model_fn):
+def wrapped_model_fn(model_fn, train_batch_size):
   """Returns a new model_fn, which wraps the TPU support."""
 
-  def _model_fn(features, labels, mode, config, params=None):
+  def _model_fn(features, labels, mode, config, params):
     """model_fn."""
 
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
@@ -427,9 +485,8 @@ def wrapped_model_fn(model_fn):
       return _call_model_fn_without_tpu(
           model_fn, features, labels, mode, config, params)
 
-    # Now for TPU training.
-    if params is not None and _BATCH_SIZE_KEY in params:
-      params[_BATCH_SIZE_KEY] //= config.tpu_config.num_shards
+    # Now for TPU training. `params` is never `None`.
+    params[_BATCH_SIZE_KEY] = _per_shard_batch_size(train_batch_size, config)
 
     assert isinstance(features, _PerShardOutput)
     features = features.as_list()
-- 
GitLab


From 2559fda88090e78758fbf4b6b35353f49c1753e6 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 6 Jul 2017 11:22:33 -0700
Subject: [PATCH 0748/1375] Disable two  timeseries tests that are failing in
 PIP GPU builds

PiperOrigin-RevId: 161105334
---
 tensorflow/contrib/timeseries/examples/BUILD          | 3 +++
 tensorflow/contrib/timeseries/python/timeseries/BUILD | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD
index de85c0d5ad..1d8231d26e 100644
--- a/tensorflow/contrib/timeseries/examples/BUILD
+++ b/tensorflow/contrib/timeseries/examples/BUILD
@@ -32,6 +32,9 @@ py_test(
         "data/period_trend.csv",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":predict",
     ],
diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index 103b9ea541..eafaee8ac4 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -268,6 +268,9 @@ py_test(
         "ar_model_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":ar_model",
         ":estimators",
-- 
GitLab


From 06d25a7e62465fd2e663b1b9e79280d972475ac8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 11:24:47 -0700
Subject: [PATCH 0749/1375] Add a HVX graph execution test for quantized
 inception v3 model with quantized input

PiperOrigin-RevId: 161105730
---
 .../hexagon/hexagon_graph_execution_test.cc   | 88 ++++++++++++++++---
 1 file changed, 75 insertions(+), 13 deletions(-)

diff --git a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
index 130109b813..9dcdfd6aa4 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
@@ -24,6 +24,9 @@ https://storage.googleapis.com/download.tensorflow.org/models/imagenet_comp_grap
 adb push /tmp/imagenet_comp_graph_label_strings.txt /data/local/tmp
 */
 
+// define EIGEN_USE_THREADS to include quantization_utils.h
+#define EIGEN_USE_THREADS
+
 #include <memory>
 
 #include "tensorflow/core/framework/tensor_shape.pb.h"
@@ -34,6 +37,7 @@ adb push /tmp/imagenet_comp_graph_label_strings.txt /data/local/tmp
 #include "tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h"
 #include "tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h"
 #include "tensorflow/core/kernels/i_remote_fused_graph_executor.h"
+#include "tensorflow/core/kernels/quantization_utils.h"
 #include "tensorflow/core/lib/core/casts.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -52,6 +56,10 @@ using ByteArray = HexagonControlWrapper::ByteArray;
 constexpr const char* const IMAGE_FILENAME = "/data/local/tmp/img_299x299.bmp";
 constexpr const char* const MODEL_FILENAME =
     "/data/local/tmp/tensorflow_inception_v3_stripped_optimized_quantized.pb";
+constexpr const char* const MODEL_WITH_QUANTIZED_INPUT_FILENAME =
+    "/data/local/tmp/"
+    "tensorflow_inception_v3_stripped_optimized_quantized_with_quantized_input."
+    "pb";
 constexpr const char* const FUSED_MODEL_FILENAME =
     "/data/local/tmp/"
     "tensorflow_inception_v3_stripped_optimized_quantized_fused_hexagon.pb";
@@ -64,7 +72,7 @@ const int WIDTH = 299;
 const int HEIGHT = 299;
 const int DEPTH = 3;
 const int EXPECTED_FIRST_RESULT_ID = 59;
-const int EXECUTION_REPEAT_COUNT = 3;
+const int EXECUTION_REPEAT_COUNT = 10;
 
 static void CheckHexagonControllerVersion() {
   HexagonControlWrapper hexagon_control_wrapper;
@@ -165,8 +173,16 @@ static void LoadImage(std::vector<float>* img_floats_ptr) {
   }
 }
 
+static void QuantizeImage(const std::vector<float>& float_vec,
+                          std::vector<quint8>* quint8_vec) {
+  quint8_vec->resize(float_vec.size());
+  for (int i = 0; i < float_vec.size(); ++i) {
+    quint8_vec->at(i) = FloatToQuantized<quint8>(float_vec[i], -1.0f, 1.0f);
+  }
+}
+
 static Tensor BuildImageTensor(const std::vector<float>& img_floats) {
-  LOG(INFO) << "Ioading image finished.";
+  LOG(INFO) << "Loading image finished.";
   Tensor img_tensor(DT_FLOAT, {1, WIDTH, HEIGHT, DEPTH});
   CHECK_EQ(WIDTH * HEIGHT * DEPTH, img_floats.size());
   CHECK_EQ(img_tensor.TotalBytes(), img_floats.size() * sizeof(float));
@@ -176,6 +192,18 @@ static Tensor BuildImageTensor(const std::vector<float>& img_floats) {
   return img_tensor;
 }
 
+static Tensor BuildQuantizedImageTensor(
+    const std::vector<quint8>& quantized_img) {
+  LOG(INFO) << "Loading image finished.";
+  Tensor img_tensor(DT_QUINT8, {1, WIDTH, HEIGHT, DEPTH});
+  CHECK_EQ(WIDTH * HEIGHT * DEPTH, quantized_img.size());
+  CHECK_EQ(img_tensor.TotalBytes(), quantized_img.size() * sizeof(quint8));
+  LOG(INFO) << "Copy data to tensor.";
+  std::memcpy(img_tensor.flat<quint8>().data(), quantized_img.data(),
+              img_tensor.TotalBytes());
+  return img_tensor;
+}
+
 /* static */ RemoteFusedGraphExecuteInfo
 BuildRemoteFusedGraphExecuteInfoWithGraphTransferInfo(
     const GraphTransferInfo& graph_transfer_info) {
@@ -210,10 +238,8 @@ BuildRemoteFusedGraphExecuteInfoWithGraphTransferInfo(
   return execute_info;
 }
 
-static void RunInferenceByHexagonControlWrapper(
-    const GraphTransferer& gt, const std::vector<float>& img_floats) {
-  const Tensor img_tensor = BuildImageTensor(img_floats);
-
+static void RunInferenceByHexagonControlWrapper(const GraphTransferer& gt,
+                                                const Tensor& img_tensor) {
   const RemoteFusedGraphExecuteInfo execute_info =
       BuildRemoteFusedGraphExecuteInfoWithGraphTransferInfo(
           gt.GetGraphTransferInfo());
@@ -229,13 +255,11 @@ static void RunInferenceByHexagonControlWrapper(
   hexagon_control_wrapper.FillInputNode("Mul", img_tensor);
 
   // 4. Execute graph
-  profile_utils::CpuUtils::EnableClockCycleProfiling(true);
-  ClockCycleProfiler prof;
+  const int64 start_time_us = Env::Default()->NowMicros();
   for (int i = 0; i < EXECUTION_REPEAT_COUNT; ++i) {
-    prof.Start();
     hexagon_control_wrapper.ExecuteGraph();
-    prof.Stop();
   }
+  const int64 end_time_us = Env::Default()->NowMicros();
 
   // 5-1. Read output node's outputs
   std::vector<ByteArray> outputs;
@@ -244,7 +268,8 @@ static void RunInferenceByHexagonControlWrapper(
   // 5-2. Dump results
   DumpTop10Results(outputs);
   CheckFirstResult(outputs, EXPECTED_FIRST_RESULT_ID);
-  prof.DumpStatistics("Graph Execution");
+  LOG(INFO) << "Average execution time = "
+            << (end_time_us - start_time_us) / EXECUTION_REPEAT_COUNT << "us";
 
   // 6. Teardown graph in hexagon
   hexagon_control_wrapper.TeardownGraph();
@@ -405,7 +430,43 @@ TEST(GraphTransferer,
 
   std::vector<float> img_floats;
   LoadImage(&img_floats);
-  RunInferenceByHexagonControlWrapper(gt, img_floats);
+  const Tensor img_tensor = BuildImageTensor(img_floats);
+  RunInferenceByHexagonControlWrapper(gt, img_tensor);
+}
+
+TEST(GraphTransferer,
+     DISABLED_RunInceptionV3OnHexagonExampleWithHexagonWrapperQuantizedInput) {
+  LOG(INFO) << "Run inception v3 on hexagon with hexagon controller "
+            << "with quantized input";
+  CheckHexagonControllerVersion();
+
+  const IGraphTransferOpsDefinitions* ops_definitions =
+      &HexagonOpsDefinitions::getInstance();
+  std::vector<std::pair<string, Tensor>> inputs;
+  inputs.emplace_back("Mul", Tensor(DT_QUINT8, {1, WIDTH, HEIGHT, DEPTH}));
+  std::vector<string> output_node_names = {"softmax"};
+
+  GraphTransferer gt;
+  gt.EnableStrictCheckMode(false);
+  profile_utils::CpuUtils::EnableClockCycleProfiling(true);
+  ClockCycleProfiler prof;
+  prof.Start();
+  Status status = gt.LoadGraphFromProtoFile(
+      *ops_definitions, MODEL_WITH_QUANTIZED_INPUT_FILENAME, inputs,
+      output_node_names,
+      /*is_text_proto=*/false,
+      /*shape_inference_for_unknown_shape=*/false,
+      /*dry_run_for_unknown_shape=*/true);
+  ASSERT_TRUE(status.ok()) << status;
+  prof.Stop();
+  prof.DumpStatistics("LoadGraphFromProtoFile");
+
+  std::vector<float> img_floats;
+  LoadImage(&img_floats);
+  std::vector<quint8> quantized_img;
+  QuantizeImage(img_floats, &quantized_img);
+  const Tensor img_tensor = BuildQuantizedImageTensor(quantized_img);
+  RunInferenceByHexagonControlWrapper(gt, img_tensor);
 }
 
 TEST(GraphTransferer,
@@ -436,7 +497,8 @@ TEST(GraphTransferer,
 
   std::vector<float> img_floats;
   LoadImage(&img_floats);
-  RunInferenceByHexagonControlWrapper(gt, img_floats);
+  const Tensor img_tensor = BuildImageTensor(img_floats);
+  RunInferenceByHexagonControlWrapper(gt, img_tensor);
 }
 
 TEST(GraphTransferer, RunInceptionV3OnHexagonExampleWithTfRuntime) {
-- 
GitLab


From cab048ecde3c3567b188a93f62d51af3b6b5b078 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 11:26:54 -0700
Subject: [PATCH 0750/1375] Use C API to implement Operation.control_inputs

Operation.control_inputs must return Python Operation instances,
but C API returns TF_Operation*s. After retrieving TF_Operation*s,
we get the Python Operation object from the graph using TF_Operation's
name.

There is currently just one test for this functionality, namely
ControlDependenciesTest.testBasic. We can't enable other tests
because C API does not accept unregistered operations.
In following changes we will deal with this problem thereby enabling
more tests.

PiperOrigin-RevId: 161106091
---
 tensorflow/python/client/tf_session.i         | 21 ++++++++++++
 tensorflow/python/client/tf_session_helper.cc |  8 +++++
 tensorflow/python/client/tf_session_helper.h  |  5 +++
 tensorflow/python/framework/ops.py            | 32 ++++++++++++++++++-
 4 files changed, 65 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index 284c98639d..243c870e0d 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -68,6 +68,27 @@ tensorflow::ImportNumpy();
   $result = PyUnicode_FromString($1);
 }
 
+// We use TF_OperationGetControlInputs_wrapper instead of
+// TF_OperationGetControlInputs
+%ignore TF_OperationGetControlInputs;
+%unignore TF_OperationGetControlInputs_wrapper;
+// See comment for "%noexception TF_SessionRun_wrapper;"
+%noexception TF_OperationGetControlInputs_wrapper;
+
+// Build a Python list of TF_Operation* and return it.
+%typemap(out) std::vector<TF_Operation*> tensorflow::TF_OperationGetControlInputs_wrapper {
+  $result = PyList_New($1.size());
+  if (!$result) {
+    SWIG_exception_fail(SWIG_MemoryError, "$symname: couldn't create list");
+  }
+
+  for (size_t i = 0; i < $1.size(); ++i) {
+    PyList_SET_ITEM($result, i, SWIG_NewPointerObj(
+                            $1[i], SWIGTYPE_p_TF_Operation, 0));
+  }
+}
+
+
 ////////////////////////////////////////////////////////////////////////////////
 // BEGIN TYPEMAPS FOR tensorflow::TF_Run_wrapper()
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc
index 7ebb1a7fe4..0e89ae2426 100644
--- a/tensorflow/python/client/tf_session_helper.cc
+++ b/tensorflow/python/client/tf_session_helper.cc
@@ -766,4 +766,12 @@ void TF_SessionPRun_wrapper(TF_Session* session, const char* handle,
   ClearDecrefCache();
 }
 
+std::vector<TF_Operation*> TF_OperationGetControlInputs_wrapper(
+    TF_Operation* oper) {
+  std::vector<TF_Operation*> control_inputs(TF_OperationNumControlInputs(oper));
+  TF_OperationGetControlInputs(oper, control_inputs.data(),
+                               control_inputs.size());
+  return control_inputs;
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h
index 9937b6aeeb..f1f70a9a1d 100644
--- a/tensorflow/python/client/tf_session_helper.h
+++ b/tensorflow/python/client/tf_session_helper.h
@@ -158,6 +158,11 @@ void TF_SessionPRun_wrapper(TF_Session* session, const char* handle,
                             TF_Status* out_status,
                             std::vector<PyObject*>* py_outputs);
 
+// Retrieves control inputs of this operation.
+// control_inputs should be empty.
+std::vector<TF_Operation*> TF_OperationGetControlInputs_wrapper(
+    TF_Operation* oper);
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index f52127f119..a8c2930cbe 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1583,7 +1583,14 @@ class Operation(object):
       A list of `Operation` objects.
 
     """
-    return self._control_inputs
+    if _USE_C_API:
+      control_c_ops = c_api.TF_OperationGetControlInputs_wrapper(self._c_op)
+      # pylint: disable=protected-access
+      return [self.graph._get_operation_by_name_unsafe(
+          c_api.TF_OperationName(c_op)) for c_op in control_c_ops]
+      # pylint: enable=protected-access
+    else:
+      return self._control_inputs
 
   @property
   def type(self):
@@ -2781,6 +2788,29 @@ class Graph(object):
                       % type(name).__name__)
     return self.as_graph_element(name, allow_tensor=False, allow_operation=True)
 
+  def _get_operation_by_name_unsafe(self, name):
+    """Returns the `Operation` with the given `name`.
+
+    This is a internal unsafe version of get_operation_by_name. It skips many
+    checks and does not have user friedly error messages but runs considerably
+    faster. This method may be called concurrently from multiple threads.
+
+    Args:
+      name: The name of the `Operation` to return.
+
+    Returns:
+      The `Operation` with the given `name`.
+
+    Raises:
+      KeyError: If `name` does not correspond to an operation in this graph.
+    """
+
+    if self._finalized:
+      return self._nodes_by_name[name]
+
+    with self._lock:
+      return self._nodes_by_name[name]
+
   def get_tensor_by_name(self, name):
     """Returns the `Tensor` with the given `name`.
 
-- 
GitLab


From 27b341c800eab9c20d34394e497498d90f2b6dc9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 11:59:53 -0700
Subject: [PATCH 0751/1375] Automated g4 rollback of changelist 161087978

PiperOrigin-RevId: 161111023
---
 tensorflow/compiler/aot/BUILD                 |  25 +----
 tensorflow/compiler/aot/compile.cc            |  91 +++++++--------
 .../compiler/aot/test_graph_tfunknownop.pbtxt |  54 ++++++---
 .../aot/test_graph_tfunknownop2.config.pbtxt  |  25 -----
 .../aot/test_graph_tfunknownop3.config.pbtxt  |  26 -----
 tensorflow/compiler/aot/tfcompile.proto       |   7 --
 tensorflow/compiler/aot/tfcompile_util.cc     | 106 +-----------------
 tensorflow/compiler/aot/tfcompile_util.h      |  15 ---
 8 files changed, 89 insertions(+), 260 deletions(-)
 delete mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
 delete mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index b12b5318ec..12179b7637 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -156,7 +156,7 @@ tf_library(
 )
 
 # A test of tf_library that includes a graph with an unknown op, but where
-# the compilation works because the unknown op is not needed for the fetches.
+# the compilation works because the the unknown op is not needed for the fetches.
 tf_library(
     name = "test_graph_tfunknownop",
     testonly = 1,
@@ -166,29 +166,6 @@ tf_library(
     tags = ["manual"],
 )
 
-# A test of tf_library that includes a graph with an unknown op, but where
-# the compilation works because the op between the unknown op and the
-# fetches is a feed.
-tf_library(
-    name = "test_graph_tfunknownop2",
-    testonly = 1,
-    config = "test_graph_tfunknownop2.config.pbtxt",
-    cpp_class = "UnknownOpAddComp",
-    graph = "test_graph_tfunknownop.pbtxt",
-    tags = ["manual"],
-)
-
-# A test of tf_library that includes a graph with an unknown op, but where
-# the compilation works because the unknown op is fed.
-tf_library(
-    name = "test_graph_tfunknownop3",
-    testonly = 1,
-    config = "test_graph_tfunknownop3.config.pbtxt",
-    cpp_class = "UnknownOpAddComp",
-    graph = "test_graph_tfunknownop.pbtxt",
-    tags = ["manual"],
-)
-
 # Utility library for benchmark binaries, used by the *_benchmark rules that are
 # added by the tfcompile bazel macro.
 cc_library(
diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc
index 51d08eaa01..59ff14600b 100644
--- a/tensorflow/compiler/aot/compile.cc
+++ b/tensorflow/compiler/aot/compile.cc
@@ -78,51 +78,66 @@ Status DumpGraph(const MainFlags& flags, const string& name,
   return WriteTextProto(Env::Default(), file, graph_def);
 }
 
+string TensorIdToString(const TensorId& id) {
+  return strings::StrCat(id.node_name(), ":", id.output_index());
+}
+
 typedef std::unordered_map<string, Node*> NodeMap;
 
 // Each feed id identifies the positional output of some node, which may consist
-// of multiple edges. AddPlaceholdersForFeeds has already replaced each fed
-// tensor with a placeholder.  For each feed tensor, replaces all edges so they
-// point from a new _Arg node instead.
+// of multiple edges.  For each feed node, replaces all matching edges so that
+// they point from a new _Arg node instead.
 Status AddArgNodes(Graph* graph, const NodeMap& node_map,
-                   const protobuf::RepeatedPtrField<Feed>& feeds,
-                   const std::unordered_map<string, string>& feed_remapping) {
+                   const protobuf::RepeatedPtrField<Feed>& feeds) {
   for (int arg_index = 0; arg_index < feeds.size(); ++arg_index) {
     const Feed& feed = feeds[arg_index];
-    // All feeds have been replaced by placeholders.
-    const int output_index = 0;
-
-    const auto remap_it = feed_remapping.find(TensorIdToString(feed.id()));
-    auto node_it = node_map.find(remap_it->second);
-    const Node* feed_node = node_it->second;
-
-    // TODO(toddw): Invoke shape inference in AddPlaceholdersForFeeds and add a
-    // "_shape" attr if we can determine it.  That way the graph will be
-    // initialized with whatever shapes we can infer, while the user can still
-    // explicitly specify or override them.
+    const TensorId& id = feed.id();
+    auto it = node_map.find(id.node_name());
+    if (it == node_map.end()) {
+      return errors::NotFound("Can't find feed id: ", TensorIdToString(id));
+    }
+    const Node* feed_node = it->second;
+    if (id.output_index() >= feed_node->num_outputs()) {
+      return errors::InvalidArgument("Invalid feed id: ", TensorIdToString(id),
+                                     ", output index should be < ",
+                                     feed_node->num_outputs());
+    }
+    // TODO(toddw): Invoke shape inference on the graph and add a "_shape" attr
+    // if we can determine it.  That way the graph will be initialized with
+    // whatever shapes we can infer, while the user can still explicitly specify
+    // or override them.
     Node* arg_node = nullptr;
     TF_RETURN_IF_ERROR(
         NodeBuilder(strings::StrCat("_arg_", arg_index), kArgOp)
-            .Attr("T", BaseType(feed_node->output_type(output_index)))
+            .Attr("T", BaseType(feed_node->output_type(id.output_index())))
             .Attr("index", arg_index)
-            .Attr(kFeedIdAttr, TensorIdToString(feed.id()))
+            .Attr(kFeedIdAttr, TensorIdToString(id))
             .Attr(kShapeAttr, TensorShape(feed.shape()))
             .Attr(kDebugNameAttr, feed.name())
             .Finalize(graph, &arg_node));
-
     // Collects out-edges from the feed node that have a matching edge index;
-    // these will be replaced with edges from the arg node instead.
+    // these will be replaced with edges from the arg node instead.  Also
+    // replaces all control edges from Placeholder feed nodes; similar code
+    // exists in subgraph::RewriteGraphForExecution.
+    // TODO(toddw): Why only replace control edges from Placeholder?
     //
     // We must collect the edges first and process them in a second pass, since
     // removing the edge from the graph invalidates feed_node->out_edges.
     std::vector<const Edge*> feed_edges;
     for (const Edge* edge : feed_node->out_edges()) {
-      if (edge->src_output() == output_index) {
+      if (edge->src_output() == id.output_index() ||
+          (edge->src_output() == Graph::kControlSlot &&
+           feed_node->type_string() == "Placeholder")) {
         feed_edges.push_back(edge);
       }
     }
     for (const Edge* edge : feed_edges) {
-      graph->AddEdge(arg_node, 0, edge->dst(), edge->dst_input());
+      if (edge->src_output() == id.output_index()) {
+        graph->AddEdge(arg_node, 0, edge->dst(), edge->dst_input());
+      } else {
+        CHECK_EQ(edge->src_output(), Graph::kControlSlot);
+        graph->AddControlEdge(arg_node, edge->dst());
+      }
       graph->RemoveEdge(edge);
     }
   }
@@ -164,16 +179,13 @@ Status AddRetvalNodes(Graph* graph, const NodeMap& node_map,
 // fetch ids respectively), and rewrites the edges so that inputs flow from _Arg
 // nodes, and outputs flow to _Retval nodes.  This allows the symbolic graph
 // execution to know the input and output args for the generated function.
-Status RewriteAndPruneGraph(
-    Graph* graph, const Config& config,
-    const std::unordered_map<string, string>& feed_remapping,
-    const MainFlags& flags) {
+Status RewriteAndPruneGraph(Graph* graph, const Config& config,
+                            const MainFlags& flags) {
   NodeMap node_map;
   for (Node* n : graph->nodes()) {
     node_map[n->name()] = n;
   }
-  TF_RETURN_IF_ERROR(
-      AddArgNodes(graph, node_map, config.feed(), feed_remapping));
+  TF_RETURN_IF_ERROR(AddArgNodes(graph, node_map, config.feed()));
   std::unordered_set<const Node*> retval_nodes;
   TF_RETURN_IF_ERROR(
       AddRetvalNodes(graph, node_map, config.fetch(), &retval_nodes));
@@ -371,28 +383,17 @@ Status InitGraph(const GraphDef& graph_def, const Config& config,
   FunctionLibraryDefinition flib_def(OpRegistry::Global(), graph_def.library());
   std::unique_ptr<Graph> g(new Graph(flib_def));
 
-  // Replace references to fed tensors with references to newly added
-  // placeholders.
-  GraphDef first_copy_def = graph_def;
-
-  // Maps from name:port of a feed to the name:port of the placeholder to use.
-  std::unordered_map<string, string> feed_remapping;
-  TF_RETURN_IF_ERROR(AddPlaceholdersForFeeds(config, g->op_registry(),
-                                             &feed_remapping, &first_copy_def));
+  GraphDef copy_def;
 
   // Prune the GraphDef first so that unknown ops that we aren't compiling get
   // filtered out.
-  GraphDef second_copy_def;
-  TF_RETURN_IF_ERROR(
-      PruneGraphDefInto(config, first_copy_def, &second_copy_def));
-
-  TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(
-      &second_copy_def, *g->op_registry(), 0 /*node_offset*/));
+  TF_RETURN_IF_ERROR(PruneGraphDefInto(config, graph_def, &copy_def));
 
-  TF_RETURN_IF_ERROR(ConvertGraphDefToGraph(GraphConstructorOptions(),
-                                            second_copy_def, g.get()));
+  TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&copy_def, *g->op_registry(),
+                                               0 /*node_offset*/));
   TF_RETURN_IF_ERROR(
-      RewriteAndPruneGraph(g.get(), config, feed_remapping, flags));
+      ConvertGraphDefToGraph(GraphConstructorOptions(), copy_def, g.get()));
+  TF_RETURN_IF_ERROR(RewriteAndPruneGraph(g.get(), config, flags));
   *graph = std::move(g);
   return Status::OK();
 }
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
index 48b881bb94..212ffbb5ff 100644
--- a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
@@ -6,12 +6,21 @@ node {
     value {
       tensor {
         dtype: DT_INT32
-        tensor_shape { dim { size: 1 } }
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
         int_val: 1
       }
     }
   }
-  attr { key  : "dtype" value { type: DT_INT32 } }
+  attr {
+    key  : "dtype"
+    value {
+      type       : DT_INT32
+    }
+  }
 }
 node {
   name  : "y_const"
@@ -21,37 +30,56 @@ node {
     value {
       tensor {
         dtype: DT_INT32
-        tensor_shape { dim { size: 1 } }
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
         int_val: 2
       }
     }
   }
-  attr { key: "dtype" value { type: DT_INT32 } }
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
 }
 node {
   name  : "x_y_sum"
   op    : "Add"
   input : "x_const"
   input : "y_const"
-  attr { key  : "T" value { type: DT_INT32 } }
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
 }
 node {
   name  : "z"
   op    : "SomeUnknownOp"
   input : "x_const"
-}
-node {
-  name  : "z_identity"
-  op    : "Identity"
-  input : "z:1"
-  attr { key  : "T" value { type: DT_INT32 } }
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
 }
 node {
   name  : "x_z_sum"
   op    : "Add"
   input : "x_const"
-  input : "z_identity"
-  attr { key  : "T" value { type: DT_INT32 } }
+  input : "z"
+  attr {
+    key  : "T"
+    value {
+      type: DT_INT32
+    }
+  }
 }
 versions {
   producer: 15
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
deleted file mode 100644
index 7370ed370d..0000000000
--- a/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
+++ /dev/null
@@ -1,25 +0,0 @@
-# Text form of tensorflow.tfcompile.Config proto.
-feed {
-  id { node_name: "x_const" }
-  shape {
-    dim { size: 1 }
-  }
-}
-feed {
-  id { node_name: "y_const" }
-  shape {
-    dim { size: 1 }
-  }
-}
-feed {
-  id { node_name: "z_identity"}
-  shape {
-    dim { size: 1 }
-  }
-}
-fetch {
-  id { node_name: "x_y_sum" }
-}
-fetch {
-  id { node_name: "x_z_sum" }
-}
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt
deleted file mode 100644
index b2d7d54574..0000000000
--- a/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt
+++ /dev/null
@@ -1,26 +0,0 @@
-# Text form of tensorflow.tfcompile.Config proto.
-feed {
-  id { node_name: "x_const" }
-  shape {
-    dim { size: 1 }
-  }
-}
-feed {
-  id { node_name: "y_const" }
-  shape {
-    dim { size: 1 }
-  }
-}
-feed {
-  id { node_name: "z" output_index: 1}
-  shape {
-    dim { size: 1 }
-  }
-  type: DT_INT32
-}
-fetch {
-  id { node_name: "x_y_sum" }
-}
-fetch {
-  id { node_name: "x_z_sum" }
-}
diff --git a/tensorflow/compiler/aot/tfcompile.proto b/tensorflow/compiler/aot/tfcompile.proto
index cd83840d89..be3f504350 100644
--- a/tensorflow/compiler/aot/tfcompile.proto
+++ b/tensorflow/compiler/aot/tfcompile.proto
@@ -7,7 +7,6 @@ option java_multiple_files = true;
 option java_package = "org.tensorflow.tfcompile";
 
 import "tensorflow/core/framework/tensor_shape.proto";
-import "tensorflow/core/framework/types.proto";
 
 // TensorId identifies a tensor in a TensorFlow graph, by specifying the output
 // index of a particular node in the graph.  If the output of the named node
@@ -24,12 +23,6 @@ message Feed {
   TensorId id = 1;
   TensorShapeProto shape = 2;
   string name = 3;  // Optional name for generated code.
-
-  // Optional data type. This is not normally required, as the graph itself
-  // contains this information. However, if the node being fed is an op that
-  // is not linked into the tfcompile binary, then the type cannot be inferred
-  // from the node; in this case, the type should be set here.
-  DataType type = 4;
 };
 
 // Fetch represents a single fetch tensor in the graph, which corresponds to an
diff --git a/tensorflow/compiler/aot/tfcompile_util.cc b/tensorflow/compiler/aot/tfcompile_util.cc
index 4476baae63..8774a02128 100644
--- a/tensorflow/compiler/aot/tfcompile_util.cc
+++ b/tensorflow/compiler/aot/tfcompile_util.cc
@@ -26,7 +26,6 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
-#include "tensorflow/core/lib/strings/strcat.h"
 
 namespace tensorflow {
 namespace tfcompile {
@@ -120,105 +119,17 @@ Status ValidateConfig(const Config& config) {
   return Status::OK();
 }
 
-Status AddPlaceholdersForFeeds(
-    const Config& config, const OpRegistryInterface* op_registry,
-    std::unordered_map<string, string>* feed_remapping, GraphDef* graph_def) {
-  struct PlaceholderInfo {
-    const Feed* feed = nullptr;  // point to Feed in <config>.
-    string placeholder_name;
-    DataType data_type = DT_INVALID;
-  };
-
-  // Put each fed tensor into a map by name:port. A map is used for determinism
-  // when creating placeholders (genrules want deterministic output).
-  std::map<string, PlaceholderInfo> placeholder_info;
-  for (int i = 0; i < config.feed_size(); ++i) {
-    const Feed* feed = &config.feed(i);
-    const string name_port = TensorIdToString(feed->id());
-    auto& info = placeholder_info[name_port];
-    info.feed = feed;
-    info.placeholder_name = strings::StrCat(
-        "aot_feed_", feed->id().output_index(), "/", feed->id().node_name());
-    (*feed_remapping)[name_port] = info.placeholder_name;
-  }
-
-  // Verify node exists and determine data type.
-  std::unordered_map<string, const NodeDef*> name_to_node;
-  for (int i = 0; i < graph_def->node_size(); ++i) {
-    name_to_node[graph_def->node(i).name()] = &graph_def->node(i);
-  }
-  for (auto it = placeholder_info.begin(); it != placeholder_info.end(); ++it) {
-    PlaceholderInfo& info = it->second;
-    const TensorId& feed_id = info.feed->id();
-
-    // Find the existing node and determine data type.
-    auto node_it = name_to_node.find(feed_id.node_name());
-    if (node_it == name_to_node.end()) {
-      return errors::NotFound("Can't find feed node: ",
-                              TensorIdToString(feed_id));
-    }
-    const NodeDef* existing = node_it->second;
-
-    if (info.feed->type() != DT_INVALID) {
-      info.data_type = info.feed->type();
-    } else {
-      // Build the node in order to infer its type.
-      Graph g(op_registry);
-      Status status;
-      Node* feed_node = g.AddNode(*existing, &status);
-      TF_RETURN_IF_ERROR(status);
-      info.data_type =
-          BaseType(feed_node->output_type(info.feed->id().output_index()));
-    }
-  }
-
-  // Create placeholders. Note that we could avoid creating a placeholder for
-  // feeds which are already placeholders, but we omit that to avoid more cases
-  // in this code.
-  for (auto it = placeholder_info.begin(); it != placeholder_info.end(); ++it) {
-    const PlaceholderInfo& info = it->second;
-    NodeDef* d = graph_def->add_node();
-    d->set_name(info.placeholder_name);
-    d->set_op("PlaceholderV2");
-    auto& attr_map = *d->mutable_attr();
-    attr_map["dtype"].set_type(info.data_type);
-    *attr_map["shape"].mutable_shape() = info.feed->shape();
-  }
-
-  // Rewrite references to the fed tensors to refer to the placeholder.
-  for (int i = 0; i < graph_def->node_size(); ++i) {
-    NodeDef* node_def = graph_def->mutable_node(i);
-    for (int j = 0; j < node_def->input_size(); ++j) {
-      auto id = ParseTensorName(node_def->input(j));
-      auto it = placeholder_info.find(id.ToString());
-      if (it != placeholder_info.end()) {
-        node_def->set_input(j, it->second.placeholder_name);
-      }
-    }
-  }
-
-  return Status::OK();
-}
-
 Status PruneGraphDefInto(const Config& config, const GraphDef& in,
                          GraphDef* out) {
   *out = in;
   out->clear_node();
 
-  // Tensors needed for feeding.
-  std::set<std::pair<string, int>> feed_tensors;
-  for (const auto& feed_config : config.feed()) {
-    feed_tensors.insert(std::make_pair(feed_config.id().node_name(),
-                                       feed_config.id().output_index()));
-  }
-
   // Maps node name to reachability.
   std::unordered_map<string, std::pair<bool, const NodeDef*>> node_by_name;
   for (const NodeDef& node : in.node()) {
     node_by_name[node.name()] = std::pair<bool, const NodeDef*>(false, &node);
   }
 
-  // Traverse.
   std::queue<string> name_queue;
   for (int i = 0; i < config.fetch_size(); ++i) {
     name_queue.push(config.fetch(i).id().node_name());
@@ -238,19 +149,8 @@ Status PruneGraphDefInto(const Config& config, const GraphDef& in,
     }
     map_entry.first = true;
 
-    // Push input nodes of the currently visited node to name_queue.
     for (const string& in_edge : map_entry.second->input()) {
-      auto id = ParseTensorName(in_edge);
-      const string node_name = id.first.ToString();
-      if (feed_tensors.find(std::make_pair(node_name, id.second)) ==
-          feed_tensors.end()) {
-        name_queue.push(node_name);
-      } else {
-        // The input tensor is from an edge that is being fed. Therefore,
-        // we skip recursing down that edge, to avoid requiring nodes that
-        // may not be needed (note that the input node may still be added
-        // to name_queue later if one of its output edges is not being fed).
-      }
+      name_queue.push(ParseTensorName(in_edge).first.ToString());
     }
   }
 
@@ -265,9 +165,5 @@ Status PruneGraphDefInto(const Config& config, const GraphDef& in,
   return Status::OK();
 }
 
-string TensorIdToString(const TensorId& id) {
-  return strings::StrCat(id.node_name(), ":", id.output_index());
-}
-
 }  // namespace tfcompile
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/aot/tfcompile_util.h b/tensorflow/compiler/aot/tfcompile_util.h
index 365f7b0e7b..84060c0761 100644
--- a/tensorflow/compiler/aot/tfcompile_util.h
+++ b/tensorflow/compiler/aot/tfcompile_util.h
@@ -16,11 +16,8 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 #define TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 
-#include <unordered_map>
-
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
-#include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
@@ -34,23 +31,11 @@ Status ValidateCppIdent(StringPiece ident, StringPiece msg);
 // ValidateConfig returns OK iff config is valid.
 Status ValidateConfig(const Config& config);
 
-// Modifies <graph_def> to include placeholders for each fed tensor, and
-// update references to the fed tensors to refer to the placeholders.
-// The existing nodes referenced by the feeds are not removed or modified
-// (except where their input edges are modified by the replacement of other
-// feeds).
-Status AddPlaceholdersForFeeds(
-    const Config& config, const OpRegistryInterface* op_registry,
-    std::unordered_map<string, string>* feed_remapping, GraphDef* graph_def);
-
 // Returns in <out> a copy of <in>, pruned to only include fetches from
 // <config>.
 Status PruneGraphDefInto(const Config& config, const GraphDef& in,
                          GraphDef* out);
 
-// Returns node:port for the given <id>.
-string TensorIdToString(const TensorId& id);
-
 }  // namespace tfcompile
 }  // namespace tensorflow
 
-- 
GitLab


From 70aa8daacfd45c24e80d584c537e1badde74b4a7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 12:14:45 -0700
Subject: [PATCH 0752/1375] [framework] Add 'most_specific_compatible_shape'
 for the most specific TensorShape compatible with given two TensorShapes.

PiperOrigin-RevId: 161112884
---
 .../contrib/data/python/ops/dataset_ops.py    | 112 +++++++-----------
 tensorflow/python/framework/tensor_shape.py   |  30 +++++
 .../python/framework/tensor_shape_test.py     |  20 ++++
 .../api/golden/tensorflow.-tensor-shape.pbtxt |   4 +
 4 files changed, 100 insertions(+), 66 deletions(-)

diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index fde85d8783..5e43aab04a 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -350,8 +350,7 @@ def _estimate_data_distribution(c, num_examples_per_class_seen):
   # cross-device round-trip.  Just use the cached value.
   num_examples_per_class_seen = num_examples_per_class_seen.assign_add(
       math_ops.reduce_sum(
-          array_ops.one_hot(c, num_classes, dtype=dtypes.int64),
-          0))
+          array_ops.one_hot(c, num_classes, dtype=dtypes.int64), 0))
   init_prob_estimate = math_ops.truediv(
       num_examples_per_class_seen,
       math_ops.reduce_sum(num_examples_per_class_seen))
@@ -445,8 +444,8 @@ class Dataset(object):
     output_shapes = str(output_shapes).replace("'", "")
     output_types = nest.map_structure(repr, self.output_types)
     output_types = str(output_types).replace("'", "")
-    return ("<%s shapes: %s, types: %s>"
-            % (type(self).__name__, output_shapes, output_types))
+    return ("<%s shapes: %s, types: %s>" % (type(self).__name__, output_shapes,
+                                            output_types))
 
   @staticmethod
   def from_tensors(tensors):
@@ -595,9 +594,7 @@ class Dataset(object):
     dataset = dataset.repeat(num_epochs)
     if randomize_input:
       dataset = dataset.shuffle(capacity)
-    dataset = dataset.map(
-        lambda x: _parse_example(nest.flatten(x), features)
-    )
+    dataset = dataset.map(lambda x: _parse_example(nest.flatten(x), features))
     dataset = dataset.batch(batch_size)
     return dataset
 
@@ -897,7 +894,7 @@ class Dataset(object):
       A `Dataset`.
     """
     return self.flat_map(
-      map_func=lambda *args: Dataset.from_tensor_slices(args))
+        map_func=lambda *args: Dataset.from_tensor_slices(args))
 
   def filter(self, predicate):
     """Filters this dataset according to `predicate`.
@@ -1031,12 +1028,14 @@ class ZipDataset(Dataset):
   @property
   def output_shapes(self):
     return nest.pack_sequence_as(self._datasets, [
-        ds.output_shapes for ds in nest.flatten(self._datasets)])
+        ds.output_shapes for ds in nest.flatten(self._datasets)
+    ])
 
   @property
   def output_types(self):
     return nest.pack_sequence_as(self._datasets, [
-        ds.output_types for ds in nest.flatten(self._datasets)])
+        ds.output_types for ds in nest.flatten(self._datasets)
+    ])
 
 
 class RepeatDataset(Dataset):
@@ -1049,8 +1048,8 @@ class RepeatDataset(Dataset):
     if count is None:
       self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
     else:
-      self._count = ops.convert_to_tensor(count, dtype=dtypes.int64,
-                                          name="count")
+      self._count = ops.convert_to_tensor(
+          count, dtype=dtypes.int64, name="count")
 
   def make_dataset_resource(self):
     return gen_dataset_ops.repeat_dataset(
@@ -1155,8 +1154,8 @@ class ShuffleDataset(Dataset):
     if seed2 is None:
       self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
     else:
-      self._seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64,
-                                          name="seed2")
+      self._seed2 = ops.convert_to_tensor(
+          seed2, dtype=dtypes.int64, name="seed2")
 
   def make_dataset_resource(self):
     return gen_dataset_ops.shuffle_dataset(
@@ -1310,12 +1309,10 @@ def _padding_value_to_tensor(value, output_type):
   """
   value = ops.convert_to_tensor(value, name="padding_value")
   if not value.shape.is_compatible_with(tensor_shape.scalar()):
-    raise ValueError(
-        "Padding value should be a scalar, but is not: %s" % value)
+    raise ValueError("Padding value should be a scalar, but is not: %s" % value)
   if value.dtype != output_type:
-    raise TypeError(
-        "Padding value tensor (%s) does not match output type: %s"
-        % (value, output_type))
+    raise TypeError("Padding value tensor (%s) does not match output type: %s" %
+                    (value, output_type))
   return value
 
 
@@ -1329,20 +1326,20 @@ class PaddedBatchDataset(Dataset):
     self._batch_size = batch_size
     padding_values = (padding_values if padding_values is not None else
                       self._default_padding(input_dataset))
-    self._padded_shapes = nest.map_structure_up_to(input_dataset.output_shapes,
-                                                   _partial_shape_to_tensor,
-                                                   padded_shapes)
-    self._padding_values = nest.map_structure_up_to(input_dataset.output_shapes,
-                                                    _padding_value_to_tensor,
-                                                    padding_values,
-                                                    input_dataset.output_types)
+    self._padded_shapes = nest.map_structure_up_to(
+        input_dataset.output_shapes, _partial_shape_to_tensor, padded_shapes)
+    self._padding_values = nest.map_structure_up_to(
+        input_dataset.output_shapes, _padding_value_to_tensor, padding_values,
+        input_dataset.output_types)
 
   def _default_padding(self, input_dataset):
+
     def make_zero(t):
       if t.base_dtype == dtypes.string:
         return ""
       else:
         return np.zeros_like(t.as_numpy_dtype())
+
     return nest.map_structure(make_zero, input_dataset.output_types)
 
   def make_dataset_resource(self):
@@ -1358,9 +1355,11 @@ class PaddedBatchDataset(Dataset):
 
   @property
   def output_shapes(self):
+
     def _padded_shape_to_batch_shape(s):
       return tensor_shape.vector(None).concatenate(
           tensor_util.constant_value_as_shape(s))
+
     return nest.map_structure(_padded_shape_to_batch_shape, self._padded_shapes)
 
   @property
@@ -1376,8 +1375,8 @@ class DenseToSparseBatchDataset(Dataset):
     super(DenseToSparseBatchDataset, self).__init__()
     if not isinstance(input_dataset.output_types, dtypes.DType):
       raise TypeError("DenseToSparseDataset requires an input whose elements "
-                      "have a single component, whereas the input has %r."
-                      % input_dataset.output_types)
+                      "have a single component, whereas the input has %r." %
+                      input_dataset.output_types)
     self._input_dataset = input_dataset
     self._batch_size = batch_size
     self._row_shape = _partial_shape_to_tensor(row_shape)
@@ -1493,22 +1492,6 @@ class GroupByWindowDataset(Dataset):
     return self._output_types
 
 
-def _most_specific_compatible_shape(s1, s2):
-  """Returns the most specific shape compatible with `s1` and `s2`."""
-  if s1.dims is None:
-    return s1
-  if s2.dims is None:
-    return s2
-  s1.assert_same_rank(s2)
-  dims = []
-  for dim1, dim2 in zip(s1, s2):
-    if dim1.value is None or dim2.value is None or dim1.value != dim2.value:
-      dims.append(tensor_shape.Dimension(None))
-    else:
-      dims.append(dim1.value)
-  return tensor_shape.TensorShape(dims)
-
-
 class MapDataset(Dataset):
   """A `Dataset` that maps a function over elements in its input."""
 
@@ -1593,9 +1576,7 @@ class MapDataset(Dataset):
 class FlatMapDataset(Dataset):
   """A `Dataset` that maps a function over its input and flattens the result."""
 
-  def __init__(self,
-               input_dataset,
-               map_func):
+  def __init__(self, input_dataset, map_func):
     """See `Dataset.flat_map()` for details."""
     super(FlatMapDataset, self).__init__()
     self._input_dataset = input_dataset
@@ -1799,8 +1780,11 @@ class FixedLengthRecordDataset(Dataset):
     return dtypes.string
 
 
-def rejection_resample(dataset, class_func, target_dist,
-                       initial_dist=None, seed=None):
+def rejection_resample(dataset,
+                       class_func,
+                       target_dist,
+                       initial_dist=None,
+                       seed=None):
   """Resamples this dataset to achieve a target class distribution.
 
   **NOTE** Resampling is performed via rejection sampling; some fraction
@@ -1825,36 +1809,34 @@ def rejection_resample(dataset, class_func, target_dist,
   target_dist = ops.convert_to_tensor(target_dist, name="initial_dist")
   class_values_ds = dataset.map(class_func)
   if initial_dist is not None:
-    initial_dist = ops.convert_to_tensor(
-        initial_dist, name="initial_dist")
+    initial_dist = ops.convert_to_tensor(initial_dist, name="initial_dist")
     acceptance_dist = _calculate_acceptance_probs(initial_dist, target_dist)
     initial_dist_ds = Dataset.from_tensors(initial_dist).repeat()
     acceptance_dist_ds = Dataset.from_tensors(acceptance_dist).repeat()
   else:
-    num_classes = (target_dist.shape[0].value
-                   or array_ops.shape(target_dist)[0])
+    num_classes = (target_dist.shape[0].value or
+                   array_ops.shape(target_dist)[0])
     smoothing_constant = 10
     num_examples_per_class_seen = resource_variable_ops.ResourceVariable(
-        initial_value=array_ops.fill(
-            [num_classes], np.int64(smoothing_constant)),
+        initial_value=array_ops.fill([num_classes],
+                                     np.int64(smoothing_constant)),
         trainable=False,
         name="class_count",
         dtype=dtypes.int64)
+
     def update_estimate_and_tile(c):
       return array_ops.tile(
           array_ops.expand_dims(
               _estimate_data_distribution(c, num_examples_per_class_seen), 0),
           [dist_estimation_batch_size, 1])
-    initial_dist_ds = (class_values_ds
-                       .batch(dist_estimation_batch_size)
-                       .map(update_estimate_and_tile)
-                       .unbatch())
+
+    initial_dist_ds = (class_values_ds.batch(dist_estimation_batch_size)
+                       .map(update_estimate_and_tile).unbatch())
     acceptance_dist_ds = initial_dist_ds.map(
         lambda initial: _calculate_acceptance_probs(initial, target_dist))
 
   def maybe_warn_on_large_rejection(accept_dist, initial_dist):
-    proportion_rejected = math_ops.reduce_sum(
-        (1 - accept_dist) * initial_dist)
+    proportion_rejected = math_ops.reduce_sum((1 - accept_dist) * initial_dist)
     return control_flow_ops.cond(
         math_ops.less(proportion_rejected, .5),
         lambda: accept_dist,
@@ -1864,12 +1846,10 @@ def rejection_resample(dataset, class_func, target_dist,
             summarize=100,
             first_n=10))
 
-  acceptance_dist_ds = (
-      Dataset.zip((acceptance_dist_ds, initial_dist_ds))
-      .map(maybe_warn_on_large_rejection))
+  acceptance_dist_ds = (Dataset.zip((acceptance_dist_ds, initial_dist_ds))
+                        .map(maybe_warn_on_large_rejection))
 
-  current_probabilities_ds = (Dataset
-                              .zip((acceptance_dist_ds, class_values_ds))
+  current_probabilities_ds = (Dataset.zip((acceptance_dist_ds, class_values_ds))
                               .map(array_ops.gather))
   filtered_ds = (
       Dataset.zip((class_values_ds, current_probabilities_ds, dataset))
diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py
index 3aedbfef0d..155fa4719b 100644
--- a/tensorflow/python/framework/tensor_shape.py
+++ b/tensorflow/python/framework/tensor_shape.py
@@ -736,6 +736,36 @@ class TensorShape(object):
     if not self.is_compatible_with(other):
       raise ValueError("Shapes %s and %s are incompatible" % (self, other))
 
+  def most_specific_compatible_shape(self, other):
+    """Returns the most specific TensorShape compatible with `self` and `other`.
+
+    * TensorShape([None, 1]) is the most specific TensorShape compatible with
+      both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
+      TensorShape(None) is also compatible with above mentioned TensorShapes.
+
+    * TensorShape([1, 2, 3]) is the most specific TensorShape compatible with
+      both TensorShape([1, 2, 3]) and TensorShape([1, 2, 3]). There are more
+      less specific TensorShapes compatible with above mentioned TensorShapes,
+      e.g. TensorShape([1, 2, None]), TensorShape(None).
+
+    Args:
+      other: Another `TensorShape`.
+
+    Returns:
+      A `TensorShape` which is the most specific compatible shape of `self`
+      and `other`.
+    """
+
+    other = as_shape(other)
+    if self._dims is None or other.dims is None or self.ndims != other.ndims:
+      return unknown_shape()
+
+    dims = [(Dimension(None))] * self.ndims
+    for i, (d1, d2) in enumerate(zip(self._dims, other.dims)):
+      if d1 is not None and d2 is not None and d1 == d2:
+        dims[i] = d1
+    return TensorShape(dims)
+
   def is_fully_defined(self):
     """Returns True iff `self` is fully defined in every dimension."""
     return (self._dims is not None and all(dim.value is not None
diff --git a/tensorflow/python/framework/tensor_shape_test.py b/tensorflow/python/framework/tensor_shape_test.py
index 0ae8d6b821..fffd86c7a6 100644
--- a/tensorflow/python/framework/tensor_shape_test.py
+++ b/tensorflow/python/framework/tensor_shape_test.py
@@ -275,6 +275,26 @@ class ShapeTest(test_util.TensorFlowTestCase):
         tensor_shape.TensorShape([1, 2]).concatenate(
             tensor_shape.Dimension(3)))
 
+  def _testMostSpecificCompatibleShapeHelper(self, x, y, expected):
+    mcs = tensor_shape.TensorShape(x).most_specific_compatible_shape(
+        tensor_shape.TensorShape(y))
+    mcs_dims = mcs.dims
+    if expected is None or mcs_dims is None:
+      self.assertIs(expected, mcs_dims)
+    else:
+      self.assertEqual(expected, mcs.as_list())
+
+  def testMostSpecificCompatibleShape(self):
+    self._testMostSpecificCompatibleShapeHelper([1, 2], None, None)
+    self._testMostSpecificCompatibleShapeHelper(None, [1, 2], None)
+    self._testMostSpecificCompatibleShapeHelper([1, 2], [1, 2, 3, 4], None)
+    self._testMostSpecificCompatibleShapeHelper([1, 2, 3, 4], [1, 2], None)
+    self._testMostSpecificCompatibleShapeHelper([1, 2], [1, 2], [1, 2])
+    self._testMostSpecificCompatibleShapeHelper([None, 2, 3], [1, 1, 3],
+                                                [None, None, 3])
+    self._testMostSpecificCompatibleShapeHelper([1, 1, 3], [None, 2, 3],
+                                                [None, None, 3])
+
   def testHelpers(self):
     tensor_shape.TensorShape([]).assert_is_compatible_with(
         tensor_shape.scalar())
diff --git a/tensorflow/tools/api/golden/tensorflow.-tensor-shape.pbtxt b/tensorflow/tools/api/golden/tensorflow.-tensor-shape.pbtxt
index d5b9cb8f5e..8e3598fb24 100644
--- a/tensorflow/tools/api/golden/tensorflow.-tensor-shape.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-tensor-shape.pbtxt
@@ -54,6 +54,10 @@ tf_class {
     name: "merge_with"
     argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "most_specific_compatible_shape"
+    argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "num_elements"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-- 
GitLab


From e7fc162658db3c5435fbca17e98fcce553e1648d Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Thu, 6 Jul 2017 12:18:21 -0700
Subject: [PATCH 0753/1375] Consolidate/Merge the fn_args in multiple places
 (with double partial) and add full tests. RELNOTES: n/a

PiperOrigin-RevId: 161113304
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   |   5 +-
 tensorflow/python/estimator/BUILD             |  23 ++++
 tensorflow/python/estimator/estimator.py      |  34 +----
 tensorflow/python/estimator/util.py           |  57 +++++++++
 tensorflow/python/estimator/util_test.py      | 119 ++++++++++++++++++
 5 files changed, 206 insertions(+), 32 deletions(-)
 create mode 100644 tensorflow/python/estimator/util.py
 create mode 100644 tensorflow/python/estimator/util_test.py

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index b9da8dc35a..e001d866c3 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -30,6 +30,7 @@ from tensorflow.contrib.tpu.python.tpu import training_loop
 
 from tensorflow.python.estimator import estimator as estimator_lib
 from tensorflow.python.estimator import model_fn as model_fn_lib
+from tensorflow.python.estimator import util
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
@@ -303,7 +304,7 @@ class TpuEstimator(estimator_lib.Estimator):
     if not self._use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
       return super(TpuEstimator, self)._call_input_fn(input_fn, mode)
 
-    input_fn_args = estimator_lib._fn_args(input_fn)  # pylint: disable=protected-access
+    input_fn_args = util.fn_args(input_fn)
     config = self.config  # a deep copy.
     kwargs = {}
     if 'params' in input_fn_args:
@@ -357,7 +358,7 @@ def _verify_estimator_spec(estimator_spec):
 def _call_model_fn(model_fn, features, labels, mode, config, params,
                    require_params=False):
   """Calls the model_fn with required parameters."""
-  model_fn_args = estimator_lib._fn_args(model_fn)  # pylint: disable=protected-access
+  model_fn_args = util.fn_args(model_fn)
   kwargs = {}
   if 'mode' in model_fn_args:
     kwargs['mode'] = mode
diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index ca46daf306..627fa8b25e 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -26,6 +26,7 @@ py_library(
         ":model_fn",
         ":parsing_utils",
         ":run_config",
+        ":util",
         "//tensorflow/python:util",
     ],
 )
@@ -212,6 +213,27 @@ py_test(
     ],
 )
 
+py_library(
+    name = "util",
+    srcs = [
+        "util.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:util",
+    ],
+)
+
+py_test(
+    name = "util_test",
+    srcs = ["util_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":util",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 py_library(
     name = "estimator",
     srcs = [
@@ -222,6 +244,7 @@ py_library(
         ":export",
         ":model_fn",
         ":run_config",
+        ":util",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:control_flow_ops",
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index bc11a6c84f..0c1bf7ccf5 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -31,6 +31,7 @@ from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session as tf_session
 from tensorflow.python.estimator import model_fn as model_fn_lib
 from tensorflow.python.estimator import run_config
+from tensorflow.python.estimator import util
 from tensorflow.python.estimator.export.export import build_all_signature_defs
 from tensorflow.python.estimator.export.export import get_timestamped_export_dir
 from tensorflow.python.framework import ops
@@ -47,7 +48,6 @@ from tensorflow.python.training import monitored_session
 from tensorflow.python.training import saver
 from tensorflow.python.training import training
 from tensorflow.python.util import compat
-from tensorflow.python.util import tf_decorator
 from tensorflow.python.util import tf_inspect
 
 
@@ -575,7 +575,7 @@ class Estimator(object):
       ValueError: if input_fn takes invalid arguments.
     """
     del mode  # unused
-    input_fn_args = _fn_args(input_fn)
+    input_fn_args = util.fn_args(input_fn)
     kwargs = {}
     if 'params' in input_fn_args:
       kwargs['params'] = self.params
@@ -598,7 +598,7 @@ class Estimator(object):
     Raises:
       ValueError: if model_fn returns invalid objects.
     """
-    model_fn_args = _fn_args(self._model_fn)
+    model_fn_args = util.fn_args(self._model_fn)
     kwargs = {}
     if 'labels' in model_fn_args:
       kwargs['labels'] = labels
@@ -791,35 +791,9 @@ def _get_replica_device_setter(config):
     return None
 
 
-def _fn_args(fn):
-  """Get argument names for function-like object.
-
-  Args:
-    fn: Function, or function-like object (e.g., result of `functools.partial`).
-
-  Returns:
-    `tuple` of string argument names.
-
-  Raises:
-    ValueError: if partial function has positionally bound arguments
-  """
-  _, fn = tf_decorator.unwrap(fn)
-  if hasattr(fn, '__call__') and tf_inspect.ismethod(fn.__call__):
-    # Handle callables.
-    return tuple(tf_inspect.getargspec(fn.__call__).args)
-  if hasattr(fn, 'func') and hasattr(fn, 'keywords') and hasattr(fn, 'args'):
-    # Handle functools.partial and similar objects.
-    return tuple([
-        arg for arg in tf_inspect.getargspec(fn.func).args[len(fn.args):]
-        if arg not in set(fn.keywords.keys())
-    ])
-  # Handle function.
-  return tuple(tf_inspect.getargspec(fn).args)
-
-
 def _verify_model_fn_args(model_fn, params):
   """Verifies model fn arguments."""
-  args = set(_fn_args(model_fn))
+  args = set(util.fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
   if params is not None and 'params' not in args:
diff --git a/tensorflow/python/estimator/util.py b/tensorflow/python/estimator/util.py
new file mode 100644
index 0000000000..de35e66bdf
--- /dev/null
+++ b/tensorflow/python/estimator/util.py
@@ -0,0 +1,57 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Utility to retrieve function args.."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.util import tf_decorator
+from tensorflow.python.util import tf_inspect
+
+
+def fn_args(fn):
+  """Get argument names for function-like object.
+
+  Args:
+    fn: Function, or function-like object (e.g., result of `functools.partial`).
+
+  Returns:
+    `tuple` of string argument names.
+
+  Raises:
+    ValueError: if partial function has positionally bound arguments
+  """
+  _, fn = tf_decorator.unwrap(fn)
+
+  # Handle callables.
+  if hasattr(fn, '__call__') and tf_inspect.ismethod(fn.__call__):
+    return tuple(tf_inspect.getargspec(fn.__call__).args)
+
+  # Handle functools.partial and similar objects.
+  if hasattr(fn, 'func') and hasattr(fn, 'keywords') and hasattr(fn, 'args'):
+    # Handle nested partial.
+    original_args = fn_args(fn.func)
+    if not original_args:
+      return tuple()
+
+    return tuple([
+        arg for arg in original_args[len(fn.args):]
+        if arg not in set((fn.keywords or {}).keys())
+    ])
+
+  # Handle function.
+  return tuple(tf_inspect.getargspec(fn).args)
diff --git a/tensorflow/python/estimator/util_test.py b/tensorflow/python/estimator/util_test.py
new file mode 100644
index 0000000000..3f8122c407
--- /dev/null
+++ b/tensorflow/python/estimator/util_test.py
@@ -0,0 +1,119 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Estimator related util."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+
+from tensorflow.python.estimator import util
+from tensorflow.python.platform import test
+
+
+class FnArgsTest(test.TestCase):
+
+  def test_simple_function(self):
+    def fn(a, b):
+      return a + b
+    self.assertEqual(('a', 'b'), util.fn_args(fn))
+
+  def test_callable(self):
+
+    class Foo(object):
+
+      def __call__(self, a, b):
+        return a + b
+
+    self.assertEqual(('self', 'a', 'b'), util.fn_args(Foo()))
+
+  def test_partial_function(self):
+    expected_test_arg = 123
+
+    def fn(a, test_arg):
+      if test_arg != expected_test_arg:
+        return ValueError('partial fn does not work correctly')
+      return a
+
+    wrapped_fn = functools.partial(fn, test_arg=123)
+
+    self.assertEqual(('a',), util.fn_args(wrapped_fn))
+
+  def test_partial_function_with_positional_args(self):
+    expected_test_arg = 123
+
+    def fn(test_arg, a):
+      if test_arg != expected_test_arg:
+        return ValueError('partial fn does not work correctly')
+      return a
+
+    wrapped_fn = functools.partial(fn, 123)
+
+    self.assertEqual(('a',), util.fn_args(wrapped_fn))
+
+    self.assertEqual(3, wrapped_fn(3))
+    self.assertEqual(3, wrapped_fn(a=3))
+
+  def test_double_partial(self):
+    expected_test_arg1 = 123
+    expected_test_arg2 = 456
+
+    def fn(a, test_arg1, test_arg2):
+      if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
+        return ValueError('partial does not work correctly')
+      return a
+
+    wrapped_fn = functools.partial(fn, test_arg2=456)
+    double_wrapped_fn = functools.partial(wrapped_fn, test_arg1=123)
+
+    self.assertEqual(('a',), util.fn_args(double_wrapped_fn))
+
+  def test_double_partial_with_positional_args_in_outer_layer(self):
+    expected_test_arg1 = 123
+    expected_test_arg2 = 456
+
+    def fn(test_arg1, a, test_arg2):
+      if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
+        return ValueError('partial fn does not work correctly')
+      return a
+
+    wrapped_fn = functools.partial(fn, test_arg2=456)
+    double_wrapped_fn = functools.partial(wrapped_fn, 123)
+
+    self.assertEqual(('a',), util.fn_args(double_wrapped_fn))
+
+    self.assertEqual(3, double_wrapped_fn(3))
+    self.assertEqual(3, double_wrapped_fn(a=3))
+
+  def test_double_partial_with_positional_args_in_both_layers(self):
+    expected_test_arg1 = 123
+    expected_test_arg2 = 456
+
+    def fn(test_arg1, test_arg2, a):
+      if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
+        return ValueError('partial fn does not work correctly')
+      return a
+
+    wrapped_fn = functools.partial(fn, 123)  # binds to test_arg1
+    double_wrapped_fn = functools.partial(wrapped_fn, 456)  # binds to test_arg2
+
+    self.assertEqual(('a',), util.fn_args(double_wrapped_fn))
+
+    self.assertEqual(3, double_wrapped_fn(3))
+    self.assertEqual(3, double_wrapped_fn(a=3))
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 27b15d8b590bab67857c68cdd2d9929b5201e71b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 12:24:33 -0700
Subject: [PATCH 0754/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 161114057
---
 tensorflow/contrib/data/python/ops/BUILD  | 17 +++++++++++++++--
 tensorflow/contrib/data/python/util/BUILD |  2 +-
 tensorflow/contrib/image/BUILD            |  6 +++++-
 3 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD
index 08a2774ece..f49350505a 100644
--- a/tensorflow/contrib/data/python/ops/BUILD
+++ b/tensorflow/contrib/data/python/ops/BUILD
@@ -11,10 +11,23 @@ py_library(
     deps = [
         "//tensorflow/contrib/data/python/framework:function",
         "//tensorflow/contrib/data/python/util:nest",
-        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:dataset_ops_gen",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:logging_ops",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:tensor_util",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/contrib/data/python/util/BUILD b/tensorflow/contrib/data/python/util/BUILD
index b9691c8e49..a2b80590ba 100644
--- a/tensorflow/contrib/data/python/util/BUILD
+++ b/tensorflow/contrib/data/python/util/BUILD
@@ -12,6 +12,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:util",
+        "@six_archive//:six",
     ],
 )
 
@@ -26,7 +27,6 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:util",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/contrib/image/BUILD b/tensorflow/contrib/image/BUILD
index a095f0e048..a857f947ea 100755
--- a/tensorflow/contrib/image/BUILD
+++ b/tensorflow/contrib/image/BUILD
@@ -122,7 +122,11 @@ py_library(
     data = [":python/ops/_single_image_random_dot_stereograms.so"],
     srcs_version = "PY2AND3",
     deps = [
-        ":single_image_random_dot_stereograms_ops",
+        ":image_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
-- 
GitLab


From a22dad9836b74a20e900d426f6e9e611df36a056 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 12:53:33 -0700
Subject: [PATCH 0755/1375] [XLA] Add ReducePrecisionInsertion pass.

This new HLO pass, intended for experimental purposes rather than optimization, inserts ReducePrecision instructions (with user-specified bitsizes) after all instructions of opcode types specified by the user.  This makes it possible to do experiments on the numerical effects storing intermediate values in reduced precision without changing the HLO graph definition.

PiperOrigin-RevId: 161117760
---
 tensorflow/compiler/xla/service/BUILD         |  32 +++
 .../compiler/xla/service/hlo_matchers.h       |   1 +
 .../xla/service/reduce_precision_insertion.cc |  61 ++++++
 .../xla/service/reduce_precision_insertion.h  |  67 +++++++
 .../reduce_precision_insertion_test.cc        | 186 ++++++++++++++++++
 5 files changed, 347 insertions(+)
 create mode 100644 tensorflow/compiler/xla/service/reduce_precision_insertion.cc
 create mode 100644 tensorflow/compiler/xla/service/reduce_precision_insertion.h
 create mode 100644 tensorflow/compiler/xla/service/reduce_precision_insertion_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 9cf8b83d5b..d08bc61e37 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1887,6 +1887,38 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "reduce_precision_insertion",
+    srcs = ["reduce_precision_insertion.cc"],
+    hdrs = ["reduce_precision_insertion.h"],
+    deps = [
+        ":buffer_liveness",
+        ":hlo",
+        ":hlo_pass",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_test(
+    name = "reduce_precision_insertion_test",
+    size = "small",
+    srcs = ["reduce_precision_insertion_test.cc"],
+    deps = [
+        ":hlo",
+        ":hlo_matchers",
+        ":reduce_precision_insertion",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:test_helpers",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+    ],
+)
+
 # -----------------------------------------------------------------------------
 
 filegroup(
diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h
index 141251011c..79f17bbb6b 100644
--- a/tensorflow/compiler/xla/service/hlo_matchers.h
+++ b/tensorflow/compiler/xla/service/hlo_matchers.h
@@ -95,6 +95,7 @@ HLO_MATCHER(Parameter);
 HLO_MATCHER(Power);
 HLO_MATCHER(Recv);
 HLO_MATCHER(Reduce);
+HLO_MATCHER(ReducePrecision);
 HLO_MATCHER(ReduceWindow);
 HLO_MATCHER(Remainder);
 HLO_MATCHER(Reshape);
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
new file mode 100644
index 0000000000..dafefdc491
--- /dev/null
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
@@ -0,0 +1,61 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
+
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+StatusOr<bool> ReducePrecisionInsertion::Run(HloModule* module) {
+  bool changed = false;
+  VLOG(1) << "Running ReducePrecisionInsertion pass on " << module->name();
+
+  for (auto& computation : module->computations()) {
+    std::vector<HloInstruction*> instructions_to_suffix;
+
+    for (auto& instruction : computation->instructions()) {
+      VLOG(3) << "Visited instruction: " << instruction->ToString();
+
+      // For now, ReducePrecision is only implemented for F32 data, so this
+      // ignore instructions that produce other data.  In particular, this
+      // currently ignores instructions producing tuples, even if those tuples
+      // contain F32 data inside them.  The assumption is that in most cases
+      // equivalent behavior can be obtained by adding ReducePrecision
+      // instructions after the instructions that pull the F32 data out of the
+      // tuples.
+      if (instruction->shape().element_type() == PrimitiveType::F32 &&
+          should_reduce_output_precision_(instruction->opcode())) {
+        instructions_to_suffix.push_back(instruction.get());
+      }
+    }
+
+    for (auto& instruction : instructions_to_suffix) {
+      HloInstruction* reduced =
+          computation->AddInstruction(HloInstruction::CreateReducePrecision(
+              instruction->shape(), instruction, exponent_bits_,
+              mantissa_bits_));
+      TF_RETURN_IF_ERROR(
+          computation->ReplaceUsesOfInstruction(instruction, reduced));
+      VLOG(2) << "Inserted new op after instruction: "
+              << instruction->ToString();
+      changed = true;
+    }
+  }
+  return changed;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.h b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
new file mode 100644
index 0000000000..e9c8bba031
--- /dev/null
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
@@ -0,0 +1,67 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_REDUCE_PRECISION_INSERTION_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_REDUCE_PRECISION_INSERTION_H_
+
+#include "tensorflow/compiler/xla/service/buffer_liveness.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
+
+namespace xla {
+
+// HLO pass which inserts reduce-precision instructions into the HLO graph, for
+// purposes of experimenting with the effects of reduced-precision storage of
+// intermediate values.
+class ReducePrecisionInsertion : public HloPassInterface {
+  using OpcodeFilterFunction = std::function<bool(HloOpcode)>;
+
+ public:
+  // The exponent_bits and mantissa_bits arguments specify the parameters of
+  // the instructions to insert.  The instructions will be inserted after each
+  // instruction with an opcode for which the should_reduce_output_precision
+  // function returns true and the output type is F32.
+  explicit ReducePrecisionInsertion(
+      const int exponent_bits, const int mantissa_bits,
+      const OpcodeFilterFunction& should_reduce_output_precision)
+      : exponent_bits_(exponent_bits),
+        mantissa_bits_(mantissa_bits),
+        should_reduce_output_precision_(should_reduce_output_precision) {}
+  ~ReducePrecisionInsertion() override{};
+
+  tensorflow::StringPiece name() const override {
+    return "reduce-precision-insertion";
+  }
+
+  // Run the pass on the given module. Returns whether the module was changed
+  // (reduce-precision instructions were inserted).
+  StatusOr<bool> Run(HloModule* module) override;
+
+ private:
+  // Parameters for the precision reduction to be added.
+  const int exponent_bits_;
+  const int mantissa_bits_;
+
+  // Function to determine (from the opcode) whether a given instruction should
+  // have a reduce-precision instruction inserted in its output stream.
+  const OpcodeFilterFunction& should_reduce_output_precision_;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_REDUCE_PRECISION_INSERTION_H_
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion_test.cc b/tensorflow/compiler/xla/service/reduce_precision_insertion_test.cc
new file mode 100644
index 0000000000..80717ec2e3
--- /dev/null
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion_test.cc
@@ -0,0 +1,186 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
+
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_matchers.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/test_helpers.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+
+namespace op = xla::testing::opcode_matchers;
+
+namespace xla {
+
+using ::testing::UnorderedElementsAre;
+
+class ReducePrecisionInsertionTest : public HloTestBase {
+ protected:
+  bool InsertOps(HloModule* module,
+                 const std::function<bool(HloOpcode)>& filter) {
+    ReducePrecisionInsertion op_insertion(5, 10, filter);
+    StatusOr<bool> result = op_insertion.Run(module);
+    EXPECT_IS_OK(result.status());
+    return result.ValueOrDie();
+  }
+};
+
+TEST_F(ReducePrecisionInsertionTest, RootInstruction) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4});
+
+  // Create a simple graph with a parameter feeding a unary cosine function.
+  HloInstruction* a =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b = builder.AddInstruction(
+      HloInstruction::CreateUnary(shape, HloOpcode::kCos, a));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  // Confirm expected state before adding ops.
+  EXPECT_EQ(computation->root_instruction(), b);
+
+  EXPECT_TRUE(InsertOps(module.get(),
+                        [](HloOpcode h) { return h == HloOpcode::kCos; }));
+
+  // Confirm expected graph after adding ops.
+  EXPECT_THAT(computation->root_instruction(), op::ReducePrecision());
+  EXPECT_EQ(computation->root_instruction()->operand(0), b);
+}
+
+TEST_F(ReducePrecisionInsertionTest, NonRootInstruction) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4});
+
+  // Create a graph with two parameters feeding into unary cosine functions,
+  // and the output of those feeds into an add function.  Feeding the outputs
+  // from the suffixed cosine functions into a binary add function allows us to
+  // confirm that the separate operand streams are not crossed when the new
+  // instructions are inserted.
+  HloInstruction* a =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* a_cos = builder.AddInstruction(
+      HloInstruction::CreateUnary(shape, HloOpcode::kCos, a));
+
+  HloInstruction* b =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* b_cos = builder.AddInstruction(
+      HloInstruction::CreateUnary(shape, HloOpcode::kCos, b));
+
+  HloInstruction* c = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a_cos, b_cos));
+
+  auto module = CreateNewModule();
+  module->AddEntryComputation(builder.Build());
+
+  // Confirm expected graph before adding ops.
+  EXPECT_EQ(c->operand(0), a_cos);
+  EXPECT_EQ(c->operand(1), b_cos);
+
+  EXPECT_TRUE(InsertOps(module.get(),
+                        [](HloOpcode h) { return h == HloOpcode::kCos; }));
+
+  // Confirm expected graph after adding ops.
+  EXPECT_THAT(c->operand(0), op::ReducePrecision());
+  EXPECT_EQ(c->operand(0)->operand(0), a_cos);
+  EXPECT_THAT(c->operand(1), op::ReducePrecision());
+  EXPECT_EQ(c->operand(1)->operand(0), b_cos);
+}
+
+TEST_F(ReducePrecisionInsertionTest, OutputIsNotFloat) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(S32, {4});
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x"));
+  HloInstruction* y = builder.AddInstruction(
+      HloInstruction::CreateUnary(shape, HloOpcode::kCos, x));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  // Confirm expected graph before adding ops.
+  EXPECT_THAT(x->users(), UnorderedElementsAre(y));
+  EXPECT_EQ(computation->root_instruction(), y);
+
+  // Since none of the instructions produce F32 data, this should not change
+  // the graph.
+  EXPECT_FALSE(InsertOps(module.get(), [](HloOpcode) { return true; }));
+
+  // Confirm that graph has not changed.
+  EXPECT_THAT(x->users(), UnorderedElementsAre(y));
+  EXPECT_EQ(computation->root_instruction(), y);
+}
+
+TEST_F(ReducePrecisionInsertionTest, ShouldReduceOutputPrecisionIsFalse) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4});
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x"));
+  HloInstruction* y = builder.AddInstruction(
+      HloInstruction::CreateUnary(shape, HloOpcode::kCos, x));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  // Confirm expected graph before adding ops.
+  EXPECT_THAT(x->users(), UnorderedElementsAre(y));
+  EXPECT_EQ(computation->root_instruction(), y);
+
+  // Since none of the instructions match the should_reduce_output_precision
+  // function, this should not change the graph.
+  EXPECT_FALSE(InsertOps(module.get(), [](HloOpcode h) { return false; }));
+
+  // Confirm that graph has not changed.
+  EXPECT_THAT(x->users(), UnorderedElementsAre(y));
+  EXPECT_EQ(computation->root_instruction(), y);
+}
+
+TEST_F(ReducePrecisionInsertionTest, InsertionIsNotRecursive) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4});
+  HloInstruction* a =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b = builder.AddInstruction(
+      HloInstruction::CreateReducePrecision(shape, a, 9, 23));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  // Confirm expected state before adding ops.
+  EXPECT_EQ(computation->root_instruction(), b);
+
+  // This should insert a new ReducePrecision after the existing one, but
+  // should not then recurse by adding another after the just-inserted one.
+  EXPECT_TRUE(InsertOps(module.get(), [](HloOpcode h) {
+    return h == HloOpcode::kReducePrecision;
+  }));
+
+  // Confirm expected graph after adding ops.
+  EXPECT_THAT(computation->root_instruction(), op::ReducePrecision());
+  EXPECT_EQ(computation->root_instruction()->operand(0), b);
+}
+
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  return xla::ParseDebugOptionsFlagsAndRunTests(argc, argv);
+}
-- 
GitLab


From b87774c9d8f0eae067bd65793f52f90f0b2d8228 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 13:17:39 -0700
Subject: [PATCH 0756/1375] [TF:XLA] Remove flag --xla_enable_buffer_reuse.

The corresponding issues have been fixed, so this should not be needed anymore.

PiperOrigin-RevId: 161120874
---
 tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc | 6 ------
 tensorflow/compiler/xla/service/buffer_assignment.cc        | 3 +--
 tensorflow/compiler/xla/xla.proto                           | 3 ---
 3 files changed, 1 insertion(+), 11 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 4a6b0b581c..2f3ec403a0 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -42,7 +42,6 @@ struct DebugOptionsFlags {
   string xla_dump_ir_to;
   string xla_dump_debug_json_to;
   bool xla_eliminate_hlo_implicit_broadcast;
-  bool xla_enable_buffer_reuse;
 
   bool xla_cpu_multi_thread_eigen;
 
@@ -79,7 +78,6 @@ void AllocateFlags() {
   flag_values->xla_dump_ir_to = "";
   flag_values->xla_dump_debug_json_to = "";
   flag_values->xla_eliminate_hlo_implicit_broadcast = false;
-  flag_values->xla_enable_buffer_reuse = true;
   flag_values->xla_cpu_multi_thread_eigen = true;
   flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
   flag_values->xla_gpu_ftz = false;
@@ -146,9 +144,6 @@ void AllocateFlags() {
                         "Eliminate implicit broadcasts when lowering user "
                         "computations to HLO instructions; use explicit "
                         "broadcast instead."),
-       tensorflow::Flag("xla_enable_buffer_reuse",
-                        &flag_values->xla_enable_buffer_reuse,
-                        "Enable reuse of buffers between HLO operations."),
        tensorflow::Flag("xla_cpu_multi_thread_eigen",
                         &flag_values->xla_cpu_multi_thread_eigen,
                         "When generating calls to Eigen in the CPU backend, "
@@ -207,7 +202,6 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
   options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
   options.set_xla_eliminate_hlo_implicit_broadcast(
       flag_values->xla_eliminate_hlo_implicit_broadcast);
-  options.set_xla_enable_buffer_reuse(flag_values->xla_enable_buffer_reuse);
   options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
 
   options.set_xla_cpu_multi_thread_eigen(
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index d00749247e..f372b18f7e 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -796,8 +796,7 @@ Status BufferAssigner::AssignBuffersForComputation(
       continue;
     }
 
-    if (!debug_options.xla_enable_buffer_reuse() || is_thread_local ||
-        instruction->opcode() == HloOpcode::kCustomCall) {
+    if (is_thread_local || instruction->opcode() == HloOpcode::kCustomCall) {
       // Custom call operations never have reusable buffers. Also we do not
       // reuse thread-local buffers for now, because they are dynamically
       // allocated and their lifetimes are hard to compute.
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index c4badfaede..72b47c4223 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -99,9 +99,6 @@ message DebugOptions {
   // the generated IR.
   bool xla_llvm_enable_invariant_load_metadata = 65;
 
-  // Enable reuse of buffers between HLO operations.
-  bool xla_enable_buffer_reuse = 66;
-
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
   map<string, string> xla_backend_extra_options = 500;
-- 
GitLab


From 87d86dbbf4eadf1f7be414d1192cd5ef72db6026 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 6 Jul 2017 13:20:29 -0700
Subject: [PATCH 0757/1375] [XLA] Fix crash in algebraic simplifier when
 sinking a reshape-to-scalar after an elementwise operation.

The test case in the change previously failed with:
algebraic_simplifier.cc:1091] Check failed: user->operand(reshape_or_broadcast_operand_index) == reshape_or_broadcast

PiperOrigin-RevId: 161121259
---
 .../xla/service/algebraic_simplifier.cc       |  3 ++
 .../xla/service/algebraic_simplifier_test.cc  | 28 +++++++++++++++++++
 2 files changed, 31 insertions(+)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index bc6a879b8a..b351861425 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -1055,6 +1055,9 @@ StatusOr<bool> AlgebraicSimplifierVisitor::
     TryToSinkReshapeOrBroadcastAfterOpWithUniqueNonScalarOperand(
         HloInstruction* reshape_or_broadcast) {
   bool changed = false;
+  if (ShapeUtil::IsScalar(reshape_or_broadcast->shape())) {
+    return false;
+  }
   HloInstruction* operand = reshape_or_broadcast->mutable_operand(0);
   for (HloInstruction* user : reshape_or_broadcast->users()) {
     if (user->user_count() == 0 && user != computation_->root_instruction()) {
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index aceef6cd5d..ff119c0098 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -983,6 +983,34 @@ TEST_F(AlgebraicSimplifierTest, ReshapeAfterEffectiveUnary) {
               op::Reshape(op::Maximum(param, zero)));
 }
 
+// Regression test for a bug in the reshape sinking transformation, where
+// moving a reshape to a scalar led to a crash.
+TEST_F(AlgebraicSimplifierTest, ReshapeToScalarNotHoistedAfterEffectiveUnary) {
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param =
+      builder.AddInstruction(HloInstruction::CreateParameter(
+          0, ShapeUtil::MakeShape(F32, {1, 1}), "param"));
+  HloInstruction* reshape = builder.AddInstruction(
+      HloInstruction::CreateReshape(ShapeUtil::MakeShape(F32, {}), param));
+  HloInstruction* zero = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({1., 2., 3.})));
+  builder.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {}), HloOpcode::kMaximum, reshape, zero));
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Maximum(op::Reshape(param), zero));
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 bitcasting_callback());
+
+  simplifier.Run(module.get()).ValueOrDie();
+
+  EXPECT_THAT(computation->root_instruction(),
+              op::Maximum(op::Reshape(param), zero));
+}
+
 TEST_F(AlgebraicSimplifierTest, TransposeEqualsBitcast1) {
   HloComputation::Builder builder(TestName());
   HloInstruction* param =
-- 
GitLab


From 75f03e2d509d016021f8508555f9ab96af2c7cfe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 13:29:22 -0700
Subject: [PATCH 0758/1375] Add option to TensorForest to initialize splits
 with the average of 2 examples.

PiperOrigin-RevId: 161122345
---
 .../kernels/v4/fertile-stats-resource.cc      |  2 +-
 .../kernels/v4/graph_collection_operator.cc   |  6 +-
 .../kernels/v4/graph_collection_operator.h    |  4 +-
 .../tensor_forest/kernels/v4/grow_stats.cc    | 56 ++++++++++++++++---
 .../tensor_forest/kernels/v4/grow_stats.h     | 35 ++++++++++--
 .../kernels/v4/grow_stats_test.cc             | 16 +++---
 .../kernels/v4/split_collection_operators.cc  |  7 +--
 .../kernels/v4/split_collection_operators.h   |  4 +-
 .../proto/tensor_forest_params.proto          |  1 +
 .../tensor_forest/python/tensor_forest_v4.py  |  3 +
 10 files changed, 100 insertions(+), 34 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
index 9f5d948514..5c1b7454ae 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
@@ -36,7 +36,7 @@ void FertileStatsResource::AddExampleToStatsAndInitialize(
     // the top but gradually becomes less of an issue as the tree grows.
     for (int example : examples) {
       collection_op_->CreateAndInitializeCandidateWithExample(
-          input_data, example, node_id);
+          input_data, target, example, node_id);
       if (collection_op_->IsInitialized(node_id)) {
         break;
       }
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
index 2c925b5dd7..c7faea0aef 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.cc
@@ -96,8 +96,8 @@ void GraphRunnerSplitCollectionOperator::AddExample(
 
 void GraphRunnerSplitCollectionOperator::
     CreateAndInitializeCandidateWithExample(
-        const std::unique_ptr<TensorDataSet>& input_data, int example,
-        int32 node_id) const {
+        const std::unique_ptr<TensorDataSet>& input_data,
+        const InputTarget* target, int example, int32 node_id) const {
   auto* slot = stats_.at(node_id).get();
   int cand_num = slot->num_splits();
   const int64 unique_id = UniqueId(node_id, cand_num);
@@ -125,7 +125,7 @@ void GraphRunnerSplitCollectionOperator::
     }
   }
 
-  slot->AddSplit(split);
+  slot->AddSplit(split, input_data, target, example);
 
   runners_[unique_id].reset(new CandidateGraphRunner(graph_dir_, split));
   runners_[unique_id]->Init();
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
index 9b18e3e969..2ae3a79b3d 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/graph_collection_operator.h
@@ -56,8 +56,8 @@ class GraphRunnerSplitCollectionOperator : public SplitCollectionOperator {
 
   // Create a new candidate and initialize it with the given example.
   void CreateAndInitializeCandidateWithExample(
-      const std::unique_ptr<TensorDataSet>& input_data, int example,
-      int32 node_id) const override;
+      const std::unique_ptr<TensorDataSet>& input_data,
+      const InputTarget* target, int example, int32 node_id) const override;
 
   bool BestSplit(int32 node_id, SplitCandidate* best,
                  int32* depth) const override;
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
index 226376c571..81b4534f10 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
@@ -38,11 +38,23 @@ GrowStats::GrowStats(const TensorForestParams& params, int32 depth)
           ResolveParam(params.num_splits_to_consider(), depth)),
       num_outputs_(params.num_outputs()) {}
 
-void GrowStats::AddSplit(const decision_trees::BinaryNode& split) {
-  splits_.push_back(split);
-  evaluators_.emplace_back(
-      CreateBinaryDecisionNodeEvaluator(split, LEFT_INDEX, RIGHT_INDEX));
-  AddSplitStats();
+void GrowStats::AddSplit(const decision_trees::BinaryNode& split,
+                         const std::unique_ptr<TensorDataSet>& input_data,
+                         const InputTarget* target, int example) {
+  // It's possible that the split collection calls AddSplit, but we actually
+  // have all the splits we need and are just waiting for them to be fully
+  // initialized.
+  if (splits_.size() < num_splits_to_consider_) {
+    splits_.push_back(split);
+    evaluators_.emplace_back(
+        CreateBinaryDecisionNodeEvaluator(split, LEFT_INDEX, RIGHT_INDEX));
+    AddSplitStats(target, example);
+  }
+
+  if (input_data != nullptr && target != nullptr &&
+      params_.initialize_average_splits()) {
+    AdditionalInitializationExample(input_data, target, example);
+  }
 }
 
 void GrowStats::RemoveSplit(int split_num) {
@@ -118,6 +130,34 @@ ClassificationStats::ClassificationStats(const TensorForestParams& params,
       new random::SimplePhilox(single_rand_.get()));
 }
 
+void ClassificationStats::AdditionalInitializationExample(
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    int example) {
+  const int32 new_target = target->GetTargetAsClassIndex(example, 0);
+  std::unordered_set<int> to_erase;
+  for (auto it = half_initialized_splits_.begin();
+       it != half_initialized_splits_.end(); ++it) {
+    if (it->second != new_target) {
+      auto& split = splits_[it->first];
+      if (split.has_inequality_left_child_test()) {
+        auto& test = split.inequality_left_child_test();
+        auto* thresh =
+            split.mutable_inequality_left_child_test()->mutable_threshold();
+        if (test.has_feature_id()) {
+          const float val =
+              input_data->GetExampleValue(example, test.feature_id());
+          thresh->set_float_value((thresh->float_value() + val) / 2);
+        }
+      }
+      to_erase.insert(it->first);
+    }
+  }
+
+  for (const int split_id : to_erase) {
+    half_initialized_splits_.erase(split_id);
+  }
+}
+
 bool ClassificationStats::IsFinished() const {
   bool basic = weight_sum_ >= split_after_samples_ && num_outputs_seen() > 1;
   return basic || finish_early_;
@@ -353,7 +393,7 @@ void DenseClassificationGrowStats::ExtractFromProto(const FertileSlot& slot) {
   // Candidate counts and splits.
   int split_num = 0;
   for (const auto& cand : slot.candidates()) {
-    AddSplit(cand.split());
+    AddSplit(cand.split(), nullptr, nullptr, -1);
     const auto& left_stats = cand.left_stats().classification().dense_counts();
     for (int i = 0; i < num_classes; ++i) {
       const float val = left_stats.value(i).float_value();
@@ -474,7 +514,7 @@ void SparseClassificationGrowStats::ExtractFromProto(const FertileSlot& slot) {
   // Candidate counts and splits.
   int split_num = 0;
   for (const auto& cand : slot.candidates()) {
-    AddSplit(cand.split());
+    AddSplit(cand.split(), nullptr, nullptr, -1);
     const auto& left_stats = cand.left_stats().classification().sparse_counts();
     for (auto const& entry : left_stats.sparse_value()) {
       const float val = entry.second.float_value();
@@ -622,7 +662,7 @@ void LeastSquaresRegressionGrowStats::ExtractFromProto(
   // Candidate counts and splits.
   int split_num = 0;
   for (const auto& cand : slot.candidates()) {
-    AddSplit(cand.split());
+    AddSplit(cand.split(), nullptr, nullptr, -1);
     const auto& sums = cand.left_stats().regression().mean_output();
     const auto& squares = cand.left_stats().regression().mean_output_squares();
     for (int i = 0; i < num_outputs; ++i) {
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
index 8d32b4961b..6702b81c79 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
@@ -65,7 +65,12 @@ class GrowStats {
   virtual void PackToProto(FertileSlot* slot) const = 0;
 
   // Add split to the list of candidate splits.
-  void AddSplit(const decision_trees::BinaryNode& split);
+  void AddSplit(const decision_trees::BinaryNode& split,
+                const std::unique_ptr<TensorDataSet>& input_data,
+                const InputTarget* target, int example);
+  virtual void AdditionalInitializationExample(
+      const std::unique_ptr<TensorDataSet>& input_data,
+      const InputTarget* target, int example) {}
   void RemoveSplit(int split_num);
 
   int num_splits() const {
@@ -76,7 +81,7 @@ class GrowStats {
     return weight_sum_;
   }
 
-  bool IsInitialized() const {
+  virtual bool IsInitialized() const {
     return weight_sum_ > 0 || splits_.size() == num_splits_to_consider_;
   }
 
@@ -88,7 +93,7 @@ class GrowStats {
   GrowStats(const TensorForestParams& params, int32 depth);
 
   // Function called by AddSplit for subclasses to initialize stats for a split.
-  virtual void AddSplitStats() = 0;
+  virtual void AddSplitStats(const InputTarget* target, int example) = 0;
 
   virtual void RemoveSplitStats(int split_num) = 0;
 
@@ -134,7 +139,7 @@ class SimpleStats : public GrowStats {
   }
 
  protected:
-  void AddSplitStats() override {}
+  void AddSplitStats(const InputTarget* target, int example) override {}
   void RemoveSplitStats(int split_num) override {}
   void ClearInternal() override {}
 };
@@ -175,6 +180,15 @@ class ClassificationStats : public GrowStats {
   void AddExample(const std::unique_ptr<TensorDataSet>& input_data,
                   const InputTarget* target, int example) override;
 
+  void AdditionalInitializationExample(
+      const std::unique_ptr<TensorDataSet>& input_data,
+      const InputTarget* target, int example) override;
+
+  bool IsInitialized() const override {
+    return weight_sum_ > 0 || (splits_.size() == num_splits_to_consider_ &&
+                               half_initialized_splits_.empty());
+  }
+
  protected:
   virtual float GiniScore(int split, float* left_sum,
                           float* right_sum) const = 0;
@@ -189,11 +203,17 @@ class ClassificationStats : public GrowStats {
   virtual void ClassificationAddSplitStats() = 0;
   virtual void ClassificationRemoveSplitStats(int split) = 0;
 
-  void AddSplitStats() override {
+  void AddSplitStats(const InputTarget* target, int example) override {
     if (left_gini_ != nullptr) {
       left_gini_->add_split();
       right_gini_->add_split();
     }
+    if (params_.initialize_average_splits()) {
+      if (splits_[splits_.size() - 1].has_inequality_left_child_test()) {
+        half_initialized_splits_[splits_.size() - 1] =
+            target->GetTargetAsClassIndex(example, 0);
+      }
+    }
     ClassificationAddSplitStats();
   }
   void RemoveSplitStats(int split) override {
@@ -262,6 +282,9 @@ class ClassificationStats : public GrowStats {
 
   std::unique_ptr<RunningGiniScores> left_gini_;
   std::unique_ptr<RunningGiniScores> right_gini_;
+
+  // Stores split number -> class that was first seen.
+  std::unordered_map<int, int32> half_initialized_splits_;
 };
 
 // Tracks classification stats by storing class counts densely.
@@ -413,7 +436,7 @@ class LeastSquaresRegressionGrowStats : public GrowStats {
   // Returns the variance of split.
   float SplitVariance(int split) const;
 
-  void AddSplitStats() override {
+  void AddSplitStats(const InputTarget* target, int example) override {
     left_sums_.resize(num_outputs_ * num_splits());
     left_squares_.resize(num_outputs_ * num_splits());
     left_counts_.push_back(0);
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
index 7cd16d222a..fa959e8373 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc
@@ -52,13 +52,13 @@ BinaryNode MakeSplit(const string& feat, float val) {
 
 void RunBatch(GrowStats* stats,
               const TestableInputTarget* target) {
-  stats->AddSplit(MakeSplit("0", 10.0));
-  stats->AddSplit(MakeSplit("1", 4.0));
-
   std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
       new tensorflow::tensorforest::TestableDataSet(
           {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, 2));
 
+  stats->AddSplit(MakeSplit("0", 10.0), dataset, target, 0);
+  stats->AddSplit(MakeSplit("1", 4.0), dataset, target, 0);
+
   for (int i = 0; i < target->NumItems(); ++i) {
     stats->AddExample(dataset, target, i);
   }
@@ -225,11 +225,6 @@ TEST(GrowStatsDenseClassificationTest, TestCheckPruneHoeffding) {
   params.mutable_pruning_type()->mutable_prune_every_samples()
       ->set_constant_value(1);
 
-  DenseClassificationGrowStats stats(params, 1);
-  stats.Initialize();
-  stats.AddSplit(MakeSplit("0", 0.0));
-  stats.AddSplit(MakeSplit("1", 0.0));
-
   // On each iteration, we add two examples, one of class 0 and one
   // of class 1.  Split #0 classifies them perfectly, while split #1
   // sends them both to the left.
@@ -240,6 +235,11 @@ TEST(GrowStatsDenseClassificationTest, TestCheckPruneHoeffding) {
       new tensorflow::tensorforest::TestableDataSet(
           {-1.0, -1.0, 1.0, -1.0}, 2));
 
+  DenseClassificationGrowStats stats(params, 1);
+  stats.Initialize();
+  stats.AddSplit(MakeSplit("0", 0.0), dataset, &target, 0);
+  stats.AddSplit(MakeSplit("1", 0.0), dataset, &target, 0);
+
   // Math time!
   // After 2n samples,
   // split 0 has smoothed counts (n+1,1);(1,n+1) and
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
index c207c0859d..632408fd71 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
@@ -100,8 +100,8 @@ bool SplitCollectionOperator::IsInitialized(int32 node_id) const {
 }
 
 void SplitCollectionOperator::CreateAndInitializeCandidateWithExample(
-    const std::unique_ptr<TensorDataSet>& input_data, int example,
-    int32 node_id) const {
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    int example, int32 node_id) const {
   // Assumes split_initializations_per_input == 1.
   decision_trees::BinaryNode split;
   float bias;
@@ -124,7 +124,7 @@ void SplitCollectionOperator::CreateAndInitializeCandidateWithExample(
     LOG(ERROR) << "Unknown feature type " << type << ", not sure which "
                << "node type to use.";
   }
-  stats_.at(node_id)->AddSplit(split);
+  stats_.at(node_id)->AddSplit(split, input_data, target, example);
 }
 
 bool SplitCollectionOperator::BestSplit(int32 node_id,
@@ -134,6 +134,5 @@ bool SplitCollectionOperator::BestSplit(int32 node_id,
   *depth = slot->depth();
   return slot->BestSplit(best);
 }
-
 }  // namespace tensorforest
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
index 81d820a6b2..6990e82678 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
@@ -56,8 +56,8 @@ class SplitCollectionOperator {
 
   // Create a new candidate and initialize it with the given example.
   virtual void CreateAndInitializeCandidateWithExample(
-      const std::unique_ptr<TensorDataSet>& input_data, int example,
-      int32 node_id) const;
+      const std::unique_ptr<TensorDataSet>& input_data,
+      const InputTarget* target, int example, int32 node_id) const;
 
   // Create a new GrowStats for the given node id and initialize it.
   virtual void InitializeSlot(int32 node_id, int32 depth);
diff --git a/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto b/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
index 49b19e0b62..58c5b9bbe7 100644
--- a/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
+++ b/tensorflow/contrib/tensor_forest/proto/tensor_forest_params.proto
@@ -130,6 +130,7 @@ message TensorForestParams {
   bool collate_examples = 10;
   bool checkpoint_stats = 11;
   bool use_running_stats_method = 20;
+  bool initialize_average_splits = 22;
 
   // Number of classes (classification) or targets (regression)
   int32 num_outputs = 12;
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
index 5773b5d729..7e6f00a13d 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
@@ -90,6 +90,7 @@ def build_params_proto(params):
   proto.collate_examples = params.v4_collate_examples
   proto.checkpoint_stats = params.v4_checkpoint_stats
   proto.use_running_stats_method = params.v4_use_running_stats_method
+  proto.initialize_average_splits = params.v4_initialize_average_splits
 
   if params.v4_prune_every_samples:
     text_format.Merge(params.v4_prune_every_samples,
@@ -174,6 +175,8 @@ class V4ForestHParams(object):
     self.v4_checkpoint_stats = getattr(self, 'v4_checkpoint_stats', False)
     self.v4_use_running_stats_method = getattr(
         self, 'v4_use_running_stats_method', False)
+    self.v4_initialize_average_splits = getattr(
+        self, 'v4_initialize_average_splits', False)
 
     self.v4_param_file = getattr(self, 'v4_param_file', None)
 
-- 
GitLab


From 0597c4189a07e4ba1141a90ead76c9900a99d773 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 6 Jul 2017 13:36:57 -0700
Subject: [PATCH 0759/1375] Add -no_oss test tag filter to
 ci_parameterized_build.sh

PiperOrigin-RevId: 161123471
---
 tensorflow/tools/ci_build/ci_parameterized_build.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index 1cf87d7c7c..4ca08052ba 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -358,7 +358,7 @@ if [[ "${TF_BUILD_APPEND_ARGUMENTS}" == *"--test_tag_filters="* ]]; then
     fi
   done
 else
-  EXTRA_ARGS="${TF_BUILD_APPEND_ARGUMENTS} --test_tag_filters=-benchmark-test"
+  EXTRA_ARGS="${TF_BUILD_APPEND_ARGUMENTS} --test_tag_filters=-no_oss,-benchmark-test"
   if [[ ${IS_MAC} == "1" ]]; then
     EXTRA_ARGS="${EXTRA_ARGS},-nomac"
   fi
-- 
GitLab


From 43a122beb6a7e8ad4f9e6985e2c87309bf881ea6 Mon Sep 17 00:00:00 2001
From: David Norman <DavidNorman@users.noreply.github.com>
Date: Thu, 6 Jul 2017 21:44:10 +0100
Subject: [PATCH 0760/1375] [XLA] Add scaffolding to allow XLA unit tests to
 run for other devices (#11262)

* Add scaffolding to allow XLA unit tests to run for other devices

* Remove poplar, and add as an example

* Change the example to be less poplar specific

* Fix indent.
---
 tensorflow/compiler/xla/tests/build_defs.bzl |  8 ++++-
 tensorflow/compiler/xla/tests/plugin.bzl     | 32 ++++++++++++++++++++
 2 files changed, 39 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/compiler/xla/tests/plugin.bzl

diff --git a/tensorflow/compiler/xla/tests/build_defs.bzl b/tensorflow/compiler/xla/tests/build_defs.bzl
index 50edd8ea5b..a297132dd3 100644
--- a/tensorflow/compiler/xla/tests/build_defs.bzl
+++ b/tensorflow/compiler/xla/tests/build_defs.bzl
@@ -1,8 +1,9 @@
 """Build rules for XLA testing."""
 
 load("@local_config_cuda//cuda:build_defs.bzl", "cuda_is_configured")
+load("//tensorflow/compiler/xla/tests:plugin.bzl", "plugins")
 
-all_backends = ["cpu", "cpu_parallel", "gpu"]
+all_backends = ["cpu", "cpu_parallel", "gpu"] + plugins.keys()
 
 def filter_backends(backends):
   """Removes "gpu" from a backend list if CUDA is not enabled.
@@ -121,6 +122,11 @@ def xla_test(name,
       backend_deps = ["//tensorflow/compiler/xla/service:gpu_plugin"]
       backend_deps += ["//tensorflow/compiler/xla/tests:test_macros_gpu"]
       this_backend_tags += ["requires-gpu-sm35"]
+    elif backend in plugins:
+      backend_deps = plugins[backend]["deps"]
+      this_backend_copts += plugins[backend]["copts"]
+      this_backend_tags += plugins[backend]["tags"]
+      this_backend_args += plugins[backend]["args"]
     else:
       fail("Unknown backend %s" % backend)
 
diff --git a/tensorflow/compiler/xla/tests/plugin.bzl b/tensorflow/compiler/xla/tests/plugin.bzl
new file mode 100644
index 0000000000..1b10c778ce
--- /dev/null
+++ b/tensorflow/compiler/xla/tests/plugin.bzl
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Additional XLA devices to be included in the unit test suite."""
+
+# Example:
+#
+# plugins = {
+#   "foo": {
+#     "deps": [
+#       "//tensorflow/compiler/plugin/foo:foo_lib",
+#       "//tensorflow/compiler/plugin/foo:test_macros",
+#     ],
+#     "copts": [],
+#     "tags": [],
+#     "args": []
+#   },
+# }
+
+plugins = {}
+
-- 
GitLab


From 0b5cce367cf95a5d7fbb6b037e7a8646f6c47b70 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 6 Jul 2017 13:41:47 -0700
Subject: [PATCH 0761/1375] Get TopK op working on GPU again.  Extend using
 cub's radix sort.

1. Undo rollback of Andreas Kirsch's initial implementation.
2. Use cub segmented radix sort if Andreas' heap-based impl
   for large k and small num_cols (thresholds of k=100, n=1000
   determined empirically).
3. Use cub segmented radix sort if k == num_cols (this case is always faster).
4. Added benchmarks.

Benchmarks show that the GPU implementation is up to 3x slower for small k but
can be 10x faster for large num_cols and k.

Benchmarks:

Benchmark: m_128_n_10_k_5_use_gpu_False          wall_time: 0.000166 s   Throughput: 0.0077 GB/s
Benchmark: m_128_n_10_k_5_use_gpu_True   wall_time: 0.000796 s   Throughput: 0.00161 GB/s
Benchmark: m_128_n_10_k_9_use_gpu_False          wall_time: 0.00017 s    Throughput: 0.00751 GB/s
Benchmark: m_128_n_10_k_9_use_gpu_True   wall_time: 0.000796 s   Throughput: 0.00161 GB/s
Benchmark: m_128_n_10_k_10_use_gpu_False         wall_time: 0.00017 s    Throughput: 0.00753 GB/s
Benchmark: m_128_n_10_k_10_use_gpu_True          wall_time: 0.000775 s   Throughput: 0.00165 GB/s
Benchmark: m_128_n_100_k_1_use_gpu_False         wall_time: 0.000155 s   Throughput: 0.0826 GB/s
Benchmark: m_128_n_100_k_1_use_gpu_True          wall_time: 0.000796 s   Throughput: 0.0161 GB/s
Benchmark: m_128_n_100_k_50_use_gpu_False        wall_time: 0.000247 s   Throughput: 0.0519 GB/s
Benchmark: m_128_n_100_k_50_use_gpu_True         wall_time: 0.0008 s     Throughput: 0.016 GB/s
Benchmark: m_128_n_100_k_99_use_gpu_False        wall_time: 0.000261 s   Throughput: 0.049 GB/s
Benchmark: m_128_n_100_k_99_use_gpu_True         wall_time: 0.000794 s   Throughput: 0.0161 GB/s
Benchmark: m_128_n_100_k_100_use_gpu_False       wall_time: 0.000239 s   Throughput: 0.0536 GB/s
Benchmark: m_128_n_100_k_100_use_gpu_True        wall_time: 0.000777 s   Throughput: 0.0165 GB/s
Benchmark: m_128_n_1000_k_1_use_gpu_False        wall_time: 0.000324 s   Throughput: 0.395 GB/s
Benchmark: m_128_n_1000_k_1_use_gpu_True         wall_time: 0.000916 s   Throughput: 0.14 GB/s
Benchmark: m_128_n_1000_k_10_use_gpu_False       wall_time: 0.00042 s    Throughput: 0.305 GB/s
Benchmark: m_128_n_1000_k_10_use_gpu_True        wall_time: 0.000902 s   Throughput: 0.142 GB/s
Benchmark: m_128_n_1000_k_500_use_gpu_False      wall_time: 0.0011 s     Throughput: 0.116 GB/s
Benchmark: m_128_n_1000_k_500_use_gpu_True       wall_time: 0.00097 s    Throughput: 0.132 GB/s
Benchmark: m_128_n_1000_k_990_use_gpu_False      wall_time: 0.00133 s    Throughput: 0.0962 GB/s
Benchmark: m_128_n_1000_k_990_use_gpu_True       wall_time: 0.000993 s   Throughput: 0.129 GB/s
Benchmark: m_128_n_1000_k_1000_use_gpu_False     wall_time: 0.00102 s    Throughput: 0.126 GB/s
Benchmark: m_128_n_1000_k_1000_use_gpu_True      wall_time: 0.000964 s   Throughput: 0.133 GB/s
Benchmark: m_128_n_10000_k_10_use_gpu_False      wall_time: 0.002 s      Throughput: 0.64 GB/s
Benchmark: m_128_n_10000_k_10_use_gpu_True       wall_time: 0.00288 s    Throughput: 0.445 GB/s
Benchmark: m_128_n_10000_k_100_use_gpu_False     wall_time: 0.00233 s    Throughput: 0.549 GB/s
Benchmark: m_128_n_10000_k_100_use_gpu_True      wall_time: 0.00325 s    Throughput: 0.394 GB/s
Benchmark: m_128_n_10000_k_5000_use_gpu_False    wall_time: 0.0127 s     Throughput: 0.101 GB/s
Benchmark: m_128_n_10000_k_5000_use_gpu_True     wall_time: 0.00381 s    Throughput: 0.336 GB/s
Benchmark: m_128_n_10000_k_9900_use_gpu_False    wall_time: 0.015 s      Throughput: 0.0853 GB/s
Benchmark: m_128_n_10000_k_9900_use_gpu_True     wall_time: 0.00438 s    Throughput: 0.292 GB/s
Benchmark: m_128_n_10000_k_10000_use_gpu_False   wall_time: 0.0104 s     Throughput: 0.123 GB/s
Benchmark: m_128_n_10000_k_10000_use_gpu_True    wall_time: 0.00427 s    Throughput: 0.3 GB/s
Benchmark: m_128_n_100000_k_100_use_gpu_False    wall_time: 0.0148 s     Throughput: 0.865 GB/s
Benchmark: m_128_n_100000_k_100_use_gpu_True     wall_time: 0.0262 s     Throughput: 0.488 GB/s
Benchmark: m_128_n_100000_k_1000_use_gpu_False   wall_time: 0.0201 s     Throughput: 0.636 GB/s
Benchmark: m_128_n_100000_k_1000_use_gpu_True    wall_time: 0.0263 s     Throughput: 0.486 GB/s
Benchmark: m_128_n_100000_k_50000_use_gpu_False          wall_time: 0.214 s      Throughput: 0.0599 GB/s
Benchmark: m_128_n_100000_k_50000_use_gpu_True   wall_time: 0.0322 s     Throughput: 0.398 GB/s
Benchmark: m_128_n_100000_k_99000_use_gpu_False          wall_time: 0.262 s      Throughput: 0.0489 GB/s
Benchmark: m_128_n_100000_k_99000_use_gpu_True   wall_time: 0.0377 s     Throughput: 0.34 GB/s
Benchmark: m_128_n_100000_k_100000_use_gpu_False         wall_time: 0.118 s      Throughput: 0.108 GB/s
Benchmark: m_128_n_100000_k_100000_use_gpu_True          wall_time: 0.0365 s     Throughput: 0.351 GB/s

END_PUBLIC

BEGIN_PUBLIC
Automated g4 rollback of changelist 157169178

PiperOrigin-RevId: 161124193
---
 tensorflow/core/kernels/BUILD                 |   3 +-
 tensorflow/core/kernels/topk_op.cc            | 105 +++-
 tensorflow/core/kernels/topk_op.h             |  42 ++
 tensorflow/core/kernels/topk_op_gpu.cu.cc     | 573 ++++++++++++++++++
 tensorflow/python/kernel_tests/BUILD          |   2 +-
 .../python/kernel_tests/topk_op_test.py       | 141 ++++-
 .../python/kernel_tests/where_op_test.py      |   2 +-
 7 files changed, 829 insertions(+), 39 deletions(-)
 create mode 100644 tensorflow/core/kernels/topk_op.h
 create mode 100644 tensorflow/core/kernels/topk_op_gpu.cu.cc

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 04f7bd0d47..9924cbb49d 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -2805,7 +2805,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "topk_op",
     prefix = "topk_op",
-    deps = NN_DEPS,
+    deps = NN_DEPS + if_cuda(["@cub_archive//:cub"]),
 )
 
 tf_kernel_library(
@@ -4164,6 +4164,7 @@ filegroup(
         "tile_functor.h",
         "tile_ops_cpu_impl.h",
         "tile_ops_impl.h",
+        "topk_op.h",
         "training_op_helpers.h",
         "training_ops.h",
         "transpose_functor.h",
diff --git a/tensorflow/core/kernels/topk_op.cc b/tensorflow/core/kernels/topk_op.cc
index 5c89eaef5f..05fee56335 100644
--- a/tensorflow/core/kernels/topk_op.cc
+++ b/tensorflow/core/kernels/topk_op.cc
@@ -17,6 +17,8 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
+#include "tensorflow/core/kernels/topk_op.h"
+
 #include <algorithm>
 #include <numeric>
 #include <vector>
@@ -25,6 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/gtl/top_n.h"
 #include "tensorflow/core/util/work_sharder.h"
 
@@ -33,7 +36,7 @@ namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 
-template <typename T>
+template <typename Device, typename T>
 class TopK : public OpKernel {
  public:
   explicit TopK(OpKernelConstruction* context) : OpKernel(context) {
@@ -82,7 +85,25 @@ class TopK : public OpKernel {
 
     auto values = values_out->flat_inner_dims<T>();
     auto indices = indices_out->flat_inner_dims<int32>();
+    Status s = functor::TopKFunctor<Device, T>::Compute(
+        context, sorted_, k, input, num_rows, num_cols, values, indices);
+    OP_REQUIRES_OK(context, s);
+  }
 
+ private:
+  int k_;
+  bool sorted_;
+};
+
+namespace functor {
+
+template <typename T>
+struct TopKFunctor<CPUDevice, T> {
+  static EIGEN_ALWAYS_INLINE Status
+  Compute(OpKernelContext* context, bool sorted, int k,
+          const typename TTypes<T, 2>::ConstTensor& input, const int64 num_rows,
+          const int64 num_cols, typename TTypes<T, 2>::Tensor values,
+          typename TTypes<int, 2>::Tensor indices) {
     const CPUDevice& d = context->eigen_device<CPUDevice>();
 
     // Special case for k == 1.
@@ -108,7 +129,7 @@ class TopK : public OpKernel {
         }
       }
 
-      return;
+      return Status::OK();
     }
 
     auto SortIndices = [&, context](int start_batch, int limit_batch) {
@@ -117,7 +138,6 @@ class TopK : public OpKernel {
         const auto comp = [input_data](const int32 a, const int32 b) {
           return input_data[a] > input_data[b];
         };
-        gtl::TopN<int32, decltype(comp)> filter(k, comp);
         // TODO(ebrevdo): For large k < num_cols, instead of using
         // TopN, it may be faster to create a temporary vector of
         // values 0..num_cols - 1 and then use std::partial_sort_copy
@@ -130,13 +150,14 @@ class TopK : public OpKernel {
           std::sort(&indices(b, 0), &indices(b, k), comp);
         } else {
           // Use the TopN heap object to sort.
+          gtl::TopN<int32, decltype(comp)> filter(k, comp);
           filter.reserve(num_cols);
           for (int32 c = 0; c < num_cols; ++c) {
             filter.push(c);
           }
 
           int32 i = 0;
-          if (sorted_) {
+          if (sorted) {
             std::unique_ptr<std::vector<int32>> top_k(filter.Extract());
             for (auto top_k_it = top_k->begin(); top_k_it != top_k->end();
                  ++top_k_it, ++i) {
@@ -158,35 +179,75 @@ class TopK : public OpKernel {
 
     // Guesstimate of cost; 4*N*log(K) where N == num_cols.
     // If K == N, assume the cost is N*log(K + 1).
-    const int64 cmp_cost = 3 * Eigen::TensorOpCost::AddCost<int32>() +
-                           Eigen::TensorOpCost::AddCost<T>();
-    const int64 base_cost =
+    const double cmp_cost = 3 * Eigen::TensorOpCost::AddCost<int32>() +
+                            Eigen::TensorOpCost::AddCost<T>();
+    const double base_cost =
         cmp_cost *
-        static_cast<int64>(num_cols *
-                           Eigen::numext::log2(static_cast<float>(k + 1)));
-    const int64 sort_cost = (k == num_cols) ? base_cost : 4 * base_cost;
-    const int64 copy_cost = 2 * k * Eigen::TensorOpCost::AddCost<T>();
-    const int64 total_cost = sort_cost + copy_cost;
+        static_cast<double>(num_cols *
+                            Eigen::numext::log2(static_cast<float>(k + 1)));
+    const double sort_cost = (k == num_cols) ? base_cost : 4 * base_cost;
+    const double copy_cost = 2 * k * Eigen::TensorOpCost::AddCost<T>();
+    const double total_cost = sort_cost + copy_cost;
+    const int64 final_cost = (total_cost >= static_cast<double>(kint64max))
+                                 ? kint64max
+                                 : static_cast<int64>(total_cost);
     auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
     Shard(worker_threads.num_threads, worker_threads.workers, num_rows,
-          total_cost, SortIndices);
-  }
+          final_cost, SortIndices);
 
- private:
-  int k_;
-  bool sorted_;
+    return Status::OK();
+  }
 };
 
-#define REGISTER_KERNELS_NAME(name, type) \
-  REGISTER_KERNEL_BUILDER(                \
-      Name(#name).Device(DEVICE_CPU).TypeConstraint<type>("T"), TopK<type>)
+}  // namespace functor
+
+#define REGISTER_KERNELS_NAME(name, type)                       \
+  REGISTER_KERNEL_BUILDER(                                      \
+      Name(#name).Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      TopK<CPUDevice, type>)
 
 #define REGISTER_KERNELS(type)       \
   REGISTER_KERNELS_NAME(TopK, type); \
   REGISTER_KERNELS_NAME(TopKV2, type)
 
 TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS_TO_NAME
+#undef REGISTER_KERNELS_NAME
 #undef REGISTER_KERNELS
 
-}  // namespace tensorflow
+#ifdef GOOGLE_CUDA
+
+namespace functor {
+#define DECLARE_GPU_SPEC(T)                                                  \
+  template <>                                                                \
+  Status TopKFunctor<GPUDevice, T>::Compute(                                 \
+      OpKernelContext* context, bool sorted, int k,                          \
+      const typename TTypes<T, 2>::ConstTensor& input, const int64 num_rows, \
+      const int64 num_cols, typename TTypes<T, 2>::Tensor values,            \
+      typename TTypes<int, 2>::Tensor indices);                              \
+  extern template struct functor::TopKFunctor<GPUDevice, T>;
+
+TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
+TF_CALL_INTEGRAL_TYPES(DECLARE_GPU_SPEC);
+
+#undef DECLARE_GPU_SPEC
+
+}  // namespace functor
+
+#define REGISTER_KERNELS(type)                                   \
+  REGISTER_KERNEL_BUILDER(                                       \
+      Name("TopK").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
+      TopK<GPUDevice, type>)                                     \
+  REGISTER_KERNEL_BUILDER(Name("TopKV2")                         \
+                              .Device(DEVICE_GPU)                \
+                              .TypeConstraint<type>("T")         \
+                              .HostMemory("k"),                  \
+                          TopK<GPUDevice, type>)
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_KERNELS);
+TF_CALL_INTEGRAL_TYPES(REGISTER_KERNELS);
+
+#undef REGISTER_KERNELS
+
+#endif  // end GOOGLE_CUDA
+
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/topk_op.h b/tensorflow/core/kernels/topk_op.h
new file mode 100644
index 0000000000..a53e3ec8d4
--- /dev/null
+++ b/tensorflow/core/kernels/topk_op.h
@@ -0,0 +1,42 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_TOPK_OP_H_
+#define TENSORFLOW_TOPK_OP_H_
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/errors.h"
+
+namespace tensorflow {
+
+namespace functor {
+
+template <typename Device, typename T>
+struct TopKFunctor {
+  static Status Compute(OpKernelContext* context, bool sorted, int k,
+                        const typename TTypes<T, 2>::ConstTensor& input,
+                        const int64 num_rows, const int64 num_cols,
+                        typename TTypes<T, 2>::Tensor values,
+                        typename TTypes<int, 2>::Tensor indices);
+};
+
+}  // end namespace functor
+
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_TOPK_OP_H_
diff --git a/tensorflow/core/kernels/topk_op_gpu.cu.cc b/tensorflow/core/kernels/topk_op_gpu.cu.cc
new file mode 100644
index 0000000000..e4b4a3cb49
--- /dev/null
+++ b/tensorflow/core/kernels/topk_op_gpu.cu.cc
@@ -0,0 +1,573 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include <cmath>
+#include <vector>
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "external/cub_archive/cub/device/device_segmented_radix_sort.cuh"
+#include "external/cub_archive/cub/iterator/counting_input_iterator.cuh"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/kernels/topk_op.h"
+#include "tensorflow/core/lib/gtl/top_n.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/types.h"
+
+// Required for sorting Eigen::half
+namespace cub {
+template <>
+struct NumericTraits<Eigen::half>
+    : BaseTraits<FLOATING_POINT, true, false, unsigned short int, Eigen::half> {
+};
+}  // namespace cub
+
+namespace tensorflow {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace impl {
+
+enum class HeapType { kMinHeap, kMaxHeap };
+enum class PreferIndices { kLower, kHigher };
+
+template <typename T>
+struct Entry {
+  int index;
+  T value;
+
+  // Test-only.
+  static bool greater(const Entry<T>& a, const Entry<T>& b) {
+    if (a.value == b.value) {
+      return a.index < b.index;
+    }
+    return a.value > b.value;
+  }
+};
+
+template <typename T>
+struct LinearData {
+  typedef impl::Entry<T> Entry;
+
+  __device__ Entry& operator[](std::size_t index) const { return data[index]; }
+
+  __device__ int get_index(int i) const { return data[i].index; }
+  __device__ T get_value(int i) const { return data[i].value; }
+
+  Entry* const data;
+};
+
+template <typename T>
+struct IndirectLinearData {
+  typedef impl::Entry<T> Entry;
+
+  __device__ Entry& operator[](std::size_t index) const { return data[index]; }
+
+  __device__ int get_index(int i) const {
+    return backing_data[data[i].index].index;
+  }
+  __device__ T get_value(int i) const { return data[i].value; }
+
+  Entry* const data;
+  Entry* const backing_data;
+};
+
+#if GOOGLE_CUDA
+template <typename T>
+struct StridedData {
+  typedef impl::Entry<T> Entry;
+
+  __device__ Entry& operator[](std::size_t index) const {
+    return data[index * blockDim.x + threadIdx.x];
+  }
+
+  __device__ int get_index(int i) const { return (*this)[i].index; }
+  __device__ T get_value(int i) const { return (*this)[i].value; }
+
+  Entry* const data;
+};
+#endif
+
+// A heap of Entry<T> that can either work as a min-heap or as a max-heap.
+template <HeapType heapType, PreferIndices preferIndices,
+          template <typename> class Data, typename T>
+struct IndexedHeap {
+  typedef typename Data<T>::Entry Entry;
+  const Data<T> data;
+
+  __device__ bool is_above(int left, int right) {
+    T left_value = data.get_value(left);
+    T right_value = data.get_value(right);
+    if (left_value == right_value) {
+      if (preferIndices == PreferIndices::kLower) {
+        return data.get_index(left) < data.get_index(right);
+      } else {
+        return data.get_index(left) > data.get_index(right);
+      }
+    }
+    if (heapType == HeapType::kMinHeap) {
+      return left_value < right_value;
+    } else {
+      return left_value > right_value;
+    }
+  }
+
+  __device__ void assign(int i, const Entry& entry) { data[i] = entry; }
+
+  __device__ void push_up(int i) {
+    int child = i;
+    int parent;
+    for (; child > 0; child = parent) {
+      parent = (child - 1) / 2;
+      if (!is_above(child, parent)) {
+        // Heap property satisfied.
+        break;
+      }
+      swap(child, parent);
+    }
+  }
+
+  __device__ void swap(int a, int b) {
+    auto tmp = data[b];
+    data[b] = data[a];
+    data[a] = tmp;
+  }
+
+  __device__ void push_root_down(int k) { push_down(0, k); }
+
+  // MAX-HEAPIFY in Cormen
+  __device__ void push_down(int node, int k) {
+    while (true) {
+      const int left = 2 * node + 1;
+      const int right = left + 1;
+      int smallest = node;
+      if (left < k && is_above(left, smallest)) {
+        smallest = left;
+      }
+      if (right < k && is_above(right, smallest)) {
+        smallest = right;
+      }
+      if (smallest == node) {
+        break;
+      }
+      swap(smallest, node);
+      node = smallest;
+    }
+  }
+
+  // BUILD-MAX-HEAPIFY in Cormen
+  __device__ void build(int k) {
+    for (int node = (k - 1) / 2; node >= 0; node--) {
+      push_down(node, k);
+    }
+  }
+
+  // HEAP-EXTRACT-MAX in Cormen
+  __device__ void remove_root(int k) {
+    data[0] = data[k - 1];
+    push_root_down(k - 1);
+  }
+
+  // in-place HEAPSORT in Cormen
+  // This method destroys the heap property.
+  __device__ void sort(int k) {
+    for (int slot = k - 1; slot > 0; slot--) {
+      // This is like remove_root but we insert the element at the end.
+      swap(slot, 0);
+      // Heap is now an element smaller.
+      push_root_down(/*k=*/slot);
+    }
+  }
+
+  __device__ void replace_root(const Entry& entry, int k) {
+    data[0] = entry;
+    push_root_down(k);
+  }
+
+  __device__ const Entry& root() { return data[0]; }
+};
+
+template <HeapType heapType, PreferIndices preferIndices,
+          template <typename> class Data, typename T>
+__device__ IndexedHeap<heapType, preferIndices, Data, T> make_indexed_heap(
+    typename Data<T>::Entry* data) {
+  return IndexedHeap<heapType, preferIndices, Data, T>{Data<T>{data}};
+}
+
+// heapTopK walks over [input, input+length) with `step_size` stride starting at
+// `start_index`.
+// It builds a top-`k` heap that is stored in `heap_entries` using `Accessor` to
+// access elements in `heap_entries`. If sorted=true, the elements will be
+// sorted at the end.
+template <typename T, template <typename> class Data = LinearData>
+__device__ void heapTopK(const T* __restrict__ input, int length, int k,
+                         Entry<T>* __restrict__ heap_entries,
+                         bool sorted = false, int start_index = 0,
+                         int step_size = 1) {
+  assert(k <= length);
+
+  auto heap =
+      make_indexed_heap<HeapType::kMinHeap, PreferIndices::kHigher, Data, T>(
+          heap_entries);
+
+  int heap_end_index = start_index + k * step_size;
+  if (heap_end_index > length) {
+    heap_end_index = length;
+  }
+  // Initialize the min-heap.
+  for (int index = start_index, slot = 0; index < heap_end_index;
+       index += step_size, slot++) {
+    heap.assign(slot, {index, input[index]});
+  }
+
+  heap.build(k);
+
+  // Now iterate over the remaining items.
+  // If an item is smaller than the min element, it is not amongst the top k.
+  // Otherwise, replace the min element with it and push upwards.
+  for (int index = heap_end_index; index < length; index += step_size) {
+    // We prefer elements with lower indices. This is given here.
+    // Later elements automatically have higher indices, so can be discarded.
+    if (input[index] > heap.root().value) {
+      // This element should replace the min.
+      heap.replace_root({index, input[index]}, k);
+    }
+  }
+
+  // Sort if wanted.
+  if (sorted) {
+    heap.sort(k);
+  }
+}
+
+// mergeShards performs a top-k merge on `num_shards` many sorted streams that
+// are sorted and stored in `entries` in a strided way:
+// |s_1 1st|s_2 1st|...s_{num_shards} 1st|s_1 2nd|s_2 2nd|...
+// The overall top k elements are written to `top_k_values` and their indices
+// to top_k_indices.
+// `top_k_heap` is used as temporary storage for the merge heap.
+template <typename T>
+__device__ void mergeShards(int num_shards, int k,
+                            Entry<T>* __restrict__ entries,
+                            Entry<T>* __restrict__ top_k_heap, T* top_k_values,
+                            int* top_k_indices) {
+  // If k < num_shards, we can use a min-heap with k elements to get the top k
+  // of the sorted blocks.
+  // If k > num_shards, we can initialize a min-heap with the top element from
+  // each sorted block.
+  const int heap_size = k < num_shards ? k : num_shards;
+
+  // Min-heap part.
+  {
+    auto min_heap = IndexedHeap<HeapType::kMinHeap, PreferIndices::kHigher,
+                                IndirectLinearData, T>{
+        IndirectLinearData<T>{top_k_heap, entries}};
+    // Initialize the heap as a min-heap.
+    for (int slot = 0; slot < heap_size; slot++) {
+      min_heap.assign(slot, {slot, entries[slot].value});
+    }
+    min_heap.build(heap_size);
+
+    // Now perform top k with the remaining shards (if num_shards > heap_size).
+    for (int shard = heap_size; shard < num_shards; shard++) {
+      const auto entry = entries[shard];
+      const auto root = min_heap.root();
+      if (entry.value < root.value) {
+        continue;
+      }
+      if (entry.value == root.value &&
+          entry.index > entries[root.index].index) {
+        continue;
+      }
+      // This element should replace the min.
+      min_heap.replace_root({shard, entry.value}, heap_size);
+    }
+  }
+
+  // Max-part.
+  {
+    // Turn the min-heap into a max-heap in-place.
+    auto max_heap = IndexedHeap<HeapType::kMaxHeap, PreferIndices::kLower,
+                                IndirectLinearData, T>{
+        IndirectLinearData<T>{top_k_heap, entries}};
+    // Heapify into a max heap.
+    max_heap.build(heap_size);
+
+    // Now extract the minimum k-1 times.
+    // k is treated specially.
+    const int last_k = k - 1;
+    for (int rank = 0; rank < last_k; rank++) {
+      const Entry<T>& max_element = max_heap.root();
+      top_k_values[rank] = max_element.value;
+      int shard_index = max_element.index;
+      top_k_indices[rank] = entries[shard_index].index;
+      int next_shard_index = shard_index + num_shards;
+      // For rank < k-1, each top k heap still contains at least 1 element,
+      // so we can draw a replacement.
+      max_heap.replace_root({next_shard_index, entries[next_shard_index].value},
+                            heap_size);
+    }
+
+    // rank == last_k.
+    const Entry<T>& max_element = max_heap.root();
+    top_k_values[last_k] = max_element.value;
+    int shard_index = max_element.index;
+    top_k_indices[last_k] = entries[shard_index].index;
+  }
+}
+
+extern __shared__ char shared_memory[];
+
+template <typename T>
+__global__ void TopKKernel(const T* input, int length, int k, bool sorted,
+                           T* output, int* indices) {
+  const int batch_index = blockIdx.x;
+  const T* batch_input = input + batch_index * length;
+
+  const int thread_index = threadIdx.x;
+  const int thread_count = blockDim.x;
+
+  Entry<T>* shared_entries = (Entry<T>*)shared_memory;
+
+  heapTopK<T, StridedData>(batch_input, length, k, shared_entries, true,
+                           thread_index, thread_count);
+
+  __syncthreads();
+  if (thread_index == 0) {
+    const int offset = batch_index * k;
+    auto batch_output = output + offset;
+    auto batch_indices = indices + offset;
+    Entry<T>* top_k_heap = shared_entries + thread_count * k;
+
+    // TODO(blackhc): Erich says: Performance can likely be improved
+    // significantly by having the merge be done by multiple threads rather than
+    // just one.  ModernGPU has some nice primitives that could help with this.
+    mergeShards(thread_count, k, shared_entries, top_k_heap, batch_output,
+                batch_indices);
+  }
+}
+
+template <typename T>
+cudaError LaunchTopKKernel(cudaStream_t stream, int num_shards, const T* input,
+                           int batch_size, int length, int k, bool sorted,
+                           T* output, int* indices) {
+  // This code assumes that k is small enough that the computation
+  // fits inside shared memory (hard coded to 48KB).  In practice this
+  // means k <= 3072 for T=float/int32 and k <= 2048 for T=double/int64.
+  // The calculation is:
+  //   shared_memory_size / (2 * (sizeof(int) + sizeof(T))) < k.
+
+  // Use as many shards as possible.
+  if (num_shards <= 0) {
+    constexpr auto shared_memory_size = 48 << 10;  // 48 KB
+    const auto heap_size = k * (sizeof(int) + sizeof(T));
+    // shared_memory_size = (num_shards + 1) * heap_size <=>
+    num_shards = shared_memory_size / heap_size - 1;
+    if (num_shards <= 0) {
+      num_shards = 1;
+    }
+    auto shard_size = length / num_shards;
+    auto min_shard_size = 2 * k;
+    if (shard_size < min_shard_size) {
+      num_shards = length / min_shard_size;
+    }
+    if (num_shards <= 0) {
+      num_shards = 1;
+    } else if (num_shards > 1024) {
+      num_shards = 1024;
+    }
+  }
+  // We are limited by the amount of shared memory we have per block.
+  auto shared_memory_size = (num_shards + 1) * k * sizeof(Entry<T>);
+
+  TopKKernel<<<batch_size, num_shards, shared_memory_size, stream>>>(
+      input, length, k, sorted, output, indices);
+  return cudaGetLastError();
+}
+
+struct SegmentOffsetCreator {
+  SegmentOffsetCreator(int num_cols) : num_cols_(num_cols) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator()(
+      const Eigen::array<int, 1>& ix) const {
+    return ix[0] * num_cols_;
+  };
+  int num_cols_;
+};
+
+struct ColumnIndexCreator {
+  ColumnIndexCreator(int num_cols) : num_cols_(num_cols) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator()(
+      const Eigen::array<int, 1>& ix) const {
+    return ix[0] % num_cols_;
+  }
+
+  int num_cols_;
+};
+
+template <typename T>
+Status LaunchSortKernel(OpKernelContext* ctx, const T* input, int num_rows,
+                        int num_cols, int k,
+                        typename TTypes<T, 2>::Tensor values,
+                        TTypes<int, 2>::Tensor indices) {
+  const GPUDevice& d = ctx->eigen_device<GPUDevice>();
+  auto stream = ctx->eigen_gpu_device().stream();
+  size_t temp_storage_bytes = -1;
+
+  // TODO(ebrevdo): Once cub supports iterators for the ValueT and
+  // segment_offsets, replace these tensors with iterators that
+  // directly return the correct value.
+  Tensor input_indices;
+  TF_RETURN_IF_ERROR(ctx->allocate_temp(
+      DT_INT32, TensorShape({num_rows, num_cols}), &input_indices));
+  auto input_indices_t = To32Bit(input_indices.flat<int32>());
+  input_indices_t.device(d) =
+      input_indices_t.generate(ColumnIndexCreator(num_cols));
+
+  Tensor segment_offsets;
+  TF_RETURN_IF_ERROR(ctx->allocate_temp(DT_INT32, TensorShape({num_rows + 1}),
+                                        &segment_offsets));
+  auto segment_offsets_t = To32Bit(segment_offsets.flat<int32>());
+  segment_offsets_t.device(d) =
+      segment_offsets_t.generate(SegmentOffsetCreator(num_cols));
+
+  Tensor temp_values;
+  Tensor temp_indices;
+  T* sorted_values_ptr;
+  int* sorted_indices_ptr;
+  if (k == num_cols) {
+    // Doing a full sort, no intermediate values needed.
+    sorted_values_ptr = values.data();
+    sorted_indices_ptr = indices.data();
+  } else {
+    // Need to create intermediate values for sorting.
+    TF_RETURN_IF_ERROR(ctx->allocate_temp(
+        DT_INT32, TensorShape({num_rows, num_cols}), &temp_indices));
+    TF_RETURN_IF_ERROR(ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                          TensorShape({num_rows, num_cols}),
+                                          &temp_values));
+    sorted_indices_ptr = temp_indices.flat<int32>().data();
+    sorted_values_ptr = temp_values.flat<T>().data();
+  }
+
+  auto err = cub::DeviceSegmentedRadixSort::SortPairsDescending(
+      /* d_temp_storage */ nullptr,
+      /* temp_storage_bytes */ temp_storage_bytes,
+      /* d_keys_in */ input,
+      /* d_keys_out */ sorted_values_ptr,
+      /* d_values_in */ input_indices_t.data(),
+      /* d_values_out */ sorted_indices_ptr,
+      /* num_items */ num_cols * num_rows,
+      /* num_segments */ num_rows,
+      /* d_begin_offsets */ segment_offsets_t.data(),
+      /* d_end_offsets */ segment_offsets_t.data() + 1,
+      /* begin_bit */ 0,
+      /* end_bit */ sizeof(T) * 8,
+      /* stream */ stream);
+  if (err != cudaSuccess) {
+    return errors::Internal(
+        "TopKOp: Could not launch "
+        "cub::DeviceSegmentedRadixSort::SortPairsDescending to calculate "
+        "temp_storage_bytes, status: ",
+        cudaGetErrorString(err));
+  }
+  Tensor temp_storage;
+  TF_RETURN_IF_ERROR(ctx->allocate_temp(
+      DT_INT8, TensorShape({static_cast<int64>(temp_storage_bytes)}),
+      &temp_storage));
+  err = cub::DeviceSegmentedRadixSort::SortPairsDescending(
+      /* d_temp_storage */ temp_storage.flat<int8>().data(),
+      /* temp_storage_bytes */ temp_storage_bytes,
+      /* d_keys_in */ input,
+      /* d_keys_out */ sorted_values_ptr,
+      /* d_values_in */ input_indices_t.data(),
+      /* d_values_out */ sorted_indices_ptr,
+      /* num_items */ num_cols * num_rows,
+      /* num_segments */ num_rows,
+      /* d_begin_offsets */ segment_offsets_t.data(),
+      /* d_end_offsets */ segment_offsets_t.data() + 1,
+      /* begin_bit */ 0,
+      /* end_bit */ sizeof(T) * 8,
+      /* stream */ stream);
+  if (err != cudaSuccess) {
+    return errors::Internal(
+        "TopKOp: Could not launch "
+        "cub::DeviceSegmentedRadixSort::SortPairsDescending to sort input, "
+        "temp_storage_bytes: ",
+        temp_storage_bytes, ", status: ", cudaGetErrorString(err));
+  }
+  if (k < num_cols) {
+    // Need to copy subsets of sorted_indices and sorted_outputs to
+    // indices and outputs.
+    const Eigen::DSizes<Eigen::DenseIndex, 2> slice_indices{0, 0};
+    const Eigen::DSizes<Eigen::DenseIndex, 2> slice_sizes{num_rows, k};
+    To32Bit(indices).device(d) =
+        To32Bit(temp_indices.matrix<int32>()).slice(slice_indices, slice_sizes);
+    To32Bit(values).device(d) =
+        To32Bit(temp_values.matrix<T>()).slice(slice_indices, slice_sizes);
+  }
+  return Status::OK();
+}
+
+}  // end namespace impl
+
+namespace functor {
+
+template <typename T>
+struct TopKFunctor<GPUDevice, T> {
+  static EIGEN_ALWAYS_INLINE Status
+  Compute(OpKernelContext* context, bool sorted, int k,
+          const typename TTypes<T, 2>::ConstTensor& input, const int64 num_rows,
+          const int64 num_cols, typename TTypes<T, 2>::Tensor values,
+          typename TTypes<int, 2>::Tensor indices) {
+    // For small k, use the heap implementation.  For larger k, use
+    // the in-place cub sort.  For k == num_cols, always use the
+    // in-place cub sort.  The thresholds for n and k were determined
+    // empirically.
+    if (num_cols <= 1000 || k == num_cols || k >= 100) {
+      return impl::LaunchSortKernel(context, input.data(), num_rows, num_cols,
+                                    k, values, indices);
+    } else {
+      auto stream = context->eigen_gpu_device().stream();
+      auto err = impl::LaunchTopKKernel(stream, /* num_shards */ 0,
+                                        input.data(), num_rows, num_cols, k,
+                                        sorted, values.data(), indices.data());
+      if (err != cudaSuccess) {
+        return errors::Internal(
+            "Could not launch TopKKernel: ", cudaGetErrorString(err), ".");
+      } else {
+        return Status::OK();
+      }
+    }
+  }
+};
+
+}  // end namespace functor
+
+#define INSTANTIATE_TEMPLATE(type) \
+  template struct functor::TopKFunctor<GPUDevice, type>;
+
+TF_CALL_GPU_NUMBER_TYPES(INSTANTIATE_TEMPLATE);
+TF_CALL_INTEGRAL_TYPES(INSTANTIATE_TEMPLATE);
+#undef INSTANTIATE_TEMPLATE
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 64d0b8fa52..2c206f330b 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -835,7 +835,7 @@ tf_py_test(
     ],
 )
 
-tf_py_test(
+cuda_py_test(
     name = "topk_op_test",
     size = "small",
     srcs = ["topk_op_test.py"],
diff --git a/tensorflow/python/kernel_tests/topk_op_test.py b/tensorflow/python/kernel_tests/topk_op_test.py
index b3f737c884..034b8be4dd 100644
--- a/tensorflow/python/kernel_tests/topk_op_test.py
+++ b/tensorflow/python/kernel_tests/topk_op_test.py
@@ -18,13 +18,20 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import itertools
+import sys
+
 import numpy as np
 
+from tensorflow.python.client import session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import resource_variable_ops
 import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
 
@@ -36,25 +43,103 @@ class TopKTest(test.TestCase):
                     k,
                     expected_values,
                     expected_indices,
-                    sorted=True):
-    np_values = np.array(expected_values)
-    np_indices = np.array(expected_indices)
-    with self.test_session():
+                    sorted=True):  # pylint: disable=redefined-builtin
+    np_expected_values = np.array(expected_values)
+    np_expected_indices = np.array(expected_indices)
+    with self.test_session(use_gpu=True) as sess:
       values_op, indices_op = nn_ops.top_k(inputs, k, sorted=sorted)
-      values = values_op.eval()
-      indices = indices_op.eval()
-      self.assertShapeEqual(np_values, values_op)
-      self.assertShapeEqual(np_indices, indices_op)
-      self.assertAllEqual(np_indices, indices)
-      self.assertAllClose(np_values, values)
+      values, indices = sess.run([values_op, indices_op])
+
+      self.assertShapeEqual(np_expected_values, values_op)
+      self.assertShapeEqual(np_expected_indices, indices_op)
+
+      if sorted:
+        self.assertAllClose(np_expected_values, values)
+        # Do some special casing of equality of indices: if indices
+        # are not the same, but values are floating type, ensure that
+        # the values are within epsilon of each other.
+        if not np.issubdtype(np_expected_values.dtype, np.float):
+          # Values are not floating point type; check indices exactly
+          self.assertAllEqual(np_expected_indices, indices)
+        else:
+          # Values are floating point; indices may be swapped for
+          # values near each other.
+          indices_not_equal = np_expected_indices != indices
+          if np.any(indices_not_equal):
+            values_unsure = values[indices_not_equal]
+            expected_values_unsure = expected_values[indices_not_equal]
+            self.assertAllClose(expected_values_unsure, values_unsure)
+      else:
+        np_inputs = np.array(inputs)
+
+        # Check that the indices are valid.
+        for result_index, src_index in np.ndenumerate(indices):
+          value = values[result_index]
+          expected_value = np_inputs[result_index[0], src_index]
+          np.testing.utils.assert_almost_equal(value, expected_value)
+
+        # Check that if two elements are equal, the lower-index element appears
+        # first.
+        shape = values.shape
+        for batch_index in range(shape[0]):
+          for index in range(shape[1] - 1):
+            if np.isclose(values[batch_index, index],
+                          values[batch_index, index + 1]):
+              self.assertLess(indices[batch_index, index],
+                              indices[batch_index, index + 1])
+
+        # Now check the results, ignoring order.
+        self.assertAllEqual(np.sort(np_expected_indices), np.sort(indices))
+        self.assertAllClose(np.sort(np_expected_values), np.sort(values))
 
   def testTop1(self):
     inputs = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.3, 0.3, 0.2]]
     self._validateTopK(inputs, 1, [[0.4], [0.3]], [[3], [1]])
 
   def testTop2(self):
-    inputs = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.3, 0.3, 0.2]]
-    self._validateTopK(inputs, 2, [[0.4, 0.3], [0.3, 0.3]], [[3, 1], [2, 1]])
+    inputs = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.3, 0.4, 0.2]]
+    self._validateTopK(inputs, 2, [[0.4, 0.3], [0.4, 0.3]], [[3, 1], [2, 1]])
+
+  def _testLargeSort(self, dtype):
+    b = 10
+    n = 5000
+    inputs = np.random.permutation(
+        np.linspace(0, 100, b * n, dtype=dtype)).reshape(b, n)
+    indices = np.argsort(-inputs, axis=1)
+    values = -np.sort(-inputs, axis=1)
+    self._validateTopK(inputs, n, values, indices)
+
+  def testLargeSort(self):
+    self._testLargeSort(np.float32)
+    self._testLargeSort(np.float16)
+
+  def _testLargeTopK(self, dtype):
+    b = 10
+    n = 5000
+    k = n - 1
+    inputs = np.random.permutation(
+        np.linspace(0, 100, b * n, dtype=dtype)).reshape(b, n)
+    indices = np.argsort(-inputs, axis=1)[:, :k]
+    values = -np.sort(-inputs, axis=1)[:, :k]
+    self._validateTopK(inputs, k, values, indices)
+
+  def testLargeTopK(self):
+    self._testLargeTopK(np.float32)
+    self._testLargeTopK(np.float16)
+
+  def _testMediumTopK(self, dtype):
+    b = 5
+    n = 500
+    k = 50
+    inputs = np.random.permutation(
+        np.linspace(0, 100, b * n, dtype=dtype)).reshape(b, n)
+    indices = np.argsort(-inputs, axis=1)[:, :k]
+    values = -np.sort(-inputs, axis=1)[:, :k]
+    self._validateTopK(inputs, k, values, indices)
+
+  def testMediumTopK(self):
+    self._testMediumTopK(np.float32)
+    self._testMediumTopK(np.float16)
 
   def testTopAll(self):
     inputs = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.3, 0.3, 0.2]]
@@ -79,7 +164,7 @@ class TopKTest(test.TestCase):
 
   def testKNegative(self):
     inputs = [[0.1, 0.2], [0.3, 0.4]]
-    with self.test_session():
+    with self.test_session(use_gpu=True):
       k = array_ops.placeholder(dtypes.int32)
       values, _ = nn_ops.top_k(inputs, k)
       with self.assertRaisesOpError("Need k >= 0, got -7"):
@@ -92,7 +177,7 @@ class TopKTest(test.TestCase):
       nn_ops.top_k(inputs, 4)
 
   def testTopKGradients(self):
-    with self.test_session() as sess:
+    with self.test_session(use_gpu=True) as sess:
       inputs = array_ops.placeholder(dtypes.int32, shape=[2, 5])
       values, _ = nn_ops.top_k(inputs, 3)
       grad = sess.run(
@@ -102,5 +187,33 @@ class TopKTest(test.TestCase):
     self.assertEqual(grad.tolist(), [[0, 0, 1, 3, 2], [0, 4, 0, 5, 6]])
 
 
+class TopKBenchmark(test.Benchmark):
+
+  def benchmarkTopK(self):
+    for (m, n, p, use_gpu) in itertools.product(
+        [128],
+        [10, 100, 1000, 10000, 100000],
+        [0.001, 0.01, 0.5, 0.99, 1.0],
+        [False, True]):
+      k = int(p * n)
+      if k == 0:
+        continue
+      name = "m_%d_n_%d_k_%g_use_gpu_%s" % (m, n, k, use_gpu)
+      device = "/%s:0" % ("gpu" if use_gpu else "cpu")
+      with ops.Graph().as_default():
+        with ops.device(device):
+          x = random_ops.random_uniform((m, n))
+          v = resource_variable_ops.ResourceVariable(x)
+          op = nn_ops.top_k(v, k)
+        with session.Session() as sess:
+          v.initializer.run()
+          r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
+          gb_processed_input = m * n / 1.0e9
+          throughput = gb_processed_input / r["wall_time"]
+          print("Benchmark: %s \t wall_time: %0.03g s \t "
+                "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
+          sys.stdout.flush()
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/kernel_tests/where_op_test.py b/tensorflow/python/kernel_tests/where_op_test.py
index a428d26996..3e1fa0a287 100644
--- a/tensorflow/python/kernel_tests/where_op_test.py
+++ b/tensorflow/python/kernel_tests/where_op_test.py
@@ -100,7 +100,7 @@ class WhereOpTest(test.TestCase):
 
 class WhereBenchmark(test.Benchmark):
 
-  def benchmarkWhereCPU(self):
+  def benchmarkWhere(self):
     for (m, n, p, use_gpu) in itertools.product(
         [10],
         [10, 100, 1000, 10000, 100000, 1000000],
-- 
GitLab


From 2caec3af18a5c81f4c02f049cb5f39ca71700795 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 6 Jul 2017 13:46:19 -0700
Subject: [PATCH 0762/1375] Disable more timeseries py tests failing in OSS PIP
 GPU builds

PiperOrigin-RevId: 161124799
---
 tensorflow/contrib/timeseries/examples/BUILD  |  6 ++++++
 .../timeseries/state_space_models/BUILD       | 21 +++++++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD
index 1d8231d26e..c3a8d60484 100644
--- a/tensorflow/contrib/timeseries/examples/BUILD
+++ b/tensorflow/contrib/timeseries/examples/BUILD
@@ -62,6 +62,9 @@ py_test(
         "known_anomaly_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":known_anomaly",
     ],
@@ -90,6 +93,9 @@ py_test(
         "multivariate_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":multivariate",
     ],
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
index a1afa30e3b..a7f5da56c7 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -60,6 +60,9 @@ tf_py_test(
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
     ],
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
 )
 
 py_library(
@@ -95,6 +98,9 @@ tf_py_test(
         "//tensorflow/python:dtypes",
         "//tensorflow/python:math_ops",
     ],
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
 )
 
 py_library(
@@ -129,6 +135,9 @@ tf_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
     ],
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
 )
 
 py_library(
@@ -159,6 +168,9 @@ tf_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
     ],
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
 )
 
 py_library(
@@ -193,6 +205,9 @@ tf_py_test(
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
     ],
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
 )
 
 py_library(
@@ -228,6 +243,9 @@ tf_py_test(
         "//tensorflow/python:variables",
         "//tensorflow/python/estimator:estimator_py",
     ],
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
 )
 
 py_library(
@@ -258,6 +276,9 @@ tf_py_test(
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
     ],
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
 )
 
 py_library(
-- 
GitLab


From 7d5c74a9c89333f4f5b0524856907ac1b9f43a7b Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Thu, 6 Jul 2017 13:59:28 -0700
Subject: [PATCH 0763/1375] Move duplicate detection logic from Graph to
 FunctionLibraryDefinition

Turns out this is more useful, since there are many function libraries
that don't belong to a graph. This will be used in a future
change. Note that this maintains the current behavior of Graph.

In addition, updates FunctionDefsEqual() to handle unset attr entries
(I ran into this when using this in said future change).

PiperOrigin-RevId: 161126628
---
 tensorflow/core/framework/function.cc      | 61 +++++++++++++++++-----
 tensorflow/core/framework/function.h       |  6 ++-
 tensorflow/core/framework/function_test.cc | 47 ++++++++++++-----
 tensorflow/core/graph/graph.cc             | 30 +----------
 4 files changed, 86 insertions(+), 58 deletions(-)

diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index 9d43aab5a5..c6a20bf3ce 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -724,6 +724,23 @@ string DebugStringWhole(const GraphDef& gdef) {
   return ret;
 }
 
+namespace {
+
+// Returns the name -> attr mapping of fdef's attrs that have a value set. In
+// Python, it's possible to access unset attrs, which returns a default value
+// and adds an unset attr to the map.
+std::map<StringPiece, AttrValue> GetSetAttrs(const FunctionDef& fdef) {
+  std::map<StringPiece, AttrValue> set_attrs;
+  for (auto iter : fdef.attr()) {
+    if (iter.second.value_case() != AttrValue::VALUE_NOT_SET) {
+      set_attrs[iter.first] = iter.second;
+    }
+  }
+  return set_attrs;
+}
+
+}  // end namespace
+
 bool FunctionDefsEqual(const FunctionDef& f1, const FunctionDef& f2) {
   // NOTE(skyewm): Using MessageDifferencer would be better here, but that is
   // currently not included in tensorflow/core/platform/default/protobuf.h, so
@@ -736,10 +753,12 @@ bool FunctionDefsEqual(const FunctionDef& f1, const FunctionDef& f2) {
   f2.signature().SerializeToString(&sig2);
   if (sig1 != sig2) return false;
 
-  if (f1.attr().size() != f2.attr().size()) return false;
-  for (auto iter1 : f1.attr()) {
-    auto iter2 = f2.attr().find(iter1.first);
-    if (iter2 == f2.attr().end()) return false;
+  std::map<StringPiece, AttrValue> f1_attrs = GetSetAttrs(f1);
+  std::map<StringPiece, AttrValue> f2_attrs = GetSetAttrs(f2);
+  if (f1_attrs.size() != f2_attrs.size()) return false;
+  for (auto iter1 : f1_attrs) {
+    auto iter2 = f2_attrs.find(iter1.first);
+    if (iter2 == f2_attrs.end()) return false;
     if (!AreAttrValuesEqual(iter1.second, iter2->second)) return false;
   }
 
@@ -883,11 +902,17 @@ const FunctionDef* FunctionLibraryDefinition::Find(const string& name) const {
 }
 
 Status FunctionLibraryDefinition::AddFunctionDef(const FunctionDef& fdef) {
-  auto& ptr = function_defs_[fdef.signature().name()];
-  if (ptr != nullptr) {
-    return errors::InvalidArgument("Function with name: ",
-                                   fdef.signature().name(),
-                                   " already exists in function library.");
+  std::unique_ptr<FunctionDefAndOpRegistration>* entry =
+      &function_defs_[fdef.signature().name()];
+  if (*entry != nullptr) {
+    if (!FunctionDefsEqual((*entry)->fdef, fdef)) {
+      return errors::InvalidArgument(
+          "Cannot add function '", fdef.signature().name(),
+          "' because a different function with the same name already "
+          "exists.");
+    }
+    // Ignore duplicate FunctionDefs
+    return Status::OK();
   }
   const OpDef* op_def;
   if (default_registry_->LookUpOpDef(fdef.signature().name(), &op_def).ok()) {
@@ -895,19 +920,27 @@ Status FunctionLibraryDefinition::AddFunctionDef(const FunctionDef& fdef) {
         "Cannot add function '", fdef.signature().name(),
         "' because an op with the same name already exists.");
   }
-  ptr.reset(new FunctionDefAndOpRegistration(fdef));
+  entry->reset(new FunctionDefAndOpRegistration(fdef));
   return Status::OK();
 }
 
 Status FunctionLibraryDefinition::AddGradientDef(const GradientDef& grad) {
-  if (func_grad_.count(grad.function_name()) > 0) {
-    return errors::InvalidArgument("Gradient for function '",
-                                   grad.function_name(), "' already exists.");
+  string* entry = &func_grad_[grad.function_name()];
+  if (!entry->empty()) {
+    if (*entry != grad.gradient_func()) {
+      return errors::InvalidArgument(
+          "Cannot assign gradient function '", grad.gradient_func(), "' to '",
+          grad.function_name(), "' because it already has gradient function ",
+          "'", *entry, "'");
+    }
+    // Ignore duplicate GradientDefs
+    return Status::OK();
   }
-  func_grad_[grad.function_name()] = grad.gradient_func();
+  *entry = grad.gradient_func();
   return Status::OK();
 }
 
+// TODO(skyewm): don't modify FunctionLibraryDefinition in case of error
 Status FunctionLibraryDefinition::AddLibrary(
     const FunctionLibraryDefinition& other) {
   for (auto iter : other.function_defs_) {
diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h
index b8d5b8797a..2342e08b38 100644
--- a/tensorflow/core/framework/function.h
+++ b/tensorflow/core/framework/function.h
@@ -287,20 +287,24 @@ class FunctionLibraryDefinition : public OpRegistryInterface {
   const FunctionDef* Find(const string& func) const;
 
   // Adds function definition 'fdef' to this function library.
-  // Returns status 'ok' on success, or error otherwise.
+  // Returns status 'ok' on success, or error otherwise. This is a no-op if
+  // 'fdef' already exists in this function library.
   // If 'fdef' is successfully added to the library, it will be accessible
   // from 'LookUp' and included in the proto returned by 'ToProto'.
   Status AddFunctionDef(const FunctionDef& fdef);
 
   // Adds gradient definition 'grad' to this function library.
+  // This is a no-op if 'grad' already exists in this function library.
   // If 'grad' is successfully added, it will be accessible via 'FindGradient'
   // and included in the proto returned by 'ToProto'.
   Status AddGradientDef(const GradientDef& grad);
 
   // Adds the functions and gradients in 'other' to this function library.
+  // Duplicate functions and gradients are ignored.
   Status AddLibrary(const FunctionLibraryDefinition& other);
 
   // Adds the functions and gradients in 'lib_def' to this function library.
+  // Duplicate functions and gradients are ignored.
   Status AddLibrary(const FunctionDefLibrary& lib_def);
 
   // If the gradient function for 'func' is specified explicitly in
diff --git a/tensorflow/core/framework/function_test.cc b/tensorflow/core/framework/function_test.cc
index 2ecdc36c11..1173384a1e 100644
--- a/tensorflow/core/framework/function_test.cc
+++ b/tensorflow/core/framework/function_test.cc
@@ -971,6 +971,10 @@ TEST(FunctionLibraryDefinitionTest, AddFunctionDef) {
   EXPECT_EQ(s.error_message(),
             "Cannot add function 'Add' because an op with the same name "
             "already exists.");
+
+  // Already-added functions don't produce error
+  TF_EXPECT_OK(lib_def.AddFunctionDef(test::function::XTimesTwo()));
+  TF_EXPECT_OK(lib_def.AddFunctionDef(test::function::WXPlusB()));
 }
 
 TEST(FunctionLibraryDefinitionTest, AddGradientDef) {
@@ -984,12 +988,16 @@ TEST(FunctionLibraryDefinitionTest, AddGradientDef) {
   grad.set_gradient_func(test::function::XTimesFour().signature().name());
   TF_EXPECT_OK(lib_def.AddGradientDef(grad));
 
+  // Already-added gradients don't produce error
+  TF_EXPECT_OK(lib_def.AddGradientDef(grad));
+
   // Test that adding a duplicate gradient fails
   grad.set_gradient_func(test::function::XTimes16().signature().name());
   Status s = lib_def.AddGradientDef(grad);
   EXPECT_EQ(s.code(), error::Code::INVALID_ARGUMENT);
   EXPECT_EQ(s.error_message(),
-            "Gradient for function 'XTimesTwo' already exists.");
+            "Cannot assign gradient function 'XTimes16' to 'XTimesTwo' because "
+            "it already has gradient function 'XTimesFour'");
 }
 
 TEST(FunctionLibraryDefinitionTest, AddLibrary) {
@@ -998,35 +1006,46 @@ TEST(FunctionLibraryDefinitionTest, AddLibrary) {
   *proto.add_function() = test::function::XTimesTwo();
   FunctionLibraryDefinition lib_def(OpRegistry::Global(), proto);
 
-  // Error if you try to add the same function twice
-  Status s = lib_def.AddLibrary(lib_def);
-  EXPECT_EQ(s.code(), error::Code::INVALID_ARGUMENT);
-  EXPECT_EQ(s.error_message(),
-            "Function with name: XTimesTwo already exists in function "
-            "library.");
-
   // Add gradient
   GradientDef grad;
   grad.set_function_name(test::function::XTimesTwo().signature().name());
   grad.set_gradient_func(test::function::XTimesFour().signature().name());
   TF_EXPECT_OK(lib_def.AddGradientDef(grad));
 
-  // Error if you try to add the same library function twice
+  // Error if you try to add conflicting function
   proto.Clear();
-  *proto.add_gradient() = grad;
+  FunctionDef fdef = test::function::XTimesFour();
+  fdef.mutable_signature()->set_name(
+      test::function::XTimesTwo().signature().name());
+  *proto.add_function() = fdef;
   FunctionLibraryDefinition lib_def2(OpRegistry::Global(), proto);
-  s = lib_def.AddLibrary(lib_def2);
+  Status s = lib_def.AddLibrary(lib_def2);
   EXPECT_EQ(s.code(), error::Code::INVALID_ARGUMENT);
   EXPECT_EQ(s.error_message(),
-            "Gradient for function 'XTimesTwo' already exists.");
+            "Cannot add function 'XTimesTwo' because a different function with "
+            "the same name already exists.");
+
+  // Error if you try to add conflicting gradient
+  proto.Clear();
+  grad.set_gradient_func(test::function::XTimes16().signature().name());
+  *proto.add_gradient() = grad;
+  FunctionLibraryDefinition lib_def3(OpRegistry::Global(), proto);
+  s = lib_def.AddLibrary(lib_def3);
+  EXPECT_EQ(s.code(), error::Code::INVALID_ARGUMENT);
+  EXPECT_EQ(s.error_message(),
+            "Cannot assign gradient function 'XTimes16' to 'XTimesTwo' because "
+            "it already has gradient function 'XTimesFour'");
 
   // No conflicting functions or gradients OK
   proto.Clear();
   *proto.add_function() = test::function::XTimesFour();
   grad.set_function_name(test::function::XTimes16().signature().name());
   *proto.add_gradient() = grad;
-  FunctionLibraryDefinition lib_def3(OpRegistry::Global(), proto);
-  TF_EXPECT_OK(lib_def.AddLibrary(lib_def3));
+  FunctionLibraryDefinition lib_def4(OpRegistry::Global(), proto);
+  TF_EXPECT_OK(lib_def.AddLibrary(lib_def4));
+
+  // OK to add the same functions and gradients twice
+  TF_EXPECT_OK(lib_def.AddLibrary(lib_def));
 }
 
 TEST(FunctionLibraryDefinitionTest, ToProto) {
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 7a5e76fdd0..9691326c99 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -413,35 +413,7 @@ void Graph::RemoveEdge(const Edge* e) {
 }
 
 Status Graph::AddFunctionLibrary(const FunctionDefLibrary& fdef_lib) {
-  for (const FunctionDef& fdef : fdef_lib.function()) {
-    const FunctionDef* preexisting_fdef = ops_.Find(fdef.signature().name());
-    if (preexisting_fdef != nullptr) {
-      if (!FunctionDefsEqual(*preexisting_fdef, fdef)) {
-        return errors::InvalidArgument(
-            "Cannot add function '", fdef.signature().name(),
-            "' because a different function with the same name already "
-            "exists.");
-      }
-      // Ignore duplicate FunctionDefs
-      continue;
-    }
-    TF_RETURN_IF_ERROR(ops_.AddFunctionDef(fdef));
-  }
-  for (const GradientDef& grad : fdef_lib.gradient()) {
-    string preexisting_grad_func = ops_.FindGradient(grad.function_name());
-    if (!preexisting_grad_func.empty()) {
-      if (preexisting_grad_func != grad.gradient_func()) {
-        return errors::InvalidArgument(
-            "Cannot assign gradient function '", grad.gradient_func(), "' to '",
-            grad.function_name(), "' because it already has gradient function ",
-            "'", preexisting_grad_func, "'");
-      }
-      // Ignore duplicate GradientDefs
-      continue;
-    }
-    TF_RETURN_IF_ERROR(ops_.AddGradientDef(grad));
-  }
-  return Status::OK();
+  return ops_.AddLibrary(fdef_lib);
 }
 
 namespace {
-- 
GitLab


From 9f57dc8dd752b78b402d768187f76d8ccb5f7254 Mon Sep 17 00:00:00 2001
From: Bruno Rosa <bruno.rosa@eldorado.org.br>
Date: Thu, 6 Jul 2017 18:06:41 -0300
Subject: [PATCH 0764/1375] Use mcpu instead of march for ppc64le

march is not support by gcc on ppc64le
---
 configure | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/configure b/configure
index 4bcf11e0a9..4c6cba2169 100755
--- a/configure
+++ b/configure
@@ -25,6 +25,10 @@ function is_windows() {
   [[ "${PLATFORM}" =~ msys_nt*|mingw*|cygwin*|uwin* ]]
 }
 
+function is_ppc64le() {
+  [[ "${uname -m}" == "ppc64le" ]]
+}
+
 function sed_in_place() {
   sed -e $1 $2 > "$2.bak"
   mv "$2.bak" $2
@@ -294,7 +298,12 @@ fi # TF_NEED_MKL
 
 ## Set up architecture-dependent optimization flags.
 if [ -z "$CC_OPT_FLAGS" ]; then
-  default_cc_opt_flags="-march=native"
+  if [ is_ppc64le ]; then
+    # gcc on ppc64le does not support -march, use mcpu instead
+    default_cc_opt_flags="-mcpu=native"
+  else
+    default_cc_opt_flags="-march=native"
+  fi
   read -p "Please specify optimization flags to use during compilation when bazel option "\
 "\"--config=opt\" is specified [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
   if [ -z "$CC_OPT_FLAGS" ]; then
-- 
GitLab


From 53604916ed2acbe931d7773a6f6edcfc191956e5 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Thu, 6 Jul 2017 14:29:37 -0700
Subject: [PATCH 0765/1375] Fixed the missing labels test in TPUEstimator.

PiperOrigin-RevId: 161131282
---
 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index e001d866c3..193d14e1ce 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -360,6 +360,12 @@ def _call_model_fn(model_fn, features, labels, mode, config, params,
   """Calls the model_fn with required parameters."""
   model_fn_args = util.fn_args(model_fn)
   kwargs = {}
+  if 'labels' in model_fn_args:
+    kwargs['labels'] = labels
+  else:
+    if labels is not None:
+      raise ValueError(
+          'model_fn does not take labels, but input_fn returns labels.')
   if 'mode' in model_fn_args:
     kwargs['mode'] = mode
   if 'config' in model_fn_args:
@@ -371,7 +377,7 @@ def _call_model_fn(model_fn, features, labels, mode, config, params,
         'model_fn ({}) does not include params argument, '
         'required by TPUEstimator to pass batch size as '
         'params[\'batch_size\']'.format(model_fn))
-  return model_fn(features=features, labels=labels, **kwargs)
+  return model_fn(features=features, **kwargs)
 
 
 def _call_model_fn_with_tpu(model_fn, features, labels, mode, config, params):
-- 
GitLab


From 335f1f14d3b17d93ed7f575546cdad035dc22426 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 14:37:27 -0700
Subject: [PATCH 0766/1375] Extend static shape inference for SparseTensors
 with dense_shapes constructed using slicing.

PiperOrigin-RevId: 161132391
---
 tensorflow/python/framework/tensor_util.py    | 47 +++++++++--
 .../python/framework/tensor_util_test.py      | 77 +++++++++++++++++++
 2 files changed, 117 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py
index 1081110001..323802e57f 100644
--- a/tensorflow/python/framework/tensor_util.py
+++ b/tensorflow/python/framework/tensor_util.py
@@ -770,13 +770,46 @@ def constant_value_as_shape(tensor):  # pylint: disable=invalid-name
       # and concatenate it with `ret`.
       ret = ret.concatenate(constant_value_as_shape(concat_input))
     return ret
-  else:
-    ret = tensor_shape.unknown_shape(shape[0].value)
-    value = constant_value(tensor)
-    if value is not None:
-      ret = ret.merge_with(tensor_shape.TensorShape(
-          [d if d != -1 else None for d in value]))
-    return ret
+  elif tensor.op.type == "StridedSlice":
+    try:
+      begin = constant_value(tensor.op.inputs[1])
+      end = constant_value(tensor.op.inputs[2])
+      strides = constant_value(tensor.op.inputs[3])
+      if begin is not None and end is not None and strides is not None:
+        begin = begin[0]
+        end = end[0]
+        strides = strides[0]
+        begin_mask = tensor.op.get_attr("begin_mask")
+        if begin_mask == 1:
+          begin = None
+        end_mask = tensor.op.get_attr("end_mask")
+        if end_mask == 1:
+          end = None
+
+        ellipsis_mask = tensor.op.get_attr("ellipsis_mask")
+        new_axis_mask = tensor.op.get_attr("new_axis_mask")
+        shrink_axis_mask = tensor.op.get_attr("shrink_axis_mask")
+        valid_attributes = (not ellipsis_mask and not new_axis_mask and
+                            not shrink_axis_mask and
+                            (not begin_mask or (begin_mask == 1)) and
+                            (not end_mask or (end_mask == 1)))
+        if valid_attributes:  # additional inputs not supported
+          prev = constant_value_as_shape(tensor.op.inputs[0])
+          prev = prev[begin:end:strides]
+          ret = tensor_shape.TensorShape(prev)
+          return ret
+
+    except ValueError:  # Could come from get_attr or slicing prev.
+      pass
+    except TypeError:  # Could come from slicing prev.
+      pass
+
+  ret = tensor_shape.unknown_shape(shape[0].value)
+  value = constant_value(tensor)
+  if value is not None:
+    ret = ret.merge_with(tensor_shape.TensorShape(
+        [d if d >= 0 else None for d in value]))
+  return ret
 
 
 def is_tensor(x):  # pylint: disable=invalid-name
diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py
index 8949702b87..b0a117a21e 100644
--- a/tensorflow/python/framework/tensor_util_test.py
+++ b/tensorflow/python/framework/tensor_util_test.py
@@ -832,6 +832,83 @@ class ConstantValueAsShapeTest(test.TestCase):
     c_val = tensor_util.constant_value_as_shape(tf_val)
     self.assertEqual([16, 37, None, 48], c_val.as_list())
 
+  def testSlice(self):
+    tf_val = array_ops.placeholder(dtypes.int32, shape=(4,))[0:2]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([None, None], c_val.as_list())
+
+    # begin:end
+    tf_val = constant_op.constant([10, 20, 30])[1:3]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([20, 30], c_val.as_list())
+
+    # begin:end:stride
+    tf_val = array_ops.strided_slice(
+        constant_op.constant([10, 20, 30]), [1], [3], strides=[2])
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([20], c_val.as_list())
+
+    # [1, 2, 16, 37, None, 48]
+    tf_val_orig = array_ops.concat(
+        [[1, 2, 16, 37], array_ops.placeholder(
+            dtypes.int32, shape=(1,)), [48]], 0)
+
+    # begin: no end
+    tf_val = tf_val_orig[2:]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([16, 37, None, 48], c_val.as_list())
+
+    # begin::negative slice
+    tf_val = tf_val_orig[2::-1]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([16, 2, 1], c_val.as_list())
+
+    # :end:negative slice
+    tf_val = tf_val_orig[:1:-2]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([48, 37], c_val.as_list())
+
+    # begin:end:negative slice
+    tf_val = tf_val_orig[3:1:-1]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([37, 16], c_val.as_list())
+
+    # begin:negative end:slice
+    tf_val = tf_val_orig[1:-3:1]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([2, 16], c_val.as_list())
+
+    # negative begin::slice
+    tf_val = tf_val_orig[-3::1]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([37, None, 48], c_val.as_list())
+
+    # negative begin::negative slice
+    tf_val = tf_val_orig[-3::-1]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([37, 16, 2, 1], c_val.as_list())
+
+    # negative begin:negative end:negative slice
+    tf_val = tf_val_orig[-3:-5:-1]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([37, 16], c_val.as_list())
+
+    # Do not support shape inference for additional arguments
+    tf_val = constant_op.constant([10, 20, 30])[...]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual([None, None, None], c_val.as_list())
+
+    # Do not support shape inference for tensor slices.
+    tf_val = constant_op.constant([10, 20, 30])[
+        array_ops.placeholder(dtypes.int32, shape=()):]
+    c_val = tensor_util.constant_value_as_shape(tf_val)
+    self.assertEqual(tensor_shape.unknown_shape(), c_val)
+
+    # Do not support shape inference for higher rank
+    with self.assertRaises(ValueError):
+      tf_val = constant_op.constant([[10], [20], [30]])[:, 0:]
+      c_val = tensor_util.constant_value_as_shape(tf_val)
+
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 0cbd249e8ba045ddf3e7dc6eeab3035363bb92da Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 14:37:39 -0700
Subject: [PATCH 0767/1375] Add complex tensors support to
 `matrix_determinant`.

PiperOrigin-RevId: 161132422
---
 .../python/ops/linear_operator_test_util.py   |  8 -----
 tensorflow/core/kernels/determinant_op.cc     |  8 +++++
 tensorflow/core/ops/linalg_ops.cc             |  4 +--
 .../kernel_tests/determinant_op_test.py       | 35 +++++++++++++++++++
 4 files changed, 45 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
index 1924ea0f66..af14f34600 100644
--- a/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
+++ b/tensorflow/contrib/linalg/python/ops/linear_operator_test_util.py
@@ -169,10 +169,6 @@ class LinearOperatorDerivedClassTest(test.TestCase):
     for use_placeholder in False, True:
       for shape in self._shapes_to_test:
         for dtype in self._dtypes_to_test:
-          if dtype.is_complex:
-            self.skipTest(
-                "tf.matrix_determinant does not work with complex, so this "
-                "test is being skipped.")
           with self.test_session(graph=ops.Graph()) as sess:
             sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
             operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
@@ -190,10 +186,6 @@ class LinearOperatorDerivedClassTest(test.TestCase):
     for use_placeholder in False, True:
       for shape in self._shapes_to_test:
         for dtype in self._dtypes_to_test:
-          if dtype.is_complex:
-            self.skipTest(
-                "tf.matrix_determinant does not work with complex, so this "
-                "test is being skipped.")
           with self.test_session(graph=ops.Graph()) as sess:
             sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
             operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
diff --git a/tensorflow/core/kernels/determinant_op.cc b/tensorflow/core/kernels/determinant_op.cc
index d51563580b..d7e55a8ba2 100644
--- a/tensorflow/core/kernels/determinant_op.cc
+++ b/tensorflow/core/kernels/determinant_op.cc
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include "third_party/eigen3/Eigen/LU"
 #include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/linalg_ops_common.h"
@@ -62,7 +63,14 @@ class DeterminantOp : public LinearAlgebraOp<Scalar> {
 
 REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<float>), float);
 REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<double>), double);
+REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<complex64>), complex64);
+REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<complex128>),
+                   complex128);
 REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<float>), float);
 REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<double>), double);
+REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<complex64>),
+                   complex64);
+REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<complex128>),
+                   complex128);
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/linalg_ops.cc b/tensorflow/core/ops/linalg_ops.cc
index 6e1f2dc052..b0f95c91fd 100644
--- a/tensorflow/core/ops/linalg_ops.cc
+++ b/tensorflow/core/ops/linalg_ops.cc
@@ -189,7 +189,7 @@ Status SvdShapeFn(InferenceContext* c) {
 REGISTER_OP("MatrixDeterminant")
     .Input("input: T")
     .Output("output: T")
-    .Attr("T: {float, double}")
+    .Attr("T: {float, double, complex64, complex128}")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 2, &input));
@@ -560,7 +560,7 @@ REGISTER_OP("BatchSelfAdjointEig")
 REGISTER_OP("BatchMatrixDeterminant")
     .Input("input: T")
     .Output("output: T")
-    .Attr("T: {float, double}")
+    .Attr("T: {float, double, complex64, complex128}")
     .Deprecated(13, "Use MatrixDeterminant instead.");
 
 REGISTER_OP("BatchMatrixInverse")
diff --git a/tensorflow/python/kernel_tests/determinant_op_test.py b/tensorflow/python/kernel_tests/determinant_op_test.py
index 2d05ab6139..089ec0de79 100644
--- a/tensorflow/python/kernel_tests/determinant_op_test.py
+++ b/tensorflow/python/kernel_tests/determinant_op_test.py
@@ -66,6 +66,41 @@ class DeterminantOpTest(test.TestCase):
     # A multidimensional batch of 2x2 matrices
     self._compareDeterminant(np.random.rand(3, 4, 5, 2, 2).astype(np.float64))
 
+  def testBasicComplex64(self):
+    # 2x2 matrices
+    self._compareDeterminant(
+        np.array([[2., 3.], [3., 4.]]).astype(np.complex64))
+    self._compareDeterminant(
+        np.array([[0., 0.], [0., 0.]]).astype(np.complex64))
+    self._compareDeterminant(
+        np.array([[1. + 1.j, 1. - 1.j], [-1. + 1.j, -1. - 1.j]]).astype(
+            np.complex64))
+    # 5x5 matrices (Eigen forces LU decomposition)
+    self._compareDeterminant(
+        np.array([[2., 3., 4., 5., 6.], [3., 4., 9., 2., 0.], [
+            2., 5., 8., 3., 8.
+        ], [1., 6., 7., 4., 7.], [2., 3., 4., 5., 6.]]).astype(np.complex64))
+    # A multidimensional batch of 2x2 matrices
+    self._compareDeterminant(np.random.rand(3, 4, 5, 2, 2).astype(np.complex64))
+
+  def testBasicComplex128(self):
+    # 2x2 matrices
+    self._compareDeterminant(
+        np.array([[2., 3.], [3., 4.]]).astype(np.complex128))
+    self._compareDeterminant(
+        np.array([[0., 0.], [0., 0.]]).astype(np.complex128))
+    self._compareDeterminant(
+        np.array([[1. + 1.j, 1. - 1.j], [-1. + 1.j, -1. - 1.j]]).astype(
+            np.complex128))
+    # 5x5 matrices (Eigen forces LU decomposition)
+    self._compareDeterminant(
+        np.array([[2., 3., 4., 5., 6.], [3., 4., 9., 2., 0.], [
+            2., 5., 8., 3., 8.
+        ], [1., 6., 7., 4., 7.], [2., 3., 4., 5., 6.]]).astype(np.complex128))
+    # A multidimensional batch of 2x2 matrices
+    self._compareDeterminant(
+        np.random.rand(3, 4, 5, 2, 2).astype(np.complex128))
+
   def testOverflow(self):
     max_double = np.finfo("d").max
     huge_matrix = np.array([[max_double, 0.0], [0.0, max_double]])
-- 
GitLab


From dabcb60bc943946f19a6af1fb13415f958053e43 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 15:04:41 -0700
Subject: [PATCH 0768/1375] [XLA] Add reasonable error messages to
 Builder::Build for bad parameter numbers.

PiperOrigin-RevId: 161136262
---
 tensorflow/compiler/xla/service/hlo_computation.cc | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index fef6cfc95a..6a5533c469 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -73,15 +73,19 @@ HloComputation::HloComputation(
   for (auto& instruction : *instructions) {
     if (instruction->opcode() == HloOpcode::kParameter) {
       int64 param_no = instruction->parameter_number();
-      CHECK_GE(param_no, 0);
-      CHECK_LT(param_no, param_instructions_.size());
-      CHECK_EQ(nullptr, param_instructions_[param_no]);
+      CHECK(param_no >= 0 && param_no < parameter_count)
+          << "\nERROR: invalid parameter number.  Expected [0, "
+          << parameter_count << "), got " << param_no;
+      CHECK(param_instructions_[param_no] == nullptr)
+          << "\nERROR: parameter number " << param_no
+          << " already allocated in this computation";
       param_instructions_[param_no] = instruction.get();
     }
     root_found |= instruction.get() == root_instruction_;
     AddInstructionInternal(std::move(instruction));
   }
-  CHECK(root_found);
+  CHECK(root_found)
+      << "\nERROR: root instruction is not present in computation.";
 }
 
 HloInstruction* HloComputation::AddInstruction(
-- 
GitLab


From 97cbcac4535de361d0f04d5b30e9653e3b229c94 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 6 Jul 2017 15:07:04 -0700
Subject: [PATCH 0769/1375] [TF:XLA] Fix failure in functionalize_control_flow
 rewrite for Enter nodes that are unused. Make sure we ignore such nodes
 without producing an error.

PiperOrigin-RevId: 161136545
---
 tensorflow/compiler/tf2xla/functionalize_control_flow.cc     | 5 ++++-
 .../compiler/tf2xla/functionalize_control_flow_test.cc       | 3 +++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
index 623c52120f..1c2c327347 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
@@ -261,7 +261,10 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame,
       std::vector<const Edge*> edges(arg.enter->out_edges().begin(),
                                      arg.enter->out_edges().end());
       for (int i = 0; i < edges.size(); ++i) {
-        TF_RET_CHECK(!edges[i]->IsControlEdge());
+        if (edges[i]->IsControlEdge() && edges[i]->dst()->IsSink()) {
+          continue;
+        }
+        TF_RET_CHECK(!edges[i]->IsControlEdge()) << edges[i]->src()->name();
         Arg new_arg;
         new_arg.is_loop_invariant = false;
         if (i == 0) {
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
index 7f6717ffaf..2fb1cc0454 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
@@ -65,6 +65,9 @@ TEST(FunctionalizeControlFlow, OneLoopVar) {
     auto source = ops::Placeholder(scope.WithOpName("source"), DT_INT32);
     auto enter =
         ops::internal::Enter(scope.WithOpName("while/Enter"), source, "aloop");
+    // Add an unused Enter node. These should be ignored.
+    auto enter2 =
+        ops::internal::Enter(scope.WithOpName("while/Enter2"), source, "aloop");
     auto merge = ops::Merge(scope.WithOpName("while/Merge"),
                             std::initializer_list<Input>{enter, dummy});
     auto ten = ops::Const<int32>(
-- 
GitLab


From 755fa7b501b5a1dadf2b8a1814d74d4451a05975 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Thu, 6 Jul 2017 15:13:23 -0700
Subject: [PATCH 0770/1375] Block generate_test, and docs generating from
 running in python3.

- Doc generation is currently unsupported in python3

- These both end in errors in python 3.5.1+

PiperOrigin-RevId: 161137467
---
 tensorflow/tools/docs/generate_lib.py      | 2 +-
 tensorflow/tools/docs/generate_lib_test.py | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index 23c1ea9d60..a8ac9f13b1 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -421,7 +421,7 @@ class DocGenerator(object):
 
   def __init__(self):
     if sys.version_info >= (3, 0):
-      print('Warning: Doc generation is not supported from python3.')
+      sys.exit('Doc generation is not supported from python3.')
     self.argument_parser = argparse.ArgumentParser()
     self._py_modules = None
     self._private_map = _get_default_private_map()
diff --git a/tensorflow/tools/docs/generate_lib_test.py b/tensorflow/tools/docs/generate_lib_test.py
index ea6d28a02b..1ceaf31f1c 100644
--- a/tensorflow/tools/docs/generate_lib_test.py
+++ b/tensorflow/tools/docs/generate_lib_test.py
@@ -52,6 +52,9 @@ class DummyVisitor(object):
 class GenerateTest(googletest.TestCase):
 
   def test_write(self):
+    if sys.version_info >= (3, 0):
+      self.skipTest('Warning: Doc generation is not supported from python3.')
+
     module = sys.modules[__name__]
 
     index = {
-- 
GitLab


From a2ee8bca3f3fa08bf022f5c8a68c3e1cf829f2cd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 15:15:27 -0700
Subject: [PATCH 0771/1375] Add support for int8 x int8 -> int32 matrix
 multiplication via cublasGemmEx to stream_executor.

PiperOrigin-RevId: 161137741
---
 tensorflow/stream_executor/blas.h            | 25 +++++++++++++-
 tensorflow/stream_executor/cuda/cuda_blas.cc | 36 ++++++++++++++++----
 tensorflow/stream_executor/cuda/cuda_blas.h  | 18 +++++-----
 tensorflow/stream_executor/stream.cc         | 21 ++++++++++++
 tensorflow/stream_executor/stream.h          |  9 +++++
 5 files changed, 91 insertions(+), 18 deletions(-)

diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h
index 07a0f7ccd6..cfff3649c8 100644
--- a/tensorflow/stream_executor/blas.h
+++ b/tensorflow/stream_executor/blas.h
@@ -97,8 +97,9 @@ enum class ComputationType {
   kF16,         // 16-bit floating-point
   kF32,         // 32-bit floating-point
   kF64,         // 64-bit floating-point
+  kI32,         // 32-bit integer
   kComplexF32,  // Complex number comprised of two f32s.
-  kComplexF64   // Complex number comprised of two f64s.
+  kComplexF64,  // Complex number comprised of two f64s.
 };
 
 // Converts a ComputationType to a string.
@@ -108,6 +109,15 @@ string ComputationTypeString(ComputationType ty);
 // as a hint to the blas library.
 typedef int64 AlgorithmType;
 
+// blas uses -1 to represent the default algorithm. This happens to match up
+// with the CUBLAS_GEMM_DFALT constant, so cuda_blas.cc is using static_cast
+// to convert from AlgorithmType to cublasGemmAlgo_t, and uses a static_assert
+// to ensure that this assumption does not break.
+// If another blas implementation uses a different value for the default
+// algorithm, then it needs to convert kDefaultGemmAlgo to that value
+// (e.g. via a function called ToWhateverGemmAlgo).
+constexpr AlgorithmType kDefaultGemmAlgo = -1;
+
 // Describes the result of a performance experiment, usually timing the speed of
 // a particular AlgorithmType.
 //
@@ -944,6 +954,12 @@ class BlasSupport {
   // output_profile_result->is_valid().  This lets you use this function for
   // choosing the best algorithm among many (some of which may fail) without
   // creating a new Stream for each attempt.
+  virtual bool DoBlasGemmWithAlgorithm(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, int alpha, const DeviceMemory<int8> &a, int lda,
+      const DeviceMemory<int8> &b, int ldb, int beta, DeviceMemory<int32> *c,
+      int ldc, ComputationType computation_type, AlgorithmType algorithm,
+      ProfileResult *output_profile_result) = 0;
   virtual bool DoBlasGemmWithAlgorithm(
       Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
       uint64 n, uint64 k, const Eigen::half &alpha,
@@ -1737,6 +1753,13 @@ class BlasSupport {
                   DeviceMemory<std::complex<double>> *c, int ldc) override;    \
   bool GetBlasGemmAlgorithms(std::vector<blas::AlgorithmType> *out_algorithms) \
       override;                                                                \
+  bool DoBlasGemmWithAlgorithm(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, int alpha, const DeviceMemory<int8> &a,    \
+      int lda, const DeviceMemory<int8> &b, int ldb, int beta,                 \
+      DeviceMemory<int> *c, int ldc, blas::ComputationType computation_type,   \
+      blas::AlgorithmType algorithm,                                           \
+      blas::ProfileResult *output_profile_result) override;                    \
   bool DoBlasGemmWithAlgorithm(                                                \
       Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
       uint64 m, uint64 n, uint64 k, const Eigen::half &alpha,                  \
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 2c650afc70..2817364e97 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -33,6 +33,7 @@ limitations under the License.
 
 #include "tensorflow/stream_executor/cuda/cuda_blas.h"
 
+#include <assert.h>
 #include <complex>
 
 #include "tensorflow/stream_executor/cuda/cuda_activation.h"
@@ -483,6 +484,11 @@ struct CUDADataType<std::complex<double>> {
   static constexpr cudaDataType_t type = CUDA_C_64F;
 };
 
+template <>
+struct CUDADataType<int> {
+  static constexpr cudaDataType_t type = CUDA_R_32I;
+};
+
 template <>
 struct CUDADataType<int8> {
   static constexpr cudaDataType_t type = CUDA_R_8I;
@@ -511,6 +517,8 @@ cudaDataType_t CUDAComputationType(blas::ComputationType ty) {
       return CUDA_R_32F;
     case blas::ComputationType::kF64:
       return CUDA_R_64F;
+    case blas::ComputationType::kI32:
+      return CUDA_R_32I;
     case blas::ComputationType::kComplexF32:
       return CUDA_C_32F;
     case blas::ComputationType::kComplexF64:
@@ -1849,12 +1857,12 @@ bool CUDABlas::DoBlasGemm(Stream *stream, blas::Transpose transa,
       CUDAComplex(CUDAMemoryMutable(c)), ldc);
 }
 
-template <typename T>
+template <typename InT, typename OutT, typename CompT>
 bool CUDABlas::DoBlasGemmWithAlgorithmImpl(
     Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-    uint64 n, uint64 k, const T &alpha, const DeviceMemory<T> &a, int lda,
-    const DeviceMemory<T> &b, int ldb, const T &beta, DeviceMemory<T> *c,
-    int ldc, blas::ComputationType computation_type,
+    uint64 n, uint64 k, const CompT &alpha, const DeviceMemory<InT> &a, int lda,
+    const DeviceMemory<InT> &b, int ldb, const CompT &beta,
+    DeviceMemory<OutT> *c, int ldc, blas::ComputationType computation_type,
     blas::AlgorithmType algorithm, blas::ProfileResult *output_profile_result) {
 // CUDA < version 8 and GPUs < sm_50 don't support cublasGemmEx.
 #if CUDA_VERSION < 8000
@@ -1881,12 +1889,15 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl(
     }
   }
 
-  cudaDataType_t data_type = CUDADataType<T>::type;
+  cudaDataType_t cuda_in_type = CUDADataType<InT>::type;
+  // Since we are converting 'algorithm' to cublasGemmAlgo_t by static_cast,
+  // we do the following compile-time check on the default value:
+  static_assert(blas::kDefaultGemmAlgo == CUBLAS_GEMM_DFALT, "");
   bool result = DoBlasInternalFailureOK(
       wrap::cublasGemmEx, stream, /* pointer_mode_host = */ true,
       CUDABlasTranspose(transa), CUDABlasTranspose(transb), m, n, k, &alpha,
-      CUDAMemory(a), data_type, lda, CUDAMemory(b), data_type, ldb, &beta,
-      CUDAMemoryMutable(c), data_type, ldc,
+      CUDAMemory(a), cuda_in_type, lda, CUDAMemory(b), cuda_in_type, ldb, &beta,
+      CUDAMemoryMutable(c), CUDADataType<OutT>::type, ldc,
       CUDAComputationType(computation_type),
       static_cast<cublasGemmAlgo_t>(algorithm));
 
@@ -1920,6 +1931,17 @@ bool CUDABlas::GetBlasGemmAlgorithms(
   return true;
 }
 
+bool CUDABlas::DoBlasGemmWithAlgorithm(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, int alpha, const DeviceMemory<int8> &a, int lda,
+    const DeviceMemory<int8> &b, int ldb, int beta, DeviceMemory<int> *c,
+    int ldc, blas::ComputationType computation_type,
+    blas::AlgorithmType algorithm, blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithAlgorithmImpl(
+      stream, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+      computation_type, algorithm, output_profile_result);
+}
+
 bool CUDABlas::DoBlasGemmWithAlgorithm(
     Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
     uint64 n, uint64 k, const Eigen::half &alpha,
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.h b/tensorflow/stream_executor/cuda/cuda_blas.h
index 6a33cd746b..4a8641b300 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.h
+++ b/tensorflow/stream_executor/cuda/cuda_blas.h
@@ -118,16 +118,14 @@ class CUDABlas : public blas::BlasSupport {
   // and we want to avoid pulling in a dependency on Eigen.  When we pass the
   // references to cublas, we essentially reinterpret_cast to __half, which is
   // safe because Eigen::half inherits from __half.
-  template <typename T>
-  bool DoBlasGemmWithAlgorithmImpl(Stream *stream, blas::Transpose transa,
-                                   blas::Transpose transb, uint64 m, uint64 n,
-                                   uint64 k, const T &alpha,
-                                   const DeviceMemory<T> &a, int lda,
-                                   const DeviceMemory<T> &b, int ldb,
-                                   const T &beta, DeviceMemory<T> *c, int ldc,
-                                   blas::ComputationType computation_type,
-                                   blas::AlgorithmType algorithm,
-                                   blas::ProfileResult *output_profile_result);
+  template <typename InT, typename OutT, typename CompT>
+  bool DoBlasGemmWithAlgorithmImpl(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, const CompT &alpha, const DeviceMemory<InT> &a,
+      int lda, const DeviceMemory<InT> &b, int ldb, const CompT &beta,
+      DeviceMemory<OutT> *c, int ldc, blas::ComputationType computation_type,
+      blas::AlgorithmType algorithm,
+      blas::ProfileResult *output_profile_result);
 
   // mutex that guards the cuBLAS handle for this device.
   mutex mu_;
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index 97193af777..9b4a4c4fb1 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -3482,6 +3482,27 @@ Stream &Stream::ThenBlasGemmWithAlgorithm(
               algorithm, output_profile_result);
 }
 
+Stream &Stream::ThenBlasGemmWithAlgorithm(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, int alpha, const DeviceMemory<int8> &a, int lda,
+    const DeviceMemory<int8> &b, int ldb, int beta, DeviceMemory<int> *c,
+    int ldc, blas::ComputationType computation_type,
+    blas::AlgorithmType algorithm, blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc), PARAM(computation_type),
+            PARAM(algorithm));
+
+  ThenBlasWithProfileImpl<
+      blas::Transpose, blas::Transpose, uint64, uint64, uint64, int,
+      const DeviceMemory<int8> &, int, const DeviceMemory<int8> &, int, int,
+      DeviceMemory<int> *, int, blas::ComputationType, blas::AlgorithmType>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithAlgorithm, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, computation_type,
+              algorithm, output_profile_result);
+}
+
 Stream &Stream::ThenBlasGemmWithAlgorithm(
     blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
     uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index 2ab3f44af5..ab6b866744 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -1257,6 +1257,15 @@ class Stream {
       const Eigen::half &beta, DeviceMemory<Eigen::half> *c, int ldc,
       blas::ComputationType computation_type, blas::AlgorithmType algorithm,
       blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithAlgorithm(blas::Transpose transa,
+                                    blas::Transpose transb, uint64 m, uint64 n,
+                                    uint64 k, int alpha,
+                                    const DeviceMemory<int8> &a, int lda,
+                                    const DeviceMemory<int8> &b, int ldb,
+                                    int beta, DeviceMemory<int> *c, int ldc,
+                                    blas::ComputationType computation_type,
+                                    blas::AlgorithmType algorithm,
+                                    blas::ProfileResult *output_profile_result);
   Stream &ThenBlasGemmWithAlgorithm(blas::Transpose transa,
                                     blas::Transpose transb, uint64 m, uint64 n,
                                     uint64 k, float alpha,
-- 
GitLab


From 68d666737012dc35cbfc9b7e4bdfac7f027ebf35 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Thu, 6 Jul 2017 15:15:45 -0700
Subject: [PATCH 0772/1375] Fixes a reentrant lock issue with tensors using
 ndarray memory which uses tensor memory.

PiperOrigin-RevId: 161137788
---
 .../python/kernel_tests/py_func_test.py       | 22 ++++++++++++++++---
 .../python/lib/core/ndarray_tensor_bridge.cc  |  9 +++++---
 2 files changed, 25 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py
index e098cf3ff9..43c0fe7837 100644
--- a/tensorflow/python/kernel_tests/py_func_test.py
+++ b/tensorflow/python/kernel_tests/py_func_test.py
@@ -113,8 +113,8 @@ class PyOpTest(test.TestCase):
     # returns a tuple, Tout and inp a tuple
     with self.test_session():
       x = constant_op.constant(0.0, dtypes.float64)
-      y, z = script_ops.py_func(tuple_func, (x,),
-                                (dtypes.float64, dtypes.float64))
+      y, z = script_ops.py_func(tuple_func, (x,), (dtypes.float64,
+                                                   dtypes.float64))
       self.assertAllClose(y.eval(), 0.0)
       self.assertAllClose(z.eval(), 1.0)
 
@@ -194,6 +194,21 @@ class PyOpTest(test.TestCase):
                                    "Unsupported object type"):
         z.eval()
 
+  def testReturnInput(self):
+    with self.test_session():
+
+      def ident(x):
+        return x[0]
+
+      p = array_ops.placeholder(dtypes.float32)
+
+      # Create a numpy array aliasing a tensor and a tensor aliasing this array
+      z, = script_ops.py_func(ident, [p], [dtypes.float32])
+      z += 0.0  # Makes sure we release the tensor aliasing the numpy array x[0]
+                # above instead of using its memory as the return value of
+                # session.run
+      self.assertEqual(0.0, z.eval(feed_dict={p: [0.0]}))
+
   def testStateful(self):
     # Not using self.test_session(), which disables optimization.
     with session_lib.Session() as sess:
@@ -225,7 +240,8 @@ class PyOpTest(test.TestCase):
   def testCOrder(self):
     with self.test_session():
       val = [[1, 2], [3, 4]]
-      x, = script_ops.py_func(lambda: np.array(val, order="F"), [], [dtypes.int64])
+      x, = script_ops.py_func(lambda: np.array(val, order="F"), [],
+                              [dtypes.int64])
       self.assertAllEqual(val, x.eval())
 
   def testParallel(self):
diff --git a/tensorflow/python/lib/core/ndarray_tensor_bridge.cc b/tensorflow/python/lib/core/ndarray_tensor_bridge.cc
index 8934d39e47..f468e0b70e 100644
--- a/tensorflow/python/lib/core/ndarray_tensor_bridge.cc
+++ b/tensorflow/python/lib/core/ndarray_tensor_bridge.cc
@@ -49,11 +49,14 @@ void DelayedNumpyDecref(void* data, size_t len, void* obj) {
 // Actually dereferences cached numpy arrays. REQUIRES being called while
 // holding the GIL.
 void ClearDecrefCache() {
-  mutex_lock ml(*DelayedDecrefLock());
-  for (void* obj : *DecrefCache()) {
+  std::vector<void*> cache_copy;
+  {
+    mutex_lock ml(*DelayedDecrefLock());
+    cache_copy.swap(*DecrefCache());
+  }
+  for (void* obj : cache_copy) {
     Py_DECREF(reinterpret_cast<PyObject*>(obj));
   }
-  DecrefCache()->clear();
 }
 
 // Structure which keeps a reference to a Tensor alive while numpy has a pointer
-- 
GitLab


From ba297aec99a314a4bab9647b024e39666c6d3f3e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 15:19:21 -0700
Subject: [PATCH 0773/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161138258
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 49 +++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 |  4 ++
 2 files changed, 53 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 4da4f6bbb4..8ee032d8bf 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -3144,6 +3144,32 @@ op {
     version: 13
   }
 }
+op {
+  name: "BatchMatrixDeterminant"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  deprecation {
+    version: 13
+  }
+}
 op {
   name: "BatchMatrixDiag"
   input_arg {
@@ -12354,6 +12380,29 @@ op {
     }
   }
 }
+op {
+  name: "MatrixDeterminant"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "MatrixDiag"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 4461244062..d2f65e0d87 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -2949,6 +2949,8 @@ op {
       list {
         type: DT_FLOAT
         type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
       }
     }
   }
@@ -11799,6 +11801,8 @@ op {
       list {
         type: DT_FLOAT
         type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
       }
     }
   }
-- 
GitLab


From f9c9cacb06964f68737bf78f51e61452a3b480a8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 15:36:56 -0700
Subject: [PATCH 0774/1375] Refactor ElementalIrEmitter's slice index finding
 code into IrArray::Index::SourceIndexOfSlice().

PiperOrigin-RevId: 161140653
---
 .../xla/service/elemental_ir_emitter.cc       | 20 +++--------------
 .../compiler/xla/service/llvm_ir/ir_array.cc  | 22 +++++++++++++++++++
 .../compiler/xla/service/llvm_ir/ir_array.h   | 10 +++++++++
 3 files changed, 35 insertions(+), 17 deletions(-)

diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index 0a45e52811..9c4380b39d 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -948,23 +948,9 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
     case HloOpcode::kSlice:
       return [this, hlo, &operand_to_generator](
                  const IrArray::Index& index) -> StatusOr<llvm::Value*> {
-        IrArray::Index sliced_index(index.size());
-        for (int i = 0; i < index.size(); ++i) {
-          int64 stride = hlo->slice_stride(i);
-          if (stride != 1) {
-            sliced_index[i] = ir_builder_->CreateAdd(
-                ir_builder_->CreateMul(
-                    index[i], llvm::ConstantInt::get(index[i]->getType(),
-                                                     stride)),
-                llvm::ConstantInt::get(index[i]->getType(),
-                                       hlo->slice_starts(i)));
-          } else {
-            sliced_index[i] = ir_builder_->CreateAdd(
-                    index[i],
-                    llvm::ConstantInt::get(index[i]->getType(),
-                                           hlo->slice_starts(i)));
-          }
-        }
+        IrArray::Index sliced_index = index.SourceIndexOfSlice(
+            /*shape=*/hlo->shape(), /*starts=*/hlo->slice_starts(),
+            /*strides=*/hlo->slice_strides(), /*builder=*/ir_builder_);
         return operand_to_generator.at(hlo->operand(0))(sliced_index);
       };
     case HloOpcode::kDynamicSlice:
diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index 5268e89e0f..dd13a39232 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -153,6 +153,28 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape(
   return Index(source_multidim_index);
 }
 
+IrArray::Index IrArray::Index::SourceIndexOfSlice(
+    const Shape& shape, tensorflow::gtl::ArraySlice<int64> starts,
+    tensorflow::gtl::ArraySlice<int64> strides,
+    llvm::IRBuilder<>* builder) const {
+  Index source_index(multidim_.size());
+  for (int i = 0; i < multidim_.size(); ++i) {
+    int64 stride = strides[i];
+    auto type = multidim_[i]->getType();
+
+    if (stride != 1) {
+      source_index[i] = builder->CreateAdd(
+          builder->CreateMul(multidim_[i],
+                             llvm::ConstantInt::get(type, stride)),
+          llvm::ConstantInt::get(type, starts[i]));
+    } else {
+      source_index[i] = builder->CreateAdd(
+          multidim_[i], llvm::ConstantInt::get(type, starts[i]));
+    }
+  }
+  return source_index;
+}
+
 IrArray::Index IrArray::Index::SourceIndexOfTranspose(
     const Shape& shape, const Shape& operand_shape,
     tensorflow::gtl::ArraySlice<int64> dimension_mapping,
diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h
index 91cb3a679f..e72f651872 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h
@@ -115,6 +115,16 @@ class IrArray {
     Index SourceIndexOfReshape(const Shape& shape, const Shape& operand_shape,
                                llvm::IRBuilder<>* builder) const;
 
+    // Returns the index into the source operand from which a slice operation
+    // selects a value to be placed into index "this". The slice is described
+    // by starting indices `starts` and stride values `strides`.
+    //
+    // Precondition: "this" is an index into a slice whose shape is `shape`.
+    Index SourceIndexOfSlice(const Shape& shape,
+                             tensorflow::gtl::ArraySlice<int64> starts,
+                             tensorflow::gtl::ArraySlice<int64> strides,
+                             llvm::IRBuilder<>* builder) const;
+
     // Given that "this" is the target index of a transpose from `operand_shape`
     // to `shape` with the given dimension mapping, returns the source index.
     Index SourceIndexOfTranspose(
-- 
GitLab


From eb8bebd6709374dc6961e350d9988b8c15b42d31 Mon Sep 17 00:00:00 2001
From: Igor Saprykin <isaprykin@google.com>
Date: Thu, 6 Jul 2017 15:59:14 -0700
Subject: [PATCH 0775/1375] Fix ValueError("None values not supported.") in the
 Abalone tutorial.

PiperOrigin-RevId: 161143481
---
 tensorflow/docs_src/extend/estimators.md      |  9 +++++++--
 .../examples/tutorials/estimators/abalone.py  | 19 ++++++++++++-------
 2 files changed, 19 insertions(+), 9 deletions(-)

diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index a18da1708d..5265e5889b 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -578,7 +578,12 @@ def model_fn(features, labels, mode, params):
 
   # Reshape output layer to 1-dim Tensor to return predictions
   predictions = tf.reshape(output_layer, [-1])
-  predictions_dict = {"ages": predictions}
+
+  # Provide an estimator spec for `ModeKeys.PREDICT`.
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={"ages": predictions})
 
   # Calculate loss using mean squared error
   loss = tf.losses.mean_squared_error(labels, predictions)
@@ -594,9 +599,9 @@ def model_fn(features, labels, mode, params):
   train_op = optimizer.minimize(
       loss=loss, global_step=tf.train.get_global_step())
 
+  # Provide an estimator spec for `ModeKeys.EVAL` and `ModeKeys.TRAIN` modes.
   return tf.estimator.EstimatorSpec(
       mode=mode,
-      predictions=predictions_dict,
       loss=loss,
       train_op=train_op,
       eval_metric_ops=eval_metric_ops)
diff --git a/tensorflow/examples/tutorials/estimators/abalone.py b/tensorflow/examples/tutorials/estimators/abalone.py
index 4765d5dabf..737b3ee5d6 100644
--- a/tensorflow/examples/tutorials/estimators/abalone.py
+++ b/tensorflow/examples/tutorials/estimators/abalone.py
@@ -87,25 +87,30 @@ def model_fn(features, labels, mode, params):
 
   # Reshape output layer to 1-dim Tensor to return predictions
   predictions = tf.reshape(output_layer, [-1])
-  predictions_dict = {"ages": predictions}
+
+  # Provide an estimator spec for `ModeKeys.PREDICT`.
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions={"ages": predictions})
 
   # Calculate loss using mean squared error
   loss = tf.losses.mean_squared_error(labels, predictions)
 
+  optimizer = tf.train.GradientDescentOptimizer(
+      learning_rate=params["learning_rate"])
+  train_op = optimizer.minimize(
+      loss=loss, global_step=tf.train.get_global_step())
+
   # Calculate root mean squared error as additional eval metric
   eval_metric_ops = {
       "rmse": tf.metrics.root_mean_squared_error(
           tf.cast(labels, tf.float64), predictions)
   }
 
-  optimizer = tf.train.GradientDescentOptimizer(
-      learning_rate=params["learning_rate"])
-  train_op = optimizer.minimize(
-      loss=loss, global_step=tf.train.get_global_step())
-
+  # Provide an estimator spec for `ModeKeys.EVAL` and `ModeKeys.TRAIN` modes.
   return tf.estimator.EstimatorSpec(
       mode=mode,
-      predictions=predictions_dict,
       loss=loss,
       train_op=train_op,
       eval_metric_ops=eval_metric_ops)
-- 
GitLab


From 0ffc3789071428b433ffce19dd809341e423bb35 Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Thu, 6 Jul 2017 16:47:48 -0700
Subject: [PATCH 0776/1375] Removing second declaration of functions.

---
 tensorflow/go/op/wrappers.go | 48 ------------------------------------
 1 file changed, 48 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index d80e7e500c..9e9656a394 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14195,54 +14195,6 @@ func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
   return op.Output(0)
 }
 
-// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad.
-type AvgPool3DGradAttr func(optionalAttr)
-
-// AvgPool3DGradDataFormat sets the optional data_format attribute to value.
-//
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-//     [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-//     [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr {
-	return func(m optionalAttr) {
-		m["data_format"] = value
-	}
-}
-
-// Computes gradients of average pooling function.
-//
-// Arguments:
-//	orig_input_shape: The original input dimensions.
-//	grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
-//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-//	strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-//	padding: The type of padding algorithm to use.
-//
-// Returns The backprop for input.
-func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "AvgPool3DGrad",
-		Input: []tf.Input{
-			orig_input_shape, grad,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
 type TextLineReaderV2Attr func(optionalAttr)
 
-- 
GitLab


From b031107cdfa13a16b5f8ad2e4fe41d60abb6d0d3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 17:28:37 -0700
Subject: [PATCH 0777/1375] VirtualPlacer use default device localhost if
 device is empty.

PiperOrigin-RevId: 161153235
---
 .../core/grappler/costs/virtual_placer.cc     |  3 +++
 .../grappler/costs/virtual_placer_test.cc     | 23 +++++++++++++++++++
 2 files changed, 26 insertions(+)

diff --git a/tensorflow/core/grappler/costs/virtual_placer.cc b/tensorflow/core/grappler/costs/virtual_placer.cc
index a3dfb278e2..64e2778fc9 100644
--- a/tensorflow/core/grappler/costs/virtual_placer.cc
+++ b/tensorflow/core/grappler/costs/virtual_placer.cc
@@ -77,6 +77,9 @@ string VirtualPlacer::get_canonical_device_name(const NodeDef& node) const {
     if (!parsed) {
       return get_default_device_name();
     } else {
+      if (parsed_name.job.empty()) {
+        parsed_name.job = "localhost";
+      }
       device = strings::StrCat(
           "/job:", parsed_name.job, "/replica:", parsed_name.replica,
           "/task:", parsed_name.task, "/",
diff --git a/tensorflow/core/grappler/costs/virtual_placer_test.cc b/tensorflow/core/grappler/costs/virtual_placer_test.cc
index 31592cd822..65a03fb557 100644
--- a/tensorflow/core/grappler/costs/virtual_placer_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_placer_test.cc
@@ -51,6 +51,29 @@ TEST(VirtualPlacerTest, LocalDevices) {
             placer.get_canonical_device_name(node));
 }
 
+TEST(VirtualPlacerTest, EmptyJobBecomesLocalhost) {
+  // Virtual placer should use "localhost" if device is empty.
+  // First create a cluster with only localhost devices.
+  std::unordered_map<string, DeviceProperties> devices;
+  DeviceProperties cpu_device;
+  cpu_device.set_type("CPU");
+  devices["/job:localhost/replica:0/task:0/cpu:0"] = cpu_device;
+  DeviceProperties gpu_device;
+  gpu_device.set_type("GPU");
+  devices["/job:localhost/replica:0/task:0/gpu:0"] = gpu_device;
+  VirtualCluster cluster(devices);
+  VirtualPlacer placer(&cluster);
+
+  NodeDef node;
+  node.set_op("Conv2D");
+  node.set_device("/device:CPU:0");
+  EXPECT_EQ("/job:localhost/replica:0/task:0/cpu:0",
+            placer.get_canonical_device_name(node));
+  node.set_device("/device:GPU:0");
+  EXPECT_EQ("/job:localhost/replica:0/task:0/gpu:0",
+            placer.get_canonical_device_name(node));
+}
+
 TEST(VirtualPlacerTest, FallBackUnknown) {
   // Virtual placer falls back to "UNKNOWN" only if there are no devices in the
   // cluster.
-- 
GitLab


From 3926a3aca125c42987adc602a5cb006d97b0261e Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Thu, 6 Jul 2017 18:14:10 -0700
Subject: [PATCH 0778/1375] TFTS: Fix race conditions in adaptive statistics.

Switches variables to ResourceVariables. Existing control dependencies are then
sufficient for preventing single-machine race conditions.

PiperOrigin-RevId: 161157061
---
 .../contrib/timeseries/python/timeseries/math_utils.py       | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/math_utils.py b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
index b7832a10cd..e65e3eab3d 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
@@ -696,9 +696,10 @@ class InputStatisticsFromMiniBatch(object):
     # Create/retreive variables representing input statistics, initialized
     # without data to avoid deadlocking if variables are initialized before
     # queue runners are started.
-    with variable_scope.variable_scope("input_statistics"):
+    with variable_scope.variable_scope("input_statistics", use_resource=True):
       statistics = self._create_variable_statistics_object()
-    with variable_scope.variable_scope("input_statistics_auxiliary"):
+    with variable_scope.variable_scope(
+        "input_statistics_auxiliary", use_resource=True):
       # Secondary statistics, necessary for the incremental computation of the
       # primary statistics (e.g. counts and sums for computing a mean
       # incrementally).
-- 
GitLab


From 172b8740236c88acf06fc9fa01e8ca52e5482edf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 18:15:00 -0700
Subject: [PATCH 0779/1375] Make the pruning of the graph in ahead-of-time
 compilation also prune out dependencies of fed tensors.

Remove special handling of control dependencies from the introduced
placeholders.  For a compilation going to XLA, those control dependencies would
not really be doing anything anyway (and the current code was only handling
Placeholder, not PlaceholderV2).

PiperOrigin-RevId: 161157126
---
 tensorflow/compiler/aot/BUILD                 |  25 +++-
 tensorflow/compiler/aot/compile.cc            |  91 +++++++-------
 .../aot/test_graph_tfadd.config.pbtxt         |   2 +-
 .../compiler/aot/test_graph_tfadd.pbtxt       |  31 ++---
 .../compiler/aot/test_graph_tfunknownop.pbtxt |  54 ++------
 .../aot/test_graph_tfunknownop2.config.pbtxt  |  25 ++++
 .../aot/test_graph_tfunknownop3.config.pbtxt  |  26 ++++
 tensorflow/compiler/aot/tfcompile.proto       |   7 ++
 tensorflow/compiler/aot/tfcompile_util.cc     | 117 +++++++++++++++++-
 tensorflow/compiler/aot/tfcompile_util.h      |  15 +++
 10 files changed, 284 insertions(+), 109 deletions(-)
 create mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
 create mode 100644 tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 12179b7637..b12b5318ec 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -156,7 +156,7 @@ tf_library(
 )
 
 # A test of tf_library that includes a graph with an unknown op, but where
-# the compilation works because the the unknown op is not needed for the fetches.
+# the compilation works because the unknown op is not needed for the fetches.
 tf_library(
     name = "test_graph_tfunknownop",
     testonly = 1,
@@ -166,6 +166,29 @@ tf_library(
     tags = ["manual"],
 )
 
+# A test of tf_library that includes a graph with an unknown op, but where
+# the compilation works because the op between the unknown op and the
+# fetches is a feed.
+tf_library(
+    name = "test_graph_tfunknownop2",
+    testonly = 1,
+    config = "test_graph_tfunknownop2.config.pbtxt",
+    cpp_class = "UnknownOpAddComp",
+    graph = "test_graph_tfunknownop.pbtxt",
+    tags = ["manual"],
+)
+
+# A test of tf_library that includes a graph with an unknown op, but where
+# the compilation works because the unknown op is fed.
+tf_library(
+    name = "test_graph_tfunknownop3",
+    testonly = 1,
+    config = "test_graph_tfunknownop3.config.pbtxt",
+    cpp_class = "UnknownOpAddComp",
+    graph = "test_graph_tfunknownop.pbtxt",
+    tags = ["manual"],
+)
+
 # Utility library for benchmark binaries, used by the *_benchmark rules that are
 # added by the tfcompile bazel macro.
 cc_library(
diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc
index 59ff14600b..51d08eaa01 100644
--- a/tensorflow/compiler/aot/compile.cc
+++ b/tensorflow/compiler/aot/compile.cc
@@ -78,66 +78,51 @@ Status DumpGraph(const MainFlags& flags, const string& name,
   return WriteTextProto(Env::Default(), file, graph_def);
 }
 
-string TensorIdToString(const TensorId& id) {
-  return strings::StrCat(id.node_name(), ":", id.output_index());
-}
-
 typedef std::unordered_map<string, Node*> NodeMap;
 
 // Each feed id identifies the positional output of some node, which may consist
-// of multiple edges.  For each feed node, replaces all matching edges so that
-// they point from a new _Arg node instead.
+// of multiple edges. AddPlaceholdersForFeeds has already replaced each fed
+// tensor with a placeholder.  For each feed tensor, replaces all edges so they
+// point from a new _Arg node instead.
 Status AddArgNodes(Graph* graph, const NodeMap& node_map,
-                   const protobuf::RepeatedPtrField<Feed>& feeds) {
+                   const protobuf::RepeatedPtrField<Feed>& feeds,
+                   const std::unordered_map<string, string>& feed_remapping) {
   for (int arg_index = 0; arg_index < feeds.size(); ++arg_index) {
     const Feed& feed = feeds[arg_index];
-    const TensorId& id = feed.id();
-    auto it = node_map.find(id.node_name());
-    if (it == node_map.end()) {
-      return errors::NotFound("Can't find feed id: ", TensorIdToString(id));
-    }
-    const Node* feed_node = it->second;
-    if (id.output_index() >= feed_node->num_outputs()) {
-      return errors::InvalidArgument("Invalid feed id: ", TensorIdToString(id),
-                                     ", output index should be < ",
-                                     feed_node->num_outputs());
-    }
-    // TODO(toddw): Invoke shape inference on the graph and add a "_shape" attr
-    // if we can determine it.  That way the graph will be initialized with
-    // whatever shapes we can infer, while the user can still explicitly specify
-    // or override them.
+    // All feeds have been replaced by placeholders.
+    const int output_index = 0;
+
+    const auto remap_it = feed_remapping.find(TensorIdToString(feed.id()));
+    auto node_it = node_map.find(remap_it->second);
+    const Node* feed_node = node_it->second;
+
+    // TODO(toddw): Invoke shape inference in AddPlaceholdersForFeeds and add a
+    // "_shape" attr if we can determine it.  That way the graph will be
+    // initialized with whatever shapes we can infer, while the user can still
+    // explicitly specify or override them.
     Node* arg_node = nullptr;
     TF_RETURN_IF_ERROR(
         NodeBuilder(strings::StrCat("_arg_", arg_index), kArgOp)
-            .Attr("T", BaseType(feed_node->output_type(id.output_index())))
+            .Attr("T", BaseType(feed_node->output_type(output_index)))
             .Attr("index", arg_index)
-            .Attr(kFeedIdAttr, TensorIdToString(id))
+            .Attr(kFeedIdAttr, TensorIdToString(feed.id()))
             .Attr(kShapeAttr, TensorShape(feed.shape()))
             .Attr(kDebugNameAttr, feed.name())
             .Finalize(graph, &arg_node));
+
     // Collects out-edges from the feed node that have a matching edge index;
-    // these will be replaced with edges from the arg node instead.  Also
-    // replaces all control edges from Placeholder feed nodes; similar code
-    // exists in subgraph::RewriteGraphForExecution.
-    // TODO(toddw): Why only replace control edges from Placeholder?
+    // these will be replaced with edges from the arg node instead.
     //
     // We must collect the edges first and process them in a second pass, since
     // removing the edge from the graph invalidates feed_node->out_edges.
     std::vector<const Edge*> feed_edges;
     for (const Edge* edge : feed_node->out_edges()) {
-      if (edge->src_output() == id.output_index() ||
-          (edge->src_output() == Graph::kControlSlot &&
-           feed_node->type_string() == "Placeholder")) {
+      if (edge->src_output() == output_index) {
         feed_edges.push_back(edge);
       }
     }
     for (const Edge* edge : feed_edges) {
-      if (edge->src_output() == id.output_index()) {
-        graph->AddEdge(arg_node, 0, edge->dst(), edge->dst_input());
-      } else {
-        CHECK_EQ(edge->src_output(), Graph::kControlSlot);
-        graph->AddControlEdge(arg_node, edge->dst());
-      }
+      graph->AddEdge(arg_node, 0, edge->dst(), edge->dst_input());
       graph->RemoveEdge(edge);
     }
   }
@@ -179,13 +164,16 @@ Status AddRetvalNodes(Graph* graph, const NodeMap& node_map,
 // fetch ids respectively), and rewrites the edges so that inputs flow from _Arg
 // nodes, and outputs flow to _Retval nodes.  This allows the symbolic graph
 // execution to know the input and output args for the generated function.
-Status RewriteAndPruneGraph(Graph* graph, const Config& config,
-                            const MainFlags& flags) {
+Status RewriteAndPruneGraph(
+    Graph* graph, const Config& config,
+    const std::unordered_map<string, string>& feed_remapping,
+    const MainFlags& flags) {
   NodeMap node_map;
   for (Node* n : graph->nodes()) {
     node_map[n->name()] = n;
   }
-  TF_RETURN_IF_ERROR(AddArgNodes(graph, node_map, config.feed()));
+  TF_RETURN_IF_ERROR(
+      AddArgNodes(graph, node_map, config.feed(), feed_remapping));
   std::unordered_set<const Node*> retval_nodes;
   TF_RETURN_IF_ERROR(
       AddRetvalNodes(graph, node_map, config.fetch(), &retval_nodes));
@@ -383,17 +371,28 @@ Status InitGraph(const GraphDef& graph_def, const Config& config,
   FunctionLibraryDefinition flib_def(OpRegistry::Global(), graph_def.library());
   std::unique_ptr<Graph> g(new Graph(flib_def));
 
-  GraphDef copy_def;
+  // Replace references to fed tensors with references to newly added
+  // placeholders.
+  GraphDef first_copy_def = graph_def;
+
+  // Maps from name:port of a feed to the name:port of the placeholder to use.
+  std::unordered_map<string, string> feed_remapping;
+  TF_RETURN_IF_ERROR(AddPlaceholdersForFeeds(config, g->op_registry(),
+                                             &feed_remapping, &first_copy_def));
 
   // Prune the GraphDef first so that unknown ops that we aren't compiling get
   // filtered out.
-  TF_RETURN_IF_ERROR(PruneGraphDefInto(config, graph_def, &copy_def));
+  GraphDef second_copy_def;
+  TF_RETURN_IF_ERROR(
+      PruneGraphDefInto(config, first_copy_def, &second_copy_def));
+
+  TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(
+      &second_copy_def, *g->op_registry(), 0 /*node_offset*/));
 
-  TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&copy_def, *g->op_registry(),
-                                               0 /*node_offset*/));
+  TF_RETURN_IF_ERROR(ConvertGraphDefToGraph(GraphConstructorOptions(),
+                                            second_copy_def, g.get()));
   TF_RETURN_IF_ERROR(
-      ConvertGraphDefToGraph(GraphConstructorOptions(), copy_def, g.get()));
-  TF_RETURN_IF_ERROR(RewriteAndPruneGraph(g.get(), config, flags));
+      RewriteAndPruneGraph(g.get(), config, feed_remapping, flags));
   *graph = std::move(g);
   return Status::OK();
 }
diff --git a/tensorflow/compiler/aot/test_graph_tfadd.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfadd.config.pbtxt
index 5625c0ab03..f2d9c34b2d 100644
--- a/tensorflow/compiler/aot/test_graph_tfadd.config.pbtxt
+++ b/tensorflow/compiler/aot/test_graph_tfadd.config.pbtxt
@@ -6,7 +6,7 @@ feed {
   }
 }
 feed {
-  id { node_name: "y_const" }
+  id { node_name: "y_reshape" }
   shape {
     dim { size: 1 }
   }
diff --git a/tensorflow/compiler/aot/test_graph_tfadd.pbtxt b/tensorflow/compiler/aot/test_graph_tfadd.pbtxt
index 91c900e06d..665c9fe287 100644
--- a/tensorflow/compiler/aot/test_graph_tfadd.pbtxt
+++ b/tensorflow/compiler/aot/test_graph_tfadd.pbtxt
@@ -4,15 +4,7 @@ node {
   attr {
     key: "value"
     value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
-        int_val: 1
-      }
+      tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } int_val: 1 }
     }
   }
   attr {
@@ -28,15 +20,7 @@ node {
   attr {
     key: "value"
     value {
-      tensor {
-        dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
-        int_val: 2
-      }
+      tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } int_val: 2 }
     }
   }
   attr {
@@ -46,11 +30,20 @@ node {
     }
   }
 }
+node {
+  name  : "y_reshape"
+  op    : "Reshape"
+  input : "y_const"
+  input : "y_shape"
+  attr { key: "T" value { type: DT_INT32 } }
+  # Attribute TShape not specified; needs to be set to its default
+  # by tfcompile.
+}
 node {
   name  : "x_y_sum"
   op    : "Add"
   input : "x_const"
-  input : "y_const"
+  input : "y_reshape"
   attr {
     key  : "T"
     value {
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
index 212ffbb5ff..48b881bb94 100644
--- a/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop.pbtxt
@@ -6,21 +6,12 @@ node {
     value {
       tensor {
         dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
+        tensor_shape { dim { size: 1 } }
         int_val: 1
       }
     }
   }
-  attr {
-    key  : "dtype"
-    value {
-      type       : DT_INT32
-    }
-  }
+  attr { key  : "dtype" value { type: DT_INT32 } }
 }
 node {
   name  : "y_const"
@@ -30,56 +21,37 @@ node {
     value {
       tensor {
         dtype: DT_INT32
-        tensor_shape {
-          dim {
-            size: 1
-          }
-        }
+        tensor_shape { dim { size: 1 } }
         int_val: 2
       }
     }
   }
-  attr {
-    key: "dtype"
-    value {
-      type: DT_INT32
-    }
-  }
+  attr { key: "dtype" value { type: DT_INT32 } }
 }
 node {
   name  : "x_y_sum"
   op    : "Add"
   input : "x_const"
   input : "y_const"
-  attr {
-    key  : "T"
-    value {
-      type: DT_INT32
-    }
-  }
+  attr { key  : "T" value { type: DT_INT32 } }
 }
 node {
   name  : "z"
   op    : "SomeUnknownOp"
   input : "x_const"
-  attr {
-    key  : "T"
-    value {
-      type: DT_INT32
-    }
-  }
+}
+node {
+  name  : "z_identity"
+  op    : "Identity"
+  input : "z:1"
+  attr { key  : "T" value { type: DT_INT32 } }
 }
 node {
   name  : "x_z_sum"
   op    : "Add"
   input : "x_const"
-  input : "z"
-  attr {
-    key  : "T"
-    value {
-      type: DT_INT32
-    }
-  }
+  input : "z_identity"
+  attr { key  : "T" value { type: DT_INT32 } }
 }
 versions {
   producer: 15
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
new file mode 100644
index 0000000000..7370ed370d
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop2.config.pbtxt
@@ -0,0 +1,25 @@
+# Text form of tensorflow.tfcompile.Config proto.
+feed {
+  id { node_name: "x_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "y_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "z_identity"}
+  shape {
+    dim { size: 1 }
+  }
+}
+fetch {
+  id { node_name: "x_y_sum" }
+}
+fetch {
+  id { node_name: "x_z_sum" }
+}
diff --git a/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt b/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt
new file mode 100644
index 0000000000..b2d7d54574
--- /dev/null
+++ b/tensorflow/compiler/aot/test_graph_tfunknownop3.config.pbtxt
@@ -0,0 +1,26 @@
+# Text form of tensorflow.tfcompile.Config proto.
+feed {
+  id { node_name: "x_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "y_const" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "z" output_index: 1}
+  shape {
+    dim { size: 1 }
+  }
+  type: DT_INT32
+}
+fetch {
+  id { node_name: "x_y_sum" }
+}
+fetch {
+  id { node_name: "x_z_sum" }
+}
diff --git a/tensorflow/compiler/aot/tfcompile.proto b/tensorflow/compiler/aot/tfcompile.proto
index be3f504350..cd83840d89 100644
--- a/tensorflow/compiler/aot/tfcompile.proto
+++ b/tensorflow/compiler/aot/tfcompile.proto
@@ -7,6 +7,7 @@ option java_multiple_files = true;
 option java_package = "org.tensorflow.tfcompile";
 
 import "tensorflow/core/framework/tensor_shape.proto";
+import "tensorflow/core/framework/types.proto";
 
 // TensorId identifies a tensor in a TensorFlow graph, by specifying the output
 // index of a particular node in the graph.  If the output of the named node
@@ -23,6 +24,12 @@ message Feed {
   TensorId id = 1;
   TensorShapeProto shape = 2;
   string name = 3;  // Optional name for generated code.
+
+  // Optional data type. This is not normally required, as the graph itself
+  // contains this information. However, if the node being fed is an op that
+  // is not linked into the tfcompile binary, then the type cannot be inferred
+  // from the node; in this case, the type should be set here.
+  DataType type = 4;
 };
 
 // Fetch represents a single fetch tensor in the graph, which corresponds to an
diff --git a/tensorflow/compiler/aot/tfcompile_util.cc b/tensorflow/compiler/aot/tfcompile_util.cc
index 8774a02128..e6a4705b6c 100644
--- a/tensorflow/compiler/aot/tfcompile_util.cc
+++ b/tensorflow/compiler/aot/tfcompile_util.cc
@@ -20,12 +20,14 @@ limitations under the License.
 #include <unordered_map>
 
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
+#include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/graph/tensor_id.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 
 namespace tensorflow {
 namespace tfcompile {
@@ -119,17 +121,115 @@ Status ValidateConfig(const Config& config) {
   return Status::OK();
 }
 
+Status AddPlaceholdersForFeeds(
+    const Config& config, const OpRegistryInterface* op_registry,
+    std::unordered_map<string, string>* feed_remapping, GraphDef* graph_def) {
+  struct PlaceholderInfo {
+    const Feed* feed = nullptr;  // point to Feed in <config>.
+    string placeholder_name;
+    DataType data_type = DT_INVALID;
+  };
+
+  // Put each fed tensor into a map by name:port. A map is used for determinism
+  // when creating placeholders (genrules want deterministic output).
+  std::map<string, PlaceholderInfo> placeholder_info;
+  for (int i = 0; i < config.feed_size(); ++i) {
+    const Feed* feed = &config.feed(i);
+    const string name_port = TensorIdToString(feed->id());
+    auto& info = placeholder_info[name_port];
+    info.feed = feed;
+    info.placeholder_name = strings::StrCat(
+        "aot_feed_", feed->id().output_index(), "/", feed->id().node_name());
+    (*feed_remapping)[name_port] = info.placeholder_name;
+  }
+
+  // Verify node exists and determine data type.
+  std::unordered_map<string, const NodeDef*> name_to_node;
+  for (int i = 0; i < graph_def->node_size(); ++i) {
+    name_to_node[graph_def->node(i).name()] = &graph_def->node(i);
+  }
+  for (auto it = placeholder_info.begin(); it != placeholder_info.end(); ++it) {
+    PlaceholderInfo& info = it->second;
+    const TensorId& feed_id = info.feed->id();
+
+    // Find the existing node and determine data type.
+    auto node_it = name_to_node.find(feed_id.node_name());
+    if (node_it == name_to_node.end()) {
+      return errors::NotFound("Can't find feed node: ",
+                              TensorIdToString(feed_id));
+    }
+    const NodeDef* existing = node_it->second;
+
+    if (info.feed->type() != DT_INVALID) {
+      info.data_type = info.feed->type();
+    } else {
+      // Build the node in order to infer its type.
+
+      // Must first add default attrs as well, so do this in a copied GraphDef.
+      GraphDef gd;
+      *gd.mutable_versions() = graph_def->versions();
+      *gd.add_node() = *existing;
+      TF_RETURN_IF_ERROR(
+          AddDefaultAttrsToGraphDef(&gd, *op_registry, 0 /*node_offset*/));
+
+      // Now build the node from the copied node def.
+      Graph g(op_registry);
+      g.set_versions(graph_def->versions());
+      Status status;
+      Node* feed_node = g.AddNode(gd.node(0), &status);
+      TF_RETURN_IF_ERROR(status);
+      info.data_type =
+          BaseType(feed_node->output_type(info.feed->id().output_index()));
+    }
+  }
+
+  // Create placeholders. Note that we could avoid creating a placeholder for
+  // feeds which are already placeholders, but we omit that to avoid more cases
+  // in this code.
+  for (auto it = placeholder_info.begin(); it != placeholder_info.end(); ++it) {
+    const PlaceholderInfo& info = it->second;
+    NodeDef* d = graph_def->add_node();
+    d->set_name(info.placeholder_name);
+    d->set_op("PlaceholderV2");
+    auto& attr_map = *d->mutable_attr();
+    attr_map["dtype"].set_type(info.data_type);
+    *attr_map["shape"].mutable_shape() = info.feed->shape();
+  }
+
+  // Rewrite references to the fed tensors to refer to the placeholder.
+  for (int i = 0; i < graph_def->node_size(); ++i) {
+    NodeDef* node_def = graph_def->mutable_node(i);
+    for (int j = 0; j < node_def->input_size(); ++j) {
+      auto id = ParseTensorName(node_def->input(j));
+      auto it = placeholder_info.find(id.ToString());
+      if (it != placeholder_info.end()) {
+        node_def->set_input(j, it->second.placeholder_name);
+      }
+    }
+  }
+
+  return Status::OK();
+}
+
 Status PruneGraphDefInto(const Config& config, const GraphDef& in,
                          GraphDef* out) {
   *out = in;
   out->clear_node();
 
+  // Tensors needed for feeding.
+  std::set<std::pair<string, int>> feed_tensors;
+  for (const auto& feed_config : config.feed()) {
+    feed_tensors.insert(std::make_pair(feed_config.id().node_name(),
+                                       feed_config.id().output_index()));
+  }
+
   // Maps node name to reachability.
   std::unordered_map<string, std::pair<bool, const NodeDef*>> node_by_name;
   for (const NodeDef& node : in.node()) {
     node_by_name[node.name()] = std::pair<bool, const NodeDef*>(false, &node);
   }
 
+  // Traverse.
   std::queue<string> name_queue;
   for (int i = 0; i < config.fetch_size(); ++i) {
     name_queue.push(config.fetch(i).id().node_name());
@@ -149,8 +249,19 @@ Status PruneGraphDefInto(const Config& config, const GraphDef& in,
     }
     map_entry.first = true;
 
+    // Push input nodes of the currently visited node to name_queue.
     for (const string& in_edge : map_entry.second->input()) {
-      name_queue.push(ParseTensorName(in_edge).first.ToString());
+      auto id = ParseTensorName(in_edge);
+      const string node_name = id.first.ToString();
+      if (feed_tensors.find(std::make_pair(node_name, id.second)) ==
+          feed_tensors.end()) {
+        name_queue.push(node_name);
+      } else {
+        // The input tensor is from an edge that is being fed. Therefore,
+        // we skip recursing down that edge, to avoid requiring nodes that
+        // may not be needed (note that the input node may still be added
+        // to name_queue later if one of its output edges is not being fed).
+      }
     }
   }
 
@@ -165,5 +276,9 @@ Status PruneGraphDefInto(const Config& config, const GraphDef& in,
   return Status::OK();
 }
 
+string TensorIdToString(const TensorId& id) {
+  return strings::StrCat(id.node_name(), ":", id.output_index());
+}
+
 }  // namespace tfcompile
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/aot/tfcompile_util.h b/tensorflow/compiler/aot/tfcompile_util.h
index 84060c0761..365f7b0e7b 100644
--- a/tensorflow/compiler/aot/tfcompile_util.h
+++ b/tensorflow/compiler/aot/tfcompile_util.h
@@ -16,8 +16,11 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 #define TENSORFLOW_COMPILER_AOT_TFCOMPILE_UTIL_H_
 
+#include <unordered_map>
+
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 
@@ -31,11 +34,23 @@ Status ValidateCppIdent(StringPiece ident, StringPiece msg);
 // ValidateConfig returns OK iff config is valid.
 Status ValidateConfig(const Config& config);
 
+// Modifies <graph_def> to include placeholders for each fed tensor, and
+// update references to the fed tensors to refer to the placeholders.
+// The existing nodes referenced by the feeds are not removed or modified
+// (except where their input edges are modified by the replacement of other
+// feeds).
+Status AddPlaceholdersForFeeds(
+    const Config& config, const OpRegistryInterface* op_registry,
+    std::unordered_map<string, string>* feed_remapping, GraphDef* graph_def);
+
 // Returns in <out> a copy of <in>, pruned to only include fetches from
 // <config>.
 Status PruneGraphDefInto(const Config& config, const GraphDef& in,
                          GraphDef* out);
 
+// Returns node:port for the given <id>.
+string TensorIdToString(const TensorId& id);
+
 }  // namespace tfcompile
 }  // namespace tensorflow
 
-- 
GitLab


From 4ea68da041c78476f561fdff53a85ae05b737ae6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 18:18:19 -0700
Subject: [PATCH 0780/1375] Make ClientLibraryTestBase able to test all
 combinations of output layouts.

PiperOrigin-RevId: 161157385
---
 .../xla/legacy_flags/debug_options_flags.cc   | 11 +++++
 .../xla/tests/client_library_test_base.cc     | 47 +++++++++++++++++--
 .../xla/tests/client_library_test_base.h      |  7 +++
 tensorflow/compiler/xla/xla.proto             |  6 +++
 4 files changed, 67 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 2f3ec403a0..9442a16ee5 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -48,6 +48,8 @@ struct DebugOptionsFlags {
   string xla_gpu_cuda_data_dir;
   bool xla_gpu_ftz;
 
+  bool xla_test_all_output_layouts;
+
   string xla_backend_extra_options;
 };
 
@@ -81,6 +83,7 @@ void AllocateFlags() {
   flag_values->xla_cpu_multi_thread_eigen = true;
   flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
   flag_values->xla_gpu_ftz = false;
+  flag_values->xla_test_all_output_layouts = false;
   flag_values->xla_backend_extra_options = "";
 
   flag_objects = new std::vector<tensorflow::Flag>(
@@ -159,6 +162,12 @@ void AllocateFlags() {
        tensorflow::Flag(
            "xla_dump_debug_json_to", &flag_values->xla_dump_debug_json_to,
            "Dump compilation artifacts as JSON into this directory."),
+       tensorflow::Flag("xla_test_all_output_layouts",
+                        &flag_values->xla_test_all_output_layouts,
+                        "Let ClientLibraryTestBase::ComputeAndCompare* test "
+                        "all permutations of output layouts. For example, with "
+                        "a 3D shape, all permutations of the set {0, 1, 2} are "
+                        "tried."),
        tensorflow::Flag("xla_backend_extra_options",
                         &flag_values->xla_backend_extra_options,
                         "Extra options to pass to a backend; "
@@ -214,6 +223,8 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
       flag_values->xla_llvm_enable_noalias_metadata);
   options.set_xla_llvm_enable_invariant_load_metadata(
       flag_values->xla_llvm_enable_invariant_load_metadata);
+  options.set_xla_test_all_output_layouts(
+      flag_values->xla_test_all_output_layouts);
 
   std::vector<string> extra_options_parts =
       tensorflow::str_util::Split(flag_values->xla_backend_extra_options, ',');
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index 1f7a0ec9e7..04cc3e310d 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -151,18 +151,50 @@ void ClientLibraryTestBase::ComputeAndCompareLiteral(
                                                   error, shape_with_layout));
 }
 
+tensorflow::Status
+ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
+    const xla::Computation& computation, const Literal& expected,
+    tensorflow::gtl::ArraySlice<GlobalData*> arguments,
+    const std::function<void(const Literal& actual)>& expect_equal_or_near) {
+  // Try with no layout requirement.
+  TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments));
+  expect_equal_or_near(*actual);
+
+  // Try with all output layouts.
+  std::vector<int64> minor_to_major(ShapeUtil::Rank(expected.shape()));
+  std::iota(minor_to_major.begin(), minor_to_major.end(), 0);
+  do {
+    auto layout = ShapeUtil::MakeShapeWithLayout(
+        expected.shape().element_type(),
+        AsInt64Slice(expected.shape().dimensions()), minor_to_major);
+    VLOG(1) << "Test with output layout: "
+            << ShapeUtil::HumanStringWithLayout(layout);
+    TF_ASSIGN_OR_RETURN(auto actual,
+                        ExecuteAndTransfer(computation, arguments, &layout));
+    expect_equal_or_near(*actual);
+  } while (std::next_permutation(minor_to_major.begin(), minor_to_major.end()));
+  return tensorflow::Status::OK();
+}
+
 tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     ComputationBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments,
     const Shape* shape_with_layout) {
-  TF_ASSIGN_OR_RETURN(
-      auto actual, ExecuteAndTransfer(builder, arguments, shape_with_layout));
+  TF_ASSIGN_OR_RETURN(auto computation, builder->Build());
   if (ShapeUtil::ElementIsFloating(expected.shape())) {
     LOG(WARNING) << "performing exact comparison of floating point numbers";
   } else {
     TF_RET_CHECK(ShapeUtil::ElementIsIntegral(expected.shape()) ||
                  expected.shape().element_type() == PRED);
   }
+  if (execution_options_.debug_options().xla_test_all_output_layouts()) {
+    return ComputeAndCompareLiteralWithAllOutputLayouts(
+        computation, expected, arguments, [&](const Literal& actual) {
+          LiteralTestUtil::ExpectEqual(expected, actual);
+        });
+  }
+  TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
+                                                      shape_with_layout));
   LiteralTestUtil::ExpectEqual(expected, *actual);
   return tensorflow::Status::OK();
 }
@@ -171,9 +203,16 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     ComputationBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments, ErrorSpec error,
     const Shape* shape_with_layout) {
-  TF_ASSIGN_OR_RETURN(
-      auto actual, ExecuteAndTransfer(builder, arguments, shape_with_layout));
   TF_RET_CHECK(ShapeUtil::ElementIsFloating(expected.shape()));
+  TF_ASSIGN_OR_RETURN(auto computation, builder->Build());
+  if (execution_options_.debug_options().xla_test_all_output_layouts()) {
+    return ComputeAndCompareLiteralWithAllOutputLayouts(
+        computation, expected, arguments, [&](const Literal& actual) {
+          LiteralTestUtil::ExpectNear(expected, actual, error);
+        });
+  }
+  TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
+                                                      shape_with_layout));
   LiteralTestUtil::ExpectNear(expected, *actual, error);
   return tensorflow::Status::OK();
 }
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index f665bb4514..09640672d3 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -280,6 +280,13 @@ class ClientLibraryTestBase : public ::testing::Test {
 
   Client* client_;
   ExecutionOptions execution_options_;
+
+ private:
+  // Build and run the computation with all permutations of output layouts.
+  tensorflow::Status ComputeAndCompareLiteralWithAllOutputLayouts(
+      const xla::Computation& computation, const Literal& expected,
+      tensorflow::gtl::ArraySlice<GlobalData*> arguments,
+      const std::function<void(const Literal& actual)>& expect_equal_or_near);
 };
 
 template <typename NativeT>
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 72b47c4223..08c208d85e 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -99,6 +99,12 @@ message DebugOptions {
   // the generated IR.
   bool xla_llvm_enable_invariant_load_metadata = 65;
 
+  // This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the
+  // computation will run n! times with all permunations of layouts for the
+  // output shape in rank n. For example, with a 3D shape, all permutations of
+  // the set {0, 1, 2} are tried.
+  bool xla_test_all_output_layouts = 70;
+
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
   map<string, string> xla_backend_extra_options = 500;
-- 
GitLab


From 5ff65ca53d37bf7fbc25e559a3b164796e9e7d05 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 6 Jul 2017 18:30:51 -0700
Subject: [PATCH 0781/1375] Bugfix for
 https://github.com/tensorflow/models/issues/1050

When a layer undergoes a deep copy; any internal object that contains tensors
gets moved over shallowly.  Fixes a bug where trained layers whose attributes
contain Tensors break in legacy_seq2seq code.

PiperOrigin-RevId: 161158127
---
 tensorflow/python/layers/base.py      | 10 ++++++++++
 tensorflow/python/layers/base_test.py |  2 ++
 2 files changed, 12 insertions(+)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index a37308f702..36491fc9c7 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -40,6 +40,14 @@ from tensorflow.python.util import nest
 from tensorflow.python.util import tf_inspect
 
 
+def _is_tensor_or_tensor_list(v):
+  v = nest.flatten(v)
+  if v and isinstance(v[0], ops.Tensor):
+    return True
+  else:
+    return False
+
+
 class Layer(object):
   """Base layer class.
 
@@ -472,6 +480,8 @@ class Layer(object):
         setattr(result, k, v)
       elif k in shallow_copy:
         setattr(result, k, copy.copy(v))
+      elif _is_tensor_or_tensor_list(v):
+        setattr(result, k, v)
       else:
         setattr(result, k, copy.deepcopy(v, memo))
     return result
diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py
index 81fbe5fbf7..5f93f58f8c 100644
--- a/tensorflow/python/layers/base_test.py
+++ b/tensorflow/python/layers/base_test.py
@@ -192,6 +192,7 @@ class BaseLayerTest(test.TestCase):
         return math_ops.square(inputs)
 
     layer = MyLayer(name='my_layer')
+    layer._private_tensor = random_ops.random_uniform(())
     inputs = random_ops.random_uniform((5,), seed=1)
     outputs = layer.apply(inputs)
     self.assertEqual(layer.built, True)
@@ -201,6 +202,7 @@ class BaseLayerTest(test.TestCase):
     self.assertEqual(layer_copy.name, layer.name)
     self.assertEqual(layer_copy._scope.name, layer._scope.name)
     self.assertEqual(layer_copy._graph, layer._graph)
+    self.assertEqual(layer_copy._private_tensor, layer._private_tensor)
 
   def testScopeNaming(self):
 
-- 
GitLab


From 154df32a959df74b3a1c377ff72f955d755b3d34 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 19:07:18 -0700
Subject: [PATCH 0782/1375] Follow up changes of tfprof migration: 1. Add a
 option_builder module to build options for Python API. 2. Expose necessary
 profiler protos. 3. Rename command line tool from tfprof to profiler.

PiperOrigin-RevId: 161160274
---
 tensorflow/contrib/tfprof/model_analyzer.py   |  69 ++-
 tensorflow/contrib/tfprof/tfprof_logger.py    |   4 +-
 tensorflow/core/profiler/BUILD                |   4 +-
 tensorflow/core/profiler/g3doc/advise.md      |   2 +-
 .../core/profiler/g3doc/command_line.md       |  26 +-
 tensorflow/core/profiler/g3doc/options.md     |   4 +-
 .../g3doc/profile_model_architecture.md       |   8 +-
 tensorflow/core/profiler/g3doc/python_api.md  |  29 +-
 .../advisor/expensive_operation_checker.h     |  18 +-
 .../profiler/internal/print_model_analysis.cc |  14 +-
 .../profiler/internal/print_model_analysis.h  |   2 +-
 .../core/profiler/internal/tfprof_code.cc     |   4 +-
 .../core/profiler/internal/tfprof_code.h      |   2 +-
 .../core/profiler/internal/tfprof_graph.cc    |   6 +-
 .../core/profiler/internal/tfprof_graph.h     |   2 +-
 .../profiler/internal/tfprof_node_show.cc     |  12 +-
 .../core/profiler/internal/tfprof_node_show.h |  12 +-
 .../core/profiler/internal/tfprof_scope.cc    |   4 +-
 .../core/profiler/internal/tfprof_scope.h     |   2 +-
 .../core/profiler/internal/tfprof_show.cc     |   2 +-
 .../core/profiler/internal/tfprof_show.h      |   2 +-
 .../profiler/internal/tfprof_show_multi.cc    |   7 +-
 .../profiler/internal/tfprof_show_multi.h     |   4 +-
 .../profiler/internal/tfprof_show_test.cc     |   2 +-
 .../core/profiler/internal/tfprof_stats.cc    |  12 +-
 .../core/profiler/internal/tfprof_stats.h     |  17 +-
 .../profiler/internal/tfprof_stats_test.cc    |  26 +-
 .../profiler/internal/tfprof_tensor_test.cc   |   6 +-
 .../core/profiler/internal/tfprof_utils.cc    |   4 +-
 .../profiler/{tfprof_main.cc => profiler.cc}  |   6 +-
 tensorflow/core/profiler/tfprof_log.proto     |   2 +-
 tensorflow/core/profiler/tfprof_output.proto  |  14 +-
 tensorflow/python/profiler/BUILD              |  10 +
 .../internal/print_model_analysis_test.py     |   4 +-
 .../profiler/internal/run_metadata_test.py    |   7 +-
 tensorflow/python/profiler/model_analyzer.py  | 122 +-----
 .../python/profiler/model_analyzer_test.py    |  86 ++--
 tensorflow/python/profiler/option_builder.py  | 400 ++++++++++++++++++
 tensorflow/python/profiler/profiler.py        |  45 +-
 tensorflow/python/profiler/profiler_test.py   |  16 +-
 tensorflow/python/profiler/tfprof_logger.py   |  17 +-
 ...flow.profiler.-advice-proto.-checker.pbtxt |  80 ++++
 ...ofiler.-advice-proto.-checkers-entry.pbtxt |  84 ++++
 .../tensorflow.profiler.-advice-proto.pbtxt   |  88 ++++
 ...graph-node-proto.-input-shapes-entry.pbtxt |  84 ++++
 ...ensorflow.profiler.-graph-node-proto.pbtxt | 164 +++++++
 ...low.profiler.-multi-graph-node-proto.pbtxt | 136 ++++++
 .../tensorflow.profiler.-op-log-proto.pbtxt   |  80 ++++
 ...low.profiler.-profile-option-builder.pbtxt |  89 ++++
 .../api/golden/tensorflow.profiler.pbtxt      |  20 +
 50 files changed, 1556 insertions(+), 304 deletions(-)
 rename tensorflow/core/profiler/{tfprof_main.cc => profiler.cc} (97%)
 create mode 100644 tensorflow/python/profiler/option_builder.py
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.profiler.-profile-option-builder.pbtxt

diff --git a/tensorflow/contrib/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/model_analyzer.py
index 04b5063218..a7b609a031 100644
--- a/tensorflow/contrib/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/model_analyzer.py
@@ -23,17 +23,80 @@ from __future__ import print_function
 
 # Import the names here for existing users.
 # pylint: disable=unused-import
+from tensorflow.python.profiler import tfprof_logger
 from tensorflow.python.profiler.model_analyzer import advise as _advise
 from tensorflow.python.profiler.model_analyzer import ALL_ADVICE
-from tensorflow.python.profiler.model_analyzer import FLOAT_OPS_OPTIONS
-from tensorflow.python.profiler.model_analyzer import PRINT_ALL_TIMING_MEMORY
 from tensorflow.python.profiler.model_analyzer import profile as _profile
 from tensorflow.python.profiler.model_analyzer import Profiler
-from tensorflow.python.profiler.model_analyzer import TRAINABLE_VARS_PARAMS_STAT_OPTIONS
 
 _DEFAULT_PROFILE_OPTIONS = 0
 _DEFAULT_ADVISE_OPTIONS = 0
 
+# pylint: disable=bad-whitespace
+# pylint: disable=bad-continuation
+# options examples for profiling API.
+#
+# Show the parameter statistics of trainable variables.
+TRAINABLE_VARS_PARAMS_STAT_OPTIONS = {
+    'max_depth': 10000,
+    'min_bytes': 0,
+    'min_micros': 0,
+    'min_params': 0,
+    'min_float_ops': 0,
+    'order_by': 'name',
+    'account_type_regexes': [tfprof_logger.TRAINABLE_VARIABLES],
+    'start_name_regexes': ['.*'],
+    'trim_name_regexes': [],
+    'show_name_regexes': ['.*'],
+    'hide_name_regexes': [],
+    'account_displayed_op_only': True,
+    'select': ['params'],
+    'output': 'stdout',
+    'dump_to_file': ''  # Deprecated, use 'output': 'file:outfile=<name>'
+}
+
+# Show the number float operations.
+FLOAT_OPS_OPTIONS = {
+    'max_depth': 10000,
+    'min_bytes': 0,
+    'min_micros': 0,
+    'min_params': 0,
+    'min_float_ops': 1,
+    'order_by': 'float_ops',
+    'account_type_regexes': ['.*'],
+    'start_name_regexes': ['.*'],
+    'trim_name_regexes': [],
+    'show_name_regexes': ['.*'],
+    'hide_name_regexes': [],
+    'account_displayed_op_only': True,
+    'select': ['float_ops'],
+    'output': 'stdout',
+    'dump_to_file': ''  # Deprecated, use 'output': 'file:outfile=<name>'
+}
+
+
+# Show the timing stats and memory demands.
+PRINT_ALL_TIMING_MEMORY = {
+    'max_depth': 10000,
+    'min_bytes': 1,  # Only >=1
+    'min_micros': 1,  # Only >=1
+    'min_params': 0,
+    'min_float_ops': 0,
+    'order_by': 'name',
+    'account_type_regexes': ['.*'],
+    'start_name_regexes': ['.*'],
+    'trim_name_regexes': [],
+    'show_name_regexes': ['.*'],
+    'hide_name_regexes': [],
+    'account_displayed_op_only': True,
+    'select': ['micros', 'bytes'],
+    'output': 'stdout',
+    'dump_to_file': ''  # Deprecated, use 'output': 'file:outfile=<name>'
+}
+
+# pylint: enable=bad-whitespace
+# pylint: enable=bad-continuation
+
 
 def advise(graph, run_meta=None, tfprof_options=_DEFAULT_ADVISE_OPTIONS):
   return _advise(graph, run_meta, tfprof_options)
diff --git a/tensorflow/contrib/tfprof/tfprof_logger.py b/tensorflow/contrib/tfprof/tfprof_logger.py
index 9588bd2985..321ac5ad9f 100644
--- a/tensorflow/contrib/tfprof/tfprof_logger.py
+++ b/tensorflow/contrib/tfprof/tfprof_logger.py
@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Logging tensorflow::tfprof::OpLog.
+"""Logging tensorflow::tfprof::OpLogProto.
 
-OpLog is used to add extra model information for offline analysis by tfprof.
+OpLogProto is used to add extra model information for offline analysis.
 """
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/core/profiler/BUILD b/tensorflow/core/profiler/BUILD
index 5c75eb9a83..c930ae3bd2 100644
--- a/tensorflow/core/profiler/BUILD
+++ b/tensorflow/core/profiler/BUILD
@@ -20,8 +20,8 @@ filegroup(
 )
 
 cc_binary(
-    name = "tfprof",
-    srcs = ["tfprof_main.cc"],
+    name = "profiler",
+    srcs = ["profiler.cc"],
     deps = [
         ":protos_all_cc",
         "//tensorflow/c:c_api",
diff --git a/tensorflow/core/profiler/g3doc/advise.md b/tensorflow/core/profiler/g3doc/advise.md
index 6973c6a84c..cc16c8fdff 100644
--- a/tensorflow/core/profiler/g3doc/advise.md
+++ b/tensorflow/core/profiler/g3doc/advise.md
@@ -15,7 +15,7 @@ _ = sess.run(r1,
 profiler.add_step(1, run_meta)
 
 # Then Start advise.
-profiler.advise(tf.contrib.tfprof.model_analyzer.ALL_ADVICE)
+profiler.advise()
 
 # For one-shot API
 tf.profiler.advise(
diff --git a/tensorflow/core/profiler/g3doc/command_line.md b/tensorflow/core/profiler/g3doc/command_line.md
index 06ebe8e949..48d8078cdf 100644
--- a/tensorflow/core/profiler/g3doc/command_line.md
+++ b/tensorflow/core/profiler/g3doc/command_line.md
@@ -36,11 +36,11 @@ with tf.gfile.Open(os.path.join(output_dir, "run_meta"), "w") as f:
 ```
 
 <b>--op_log_path:</b>
-tensorflow::tfprof::OpLog (optional). A proto used to provide extra operation
+tensorflow::tfprof::OpLogProto (optional). A proto used to provide extra operation
 information. 1) float operations. 2) code traces. 3) define customized operation
 type for -account_type_regexes option.
 
-The following code snippet writes a OpLog file.
+The following code snippet writes a OpLogProto file.
 
 ```python
 tf.profiler.write_op_log(graph, log_dir, op_log=None)
@@ -59,7 +59,7 @@ It defines _checkpoint_variable op type. It also provides checkpointed tensors'
 bazel build --config opt tensorflow/core/profiler/...
 
 # Help information, including detail 'option' instructions.
-bazel-bin/tensorflow/core/profiler/tfprof help
+bazel-bin/tensorflow/core/profiler/profiler help
 ```
 
 #### Start `tfprof` Interactive Mode
@@ -72,17 +72,17 @@ bazel-bin/tensorflow/core/profiler/tfprof help
 # --checkpoint_path contains the model checkpoint data.
 #
 # Only includes model architecture, parameters and shapes.
-bazel-bin/tensorflow/core/profiler/tfprof \
+bazel-bin/tensorflow/core/profiler/profiler \
     --graph_path=graph.pbtxt
 #
 # Additionally profile ops memory and timing.
-bazel-bin/tensorflow/core/profiler/tfprof \
+bazel-bin/tensorflow/core/profiler/profiler \
     --graph_path=graph.pbtxt \
     --run_meta_path=run_meta \
 #
 # tfprof_log is used to define customized op types, float ops and code traces.
 # Use tfprof_logger.write_op_log() to create tfprof_log.
-bazel-bin/tensorflow/core/profiler/tfprof \
+bazel-bin/tensorflow/core/profiler/profiler \
     --graph_path=graph.pbtxt \
     --run_meta_path=run_meta \
     --op_log_path=tfprof_log \
@@ -90,7 +90,7 @@ bazel-bin/tensorflow/core/profiler/tfprof \
 # Additionally profile checkpoint statistics and values.
 # Use '-account_type_regexes _checkpoint_variables' to select
 # checkpoint tensors.
-bazel-bin/tensorflow/core/profiler/tfprof \
+bazel-bin/tensorflow/core/profiler/profiler \
     --graph_path=graph.pbtxt \
     --run_meta_path=run_meta \
     --op_log_path=tfprof_log \
@@ -101,7 +101,7 @@ bazel-bin/tensorflow/core/profiler/tfprof \
 
 ```python
 # Runs tfprof in one-shot.
-bazel-bin/tensorflow/core/profiler/tfprof scope \
+bazel-bin/tensorflow/core/profiler/profiler scope \
     --graph_path=graph.pbtxt \
     --max_depth=3
 ```
@@ -211,7 +211,7 @@ _TFProfRoot (--/464.15k params)
 ```
 
 Where does `_trainable_variables` come from? It is customized operation type
-defined through the OpLog file.
+defined through the OpLogProto file.
 Users can [Define Customized Operation Type](#define-customized-operation-type)
 
 <b>Following example shows importance of defining customized operation type.</b>
@@ -243,7 +243,7 @@ In tfprof, 'device' is an op_type. For example, if op1 and op2 are placed on
 gpu:0. They share an operation type.
 
 ```shell
-bazel-bin/tensorflow/core/profiler/tfprof \
+bazel-bin/tensorflow/core/profiler/profiler \
   --graph_path=/tmp/graph.pbtxt  \
   --run_meta_path=/tmp/run_meta
 
@@ -256,12 +256,12 @@ _TFProfRoot (--/58.84m params)
 
 #### Define Customized Operation Type
 
-First, in Python code, create an `OpLog` proto and add op type
+First, in Python code, create an `OpLogProto` proto and add op type
 information to it:
 
 ```python
 
-op_log = tfprof_log_pb2.OpLog()
+op_log = tfprof_log_pb2.OpLogProto()
 entry = op_log.log_entries.add()
 entry.name = 'pool_logit/DW'
 entry.types.append('pool_logit')
@@ -270,7 +270,7 @@ entry.name = 'pool_logit/biases'
 entry.types.append('pool_logit')
 ```
 
-Second, call write_op_log to write the OpLog proto.
+Second, call write_op_log to write the OpLogProto proto.
 
 ```python
 tf.profiler.write_op_log(
diff --git a/tensorflow/core/profiler/g3doc/options.md b/tensorflow/core/profiler/g3doc/options.md
index 57f67c66fa..051d72493e 100644
--- a/tensorflow/core/profiler/g3doc/options.md
+++ b/tensorflow/core/profiler/g3doc/options.md
@@ -43,7 +43,7 @@ In graph view, in means the number of hops in the <b>graph</b>.
 
 `-min_params`: Show ops that contains at least this number of parameters.
 
-`-min_float_ops`: Show ops that contain at least this number of float operations. Only available if an op has op.RegisterStatistics() defined and OpLog is provided
+`-min_float_ops`: Show ops that contain at least this number of float operations. Only available if an op has op.RegisterStatistics() defined and OpLogProto is provided
 
 `-min_occurrence`: Show ops that appear at least this number of times. Only available in "op" view.
 
@@ -51,7 +51,7 @@ In graph view, in means the number of hops in the <b>graph</b>.
 
 `-order_by`: Order the results by [name|depth|bytes|micros|accelerator_micros|cpu_micros|params|float_ops|occurrence]
 
-`-account_type_regexes`: Account and display the ops whose types match one of the type regexes specified. tfprof allow user to define extra op types for ops through tensorflow.tfprof.OpLog proto. regexes are comma-sperated.
+`-account_type_regexes`: Account and display the ops whose types match one of the type regexes specified. tfprof allow user to define extra op types for ops through tensorflow.tfprof.OpLogProto proto. regexes are comma-sperated.
 
 `-start_name_regexes`: Show ops starting from the ops that matches the regexes, recursively. regexes are comma-separated.
 
diff --git a/tensorflow/core/profiler/g3doc/profile_model_architecture.md b/tensorflow/core/profiler/g3doc/profile_model_architecture.md
index 6d49cdcb75..a42b2e918d 100644
--- a/tensorflow/core/profiler/g3doc/profile_model_architecture.md
+++ b/tensorflow/core/profiler/g3doc/profile_model_architecture.md
@@ -34,8 +34,8 @@ _TFProfRoot (--/930.58k params)
 # to write to file. The result is always returned as a proto buffer.
 param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    options=tf.contrib.tfprof.model_analyzer.
-        TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
+    options=tf.profiler.ProfileOptionBuilder
+        .trainable_variables_parameter())
 sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
 ```
 
@@ -52,7 +52,7 @@ use the definition to calculate float operations. Contributes are welcome.
 to calculate the statistics. It is suggested to pass in `-run_meta_path` if
 shape is only known during runtime. tfprof can fill in the missing shape with
 the runtime shape information from RunMetadata.
-Hence, it is suggested to use `-account_displayed_name_only`
+Hence, it is suggested to use `-account_displayed_op_only`
 option so that you know the statistics are only for the operations printed out.
 
 * If no RunMetadata provided, tfprof count float_ops of each graph node once,
@@ -88,5 +88,5 @@ BiasAdd                  1.28k float_ops (0.00%, 0.00%)
 # You can also do that in Python API.
 tf.profiler.profile(
     tf.get_default_graph(),
-    options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
+    options=tf.profiler.ProfileOptionBuilder.float_operation())
 ```
diff --git a/tensorflow/core/profiler/g3doc/python_api.md b/tensorflow/core/profiler/g3doc/python_api.md
index 5dda419711..4f7e1c2d8d 100644
--- a/tensorflow/core/profiler/g3doc/python_api.md
+++ b/tensorflow/core/profiler/g3doc/python_api.md
@@ -11,21 +11,24 @@
 ### Parameters and Shapes.
 ```python
 # Print trainable variable parameter statistics to stdout.
+ProfileOptionBuilder = tf.profiler.ProfileOptionBuilder
+
 param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    options=tf.contrib.tfprof.model_analyzer.
-        TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
+    options=ProfileOptionBuilder.trainable_variables_parameter())
 
 # Use code view to associate statistics with Python codes.
-opts = tf.contrib.tfprof.model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
-opts['show_name_regexes'] = ['.*my_code1.py.*', '.*my_code2.py.*']
+opts = ProfileOptionBuilder(
+    ProfileOptionBuilder.trainable_variables_parameter()
+    ).with_node_names(show_name_regexes=['.*my_code1.py.*', '.*my_code2.py.*']
+    ).build()
 param_stats = tf.profiler.profile(
     tf.get_default_graph(),
     cmd='code'
     options=opts)
 
-# param_stats can be tensorflow.tfprof.TFGraphNodeProto or
-# tensorflow.tfprof.TFMultiGraphNodeProto, depending on the view.
+# param_stats can be tensorflow.tfprof.GraphNodeProto or
+# tensorflow.tfprof.MultiGraphNodeProto, depending on the view.
 # Let's print the root below.
 sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
 ```
@@ -38,7 +41,7 @@ sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
 # model broken down by individual operations.
 tf.profiler.profile(
     tf.get_default_graph(),
-    options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
+    options=tf.profiler.ProfileOptionBuilder.float_operation())
 ```
 
 ### Time and Memory
@@ -66,8 +69,10 @@ information of the model.
 #
 # Print to stdout an analysis of the memory usage and the timing information
 # broken down by python codes.
-opts = tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
-opts['show_name_regexes'] = ['.*my_code.py.*']
+ProfileOptionBuilder = tf.profiler.ProfileOptionBuilder
+opts = ProfileOptionBuilder(ProfileOptionBuilder.time_and_memory()
+    ).with_node_names(show_name_regexes=['.*my_code.py.*']).build()
+
 tf.profiler.profile(
     tf.get_default_graph(),
     run_meta=run_metadata,
@@ -79,14 +84,14 @@ tf.profiler.profile(
 tf.profiler.profile(
     tf.get_default_graph(),
     run_meta=run_metadata,
-    options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
+    options=tf.profiler.ProfileOptionBuilder.time_and_memory())
 ```
 
 ### Visualize
 
 ```
 To visualize the result of Python API results:
-Set opts['output'] = 'timeline:outfile=<filename>' to generate a timeline json file.
+Call with_step(0).with_timeline_output(filename)' to generate a timeline json file.
 Open a Chrome Browser, open URL chrome://tracing, and load the json file.
 ```
 
@@ -141,4 +146,4 @@ with session.Session() as sess:
   # Add run_meta of step 3.
   profiler.add_step(3, run_meta3)
   pb3 = profiler.profile_name_scope(opts)
-```
\ No newline at end of file
+```
diff --git a/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h b/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
index bc94346d2d..53c6fd2d7b 100644
--- a/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
+++ b/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
@@ -49,11 +49,11 @@ class ExpensiveOperationChecker : public Checker {
     }
     Options opts(3, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"}, {},
                  false, {"micros", "occurrence"}, "none", {});
-    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("op", opts);
+    const MultiGraphNodeProto root = stats->ShowMultiGraphNode("op", opts);
     if (root.children_size() == 0) {
       return;
     }
-    const TFMultiGraphNodeProto* node = &root;
+    const MultiGraphNodeProto* node = &root;
     std::vector<string> outputs;
     for (int i = 0; i < 3 && node->children_size() > 0; ++i) {
       node = &node->children(0);
@@ -76,8 +76,8 @@ class ExpensiveOperationChecker : public Checker {
     }
     Options opts(100, 0, 1, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
                  {}, false, {"micros"}, "none", {});
-    const TFMultiGraphNodeProto root = stats->ShowMultiGraphNode("code", opts);
-    const TFMultiGraphNodeProto* node = &root;
+    const MultiGraphNodeProto root = stats->ShowMultiGraphNode("code", opts);
+    const MultiGraphNodeProto* node = &root;
     // A trick here is: Usually, codes in library file are usually referenced
     // only once, while user's own code are referenced multiple times.
     while (node->children_size() == 1) {
@@ -95,14 +95,14 @@ class ExpensiveOperationChecker : public Checker {
   void CheckScopeView(const TFStats* stats) {
     Options opts(100, 0, 100, 0, 0, 0, -1, "micros", {".*"}, {".*"}, {}, {".*"},
                  {}, false, {"micros"}, "none", {});
-    const TFGraphNodeProto root = stats->ShowGraphNode("scope", opts);
+    const GraphNodeProto root = stats->ShowGraphNode("scope", opts);
     if (root.children_size() == 0) {
       return;
     }
     std::vector<string> outputs;
-    const TFGraphNodeProto* node = &root;
+    const GraphNodeProto* node = &root;
     for (int i = 0; i < 3 && i < root.children_size(); ++i) {
-      const TFGraphNodeProto& node = root.children(i);
+      const GraphNodeProto& node = root.children(i);
       outputs.push_back(strings::Printf(
           "top %d graph node: %s, cpu: %s, accelerator: %s, total: %s", i + 1,
           node.name().c_str(), FormatTime(node.cpu_exec_micros()).c_str(),
@@ -112,13 +112,13 @@ class ExpensiveOperationChecker : public Checker {
     reports_.add_reports(str_util::Join(outputs, "\n"));
   }
 
-  void CodeViewHelper(const TFMultiGraphNodeProto* node, int depth,
+  void CodeViewHelper(const MultiGraphNodeProto* node, int depth,
                       std::vector<string>* outputs) {
     if (node->children_size() <= 1 || depth > 4) {
       return;
     }
     for (int j = 0; j < 3 && j < node->children_size(); ++j) {
-      const TFMultiGraphNodeProto* c = &node->children(j);
+      const MultiGraphNodeProto* c = &node->children(j);
       outputs->push_back(strings::Printf(
           "%s%s, cpu: %s, accelerator: %s, total: %s",
           string(depth * 2, ' ').c_str(), c->name().c_str(),
diff --git a/tensorflow/core/profiler/internal/print_model_analysis.cc b/tensorflow/core/profiler/internal/print_model_analysis.cc
index a912f59837..65b54f01aa 100644
--- a/tensorflow/core/profiler/internal/print_model_analysis.cc
+++ b/tensorflow/core/profiler/internal/print_model_analysis.cc
@@ -89,9 +89,9 @@ bool NewProfiler(const string* graph, const string* op_log) {
   std::unique_ptr<GraphDef> graph_ptr(new GraphDef());
   graph_ptr->ParseFromString(*graph);
 
-  std::unique_ptr<OpLog> op_log_ptr;
+  std::unique_ptr<OpLogProto> op_log_ptr;
   if (op_log && !op_log->empty()) {
-    op_log_ptr.reset(new OpLog());
+    op_log_ptr.reset(new OpLogProto());
     op_log_ptr->ParseFromString(*op_log);
   }
   tf_stat = new TFStats(std::move(graph_ptr), nullptr, std::move(op_log_ptr),
@@ -112,12 +112,12 @@ void AddStep(int64 step, const string* run_meta, const string* op_log) {
   run_meta_ptr->ParseFromString(*run_meta);
   tf_stat->AddRunMeta(step, std::move(run_meta_ptr));
 
-  std::unique_ptr<OpLog> op_log_ptr;
+  std::unique_ptr<OpLogProto> op_log_ptr;
   if (op_log && !op_log->empty()) {
-    op_log_ptr.reset(new OpLog());
+    op_log_ptr.reset(new OpLogProto());
     op_log_ptr->ParseFromString(*op_log);
   }
-  tf_stat->AddOpLog(std::move(op_log_ptr));
+  tf_stat->AddOpLogProto(std::move(op_log_ptr));
 }
 
 string Profile(const string* command, const string* options) {
@@ -142,9 +142,9 @@ string PrintModelAnalysis(const string* graph, const string* run_meta,
     run_meta_ptr->ParseFromString(*run_meta);
   }
 
-  std::unique_ptr<OpLog> op_log_ptr;
+  std::unique_ptr<OpLogProto> op_log_ptr;
   if (op_log && !op_log->empty()) {
-    op_log_ptr.reset(new OpLog());
+    op_log_ptr.reset(new OpLogProto());
     op_log_ptr->ParseFromString(*op_log);
   }
 
diff --git a/tensorflow/core/profiler/internal/print_model_analysis.h b/tensorflow/core/profiler/internal/print_model_analysis.h
index de8216d54b..76a436f71c 100644
--- a/tensorflow/core/profiler/internal/print_model_analysis.h
+++ b/tensorflow/core/profiler/internal/print_model_analysis.h
@@ -43,7 +43,7 @@ string Profile(const string* command, const string* options);
 //
 // Interface defined for Python API swig. Calls the tfprof core API.
 // 'graph', 'run_meta', 'op_log' are serialized GraphDef, RunMetadata,
-// OpLog strings, respectively.
+// OpLogProto strings, respectively.
 // 'graph', 'command' and 'options' are required. Others can be nullptr
 // if not available.
 string PrintModelAnalysis(const string* graph, const string* run_meta,
diff --git a/tensorflow/core/profiler/internal/tfprof_code.cc b/tensorflow/core/profiler/internal/tfprof_code.cc
index 564890a6fb..1746076f79 100644
--- a/tensorflow/core/profiler/internal/tfprof_code.cc
+++ b/tensorflow/core/profiler/internal/tfprof_code.cc
@@ -130,10 +130,10 @@ const ShowMultiNode* TFCode::ShowInternal(const Options& opts,
 }
 
 void TFCode::Format(const std::vector<CodeNode*> roots, string* display_str,
-                    TFMultiGraphNodeProto* proto) {
+                    MultiGraphNodeProto* proto) {
   for (CodeNode* node : roots) {
     display_str->append(node->formatted_str);
-    TFMultiGraphNodeProto* child = proto->add_children();
+    MultiGraphNodeProto* child = proto->add_children();
     child->MergeFrom(node->proto());
     Format(node->show_children, display_str, child);
   }
diff --git a/tensorflow/core/profiler/internal/tfprof_code.h b/tensorflow/core/profiler/internal/tfprof_code.h
index cc5a4fe6ba..7338297221 100644
--- a/tensorflow/core/profiler/internal/tfprof_code.h
+++ b/tensorflow/core/profiler/internal/tfprof_code.h
@@ -64,7 +64,7 @@ class TFCode : public TFMultiShow {
                                  const Options& opts);
 
   void Format(const std::vector<CodeNode*> roots, string* display_str,
-              TFMultiGraphNodeProto* proto);
+              MultiGraphNodeProto* proto);
 
   string FormatNode(CodeNode* node, const Options& opts, int64 indent);
 
diff --git a/tensorflow/core/profiler/internal/tfprof_graph.cc b/tensorflow/core/profiler/internal/tfprof_graph.cc
index 9b7063553c..8d7c44b219 100644
--- a/tensorflow/core/profiler/internal/tfprof_graph.cc
+++ b/tensorflow/core/profiler/internal/tfprof_graph.cc
@@ -94,7 +94,7 @@ const ShowNode* TFGraph::ShowInternal(const Options& opts, Timeline* timeline) {
   }
 
   // 3. Trim the nodes not matching show/hide/trim_name_regexes.
-  // If account_displayed_name_only=true, redo the accounting.
+  // If account_displayed_op_only=true, redo the accounting.
   visits.clear();
   root_->show_children.assign(roots.begin(), roots.end());
   GraphNode* root = PrintGraph({root_}, opts, 1, 0, &visits)[0];
@@ -141,10 +141,10 @@ std::vector<GraphNode*> TFGraph::SearchRoot(
 }
 
 void TFGraph::Format(const std::vector<GraphNode*> roots, string* display_str,
-                     TFGraphNodeProto* proto) {
+                     GraphNodeProto* proto) {
   for (GraphNode* node : roots) {
     display_str->append(node->formatted_str);
-    TFGraphNodeProto* child = proto->add_children();
+    GraphNodeProto* child = proto->add_children();
     child->MergeFrom(node->proto());
     Format(node->show_children, display_str, child);
   }
diff --git a/tensorflow/core/profiler/internal/tfprof_graph.h b/tensorflow/core/profiler/internal/tfprof_graph.h
index 422b67aa62..8dac4aee77 100644
--- a/tensorflow/core/profiler/internal/tfprof_graph.h
+++ b/tensorflow/core/profiler/internal/tfprof_graph.h
@@ -74,7 +74,7 @@ class TFGraph : public TFShow {
                                   std::set<string>* visits);
 
   void Format(const std::vector<GraphNode*> roots, string* display_str,
-              TFGraphNodeProto* proto);
+              GraphNodeProto* proto);
 
   MemoryTracker memory_tracker_;
   GraphNode* root_;
diff --git a/tensorflow/core/profiler/internal/tfprof_node_show.cc b/tensorflow/core/profiler/internal/tfprof_node_show.cc
index 8dc88166d0..5ca57412e5 100644
--- a/tensorflow/core/profiler/internal/tfprof_node_show.cc
+++ b/tensorflow/core/profiler/internal/tfprof_node_show.cc
@@ -66,12 +66,12 @@ void ShowNode::ReInit(int64 step) {
   }
 }
 
-TFGraphNodeProto* ShowNode::mutable_proto() { return &proto_; }
+GraphNodeProto* ShowNode::mutable_proto() { return &proto_; }
 
-const TFGraphNodeProto& ShowNode::proto() const { return proto_; }
+const GraphNodeProto& ShowNode::proto() const { return proto_; }
 
 void ShowNode::AggregateTotalStats(ShowNode* node) {
-  TFGraphNodeProto* node_pb = node->mutable_proto();
+  GraphNodeProto* node_pb = node->mutable_proto();
   mutable_proto()->set_total_run_count(proto().total_run_count() +
                                        node_pb->total_run_count());
   mutable_proto()->set_total_definition_count(
@@ -176,12 +176,12 @@ bool ShowMultiNode::ReInit(int64 step,
   return has_matched_type;
 }
 
-TFMultiGraphNodeProto* ShowMultiNode::mutable_proto() { return &proto_; }
+MultiGraphNodeProto* ShowMultiNode::mutable_proto() { return &proto_; }
 
-const TFMultiGraphNodeProto& ShowMultiNode::proto() const { return proto_; }
+const MultiGraphNodeProto& ShowMultiNode::proto() const { return proto_; }
 
 void ShowMultiNode::AggregateTotalStats(ShowMultiNode* node) {
-  TFMultiGraphNodeProto* node_pb = node->mutable_proto();
+  MultiGraphNodeProto* node_pb = node->mutable_proto();
   mutable_proto()->set_total_exec_micros(proto().total_exec_micros() +
                                          node_pb->total_exec_micros());
   mutable_proto()->set_total_accelerator_exec_micros(
diff --git a/tensorflow/core/profiler/internal/tfprof_node_show.h b/tensorflow/core/profiler/internal/tfprof_node_show.h
index 97d22a29d6..fea19bdca3 100644
--- a/tensorflow/core/profiler/internal/tfprof_node_show.h
+++ b/tensorflow/core/profiler/internal/tfprof_node_show.h
@@ -45,8 +45,8 @@ class ShowNode {
   virtual ~ShowNode() {}
 
   const string& name() const { return node->name(); }
-  TFGraphNodeProto* mutable_proto();
-  const TFGraphNodeProto& proto() const;
+  GraphNodeProto* mutable_proto();
+  const GraphNodeProto& proto() const;
 
   void ReInit(int64 step);
 
@@ -61,7 +61,7 @@ class ShowNode {
   string formatted_str;
 
  protected:
-  TFGraphNodeProto proto_;
+  GraphNodeProto proto_;
 };
 
 class GraphNode : public ShowNode {
@@ -91,8 +91,8 @@ class ShowMultiNode {
   bool ReInit(int64 step, const std::vector<string>& type_regexes);
 
   const string& name() const { return node->name(); }
-  TFMultiGraphNodeProto* mutable_proto();
-  const TFMultiGraphNodeProto& proto() const;
+  MultiGraphNodeProto* mutable_proto();
+  const MultiGraphNodeProto& proto() const;
 
   void AggregateTotalStats(ShowMultiNode* node);
 
@@ -106,7 +106,7 @@ class ShowMultiNode {
   string formatted_str;
 
  protected:
-  TFMultiGraphNodeProto proto_;
+  MultiGraphNodeProto proto_;
 };
 
 class CodeNode : public ShowMultiNode {
diff --git a/tensorflow/core/profiler/internal/tfprof_scope.cc b/tensorflow/core/profiler/internal/tfprof_scope.cc
index 68becefad6..0f7e079098 100644
--- a/tensorflow/core/profiler/internal/tfprof_scope.cc
+++ b/tensorflow/core/profiler/internal/tfprof_scope.cc
@@ -103,10 +103,10 @@ const ShowNode* TFScope::ShowInternal(const Options& opts, Timeline* timeline) {
 }
 
 void TFScope::Format(const std::vector<ScopeNode*> roots, string* display_str,
-                     TFGraphNodeProto* proto) {
+                     GraphNodeProto* proto) {
   for (ScopeNode* node : roots) {
     display_str->append(node->formatted_str);
-    TFGraphNodeProto* child = proto->add_children();
+    GraphNodeProto* child = proto->add_children();
     child->MergeFrom(node->proto());
     Format(node->show_children, display_str, child);
   }
diff --git a/tensorflow/core/profiler/internal/tfprof_scope.h b/tensorflow/core/profiler/internal/tfprof_scope.h
index 3c87caa92b..5e1fa2a32a 100644
--- a/tensorflow/core/profiler/internal/tfprof_scope.h
+++ b/tensorflow/core/profiler/internal/tfprof_scope.h
@@ -64,7 +64,7 @@ class TFScope : public TFShow {
                                   const Options& opts);
 
   void Format(const std::vector<ScopeNode*> roots, string* display_str,
-              TFGraphNodeProto* proto);
+              GraphNodeProto* proto);
 
   ScopeNode* root_;
   std::vector<std::unique_ptr<NodeDef>> node_defs_;
diff --git a/tensorflow/core/profiler/internal/tfprof_show.cc b/tensorflow/core/profiler/internal/tfprof_show.cc
index aae7517d6f..2828217d3d 100644
--- a/tensorflow/core/profiler/internal/tfprof_show.cc
+++ b/tensorflow/core/profiler/internal/tfprof_show.cc
@@ -25,7 +25,7 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-const TFGraphNodeProto& TFShow::Show(const Options& opts) {
+const GraphNodeProto& TFShow::Show(const Options& opts) {
   if (opts.output_type == kOutput[3]) {
     return ShowInternal(opts, nullptr)->proto();
   } else if (opts.output_type == kOutput[0]) {
diff --git a/tensorflow/core/profiler/internal/tfprof_show.h b/tensorflow/core/profiler/internal/tfprof_show.h
index 4c97fad1af..113862ba95 100644
--- a/tensorflow/core/profiler/internal/tfprof_show.h
+++ b/tensorflow/core/profiler/internal/tfprof_show.h
@@ -44,7 +44,7 @@ class TFShow {
   virtual ~TFShow() {}
   virtual void AddNode(TFGraphNode* node) = 0;
   virtual void Build() = 0;
-  const TFGraphNodeProto& Show(const Options& opts);
+  const GraphNodeProto& Show(const Options& opts);
 
  protected:
   virtual const ShowNode* ShowInternal(const Options& opts,
diff --git a/tensorflow/core/profiler/internal/tfprof_show_multi.cc b/tensorflow/core/profiler/internal/tfprof_show_multi.cc
index 651b754386..199aef7685 100644
--- a/tensorflow/core/profiler/internal/tfprof_show_multi.cc
+++ b/tensorflow/core/profiler/internal/tfprof_show_multi.cc
@@ -27,7 +27,7 @@ limitations under the License.
 namespace tensorflow {
 namespace tfprof {
 
-const TFMultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
+const MultiGraphNodeProto& TFMultiShow::Show(const Options& opts) {
   if (opts.output_type == kOutput[3]) {
     return ShowInternal(opts, nullptr)->proto();
   } else if (opts.output_type == kOutput[0]) {
@@ -144,12 +144,11 @@ string TFMultiShow::FormatLegend(const Options& opts) const {
                          str_util::Join(legends, " | ").c_str());
 }
 
-string TFMultiShow::FormatInputShapes(
-    const TFMultiGraphNodeProto& proto) const {
+string TFMultiShow::FormatInputShapes(const MultiGraphNodeProto& proto) const {
   // input_shape string -> (static defined count, run count, run_micros)
   std::map<string, std::tuple<int64, int64, int64>> input_shapes_attr;
   for (int i = 0; i < proto.graph_nodes_size(); ++i) {
-    const TFGraphNodeProto& gnode = proto.graph_nodes(i);
+    const GraphNodeProto& gnode = proto.graph_nodes(i);
     // Convert and sort by input_idx.
     std::map<int, std::vector<int64>> input_shapes;
     for (const auto& inp : gnode.input_shapes()) {
diff --git a/tensorflow/core/profiler/internal/tfprof_show_multi.h b/tensorflow/core/profiler/internal/tfprof_show_multi.h
index e89ee4f95b..f731f6afbb 100644
--- a/tensorflow/core/profiler/internal/tfprof_show_multi.h
+++ b/tensorflow/core/profiler/internal/tfprof_show_multi.h
@@ -45,7 +45,7 @@ class TFMultiShow {
   virtual ~TFMultiShow() {}
   virtual void AddNode(TFGraphNode* node) = 0;
   virtual void Build() = 0;
-  const TFMultiGraphNodeProto& Show(const Options& opts);
+  const MultiGraphNodeProto& Show(const Options& opts);
 
  protected:
   virtual const ShowMultiNode* ShowInternal(const Options& opts,
@@ -69,7 +69,7 @@ class TFMultiShow {
   bool ReAccount(ShowMultiNode* node, const Options& opts);
 
   string FormatLegend(const Options& opts) const;
-  string FormatInputShapes(const TFMultiGraphNodeProto& proto) const;
+  string FormatInputShapes(const MultiGraphNodeProto& proto) const;
   std::vector<string> FormatTimes(const ShowMultiNode* node,
                                   const Options& opts) const;
 
diff --git a/tensorflow/core/profiler/internal/tfprof_show_test.cc b/tensorflow/core/profiler/internal/tfprof_show_test.cc
index c3e98f8ea2..ff827ecab8 100644
--- a/tensorflow/core/profiler/internal/tfprof_show_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_show_test.cc
@@ -49,7 +49,7 @@ class TFProfShowTest : public ::testing::Test {
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
-    std::unique_ptr<OpLog> op_log_pb(new OpLog());
+    std::unique_ptr<OpLogProto> op_log_pb(new OpLogProto());
     string op_log_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "core/profiler/internal/testdata/tfprof_log");
diff --git a/tensorflow/core/profiler/internal/tfprof_stats.cc b/tensorflow/core/profiler/internal/tfprof_stats.cc
index 175cdb5eba..f0db8edd4a 100644
--- a/tensorflow/core/profiler/internal/tfprof_stats.cc
+++ b/tensorflow/core/profiler/internal/tfprof_stats.cc
@@ -27,7 +27,7 @@ namespace tensorflow {
 namespace tfprof {
 TFStats::TFStats(std::unique_ptr<GraphDef> graph,
                  std::unique_ptr<RunMetadata> run_meta,
-                 std::unique_ptr<OpLog> op_log,
+                 std::unique_ptr<OpLogProto> op_log,
                  std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader)
     : has_code_traces_(false),
       graph_(std::move(graph)),
@@ -39,7 +39,7 @@ TFStats::TFStats(std::unique_ptr<GraphDef> graph,
   if (run_meta && run_meta->has_step_stats()) {
     AddRunMeta(0, std::move(run_meta));
   }
-  AddOpLog(std::move(op_log));
+  AddOpLogProto(std::move(op_log));
 
   if (ckpt_reader_) {
     for (const auto& v : ckpt_reader_->GetVariableToShapeMap()) {
@@ -89,8 +89,8 @@ void TFStats::BuildAllViews() {
   }
 }
 
-const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
-                                               const Options& opts) const {
+const GraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
+                                             const Options& opts) const {
   if (!Validate(opts)) {
     return empty_graph_node_;
   }
@@ -104,7 +104,7 @@ const TFGraphNodeProto& TFStats::ShowGraphNode(const string& cmd,
   }
 }
 
-const TFMultiGraphNodeProto& TFStats::ShowMultiGraphNode(
+const MultiGraphNodeProto& TFStats::ShowMultiGraphNode(
     const string& cmd, const Options& opts) const {
   if (!Validate(opts)) {
     return empty_multi_graph_node_;
@@ -152,7 +152,7 @@ void TFStats::ParseGraph() {
   }
 }
 
-void TFStats::AddOpLog(std::unique_ptr<OpLog> op_log) {
+void TFStats::AddOpLogProto(std::unique_ptr<OpLogProto> op_log) {
   if (!op_log) {
     return;
   }
diff --git a/tensorflow/core/profiler/internal/tfprof_stats.h b/tensorflow/core/profiler/internal/tfprof_stats.h
index fd2628fd8b..8dbab16fe4 100644
--- a/tensorflow/core/profiler/internal/tfprof_stats.h
+++ b/tensorflow/core/profiler/internal/tfprof_stats.h
@@ -52,7 +52,8 @@ namespace tfprof {
 class TFStats {
  public:
   TFStats(std::unique_ptr<GraphDef> graph,
-          std::unique_ptr<RunMetadata> run_meta, std::unique_ptr<OpLog> op_log,
+          std::unique_ptr<RunMetadata> run_meta,
+          std::unique_ptr<OpLogProto> op_log,
           std::unique_ptr<checkpoint::CheckpointReader> ckpt_reader);
   ~TFStats() {}
 
@@ -70,16 +71,16 @@ class TFStats {
   // Organize the TensorFlow model as different types of views, and generate
   // outputs for profiling.
   // TODO(xpan): Should it return reference here?
-  const TFGraphNodeProto& ShowGraphNode(const string& cmd,
-                                        const Options& opts) const;
-  const TFMultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
-                                                  const Options& opts) const;
+  const GraphNodeProto& ShowGraphNode(const string& cmd,
+                                      const Options& opts) const;
+  const MultiGraphNodeProto& ShowMultiGraphNode(const string& cmd,
+                                                const Options& opts) const;
 
   // Add a step of run time meta data.
   void AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta);
   // Add tfprof operation meta data, such as customized op type, float_ops,
   // and code traces.
-  void AddOpLog(std::unique_ptr<OpLog> op_log);
+  void AddOpLogProto(std::unique_ptr<OpLogProto> op_log);
 
   // For test purpose only.
   void AddNodeForTest(int64 step, std::unique_ptr<TFGraphNode> node);
@@ -100,8 +101,8 @@ class TFStats {
   // Store TFGraphNode instead of TFGraphNode* to avoid large number of
   // dynamic alloc.
   std::map<string, std::unique_ptr<TFGraphNode>> nodes_map_;
-  TFGraphNodeProto empty_graph_node_;
-  TFMultiGraphNodeProto empty_multi_graph_node_;
+  GraphNodeProto empty_graph_node_;
+  MultiGraphNodeProto empty_multi_graph_node_;
 };
 
 }  // namespace tfprof
diff --git a/tensorflow/core/profiler/internal/tfprof_stats_test.cc b/tensorflow/core/profiler/internal/tfprof_stats_test.cc
index 4f7db87a00..8744f5be28 100644
--- a/tensorflow/core/profiler/internal/tfprof_stats_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_stats_test.cc
@@ -50,7 +50,7 @@ class TFProfStatsTest : public ::testing::Test {
     TF_CHECK_OK(
         ReadProtoFile(Env::Default(), run_meta_path, run_meta_pb.get(), true));
 
-    std::unique_ptr<OpLog> op_log_pb(new OpLog());
+    std::unique_ptr<OpLogProto> op_log_pb(new OpLogProto());
     string op_log_path =
         io::JoinPath(testing::TensorFlowSrcRoot(),
                      "core/profiler/internal/testdata/tfprof_log");
@@ -77,9 +77,9 @@ TEST_F(TFProfStatsTest, CustomOpType) {
                {kTrainableVarType},  // accout_type_regexes
                {".*"}, {""}, {".*"}, {""}, false,
                {"params", "bytes", "micros", "float_ops"}, "", {});
-  const TFGraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
+  const GraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
 
-  TFGraphNodeProto expected;
+  GraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 5\ntotal_requested_bytes: 1480\ntotal_parameters: "
@@ -140,9 +140,9 @@ TEST_F(TFProfStatsTest, CheckPointOpType) {
                {kCkptVarType},  // accout_type_regexes
                {".*"}, {""}, {".*"}, {""}, false,
                {"params", "bytes", "micros", "float_ops"}, "", {});
-  const TFGraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
+  const GraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
 
-  TFGraphNodeProto expected;
+  GraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 5\ntotal_requested_bytes: 1480\ntotal_parameters: "
@@ -203,9 +203,9 @@ TEST_F(TFProfStatsTest, TestGraph) {
                {"cost.*"},  // start_name_regexes
                {""}, {".*"}, {""}, false,
                {"params", "bytes", "micros", "float_ops"}, "", {});
-  const TFGraphNodeProto& root = tf_stats_->ShowGraphNode("graph", opts);
+  const GraphNodeProto& root = tf_stats_->ShowGraphNode("graph", opts);
 
-  TFGraphNodeProto expected;
+  GraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
@@ -220,9 +220,9 @@ TEST_F(TFProfStatsTest, TestGraph) {
 TEST_F(TFProfStatsTest, TestFloatOps) {
   Options opts(10, 0, 0, 0, 1, 0, -1, "name", {".*"}, {".*"}, {""}, {".*"},
                {""}, false, {"float_ops"}, "", {});
-  const TFGraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
+  const GraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
 
-  TFGraphNodeProto expected;
+  GraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
@@ -279,9 +279,9 @@ TEST_F(TFProfStatsTest, TestAccountShownNameOnly) {
                {"unit_2_1.*DW"},  // show_name_regexes.
                {""}, true,        // account_displayed_op_only.
                {"params"}, "", {});
-  const TFGraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
+  const GraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
 
-  TFGraphNodeProto expected;
+  GraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 0\ntotal_requested_bytes: 0\ntotal_parameters: "
@@ -298,8 +298,8 @@ TEST_F(TFProfStatsTest, TestShowTensorValue) {
                {"unit_1_0.*gamma"}, {""}, false,
                {"tensor_value"},  // Show tensor value from checkpoint.
                "", {});
-  const TFGraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
-  TFGraphNodeProto expected;
+  const GraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
+  GraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 97\ntotal_requested_bytes: "
diff --git a/tensorflow/core/profiler/internal/tfprof_tensor_test.cc b/tensorflow/core/profiler/internal/tfprof_tensor_test.cc
index 782f1a6117..50ef82abc9 100644
--- a/tensorflow/core/profiler/internal/tfprof_tensor_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_tensor_test.cc
@@ -38,7 +38,7 @@ class TFProfTensorTest : public ::testing::Test {
         ReadProtoFile(Env::Default(), graph_path, graph_pb.get(), false));
 
     std::unique_ptr<tensorflow::RunMetadata> run_meta_pb;
-    std::unique_ptr<OpLog> op_log_pb;
+    std::unique_ptr<OpLogProto> op_log_pb;
 
     string ckpt_path = io::JoinPath(testing::TensorFlowSrcRoot(),
                                     "core/profiler/internal/testdata/ckpt");
@@ -60,9 +60,9 @@ TEST_F(TFProfTensorTest, Basics) {
   Options opts(3, 0, 0, 0, 0, 0, -1, "name", {"VariableV2"}, {".*"}, {""},
                {".*"}, {""}, false, {"tensor_value"},  // show the tensor value.
                "", {});
-  const TFGraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
+  const GraphNodeProto& root = tf_stats_->ShowGraphNode("scope", opts);
 
-  TFGraphNodeProto expected;
+  GraphNodeProto expected;
   CHECK(protobuf::TextFormat::ParseFromString(
       "name: \"_TFProfRoot\"\nexec_micros: 0\nrequested_bytes: "
       "0\ntotal_exec_micros: 0\ntotal_requested_bytes: 0\ntotal_parameters: "
diff --git a/tensorflow/core/profiler/internal/tfprof_utils.cc b/tensorflow/core/profiler/internal/tfprof_utils.cc
index 0f0a6d0b36..a5898025be 100644
--- a/tensorflow/core/profiler/internal/tfprof_utils.cc
+++ b/tensorflow/core/profiler/internal/tfprof_utils.cc
@@ -282,7 +282,7 @@ void PrintHelp() {
       "parameters.\n\n"
       "  -min_float_ops: Show ops that contain at least this number of "
       "float operations. Only available if an op has "
-      "op.RegisterStatistics() defined and OpLog is "
+      "op.RegisterStatistics() defined and OpLogProto is "
       "provided\n\n"
       "  -min_occurrence: Show the op types that are at least used this number "
       "of times. Only available in op view.\n\n"
@@ -293,7 +293,7 @@ void PrintHelp() {
       "  -account_type_regexes: Account and display the ops whose types match "
       "one of the type regexes specified. tfprof "
       "allow user to define extra op types for ops "
-      "through tensorflow.tfprof.OpLog proto. regexes "
+      "through tensorflow.tfprof.OpLogProto proto. regexes "
       "are comma-sperated.\n\n"
       "  -start_name_regexes: Show ops starting from the ops that matches the "
       "regexes, recursively. regexes are "
diff --git a/tensorflow/core/profiler/tfprof_main.cc b/tensorflow/core/profiler/profiler.cc
similarity index 97%
rename from tensorflow/core/profiler/tfprof_main.cc
rename to tensorflow/core/profiler/profiler.cc
index 871db346af..0749db128d 100644
--- a/tensorflow/core/profiler/tfprof_main.cc
+++ b/tensorflow/core/profiler/profiler.cc
@@ -96,7 +96,7 @@ int Run(int argc, char** argv) {
            "Comma-separated list of RunMetadata proto binary "
            "files. Each file is given step number 0,1,2,etc"),
       Flag("op_log_path", &FLAGS_op_log_path,
-           "tensorflow::tfprof::OpLog proto binary file name"),
+           "tensorflow::tfprof::OpLogProto proto binary file name"),
       Flag("checkpoint_path", &FLAGS_checkpoint_path,
            "TensorFlow Checkpoint file name"),
       Flag("max_depth", &FLAGS_max_depth, "max depth"),
@@ -156,7 +156,7 @@ int Run(int argc, char** argv) {
     printf(
         "4) tfprof --graph_path <GraphDef proto text file> \\\n"
         "          --run_meta_path <RunMetadata proto binary file> \\\n"
-        "          --op_log_path <tensorflow::tfprof::OpLog proto binary file> "
+        "          --op_log_path <tensorflow::tfprof::OpLogProto proto binary file> "
         "\\\n"
         "          --checkpoint_path <TensorFlow Checkpoint file>: "
         "Profiling everything!\n");
@@ -178,7 +178,7 @@ int Run(int argc, char** argv) {
   TF_CHECK_OK(
       ReadProtoFile(Env::Default(), FLAGS_graph_path, graph.get(), false));
 
-  std::unique_ptr<OpLog> op_log(new OpLog());
+  std::unique_ptr<OpLogProto> op_log(new OpLogProto());
   if (!FLAGS_op_log_path.empty()) {
     string op_log_str;
     s = ReadFileToString(Env::Default(), FLAGS_op_log_path, &op_log_str);
diff --git a/tensorflow/core/profiler/tfprof_log.proto b/tensorflow/core/profiler/tfprof_log.proto
index 0e0fb74aae..048449b437 100644
--- a/tensorflow/core/profiler/tfprof_log.proto
+++ b/tensorflow/core/profiler/tfprof_log.proto
@@ -27,6 +27,6 @@ message OpLogEntry {
   CodeDef code_def = 4;
 }
 
-message OpLog {
+message OpLogProto {
   repeated OpLogEntry log_entries = 1;
 }
diff --git a/tensorflow/core/profiler/tfprof_output.proto b/tensorflow/core/profiler/tfprof_output.proto
index abf141f1e7..5c9f132243 100644
--- a/tensorflow/core/profiler/tfprof_output.proto
+++ b/tensorflow/core/profiler/tfprof_output.proto
@@ -15,7 +15,7 @@ message TFProfTensorProto {
 }
 
 // A node in TensorFlow graph. Used by scope/graph view.
-message TFGraphNodeProto {
+message GraphNodeProto {
   // op name.
   string name = 1;
   // tensor value restored from checkpoint.
@@ -60,17 +60,17 @@ message TFGraphNodeProto {
 
   // Descendants of the graph. The actual descendants depend on the data
   // structure used (scope, graph).
-  repeated TFGraphNodeProto children = 12;
+  repeated GraphNodeProto children = 12;
 }
 
-// A node that groups multiple TFGraphNodeProto.
+// A node that groups multiple GraphNodeProto.
 // Depending on the 'view', the semantics of the TFmultiGraphNodeProto
 // is different:
 // code view: A node groups all TensorFlow graph nodes created by the
 //            Python code.
 // op view:   A node groups all TensorFlow graph nodes that are of type
 //            of the op (e.g. MatMul, Conv2D).
-message TFMultiGraphNodeProto {
+message MultiGraphNodeProto {
   // Name of the node.
   string name = 1;
 
@@ -96,11 +96,11 @@ message TFMultiGraphNodeProto {
   int64 total_parameters = 8;
   int64 total_float_ops = 9;
 
-  // TensorFlow graph nodes contained by the TFMultiGraphNodeProto.
-  repeated TFGraphNodeProto graph_nodes = 10;
+  // TensorFlow graph nodes contained by the MultiGraphNodeProto.
+  repeated GraphNodeProto graph_nodes = 10;
   // Descendants of the node. The actual descendants depend on the data
   // structure used.
-  repeated TFMultiGraphNodeProto children = 11;
+  repeated MultiGraphNodeProto children = 11;
 }
 
 message AdviceProto {
diff --git a/tensorflow/python/profiler/BUILD b/tensorflow/python/profiler/BUILD
index 3122c0c5f4..c32cddbd6d 100644
--- a/tensorflow/python/profiler/BUILD
+++ b/tensorflow/python/profiler/BUILD
@@ -18,11 +18,21 @@ py_library(
     ],
 )
 
+py_library(
+    name = "option_builder",
+    srcs = ["option_builder.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tfprof_logger",
+    ],
+)
+
 py_library(
     name = "model_analyzer",
     srcs = ["model_analyzer.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":option_builder",
         ":tfprof_logger",
         "//tensorflow/core/profiler:protos_all_py",
         "//tensorflow/python:pywrap_tensorflow",
diff --git a/tensorflow/python/profiler/internal/print_model_analysis_test.py b/tensorflow/python/profiler/internal/print_model_analysis_test.py
index 54854e8164..797c430e99 100644
--- a/tensorflow/python/profiler/internal/print_model_analysis_test.py
+++ b/tensorflow/python/profiler/internal/print_model_analysis_test.py
@@ -95,7 +95,7 @@ class PrintModelAnalysisTest(test.TestCase):
 
     with session.Session() as sess, ops.device('/cpu:0'):
       _ = self._BuildSmallModel()
-      tfprof_pb = tfprof_output_pb2.TFGraphNodeProto()
+      tfprof_pb = tfprof_output_pb2.GraphNodeProto()
       tfprof_pb.ParseFromString(
           print_mdl.PrintModelAnalysis(
               sess.graph.as_graph_def(add_shapes=True).SerializeToString(),
@@ -104,7 +104,7 @@ class PrintModelAnalysisTest(test.TestCase):
               b'scope',
               opts.SerializeToString()))
 
-      expected_pb = tfprof_output_pb2.TFGraphNodeProto()
+      expected_pb = tfprof_output_pb2.GraphNodeProto()
       text_format.Merge(r"""name: "_TFProfRoot"
           exec_micros: 0
           requested_bytes: 0
diff --git a/tensorflow/python/profiler/internal/run_metadata_test.py b/tensorflow/python/profiler/internal/run_metadata_test.py
index a619aa6ebd..f2d1967981 100644
--- a/tensorflow/python/profiler/internal/run_metadata_test.py
+++ b/tensorflow/python/profiler/internal/run_metadata_test.py
@@ -29,12 +29,15 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
+from tensorflow.python.profiler import option_builder
 
 # pylint: disable=g-bad-import-order
 # XXX: this depends on pywrap_tensorflow and must come later
 from tensorflow.python.profiler import model_analyzer
 from tensorflow.python.profiler.internal import model_analyzer_testlib as lib
+
 SIZE = 1300
+builder = option_builder.ProfileOptionBuilder
 
 
 def _extract_node(run_meta, node_name):
@@ -54,7 +57,7 @@ def _run_model():
 
   with session.Session() as sess:
     run_metadata = config_pb2.RunMetadata()
-    opts = model_analyzer.PRINT_ALL_TIMING_MEMORY
+    opts = builder.time_and_memory()
     opts['min_micros'] = 0
     opts['min_bytes'] = 0
     _ = sess.run(y,
@@ -82,7 +85,7 @@ def _run_loop_model():
 
     tfprof_node = model_analyzer.profile(
         sess.graph, run_meta,
-        options=model_analyzer.PRINT_ALL_TIMING_MEMORY)
+        options=builder.time_and_memory())
     return tfprof_node, run_meta
 
 
diff --git a/tensorflow/python/profiler/model_analyzer.py b/tensorflow/python/profiler/model_analyzer.py
index 2e336f017e..1f61f44797 100644
--- a/tensorflow/python/profiler/model_analyzer.py
+++ b/tensorflow/python/profiler/model_analyzer.py
@@ -26,93 +26,12 @@ from tensorflow.core.profiler import tfprof_options_pb2
 from tensorflow.core.profiler import tfprof_output_pb2
 from tensorflow.python import pywrap_tensorflow as print_mdl
 from tensorflow.python.framework import errors
+from tensorflow.python.profiler import option_builder
 from tensorflow.python.profiler import tfprof_logger
 
 _DEFAULT_PROFILE_OPTIONS = 0
 _DEFAULT_ADVISE_OPTIONS = 0
 
-# pylint: disable=bad-whitespace
-# pylint: disable=bad-continuation
-# options examples for profiling API.
-#
-# Show the parameter statistics of trainable variables.
-TRAINABLE_VARS_PARAMS_STAT_OPTIONS = {
-    'max_depth': 10000,
-    'min_bytes': 0,
-    'min_micros': 0,
-    'min_params': 0,
-    'min_float_ops': 0,
-    'order_by': 'name',
-    'account_type_regexes': [tfprof_logger.TRAINABLE_VARIABLES],
-    'start_name_regexes': ['.*'],
-    'trim_name_regexes': [],
-    'show_name_regexes': ['.*'],
-    'hide_name_regexes': [],
-    'account_displayed_op_only': True,
-    'select': ['params'],
-    'output': 'stdout',
-    'dump_to_file': ''
-}
-
-# Show the number float operations.
-FLOAT_OPS_OPTIONS = {
-    'max_depth': 10000,
-    'min_bytes': 0,
-    'min_micros': 0,
-    'min_params': 0,
-    'min_float_ops': 1,
-    'order_by': 'float_ops',
-    'account_type_regexes': ['.*'],
-    'start_name_regexes': ['.*'],
-    'trim_name_regexes': [],
-    'show_name_regexes': ['.*'],
-    'hide_name_regexes': [],
-    'account_displayed_op_only': True,
-    'select': ['float_ops'],
-    'output': 'stdout',
-    'dump_to_file': ''
-}
-
-# Show number of parameters on parameter server 0.
-# It is recommended to provide`run_meta` argument
-# to have complete device placement info.
-PRINT_PARAMS_ON_DEVICE = {
-    'max_depth': 1,
-    'min_bytes': 0,
-    'min_micros': 0,
-    'min_params': 0,
-    'min_float_ops': 0,
-    'order_by': 'name',
-    'account_type_regexes': ['.*ps.*task:0.*'],
-    'start_name_regexes': ['.*'],
-    'trim_name_regexes': [],
-    'show_name_regexes': ['.*'],
-    'hide_name_regexes': [],
-    'account_displayed_op_only': False,
-    'select': ['device', 'params'],
-    'output': 'stdout',
-    'dump_to_file': ''
-}
-
-# Show the timing stats and memory demands.
-PRINT_ALL_TIMING_MEMORY = {
-    'max_depth': 10000,
-    'min_bytes': 1,  # Only >=1
-    'min_micros': 1,  # Only >=1
-    'min_params': 0,
-    'min_float_ops': 0,
-    'order_by': 'name',
-    'account_type_regexes': ['.*'],
-    'start_name_regexes': ['.*'],
-    'trim_name_regexes': [],
-    'show_name_regexes': ['.*'],
-    'hide_name_regexes': [],
-    'account_displayed_op_only': True,
-    'select': ['micros', 'bytes'],
-    'output': 'stdout',
-    'dump_to_file': ''
-}
-
 # The following options are for 'advise' cmd.
 # Show all advice.
 ALL_ADVICE = {
@@ -122,9 +41,6 @@ ALL_ADVICE = {
     'OperationChecker': {},
 }
 
-# pylint: enable=bad-whitespace
-# pylint: enable=bad-continuation
-
 
 def _build_options(options):
   """Build tfprof.OptionsProto.
@@ -192,6 +108,7 @@ class Profiler(object):
 
   https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
 
+  ```python
   Typical use case:
     # Currently we are only allowed to create 1 profiler per process.
     profiler = Profile(sess.graph)
@@ -206,7 +123,8 @@ class Profiler(object):
         profiler.add_step(i, run_meta)
 
         # Profile the parameters of your model.
-        profiler.profile_name_scope(options=TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
+        profiler.profile_name_scope(options=(option_builder.ProfileOptionBuilder
+            .trainable_variables_parameter()))
 
         # Or profile the timing of your model operations.
         opts = PRINT_ALL_TIMING_MEMORY.copy()
@@ -224,6 +142,7 @@ class Profiler(object):
         _ = sess.run(...)
     # Auto detect problems and generate advice.
     profiler.advise(model_analyzer.ALL_ADVICE)
+  ```
   """
 
   def __init__(self, graph, op_log=None):
@@ -231,7 +150,7 @@ class Profiler(object):
 
     Args:
       graph: tf.Graph.
-      op_log: optional. tensorflow::tfprof::OpLog proto. Used to define
+      op_log: optional. tensorflow::tfprof::OpLogProto proto. Used to define
           extra op types.
     """
     self._graph = graph
@@ -273,10 +192,10 @@ class Profiler(object):
     Args:
       options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
-      a TFMultiGraphNodeProto that records the results.
+      a MultiGraphNodeProto that records the results.
     """
     opts = _build_options(options)
-    tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
+    tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.Profile('code'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
@@ -287,10 +206,10 @@ class Profiler(object):
     Args:
       options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
-      a TFMultiGraphNodeProto that records the results.
+      a MultiGraphNodeProto that records the results.
     """
     opts = _build_options(options)
-    tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
+    tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.Profile('op'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
@@ -301,10 +220,10 @@ class Profiler(object):
     Args:
       options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
-      a TFGraphNodeProto that records the results.
+      a GraphNodeProto that records the results.
     """
     opts = _build_options(options)
-    tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
+    tfprof_node = tfprof_output_pb2.GraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.Profile('scope'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
@@ -315,10 +234,10 @@ class Profiler(object):
     Args:
       options: A dict of options. See core/profiler/g3doc/options.md.
     Returns:
-      a TFGraphNodeProto that records the results.
+      a GraphNodeProto that records the results.
     """
     opts = _build_options(options)
-    tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
+    tfprof_node = tfprof_output_pb2.GraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
     return tfprof_node
@@ -352,7 +271,7 @@ def profile(graph,
     run_meta: tensorflow::RunMetadata proto. When provided, also shows valid
               timing and memory information when 'select' option contains
               'micros' and 'bytes'.
-    op_log: tensorflow::tfprof::OpLog proto. users can use this proto to
+    op_log: tensorflow::tfprof::OpLogProto proto. users can use this proto to
             group together ops and use a op_type to select the group.
     cmd: string. Either 'op', 'scope', 'graph', 'code'.
          'op' view organize outputs using operation type. (e.g. MatMul)
@@ -361,12 +280,13 @@ def profile(graph,
          'code' view organize outputs using Python call stack.
     options: A dict of options. See core/profiler/g3doc/options.md.
   Returns:
-    If cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
-    If cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto.
+    If cmd is 'scope' or 'graph', returns GraphNodeProto proto.
+    If cmd is 'op' or 'code', returns MultiGraphNodeProto proto.
     Side effect: stdout/file/timeline.json depending on options['output']
   """
   if options == _DEFAULT_PROFILE_OPTIONS:
-    options = TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
+    options = (option_builder.ProfileOptionBuilder
+               .trainable_variables_parameter())
 
   # pylint: disable=protected-access
   op_log = tfprof_logger._merge_default_with_oplog(
@@ -378,7 +298,7 @@ def profile(graph,
   run_meta_str = run_meta.SerializeToString() if run_meta else b''
 
   if cmd == 'code' or cmd == 'op':
-    tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
+    tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.PrintModelAnalysis(
             graph.as_graph_def(add_shapes=True).SerializeToString(),
@@ -387,7 +307,7 @@ def profile(graph,
             cmd.encode('utf-8'),
             opts.SerializeToString()))
   elif cmd == 'graph' or cmd == 'scope':
-    tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
+    tfprof_node = tfprof_output_pb2.GraphNodeProto()
     tfprof_node.ParseFromString(
         print_mdl.PrintModelAnalysis(
             graph.as_graph_def(add_shapes=True).SerializeToString(),
diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py
index b506352d08..43da587b2c 100644
--- a/tensorflow/python/profiler/model_analyzer_test.py
+++ b/tensorflow/python/profiler/model_analyzer_test.py
@@ -25,19 +25,20 @@ from tensorflow.python.framework import ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
-
-# XXX: this depends on pywrap_tensorflow and must come later
 from tensorflow.python.profiler import model_analyzer
+from tensorflow.python.profiler import option_builder
 from tensorflow.python.profiler.internal import model_analyzer_testlib as lib
 
+builder = option_builder.ProfileOptionBuilder
+
 
 class PrintModelAnalysisTest(test.TestCase):
 
   def testDumpToFile(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
-    opts['output'] = 'file:outfile=' + outfile
+    opts = builder(builder.trainable_variables_parameter()
+                  ).with_file_output(outfile).build()
 
     with session.Session() as sess:
       _ = lib.BuildSmallModel()
@@ -53,14 +54,12 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testSelectEverything(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
-    opts['output'] = 'file:outfile=' + outfile
-    opts['account_type_regexes'] = ['.*']
-    opts['select'] = [
-        'params', 'float_ops', 'occurrence', 'device', 'op_types',
-        'input_shapes'
-    ]
+    opts = (builder(builder.trainable_variables_parameter())
+            .with_file_output(outfile)
+            .with_accounted_types(['.*'])
+            .select(['params', 'float_ops', 'occurrence', 'device', 'op_types',
+                     'input_shapes']).build())
 
     rewriter_config = rewriter_config_pb2.RewriterConfig(
         disable_model_pruning=True)
@@ -88,18 +87,16 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testSimpleCodeView(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
-    opts['output'] = 'file:outfile=' + outfile
-    opts['account_type_regexes'] = ['.*']
-    opts['show_name_regexes'] = ['.*model_analyzer_testlib.*']
-    opts['account_displayed_op_only'] = False
     # TODO(xpan): Test 'micros'. Since the execution time changes each run,
     # it's a bit difficult to test it now.
-    opts['select'] = [
-        'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device',
-        'input_shapes'
-    ]
+    opts = (builder(builder.trainable_variables_parameter())
+            .with_file_output(outfile)
+            .with_accounted_types(['.*'])
+            .with_node_names(show_name_regexes=['.*model_analyzer_testlib.*'])
+            .account_displayed_op_only(False)
+            .select(['bytes', 'params', 'float_ops', 'num_hidden_ops', 'device',
+                     'input_shapes']).build())
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -123,13 +120,14 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testComplexCodeView(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
-    opts['output'] = 'file:outfile=' + outfile
-    opts['account_type_regexes'] = ['.*']
-    opts['show_name_regexes'] = ['.*model_analyzer_testlib.py.*']
-    opts['account_displayed_op_only'] = False
-    opts['select'] = ['params', 'float_ops']
+    opts = (builder(builder.trainable_variables_parameter())
+            .with_file_output(outfile)
+            .with_accounted_types(['.*'])
+            .with_node_names(show_name_regexes=
+                             ['.*model_analyzer_testlib.py.*'])
+            .account_displayed_op_only(False)
+            .select(['params', 'float_ops']).build())
 
     with session.Session() as sess:
       x = lib.BuildFullModel()
@@ -175,13 +173,11 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testCodeViewLeafGraphNode(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
-    opts['account_type_regexes'] = ['.*']
-    opts['account_displayed_op_only'] = False
-    opts['select'] = [
-        'bytes', 'params', 'float_ops', 'device'
-    ]
-    opts['output'] = 'none'
+    opts = (builder(builder.trainable_variables_parameter())
+            .with_empty_output()
+            .with_accounted_types(['.*'])
+            .account_displayed_op_only(False)
+            .select(['bytes', 'params', 'float_ops', 'device']).build())
 
     with session.Session() as sess:
       x = lib.BuildSmallModel()
@@ -204,12 +200,13 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testTimeline(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
+    opts = builder.trainable_variables_parameter()
     outfile = os.path.join(test.get_temp_dir(), 'timeline')
-    opts['output'] = 'timeline:outfile=' + outfile
-    opts['account_type_regexes'] = ['.*']
-    opts['max_depth'] = 100000
-    opts['step'] = 0
+    opts = (builder(builder.trainable_variables_parameter())
+            .with_max_depth(100000)
+            .with_step(0)
+            .with_timeline_output(outfile)
+            .with_accounted_types(['.*']).build())
 
     with session.Session() as sess:
       x = lib.BuildFullModel()
@@ -236,13 +233,14 @@ class PrintModelAnalysisTest(test.TestCase):
 
   def testOpView(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
     outfile = os.path.join(test.get_temp_dir(), 'dump')
-    opts['output'] = 'file:outfile=' + outfile
-    opts['account_type_regexes'] = ['.*']
-    opts['min_occurrence'] = 10
-    opts['select'] = ['params', 'micros', 'occurrence', 'input_shapes']
-    opts['order_by'] = 'occurrence'
+
+    opts = (builder(builder.trainable_variables_parameter())
+            .with_file_output(outfile)
+            .with_accounted_types(['.*'])
+            .with_min_occurrence(10)
+            .order_by('occurrence')
+            .select(['params', 'micros', 'occurrence', 'input_shapes']).build())
 
     with session.Session() as sess:
       x = lib.BuildFullModel()
diff --git a/tensorflow/python/profiler/option_builder.py b/tensorflow/python/profiler/option_builder.py
new file mode 100644
index 0000000000..87f82709be
--- /dev/null
+++ b/tensorflow/python/profiler/option_builder.py
@@ -0,0 +1,400 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for building profiler options."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+from tensorflow.python.profiler import tfprof_logger
+
+
+class ProfileOptionBuilder(object):
+  # pylint: disable=line-too-long
+  """Option Builder for Profiling API.
+
+  For tutorial on the options, see
+  https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/options.md
+
+  ```python
+  # Users can use pre-built options:
+  opts = (
+      tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())
+
+  # Or, build your own options:
+  opts = (tf.profiler.ProfileOptionBuilder()
+      .with_max_depth(10)
+      .with_min_micros(1000)
+      .select(['accelerator_micros'])
+      .with_stdout_output()
+      .build()
+
+  # Or customize the pre-built options:
+  opts = (tf.profiler.ProfileOptionBuilder(
+      tf.profiler.ProfileOptionBuilder.time_and_memory())
+      .with_displaying_options(show_name_regexes=['.*rnn.*'])
+      .build())
+
+  # Finally, profiling with the options:
+  _ = tf.profiler.profile(tf.get_default_graph(),
+                          run_meta=run_meta,
+                          cmd='scope',
+                          options=opts)
+  ```
+  """
+  # pylint: enable=line-too-long
+
+  def __init__(self, options=None):
+    """Constructor.
+
+    Args:
+      options: Optional initial option dict to start with.
+    """
+    if options is not None:
+      self._options = copy.deepcopy(options)
+    else:
+      self._options = {'max_depth': 100,
+                       'min_bytes': 0,
+                       'min_micros': 0,
+                       'min_params': 0,
+                       'min_float_ops': 0,
+                       'min_occurrence': 0,
+                       'order_by': 'name',
+                       'account_type_regexes': ['.*'],
+                       'start_name_regexes': ['.*'],
+                       'trim_name_regexes': [],
+                       'show_name_regexes': ['.*'],
+                       'hide_name_regexes': [],
+                       'account_displayed_op_only': False,
+                       'select': ['micros'],
+                       'step': -1,
+                       'output': 'stdout'}
+
+  @staticmethod
+  def trainable_variables_parameter():
+    """Options used to profile trainable variable parameters.
+
+    Normally used together with 'scope' view.
+
+    Returns:
+      A dict of profiling options.
+    """
+    return {'max_depth': 10000,
+            'min_bytes': 0,
+            'min_micros': 0,
+            'min_params': 0,
+            'min_float_ops': 0,
+            'min_occurrence': 0,
+            'order_by': 'name',
+            'account_type_regexes': [tfprof_logger.TRAINABLE_VARIABLES],
+            'start_name_regexes': ['.*'],
+            'trim_name_regexes': [],
+            'show_name_regexes': ['.*'],
+            'hide_name_regexes': [],
+            'account_displayed_op_only': True,
+            'select': ['params'],
+            'step': -1,
+            'output': 'stdout'}
+
+  @staticmethod
+  def float_operation():
+    # pylint: disable=line-too-long
+    """Options used to profile float operations.
+
+    Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/profile_model_architecture.md
+    on the caveats of calculating float operations.
+
+    Returns:
+      A dict of profiling options.
+    """
+    # pylint: enable=line-too-long
+    return {'max_depth': 10000,
+            'min_bytes': 0,
+            'min_micros': 0,
+            'min_params': 0,
+            'min_float_ops': 1,
+            'min_occurrence': 0,
+            'order_by': 'float_ops',
+            'account_type_regexes': ['.*'],
+            'start_name_regexes': ['.*'],
+            'trim_name_regexes': [],
+            'show_name_regexes': ['.*'],
+            'hide_name_regexes': [],
+            'account_displayed_op_only': True,
+            'select': ['float_ops'],
+            'step': -1,
+            'output': 'stdout'}
+
+  @staticmethod
+  def time_and_memory(min_micros=1, min_bytes=1):
+    """Show operation time and memory consumptions.
+
+    Args:
+      min_micros: Only show profiler nodes with more execution time than this.
+      min_bytes: Only show profiler nodes consuming more memory than this.
+    Returns:
+      A dict of profiling options.
+    """
+    return {'max_depth': 10000,
+            'min_bytes': min_bytes,
+            'min_micros': min_micros,
+            'min_params': 0,
+            'min_float_ops': 0,
+            'min_occurrence': 0,
+            'order_by': 'name',
+            'account_type_regexes': ['.*'],
+            'start_name_regexes': ['.*'],
+            'trim_name_regexes': [],
+            'show_name_regexes': ['.*'],
+            'hide_name_regexes': [],
+            'account_displayed_op_only': True,
+            'select': ['micros', 'bytes'],
+            'step': -1,
+            'output': 'stdout'}
+
+  def build(self):
+    """Build a profiling option.
+
+    Returns:
+      A dict of profiling options.
+    """
+    return copy.deepcopy(self._options)
+
+  def with_max_depth(self, max_depth):
+    """Set the maximum depth of display.
+
+    The depth depends on profiling view. For 'scope' view, it's the
+    depth of name scope hierarchy (tree), for 'op' view, it's the number
+    of operation types (list), etc.
+
+    Args:
+      max_depth: Maximum depth of the data structure to display.
+    Returns:
+      self
+    """
+    self._options['max_depth'] = max_depth
+    return self
+
+  def with_min_memory(self, min_bytes):
+    """Only show profiler nodes consuming no less than 'min_bytes'.
+
+    Args:
+      min_bytes: Only show profiler nodes with memory consumption
+          no less than this.
+    Returns:
+      self
+    """
+    self._options['min_bytes'] = min_bytes
+    return self
+
+  def with_min_execution_time(self, min_micros):
+    """Only show profiler nodes consuming no less than 'min_micros'.
+
+    Args:
+      min_micros: Only show profiler nodes with execution time
+          no less than this.
+    Returns:
+      self
+    """
+    self._options['min_micros'] = min_micros
+    return self
+
+  def with_min_parameters(self, min_params):
+    """Only show profiler nodes holding no less than 'min_params' parameters.
+
+    'Parameters' normally refers the weights of in TensorFlow variables.
+    It reflects the 'capacity' of models.
+
+    Args:
+      min_params: Only show profiler nodes holding number parameters
+          no less than this.
+    Returns:
+      self
+    """
+    self._options['min_params'] = min_params
+    return self
+
+  def with_min_occurrence(self, min_occurrence):
+    # pylint: disable=line-too-long
+    """Only show profiler nodes including no less than 'min_occurrence' graph nodes.
+
+    A "node" means a profiler output node, which can be a python line
+    (code view), an operation type (op view), or a graph node
+    (graph/scope view). A python line includes all graph nodes created by that
+    line, while an operation type includes all graph nodes of that type.
+
+    Args:
+      min_occurrence: Only show nodes including no less than this.
+    Returns:
+      self
+    """
+    # pylint: enable=line-too-long
+    self._options['min_occurrence'] = min_occurrence
+    return self
+
+  def with_min_float_operations(self, min_float_ops):
+    # pylint: disable=line-too-long
+    """Only show profiler nodes consuming no less than 'min_float_ops'.
+
+    Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profilerg3doc/profile_model_architecture.md
+    on the caveats of calculating float operations.
+
+    Args:
+      min_float_ops: Only show profiler nodes with float operations
+          no less than this.
+    Returns:
+      self
+    """
+    # pylint: enable=line-too-long
+    self._options['min_float_ops'] = min_float_ops
+    return self
+
+  def with_accounted_types(self, account_type_regexes):
+    """Selectively counting statistics based on node types.
+
+    Here, 'types' means the profiler nodes' properties. Profiler by default
+    consider device name (e.g. /job:xx/.../gpu:0) and operation type
+    (e.g. MatMul) as profiler nodes' properties. User can also associate
+    customized 'types' to profiler nodes through OpLogProto proto.
+
+    For example, user can select profiler nodes placed on gpu:0 with:
+    `account_type_regexes=['.*gpu:0.*']`
+
+    If none of a node's properties match the specified regexes, the node is
+    not displayed nor accounted.
+
+    Args:
+      account_type_regexes: A list of regexes specifying the types.
+    Returns:
+      self.
+    """
+    self._options['account_type_regexes'] = copy.copy(account_type_regexes)
+    return self
+
+  def with_node_names(self,
+                      start_name_regexes=None,
+                      show_name_regexes=None,
+                      hide_name_regexes=None,
+                      trim_name_regexes=None):
+    """Regular expressions used to select profiler nodes to display.
+
+    After 'with_accounted_types' is evaluated, 'with_node_names' are
+    evaluated as follows:
+
+      For a profile data structure, profiler first finds the profiler
+      nodes matching 'start_name_regexes', and starts displaying profiler
+      nodes from there. Then, if a node matches 'show_name_regexes' and
+      doesn't match 'hide_name_regexes', it's displayed. If a node matches
+      'trim_name_regexes', profiler stops further searching that branch.
+
+    Args:
+      start_name_regexes: list of node name regexes to start displaying.
+      show_name_regexes: list of node names regexes to display.
+      hide_name_regexes: list of node_names regexes that should be hidden.
+      trim_name_regexes: list of node name regexes from where to stop.
+    Returns:
+      self
+    """
+    if start_name_regexes is not None:
+      self._options['start_name_regexes'] = copy.copy(start_name_regexes)
+    if show_name_regexes is not None:
+      self._options['show_name_regexes'] = copy.copy(show_name_regexes)
+    if hide_name_regexes is not None:
+      self._options['hide_name_regexes'] = copy.copy(hide_name_regexes)
+    if trim_name_regexes is not None:
+      self._options['trim_name_regexes'] = copy.copy(trim_name_regexes)
+    return self
+
+  def account_displayed_op_only(self, is_true):
+    """Whether only account the statistics of displayed profiler nodes.
+
+    Args:
+      is_true: If true, only account statistics of nodes eventually
+          displayed by the outputs.
+          Otherwise, a node's statistics are accounted by its parents
+          as long as it's types match 'account_type_regexes', even if
+          it is hidden from the output, say, by hide_name_regexes.
+    Returns:
+      self
+    """
+    self._options['account_displayed_op_only'] = is_true
+    return self
+
+  def with_empty_output(self):
+    """Do not generate side-effect outputs."""
+    self._options['output'] = 'none'
+    return self
+
+  def with_stdout_output(self):
+    """Print the result to stdout."""
+    self._options['output'] = 'stdout'
+    return self
+
+  def with_file_output(self, outfile):
+    """Print the result to a file."""
+    self._options['output'] = 'file:outfile=%s' % outfile
+    return self
+
+  def with_timeline_output(self, timeline_file):
+    """Generate a timeline json file."""
+    self._options['output'] = 'timeline:outfile=%s' % timeline_file
+    return self
+
+  def order_by(self, attribute):
+    # pylint: disable=line-too-long
+    """Order the displayed profiler nodes based on a attribute.
+
+    Supported attribute includes micros, bytes, occurrence, params, etc.
+    https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/options.md
+
+    Args:
+      attribute: An attribute the profiler node has.
+    Returns:
+      self
+    """
+    # pylint: enable=line-too-long
+    self._options['order_by'] = attribute
+    return self
+
+  def select(self, attributes):
+    # pylint: disable=line-too-long
+    """Select the attributes to display.
+
+    See https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/options.md
+    for supported attributes.
+
+    Args:
+      attributes: A list of attribute the profiler node has.
+    Returns:
+      self
+    """
+    # pylint: enable=line-too-long
+    self._options['select'] = copy.copy(attributes)
+    return self
+
+  def with_step(self, step):
+    """Which profile step to use for profiling.
+
+    The 'step' here refers to the step defined by `Profiler.add_step()` API.
+
+    Args:
+      step: When multiple steps of profiles are available, select which step's
+         profile to use. If -1, use average of all available steps.
+    Returns:
+      self
+    """
+    self._options['step'] = step
+    return self
diff --git a/tensorflow/python/profiler/profiler.py b/tensorflow/python/profiler/profiler.py
index 19b253ab2f..130dcb5134 100644
--- a/tensorflow/python/profiler/profiler.py
+++ b/tensorflow/python/profiler/profiler.py
@@ -12,21 +12,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""tfprof is a tool that profile various aspect of TensorFlow model.
-
-@@Profiler
-@@profile
-@@advise
-@@write_op_log
+"""profiler python module provides APIs to profile TensorFlow models.
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# pylint: disable=unused-import,wildcard-import
-from tensorflow.python.profiler.model_analyzer import *
-from tensorflow.python.profiler.tfprof_logger import *
+# pylint: disable=unused-import
+from tensorflow.core.profiler.tfprof_log_pb2 import OpLogProto
+from tensorflow.core.profiler.tfprof_output_pb2 import AdviceProto
+from tensorflow.core.profiler.tfprof_output_pb2 import GraphNodeProto
+from tensorflow.core.profiler.tfprof_output_pb2 import MultiGraphNodeProto
+
+from tensorflow.python.profiler.model_analyzer import advise
+from tensorflow.python.profiler.model_analyzer import profile
+from tensorflow.python.profiler.model_analyzer import Profiler
+from tensorflow.python.profiler.option_builder import ProfileOptionBuilder
+from tensorflow.python.profiler.tfprof_logger import write_op_log
+
 from tensorflow.python.util.all_util import remove_undocumented
 
 
-remove_undocumented(__name__, [])
+_allowed_symbols = [
+    'Profiler',
+    'profile',
+    'ProfileOptionBuilder',
+    'advise',
+    'write_op_log',
+]
+
+_allowed_symbols.extend([
+    'GraphNodeProto',
+    'MultiGraphNodeProto',
+    'AdviceProto',
+    'OpLogProto',
+])
+
+remove_undocumented(__name__, _allowed_symbols, [
+    Profiler,
+    profile,
+    ProfileOptionBuilder,
+    advise,
+    write_op_log,
+])
diff --git a/tensorflow/python/profiler/profiler_test.py b/tensorflow/python/profiler/profiler_test.py
index 35e7c2f12d..7d30c29264 100644
--- a/tensorflow/python/profiler/profiler_test.py
+++ b/tensorflow/python/profiler/profiler_test.py
@@ -24,22 +24,25 @@ from tensorflow.python.framework import ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
+from tensorflow.python.profiler import option_builder
 
 # pylint: disable=g-bad-import-order
 from tensorflow.python.profiler import model_analyzer
 from tensorflow.python.profiler.internal import model_analyzer_testlib as lib
 
+builder = option_builder.ProfileOptionBuilder
+
 
 class ProfilerTest(test.TestCase):
 
   def testProfileBasic(self):
     ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
-    opts['account_type_regexes'] = ['.*']
-    opts['select'] = ['params', 'float_ops', 'micros', 'bytes',
-                      'device', 'op_types', 'occurrence']
     outfile = os.path.join(test.get_temp_dir(), 'dump')
-    opts['output'] = 'file:outfile=' + outfile
+    opts = (builder(builder.trainable_variables_parameter())
+            .with_file_output(outfile)
+            .with_accounted_types(['.*'])
+            .select(['params', 'float_ops', 'micros', 'bytes',
+                     'device', 'op_types', 'occurrence']).build())
 
     # Test the output without run_meta.
     sess = session.Session()
@@ -115,8 +118,7 @@ class ProfilerTest(test.TestCase):
 
   def testMultiStepProfile(self):
     ops.reset_default_graph()
-    opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy()
-    opts['account_type_regexes'] = ['.*']
+    opts = builder.time_and_memory()
 
     with session.Session() as sess:
       r1, r2, r3 = lib.BuildSplitableModel()
diff --git a/tensorflow/python/profiler/tfprof_logger.py b/tensorflow/python/profiler/tfprof_logger.py
index 10bb6cdce5..e50c52884a 100644
--- a/tensorflow/python/profiler/tfprof_logger.py
+++ b/tensorflow/python/profiler/tfprof_logger.py
@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Logging tensorflow::tfprof::OpLog.
+"""Logging tensorflow::tfprof::OpLogProto.
 
-OpLog is used to add extra model information for offline analysis by tfprof.
+OpLogProto is used to add extra model information for offline analysis.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -131,15 +131,15 @@ def _merge_default_with_oplog(graph, op_log=None, run_meta=None,
 
   Args:
     graph: tf.Graph.
-    op_log: OpLog proto.
+    op_log: OpLogProto proto.
     run_meta: RunMetadata proto used to complete shape information.
     add_trace: Whether to add op trace information.
     add_trainable_var: Whether to assign tf.trainable_variables() op type
       '_trainable_variables'.
   Returns:
-    tmp_op_log: Merged OpLog proto.
+    tmp_op_log: Merged OpLogProto proto.
   """
-  tmp_op_log = tfprof_log_pb2.OpLog()
+  tmp_op_log = tfprof_log_pb2.OpLogProto()
   logged_ops = _get_logged_ops(
       graph, run_meta, add_trace=add_trace, add_trainable_var=add_trainable_var)
 
@@ -169,17 +169,18 @@ def write_op_log(graph, log_dir, op_log=None, run_meta=None, add_trace=True):
     '_trainable_variables'.
     The API also logs 'flops' statistics for ops with op.RegisterStatistics()
     defined. flops calculation depends on Tensor shapes defined in 'graph',
-    which might not be complete, 'run_meta', if provided, completes the shape
+    which might not be complete. 'run_meta', if provided, completes the shape
     information with best effort.
 
   Args:
     graph: tf.Graph.
     log_dir: directory to write the log file.
-    op_log: (Optional) OpLog proto to be written. If not provided, an new
+    op_log: (Optional) OpLogProto proto to be written. If not provided, an new
         one is created.
     run_meta: (Optional) RunMetadata proto that helps flops computation using
         run time shape information.
-    add_trace: Whether to add op trace information. Used to support "code" view.
+    add_trace: Whether to add python code trace information.
+        Used to support "code" view.
   """
   op_log = _merge_default_with_oplog(graph, op_log, run_meta, add_trace)
 
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt
new file mode 100644
index 0000000000..bd5c36f390
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt
@@ -0,0 +1,80 @@
+path: "tensorflow.profiler.AdviceProto.Checker"
+tf_class {
+  is_instance: "<class \'tensorflow.core.profiler.tfprof_output_pb2.Checker\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "REPORTS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt
new file mode 100644
index 0000000000..7c8c68e155
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt
@@ -0,0 +1,84 @@
+path: "tensorflow.profiler.AdviceProto.CheckersEntry"
+tf_class {
+  is_instance: "<class \'tensorflow.core.profiler.tfprof_output_pb2.CheckersEntry\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "KEY_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "VALUE_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt
new file mode 100644
index 0000000000..1b789f4fc9
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt
@@ -0,0 +1,88 @@
+path: "tensorflow.profiler.AdviceProto"
+tf_class {
+  is_instance: "<class \'tensorflow.core.profiler.tfprof_output_pb2.AdviceProto\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "CHECKERS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "Checker"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "CheckersEntry"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt
new file mode 100644
index 0000000000..f0b9605bee
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt
@@ -0,0 +1,84 @@
+path: "tensorflow.profiler.GraphNodeProto.InputShapesEntry"
+tf_class {
+  is_instance: "<class \'tensorflow.core.profiler.tfprof_output_pb2.InputShapesEntry\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "KEY_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "VALUE_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt
new file mode 100644
index 0000000000..f3bb71354e
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt
@@ -0,0 +1,164 @@
+path: "tensorflow.profiler.GraphNodeProto"
+tf_class {
+  is_instance: "<class \'tensorflow.core.profiler.tfprof_output_pb2.GraphNodeProto\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "ACCELERATOR_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "CHILDREN_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "CPU_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "DEVICES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "FLOAT_OPS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "INPUT_SHAPES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "InputShapesEntry"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "NAME_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "PARAMETERS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "REQUESTED_BYTES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "RUN_COUNT_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "SHAPES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TENSOR_VALUE_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_ACCELERATOR_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_CPU_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_DEFINITION_COUNT_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_FLOAT_OPS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_PARAMETERS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_REQUESTED_BYTES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_RUN_COUNT_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt
new file mode 100644
index 0000000000..9b88a11b2c
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt
@@ -0,0 +1,136 @@
+path: "tensorflow.profiler.MultiGraphNodeProto"
+tf_class {
+  is_instance: "<class \'tensorflow.core.profiler.tfprof_output_pb2.MultiGraphNodeProto\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "ACCELERATOR_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "CHILDREN_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "CPU_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "FLOAT_OPS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "GRAPH_NODES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "NAME_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "PARAMETERS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "REQUESTED_BYTES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_ACCELERATOR_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_CPU_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_EXEC_MICROS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_FLOAT_OPS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_PARAMETERS_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "TOTAL_REQUESTED_BYTES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt
new file mode 100644
index 0000000000..c5f9c78c9e
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt
@@ -0,0 +1,80 @@
+path: "tensorflow.profiler.OpLogProto"
+tf_class {
+  is_instance: "<class \'tensorflow.core.profiler.tfprof_log_pb2.OpLogProto\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "LOG_ENTRIES_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-profile-option-builder.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-profile-option-builder.pbtxt
new file mode 100644
index 0000000000..347187a890
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.-profile-option-builder.pbtxt
@@ -0,0 +1,89 @@
+path: "tensorflow.profiler.ProfileOptionBuilder"
+tf_class {
+  is_instance: "<class \'tensorflow.python.profiler.option_builder.ProfileOptionBuilder\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "account_displayed_op_only"
+    argspec: "args=[\'self\', \'is_true\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "float_operation"
+    argspec: "args=[], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "order_by"
+    argspec: "args=[\'self\', \'attribute\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "select"
+    argspec: "args=[\'self\', \'attributes\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "time_and_memory"
+    argspec: "args=[\'min_micros\', \'min_bytes\'], varargs=None, keywords=None, defaults=[\'1\', \'1\'], "
+  }
+  member_method {
+    name: "trainable_variables_parameter"
+    argspec: "args=[], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_accounted_types"
+    argspec: "args=[\'self\', \'account_type_regexes\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_empty_output"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_file_output"
+    argspec: "args=[\'self\', \'outfile\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_max_depth"
+    argspec: "args=[\'self\', \'max_depth\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_min_execution_time"
+    argspec: "args=[\'self\', \'min_micros\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_min_float_operations"
+    argspec: "args=[\'self\', \'min_float_ops\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_min_memory"
+    argspec: "args=[\'self\', \'min_bytes\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_min_occurrence"
+    argspec: "args=[\'self\', \'min_occurrence\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_min_parameters"
+    argspec: "args=[\'self\', \'min_params\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_node_names"
+    argspec: "args=[\'self\', \'start_name_regexes\', \'show_name_regexes\', \'hide_name_regexes\', \'trim_name_regexes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "with_stdout_output"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_step"
+    argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_timeline_output"
+    argspec: "args=[\'self\', \'timeline_file\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt
index b1a97f2e10..26b25ee3d4 100644
--- a/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt
@@ -1,5 +1,25 @@
 path: "tensorflow.profiler"
 tf_module {
+  member {
+    name: "AdviceProto"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "GraphNodeProto"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "MultiGraphNodeProto"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "OpLogProto"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member {
+    name: "ProfileOptionBuilder"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Profiler"
     mtype: "<type \'type\'>"
-- 
GitLab


From 24101b35f3baebbfff3d8057ac223b325bc415ce Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Thu, 6 Jul 2017 19:29:26 -0700
Subject: [PATCH 0783/1375] Backport changes from external Keras into
 tf.contrib.keras.

PiperOrigin-RevId: 161161308
---
 tensorflow/contrib/keras/BUILD                |  46 ++
 .../contrib/keras/python/keras/__init__.py    |   2 +-
 .../contrib/keras/python/keras/activations.py |  17 +
 .../keras/python/keras/activations_test.py    |  18 +-
 .../python/keras/applications/__init__.py     |   2 +-
 .../keras/applications/imagenet_utils_test.py | 139 ++++
 .../python/keras/applications/mobilenet.py    | 655 ++++++++++++++++++
 .../keras/applications/mobilenet_test.py      |  42 ++
 .../contrib/keras/python/keras/backend.py     | 220 +++++-
 .../contrib/keras/python/keras/callbacks.py   |  36 +-
 .../contrib/keras/python/keras/constraints.py |   6 +-
 .../keras/python/keras/engine/topology.py     |  58 +-
 .../python/keras/engine/topology_test.py      |   8 +-
 .../keras/python/keras/engine/training.py     | 407 +++++------
 .../python/keras/engine/training_test.py      |  60 +-
 .../keras/python/keras/initializers.py        |  22 +
 .../keras/python/keras/initializers_test.py   |   8 +
 .../keras/python/keras/integration_test.py    |   7 +
 .../keras/layers/advanced_activations.py      |   2 +-
 .../python/keras/layers/convolutional.py      | 161 ++++-
 .../python/keras/layers/convolutional_test.py |  60 ++
 .../contrib/keras/python/keras/layers/core.py |  15 +-
 .../keras/python/keras/layers/embeddings.py   |  28 +-
 .../python/keras/layers/embeddings_test.py    |  21 +
 .../keras/python/keras/layers/local.py        | 154 +---
 .../keras/python/keras/layers/lstm_test.py    |  44 +-
 .../keras/python/keras/layers/noise.py        |  62 ++
 .../keras/python/keras/layers/noise_test.py   |   7 +
 .../keras/python/keras/layers/recurrent.py    | 150 ++--
 .../keras/python/keras/layers/wrappers.py     |  39 +-
 .../python/keras/layers/wrappers_test.py      |  10 +
 .../contrib/keras/python/keras/metrics.py     |   5 +
 .../keras/python/keras/metrics_test.py        |  15 +
 .../contrib/keras/python/keras/models.py      | 238 ++++---
 .../contrib/keras/python/keras/models_test.py |  21 +
 .../contrib/keras/python/keras/optimizers.py  |  33 +-
 .../keras/python/keras/optimizers_test.py     |  24 +
 .../keras/python/keras/preprocessing/image.py | 152 ++--
 .../python/keras/preprocessing/image_test.py  |  12 -
 .../keras/python/keras/preprocessing/text.py  |  40 +-
 .../python/keras/preprocessing/text_test.py   |  21 +-
 .../keras/python/keras/utils/__init__.py      |   3 +
 .../keras/python/keras/utils/data_utils.py    | 385 ++++++++++
 .../python/keras/utils/data_utils_test.py     | 172 +++++
 .../keras/python/keras/utils/generic_utils.py |  18 +
 .../python/keras/utils/generic_utils_test.py  |  75 ++
 .../keras/python/keras/utils/io_utils.py      |  32 +
 .../keras/python/keras/utils/layer_utils.py   |  33 +-
 tensorflow/python/layers/convolutional.py     |  24 +
 49 files changed, 3149 insertions(+), 660 deletions(-)
 create mode 100644 tensorflow/contrib/keras/python/keras/applications/imagenet_utils_test.py
 create mode 100644 tensorflow/contrib/keras/python/keras/applications/mobilenet.py
 create mode 100644 tensorflow/contrib/keras/python/keras/applications/mobilenet_test.py
 create mode 100644 tensorflow/contrib/keras/python/keras/utils/data_utils_test.py
 create mode 100644 tensorflow/contrib/keras/python/keras/utils/generic_utils_test.py

diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index 619ebb7ce0..9e92b03399 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -52,6 +52,7 @@ py_library(
         "python/keras/applications/__init__.py",
         "python/keras/applications/imagenet_utils.py",
         "python/keras/applications/inception_v3.py",
+        "python/keras/applications/mobilenet.py",
         "python/keras/applications/resnet50.py",
         "python/keras/applications/vgg16.py",
         "python/keras/applications/vgg19.py",
@@ -239,6 +240,17 @@ py_test(
     ],
 )
 
+py_test(
+    name = "mobilenet_test",
+    size = "small",
+    srcs = ["python/keras/applications/mobilenet_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 py_test(
     name = "resnet50_test",
     size = "small",
@@ -479,6 +491,40 @@ py_test(
     ],
 )
 
+py_test(
+    name = "data_utils_test",
+    size = "small",
+    srcs = ["python/keras/utils/data_utils_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["notsan"],
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
+py_test(
+    name = "generic_utils_test",
+    size = "small",
+    srcs = ["python/keras/utils/generic_utils_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
+py_test(
+    name = "imagenet_utils_test",
+    size = "small",
+    srcs = ["python/keras/applications/imagenet_utils_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 py_test(
     name = "image_test",
     size = "medium",
diff --git a/tensorflow/contrib/keras/python/keras/__init__.py b/tensorflow/contrib/keras/python/keras/__init__.py
index 1c1485c0cd..6e0e03d7f7 100644
--- a/tensorflow/contrib/keras/python/keras/__init__.py
+++ b/tensorflow/contrib/keras/python/keras/__init__.py
@@ -37,4 +37,4 @@ from tensorflow.contrib.keras.python.keras import utils
 from tensorflow.contrib.keras.python.keras import wrappers
 from tensorflow.contrib.keras.python.keras.layers import Input
 
-__version__ = '2.0.4-tf'
+__version__ = '2.0.5-tf'
diff --git a/tensorflow/contrib/keras/python/keras/activations.py b/tensorflow/contrib/keras/python/keras/activations.py
index 35d15e74c2..7f04234e01 100644
--- a/tensorflow/contrib/keras/python/keras/activations.py
+++ b/tensorflow/contrib/keras/python/keras/activations.py
@@ -54,6 +54,23 @@ def elu(x, alpha=1.0):
   return K.elu(x, alpha)
 
 
+def selu(x):
+  """Scaled Exponential Linear Unit. (Klambauer et al., 2017).
+
+  Arguments:
+      x: A tensor or variable to compute the activation function for.
+
+  Returns:
+    Tensor with the same shape and dtype as `x`.
+
+  References:
+      - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+  """
+  alpha = 1.6732632423543772848170429916717
+  scale = 1.0507009873554804934193349852946
+  return scale * K.elu(x, alpha)
+
+
 def softplus(x):
   return K.softplus(x)
 
diff --git a/tensorflow/contrib/keras/python/keras/activations_test.py b/tensorflow/contrib/keras/python/keras/activations_test.py
index eec4d257f2..3d21610e49 100644
--- a/tensorflow/contrib/keras/python/keras/activations_test.py
+++ b/tensorflow/contrib/keras/python/keras/activations_test.py
@@ -35,7 +35,7 @@ class KerasActivationsTest(test.TestCase):
   def test_serialization(self):
     all_activations = ['softmax', 'relu', 'elu', 'tanh',
                        'sigmoid', 'hard_sigmoid', 'linear',
-                       'softplus', 'softsign']
+                       'softplus', 'softsign', 'selu']
     for name in all_activations:
       fn = keras.activations.get(name)
       ref_fn = getattr(keras.activations, name)
@@ -63,6 +63,22 @@ class KerasActivationsTest(test.TestCase):
     expected = _ref_softmax(test_values[0, 0])
     self.assertAllClose(result[0, 0], expected, rtol=1e-05)
 
+  def test_selu(self):
+    x = keras.backend.placeholder(ndim=2)
+    f = keras.backend.function([x], [keras.activations.selu(x)])
+    alpha = 1.6732632423543772848170429916717
+    scale = 1.0507009873554804934193349852946
+
+    with self.test_session():
+      positive_values = np.array([[1, 2]], dtype=keras.backend.floatx())
+      result = f([positive_values])[0]
+      self.assertAllClose(result, positive_values * scale, rtol=1e-05)
+
+      negative_values = np.array([[-1, -2]], dtype=keras.backend.floatx())
+      result = f([negative_values])[0]
+      true_result = (np.exp(negative_values) - 1) * scale * alpha
+      self.assertAllClose(result, true_result)
+
   def test_softplus(self):
     def softplus(x):
       return np.log(np.ones_like(x) + np.exp(x))
diff --git a/tensorflow/contrib/keras/python/keras/applications/__init__.py b/tensorflow/contrib/keras/python/keras/applications/__init__.py
index c6af9ea9f1..9139df30a6 100644
--- a/tensorflow/contrib/keras/python/keras/applications/__init__.py
+++ b/tensorflow/contrib/keras/python/keras/applications/__init__.py
@@ -19,8 +19,8 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.keras.python.keras.applications.inception_v3 import InceptionV3
+from tensorflow.contrib.keras.python.keras.applications.mobilenet import MobileNet
 from tensorflow.contrib.keras.python.keras.applications.resnet50 import ResNet50
 from tensorflow.contrib.keras.python.keras.applications.vgg16 import VGG16
 from tensorflow.contrib.keras.python.keras.applications.vgg19 import VGG19
 from tensorflow.contrib.keras.python.keras.applications.xception import Xception
-
diff --git a/tensorflow/contrib/keras/python/keras/applications/imagenet_utils_test.py b/tensorflow/contrib/keras/python/keras/applications/imagenet_utils_test.py
new file mode 100644
index 0000000000..f3bcf93a95
--- /dev/null
+++ b/tensorflow/contrib/keras/python/keras/applications/imagenet_utils_test.py
@@ -0,0 +1,139 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Inception V3 application."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.keras.python import keras
+from tensorflow.python.platform import test
+
+
+class ImageNetUtilsTest(test.TestCase):
+
+  def test_preprocess_input(self):
+    x = np.random.uniform(0, 255, (2, 3, 2, 3))
+    self.assertEqual(
+        keras.applications.imagenet_utils.preprocess_input(x).shape, x.shape)
+
+    out1 = keras.applications.imagenet_utils.preprocess_input(
+        x, 'channels_last')
+    out2 = keras.applications.imagenet_utils.preprocess_input(
+        np.transpose(x, (0, 3, 1, 2)), 'channels_first')
+    self.assertAllClose(out1, out2.transpose(0, 2, 3, 1))
+
+  def test_decode_predictions(self):
+    x = np.zeros((2, 1000))
+    x[0, 372] = 1.0
+    x[1, 549] = 1.0
+    outs = keras.applications.imagenet_utils.decode_predictions(x, top=1)
+    scores = [out[0][2] for out in outs]
+    self.assertEqual(scores[0], scores[1])
+
+    # the numbers of columns and ImageNet classes are not identical.
+    with self.assertRaises(ValueError):
+      keras.applications.imagenet_utils.decode_predictions(np.ones((2, 100)))
+
+  def test_obtain_input_shape(self):
+    # input_shape and default_size are not identical.
+    with self.assertRaises(ValueError):
+      keras.applications.imagenet_utils._obtain_input_shape(
+          input_shape=(224, 224, 3),
+          default_size=299,
+          min_size=139,
+          data_format='channels_last',
+          include_top=True)
+
+    # Test invalid use cases
+    for data_format in ['channels_last', 'channels_first']:
+      # input_shape is smaller than min_size.
+      shape = (100, 100)
+      if data_format == 'channels_last':
+        input_shape = shape + (3,)
+      else:
+        input_shape = (3,) + shape
+      with self.assertRaises(ValueError):
+        keras.applications.imagenet_utils._obtain_input_shape(
+            input_shape=input_shape,
+            default_size=None,
+            min_size=139,
+            data_format=data_format,
+            include_top=False)
+
+      # shape is 1D.
+      shape = (100,)
+      if data_format == 'channels_last':
+        input_shape = shape + (3,)
+      else:
+        input_shape = (3,) + shape
+      with self.assertRaises(ValueError):
+        keras.applications.imagenet_utils._obtain_input_shape(
+            input_shape=input_shape,
+            default_size=None,
+            min_size=139,
+            data_format=data_format,
+            include_top=False)
+
+      # the number of channels is 5 not 3.
+      shape = (100, 100)
+      if data_format == 'channels_last':
+        input_shape = shape + (5,)
+      else:
+        input_shape = (5,) + shape
+      with self.assertRaises(ValueError):
+        keras.applications.imagenet_utils._obtain_input_shape(
+            input_shape=input_shape,
+            default_size=None,
+            min_size=139,
+            data_format=data_format,
+            include_top=False)
+
+    assert keras.applications.imagenet_utils._obtain_input_shape(
+        input_shape=None,
+        default_size=None,
+        min_size=139,
+        data_format='channels_last',
+        include_top=False) == (None, None, 3)
+
+    assert keras.applications.imagenet_utils._obtain_input_shape(
+        input_shape=None,
+        default_size=None,
+        min_size=139,
+        data_format='channels_first',
+        include_top=False) == (3, None, None)
+
+    assert keras.applications.imagenet_utils._obtain_input_shape(
+        input_shape=None,
+        default_size=None,
+        min_size=139,
+        data_format='channels_last',
+        include_top=False) == (None, None, 3)
+
+    assert keras.applications.imagenet_utils._obtain_input_shape(
+        input_shape=(150, 150, 3),
+        default_size=None,
+        min_size=139,
+        data_format='channels_last',
+        include_top=False) == (150, 150, 3)
+
+    assert keras.applications.imagenet_utils._obtain_input_shape(
+        input_shape=(3, None, None),
+        default_size=None,
+        min_size=139,
+        data_format='channels_first',
+        include_top=False) == (3, None, None)
diff --git a/tensorflow/contrib/keras/python/keras/applications/mobilenet.py b/tensorflow/contrib/keras/python/keras/applications/mobilenet.py
new file mode 100644
index 0000000000..37240234d3
--- /dev/null
+++ b/tensorflow/contrib/keras/python/keras/applications/mobilenet.py
@@ -0,0 +1,655 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""MobileNet v1 models for Keras.
+
+MobileNet is a general architecture and can be used for multiple use cases.
+Depending on the use case, it can use different input layer size and
+different width factors. This allows different width models to reduce
+the number of multiply-adds and thereby
+reduce inference cost on mobile devices.
+
+MobileNets support any input size greater than 32 x 32, with larger image sizes
+offering better performance.
+The number of parameters and number of multiply-adds
+can be modified by using the `alpha` parameter,
+which increases/decreases the number of filters in each layer.
+By altering the image size and `alpha` parameter,
+all 16 models from the paper can be built, with ImageNet weights provided.
+
+The paper demonstrates the performance of MobileNets using `alpha` values of
+1.0 (also called 100 % MobileNet), 0.75, 0.5 and 0.25.
+For each of these `alpha` values, weights for 4 different input image sizes
+are provided (224, 192, 160, 128).
+
+The following table describes the size and accuracy of the 100% MobileNet
+on size 224 x 224:
+----------------------------------------------------------------------------
+Width Multiplier (alpha) | ImageNet Acc |  Multiply-Adds (M) |  Params (M)
+----------------------------------------------------------------------------
+|   1.0 MobileNet-224    |    70.6 %     |        529        |     4.2     |
+|   0.75 MobileNet-224   |    68.4 %     |        325        |     2.6     |
+|   0.50 MobileNet-224   |    63.7 %     |        149        |     1.3     |
+|   0.25 MobileNet-224   |    50.6 %     |        41         |     0.5     |
+----------------------------------------------------------------------------
+
+The following table describes the performance of
+the 100 % MobileNet on various input sizes:
+------------------------------------------------------------------------
+      Resolution      | ImageNet Acc | Multiply-Adds (M) | Params (M)
+------------------------------------------------------------------------
+|  1.0 MobileNet-224  |    70.6 %    |        529        |     4.2     |
+|  1.0 MobileNet-192  |    69.1 %    |        529        |     4.2     |
+|  1.0 MobileNet-160  |    67.2 %    |        529        |     4.2     |
+|  1.0 MobileNet-128  |    64.4 %    |        529        |     4.2     |
+------------------------------------------------------------------------
+
+The weights for all 16 models are obtained and translated
+from Tensorflow checkpoints found at
+https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.md
+
+# Reference
+- [MobileNets: Efficient Convolutional Neural Networks for
+   Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf))
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import warnings
+
+from tensorflow.contrib.keras.python.keras import backend as K
+from tensorflow.contrib.keras.python.keras import constraints
+from tensorflow.contrib.keras.python.keras import initializers
+from tensorflow.contrib.keras.python.keras import regularizers
+from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import _obtain_input_shape
+from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import decode_predictions  # pylint: disable=unused-import
+from tensorflow.contrib.keras.python.keras.engine import InputSpec
+from tensorflow.contrib.keras.python.keras.engine.topology import get_source_inputs
+from tensorflow.contrib.keras.python.keras.layers import Activation
+from tensorflow.contrib.keras.python.keras.layers import BatchNormalization
+from tensorflow.contrib.keras.python.keras.layers import Conv2D
+from tensorflow.contrib.keras.python.keras.layers import Dropout
+from tensorflow.contrib.keras.python.keras.layers import GlobalAveragePooling2D
+from tensorflow.contrib.keras.python.keras.layers import GlobalMaxPooling2D
+from tensorflow.contrib.keras.python.keras.layers import Input
+from tensorflow.contrib.keras.python.keras.layers import Reshape
+from tensorflow.contrib.keras.python.keras.models import Model
+from tensorflow.contrib.keras.python.keras.utils import conv_utils
+from tensorflow.contrib.keras.python.keras.utils.data_utils import get_file
+
+BASE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.6/'
+
+
+def relu6(x):
+  return K.relu(x, max_value=6)
+
+
+def preprocess_input(x):
+  x /= 255.
+  x -= 0.5
+  x *= 2.
+  return x
+
+
+class DepthwiseConv2D(Conv2D):
+  """Depthwise separable 2D convolution.
+
+  Depthwise Separable convolutions consists in performing
+  just the first step in a depthwise spatial convolution
+  (which acts on each input channel separately).
+  The `depth_multiplier` argument controls how many
+  output channels are generated per input channel in the depthwise step.
+
+  Arguments:
+      kernel_size: An integer or tuple/list of 2 integers, specifying the
+          width and height of the 2D convolution window.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+      strides: An integer or tuple/list of 2 integers,
+          specifying the strides of the convolution along the width and height.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+          Specifying any stride value != 1 is incompatible with specifying
+          any `dilation_rate` value != 1.
+      padding: one of `"valid"` or `"same"` (case-insensitive).
+      depth_multiplier: The number of depthwise convolution output channels
+          for each input channel.
+          The total number of depthwise convolution output
+          channels will be equal to `filters_in * depth_multiplier`.
+      data_format: A string,
+          one of `channels_last` (default) or `channels_first`.
+          The ordering of the dimensions in the inputs.
+          `channels_last` corresponds to inputs with shape
+          `(batch, height, width, channels)` while `channels_first`
+          corresponds to inputs with shape
+          `(batch, channels, height, width)`.
+          It defaults to the `image_data_format` value found in your
+          Keras config file at `~/.keras/keras.json`.
+          If you never set it, then it will be "channels_last".
+      activation: Activation function to use
+          (see [activations](../activations.md)).
+          If you don't specify anything, no activation is applied
+          (ie. "linear" activation: `a(x) = x`).
+      use_bias: Boolean, whether the layer uses a bias vector.
+      depthwise_initializer: Initializer for the depthwise kernel matrix
+          (see [initializers](../initializers.md)).
+      bias_initializer: Initializer for the bias vector
+          (see [initializers](../initializers.md)).
+      depthwise_regularizer: Regularizer function applied to
+          the depthwise kernel matrix
+          (see [regularizer](../regularizers.md)).
+      bias_regularizer: Regularizer function applied to the bias vector
+          (see [regularizer](../regularizers.md)).
+      activity_regularizer: Regularizer function applied to
+          the output of the layer (its "activation").
+          (see [regularizer](../regularizers.md)).
+      depthwise_constraint: Constraint function applied to
+          the depthwise kernel matrix
+          (see [constraints](../constraints.md)).
+      bias_constraint: Constraint function applied to the bias vector
+          (see [constraints](../constraints.md)).
+
+  Input shape:
+      4D tensor with shape:
+      `[batch, channels, rows, cols]` if data_format='channels_first'
+      or 4D tensor with shape:
+      `[batch, rows, cols, channels]` if data_format='channels_last'.
+
+  Output shape:
+      4D tensor with shape:
+      `[batch, filters, new_rows, new_cols]` if data_format='channels_first'
+      or 4D tensor with shape:
+      `[batch, new_rows, new_cols, filters]` if data_format='channels_last'.
+      `rows` and `cols` values might have changed due to padding.
+  """
+
+  def __init__(self,
+               kernel_size,
+               strides=(1, 1),
+               padding='valid',
+               depth_multiplier=1,
+               data_format=None,
+               activation=None,
+               use_bias=True,
+               depthwise_initializer='glorot_uniform',
+               bias_initializer='zeros',
+               depthwise_regularizer=None,
+               bias_regularizer=None,
+               activity_regularizer=None,
+               depthwise_constraint=None,
+               bias_constraint=None,
+               **kwargs):
+    super(DepthwiseConv2D, self).__init__(
+        filters=None,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        activation=activation,
+        use_bias=use_bias,
+        bias_regularizer=bias_regularizer,
+        activity_regularizer=activity_regularizer,
+        bias_constraint=bias_constraint,
+        **kwargs)
+    self.depth_multiplier = depth_multiplier
+    self.depthwise_initializer = initializers.get(depthwise_initializer)
+    self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
+    self.depthwise_constraint = constraints.get(depthwise_constraint)
+    self.bias_initializer = initializers.get(bias_initializer)
+
+  def build(self, input_shape):
+    if len(input_shape) < 4:
+      raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. '
+                       'Received input shape:', str(input_shape))
+    if self.data_format == 'channels_first':
+      channel_axis = 1
+    else:
+      channel_axis = 3
+    if input_shape[channel_axis] is None:
+      raise ValueError('The channel dimension of the inputs to '
+                       '`DepthwiseConv2D` '
+                       'should be defined. Found `None`.')
+    input_dim = int(input_shape[channel_axis])
+    depthwise_kernel_shape = (self.kernel_size[0], self.kernel_size[1],
+                              input_dim, self.depth_multiplier)
+
+    self.depthwise_kernel = self.add_weight(
+        shape=depthwise_kernel_shape,
+        initializer=self.depthwise_initializer,
+        name='depthwise_kernel',
+        regularizer=self.depthwise_regularizer,
+        constraint=self.depthwise_constraint)
+
+    if self.use_bias:
+      self.bias = self.add_weight(
+          shape=(input_dim * self.depth_multiplier,),
+          initializer=self.bias_initializer,
+          name='bias',
+          regularizer=self.bias_regularizer,
+          constraint=self.bias_constraint)
+    else:
+      self.bias = None
+    # Set input spec.
+    self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
+    self.built = True
+
+  def call(self, inputs, training=None):
+    outputs = K.depthwise_conv2d(
+        inputs,
+        self.depthwise_kernel,
+        strides=self.strides,
+        padding=self.padding,
+        dilation_rate=self.dilation_rate,
+        data_format=self.data_format)
+
+    if self.bias:
+      outputs = K.bias_add(outputs, self.bias, data_format=self.data_format)
+
+    if self.activation is not None:
+      return self.activation(outputs)
+
+    return outputs
+
+  def compute_output_shape(self, input_shape):
+    if self.data_format == 'channels_first':
+      rows = input_shape[2]
+      cols = input_shape[3]
+      out_filters = input_shape[1] * self.depth_multiplier
+    elif self.data_format == 'channels_last':
+      rows = input_shape[1]
+      cols = input_shape[2]
+      out_filters = input_shape[3] * self.depth_multiplier
+
+    rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
+                                         self.padding, self.strides[0])
+    cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
+                                         self.padding, self.strides[1])
+
+    if self.data_format == 'channels_first':
+      return (input_shape[0], out_filters, rows, cols)
+    elif self.data_format == 'channels_last':
+      return (input_shape[0], rows, cols, out_filters)
+
+  def get_config(self):
+    config = super(DepthwiseConv2D, self).get_config()
+    config.pop('filters')
+    config.pop('kernel_initializer')
+    config.pop('kernel_regularizer')
+    config.pop('kernel_constraint')
+    config['depth_multiplier'] = self.depth_multiplier
+    config['depthwise_initializer'] = initializers.serialize(
+        self.depthwise_initializer)
+    config['depthwise_regularizer'] = regularizers.serialize(
+        self.depthwise_regularizer)
+    config['depthwise_constraint'] = constraints.serialize(
+        self.depthwise_constraint)
+    return config
+
+
+def MobileNet(input_shape=None,  # pylint: disable=invalid-name
+              alpha=1.0,
+              depth_multiplier=1,
+              dropout=1e-3,
+              include_top=True,
+              weights='imagenet',
+              input_tensor=None,
+              pooling=None,
+              classes=1000):
+  """Instantiates the MobileNet architecture.
+
+  Note that only TensorFlow is supported for now,
+  therefore it only works with the data format
+  `image_data_format='channels_last'` in your Keras config
+  at `~/.keras/keras.json`.
+
+  To load a MobileNet model via `load_model`, import the custom
+  objects `relu6` and `DepthwiseConv2D` and pass them to the
+  `custom_objects` parameter.
+  E.g.
+  model = load_model('mobilenet.h5', custom_objects={
+                     'relu6': mobilenet.relu6,
+                     'DepthwiseConv2D': mobilenet.DepthwiseConv2D})
+
+  Arguments:
+      input_shape: optional shape tuple, only to be specified
+          if `include_top` is False (otherwise the input shape
+          has to be `(224, 224, 3)` (with `channels_last` data format)
+          or (3, 224, 224) (with `channels_first` data format).
+          It should have exactly 3 inputs channels,
+          and width and height should be no smaller than 32.
+          E.g. `(200, 200, 3)` would be one valid value.
+      alpha: controls the width of the network.
+          - If `alpha` < 1.0, proportionally decreases the number
+              of filters in each layer.
+          - If `alpha` > 1.0, proportionally increases the number
+              of filters in each layer.
+          - If `alpha` = 1, default number of filters from the paper
+               are used at each layer.
+      depth_multiplier: depth multiplier for depthwise convolution
+          (also called the resolution multiplier)
+      dropout: dropout rate
+      include_top: whether to include the fully-connected
+          layer at the top of the network.
+      weights: `None` (random initialization) or
+          `imagenet` (ImageNet weights)
+      input_tensor: optional Keras tensor (i.e. output of
+          `layers.Input()`)
+          to use as image input for the model.
+      pooling: Optional pooling mode for feature extraction
+          when `include_top` is `False`.
+          - `None` means that the output of the model
+              will be the 4D tensor output of the
+              last convolutional layer.
+          - `avg` means that global average pooling
+              will be applied to the output of the
+              last convolutional layer, and thus
+              the output of the model will be a
+              2D tensor.
+          - `max` means that global max pooling will
+              be applied.
+      classes: optional number of classes to classify images
+          into, only to be specified if `include_top` is True, and
+          if no `weights` argument is specified.
+
+  Returns:
+      A Keras model instance.
+
+  Raises:
+      ValueError: in case of invalid argument for `weights`,
+          or invalid input shape.
+      RuntimeError: If attempting to run this model with a
+          backend that does not support separable convolutions.
+  """
+
+  if K.backend() != 'tensorflow':
+    raise RuntimeError('Only TensorFlow backend is currently supported, '
+                       'as other backends do not support '
+                       'depthwise convolution.')
+
+  if weights not in {'imagenet', None}:
+    raise ValueError('The `weights` argument should be either '
+                     '`None` (random initialization) or `imagenet` '
+                     '(pre-training on ImageNet).')
+
+  if weights == 'imagenet' and include_top and classes != 1000:
+    raise ValueError('If using `weights` as ImageNet with `include_top` '
+                     'as true, `classes` should be 1000')
+
+  # Determine proper input shape.
+  input_shape = _obtain_input_shape(
+      input_shape,
+      default_size=224,
+      min_size=32,
+      data_format=K.image_data_format(),
+      include_top=include_top or weights)
+  if K.image_data_format() == 'channels_last':
+    row_axis, col_axis = (0, 1)
+  else:
+    row_axis, col_axis = (1, 2)
+  rows = input_shape[row_axis]
+  cols = input_shape[col_axis]
+
+  if weights == 'imagenet':
+    if depth_multiplier != 1:
+      raise ValueError('If imagenet weights are being loaded, '
+                       'depth multiplier must be 1')
+
+    if alpha not in [0.25, 0.50, 0.75, 1.0]:
+      raise ValueError('If imagenet weights are being loaded, '
+                       'alpha can be one of'
+                       '`0.25`, `0.50`, `0.75` or `1.0` only.')
+
+    if rows != cols or rows not in [128, 160, 192, 224]:
+      raise ValueError('If imagenet weights are being loaded, '
+                       'input must have a static square shape (one of '
+                       '(128,128), (160,160), (192,192), or (224, 224)).'
+                       ' Input shape provided = %s' % (input_shape,))
+
+  if K.image_data_format() != 'channels_last':
+    warnings.warn('The MobileNet family of models is only available '
+                  'for the input data format "channels_last" '
+                  '(width, height, channels). '
+                  'However your settings specify the default '
+                  'data format "channels_first" (channels, width, height).'
+                  ' You should set `image_data_format="channels_last"` '
+                  'in your Keras config located at ~/.keras/keras.json. '
+                  'The model being returned right now will expect inputs '
+                  'to follow the "channels_last" data format.')
+    K.set_image_data_format('channels_last')
+    old_data_format = 'channels_first'
+  else:
+    old_data_format = None
+
+  if input_tensor is None:
+    img_input = Input(shape=input_shape)
+  else:
+    if not K.is_keras_tensor(input_tensor):
+      img_input = Input(tensor=input_tensor, shape=input_shape)
+    else:
+      img_input = input_tensor
+
+  x = _conv_block(img_input, 32, alpha, strides=(2, 2))
+  x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)
+
+  x = _depthwise_conv_block(
+      x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2)
+  x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)
+
+  x = _depthwise_conv_block(
+      x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4)
+  x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)
+
+  x = _depthwise_conv_block(
+      x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6)
+  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)
+  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)
+  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)
+  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)
+  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)
+
+  x = _depthwise_conv_block(
+      x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12)
+  x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)
+
+  if include_top:
+    if K.image_data_format() == 'channels_first':
+      shape = (int(1024 * alpha), 1, 1)
+    else:
+      shape = (1, 1, int(1024 * alpha))
+
+    x = GlobalAveragePooling2D()(x)
+    x = Reshape(shape, name='reshape_1')(x)
+    x = Dropout(dropout, name='dropout')(x)
+    x = Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x)
+    x = Activation('softmax', name='act_softmax')(x)
+    x = Reshape((classes,), name='reshape_2')(x)
+  else:
+    if pooling == 'avg':
+      x = GlobalAveragePooling2D()(x)
+    elif pooling == 'max':
+      x = GlobalMaxPooling2D()(x)
+
+  # Ensure that the model takes into account
+  # any potential predecessors of `input_tensor`.
+  if input_tensor is not None:
+    inputs = get_source_inputs(input_tensor)
+  else:
+    inputs = img_input
+
+  # Create model.
+  model = Model(inputs, x, name='mobilenet_%0.2f_%s' % (alpha, rows))
+
+  # load weights
+  if weights == 'imagenet':
+    if K.image_data_format() == 'channels_first':
+      raise ValueError('Weights for "channels_last" format '
+                       'are not available.')
+    if alpha == 1.0:
+      alpha_text = '1_0'
+    elif alpha == 0.75:
+      alpha_text = '7_5'
+    elif alpha == 0.50:
+      alpha_text = '5_0'
+    else:
+      alpha_text = '2_5'
+
+    if include_top:
+      model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows)
+      weigh_path = BASE_WEIGHT_PATH + model_name
+      weights_path = get_file(model_name, weigh_path, cache_subdir='models')
+    else:
+      model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows)
+      weigh_path = BASE_WEIGHT_PATH + model_name
+      weights_path = get_file(model_name, weigh_path, cache_subdir='models')
+    model.load_weights(weights_path)
+
+  if old_data_format:
+    K.set_image_data_format(old_data_format)
+  return model
+
+
+def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
+  """Adds an initial convolution layer (with batch normalization and relu6).
+
+  Arguments:
+      inputs: Input tensor of shape `(rows, cols, 3)`
+          (with `channels_last` data format) or
+          (3, rows, cols) (with `channels_first` data format).
+          It should have exactly 3 inputs channels,
+          and width and height should be no smaller than 32.
+          E.g. `(224, 224, 3)` would be one valid value.
+      filters: Integer, the dimensionality of the output space
+          (i.e. the number output of filters in the convolution).
+      alpha: controls the width of the network.
+          - If `alpha` < 1.0, proportionally decreases the number
+              of filters in each layer.
+          - If `alpha` > 1.0, proportionally increases the number
+              of filters in each layer.
+          - If `alpha` = 1, default number of filters from the paper
+               are used at each layer.
+      kernel: An integer or tuple/list of 2 integers, specifying the
+          width and height of the 2D convolution window.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+      strides: An integer or tuple/list of 2 integers,
+          specifying the strides of the convolution along the width and height.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+          Specifying any stride value != 1 is incompatible with specifying
+          any `dilation_rate` value != 1.
+
+  Input shape:
+      4D tensor with shape:
+      `(samples, channels, rows, cols)` if data_format='channels_first'
+      or 4D tensor with shape:
+      `(samples, rows, cols, channels)` if data_format='channels_last'.
+
+  Output shape:
+      4D tensor with shape:
+      `(samples, filters, new_rows, new_cols)` if data_format='channels_first'
+      or 4D tensor with shape:
+      `(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
+      `rows` and `cols` values might have changed due to stride.
+
+  Returns:
+      Output tensor of block.
+  """
+  channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+  filters = int(filters * alpha)
+  x = Conv2D(
+      filters,
+      kernel,
+      padding='same',
+      use_bias=False,
+      strides=strides,
+      name='conv1')(inputs)
+  x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x)
+  return Activation(relu6, name='conv1_relu')(x)
+
+
+def _depthwise_conv_block(inputs,
+                          pointwise_conv_filters,
+                          alpha,
+                          depth_multiplier=1,
+                          strides=(1, 1),
+                          block_id=1):
+  """Adds a depthwise convolution block.
+
+  A depthwise convolution block consists of a depthwise conv,
+  batch normalization, relu6, pointwise convolution,
+  batch normalization and relu6 activation.
+
+  Arguments:
+      inputs: Input tensor of shape `(rows, cols, channels)`
+          (with `channels_last` data format) or
+          (channels, rows, cols) (with `channels_first` data format).
+      pointwise_conv_filters: Integer, the dimensionality of the output space
+          (i.e. the number output of filters in the pointwise convolution).
+      alpha: controls the width of the network.
+          - If `alpha` < 1.0, proportionally decreases the number
+              of filters in each layer.
+          - If `alpha` > 1.0, proportionally increases the number
+              of filters in each layer.
+          - If `alpha` = 1, default number of filters from the paper
+               are used at each layer.
+      depth_multiplier: The number of depthwise convolution output channels
+          for each input channel.
+          The total number of depthwise convolution output
+          channels will be equal to `filters_in * depth_multiplier`.
+      strides: An integer or tuple/list of 2 integers,
+          specifying the strides of the convolution along the width and height.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+          Specifying any stride value != 1 is incompatible with specifying
+          any `dilation_rate` value != 1.
+      block_id: Integer, a unique identification designating the block number.
+
+  Input shape:
+      4D tensor with shape:
+      `(batch, channels, rows, cols)` if data_format='channels_first'
+      or 4D tensor with shape:
+      `(batch, rows, cols, channels)` if data_format='channels_last'.
+
+  Output shape:
+      4D tensor with shape:
+      `(batch, filters, new_rows, new_cols)` if data_format='channels_first'
+      or 4D tensor with shape:
+      `(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
+      `rows` and `cols` values might have changed due to stride.
+
+  Returns:
+      Output tensor of block.
+  """
+  channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+  pointwise_conv_filters = int(pointwise_conv_filters * alpha)
+
+  x = DepthwiseConv2D(  # pylint: disable=not-callable
+      (3, 3),
+      padding='same',
+      depth_multiplier=depth_multiplier,
+      strides=strides,
+      use_bias=False,
+      name='conv_dw_%d' % block_id)(inputs)
+  x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x)
+  x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x)
+
+  x = Conv2D(
+      pointwise_conv_filters, (1, 1),
+      padding='same',
+      use_bias=False,
+      strides=(1, 1),
+      name='conv_pw_%d' % block_id)(x)
+  x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x)
+  return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)
diff --git a/tensorflow/contrib/keras/python/keras/applications/mobilenet_test.py b/tensorflow/contrib/keras/python/keras/applications/mobilenet_test.py
new file mode 100644
index 0000000000..6aa786f9b1
--- /dev/null
+++ b/tensorflow/contrib/keras/python/keras/applications/mobilenet_test.py
@@ -0,0 +1,42 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for MobileNet application."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.keras.python import keras
+from tensorflow.python.platform import test
+
+
+class MobileNetTest(test.TestCase):
+
+  def test_with_top(self):
+    model = keras.applications.MobileNet(weights=None)
+    self.assertEqual(model.output_shape, (None, 1000))
+
+  def test_no_top(self):
+    model = keras.applications.MobileNet(weights=None, include_top=False)
+    self.assertEqual(model.output_shape, (None, None, None, 1024))
+
+  def test_with_pooling(self):
+    model = keras.applications.MobileNet(weights=None,
+                                         include_top=False,
+                                         pooling='avg')
+    self.assertEqual(model.output_shape, (None, 1024))
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index 324f510301..5175bd0040 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -708,7 +708,7 @@ def dtype(x):
       'float32_ref'
   ```
   """
-  return x.dtype.name
+  return x.dtype.base_dtype.name
 
 
 def eval(x):
@@ -1309,7 +1309,7 @@ def max(x, axis=None, keepdims=False):
       A tensor with maximum values of `x`.
   """
   axis = _normalize_axis(axis, ndim(x))
-  return math_ops.reduce_max(x, reduction_indices=axis, keep_dims=keepdims)
+  return math_ops.reduce_max(x, axis=axis, keep_dims=keepdims)
 
 
 def min(x, axis=None, keepdims=False):
@@ -1327,7 +1327,7 @@ def min(x, axis=None, keepdims=False):
       A tensor with miminum values of `x`.
   """
   axis = _normalize_axis(axis, ndim(x))
-  return math_ops.reduce_min(x, reduction_indices=axis, keep_dims=keepdims)
+  return math_ops.reduce_min(x, axis=axis, keep_dims=keepdims)
 
 
 def sum(x, axis=None, keepdims=False):
@@ -1345,7 +1345,7 @@ def sum(x, axis=None, keepdims=False):
       A tensor with sum of `x`.
   """
   axis = _normalize_axis(axis, ndim(x))
-  return math_ops.reduce_sum(x, reduction_indices=axis, keep_dims=keepdims)
+  return math_ops.reduce_sum(x, axis=axis, keep_dims=keepdims)
 
 
 def prod(x, axis=None, keepdims=False):
@@ -1363,7 +1363,7 @@ def prod(x, axis=None, keepdims=False):
       A tensor with the product of elements of `x`.
   """
   axis = _normalize_axis(axis, ndim(x))
-  return math_ops.reduce_prod(x, reduction_indices=axis, keep_dims=keepdims)
+  return math_ops.reduce_prod(x, axis=axis, keep_dims=keepdims)
 
 
 def cumsum(x, axis=0):
@@ -1411,10 +1411,10 @@ def var(x, axis=None, keepdims=False):
   axis = _normalize_axis(axis, ndim(x))
   if x.dtype.base_dtype == dtypes_module.bool:
     x = math_ops.cast(x, floatx())
-  m = math_ops.reduce_mean(x, reduction_indices=axis, keep_dims=True)
+  m = math_ops.reduce_mean(x, axis=axis, keep_dims=True)
   devs_squared = math_ops.square(x - m)
   return math_ops.reduce_mean(
-      devs_squared, reduction_indices=axis, keep_dims=keepdims)
+      devs_squared, axis=axis, keep_dims=keepdims)
 
 
 def std(x, axis=None, keepdims=False):
@@ -1451,7 +1451,7 @@ def mean(x, axis=None, keepdims=False):
   axis = _normalize_axis(axis, ndim(x))
   if x.dtype.base_dtype == dtypes_module.bool:
     x = math_ops.cast(x, floatx())
-  return math_ops.reduce_mean(x, reduction_indices=axis, keep_dims=keepdims)
+  return math_ops.reduce_mean(x, axis=axis, keep_dims=keepdims)
 
 
 def any(x, axis=None, keepdims=False):
@@ -1467,7 +1467,7 @@ def any(x, axis=None, keepdims=False):
   """
   axis = _normalize_axis(axis, ndim(x))
   x = math_ops.cast(x, dtypes_module.bool)
-  return math_ops.reduce_any(x, reduction_indices=axis, keep_dims=keepdims)
+  return math_ops.reduce_any(x, axis=axis, keep_dims=keepdims)
 
 
 def all(x, axis=None, keepdims=False):
@@ -1483,7 +1483,7 @@ def all(x, axis=None, keepdims=False):
   """
   axis = _normalize_axis(axis, ndim(x))
   x = math_ops.cast(x, dtypes_module.bool)
-  return math_ops.reduce_all(x, reduction_indices=axis, keep_dims=keepdims)
+  return math_ops.reduce_all(x, axis=axis, keep_dims=keepdims)
 
 
 def argmax(x, axis=-1):
@@ -2889,13 +2889,13 @@ def categorical_crossentropy(output, target, from_logits=False):
   if not from_logits:
     # scale preds so that the class probas of each sample sum to 1
     output /= math_ops.reduce_sum(
-        output, reduction_indices=len(output.get_shape()) - 1, keep_dims=True)
+        output, axis=len(output.get_shape()) - 1, keep_dims=True)
     # manual computation of crossentropy
     epsilon = _to_tensor(_EPSILON, output.dtype.base_dtype)
     output = clip_ops.clip_by_value(output, epsilon, 1. - epsilon)
     return -math_ops.reduce_sum(
         target * math_ops.log(output),
-        reduction_indices=len(output.get_shape()) - 1)
+        axis=len(output.get_shape()) - 1)
   else:
     return nn.softmax_cross_entropy_with_logits(labels=target, logits=output)
 
@@ -2914,7 +2914,7 @@ def sparse_categorical_crossentropy(output, target, from_logits=False):
   Returns:
       Output tensor.
   """
-  # Note: nn.softmax_cross_entropy_with_logits
+  # Note: nn.sparse_softmax_cross_entropy_with_logits
   # expects logits, Keras expects probabilities.
   if not from_logits:
     epsilon = _to_tensor(_EPSILON, output.dtype.base_dtype)
@@ -3018,7 +3018,7 @@ def dropout(x, level, noise_shape=None, seed=None):
   if seed is None:
     seed = np.random.randint(10e6)
   # the dummy 1. works around a TF bug
-  # (float32_ref vs. float32 incomptability)
+  # (float32_ref vs. float32 incompatibility)
   return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
 
 
@@ -3380,6 +3380,42 @@ def separable_conv2d(x,
   return _postprocess_conv2d_output(x, data_format)
 
 
+def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
+                     data_format=None, dilation_rate=(1, 1)):
+  """2D convolution with separable filters.
+
+  Arguments:
+    x: input tensor
+    depthwise_kernel: convolution kernel for the depthwise convolution.
+    strides: strides tuple (length 2).
+    padding: string, `"same"` or `"valid"`.
+    data_format: string, `"channels_last"` or `"channels_first"`.
+    dilation_rate: tuple of integers,
+        dilation rates for the separable convolution.
+
+  Returns:
+    Output tensor.
+
+  Raises:
+    ValueError: if `data_format` is neither `channels_last`
+      or `channels_first`.
+  """
+  if data_format is None:
+    data_format = image_data_format()
+  if data_format not in {'channels_first', 'channels_last'}:
+    raise ValueError('Unknown data_format ' + str(data_format))
+
+  x = _preprocess_conv2d_input(x, data_format)
+  padding = _preprocess_padding(padding)
+  strides = (1,) + strides + (1,)
+
+  x = nn.depthwise_conv2d(x, depthwise_kernel,
+                          strides=strides,
+                          padding=padding,
+                          rate=dilation_rate)
+  return _postprocess_conv2d_output(x, data_format)
+
+
 def conv3d(x,
            kernel,
            strides=(1, 1, 1),
@@ -3515,41 +3551,177 @@ def pool3d(x,
   return _postprocess_conv3d_output(x, data_format)
 
 
+def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
+  """Apply 1D conv with un-shared weights.
+
+  Arguments:
+      inputs: 3D tensor with shape: (batch_size, steps, input_dim)
+      kernel: the unshared weight for convolution,
+              with shape (output_length, feature_dim, filters)
+      kernel_size: a tuple of a single integer,
+                   specifying the length of the 1D convolution window
+      strides: a tuple of a single integer,
+               specifying the stride length of the convolution
+      data_format: the data format, channels_first or channels_last
+
+  Returns:
+      the tensor after 1d conv with un-shared weights, with shape (batch_size,
+      output_lenght, filters)
+
+  Raises:
+      ValueError: if `data_format` is neither `channels_last` or
+      `channels_first`.
+  """
+  if data_format is None:
+    data_format = image_data_format()
+  if data_format not in {'channels_first', 'channels_last'}:
+    raise ValueError('Unknown data_format ' + str(data_format))
+
+  stride = strides[0]
+  kernel_shape = int_shape(kernel)
+  output_length = kernel_shape[0]
+  feature_dim = kernel_shape[1]
+
+  xs = []
+  for i in range(output_length):
+    slice_length = slice(i * stride, i * stride + kernel_size[0])
+    xs.append(reshape(inputs[:, slice_length, :], (1, -1, feature_dim)))
+  x_aggregate = concatenate(xs, axis=0)
+  # Shape: `(output_length, batch_size, filters)`.
+  output = batch_dot(x_aggregate, kernel)
+  return permute_dimensions(output, (1, 0, 2))
+
+
+def local_conv2d(inputs,
+                 kernel,
+                 kernel_size,
+                 strides,
+                 output_shape,
+                 data_format=None):
+  """Apply 2D conv with un-shared weights.
+
+  Arguments:
+      inputs: 4D tensor with shape:
+              (batch_size, filters, new_rows, new_cols)
+              if data_format='channels_first'
+              or 4D tensor with shape:
+              (batch_size, new_rows, new_cols, filters)
+              if data_format='channels_last'.
+      kernel: the unshared weight for convolution,
+              with shape (output_items, feature_dim, filters)
+      kernel_size: a tuple of 2 integers, specifying the
+                   width and height of the 2D convolution window.
+      strides: a tuple of 2 integers, specifying the strides
+               of the convolution along the width and height.
+      output_shape: a tuple with (output_row, output_col)
+      data_format: the data format, channels_first or channels_last
+
+  Returns:
+      A 4d tensor with shape:
+      (batch_size, filters, new_rows, new_cols)
+      if data_format='channels_first'
+      or 4D tensor with shape:
+      (batch_size, new_rows, new_cols, filters)
+      if data_format='channels_last'.
+
+  Raises:
+      ValueError: if `data_format` is neither
+                  `channels_last` or `channels_first`.
+  """
+  if data_format is None:
+    data_format = image_data_format()
+  if data_format not in {'channels_first', 'channels_last'}:
+    raise ValueError('Unknown data_format ' + str(data_format))
+
+  stride_row, stride_col = strides
+  output_row, output_col = output_shape
+  kernel_shape = int_shape(kernel)
+  feature_dim = kernel_shape[1]
+  filters = kernel_shape[2]
+
+  xs = []
+  for i in range(output_row):
+    for j in range(output_col):
+      slice_row = slice(i * stride_row, i * stride_row + kernel_size[0])
+      slice_col = slice(j * stride_col, j * stride_col + kernel_size[1])
+      if data_format == 'channels_first':
+        xs.append(
+            reshape(inputs[:, :, slice_row, slice_col], (1, -1, feature_dim)))
+      else:
+        xs.append(
+            reshape(inputs[:, slice_row, slice_col, :], (1, -1, feature_dim)))
+
+  x_aggregate = concatenate(xs, axis=0)
+  output = batch_dot(x_aggregate, kernel)
+  output = reshape(output, (output_row, output_col, -1, filters))
+
+  if data_format == 'channels_first':
+    output = permute_dimensions(output, (2, 3, 0, 1))
+  else:
+    output = permute_dimensions(output, (2, 0, 1, 3))
+  return output
+
+
 def bias_add(x, bias, data_format=None):
   """Adds a bias vector to a tensor.
 
   Arguments:
       x: Tensor or variable.
       bias: Bias tensor to add.
-      data_format: Data format for 3D, 4D or 5D tensors:
-          one of "channels_first", "channels_last".
+      data_format: string, `"channels_last"` or `"channels_first"`.
 
   Returns:
       Output tensor.
 
   Raises:
-      ValueError: In case of invalid `data_format` argument.
+      ValueError: In one of the two cases below:
+                  1. invalid `data_format` argument.
+                  2. invalid bias shape.
+                     the bias should be either a vector or
+                     a tensor with ndim(x) - 1 dimension
   """
   if data_format is None:
     data_format = image_data_format()
   if data_format not in {'channels_first', 'channels_last'}:
     raise ValueError('Unknown data_format ' + str(data_format))
+  bias_shape = int_shape(bias)
+  if len(bias_shape) != 1 and len(bias_shape) != ndim(x) - 1:
+    raise ValueError(
+        'Unexpected bias dimensions %d, expect to be 1 or %d dimensions' %
+        (len(bias_shape), ndim(x)))
   if ndim(x) == 5:
     if data_format == 'channels_first':
-      x += reshape(bias, (1, int_shape(bias)[0], 1, 1, 1))
+      if len(bias_shape) == 1:
+        x += reshape(bias, (1, bias_shape[0], 1, 1, 1))
+      else:
+        x += reshape(bias, (1, bias_shape[3]) + bias_shape[:3])
     elif data_format == 'channels_last':
-      x += reshape(bias, (1, 1, 1, 1, int_shape(bias)[0]))
+      if len(bias_shape) == 1:
+        x += reshape(bias, (1, 1, 1, bias_shape[0]))
+      else:
+        x += reshape(bias, (1,) + bias_shape)
   elif ndim(x) == 4:
     if data_format == 'channels_first':
-      # No support yet for NCHW in bias_add.
-      x += reshape(bias, (1, int_shape(bias)[0], 1, 1))
+      if len(bias_shape) == 1:
+        x += reshape(bias, (1, bias_shape[0], 1, 1))
+      else:
+        x += reshape(bias, (1, bias_shape[2]) + bias_shape[:2])
     elif data_format == 'channels_last':
-      x = nn.bias_add(x, bias, data_format='NHWC')
+      if len(bias_shape) == 1:
+        x = nn.bias_add(x, bias, data_format='NHWC')
+      else:
+        x += reshape(bias, (1,) + bias_shape)
   elif ndim(x) == 3:
     if data_format == 'channels_first':
-      x += reshape(bias, (1, int_shape(bias)[0], 1))
+      if len(bias_shape) == 1:
+        x += reshape(bias, (1, bias_shape[0], 1))
+      else:
+        x += reshape(bias, (1, bias_shape[1], bias_shape[0]))
     elif data_format == 'channels_last':
-      x += reshape(bias, (1, 1, int_shape(bias)[0]))
+      if len(bias_shape) == 1:
+        x += reshape(bias, (1, 1, bias_shape[0]))
+      else:
+        x += reshape(bias, (1,) + bias_shape)
   else:
     x = nn.bias_add(x, bias)
   return x
diff --git a/tensorflow/contrib/keras/python/keras/callbacks.py b/tensorflow/contrib/keras/python/keras/callbacks.py
index d0587a549b..6df6662081 100644
--- a/tensorflow/contrib/keras/python/keras/callbacks.py
+++ b/tensorflow/contrib/keras/python/keras/callbacks.py
@@ -513,7 +513,9 @@ class EarlyStopping(Callback):
   def on_epoch_end(self, epoch, logs=None):
     current = logs.get(self.monitor)
     if current is None:
-      logging.warning('Early stopping requires %s available!' % (self.monitor))
+      logging.warning('Early stopping conditioned on metric `%s` '
+                      'which is not available. Available metrics are: %s' %
+                      (self.monitor, ','.join(list(logs.keys()))))
 
     if self.monitor_op(current - self.min_delta, self.best):
       self.best = current
@@ -680,10 +682,11 @@ class TensorBoard(Callback):
     if self.histogram_freq and self.merged is None:
       for layer in self.model.layers:
         for weight in layer.weights:
-          tf_summary.histogram(weight.name, weight)
+          mapped_weight_name = weight.name.replace(':', '_')
+          tf_summary.histogram(mapped_weight_name, weight)
           if self.write_grads:
             grads = model.optimizer.get_gradients(model.total_loss, weight)
-            tf_summary.histogram('{}_grad'.format(weight.name), grads)
+            tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads)
           if self.write_images:
             w_img = array_ops.squeeze(weight)
             shape = K.int_shape(w_img)
@@ -708,7 +711,7 @@ class TensorBoard(Callback):
 
             shape = K.int_shape(w_img)
             assert len(shape) == 4 and shape[-1] in [1, 3, 4]
-            tf_summary.image(weight.name, w_img)
+            tf_summary.image(mapped_weight_name, w_img)
 
         if hasattr(layer, 'output'):
           tf_summary.histogram('{}_out'.format(layer.name), layer.output)
@@ -896,8 +899,9 @@ class ReduceLROnPlateau(Callback):
     logs['lr'] = K.get_value(self.model.optimizer.lr)
     current = logs.get(self.monitor)
     if current is None:
-      logging.warning('Learning Rate Plateau Reducing requires %s available!' %
-                      self.monitor)
+      logging.warning('Reduce LR on plateau conditioned on metric `%s` '
+                      'which is not available. Available metrics are: %s' %
+                      (self.monitor, ','.join(list(logs.keys()))))
     else:
       if self.in_cooldown():
         self.cooldown_counter -= 1
@@ -998,7 +1002,7 @@ class CSVLogger(Callback):
 
 
 class LambdaCallback(Callback):
-  """Callback for creating simple, custom callbacks on-the-fly.
+  r"""Callback for creating simple, custom callbacks on-the-fly.
 
   This callback is constructed with anonymous functions that will be called
   at the appropriate time. Note that the callbacks expects positional
@@ -1020,17 +1024,21 @@ class LambdaCallback(Callback):
       on_train_end: called at the end of model training.
 
   Example:
+
       ```python
       # Print the batch number at the beginning of every batch.
       batch_print_callback = LambdaCallback(
           on_batch_begin=lambda batch,logs: print(batch))
 
-      # Plot the loss after every epoch.
-      import numpy as np
-      import matplotlib.pyplot as plt
-      plot_loss_callback = LambdaCallback(
-          on_epoch_end=lambda epoch, logs: plt.plot(np.arange(epoch),
-                                                    logs['loss']))
+      # Stream the epoch loss to a file in JSON format. The file content
+      # is not well-formed JSON but rather has a JSON object per line.
+      import json
+      json_log = open('loss_log.json', mode='wt', buffering=1)
+      json_logging_callback = LambdaCallback(
+          on_epoch_end=lambda epoch, logs: json_log.write(
+              json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
+          on_train_end=lambda logs: json_log.close()
+      )
 
       # Terminate some processes after having finished model training.
       processes = ...
@@ -1040,7 +1048,7 @@ class LambdaCallback(Callback):
 
       model.fit(...,
                 callbacks=[batch_print_callback,
-                           plot_loss_callback,
+                           json_logging_callback,
                            cleanup_callback])
       ```
   """
diff --git a/tensorflow/contrib/keras/python/keras/constraints.py b/tensorflow/contrib/keras/python/keras/constraints.py
index 91d6153862..0a59dd92c1 100644
--- a/tensorflow/contrib/keras/python/keras/constraints.py
+++ b/tensorflow/contrib/keras/python/keras/constraints.py
@@ -47,7 +47,7 @@ class MaxNorm(Constraint):
           has shape `(input_dim, output_dim)`,
           set `axis` to `0` to constrain each weight vector
           of length `(input_dim,)`.
-          In a `Convolution2D` layer with `data_format="channels_last"`,
+          In a `Conv2D` layer with `data_format="channels_last"`,
           the weight tensor has shape
           `(rows, cols, input_depth, output_depth)`,
           set `axis` to `[0, 1, 2]`
@@ -92,7 +92,7 @@ class UnitNorm(Constraint):
           has shape `(input_dim, output_dim)`,
           set `axis` to `0` to constrain each weight vector
           of length `(input_dim,)`.
-          In a `Convolution2D` layer with `data_format="channels_last"`,
+          In a `Conv2D` layer with `data_format="channels_last"`,
           the weight tensor has shape
           `(rows, cols, input_depth, output_depth)`,
           set `axis` to `[0, 1, 2]`
@@ -132,7 +132,7 @@ class MinMaxNorm(Constraint):
           has shape `(input_dim, output_dim)`,
           set `axis` to `0` to constrain each weight vector
           of length `(input_dim,)`.
-          In a `Convolution2D` layer with `dim_ordering="tf"`,
+          In a `Conv2D` layer with `dim_ordering="channels_last"`,
           the weight tensor has shape
           `(rows, cols, input_depth, output_depth)`,
           set `axis` to `[0, 1, 2]`
diff --git a/tensorflow/contrib/keras/python/keras/engine/topology.py b/tensorflow/contrib/keras/python/keras/engine/topology.py
index 637d0c5a48..c8c746e8af 100644
--- a/tensorflow/contrib/keras/python/keras/engine/topology.py
+++ b/tensorflow/contrib/keras/python/keras/engine/topology.py
@@ -29,13 +29,13 @@ from six.moves import zip  # pylint: disable=redefined-builtin
 
 from tensorflow.contrib.keras.python.keras import backend as K
 from tensorflow.contrib.keras.python.keras.utils import conv_utils
+from tensorflow.contrib.keras.python.keras.utils.generic_utils import has_arg
 from tensorflow.contrib.keras.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
 from tensorflow.contrib.keras.python.keras.utils.layer_utils import print_summary as print_layer_summary
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.layers import base as tf_base_layers
 from tensorflow.python.platform import tf_logging as logging
-from tensorflow.python.util import tf_inspect
 
 
 # pylint: disable=g-import-not-at-top
@@ -386,7 +386,7 @@ class Layer(tf_base_layers.Layer):
     user_kwargs = copy.copy(kwargs)
     if not _is_all_none(previous_mask):
       # The previous layer generated a mask.
-      if 'mask' in tf_inspect.getargspec(self.call).args:
+      if has_arg(self.call, 'mask'):
         if 'mask' not in kwargs:
           # If mask is explicitly passed to __call__,
           # we should override the default mask.
@@ -1916,7 +1916,7 @@ class Container(Layer):
               kwargs = {}
             if len(computed_data) == 1:
               computed_tensor, computed_mask = computed_data[0]
-              if 'mask' in tf_inspect.getargspec(layer.call).args:
+              if has_arg(layer.call, 'mask'):
                 if 'mask' not in kwargs:
                   kwargs['mask'] = computed_mask
               output_tensors = _to_list(layer.call(computed_tensor, **kwargs))
@@ -1927,7 +1927,7 @@ class Container(Layer):
             else:
               computed_tensors = [x[0] for x in computed_data]
               computed_masks = [x[1] for x in computed_data]
-              if 'mask' in tf_inspect.getargspec(layer.call).args:
+              if has_arg(layer.call, 'mask'):
                 if 'mask' not in kwargs:
                   kwargs['mask'] = computed_masks
               output_tensors = _to_list(layer.call(computed_tensors, **kwargs))
@@ -2352,8 +2352,25 @@ class Container(Layer):
       raise ImportError('Requires yaml module installed.')
     return yaml.dump(self._updated_config(), **kwargs)
 
-  def summary(self, line_length=None, positions=None):
-    print_layer_summary(self, line_length=line_length, positions=positions)
+  def summary(self, line_length=None, positions=None, print_fn=None):
+    """Prints a string summary of the network.
+
+    Arguments:
+        line_length: Total length of printed lines
+            (e.g. set this to adapt the display to different
+            terminal window sizes).
+        positions: Relative or absolute positions of log elements
+            in each line. If not provided,
+            defaults to `[.33, .55, .67, 1.]`.
+        print_fn: Print function to use. Defaults to `print`.
+            It will be called on each line of the summary.
+            You can set it to a custom function
+            in order to capture the string summary.
+    """
+    print_layer_summary(self,
+                        line_length=line_length,
+                        positions=positions,
+                        print_fn=print_fn)
 
 
 def get_source_inputs(tensor, layer=None, node_index=None):
@@ -2610,6 +2627,35 @@ def preprocess_weights_for_loading(layer,
           recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0))
         weights = [kernel, recurrent_kernel, bias]
 
+    if layer.__class__.__name__ in ['Model', 'Sequential']:
+      new_weights = []
+      # trainable weights
+      for sublayer in layer.layers:
+        num_weights = len(sublayer.trainable_weights)
+        if num_weights > 0:
+          new_weights.extend(
+              preprocess_weights_for_loading(
+                  layer=sublayer,
+                  weights=weights[:num_weights],
+                  original_keras_version=original_keras_version,
+                  original_backend=original_backend))
+          weights = weights[num_weights:]
+
+      # non-trainable weights
+      for sublayer in layer.layers:
+        num_weights = len([
+            l for l in sublayer.weights if l not in sublayer.trainable_weights
+        ])
+        if num_weights > 0:
+          new_weights.extend(
+              preprocess_weights_for_loading(
+                  layer=sublayer,
+                  weights=weights[:num_weights],
+                  original_keras_version=original_keras_version,
+                  original_backend=original_backend))
+          weights = weights[num_weights:]
+      weights = new_weights
+
   conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D']
   if layer.__class__.__name__ in conv_layers:
     if original_backend and K.backend() != original_backend:
diff --git a/tensorflow/contrib/keras/python/keras/engine/topology_test.py b/tensorflow/contrib/keras/python/keras/engine/topology_test.py
index 531ed4be3e..ec4fa2eed8 100644
--- a/tensorflow/contrib/keras/python/keras/engine/topology_test.py
+++ b/tensorflow/contrib/keras/python/keras/engine/topology_test.py
@@ -195,13 +195,13 @@ class TopologyConstructionTest(test.TestCase):
     self.assertEqual(test_layer.output_shape, (None, 16))
 
     # pylint: disable=pointless-statement
-    with self.assertRaises(Exception):
+    with self.assertRaises(AttributeError):
       dense.input
-    with self.assertRaises(Exception):
+    with self.assertRaises(AttributeError):
       dense.output
-    with self.assertRaises(Exception):
+    with self.assertRaises(AttributeError):
       dense.input_mask
-    with self.assertRaises(Exception):
+    with self.assertRaises(AttributeError):
       dense.output_mask
     # pylint: enable=pointless-statement
 
diff --git a/tensorflow/contrib/keras/python/keras/engine/training.py b/tensorflow/contrib/keras/python/keras/engine/training.py
index 09459fd713..1563cf8c41 100644
--- a/tensorflow/contrib/keras/python/keras/engine/training.py
+++ b/tensorflow/contrib/keras/python/keras/engine/training.py
@@ -20,9 +20,6 @@ from __future__ import division
 from __future__ import print_function
 
 import copy
-import multiprocessing
-import threading
-import time
 
 import numpy as np
 import six
@@ -33,18 +30,13 @@ from tensorflow.contrib.keras.python.keras import losses
 from tensorflow.contrib.keras.python.keras import metrics as metrics_module
 from tensorflow.contrib.keras.python.keras import optimizers
 from tensorflow.contrib.keras.python.keras.engine.topology import Container
+from tensorflow.contrib.keras.python.keras.utils.data_utils import GeneratorEnqueuer
+from tensorflow.contrib.keras.python.keras.utils.data_utils import OrderedEnqueuer
+from tensorflow.contrib.keras.python.keras.utils.data_utils import Sequence
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import Progbar
 from tensorflow.python.platform import tf_logging as logging
 
 
-# pylint: disable=g-import-not-at-top
-try:
-  import queue
-except ImportError:
-  import Queue as queue
-# pylint: enable=g-import-not-at-top
-
-
 def _standardize_input_data(data,
                             names,
                             shapes=None,
@@ -115,8 +107,9 @@ def _standardize_input_data(data,
     if len(names) > 1:
       # Case: model expects multiple inputs but only received
       # a single Numpy array.
-      raise ValueError('The model expects ' + str(len(names)) + exception_prefix
-                       + ' arrays, but only received one array. '
+      raise ValueError('The model expects ' + str(len(names)) + ' ' +
+                       exception_prefix +
+                       ' arrays, but only received one array. '
                        'Found: array with shape ' + str(data.shape))
     arrays = [data]
 
@@ -205,7 +198,7 @@ def _standardize_sample_weights(sample_weight, output_names):
                                               'sample_weight')
 
 
-def _check_array_lengths(inputs, targets, weights):
+def _check_array_lengths(inputs, targets, weights=None):
   """Does user input validation for numpy arrays.
 
   Arguments:
@@ -216,29 +209,35 @@ def _check_array_lengths(inputs, targets, weights):
   Raises:
       ValueError: in case of incorrectly formatted data.
   """
-  x_lengths = [x.shape[0] for x in inputs]
-  y_lengths = [y.shape[0] for y in targets]
-  w_lengths = [w.shape[0] for w in weights]
-  set_x = set(x_lengths)
+
+  def set_of_lengths(x):
+    # return a set with the variation between
+    # different shapes, with None => 0
+    if x is None:
+      return {0}
+    else:
+      return set([0 if y is None else y.shape[0] for y in x])
+
+  set_x = set_of_lengths(inputs)
+  set_y = set_of_lengths(targets)
+  set_w = set_of_lengths(weights)
   if len(set_x) > 1:
     raise ValueError('All input arrays (x) should have '
                      'the same number of samples. Got array shapes: ' + str(
                          [x.shape for x in inputs]))
-  set_y = set(y_lengths)
   if len(set_y) > 1:
     raise ValueError('All target arrays (y) should have '
                      'the same number of samples. Got array shapes: ' + str(
                          [y.shape for y in targets]))
-  set_w = set(w_lengths)
-  if len(set_w) > 1:
-    raise ValueError('All sample_weight arrays should have '
-                     'the same number of samples. Got array shapes: ' + str(
-                         [w.shape for w in weights]))
   if set_x and set_y and list(set_x)[0] != list(set_y)[0]:
     raise ValueError('Input arrays should have '
                      'the same number of samples as target arrays. '
                      'Found ' + str(list(set_x)[0]) + ' input samples '
                      'and ' + str(list(set_y)[0]) + ' target samples.')
+  if len(set_w) > 1:
+    raise ValueError('All sample_weight arrays should have '
+                     'the same number of samples. Got array shapes: ' + str(
+                         [w.shape for w in weights]))
   if set_y and set_w and list(set_y)[0] != list(set_w)[0]:
     raise ValueError('Sample_weight arrays should have '
                      'the same number of samples as target arrays. Got ' +
@@ -261,7 +260,7 @@ def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
           is incompatible with an output.
   """
   key_losses = {
-      'mean_square_error', 'binary_crossentropy', 'categorical_crossentropy'
+      'mean_squared_error', 'binary_crossentropy', 'categorical_crossentropy'
   }
   for y, loss, shape in zip(targets, loss_fns, output_shapes):
     if loss is None:
@@ -389,21 +388,25 @@ def _slice_arrays(arrays, start=None, stop=None):
   Returns:
       A slice of the array(s).
   """
-  if isinstance(arrays, list):
+  if arrays is None:
+    return [None]
+  elif isinstance(arrays, list):
     if hasattr(start, '__len__'):
       # hdf5 datasets only support list objects as indices
       if hasattr(start, 'shape'):
         start = start.tolist()
-      return [x[start] for x in arrays]
+      return [None if x is None else x[start] for x in arrays]
     else:
-      return [x[start:stop] for x in arrays]
+      return [None if x is None else x[start:stop] for x in arrays]
   else:
     if hasattr(start, '__len__'):
       if hasattr(start, 'shape'):
         start = start.tolist()
       return arrays[start]
-    else:
+    elif hasattr(start, '__getitem__'):
       return arrays[start:stop]
+    else:
+      return [None]
 
 
 def _weighted_masked_objective(fn):
@@ -445,13 +448,12 @@ def _weighted_masked_objective(fn):
       #  to the number of unmasked samples.
       score_array /= K.mean(mask)
 
-    # reduce score_array to same ndim as weight array
-    ndim = K.ndim(score_array)
-    weight_ndim = K.ndim(weights)
-    score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
-
     # apply sample weighting
     if weights is not None:
+      # reduce score_array to same ndim as weight array
+      ndim = K.ndim(score_array)
+      weight_ndim = K.ndim(weights)
+      score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
       score_array *= weights
       score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
     return K.mean(score_array)
@@ -567,7 +569,7 @@ def _standardize_weights(y,
     return sample_weight
   elif isinstance(class_weight, dict):
     if len(y.shape) > 2:
-      raise ValueError('class_weight not supported for '
+      raise ValueError('`class_weight` not supported for '
                        '3+ dimensional targets.')
     if y.shape[1] > 1:
       y_classes = y.argmax(axis=1)
@@ -575,7 +577,18 @@ def _standardize_weights(y,
       y_classes = np.reshape(y, y.shape[0])
     else:
       y_classes = y
-    weights = np.asarray([class_weight[cls] for cls in y_classes])
+
+    weights = np.asarray(
+        [class_weight[cls] for cls in y_classes if cls in class_weight])
+
+    if len(weights) != len(y_classes):
+      # subtract the sets to pick all missing classes
+      existing_classes = set(y_classes)
+      existing_class_weight = set(class_weight.keys())
+      raise ValueError('`class_weight` must contain all classes in the data.'
+                       ' The classes %s exist in the data but not in '
+                       '`class_weight`.' %
+                       (existing_classes - existing_class_weight))
     return weights
   else:
     if sample_weight_mode is None:
@@ -584,97 +597,6 @@ def _standardize_weights(y,
       return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx())
 
 
-class GeneratorEnqueuer(object):
-  """Builds a queue out of a data generator.
-
-  Used in `fit_generator`, `evaluate_generator`, `predict_generator`.
-
-  Arguments:
-      generator: a generator function which endlessly yields data
-      pickle_safe: use multiprocessing if True, otherwise threading
-  """
-
-  def __init__(self, generator, pickle_safe=False):
-    self._generator = generator
-    self._pickle_safe = pickle_safe
-    self._threads = []
-    self._stop_event = None
-    self.queue = None
-
-  def start(self, workers=1, max_q_size=10, wait_time=0.05):
-    """Kicks off threads which add data from the generator into the queue.
-
-    Arguments:
-        workers: number of worker threads
-        max_q_size: queue size (when full, threads could block on put())
-        wait_time: time to sleep in-between calls to put()
-    """
-
-    def data_generator_task():
-      while not self._stop_event.is_set():
-        try:
-          if self._pickle_safe or self.queue.qsize() < max_q_size:
-            generator_output = next(self._generator)
-            self.queue.put(generator_output)
-          else:
-            time.sleep(wait_time)
-        except Exception:
-          self._stop_event.set()
-          raise
-
-    try:
-      if self._pickle_safe:
-        self.queue = multiprocessing.Queue(maxsize=max_q_size)
-        self._stop_event = multiprocessing.Event()
-      else:
-        self.queue = queue.Queue()
-        self._stop_event = threading.Event()
-
-      for _ in range(workers):
-        if self._pickle_safe:
-          # Reset random seed else all children processes
-          # share the same seed
-          np.random.seed()
-          thread = multiprocessing.Process(target=data_generator_task)
-          thread.daemon = True
-        else:
-          thread = threading.Thread(target=data_generator_task)
-        self._threads.append(thread)
-        thread.start()
-    except:
-      self.stop()
-      raise
-
-  def is_running(self):
-    return self._stop_event is not None and not self._stop_event.is_set()
-
-  def stop(self, timeout=None):
-    """Stop running threads and wait for them to exit, if necessary.
-
-    Should be called by the same thread which called start().
-
-    Arguments:
-        timeout: maximum time to wait on thread.join()
-    """
-    if self.is_running():
-      self._stop_event.set()
-
-    for thread in self._threads:
-      if thread.is_alive():
-        if self._pickle_safe:
-          thread.terminate()
-        else:
-          thread.join(timeout)
-
-    if self._pickle_safe:
-      if self.queue is not None:
-        self.queue.close()
-
-    self._threads = []
-    self._stop_event = None
-    self.queue = None
-
-
 class Model(Container):
   """The `Model` class adds training & evaluation routines to a `Container`.
   """
@@ -723,7 +645,7 @@ class Model(Container):
     Raises:
         ValueError: In case of invalid arguments for
             `optimizer`, `loss`, `metrics` or `sample_weight_mode`.
-        RuntimeError: If the model has no loss to optimize.
+        RuntimeError: In case of ill-formulated optimization problem.
     """
     loss = loss or {}
     self.optimizer = optimizers.get(optimizer)
@@ -981,24 +903,22 @@ class Model(Container):
     # Functions for train, test and predict will
     # be compiled lazily when required.
     # This saves time when the user is not using all functions.
+    self._function_kwargs = kwargs
+
     self.train_function = None
     self.test_function = None
     self.predict_function = None
-    self._function_kwargs = kwargs
 
-    # Collected trainable weights and sort them deterministically.
+    # Collected trainable weights, sorted in topological order.
     trainable_weights = self.trainable_weights
-    # Sort weights by name.
-    if trainable_weights:
-      trainable_weights.sort(key=lambda x: x.name)
     self._collected_trainable_weights = trainable_weights
 
   def _make_train_function(self):
     if not hasattr(self, 'train_function'):
       raise RuntimeError('You must compile your model before using it.')
     if self.train_function is None:
-      inputs = (
-          self._feed_inputs + self._feed_targets + self._feed_sample_weights)
+      inputs = (self._feed_inputs +
+                self._feed_targets + self._feed_sample_weights)
       if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
         inputs += [K.learning_phase()]
 
@@ -1016,8 +936,8 @@ class Model(Container):
     if not hasattr(self, 'test_function'):
       raise RuntimeError('You must compile your model before using it.')
     if self.test_function is None:
-      inputs = (
-          self._feed_inputs + self._feed_targets + self._feed_sample_weights)
+      inputs = (self._feed_inputs +
+                self._feed_targets + self._feed_sample_weights)
       if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
         inputs += [K.learning_phase()]
       # Return loss and metrics, no gradient updates.
@@ -1031,7 +951,6 @@ class Model(Container):
   def _make_predict_function(self):
     if not hasattr(self, 'predict_function'):
       self.predict_function = None
-      self._function_kwargs = {}
     if self.predict_function is None:
       if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
         inputs = self._feed_inputs + [K.learning_phase()]
@@ -1039,12 +958,13 @@ class Model(Container):
         inputs = self._feed_inputs
       # Gets network outputs. Does not update weights.
       # Does update the network states.
+      kwargs = getattr(self, '_function_kwargs', {})
       self.predict_function = K.function(
           inputs,
           self.outputs,
           updates=self.state_updates,
           name='predict_function',
-          **self._function_kwargs)
+          **kwargs)
 
   def _fit_loop(self,
                 f,
@@ -1430,6 +1350,7 @@ class Model(Container):
         ValueError: In case of mismatch between the provided input data
             and what the model expects.
     """
+
     # Validate user data.
     x, y, sample_weights = self._standardize_user_data(
         x,
@@ -1468,7 +1389,10 @@ class Model(Container):
 
     elif validation_split and 0. < validation_split < 1.:
       do_validation = True
-      split_at = int(len(x[0]) * (1. - validation_split))
+      if hasattr(x[0], 'shape'):
+        split_at = int(x[0].shape[0] * (1. - validation_split))
+      else:
+        split_at = int(len(x[0]) * (1. - validation_split))
       x, val_x = (_slice_arrays(x, 0, split_at), _slice_arrays(x, split_at))
       y, val_y = (_slice_arrays(y, 0, split_at), _slice_arrays(y, split_at))
       sample_weights, val_sample_weights = (_slice_arrays(
@@ -1725,18 +1649,25 @@ class Model(Container):
                     validation_data=None,
                     validation_steps=None,
                     class_weight=None,
-                    max_q_size=10,
+                    max_queue_size=10,
                     workers=1,
-                    pickle_safe=False,
-                    initial_epoch=0):
+                    use_multiprocessing=False,
+                    initial_epoch=0,
+                    **kwargs):
     """Fits the model on data yielded batch-by-batch by a Python generator.
 
     The generator is run in parallel to the model, for efficiency.
     For instance, this allows you to do real-time data augmentation
     on images on CPU in parallel to training your model on GPU.
 
+    The use of `keras.utils.Sequence` guarantees the ordering
+    and guarantees the single use of every input per epoch when
+    using `use_multiprocessing=True`.
+
     Arguments:
-        generator: a generator.
+        generator: a generator or an instance of Sequence (keras.utils.Sequence)
+                object in order to avoid duplicate data
+                when using multiprocessing.
             The output of the generator must be either
             - a tuple (inputs, targets)
             - a tuple (inputs, targets, sample_weights).
@@ -1761,10 +1692,10 @@ class Model(Container):
             to yield from `generator` before stopping.
         class_weight: dictionary mapping class indices to a weight
             for the class.
-        max_q_size: maximum size for the generator queue
+        max_queue_size: maximum size for the generator queue
         workers: maximum number of processes to spin up
             when using process based threading
-        pickle_safe: if True, use process based threading.
+        use_multiprocessing: if True, use process based threading.
             Note that because
             this implementation relies on multiprocessing,
             you should not pass
@@ -1773,6 +1704,7 @@ class Model(Container):
             easily to children processes.
         initial_epoch: epoch at which to start training
             (useful for resuming a previous training run)
+        **kwargs: support for legacy arguments.
 
     Returns:
         A `History` object.
@@ -1798,6 +1730,19 @@ class Model(Container):
         ValueError: In case the generator yields
             data in an invalid format.
     """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
     wait_time = 0.01  # in seconds
     epoch = initial_epoch
 
@@ -1809,7 +1754,8 @@ class Model(Container):
     # python 2 has 'next', 3 has '__next__'
     # avoid any explicit version checks
     val_gen = (hasattr(validation_data, 'next') or
-               hasattr(validation_data, '__next__'))
+               hasattr(validation_data, '__next__') or
+               isinstance(validation_data, Sequence))
     if val_gen and not validation_steps:
       raise ValueError('When using a generator for validation data, '
                        'you must specify a value for '
@@ -1848,7 +1794,7 @@ class Model(Container):
       elif len(validation_data) == 3:
         val_x, val_y, val_sample_weight = validation_data  # pylint: disable=unpacking-non-sequence
       else:
-        raise ValueError('validation_data should be a tuple '
+        raise ValueError('`validation_data` should be a tuple '
                          '`(val_x, val_y, val_sample_weight)` '
                          'or `(val_x, val_y)`. Found: ' + str(validation_data))
       val_x, val_y, val_sample_weights = self._standardize_user_data(
@@ -1858,11 +1804,25 @@ class Model(Container):
         val_data += [0.]
       for cbk in callbacks:
         cbk.validation_data = val_data
+    is_sequence = isinstance(generator, Sequence)
+    if not is_sequence and use_multiprocessing and workers > 1:
+      logging.warning(
+          'Using a generator with `use_multiprocessing=True`'
+          ' may duplicate your data.Please consider using '
+          'the `keras.utils.Sequence` class.')
     enqueuer = None
 
     try:
-      enqueuer = GeneratorEnqueuer(generator, pickle_safe=pickle_safe)
-      enqueuer.start(max_q_size=max_q_size, workers=workers)
+      if is_sequence:
+        enqueuer = OrderedEnqueuer(
+            generator, use_multiprocessing=use_multiprocessing)
+      else:
+        enqueuer = GeneratorEnqueuer(
+            generator,
+            use_multiprocessing=use_multiprocessing,
+            wait_time=wait_time)
+      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
+      output_generator = enqueuer.get()
 
       callback_model.stop_training = False
       while epoch < epochs:
@@ -1870,25 +1830,19 @@ class Model(Container):
         steps_done = 0
         batch_index = 0
         while steps_done < steps_per_epoch:
-          generator_output = None
-          while enqueuer.is_running():
-            if not enqueuer.queue.empty():
-              generator_output = enqueuer.queue.get()
-              break
-            else:
-              time.sleep(wait_time)
+          generator_output = next(output_generator)
 
           if not hasattr(generator_output, '__len__'):
-            raise ValueError('output of generator should be '
+            raise ValueError('Output of generator should be '
                              'a tuple `(x, y, sample_weight)` '
                              'or `(x, y)`. Found: ' + str(generator_output))
           if len(generator_output) == 2:
-            x, y = generator_output  # pylint: disable=unpacking-non-sequence
+            x, y = generator_output
             sample_weight = None
           elif len(generator_output) == 3:
-            x, y, sample_weight = generator_output  # pylint: disable=unpacking-non-sequence
+            x, y, sample_weight = generator_output
           else:
-            raise ValueError('output of generator should be '
+            raise ValueError('Output of generator should be '
                              'a tuple `(x, y, sample_weight)` '
                              'or `(x, y)`. Found: ' + str(generator_output))
           # build batch logs
@@ -1924,9 +1878,9 @@ class Model(Container):
               val_outs = self.evaluate_generator(
                   validation_data,
                   validation_steps,
-                  max_q_size=max_q_size,
+                  max_queue_size=max_queue_size,
                   workers=workers,
-                  pickle_safe=pickle_safe)
+                  use_multiprocessing=use_multiprocessing)
             else:
               # No need for try/except because
               # data has already been validated.
@@ -1957,9 +1911,10 @@ class Model(Container):
   def evaluate_generator(self,
                          generator,
                          steps,
-                         max_q_size=10,
+                         max_queue_size=10,
                          workers=1,
-                         pickle_safe=False):
+                         use_multiprocessing=False,
+                         **kwargs):
     """Evaluates the model on a data generator.
 
     The generator should return the same kind of data
@@ -1968,18 +1923,22 @@ class Model(Container):
     Arguments:
         generator: Generator yielding tuples (inputs, targets)
             or (inputs, targets, sample_weights)
+            or an instance of Sequence (keras.utils.Sequence)
+                object in order to avoid duplicate data
+                when using multiprocessing.
         steps: Total number of steps (batches of samples)
             to yield from `generator` before stopping.
-        max_q_size: maximum size for the generator queue
+        max_queue_size: maximum size for the generator queue
         workers: maximum number of processes to spin up
             when using process based threading
-        pickle_safe: if True, use process based threading.
+        use_multiprocessing: if True, use process based threading.
             Note that because
             this implementation relies on multiprocessing,
             you should not pass
             non picklable arguments to the generator
             as they can't be passed
             easily to children processes.
+        **kwargs: support for legacy arguments.
 
     Returns:
         Scalar test loss (if the model has a single output and no metrics)
@@ -1991,38 +1950,58 @@ class Model(Container):
         ValueError: In case the generator yields
             data in an invalid format.
     """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
     self._make_test_function()
 
     steps_done = 0
     wait_time = 0.01
     all_outs = []
     batch_sizes = []
+    is_sequence = isinstance(generator, Sequence)
+    if not is_sequence and use_multiprocessing and workers > 1:
+      logging.warning(
+          'Using a generator with `use_multiprocessing=True`'
+          ' may duplicate your data.Please consider using '
+          'the `keras.utils.Sequence` class.')
     enqueuer = None
 
     try:
-      enqueuer = GeneratorEnqueuer(generator, pickle_safe=pickle_safe)
-      enqueuer.start(workers=workers, max_q_size=max_q_size)
+      if is_sequence:
+        enqueuer = OrderedEnqueuer(
+            generator, use_multiprocessing=use_multiprocessing)
+      else:
+        enqueuer = GeneratorEnqueuer(
+            generator,
+            use_multiprocessing=use_multiprocessing,
+            wait_time=wait_time)
+      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
+      output_generator = enqueuer.get()
 
       while steps_done < steps:
-        generator_output = None
-        while enqueuer.is_running():
-          if not enqueuer.queue.empty():
-            generator_output = enqueuer.queue.get()
-            break
-          else:
-            time.sleep(wait_time)
-
+        generator_output = next(output_generator)
         if not hasattr(generator_output, '__len__'):
-          raise ValueError('output of generator should be a tuple '
+          raise ValueError('Output of generator should be a tuple '
                            '(x, y, sample_weight) '
                            'or (x, y). Found: ' + str(generator_output))
         if len(generator_output) == 2:
-          x, y = generator_output  # pylint: disable=unpacking-non-sequence
+          x, y = generator_output
           sample_weight = None
         elif len(generator_output) == 3:
-          x, y, sample_weight = generator_output  # pylint: disable=unpacking-non-sequence
+          x, y, sample_weight = generator_output
         else:
-          raise ValueError('output of generator should be a tuple '
+          raise ValueError('Output of generator should be a tuple '
                            '(x, y, sample_weight) '
                            'or (x, y). Found: ' + str(generator_output))
         outs = self.test_on_batch(x, y, sample_weight=sample_weight)
@@ -2033,6 +2012,9 @@ class Model(Container):
           batch_size = len(list(x.values())[0])
         else:
           batch_size = len(x)
+        if batch_size == 0:
+          raise ValueError('Received an empty batch. '
+                           'Batches should at least contain one item.')
         all_outs.append(outs)
 
         steps_done += 1
@@ -2054,23 +2036,27 @@ class Model(Container):
   def predict_generator(self,
                         generator,
                         steps,
-                        max_q_size=10,
+                        max_queue_size=10,
                         workers=1,
-                        pickle_safe=False,
-                        verbose=0):
+                        use_multiprocessing=False,
+                        verbose=0,
+                        **kwargs):
     """Generates predictions for the input samples from a data generator.
 
     The generator should return the same kind of data as accepted by
     `predict_on_batch`.
 
     Arguments:
-        generator: Generator yielding batches of input samples.
+        generator: Generator yielding batches of input samples
+                or an instance of Sequence (keras.utils.Sequence)
+                object in order to avoid duplicate data
+                when using multiprocessing.
         steps: Total number of steps (batches of samples)
             to yield from `generator` before stopping.
-        max_q_size: Maximum size for the generator queue.
+        max_queue_size: Maximum size for the generator queue.
         workers: Maximum number of processes to spin up
             when using process based threading
-        pickle_safe: If `True`, use process based threading.
+        use_multiprocessing: If `True`, use process based threading.
             Note that because
             this implementation relies on multiprocessing,
             you should not pass
@@ -2078,6 +2064,7 @@ class Model(Container):
             as they can't be passed
             easily to children processes.
         verbose: verbosity mode, 0 or 1.
+        **kwargs: support for legacy arguments.
 
     Returns:
         Numpy array(s) of predictions.
@@ -2086,38 +2073,58 @@ class Model(Container):
         ValueError: In case the generator yields
             data in an invalid format.
     """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
     self._make_predict_function()
 
     steps_done = 0
     wait_time = 0.01
     all_outs = []
+    is_sequence = isinstance(generator, Sequence)
+    if not is_sequence and use_multiprocessing and workers > 1:
+      logging.warning(
+          'Using a generator with `use_multiprocessing=True`'
+          ' may duplicate your data.Please consider using '
+          'the `keras.utils.Sequence` class.')
     enqueuer = None
 
     try:
-      enqueuer = GeneratorEnqueuer(generator, pickle_safe=pickle_safe)
-      enqueuer.start(workers=workers, max_q_size=max_q_size)
+      if is_sequence:
+        enqueuer = OrderedEnqueuer(
+            generator, use_multiprocessing=use_multiprocessing)
+      else:
+        enqueuer = GeneratorEnqueuer(
+            generator,
+            use_multiprocessing=use_multiprocessing,
+            wait_time=wait_time)
+      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
+      output_generator = enqueuer.get()
 
       if verbose == 1:
         progbar = Progbar(target=steps)
 
       while steps_done < steps:
-        generator_output = None
-        while enqueuer.is_running():
-          if not enqueuer.queue.empty():
-            generator_output = enqueuer.queue.get()
-            break
-          else:
-            time.sleep(wait_time)
-
+        generator_output = next(output_generator)
         if isinstance(generator_output, tuple):
           # Compatibility with the generators
           # used for training.
           if len(generator_output) == 2:
-            x, _ = generator_output  # pylint: disable=unpacking-non-sequence
+            x, _ = generator_output
           elif len(generator_output) == 3:
-            x, _, _ = generator_output  # pylint: disable=unpacking-non-sequence
+            x, _, _ = generator_output
           else:
-            raise ValueError('output of generator should be '
+            raise ValueError('Output of generator should be '
                              'a tuple `(x, y, sample_weight)` '
                              'or `(x, y)`. Found: ' + str(generator_output))
         else:
diff --git a/tensorflow/contrib/keras/python/keras/engine/training_test.py b/tensorflow/contrib/keras/python/keras/engine/training_test.py
index a23838f7b4..d2aac54c94 100644
--- a/tensorflow/contrib/keras/python/keras/engine/training_test.py
+++ b/tensorflow/contrib/keras/python/keras/engine/training_test.py
@@ -463,6 +463,38 @@ class LossWeightingTest(test.TestCase):
           temporal_x_test[test_ids], temporal_y_test[test_ids], verbose=0)
       self.assertLess(score, ref_score)
 
+  def test_class_weight_wrong_classes(self):
+    num_classes = 5
+    train_samples = 1000
+    test_samples = 1000
+    input_dim = 5
+    timesteps = 3
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(
+          keras.layers.TimeDistributed(
+              keras.layers.Dense(num_classes),
+              input_shape=(timesteps, input_dim)))
+      model.add(keras.layers.Activation('softmax'))
+      model.compile(
+          loss='binary_crossentropy',
+          optimizer='rmsprop')
+
+      (x_train, y_train), _ = testing_utils.get_test_data(
+          train_samples=train_samples,
+          test_samples=test_samples,
+          input_shape=(input_dim,),
+          num_classes=num_classes)
+      # convert class vectors to binary class matrices
+      y_train = keras.utils.to_categorical(y_train, num_classes)
+      class_weight = dict([(i, 1.) for i in range(num_classes)])
+
+      del class_weight[1]
+      with self.assertRaises(ValueError):
+        model.fit(x_train, y_train,
+                  epochs=0, verbose=0, class_weight=class_weight)
+
 
 class LossMaskingTest(test.TestCase):
 
@@ -654,41 +686,41 @@ class TestGeneratorMethods(test.TestCase):
                         steps_per_epoch=5,
                         epochs=1,
                         verbose=1,
-                        max_q_size=10,
+                        max_queue_size=10,
                         workers=4,
-                        pickle_safe=True)
+                        use_multiprocessing=True)
     model.fit_generator(custom_generator(),
                         steps_per_epoch=5,
                         epochs=1,
                         verbose=1,
-                        max_q_size=10,
-                        pickle_safe=False)
+                        max_queue_size=10,
+                        use_multiprocessing=False)
     model.fit_generator(custom_generator(),
                         steps_per_epoch=5,
                         epochs=1,
                         verbose=1,
-                        max_q_size=10,
-                        pickle_safe=False,
+                        max_queue_size=10,
+                        use_multiprocessing=False,
                         validation_data=custom_generator(),
                         validation_steps=10)
     model.predict_generator(custom_generator(),
                             steps=5,
-                            max_q_size=10,
+                            max_queue_size=10,
                             workers=2,
-                            pickle_safe=True)
+                            use_multiprocessing=True)
     model.predict_generator(custom_generator(),
                             steps=5,
-                            max_q_size=10,
-                            pickle_safe=False)
+                            max_queue_size=10,
+                            use_multiprocessing=False)
     model.evaluate_generator(custom_generator(),
                              steps=5,
-                             max_q_size=10,
+                             max_queue_size=10,
                              workers=2,
-                             pickle_safe=True)
+                             use_multiprocessing=True)
     model.evaluate_generator(custom_generator(),
                              steps=5,
-                             max_q_size=10,
-                             pickle_safe=False)
+                             max_queue_size=10,
+                             use_multiprocessing=False)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/keras/python/keras/initializers.py b/tensorflow/contrib/keras/python/keras/initializers.py
index b0b71e7cb4..af1dc914bb 100644
--- a/tensorflow/contrib/keras/python/keras/initializers.py
+++ b/tensorflow/contrib/keras/python/keras/initializers.py
@@ -57,6 +57,28 @@ class Identity(Initializer):
     return {'gain': self.gain}
 
 
+def lecun_normal(seed=None):
+  """LeCun normal initializer.
+
+  It draws samples from a truncated normal distribution centered on 0
+  with `stddev = sqrt(1 / fan_in)`
+  where `fan_in` is the number of input units in the weight tensor.
+
+  Arguments:
+      seed: A Python integer. Used to seed the random generator.
+
+  Returns:
+      An initializer.
+
+  References:
+      - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+      - [Efficient
+      Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
+  """
+  return VarianceScaling(
+      scale=1., mode='fan_in', distribution='normal', seed=seed)
+
+
 def lecun_uniform(seed=None):
   """LeCun uniform initializer.
 
diff --git a/tensorflow/contrib/keras/python/keras/initializers_test.py b/tensorflow/contrib/keras/python/keras/initializers_test.py
index 0a07eddd89..f39d2bfd52 100644
--- a/tensorflow/contrib/keras/python/keras/initializers_test.py
+++ b/tensorflow/contrib/keras/python/keras/initializers_test.py
@@ -103,6 +103,14 @@ class KerasInitializersTest(test.TestCase):
       self._runner(keras.initializers.he_uniform(seed=123), tensor_shape,
                    target_mean=0., target_max=scale, target_min=-scale)
 
+  def test_lecun_normal(self):
+    tensor_shape = (5, 6, 4, 2)
+    with self.test_session():
+      fan_in, _ = init_ops._compute_fans(tensor_shape)
+      scale = np.sqrt(1. / fan_in)
+      self._runner(keras.initializers.lecun_normal(seed=123), tensor_shape,
+                   target_mean=0., target_std=None, target_max=2 * scale)
+
   def test_glorot_normal(self):
     tensor_shape = (5, 6, 4, 2)
     with self.test_session():
diff --git a/tensorflow/contrib/keras/python/keras/integration_test.py b/tensorflow/contrib/keras/python/keras/integration_test.py
index 0f6db097d1..32b0a95fe3 100644
--- a/tensorflow/contrib/keras/python/keras/integration_test.py
+++ b/tensorflow/contrib/keras/python/keras/integration_test.py
@@ -228,6 +228,13 @@ class KerasIntegrationTest(test.TestCase):
                           verbose=2)
       self.assertGreater(history.history['val_acc'][-1], 0.85)
 
+  def test_embedding_with_clipnorm(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Embedding(input_dim=1, output_dim=1))
+      model.compile(optimizer=keras.optimizers.SGD(clipnorm=0.1), loss='mse')
+      model.fit(np.array([[0]]), np.array([[[0.5]]]), epochs=1)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/keras/python/keras/layers/advanced_activations.py b/tensorflow/contrib/keras/python/keras/layers/advanced_activations.py
index 2c957ece44..55f17ac4e2 100644
--- a/tensorflow/contrib/keras/python/keras/layers/advanced_activations.py
+++ b/tensorflow/contrib/keras/python/keras/layers/advanced_activations.py
@@ -57,7 +57,7 @@ class LeakyReLU(Layer):
     return K.relu(inputs, alpha=self.alpha)
 
   def get_config(self):
-    config = {'alpha': self.alpha}
+    config = {'alpha': float(self.alpha)}
     base_config = super(LeakyReLU, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
diff --git a/tensorflow/contrib/keras/python/keras/layers/convolutional.py b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
index 9ee5aa2121..24ff0baf84 100644
--- a/tensorflow/contrib/keras/python/keras/layers/convolutional.py
+++ b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
@@ -324,7 +324,7 @@ class Conv3D(tf_convolutional_layers.Conv3D, Layer):
       filters: Integer, the dimensionality of the output space
           (i.e. the number output of filters in the convolution).
       kernel_size: An integer or tuple/list of 3 integers, specifying the
-          width and height of the 3D convolution window.
+          depth, height and width of the 3D convolution window.
           Can be a single integer to specify the same value for
           all spatial dimensions.
       strides: An integer or tuple/list of 3 integers,
@@ -599,6 +599,162 @@ class Conv2DTranspose(tf_convolutional_layers.Conv2DTranspose, Layer):
     return dict(list(base_config.items()) + list(config.items()))
 
 
+class Conv3DTranspose(tf_convolutional_layers.Conv3D, Layer):
+  """Transposed convolution layer (sometimes called Deconvolution).
+
+  The need for transposed convolutions generally arises
+  from the desire to use a transformation going in the opposite direction
+  of a normal convolution, i.e., from something that has the shape of the
+  output of some convolution to something that has the shape of its input
+  while maintaining a connectivity pattern that is compatible with
+  said convolution.
+
+  When using this layer as the first layer in a model,
+  provide the keyword argument `input_shape`
+  (tuple of integers, does not include the sample axis),
+  e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels
+  if `data_format="channels_last"`.
+
+  Arguments:
+      filters: Integer, the dimensionality of the output space
+          (i.e. the number of output filters in the convolution).
+      kernel_size: An integer or tuple/list of 3 integers, specifying the
+          width and height of the 3D convolution window.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+      strides: An integer or tuple/list of 3 integers,
+          specifying the strides of the convolution along the width and height.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+          Specifying any stride value != 1 is incompatible with specifying
+          any `dilation_rate` value != 1.
+      padding: one of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string,
+          one of `channels_last` (default) or `channels_first`.
+          The ordering of the dimensions in the inputs.
+          `channels_last` corresponds to inputs with shape
+          `(batch, depth, height, width, channels)` while `channels_first`
+          corresponds to inputs with shape
+          `(batch, channels, depth, height, width)`.
+          It defaults to the `image_data_format` value found in your
+          Keras config file at `~/.keras/keras.json`.
+          If you never set it, then it will be "channels_last".
+      dilation_rate: an integer or tuple/list of 3 integers, specifying
+          the dilation rate to use for dilated convolution.
+          Can be a single integer to specify the same value for
+          all spatial dimensions.
+          Currently, specifying any `dilation_rate` value != 1 is
+          incompatible with specifying any stride value != 1.
+      activation: Activation function to use
+          (see [activations](../activations.md)).
+          If you don't specify anything, no activation is applied
+          (ie. "linear" activation: `a(x) = x`).
+      use_bias: Boolean, whether the layer uses a bias vector.
+      kernel_initializer: Initializer for the `kernel` weights matrix
+          (see [initializers](../initializers.md)).
+      bias_initializer: Initializer for the bias vector
+          (see [initializers](../initializers.md)).
+      kernel_regularizer: Regularizer function applied to
+          the `kernel` weights matrix
+          (see [regularizer](../regularizers.md)).
+      bias_regularizer: Regularizer function applied to the bias vector
+          (see [regularizer](../regularizers.md)).
+      activity_regularizer: Regularizer function applied to
+          the output of the layer (its "activation").
+          (see [regularizer](../regularizers.md)).
+      kernel_constraint: Constraint function applied to the kernel matrix
+          (see [constraints](../constraints.md)).
+      bias_constraint: Constraint function applied to the bias vector
+          (see [constraints](../constraints.md)).
+
+  Input shape:
+      5D tensor with shape:
+      `(batch, channels, depth, rows, cols)` if data_format='channels_first'
+      or 5D tensor with shape:
+      `(batch, depth, rows, cols, channels)` if data_format='channels_last'.
+
+  Output shape:
+      5D tensor with shape:
+      `(batch, filters, new_depth, new_rows, new_cols)` if
+        data_format='channels_first'
+      or 5D tensor with shape:
+      `(batch, new_depth, new_rows, new_cols, filters)` if
+        data_format='channels_last'.
+      `depth` and `rows` and `cols` values might have changed due to padding.
+
+  References:
+      - [A guide to convolution arithmetic for deep
+        learning](https://arxiv.org/abs/1603.07285v1)
+      - [Deconvolutional
+        Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
+  """
+
+  def __init__(self,
+               filters,
+               kernel_size,
+               strides=(1, 1, 1),
+               padding='valid',
+               data_format=None,
+               activation=None,
+               use_bias=True,
+               kernel_initializer='glorot_uniform',
+               bias_initializer='zeros',
+               kernel_regularizer=None,
+               bias_regularizer=None,
+               activity_regularizer=None,
+               kernel_constraint=None,
+               bias_constraint=None,
+               **kwargs):
+    if data_format is None:
+      data_format = K.image_data_format()
+    super(Conv3DTranspose, self).__init__(
+        filters=filters,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        activation=activations.get(activation),
+        use_bias=use_bias,
+        kernel_initializer=initializers.get(kernel_initializer),
+        bias_initializer=initializers.get(bias_initializer),
+        kernel_regularizer=regularizers.get(kernel_regularizer),
+        bias_regularizer=regularizers.get(bias_regularizer),
+        activity_regularizer=regularizers.get(activity_regularizer),
+        **kwargs)
+    # TODO(fchollet): move weight constraint support to core layers.
+    self.kernel_constraint = constraints.get(kernel_constraint)
+    self.bias_constraint = constraints.get(bias_constraint)
+
+  def build(self, input_shape):
+    super(Conv3DTranspose, self).build(input_shape)
+    # TODO(fchollet): move weight constraint support to core layers.
+    if self.kernel_constraint:
+      self.constraints[self.kernel] = self.kernel_constraint
+    if self.use_bias and self.bias_constraint:
+      self.constraints[self.bias] = self.bias_constraint
+
+  def get_config(self):
+    config = {
+        'filters': self.filters,
+        'kernel_size': self.kernel_size,
+        'strides': self.strides,
+        'padding': self.padding,
+        'data_format': self.data_format,
+        'activation': activations.serialize(self.activation),
+        'use_bias': self.use_bias,
+        'kernel_initializer': initializers.serialize(self.kernel_initializer),
+        'bias_initializer': initializers.serialize(self.bias_initializer),
+        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
+        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
+        'activity_regularizer':
+            regularizers.serialize(self.activity_regularizer),
+        'kernel_constraint': constraints.serialize(self.kernel_constraint),
+        'bias_constraint': constraints.serialize(self.bias_constraint)
+    }
+    base_config = super(Conv3DTranspose, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+
 class SeparableConv2D(tf_convolutional_layers.SeparableConv2D, Layer):
   """Depthwise separable 2D convolution.
 
@@ -976,7 +1132,7 @@ class ZeroPadding1D(Layer):
 class ZeroPadding2D(Layer):
   """Zero-padding layer for 2D input (e.g. picture).
 
-  This layer can add rows and columns or zeros
+  This layer can add rows and columns of zeros
   at the top, bottom, left and right side of an image tensor.
 
   Arguments:
@@ -1551,3 +1707,4 @@ Convolution3D = Conv3D
 SeparableConvolution2D = SeparableConv2D
 Convolution2DTranspose = Conv2DTranspose
 Deconvolution2D = Deconv2D = Conv2DTranspose
+Deconvolution3D = Deconv3D = Conv3DTranspose
diff --git a/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py b/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py
index 7e567d3fb0..2a05ac55db 100644
--- a/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/convolutional_test.py
@@ -239,6 +239,66 @@ class Conv2DTransposeTest(test.TestCase):
       self.assertEqual(len(layer.constraints), 2)
 
 
+class Conv3DTransposeTest(test.TestCase):
+
+  def test_conv3d_transpose(self):
+    num_samples = 2
+    filters = 2
+    stack_size = 3
+    num_row = 5
+    num_col = 6
+    depth = 4
+
+    for padding in ['valid', 'same']:
+      for strides in [(1, 1, 1), (2, 2, 2)]:
+        if padding == 'same' and strides != (1, 1, 1):
+          continue
+
+        with self.test_session(use_gpu=True):
+          testing_utils.layer_test(
+              keras.layers.Conv3DTranspose,
+              kwargs={
+                  'filters': filters,
+                  'kernel_size': 3,
+                  'padding': padding,
+                  'strides': strides,
+                  'data_format': 'channels_last'
+              },
+              input_shape=(num_samples, depth, num_row, num_col, stack_size))
+
+  def test_conv3dtranspose_regularization(self):
+    # regularizers
+    kwargs = {
+        'filters': 3,
+        'kernel_size': 3,
+        'padding': 'valid',
+        'kernel_regularizer': 'l2',
+        'bias_regularizer': 'l2',
+        'activity_regularizer': 'l2',
+        'strides': 1
+    }
+    with self.test_session(use_gpu=True):
+      layer = keras.layers.Conv3DTranspose(**kwargs)
+      layer.build((None, 5, 5, 5, 2))
+      self.assertEqual(len(layer.losses), 2)
+      layer(keras.backend.variable(np.ones((1, 5, 5, 5, 2))))
+      self.assertEqual(len(layer.losses), 3)
+
+    # constraints
+    kwargs = {
+        'filters': 3,
+        'kernel_size': 3,
+        'padding': 'valid',
+        'kernel_constraint': 'max_norm',
+        'bias_constraint': 'max_norm',
+        'strides': 1
+    }
+    with self.test_session(use_gpu=True):
+      layer = keras.layers.Conv3DTranspose(**kwargs)
+      layer.build((None, 5, 5, 5, 2))
+      self.assertEqual(len(layer.constraints), 2)
+
+
 class SeparableConv2DTest(test.TestCase):
 
   def test_separable_conv_2d(self):
diff --git a/tensorflow/contrib/keras/python/keras/layers/core.py b/tensorflow/contrib/keras/python/keras/layers/core.py
index 34548c83c5..1f9ee384c2 100644
--- a/tensorflow/contrib/keras/python/keras/layers/core.py
+++ b/tensorflow/contrib/keras/python/keras/layers/core.py
@@ -33,9 +33,9 @@ from tensorflow.contrib.keras.python.keras.engine import Layer
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import deserialize_keras_object
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import func_dump
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import func_load
+from tensorflow.contrib.keras.python.keras.utils.generic_utils import has_arg
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.layers import core as tf_core_layers
-from tensorflow.python.util import tf_inspect
 
 
 class Masking(Layer):
@@ -590,8 +590,7 @@ class Lambda(Layer):
 
   def call(self, inputs, mask=None):
     arguments = self.arguments
-    arg_spec = tf_inspect.getargspec(self.function)
-    if 'mask' in arg_spec.args:
+    if has_arg(self.function, 'mask'):
       arguments['mask'] = mask
     return self.function(inputs, **arguments)
 
@@ -634,6 +633,16 @@ class Lambda(Layer):
     else:
       raise TypeError('Unknown function type:', function_type)
 
+    # If arguments were numpy array, they have been saved as
+    # list. We need to recover the ndarray
+    if 'arguments' in config:
+      for key in config['arguments']:
+        if isinstance(config['arguments'][key], dict):
+          arg_dict = config['arguments'][key]
+          if 'type' in arg_dict and arg_dict['type'] == 'ndarray':
+            # Overwrite the argument with its numpy translation
+            config['arguments'][key] = np.array(arg_dict['value'])
+
     config['function'] = function
     return cls(**config)
 
diff --git a/tensorflow/contrib/keras/python/keras/layers/embeddings.py b/tensorflow/contrib/keras/python/keras/layers/embeddings.py
index bc0bae67d0..9f617fd3e4 100644
--- a/tensorflow/contrib/keras/python/keras/layers/embeddings.py
+++ b/tensorflow/contrib/keras/python/keras/layers/embeddings.py
@@ -96,7 +96,6 @@ class Embedding(Layer):
                mask_zero=False,
                input_length=None,
                **kwargs):
-    kwargs['dtype'] = 'int32'
     if 'input_shape' not in kwargs:
       if input_length:
         kwargs['input_shape'] = (input_length,)
@@ -120,7 +119,8 @@ class Embedding(Layer):
         initializer=self.embeddings_initializer,
         name='embeddings',
         regularizer=self.embeddings_regularizer,
-        constraint=self.embeddings_constraint)
+        constraint=self.embeddings_constraint,
+        dtype=self.dtype)
     self.built = True
 
   def compute_mask(self, inputs, mask=None):
@@ -131,12 +131,26 @@ class Embedding(Layer):
 
   def _compute_output_shape(self, input_shape):
     input_shape = tensor_shape.TensorShape(input_shape).as_list()
-    if not self.input_length:
-      input_length = input_shape[1]
+    if self.input_length is None:
+      return tensor_shape.TensorShape(input_shape + [self.output_dim])
     else:
-      input_length = self.input_length
-    return tensor_shape.TensorShape(
-        [input_shape[0], input_length, self.output_dim])
+      # input_length can be tuple if input is 3D or higher
+      if isinstance(self.input_length, (list, tuple)):
+        in_lens = list(self.input_length)
+      else:
+        in_lens = [self.input_length]
+      if len(in_lens) != len(input_shape) - 1:
+        ValueError('"input_length" is %s, but received input has shape %s' %
+                   (str(self.input_length), str(input_shape)))
+      else:
+        for i, (s1, s2) in enumerate(zip(in_lens, input_shape[1:])):
+          if s1 is not None and s2 is not None and s1 != s2:
+            ValueError('"input_length" is %s, but received input has shape %s' %
+                       (str(self.input_length), str(input_shape)))
+          elif s1 is None:
+            in_lens[i] = s2
+      return tensor_shape.TensorShape(
+          (input_shape[0],) + tuple(in_lens) + (self.output_dim,))
 
   def call(self, inputs):
     if K.dtype(inputs) != 'int32':
diff --git a/tensorflow/contrib/keras/python/keras/layers/embeddings_test.py b/tensorflow/contrib/keras/python/keras/layers/embeddings_test.py
index ca7ca3efd8..5d6d386862 100644
--- a/tensorflow/contrib/keras/python/keras/layers/embeddings_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/embeddings_test.py
@@ -46,6 +46,27 @@ class EmbeddingTest(test.TestCase):
           input_dtype='int32',
           expected_output_dtype='float32')
 
+    with self.test_session():
+      testing_utils.layer_test(
+          keras.layers.Embedding,
+          kwargs={'output_dim': 4,
+                  'input_dim': 10,
+                  'mask_zero': True},
+          input_shape=(3, 4, 2),
+          input_dtype='int32',
+          expected_output_dtype='float32')
+
+    with self.test_session():
+      testing_utils.layer_test(
+          keras.layers.Embedding,
+          kwargs={'output_dim': 4,
+                  'input_dim': 10,
+                  'mask_zero': True,
+                  'input_length': (None, 2)},
+          input_shape=(3, 4, 2),
+          input_dtype='int32',
+          expected_output_dtype='float32')
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/keras/python/keras/layers/local.py b/tensorflow/contrib/keras/python/keras/layers/local.py
index 863674c1cb..31a29cdaf4 100644
--- a/tensorflow/contrib/keras/python/keras/layers/local.py
+++ b/tensorflow/contrib/keras/python/keras/layers/local.py
@@ -154,52 +154,30 @@ class LocallyConnected1D(Layer):
     return tensor_shape.TensorShape([input_shape[0], length, self.filters])
 
   def call(self, inputs):
-    stride = self.strides[0]
-    output_length, feature_dim, filters = self.kernel_shape
-
-    xs = []
-    for i in range(output_length):
-      slice_length = slice(i * stride, i * stride + self.kernel_size[0])
-      xs.append(K.reshape(inputs[:, slice_length, :], (1, -1, feature_dim)))
-    x_aggregate = K.concatenate(xs, axis=0)
-    # Shape: `(output_length, batch_size, filters)`.
-    output = K.batch_dot(x_aggregate, self.kernel)
-    output = K.permute_dimensions(output, (1, 0, 2))
+    output = K.local_conv1d(inputs, self.kernel, self.kernel_size, self.strides)
 
     if self.use_bias:
-      output += K.reshape(self.bias, (1, output_length, filters))
+      output = K.bias_add(output, self.bias)
     if self.activation is not None:
       output = self.activation(output)
     return output
 
   def get_config(self):
     config = {
-        'filters':
-            self.filters,
-        'kernel_size':
-            self.kernel_size,
-        'strides':
-            self.strides,
-        'padding':
-            self.padding,
-        'activation':
-            activations.serialize(self.activation),
-        'use_bias':
-            self.use_bias,
-        'kernel_initializer':
-            initializers.serialize(self.kernel_initializer),
-        'bias_initializer':
-            initializers.serialize(self.bias_initializer),
-        'kernel_regularizer':
-            regularizers.serialize(self.kernel_regularizer),
-        'bias_regularizer':
-            regularizers.serialize(self.bias_regularizer),
+        'filters': self.filters,
+        'kernel_size': self.kernel_size,
+        'strides': self.strides,
+        'padding': self.padding,
+        'activation': activations.serialize(self.activation),
+        'use_bias': self.use_bias,
+        'kernel_initializer': initializers.serialize(self.kernel_initializer),
+        'bias_initializer': initializers.serialize(self.bias_initializer),
+        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
+        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
         'activity_regularizer':
             regularizers.serialize(self.activity_regularizer),
-        'kernel_constraint':
-            constraints.serialize(self.kernel_constraint),
-        'bias_constraint':
-            constraints.serialize(self.bias_constraint)
+        'kernel_constraint': constraints.serialize(self.kernel_constraint),
+        'bias_constraint': constraints.serialize(self.bias_constraint)
     }
     base_config = super(LocallyConnected1D, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
@@ -381,97 +359,35 @@ class LocallyConnected2D(Layer):
           [input_shape[0], rows, cols, self.filters])
 
   def call(self, inputs):
-    stride_row, stride_col = self.strides
-    _, feature_dim, filters = self.kernel_shape
-
-    if self.data_format == 'channels_first':
-      if K.backend() == 'theano':
-        output = []
-        for i in range(self.output_row):
-          for j in range(self.output_col):
-            slice_row = slice(i * stride_row,
-                              i * stride_row + self.kernel_size[0])
-            slice_col = slice(j * stride_col,
-                              j * stride_col + self.kernel_size[1])
-            x_flatten = K.reshape(inputs[:, :, slice_row, slice_col],
-                                  (1, -1, feature_dim))
-            output.append(
-                K.dot(x_flatten, self.kernel[i * self.output_col + j, :, :]))
-        output = K.concatenate(output, axis=0)
-      else:
-        xs = []
-        for i in range(self.output_row):
-          for j in range(self.output_col):
-            slice_row = slice(i * stride_row,
-                              i * stride_row + self.kernel_size[0])
-            slice_col = slice(j * stride_col,
-                              j * stride_col + self.kernel_size[1])
-            xs.append(
-                K.reshape(inputs[:, :, slice_row, slice_col], (1, -1,
-                                                               feature_dim)))
-        x_aggregate = K.concatenate(xs, axis=0)
-        output = K.batch_dot(x_aggregate, self.kernel)
-      output = K.reshape(output, (self.output_row, self.output_col, -1,
-                                  filters))
-      output = K.permute_dimensions(output, (2, 3, 0, 1))
-
-    elif self.data_format == 'channels_last':
-      xs = []
-      for i in range(self.output_row):
-        for j in range(self.output_col):
-          slice_row = slice(i * stride_row,
-                            i * stride_row + self.kernel_size[0])
-          slice_col = slice(j * stride_col,
-                            j * stride_col + self.kernel_size[1])
-          xs.append(
-              K.reshape(inputs[:, slice_row, slice_col, :], (1, -1, feature_dim
-                                                            )))
-      x_aggregate = K.concatenate(xs, axis=0)
-      output = K.batch_dot(x_aggregate, self.kernel)
-      output = K.reshape(output, (self.output_row, self.output_col, -1,
-                                  filters))
-      output = K.permute_dimensions(output, (2, 0, 1, 3))
-
+    output = K.local_conv2d(inputs,
+                            self.kernel,
+                            self.kernel_size,
+                            self.strides,
+                            (self.output_row, self.output_col),
+                            self.data_format)
     if self.use_bias:
-      if self.data_format == 'channels_first':
-        output += K.reshape(self.bias, (1, filters, self.output_row,
-                                        self.output_col))
-      elif self.data_format == 'channels_last':
-        output += K.reshape(self.bias, (1, self.output_row, self.output_col,
-                                        filters))
+      output = K.bias_add(output, self.bias, data_format=self.data_format)
+
     output = self.activation(output)
     return output
 
   def get_config(self):
     config = {
-        'filters':
-            self.filters,
-        'kernel_size':
-            self.kernel_size,
-        'strides':
-            self.strides,
-        'padding':
-            self.padding,
-        'data_format':
-            self.data_format,
-        'activation':
-            activations.serialize(self.activation),
-        'use_bias':
-            self.use_bias,
-        'kernel_initializer':
-            initializers.serialize(self.kernel_initializer),
-        'bias_initializer':
-            initializers.serialize(self.bias_initializer),
-        'kernel_regularizer':
-            regularizers.serialize(self.kernel_regularizer),
-        'bias_regularizer':
-            regularizers.serialize(self.bias_regularizer),
+        'filters': self.filters,
+        'kernel_size': self.kernel_size,
+        'strides': self.strides,
+        'padding': self.padding,
+        'data_format': self.data_format,
+        'activation': activations.serialize(self.activation),
+        'use_bias': self.use_bias,
+        'kernel_initializer': initializers.serialize(self.kernel_initializer),
+        'bias_initializer': initializers.serialize(self.bias_initializer),
+        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
+        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
         'activity_regularizer':
             regularizers.serialize(self.activity_regularizer),
-        'kernel_constraint':
-            constraints.serialize(self.kernel_constraint),
-        'bias_constraint':
-            constraints.serialize(self.bias_constraint)
+        'kernel_constraint': constraints.serialize(self.kernel_constraint),
+        'bias_constraint': constraints.serialize(self.bias_constraint)
     }
     base_config = super(LocallyConnected2D, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
diff --git a/tensorflow/contrib/keras/python/keras/layers/lstm_test.py b/tensorflow/contrib/keras/python/keras/layers/lstm_test.py
index 90bf95a781..04a04838e7 100644
--- a/tensorflow/contrib/keras/python/keras/layers/lstm_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/lstm_test.py
@@ -118,7 +118,7 @@ class LSTMLayerTest(test.TestCase):
       # check that container-level reset_states() works
       model.reset_states()
       out4 = model.predict(np.ones((num_samples, timesteps)))
-      np.testing.assert_allclose(out3, out4, atol=1e-5)
+      self.assertAllClose(out3, out4, atol=1e-5)
 
       # check that the call to `predict` updated the states
       out5 = model.predict(np.ones((num_samples, timesteps)))
@@ -139,7 +139,7 @@ class LSTMLayerTest(test.TestCase):
       right_padded_input[1, -2:] = 0
       out7 = model.predict(right_padded_input)
 
-      np.testing.assert_allclose(out7, out6, atol=1e-5)
+      self.assertAllClose(out7, out6, atol=1e-5)
 
   def test_regularization_LSTM(self):
     embedding_dim = 4
@@ -252,7 +252,7 @@ class LSTMLayerTest(test.TestCase):
       layer.reset_states()
       assert len(layer.states) == num_states
       assert layer.states[0] is not None
-      np.testing.assert_allclose(
+      self.assertAllClose(
           keras.backend.eval(layer.states[0]),
           np.zeros(keras.backend.int_shape(layer.states[0])),
           atol=1e-4)
@@ -261,7 +261,7 @@ class LSTMLayerTest(test.TestCase):
       if len(values) == 1:
         values = values[0]
       layer.reset_states(values)
-      np.testing.assert_allclose(
+      self.assertAllClose(
           keras.backend.eval(layer.states[0]),
           np.ones(keras.backend.int_shape(layer.states[0])),
           atol=1e-4)
@@ -292,6 +292,42 @@ class LSTMLayerTest(test.TestCase):
       targets = np.random.random((num_samples, units))
       model.train_on_batch([inputs] + initial_state, targets)
 
+  def test_return_state(self):
+    num_states = 2
+    timesteps = 3
+    embedding_dim = 4
+    units = 3
+    num_samples = 2
+
+    with self.test_session():
+      inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim))
+      layer = keras.layers.LSTM(units, return_state=True, stateful=True)
+      outputs = layer(inputs)
+      state = outputs[1:]
+      assert len(state) == num_states
+      model = keras.models.Model(inputs, state[0])
+
+      inputs = np.random.random((num_samples, timesteps, embedding_dim))
+      state = model.predict(inputs)
+      self.assertAllClose(keras.backend.eval(layer.states[0]), state, atol=1e-4)
+
+  def test_state_reuse(self):
+    timesteps = 3
+    embedding_dim = 4
+    units = 3
+    num_samples = 2
+
+    with self.test_session():
+      inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim))
+      layer = keras.layers.LSTM(units, return_state=True, return_sequences=True)
+      outputs = layer(inputs)
+      output, state = outputs[0], outputs[1:]
+      output = keras.layers.LSTM(units)(output, initial_state=state)
+      model = keras.models.Model(inputs, output)
+
+      inputs = np.random.random((num_samples, timesteps, embedding_dim))
+      outputs = model.predict(inputs)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/keras/python/keras/layers/noise.py b/tensorflow/contrib/keras/python/keras/layers/noise.py
index adc88a4fce..e3cfa1f711 100644
--- a/tensorflow/contrib/keras/python/keras/layers/noise.py
+++ b/tensorflow/contrib/keras/python/keras/layers/noise.py
@@ -109,3 +109,65 @@ class GaussianDropout(Layer):
     config = {'rate': self.rate}
     base_config = super(GaussianDropout, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
+
+
+class AlphaDropout(Layer):
+  """Applies Alpha Dropout to the input.
+
+  Alpha Dropout is a `Dropout` that keeps mean and variance of inputs
+  to their original values, in order to ensure the self-normalizing property
+  even after this dropout.
+  Alpha Dropout fits well to Scaled Exponential Linear Units
+  by randomly setting activations to the negative saturation value.
+
+  Arguments:
+      rate: float, drop probability (as with `Dropout`).
+          The multiplicative noise will have
+          standard deviation `sqrt(rate / (1 - rate))`.
+      seed: A Python integer to use as random seed.
+
+  Input shape:
+      Arbitrary. Use the keyword argument `input_shape`
+      (tuple of integers, does not include the samples axis)
+      when using this layer as the first layer in a model.
+
+  Output shape:
+      Same shape as input.
+
+  References:
+      - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+  """
+
+  def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
+    super(AlphaDropout, self).__init__(**kwargs)
+    self.rate = rate
+    self.noise_shape = noise_shape
+    self.seed = seed
+    self.supports_masking = True
+
+  def _get_noise_shape(self, inputs):
+    return self.noise_shape if self.noise_shape else K.shape(inputs)
+
+  def call(self, inputs, training=None):
+    if 0. < self.rate < 1.:
+      noise_shape = self._get_noise_shape(inputs)
+      alpha = 1.6732632423543772848170429916717
+      scale = 1.0507009873554804934193349852946
+
+      def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed):
+        alpha_p = -alpha * scale
+        kept_idx = K.greater_equal(K.random_uniform(noise_shape, seed=seed),
+                                   rate)
+        kept_idx = K.cast(kept_idx, K.floatx())
+        a = ((1 - rate) * (1 + rate * alpha_p ** 2)) ** -0.5
+        b = -a * alpha_p * rate
+        x = inputs * kept_idx + alpha_p * (1 - kept_idx)
+        return a * x + b
+
+      return K.in_train_phase(dropped_inputs, inputs, training=training)
+    return inputs
+
+  def get_config(self):
+    config = {'rate': self.rate}
+    base_config = super(AlphaDropout, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
diff --git a/tensorflow/contrib/keras/python/keras/layers/noise_test.py b/tensorflow/contrib/keras/python/keras/layers/noise_test.py
index b0257b167a..8fb1339c2e 100644
--- a/tensorflow/contrib/keras/python/keras/layers/noise_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/noise_test.py
@@ -39,6 +39,13 @@ class NoiseLayersTest(test.TestCase):
           kwargs={'rate': 0.5},
           input_shape=(3, 2, 3))
 
+  def test_AlphaDropout(self):
+    with self.test_session():
+      testing_utils.layer_test(
+          keras.layers.AlphaDropout,
+          kwargs={'rate': 0.2},
+          input_shape=(3, 2, 3))
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/keras/python/keras/layers/recurrent.py b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
index cdef55f599..592e5f5e3a 100644
--- a/tensorflow/contrib/keras/python/keras/layers/recurrent.py
+++ b/tensorflow/contrib/keras/python/keras/layers/recurrent.py
@@ -123,6 +123,8 @@ class Recurrent(Layer):
           `[(input_dim, output_dim), (output_dim, output_dim), (output_dim,)]`.
       return_sequences: Boolean. Whether to return the last output
           in the output sequence, or the full sequence.
+      return_state: Boolean. Whether to return the last state
+          in addition to the output.
       go_backwards: Boolean (default False).
           If True, process the input sequence backwards and return the
           reversed sequence.
@@ -166,6 +168,9 @@ class Recurrent(Layer):
       (Optional) 2D tensors with shape `(batch_size, output_dim)`.
 
   Output shape:
+      - if `return_state`: a list of tensors. The first tensor is
+          the output. The remaining tensors are the last states,
+          each with shape `(batch_size, units)`.
       - if `return_sequences`: 3D tensor with shape
           `(batch_size, timesteps, units)`.
       - else, 2D tensor with shape `(batch_size, units)`.
@@ -211,6 +216,7 @@ class Recurrent(Layer):
 
   def __init__(self,
                return_sequences=False,
+               return_state=False,
                go_backwards=False,
                stateful=False,
                unroll=False,
@@ -218,6 +224,7 @@ class Recurrent(Layer):
                **kwargs):
     super(Recurrent, self).__init__(**kwargs)
     self.return_sequences = return_sequences
+    self.return_state = return_state
     self.go_backwards = go_backwards
     self.stateful = stateful
     self.unroll = unroll
@@ -233,18 +240,24 @@ class Recurrent(Layer):
       input_shape = input_shape[0]
     input_shape = tensor_shape.TensorShape(input_shape).as_list()
     if self.return_sequences:
-      return tensor_shape.TensorShape(
-          [input_shape[0], input_shape[1], self.units])
+      output_shape = (input_shape[0], input_shape[1], self.units)
     else:
-      return tensor_shape.TensorShape([input_shape[0], self.units])
+      output_shape = (input_shape[0], self.units)
+
+    if self.return_state:
+      state_shape = [tensor_shape.TensorShape(
+          (input_shape[0], self.units)) for _ in self.states]
+      return [tensor_shape.TensorShape(output_shape)] + state_shape
+    return tensor_shape.TensorShape(output_shape)
 
   def compute_mask(self, inputs, mask):
-    if self.return_sequences:
-      if isinstance(mask, list):
-        return mask[0]
-      return mask
-    else:
-      return None
+    if isinstance(mask, list):
+      mask = mask[0]
+    output_mask = mask if self.return_sequences else None
+    if self.return_state:
+      state_mask = [None for _ in self.states]
+      return [output_mask] + state_mask
+    return output_mask
 
   def step(self, inputs, states):
     raise NotImplementedError
@@ -361,10 +374,16 @@ class Recurrent(Layer):
       last_output._uses_learning_phase = True
       outputs._uses_learning_phase = True
 
-    if self.return_sequences:
-      return outputs
-    else:
-      return last_output
+    if not self.return_sequences:
+      outputs = last_output
+
+    if self.return_state:
+      if not isinstance(states, (list, tuple)):
+        states = [states]
+      else:
+        states = list(states)
+      return [outputs] + states
+    return outputs
 
   def reset_states(self, states=None):
     if not self.stateful:
@@ -406,6 +425,7 @@ class Recurrent(Layer):
   def get_config(self):
     config = {
         'return_sequences': self.return_sequences,
+        'return_state': self.return_state,
         'go_backwards': self.go_backwards,
         'stateful': self.stateful,
         'unroll': self.unroll,
@@ -601,36 +621,25 @@ class SimpleRNN(Recurrent):
 
   def get_config(self):
     config = {
-        'units':
-            self.units,
-        'activation':
-            activations.serialize(self.activation),
-        'use_bias':
-            self.use_bias,
-        'kernel_initializer':
-            initializers.serialize(self.kernel_initializer),
+        'units': self.units,
+        'activation': activations.serialize(self.activation),
+        'use_bias': self.use_bias,
+        'kernel_initializer': initializers.serialize(self.kernel_initializer),
         'recurrent_initializer':
             initializers.serialize(self.recurrent_initializer),
-        'bias_initializer':
-            initializers.serialize(self.bias_initializer),
-        'kernel_regularizer':
-            regularizers.serialize(self.kernel_regularizer),
+        'bias_initializer': initializers.serialize(self.bias_initializer),
+        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
         'recurrent_regularizer':
             regularizers.serialize(self.recurrent_regularizer),
-        'bias_regularizer':
-            regularizers.serialize(self.bias_regularizer),
+        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
         'activity_regularizer':
             regularizers.serialize(self.activity_regularizer),
-        'kernel_constraint':
-            constraints.serialize(self.kernel_constraint),
+        'kernel_constraint': constraints.serialize(self.kernel_constraint),
         'recurrent_constraint':
             constraints.serialize(self.recurrent_constraint),
-        'bias_constraint':
-            constraints.serialize(self.bias_constraint),
-        'dropout':
-            self.dropout,
-        'recurrent_dropout':
-            self.recurrent_dropout
+        'bias_constraint': constraints.serialize(self.bias_constraint),
+        'dropout': self.dropout,
+        'recurrent_dropout': self.recurrent_dropout
     }
     base_config = super(SimpleRNN, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
@@ -902,38 +911,27 @@ class GRU(Recurrent):
 
   def get_config(self):
     config = {
-        'units':
-            self.units,
-        'activation':
-            activations.serialize(self.activation),
+        'units': self.units,
+        'activation': activations.serialize(self.activation),
         'recurrent_activation':
             activations.serialize(self.recurrent_activation),
-        'use_bias':
-            self.use_bias,
-        'kernel_initializer':
-            initializers.serialize(self.kernel_initializer),
+        'use_bias': self.use_bias,
+        'kernel_initializer': initializers.serialize(self.kernel_initializer),
         'recurrent_initializer':
             initializers.serialize(self.recurrent_initializer),
-        'bias_initializer':
-            initializers.serialize(self.bias_initializer),
-        'kernel_regularizer':
-            regularizers.serialize(self.kernel_regularizer),
+        'bias_initializer': initializers.serialize(self.bias_initializer),
+        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
         'recurrent_regularizer':
             regularizers.serialize(self.recurrent_regularizer),
-        'bias_regularizer':
-            regularizers.serialize(self.bias_regularizer),
+        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
         'activity_regularizer':
             regularizers.serialize(self.activity_regularizer),
-        'kernel_constraint':
-            constraints.serialize(self.kernel_constraint),
+        'kernel_constraint': constraints.serialize(self.kernel_constraint),
         'recurrent_constraint':
             constraints.serialize(self.recurrent_constraint),
-        'bias_constraint':
-            constraints.serialize(self.bias_constraint),
-        'dropout':
-            self.dropout,
-        'recurrent_dropout':
-            self.recurrent_dropout
+        'bias_constraint': constraints.serialize(self.bias_constraint),
+        'dropout': self.dropout,
+        'recurrent_dropout': self.recurrent_dropout
     }
     base_config = super(GRU, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
@@ -985,7 +983,7 @@ class LSTM(Recurrent):
 
   References:
       - [Long short-term
-        memory](http://www.bioinf.jku.at/publications/older/2604.pdf)
+        memory]((http://www.bioinf.jku.at/publications/older/2604.pdf)
         (original 1997 paper)
       - [Supervised sequence labeling with recurrent neural
         networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
@@ -1239,40 +1237,28 @@ class LSTM(Recurrent):
 
   def get_config(self):
     config = {
-        'units':
-            self.units,
-        'activation':
-            activations.serialize(self.activation),
+        'units': self.units,
+        'activation': activations.serialize(self.activation),
         'recurrent_activation':
             activations.serialize(self.recurrent_activation),
-        'use_bias':
-            self.use_bias,
-        'kernel_initializer':
-            initializers.serialize(self.kernel_initializer),
+        'use_bias': self.use_bias,
+        'kernel_initializer': initializers.serialize(self.kernel_initializer),
         'recurrent_initializer':
             initializers.serialize(self.recurrent_initializer),
-        'bias_initializer':
-            initializers.serialize(self.bias_initializer),
-        'unit_forget_bias':
-            self.unit_forget_bias,
-        'kernel_regularizer':
-            regularizers.serialize(self.kernel_regularizer),
+        'bias_initializer': initializers.serialize(self.bias_initializer),
+        'unit_forget_bias': self.unit_forget_bias,
+        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
         'recurrent_regularizer':
             regularizers.serialize(self.recurrent_regularizer),
-        'bias_regularizer':
-            regularizers.serialize(self.bias_regularizer),
+        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
         'activity_regularizer':
             regularizers.serialize(self.activity_regularizer),
-        'kernel_constraint':
-            constraints.serialize(self.kernel_constraint),
+        'kernel_constraint': constraints.serialize(self.kernel_constraint),
         'recurrent_constraint':
             constraints.serialize(self.recurrent_constraint),
-        'bias_constraint':
-            constraints.serialize(self.bias_constraint),
-        'dropout':
-            self.dropout,
-        'recurrent_dropout':
-            self.recurrent_dropout
+        'bias_constraint': constraints.serialize(self.bias_constraint),
+        'dropout': self.dropout,
+        'recurrent_dropout': self.recurrent_dropout
     }
     base_config = super(LSTM, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
diff --git a/tensorflow/contrib/keras/python/keras/layers/wrappers.py b/tensorflow/contrib/keras/python/keras/layers/wrappers.py
index dbc79fb193..91614c288d 100644
--- a/tensorflow/contrib/keras/python/keras/layers/wrappers.py
+++ b/tensorflow/contrib/keras/python/keras/layers/wrappers.py
@@ -24,8 +24,8 @@ import copy
 from tensorflow.contrib.keras.python.keras import backend as K
 from tensorflow.contrib.keras.python.keras.engine import InputSpec
 from tensorflow.contrib.keras.python.keras.engine import Layer
+from tensorflow.contrib.keras.python.keras.utils.generic_utils import has_arg
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.util import tf_inspect
 
 
 class Wrapper(Layer):
@@ -183,15 +183,29 @@ class TimeDistributed(Wrapper):
     return tensor_shape.TensorShape([child_output_shape[0], timesteps] +
                                     child_output_shape[1:])
 
-  def call(self, inputs, mask=None):
+  def call(self, inputs, training=None, mask=None):
+    kwargs = {}
+    if has_arg(self.layer.call, 'training'):
+      kwargs['training'] = training
+    uses_learning_phase = False  # pylint: disable=redefined-outer-name
+
     input_shape = K.int_shape(inputs)
     if input_shape[0]:
       # batch size matters, use rnn-based implementation
       def step(x, _):
-        output = self.layer.call(x)
+        global uses_learning_phase  # pylint: disable=global-variable-undefined
+        output = self.layer.call(x, **kwargs)
+        if hasattr(output, '_uses_learning_phase'):
+          uses_learning_phase = (output._uses_learning_phase or
+                                 uses_learning_phase)
         return output, []
 
-      _, outputs, _ = K.rnn(step, inputs, initial_states=[], unroll=False)
+      _, outputs, _ = K.rnn(
+          step,
+          inputs,
+          initial_states=[],
+          input_length=input_shape[1],
+          unroll=False)
       y = outputs
     else:
       # No batch size specified, therefore the layer will be able
@@ -202,16 +216,22 @@ class TimeDistributed(Wrapper):
         input_length = K.shape(inputs)[1]
       # Shape: (num_samples * timesteps, ...)
       inputs = K.reshape(inputs, (-1,) + input_shape[2:])
-      y = self.layer.call(inputs)  # (num_samples * timesteps, ...)
+      # (num_samples * timesteps, ...)
+      y = self.layer.call(inputs, **kwargs)
+      if hasattr(y, '_uses_learning_phase'):
+        uses_learning_phase = y._uses_learning_phase
       # Shape: (num_samples, timesteps, ...)
-      output_shape = self._compute_output_shape(input_shape).as_list()  # pylint: disable=protected-access
-      y = K.reshape(y, [-1, input_length] + output_shape[2:])
+      output_shape = self._compute_output_shape(input_shape).as_list()
+      y = K.reshape(y, (-1, input_length) + tuple(output_shape[2:]))
 
     # Apply activity regularizer if any:
     if (hasattr(self.layer, 'activity_regularizer') and
         self.layer.activity_regularizer is not None):
       regularization_loss = self.layer.activity_regularizer(y)
       self.add_loss(regularization_loss, inputs)
+
+    if uses_learning_phase:
+      y._uses_learning_phase = True
     return y
 
 
@@ -285,10 +305,9 @@ class Bidirectional(Wrapper):
 
   def call(self, inputs, training=None, mask=None):
     kwargs = {}
-    func_args = tf_inspect.getargspec(self.layer.call).args
-    if 'training' in func_args:
+    if has_arg(self.layer.call, 'training'):
       kwargs['training'] = training
-    if 'mask' in func_args:
+    if has_arg(self.layer.call, 'mask'):
       kwargs['mask'] = mask
 
     y = self.forward_layer.call(inputs, **kwargs)
diff --git a/tensorflow/contrib/keras/python/keras/layers/wrappers_test.py b/tensorflow/contrib/keras/python/keras/layers/wrappers_test.py
index b892681ada..d4cd1ccbb4 100644
--- a/tensorflow/contrib/keras/python/keras/layers/wrappers_test.py
+++ b/tensorflow/contrib/keras/python/keras/layers/wrappers_test.py
@@ -113,6 +113,16 @@ class TimeDistributedTest(test.TestCase):
       model.compile(optimizer='rmsprop', loss='mse')
       self.assertEqual(len(model.losses), 1)
 
+  def test_TimeDistributed_learning_phase(self):
+    # test layers that need learning_phase to be set
+    np.random.seed(1234)
+    x = keras.layers.Input(shape=(3, 2))
+    y = keras.layers.TimeDistributed(
+        keras.layers.Dropout(.999))(x, training=True)
+    model = keras.models.Model(x, y)
+    y = model.predict(np.random.random((10, 3, 2)))
+    self.assertAllClose(np.mean(y), 0., atol=1e-1, rtol=1e-1)
+
 
 class BidirectionalTest(test.TestCase):
 
diff --git a/tensorflow/contrib/keras/python/keras/metrics.py b/tensorflow/contrib/keras/python/keras/metrics.py
index 93c8684f91..999e9cb9d4 100644
--- a/tensorflow/contrib/keras/python/keras/metrics.py
+++ b/tensorflow/contrib/keras/python/keras/metrics.py
@@ -59,6 +59,11 @@ def top_k_categorical_accuracy(y_true, y_pred, k=5):
   return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), axis=-1)
 
 
+def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
+  return K.mean(K.in_top_k(y_pred,
+                           K.cast(K.max(y_true, axis=-1), 'int32'), k), axis=-1)
+
+
 # Aliases
 
 mse = MSE = mean_squared_error
diff --git a/tensorflow/contrib/keras/python/keras/metrics_test.py b/tensorflow/contrib/keras/python/keras/metrics_test.py
index ac0a1372c6..84c6528174 100644
--- a/tensorflow/contrib/keras/python/keras/metrics_test.py
+++ b/tensorflow/contrib/keras/python/keras/metrics_test.py
@@ -42,6 +42,21 @@ class KerasMetricsTest(test.TestCase):
       y_b = keras.backend.variable(np.random.random((6, 7)))
       self.assertEqual(keras.backend.eval(metric(y_a, y_b)).shape, (6,))
 
+  def test_sparse_top_k_categorical_accuracy(self):
+    with self.test_session():
+      y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
+                                                [0.1, 0.2, 0.7]]))
+      y_true = keras.backend.variable(np.array([[1], [0]]))
+      result = keras.backend.eval(
+          keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=3))
+      self.assertEqual(result, 1)
+      result = keras.backend.eval(
+          keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=2))
+      self.assertEqual(result, 0.5)
+      result = keras.backend.eval(
+          keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
+      self.assertEqual(result, 0.)
+
   def test_top_k_categorical_accuracy(self):
     with self.test_session():
       y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
diff --git a/tensorflow/contrib/keras/python/keras/models.py b/tensorflow/contrib/keras/python/keras/models.py
index 8786e0b97a..8864f5e69d 100644
--- a/tensorflow/contrib/keras/python/keras/models.py
+++ b/tensorflow/contrib/keras/python/keras/models.py
@@ -97,7 +97,10 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
 
     # if obj is any numpy type
     if type(obj).__module__ == np.__name__:
-      return obj.item()
+      if isinstance(obj, np.ndarray):
+        return {'type': type(obj), 'value': obj.tolist()}
+      else:
+        return obj.item()
 
     # misc functions (e.g. loss function)
     if callable(obj):
@@ -232,89 +235,79 @@ def load_model(filepath, custom_objects=None, compile=True):  # pylint: disable=
     if isinstance(obj, list):
       deserialized = []
       for value in obj:
-        if value in custom_objects:
-          deserialized.append(custom_objects[value])
-        else:
-          deserialized.append(value)
+        deserialized.append(convert_custom_objects(value))
       return deserialized
     if isinstance(obj, dict):
       deserialized = {}
       for key, value in obj.items():
-        deserialized[key] = []
-        if isinstance(value, list):
-          for element in value:
-            if element in custom_objects:
-              deserialized[key].append(custom_objects[element])
-            else:
-              deserialized[key].append(element)
-        elif value in custom_objects:
-          deserialized[key] = custom_objects[value]
-        else:
-          deserialized[key] = value
+        deserialized[key] = convert_custom_objects(value)
       return deserialized
     if obj in custom_objects:
       return custom_objects[obj]
     return obj
 
-  f = h5py.File(filepath, mode='r')
-
-  # instantiate model
-  model_config = f.attrs.get('model_config')
-  if model_config is None:
-    raise ValueError('No model found in config file.')
-  model_config = json.loads(model_config.decode('utf-8'))
-  model = model_from_config(model_config, custom_objects=custom_objects)
-
-  # set weights
-  topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)
-
-  # Early return if compilation is not required.
-  if not compile:
-    f.close()
-    return model
-
-  # instantiate optimizer
-  training_config = f.attrs.get('training_config')
-  if training_config is None:
-    logging.warning('No training configuration found in save file: '
-                    'the model was *not* compiled. Compile it manually.')
-    f.close()
-    return model
-  training_config = json.loads(training_config.decode('utf-8'))
-  optimizer_config = training_config['optimizer_config']
-  optimizer = optimizers.deserialize(
-      optimizer_config, custom_objects=custom_objects)
-
-  # Recover loss functions and metrics.
-  loss = convert_custom_objects(training_config['loss'])
-  metrics = convert_custom_objects(training_config['metrics'])
-  sample_weight_mode = training_config['sample_weight_mode']
-  loss_weights = training_config['loss_weights']
-
-  # Compile model.
-  model.compile(
-      optimizer=optimizer,
-      loss=loss,
-      metrics=metrics,
-      loss_weights=loss_weights,
-      sample_weight_mode=sample_weight_mode)
-
-  # Set optimizer weights.
-  if 'optimizer_weights' in f:
-    # Build train function (to get weight updates).
-    if isinstance(model, Sequential):
-      model.model._make_train_function()
-    else:
-      model._make_train_function()
-    optimizer_weights_group = f['optimizer_weights']
-    optimizer_weight_names = [
-        n.decode('utf8') for n in optimizer_weights_group.attrs['weight_names']
-    ]
-    optimizer_weight_values = [
-        optimizer_weights_group[n] for n in optimizer_weight_names
-    ]
-    model.optimizer.set_weights(optimizer_weight_values)
-  f.close()
+  with h5py.File(filepath, mode='r') as f:
+    # instantiate model
+    model_config = f.attrs.get('model_config')
+    if model_config is None:
+      raise ValueError('No model found in config file.')
+    model_config = json.loads(model_config.decode('utf-8'))
+    model = model_from_config(model_config, custom_objects=custom_objects)
+
+    # set weights
+    topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)
+
+    # Early return if compilation is not required.
+    if not compile:
+      return model
+
+    # instantiate optimizer
+    training_config = f.attrs.get('training_config')
+    if training_config is None:
+      logging.warning('No training configuration found in save file: '
+                      'the model was *not* compiled. Compile it manually.')
+      return model
+    training_config = json.loads(training_config.decode('utf-8'))
+    optimizer_config = training_config['optimizer_config']
+    optimizer = optimizers.deserialize(
+        optimizer_config, custom_objects=custom_objects)
+
+    # Recover loss functions and metrics.
+    loss = convert_custom_objects(training_config['loss'])
+    metrics = convert_custom_objects(training_config['metrics'])
+    sample_weight_mode = training_config['sample_weight_mode']
+    loss_weights = training_config['loss_weights']
+
+    # Compile model.
+    model.compile(
+        optimizer=optimizer,
+        loss=loss,
+        metrics=metrics,
+        loss_weights=loss_weights,
+        sample_weight_mode=sample_weight_mode)
+
+    # Set optimizer weights.
+    if 'optimizer_weights' in f:
+      # Build train function (to get weight updates).
+      if isinstance(model, Sequential):
+        model.model._make_train_function()
+      else:
+        model._make_train_function()
+      optimizer_weights_group = f['optimizer_weights']
+      optimizer_weight_names = [
+          n.decode('utf8')
+          for n in optimizer_weights_group.attrs['weight_names']
+      ]
+      optimizer_weight_values = [
+          optimizer_weights_group[n] for n in optimizer_weight_names
+      ]
+      try:
+        model.optimizer.set_weights(optimizer_weight_values)
+      except ValueError:
+        logging.warning('Error in loading the saved optimizer '
+                        'state. As a result, your model is '
+                        'starting with a freshly initialized '
+                        'optimizer.')
   return model
 
 
@@ -331,7 +324,7 @@ def model_from_config(config, custom_objects=None):
       A Keras model instance (uncompiled).
 
   Raises:
-      TypeError if `config` is not a dictionary
+      TypeError: if `config` is not a dictionary.
   """
   if isinstance(config, list):
     raise TypeError('`model_from_config` expects a dictionary, not a list. '
@@ -1018,10 +1011,11 @@ class Sequential(Model):
                     validation_data=None,
                     validation_steps=None,
                     class_weight=None,
-                    max_q_size=10,
+                    max_queue_size=10,
                     workers=1,
-                    pickle_safe=False,
-                    initial_epoch=0):
+                    use_multiprocessing=False,
+                    initial_epoch=0,
+                    **kwargs):
     """Fits the model on data generated batch-by-batch by a Python generator.
 
     The generator is run in parallel to the model, for efficiency.
@@ -1057,9 +1051,9 @@ class Sequential(Model):
             validation dataset divided by the batch size.
         class_weight: Dictionary mapping class indices to a weight
             for the class.
-        max_q_size: Maximum size for the generator queue
+        max_queue_size: Maximum size for the generator queue
         workers: Maximum number of processes to spin up
-        pickle_safe: Ff True, use process based threading.
+        use_multiprocessing: If True, use process based threading.
             Note that because
             this implementation relies on multiprocessing,
             you should not pass
@@ -1068,12 +1062,15 @@ class Sequential(Model):
             easily to children processes.
         initial_epoch: Epoch at which to start training
             (useful for resuming a previous training run)
+        **kwargs: support for legacy arguments.
 
     Returns:
         A `History` object.
 
     Raises:
         RuntimeError: if the model was never compiled.
+        ValueError: In case the generator yields
+            data in an invalid format.
 
     Example:
 
@@ -1092,6 +1089,19 @@ class Sequential(Model):
                             steps_per_epoch=1000, epochs=10)
     ```
     """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
     if self.model is None:
       raise RuntimeError('The model needs to be compiled ' 'before being used.')
     return self.model.fit_generator(
@@ -1103,17 +1113,18 @@ class Sequential(Model):
         validation_data=validation_data,
         validation_steps=validation_steps,
         class_weight=class_weight,
-        max_q_size=max_q_size,
+        max_queue_size=max_queue_size,
         workers=workers,
-        pickle_safe=pickle_safe,
+        use_multiprocessing=use_multiprocessing,
         initial_epoch=initial_epoch)
 
   def evaluate_generator(self,
                          generator,
                          steps,
-                         max_q_size=10,
+                         max_queue_size=10,
                          workers=1,
-                         pickle_safe=False):
+                         use_multiprocessing=False,
+                         **kwargs):
     """Evaluates the model on a data generator.
 
     The generator should return the same kind of data
@@ -1124,13 +1135,14 @@ class Sequential(Model):
             or (inputs, targets, sample_weights)
         steps: Total number of steps (batches of samples)
             to yield from `generator` before stopping.
-        max_q_size: maximum size for the generator queue
+        max_queue_size: maximum size for the generator queue
         workers: maximum number of processes to spin up
-        pickle_safe: if True, use process based threading.
+        use_multiprocessing: if True, use process based threading.
             Note that because this implementation
             relies on multiprocessing, you should not pass
             non picklable arguments to the generator
             as they can't be passed easily to children processes.
+        **kwargs: support for legacy arguments.
 
     Returns:
         Scalar test loss (if the model has no metrics)
@@ -1140,23 +1152,39 @@ class Sequential(Model):
 
     Raises:
         RuntimeError: if the model was never compiled.
+        ValueError: In case the generator yields
+            data in an invalid format.
     """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
     if self.model is None:
       raise RuntimeError('The model needs to be compiled ' 'before being used.')
     return self.model.evaluate_generator(
         generator,
         steps,
-        max_q_size=max_q_size,
+        max_queue_size=max_queue_size,
         workers=workers,
-        pickle_safe=pickle_safe)
+        use_multiprocessing=use_multiprocessing)
 
   def predict_generator(self,
                         generator,
                         steps,
-                        max_q_size=10,
+                        max_queue_size=10,
                         workers=1,
-                        pickle_safe=False,
-                        verbose=0):
+                        use_multiprocessing=False,
+                        verbose=0,
+                        **kwargs):
     """Generates predictions for the input samples from a data generator.
 
     The generator should return the same kind of data as accepted by
@@ -1166,26 +1194,44 @@ class Sequential(Model):
         generator: generator yielding batches of input samples.
         steps: Total number of steps (batches of samples)
             to yield from `generator` before stopping.
-        max_q_size: maximum size for the generator queue
+        max_queue_size: maximum size for the generator queue
         workers: maximum number of processes to spin up
-        pickle_safe: if True, use process based threading.
+        use_multiprocessing: if True, use process based threading.
             Note that because this implementation
             relies on multiprocessing, you should not pass
             non picklable arguments to the generator
             as they can't be passed easily to children processes.
         verbose: verbosity mode, 0 or 1.
+        **kwargs: support for legacy arguments.
 
     Returns:
         A Numpy array of predictions.
+
+    Raises:
+        ValueError: In case the generator yields
+            data in an invalid format.
     """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
     if self.model is None:
       self.build()
     return self.model.predict_generator(
         generator,
         steps,
-        max_q_size=max_q_size,
+        max_queue_size=max_queue_size,
         workers=workers,
-        pickle_safe=pickle_safe,
+        use_multiprocessing=use_multiprocessing,
         verbose=verbose)
 
   def get_config(self):
diff --git a/tensorflow/contrib/keras/python/keras/models_test.py b/tensorflow/contrib/keras/python/keras/models_test.py
index 99fd6e1cbe..f7246097ee 100644
--- a/tensorflow/contrib/keras/python/keras/models_test.py
+++ b/tensorflow/contrib/keras/python/keras/models_test.py
@@ -163,6 +163,27 @@ class TestModelSaving(test.TestCase):
       model = keras.models.load_model(fname)
       os.remove(fname)
 
+  def test_saving_lambda_numpy_array_arguments(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    mean = np.random.random((4, 2, 3))
+    std = np.abs(np.random.random((4, 2, 3))) + 1e-5
+    inputs = keras.layers.Input(shape=(4, 2, 3))
+    output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std,
+                                 arguments={'mu': mean, 'std': std})(inputs)
+    model = keras.models.Model(inputs, output)
+    model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
+
+    _, fname = tempfile.mkstemp('.h5')
+    keras.models.save_model(model, fname)
+
+    model = keras.models.load_model(fname)
+    os.remove(fname)
+
+    self.assertAllClose(mean, model.layers[1].arguments['mu'])
+    self.assertAllClose(std, model.layers[1].arguments['std'])
+
 
 class TestSequential(test.TestCase):
   """Most Sequential model API tests are covered in `training_test.py`.
diff --git a/tensorflow/contrib/keras/python/keras/optimizers.py b/tensorflow/contrib/keras/python/keras/optimizers.py
index 75fce5c96f..a1bd3be026 100644
--- a/tensorflow/contrib/keras/python/keras/optimizers.py
+++ b/tensorflow/contrib/keras/python/keras/optimizers.py
@@ -18,18 +18,49 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import copy
+
 import six
 from six.moves import zip  # pylint: disable=redefined-builtin
 
 from tensorflow.contrib.keras.python.keras import backend as K
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import deserialize_keras_object
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import serialize_keras_object
+from tensorflow.python.framework import dtypes as dtypes_module
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.training import optimizer as tf_optimizer_module
 
 
 def clip_norm(g, c, n):
+  """Clip a tensor by norm.
+
+  Arguments:
+    g: gradient tensor to clip.
+    c: clipping threshold.
+    n: norm of gradient tensor.
+
+  Returns:
+    Clipped gradient tensor.
+  """
   if c > 0:
-    g = K.switch(n >= c, g * c / n, g)
+    condition = n >= c
+    then_expression = lambda: math_ops.scalar_mul(c / n, g)
+    else_expression = lambda: g
+
+    # saving the shape to avoid converting sparse tensor to dense
+    if isinstance(g, ops.Tensor):
+      g_shape = copy.copy(g.get_shape())
+    elif isinstance(g, ops.IndexedSlices):
+      g_shape = copy.copy(g.dense_shape)
+    if condition.dtype != dtypes_module.bool:
+      condition = math_ops.cast(condition, 'bool')
+    g = control_flow_ops.cond(condition, then_expression, else_expression)
+    if isinstance(g, ops.Tensor):
+      g.set_shape(g_shape)
+    elif isinstance(g, ops.IndexedSlices):
+      g._dense_shape = g_shape  # pylint: disable=protected-access
   return g
 
 
diff --git a/tensorflow/contrib/keras/python/keras/optimizers_test.py b/tensorflow/contrib/keras/python/keras/optimizers_test.py
index af5e3c99b9..bb598f3037 100644
--- a/tensorflow/contrib/keras/python/keras/optimizers_test.py
+++ b/tensorflow/contrib/keras/python/keras/optimizers_test.py
@@ -23,6 +23,7 @@ import numpy as np
 from tensorflow.contrib.keras.python import keras
 from tensorflow.contrib.keras.python.keras import testing_utils
 from tensorflow.python.platform import test
+from tensorflow.python.training.adam import AdamOptimizer
 
 
 def _get_model(input_dim, num_hidden, output_dim):
@@ -103,5 +104,28 @@ class KerasOptimizersTest(test.TestCase):
                                            momentum=0.9,
                                            clipvalue=0.5))
 
+  def test_tfoptimizer(self):
+    optimizer = keras.optimizers.TFOptimizer(AdamOptimizer)
+    model = keras.models.Sequential()
+    model.add(keras.layers.Dense(
+        2, input_shape=(3,), kernel_constraint=keras.constraints.MaxNorm(1)))
+    # This is possible
+    model.compile(loss='mean_squared_error', optimizer=optimizer)
+    # TF optimizers do not support weights constraints
+    with self.assertRaises(ValueError):
+      model.fit(np.random.random((5, 3)),
+                np.random.random((5, 2)),
+                epochs=1,
+                batch_size=5,
+                verbose=0)
+    # not supported
+    with self.assertRaises(NotImplementedError):
+      _ = optimizer.weights
+    with self.assertRaises(NotImplementedError):
+      optimizer.get_config()
+    with self.assertRaises(NotImplementedError):
+      optimizer.from_config(None)
+
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/keras/python/keras/preprocessing/image.py b/tensorflow/contrib/keras/python/keras/preprocessing/image.py
index 0d69396e8b..4f2cff804e 100644
--- a/tensorflow/contrib/keras/python/keras/preprocessing/image.py
+++ b/tensorflow/contrib/keras/python/keras/preprocessing/image.py
@@ -21,6 +21,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from functools import partial
+import multiprocessing.pool
 import os
 import re
 import threading
@@ -178,7 +180,7 @@ def random_zoom(x,
       ValueError: if `zoom_range` isn't a tuple.
   """
   if len(zoom_range) != 2:
-    raise ValueError('zoom_range should be a tuple or list of two floats. '
+    raise ValueError('`zoom_range` should be a tuple or list of two floats. '
                      'Received arg: ', zoom_range)
 
   if zoom_range[0] == 1 and zoom_range[1] == 1:
@@ -408,8 +410,9 @@ class ImageDataGenerator(object):
       horizontal_flip: whether to randomly flip images horizontally.
       vertical_flip: whether to randomly flip images vertically.
       rescale: rescaling factor. If None or 0, no rescaling is applied,
-          otherwise we multiply the data by the value provided
-          (before applying any other transformation).
+          otherwise we multiply the data by the value provided. This is
+          applied after the `preprocessing_function` (if any provided)
+          but before any other transformation.
       preprocessing_function: function that will be implied on each input.
           The function will run before any other modification on it.
           The function should take one argument:
@@ -466,8 +469,8 @@ class ImageDataGenerator(object):
 
     if data_format not in {'channels_last', 'channels_first'}:
       raise ValueError(
-          'data_format should be "channels_last" (channel after row and '
-          'column) or "channels_first" (channel before row and column). '
+          '`data_format` should be `"channels_last"` (channel after row and '
+          'column) or `"channels_first"` (channel before row and column). '
           'Received arg: ', data_format)
     self.data_format = data_format
     if data_format == 'channels_first':
@@ -488,7 +491,7 @@ class ImageDataGenerator(object):
     elif len(zoom_range) == 2:
       self.zoom_range = [zoom_range[0], zoom_range[1]]
     else:
-      raise ValueError('zoom_range should be a float or '
+      raise ValueError('`zoom_range` should be a float or '
                        'a tuple or list of two floats. '
                        'Received arg: ', zoom_range)
 
@@ -590,11 +593,12 @@ class ImageDataGenerator(object):
                         'first by calling `.fit(numpy_data)`.')
     return x
 
-  def random_transform(self, x):
+  def random_transform(self, x, seed=None):
     """Randomly augment a single image tensor.
 
     Arguments:
         x: 3D tensor, single image.
+        seed: random seed.
 
     Returns:
         A randomly transformed version of the input (same shape).
@@ -610,6 +614,9 @@ class ImageDataGenerator(object):
     img_col_axis = self.col_axis - 1
     img_channel_axis = self.channel_axis - 1
 
+    if seed is not None:
+      np.random.seed(seed)
+
     # use composition of homographies
     # to generate final transform that needs to be applied
     if self.rotation_range:
@@ -709,8 +716,8 @@ class ImageDataGenerator(object):
     if x.ndim != 4:
       raise ValueError('Input to `.fit()` should have rank 4. '
                        'Got array with shape: ' + str(x.shape))
-    if x.shape[self.channel_axis] not in {1, 3, 4}:
-      raise ValueError(
+    if x.shape[self.channel_axis] not in {3, 4}:
+      logging.warning(
           'Expected input to be images (as Numpy array) '
           'following the data format convention "' + self.data_format + '" '
           '(channels on axis ' + str(self.channel_axis) + '), i.e. expected '
@@ -911,6 +918,81 @@ class NumpyArrayIterator(Iterator):
     return batch_x, batch_y
 
 
+def _count_valid_files_in_directory(directory, white_list_formats,
+                                    follow_links):
+  """Count files with extension in `white_list_formats` in a directory.
+
+  Arguments:
+      directory: absolute path to the directory containing files to be counted
+      white_list_formats: set of strings containing allowed extensions for
+          the files to be counted.
+      follow_links: boolean.
+
+  Returns:
+      the count of files with extension in `white_list_formats` contained in
+      the directory.
+  """
+
+  def _recursive_list(subpath):
+    return sorted(
+        os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
+
+  samples = 0
+  for _, _, files in _recursive_list(directory):
+    for fname in files:
+      is_valid = False
+      for extension in white_list_formats:
+        if fname.lower().endswith('.' + extension):
+          is_valid = True
+          break
+      if is_valid:
+        samples += 1
+  return samples
+
+
+def _list_valid_filenames_in_directory(directory, white_list_formats,
+                                       class_indices, follow_links):
+  """List paths of files in `subdir` with extensions in `white_list_formats`.
+
+  Arguments:
+      directory: absolute path to a directory containing the files to list.
+          The directory name is used as class label and must be a key of
+            `class_indices`.
+      white_list_formats: set of strings containing allowed extensions for
+          the files to be counted.
+      class_indices: dictionary mapping a class name to its index.
+      follow_links: boolean.
+
+  Returns:
+      classes: a list of class indices
+      filenames: the path of valid files in `directory`, relative from
+          `directory`'s parent (e.g., if `directory` is "dataset/class1",
+          the filenames will be ["class1/file1.jpg", "class1/file2.jpg", ...]).
+  """
+
+  def _recursive_list(subpath):
+    return sorted(
+        os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
+
+  classes = []
+  filenames = []
+  subdir = os.path.basename(directory)
+  basedir = os.path.dirname(directory)
+  for root, _, files in _recursive_list(directory):
+    for fname in files:
+      is_valid = False
+      for extension in white_list_formats:
+        if fname.lower().endswith('.' + extension):
+          is_valid = True
+          break
+      if is_valid:
+        classes.append(class_indices[subdir])
+        # add filename relative to directory
+        absolute_path = os.path.join(root, fname)
+        filenames.append(os.path.relpath(absolute_path, basedir))
+  return classes, filenames
+
+
 class DirectoryIterator(Iterator):
   """Iterator capable of reading images from a directory on disk.
 
@@ -1007,43 +1089,35 @@ class DirectoryIterator(Iterator):
     self.num_class = len(classes)
     self.class_indices = dict(zip(classes, range(len(classes))))
 
-    def _recursive_list(subpath):
-      return sorted(
-          os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
-
-    for subdir in classes:
-      subpath = os.path.join(directory, subdir)
-      for root, _, files in _recursive_list(subpath):
-        for fname in files:
-          is_valid = False
-          for extension in white_list_formats:
-            if fname.lower().endswith('.' + extension):
-              is_valid = True
-              break
-          if is_valid:
-            self.samples += 1
+    pool = multiprocessing.pool.ThreadPool()
+    function_partial = partial(
+        _count_valid_files_in_directory,
+        white_list_formats=white_list_formats,
+        follow_links=follow_links)
+    self.samples = sum(
+        pool.map(function_partial, (os.path.join(directory, subdir)
+                                    for subdir in classes)))
+
     print('Found %d images belonging to %d classes.' % (self.samples,
                                                         self.num_class))
 
     # second, build an index of the images in the different class subfolders
+    results = []
+
     self.filenames = []
     self.classes = np.zeros((self.samples,), dtype='int32')
     i = 0
-    for subdir in classes:
-      subpath = os.path.join(directory, subdir)
-      for root, _, files in _recursive_list(subpath):
-        for fname in files:
-          is_valid = False
-          for extension in white_list_formats:
-            if fname.lower().endswith('.' + extension):
-              is_valid = True
-              break
-          if is_valid:
-            self.classes[i] = self.class_indices[subdir]
-            i += 1
-            # add filename relative to directory
-            absolute_path = os.path.join(root, fname)
-            self.filenames.append(os.path.relpath(absolute_path, directory))
+    for dirpath in (os.path.join(directory, subdir) for subdir in classes):
+      results.append(
+          pool.apply_async(_list_valid_filenames_in_directory, (
+              dirpath, white_list_formats, self.class_indices, follow_links)))
+    for res in results:
+      classes, filenames = res.get()
+      self.classes[i:i + len(classes)] = classes
+      self.filenames += filenames
+      i += len(classes)
+    pool.close()
+    pool.join()
     super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle,
                                             seed)
 
diff --git a/tensorflow/contrib/keras/python/keras/preprocessing/image_test.py b/tensorflow/contrib/keras/python/keras/preprocessing/image_test.py
index 0dedf8f850..94768f5258 100644
--- a/tensorflow/contrib/keras/python/keras/preprocessing/image_test.py
+++ b/tensorflow/contrib/keras/python/keras/preprocessing/image_test.py
@@ -99,22 +99,10 @@ class TestImage(test.TestCase):
     with self.assertRaises(ValueError):
       x = np.random.random((3, 10, 10))
       generator.fit(x)
-    with self.assertRaises(ValueError):
-      x = np.random.random((32, 3, 10, 10))
-      generator.fit(x)
-    with self.assertRaises(ValueError):
-      x = np.random.random((32, 10, 10, 5))
-      generator.fit(x)
     # Test flow with invalid data
-    with self.assertRaises(ValueError):
-      x = np.random.random((32, 10, 10, 5))
-      generator.flow(np.arange(x.shape[0]))
     with self.assertRaises(ValueError):
       x = np.random.random((32, 10, 10))
       generator.flow(np.arange(x.shape[0]))
-    with self.assertRaises(ValueError):
-      x = np.random.random((32, 3, 10, 10))
-      generator.flow(np.arange(x.shape[0]))
 
   def test_image_data_generator_fit(self):
     generator = keras.preprocessing.image.ImageDataGenerator(
diff --git a/tensorflow/contrib/keras/python/keras/preprocessing/text.py b/tensorflow/contrib/keras/python/keras/preprocessing/text.py
index 93e629af17..ed00eef6ad 100644
--- a/tensorflow/contrib/keras/python/keras/preprocessing/text.py
+++ b/tensorflow/contrib/keras/python/keras/preprocessing/text.py
@@ -21,6 +21,7 @@ from __future__ import division
 from __future__ import print_function
 
 from collections import OrderedDict
+from hashlib import md5
 import string
 import sys
 
@@ -61,8 +62,45 @@ def one_hot(text,
             filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
             lower=True,
             split=' '):
+  return hashing_trick(
+      text, n, hash_function=hash, filters=filters, lower=lower, split=split)
+
+
+def hashing_trick(text,
+                  n,
+                  hash_function=None,
+                  filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
+                  lower=True,
+                  split=' '):
+  """Converts a text to a sequence of indexes in a fixed-size hashing space.
+
+  Arguments:
+      text: Input text (string).
+      n: Dimension of the hashing space.
+      hash_function: if `None` uses python `hash` function, can be 'md5' or
+          any function that takes in input a string and returns a int.
+          Note that `hash` is not a stable hashing function, so
+          it is not consistent across different runs, while 'md5'
+          is a stable hashing function.
+      filters: Sequence of characters to filter out.
+      lower: Whether to convert the input to lowercase.
+      split: Sentence split marker (string).
+
+  Returns:
+      A list of integer word indices (unicity non-guaranteed).
+
+  `0` is a reserved index that won't be assigned to any word.
+
+  Two or more words may be assigned to the same index, due to possible
+  collisions by the hashing function.
+  """
+  if hash_function is None:
+    hash_function = hash
+  elif hash_function == 'md5':
+    hash_function = lambda w: int(md5(w.encode()).hexdigest(), 16)
+
   seq = text_to_word_sequence(text, filters=filters, lower=lower, split=split)
-  return [(abs(hash(w)) % (n - 1) + 1) for w in seq]
+  return [(hash_function(w) % (n - 1) + 1) for w in seq]
 
 
 class Tokenizer(object):
diff --git a/tensorflow/contrib/keras/python/keras/preprocessing/text_test.py b/tensorflow/contrib/keras/python/keras/preprocessing/text_test.py
index e94b9019b2..7b26219e61 100644
--- a/tensorflow/contrib/keras/python/keras/preprocessing/text_test.py
+++ b/tensorflow/contrib/keras/python/keras/preprocessing/text_test.py
@@ -30,8 +30,8 @@ class TestText(test.TestCase):
     text = 'The cat sat on the mat.'
     encoded = keras.preprocessing.text.one_hot(text, 5)
     self.assertEqual(len(encoded), 6)
-    assert np.max(encoded) <= 4
-    assert np.min(encoded) >= 0
+    self.assertLessEqual(np.max(encoded), 4)
+    self.assertGreaterEqual(np.min(encoded), 0)
 
   def test_tokenizer(self):
     texts = [
@@ -45,7 +45,7 @@ class TestText(test.TestCase):
     sequences = []
     for seq in tokenizer.texts_to_sequences_generator(texts):
       sequences.append(seq)
-    assert np.max(np.max(sequences)) < 10
+    self.assertLess(np.max(np.max(sequences)), 10)
     self.assertEqual(np.min(np.min(sequences)), 1)
 
     tokenizer.fit_on_sequences(sequences)
@@ -54,6 +54,21 @@ class TestText(test.TestCase):
       matrix = tokenizer.texts_to_matrix(texts, mode)
       self.assertEqual(matrix.shape, (3, 10))
 
+  def test_hashing_trick_hash(self):
+    text = 'The cat sat on the mat.'
+    encoded = keras.preprocessing.text.hashing_trick(text, 5)
+    self.assertEqual(len(encoded), 6)
+    self.assertLessEqual(np.max(encoded), 4)
+    self.assertGreaterEqual(np.min(encoded), 1)
+
+  def test_hashing_trick_md5(self):
+    text = 'The cat sat on the mat.'
+    encoded = keras.preprocessing.text.hashing_trick(
+        text, 5, hash_function='md5')
+    self.assertEqual(len(encoded), 6)
+    self.assertLessEqual(np.max(encoded), 4)
+    self.assertGreaterEqual(np.min(encoded), 1)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/keras/python/keras/utils/__init__.py b/tensorflow/contrib/keras/python/keras/utils/__init__.py
index 68c28ab585..3b197653f3 100644
--- a/tensorflow/contrib/keras/python/keras/utils/__init__.py
+++ b/tensorflow/contrib/keras/python/keras/utils/__init__.py
@@ -23,7 +23,10 @@ from tensorflow.contrib.keras.python.keras.utils import data_utils
 from tensorflow.contrib.keras.python.keras.utils import generic_utils
 from tensorflow.contrib.keras.python.keras.utils import io_utils
 from tensorflow.contrib.keras.python.keras.utils import np_utils
+from tensorflow.contrib.keras.python.keras.utils.data_utils import GeneratorEnqueuer
 from tensorflow.contrib.keras.python.keras.utils.data_utils import get_file
+from tensorflow.contrib.keras.python.keras.utils.data_utils import OrderedEnqueuer
+from tensorflow.contrib.keras.python.keras.utils.data_utils import Sequence
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import custom_object_scope
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import CustomObjectScope
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import deserialize_keras_object
diff --git a/tensorflow/contrib/keras/python/keras/utils/data_utils.py b/tensorflow/contrib/keras/python/keras/utils/data_utils.py
index 61a11b95e8..9aa477d522 100644
--- a/tensorflow/contrib/keras/python/keras/utils/data_utils.py
+++ b/tensorflow/contrib/keras/python/keras/utils/data_utils.py
@@ -17,13 +17,21 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from abc import abstractmethod
 import hashlib
+import multiprocessing
+import multiprocessing.managers
+from multiprocessing.pool import ThreadPool
 import os
+import random
 import shutil
 import sys
 import tarfile
+import threading
+import time
 import zipfile
 
+import numpy as np
 import six
 from six.moves.urllib.error import HTTPError
 from six.moves.urllib.error import URLError
@@ -31,6 +39,11 @@ from six.moves.urllib.request import urlopen
 
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import Progbar
 
+try:
+  import queue  # pylint:disable=g-import-not-at-top
+except ImportError:
+  import Queue as queue  # pylint:disable=g-import-not-at-top
+
 
 if sys.version_info[0] == 2:
 
@@ -300,3 +313,375 @@ def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535):
     return True
   else:
     return False
+
+
+class HolderManager(multiprocessing.managers.BaseManager):
+  """Custom manager to share a Holder object."""
+  pass
+
+
+class Holder(object):
+  """Object to encapsulate a Sequence.
+
+  This allows the Sequence to be shared across multiple workers.
+
+  Arguments:
+      seq: Sequence object to be shared.
+  """
+
+  def __init__(self, seq):
+    self.seq = seq
+
+  def __getitem__(self, idx):
+    return self.seq[idx]
+
+  def __len__(self):
+    return len(self.seq)
+
+
+# Register the Holder class using the ListProxy (allows __len__ and __getitem__)
+HolderManager.register('Holder', Holder, multiprocessing.managers.ListProxy)
+
+
+class Sequence(object):
+  """Base object for fitting to a sequence of data, such as a dataset.
+
+  Every `Sequence` must implements the `__getitem__` and the `__len__` methods.
+
+  Examples:
+
+  ```python
+  from skimage.io import imread
+  from skimage.transform import resize
+  import numpy as np
+
+  # Here, `x_set` is list of path to the images
+  # and `y_set` are the associated classes.
+
+  class CIFAR10Sequence(Sequence):
+      def __init__(self, x_set, y_set, batch_size):
+          self.X,self.y = x_set,y_set
+          self.batch_size = batch_size
+
+      def __len__(self):
+          return len(self.X) // self.batch_size
+
+      def __getitem__(self,idx):
+          batch_x = self.X[idx*self.batch_size:(idx+1)*self.batch_size]
+          batch_y = self.y[idx*self.batch_size:(idx+1)*self.batch_size]
+
+          return np.array([
+              resize(imread(file_name), (200,200))
+                 for file_name in batch_x]), np.array(batch_y)
+  ```
+  """
+
+  @abstractmethod
+  def __getitem__(self, index):
+    """Gets batch at position `index`.
+
+    Arguments:
+        index: position of the batch in the Sequence.
+
+    Returns:
+        A batch
+    """
+    raise NotImplementedError
+
+  @abstractmethod
+  def __len__(self):
+    """Number of batch in the Sequence.
+
+    Returns:
+        The number of batches in the Sequence.
+    """
+    raise NotImplementedError
+
+
+def get_index(ds, i):
+  """Quick fix for Python2, otherwise, it cannot be pickled.
+
+  Arguments:
+      ds: a Holder or Sequence object.
+      i: index
+
+  Returns:
+      The value at index `i`.
+  """
+  return ds[i]
+
+
+class SequenceEnqueuer(object):
+  """Base class to enqueue inputs.
+
+  The task of an Enqueuer is to use parallelism to speed up preprocessing.
+  This is done with processes or threads.
+
+  Examples:
+
+  ```python
+  enqueuer = SequenceEnqueuer(...)
+  enqueuer.start()
+  datas = enqueuer.get()
+  for data in datas:
+      # Use the inputs; training, evaluating, predicting.
+      # ... stop sometime.
+  enqueuer.close()
+  ```
+
+  The `enqueuer.get()` should be an infinite stream of datas.
+
+  """
+
+  @abstractmethod
+  def is_running(self):
+    raise NotImplementedError
+
+  @abstractmethod
+  def start(self, workers=1, max_queue_size=10):
+    """Starts the handler's workers.
+
+    Arguments:
+        workers: number of worker threads
+        max_queue_size: queue size
+            (when full, threads could block on `put()`).
+    """
+    raise NotImplementedError
+
+  @abstractmethod
+  def stop(self, timeout=None):
+    """Stop running threads and wait for them to exit, if necessary.
+
+    Should be called by the same thread which called start().
+
+    Arguments:
+        timeout: maximum time to wait on thread.join()
+    """
+    raise NotImplementedError
+
+  @abstractmethod
+  def get(self):
+    """Creates a generator to extract data from the queue.
+
+    Skip the data if it is `None`.
+
+    Returns:
+        Generator yielding tuples `(inputs, targets)`
+            or `(inputs, targets, sample_weights)`.
+    """
+    raise NotImplementedError
+
+
+class OrderedEnqueuer(SequenceEnqueuer):
+  """Builds a Enqueuer from a Sequence.
+
+  Used in `fit_generator`, `evaluate_generator`, `predict_generator`.
+
+  Arguments:
+      sequence: A `keras.utils.data_utils.Sequence` object.
+      use_multiprocessing: use multiprocessing if True, otherwise threading
+      scheduling: Sequential querying of datas if 'sequential', random
+        otherwise.
+  """
+
+  def __init__(self,
+               sequence,
+               use_multiprocessing=False,
+               scheduling='sequential'):
+    self.manager = HolderManager()
+    self.manager.start()
+    self.sequence = self.manager.Holder(sequence)
+    self.use_multiprocessing = use_multiprocessing
+    self.scheduling = scheduling
+    self.workers = 0
+    self.executor = None
+    self.queue = None
+    self.run_thread = None
+    self.stop_signal = None
+
+  def is_running(self):
+    return self.stop_signal is not None and not self.stop_signal.is_set()
+
+  def start(self, workers=1, max_queue_size=10):
+    """Start the handler's workers.
+
+    Arguments:
+        workers: number of worker threads
+        max_queue_size: queue size
+            (when full, workers could block on `put()`)
+    """
+    if self.use_multiprocessing:
+      self.executor = multiprocessing.Pool(workers)
+    else:
+      self.executor = ThreadPool(workers)
+    self.queue = queue.Queue(max_queue_size)
+    self.stop_signal = threading.Event()
+    self.run_thread = threading.Thread(target=self._run)
+    self.run_thread.daemon = True
+    self.run_thread.start()
+
+  def _run(self):
+    """Submits requests to the executor and queues the `Future` objects."""
+    sequence = list(range(len(self.sequence)))
+    while True:
+      if self.scheduling is not 'sequential':
+        random.shuffle(sequence)
+      for i in sequence:
+        if self.stop_signal.is_set():
+          return
+        self.queue.put(
+            self.executor.apply_async(get_index, (self.sequence, i)),
+            block=True)
+
+  def get(self):
+    """Creates a generator to extract data from the queue.
+
+    Skip the data if it is `None`.
+
+    Yields:
+        Tuples (inputs, targets)
+            or (inputs, targets, sample_weights)
+    """
+    try:
+      while self.is_running():
+        inputs = self.queue.get(block=True).get()
+        if inputs is not None:
+          yield inputs
+    except Exception as e:
+      self.stop()
+      raise StopIteration(e)
+
+  def stop(self, timeout=None):
+    """Stops running threads and wait for them to exit, if necessary.
+
+    Should be called by the same thread which called `start()`.
+
+    Arguments:
+        timeout: maximum time to wait on `thread.join()`
+    """
+    self.stop_signal.set()
+    with self.queue.mutex:
+      self.queue.queue.clear()
+      self.queue.unfinished_tasks = 0
+      self.queue.not_full.notify()
+    self.executor.close()
+    self.executor.join()
+    self.run_thread.join(timeout)
+
+
+class GeneratorEnqueuer(SequenceEnqueuer):
+  """Builds a queue out of a data generator.
+
+  Used in `fit_generator`, `evaluate_generator`, `predict_generator`.
+
+  Arguments:
+      generator: a generator function which endlessly yields data
+      use_multiprocessing: use multiprocessing if True, otherwise threading
+      wait_time: time to sleep in-between calls to `put()`
+      random_seed: Initial seed for workers,
+          will be incremented by one for each workers.
+  """
+
+  def __init__(self,
+               generator,
+               use_multiprocessing=False,
+               wait_time=0.05,
+               random_seed=None):
+    self.wait_time = wait_time
+    self._generator = generator
+    self._use_multiprocessing = use_multiprocessing
+    self._threads = []
+    self._stop_event = None
+    self.queue = None
+    self.random_seed = random_seed
+
+  def start(self, workers=1, max_queue_size=10):
+    """Kicks off threads which add data from the generator into the queue.
+
+    Arguments:
+        workers: number of worker threads
+        max_queue_size: queue size
+            (when full, threads could block on `put()`)
+    """
+
+    def data_generator_task():
+      while not self._stop_event.is_set():
+        try:
+          if self._use_multiprocessing or self.queue.qsize() < max_queue_size:
+            generator_output = next(self._generator)
+            self.queue.put(generator_output)
+          else:
+            time.sleep(self.wait_time)
+        except Exception:
+          self._stop_event.set()
+          raise
+
+    try:
+      if self._use_multiprocessing:
+        self.queue = multiprocessing.Queue(maxsize=max_queue_size)
+        self._stop_event = multiprocessing.Event()
+      else:
+        self.queue = queue.Queue()
+        self._stop_event = threading.Event()
+
+      for _ in range(workers):
+        if self._use_multiprocessing:
+          # Reset random seed else all children processes
+          # share the same seed
+          np.random.seed(self.random_seed)
+          thread = multiprocessing.Process(target=data_generator_task)
+          thread.daemon = True
+          if self.random_seed is not None:
+            self.random_seed += 1
+        else:
+          thread = threading.Thread(target=data_generator_task)
+        self._threads.append(thread)
+        thread.start()
+    except:
+      self.stop()
+      raise
+
+  def is_running(self):
+    return self._stop_event is not None and not self._stop_event.is_set()
+
+  def stop(self, timeout=None):
+    """Stops running threads and wait for them to exit, if necessary.
+
+    Should be called by the same thread which called `start()`.
+
+    Arguments:
+        timeout: maximum time to wait on `thread.join()`.
+    """
+    if self.is_running():
+      self._stop_event.set()
+
+    for thread in self._threads:
+      if thread.is_alive():
+        if self._use_multiprocessing:
+          thread.terminate()
+        else:
+          thread.join(timeout)
+
+    if self._use_multiprocessing:
+      if self.queue is not None:
+        self.queue.close()
+
+    self._threads = []
+    self._stop_event = None
+    self.queue = None
+
+  def get(self):
+    """Creates a generator to extract data from the queue.
+
+    Skip the data if it is `None`.
+
+    Yields:
+        Data arrays.
+    """
+    while self.is_running():
+      if not self.queue.empty():
+        inputs = self.queue.get()
+        if inputs is not None:
+          yield inputs
+      else:
+        time.sleep(self.wait_time)
diff --git a/tensorflow/contrib/keras/python/keras/utils/data_utils_test.py b/tensorflow/contrib/keras/python/keras/utils/data_utils_test.py
new file mode 100644
index 0000000000..7b73775f46
--- /dev/null
+++ b/tensorflow/contrib/keras/python/keras/utils/data_utils_test.py
@@ -0,0 +1,172 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for data_utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from itertools import cycle
+import threading
+
+import numpy as np
+
+from tensorflow.contrib.keras.python import keras
+from tensorflow.python.platform import test
+
+
+class ThreadsafeIter(object):
+
+  def __init__(self, it):
+    self.it = it
+    self.lock = threading.Lock()
+
+  def __iter__(self):
+    return self
+
+  def __next__(self):
+    return self.next()
+
+  def next(self):
+    with self.lock:
+      return next(self.it)
+
+
+def threadsafe_generator(f):
+
+  def g(*a, **kw):
+    return ThreadsafeIter(f(*a, **kw))
+
+  return g
+
+
+class TestSequence(keras.utils.data_utils.Sequence):
+
+  def __init__(self, shape):
+    self.shape = shape
+
+  def __getitem__(self, item):
+    return np.ones(self.shape, dtype=np.uint8) * item
+
+  def __len__(self):
+    return 100
+
+
+class FaultSequence(keras.utils.data_utils.Sequence):
+
+  def __getitem__(self, item):
+    raise IndexError(item, 'item is not present')
+
+  def __len__(self):
+    return 100
+
+
+@threadsafe_generator
+def create_generator_from_sequence_threads(ds):
+  for i in cycle(range(len(ds))):
+    yield ds[i]
+
+
+def create_generator_from_sequence_pcs(ds):
+  for i in cycle(range(len(ds))):
+    yield ds[i]
+
+
+class TestEnqueuers(test.TestCase):
+
+  def test_generator_enqueuer_threads(self):
+    enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
+        create_generator_from_sequence_threads(TestSequence([3, 200, 200, 3])),
+        use_multiprocessing=False)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    acc = []
+    for _ in range(100):
+      acc.append(int(next(gen_output)[0, 0, 0, 0]))
+
+    self.assertEqual(len(set(acc) - set(range(100))), 0)
+    enqueuer.stop()
+
+  def test_generator_enqueuer_processes(self):
+    enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
+        create_generator_from_sequence_pcs(TestSequence([3, 200, 200, 3])),
+        use_multiprocessing=True)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    acc = []
+    for _ in range(100):
+      acc.append(int(next(gen_output)[0, 0, 0, 0]))
+    self.assertNotEqual(acc, list(range(100)))
+    enqueuer.stop()
+
+  def test_generator_enqueuer_fail_threads(self):
+    enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
+        create_generator_from_sequence_threads(FaultSequence()),
+        use_multiprocessing=False)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    with self.assertRaises(StopIteration):
+      next(gen_output)
+
+  def test_generator_enqueuer_fail_processes(self):
+    enqueuer = keras.utils.data_utils.GeneratorEnqueuer(
+        create_generator_from_sequence_pcs(FaultSequence()),
+        use_multiprocessing=True)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    with self.assertRaises(StopIteration):
+      next(gen_output)
+
+  def test_ordered_enqueuer_threads(self):
+    enqueuer = keras.utils.data_utils.OrderedEnqueuer(
+        TestSequence([3, 200, 200, 3]), use_multiprocessing=False)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    acc = []
+    for _ in range(100):
+      acc.append(next(gen_output)[0, 0, 0, 0])
+    self.assertEqual(acc, list(range(100)))
+    enqueuer.stop()
+
+  def test_ordered_enqueuer_processes(self):
+    enqueuer = keras.utils.data_utils.OrderedEnqueuer(
+        TestSequence([3, 200, 200, 3]), use_multiprocessing=True)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    acc = []
+    for _ in range(100):
+      acc.append(next(gen_output)[0, 0, 0, 0])
+    self.assertEqual(acc, list(range(100)))
+    enqueuer.stop()
+
+  def test_ordered_enqueuer_fail_threads(self):
+    enqueuer = keras.utils.data_utils.OrderedEnqueuer(
+        FaultSequence(), use_multiprocessing=False)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    with self.assertRaises(StopIteration):
+      next(gen_output)
+
+  def test_ordered_enqueuer_fail_processes(self):
+    enqueuer = keras.utils.data_utils.OrderedEnqueuer(
+        FaultSequence(), use_multiprocessing=True)
+    enqueuer.start(3, 10)
+    gen_output = enqueuer.get()
+    with self.assertRaises(StopIteration):
+      next(gen_output)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/keras/python/keras/utils/generic_utils.py b/tensorflow/contrib/keras/python/keras/utils/generic_utils.py
index 5cae694d54..ed57144f9c 100644
--- a/tensorflow/contrib/keras/python/keras/utils/generic_utils.py
+++ b/tensorflow/contrib/keras/python/keras/utils/generic_utils.py
@@ -227,6 +227,24 @@ def func_load(code, defaults=None, closure=None, globs=None):
       code, globs, name=code.co_name, argdefs=defaults, closure=closure)
 
 
+def has_arg(fn, name, accept_all=False):
+  """Checks if a callable accepts a given keyword argument.
+
+  Arguments:
+      fn: Callable to inspect.
+      name: Check if `fn` can be called with `name` as a keyword argument.
+      accept_all: What to return if there is no parameter called `name`
+                  but the function accepts a `**kwargs` argument.
+
+  Returns:
+      bool, whether `fn` accepts a `name` keyword argument.
+  """
+  arg_spec = tf_inspect.getargspec(fn)
+  if accept_all and arg_spec.keywords is not None:
+    return True
+  return name in arg_spec.args
+
+
 class Progbar(object):
   """Displays a progress bar.
 
diff --git a/tensorflow/contrib/keras/python/keras/utils/generic_utils_test.py b/tensorflow/contrib/keras/python/keras/utils/generic_utils_test.py
new file mode 100644
index 0000000000..8a6519f4cc
--- /dev/null
+++ b/tensorflow/contrib/keras/python/keras/utils/generic_utils_test.py
@@ -0,0 +1,75 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Keras generic Python utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.keras.python import keras
+from tensorflow.python.platform import test
+
+
+class HasArgTest(test.TestCase):
+
+  def test_has_arg(self):
+
+    def f_x(x):
+      return x
+
+    def f_x_args(x, *args):
+      _ = args
+      return x
+
+    def f_x_kwargs(x, **kwargs):
+      _ = kwargs
+      return x
+
+    self.assertTrue(keras.utils.generic_utils.has_arg(
+        f_x, 'x', accept_all=False))
+    self.assertFalse(keras.utils.generic_utils.has_arg(
+        f_x, 'y', accept_all=False))
+    self.assertTrue(keras.utils.generic_utils.has_arg(
+        f_x_args, 'x', accept_all=False))
+    self.assertFalse(keras.utils.generic_utils.has_arg(
+        f_x_args, 'y', accept_all=False))
+    self.assertTrue(keras.utils.generic_utils.has_arg(
+        f_x_kwargs, 'x', accept_all=False))
+    self.assertFalse(keras.utils.generic_utils.has_arg(
+        f_x_kwargs, 'y', accept_all=False))
+    self.assertTrue(keras.utils.generic_utils.has_arg(
+        f_x_kwargs, 'y', accept_all=True))
+
+
+class TestCustomObjectScope(test.TestCase):
+
+  def test_custom_object_scope(self):
+
+    def custom_fn():
+      pass
+
+    class CustomClass(object):
+      pass
+
+    with keras.utils.generic_utils.custom_object_scope(
+        {'CustomClass': CustomClass, 'custom_fn': custom_fn}):
+      act = keras.activations.get('custom_fn')
+      self.assertEqual(act, custom_fn)
+      cl = keras.regularizers.get('CustomClass')
+      self.assertEqual(cl.__class__, CustomClass)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/keras/python/keras/utils/io_utils.py b/tensorflow/contrib/keras/python/keras/utils/io_utils.py
index 55c135b5eb..70b2d96907 100644
--- a/tensorflow/contrib/keras/python/keras/utils/io_utils.py
+++ b/tensorflow/contrib/keras/python/keras/utils/io_utils.py
@@ -113,8 +113,40 @@ class HDF5Matrix(object):
 
   @property
   def shape(self):
+    """Gets a numpy-style shape tuple giving the dataset dimensions.
+
+    Returns:
+        A numpy-style shape tuple.
+    """
     return (self.end - self.start,) + self.data.shape[1:]
 
+  @property
+  def dtype(self):
+    """Gets the datatype of the dataset.
+
+    Returns:
+        A numpy dtype string.
+    """
+    return self.data.dtype
+
+  @property
+  def ndim(self):
+    """Gets the number of dimensions (rank) of the dataset.
+
+    Returns:
+        An integer denoting the number of dimensions (rank) of the dataset.
+    """
+    return self.data.ndim
+
+  @property
+  def size(self):
+    """Gets the total dataset size (number of elements).
+
+    Returns:
+        An integer denoting the number of elements in the dataset.
+    """
+    return np.prod(self.shape)
+
 
 def ask_to_proceed_with_overwrite(filepath):
   """Produces a prompt asking about overwriting a file.
diff --git a/tensorflow/contrib/keras/python/keras/utils/layer_utils.py b/tensorflow/contrib/keras/python/keras/utils/layer_utils.py
index 154070fb93..1c3481fdb8 100644
--- a/tensorflow/contrib/keras/python/keras/utils/layer_utils.py
+++ b/tensorflow/contrib/keras/python/keras/utils/layer_utils.py
@@ -24,15 +24,24 @@ from tensorflow.contrib.keras.python.keras import backend as K
 from tensorflow.contrib.keras.python.keras.utils.conv_utils import convert_kernel
 
 
-def print_summary(model, line_length=None, positions=None):
+def print_summary(model, line_length=None, positions=None, print_fn=None):
   """Prints a summary of a model.
 
   Arguments:
       model: Keras model instance.
-      line_length: total length of printed lines
-      positions: relative or absolute positions of log elements in each line.
+      line_length: Total length of printed lines
+          (e.g. set this to adapt the display to different
+          terminal window sizes).
+      positions: Relative or absolute positions of log elements in each line.
           If not provided, defaults to `[.33, .55, .67, 1.]`.
+      print_fn: Print function to use (defaults to `print`).
+          It will be called on each line of the summary.
+          You can set it to a custom function
+          in order to capture the string summary.
   """
+  if print_fn is None:
+    print_fn = print
+
   if model.__class__.__name__ == 'Sequential':
     sequential_like = True
   else:
@@ -70,11 +79,11 @@ def print_summary(model, line_length=None, positions=None):
       line += str(fields[i])
       line = line[:positions[i]]
       line += ' ' * (positions[i] - len(line))
-    print(line)
+    print_fn(line)
 
-  print('_' * line_length)
+  print_fn('_' * line_length)
   print_row(to_display, positions)
-  print('=' * line_length)
+  print_fn('=' * line_length)
 
   def print_layer_summary(layer):
     try:
@@ -131,19 +140,19 @@ def print_summary(model, line_length=None, positions=None):
     else:
       print_layer_summary_with_connections(layers[i])
     if i == len(layers) - 1:
-      print('=' * line_length)
+      print_fn('=' * line_length)
     else:
-      print('_' * line_length)
+      print_fn('_' * line_length)
 
   trainable_count = int(
       np.sum([K.count_params(p) for p in set(model.trainable_weights)]))
   non_trainable_count = int(
       np.sum([K.count_params(p) for p in set(model.non_trainable_weights)]))
 
-  print('Total params: {:,}'.format(trainable_count + non_trainable_count))
-  print('Trainable params: {:,}'.format(trainable_count))
-  print('Non-trainable params: {:,}'.format(non_trainable_count))
-  print('_' * line_length)
+  print_fn('Total params: {:,}'.format(trainable_count + non_trainable_count))
+  print_fn('Trainable params: {:,}'.format(trainable_count))
+  print_fn('Non-trainable params: {:,}'.format(non_trainable_count))
+  print_fn('_' * line_length)
 
 
 def convert_all_kernels_in_model(model):
diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index 63c7280b3d..5967180f5b 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -1350,6 +1350,7 @@ class Conv3DTranspose(Conv3D):
         trainable=trainable,
         name=name,
         **kwargs)
+    self.input_spec = base.InputSpec(ndim=5)
 
   def build(self, input_shape):
     if len(input_shape) != 5:
@@ -1391,6 +1392,9 @@ class Conv3DTranspose(Conv3D):
     else:
       c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
 
+    self.input_spec = base.InputSpec(ndim=5,
+                                     axes={c_axis: inputs_shape[c_axis]})
+
     depth = inputs_shape[d_axis]
     height = inputs_shape[h_axis]
     width = inputs_shape[w_axis]
@@ -1468,6 +1472,26 @@ class Conv3DTranspose(Conv3D):
       return self.activation(outputs)
     return outputs
 
+  def _compute_output_shape(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape).as_list()
+    output_shape = list(input_shape)
+    if self.data_format == 'channels_first':
+      c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
+    else:
+      c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
+
+    kernel_d, kernel_h, kernel_w = self.kernel_size
+    stride_d, stride_h, stride_w = self.strides
+
+    output_shape[c_axis] = self.filters
+    output_shape[d_axis] = utils.deconv_output_length(
+        output_shape[d_axis], stride_d, kernel_d, self.padding)
+    output_shape[h_axis] = utils.deconv_output_length(
+        output_shape[h_axis], stride_h, kernel_h, self.padding)
+    output_shape[w_axis] = utils.deconv_output_length(
+        output_shape[w_axis], stride_w, kernel_w, self.padding)
+    return tensor_shape.TensorShape(output_shape)
+
 
 def conv3d_transpose(inputs,
                      filters,
-- 
GitLab


From 7d0f6385f8e7637e155ef9c340c19aded365a6ff Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 19:38:15 -0700
Subject: [PATCH 0784/1375] [BatchNorm] Skeleton code to implement
 BatchNormGrad

This CL sets up all the boilerplate code needed to implement BatchNormGrad. None of the
backends bas been implemented yet.

RELNOTES: n/a
PiperOrigin-RevId: 161161713
---
 .../xla/client/computation_builder.cc         | 30 ++++++++++---
 .../compiler/xla/service/cpu/ir_emitter.cc    |  6 +++
 .../compiler/xla/service/cpu/ir_emitter.h     |  1 +
 .../compiler/xla/service/dfs_hlo_visitor.h    |  2 +
 .../service/dfs_hlo_visitor_with_default.h    |  4 ++
 .../compiler/xla/service/hlo_cost_analysis.cc |  7 ++-
 .../compiler/xla/service/hlo_cost_analysis.h  |  1 +
 .../compiler/xla/service/hlo_graph_dumper.cc  |  4 ++
 .../compiler/xla/service/hlo_instruction.cc   | 23 +++++++++-
 .../compiler/xla/service/hlo_instruction.h    |  6 +++
 tensorflow/compiler/xla/service/hlo_opcode.cc |  2 +
 tensorflow/compiler/xla/service/hlo_opcode.h  |  1 +
 .../xla/service/instruction_fusion.cc         |  1 +
 tensorflow/compiler/xla/service/service.cc    |  4 ++
 .../compiler/xla/service/shape_inference.cc   |  9 ++++
 .../compiler/xla/service/shape_inference.h    |  8 ++++
 .../compiler/xla/service/user_computation.cc  | 45 +++++++++++++++++++
 .../compiler/xla/service/user_computation.h   |  4 ++
 tensorflow/compiler/xla/xla_data.proto        | 13 +++++-
 19 files changed, 163 insertions(+), 8 deletions(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index dcc313707b..9810ccc042 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -1453,13 +1453,33 @@ ComputationDataHandle ComputationBuilder::BatchNormInference(
 
 ComputationDataHandle ComputationBuilder::BatchNormGrad(
     const ComputationDataHandle& operand, const ComputationDataHandle& scale,
-    const ComputationDataHandle& batch_mean,
-    const ComputationDataHandle& batch_var,
+    const ComputationDataHandle& mean, const ComputationDataHandle& var,
     const ComputationDataHandle& grad_output, float epsilon,
     int64 feature_index) {
-  // TODO(b/62843645): Implement BatchNormGrad.
-  NoteError(Unimplemented("BatchNormGrad is not implemented yet."));
-  return ComputationDataHandle();
+  if (!first_error_.ok() || !PrepareComputation().ok()) {
+    return ComputationDataHandle();
+  }
+  BatchNormGradRequest request;
+  *request.mutable_operand() = operand;
+  *request.mutable_scale() = scale;
+  *request.mutable_mean() = mean;
+  *request.mutable_variance() = var;
+  *request.mutable_grad_output() = grad_output;
+  request.set_epsilon(epsilon);
+  request.set_feature_index(feature_index);
+
+  OpRequest op_request;
+  *op_request.mutable_batch_norm_grad_request() = request;
+  *op_request.mutable_computation() = computation_.handle();
+  AddOpMetadata(&op_request);
+
+  OpResponse response;
+
+  VLOG(2) << "making BatchNormGrad request";
+
+  Status s = client_->stub()->Op(&op_request, &response);
+
+  return ParseOpResponse(s, &response);
 }
 
 ComputationDataHandle ComputationBuilder::CrossReplicaSum(
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index cc27db8993..590a23562b 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -1321,6 +1321,12 @@ Status IrEmitter::HandleBatchNormTraining(HloInstruction* batch_norm_training) {
   return Status::OK();
 }
 
+Status IrEmitter::HandleBatchNormGrad(HloInstruction* batch_norm_grad) {
+  // TODO(b/62843645) Implement BatchNormGrad on CPU backend.
+  return Unimplemented(
+      "BatchNormGrad is not implemented on CPU. See b/62843645.");
+}
+
 Status IrEmitter::HandleParameter(HloInstruction* parameter) {
   VLOG(2) << "HandleParameter: " << parameter->ToString();
   auto param_number = parameter->parameter_number();
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index ff1ce7218b..dd3c55f408 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -107,6 +107,7 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   Status HandleConvolution(HloInstruction* convolution, HloInstruction* lhs,
                            HloInstruction* rhs, const Window& window) override;
   Status HandleBatchNormTraining(HloInstruction* batch_norm_training) override;
+  Status HandleBatchNormGrad(HloInstruction* batch_norm_grad) override;
   Status HandleCrossReplicaSum(HloInstruction* crs) override;
   Status HandleInfeed(HloInstruction* infeed) override;
   Status HandleOutfeed(HloInstruction* infeed) override;
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index fcc4f85f01..0deedcf184 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -227,6 +227,8 @@ class DfsHloVisitor {
 
   virtual Status HandleBatchNormTraining(HloInstruction* batchNormTraining) = 0;
 
+  virtual Status HandleBatchNormGrad(HloInstruction* batchNormGrad) = 0;
+
   // Invoked to inform the visitor that the traversal has completed, and that
   // the root was "root".
   virtual Status FinishVisit(HloInstruction* root) = 0;
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
index 2970ba8cc4..c447165cec 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
@@ -54,6 +54,10 @@ class DfsHloVisitorWithDefault : public DfsHloVisitor {
     return DefaultAction(hlo);
   }
 
+  Status HandleBatchNormGrad(HloInstruction* hlo) override {
+    return DefaultAction(hlo);
+  }
+
   Status HandleClamp(HloInstruction* clamp, HloInstruction* /*min*/,
                      HloInstruction* /*arg*/,
                      HloInstruction* /*max*/) override {
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index 522dddea4e..efc3b1c49c 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -365,7 +365,12 @@ Status HloCostAnalysis::HandleReshape(HloInstruction* reshape) {
 
 Status HloCostAnalysis::HandleBatchNormTraining(
     HloInstruction* batchNormTraining) {
-  // TODO(b/62294698): Implement cost analysis for batch-norm-learning.
+  // TODO(b/62294698): Implement cost analysis for batch-norm-training.
+  return Status::OK();
+}
+
+Status HloCostAnalysis::HandleBatchNormGrad(HloInstruction* batchNormGrad) {
+  // TODO(b/62294698): Implement cost analysis for batch-norm-grad.
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index 17c26fc1a1..3c2e9503aa 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -89,6 +89,7 @@ class HloCostAnalysis : public DfsHloVisitor {
                       tensorflow::gtl::ArraySlice<int64> dimensions,
                       HloComputation* function_handle) override;
   Status HandleBatchNormTraining(HloInstruction* batchNormTraining) override;
+  Status HandleBatchNormGrad(HloInstruction* batchNormGrad) override;
   Status HandleFusion(HloInstruction* fusion) override;
   Status HandleCall(HloInstruction* call) override;
   Status HandleCustomCall(HloInstruction* custom_call,
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 7bb0ca2329..6138496fe5 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -287,6 +287,10 @@ string InstructionSequenceGraph(
         StrAppend(&name, " feature_index=", instruction->feature_index());
         color = kPurple;
         break;
+      case HloOpcode::kBatchNormGrad:
+        StrAppend(&name, " feature_index=", instruction->feature_index());
+        color = kPurple;
+        break;
       case HloOpcode::kReduce:
         StrAppend(&name, " dims=", Join(instruction->dimensions(), ","));
         color = kPurple;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index f0f97c80f7..c8bb50173c 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -407,6 +407,24 @@ HloInstruction::CreateBatchNormTraining(const Shape& shape,
   return instruction;
 }
 
+/* static */ std::unique_ptr<HloInstruction>
+HloInstruction::CreateBatchNormGrad(const Shape& shape, HloInstruction* operand,
+                                    HloInstruction* scale, HloInstruction* mean,
+                                    HloInstruction* variance,
+                                    HloInstruction* grad_output, float epsilon,
+                                    int64 feature_index) {
+  auto instruction =
+      WrapUnique(new HloInstruction(HloOpcode::kBatchNormGrad, shape));
+  instruction->AppendOperand(operand);
+  instruction->AppendOperand(scale);
+  instruction->AppendOperand(mean);
+  instruction->AppendOperand(variance);
+  instruction->AppendOperand(grad_output);
+  instruction->epsilon_ = epsilon;
+  instruction->feature_index_ = feature_index;
+  return instruction;
+}
+
 /* static */ std::unique_ptr<HloInstruction>
 HloInstruction::CreateSelectAndScatter(
     const Shape& shape, HloInstruction* operand, HloComputation* select,
@@ -889,7 +907,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CreateBatchNormTraining(shape, new_operands[0], new_operands[1],
                                      new_operands[2], epsilon(),
                                      feature_index());
-    // Unsupported ops for cloning.
+    case HloOpcode::kBatchNormGrad:
     case HloOpcode::kRecv:
     case HloOpcode::kSend:
     case HloOpcode::kUpdate:
@@ -1189,6 +1207,7 @@ bool HloInstruction::Identical(
              ShapeUtil::Compatible(shape(), other.shape());
 
     case HloOpcode::kBatchNormTraining:
+    case HloOpcode::kBatchNormGrad:
       return feature_index() == other.feature_index() &&
              epsilon() == other.epsilon();
 
@@ -1788,6 +1807,8 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
       return visitor->HandleAbs(this, operands_[0]);
     case HloOpcode::kBatchNormTraining:
       return visitor->HandleBatchNormTraining(this);
+    case HloOpcode::kBatchNormGrad:
+      return visitor->HandleBatchNormGrad(this);
     case HloOpcode::kSign:
       return visitor->HandleSign(this, operands_[0]);
     case HloOpcode::kConstant:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 44eacc450c..9b0eac32e2 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -224,6 +224,12 @@ class HloInstruction {
       const Shape& shape, HloInstruction* operand, HloInstruction* scale,
       HloInstruction* offset, float epsilon, int64 feature_index);
 
+  // Creates a batch-norm-grad instruction.
+  static std::unique_ptr<HloInstruction> CreateBatchNormGrad(
+      const Shape& shape, HloInstruction* operand, HloInstruction* scale,
+      HloInstruction* mean, HloInstruction* variance,
+      HloInstruction* grad_output, float epsilon, int64 feature_index);
+
   // Creates a scatter computation that scatters the `source` array to the
   // selected indices of each window.
   static std::unique_ptr<HloInstruction> CreateSelectAndScatter(
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.cc b/tensorflow/compiler/xla/service/hlo_opcode.cc
index 53a93f3dac..ac637dfd3e 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.cc
+++ b/tensorflow/compiler/xla/service/hlo_opcode.cc
@@ -33,6 +33,8 @@ string HloOpcodeString(HloOpcode opcode) {
       return "add";
     case HloOpcode::kBatchNormTraining:
       return "batch-norm-training";
+    case HloOpcode::kBatchNormGrad:
+      return "batch-norm-grad";
     case HloOpcode::kBitcast:
       return "bitcast";
     case HloOpcode::kBroadcast:
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index d1263219c0..7086dfff42 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -31,6 +31,7 @@ enum class HloOpcode {
   kAbs,
   kAdd,
   kBatchNormTraining,
+  kBatchNormGrad,
   kBitcast,
   kBroadcast,
   kCall,
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 2a66943f9b..e7e2e4f9d3 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -77,6 +77,7 @@ namespace xla {
 
     // Expensive instructions.
     case HloOpcode::kBatchNormTraining:
+    case HloOpcode::kBatchNormGrad:
     case HloOpcode::kCall:
     case HloOpcode::kConvolution:
     case HloOpcode::kCrossReplicaSum:
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 44e0fabe77..443051e6d1 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -1218,6 +1218,10 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
       handle_status = computation->AddBatchNormTrainingInstruction(
           arg->batch_norm_training_request());
       break;
+    case OpRequest::kBatchNormGradRequest:
+      handle_status = computation->AddBatchNormGradInstruction(
+          arg->batch_norm_grad_request());
+      break;
     case OpRequest::kBinaryOpRequest:
       handle_status =
           computation->AddBinaryInstruction(arg->binary_op_request());
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index f02df232d8..7024948cee 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -884,6 +884,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
                                     output_shape_for_mean_and_var});
 }
 
+/* static */ StatusOr<Shape> ShapeInference::InferBatchNormGradShape(
+    const Shape& operand_shape, const Shape& scale_shape,
+    const Shape& mean_shape, const Shape& var_shape,
+    const Shape& output_grad_shape, int64 feature_index) {
+  // TODO(b/62843645) Implement shape inference.
+  return Unimplemented(
+      "Shape inference on BatchNormGrad is not implemented yet.");
+}
+
 /* static */ StatusOr<Shape> ShapeInference::InferConvolveShape(
     const Shape& lhs, const Shape& rhs, const Window& window,
     const ConvolutionDimensionNumbers& dnums) {
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index 42e4c7d39d..f3f0176a43 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -71,6 +71,14 @@ class ShapeInference {
                                                      const Shape& scale_shape,
                                                      int64 feature_index);
 
+  // Infers the shape produced by InferBatchNormGrad with the given operands.
+  static StatusOr<Shape> InferBatchNormGradShape(const Shape& operand_shape,
+                                                 const Shape& scale_shape,
+                                                 const Shape& mean_shape,
+                                                 const Shape& var_shape,
+                                                 const Shape& output_grad_shape,
+                                                 int64 feature_index);
+
   // Infers the shape produced by applying the given convolutional
   // filter (rhs) to lhs in the way specified by the fields on window.
   static StatusOr<Shape> InferConvolveShape(
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 90a24fb44d..2373b5e32e 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -505,6 +505,51 @@ UserComputation::AddBatchNormTrainingInstruction(
   return handle;
 }
 
+StatusOr<ComputationDataHandle> UserComputation::AddBatchNormGradInstruction(
+    const BatchNormGradRequest& batch_norm_grad_request) {
+  tensorflow::mutex_lock lock(mutex_);
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* operand,
+                      LookUpRequest(batch_norm_grad_request.operand()));
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* scale,
+                      LookUpRequest(batch_norm_grad_request.scale()));
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* mean,
+                      LookUpRequest(batch_norm_grad_request.mean()));
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* variance,
+                      LookUpRequest(batch_norm_grad_request.variance()));
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* grad_output,
+                      LookUpRequest(batch_norm_grad_request.grad_output()));
+
+  ComputationDataHandle handle = CreateComputationDataHandle();
+
+  OperationRequest& request =
+      (*session_computation_.mutable_requests())[handle.handle()];
+
+  TF_ASSIGN_OR_RETURN(
+      Shape inferred_shape,
+      ShapeInference::InferBatchNormGradShape(
+          operand->output_shape(), scale->output_shape(), mean->output_shape(),
+          variance->output_shape(), grad_output->output_shape(),
+          batch_norm_grad_request.feature_index()));
+
+  *request.mutable_output_shape() = inferred_shape;
+
+  *request.mutable_output_handle() = handle;
+
+  *request.mutable_request()->mutable_batch_norm_grad_request() =
+      batch_norm_grad_request;
+
+  VLOG(1) << "AddBatchNormGradInstruction (" << GetVersionedHandleInternal()
+          << "), data handle " << handle.handle() << ": "
+          << batch_norm_grad_request.ShortDebugString();
+
+  return handle;
+}
+
 StatusOr<ComputationDataHandle> UserComputation::AddReduceWindowInstruction(
     const ReduceWindowRequest& reduce_window_request,
     const UserComputation& to_apply_computation) {
diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h
index 3cc3bd0918..36b1d34e05 100644
--- a/tensorflow/compiler/xla/service/user_computation.h
+++ b/tensorflow/compiler/xla/service/user_computation.h
@@ -89,6 +89,10 @@ class UserComputation {
   StatusOr<ComputationDataHandle> AddBatchNormTrainingInstruction(
       const BatchNormTrainingRequest& batch_norm_training_request);
 
+  // Enqueues a batch norm grad instruction onto this user computation.
+  StatusOr<ComputationDataHandle> AddBatchNormGradInstruction(
+      const BatchNormGradRequest& batch_norm_grad_request);
+
   // Enqueues a binary instruction onto this user computation.
   // Returns an error status if the operand indices are out of bounds.
   StatusOr<ComputationDataHandle> AddBinaryInstruction(
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 86c72b3449..baecfda1c8 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -491,6 +491,16 @@ message BatchNormTrainingRequest {
   int64 feature_index = 5;
 }
 
+message BatchNormGradRequest {
+  ComputationDataHandle operand = 1;
+  ComputationDataHandle scale = 2;
+  ComputationDataHandle mean = 3;
+  ComputationDataHandle variance = 4;
+  ComputationDataHandle grad_output = 5;
+  float epsilon = 6;
+  int64 feature_index = 7;
+}
+
 message CrossReplicaSumRequest {
   ComputationDataHandle operand = 2;
 }
@@ -799,7 +809,8 @@ message OpRequest {
     RecvRequest recv_request = 31;
     OutfeedRequest outfeed_request = 32;
     BatchNormTrainingRequest batch_norm_training_request = 35;
-    // Next: 37
+    BatchNormGradRequest batch_norm_grad_request = 37;
+    // Next: 38
   }
 }
 
-- 
GitLab


From 01383b946e4558431830546a325b8d828679b2a7 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 6 Jul 2017 23:25:44 -0400
Subject: [PATCH 0785/1375] Adapt TensorFlowTestCase.setUp() to new
 reset_default_graph() semantics

Avoid calling reset_default_graph() directly to prevent exceptions in
cases where test methods error out from within nested graph contexts,
which can leave _default_graph_stack non-empty in certain Python
versions.
---
 tensorflow/python/framework/test_util.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 71328a7f88..401c5c7f12 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -266,7 +266,12 @@ class TensorFlowTestCase(googletest.TestCase):
     self._ClearCachedSession()
     random.seed(random_seed.DEFAULT_GRAPH_SEED)
     np.random.seed(random_seed.DEFAULT_GRAPH_SEED)
-    ops.reset_default_graph()
+    # Note: we avoid calling ops.reset_default_graph() here due to the fact that
+    # under certain Python versions, test methods that error out from within
+    # nested graph contexts may leave ops._default_graph_stack non-empty,
+    # which would cause ops.reset_default_graph() to throw an exception if it
+    # were used in the following line.
+    ops._default_graph_stack.reset()  # pylint: disable=protected-access
     ops.get_default_graph().seed = random_seed.DEFAULT_GRAPH_SEED
 
   def tearDown(self):
-- 
GitLab


From f8cd1283e541f8f0366d3816091d144fcafc786a Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 7 Jul 2017 00:08:00 -0400
Subject: [PATCH 0786/1375] Chaser

---
 tensorflow/python/framework/test_util.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 401c5c7f12..5ba226ce07 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -266,12 +266,14 @@ class TensorFlowTestCase(googletest.TestCase):
     self._ClearCachedSession()
     random.seed(random_seed.DEFAULT_GRAPH_SEED)
     np.random.seed(random_seed.DEFAULT_GRAPH_SEED)
-    # Note: we avoid calling ops.reset_default_graph() here due to the fact that
-    # under certain Python versions, test methods that error out from within
-    # nested graph contexts may leave ops._default_graph_stack non-empty,
-    # which would cause ops.reset_default_graph() to throw an exception if it
-    # were used in the following line.
+    # Note: The following line is necessary because some test methods may error
+    # out from within nested graph contexts (e.g., via assertRaises and
+    # assertRaisesRegexp), which may leave ops._default_graph_stack non-empty
+    # under certain versions of Python. That would cause
+    # ops.reset_default_graph() to throw an exception if the stack were not
+    # cleared first.
     ops._default_graph_stack.reset()  # pylint: disable=protected-access
+    ops.reset_default_graph()
     ops.get_default_graph().seed = random_seed.DEFAULT_GRAPH_SEED
 
   def tearDown(self):
-- 
GitLab


From 764c9b6b44c82ef7cf9bf2da20a4bd859b676ca9 Mon Sep 17 00:00:00 2001
From: Louis Tiao <ltiao@users.noreply.github.com>
Date: Fri, 7 Jul 2017 14:17:40 +1000
Subject: [PATCH 0787/1375] Fixed typo in docstring

---
 tensorflow/python/ops/distributions/distribution.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/distributions/distribution.py b/tensorflow/python/ops/distributions/distribution.py
index a0be433a61..1684e85cc7 100644
--- a/tensorflow/python/ops/distributions/distribution.py
+++ b/tensorflow/python/ops/distributions/distribution.py
@@ -176,7 +176,7 @@ class _DistributionMeta(abc.ABCMeta):
 class ReparameterizationType(object):
   """Instances of this class represent how sampling is reparameterized.
 
-  Two static instances exist in the distritributions library, signifying
+  Two static instances exist in the distributions library, signifying
   one of two possible properties for samples from a distribution:
 
   `FULLY_REPARAMETERIZED`: Samples from the distribution are fully
-- 
GitLab


From 193cea2b6183e048497a0194449da29b4ee86306 Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Thu, 6 Jul 2017 21:30:07 -0700
Subject: [PATCH 0788/1375] Include grappler optimizations in
 Cluster::DisableOptimizations.

PiperOrigin-RevId: 161166683
---
 tensorflow/core/grappler/clusters/cluster.cc | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/tensorflow/core/grappler/clusters/cluster.cc b/tensorflow/core/grappler/clusters/cluster.cc
index dec51842e4..930f7d83be 100644
--- a/tensorflow/core/grappler/clusters/cluster.cc
+++ b/tensorflow/core/grappler/clusters/cluster.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/clusters/cluster.h"
+#include "tensorflow/core/protobuf/rewriter_config.pb.h"
 
 namespace tensorflow {
 namespace grappler {
@@ -49,6 +50,15 @@ void Cluster::DisableOptimizer(bool disable) {
       options_.config.mutable_graph_options()->mutable_optimizer_options();
   if (disable) {
     options->set_opt_level(OptimizerOptions::L0);
+    // Disable Grappler optimizations.
+    auto rewriter_config =
+        options_.config.mutable_graph_options()->mutable_rewrite_options();
+    rewriter_config->set_optimize_tensor_layout(false);
+    rewriter_config->set_disable_model_pruning(true);
+    rewriter_config->set_constant_folding(false);
+    rewriter_config->set_memory_optimization(RewriterConfig::NO_MEM_OPT);
+    rewriter_config->mutable_auto_parallel()->set_enable(false);
+    rewriter_config->clear_optimizers();
   } else {
     options->set_opt_level(OptimizerOptions::L1);
   }
-- 
GitLab


From eb27277809c038d93edcfb7221295608654589d7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 6 Jul 2017 23:38:21 -0700
Subject: [PATCH 0789/1375] Support SparseTensors transparently in
 saved_model.utils: - build_tensor_info(tensor) now accepts a SparseTensor
 argument. - A new function get_tensor_from_info(tensor_info) provides the
 inverse   transformation: looking up the Tensor or SparseTensor described by 
  tensor_info.

PiperOrigin-RevId: 161172484
---
 tensorflow/python/saved_model/utils.py        |  3 +-
 tensorflow/python/saved_model/utils_impl.py   | 52 +++++++++++--
 tensorflow/python/saved_model/utils_test.py   | 75 ++++++++++++++++++-
 .../golden/tensorflow.saved_model.utils.pbtxt |  4 +
 4 files changed, 127 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/saved_model/utils.py b/tensorflow/python/saved_model/utils.py
index 8e970e96ae..8e750d8708 100644
--- a/tensorflow/python/saved_model/utils.py
+++ b/tensorflow/python/saved_model/utils.py
@@ -22,8 +22,9 @@ from __future__ import print_function
 
 # pylint: disable=unused-import
 from tensorflow.python.saved_model.utils_impl import build_tensor_info
+from tensorflow.python.saved_model.utils_impl import get_tensor_from_tensor_info
 # pylint: enable=unused-import
 from tensorflow.python.util.all_util import remove_undocumented
 
-_allowed_symbols = ["build_tensor_info",]
+_allowed_symbols = ["build_tensor_info", "get_tensor_from_tensor_info"]
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/python/saved_model/utils_impl.py b/tensorflow/python/saved_model/utils_impl.py
index fcb6fc91b6..73ca8c9c1c 100644
--- a/tensorflow/python/saved_model/utils_impl.py
+++ b/tensorflow/python/saved_model/utils_impl.py
@@ -20,6 +20,8 @@ from __future__ import print_function
 
 from tensorflow.core.protobuf import meta_graph_pb2
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
 
 
 # TensorInfo helpers.
@@ -29,13 +31,53 @@ def build_tensor_info(tensor):
   """Utility function to build TensorInfo proto.
 
   Args:
-    tensor: Tensor whose name, dtype and shape are used to build the TensorInfo.
+    tensor: Tensor or SparseTensor whose name, dtype and shape are used to
+        build the TensorInfo. For SparseTensors, the names of the three
+        constitutent Tensors are used.
 
   Returns:
     A TensorInfo protocol buffer constructed based on the supplied argument.
   """
-  dtype_enum = dtypes.as_dtype(tensor.dtype).as_datatype_enum
-  return meta_graph_pb2.TensorInfo(
-      name=tensor.name,
-      dtype=dtype_enum,
+  tensor_info = meta_graph_pb2.TensorInfo(
+      dtype=dtypes.as_dtype(tensor.dtype).as_datatype_enum,
       tensor_shape=tensor.get_shape().as_proto())
+  if isinstance(tensor, sparse_tensor.SparseTensor):
+    tensor_info.coo_sparse.values_tensor_name = tensor.values.name
+    tensor_info.coo_sparse.indices_tensor_name = tensor.indices.name
+    tensor_info.coo_sparse.dense_shape_tensor_name = tensor.dense_shape.name
+  else:
+    tensor_info.name = tensor.name
+  return tensor_info
+
+
+def get_tensor_from_tensor_info(tensor_info, graph=None, import_scope=None):
+  """Returns the Tensor or SparseTensor described by a TensorInfo proto.
+
+  Args:
+    tensor_info: A TensorInfo proto describing a Tensor or SparseTensor.
+    graph: The tf.Graph in which tensors are looked up. If None, the
+        current default graph is used.
+    import_scope: If not None, names in `tensor_info` are prefixed with this
+        string before lookup.
+
+  Returns:
+    The Tensor or SparseTensor in `graph` described by `tensor_info`.
+
+  Raises:
+    KeyError: If `tensor_info` does not correspond to a tensor in `graph`.
+    ValueError: If `tensor_info` is malformed.
+  """
+  graph = graph if graph is not None else ops.get_default_graph()
+  def _get_tensor(name):
+    return graph.get_tensor_by_name(
+        ops.prepend_name_scope(name, import_scope=import_scope))
+  encoding = tensor_info.WhichOneof("encoding")
+  if encoding == "name":
+    return _get_tensor(tensor_info.name)
+  elif encoding == "coo_sparse":
+    return sparse_tensor.SparseTensor(
+        _get_tensor(tensor_info.coo_sparse.indices_tensor_name),
+        _get_tensor(tensor_info.coo_sparse.values_tensor_name),
+        _get_tensor(tensor_info.coo_sparse.dense_shape_tensor_name))
+  else:
+    raise ValueError("Invalid TensorInfo.encoding: %s" % encoding)
diff --git a/tensorflow/python/saved_model/utils_test.py b/tensorflow/python/saved_model/utils_test.py
index 39c87e48ce..85e6757b26 100644
--- a/tensorflow/python/saved_model/utils_test.py
+++ b/tensorflow/python/saved_model/utils_test.py
@@ -20,6 +20,8 @@ from __future__ import print_function
 
 from tensorflow.core.framework import types_pb2
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import test
 from tensorflow.python.saved_model import utils
@@ -27,7 +29,7 @@ from tensorflow.python.saved_model import utils
 
 class UtilsTest(test.TestCase):
 
-  def testBuildTensorInfo(self):
+  def testBuildTensorInfoDense(self):
     x = array_ops.placeholder(dtypes.float32, 1, name="x")
     x_tensor_info = utils.build_tensor_info(x)
     self.assertEqual("x:0", x_tensor_info.name)
@@ -35,6 +37,77 @@ class UtilsTest(test.TestCase):
     self.assertEqual(1, len(x_tensor_info.tensor_shape.dim))
     self.assertEqual(1, x_tensor_info.tensor_shape.dim[0].size)
 
+  def testBuildTensorInfoSparse(self):
+    x = sparse_tensor.SparseTensor(indices=[[3, 3], [4, 4], [5, 5]],
+                                   values=[103.0, 104.0, 105.0],
+                                   dense_shape=[42, 69])
+    x_tensor_info = utils.build_tensor_info(x)
+    self.assertEqual(x.values.name,
+                     x_tensor_info.coo_sparse.values_tensor_name)
+    self.assertEqual(x.indices.name,
+                     x_tensor_info.coo_sparse.indices_tensor_name)
+    self.assertEqual(x.dense_shape.name,
+                     x_tensor_info.coo_sparse.dense_shape_tensor_name)
+    self.assertEqual(types_pb2.DT_FLOAT, x_tensor_info.dtype)
+    self.assertEqual(2, len(x_tensor_info.tensor_shape.dim))
+    self.assertEqual(42, x_tensor_info.tensor_shape.dim[0].size)
+    self.assertEqual(69, x_tensor_info.tensor_shape.dim[1].size)
+
+  def testGetTensorFromInfoDense(self):
+    expected = array_ops.placeholder(dtypes.float32, 1, name="x")
+    tensor_info = utils.build_tensor_info(expected)
+    actual = utils.get_tensor_from_tensor_info(tensor_info)
+    self.assertIsInstance(actual, ops.Tensor)
+    self.assertEqual(expected.name, actual.name)
+
+  def testGetTensorFromInfoSparse(self):
+    expected = array_ops.sparse_placeholder(dtypes.float32, name="x")
+    tensor_info = utils.build_tensor_info(expected)
+    actual = utils.get_tensor_from_tensor_info(tensor_info)
+    self.assertIsInstance(actual, sparse_tensor.SparseTensor)
+    self.assertEqual(expected.values.name, actual.values.name)
+    self.assertEqual(expected.indices.name, actual.indices.name)
+    self.assertEqual(expected.dense_shape.name, actual.dense_shape.name)
+
+  def testGetTensorFromInfoInOtherGraph(self):
+    with ops.Graph().as_default() as expected_graph:
+      expected = array_ops.placeholder(dtypes.float32, 1, name="right")
+      tensor_info = utils.build_tensor_info(expected)
+    with ops.Graph().as_default():  # Some other graph.
+      array_ops.placeholder(dtypes.float32, 1, name="other")
+    actual = utils.get_tensor_from_tensor_info(tensor_info,
+                                               graph=expected_graph)
+    self.assertIsInstance(actual, ops.Tensor)
+    self.assertIs(actual.graph, expected_graph)
+    self.assertEqual(expected.name, actual.name)
+
+  def testGetTensorFromInfoInScope(self):
+    # Build a TensorInfo with name "bar/x:0".
+    with ops.Graph().as_default():
+      with ops.name_scope("bar"):
+        unscoped = array_ops.placeholder(dtypes.float32, 1, name="x")
+        tensor_info = utils.build_tensor_info(unscoped)
+        self.assertEqual("bar/x:0", tensor_info.name)
+    # Build a graph with node "foo/bar/x:0", akin to importing into scope foo.
+    with ops.Graph().as_default():
+      with ops.name_scope("foo"):
+        with ops.name_scope("bar"):
+          expected = array_ops.placeholder(dtypes.float32, 1, name="x")
+      self.assertEqual("foo/bar/x:0", expected.name)
+      # Test that tensor is found by prepending the import scope.
+      actual = utils.get_tensor_from_tensor_info(tensor_info,
+                                                 import_scope="foo")
+      self.assertEqual(expected.name, actual.name)
+
+  def testGetTensorFromInfoRaisesErrors(self):
+    expected = array_ops.placeholder(dtypes.float32, 1, name="x")
+    tensor_info = utils.build_tensor_info(expected)
+    tensor_info.name = "blah:0"  # Nonexistant name.
+    with self.assertRaises(KeyError):
+      utils.get_tensor_from_tensor_info(tensor_info)
+    tensor_info.ClearField("name")  # Malformed (missing encoding).
+    with self.assertRaises(ValueError):
+      utils.get_tensor_from_tensor_info(tensor_info)
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.utils.pbtxt b/tensorflow/tools/api/golden/tensorflow.saved_model.utils.pbtxt
index bc150e56a3..d95c946682 100644
--- a/tensorflow/tools/api/golden/tensorflow.saved_model.utils.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.saved_model.utils.pbtxt
@@ -4,4 +4,8 @@ tf_module {
     name: "build_tensor_info"
     argspec: "args=[\'tensor\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "get_tensor_from_tensor_info"
+    argspec: "args=[\'tensor_info\', \'graph\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
 }
-- 
GitLab


From 5f81e2b404d3d3d721a6d16f6809ac4511ef7d92 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <taehoonlee@snu.ac.kr>
Date: Fri, 7 Jul 2017 17:49:40 +0900
Subject: [PATCH 0790/1375] Improve examples for Python 3 compatibility

---
 tensorflow/contrib/learn/python/learn/utils/gc.py | 8 ++++----
 tensorflow/contrib/lookup/lookup_ops.py           | 4 ++--
 tensorflow/contrib/session_bundle/gc.py           | 8 ++++----
 tensorflow/python/ops/array_ops.py                | 4 ++--
 tensorflow/python/ops/lookup_ops.py               | 4 ++--
 5 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/utils/gc.py b/tensorflow/contrib/learn/python/learn/utils/gc.py
index 5af9e8b9e2..b890129064 100644
--- a/tensorflow/contrib/learn/python/learn/utils/gc.py
+++ b/tensorflow/contrib/learn/python/learn/utils/gc.py
@@ -46,14 +46,14 @@ For example,
   path_list = gc.get_paths("/tmp", parser)  # contains all ten Paths
 
   every_fifth = gc.mod_export_version(5)
-  print every_fifth(path_list) # shows ["/tmp/0", "/tmp/5"]
+  print(every_fifth(path_list))  # shows ["/tmp/0", "/tmp/5"]
 
   largest_three = gc.largest_export_versions(3)
-  print largest_three(all_paths)  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
+  print(largest_three(all_paths))  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
 
   both = gc.union(every_fifth, largest_three)
-  print both(all_paths)  # shows ["/tmp/0", "/tmp/5",
-                         #        "/tmp/7", "/tmp/8", "/tmp/9"]
+  print(both(all_paths))  # shows ["/tmp/0", "/tmp/5",
+                          #        "/tmp/7", "/tmp/8", "/tmp/9"]
   # delete everything not in 'both'
   to_delete = gc.negation(both)
   for p in to_delete(all_paths):
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index f53f38f3cf..66caa6a2e5 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -300,7 +300,7 @@ class MutableHashTable(LookupInterface):
                                              default_value=-1)
   table.insert(keys, values)
   out = table.lookup(query_keys)
-  print out.eval()
+  print(out.eval())
   ```
   """
 
@@ -502,7 +502,7 @@ class MutableDenseHashTable(LookupInterface):
                                                   empty_key=0)
   table.insert(keys, values)
   out = table.lookup(query_keys)
-  print out.eval()
+  print(out.eval())
   ```
   """
 
diff --git a/tensorflow/contrib/session_bundle/gc.py b/tensorflow/contrib/session_bundle/gc.py
index 885b888e27..246ae77139 100644
--- a/tensorflow/contrib/session_bundle/gc.py
+++ b/tensorflow/contrib/session_bundle/gc.py
@@ -46,14 +46,14 @@ For example,
   path_list = gc.get_paths("/tmp", parser)  # contains all ten Paths
 
   every_fifth = gc.mod_export_version(5)
-  print every_fifth(path_list) # shows ["/tmp/0", "/tmp/5"]
+  print(every_fifth(path_list))  # shows ["/tmp/0", "/tmp/5"]
 
   largest_three = gc.largest_export_versions(3)
-  print largest_three(all_paths)  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
+  print(largest_three(all_paths))  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
 
   both = gc.union(every_fifth, largest_three)
-  print both(all_paths)  # shows ["/tmp/0", "/tmp/5",
-                         #        "/tmp/7", "/tmp/8", "/tmp/9"]
+  print(both(all_paths))  # shows ["/tmp/0", "/tmp/5",
+                          #        "/tmp/7", "/tmp/8", "/tmp/9"]
   # delete everything not in 'both'
   to_delete = gc.negation(both)
   for p in to_delete(all_paths):
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 8db5efb447..0b09293385 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -713,10 +713,10 @@ def _SliceHelperVar(var, slice_spec):
   A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
   with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
-    print sess.run(A[:2, :2]) # => [[1,2], [4,5]]
+    print(sess.run(A[:2, :2]))  # => [[1,2], [4,5]]
 
     op = A[:2,:2].assign(22. * tf.ones((2, 2)))
-    print sess.run(op) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
+    print(sess.run(op))  # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
   ```
 
   Note that assignments currently do not support NumPy broadcasting
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index dade053589..e0cf8bc5e1 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -240,7 +240,7 @@ class HashTable(InitializableLookupTableBase):
       tf.contrib.lookup.KeyValueTensorInitializer(keys, values), -1)
   out = table.lookup(input_tensor).
   table.init.run()
-  print out.eval()
+  print(out.eval())
   ```
   """
 
@@ -709,7 +709,7 @@ class IdTableWithHashBuckets(LookupInterface):
       num_oov_buckets)
   out = table.lookup(input_tensor).
   table.init.run()
-  print out.eval()
+  print(out.eval())
   ```
 
   The hash function used for generating out-of-vocabulary buckets ID is handled
-- 
GitLab


From 02fcf564eaaab9597281db579847b4c053c9f9a7 Mon Sep 17 00:00:00 2001
From: Chris Song <sjhshy@gmail.com>
Date: Fri, 7 Jul 2017 20:00:21 +0900
Subject: [PATCH 0791/1375] Fix misspells.

---
 .../compiler/xla/service/cpu/parallel_cpu_executable.cc       | 2 +-
 tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc    | 2 +-
 .../stochastic/handlers/categorical-feature-column-handler.cc | 2 +-
 .../handlers/dense-quantized-feature-column-handler.cc        | 2 +-
 .../handlers/sparse-quantized-feature-column-handler.cc       | 4 ++--
 .../contrib/factorization/python/ops/factorization_ops.py     | 2 +-
 tensorflow/contrib/timeseries/python/timeseries/math_utils.py | 4 ++--
 .../contrib/timeseries/python/timeseries/state_management.py  | 4 ++--
 .../timeseries/state_space_models/filtering_postprocessor.py  | 2 +-
 .../timeseries/state_space_models/structural_ensemble.py      | 2 +-
 tensorflow/contrib/tpu/python/tpu/tpu_feed.py                 | 2 +-
 tensorflow/core/framework/op_gen_lib.cc                       | 2 +-
 tensorflow/examples/ios/README.md                             | 2 +-
 tensorflow/python/util/nest.py                                | 2 +-
 tensorflow/tools/docs/parser.py                               | 2 +-
 15 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
index 19909f4bed..598858d4ed 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
@@ -440,7 +440,7 @@ Status ParallelCpuExecutable::ExecuteComputeFunctions(
   // TODO(b/27458679) Manage scheduling based on in-flight concurrency limits.
   // For example, if we expect a library conv/matmul call to run at max
   // concurrency, we should not dispatch runnable instructions until the
-  // libary call is finished (to avoid expensive cache invalidation).
+  // library call is finished (to avoid expensive cache invalidation).
   Executor executor(functions, run_options, &pending, &results,
                     buffer_pointers.data(), profile_counters.data(),
                     assignment_.get());
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
index 442585772f..d67b48dff0 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
@@ -495,7 +495,7 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
   };
   // Build separate condition computations so the call graph is flat. The
   // callgraph is always flattened in the compiler pipeline, and the flattened
-  // callgraph enables representive interference analysis.
+  // callgraph enables representative interference analysis.
   HloComputation* condition1 =
       module_->AddEmbeddedComputation(build_cond_computation());
   HloComputation* condition2 =
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
index 2f76224ff0..3a6c409f84 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
@@ -106,7 +106,7 @@ void CategoricalFeatureColumnHandler::GenerateFeatureSplitCandidates(
     NodeStats left_node_stats(learner_config, left_gradient_stats);
     NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-    // Generate split candiate and update best split candidate for the
+    // Generate split candidate and update best split candidate for the
     // current root if needed.
     FeatureSplitCandidate split_candidate(
         slot_id_,
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
index f43112c8a4..ca7bb71e7d 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
@@ -93,7 +93,7 @@ void DenseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
       NodeStats left_node_stats(learner_config, left_gradient_stats);
       NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-      // Generate split candiate.
+      // Generate split candidate.
       const float threshold = dense_quantiles_(bucket_id);
       FeatureSplitCandidate split_candidate(
           slot_id_, CreateDenseSplitNode(dense_feature_column_, threshold),
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
index c7f49a4699..a0e9efbbc5 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
@@ -109,7 +109,7 @@ void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
       NodeStats left_node_stats(learner_config, left_gradient_stats);
       NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-      // Generate split candiate.
+      // Generate split candidate.
       const float threshold = sparse_quantiles_(bucket_id);
       FeatureSplitCandidate split_candidate(
           slot_id_,
@@ -147,7 +147,7 @@ void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
         NodeStats left_node_stats(learner_config, left_gradient_stats);
         NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-        // Generate split candiate.
+        // Generate split candidate.
         const float threshold = sparse_quantiles_(bucket_id - 1);
         FeatureSplitCandidate split_candidate(
             slot_id_,
diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops.py b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
index 14a2311a42..fb400dbcea 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
@@ -800,7 +800,7 @@ class WALSModel(object):
       regularization: A tensor (scalar) that contains the normalized
         regularization term for the minibatch loss corresponding to sp_input.
       sum_weights: The sum of the weights corresponding to sp_input. This
-        can be used with unregularized loss to caluclate the root weighted
+        can be used with unregularized loss to calculate the root weighted
         squared error.
     """
     assert isinstance(sp_input, sparse_tensor.SparseTensor)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/math_utils.py b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
index b7832a10cd..2d3d8c3b1e 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
@@ -689,11 +689,11 @@ class InputStatisticsFromMiniBatch(object):
       values = features[TrainEvalFeatures.VALUES]
     else:
       # times and values may not be available, for example during prediction. We
-      # still need to retreive our variables so that they can be read from, even
+      # still need to retrieve our variables so that they can be read from, even
       # if we're not going to update them.
       times = None
       values = None
-    # Create/retreive variables representing input statistics, initialized
+    # Create/retrieve variables representing input statistics, initialized
     # without data to avoid deadlocking if variables are initialized before
     # queue runners are started.
     with variable_scope.variable_scope("input_statistics"):
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_management.py b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
index ba51a82f4c..dc8f8fdb92 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_management.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
@@ -196,7 +196,7 @@ class ChainingStateManager(_OverridableStateManager):
     return time // self._state_saving_interval
 
   def _get_cached_states(self, times):
-    """Retreive cached states for a batch of times."""
+    """Retrieve cached states for a batch of times."""
     read_chunk_numbers = self._get_chunk_number(times)
     looked_up_state = list(self._cached_states.lookup(
         math_ops.cast(read_chunk_numbers, dtypes.int64)))
@@ -242,7 +242,7 @@ class ChainingStateManager(_OverridableStateManager):
     # written to the next bucket). This assumes fixed missing times (i.e. if we
     # were presented with times [10, 50] we will never see times [30, 50]).
     #
-    # TODO(allenl): Retreive the highest time less than the current time rather
+    # TODO(allenl): Retrieve the highest time less than the current time rather
     # than relying on fixed bucketing.
     write_chunk_numbers = math_ops.maximum(
         self._get_chunk_number(array_ops.concat(
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
index 163819ae2f..7fa538a16e 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
@@ -150,7 +150,7 @@ class StateInterpolatingAnomalyDetector(FilteringStepPostprocessor):
   This is simply Bayes' theorem, where p(data | anomaly) is the
   alternative/anomaly distribution, p(data | not anomaly) is the model's
   predicted distribution, and anomaly_prior_probability is the prior probability
-  of an anomaly occuring (user-specified, defaulting to 1%).
+  of an anomaly occurring (user-specified, defaulting to 1%).
 
   Rather than computing p(anomaly | data) directly, we use the odds ratio:
 
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
index 26ab726591..a7a80a8e3e 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
@@ -70,7 +70,7 @@ class StructuralEnsemble(state_space_model.StateSpaceIndependentEnsemble):
 
   `observation_noise`, `level_noise`, `trend noise`, `seasonality_noise`, and
   `transient` are (typically scalar) Gaussian random variables whose variance is
-  learned from data, and that variance is not time dependant in this
+  learned from data, and that variance is not time dependent in this
   implementation. Level noise is optional due to its similarity with observation
   noise in some cases. Seasonality is enforced by constraining a full cycle of
   seasonal variables to have zero expectation, allowing seasonality to adapt
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
index c955de524c..3d0a532010 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
@@ -298,7 +298,7 @@ class InfeedQueue(object):
     input_tensors is a list of lists of Tensors whose types and shapes are used
     to set the queue configuration. The length of the outer list is the number
     of shards required, and each inner list is the tuple of Tensors to use to
-    determine the types and shapes of the correponding shard. This method
+    determine the types and shapes of the corresponding shard. This method
     depends on the shard dimension, and calling it freezes the shard policy.
 
     Args:
diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc
index cedfd6fc9c..143da996a1 100644
--- a/tensorflow/core/framework/op_gen_lib.cc
+++ b/tensorflow/core/framework/op_gen_lib.cc
@@ -73,7 +73,7 @@ bool ConsumeEquals(StringPiece* description) {
   return false;
 }
 
-// Split `*orig` into two pieces at the first occurence of `split_ch`.
+// Split `*orig` into two pieces at the first occurrence of `split_ch`.
 // Returns whether `split_ch` was found. Afterwards, `*before_split`
 // contains the maximum prefix of the input `*orig` that doesn't
 // contain `split_ch`, and `*orig` contains everything after the
diff --git a/tensorflow/examples/ios/README.md b/tensorflow/examples/ios/README.md
index a412381196..7974b8c879 100644
--- a/tensorflow/examples/ios/README.md
+++ b/tensorflow/examples/ios/README.md
@@ -66,7 +66,7 @@ target 'YourProjectName'
 ```
 
  - Then you run ```pod install``` to download and install the
- TensorFlow-experimental pod, and finaly perform
+ TensorFlow-experimental pod, and finally perform
  ```open YourProjectName.xcworkspace``` and add your code.
 
  - In your apps "Build Settings", make sure to add $(inherited) to sections
diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py
index aeb4405421..b93842b3a7 100644
--- a/tensorflow/python/util/nest.py
+++ b/tensorflow/python/util/nest.py
@@ -180,7 +180,7 @@ def assert_same_structure(nest1, nest2, check_types=True):
     nest1: an arbitrarily nested structure.
     nest2: an arbitrarily nested structure.
     check_types: if `True` (default) types of sequences are checked as
-        well, incuding the keys of dictionaries. If set to `False`, for example
+        well, including the keys of dictionaries. If set to `False`, for example
         a list and a tuple of objects will look the same if they have the same
         size.
 
diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py
index 1bf8a2979c..18c3c98dc2 100644
--- a/tensorflow/tools/docs/parser.py
+++ b/tensorflow/tools/docs/parser.py
@@ -54,7 +54,7 @@ class _Errors(object):
     """Add an error to the collection.
 
     Args:
-      full_name: The path to the file in which the error occured.
+      full_name: The path to the file in which the error occurred.
       message: The message to display with the error.
     """
     self._errors.append((full_name, message))
-- 
GitLab


From 4e0f4e462f80e2dc84aa38da3fbc39cb15da3482 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Fri, 7 Jul 2017 06:00:28 -0700
Subject: [PATCH 0792/1375] [TF:XLA] Fix crash in graph_to_functiondef.cc if
 there was a control dependency onto a _Retval node.

Move graph_to_functiondef_test into its own BUILD target.

PiperOrigin-RevId: 161193674
---
 tensorflow/compiler/jit/BUILD                 | 24 ++++++++++++--
 .../compiler/jit/graph_to_functiondef.cc      |  4 +--
 .../compiler/jit/graph_to_functiondef_test.cc | 33 +++++++++++++++++++
 3 files changed, 57 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 8b2d0b7659..625eb08f1b 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -233,18 +233,38 @@ cc_library(
     hdrs = ["union_find.h"],
 )
 
+cc_test(
+    name = "graph_to_functiondef_test",
+    size = "small",
+    srcs = [
+        "graph_to_functiondef_test.cc",
+    ],
+    deps = [
+        ":graph_to_functiondef",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:cc_ops_internal",
+        "//tensorflow/cc:function_ops",
+        "//tensorflow/cc:ops",
+        "//tensorflow/compiler/tf2xla:xla_compiler",
+        "//tensorflow/compiler/tf2xla/kernels:xla_ops",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+    ],
+)
+
 cc_test(
     name = "compilation_passes_test",
     size = "small",
     srcs = [
         "encapsulate_subgraphs_pass_test.cc",
-        "graph_to_functiondef_test.cc",
         "mark_for_compilation_pass_test.cc",
     ],
     deps = [
         ":common",
         ":compilation_passes",
-        ":graph_to_functiondef",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/cc:cc_ops_internal",
         "//tensorflow/cc:function_ops",
diff --git a/tensorflow/compiler/jit/graph_to_functiondef.cc b/tensorflow/compiler/jit/graph_to_functiondef.cc
index 5cdbebd88e..6fa21fa620 100644
--- a/tensorflow/compiler/jit/graph_to_functiondef.cc
+++ b/tensorflow/compiler/jit/graph_to_functiondef.cc
@@ -151,8 +151,8 @@ Status GraphToFunctionDef(const Graph& graph, const string& name,
       argdef->set_type(type);
       const string normalized = node_names.Normalize(node->name());
       argdef->set_name(normalized);
-      CHECK_EQ(node->in_edges().size(), 1) << node->DebugString();
-      Edge const* edge = *node->in_edges().begin();
+      Edge const* edge;
+      TF_CHECK_OK(node->input_edge(0, &edge));
       return_values[normalized] =
           strings::StrCat(edge->src()->name(), ":", edge->src_output());
       continue;
diff --git a/tensorflow/compiler/jit/graph_to_functiondef_test.cc b/tensorflow/compiler/jit/graph_to_functiondef_test.cc
index 5c09e96a4c..676db7c4dd 100644
--- a/tensorflow/compiler/jit/graph_to_functiondef_test.cc
+++ b/tensorflow/compiler/jit/graph_to_functiondef_test.cc
@@ -82,5 +82,38 @@ TEST(GraphToFunctionDefTest, Basics) {
   EXPECT_TRUE(fdefs_equal) << diff;
 }
 
+// Regression test for a crash if there was a control edge to a _Retval node.
+TEST(GraphToFunctionDefTest, ControlDependencies) {
+  Scope root = Scope::NewRootScope().ExitOnError();
+  auto a = ops::_Arg(root.WithOpName("a"), DT_FLOAT, 0);
+  auto b = ops::Neg(root.WithOpName("b").WithControlDependencies(a), a);
+  auto c = ops::_Retval(root.WithOpName("c").WithControlDependencies(b), b, 0);
+
+  GraphDef graph_def;
+  TF_EXPECT_OK(root.ToGraphDef(&graph_def));
+
+  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
+  GraphConstructorOptions options;
+  TF_EXPECT_OK(ConvertGraphDefToGraph(options, graph_def, graph.get()));
+
+  FunctionDef fdef;
+  TF_EXPECT_OK(GraphToFunctionDef(*graph, "test_fn", &fdef));
+
+  FunctionDef fdef_expected = FunctionDefHelper::Create(
+      "test_fn",     // function name
+      {"a: float"},  // inputs
+      {"c: float"},  // outputs
+      {},            // attrs
+      {
+          // nodes in the function body
+          {{"b"}, "Neg", {"a", "^a"}, {{"T", DT_FLOAT}}},
+      },
+      {{"c", "b:y:0"}});  // return values
+
+  string diff;
+  bool fdefs_equal = EqualFunctionDef(fdef_expected, fdef, &diff);
+  EXPECT_TRUE(fdefs_equal) << diff;
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From 7c2cfdef2693f17295471f717f86f31a3d308659 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Fri, 7 Jul 2017 06:08:53 -0700
Subject: [PATCH 0793/1375] Add Stack*V2 operators, which use a resource handle
 instead of a Ref tensor to store the stack handle.

Change in preparation for adding support for loop gradients in XLA-compiled graphs. XLA compilation requires the use of resource types instead of Ref types, and also requires statically known shapes.

PiperOrigin-RevId: 161194280
---
 tensorflow/cc/ops/op_gen_overrides.pbtxt      |   4 +
 tensorflow/core/kernels/stack_ops.cc          | 152 +++++++++++++----
 tensorflow/core/ops/data_flow_ops.cc          |  61 ++++++-
 tensorflow/core/public/version.h              |   1 +
 .../python/kernel_tests/stack_ops_test.py     | 159 ++++++++++++++++--
 tensorflow/python/ops/hidden_ops.txt          |   4 +
 6 files changed, 325 insertions(+), 56 deletions(-)

diff --git a/tensorflow/cc/ops/op_gen_overrides.pbtxt b/tensorflow/cc/ops/op_gen_overrides.pbtxt
index 1dffb10c03..a1f79177f7 100644
--- a/tensorflow/cc/ops/op_gen_overrides.pbtxt
+++ b/tensorflow/cc/ops/op_gen_overrides.pbtxt
@@ -100,6 +100,10 @@ op { name: "Stack" skip: true }
 op { name: "StackClose" skip: true }
 op { name: "StackPop" skip: true }
 op { name: "StackPush" skip: true }
+op { name: "StackV2" skip: true }
+op { name: "StackCloseV2" skip: true }
+op { name: "StackPopV2" skip: true }
+op { name: "StackPushV2" skip: true }
 
 op { name: "TensorArrayCloseV2" skip: true }
 op { name: "TensorArrayCloseV3" rename_to: "TensorArrayClose" }
diff --git a/tensorflow/core/kernels/stack_ops.cc b/tensorflow/core/kernels/stack_ops.cc
index 2db3e5ef77..a474e75d6a 100644
--- a/tensorflow/core/kernels/stack_ops.cc
+++ b/tensorflow/core/kernels/stack_ops.cc
@@ -54,12 +54,19 @@ class Stack : public ResourceBase {
     bool swapped_to_cpu;
   };
 
-  Stack(const DataType& elem_type, const Tensor& handle)
-      : elem_type_(elem_type), handle_(handle), closed_(false) {}
+  Stack(const DataType& elem_type, const string& stack_name, int max_size)
+      : elem_type_(elem_type),
+        stack_name_(stack_name),
+        max_size_(max_size),
+        closed_(false) {}
 
   Status Push(const TensorAndAllocation& value) {
     mutex_lock l(mu_);
     TF_RETURN_IF_ERROR(CheckNotClosed());
+    if (max_size_ >= 0 && stack_.size() >= max_size_) {
+      return errors::InvalidArgument("Stack[", stack_name_, "] overflowed ",
+                                     "its max_size (", max_size_, ")");
+    }
     stack_.push_back(value);
     return Status::OK();
   }
@@ -68,8 +75,7 @@ class Stack : public ResourceBase {
     mutex_lock l(mu_);
     TF_RETURN_IF_ERROR(CheckNotClosed());
     if (stack_.empty()) {
-      const string& stack_name = handle_.vec<string>()(1);
-      return errors::InvalidArgument("Stack[", stack_name,
+      return errors::InvalidArgument("Stack[", stack_name_,
                                      "] is empty when calling Pop().");
     }
     *value = stack_.back();
@@ -98,25 +104,26 @@ class Stack : public ResourceBase {
 
   string DebugString() override {
     mutex_lock l(mu_);
-    const string& stack_name = handle_.vec<string>()(1);
-    return strings::StrCat("Stack[", stack_name, "]");
+    return strings::StrCat("Stack[", stack_name_, "]");
   }
 
+  const string& stack_name() { return stack_name_; }
+
  private:
   friend class StackOp;
   mutex* mu() { return &mu_; }
-  Tensor* handle() { return &handle_; }
 
   mutable mutex mu_;
   DataType elem_type_;
+  const string stack_name_;
   Tensor handle_;
+  int max_size_;
   bool closed_ GUARDED_BY(mu_);
   std::vector<TensorAndAllocation> stack_ GUARDED_BY(mu_);
 
   Status CheckNotClosed() const EXCLUSIVE_LOCKS_REQUIRED(mu_) {
     if (closed_) {
-      const string& stack_name = handle_.vec<string>()(1);
-      return errors::InvalidArgument("Stack[", stack_name,
+      return errors::InvalidArgument("Stack[", stack_name_,
                                      "] has already been closed.");
     }
     return Status::OK();
@@ -124,20 +131,26 @@ class Stack : public ResourceBase {
 };
 
 Status GetStack(OpKernelContext* ctx, Stack** stack) {
-  Tensor Tstack_handle = ctx->mutable_input(0, false);
-  if (Tstack_handle.NumElements() != 2) {
-    return errors::InvalidArgument(
-        "Stack handle must have two elements, but had shape: ",
-        Tstack_handle.shape().DebugString());
+  string key;
+  if (ctx->input_dtype(0) == DT_RESOURCE) {
+    auto resource = ctx->input(0).flat<ResourceHandle>()(0);
+    key = resource.name();
+  } else {
+    Tensor Tstack_handle = ctx->mutable_input(0, false);
+    if (Tstack_handle.NumElements() != 2) {
+      return errors::InvalidArgument(
+          "Stack handle must have two elements, but had shape: ",
+          Tstack_handle.shape().DebugString());
+    }
+    const string& container = Tstack_handle.flat<string>()(0);
+    const string& stack_name = Tstack_handle.flat<string>()(1);
+    key = strings::StrCat(container, stack_name);
   }
-  const string& container = Tstack_handle.flat<string>()(0);
-  const string& stack_name = Tstack_handle.flat<string>()(1);
   ResourceMgr* rm = ctx->resource_manager();
   if (rm == nullptr) {
     return errors::Internal("No resource manager.");
   }
-  TF_RETURN_IF_ERROR(rm->Lookup(ctx->step_container()->name(),
-                                strings::StrCat(container, stack_name), stack));
+  TF_RETURN_IF_ERROR(rm->Lookup(ctx->step_container()->name(), key, stack));
   return Status::OK();
 }
 
@@ -154,25 +167,48 @@ class StackOp : public OpKernel {
   }
 
   void Compute(OpKernelContext* ctx) override {
-    // Create the stack handle.
-    Tensor stack_handle;
-    AllocatorAttributes alloc_attr;
-    alloc_attr.set_on_host(true);
-    OP_REQUIRES_OK(ctx, ctx->allocate_temp(tensorflow::DT_STRING,
-                                           tensorflow::TensorShape({2}),
-                                           &stack_handle, alloc_attr));
+    int32 size = std::numeric_limits<int32>::max();
+    if (ctx->num_inputs() > 0) {
+      const Tensor* tensor_size;
+      OP_REQUIRES_OK(ctx, ctx->input("max_size", &tensor_size));
+
+      OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(tensor_size->shape()),
+                  errors::InvalidArgument(
+                      "Stack size must be a scalar, but had shape: ",
+                      tensor_size->shape().DebugString()));
+
+      int32 size_value = tensor_size->scalar<int32>()();
+      if (size_value >= 0) {
+        size = size_value;
+      }
+    }
+
+    static const char kContainer[] = "_stacks";
     auto stack_id = Stack::stack_counter.fetch_add(1);
-    auto handle = stack_handle.flat<string>();
-    handle(0) = "_stacks";
-    handle(1) = strings::StrCat(stack_name_, "_", stack_id);
+    string stack_name = strings::StrCat(stack_name_, "_", stack_id);
     // Store the handle in a per-step container.
     ResourceMgr* rm = ctx->resource_manager();
     OP_REQUIRES(ctx, rm != nullptr, errors::Internal("No resource manager."));
-    Stack* stack = new Stack(elem_type_, stack_handle);
-    OP_REQUIRES_OK(ctx,
-                   rm->Create(ctx->step_container()->name(),
-                              strings::StrCat(handle(0), handle(1)), stack));
-    ctx->set_output_ref(0, stack->mu(), stack->handle());
+    string key = strings::StrCat(kContainer, stack_name);
+    Stack* stack = new Stack(elem_type_, stack_name, size);
+    OP_REQUIRES_OK(ctx, rm->Create(ctx->step_container()->name(), key, stack));
+    if (IsRefType(ctx->expected_output_dtype(0))) {
+      // Create the stack handle.
+      AllocatorAttributes alloc_attr;
+      alloc_attr.set_on_host(true);
+      OP_REQUIRES_OK(ctx, ctx->allocate_temp(tensorflow::DT_STRING,
+                                             tensorflow::TensorShape({2}),
+                                             &stack->handle_, alloc_attr));
+      auto handle = stack->handle_.flat<string>();
+      handle(0) = kContainer;
+      handle(1) = std::move(stack_name);
+      ctx->set_output_ref(0, stack->mu(), &stack->handle_);
+    } else {
+      Tensor* handle;
+      OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &handle));
+      handle->flat<ResourceHandle>()(0) =
+          MakePerStepResourceHandle<Stack>(ctx, key);
+    }
   }
 
  private:
@@ -185,9 +221,20 @@ class StackOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_CPU), StackOp);
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_GPU).HostMemory("handle"),
                         StackOp);
+REGISTER_KERNEL_BUILDER(Name("StackV2").Device(DEVICE_CPU), StackOp);
+REGISTER_KERNEL_BUILDER(Name("StackV2")
+                            .Device(DEVICE_GPU)
+                            .HostMemory("max_size")
+                            .HostMemory("handle"),
+                        StackOp);
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(Name("Stack").Device(DEVICE_SYCL).HostMemory("handle"),
                         StackOp);
+REGISTER_KERNEL_BUILDER(Name("StackV2")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("max_size")
+                            .HostMemory("handle"),
+                        StackOp);
 #endif // TENSORFLOW_USE_SYCL
 
 template <typename Device>
@@ -272,12 +319,19 @@ class StackPushOp : public AsyncOpKernel {
 
 REGISTER_KERNEL_BUILDER(Name("StackPush").Device(DEVICE_CPU),
                         StackPushOp<CPUDevice>);
+REGISTER_KERNEL_BUILDER(Name("StackPushV2").Device(DEVICE_CPU),
+                        StackPushOp<CPUDevice>);
 
 #define REGISTER_GPU_KERNEL(type)                         \
   REGISTER_KERNEL_BUILDER(Name("StackPush")               \
                               .Device(DEVICE_GPU)         \
                               .HostMemory("handle")       \
                               .TypeConstraint<type>("T"), \
+                          StackPushOp<GPUDevice>);        \
+  REGISTER_KERNEL_BUILDER(Name("StackPushV2")             \
+                              .Device(DEVICE_GPU)         \
+                              .HostMemory("handle")       \
+                              .TypeConstraint<type>("T"), \
                           StackPushOp<GPUDevice>);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
@@ -293,7 +347,14 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
                               .HostMemory("elem")         \
                               .HostMemory("output")       \
                               .TypeConstraint<type>("T"), \
-                          StackPushOp<GPUDevice>)
+                          StackPushOp<GPUDevice>);        \
+  REGISTER_KERNEL_BUILDER(Name("StackPushV2")             \
+                              .Device(DEVICE_GPU)         \
+                              .HostMemory("handle")       \
+                              .HostMemory("elem")         \
+                              .HostMemory("output")       \
+                              .TypeConstraint<type>("T"), \
+                          StackPushOp<GPUDevice>);
 
 REGISTER_GPU_HOST_KERNEL(int32);
 REGISTER_GPU_HOST_KERNEL(bool);
@@ -368,13 +429,19 @@ class StackPopOp : public AsyncOpKernel {
 };
 
 REGISTER_KERNEL_BUILDER(Name("StackPop").Device(DEVICE_CPU), StackPopOp);
+REGISTER_KERNEL_BUILDER(Name("StackPopV2").Device(DEVICE_CPU), StackPopOp);
 
 #define REGISTER_GPU_KERNEL(type)                                 \
   REGISTER_KERNEL_BUILDER(Name("StackPop")                        \
                               .Device(DEVICE_GPU)                 \
                               .HostMemory("handle")               \
                               .TypeConstraint<type>("elem_type"), \
-                          StackPopOp)
+                          StackPopOp);                            \
+  REGISTER_KERNEL_BUILDER(Name("StackPopV2")                      \
+                              .Device(DEVICE_GPU)                 \
+                              .HostMemory("handle")               \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp);
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
 #undef REGISTER_GPU_KERNEL
@@ -388,7 +455,13 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
                               .HostMemory("handle")               \
                               .HostMemory("elem")                 \
                               .TypeConstraint<type>("elem_type"), \
-                          StackPopOp)
+                          StackPopOp);                            \
+  REGISTER_KERNEL_BUILDER(Name("StackPopV2")                      \
+                              .Device(DEVICE_GPU)                 \
+                              .HostMemory("handle")               \
+                              .HostMemory("elem")                 \
+                              .TypeConstraint<type>("elem_type"), \
+                          StackPopOp);
 
 REGISTER_GPU_HOST_KERNEL(int32);
 REGISTER_GPU_HOST_KERNEL(bool);
@@ -437,8 +510,15 @@ class StackCloseOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("StackClose").Device(DEVICE_CPU), StackCloseOp);
 REGISTER_KERNEL_BUILDER(
     Name("StackClose").Device(DEVICE_GPU).HostMemory("handle"), StackCloseOp);
+REGISTER_KERNEL_BUILDER(Name("StackCloseV2").Device(DEVICE_CPU), StackCloseOp);
+REGISTER_KERNEL_BUILDER(
+    Name("StackCloseV2").Device(DEVICE_GPU).HostMemory("handle"), StackCloseOp);
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER_KERNEL_BUILDER(
     Name("StackClose").Device(DEVICE_SYCL).HostMemory("handle"), StackCloseOp);
+REGISTER_KERNEL_BUILDER(
+    Name("StackCloseV2").Device(DEVICE_SYCL).HostMemory("handle"),
+    StackCloseOp);
 #endif // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index 4bf3d24324..51a964e3e3 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -1114,8 +1114,9 @@ dtype: The data type of accumulated gradients. Needs to correspond to the type
 
 // --------------------------------------------------------------------------
 
-REGISTER_OP("Stack")
-    .Output("handle: Ref(string)")
+REGISTER_OP("StackV2")
+    .Input("max_size: int32")
+    .Output("handle: resource")
     .Attr("elem_type: type")
     .Attr("stack_name: string = ''")
     .SetIsStateful()
@@ -1123,14 +1124,16 @@ REGISTER_OP("Stack")
     .Doc(R"doc(
 A stack that produces elements in first-in last-out order.
 
+max_size: The maximum size of the stack if non-negative. If negative, the stack
+  size is unlimited.
 handle: The handle to the stack.
 elem_type: The type of the elements on the stack.
 stack_name: Overrides the name used for the temporary stack resource. Default
 value is the name of the 'Stack' op (which is guaranteed unique).
 )doc");
 
-REGISTER_OP("StackPush")
-    .Input("handle: Ref(string)")
+REGISTER_OP("StackPushV2")
+    .Input("handle: resource")
     .Input("elem: T")
     .Output("output: T")
     .Attr("T: type")
@@ -1148,8 +1151,8 @@ output: The same tensor as the input 'elem'.
 swap_memory: Swap `elem` to CPU. Default to false.
 )doc");
 
-REGISTER_OP("StackPop")
-    .Input("handle: Ref(string)")
+REGISTER_OP("StackPopV2")
+    .Input("handle: resource")
     .Output("elem: elem_type")
     .Attr("elem_type: type")
     .SetShapeFn(shape_inference::UnknownShape)
@@ -1161,8 +1164,8 @@ elem: The tensor that is popped from the top of the stack.
 elem_type: The type of the elem that is popped.
 )doc");
 
-REGISTER_OP("StackClose")
-    .Input("handle: Ref(string)")
+REGISTER_OP("StackCloseV2")
+    .Input("handle: resource")
     .SetShapeFn(TwoElementVectorInputsAndScalarOutputs)
     .Doc(R"doc(
 Delete the stack from its resource container.
@@ -1170,6 +1173,48 @@ Delete the stack from its resource container.
 handle: The handle to a stack.
 )doc");
 
+// Deprecated ref-typed variants of stack.
+
+REGISTER_OP("Stack")
+    .Output("handle: Ref(string)")
+    .Attr("elem_type: type")
+    .Attr("stack_name: string = ''")
+    .SetIsStateful()
+    .SetShapeFn(TwoElementOutput)
+    .Doc(R"doc(
+Deprecated, use StackV2.
+)doc");
+
+REGISTER_OP("StackPush")
+    .Input("handle: Ref(string)")
+    .Input("elem: T")
+    .Output("output: T")
+    .Attr("T: type")
+    .Attr("swap_memory: bool = false")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      c->set_output(0, c->input(1));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Deprecated, use StackPushV2.
+)doc");
+
+REGISTER_OP("StackPop")
+    .Input("handle: Ref(string)")
+    .Output("elem: elem_type")
+    .Attr("elem_type: type")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Deprecated, use StackPopV2.
+)doc");
+
+REGISTER_OP("StackClose")
+    .Input("handle: Ref(string)")
+    .SetShapeFn(TwoElementVectorInputsAndScalarOutputs)
+    .Doc(R"doc(
+Deprecated, use StackCloseV2.
+)doc");
+
 // --------------------------------------------------------------------------
 
 REGISTER_OP("TensorArrayV3")
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 0e5611e359..e76876c9dc 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -89,6 +89,7 @@ limitations under the License.
 //     produced at version 22 or later.  (04/10/2016)
 // 23. Remove NonMaxSuppression in favor of NonMaxSuppressionV2.
 // 24. Deprecate lookup ops (v1) ops in favor of v2 (30may2017)
+// 25. Deprecate stack (v1) ops in favor of v2 (2017/6/15).
 
 #define TF_GRAPH_DEF_VERSION_MIN_PRODUCER 0
 #define TF_GRAPH_DEF_VERSION_MIN_CONSUMER 0
diff --git a/tensorflow/python/kernel_tests/stack_ops_test.py b/tensorflow/python/kernel_tests/stack_ops_test.py
index 441256df26..aa409336f5 100644
--- a/tensorflow/python/kernel_tests/stack_ops_test.py
+++ b/tensorflow/python/kernel_tests/stack_ops_test.py
@@ -22,7 +22,6 @@ import numpy as np
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import control_flow_ops
@@ -35,10 +34,11 @@ class StackOpTest(test.TestCase):
 
   def _testStackPushPop(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
-      h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]])
+      h = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]])
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32)
+        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
       self.assertAllClose([[4.0, 5.0]], c1.eval())
 
   def testStackPushPop(self):
@@ -49,10 +49,11 @@ class StackOpTest(test.TestCase):
     with self.test_session(use_gpu=use_gpu):
       a = np.arange(2000)
       x = constant_op.constant(a, dtype=dtypes.float32)
-      h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push(h, x, swap_memory=True)
+      h = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True)
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32)
+        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
       self.assertAllClose(a, c1.eval())
 
   def testStackPushPopSwap(self):
@@ -62,7 +63,8 @@ class StackOpTest(test.TestCase):
   def _testStackWhileSwap(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       n = constant_op.constant(0)
-      h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
+      h = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
 
       def c(x):
         return math_ops.less(x, 10)
@@ -70,7 +72,7 @@ class StackOpTest(test.TestCase):
       def b(x):
         with ops.control_dependencies([x]):
           a = constant_op.constant(np.ones(2000), dtype=dtypes.float32)
-          v = gen_data_flow_ops._stack_push(h, a, swap_memory=True)
+          v = gen_data_flow_ops._stack_push_v2(h, a, swap_memory=True)
         with ops.control_dependencies([v]):
           return math_ops.add(x, 1)
 
@@ -79,14 +81,15 @@ class StackOpTest(test.TestCase):
       v = constant_op.constant(np.zeros(2000), dtype=dtypes.float32)
 
       def c1(x, y):
+        del y
         return math_ops.greater(x, 0)
 
       def b1(x, y):
         nx = math_ops.subtract(x, 1)
-        ny = y + gen_data_flow_ops._stack_pop(h, dtypes.float32)
+        ny = y + gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
         return [nx, ny]
 
-      rx, ry = control_flow_ops.while_loop(
+      _, ry = control_flow_ops.while_loop(
           c1, b1, [r, v], [r.get_shape(), tensor_shape.unknown_shape()])
       self.assertAllClose(np.ones(2000) * 10.0, ry.eval())
 
@@ -94,6 +97,102 @@ class StackOpTest(test.TestCase):
     self._testStackWhileSwap(use_gpu=False)
     self._testStackWhileSwap(use_gpu=True)
 
+  def _testMultiStack(self, use_gpu):
+    with self.test_session(use_gpu=use_gpu):
+      h1 = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
+      c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0)
+      with ops.control_dependencies([c1]):
+        c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
+      h2 = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="bar")
+      c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0)
+      with ops.control_dependencies([c2]):
+        c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+      r = c1 + c2
+      self.assertAllClose(9.0, r.eval())
+
+  def testMultiStack(self):
+    self._testMultiStack(use_gpu=False)
+    self._testMultiStack(use_gpu=True)
+
+  def _testSameNameStacks(self, use_gpu):
+    """Different stacks with the same name do not interfere."""
+    with self.test_session(use_gpu=use_gpu) as sess:
+      h1 = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
+      h2 = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
+
+      c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0)
+      with ops.control_dependencies([c1]):
+        c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0)
+      with ops.control_dependencies([c2]):
+        pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
+        pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+
+      out1, out2 = sess.run([pop1, pop2])
+      self.assertAllClose(out1, 4.0)
+      self.assertAllClose(out2, 5.0)
+
+  def testSameNameStacks(self):
+    self._testSameNameStacks(use_gpu=False)
+    self._testSameNameStacks(use_gpu=True)
+
+  def _testCloseStack(self, use_gpu):
+    with self.test_session(use_gpu=use_gpu) as sess:
+      h = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
+      c1 = gen_data_flow_ops._stack_close_v2(h)
+      sess.run(c1)
+
+  def testCloseStack(self):
+    self._testCloseStack(use_gpu=False)
+    self._testCloseStack(use_gpu=True)
+
+  def _testPushCloseStack(self, use_gpu):
+    with self.test_session(use_gpu=use_gpu) as sess:
+      h = gen_data_flow_ops._stack_v2(
+          -1, elem_type=dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]])
+      with ops.control_dependencies([c]):
+        c1 = gen_data_flow_ops._stack_close_v2(h)
+      sess.run(c1)
+
+  def testPushCloseStack(self):
+    self._testPushCloseStack(use_gpu=False)
+    self._testPushCloseStack(use_gpu=True)
+
+
+class StackOpRefTest(test.TestCase):
+  """Tests for deprecated non-resource variant of stack ops."""
+
+  def _testStackPushPop(self, use_gpu):
+    with self.test_session(use_gpu=use_gpu):
+      h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]])
+      with ops.control_dependencies([c]):
+        c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32)
+      self.assertAllClose([[4.0, 5.0]], c1.eval())
+
+  def testStackPushPop(self):
+    self._testStackPushPop(use_gpu=False)
+    self._testStackPushPop(use_gpu=True)
+
+  def _testStackPushPopSwap(self, use_gpu):
+    with self.test_session(use_gpu=use_gpu):
+      a = np.arange(2000)
+      x = constant_op.constant(a, dtype=dtypes.float32)
+      h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push(h, x, swap_memory=True)
+      with ops.control_dependencies([c]):
+        c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32)
+      self.assertAllClose(a, c1.eval())
+
+  def testStackPushPopSwap(self):
+    self._testStackPushPopSwap(use_gpu=False)
+    self._testStackPushPopSwap(use_gpu=True)
+
   def _testMultiStack(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       h1 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
@@ -107,6 +206,42 @@ class StackOpTest(test.TestCase):
       r = c1 + c2
       self.assertAllClose(9.0, r.eval())
 
+  def _testStackWhileSwap(self, use_gpu):
+    with self.test_session(use_gpu=use_gpu):
+      n = constant_op.constant(0)
+      h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
+
+      def c(x):
+        return math_ops.less(x, 10)
+
+      def b(x):
+        with ops.control_dependencies([x]):
+          a = constant_op.constant(np.ones(2000), dtype=dtypes.float32)
+          v = gen_data_flow_ops._stack_push(h, a, swap_memory=True)
+        with ops.control_dependencies([v]):
+          return math_ops.add(x, 1)
+
+      r = control_flow_ops.while_loop(c, b, [n])
+
+      v = constant_op.constant(np.zeros(2000), dtype=dtypes.float32)
+
+      def c1(x, y):
+        del y
+        return math_ops.greater(x, 0)
+
+      def b1(x, y):
+        nx = math_ops.subtract(x, 1)
+        ny = y + gen_data_flow_ops._stack_pop(h, dtypes.float32)
+        return [nx, ny]
+
+      _, ry = control_flow_ops.while_loop(
+          c1, b1, [r, v], [r.get_shape(), tensor_shape.unknown_shape()])
+      self.assertAllClose(np.ones(2000) * 10.0, ry.eval())
+
+  def testStackWhileSwap(self):
+    self._testStackWhileSwap(use_gpu=False)
+    self._testStackWhileSwap(use_gpu=True)
+
   def testMultiStack(self):
     self._testMultiStack(use_gpu=False)
     self._testMultiStack(use_gpu=True)
@@ -117,7 +252,7 @@ class StackOpTest(test.TestCase):
       c1 = gen_data_flow_ops._stack_push(h1, 4.0)
       h2 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
       c2 = gen_data_flow_ops._stack_push(h2, 5.0)
-      r = c1 + c2
+      _ = c1 + c2
       self.assertNotEqual(h1.eval()[1], h2.eval()[1])
 
   def testSameNameStacks(self):
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index 9aef6bffde..6408d52d8c 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -111,6 +111,10 @@ Stack
 StackClose
 StackPop
 StackPush
+StackV2
+StackCloseV2
+StackPopV2
+StackPushV2
 TensorArray
 TensorArrayClose
 TensorArrayCloseV2
-- 
GitLab


From 5069ec7b0791edd66bb5ae144e3a0a710fb3d538 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 06:20:23 -0700
Subject: [PATCH 0794/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161194969
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 74 +++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 93 +++++++++++++++++--
 2 files changed, 160 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 8ee032d8bf..4c2af8bd46 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -26552,6 +26552,14 @@ op {
     is_ref: true
   }
 }
+op {
+  name: "StackCloseV2"
+  input_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  is_stateful: true
+}
 op {
   name: "StackPop"
   input_arg {
@@ -26568,6 +26576,22 @@ op {
     type: "type"
   }
 }
+op {
+  name: "StackPopV2"
+  input_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "elem"
+    type_attr: "elem_type"
+  }
+  attr {
+    name: "elem_type"
+    type: "type"
+  }
+  is_stateful: true
+}
 op {
   name: "StackPush"
   input_arg {
@@ -26595,6 +26619,56 @@ op {
     }
   }
 }
+op {
+  name: "StackPushV2"
+  input_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "elem"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "swap_memory"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  is_stateful: true
+}
+op {
+  name: "StackV2"
+  input_arg {
+    name: "max_size"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "elem_type"
+    type: "type"
+  }
+  attr {
+    name: "stack_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "Stage"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index d2f65e0d87..765eb763be 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -26770,14 +26770,12 @@ op {
   name: "Stack"
   output_arg {
     name: "handle"
-    description: "The handle to the stack."
     type: DT_STRING
     is_ref: true
   }
   attr {
     name: "elem_type"
     type: "type"
-    description: "The type of the elements on the stack."
   }
   attr {
     name: "stack_name"
@@ -26785,29 +26783,53 @@ op {
     default_value {
       s: ""
     }
-    description: "Overrides the name used for the temporary stack resource. Default\nvalue is the name of the \'Stack\' op (which is guaranteed unique)."
   }
-  summary: "A stack that produces elements in first-in last-out order."
+  summary: "Deprecated, use StackV2."
   is_stateful: true
 }
 op {
   name: "StackClose"
   input_arg {
     name: "handle"
-    description: "The handle to a stack."
     type: DT_STRING
     is_ref: true
   }
+  summary: "Deprecated, use StackCloseV2."
+}
+op {
+  name: "StackCloseV2"
+  input_arg {
+    name: "handle"
+    description: "The handle to a stack."
+    type: DT_RESOURCE
+  }
   summary: "Delete the stack from its resource container."
+  is_stateful: true
 }
 op {
   name: "StackPop"
   input_arg {
     name: "handle"
-    description: "The handle to a stack."
     type: DT_STRING
     is_ref: true
   }
+  output_arg {
+    name: "elem"
+    type_attr: "elem_type"
+  }
+  attr {
+    name: "elem_type"
+    type: "type"
+  }
+  summary: "Deprecated, use StackPopV2."
+}
+op {
+  name: "StackPopV2"
+  input_arg {
+    name: "handle"
+    description: "The handle to a stack."
+    type: DT_RESOURCE
+  }
   output_arg {
     name: "elem"
     description: "The tensor that is popped from the top of the stack."
@@ -26819,15 +26841,43 @@ op {
     description: "The type of the elem that is popped."
   }
   summary: "Pop the element at the top of the stack."
+  is_stateful: true
 }
 op {
   name: "StackPush"
   input_arg {
     name: "handle"
-    description: "The handle to a stack."
     type: DT_STRING
     is_ref: true
   }
+  input_arg {
+    name: "elem"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "swap_memory"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  summary: "Deprecated, use StackPushV2."
+}
+op {
+  name: "StackPushV2"
+  input_arg {
+    name: "handle"
+    description: "The handle to a stack."
+    type: DT_RESOURCE
+  }
   input_arg {
     name: "elem"
     description: "The tensor to be pushed onto the stack."
@@ -26851,6 +26901,35 @@ op {
     description: "Swap `elem` to CPU. Default to false."
   }
   summary: "Push an element onto the stack."
+  is_stateful: true
+}
+op {
+  name: "StackV2"
+  input_arg {
+    name: "max_size"
+    description: "The maximum size of the stack if non-negative. If negative, the stack\nsize is unlimited."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "handle"
+    description: "The handle to the stack."
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "elem_type"
+    type: "type"
+    description: "The type of the elements on the stack."
+  }
+  attr {
+    name: "stack_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "Overrides the name used for the temporary stack resource. Default\nvalue is the name of the \'Stack\' op (which is guaranteed unique)."
+  }
+  summary: "A stack that produces elements in first-in last-out order."
+  is_stateful: true
 }
 op {
   name: "Stage"
-- 
GitLab


From a62244ed4945fd2444b35289ed46f56dfc340789 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 06:26:11 -0700
Subject: [PATCH 0795/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161195304
---
 tensorflow/go/op/wrappers.go | 122 +++++++++++++++++++++++++++++++++++
 1 file changed, 122 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 759484bfa3..40c3a6d1b5 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -4352,6 +4352,86 @@ func TensorArrayGradV3(scope *Scope, handle tf.Output, flow_in tf.Output, source
 	return op.Output(0), op.Output(1)
 }
 
+// StackPushV2Attr is an optional argument to StackPushV2.
+type StackPushV2Attr func(optionalAttr)
+
+// StackPushV2SwapMemory sets the optional swap_memory attribute to value.
+//
+// value: Swap `elem` to CPU. Default to false.
+// If not specified, defaults to false
+func StackPushV2SwapMemory(value bool) StackPushV2Attr {
+	return func(m optionalAttr) {
+		m["swap_memory"] = value
+	}
+}
+
+// Push an element onto the stack.
+//
+// Arguments:
+//	handle: The handle to a stack.
+//	elem: The tensor to be pushed onto the stack.
+//
+// Returns The same tensor as the input 'elem'.
+func StackPushV2(scope *Scope, handle tf.Output, elem tf.Output, optional ...StackPushV2Attr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "StackPushV2",
+		Input: []tf.Input{
+			handle, elem,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// StackV2Attr is an optional argument to StackV2.
+type StackV2Attr func(optionalAttr)
+
+// StackV2StackName sets the optional stack_name attribute to value.
+//
+// value: Overrides the name used for the temporary stack resource. Default
+// value is the name of the 'Stack' op (which is guaranteed unique).
+// If not specified, defaults to ""
+func StackV2StackName(value string) StackV2Attr {
+	return func(m optionalAttr) {
+		m["stack_name"] = value
+	}
+}
+
+// A stack that produces elements in first-in last-out order.
+//
+// Arguments:
+//	max_size: The maximum size of the stack if non-negative. If negative, the stack
+// size is unlimited.
+//	elem_type: The type of the elements on the stack.
+//
+// Returns The handle to the stack.
+func StackV2(scope *Scope, max_size tf.Output, elem_type tf.DataType, optional ...StackV2Attr) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"elem_type": elem_type}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "StackV2",
+		Input: []tf.Input{
+			max_size,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Returns the batched diagonal part of a batched tensor.
 //
 // This operation returns a tensor with the `diagonal` part
@@ -10450,6 +10530,25 @@ func SigmoidGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
+// Delete the stack from its resource container.
+//
+// Arguments:
+//	handle: The handle to a stack.
+//
+// Returns the created operation.
+func StackCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "StackCloseV2",
+		Input: []tf.Input{
+			handle,
+		},
+	}
+	return scope.AddOperation(opspec)
+}
+
 // Generate a sharded filename. The filename is printf formatted as
 //
 //    %s-%05d-of-%05d, basename, shard, num_shards.
@@ -24412,6 +24511,29 @@ func AvgPool(scope *Scope, value tf.Output, ksize []int64, strides []int64, padd
 	return op.Output(0)
 }
 
+// Pop the element at the top of the stack.
+//
+// Arguments:
+//	handle: The handle to a stack.
+//	elem_type: The type of the elem that is popped.
+//
+// Returns The tensor that is popped from the top of the stack.
+func StackPopV2(scope *Scope, handle tf.Output, elem_type tf.DataType) (elem tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"elem_type": elem_type}
+	opspec := tf.OpSpec{
+		Type: "StackPopV2",
+		Input: []tf.Input{
+			handle,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the mean along sparse segments of a tensor.
 //
 // Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-- 
GitLab


From 638aa8d7b7aa8fa15f745bf786b873408e416a54 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 06:29:12 -0700
Subject: [PATCH 0796/1375] fix path to saved model constant in docs.

PiperOrigin-RevId: 161195442
---
 tensorflow/python/saved_model/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/saved_model/README.md b/tensorflow/python/saved_model/README.md
index 38203da5b6..8213e52ce9 100644
--- a/tensorflow/python/saved_model/README.md
+++ b/tensorflow/python/saved_model/README.md
@@ -106,7 +106,7 @@ builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
 with tf.Session(graph=tf.Graph()) as sess:
   ...
   builder.add_meta_graph_and_variables(sess,
-                                       [tag_constants.TRAINING],
+                                       [tf.saved_model.tag_constants.TRAINING],
                                        signature_def_map=foo_signatures,
                                        assets_collection=foo_assets)
 ...
-- 
GitLab


From b760c0cade03186a8f194390f6cba46fb363bfca Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 06:33:59 -0700
Subject: [PATCH 0797/1375] Update xla compiler after upstream Orc API change
 r307350: https://reviews.llvm.org/rL307350

PiperOrigin-RevId: 161195744
---
 tensorflow/compiler/xla/service/cpu/cpu_executable.cc       | 3 ++-
 .../compiler/xla/service/cpu/parallel_cpu_executable.cc     | 5 +++--
 tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc       | 6 +++---
 3 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
index 671d6957a3..8787336ed0 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
@@ -66,7 +66,8 @@ CpuExecutable::CpuExecutable(
   CHECK(sym) << "Symbol " << entry_function_name << " not found.";
   // getAddress can do work under the hood in the jit, so it needs to be
   // guarded by the mutex.
-  compute_function_ = reinterpret_cast<ComputeFunctionType>(sym.getAddress());
+  compute_function_ =
+      reinterpret_cast<ComputeFunctionType>(cantFail(sym.getAddress()));
 }
 
 // Given a pointer to an output buffer (following the CPU JIT calling
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
index 19909f4bed..85fccd327f 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
@@ -407,8 +407,9 @@ Status ParallelCpuExecutable::ExecuteComputeFunctions(
     HloInstruction* instruction = entry.first;
     llvm::JITSymbol sym = jit_->FindSymbol(entry.second);
     TF_RET_CHECK(sym);
-    InsertOrDie(&functions, instruction,
-                reinterpret_cast<ComputeFunctionType>(sym.getAddress()));
+    InsertOrDie(
+        &functions, instruction,
+        reinterpret_cast<ComputeFunctionType>(cantFail(sym.getAddress())));
   }
 
   // Map containing pointers to result buffers for each instruction.
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index 8f567b4f8c..b25ecca45c 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -168,8 +168,8 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
 
 SimpleOrcJIT::ModuleHandleT SimpleOrcJIT::AddModule(
     std::unique_ptr<llvm::Module> module) {
-  auto handle =
-      compile_layer_.addModule(std::move(module), MakeUnique<SimpleResolver>());
+  auto handle = cantFail(compile_layer_.addModule(
+      std::move(module), MakeUnique<SimpleResolver>()));
   module_handles_.push_back(handle);
   return handle;
 }
@@ -178,7 +178,7 @@ void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::ModuleHandleT handle) {
   module_handles_.erase(
       std::remove(module_handles_.begin(), module_handles_.end(), handle),
       module_handles_.end());
-  compile_layer_.removeModule(handle);
+  cantFail(compile_layer_.removeModule(handle));
 }
 
 llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string &name) {
-- 
GitLab


From 97617f139f814dade30d1b0758400ea25d502757 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 07:27:30 -0700
Subject: [PATCH 0798/1375] Update llvm revision to r307383. Need this because
 of an Orc api change r307350: https://reviews.llvm.org/rL307350
 https://reviews.llvm.org/rL307383

PiperOrigin-RevId: 161199511
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index a6b062ea22..bb5a28bab1 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -484,11 +484,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   temp_workaround_http_archive(
       name = "llvm",
       urls = [
-          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/9e886358ff35a13de549b4adf49b52f933b9ec37.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/9e886358ff35a13de549b4adf49b52f933b9ec37.tar.gz",
+          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/9889fe2290766430b99a2d4fadbc5ba92f8004b6.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/9889fe2290766430b99a2d4fadbc5ba92f8004b6.tar.gz",
       ],
-      sha256 = "5a56369e906e5af2d4baf5a92317a3db085800e848def7114aba176c80432ea0",
-      strip_prefix = "llvm-9e886358ff35a13de549b4adf49b52f933b9ec37",
+      sha256 = "00fb4a83a4dd1c046b19730a80e2183acc647715b7a8dcc8e808d49ea5530ca8",
+      strip_prefix = "llvm-9889fe2290766430b99a2d4fadbc5ba92f8004b6",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
       repository = tf_repo_name,
   )
-- 
GitLab


From d0f53f77fbed8506dd3a3fe23ea8e9ac0028a577 Mon Sep 17 00:00:00 2001
From: Penghao Cen <scorpiocph@gmail.com>
Date: Fri, 7 Jul 2017 22:56:40 +0800
Subject: [PATCH 0799/1375] Minor fix typo (#11323)

---
 tensorflow/core/lib/gtl/array_slice.h | 2 +-
 tensorflow/core/protobuf/worker.proto | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/lib/gtl/array_slice.h b/tensorflow/core/lib/gtl/array_slice.h
index 30ef19ebe8..002d166c72 100644
--- a/tensorflow/core/lib/gtl/array_slice.h
+++ b/tensorflow/core/lib/gtl/array_slice.h
@@ -191,7 +191,7 @@ class ArraySlice {
   void pop_front() { remove_prefix(1); }
 
   // These relational operators have the same semantics as the
-  // std::vector<T> relational operators: they do deep (elementwise)
+  // std::vector<T> relational operators: they do deep (element-wise)
   // comparisons.  Array slices are equal iff their size is the same
   // and all their elements are equal.
   bool operator==(ArraySlice<T> other) const { return impl_ == other.impl_; }
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index e476a84a13..9d4a417aa3 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -141,7 +141,7 @@ message DeregisterGraphResponse {
 message CleanupAllRequest {
   // A list of container names.
   //
-  // If 'container' is not empty, releases resoures in the given
+  // If 'container' is not empty, releases resources in the given
   // containers in all devices.
   //
   // If 'container' is empty, releases resources in the default
-- 
GitLab


From aee58d07201e6cd247cd367ca34973c6d8611564 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Fri, 7 Jul 2017 08:15:14 -0700
Subject: [PATCH 0800/1375] Delete merged docs

`versioning_semantics` and `data_versioning` have been into `version_compat`

Also fix all links to the old docs.

PiperOrigin-RevId: 161203112
---
 tensorflow/docs_src/extend/adding_an_op.md    |   4 +-
 .../programmers_guide/data_versions.md        | 132 --------------
 .../docs_src/programmers_guide/index.md       |  12 +-
 .../programmers_guide/version_semantics.md    | 161 ------------------
 4 files changed, 6 insertions(+), 303 deletions(-)
 delete mode 100644 tensorflow/docs_src/programmers_guide/data_versions.md
 delete mode 100644 tensorflow/docs_src/programmers_guide/version_semantics.md

diff --git a/tensorflow/docs_src/extend/adding_an_op.md b/tensorflow/docs_src/extend/adding_an_op.md
index a8c28e98c9..e8441d51c2 100644
--- a/tensorflow/docs_src/extend/adding_an_op.md
+++ b/tensorflow/docs_src/extend/adding_an_op.md
@@ -1100,7 +1100,7 @@ In general, changes to existing, checked-in specifications must be
 backwards-compatible: changing the specification of an op must not break prior
 serialized `GraphDef` protocol buffers constructed from older specifications.
 The details of `GraphDef` compatibility are
-@{$version_semantics#graphs$described here}.
+@{$version_compat#compatibility_of_graphs_and_checkpoints$described here}.
 
 There are several ways to preserve backwards-compatibility.
 
@@ -1150,7 +1150,7 @@ callers.  The Python API may be kept compatible by careful changes in a
 hand-written Python wrapper, by keeping the old signature except possibly adding
 new optional arguments to the end.  Generally incompatible changes may only be
 made when TensorFlow's changes major versions, and must conform to the
-@{$version_semantics#graphs$`GraphDef` version semantics}.
+@{$version_compat#compatibility_of_graphs_and_checkpoints$`GraphDef` version semantics}.
 
 ### GPU Support {#gpu-support}
 
diff --git a/tensorflow/docs_src/programmers_guide/data_versions.md b/tensorflow/docs_src/programmers_guide/data_versions.md
deleted file mode 100644
index 006aa2a826..0000000000
--- a/tensorflow/docs_src/programmers_guide/data_versions.md
+++ /dev/null
@@ -1,132 +0,0 @@
-# TensorFlow Data Versioning: GraphDefs and Checkpoints
-
-As described in
-@{$version_semantics#compatibility-for-graphs-and-checkpoints$Compatibility for Graphs and Checkpoints},
-TensorFlow marks each kind of data with version information in order to maintain
-backward compatibility. This document provides additional details about the
-versioning mechanism, and how to use it to safely change data formats.
-
-## Backward and partial forward compatibility
-
-The two core artifacts exported from and imported into TensorFlow are
-checkpoints (serialized variable states) and `GraphDef`s (serialized computation
-graphs). Any approach to versioning these artifacts must take into account the
-following requirements:
-
-*   **Backward compatibility** to support loading `GraphDefs` created with older
-    versions of TensorFlow.
-*   **Forward compatibility** to support scenarios where the producer of a
-    `GraphDef` is upgraded to a newer version of TensorFlow before the consumer.
-*   Enable evolving TensorFlow in incompatible ways. For example, removing Ops,
-    adding attributes, and removing attributes.
-
-For `GraphDef`s, backward compatibility is enforced within a major version. This
-means functionality can only be removed between major versions. Forward
-compatibility is enforced within Patch releases (1.x.1 -> 1.x.2, for example).
-
-
-In order to achieve backward and forward compatibility as well as know when to
-enforce changes in formats, the serialized representations of graphs and
-variable state need to have metadata that describes when they were produced. The
-sections below detail the TensorFlow implementation and guidelines for evolving
-`GraphDef` versions.
-
-### Independent data version schemes
-
-There are data versions for `GraphDef`s and checkpoints. Both data formats
-evolve at different rates, and also at different speeds than the version of
-TensorFlow. Both versioning systems are defined in
-[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h).
-Whenever a new version is added a note is added to the header detailing what
-changed and the date.
-
-### Data, producers, and consumers
-
-This section discusses version information for **data**, binaries that produce
-data (**producers**), and binaries that consume data (**consumers**):
-
-*   Producer binaries have a version (`producer`) and a minimum consumer version
-    that they are compatible with (`min_consumer`).
-*   Consumer binaries have a version (`consumer`) and a minimum producer version
-    that they are compatible with (`min_producer`).
-*   Each piece of versioned data has a [`VersionDef
-    versions`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/versions.proto)
-    field which records the `producer` that made the data, the `min_consumer`
-    that it is compatible with, and a list of `bad_consumers` versions that are
-    disallowed.
-
-By default, when a producer makes some data, the data inherits the producer's
-`producer` and `min_consumer` versions. `bad_consumers` can be set if specific
-consumer versions are known to contain bugs and must be avoided. A consumer can
-accept a piece of data if
-
-*   `consumer` >= data's `min_consumer`
-*   data's `producer` >= consumer's `min_producer`
-*   `consumer` not in data's `bad_consumers`
-
-Since both producers and consumers come from the same TensorFlow code base,
-[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h)
-contains a main binary version which is treated as either `producer` or
-`consumer` depending on context and both `min_consumer` and `min_producer`
-(needed by producers and consumers, respectively). Specifically,
-
-*   For `GraphDef` versions, we have `TF_GRAPH_DEF_VERSION`,
-    `TF_GRAPH_DEF_VERSION_MIN_CONSUMER`, and
-    `TF_GRAPH_DEF_VERSION_MIN_PRODUCER`.
-*   For checkpoint versions, we have `TF_CHECKPOINT_VERSION`,
-    `TF_CHECKPOINT_VERSION_MIN_CONSUMER`, and
-    `TF_CHECKPOINT_VERSION_MIN_PRODUCER`.
-
-### Evolving GraphDef versions
-
-This section presents examples of using this versioning mechanism to make
-changes to the `GraphDef` format.
-
-**Adding a new Op:**
-
-1.  Add the new Op to both consumers and producers at the same time, and do not
-    change any `GraphDef` versions. This type of change is automatically
-    backward compatible, and does not impact forward compatibility plan since
-    existing producer scripts will not suddenly use the new functionality.
-
-**Adding a new Op and switching existing Python wrappers to use it:**
-
-1.  Implement new consumer functionality and increment the binary version.
-2.  If it is possible to make the wrappers use the new functionality only in
-    cases that did not work before, the wrappers can be updated now.
-3.  Change Python wrappers to use the new functionality. Do not increment
-    `min_consumer`, since models which do not use this Op should not break.
-
-**Removing an Op or restricting the functionality of an Op:**
-
-1.  Fix all producer scripts (not TensorFlow itself) to not use the banned Op or
-    functionality.
-2.  Increment the binary version and implement new consumer functionality that
-    bans the removed Op or functionality for GraphDefs at the new version and
-    above. If possible, make TensorFlow stop producing `GraphDefs` with the
-    banned functionality. This can be done with
-    [`REGISTER_OP(...).Deprecated(deprecated_at_version,
-    message)`](https://github.com/tensorflow/tensorflow/blob/b289bc7a50fc0254970c60aaeba01c33de61a728/tensorflow/core/ops/array_ops.cc#L1009).
-3.  Wait for a major release for backward compatibility purposes.
-4.  Increase `min_producer` to the GraphDef version from (2) and remove the
-    functionality entirely.
-
-**Changing the functionality of an Op:**
-
-1.  Add a new similar Op named `SomethingV2` or similar and go through the
-    process of adding it and switching existing Python wrappers to use it (may
-    take 3 weeks if forward compatibility is desired).
-2.  Remove the old Op (Can only take place with a major version change due to
-    backward compatibility).
-3.  Increase `min_consumer` to rule out consumers with the old Op, add back the
-    old Op as an alias for `SomethingV2`, and go through the process to switch
-    existing Python wrappers to use it.
-4.  Go through the process to remove `SomethingV2`.
-
-**Banning a single consumer version that cannot run safely:**
-
-1.  Bump the binary version and add the bad version to `bad_consumers` for all
-    new GraphDefs. If possible, add to `bad_consumers` only for GraphDefs which
-    contain a certain Op or similar.
-2.  If existing consumers have the bad version, push them out as soon as
-    possible.
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md
index 6074b2e92d..52c2aaae34 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/programmers_guide/index.md
@@ -40,14 +40,10 @@ documented in the following guide:
 
   * @{$saved_model_cli$SavedModel CLI (Command-Line Interface)}.
 
-To learn about the TensorFlow versioning scheme, consult the following two
-guides:
-
-  * @{$version_semantics$TensorFlow Version Semantics}, which explains
-    TensorFlow's versioning nomenclature and compatibility rules.
-  * @{$data_versions$TensorFlow Data Versioning: GraphDefs and Checkpoints},
-    which explains how TensorFlow adds versioning information to computational
-    graphs and checkpoints in order to support compatibility across versions.
+To learn about the TensorFlow versioning scheme consult:
+
+  * @{$version_compat$The TensorFlow Version Compatibility Guide}, which explains
+TensorFlow's versioning nomenclature and compatibility rules.
 
 We conclude this section with a FAQ about TensorFlow programming:
 
diff --git a/tensorflow/docs_src/programmers_guide/version_semantics.md b/tensorflow/docs_src/programmers_guide/version_semantics.md
deleted file mode 100644
index cee3b105de..0000000000
--- a/tensorflow/docs_src/programmers_guide/version_semantics.md
+++ /dev/null
@@ -1,161 +0,0 @@
-# TensorFlow Version Semantics
-
-## Semantic Versioning 2.0
-
-TensorFlow follows Semantic Versioning 2.0 ([semver](http://semver.org)) for its
-public API. Each release version of TensorFlow has the form `MAJOR.MINOR.PATCH`.
-Changes to the each number have the following meaning:
-
-* **MAJOR**:  Backwards incompatible changes.  Code and data that worked with
-  a previous major release will not necessarily work with a new release.
-  However, in some cases existing TensorFlow data (graphs, checkpoints, and
-  other protobufs) may be migratable to the newer release; see below for details
-  on data compatibility.
-
-* **MINOR**: Backwards compatible features, speed improvements, etc.  Code and
-  data that worked with a previous minor release *and* which depends only the
-  public API will continue to work unchanged.  For details on what is and is
-  not the public API, see below.
-
-* **PATCH**: Backwards compatible bug fixes.
-
-## What is covered
-
-Only the public APIs of TensorFlow are backwards compatible across minor and
-patch versions.  The public APIs consist of
-
-* The documented public [Python](../api_docs/python) API, excluding `tf.contrib`.
-  This includes all public functions and classes (whose names do not start with
-  `_`) in the tensorflow module and its submodules. Note that the code in
-  the `examples/` to `tools/` directories is not reachable through the
-  tensorflow Python module and is thus not covered by the compatibility
-  guarantee.
-
-  If a symbol is available through the tensorflow Python module or its
-  submodules, but is not documented, then it is _not_ considered part of the
-  public API.
-
-* The [C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h).
-
-* The following protocol buffer files:
-  [`attr_value`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto),
-  [`config`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto),
-  [`event`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/event.proto),
-  [`graph`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto),
-  [`op_def`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto),
-  [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/reader_base.proto),
-  [`summary`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto),
-  [`tensor`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto),
-  [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto),
-  and [`types`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto).
-
-## What is *not* covered
-
-Some API functions are explicitly marked as "experimental" and can change in
-backward incompatible ways between minor releases. These include:
-
-*   **Experimental APIs**: The @{tf.contrib} module and its submodules in Python
-    and any functions in the C API or fields in protocol buffers that are
-    explicitly commented as being experimental.
-
-*   **Other languages**: TensorFlow APIs in languages other than Python and C,
-    such as:
-
-  - @{$cc/guide$C++} (exposed through header files in
-    [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc)).
-  - [Java](../api_docs/java/reference/org/tensorflow/package-summary), and
-  - [Go](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
-
-*   **Details of composite ops:** Many public functions in Python expand to
-    several primitive ops in the graph, and these details will be part of any
-    graphs saved to disk as `GraphDef`s. These details are allowed to change for
-    minor releases. In particular, regressions tests that check for exact
-    matching between graphs are likely to break across minor releases, even
-    though the behavior of the graph should be unchanged and existing
-    checkpoints will still work.
-
-*   **Floating point numerical details:** The specific floating point values
-    computed by ops may change at any time: users should rely only on
-    approximate accuracy and numerical stability, not on the specific bits
-    computed. Changes to numerical formulas in minor and patch releases should
-    result in comparable or improved accuracy, with the caveat that in machine
-    learning improved accuracy of specific formulas may result in worse accuracy
-    for the overall system.
-
-*   **Random numbers:** The specific random numbers computed by the
-    @{$python/constant_op#Random_Tensors$random ops} may change at any time:
-    users should rely only on approximately correct distributions and
-    statistical strength, not the specific bits computed. However, we will make
-    changes to random bits rarely and ideally never for patch releases, and all
-    such intended changes will be documented.
-
-*   **Distributed Tensorflow:** Running 2 different versions of TensorFlow in a
-    single cluster is unsupported. There are no guarantees about backwards
-    compatibility of the wire protocol.
-
-*   **Bugs:** We reserve the right to make backwards incompatible behavior
-    (though not API) changes if the current implementation is clearly broken,
-    i.e., if it is contradicting the documentation, or if a well-known and
-    well-defined intended behavior is not properly implemented due to a bug.
-    For example, if an optimizer claims to implement a well-known optimization
-    algorithm but, due to a bug, does not match that algorithm we will fix the
-    optimizer. This may break code relying on the wrong behavior for
-    convergence. We will note such changes in the release notes.
-
-*   **Error messages:** We reserve the right to change the text of error
-    messages. In addition, the type of an error may change unless the type is
-    specified in the documentation. For example, a function that says in some
-    condition it will raise an `InvalidArgument` exception, it will continue to
-    raise `InvalidArgument`, but the human-readable message contents can change.
-
-
-Furthermore, any API methods marked "deprecated" in the 1.0 release can
-be deleted in any subsequent minor release.
-
-## Compatibility for Graphs and Checkpoints
-
-Many users of TensorFlow will be saving graphs and trained models to disk for
-later evaluation or more training, often changing versions of TensorFlow in the
-process.  First, following semver, any graph or checkpoint written out with one
-version of TensorFlow can be loaded and evaluated with a later version of
-TensorFlow with the same major release.  However, we will endeavor to preserve
-backwards compatibility even across major releases when possible, so that the
-serialized files are usable over long periods of time.
-
-There are two main classes of saved TensorFlow data: graphs and checkpoints.
-Graphs describe the data flow graphs of ops to be run during training and
-inference, and checkpoints contain the saved tensor values of variables in a
-graph.
-
-Graphs are serialized via the `GraphDef` protocol buffer.  To facilitate (rare)
-backwards incompatible changes to graphs, each `GraphDef` has an integer version
-separate from the TensorFlow version.  The semantics are:
-
-* Each version of TensorFlow supports an interval of `GraphDef` versions.  This
-  interval with be constant across patch releases, and will only grow across
-  minor releases.  Dropping support for a `GraphDef` version will only occur
-  for a major release of TensorFlow.
-
-* Newly created graphs use the newest `GraphDef` version.
-
-* If a given version of TensorFlow supports the `GraphDef` version of a graph,
-  it will load and evaluate with the same behavior as when it was written out
-  (except for floating point numerical details and random numbers), regardless
-  of the major version of TensorFlow.  In particular, all checkpoint files will
-  be compatible.
-
-* If the `GraphDef` upper bound is increased to X in a (minor) release, there
-  will be at least six months before the lower bound is increased to X.
-
-For example (numbers and versions hypothetical), TensorFlow 1.2 might support
-`GraphDef` versions 4 to 7.  TensorFlow 1.3 could add `GraphDef` version 8 and
-support versions 4 to 8.  At least six months later, TensorFlow 2.0.0 could drop
-support for versions 4 to 7, leaving version 8 only.
-
-Finally, when support for a `GraphDef` version is dropped, we will attempt to
-provide tools for automatically converting graphs to a newer supported
-`GraphDef` version.
-
-For developer-level details about `GraphDef` versioning, including how to evolve
-the versions to account for changes, see
-@{$data_versions$TensorFlow Data Versioning}.
-- 
GitLab


From 81e81b796dd40a8294d166eb457e609aaedb4540 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 08:20:20 -0700
Subject: [PATCH 0801/1375] Fix dependencies and import statements for
 predictor module.

PiperOrigin-RevId: 161203536
---
 tensorflow/contrib/BUILD                      |  1 +
 tensorflow/contrib/__init__.py                |  1 +
 tensorflow/contrib/predictor/BUILD            |  3 +-
 tensorflow/contrib/predictor/__init__.py      |  6 ++--
 .../predictor/saved_model_predictor.py        | 32 ++++++++++++++++---
 5 files changed, 34 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index 7ada956124..c9065b0a89 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -51,6 +51,7 @@ py_library(
         "//tensorflow/contrib/ndlstm",
         "//tensorflow/contrib/nn:nn_py",
         "//tensorflow/contrib/opt:opt_py",
+        "//tensorflow/contrib/predictor",
         "//tensorflow/contrib/quantization:quantization_py",
         "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py",
         "//tensorflow/contrib/resampler:resampler_py",
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index f70b810ec6..513e657a33 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -50,6 +50,7 @@ from tensorflow.contrib import metrics
 from tensorflow.contrib import nccl
 from tensorflow.contrib import nn
 from tensorflow.contrib import opt
+from tensorflow.contrib import predictor
 from tensorflow.contrib import quantization
 from tensorflow.contrib import resampler
 from tensorflow.contrib import rnn
diff --git a/tensorflow/contrib/predictor/BUILD b/tensorflow/contrib/predictor/BUILD
index c4b46551c1..e298fd3cb2 100644
--- a/tensorflow/contrib/predictor/BUILD
+++ b/tensorflow/contrib/predictor/BUILD
@@ -1,6 +1,6 @@
 # `Predictor` classes provide an interface for efficient, repeated inference.
 
-package(default_visibility = ["//third_party/tensroflow/contrib/predictor:__subpackages__"])
+package(default_visibility = ["//tensorflow/contrib/predictor:__subpackages__"])
 
 licenses(["notice"])  # Apache 2.0
 
@@ -62,7 +62,6 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":base_predictor",
-        "//tensorflow/python/tools:saved_model_cli",
     ],
 )
 
diff --git a/tensorflow/contrib/predictor/__init__.py b/tensorflow/contrib/predictor/__init__.py
index d270c3f798..e0a2152b37 100644
--- a/tensorflow/contrib/predictor/__init__.py
+++ b/tensorflow/contrib/predictor/__init__.py
@@ -19,6 +19,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.predictor import from_contrib_estimator
-from tensorflow.contrib.predictor import from_estimator
-from tensorflow.contrib.predictor import from_saved_model
+from tensorflow.contrib.predictor.predictor_factories import from_contrib_estimator
+from tensorflow.contrib.predictor.predictor_factories import from_estimator
+from tensorflow.contrib.predictor.predictor_factories import from_saved_model
diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py
index ab2bafa0c8..0dbca0f813 100644
--- a/tensorflow/contrib/predictor/saved_model_predictor.py
+++ b/tensorflow/contrib/predictor/saved_model_predictor.py
@@ -22,12 +22,12 @@ from __future__ import print_function
 import logging
 
 from tensorflow.contrib.predictor import predictor
+from tensorflow.contrib.saved_model.python.saved_model import reader
 from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils
 from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.saved_model import loader
 from tensorflow.python.saved_model import signature_constants
-from tensorflow.python.tools import saved_model_cli
 
 
 DEFAULT_TAGS = 'serve'
@@ -35,13 +35,37 @@ DEFAULT_TAGS = 'serve'
 _DEFAULT_INPUT_ALTERNATIVE_FORMAT = 'default_input_alternative:{}'
 
 
+def get_meta_graph_def(saved_model_dir, tags):
+  """Gets `MetaGraphDef` from a directory containing a `SavedModel`.
+
+  Returns the `MetaGraphDef` for the given tag-set and SavedModel directory.
+
+  Args:
+    saved_model_dir: Directory containing the SavedModel.
+    tags: Comma separated list of tags used to identify the correct
+      `MetaGraphDef`.
+
+  Raises:
+    ValueError: An error when the given tags cannot be found.
+
+  Returns:
+    A `MetaGraphDef` corresponding to the given tags.
+  """
+  saved_model = reader.read_saved_model(saved_model_dir)
+  set_of_tags = set([tag.strip() for tag in tags.split(',')])
+  for meta_graph_def in saved_model.meta_graphs:
+    if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
+      return meta_graph_def
+  raise ValueError('Could not find MetaGraphDef with tags {}'.format(tags))
+
+
 def _get_signature_def(signature_def_key, export_dir, tags):
   """Construct a `SignatureDef` proto."""
   signature_def_key = (
       signature_def_key or
       signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY)
 
-  metagraph_def = saved_model_cli.get_meta_graph_def(export_dir, tags)
+  metagraph_def = get_meta_graph_def(export_dir, tags)
 
   try:
     signature_def = signature_def_utils.get_signature_def_by_key(
@@ -114,8 +138,8 @@ class SavedModelPredictor(predictor.Predictor):
       output_names: A dictionary mapping strings to `Tensor`s in the
         `SavedModel` that represent the output. The keys can be any string of
         the user's choosing.
-      tags: Optional. Tags that will be used to retrieve the correct
-        `SignatureDef`. Defaults to `DEFAULT_TAGS`.
+      tags: Optional. Comma separated list of tags that will be used to retrieve
+        the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`.
       graph: Optional. The Tensorflow `graph` in which prediction should be
         done.
     Raises:
-- 
GitLab


From a86d439070c130cb40f049b69090fc3c72b4f146 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Fri, 7 Jul 2017 08:20:48 -0700
Subject: [PATCH 0802/1375] Edits to "Threading and Queues" section of
 programmers guide.

PiperOrigin-RevId: 161203568
---
 .../programmers_guide/threading_and_queues.md | 72 ++++++++++++-------
 1 file changed, 45 insertions(+), 27 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/threading_and_queues.md b/tensorflow/docs_src/programmers_guide/threading_and_queues.md
index 7d3edb788e..7f3e6f0da5 100644
--- a/tensorflow/docs_src/programmers_guide/threading_and_queues.md
+++ b/tensorflow/docs_src/programmers_guide/threading_and_queues.md
@@ -1,28 +1,44 @@
 # Threading and Queues
 
-Queues are a powerful mechanism for asynchronous computation using TensorFlow.
-
-Like everything in TensorFlow, a queue is a node in a TensorFlow graph. It's a
-stateful node, like a variable: other nodes can modify its content. In
-particular, nodes can enqueue new items in to the queue, or dequeue existing
-items from the queue.
-
-To get a feel for queues, let's consider a simple example. We will create a
-"first in, first out" queue (`FIFOQueue`) and fill it with zeros.
-Then we'll construct a graph
-that takes an item off the queue, adds one to that item, and puts it back on the
-end of the queue. Slowly, the numbers on the queue increase.
+Note: In versions of TensorFlow before 1.2, we recommended using multi-threaded,
+queue-based input pipelines for performance. Beginning with TensorFlow 1.2,
+however, we recommend using the `tf.contrib.data` module instead. (See
+[Datasets](datasets) for details.) The `tf.contrib.data` module offers an
+easier-to-use interface for constructing efficient input pipelines. Furthermore,
+we've stopped developing the old multi-threaded, queue-based input pipelines.
+We've retained the documentation in this file to help developers who are still
+maintaining older code.
+
+Multithreaded queues are a powerful and widely used mechanism supporting
+asynchronous computation.
+
+Following the [dataflow programming model](graphs.md), TensorFlow's queues are
+implemented using nodes in the computation graph.  A queue is a stateful node,
+like a variable: other nodes can modify its content. In particular, nodes can
+enqueue new items in to the queue, or dequeue existing items from the
+queue. TensorFlow's queues provide a way to coordinate multiple steps of a
+computation: a queue will **block** any step that attempts to dequeue from it
+when it is empty, or enqueue to it when it is full. When that condition no
+longer holds, the queue will unblock the step and allow execution to proceed.
+
+TensorFlow implements several classes of queue. The principal difference between
+these classes is the order that items are removed from the queue.  To get a feel
+for queues, let's consider a simple example. We will create a "first in, first
+out" queue (@{tf.FIFOQueue}) and fill it with zeros.  Then we'll construct a
+graph that takes an item off the queue, adds one to that item, and puts it back
+on the end of the queue. Slowly, the numbers on the queue increase.
 
 <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
 <img style="width:100%" src="https://www.tensorflow.org/images/IncremeterFifoQueue.gif">
 </div>
 
 `Enqueue`, `EnqueueMany`, and `Dequeue` are special nodes. They take a pointer
-to the queue instead of a normal value, allowing them to change it. We recommend
-you think of these as being like methods of the queue. In fact, in the Python
-API, they are methods of the queue object (e.g. `q.enqueue(...)`).
+to the queue instead of a normal value, allowing them to mutate its state. We
+recommend that you think of these operations as being like methods of the queue
+in an object-oriented sense. In fact, in the Python API, these operations are
+created by calling methods on a queue object (e.g. `q.enqueue(...)`).
 
-**N.B.** Queue methods (such as `q.enqueue(...)`) *must* run on the same device
+Note: Queue methods (such as `q.enqueue(...)`) *must* run on the same device
 as the queue. Incompatible device placement directives will be ignored when
 creating these operations.
 
@@ -32,13 +48,13 @@ Now that you have a bit of a feel for queues, let's dive into the details...
 
 Queues, such as @{tf.FIFOQueue}
 and @{tf.RandomShuffleQueue},
-are important TensorFlow objects for computing tensors asynchronously in a
-graph.
+are important TensorFlow objects that aid in computing tensors asynchronously
+in a graph.
 
-For example, a typical input architecture is to use a `RandomShuffleQueue` to
+For example, a typical input pipeline uses a `RandomShuffleQueue` to
 prepare inputs for training a model:
 
-* Multiple threads prepare training examples and push them in the queue.
+* Multiple threads prepare training examples and enqueue them in the queue.
 * A training thread executes a training op that dequeues mini-batches from the
   queue
 
@@ -46,7 +62,8 @@ This architecture has many benefits, as highlighted in the
 @{$reading_data$Reading data how to}, which also gives an overview of
 functions that simplify the construction of input pipelines.
 
-The TensorFlow `Session` object is multithreaded, so multiple threads can
+The TensorFlow `Session` object is multithreaded and thread-safe, so multiple
+threads can
 easily use the same session and run ops in parallel.  However, it is not always
 easy to implement a Python program that drives threads as described above.  All
 threads must be able to stop together, exceptions must be caught and
@@ -62,11 +79,12 @@ enqueue tensors in the same queue.
 
 ## Coordinator
 
-The `Coordinator` class helps multiple threads stop together.
+The @{tf.train.Coordinator} class manages background threads in a TensorFlow
+program and helps multiple threads stop together.
 
 Its key methods are:
 
-* @{tf.train.Coordinator.should_stop}: returns True if the threads should stop.
+* @{tf.train.Coordinator.should_stop}: returns `True` if the threads should stop.
 * @{tf.train.Coordinator.request_stop}: requests that threads should stop.
 * @{tf.train.Coordinator.join}: waits until the specified threads have stopped.
 
@@ -105,10 +123,10 @@ also has support to capture and report exceptions.  See the @{tf.train.Coordinat
 
 ## QueueRunner
 
-The `QueueRunner` class creates a number of threads that repeatedly run an
-enqueue op.  These threads can use a coordinator to stop together.  In
-addition, a queue runner runs a *closer thread* that automatically closes the
-queue if an exception is reported to the coordinator.
+The @{tf.train.QueueRunner} class creates a number of threads that repeatedly
+run an enqueue op.  These threads can use a coordinator to stop together.  In
+addition, a queue runner will run a *closer operation* that closes the queue if
+an exception is reported to the coordinator.
 
 You can use a queue runner to implement the architecture described above.
 
-- 
GitLab


From cdc4b177f6d12f63aad91bb0bc44f6e00e00d3ec Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 09:31:48 -0700
Subject: [PATCH 0803/1375] BUILD cleanup of tensorflow/contrib/learn

PiperOrigin-RevId: 161209972
---
 tensorflow/contrib/learn/BUILD | 167 +++++++++++++++------------------
 1 file changed, 74 insertions(+), 93 deletions(-)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 980f971c50..44c322fba2 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -29,6 +29,7 @@ py_library(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/contrib/linear_optimizer:sdca_ops_py",
+        "//tensorflow/contrib/lookup:lookup_py",
         "//tensorflow/contrib/losses:losses_py",
         "//tensorflow/contrib/metrics:metrics_py",
         "//tensorflow/contrib/rnn:rnn_py",
@@ -50,24 +51,37 @@ py_library(
         "//tensorflow/python:init_ops",
         "//tensorflow/python:io_ops",
         "//tensorflow/python:logging_ops",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:partitioned_variables",
         "//tensorflow/python:platform",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:resources",
         "//tensorflow/python:rnn",
         "//tensorflow/python:session",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:string_ops",
         "//tensorflow/python:summary",
+        "//tensorflow/python:tensor_util",
         "//tensorflow/python:training",
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python:weights_broadcast_ops",
+        "//tensorflow/python/estimator",
         "//tensorflow/python/estimator:estimator_py",
+        "//tensorflow/python/estimator:export_output",
         "//tensorflow/python/estimator:inputs",
+        "//tensorflow/python/estimator:inputs_queues",
+        "//tensorflow/python/estimator:model_fn",
+        "//tensorflow/python/estimator:numpy_io",
+        "//tensorflow/python/estimator:pandas_io",
+        "//tensorflow/python/estimator:run_config",
+        "//tensorflow/python/feature_column",
         "//tensorflow/python/feature_column:feature_column_py",
         "//tensorflow/python/saved_model:builder",
         "//tensorflow/python/saved_model:loader",
@@ -107,14 +121,11 @@ py_test(
 py_test(
     name = "feeding_functions_test",
     size = "small",
-    srcs = [
-        "python/learn/tests/dataframe/feeding_functions_test.py",
-    ],
+    srcs = ["python/learn/tests/dataframe/feeding_functions_test.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
     ],
 )
@@ -122,16 +133,13 @@ py_test(
 py_test(
     name = "feeding_queue_runner_test",
     size = "small",
-    srcs = [
-        "python/learn/tests/dataframe/feeding_queue_runner_test.py",
-    ],
+    srcs = ["python/learn/tests/dataframe/feeding_queue_runner_test.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -146,7 +154,6 @@ py_test(
         ":learn",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -160,7 +167,6 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:parsing_ops",
     ],
 )
@@ -174,7 +180,6 @@ py_test(
         ":learn",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -186,7 +191,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -201,7 +205,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:io_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
@@ -217,7 +220,6 @@ py_test(
         ":learn",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
     ],
 )
@@ -230,7 +232,7 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow:tensorflow_py",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/core:protos_all_py",
         "//third_party/py/numpy",
     ],
 )
@@ -245,7 +247,6 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -259,7 +260,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -273,7 +273,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -287,9 +286,9 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:lib",
         "//tensorflow/python:parsing_ops",
         "//third_party/py/numpy",
@@ -304,7 +303,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
     ],
 )
@@ -318,7 +316,6 @@ py_test(
         ":learn",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -333,7 +330,6 @@ py_test(
         ":learn",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -347,9 +343,8 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:sparse_tensor",
         "//third_party/py/numpy",
     ],
 )
@@ -361,11 +356,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
+        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -380,7 +374,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -394,11 +387,11 @@ py_test(
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform",
         "//tensorflow/python:training",
         "//tensorflow/python:util",
         "//tensorflow/python:variables",
+        "//tensorflow/python/estimator",
     ],
 )
 
@@ -414,9 +407,7 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:extra_py_tests_deps",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:resources",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:summary",
@@ -433,8 +424,8 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
+        "//tensorflow/contrib/training:training_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform",
     ],
 )
@@ -458,21 +449,21 @@ py_test(
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
+        "//tensorflow/python/estimator",
     ],
 )
 
 py_test(
     name = "run_config_test",
     size = "small",
-    srcs = [
-        "python/learn/estimators/run_config_test.py",
-    ],
+    srcs = ["python/learn/estimators/run_config_test.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
+        "//tensorflow/python/estimator:run_config",
     ],
 )
 
@@ -485,9 +476,8 @@ py_test(
         ":learn",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:sparse_tensor",
     ],
 )
 
@@ -506,14 +496,18 @@ py_test(
         "//tensorflow/contrib/metrics:metrics_py",
         "//tensorflow/contrib/testing:testing_py",
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:platform",
+        "//tensorflow/python:protos_all_py",
         "//tensorflow/python:training",
         "//tensorflow/python:util",
         "//tensorflow/python:variables",
@@ -559,7 +553,6 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
         "//third_party/py/numpy",
@@ -574,18 +567,17 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
-        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/contrib/metrics:metrics_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
+        "//tensorflow/python/feature_column",
         "//tensorflow/python/ops/losses",
         "//third_party/py/numpy",
     ],
@@ -598,12 +590,12 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:lookup_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:variables",
         "//tensorflow/python/ops/losses",
         "//third_party/py/numpy",
@@ -624,12 +616,12 @@ py_test(
         "//tensorflow/contrib/metrics:metrics_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
+        "//tensorflow/python/feature_column",
         "//third_party/py/numpy",
     ],
 )
@@ -655,9 +647,8 @@ py_test(
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
     ],
 )
@@ -669,14 +660,16 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
-        "//tensorflow/contrib/factorization:factorization_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:platform_benchmark",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
 )
@@ -700,9 +693,8 @@ py_test(
         ":learn",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -720,13 +712,14 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:functional_ops",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:random_seed",
+        "//tensorflow/python:rnn_cell",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
@@ -740,17 +733,15 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/rnn:rnn_py",
+        "//tensorflow/contrib/lookup:lookup_py",
         "//tensorflow/python:array_ops",
-        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:data_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:functional_ops",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
@@ -771,11 +762,11 @@ py_test(
         "//tensorflow/contrib/metrics:metrics_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
+        "//tensorflow/python/feature_column",
         "//third_party/py/numpy",
     ],
 )
@@ -813,9 +804,8 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
     ],
 )
@@ -829,9 +819,8 @@ py_test(
         ":learn",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:sparse_tensor",
     ],
 )
 
@@ -843,7 +832,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -867,9 +855,14 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
+        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
-        "//third_party/py/numpy",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python/estimator:export_output",
+        "//tensorflow/python/saved_model:signature_constants",
+        "@six_archive//:six",
     ],
 )
 
@@ -881,7 +874,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
     ],
 )
@@ -896,8 +888,7 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:random_seed",
     ],
 )
 
@@ -909,7 +900,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
     ],
 )
@@ -921,12 +911,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
-        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:math_ops",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -940,9 +928,8 @@ py_test(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:random_seed",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
@@ -955,11 +942,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
-        "//tensorflow/contrib/rnn:rnn_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:rnn_cell",
         "//third_party/py/numpy",
     ],
 )
@@ -972,7 +958,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
     ],
 )
@@ -985,7 +970,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -997,7 +981,6 @@ py_test(
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
     ],
 )
 
@@ -1015,6 +998,7 @@ py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:io_ops",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:training",
@@ -1031,7 +1015,6 @@ py_test(
         ":learn",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -1046,7 +1029,6 @@ py_test(
         ":learn",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -1066,12 +1048,11 @@ py_test(
         ":learn",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/session_bundle:exporter",
+        "//tensorflow/contrib/session_bundle:manifest_proto_py_pb2",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:training",
@@ -1091,6 +1072,7 @@ py_test(
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -1101,12 +1083,13 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":learn",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform",
+        "//tensorflow/python:util",
         "//tensorflow/python/saved_model:signature_constants",
         "//tensorflow/python/saved_model:signature_def_utils",
     ],
@@ -1124,9 +1107,7 @@ py_test(
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:training",
     ],
-- 
GitLab


From cc342cfb7a5a8cc174f1f84df0319161cddf48ea Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 7 Jul 2017 10:13:30 -0700
Subject: [PATCH 0804/1375] Internal-only changes.

PiperOrigin-RevId: 161214648
---
 third_party/boringssl/BUILD | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 third_party/boringssl/BUILD

diff --git a/third_party/boringssl/BUILD b/third_party/boringssl/BUILD
new file mode 100644
index 0000000000..e69de29bb2
-- 
GitLab


From ac1c5224a1dd0c3142ef06d76f6b62097210959e Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Fri, 7 Jul 2017 10:42:20 -0700
Subject: [PATCH 0805/1375] Automated g4 rollback of changelist 161203536

PiperOrigin-RevId: 161218103
---
 tensorflow/contrib/BUILD                      |  1 -
 tensorflow/contrib/__init__.py                |  1 -
 tensorflow/contrib/predictor/BUILD            |  3 +-
 tensorflow/contrib/predictor/__init__.py      |  6 ++--
 .../predictor/saved_model_predictor.py        | 32 +++----------------
 5 files changed, 9 insertions(+), 34 deletions(-)

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index c9065b0a89..7ada956124 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -51,7 +51,6 @@ py_library(
         "//tensorflow/contrib/ndlstm",
         "//tensorflow/contrib/nn:nn_py",
         "//tensorflow/contrib/opt:opt_py",
-        "//tensorflow/contrib/predictor",
         "//tensorflow/contrib/quantization:quantization_py",
         "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py",
         "//tensorflow/contrib/resampler:resampler_py",
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index 513e657a33..f70b810ec6 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -50,7 +50,6 @@ from tensorflow.contrib import metrics
 from tensorflow.contrib import nccl
 from tensorflow.contrib import nn
 from tensorflow.contrib import opt
-from tensorflow.contrib import predictor
 from tensorflow.contrib import quantization
 from tensorflow.contrib import resampler
 from tensorflow.contrib import rnn
diff --git a/tensorflow/contrib/predictor/BUILD b/tensorflow/contrib/predictor/BUILD
index e298fd3cb2..c4b46551c1 100644
--- a/tensorflow/contrib/predictor/BUILD
+++ b/tensorflow/contrib/predictor/BUILD
@@ -1,6 +1,6 @@
 # `Predictor` classes provide an interface for efficient, repeated inference.
 
-package(default_visibility = ["//tensorflow/contrib/predictor:__subpackages__"])
+package(default_visibility = ["//third_party/tensroflow/contrib/predictor:__subpackages__"])
 
 licenses(["notice"])  # Apache 2.0
 
@@ -62,6 +62,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":base_predictor",
+        "//tensorflow/python/tools:saved_model_cli",
     ],
 )
 
diff --git a/tensorflow/contrib/predictor/__init__.py b/tensorflow/contrib/predictor/__init__.py
index e0a2152b37..d270c3f798 100644
--- a/tensorflow/contrib/predictor/__init__.py
+++ b/tensorflow/contrib/predictor/__init__.py
@@ -19,6 +19,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.predictor.predictor_factories import from_contrib_estimator
-from tensorflow.contrib.predictor.predictor_factories import from_estimator
-from tensorflow.contrib.predictor.predictor_factories import from_saved_model
+from tensorflow.contrib.predictor import from_contrib_estimator
+from tensorflow.contrib.predictor import from_estimator
+from tensorflow.contrib.predictor import from_saved_model
diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py
index 0dbca0f813..ab2bafa0c8 100644
--- a/tensorflow/contrib/predictor/saved_model_predictor.py
+++ b/tensorflow/contrib/predictor/saved_model_predictor.py
@@ -22,12 +22,12 @@ from __future__ import print_function
 import logging
 
 from tensorflow.contrib.predictor import predictor
-from tensorflow.contrib.saved_model.python.saved_model import reader
 from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils
 from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.saved_model import loader
 from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.tools import saved_model_cli
 
 
 DEFAULT_TAGS = 'serve'
@@ -35,37 +35,13 @@ DEFAULT_TAGS = 'serve'
 _DEFAULT_INPUT_ALTERNATIVE_FORMAT = 'default_input_alternative:{}'
 
 
-def get_meta_graph_def(saved_model_dir, tags):
-  """Gets `MetaGraphDef` from a directory containing a `SavedModel`.
-
-  Returns the `MetaGraphDef` for the given tag-set and SavedModel directory.
-
-  Args:
-    saved_model_dir: Directory containing the SavedModel.
-    tags: Comma separated list of tags used to identify the correct
-      `MetaGraphDef`.
-
-  Raises:
-    ValueError: An error when the given tags cannot be found.
-
-  Returns:
-    A `MetaGraphDef` corresponding to the given tags.
-  """
-  saved_model = reader.read_saved_model(saved_model_dir)
-  set_of_tags = set([tag.strip() for tag in tags.split(',')])
-  for meta_graph_def in saved_model.meta_graphs:
-    if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
-      return meta_graph_def
-  raise ValueError('Could not find MetaGraphDef with tags {}'.format(tags))
-
-
 def _get_signature_def(signature_def_key, export_dir, tags):
   """Construct a `SignatureDef` proto."""
   signature_def_key = (
       signature_def_key or
       signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY)
 
-  metagraph_def = get_meta_graph_def(export_dir, tags)
+  metagraph_def = saved_model_cli.get_meta_graph_def(export_dir, tags)
 
   try:
     signature_def = signature_def_utils.get_signature_def_by_key(
@@ -138,8 +114,8 @@ class SavedModelPredictor(predictor.Predictor):
       output_names: A dictionary mapping strings to `Tensor`s in the
         `SavedModel` that represent the output. The keys can be any string of
         the user's choosing.
-      tags: Optional. Comma separated list of tags that will be used to retrieve
-        the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`.
+      tags: Optional. Tags that will be used to retrieve the correct
+        `SignatureDef`. Defaults to `DEFAULT_TAGS`.
       graph: Optional. The Tensorflow `graph` in which prediction should be
         done.
     Raises:
-- 
GitLab


From 26a6260dd5d9dce10567a1a9f48a93b63751d191 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 7 Jul 2017 11:07:34 -0700
Subject: [PATCH 0806/1375] TFTS: Structural ensemble test timeout adjustment
 for asan/tsan

PiperOrigin-RevId: 161221736
---
 .../timeseries/state_space_models/BUILD       | 20 ++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
index a7f5da56c7..ab6bccd164 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -1,5 +1,6 @@
 # State space components and ensembles
 load("//tensorflow:tensorflow.bzl", "tf_py_test")
+load("//tensorflow:tensorflow.bzl", "py_test")
 
 package(
     default_visibility = [
@@ -188,25 +189,26 @@ py_library(
     ],
 )
 
-tf_py_test(
+py_test(
     name = "structural_ensemble_test",
+    timeout = "long",  # Moderate but for asan/tsan timeouts
     srcs = [
         "structural_ensemble_test.py",
     ],
-    additional_deps = [
-        ":structural_ensemble",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
+    deps = [
         ":state_space_model",
-        "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
-        "//tensorflow/contrib/timeseries/python/timeseries:estimators",
-        "//third_party/py/numpy",
+        ":structural_ensemble",
         "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/contrib/timeseries/python/timeseries:estimators",
+        "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
-    ],
-    tags = [
-        "no_pip_gpu",  # b/63391119
+        "//third_party/py/numpy",
     ],
 )
 
-- 
GitLab


From b82b18ee5e532151cd65f7e5aea74ca73c0ccb18 Mon Sep 17 00:00:00 2001
From: Yann Henon <yannhenon@gmail.com>
Date: Fri, 7 Jul 2017 14:25:41 -0400
Subject: [PATCH 0807/1375] add h5py to dockerfile (#11360)

* add h5py to dockerfile

* alphabetical ordering of list

* Further alphabetical order fix

* Further alphabetical order fix.
---
 tensorflow/tools/docker/Dockerfile     | 5 +++--
 tensorflow/tools/docker/Dockerfile.gpu | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/tensorflow/tools/docker/Dockerfile b/tensorflow/tools/docker/Dockerfile
index 5b3f1f936a..07a972400d 100644
--- a/tensorflow/tools/docker/Dockerfile
+++ b/tensorflow/tools/docker/Dockerfile
@@ -24,14 +24,15 @@ RUN curl -O https://bootstrap.pypa.io/get-pip.py && \
     rm get-pip.py
 
 RUN pip --no-cache-dir install \
+        Pillow \
+        h5py \
         ipykernel \
         jupyter \
         matplotlib \
         numpy \
+        pandas \
         scipy \
         sklearn \
-        pandas \
-        Pillow \
         && \
     python -m ipykernel.kernelspec
 
diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu
index 3ba1e963f9..da83a30058 100644
--- a/tensorflow/tools/docker/Dockerfile.gpu
+++ b/tensorflow/tools/docker/Dockerfile.gpu
@@ -24,14 +24,15 @@ RUN curl -O https://bootstrap.pypa.io/get-pip.py && \
     rm get-pip.py
 
 RUN pip --no-cache-dir install \
+        Pillow \
+        h5py \
         ipykernel \
         jupyter \
         matplotlib \
         numpy \
+        pandas \
         scipy \
         sklearn \
-        pandas \
-        Pillow \
         && \
     python -m ipykernel.kernelspec
 
-- 
GitLab


From 07adc2ea910de715d31e16a019fcbcccb575e931 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 7 Jul 2017 11:39:44 -0700
Subject: [PATCH 0808/1375] Fixed initialization of variables: when a variable
 w is initialized with the value of another variable v, make sure that the
 initializer of v is run first.

PiperOrigin-RevId: 161226052
---
 .../python/ops/resource_variable_ops.py       |  4 +-
 tensorflow/python/ops/variables.py            | 90 ++++++++++++++++++-
 .../python/training/session_manager_test.py   | 90 +++++++++++++++++--
 3 files changed, 174 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index 465b73bda5..38c4dacef0 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -204,7 +204,9 @@ class ResourceVariable(variables.Variable):
         if initial_value is not None:
           with ops.name_scope("Assign") as n, ops.colocate_with(self._handle):
             self._initializer_op = gen_resource_variable_ops.assign_variable_op(
-                self._handle, self._initial_value, name=n)
+                self._handle,
+                self._build_initializer_expr(self._initial_value),
+                name=n)
         with ops.name_scope("Read"), ops.colocate_with(self._handle):
           # Manually assign reads to the handle's device to avoid log messages.
           with ops.device(self._handle.device):
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 190c823314..839b1ea882 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -311,9 +311,12 @@ class Variable(object):
             raise ValueError("initial_value must have a shape specified: %s" %
                              self._initial_value)
 
-        # Assigns initial value.
+        # If 'initial_value' makes use of other variables, make sure we don't
+        # have an issue if these other variables aren't initialized first by
+        # using their initialized_value() method.
         self._initializer_op = state_ops.assign(
-            self._variable, self._initial_value,
+            self._variable,
+            self._build_initializer_expr(self._initial_value),
             validate_shape=validate_shape).op
 
         # TODO(vrv): Change this class to not take caching_device, but
@@ -708,6 +711,89 @@ class Variable(object):
 
     setattr(Variable, operator, _run_op)
 
+  def _build_initializer_expr(self, initial_value):
+    """Build an expression suitable to initialize a variable.
+
+    Replace references to variables in initial_value with references to the
+    variable initial values instead.
+
+    Args:
+      initial_value: original expression
+    Returns:
+      A tensorflow expression suitable to initialize a variable.
+    """
+    if isinstance(initial_value, Variable):
+      return initial_value.initialized_value()
+    elif isinstance(initial_value, ops.Tensor):
+      new_op = self._build_initializer_expr(initial_value.op)
+      if new_op != initial_value.op:
+        if isinstance(new_op, ops.Tensor):
+          return new_op
+        else:
+          return ops.Tensor(new_op, initial_value.value_index,
+                            initial_value.dtype)
+      else:
+        return initial_value
+    elif isinstance(initial_value, ops.Operation):
+      if initial_value.node_def.op in [
+          "IsVariableInitialized", "ReadVariableOp"
+      ]:
+        return initial_value
+      if initial_value.node_def.op in ["Variable", "VariableV2", "VarHandleOp"]:
+        return self._find_initialized_value_for_variable(initial_value)
+      modified = False
+      new_inputs = []
+      for tensor in initial_value.inputs:
+        new_tensor = self._build_initializer_expr(tensor)
+        new_inputs.append(new_tensor)
+        if new_tensor != tensor:
+          modified = True
+
+      if modified:
+        new_name = initial_value.node_def.name + "_" + self.name
+        new_name = new_name.replace(":", "_")
+        new_op = initial_value.node_def.op
+        new_op = new_op.replace("RefSwitch", "Switch")
+        new_value = self.graph.create_op(
+            new_op,
+            new_inputs,
+            # pylint: disable=protected-access
+            initial_value._output_types,
+            # pylint: enable=protected-access
+            name=new_name,
+            attrs=initial_value.node_def.attr)
+        return new_value
+      else:
+        return initial_value
+    else:
+      return initial_value
+
+  def _find_initialized_value_for_variable(self, variable_op):
+    """Find the initial value for a variable op.
+
+    To do so, lookup the variable op in the variables collection.
+
+    Args:
+      variable_op: a TensorFlow variable Operation
+    Returns:
+      The initial value for the variable.
+    """
+    try:
+      var_names = [variable_op.node_def.name, variable_op.node_def.name + ":0"]
+      global_vars = self.graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+      for var in global_vars:
+        if var.name in var_names:
+          return var.initialized_value()
+      local_vars = self.graph.get_collection(ops.GraphKeys.LOCAL_VARIABLES)
+      for var in local_vars:
+        if var.name == var_names:
+          return var.initialized_value()
+    except AttributeError:
+      # Return the variable itself when an incomplete user defined variable type
+      # was put in the collection.
+      return variable_op
+    return variable_op
+
   # NOTE(mrry): This enables the Variable's overloaded "right" binary
   # operators to run when the left operand is an ndarray, because it
   # accords the Variable class higher priority than an ndarray, or a
diff --git a/tensorflow/python/training/session_manager_test.py b/tensorflow/python/training/session_manager_test.py
index 4dc1d5abb7..5879fd330a 100644
--- a/tensorflow/python/training/session_manager_test.py
+++ b/tensorflow/python/training/session_manager_test.py
@@ -448,8 +448,8 @@ class SessionManagerTest(test.TestCase):
           ready_op=variables.report_uninitialized_variables(),
           ready_for_local_init_op=None,
           local_init_op=w.initializer)
-    with self.assertRaisesRegexp(errors_impl.FailedPreconditionError,
-                                 "Attempting to use uninitialized value v"):
+    with self.assertRaisesRegexp(errors_impl.DeadlineExceededError,
+                                 "Session was not ready after waiting.*"):
       sm.wait_for_session("", max_wait_secs=3)
 
   def testPrepareSessionWithReadyForLocalInitOp(self):
@@ -460,14 +460,20 @@ class SessionManagerTest(test.TestCase):
           trainable=False,
           collections=[ops.GraphKeys.LOCAL_VARIABLES],
           name="w")
+      x = variables.Variable(
+          3 * v,
+          trainable=False,
+          collections=[ops.GraphKeys.LOCAL_VARIABLES],
+          name="x")
       with self.test_session():
         self.assertEqual(False, variables.is_variable_initialized(v).eval())
         self.assertEqual(False, variables.is_variable_initialized(w).eval())
+        self.assertEqual(False, variables.is_variable_initialized(x).eval())
       sm2 = session_manager.SessionManager(
           ready_op=variables.report_uninitialized_variables(),
           ready_for_local_init_op=variables.report_uninitialized_variables(
               variables.global_variables()),
-          local_init_op=w.initializer)
+          local_init_op=[w.initializer, x.initializer])
       sess = sm2.prepare_session("", init_op=v.initializer)
       self.assertEqual(
           True,
@@ -477,8 +483,78 @@ class SessionManagerTest(test.TestCase):
           True,
           variables.is_variable_initialized(
               sess.graph.get_tensor_by_name("w:0")).eval(session=sess))
+      self.assertEqual(
+          True,
+          variables.is_variable_initialized(
+              sess.graph.get_tensor_by_name("x:0")).eval(session=sess))
       self.assertEquals(1, sess.run(v))
       self.assertEquals(1, sess.run(w))
+      self.assertEquals(3, sess.run(x))
+
+  def testPrepareSessionWithPartialInitOp(self):
+    with ops.Graph().as_default():
+      v = variables.Variable(1, name="v")
+      w = variables.Variable(
+          v,
+          trainable=False,
+          collections=[ops.GraphKeys.LOCAL_VARIABLES],
+          name="w")
+      x = variables.Variable(
+          3 * v,
+          trainable=False,
+          collections=[ops.GraphKeys.LOCAL_VARIABLES],
+          name="x")
+      v_res = variables.Variable(1, name="v_res")
+      w_res = variables.Variable(
+          v_res,
+          trainable=False,
+          collections=[ops.GraphKeys.LOCAL_VARIABLES],
+          name="w_res")
+      x_res = variables.Variable(
+          3 * v_res,
+          trainable=False,
+          collections=[ops.GraphKeys.LOCAL_VARIABLES],
+          name="x_res")
+
+      with self.test_session():
+        self.assertEqual(False, variables.is_variable_initialized(v).eval())
+        self.assertEqual(False, variables.is_variable_initialized(w).eval())
+        self.assertEqual(False, variables.is_variable_initialized(x).eval())
+        self.assertEqual(False, variables.is_variable_initialized(v_res).eval())
+        self.assertEqual(False, variables.is_variable_initialized(w_res).eval())
+        self.assertEqual(False, variables.is_variable_initialized(x_res).eval())
+      sm2 = session_manager.SessionManager(local_init_op=[
+          w.initializer, x.initializer, w_res.initializer, x_res.initializer
+      ])
+      sess = sm2.prepare_session("", init_op=None)
+      self.assertEqual(
+          False,
+          variables.is_variable_initialized(
+              sess.graph.get_tensor_by_name("v:0")).eval(session=sess))
+      self.assertEqual(
+          True,
+          variables.is_variable_initialized(
+              sess.graph.get_tensor_by_name("w:0")).eval(session=sess))
+      self.assertEqual(
+          True,
+          variables.is_variable_initialized(
+              sess.graph.get_tensor_by_name("x:0")).eval(session=sess))
+      self.assertEquals(1, sess.run(w))
+      self.assertEquals(3, sess.run(x))
+      self.assertEqual(
+          False,
+          variables.is_variable_initialized(
+              sess.graph.get_tensor_by_name("v_res:0")).eval(session=sess))
+      self.assertEqual(
+          True,
+          variables.is_variable_initialized(
+              sess.graph.get_tensor_by_name("w_res:0")).eval(session=sess))
+      self.assertEqual(
+          True,
+          variables.is_variable_initialized(
+              sess.graph.get_tensor_by_name("x_res:0")).eval(session=sess))
+      self.assertEquals(1, sess.run(w_res))
+      self.assertEquals(3, sess.run(x_res))
 
   def testPrepareSessionDidNotInitLocalVariable(self):
     with ops.Graph().as_default():
@@ -493,8 +569,8 @@ class SessionManagerTest(test.TestCase):
         self.assertEqual(False, variables.is_variable_initialized(w).eval())
       sm2 = session_manager.SessionManager(
           ready_op=variables.report_uninitialized_variables())
-      with self.assertRaisesRegexp(RuntimeError,
-                                   "Init operations did not make model ready"):
+      with self.assertRaisesRegexp(
+          RuntimeError, "Init operations did not make model ready.*"):
         sm2.prepare_session("", init_op=v.initializer)
 
   def testPrepareSessionDidNotInitLocalVariableList(self):
@@ -550,8 +626,8 @@ class SessionManagerTest(test.TestCase):
           ready_op=variables.report_uninitialized_variables(),
           ready_for_local_init_op=None,
           local_init_op=w.initializer)
-    with self.assertRaisesRegexp(errors_impl.FailedPreconditionError,
-                                 "Attempting to use uninitialized value v"):
+    with self.assertRaisesRegexp(RuntimeError,
+                                 "Init operations did not make model ready.*"):
       sm2.prepare_session("", init_op=None)
 
 
-- 
GitLab


From 7a4c19ec2bdef3549aad11556fcf14fde6e3946c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 12:50:40 -0700
Subject: [PATCH 0809/1375] Updating tf/contrib/boosted_trees to support
 multiclass diagonal hessian strategy.

PiperOrigin-RevId: 161233946
---
 .../contrib/boosted_trees/python/ops/quantile_ops.py       | 5 +++++
 .../boosted_trees/python/ops/stats_accumulator_ops.py      | 7 ++++++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
index d8ab6cdbb3..fa62c9428b 100644
--- a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
+++ b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
@@ -16,6 +16,7 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+import re
 
 from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
 from tensorflow.contrib.boosted_trees.python.ops import gen_quantile_ops
@@ -33,6 +34,9 @@ from tensorflow.python.ops import resources
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.training import saver
 
+# Pattern to remove all non alpha numeric from a string.
+_PATTERN = re.compile(r"[\W_]+")
+
 
 class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
   """A resource that allows distributed quantile computation."""
@@ -54,6 +58,7 @@ class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
     """
     self._epsilon = epsilon
 
+    name = _PATTERN.sub("", name)
     with ops.name_scope(name, "QuantileAccumulator") as name:
       self._quantile_accumulator_handle = (
           gen_quantile_ops.quantile_stream_resource_handle_op(
diff --git a/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
index ea6c4275f1..c24f8b5173 100644
--- a/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
+++ b/tensorflow/contrib/boosted_trees/python/ops/stats_accumulator_ops.py
@@ -16,7 +16,7 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
+import re
 from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
 from tensorflow.contrib.boosted_trees.python.ops import gen_stats_accumulator_ops
 from tensorflow.contrib.util import loader
@@ -27,6 +27,9 @@ from tensorflow.python.ops import resources
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.training import saver
 
+# Pattern to remove all non alpha numeric from a string.
+_PATTERN = re.compile(r"[\W_]+")
+
 
 class StatsAccumulator(saver.BaseSaverBuilder.SaveableObject):
   """A resource that allows to accumulate gradients and hessians.
@@ -55,6 +58,8 @@ class StatsAccumulator(saver.BaseSaverBuilder.SaveableObject):
     Returns:
       A `Tensor` of type mutable `string`. The handle to the stats accumulator.
     """
+    if name is not None:
+      name = _PATTERN.sub("", name)
     with ops.name_scope(name, "StatsAccumulator") as name:
       # Both values are scalars.
       if (gradient_shape == tensor_shape.scalar() and
-- 
GitLab


From 6629e3a9863d25a81764f3d3115c1bfc2a7d8e67 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 7 Jul 2017 12:51:51 -0700
Subject: [PATCH 0810/1375] Enabled model pruning by default

PiperOrigin-RevId: 161234072
---
 .../grappler/optimizers/graph_rewriter.cc     | 74 ++++++++++++-------
 .../core/grappler/optimizers/graph_rewriter.h |  7 ++
 .../grappler/optimizers/meta_optimizer.cc     |  6 +-
 .../core/grappler/optimizers/model_pruner.cc  |  5 ++
 tensorflow/python/kernel_tests/BUILD          |  1 +
 5 files changed, 65 insertions(+), 28 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.cc b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
index c8b100abae..91222f1c80 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.cc
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
@@ -29,16 +29,7 @@ GraphRewriter::GraphRewriter(const GrapplerItem& item) {
   }
 
   for (auto& node : item.graph.node()) {
-    for (const auto& input : node.input()) {
-      int position = 0;
-      string input_node_name = ParseNodeName(input, &position);
-      if (position < 0) {
-        // This is a control edge
-        auto itr = nodes_.find(input_node_name);
-        CHECK(itr != nodes_.end());
-        control_dependency_drivers_.insert(itr->second);
-      }
-    }
+    RecordControlDependencyDrivers(node);
   }
 }
 
@@ -46,21 +37,9 @@ void GraphRewriter::ForwardInputs(
     const NodeDef& original_node,
     const std::unordered_set<const NodeDef*>& nodes_to_delete,
     NodeDef* new_node) {
-  for (const auto& input : original_node.input()) {
-    string input_node_name = NodeName(input);
-    auto itr = nodes_.find(input_node_name);
-    if (itr == nodes_.end()) {
-      // Invalid input, preserve it as is.
-      *new_node->add_input() = input;
-    }
-    const NodeDef* input_node = itr->second;
-    if ((input_node->device().empty() || original_node.device().empty() ||
-         input_node->device() == original_node.device()) &&
-        nodes_to_delete.find(input_node) != nodes_to_delete.end()) {
-      ForwardInputs(*input_node, nodes_to_delete, new_node);
-    } else {
-      *new_node->add_input() = input;
-    }
+  ForwardInputsInternal(original_node, nodes_to_delete, new_node);
+  if (!new_node->name().empty()) {
+    optimized_nodes_[new_node->name()] = new_node;
   }
 }
 
@@ -79,5 +58,50 @@ bool GraphRewriter::IsDrivenByControlDependency(const NodeDef& node) const {
   return false;
 }
 
+void GraphRewriter::RecordControlDependencyDrivers(const NodeDef& node) {
+  for (const auto& input : node.input()) {
+    int position = 0;
+    string input_node_name = ParseNodeName(input, &position);
+    if (position < 0) {
+      // This is a control edge
+      auto itr = nodes_.find(input_node_name);
+      CHECK(itr != nodes_.end());
+      control_dependency_drivers_.insert(itr->second);
+    }
+  }
+}
+
+void GraphRewriter::ForwardInputsInternal(
+    const NodeDef& node,
+    const std::unordered_set<const NodeDef*>& nodes_to_delete,
+    NodeDef* new_node) {
+  // To speed things up, use the optimized version of the node if
+  // available.
+  auto itr = optimized_nodes_.find(node.name());
+  if (itr != optimized_nodes_.end()) {
+    for (const string& input : itr->second->input()) {
+      *new_node->add_input() = input;
+    }
+    return;
+  }
+  for (const auto& input : node.input()) {
+    string input_node_name = NodeName(input);
+    auto itr = nodes_.find(input_node_name);
+    if (itr == nodes_.end()) {
+      // Invalid input, preserve it as is.
+      *new_node->add_input() = input;
+      continue;
+    }
+    const NodeDef* input_node = itr->second;
+    if ((input_node->device().empty() || node.device().empty() ||
+         input_node->device() == node.device()) &&
+        nodes_to_delete.find(input_node) != nodes_to_delete.end()) {
+      ForwardInputsInternal(*input_node, nodes_to_delete, new_node);
+    } else {
+      *new_node->add_input() = input;
+    }
+  }
+}
+
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.h b/tensorflow/core/grappler/optimizers/graph_rewriter.h
index adbe5a24c8..874e461831 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.h
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.h
@@ -48,7 +48,14 @@ class GraphRewriter {
   bool IsDrivenByControlDependency(const NodeDef& node) const;
 
  private:
+  void RecordControlDependencyDrivers(const NodeDef& node);
+  void ForwardInputsInternal(
+      const NodeDef& original_node,
+      const std::unordered_set<const NodeDef*>& nodes_to_delete,
+      NodeDef* new_node);
+
   std::unordered_map<string, const NodeDef*> nodes_;
+  std::unordered_map<string, const NodeDef*> optimized_nodes_;
   std::unordered_set<const NodeDef*> control_dependency_drivers_;
 };
 
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 4007c8802b..f094b86ca7 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -120,9 +120,9 @@ void MetaOptimizer::Feedback(Cluster* cluster, const GrapplerItem& item,
 }
 
 bool MetaOptimizerEnabled(const RewriterConfig& cfg) {
-  return cfg.optimize_tensor_layout() || cfg.constant_folding() ||
-         cfg.auto_parallel().enable() || cfg.memory_optimization() > 0 ||
-         !cfg.optimizers().empty();
+  return !cfg.disable_model_pruning() || cfg.optimize_tensor_layout() ||
+         cfg.constant_folding() || cfg.auto_parallel().enable() ||
+         cfg.memory_optimization() > 0 || !cfg.optimizers().empty();
 }
 
 Status RunMetaOptimizer(const GrapplerItem& item, const RewriterConfig& cfg,
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index caf521d979..dd634a3255 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -63,6 +63,11 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     }
   }
 
+  if (nodes_to_delete.empty()) {
+    *pruned_graph = item.graph;
+    return Status::OK();
+  }
+
   for (auto& node : item.graph.node()) {
     NodeDef* new_node = pruned_graph->add_node();
     *new_node = node;
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 2c206f330b..dc52cedb80 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -56,6 +56,7 @@ tf_py_test(
         "//tensorflow/python:errors",
         "//tensorflow/python:framework_for_generated_wrappers",
     ],
+    shard_count = 20,
 )
 
 tf_py_test(
-- 
GitLab


From 50de7fa55d4f179e66f7daeae8354f264969928a Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 7 Jul 2017 13:04:50 -0700
Subject: [PATCH 0811/1375] TFTS: Add srcs_version to structural_ensemble_test

Switched it to py_test for timeout=, but forgot to add PY2AND3.

PiperOrigin-RevId: 161235480
---
 .../timeseries/python/timeseries/state_space_models/BUILD        | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
index ab6bccd164..7c0c07972f 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -195,6 +195,7 @@ py_test(
     srcs = [
         "structural_ensemble_test.py",
     ],
+    srcs_version = "PY2AND3",
     tags = [
         "no_pip_gpu",  # b/63391119
     ],
-- 
GitLab


From 996605b0e4ef96e6732f7496abf44b6e5e1eb504 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Fri, 7 Jul 2017 13:49:18 -0700
Subject: [PATCH 0812/1375] PiperOrigin-RevId: 161240586

---
 tensorflow/core/common_runtime/gpu/gpu_device.cc | 1 +
 tensorflow/core/framework/node_def_util_test.cc  | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index 991f81c5fd..c4ee937249 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -41,6 +41,7 @@ limitations under the License.
 #include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/graph/types.h"
 #include "tensorflow/core/lib/core/errors.h"
diff --git a/tensorflow/core/framework/node_def_util_test.cc b/tensorflow/core/framework/node_def_util_test.cc
index b60709c5a4..bfd598a972 100644
--- a/tensorflow/core/framework/node_def_util_test.cc
+++ b/tensorflow/core/framework/node_def_util_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/node_def_util.h"
 
+#include "tensorflow/core/framework/attr_value.pb.h"  // NOLINT
 #include "tensorflow/core/framework/fake_input.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/op_def_builder.h"
-- 
GitLab


From b1b7d5930ecdc9412e7a3035bdd2be49e9cfc230 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 14:03:48 -0700
Subject: [PATCH 0813/1375] Add a tag constant, gpu, to present graph with GPU
 support.

PiperOrigin-RevId: 161242660
---
 .../saved_model/python/saved_model/reader_test.py  | 11 +++++++++--
 tensorflow/python/saved_model/saved_model_test.py  | 14 ++++++++++++++
 tensorflow/python/saved_model/tag_constants.py     |  5 ++++-
 .../tensorflow.saved_model.tag_constants.pbtxt     |  4 ++++
 4 files changed, 31 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/saved_model/python/saved_model/reader_test.py b/tensorflow/contrib/saved_model/python/saved_model/reader_test.py
index 76d5a3e96d..a8331cbc8f 100644
--- a/tensorflow/contrib/saved_model/python/saved_model/reader_test.py
+++ b/tensorflow/contrib/saved_model/python/saved_model/reader_test.py
@@ -81,16 +81,23 @@ class ReaderTest(test.TestCase):
 
     # Graph that updates the single variable. SavedModel is invoked:
     # - to add the model (weights are not updated).
-    # - multiple custom tags.
+    # - multiple predefined tags.
     with self.test_session(graph=ops.Graph()) as sess:
       self._init_and_validate_variable(sess, "v", 44)
+      builder.add_meta_graph([tag_constants.SERVING, tag_constants.GPU])
+
+    # Graph that updates the single variable. SavedModel is invoked:
+    # - to add the model (weights are not updated).
+    # - multiple custom tags.
+    with self.test_session(graph=ops.Graph()) as sess:
+      self._init_and_validate_variable(sess, "v", 45)
       builder.add_meta_graph(["foo", "bar"])
 
     # Save the SavedModel to disk.
     builder.save()
 
     actual_tags = reader.get_saved_model_tag_sets(saved_model_dir)
-    expected_tags = [["train"], ["serve"], ["foo", "bar"]]
+    expected_tags = [["train"], ["serve"], ["serve", "gpu"], ["foo", "bar"]]
     self.assertEqual(expected_tags, actual_tags)
 
 
diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py
index fcd6bc3954..5639e6855d 100644
--- a/tensorflow/python/saved_model/saved_model_test.py
+++ b/tensorflow/python/saved_model/saved_model_test.py
@@ -207,6 +207,13 @@ class SavedModelTest(test.TestCase):
       self._init_and_validate_variable(sess, "v", 43)
       builder.add_meta_graph([tag_constants.SERVING])
 
+    # Graph that updates the single variable. SavedModel invoked to:
+    # - simply add the model (weights are not updated).
+    # - multiple tags (from predefined constants).
+    with self.test_session(graph=ops.Graph()) as sess:
+      self._init_and_validate_variable(sess, "v", 45)
+      builder.add_meta_graph([tag_constants.SERVING, tag_constants.GPU])
+
     # Graph that updates the single variable. SavedModel is invoked:
     # - to add the model (weights are not updated).
     # - multiple custom tags.
@@ -230,6 +237,13 @@ class SavedModelTest(test.TestCase):
       self.assertEqual(
           42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())
 
+    # Restore the graph with multiple predefined tags whose variables were not
+    # saved.
+    with self.test_session(graph=ops.Graph()) as sess:
+      loader.load(sess, [tag_constants.SERVING, tag_constants.GPU], export_dir)
+      self.assertEqual(
+          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())
+
     # Restore the graph with multiple tags. Provide duplicate tags to test set
     # semantics.
     with self.test_session(graph=ops.Graph()) as sess:
diff --git a/tensorflow/python/saved_model/tag_constants.py b/tensorflow/python/saved_model/tag_constants.py
index 4fb9645dea..52868bdf99 100644
--- a/tensorflow/python/saved_model/tag_constants.py
+++ b/tensorflow/python/saved_model/tag_constants.py
@@ -28,9 +28,12 @@ SERVING = "serve"
 # Tag for the `training` graph.
 TRAINING = "train"
 
+# Tag for the `gpu` graph.
+GPU = "gpu"
 
 _allowed_symbols = [
     "SERVING",
-    "TRAINING"
+    "TRAINING",
+    "GPU"
 ]
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.tag_constants.pbtxt b/tensorflow/tools/api/golden/tensorflow.saved_model.tag_constants.pbtxt
index 7c24b7ad3c..35e49ee9f4 100644
--- a/tensorflow/tools/api/golden/tensorflow.saved_model.tag_constants.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.saved_model.tag_constants.pbtxt
@@ -1,5 +1,9 @@
 path: "tensorflow.saved_model.tag_constants"
 tf_module {
+  member {
+    name: "GPU"
+    mtype: "<type \'str\'>"
+  }
   member {
     name: "SERVING"
     mtype: "<type \'str\'>"
-- 
GitLab


From e729dd303a80db6fb628a2e63f15aa12c28c956b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 14:20:57 -0700
Subject: [PATCH 0814/1375] Added missing square on the L2 term in the python
 doc for the FTRL optimizer.

PiperOrigin-RevId: 161245173
---
 tensorflow/python/training/ftrl.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/training/ftrl.py b/tensorflow/python/training/ftrl.py
index 81679cd724..c64a1b3f79 100644
--- a/tensorflow/python/training/ftrl.py
+++ b/tensorflow/python/training/ftrl.py
@@ -66,10 +66,16 @@ class FtrlOptimizer(optimizer.Optimizer):
       l2_shrinkage_regularization_strength: A float value, must be greater than
         or equal to zero. This differs from L2 above in that the L2 above is a
         stabilization penalty, whereas this L2 shrinkage is a magnitude penalty.
-        Specifically, the FTRL formulation can be written as:
-        w_{t+1} = argmin_w(\hat{g}_{1:t}w + L1*||w||_1 + L2*||w||_2), where
+        The FTRL formulation can be written as:
+        w_{t+1} = argmin_w(\hat{g}_{1:t}w + L1*||w||_1 + L2*||w||_2^2), where
         \hat{g} = g + (2*L2_shrinkage*w), and g is the gradient of the loss
         function w.r.t. the weights w.
+        Specifically, in the absence of L1 regularization, it is equivalent to
+        the following update rule:
+        w_{t+1} = w_t - lr_t / (1 + 2*L2*lr_t) * g_t -
+                  2*L2_shrinkage*lr_t / (1 + 2*L2*lr_t) * w_t
+        where lr_t is the learning rate at t.
+        When input is sparse shrinkage will only happen on the active weights.
 
     Raises:
       ValueError: If one of the arguments is invalid.
-- 
GitLab


From 204c367ab1a38cee71dac2a64164e96abaeffbf2 Mon Sep 17 00:00:00 2001
From: Igor Saprykin <isaprykin@google.com>
Date: Fri, 7 Jul 2017 14:33:47 -0700
Subject: [PATCH 0815/1375] Fix KeyError when looking for 'softmax_tensor' in
 the layers tutorial.

PiperOrigin-RevId: 161247019
---
 tensorflow/docs_src/tutorials/layers.md       | 27 ++++++++++---------
 .../examples/tutorials/layers/cnn_mnist.py    | 15 ++++++-----
 2 files changed, 22 insertions(+), 20 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index 37df19a8af..acf33afe6d 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -157,13 +157,15 @@ def cnn_model_fn(features, labels, mode):
   # Logits Layer
   logits = tf.layers.dense(inputs=dropout, units=10)
 
-  # Generate Predictions (for PREDICT mode)
-  predicted_classes = tf.argmax(input=logits, axis=1)
+  predictions = {
+      # Generate predictions (for PREDICT and EVAL mode)
+      "classes": tf.argmax(input=logits, axis=1),
+      # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
+      # `logging_hook`.
+      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+  }
+
   if mode == tf.estimator.ModeKeys.PREDICT:
-    predictions = {
-        "classes": predicted_classes,
-        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
-    }
     return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
   # Calculate Loss (for both TRAIN and EVAL modes)
@@ -182,7 +184,7 @@ def cnn_model_fn(features, labels, mode):
   # Add evaluation metrics (for EVAL mode)
   eval_metric_ops = {
       "accuracy": tf.metrics.accuracy(
-          labels=labels, predictions=predicted_classes)}
+          labels=labels, predictions=predictions["classes"])}
   return tf.estimator.EstimatorSpec(
       mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 ```
@@ -451,12 +453,11 @@ tf.nn.softmax(logits, name="softmax_tensor")
 We compile our predictions in a dict, and return an `EstimatorSpec` object:
 
 ```python
-predicted_classes = tf.argmax(input=logits, axis=1)
+predictions = {
+    "classes": tf.argmax(input=logits, axis=1),
+    "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+}
 if mode == tf.estimator.ModeKeys.PREDICT:
-  predictions = {
-      "classes": predicted_classes,
-      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
-  }
   return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 ```
 
@@ -547,7 +548,7 @@ mode as follows:
 ```python
 eval_metric_ops = {
     "accuracy": tf.metrics.accuracy(
-        labels=labels, predictions=predicted_classes)}
+        labels=labels, predictions=predictions["classes"])}
 return tf.estimator.EstimatorSpec(
     mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 ```
diff --git a/tensorflow/examples/tutorials/layers/cnn_mnist.py b/tensorflow/examples/tutorials/layers/cnn_mnist.py
index f92277dac7..2124843fcb 100644
--- a/tensorflow/examples/tutorials/layers/cnn_mnist.py
+++ b/tensorflow/examples/tutorials/layers/cnn_mnist.py
@@ -86,13 +86,14 @@ def cnn_model_fn(features, labels, mode):
   # Output Tensor Shape: [batch_size, 10]
   logits = tf.layers.dense(inputs=dropout, units=10)
 
-  # Generate Predictions (for PREDICT mode)
-  predicted_classes = tf.argmax(input=logits, axis=1)
+  predictions = {
+      # Generate predictions (for PREDICT and EVAL mode)
+      "classes": tf.argmax(input=logits, axis=1),
+      # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
+      # `logging_hook`.
+      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+  }
   if mode == tf.estimator.ModeKeys.PREDICT:
-    predictions = {
-        "classes": predicted_classes,
-        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
-    }
     return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
   # Calculate Loss (for both TRAIN and EVAL modes)
@@ -111,7 +112,7 @@ def cnn_model_fn(features, labels, mode):
   # Add evaluation metrics (for EVAL mode)
   eval_metric_ops = {
       "accuracy": tf.metrics.accuracy(
-          labels=labels, predictions=predicted_classes)}
+          labels=labels, predictions=predictions["classes"])}
   return tf.estimator.EstimatorSpec(
       mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
-- 
GitLab


From e9bea40511b1fe5d2e1e7761f640943d0e17a7df Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Fri, 7 Jul 2017 15:19:40 -0700
Subject: [PATCH 0816/1375] Refactor Keras Sequence utility. Correct Keras
 version number.

PiperOrigin-RevId: 161252947
---
 .../contrib/keras/python/keras/__init__.py    |  2 +-
 .../keras/python/keras/utils/data_utils.py    | 33 +------------------
 2 files changed, 2 insertions(+), 33 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/__init__.py b/tensorflow/contrib/keras/python/keras/__init__.py
index 6e0e03d7f7..19380bc8c5 100644
--- a/tensorflow/contrib/keras/python/keras/__init__.py
+++ b/tensorflow/contrib/keras/python/keras/__init__.py
@@ -37,4 +37,4 @@ from tensorflow.contrib.keras.python.keras import utils
 from tensorflow.contrib.keras.python.keras import wrappers
 from tensorflow.contrib.keras.python.keras.layers import Input
 
-__version__ = '2.0.5-tf'
+__version__ = '2.0.6-tf'
diff --git a/tensorflow/contrib/keras/python/keras/utils/data_utils.py b/tensorflow/contrib/keras/python/keras/utils/data_utils.py
index 9aa477d522..853625e7c4 100644
--- a/tensorflow/contrib/keras/python/keras/utils/data_utils.py
+++ b/tensorflow/contrib/keras/python/keras/utils/data_utils.py
@@ -20,7 +20,6 @@ from __future__ import print_function
 from abc import abstractmethod
 import hashlib
 import multiprocessing
-import multiprocessing.managers
 from multiprocessing.pool import ThreadPool
 import os
 import random
@@ -315,34 +314,6 @@ def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535):
     return False
 
 
-class HolderManager(multiprocessing.managers.BaseManager):
-  """Custom manager to share a Holder object."""
-  pass
-
-
-class Holder(object):
-  """Object to encapsulate a Sequence.
-
-  This allows the Sequence to be shared across multiple workers.
-
-  Arguments:
-      seq: Sequence object to be shared.
-  """
-
-  def __init__(self, seq):
-    self.seq = seq
-
-  def __getitem__(self, idx):
-    return self.seq[idx]
-
-  def __len__(self):
-    return len(self.seq)
-
-
-# Register the Holder class using the ListProxy (allows __len__ and __getitem__)
-HolderManager.register('Holder', Holder, multiprocessing.managers.ListProxy)
-
-
 class Sequence(object):
   """Base object for fitting to a sequence of data, such as a dataset.
 
@@ -488,9 +459,7 @@ class OrderedEnqueuer(SequenceEnqueuer):
                sequence,
                use_multiprocessing=False,
                scheduling='sequential'):
-    self.manager = HolderManager()
-    self.manager.start()
-    self.sequence = self.manager.Holder(sequence)
+    self.sequence = sequence
     self.use_multiprocessing = use_multiprocessing
     self.scheduling = scheduling
     self.workers = 0
-- 
GitLab


From f887930d8cdeada252b1a7aa277ce9c9c624cf82 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 15:35:03 -0700
Subject: [PATCH 0817/1375] Improving TF documentation for the convolution
 operation with padding scheme SAME.

This CL introduces
- a new expression for the total padding which is much easier to parse than the previous one
- a new section which derives this expression, explaining why it is used.

PiperOrigin-RevId: 161254603
---
 tensorflow/docs_src/api_guides/python/nn.md | 151 ++++++++++++++++++--
 1 file changed, 138 insertions(+), 13 deletions(-)

diff --git a/tensorflow/docs_src/api_guides/python/nn.md b/tensorflow/docs_src/api_guides/python/nn.md
index 44a2696e5c..4f188372a0 100644
--- a/tensorflow/docs_src/api_guides/python/nn.md
+++ b/tensorflow/docs_src/api_guides/python/nn.md
@@ -47,28 +47,39 @@ strided according to the `strides` argument.  `strides = [1, 1, 1, 1]` applies
 the filter to a patch at every offset, `strides = [1, 2, 2, 1]` applies the
 filter to every other image patch in each dimension, etc.
 
-Ignoring channels for the moment, and assume that the 4-D `input` has shape
+Ignoring channels for the moment, assume that the 4-D `input` has shape
 `[batch, in_height, in_width, ...]` and the 4-D `filter` has shape
-`[filter_height, filter_width, ...]`, then the spatial semantics of the
-convolution ops are as follows: first, according to the padding scheme chosen
-as `'SAME'` or `'VALID'`, the output size and the padding pixels are computed.
-For the `'SAME'` padding, the output height and width are computed as:
+`[filter_height, filter_width, ...]`. The spatial semantics of the
+convolution ops depend on the padding scheme chosen: `'SAME'` or `'VALID'`.
+Note that the padding values are always zero.
+
+First, consider the `'SAME'` padding scheme. A detailed explanation of the
+reasoning behind it is given in
+[these notes](#Notes_on_SAME_Convolution_Padding). Here, we summarize the
+mechanics of this padding scheme. When using `'SAME'`, the output height and
+width are computed as:
 
     out_height = ceil(float(in_height) / float(strides[1]))
     out_width  = ceil(float(in_width) / float(strides[2]))
 
-and the padding on the top and left are computed as:
+The total padding applied along the height and width is computed as:
+
+    if (in_height % strides[1] == 0):
+      pad_along_height = max(filter_height - strides[1], 0)
+    else:
+      pad_along_height = max(filter_height - (in_height % strides[1]), 0)
+    if (in_width % strides[2] == 0):
+      pad_along_width = max(filter_width - strides[2], 0)
+    else:
+      pad_along_width = max(filter_width - (in_width % strides[2]), 0)
+    
+Finally, the padding on the top, bottom, left and right are:
 
-    pad_along_height = max((out_height - 1) * strides[1] +
-                        filter_height - in_height, 0)
-    pad_along_width = max((out_width - 1) * strides[2] +
-                       filter_width - in_width, 0)
     pad_top = pad_along_height // 2
     pad_bottom = pad_along_height - pad_top
     pad_left = pad_along_width // 2
     pad_right = pad_along_width - pad_left
 
-
 Note that the division by 2 means that there might be cases when the padding on
 both sides (top vs bottom, right vs left) are off by one. In this case, the
 bottom and right sides always get the one additional padded pixel. For example,
@@ -77,12 +88,14 @@ bottom. Note that this is different from existing libraries such as cuDNN and
 Caffe, which explicitly specify the number of padded pixels and always pad the
 same number of pixels on both sides.
 
-For the `'VALID`' padding, the output height and width are computed as:
+For the `'VALID`' scheme, the output height and width are computed as:
 
     out_height = ceil(float(in_height - filter_height + 1) / float(strides[1]))
     out_width  = ceil(float(in_width - filter_width + 1) / float(strides[2]))
 
-and the padding values are always zero. The output is then computed as
+and no padding is used.
+
+Given the output size and the padding, the output can be computed as
 
     output[b, i, j, :] =
         sum_{di, dj} input[b, strides[1] * i + di - pad_top,
@@ -288,3 +301,115 @@ classes when using one of the sampled loss functions above.
 *   @{tf.nn.quantized_relu_x}
 *   @{tf.nn.quantized_max_pool}
 *   @{tf.nn.quantized_avg_pool}
+
+## Notes on SAME Convolution Padding
+
+In these notes, we provide more background on the use of the `'SAME'` padding
+scheme for convolution operations.
+
+Tensorflow uses the smallest possible padding to achieve the desired output
+size. To understand what is done, consider the \\(1\\)-dimensional case. Denote
+\\(n_i\\) and \\(n_o\\) the input and output sizes, respectively, and denote the
+kernel size \\(k\\) and stride \\(s\\). As discussed in the
+[Convolution section](#Convolution), for `'SAME'`,
+\\(n_o = \left \lceil{\frac{n_i}{s}}\right \rceil\\).
+
+To achieve a desired output size \\(n_o\\), we need to pad the input such that the
+output size after a `'VALID'` convolution is \\(n_o\\). In other words, we need to
+have padding \\(p_i\\) such that:
+
+\begin{equation}
+\left \lceil{\frac{n_i + p_i - k + 1}{s}}\right \rceil = n_o
+\label{eq:tf_pad_1}
+\end{equation}
+
+What is the smallest \\(p_i\\) that we could possibly use? In general, \\(\left
+\lceil{\frac{x}{a}}\right \rceil = b\\) (with \\(a > 0\\)) means that \\(b-1 <
+\frac{x}{a} \leq b\\), and the smallest integer \\(x\\) we can choose to satisfy
+this is \\(x = a\cdot (b-1) + 1\\). The same applies to our problem; we need
+\\(p_i\\) such that:
+
+\begin{equation}
+n_i + p_i - k + 1 = s\cdot (n_o - 1) + 1
+\label{eq:tf_pad_2}
+\end{equation}
+
+which leads to:
+
+\begin{equation}
+p_i = s\cdot (n_o - 1) + k - n_i
+\label{eq:tf_pad_3}
+\end{equation}
+
+Note that this might lead to negative \\(p_i\\), since in some cases we might
+already have more input samples than we actually need. Thus,
+
+\begin{equation}
+p_i = max(s\cdot (n_o - 1) + k - n_i, 0)
+\label{eq:tf_pad_4}
+\end{equation}
+
+Remember that, for `'SAME'` padding,
+\\(n_o = \left \lceil{\frac{n_i}{s}}\right \rceil\\), as mentioned above. 
+We need to analyze in detail two cases:
+
+- \\(n_i \text{ mod } s = 0\\)
+
+In this simple case, \\(n_o = \frac{n_i}{s}\\), and the expression for \\(p_i\\)
+becomes:
+
+\begin{equation}
+p_i = max(k - s, 0)
+\label{eq:tf_pad_5}
+\end{equation}
+
+- \\(n_i \text{ mod } s \neq 0\\)
+
+This case is more involved to parse. First, we write:
+
+\begin{equation}
+n_i = s\cdot\left \lceil{\frac{n_i}{s}}\right \rceil
+- s \left(\left \lceil{\frac{n_i}{s}}\right \rceil -
+          \left \lfloor{\frac{n_i}{s}}\right \rfloor\right)
++ (n_i \text{ mod } s)
+\label{eq:tf_pad_6}
+\end{equation}
+
+For the case where \\((n_i \text{ mod } s) \neq 0\\), we have \\(\left
+\lceil{\frac{n_i}{s}}\right \rceil -\left \lfloor{\frac{n_i}{s}}\right \rfloor =
+1\\), leading to:
+
+\begin{equation}
+n_i = s\cdot\left \lceil{\frac{n_i}{s}}\right \rceil
+- s
++ (n_i \text{ mod } s)
+\label{eq:tf_pad_7}
+\end{equation}
+
+We can use this expression to substitute \\(n_o = \left
+\lceil{\frac{n_i}{s}}\right \rceil\\) and get:
+
+$$\begin{align}
+p_i &= max\left(s\cdot \left(\frac{n_i + s - (n_i \text{ mod } s)}{s}
+  - 1\right) + k - n_i, 0\right) \nonumber\\
+&= max(n_i + s - (n_i \text{ mod } s) - s + k - n_i,0) \nonumber \\
+&= max(k - (n_i \text{ mod } s),0)
+\label{eq:tf_pad_8}
+\end{align}$$
+
+### Final expression
+
+Putting all together, the total padding used by tensorflow's convolution with
+`'SAME'` mode is:
+
+$$\begin{align}
+p_i =
+ \begin{cases}
+ max(k - s, 0),  & \text{if $(n_i \text{ mod } s) = 0$} \\
+ max(k - (n_i \text{ mod } s),0), & \text{if $(n_i \text{ mod } s) \neq 0$}
+ \end{cases}
+ \label{eq:tf_pad_9}
+\end{align}$$
+
+This expression is exactly equal to the ones presented for `pad_along_height`
+and `pad_along_width` in the [Convolution section](#Convolution).
-- 
GitLab


From 8d2cd4173720795638a4ecf98de012fa27f399c6 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Fri, 7 Jul 2017 15:35:16 -0700
Subject: [PATCH 0818/1375] Add a README to the TPU contrib directory.

PiperOrigin-RevId: 161254616
---
 tensorflow/contrib/tpu/README.md | 44 ++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)
 create mode 100644 tensorflow/contrib/tpu/README.md

diff --git a/tensorflow/contrib/tpu/README.md b/tensorflow/contrib/tpu/README.md
new file mode 100644
index 0000000000..69fb11e344
--- /dev/null
+++ b/tensorflow/contrib/tpu/README.md
@@ -0,0 +1,44 @@
+# TPU support for TensorFlow #
+
+This directory contains code required to re-target a TensorFlow model to run
+on TPUs.
+
+## Example usage - TPU Estimator
+
+Below shows example usage of the TPU Estimator for a simple convolutional
+network.
+
+```python
+import tensorflow as tf
+
+from tensorflow.contrib.tpu.python.tpu import tpu_config
+from tensorflow.contrib.tpu.python.tpu import tpu_estimator
+from tensorflow.contrib.tpu.python.tpu import tpu_optimizer
+
+def model_fn(features, labels, mode, params):
+  # Define the model to construct the logits
+  logits = # ...
+  loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
+  optimizer = tpu_optimizer.CrossShardOptimizer(
+    tf.train.GradientDescentOptimizer(learning_rate=FLAGS.learning_rate))
+  train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
+  return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
+
+def input_fn(params):
+  # ...
+  pass
+
+def main():
+  run_config = tpu_config.RunConfig(
+    master=FLAGS.master,
+    # ...
+  )
+  estimator = tpu_estimator.TpuEstimator(
+    model_fn=model_fn,
+    use_tpu=FLAGS.use_tpu,
+    config=run_config,
+    batch_size=FLAGS.batch_size)
+  estimator.train(input_fn=input_fn, max_steps=FLAGS.train_steps)
+```
+
+For the complete [executable] example, see our open source TPU models.
-- 
GitLab


From 827a47fc1a2638b13470f063f520f72be66ce8c3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 15:38:54 -0700
Subject: [PATCH 0819/1375] [tf.contrib.data] Add `ConcatenateDataset` to
 concatenate two datasets.

This change also adds helper class BinaryDatasetOpKernel, that simplifies parsing two input
datasets.

RELNOTES: Add `ConcatenateDataset` to concatenate two datasets.
PiperOrigin-RevId: 161254998
---
 .../contrib/data/python/kernel_tests/BUILD    |  15 ++
 .../concatenate_dataset_op_test.py            | 134 +++++++++++++++++
 .../contrib/data/python/ops/dataset_ops.py    |  65 +++++++++
 tensorflow/core/kernels/BUILD                 |  13 ++
 .../core/kernels/concatenate_dataset_op.cc    | 135 ++++++++++++++++++
 tensorflow/core/kernels/dataset.cc            |  13 ++
 tensorflow/core/kernels/dataset.h             |  13 ++
 tensorflow/core/ops/dataset_ops.cc            |  11 ++
 8 files changed, 399 insertions(+)
 create mode 100644 tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py
 create mode 100644 tensorflow/core/kernels/concatenate_dataset_op.cc

diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index 9909ea41c9..655c6f23dd 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -240,6 +240,21 @@ py_test(
     ],
 )
 
+py_test(
+    name = "concatenate_dataset_op_test",
+    size = "small",
+    srcs = ["concatenate_dataset_op_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/data",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py
new file mode 100644
index 0000000000..3407a06a98
--- /dev/null
+++ b/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py
@@ -0,0 +1,134 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the experimental input pipeline ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.data.python.ops import dataset_ops
+from tensorflow.contrib.data.python.util import nest
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.platform import test
+
+
+class ConcatenateDatasetTest(test.TestCase):
+
+  def testConcatenateDataset(self):
+    input_components = (
+        np.tile(np.array([[1], [2], [3], [4]]), 20),
+        np.tile(np.array([[12], [13], [14], [15]]), 15),
+        np.array([37.0, 38.0, 39.0, 40.0]))
+    to_concatenate_components = (
+        np.tile(np.array([[1], [2], [3], [4], [5]]), 20),
+        np.tile(np.array([[12], [13], [14], [15], [16]]), 15),
+        np.array([37.0, 38.0, 39.0, 40.0, 41.0]))
+
+    input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components)
+    dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices(
+        to_concatenate_components)
+    concatenated = input_dataset.concatenate(dataset_to_concatenate)
+    self.assertEqual(concatenated.output_shapes, (tensor_shape.TensorShape(
+        [20]), tensor_shape.TensorShape([15]), tensor_shape.TensorShape([])))
+
+    iterator = concatenated.make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for i in range(9):
+        result = sess.run(get_next)
+        if i < 4:
+          for component, result_component in zip(input_components, result):
+            self.assertAllEqual(component[i], result_component)
+        else:
+          for component, result_component in zip(to_concatenate_components,
+                                                 result):
+            self.assertAllEqual(component[i - 4], result_component)
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+  def testConcatenateDatasetDifferentShape(self):
+    input_components = (
+        np.tile(np.array([[1], [2], [3], [4]]), 20),
+        np.tile(np.array([[12], [13], [14], [15]]), 4))
+    to_concatenate_components = (
+        np.tile(np.array([[1], [2], [3], [4], [5]]), 20),
+        np.tile(np.array([[12], [13], [14], [15], [16]]), 15))
+
+    input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components)
+    dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices(
+        to_concatenate_components)
+    concatenated = input_dataset.concatenate(dataset_to_concatenate)
+    self.assertEqual(
+        [ts.as_list()
+         for ts in nest.flatten(concatenated.output_shapes)], [[20], [None]])
+
+    iterator = concatenated.make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for i in range(9):
+        result = sess.run(get_next)
+        if i < 4:
+          for component, result_component in zip(input_components, result):
+            self.assertAllEqual(component[i], result_component)
+        else:
+          for component, result_component in zip(to_concatenate_components,
+                                                 result):
+            self.assertAllEqual(component[i - 4], result_component)
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+  def testConcatenateDatasetDifferentStructure(self):
+    input_components = (
+        np.tile(np.array([[1], [2], [3], [4]]), 5),
+        np.tile(np.array([[12], [13], [14], [15]]), 4))
+    to_concatenate_components = (
+        np.tile(np.array([[1], [2], [3], [4], [5]]), 20),
+        np.tile(np.array([[12], [13], [14], [15], [16]]), 15),
+        np.array([37.0, 38.0, 39.0, 40.0, 41.0]))
+
+    input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components)
+    dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices(
+        to_concatenate_components)
+
+    with self.assertRaisesRegexp(ValueError,
+                                 "don't have the same number of elements"):
+      input_dataset.concatenate(dataset_to_concatenate)
+
+  def testConcatenateDatasetDifferentType(self):
+    input_components = (
+        np.tile(np.array([[1], [2], [3], [4]]), 5),
+        np.tile(np.array([[12], [13], [14], [15]]), 4))
+    to_concatenate_components = (
+        np.tile(np.array([[1.0], [2.0], [3.0], [4.0]]), 5),
+        np.tile(np.array([[12], [13], [14], [15]]), 15))
+
+    input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components)
+    dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices(
+        to_concatenate_components)
+
+    with self.assertRaisesRegexp(TypeError, "have different types"):
+      input_dataset.concatenate(dataset_to_concatenate)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index 5e43aab04a..a99bd5fe58 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -554,6 +554,32 @@ class Dataset(object):
     """
     return ZipDataset(datasets)
 
+  def concatenate(self, dataset):
+    """Creates a `Dataset` by concatenating given dataset with this dataset.
+
+    ```python
+    # NOTE: The following examples use `{ ... }` to represent the
+    # contents of a dataset.
+    a = { 1, 2, 3 }
+    b = { 4, 5, 6, 7 }
+
+    # Input dataset and dataset to be concatenated should have same
+    # nested structures and output types.
+    # c = { (8, 9), (10, 11), (12, 13) }
+    # d = { 14.0, 15.0, 16.0 }
+    # a.concatenate(c) and a.concatenate(d) would result in error.
+
+    a.concatenate(b) == { 1, 2, 3, 4, 5, 6, 7 }
+    ```
+
+    Args:
+      dataset: `Dataset` to be concatenated.
+
+    Returns:
+      A `Dataset`.
+    """
+    return ConcatenateDataset(self, dataset)
+
   @staticmethod
   def read_batch_features(file_pattern,
                           batch_size,
@@ -1038,6 +1064,45 @@ class ZipDataset(Dataset):
     ])
 
 
+class ConcatenateDataset(Dataset):
+  """A `Dataset` that concatenates its input with given dataset."""
+
+  def __init__(self, input_dataset, dataset_to_concatenate):
+    """See `Dataset.concatenate()` for details."""
+    super(ConcatenateDataset, self).__init__()
+    self._input_dataset = input_dataset
+    self._dataset_to_concatenate = dataset_to_concatenate
+    nest.assert_same_structure(input_dataset.output_types,
+                               dataset_to_concatenate.output_types)
+    for a, b in zip(
+        nest.flatten(input_dataset.output_types),
+        nest.flatten(dataset_to_concatenate.output_types)):
+      if a != b:
+        raise TypeError(
+            "Two datasets to concatenate have different types %s and %s" %
+            (input_dataset.output_types, dataset_to_concatenate.output_types))
+
+  def make_dataset_resource(self):
+    return gen_dataset_ops.concatenate_dataset(
+        self._input_dataset.make_dataset_resource(),
+        self._dataset_to_concatenate.make_dataset_resource(),
+        output_shapes=nest.flatten(self.output_shapes),
+        output_types=nest.flatten(self.output_types))
+
+  @property
+  def output_shapes(self):
+    return nest.pack_sequence_as(self._input_dataset.output_shapes, [
+        ts1.most_specific_compatible_shape(ts2)
+        for (ts1, ts2) in zip(
+            nest.flatten(self._input_dataset.output_shapes),
+            nest.flatten(self._dataset_to_concatenate.output_shapes))
+    ])
+
+  @property
+  def output_types(self):
+    return self._input_dataset.output_types
+
+
 class RepeatDataset(Dataset):
   """A `Dataset` that repeats its input several times."""
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 9924cbb49d..3ab61b71b8 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -5520,6 +5520,18 @@ tf_kernel_library(
     ],
 )
 
+tf_kernel_library(
+    name = "concatenate_dataset_op",
+    srcs = ["concatenate_dataset_op.cc"],
+    deps = [
+        ":dataset",
+        "//tensorflow/core:dataset_ops_op_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
 tf_kernel_library(
     name = "reader_dataset_ops",
     srcs = ["reader_dataset_ops.cc"],
@@ -5564,6 +5576,7 @@ tf_kernel_library(
     deps = [
         ":batch_dataset_op",
         ":cache_dataset_ops",
+        ":concatenate_dataset_op",
         ":dense_to_sparse_batch_dataset_op",
         ":filter_dataset_op",
         ":flat_map_dataset_op",
diff --git a/tensorflow/core/kernels/concatenate_dataset_op.cc b/tensorflow/core/kernels/concatenate_dataset_op.cc
new file mode 100644
index 0000000000..0a206e3653
--- /dev/null
+++ b/tensorflow/core/kernels/concatenate_dataset_op.cc
@@ -0,0 +1,135 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/kernels/dataset.h"
+
+#include "tensorflow/core/framework/partial_tensor_shape.h"
+#include "tensorflow/core/framework/tensor.h"
+
+namespace tensorflow {
+
+namespace {
+
+// See documentation in ../ops/dataset_ops.cc for a high-level
+// description of the following op.
+
+class ConcatenateDatasetOp : public BinaryDatasetOpKernel {
+ public:
+  explicit ConcatenateDatasetOp(OpKernelConstruction* ctx)
+      : BinaryDatasetOpKernel(ctx) {}
+  void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
+                   DatasetBase* to_concatenate, DatasetBase** output) override {
+    OP_REQUIRES(ctx, input->output_dtypes() == to_concatenate->output_dtypes(),
+                errors::InvalidArgument(
+                    "input dataset and dataset to concatenate"
+                    " have different output_types %s and %s",
+                    (DataTypeVectorString(input->output_dtypes()),
+                     DataTypeVectorString(to_concatenate->output_dtypes()))));
+    *output = new Dataset(input, to_concatenate);
+  }
+
+ private:
+  class Dataset : public DatasetBase {
+   public:
+    explicit Dataset(const DatasetBase* input,
+                     const DatasetBase* to_concatenate)
+        : input_(input), to_concatenate_(to_concatenate) {
+      input_->Ref();
+      to_concatenate_->Ref();
+
+      auto os_input = input->output_shapes();
+      auto os_concatenate = to_concatenate->output_shapes();
+      for (int i = 0; i < os_input.size(); i++) {
+        output_shapes_.push_back(
+            MostSpecificCompatibleShape(os_input[i], os_concatenate[i]));
+      }
+    }
+    ~Dataset() override {
+      input_->Unref();
+      to_concatenate_->Unref();
+    }
+
+    std::unique_ptr<IteratorBase> MakeIterator() const override {
+      return std::unique_ptr<IteratorBase>(new Iterator(this));
+    }
+
+    const DataTypeVector& output_dtypes() const override {
+      return input_->output_dtypes();
+    }
+    const std::vector<PartialTensorShape>& output_shapes() const override {
+      return output_shapes_;
+    }
+
+    string DebugString() override { return "ConcatenateDatasetOp::Dataset"; }
+
+   private:
+    class Iterator : public DatasetIterator<Dataset> {
+     public:
+      explicit Iterator(const Dataset* dataset)
+          : DatasetIterator<Dataset>(dataset),
+            i_(0),
+            input_impl_(dataset->input_->MakeIterator()) {}
+
+      Status GetNext(IteratorContext* ctx, std::vector<Tensor>* out_tensors,
+                     bool* end_of_sequence) override {
+        mutex_lock l(mu_);
+        while (i_ < 2) {
+          TF_RETURN_IF_ERROR(
+              input_impl_->GetNext(ctx, out_tensors, end_of_sequence));
+          if (!*end_of_sequence) {
+            return Status::OK();
+          }
+          if (++i_ < 2) {
+            input_impl_ = dataset()->to_concatenate_->MakeIterator();
+          }
+        }
+        *end_of_sequence = true;
+        input_impl_.reset();
+        return Status::OK();
+      }
+
+     private:
+      mutex mu_;
+      int64 i_ GUARDED_BY(mu_);
+      std::unique_ptr<IteratorBase> input_impl_ GUARDED_BY(mu_);
+    };
+
+    static PartialTensorShape MostSpecificCompatibleShape(
+        const PartialTensorShape& ts1, const PartialTensorShape& ts2) {
+      PartialTensorShape output_tensorshape;
+      if (ts1.dims() != ts2.dims() || ts1.unknown_rank() || ts2.unknown_rank())
+        return output_tensorshape;
+      auto dims1 = ts1.dim_sizes();
+      auto dims2 = ts2.dim_sizes();
+      for (int d = 0; d < ts1.dims(); d++) {
+        if (dims1[d] == dims2[d])
+          output_tensorshape.Concatenate(dims1[d]);
+        else
+          output_tensorshape.Concatenate(-1);
+      }
+      return output_tensorshape;
+    }
+
+    const DatasetBase* input_;
+    const DatasetBase* to_concatenate_;
+    std::vector<PartialTensorShape> output_shapes_;
+  };
+};
+
+REGISTER_KERNEL_BUILDER(Name("ConcatenateDataset").Device(DEVICE_CPU),
+                        ConcatenateDatasetOp);
+
+}  // namespace
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/dataset.cc b/tensorflow/core/kernels/dataset.cc
index 925cbda56e..f99684b1ca 100644
--- a/tensorflow/core/kernels/dataset.cc
+++ b/tensorflow/core/kernels/dataset.cc
@@ -39,4 +39,17 @@ void UnaryDatasetOpKernel::MakeDataset(OpKernelContext* ctx,
   MakeDataset(ctx, input, output);
 }
 
+void BinaryDatasetOpKernel::MakeDataset(OpKernelContext* ctx,
+                                        DatasetBase** output) {
+  DatasetBase* input;
+  DatasetBase* another_input;
+  OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &input));
+  OP_REQUIRES_OK(ctx,
+                 LookupResource(ctx, HandleFromInput(ctx, 1), &another_input));
+  core::ScopedUnref unref_input(input);
+  core::ScopedUnref unref_another_input(another_input);
+
+  MakeDataset(ctx, input, another_input, output);
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/dataset.h b/tensorflow/core/kernels/dataset.h
index da56844dbe..b4d69657d8 100644
--- a/tensorflow/core/kernels/dataset.h
+++ b/tensorflow/core/kernels/dataset.h
@@ -199,6 +199,19 @@ class UnaryDatasetOpKernel : public DatasetOpKernel {
                            DatasetBase** output) = 0;
 };
 
+// Encapsulates the work required to plug binary Datasets into the core
+// TensorFlow graph execution engine.
+class BinaryDatasetOpKernel : public DatasetOpKernel {
+ public:
+  BinaryDatasetOpKernel(OpKernelConstruction* ctx) : DatasetOpKernel(ctx) {}
+
+ protected:
+  void MakeDataset(OpKernelContext* ctx, DatasetBase** output) final;
+  virtual void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
+                           DatasetBase* another_input,
+                           DatasetBase** output) = 0;
+};
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_DATASET_H_
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index 8b87429705..a490a30aaa 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -75,6 +75,17 @@ REGISTER_OP("ZipDataset")
 Creates a dataset that zips together `input_datasets`.
 )doc");
 
+REGISTER_OP("ConcatenateDataset")
+    .Input("input_dataset: resource")
+    .Input("another_dataset: resource")
+    .Output("handle: resource")
+    .Attr("output_types: list(type) >= 1")
+    .Attr("output_shapes: list(shape) >= 1")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Creates a dataset that concatenates `input_dataset` with `another_dataset`.
+)doc");
+
 REGISTER_OP("RepeatDataset")
     .Input("input_dataset: resource")
     .Input("count: int64")
-- 
GitLab


From 8d31d4d909ef854e53d2cce5ff201e2bc6bc87a2 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Fri, 7 Jul 2017 15:42:08 -0700
Subject: [PATCH 0820/1375] Double MinSystemMemory for non-opt builds.  This
 allows more complicated kernels (i.e., CUB reduces) a more GPU memory to
 launch.  Prior to this, calling WhereOp with the BFC allocator in non-opt
 mode led to "out of memory" error when launching the kernel.

PiperOrigin-RevId: 161255358
---
 .../core/common_runtime/gpu/gpu_device.cc      | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index c4ee937249..082e33b713 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -605,14 +605,28 @@ int64 MinSystemMemory(int64 available_memory) {
   // Otherwise, allocate max(300MiB, 0.05 * available_memory) to system memory.
   //
   // In the future we could be more sophisticated by using a table of devices.
+  int64 min_system_memory;
   if (available_memory < (1LL << 31)) {
     // 225MiB
-    return 225 * 1024 * 1024;
+    min_system_memory = 225 * 1024 * 1024;
   } else {
     // max(300 MiB, 0.05 * available_memory)
-    return std::max(314572800LL, static_cast<int64>(available_memory * 0.05));
+    min_system_memory =
+        std::max(314572800LL, static_cast<int64>(available_memory * 0.05));
   }
+#if defined(__GNUC__) && defined(__OPTIMIZE__)
+// Do nothing
+#elif !defined(__GNUC__) && defined(NDEBUG)
+// Do nothing
+#else
+  // Double the amount of available GPU memory in non-opt builds (debug
+  // builds in windows); because in non-opt builds more system memory
+  // is necessary.
+  min_system_memory *= 2;
+#endif
+  return min_system_memory;
 }
+
 }  // namespace
 
 static string GetShortDeviceDescription(int device_id,
-- 
GitLab


From 3234c6246d7cabb0227cee223f381ad323849b78 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 16:05:12 -0700
Subject: [PATCH 0821/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161257957
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 28 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 29 +++++++++++++++++++
 2 files changed, 57 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 4c2af8bd46..91b9a9a7ee 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -4673,6 +4673,34 @@ op {
     }
   }
 }
+op {
+  name: "ConcatenateDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "another_dataset"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  is_stateful: true
+}
 op {
   name: "ConditionalAccumulator"
   output_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 765eb763be..3a853da189 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -4561,6 +4561,35 @@ op {
   }
   summary: "Concatenates tensors along one dimension."
 }
+op {
+  name: "ConcatenateDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "another_dataset"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  summary: "Creates a dataset that concatenates `input_dataset` with `another_dataset`."
+  is_stateful: true
+}
 op {
   name: "ConditionalAccumulator"
   output_arg {
-- 
GitLab


From d9c4732a1dd7466c5c98e09ed034876c45724f8b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 16:12:37 -0700
Subject: [PATCH 0822/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161258773
---
 tensorflow/go/op/wrappers.go | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 40c3a6d1b5..b70c398b2d 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -5747,6 +5747,23 @@ func IgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []t
 	return op.Output(0)
 }
 
+// Creates a dataset that concatenates `input_dataset` with `another_dataset`.
+func ConcatenateDataset(scope *Scope, input_dataset tf.Output, another_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+	opspec := tf.OpSpec{
+		Type: "ConcatenateDataset",
+		Input: []tf.Input{
+			input_dataset, another_dataset,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Creates a dataset that splits a SparseTensor into elements row-wise.
 func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (handle tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From 305712c02e70bc860812e7c151a3842f028cacb1 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Fri, 7 Jul 2017 16:35:46 -0700
Subject: [PATCH 0823/1375] More WhereOp/TopK GPU bugfixes: use the direct cuda
 stream for CUB GPU kernel.

Turns out using the StreamInterface objects leads to "invalid resource handle"
errors, so we have to use the cudaStream_t directly.  This change is based on
similar code in cuda_solvers.cc.

PiperOrigin-RevId: 161261085
---
 .../core/kernels/concat_lib_gpu_impl.cu.cc    | 31 ++++++++++---------
 tensorflow/core/kernels/split_lib_gpu.cu.cc   | 27 ++++++++--------
 tensorflow/core/kernels/topk_op_gpu.cu.cc     | 17 +++++-----
 tensorflow/core/kernels/where_op_gpu.cu.cc    | 13 +++++---
 tensorflow/core/util/cuda_kernel_helper.h     | 25 ++++++++++++---
 5 files changed, 67 insertions(+), 46 deletions(-)

diff --git a/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc b/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
index 3ed6241b7a..d120c69c4f 100644
--- a/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
+++ b/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
@@ -88,7 +88,8 @@ __global__ void concat_variable_kernel(
   // do an initial binary search and then scan linearly from there
   // works well when there are many small segments and when the
   // segments are much longer
-  IntType segment = gpu::upper_bound<IntType>(col_scan, num_inputs, gidx) - 1;
+  IntType segment =
+      cuda_helper::upper_bound<IntType>(col_scan, num_inputs, gidx) - 1;
 
   IntType curr_offset = col_scan[segment];
   IntType curr_segment = segment;
@@ -142,10 +143,10 @@ void ConcatGPUImpl(const Eigen::GpuDevice& gpu_device,
                                       output->dimension(0), gpu_device);
 
   if (fixed_size) {
-    concat_fixed_kernel<T, IntType><<<
-        config.block_count, config.thread_per_block, 0, gpu_device.stream()>>>(
-        input_ptrs, split_size, output->dimension(0), output->dimension(1),
-        output->data());
+    concat_fixed_kernel<T, IntType>
+        <<<config.block_count, config.thread_per_block, 0,
+           gpu_device.stream()>>>(input_ptrs, split_size, output->dimension(0),
+                                  output->dimension(1), output->data());
   } else {
     IntType smem_max = gpu_device.sharedMemPerBlock();
     IntType smem_usage = output_scan.size * sizeof(IntType);
@@ -155,17 +156,17 @@ void ConcatGPUImpl(const Eigen::GpuDevice& gpu_device,
     // 4096 inputs is a lot, most code will take the smem path
     const int32 kMaxSmemBytesPerformance = 16384;
     if (smem_usage < smem_max && smem_usage < kMaxSmemBytesPerformance)
-      concat_variable_kernel<
-          T, IntType, true><<<config.block_count, config.thread_per_block,
-                              smem_usage, gpu_device.stream()>>>(
-          input_ptrs, output_scan, output->dimension(0), output->dimension(1),
-          output->data());
+      concat_variable_kernel<T, IntType, true>
+          <<<config.block_count, config.thread_per_block, smem_usage,
+             gpu_device.stream()>>>(input_ptrs, output_scan,
+                                    output->dimension(0), output->dimension(1),
+                                    output->data());
     else
-      concat_variable_kernel<
-          T, IntType, false><<<config.block_count, config.thread_per_block, 0,
-                               gpu_device.stream()>>>(
-          input_ptrs, output_scan, output->dimension(0), output->dimension(1),
-          output->data());
+      concat_variable_kernel<T, IntType, false>
+          <<<config.block_count, config.thread_per_block, 0,
+             gpu_device.stream()>>>(input_ptrs, output_scan,
+                                    output->dimension(0), output->dimension(1),
+                                    output->data());
   }
 }
 
diff --git a/tensorflow/core/kernels/split_lib_gpu.cu.cc b/tensorflow/core/kernels/split_lib_gpu.cu.cc
index 3c0b5d113b..dd6fc6115f 100644
--- a/tensorflow/core/kernels/split_lib_gpu.cu.cc
+++ b/tensorflow/core/kernels/split_lib_gpu.cu.cc
@@ -138,7 +138,8 @@ __global__ void split_v_kernel(const T* input_ptr,
   // do an initial binary search and then scan linearly from there
   // works well when there are many small segments and when the
   // segments are much longer
-  IntType segment = gpu::upper_bound<IntType>(col_scan, num_outputs, gidx) - 1;
+  IntType segment =
+      cuda_helper::upper_bound<IntType>(col_scan, num_outputs, gidx) - 1;
 
   IntType curr_offset = col_scan[segment];
   IntType curr_segment = segment;
@@ -195,10 +196,10 @@ struct SplitOpGPULaunch {
     CudaLaunchConfig config = GetCudaLaunchConfig(
         prefix_dim_size * split_dim_size * suffix_dim_size, d);
 
-    SplitOpKernel<
-        T><<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
-        input, prefix_dim_size, split_dim_size, suffix_dim_size,
-        output_ptr_data);
+    SplitOpKernel<T>
+        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+            input, prefix_dim_size, split_dim_size, suffix_dim_size,
+            output_ptr_data);
   }
 };
 
@@ -224,15 +225,15 @@ struct SplitVOpGPULaunch {
       // 4096 inputs is a lot, most code will take the smem path
       const int32 kMaxSmemBytesPerformance = 16384;
       if (smem_usage < smem_max && smem_usage < kMaxSmemBytesPerformance)
-        split_v_kernel<T, IntType,
-                       true><<<config.block_count, config.thread_per_block,
-                               smem_usage, gpu_device.stream()>>>(
-            input_ptr, output_scan, total_rows, total_cols, output_ptr_data);
+        split_v_kernel<T, IntType, true>
+            <<<config.block_count, config.thread_per_block, smem_usage,
+               gpu_device.stream()>>>(input_ptr, output_scan, total_rows,
+                                      total_cols, output_ptr_data);
       else
-        split_v_kernel<T, IntType,
-                       false><<<config.block_count, config.thread_per_block, 0,
-                                gpu_device.stream()>>>(
-            input_ptr, output_scan, total_rows, total_cols, output_ptr_data);
+        split_v_kernel<T, IntType, false>
+            <<<config.block_count, config.thread_per_block, 0,
+               gpu_device.stream()>>>(input_ptr, output_scan, total_rows,
+                                      total_cols, output_ptr_data);
     }
   }
 };
diff --git a/tensorflow/core/kernels/topk_op_gpu.cu.cc b/tensorflow/core/kernels/topk_op_gpu.cu.cc
index e4b4a3cb49..4b3f5ccacc 100644
--- a/tensorflow/core/kernels/topk_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/topk_op_gpu.cu.cc
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/top_n.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
 
 // Required for sorting Eigen::half
 namespace cub {
@@ -365,9 +366,9 @@ __global__ void TopKKernel(const T* input, int length, int k, bool sorted,
 }
 
 template <typename T>
-cudaError LaunchTopKKernel(cudaStream_t stream, int num_shards, const T* input,
-                           int batch_size, int length, int k, bool sorted,
-                           T* output, int* indices) {
+cudaError LaunchTopKKernel(const cudaStream_t& stream, int num_shards,
+                           const T* input, int batch_size, int length, int k,
+                           bool sorted, T* output, int* indices) {
   // This code assumes that k is small enough that the computation
   // fits inside shared memory (hard coded to 48KB).  In practice this
   // means k <= 3072 for T=float/int32 and k <= 2048 for T=double/int64.
@@ -428,7 +429,7 @@ Status LaunchSortKernel(OpKernelContext* ctx, const T* input, int num_rows,
                         typename TTypes<T, 2>::Tensor values,
                         TTypes<int, 2>::Tensor indices) {
   const GPUDevice& d = ctx->eigen_device<GPUDevice>();
-  auto stream = ctx->eigen_gpu_device().stream();
+  const cudaStream_t& cu_stream = GetCudaStream(ctx);
   size_t temp_storage_bytes = -1;
 
   // TODO(ebrevdo): Once cub supports iterators for the ValueT and
@@ -480,7 +481,7 @@ Status LaunchSortKernel(OpKernelContext* ctx, const T* input, int num_rows,
       /* d_end_offsets */ segment_offsets_t.data() + 1,
       /* begin_bit */ 0,
       /* end_bit */ sizeof(T) * 8,
-      /* stream */ stream);
+      /* stream */ cu_stream);
   if (err != cudaSuccess) {
     return errors::Internal(
         "TopKOp: Could not launch "
@@ -505,7 +506,7 @@ Status LaunchSortKernel(OpKernelContext* ctx, const T* input, int num_rows,
       /* d_end_offsets */ segment_offsets_t.data() + 1,
       /* begin_bit */ 0,
       /* end_bit */ sizeof(T) * 8,
-      /* stream */ stream);
+      /* stream */ cu_stream);
   if (err != cudaSuccess) {
     return errors::Internal(
         "TopKOp: Could not launch "
@@ -545,8 +546,8 @@ struct TopKFunctor<GPUDevice, T> {
       return impl::LaunchSortKernel(context, input.data(), num_rows, num_cols,
                                     k, values, indices);
     } else {
-      auto stream = context->eigen_gpu_device().stream();
-      auto err = impl::LaunchTopKKernel(stream, /* num_shards */ 0,
+      const cudaStream_t& cu_stream = GetCudaStream(context);
+      auto err = impl::LaunchTopKKernel(cu_stream, /* num_shards */ 0,
                                         input.data(), num_rows, num_cols, k,
                                         sorted, values.data(), indices.data());
       if (err != cudaSuccess) {
diff --git a/tensorflow/core/kernels/where_op_gpu.cu.cc b/tensorflow/core/kernels/where_op_gpu.cu.cc
index ca147cc21f..c7c54ccbb4 100644
--- a/tensorflow/core/kernels/where_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/where_op_gpu.cu.cc
@@ -56,8 +56,9 @@ struct NumTrue<GPUDevice, TIndex> {
   EIGEN_ALWAYS_INLINE static Status Compute(
       OpKernelContext* ctx, const GPUDevice& d, TTypes<bool>::ConstFlat input,
       typename TTypes<TIndex>::Scalar num_true) {
-    std::size_t temp_storage_bytes = 0;
+    const cudaStream_t& cu_stream = GetCudaStream(ctx);
 
+    std::size_t temp_storage_bytes = 0;
     const bool* input_data = input.data();
     TIndex* num_true_data = num_true.data();
 
@@ -66,7 +67,7 @@ struct NumTrue<GPUDevice, TIndex> {
                                /*d_in*/ input_data,
                                /*d_out*/ num_true_data,
                                /*num_items*/ input.size(),
-                               /*stream*/ d.stream());
+                               /*stream*/ cu_stream);
 
     if (first_success != cudaSuccess) {
       return errors::Internal(
@@ -85,7 +86,7 @@ struct NumTrue<GPUDevice, TIndex> {
         /*d_in*/ input_data,
         /*d_out*/ num_true_data,
         /*num_items*/ input.size(),
-        /*stream*/ d.stream());
+        /*stream*/ cu_stream);
 
     if (second_success != cudaSuccess) {
       return errors::Internal(
@@ -168,6 +169,8 @@ struct Where<GPUDevice, NDIM, Tindex> {
       return Status::OK();
     }
 
+    const cudaStream_t& cu_stream = GetCudaStream(ctx);
+
     std::size_t temp_storage_bytes = 0;
 
     cub::CountingInputIterator<Tindex> select_counter(0);
@@ -188,7 +191,7 @@ struct Where<GPUDevice, NDIM, Tindex> {
                                    /*d_out*/ output_iterator,
                                    /*d_num_selected_out*/ found_true_device,
                                    /*num_items*/ input.size(),
-                                   /*stream*/ d.stream());
+                                   /*stream*/ cu_stream);
     if (first_success != cudaSuccess) {
       return errors::Internal(
           "WhereOp: Could not launch cub::DeviceSelect::Flagged to calculate "
@@ -208,7 +211,7 @@ struct Where<GPUDevice, NDIM, Tindex> {
         /*d_out*/ output_iterator,
         /*d_num_selected_out*/ found_true_device,
         /*num_items*/ input.size(),
-        /*stream*/ d.stream());
+        /*stream*/ cu_stream);
 
     if (second_success != cudaSuccess) {
       return errors::Internal(
diff --git a/tensorflow/core/util/cuda_kernel_helper.h b/tensorflow/core/util/cuda_kernel_helper.h
index c86c6e4a5d..ee65194405 100644
--- a/tensorflow/core/util/cuda_kernel_helper.h
+++ b/tensorflow/core/util/cuda_kernel_helper.h
@@ -20,9 +20,11 @@ limitations under the License.
 
 #include <algorithm>
 
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor.h"
 #include "tensorflow/core/platform/types.h"
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 
 // Usage of GetCudaLaunchConfig, GetCuda2DLaunchConfig, and
 // GetCuda3DLaunchConfig:
@@ -95,7 +97,8 @@ void MyDriverFunc(const GPUDevice &d) {
 }
 
 // See the test for this for more example:
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
+//
+https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/cuda_kernel_helper_test.cu.cc
 
 */
 
@@ -107,7 +110,7 @@ void MyDriverFunc(const GPUDevice &d) {
   for (int i = blockIdx.axis * blockDim.axis + threadIdx.axis; i < n.axis; \
        i += blockDim.axis * gridDim.axis)
 
-#define DIV_UP(a, b) (((a) + (b) - 1) / (b))
+#define DIV_UP(a, b) (((a) + (b)-1) / (b))
 
 namespace tensorflow {
 
@@ -277,7 +280,19 @@ inline Cuda2DLaunchConfig GetCuda2DLaunchConfig(
                                dynamic_shared_memory_size, block_size_limit);
 }
 
-namespace gpu {
+// Returns a raw reference to the current cuda stream.  Required by a
+// number of kernel calls (for which StreamInterface* does not work), i.e.
+// CUB and certain cublas primitives.
+inline const cudaStream_t& GetCudaStream(OpKernelContext* context) {
+  const cudaStream_t* ptr = CHECK_NOTNULL(
+      reinterpret_cast<const cudaStream_t*>(context->op_device_context()
+                                                ->stream()
+                                                ->implementation()
+                                                ->CudaStreamMemberHack()));
+  return *ptr;
+}
+
+namespace cuda_helper {
 
 template <typename IntType>
 __device__ IntType upper_bound(IntType* first, IntType count, IntType val) {
@@ -299,7 +314,7 @@ __device__ IntType upper_bound(IntType* first, IntType count, IntType val) {
   return first - orig;
 }
 
-}  // namespace gpu
+}  // namespace cuda_helper
 
 template <typename T>
 __device__ __host__ inline T ldg(const T* address) {
-- 
GitLab


From dc07426d5c25e7bf87f53b6536e74d8ee01ca2e8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 17:22:46 -0700
Subject: [PATCH 0824/1375] Fix FunctionDefsEqual to handle multiple
 attributes.

The fix is to change StringPiece to string in our temporary maps, since the
backing memory for the StringPiece was actually dead by the time we used the
temporary maps.

Note that FunctionDef::attr() is a proto2::Map, which has a value_type that
behaves similarly to std::pair.  Here's a re-written snippet of the original
GetSetAttrs code, which makes the bug more apparent:

  std::map<StringPiece, AttrValue> GetSetAttrs(const FunctionDef& fdef) {
    std::map<StringPiece, AttrValue> attrs;
	  for (std::pair<string, AttrValue> pair : fdef.attr()) {
		  attrs[pair.first] = pair.second;
	  }
		return attrs;
	}

The problem is that the `pair` that captures each entry of fdef.attr() creates a
copy of the underlying string, and we're converting that into a StringPiece.
The underlying memory for the string is dead on each iteration of the loop.

I've also added a test that fails with the old implementation, and passes with
the new implementation.

PiperOrigin-RevId: 161265130
---
 tensorflow/core/framework/function.cc      | 14 +++++++-------
 tensorflow/core/framework/function_test.cc | 18 +++++++++++++++++-
 2 files changed, 24 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index c6a20bf3ce..1774f74ca8 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -729,11 +729,11 @@ namespace {
 // Returns the name -> attr mapping of fdef's attrs that have a value set. In
 // Python, it's possible to access unset attrs, which returns a default value
 // and adds an unset attr to the map.
-std::map<StringPiece, AttrValue> GetSetAttrs(const FunctionDef& fdef) {
-  std::map<StringPiece, AttrValue> set_attrs;
-  for (auto iter : fdef.attr()) {
-    if (iter.second.value_case() != AttrValue::VALUE_NOT_SET) {
-      set_attrs[iter.first] = iter.second;
+std::map<string, AttrValue> GetSetAttrs(const FunctionDef& fdef) {
+  std::map<string, AttrValue> set_attrs;
+  for (auto pair : fdef.attr()) {
+    if (pair.second.value_case() != AttrValue::VALUE_NOT_SET) {
+      set_attrs[pair.first] = pair.second;
     }
   }
   return set_attrs;
@@ -753,8 +753,8 @@ bool FunctionDefsEqual(const FunctionDef& f1, const FunctionDef& f2) {
   f2.signature().SerializeToString(&sig2);
   if (sig1 != sig2) return false;
 
-  std::map<StringPiece, AttrValue> f1_attrs = GetSetAttrs(f1);
-  std::map<StringPiece, AttrValue> f2_attrs = GetSetAttrs(f2);
+  std::map<string, AttrValue> f1_attrs = GetSetAttrs(f1);
+  std::map<string, AttrValue> f2_attrs = GetSetAttrs(f2);
   if (f1_attrs.size() != f2_attrs.size()) return false;
   for (auto iter1 : f1_attrs) {
     auto iter2 = f2_attrs.find(iter1.first);
diff --git a/tensorflow/core/framework/function_test.cc b/tensorflow/core/framework/function_test.cc
index 1173384a1e..140dbd8932 100644
--- a/tensorflow/core/framework/function_test.cc
+++ b/tensorflow/core/framework/function_test.cc
@@ -1156,7 +1156,7 @@ TEST(FunctionLibraryDefinitionTest, GetAttr_Gradient) {
 // TODO(skyewm): this could be more thorough
 TEST(FunctionDefsEqualTest, TestFunctionDefsEqual) {
   // Equal functions
-  FunctionDef fdef1 = test::function::XTimesTwo();
+  const FunctionDef fdef1 = test::function::XTimesTwo();
   FunctionDef fdef2 = test::function::XTimesTwo();
   EXPECT_TRUE(FunctionDefsEqual(fdef1, fdef2));
 
@@ -1183,6 +1183,22 @@ TEST(FunctionDefsEqualTest, TestFunctionDefsEqual) {
   fdef2 = test::function::XTimesTwo();
   (*fdef2.mutable_ret())["y"] = "y:z:1";  // originally is "y:z:0"
   EXPECT_FALSE(FunctionDefsEqual(fdef1, fdef2));
+
+  // Different attributes
+  fdef2 = test::function::XTimesTwo();
+  SetAttrValue(&fdef2, "ExtraAttr", true);
+  EXPECT_FALSE(FunctionDefsEqual(fdef1, fdef2));
+
+  // Multiple equivalent attributes; the two functions should be equal.
+  fdef2 = test::function::XTimesTwo();
+  FunctionDef fdef3 = test::function::XTimesTwo();
+  SetAttrValue(&fdef2, "Foo", true);
+  SetAttrValue(&fdef3, "Foo", true);
+  SetAttrValue(&fdef2, "Bar", 123);
+  SetAttrValue(&fdef3, "Bar", 123);
+  SetAttrValue(&fdef2, "Baz", "abc");
+  SetAttrValue(&fdef3, "Baz", "abc");
+  EXPECT_TRUE(FunctionDefsEqual(fdef2, fdef3));
 }
 
 }  // end namespace
-- 
GitLab


From 2177491c1a5bfb2aefef2358927487c4549cdb0f Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 7 Jul 2017 17:29:15 -0700
Subject: [PATCH 0825/1375] Disable graph optimizations in
 delay_compensated_gradient_descent_test since they appear to make the test
 more flaky

PiperOrigin-RevId: 161265638
---
 .../delay_compensated_gradient_descent_test.py | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/opt/python/training/delay_compensated_gradient_descent_test.py b/tensorflow/contrib/opt/python/training/delay_compensated_gradient_descent_test.py
index 1dbd8416a0..1fa76a360c 100644
--- a/tensorflow/contrib/opt/python/training/delay_compensated_gradient_descent_test.py
+++ b/tensorflow/contrib/opt/python/training/delay_compensated_gradient_descent_test.py
@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import resource_variable_ops
@@ -26,11 +28,19 @@ from tensorflow.python.platform import test
 from tensorflow.contrib.opt.python.training import delay_compensated_gradient_descent
 
 
+def build_session_config():
+  rewriter_config = rewriter_config_pb2.RewriterConfig(
+      disable_model_pruning=True)
+  graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+  config = config_pb2.ConfigProto(graph_options=graph_options)
+  return config
+
+
 class DelayCompensatedGradientDescentOptimizerTest(test.TestCase):
 
   def testBasic(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.test_session(config=build_session_config()):
         var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -56,7 +66,7 @@ class DelayCompensatedGradientDescentOptimizerTest(test.TestCase):
 
   def testTensorLearningRate(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.test_session(config=build_session_config()):
         var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -83,7 +93,7 @@ class DelayCompensatedGradientDescentOptimizerTest(test.TestCase):
 
     def testGradWrtRef(self):
       for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-        with self.test_session():
+        with self.test_session(cnofig=build_session_config()):
           optimizer = (delay_compensated_gradient_descent.
                        DelayCompensatedGradientDescentOptimizer)(
                            learning_rate=3.0,
@@ -99,7 +109,7 @@ class DelayCompensatedGradientDescentOptimizerTest(test.TestCase):
 
   def testWithGlobalStep(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.test_session(config=build_session_config()):
         global_step = variables.Variable(0, trainable=False)
         var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
-- 
GitLab


From fa10410de99782e90aa46623b0d55e1bfcaeb16b Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 7 Jul 2017 19:08:37 -0700
Subject: [PATCH 0826/1375] Don't mess with nodes in the fanin or the fanout of
 a function since that can lead to inlining failures.

PiperOrigin-RevId: 161271376
---
 .../grappler/optimizers/graph_rewriter.cc     | 36 ++++++++++++++++---
 .../core/grappler/optimizers/graph_rewriter.h |  8 ++++-
 .../core/grappler/optimizers/model_pruner.cc  |  5 ++-
 3 files changed, 42 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.cc b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
index 91222f1c80..2eba753fa4 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.cc
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
@@ -16,7 +16,9 @@ limitations under the License.
 #include "tensorflow/core/grappler/optimizers/graph_rewriter.h"
 #include <unordered_map>
 #include <unordered_set>
+#include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/utils.h"
 
@@ -28,8 +30,13 @@ GraphRewriter::GraphRewriter(const GrapplerItem& item) {
     nodes_[node.name()] = &node;
   }
 
+  std::unordered_set<string> function_names;
+  for (const auto& function : item.graph.library().function()) {
+    function_names.insert(function.signature().name());
+  }
+
   for (auto& node : item.graph.node()) {
-    RecordControlDependencyDrivers(node);
+    RecordConnectivity(node, function_names);
   }
 }
 
@@ -58,15 +65,34 @@ bool GraphRewriter::IsDrivenByControlDependency(const NodeDef& node) const {
   return false;
 }
 
-void GraphRewriter::RecordControlDependencyDrivers(const NodeDef& node) {
+bool GraphRewriter::IsConnectedToFunction(const NodeDef& node) const {
+  return function_neighbors_.find(&node) != function_neighbors_.end();
+}
+
+void GraphRewriter::RecordConnectivity(
+    const NodeDef& node, const std::unordered_set<string>& function_names) {
+  const bool is_function =
+      function_names.find(node.op()) != function_names.end();
+
   for (const auto& input : node.input()) {
     int position = 0;
     string input_node_name = ParseNodeName(input, &position);
+    auto itr = nodes_.find(input_node_name);
+    if (itr == nodes_.end()) {
+      continue;
+    }
+    const NodeDef* fanin = itr->second;
     if (position < 0) {
       // This is a control edge
-      auto itr = nodes_.find(input_node_name);
-      CHECK(itr != nodes_.end());
-      control_dependency_drivers_.insert(itr->second);
+      control_dependency_drivers_.insert(fanin);
+    } else {
+      // This is a regular edge
+      if (function_names.find(fanin->op()) != function_names.end()) {
+        function_neighbors_.insert(&node);
+      }
+      if (is_function) {
+        function_neighbors_.insert(fanin);
+      }
     }
   }
 }
diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.h b/tensorflow/core/grappler/optimizers/graph_rewriter.h
index 874e461831..9a61d768b2 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.h
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.h
@@ -47,8 +47,13 @@ class GraphRewriter {
   // edge.
   bool IsDrivenByControlDependency(const NodeDef& node) const;
 
+  // Returns true if at least one of the nodes in the direct fanin or the direct
+  // fanout (excluding control dependencies) of 'node' is a function.
+  bool IsConnectedToFunction(const NodeDef& node) const;
+
  private:
-  void RecordControlDependencyDrivers(const NodeDef& node);
+  void RecordConnectivity(const NodeDef& node,
+                          const std::unordered_set<string>& function_names);
   void ForwardInputsInternal(
       const NodeDef& original_node,
       const std::unordered_set<const NodeDef*>& nodes_to_delete,
@@ -57,6 +62,7 @@ class GraphRewriter {
   std::unordered_map<string, const NodeDef*> nodes_;
   std::unordered_map<string, const NodeDef*> optimized_nodes_;
   std::unordered_set<const NodeDef*> control_dependency_drivers_;
+  std::unordered_set<const NodeDef*> function_neighbors_;
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index dd634a3255..5abada3d6a 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -57,8 +57,11 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     // dependency edges by deleting them (for example, removing a node driven by
     // 10 control edges and driving 10 control edges would result in the
     // creation of 100 edges).
+    // Don't modify nodes that are connected to functions since that can result
+    // in inlining failures later on.
     if (!rewriter.DrivesControlDependency(node) &&
-        !rewriter.IsDrivenByControlDependency(node)) {
+        !rewriter.IsDrivenByControlDependency(node) &&
+        !rewriter.IsConnectedToFunction(node)) {
       nodes_to_delete.insert(&node);
     }
   }
-- 
GitLab


From 50cd64cbb91997a6ef701e4d5ada32f6e55e0d29 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 19:39:07 -0700
Subject: [PATCH 0827/1375] Rename HloInstruction::slice_stride(int64) to
 HloInstruction::slide_strides(int64).

This is so that the function matches the naming
convention of the other slice accessors.

PiperOrigin-RevId: 161272516
---
 tensorflow/compiler/xla/service/hlo_instruction.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 9b0eac32e2..e2e77e5219 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -670,7 +670,7 @@ class HloInstruction {
   // Returns the stride in the given dimension for a slice node.
   //
   // Precondition: opcode() == HloOpcode::kSlice
-  int64 slice_stride(int64 dimension) const {
+  int64 slice_strides(int64 dimension) const {
     CHECK_EQ(HloOpcode::kSlice, opcode_);
     return slice_strides_[dimension];
   }
-- 
GitLab


From 591eaa548218c664632b6ee1fe0732650094116d Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 7 Jul 2017 19:47:13 -0700
Subject: [PATCH 0828/1375] Account for the fact that the equivallent of the
 IsVariableInitialized op is VarIsInitializedOp when working on
 ResourceVariables

PiperOrigin-RevId: 161272800
---
 tensorflow/python/ops/variables.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 839b1ea882..5381e3abbe 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -736,7 +736,7 @@ class Variable(object):
         return initial_value
     elif isinstance(initial_value, ops.Operation):
       if initial_value.node_def.op in [
-          "IsVariableInitialized", "ReadVariableOp"
+          "IsVariableInitialized", "VarIsInitializedOp", "ReadVariableOp"
       ]:
         return initial_value
       if initial_value.node_def.op in ["Variable", "VariableV2", "VarHandleOp"]:
-- 
GitLab


From 1fa7aae058f3d4ee85966163b1b27d21ca209f5f Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Fri, 7 Jul 2017 22:18:44 -0700
Subject: [PATCH 0829/1375] Add new transformation
 `tf.contrib.data.Dataset.interleave()`.

This transformation acts like `tf.contrib.data.Dataset.flat_map()` but
produces output elements from `k` input elements in an interleaved fashion,
optionally in blocks of consecutive elements of length `b`.

PiperOrigin-RevId: 161277614
---
 .../kernel_tests/flat_map_dataset_op_test.py  | 152 ++++++++++
 .../contrib/data/python/ops/dataset_ops.py    | 123 ++++++++
 tensorflow/core/kernels/BUILD                 |  15 +
 .../core/kernels/interleave_dataset_op.cc     | 270 ++++++++++++++++++
 tensorflow/core/ops/dataset_ops.cc            |  25 ++
 5 files changed, 585 insertions(+)
 create mode 100644 tensorflow/core/kernels/interleave_dataset_op.cc

diff --git a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py
index ace0dd3668..2a582ae662 100644
--- a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py
@@ -17,13 +17,16 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import itertools
 import random
 
 import numpy as np
 
 from tensorflow.contrib.data.python.ops import dataset_ops
 from tensorflow.python.client import session
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
+from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import test
 from tensorflow.python.training import server_lib
 
@@ -121,5 +124,154 @@ class FlatMapDatasetTest(test.TestCase):
   # pylint: enable=g-long-lambda
 
 
+class InterleaveDatasetTest(test.TestCase):
+
+  def _interleave(self, lists, cycle_length, block_length):
+    num_open = 0
+
+    # `all_iterators` acts as a queue of iterators over each element of `lists`.
+    all_iterators = [iter(l) for l in lists]
+
+    # `open_iterators` are the iterators whose elements are currently being
+    # interleaved.
+    open_iterators = []
+    for i in range(cycle_length):
+      if all_iterators:
+        open_iterators.append(all_iterators.pop(0))
+        num_open += 1
+      else:
+        open_iterators.append(None)
+
+    while num_open or all_iterators:
+      for i in range(cycle_length):
+        if open_iterators[i] is None:
+          if all_iterators:
+            open_iterators[i] = all_iterators.pop(0)
+            num_open += 1
+          else:
+            continue
+        for _ in range(block_length):
+          try:
+            yield next(open_iterators[i])
+          except StopIteration:
+            open_iterators[i] = None
+            num_open -= 1
+            break
+
+  def testPythonImplementation(self):
+    input_lists = [[4, 4, 4, 4], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6],
+                   [4, 4, 4, 4], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]]
+
+    # Cycle length 1 acts like `Dataset.flat_map()`.
+    expected_elements = itertools.chain(*input_lists)
+    for expected, produced in zip(
+        expected_elements, self._interleave(input_lists, 1, 1)):
+      self.assertEqual(expected, produced)
+
+    # Cycle length > 1.
+    expected_elements = [4, 5, 4, 5, 4, 5, 4,
+                         5, 5, 6, 6,  # NOTE(mrry): When we cycle back
+                                      # to a list and are already at
+                                      # the end of that list, we move
+                                      # on to the next element.
+                         4, 6, 4, 6, 4, 6, 4, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5]
+    for expected, produced in zip(
+        expected_elements, self._interleave(input_lists, 2, 1)):
+      self.assertEqual(expected, produced)
+
+    # Cycle length > 1 and block length > 1.
+    expected_elements = [4, 4, 4, 5, 5, 5, 4, 5, 5, 6, 6, 6, 4, 4, 4, 6, 6, 6,
+                         4, 5, 5, 5, 6, 6, 6, 5, 5, 6, 6, 6]
+    for expected, produced in zip(
+        expected_elements, self._interleave(input_lists, 2, 3)):
+      self.assertEqual(expected, produced)
+
+    # Cycle length > len(input_values).
+    expected_elements = [4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6,
+                         4, 4, 5, 5, 6, 6, 5, 6, 6, 5, 6, 6]
+    for expected, produced in zip(
+        expected_elements, self._interleave(input_lists, 7, 2)):
+      self.assertEqual(expected, produced)
+
+  def testInterleaveDataset(self):
+    input_values = array_ops.placeholder(dtypes.int64, shape=[None])
+    cycle_length = array_ops.placeholder(dtypes.int64, shape=[])
+    block_length = array_ops.placeholder(dtypes.int64, shape=[])
+
+    repeat_count = 2
+
+    dataset = (
+        dataset_ops.Dataset.from_tensor_slices(input_values)
+        .repeat(repeat_count)
+        .interleave(lambda x: dataset_ops.Dataset.from_tensors(x).repeat(x),
+                    cycle_length, block_length))
+    iterator = dataset.make_initializable_iterator()
+    init_op = iterator.initializer
+    next_element = iterator.get_next()
+
+    with self.test_session() as sess:
+      # Cycle length 1 acts like `Dataset.flat_map()`.
+      sess.run(init_op, feed_dict={input_values: [4, 5, 6],
+                                   cycle_length: 1, block_length: 3})
+
+      for expected_element in self._interleave(
+          [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 1, 3):
+        self.assertEqual(expected_element, sess.run(next_element))
+
+      # Cycle length > 1.
+      # expected: [4, 5, 4, 5, 4, 5, 4, 5, 5, 6, 6, 4, 6, 4, 6, 4, 6, 4, 6, 5,
+      #            6, 5, 6, 5, 6, 5, 6, 5]
+      sess.run(init_op, feed_dict={input_values: [4, 5, 6],
+                                   cycle_length: 2, block_length: 1})
+      for expected_element in self._interleave(
+          [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 2, 1):
+        self.assertEqual(expected_element, sess.run(next_element))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+      # Cycle length > 1 and block length > 1.
+      # expected: [4, 4, 4, 5, 5, 5, 4, 5, 5, 6, 6, 6, 4, 4, 4, 6, 6, 6, 4, 5,
+      #            5, 5, 6, 6, 6, 5, 5, 6, 6, 6]
+      sess.run(init_op, feed_dict={input_values: [4, 5, 6],
+                                   cycle_length: 2, block_length: 3})
+      for expected_element in self._interleave(
+          [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 2, 3):
+        self.assertEqual(expected_element, sess.run(next_element))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+      # Cycle length > len(input_values) * repeat_count.
+      # expected: [4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6, 4, 4, 5, 5, 6, 6, 4, 4,
+      #            5, 5, 6, 6, 5, 6, 6, 5, 6, 6]
+      sess.run(init_op, feed_dict={input_values: [4, 5, 6],
+                                   cycle_length: 7, block_length: 2})
+      for expected_element in self._interleave(
+          [[4] * 4, [5] * 5, [6] * 6] * repeat_count, 7, 2):
+        self.assertEqual(expected_element, sess.run(next_element))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+      # Empty input.
+      sess.run(init_op, feed_dict={input_values: [],
+                                   cycle_length: 2, block_length: 3})
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+      # Non-empty input leading to empty output.
+      sess.run(init_op, feed_dict={input_values: [0, 0, 0],
+                                   cycle_length: 2, block_length: 3})
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+      # Mixture of non-empty and empty interleaved datasets.
+      sess.run(init_op, feed_dict={input_values: [4, 0, 6],
+                                   cycle_length: 2, block_length: 3})
+      for expected_element in self._interleave(
+          [[4] * 4, [], [6] * 6] * repeat_count, 2, 3):
+        self.assertEqual(expected_element, sess.run(next_element))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index a99bd5fe58..e6b0f25d74 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -908,6 +908,70 @@ class Dataset(object):
     """
     return FlatMapDataset(self, map_func)
 
+  def interleave(self, map_func, cycle_length, block_length=1):
+    """Maps `map_func` across this dataset, and interleaves the results.
+
+    For example, you can use `Dataset.interleave()` to process many input files
+    concurrently:
+
+    ```python
+    # Preprocess 4 files concurrently, and interleave blocks of 16 records from
+    # each file.
+    filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ..."]
+    dataset = (Dataset.from_tensor_slices(filenames)
+               .interleave(
+                   lambda x: TextLineDataset(x).map(parse_fn, num_threads=1)
+                   cycle_length=4, block_length=16))
+    ```
+
+    The `cycle_length` and `block_length` arguments control the order in which
+    elements are produced. `cycle_length` controls the number of input elements
+    that are processed concurrently. If you set `cycle_length` to 1, this
+    transformation will handle one input element at a time, and will produce
+    identical results = to @{tf.contrib.data.Dataset.flat_map}. In general,
+    this transformation will apply `map_func` to `cycle_length` input elements,
+    open iterators on the returned `Dataset` objects, and cycle through them
+    producing `block_length` consecutive elements from each iterator, and
+    consuming the next input element each time it reaches the end of an
+    iterator.
+
+    For example:
+
+    ```python
+    # NOTE: The following examples use `{ ... }` to represent the
+    # contents of a dataset.
+    a = { 1, 2, 3, 4, 5 }
+
+    # NOTE: New lines indicate "block" boundaries.
+    a.interleave(lambda x: Dataset.from_tensors(x).repeat(6),
+                 cycle_length=2, block_length=4) == {
+        1, 1, 1, 1,
+        2, 2, 2, 2,
+        1, 1,
+        2, 2,
+        3, 3, 3, 3,
+        4, 4, 4, 4,
+        3, 3,
+        4, 4,
+        5, 5, 5, 5,
+        5, 5,
+    }
+    ```
+
+    Args:
+      map_func: A function mapping a nested structure of tensors (having shapes
+        and types defined by `self.output_shapes` and `self.output_types`) to a
+        `Dataset`.
+      cycle_length: The number of elements from this dataset that will be
+        processed concurrently.
+      block_length: The number of consecutive elements to produce from each
+        input element before cycling to another input element.
+
+    Returns:
+      A `Dataset`.
+    """
+    return InterleaveDataset(self, map_func, cycle_length, block_length)
+
   def unbatch(self):
     """Splits elements of this dataset into sequences of consecutive elements.
 
@@ -1688,6 +1752,65 @@ class FlatMapDataset(Dataset):
     return self._output_types
 
 
+class InterleaveDataset(Dataset):
+  """A `Dataset` that maps a function over its input and flattens the result."""
+
+  def __init__(self,
+               input_dataset,
+               map_func,
+               cycle_length,
+               block_length):
+    """See `Dataset.interleave()` for details."""
+    super(InterleaveDataset, self).__init__()
+    self._input_dataset = input_dataset
+
+    @function.Defun(*nest.flatten(input_dataset.output_types))
+    def tf_map_func(*args):
+      """A wrapper for Defun that facilitates shape inference."""
+      # Pass in shape information from the input_dataset.
+      for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
+        arg.set_shape(shape)
+
+      nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
+
+      if nest.is_sequence(nested_args):
+        dataset = map_func(*nested_args)
+      else:
+        dataset = map_func(nested_args)
+
+      if not isinstance(dataset, Dataset):
+        raise TypeError("`map_func` must return a `Dataset` object.")
+
+      self._output_types = dataset.output_types
+      self._output_shapes = dataset.output_shapes
+
+      return dataset.make_dataset_resource()
+
+    self._map_func = tf_map_func
+    self._map_func.add_to_graph(ops.get_default_graph())
+
+    self._cycle_length = ops.convert_to_tensor(cycle_length, dtype=dtypes.int64)
+    self._block_length = ops.convert_to_tensor(block_length, dtype=dtypes.int64)
+
+  def make_dataset_resource(self):
+    return gen_dataset_ops.interleave_dataset(
+        self._input_dataset.make_dataset_resource(),
+        self._map_func.captured_inputs,
+        self._cycle_length,
+        self._block_length,
+        f=self._map_func,
+        output_types=nest.flatten(self.output_types),
+        output_shapes=nest.flatten(self.output_shapes))
+
+  @property
+  def output_shapes(self):
+    return self._output_shapes
+
+  @property
+  def output_types(self):
+    return self._output_types
+
+
 class FilterDataset(Dataset):
   """A `Dataset` that filters its input according to a predicate function."""
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 3ab61b71b8..96a7999c11 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -5400,6 +5400,20 @@ tf_kernel_library(
     ],
 )
 
+tf_kernel_library(
+    name = "interleave_dataset_op",
+    srcs = ["interleave_dataset_op.cc"],
+    deps = [
+        ":captured_function",
+        ":dataset",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:dataset_ops_op_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
 tf_kernel_library(
     name = "repeat_dataset_op",
     srcs = ["repeat_dataset_op.cc"],
@@ -5582,6 +5596,7 @@ tf_kernel_library(
         ":flat_map_dataset_op",
         ":group_by_window_dataset_op",
         ":ignore_errors_dataset_op",
+        ":interleave_dataset_op",
         ":iterator_ops",
         ":map_dataset_op",
         ":padded_batch_dataset_op",
diff --git a/tensorflow/core/kernels/interleave_dataset_op.cc b/tensorflow/core/kernels/interleave_dataset_op.cc
new file mode 100644
index 0000000000..bfbb5340d2
--- /dev/null
+++ b/tensorflow/core/kernels/interleave_dataset_op.cc
@@ -0,0 +1,270 @@
+
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/kernels/dataset.h"
+
+#include "tensorflow/core/common_runtime/function.h"
+#include "tensorflow/core/framework/partial_tensor_shape.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/random/random.h"
+
+#include "tensorflow/core/kernels/captured_function.h"
+
+namespace tensorflow {
+
+namespace {
+
+// See documentation in ../ops/dataset_ops.cc for a high-level
+// description of the following op.
+
+class InterleaveDatasetOp : public OpKernel {
+ public:
+  explicit InterleaveDatasetOp(OpKernelConstruction* ctx)
+      : OpKernel(ctx), graph_def_version_(ctx->graph_def_version()) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("f", &func_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_));
+  }
+
+  void Compute(OpKernelContext* ctx) override {
+    DatasetBase* input;
+    OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &input));
+    core::ScopedUnref unref_input(input);
+
+    OpInputList inputs;
+    OP_REQUIRES_OK(ctx, ctx->input_list("other_arguments", &inputs));
+    std::vector<Tensor> other_arguments;
+    other_arguments.reserve(inputs.size());
+    for (const Tensor& t : inputs) {
+      other_arguments.push_back(t);
+    }
+
+    const Tensor* cycle_length_t;
+    OP_REQUIRES_OK(ctx, ctx->input("cycle_length", &cycle_length_t));
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(cycle_length_t->shape()),
+                errors::InvalidArgument("cycle_length must be a scalar."));
+    const int64 cycle_length = cycle_length_t->flat<int64>()(0);
+    OP_REQUIRES(
+        ctx, cycle_length > 0,
+        errors::InvalidArgument("cycle_length must be greater than zero."));
+
+    const Tensor* block_length_t;
+    OP_REQUIRES_OK(ctx, ctx->input("block_length", &block_length_t));
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(block_length_t->shape()),
+                errors::InvalidArgument("block_length must be a scalar."));
+    const int64 block_length = block_length_t->flat<int64>()(0);
+    OP_REQUIRES(
+        ctx, block_length > 0,
+        errors::InvalidArgument("block_length must be greater than zero."));
+
+    std::unique_ptr<CapturedFunction> captured_func;
+    OP_REQUIRES_OK(ctx, CapturedFunction::Create(ctx, func_, graph_def_version_,
+                                                 std::move(other_arguments),
+                                                 &captured_func));
+
+    DatasetBase* dataset =
+        new Dataset(input, std::move(captured_func), cycle_length, block_length,
+                    output_types_, output_shapes_);
+
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &output));
+    ResourceHandle handle = MakeResourceHandle<DatasetBase>(
+        ctx, ctx->step_container()->name(), name());
+    OP_REQUIRES_OK(ctx, CreateResource(ctx, handle, dataset));
+    output->flat<ResourceHandle>()(0) = handle;
+  }
+
+ private:
+  class Dataset : public DatasetBase {
+   public:
+    Dataset(const DatasetBase* input,
+            std::unique_ptr<CapturedFunction> captured_func, int64 cycle_length,
+            int64 block_length, const DataTypeVector& output_types,
+            const std::vector<PartialTensorShape>& output_shapes)
+        : input_(input),
+          captured_func_(std::move(captured_func)),
+          cycle_length_(cycle_length),
+          block_length_(block_length),
+          output_types_(output_types),
+          output_shapes_(output_shapes) {
+      input_->Ref();
+    }
+
+    ~Dataset() override { input_->Unref(); }
+
+    std::unique_ptr<IteratorBase> MakeIterator() const override {
+      return std::unique_ptr<IteratorBase>(new Iterator(this));
+    }
+
+    const DataTypeVector& output_dtypes() const override {
+      return output_types_;
+    }
+    const std::vector<PartialTensorShape>& output_shapes() const override {
+      return output_shapes_;
+    }
+
+    string DebugString() override { return "InterleaveDatasetOp::Dataset"; }
+
+   private:
+    class Iterator : public DatasetIterator<Dataset> {
+     public:
+      explicit Iterator(const Dataset* dataset)
+          : DatasetIterator<Dataset>(dataset),
+            input_impl_(dataset->input_->MakeIterator()),
+            current_elements_(dataset->cycle_length_) {}
+
+      void AdvanceToNextInCycle() EXCLUSIVE_LOCKS_REQUIRED(mu_) {
+        block_index_ = 0;
+        cycle_index_ = (cycle_index_ + 1) % dataset()->cycle_length_;
+      }
+
+      void AdvancePosition() EXCLUSIVE_LOCKS_REQUIRED(mu_) {
+        ++block_index_;
+        if (block_index_ == dataset()->block_length_) {
+          AdvanceToNextInCycle();
+        }
+      }
+
+      Status GetNext(IteratorContext* ctx, std::vector<Tensor>* out_tensors,
+                     bool* end_of_sequence) override {
+        mutex_lock l(mu_);
+        while (!end_of_input_ || num_open_ > 0) {
+          if (current_elements_[cycle_index_]) {
+            // We are currently processing a mapped element, so try to get the
+            // next subelement.
+            bool end_of_element;
+            TF_RETURN_IF_ERROR(current_elements_[cycle_index_]->GetNext(
+                ctx, out_tensors, &end_of_element));
+            if (!end_of_element) {
+              // Produce the subelement as output.
+              AdvancePosition();
+              *end_of_sequence = false;
+              return Status::OK();
+            }
+            // We have reached the end of the current element, so move
+            // on to the next element in the cycle.
+            current_elements_[cycle_index_].reset();
+            --num_open_;
+            AdvanceToNextInCycle();
+          } else if (!end_of_input_) {
+            // Get the next element from the input dataset, and create
+            // an iterator from it.
+            std::vector<Tensor> args;
+            TF_RETURN_IF_ERROR(
+                input_impl_->GetNext(ctx, &args, &end_of_input_));
+            if (!end_of_input_) {
+              TF_RETURN_IF_ERROR(MakeIteratorFromInputElement(
+                  ctx, args, &current_elements_[cycle_index_]));
+              ++num_open_;
+            }
+          } else {
+            AdvanceToNextInCycle();
+          }
+        }
+
+        *end_of_sequence = true;
+        return Status::OK();
+      }
+
+     private:
+      Status MakeIteratorFromInputElement(
+          IteratorContext* ctx, const std::vector<Tensor>& input_element,
+          std::unique_ptr<IteratorBase>* out_iterator) {
+        FunctionLibraryRuntime::Options opts;
+        opts.runner = ctx->runner();
+        // Choose a step ID that is guaranteed not to clash with any
+        // Session-generated step ID. DirectSession only generates
+        // non-negative step IDs (contiguous, starting from 0), and
+        // MasterSession generates 56-bit random step IDs whose MSB
+        // is always 0, so a negative random step ID should suffice.
+        opts.step_id = -std::abs(static_cast<int64>(random::New64()));
+        ScopedStepContainer step_container(
+            opts.step_id, [this, ctx](const string& name) {
+              dataset()
+                  ->captured_func_->resource_manager()
+                  ->Cleanup(name)
+                  .IgnoreError();
+            });
+        opts.step_container = &step_container;
+        std::vector<Tensor> return_values;
+        TF_RETURN_IF_ERROR(dataset()->captured_func_->Run(opts, input_element,
+                                                          &return_values));
+
+        if (!(return_values.size() == 1 &&
+              return_values[0].dtype() == DT_RESOURCE &&
+              TensorShapeUtils::IsScalar(return_values[0].shape()))) {
+          return errors::InvalidArgument(
+              "`f` must return a single scalar of dtype DT_RESOURCE.");
+        }
+
+        // Retrieve the dataset that was created in `f`.
+        DatasetBase* returned_dataset;
+        const ResourceHandle& dataset_resource =
+            return_values[0].scalar<ResourceHandle>()();
+
+        // NOTE(mrry): We cannot use the core `LookupResource()` or
+        // `DeleteResource()` functions, because we have an
+        // `IteratorContext*` and not an `OpKernelContext*`, so we
+        // replicate the necessary functionality here.
+        auto type_index = MakeTypeIndex<DatasetBase>();
+        if (type_index.hash_code() != dataset_resource.hash_code()) {
+          return errors::InvalidArgument("`f` must return a Dataset resource.");
+        }
+        TF_RETURN_IF_ERROR(
+            dataset()->captured_func_->resource_manager()->Lookup(
+                dataset_resource.container(), dataset_resource.name(),
+                &returned_dataset));
+        core::ScopedUnref unref_dataset(returned_dataset);
+
+        // Create an iterator for the dataset that was returned by
+        // `f`. This transfers ownership of the dataset to the
+        // iterator, so we can delete it from the resource manager.
+        *out_iterator = returned_dataset->MakeIterator();
+        TF_RETURN_IF_ERROR(
+            dataset()->captured_func_->resource_manager()->Delete<DatasetBase>(
+                dataset_resource.container(), dataset_resource.name()));
+        return Status::OK();
+      }
+
+      mutex mu_;
+      const std::unique_ptr<IteratorBase> input_impl_ GUARDED_BY(mu_);
+      std::vector<std::unique_ptr<IteratorBase>> current_elements_
+          GUARDED_BY(mu_);
+      size_t cycle_index_ GUARDED_BY(mu_) = 0;
+      int64 block_index_ GUARDED_BY(mu_) = 0;
+      bool end_of_input_ GUARDED_BY(mu_) = false;
+      size_t num_open_ GUARDED_BY(mu_) = 0;
+    };
+
+    const DatasetBase* const input_;
+    const std::unique_ptr<CapturedFunction> captured_func_;
+    const int64 cycle_length_;
+    const int64 block_length_;
+    const DataTypeVector output_types_;
+    const std::vector<PartialTensorShape> output_shapes_;
+  };
+
+  const int graph_def_version_;
+  DataTypeVector output_types_;
+  std::vector<PartialTensorShape> output_shapes_;
+  const NameAttrList* func_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("InterleaveDataset").Device(DEVICE_CPU),
+                        InterleaveDatasetOp);
+
+}  // namespace
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index a490a30aaa..789d8a9990 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -198,6 +198,31 @@ f: A function mapping elements of `input_dataset`, concatenated with
   `output_types` and `output_shapes`.
 )doc");
 
+REGISTER_OP("InterleaveDataset")
+    .Input("input_dataset: resource")
+    .Input("other_arguments: Targuments")
+    .Input("cycle_length: int64")
+    .Input("block_length: int64")
+    .Output("handle: resource")
+    .Attr("f: func")
+    .Attr("Targuments: list(type) >= 0")
+    .Attr("output_types: list(type) >= 1")
+    .Attr("output_shapes: list(shape) >= 1")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Creates a dataset that applies `f` to the outputs of `input_dataset`.
+
+Unlike MapDataset, the `f` in InterleaveDataset is expected to return
+a Dataset resource, and InterleaveDataset will flatten successive
+results into a single Dataset. Unlike FlatMapDataset,
+InterleaveDataset will interleave sequences of up to `block_length`
+consecutive elements from `cycle_length` input elements.
+
+f: A function mapping elements of `input_dataset`, concatenated with
+  `other_arguments`, to a Dataset resource that contains elements matching
+  `output_types` and `output_shapes`.
+)doc");
+
 REGISTER_OP("GroupByWindowDataset")
     .Input("input_dataset: resource")
     .Input("key_func_other_arguments: Tkey_func_other_arguments")
-- 
GitLab


From 936accf07d65e85c78f930d7437d4aaaf8d066fd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 7 Jul 2017 22:49:51 -0700
Subject: [PATCH 0830/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161278460
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 45 +++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 48 +++++++++++++++++++
 2 files changed, 93 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 91b9a9a7ee..e7a72b03c4 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -10762,6 +10762,51 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "InterleaveDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "other_arguments"
+    type_list_attr: "Targuments"
+  }
+  input_arg {
+    name: "cycle_length"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "block_length"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "f"
+    type: "func"
+  }
+  attr {
+    name: "Targuments"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  is_stateful: true
+}
 op {
   name: "Inv"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 3a853da189..02553df448 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -10134,6 +10134,54 @@ op {
   summary: "Table initializer that takes two tensors for keys and values respectively."
   is_stateful: true
 }
+op {
+  name: "InterleaveDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "other_arguments"
+    type_list_attr: "Targuments"
+  }
+  input_arg {
+    name: "cycle_length"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "block_length"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "f"
+    type: "func"
+    description: "A function mapping elements of `input_dataset`, concatenated with\n`other_arguments`, to a Dataset resource that contains elements matching\n`output_types` and `output_shapes`."
+  }
+  attr {
+    name: "Targuments"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  summary: "Creates a dataset that applies `f` to the outputs of `input_dataset`."
+  description: "Unlike MapDataset, the `f` in InterleaveDataset is expected to return\na Dataset resource, and InterleaveDataset will flatten successive\nresults into a single Dataset. Unlike FlatMapDataset,\nInterleaveDataset will interleave sequences of up to `block_length`\nconsecutive elements from `cycle_length` input elements."
+  is_stateful: true
+}
 op {
   name: "Inv"
   input_arg {
-- 
GitLab


From 660f2ae51948b3ffd27afd19d7fbafdc4c06e674 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Sat, 8 Jul 2017 10:51:37 -0400
Subject: [PATCH 0831/1375] Disable more timeseries py tests failing under GPU
 PIP

---
 .../contrib/timeseries/python/timeseries/BUILD    | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index eafaee8ac4..8edc209eb4 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -82,6 +82,9 @@ py_test(
         "estimators_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":estimators",
         ":feature_keys",
@@ -122,6 +125,9 @@ py_test(
         "model_utils_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":model_utils",
         ":state_management",
@@ -153,6 +159,9 @@ py_test(
         "state_management_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":input_pipeline",
         ":model",
@@ -203,6 +212,9 @@ py_test(
         "input_pipeline_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":input_pipeline",
         ":test_utils",
@@ -315,6 +327,9 @@ py_test(
         "math_utils_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":math_utils",
         "//tensorflow/python:array_ops",
-- 
GitLab


From 2662d4e80c4892fb283d1cc2181fe017a71c8516 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 8 Jul 2017 12:56:19 -0700
Subject: [PATCH 0832/1375] Reduce code duplication in the
 CreateScalarXXXComputation routines.

PiperOrigin-RevId: 161300540
---
 tensorflow/compiler/xla/client/lib/BUILD      |  1 +
 .../compiler/xla/client/lib/arithmetic.cc     | 80 +++++++++++--------
 2 files changed, 47 insertions(+), 34 deletions(-)

diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD
index edd971e114..ee34682087 100644
--- a/tensorflow/compiler/xla/client/lib/BUILD
+++ b/tensorflow/compiler/xla/client/lib/BUILD
@@ -24,6 +24,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/core:lib",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc
index a45974b86b..3ad769a040 100644
--- a/tensorflow/compiler/xla/client/lib/arithmetic.cc
+++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc
@@ -22,65 +22,77 @@ limitations under the License.
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 
 namespace xla {
+namespace {
+using InstructionGenerator =
+    ComputationDataHandle (*)(ComputationBuilder*, const ComputationDataHandle&,
+                              const ComputationDataHandle&);
+
+Computation CreateScalarComputation(const string& name, PrimitiveType type,
+                                    ComputationBuilder* builder,
+                                    InstructionGenerator generator) {
+  std::unique_ptr<ComputationBuilder> b;
+  if (type == PRED) {
+    b = builder->CreateSubBuilder(name);
+  } else {
+    b = builder->CreateSubBuilder(
+        tensorflow::strings::StrCat(name, "_", PrimitiveType_Name(type)));
+  }
 
-Computation CreateScalarAddComputation(PrimitiveType type,
-                                       ComputationBuilder* builder) {
   const Shape scalar = ShapeUtil::MakeShape(type, {});
-  auto b = builder->CreateSubBuilder("add_" + PrimitiveType_Name(type));
   auto lhs = b->Parameter(0, scalar, "lhs");
   auto rhs = b->Parameter(1, scalar, "rhs");
-  b->Add(lhs, rhs);
+  generator(b.get(), lhs, rhs);
   return b->BuildAndNoteError();
 }
+}  // namespace
+
+Computation CreateScalarAddComputation(PrimitiveType type,
+                                       ComputationBuilder* builder) {
+  return CreateScalarComputation(
+      "add", type, builder,
+      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
+         const ComputationDataHandle& rhs) { return b->Add(lhs, rhs); });
+}
 
 Computation CreateScalarGeComputation(PrimitiveType type,
                                       ComputationBuilder* builder) {
-  const Shape scalar = ShapeUtil::MakeShape(type, {});
-  auto b = builder->CreateSubBuilder("ge_" + PrimitiveType_Name(type));
-  auto lhs = b->Parameter(0, scalar, "lhs");
-  auto rhs = b->Parameter(1, scalar, "rhs");
-  b->Ge(lhs, rhs);
-  return b->BuildAndNoteError();
+  return CreateScalarComputation(
+      "ge", type, builder,
+      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
+         const ComputationDataHandle& rhs) { return b->Ge(lhs, rhs); });
 }
 
 Computation CreateScalarMaxComputation(PrimitiveType type,
                                        ComputationBuilder* builder) {
-  const Shape scalar = ShapeUtil::MakeShape(type, {});
-  auto b = builder->CreateSubBuilder("max_" + PrimitiveType_Name(type));
-  auto lhs = b->Parameter(0, scalar, "lhs");
-  auto rhs = b->Parameter(1, scalar, "rhs");
-  b->Max(lhs, rhs);
-  return b->BuildAndNoteError();
+  return CreateScalarComputation(
+      "max", type, builder,
+      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
+         const ComputationDataHandle& rhs) { return b->Max(lhs, rhs); });
 }
 
 Computation CreateScalarMinComputation(PrimitiveType type,
                                        ComputationBuilder* builder) {
-  const Shape scalar = ShapeUtil::MakeShape(type, {});
-  auto b = builder->CreateSubBuilder("min_" + PrimitiveType_Name(type));
-  auto lhs = b->Parameter(0, scalar, "lhs");
-  auto rhs = b->Parameter(1, scalar, "rhs");
-  b->Min(lhs, rhs);
-  return b->BuildAndNoteError();
+  return CreateScalarComputation(
+      "min", type, builder,
+      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
+         const ComputationDataHandle& rhs) { return b->Min(lhs, rhs); });
 }
 
 Computation CreateScalarLogicalAndComputation(ComputationBuilder* builder) {
-  const Shape scalar = ShapeUtil::MakeShape(PRED, {});
-  auto b = builder->CreateSubBuilder("logical_and");
-  auto lhs = b->Parameter(0, scalar, "lhs");
-  auto rhs = b->Parameter(1, scalar, "rhs");
-  b->LogicalAnd(lhs, rhs);
-  return b->BuildAndNoteError();
+  return CreateScalarComputation(
+      "logical_and", PRED, builder,
+      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
+         const ComputationDataHandle& rhs) { return b->LogicalAnd(lhs, rhs); });
 }
 
 Computation CreateScalarLogicalOrComputation(ComputationBuilder* builder) {
-  const Shape scalar = ShapeUtil::MakeShape(PRED, {});
-  auto b = builder->CreateSubBuilder("logical_or");
-  auto lhs = b->Parameter(0, scalar, "lhs");
-  auto rhs = b->Parameter(1, scalar, "rhs");
-  b->LogicalOr(lhs, rhs);
-  return b->BuildAndNoteError();
+  return CreateScalarComputation(
+      "logical_or", PRED, builder,
+      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
+         const ComputationDataHandle& rhs) { return b->LogicalOr(lhs, rhs); });
 }
 
 StatusOr<ComputationDataHandle> Any(const ComputationDataHandle& predicates,
-- 
GitLab


From d22c433216254d2e6ab33a849f922b4545d1f7dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 8 Jul 2017 16:49:51 -0700
Subject: [PATCH 0833/1375] Fix typos in comments.

PiperOrigin-RevId: 161305803
---
 .../python/training/monitored_session.py      |  2 +-
 .../python/training/session_run_hook.py       | 19 ++++++++++++-------
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index e7b296374a..2feb6bd3c3 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -92,7 +92,7 @@ class Scaffold(object):
 
   * `init_feed_dict`: A session feed dictionary that should be used when
      running the init op.
-  * `init_fn`: A callable to run run after the init op to perform additional
+  * `init_fn`: A callable to run after the init op to perform additional
     initializations.  The callable will be called as
     `init_fn(scaffold, session)`.
 
diff --git a/tensorflow/python/training/session_run_hook.py b/tensorflow/python/training/session_run_hook.py
index 09da63eb68..dbeabd250e 100644
--- a/tensorflow/python/training/session_run_hook.py
+++ b/tensorflow/python/training/session_run_hook.py
@@ -42,23 +42,28 @@ For more specific needs, you can create custom hooks:
       print('Starting the session.')
       self.your_tensor = ...
 
-    def end(self, session):
-      print('Done with the session.')
+    def after_create_session(self, session, coord):
+      # When this is called, the graph is finalized and
+      # ops can no longer be added to the graph.
+      print('Session created.')
 
     def before_run(self, run_context):
-      print('before calling session.run)
+      print('Before calling session.run().')
       return SessionRunArgs(self.your_tensor)
 
-    def after_run(self, run_context, run_values)
+    def after_run(self, run_context, run_values):
       print('Done running one step. The value of my tensor: %s',
             run_values.results)
       if you-need-to-stop-loop:
         run_context.request_stop()
 
+    def end(self, session):
+      print('Done with the session.')
+
 To understand how hooks interact with calls to `MonitoredSession.run()`,
 look at following code:
-  with SupervisedSession(hooks=your_hooks, ...) as sess
-    while not sess.should_stop()
+  with MonitoredTrainingSession(hooks=your_hooks, ...) as sess:
+    while not sess.should_stop():
       sess.run(your_fetches)
 
 Above user code leads to following execution:
@@ -68,7 +73,7 @@ Above user code leads to following execution:
   while not stop is requested:
     call hooks.before_run()
     try:
-      results = sess.run(merged_fetches)
+      results = sess.run(merged_fetches, feed_dict=merged_feeds)
     except (errors.OutOfRangeError, StopIteration):
       break
     call hooks.after_run()
-- 
GitLab


From 23caae7408cf6bb961d1bab0c8b819d88ccf5f3c Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Sun, 9 Jul 2017 13:09:51 -0700
Subject: [PATCH 0834/1375] Allow adding new functions to an already-run
 session

PiperOrigin-RevId: 161337922
---
 .../core/common_runtime/direct_session.cc     |  1 +
 .../simple_graph_execution_state.cc           | 19 ++++++++-------
 tensorflow/python/client/session_test.py      | 23 +++++++++++++++++++
 3 files changed, 33 insertions(+), 10 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index f6eac2d698..ada2e32f38 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -390,6 +390,7 @@ Status DirectSession::ExtendLocked(const GraphDef& graph) {
   TF_RETURN_IF_ERROR(
       MaybeInitializeExecutionState(graph, &already_initialized));
   if (already_initialized) {
+    TF_RETURN_IF_ERROR(flib_def_->AddLibrary(graph.library()));
     std::unique_ptr<SimpleGraphExecutionState> state;
     TF_RETURN_IF_ERROR(execution_state_->Extend(graph, &state));
     execution_state_.swap(state);
diff --git a/tensorflow/core/common_runtime/simple_graph_execution_state.cc b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
index 5a69d2440a..41e685bdc7 100644
--- a/tensorflow/core/common_runtime/simple_graph_execution_state.cc
+++ b/tensorflow/core/common_runtime/simple_graph_execution_state.cc
@@ -117,16 +117,21 @@ SimpleGraphExecutionState::~SimpleGraphExecutionState() {
 Status SimpleGraphExecutionState::Extend(
     const GraphDef& extension_def,
     std::unique_ptr<SimpleGraphExecutionState>* out) const {
+  GraphDef gdef;
+
+  // 1. Copy the function library.
+  TF_RETURN_IF_ERROR(flib_def_->AddLibrary(extension_def.library()));
+  *gdef.mutable_library() = flib_def_->ToProto();
+
+  // 2. Build an index of the new node names.
   std::unordered_set<string> new_names;
-  // 1. Build an index of the new node names.
   for (const NodeDef& node : extension_def.node()) {
     new_names.insert(node.name());
   }
 
-  // 2. Add the non-duplicates from the old graph to the new graph.
+  // 3. Add the non-duplicates from the old graph to the new graph.
   //    Return an error if the same node name appears in both the
   //    old graph and the extension.
-  GraphDef gdef;
   for (const NodeDef& node : original_graph_def_.node()) {
     if (new_names.count(node.name()) == 0) {
       *gdef.add_node() = node;
@@ -138,7 +143,7 @@ Status SimpleGraphExecutionState::Extend(
     }
   }
 
-  // 3. Merge the versions field.
+  // 4. Merge the versions field.
   int old_node_size = gdef.node_size();
   gdef.mutable_node()->MergeFrom(extension_def.node());
   TF_RETURN_IF_ERROR(
@@ -174,12 +179,6 @@ Status SimpleGraphExecutionState::Extend(
     gdef.mutable_versions()->CopyFrom(extension_def.versions());
   }
 
-  // 4. Copy the function library from this execution state.
-  // NOTE(mrry): To match the previous behavior, the first GraphDef
-  // passed to a session will contain the function library that is
-  // used for all subsequent execution states.
-  *gdef.mutable_library() = flib_def_->ToProto();
-
   // 5. Validate that the final graphdef is valid.
   if (gdef.versions().producer() >= 5) {
     // Validate the graph: we assume that merging two valid graphs
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index 571b9a4668..83c52b7cf7 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -36,6 +36,7 @@ from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
+from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_util
@@ -1752,6 +1753,28 @@ class SessionTest(test_util.TensorFlowTestCase):
     str_repr = '%s' % attrs
     self.assertTrue(str_repr.startswith('_DeviceAttributes'), str_repr)
 
+  def runTestAddFunctionToSession(self, target=''):
+    """Add a function to a session after the graph has already been run."""
+    @function.Defun(dtypes.float32)
+    def foo(x):
+      return x + 1
+
+    x = constant_op.constant(1.0)
+    with session.Session(target=target) as sess:
+      sess.run(x)
+      f = foo(x)
+      result = sess.run(f)
+      self.assertEqual(result, 2.0)
+
+  @test_util.disable_c_api  # functions don't work with C API
+  def testAddFunctionToSession(self):
+    self.runTestAddFunctionToSession()
+
+  @test_util.disable_c_api  # functions don't work with C API
+  def testAddFunctionToGrpcSession(self):
+    server = server_lib.Server.create_local_server()
+    self.runTestAddFunctionToSession(server.target)
+
 
 if __name__ == '__main__':
   googletest.main()
-- 
GitLab


From fed833ebb94b60a175d9d117f13bf303c3c14dfe Mon Sep 17 00:00:00 2001
From: Brett Koonce <koonce@hello.com>
Date: Sun, 9 Jul 2017 12:56:53 -0700
Subject: [PATCH 0835/1375] update folder link in docs

---
 tensorflow/contrib/makefile/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md
index 9ba5c035a2..0306ecb214 100644
--- a/tensorflow/contrib/makefile/README.md
+++ b/tensorflow/contrib/makefile/README.md
@@ -130,7 +130,7 @@ For more details, see the [benchmark documentation](../../tools/benchmark).
 ## iOS
 
 _Note: To use this library in an iOS application, see related instructions in
-the [iOS examples](../ios_examples/) directory._
+the [iOS examples](../../examples/ios/) directory._
 
 Install XCode 7.3 or more recent. If you have not already, you will need to
 install the command-line tools using `xcode-select`:
@@ -176,7 +176,7 @@ benchmark program. Although successfully compiling the benchmark program is a
 sign of success, the program is not a complete iOS app.
 
 To see TensorFlow running on iOS, the example Xcode project in
-[tensorflow/contrib/ios_examples](../ios_examples) shows how to use the static
+[tensorflow/examples/ios](../../examples/ios/) shows how to use the static
 library in a simple app.
 
 ### Building by hand
@@ -214,7 +214,7 @@ benchmark program. Although successfully compiling the benchmark program is a
 sign of success, the program is not a complete iOS app. 
 
 To see TensorFlow running on iOS, the example Xcode project in
-[tensorflow/contrib/ios_examples](../ios_examples) shows how to use the static
+[tensorflow/examples/ios](../../examples/ios/) shows how to use the static
 library in a simple app.
 
 #### Universal binaries
-- 
GitLab


From bdabccd483a3ec5db66107239066ffea6faa085b Mon Sep 17 00:00:00 2001
From: Taehoon Lee <taehoonlee@snu.ac.kr>
Date: Mon, 10 Jul 2017 09:13:03 +0900
Subject: [PATCH 0836/1375] Fix typos

---
 tensorflow/contrib/factorization/kernels/wals_solver_ops.cc | 2 +-
 tensorflow/contrib/framework/python/ops/ops.py              | 2 +-
 tensorflow/python/kernel_tests/sparse_ops_test.py           | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc b/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc
index cd1f23bba2..bb9b835889 100644
--- a/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc
+++ b/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc
@@ -213,7 +213,7 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel {
       CHECK_LE(shard.second, perm.size());
       CHECK_LE(shard.first, shard.second);
       const int64 input_index = get_input_index(perm[shard.first]);
-      // Acccumulate the rhs and lhs terms in the normal equations
+      // Accumulate the rhs and lhs terms in the normal equations
       // for the non-zero elements in the row or column of the sparse matrix
       // corresponding to input_index.
       int num_batched = 0;
diff --git a/tensorflow/contrib/framework/python/ops/ops.py b/tensorflow/contrib/framework/python/ops/ops.py
index 4fccc2ceac..ac451a974d 100644
--- a/tensorflow/contrib/framework/python/ops/ops.py
+++ b/tensorflow/contrib/framework/python/ops/ops.py
@@ -68,6 +68,6 @@ def get_name_scope():
     would print the string `scope1/scope2`.
 
   Returns:
-    A string represnting the current name scope.
+    A string representing the current name scope.
   """
   return ops.get_default_graph().get_name_scope()
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 14eb2cba68..cb3d8fcb55 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -549,7 +549,7 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
 
   # [[1, ?, 2]
   #  [?, 3, ?]]
-  # where ? is implictly-zero.
+  # where ? is implicitly-zero.
   ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64)
   vals = np.array([1, 1, 1]).astype(np.int32)
   dense_shape = np.array([2, 3]).astype(np.int64)
-- 
GitLab


From 0153efbc231c73c7dbed4398a7c57111a70cf68c Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Sun, 9 Jul 2017 20:11:08 -0700
Subject: [PATCH 0837/1375] tfdbg CLI: add text summary of numeric values

For the `pt` or `print_tensor` command, the numeric summary can be activated with the `-s` option flag, e.g.,

tfdbg> pt -s cross_entropy/Log:0

Numeric summaries are applicable only to non-empty tensors of Boolean and numeric (int*, float*, complex*) types.

PiperOrigin-RevId: 161350396
---
 .../docs_src/programmers_guide/debugger.md    |  11 ++
 tensorflow/python/debug/cli/analyzer_cli.py   |   9 +-
 .../python/debug/cli/analyzer_cli_test.py     |  28 +++
 tensorflow/python/debug/cli/cli_shared.py     |   6 +-
 tensorflow/python/debug/cli/tensor_format.py  | 111 +++++++++++-
 .../python/debug/cli/tensor_format_test.py    | 159 ++++++++++++++++++
 6 files changed, 315 insertions(+), 9 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 2dbccb6363..72209896cb 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -147,6 +147,7 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | | `pt <tensor>[slicing]` | Print a subarray of tensor, using [numpy](http://www.numpy.org/)-style array slicing. | `pt hidden/Relu:0[0:50,:]` |
 | | `-a` | Print the entirety of a large tensor, without using ellipses. (May take a long time for large tensors.) | `pt -a hidden/Relu:0[0:50,:]` |
 | | `-r <range>` | Highlight elements falling into specified numerical range. Multiple ranges can be used in conjunction. | `pt hidden/Relu:0 -a -r [[-inf,-1],[1,inf]]` |
+| | `-s` | Include a summary of the numeric values of the tensor (applicable only to non-empty tensors with Boolean and numeric types such as `int*` and `float*`.) | `pt -s hidden/Relu:0[0:50,:]` |
 | **`@[coordinates]`** | | Navigate to specified element in `pt` output. | `@[10,0]` or `@10,0` |
 | **`/regex`** | |  [less](https://linux.die.net/man/1/less)-style search for given regular expression. | `/inf` |
 | **`/`** | | Scroll to the next line with matches to the searched regex (if any). | `/` |
@@ -289,6 +290,16 @@ Or, alternatively:
 tfdbg> /(inf|nan)
 ```
 
+You can also use the `-s` or `--numeric_summary` command to get a quick summary
+of the types of numeric values in the tensor:
+
+``` none
+tfdbg> pt -s cross_entropy/Log:0
+```
+
+From the summary, you can see that several of the 1000 elements of the
+`cross_entropy/Log:0` tensor are `-inf`s (negative infinities).
+
 Why did these infinities appear? To further debug, display more information
 about the node `cross_entropy/Log` by clicking the underlined `node_info` menu
 item on the top or entering the equivalent node_info (`ni`) command:
diff --git a/tensorflow/python/debug/cli/analyzer_cli.py b/tensorflow/python/debug/cli/analyzer_cli.py
index d8f57ad19a..45154bee58 100644
--- a/tensorflow/python/debug/cli/analyzer_cli.py
+++ b/tensorflow/python/debug/cli/analyzer_cli.py
@@ -311,6 +311,12 @@ class DebugAnalyzer(object):
         dest="print_all",
         action="store_true",
         help="Print the tensor in its entirety, i.e., do not use ellipses.")
+    ap.add_argument(
+        "-s",
+        "--numeric_summary",
+        action="store_true",
+        help="Include summary for non-empty tensors of numeric (int*, float*, "
+        "complex*) and Boolean types.")
     self._arg_parsers["print_tensor"] = ap
 
     # Parser for print_source.
@@ -910,7 +916,8 @@ class DebugAnalyzer(object):
             np_printoptions,
             print_all=parsed.print_all,
             tensor_slicing=tensor_slicing,
-            highlight_options=highlight_options)
+            highlight_options=highlight_options,
+            include_numeric_summary=parsed.numeric_summary)
       else:
         output = cli_shared.error(
             "Invalid number (%d) for tensor %s, which generated one dump." %
diff --git a/tensorflow/python/debug/cli/analyzer_cli_test.py b/tensorflow/python/debug/cli/analyzer_cli_test.py
index 8302a4b3cb..9e1f459915 100644
--- a/tensorflow/python/debug/cli/analyzer_cli_test.py
+++ b/tensorflow/python/debug/cli/analyzer_cli_test.py
@@ -1001,6 +1001,34 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase):
         list_inputs_node_name=node_name,
         list_outputs_node_name=node_name)
 
+  def testPrintTensorHighlightingRangesAndIncludingNumericSummary(self):
+    node_name = "simple_mul_add/matmul"
+    tensor_name = node_name + ":0"
+    out = self._registry.dispatch_command(
+        "print_tensor", [tensor_name, "--ranges", "[-inf, 0.0]", "-s"],
+        screen_info={"cols": 80})
+
+    self.assertEqual([
+        "Tensor \"%s:DebugIdentity\": " % tensor_name +
+        "Highlighted([-inf, 0.0]): 1 of 2 element(s) (50.00%)",
+        "  dtype: float64",
+        "  shape: (2, 1)",
+        "",
+        "Numeric summary:",
+        "| - + | total |",
+        "| 1 1 |     2 |",
+        "|  min  max mean  std |",
+        "| -2.0  7.0  2.5  4.5 |",
+        "",
+        "array([[ 7.],",
+        "       [-2.]])",
+    ], out.lines)
+
+    self.assertIn("tensor_metadata", out.annotations)
+    self.assertIn(10, out.annotations)
+    self.assertIn(11, out.annotations)
+    self.assertEqual([(8, 11, "bold")], out.font_attr_segs[11])
+
   def testPrintTensorWithSlicing(self):
     node_name = "simple_mul_add/matmul"
     tensor_name = node_name + ":0"
diff --git a/tensorflow/python/debug/cli/cli_shared.py b/tensorflow/python/debug/cli/cli_shared.py
index abd2e84fde..888080c634 100644
--- a/tensorflow/python/debug/cli/cli_shared.py
+++ b/tensorflow/python/debug/cli/cli_shared.py
@@ -143,7 +143,8 @@ def format_tensor(tensor,
                   np_printoptions,
                   print_all=False,
                   tensor_slicing=None,
-                  highlight_options=None):
+                  highlight_options=None,
+                  include_numeric_summary=False):
   """Generate formatted str to represent a tensor or its slices.
 
   Args:
@@ -161,6 +162,8 @@ def format_tensor(tensor,
     highlight_options: (tensor_format.HighlightOptions) options to highlight
       elements of the tensor. See the doc of tensor_format.format_tensor()
       for more details.
+    include_numeric_summary: Whether a text summary of the numeric values (if
+      applicable) will be included.
 
   Returns:
     (str) Formatted str representing the (potentially sliced) tensor.
@@ -183,6 +186,7 @@ def format_tensor(tensor,
       value,
       sliced_name,
       include_metadata=True,
+      include_numeric_summary=include_numeric_summary,
       np_printoptions=np_printoptions,
       highlight_options=highlight_options)
 
diff --git a/tensorflow/python/debug/cli/tensor_format.py b/tensorflow/python/debug/cli/tensor_format.py
index bb7ac31430..7a5597db12 100644
--- a/tensorflow/python/debug/cli/tensor_format.py
+++ b/tensorflow/python/debug/cli/tensor_format.py
@@ -72,6 +72,7 @@ class HighlightOptions(object):
 def format_tensor(tensor,
                   tensor_label,
                   include_metadata=False,
+                  include_numeric_summary=False,
                   np_printoptions=None,
                   highlight_options=None):
   """Generate a RichTextLines object showing a tensor in formatted style.
@@ -83,6 +84,8 @@ def format_tensor(tensor,
       suppress the tensor name line in the return value.
     include_metadata: Whether metadata such as dtype and shape are to be
       included in the formatted text.
+    include_numeric_summary: Whether a text summary of the numeric values (if
+      applicable) will be included.
     np_printoptions: A dictionary of keyword arguments that are passed to a
       call of np.set_printoptions() to set the text format for display numpy
       ndarrays.
@@ -132,25 +135,31 @@ def format_tensor(tensor,
 
   if lines:
     lines.append("")
-  hlines = len(lines)
+  formatted = debugger_cli_common.RichTextLines(
+      lines, font_attr_segs=font_attr_segs)
+
+  if include_numeric_summary:
+    formatted.append("Numeric summary:")
+    formatted.extend(numeric_summary(tensor))
+    formatted.append("")
 
   # Apply custom string formatting options for numpy ndarray.
   if np_printoptions is not None:
     np.set_printoptions(**np_printoptions)
 
   array_lines = repr(tensor).split("\n")
-  lines.extend(array_lines)
-
   if tensor.dtype.type is not np.string_:
     # Parse array lines to get beginning indices for each line.
 
     # TODO(cais): Currently, we do not annotate string-type tensors due to
     #   difficulty in escaping sequences. Address this issue.
     annotations = _annotate_ndarray_lines(
-        array_lines, tensor, np_printoptions=np_printoptions, offset=hlines)
-
-  formatted = debugger_cli_common.RichTextLines(
-      lines, font_attr_segs=font_attr_segs, annotations=annotations)
+        array_lines, tensor, np_printoptions=np_printoptions)
+  else:
+    annotations = None
+  formatted_array = debugger_cli_common.RichTextLines(
+      array_lines, annotations=annotations)
+  formatted.extend(formatted_array)
 
   # Perform optional highlighting.
   if highlight_options is not None:
@@ -464,3 +473,91 @@ def _locate_elements_in_line(line, indices_list, ref_indices):
     offset_counter += 1
 
   return start_columns, end_columns
+
+
+def _pad_string_to_length(string, length):
+  return " " * (length - len(string)) + string
+
+
+def numeric_summary(tensor):
+  """Get a text summmary of a numeric tensor.
+
+  This summary is only available for numeric (int*, float*, complex*) and
+  Boolean tensors.
+
+  Args:
+    tensor: (`numpy.ndarray`) the tensor value object to be summarized.
+
+  Returns:
+    The summary text as a `RichTextLines` object. If the type of `tensor` is not
+    numeric or Boolean, a single-line `RichTextLines` object containing a
+    warning message will reflect that.
+  """
+
+  def _counts_summary(counts, skip_zeros=True, total_count=None):
+    """Format values as a two-row table."""
+    if skip_zeros:
+      counts = [(count_key, count_val) for count_key, count_val in counts
+                if count_val]
+    max_common_len = 0
+    for count_key, count_val in counts:
+      count_val_str = str(count_val)
+      common_len = max(len(count_key) + 1, len(count_val_str) + 1)
+      max_common_len = max(common_len, max_common_len)
+
+    key_line = debugger_cli_common.RichLine("|")
+    val_line = debugger_cli_common.RichLine("|")
+    for count_key, count_val in counts:
+      count_val_str = str(count_val)
+      key_line += _pad_string_to_length(count_key, max_common_len)
+      val_line += _pad_string_to_length(count_val_str, max_common_len)
+    key_line += " |"
+    val_line += " |"
+
+    if total_count is not None:
+      total_key_str = "total"
+      total_val_str = str(total_count)
+      max_common_len = max(len(total_key_str) + 1, len(total_val_str))
+      total_key_str = _pad_string_to_length(total_key_str, max_common_len)
+      total_val_str = _pad_string_to_length(total_val_str, max_common_len)
+      key_line += total_key_str + " |"
+      val_line += total_val_str + " |"
+
+    return debugger_cli_common.rich_text_lines_from_rich_line_list(
+        [key_line, val_line])
+
+  if not isinstance(tensor, np.ndarray) or not np.size(tensor):
+    return debugger_cli_common.RichTextLines([
+        "No numeric summary available due to empty tensor."])
+  elif (np.issubdtype(tensor.dtype, np.float) or
+        np.issubdtype(tensor.dtype, np.complex) or
+        np.issubdtype(tensor.dtype, np.integer)):
+    counts = [
+        ("nan", np.sum(np.isnan(tensor))),
+        ("-inf", np.sum(np.isneginf(tensor))),
+        ("-", np.sum(np.logical_and(
+            tensor < 0.0, np.logical_not(np.isneginf(tensor))))),
+        ("0", np.sum(tensor == 0.0)),
+        ("+", np.sum(np.logical_and(
+            tensor > 0.0, np.logical_not(np.isposinf(tensor))))),
+        ("+inf", np.sum(np.isposinf(tensor)))]
+    output = _counts_summary(counts, total_count=np.size(tensor))
+
+    valid_array = tensor[
+        np.logical_not(np.logical_or(np.isinf(tensor), np.isnan(tensor)))]
+    if np.size(valid_array):
+      stats = [
+          ("min", np.min(valid_array)),
+          ("max", np.max(valid_array)),
+          ("mean", np.mean(valid_array)),
+          ("std", np.std(valid_array))]
+      output.extend(_counts_summary(stats, skip_zeros=False))
+    return output
+  elif tensor.dtype == np.bool:
+    counts = [
+        ("False", np.sum(tensor == 0)),
+        ("True", np.sum(tensor > 0)),]
+    return _counts_summary(counts, total_count=np.size(tensor))
+  else:
+    return debugger_cli_common.RichTextLines([
+        "No numeric summary available due to tensor dtype: %s." % tensor.dtype])
diff --git a/tensorflow/python/debug/cli/tensor_format_test.py b/tensorflow/python/debug/cli/tensor_format_test.py
index ec80bb998e..d3beb5f7bc 100644
--- a/tensorflow/python/debug/cli/tensor_format_test.py
+++ b/tensorflow/python/debug/cli/tensor_format_test.py
@@ -665,6 +665,73 @@ class RichTextLinesTest(test_util.TensorFlowTestCase):
         ValueError, "Dimensions mismatch"):
       tensor_format.locate_tensor_element(out, [0])
 
+  def testLocateTensorElement2DNoEllipsisWithNumericSummary(self):
+    a = np.linspace(0.0, 1.0 - 1.0 / 16.0, 16).reshape([4, 4])
+
+    out = tensor_format.format_tensor(a, "a", include_numeric_summary=True)
+
+    self.assertEqual([
+        "Tensor \"a\":",
+        "",
+        "Numeric summary:",
+        "|  0  + | total |",
+        "|  1 15 |    16 |",
+        "|           min           max          mean           std |",
+        "|           0.0        0.9375       0.46875 0.28811076429 |",
+        "",
+        "array([[ 0.    ,  0.0625,  0.125 ,  0.1875],",
+        "       [ 0.25  ,  0.3125,  0.375 ,  0.4375],",
+        "       [ 0.5   ,  0.5625,  0.625 ,  0.6875],",
+        "       [ 0.75  ,  0.8125,  0.875 ,  0.9375]])",
+    ], out.lines)
+
+    is_omitted, row, start_col, end_col = tensor_format.locate_tensor_element(
+        out, [0, 0])
+    self.assertFalse(is_omitted)
+    self.assertEqual(8, row)
+    self.assertEqual(9, start_col)
+    self.assertEqual(11, end_col)
+
+    is_omitted, row, start_col, end_col = tensor_format.locate_tensor_element(
+        out, [0, 3])
+    self.assertFalse(is_omitted)
+    self.assertEqual(8, row)
+    self.assertEqual(36, start_col)
+    self.assertEqual(42, end_col)
+
+    is_omitted, row, start_col, end_col = tensor_format.locate_tensor_element(
+        out, [1, 0])
+    self.assertFalse(is_omitted)
+    self.assertEqual(9, row)
+    self.assertEqual(9, start_col)
+    self.assertEqual(13, end_col)
+
+    is_omitted, row, start_col, end_col = tensor_format.locate_tensor_element(
+        out, [1, 3])
+    self.assertFalse(is_omitted)
+    self.assertEqual(9, row)
+    self.assertEqual(36, start_col)
+    self.assertEqual(42, end_col)
+
+    is_omitted, row, start_col, end_col = tensor_format.locate_tensor_element(
+        out, [3, 3])
+    self.assertFalse(is_omitted)
+    self.assertEqual(11, row)
+    self.assertEqual(36, start_col)
+    self.assertEqual(42, end_col)
+
+    with self.assertRaisesRegexp(
+        ValueError, "Indices exceed tensor dimensions"):
+      tensor_format.locate_tensor_element(out, [1, 4])
+
+    with self.assertRaisesRegexp(
+        ValueError, "Indices contain negative"):
+      tensor_format.locate_tensor_element(out, [-1, 2])
+
+    with self.assertRaisesRegexp(
+        ValueError, "Dimensions mismatch"):
+      tensor_format.locate_tensor_element(out, [0])
+
   def testLocateTensorElement3DWithEllipses(self):
     a = np.zeros([11, 11, 11])
 
@@ -858,5 +925,97 @@ class RichTextLinesTest(test_util.TensorFlowTestCase):
       tensor_format.locate_tensor_element(out, [0])
 
 
+class NumericSummaryTest(test_util.TensorFlowTestCase):
+
+  def testNumericSummaryOnFloatFullHouse(self):
+    x = np.array([np.nan, np.nan, -np.inf, np.inf, np.inf, np.inf, -2, -3, -4,
+                  0, 1, 2, 2, 2, 2, 0, 0, 0, np.inf, np.inf, np.inf])
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(
+        "|  nan -inf    -    0    + +inf | total |", out.lines[0])
+    self.assertEqual(
+        "|    2    1    3    4    5    6 |    21 |", out.lines[1])
+    self.assertEqual(
+        "|           min           max          mean           std |",
+        out.lines[2])
+    self.assertEqual(
+        "|          -4.0           2.0           0.0 1.95789002075 |",
+        out.lines[3])
+
+  def testNumericSummaryOnFloatMissingCategories(self):
+    x = np.array([np.nan, np.nan])
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(2, len(out.lines))
+    self.assertEqual("| nan | total |", out.lines[0])
+    self.assertEqual("|   2 |     2 |", out.lines[1])
+
+    x = np.array([-np.inf, np.inf, 0, 0, np.inf, np.inf])
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual("| -inf    0 +inf | total |", out.lines[0])
+    self.assertEqual("|    1    2    3 |     6 |", out.lines[1])
+    self.assertEqual("|  min  max mean  std |", out.lines[2])
+    self.assertEqual("|  0.0  0.0  0.0  0.0 |", out.lines[3])
+
+    x = np.array([-120, 120, 130])
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual("| - + | total |", out.lines[0])
+    self.assertEqual("| 1 2 |     3 |", out.lines[1])
+    self.assertEqual(
+        "|           min           max          mean           std |",
+        out.lines[2])
+    self.assertEqual(
+        "|          -120           130 43.3333333333 115.566238822 |",
+        out.lines[3])
+
+  def testNumericSummaryOnEmptyFloat(self):
+    x = np.array([], dtype=np.float32)
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(["No numeric summary available due to empty tensor."],
+                     out.lines)
+
+  def testNumericSummaryOnInt(self):
+    x = np.array([-3] * 50 + [3] * 200 + [0], dtype=np.int32)
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual("|   -   0   + | total |", out.lines[0])
+    self.assertEqual("|  50   1 200 |   251 |", out.lines[1])
+    self.assertEqual(
+        "|           min           max          mean           std |",
+        out.lines[2])
+    self.assertEqual(
+        "|            -3             3 1.79282868526 2.39789673081 |",
+        out.lines[3])
+
+  def testNumericSummaryOnBool(self):
+    x = np.array([False, True, True, False], dtype=np.bool)
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(2, len(out.lines))
+    self.assertEqual("| False  True | total |", out.lines[0])
+    self.assertEqual("|     2     2 |     4 |", out.lines[1])
+
+    x = np.array([True] * 10, dtype=np.bool)
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(2, len(out.lines))
+    self.assertEqual("| True | total |", out.lines[0])
+    self.assertEqual("|   10 |    10 |", out.lines[1])
+
+    x = np.array([False] * 10, dtype=np.bool)
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(2, len(out.lines))
+    self.assertEqual("| False | total |", out.lines[0])
+    self.assertEqual("|    10 |    10 |", out.lines[1])
+
+    x = np.array([], dtype=np.bool)
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(["No numeric summary available due to empty tensor."],
+                     out.lines)
+
+  def testNumericSummaryOnStrTensor(self):
+    x = np.array(["spam", "egg"], dtype=np.object)
+    out = tensor_format.numeric_summary(x)
+    self.assertEqual(
+        ["No numeric summary available due to tensor dtype: object."],
+        out.lines)
+
+
 if __name__ == "__main__":
   googletest.main()
-- 
GitLab


From 9fec6009f114eba319355dd07489900e5baed6dc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jeroen=20B=C3=A9dorf?= <jeroen@minds.ai>
Date: Mon, 10 Jul 2017 03:08:43 -0700
Subject: [PATCH 0838/1375] - Fix the mixup of replica and task-IDs - Clean-up
 and simplified received tensor selection call-map

---
 tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc | 13 +++-----
 tensorflow/contrib/mpi/mpi_rendezvous_mgr.h  | 33 +++++++-------------
 2 files changed, 16 insertions(+), 30 deletions(-)

diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc
index e97e8d0163..1a2563d20f 100644
--- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc
+++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc
@@ -44,7 +44,8 @@ MPIRendezvousMgr::MPIRendezvousMgr(const WorkerEnv* env)
 
   // extract worker-name
   auto parsed = env->local_devices[0]->parsed_name();
-  const std::string task_id = strings::StrCat(parsed.job, ":", parsed.replica);
+  const std::string task_id =
+      strings::StrCat(parsed.job, ":", parsed.replica, ":", parsed.task);
 
   mpiutils_ = new MPIUtils(task_id);
   background_thread_ =
@@ -66,8 +67,8 @@ void MPIRemoteRendezvous::RecvFromRemoteAsync(
   VLOG(2) << "MPI User requested " << parsed.FullKey()
           << " @ step: " << step_id_;
 
-  std::string src_task =
-      strings::StrCat(parsed.src.job, ":", parsed.src.replica);
+  std::string src_task = strings::StrCat(
+      parsed.src.job, ":", parsed.src.replica, ":", parsed.src.task);
   const int dst = mpiutils_->GetSourceID(src_task);
 
   Device* dst_device;
@@ -138,11 +139,7 @@ void MPIRemoteRendezvous::RecvFromRemoteAsync(
                     std::move(request_call), rendezvous_call);
 }
 
-MPIRemoteRendezvous::~MPIRemoteRendezvous() {
-  MPIRendezvousMgr* mgr =
-      reinterpret_cast<MPIRendezvousMgr*>(this->rendezvous_mgr_);
-  mgr->RemoveStepID(step_id_);
-}
+MPIRemoteRendezvous::~MPIRemoteRendezvous() {}
 
 /*
  * Add the request for one of our Tensors by a remote process
diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
index 50fc380496..24e784df3e 100644
--- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
+++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
@@ -147,15 +147,8 @@ class MPIRendezvousMgr : public BaseRendezvousMgr {
                     MPIRequestTensorCall* rCall) {
     mutex_lock l(mrq_);
     request_queue_.push(RequestQueueEntry(key, std::move(request_call)));
-    recv_tensor_map_[step_id][key] =
-        std::shared_ptr<MPIRequestTensorCall>(rCall);
-  }
-
-  void RemoveStepID(const int64 step_id) {
-    mutex_lock l(mrq_);
-    CHECK(recv_tensor_map_[step_id].size() == 0) << "Removing unfinished step";
-    recv_tensor_map_.erase(step_id);
-    // TODO(jbedorf) Should we verify that the step_id is clear before remove?
+    const std::string key_id = strings::StrCat(key, "_", step_id);
+    recv_tensor_map_[key_id] = std::shared_ptr<MPIRequestTensorCall>(rCall);
   }
 
  protected:
@@ -181,9 +174,8 @@ class MPIRendezvousMgr : public BaseRendezvousMgr {
 
   std::queue<SendQueueEntry> send_queue_ GUARDED_BY(msq_);
   std::queue<RequestQueueEntry> request_queue_ GUARDED_BY(mrq_);
-  std::map<int64, std::unordered_map<std::string,
-                                     std::shared_ptr<MPIRequestTensorCall>>>
-      recv_tensor_map_ GUARDED_BY(mrq_);
+  std::map<std::string, std::shared_ptr<MPIRequestTensorCall>> recv_tensor_map_
+      GUARDED_BY(mrq_);
 
   void AddRequest(RecvTensorRequest, const int);
   void MPIBackgroundThread();
@@ -196,22 +188,19 @@ class MPIRendezvousMgr : public BaseRendezvousMgr {
   void GetRecvCall(const int64 step_id, const std::string& key,
                    std::shared_ptr<MPIRequestTensorCall>* call) {
     mutex_lock l(mrq_);
-    if (recv_tensor_map_.find(step_id) == recv_tensor_map_.end()) {
-      LOG(FATAL) << "Step not found in recv_tensor_map_, step: " << step_id
-                 << " key:  " << key << std::endl;
-    }
-    if (recv_tensor_map_[step_id].find(key) !=
-        recv_tensor_map_[step_id].end()) {
-      *call = recv_tensor_map_[step_id][key];
-    } else {
-      LOG(FATAL) << "Key not found in recv_tensor_map_, step: " << step_id
+
+    const std::string key_id = strings::StrCat(key, "_", step_id);
+    if (recv_tensor_map_.find(key_id) == recv_tensor_map_.end()) {
+      LOG(FATAL) << "Key/step not found in recv_tensor_map_, step: " << step_id
                  << " key:  " << key << std::endl;
     }
+    *call = recv_tensor_map_[key_id];
   }
 
   void RemoveRecvCall(const int64 step_id, const std::string& key) {
     mutex_lock l(mrq_);
-    recv_tensor_map_[step_id].erase(key);
+    const std::string key_id = strings::StrCat(key, "_", step_id);
+    recv_tensor_map_.erase(key_id);
   }
 
   bool GetRequest(RequestQueueEntry* req) {
-- 
GitLab


From eff704b61d8665232cf60f082f64077054fe7917 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 06:48:51 -0700
Subject: [PATCH 0839/1375] Update deprecated ByteSize to ByteSizeLong

PiperOrigin-RevId: 161388633
---
 .../kernels/hexagon/hexagon_graph_execution_test.cc  | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
index 9dcdfd6aa4..218cdff0c6 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
@@ -328,7 +328,7 @@ static void CompareGraphTransferInfo(const GraphTransferInfo& gfi0,
     const GraphTransferInfo::NodeInfo& ni0 = gfi0.node_info(i);
     const GraphTransferInfo::NodeInfo& ni1 = gfi1.node_info(i);
     EXPECT_EQ(ni0.DebugString(), ni1.DebugString());
-    EXPECT_EQ(ni0.ByteSize(), ni1.ByteSize());
+    EXPECT_EQ(ni0.ByteSizeLong(), ni1.ByteSizeLong());
   }
 
   // 2. check const_node_info
@@ -340,7 +340,7 @@ static void CompareGraphTransferInfo(const GraphTransferInfo& gfi0,
     for (int j = 0; j < cni0.shape_size(); ++j) {
       EXPECT_EQ(cni0.shape(j), cni1.shape(j));
     }
-    EXPECT_EQ(cni0.ByteSize(), cni1.ByteSize());
+    EXPECT_EQ(cni0.ByteSizeLong(), cni1.ByteSizeLong());
     EXPECT_EQ(cni0.DebugString(), cni1.DebugString());
   }
 
@@ -349,7 +349,7 @@ static void CompareGraphTransferInfo(const GraphTransferInfo& gfi0,
   for (int i = 0; i < gfi0.node_input_info_size(); ++i) {
     const GraphTransferInfo::NodeInputInfo& nii0 = gfi0.node_input_info(i);
     const GraphTransferInfo::NodeInputInfo& nii1 = gfi1.node_input_info(i);
-    EXPECT_EQ(nii0.ByteSize(), nii1.ByteSize());
+    EXPECT_EQ(nii0.ByteSizeLong(), nii1.ByteSizeLong());
     EXPECT_EQ(nii0.DebugString(), nii1.DebugString());
   }
 
@@ -362,7 +362,7 @@ static void CompareGraphTransferInfo(const GraphTransferInfo& gfi0,
     for (int j = 0; j < noi0.max_byte_size_size(); ++j) {
       EXPECT_EQ(noi0.max_byte_size(j), noi1.max_byte_size(j));
     }
-    EXPECT_EQ(noi0.ByteSize(), noi1.ByteSize());
+    EXPECT_EQ(noi0.ByteSizeLong(), noi1.ByteSizeLong());
     EXPECT_EQ(noi0.DebugString(), noi1.DebugString());
   }
 
@@ -374,7 +374,7 @@ static void CompareGraphTransferInfo(const GraphTransferInfo& gfi0,
         gfi0.graph_input_node_info(i);
     const GraphTransferInfo::GraphInputNodeInfo& gini1 =
         gfi0.graph_input_node_info(i);
-    EXPECT_EQ(gini0.ByteSize(), gini1.ByteSize());
+    EXPECT_EQ(gini0.ByteSizeLong(), gini1.ByteSizeLong());
     EXPECT_EQ(gini0.DebugString(), gini1.DebugString());
   }
 
@@ -386,7 +386,7 @@ static void CompareGraphTransferInfo(const GraphTransferInfo& gfi0,
         gfi0.graph_output_node_info(i);
     const GraphTransferInfo::GraphOutputNodeInfo& goni1 =
         gfi0.graph_output_node_info(i);
-    EXPECT_EQ(goni0.ByteSize(), goni1.ByteSize());
+    EXPECT_EQ(goni0.ByteSizeLong(), goni1.ByteSizeLong());
     EXPECT_EQ(goni0.DebugString(), goni1.DebugString());
   }
 
-- 
GitLab


From bf267f366b5f4ed4f27ee36e99fae2f6661891bb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 07:24:18 -0700
Subject: [PATCH 0840/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 161391365
---
 tensorflow/contrib/batching/BUILD             |  17 +--
 tensorflow/contrib/imperative/BUILD           |  25 +++-
 tensorflow/contrib/tensor_forest/BUILD        |  53 +++-----
 tensorflow/contrib/tensor_forest/hybrid/BUILD | 118 ++++--------------
 .../contrib/tensor_forest/kernels/v4/BUILD    |   1 -
 tensorflow/contrib/tpu/BUILD                  |  44 +++++--
 6 files changed, 103 insertions(+), 155 deletions(-)

diff --git a/tensorflow/contrib/batching/BUILD b/tensorflow/contrib/batching/BUILD
index 3cf8c318eb..fac4c41d83 100644
--- a/tensorflow/contrib/batching/BUILD
+++ b/tensorflow/contrib/batching/BUILD
@@ -26,9 +26,7 @@ cc_library(
 cc_test(
     name = "batch_scheduler_test",
     size = "small",
-    srcs = [
-        "batch_scheduler_test.cc",
-    ],
+    srcs = ["batch_scheduler_test.cc"],
     deps = [
         ":batch_scheduler",
         "//tensorflow/core:lib",
@@ -52,7 +50,6 @@ cc_library(
     hdrs = ["shared_batch_scheduler.h"],
     deps = [
         ":batch_scheduler",
-        ":shared_batch_scheduler_hdrs",
         "//tensorflow/contrib/batching/util:periodic_function_dynamic",
         "//tensorflow/core:lib",
     ],
@@ -62,9 +59,7 @@ cc_library(
 cc_test(
     name = "shared_batch_scheduler_test",
     size = "small",
-    srcs = [
-        "shared_batch_scheduler_test.cc",
-    ],
+    srcs = ["shared_batch_scheduler_test.cc"],
     deps = [
         ":shared_batch_scheduler",
         "//tensorflow/contrib/batching/test_util:fake_clock_env",
@@ -86,9 +81,7 @@ cc_library(
 cc_test(
     name = "basic_batch_scheduler_test",
     size = "small",
-    srcs = [
-        "basic_batch_scheduler_test.cc",
-    ],
+    srcs = ["basic_batch_scheduler_test.cc"],
     deps = [
         ":basic_batch_scheduler",
         ":batch_scheduler",
@@ -125,9 +118,7 @@ load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
 
 tf_custom_op_library(
     name = "python/ops/_batch_ops.so",
-    srcs = [
-        "ops/batch_ops.cc",
-    ],
+    srcs = ["ops/batch_ops.cc"],
     deps = [
         "//tensorflow/contrib/batching/kernels:batch_kernels",
     ],
diff --git a/tensorflow/contrib/imperative/BUILD b/tensorflow/contrib/imperative/BUILD
index c01d3f2d05..df6e5c4e0a 100644
--- a/tensorflow/contrib/imperative/BUILD
+++ b/tensorflow/contrib/imperative/BUILD
@@ -20,6 +20,18 @@ py_library(
     deps = [
         ":imperative_graph",
         ":imperative_mode",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:util",
+        "//tensorflow/python:variables",
     ],
 )
 
@@ -28,9 +40,16 @@ py_library(
     srcs = ["imperative_graph.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:framework",
-        "//tensorflow/python:ops",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:tensor_shape",
         "//tensorflow/python:util",
+        "//tensorflow/python:variables",
     ],
 )
 
@@ -41,7 +60,9 @@ py_library(
     deps = [
         ":imperative_graph",
         "//tensorflow/python:client",
+        "//tensorflow/python:errors",
         "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
     ],
 )
 
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 1115f39b2a..3ef7f30f8a 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -1,11 +1,6 @@
 # TensorFlow code for training random forests.
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
 
-package(default_visibility = [
-    "//visibility:public",
-])
+licenses(["notice"])  # Apache 2.0
 
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs")
@@ -14,6 +9,10 @@ load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
 load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
 
+package(default_visibility = ["//visibility:public"])
+
+exports_files(["LICENSE"])
+
 filegroup(
     name = "all_files",
     srcs = glob(
@@ -68,25 +67,22 @@ cc_library(
 
 py_library(
     name = "constants",
-    srcs = [
-        "python/constants.py",
-    ],
+    srcs = ["python/constants.py"],
     srcs_version = "PY2AND3",
 )
 
 py_library(
     name = "data_ops_py",
-    srcs = [
-        "python/ops/data_ops.py",
-    ],
+    srcs = ["python/ops/data_ops.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":tensor_forest_ops_py",
         "//tensorflow/python:array_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
     ],
 )
 
@@ -131,9 +127,7 @@ py_library(
 
 tf_kernel_library(
     name = "tensor_forest_kernels",
-    srcs = [
-        ":v2_op_sources",
-    ],
+    srcs = [":v2_op_sources"],
     deps = [
         ":tree_utils",
         "//tensorflow/core:framework_headers_lib",
@@ -248,12 +242,8 @@ tf_kernel_library(
 
 tf_custom_op_library(
     name = "python/ops/_model_ops.so",
-    srcs = [
-        "ops/model_ops.cc",
-    ],
-    deps = [
-        ":model_ops_lib",
-    ],
+    srcs = ["ops/model_ops.cc"],
+    deps = [":model_ops_lib"],
 )
 
 tf_custom_op_py_library(
@@ -332,12 +322,8 @@ tf_kernel_library(
 
 tf_custom_op_library(
     name = "python/ops/_stats_ops.so",
-    srcs = [
-        "ops/stats_ops.cc",
-    ],
-    deps = [
-        ":stats_ops_lib",
-    ],
+    srcs = ["ops/stats_ops.cc"],
+    deps = [":stats_ops_lib"],
 )
 
 tf_custom_op_py_library(
@@ -520,9 +506,8 @@ py_test(
     srcs = ["python/kernel_tests/tree_predictions_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":constants",
+        ":data_ops_py",
         ":tensor_forest_ops_py",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
     ],
@@ -549,10 +534,10 @@ py_library(
         ":constants",
         ":data_ops_py",
         ":tensor_forest_ops_py",
+        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/losses:losses_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
@@ -586,10 +571,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":tensor_forest_py",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
     ],
 )
 
@@ -599,13 +584,14 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":client_lib",
-        ":data_ops_py",
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/learn",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform",
         "//tensorflow/python:state_ops",
+        "//tensorflow/python:training",
     ],
 )
 
@@ -619,7 +605,6 @@ py_test(
         ":tensor_forest_py",
         "//tensorflow/contrib/learn/python/learn/datasets",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/contrib/tensor_forest/hybrid/BUILD b/tensorflow/contrib/tensor_forest/hybrid/BUILD
index 41a815bdec..f9b9914341 100644
--- a/tensorflow/contrib/tensor_forest/hybrid/BUILD
+++ b/tensorflow/contrib/tensor_forest/hybrid/BUILD
@@ -2,17 +2,15 @@
 
 licenses(["notice"])  # Apache 2.0
 
-exports_files(["LICENSE"])
-
-package(default_visibility = [
-    "//visibility:public",
-])
-
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
 
+package(default_visibility = ["//visibility:public"])
+
+exports_files(["LICENSE"])
+
 filegroup(
     name = "all_files",
     srcs = glob(
@@ -28,20 +26,14 @@ filegroup(
 filegroup(
     name = "custom_op_sources",
     srcs = glob(
-        [
-            "core/ops/*.cc",
-        ],
+        ["core/ops/*.cc"],
         exclude = ["core/ops/*_test.cc"],
     ),
 )
 
 filegroup(
     name = "custom_op_headers",
-    srcs = glob(
-        [
-            "core/ops/*.h",
-        ],
-    ),
+    srcs = glob(["core/ops/*.h"]),
 )
 
 cc_library(
@@ -92,9 +84,7 @@ tf_custom_op_library(
 cc_library(
     name = "utils",
     srcs = ["core/ops/utils.cc"],
-    hdrs = [
-        "core/ops/utils.h",
-    ],
+    hdrs = ["core/ops/utils.h"],
     deps = [
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
@@ -108,9 +98,7 @@ tf_custom_op_py_library(
         "__init__.py",
         "python/ops/training_ops.py",
     ],
-    dso = [
-        "python/ops/_training_ops.so",
-    ],
+    dso = ["python/ops/_training_ops.so"],
     kernels = [
         ":all_kernels",
     ],
@@ -139,22 +127,16 @@ py_library(
         "python/hybrid_layer.py",
     ],
     srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
-    ],
+    deps = ["//tensorflow/contrib/framework:framework_py"],
 )
 
 py_test(
     name = "hybrid_layer_test",
     size = "small",
-    srcs = [
-        "python/hybrid_layer_test.py",
-    ],
+    srcs = ["python/hybrid_layer_test.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":fully_connected_layer",
-        ":hybrid_layer",
         ":hybrid_model",
         "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:framework_test_lib",
@@ -164,14 +146,11 @@ py_test(
 
 py_library(
     name = "hybrid_model",
-    srcs = [
-        "python/hybrid_model.py",
-    ],
+    srcs = ["python/hybrid_model.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow:tensorflow_py",
+        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
@@ -182,13 +161,10 @@ py_library(
 
 py_library(
     name = "fully_connected_layer",
-    srcs = [
-        "python/layers/fully_connected.py",
-    ],
+    srcs = ["python/layers/fully_connected.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":hybrid_layer",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
@@ -219,7 +195,6 @@ py_test(
     deps = [
         ":ops_lib",
         ":training_ops",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
@@ -228,15 +203,12 @@ py_test(
 
 py_library(
     name = "decisions_to_data_layer",
-    srcs = [
-        "python/layers/decisions_to_data.py",
-    ],
+    srcs = ["python/layers/decisions_to_data.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":hybrid_layer",
         ":ops_lib",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
+        ":training_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:init_ops",
@@ -246,13 +218,10 @@ py_library(
 
 py_test(
     name = "decisions_to_data_test",
-    srcs = [
-        "python/layers/decisions_to_data_test.py",
-    ],
+    srcs = ["python/layers/decisions_to_data_test.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_layer",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
@@ -263,35 +232,25 @@ py_test(
 
 py_library(
     name = "decisions_to_data_then_nn",
-    srcs = [
-        "python/models/decisions_to_data_then_nn.py",
-    ],
+    srcs = ["python/models/decisions_to_data_then_nn.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_layer",
         ":fully_connected_layer",
         ":hybrid_model",
-        ":ops_lib",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:training",
     ],
 )
 
 py_library(
     name = "hard_decisions_to_data_then_nn",
-    srcs = [
-        "python/models/hard_decisions_to_data_then_nn.py",
-    ],
+    srcs = ["python/models/hard_decisions_to_data_then_nn.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_layer",
         ":fully_connected_layer",
         ":hybrid_model",
-        ":ops_lib",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:training",
     ],
@@ -304,7 +263,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_then_nn",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
@@ -315,17 +273,12 @@ py_test(
 
 py_library(
     name = "k_feature_decisions_to_data_then_nn",
-    srcs = [
-        "python/models/k_feature_decisions_to_data_then_nn.py",
-    ],
+    srcs = ["python/models/k_feature_decisions_to_data_then_nn.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_layer",
         ":fully_connected_layer",
         ":hybrid_model",
-        ":ops_lib",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:training",
     ],
 )
@@ -337,7 +290,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":k_feature_decisions_to_data_then_nn",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
@@ -348,17 +300,12 @@ py_test(
 
 py_library(
     name = "forest_to_data_then_nn",
-    srcs = [
-        "python/models/forest_to_data_then_nn.py",
-    ],
+    srcs = ["python/models/forest_to_data_then_nn.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_layer",
         ":fully_connected_layer",
         ":hybrid_model",
-        ":ops_lib",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:training",
     ],
 )
@@ -370,7 +317,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":forest_to_data_then_nn",
-        "//tensorflow:tensorflow_py",
         "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
@@ -381,52 +327,36 @@ py_test(
 
 py_library(
     name = "nn",
-    srcs = [
-        "python/models/nn.py",
-    ],
+    srcs = ["python/models/nn.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":fully_connected_layer",
         ":hybrid_model",
-        ":ops_lib",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:training",
     ],
 )
 
 py_library(
     name = "stochastic_hard_decisions_to_data_then_nn",
-    srcs = [
-        "python/models/stochastic_hard_decisions_to_data_then_nn.py",
-    ],
+    srcs = ["python/models/stochastic_hard_decisions_to_data_then_nn.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_layer",
         ":fully_connected_layer",
         ":hard_decisions_to_data_then_nn",
-        ":hybrid_model",
-        ":ops_lib",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
         "//tensorflow/python:training",
     ],
 )
 
 py_library(
     name = "stochastic_soft_decisions_to_data_then_nn",
-    srcs = [
-        "python/models/stochastic_soft_decisions_to_data_then_nn.py",
-    ],
+    srcs = ["python/models/stochastic_soft_decisions_to_data_then_nn.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":decisions_to_data_layer",
         ":fully_connected_layer",
         ":hard_decisions_to_data_then_nn",
-        ":hybrid_model",
-        ":ops_lib",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/tensor_forest:tensor_forest_py",
+        "//tensorflow/python:training",
     ],
 )
 
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index 06856c3c07..7b3fd63a7a 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -198,7 +198,6 @@ cc_library(
         ":leaf_model_operators",
         ":params",
         ":split_collection_operators",
-        ":stat_utils",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
         "//tensorflow/contrib/tensor_forest:tree_utils",
diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD
index 0a83070c34..210bfc766e 100644
--- a/tensorflow/contrib/tpu/BUILD
+++ b/tensorflow/contrib/tpu/BUILD
@@ -1,12 +1,5 @@
 # Description: Operations defined for Cloud TPUs
 
-package(
-    default_visibility = [
-        "//learning/brain:__subpackages__",
-        "//tensorflow:__subpackages__",
-    ],
-)
-
 licenses(["notice"])  # Apache 2.0
 
 load(
@@ -18,6 +11,13 @@ load(
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
 load("//tensorflow:tensorflow.bzl", "tf_py_test")
 
+package(
+    default_visibility = [
+        "//learning/brain:__subpackages__",
+        "//tensorflow:__subpackages__",
+    ],
+)
+
 cc_library(
     name = "all_ops",
     deps = [
@@ -41,9 +41,17 @@ py_library(
         ":tpu",
         ":tpu_py",
         ":training_loop",
-        "//tensorflow/python:framework",
+        "//tensorflow/contrib/learn",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:init_ops",
         "//tensorflow/python:platform",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:model_fn",
     ],
 )
 
@@ -126,8 +134,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":tpu_feed",
-        ":tpu_py",
-        "//tensorflow/python:framework",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -139,9 +146,15 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":tpu_function",
         ":tpu_py",
         ":training_loop",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:variable_scope",
     ],
 )
 
@@ -151,6 +164,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:framework",
+        "//tensorflow/python:tensor_shape",
     ],
 )
 
@@ -161,7 +175,11 @@ py_library(
     deps = [
         ":tpu_py",
         ":tpu_sharding",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:tensor_shape",
     ],
 )
 
@@ -174,7 +192,11 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":tpu_function",
-        "//tensorflow/python:framework",
+        ":tpu_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:training",
     ],
 )
 
-- 
GitLab


From 5b9bff4a04a331aa551ba5fc5bd713d9cb9dc684 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Mon, 10 Jul 2017 07:29:34 -0700
Subject: [PATCH 0841/1375] [TF:XLA] Use xla::Shape instead of TensorShape for
 representing argument/resource update shapes to XlaCompiler.

Change in preparation for adding implementations of the Stack... operators in the Tensorflow/XLA bridge. The representation of a Stack will be a tuple of (stack value, stack size). xla::Shape can represent tuples, but TensorShape cannot.

PiperOrigin-RevId: 161391774
---
 tensorflow/compiler/aot/compile.cc            |  5 +-
 tensorflow/compiler/jit/kernels/BUILD         |  1 +
 .../jit/kernels/xla_device_launch_op.cc       | 31 ++++++----
 .../compiler/jit/xla_compilation_cache.cc     | 11 ++--
 tensorflow/compiler/tf2xla/BUILD              |  1 +
 .../compiler/tf2xla/kernels/while_op.cc       | 14 +++--
 tensorflow/compiler/tf2xla/xla_compiler.cc    | 59 +++++++++----------
 tensorflow/compiler/tf2xla/xla_compiler.h     | 10 ++--
 .../compiler/tf2xla/xla_compiler_test.cc      |  9 +--
 tensorflow/contrib/xla_tf_graph/BUILD         |  1 +
 .../xla_tf_graph/xla_tf_graph_util_test.cc    |  5 +-
 11 files changed, 81 insertions(+), 66 deletions(-)

diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc
index 51d08eaa01..03bdd63623 100644
--- a/tensorflow/compiler/aot/compile.cc
+++ b/tensorflow/compiler/aot/compile.cc
@@ -24,6 +24,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/aot/flags.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/tf2xla/xla_compiler.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/compiler/xla/client/client_library.h"
@@ -254,7 +255,9 @@ Status CreateXlaArgs(const Graph& graph,
     XlaCompiler::Argument arg;
     arg.kind = XlaCompiler::Argument::kParameter;
     TF_RETURN_IF_ERROR(GetNodeAttr(node->attrs(), "T", &arg.type));
-    TF_RETURN_IF_ERROR(GetNodeAttr(node->attrs(), kShapeAttr, &arg.shape));
+    TensorShape shape;
+    TF_RETURN_IF_ERROR(GetNodeAttr(node->attrs(), kShapeAttr, &shape));
+    TF_RETURN_IF_ERROR(TensorShapeToXLAShape(arg.type, shape, &arg.shape));
     TF_RETURN_IF_ERROR(GetNodeAttr(node->attrs(), kDebugNameAttr, &arg.name));
     xla_args->push_back(arg);
   }
diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD
index 97f3512a6c..354c0fabfc 100644
--- a/tensorflow/compiler/jit/kernels/BUILD
+++ b/tensorflow/compiler/jit/kernels/BUILD
@@ -36,6 +36,7 @@ cc_library(
         "//tensorflow/compiler/jit:common",
         "//tensorflow/compiler/jit:xla_compilation_cache",
         "//tensorflow/compiler/jit:xla_device",
+        "//tensorflow/compiler/tf2xla:common",
         "//tensorflow/compiler/tf2xla:xla_compiler",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla/client:local_client",
diff --git a/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
index bd051d06ae..2b77e5aaf4 100644
--- a/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
+++ b/tensorflow/compiler/jit/kernels/xla_device_launch_op.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/compiler/jit/defs.h"
 #include "tensorflow/compiler/jit/xla_device.h"
 #include "tensorflow/compiler/jit/xla_device_context.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -210,6 +211,9 @@ void XlaDeviceLaunchOp::Compute(OpKernelContext* ctx) {
     OP_REQUIRES(ctx,
                 write.input_index >= 0 && write.input_index < ctx->num_inputs(),
                 errors::Internal("Invalid input index for variable write."));
+    TensorShape write_shape;
+    OP_REQUIRES_OK(ctx, XLAShapeToTensorShape(write.shape, &write_shape));
+
     // This code is very close to being a clone of AssignVariableOp, but the
     // key difference is that the contents of an XLA device tensor cannot be
     // copied safely; instead we must use
@@ -217,26 +221,27 @@ void XlaDeviceLaunchOp::Compute(OpKernelContext* ctx) {
     Var* variable = nullptr;
     // TODO(b/35625933): tensorflow::Var should contain a PersistentTensor, not
     // a Tensor.
-    OP_REQUIRES_OK(ctx, LookupOrCreateResource<Var>(
-                            ctx, HandleFromInput(ctx, write.input_index),
-                            &variable, [this, ctx, &write](Var** ptr) {
-                              *ptr = new Var(write.type);
-                              PersistentTensor unused;
-                              Tensor* tmp;
-                              TF_RETURN_IF_ERROR(ctx->allocate_persistent(
-                                  write.type, write.shape, &unused, &tmp));
-                              *(*ptr)->tensor() = *tmp;
-                              return Status::OK();
-                            }));
+    OP_REQUIRES_OK(ctx,
+                   LookupOrCreateResource<Var>(
+                       ctx, HandleFromInput(ctx, write.input_index), &variable,
+                       [this, ctx, &write, &write_shape](Var** ptr) {
+                         *ptr = new Var(write.type);
+                         PersistentTensor unused;
+                         Tensor* tmp;
+                         TF_RETURN_IF_ERROR(ctx->allocate_persistent(
+                             write.type, write_shape, &unused, &tmp));
+                         *(*ptr)->tensor() = *tmp;
+                         return Status::OK();
+                       }));
     core::ScopedUnref s(variable);
 
     mutex_lock ml(*variable->mu());
     OP_REQUIRES(ctx, variable->tensor()->dtype() == write.type,
                 errors::Internal("Mismatched type in variable write"));
-    if (!variable->tensor()->shape().IsSameSize(write.shape)) {
+    if (!variable->tensor()->shape().IsSameSize(write_shape)) {
       PersistentTensor unused;
       Tensor* tmp;
-      OP_REQUIRES_OK(ctx, ctx->allocate_persistent(write.type, write.shape,
+      OP_REQUIRES_OK(ctx, ctx->allocate_persistent(write.type, write_shape,
                                                    &unused, &tmp));
       *variable->tensor() = *tmp;
     }
diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc
index 2325217b97..3c52316cce 100644
--- a/tensorflow/compiler/jit/xla_compilation_cache.cc
+++ b/tensorflow/compiler/jit/xla_compilation_cache.cc
@@ -148,7 +148,8 @@ Status BuildArguments(int num_constant_args,
     XlaCompiler::Argument& arg = (*args)[input_num];
     arg.kind = XlaCompiler::Argument::kConstant;
     arg.type = input.dtype();
-    arg.shape = input.shape();
+    TF_RETURN_IF_ERROR(
+        TensorShapeToXLAShape(input.dtype(), input.shape(), &arg.shape));
     arg.constant_value = input;
     ++input_num;
   }
@@ -169,7 +170,8 @@ Status BuildArguments(int num_constant_args,
       arg.constant_value = input;
     }
     arg.type = input.dtype();
-    arg.shape = input.shape();
+    TF_RETURN_IF_ERROR(
+        TensorShapeToXLAShape(input.dtype(), input.shape(), &arg.shape));
     ++input_num;
   }
 
@@ -186,7 +188,8 @@ Status BuildArguments(int num_constant_args,
     if (variable_args[variable_id].present) {
       const Tensor& value = variable_args[variable_id].value;
       arg.type = value.dtype();
-      arg.shape = value.shape();
+      TF_RETURN_IF_ERROR(
+          TensorShapeToXLAShape(value.dtype(), value.shape(), &arg.shape));
       arg.initialized = true;
     } else {
       // The values of uninitialized variables are not passed as inputs, since
@@ -195,7 +198,7 @@ Status BuildArguments(int num_constant_args,
       // uninitialized variables.
       arg.initialized = false;
       arg.type = DT_INVALID;
-      arg.shape = TensorShape();
+      arg.shape = xla::Shape();
     }
     ++input_num;
   }
diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD
index 997ecd7ebb..60e68db2d6 100644
--- a/tensorflow/compiler/tf2xla/BUILD
+++ b/tensorflow/compiler/tf2xla/BUILD
@@ -102,6 +102,7 @@ cc_test(
         "//tensorflow/cc:ops",
         "//tensorflow/compiler/tf2xla/kernels:xla_ops",
         "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/tests:literal_test_util",
diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc
index 37c611d147..ae018515ad 100644
--- a/tensorflow/compiler/tf2xla/kernels/while_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc
@@ -63,8 +63,7 @@ Status MakeXlaCompilerArgumentsFromInputs(
       if (arg.initialized) {
         auto shape = ctx->builder()->GetShape(resource->value);
         TF_RETURN_IF_ERROR(shape.status());
-        TF_RETURN_IF_ERROR(
-            XLAShapeToTensorShape(*shape.ValueOrDie(), &arg.shape));
+        arg.shape = *shape.ValueOrDie();
       } else {
         *has_uninitialized_vars = true;
       }
@@ -79,7 +78,8 @@ Status MakeXlaCompilerArgumentsFromInputs(
     } else {
       arg.kind = XlaCompiler::Argument::kParameter;
       arg.type = ctx->input_type(i);
-      arg.shape = ctx->InputShape(i);
+      TF_RETURN_IF_ERROR(
+          TensorShapeToXLAShape(arg.type, ctx->InputShape(i), &arg.shape));
     }
   }
   return Status::OK();
@@ -143,6 +143,8 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
       const XlaCompiler::ResourceUpdate& update = body.resource_updates[i];
       XlaCompiler::Argument& arg = arguments[update.input_index];
       if (!arg.initialized) {
+        VLOG(2) << "Update shape for argument " << update.input_index << " "
+                << xla::ShapeUtil::HumanString(update.shape);
         arg.initialized = true;
         arg.shape = update.shape;
 
@@ -150,11 +152,13 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
         OP_REQUIRES_OK(ctx,
                        ctx->GetResourceInput(update.input_index, &resource));
 
-        xla::ComputationDataHandle zero = XlaHelpers::Zero(builder, arg.type);
-        resource->value = builder->Broadcast(zero, update.shape.dim_sizes());
+        std::unique_ptr<xla::Literal> zero =
+            xla::Literal::CreateFromShape(update.shape);
+        resource->value = builder->ConstantLiteral(*zero);
       }
     }
     // Recompile the body with the "correct" shapes.
+    VLOG(1) << "Recompiling body with non-placeholder shapes";
     body = {};
     OP_REQUIRES_OK(ctx, compiler->CompileFunction(body_options, body_name_attr_,
                                                   arguments, &body));
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index c1af731986..d1dc85a0eb 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -60,9 +60,11 @@ Status CheckSignature(const DataTypeVector& types,
 
 bool XlaCompiler::Argument::operator==(
     const XlaCompiler::Argument& other) const {
-  if (std::tie(kind, type, shape, name, tensor_array_size) !=
-      std::tie(other.kind, other.type, other.shape, other.name,
-               other.tensor_array_size)) {
+  if (std::tie(kind, type, name, tensor_array_size) !=
+      std::tie(other.kind, other.type, other.name, other.tensor_array_size)) {
+    return false;
+  }
+  if (!xla::ShapeUtil::Equal(shape, other.shape)) {
     return false;
   }
   if (constant_value.shape() != other.constant_value.shape()) {
@@ -297,10 +299,7 @@ Status BuildArguments(const std::vector<XlaCompiler::Argument>& args,
   for (std::vector<int>::size_type i = 0; i < input_shapes->size(); ++i) {
     const XlaCompiler::Argument& arg = args[parameters[i]];
     // Computes the shapes of non-constant arguments.
-    xla::PrimitiveType type;
-    TF_RETURN_IF_ERROR(DataTypeToPrimitiveType(arg.type, &type));
-    xla::ShapeUtil::PopulateShape(type, arg.shape.dim_sizes(),
-                                  &(*input_shapes)[i]);
+    (*input_shapes)[i] = arg.shape;
     (*input_mapping)[i] = parameters[i];
   }
 
@@ -342,7 +341,7 @@ Status BuildComputation(
     const std::vector<std::unique_ptr<XlaResource>>& resources,
     bool has_side_effects, bool return_updated_values_for_all_resources,
     xla::ComputationBuilder* builder, xla::Computation* computation,
-    int* num_nonconst_outputs,
+    int* num_computation_outputs, int* num_nonconst_outputs,
     std::vector<XlaCompiler::ResourceUpdate>* resource_updates) {
   std::vector<xla::ComputationDataHandle> elems;
   elems.reserve(retvals.size());
@@ -378,6 +377,7 @@ Status BuildComputation(
     }
   }
 
+  *num_computation_outputs = elems.size();
   if (!elems.empty() || has_side_effects) {
     // Builds a empty tuple return value for computations that have side effects
     // but have no return values.
@@ -400,6 +400,18 @@ Status BuildComputation(
   return Status::OK();
 }
 
+void AssignMajorToMinorLayout(xla::Shape* shape) {
+  if (xla::ShapeUtil::IsTuple(*shape)) {
+    for (xla::Shape& elem_shape : *shape->mutable_tuple_shapes()) {
+      AssignMajorToMinorLayout(&elem_shape);
+    }
+  } else {
+    auto& minor_to_major = *shape->mutable_layout()->mutable_minor_to_major();
+    minor_to_major.Resize(xla::ShapeUtil::Rank(*shape), 0);
+    std::iota(minor_to_major.rbegin(), minor_to_major.rend(), 0);
+  }
+}
+
 }  // namespace
 
 Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
@@ -440,12 +452,13 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
                                   flib_runtime_.get(), NextStepId()));
 
   int num_nonconst_outputs;
+  int num_computation_outputs;
   result->computation = std::make_shared<xla::Computation>();
   TF_RETURN_IF_ERROR(BuildComputation(
       context->retvals(), context->resources(), context->has_side_effects(),
       options.return_updated_values_for_all_resources, &builder,
-      result->computation.get(), &num_nonconst_outputs,
-      &result->resource_updates));
+      result->computation.get(), &num_computation_outputs,
+      &num_nonconst_outputs, &result->resource_updates));
 
   result->requires_runtime_context = context->has_context_parameter();
 
@@ -482,23 +495,8 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
   VLOG(2) << "XLA output shape: "
           << xla::ShapeUtil::HumanString(result->xla_output_shape);
 
-  auto num_computation_outputs =
-      (xla::ShapeUtil::IsTuple(result->xla_output_shape))
-          ? xla::ShapeUtil::TupleElementCount(result->xla_output_shape)
-          : 1;
   // Tensorflow expects a major-to-minor order of results.
-  if (1 == num_computation_outputs) {
-    xla::Shape& s = result->xla_output_shape;
-    auto& minor_to_major = *s.mutable_layout()->mutable_minor_to_major();
-    minor_to_major.Resize(xla::ShapeUtil::Rank(s), 0);
-    std::iota(minor_to_major.rbegin(), minor_to_major.rend(), 0);
-  } else {
-    for (xla::Shape& s : *result->xla_output_shape.mutable_tuple_shapes()) {
-      auto& minor_to_major = *s.mutable_layout()->mutable_minor_to_major();
-      minor_to_major.Resize(xla::ShapeUtil::Rank(s), 0);
-      std::iota(minor_to_major.rbegin(), minor_to_major.rend(), 0);
-    }
-  }
+  AssignMajorToMinorLayout(&result->xla_output_shape);
 
   // Converts the output shapes to TensorShapes.
   int computation_output = 0;
@@ -525,14 +523,11 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
   for (std::vector<ResourceUpdate>::size_type i = 0;
        i < result->resource_updates.size(); ++i) {
     if (num_computation_outputs > 1) {
-      TF_RETURN_IF_ERROR(XLAShapeToTensorShape(
-          xla::ShapeUtil::GetTupleElementShape(result->xla_output_shape,
-                                               computation_output),
-          &result->resource_updates[i].shape));
+      result->resource_updates[i].shape = xla::ShapeUtil::GetTupleElementShape(
+          result->xla_output_shape, computation_output);
     } else {
       CHECK_EQ(0, computation_output);
-      TF_RETURN_IF_ERROR(XLAShapeToTensorShape(
-          result->xla_output_shape, &result->resource_updates[i].shape));
+      result->resource_updates[i].shape = result->xla_output_shape;
     }
     ++computation_output;
   }
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h
index 197e45617c..968f667bba 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.h
+++ b/tensorflow/compiler/tf2xla/xla_compiler.h
@@ -99,13 +99,13 @@ class XlaCompiler {
 
     Kind kind = kInvalid;
 
-    // The type of the argument. If the argument is a resource variable, this
+    // The type of the argument. If the argument is a resource, this
     // is the type of the variable's value, not DT_RESOURCE.
     DataType type;
 
-    // The shape of the argument. If the argument is a resource variable, this
-    // is the shape of the variable's value.
-    TensorShape shape;
+    // The shape of the argument. If the argument is a resource, this is the
+    // shape of the resource's value.
+    xla::Shape shape;
 
     // The value of the argument, if it is a compile-time constant. Must be a
     // host-memory tensor.
@@ -142,7 +142,7 @@ class XlaCompiler {
 
     // Type and shape of the tensor to be written back.
     DataType type;
-    TensorShape shape;
+    xla::Shape shape;
 
     // Was the value of the variable modified by the computation?
     // (Always true, unless `return_updated_values_for_all_resources` is true.)
diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
index 97cd951000..42bbccd1d3 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/framework/resource_mgr.h"
@@ -149,10 +150,10 @@ TEST_F(XlaCompilerTest, Simple) {
   std::vector<XlaCompiler::Argument> args(2);
   args[0].kind = XlaCompiler::Argument::kParameter;
   args[0].type = DT_INT32;
-  args[0].shape = TensorShape({2});
+  args[0].shape = xla::ShapeUtil::MakeShape(xla::S32, {2});
   args[1].kind = XlaCompiler::Argument::kParameter;
   args[1].type = DT_INT32;
-  args[1].shape = TensorShape({2});
+  args[1].shape = xla::ShapeUtil::MakeShape(xla::S32, {2});
 
   // Compiles the graph.
   XlaCompiler compiler(DefaultOptions());
@@ -201,7 +202,7 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
   std::vector<XlaCompiler::Argument> args(1);
   args[0].kind = XlaCompiler::Argument::kParameter;
   args[0].type = DT_INT32;
-  args[0].shape = TensorShape({2});
+  args[0].shape = xla::ShapeUtil::MakeShape(xla::S32, {2});
 
   XlaCompiler::Options options = DefaultOptions();
   XlaCompiler compiler(options);
@@ -290,7 +291,7 @@ TEST_F(XlaCompilerTest, ResourceManager) {
   std::vector<XlaCompiler::Argument> args(1);
   args[0].kind = XlaCompiler::Argument::kParameter;
   args[0].type = DT_INT32;
-  args[0].shape = TensorShape({2});
+  args[0].shape = xla::ShapeUtil::MakeShape(xla::S32, {2});
 
   DummyResourceForTest* resource = new DummyResourceForTest();
 
diff --git a/tensorflow/contrib/xla_tf_graph/BUILD b/tensorflow/contrib/xla_tf_graph/BUILD
index 0487e79334..33373e0eb4 100644
--- a/tensorflow/contrib/xla_tf_graph/BUILD
+++ b/tensorflow/contrib/xla_tf_graph/BUILD
@@ -51,6 +51,7 @@ cc_test(
         "//tensorflow/cc:scope",
         "//tensorflow/compiler/jit:xla_cpu_jit",
         "//tensorflow/compiler/tf2xla:xla_compiler",
+        "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/service:hlo_module_config",
         "//tensorflow/core:core_cpu_internal",
diff --git a/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc b/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc
index 270e062db6..32336a767a 100644
--- a/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc
+++ b/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/service/hlo_module_config.h"
+#include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
@@ -45,10 +46,10 @@ static std::vector<XlaCompiler::Argument> BuildAddGraphArguments() {
   // Difference of dimension will add extra broadcast_dimensions.
   // broadcast_dimension generates an additional HloInstruction
   // in user_computation.cc
-  args[0].shape = TensorShape({2, 2});
+  args[0].shape = xla::ShapeUtil::MakeShape(xla::S32, {2, 2});
   args[1].kind = XlaCompiler::Argument::kParameter;
   args[1].type = DT_INT32;
-  args[1].shape = TensorShape({2});
+  args[1].shape = xla::ShapeUtil::MakeShape(xla::S32, {2});
   return args;
 }
 
-- 
GitLab


From 3d4708dad0a3ef52775a4f0e0de8ccbfc16434e0 Mon Sep 17 00:00:00 2001
From: Guillaume Klein <guillaumekln@users.noreply.github.com>
Date: Mon, 10 Jul 2017 17:20:32 +0200
Subject: [PATCH 0842/1375] Fix minor docstring formatting issue

---
 tensorflow/contrib/copy_graph/python/util/copy_elements.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/copy_graph/python/util/copy_elements.py b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
index 3f124be771..3b20a3e83b 100644
--- a/tensorflow/contrib/copy_graph/python/util/copy_elements.py
+++ b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
@@ -100,7 +100,7 @@ def copy_variable_to_graph(org_instance, to_graph, scope=""):
 
 def copy_op_to_graph(org_instance, to_graph, variables,
                      scope=""):
-  """Given an `Operation` 'org_instance` from one `Graph`,
+  """Given an `Operation` `org_instance` from one `Graph`,
   initializes and returns a copy of it from another `Graph`,
   under the specified scope (default `""`).
 
-- 
GitLab


From b6becb44dd1a4494aa7c3acbb6961d23900f614b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 08:26:10 -0700
Subject: [PATCH 0843/1375] Fix tensorforest for using sparse-only data.

PiperOrigin-RevId: 161396592
---
 tensorflow/contrib/tensor_forest/kernels/model_ops.cc     | 3 ++-
 tensorflow/contrib/tensor_forest/kernels/stats_ops.cc     | 3 ++-
 tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc | 4 +++-
 tensorflow/contrib/tensor_forest/kernels/v4/input_data.h  | 6 ++++--
 tensorflow/contrib/tensor_forest/ops/model_ops.cc         | 4 +++-
 5 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
index 5b265a869d..0f92f05e2c 100644
--- a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
@@ -165,9 +165,10 @@ class TreePredictionsV4Op : public OpKernel {
     const Tensor& input_data = context->input(1);
     const Tensor& sparse_input_indices = context->input(2);
     const Tensor& sparse_input_values = context->input(3);
+    const Tensor& sparse_input_shape = context->input(4);
 
     data_set_->set_input_tensors(input_data, sparse_input_indices,
-                                 sparse_input_values);
+                                 sparse_input_values, sparse_input_shape);
 
     DecisionTreeResource* decision_tree_resource;
     OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
index 1fd85fd81f..260e03df26 100644
--- a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -258,11 +258,12 @@ class ProcessInputOp : public OpKernel {
     const Tensor& input_data = context->input(2);
     const Tensor& sparse_input_indices = context->input(3);
     const Tensor& sparse_input_values = context->input(4);
+    const Tensor& sparse_input_shape = context->input(5);
     const Tensor& input_labels = context->input(6);
     const Tensor& input_weights = context->input(7);
 
     data_set_->set_input_tensors(input_data, sparse_input_indices,
-                                 sparse_input_values);
+                                 sparse_input_values, sparse_input_shape);
 
     FertileStatsResource* fertile_stats_resource;
     OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 1),
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
index f5f07bea5c..14cb19d36f 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
@@ -105,7 +105,8 @@ float TensorDataSet::GetExampleValue(int example, int32 feature_id) const {
 
 void TensorDataSet::set_input_tensors(const Tensor& dense,
                                       const Tensor& sparse_indices,
-                                      const Tensor& sparse_values) {
+                                      const Tensor& sparse_values,
+                                      const Tensor& sparse_shape) {
   if (dense.shape().dims() == 2) {
     dense_data_.reset(new DenseStorageType(dense.tensor<float, 2>()));
   }
@@ -114,6 +115,7 @@ void TensorDataSet::set_input_tensors(const Tensor& dense,
         sparse_indices.tensor<int64, 2>()));
     sparse_values_.reset(new SparseValuesStorageType(
         sparse_values.tensor<float, 1>()));
+    sparse_batch_size_ = sparse_shape.tensor<int64, 1>()(0);
   }
   original_dense_tensor_ = dense;
 }
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h
index 261a1f2d5e..e3d4edbf8a 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h
@@ -67,7 +67,8 @@ class TensorDataSet {
   virtual ~TensorDataSet() {}
 
   void set_input_tensors(const Tensor& dense, const Tensor& sparse_indices,
-                         const Tensor& sparse_values);
+                         const Tensor& sparse_values,
+                         const Tensor& sparse_shape);
 
   float get_input_value(int offset, int col) {
     return (*dense_data_)(offset, col);
@@ -77,7 +78,7 @@ class TensorDataSet {
     if (dense_data_ != nullptr) {
       return dense_data_->dimensions()[0];
     } else if (sparse_indices_ != nullptr) {
-      return sparse_indices_->dimensions()[0];
+      return sparse_batch_size_;
     } else {
       return 0;
     }
@@ -109,6 +110,7 @@ class TensorDataSet {
   std::unique_ptr<DenseStorageType> dense_data_;
   std::unique_ptr<SparseIndicesStorageType> sparse_indices_;
   std::unique_ptr<SparseValuesStorageType> sparse_values_;
+  int sparse_batch_size_;
 
   Tensor original_dense_tensor_;
   const tensorforest::TensorForestDataSpec input_spec_;
diff --git a/tensorflow/contrib/tensor_forest/ops/model_ops.cc b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
index c9acc4a6ae..168f079f52 100644
--- a/tensorflow/contrib/tensor_forest/ops/model_ops.cc
+++ b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
@@ -20,6 +20,7 @@
 namespace tensorflow {
 using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
 
 namespace tensorforest {
 
@@ -93,7 +94,8 @@ REGISTER_OP("TreePredictionsV4")
     .SetShapeFn([](InferenceContext* c) {
       DimensionHandle num_points = c->UnknownDim();
 
-      if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0) {
+      if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0 &&
+          c->Value(c->Dim(c->input(1), 0)) > 0) {
         num_points = c->Dim(c->input(1), 0);
       }
 
-- 
GitLab


From 48986f20fb27e04e0925ae44615729a9472704a9 Mon Sep 17 00:00:00 2001
From: Andy Kernahan <andrew.kernahan@gmail.com>
Date: Thu, 6 Jul 2017 11:07:19 +0100
Subject: [PATCH 0844/1375] Fixes WAV encoding/decoding for multi-channel data.

---
 tensorflow/core/lib/wav/wav_io.cc      |  6 +--
 tensorflow/core/lib/wav/wav_io_test.cc | 58 ++++++++++++++++++++++++++
 2 files changed, 61 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/lib/wav/wav_io.cc b/tensorflow/core/lib/wav/wav_io.cc
index 028ff26ffb..c72f1b961d 100644
--- a/tensorflow/core/lib/wav/wav_io.cc
+++ b/tensorflow/core/lib/wav/wav_io.cc
@@ -147,7 +147,8 @@ Status EncodeAudioAsS16LEWav(const float* audio, size_t sample_rate,
     return errors::InvalidArgument("num_frames must be positive.");
   }
 
-  const size_t bytes_per_second = sample_rate * kBytesPerSample;
+  const size_t bytes_per_second =
+    sample_rate * kBytesPerSample * num_channels;
   const size_t num_samples = num_frames * num_channels;
   const size_t data_size = num_samples * kBytesPerSample;
   const size_t file_size = kHeaderSize + num_samples * kBytesPerSample;
@@ -242,8 +243,7 @@ Status DecodeLin16WaveAsFloatVector(const string& wav_string,
         "Bad bytes per sample in WAV header: Expected ",
         expected_bytes_per_sample, " but got ", bytes_per_sample);
   }
-  const uint32 expected_bytes_per_second =
-      (bytes_per_sample * (*sample_rate)) / *channel_count;
+  const uint32 expected_bytes_per_second = bytes_per_sample * *sample_rate;
   if (bytes_per_second != expected_bytes_per_second) {
     return errors::InvalidArgument(
         "Bad bytes per second in WAV header: Expected ",
diff --git a/tensorflow/core/lib/wav/wav_io_test.cc b/tensorflow/core/lib/wav/wav_io_test.cc
index e54b9445ab..2150799d69 100644
--- a/tensorflow/core/lib/wav/wav_io_test.cc
+++ b/tensorflow/core/lib/wav/wav_io_test.cc
@@ -97,5 +97,63 @@ TEST(WavIO, EncodeThenDecode) {
   }
 }
 
+TEST(WavIO, BasicMono) {
+  std::vector<uint8> wav_data = {
+      'R',  'I',  'F', 'F', // ChunkID
+      44, 0, 0, 0,          // ChunkSize: 36 + SubChunk2Size
+      'W', 'A', 'V', 'E',   // Format
+      'f', 'm', 't', ' ',   // Subchunk1ID
+      16, 0, 0, 0,          // Subchunk1Size
+      1, 0,                 // AudioFormat: 1=PCM
+      1, 0,                 // NumChannels
+      0x44, 0xac, 0, 0,     // SampleRate: 44100
+      0x88, 0x58, 0x1, 0,   // BytesPerSecond: SampleRate * NumChannels *
+                            //                 BitsPerSample/8
+      2, 0,                 // BytesPerSample: NumChannels * BitsPerSample/8
+      16, 0,                // BitsPerSample
+      'd', 'a', 't', 'a',   // Subchunk2ID
+      8, 0, 0, 0,           // Subchunk2Size: NumSamples * NumChannels *
+                            //                BitsPerSample/8
+      0, 0,                 // Sample 1: 0
+      0xff, 0x7f,           // Sample 2: 32767 (saturated)
+      0, 0,                 // Sample 3: 0
+      0x00, 0x80,           // Sample 4: -32768 (saturated)
+  };
+  string expected(wav_data.begin(), wav_data.end());
+  float audio[] = {0.0f, 1.0f, 0.0f, -1.0f};
+  string result;
+  TF_EXPECT_OK(EncodeAudioAsS16LEWav(audio, 44100, 1, 4, &result));
+  EXPECT_EQ(expected, result);
+}
+
+TEST(WavIO, BasicStereo) {
+  std::vector<uint8> wav_data = {
+      'R',  'I',  'F', 'F', // ChunkID
+      44, 0, 0, 0,          // ChunkSize: 36 + SubChunk2Size
+      'W', 'A', 'V', 'E',   // Format
+      'f', 'm', 't', ' ',   // Subchunk1ID
+      16, 0, 0, 0,          // Subchunk1Size
+      1, 0,                 // AudioFormat: 1=PCM
+      2, 0,                 // NumChannels
+      0x44, 0xac, 0, 0,     // SampleRate: 44100
+      0x10, 0xb1, 0x2, 0,   // BytesPerSecond: SampleRate * NumChannels *
+                            //                 BitsPerSample/8
+      4, 0,                 // BytesPerSample: NumChannels * BitsPerSample/8
+      16, 0,                // BitsPerSample
+      'd', 'a', 't', 'a',   // Subchunk2ID
+      8, 0, 0, 0,           // Subchunk2Size: NumSamples * NumChannels *
+                            //                BitsPerSample/8
+      0, 0,                 // Sample 1: 0
+      0xff, 0x7f,           // Sample 2: 32767 (saturated)
+      0, 0,                 // Sample 3: 0
+      0x00, 0x80,           // Sample 4: -32768 (saturated)
+  };
+  string expected(wav_data.begin(), wav_data.end());
+  float audio[] = {0.0f, 1.0f, 0.0f, -1.0f};
+  string result;
+  TF_EXPECT_OK(EncodeAudioAsS16LEWav(audio, 44100, 2, 2, &result));
+  EXPECT_EQ(expected, result);
+}
+
 }  // namespace wav
 }  // namespace tensorflow
-- 
GitLab


From d63e799a75db89b7cabadbd3f82bfb9a4ea20a61 Mon Sep 17 00:00:00 2001
From: Andy Kernahan <andrew.kernahan@gmail.com>
Date: Mon, 10 Jul 2017 16:21:08 +0100
Subject: [PATCH 0845/1375] Fixes default value handling of the
 desired_{samples,channels} attributes.

---
 tensorflow/core/kernels/decode_wav_op_test.cc | 40 +++++++++++++++++++
 tensorflow/core/ops/audio_ops.cc              |  4 +-
 2 files changed, 42 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/decode_wav_op_test.cc b/tensorflow/core/kernels/decode_wav_op_test.cc
index c282d53a5a..9023d165b7 100644
--- a/tensorflow/core/kernels/decode_wav_op_test.cc
+++ b/tensorflow/core/kernels/decode_wav_op_test.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/audio_ops.h"
 #include "tensorflow/cc/ops/const_op.h"
 #include "tensorflow/cc/ops/math_ops.h"
+#include "tensorflow/core/framework/shape_inference_testutil.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/types.pb.h"
@@ -83,4 +84,43 @@ TEST(DecodeWavOpTest, DecodeWavTest) {
   EXPECT_EQ(14099, sample_rate);
 }
 
+TEST(DecodeWavOpTest, DecodeWav_ShapeFn) {
+  const std::string op_name = "DecodeWav";
+  ShapeInferenceTestOp op(op_name);
+
+  INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");
+
+  // audio shape is unknown when desired_{samples,channels} are default.
+  TF_ASSERT_OK(NodeDefBuilder("test", op_name)
+                    .Input({"a", 0, DT_STRING})
+                    .Finalize(&op.node_def));
+  INFER_OK(op, "[]", "[?,?];[]");
+
+  TF_ASSERT_OK(NodeDefBuilder("test", op_name)
+                    .Input({"a", 0, DT_STRING})
+                    .Attr("desired_samples", 42)
+                    .Finalize(&op.node_def));
+  INFER_OK(op, "[]", "[42,?];[]");
+
+  // Negative sample value is rejected.
+  TF_ASSERT_OK(NodeDefBuilder("test", op_name)
+                    .Input({"a", 0, DT_STRING})
+                    .Attr("desired_samples", -2)
+                    .Finalize(&op.node_def));
+  INFER_ERROR("samples must be non-negative, got -2", op, "[]");
+
+  TF_ASSERT_OK(NodeDefBuilder("test", op_name)
+                    .Input({"a", 0, DT_STRING})
+                    .Attr("desired_channels", 2)
+                    .Finalize(&op.node_def));
+  INFER_OK(op, "[]", "[?,2];[]");
+
+  // Negative channel value is rejected.
+  TF_ASSERT_OK(NodeDefBuilder("test", op_name)
+                    .Input({"a", 0, DT_STRING})
+                    .Attr("desired_channels", -2)
+                    .Finalize(&op.node_def));
+  INFER_ERROR("channels must be non-negative, got -2", op, "[]");
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/audio_ops.cc b/tensorflow/core/ops/audio_ops.cc
index 02b13a455c..91e81f2579 100644
--- a/tensorflow/core/ops/audio_ops.cc
+++ b/tensorflow/core/ops/audio_ops.cc
@@ -33,7 +33,7 @@ Status DecodeWavShapeFn(InferenceContext* c) {
   DimensionHandle channels_dim;
   int32 desired_channels;
   TF_RETURN_IF_ERROR(c->GetAttr("desired_channels", &desired_channels));
-  if (desired_channels == 0) {
+  if (desired_channels == -1) {
     channels_dim = c->UnknownDim();
   } else {
     if (desired_channels < 0) {
@@ -45,7 +45,7 @@ Status DecodeWavShapeFn(InferenceContext* c) {
   DimensionHandle samples_dim;
   int32 desired_samples;
   TF_RETURN_IF_ERROR(c->GetAttr("desired_samples", &desired_samples));
-  if (desired_samples == 0) {
+  if (desired_samples == -1) {
     samples_dim = c->UnknownDim();
   } else {
     if (desired_samples < 0) {
-- 
GitLab


From b8e6bc701240ac76acf7fe35a1487564cc8689d5 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Mon, 10 Jul 2017 09:10:03 -0700
Subject: [PATCH 0846/1375] Debugger.md: Fix missing line breaks

PiperOrigin-RevId: 161401043
---
 tensorflow/docs_src/programmers_guide/debugger.md | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 72209896cb..74b8d4ca61 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -35,11 +35,13 @@ This code trains a simple neural network for MNIST digit image recognition.
 Notice that the accuracy increases slightly after the first training step, but
 then gets stuck at a low (near-chance) level:
 
-> Accuracy at step 0: 0.1113
-> Accuracy at step 1: 0.3183
-> Accuracy at step 2: 0.098
-> Accuracy at step 3: 0.098
-> Accuracy at step 4: 0.098
+```none
+Accuracy at step 0: 0.1113
+Accuracy at step 1: 0.3183
+Accuracy at step 2: 0.098
+Accuracy at step 3: 0.098
+Accuracy at step 4: 0.098
+```
 
 Wondering what might have gone wrong, you suspect that certain nodes in the
 training graph generated bad numeric values such as `inf`s and `nan`s, because
-- 
GitLab


From 792685e91054301931131d6246ea2b31ea6cba9e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 09:48:17 -0700
Subject: [PATCH 0847/1375] Fix output type of IOUGreaterThanThreshold to be
 bool.

PiperOrigin-RevId: 161404589
---
 tensorflow/core/kernels/non_max_suppression_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index 977d1dda64..ba597f5c67 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -67,7 +67,7 @@ static inline void DecreasingArgSort(const std::vector<float>& values,
 
 // Return true if intersection-over-union overlap between boxes i and j
 // is greater than iou_threshold.
-static inline float IOUGreaterThanThreshold(
+static inline bool IOUGreaterThanThreshold(
     typename TTypes<float, 2>::ConstTensor boxes, int i, int j,
     float iou_threshold) {
   const float ymin_i = std::min<float>(boxes(i, 0), boxes(i, 2));
-- 
GitLab


From 59595ff9a88e2f0a826d988d7336bd6c158add23 Mon Sep 17 00:00:00 2001
From: David Norman <DavidNorman@users.noreply.github.com>
Date: Mon, 10 Jul 2017 18:39:50 +0100
Subject: [PATCH 0848/1375] [XLA] Ensure that the 2 data types chosen by the
 test are both valid for the device (#11172)

* Ensure that the 2 data types chosen by the test are both valid for the device

* Include a comment about the test

* Fix punctuation in comments throughout.

* Relax the regular expression matching for the exception
---
 .../compiler/tests/tensor_array_ops_test.py   | 57 ++++++++++---------
 1 file changed, 31 insertions(+), 26 deletions(-)

diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index b3067be51d..788d4bb291 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -139,7 +139,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
 
-      # Unpack a matrix into vectors
+      # Unpack a matrix into vectors.
       w1 = ta.unstack(convert([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1]]))
       r0 = w1.read(0)
       r1 = w1.read(1)
@@ -180,7 +180,7 @@ class TensorArrayTest(xla_test.XLATestCase):
 
       convert = _make_converter(tf_dtype)
 
-      # Split an empty vector
+      # Split an empty vector.
       lengths = constant_op.constant([0, 0, 0])
       w0 = ta.split(convert([]), lengths=lengths)
       r0 = w0.read(0)
@@ -192,7 +192,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       self.assertAllEqual(convert([]), d1)
       self.assertAllEqual(convert([]), d2)
 
-      # Split a vector
+      # Split a vector.
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
       lengths = constant_op.constant([1, 1, 1])
@@ -206,7 +206,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       self.assertAllEqual(convert([2.0]), d1)
       self.assertAllEqual(convert([3.0]), d2)
 
-      # Split a matrix
+      # Split a matrix.
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
       lengths = constant_op.constant([1, 1, 1])
@@ -319,27 +319,32 @@ class TensorArrayTest(xla_test.XLATestCase):
       ta = tensor_array_ops.TensorArray(
           dtype=dtypes.float32, tensor_array_name="foo", size=3)
 
-      # Test writing the wrong datatype
+      # Test writing the wrong datatype.
       with self.assertRaisesOpError(
           "TensorArray dtype is float but op has dtype int32"):
         ta.write(-1, np.int32(7)).flow.eval()
 
   def testTensorArrayReadWrongIndexOrDataTypeFails(self):
-    with self.test_session(), self.test_scope():
-      ta = tensor_array_ops.TensorArray(
-          dtype=dtypes.float32, tensor_array_name="foo", size=3)
-
-      w0 = ta.write(0, [[4.0, 5.0]])
-
-      # Test reading wrong datatype
-      r0_bad = gen_data_flow_ops._tensor_array_read_v3(
-          handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow)
-      with self.assertRaisesOpError(
-          "TensorArray dtype is float but op has dtype double."):
-        r0_bad.eval()
-
-      # Test reading from a different index than the one we wrote to
-      w0.read(1)
+    # Find two different floating point types, create an array of
+    # the first type, but try to read the other type.
+    if len(self.float_types) > 1:
+      dtype1 = self.float_types[0]
+      dtype2 = self.float_types[1]
+      with self.test_session(), self.test_scope():
+        ta = tensor_array_ops.TensorArray(
+            dtype=dtype1, tensor_array_name="foo", size=3)
+
+        w0 = ta.write(0, [[4.0, 5.0]])
+
+        # Test reading wrong datatype.
+        r0_bad = gen_data_flow_ops._tensor_array_read_v3(
+            handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow)
+        with self.assertRaisesOpError(
+            "TensorArray dtype is "):
+          r0_bad.eval()
+
+        # Test reading from a different index than the one we wrote to
+        w0.read(1)
 
   def testTensorArraySplitIncompatibleShapesFails(self):
     with self.test_session(), self.test_scope():
@@ -487,7 +492,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w1.read(0)
       s0 = w1.concat()
 
-      # Test gradient accumulation between read(0), pack(), and concat()
+      # Test gradient accumulation between read(0), pack(), and concat().
       with ops.control_dependencies([p0, r0, s0]):
         grad_r = gradients_impl.gradients(
             ys=[p0, r0, s0],
@@ -536,7 +541,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0_1 = w.read(0)
       r1 = w.read(1)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[r0, r0_1, r1],
           xs=[value],
@@ -744,7 +749,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       grad_b_t, = session.run([grad_b])
       self.assertAllEqual(grad_b_t, g0)
 
-      # Test gradients calculated jointly
+      # Test gradients calculated jointly.
       joint_grad_a_t, joint_grad_b_t = session.run([grad_a, grad_b])
       self.assertAllEqual(joint_grad_a_t, g0)
       self.assertAllEqual(joint_grad_b_t, g0)
@@ -877,7 +882,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       x = constant_op.constant([2.0, 3.0])
       w = ta.unstack(x)
       r0 = w.read(0)
-      # calculate (dr0/dx0, dr0/dx1).  since r0 = x0, gradients are (1, 0).
+      # Calculate (dr0/dx0, dr0/dx1).  since r0 = x0, gradients are (1, 0).
       grad_r0 = gradients_impl.gradients(ys=[r0], xs=[x], grad_ys=[1.0])
       grad_r0_vals = session.run(grad_r0)[0]
       self.assertAllEqual(grad_r0_vals, [1.0, 0.0])
@@ -927,7 +932,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w.read(1)
       r1 = w.read(8)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[r0, r1], xs=[value], grad_ys=[[2.0, 3.0], [4.0, 5.0]])
       read_vals, grad_vals = session.run([[r0, r1], grad])
@@ -951,7 +956,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       w = ta.unstack(values)
       g = w.gather(indices)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[g], xs=[values], grad_ys=[[[2.0, 3.0], [4.0, 5.0]]])
       g_vals, grad_vals = session.run([[g], grad])
-- 
GitLab


From 894aac266b7d3825f32a2cfc49b0cb994d2238ec Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Mon, 10 Jul 2017 10:42:31 -0700
Subject: [PATCH 0849/1375] Fix SliceHelper _baseslice case to use the same
 dtype for begin and strides.

Using '1' automatically gets converted to int32, but when the user explicitly
uses an int64 for the 'begin' argument, we want to make sure that 'strides'
is of the same dtype, so we make sure we use a 1 of the proper dtype.

Adds a test that failed before and passes with this change.  Also
tested the indexing case in addition to the _baseslice case, and added
tests for both 'begin' as a Tensor as well as a non-Tensor.

Fixes #11380.

RELNOTES: Fixes 'strides' and 'begin' dtype mismatch when slicing using int64 Tensor index in python. See #11380.
PiperOrigin-RevId: 161411479
---
 .../python/kernel_tests/slice_op_test.py      | 22 +++++++++++++++++++
 tensorflow/python/ops/array_ops.py            | 18 ++++++++++-----
 2 files changed, 35 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/kernel_tests/slice_op_test.py b/tensorflow/python/kernel_tests/slice_op_test.py
index c11f78b77e..f6997e9c61 100644
--- a/tensorflow/python/kernel_tests/slice_op_test.py
+++ b/tensorflow/python/kernel_tests/slice_op_test.py
@@ -49,6 +49,28 @@ class SliceTest(test.TestCase):
         slice_val = slice_t.eval()
       self.assertAllEqual(slice_val, inp[2, k:k])
 
+  def testInt64Slicing(self):
+    with self.test_session(use_gpu=True):
+      a = constant_op.constant([0, 1, 2], dtype=dtypes.int64)
+
+      # Slice using int64 Tensor.
+      i = constant_op.constant(1, dtype=dtypes.int64)
+      slice_t = a[i]
+      slice_val = slice_t.eval()
+      self.assertAllEqual(1, slice_val)
+      slice_t = a[i:i+1]
+      slice_val = slice_t.eval()
+      self.assertAllEqual([1], slice_val)
+
+      # Slice using int64 integer.
+      i = np.asarray(1).astype(np.int64)
+      slice_t = a[i]
+      slice_val = slice_t.eval()
+      self.assertAllEqual(1, slice_val)
+      slice_t = a[i:i+1]
+      slice_val = slice_t.eval()
+      self.assertAllEqual([1], slice_val)
+
   def testSelectAll(self):
     for _ in range(10):
       with self.test_session(use_gpu=True):
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 8db5efb447..a963bd265c 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -381,6 +381,13 @@ def rank_internal(input, name=None, optimize=True):
       return gen_array_ops.rank(input, name=name)
 
 
+def _one_like_dtype(other):
+  if isinstance(other, ops.Tensor):
+    return constant(1, other.dtype)
+  else:
+    return np.ones_like(other).dtype.type(1)
+
+
 def _SliceHelper(tensor, slice_spec, var=None):
   """Overload for Tensor.__getitem__.
 
@@ -444,7 +451,6 @@ def _SliceHelper(tensor, slice_spec, var=None):
   ellipsis_mask = 0
   for s in slice_spec:
     if isinstance(s, _baseslice):
-      strides.append(s.step if s.step is not None else 1)
       # python doesn't always use None when constructing ranges
       # for example a[:] gives slice(None,sys.maxsize,None)
       # whereas a[::1] gives slice(None,None,None)
@@ -458,6 +464,11 @@ def _SliceHelper(tensor, slice_spec, var=None):
       else:
         end.append(0)
         end_mask |= (1 << index)
+      if s.step is not None:
+        strides.append(s.step)
+      else:
+        # Use a 1 of the same dtype as begin.
+        strides.append(_one_like_dtype(begin[-1]))
     elif s is Ellipsis:
       begin.append(0)
       end.append(0)
@@ -471,10 +482,7 @@ def _SliceHelper(tensor, slice_spec, var=None):
     else:
       begin.append(s)
       end.append(s + 1)
-      if isinstance(s, ops.Tensor):
-        strides.append(constant(1, s.dtype))
-      else:
-        strides.append(np.ones_like(s).dtype.type(1))
+      strides.append(_one_like_dtype(s))
       shrink_axis_mask |= (1 << index)
     index += 1
 
-- 
GitLab


From 51ce2db3f178501855d6185aa521b5cdca8bb4ee Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Mon, 10 Jul 2017 10:58:12 -0700
Subject: [PATCH 0850/1375] Speed up CreateMemoryMinimizingSequence.

In particular, BytesFreedIfScheduled is the O(n^2) kernel of our
scheduling algorithm, and it used to do one hashtable lookup for each
input and output buffer of its instruction.  This patch gets rid of all
of the hashtable lookups, replacing them with simple pointer
dereferences.

PiperOrigin-RevId: 161413495
---
 .../compiler/xla/service/hlo_scheduling.cc    | 91 +++++++++++++------
 1 file changed, 61 insertions(+), 30 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc
index f8e05448da..17f55f9cfb 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling.cc
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc
@@ -91,14 +91,15 @@ class ListScheduler {
     // LogicalBuffer is in an operand of the instruction as indicated by
     // points-to analysis.
     for (auto& instruction : computation.instructions()) {
-      buffer_uses_.insert(
-          {instruction.get(), std::unordered_set<const LogicalBuffer*>()});
+      std::unordered_set<const LogicalBuffer*> instr_uses;
       for (auto* operand : instruction->operands()) {
         for (const LogicalBuffer* buffer :
              points_to_analysis.GetBuffersDefinedByInstruction(operand)) {
-          buffer_uses_[instruction.get()].insert(buffer);
+          instr_uses.insert(buffer);
         }
       }
+      buffer_uses_[instruction.get()] = std::vector<const LogicalBuffer*>(
+          instr_uses.begin(), instr_uses.end());
     }
 
     // Create map containing the number of unscheduled uses (hlo instructions)
@@ -131,33 +132,64 @@ class ListScheduler {
            buffer.instruction()->opcode() == HloOpcode::kConstant;
   }
 
-  // Return the number of bytes freed if the HLO instruction is scheduled.
-  int64 BytesFreedIfScheduled(const HloInstruction* instruction) {
-    int64 freed_bytes = 0;
-    // Sum the total size of the values last used by this instruction.
+  // An entry in the worklist used by CreateSchedule.  Corresponds to one
+  // HloInstruction, plus some cached metadata, saved for the purposes of making
+  // BytesFreedIfScheduled fast.
+  struct ReadyListEntry {
+    const HloInstruction* instruction;
+
+    // The total size of all buffers defined by this instruction.
+    int64 bytes_defined;
+
+    // For each buffer B used by this instruction, we keep a pair (B, U), where
+    // U is the number of uses of B that have not yet been scheduled.  This pair
+    // is a pointer into the unscheduled_use_count_ map, so it gets updated for
+    // free when we update counts in the map.
+    std::vector<const std::pair<const LogicalBuffer* const, int64>*>
+        used_buffer_unscheduled_use_counts;
+  };
+
+  // Creates a ReadyListEntry for the given instruction.
+  ReadyListEntry MakeReadyListEntry(const HloInstruction* instruction) {
+    ReadyListEntry entry;
+    entry.instruction = instruction;
+
+    entry.bytes_defined = 0;
+    for (auto* buffer :
+         points_to_analysis_.GetBuffersDefinedByInstruction(instruction)) {
+      if (!IgnoreBuffer(*buffer)) {
+        entry.bytes_defined += size_function_(*buffer);
+      }
+    }
+
     for (auto* buffer : buffer_uses_.at(instruction)) {
       if (IgnoreBuffer(*buffer)) {
         continue;
       }
-      CHECK_GE(unscheduled_use_count_.at(buffer), 1);
-      if (unscheduled_use_count_.at(buffer) == 1) {
-        // This is the last use of the logical buffer.
-        freed_bytes += size_function_(*buffer);
-      }
+      auto unscheduled_use_count_it = unscheduled_use_count_.find(buffer);
+      CHECK(unscheduled_use_count_it != unscheduled_use_count_.end());
+      entry.used_buffer_unscheduled_use_counts.push_back(
+          &*unscheduled_use_count_it);
     }
-    // Then subtract the size of the value(s) defined by this instruction.
-    for (auto* buffer :
-         points_to_analysis_.GetBuffersDefinedByInstruction(instruction)) {
-      if (!IgnoreBuffer(*buffer)) {
-        freed_bytes -= size_function_(*buffer);
+    return entry;
+  }
+
+  // Returns the number of bytes freed if the HLO instruction is scheduled.
+  int64 BytesFreedIfScheduled(const ReadyListEntry& entry) {
+    int64 freed_bytes = 0;
+    for (const auto& kv : entry.used_buffer_unscheduled_use_counts) {
+      auto buffer = kv->first;
+      auto use_count = kv->second;
+      if (use_count == 1) {
+        freed_bytes += size_function_(*buffer);
       }
     }
-    return freed_bytes;
+    return freed_bytes - entry.bytes_defined;
   }
 
-  // Construct the scheduling priority of the given instruction.
-  Priority GetPriority(const HloInstruction* instruction) {
-    return {BytesFreedIfScheduled(instruction), instruction->user_count()};
+  // Constructs the scheduling priority of the given instruction.
+  Priority GetPriority(const ReadyListEntry& entry) {
+    return {BytesFreedIfScheduled(entry), entry.instruction->user_count()};
   }
 
   std::vector<const HloInstruction*> CreateSchedule() {
@@ -166,7 +198,6 @@ class ListScheduler {
     // Populate the ready list with instructions which have no operands or
     // control predecessors.
     std::unordered_map<const HloInstruction*, int64> unscheduled_pred_count;
-    std::list<const HloInstruction*> ready_list;
     for (auto& instruction : computation_.instructions()) {
       // TODO(b/34466113): Replace this and above with successors() or
       // predecessors() when these methods are added to HloInstruction.
@@ -177,11 +208,13 @@ class ListScheduler {
         unscheduled_pred_count[succ]++;
       }
     }
+
+    std::list<ReadyListEntry> ready_list;
     for (auto& instruction : computation_.instructions()) {
       // Instruction with no operands or control predecessors will
       // not be in the map.
       if (unscheduled_pred_count.count(instruction.get()) == 0) {
-        ready_list.push_back(instruction.get());
+        ready_list.push_back(MakeReadyListEntry(instruction.get()));
       }
     }
 
@@ -200,7 +233,7 @@ class ListScheduler {
 
       // Remove the selected instruction from the ready list and add it to the
       // schedule.
-      const HloInstruction* best = *best_it;
+      const HloInstruction* best = best_it->instruction;
       ready_list.erase(best_it);
       schedule.push_back(best);
       scheduled_instructions_.insert(best);
@@ -212,12 +245,11 @@ class ListScheduler {
       }
 
       // Add new instructions to ready list.
-      auto update_pred_count = [&unscheduled_pred_count,
-                                &ready_list](HloInstruction* inst) {
+      auto update_pred_count = [&](HloInstruction* inst) {
         int64 pred_count = --unscheduled_pred_count.at(inst);
         CHECK_GE(pred_count, 0);
         if (pred_count == 0) {
-          ready_list.push_back(inst);
+          ready_list.push_back(MakeReadyListEntry(inst));
         }
       };
       // TODO(b/34466113): Replace this and above with successors() or
@@ -241,12 +273,11 @@ class ListScheduler {
   const LogicalBuffer::SizeFunction& size_function_;
 
   // A map containing the LogicalBuffers that each instruction uses.
-  std::unordered_map<const HloInstruction*,
-                     std::unordered_set<const LogicalBuffer*>>
+  std::unordered_map<const HloInstruction*, std::vector<const LogicalBuffer*>>
       buffer_uses_;
 
   // A map containing the count of unscheduled HLOs which using a particular
-  // LogicalBuffer.
+  // LogicalBuffer.  We rely on iterator stability in this map.
   std::unordered_map<const LogicalBuffer*, int64> unscheduled_use_count_;
 
   // Set of instructions which have been scheduled.
-- 
GitLab


From b39ac077fff64fc9491cc115cc676aad600d5d6c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 12:43:55 -0700
Subject: [PATCH 0851/1375] Add documentation for build targets generated by
 tflibrary.

PiperOrigin-RevId: 161426590
---
 tensorflow/compiler/aot/tfcompile.bzl | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tensorflow/compiler/aot/tfcompile.bzl b/tensorflow/compiler/aot/tfcompile.bzl
index 4be4e0fbb3..12e1485b48 100644
--- a/tensorflow/compiler/aot/tfcompile.bzl
+++ b/tensorflow/compiler/aot/tfcompile.bzl
@@ -27,6 +27,15 @@ def tf_library(name, graph, config,
                deps=None, tags=None):
   """Runs tfcompile to compile a TensorFlow graph into executable code.
 
+  Given an invocation of tf_library(name="foo", ...), generates the following
+  build targets:
+    foo:           A cc_library containing the generated header and computation.
+    foo_test:      A cc_test with simple tests and benchmarks. Only created if
+                   gen_test=True.
+    foo_benchmark: A cc_binary that runs a minimal-dependency benchmark, useful
+                   for mobile devices or other platforms that can't compile the
+                   full test libraries. Only created if gen_benchmark=True.
+
   Args:
     name: The name of the build rule.
     graph: The TensorFlow GraphDef to compile.  If the file ends in '.pbtxt' it
-- 
GitLab


From e121535a7d04cfc7c7dbb09d8694c01eb29da26f Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 10 Jul 2017 12:47:38 -0700
Subject: [PATCH 0852/1375] Correct elu grad grad.

Fixes issue #11091

PiperOrigin-RevId: 161427084
---
 tensorflow/python/kernel_tests/relu_op_test.py | 18 ++++++++++++++++++
 tensorflow/python/ops/nn_grad.py               |  9 +++++----
 2 files changed, 23 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py
index 229f27e9ca..63ac743843 100644
--- a/tensorflow/python/kernel_tests/relu_op_test.py
+++ b/tensorflow/python/kernel_tests/relu_op_test.py
@@ -22,6 +22,7 @@ import numpy as np
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import nn_ops
@@ -32,6 +33,12 @@ from tensorflow.python.platform import test
 from tensorflow.python.training import gradient_descent
 
 
+def _elu_grad_grad(activation):
+  if activation < 0:
+    return np.exp(activation)
+  return 0
+
+
 class ReluTest(test.TestCase):
 
   def _npRelu(self, np_features):
@@ -266,6 +273,17 @@ class EluTest(test.TestCase):
     print("elu (float64) gradient err = ", err)
     self.assertLess(err, 1e-6)
 
+  def testGradGrad(self):
+    with self.test_session():
+      x = array_ops.placeholder(dtype=dtypes.float32)
+      elu = nn_ops.elu(x)
+      g, = gradients_impl.gradients(elu, x)
+      gg, = gradients_impl.gradients(g, x)
+
+      for x_val in [-1, -0.5, 0.5, 1]:
+        err = np.abs(gg.eval(feed_dict={x: x_val}) - _elu_grad_grad(x_val))
+        self.assertLess(err, 1e-4)
+
   def testGradGradFloat32(self):
     with self.test_session():
       x = constant_op.constant(
diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py
index 028d82aa4d..f1453f9ef0 100644
--- a/tensorflow/python/ops/nn_grad.py
+++ b/tensorflow/python/ops/nn_grad.py
@@ -327,11 +327,12 @@ def _ReluGrad(op, grad):
 
 @ops.RegisterGradient("EluGrad")
 def _EluGradGrad(op, grad):
-  x = op.inputs[1]
+  elu_x = op.inputs[1]
   return (gen_nn_ops._elu_grad(grad, op.outputs[0]),
-          array_ops.where(
-              x < 0., gen_nn_ops._elu_grad(grad, op.outputs[0] + 1),
-              array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)))
+          array_ops.where(elu_x < 0,
+                          grad * op.inputs[0],
+                          array_ops.zeros(shape=array_ops.shape(elu_x),
+                                          dtype=elu_x.dtype)))
 
 
 @ops.RegisterGradient("Relu6")
-- 
GitLab


From 8b53854ea5e4b7ebeec4a8636580f54424cb1952 Mon Sep 17 00:00:00 2001
From: Frank Chen <frankchn@google.com>
Date: Mon, 10 Jul 2017 14:46:57 -0700
Subject: [PATCH 0853/1375] Adds support for retrieving instances from the
 Google Compute Engine instance group APIs, with support (in conjunction with
 UnionClusterResolver) for mapping multiple instance groups into one
 TensorFlow job (see the `testUnionMultipleInstanceRetrieval` test for
 details).

This should simplify creating and using standardized grpc TensorFlow server based instances using Compute Engine instance groups for distributed training.

PiperOrigin-RevId: 161443891
---
 tensorflow/contrib/cluster_resolver/BUILD     |  34 ++-
 .../python/training/__init__.py               |   1 +
 .../python/training/gce_cluster_resolver.py   | 129 ++++++++++
 .../training/gce_cluster_resolver_test.py     | 234 ++++++++++++++++++
 tensorflow/contrib/cmake/tf_python.cmake      |   3 +
 tensorflow/tools/pip_package/BUILD            |   1 +
 6 files changed, 401 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
 create mode 100644 tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver_test.py

diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD
index 0dfc5a81d5..1e2c17eb52 100644
--- a/tensorflow/contrib/cluster_resolver/BUILD
+++ b/tensorflow/contrib/cluster_resolver/BUILD
@@ -21,10 +21,19 @@ filegroup(
     ),
 )
 
+py_library(
+    name = "cluster_resolver_pip",
+    srcs = ["python/training/__init__.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":cluster_resolver_py",
+        ":gce_cluster_resolver_py",
+    ],
+)
+
 py_library(
     name = "cluster_resolver_py",
     srcs = [
-        "python/training/__init__.py",
         "python/training/cluster_resolver.py",
     ],
     srcs_version = "PY2AND3",
@@ -33,6 +42,15 @@ py_library(
     ],
 )
 
+py_library(
+    name = "gce_cluster_resolver_py",
+    srcs = [
+        "python/training/gce_cluster_resolver.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [":cluster_resolver_py"],
+)
+
 tf_py_test(
     name = "cluster_resolver_py_test",
     size = "small",
@@ -46,3 +64,17 @@ tf_py_test(
     ],
     main = "python/training/cluster_resolver_test.py",
 )
+
+tf_py_test(
+    name = "gce_cluster_resolver_py_test",
+    size = "small",
+    srcs = ["python/training/gce_cluster_resolver_test.py"],
+    additional_deps = [
+        ":gce_cluster_resolver_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+    main = "python/training/gce_cluster_resolver_test.py",
+)
diff --git a/tensorflow/contrib/cluster_resolver/python/training/__init__.py b/tensorflow/contrib/cluster_resolver/python/training/__init__.py
index 3520467bc6..fbf7ca3a5d 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/__init__.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/__init__.py
@@ -21,3 +21,4 @@ from __future__ import print_function
 from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import ClusterResolver
 from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import SimpleClusterResolver
 from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import UnionClusterResolver
+from tensorflow.contrib.cluster_resolver.python.training.gce_cluster_resolver import GceClusterResolver
diff --git a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
new file mode 100644
index 0000000000..2603d59920
--- /dev/null
+++ b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
@@ -0,0 +1,129 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implementation of Cluster Resolvers for GCE Instance Groups."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import ClusterResolver
+from tensorflow.python.training.server_lib import ClusterSpec
+
+_GOOGLE_API_CLIENT_INSTALLED = True
+try:
+  from googleapiclient import discovery  # pylint: disable=g-import-not-at-top
+except ImportError:
+  _GOOGLE_API_CLIENT_INSTALLED = False
+
+
+class GceClusterResolver(ClusterResolver):
+  """Cluster Resolver for Google Compute Engine.
+
+  This is an implementation of cluster resolvers for the Google Compute Engine
+  instance group platform. By specifying a project, zone, and instance group,
+  this will retrieve the IP address of all the instances within the instance
+  group and return a Cluster Resolver object suitable for use for distributed
+  TensorFlow.
+  """
+
+  def __init__(self,
+               project,
+               zone,
+               instance_group,
+               port,
+               job_name='worker',
+               credentials=None,
+               service=None):
+    """Creates a new GceClusterResolver object.
+
+    This takes in a few parameters and creates a GceClusterResolver project. It
+    will then use these parameters to query the GCE API for the IP addresses of
+    each instance in the instance group.
+
+    Args:
+      project: Name of the GCE project
+      zone: Zone of the GCE instance group
+      instance_group: Name of the GCE instance group
+      port: Port of the listening TensorFlow server (default: 8470)
+      job_name: Name of the TensorFlow job this set of instances belongs to
+      credentials: GCE Credentials. This defaults to
+        GoogleCredentials.get_application_default()
+      service: The GCE API object returned by the googleapiclient.discovery
+        function. (Default: discovery.build('compute', 'v1')). If you specify a
+        custom service object, then the credentials parameter will be ignored.
+
+    Raises:
+      ImportError: If the googleapiclient is not installed.
+    """
+    self._project = project
+    self._zone = zone
+    self._instance_group = instance_group
+    self._job_name = job_name
+    self._port = port
+    if service is None:
+      if _GOOGLE_API_CLIENT_INSTALLED is True:
+        self._service = discovery.build('compute', 'v1',
+                                        credentials=credentials)
+      else:
+        raise ImportError('googleapiclient must be installed before using the '
+                          'GCE cluster resolver')
+    else:
+      self._service = service
+
+  def cluster_spec(self):
+    """Returns a ClusterSpec object based on the latest instance group info.
+
+    This returns a ClusterSpec object for use based on information from the
+    specified instance group. We will retrieve the information from the GCE APIs
+    every time this method is called.
+
+    Returns:
+      A ClusterSpec containing host information retrieved from GCE.
+    """
+    request_body = {'instanceState': 'RUNNING'}
+    request = self._service.instanceGroups().listInstances(
+        project=self._project,
+        zone=self._zone,
+        instanceGroups=self._instance_group,
+        body=request_body,
+        orderBy='name')
+
+    worker_list = []
+
+    while request is not None:
+      response = request.execute()
+
+      items = response['items']
+      for instance in items:
+        instance_name = instance['instance'].split('/')[-1]
+
+        instance_request = self._service.instances().get(
+            project=self._project,
+            zone=self._zone,
+            instance=instance_name)
+
+        if instance_request is not None:
+          instance_details = instance_request.execute()
+          ip_address = instance_details['networkInterfaces'][0]['networkIP']
+          instance_url = '%s:%s' % (ip_address, self._port)
+          worker_list.append(instance_url)
+
+      request = self._service.instanceGroups().listInstances_next(
+          previous_request=request,
+          previous_response=response)
+
+    worker_list.sort()
+    return ClusterSpec({self._job_name: worker_list})
diff --git a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver_test.py
new file mode 100644
index 0000000000..f2deacbc26
--- /dev/null
+++ b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver_test.py
@@ -0,0 +1,234 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for GceClusterResolver."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import UnionClusterResolver
+from tensorflow.contrib.cluster_resolver.python.training.gce_cluster_resolver import GceClusterResolver
+from tensorflow.python.platform import test
+from tensorflow.python.training import server_lib
+
+
+mock = test.mock
+
+
+class GceClusterResolverTest(test.TestCase):
+
+  def _verifyClusterSpecEquality(self, cluster_spec, expected_proto):
+    self.assertProtoEquals(expected_proto, cluster_spec.as_cluster_def())
+    self.assertProtoEquals(
+        expected_proto, server_lib.ClusterSpec(cluster_spec).as_cluster_def())
+    self.assertProtoEquals(
+        expected_proto,
+        server_lib.ClusterSpec(cluster_spec.as_cluster_def()).as_cluster_def())
+    self.assertProtoEquals(
+        expected_proto,
+        server_lib.ClusterSpec(cluster_spec.as_dict()).as_cluster_def())
+
+  def standard_mock_instance_groups(self, instance_map=None):
+    if instance_map is None:
+      instance_map = [
+          {'instance': 'https://gce.example.com/res/gce-instance-1'}
+      ]
+
+    mock_instance_group_request = mock.MagicMock()
+    mock_instance_group_request.execute.return_value = {
+        'items': instance_map
+    }
+
+    service_attrs = {
+        'listInstances.return_value': mock_instance_group_request,
+        'listInstances_next.return_value': None,
+    }
+    mock_instance_groups = mock.Mock(**service_attrs)
+    return mock_instance_groups
+
+  def standard_mock_instances(self, instance_to_ip_map=None):
+    if instance_to_ip_map is None:
+      instance_to_ip_map = {
+          'gce-instance-1': '10.123.45.67'
+      }
+
+    mock_get_request = mock.MagicMock()
+    mock_get_request.execute.return_value = {
+        'networkInterfaces': [
+            {'networkIP': '10.123.45.67'}
+        ]
+    }
+
+    def get_side_effect(project, zone, instance):
+      del project, zone  # Unused
+
+      if instance in instance_to_ip_map:
+        mock_get_request = mock.MagicMock()
+        mock_get_request.execute.return_value = {
+            'networkInterfaces': [
+                {'networkIP': instance_to_ip_map[instance]}
+            ]
+        }
+        return mock_get_request
+      else:
+        raise RuntimeError('Instance %s not found!' % instance)
+
+    service_attrs = {
+        'get.side_effect': get_side_effect,
+    }
+    mock_instances = mock.MagicMock(**service_attrs)
+    return mock_instances
+
+  def standard_mock_service_client(
+      self,
+      mock_instance_groups=None,
+      mock_instances=None):
+
+    if mock_instance_groups is None:
+      mock_instance_groups = self.standard_mock_instance_groups()
+    if mock_instances is None:
+      mock_instances = self.standard_mock_instances()
+
+    mock_client = mock.MagicMock()
+    mock_client.instanceGroups.return_value = mock_instance_groups
+    mock_client.instances.return_value = mock_instances
+    return mock_client
+
+  def gen_standard_mock_service_client(self, instances=None):
+    name_to_ip = {}
+    instance_list = []
+    for instance in instances:
+      name_to_ip[instance['name']] = instance['ip']
+      instance_list.append({
+          'instance': 'https://gce.example.com/gce/res/' + instance['name']
+      })
+
+    mock_instance = self.standard_mock_instances(name_to_ip)
+    mock_instance_group = self.standard_mock_instance_groups(instance_list)
+
+    return self.standard_mock_service_client(mock_instance_group, mock_instance)
+
+  def testSimpleSuccessfulRetrieval(self):
+    gce_cluster_resolver = GceClusterResolver(
+        project='test-project',
+        zone='us-east1-d',
+        instance_group='test-instance-group',
+        port=8470,
+        service=self.standard_mock_service_client())
+
+    actual_cluster_spec = gce_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job { name: 'worker' tasks { key: 0 value: '10.123.45.67:8470' } }
+    """
+    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+
+  def testCustomJobNameAndPortRetrieval(self):
+    gce_cluster_resolver = GceClusterResolver(
+        project='test-project',
+        zone='us-east1-d',
+        instance_group='test-instance-group',
+        job_name='custom',
+        port=2222,
+        service=self.standard_mock_service_client())
+
+    actual_cluster_spec = gce_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job { name: 'custom' tasks { key: 0 value: '10.123.45.67:2222' } }
+    """
+    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+
+  def testMultipleInstancesRetrieval(self):
+    name_to_ip = [
+        {'name': 'instance1', 'ip': '10.1.2.3'},
+        {'name': 'instance2', 'ip': '10.2.3.4'},
+        {'name': 'instance3', 'ip': '10.3.4.5'},
+    ]
+
+    gce_cluster_resolver = GceClusterResolver(
+        project='test-project',
+        zone='us-east1-d',
+        instance_group='test-instance-group',
+        port=8470,
+        service=self.gen_standard_mock_service_client(name_to_ip))
+
+    actual_cluster_spec = gce_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' }
+                         tasks { key: 1 value: '10.2.3.4:8470' }
+                         tasks { key: 2 value: '10.3.4.5:8470' } }
+    """
+    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+
+  def testUnionMultipleInstanceRetrieval(self):
+    worker1_name_to_ip = [
+        {'name': 'instance1', 'ip': '10.1.2.3'},
+        {'name': 'instance2', 'ip': '10.2.3.4'},
+        {'name': 'instance3', 'ip': '10.3.4.5'},
+    ]
+
+    worker2_name_to_ip = [
+        {'name': 'instance4', 'ip': '10.4.5.6'},
+        {'name': 'instance5', 'ip': '10.5.6.7'},
+        {'name': 'instance6', 'ip': '10.6.7.8'},
+    ]
+
+    ps_name_to_ip = [
+        {'name': 'ps1', 'ip': '10.100.1.2'},
+        {'name': 'ps2', 'ip': '10.100.2.3'},
+    ]
+
+    worker1_gce_cluster_resolver = GceClusterResolver(
+        project='test-project',
+        zone='us-east1-d',
+        instance_group='test-instance-group',
+        job_name='worker',
+        port=8470,
+        service=self.gen_standard_mock_service_client(worker1_name_to_ip))
+
+    worker2_gce_cluster_resolver = GceClusterResolver(
+        project='test-project',
+        zone='us-east1-d',
+        instance_group='test-instance-group',
+        job_name='worker',
+        port=8470,
+        service=self.gen_standard_mock_service_client(worker2_name_to_ip))
+
+    ps_gce_cluster_resolver = GceClusterResolver(
+        project='test-project',
+        zone='us-east1-d',
+        instance_group='test-instance-group',
+        job_name='ps',
+        port=2222,
+        service=self.gen_standard_mock_service_client(ps_name_to_ip))
+
+    union_cluster_resolver = UnionClusterResolver(worker1_gce_cluster_resolver,
+                                                  worker2_gce_cluster_resolver,
+                                                  ps_gce_cluster_resolver)
+
+    actual_cluster_spec = union_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job { name: 'ps' tasks { key: 0 value: '10.100.1.2:2222' }
+                     tasks { key: 1 value: '10.100.2.3:2222' } }
+    job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' }
+                         tasks { key: 1 value: '10.2.3.4:8470' }
+                         tasks { key: 2 value: '10.3.4.5:8470' }
+                         tasks { key: 3 value: '10.4.5.6:8470' }
+                         tasks { key: 4 value: '10.5.6.7:8470' }
+                         tasks { key: 5 value: '10.6.7.8:8470' } }
+    """
+    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index c9666a776c..0ffa0200db 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -258,6 +258,9 @@ add_python_module("tensorflow/contrib/cloud/kernels")
 add_python_module("tensorflow/contrib/cloud/ops")
 add_python_module("tensorflow/contrib/cloud/python")
 add_python_module("tensorflow/contrib/cloud/python/ops")
+add_python_module("tensorflow/contrib/cluster_resolver")
+add_python_module("tensorflow/contrib/cluster_resolver/python")
+add_python_module("tensorflow/contrib/cluster_resolver/python/training")
 add_python_module("tensorflow/contrib/compiler")
 add_python_module("tensorflow/contrib/copy_graph")
 add_python_module("tensorflow/contrib/copy_graph/python")
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 78a652ccae..d1f83939b8 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -136,6 +136,7 @@ sh_binary(
             ":simple_console",
             "//tensorflow:tensorflow_py",
             "//tensorflow/contrib/boosted_trees:boosted_trees_pip",
+            "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip",
             "//tensorflow/contrib/graph_editor:graph_editor_pip",
             "//tensorflow/contrib/keras:keras",
             "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip",
-- 
GitLab


From 0344d9c9cf2f15f64eaabf72a102acd759541850 Mon Sep 17 00:00:00 2001
From: Pete Warden <petewarden@google.com>
Date: Mon, 10 Jul 2017 14:58:04 -0700
Subject: [PATCH 0854/1375] Added Mobilenet support to TensorFlow tooling

PiperOrigin-RevId: 161445783
---
 tensorflow/examples/label_image/main.cc       | 15 +++++------
 tensorflow/tools/benchmark/benchmark_model.cc | 27 ++++++++++++++++---
 tensorflow/tools/graph_transforms/README.md   |  8 ++++--
 .../graph_transforms/quantize_weights.cc      | 12 ++++++---
 .../graph_transforms/quantize_weights_test.cc | 14 +++++++---
 .../graph_transforms/summarize_graph_main.cc  | 12 ++++++++-
 6 files changed, 66 insertions(+), 22 deletions(-)

diff --git a/tensorflow/examples/label_image/main.cc b/tensorflow/examples/label_image/main.cc
index a98c0817e3..63bc39de6c 100644
--- a/tensorflow/examples/label_image/main.cc
+++ b/tensorflow/examples/label_image/main.cc
@@ -89,7 +89,6 @@ Status ReadLabelsFile(const string& file_name, std::vector<string>* result,
 
 static Status ReadEntireFile(tensorflow::Env* env, const string& filename,
                              Tensor* output) {
-
   tensorflow::uint64 file_size = 0;
   TF_RETURN_IF_ERROR(env->GetFileSize(filename, &file_size));
 
@@ -124,15 +123,15 @@ Status ReadTensorFromImageFile(const string& file_name, const int input_height,
 
   // read file_name into a tensor named input
   Tensor input(tensorflow::DT_STRING, tensorflow::TensorShape());
-  TF_RETURN_IF_ERROR(ReadEntireFile(tensorflow::Env::Default(), file_name,
-                                    &input));
+  TF_RETURN_IF_ERROR(
+      ReadEntireFile(tensorflow::Env::Default(), file_name, &input));
 
   // use a placeholder to read input data
-  auto file_reader = Placeholder(root.WithOpName("input"),
-                                 tensorflow::DataType::DT_STRING);
+  auto file_reader =
+      Placeholder(root.WithOpName("input"), tensorflow::DataType::DT_STRING);
 
   std::vector<std::pair<string, tensorflow::Tensor>> inputs = {
-    {"input", input},
+      {"input", input},
   };
 
   // Now try to figure out what kind of file it is and decode it.
@@ -285,8 +284,8 @@ int main(int argc, char* argv[]) {
       "tensorflow/examples/label_image/data/imagenet_slim_labels.txt";
   int32 input_width = 299;
   int32 input_height = 299;
-  int32 input_mean = 0;
-  int32 input_std = 255;
+  float input_mean = 0;
+  float input_std = 255;
   string input_layer = "input";
   string output_layer = "InceptionV3/Predictions/Reshape_1";
   bool self_test = false;
diff --git a/tensorflow/tools/benchmark/benchmark_model.cc b/tensorflow/tools/benchmark/benchmark_model.cc
index 81892aef96..38c1bd3fb5 100644
--- a/tensorflow/tools/benchmark/benchmark_model.cc
+++ b/tensorflow/tools/benchmark/benchmark_model.cc
@@ -138,8 +138,21 @@ Status GetOutputShapes(const std::vector<InputLayerInfo>& inputs,
   std::vector<std::pair<string, tensorflow::Tensor> > input_tensors;
   CreateTensorsFromInputInfo(inputs, &input_tensors);
   std::vector<tensorflow::Tensor> output_tensors;
-  std::vector<string> output_tensor_names(wanted_shapes.begin(),
-                                          wanted_shapes.end());
+  std::vector<string> output_tensor_names;
+  for (const string& wanted_shape : wanted_shapes) {
+    bool is_input = false;
+    for (const std::pair<string, tensorflow::Tensor>& input_tensor :
+         input_tensors) {
+      if (input_tensor.first == wanted_shape) {
+        (*node_shapes)[wanted_shape] = input_tensor.second.shape();
+        is_input = true;
+        break;
+      }
+    }
+    if (!is_input) {
+      output_tensor_names.push_back(wanted_shape);
+    }
+  }
   TF_RETURN_IF_ERROR(
       session->Run(input_tensors, output_tensor_names, {}, &output_tensors));
   CHECK_EQ(output_tensors.size(), output_tensor_names.size());
@@ -156,7 +169,8 @@ Status CalculateFlops(const GraphDef& graph,
                       Session* session, int64* total_flops,
                       std::unordered_map<string, int64>* flops_by_op) {
   std::unordered_set<string> floppable_ops = {
-      "Conv2D", "MatMul", "QuantizedConv2D", "QuantizedMatMul"};
+      "Conv2D", "MatMul", "QuantizedConv2D", "QuantizedMatMul",
+      "DepthwiseConv2dNative"};
 
   std::set<string> wanted_shapes;
   for (const NodeDef& node : graph.node()) {
@@ -201,6 +215,13 @@ Status CalculateFlops(const GraphDef& graph,
         }
         int64 output_count = output_shape.num_elements();
         current_flops = k * output_count * 2;
+      } else if (node.op() == "DepthwiseConv2dNative") {
+        const TensorShape& filter_shape = found_shapes[node.input(1)];
+        const TensorShape& output_shape = found_shapes[node.name()];
+        int64 filter_height = filter_shape.dim_size(0);
+        int64 filter_width = filter_shape.dim_size(1);
+        int64 output_count = output_shape.num_elements();
+        current_flops = output_count * filter_height * filter_width * 2;
       }
       (*flops_by_op)[node.op()] += current_flops;
       *total_flops += current_flops;
diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index b4274e67df..18d1523070 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -578,10 +578,14 @@ eight-bit form.
 
 ### quantize_weights
 
-Args: None \
+Args:
+
+*   minimum_size: Tensors with fewer elements than this won't be quantized
+(defaults to 1024)
+
 Prerequisites: None
 
-Converts any large (more than 15 element) float Const op into an eight-bit
+Converts any large (more than minimum_size) float Const op into an eight-bit
 equivalent, followed by a float conversion op so that the result is usable by
 subsequent nodes. This is mostly useful for [shrinking file
 sizes](#shrinking-file-size), but also helps with the more advanced
diff --git a/tensorflow/tools/graph_transforms/quantize_weights.cc b/tensorflow/tools/graph_transforms/quantize_weights.cc
index 66d800f0da..cccae8a992 100644
--- a/tensorflow/tools/graph_transforms/quantize_weights.cc
+++ b/tensorflow/tools/graph_transforms/quantize_weights.cc
@@ -35,11 +35,15 @@ namespace graph_transforms {
 Status QuantizeWeights(const GraphDef& input_graph_def,
                        const TransformFuncContext& context,
                        GraphDef* output_graph_def) {
+  int32 minimum_size;
+  TF_RETURN_IF_ERROR(
+      context.GetOneInt32Parameter("minimum_size", 1024, &minimum_size));
   TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes(
       input_graph_def, {"Const"},
-      [](const NodeMatch& match, const std::set<string>& input_nodes,
-         const std::set<string>& output_nodes,
-         std::vector<NodeDef>* new_nodes) {
+      [minimum_size](const NodeMatch& match,
+                     const std::set<string>& input_nodes,
+                     const std::set<string>& output_nodes,
+                     std::vector<NodeDef>* new_nodes) {
         const NodeDef& old_const_node = match.node;
         if (!old_const_node.attr().count("dtype")) {
           return errors::InvalidArgument("No 'dtype' attribute for Const node ",
@@ -58,7 +62,7 @@ Status QuantizeWeights(const GraphDef& input_graph_def,
         const size_t num_elements = old_tensor.NumElements();
         // If this isn't a float constant, or it's too small, then reuse the
         // same node with no changes.
-        if ((old_dtype != DT_FLOAT) || (num_elements < 16)) {
+        if ((old_dtype != DT_FLOAT) || (num_elements < minimum_size)) {
           new_nodes->push_back(old_const_node);
           return Status::OK();
         }
diff --git a/tensorflow/tools/graph_transforms/quantize_weights_test.cc b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
index 63c5b5a64d..e1828831db 100644
--- a/tensorflow/tools/graph_transforms/quantize_weights_test.cc
+++ b/tensorflow/tools/graph_transforms/quantize_weights_test.cc
@@ -70,9 +70,12 @@ class QuantizeWeightsTest : public ::testing::Test {
                    0.3f, 0.4f, 1.0f, 2.0f, 3.0f, 4.0f, 0.1f, 0.2f, 0.3f, 0.4f},
                   &original_graph_def);
 
+    TransformFuncContext context;
+    context.output_names = {"output"};
+    context.params["minimum_size"] = {"16"};
     GraphDef quantized_graph_def;
-    TF_ASSERT_OK(QuantizeWeights(original_graph_def, {{}, {"output"}},
-                                 &quantized_graph_def));
+    TF_ASSERT_OK(
+        QuantizeWeights(original_graph_def, context, &quantized_graph_def));
 
     // Verify the structure of the quantized graph.
     std::map<string, const NodeDef*> node_lookup;
@@ -122,9 +125,12 @@ TEST_F(QuantizeWeightsTest, RangesAlwaysIncludeZero) {
                  0.1f, 0.2f, 0.3f, 0.4f, 1.0f, 2.0f, 3.0f, 4.0f, 0.1f, 0.2f,
                  0.3f, 0.4f, 1.0f, 2.0f, 3.0f, 4.0f, 0.1f, 0.2f, 0.3f, 0.4f},
                 &original_graph_def);
+  TransformFuncContext context;
+  context.output_names = {"output"};
+  context.params["minimum_size"] = {"16"};
   GraphDef quantized_graph_def;
-  TF_ASSERT_OK(QuantizeWeights(original_graph_def, {{}, {"output"}},
-                               &quantized_graph_def));
+  TF_ASSERT_OK(
+      QuantizeWeights(original_graph_def, context, &quantized_graph_def));
 
   std::map<string, const NodeDef*> node_lookup;
   MapNamesToNodes(quantized_graph_def, &node_lookup);
diff --git a/tensorflow/tools/graph_transforms/summarize_graph_main.cc b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
index e79e7ba121..50353c3654 100644
--- a/tensorflow/tools/graph_transforms/summarize_graph_main.cc
+++ b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
@@ -118,7 +118,17 @@ Status PrintStructure(const GraphDef& graph) {
   TF_RETURN_IF_ERROR(SortByExecutionOrder(graph, &sorted_graph));
   for (const NodeDef& node : sorted_graph.node()) {
     std::cout << node.name() << " (" << node.op() << "): ["
-              << str_util::Join(node.input(), ", ") << "]" << std::endl;
+              << str_util::Join(node.input(), ", ") << "]";
+    if (node.op() == "Const") {
+      Tensor tensor;
+      if (node.attr().count("value") &&
+          tensor.FromProto(node.attr().at("value").tensor())) {
+        std::cout << ", value=" << tensor.DebugString();
+      } else {
+        LOG(WARNING) << "Decoding Tensor failed for node" << node.name();
+      }
+    }
+    std::cout << std::endl;
   }
   return Status::OK();
 }
-- 
GitLab


From 5397130e5fdff8e783cf677a0fdaf2c8f4b5b142 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 15:45:39 -0700
Subject: [PATCH 0855/1375] Fix bug with graph_replace on graphs with
 gradients. Added a test to verify the fix.

PiperOrigin-RevId: 161452438
---
 .../graph_editor/tests/transform_test.py      | 29 +++++++++++++++++++
 tensorflow/contrib/graph_editor/transform.py  | 19 ++++++++----
 2 files changed, 42 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/graph_editor/tests/transform_test.py b/tensorflow/contrib/graph_editor/tests/transform_test.py
index a4105645c6..ab5776b9dd 100644
--- a/tensorflow/contrib/graph_editor/tests/transform_test.py
+++ b/tensorflow/contrib/graph_editor/tests/transform_test.py
@@ -26,6 +26,7 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -181,6 +182,34 @@ class TransformTest(test.TestCase):
     self.assertEqual(res[0].name, "b:0")
     self.assertEqual(res[1].name, "add_1:0")
 
+  def test_graph_replace_gradients(self):
+    ops.reset_default_graph()
+    w = variables.Variable(0.0, name="w")
+    y = math_ops.multiply(math_ops.multiply(w, w, name="mul1"), w, name="mul2")
+    g = gradients_impl.gradients(y, w, name="grad")[0]
+
+    # Extract the operations.
+    replacement_ts = {w.value(): g}
+    original_mul1_grad = (ops.get_default_graph().
+                          get_operation_by_name("grad/mul1_grad/mul_1"))
+
+    # Should not raise exception.
+    res = ge.graph_replace(g, replacement_ts, dst_scope="res")
+
+    # Extract the operations after graph_replace.
+    result_mul1_grad = (ops.get_default_graph().
+                        get_operation_by_name("res/grad/mul1_grad/mul_1"))
+
+    # Make sure _original_ops are as expected.
+    self.assertEquals(original_mul1_grad._original_op.name, u"mul1")
+    self.assertEquals(result_mul1_grad._original_op.name, u"res/mul1")
+    self.assertNotEquals(res.name, g.name)
+    with session.Session() as sess:
+      sess.run(variables.global_variables_initializer())
+      g_val, res_val = sess.run([g, res])
+    self.assertNear(g_val, 0.0, ERROR_TOLERANCE)
+    self.assertNear(res_val, 0.0, ERROR_TOLERANCE)
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index 2234400fdc..14ac529665 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -166,13 +166,12 @@ def copy_op_handler(info, op, copy_shape=True):
     for t, t_ in zip(op.outputs, op_.outputs):
       t_.set_shape(t.get_shape())
 
-  # Finalize original op.
+  # Original op cannot be finalised here yet. Because some ops require this
+  # attribute to exist, we will create a dummy original_op first and then
+  # later finalise it with the actual original_op when all the ops have
+  # been copied.
   if op._original_op:
-    original_op = info.transform_original_op_handler(info, op._original_op)
-    if original_op is None:
-      logging.debug("Could not find original op of: %s", op_.name)
-    else:
-      op_._original_op = original_op
+    op_._original_op = op._original_op
 
   # Add op to the graph
   info.graph_._add_op(op_)
@@ -471,6 +470,14 @@ class Transformer(object):
       for t in inputs_:
         op_._add_input(t)
 
+      # Finalize original op.
+      if op._original_op:
+        original_op = info.transform_original_op_handler(info, op._original_op)
+        if original_op is None:
+          logging.debug("Could not find original op for: %s", op_.name)
+        else:
+          op_._original_op = original_op
+
       # Finalize control inputs:
       control_inputs_ = [self.transform_control_input_handler(info, ci)
                          for ci in op.control_inputs]
-- 
GitLab


From 8905db7881d9118b0267ac948092e56a63cd680e Mon Sep 17 00:00:00 2001
From: Pete Warden <petewarden@google.com>
Date: Mon, 10 Jul 2017 15:45:40 -0700
Subject: [PATCH 0856/1375] Fixed crash in graph summarization tool

PiperOrigin-RevId: 161452440
---
 .../tools/graph_transforms/summarize_graph_main.cc | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/tensorflow/tools/graph_transforms/summarize_graph_main.cc b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
index 50353c3654..d9806e8ed2 100644
--- a/tensorflow/tools/graph_transforms/summarize_graph_main.cc
+++ b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
@@ -83,11 +83,17 @@ void PrintBenchmarkUsage(const std::vector<const NodeDef*>& placeholders,
         shape = PartialTensorShape(shape_proto);
       }
     }
-    sizes.reserve(shape.dims());
-    for (int i = 0; i < shape.dims(); ++i) {
-      sizes.push_back(shape.dim_size(i));
+    string sizes_string;
+    if (shape.dims() == -1) {
+      // Unknown shapes can have -1 for dims, so leave these blank.
+      sizes_string = "";
+    } else {
+      sizes.reserve(shape.dims());
+      for (int i = 0; i < shape.dims(); ++i) {
+        sizes.push_back(shape.dim_size(i));
+      }
+      sizes_string = str_util::Join(sizes, ",");
     }
-    string sizes_string = str_util::Join(sizes, ",");
     input_layer_shapes.push_back(sizes_string);
   }
   std::vector<string> output_layers;
-- 
GitLab


From 2874780e5dcec9680f5474efcee10cadc37cf753 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Mon, 10 Jul 2017 15:50:11 -0700
Subject: [PATCH 0857/1375] [TF:XLA] Remove unneeded backend parameter in
 CreateModuleConfig.

PiperOrigin-RevId: 161453063
---
 tensorflow/compiler/xla/service/service.cc | 22 +++++++++-------------
 tensorflow/compiler/xla/service/service.h  |  2 +-
 2 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 443051e6d1..1eaa56867f 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -293,7 +293,7 @@ StatusOr<std::vector<const Allocation*>> Service::ResolveAndValidateArguments(
 StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig(
     const ProgramShape& program_shape,
     tensorflow::gtl::ArraySlice<const Allocation*> arguments,
-    const ExecutionOptions& execution_options, Backend* backend) {
+    const ExecutionOptions& execution_options) {
   auto module_config = MakeUnique<HloModuleConfig>(program_shape);
   auto* computation_layout = module_config->mutable_entry_computation_layout();
 
@@ -678,8 +678,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
     // the program and the argument allocations.
     TF_ASSIGN_OR_RETURN(std::unique_ptr<HloModuleConfig> module_config,
                         CreateModuleConfig(*program_shape, arg_allocations,
-                                           request.execution_options(),
-                                           execute_backend_.get()));
+                                           request.execution_options()));
     VLOG(3) << "ExecuteParallel created HloModuleConfig computation layout: "
             << module_config->entry_computation_layout().ToString();
 
@@ -768,10 +767,9 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg,
       ResolveAndValidateArguments(arg->arguments(), execute_backend_.get(),
                                   execute_backend_->default_device_ordinal()));
 
-  TF_ASSIGN_OR_RETURN(
-      std::unique_ptr<HloModuleConfig> module_config,
-      CreateModuleConfig(*program_shape, arg_allocations,
-                         arg->execution_options(), execute_backend_.get()));
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<HloModuleConfig> module_config,
+                      CreateModuleConfig(*program_shape, arg_allocations,
+                                         arg->execution_options()));
 
   VLOG(3) << "Execute created HloModuleConfig computation layout: "
           << module_config->entry_computation_layout().ToString();
@@ -837,10 +835,9 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg,
       ResolveAndValidateArguments(arg->arguments(), execute_backend_.get(),
                                   execute_backend_->default_device_ordinal()));
 
-  TF_ASSIGN_OR_RETURN(
-      std::unique_ptr<HloModuleConfig> module_config,
-      CreateModuleConfig(*program_shape, arg_allocations,
-                         arg->execution_options(), execute_backend_.get()));
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<HloModuleConfig> module_config,
+                      CreateModuleConfig(*program_shape, arg_allocations,
+                                         arg->execution_options()));
 
   VLOG(3) << "ExecuteAsync created HloModuleConfig computation layout: "
           << module_config->entry_computation_layout().ToString();
@@ -1112,8 +1109,7 @@ tensorflow::Status Service::ComputeConstant(const ComputeConstantRequest* arg,
   }
 
   TF_ASSIGN_OR_RETURN(std::unique_ptr<HloModuleConfig> module_config,
-                      CreateModuleConfig(program_shape, {}, execution_options,
-                                         compute_constant_backend_.get()));
+                      CreateModuleConfig(program_shape, {}, execution_options));
 
   TF_ASSIGN_OR_RETURN(
       std::shared_ptr<Executable> executable,
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 48cb0bec47..034604dfd1 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -263,7 +263,7 @@ class Service : public ServiceInterface {
   StatusOr<std::unique_ptr<HloModuleConfig>> CreateModuleConfig(
       const ProgramShape& program_shape,
       tensorflow::gtl::ArraySlice<const Allocation*> arguments,
-      const ExecutionOptions& execution_options, Backend* backend);
+      const ExecutionOptions& execution_options);
 
   // Builds an Executable for the given parameters. If
   // executable_for_compute_constant is true, then the executable is intended to
-- 
GitLab


From 9b94d1091134e203f4a57d10b4a86f63d0e1ce19 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 16:18:53 -0700
Subject: [PATCH 0858/1375] Add check py_test to sanity to make sure BUILD
 files with py_test targets loads 'py_test' from tensorflow.bzl.

PiperOrigin-RevId: 161457087
---
 tensorflow/contrib/boosted_trees/BUILD        | 10 +--
 tensorflow/tools/ci_build/ci_sanity.sh        | 16 +++-
 .../ci_build/install/install_buildifier.sh    | 18 ++--
 tensorflow/tools/pip_package/BUILD            |  8 ++
 .../tools/pip_package/check_load_py_test.py   | 89 +++++++++++++++++++
 5 files changed, 120 insertions(+), 21 deletions(-)
 create mode 100644 tensorflow/tools/pip_package/check_load_py_test.py

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 5f726cc18e..5f5a9488e6 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -7,6 +7,7 @@ package(default_visibility = [
     "//visibility:public",
 ])
 
+load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs")
 load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
@@ -73,7 +74,6 @@ py_test(
     srcs = ["python/kernel_tests/ensemble_optimizer_ops_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip",  # b/63386583
         "nomac",  # b/63258195
     ],
     deps = [
@@ -95,7 +95,6 @@ py_test(
     srcs = ["python/kernel_tests/model_ops_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip",  # b/63386583
         "nomac",  # b/63258195
     ],
     deps = [
@@ -119,7 +118,6 @@ py_test(
     srcs = ["python/kernel_tests/prediction_ops_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip",  # b/63386583
         "nomac",  # b/63258195
     ],
     deps = [
@@ -141,7 +139,6 @@ py_test(
     srcs = ["python/kernel_tests/quantile_ops_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip",  # b/63386583
         "nomac",  # b/63258195
     ],
     deps = [
@@ -160,9 +157,6 @@ py_test(
     size = "small",
     srcs = ["python/kernel_tests/split_handler_ops_test.py"],
     srcs_version = "PY2AND3",
-    tags = [
-        "no_pip",  # b/63386583
-    ],
     deps = [
         ":split_handler_ops_py",
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
@@ -179,7 +173,6 @@ py_test(
     srcs = ["python/kernel_tests/stats_accumulator_ops_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip",  # b/63386583
         "nomac",  # b/63258195
     ],
     deps = [
@@ -198,7 +191,6 @@ py_test(
     srcs = ["python/kernel_tests/training_ops_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_pip",  # b/63386583
         "nomac",  # b/63258195
     ],
     deps = [
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index e428766a40..5bea031b68 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -418,9 +418,21 @@ do_pip_smoke_test() {
     "The pip smoke test failed."
 }
 
+do_check_load_py_test() {
+  BUILD_CMD="bazel build //tensorflow/tools/pip_package:check_load_py_test"
+  ${BUILD_CMD}
+  cmd_status \
+    "check_load_py_test failed to build."
+
+  BUILD_CMD="bazel-bin/tensorflow/tools/pip_package/check_load_py_test"
+  ${BUILD_CMD}
+  cmd_status \
+    "check_load_py_test failed."
+}
+
 # Supply all sanity step commands and descriptions
-SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test")
-SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package")
+SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test")
+SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test")
 
 INCREMENTAL_FLAG=""
 
diff --git a/tensorflow/tools/ci_build/install/install_buildifier.sh b/tensorflow/tools/ci_build/install/install_buildifier.sh
index b2dfcf8db7..967c62bac0 100755
--- a/tensorflow/tools/ci_build/install/install_buildifier.sh
+++ b/tensorflow/tools/ci_build/install/install_buildifier.sh
@@ -15,14 +15,12 @@
 # ==============================================================================
 
 set -e
-BUILDIFIER_DIR="buildifier"
-mkdir ${BUILDIFIER_DIR}
-curl -Ls https://github.com/bazelbuild/buildifier/archive/0.4.5.tar.gz | \
-    tar -C "${BUILDIFIER_DIR}" --strip-components=1 -xz
-pushd ${BUILDIFIER_DIR}
+# Download buildifier.
+wget https://github.com/bazelbuild/buildtools/releases/download/0.4.5/buildifier
+chmod +x buildifier
+sudo mv buildifier /usr/local/bin/.
 
-bazel build buildifier:buildifier --spawn_strategy=standalone --genrule_strategy=standalone
-sudo cp bazel-bin/buildifier/buildifier /usr/local/bin/
-
-popd
-rm -rf ${BUILDIFIER_DIR}
+# Download buildozer.
+wget https://github.com/bazelbuild/buildtools/releases/download/0.4.5/buildozer
+chmod +x buildozer
+sudo mv buildozer /usr/local/bin/.
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index d1f83939b8..c94d2e4abc 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -55,6 +55,14 @@ py_test(
     ],
 )
 
+py_binary(
+    name = "check_load_py_test",
+    srcs = ["check_load_py_test.py"],
+    data = [
+        "//tensorflow:all_opensource_files",
+    ],
+)
+
 # On Windows, python binary is a zip file of runfiles tree.
 # Add everything to its data dependency for generating a runfiles tree
 # for building the pip package on Windows.
diff --git a/tensorflow/tools/pip_package/check_load_py_test.py b/tensorflow/tools/pip_package/check_load_py_test.py
new file mode 100644
index 0000000000..7a132a8de3
--- /dev/null
+++ b/tensorflow/tools/pip_package/check_load_py_test.py
@@ -0,0 +1,89 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests to check that py_test are properly loaded in BUILD files."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import subprocess
+
+
+def check_output_despite_error(args):
+  """Get output of args from command line, even if there are errors.
+
+  Args:
+    args: a list of command line args.
+
+  Returns:
+    output as string.
+  """
+  try:
+    output = subprocess.check_output(args, stderr=subprocess.STDOUT)
+  except subprocess.CalledProcessError as e:
+    output = e.output
+  return output.strip()
+
+
+def main():
+  # Get all py_test target, note bazel query result will also include
+  # cuda_py_test etc.
+  try:
+    targets = subprocess.check_output(
+        'bazel query "kind(py_test, //tensorflow/contrib/... + '
+        '//tensorflow/python/... - '
+        '//tensorflow/contrib/tensorboard/...)"',
+        shell=True).strip()
+  except subprocess.CalledProcessError as e:
+    targets = e.output
+
+  # Only keep py_test targets, and filter out targets with 'no_pip' tag.
+  valid_targets = []
+  for target in targets.split('\n'):
+    kind = check_output_despite_error(['buildozer', 'print kind', target])
+    if kind == 'py_test':
+      tags = check_output_despite_error(['buildozer', 'print tags', target])
+      if 'no_pip' not in tags:
+        valid_targets.append(target)
+
+  # Get all BUILD files for all valid targets.
+  build_files = set()
+  for target in valid_targets:
+    build_files.add(os.path.join(target[2:].split(':')[0], 'BUILD'))
+
+  # Check if BUILD files load py_test.
+  files_missing_load = []
+  for build_file in build_files:
+    updated_build_file = subprocess.check_output(
+        'buildozer -stdout "new_load //tensorflow:tensorflow.bzl py_test" ' +
+        build_file,
+        shell=True)
+    with open(build_file, 'r') as f:
+      if f.read() != updated_build_file:
+        files_missing_load.append(build_file)
+
+  if files_missing_load:
+    raise RuntimeError('The following files are missing %s:\n %s' % (
+        'load("//tensorflow:tensorflow.bzl", "py_test").\nThis load statement'
+        ' is needed because otherwise pip tests will try to use their '
+        'dependencies, which are not visible to them.',
+        '\n'.join(files_missing_load)))
+  else:
+    print('TEST PASSED.')
+
+
+if __name__ == '__main__':
+  main()
-- 
GitLab


From 0bd57e3e315956e5751987f5601edd95d5566bc5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 16:37:45 -0700
Subject: [PATCH 0859/1375] Dump LLVM IR to disk (when asked to) as individual
 files, and not a single concatenated file.  This is a bit more usable since
 we can feed these files directly to opt, llc etc.

PiperOrigin-RevId: 161459565
---
 .../xla/legacy_flags/debug_options_flags.cc   |  5 +-
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 61 ++++++++++++++++---
 tensorflow/compiler/xla/xla.proto             |  2 +-
 3 files changed, 56 insertions(+), 12 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 9442a16ee5..e7c037f3f5 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -140,8 +140,9 @@ void AllocateFlags() {
        tensorflow::Flag("xla_embed_ir_in_executable",
                         &flag_values->xla_embed_ir_in_executable,
                         "Embed the compiler IR as a string in the executable."),
-       tensorflow::Flag("xla_dump_ir_to", &flag_values->xla_dump_ir_to,
-                        "Dump the compiler IR into this file/path."),
+       tensorflow::Flag(
+           "xla_dump_ir_to", &flag_values->xla_dump_ir_to,
+           "Dump the compiler IR into this directory as individual files."),
        tensorflow::Flag("xla_eliminate_hlo_implicit_broadcast",
                         &flag_values->xla_eliminate_hlo_implicit_broadcast,
                         "Eliminate implicit broadcasts when lowering user "
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 45da0cea33..4d4cf794cc 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -81,7 +81,9 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/env.h"
 
 namespace se = ::perftools::gputools;
@@ -355,6 +357,49 @@ Status AppendIRToFile(const string& file_name, const string& ir_module_string) {
   return Status::OK();
 }
 
+Status InitializeIRDumpHooks(
+    const HloModule& module,
+    SimpleOrcJIT::OptimizationCallback* pre_optimization_ir_dump_hook,
+    SimpleOrcJIT::OptimizationCallback* post_optimization_ir_dump_hook) {
+  const string& dump_ir_to = module.config().debug_options().xla_dump_ir_to();
+  if (dump_ir_to.empty()) {
+    return Status::OK();
+  }
+
+  TF_RETURN_IF_ERROR(
+      tensorflow::Env::Default()->RecursivelyCreateDir(dump_ir_to));
+
+  string safe_file_name_base = module.name();
+  std::replace_if(safe_file_name_base.begin(), safe_file_name_base.end(),
+                  [](char c) { return c == '/' || c == '\\'; }, '_');
+
+  string unoptimized_ir_file_name = tensorflow::io::JoinPath(
+      dump_ir_to,
+      tensorflow::strings::StrCat("ir-", safe_file_name_base, "-no-opt.ll"));
+  string optimized_ir_file_name = tensorflow::io::JoinPath(
+      dump_ir_to,
+      tensorflow::strings::StrCat("ir-", safe_file_name_base, "-opt.ll"));
+
+  // We still want to append to avoid overwriting possibly important information
+  // due to operator error.
+
+  *pre_optimization_ir_dump_hook =
+      [unoptimized_ir_file_name](const llvm::Module& module) {
+        TF_RETURN_IF_ERROR(AppendIRToFile(unoptimized_ir_file_name,
+                                          llvm_ir::DumpModuleToString(module)));
+        return Status::OK();
+      };
+
+  *post_optimization_ir_dump_hook =
+      [optimized_ir_file_name](const llvm::Module& module) {
+        TF_RETURN_IF_ERROR(AppendIRToFile(optimized_ir_file_name,
+                                          llvm_ir::DumpModuleToString(module)));
+        return Status::OK();
+      };
+
+  return Status::OK();
+}
+
 }  // namespace
 
 StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
@@ -364,15 +409,12 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
   std::call_once(llvm_command_line_options_initialized,
                  &InitializeLLVMCommandLineOptions, module->config());
 
-  const string dump_ir_to = module->config().debug_options().xla_dump_ir_to();
+  SimpleOrcJIT::OptimizationCallback pre_optimization_ir_dump_hook;
+  SimpleOrcJIT::OptimizationCallback post_optimization_ir_dump_hook;
 
-  auto dump_ir_to_disk = [dump_ir_to](const llvm::Module& module) {
-    if (!dump_ir_to.empty()) {
-      TF_RETURN_IF_ERROR(
-          AppendIRToFile(dump_ir_to, llvm_ir::DumpModuleToString(module)));
-    }
-    return Status::OK();
-  };
+  TF_RETURN_IF_ERROR(InitializeIRDumpHooks(*module,
+                                           &pre_optimization_ir_dump_hook,
+                                           &post_optimization_ir_dump_hook));
 
   // Compile must be thread-safe so create a new LLVM context for the module.
   auto llvm_context = MakeUnique<llvm::LLVMContext>();
@@ -380,7 +422,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
       MakeUnique<llvm::Module>("__compute_module", *llvm_context);
   auto jit = MakeUnique<SimpleOrcJIT>(CompilerTargetOptions(module->config()),
                                       CodeGenOptLevel(module->config()),
-                                      dump_ir_to_disk, dump_ir_to_disk);
+                                      pre_optimization_ir_dump_hook,
+                                      post_optimization_ir_dump_hook);
   llvm_module->setDataLayout(jit->data_layout());
   llvm_module->setTargetTriple(jit->target_triple().getTriple());
 
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 08c208d85e..ea065532d6 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -70,7 +70,7 @@ message DebugOptions {
   // Embed the compiler IR as a string in the executable.
   bool xla_embed_ir_in_executable = 33;
 
-  // Dump the compiler IR into this file/path.
+  // Dump the compiler IR into this directory as individual files.
   string xla_dump_ir_to = 34;
 
   // Eliminate implicit broadcasts when lowering user computations to HLO
-- 
GitLab


From 285f9766471e10fa9fee4299940225a33515c010 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 16:38:11 -0700
Subject: [PATCH 0860/1375] Made the collections argument of text_summary
 un-deprecated.

The argument is actually still used for adding summaries to collections.

PiperOrigin-RevId: 161459624
---
 tensorflow/python/summary/text_summary.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/tensorflow/python/summary/text_summary.py b/tensorflow/python/summary/text_summary.py
index 2132dc6eb8..72799f2c6c 100644
--- a/tensorflow/python/summary/text_summary.py
+++ b/tensorflow/python/summary/text_summary.py
@@ -30,7 +30,6 @@ from tensorflow.core.framework import summary_pb2
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops.summary_ops import _tensor_summary_v2
 from tensorflow.python.summary import plugin_asset
-from tensorflow.python.util import deprecation
 
 PLUGIN_NAME = "text"
 
@@ -38,11 +37,6 @@ PLUGIN_NAME = "text"
 _TextPluginData = namedtuple("_TextPluginData", [])
 
 
-@deprecation.deprecated_args(
-    "2017-06-13",
-    "collections is deprecated. Instead of using collections to associate "
-    "plugins to events, add a PluginData field to the SummaryMetadata of a "
-    "Value proto.", "collections")
 def text_summary(name, tensor, collections=None):
   """Summarizes textual data.
 
-- 
GitLab


From a6773e98e97956b7adf3aa51eb3548261f51d6f7 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Mon, 10 Jul 2017 16:41:33 -0700
Subject: [PATCH 0861/1375] Add a PadV2 op with support for specifying a pad
 value.

Added a `constant_values` keyword argument to the tf.pad Python API for compatibility with numpy.pad. For now, only scalar values are supported. To efficiently support specifying a `[D, 2]` tensor for `constant_values` to pick per-dimension pre/post constant values will require adding Eigen and XLA support first.

PiperOrigin-RevId: 161460091
---
 tensorflow/cc/gradients/array_grad.cc         |  8 +-
 tensorflow/compiler/tf2xla/kernels/pad_op.cc  | 14 ++-
 tensorflow/core/kernels/pad_op.cc             | 91 ++++++++++++++-----
 tensorflow/core/kernels/pad_op.h              | 13 +--
 tensorflow/core/ops/array_ops.cc              | 39 ++++++++
 tensorflow/core/ops/array_ops_test.cc         | 30 ++++++
 tensorflow/python/kernel_tests/pad_op_test.py | 56 ++++++++----
 tensorflow/python/ops/array_grad.py           | 10 +-
 tensorflow/python/ops/array_ops.py            | 12 ++-
 tensorflow/python/ops/hidden_ops.txt          |  1 +
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  2 +-
 11 files changed, 224 insertions(+), 52 deletions(-)

diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc
index 48185db3cb..6545e4ee3e 100644
--- a/tensorflow/cc/gradients/array_grad.cc
+++ b/tensorflow/cc/gradients/array_grad.cc
@@ -269,6 +269,7 @@ Status ScatterNdNonAliasingAddGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("ScatterNdNonAliasingAdd", ScatterNdNonAliasingAddGrad);
 
+template <bool IsPadV2>
 Status PadGrad(const Scope& scope, const Operation& op,
                const std::vector<Output>& grad_inputs,
                std::vector<Output>* grad_outputs) {
@@ -281,9 +282,14 @@ Status PadGrad(const Scope& scope, const Operation& op,
   auto begin = Reshape(scope, pad_before, {-1});
   grad_outputs->push_back(Slice(scope, grad_inputs[0], begin, Shape(scope, x)));
   grad_outputs->push_back(NoGradient());
+  // PadV2 adds a "constant_values" input.
+  if (IsPadV2) {
+    grad_outputs->push_back(NoGradient());
+  }
   return scope.status();
 }
-REGISTER_GRADIENT_OP("Pad", PadGrad);
+REGISTER_GRADIENT_OP("Pad", PadGrad<false>);
+REGISTER_GRADIENT_OP("PadV2", PadGrad<true>);
 
 Status SpaceToBatchGrad(const Scope& scope, const Operation& op,
                         const std::vector<Output>& grad_inputs,
diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc
index cc13ab0203..d841bd37b3 100644
--- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc
@@ -69,12 +69,22 @@ class PadOp : public XlaOpKernel {
       dim->set_edge_padding_high(after);
     }
 
-    auto zero = XlaHelpers::Zero(ctx->builder(), input_type(0));
-    ctx->SetOutput(0, ctx->builder()->Pad(ctx->Input(0), zero, config));
+    // PadV2 added a "constant_values" input that indicates the pad value.
+    xla::ComputationDataHandle constant_values;
+    if (ctx->num_inputs() == 3) {
+      OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(ctx->InputShape(2)),
+                  errors::InvalidArgument("constant_values must be a scalar."));
+      ctx->SetOutput(0,
+                     ctx->builder()->Pad(ctx->Input(0), ctx->Input(2), config));
+    } else {
+      auto zero = XlaHelpers::Zero(ctx->builder(), input_type(0));
+      ctx->SetOutput(0, ctx->builder()->Pad(ctx->Input(0), zero, config));
+    }
   }
 };
 
 REGISTER_XLA_OP(Name("Pad"), PadOp);
+REGISTER_XLA_OP(Name("PadV2"), PadOp);
 
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/pad_op.cc b/tensorflow/core/kernels/pad_op.cc
index 4c43193579..6e8b09d050 100644
--- a/tensorflow/core/kernels/pad_op.cc
+++ b/tensorflow/core/kernels/pad_op.cc
@@ -70,6 +70,16 @@ class PadOp : public OpKernel {
             "The first dimension of paddings must be the rank of inputs",
             in1.shape().DebugString(), " ", in0.shape().DebugString()));
 
+    T pad_value(0);
+    if (context->num_inputs() == 3) {
+      const Tensor& constant_values = context->input(2);
+      OP_REQUIRES(
+          context, TensorShapeUtils::IsScalar(constant_values.shape()),
+          errors::InvalidArgument("constant_values must be a scalar. Found: ",
+                                  constant_values.shape().DebugString()));
+      pad_value = context->input(2).scalar<T>()();
+    }
+
     // Compute the shape of the output tensor, and allocate it.
     TensorShape output_shape;
     TTypes<int32>::ConstMatrix paddings = in1.matrix<int32>();
@@ -99,27 +109,27 @@ class PadOp : public OpKernel {
     // Invoke the dims-specific implementation.
     switch (fixed_dims) {
       case 0:
-        Operate<0>(context, in0.tensor<T, 0>(), paddings, output);
+        Operate<0>(context, in0.tensor<T, 0>(), paddings, pad_value, output);
         break;
       case 1:
         // TODO(irving): Once Pad doesn't need a scalar special case,
         // change flat to tensor.  That is, once !allow_legacy_scalars().
-        Operate<1>(context, in0.flat<T>(), paddings, output);
+        Operate<1>(context, in0.flat<T>(), paddings, pad_value, output);
         break;
       case 2:
-        Operate<2>(context, in0.tensor<T, 2>(), paddings, output);
+        Operate<2>(context, in0.tensor<T, 2>(), paddings, pad_value, output);
         break;
       case 3:
-        Operate<3>(context, in0.tensor<T, 3>(), paddings, output);
+        Operate<3>(context, in0.tensor<T, 3>(), paddings, pad_value, output);
         break;
       case 4:
-        Operate<4>(context, in0.tensor<T, 4>(), paddings, output);
+        Operate<4>(context, in0.tensor<T, 4>(), paddings, pad_value, output);
         break;
       case 5:
-        Operate<5>(context, in0.tensor<T, 5>(), paddings, output);
+        Operate<5>(context, in0.tensor<T, 5>(), paddings, pad_value, output);
         break;
       case 6:
-        Operate<6>(context, in0.tensor<T, 6>(), paddings, output);
+        Operate<6>(context, in0.tensor<T, 6>(), paddings, pad_value, output);
         break;
       default:
         OP_REQUIRES(context, false,
@@ -132,7 +142,8 @@ class PadOp : public OpKernel {
   template <int Dims>
   void Operate(OpKernelContext* context,
                typename TTypes<T, Dims>::ConstTensor input,
-               TTypes<int32>::ConstMatrix paddings, Tensor* output) {
+               TTypes<int32>::ConstMatrix paddings, T pad_value,
+               Tensor* output) {
     CHECK_EQ(Dims, paddings.dimension(0));
     CHECK_EQ(2, paddings.dimension(1));
     Eigen::array<std::pair<int32, int32>, Dims> paddings_array;
@@ -141,16 +152,22 @@ class PadOp : public OpKernel {
     }
     functor::Pad<Device, T, Dims> functor;
     functor(context->eigen_device<Device>(), output->tensor<T, Dims>(), input,
-            paddings_array);
+            paddings_array, pad_value);
   }
 };
 
-#define REGISTER_KERNEL(type)                            \
-  REGISTER_KERNEL_BUILDER(Name("Pad")                    \
-                              .Device(DEVICE_CPU)        \
-                              .TypeConstraint<type>("T") \
-                              .HostMemory("paddings"),   \
-                          PadOp<CPUDevice, type>)
+#define REGISTER_KERNEL(type)                                 \
+  REGISTER_KERNEL_BUILDER(Name("Pad")                         \
+                              .Device(DEVICE_CPU)             \
+                              .TypeConstraint<type>("T")      \
+                              .HostMemory("paddings"),        \
+                          PadOp<CPUDevice, type>);            \
+  REGISTER_KERNEL_BUILDER(Name("PadV2")                       \
+                              .Device(DEVICE_CPU)             \
+                              .TypeConstraint<type>("T")      \
+                              .HostMemory("paddings")         \
+                              .HostMemory("constant_values"), \
+                          PadOp<CPUDevice, type>);
 
 TF_CALL_POD_TYPES(REGISTER_KERNEL);
 #undef REGISTER_KERNEL
@@ -158,12 +175,12 @@ TF_CALL_POD_TYPES(REGISTER_KERNEL);
 #if GOOGLE_CUDA
 // Forward declarations of the functor specializations for GPU.
 namespace functor {
-#define DECLARE_GPU_SPEC(T, Dims)                                  \
-  template <>                                                      \
-  void Pad<GPUDevice, T, Dims>::operator()(                        \
-      const GPUDevice& d, typename TTypes<T, Dims>::Tensor output, \
-      typename TTypes<T, Dims>::ConstTensor input,                 \
-      Eigen::array<std::pair<int32, int32>, Dims> paddings);       \
+#define DECLARE_GPU_SPEC(T, Dims)                                         \
+  template <>                                                             \
+  void Pad<GPUDevice, T, Dims>::operator()(                               \
+      const GPUDevice& d, typename TTypes<T, Dims>::Tensor output,        \
+      typename TTypes<T, Dims>::ConstTensor input,                        \
+      Eigen::array<std::pair<int32, int32>, Dims> paddings, T pad_value); \
   extern template struct Pad<GPUDevice, T, Dims>;
 
 #define DECLARE_GPU_SPECS(T) \
@@ -185,6 +202,13 @@ TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS);
                               .TypeConstraint<T>("T")             \
                               .TypeConstraint<int32>("Tpaddings") \
                               .HostMemory("paddings"),            \
+                          PadOp<GPUDevice, T>);                   \
+  REGISTER_KERNEL_BUILDER(Name("PadV2")                           \
+                              .Device(DEVICE_GPU)                 \
+                              .TypeConstraint<T>("T")             \
+                              .TypeConstraint<int32>("Tpaddings") \
+                              .HostMemory("paddings")             \
+                              .HostMemory("constant_values"),     \
                           PadOp<GPUDevice, T>)
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNEL);
@@ -200,6 +224,15 @@ REGISTER_KERNEL_BUILDER(Name("Pad")
                             .HostMemory("paddings")
                             .HostMemory("output"),
                         PadOp<CPUDevice, int32>);
+REGISTER_KERNEL_BUILDER(Name("PadV2")
+                            .Device(DEVICE_GPU)
+                            .TypeConstraint<int32>("T")
+                            .TypeConstraint<int32>("Tpaddings")
+                            .HostMemory("input")
+                            .HostMemory("paddings")
+                            .HostMemory("constant_values")
+                            .HostMemory("output"),
+                        PadOp<CPUDevice, int32>);
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
@@ -210,6 +243,13 @@ REGISTER_KERNEL_BUILDER(Name("Pad")
                               .TypeConstraint<T>("T")             \
                               .TypeConstraint<int32>("Tpaddings") \
                               .HostMemory("paddings"),            \
+                          PadOp<SYCLDevice, T>);                  \
+  REGISTER_KERNEL_BUILDER(Name("PadV2")                           \
+                              .Device(DEVICE_SYCL)                \
+                              .TypeConstraint<T>("T")             \
+                              .TypeConstraint<int32>("Tpaddings") \
+                              .HostMemory("paddings")             \
+                              .HostMemory("constant_values"),     \
                           PadOp<SYCLDevice, T>)
 
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNEL);
@@ -221,6 +261,15 @@ REGISTER_KERNEL_BUILDER(Name("Pad")
                             .HostMemory("paddings")
                             .HostMemory("output"),
                         PadOp<CPUDevice, int32>);
+REGISTER_KERNEL_BUILDER(Name("PadV2")
+                            .Device(DEVICE_SYCL)
+                            .TypeConstraint<int32>("T")
+                            .TypeConstraint<int32>("Tpaddings")
+                            .HostMemory("input")
+                            .HostMemory("paddings")
+                            .HostMemory("constant_values")
+                            .HostMemory("output"),
+                        PadOp<CPUDevice, int32>);
 #undef REGISTER_SYCL_KERNEL
 #endif // TENSORFLOW_USE_SYCL
 
diff --git a/tensorflow/core/kernels/pad_op.h b/tensorflow/core/kernels/pad_op.h
index 733e0f3083..6a973833e2 100644
--- a/tensorflow/core/kernels/pad_op.h
+++ b/tensorflow/core/kernels/pad_op.h
@@ -27,16 +27,17 @@ namespace functor {
 // Functor used by PadOp to do the computations.
 template <typename Device, typename T, int Dims>
 struct Pad {
-  // Pad "input" into "output", as specified by "paddings".  See pad_op.cc for
-  // details.
+  // Pad "input" into "output", as specified by "paddings" and "pad_value".
+  // See pad_op.cc for details.
   void operator()(const Device& d, typename TTypes<T, Dims>::Tensor output,
                   typename TTypes<T, Dims>::ConstTensor input,
-                  Eigen::array<std::pair<int32, int32>, Dims> paddings) {
+                  Eigen::array<std::pair<int32, int32>, Dims> paddings,
+                  T pad_value) {
     if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value &&
         (output.size() <= std::numeric_limits<int32>::max())) {
-      To32Bit(output).device(d) = To32Bit(input).pad(paddings);
+      To32Bit(output).device(d) = To32Bit(input).pad(paddings, pad_value);
     } else {
-      output.device(d) = input.pad(paddings);
+      output.device(d) = input.pad(paddings, pad_value);
     }
   }
 };
@@ -46,7 +47,7 @@ struct Pad<Device, T, 0> {
   // In the scalar case we simply copy the input.
   void operator()(const Device& d, typename TTypes<T, 0>::Tensor output,
                   typename TTypes<T, 0>::ConstTensor input,
-                  Eigen::array<std::pair<int32, int32>, 0>) {
+                  Eigen::array<std::pair<int32, int32>, 0>, T) {
     output.device(d) = input;
   }
 };
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 7dd3ad31a0..882b4b0cc4 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -2719,6 +2719,45 @@ pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
 
 )doc");
 
+// --------------------------------------------------------------------------
+REGISTER_OP("PadV2")
+    .Input("input: T")
+    .Input("paddings: Tpaddings")
+    .Input("constant_values: T")
+    .Output("output: T")
+    .Attr("T: type")
+    .Attr("Tpaddings: {int32, int64} = DT_INT32")
+    .SetShapeFn(PadShapeFn)
+    .Doc(R"doc(
+Pads a tensor.
+
+This operation pads `input` according to the `paddings` and `constant_values`
+you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is
+the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
+how many padding values to add before the contents of `input` in that dimension,
+and `paddings[D, 1]` indicates how many padding values to add after the contents
+of `input` in that dimension. `constant_values` is a scalar tensor of the same
+type as `input` that indicates the value to use for padding `input`.
+
+The padded size of each dimension D of the output is:
+
+`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
+
+For example:
+
+```
+# 't' is [[1, 1], [2, 2]]
+# 'paddings' is [[1, 1], [2, 2]]
+# 'constant_values' is 0
+# rank of 't' is 2
+pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
+                      [0, 0, 1, 1, 0, 0]
+                      [0, 0, 2, 2, 0, 0]
+                      [0, 0, 0, 0, 0, 0]]
+```
+
+)doc");
+
 // --------------------------------------------------------------------------
 REGISTER_OP("MirrorPad")
     .Input("input: T")
diff --git a/tensorflow/core/ops/array_ops_test.cc b/tensorflow/core/ops/array_ops_test.cc
index dc5d46e6fa..1351a2f177 100644
--- a/tensorflow/core/ops/array_ops_test.cc
+++ b/tensorflow/core/ops/array_ops_test.cc
@@ -349,6 +349,36 @@ TEST(ArrayOpsTest, PadD_ShapeFn) {
   }
 }
 
+TEST(ArrayOpsTest, PadV2_ShapeFn) {
+  ShapeInferenceTestOp op("PadV2");
+  op.input_tensors.resize(3);
+
+  // Inputs are input, paddings and constant_values.
+
+  INFER_OK(op, "?;?;?", "?");
+
+  // Check shape of paddings.
+  INFER_ERROR("Shape must be rank 2 but is rank 3", op, "?;[1,2,3];?");
+  INFER_ERROR("Dimension must be 2 but is 4", op, "?;[1,4];?");
+
+  // input.rank and paddings.dim(0) are equal. This is the number of dims in
+  // output.
+  INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];[4,2];[]");
+  INFER_OK(op, "[1,2,3];?;[]", "[?,?,?]");
+  INFER_OK(op, "?;[3,2];[]", "[?,?,?]");
+
+  // Make the paddings tensor known and verify padding values get added.
+  // E.g., if padding is ((1,10),(2,20),(3,30)) then values 11,22,23 are added
+  // to input dims to get output.
+  Tensor paddings_t(DT_INT64, TensorShape{3, 2});
+  test::FillValues<int64>(&paddings_t, {1, 10, 2, 20, 3, 30});
+  op.input_tensors[1] = &paddings_t;
+  INFER_OK(op, "[100,200,300];[3,2];[]", "[111,222,333]");
+  INFER_OK(op, "[100,?,300];[3,2];[]", "[111,?,333]");
+  INFER_OK(op, "?;[3,2];[]", "[?,?,?]");
+  INFER_OK(op, "?;?;[]", "[?,?,?]");
+}
+
 TEST(ArrayOpsTest, MirrorPadGrad_ShapeFn) {
   ShapeInferenceTestOp op("MirrorPadGrad");
   op.input_tensors.resize(2);
diff --git a/tensorflow/python/kernel_tests/pad_op_test.py b/tensorflow/python/kernel_tests/pad_op_test.py
index c709be0b5b..b774c69ceb 100644
--- a/tensorflow/python/kernel_tests/pad_op_test.py
+++ b/tensorflow/python/kernel_tests/pad_op_test.py
@@ -30,8 +30,12 @@ from tensorflow.python.platform import test
 
 class PadOpTest(test.TestCase):
 
-  def _npPad(self, inp, paddings, mode):
-    return np.pad(inp, paddings, mode=mode.lower())
+  def _npPad(self, inp, paddings, mode, constant_values=0):
+    mode = mode.lower()
+    if mode == "constant":
+      return np.pad(inp, paddings, mode=mode, constant_values=constant_values)
+    else:
+      return np.pad(inp, paddings, mode=mode)
 
   def testNpPad(self):
     self.assertAllEqual(
@@ -46,6 +50,18 @@ class PadOpTest(test.TestCase):
             [[1, 2], [1, 3]],
             mode="constant"))
 
+    self.assertAllEqual(
+        np.array([[1, 1, 1, 1, 1, 1],
+                  [1, 3, 3, 1, 1, 1],
+                  [1, 4, 4, 1, 1, 1],
+                  [1, 5, 5, 1, 1, 1],
+                  [1, 1, 1, 1, 1, 1],
+                  [1, 1, 1, 1, 1, 1]]),
+        self._npPad(
+            np.array([[3, 3], [4, 4], [5, 5]]),
+            [[1, 2], [1, 3]],
+            mode="constant", constant_values=1))
+
     self.assertAllEqual(
         np.array([[4, 3, 4, 9, 4, 3],
                   [1, 0, 1, 2, 1, 0],
@@ -66,35 +82,39 @@ class PadOpTest(test.TestCase):
             [[1, 1], [1, 2]],
             mode="symmetric"))
 
-  def _testPad(self, np_inputs, paddings, mode):
-    np_val = self._npPad(np_inputs, paddings, mode=mode)
+  def _testPad(self, np_inputs, paddings, mode, constant_values):
+    np_val = self._npPad(np_inputs, paddings, mode=mode,
+                         constant_values=constant_values)
     with self.test_session(use_gpu=True):
-      tf_val = array_ops.pad(np_inputs, paddings, mode=mode)
+      tf_val = array_ops.pad(np_inputs, paddings, mode=mode,
+                             constant_values=constant_values)
       out = tf_val.eval()
     self.assertAllEqual(np_val, out)
     self.assertShapeEqual(np_val, tf_val)
 
-  def _testGradient(self, x, a, mode):
+  def _testGradient(self, x, a, mode, constant_values):
     with self.test_session(use_gpu=True):
       inx = ops.convert_to_tensor(x)
       xs = list(x.shape)
       ina = ops.convert_to_tensor(a)
-      y = array_ops.pad(inx, ina, mode=mode)
+      y = array_ops.pad(inx, ina, mode=mode, constant_values=constant_values)
       # Expected y's shape to be:
       ys = list(np.array(x.shape) + np.sum(np.array(a), axis=1))
       jacob_t, jacob_n = gradient_checker.compute_gradient(
           inx, xs, y, ys, x_init_value=x)
     self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5)
 
-  def _testAll(self, np_inputs, paddings):
+  def _testAll(self, np_inputs, paddings, constant_values):
     for mode in ("CONSTANT", "REFLECT", "SYMMETRIC", "reflect", "symmetric",
                  "constant"):
       # Zero-sized input is not allowed for REFLECT mode, but we still want
       # zero-sized input test cases for the other modes.
       if np_inputs.size or mode.upper() != "REFLECT":
-        self._testPad(np_inputs, paddings, mode=mode)
+        self._testPad(np_inputs, paddings, mode=mode,
+                      constant_values=constant_values)
         if np_inputs.dtype == np.float32:
-          self._testGradient(np_inputs, paddings, mode=mode)
+          self._testGradient(np_inputs, paddings, mode=mode,
+                             constant_values=constant_values)
 
   def testInputDims(self):
     with self.test_session(use_gpu=True):
@@ -179,23 +199,25 @@ class PadOpTest(test.TestCase):
     for t in [np.int32, np.int64]:
       self._testAll(
           np.random.randint(-100, 100, (4, 4, 3)).astype(t),
-          [[1, 0], [2, 3], [0, 2]])
+          [[1, 0], [2, 3], [0, 2]], 0)
       self._testAll(
           np.random.randint(-100, 100, (4, 2, 1, 3)).astype(t),
-          [[0, 0], [0, 0], [0, 0], [0, 0]])
+          [[0, 0], [0, 0], [0, 0], [0, 0]], -1234)
 
   def testFloatTypes(self):
     for t in [np.float32, np.float64]:
-      self._testAll(np.random.rand(2, 5).astype(t), [[1, 0], [2, 0]])
-      self._testAll(np.random.rand(2, 3, 4).astype(t), [[0, 0], [0, 0], [0, 0]])
-      self._testAll(np.random.rand(0, 3, 4).astype(t), [[0, 0], [2, 1], [2, 3]])
+      self._testAll(np.random.rand(2, 5).astype(t), [[1, 0], [2, 0]], 0.0)
+      self._testAll(np.random.rand(2, 3, 4).astype(t),
+                    [[0, 0], [0, 0], [0, 0]], -1234.0)
+      self._testAll(np.random.rand(0, 3, 4).astype(t),
+                    [[0, 0], [2, 1], [2, 3]], 0.0)
 
   def testComplexTypes(self):
     for t in [np.complex64, np.complex128]:
       x = np.random.rand(2, 5).astype(t)
-      self._testAll(x + 1j * x, [[1, 0], [2, 0]])
+      self._testAll(x + 1j * x, [[1, 0], [2, 0]], 1234.0 - 1234.0j)
       x = np.random.rand(3, 2, 1, 1).astype(t)
-      self._testAll(x + 1j * x, [[0, 0], [0, 0], [0, 0], [0, 0]])
+      self._testAll(x + 1j * x, [[0, 0], [0, 0], [0, 0], [0, 0]], 0 + 0j)
 
   def testShapeFunctionEdgeCases(self):
     # Unknown paddings shape.
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index 51b3b81500..73a4b7db9f 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -470,7 +470,6 @@ def _TileGrad(op, grad):
 ops.NotDifferentiable("BroadcastGradientArgs")
 
 
-@ops.RegisterGradient("Pad")
 def _PadGrad(op, grad):
   """Gradient for Pad."""
   # Pad introduces values around the original tensor, so the gradient function
@@ -483,7 +482,14 @@ def _PadGrad(op, grad):
   # Make it a 1-D tensor.
   begin = array_ops.reshape(pad_before, [-1])
   sizes = array_ops.shape(x)
-  return array_ops.slice(grad, begin, sizes), None
+  x_grad = array_ops.slice(grad, begin, sizes)
+  if len(op.inputs) == 3:
+    return x_grad, None, None
+  else:
+    return x_grad, None
+
+ops.RegisterGradient("Pad")(_PadGrad)
+ops.RegisterGradient("PadV2")(_PadGrad)
 
 
 # ReverseSequence is just a permutation.  The gradient permutes back.
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index a963bd265c..be3cadbd21 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1613,7 +1613,7 @@ def sparse_placeholder(dtype, shape=None, name=None):
 # pylint: enable=redefined-outer-name
 
 
-def pad(tensor, paddings, mode="CONSTANT", name=None):  # pylint: disable=invalid-name
+def pad(tensor, paddings, mode="CONSTANT", name=None, constant_values=0):  # pylint: disable=invalid-name
   """Pads a tensor.
 
   This operation pads a `tensor` according to the `paddings` you specify.
@@ -1635,6 +1635,7 @@ def pad(tensor, paddings, mode="CONSTANT", name=None):  # pylint: disable=invali
   ```python
   # 't' is [[1, 2, 3], [4, 5, 6]].
   # 'paddings' is [[1, 1,], [2, 2]].
+  # 'constant_values' is 0.
   # rank of 't' is 2.
   pad(t, paddings, "CONSTANT") ==> [[0, 0, 0, 0, 0, 0, 0],
                                     [0, 0, 1, 2, 3, 0, 0],
@@ -1657,6 +1658,8 @@ def pad(tensor, paddings, mode="CONSTANT", name=None):  # pylint: disable=invali
     paddings: A `Tensor` of type `int32`.
     mode: One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)
     name: A name for the operation (optional).
+    constant_values: In "CONSTANT" mode, the scalar pad value to use. Must be
+      same type as `tensor`.
 
   Returns:
     A `Tensor`. Has the same type as `tensor`.
@@ -1669,7 +1672,12 @@ def pad(tensor, paddings, mode="CONSTANT", name=None):  # pylint: disable=invali
   # NumPy uses all lower-case modes.
   mode = mode.upper()
   if mode == "CONSTANT":
-    return gen_array_ops._pad(tensor, paddings, name=name)
+    # TODO(rjryan): Once the forward compatibility period (3 weeks) have passed
+    # remove the "Pad" fallback here.
+    if constant_values != 0:
+      return gen_array_ops._pad_v2(tensor, paddings, constant_values, name=name)
+    else:
+      return gen_array_ops._pad(tensor, paddings, name=name)
   if mode == "REFLECT":
     return gen_array_ops._mirror_pad(tensor,
                                      paddings,
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index 6408d52d8c..bec6b82a4c 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -14,6 +14,7 @@ MirrorPadGrad
 OneHot
 Pack
 Pad
+PadV2
 ParallelConcat
 Placeholder
 RefIdentity
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 1857589417..cb0b383934 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -1362,7 +1362,7 @@ tf_module {
   }
   member_method {
     name: "pad"
-    argspec: "args=[\'tensor\', \'paddings\', \'mode\', \'name\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\'], "
+    argspec: "args=[\'tensor\', \'paddings\', \'mode\', \'name\', \'constant_values\'], varargs=None, keywords=None, defaults=[\'CONSTANT\', \'None\', \'0\'], "
   }
   member_method {
     name: "parallel_stack"
-- 
GitLab


From 0bfccd3994d7ee9cfee4dd1e337047ab6abbe25c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 16:45:28 -0700
Subject: [PATCH 0862/1375] Use C API to implement Operation.type

The code is excercised by many tests in ops_test.py, math_ops_test*.py,
session_*test.py.

PiperOrigin-RevId: 161460668
---
 tensorflow/python/client/tf_session.i |  5 +++++
 tensorflow/python/framework/ops.py    | 14 +++++++++++++-
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index 243c870e0d..d3dc8458ce 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -68,6 +68,11 @@ tensorflow::ImportNumpy();
   $result = PyUnicode_FromString($1);
 }
 
+// Convert TF_OperationOpType output to unicode python string
+%typemap(out) const char* TF_OperationOpType {
+  $result = PyUnicode_FromString($1);
+}
+
 // We use TF_OperationGetControlInputs_wrapper instead of
 // TF_OperationGetControlInputs
 %ignore TF_OperationGetControlInputs;
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index a8c2930cbe..7fbbe204b0 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1595,7 +1595,19 @@ class Operation(object):
   @property
   def type(self):
     """The type of the op (e.g. `"MatMul"`)."""
-    return self._node_def.op
+    if _USE_C_API:
+      op_type = c_api.TF_OperationOpType(self._c_op)
+      # TODO(iga): Remove these asserts after converting to C API by default.
+      # Just being a bit paranoid here.
+      # pylint: disable=unidiomatic-typecheck
+      assert type(op_type) == type(self._node_def.op), (
+          "Expected same types %s vs %s" % (type(op_type),
+                                            type(self._node_def.op)))
+      # pylint: enable=unidiomatic-typecheck
+      assert op_type == self._node_def.op
+      return op_type
+    else:
+      return self._node_def.op
 
   @property
   def graph(self):
-- 
GitLab


From 277e0502a2afbff51dc21c496565b63d71c6a273 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 16:50:01 -0700
Subject: [PATCH 0863/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161461239
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 36 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 38 +++++++++++++++++++
 2 files changed, 74 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index e7a72b03c4..f872ef3cb5 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -15265,6 +15265,42 @@ op {
     }
   }
 }
+op {
+  name: "PadV2"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "paddings"
+    type_attr: "Tpaddings"
+  }
+  input_arg {
+    name: "constant_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "Tpaddings"
+    type: "type"
+    default_value {
+      type: DT_INT32
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "PaddedBatchDataset"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 02553df448..0b10382471 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -14477,6 +14477,44 @@ op {
   summary: "Pads a tensor with zeros."
   description: "This operation pads a `input` with zeros according to the `paddings` you\nspecify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the\nrank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates\nhow many zeros to add before the contents of `input` in that dimension, and\n`paddings[D, 1]` indicates how many zeros to add after the contents of `input`\nin that dimension.\n\nThe padded size of each dimension D of the output is:\n\n`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`\n\nFor example:\n\n```\n# \'t\' is [[1, 1], [2, 2]]\n# \'paddings\' is [[1, 1], [2, 2]]\n# rank of \'t\' is 2\npad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]\n                      [0, 0, 1, 1, 0, 0]\n                      [0, 0, 2, 2, 0, 0]\n                      [0, 0, 0, 0, 0, 0]]\n```"
 }
+op {
+  name: "PadV2"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "paddings"
+    type_attr: "Tpaddings"
+  }
+  input_arg {
+    name: "constant_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "Tpaddings"
+    type: "type"
+    default_value {
+      type: DT_INT32
+    }
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Pads a tensor."
+  description: "This operation pads `input` according to the `paddings` and `constant_values`\nyou specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is\nthe rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates\nhow many padding values to add before the contents of `input` in that dimension,\nand `paddings[D, 1]` indicates how many padding values to add after the contents\nof `input` in that dimension. `constant_values` is a scalar tensor of the same\ntype as `input` that indicates the value to use for padding `input`.\n\nThe padded size of each dimension D of the output is:\n\n`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`\n\nFor example:\n\n```\n# \'t\' is [[1, 1], [2, 2]]\n# \'paddings\' is [[1, 1], [2, 2]]\n# \'constant_values\' is 0\n# rank of \'t\' is 2\npad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]\n                      [0, 0, 1, 1, 0, 0]\n                      [0, 0, 2, 2, 0, 0]\n                      [0, 0, 0, 0, 0, 0]]\n```"
+}
 op {
   name: "PaddedBatchDataset"
   input_arg {
-- 
GitLab


From b7181debf5ca40fe67e21e5d93ac2ff6230019b6 Mon Sep 17 00:00:00 2001
From: Kiril Gorovoy <kgorovoy@google.com>
Date: Mon, 10 Jul 2017 16:54:01 -0700
Subject: [PATCH 0864/1375] Add a call to AddDefaultAttrsToGraphDef when
 loading deprecated SessionBundles that may run into a problem with undefined
 attributes.

PiperOrigin-RevId: 161461770
---
 tensorflow/contrib/session_bundle/session_bundle.cc | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/tensorflow/contrib/session_bundle/session_bundle.cc b/tensorflow/contrib/session_bundle/session_bundle.cc
index bc6fdcd4de..cf26e3cae7 100644
--- a/tensorflow/contrib/session_bundle/session_bundle.cc
+++ b/tensorflow/contrib/session_bundle/session_bundle.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "google/protobuf/any.pb.h"
 #include "tensorflow/contrib/session_bundle/manifest.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
@@ -163,6 +164,13 @@ Status LoadSessionBundleFromPathUsingRunOptionsInternal(
   TF_RETURN_IF_ERROR(
       GetMetaGraphDefFromExport(export_dir, &(bundle->meta_graph_def)));
 
+  // Deprecated SessionBundle models may fail to load because newly added
+  // attributes are not added to the Graph in the default Session initialization
+  // flow. Add an explicit call here when first loading the graph from disk.
+  TF_RETURN_IF_ERROR(
+      AddDefaultAttrsToGraphDef(bundle->meta_graph_def.mutable_graph_def(),
+                                *OpRegistry::Global(), 0 /* node_offset */));
+
   const auto& collection_def_map = bundle->meta_graph_def.collection_def();
   const auto graph_it = bundle->meta_graph_def.collection_def().find(kGraphKey);
   if (graph_it != collection_def_map.end()) {
-- 
GitLab


From bdd6d2ebb6364939a89d49750c64bd10793c3cd9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 16:55:46 -0700
Subject: [PATCH 0865/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161461980
---
 tensorflow/go/op/wrappers.go | 40 ++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index b70c398b2d..0025692146 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -1268,6 +1268,46 @@ func MirrorPad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (
 	return op.Output(0)
 }
 
+// Pads a tensor.
+//
+// This operation pads `input` according to the `paddings` and `constant_values`
+// you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is
+// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
+// how many padding values to add before the contents of `input` in that dimension,
+// and `paddings[D, 1]` indicates how many padding values to add after the contents
+// of `input` in that dimension. `constant_values` is a scalar tensor of the same
+// type as `input` that indicates the value to use for padding `input`.
+//
+// The padded size of each dimension D of the output is:
+//
+// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
+//
+// For example:
+//
+// ```
+// # 't' is [[1, 1], [2, 2]]
+// # 'paddings' is [[1, 1], [2, 2]]
+// # 'constant_values' is 0
+// # rank of 't' is 2
+// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
+//                       [0, 0, 1, 1, 0, 0]
+//                       [0, 0, 2, 2, 0, 0]
+//                       [0, 0, 0, 0, 0, 0]]
+// ```
+func PadV2(scope *Scope, input tf.Output, paddings tf.Output, constant_values tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "PadV2",
+		Input: []tf.Input{
+			input, paddings, constant_values,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Return the reduction indices for computing gradients of s0 op s1 with broadcast.
 //
 // This is typically used by gradient computations for a broadcasting operation.
-- 
GitLab


From 7624e8e9336e9b9f9a066292b11cf75f8fdeea05 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 18:06:59 -0700
Subject: [PATCH 0866/1375] To add an arg 'name' to Experiment.evaluate() in
 order to distinguish multiple 'one-pass' evaluations.

PiperOrigin-RevId: 161470441
---
 tensorflow/contrib/learn/python/learn/experiment.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index cd47b82d82..3b6399c7e6 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -274,7 +274,7 @@ class Experiment(object):
                             max_steps=self._train_steps,
                             hooks=self._train_monitors + extra_hooks)
 
-  def evaluate(self, delay_secs=None):
+  def evaluate(self, delay_secs=None, name=None):
     """Evaluate on the evaluation data.
 
     Runs evaluation on the evaluation data and returns the result. Runs for
@@ -286,6 +286,8 @@ class Experiment(object):
     Args:
       delay_secs: Start evaluating after this many seconds. If `None`, defaults
         to using `self._eval_delays_secs`.
+      name: Gives the name to the evauation for the case multiple evaluation is
+        run for the same experiment.
 
     Returns:
       The result of the `evaluate` call to the `Estimator`.
@@ -300,7 +302,7 @@ class Experiment(object):
     return self._call_evaluate(input_fn=self._eval_input_fn,
                                steps=self._eval_steps,
                                metrics=self._eval_metrics,
-                               name="one_pass",
+                               name=(name or "one_pass"),
                                hooks=self._eval_hooks)
 
   @deprecated(
-- 
GitLab


From aa239529c47c09599d1085791fc849f03efbddfe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 18:39:30 -0700
Subject: [PATCH 0867/1375] [XLA] Fix bugs and incomplete comment in
 reduce_precision_test.cc.

We want a bitwise or, not bitwise and, to add in the sign bit.  This was previously just creating zeros instead of negative test values.

PiperOrigin-RevId: 161473273
---
 tensorflow/compiler/xla/tests/reduce_precision_test.cc | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
index a66c9b4487..48212dc7d1 100644
--- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
@@ -214,11 +214,12 @@ XLA_TEST_P(ReducePrecisionTest, ReducePrecisionF32) {
     // Add positive values.
     input_values.push_back(tensorflow::bit_cast<float>(test_value[0]));
     expected_values.push_back(tensorflow::bit_cast<float>(test_value[index]));
-    // Add negative values.  We do this in the
+    // Add negative values.  We do this in the bitwise representation so as to
+    // avoid problems with NaN handling.
     input_values.push_back(
-        tensorflow::bit_cast<float>(test_value[0] & sign_bit));
+        tensorflow::bit_cast<float>(test_value[0] ^ sign_bit));
     expected_values.push_back(
-        tensorflow::bit_cast<float>(test_value[index] & sign_bit));
+        tensorflow::bit_cast<float>(test_value[index] ^ sign_bit));
   }
 
   // This is required for proper handling of NaN values.
-- 
GitLab


From 90d6421c5e0898fb840197d9533c2f8ba1a7c651 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Mon, 10 Jul 2017 19:22:04 -0700
Subject: [PATCH 0868/1375] Merge changes from github. END_PUBLIC

---
Commit d0f53f77f authored by Penghao Cen<scorpiocph@gmail.com>
Committed by Shanqing Cai<cais@google.com>:
Minor fix typo (#11323)

---
Commit 02fcf564e authored by Chris Song<sjhshy@gmail.com>
Committed by Chris Song<sjhshy@gmail.com>:
Fix misspells.

---
Commit 764c9b6b4 authored by Louis Tiao<ltiao@users.noreply.github.com>
Committed by GitHub<noreply@github.com>:
Fixed typo in docstring
---
Commit f8cd1283e authored by Shanqing Cai<cais@google.com>
Committed by Shanqing Cai<cais@google.com>:
Chaser

---
Commit 01383b946 authored by Shanqing Cai<cais@google.com>
Committed by Shanqing Cai<cais@google.com>:
Adapt TensorFlowTestCase.setUp() to new reset_default_graph() semantics

Avoid calling reset_default_graph() directly to prevent exceptions in
cases where test methods error out from within nested graph contexts,
which can leave _default_graph_stack non-empty in certain Python
versions.

---
Commit 0ffc37890 authored by Amit Patankar<amitpatankar@google.com>
Committed by Amit Patankar<amitpatankar@google.com>:
Removing second declaration of functions.

---
Commit f9c9cacb0 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Refactor ElementalIrEmitter's slice index finding code into
IrArray::Index::SourceIndexOfSlice().

PiperOrigin-RevId: 161140653

---
Commit ba297aec9 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Update ops-related pbtxt files.

PiperOrigin-RevId: 161138258

---
Commit 68d666737 authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fixes a reentrant lock issue with tensors using ndarray memory which uses tensor memory.

PiperOrigin-RevId: 161137788

---
Commit a2ee8bca3 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add support for int8 x int8 -> int32 matrix multiplication via cublasGemmEx to stream_executor.

PiperOrigin-RevId: 161137741

---
Commit 755fa7b50 authored by Mark Daoust<markdaoust@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Block generate_test, and docs generating from running in python3.

- Doc generation is currently unsupported in python3

- These both end in errors in python 3.5.1+

PiperOrigin-RevId: 161137467

---
Commit 97cbcac45 authored by Peter Hawkins<phawkins@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Fix failure in functionalize_control_flow rewrite for Enter nodes that are unused. Make sure we ignore such nodes without producing an error.

PiperOrigin-RevId: 161136545

---
Commit dabcb60bc authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Add reasonable error messages to Builder::Build for bad parameter numbers.

PiperOrigin-RevId: 161136262

---
Commit 0cbd249e8 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add complex tensors support to `matrix_determinant`.

PiperOrigin-RevId: 161132422

---
Commit 335f1f14d authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Extend static shape inference for SparseTensors with dense_shapes constructed using slicing.

PiperOrigin-RevId: 161132391

---
Commit 53604916e authored by Jianwei Xie<xiejw@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fixed the missing labels test in TPUEstimator.

PiperOrigin-RevId: 161131282

---
Commit 9f57dc8dd authored by Bruno Rosa<bruno.rosa@eldorado.org.br>
Committed by Bruno Rosa<bruno.rosa@eldorado.org.br>:
Use mcpu instead of march for ppc64le

march is not support by gcc on ppc64le

---
Commit 7d5c74a9c authored by Skye Wanderman-Milne<skyewm@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Move duplicate detection logic from Graph to FunctionLibraryDefinition

Turns out this is more useful, since there are many function libraries
that don't belong to a graph. This will be used in a future
change. Note that this maintains the current behavior of Graph.

In addition, updates FunctionDefsEqual() to handle unset attr entries
(I ran into this when using this in said future change).

PiperOrigin-RevId: 161126628

---
Commit 2caec3af1 authored by Shanqing Cai<cais@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Disable more timeseries py tests failing in OSS PIP GPU builds

PiperOrigin-RevId: 161124799

---
Commit 0b5cce367 authored by Eugene Brevdo<ebrevdo@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Get TopK op working on GPU again.  Extend using cub's radix sort.

1. Undo rollback of Andreas Kirsch's initial implementation.
2. Use cub segmented radix sort if Andreas' heap-based impl
   for large k and small num_cols (thresholds of k=100, n=1000
   determined empirically).
3. Use cub segmented radix sort if k == num_cols (this case is always faster).
4. Added benchmarks.

Benchmarks show that the GPU implementation is up to 3x slower for small k but
can be 10x faster for large num_cols and k.

Benchmarks:

Benchmark: m_128_n_10_k_5_use_gpu_False          wall_time: 0.000166 s   Throughput: 0.0077 GB/s
Benchmark: m_128_n_10_k_5_use_gpu_True   wall_time: 0.000796 s   Throughput: 0.00161 GB/s
Benchmark: m_128_n_10_k_9_use_gpu_False          wall_time: 0.00017 s    Throughput: 0.00751 GB/s
Benchmark: m_128_n_10_k_9_use_gpu_True   wall_time: 0.000796 s   Throughput: 0.00161 GB/s
Benchmark: m_128_n_10_k_10_use_gpu_False         wall_time: 0.00017 s    Throughput: 0.00753 GB/s
Benchmark: m_128_n_10_k_10_use_gpu_True          wall_time: 0.000775 s   Throughput: 0.00165 GB/s
Benchmark: m_128_n_100_k_1_use_gpu_False         wall_time: 0.000155 s   Throughput: 0.0826 GB/s
Benchmark: m_128_n_100_k_1_use_gpu_True          wall_time: 0.000796 s   Throughput: 0.0161 GB/s
Benchmark: m_128_n_100_k_50_use_gpu_False        wall_time: 0.000247 s   Throughput: 0.0519 GB/s
Benchmark: m_128_n_100_k_50_use_gpu_True         wall_time: 0.0008 s     Throughput: 0.016 GB/s
Benchmark: m_128_n_100_k_99_use_gpu_False        wall_time: 0.000261 s   Throughput: 0.049 GB/s
Benchmark: m_128_n_100_k_99_use_gpu_True         wall_time: 0.000794 s   Throughput: 0.0161 GB/s
Benchmark: m_128_n_100_k_100_use_gpu_False       wall_time: 0.000239 s   Throughput: 0.0536 GB/s
Benchmark: m_128_n_100_k_100_use_gpu_True        wall_time: 0.000777 s   Throughput: 0.0165 GB/s
Benchmark: m_128_n_1000_k_1_use_gpu_False        wall_time: 0.000324 s   Throughput: 0.395 GB/s
Benchmark: m_128_n_1000_k_1_use_gpu_True         wall_time: 0.000916 s   Throughput: 0.14 GB/s
Benchmark: m_128_n_1000_k_10_use_gpu_False       wall_time: 0.00042 s    Throughput: 0.305 GB/s
Benchmark: m_128_n_1000_k_10_use_gpu_True        wall_time: 0.000902 s   Throughput: 0.142 GB/s
Benchmark: m_128_n_1000_k_500_use_gpu_False      wall_time: 0.0011 s     Throughput: 0.116 GB/s
Benchmark: m_128_n_1000_k_500_use_gpu_True       wall_time: 0.00097 s    Throughput: 0.132 GB/s
Benchmark: m_128_n_1000_k_990_use_gpu_False      wall_time: 0.00133 s    Throughput: 0.0962 GB/s
Benchmark: m_128_n_1000_k_990_use_gpu_True       wall_time: 0.000993 s   Throughput: 0.129 GB/s
Benchmark: m_128_n_1000_k_1000_use_gpu_False     wall_time: 0.00102 s    Throughput: 0.126 GB/s
Benchmark: m_128_n_1000_k_1000_use_gpu_True      wall_time: 0.000964 s   Throughput: 0.133 GB/s
Benchmark: m_128_n_10000_k_10_use_gpu_False      wall_time: 0.002 s      Throughput: 0.64 GB/s
Benchmark: m_128_n_10000_k_10_use_gpu_True       wall_time: 0.00288 s    Throughput: 0.445 GB/s
Benchmark: m_128_n_10000_k_100_use_gpu_False     wall_time: 0.00233 s    Throughput: 0.549 GB/s
Benchmark: m_128_n_10000_k_100_use_gpu_True      wall_time: 0.00325 s    Throughput: 0.394 GB/s
Benchmark: m_128_n_10000_k_5000_use_gpu_False    wall_time: 0.0127 s     Throughput: 0.101 GB/s
Benchmark: m_128_n_10000_k_5000_use_gpu_True     wall_time: 0.00381 s    Throughput: 0.336 GB/s
Benchmark: m_128_n_10000_k_9900_use_gpu_False    wall_time: 0.015 s      Throughput: 0.0853 GB/s
Benchmark: m_128_n_10000_k_9900_use_gpu_True     wall_time: 0.00438 s    Throughput: 0.292 GB/s
Benchmark: m_128_n_10000_k_10000_use_gpu_False   wall_time: 0.0104 s     Throughput: 0.123 GB/s
Benchmark: m_128_n_10000_k_10000_use_gpu_True    wall_time: 0.00427 s    Throughput: 0.3 GB/s
Benchmark: m_128_n_100000_k_100_use_gpu_False    wall_time: 0.0148 s     Throughput: 0.865 GB/s
Benchmark: m_128_n_100000_k_100_use_gpu_True     wall_time: 0.0262 s     Throughput: 0.488 GB/s
Benchmark: m_128_n_100000_k_1000_use_gpu_False   wall_time: 0.0201 s     Throughput: 0.636 GB/s
Benchmark: m_128_n_100000_k_1000_use_gpu_True    wall_time: 0.0263 s     Throughput: 0.486 GB/s
Benchmark: m_128_n_100000_k_50000_use_gpu_False          wall_time: 0.214 s      Throughput: 0.0599 GB/s
Benchmark: m_128_n_100000_k_50000_use_gpu_True   wall_time: 0.0322 s     Throughput: 0.398 GB/s
Benchmark: m_128_n_100000_k_99000_use_gpu_False          wall_time: 0.262 s      Throughput: 0.0489 GB/s
Benchmark: m_128_n_100000_k_99000_use_gpu_True   wall_time: 0.0377 s     Throughput: 0.34 GB/s
Benchmark: m_128_n_100000_k_100000_use_gpu_False         wall_time: 0.118 s      Throughput: 0.108 GB/s
Benchmark: m_128_n_100000_k_100000_use_gpu_True          wall_time: 0.0365 s     Throughput: 0.351 GB/s

END_PUBLIC

BEGIN_PUBLIC
BEGIN_PUBLIC
Automated g4 rollback of changelist 157169178

PiperOrigin-RevId: 161476569
---
 ISSUE_TEMPLATE.md                             |   1 +
 README.md                                     |  16 +-
 RELEASE.md                                    |   6 +
 configure                                     |  11 +-
 tensorflow/c/c_api_test.cc                    |   2 +
 tensorflow/cc/framework/gradients.cc          |   6 +-
 tensorflow/cc/gradients/math_grad.cc          |  40 +++
 tensorflow/cc/gradients/math_grad_test.cc     |  82 ++++++
 .../tf2xla/functionalize_control_flow.cc      |   2 +-
 .../cpu/cpu_parallelization_preparation.h     |   2 +-
 .../compiler/xla/service/cpu/infeed_manager.h |   1 +
 .../service/cpu/parallel_cpu_executable.cc    |   2 +-
 .../xla/service/cpu/shape_partition.cc        |   2 +-
 .../xla/service/cpu/shape_partition.h         |   2 +-
 .../xla/service/hlo_alias_analysis_test.cc    |   2 +-
 tensorflow/compiler/xla/tests/BUILD           |   2 +-
 tensorflow/compiler/xla/tests/build_defs.bzl  |   8 +-
 tensorflow/compiler/xla/tests/plugin.bzl      |  32 +++
 .../contrib/batching/kernels/batch_kernels.cc |   6 +-
 .../categorical-feature-column-handler.cc     |   2 +-
 .../dense-quantized-feature-column-handler.cc |   2 +-
 ...sparse-quantized-feature-column-handler.cc |   6 +-
 .../kernel_tests/prediction_ops_test.py       |   2 +-
 tensorflow/contrib/cmake/external/cub.cmake   |   3 +-
 .../contrib/cmake/tf_core_kernels.cmake       |   4 +-
 tensorflow/contrib/cmake/tf_tests.cmake       |   5 +
 .../distributions/python/ops/binomial.py      |   2 +-
 .../python/ops/vector_student_t.py            |   2 +-
 .../python/ops/factorization_ops.py           |   2 +-
 tensorflow/contrib/image/kernels/image_ops.cc |  12 +-
 .../contrib/keras/python/keras/backend.py     |   4 +
 .../python/keras/layers/convolutional.py      |   2 +-
 .../keras/python/keras/testing_utils.py       |   2 +-
 .../kernels/sparse_feature_cross_kernel.cc    |   6 +-
 .../layers/python/layers/embedding_ops.py     |   2 +-
 .../layers/python/layers/feature_column.py    |   4 +-
 .../contrib/layers/python/layers/layers.py    |   2 +-
 .../layers/python/layers/layers_test.py       |   4 +-
 .../contrib/learn/python/learn/experiment.py  |   2 +-
 .../contrib/makefile/create_ios_frameworks.sh |   2 +-
 .../seq2seq/python/ops/attention_wrapper.py   |   2 +-
 tensorflow/contrib/slim/README.md             |   2 +-
 .../tensor_forest/client/random_forest.py     |   2 +-
 .../tensor_forest/kernels/v4/grow_stats.h     |   2 +-
 .../python/timeseries/math_utils.py           |   4 +-
 .../python/timeseries/state_management.py     |   4 +-
 .../filtering_postprocessor.py                |   2 +-
 .../state_space_models/structural_ensemble.py |   2 +-
 .../contrib/tpu/python/tpu/tpu_estimator.py   |   2 +-
 tensorflow/contrib/tpu/python/tpu/tpu_feed.py |   2 +-
 .../training/python/training/bucket_ops.py    |   2 +-
 .../python/training/evaluation_test.py        |   4 +-
 tensorflow/contrib/verbs/rdma.cc              |  16 +-
 tensorflow/core/BUILD                         |   4 +-
 tensorflow/core/framework/op.cc               |   7 +-
 tensorflow/core/framework/op_gen_lib.cc       |   2 +-
 tensorflow/core/framework/op_kernel.h         |   2 +-
 tensorflow/core/framework/queue_interface.h   |   3 +
 tensorflow/core/graph/graph.cc                |   2 +-
 .../core/grappler/clusters/single_machine.cc  |   2 +-
 .../core/grappler/costs/graph_properties.cc   |   4 +-
 .../core/grappler/costs/virtual_scheduler.cc  |   2 +-
 .../grappler/optimizers/constant_folding.cc   |   2 +-
 tensorflow/core/kernels/BUILD                 |  13 +-
 tensorflow/core/kernels/barrier_ops.cc        |   2 +-
 tensorflow/core/kernels/cast_op_impl.h        |  40 +--
 tensorflow/core/kernels/cast_op_impl_bool.cc  |   5 +-
 .../core/kernels/cast_op_impl_double.cc       |   5 +-
 tensorflow/core/kernels/cast_op_impl_float.cc |   5 +-
 tensorflow/core/kernels/cast_op_impl_int16.cc |   9 +
 tensorflow/core/kernels/cast_op_impl_int32.cc |   4 +-
 tensorflow/core/kernels/cast_op_impl_int64.cc |   4 +-
 tensorflow/core/kernels/cast_op_impl_int8.cc  |   9 +
 .../core/kernels/cast_op_impl_uint16.cc       |   9 +
 tensorflow/core/kernels/cast_op_impl_uint8.cc |   9 +
 tensorflow/core/kernels/cwise_op_abs.cc       |  20 +-
 tensorflow/core/kernels/cwise_op_acos.cc      |  16 +-
 tensorflow/core/kernels/cwise_op_acosh.cc     |  38 +++
 tensorflow/core/kernels/cwise_op_add_1.cc     |  31 +--
 tensorflow/core/kernels/cwise_op_asin.cc      |  16 +-
 tensorflow/core/kernels/cwise_op_asinh.cc     |  38 +++
 tensorflow/core/kernels/cwise_op_atan.cc      |  16 +-
 tensorflow/core/kernels/cwise_op_atanh.cc     |  38 +++
 tensorflow/core/kernels/cwise_op_ceil.cc      |  16 +-
 tensorflow/core/kernels/cwise_op_cos.cc       |  16 +-
 tensorflow/core/kernels/cwise_op_div.cc       |  37 +--
 .../core/kernels/cwise_op_equal_to_1.cc       |   4 +-
 tensorflow/core/kernels/cwise_op_exp.cc       |  16 +-
 tensorflow/core/kernels/cwise_op_expm1.cc     |   2 +-
 tensorflow/core/kernels/cwise_op_floor.cc     |  15 +-
 .../core/kernels/cwise_op_gpu_acosh.cu.cc     |  27 ++
 .../core/kernels/cwise_op_gpu_asinh.cu.cc     |  27 ++
 .../core/kernels/cwise_op_gpu_atanh.cu.cc     |  27 ++
 tensorflow/core/kernels/cwise_op_greater.cc   |   6 +-
 .../core/kernels/cwise_op_greater_equal.cc    |   2 +-
 tensorflow/core/kernels/cwise_op_isfinite.cc  |  16 +-
 tensorflow/core/kernels/cwise_op_isinf.cc     |  16 +-
 tensorflow/core/kernels/cwise_op_isnan.cc     |  16 +-
 tensorflow/core/kernels/cwise_op_less.cc      |   1 -
 .../core/kernels/cwise_op_less_equal.cc       |   5 +-
 tensorflow/core/kernels/cwise_op_log.cc       |  16 +-
 tensorflow/core/kernels/cwise_op_log1p.cc     |  16 +-
 tensorflow/core/kernels/cwise_op_maximum.cc   |   7 +-
 tensorflow/core/kernels/cwise_op_minimum.cc   |   3 +-
 tensorflow/core/kernels/cwise_op_mul_1.cc     |  28 +-
 tensorflow/core/kernels/cwise_op_neg.cc       |  17 +-
 tensorflow/core/kernels/cwise_op_pow.cc       |  15 +-
 tensorflow/core/kernels/cwise_op_round.cc     |   3 -
 tensorflow/core/kernels/cwise_op_rsqrt.cc     |  19 +-
 tensorflow/core/kernels/cwise_op_select.cc    |   2 +
 tensorflow/core/kernels/cwise_op_sign.cc      |   5 +-
 tensorflow/core/kernels/cwise_op_sin.cc       |  16 +-
 tensorflow/core/kernels/cwise_op_sqrt.cc      |  20 +-
 tensorflow/core/kernels/cwise_op_square.cc    |  22 +-
 tensorflow/core/kernels/cwise_op_sub.cc       |  31 +--
 tensorflow/core/kernels/cwise_op_tan.cc       |  16 +-
 tensorflow/core/kernels/cwise_op_tanh.cc      |  16 +-
 tensorflow/core/kernels/cwise_ops.h           |  42 +++
 .../kernels/fixed_length_record_reader_op.cc  | 132 ++++++---
 .../core/kernels/fused_batch_norm_op.cc       |   2 +-
 .../kernels/hexagon/hexagon_ops_definitions.h |   2 +-
 tensorflow/core/kernels/map_stage_op.cc       |  15 +-
 tensorflow/core/kernels/ops_util.h            |  14 +
 ...arameterized_truncated_normal_op_gpu.cu.cc |   2 +-
 tensorflow/core/kernels/queue_base.h          |   2 +-
 tensorflow/core/kernels/queue_ops.cc          |  21 ++
 .../remote_fused_graph_execute_utils.cc       |  14 +-
 .../remote_fused_graph_rewriter_transform.cc  |   6 +-
 .../sample_distorted_bounding_box_op.cc       |  53 +++-
 tensorflow/core/kernels/sparse_slice_op.cc    | 104 ++++++++
 tensorflow/core/kernels/tile_functor.h        |  15 --
 tensorflow/core/kernels/tile_functor_cpu.cc   |   1 +
 .../core/kernels/tile_functor_gpu.cu.cc       |   1 +
 tensorflow/core/kernels/transpose_functor.h   |  24 +-
 .../core/kernels/transpose_functor_cpu.cc     |  51 ++--
 .../core/kernels/transpose_functor_gpu.cu.cc  |  25 +-
 tensorflow/core/lib/gtl/array_slice.h         |   2 +-
 tensorflow/core/lib/gtl/flatrep.h             |   2 +-
 tensorflow/core/ops/data_flow_ops.cc          |  26 ++
 tensorflow/core/ops/image_ops.cc              | 104 +++++++-
 tensorflow/core/ops/io_ops.cc                 |   3 +
 tensorflow/core/ops/math_grad.cc              |  36 +++
 tensorflow/core/ops/math_grad_test.cc         |  38 +++
 tensorflow/core/ops/math_ops.cc               |  12 +
 tensorflow/core/ops/ops.pbtxt                 |  99 +++++++
 tensorflow/core/ops/sparse_ops.cc             |  66 ++++-
 .../core/platform/default/build_config.bzl    |   5 +-
 .../core/platform/profile_utils/cpu_utils.cc  |  13 +-
 .../core/platform/profile_utils/cpu_utils.h   |   6 +-
 .../platform/profile_utils/cpu_utils_test.cc  |  12 +-
 tensorflow/core/profiler/internal/BUILD       |   5 +-
 .../core/profiler/internal/tfprof_timeline.cc |   2 +-
 tensorflow/core/protobuf/worker.proto         |   2 +-
 tensorflow/core/public/version.h              |   2 +-
 .../core/util/memmapped_file_system_test.cc   |   2 +-
 tensorflow/core/util/sparse/sparse_tensor.h   |  90 ++++++-
 .../core/util/sparse/sparse_tensor_test.cc    |  46 +++-
 tensorflow/core/util/tensor_bundle/BUILD      |   3 +-
 .../docs_src/api_guides/python/math_ops.md    |   3 +
 .../docs_src/community/documentation.md       |   2 +-
 tensorflow/docs_src/community/welcome.md      |   4 +
 .../docs_src/get_started/get_started.md       |  23 +-
 .../docs_src/get_started/mnist/beginners.md   |  10 +-
 tensorflow/docs_src/install/install_c.md      |   4 +-
 tensorflow/docs_src/install/install_go.md     |   2 +-
 tensorflow/docs_src/install/install_java.md   |  18 +-
 tensorflow/docs_src/install/install_linux.md  |  26 +-
 tensorflow/docs_src/install/install_mac.md    |  14 +-
 .../docs_src/install/install_sources.md       |   4 +-
 .../docs_src/install/install_windows.md       |   4 +-
 .../docs_src/programmers_guide/embedding.md   |   4 +-
 .../programmers_guide/threading_and_queues.md |   3 +
 .../docs_src/programmers_guide/variables.md   |   4 +-
 .../programmers_guide/version_compat.md       |   2 +-
 tensorflow/docs_src/tutorials/deep_cnn.md     |   2 +-
 tensorflow/docs_src/tutorials/seq2seq.md      |   2 +-
 tensorflow/examples/ios/README.md             |   2 +-
 tensorflow/go/op/wrappers.go                  |  45 ++++
 .../src/main/java/org/tensorflow/Input.java   |   2 +-
 .../main/java/org/tensorflow/Operation.java   |   2 +-
 .../java/org/tensorflow/OperationBuilder.java |  31 ++-
 .../src/main/java/org/tensorflow/Session.java |   2 +-
 .../src/main/java/org/tensorflow/Shape.java   |   2 +-
 .../src/main/native/operation_builder_jni.cc  |  27 ++
 .../src/main/native/operation_builder_jni.h   |   8 +
 tensorflow/python/BUILD                       |   4 +-
 tensorflow/python/client/tf_session_helper.cc |   4 +-
 .../estimator/canned/linear_testing_utils.py  |   2 +-
 tensorflow/python/framework/ops.py            |   9 +
 tensorflow/python/framework/ops_test.py       |   6 +
 tensorflow/python/framework/tensor_shape.py   |   2 +-
 tensorflow/python/framework/test_util.py      |   7 +
 tensorflow/python/kernel_tests/BUILD          |  12 +
 .../python/kernel_tests/basic_gpu_test.py     |   4 +
 .../python/kernel_tests/cast_op_test.py       |  14 +-
 .../python/kernel_tests/cwise_ops_test.py     |  20 ++
 .../python/kernel_tests/map_stage_op_test.py  |   4 +-
 .../python/kernel_tests/pooling_ops_test.py   |   2 +-
 .../python/kernel_tests/reader_ops_test.py    | 159 +++++++++--
 .../kernel_tests/sparse_slice_op_test.py      | 251 ++++++++++++++++++
 .../python/kernel_tests/stage_op_test.py      |   4 +-
 tensorflow/python/layers/base.py              |   6 +-
 tensorflow/python/layers/convolutional.py     |  15 +-
 tensorflow/python/ops/data_flow_ops.py        |  19 ++
 .../distributions/dirichlet_multinomial.py    |   2 +-
 .../python/ops/distributions/distribution.py  |   2 +-
 .../python/ops/distributions/multinomial.py   |   2 +-
 tensorflow/python/ops/distributions/util.py   |   2 +-
 tensorflow/python/ops/gradients_impl.py       |   2 +-
 tensorflow/python/ops/hidden_ops.txt          |   2 +
 tensorflow/python/ops/image_ops_impl.py       | 102 +++++++
 tensorflow/python/ops/image_ops_test.py       |  49 +++-
 tensorflow/python/ops/io_ops.py               |   5 +-
 tensorflow/python/ops/math_grad.py            |  34 ++-
 tensorflow/python/ops/math_grad_test.py       |  23 ++
 tensorflow/python/ops/math_ops.py             |   7 +-
 tensorflow/python/ops/sets_impl.py            |   2 +-
 tensorflow/python/ops/sparse_ops.py           |  45 ++++
 tensorflow/python/ops/summary_op_util.py      |   2 +-
 tensorflow/python/training/adam.py            |   2 +-
 tensorflow/python/util/deprecation_test.py    |   2 +-
 tensorflow/python/util/nest.py                |   6 +-
 tensorflow/python/util/tfprof.i               |   1 -
 tensorflow/tensorflow.bzl                     |  12 +-
 .../golden/tensorflow.-f-i-f-o-queue.pbtxt    |   4 +
 ...nsorflow.-fixed-length-record-reader.pbtxt |   2 +-
 .../tensorflow.-padding-f-i-f-o-queue.pbtxt   |   4 +
 .../golden/tensorflow.-priority-queue.pbtxt   |   4 +
 .../api/golden/tensorflow.-queue-base.pbtxt   |   4 +
 .../tensorflow.-random-shuffle-queue.pbtxt    |   4 +
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  16 ++
 .../tools/dist_test/python/mnist_replica.py   |   6 +-
 tensorflow/tools/docker/Dockerfile.devel-gpu  |   1 +
 tensorflow/tools/docker/README.md             |   2 +-
 tensorflow/tools/docs/generate_lib.py         |   2 +-
 tensorflow/tools/docs/parser.py               |   2 +-
 tensorflow/tools/pip_package/setup.py         |   2 +-
 tensorflow/workspace.bzl                      |   5 +-
 third_party/lmdb.BUILD                        |   4 +-
 third_party/snappy.BUILD                      |  24 +-
 third_party/sycl/crosstool/computecpp.tpl     |   2 +-
 third_party/toolchains/cpus/CROSSTOOL         |   6 +
 third_party/zlib.BUILD                        |  24 +-
 243 files changed, 2707 insertions(+), 814 deletions(-)
 create mode 100644 tensorflow/compiler/xla/tests/plugin.bzl
 create mode 100644 tensorflow/core/kernels/cwise_op_acosh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_asinh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_atanh.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc
 create mode 100644 tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc
 create mode 100644 tensorflow/core/kernels/sparse_slice_op.cc
 create mode 100644 tensorflow/python/kernel_tests/sparse_slice_op_test.py

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 4f1666c2e7..5bf13ee152 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -17,6 +17,7 @@ If you open a GitHub issue, here is our policy:
 - **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
 - **TensorFlow installed from (source or binary)**:
 - **TensorFlow version (use command below)**:
+- **Python version**: 
 - **Bazel version (if compiling from source)**:
 - **CUDA/cuDNN version**:
 - **GPU model and memory**:
diff --git a/README.md b/README.md
index abbead98a7..90d50f6768 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
 code.  TensorFlow also includes TensorBoard, a data visualization toolkit.
 
 TensorFlow was originally developed by researchers and engineers
-working on the Google Brain team within Google's Machine Intelligence research
+working on the Google Brain team within Google's Machine Intelligence Research
 organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
@@ -34,13 +34,13 @@ and discussion.**
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
diff --git a/RELEASE.md b/RELEASE.md
index 9875838d7e..0cd4eef5d6 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,3 +1,9 @@
+# Release 1.2.1
+
+## Bug Fixes and Other Changes
+* Updating markdown version required to >= 2.6.8.
+* Support tensors as dropout rates again, by removing the min(max(..))
+
 # Release 1.2.0
 
 ## Major Features and Improvements
diff --git a/configure b/configure
index 4bcf11e0a9..4c6cba2169 100755
--- a/configure
+++ b/configure
@@ -25,6 +25,10 @@ function is_windows() {
   [[ "${PLATFORM}" =~ msys_nt*|mingw*|cygwin*|uwin* ]]
 }
 
+function is_ppc64le() {
+  [[ "${uname -m}" == "ppc64le" ]]
+}
+
 function sed_in_place() {
   sed -e $1 $2 > "$2.bak"
   mv "$2.bak" $2
@@ -294,7 +298,12 @@ fi # TF_NEED_MKL
 
 ## Set up architecture-dependent optimization flags.
 if [ -z "$CC_OPT_FLAGS" ]; then
-  default_cc_opt_flags="-march=native"
+  if [ is_ppc64le ]; then
+    # gcc on ppc64le does not support -march, use mcpu instead
+    default_cc_opt_flags="-mcpu=native"
+  else
+    default_cc_opt_flags="-march=native"
+  fi
   read -p "Please specify optimization flags to use during compilation when bazel option "\
 "\"--config=opt\" is specified [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
   if [ -z "$CC_OPT_FLAGS" ]; then
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 35d6e295c2..736f56837c 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -912,11 +912,13 @@ class CSession {
     for (TF_Operation* o : outputs) {
       outputs_.emplace_back(TF_Output{o, 0});
     }
+    output_values_.resize(outputs_.size());
   }
 
   void SetOutputs(const std::vector<TF_Output>& outputs) {
     ResetOutputValues();
     outputs_ = outputs;
+    output_values_.resize(outputs_.size());
   }
 
   void SetTargets(std::initializer_list<TF_Operation*> targets) {
diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index 8c00a6f704..29ad8a934b 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -152,12 +152,12 @@ Status SymbolicGradientBuilder::Initialize() {
   grad_outputs_->resize(inputs_.size());
   // Populate `output_nodes_` from node ids in `outputs_`.
   output_nodes_.reserve(outputs_.size());
-  for (int i = 0; i < outputs_.size(); ++i) {
+  for (size_t i = 0; i < outputs_.size(); ++i) {
     output_nodes_.insert(outputs_[i].node()->id());
   }
   // Populate `input_nodes_` from Outputs in `inputs_`.
   input_nodes_.reserve(inputs_.size());
-  for (int i = 0; i < inputs_.size(); ++i) {
+  for (size_t i = 0; i < inputs_.size(); ++i) {
     input_nodes_.insert({inputs_[i], i});
   }
 
@@ -341,7 +341,7 @@ Status SymbolicGradientBuilder::AddGradients() {
     // gradient function to the src node/output to which it should be
     // backproped. Maybe grad functions can return a vector of Output pairs to
     // make this association explicit.
-    int dx_index = 0;
+    size_t dx_index = 0;
     for (const Edge* e : n->in_edges()) {
       if (e->IsControlEdge()) continue;
       if (dx_index == dx.size()) {
diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc
index 71d9a8ed7b..0b9b665b1e 100644
--- a/tensorflow/cc/gradients/math_grad.cc
+++ b/tensorflow/cc/gradients/math_grad.cc
@@ -203,6 +203,46 @@ Status TanhGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Tanh", TanhGrad);
 
+Status AsinhGrad(const Scope& scope, const Operation& op,
+                 const std::vector<Output>& grad_inputs,
+                 std::vector<Output>* grad_outputs) {
+  // y = asinh(x)
+  // dy/dx = 1 / cosh(y)
+  auto dydx = Reciprocal(scope, Cosh(scope, op.output(0)));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Asinh", AsinhGrad);
+
+Status AcoshGrad(const Scope& scope, const Operation& op,
+                 const std::vector<Output>& grad_inputs,
+                 std::vector<Output>* grad_outputs) {
+  // y = acosh(x)
+  // dy/dx = 1 / sinh(y)
+  auto dydx = Reciprocal(scope, Sinh(scope, op.output(0)));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Acosh", AcoshGrad);
+
+Status AtanhGrad(const Scope& scope, const Operation& op,
+                 const std::vector<Output>& grad_inputs,
+                 std::vector<Output>* grad_outputs) {
+  // y = atanh(x)
+  // dy/dx = 1 / (1 - x^2)
+  auto one = Cast(scope, Const(scope, 1.0), op.input(0).type());
+  auto dydx = Reciprocal(scope, Sub(scope, one, Square(scope, op.input(0))));
+  // grad(x) = grad(y) * conj(dy/dx)
+  grad_outputs->push_back(
+      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Atanh", AtanhGrad);
+
 Status SigmoidGrad(const Scope& scope, const Operation& op,
                    const std::vector<Output>& grad_inputs,
                    std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc
index 1653b04378..48b3ddbe90 100644
--- a/tensorflow/cc/gradients/math_grad_test.cc
+++ b/tensorflow/cc/gradients/math_grad_test.cc
@@ -48,6 +48,9 @@ class CWiseUnaryGradTest : public ::testing::Test {
     SINH,
     COSH,
     TANH,
+    ASINH,
+    ACOSH,
+    ATANH,
     SIGMOID,
     SIGN,
     SIN,
@@ -122,6 +125,15 @@ class CWiseUnaryGradTest : public ::testing::Test {
       case TANH:
         y = Tanh(scope_, x);
         break;
+      case ASINH:
+        y = Asinh(scope_, x);
+        break;
+      case ACOSH:
+        y = Acosh(scope_, x);
+        break;
+      case ATANH:
+        y = Atanh(scope_, x);
+        break;
       case SIGMOID:
         y = Sigmoid(scope_, x);
         break;
@@ -413,6 +425,76 @@ TEST_F(CWiseUnaryGradTest, Tanh_Complex) {
   TestCWiseGrad<complex64>(TANH, x_fn, dy_fn, dx_fn);
 }
 
+TEST_F(CWiseUnaryGradTest, Asinh) {
+  auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    auto y = std::asinh(x);
+    return dy / std::cosh(y);
+  };
+  TestCWiseGrad<float>(ASINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Asinh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    auto y = std::asinh(x);
+    return dy / conjugate(std::cosh(y));
+  };
+  TestCWiseGrad<complex64>(ASINH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Acosh) {
+  auto x_fn = [this](const int i) { return RV({1, 2, 3, 4, 5, 6, 7}); };
+  auto dy_fn = [this](const float x) { return x + RV({8, 9, 10, 11, 12, 13, 14}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    auto y = std::acosh(x);
+    return dy / std::sinh(y);
+  };
+  TestCWiseGrad<float>(ACOSH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Acosh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{1, 1}, {2, 1}, {1, 4}, {1, 2}, {3, 4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{2, 2}, {3, 3}, {1, 4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    auto y = std::acosh(x);
+    return dy / conjugate(std::sinh(y));
+  };
+  TestCWiseGrad<complex64>(ACOSH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Atanh) {
+  auto x_fn = [this](const int i) { return RV({0, -0.5, 0.5, -0.1, 0.1}); };
+  auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
+  auto dx_fn = [this](const float x, const float dy) {
+    return dy * (1. / (1. - x * x));
+  };
+  TestCWiseGrad<float>(ATANH, x_fn, dy_fn, dx_fn);
+}
+
+TEST_F(CWiseUnaryGradTest, Atanh_Complex) {
+  auto x_fn = [this](const int i) {
+    return CRV({{0.1, 0}, {0, 0.1}, {0.2, -0.1}, {0.1, 0.2}, {0.3, 0.4}});
+  };
+  auto dy_fn = [this](const complex64& x) {
+    return x + CRV({{-2, 2}, {-3, 3}, {1, -4}});
+  };
+  auto dx_fn = [this](const complex64& x, const complex64& dy) {
+    return dy / conjugate(one_ - x * x);
+  };
+  TestCWiseGrad<complex64>(ATANH, x_fn, dy_fn, dx_fn);
+}
+
 TEST_F(CWiseUnaryGradTest, Sigmoid) {
   auto x_fn = [this](const int i) { return RV({0, -1, 1, -2, 2, -3, 3}); };
   auto dy_fn = [this](const float x) { return x + RV({-2, 2, -3, 3, -4, 4}); };
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
index 1c2c327347..faa88ecfe2 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
@@ -383,7 +383,7 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame,
         }
       }
       if (arg.exit == nullptr) {
-        return errors::InvalidArgument("Mising Exit successor to ",
+        return errors::InvalidArgument("Missing Exit successor to ",
                                        arg.switch_node->name());
       }
     }
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
index 70e3492934..d53fc46150 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h
@@ -63,7 +63,7 @@ class ParallelizationPreparation : public HloPassInterface {
 
   // Outlines 'instruction' from entry computation, if it had
   // been assigned parallel tasks in an earlier pass through the computation.
-  // Returns true if 'instruction' was succesfully outlined, false otherwise.
+  // Returns true if 'instruction' was successfully outlined, false otherwise.
   bool OutlineParallelizableInstruction(HloInstruction* instruction);
 
   // Returns true if 'instruction' can be outlined into the same sub-computation
diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager.h b/tensorflow/compiler/xla/service/cpu/infeed_manager.h
index 756582ef78..e965988453 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager.h
+++ b/tensorflow/compiler/xla/service/cpu/infeed_manager.h
@@ -21,6 +21,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_INFEED_MANAGER_H_
 
 #include <deque>
+#include <vector>
 
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/platform/mutex.h"
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
index 85fccd327f..f0af3e7b89 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
@@ -441,7 +441,7 @@ Status ParallelCpuExecutable::ExecuteComputeFunctions(
   // TODO(b/27458679) Manage scheduling based on in-flight concurrency limits.
   // For example, if we expect a library conv/matmul call to run at max
   // concurrency, we should not dispatch runnable instructions until the
-  // libary call is finished (to avoid expensive cache invalidation).
+  // library call is finished (to avoid expensive cache invalidation).
   Executor executor(functions, run_options, &pending, &results,
                     buffer_pointers.data(), profile_counters.data(),
                     assignment_.get());
diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition.cc b/tensorflow/compiler/xla/service/cpu/shape_partition.cc
index e27ff13edd..61b408b8c2 100644
--- a/tensorflow/compiler/xla/service/cpu/shape_partition.cc
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition.cc
@@ -20,7 +20,7 @@ namespace cpu {
 
 std::vector<int64> ShapePartitionAssigner::Run(int64 target_partition_count) {
   // Gather outer-most dims where dim_size >= 'target_partition_count'.
-  // Note: always leave inner-dim static for vectorization/optimzations.
+  // Note: always leave inner-dim static for vectorization/optimizations.
   std::vector<int64> outer_dims;
   int64 outer_dim_size = 1;
   // TODO(b/27458679) Consider reserving enough minor dimensions (based on
diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition.h b/tensorflow/compiler/xla/service/cpu/shape_partition.h
index bdbcb874c1..7a2d00421c 100644
--- a/tensorflow/compiler/xla/service/cpu/shape_partition.h
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition.h
@@ -38,7 +38,7 @@ namespace cpu {
 //
 //     [0, 1), [1, 2), [2, 3), [3, 4), [4, 5) [5, 8)
 //
-//   Note that the last parition has residule because the dimension size is
+//   Note that the last partition has residule because the dimension size is
 //   not a multiple of the partition count.
 //
 //
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
index 442585772f..d67b48dff0 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
@@ -495,7 +495,7 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) {
   };
   // Build separate condition computations so the call graph is flat. The
   // callgraph is always flattened in the compiler pipeline, and the flattened
-  // callgraph enables representive interference analysis.
+  // callgraph enables representative interference analysis.
   HloComputation* condition1 =
       module_->AddEmbeddedComputation(build_cond_computation());
   HloComputation* condition2 =
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 7a899d6e23..5298af788e 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -527,7 +527,7 @@ xla_test(
 )
 
 # Tests the dot operation in some cases that can be performed via a
-# runtime call on some backends - e.g. a runtime call to to Eigen.
+# runtime call on some backends - e.g. a runtime call to Eigen.
 xla_test(
     name = "dot_operation_runtime_test",
     srcs = ["dot_operation_test.cc"],
diff --git a/tensorflow/compiler/xla/tests/build_defs.bzl b/tensorflow/compiler/xla/tests/build_defs.bzl
index 50edd8ea5b..a297132dd3 100644
--- a/tensorflow/compiler/xla/tests/build_defs.bzl
+++ b/tensorflow/compiler/xla/tests/build_defs.bzl
@@ -1,8 +1,9 @@
 """Build rules for XLA testing."""
 
 load("@local_config_cuda//cuda:build_defs.bzl", "cuda_is_configured")
+load("//tensorflow/compiler/xla/tests:plugin.bzl", "plugins")
 
-all_backends = ["cpu", "cpu_parallel", "gpu"]
+all_backends = ["cpu", "cpu_parallel", "gpu"] + plugins.keys()
 
 def filter_backends(backends):
   """Removes "gpu" from a backend list if CUDA is not enabled.
@@ -121,6 +122,11 @@ def xla_test(name,
       backend_deps = ["//tensorflow/compiler/xla/service:gpu_plugin"]
       backend_deps += ["//tensorflow/compiler/xla/tests:test_macros_gpu"]
       this_backend_tags += ["requires-gpu-sm35"]
+    elif backend in plugins:
+      backend_deps = plugins[backend]["deps"]
+      this_backend_copts += plugins[backend]["copts"]
+      this_backend_tags += plugins[backend]["tags"]
+      this_backend_args += plugins[backend]["args"]
     else:
       fail("Unknown backend %s" % backend)
 
diff --git a/tensorflow/compiler/xla/tests/plugin.bzl b/tensorflow/compiler/xla/tests/plugin.bzl
new file mode 100644
index 0000000000..1b10c778ce
--- /dev/null
+++ b/tensorflow/compiler/xla/tests/plugin.bzl
@@ -0,0 +1,32 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Additional XLA devices to be included in the unit test suite."""
+
+# Example:
+#
+# plugins = {
+#   "foo": {
+#     "deps": [
+#       "//tensorflow/compiler/plugin/foo:foo_lib",
+#       "//tensorflow/compiler/plugin/foo:test_macros",
+#     ],
+#     "copts": [],
+#     "tags": [],
+#     "args": []
+#   },
+# }
+
+plugins = {}
+
diff --git a/tensorflow/contrib/batching/kernels/batch_kernels.cc b/tensorflow/contrib/batching/kernels/batch_kernels.cc
index 3c06325651..3b7c538fcc 100644
--- a/tensorflow/contrib/batching/kernels/batch_kernels.cc
+++ b/tensorflow/contrib/batching/kernels/batch_kernels.cc
@@ -51,7 +51,7 @@ Status Concat(OpKernelContext* context, const gtl::ArraySlice<Tensor>& inputs,
   std::vector<std::unique_ptr<typename TTypes<T, 2>::ConstMatrix>> inputs_flat;
   inputs_flat.reserve(inputs.size());
   int64 output_dim0 = 0;
-  for (int i = 0; i < inputs.size(); ++i) {
+  for (size_t i = 0; i < inputs.size(); ++i) {
     const Tensor& input = inputs[i];
     if (input.dims() != input_dims) {
       return errors::InvalidArgument(
@@ -548,7 +548,7 @@ class BatchKernel : public AsyncOpKernel {
       return Status::OK();
     }
     int32 last_size = 0;
-    for (int i = 0; i < allowed_batch_sizes_.size(); ++i) {
+    for (size_t i = 0; i < allowed_batch_sizes_.size(); ++i) {
       const int32 size = allowed_batch_sizes_.at(i);
       if (i > 0 && size <= last_size) {
         return errors::InvalidArgument(
@@ -675,7 +675,7 @@ class UnbatchResource : public ResourceBase {
       // If we have a non-empty tensor, finish the waitlisted runs,
       // and store any remaining pieces.
       if (nonempty_input) {
-        for (int i = 0; i < batch_keys.size(); ++i) {
+        for (size_t i = 0; i < batch_keys.size(); ++i) {
           auto runs_it = waiting_callbacks_.find(batch_keys[i]);
           if (runs_it != waiting_callbacks_.end()) {
             runs_it->second.context->set_output(0, split_inputs[i]);
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
index 2f76224ff0..3a6c409f84 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc
@@ -106,7 +106,7 @@ void CategoricalFeatureColumnHandler::GenerateFeatureSplitCandidates(
     NodeStats left_node_stats(learner_config, left_gradient_stats);
     NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-    // Generate split candiate and update best split candidate for the
+    // Generate split candidate and update best split candidate for the
     // current root if needed.
     FeatureSplitCandidate split_candidate(
         slot_id_,
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
index f43112c8a4..ca7bb71e7d 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc
@@ -93,7 +93,7 @@ void DenseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
       NodeStats left_node_stats(learner_config, left_gradient_stats);
       NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-      // Generate split candiate.
+      // Generate split candidate.
       const float threshold = dense_quantiles_(bucket_id);
       FeatureSplitCandidate split_candidate(
           slot_id_, CreateDenseSplitNode(dense_feature_column_, threshold),
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
index 76da16ab93..a0e9efbbc5 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
+++ b/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc
@@ -109,7 +109,7 @@ void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
       NodeStats left_node_stats(learner_config, left_gradient_stats);
       NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-      // Generate split candiate.
+      // Generate split candidate.
       const float threshold = sparse_quantiles_(bucket_id);
       FeatureSplitCandidate split_candidate(
           slot_id_,
@@ -124,7 +124,7 @@ void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
 
     // Determine if we need a backward pass by checking if the residual gradient
     // after forward aggregation is almost the same as the aggregated gradient.
-    // for the current root. This helps avoid unecessary computation as well
+    // for the current root. This helps avoid unnecessary computation as well
     // as consistency due to floating point precision.
     if (!right_gradient_stats.IsAlmostZero()) {
       // Backward pass with left default direction.
@@ -147,7 +147,7 @@ void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
         NodeStats left_node_stats(learner_config, left_gradient_stats);
         NodeStats right_node_stats(learner_config, right_gradient_stats);
 
-        // Generate split candiate.
+        // Generate split candidate.
         const float threshold = sparse_quantiles_(bucket_id - 1);
         FeatureSplitCandidate split_candidate(
             slot_id_,
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
index aa665d95d7..e5624da965 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
@@ -1149,7 +1149,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
       adjusted_tree_ensemble_config = (
           tree_config_pb2.DecisionTreeEnsembleConfig())
       # When we say to average over more trees than possible, it is averaging
-      # accross all trees.
+      # across all trees.
       total_num = 100
       for i in range(0, total_num):
         tree = tree_ensemble_config.trees.add()
diff --git a/tensorflow/contrib/cmake/external/cub.cmake b/tensorflow/contrib/cmake/external/cub.cmake
index 54329dbd70..1119f0ccba 100644
--- a/tensorflow/contrib/cmake/external/cub.cmake
+++ b/tensorflow/contrib/cmake/external/cub.cmake
@@ -18,6 +18,7 @@ set(cub_URL https://github.com/NVlabs/cub/archive/1.6.4.zip)
 set(cub_HASH SHA256=966d0c4f41e2bdc81aebf9ccfbf0baffaac5a74f00b826b06f4dee79b2bb8cee)
 set(cub_BUILD ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
 set(cub_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
+set(cub_ARCHIVE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/cub_archive)
 
 ExternalProject_Add(cub
     PREFIX cub
@@ -26,4 +27,4 @@ ExternalProject_Add(cub
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
     PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/cub/CMakeLists.txt ${cub_BUILD}
-    INSTALL_COMMAND "")
+    INSTALL_COMMAND  ${CMAKE_COMMAND} -E copy_directory  ${cub_INCLUDE_DIR}/cub ${cub_ARCHIVE_DIR}/cub)
diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 6b8c130490..57df33c13e 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -126,13 +126,15 @@ if(WIN32)
       "${tensorflow_source_dir}/tensorflow/core/kernels/*quantiz*.h"
       "${tensorflow_source_dir}/tensorflow/core/kernels/*quantiz*.cc"
       "${tensorflow_source_dir}/tensorflow/core/kernels/neon/*"
-      # no in tensorflow.dll - comes from .so
+      # not in core - those are loaded dynamically as dll
       "${tensorflow_source_dir}/tensorflow/contrib/resampler/kernels/resampler_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/blas_gemm.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/lstm_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/gru_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/rnn/ops/lstm_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc"
       # temporarily disable nccl (nccl itself needs to be ported to windows first)
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_manager.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_ops.cc"
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 0d49ecd0fd..b7023dbac8 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -148,6 +148,7 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/contrib/data/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/factorization/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/integration_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/python/kernel_tests/*_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/stateless/python/kernel_tests/*_test.py"
     # NOTE: tensor_forest tests in tensor_forest/hybrid/... still don't pass.
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/*_test.py"
@@ -171,6 +172,10 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/python/saved_model/saved_model_test.py"
     # requires scipy
     "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/preprocessing/*_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py"
+    # flaky tests
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/cwise_ops_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py
index d8bc77eaa4..6a1bb39ab2 100644
--- a/tensorflow/contrib/distributions/python/ops/binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/binomial.py
@@ -196,7 +196,7 @@ class Binomial(distribution.Distribution):
 
   @property
   def probs(self):
-    """Probability of of drawing a `1`."""
+    """Probability of drawing a `1`."""
     return self._probs
 
   def _batch_shape_tensor(self):
diff --git a/tensorflow/contrib/distributions/python/ops/vector_student_t.py b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
index ae804b6172..507493560b 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_student_t.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
@@ -160,7 +160,7 @@ class _VectorStudentT(transformed_distribution.TransformedDistribution):
   #### Examples
 
   A single instance of a "Vector Student's t-distribution" is defined by a mean
-  vector of of length `k` and a scale matrix of shape `k x k`.
+  vector of length `k` and a scale matrix of shape `k x k`.
 
   Extra leading dimensions, if provided, allow for batches.
 
diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops.py b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
index 14a2311a42..fb400dbcea 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
@@ -800,7 +800,7 @@ class WALSModel(object):
       regularization: A tensor (scalar) that contains the normalized
         regularization term for the minibatch loss corresponding to sp_input.
       sum_weights: The sum of the weights corresponding to sp_input. This
-        can be used with unregularized loss to caluclate the root weighted
+        can be used with unregularized loss to calculate the root weighted
         squared error.
     """
     assert isinstance(sp_input, sparse_tensor.SparseTensor)
diff --git a/tensorflow/contrib/image/kernels/image_ops.cc b/tensorflow/contrib/image/kernels/image_ops.cc
index 8a97f07732..6adf837ca0 100644
--- a/tensorflow/contrib/image/kernels/image_ops.cc
+++ b/tensorflow/contrib/image/kernels/image_ops.cc
@@ -32,11 +32,11 @@ namespace functor {
 // Explicit instantiation of the CPU functor.
 typedef Eigen::ThreadPoolDevice CPUDevice;
 
-template class FillProjectiveTransform<CPUDevice, uint8>;
-template class FillProjectiveTransform<CPUDevice, int32>;
-template class FillProjectiveTransform<CPUDevice, int64>;
-template class FillProjectiveTransform<CPUDevice, float>;
-template class FillProjectiveTransform<CPUDevice, double>;
+template struct FillProjectiveTransform<CPUDevice, uint8>;
+template struct FillProjectiveTransform<CPUDevice, int32>;
+template struct FillProjectiveTransform<CPUDevice, int64>;
+template struct FillProjectiveTransform<CPUDevice, float>;
+template struct FillProjectiveTransform<CPUDevice, double>;
 
 }  // end namespace functor
 
@@ -116,7 +116,7 @@ namespace functor {
   void FillProjectiveTransform<GPUDevice, TYPE>::operator()(                \
       const GPUDevice& device, OutputType* output, const InputType& images, \
       const TransformsType& transform) const;                               \
-  extern template class FillProjectiveTransform<GPUDevice, TYPE>
+  extern template struct FillProjectiveTransform<GPUDevice, TYPE>
 
 TF_CALL_uint8(DECLARE_FUNCTOR);
 TF_CALL_int32(DECLARE_FUNCTOR);
diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py
index 5175bd0040..4fa4ec0dd4 100644
--- a/tensorflow/contrib/keras/python/keras/backend.py
+++ b/tensorflow/contrib/keras/python/keras/backend.py
@@ -21,6 +21,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import json
 import os
 
@@ -263,6 +264,9 @@ def get_uid(prefix=''):
   ```
   """
   graph = ops.get_default_graph()
+  if graph not in tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS:
+    tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS[graph] = collections.defaultdict(
+      int)
   layer_name_uids = tf_base_layers.PER_GRAPH_LAYER_NAME_UIDS[graph]
   layer_name_uids[prefix] += 1
   return layer_name_uids[prefix]
diff --git a/tensorflow/contrib/keras/python/keras/layers/convolutional.py b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
index 24ff0baf84..b5e44c89c0 100644
--- a/tensorflow/contrib/keras/python/keras/layers/convolutional.py
+++ b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
@@ -316,7 +316,7 @@ class Conv3D(tf_convolutional_layers.Conv3D, Layer):
   When using this layer as the first layer in a model,
   provide the keyword argument `input_shape`
   (tuple of integers, does not include the sample axis),
-  e.g. `input_shape=(128, 128, 128, 3)` for 128x128x128 volumes
+  e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
   with a single channel,
   in `data_format="channels_last"`.
 
diff --git a/tensorflow/contrib/keras/python/keras/testing_utils.py b/tensorflow/contrib/keras/python/keras/testing_utils.py
index bf6f661adf..2f51ace945 100644
--- a/tensorflow/contrib/keras/python/keras/testing_utils.py
+++ b/tensorflow/contrib/keras/python/keras/testing_utils.py
@@ -78,7 +78,7 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
       if e is None:
         input_data_shape[i] = np.random.randint(1, 4)
     input_data = 10 * np.random.random(input_data_shape)
-    if input_dtype[:4] == 'float':
+    if input_dtype[:5] == 'float':
       input_data -= 0.5
     input_data = input_data.astype(input_dtype)
   elif input_shape is None:
diff --git a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
index badf9d486a..932c5ab992 100644
--- a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
+++ b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
@@ -177,7 +177,7 @@ class StringCrosser {
     static const auto k_feature_separator = "_X_";
 
     gtl::InlinedVector<InternalType, 6> cross_vec(columns_.size());
-    for (int i = 0; i < permutation.size(); i++) {
+    for (size_t i = 0; i < permutation.size(); i++) {
       cross_vec[i] = columns_[i]->Feature(batch_index, permutation[i]);
     }
     // TODO(zakaria): this will copy the string twice, might effect
@@ -267,7 +267,7 @@ class ProductIterator {
     next_permutation_.resize(columns_.size(), 0);
     // Sets has_next_ to false if any feature column has 0 features.
     has_next_ = true;
-    for (int i = 0; i < columns_.size(); i++) {
+    for (size_t i = 0; i < columns_.size(); i++) {
       if (columns_[i]->FeatureCount(batch_index_) == 0) {
         has_next_ = false;
         break;
@@ -581,7 +581,7 @@ class SparseFeatureCrossOp : public OpKernel {
           columns,
       int batch_index) {
     int64 cross_count = 1;
-    for (int i = 0; i < columns.size(); i++) {
+    for (size_t i = 0; i < columns.size(); i++) {
       const auto feature_count = columns[i]->FeatureCount(batch_index);
       // If one column is missing any feature, there won't be any cross.
       if (feature_count == 0) {
diff --git a/tensorflow/contrib/layers/python/layers/embedding_ops.py b/tensorflow/contrib/layers/python/layers/embedding_ops.py
index f8f4122d1d..b62e3050cd 100644
--- a/tensorflow/contrib/layers/python/layers/embedding_ops.py
+++ b/tensorflow/contrib/layers/python/layers/embedding_ops.py
@@ -871,7 +871,7 @@ def _embedding_lookup_with_distributed_aggregation(params,
           p_segment_ids = array_ops.gather(segment_ids, pindices[p])
           # Number the p_segment_ids to meet segment_sum's requirements. Note
           # that unique_p_segment_ids contains unique segment ids of this
-          # partiton and these ids' order is unchanged.
+          # partition and these ids' order is unchanged.
           unique_p_segment_ids, unique_p_segment_idx = array_ops.unique(
               p_segment_ids)
           partitioned_segment_ids.append(unique_p_segment_ids)
diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index 68159fe9b9..4cbd198c02 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -165,7 +165,7 @@ class _LinearEmbeddingLookupArguments(
                             "combiner"])):
   """Represents the information needed from a column for embedding lookup.
 
-  Used to to compute DNN inputs and weighted sum.
+  Used to compute DNN inputs and weighted sum.
   """
   pass
 
@@ -184,7 +184,7 @@ class _DeepEmbeddingLookupArguments(
                             "trainable"])):
   """Represents the information needed from a column for embedding lookup.
 
-  Used to to compute DNN inputs and weighted sum.
+  Used to compute DNN inputs and weighted sum.
   """
   pass
 
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index ed4b723ca7..8b3ccea995 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -938,7 +938,7 @@ def convolution(inputs,
       with "NC".
     num_outputs: Integer, the number of output filters.
     kernel_size: A sequence of N positive integers specifying the spatial
-      dimensions of of the filters.  Can be a single integer to specify the same
+      dimensions of the filters.  Can be a single integer to specify the same
       value for all spatial dimensions.
     stride: A sequence of N positive integers specifying the stride at which to
       compute output.  Can be a single integer to specify the same value for all
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 15809ea180..2d08a0cb91 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -1493,12 +1493,12 @@ class PartialFlattenTest(test.TestCase):
 
   def testSparsePartialFlatten(self):
     """Test `_inner_flatten` on `SparseTensor`s."""
-    shape = [4, 3, 11, 6, 1, 3]
+    shape = [4, 3, 11, 6]
     np.random.seed(10301)
     random_ = np.random.rand(*shape)
     indices, values, _ = _sparsify(random_)
 
-    for new_rank in [1, 2, 3, 4, 5]:
+    for new_rank in [1, 2, 3]:
       expected_shape = (shape[:new_rank - 1] + [np.prod(shape[new_rank - 1:])])
       reshaped_random_ = np.reshape(random_, expected_shape)
       expected_indices, expected_values, _ = _sparsify(reshaped_random_)
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 3b6399c7e6..475f5beefd 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -525,7 +525,7 @@ class Experiment(object):
       differences in resource control. First, the resources (e.g., memory) used
       by training will be released before evaluation (`train_and_evaluate` takes
       double resources). Second, more checkpoints will be saved as a checkpoint
-      is generated at the end of each trainning iteration.
+      is generated at the end of each training iteration.
 
       3. As the estimator.train starts from scratch (new graph, new states for
       input, etc) at each iteration, it is recommended to have the
diff --git a/tensorflow/contrib/makefile/create_ios_frameworks.sh b/tensorflow/contrib/makefile/create_ios_frameworks.sh
index 2ad095b397..2bbde6aa88 100644
--- a/tensorflow/contrib/makefile/create_ios_frameworks.sh
+++ b/tensorflow/contrib/makefile/create_ios_frameworks.sh
@@ -79,7 +79,7 @@ cd $SCRIPT_DIR/gen/proto
 tar cf $FW_DIR_TFCORE_HDRS/tmp.tar tensorflow
 cd $FW_DIR_TFCORE_HDRS
 tar xf tmp.tar
-# Dont include the auto downloaded/generated to build this library
+# Don't include the auto downloaded/generated to build this library
 rm -rf tensorflow/contrib/makefile
 rm -f tmp.tar
 
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index 0b1692ef2e..cd65a54b83 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -283,7 +283,7 @@ def _luong_score(query, keys, scale):
     raise ValueError(
         "Incompatible or unknown inner dimensions between query and keys.  "
         "Query (%s) has units: %s.  Keys (%s) have units: %s.  "
-        "Perhaps you need to set num_units to the the keys' dimension (%s)?"
+        "Perhaps you need to set num_units to the keys' dimension (%s)?"
         % (query, depth, keys, key_units, key_units))
   dtype = query.dtype
 
diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index 4bde661698..c0aa6d445a 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -775,7 +775,7 @@ images, labels = LoadTestData(...)
 predictions = MyModel(images)
 
 mae_value_op, mae_update_op = slim.metrics.streaming_mean_absolute_error(predictions, labels)
-mre_value_op, mre_update_op = slim.metrics.streaming_mean_relative_error(predictions, labels, labels)
+mre_value_op, mre_update_op = slim.metrics.streaming_mean_relative_error(predictions, labels)
 pl_value_op, pl_update_op = slim.metrics.percentage_less(mean_relative_errors, 0.3)
 ```
 
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index 47685df4a5..1221165baa 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -315,7 +315,7 @@ class TensorForestEstimator(estimator.Estimator):
         though training might be distributed.
       version: String indicating TensorForest version to use, for backward
         compatibility. Either 'v2', 'v4', or None to let system pick.
-        Overrides grpah_builder_class.
+        Overrides graph_builder_class.
 
     Returns:
       A `TensorForestEstimator` instance.
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
index 6702b81c79..ba73d1d246 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
@@ -109,7 +109,7 @@ class GrowStats {
 
   const TensorForestParams& params_;
 
-  // We cache these beacuse they're used often.
+  // We cache these because they're used often.
   const int split_after_samples_;
   const int num_splits_to_consider_;
 
diff --git a/tensorflow/contrib/timeseries/python/timeseries/math_utils.py b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
index e65e3eab3d..c70da3e082 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/math_utils.py
@@ -689,11 +689,11 @@ class InputStatisticsFromMiniBatch(object):
       values = features[TrainEvalFeatures.VALUES]
     else:
       # times and values may not be available, for example during prediction. We
-      # still need to retreive our variables so that they can be read from, even
+      # still need to retrieve our variables so that they can be read from, even
       # if we're not going to update them.
       times = None
       values = None
-    # Create/retreive variables representing input statistics, initialized
+    # Create/retrieve variables representing input statistics, initialized
     # without data to avoid deadlocking if variables are initialized before
     # queue runners are started.
     with variable_scope.variable_scope("input_statistics", use_resource=True):
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_management.py b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
index ba51a82f4c..dc8f8fdb92 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_management.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
@@ -196,7 +196,7 @@ class ChainingStateManager(_OverridableStateManager):
     return time // self._state_saving_interval
 
   def _get_cached_states(self, times):
-    """Retreive cached states for a batch of times."""
+    """Retrieve cached states for a batch of times."""
     read_chunk_numbers = self._get_chunk_number(times)
     looked_up_state = list(self._cached_states.lookup(
         math_ops.cast(read_chunk_numbers, dtypes.int64)))
@@ -242,7 +242,7 @@ class ChainingStateManager(_OverridableStateManager):
     # written to the next bucket). This assumes fixed missing times (i.e. if we
     # were presented with times [10, 50] we will never see times [30, 50]).
     #
-    # TODO(allenl): Retreive the highest time less than the current time rather
+    # TODO(allenl): Retrieve the highest time less than the current time rather
     # than relying on fixed bucketing.
     write_chunk_numbers = math_ops.maximum(
         self._get_chunk_number(array_ops.concat(
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
index 163819ae2f..7fa538a16e 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py
@@ -150,7 +150,7 @@ class StateInterpolatingAnomalyDetector(FilteringStepPostprocessor):
   This is simply Bayes' theorem, where p(data | anomaly) is the
   alternative/anomaly distribution, p(data | not anomaly) is the model's
   predicted distribution, and anomaly_prior_probability is the prior probability
-  of an anomaly occuring (user-specified, defaulting to 1%).
+  of an anomaly occurring (user-specified, defaulting to 1%).
 
   Rather than computing p(anomaly | data) directly, we use the odds ratio:
 
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
index 26ab726591..a7a80a8e3e 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/structural_ensemble.py
@@ -70,7 +70,7 @@ class StructuralEnsemble(state_space_model.StateSpaceIndependentEnsemble):
 
   `observation_noise`, `level_noise`, `trend noise`, `seasonality_noise`, and
   `transient` are (typically scalar) Gaussian random variables whose variance is
-  learned from data, and that variance is not time dependant in this
+  learned from data, and that variance is not time dependent in this
   implementation. Level noise is optional due to its similarity with observation
   noise in some cases. Seasonality is enforced by constraining a full cycle of
   seasonal variables to have zero expectation, allowing seasonality to adapt
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 193d14e1ce..24a3d01272 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -544,7 +544,7 @@ def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config,
 
     # TODO(xiejw): how to do we support hook and savers in the original
     # model_fn. Realistically, the original
-    # model_fn will be excuted on TPU chips in a replica way. The hooks
+    # model_fn will be executed on TPU chips in a replica way. The hooks
     # returned by the model_fn cannot be supported at all. If we have to,
     # the graph construction part in the model_fn should be separated from the
     # control part (such as hooks and savers). By that the graph construction
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
index c955de524c..3d0a532010 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py
@@ -298,7 +298,7 @@ class InfeedQueue(object):
     input_tensors is a list of lists of Tensors whose types and shapes are used
     to set the queue configuration. The length of the outer list is the number
     of shards required, and each inner list is the tuple of Tensors to use to
-    determine the types and shapes of the correponding shard. This method
+    determine the types and shapes of the corresponding shard. This method
     depends on the shard dimension, and calling it freezes the shard policy.
 
     Args:
diff --git a/tensorflow/contrib/training/python/training/bucket_ops.py b/tensorflow/contrib/training/python/training/bucket_ops.py
index 7e293da551..5523cc375f 100644
--- a/tensorflow/contrib/training/python/training/bucket_ops.py
+++ b/tensorflow/contrib/training/python/training/bucket_ops.py
@@ -88,7 +88,7 @@ def bucket(tensors,
   This function is implemented using several queues. A `QueueRunner` for the
   queues is added to the current `Graph`'s `QUEUE_RUNNER` collection.
 
-  As the returned tensors are the result of of a dequeue operation, evaluating
+  As the returned tensors are the result of a dequeue operation, evaluating
   them will throw a `tf.errors.OutOfRangeError` when the input queue is
   exhausted.  If these tensors are feeding another input queue, its queue runner
   will catch this exception, however, if they are used in your main thread
diff --git a/tensorflow/contrib/training/python/training/evaluation_test.py b/tensorflow/contrib/training/python/training/evaluation_test.py
index babd2239b6..b07039916c 100644
--- a/tensorflow/contrib/training/python/training/evaluation_test.py
+++ b/tensorflow/contrib/training/python/training/evaluation_test.py
@@ -329,7 +329,7 @@ class EvaluateRepeatedlyTest(test.TestCase):
     if not gfile.Exists(checkpoint_dir):
       gfile.MakeDirs(checkpoint_dir)
 
-    # We need a variable that that the saver will try to restore.
+    # We need a variable that the saver will try to restore.
     variables.get_or_create_global_step()
 
     # Run with placeholders. If we actually try to evaluate this, we'd fail
@@ -394,7 +394,7 @@ class EvaluateRepeatedlyTest(test.TestCase):
                                   'evaluate_with_eval_feed_dict')
     self._train_model(checkpoint_dir, num_steps=1)
 
-    # We need a variable that that the saver will try to restore.
+    # We need a variable that the saver will try to restore.
     variables.get_or_create_global_step()
 
     # Create a variable and an eval op that increments it with a placeholder.
diff --git a/tensorflow/contrib/verbs/rdma.cc b/tensorflow/contrib/verbs/rdma.cc
index 6f3a616fe8..445cbe290a 100644
--- a/tensorflow/contrib/verbs/rdma.cc
+++ b/tensorflow/contrib/verbs/rdma.cc
@@ -761,8 +761,8 @@ void RdmaTensorBuffer::SendNextItem() {
           (buffer_size > size_ && local_status_ == idle &&
            remote_status_ == idle)) {
         if ((local_status_ != none) && (buffer_size > size_)) {
-          CHECK(rm.data_type_ == DT_STRING)
-              << "Only string tensor allows to change size";
+          VLOG(2) << "Extend RDMA buffer from " << size_ << " to "
+                  << buffer_size;
         }
         CreateCPUBuffer(buffer_size, false);
         mu_.unlock();
@@ -782,11 +782,13 @@ void RdmaTensorBuffer::SendNextItem() {
         // local/remote_status_ won't be set back to idle
         // unitl Write() is successful
         mu_.unlock();
-        CHECK((buffer_size == size_ && rm.data_type_ != DT_STRING) ||
-              (buffer_size <= size_ && rm.data_type_ == DT_STRING))
-            << "tensor and buffer size do not agree!"
-            << " buffer_size = " << size_
-            << " requested tensor size = " << buffer_size << in.DebugString();
+        if (!((buffer_size == size_ && rm.data_type_ != DT_STRING) ||
+              (buffer_size <= size_ && rm.data_type_ == DT_STRING))) {
+          VLOG(2) << "Tensor and buffer size do not agree,"
+                  << " buffer_size = " << size_
+                  << " requested tensor size = "
+                  << buffer_size << in.DebugString();
+        }
         uint32_t imm_data = LookupBufferIndex(key);
         rm.type_ = RDMA_MESSAGE_TENSOR_WRITE;
         string message = RdmaMessage::CreateMessage(rm);
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index a969a39edc..9411377733 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1221,6 +1221,9 @@ LIB_INTERNAL_WINDOWS_DEPS = glob(
         "platform/*.cc",
         "platform/profile_utils/**/*.h",
         "platform/profile_utils/**/*.cc",
+    ] + [
+        "framework/resource_handle.h",
+        "framework/resource_handle.cc",
     ],
     exclude = [
         "**/*test*",
@@ -1250,7 +1253,6 @@ cc_library(
                 "platform/*.cc",
                 "platform/profile_utils/**/*.h",
                 "platform/profile_utils/**/*.cc",
-            ] + [
                 "framework/resource_handle.h",
                 "framework/resource_handle.cc",
             ],
diff --git a/tensorflow/core/framework/op.cc b/tensorflow/core/framework/op.cc
index fe333dc9ff..4f5a1f80a0 100644
--- a/tensorflow/core/framework/op.cc
+++ b/tensorflow/core/framework/op.cc
@@ -77,9 +77,10 @@ Status OpRegistry::LookUp(const string& op_type_name,
     if (first_unregistered) {
       OpList op_list;
       Export(true, &op_list);
-      VLOG(1) << "All registered Ops:";
-      for (const auto& op : op_list.op()) {
-        VLOG(1) << SummarizeOpDef(op);
+      if (VLOG_IS_ON(3)) {
+         LOG(INFO) << "All registered Ops:";
+         for (const auto& op : op_list.op())
+            LOG(INFO) << SummarizeOpDef(op);
       }
       first_unregistered = false;
     }
diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc
index cedfd6fc9c..143da996a1 100644
--- a/tensorflow/core/framework/op_gen_lib.cc
+++ b/tensorflow/core/framework/op_gen_lib.cc
@@ -73,7 +73,7 @@ bool ConsumeEquals(StringPiece* description) {
   return false;
 }
 
-// Split `*orig` into two pieces at the first occurence of `split_ch`.
+// Split `*orig` into two pieces at the first occurrence of `split_ch`.
 // Returns whether `split_ch` was found. Afterwards, `*before_split`
 // contains the maximum prefix of the input `*orig` that doesn't
 // contain `split_ch`, and `*orig` contains everything after the
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index 1b716c5a5a..bf52c53b88 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -716,7 +716,7 @@ class OpKernelContext {
       StringPiece output_name, const TensorShape& output_shape,
       Tensor** output) TF_MUST_USE_RESULT;
 
-  // Tries to reuse one of of the inputs given in input_indices as a temporary.
+  // Tries to reuse one of the inputs given in input_indices as a temporary.
   // If none of the given inputs can be forwarded, calls
   // allocate_temp() to allocate a new temporary buffer.
   Status forward_input_or_allocate_temp(
diff --git a/tensorflow/core/framework/queue_interface.h b/tensorflow/core/framework/queue_interface.h
index baddf0bbfa..4aeaab3d9b 100644
--- a/tensorflow/core/framework/queue_interface.h
+++ b/tensorflow/core/framework/queue_interface.h
@@ -77,6 +77,9 @@ class QueueInterface : public ResourceBase {
   virtual void Close(OpKernelContext* ctx, bool cancel_pending_enqueues,
                      DoneCallback callback) = 0;
 
+  // Returns true if a given queue is closed and false if it is open.
+  virtual bool is_closed() const = 0;
+
   // Assuming *this represents a shared queue, verify that it matches
   // another instantiation indicated by node_def.
   virtual Status MatchesNodeDef(const NodeDef& node_def) = 0;
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 9691326c99..a840ef39d2 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -31,7 +31,7 @@ namespace tensorflow {
 
 const int Graph::kControlSlot = -1;
 
-struct NodeProperties {
+class NodeProperties {
  public:
   NodeProperties(const OpDef* op_def, const NodeDef& node_def,
                  const DataTypeSlice inputs, const DataTypeSlice outputs)
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index 58b9fc6429..a1531f1cfc 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -158,7 +158,7 @@ Status SingleMachine::Run(const GraphDef& graph_def,
         // Also clear the timeline to save memory
         init_metadata_.clear_step_stats();
       }
-      for (int i = 0; i < queue_runner_defs_.size(); ++i) {
+      for (size_t i = 0; i < queue_runner_defs_.size(); ++i) {
         std::unique_ptr<QueueRunner> queue_runner;
         TF_RETURN_IF_ERROR(QueueRunner::New(queue_runner_defs_[i],
                                             coordinator_.get(), &queue_runner));
diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index cd673a64a0..e29f32b270 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -141,7 +141,7 @@ Status GraphProperties::MergeEnqueueShapesAndTypes(
         "Enqueue nodes mixed number of tensors: ", shapes_and_types.size(),
         "  vs ", queue_shapes_and_types->size());
   }
-  for (int i = 0; i < shapes_and_types.size(); ++i) {
+  for (size_t i = 0; i < shapes_and_types.size(); ++i) {
     const ShapeAndType& a = shapes_and_types[i];
     ShapeAndType& b = (*queue_shapes_and_types)[i];
     if (a.dtype != b.dtype) {
@@ -163,7 +163,7 @@ Status GraphProperties::RelaxEnqueueShapesAndMergeTypes(
         "Enqueue nodes mixed number of tensors: ", shapes_and_types.size(),
         "  vs ", queue_shapes_and_types->size());
   }
-  for (int i = 0; i < shapes_and_types.size(); ++i) {
+  for (size_t i = 0; i < shapes_and_types.size(); ++i) {
     const ShapeAndType& a = shapes_and_types[i];
     ShapeAndType& b = (*queue_shapes_and_types)[i];
     if (a.dtype != b.dtype) {
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 11650cb6a2..15ebef188f 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -365,7 +365,7 @@ NodeState& VirtualScheduler::GetNodeStateOrCreateIt(const NodeDef* node) {
     // Initialize output port related data:
     // Assume the size of OutputProperties represents the number of output ports
     // of this node.
-    for (int i = 0; i < node_state.output_properties.size(); ++i) {
+    for (size_t i = 0; i < node_state.output_properties.size(); ++i) {
       node_state.time_no_references[i] = Costs::Duration::max();
       node_state.num_outputs_executed[i] = 0;
       // Populate an empty vector for each port. The caller will add nodes
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 63159dc3aa..715b6e97d2 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -396,7 +396,7 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
   if (output_tensors.empty()) {
     Status(error::INVALID_ARGUMENT, "Expected at least one output.");
   }
-  for (int i = 0; i < output_tensors.size(); i++) {
+  for (size_t i = 0; i < output_tensors.size(); i++) {
     string node_name = AddPrefixToNodeName(node.name(), kConstantFoldingConst);
     if (output_tensors.size() > 1) {
       node_name = strings::StrCat(node_name, "-", i);
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 96a7999c11..6eee2e16eb 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -32,6 +32,7 @@ load(
     "tf_kernel_library",
     "tf_mkl_kernel_library",
     "cc_header_only_library",
+    "if_not_windows",
 )
 load("@local_config_sycl//sycl:build_defs.bzl", "if_sycl")
 load("//tensorflow:tensorflow.bzl", "tf_cuda_cc_test")
@@ -231,7 +232,7 @@ cc_library(
     name = "ops_util",
     srcs = ["ops_util.cc"],
     hdrs = ["ops_util.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -344,7 +345,7 @@ cc_library(
     name = "save_restore_tensor",
     srcs = ["save_restore_tensor.cc"],
     hdrs = ["save_restore_tensor.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         ":bounds_check",
         "//tensorflow/core:framework",
@@ -1222,6 +1223,7 @@ tf_kernel_library(
     ],
     visibility = ["//visibility:private"],
     deps = [
+        ":ops_util",
         "//tensorflow/core:framework",
         "//tensorflow/core/kernels:conv_ops",
         "//third_party/eigen3",
@@ -3219,6 +3221,7 @@ cc_library(
         ":sparse_reduce_op",
         ":sparse_reorder_op",
         ":sparse_reshape_op",
+        ":sparse_slice_op",
         ":sparse_softmax",
         ":sparse_sparse_binary_op_shared",
         ":sparse_split_op",
@@ -3301,6 +3304,12 @@ tf_kernel_library(
     deps = SPARSE_DEPS,
 )
 
+tf_kernel_library(
+    name = "sparse_slice_op",
+    prefix = "sparse_slice_op",
+    deps = SPARSE_DEPS,
+)
+
 tf_kernel_library(
     name = "sparse_softmax",
     prefix = "sparse_softmax",
diff --git a/tensorflow/core/kernels/barrier_ops.cc b/tensorflow/core/kernels/barrier_ops.cc
index 83633a1dd9..3b880a9635 100644
--- a/tensorflow/core/kernels/barrier_ops.cc
+++ b/tensorflow/core/kernels/barrier_ops.cc
@@ -413,7 +413,7 @@ class Barrier : public ResourceBase {
     }
     queue_closed_ = true;
     if (cancel_pending_enqueues) queue_cancelled_ = true;
-    if (!ready_queue_->closed()) {
+    if (!ready_queue_->is_closed()) {
       ready_queue_->Close(ctx, cancel_pending_enqueues, callback);
     }
   }
diff --git a/tensorflow/core/kernels/cast_op_impl.h b/tensorflow/core/kernels/cast_op_impl.h
index 1ee0796ac1..6309e4a4dc 100644
--- a/tensorflow/core/kernels/cast_op_impl.h
+++ b/tensorflow/core/kernels/cast_op_impl.h
@@ -45,20 +45,23 @@ struct CastFunctor<Eigen::SyclDevice, O, I> {
 
 }  // namespace functor
 
-#define CURRY_TYPES3(FN, arg0, arg1)   \
-  FN(arg0, arg1, bool);                \
-  FN(arg0, arg1, uint8);               \
-  FN(arg0, arg1, int8);                \
-  FN(arg0, arg1, uint16);              \
-  FN(arg0, arg1, int16);               \
-  FN(arg0, arg1, int32);               \
-  FN(arg0, arg1, int64);               \
-  FN(arg0, arg1, Eigen::half);         \
-  FN(arg0, arg1, float);               \
-  FN(arg0, arg1, double);              \
-  FN(arg0, arg1, std::complex<float>); \
+#define CURRY_TYPES3_NO_HALF(FN, arg0, arg1)   \
+  FN(arg0, arg1, bool);                        \
+  FN(arg0, arg1, uint8);                       \
+  FN(arg0, arg1, int8);                        \
+  FN(arg0, arg1, uint16);                      \
+  FN(arg0, arg1, int16);                       \
+  FN(arg0, arg1, int32);                       \
+  FN(arg0, arg1, int64);                       \
+  FN(arg0, arg1, float);                       \
+  FN(arg0, arg1, double);                      \
+  FN(arg0, arg1, std::complex<float>);         \
   FN(arg0, arg1, std::complex<double>)
 
+#define CURRY_TYPES3(FN, arg0, arg1)           \
+  CURRY_TYPES3_NO_HALF(FN, arg0, arg1)         \
+  FN(arg0, arg1, Eigen::half);
+
 #define CAST_CASE(DEVICE, IN, OUT)                                         \
   if (DataTypeToEnum<OUT>::value == dst_dtype) {                           \
     return [](OpKernelContext* ctx, const Tensor& inp, Tensor* out) {      \
@@ -154,6 +157,15 @@ GetGpuCastFromBfloat(DataType dst_dtype);
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromBool(DataType dst_dtype);
 
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint8(DataType dst_dtype);
+
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint16(DataType dst_dtype);
+
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromInt16(DataType dst_dtype);
+
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromInt32(DataType dst_dtype);
 
@@ -165,10 +177,8 @@ GetSyclCastFromFloat(DataType dst_dtype);
 
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromDouble(DataType dst_dtype);
-
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_CAST_OP_IMPL_H_
-
diff --git a/tensorflow/core/kernels/cast_op_impl_bool.cc b/tensorflow/core/kernels/cast_op_impl_bool.cc
index a13f163009..5cd63f2458 100644
--- a/tensorflow/core/kernels/cast_op_impl_bool.cc
+++ b/tensorflow/core/kernels/cast_op_impl_bool.cc
@@ -38,10 +38,9 @@ GetGpuCastFromBool(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromBool(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, bool);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, bool);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
-
diff --git a/tensorflow/core/kernels/cast_op_impl_double.cc b/tensorflow/core/kernels/cast_op_impl_double.cc
index fdc8d51158..1203f066a2 100644
--- a/tensorflow/core/kernels/cast_op_impl_double.cc
+++ b/tensorflow/core/kernels/cast_op_impl_double.cc
@@ -38,10 +38,9 @@ GetGpuCastFromDouble(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromDouble(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, double);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, double);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYC
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
-
diff --git a/tensorflow/core/kernels/cast_op_impl_float.cc b/tensorflow/core/kernels/cast_op_impl_float.cc
index 1241dcd8f2..2ff9af21f2 100644
--- a/tensorflow/core/kernels/cast_op_impl_float.cc
+++ b/tensorflow/core/kernels/cast_op_impl_float.cc
@@ -53,10 +53,9 @@ GetGpuCastFromFloat(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromFloat(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, float);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, float);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
-
diff --git a/tensorflow/core/kernels/cast_op_impl_int16.cc b/tensorflow/core/kernels/cast_op_impl_int16.cc
index 3c2d6185e3..f12d852e95 100644
--- a/tensorflow/core/kernels/cast_op_impl_int16.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int16.cc
@@ -34,4 +34,13 @@ GetGpuCastFromInt16(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromInt16(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int16);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_int32.cc b/tensorflow/core/kernels/cast_op_impl_int32.cc
index 69ed760455..2a4b27a12d 100644
--- a/tensorflow/core/kernels/cast_op_impl_int32.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int32.cc
@@ -38,9 +38,9 @@ GetGpuCastFromInt32(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromInt32(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, int32);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int32);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_int64.cc b/tensorflow/core/kernels/cast_op_impl_int64.cc
index 7a8363ca39..065defabbb 100644
--- a/tensorflow/core/kernels/cast_op_impl_int64.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int64.cc
@@ -38,9 +38,9 @@ GetGpuCastFromInt64(DataType dst_dtype) {
 typedef Eigen::SyclDevice SYCLDevice;
 std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
 GetSyclCastFromInt64(DataType dst_dtype) {
-  CURRY_TYPES3(CAST_CASE, SYCLDevice, int64);
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int64);
   return nullptr;
 }
-#endif // TENSORFLOW_USE_SYCL
+#endif  // TENSORFLOW_USE_SYCL
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_int8.cc b/tensorflow/core/kernels/cast_op_impl_int8.cc
index 62971fa95c..8d678c4733 100644
--- a/tensorflow/core/kernels/cast_op_impl_int8.cc
+++ b/tensorflow/core/kernels/cast_op_impl_int8.cc
@@ -34,4 +34,13 @@ GetGpuCastFromInt8(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromInt8(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int8);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_uint16.cc b/tensorflow/core/kernels/cast_op_impl_uint16.cc
index 529d9758f0..c917aaf7bd 100644
--- a/tensorflow/core/kernels/cast_op_impl_uint16.cc
+++ b/tensorflow/core/kernels/cast_op_impl_uint16.cc
@@ -34,4 +34,13 @@ GetGpuCastFromUint16(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint16(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint16);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cast_op_impl_uint8.cc b/tensorflow/core/kernels/cast_op_impl_uint8.cc
index 1a5025b054..377c8ca953 100644
--- a/tensorflow/core/kernels/cast_op_impl_uint8.cc
+++ b/tensorflow/core/kernels/cast_op_impl_uint8.cc
@@ -34,4 +34,13 @@ GetGpuCastFromUint8(DataType dst_dtype) {
 }
 #endif  // GOOGLE_CUDA
 
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+std::function<void(OpKernelContext*, const Tensor&, Tensor*)>
+GetSyclCastFromUint8(DataType dst_dtype) {
+  CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint8);
+  return nullptr;
+}
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_abs.cc b/tensorflow/core/kernels/cwise_op_abs.cc
index 8cf1eac41e..5fd38d9dc2 100644
--- a/tensorflow/core/kernels/cwise_op_abs.cc
+++ b/tensorflow/core/kernels/cwise_op_abs.cc
@@ -22,17 +22,6 @@ REGISTER5(UnaryOp, CPU, "Abs", functor::abs, float, Eigen::half, double, int32,
 REGISTER2(UnaryOp, CPU, "ComplexAbs", functor::abs, complex64, complex128);
 #endif
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Abs")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::abs<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER4(UnaryOp, GPU, "Abs", functor::abs, float, Eigen::half, double, int64);
 REGISTER2(UnaryOp, GPU, "ComplexAbs", functor::abs, complex64, complex128);
@@ -48,4 +37,13 @@ REGISTER_KERNEL_BUILDER(Name("Abs")
                         UnaryOp<CPUDevice, functor::abs<int32>>);
 #endif
 
+#if TENSORFLOW_USE_SYCL
+REGISTER3(UnaryOp, SYCL, "Abs", functor::abs, float, double, int64);
+REGISTER_KERNEL_BUILDER(Name("Abs")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .TypeConstraint<int32>("T"),
+                        UnaryOp<CPUDevice, functor::abs<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_acos.cc b/tensorflow/core/kernels/cwise_op_acos.cc
index 65801da3c7..12cc6c8bdd 100644
--- a/tensorflow/core/kernels/cwise_op_acos.cc
+++ b/tensorflow/core/kernels/cwise_op_acos.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Acos", functor::acos, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Acos")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::acos<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Acos", functor::acos, float, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Acos", functor::acos, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_acosh.cc b/tensorflow/core/kernels/cwise_op_acosh.cc
new file mode 100644
index 0000000000..7bdd8d22a3
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_acosh.cc
@@ -0,0 +1,38 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+#include "tensorflow/core/kernels/cwise_ops_gradients.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Acosh", functor::acosh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Acosh")                               \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::acosh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYCL
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Acosh", functor::acosh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_add_1.cc b/tensorflow/core/kernels/cwise_op_add_1.cc
index f6e9b59cf8..acf1f2ad49 100644
--- a/tensorflow/core/kernels/cwise_op_add_1.cc
+++ b/tensorflow/core/kernels/cwise_op_add_1.cc
@@ -19,26 +19,6 @@ namespace tensorflow {
 REGISTER5(BinaryOp, CPU, "Add", functor::add, float, Eigen::half, double, int32,
           int64);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Add")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::add<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-
-REGISTER_KERNEL_BUILDER(Name("Add")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::add<int32>>);
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(BinaryOp, GPU, "Add", functor::add, float, Eigen::half, double);
 
@@ -54,4 +34,15 @@ REGISTER_KERNEL_BUILDER(Name("Add")
                         BinaryOp<CPUDevice, functor::add<int32>>);
 #endif
 
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(BinaryOp, SYCL, "Add", functor::add, float, double);
+REGISTER_KERNEL_BUILDER(Name("Add")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::add<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_asin.cc b/tensorflow/core/kernels/cwise_op_asin.cc
index c9ebfe759b..c28e27d95a 100644
--- a/tensorflow/core/kernels/cwise_op_asin.cc
+++ b/tensorflow/core/kernels/cwise_op_asin.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Asin", functor::asin, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Asin")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::asin<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Asin", functor::asin, float, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Asin", functor::asin, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_asinh.cc b/tensorflow/core/kernels/cwise_op_asinh.cc
new file mode 100644
index 0000000000..e0644323c0
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_asinh.cc
@@ -0,0 +1,38 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+#include "tensorflow/core/kernels/cwise_ops_gradients.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Asinh", functor::asinh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Asinh")                               \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::asinh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYC
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Asinh", functor::asinh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_atan.cc b/tensorflow/core/kernels/cwise_op_atan.cc
index 72645b303f..7d73de4810 100644
--- a/tensorflow/core/kernels/cwise_op_atan.cc
+++ b/tensorflow/core/kernels/cwise_op_atan.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Atan", functor::atan, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Atan")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::atan<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Atan", functor::atan, float, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Atan", functor::atan, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_atanh.cc b/tensorflow/core/kernels/cwise_op_atanh.cc
new file mode 100644
index 0000000000..058f5140c5
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_atanh.cc
@@ -0,0 +1,38 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/cwise_ops_common.h"
+#include "tensorflow/core/kernels/cwise_ops_gradients.h"
+
+namespace tensorflow {
+REGISTER4(UnaryOp, CPU, "Atanh", functor::atanh, float, double,
+          complex64, complex128);
+
+#if TENSORFLOW_USE_SYCL
+#define REGISTER_SYCL_KERNEL(TYPE)                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+                          Name("Atanh")                               \
+                          .Device(DEVICE_SYCL)                        \
+                          .TypeConstraint<TYPE>("T"),                 \
+                          UnaryOp<SYCLDevice, functor::atanh<TYPE>>);
+REGISTER_SYCL_KERNEL(float);
+REGISTER_SYCL_KERNEL(double);
+#undef REGISTER_SYCL_KERNEL
+#endif // TENSORFLOW_USE_SYC
+
+#if GOOGLE_CUDA
+REGISTER2(UnaryOp, GPU, "Atanh", functor::atanh, float, double);
+#endif
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_ceil.cc b/tensorflow/core/kernels/cwise_op_ceil.cc
index c74e10576d..0111e9d5fd 100644
--- a/tensorflow/core/kernels/cwise_op_ceil.cc
+++ b/tensorflow/core/kernels/cwise_op_ceil.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "Ceil", functor::ceil, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Ceil")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::ceil<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Ceil", functor::ceil, float, Eigen::half, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Ceil", functor::ceil, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_cos.cc b/tensorflow/core/kernels/cwise_op_cos.cc
index 634c90adc6..d4b3b0e393 100644
--- a/tensorflow/core/kernels/cwise_op_cos.cc
+++ b/tensorflow/core/kernels/cwise_op_cos.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Cos", functor::cos, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Cos")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::cos<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Cos", functor::cos, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Cos", functor::cos, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_div.cc b/tensorflow/core/kernels/cwise_op_div.cc
index 1e2300832f..d44c1bf473 100644
--- a/tensorflow/core/kernels/cwise_op_div.cc
+++ b/tensorflow/core/kernels/cwise_op_div.cc
@@ -24,32 +24,6 @@ REGISTER5(BinaryOp, CPU, "TruncateDiv", functor::safe_div, uint8, uint16, int16,
           int32, int64);
 REGISTER5(BinaryOp, CPU, "RealDiv", functor::div, float, Eigen::half, double,
           complex64, complex128);
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Div")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::div<TYPE>>);  \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("RealDiv")                             \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::div<TYPE>>);
-REGISTER_SYCL_KERNEL(float)
-REGISTER_SYCL_KERNEL(double)
-#undef REGISTER_SYCL_KERNEL
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
-REGISTER_KERNEL_BUILDER(Name("Div")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::safe_div<int32>>);
-#endif // TENSORFLOW_USE_SYCL
 #if GOOGLE_CUDA
 REGISTER9(BinaryOp, GPU, "Div", functor::div, float, Eigen::half, double, uint8,
           uint16, int16, int64, complex64, complex128);
@@ -70,4 +44,15 @@ REGISTER_KERNEL_BUILDER(Name("Div")
                         BinaryOp<CPUDevice, functor::safe_div<int32>>);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(BinaryOp, SYCL, "Div", functor::div, float, double);
+REGISTER2(BinaryOp, SYCL, "RealDiv", functor::div, float, double);
+REGISTER_KERNEL_BUILDER(Name("Div")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::safe_div<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_equal_to_1.cc b/tensorflow/core/kernels/cwise_op_equal_to_1.cc
index 7049305deb..ea10ebe9a0 100644
--- a/tensorflow/core/kernels/cwise_op_equal_to_1.cc
+++ b/tensorflow/core/kernels/cwise_op_equal_to_1.cc
@@ -47,8 +47,8 @@ REGISTER_KERNEL_BUILDER(Name("Equal")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER2(BinaryOp, SYCL, "Equal", functor::equal_to, float, double);
-
+REGISTER5(BinaryOp, SYCL, "Equal", functor::equal_to, float, double, uint8,
+          int8, int16);
 REGISTER_KERNEL_BUILDER(Name("Equal")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_exp.cc b/tensorflow/core/kernels/cwise_op_exp.cc
index 2e3a60cf79..9d4d654427 100644
--- a/tensorflow/core/kernels/cwise_op_exp.cc
+++ b/tensorflow/core/kernels/cwise_op_exp.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Exp", functor::exp, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Exp")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::exp<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Exp", functor::exp, float, Eigen::half, double);
 #endif
+
+#if TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Exp", functor::exp, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_expm1.cc b/tensorflow/core/kernels/cwise_op_expm1.cc
index 5573c2bcc2..4f72308006 100644
--- a/tensorflow/core/kernels/cwise_op_expm1.cc
+++ b/tensorflow/core/kernels/cwise_op_expm1.cc
@@ -22,6 +22,6 @@ REGISTER5(UnaryOp, CPU, "Expm1", functor::expm1, float, Eigen::half, double,
 REGISTER3(UnaryOp, GPU, "Expm1", functor::expm1, float, Eigen::half, double);
 #endif
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(UnaryOp, SYCL, "Expm1", functor::expm1, float);
+REGISTER2(UnaryOp, SYCL, "Expm1", functor::expm1, float, double);
 #endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_floor.cc b/tensorflow/core/kernels/cwise_op_floor.cc
index 59e32d7f6f..5a142b9ce9 100644
--- a/tensorflow/core/kernels/cwise_op_floor.cc
+++ b/tensorflow/core/kernels/cwise_op_floor.cc
@@ -18,19 +18,10 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "Floor", functor::floor, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Floor")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::floor<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Floor", functor::floor, float, Eigen::half, double);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Floor", functor::floor, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc
new file mode 100644
index 0000000000..a29c9a374d
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_acosh.cu.cc
@@ -0,0 +1,27 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+#include "tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(acosh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc
new file mode 100644
index 0000000000..c78f09e5e9
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_asinh.cu.cc
@@ -0,0 +1,27 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+#include "tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(asinh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc
new file mode 100644
index 0000000000..895dcbff02
--- /dev/null
+++ b/tensorflow/core/kernels/cwise_op_gpu_atanh.cu.cc
@@ -0,0 +1,27 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h"
+#include "tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h"
+
+namespace tensorflow {
+namespace functor {
+DEFINE_UNARY2(atanh, float, double);
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_greater.cc b/tensorflow/core/kernels/cwise_op_greater.cc
index 6b5a806aa2..ba89899fb3 100644
--- a/tensorflow/core/kernels/cwise_op_greater.cc
+++ b/tensorflow/core/kernels/cwise_op_greater.cc
@@ -34,11 +34,8 @@ REGISTER_KERNEL_BUILDER(Name("Greater")
                         BinaryOp<CPUDevice, functor::greater<int32>>);
 #endif
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "Greater", functor::greater, float);
+REGISTER2(BinaryOp, SYCL, "Greater", functor::greater, float, double);
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
 REGISTER_KERNEL_BUILDER(Name("Greater")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
@@ -47,5 +44,4 @@ REGISTER_KERNEL_BUILDER(Name("Greater")
                             .TypeConstraint<int32>("T"),
                         BinaryOp<CPUDevice, functor::greater<int32>>);
 #endif // TENSORFLOW_USE_SYCL
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_greater_equal.cc b/tensorflow/core/kernels/cwise_op_greater_equal.cc
index ac21528256..8f0c483aec 100644
--- a/tensorflow/core/kernels/cwise_op_greater_equal.cc
+++ b/tensorflow/core/kernels/cwise_op_greater_equal.cc
@@ -35,7 +35,7 @@ REGISTER_KERNEL_BUILDER(Name("GreaterEqual")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float);
+REGISTER2(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float, double);
 
 REGISTER_KERNEL_BUILDER(Name("GreaterEqual")
                             .Device(DEVICE_SYCL)
diff --git a/tensorflow/core/kernels/cwise_op_isfinite.cc b/tensorflow/core/kernels/cwise_op_isfinite.cc
index 0faeffa95c..53ec1c1c63 100644
--- a/tensorflow/core/kernels/cwise_op_isfinite.cc
+++ b/tensorflow/core/kernels/cwise_op_isfinite.cc
@@ -19,20 +19,12 @@ namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "IsFinite", functor::isfinite, float, Eigen::half,
           double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("IsFinite")                            \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::isfinite<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "IsFinite", functor::isfinite, float, Eigen::half,
           double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "IsFinite", functor::isfinite, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_isinf.cc b/tensorflow/core/kernels/cwise_op_isinf.cc
index df63006b3f..4b34744304 100644
--- a/tensorflow/core/kernels/cwise_op_isinf.cc
+++ b/tensorflow/core/kernels/cwise_op_isinf.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "IsInf", functor::isinf, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("IsInf")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::isinf<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "IsInf", functor::isinf, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "IsInf", functor::isinf, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_isnan.cc b/tensorflow/core/kernels/cwise_op_isnan.cc
index e1cf7a8637..ad2dd3f722 100644
--- a/tensorflow/core/kernels/cwise_op_isnan.cc
+++ b/tensorflow/core/kernels/cwise_op_isnan.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER3(UnaryOp, CPU, "IsNan", functor::isnan, float, Eigen::half, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("IsNan")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::isnan<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "IsNan", functor::isnan, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "IsNan", functor::isnan, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_less.cc b/tensorflow/core/kernels/cwise_op_less.cc
index a38f1024a9..136c3666df 100644
--- a/tensorflow/core/kernels/cwise_op_less.cc
+++ b/tensorflow/core/kernels/cwise_op_less.cc
@@ -35,7 +35,6 @@ REGISTER_KERNEL_BUILDER(Name("Less")
 #endif
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER3(BinaryOp, SYCL, "Less", functor::less, float, double, int64);
-
 REGISTER_KERNEL_BUILDER(Name("Less")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_less_equal.cc b/tensorflow/core/kernels/cwise_op_less_equal.cc
index 3a2cc2ae0e..97a2508d12 100644
--- a/tensorflow/core/kernels/cwise_op_less_equal.cc
+++ b/tensorflow/core/kernels/cwise_op_less_equal.cc
@@ -35,8 +35,8 @@ REGISTER_KERNEL_BUILDER(Name("LessEqual")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "LessEqual", functor::less_equal, float);
-
+REGISTER6(BinaryOp, SYCL, "LessEqual", functor::less_equal, float, double,
+          int64, uint8, int8, int16);
 REGISTER_KERNEL_BUILDER(Name("LessEqual")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
@@ -45,5 +45,4 @@ REGISTER_KERNEL_BUILDER(Name("LessEqual")
                             .TypeConstraint<int32>("T"),
                         BinaryOp<CPUDevice, functor::less_equal<int32>>);
 #endif // TENSORFLOW_USE_SYCL
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_log.cc b/tensorflow/core/kernels/cwise_op_log.cc
index 5e74e778c7..7fdfdff0e3 100644
--- a/tensorflow/core/kernels/cwise_op_log.cc
+++ b/tensorflow/core/kernels/cwise_op_log.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Log", functor::log, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Log")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::log<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Log", functor::log, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Log", functor::log, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_log1p.cc b/tensorflow/core/kernels/cwise_op_log1p.cc
index edb821318e..25ad7b24bb 100644
--- a/tensorflow/core/kernels/cwise_op_log1p.cc
+++ b/tensorflow/core/kernels/cwise_op_log1p.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Log1p", functor::log1p, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Log1p")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::log1p<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Log1p", functor::log1p, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Log1p", functor::log1p, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_maximum.cc b/tensorflow/core/kernels/cwise_op_maximum.cc
index 7311f25ec0..87d54e380b 100644
--- a/tensorflow/core/kernels/cwise_op_maximum.cc
+++ b/tensorflow/core/kernels/cwise_op_maximum.cc
@@ -35,11 +35,7 @@ REGISTER_KERNEL_BUILDER(Name("Maximum")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "Maximum", functor::maximum, float);
-
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
+REGISTER3(BinaryOp, SYCL, "Maximum", functor::maximum, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Maximum")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
@@ -48,5 +44,4 @@ REGISTER_KERNEL_BUILDER(Name("Maximum")
                             .TypeConstraint<int32>("T"),
                         BinaryOp<CPUDevice, functor::maximum<int32>>);
 #endif // TENSORFLOW_USE_SYCL
-
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_minimum.cc b/tensorflow/core/kernels/cwise_op_minimum.cc
index 99e5a76620..442171193b 100644
--- a/tensorflow/core/kernels/cwise_op_minimum.cc
+++ b/tensorflow/core/kernels/cwise_op_minimum.cc
@@ -35,8 +35,7 @@ REGISTER_KERNEL_BUILDER(Name("Minimum")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(BinaryOp, SYCL, "Minimum", functor::minimum, float);
-
+REGISTER3(BinaryOp, SYCL, "Minimum", functor::minimum, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Minimum")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_mul_1.cc b/tensorflow/core/kernels/cwise_op_mul_1.cc
index a3cdfa5f84..023eb07ca3 100644
--- a/tensorflow/core/kernels/cwise_op_mul_1.cc
+++ b/tensorflow/core/kernels/cwise_op_mul_1.cc
@@ -26,24 +26,6 @@ REGISTER5(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double,
 REGISTER(BinaryOp, CPU, "Mul", functor::mul, int32);
 #endif  // __ANDROID_TYPES_SLIM__
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Mul")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::mul<TYPE>>);
-REGISTER_SYCL_KERNEL(float)
-REGISTER_SYCL_KERNEL(double)
-#undef REGISTER_SYCL_KERNEL
-REGISTER_KERNEL_BUILDER(Name("Mul")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::mul<int32>>);
-#endif // TENSORFLOW_USE_SYCL
 #if GOOGLE_CUDA
 REGISTER4(BinaryOp, GPU, "Mul", functor::mul, float, Eigen::half, double,
            uint8);
@@ -59,4 +41,14 @@ REGISTER_KERNEL_BUILDER(Name("Mul")
                         BinaryOp<CPUDevice, functor::mul<int32>>);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(BinaryOp, SYCL, "Mul", functor::mul, float, double, uint8);
+REGISTER_KERNEL_BUILDER(Name("Mul")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::mul<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_neg.cc b/tensorflow/core/kernels/cwise_op_neg.cc
index eb7e3764d9..536891b548 100644
--- a/tensorflow/core/kernels/cwise_op_neg.cc
+++ b/tensorflow/core/kernels/cwise_op_neg.cc
@@ -19,27 +19,14 @@ namespace tensorflow {
 REGISTER7(UnaryOp, CPU, "Neg", functor::neg, float, Eigen::half, double, int32,
           complex64, int64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Neg")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::neg<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(UnaryOp, SYCL, "Neg", functor::neg, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Neg")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
                             .HostMemory("y")
                             .TypeConstraint<int32>("T"),
                         UnaryOp<CPUDevice, functor::neg<int32>>);
-
-#undef REGISTER_SYCL_KERNEL
 #endif // TENSORFLOW_USE_SYCL
 
 #if GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_pow.cc b/tensorflow/core/kernels/cwise_op_pow.cc
index f1780168e4..5fb0735ac1 100644
--- a/tensorflow/core/kernels/cwise_op_pow.cc
+++ b/tensorflow/core/kernels/cwise_op_pow.cc
@@ -19,20 +19,11 @@ namespace tensorflow {
 REGISTER7(BinaryOp, CPU, "Pow", functor::pow, float, Eigen::half, double, int32,
           int64, complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Pow")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::pow<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER4(BinaryOp, GPU, "Pow", functor::pow, float, Eigen::half, double,
           int64);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(BinaryOp, SYCL, "Pow", functor::pow, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_round.cc b/tensorflow/core/kernels/cwise_op_round.cc
index e192f89782..163814aac4 100644
--- a/tensorflow/core/kernels/cwise_op_round.cc
+++ b/tensorflow/core/kernels/cwise_op_round.cc
@@ -21,9 +21,6 @@ REGISTER5(UnaryOp, CPU, "Round", functor::round, Eigen::half, float, double,
 
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER2(UnaryOp, SYCL, "Round", functor::round, float, double);
-namespace functor {
-DEFINE_UNARY2(round, float, double);
-}  // namespace functor
 #endif
 
 #if GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_op_rsqrt.cc b/tensorflow/core/kernels/cwise_op_rsqrt.cc
index f23725f48e..a434538fbf 100644
--- a/tensorflow/core/kernels/cwise_op_rsqrt.cc
+++ b/tensorflow/core/kernels/cwise_op_rsqrt.cc
@@ -19,21 +19,12 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Rsqrt", functor::rsqrt, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Rsqrt")                               \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::rsqrt<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Rsqrt", functor::rsqrt, float, Eigen::half, double);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Rsqrt", functor::rsqrt, float, double);
+#endif  // TENSORFLOW_USE_SYCL
 
 REGISTER5(SimpleBinaryOp, CPU, "RsqrtGrad", functor::rsqrt_grad, float,
           Eigen::half, double, complex64, complex128);
@@ -41,4 +32,8 @@ REGISTER5(SimpleBinaryOp, CPU, "RsqrtGrad", functor::rsqrt_grad, float,
 REGISTER3(SimpleBinaryOp, GPU, "RsqrtGrad", functor::rsqrt_grad, float,
           Eigen::half, double);
 #endif
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(SimpleBinaryOp, SYCL, "RsqrtGrad", functor::rsqrt_grad, float,
+          double);
+#endif  //  TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_select.cc b/tensorflow/core/kernels/cwise_op_select.cc
index 709628da13..3dd9de8d89 100644
--- a/tensorflow/core/kernels/cwise_op_select.cc
+++ b/tensorflow/core/kernels/cwise_op_select.cc
@@ -181,7 +181,9 @@ REGISTER_SELECT_GPU(complex128);
       SelectOp<SYCLDevice, type>);
 
 REGISTER_SELECT_SYCL(float);
+REGISTER_SELECT_SYCL(double);
 REGISTER_SELECT_SYCL(int32);
+REGISTER_SELECT_SYCL(int64);
 #undef REGISTER_SELECT_SYCL
 #endif // TENSORFLOW_USE_SYCL
 
diff --git a/tensorflow/core/kernels/cwise_op_sign.cc b/tensorflow/core/kernels/cwise_op_sign.cc
index dedd414db5..a4084d5ad1 100644
--- a/tensorflow/core/kernels/cwise_op_sign.cc
+++ b/tensorflow/core/kernels/cwise_op_sign.cc
@@ -34,10 +34,7 @@ REGISTER_KERNEL_BUILDER(Name("Sign")
 #endif
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER(UnaryOp, SYCL, "Sign", functor::sign, float);
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
+REGISTER3(UnaryOp, SYCL, "Sign", functor::sign, float, double, int64);
 REGISTER_KERNEL_BUILDER(Name("Sign")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/kernels/cwise_op_sin.cc b/tensorflow/core/kernels/cwise_op_sin.cc
index ab54c61b56..b91ff1ac30 100644
--- a/tensorflow/core/kernels/cwise_op_sin.cc
+++ b/tensorflow/core/kernels/cwise_op_sin.cc
@@ -19,19 +19,11 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Sin", functor::sin, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Sin")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::sin<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Sin", functor::sin, float, Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Sin", functor::sin, float, double);
+#endif // TENSORFLOW_USE_SYC
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_sqrt.cc b/tensorflow/core/kernels/cwise_op_sqrt.cc
index 55acf648db..00efbb00f1 100644
--- a/tensorflow/core/kernels/cwise_op_sqrt.cc
+++ b/tensorflow/core/kernels/cwise_op_sqrt.cc
@@ -19,26 +19,22 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Sqrt", functor::sqrt, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Sqrt")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::sqrt<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Sqrt", functor::sqrt, float, Eigen::half, double);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Sqrt", functor::sqrt, float, double);
+#endif // TENSORFLOW_USE_SYCL
+
 REGISTER5(SimpleBinaryOp, CPU, "SqrtGrad", functor::sqrt_grad, float,
           Eigen::half, double, complex64, complex128);
 #if GOOGLE_CUDA
 REGISTER3(SimpleBinaryOp, GPU, "SqrtGrad", functor::sqrt_grad, float,
           Eigen::half, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(SimpleBinaryOp, SYCL, "SqrtGrad", functor::sqrt_grad, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_square.cc b/tensorflow/core/kernels/cwise_op_square.cc
index afcacfec1c..07a4b0b084 100644
--- a/tensorflow/core/kernels/cwise_op_square.cc
+++ b/tensorflow/core/kernels/cwise_op_square.cc
@@ -19,18 +19,6 @@ namespace tensorflow {
 REGISTER7(UnaryOp, CPU, "Square", functor::square, float, Eigen::half, double,
           int32, int64, complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Square")                              \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::square<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER4(UnaryOp, GPU, "Square", functor::square, float, Eigen::half, double,
           int64);
@@ -45,4 +33,14 @@ REGISTER_KERNEL_BUILDER(Name("Square")
                             .TypeConstraint<int32>("T"),
                         UnaryOp<CPUDevice, functor::square<int32>>);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(UnaryOp, SYCL, "Square", functor::square, float, double, int64);
+REGISTER_KERNEL_BUILDER(Name("Square")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .TypeConstraint<int32>("T"),
+                        UnaryOp<CPUDevice, functor::square<int32>>);
+#endif // TENSORFLOW_USE_SYC
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_sub.cc b/tensorflow/core/kernels/cwise_op_sub.cc
index eab1e2a09c..eb173c7040 100644
--- a/tensorflow/core/kernels/cwise_op_sub.cc
+++ b/tensorflow/core/kernels/cwise_op_sub.cc
@@ -24,28 +24,7 @@ REGISTER7(BinaryOp, CPU, "Sub", functor::sub, float, Eigen::half, double, int32,
 // int32 version of this op is needed, so explicitly include it.
 REGISTER(BinaryOp, CPU, "Sub", functor::sub, int32);
 #endif  // __ANDROID_TYPES_SLIM__
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Sub")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          BinaryOp<SYCLDevice, functor::sub<TYPE>>);
-  REGISTER_SYCL_KERNEL(float);
-  REGISTER_SYCL_KERNEL(double);
 
-// A special GPU kernel for int32.
-// TODO(b/25387198): Also enable int32 in device memory. This kernel
-// registration requires all int32 inputs and outputs to be in host memory.
-REGISTER_KERNEL_BUILDER(Name("Sub")
-                            .Device(DEVICE_SYCL)
-                            .HostMemory("x")
-                            .HostMemory("y")
-                            .HostMemory("z")
-                            .TypeConstraint<int32>("T"),
-                        BinaryOp<CPUDevice, functor::sub<int32>>);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYCL
 #if GOOGLE_CUDA
 REGISTER6(BinaryOp, GPU, "Sub", functor::sub, float, Eigen::half, double, int64,
           complex64, complex128);
@@ -62,4 +41,14 @@ REGISTER_KERNEL_BUILDER(Name("Sub")
                         BinaryOp<CPUDevice, functor::sub<int32>>);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER3(BinaryOp, SYCL, "Sub", functor::sub, float, double, int64);
+REGISTER_KERNEL_BUILDER(Name("Sub")
+                            .Device(DEVICE_SYCL)
+                            .HostMemory("x")
+                            .HostMemory("y")
+                            .HostMemory("z")
+                            .TypeConstraint<int32>("T"),
+                        BinaryOp<CPUDevice, functor::sub<int32>>);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_tan.cc b/tensorflow/core/kernels/cwise_op_tan.cc
index 9c850c9420..7891b1183d 100644
--- a/tensorflow/core/kernels/cwise_op_tan.cc
+++ b/tensorflow/core/kernels/cwise_op_tan.cc
@@ -18,19 +18,11 @@ limitations under the License.
 namespace tensorflow {
 REGISTER2(UnaryOp, CPU, "Tan", functor::tan, float, double);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Tan")                                 \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::tan<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER2(UnaryOp, GPU, "Tan", functor::tan, float, double);
 #endif
+
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Tan", functor::tan, float, double);
+#endif // TENSORFLOW_USE_SYCL
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_op_tanh.cc b/tensorflow/core/kernels/cwise_op_tanh.cc
index 1dbc13061b..8b3900892c 100644
--- a/tensorflow/core/kernels/cwise_op_tanh.cc
+++ b/tensorflow/core/kernels/cwise_op_tanh.cc
@@ -20,22 +20,14 @@ namespace tensorflow {
 REGISTER5(UnaryOp, CPU, "Tanh", functor::tanh, float, Eigen::half, double,
           complex64, complex128);
 
-#if TENSORFLOW_USE_SYCL
-#define REGISTER_SYCL_KERNEL(TYPE)                                    \
-  REGISTER_KERNEL_BUILDER(                                            \
-                          Name("Tanh")                                \
-                          .Device(DEVICE_SYCL)                        \
-                          .TypeConstraint<TYPE>("T"),                 \
-                          UnaryOp<SYCLDevice, functor::tanh<TYPE>>);
-REGISTER_SYCL_KERNEL(float);
-REGISTER_SYCL_KERNEL(double);
-#undef REGISTER_SYCL_KERNEL
-#endif // TENSORFLOW_USE_SYC
-
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "Tanh", functor::tanh, float, Eigen::half, double);
 #endif
 
+#ifdef TENSORFLOW_USE_SYCL
+REGISTER2(UnaryOp, SYCL, "Tanh", functor::tanh, float, double);
+#endif // TENSORFLOW_USE_SYCL
+
 REGISTER5(SimpleBinaryOp, CPU, "TanhGrad", functor::tanh_grad, float,
           Eigen::half, double, complex64, complex128);
 #if GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h
index c11d6cfabb..e198fda473 100644
--- a/tensorflow/core/kernels/cwise_ops.h
+++ b/tensorflow/core/kernels/cwise_ops.h
@@ -43,6 +43,39 @@ struct functor_traits<scalar_fmod2_op<T>> {
   };
 };
 
+template<typename T> struct scalar_asinh_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
+    return std::asinh(a);
+  }
+};
+template<typename T>
+struct functor_traits<scalar_asinh_op<T>> {
+  enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
+};
+
+template<typename T> struct scalar_acosh_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_acosh_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
+    return std::acosh(a);
+  }
+};
+template<typename T>
+struct functor_traits<scalar_acosh_op<T>> {
+  enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
+};
+
+template<typename T> struct scalar_atanh_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_atanh_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
+    return std::atanh(a);
+  }
+};
+template<typename T>
+struct functor_traits<scalar_atanh_op<T>> {
+  enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
+};
+
 // TODO(rmlarsen): This is a workaround for upstream change
 // https://bitbucket.org/eigen/eigen/commits/f339468d04d0f87caeb6cab9aef568627e9f6ea9
 // that renamed scalar_binary_pow_op to scalar_pow_op and deleted the unary
@@ -492,6 +525,15 @@ struct cosh : base<T, Eigen::internal::scalar_cosh_op<T> > {};
 template <typename T>
 struct tanh : base<T, Eigen::internal::scalar_tanh_op<T> > {};
 
+template <typename T>
+struct asinh : base<T, Eigen::internal::scalar_asinh_op<T> > {};
+
+template <typename T>
+struct acosh : base<T, Eigen::internal::scalar_acosh_op<T> > {};
+
+template <typename T>
+struct atanh : base<T, Eigen::internal::scalar_atanh_op<T> > {};
+
 template <typename T>
 struct lgamma : base<T, Eigen::internal::scalar_lgamma_op<T> > {};
 
diff --git a/tensorflow/core/kernels/fixed_length_record_reader_op.cc b/tensorflow/core/kernels/fixed_length_record_reader_op.cc
index ce7fb9c332..298a4e3678 100644
--- a/tensorflow/core/kernels/fixed_length_record_reader_op.cc
+++ b/tensorflow/core/kernels/fixed_length_record_reader_op.cc
@@ -19,83 +19,118 @@ limitations under the License.
 #include "tensorflow/core/framework/reader_base.h"
 #include "tensorflow/core/framework/reader_op_kernel.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/lib/io/inputbuffer.h"
+#include "tensorflow/core/lib/io/buffered_inputstream.h"
+#include "tensorflow/core/lib/io/random_inputstream.h"
+#include "tensorflow/core/lib/io/zlib_compression_options.h"
+#include "tensorflow/core/lib/io/zlib_inputstream.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/env.h"
 
 namespace tensorflow {
 
+// In the constructor hop_bytes_ is set to record_bytes_ if it was 0,
+// so that we will always "hop" after each read (except first).
 class FixedLengthRecordReader : public ReaderBase {
  public:
   FixedLengthRecordReader(const string& node_name, int64 header_bytes,
                           int64 record_bytes, int64 footer_bytes,
-                          int64 hop_bytes, Env* env)
+                          int64 hop_bytes, const string& encoding, Env* env)
       : ReaderBase(
             strings::StrCat("FixedLengthRecordReader '", node_name, "'")),
         header_bytes_(header_bytes),
         record_bytes_(record_bytes),
         footer_bytes_(footer_bytes),
-        hop_bytes_(hop_bytes),
+        hop_bytes_(hop_bytes == 0 ? record_bytes : hop_bytes),
         env_(env),
-        file_pos_limit_(-1),
-        record_number_(0) {}
+        record_number_(0),
+        encoding_(encoding) {}
 
   // On success:
-  // * input_buffer_ != nullptr,
-  // * input_buffer_->Tell() == header_bytes_
-  // * file_pos_limit_ == file size - footer_bytes_
+  // * buffered_inputstream_ != nullptr,
+  // * buffered_inputstream_->Tell() == header_bytes_
   Status OnWorkStartedLocked() override {
     record_number_ = 0;
-    uint64 file_size = 0;
-    TF_RETURN_IF_ERROR(env_->GetFileSize(current_work(), &file_size));
-    file_pos_limit_ = file_size - footer_bytes_;
+
+    lookahead_cache_.clear();
 
     TF_RETURN_IF_ERROR(env_->NewRandomAccessFile(current_work(), &file_));
+    if (encoding_ == "ZLIB" || encoding_ == "GZIP") {
+      const io::ZlibCompressionOptions zlib_options =
+          encoding_ == "ZLIB" ? io::ZlibCompressionOptions::DEFAULT()
+                              : io::ZlibCompressionOptions::GZIP();
+      file_stream_.reset(new io::RandomAccessInputStream(file_.get()));
+      buffered_inputstream_.reset(
+          new io::ZlibInputStream(file_stream_.get(), (size_t)kBufferSize,
+                                  (size_t)kBufferSize, zlib_options));
+    } else {
+      buffered_inputstream_.reset(
+          new io::BufferedInputStream(file_.get(), kBufferSize));
+    }
+    // header_bytes_ is always skipped.
+    TF_RETURN_IF_ERROR(buffered_inputstream_->SkipNBytes(header_bytes_));
 
-    input_buffer_.reset(new io::InputBuffer(file_.get(), kBufferSize));
-    TF_RETURN_IF_ERROR(input_buffer_->SkipNBytes(header_bytes_));
     return Status::OK();
   }
 
   Status OnWorkFinishedLocked() override {
-    input_buffer_.reset(nullptr);
+    buffered_inputstream_.reset(nullptr);
     return Status::OK();
   }
 
   Status ReadLocked(string* key, string* value, bool* produced,
                     bool* at_end) override {
-    // The condition `input_buffer_->Tell() + record_bytes_ > file_pos_limit_`
-    // is to confirm that none of record bytes is out of the range of
-    // file_pos_limit_.
-    // This is necessary for the condition `hop_bytes > 0`. For example.
-    // File: "0123456"
-    // Reader setting: `record_bytes=3`, `hop_bytes=2`, `footer_bytes=0`,
-    //     `header_bytes=0`
-    // Without this checking condition, the forth time the reader will at
-    // this position: "012345|6" and the reading operation will result in
-    // an error.
-    if (input_buffer_->Tell() >= file_pos_limit_ ||
-        input_buffer_->Tell() + record_bytes_ > file_pos_limit_) {
+    // We will always "hop" the hop_bytes_ except the first record
+    // where record_number_ == 0
+    if (record_number_ != 0) {
+      if (hop_bytes_ <= lookahead_cache_.size()) {
+        // If hop_bytes_ is smaller than the cached data we skip the
+        // hop_bytes_ from the cache.
+        lookahead_cache_ = lookahead_cache_.substr(hop_bytes_);
+      } else {
+        // If hop_bytes_ is larger than the cached data, we clean up
+        // the cache, then skip hop_bytes_ - cache_size from the file
+        // as the cache_size has been skipped through cache.
+        int64 cache_size = lookahead_cache_.size();
+        lookahead_cache_.clear();
+        Status s = buffered_inputstream_->SkipNBytes(hop_bytes_ - cache_size);
+        if (!s.ok()) {
+          if (!errors::IsOutOfRange(s)) {
+            return s;
+          }
+          *at_end = true;
+          return Status::OK();
+        }
+      }
+    }
+
+    // Fill up lookahead_cache_ to record_bytes_ + footer_bytes_
+    int bytes_to_read = record_bytes_ + footer_bytes_ - lookahead_cache_.size();
+    Status s = buffered_inputstream_->ReadNBytes(bytes_to_read, value);
+    if (!s.ok()) {
+      value->clear();
+      if (!errors::IsOutOfRange(s)) {
+        return s;
+      }
       *at_end = true;
       return Status::OK();
     }
-    const int64 pos_before_read = input_buffer_->Tell();
-    TF_RETURN_IF_ERROR(input_buffer_->ReadNBytes(record_bytes_, value));
+    lookahead_cache_.append(*value, 0, bytes_to_read);
+    value->clear();
+
+    // Copy first record_bytes_ from cache to value
+    *value = lookahead_cache_.substr(0, record_bytes_);
+
     *key = strings::StrCat(current_work(), ":", record_number_);
     *produced = true;
     ++record_number_;
 
-    if (hop_bytes_ > 0) {
-      input_buffer_->Seek(pos_before_read + hop_bytes_).IgnoreError();
-    }
-
     return Status::OK();
   }
 
   Status ResetLocked() override {
-    file_pos_limit_ = -1;
     record_number_ = 0;
-    input_buffer_.reset(nullptr);
+    buffered_inputstream_.reset(nullptr);
+    lookahead_cache_.clear();
     return ReaderBase::ResetLocked();
   }
 
@@ -107,11 +142,21 @@ class FixedLengthRecordReader : public ReaderBase {
   const int64 record_bytes_;
   const int64 footer_bytes_;
   const int64 hop_bytes_;
+  // The purpose of lookahead_cache_ is to allows "one-pass" processing
+  // without revisit previous processed data of the stream. This is needed
+  // because certain compression like zlib does not allow random access
+  // or even obtain the uncompressed stream size before hand.
+  // The max size of the lookahead_cache_ could be
+  // record_bytes_ + footer_bytes_
+  string lookahead_cache_;
   Env* const env_;
-  int64 file_pos_limit_;
   int64 record_number_;
-  std::unique_ptr<RandomAccessFile> file_;  // must outlive input_buffer_
-  std::unique_ptr<io::InputBuffer> input_buffer_;
+  string encoding_;
+  // must outlive buffered_inputstream_
+  std::unique_ptr<RandomAccessFile> file_;
+  // must outlive buffered_inputstream_
+  std::unique_ptr<io::RandomAccessInputStream> file_stream_;
+  std::unique_ptr<io::InputStreamInterface> buffered_inputstream_;
 };
 
 class FixedLengthRecordReaderOp : public ReaderOpKernel {
@@ -137,11 +182,14 @@ class FixedLengthRecordReaderOp : public ReaderOpKernel {
         context, hop_bytes >= 0,
         errors::InvalidArgument("hop_bytes must be >= 0 not ", hop_bytes));
     Env* env = context->env();
-    SetReaderFactory(
-        [this, header_bytes, record_bytes, footer_bytes, hop_bytes, env]() {
-          return new FixedLengthRecordReader(name(), header_bytes, record_bytes,
-                                             footer_bytes, hop_bytes, env);
-        });
+    string encoding;
+    TF_CHECK_OK(context->GetAttr("encoding", &encoding));
+    SetReaderFactory([this, header_bytes, record_bytes, footer_bytes, hop_bytes,
+                      encoding, env]() {
+      return new FixedLengthRecordReader(name(), header_bytes, record_bytes,
+                                         footer_bytes, hop_bytes, encoding,
+                                         env);
+    });
   }
 };
 
diff --git a/tensorflow/core/kernels/fused_batch_norm_op.cc b/tensorflow/core/kernels/fused_batch_norm_op.cc
index 37758e82eb..81551ee26f 100644
--- a/tensorflow/core/kernels/fused_batch_norm_op.cc
+++ b/tensorflow/core/kernels/fused_batch_norm_op.cc
@@ -149,7 +149,7 @@ struct FusedBatchNormGrad<CPUDevice, T> {
     typename TTypes<T>::Vec scale_backprop(scale_backprop_output->vec<T>());
     typename TTypes<T>::Vec offset_backprop(offset_backprop_output->vec<T>());
 
-    // Note: the following formulas are used to to compute the gradients for
+    // Note: the following formulas are used to compute the gradients for
     // back propagation.
     // x_backprop = scale * rsqrt(variance + epsilon) *
     //              [y_backprop - mean(y_backprop) - (x - mean(x)) *
diff --git a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
index bd1120e1df..d0689428dd 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
+++ b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 namespace tensorflow {
 
-// HexagonOpsDefinitions provides ops definitons supported in hexagon library
+// HexagonOpsDefinitions provides ops definitions supported in hexagon library
 // TODO(satok): add a functionality to call functions in hexagon library
 class HexagonOpsDefinitions final : public IGraphTransferOpsDefinitions {
  public:
diff --git a/tensorflow/core/kernels/map_stage_op.cc b/tensorflow/core/kernels/map_stage_op.cc
index 46eaf3d9e7..0168b57d35 100644
--- a/tensorflow/core/kernels/map_stage_op.cc
+++ b/tensorflow/core/kernels/map_stage_op.cc
@@ -550,12 +550,17 @@ REGISTER_KERNEL_BUILDER(Name("OrderedMapStage")
 #endif // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
-REGISTER_KERNEL_BUILDER(Name("MapStage").HostMemory("key").Device(DEVICE_SYCL),
+REGISTER_KERNEL_BUILDER(Name("MapStage")
+                            .HostMemory("key")
+                            .HostMemory("indices")
+                            .Device(DEVICE_SYCL),
                         MapStageOp<false>);
-REGISTER_KERNEL_BUILDER(
-    Name("OrderedMapStage").HostMemory("key").Device(DEVICE_SYCL),
-    MapStageOp<true>);
-#endif // TENSORFLOW_USE_SYCL
+REGISTER_KERNEL_BUILDER(Name("OrderedMapStage")
+                            .HostMemory("key")
+                            .HostMemory("indices")
+                            .Device(DEVICE_SYCL),
+                        MapStageOp<true>);
+#endif  // TENSORFLOW_USE_SYCL
 
 template <bool Ordered>
 class MapUnstageOp : public OpKernel {
diff --git a/tensorflow/core/kernels/ops_util.h b/tensorflow/core/kernels/ops_util.h
index 68a9c37406..d3d1b56c9d 100644
--- a/tensorflow/core/kernels/ops_util.h
+++ b/tensorflow/core/kernels/ops_util.h
@@ -84,6 +84,20 @@ bool IsDim0SliceAligned(const TensorShape& s, int64 start, int64 end_or_size) {
 // Returns <suffix> sanitized to have only [a-zA-Z0-9-_].
 string SanitizeThreadSuffix(string suffix);
 
+// Helper to compute 'strides' given a tensor 'shape'. I.e.,
+// strides[i] = prod(shape.dim_size[(i+1):])
+template <typename T>
+gtl::InlinedVector<T, 8> ComputeStride(const TensorShape& shape) {
+  const int ndims = shape.dims();
+  gtl::InlinedVector<T, 8> strides(ndims);
+  T stride = 1;
+  for (int i = ndims - 1; i >= 0; --i) {
+    strides[i] = stride;
+    stride *= static_cast<T>(shape.dim_size(i));
+  }
+  return strides;
+}
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_KERNELS_OPS_UTIL_H_
diff --git a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
index 8b85bd4ebe..933de65c15 100644
--- a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
@@ -32,7 +32,7 @@ limitations under the License.
 #ifdef COMPILER_MSVC
 // msvc does not support unroll. One could try the loop pragma but we need to
 // take a closer look if this generates better code in this case. For now let
-// the compiler take care of of it.
+// the compiler take care of it.
 #define UNROLL
 #else
 #define UNROLL _Pragma("unroll")
diff --git a/tensorflow/core/kernels/queue_base.h b/tensorflow/core/kernels/queue_base.h
index 0a0e51a7c3..c101fb3579 100644
--- a/tensorflow/core/kernels/queue_base.h
+++ b/tensorflow/core/kernels/queue_base.h
@@ -69,7 +69,7 @@ class QueueBase : public QueueInterface {
 
   int32 capacity() const { return capacity_; }
 
-  bool closed() {
+  bool is_closed() const override {
     mutex_lock lock(mu_);
     return closed_;
   }
diff --git a/tensorflow/core/kernels/queue_ops.cc b/tensorflow/core/kernels/queue_ops.cc
index f2ac09c4e6..d51dc4ecb0 100644
--- a/tensorflow/core/kernels/queue_ops.cc
+++ b/tensorflow/core/kernels/queue_ops.cc
@@ -425,6 +425,27 @@ class QueueSizeOp : public QueueOpKernel {
 REGISTER_KERNEL_BUILDER(Name("QueueSize").Device(DEVICE_CPU), QueueSizeOp);
 REGISTER_KERNEL_BUILDER(Name("QueueSizeV2").Device(DEVICE_CPU), QueueSizeOp);
 
+class QueueIsClosedOp : public QueueOpKernel {
+ public:
+  explicit QueueIsClosedOp(OpKernelConstruction* context)
+     : QueueOpKernel(context) {}
+ 
+ protected:
+  void ComputeAsync(OpKernelContext* ctx, QueueInterface* queue,
+                    DoneCallback callback) override {
+    Tensor* Tqueue_is_closed = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &Tqueue_is_closed));
+    Tqueue_is_closed->flat<bool>().setConstant(queue->is_closed());
+    callback();
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(QueueIsClosedOp);
+};
+
+REGISTER_KERNEL_BUILDER(Name("QueueIsClosed").Device(DEVICE_CPU), QueueIsClosedOp);
+REGISTER_KERNEL_BUILDER(Name("QueueIsClosedV2").Device(DEVICE_CPU), QueueIsClosedOp);
+
 class FakeQueueOp : public OpKernel {
  public:
   explicit FakeQueueOp(OpKernelConstruction* context) : OpKernel(context) {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index ed088bfba7..a79092e8a8 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -104,7 +104,7 @@ void ConvertMapToVector(const std::unordered_map<int, string>& in,
                         std::vector<string>* out) {
   CHECK_NOTNULL(out);
   out->resize(in.size());
-  for (int i = 0; i < in.size(); ++i) {
+  for (size_t i = 0; i < in.size(); ++i) {
     CHECK(in.count(i) > 0);
     out->at(i) = in.at(i);
   }
@@ -993,7 +993,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   for (const string& output : outputs) {
     const TensorId output_tid = ParseTensorName(output);
     const string output_name = output_tid.first.ToString();
-    for (int i = 0; i < border_outputs.size(); ++i) {
+    for (size_t i = 0; i < border_outputs.size(); ++i) {
       const TensorId subgraph_output_tid =
           ParseTensorName(border_outputs.at(i));
       const string& subgraph_output_name = subgraph_output_tid.first.ToString();
@@ -1040,7 +1040,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::ClusterizeNodes(
       subgraph_nodes, input_graph_def, &ci_vec));
 
-  for (int i = 0; i < ci_vec.size(); ++i) {
+  for (size_t i = 0; i < ci_vec.size(); ++i) {
     const string remote_fused_graph_node_name =
         strings::StrCat(remote_fused_graph_node_name_prefix, "/", i);
     TF_RETURN_IF_ERROR(FuseCluster(input_graph_def, inputs, outputs,
@@ -1100,7 +1100,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   for (NodeDef& node_def : *graph_def->mutable_node()) {
     string attr_str;
     TensorId tid;
-    for (int i = 0; i < inputs.size(); ++i) {
+    for (size_t i = 0; i < inputs.size(); ++i) {
       if (IsSameNodeName(node_def, inputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::GRAPH_INPUT,
@@ -1108,7 +1108,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
                                       remote_fused_graph_node_name);
       }
     }
-    for (int i = 0; i < outputs.size(); ++i) {
+    for (size_t i = 0; i < outputs.size(); ++i) {
       if (IsSameNodeName(node_def, outputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::GRAPH_OUTPUT,
@@ -1127,14 +1127,14 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::FUSED_NODE);
       }
     }
-    for (int i = 0; i < border_inputs.size(); ++i) {
+    for (size_t i = 0; i < border_inputs.size(); ++i) {
       if (IsSameNodeName(node_def, border_inputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(RemoteFusedGraphExecuteInfo::BORDER_INPUT,
                                       tid.second, i);
       }
     }
-    for (int i = 0; i < border_outputs.size(); ++i) {
+    for (size_t i = 0; i < border_outputs.size(); ++i) {
       if (IsSameNodeName(node_def, border_outputs.at(i), &tid)) {
         AppendDeliminator(&attr_str);
         attr_str += BuildNodeTypeAttr(
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
index 1f8c2a7684..11670e80c7 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
@@ -111,7 +111,7 @@ static Status PlaceShapeType(const std::vector<string>& inputs,
   CHECK_EQ(inputs.size(), input_types_strs.size());
   CHECK_EQ(inputs.size(), input_shapes_strs.size());
   std::vector<std::pair<string, Tensor>> input_tensors;
-  for (int i = 0; i < inputs.size(); ++i) {
+  for (size_t i = 0; i < inputs.size(); ++i) {
     const string& name = inputs.at(i);
     std::vector<int64> dims;
     CHECK(str_util::SplitAndParseAsInts(input_shapes_strs.at(i), ',', &dims));
@@ -168,10 +168,10 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
         str_util::Split(border_inputs_str, ",");
     const std::vector<string> border_outputs =
         str_util::Split(border_outputs_str, ",");
-    for (int i = 0; i < border_inputs.size(); ++i) {
+    for (size_t i = 0; i < border_inputs.size(); ++i) {
       VLOG(2) << "Border Input(" << i << "): " << border_inputs.at(i);
     }
-    for (int i = 0; i < border_outputs.size(); ++i) {
+    for (size_t i = 0; i < border_outputs.size(); ++i) {
       VLOG(2) << "Border Output(" << i << "): " << border_outputs.at(i);
     }
     TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::FuseRemoteGraphByBorder(
diff --git a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
index 4dae5da635..44a817a5c7 100644
--- a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
+++ b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
@@ -192,18 +192,20 @@ bool GenerateRandomCrop(int original_width, int original_height,
 }  // namespace
 
 template <typename T>
-class SampleDistortedBoundingBoxOp : public OpKernel {
+class SampleDistortedBoundingBoxV2Op : public OpKernel {
  public:
-  explicit SampleDistortedBoundingBoxOp(OpKernelConstruction* context)
+  explicit SampleDistortedBoundingBoxV2Op(OpKernelConstruction* context)
       : OpKernel(context) {
     OP_REQUIRES_OK(context, generator_.Init(context));
 
-    OP_REQUIRES_OK(
-        context, context->GetAttr("min_object_covered", &min_object_covered_));
-    OP_REQUIRES(
-        context, min_object_covered_ >= 0,
-        errors::InvalidArgument("Min object covered must be non-negative: ",
-                                min_object_covered_));
+    if (context->num_inputs() == 2) {
+      OP_REQUIRES_OK(context, context->GetAttr("min_object_covered",
+                                               &min_object_covered_));
+      OP_REQUIRES(
+          context, min_object_covered_ >= 0,
+          errors::InvalidArgument("Min object covered must be non-negative: ",
+                                  min_object_covered_));
+    }
 
     OP_REQUIRES_OK(context, context->GetAttr("use_image_if_no_bounding_boxes",
                                              &use_image_if_no_bounding_boxes_));
@@ -275,6 +277,25 @@ class SampleDistortedBoundingBoxOp : public OpKernel {
                     "bounding boxes must have shape [4] or [*, 4], got ",
                     input_boxes.shape().DebugString()));
 
+    float min_object_covered_val = 0.0;
+    if (context->num_inputs() == 3) {
+      const Tensor& min_object_covered = context->input(2);
+
+      OP_REQUIRES(
+          context, TensorShapeUtils::IsScalar(min_object_covered.shape()),
+          errors::InvalidArgument("min_object_covered must be 0-D, got shape ",
+                                  min_object_covered.shape().DebugString()));
+
+      min_object_covered_val = min_object_covered.scalar<float>()();
+
+      OP_REQUIRES(
+          context, min_object_covered_val >= 0,
+          errors::InvalidArgument("Min object covered must be non-negative: ",
+                                  min_object_covered_val));
+    } else {
+      min_object_covered_val = min_object_covered_;
+    }
+
     std::vector<Rectangle> bounding_boxes;
     if (input_boxes.NumElements() > 0) {
       TTypes<float>::ConstMatrix boxes = input_boxes.flat_inner_dims<float>();
@@ -325,7 +346,7 @@ class SampleDistortedBoundingBoxOp : public OpKernel {
 
       if (GenerateRandomCrop(width, height, min_sample_area, max_sample_area,
                              sample_aspect_ratio, &random, &crop_rect)) {
-        if (SatisfiesOverlapConstraints(crop_rect, min_object_covered_,
+        if (SatisfiesOverlapConstraints(crop_rect, min_object_covered_val,
                                         bounding_boxes)) {
           sample_generated = true;
           break;
@@ -399,11 +420,15 @@ class SampleDistortedBoundingBoxOp : public OpKernel {
   bool use_image_if_no_bounding_boxes_;
 };
 
-#define REGISTER_KERNELS(type)                               \
-  REGISTER_KERNEL_BUILDER(                                   \
-      Name("SampleDistortedBoundingBox").Device(DEVICE_CPU)   \
-           .TypeConstraint<type>("T"),                       \
-      SampleDistortedBoundingBoxOp<type>)
+#define REGISTER_KERNELS(type)                                  \
+  REGISTER_KERNEL_BUILDER(Name("SampleDistortedBoundingBox")    \
+                              .Device(DEVICE_CPU)               \
+                              .TypeConstraint<type>("T"),       \
+                          SampleDistortedBoundingBoxV2Op<type>) \
+  REGISTER_KERNEL_BUILDER(Name("SampleDistortedBoundingBoxV2")  \
+                              .Device(DEVICE_CPU)               \
+                              .TypeConstraint<type>("T"),       \
+                          SampleDistortedBoundingBoxV2Op<type>)
 
 TF_CALL_INTEGRAL_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
diff --git a/tensorflow/core/kernels/sparse_slice_op.cc b/tensorflow/core/kernels/sparse_slice_op.cc
new file mode 100644
index 0000000000..10dc208ab6
--- /dev/null
+++ b/tensorflow/core/kernels/sparse_slice_op.cc
@@ -0,0 +1,104 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include <vector>
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/util/sparse/sparse_tensor.h"
+
+namespace tensorflow {
+
+template <typename T>
+class SparseSliceOp : public OpKernel {
+ public:
+  explicit SparseSliceOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_indices = context->input(0);
+    const Tensor& input_values = context->input(1);
+    const Tensor& input_shape = context->input(2);
+    const Tensor& input_start = context->input(3);
+    const Tensor& input_size = context->input(4);
+
+    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices.shape()),
+                errors::InvalidArgument(
+                    "Input indices should be a matrix but received shape ",
+                    input_indices.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_values.shape()),
+                errors::InvalidArgument(
+                    "Input values should be a vector but received shape ",
+                    input_indices.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape.shape()),
+                errors::InvalidArgument(
+                    "Input shape should be a vector but received shape ",
+                    input_shape.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_start.shape()),
+                errors::InvalidArgument(
+                    "Input start should be a vector but received shape ",
+                    input_start.shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_size.shape()),
+                errors::InvalidArgument(
+                    "Input size should be a vector but received shape ",
+                    input_size.shape().DebugString()));
+
+    const int input_dims = input_shape.NumElements();
+    OP_REQUIRES(context, input_dims == input_start.NumElements(),
+                errors::InvalidArgument(
+                    "Expected start to be a vector of length ", input_dims,
+                    " but got length ", input_start.NumElements()));
+
+    OP_REQUIRES(context, input_dims == input_size.NumElements(),
+                errors::InvalidArgument(
+                    "Expected size to be a vector of length ", input_dims,
+                    " but got length ", input_size.NumElements()));
+
+    sparse::SparseTensor sparse_tensor(input_indices, input_values,
+                                       TensorShape(input_shape.vec<int64>()));
+
+    const gtl::ArraySlice<int64> start(input_start.flat<int64>().data(),
+                                       input_dims);
+    const gtl::ArraySlice<int64> size(input_size.flat<int64>().data(),
+                                      input_dims);
+
+    const sparse::SparseTensor output =
+        sparse::SparseTensor::Slice<T>(sparse_tensor, start, size);
+
+    context->set_output(0, output.indices());
+    context->set_output(1, output.values());
+
+    const TensorShape output_shape(output.shape());
+
+    Tensor* shape = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(2, {output_shape.dims()}, &shape));
+    for (int dim = 0; dim < output_shape.dims(); ++dim) {
+      shape->vec<int64>()(dim) = output_shape.dim_size(dim);
+    }
+  }
+
+ private:
+};
+
+#define REGISTER_KERNELS(type)                                          \
+  REGISTER_KERNEL_BUILDER(                                              \
+      Name("SparseSlice").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      SparseSliceOp<type>)
+
+TF_CALL_ALL_TYPES(REGISTER_KERNELS);
+#undef REGISTER_KERNELS
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/tile_functor.h b/tensorflow/core/kernels/tile_functor.h
index d80898b24f..28af2dace3 100644
--- a/tensorflow/core/kernels/tile_functor.h
+++ b/tensorflow/core/kernels/tile_functor.h
@@ -25,21 +25,6 @@ namespace tensorflow {
 
 namespace internal {
 
-// Helper to compute 'strides' given a tensor 'shape'. I.e.,
-// strides[i] = prod(shape.dim_size[(i+1):])
-template <typename Index>
-gtl::InlinedVector<Index, 8> ComputeStride(const TensorShape& shape) {
-  const int ndims = shape.dims();
-  gtl::InlinedVector<Index, 8> strides(ndims);
-  Index stride = 1;
-  for (int i = ndims - 1; i >= 0; --i) {
-    strides[i] = stride;
-    stride *= static_cast<Index>(shape.dim_size(i));
-  }
-  return strides;
-}
-
-
 // Device-specific naive implementation for tile.
 template <typename Device, typename T>
 void TileSimple(const Device& d, Tensor* out, const Tensor& in);
diff --git a/tensorflow/core/kernels/tile_functor_cpu.cc b/tensorflow/core/kernels/tile_functor_cpu.cc
index 3af00350b9..c5418fa142 100644
--- a/tensorflow/core/kernels/tile_functor_cpu.cc
+++ b/tensorflow/core/kernels/tile_functor_cpu.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/tile_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/tile_functor_gpu.cu.cc b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
index 08aca8c731..1c61c3030a 100644
--- a/tensorflow/core/kernels/tile_functor_gpu.cu.cc
+++ b/tensorflow/core/kernels/tile_functor_gpu.cu.cc
@@ -19,6 +19,7 @@ limitations under the License.
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/kernels/tile_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/util/cuda_kernel_helper.h"
 #include "tensorflow/core/framework/register_types.h"
 
diff --git a/tensorflow/core/kernels/transpose_functor.h b/tensorflow/core/kernels/transpose_functor.h
index f1ab770eeb..498030fdfe 100644
--- a/tensorflow/core/kernels/transpose_functor.h
+++ b/tensorflow/core/kernels/transpose_functor.h
@@ -38,18 +38,6 @@ namespace internal {
 typedef gtl::InlinedVector<int64, 8> TransposeDimsVec;
 typedef gtl::InlinedVector<int32, 8> TransposePermsVec;
 
-// Helper to compute 'strides' given a tensor 'shape'. I.e.,
-// strides[i] = prod(shape.dim_size[(i+1):])
-template <typename Index>
-void ComputeStride(const TensorShape& shape, Index* strides) {
-  const int ndims = shape.dims();
-  Index stride = 1;
-  for (int i = ndims - 1; i >= 0; --i) {
-    strides[i] = stride;
-    stride *= static_cast<Index>(shape.dim_size(i));
-  }
-}
-
 // Helper function that takes a tensor shape, a permutation, combines the
 // neighboring shapes if their indices in the permutation are consecutive.
 // The function outputs the combined shape and new permutation.
@@ -130,7 +118,17 @@ void TransposeSimple(const Device& d, const Tensor& in,
 // Uses Eigen to transpose.
 template <typename Device, typename T, int NDIMS>
 void TransposeUsingEigen(const Device& d, const Tensor& in,
-                         const gtl::ArraySlice<int32> perm, Tensor* out);
+                         const gtl::ArraySlice<int32> perm, Tensor* out) {
+  Eigen::array<int, NDIMS> p;
+  for (int i = 0; i < NDIMS; ++i) p[i] = perm[i];
+  auto x = typename TTypes<T, NDIMS>::ConstTensor(
+      reinterpret_cast<const T*>(in.tensor_data().data()),
+      in.shape().AsEigenDSizes<NDIMS>());
+  auto y = typename TTypes<T, NDIMS>::Tensor(
+      reinterpret_cast<T*>(const_cast<char*>(out->tensor_data().data())),
+      out->shape().AsEigenDSizes<NDIMS>());
+  y.device(d) = x.shuffle(p);
+}
 
 
 #ifdef TENSORFLOW_USE_SYCL
diff --git a/tensorflow/core/kernels/transpose_functor_cpu.cc b/tensorflow/core/kernels/transpose_functor_cpu.cc
index 248c11976e..a004cb2293 100644
--- a/tensorflow/core/kernels/transpose_functor_cpu.cc
+++ b/tensorflow/core/kernels/transpose_functor_cpu.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #define EIGEN_USE_THREADS
 
 #include "tensorflow/core/kernels/transpose_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 
 namespace tensorflow {
 namespace internal {
@@ -24,10 +25,8 @@ template <typename Device, typename T>
 void TransposeSimple(const Device& d, const Tensor& in,
                      const gtl::ArraySlice<int32> perm, Tensor* out) {
   const int ndims = in.dims();
-  gtl::InlinedVector<int64, 8> in_strides(ndims);
-  ComputeStride(in.shape(), in_strides.data());
-  gtl::InlinedVector<int64, 8> out_strides(ndims);
-  ComputeStride(out->shape(), out_strides.data());
+  gtl::InlinedVector<int64, 8> in_strides = ComputeStride<int64>(in.shape());
+  gtl::InlinedVector<int64, 8> out_strides = ComputeStride<int64>(out->shape());
   const int64 nelem = in.NumElements();
   const T* p = reinterpret_cast<const T*>(in.tensor_data().data());
   T* q = reinterpret_cast<T*>(const_cast<char*>((out->tensor_data().data())));
@@ -45,20 +44,6 @@ void TransposeSimple(const Device& d, const Tensor& in,
   }
 }
 
-template <typename Device, typename T, int NDIMS>
-void TransposeUsingEigen(const Device& d, const Tensor& in,
-                         const gtl::ArraySlice<int32> perm, Tensor* out) {
-  Eigen::array<int, NDIMS> p;
-  for (int i = 0; i < NDIMS; ++i) p[i] = perm[i];
-  auto x = typename TTypes<T, NDIMS>::ConstTensor(
-      reinterpret_cast<const T*>(in.tensor_data().data()),
-      in.shape().AsEigenDSizes<NDIMS>());
-  auto y = typename TTypes<T, NDIMS>::Tensor(
-      reinterpret_cast<T*>(const_cast<char*>(out->tensor_data().data())),
-      out->shape().AsEigenDSizes<NDIMS>());
-  y.device(d) = x.shuffle(p);
-}
-
 }  // end namespace internal
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -182,7 +167,35 @@ template <typename T>
 struct Transpose<SYCLDevice, T> {
   static void run(const SYCLDevice& d, const Tensor& in,
                   const gtl::ArraySlice<int32> perm, Tensor* out) {
-    // Should add a specialized implementation for SYCLDevice here.
+    switch (in.dims()) {
+      case 1:
+        internal::TransposeUsingEigen<SYCLDevice, T, 1>(d, in, perm, out);
+        break;
+      case 2:
+        internal::TransposeUsingEigen<SYCLDevice, T, 2>(d, in, perm, out);
+        break;
+      case 3:
+        internal::TransposeUsingEigen<SYCLDevice, T, 3>(d, in, perm, out);
+        break;
+      case 4:
+        internal::TransposeUsingEigen<SYCLDevice, T, 4>(d, in, perm, out);
+        break;
+      case 5:
+        internal::TransposeUsingEigen<SYCLDevice, T, 5>(d, in, perm, out);
+        break;
+      case 6:
+        internal::TransposeUsingEigen<SYCLDevice, T, 6>(d, in, perm, out);
+        break;
+      case 7:
+        internal::TransposeUsingEigen<SYCLDevice, T, 7>(d, in, perm, out);
+        break;
+      case 8:
+        internal::TransposeUsingEigen<SYCLDevice, T, 8>(d, in, perm, out);
+        break;
+      default:
+        LOG(FATAL) << "Unsupported TransposeUsingEigen for: " << in.dims();
+        break;
+    }
   }
 };
 
diff --git a/tensorflow/core/kernels/transpose_functor_gpu.cu.cc b/tensorflow/core/kernels/transpose_functor_gpu.cu.cc
index bc72bfb2fd..a118cc80c9 100644
--- a/tensorflow/core/kernels/transpose_functor_gpu.cu.cc
+++ b/tensorflow/core/kernels/transpose_functor_gpu.cu.cc
@@ -19,6 +19,7 @@ limitations under the License.
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/kernels/transpose_functor.h"
+#include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/util/cuda_kernel_helper.h"
 
 // TODO(yangzihao): Remove the dependency of conv_2d.h once we move all
@@ -53,13 +54,13 @@ void TransposeSimple(const Device& d, const Tensor& in,
   CHECK_LT(nelem, kint32max) << "Tensor too large to transpose on GPU";
   // Pack strides and permutation into one buffer.
   const int32 ndims = in.dims();
-  gtl::InlinedVector<int32, 16> host_buf(ndims * 3);
-  // Input strides.
-  ComputeStride(in.shape(), &host_buf[0]);
-  // Output strides.
-  ComputeStride(out->shape(), &host_buf[ndims]);
+  gtl::InlinedVector<int32, 24> host_buf(ndims * 3);
+  gtl::InlinedVector<int32, 8> in_strides = ComputeStride<int32>(in.shape());
+  gtl::InlinedVector<int32, 8> out_strides = ComputeStride<int32>(out->shape());
   // Dimension permutation.
   for (int i = 0; i < ndims; ++i) {
+    host_buf[i] = in_strides[i];
+    host_buf[ndims + i] = out_strides[i];
     host_buf[ndims * 2 + i] = perm[i];
   }
   // Copies the input strides, output strides and permutation to the device.
@@ -79,20 +80,6 @@ void TransposeSimple(const Device& d, const Tensor& in,
   d.deallocate(dev_buf);
 }
 
-template <typename Device, typename T, int NDIMS>
-void TransposeUsingEigen(const Device& d, const Tensor& in,
-                         const gtl::ArraySlice<int32> perm, Tensor* out) {
-  Eigen::array<int, NDIMS> p;
-  for (int i = 0; i < NDIMS; ++i) p[i] = perm[i];
-  auto x = typename TTypes<T, NDIMS>::ConstTensor(
-      reinterpret_cast<const T*>(in.tensor_data().data()),
-      in.shape().AsEigenDSizes<NDIMS>());
-  auto y = typename TTypes<T, NDIMS>::Tensor(
-      reinterpret_cast<T*>(const_cast<char*>(out->tensor_data().data())),
-      out->shape().AsEigenDSizes<NDIMS>());
-  y.device(d) = x.shuffle(p);
-}
-
 // TransposeUsingTile tries to reduce the dimension of the input tensor to 3 and
 // then call special kernels to swap either dimension 1 and dimension 2 or
 // dimension 0 and dimension 2. It returns true if the operation is success,
diff --git a/tensorflow/core/lib/gtl/array_slice.h b/tensorflow/core/lib/gtl/array_slice.h
index 30ef19ebe8..002d166c72 100644
--- a/tensorflow/core/lib/gtl/array_slice.h
+++ b/tensorflow/core/lib/gtl/array_slice.h
@@ -191,7 +191,7 @@ class ArraySlice {
   void pop_front() { remove_prefix(1); }
 
   // These relational operators have the same semantics as the
-  // std::vector<T> relational operators: they do deep (elementwise)
+  // std::vector<T> relational operators: they do deep (element-wise)
   // comparisons.  Array slices are equal iff their size is the same
   // and all their elements are equal.
   bool operator==(ArraySlice<T> other) const { return impl_ == other.impl_; }
diff --git a/tensorflow/core/lib/gtl/flatrep.h b/tensorflow/core/lib/gtl/flatrep.h
index f5e318be1f..bb405b327a 100644
--- a/tensorflow/core/lib/gtl/flatrep.h
+++ b/tensorflow/core/lib/gtl/flatrep.h
@@ -29,7 +29,7 @@ namespace internal {
 //
 // The representation is an open-addressed hash table.  Conceptually,
 // the representation is a flat array of entries.  However we
-// structure it as an array of of buckets where each bucket holds
+// structure it as an array of buckets where each bucket holds
 // kWidth entries along with metadata for the kWidth entries.  The
 // metadata marker is
 //
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index 51a964e3e3..fe709405f4 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -881,6 +881,32 @@ cancel_pending_enqueues: If true, all pending enqueue requests that are
   blocked on the given queue will be canceled.
 )doc");
 
+REGISTER_OP("QueueIsClosed")
+    .Input("handle: Ref(string)")
+    .Output("is_closed: bool")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Returns true if queue is closed.
+
+This operation returns true if the queue is closed and false if the queue
+is open.
+
+handle: The handle to a queue.
+)doc");
+
+REGISTER_OP("QueueIsClosedV2")
+    .Input("handle: resource")
+    .Output("is_closed: bool")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Returns true if queue is closed.
+
+This operation returns true if the queue is closed and false if the queue
+is open.
+
+handle: The handle to a queue.
+)doc");
+
 REGISTER_OP("QueueSize")
     .Input("handle: Ref(string)")
     .Output("size: int32")
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index 68f4863026..0f8563a2da 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -82,9 +82,8 @@ Status DecodeImageShapeFn(InferenceContext* c) {
     channels_dim = c->MakeDim(channels);
   }
 
-  c->set_output(0,
-                c->MakeShape({InferenceContext::kUnknownDim,
-                              InferenceContext::kUnknownDim, channels_dim}));
+  c->set_output(0, c->MakeShape({InferenceContext::kUnknownDim,
+                                 InferenceContext::kUnknownDim, channels_dim}));
   return Status::OK();
 }
 
@@ -628,10 +627,9 @@ REGISTER_OP("DecodeGif")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
-      c->set_output(0,
-                    c->MakeShape({InferenceContext::kUnknownDim,
-                                  InferenceContext::kUnknownDim,
-                                  InferenceContext::kUnknownDim, 3}));
+      c->set_output(0, c->MakeShape({InferenceContext::kUnknownDim,
+                                     InferenceContext::kUnknownDim,
+                                     InferenceContext::kUnknownDim, 3}));
       return Status::OK();
     })
     .Doc(R"doc(
@@ -813,6 +811,98 @@ use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes supplied.
   raise an error.
 )doc");
 
+REGISTER_OP("SampleDistortedBoundingBoxV2")
+  .Input("image_size: T")
+  .Input("bounding_boxes: float")
+  .Input("min_object_covered: float")
+  .Output("begin: T")
+  .Output("size: T")
+  .Output("bboxes: float")
+  .Attr("T: {uint8, int8, int16, int32, int64}")
+  .Attr("seed: int = 0")
+  .Attr("seed2: int = 0")
+  .Attr("aspect_ratio_range: list(float) = [0.75, 1.33]")
+  .Attr("area_range: list(float) = [0.05, 1.0]")
+  .Attr("max_attempts: int = 100")
+  .Attr("use_image_if_no_bounding_boxes: bool = false")
+  .SetIsStateful()
+  .SetShapeFn([](InferenceContext* c) {
+    c->set_output(0, c->Vector(3));
+    c->set_output(1, c->Vector(3));
+    c->set_output(2, c->MakeShape({1, 1, 4}));
+    return Status::OK();
+  })
+  .Doc(R"doc(
+Generate a single randomly distorted bounding box for an image.
+
+Bounding box annotations are often supplied in addition to ground-truth labels
+in image recognition or object localization tasks. A common technique for
+training such a system is to randomly distort an image while preserving
+its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+localization of an object, i.e. bounding box, given an `image_size`,
+`bounding_boxes` and a series of constraints.
+
+The output of this Op is a single bounding box that may be used to crop the
+original image. The output is returned as 3 tensors: `begin`, `size` and
+`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
+what the bounding box looks like.
+
+Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+height of the underlying image.
+
+For example,
+
+```python
+    # Generate a single distorted bounding box.
+    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+        tf.shape(image),
+        bounding_boxes=bounding_boxes)
+
+    # Draw the bounding box in an image summary.
+    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+                                                  bbox_for_draw)
+    tf.image_summary('images_with_box', image_with_box)
+
+    # Employ the bounding box to distort the image.
+    distorted_image = tf.slice(image, begin, size)
+```
+
+Note that if no bounding box information is available, setting
+`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+false and no bounding boxes are supplied, an error is raised.
+
+image_size: 1-D, containing `[height, width, channels]`.
+bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
+  associated with the image.
+min_object_covered: The cropped area of the image must contain at least this
+  fraction of any bounding box supplied. The value of this parameter should be
+  non-negative. In the case of 0, the cropped area does not need to overlap
+  any of the bounding boxes supplied.
+begin: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+  `tf.slice`.
+size: 1-D, containing `[target_height, target_width, -1]`. Provide as input to
+  `tf.slice`.
+bboxes: 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+  Provide as input to `tf.image.draw_bounding_boxes`.
+seed: If either `seed` or `seed2` are set to non-zero, the random number
+  generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
+  seed.
+seed2: A second seed to avoid seed collision.
+aspect_ratio_range: The cropped area of the image must have an aspect ratio =
+  width / height within this range.
+area_range: The cropped area of the image must contain a fraction of the
+  supplied image within in this range.
+max_attempts: Number of attempts at generating a cropped region of the image
+  of the specified constraints. After `max_attempts` failures, return the entire
+  image.
+use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes supplied.
+  If true, assume an implicit bounding box covering the whole input. If false,
+  raise an error.
+)doc");
+
 // --------------------------------------------------------------------------
 
 // glimpse = extract_glimpse(input, size, offsets) extract the glimpse
diff --git a/tensorflow/core/ops/io_ops.cc b/tensorflow/core/ops/io_ops.cc
index b92c18416f..350aefe2c8 100644
--- a/tensorflow/core/ops/io_ops.cc
+++ b/tensorflow/core/ops/io_ops.cc
@@ -474,6 +474,7 @@ REGISTER_OP("FixedLengthRecordReaderV2")
     .Attr("hop_bytes: int = 0")
     .Attr("container: string = ''")
     .Attr("shared_name: string = ''")
+    .Attr("encoding: string = ''")
     .SetIsStateful()
     .SetShapeFn(shape_inference::ScalarShape)
     .Doc(R"doc(
@@ -489,6 +490,8 @@ container: If non-empty, this reader is placed in the given container.
         Otherwise, a default container is used.
 shared_name: If non-empty, this reader is named in the given bucket
              with this shared_name. Otherwise, the node name is used instead.
+encoding: The type of encoding for the file. Currently ZLIB and GZIP
+        are supported. Defaults to none.
 )doc");
 
 // TODO(cwhipkey): mark this deprecated in favor of V2.
diff --git a/tensorflow/core/ops/math_grad.cc b/tensorflow/core/ops/math_grad.cc
index 9a58a31757..5e082ce8f5 100644
--- a/tensorflow/core/ops/math_grad.cc
+++ b/tensorflow/core/ops/math_grad.cc
@@ -189,6 +189,42 @@ Status TanhGrad(const AttrSlice& attrs, FunctionDef* g) {
 }
 REGISTER_OP_GRADIENT("Tanh", TanhGrad);
 
+Status AsinhGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"y"}, "Asinh", {"x"}},
+      {{"cosh"}, "Cosh", {"y"}},
+      {{"dx"}, "Mul", {"dy", "cosh"}},  // dy * cosh(y)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Asinh", AsinhGrad);
+
+Status AcoshGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+      {{"y"}, "Acosh", {"x"}},
+      {{"sinh"}, "Sinh", {"y"}},
+      {{"dx"}, "Mul", {"dy", "sinh"}},  // dy * sinh(y)
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Acosh", AcoshGrad);
+
+Status AtanhGrad(const AttrSlice& attrs, FunctionDef* g) {
+  // clang-format off
+  return GradForUnaryCwise(g, {
+    {{"x2"}, "Square", {"x"}},
+    FDH::Const("const", 1.0f),
+    {{"one"}, "Cast", {"const"}, {{"SrcT", DT_FLOAT}, {"DstT", "$T"}}},
+    {{"a"}, "Sub", {"one", "x2"}}, // 1 - x^2
+    {{"inv"}, "Reciprocal", {"a"}},
+    {{"dx"}, "Mul", {"dy", "inv"}}
+  });
+  // clang-format on
+}
+REGISTER_OP_GRADIENT("Atanh", AtanhGrad);
+
 Status SigmoidGrad(const AttrSlice& attrs, FunctionDef* g) {
   // clang-format off
   return GradForUnaryCwise(g, {
diff --git a/tensorflow/core/ops/math_grad_test.cc b/tensorflow/core/ops/math_grad_test.cc
index aa9706a328..1393bffb91 100644
--- a/tensorflow/core/ops/math_grad_test.cc
+++ b/tensorflow/core/ops/math_grad_test.cc
@@ -528,6 +528,44 @@ TEST_F(MathGradTest, Tanh) {
   test::ExpectClose(ans, dx);
 }
 
+TEST_F(MathGradTest, Asinh) {
+  auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) {
+    auto y = std::asinh(x);
+    return std::cosh(y);
+  };
+  auto dx = test::AsTensor<float>(
+      {g(-3.f), g(-2.f), g(-1.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Asinh", x);
+  test::ExpectClose(ans, dx);
+}
+
+TEST_F(MathGradTest, Acosh) {
+  auto x = test::AsTensor<float>({6.f, 5.f, 4.f, 1.f, 2.f, 3.f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) {
+    auto y = std::acosh(x);
+    return std::sinh(y);
+  };
+  auto dx = test::AsTensor<float>(
+      {g(6.f), g(5.f), g(4.f), g(1.f), g(2.f), g(3.f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Acosh", x);
+  test::ExpectClose(ans, dx);
+}
+
+TEST_F(MathGradTest, Atanh) {
+  auto x = test::AsTensor<float>({-0.3f, -0.2f, -0.1f, 0.1f, 0.2f, 0.3f},
+                                 TensorShape({2, 3}));
+  auto g = [](float x) {
+    return 1.f / (1.f - x * x);
+  };
+  auto dx = test::AsTensor<float>(
+      {g(-0.3f), g(-0.2f), g(-0.1f), g(0.1f), g(0.2f), g(0.3f)}, TensorShape({2, 3}));
+  auto ans = SymGrad("Atanh", x);
+  test::ExpectClose(ans, dx);
+}
+
 TEST_F(MathGradTest, Sigmoid) {
   auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
                                  TensorShape({2, 3}));
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 30d6987707..89818bc210 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -305,6 +305,18 @@ REGISTER_OP("Tanh").UNARY_COMPLEX().Doc(R"doc(
 Computes hyperbolic tangent of `x` element-wise.
 )doc");
 
+REGISTER_OP("Asinh").UNARY_COMPLEX().Doc(R"doc(
+Computes inverse hyperbolic sine of x element-wise.
+)doc");
+
+REGISTER_OP("Acosh").UNARY_COMPLEX().Doc(R"doc(
+Computes inverse hyperbolic cosine of x element-wise.
+)doc");
+
+REGISTER_OP("Atanh").UNARY_COMPLEX().Doc(R"doc(
+Computes inverse hyperbolic tangent of x element-wise.
+)doc");
+
 REGISTER_OP("TanhGrad").UNARY_GRADIENT_COMPLEX().Doc(R"doc(
 Computes the gradient for the tanh of `x` wrt its input.
 
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 0b10382471..7270e31fe4 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -190,6 +190,30 @@ op {
   }
   summary: "Computes acos of x element-wise."
 }
+op {
+  name: "Acosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes acosh of x element-wise."
+}
 op {
   name: "Add"
   input_arg {
@@ -1885,6 +1909,30 @@ op {
   }
   summary: "Computes asin of x element-wise."
 }
+op {
+  name: "Asinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes asinh of x element-wise."
+}
 op {
   name: "Assert"
   input_arg {
@@ -2116,6 +2164,30 @@ op {
   summary: "Computes arctangent of `y/x` element-wise, respecting signs of the arguments."
   description: "This is the angle \\( \\theta \\in [-\\pi, \\pi] \\) such that\n\\[ x = r \\cos(\\theta) \\]\nand\n\\[ y = r \\sin(\\theta) \\]\nwhere \\(r = \\sqrt(x^2 + y^2) \\)."
 }
+op {
+  name: "Atanh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  summary: "Computes atanh of x element-wise."
+}
 op {
   name: "AudioSpectrogram"
   input_arg {
@@ -28120,6 +28192,33 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
+op {
+  name: "LMDBReader"
+  output_arg {
+    name: "reader_handle"
+    description: "The handle to reference the Reader."
+    type: DT_STRING
+    is_ref: true
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
+  }
+  summary: "A Reader that outputs the records from a LMDB database."
+  is_stateful: true
+}
 op {
   name: "TakeDataset"
   input_arg {
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 6aca2c3b01..646c379586 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -597,6 +597,60 @@ output_shape: A list of 1-D tensors represents the shape of the output sparse
   tensors.
 )doc");
 
+REGISTER_OP("SparseSlice")
+    .Input("indices: int64")
+    .Input("values: T")
+    .Input("shape: int64")
+    .Input("start: int64")
+    .Input("size: int64")
+    .Output("output_indices: int64")
+    .Output("output_values: T")
+    .Output("output_shape: int64")
+    .Attr("T: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input_shape = c->input(2);
+      ShapeHandle output_indices =
+          c->Matrix(InferenceContext::kUnknownDim, c->NumElements(input_shape));
+      ShapeHandle output_values = c->Vector(InferenceContext::kUnknownDim);
+      ShapeHandle output_shape = input_shape;
+
+      c->set_output(0, output_indices);
+      c->set_output(1, output_values);
+      c->set_output(2, output_shape);
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Slice a `SparseTensor` based on the `start` and `size`.
+
+For example, if the input is
+
+    input_tensor = shape = [2, 7]
+    [    a   d e  ]
+    [b c          ]
+
+Graphically the output tensors are:
+
+    sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
+    [    a  ]
+    [b c    ]
+
+    sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
+    [ d e  ]
+    [      ]
+
+indices: 2-D tensor represents the indices of the sparse tensor.
+values: 1-D tensor represents the values of the sparse tensor.
+shape: 1-D. tensor represents the shape of the sparse tensor.
+start: 1-D. tensor represents the start of the slice.
+size: 1-D. tensor represents the size of the slice.
+output indices: A list of 1-D tensors represents the indices of the output
+sparse tensors.
+output_values: A list of 1-D tensors represents the values of the output sparse
+  tensors.
+output_shape: A list of 1-D tensors represents the shape of the output sparse
+  tensors.
+)doc");
+
 REGISTER_OP("SparseReorder")
     .Input("input_indices: int64")
     .Input("input_values: T")
@@ -862,7 +916,9 @@ keep_dims: If true, retain reduced dimensions with length 1.
         return Status::OK();                                     \
       })
 
-REGISTER_OP("SparseDenseCwiseMul").SPARSE_DENSE_CWISE_SIGNATURE().Doc(R"doc(
+REGISTER_OP("SparseDenseCwiseMul")
+    .SPARSE_DENSE_CWISE_SIGNATURE()
+    .Doc(R"doc(
 Component-wise multiplies a SparseTensor by a dense Tensor.
 
 The output locations corresponding to the implicitly zero elements in the sparse
@@ -880,7 +936,9 @@ dense: `R`-D.  The dense Tensor operand.
 output: 1-D.  The `N` values that are operated on.
 )doc");
 
-REGISTER_OP("SparseDenseCwiseDiv").SPARSE_DENSE_CWISE_SIGNATURE().Doc(R"doc(
+REGISTER_OP("SparseDenseCwiseDiv")
+    .SPARSE_DENSE_CWISE_SIGNATURE()
+    .Doc(R"doc(
 Component-wise divides a SparseTensor by a dense Tensor.
 
 *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
@@ -894,7 +952,9 @@ dense: `R`-D.  The dense Tensor operand.
 output: 1-D.  The `N` values that are operated on.
 )doc");
 
-REGISTER_OP("SparseDenseCwiseAdd").SPARSE_DENSE_CWISE_SIGNATURE().Doc(R"doc(
+REGISTER_OP("SparseDenseCwiseAdd")
+    .SPARSE_DENSE_CWISE_SIGNATURE()
+    .Doc(R"doc(
 Adds up a SparseTensor and a dense Tensor, using these special rules:
 
 (1) Broadcasts the dense side to have the same shape as the sparse side, if
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 94f255663e..f9c876cc21 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -3,6 +3,7 @@
 load("@protobuf//:protobuf.bzl", "cc_proto_library")
 load("@protobuf//:protobuf.bzl", "py_proto_library")
 load("//tensorflow:tensorflow.bzl", "if_not_mobile")
+load("//tensorflow:tensorflow.bzl", "if_not_windows")
 
 # Appends a suffix to a list of deps.
 def tf_deps(deps, suffix):
@@ -45,11 +46,11 @@ def tf_proto_library_cc(name, srcs = [], has_services = None,
       srcs = srcs,
       deps = tf_deps(protodeps, "_cc") + ["@protobuf//:cc_wkt_protos"],
       cc_libs = cc_libs + ["@protobuf//:protobuf"],
-      copts = [
+      copts = if_not_windows([
           "-Wno-unknown-warning-option",
           "-Wno-unused-but-set-variable",
           "-Wno-sign-compare",
-      ],
+      ]),
       protoc = "@protobuf//:protoc",
       default_runtime = "@protobuf//:protobuf",
       use_grpc_plugin = use_grpc_plugin,
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.cc b/tensorflow/core/platform/profile_utils/cpu_utils.cc
index 22400565d6..52df84e81c 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.cc
@@ -28,10 +28,17 @@ namespace profile_utils {
 
 static ICpuUtilsHelper* cpu_utils_helper_instance_ = nullptr;
 
-/* static */ int64 CpuUtils::GetCycleCounterFrequency() {
-  static const int64 cpu_frequency = GetCycleCounterFrequencyImpl();
-  return cpu_frequency;
+#if defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
+   /* static */ uint64 CpuUtils::GetCycleCounterFrequency() {
+     static const uint64 cpu_frequency = GetCycleCounterFrequencyImpl();
+     return cpu_frequency;
 }
+#else
+   /* static */ int64 CpuUtils::GetCycleCounterFrequency() {
+     static const int64 cpu_frequency = GetCycleCounterFrequencyImpl();
+     return cpu_frequency;
+}
+#endif
 
 /* static */ double CpuUtils::GetMicroSecPerClock() {
   static const double micro_sec_per_clock =
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h
index 19471ec858..8979a40ea1 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils.h
+++ b/tensorflow/core/platform/profile_utils/cpu_utils.h
@@ -97,7 +97,11 @@ class CpuUtils {
   // Return cycle counter frequency.
   // As this method caches the cpu frequency internally,
   // the first call will incur overhead, but not subsequent calls.
-  static int64 GetCycleCounterFrequency();
+  #if defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
+     static uint64 GetCycleCounterFrequency();
+  #else
+     static int64 GetCycleCounterFrequency();
+  #endif
 
   // Return micro secound per each clock
   // As this method caches the cpu frequency internally,
diff --git a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
index ca487965a0..e1ec4aaac0 100644
--- a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
+++ b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc
@@ -53,9 +53,15 @@ TEST_F(CpuUtilsTest, CheckGetCurrentClockCycle) {
 }
 
 TEST_F(CpuUtilsTest, CheckCycleCounterFrequency) {
-  const int64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
-  CHECK_GT(cpu_frequency, 0);
-  CHECK_NE(cpu_frequency, CpuUtils::INVALID_FREQUENCY);
+  #if defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
+     const uint64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
+     CHECK_GT(cpu_frequency, 0);
+     CHECK_NE(cpu_frequency, unsigned(CpuUtils::INVALID_FREQUENCY));
+  #else
+     const int64 cpu_frequency = CpuUtils::GetCycleCounterFrequency();
+     CHECK_GT(cpu_frequency, 0);
+     CHECK_NE(cpu_frequency, CpuUtils::INVALID_FREQUENCY);
+  #endif
   if (DBG) {
     LOG(INFO) << "Cpu frequency = " << cpu_frequency;
   }
diff --git a/tensorflow/core/profiler/internal/BUILD b/tensorflow/core/profiler/internal/BUILD
index abb4534f77..129a42deeb 100644
--- a/tensorflow/core/profiler/internal/BUILD
+++ b/tensorflow/core/profiler/internal/BUILD
@@ -9,6 +9,7 @@ package(
 licenses(["notice"])  # Apache 2.0
 
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
+load("//tensorflow:tensorflow.bzl", "if_not_windows")
 
 cc_library(
     name = "tfprof_stats",
@@ -243,7 +244,7 @@ cc_library(
     name = "tfprof_utils",
     srcs = ["tfprof_utils.cc"],
     hdrs = ["tfprof_utils.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         ":tfprof_options",
         "//tensorflow/core:lib",
@@ -315,7 +316,7 @@ cc_library(
     name = "tfprof_tensor",
     srcs = ["tfprof_tensor.cc"],
     hdrs = ["tfprof_tensor.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
diff --git a/tensorflow/core/profiler/internal/tfprof_timeline.cc b/tensorflow/core/profiler/internal/tfprof_timeline.cc
index ce0d8b7c7b..cfd80b875a 100644
--- a/tensorflow/core/profiler/internal/tfprof_timeline.cc
+++ b/tensorflow/core/profiler/internal/tfprof_timeline.cc
@@ -327,7 +327,7 @@ void Timeline::AllocateLanes() {
       int64 start_time = tnode.second->start_micros;
       int64 end_time = tnode.second->start_micros + tnode.second->exec_micros;
       int64 l = -1;
-      for (int i = 0; i < p->lanes.size(); ++i) {
+      for (int64 i = 0; i < p->lanes.size(); ++i) {
         const auto& lane = p->lanes[i];
         l = i;
         for (auto cur_it = lane.rbegin(); cur_it != lane.rend(); ++cur_it) {
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index e476a84a13..9d4a417aa3 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -141,7 +141,7 @@ message DeregisterGraphResponse {
 message CleanupAllRequest {
   // A list of container names.
   //
-  // If 'container' is not empty, releases resoures in the given
+  // If 'container' is not empty, releases resources in the given
   // containers in all devices.
   //
   // If 'container' is empty, releases resources in the default
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index e76876c9dc..8fc10111a6 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -20,7 +20,7 @@ limitations under the License.
 
 #define TF_MAJOR_VERSION 1
 #define TF_MINOR_VERSION 2
-#define TF_PATCH_VERSION 0
+#define TF_PATCH_VERSION 1
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
diff --git a/tensorflow/core/util/memmapped_file_system_test.cc b/tensorflow/core/util/memmapped_file_system_test.cc
index 24ce5ebafc..a5f24b08b3 100644
--- a/tensorflow/core/util/memmapped_file_system_test.cc
+++ b/tensorflow/core/util/memmapped_file_system_test.cc
@@ -110,7 +110,7 @@ TEST(MemmappedFileSystemTest, SimpleTest) {
             memmapped_env.FileExists("bla-bla-bla").code());
 }
 
-TEST(MemmappedFileSystemTest, NotInitalized) {
+TEST(MemmappedFileSystemTest, NotInitialized) {
   MemmappedEnv memmapped_env(Env::Default());
   std::unique_ptr<ReadOnlyMemoryRegion> memory_region;
   EXPECT_EQ(
diff --git a/tensorflow/core/util/sparse/sparse_tensor.h b/tensorflow/core/util/sparse/sparse_tensor.h
index d8e5d90142..0ea74c38b1 100644
--- a/tensorflow/core/util/sparse/sparse_tensor.h
+++ b/tensorflow/core/util/sparse/sparse_tensor.h
@@ -20,7 +20,6 @@ limitations under the License.
 #include <numeric>
 #include <vector>
 
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/types.h"
@@ -32,6 +31,7 @@ limitations under the License.
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/util/sparse/dim_comparator.h"
 #include "tensorflow/core/util/sparse/group_iterator.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 
 namespace tensorflow {
 namespace sparse {
@@ -59,8 +59,8 @@ class SparseTensor {
         shape_(shape.begin(), shape.end()),
         order_(order.begin(), order.end()),
         dims_(GetDimsFromIx(ix)) {
-    CHECK_EQ(ix.dtype(), DT_INT64)
-        << "indices must be type int64 but got: " << ix.dtype();
+    CHECK_EQ(ix.dtype(), DT_INT64) << "indices must be type int64 but got: "
+                                   << ix.dtype();
     CHECK(TensorShapeUtils::IsVector(vals.shape()))
         << "vals must be a vec, but got: " << vals.shape().DebugString();
     CHECK_EQ(ix.shape().dim_size(0), vals.shape().dim_size(0))
@@ -155,6 +155,15 @@ class SparseTensor {
                                          const int split_dim,
                                          const int num_split);
 
+  // Slice() will slice the input SparseTensor into a SparseTensor based on
+  // specified start and size. Both start and size are 1-D array with each
+  // element of the array representing one dimension. The start is the start
+  // index at each dimension and the size is the size at each dimension.
+  template <typename T>
+  static SparseTensor Slice(const SparseTensor& tensor,
+                            const gtl::ArraySlice<int64>& start,
+                            const gtl::ArraySlice<int64>& size);
+
   // Picks out the dimensions according to `dim_indices`.
   std::vector<int64> PickDims(gtl::ArraySlice<int64> dim_indices) const {
     std::vector<int64> res(dim_indices.size());
@@ -541,6 +550,81 @@ std::vector<SparseTensor> SparseTensor::Split(const SparseTensor& input_tensor,
   return output_tensors;
 }
 
+template <typename T>
+SparseTensor SparseTensor::Slice(const SparseTensor& input_tensor,
+                                 const gtl::ArraySlice<int64>& start,
+                                 const gtl::ArraySlice<int64>& size) {
+  TensorShape output_shape(input_tensor.shape());
+
+  const int dims = input_tensor.dims();
+  for (int dim = 0; dim < dims; dim++) {
+    int64 dim_size = start[dim] + size[dim] < output_shape.dim_size(dim)
+                         ? size[dim]
+                         : output_shape.dim_size(dim) - start[dim];
+    output_shape.set_dim(dim, dim_size);
+  }
+
+  auto input_indices_t = input_tensor.indices().matrix<int64>();
+  auto input_values_t = input_tensor.values().vec<T>();
+
+  // Find the number of indices that fall inside start and size.
+  int count = 0;
+  for (int i = 0; i < input_tensor.indices().dim_size(0); i++) {
+    // The following will check to see if an input is within the
+    // range specified by start and size.
+    // The for loop below iterates through all dimensions. In case
+    // the index falls outside of the start and size at any dimension,
+    // it will be considered as a "no hit" (hit = false). In this
+    // case, it will not be counted as the index that fall inside
+    // the range specified by start and size.
+    bool hit = true;
+    for (int dim = 0; dim < dims; dim++) {
+      if (!(start[dim] <= input_indices_t(i, dim) &&
+            input_indices_t(i, dim) < start[dim] + size[dim])) {
+        hit = false;
+        break;
+      }
+    }
+    if (!hit) {
+      continue;
+    }
+    count++;
+  }
+
+  Tensor output_values(DataTypeToEnum<T>::v(), TensorShape({count}));
+  Tensor output_indices(DT_INT64, TensorShape({count, dims}));
+
+  auto output_values_t = output_values.vec<T>();
+  auto output_indices_t = output_indices.matrix<int64>();
+
+  // Obtain the output indices that fall inside start and size.
+  int index = 0;
+  for (int i = 0; i < input_tensor.indices().dim_size(0) && index < count;
+       i++) {
+    // The logic here is similiar as the above except that the above
+    // only count the number of indices while here we actually generate
+    // the output.
+    bool hit = true;
+    for (int dim = 0; dim < dims; dim++) {
+      if (!(start[dim] <= input_indices_t(i, dim) &&
+            input_indices_t(i, dim) < start[dim] + size[dim])) {
+        hit = false;
+        break;
+      }
+    }
+    if (!hit) {
+      continue;
+    }
+    output_values_t(index) = input_values_t(i);
+    for (int dim = 0; dim < dims; dim++) {
+      output_indices_t(index, dim) = input_indices_t(i, dim) - start[dim];
+    }
+    index++;
+  }
+
+  return SparseTensor(output_indices, output_values, output_shape);
+}
+
 }  // namespace sparse
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/util/sparse/sparse_tensor_test.cc b/tensorflow/core/util/sparse/sparse_tensor_test.cc
index 5edd6cb1d8..efdd97fd3d 100644
--- a/tensorflow/core/util/sparse/sparse_tensor_test.cc
+++ b/tensorflow/core/util/sparse/sparse_tensor_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include <string>
 #include <vector>
 
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -26,6 +25,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/test_benchmark.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 
 namespace tensorflow {
 namespace sparse {
@@ -612,6 +612,50 @@ TEST(SparseTensorTest, Split) {
   EXPECT_EQ(st_list[1].indices().matrix<int64>()(0, 1), 0);
 }
 
+TEST(SparseTensorTest, Slice) {
+  const int N = 4;
+  const int DIM = 2;
+
+  Tensor ids(DT_INT64, TensorShape({N, DIM}));
+  Tensor vals(DT_INT64, TensorShape({N}));
+
+  ids.matrix<int64>()(0, 0) = 0;
+  ids.matrix<int64>()(0, 1) = 0;
+  ids.matrix<int64>()(1, 0) = 1;
+  ids.matrix<int64>()(1, 1) = 1;
+  ids.matrix<int64>()(2, 0) = 1;
+  ids.matrix<int64>()(2, 1) = 2;
+  ids.matrix<int64>()(3, 0) = 3;
+  ids.matrix<int64>()(3, 1) = 0;
+
+  vals.vec<int64>()(0) = 1;
+  vals.vec<int64>()(1) = 2;
+  vals.vec<int64>()(2) = 3;
+  vals.vec<int64>()(3) = 4;
+
+  SparseTensor st(ids, vals, TensorShape({4, 3}));
+
+  std::vector<int64> start(2, 0);
+  std::vector<int64> size(2);
+  size[0] = 2;
+  size[1] = 3;
+
+  SparseTensor slice = SparseTensor::Slice<int64>(st, start, size);
+
+  EXPECT_EQ(TensorShape(slice.shape()), TensorShape({2, 3}));
+  EXPECT_EQ(slice.values().NumElements(), 3);
+  EXPECT_EQ(slice.values().vec<int64>()(0), 1);
+  EXPECT_EQ(slice.values().vec<int64>()(1), 2);
+  EXPECT_EQ(slice.values().vec<int64>()(2), 3);
+  EXPECT_EQ(slice.indices().NumElements(), 6);
+  EXPECT_EQ(slice.indices().matrix<int64>()(0, 0), 0);
+  EXPECT_EQ(slice.indices().matrix<int64>()(0, 1), 0);
+  EXPECT_EQ(slice.indices().matrix<int64>()(1, 0), 1);
+  EXPECT_EQ(slice.indices().matrix<int64>()(1, 1), 1);
+  EXPECT_EQ(slice.indices().matrix<int64>()(2, 0), 1);
+  EXPECT_EQ(slice.indices().matrix<int64>()(2, 1), 2);
+}
+
 TEST(SparseTensorTest, Dim0SparseTensorToDenseTensor) {
   Tensor ix(DT_INT64, TensorShape({1, 0}));
   Tensor vals(DT_INT32, TensorShape({1}));
diff --git a/tensorflow/core/util/tensor_bundle/BUILD b/tensorflow/core/util/tensor_bundle/BUILD
index ca92713a62..1045cf84b4 100644
--- a/tensorflow/core/util/tensor_bundle/BUILD
+++ b/tensorflow/core/util/tensor_bundle/BUILD
@@ -11,6 +11,7 @@ licenses(["notice"])  # Apache 2.0
 load(
     "//tensorflow:tensorflow.bzl",
     "cc_header_only_library",
+    "if_not_windows",
     "tf_copts",
 )
 
@@ -34,7 +35,7 @@ cc_library(
     name = "tensor_bundle",
     srcs = ["tensor_bundle.cc"],
     hdrs = ["tensor_bundle.h"],
-    copts = tf_copts() + ["-Wno-sign-compare"],
+    copts = tf_copts() + if_not_windows(["-Wno-sign-compare"]),
     deps = [
         ":naming",
         "//tensorflow/core:core_cpu_internal",
diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md
index 3d9f203297..b3c7a0c010 100644
--- a/tensorflow/docs_src/api_guides/python/math_ops.md
+++ b/tensorflow/docs_src/api_guides/python/math_ops.md
@@ -61,6 +61,9 @@ mathematical functions to your graph.
 *   @{tf.atan}
 *   @{tf.cosh}
 *   @{tf.sinh}
+*   @{tf.asinh}
+*   @{tf.acosh}
+*   @{tf.atanh}
 *   @{tf.lgamma}
 *   @{tf.digamma}
 *   @{tf.erf}
diff --git a/tensorflow/docs_src/community/documentation.md b/tensorflow/docs_src/community/documentation.md
index 31a10d1f15..a757d8c66d 100644
--- a/tensorflow/docs_src/community/documentation.md
+++ b/tensorflow/docs_src/community/documentation.md
@@ -276,7 +276,7 @@ __init__.py:
     # Otherwise import symbols directly
     from tensorflow.some_module.some_other_file import some_symbol
 
-    from tensorflow.platform.all_util import remove_undocumented
+    from tensorflow.python.util.all_util import remove_undocumented
 
     _allowed_symbols = [‘some_symbol’, ‘some_other_symbol’]
 
diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md
index 4c8c4e1a97..194649a304 100644
--- a/tensorflow/docs_src/community/welcome.md
+++ b/tensorflow/docs_src/community/welcome.md
@@ -25,6 +25,10 @@ The TensorFlow community has created many great projects around TensorFlow, incl
 * [Bitfusion's` GPU-enabled AWS EC2 TensorFlow AMI](https://github.com/bitfusionio/amis/tree/master/awsmrkt-bfboost-ubuntu14-cuda75-tensorflow) ([Launch AMI](https://aws.amazon.com/marketplace/pp/B01EYKBEQ0))
 * [Rust language bindings](https://github.com/google/tensorflow-rust)
 * [Operator Vectorization Library](https://github.com/opveclib/opveclib)
+* [Swift language bindings](https://github.com/PerfectlySoft/Perfect-TensorFlow)
+* [Sublime Tensorflow - A plugin for Sublime Text](https://github.com/baptisteArnaud/Sublime-Tensorflow)
+* [Edward - A library for probabilistic modeling, inference, and criticism](http://edwardlib.org) ([Github](https://github.com/blei-lab/edward), [Forum](https://discourse.edwardlib.org))
+* [GPflow - Gaussian processes in TensorFlow](https://github.com/GPflow/GPflow)
 
 ## TensorFlow Communities Around the World
 
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index d1c9cd696c..17d07cc582 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -295,7 +295,6 @@ next section.
 The completed trainable linear regression model is shown here:
 
 ```python
-import numpy as np
 import tensorflow as tf
 
 # Model parameters
@@ -305,14 +304,16 @@ b = tf.Variable([-.3], dtype=tf.float32)
 x = tf.placeholder(tf.float32)
 linear_model = W * x + b
 y = tf.placeholder(tf.float32)
+
 # loss
 loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
 # optimizer
 optimizer = tf.train.GradientDescentOptimizer(0.01)
 train = optimizer.minimize(loss)
+
 # training data
-x_train = [1,2,3,4]
-y_train = [0,-1,-2,-3]
+x_train = [1, 2, 3, 4]
+y_train = [0, -1, -2, -3]
 # training loop
 init = tf.global_variables_initializer()
 sess = tf.Session()
@@ -329,9 +330,9 @@ When run, it produces
 W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11
 ```
 
-Notice that the loss is a very small number (close to zero). If you run this
-program your loss will not be exactly the same, because the model is initialized
-with random values.
+Notice that the loss is a very small number (very close to zero). If you run this
+program, your loss may not be the exact same because the model is initialized
+with pseudorandom values.
 
 This more complicated program can still be visualized in TensorBoard
 ![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png)
@@ -394,8 +395,8 @@ print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-    train metrics: {'global_step': 1000, 'loss': 4.3049088e-08}
-    eval metrics: {'global_step': 1000, 'loss': 0.0025487561}
+train metrics: {'loss': 1.2712867e-09, 'global_step': 1000}
+eval metrics: {'loss': 0.0025279333, 'global_step': 1000}
 ```
 Notice how our eval data has a higher loss, but it is still close to zero.
 That means we are learning properly.
@@ -425,7 +426,7 @@ def model_fn(features, labels, mode):
   # Build a linear model and predict values
   W = tf.get_variable("W", [1], dtype=tf.float64)
   b = tf.get_variable("b", [1], dtype=tf.float64)
-  y = W*features['x'] + b
+  y = W * features['x'] + b
   # Loss sub-graph
   loss = tf.reduce_sum(tf.square(y - labels))
   # Training sub-graph
@@ -464,8 +465,8 @@ print("eval metrics: %r"% eval_metrics)
 ```
 When run, it produces
 ```
-train metrics: {'global_step': 1000, 'loss': 4.9380226e-11}
-eval metrics: {'global_step': 1000, 'loss': 0.01010081}
+train metrics: {'loss': 1.227995e-11, 'global_step': 1000}
+eval metrics: {'loss': 0.01010036, 'global_step': 1000}
 ```
 
 Notice how the contents of the custom `model_fn()` function are very similar
diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 4858b3cf92..193dd41b2a 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -95,7 +95,7 @@ We can flatten this array into a vector of 28x28 = 784 numbers. It doesn't
 matter how we flatten the array, as long as we're consistent between images.
 From this perspective, the MNIST images are just a bunch of points in a
 784-dimensional vector space, with a
-[very rich structure](http://colah.github.io/posts/2014-10-Visualizing-MNIST/)
+[very rich structure](https://colah.github.io/posts/2014-10-Visualizing-MNIST/)
 (warning: computationally intensive visualizations).
 
 Flattening the data throws away information about the 2D structure of the image.
@@ -231,7 +231,7 @@ Now let's turn that into something that TensorFlow can use.
 
 
 To do efficient numerical computing in Python, we typically use libraries like
-[NumPy](http://www.numpy.org/) that do expensive operations such as matrix
+[NumPy](http://www.numpy.org) that do expensive operations such as matrix
 multiplication outside Python, using highly efficient code implemented in
 another language.  Unfortunately, there can still be a lot of overhead from
 switching back to Python every operation. This overhead is especially bad if you
@@ -324,7 +324,7 @@ distribution (the one-hot vector with the digit labels).  In some rough sense, t
 cross-entropy is measuring how inefficient our predictions are for describing
 the truth. Going into more detail about cross-entropy is beyond the scope of
 this tutorial, but it's well worth
-[understanding](http://colah.github.io/posts/2015-09-Visual-Information/).
+[understanding](https://colah.github.io/posts/2015-09-Visual-Information).
 
 To implement cross-entropy we need to first add a new placeholder to input the
 correct answers:
@@ -356,7 +356,7 @@ instead.
 Now that we know what we want our model to do, it's very easy to have TensorFlow
 train it to do so.  Because TensorFlow knows the entire graph of your
 computations, it can automatically use the
-[backpropagation algorithm](http://colah.github.io/posts/2015-08-Backprop/) to
+[backpropagation algorithm](https://colah.github.io/posts/2015-08-Backprop) to
 efficiently determine how your variables affect the loss you ask it to
 minimize. Then it can apply your choice of optimization algorithm to modify the
 variables and reduce the loss.
@@ -447,7 +447,7 @@ Is that good? Well, not really. In fact, it's pretty bad. This is because we're
 using a very simple model. With some small changes, we can get to 97%. The best
 models can get to over 99.7% accuracy! (For more information, have a look at
 this
-[list of results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html).)
+[list of results](https://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results).)
 
 What matters is that we learned from this model. Still, if you're feeling a bit
 down about these results, check out
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 81aa6e3f76..e971a03d36 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -1,7 +1,7 @@
 # Installing TensorFlow for C
 
 TensorFlow provides a C API defined in
-[`c_api.h`](https://github.com/tensorflow/tensorflow/tree/master/c/c_api.h),
+[`c_api.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h),
 which is suitable for
 [building bindings for other languages](https://www.tensorflow.org/extend/language_bindings).
 The API leans towards simplicity and uniformity rather than convenience.
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.1.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 3f9096b822..6fb8f7eb55 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.1.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 40ed9e1826..a8e6356f9d 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0</version>
+  <version>1.2.1</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0</version>
+                 <version>1.2.1</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.1.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.1.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.1.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.1.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.1.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.1.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 99f27d7b85..c371712f57 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -171,8 +171,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
-     <pre>(tensorflow)$ <b>pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
+     <pre>(tensorflow)$ <b>pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -276,8 +276,8 @@ take the following steps:
      the following command:
 
      <pre>
-     $ <b>sudo pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
+     $ <b>sudo pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -632,14 +632,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -651,14 +651,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -670,14 +670,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -689,14 +689,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 8ff0fb872f..c8a9c49559 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -108,8 +108,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
+     <pre> $ <b>pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -229,8 +229,8 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-     <pre> $ <b>sudo pip install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
+     <pre> $ <b>sudo pip3 install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index a082c3ce78..37ef3816af 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -342,10 +342,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0 on Linux:
+for TensorFlow 1.2.1 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.1-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 8282afaab4..221e6d4ff1 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -115,12 +115,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/docs_src/programmers_guide/embedding.md b/tensorflow/docs_src/programmers_guide/embedding.md
index 975850349f..91beb36f96 100644
--- a/tensorflow/docs_src/programmers_guide/embedding.md
+++ b/tensorflow/docs_src/programmers_guide/embedding.md
@@ -84,7 +84,7 @@ labels/images to the data points. You can do this by generating a
 [metadata file](#metadata) containing the labels for each point and configuring
 the projector either by using our Python API, or manually constructing and
 saving a
-<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/plugins/projector/projector_config.proto)</code>
+<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto)</code>
 in the same directory as your checkpoint file.
 
 ### Setup
@@ -113,7 +113,7 @@ saver.save(session, os.path.join(LOG_DIR, "model.ckpt"), step)
 
 If you have any metadata (labels, images) associated with your embedding, you
 can tell TensorBoard about it either by directly storing a
-<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/plugins/projector/projector_config.proto)</code>
+<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto)</code>
 in the <code>LOG_DIR</code>, or use our python API.
 
 For instance, the following <code>projector_config.ptxt</code> associates the
diff --git a/tensorflow/docs_src/programmers_guide/threading_and_queues.md b/tensorflow/docs_src/programmers_guide/threading_and_queues.md
index 7f3e6f0da5..3483c7533c 100644
--- a/tensorflow/docs_src/programmers_guide/threading_and_queues.md
+++ b/tensorflow/docs_src/programmers_guide/threading_and_queues.md
@@ -97,6 +97,9 @@ Any thread can decide that the computation should stop.  It only has to call
 return `True`.
 
 ```python
+# Using Python's threading library.
+import threading
+
 # Thread body: loop until the coordinator indicates a stop was requested.
 # If some condition becomes true, ask the coordinator to stop.
 def MyLoop(coord):
diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index a7b7db626a..0ccd2c4899 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -410,7 +410,7 @@ calling this function repeatedly would not work:
 ``` python
 input1 = tf.random_normal([1,10,10,32])
 input2 = tf.random_normal([1,20,20,32])
-x = conv_relu(input1, kernel_shape=[5, 5, 32, 32], bias_shape=[32])
+x = conv_relu(input1, kernel_shape=[5, 5, 1, 32], bias_shape=[32])
 x = conv_relu(x, kernel_shape=[5, 5, 32, 32], bias_shape = [32])  # This fails.
 ```
 
@@ -422,7 +422,7 @@ however, clarifies that we want to create new variables:
 def my_image_filter(input_images):
     with tf.variable_scope("conv1"):
         # Variables created here will be named "conv1/weights", "conv1/biases".
-        relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
+        relu1 = conv_relu(input_images, [5, 5, 1, 32], [32])
     with tf.variable_scope("conv2"):
         # Variables created here will be named "conv2/weights", "conv2/biases".
         return conv_relu(relu1, [5, 5, 32, 32], [32])
diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/programmers_guide/version_compat.md
index 13c410fada..db6d596acf 100644
--- a/tensorflow/docs_src/programmers_guide/version_compat.md
+++ b/tensorflow/docs_src/programmers_guide/version_compat.md
@@ -54,7 +54,7 @@ patch versions.  The public APIs consist of
     * [`event`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/event.proto)
     * [`graph`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto)
     * [`op_def`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto)
-    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/reader_base.proto)
+    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/reader_base.proto)
     * [`summary`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto)
     * [`tensor`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto)
     * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto)
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index adf66b904e..a9802b0849 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -178,7 +178,7 @@ the network architecture to return normalized predictions using
 @{tf.nn.softmax}.
 
 The `inputs()` and `inference()` functions provide all the components
-necessary to perform evaluation on a model. We now shift our focus towards
+necessary to perform an evaluation of a model. We now shift our focus towards
 building operations for training a model.
 
 > **EXERCISE:** The model architecture in `inference()` differs slightly from
diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md
index dd2ca8d524..84c8a9c9f3 100644
--- a/tensorflow/docs_src/tutorials/seq2seq.md
+++ b/tensorflow/docs_src/tutorials/seq2seq.md
@@ -140,7 +140,7 @@ When training models with large output vocabularies, i.e., when
 tensors. Instead, it is better to return smaller output tensors, which will
 later be projected onto a large output tensor using `output_projection`.
 This allows to use our seq2seq models with a sampled softmax loss, as described
-in [Jean et. al., 2014](http://arxiv.org/abs/1412.2007)
+in [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
 ([pdf](http://arxiv.org/pdf/1412.2007.pdf)).
 
 In addition to `basic_rnn_seq2seq` and `embedding_rnn_seq2seq` there are a few
diff --git a/tensorflow/examples/ios/README.md b/tensorflow/examples/ios/README.md
index a412381196..7974b8c879 100644
--- a/tensorflow/examples/ios/README.md
+++ b/tensorflow/examples/ios/README.md
@@ -66,7 +66,7 @@ target 'YourProjectName'
 ```
 
  - Then you run ```pod install``` to download and install the
- TensorFlow-experimental pod, and finaly perform
+ TensorFlow-experimental pod, and finally perform
  ```open YourProjectName.xcworkspace``` and add your code.
 
  - In your apps "Build Settings", make sure to add $(inherited) to sections
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 0025692146..5b53bd81f7 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14306,6 +14306,51 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// Computes inverse hyperbolic sine of x element-wise.
+func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Asinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes inverse hyperbolic cosine of x element-wise.
+func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Acosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes inverse hyperbolic tangent of x element-wise.
+func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
+  if scope.Err() != nil {
+    return
+  }
+  opspec := tf.OpSpec{
+    Type: "Atanh",
+    Input: []tf.Input{
+      x,
+    },
+  }
+  op := scope.AddOperation(opspec)
+  return op.Output(0)
+}
+
 // TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
 type TextLineReaderV2Attr func(optionalAttr)
 
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
index dff3a45463..8e6685ee0f 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Input.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Input.java
@@ -42,7 +42,7 @@ public interface Input {
    * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
    * used to obtain a symbolic handle that represents the computation of the input.
    *
-   * @see {@link OperationBuilder#addInput(Output)}.
+   * @see OperationBuilder#addInput(Output)
    */
   Output asOutput();
 }
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index 200350f7ae..d584085282 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -139,7 +139,7 @@ public final class Operation {
    *
    * @param name identifier of the list of tensors (of which there may be many) inputs to this
    *     operation.
-   * @returns the size of the list of Tensors produced by this named input.
+   * @return the size of the list of Tensors produced by this named input.
    * @throws IllegalArgumentException if this operation has no input with the provided name.
    */
   public int inputListLength(final String name) {
diff --git a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
index 8f7559d39e..15077ce439 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/OperationBuilder.java
@@ -63,6 +63,16 @@ public final class OperationBuilder {
     }
   }
 
+
+  /**
+   * Returns the builder to create an operation.
+   *
+   * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
+   * used to add a input to a {@link OperationBuilder}.
+   *
+   * @param input {@link Output} supposed to be the input of the OperationBuilder.
+   * @return the OperationBuilder instance for chaining.
+   */
   public OperationBuilder addInput(Output input) {
     Graph.Reference r = graph.ref();
     try {
@@ -256,6 +266,21 @@ public final class OperationBuilder {
     return this;
   }
 
+  public OperationBuilder setAttr(String name,  String[] value) {
+    Charset utf8 = Charset.forName("UTF-8");
+    Object[] objects = new Object[value.length];
+    for (int i = 0; i < value.length; ++i) {
+      objects[i] = value[i].getBytes(utf8);
+    }
+    Graph.Reference r = graph.ref();
+    try {
+      setAttrStringList(unsafeNativeHandle, name, objects);
+    } finally {
+      r.close();
+    }
+    return this;
+  }
+
   private long unsafeNativeHandle;
   private Graph graph;
 
@@ -273,10 +298,7 @@ public final class OperationBuilder {
 
   // The names of all the setAttr* family functions below correspond to the C library types, not the
   // Java library types. Roughly, setAttrFoo calls the TensorFlow C library function: TF_SetAttrFoo.
-  //
   // TODO(ashankar):
-  // - setAttrStringList: Which would take in an array of byte[] (java Strings will need to be UTF-8
-  //   encoded?)
   // - setAttrShapeList: Which would take in a long[][]
 
   private static native void setAttrString(long handle, String name, byte[] value);
@@ -302,4 +324,7 @@ public final class OperationBuilder {
   private static native void setAttrTensorList(long handle, String name, long[] tensorHandle);
 
   private static native void setAttrShape(long handle, String name, long[] shape, int numDims);
+
+  private static native void setAttrStringList(long handle, String name, Object[] value);
+
 }
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Session.java b/tensorflow/java/src/main/java/org/tensorflow/Session.java
index f73cded4e3..83a300a560 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Session.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Session.java
@@ -113,7 +113,7 @@ public final class Session implements AutoCloseable {
    *
    * <p>A Runner runs the necessary graph fragments to execute every {@link Operation} required to
    * evaluate the {@link Tensor}s to fetch. The {@link #feed(String,int,Tensor)} call allows callers
-   * to override the value of {@link Tensor}s in the graph by substituing the provided {@link
+   * to override the value of {@link Tensor}s in the graph by substituting the provided {@link
    * Tensor}s for the outputs of the operations provided to {@link #feed(String,int,Tensor)}.
    */
   public final class Runner {
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Shape.java b/tensorflow/java/src/main/java/org/tensorflow/Shape.java
index 90d6cf7b85..9aa92be111 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Shape.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Shape.java
@@ -77,7 +77,7 @@ public final class Shape {
     return shape[i];
   }
 
-  /** Succint description of the shape meant for debugging. */
+  /** Succinct description of the shape meant for debugging. */
   @Override
   public String toString() {
     if (shape == null) {
diff --git a/tensorflow/java/src/main/native/operation_builder_jni.cc b/tensorflow/java/src/main/native/operation_builder_jni.cc
index a7696182c7..37f01a943a 100644
--- a/tensorflow/java/src/main/native/operation_builder_jni.cc
+++ b/tensorflow/java/src/main/native/operation_builder_jni.cc
@@ -257,3 +257,30 @@ JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrShape(
   TF_SetAttrShape(d, cname, cvalue.get(), static_cast<int>(num_dims));
   env->ReleaseStringUTFChars(name, cname);
 }
+
+JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrStringList(
+        JNIEnv* env, jclass object, jlong handle, jstring name, jobjectArray values) {
+  TF_OperationDescription* d = requireHandle(env, handle);
+  if (d == nullptr) return;
+  const char* cname = env->GetStringUTFChars(name, nullptr);
+  int num_values = env->GetArrayLength(values);
+  static_assert(sizeof(jbyte) == 1,
+                "Require Java byte to be represented as a single byte");
+  std::unique_ptr<jbyteArray[]> jarrays(new jbyteArray[num_values]);
+  std::unique_ptr<jbyte*[]> jvalues(new jbyte*[num_values]);
+  std::unique_ptr<void*[]> cvalues(new void*[num_values]);
+  std::unique_ptr<size_t[]> lengths(new size_t[num_values]);
+
+  for (int i = 0; i < num_values; ++i) {
+    jbyteArray v = static_cast<jbyteArray>(env->GetObjectArrayElement(values, i));
+    jarrays[i] = v;
+    jvalues[i] = env->GetByteArrayElements(v, nullptr);
+    cvalues[i] = jvalues[i];
+    lengths[i] = static_cast<size_t>(env->GetArrayLength(v));
+  }
+  TF_SetAttrStringList(d, cname, cvalues.get(), lengths.get(), num_values);
+  for (int i = 0; i < num_values; ++i) {
+    env->ReleaseByteArrayElements(jarrays[i], jvalues[i], JNI_ABORT);
+  }
+  env->ReleaseStringUTFChars(name, cname);
+}
diff --git a/tensorflow/java/src/main/native/operation_builder_jni.h b/tensorflow/java/src/main/native/operation_builder_jni.h
index 9b64c32820..2e72bd68da 100644
--- a/tensorflow/java/src/main/native/operation_builder_jni.h
+++ b/tensorflow/java/src/main/native/operation_builder_jni.h
@@ -169,6 +169,14 @@ JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrTensorList(
 JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrShape(
     JNIEnv *, jclass, jlong, jstring, jlongArray, jint);
 
+/*
+ * Class:     org_tensorflow_OperationBuilder
+ * Method:    setAttrStringList
+ * Signature: (JLjava/lang/String;[L)V
+ */
+JNIEXPORT void JNICALL Java_org_tensorflow_OperationBuilder_setAttrStringList(
+    JNIEnv *, jclass, jlong, jstring, jobjectArray);
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif  // __cplusplus
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 40667dc92d..9a5f059061 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -267,7 +267,7 @@ cc_library(
 cc_binary(
     name = "framework/test_file_system.so",
     srcs = ["framework/test_file_system.cc"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     linkopts = select({
         "//conditions:default": [
             "-lm",
@@ -2689,7 +2689,7 @@ cc_library(
     name = "cpp_shape_inference",
     srcs = ["framework/cpp_shape_inference.cc"],
     hdrs = ["framework/cpp_shape_inference.h"],
-    copts = ["-Wno-sign-compare"],
+    copts = if_not_windows(["-Wno-sign-compare"]),
     visibility = ["//visibility:public"],
     deps = [
         ":cpp_shape_inference_proto_cc",
diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc
index 0e89ae2426..5c04a8c2a5 100644
--- a/tensorflow/python/client/tf_session_helper.cc
+++ b/tensorflow/python/client/tf_session_helper.cc
@@ -690,7 +690,7 @@ void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
 
   // Convert outputs to ndarrays (in scoped containers)
   std::vector<Safe_PyObjectPtr> py_outputs_safe;
-  for (int i = 0; i < outputs.size(); ++i) {
+  for (size_t i = 0; i < outputs.size(); ++i) {
     PyObject* py_array;
     s = TFTensorToPyArray(std::move(output_vals_safe[i]), &py_array);
     if (!s.ok()) {
@@ -702,7 +702,7 @@ void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
 
   // If we reach this point, we have successfully built a list of objects so we
   // can release them from the safe container into the return vector.
-  for (int i = 0; i < outputs.size(); ++i) {
+  for (size_t i = 0; i < outputs.size(); ++i) {
     py_outputs->push_back(py_outputs_safe[i].release());
   }
 }
diff --git a/tensorflow/python/estimator/canned/linear_testing_utils.py b/tensorflow/python/estimator/canned/linear_testing_utils.py
index 965ac8cbdd..c9bde91f9b 100644
--- a/tensorflow/python/estimator/canned/linear_testing_utils.py
+++ b/tensorflow/python/estimator/canned/linear_testing_utils.py
@@ -124,7 +124,7 @@ def sigmoid(x):
 
 
 class CheckPartitionerVarHook(session_run_hook.SessionRunHook):
-  """A `SessionRunHook` to check a paritioned variable."""
+  """A `SessionRunHook` to check a partitioned variable."""
 
   def __init__(self, test_case, var_name, var_dim, partitions):
     self._test_case = test_case
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 7fbbe204b0..638ed7ef4b 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -3915,6 +3915,9 @@ class _DefaultStack(threading.local):
   def reset(self):
     self.stack = []
 
+  def is_cleared(self):
+    return self.stack == []
+
   @property
   def enforce_nesting(self):
     return self._enforce_nesting
@@ -4120,7 +4123,13 @@ def reset_default_graph():
   a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
   behavior. Using any previously created `tf.Operation` or `tf.Tensor` objects
   after calling this function will result in undefined behavior.
+  Raises:
+    AssertionError: If this function is called within a nested graph.
   """
+  if not _default_graph_stack.is_cleared():
+    raise AssertionError("Do not use tf.reset_default_graph() to clear "
+                         "nested graphs. If you need a cleared graph, "
+                         "exit the nesting and create a new graph.")
   _default_graph_stack.reset()
 
 
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 2d0a6cbf8a..01841e6306 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -1315,6 +1315,12 @@ class GraphTest(test_util.TensorFlowTestCase):
   def _AssertDefault(self, expected):
     self.assertIs(expected, ops.get_default_graph())
 
+  def testResetDefaultGraphNesting(self):
+    g0 = ops.Graph()
+    with self.assertRaises(AssertionError):
+      with g0.as_default():
+        ops.reset_default_graph()
+
   def testGraphContextManager(self):
     g0 = ops.Graph()
     with g0.as_default() as g1:
diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py
index 155fa4719b..66c05335b4 100644
--- a/tensorflow/python/framework/tensor_shape.py
+++ b/tensorflow/python/framework/tensor_shape.py
@@ -365,7 +365,7 @@ class Dimension(object):
 def as_dimension(value):
   """Converts the given value to a Dimension.
 
-  A Dimenson input will be returned unmodified.
+  A Dimension input will be returned unmodified.
   An input of `None` will be converted to an unknown Dimension.
   An integer input will be converted to a Dimension with that value.
 
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 71328a7f88..5ba226ce07 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -266,6 +266,13 @@ class TensorFlowTestCase(googletest.TestCase):
     self._ClearCachedSession()
     random.seed(random_seed.DEFAULT_GRAPH_SEED)
     np.random.seed(random_seed.DEFAULT_GRAPH_SEED)
+    # Note: The following line is necessary because some test methods may error
+    # out from within nested graph contexts (e.g., via assertRaises and
+    # assertRaisesRegexp), which may leave ops._default_graph_stack non-empty
+    # under certain versions of Python. That would cause
+    # ops.reset_default_graph() to throw an exception if the stack were not
+    # cleared first.
+    ops._default_graph_stack.reset()  # pylint: disable=protected-access
     ops.reset_default_graph()
     ops.get_default_graph().seed = random_seed.DEFAULT_GRAPH_SEED
 
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index dc52cedb80..17b646d17c 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -728,6 +728,18 @@ tf_py_test(
     ],
 )
 
+tf_py_test(
+    name = "sparse_slice_op_test",
+    size = "small",
+    srcs = ["sparse_slice_op_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:sparse_ops",
+    ],
+)
+
 tf_py_test(
     name = "sparse_to_dense_op_py_test",
     size = "small",
diff --git a/tensorflow/python/kernel_tests/basic_gpu_test.py b/tensorflow/python/kernel_tests/basic_gpu_test.py
index b5b17ff80a..155aad8bd9 100644
--- a/tensorflow/python/kernel_tests/basic_gpu_test.py
+++ b/tensorflow/python/kernel_tests/basic_gpu_test.py
@@ -107,9 +107,12 @@ class MathBuiltinUnaryTest(test.TestCase):
 
   def _testDtype(self, dtype, use_gpu):
     data = (np.arange(-3, 3) / 4.).reshape([1, 3, 2]).astype(dtype)
+    data_gt_1 = data + 2 # for x > 1
     self._compare(data, np.abs, math_ops.abs, use_gpu)
     self._compare(data, np.arccos, math_ops.acos, use_gpu)
     self._compare(data, np.arcsin, math_ops.asin, use_gpu)
+    self._compare(data, np.arcsinh, math_ops.asinh, use_gpu)
+    self._compare(data_gt_1, np.arccosh, math_ops.acosh, use_gpu)
     self._compare(data, np.arctan, math_ops.atan, use_gpu)
     self._compare(data, np.ceil, math_ops.ceil, use_gpu)
     self._compare(data, np.cos, math_ops.cos, use_gpu)
@@ -126,6 +129,7 @@ class MathBuiltinUnaryTest(test.TestCase):
     self._compare(data, np.square, math_ops.square, use_gpu)
     self._compare(data, np.tan, math_ops.tan, use_gpu)
     self._compare(data, np.tanh, math_ops.tanh, use_gpu)
+    self._compare(data, np.arctanh, math_ops.atanh, use_gpu)
 
   def testTypes(self):
     for dtype in [np.float32]:
diff --git a/tensorflow/python/kernel_tests/cast_op_test.py b/tensorflow/python/kernel_tests/cast_op_test.py
index 17771e0572..c785f2358d 100644
--- a/tensorflow/python/kernel_tests/cast_op_test.py
+++ b/tensorflow/python/kernel_tests/cast_op_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 import sys
+import platform
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -146,9 +147,16 @@ class CastOpTest(test.TestCase):
     if sys.byteorder == "big":  
       self._compare(np.inf, np.int32, i4.max, False)  
       self._compare(np.inf, np.int64, i8.max, False)  
-    else:  
-      self._compare(np.inf, np.int32, i4.min, False)  
-      self._compare(np.inf, np.int64, i8.min, False)  
+    else:
+      # np.float64("np.inf").astype(np.int32) is negative on x86 but positive on ppc64le
+      # Numpy link to relevant discussion - https://github.com/numpy/numpy/issues/9040
+      # Tensorflow link to relevant discussion - https://github.com/tensorflow/tensorflow/issues/9360
+      if platform.machine() == "ppc64le":
+        self._compare(-np.inf, np.int32, i4.min, False)
+        self._compare(-np.inf, np.int64, i8.min, False)
+      else:
+        self._compare(np.inf, np.int32, i4.min, False)
+        self._compare(np.inf, np.int64, i8.min, False)  
     self._compare(-np.inf, np.float32, -np.inf, False)
     self._compare(-np.inf, np.float64, -np.inf, False)
     self._compare(-np.inf, np.int32, i4.min, False)
diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py
index b47139e6b8..5a0fb1879a 100644
--- a/tensorflow/python/kernel_tests/cwise_ops_test.py
+++ b/tensorflow/python/kernel_tests/cwise_ops_test.py
@@ -184,6 +184,7 @@ class UnaryOpTest(test.TestCase):
 
   def testFloatBasic(self):
     x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32)
+    w = x - x.min() + 1.01 # all greater than 1
     y = (x + .5).astype(np.float32)  # no zero
     z = (x + 15.5).astype(np.float32)  # all positive
     k = np.arange(-0.90, 0.90, 0.25).astype(np.float32)  # between -1 and 1
@@ -203,6 +204,10 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.sinh, math_ops.sinh)
     self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
+    # b/63457572: failing under CUDA.
+    # self._compareBoth(x, np.arcsinh, math_ops.asinh)
+    # self._compareBoth(w, np.arccosh, math_ops.acosh)
+    # self._compareBoth(k, np.arctanh, math_ops.atanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(x, self._log_sigmoid, math_ops.log_sigmoid)
     self._compareBoth(y, np.sign, math_ops.sign)
@@ -248,6 +253,8 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.log, math_ops.log)
     self._compareBoth(x, np.log1p, math_ops.log1p)
     self._compareBoth(x, np.sinh, math_ops.sinh)
+    # b/63457572.
+    # self._compareBoth(x, np.arcsinh, math_ops.asinh)
     self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
@@ -273,6 +280,7 @@ class UnaryOpTest(test.TestCase):
 
   def testDoubleBasic(self):
     x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64)
+    w = x - x.min() + 1.01 # all greater than 1
     y = (x + .5).astype(np.float64)  # no zero
     z = (x + 15.5).astype(np.float64)  # all positive
     k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype(
@@ -292,6 +300,10 @@ class UnaryOpTest(test.TestCase):
     self._compareBoth(x, np.sinh, math_ops.sinh)
     self._compareBoth(x, np.cosh, math_ops.cosh)
     self._compareBoth(x, np.tanh, math_ops.tanh)
+    # b/63457572: failing under CUDA.
+    # self._compareBoth(x, np.arcsinh, math_ops.asinh)
+    # self._compareBoth(w, np.arccosh, math_ops.acosh)
+    # self._compareBoth(k, np.arctanh, math_ops.atanh)
     self._compareBoth(x, self._sigmoid, math_ops.sigmoid)
     self._compareBoth(y, np.sign, math_ops.sign)
     self._compareBoth(x, np.sin, math_ops.sin)
@@ -398,6 +410,10 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.sinh, math_ops.sinh)
     self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
+    # b/63457572: failing under CUDA.
+    # self._compareCpu(x, np.arcsinh, math_ops.asinh)
+    # self._compareCpu(x, np.arccosh, math_ops.acosh)
+    # self._compareCpu(x, np.arctanh, math_ops.atanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
     self._compareCpu(x, np.cos, math_ops.cos)
@@ -434,6 +450,10 @@ class UnaryOpTest(test.TestCase):
     self._compareCpu(x, np.sinh, math_ops.sinh)
     self._compareCpu(x, np.cosh, math_ops.cosh)
     self._compareCpu(x, np.tanh, math_ops.tanh)
+    # b/63457572, failing under CUDA.
+    # self._compareCpu(x, np.arcsinh, math_ops.asinh)
+    # self._compareCpu(x, np.arccosh, math_ops.acosh)
+    # self._compareCpu(x, np.arctanh, math_ops.atanh)
     self._compareCpu(x, self._sigmoid, math_ops.sigmoid)
     self._compareCpu(x, np.sin, math_ops.sin)
     self._compareCpu(x, np.cos, math_ops.cos)
diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py
index 4ceb24862f..64f8f4bb6d 100644
--- a/tensorflow/python/kernel_tests/map_stage_op_test.py
+++ b/tensorflow/python/kernel_tests/map_stage_op_test.py
@@ -108,8 +108,8 @@ class MapStageTest(test.TestCase):
       with ops.device(gpu_dev):
         stager = data_flow_ops.MapStagingArea([dtypes.float32])
         y = stager.put(1, [v], [0])
-        self.assertEqual(y.device, '/device:GPU:0' if gpu_dev
-                                                   else gpu_dev)
+        expected_name = gpu_dev if 'gpu' not in gpu_dev else '/device:GPU:0'
+        self.assertEqual(y.device, expected_name)
       with ops.device('/cpu:0'):
         _, x = stager.get(1)
         y = stager.peek(1)
diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py
index 1b6c8bef98..f5fb7e4e03 100644
--- a/tensorflow/python/kernel_tests/pooling_ops_test.py
+++ b/tensorflow/python/kernel_tests/pooling_ops_test.py
@@ -521,7 +521,7 @@ class PoolingTest(test.TestCase):
               padding="SAME").eval()
 
   # The following are tests that verify that the CPU and GPU implementations
-  # produce the same resuts.
+  # produce the same results.
   def _CompareMaxPoolingFwd(self, input_shape, ksize, strides, padding):
     for dtype in np.float64, np.float32, np.float16:
       tensor_input = np.random.rand(*input_shape).astype(dtype)
diff --git a/tensorflow/python/kernel_tests/reader_ops_test.py b/tensorflow/python/kernel_tests/reader_ops_test.py
index c7e1d88360..5630259b7b 100644
--- a/tensorflow/python/kernel_tests/reader_ops_test.py
+++ b/tensorflow/python/kernel_tests/reader_ops_test.py
@@ -350,13 +350,11 @@ class FixedLengthRecordReaderTest(test.TestCase):
   def setUp(self):
     super(FixedLengthRecordReaderTest, self).setUp()
     self._num_files = 2
-    self._num_records = 7
     self._header_bytes = 5
     self._record_bytes = 3
     self._footer_bytes = 2
 
     self._hop_bytes = 2
-    self._num_overlapped_records = 3
 
   def _Record(self, f, r):
     return compat.as_bytes(str(f * 2 + r) * self._record_bytes)
@@ -369,19 +367,24 @@ class FixedLengthRecordReaderTest(test.TestCase):
     ])
     return compat.as_bytes(record_str)
 
-  def _CreateFiles(self):
+  # gap_bytes=hop_bytes-record_bytes
+  def _CreateFiles(self, num_records, gap_bytes):
     filenames = []
     for i in range(self._num_files):
       fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i)
       filenames.append(fn)
       with open(fn, "wb") as f:
         f.write(b"H" * self._header_bytes)
-        for j in range(self._num_records):
+        if num_records > 0:
+          f.write(self._Record(i, 0))
+        for j in range(1, num_records):
+          if gap_bytes > 0:
+            f.write(b"G" * gap_bytes)
           f.write(self._Record(i, j))
         f.write(b"F" * self._footer_bytes)
     return filenames
 
-  def _CreateOverlappedRecordFiles(self):
+  def _CreateOverlappedRecordFiles(self, num_overlapped_records):
     filenames = []
     for i in range(self._num_files):
       fn = os.path.join(self.get_temp_dir(),
@@ -389,23 +392,104 @@ class FixedLengthRecordReaderTest(test.TestCase):
       filenames.append(fn)
       with open(fn, "wb") as f:
         f.write(b"H" * self._header_bytes)
-        all_records_str = "".join([
-            str(i)[0]
-            for i in range(self._record_bytes + self._hop_bytes *
-                           (self._num_overlapped_records - 1))
-        ])
-        f.write(compat.as_bytes(all_records_str))
+        if num_overlapped_records > 0:
+          all_records_str = "".join([
+              str(i)[0]
+              for i in range(self._record_bytes + self._hop_bytes *
+                             (num_overlapped_records - 1))
+          ])
+          f.write(compat.as_bytes(all_records_str))
         f.write(b"F" * self._footer_bytes)
     return filenames
 
-  def testOneEpoch(self):
-    files = self._CreateFiles()
+  # gap_bytes=hop_bytes-record_bytes
+  def _CreateGzipFiles(self, num_records, gap_bytes):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i)
+      filenames.append(fn)
+      with gzip.GzipFile(fn, "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_records > 0:
+          f.write(self._Record(i, 0))
+        for j in range(1, num_records):
+          if gap_bytes > 0:
+            f.write(b"G" * gap_bytes)
+          f.write(self._Record(i, j))
+        f.write(b"F" * self._footer_bytes)
+    return filenames
+
+  # gap_bytes=hop_bytes-record_bytes
+  def _CreateZlibFiles(self, num_records, gap_bytes):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i)
+      filenames.append(fn)
+      with open(fn+".tmp", "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_records > 0:
+          f.write(self._Record(i, 0))
+        for j in range(1, num_records):
+          if gap_bytes > 0:
+            f.write(b"G" * gap_bytes)
+          f.write(self._Record(i, j))
+        f.write(b"F" * self._footer_bytes)
+      with open(fn+".tmp", "rb") as f:
+        cdata = zlib.compress(f.read())
+        with open(fn, "wb") as zf:
+          zf.write(cdata)
+    return filenames
+
+  def _CreateGzipOverlappedRecordFiles(self, num_overlapped_records):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(),
+                        "fixed_length_overlapped_record.%d.txt" % i)
+      filenames.append(fn)
+      with gzip.GzipFile(fn, "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_overlapped_records > 0:
+          all_records_str = "".join([
+              str(i)[0]
+              for i in range(self._record_bytes + self._hop_bytes *
+                           (num_overlapped_records - 1))
+          ])
+          f.write(compat.as_bytes(all_records_str))
+        f.write(b"F" * self._footer_bytes)
+    return filenames
+
+  def _CreateZlibOverlappedRecordFiles(self, num_overlapped_records):
+    filenames = []
+    for i in range(self._num_files):
+      fn = os.path.join(self.get_temp_dir(),
+                        "fixed_length_overlapped_record.%d.txt" % i)
+      filenames.append(fn)
+      with open(fn+".tmp", "wb") as f:
+        f.write(b"H" * self._header_bytes)
+        if num_overlapped_records > 0:
+          all_records_str = "".join([
+              str(i)[0]
+              for i in range(self._record_bytes + self._hop_bytes *
+                             (num_overlapped_records - 1))
+          ])
+          f.write(compat.as_bytes(all_records_str))
+        f.write(b"F" * self._footer_bytes)
+      with open(fn+".tmp", "rb") as f:
+        cdata = zlib.compress(f.read())
+        with open(fn, "wb") as zf:
+          zf.write(cdata)
+    return filenames
+
+  # gap_bytes=hop_bytes-record_bytes
+  def _TestOneEpoch(self, files, num_records, gap_bytes, encoding=None):
+    hop_bytes = 0 if gap_bytes == 0 else self._record_bytes + gap_bytes
     with self.test_session() as sess:
       reader = io_ops.FixedLengthRecordReader(
           header_bytes=self._header_bytes,
           record_bytes=self._record_bytes,
           footer_bytes=self._footer_bytes,
-          hop_bytes=0,
+          hop_bytes=hop_bytes,
+          encoding=encoding,
           name="test_reader")
       queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
       key, value = reader.read(queue)
@@ -413,7 +497,7 @@ class FixedLengthRecordReaderTest(test.TestCase):
       queue.enqueue_many([files]).run()
       queue.close().run()
       for i in range(self._num_files):
-        for j in range(self._num_records):
+        for j in range(num_records):
           k, v = sess.run([key, value])
           self.assertAllEqual("%s:%d" % (files[i], j), compat.as_text(k))
           self.assertAllEqual(self._Record(i, j), v)
@@ -422,14 +506,14 @@ class FixedLengthRecordReaderTest(test.TestCase):
                                     "\\(requested 1, current size 0\\)"):
         k, v = sess.run([key, value])
 
-  def testOneEpochWithHopBytes(self):
-    files = self._CreateOverlappedRecordFiles()
+  def _TestOneEpochWithHopBytes(self, files, num_overlapped_records, encoding=None):
     with self.test_session() as sess:
       reader = io_ops.FixedLengthRecordReader(
           header_bytes=self._header_bytes,
           record_bytes=self._record_bytes,
           footer_bytes=self._footer_bytes,
           hop_bytes=self._hop_bytes,
+          encoding=encoding,
           name="test_reader")
       queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=())
       key, value = reader.read(queue)
@@ -437,7 +521,7 @@ class FixedLengthRecordReaderTest(test.TestCase):
       queue.enqueue_many([files]).run()
       queue.close().run()
       for i in range(self._num_files):
-        for j in range(self._num_overlapped_records):
+        for j in range(num_overlapped_records):
           k, v = sess.run([key, value])
           print(v)
           self.assertAllEqual("%s:%d" % (files[i], j), compat.as_text(k))
@@ -447,6 +531,45 @@ class FixedLengthRecordReaderTest(test.TestCase):
                                     "\\(requested 1, current size 0\\)"):
         k, v = sess.run([key, value])
 
+  def testOneEpoch(self):
+    for num_records in [0, 7]:
+      # gap_bytes=0: hop_bytes=0
+      # gap_bytes=1: hop_bytes=record_bytes+1
+      for gap_bytes in [0, 1]:
+        files = self._CreateFiles(num_records, gap_bytes)
+        self._TestOneEpoch(files, num_records, gap_bytes)
+
+  def testGzipOneEpoch(self):
+    for num_records in [0, 7]:
+      # gap_bytes=0: hop_bytes=0
+      # gap_bytes=1: hop_bytes=record_bytes+1
+      for gap_bytes in [0, 1]:
+        files = self._CreateGzipFiles(num_records, gap_bytes)
+        self._TestOneEpoch(files, num_records, gap_bytes, encoding="GZIP")
+
+  def testZlibOneEpoch(self):
+    for num_records in [0, 7]:
+      # gap_bytes=0: hop_bytes=0
+      # gap_bytes=1: hop_bytes=record_bytes+1
+      for gap_bytes in [0, 1]:
+        files = self._CreateZlibFiles(num_records, gap_bytes)
+        self._TestOneEpoch(files, num_records, gap_bytes, encoding="ZLIB")
+
+  def testOneEpochWithHopBytes(self):
+    for num_overlapped_records in [0, 2]:
+      files = self._CreateOverlappedRecordFiles(num_overlapped_records)
+      self._TestOneEpochWithHopBytes(files, num_overlapped_records)
+
+  def testGzipOneEpochWithHopBytes(self):
+    for num_overlapped_records in [0, 2]:
+      files = self._CreateGzipOverlappedRecordFiles(num_overlapped_records, )
+      self._TestOneEpochWithHopBytes(files, num_overlapped_records, encoding="GZIP")
+
+  def testZlibOneEpochWithHopBytes(self):
+    for num_overlapped_records in [0, 2]:
+      files = self._CreateZlibOverlappedRecordFiles(num_overlapped_records)
+      self._TestOneEpochWithHopBytes(files, num_overlapped_records, encoding="ZLIB")
+
 
 class TFRecordReaderTest(test.TestCase):
 
diff --git a/tensorflow/python/kernel_tests/sparse_slice_op_test.py b/tensorflow/python/kernel_tests/sparse_slice_op_test.py
new file mode 100644
index 0000000000..762e400447
--- /dev/null
+++ b/tensorflow/python/kernel_tests/sparse_slice_op_test.py
@@ -0,0 +1,251 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for SparseReorder."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import sparse_ops
+from tensorflow.python.platform import test
+
+
+class SparseSliceOpTest(test.TestCase):
+
+  def _SparseTensor_4x6(self):
+    # [0 |  |2 |  |4 |5 ]
+    # [  |11|  |13|14|  ]
+    # [20|  |  |23|  |25]
+    # [30|  |32|33|  |35]
+    ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4],
+                    [2, 0], [2, 3], [2, 5], [3, 0], [3, 2], [3, 3],
+                    [3, 5]]).astype(np.int64)
+    val = np.array(
+        [0, 2, 4, 5, 11, 13, 14, 20, 23, 25, 30, 32, 33, 35]).astype(np.int64)
+    shape = np.array([4, 6]).astype(np.int64)
+    return sparse_tensor.SparseTensor(ind, val, shape)
+
+  def _SparseTensor_5x7(self):
+    # [0 |  |2 |  |4 |5 |  ]
+    # [  |11|  |13|14|  |16]
+    # [20|  |  |23|  |25|  ]
+    # [30|  |32|33|  |35|  ]
+    # [  |41|  |  |44|  |46]
+    ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4],
+                    [1, 6], [2, 0], [2, 3], [2, 5], [3, 0], [3, 2], [3, 3],
+                    [3, 5], [4, 1], [4, 4], [4, 6]]).astype(np.int64)
+    val = np.array(
+        [0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25, 30, 32, 33, 35, 41, 44,
+         46]).astype(np.int64)
+    shape = np.array([5, 7]).astype(np.int64)
+    return sparse_tensor.SparseTensor(ind, val, shape)
+
+  def _SparseTensorValue_3x4x2(self):
+    #  slice(:,:, 0)
+    #  ['a0'|    |'b0'|    ]
+    #  [    |'c0'|    |'d0']
+    #  [    |    |'e0'|    ]
+    #  slice(:,:, 1)
+    #  ['a1'|    |'b1'|    ]
+    #  [    |'c1'|    |'d1']
+    #  [    |    |'e1'|    ]
+    ind = np.array([[0, 0, 0], [0, 0, 1], [0, 2, 0], [0, 2, 1], [1, 1, 0],
+                    [1, 1, 1], [1, 3, 0], [1, 3, 1], [2, 2, 0],
+                    [2, 2, 1]]).astype(np.int64)
+    val = np.array(['a0', 'a1', 'b0', 'b1', 'c0', 'c1', 'd0', 'd1', 'e0', 'e1'])
+    shape = np.array([3, 4, 2]).astype(np.int64)
+    return sparse_tensor.SparseTensorValue(ind, val, shape)
+
+  def _SparseTensor_3x4x2(self):
+    return sparse_tensor.SparseTensor.from_value(self._SparseTensorValue_3x4x2(
+    ))
+
+  def testSliceMatrixRows(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [2, 6])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [2, 0], [3, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(), [[0, 0], [0, 2], [0, 4],
+                                                      [0, 5], [1, 1], [1, 3],
+                                                      [1, 4]])
+      self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5, 11, 13, 14])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [2, 6])
+      self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 0], [0, 3], [0, 5],
+                                                      [1, 0], [1, 2], [1, 3],
+                                                      [1, 5]])
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [20, 23, 25, 30, 32, 33, 35])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 6])
+
+  def testSliceMatrixUnevenCols(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_5x7()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [5, 3])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 3], [5, 2])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 5], [5, 2])
+
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [0, 2], [1, 1], [2, 0], [3, 0], [3, 2],
+                           [4, 1]])
+      self.assertAllEqual(sp_tensor0.values.eval(),
+                          [0, 2, 11, 20, 30, 32, 41])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [5, 3])
+      self.assertAllEqual(sp_tensor1.indices.eval(),
+                          [[0, 1], [1, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [4, 13, 14, 23, 33, 44])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor2.indices.eval(),
+                          [[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [5, 16, 25, 35, 46])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [5, 2])
+
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [5, 2])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 2], [5, 2])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 2])
+      sp_tensor3 = sparse_ops.sparse_slice(sp_input, [0, 6], [5, 2])
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
+      self.assertAllEqual(sp_tensor0.values.eval(), [0, 11, 20, 30, 41])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor1.indices.eval(),
+                          [[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
+      self.assertAllEqual(sp_tensor1.values.eval(), [2, 13, 23, 32, 33])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor2.indices.eval(),
+                          [[0, 0], [0, 1], [1, 0], [2, 1], [3, 1], [4, 0]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [4, 5, 14, 25, 35, 44])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [5, 2])
+      self.assertAllEqual(sp_tensor3.indices.eval(), [[1, 0], [4, 0]])
+      self.assertAllEqual(sp_tensor3.values.eval(), [16, 46])
+      self.assertAllEqual(sp_tensor3.dense_shape.eval(), [5, 1])
+
+  def testSliceMatrixUnevenRows(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_5x7()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [3, 7])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [3, 0], [3, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
+                           [1, 4], [1, 6], [2, 0], [2, 3], [2, 5]])
+      self.assertAllEqual(sp_tensor0.values.eval(),
+                          [0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [3, 7])
+      self.assertAllEqual(sp_tensor1.indices.eval(),
+                          [[0, 0], [0, 2], [0, 3], [0, 5], [1, 1], [1, 4],
+                           [1, 6]])
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [30, 32, 33, 35, 41, 44, 46])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 7])
+
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [2, 7])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [2, 0], [2, 7])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [4, 0], [2, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(),
+                          [[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
+                           [1, 4], [1, 6]])
+      self.assertAllEqual(sp_tensor0.values.eval(),
+                          [0, 2, 4, 5, 11, 13, 14, 16])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [2, 7])
+
+      self.assertAllEqual(sp_tensor1.values.eval(),
+                          [20, 23, 25, 30, 32, 33, 35])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 7])
+      self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 1], [0, 4],
+                                                           [0, 6]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [41, 44, 46])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [1, 7])
+    return
+
+  def testSliceAllRows(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [1, 6])
+      sp_tensor1 = sparse_ops.sparse_slice(sp_input, [1, 0], [1, 6])
+      sp_tensor2 = sparse_ops.sparse_slice(sp_input, [2, 0], [1, 7])
+      sp_tensor3 = sparse_ops.sparse_slice(sp_input, [3, 0], [2, 7])
+      self.assertAllEqual(sp_tensor0.indices.eval(), [[0, 0], [0, 2], [0, 4],
+                                                         [0, 5]])
+      self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5])
+      self.assertAllEqual(sp_tensor0.dense_shape.eval(), [1, 6])
+      self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 1], [0, 3], [0,
+                                                                          4]])
+      self.assertAllEqual(sp_tensor1.values.eval(), [11, 13, 14])
+      self.assertAllEqual(sp_tensor1.dense_shape.eval(), [1, 6])
+      self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 0], [0, 3], [0,
+                                                                          5]])
+      self.assertAllEqual(sp_tensor2.values.eval(), [20, 23, 25])
+      self.assertAllEqual(sp_tensor2.dense_shape.eval(), [1, 6])
+      self.assertAllEqual(sp_tensor3.indices.eval(), [[0, 0], [0, 2], [0, 3],
+                                                         [0, 5]])
+      self.assertAllEqual(sp_tensor3.values.eval(), [30, 32, 33, 35])
+      self.assertAllEqual(sp_tensor3.dense_shape.eval(), [1, 6])
+
+  def testSliceColumns(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sparse_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [4, 2])
+      sparse_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 2], [5, 2])
+      sparse_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 3])
+
+      self.assertAllEqual(sparse_tensor0.indices.eval(), [[0, 0], [1, 1],
+                                                             [2, 0], [3, 0]])
+      self.assertAllEqual(sparse_tensor0.values.eval(), [0, 11, 20, 30])
+      self.assertAllEqual(sparse_tensor0.dense_shape.eval(), [4, 2])
+      self.assertAllEqual(sparse_tensor1.indices.eval(),
+                          [[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
+      self.assertAllEqual(sparse_tensor1.values.eval(), [2, 13, 23, 32, 33])
+      self.assertAllEqual(sparse_tensor1.dense_shape.eval(), [4, 2])
+      self.assertAllEqual(sparse_tensor2.indices.eval(),
+                          [[0, 0], [0, 1], [1, 0], [2, 1], [3, 1]])
+      self.assertAllEqual(sparse_tensor2.values.eval(), [4, 5, 14, 25, 35])
+      self.assertAllEqual(sparse_tensor2.dense_shape.eval(), [4, 2])
+
+  def testSliceAllColumns(self):
+    with self.test_session(use_gpu=False):
+      sp_input=self._SparseTensor_4x6()
+      sparse_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [4, 1])
+      sparse_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 1], [4, 1])
+      sparse_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 2], [4, 1])
+      sparse_tensor3 = sparse_ops.sparse_slice(sp_input, [0, 3], [4, 1])
+      sparse_tensor4 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 1])
+      sparse_tensor5 = sparse_ops.sparse_slice(sp_input, [0, 5], [6, 3])
+      self.assertAllEqual(sparse_tensor0.indices.eval(), [[0, 0], [2, 0],
+                                                             [3, 0]])
+      self.assertAllEqual(sparse_tensor0.values.eval(), [0, 20, 30])
+      self.assertAllEqual(sparse_tensor0.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor1.indices.eval(), [[1, 0]])
+      self.assertAllEqual(sparse_tensor1.values.eval(), [11])
+      self.assertAllEqual(sparse_tensor1.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor2.indices.eval(), [[0, 0], [3, 0]])
+      self.assertAllEqual(sparse_tensor2.values.eval(), [2, 32])
+      self.assertAllEqual(sparse_tensor2.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor3.indices.eval(), [[1, 0], [2, 0],
+                                                             [3, 0]])
+      self.assertAllEqual(sparse_tensor3.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor3.values.eval(), [13, 23, 33])
+      self.assertAllEqual(sparse_tensor4.indices.eval(), [[0, 0], [1, 0]])
+      self.assertAllEqual(sparse_tensor4.values.eval(), [4, 14])
+      self.assertAllEqual(sparse_tensor4.dense_shape.eval(), [4, 1])
+      self.assertAllEqual(sparse_tensor5.indices.eval(), [[0, 0], [2, 0],
+                                                             [3, 0]])
+      self.assertAllEqual(sparse_tensor5.values.eval(), [5, 25, 35])
+      self.assertAllEqual(sparse_tensor5.dense_shape.eval(), [4, 1])
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py
index 1a6a869e3d..4073fc6b79 100644
--- a/tensorflow/python/kernel_tests/stage_op_test.py
+++ b/tensorflow/python/kernel_tests/stage_op_test.py
@@ -102,8 +102,8 @@ class StageTest(test.TestCase):
       with ops.device(gpu_dev):
         stager = data_flow_ops.StagingArea([dtypes.float32])
         y = stager.put([v])
-        self.assertEqual(y.device, '/device:GPU:0' if gpu_dev
-                                                   else gpu_dev)
+        expected_name = gpu_dev if 'gpu' not in gpu_dev else '/device:GPU:0'
+        self.assertEqual(y.device, expected_name)
       with ops.device('/cpu:0'):
         x = stager.get()
         self.assertEqual(x.device, '/device:CPU:0')
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index 36491fc9c7..ebf108d8ca 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -27,6 +27,7 @@ import collections
 import copy
 import functools
 import re
+import weakref
 
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import numpy as np
@@ -681,8 +682,7 @@ def _object_list_uid(object_list):
 # A global dictionary mapping graph objects to an index of counters used
 # for various layer names in each graph.
 # Allows to give unique autogenerated names to layers, in a graph-specific way.
-PER_GRAPH_LAYER_NAME_UIDS = collections.defaultdict(
-    lambda: collections.defaultdict(int))
+PER_GRAPH_LAYER_NAME_UIDS = weakref.WeakKeyDictionary()
 
 
 def _unique_layer_name(name):
@@ -704,6 +704,8 @@ def _unique_layer_name(name):
   ```
   """
   graph = ops.get_default_graph()
+  if graph not in PER_GRAPH_LAYER_NAME_UIDS:
+    PER_GRAPH_LAYER_NAME_UIDS[graph] = collections.defaultdict(int)
   layer_name_uids = PER_GRAPH_LAYER_NAME_UIDS[graph]
   layer_name_uids[name] += 1
   return name + '_' + str(layer_name_uids[name])
diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index 5967180f5b..3594ced259 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -741,7 +741,7 @@ class SeparableConv2D(Conv2D):
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -950,7 +950,7 @@ def separable_conv2d(inputs,
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -1033,7 +1033,7 @@ class Conv2DTranspose(Conv2D):
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 positive integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -1233,7 +1233,7 @@ def conv2d_transpose(inputs,
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 2 positive integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 2 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -1516,7 +1516,7 @@ def conv3d_transpose(inputs,
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
     kernel_size: A tuple or list of 3 positive integers specifying the spatial
-      dimensions of of the filters. Can be a single integer to specify the same
+      dimensions of the filters. Can be a single integer to specify the same
       value for all spatial dimensions.
     strides: A tuple or list of 3 positive integers specifying the strides
       of the convolution. Can be a single integer to specify the same value for
@@ -1525,8 +1525,9 @@ def conv3d_transpose(inputs,
     data_format: A string, one of `channels_last` (default) or `channels_first`.
       The ordering of the dimensions in the inputs.
       `channels_last` corresponds to inputs with shape
-      `(batch, height, width, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, height, width)`.
+      `(batch, depth, height, width, channels)` while `channels_first`
+      corresponds to inputs with shape
+      `(batch, channels, depth, height, width)`.
     activation: Activation function. Set it to None to maintain a
       linear activation.
     use_bias: Boolean, whether the layer uses a bias.
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 1dc5eb2f90..3ff93b61c5 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -537,6 +537,25 @@ class QueueBase(object):
           self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues,
           name=name)
 
+  def is_closed(self, name=None):
+    """ Returns true if queue is closed.
+
+    This operation returns true if the queue is closed and false if the queue
+    is open.
+
+    Args:
+      name: A name for the operation (optional).
+	
+    Returns:
+      True if the queue is closed and false if the queue is open.
+    """
+    if name is None:
+      name = "%s_Is_Closed" % self._name
+    if self._queue_ref.dtype == _dtypes.resource:
+      return gen_data_flow_ops.queue_is_closed_v2(self._queue_ref,name=name)
+    else:
+      return gen_data_flow_ops.queue_is_closed_(self._queue_ref,name=name)
+
   def size(self, name=None):
     """Compute the number of elements in this queue.
 
diff --git a/tensorflow/python/ops/distributions/dirichlet_multinomial.py b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
index f1b005f418..d792e9fe52 100644
--- a/tensorflow/python/ops/distributions/dirichlet_multinomial.py
+++ b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
@@ -95,7 +95,7 @@ class DirichletMultinomial(distribution.Distribution):
   The last `concentration` dimension parametrizes a single Dirichlet-Multinomial
   distribution. When calling distribution functions (e.g., `dist.prob(counts)`),
   `concentration`, `total_count` and `counts` are broadcast to the same shape.
-  The last dimension of of `counts` corresponds single Dirichlet-Multinomial
+  The last dimension of `counts` corresponds single Dirichlet-Multinomial
   distributions.
 
   Distribution parameters are automatically broadcast in all functions; see
diff --git a/tensorflow/python/ops/distributions/distribution.py b/tensorflow/python/ops/distributions/distribution.py
index a0be433a61..1684e85cc7 100644
--- a/tensorflow/python/ops/distributions/distribution.py
+++ b/tensorflow/python/ops/distributions/distribution.py
@@ -176,7 +176,7 @@ class _DistributionMeta(abc.ABCMeta):
 class ReparameterizationType(object):
   """Instances of this class represent how sampling is reparameterized.
 
-  Two static instances exist in the distritributions library, signifying
+  Two static instances exist in the distributions library, signifying
   one of two possible properties for samples from a distribution:
 
   `FULLY_REPARAMETERIZED`: Samples from the distribution are fully
diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index aa691c565b..9b15d4c76e 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -213,7 +213,7 @@ class Multinomial(distribution.Distribution):
 
   @property
   def probs(self):
-    """Probability of of drawing a `1` in that coordinate."""
+    """Probability of drawing a `1` in that coordinate."""
     return self._probs
 
   def _batch_shape_tensor(self):
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 2caa341cd2..c808f1fa7a 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -37,7 +37,7 @@ from tensorflow.python.ops import nn
 
 def assert_close(
     x, y, data=None, summarize=None, message=None, name="assert_close"):
-  """Assert that that x and y are within machine epsilon of each other.
+  """Assert that x and y are within machine epsilon of each other.
 
   Args:
     x: Floating-point `Tensor`
diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index 52ce451238..e073dbc640 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -917,7 +917,7 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False,
     aggregation_method: See `gradients()` documentation for details.
 
   Returns:
-    A list of Hessian matrices of `sum(y)` for each `x` in `xs`.
+    A list of Hessian matrices of `sum(ys)` for each `x` in `xs`.
 
   Raises:
     LookupError: if one of the operations between `xs` and `ys` does not
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index bec6b82a4c..28817e99e8 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -162,6 +162,8 @@ ResizeBilinearGrad
 ResizeNearestNeighborGrad
 AdjustContrastv2
 ScaleImageGrad
+SampleDistortedBoundingBox
+SampleDistortedBoundingBoxV2
 
 # io_ops
 FixedLengthRecordReader
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 65a1399c5b..2be92fac63 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -47,6 +47,7 @@ ops.NotDifferentiable('RGBToHSV')
 ops.NotDifferentiable('HSVToRGB')
 ops.NotDifferentiable('DrawBoundingBoxes')
 ops.NotDifferentiable('SampleDistortedBoundingBox')
+ops.NotDifferentiable('SampleDistortedBoundingBoxV2')
 # TODO(bsteiner): Implement the gradient function for extract_glimpse
 # TODO(b/31222613): This op may be differentiable, and there may be
 # latent bugs here.
@@ -1472,3 +1473,104 @@ def total_variation(images, name=None):
                math_ops.reduce_sum(math_ops.abs(pixel_dif2), axis=sum_axis))
 
   return tot_var
+
+
+def sample_distorted_bounding_box(image_size, bounding_boxes, seed=None,
+                                  seed2=None, min_object_covered=None,
+                                  aspect_ratio_range=None, area_range=None,
+                                  max_attempts=None,
+                                  use_image_if_no_bounding_boxes=None,
+                                  name=None):
+  """Generate a single randomly distorted bounding box for an image.
+
+  Bounding box annotations are often supplied in addition to ground-truth labels
+  in image recognition or object localization tasks. A common technique for
+  training such a system is to randomly distort an image while preserving
+  its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+  localization of an object, i.e. bounding box, given an `image_size`,
+  `bounding_boxes` and a series of constraints.
+
+  The output of this Op is a single bounding box that may be used to crop the
+  original image. The output is returned as 3 tensors: `begin`, `size` and
+  `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+  image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
+  what the bounding box looks like.
+
+  Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+  bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+  height of the underlying image.
+
+  For example,
+
+  ```python
+      # Generate a single distorted bounding box.
+      begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+          tf.shape(image),
+          bounding_boxes=bounding_boxes)
+
+      # Draw the bounding box in an image summary.
+      image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+                                                    bbox_for_draw)
+      tf.image_summary('images_with_box', image_with_box)
+
+      # Employ the bounding box to distort the image.
+      distorted_image = tf.slice(image, begin, size)
+  ```
+
+  Note that if no bounding box information is available, setting
+  `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+  bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+  false and no bounding boxes are supplied, an error is raised.
+
+  Args:
+    image_size: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
+      1-D, containing `[height, width, channels]`.
+    bounding_boxes: A `Tensor` of type `float32`.
+      3-D with shape `[batch, N, 4]` describing the N bounding boxes
+      associated with the image.
+    seed: An optional `int`. Defaults to `0`.
+      If either `seed` or `seed2` are set to non-zero, the random number
+      generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
+      seed.
+    seed2: An optional `int`. Defaults to `0`.
+      A second seed to avoid seed collision.
+    min_object_covered: An optional `float`. Defaults to `0.1`.
+      The cropped area of the image must contain at least this
+      fraction of any bounding box supplied. The value of this parameter should be
+      non-negative. In the case of 0, the cropped area does not need to overlap
+      any of the bounding boxes supplied.
+    aspect_ratio_range: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
+      The cropped area of the image must have an aspect ratio =
+      width / height within this range.
+    area_range: An optional list of `floats`. Defaults to `[0.05, 1]`.
+      The cropped area of the image must contain a fraction of the
+      supplied image within in this range.
+    max_attempts: An optional `int`. Defaults to `100`.
+      Number of attempts at generating a cropped region of the image
+      of the specified constraints. After `max_attempts` failures, return the entire
+      image.
+    use_image_if_no_bounding_boxes: An optional `bool`. Defaults to `False`.
+      Controls behavior if no bounding boxes supplied.
+      If true, assume an implicit bounding box covering the whole input. If false,
+      raise an error.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (begin, size, bboxes).
+
+    begin: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+      `tf.slice`.
+    size: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
+      `tf.slice`.
+    bboxes: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+      Provide as input to `tf.image.draw_bounding_boxes`.
+  """
+  with ops.name_scope(name, 'sample_distorted_bounding_box'):
+    # TODO (yongtang): Need to switch to v2 after 3 weeks.
+    return gen_image_ops._sample_distorted_bounding_box(image_size,
+                bounding_boxes, seed=seed,
+                seed2=seed2, min_object_covered=min_object_covered,
+                aspect_ratio_range=aspect_ratio_range, area_range=area_range,
+                max_attempts=max_attempts,
+                use_image_if_no_bounding_boxes=use_image_if_no_bounding_boxes,
+                name=name)
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py
index 5588d18ef1..5564194ea1 100644
--- a/tensorflow/python/ops/image_ops_test.py
+++ b/tensorflow/python/ops/image_ops_test.py
@@ -1501,16 +1501,41 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
           image_size_np, shape=image_size_np.shape)
       bounding_box_tf = constant_op.constant(
           bounding_box_np, dtype=dtypes.float32, shape=bounding_box_np.shape)
-      begin, size, _ = image_ops.sample_distorted_bounding_box(
+
+      # sample_distorted_bounding_box and sample_distorted_bounding_box_v2
+      for op in [image_ops.sample_distorted_bounding_box,
+                 gen_image_ops._sample_distorted_bounding_box_v2]:
+        begin, size, _ = op(
+            image_size=image_size_tf,
+            bounding_boxes=bounding_box_tf,
+            min_object_covered=min_object_covered,
+            aspect_ratio_range=aspect_ratio_range,
+            area_range=area_range)
+        y = array_ops.strided_slice(image_tf, begin, begin + size)
+
+        for _ in xrange(num_iter):
+          y_tf = y.eval()
+          crop_height = y_tf.shape[0]
+          crop_width = y_tf.shape[1]
+          aspect_ratio = float(crop_width) / float(crop_height)
+          area = float(crop_width * crop_height)
+
+          aspect_ratios.append(aspect_ratio)
+          area_ratios.append(area / original_area)
+          fraction_object_covered.append(float(np.sum(y_tf)) / bounding_box_area)
+
+      # sample_distorted_bounding_box_v2
+      min_object_covered_placeholder = array_ops.placeholder(dtypes.float32)
+      begin, size, _ = gen_image_ops._sample_distorted_bounding_box_v2(
           image_size=image_size_tf,
           bounding_boxes=bounding_box_tf,
-          min_object_covered=min_object_covered,
+          min_object_covered=min_object_covered_placeholder,
           aspect_ratio_range=aspect_ratio_range,
           area_range=area_range)
       y = array_ops.strided_slice(image_tf, begin, begin + size)
 
       for _ in xrange(num_iter):
-        y_tf = y.eval()
+        y_tf = y.eval(feed_dict={min_object_covered_placeholder: min_object_covered})
         crop_height = y_tf.shape[0]
         crop_width = y_tf.shape[1]
         aspect_ratio = float(crop_width) / float(crop_height)
@@ -1605,10 +1630,24 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
           [0.0, 0.0, 1.0, 1.0],
           shape=[4],
           dtype=dtypes.float32,)
-      begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box(
+      for op in [image_ops.sample_distorted_bounding_box,
+                 gen_image_ops._sample_distorted_bounding_box_v2]:
+        begin, end, bbox_for_drawing = op(
+            image_size=image_size,
+            bounding_boxes=bounding_box,
+            min_object_covered=0.1,
+            aspect_ratio_range=(0.75, 1.33),
+            area_range=(0.05, 1.0))
+
+        # Test that the shapes are correct.
+        self.assertAllEqual([3], begin.get_shape().as_list())
+        self.assertAllEqual([3], end.get_shape().as_list())
+        self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list())
+
+      begin, end, bbox_for_drawing = op(
           image_size=image_size,
           bounding_boxes=bounding_box,
-          min_object_covered=0.1,
+          min_object_covered=array_ops.placeholder(dtypes.float32),
           aspect_ratio_range=(0.75, 1.33),
           area_range=(0.05, 1.0))
 
diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index 8f5d0e5cd4..975494e45f 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -397,7 +397,8 @@ class FixedLengthRecordReader(ReaderBase):
                header_bytes=None,
                footer_bytes=None,
                hop_bytes=None,
-               name=None):
+               name=None,
+               encoding=None):
     """Create a FixedLengthRecordReader.
 
     Args:
@@ -406,12 +407,14 @@ class FixedLengthRecordReader(ReaderBase):
       footer_bytes: An optional int. Defaults to 0.
       hop_bytes: An optional int. Defaults to 0.
       name: A name for the operation (optional).
+      encoding: The type of encoding for the file. Defaults to none.
     """
     rr = gen_io_ops._fixed_length_record_reader_v2(
         record_bytes=record_bytes,
         header_bytes=header_bytes,
         footer_bytes=footer_bytes,
         hop_bytes=hop_bytes,
+        encoding=encoding,
         name=name)
     super(FixedLengthRecordReader, self).__init__(rr)
 
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index a0f505e47b..b8266a527d 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -122,8 +122,10 @@ def _ProdGrad(op, grad):
   # so we need to cast here.  We put all the shape-related ops on CPU to avoid
   # copying back and forth, and since listdiff is CPU only.
   with ops.device("/cpu:0"):
+    rank = array_ops.rank(op.inputs[0])
+    reduction_indices = (reduction_indices + rank) % rank
     reduced = math_ops.cast(reduction_indices, dtypes.int32)
-    idx = math_ops.range(0, array_ops.rank(op.inputs[0]))
+    idx = math_ops.range(0, rank)
     other, _ = array_ops.setdiff1d(idx, reduced)
     perm = array_ops.concat([reduced, other], 0)
     reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
@@ -397,6 +399,36 @@ def _TanhGrad(op, grad):
     return gen_math_ops._tanh_grad(y, grad)
 
 
+@ops.RegisterGradient("Asinh")
+def _AsinhGrad(op, grad):
+  """Returns grad * 1/cosh(y)."""
+  y = op.outputs[0]
+  with ops.control_dependencies([grad.op]):
+    y = math_ops.conj(y)
+    return grad / math_ops.cosh(y)
+
+
+@ops.RegisterGradient("Acosh")
+def _AcoshGrad(op, grad):
+  """Returns grad * 1/sinh(y)."""
+  y = op.outputs[0]
+  with ops.control_dependencies([grad.op]):
+    y = math_ops.conj(y)
+    return grad / math_ops.sinh(y)
+
+
+@ops.RegisterGradient("Atanh")
+def _AtanhGrad(op, grad):
+  """Returns grad * 1/ (1 - x^2)."""
+  x = op.inputs[0]
+  with ops.control_dependencies([grad.op]):
+    x = math_ops.conj(x)
+    x2 = math_ops.square(x)
+    one = constant_op.constant(1, dtype=grad.dtype)
+    inv = math_ops.reciprocal(math_ops.subtract(one, x2))
+    return grad * inv
+
+
 @ops.RegisterGradient("TanhGrad")
 def _TanhGradGrad(op, grad):
   with ops.control_dependencies([grad.op]):
diff --git a/tensorflow/python/ops/math_grad_test.py b/tensorflow/python/ops/math_grad_test.py
index 1fa15957b0..da3e0d7294 100644
--- a/tensorflow/python/ops/math_grad_test.py
+++ b/tensorflow/python/ops/math_grad_test.py
@@ -113,6 +113,29 @@ class MinOrMaxGradientTest(test.TestCase):
       self.assertLess(error, 1e-4)
 
 
+class ProdGradientTest(test.TestCase):
+
+  def testProdGradient(self):
+    inputs = constant_op.constant([[1., 2.], [3., 4.]],
+                                  dtype=dtypes.float32)
+    outputs = math_ops.reduce_prod(inputs)
+    with self.test_session():
+      error = gradient_checker.compute_gradient_error(
+          inputs, inputs.get_shape().as_list(),
+          outputs, outputs.get_shape().as_list())
+      self.assertLess(error, 1e-4)
+
+  def testProdGradientForNegativeAxis(self):
+    inputs = constant_op.constant([[1., 2.], [3., 4.]],
+                                  dtype=dtypes.float32)
+    outputs = math_ops.reduce_prod(inputs, -1)
+    with self.test_session():
+      error = gradient_checker.compute_gradient_error(
+          inputs, inputs.get_shape().as_list(),
+          outputs, outputs.get_shape().as_list())
+      self.assertLess(error, 1e-4)
+
+
 class SegmentMinOrMaxGradientTest(test.TestCase):
 
   def testSegmentMinGradient(self):
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index bf46bbdcc2..7edd1c47d2 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -47,6 +47,9 @@ See the @{$python/math_ops} guide.
 @@log1p
 @@sinh
 @@cosh
+@@asinh
+@@acosh
+@@atanh
 @@ceil
 @@floor
 @@maximum
@@ -1691,8 +1694,8 @@ def matmul(a,
            name=None):
   """Multiplies matrix `a` by matrix `b`, producing `a` * `b`.
 
-  The inputs must, following any transpositions, be tensors of rank >= 2 
-  where the inner 2 dimensions specify valid matrix multiplication arguments, 
+  The inputs must, following any transpositions, be tensors of rank >= 2
+  where the inner 2 dimensions specify valid matrix multiplication arguments,
   and any further outer dimensions match.
 
   Both matrices must be of the same type. The supported types are:
diff --git a/tensorflow/python/ops/sets_impl.py b/tensorflow/python/ops/sets_impl.py
index f3b52636d4..e623c4f2ee 100644
--- a/tensorflow/python/ops/sets_impl.py
+++ b/tensorflow/python/ops/sets_impl.py
@@ -99,7 +99,7 @@ def _set_operation(a, b, set_operation, validate_indices=True):
     b: `Tensor` or `SparseTensor` of the same type as `a`. Must be
         `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
         sorted in row-major order.
-    set_operation: String indicating set operaiton. See
+    set_operation: String indicating set operation. See
         SetOperationOp::SetOperationFromContext for valid values.
     validate_indices: Whether to validate the order and range of sparse indices
        in `a` and `b`.
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 93a9656950..d05fabce86 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -25,6 +25,7 @@
 @@sparse_concat
 @@sparse_reorder
 @@sparse_reshape
+@@sparse_slice
 @@sparse_split
 @@sparse_retain
 @@sparse_reset_shape
@@ -657,6 +658,50 @@ def sparse_split(keyword_required=KeywordRequired(),
   return sparse_tensors
 
 
+def sparse_slice(sp_input, start, size, name=None):
+  """Slice a `SparseTensor` based on the `start` and `size.
+
+  For example, if the input is
+
+      input_tensor = shape = [2, 7]
+      [    a   d e  ]
+      [b c          ]
+
+  Graphically the output tensors are:
+
+      sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
+      [    a  ]
+      [b c    ]
+
+      sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
+      [ d e  ]
+      [      ]
+
+  Args:
+    sp_input: The `SparseTensor` to split.
+    start: 1-D. tensor represents the start of the slice.
+    size: 1-D. tensor represents the size of the slice.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `SparseTensor` objects resulting from splicing.
+
+  Raises:
+    TypeError: If `sp_input` is not a `SparseTensor`.
+  """
+  sp_input = _convert_to_sparse_tensor(sp_input)
+  start = ops.convert_to_tensor(start, dtypes.int64)
+  size = ops.convert_to_tensor(size, dtypes.int64)
+
+  with ops.name_scope(name, "SparseSlice", [sp_input]) as name:
+    output_indices, output_values, output_shape = gen_sparse_ops.sparse_slice(
+        sp_input.indices, sp_input.values, sp_input.dense_shape, start, size, name=name)
+
+    return sparse_tensor.SparseTensor(
+        output_indices,
+        output_values,
+        output_shape)
+
 def sparse_to_dense(sparse_indices,
                     output_shape,
                     sparse_values,
diff --git a/tensorflow/python/ops/summary_op_util.py b/tensorflow/python/ops/summary_op_util.py
index a3f6616902..06ea63704d 100644
--- a/tensorflow/python/ops/summary_op_util.py
+++ b/tensorflow/python/ops/summary_op_util.py
@@ -78,7 +78,7 @@ def summary_scope(name, family=None, default_name=None, values=None):
   If `family` is set, then the tag name will be '<family>/<scope_name>', where
   `scope_name` is `<outer_scope>/<family>/<name>`. This ensures that `family`
   is always the prefix of the tag (and unmodified), while ensuring the scope
-  respects the outer scope from this this summary was created.
+  respects the outer scope from this summary was created.
 
   Args:
     name: A name for the generated summary node.
diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py
index 459c735ea3..796402425a 100644
--- a/tensorflow/python/training/adam.py
+++ b/tensorflow/python/training/adam.py
@@ -31,7 +31,7 @@ from tensorflow.python.training import training_ops
 class AdamOptimizer(optimizer.Optimizer):
   """Optimizer that implements the Adam algorithm.
 
-  See [Kingma et. al., 2014](http://arxiv.org/abs/1412.6980)
+  See [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
   ([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
   """
 
diff --git a/tensorflow/python/util/deprecation_test.py b/tensorflow/python/util/deprecation_test.py
index e2d9a594a3..16d246c4df 100644
--- a/tensorflow/python/util/deprecation_test.py
+++ b/tensorflow/python/util/deprecation_test.py
@@ -608,7 +608,7 @@ class DeprecatedArgsTest(test.TestCase):
     self._assert_subset(set(["after " + date, instructions, "d2"]),
                         set(args2[1:]))
 
-    # Assert calls with the deprecated arguments dont log warnings if
+    # Assert calls with the deprecated arguments don't log warnings if
     # the value matches the 'ok_val'.
     mock_warning.reset_mock()
     self.assertEqual(3, _fn(1, None, 2, d2="my_ok_val"))
diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py
index 8d77f8c845..b93842b3a7 100644
--- a/tensorflow/python/util/nest.py
+++ b/tensorflow/python/util/nest.py
@@ -180,7 +180,7 @@ def assert_same_structure(nest1, nest2, check_types=True):
     nest1: an arbitrarily nested structure.
     nest2: an arbitrarily nested structure.
     check_types: if `True` (default) types of sequences are checked as
-        well, incuding the keys of dictionaries. If set to `False`, for example
+        well, including the keys of dictionaries. If set to `False`, for example
         a list and a tuple of objects will look the same if they have the same
         size.
 
@@ -333,9 +333,9 @@ def map_structure(func, *structure, **check_types_dict):
   and the return value will contain the results in the same structure.
 
   Args:
-    func: A callable that acceps as many arguments are there are structures.
+    func: A callable that accepts as many arguments as there are structures.
     *structure: scalar, or tuple or list of constructed scalars and/or other
-      tuples/lists, or scalars.  Note: numpy arrays are considered scalars.
+      tuples/lists, or scalars.  Note: numpy arrays are considered  as scalars.
     **check_types_dict: only valid keyword argument is `check_types`. If set to
       `True` (default) the types of iterables within the  structures have to be
       same (e.g. `map_structure(func, [1], (1,))` raises a `TypeError`
diff --git a/tensorflow/python/util/tfprof.i b/tensorflow/python/util/tfprof.i
index 3949fec4e6..45105298e5 100644
--- a/tensorflow/python/util/tfprof.i
+++ b/tensorflow/python/util/tfprof.i
@@ -47,7 +47,6 @@ using tensorflow::int64;
 %unignore tensorflow::tfprof::DeleteProfiler;
 %unignore tensorflow::tfprof::AddStep;
 %unignore tensorflow::tfprof::Profile;
-%unignore tensorflow::tfprof::Advise;
 
 %include "tensorflow/core/profiler/internal/print_model_analysis.h"
 
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 5b1d9a517e..83a9313e50 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -138,16 +138,20 @@ WIN_COPTS = [
     "/DTF_COMPILE_LIBRARY",
     "/DEIGEN_HAS_C99_MATH",
     "/DTENSORFLOW_USE_EIGEN_THREADPOOL",
+    "/DEIGEN_AVOID_STL_ARRAY",
+    "/Iexternal/gemmlowp",
+    "/wd4018", # -Wno-sign-compare
+    "/U_HAS_EXCEPTIONS", "/D_HAS_EXCEPTIONS=1", "/EHsc", # -fno-exceptions
 ]
 
 # LINT.IfChange
 def tf_copts():
-  return ([
+  return (if_not_windows([
       "-DEIGEN_AVOID_STL_ARRAY",
       "-Iexternal/gemmlowp",
       "-Wno-sign-compare",
       "-fno-exceptions",
-  ] + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
+  ]) + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
       ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
@@ -1021,9 +1025,9 @@ def tf_py_wrap_cc(name,
   native.cc_binary(
       name=cc_library_name,
       srcs=[module_name + ".cc"],
-      copts=(copts + [
+      copts=(copts + if_not_windows([
           "-Wno-self-assign", "-Wno-sign-compare", "-Wno-write-strings"
-      ] + tf_extension_copts()),
+      ]) + tf_extension_copts()),
       linkopts=tf_extension_linkopts() + extra_linkopts,
       linkstatic=1,
       linkshared=1,
diff --git a/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt
index 72cc532447..a095616c00 100644
--- a/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt b/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt
index 5c77b3dd5c..260c796fd6 100644
--- a/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt
@@ -13,7 +13,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'record_bytes\', \'header_bytes\', \'footer_bytes\', \'hop_bytes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'record_bytes\', \'header_bytes\', \'footer_bytes\', \'hop_bytes\', \'name\', \'encoding\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "num_records_produced"
diff --git a/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt
index 1bfe723ce7..8fed133561 100644
--- a/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-padding-f-i-f-o-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt
index dbe25f3a5b..ebb017e81b 100644
--- a/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-priority-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt b/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt
index 9263d73a51..761f90989f 100644
--- a/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-queue-base.pbtxt
@@ -54,6 +54,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt b/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt
index ec783ffe5a..f3ca841393 100644
--- a/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-random-shuffle-queue.pbtxt
@@ -55,6 +55,10 @@ tf_class {
     name: "from_list"
     argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "is_closed"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "size"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index cb0b383934..8cf40e3c4b 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -516,6 +516,10 @@ tf_module {
     name: "acos"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "acosh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "add"
     argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -564,6 +568,10 @@ tf_module {
     name: "asin"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "asinh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "assert_equal"
     argspec: "args=[\'x\', \'y\', \'data\', \'summarize\', \'message\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
@@ -656,6 +664,10 @@ tf_module {
     name: "atan2"
     argspec: "args=[\'y\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "atanh"
+    argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "batch_to_space"
     argspec: "args=[\'input\', \'crops\', \'block_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -1764,6 +1776,10 @@ tf_module {
     name: "sparse_segment_sum"
     argspec: "args=[\'data\', \'indices\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "sparse_slice"
+    argspec: "args=[\'sp_input\', \'start\', \'size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "sparse_softmax"
     argspec: "args=[\'sp_input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/dist_test/python/mnist_replica.py b/tensorflow/tools/dist_test/python/mnist_replica.py
index f7dbfea7fb..e40ecb43f9 100644
--- a/tensorflow/tools/dist_test/python/mnist_replica.py
+++ b/tensorflow/tools/dist_test/python/mnist_replica.py
@@ -17,7 +17,7 @@
 
 A simple softmax model with one hidden layer is defined. The parameters
 (weights and biases) are located on one parameter server (ps), while the ops
-are executed on two worker nodes by default. The TF sessions also run on the 
+are executed on two worker nodes by default. The TF sessions also run on the
 worker node.
 Multiple invocations of this script can be done in parallel, with different
 values for --task_index. There should be exactly one invocation with
@@ -123,9 +123,7 @@ def main(unused_argv):
 
   is_chief = (FLAGS.task_index == 0)
   if FLAGS.num_gpus > 0:
-    if FLAGS.num_gpus < num_workers:
-      raise ValueError("number of gpus is less than number of workers")
-    # Avoid gpu allocation conflict: now allocate task_num -> #gpu 
+    # Avoid gpu allocation conflict: now allocate task_num -> #gpu
     # for each worker in the corresponding machine
     gpu = (FLAGS.task_index % FLAGS.num_gpus)
     worker_device = "/job:worker/task:%d/gpu:%d" % (FLAGS.task_index, gpu)
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index d0a038a9db..e10565a064 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -19,6 +19,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         zlib1g-dev \
         openjdk-8-jdk \
         openjdk-8-jre-headless \
+        wget \
         && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index 6d5a9bdc4c..3780bde2be 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -55,7 +55,7 @@ for additional containers, such as release candidates or nightly builds.
 ## Rebuilding the containers
 
 Building TensorFlow Docker containers should be done through the
-[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/README.md)
+[parameterized_docker_build.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/parameterized_docker_build.sh)
 script. The raw Dockerfiles should not be used directly as they contain strings
 to be replaced by the script during the build.
 
diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index a8ac9f13b1..bbeb3921d7 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -449,7 +449,7 @@ class DocGenerator(object):
         '--base_dir',
         type=str,
         default=default_base_dir,
-        help='Base directory to to strip from file names referenced in docs.')
+        help='Base directory to strip from file names referenced in docs.')
 
   def parse_known_args(self):
     flags, _ = self.argument_parser.parse_known_args()
diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py
index 1bf8a2979c..18c3c98dc2 100644
--- a/tensorflow/tools/docs/parser.py
+++ b/tensorflow/tools/docs/parser.py
@@ -54,7 +54,7 @@ class _Errors(object):
     """Add an error to the collection.
 
     Args:
-      full_name: The path to the file in which the error occured.
+      full_name: The path to the file in which the error occurred.
       message: The message to display with the error.
     """
     self._errors.append((full_name, message))
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 54ba8064e8..39499e1775 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.0'
+_VERSION = '1.2.1'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index bb5a28bab1..20d45a7fdc 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -520,7 +520,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       actual = "@jsoncpp_git//:jsoncpp",
   )
 
-  native.http_archive(
+  patched_http_archive(
       name = "boringssl",
       urls = [
           "http://mirror.bazel.build/github.com/google/boringssl/archive/bbcaa15b0647816b9a1a9b9e0d209cd6712f0105.tar.gz",
@@ -528,6 +528,9 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       ],
       sha256 = "025264d6e9a7ad371f2f66d17a28b6627de0c9592dc2eb54afd062f68f1f9aa3",
       strip_prefix = "boringssl-bbcaa15b0647816b9a1a9b9e0d209cd6712f0105",
+
+      # Add patch to boringssl code to support s390x
+      patch_file = str(Label("//third_party/boringssl:add_boringssl_s390x.patch")),
   )
 
   native.new_http_archive(
diff --git a/third_party/lmdb.BUILD b/third_party/lmdb.BUILD
index 7c6e3dc3f0..9b3e1d97c8 100644
--- a/third_party/lmdb.BUILD
+++ b/third_party/lmdb.BUILD
@@ -19,8 +19,8 @@ cc_library(
         "-w",
     ],
     linkopts = select({
-        ":windows": ["-Wl,advapi32.lib"],  # InitializeSecurityDescriptor, SetSecurityDescriptorDacl
-        ":windows_msvc": ["-Wl,advapi32.lib"],
+        ":windows": ["-DEFAULTLIB:advapi32.lib"],  # InitializeSecurityDescriptor, SetSecurityDescriptorDacl
+        ":windows_msvc": ["-DEFAULTLIB:advapi32.lib"],
         "//conditions:default": ["-lpthread"],
     }),
     visibility = ["//visibility:public"],
diff --git a/third_party/snappy.BUILD b/third_party/snappy.BUILD
index 120028dc52..9c00b7068a 100644
--- a/third_party/snappy.BUILD
+++ b/third_party/snappy.BUILD
@@ -4,6 +4,18 @@ licenses(["notice"])  # BSD 3-Clause
 
 exports_files(["COPYING"])
 
+config_setting(
+    name = "windows",
+    values = {"cpu": "x64_windows"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "windows_msvc",
+    values = {"cpu": "x64_windows_msvc"},
+    visibility = ["//visibility:public"],
+)
+
 cc_library(
     name = "snappy",
     srcs = [
@@ -19,10 +31,14 @@ cc_library(
         "snappy-stubs-public.h",
     ],
     hdrs = ["snappy.h"],
-    copts = [
-        "-Wno-shift-negative-value",
-        "-Wno-implicit-function-declaration",
-    ],
+    copts = select({
+        ":windows": [],
+        ":windows_msvc": [],
+        "//conditions:default": [
+            "-Wno-shift-negative-value",
+            "-Wno-implicit-function-declaration",
+        ],
+    }),
 )
 
 genrule(
diff --git a/third_party/sycl/crosstool/computecpp.tpl b/third_party/sycl/crosstool/computecpp.tpl
index 8b0a7a7d4a..c699eabb6f 100755
--- a/third_party/sycl/crosstool/computecpp.tpl
+++ b/third_party/sycl/crosstool/computecpp.tpl
@@ -73,7 +73,7 @@ def main():
   bc_out = filename + '.sycl'
 
   # strip asan for the device
-  computecpp_device_compiler_flags = ['-sycl-compress-name', '-Wno-unused-variable',
+  computecpp_device_compiler_flags = ['-sycl-compress-name', '-Wno-unused-variable', '-Wno-c++11-narrowing',
                                       '-I', COMPUTECPP_INCLUDE, '-isystem', COMPUTECPP_INCLUDE,
                                       '-std=c++11', '-sycl', '-emit-llvm', '-no-serial-memop',
                                       '-Xclang', '-cl-denorms-are-zero', '-Xclang', '-cl-fp32-correctly-rounded-divide-sqrt']
diff --git a/third_party/toolchains/cpus/CROSSTOOL b/third_party/toolchains/cpus/CROSSTOOL
index 0e2d0119af..0fff8191e0 100644
--- a/third_party/toolchains/cpus/CROSSTOOL
+++ b/third_party/toolchains/cpus/CROSSTOOL
@@ -374,12 +374,15 @@ toolchain {
       action: 'c++-header-preprocessing'
       action: 'c++-module-compile'
       flag_group {
+        iterate_over: 'quote_include_paths'
         flag: '/I%{quote_include_paths}'
       }
       flag_group {
+        iterate_over: 'include_paths'
         flag: '/I%{include_paths}'
       }
       flag_group {
+        iterate_over: 'system_include_paths'
         flag: '/I%{system_include_paths}'
       }
     }
@@ -630,6 +633,7 @@ toolchain {
       action: 'c++-link-dynamic-library'
       expand_if_all_available: 'linkstamp_paths'
       flag_group {
+        iterate_over: 'linkstamp_paths'
         flag: '%{linkstamp_paths}'
       }
     }
@@ -671,6 +675,7 @@ toolchain {
       action: 'c++-link-pic-static-library'
       action: 'c++-link-alwayslink-pic-static-library'
       flag_group {
+        iterate_over: 'libopts'
         flag: '%{libopts}'
       }
     }
@@ -770,6 +775,7 @@ toolchain {
       action: 'c++-link-executable'
       action: 'c++-link-dynamic-library'
       flag_group {
+        iterate_over: 'legacy_link_flags'
         flag: '%{legacy_link_flags}'
       }
     }
diff --git a/third_party/zlib.BUILD b/third_party/zlib.BUILD
index 279e6395b0..8509668891 100644
--- a/third_party/zlib.BUILD
+++ b/third_party/zlib.BUILD
@@ -2,6 +2,18 @@ package(default_visibility = ["//visibility:public"])
 
 licenses(["notice"])  # BSD/MIT-like license (for zlib)
 
+config_setting(
+    name = "windows",
+    values = {"cpu": "x64_windows"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "windows_msvc",
+    values = {"cpu": "x64_windows_msvc"},
+    visibility = ["//visibility:public"],
+)
+
 cc_library(
     name = "zlib",
     srcs = [
@@ -32,9 +44,13 @@ cc_library(
         "zutil.h",
     ],
     hdrs = ["zlib.h"],
-    copts = [
-        "-Wno-shift-negative-value",
-        "-Wno-implicit-function-declaration",
-    ],
+    copts = select({
+        ":windows": [],
+        ":windows_msvc": [],
+        "//conditions:default": [
+            "-Wno-shift-negative-value",
+            "-Wno-implicit-function-declaration",
+        ],
+    }),
     includes = ["."],
 )
-- 
GitLab


From e2930aeebc94c385f03c8c06907d16861d6fd4c3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 19:49:44 -0700
Subject: [PATCH 0869/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161478409
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 278 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 225 ++++++++++++--
 2 files changed, 473 insertions(+), 30 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index f872ef3cb5..0198d4ff22 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -165,6 +165,30 @@ op {
     }
   }
 }
+op {
+  name: "Acosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "Add"
   input_arg {
@@ -1879,6 +1903,30 @@ op {
     }
   }
 }
+op {
+  name: "Asinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "Assert"
   input_arg {
@@ -2083,6 +2131,30 @@ op {
     }
   }
 }
+op {
+  name: "Atanh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "AudioSpectrogram"
   input_arg {
@@ -9066,6 +9138,60 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "FixedLengthRecordReaderV2"
+  output_arg {
+    name: "reader_handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "header_bytes"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "record_bytes"
+    type: "int"
+  }
+  attr {
+    name: "footer_bytes"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "hop_bytes"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "encoding"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "FixedUnigramCandidateSampler"
   input_arg {
@@ -18031,6 +18157,30 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "QueueIsClosed"
+  input_arg {
+    name: "handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+}
+op {
+  name: "QueueIsClosedV2"
+  input_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+  is_stateful: true
+}
 op {
   name: "QueueSize"
   input_arg {
@@ -21967,6 +22117,95 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "SampleDistortedBoundingBoxV2"
+  input_arg {
+    name: "image_size"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "bounding_boxes"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "min_object_covered"
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "begin"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "size"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "bboxes"
+    type: DT_FLOAT
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "seed"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "seed2"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "aspect_ratio_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.75
+        f: 1.33
+      }
+    }
+  }
+  attr {
+    name: "area_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.05
+        f: 1
+      }
+    }
+  }
+  attr {
+    name: "max_attempts"
+    type: "int"
+    default_value {
+      i: 100
+    }
+  }
+  attr {
+    name: "use_image_if_no_bounding_boxes"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "Save"
   input_arg {
@@ -25923,6 +26162,45 @@ op {
     }
   }
 }
+op {
+  name: "SparseSlice"
+  input_arg {
+    name: "indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "start"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "size"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+}
 op {
   name: "SparseSoftmax"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 7270e31fe4..064b5c62bb 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -205,6 +205,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -212,7 +213,7 @@ op {
       }
     }
   }
-  summary: "Computes acosh of x element-wise."
+  summary: "Computes inverse hyperbolic cosine of x element-wise."
 }
 op {
   name: "Add"
@@ -1924,6 +1925,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -1931,7 +1933,7 @@ op {
       }
     }
   }
-  summary: "Computes asinh of x element-wise."
+  summary: "Computes inverse hyperbolic sine of x element-wise."
 }
 op {
   name: "Assert"
@@ -2179,6 +2181,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -2186,7 +2189,7 @@ op {
       }
     }
   }
-  summary: "Computes atanh of x element-wise."
+  summary: "Computes inverse hyperbolic tangent of x element-wise."
 }
 op {
   name: "AudioSpectrogram"
@@ -8391,6 +8394,14 @@ op {
     }
     description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
   }
+  attr {
+    name: "encoding"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "The type of encoding for the file. Currently ZLIB and GZIP\nare supported. Defaults to none."
+  }
   summary: "A Reader that outputs fixed-length records from a file."
   is_stateful: true
 }
@@ -17532,6 +17543,36 @@ op {
   description: "The components input has k elements, which correspond to the components of\ntuples stored in the given queue.\n\nN.B. If the queue is full, this operation will block until the given\nelement has been enqueued (or \'timeout_ms\' elapses, if specified)."
   is_stateful: true
 }
+op {
+  name: "QueueIsClosed"
+  input_arg {
+    name: "handle"
+    description: "The handle to a queue."
+    type: DT_STRING
+    is_ref: true
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+  summary: "Returns true if queue is closed."
+  description: "This operation returns true if the queue is closed and false if the queue\nis open."
+}
+op {
+  name: "QueueIsClosedV2"
+  input_arg {
+    name: "handle"
+    description: "The handle to a queue."
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+  summary: "Returns true if queue is closed."
+  description: "This operation returns true if the queue is closed and false if the queue\nis open."
+  is_stateful: true
+}
 op {
   name: "QueueSize"
   input_arg {
@@ -21802,6 +21843,109 @@ op {
   description: "Bounding box annotations are often supplied in addition to ground-truth labels\nin image recognition or object localization tasks. A common technique for\ntraining such a system is to randomly distort an image while preserving\nits content, i.e. *data augmentation*. This Op outputs a randomly distorted\nlocalization of an object, i.e. bounding box, given an `image_size`,\n`bounding_boxes` and a series of constraints.\n\nThe output of this Op is a single bounding box that may be used to crop the\noriginal image. The output is returned as 3 tensors: `begin`, `size` and\n`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the\nimage. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize\nwhat the bounding box looks like.\n\nBounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The\nbounding box coordinates are floats in `[0.0, 1.0]` relative to the width and\nheight of the underlying image.\n\nFor example,\n\n```python\n    # Generate a single distorted bounding box.\n    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(\n        tf.shape(image),\n        bounding_boxes=bounding_boxes)\n\n    # Draw the bounding box in an image summary.\n    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),\n                                                  bbox_for_draw)\n    tf.image_summary(\'images_with_box\', image_with_box)\n\n    # Employ the bounding box to distort the image.\n    distorted_image = tf.slice(image, begin, size)\n```\n\nNote that if no bounding box information is available, setting\n`use_image_if_no_bounding_boxes = true` will assume there is a single implicit\nbounding box covering the whole image. If `use_image_if_no_bounding_boxes` is\nfalse and no bounding boxes are supplied, an error is raised."
   is_stateful: true
 }
+op {
+  name: "SampleDistortedBoundingBoxV2"
+  input_arg {
+    name: "image_size"
+    description: "1-D, containing `[height, width, channels]`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "bounding_boxes"
+    description: "3-D with shape `[batch, N, 4]` describing the N bounding boxes\nassociated with the image."
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "min_object_covered"
+    description: "The cropped area of the image must contain at least this\nfraction of any bounding box supplied. The value of this parameter should be\nnon-negative. In the case of 0, the cropped area does not need to overlap\nany of the bounding boxes supplied."
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "begin"
+    description: "1-D, containing `[offset_height, offset_width, 0]`. Provide as input to\n`tf.slice`."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "size"
+    description: "1-D, containing `[target_height, target_width, -1]`. Provide as input to\n`tf.slice`."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "bboxes"
+    description: "3-D with shape `[1, 1, 4]` containing the distorted bounding box.\nProvide as input to `tf.image.draw_bounding_boxes`."
+    type: DT_FLOAT
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "seed"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "If either `seed` or `seed2` are set to non-zero, the random number\ngenerator is seeded by the given `seed`.  Otherwise, it is seeded by a random\nseed."
+  }
+  attr {
+    name: "seed2"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "A second seed to avoid seed collision."
+  }
+  attr {
+    name: "aspect_ratio_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.75
+        f: 1.33
+      }
+    }
+    description: "The cropped area of the image must have an aspect ratio =\nwidth / height within this range."
+  }
+  attr {
+    name: "area_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.05
+        f: 1
+      }
+    }
+    description: "The cropped area of the image must contain a fraction of the\nsupplied image within in this range."
+  }
+  attr {
+    name: "max_attempts"
+    type: "int"
+    default_value {
+      i: 100
+    }
+    description: "Number of attempts at generating a cropped region of the image\nof the specified constraints. After `max_attempts` failures, return the entire\nimage."
+  }
+  attr {
+    name: "use_image_if_no_bounding_boxes"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "Controls behavior if no bounding boxes supplied.\nIf true, assume an implicit bounding box covering the whole input. If false,\nraise an error."
+  }
+  summary: "Generate a single randomly distorted bounding box for an image."
+  description: "Bounding box annotations are often supplied in addition to ground-truth labels\nin image recognition or object localization tasks. A common technique for\ntraining such a system is to randomly distort an image while preserving\nits content, i.e. *data augmentation*. This Op outputs a randomly distorted\nlocalization of an object, i.e. bounding box, given an `image_size`,\n`bounding_boxes` and a series of constraints.\n\nThe output of this Op is a single bounding box that may be used to crop the\noriginal image. The output is returned as 3 tensors: `begin`, `size` and\n`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the\nimage. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize\nwhat the bounding box looks like.\n\nBounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The\nbounding box coordinates are floats in `[0.0, 1.0]` relative to the width and\nheight of the underlying image.\n\nFor example,\n\n```python\n    # Generate a single distorted bounding box.\n    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(\n        tf.shape(image),\n        bounding_boxes=bounding_boxes)\n\n    # Draw the bounding box in an image summary.\n    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),\n                                                  bbox_for_draw)\n    tf.image_summary(\'images_with_box\', image_with_box)\n\n    # Employ the bounding box to distort the image.\n    distorted_image = tf.slice(image, begin, size)\n```\n\nNote that if no bounding box information is available, setting\n`use_image_if_no_bounding_boxes = true` will assume there is a single implicit\nbounding box covering the whole image. If `use_image_if_no_bounding_boxes` is\nfalse and no bounding boxes are supplied, an error is raised."
+  is_stateful: true
+}
 op {
   name: "Save"
   input_arg {
@@ -26186,6 +26330,54 @@ op {
   summary: "Computes the sum along sparse segments of a tensor."
   description: "Read @{$math_ops#segmentation$the section on segmentation} for an explanation of\nsegments.\n\nLike `SegmentSum`, but `segment_ids` can have rank less than `data`\'s first\ndimension, selecting a subset of dimension 0, specified by `indices`.\n\nFor example:\n\n```python\nc = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])\n\n# Select two rows, one segment.\ntf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))\n# => [[0 0 0 0]]\n\n# Select two rows, two segment.\ntf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))\n# => [[ 1  2  3  4]\n#     [-1 -2 -3 -4]]\n\n# Select all rows, two segments.\ntf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))\n# => [[0 0 0 0]\n#     [5 6 7 8]]\n\n# Which is equivalent to:\ntf.segment_sum(c, tf.constant([0, 0, 1]))\n```"
 }
+op {
+  name: "SparseSlice"
+  input_arg {
+    name: "indices"
+    description: "2-D tensor represents the indices of the sparse tensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "values"
+    description: "1-D tensor represents the values of the sparse tensor."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "shape"
+    description: "1-D. tensor represents the shape of the sparse tensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "start"
+    description: "1-D. tensor represents the start of the slice."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "size"
+    description: "1-D. tensor represents the size of the slice.\noutput indices: A list of 1-D tensors represents the indices of the output\nsparse tensors."
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    description: "A list of 1-D tensors represents the values of the output sparse\ntensors."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    description: "A list of 1-D tensors represents the shape of the output sparse\ntensors."
+    type: DT_INT64
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  summary: "Slice a `SparseTensor` based on the `start` and `size`."
+  description: "For example, if the input is\n\n    input_tensor = shape = [2, 7]\n    [    a   d e  ]\n    [b c          ]\n\nGraphically the output tensors are:\n\n    sparse_slice([0, 0], [2, 4]) = shape = [2, 4]\n    [    a  ]\n    [b c    ]\n\n    sparse_slice([0, 4], [2, 3]) = shape = [2, 3]\n    [ d e  ]\n    [      ]"
+}
 op {
   name: "SparseSoftmax"
   input_arg {
@@ -28192,33 +28384,6 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
-op {
-  name: "LMDBReader"
-  output_arg {
-    name: "reader_handle"
-    description: "The handle to reference the Reader."
-    type: DT_STRING
-    is_ref: true
-  }
-  attr {
-    name: "container"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
-  }
-  attr {
-    name: "shared_name"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
-  }
-  summary: "A Reader that outputs the records from a LMDB database."
-  is_stateful: true
-}
 op {
   name: "TakeDataset"
   input_arg {
-- 
GitLab


From aee7408dca2c69a5b108bd1fad6036feb772a867 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 19:56:56 -0700
Subject: [PATCH 0870/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161478803
---
 tensorflow/go/op/wrappers.go | 1220 ++++++++++++++++++++--------------
 1 file changed, 720 insertions(+), 500 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 5b53bd81f7..1fb34bb603 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -4522,6 +4522,27 @@ func MatrixDiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
 	return op.Output(0)
 }
 
+// Returns true if queue is closed.
+//
+// This operation returns true if the queue is closed and false if the queue
+// is open.
+//
+// Arguments:
+//	handle: The handle to a queue.
+func QueueIsClosedV2(scope *Scope, handle tf.Output) (is_closed tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "QueueIsClosedV2",
+		Input: []tf.Input{
+			handle,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2.
 type QueueDequeueUpToV2Attr func(optionalAttr)
 
@@ -6186,6 +6207,150 @@ func ExtractGlimpse(scope *Scope, input tf.Output, size tf.Output, offsets tf.Ou
 	return op.Output(0)
 }
 
+// SampleDistortedBoundingBoxV2Attr is an optional argument to SampleDistortedBoundingBoxV2.
+type SampleDistortedBoundingBoxV2Attr func(optionalAttr)
+
+// SampleDistortedBoundingBoxV2Seed sets the optional seed attribute to value.
+//
+// value: If either `seed` or `seed2` are set to non-zero, the random number
+// generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
+// seed.
+// If not specified, defaults to 0
+func SampleDistortedBoundingBoxV2Seed(value int64) SampleDistortedBoundingBoxV2Attr {
+	return func(m optionalAttr) {
+		m["seed"] = value
+	}
+}
+
+// SampleDistortedBoundingBoxV2Seed2 sets the optional seed2 attribute to value.
+//
+// value: A second seed to avoid seed collision.
+// If not specified, defaults to 0
+func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2Attr {
+	return func(m optionalAttr) {
+		m["seed2"] = value
+	}
+}
+
+// SampleDistortedBoundingBoxV2AspectRatioRange sets the optional aspect_ratio_range attribute to value.
+//
+// value: The cropped area of the image must have an aspect ratio =
+// width / height within this range.
+// If not specified, defaults to <f:0.75 f:1.33 >
+func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
+	return func(m optionalAttr) {
+		m["aspect_ratio_range"] = value
+	}
+}
+
+// SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value.
+//
+// value: The cropped area of the image must contain a fraction of the
+// supplied image within in this range.
+// If not specified, defaults to <f:0.05 f:1 >
+func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
+	return func(m optionalAttr) {
+		m["area_range"] = value
+	}
+}
+
+// SampleDistortedBoundingBoxV2MaxAttempts sets the optional max_attempts attribute to value.
+//
+// value: Number of attempts at generating a cropped region of the image
+// of the specified constraints. After `max_attempts` failures, return the entire
+// image.
+// If not specified, defaults to 100
+func SampleDistortedBoundingBoxV2MaxAttempts(value int64) SampleDistortedBoundingBoxV2Attr {
+	return func(m optionalAttr) {
+		m["max_attempts"] = value
+	}
+}
+
+// SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
+//
+// value: Controls behavior if no bounding boxes supplied.
+// If true, assume an implicit bounding box covering the whole input. If false,
+// raise an error.
+// If not specified, defaults to false
+func SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxV2Attr {
+	return func(m optionalAttr) {
+		m["use_image_if_no_bounding_boxes"] = value
+	}
+}
+
+// Generate a single randomly distorted bounding box for an image.
+//
+// Bounding box annotations are often supplied in addition to ground-truth labels
+// in image recognition or object localization tasks. A common technique for
+// training such a system is to randomly distort an image while preserving
+// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+// localization of an object, i.e. bounding box, given an `image_size`,
+// `bounding_boxes` and a series of constraints.
+//
+// The output of this Op is a single bounding box that may be used to crop the
+// original image. The output is returned as 3 tensors: `begin`, `size` and
+// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
+// what the bounding box looks like.
+//
+// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+// height of the underlying image.
+//
+// For example,
+//
+// ```python
+//     # Generate a single distorted bounding box.
+//     begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+//         tf.shape(image),
+//         bounding_boxes=bounding_boxes)
+//
+//     # Draw the bounding box in an image summary.
+//     image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+//                                                   bbox_for_draw)
+//     tf.image_summary('images_with_box', image_with_box)
+//
+//     # Employ the bounding box to distort the image.
+//     distorted_image = tf.slice(image, begin, size)
+// ```
+//
+// Note that if no bounding box information is available, setting
+// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+// false and no bounding boxes are supplied, an error is raised.
+//
+// Arguments:
+//	image_size: 1-D, containing `[height, width, channels]`.
+//	bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
+// associated with the image.
+//	min_object_covered: The cropped area of the image must contain at least this
+// fraction of any bounding box supplied. The value of this parameter should be
+// non-negative. In the case of 0, the cropped area does not need to overlap
+// any of the bounding boxes supplied.
+//
+// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to
+// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+// Provide as input to `tf.image.draw_bounding_boxes`.
+func SampleDistortedBoundingBoxV2(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, min_object_covered tf.Output, optional ...SampleDistortedBoundingBoxV2Attr) (begin tf.Output, size tf.Output, bboxes tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "SampleDistortedBoundingBoxV2",
+		Input: []tf.Input{
+			image_size, bounding_boxes, min_object_covered,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
+}
+
 // Draw bounding boxes on a batch of images.
 //
 // Outputs a copy of `images` but draws on top of the pixels zero or more bounding
@@ -6759,49 +6924,43 @@ func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_ha
 	return op.Output(0)
 }
 
-// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul.
-type SparseTensorDenseMatMulAttr func(optionalAttr)
+// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2.
+type TFRecordReaderV2Attr func(optionalAttr)
 
-// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value.
+// TFRecordReaderV2Container sets the optional container attribute to value.
 //
-// value: Use the adjoint of A in the matrix multiply.  If A is complex, this
-// is transpose(conj(A)).  Otherwise it's transpose(A).
-// If not specified, defaults to false
-func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr {
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr {
 	return func(m optionalAttr) {
-		m["adjoint_a"] = value
+		m["container"] = value
 	}
 }
 
-// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value.
+// TFRecordReaderV2SharedName sets the optional shared_name attribute to value.
 //
-// value: Use the adjoint of B in the matrix multiply.  If B is complex, this
-// is transpose(conj(B)).  Otherwise it's transpose(B).
-// If not specified, defaults to false
-func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr {
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr {
 	return func(m optionalAttr) {
-		m["adjoint_b"] = value
+		m["shared_name"] = value
 	}
 }
 
-// Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
-//
-// No validity checking is performed on the indices of A.  However, the following
-// input format is recommended for optimal behavior:
-//
-// if adjoint_a == false:
-//   A should be sorted in lexicographically increasing order.  Use SparseReorder
-//   if you're not sure.
-// if adjoint_a == true:
-//   A should be sorted in order of increasing dimension 1 (i.e., "column major"
-//   order instead of "row major" order).
+// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value.
+// If not specified, defaults to ""
+func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr {
+	return func(m optionalAttr) {
+		m["compression_type"] = value
+	}
+}
+
+// A Reader that outputs the records from a TensorFlow Records file.
 //
-// Arguments:
-//	a_indices: 2-D.  The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
-//	a_values: 1-D.  The `values` of the `SparseTensor`, size `[nnz]` Vector.
-//	a_shape: 1-D.  The `shape` of the `SparseTensor`, size `[2]` Vector.
-//	b: 2-D.  A dense Matrix.
-func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) {
+// Returns The handle to reference the Reader.
+func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -6810,10 +6969,8 @@ func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Outp
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "SparseTensorDenseMatMul",
-		Input: []tf.Input{
-			a_indices, a_values, a_shape, b,
-		},
+		Type: "TFRecordReaderV2",
+
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
@@ -7634,147 +7791,6 @@ func RsqrtGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
-// MapClearAttr is an optional argument to MapClear.
-type MapClearAttr func(optionalAttr)
-
-// MapClearCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func MapClearCapacity(value int64) MapClearAttr {
-	return func(m optionalAttr) {
-		m["capacity"] = value
-	}
-}
-
-// MapClearMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func MapClearMemoryLimit(value int64) MapClearAttr {
-	return func(m optionalAttr) {
-		m["memory_limit"] = value
-	}
-}
-
-// MapClearContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func MapClearContainer(value string) MapClearAttr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// MapClearSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func MapClearSharedName(value string) MapClearAttr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// Op removes all elements in the underlying container.
-//
-// Returns the created operation.
-func MapClear(scope *Scope, dtypes []tf.DataType, optional ...MapClearAttr) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"dtypes": dtypes}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "MapClear",
-
-		Attrs: attrs,
-	}
-	return scope.AddOperation(opspec)
-}
-
-// TensorArrayV2Attr is an optional argument to TensorArrayV2.
-type TensorArrayV2Attr func(optionalAttr)
-
-// TensorArrayV2ElementShape sets the optional element_shape attribute to value.
-// If not specified, defaults to <unknown_rank:true >
-func TensorArrayV2ElementShape(value tf.Shape) TensorArrayV2Attr {
-	return func(m optionalAttr) {
-		m["element_shape"] = value
-	}
-}
-
-// TensorArrayV2DynamicSize sets the optional dynamic_size attribute to value.
-// If not specified, defaults to false
-func TensorArrayV2DynamicSize(value bool) TensorArrayV2Attr {
-	return func(m optionalAttr) {
-		m["dynamic_size"] = value
-	}
-}
-
-// TensorArrayV2ClearAfterRead sets the optional clear_after_read attribute to value.
-// If not specified, defaults to true
-func TensorArrayV2ClearAfterRead(value bool) TensorArrayV2Attr {
-	return func(m optionalAttr) {
-		m["clear_after_read"] = value
-	}
-}
-
-// TensorArrayV2TensorArrayName sets the optional tensor_array_name attribute to value.
-// If not specified, defaults to ""
-func TensorArrayV2TensorArrayName(value string) TensorArrayV2Attr {
-	return func(m optionalAttr) {
-		m["tensor_array_name"] = value
-	}
-}
-
-// Deprecated. Use TensorArrayV3
-func TensorArrayV2(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV2Attr) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"dtype": dtype}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "TensorArrayV2",
-		Input: []tf.Input{
-			size,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Tensor`.
-//
-// The `SparseTensor` must have rank `R` greater than 1, and the first dimension
-// is treated as the minibatch dimension.  Elements of the `SparseTensor`
-// must be sorted in increasing order of this first dimension.  The serialized
-// `SparseTensor` objects going into each row of `serialized_sparse` will have
-// rank `R-1`.
-//
-// The minibatch size `N` is extracted from `sparse_shape[0]`.
-//
-// Arguments:
-//	sparse_indices: 2-D.  The `indices` of the minibatch `SparseTensor`.
-//	sparse_values: 1-D.  The `values` of the minibatch `SparseTensor`.
-//	sparse_shape: 1-D.  The `shape` of the minibatch `SparseTensor`.
-func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) (serialized_sparse tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SerializeManySparse",
-		Input: []tf.Input{
-			sparse_indices, sparse_values, sparse_shape,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Makes its input available to the next iteration.
 //
 // Arguments:
@@ -8609,92 +8625,38 @@ func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.
 // *  The empty string, in which case the corresponding tensor is
 //    saved normally.
 // *  A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the
-//    `dimI` are the dimensions of the larger tensor and `slice-spec`
-//    specifies what part is covered by the tensor to save.
-//
-// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
-// where each `sliceI` is either:
-//
-// *  The string `-` meaning that the slice covers all indices of this dimension
-// *  `start,length` where `start` and `length` are integers.  In that
-//    case the slice covers `length` indices starting at `start`.
-//
-// See also `Save`.
-//
-// Arguments:
-//	filename: Must have a single element. The name of the file to which we write the
-// tensor.
-//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
-//	shapes_and_slices: Shape `[N]`.  The shapes and slice specifications to use when
-// saving the tensors.
-//	data: `N` tensors to save.
-//
-// Returns the created operation.
-func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SaveSlices",
-		Input: []tf.Input{
-			filename, tensor_names, shapes_and_slices, tf.OutputList(data),
-		},
-	}
-	return scope.AddOperation(opspec)
-}
-
-// Writes contents to the file at input filename. Creates file if not existing.
-//
-// Arguments:
-//	filename: scalar. The name of the file to which we write the contents.
-//	contents: scalar. The content to be written to the output file.
-//
-// Returns the created operation.
-func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "WriteFile",
-		Input: []tf.Input{
-			filename, contents,
-		},
-	}
-	return scope.AddOperation(opspec)
-}
-
-// Computes the Cholesky decomposition of one or more square matrices.
-//
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices.
+//    `dimI` are the dimensions of the larger tensor and `slice-spec`
+//    specifies what part is covered by the tensor to save.
 //
-// The input has to be symmetric and positive definite. Only the lower-triangular
-// part of the input will be used for this operation. The upper-triangular part
-// will not be read.
+// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
+// where each `sliceI` is either:
 //
-// The output is a tensor of the same shape as the input
-// containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
+// *  The string `-` meaning that the slice covers all indices of this dimension
+// *  `start,length` where `start` and `length` are integers.  In that
+//    case the slice covers `length` indices starting at `start`.
 //
-// **Note**: The gradient computation on GPU is faster for large matrices but
-// not for large batch dimensions when the submatrices are small. In this
-// case it might be faster to use the CPU.
+// See also `Save`.
 //
 // Arguments:
-//	input: Shape is `[..., M, M]`.
+//	filename: Must have a single element. The name of the file to which we write the
+// tensor.
+//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
+//	shapes_and_slices: Shape `[N]`.  The shapes and slice specifications to use when
+// saving the tensors.
+//	data: `N` tensors to save.
 //
-// Returns Shape is `[..., M, M]`.
-func Cholesky(scope *Scope, input tf.Output) (output tf.Output) {
+// Returns the created operation.
+func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Cholesky",
+		Type: "SaveSlices",
 		Input: []tf.Input{
-			input,
+			filename, tensor_names, shapes_and_slices, tf.OutputList(data),
 		},
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return scope.AddOperation(opspec)
 }
 
 // Returns the rank of a tensor.
@@ -10929,6 +10891,104 @@ func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output,
 	return op.Output(0)
 }
 
+// Writes contents to the file at input filename. Creates file if not existing.
+//
+// Arguments:
+//	filename: scalar. The name of the file to which we write the contents.
+//	contents: scalar. The content to be written to the output file.
+//
+// Returns the created operation.
+func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "WriteFile",
+		Input: []tf.Input{
+			filename, contents,
+		},
+	}
+	return scope.AddOperation(opspec)
+}
+
+// Computes the Cholesky decomposition of one or more square matrices.
+//
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices.
+//
+// The input has to be symmetric and positive definite. Only the lower-triangular
+// part of the input will be used for this operation. The upper-triangular part
+// will not be read.
+//
+// The output is a tensor of the same shape as the input
+// containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
+//
+// **Note**: The gradient computation on GPU is faster for large matrices but
+// not for large batch dimensions when the submatrices are small. In this
+// case it might be faster to use the CPU.
+//
+// Arguments:
+//	input: Shape is `[..., M, M]`.
+//
+// Returns Shape is `[..., M, M]`.
+func Cholesky(scope *Scope, input tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Cholesky",
+		Input: []tf.Input{
+			input,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Slice a `SparseTensor` based on the `start` and `size`.
+//
+// For example, if the input is
+//
+//     input_tensor = shape = [2, 7]
+//     [    a   d e  ]
+//     [b c          ]
+//
+// Graphically the output tensors are:
+//
+//     sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
+//     [    a  ]
+//     [b c    ]
+//
+//     sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
+//     [ d e  ]
+//     [      ]
+//
+// Arguments:
+//	indices: 2-D tensor represents the indices of the sparse tensor.
+//	values: 1-D tensor represents the values of the sparse tensor.
+//	shape: 1-D. tensor represents the shape of the sparse tensor.
+//	start: 1-D. tensor represents the start of the slice.
+//	size: 1-D. tensor represents the size of the slice.
+// output indices: A list of 1-D tensors represents the indices of the output
+// sparse tensors.
+//
+// Returns A list of 1-D tensors represents the values of the output sparse
+// tensors.A list of 1-D tensors represents the shape of the output sparse
+// tensors.
+func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseSlice",
+		Input: []tf.Input{
+			indices, values, shape, start, size,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
+}
+
 // ListDiffAttr is an optional argument to ListDiff.
 type ListDiffAttr func(optionalAttr)
 
@@ -11295,34 +11355,6 @@ func FusedPadConv2D(scope *Scope, input tf.Output, paddings tf.Output, filter tf
 	return op.Output(0)
 }
 
-// Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
-//
-// For an explanation see "Differentiation of the Cholesky algorithm" by
-// Iain Murray http://arxiv.org/abs/1602.07527.
-//
-// Arguments:
-//	l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`.
-// Algorithm depends only on lower triangular part of the innermost matrices of
-// this tensor.
-//	grad: df/dl where f is some scalar function. Shape is `[..., M, M]`.
-// Algorithm depends only on lower triangular part of the innermost matrices of
-// this tensor.
-//
-// Returns Symmetrized version of df/dA . Shape is `[..., M, M]`
-func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "CholeskyGrad",
-		Input: []tf.Input{
-			l, grad,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Returns immutable tensor from memory region.
 //
 // The current implementation memmaps the tensor from a file.
@@ -11410,6 +11442,67 @@ func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.D
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul.
+type SparseTensorDenseMatMulAttr func(optionalAttr)
+
+// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value.
+//
+// value: Use the adjoint of A in the matrix multiply.  If A is complex, this
+// is transpose(conj(A)).  Otherwise it's transpose(A).
+// If not specified, defaults to false
+func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr {
+	return func(m optionalAttr) {
+		m["adjoint_a"] = value
+	}
+}
+
+// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value.
+//
+// value: Use the adjoint of B in the matrix multiply.  If B is complex, this
+// is transpose(conj(B)).  Otherwise it's transpose(B).
+// If not specified, defaults to false
+func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr {
+	return func(m optionalAttr) {
+		m["adjoint_b"] = value
+	}
+}
+
+// Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
+//
+// No validity checking is performed on the indices of A.  However, the following
+// input format is recommended for optimal behavior:
+//
+// if adjoint_a == false:
+//   A should be sorted in lexicographically increasing order.  Use SparseReorder
+//   if you're not sure.
+// if adjoint_a == true:
+//   A should be sorted in order of increasing dimension 1 (i.e., "column major"
+//   order instead of "row major" order).
+//
+// Arguments:
+//	a_indices: 2-D.  The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
+//	a_values: 1-D.  The `values` of the `SparseTensor`, size `[nnz]` Vector.
+//	a_shape: 1-D.  The `shape` of the `SparseTensor`, size `[2]` Vector.
+//	b: 2-D.  A dense Matrix.
+func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseTensorDenseMatMul",
+		Input: []tf.Input{
+			a_indices, a_values, a_shape, b,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Encode audio data using the WAV file format.
 //
 // This operation will generate a string suitable to be saved out to create a .wav
@@ -14306,51 +14399,6 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
-// Computes inverse hyperbolic sine of x element-wise.
-func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Asinh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes inverse hyperbolic cosine of x element-wise.
-func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Acosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes inverse hyperbolic tangent of x element-wise.
-func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
-  if scope.Err() != nil {
-    return
-  }
-  opspec := tf.OpSpec{
-    Type: "Atanh",
-    Input: []tf.Input{
-      x,
-    },
-  }
-  op := scope.AddOperation(opspec)
-  return op.Output(0)
-}
-
 // TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
 type TextLineReaderV2Attr func(optionalAttr)
 
@@ -16193,12 +16241,23 @@ func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2A
 
 // FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value.
 //
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value.
+//
+// value: The type of encoding for the file. Currently ZLIB and GZIP
+// are supported. Defaults to none.
 // If not specified, defaults to ""
-func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr {
+func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr {
 	return func(m optionalAttr) {
-		m["shared_name"] = value
+		m["encoding"] = value
 	}
 }
 
@@ -18142,6 +18201,21 @@ func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *
 	return scope.AddOperation(opspec)
 }
 
+// Computes inverse hyperbolic sine of x element-wise.
+func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Asinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Real-valued fast Fourier transform.
 //
 // Computes the 1-dimensional discrete Fourier transform of a real-valued signal
@@ -19377,6 +19451,48 @@ func FFT3D(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
+// Returns the truth value of (x <= y) element-wise.
+//
+// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "LessEqual",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes log softmax activations.
+//
+// For each batch `i` and class `j` we have
+//
+//     logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
+//
+// Arguments:
+//	logits: 2-D with shape `[batch_size, num_classes]`.
+//
+// Returns Same shape as `logits`.
+func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "LogSoftmax",
+		Input: []tf.Input{
+			logits,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Given a quantized tensor described by (input, input_min, input_max), outputs a
 //
 // range that covers the actual values present in that tensor.  This op is
@@ -20696,14 +20812,213 @@ func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...R
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResizeBicubic",
+		Input: []tf.Input{
+			images, size,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes rectified linear 6 gradients for a Relu6 operation.
+//
+// Arguments:
+//	gradients: The backpropagated gradients to the corresponding Relu6 operation.
+//	features: The features passed as input to the corresponding Relu6 operation.
+//
+// Returns The gradients:
+// `gradients * (features > 0) * (features < 6)`.
+func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Relu6Grad",
+		Input: []tf.Input{
+			gradients, features,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes natural logarithm of (1 + x) element-wise.
+//
+// I.e., \\(y = \log_e (1 + x)\\).
+func Log1p(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Log1p",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Creates a dataset that emits each dim-0 slice of `components` once.
+func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"output_shapes": output_shapes}
+	opspec := tf.OpSpec{
+		Type: "TensorSliceDataset",
+		Input: []tf.Input{
+			tf.OutputList(components),
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes tan of x element-wise.
+func Tan(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Tan",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes hyperbolic cosine of x element-wise.
+func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Cosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// MapClearAttr is an optional argument to MapClear.
+type MapClearAttr func(optionalAttr)
+
+// MapClearCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func MapClearCapacity(value int64) MapClearAttr {
+	return func(m optionalAttr) {
+		m["capacity"] = value
+	}
+}
+
+// MapClearMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func MapClearMemoryLimit(value int64) MapClearAttr {
+	return func(m optionalAttr) {
+		m["memory_limit"] = value
+	}
+}
+
+// MapClearContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func MapClearContainer(value string) MapClearAttr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// MapClearSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func MapClearSharedName(value string) MapClearAttr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// Op removes all elements in the underlying container.
+//
+// Returns the created operation.
+func MapClear(scope *Scope, dtypes []tf.DataType, optional ...MapClearAttr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"dtypes": dtypes}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "MapClear",
+
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// TensorArrayV2Attr is an optional argument to TensorArrayV2.
+type TensorArrayV2Attr func(optionalAttr)
+
+// TensorArrayV2ElementShape sets the optional element_shape attribute to value.
+// If not specified, defaults to <unknown_rank:true >
+func TensorArrayV2ElementShape(value tf.Shape) TensorArrayV2Attr {
+	return func(m optionalAttr) {
+		m["element_shape"] = value
+	}
+}
+
+// TensorArrayV2DynamicSize sets the optional dynamic_size attribute to value.
+// If not specified, defaults to false
+func TensorArrayV2DynamicSize(value bool) TensorArrayV2Attr {
+	return func(m optionalAttr) {
+		m["dynamic_size"] = value
+	}
+}
+
+// TensorArrayV2ClearAfterRead sets the optional clear_after_read attribute to value.
+// If not specified, defaults to true
+func TensorArrayV2ClearAfterRead(value bool) TensorArrayV2Attr {
+	return func(m optionalAttr) {
+		m["clear_after_read"] = value
+	}
+}
+
+// TensorArrayV2TensorArrayName sets the optional tensor_array_name attribute to value.
+// If not specified, defaults to ""
+func TensorArrayV2TensorArrayName(value string) TensorArrayV2Attr {
+	return func(m optionalAttr) {
+		m["tensor_array_name"] = value
+	}
+}
+
+// Deprecated. Use TensorArrayV3
+func TensorArrayV2(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV2Attr) (handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"dtype": dtype}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "ResizeBicubic",
+		Type: "TensorArrayV2",
 		Input: []tf.Input{
-			images, size,
+			size,
 		},
 		Attrs: attrs,
 	}
@@ -20711,84 +21026,84 @@ func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...R
 	return op.Output(0)
 }
 
-// Computes rectified linear 6 gradients for a Relu6 operation.
+// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Tensor`.
 //
-// Arguments:
-//	gradients: The backpropagated gradients to the corresponding Relu6 operation.
-//	features: The features passed as input to the corresponding Relu6 operation.
+// The `SparseTensor` must have rank `R` greater than 1, and the first dimension
+// is treated as the minibatch dimension.  Elements of the `SparseTensor`
+// must be sorted in increasing order of this first dimension.  The serialized
+// `SparseTensor` objects going into each row of `serialized_sparse` will have
+// rank `R-1`.
 //
-// Returns The gradients:
-// `gradients * (features > 0) * (features < 6)`.
-func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Relu6Grad",
-		Input: []tf.Input{
-			gradients, features,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes natural logarithm of (1 + x) element-wise.
+// The minibatch size `N` is extracted from `sparse_shape[0]`.
 //
-// I.e., \\(y = \log_e (1 + x)\\).
-func Log1p(scope *Scope, x tf.Output) (y tf.Output) {
+// Arguments:
+//	sparse_indices: 2-D.  The `indices` of the minibatch `SparseTensor`.
+//	sparse_values: 1-D.  The `values` of the minibatch `SparseTensor`.
+//	sparse_shape: 1-D.  The `shape` of the minibatch `SparseTensor`.
+func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) (serialized_sparse tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Log1p",
+		Type: "SerializeManySparse",
 		Input: []tf.Input{
-			x,
+			sparse_indices, sparse_values, sparse_shape,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Creates a dataset that emits each dim-0 slice of `components` once.
-func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
+// Computes inverse hyperbolic cosine of x element-wise.
+func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"output_shapes": output_shapes}
 	opspec := tf.OpSpec{
-		Type: "TensorSliceDataset",
+		Type: "Acosh",
 		Input: []tf.Input{
-			tf.OutputList(components),
+			x,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes tan of x element-wise.
-func Tan(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
+//
+// For an explanation see "Differentiation of the Cholesky algorithm" by
+// Iain Murray http://arxiv.org/abs/1602.07527.
+//
+// Arguments:
+//	l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`.
+// Algorithm depends only on lower triangular part of the innermost matrices of
+// this tensor.
+//	grad: df/dl where f is some scalar function. Shape is `[..., M, M]`.
+// Algorithm depends only on lower triangular part of the innermost matrices of
+// this tensor.
+//
+// Returns Symmetrized version of df/dA . Shape is `[..., M, M]`
+func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Tan",
+		Type: "CholeskyGrad",
 		Input: []tf.Input{
-			x,
+			l, grad,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes hyperbolic cosine of x element-wise.
-func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
+// Computes inverse hyperbolic tangent of x element-wise.
+func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "Cosh",
+		Type: "Atanh",
 		Input: []tf.Input{
 			x,
 		},
@@ -22104,48 +22419,6 @@ func Betainc(scope *Scope, a tf.Output, b tf.Output, x tf.Output) (z tf.Output)
 	return op.Output(0)
 }
 
-// Computes log softmax activations.
-//
-// For each batch `i` and class `j` we have
-//
-//     logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
-//
-// Arguments:
-//	logits: 2-D with shape `[batch_size, num_classes]`.
-//
-// Returns Same shape as `logits`.
-func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "LogSoftmax",
-		Input: []tf.Input{
-			logits,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Returns the truth value of (x <= y) element-wise.
-//
-// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "LessEqual",
-		Input: []tf.Input{
-			x, y,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Returns the truth value of x OR y element-wise.
 //
 // *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
@@ -22642,6 +22915,44 @@ func ImageSummary(scope *Scope, tag tf.Output, tensor tf.Output, optional ...Ima
 	return op.Output(0)
 }
 
+// Bucketizes 'input' based on 'boundaries'.
+//
+// For example, if the inputs are
+//     boundaries = [0, 10, 100]
+//     input = [[-5, 10000]
+//              [150,   10]
+//              [5,    100]]
+//
+// then the output will be
+//     output = [[0, 3]
+//               [3, 2]
+//               [1, 3]]
+//
+// Arguments:
+//	input: Any shape of Tensor contains with int or float type.
+//	boundaries: A sorted list of floats gives the boundary of the buckets.
+//
+// Returns Same shape with 'input', each value of input replaced with bucket index.
+//
+// @compatibility(numpy)
+// Equivalent to np.digitize.
+// @end_compatibility
+func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"boundaries": boundaries}
+	opspec := tf.OpSpec{
+		Type: "Bucketize",
+		Input: []tf.Input{
+			input,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Reshapes a SparseTensor to represent values in a new dense shape.
 //
 // This operation has the same semantics as reshape on the represented dense
@@ -22684,44 +22995,6 @@ func SparseReshape(scope *Scope, input_indices tf.Output, input_shape tf.Output,
 	return op.Output(0), op.Output(1)
 }
 
-// Bucketizes 'input' based on 'boundaries'.
-//
-// For example, if the inputs are
-//     boundaries = [0, 10, 100]
-//     input = [[-5, 10000]
-//              [150,   10]
-//              [5,    100]]
-//
-// then the output will be
-//     output = [[0, 3]
-//               [3, 2]
-//               [1, 3]]
-//
-// Arguments:
-//	input: Any shape of Tensor contains with int or float type.
-//	boundaries: A sorted list of floats gives the boundary of the buckets.
-//
-// Returns Same shape with 'input', each value of input replaced with bucket index.
-//
-// @compatibility(numpy)
-// Equivalent to np.digitize.
-// @end_compatibility
-func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"boundaries": boundaries}
-	opspec := tf.OpSpec{
-		Type: "Bucketize",
-		Input: []tf.Input{
-			input,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes the product along segments of a tensor.
 //
 // Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
@@ -24161,59 +24434,6 @@ func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr)
 	return op.Output(0)
 }
 
-// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2.
-type TFRecordReaderV2Attr func(optionalAttr)
-
-// TFRecordReaderV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// TFRecordReaderV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value.
-// If not specified, defaults to ""
-func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr {
-	return func(m optionalAttr) {
-		m["compression_type"] = value
-	}
-}
-
-// A Reader that outputs the records from a TensorFlow Records file.
-//
-// Returns The handle to reference the Reader.
-func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "TFRecordReaderV2",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Adjust the saturation of one or more images.
 //
 // `images` is a tensor of at least 3 dimensions.  The last dimension is
-- 
GitLab


From 03ecff186bf9396d1aca0b9482975fde1b144dc0 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Tue, 11 Jul 2017 00:23:49 -0400
Subject: [PATCH 0871/1375] Remove duplicate op definitions: Acosh, Asinh and
 Atanh

---
 tensorflow/go/op/wrappers.go | 67 ++++++------------------------------
 1 file changed, 11 insertions(+), 56 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 9cf5bf2d77..7e76c3b5f0 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14431,17 +14431,17 @@ func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
 
 // Computes inverse hyperbolic tangent of x element-wise.
 func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
-  if scope.Err() != nil {
-    return
-  }
-  opspec := tf.OpSpec{
-    Type: "Atanh",
-    Input: []tf.Input{
-      x,
-    },
-  }
-  op := scope.AddOperation(opspec)
-  return op.Output(0)
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Atanh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
 // TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
@@ -18246,21 +18246,6 @@ func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *
 	return scope.AddOperation(opspec)
 }
 
-// Computes inverse hyperbolic sine of x element-wise.
-func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Asinh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Real-valued fast Fourier transform.
 //
 // Computes the 1-dimensional discrete Fourier transform of a real-valued signal
@@ -21099,21 +21084,6 @@ func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values t
 	return op.Output(0)
 }
 
-// Computes inverse hyperbolic cosine of x element-wise.
-func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Acosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
 //
 // For an explanation see "Differentiation of the Cholesky algorithm" by
@@ -21142,21 +21112,6 @@ func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output)
 	return op.Output(0)
 }
 
-// Computes inverse hyperbolic tangent of x element-wise.
-func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Atanh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes the log of the absolute value of `Gamma(x)` element-wise.
 func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From ea3d9ab10f6391ce131f0fd39a214c66fdf06ae6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 22:01:27 -0700
Subject: [PATCH 0872/1375] Removed the _tensor_summary_v2 op.

Made the tensor_summary op directly use the TensorSummaryV2 kernel.

PiperOrigin-RevId: 161486007
---
 .../kernel_tests/summary_tensor_op_test.py    | 10 +--
 tensorflow/python/ops/summary_ops.py          | 70 +------------------
 tensorflow/python/summary/summary.py          |  2 -
 tensorflow/python/summary/text_summary.py     |  4 +-
 .../tools/api/golden/tensorflow.summary.pbtxt |  2 +-
 5 files changed, 11 insertions(+), 77 deletions(-)

diff --git a/tensorflow/python/kernel_tests/summary_tensor_op_test.py b/tensorflow/python/kernel_tests/summary_tensor_op_test.py
index ee3f5aa250..48489433a0 100644
--- a/tensorflow/python/kernel_tests/summary_tensor_op_test.py
+++ b/tensorflow/python/kernel_tests/summary_tensor_op_test.py
@@ -41,7 +41,7 @@ class SummaryOpsTest(test.TestCase):
   def _AssertNumpyEq(self, actual, expected):
     self.assertTrue(np.array_equal(actual, expected))
 
-  def testNodeNames(self):
+  def testTags(self):
     with self.test_session() as sess:
       c = constant_op.constant(1)
       s1 = summary_ops.tensor_summary("s1", c)
@@ -53,16 +53,16 @@ class SummaryOpsTest(test.TestCase):
       summ1, summ2, summ3, summ4 = sess.run([s1, s2, s3, s4])
 
     v1 = self._SummarySingleValue(summ1)
-    self.assertEqual(v1.node_name, "s1")
+    self.assertEqual(v1.tag, "s1")
 
     v2 = self._SummarySingleValue(summ2)
-    self.assertEqual(v2.node_name, "foo/s2")
+    self.assertEqual(v2.tag, "foo/s2")
 
     v3 = self._SummarySingleValue(summ3)
-    self.assertEqual(v3.node_name, "foo/zod/s3")
+    self.assertEqual(v3.tag, "foo/zod/s3")
 
     v4 = self._SummarySingleValue(summ4)
-    self.assertEqual(v4.node_name, "foo/zod/TensorSummary")
+    self.assertEqual(v4.tag, "foo/zod/TensorSummary")
 
   def testScalarSummary(self):
     with self.test_session() as sess:
diff --git a/tensorflow/python/ops/summary_ops.py b/tensorflow/python/ops/summary_ops.py
index 3d19bc2526..02bb394822 100644
--- a/tensorflow/python/ops/summary_ops.py
+++ b/tensorflow/python/ops/summary_ops.py
@@ -18,8 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from google.protobuf import json_format
-from tensorflow.core.framework import summary_pb2
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import gen_logging_ops
 from tensorflow.python.ops import summary_op_util
@@ -29,77 +27,15 @@ from tensorflow.python.ops.gen_logging_ops import *
 # pylint: enable=wildcard-import
 
 
-# TODO(dandelion): As currently implemented, this op has several problems.
-# The 'summary_description' field is passed but not used by the kernel.
-# The 'name' field is used to creat a scope and passed down via name=scope,
-# but gen_logging_ops._tensor_summary ignores this parameter and uses the
-# kernel's op name as the name. This is ok because scope and the op name
-# are identical, but it's probably worthwhile to fix.
-# Finally, because of the complications above, this currently does not
-# support the family= attribute added to other summaries in cl/156791589.
-def tensor_summary(  # pylint: disable=invalid-name
-    name,
-    tensor,
-    summary_description=None,
-    collections=None):
-  # pylint: disable=line-too-long
-  """Outputs a `Summary` protocol buffer with a serialized tensor.proto.
-
-  The generated
-  [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-  has one summary value containing the input tensor.
-
-  Args:
-    name: A name for the generated node. Will also serve as the series name in
-      TensorBoard.
-    tensor: A tensor of any type and shape to serialize.
-    summary_description: Optional summary_pb2.SummaryDescription()
-    collections: Optional list of graph collections keys. The new summary op is
-      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
-
-  Returns:
-    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
-    buffer.
-  """
-  # pylint: enable=line-too-long
-
-  if summary_description is None:
-    summary_description = summary_pb2.SummaryDescription()
-
-  description = json_format.MessageToJson(summary_description)
-  with ops.name_scope(name, None, [tensor]) as scope:
-    val = gen_logging_ops._tensor_summary(
-        tensor=tensor,
-        description=description,
-        name=scope)
-    summary_op_util.collect(val, collections, [ops.GraphKeys.SUMMARIES])
-  return val
-
-ops.NotDifferentiable("TensorSummary")
-
-
-def _tensor_summary_v2(  # pylint: disable=invalid-name
+def tensor_summary(
     name,
     tensor,
     summary_description=None,
     collections=None,
     summary_metadata=None,
     family=None):
-  # pylint: disable=line-too-long
   """Outputs a `Summary` protocol buffer with a serialized tensor.proto.
 
-  NOTE(chizeng): This method is temporary. It should never make it into
-  TensorFlow 1.3, and nothing should depend on it. This method should be deleted
-  before August 2017 (ideally, earlier). This method exists to unblock the
-  TensorBoard plugin refactoring effort. We will later modify the tensor_summary
-  method to directly make use of the TensorSummaryV2 op. There must be a 3-week
-  difference between adding a new op (C++) and changing a python interface to
-  use it.
-
-  The generated
-  [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-  has one summary value containing the input tensor.
-
   Args:
     name: A name for the generated node. Will also serve as the series name in
       TensorBoard.
@@ -117,8 +53,6 @@ def _tensor_summary_v2(  # pylint: disable=invalid-name
     A scalar `Tensor` of type `string`. The serialized `Summary` protocol
     buffer.
   """
-  # pylint: enable=line-too-long
-
   # The summary description is unused now.
   del summary_description
 
@@ -135,3 +69,5 @@ def _tensor_summary_v2(  # pylint: disable=invalid-name
         serialized_summary_metadata=serialized_summary_metadata)
     summary_op_util.collect(val, collections, [ops.GraphKeys.SUMMARIES])
   return val
+
+ops.NotDifferentiable("TensorSummary")
diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py
index f3600793a6..90afcc0a11 100644
--- a/tensorflow/python/summary/summary.py
+++ b/tensorflow/python/summary/summary.py
@@ -20,7 +20,6 @@ See the @{$python/summary} guide.
 @@FileWriter
 @@FileWriterCache
 @@tensor_summary
-@@_tensor_summary_v2
 @@scalar
 @@histogram
 @@audio
@@ -56,7 +55,6 @@ from tensorflow.python.ops import summary_op_util as _summary_op_util
 
 # exports tensor-related summaries
 # pylint: disable=unused-import
-from tensorflow.python.ops.summary_ops import _tensor_summary_v2
 from tensorflow.python.ops.summary_ops import tensor_summary
 # pylint: enable=unused-import
 
diff --git a/tensorflow/python/summary/text_summary.py b/tensorflow/python/summary/text_summary.py
index 72799f2c6c..b97c02666c 100644
--- a/tensorflow/python/summary/text_summary.py
+++ b/tensorflow/python/summary/text_summary.py
@@ -28,7 +28,7 @@ import json
 
 from tensorflow.core.framework import summary_pb2
 from tensorflow.python.framework import dtypes
-from tensorflow.python.ops.summary_ops import _tensor_summary_v2
+from tensorflow.python.ops.summary_ops import tensor_summary
 from tensorflow.python.summary import plugin_asset
 
 PLUGIN_NAME = "text"
@@ -72,7 +72,7 @@ def text_summary(name, tensor, collections=None):
   data_dict = text_plugin_data._asdict()  # pylint: disable=protected-access
   summary_metadata.plugin_data.add(
       plugin_name=PLUGIN_NAME, content=json.dumps(data_dict))
-  t_summary = _tensor_summary_v2(
+  t_summary = tensor_summary(
       name=name,
       tensor=tensor,
       summary_metadata=summary_metadata,
diff --git a/tensorflow/tools/api/golden/tensorflow.summary.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.pbtxt
index 19d822e61b..b3748d2c6f 100644
--- a/tensorflow/tools/api/golden/tensorflow.summary.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.summary.pbtxt
@@ -58,7 +58,7 @@ tf_module {
   }
   member_method {
     name: "tensor_summary"
-    argspec: "args=[\'name\', \'tensor\', \'summary_description\', \'collections\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+    argspec: "args=[\'name\', \'tensor\', \'summary_description\', \'collections\', \'summary_metadata\', \'family\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "text"
-- 
GitLab


From 8281e234c1dd741f30f657b66d129089e81f63e8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 10 Jul 2017 22:11:52 -0700
Subject: [PATCH 0873/1375] tfprof further clean ups

PiperOrigin-RevId: 161486865
---
 tensorflow/contrib/tfprof/BUILD               |   1 +
 tensorflow/contrib/tfprof/model_analyzer.py   |   7 +
 tensorflow/contrib/tfprof/tfprof_logger.py    |   9 +-
 tensorflow/core/profiler/README.md            | 126 +++++++++++++++---
 .../core/profiler/g3doc/command_line.md       |  16 +--
 tensorflow/core/profiler/g3doc/options.md     |  67 +++++-----
 tensorflow/core/profiler/g3doc/python_api.md  |  20 +--
 .../advisor/expensive_operation_checker.h     |   7 +-
 .../core/profiler/internal/tfprof_show.h      |   8 +-
 .../profiler/internal/tfprof_show_test.cc     |  74 +++++-----
 .../core/profiler/internal/tfprof_utils.cc    |  93 +++----------
 tensorflow/core/profiler/profiler.cc          |  15 +--
 tensorflow/python/profiler/model_analyzer.py  |  49 ++++---
 13 files changed, 266 insertions(+), 226 deletions(-)

diff --git a/tensorflow/contrib/tfprof/BUILD b/tensorflow/contrib/tfprof/BUILD
index 9b9215234e..4ff97e5d76 100644
--- a/tensorflow/contrib/tfprof/BUILD
+++ b/tensorflow/contrib/tfprof/BUILD
@@ -14,6 +14,7 @@ py_library(
     srcs_version = "PY2AND3",
     visibility = ["//tensorflow:__subpackages__"],
     deps = [
+        "//tensorflow/python:util",
         "//tensorflow/python/profiler:model_analyzer",
         "//tensorflow/python/profiler:tfprof_logger",
     ],
diff --git a/tensorflow/contrib/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/model_analyzer.py
index a7b609a031..d45c8f54c4 100644
--- a/tensorflow/contrib/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/model_analyzer.py
@@ -28,6 +28,7 @@ from tensorflow.python.profiler.model_analyzer import advise as _advise
 from tensorflow.python.profiler.model_analyzer import ALL_ADVICE
 from tensorflow.python.profiler.model_analyzer import profile as _profile
 from tensorflow.python.profiler.model_analyzer import Profiler
+from tensorflow.python.util.deprecation import deprecated
 
 _DEFAULT_PROFILE_OPTIONS = 0
 _DEFAULT_ADVISE_OPTIONS = 0
@@ -98,10 +99,16 @@ PRINT_ALL_TIMING_MEMORY = {
 # pylint: enable=bad-continuation
 
 
+@deprecated('2018-01-01',
+            'Use `tf.profiler.advise(graph, run_meta, options)`. See README.md')
 def advise(graph, run_meta=None, tfprof_options=_DEFAULT_ADVISE_OPTIONS):
   return _advise(graph, run_meta, tfprof_options)
 
 
+@deprecated('2018-01-01',
+            'Use `tf.profiler.profile(graph, run_meta, op_log, cmd, options)`. '
+            'Build `options` with `tf.profiler.ProfileOptionBuilder`. '
+            'See README.md for details')
 def print_model_analysis(graph,
                          run_meta=None,
                          op_log=None,
diff --git a/tensorflow/contrib/tfprof/tfprof_logger.py b/tensorflow/contrib/tfprof/tfprof_logger.py
index 321ac5ad9f..78eaca37a1 100644
--- a/tensorflow/contrib/tfprof/tfprof_logger.py
+++ b/tensorflow/contrib/tfprof/tfprof_logger.py
@@ -20,5 +20,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# pylint: disable=unused-import
-from tensorflow.python.profiler.tfprof_logger import write_op_log
+from tensorflow.python.profiler.tfprof_logger import write_op_log as _write_op_log
+from tensorflow.python.util.deprecation import deprecated
+
+
+@deprecated("2018-01-01", "Use `tf.profiler.write_op_log. go/tfprof`")
+def write_op_log(graph, log_dir, op_log=None, run_meta=None, add_trace=True):
+  _write_op_log(graph, log_dir, op_log, run_meta, add_trace)
diff --git a/tensorflow/core/profiler/README.md b/tensorflow/core/profiler/README.md
index e86584acd1..d55824e6f2 100644
--- a/tensorflow/core/profiler/README.md
+++ b/tensorflow/core/profiler/README.md
@@ -1,12 +1,19 @@
-# tfprof: TensorFlow Profiler and Beyond
+<h1>TensorFlow Profiler and Advisor</h1>
+
+* [Features](#features)
+* [Interfaces](#interfaces)
+* [Tutorials](#tutorials)
+* [Demo](#demo)
+* [Feature Request and Bug Report](#feature-request-and-bug-report)
+
+Contact for bug report and feature request (xpan@google.com)
 
 ### Features
 
-* Profile model architectures
+* Profile model architectures.
   * parameters, tensor shapes, float operations, device placement, etc.
-* Profile model performance
-  * execution time, memory consumption
-  * Profile multiple steps.
+* Profile multiple-steps model performance.
+  * execution time, memory consumption.
 * Auto profile and advise.
   * accelerator utilization check
   * expensive operation check
@@ -20,9 +27,7 @@
 * Visualization
 * C++ API (Not public, contact us if needed.)
 
-### Views and Options
-
-tfprof provides 4 different views to organize the statistics.
+tfprof provides 4 different views to organize the profiles.
 
     *  code view: operations are grouped by Python codes that generate them.
     *  op view: operations are grouped by operation type (E.g. MatMul, Conv2D).
@@ -101,26 +106,105 @@ _TFProfRoot (--/930.58k params)
 ### Show the most expensive operation types.
 ```
 tfprof> op -select micros,bytes,occurrence -order_by micros
-SoftmaxCrossEntropyWithLogits      36.58MB (100.00%, 0.05%),      1.37sec (100.00%, 23.56%),         30
-MatMul                        2720.57MB (99.95%, 3.66%),      988.90ms (76.44%, 17.05%),       3450
-ConcatV2                       741.37MB (96.29%, 1.00%),       421.44ms (59.38%, 7.27%),       6098
-Mul                           3957.24MB (95.29%, 5.33%),       418.90ms (52.12%, 7.22%),       9427
-Add                            740.05MB (89.96%, 1.00%),       335.26ms (44.89%, 5.78%),       2180
-Sub                             32.46MB (88.97%, 0.04%),       216.44ms (39.11%, 3.73%),       4372
-AddN                           733.21MB (88.92%, 0.99%),       208.46ms (35.38%, 3.59%),       5481
-Slice                          708.07MB (87.94%, 0.95%),       205.27ms (31.78%, 3.54%),       7277
-Fill                           954.27MB (86.98%, 1.28%),       154.50ms (28.24%, 2.66%),       9686
-Select                         312.33MB (85.70%, 0.42%),       123.04ms (25.58%, 2.12%),       5746
-Sigmoid                        152.57MB (85.28%, 0.21%),        96.66ms (23.46%, 1.67%),       2970
+node name | output bytes | total execution time | accelerator execution time | cpu execution time | op occurrence (run|defined)
+SoftmaxCrossEntropyWithLogits      36.58MB (100.00%, 0.05%),      1.37sec (100.00%, 26.68%),           0us (100.00%, 0.00%),      1.37sec (100.00%, 30.75%),      30|30
+MatMul                        2720.57MB (99.95%, 3.66%),      708.14ms (73.32%, 13.83%),     280.76ms (100.00%, 41.42%),       427.39ms (69.25%, 9.62%),  2694|3450
+ConcatV2                       741.37MB (96.29%, 1.00%),       389.63ms (59.49%, 7.61%),        31.80ms (58.58%, 4.69%),       357.83ms (59.63%, 8.05%),  4801|6098
+Mul                           3957.24MB (95.29%, 5.33%),       338.02ms (51.88%, 6.60%),       80.88ms (53.88%, 11.93%),       257.14ms (51.58%, 5.79%),  7282|9427
+Add                            740.05MB (89.96%, 1.00%),       321.76ms (45.28%, 6.28%),        13.50ms (41.95%, 1.99%),       308.26ms (45.79%, 6.94%),  1699|2180
+Sub                             32.46MB (88.97%, 0.04%),       216.20ms (39.00%, 4.22%),          241us (39.96%, 0.04%),       215.96ms (38.85%, 4.86%),  1780|4372
+Slice                          708.07MB (88.92%, 0.95%),       179.88ms (34.78%, 3.51%),        25.38ms (39.92%, 3.74%),       154.50ms (33.99%, 3.48%),  5800|7277
+AddN                           733.21MB (87.97%, 0.99%),       158.36ms (31.26%, 3.09%),        50.10ms (36.18%, 7.39%),       108.26ms (30.51%, 2.44%),  4567|5481
+Fill                           954.27MB (86.98%, 1.28%),       138.29ms (28.17%, 2.70%),        16.21ms (28.79%, 2.39%),       122.08ms (28.08%, 2.75%),  3278|9686
+Select                         312.33MB (85.70%, 0.42%),       104.75ms (25.47%, 2.05%),        18.30ms (26.40%, 2.70%),        86.45ms (25.33%, 1.95%),  2880|5746
+ApplyAdam                      231.65MB (85.28%, 0.31%),        92.66ms (23.43%, 1.81%),            0us (23.70%, 0.00%),        92.66ms (23.38%, 2.09%),      27|27
+```
+
+### Auto-profile.
+
+```
+tfprof> advise
+Not running under xxxx. Skip JobChecker.
+
+AcceleratorUtilizationChecker:
+device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03
+device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08
+device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04
+device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21
+
+OperationChecker:
+Found operation using NHWC data_format on GPU. Maybe NCHW is faster.
+
+JobChecker:
+
+ExpensiveOperationChecker:
+top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%)
+top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%)
+top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%)
+top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms
+top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms
+top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms
+seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec
+  seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec
+    seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec
+      seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec
+      seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms
+  seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms
+    seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms
+    seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms
+      core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms
+    seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms
+  seq2seq_attention_model.py:253:_add_seq2seq:initial_state_att..., cpu: 32.48ms, accelerator: 50.01ms, total: 82.50ms
+    seq2seq.py:693:attention_decoder:attns = attention..., cpu: 11.73ms, accelerator: 38.41ms, total: 50.14ms
+      seq2seq.py:653:attention:s = math_ops.redu..., cpu: 2.62ms, accelerator: 17.80ms, total: 20.41ms
+      seq2seq.py:658:attention:array_ops.reshape..., cpu: 1.90ms, accelerator: 12.08ms, total: 13.98ms
+      seq2seq.py:655:attention:a = nn_ops.softma..., cpu: 4.15ms, accelerator: 4.25ms, total: 8.40ms
+    seq2seq.py:686:attention_decoder:cell_output, stat..., cpu: 14.43ms, accelerator: 4.85ms, total: 19.27ms
+    seq2seq.py:696:attention_decoder:output = linear([..., cpu: 3.04ms, accelerator: 2.88ms, total: 5.93ms
+      core_rnn_cell_impl.py:1009:_linear:res = math_ops.ma..., cpu: 2.33ms, accelerator: 2.71ms, total: 5.04ms
+seq2seq_attention_model.py:363:build_graph:self._add_train_o..., cpu: 1.28sec, accelerator: 462.93ms, total: 1.74sec
+  seq2seq_attention_model.py:307:_add_train_op:tf.gradients(self..., cpu: 967.84ms, accelerator: 462.88ms, total: 1.43sec
+    gradients_impl.py:563:gradients:grad_scope, op, f..., cpu: 692.60ms, accelerator: 390.75ms, total: 1.08sec
+    gradients_impl.py:554:gradients:out_grads[i] = co..., cpu: 164.71ms, accelerator: 16.21ms, total: 180.92ms
+      control_flow_ops.py:1314:ZerosLikeOutsideL...:return array_ops...., cpu: 121.85ms, accelerator: 16.21ms, total: 138.05ms
+      control_flow_ops.py:1313:ZerosLikeOutsideL...:zeros_shape = arr..., cpu: 22.85ms, accelerator: 0us, total: 22.85ms
+      control_flow_ops.py:1312:ZerosLikeOutsideL...:switch_val = swit..., cpu: 20.02ms, accelerator: 0us, total: 20.02ms
+    gradients_impl.py:515:gradients:out_grads = _Aggr..., cpu: 108.69ms, accelerator: 51.92ms, total: 160.61ms
+      gradients_impl.py:846:_AggregatedGrads:out_grads[i] = _M..., cpu: 107.99ms, accelerator: 50.05ms, total: 158.04ms
+      gradients_impl.py:856:_AggregatedGrads:array_ops.concat(..., cpu: 340us, accelerator: 1.87ms, total: 2.21ms
+  seq2seq_attention_model.py:322:_add_train_op:zip(grads, tvars)..., cpu: 307.56ms, accelerator: 0us, total: 307.56ms
+    optimizer.py:456:apply_gradients:update_ops.append..., cpu: 307.43ms, accelerator: 0us, total: 307.43ms
+      optimizer.py:102:update_op:return optimizer...., cpu: 222.66ms, accelerator: 0us, total: 222.66ms
+      optimizer.py:97:update_op:return optimizer...., cpu: 84.76ms, accelerator: 0us, total: 84.76ms
 ```
 
 ### Visualize time and memory.
+```
+tfprof> graph -step 0 -max_depth 100000 -output timeline:outfile=<filename>
+
+generating trace file.
+
+******************************************************
+Timeline file is written to <filename>.
+Open a Chrome browser, enter URL chrome://tracing and load the timeline file.
+******************************************************
+```
 <left>
 [CodeTimeline](g3doc/graph_timeline.png)
 </left>
 
-### Teams
+
+### Feature Request and Bug Report
+
+Contact: xpan@google.com
+
+Providing `GraphDef` and `RunMetadata` file will greatly help
+bug fix. `OpLogProto` is a good plus if it is used.
+
+
+#### Teams
 
 * Xin Pan (xpan@google.com, github: panyx0718)
-* Jon Shlens
 * Yao Zhang
+* Jon Shlens
diff --git a/tensorflow/core/profiler/g3doc/command_line.md b/tensorflow/core/profiler/g3doc/command_line.md
index 48d8078cdf..857b5e6459 100644
--- a/tensorflow/core/profiler/g3doc/command_line.md
+++ b/tensorflow/core/profiler/g3doc/command_line.md
@@ -16,12 +16,12 @@
 
 tfprof command line tool uses the following inputs:
 
-<b>--graph_path:</b> GraphDef text file (required). Used to build in-memory
-architecture of the model. For example, graph.pbtxt written by tf.Supervisor
+<b>--graph_path:</b> GraphDef proto file (required). Used to build in-memory
+data structure of the model. For example, graph.pbtxt written by tf.Supervisor
 can be passed to --graph_path. You can also easily get GraphDef using
 tf.get_default_graph().as_graph_def(add_shapes=True) or other API.
 
-<b>--run_meta_path:</b> tensorflow::RunMetadata (optional).
+<b>--run_meta_path:</b> RunMetadata proto file (optional).
 Used to get the memory consumption and execution time of
 each op of the model.
 
@@ -36,9 +36,9 @@ with tf.gfile.Open(os.path.join(output_dir, "run_meta"), "w") as f:
 ```
 
 <b>--op_log_path:</b>
-tensorflow::tfprof::OpLogProto (optional). A proto used to provide extra operation
+tensorflow.tfprof.OpLogProto (optional). A proto used to provide extra operation
 information. 1) float operations. 2) code traces. 3) define customized operation
-type for -account_type_regexes option.
+type for `-account_type_regexes` option.
 
 The following code snippet writes a OpLogProto file.
 
@@ -48,6 +48,7 @@ tf.profiler.write_op_log(graph, log_dir, op_log=None)
 
 <b>--checkpoint_path:</b> TensorFlow checkpoint (optional).
 It defines _checkpoint_variable op type. It also provides checkpointed tensors' values.
+Note: this feature is not well maintained now.
 
 
 ###Start `tfprof`
@@ -56,7 +57,7 @@ It defines _checkpoint_variable op type. It also provides checkpointed tensors'
 
 ```shell
 # Build the tool.
-bazel build --config opt tensorflow/core/profiler/...
+bazel build --config opt third_party/tensorflow/core/profiler/...
 
 # Help information, including detail 'option' instructions.
 bazel-bin/tensorflow/core/profiler/profiler help
@@ -260,7 +261,6 @@ First, in Python code, create an `OpLogProto` proto and add op type
 information to it:
 
 ```python
-
 op_log = tfprof_log_pb2.OpLogProto()
 entry = op_log.log_entries.add()
 entry.name = 'pool_logit/DW'
@@ -282,7 +282,7 @@ tf.profiler.write_op_log(
 ```
 
 Third, when starting the tfprof tool, specify
-"--op_log_path /tmp/my_op_log_dir/op_log"
+"--op_log_path=/tmp/my_op_log_dir/op_log"
 
 ```shell
 tfprof> scope -account_type_regexes pool_logit -max_depth 4 -select params
diff --git a/tensorflow/core/profiler/g3doc/options.md b/tensorflow/core/profiler/g3doc/options.md
index 051d72493e..9508379324 100644
--- a/tensorflow/core/profiler/g3doc/options.md
+++ b/tensorflow/core/profiler/g3doc/options.md
@@ -2,28 +2,37 @@
 
 ###Overview
 
-For all tfprof views, the statistics are processed with the following procedures
+For all tfprof views, the profiles are processed with the following procedures
 
-1) An in-memory data structure is used represent the view.
+1) An in-memory data structure is built represent the view.
 
-2) `-account_type_regexes` is used to first select the operations that match
+   *  graph view. Graph. Each profiler node corresponds to a
+      TensorFlow graph node.
+   *  scope view. Tree. Each profiler node corresponds to a
+      TensorFlow graph node.
+   *  code view. Tree. Each profiler node includes to all TensorFlow
+      graph nodes created by the profiler node (python code).
+   *  op view. List. Each profiler node includes to all TensorFlow
+      graph nodes belonging to an operation type.
+
+2) `-account_type_regexes` is used to first select the nodes that includes
    the specified operation types. An operation has its default type
    (e.g. MatMul, Conv2D). `tfprof` also considers device as operation type.
    User can also define customized operation type. Hence, an operation has
-   multiple types. Operations with matched
-   types are selected for display and their statistics are aggregated
-   by the in-memory data structure.
+   multiple types. Profiler nodes containing matched
+   types are selected for display and their statistics are aggregated by the
+   parents of the in-memory data structure.
 
 3) Various `-xxx_name_regexes`,  `-min_xxx`, `-max_depth` etc options are then
-   applied to further filter based on names and values.
+   applied to further filter based on profiler node names and statistics.
    It's no limited operation name. In code view,
-   it's the code trace. In op view, it's the operation type name. Different
-   from `-account_type_regexes`, Statistics are used even if a name is not displayed.
+   it's the code string. In op view, it's the operation type name. Different
+   from `-account_type_regexes`, Statistics are used even if a profiler node is not displayed.
    For example, in code view, a callee might be hidden, but its statistics is
    still aggregated by it's caller. `-account_displayed_op_only`, however,
-   breaks the rule and only use statistics of displayed names.
+   breaks the rule and only aggregates statistics of displayed names.
 
-4) Finally, the filtered data structure is displayed in a format depending
+4) Finally, the filtered data structure is output in a format depending
    on the `-output` option.
 
 ####Option Semantics In Different View
@@ -35,44 +44,38 @@ In graph view, in means the number of hops in the <b>graph</b>.
 
 ###Docs
 
-`-max_depth`: Show ops that are at most this number of hops from starting op in the tree/graph structure.
+`-max_depth`: Show nodes that are at most this number of hops from starting node in the data structure.
 
-`-min_bytes`: Show ops that request at least this number of bytes.
+`-min_bytes`: Show nodes that request at least this number of bytes.
 
-`-min_micros`: Show ops that spend at least this number of microseconds to run.
+`-min_micros`: Show nodes that spend at least this number of microseconds to run.
 
-`-min_params`: Show ops that contains at least this number of parameters.
+`-min_params`: Show nodes that contains at least this number of parameters.
 
-`-min_float_ops`: Show ops that contain at least this number of float operations. Only available if an op has op.RegisterStatistics() defined and OpLogProto is provided
+`-min_float_ops`: Show nodes that contain at least this number of float operations. Only available if an node has op.RegisterStatistics() defined and OpLogProto is provided
 
-`-min_occurrence`: Show ops that appear at least this number of times. Only available in "op" view.
+`-min_occurrence`: Show nodes that appear at least this number of times..
 
 `-step`: Show the stats of the this step when multiple steps of RunMetadata were added. By default, show the average of all steps."
 
 `-order_by`: Order the results by [name|depth|bytes|micros|accelerator_micros|cpu_micros|params|float_ops|occurrence]
 
-`-account_type_regexes`: Account and display the ops whose types match one of the type regexes specified. tfprof allow user to define extra op types for ops through tensorflow.tfprof.OpLogProto proto. regexes are comma-sperated.
-
-`-start_name_regexes`: Show ops starting from the ops that matches the regexes, recursively. regexes are comma-separated.
+`-account_type_regexes`: Account and display the nodes whose types match one of the type regexes specified. tfprof allow user to define extra operation types for graph nodes through tensorflow.tfprof.OpLogProto proto. regexes are comma-sperated.
 
-`-trim_name_regexes`: Hide ops starting from the ops that matches the regexes, recursively, regexes are comma-seprated.
+`-start_name_regexes`: Show node starting from the node that matches the regexes, recursively. regexes are comma-separated.
 
-`-show_name_regexes`: Show ops that match the regexes. regexes are comma-seprated.
+`-trim_name_regexes`: Hide node starting from the node that matches the regexes, recursively, regexes are comma-seprated.
 
-`-hide_name_regexes`: Hide ops that match the regexes. regexes are comma-seprated.
+`-show_name_regexes`: Show node that match the regexes. regexes are comma-seprated.
 
-Notes: For each op, `-account_type_regexes` is first evaluated, only ops with
-types matching the specified regexes are accounted and selected for displayed.
-`-start/trim/show/hide_name_regexes` are used to further filter ops for display.
-`-start_name_regexes` is evaluated first to search the starting ops to display.
-Descendants of starting ops are then evaluated against `-show/hide_name_regexes`
-to make display decision. If an op matches trim_name_regexes, all its
-descendants are hidden. Ops statistics are *accounted even if they are hidden*
-as long as they match the `-account_xxx` options.
+`-hide_name_regexes`: Hide node that match the regexes. regexes are comma-seprated.
 
 `-account_displayed_op_only`: If True, only account the statistics of ops eventually displayed. If False, account all op statistics matching -account_type_regexes recursively.
 
-`-select`: Comma-separated list of metrics to show:
+
+Notes: See <b>overview</b> sesion on how does above options play with each other to decide the output and counting.
+
+`-select`: Comma-separated list of attributes to show. Supported attributes:
 [bytes|micros|accelerator_micros|cpu_micros|params|float_ops|occurrence|tensor_value|device|op_types|input_shapes].
 
 `-output`: Output results as stdout, file or timeline.
diff --git a/tensorflow/core/profiler/g3doc/python_api.md b/tensorflow/core/profiler/g3doc/python_api.md
index 4f7e1c2d8d..42a89d22a1 100644
--- a/tensorflow/core/profiler/g3doc/python_api.md
+++ b/tensorflow/core/profiler/g3doc/python_api.md
@@ -51,8 +51,11 @@ compute the memory and timing statistics.
 ```python
 # Generate the RunMetadata that contains the memory and timing information.
 #
-# Note: When run on GPU, a kernel is first scheduled (enqueued) and then
-#       executed asynchronously. tfprof only tracks the execution time.
+# Note: When run on accelerator (e.g. GPU), an operation might perform some
+#       cpu computation, enqueue the accelerator computation. The accelerator
+#       computation is then run asynchronously. The profiler considers 3
+#       times: 1) accelerator computation. 2) cpu computation (might wait on
+#       accelerator). 3) the sum of 1 and 2.
 #
 run_metadata = tf.RunMetadata()
 with tf.Session() as sess:
@@ -61,12 +64,10 @@ with tf.Session() as sess:
                run_metadata=run_metadata)
 ```
 
-Finally, you may run `print_model_analysis` to explore the timing and memory
+Finally, you may run `tf.profiler.profile` to explore the timing and memory
 information of the model.
 
 ``` python
-# See model_analyzer_test.py for more examples.
-#
 # Print to stdout an analysis of the memory usage and the timing information
 # broken down by python codes.
 ProfileOptionBuilder = tf.profiler.ProfileOptionBuilder
@@ -80,10 +81,11 @@ tf.profiler.profile(
     options=opts)
 
 # Print to stdout an analysis of the memory usage and the timing information
-# broken down by operations.
+# broken down by operation types.
 tf.profiler.profile(
     tf.get_default_graph(),
     run_meta=run_metadata,
+    cmd='op',
     options=tf.profiler.ProfileOptionBuilder.time_and_memory())
 ```
 
@@ -91,11 +93,11 @@ tf.profiler.profile(
 
 ```
 To visualize the result of Python API results:
-Call with_step(0).with_timeline_output(filename)' to generate a timeline json file.
-Open a Chrome Browser, open URL chrome://tracing, and load the json file.
+Call `with_step(0).with_timeline_output(filename)` to generate a timeline json file.
+Open a Chrome Browser, type URL `chrome://tracing`, and load the json file.
 ```
 
-Below are 2 examples of graph view and scope view. See code view example in later examples.
+Below are 2 examples of graph view and scope view.
 
 <left>
 ![CodeTimeline](graph_timeline.png)
diff --git a/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h b/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
index 53c6fd2d7b..85b99dc951 100644
--- a/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
+++ b/tensorflow/core/profiler/internal/advisor/expensive_operation_checker.h
@@ -37,8 +37,8 @@ class ExpensiveOperationChecker : public Checker {
       fprintf(stderr, "Missing RunMetadata info. Skip %s\n", name().c_str());
     }
     CheckOpView(stats);
-    CheckCodeView(stats);
     CheckScopeView(stats);
+    CheckCodeView(stats);
     return reports_;
   }
 
@@ -114,11 +114,14 @@ class ExpensiveOperationChecker : public Checker {
 
   void CodeViewHelper(const MultiGraphNodeProto* node, int depth,
                       std::vector<string>* outputs) {
-    if (node->children_size() <= 1 || depth > 4) {
+    if (node->children_size() <= 1 || depth > 3) {
       return;
     }
     for (int j = 0; j < 3 && j < node->children_size(); ++j) {
       const MultiGraphNodeProto* c = &node->children(j);
+      if (c->total_exec_micros() < 1000) {
+        continue;
+      }
       outputs->push_back(strings::Printf(
           "%s%s, cpu: %s, accelerator: %s, total: %s",
           string(depth * 2, ' ').c_str(), c->name().c_str(),
diff --git a/tensorflow/core/profiler/internal/tfprof_show.h b/tensorflow/core/profiler/internal/tfprof_show.h
index 113862ba95..2f7e0e6211 100644
--- a/tensorflow/core/profiler/internal/tfprof_show.h
+++ b/tensorflow/core/profiler/internal/tfprof_show.h
@@ -90,8 +90,14 @@ class TFShow {
         return n1->proto().total_exec_micros() >
                n2->proto().total_exec_micros();
       } else if (opts.order_by == kOrderBy[3]) {
-        return n1->proto().total_parameters() > n2->proto().total_parameters();
+        return n1->proto().total_accelerator_exec_micros() >
+               n2->proto().total_accelerator_exec_micros();
       } else if (opts.order_by == kOrderBy[4]) {
+        return n1->proto().total_cpu_exec_micros() >
+               n2->proto().total_cpu_exec_micros();
+      } else if (opts.order_by == kOrderBy[5]) {
+        return n1->proto().total_parameters() > n2->proto().total_parameters();
+      } else if (opts.order_by == kOrderBy[6]) {
         return n1->proto().total_float_ops() > n2->proto().total_float_ops();
       }
       return name_cmp;
diff --git a/tensorflow/core/profiler/internal/tfprof_show_test.cc b/tensorflow/core/profiler/internal/tfprof_show_test.cc
index ff827ecab8..e2ba113e9b 100644
--- a/tensorflow/core/profiler/internal/tfprof_show_test.cc
+++ b/tensorflow/core/profiler/internal/tfprof_show_test.cc
@@ -113,43 +113,43 @@ TEST_F(TFProfShowTest, DumpAcceleratorAndCPUMicros) {
       "conv2d/convolution (0us/0us, 60us/60us)\n      conv2d/convolution/Shape "
       "(0us/0us, 0us/0us)\n      conv2d/convolution/dilation_rate (0us/0us, "
       "0us/0us)\n    conv2d/BiasAdd (0us/0us, 12us/12us)\n    conv2d/bias "
-      "(0us/0us, 1us/2us)\n      conv2d/bias/Assign (0us/0us, 0us/0us)\n      "
-      "conv2d/bias/Initializer (0us/0us, 0us/0us)\n        "
-      "conv2d/bias/Initializer/Const (0us/0us, 0us/0us)\n      "
-      "conv2d/bias/read (0us/0us, 1us/1us)\n    conv2d/kernel (0us/0us, "
-      "1us/2us)\n      conv2d/kernel/Assign (0us/0us, 0us/0us)\n      "
-      "conv2d/kernel/Initializer (0us/0us, 0us/0us)\n        "
-      "conv2d/kernel/Initializer/random_uniform (0us/0us, 0us/0us)\n      "
-      "conv2d/kernel/read (0us/0us, 1us/1us)\n  conv2d_2 (0us/0us, 0us/15us)\n "
-      "   conv2d_2/convolution (0us/0us, 13us/13us)\n      "
-      "conv2d_2/convolution/Shape (0us/0us, 0us/0us)\n      "
-      "conv2d_2/convolution/dilation_rate (0us/0us, 0us/0us)\n    "
-      "conv2d_2/BiasAdd (0us/0us, 2us/2us)\n  conv2d_1 (0us/0us, 0us/5us)\n    "
-      "conv2d_1/bias (0us/0us, 1us/2us)\n      conv2d_1/bias/Assign (0us/0us, "
-      "0us/0us)\n      conv2d_1/bias/Initializer (0us/0us, 0us/0us)\n        "
-      "conv2d_1/bias/Initializer/Const (0us/0us, 0us/0us)\n      "
-      "conv2d_1/bias/read (0us/0us, 1us/1us)\n    conv2d_1/kernel (0us/0us, "
-      "2us/3us)\n      conv2d_1/kernel/Assign (0us/0us, 0us/0us)\n      "
-      "conv2d_1/kernel/Initializer (0us/0us, 0us/0us)\n        "
-      "conv2d_1/kernel/Initializer/random_uniform (0us/0us, 0us/0us)\n      "
-      "conv2d_1/kernel/read (0us/0us, 1us/1us)\n  init (0us/0us, 0us/0us)\n  "
-      "save (0us/0us, 0us/0us)\n    save/Assign (0us/0us, 0us/0us)\n    "
-      "save/Assign_1 (0us/0us, 0us/0us)\n    save/Assign_2 (0us/0us, "
-      "0us/0us)\n    save/Assign_3 (0us/0us, 0us/0us)\n    save/Const "
-      "(0us/0us, 0us/0us)\n    save/RestoreV2 (0us/0us, 0us/0us)\n      "
-      "save/RestoreV2/shape_and_slices (0us/0us, 0us/0us)\n      "
-      "save/RestoreV2/tensor_names (0us/0us, 0us/0us)\n    save/RestoreV2_1 "
-      "(0us/0us, 0us/0us)\n      save/RestoreV2_1/shape_and_slices (0us/0us, "
-      "0us/0us)\n      save/RestoreV2_1/tensor_names (0us/0us, 0us/0us)\n    "
-      "save/RestoreV2_2 (0us/0us, 0us/0us)\n      "
-      "save/RestoreV2_2/shape_and_slices (0us/0us, 0us/0us)\n      "
-      "save/RestoreV2_2/tensor_names (0us/0us, 0us/0us)\n    save/RestoreV2_3 "
-      "(0us/0us, 0us/0us)\n      save/RestoreV2_3/shape_and_slices (0us/0us, "
-      "0us/0us)\n      save/RestoreV2_3/tensor_names (0us/0us, 0us/0us)\n    "
-      "save/SaveV2 (0us/0us, 0us/0us)\n      save/SaveV2/shape_and_slices "
-      "(0us/0us, 0us/0us)\n      save/SaveV2/tensor_names (0us/0us, 0us/0us)\n "
-      "   save/control_dependency (0us/0us, 0us/0us)\n    save/restore_all "
-      "(0us/0us, 0us/0us)\n  zeros (0us/0us, 1us/1us)\n",
+      "(0us/0us, 1us/2us)\n      conv2d/bias/read (0us/0us, 1us/1us)\n      "
+      "conv2d/bias/Assign (0us/0us, 0us/0us)\n      conv2d/bias/Initializer "
+      "(0us/0us, 0us/0us)\n        conv2d/bias/Initializer/Const (0us/0us, "
+      "0us/0us)\n    conv2d/kernel (0us/0us, 1us/2us)\n      "
+      "conv2d/kernel/read (0us/0us, 1us/1us)\n      conv2d/kernel/Assign "
+      "(0us/0us, 0us/0us)\n      conv2d/kernel/Initializer (0us/0us, "
+      "0us/0us)\n        conv2d/kernel/Initializer/random_uniform (0us/0us, "
+      "0us/0us)\n  conv2d_2 (0us/0us, 0us/15us)\n    conv2d_2/convolution "
+      "(0us/0us, 13us/13us)\n      conv2d_2/convolution/Shape (0us/0us, "
+      "0us/0us)\n      conv2d_2/convolution/dilation_rate (0us/0us, 0us/0us)\n "
+      "   conv2d_2/BiasAdd (0us/0us, 2us/2us)\n  conv2d_1 (0us/0us, 0us/5us)\n "
+      "   conv2d_1/kernel (0us/0us, 2us/3us)\n      conv2d_1/kernel/read "
+      "(0us/0us, 1us/1us)\n      conv2d_1/kernel/Assign (0us/0us, 0us/0us)\n   "
+      "   conv2d_1/kernel/Initializer (0us/0us, 0us/0us)\n        "
+      "conv2d_1/kernel/Initializer/random_uniform (0us/0us, 0us/0us)\n    "
+      "conv2d_1/bias (0us/0us, 1us/2us)\n      conv2d_1/bias/read (0us/0us, "
+      "1us/1us)\n      conv2d_1/bias/Assign (0us/0us, 0us/0us)\n      "
+      "conv2d_1/bias/Initializer (0us/0us, 0us/0us)\n        "
+      "conv2d_1/bias/Initializer/Const (0us/0us, 0us/0us)\n  zeros (0us/0us, "
+      "1us/1us)\n  init (0us/0us, 0us/0us)\n  save (0us/0us, 0us/0us)\n    "
+      "save/Assign (0us/0us, 0us/0us)\n    save/Assign_1 (0us/0us, 0us/0us)\n  "
+      "  save/Assign_2 (0us/0us, 0us/0us)\n    save/Assign_3 (0us/0us, "
+      "0us/0us)\n    save/Const (0us/0us, 0us/0us)\n    save/RestoreV2 "
+      "(0us/0us, 0us/0us)\n      save/RestoreV2/shape_and_slices (0us/0us, "
+      "0us/0us)\n      save/RestoreV2/tensor_names (0us/0us, 0us/0us)\n    "
+      "save/RestoreV2_1 (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2_1/shape_and_slices (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2_1/tensor_names (0us/0us, 0us/0us)\n    save/RestoreV2_2 "
+      "(0us/0us, 0us/0us)\n      save/RestoreV2_2/shape_and_slices (0us/0us, "
+      "0us/0us)\n      save/RestoreV2_2/tensor_names (0us/0us, 0us/0us)\n    "
+      "save/RestoreV2_3 (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2_3/shape_and_slices (0us/0us, 0us/0us)\n      "
+      "save/RestoreV2_3/tensor_names (0us/0us, 0us/0us)\n    save/SaveV2 "
+      "(0us/0us, 0us/0us)\n      save/SaveV2/shape_and_slices (0us/0us, "
+      "0us/0us)\n      save/SaveV2/tensor_names (0us/0us, 0us/0us)\n    "
+      "save/control_dependency (0us/0us, 0us/0us)\n    save/restore_all "
+      "(0us/0us, 0us/0us)\n",
       dump_str);
 }
 
diff --git a/tensorflow/core/profiler/internal/tfprof_utils.cc b/tensorflow/core/profiler/internal/tfprof_utils.cc
index a5898025be..464a13f7df 100644
--- a/tensorflow/core/profiler/internal/tfprof_utils.cc
+++ b/tensorflow/core/profiler/internal/tfprof_utils.cc
@@ -260,81 +260,24 @@ tensorflow::Status ParseCmdLine(const string& line, string* cmd,
 
 void PrintHelp() {
   printf(
-      "\nSee go/tfprof for detail tutorial.\n"
-      "\nCommands\n\n"
-      "  scope: Each op has its op name in TensorFlow, such as 'n1', 'n1/n2', "
-      "'n1/n2/n3'. 'n1/n2' is a child of 'n1'. 'scope' command builds "
-      "a name scope tree and aggregates statistics based on it.\n\n"
-      "  graph: ops in TensorFlow are organized as a graph based on their "
-      "the source (inputs) and sink (outputs). 'graph' command builds "
-      "a graph pointing *from output to input*, and aggregates "
-      "statistics based on it.\n\n"
-      "  set: Set options that will be default for follow up commands.\n\n"
-      "  help: Show helps.\n"
-      "\nOptions\n\n"
-      "Press Enter in CLI to see default option values.\n\n"
-      "  -max_depth: Show ops that are at most this number of hops from "
-      "starting op in the tree/graph structure.\n\n"
-      "  -min_bytes: Show ops that request at least this number of bytes.\n\n"
-      "  -min_micros: Show ops that spend at least this number of micros to "
-      "run.\n\n"
-      "  -min_params: Show ops that contains at least this number of "
-      "parameters.\n\n"
-      "  -min_float_ops: Show ops that contain at least this number of "
-      "float operations. Only available if an op has "
-      "op.RegisterStatistics() defined and OpLogProto is "
-      "provided\n\n"
-      "  -min_occurrence: Show the op types that are at least used this number "
-      "of times. Only available in op view.\n\n"
-      "  -step: Show the stats of a step when multiple steps of "
-      "RunMetadata were added. By default (-1), show the average of all steps."
-      "  -order_by: Order the results by [name|depth|bytes|micros"
-      "|accelerator_micros|cpu_micros|params|float_ops]\n\n"
-      "  -account_type_regexes: Account and display the ops whose types match "
-      "one of the type regexes specified. tfprof "
-      "allow user to define extra op types for ops "
-      "through tensorflow.tfprof.OpLogProto proto. regexes "
-      "are comma-sperated.\n\n"
-      "  -start_name_regexes: Show ops starting from the ops that matches the "
-      "regexes, recursively. regexes are "
-      "comma-separated.\n\n"
-      "  -trim_name_regexes: Hide ops starting from the ops that matches the "
-      "regexes, recursively, regexes are comma-seprated. "
-      "\n\n"
-      "  -show_name_regexes: Show ops that match the regexes. regexes are "
-      "comma-seprated.\n\n"
-      "  -hide_name_regexes: Hide ops that match the regexes. regexes are "
-      "comma-seprated.\n\n"
-      ""
-      "  Notes: For each op, -acount_type_regexes is first evaluated, "
-      "only ops with types matching the specified regexes are accounted and "
-      "selected for displayed. -start/trim/show/hide_name_regexes are used "
-      "to further filter ops for display. -start_name_regexes is evaluated "
-      "first to search the starting ops to display. Descendants of starting "
-      "ops are then evaluated against show/hide_name_regexes to make display "
-      "decision. If an op matches trim_name_regexes, all its descendants are "
-      "hidden.\n"
-      "Ops statistics are *accounted even if they are hidden* as long as "
-      "they match the -account_xxx options.\n\n"
-      "  -account_displayed_op_only: If True, only account the statistics of "
-      "ops eventually displayed. If False, account all "
-      "op statistics matching -account_type_regexes recursively.\n\n"
-      "  -select: Comma-separated list of metrics to show: [bytes|micros|"
-      "accelerator_micros|cpu_micros|params|float_ops|tensor_value|device|"
-      "op_types|input_shapes]."
-      "\n\n"
-      "  -dump_to_file: Dump the output to a file, instead of terminal.\n\n"
-      ""
-      "Examples\n"
-      "  Assuming a toy model:\n"
-      "    intput(typeB)->conv2d_1(typeA)->conv2d_2(typeA)->"
-      "fc(typeA)->cost(typeA)->summarize(typeC)\n"
-      "  Command:\n"
-      "    tfprof> graph -account_type_regexes typeA -start_name_regexes "
-      "cost.* -show_name_regexes conv2d.* -max_depth 10\n\n"
-      "  The above command only aggregate statistics of all ops of typeA ("
-      "hence ignoring input(typeB)). It will start looking for candidate to "
-      "display from cost.* and finally displays conv2d_1 and conv2d_2.\n\n");
+      "See https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/"
+      "README.md for profiler tutorial.\n");
+  printf(
+      "See https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/"
+      "g3doc/command_line.md for command line tool tutorial.\n");
+  printf(
+      "profiler --graph_path=<GraphDef proto file>  # required\n"
+      "         --run_meta_patn=<RunMetadata proto file>  # optional\n"
+      "         --run_log_path=<OpLogProto proto file>  # optional\n\n");
+  printf(
+      "\nCommands:\n"
+      "  scope: Organize profiles based on name scopes.\n"
+      "  graph: Organize profiles based on graph node input/output.\n"
+      "  op: Organize profiles based on operation type.\n"
+      "  code: Organize profiles based on python codes (need op_log_path).\n"
+      "  advise: Auto-profile and advise.\n"
+      "  set: Set options that will be default for follow up commands.\n"
+      "  help: Show helps.\n");
   fflush(stdout);
 }
 
diff --git a/tensorflow/core/profiler/profiler.cc b/tensorflow/core/profiler/profiler.cc
index 0749db128d..ade478367e 100644
--- a/tensorflow/core/profiler/profiler.cc
+++ b/tensorflow/core/profiler/profiler.cc
@@ -126,8 +126,6 @@ int Run(int argc, char** argv) {
   }
   port::InitMain(argv[0], &argc, &argv);
 
-  fprintf(stderr, "%s\n", FLAGS_graph_path.c_str());
-
   std::vector<string> account_type_regexes =
       str_util::Split(FLAGS_account_type_regexes, ',', str_util::SkipEmpty());
   std::vector<string> start_name_regexes =
@@ -148,18 +146,7 @@ int Run(int argc, char** argv) {
 
   string cmd = "";
   if (argc == 1 && FLAGS_graph_path.empty()) {
-    printf("1) go/tfprof: Tutorial.\n");
-    printf("2) tfprof help: Detail help information.\n");
-    printf(
-        "3) tfprof --graph_path <GraphDef proto text file>: "
-        "Profiling model structure, tensor shape and # parameters.\n");
-    printf(
-        "4) tfprof --graph_path <GraphDef proto text file> \\\n"
-        "          --run_meta_path <RunMetadata proto binary file> \\\n"
-        "          --op_log_path <tensorflow::tfprof::OpLogProto proto binary file> "
-        "\\\n"
-        "          --checkpoint_path <TensorFlow Checkpoint file>: "
-        "Profiling everything!\n");
+    PrintHelp();
     return 0;
   } else if (argc > 1) {
     if (string(argv[1]) == kCmds[6]) {
diff --git a/tensorflow/python/profiler/model_analyzer.py b/tensorflow/python/profiler/model_analyzer.py
index 1f61f44797..2268472fdd 100644
--- a/tensorflow/python/profiler/model_analyzer.py
+++ b/tensorflow/python/profiler/model_analyzer.py
@@ -127,21 +127,19 @@ class Profiler(object):
             .trainable_variables_parameter()))
 
         # Or profile the timing of your model operations.
-        opts = PRINT_ALL_TIMING_MEMORY.copy()
-        opts['order_by'] = 'micros'
-        opts['select'] = ['micros', 'occurrence']
-        opts['max_depth'] = 20
+        opts = option_builder.ProfileOptionBuilder.time_and_memory()
         profiler.profile_operations(options=opts)
 
         # Or you can generate a timeline:
-        opts = PRINT_ALL_TIMING_MEMORY.copy()
-        opts['output'] = 'timeline:outfile=' + filename
-        opts['step'] = i
+        opts = (option_builder.ProfileOptionBuilder(
+                option_builder.ProfileOptionBuilder.time_and_memory())
+                .with_step(i)
+                .with_timeline_output(filename).build())
         profiler.profile_graph(options=opts)
       else:
         _ = sess.run(...)
     # Auto detect problems and generate advice.
-    profiler.advise(model_analyzer.ALL_ADVICE)
+    profiler.advise()
   ```
   """
 
@@ -262,22 +260,23 @@ def profile(graph,
             op_log=None,
             cmd='scope',
             options=_DEFAULT_PROFILE_OPTIONS):
-  """Print model statistics.
+  """Profile model.
 
+    Tutorials and examples can be found in:
     https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
 
   Args:
-    graph: tf.Graph.
-    run_meta: tensorflow::RunMetadata proto. When provided, also shows valid
-              timing and memory information when 'select' option contains
-              'micros' and 'bytes'.
-    op_log: tensorflow::tfprof::OpLogProto proto. users can use this proto to
-            group together ops and use a op_type to select the group.
-    cmd: string. Either 'op', 'scope', 'graph', 'code'.
-         'op' view organize outputs using operation type. (e.g. MatMul)
-         'scope' view organize outputs using graph node name scope.
-         'graph' view organize outputs using graph node inputs/outputs.
-         'code' view organize outputs using Python call stack.
+    graph: required tf.Graph.
+    run_meta: optional tensorflow.RunMetadata proto. It is necessary to
+        to support run time information profiling, such as time and memory.
+    op_log: tensorflow.tfprof.OpLogProto proto. User can assign "types" to
+        graph nodes with op_log. "types" allow user to flexibly group and
+        account profiles using options['accounted_type_regexes'].
+    cmd: string. Either 'op', 'scope', 'graph' or 'code'.
+        'op' view organizes profile using operation type. (e.g. MatMul)
+        'scope' view organizes profile using graph node name scope.
+        'graph' view organizes profile using graph node inputs/outputs.
+        'code' view organizes profile using Python call stack.
     options: A dict of options. See core/profiler/g3doc/options.md.
   Returns:
     If cmd is 'scope' or 'graph', returns GraphNodeProto proto.
@@ -325,15 +324,15 @@ def profile(graph,
 def advise(graph, run_meta=None, options=_DEFAULT_ADVISE_OPTIONS):
   """Auto profile and advise.
 
-    Builds profiles and automatically check anormalies of various
+    Builds profiles and automatically check anomalies of various
     aspects. For more details:
     https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
 
   Args:
-    graph: tf.Graph.
-    run_meta: tensorflow::RunMetadata proto. Allows auto-profile
-              time and memroy.
-    options: see ALL_ADVICE example above.
+    graph: required tf.Graph.
+    run_meta: optional tensorflow.RunMetadata proto. It is necessary to
+        to support run time information profiling, such as time and memory.
+    options: see ALL_ADVICE example above. Default checks everything.
   Returns:
     Returns AdviceProto proto
   """
-- 
GitLab


From 9e89636e6aa2be508fad22089c61659ce87f6e67 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 02:10:52 -0700
Subject: [PATCH 0874/1375] [XLA:CPU] Support for CPU outfeed and a xfeed
 (infeed/outfeed) test.

Note: does not yet support nested tuples, for symmetry with the current infeed
limitations.
PiperOrigin-RevId: 161502502
---
 tensorflow/compiler/xla/literal_util.cc       |  12 ++
 tensorflow/compiler/xla/literal_util.h        |  10 ++
 tensorflow/compiler/xla/service/cpu/BUILD     |   8 +-
 .../compiler/xla/service/cpu/cpu_runtime.cc   |  35 ++++--
 .../compiler/xla/service/cpu/cpu_runtime.h    |  25 +++-
 .../compiler/xla/service/cpu/ir_emitter.cc    |  90 ++++++++++----
 .../compiler/xla/service/cpu/ir_emitter.h     |  13 +-
 .../{infeed_manager.cc => xfeed_manager.cc}   |  42 ++++---
 .../cpu/{infeed_manager.h => xfeed_manager.h} |  66 ++++++----
 ..._manager_test.cc => xfeed_manager_test.cc} |  22 ++--
 .../xla/service/cpu_transfer_manager.cc       | 116 +++++++++++++++---
 .../xla/service/cpu_transfer_manager.h        |  10 +-
 .../xla/service/generic_transfer_manager.cc   |   3 +-
 .../compiler/xla/service/hlo_computation.cc   |  16 ++-
 .../compiler/xla/service/hlo_instruction.cc   |   8 +-
 15 files changed, 361 insertions(+), 115 deletions(-)
 rename tensorflow/compiler/xla/service/cpu/{infeed_manager.cc => xfeed_manager.cc} (62%)
 rename tensorflow/compiler/xla/service/cpu/{infeed_manager.h => xfeed_manager.h} (59%)
 rename tensorflow/compiler/xla/service/cpu/{infeed_manager_test.cc => xfeed_manager_test.cc} (82%)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 6760f72e55..0c38b6d3e7 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -631,6 +631,18 @@ string Literal::ToString() const {
   return literal;
 }
 
+/* static */ std::unique_ptr<Literal> Literal::MakeTupleOwned(
+    std::vector<std::unique_ptr<Literal>> elements) {
+  auto literal = MakeUnique<Literal>();
+  std::vector<Shape> shape;
+  for (auto& tuple_element : elements) {
+    shape.push_back(tuple_element->shape());
+    literal->add_tuple_literals()->Swap(tuple_element.get());
+  }
+  *literal->mutable_shape() = ShapeUtil::MakeTupleShape(shape);
+  return literal;
+}
+
 const void* Literal::InternalData() const {
   return const_cast<const void*>(
       const_cast<Literal*>(this)->MutableInternalData());
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 8266511614..4b1464a132 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -481,6 +481,16 @@ class Literal {
   static std::unique_ptr<Literal> MakeTuple(
       tensorflow::gtl::ArraySlice<const Literal*> elements);
 
+  // As above, but intended to be invoked with move semantics; i.e.
+  //
+  //  std::vector<std::unique_ptr<Literal>> elements = ...;
+  //  auto result = Literal::MakeTupleOwned(std::move(elements));
+  //
+  // This would have been declared as an overload, but there is ambiguity
+  // in invocation between the above signature and this one.
+  static std::unique_ptr<Literal> MakeTupleOwned(
+      std::vector<std::unique_ptr<Literal>> elements);
+
   // Validates that the data payload of the literal matches the literal shape;
   // if it does not, an appropriate status is returned.
   tensorflow::Status ValidateLiteral() const;
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 53410b09c8..ddf45f03f0 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -326,11 +326,11 @@ cc_library(
     name = "cpu_runtime",
     srcs = [
         "cpu_runtime.cc",
-        "infeed_manager.cc",
+        "xfeed_manager.cc",
     ],
     hdrs = [
         "cpu_runtime.h",
-        "infeed_manager.h",
+        "xfeed_manager.h",
     ],
     copts = runtime_copts(),
     deps = [
@@ -416,9 +416,9 @@ cc_test(
 )
 
 cc_test(
-    name = "infeed_manager_test",
+    name = "xfeed_manager_test",
     size = "small",
-    srcs = ["infeed_manager_test.cc"],
+    srcs = ["xfeed_manager_test.cc"],
     deps = [
         ":cpu_runtime",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
index 253de20f25..40cdf079e3 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
@@ -24,8 +24,8 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-InfeedManager* GetInfeedManager() {
-  static InfeedManager* manager = new InfeedManager;
+XfeedManager* GetXfeedManager() {
+  static XfeedManager* manager = new XfeedManager;
   return manager;
 }
 
@@ -35,17 +35,36 @@ InfeedManager* GetInfeedManager() {
 
 void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(
     xla::int32 buffer_length) {
-  xla::cpu::runtime::InfeedManager* infeed =
-      xla::cpu::runtime::GetInfeedManager();
+  VLOG(2) << "AcquireInfeedBufferForDequeue";
+  xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
   // Wait until there's a buffer to dequeue.
-  xla::cpu::runtime::InfeedBuffer* buffer = infeed->BlockingDequeueBuffer();
+  xla::cpu::runtime::XfeedBuffer* buffer =
+      xfeed->infeed()->BlockingDequeueBuffer();
   CHECK_EQ(buffer->length(), buffer_length);
   return buffer->data();
 }
 
 void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(xla::int32 buffer_length,
                                                        void* buffer_ptr) {
-  xla::cpu::runtime::InfeedManager* infeed =
-      xla::cpu::runtime::GetInfeedManager();
-  infeed->ReleaseCurrentBuffer(buffer_length, buffer_ptr);
+  VLOG(2) << "ReleaseInfeedBufferAfterDequeue";
+  xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
+  xfeed->infeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr);
+}
+
+void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
+    xla::int32 buffer_length) {
+  VLOG(2) << "AcquireOutfeedBufferForPopulation";
+  xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
+  // Wait until there's a buffer to dequeue.
+  xla::cpu::runtime::XfeedBuffer* buffer =
+      xfeed->outfeed()->BlockingDequeueBuffer();
+  CHECK_EQ(buffer->length(), buffer_length);
+  return buffer->data();
+}
+
+void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
+    xla::int32 buffer_length, void* buffer_ptr) {
+  VLOG(2) << "ReleaseOutfeedBufferAfterPopulation";
+  xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
+  xfeed->outfeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr);
 }
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
index 8eae210230..04126e062e 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
@@ -26,7 +26,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_H_
 
-#include "tensorflow/compiler/xla/service/cpu/infeed_manager.h"
+#include "tensorflow/compiler/xla/service/cpu/xfeed_manager.h"
 #include "tensorflow/compiler/xla/types.h"
 
 namespace xla {
@@ -54,9 +54,13 @@ constexpr char kAcquireInfeedBufferForDequeueSymbolName[] =
     "__xla_cpu_runtime_AcquireInfeedBufferForDequeue";
 constexpr char kReleaseInfeedBufferAfterDequeueSymbolName[] =
     "__xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue";
+constexpr char kAcquireOutfeedBufferForPopulationSymbolName[] =
+    "__xla_cpu_runtime_AcquireOutfeedBufferForPopulation";
+constexpr char kReleaseOutfeedBufferAfterPopulationSymbolName[] =
+    "__xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation";
 
 // Returns the infeed manager used by the CPU runtime.
-InfeedManager* GetInfeedManager();
+XfeedManager* GetXfeedManager();
 
 }  // namespace runtime
 }  // namespace cpu
@@ -86,6 +90,23 @@ extern void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(
 // that can be returned out of order.
 extern void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
     xla::int32 buffer_length, void* buffer_ptr);
+
+// Blocks until the next outfeed buffer is available to be populated, then
+// returns it.
+extern void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
+    xla::int32 buffer_length);
+
+// Relinquishes the outfeed buffer after it has been populated.
+// buffer_ptr must have been previously returned by
+// __xla_cpu_runtime_AcquireOutfeedBufferForPopulation.
+// Once this call completes, buffer_ptr may no longer be accessed.
+// buffer_length must match the length passed to the call to
+// __xla_cpu_runtime_AcquireInfeedBufferForDequeue that returned
+// buffer_ptr. This function must be called before the next buffer is
+// acquired, i.e., there may only be one outstanding outfeed buffer in
+// use by the runtime.
+extern void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
+    xla::int32 buffer_length, void* buffer_ptr);
 }
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_H_
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 590a23562b..077d1ee2a1 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -83,7 +83,8 @@ StatusOr<llvm::Function*> IrEmitter::EmitComputation(
     bool is_entry_computation,
     std::vector<const HloInstruction*>* instruction_order) {
   string function_name = name_uniquer_.GetUniqueName(function_name_prefix);
-  VLOG(2) << "Emitting IR for CPU function [" << function_name_prefix << "]";
+  VLOG(2) << "Emitting IR for CPU function [" << function_name_prefix
+          << "]; ordered? " << (instruction_order != nullptr);
   num_dynamic_loop_bounds_ = 0;
   if (!computation->root_instruction()->outer_dimension_partitions().empty()) {
     num_dynamic_loop_bounds_ =
@@ -97,11 +98,10 @@ StatusOr<llvm::Function*> IrEmitter::EmitComputation(
                     arch_type_ == llvm::Triple::ArchType::x86_64;
   profiling_state_ = ProfilingState(is_entry_computation, use_rdtscp,
                                     GetProfileCountersArgument());
-  if (instruction_order != nullptr) {
-    TF_RETURN_IF_ERROR(computation->root_instruction()->AcceptOrdered(
-        this, *instruction_order));
+  if (instruction_order == nullptr) {
+    TF_RETURN_IF_ERROR(computation->Accept(this));
   } else {
-    TF_RETURN_IF_ERROR(computation->root_instruction()->Accept(this));
+    TF_RETURN_IF_ERROR(computation->AcceptOrdered(this, *instruction_order));
   }
   InsertOrDie(&emitted_functions_, computation, compute_function_);
 
@@ -376,6 +376,8 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
 
   const Shape& shape = infeed->shape();
 
+  // The infeed operation produces data (dequeued from the infeed queue) at this
+  // address, which has been provided by buffer assignment.
   TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
                       EmitTargetAddressForOp(infeed));
 
@@ -401,8 +403,9 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
       llvm::Value* tuple_element_address =
           EmitTempBufferPointer(buffer, tuple_element_shape);
 
-      TF_RETURN_IF_ERROR(EmitInfeedTransfer(ByteSizeOf(tuple_element_shape),
-                                            tuple_element_address));
+      TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kInfeed,
+                                           ByteSizeOf(tuple_element_shape),
+                                           tuple_element_address));
 
       tuple_element_addresses.push_back(tuple_element_address);
     }
@@ -410,7 +413,8 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
     llvm_ir::EmitTuple(llvm_ir::IrArray(target_address, shape),
                        tuple_element_addresses, &ir_builder_);
   } else {
-    TF_RETURN_IF_ERROR(EmitInfeedTransfer(ByteSizeOf(shape), target_address));
+    TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kInfeed, ByteSizeOf(shape),
+                                         target_address));
   }
 
   emitted_value_[infeed] = target_address;
@@ -418,10 +422,13 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
   return Status::OK();
 }
 
-Status IrEmitter::EmitInfeedTransfer(int64 length,
-                                     llvm::Value* target_address) {
-  if (length > std::numeric_limits<int32>::max()) {
-    return InvalidArgument("infeed buffer length %lld is too large", length);
+Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, int64 length,
+                                    llvm::Value* program_buffer_address) {
+  if (length <= 0 || length > std::numeric_limits<int32>::max()) {
+    return InvalidArgument(
+        "xfeed (infeed or outfeed) buffer length %lld is outside the valid "
+        "size range",
+        length);
   }
   int32 length_32 = static_cast<int32>(length);
 
@@ -434,9 +441,14 @@ Status IrEmitter::EmitInfeedTransfer(int64 length,
       llvm::FunctionType::get(i8_ptr_type, {int32_type},
                               /*isVarArg=*/false);
 
-  llvm::Function* acquire_func =
-      llvm::cast<llvm::Function>(module_->getOrInsertFunction(
-          runtime::kAcquireInfeedBufferForDequeueSymbolName, acquire_type));
+  llvm::Function* acquire_func;
+  if (kind == XfeedKind::kInfeed) {
+    acquire_func = llvm::cast<llvm::Function>(module_->getOrInsertFunction(
+        runtime::kAcquireInfeedBufferForDequeueSymbolName, acquire_type));
+  } else {
+    acquire_func = llvm::cast<llvm::Function>(module_->getOrInsertFunction(
+        runtime::kAcquireOutfeedBufferForPopulationSymbolName, acquire_type));
+  }
   acquire_func->setCallingConv(llvm::CallingConv::C);
 
   // The signature of the release infeed buffer function is:
@@ -446,15 +458,28 @@ Status IrEmitter::EmitInfeedTransfer(int64 length,
       ir_builder_.getVoidTy(), {int32_type, i8_ptr_type},
       /*isVarArg=*/false);
 
-  llvm::Function* release_func =
-      llvm::cast<llvm::Function>(module_->getOrInsertFunction(
-          runtime::kReleaseInfeedBufferAfterDequeueSymbolName, release_type));
+  llvm::Function* release_func;
+  if (kind == XfeedKind::kInfeed) {
+    release_func = llvm::cast<llvm::Function>(module_->getOrInsertFunction(
+        runtime::kReleaseInfeedBufferAfterDequeueSymbolName, release_type));
+  } else {
+    release_func = llvm::cast<llvm::Function>(module_->getOrInsertFunction(
+        runtime::kReleaseOutfeedBufferAfterPopulationSymbolName, release_type));
+  }
   release_func->setCallingConv(llvm::CallingConv::C);
 
   llvm::Value* acquired_pointer =
       ir_builder_.CreateCall(acquire_func, {ir_builder_.getInt32(length_32)});
 
-  ir_builder_.CreateMemCpy(target_address, acquired_pointer, length_32, 1);
+  if (kind == XfeedKind::kInfeed) {
+    // Copy to the program buffer address from the acquired buffer.
+    ir_builder_.CreateMemCpy(program_buffer_address, acquired_pointer,
+                             length_32, 1);
+  } else {
+    // Outfeed -- copy from the in-program address to the acquired buffer.
+    ir_builder_.CreateMemCpy(acquired_pointer, program_buffer_address,
+                             length_32, 1);
+  }
 
   ir_builder_.CreateCall(release_func,
                          {ir_builder_.getInt32(length_32), acquired_pointer});
@@ -463,13 +488,33 @@ Status IrEmitter::EmitInfeedTransfer(int64 length,
 }
 
 Status IrEmitter::HandleOutfeed(HloInstruction* outfeed) {
-  // TODO(b/34359662): Implement outfeed on CPU.
-  return Unimplemented("Outfeed is not supported on CPU (b/34359662).");
+  HloInstruction* operand = outfeed->operands()[0];
+  const Shape& operand_shape = operand->shape();
+
+  llvm::Value* value = GetEmittedValueFor(operand);
+  if (!ShapeUtil::IsTuple(operand_shape)) {
+    return EmitXfeedTransfer(XfeedKind::kOutfeed, ByteSizeOf(operand_shape),
+                             value);
+  }
+
+  TF_RET_CHECK(!ShapeUtil::IsNestedTuple(operand_shape));
+
+  for (int64 i = 0; i < operand_shape.tuple_shapes_size(); ++i) {
+    const Shape& tuple_element_shape =
+        ShapeUtil::GetTupleElementShape(operand_shape, i);
+    llvm::Value* tuple_element = llvm_ir::EmitGetTupleElement(
+        tuple_element_shape, i, MinimumAlignmentForShape(tuple_element_shape),
+        value, &ir_builder_);
+    TF_RETURN_IF_ERROR(EmitXfeedTransfer(
+        XfeedKind::kOutfeed, ByteSizeOf(tuple_element_shape), tuple_element));
+  }
+
+  return Status::OK();
 }
 
 Status IrEmitter::HandleSort(HloInstruction* sort, HloInstruction* operand) {
   // TODO(b/26783907): Implement sort on CPU.
-  return Unimplemented("Sort is not supported on GPU (b/26783907).");
+  return Unimplemented("Sort is not supported on CPU (b/26783907).");
 }
 
 Status IrEmitter::HandleTuple(
@@ -1856,6 +1901,7 @@ void IrEmitter::ProfilingState::RecordCompleteComputation(
 }
 
 Status IrEmitter::Preprocess(HloInstruction* hlo) {
+  VLOG(3) << "Visiting: " << hlo->ToString();
   if (hlo_to_profile_idx_ && hlo_to_profile_idx_->count(hlo)) {
     profiling_state_.RecordCycleStart(&ir_builder_, hlo);
   }
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index dd3c55f408..4e1308384b 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -110,7 +110,7 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   Status HandleBatchNormGrad(HloInstruction* batch_norm_grad) override;
   Status HandleCrossReplicaSum(HloInstruction* crs) override;
   Status HandleInfeed(HloInstruction* infeed) override;
-  Status HandleOutfeed(HloInstruction* infeed) override;
+  Status HandleOutfeed(HloInstruction* outfeed) override;
   Status HandleSort(HloInstruction* sort, HloInstruction* operand) override;
   Status HandleParameter(HloInstruction* parameter) override;
   Status HandleReduce(HloInstruction* reduce, HloInstruction* arg,
@@ -425,8 +425,15 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   // Returns the number of bytes within the shape.
   int64 ByteSizeOf(const Shape& shape) const;
 
-  // Emit IR to transfer an infeed buffer to the target address.
-  Status EmitInfeedTransfer(int64 length, llvm::Value* target_address);
+  enum class XfeedKind {
+    kInfeed,
+    kOutfeed,
+  };
+
+  // Emit IR to transfer between a {infeed,outfeed} buffer and an in-program
+  // address.
+  Status EmitXfeedTransfer(XfeedKind kind, int64 length,
+                           llvm::Value* program_buffer_address);
 
   const HloModuleConfig& hlo_module_config_;
 
diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager.cc b/tensorflow/compiler/xla/service/cpu/xfeed_manager.cc
similarity index 62%
rename from tensorflow/compiler/xla/service/cpu/infeed_manager.cc
rename to tensorflow/compiler/xla/service/cpu/xfeed_manager.cc
index 2ce27d22c7..0aa97b7cce 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu/xfeed_manager.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/compiler/xla/service/cpu/infeed_manager.h"
+#include "tensorflow/compiler/xla/service/cpu/xfeed_manager.h"
 
 #include "tensorflow/core/platform/logging.h"
 
@@ -21,26 +21,28 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-InfeedBuffer::~InfeedBuffer() = default;
-
-InfeedManager::InfeedManager() : current_buffer_(nullptr) {}
+void XfeedManager::Reset() {
+  infeed()->Reset();
+  outfeed()->Reset();
+}
 
-void InfeedManager::Reset() {
+void XfeedQueueManager::Reset() {
   tensorflow::mutex_lock l(mu_);
-  CHECK(!current_buffer_);
-  for (auto buffer : enqueued_buffer_) {
+  CHECK(current_buffer_ == nullptr);
+  for (auto buffer : enqueued_buffers_) {
     buffer->Done();
   }
-  enqueued_buffer_.clear();
+  enqueued_buffers_.clear();
 }
 
-void InfeedManager::EnqueueBuffers(const std::vector<InfeedBuffer*>& buffers) {
+void XfeedQueueManager::EnqueueBuffers(
+    tensorflow::gtl::ArraySlice<XfeedBuffer*> buffers) {
   tensorflow::mutex_lock l(mu_);
-  bool was_empty = enqueued_buffer_.empty();
-  for (InfeedBuffer* b : buffers) {
-    enqueued_buffer_.push_back(b);
+  bool was_empty = enqueued_buffers_.empty();
+  for (XfeedBuffer* b : buffers) {
+    enqueued_buffers_.push_back(b);
   }
-  if (was_empty) {
+  if (was_empty && !buffers.empty()) {
     // This has the potential to suffer from the notified thread
     // immediately trying and failing to acquire mu_, but seems
     // preferable to the alternative of notifying outside the lock
@@ -49,20 +51,20 @@ void InfeedManager::EnqueueBuffers(const std::vector<InfeedBuffer*>& buffers) {
   }
 }
 
-InfeedBuffer* InfeedManager::BlockingDequeueBuffer() {
+XfeedBuffer* XfeedQueueManager::BlockingDequeueBuffer() {
   tensorflow::mutex_lock l(mu_);
-  while (enqueued_buffer_.empty()) {
+  while (enqueued_buffers_.empty()) {
     cv_.wait(l);
   }
-  CHECK(!current_buffer_);
-  current_buffer_ = enqueued_buffer_.front();
-  enqueued_buffer_.pop_front();
+  CHECK(current_buffer_ == nullptr);
+  current_buffer_ = enqueued_buffers_.front();
+  enqueued_buffers_.pop_front();
   return current_buffer_;
 }
 
-void InfeedManager::ReleaseCurrentBuffer(int32 length, void* data) {
+void XfeedQueueManager::ReleaseCurrentBuffer(int32 length, void* data) {
   tensorflow::mutex_lock l(mu_);
-  CHECK(current_buffer_);
+  CHECK(current_buffer_ != nullptr);
   CHECK_EQ(length, current_buffer_->length());
   CHECK_EQ(data, current_buffer_->data());
   current_buffer_->Done();
diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager.h b/tensorflow/compiler/xla/service/cpu/xfeed_manager.h
similarity index 59%
rename from tensorflow/compiler/xla/service/cpu/infeed_manager.h
rename to tensorflow/compiler/xla/service/cpu/xfeed_manager.h
index e965988453..efeb5eb980 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager.h
+++ b/tensorflow/compiler/xla/service/cpu/xfeed_manager.h
@@ -17,13 +17,13 @@ limitations under the License.
 // is used by the CPU runtime to transfer buffers into an executing
 // CPU computation, e.g., to feed data into a while loop.
 
-#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_INFEED_MANAGER_H_
-#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_INFEED_MANAGER_H_
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_XFEED_MANAGER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_XFEED_MANAGER_H_
 
 #include <deque>
-#include <vector>
 
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/mutex.h"
 
 namespace xla {
@@ -32,41 +32,39 @@ namespace runtime {
 
 // Abstract class defining an infeed buffer that is passed to the
 // runtime by a client. The client manages the storage of the buffer.
-class InfeedBuffer {
+class XfeedBuffer {
  public:
-  virtual ~InfeedBuffer();
+  virtual ~XfeedBuffer() = default;
 
   virtual int32 length() = 0;
   virtual void* data() = 0;
   virtual void Done() = 0;
 };
 
-// Client-side class used to enqueue infeed buffers.
-class InfeedManager {
+// Reusable component for managing the infeed and outfeed queue state.
+class XfeedQueueManager {
  public:
-  InfeedManager();
+  XfeedQueueManager() = default;
 
   // Calls the completion callback for any enqueued buffers that have
-  // not been dequeued by the runtime, and empties the infeed
+  // not been dequeued by the runtime, and empties the
   // queue. Reset may not be called while a runtime computation is
   // processing a dequeued buffer. The only safe way to ensure this
   // condition is to call Reset when no computation is taking place.
   void Reset();
 
-  // Adds a set of buffers to the infeed queue
-  // atomically. buffer->Done will be called when the buffer will no
-  // longer be accessed by the InfeedManager, either as a result of a
-  // call to Reset or because the runtime has dequeued and used the
-  // buffer.
-  void EnqueueBuffers(const std::vector<InfeedBuffer*>& buffers);
+  // Adds a sequence of buffers to the queue atomically. buffer->Done will be
+  // called when the buffer will no longer be accessed by the XfeedManager,
+  // either as a result of a call to Reset or because the runtime has dequeued
+  // and used the buffer.
+  void EnqueueBuffers(tensorflow::gtl::ArraySlice<XfeedBuffer*> buffers);
 
-  // Blocks until the infeed queue is non-empty, then returns the
-  // buffer at the head of the queue. Sets the current buffer to be
-  // the returned buffer. It is an error to call BlockingDequeueBuffer
-  // if there is an unreleased current buffer, i.e.,
-  // ReleaseCurrentBuffer must be called between calls to
+  // Blocks until the queue is non-empty, then returns the buffer at the head of
+  // the queue. Sets the current buffer to be the returned buffer. It is an
+  // error to call BlockingDequeueBuffer if there is an unreleased current
+  // buffer, i.e., ReleaseCurrentBuffer must be called between calls to
   // BlockingDequeueBuffer.
-  InfeedBuffer* BlockingDequeueBuffer();
+  XfeedBuffer* BlockingDequeueBuffer();
 
   // Releases the current buffer, which is the last buffer returned by
   // BlockingDequeuBuffer and not yet released. length and data must
@@ -76,19 +74,37 @@ class InfeedManager {
 
  private:
   tensorflow::mutex mu_;
+
   // Condition variable that is signaled every time a buffer is
   // enqueued to an empty queue.
   tensorflow::condition_variable cv_;
-  // InfeedBuffer* queue contents are not owned, but buffer->Done must
+
+  // XfeedBuffer* queue contents are not owned, but buffer->Done must
   // be called when the buffer is no longer needed by the runtime.
-  std::deque<InfeedBuffer*> enqueued_buffer_;
+  std::deque<XfeedBuffer*> enqueued_buffers_;
+
   // If non-NULL, the buffer that is currently being processed by the
   // runtime. Not owned.
-  InfeedBuffer* current_buffer_;
+  XfeedBuffer* current_buffer_ = nullptr;
+};
+
+// Client-side class used to enqueue infeed buffers.
+class XfeedManager {
+ public:
+  XfeedManager() = default;
+
+  void Reset();
+
+  XfeedQueueManager* infeed() { return &infeed_; }
+  XfeedQueueManager* outfeed() { return &outfeed_; }
+
+ private:
+  XfeedQueueManager infeed_;
+  XfeedQueueManager outfeed_;
 };
 
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
 
-#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_INFEED_MANAGER_H_
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_XFEED_MANAGER_H_
diff --git a/tensorflow/compiler/xla/service/cpu/infeed_manager_test.cc b/tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc
similarity index 82%
rename from tensorflow/compiler/xla/service/cpu/infeed_manager_test.cc
rename to tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc
index a59fa35fdb..fddb59fc2d 100644
--- a/tensorflow/compiler/xla/service/cpu/infeed_manager_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/compiler/xla/service/cpu/infeed_manager.h"
+#include "tensorflow/compiler/xla/service/cpu/xfeed_manager.h"
 
 #include <memory>
 
@@ -28,7 +28,7 @@ namespace {
 
 class InfeedManagerTest : public ::testing::Test {};
 
-class TestInfeedBuffer : public cpu::runtime::InfeedBuffer {
+class TestInfeedBuffer : public cpu::runtime::XfeedBuffer {
  public:
   explicit TestInfeedBuffer(int32 length)
       : done_called_(false), length_(length) {}
@@ -55,10 +55,10 @@ TEST_F(InfeedManagerTest, SingleThreadedSequential) {
   TestInfeedBuffer* a = new TestInfeedBuffer(64);
   TestInfeedBuffer* b = new TestInfeedBuffer(32);
 
-  cpu::runtime::InfeedManager* infeed = cpu::runtime::GetInfeedManager();
+  cpu::runtime::XfeedManager* xfeed = cpu::runtime::GetXfeedManager();
 
-  infeed->EnqueueBuffers({a});
-  infeed->EnqueueBuffers({b});
+  xfeed->infeed()->EnqueueBuffers({a});
+  xfeed->infeed()->EnqueueBuffers({b});
   ProcessNextBuffer(a->length());
   ProcessNextBuffer(b->length());
 }
@@ -67,22 +67,22 @@ TEST_F(InfeedManagerTest, SingleThreadedInterleaved) {
   TestInfeedBuffer* a = new TestInfeedBuffer(64);
   TestInfeedBuffer* b = new TestInfeedBuffer(32);
 
-  cpu::runtime::InfeedManager* infeed = cpu::runtime::GetInfeedManager();
+  cpu::runtime::XfeedManager* xfeed = cpu::runtime::GetXfeedManager();
 
-  infeed->EnqueueBuffers({a});
+  xfeed->infeed()->EnqueueBuffers({a});
   ProcessNextBuffer(a->length());
-  infeed->EnqueueBuffers({b});
+  xfeed->infeed()->EnqueueBuffers({b});
   ProcessNextBuffer(b->length());
 }
 
 TEST_F(InfeedManagerTest, MultiThreaded) {
   tensorflow::thread::ThreadPool pool(tensorflow::Env::Default(), "test", 2);
 
-  cpu::runtime::InfeedManager* infeed = cpu::runtime::GetInfeedManager();
+  cpu::runtime::XfeedManager* xfeed = cpu::runtime::GetXfeedManager();
 
   const int32 length = 64;
 
-  pool.Schedule([infeed]() {
+  pool.Schedule([xfeed]() {
     // Spin for 100 milliseconds
     int64 start_micros = tensorflow::Env::Default()->NowMicros();
     while (true) {
@@ -92,7 +92,7 @@ TEST_F(InfeedManagerTest, MultiThreaded) {
       }
     }
     TestInfeedBuffer* a = new TestInfeedBuffer(length);
-    infeed->EnqueueBuffers({a});
+    xfeed->infeed()->EnqueueBuffers({a});
   });
 
   ProcessNextBuffer(length);
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
index 262ba83f3d..80e918970c 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
@@ -27,9 +27,11 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/casts.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/notification.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
 
 namespace se = ::perftools::gputools;
@@ -38,7 +40,7 @@ namespace xla {
 
 namespace {
 
-class CpuInfeedBuffer : public cpu::runtime::InfeedBuffer {
+class CpuInfeedBuffer : public cpu::runtime::XfeedBuffer {
  public:
   explicit CpuInfeedBuffer(int32 length)
       : length_(length),
@@ -58,6 +60,23 @@ class CpuInfeedBuffer : public cpu::runtime::InfeedBuffer {
   se::DeviceMemoryBase device_memory_;
 };
 
+class CpuOutfeedBuffer : public cpu::runtime::XfeedBuffer {
+ public:
+  CpuOutfeedBuffer(void* destination, int32 length)
+      : destination_(destination), length_(length) {}
+
+  void WaitForNotification() { return done_.WaitForNotification(); }
+
+  int32 length() override { return length_; }
+  void* data() override { return destination_; }
+  void Done() override { done_.Notify(); }
+
+ private:
+  void* destination_;
+  int32 length_;
+  tensorflow::Notification done_;
+};
+
 }  // namespace
 
 CpuTransferManager::CpuTransferManager()
@@ -83,10 +102,10 @@ Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
   // For a tuple, we transfer each of its elements to the device and
   // enqueue the resulting destination device addresses with the
   // infeed manager.
-  std::vector<cpu::runtime::InfeedBuffer*> buffers;
+  std::vector<cpu::runtime::XfeedBuffer*> buffers;
   buffers.reserve(literal.tuple_literals_size());
   auto cleanup = tensorflow::gtl::MakeCleanup([buffers]() {
-    for (cpu::runtime::InfeedBuffer* b : buffers) {
+    for (cpu::runtime::XfeedBuffer* b : buffers) {
       b->Done();
     }
   });
@@ -95,15 +114,14 @@ Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
     const Shape& tuple_element_shape = tuple_element.shape();
     int64 tuple_element_size = GetByteSizeRequirement(tuple_element_shape);
     TF_ASSIGN_OR_RETURN(
-        cpu::runtime::InfeedBuffer * buffer,
+        cpu::runtime::XfeedBuffer * buffer,
         TransferBufferToInfeedInternal(executor, tuple_element_size,
                                        tuple_element.InternalData()));
     buffers.push_back(buffer);
   }
 
-  cpu::runtime::InfeedManager* infeed_manager =
-      cpu::runtime::GetInfeedManager();
-  infeed_manager->EnqueueBuffers(buffers);
+  cpu::runtime::XfeedManager* xfeed_manager = cpu::runtime::GetXfeedManager();
+  xfeed_manager->infeed()->EnqueueBuffers(buffers);
 
   cleanup.release();
   return Status::OK();
@@ -112,17 +130,16 @@ Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
 Status CpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor,
                                                   int64 size,
                                                   const void* source) {
-  TF_ASSIGN_OR_RETURN(cpu::runtime::InfeedBuffer * buffer,
+  TF_ASSIGN_OR_RETURN(cpu::runtime::XfeedBuffer * buffer,
                       TransferBufferToInfeedInternal(executor, size, source));
 
-  cpu::runtime::InfeedManager* infeed_manager =
-      cpu::runtime::GetInfeedManager();
-  infeed_manager->EnqueueBuffers({buffer});
+  cpu::runtime::XfeedManager* xfeed_manager = cpu::runtime::GetXfeedManager();
+  xfeed_manager->infeed()->EnqueueBuffers({buffer});
 
   return Status::OK();
 }
 
-StatusOr<cpu::runtime::InfeedBuffer*>
+StatusOr<cpu::runtime::XfeedBuffer*>
 CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor,
                                                    int64 size,
                                                    const void* source) {
@@ -130,8 +147,9 @@ CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor,
     return InvalidArgument("Infeed shape is too large: needs %lld bytes", size);
   }
 
-  if (size == 0) {
-    return InvalidArgument("Infeed shape needs 0 bytes");
+  if (size <= 0) {
+    return InvalidArgument("Infeed shape must have positive size; got %lld",
+                           size);
   }
 
   int32 size_32 = static_cast<int32>(size);
@@ -147,6 +165,76 @@ CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor,
   return queued_buffer;
 }
 
+Status CpuTransferManager::TransferLiteralFromOutfeed(
+    se::StreamExecutor* executor, const Shape& literal_shape,
+    Literal* literal) {
+  if (!ShapeUtil::IsTuple(literal_shape)) {
+    int64 size = GetByteSizeRequirement(literal_shape);
+    // Note: OSS build didn't like implicit conversion from
+    // literal_shape.dimensions() to the array slice on 2017-07-10.
+    tensorflow::gtl::ArraySlice<int64> dimensions(
+        tensorflow::bit_cast<const int64*>(literal_shape.dimensions().data()),
+        literal_shape.dimensions().size());
+    auto empty =
+        Literal::CreateFromDimensions(literal_shape.element_type(), dimensions);
+    literal->Swap(empty.get());
+    return TransferBufferFromOutfeed(executor, size,
+                                     literal->MutableInternalData());
+  }
+
+  if (ShapeUtil::IsNestedTuple(literal_shape)) {
+    return Unimplemented(
+        "Nested tuple outfeeds are not yet implemented on CPU.");
+  }
+
+  std::vector<std::unique_ptr<Literal>> elements;
+  for (int64 i = 0; i < literal_shape.tuple_shapes_size(); ++i) {
+    const Shape& tuple_element_shape =
+        ShapeUtil::GetTupleElementShape(literal_shape, i);
+    // Note: OSS build didn't like implicit conversion from
+    // literal_shape.dimensions() to the array slice on 2017-07-10.
+    tensorflow::gtl::ArraySlice<int64> dimensions(
+        tensorflow::bit_cast<const int64*>(
+            tuple_element_shape.dimensions().data()),
+        tuple_element_shape.dimensions().size());
+    auto empty = Literal::CreateFromDimensions(
+        tuple_element_shape.element_type(), dimensions);
+    TF_RETURN_IF_ERROR(TransferBufferFromOutfeed(
+        executor, GetByteSizeRequirement(tuple_element_shape),
+        empty->MutableInternalData()));
+    elements.push_back(std::move(empty));
+  }
+  auto result = Literal::MakeTupleOwned(std::move(elements));
+  literal->Swap(result.get());
+  TF_RET_CHECK(ShapeUtil::Equal(literal->shape(), literal_shape));
+  return Status::OK();
+}
+
+Status CpuTransferManager::TransferBufferFromOutfeed(
+    perftools::gputools::StreamExecutor* executor, int64 size,
+    void* destination) {
+  if (size > std::numeric_limits<int32>::max()) {
+    return InvalidArgument("Outfeed shape is too large: needs %lld bytes",
+                           size);
+  }
+
+  if (size <= 0) {
+    return InvalidArgument("Outfeed shape must have positive size; got %lld",
+                           size);
+  }
+
+  int32 size_32 = static_cast<int32>(size);
+  cpu::runtime::XfeedManager* xfeed_manager = cpu::runtime::GetXfeedManager();
+  CpuOutfeedBuffer buffer(destination, size_32);
+  VLOG(2) << "Enqueueing outfeed buffer (for the device to populate) of length "
+          << size_32 << "B";
+  xfeed_manager->outfeed()->EnqueueBuffers({&buffer});
+  VLOG(2) << "Waiting for buffer to be notified as populated.";
+  buffer.WaitForNotification();
+  VLOG(2) << "Buffer is populated, returning from outfeed buffer request.";
+  return Status::OK();
+}
+
 }  // namespace xla
 
 static std::unique_ptr<xla::TransferManager> CreateCpuTransferManager() {
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.h b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
index 96ffb94d71..f133a0ea49 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
@@ -18,7 +18,7 @@ limitations under the License.
 
 #include <vector>
 
-#include "tensorflow/compiler/xla/service/cpu/infeed_manager.h"
+#include "tensorflow/compiler/xla/service/cpu/xfeed_manager.h"
 #include "tensorflow/compiler/xla/service/generic_transfer_manager.h"
 #include "tensorflow/compiler/xla/service/transfer_manager.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -40,13 +40,19 @@ class CpuTransferManager : public GenericTransferManager {
                                  const Literal& literal) override;
   Status TransferBufferToInfeed(perftools::gputools::StreamExecutor* executor,
                                 int64 size, const void* source) override;
+  Status TransferLiteralFromOutfeed(
+      perftools::gputools::StreamExecutor* executor, const Shape& literal_shape,
+      Literal* literal) override;
 
  private:
   // Transfers infeed data to device. InfeedBuffer->Done() must be
   // called to clean up the memory allocated for InfeedBuffer.
-  StatusOr<cpu::runtime::InfeedBuffer*> TransferBufferToInfeedInternal(
+  StatusOr<cpu::runtime::XfeedBuffer*> TransferBufferToInfeedInternal(
       perftools::gputools::StreamExecutor* executor, int64 size,
       const void* source);
+  Status TransferBufferFromOutfeed(
+      perftools::gputools::StreamExecutor* executor, int64 size,
+      void* destination);
 
   TF_DISALLOW_COPY_AND_ASSIGN(CpuTransferManager);
 };
diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
index 476b2b8d6f..cc7ff83d5e 100644
--- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
@@ -170,7 +170,8 @@ Status GenericTransferManager::TransferBufferToInfeed(
 Status GenericTransferManager::TransferLiteralFromOutfeed(
     perftools::gputools::StreamExecutor* executor, const Shape& literal_shape,
     Literal* literal) {
-  return Unimplemented("Outfeed is not supported on CPU/GPU (b/30467474)");
+  return Unimplemented(
+      "Outfeed is not supported on this platform (b/30467474)");
 }
 
 Status GenericTransferManager::ResetDevices(
diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index 6a5533c469..119cf7dde5 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -211,7 +211,8 @@ Status HloComputation::RemoveInstructionAndUnusedOperands(
 Status HloComputation::RemoveInstruction(HloInstruction* instruction) {
   VLOG(2) << "Removing instruction " << instruction->name()
           << " from computation " << name();
-  TF_RET_CHECK(IsRemovable(instruction));
+  TF_RET_CHECK(IsRemovable(instruction))
+      << "cannot remove instruction: " << instruction->ToString();
   TF_RET_CHECK(root_instruction() != instruction)
       << "cannot remove root instruction " << instruction->name();
   TF_RET_CHECK(instruction->user_count() == 0)
@@ -593,6 +594,12 @@ std::vector<HloInstruction*> HloComputation::CollectUnreachableRoots() const {
       unreachable_roots.push_back(instruction.get());
     }
   }
+  VLOG(3) << "Unreachable roots:"
+          << tensorflow::str_util::Join(
+                 unreachable_roots, "\n\t",
+                 [](string* out, const HloInstruction* hlo) {
+                   tensorflow::strings::StrAppend(out, hlo->ToString());
+                 });
   return unreachable_roots;
 }
 
@@ -601,6 +608,7 @@ Status HloComputation::Accept(DfsHloVisitor* visitor) const {
   // visited root, which would invalidate iterators if the unreachable roots
   // weren't computed ahead of time.
   for (HloInstruction* root : CollectUnreachableRoots()) {
+    VLOG(3) << "Traversing unreachable root: " << root->ToString();
     // Call FinishVisit only at the end.
     TF_RETURN_IF_ERROR(root->Accept(visitor, /*call_finish_visit=*/false));
   }
@@ -627,9 +635,15 @@ Status HloComputation::AcceptWithOperandOrder(
 Status HloComputation::AcceptOrdered(
     DfsHloVisitor* visitor,
     const std::vector<const HloInstruction*>& order) const {
+  VLOG(3) << "Accepting visitor with order.";
+  for (HloInstruction* root : CollectUnreachableRoots()) {
+    TF_RET_CHECK(std::find(order.begin(), order.end(), root) != order.end())
+        << root->ToString();
+  }
   TF_RET_CHECK(order.size() == instruction_count());
   std::unordered_set<const HloInstruction*> visited;
   for (const HloInstruction* instruction : order) {
+    VLOG(3) << "Visiting ordered: " << instruction->ToString();
     TF_RET_CHECK(instruction_iterators_.count(instruction) == 1)
         << "Instruction " << instruction->name() << " is not in computation "
         << name();
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index c8bb50173c..73bae5cb31 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -907,13 +907,17 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CreateBatchNormTraining(shape, new_operands[0], new_operands[1],
                                      new_operands[2], epsilon(),
                                      feature_index());
+    case HloOpcode::kInfeed:
+      CHECK_EQ(new_operands.size(), 0);
+      return CreateInfeed(shape, infeed_config());
+    case HloOpcode::kOutfeed:
+      CHECK_EQ(new_operands.size(), 1);
+      return CreateOutfeed(shape, new_operands[0], outfeed_config());
     case HloOpcode::kBatchNormGrad:
     case HloOpcode::kRecv:
     case HloOpcode::kSend:
     case HloOpcode::kUpdate:
     case HloOpcode::kIndex:
-    case HloOpcode::kInfeed:
-    case HloOpcode::kOutfeed:
     case HloOpcode::kTrace:
       LOG(FATAL) << "Not yet implemented, clone: " << HloOpcodeString(opcode_);
   }
-- 
GitLab


From ba45775be000f0923ae620fff6760f15ca56dc53 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 11 Jul 2017 07:46:56 -0700
Subject: [PATCH 0875/1375] [TF:XLA] Fix copy-and-paste bug in CHECK statement.

PiperOrigin-RevId: 161527685
---
 tensorflow/compiler/tf2xla/kernels/while_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc
index ae018515ad..6d1af50053 100644
--- a/tensorflow/compiler/tf2xla/kernels/while_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc
@@ -180,7 +180,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) {
   } else {
     CHECK(!body.xla_input_shapes.empty());
     body_input_shape = body.xla_input_shapes[0];
-    CHECK(!body.xla_input_shapes.empty());
+    CHECK(!cond.xla_input_shapes.empty());
     cond_input_shape = cond.xla_input_shapes[0];
   }
 
-- 
GitLab


From 18a5510e67ef536c947512b70030c5c995ce7875 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 08:29:11 -0700
Subject: [PATCH 0876/1375] Update toolchain configuration artifacts to work
 with latest version of bazel

PiperOrigin-RevId: 161531749
---
 third_party/gpus/cuda/BUILD.tpl        |   2 +-
 third_party/py/BUILD.tpl               |  16 ++--
 third_party/toolchains/cpus/CROSSTOOL  | 118 +++++++++++--------------
 third_party/toolchains/cpus/py/BUILD   |  16 +++-
 third_party/toolchains/gpus/cuda/BUILD |  28 +++---
 5 files changed, 91 insertions(+), 89 deletions(-)

diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index 51d9e4e994..b752734a08 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -152,6 +152,7 @@ cc_library(
 
 cc_library(
     name = "cuda",
+    visibility = ["//visibility:public"],
     deps = [
         ":cublas",
         ":cuda_headers",
@@ -160,7 +161,6 @@ cc_library(
         ":cufft",
         ":curand",
     ],
-    visibility = ["//visibility:public"],
 )
 
 cc_library(
diff --git a/third_party/py/BUILD.tpl b/third_party/py/BUILD.tpl
index 2f3503e793..de06ad5f27 100644
--- a/third_party/py/BUILD.tpl
+++ b/third_party/py/BUILD.tpl
@@ -5,16 +5,16 @@ package(default_visibility = ["//visibility:public"])
 cc_library(
     name = "python_headers",
     hdrs = [":python_include"],
-    includes = ["python_include"],
     data = select({
-      ":windows" : [":python_import_lib"],
-      "//conditions:default": [],
+        ":windows": [":python_import_lib"],
+        "//conditions:default": [],
     }),
+    includes = ["python_include"],
     linkopts = select({
-      # TODO(pcloudy): Ideally, this should just go into deps after resolving
-      # https://github.com/bazelbuild/bazel/issues/3237,
-      ":windows" : ["$(locations :python_import_lib)"],
-      "//conditions:default": [],
+        # TODO(pcloudy): Ideally, this should just go into deps after resolving
+        # https://github.com/bazelbuild/bazel/issues/3237,
+        ":windows": ["$(locations :python_import_lib)"],
+        "//conditions:default": [],
     }),
 )
 
@@ -31,5 +31,5 @@ config_setting(
 )
 
 %{PYTHON_INCLUDE_GENRULE}
-%{PYTHON_IMPORT_LIB_GENRULE}
 %{NUMPY_INCLUDE_GENRULE}
+%{PYTHON_IMPORT_LIB_GENRULE}
diff --git a/third_party/toolchains/cpus/CROSSTOOL b/third_party/toolchains/cpus/CROSSTOOL
index 0fff8191e0..66039c2135 100644
--- a/third_party/toolchains/cpus/CROSSTOOL
+++ b/third_party/toolchains/cpus/CROSSTOOL
@@ -185,7 +185,10 @@ toolchain {
         flag_group {
           flag: '-fprofile-arcs'
           flag: '-ftest-coverage'
-        }
+      }
+
+
+
       }
       flag_set {
         action: 'c++-link-interface-dynamic-library'
@@ -193,10 +196,9 @@ toolchain {
         action: 'c++-link-executable'
         flag_group {
           flag: '-lgcov'
-        }
+      }
       }
     }
-  
 }
 
 toolchain {
@@ -215,15 +217,15 @@ toolchain {
 
   tool_path {
     name: "ar"
-    path: "wrapper/bin/msvc_link.bat"
+    path: ""
   }
   tool_path {
     name: "cpp"
-    path: "wrapper/bin/msvc_cl.bat"
+    path: ""
   }
   tool_path {
     name: "gcc"
-    path: "wrapper/bin/msvc_cl.bat"
+    path: ""
   }
   tool_path {
     name: "gcov"
@@ -231,7 +233,7 @@ toolchain {
   }
   tool_path {
     name: "ld"
-    path: "wrapper/bin/msvc_link.bat"
+    path: ""
   }
   tool_path {
     name: "nm"
@@ -285,8 +287,6 @@ toolchain {
   compiler_flag: "/Gy"
   # Use string pooling.
   compiler_flag: "/GF"
-  # Warning level 3 (could possibly go to 4 in the future).
-  compiler_flag: "/W3"
   # Catch both asynchronous (structured) and synchronous (C++) exceptions.
   compiler_flag: "/EHsc"
 
@@ -304,6 +304,10 @@ toolchain {
 
   linker_flag: "/SUBSYSTEM:CONSOLE"
 
+  feature {
+    name: "no_legacy_features"
+  }
+
   # Suppress startup banner.
   feature {
     name: "nologo"
@@ -364,6 +368,18 @@ toolchain {
     }
   }
 
+  feature {
+    name: "use_linker"
+    env_set {
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      env_entry {
+        key: "USE_LINKER"
+        value: "1"
+      }
+    }
+  }
+
   feature {
     name: 'include_paths'
     flag_set {
@@ -425,7 +441,7 @@ toolchain {
     config_name: 'c-compile'
     action_name: 'c-compile'
     tool {
-      tool_path: 'wrapper/bin/msvc_cl.bat'
+      tool_path: ''
     }
     flag_set {
       flag_group {
@@ -461,7 +477,7 @@ toolchain {
     config_name: 'c++-compile'
     action_name: 'c++-compile'
     tool {
-      tool_path: 'wrapper/bin/msvc_cl.bat'
+      tool_path: ''
     }
     flag_set {
       flag_group {
@@ -497,26 +513,25 @@ toolchain {
     config_name: 'c++-link-executable'
     action_name: 'c++-link-executable'
     tool {
-      tool_path: 'wrapper/bin/msvc_link.bat'
+      tool_path: ''
     }
     implies: 'nologo'
-    implies: 'strip_debug_symbols'
     implies: 'linkstamps'
     implies: 'output_execpath_flags'
     implies: 'input_param_flags'
     implies: 'legacy_link_flags'
     implies: 'linker_param_file'
     implies: 'msvc_env'
+    implies: 'use_linker'
   }
 
   action_config {
     config_name: 'c++-link-dynamic-library'
     action_name: 'c++-link-dynamic-library'
     tool {
-      tool_path: 'wrapper/bin/msvc_link.bat'
+      tool_path: ''
     }
     implies: 'nologo'
-    implies: 'strip_debug_symbols'
     implies: 'shared_flag'
     implies: 'linkstamps'
     implies: 'output_execpath_flags'
@@ -525,15 +540,17 @@ toolchain {
     implies: 'legacy_link_flags'
     implies: 'linker_param_file'
     implies: 'msvc_env'
+    implies: 'use_linker'
   }
 
   action_config {
     config_name: 'c++-link-static-library'
     action_name: 'c++-link-static-library'
     tool {
-      tool_path: 'wrapper/bin/msvc_link.bat'
+      tool_path: ''
     }
     implies: 'nologo'
+    implies: 'archiver_flags'
     implies: 'input_param_flags'
     implies: 'linker_param_file'
     implies: 'msvc_env'
@@ -543,9 +560,10 @@ toolchain {
     config_name: 'c++-link-alwayslink-static-library'
     action_name: 'c++-link-alwayslink-static-library'
     tool {
-      tool_path: 'wrapper/bin/msvc_link.bat'
+      tool_path: ''
     }
     implies: 'nologo'
+    implies: 'archiver_flags'
     implies: 'input_param_flags'
     implies: 'linker_param_file'
     implies: 'msvc_env'
@@ -557,9 +575,10 @@ toolchain {
     config_name: 'c++-link-pic-static-library'
     action_name: 'c++-link-pic-static-library'
     tool {
-      tool_path: 'wrapper/bin/msvc_link.bat'
+      tool_path: ''
     }
     implies: 'nologo'
+    implies: 'archiver_flags'
     implies: 'input_param_flags'
     implies: 'linker_param_file'
     implies: 'msvc_env'
@@ -569,9 +588,10 @@ toolchain {
     config_name: 'c++-link-alwayslink-pic-static-library'
     action_name: 'c++-link-alwayslink-pic-static-library'
     tool {
-      tool_path: 'wrapper/bin/msvc_link.bat'
+      tool_path: ''
     }
     implies: 'nologo'
+    implies: 'archiver_flags'
     implies: 'input_param_flags'
     implies: 'linker_param_file'
     implies: 'msvc_env'
@@ -581,10 +601,9 @@ toolchain {
     config_name: 'c++-link-interface-dynamic-library'
     action_name: 'c++-link-interface-dynamic-library'
     tool {
-      tool_path: 'wrapper/bin/msvc_link.bat'
+      tool_path: ''
     }
     implies: 'nologo'
-    implies: 'strip_debug_symbols'
     implies: 'linker_param_file'
     implies: 'msvc_env'
   }
@@ -603,19 +622,6 @@ toolchain {
     name: 'has_configured_linker_path'
   }
 
-  feature {
-    name: 'strip_debug_symbols'
-    flag_set {
-      action: 'c++-link-executable'
-      action: 'c++-link-dynamic-library'
-      action: 'c++-link-interface-dynamic-library'
-      flag_group {
-        expand_if_all_available: 'strip_debug_symbols'
-        flag: '-Wl,-S'
-      }
-    }
-  }
-
   feature {
     name: 'shared_flag'
     flag_set {
@@ -652,15 +658,25 @@ toolchain {
   }
 
   feature {
-    name: 'input_param_flags'
+    name: 'archiver_flags'
     flag_set {
-      expand_if_all_available: 'library_search_directories'
-      action: 'c++-link-executable'
-      action: 'c++-link-dynamic-library'
+      expand_if_all_available: 'output_execpath'
       action: 'c++-link-static-library'
       action: 'c++-link-alwayslink-static-library'
       action: 'c++-link-pic-static-library'
       action: 'c++-link-alwayslink-pic-static-library'
+      flag_group {
+        flag: '/OUT:%{output_execpath}'
+      }
+    }
+  }
+
+  feature {
+    name: 'input_param_flags'
+    flag_set {
+      expand_if_all_available: 'library_search_directories'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
       flag_group {
         iterate_over: 'library_search_directories'
         flag: "-L%{library_search_directories}"
@@ -670,10 +686,6 @@ toolchain {
       expand_if_all_available: 'libopts'
       action: 'c++-link-executable'
       action: 'c++-link-dynamic-library'
-      action: 'c++-link-static-library'
-      action: 'c++-link-alwayslink-static-library'
-      action: 'c++-link-pic-static-library'
-      action: 'c++-link-alwayslink-pic-static-library'
       flag_group {
         iterate_over: 'libopts'
         flag: '%{libopts}'
@@ -787,12 +799,6 @@ toolchain {
       expand_if_all_available: 'linker_param_file'
       action: 'c++-link-executable'
       action: 'c++-link-dynamic-library'
-      flag_group {
-        flag: '-Wl,@%{linker_param_file}'
-      }
-    }
-    flag_set {
-      expand_if_all_available: 'linker_param_file'
       action: 'c++-link-static-library'
       action: 'c++-link-alwayslink-static-library'
       action: 'c++-link-pic-static-library'
@@ -907,22 +913,6 @@ toolchain {
     implies: 'link_crt_library'
   }
 
-  compilation_mode_flags {
-    mode: DBG
-    compiler_flag: "-Xcompilation-mode=dbg"
-    linker_flag: "-Xcompilation-mode=dbg"
-  }
-
-  compilation_mode_flags {
-    mode: FASTBUILD
-    compiler_flag: "-Xcompilation-mode=fastbuild"
-    linker_flag: "-Xcompilation-mode=fastbuild"
-  }
 
-  compilation_mode_flags {
-    mode: OPT
-    compiler_flag: "-Xcompilation-mode=opt"
-    linker_flag: "-Xcompilation-mode=opt"
-  }
 
 }
diff --git a/third_party/toolchains/cpus/py/BUILD b/third_party/toolchains/cpus/py/BUILD
index d54eebb8e7..c175742cbf 100644
--- a/third_party/toolchains/cpus/py/BUILD
+++ b/third_party/toolchains/cpus/py/BUILD
@@ -9,7 +9,17 @@ package(default_visibility = ["//visibility:public"])
 cc_library(
     name = "python_headers",
     hdrs = [":python_include"],
+    data = select({
+        ":windows": [":python_import_lib"],
+        "//conditions:default": [],
+    }),
     includes = ["python_include"],
+    linkopts = select({
+        # TODO(pcloudy): Ideally, this should just go into deps after resolving
+        # https://github.com/bazelbuild/bazel/issues/3237,
+        ":windows": ["$(locations :python_import_lib)"],
+        "//conditions:default": [],
+    }),
 )
 
 cc_library(
@@ -147,7 +157,8 @@ genrule(
         "python_include/Python-ast.h",
     ],
     cmd = """
-cp  "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp  "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp  "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp  "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp  "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp  "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp  "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp  "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp  "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp  "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp  "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp  "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp  "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp  "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp  "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp  "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp  "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp  "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp  "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp  "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp  "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp  "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp  "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp  "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp  "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp  "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp  "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp  "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp  "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp  "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp  "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp  "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp  "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp  "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" && cp  "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp  "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp  "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp  "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp  "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp  "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp  "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp  "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp  "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp  "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp  "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp  "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp  "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp  "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp  "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp  "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp  "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp  "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp  "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp  "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp  "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp  "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp  "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp  "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp  "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp  "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp  "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp  "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp  "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp  "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp  "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp  "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp  "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp  "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp  "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp  "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp  "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp  "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp  "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp  "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp  "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp  "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp  "/usr/include/python2.7/numpy/oldnumeric.h" "$(@D)/python_include/numpy/oldnumeric.h" && cp  "/usr/include/python2.7/numpy/npy_1_7_deprecated_api.h" "$(@D)/python_include/numpy/npy_1_7_deprecated_api.h" && cp  "/usr/include/python2.7/numpy/ufunc_api.txt" "$(@D)/python_include/numpy/ufunc_api.txt" && cp  "/usr/include/python2.7/numpy/multiarray_api.txt" "$(@D)/python_include/numpy/multiarray_api.txt" && cp  "/usr/include/python2.7/numpy/halffloat.h" "$(@D)/python_include/numpy/halffloat.h" && cp  "/usr/include/python2.7/numpy/npy_common.h" "$(@D)/python_include/numpy/npy_common.h" && cp  "/usr/include/python2.7/numpy/utils.h" "$(@D)/python_include/numpy/utils.h" && cp  "/usr/include/python2.7/numpy/npy_interrupt.h" "$(@D)/python_include/numpy/npy_interrupt.h" && cp  "/usr/include/python2.7/numpy/npy_endian.h" "$(@D)/python_include/numpy/npy_endian.h" && cp  "/usr/include/python2.7/numpy/__ufunc_api.h" "$(@D)/python_include/numpy/__ufunc_api.h" && cp  "/usr/include/python2.7/numpy/_neighborhood_iterator_imp.h" "$(@D)/python_include/numpy/_neighborhood_iterator_imp.h" && cp  "/usr/include/python2.7/numpy/ufuncobject.h" "$(@D)/python_include/numpy/ufuncobject.h" && cp  "/usr/include/python2.7/numpy/ndarraytypes.h" "$(@D)/python_include/numpy/ndarraytypes.h" && cp  "/usr/include/python2.7/numpy/npy_math.h" "$(@D)/python_include/numpy/npy_math.h" && cp  "/usr/include/python2.7/numpy/noprefix.h" "$(@D)/python_include/numpy/noprefix.h" && cp  "/usr/include/python2.7/numpy/npy_3kcompat.h" "$(@D)/python_include/numpy/npy_3kcompat.h" && cp  "/usr/include/python2.7/numpy/arrayscalars.h" "$(@D)/python_include/numpy/arrayscalars.h" && cp  "/usr/include/python2.7/numpy/npy_os.h" "$(@D)/python_include/numpy/npy_os.h" && cp  "/usr/include/python2.7/numpy/ndarrayobject.h" "$(@D)/python_include/numpy/ndarrayobject.h" && cp  "/usr/include/python2.7/numpy/npy_no_deprecated_api.h" "$(@D)/python_include/numpy/npy_no_deprecated_api.h" && cp  "/usr/include/python2.7/numpy/arrayobject.h" "$(@D)/python_include/numpy/arrayobject.h" && cp  "/usr/include/python2.7/numpy/_numpyconfig.h" "$(@D)/python_include/numpy/_numpyconfig.h" && cp  "/usr/include/python2.7/numpy/__multiarray_api.h" "$(@D)/python_include/numpy/__multiarray_api.h" && cp  "/usr/include/python2.7/numpy/npy_cpu.h" "$(@D)/python_include/numpy/npy_cpu.h" && cp  "/usr/include/python2.7/numpy/old_defines.h" "$(@D)/python_include/numpy/old_defines.h" && cp  "/usr/include/python2.7/numpy/numpyconfig.h" "$(@D)/python_include/numpy/numpyconfig.h" && cp  "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp  "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp  "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp  "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp  "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp  "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp  "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp  "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp  "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp  "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp  "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp  "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp  "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp  "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp  "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp  "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h"    """,
+cp "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" && cp "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/usr/include/python2.7/numpy/oldnumeric.h" "$(@D)/python_include/numpy/oldnumeric.h" && cp "/usr/include/python2.7/numpy/npy_1_7_deprecated_api.h" "$(@D)/python_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/include/python2.7/numpy/ufunc_api.txt" "$(@D)/python_include/numpy/ufunc_api.txt" && cp "/usr/include/python2.7/numpy/multiarray_api.txt" "$(@D)/python_include/numpy/multiarray_api.txt" && cp "/usr/include/python2.7/numpy/halffloat.h" "$(@D)/python_include/numpy/halffloat.h" && cp "/usr/include/python2.7/numpy/npy_common.h" "$(@D)/python_include/numpy/npy_common.h" && cp "/usr/include/python2.7/numpy/utils.h" "$(@D)/python_include/numpy/utils.h" && cp "/usr/include/python2.7/numpy/npy_interrupt.h" "$(@D)/python_include/numpy/npy_interrupt.h" && cp "/usr/include/python2.7/numpy/npy_endian.h" "$(@D)/python_include/numpy/npy_endian.h" && cp "/usr/include/python2.7/numpy/__ufunc_api.h" "$(@D)/python_include/numpy/__ufunc_api.h" && cp "/usr/include/python2.7/numpy/_neighborhood_iterator_imp.h" "$(@D)/python_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/include/python2.7/numpy/ufuncobject.h" "$(@D)/python_include/numpy/ufuncobject.h" && cp "/usr/include/python2.7/numpy/ndarraytypes.h" "$(@D)/python_include/numpy/ndarraytypes.h" && cp "/usr/include/python2.7/numpy/npy_math.h" "$(@D)/python_include/numpy/npy_math.h" && cp "/usr/include/python2.7/numpy/noprefix.h" "$(@D)/python_include/numpy/noprefix.h" && cp "/usr/include/python2.7/numpy/npy_3kcompat.h" "$(@D)/python_include/numpy/npy_3kcompat.h" && cp "/usr/include/python2.7/numpy/arrayscalars.h" "$(@D)/python_include/numpy/arrayscalars.h" && cp "/usr/include/python2.7/numpy/npy_os.h" "$(@D)/python_include/numpy/npy_os.h" && cp "/usr/include/python2.7/numpy/ndarrayobject.h" "$(@D)/python_include/numpy/ndarrayobject.h" && cp "/usr/include/python2.7/numpy/npy_no_deprecated_api.h" "$(@D)/python_include/numpy/npy_no_deprecated_api.h" && cp "/usr/include/python2.7/numpy/arrayobject.h" "$(@D)/python_include/numpy/arrayobject.h" && cp "/usr/include/python2.7/numpy/_numpyconfig.h" "$(@D)/python_include/numpy/_numpyconfig.h" && cp "/usr/include/python2.7/numpy/__multiarray_api.h" "$(@D)/python_include/numpy/__multiarray_api.h" && cp "/usr/include/python2.7/numpy/npy_cpu.h" "$(@D)/python_include/numpy/npy_cpu.h" && cp "/usr/include/python2.7/numpy/old_defines.h" "$(@D)/python_include/numpy/old_defines.h" && cp "/usr/include/python2.7/numpy/numpyconfig.h" "$(@D)/python_include/numpy/numpyconfig.h" && cp "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h"
+   """,
 )
 
 genrule(
@@ -181,5 +192,6 @@ genrule(
         "numpy_include/numpy/numpyconfig.h",
     ],
     cmd = """
-cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp  "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h"    """,
+cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h"
+   """,
 )
diff --git a/third_party/toolchains/gpus/cuda/BUILD b/third_party/toolchains/gpus/cuda/BUILD
index 9f8f8754ff..36be86cd10 100644
--- a/third_party/toolchains/gpus/cuda/BUILD
+++ b/third_party/toolchains/gpus/cuda/BUILD
@@ -121,8 +121,8 @@ cc_library(
 
 cc_library(
     name = "cudnn",
-    srcs = ["cuda/lib/libcudnn.so.5"],
-    data = ["cuda/lib/libcudnn.so.5"],
+    srcs = ["cuda/lib/libcudnn.so.6"],
+    data = ["cuda/lib/libcudnn.so.6"],
     includes = [
         ".",
         "cuda/include",
@@ -186,7 +186,7 @@ cc_library(
     data = ["cuda/lib/libcupti.so.8.0"],
     includes = [
         ".",
-        "cuda/extras/CUPTI/include/",
+        "cuda/include",
     ],
     visibility = ["//visibility:public"],
 )
@@ -194,10 +194,6 @@ cc_library(
 cc_library(
     name = "libdevice_root",
     data = [":cuda-nvvm"],
-    includes = [
-        ".",
-        "cuda/extras/CUPTI/include/",
-    ],
     visibility = ["//visibility:public"],
 )
 
@@ -1260,8 +1256,8 @@ genrule(
         "cuda/include/nvfunctional",
     ],
     cmd = """
-cp "/usr/local/cuda-8.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-8.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-8.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-8.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-8.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-8.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-8.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-8.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-8.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-8.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-8.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-8.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-8.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-8.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-8.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-8.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-8.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-8.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-8.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-8.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-8.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-8.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-8.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-8.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-8.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-8.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-8.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-8.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-8.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-8.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-8.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-8.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-8.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-8.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-8.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-8.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-8.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-8.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-8.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuviddec.h" "$(@D)/cuda/include/cuviddec.h" && cp "/usr/local/cuda-8.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-8.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-8.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-8.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-8.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-8.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-8.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-8.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-8.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk.h" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cuda_launch_config.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cuda_launch_config.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cub.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_symmetric_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.h" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extern_shared_ptr.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/balanced_path.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/balanced_path.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/alignment.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/uninitialized.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/uninitialized.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/throw_on_error.h" "$(@D)/cuda/include/thrust/system/cuda/detail/throw_on_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execute_on_stream.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execute_on_stream.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.h" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_union.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_union.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_intersection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_intersection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/malloc.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/async.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/bulk.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/async.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/async.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/future.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/future.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/inclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merging_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merging_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/exclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/odd_even_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-8.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-8.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-8.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-8.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-8.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-8.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-8.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-8.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-8.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-8.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-8.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-8.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-8.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-8.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-8.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-8.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-8.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-8.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-8.0/include/nvcuvid.h" "$(@D)/cuda/include/nvcuvid.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-8.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-8.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-8.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-8.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-8.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-8.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" && cp "/usr/local/cuda-8.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-8.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-8.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-8.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-8.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-8.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-8.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-8.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/crt/device_runtime.h" "$(@D)/cuda/include/crt/device_runtime.h" && cp "/usr/local/cuda-8.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-8.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-8.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-8.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-8.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-8.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-8.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-8.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-8.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-8.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-8.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-8.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-8.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-8.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-8.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-8.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-8.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-8.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-8.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-8.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-8.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional"    """,
-    local = 1,
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-8.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-8.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-8.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-8.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-8.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-8.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-8.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-8.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-8.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-8.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-8.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-8.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-8.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-8.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-8.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-8.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-8.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-8.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-8.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-8.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-8.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-8.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-8.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-8.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-8.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-8.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-8.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-8.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-8.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-8.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-8.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-8.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-8.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-8.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-8.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-8.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-8.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-8.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuviddec.h" "$(@D)/cuda/include/cuviddec.h" && cp "/usr/local/cuda-8.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-8.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-8.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-8.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-8.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-8.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-8.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-8.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-8.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk.h" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cuda_launch_config.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cuda_launch_config.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cub.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_symmetric_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.h" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extern_shared_ptr.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/balanced_path.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/balanced_path.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/alignment.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/uninitialized.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/uninitialized.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/throw_on_error.h" "$(@D)/cuda/include/thrust/system/cuda/detail/throw_on_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execute_on_stream.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execute_on_stream.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.h" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_union.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_union.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_intersection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_intersection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/malloc.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/async.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/bulk.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/async.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/async.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/future.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/future.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/inclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merging_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merging_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/exclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/odd_even_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-8.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-8.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-8.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-8.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-8.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-8.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-8.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-8.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-8.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-8.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-8.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-8.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-8.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-8.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-8.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-8.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-8.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-8.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-8.0/include/nvcuvid.h" "$(@D)/cuda/include/nvcuvid.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-8.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-8.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-8.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-8.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-8.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-8.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" && cp "/usr/local/cuda-8.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-8.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-8.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-8.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-8.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-8.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-8.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-8.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/crt/device_runtime.h" "$(@D)/cuda/include/crt/device_runtime.h" && cp "/usr/local/cuda-8.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-8.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-8.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-8.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-8.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-8.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-8.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-8.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-8.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-8.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-8.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-8.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-8.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-8.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-8.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-8.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-8.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-8.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-8.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-8.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-8.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional"
+   """,
 )
 
 genrule(
@@ -1296,7 +1292,8 @@ genrule(
         "cuda/nvvm/libnvvm-samples/CMakeLists.txt",
     ],
     cmd = """
-cp "/usr/local/cuda-8.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_50.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_50.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_30.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_30.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_20.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_20.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_35.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_35.10.bc" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3.1.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.1.0" && cp "/usr/local/cuda-8.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt"    """,
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_50.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_50.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_30.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_30.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_20.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_20.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_35.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_35.10.bc" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3.1.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.1.0" && cp "/usr/local/cuda-8.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt"
+   """,
 )
 
 genrule(
@@ -1332,7 +1329,8 @@ genrule(
         "cuda/extras/CUPTI/include/generated_nvtx_meta.h",
     ],
     cmd = """
-cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h"    """,
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h"
+   """,
 )
 
 genrule(
@@ -1345,11 +1343,12 @@ genrule(
         "cuda/lib/libcusolver.so.8.0",
         "cuda/lib/libcurand.so.8.0",
         "cuda/lib/libcufft.so.8.0",
-        "cuda/lib/libcudnn.so.5",
+        "cuda/lib/libcudnn.so.6",
         "cuda/lib/libcupti.so.8.0",
     ],
     cmd = """
-cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart.so.8.0.61" "$(@D)/cuda/lib/libcudart.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcublas.so.8.0.71" "$(@D)/cuda/lib/libcublas.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0.61" "$(@D)/cuda/lib/libcusolver.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcurand.so.8.0.61" "$(@D)/cuda/lib/libcurand.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcufft.so.8.0.61" "$(@D)/cuda/lib/libcufft.so.8.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.5.1.10" "$(@D)/cuda/lib/libcudnn.so.5" && cp "/usr/local/cuda-8.0/extras/CUPTI/lib64/libcupti.so.8.0.61" "$(@D)/cuda/lib/libcupti.so.8.0"    """,
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart.so.8.0.61" "$(@D)/cuda/lib/libcudart.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcublas.so.8.0.71" "$(@D)/cuda/lib/libcublas.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0.61" "$(@D)/cuda/lib/libcusolver.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcurand.so.8.0.61" "$(@D)/cuda/lib/libcurand.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcufft.so.8.0.61" "$(@D)/cuda/lib/libcufft.so.8.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.6.0.21" "$(@D)/cuda/lib/libcudnn.so.6" && cp "/usr/local/cuda-8.0/extras/CUPTI/lib64/libcupti.so.8.0.61" "$(@D)/cuda/lib/libcupti.so.8.0"
+   """,
 )
 
 genrule(
@@ -1358,5 +1357,6 @@ genrule(
         "cuda/include/cudnn.h",
     ],
     cmd = """
-cp "/usr/include/cudnn.h" "$(@D)/cudnn.h"    """,
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/include/cudnn.h" "$(@D)/cudnn.h"
+   """,
 )
-- 
GitLab


From b1f9e2c89eb007cb4b9483d08dcace1e45e84164 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Tue, 11 Jul 2017 09:51:54 -0700
Subject: [PATCH 0877/1375] Add an axis parameter to tf.gather. Fixes GitHub
 issue #11223.

This brings tf.gather closer to compatibility with numpy.take.

To emulate gathering over an axis generally requires inefficient workarounds, e.g. transpose/gather/transpose. This technique is gaining popularity (hundreds of uses inside and outside of Google), so it is worth supporting efficiently.

For an `[a_0, ..., a_i, ..., a_n]` tensor, gathering `N` elements from axis `i` requires `(a_0*...*a_i-1) * N` copies of `(a_i+1 * ... * a_n)` elements each. The CPU kernel does this with memcpy which is far more efficient than transpose/gather/transpose since it requires no intermediate allocations and copies. The GPU kernel does the same number of copies but in parallel across multiple hardware threads.

Since this is a backwards incompatible change, this adds a "GatherV2" op with an axis input, and simultaneously supports backwards compatibility with "Gather" ops by defaulting to axis 0 if a 3rd input is not present.

PiperOrigin-RevId: 161541416
---
 .../compiler/tf2xla/kernels/gather_op.cc      |  62 ++++--
 .../kernels/gather_op_kernel_float_int32.cc   |  21 +-
 .../kernels/gather_op_kernel_float_int64.cc   |  21 +-
 tensorflow/core/graph/testlib.cc              |   6 +-
 tensorflow/core/graph/testlib.h               |   4 +-
 .../grappler/optimizers/layout_optimizer.cc   |  30 ++-
 tensorflow/core/kernels/gather_functor.cc     |  12 +-
 tensorflow/core/kernels/gather_functor.h      |  78 ++++---
 .../core/kernels/gather_functor_gpu.cu.h      |  69 +++++--
 tensorflow/core/kernels/gather_op.cc          |  73 +++++--
 tensorflow/core/kernels/gather_op_test.cc     |  35 +++-
 .../core/kernels/resource_variable_ops.cc     |   9 +-
 tensorflow/core/ops/array_ops.cc              |  99 +++++++++
 tensorflow/core/ops/array_ops_test.cc         |  55 +++++
 tensorflow/python/kernel_tests/BUILD          |   2 +-
 .../python/kernel_tests/gather_op_test.py     | 192 +++++++++++++-----
 .../resource_variable_ops_test.py             |  10 +
 tensorflow/python/ops/array_grad.py           |  63 +++++-
 tensorflow/python/ops/array_ops.py            |  11 +
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   2 +-
 .../tools/graph_transforms/sparsify_gather.cc |  87 ++++++--
 .../graph_transforms/sparsify_gather_test.cc  |  37 +++-
 22 files changed, 772 insertions(+), 206 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc
index 3c1cdef5f8..184b5119f8 100644
--- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc
@@ -29,6 +29,7 @@ class GatherOp : public XlaOpKernel {
 
   void Compile(XlaOpKernelContext* ctx) override {
     const TensorShape params_shape = ctx->InputShape(0);
+    const auto params_dims = params_shape.dims();
     const TensorShape indices_shape = ctx->InputShape(1);
     OP_REQUIRES(
         ctx, TensorShapeUtils::IsVectorOrHigher(params_shape),
@@ -38,20 +39,51 @@ class GatherOp : public XlaOpKernel {
     OP_REQUIRES(ctx, index_type == DT_INT32 || index_type == DT_INT64,
                 errors::InvalidArgument("index must be int32 or int64"));
 
+    // GatherV2 added an axis argument. We support both Gather and GatherV2 in
+    // this kernel by defaulting axis to 0 if there are 2 inputs.
+    int64 axis = 0;
+    if (ctx->num_inputs() == 3) {
+      const TensorShape axis_shape = ctx->InputShape(2);
+      OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(axis_shape),
+                  errors::InvalidArgument("axis must be scalar"));
+      DataType axis_type = input_type(2);
+      OP_REQUIRES(ctx, axis_type == DT_INT32 || axis_type == DT_INT64,
+                  errors::InvalidArgument("axis must be int32 or int64"));
+
+      xla::Literal literal;
+      OP_REQUIRES_OK(ctx, ctx->ConstantInput(2, &literal));
+      int64 axis_input = axis_type == DT_INT32 ? literal.Get<int32>({})
+                                               : literal.Get<int64>({});
+      axis = axis_input < 0 ? axis_input + params_dims : axis_input;
+      OP_REQUIRES(ctx, 0 <= axis && axis < params_dims,
+                  errors::InvalidArgument("Expected axis in the range [",
+                                          -params_dims, ", ", params_dims,
+                                          "), but got ", axis_input));
+    }
+
     // Check that we have enough index space.
     const int64 limit = index_type == DT_INT32
                             ? std::numeric_limits<int32>::max()
                             : std::numeric_limits<int64>::max();
-    OP_REQUIRES(
-        ctx, params_shape.dim_size(0) <= limit,
-        errors::InvalidArgument("params.shape[0] too large for ",
-                                DataTypeString(index_type), " indexing: ",
-                                params_shape.dim_size(0), " > ", limit));
-
-    // The result shape is indices.shape + params.shape[1:].
-    TensorShape result_shape = indices_shape;
-    for (int i = 1; i < params_shape.dims(); i++) {
+    OP_REQUIRES(ctx, params_shape.dim_size(axis) <= limit,
+                errors::InvalidArgument(
+                    "params.shape[", axis, "] too large for ",
+                    DataTypeString(index_type),
+                    " indexing: ", params_shape.dim_size(axis), " > ", limit));
+
+    // The result shape is params.shape[0:axis] + indices.shape +
+    // params.shape[axis + 1:].
+    TensorShape result_shape;
+    int64 outer_size = 1;
+    int64 inner_size = 1;
+    for (int i = 0; i < axis; i++) {
+      result_shape.AddDim(params_shape.dim_size(i));
+      outer_size *= params_shape.dim_size(i);
+    }
+    result_shape.AppendShape(indices_shape);
+    for (int i = axis + 1; i < params_dims; i++) {
       result_shape.AddDim(params_shape.dim_size(i));
+      inner_size *= params_shape.dim_size(i);
     }
 
     XlaContext& tc = XlaContext::Get(ctx);
@@ -67,10 +99,12 @@ class GatherOp : public XlaOpKernel {
     args.push_back(tc.GetOrCreateRuntimeContextParameter());
     args.push_back(b.ConstantLiteral(
         *xla::Literal::CreateR0<int64>(indices_shape.num_elements())));
+    args.push_back(
+        b.ConstantLiteral(*xla::Literal::CreateR0<int64>(outer_size)));
     args.push_back(b.ConstantLiteral(
-        *xla::Literal::CreateR0<int64>(params_shape.dim_size(0))));
-    args.push_back(b.ConstantLiteral(*xla::Literal::CreateR0<int64>(
-        params_shape.num_elements() / params_shape.dim_size(0))));
+        *xla::Literal::CreateR0<int64>(params_shape.dim_size(axis))));
+    args.push_back(
+        b.ConstantLiteral(*xla::Literal::CreateR0<int64>(inner_size)));
     args.push_back(ctx->Input(0));
     args.push_back(ctx->Input(1));
 
@@ -97,6 +131,10 @@ REGISTER_XLA_OP(Name("Gather")
                     .TypeConstraint("Tparams", DT_FLOAT)
                     .Device(DEVICE_CPU_XLA_JIT),
                 GatherOp);
+REGISTER_XLA_OP(Name("GatherV2")
+                    .TypeConstraint("Tparams", DT_FLOAT)
+                    .Device(DEVICE_CPU_XLA_JIT),
+                GatherOp);
 
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int32.cc b/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int32.cc
index 691a0b972d..33b1b087d0 100644
--- a/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int32.cc
+++ b/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int32.cc
@@ -26,28 +26,31 @@ namespace tensorflow {
 
 EIGEN_STRONG_INLINE void gather_float_int32_xla_impl(float* out, void** data) {
   // data is managed by the JIT code so msan can't tell it's initialized.
-  TF_ANNOTATE_MEMORY_IS_INITIALIZED(data, 6 * sizeof(void*));
+  TF_ANNOTATE_MEMORY_IS_INITIALIZED(data, 7 * sizeof(void*));
 
   int64 indices_size = *static_cast<int64*>(data[1]);
   int64 params_x = *static_cast<int64*>(data[2]);
   int64 params_y = *static_cast<int64*>(data[3]);
+  int64 params_z = *static_cast<int64*>(data[4]);
 
-  float* in = static_cast<float*>(data[4]);
+  float* in = static_cast<float*>(data[5]);
 
-  int32* indices = static_cast<int32*>(data[5]);
-  Eigen::DSizes<Eigen::DenseIndex, 2> in_eig_sizes;
+  int32* indices = static_cast<int32*>(data[6]);
+  Eigen::DSizes<Eigen::DenseIndex, 3> in_eig_sizes;
   in_eig_sizes[0] = params_x;
   in_eig_sizes[1] = params_y;
-  tensorflow::TTypes<float, 2>::ConstMatrix in_eig(in, in_eig_sizes);
+  in_eig_sizes[2] = params_z;
+  tensorflow::TTypes<float, 3>::ConstTensor in_eig(in, in_eig_sizes);
 
   Eigen::DSizes<Eigen::DenseIndex, 1> indices_eig_sizes;
   indices_eig_sizes[0] = indices_size;
   tensorflow::TTypes<int32>::ConstFlat indices_eig(indices, indices_eig_sizes);
 
-  Eigen::DSizes<Eigen::DenseIndex, 2> out_eig_sizes;
-  out_eig_sizes[0] = indices_size;
-  out_eig_sizes[1] = params_y;
-  tensorflow::TTypes<float>::Matrix out_eig(out, out_eig_sizes);
+  Eigen::DSizes<Eigen::DenseIndex, 3> out_eig_sizes;
+  out_eig_sizes[0] = params_x;
+  out_eig_sizes[1] = indices_size;
+  out_eig_sizes[2] = params_z;
+  tensorflow::TTypes<float, 3>::Tensor out_eig(out, out_eig_sizes);
 
   tensorflow::functor::GatherFunctorCPU<float, int32> f;
   const int64 bad_i = f(in_eig, indices_eig, out_eig);
diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int64.cc b/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int64.cc
index 3dff6e2737..5e2d872ce0 100644
--- a/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int64.cc
+++ b/tensorflow/compiler/tf2xla/kernels/gather_op_kernel_float_int64.cc
@@ -26,28 +26,31 @@ namespace tensorflow {
 
 EIGEN_STRONG_INLINE void gather_float_int64_xla_impl(float* out, void** data) {
   // data is managed by the JIT code so msan can't tell it's initialized.
-  TF_ANNOTATE_MEMORY_IS_INITIALIZED(data, 6 * sizeof(void*));
+  TF_ANNOTATE_MEMORY_IS_INITIALIZED(data, 7 * sizeof(void*));
 
   int64 indices_size = *static_cast<int64*>(data[1]);
   int64 params_x = *static_cast<int64*>(data[2]);
   int64 params_y = *static_cast<int64*>(data[3]);
+  int64 params_z = *static_cast<int64*>(data[4]);
 
-  float* in = static_cast<float*>(data[4]);
+  float* in = static_cast<float*>(data[5]);
 
-  int64* indices = static_cast<int64*>(data[5]);
-  Eigen::DSizes<Eigen::DenseIndex, 2> in_eig_sizes;
+  int64* indices = static_cast<int64*>(data[6]);
+  Eigen::DSizes<Eigen::DenseIndex, 3> in_eig_sizes;
   in_eig_sizes[0] = params_x;
   in_eig_sizes[1] = params_y;
-  tensorflow::TTypes<float, 2>::ConstMatrix in_eig(in, in_eig_sizes);
+  in_eig_sizes[2] = params_z;
+  tensorflow::TTypes<float, 3>::ConstTensor in_eig(in, in_eig_sizes);
 
   Eigen::DSizes<Eigen::DenseIndex, 1> indices_eig_sizes;
   indices_eig_sizes[0] = indices_size;
   tensorflow::TTypes<int64>::ConstFlat indices_eig(indices, indices_eig_sizes);
 
-  Eigen::DSizes<Eigen::DenseIndex, 2> out_eig_sizes;
-  out_eig_sizes[0] = indices_size;
-  out_eig_sizes[1] = params_y;
-  tensorflow::TTypes<float>::Matrix out_eig(out, out_eig_sizes);
+  Eigen::DSizes<Eigen::DenseIndex, 3> out_eig_sizes;
+  out_eig_sizes[0] = params_x;
+  out_eig_sizes[1] = indices_size;
+  out_eig_sizes[2] = params_z;
+  tensorflow::TTypes<float, 3>::Tensor out_eig(out, out_eig_sizes);
 
   tensorflow::functor::GatherFunctorCPU<float, int64> f;
   const int64 bad_i = f(in_eig, indices_eig, out_eig);
diff --git a/tensorflow/core/graph/testlib.cc b/tensorflow/core/graph/testlib.cc
index c59c44c80e..be52438747 100644
--- a/tensorflow/core/graph/testlib.cc
+++ b/tensorflow/core/graph/testlib.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <vector>
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/types.h"
@@ -420,11 +421,12 @@ Node* Cast(Graph* g, Node* in, DataType dst) {
   return ret;
 }
 
-Node* Gather(Graph* g, Node* in0, Node* in1) {
+Node* Gather(Graph* g, Node* in0, Node* in1, Node* axis) {
   Node* ret;
-  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Gather")
+  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "GatherV2")
                   .Input(in0)
                   .Input(in1)
+                  .Input(axis)
                   .Finalize(g, &ret));
   return ret;
 }
diff --git a/tensorflow/core/graph/testlib.h b/tensorflow/core/graph/testlib.h
index 48250fef0f..a38809e6b4 100644
--- a/tensorflow/core/graph/testlib.h
+++ b/tensorflow/core/graph/testlib.h
@@ -171,8 +171,8 @@ Node* Select(Graph* g, Node* c, Node* inx, Node* iny);
 // Casts "in" into data type "dst".
 Node* Cast(Graph* g, Node* in, DataType dst);
 
-// Perform gather op on params "in0" with indices "in1".
-Node* Gather(Graph* g, Node* in0, Node* in1);
+// Perform gather op on params "in0" with indices "in1" and axis "axis".
+Node* Gather(Graph* g, Node* in0, Node* in1, Node* axis);
 
 // Gets a tensor stored in the session state.
 Node* GetSessionTensor(Graph* g, Node* in);
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index c6f3ec3950..55da0f710d 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -35,6 +35,7 @@ namespace grappler {
 const char kConcatConst[] = "LayoutOptimizerConcatConst";
 const char kPermNHWCToNCHW[] = "LayoutOptimizerPermConstNHWCToNCHW";
 const char kPermNCHWToNHWC[] = "LayoutOptimizerPermConstNCHWToNHWC";
+const char kGatherAxisConst[] = "LayoutOptimizerGatherAxisConst";
 const char kTransposeNHWCToNCHW[] = "LayoutOptimizerTransposeNHWCToNCHW";
 const char kTransposeNCHWToNHWC[] = "LayoutOptimizerTransposeNCHWToNHWC";
 const char kPermVecNHWCToNCHW[] = "LayoutOptimizerPermVecNHWCToNCHW";
@@ -755,12 +756,17 @@ class SliceProcessor : public AgnosticNodeProcessor {
     node->set_name(node_name);
     *node->add_input() = input_name;
     *node->add_input() = NHWCToNCHW ? kPermNHWCToNCHW : kPermNCHWToNHWC;
-    node->set_op("Gather");
+    *node->add_input() = kGatherAxisConst;
+    node->set_op("GatherV2");
 
     AttrValue attr_type_indices;
     attr_type_indices.set_type(DT_INT32);
     node->mutable_attr()->insert({"Tindices", attr_type_indices});
 
+    AttrValue attr_type_axis;
+    attr_type_axis.set_type(DT_INT32);
+    node->mutable_attr()->insert({"Taxis", attr_type_axis});
+
     AttrValue attr_type_params;
     attr_type_params.set_type(data_type);
     node->mutable_attr()->insert({"Tparams", attr_type_params});
@@ -979,22 +985,30 @@ class DataLayoutOptimizer {
     return node;
   }
 
-  NodeDef* AddNodeConcatConst() {
+  NodeDef* AddConstScalar(const char* name, DataType dtype, int value) {
     NodeDef* node = graph_->add_node();
-    node_map_.AddNode(kConcatConst, node);
-    node->set_name(kConcatConst);
+    node_map_.AddNode(name, node);
+    node->set_name(name);
     node->set_op("Const");
     AttrValue attr_data_type;
-    attr_data_type.set_type(DT_INT32);
+    attr_data_type.set_type(dtype);
     node->mutable_attr()->insert({"dtype", attr_data_type});
     AttrValue attr_tensor;
-    Tensor tensor(DT_INT32, TensorShape({}));
-    tensor.scalar<int>()() = 1;
+    Tensor tensor(dtype, TensorShape({}));
+    tensor.scalar<int>()() = value;
     tensor.AsProtoTensorContent(attr_tensor.mutable_tensor());
     node->mutable_attr()->insert({"value", attr_tensor});
     return node;
   }
 
+  NodeDef* AddNodeConcatConst() {
+    return AddConstScalar(kConcatConst, DT_INT32, 1);
+  }
+
+  NodeDef* AddGatherAxisConst() {
+    return AddConstScalar(kGatherAxisConst, DT_INT32, 0);
+  }
+
   NodeDef* AddNodeReductionConst() {
     NodeDef* node = graph_->add_node();
     node_map_.AddNode(kReductionConst, node);
@@ -1063,6 +1077,8 @@ class DataLayoutOptimizer {
       n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       n = AddNodeConcatConst();
       n->set_device("/job:localhost/replica:0/task:0/cpu:0");
+      n = AddGatherAxisConst();
+      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       n = AddNodeReductionConst();
       n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       std::set<string> ops_format_agnostic = GetOpsFormatAgnostic();
diff --git a/tensorflow/core/kernels/gather_functor.cc b/tensorflow/core/kernels/gather_functor.cc
index c1d58733a2..1b8be9b2ce 100644
--- a/tensorflow/core/kernels/gather_functor.cc
+++ b/tensorflow/core/kernels/gather_functor.cc
@@ -25,12 +25,12 @@ typedef Eigen::GpuDevice GPUDevice;
 namespace functor {
 
 // Forward declarations of the functor specializations for GPU.
-#define DECLARE_GPU_SPECS_INDEX(T, Index)                          \
-  template <>                                                      \
-  int64 GatherFunctor<GPUDevice, T, Index>::operator()(            \
-      const GPUDevice& d, typename TTypes<T>::ConstMatrix Tparams, \
-      typename TTypes<Index>::ConstFlat Tindices,                  \
-      typename TTypes<T>::Matrix Tout);                            \
+#define DECLARE_GPU_SPECS_INDEX(T, Index)                             \
+  template <>                                                         \
+  int64 GatherFunctor<GPUDevice, T, Index>::operator()(               \
+      const GPUDevice& d, typename TTypes<T, 3>::ConstTensor Tparams, \
+      typename TTypes<Index>::ConstFlat Tindices,                     \
+      typename TTypes<T, 3>::Tensor Tout);                            \
   extern template struct GatherFunctor<GPUDevice, T, Index>;
 
 #define DECLARE_GPU_SPECS(T)         \
diff --git a/tensorflow/core/kernels/gather_functor.h b/tensorflow/core/kernels/gather_functor.h
index 1ad4d2da8d..dfa1a5f1f9 100644
--- a/tensorflow/core/kernels/gather_functor.h
+++ b/tensorflow/core/kernels/gather_functor.h
@@ -32,40 +32,51 @@ namespace functor {
 // Helper method to copy using memcpy.
 template <typename T, typename Index, typename SliceIndex,
           SliceIndex static_slice_elems>
-SliceIndex HandleCopies(typename TTypes<T>::ConstMatrix params,
+SliceIndex HandleCopies(typename TTypes<T, 3>::ConstTensor params,
                         typename TTypes<Index>::ConstFlat indices,
                         SliceIndex slice_elems,
-                        typename TTypes<T>::Matrix out) {
-  const SliceIndex first_dim_size =
-      static_cast<SliceIndex>(indices.dimension(0));
-  const Index limit = static_cast<Index>(params.dimension(0));
-  T* out_base = &out(0, 0);
-  const T* params_base = &params(0, 0);
+                        typename TTypes<T, 3>::Tensor out) {
+  const SliceIndex indices_size = static_cast<SliceIndex>(indices.dimension(0));
+  const SliceIndex batch_size = static_cast<SliceIndex>(params.dimension(0));
+  const Index limit = static_cast<Index>(params.dimension(1));
+  T* out_base = &out(0, 0, 0);
+  const T* params_base = &params(0, 0, 0);
   if (static_slice_elems >= 0) {
     // Give compiler static knowledge of the number of elements/bytes
     slice_elems = static_slice_elems;
   }
   // Compute slice_bytes here so that static knowledge is available
   const size_t slice_bytes = slice_elems * sizeof(T);
-  for (SliceIndex i = 0; i < first_dim_size; i++) {
-    const SliceIndex j = i + 1;
-    if (j < first_dim_size) {
-      port::prefetch<port::PREFETCH_HINT_T0>(&params(indices(j), 0));
-      port::prefetch<port::PREFETCH_HINT_T0>(&out(j, 0));
-    }
-    // Grab the index and check its validity.  An earlier version of the
-    // code checked it and then grabbed it from memory a second time, which
-    // was a security risk since it could have changed in between.
-    const Index index = internal::SubtleMustCopy(indices(i));
-    if (!FastBoundsCheck(index, limit)) return i;
-    // Copy using memcpy if possible, otherwise an Eigen loop
-    // TODO(cwhipkey): avoid linking to framework to get Allocator (to improve
-    // ahead-of-time compilation binary size).
-    if (is_simple_type<T>::value) {
-      memcpy(out_base + i * slice_elems, params_base + index * slice_elems,
-             slice_bytes);
-    } else {
-      out.template chip<0>(i) = params.template chip<0>(index);
+  for (SliceIndex b = 0; b < batch_size; b++) {
+    for (SliceIndex i = 0; i < indices_size; i++) {
+      const SliceIndex i_next = i + 1;
+      const SliceIndex b_next = b + 1;
+      if (i_next < indices_size) {
+        port::prefetch<port::PREFETCH_HINT_T0>(&params(b, indices(i_next), 0));
+        port::prefetch<port::PREFETCH_HINT_T0>(&out(b, i_next, 0));
+      } else if (b_next < batch_size) {
+        port::prefetch<port::PREFETCH_HINT_T0>(&params(b_next, indices(0), 0));
+        port::prefetch<port::PREFETCH_HINT_T0>(&out(b_next, 0, 0));
+      }
+      // Grab the index and check its validity.  An earlier version of the
+      // code checked it and then grabbed it from memory a second time, which
+      // was a security risk since it could have changed in between.
+      const Index index = internal::SubtleMustCopy(indices(i));
+      if (!FastBoundsCheck(index, limit)) return i;
+      // Copy using memcpy if possible, otherwise an Eigen loop
+      // TODO(cwhipkey): avoid linking to framework to get Allocator (to improve
+      // ahead-of-time compilation binary size).
+      if (is_simple_type<T>::value) {
+        // Avoid auto-promotion to Index from SliceIndex by casting.
+        memcpy(out_base + (b * indices_size + i) * slice_elems,
+               params_base + (b * static_cast<SliceIndex>(limit) +
+                              static_cast<SliceIndex>(index)) *
+                                 slice_elems,
+               slice_bytes);
+      } else {
+        // For non-"simple" types (e.g. strings).
+        out.template chip<1>(i) = params.template chip<1>(index);
+      }
     }
   }
   return -1;
@@ -73,11 +84,11 @@ SliceIndex HandleCopies(typename TTypes<T>::ConstMatrix params,
 
 template <typename T, typename Index>
 struct GatherFunctorCPU {
-  int64 operator()(typename TTypes<T>::ConstMatrix params,
+  int64 operator()(typename TTypes<T, 3>::ConstTensor params,
                    typename TTypes<Index>::ConstFlat indices,
-                   typename TTypes<T>::Matrix out) {
+                   typename TTypes<T, 3>::Tensor out) {
     const int64 N = indices.size();
-    const int64 slice_size = out.size() / N;
+    const int64 slice_size = out.dimension(2);
     int64 bad_i;
 
     bool use_large = (slice_size > std::numeric_limits<int32>::max() ||
@@ -109,16 +120,17 @@ struct GatherFunctorCPU {
 
 template <typename Device, typename T, typename Index>
 struct GatherFunctor {
-  int64 operator()(const Device& d, typename TTypes<T>::ConstMatrix params,
+  int64 operator()(const Device& d, typename TTypes<T, 3>::ConstTensor params,
                    typename TTypes<Index>::ConstFlat indices,
-                   typename TTypes<T>::Matrix out);
+                   typename TTypes<T, 3>::Tensor out);
 };
 
 template <typename T, typename Index>
 struct GatherFunctor<CPUDevice, T, Index> {
-  int64 operator()(const CPUDevice& d, typename TTypes<T>::ConstMatrix params,
+  int64 operator()(const CPUDevice& d,
+                   typename TTypes<T, 3>::ConstTensor params,
                    typename TTypes<Index>::ConstFlat indices,
-                   typename TTypes<T>::Matrix out) {
+                   typename TTypes<T, 3>::Tensor out) {
     return GatherFunctorCPU<T, Index>()(params, indices, out);
   }
 };
diff --git a/tensorflow/core/kernels/gather_functor_gpu.cu.h b/tensorflow/core/kernels/gather_functor_gpu.cu.h
index ff6779b648..e2384ef011 100644
--- a/tensorflow/core/kernels/gather_functor_gpu.cu.h
+++ b/tensorflow/core/kernels/gather_functor_gpu.cu.h
@@ -29,21 +29,41 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-template <typename T, typename Index>
+template <typename T, typename Index, bool is_axis_zero>
 __global__ void GatherOpKernel(const T* params, const Index* indices, T* out,
-                               int64 first_dim_size, int64 indices_size,
-                               int64 out_size) {
-  const int32 slice_size = out_size / indices_size;
+                               int64 gather_dim_size, int64 indices_size,
+                               int64 slice_size, int64 out_size) {
   CUDA_1D_KERNEL_LOOP(i, out_size) {
-    Index indices_i = i / slice_size;
-    Index indices_slice_i = i - indices_i * slice_size;
-    Index params_first_index = ldg(indices + indices_i);
-    if (!(params_first_index >= 0 && params_first_index < first_dim_size)) {
+    Index batch_i = 0;
+    Index indices_i = 0;
+    Index slice_i = 0;
+    if (is_axis_zero) {
+      indices_i = i / slice_size;
+      slice_i = i - indices_i * slice_size;
+    } else {
+      Index batch_indices_i = i / slice_size;
+      // The batch index into params to use for i.
+      batch_i = batch_indices_i / indices_size;
+      // The index into indices to use for i.
+      indices_i = batch_indices_i - batch_i * indices_size;
+      // Index into the current slice in params to use for i.
+      slice_i = i - batch_indices_i * slice_size;
+    }
+
+    // Index into the gather axis to use for i.
+    Index gather_i = ldg(indices + indices_i);
+
+    // Check gather_i is in [0, gather_dim_size).
+    if (!FastBoundsCheck(gather_i, gather_dim_size)) {
       // Set indices out of range to zero
       // TODO(fpmc): Log an error for transfer back to host.
       out[i] = T(0);
     } else {
-      Index params_i = params_first_index * slice_size + indices_slice_i;
+      // params is a [batch_size, gather_dim_size, slice_size] tensor. Read
+      // params[batch_i, gather_i, slice_i] and write it to the i'th position in
+      // out.
+      Index params_i =
+          (batch_i * gather_dim_size + gather_i) * slice_size + slice_i;
       out[i] = ldg(params + params_i);
     }
   }
@@ -52,9 +72,10 @@ __global__ void GatherOpKernel(const T* params, const Index* indices, T* out,
 namespace functor {
 template <typename T, typename Index>
 struct GatherFunctor<GPUDevice, T, Index> {
-  int64 operator()(const GPUDevice& d, typename TTypes<T>::ConstMatrix params,
+  int64 operator()(const GPUDevice& d,
+                   typename TTypes<T, 3>::ConstTensor params,
                    typename TTypes<Index>::ConstFlat indices,
-                   typename TTypes<T>::Matrix out) {
+                   typename TTypes<T, 3>::Tensor out) {
     const int64 out_size = out.size();
     if (out_size == 0) {
       // We need a check here since the CPU version does useful error checking
@@ -63,15 +84,27 @@ struct GatherFunctor<GPUDevice, T, Index> {
       // checking, so we skip the loop entirely.
       return -1;
     }
-    const int64 first_dim_size = params.dimension(0);
+    const bool is_axis_zero = params.dimension(0) == 1;
+    const int64 gather_dim_size = params.dimension(1);
     const int64 indices_size = indices.size();
+    const int64 slice_size = params.dimension(2);
+
     CudaLaunchConfig config = GetCudaLaunchConfig(out_size, d);
-    // clang-format off
-    GatherOpKernel<T, Index>
-        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
-            params.data(), indices.data(), out.data(), first_dim_size,
-            indices_size, out_size);
-    // clang-format on
+    if (is_axis_zero) {
+      // clang-format off
+      GatherOpKernel<T, Index, true>
+          <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+              params.data(), indices.data(), out.data(), gather_dim_size,
+              indices_size, slice_size, out_size);
+      // clang-format on
+    } else {
+      // clang-format off
+      GatherOpKernel<T, Index, false>
+          <<<config.block_count, config.thread_per_block, 0, d.stream()>>>(
+              params.data(), indices.data(), out.data(), gather_dim_size,
+              indices_size, slice_size, out_size);
+      // clang-format on
+    }
     // TODO(fpmc): enable indices validation on GPU.
     // Right now checking for indicies out of bound in the kernel would
     // require copying code between GPU/CPU, and thus slow.
diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc
index dd25f58957..e649c54fa8 100644
--- a/tensorflow/core/kernels/gather_op.cc
+++ b/tensorflow/core/kernels/gather_op.cc
@@ -37,15 +37,7 @@ class GatherOp : public OpKernel {
   //   we have the framework do some sort of integer promotion
   //   automatically, or should that be something that users have to
   //   do explicitly with a conversion operator in the graph?
-  explicit GatherOp(OpKernelConstruction* c) : OpKernel(c) {
-    const DataType dt = DataTypeToEnum<T>::v();
-    const DataType index_t = DataTypeToEnum<Index>::v();
-    OP_REQUIRES_OK(c, c->MatchSignature({dt, index_t}, {dt}));
-    // We used to grab the validate_indices attribute here, but now we
-    // always validate indices since the speed difference was only 1.5%.
-    // TODO(irving): Remove the validate_indices attribute once we have
-    // support for removing attrs in a backwards compatible way.
-  }
+  explicit GatherOp(OpKernelConstruction* c) : OpKernel(c) {}
 
   void Compute(OpKernelContext* c) override {
     const Tensor& params = c->input(0);
@@ -54,27 +46,64 @@ class GatherOp : public OpKernel {
         c, TensorShapeUtils::IsVectorOrHigher(params.shape()),
         errors::InvalidArgument("params must be at least 1 dimensional"));
 
+    // GatherV2 added an axis argument. For backwards compatibility with Gather,
+    // fall back to axis 0 if the op does not have an axis input.
+    int64 axis = 0;
+    if (c->num_inputs() == 3) {
+      const Tensor& axis_tensor = c->input(2);
+      OP_REQUIRES(c, TensorShapeUtils::IsScalar(axis_tensor.shape()),
+                  errors::InvalidArgument("axis must be scalar"));
+
+      if (axis_tensor.dtype() == DT_INT32) {
+        axis = axis_tensor.scalar<int32>()();
+      } else if (axis_tensor.dtype() == DT_INT64) {
+        axis = axis_tensor.scalar<int64>()();
+      } else {
+        OP_REQUIRES(c, false,
+                    errors::InvalidArgument("axis must be int32 or int64."));
+      }
+    }
+
+    OP_REQUIRES(
+        c, axis >= -params.dims() && axis < params.dims(),
+        errors::InvalidArgument("Expected axis in the range [", -params.dims(),
+                                ", ", params.dims(), "), but got ", axis));
+    if (axis < 0) {
+      axis = params.dims() + axis;
+    }
+
     // Check that we have enough index space
+    const int64 gather_dim_size = params.dim_size(axis);
     const int64 N = indices.NumElements();
     OP_REQUIRES(
-        c, params.dim_size(0) <= std::numeric_limits<Index>::max(),
-        errors::InvalidArgument("params.shape[0] too large for ",
+        c, gather_dim_size <= std::numeric_limits<Index>::max(),
+        errors::InvalidArgument("params.shape[", axis, "] too large for ",
                                 DataTypeString(DataTypeToEnum<Index>::v()),
-                                " indexing: ", params.dim_size(0), " > ",
+                                " indexing: ", gather_dim_size, " > ",
                                 std::numeric_limits<Index>::max()));
 
-    // The result shape is indices.shape + params.shape[1:].
-    TensorShape result_shape = indices.shape();
-    for (int i = 1; i < params.dims(); i++) {
+    // The result shape is params.shape[0:axis] + indices.shape +
+    // params.shape[axis + 1:].
+    TensorShape result_shape;
+    int64 outer_size = 1;
+    int64 inner_size = 1;
+    for (int i = 0; i < axis; i++) {
+      result_shape.AddDim(params.dim_size(i));
+      outer_size *= params.dim_size(i);
+    }
+    result_shape.AppendShape(indices.shape());
+    for (int i = axis + 1; i < params.dims(); i++) {
       result_shape.AddDim(params.dim_size(i));
+      inner_size *= params.dim_size(i);
     }
 
     Tensor* out = nullptr;
     OP_REQUIRES_OK(c, c->allocate_output(0, result_shape, &out));
-    if (N > 0) {
-      auto params_flat = params.flat_outer_dims<T>();
+    if (N > 0 && outer_size > 0 && inner_size > 0) {
+      auto params_flat =
+          params.shaped<T, 3>({outer_size, gather_dim_size, inner_size});
       auto indices_flat = indices.flat<Index>();
-      auto out_flat = out->shaped<T, 2>({N, out->NumElements() / N});
+      auto out_flat = out->shaped<T, 3>({outer_size, N, inner_size});
 
       functor::GatherFunctor<Device, T, Index> functor;
       int64 bad_i = functor(c->eigen_device<Device>(), params_flat,
@@ -84,7 +113,7 @@ class GatherOp : public OpKernel {
           c, bad_i < 0,
           errors::InvalidArgument(
               "indices", SliceDebugString(indices.shape(), bad_i), " = ",
-              indices_flat(bad_i), " is not in [0, ", params.dim_size(0), ")"));
+              indices_flat(bad_i), " is not in [0, ", gather_dim_size, ")"));
     }
   }
 };
@@ -94,6 +123,12 @@ class GatherOp : public OpKernel {
                               .Device(DEVICE_##dev)                    \
                               .TypeConstraint<type>("Tparams")         \
                               .TypeConstraint<index_type>("Tindices"), \
+                          GatherOp<dev##Device, type, index_type>);    \
+  REGISTER_KERNEL_BUILDER(Name("GatherV2")                             \
+                              .Device(DEVICE_##dev)                    \
+                              .TypeConstraint<type>("Tparams")         \
+                              .TypeConstraint<index_type>("Tindices")  \
+                              .HostMemory("axis"),                     \
                           GatherOp<dev##Device, type, index_type>)
 
 #define REGISTER_GATHER_ALL_INDICES(dev, type) \
diff --git a/tensorflow/core/kernels/gather_op_test.cc b/tensorflow/core/kernels/gather_op_test.cc
index 10d5aefe43..3edcb34bca 100644
--- a/tensorflow/core/kernels/gather_op_test.cc
+++ b/tensorflow/core/kernels/gather_op_test.cc
@@ -40,9 +40,10 @@ namespace {
 class GatherOpTest : public OpsTestBase {
  protected:
   void MakeOp(DataType data_type, DataType index_type) {
-    TF_ASSERT_OK(NodeDefBuilder("myop", "Gather")
+    TF_ASSERT_OK(NodeDefBuilder("myop", "GatherV2")
                      .Input(FakeInput(data_type))
                      .Input(FakeInput(index_type))
+                     .Input(FakeInput(index_type))
                      .Finalize(node_def()));
     TF_ASSERT_OK(InitOp());
   }
@@ -54,6 +55,7 @@ TEST_F(GatherOpTest, ScalarIndices) {
   // Feed and run
   AddInputFromArray<float>(TensorShape({5}), {0, 1, 2, 3, 4});
   AddInputFromArray<int32>(TensorShape({}), {3});
+  AddInputFromArray<int32>(TensorShape({}), {0});
   TF_ASSERT_OK(RunOpKernel());
 
   // Check the output.
@@ -71,6 +73,7 @@ TEST_F(GatherOpTest, ScalarIndices_Complex) {
                          std::complex<float>(2, 12), std::complex<float>(3, 13),
                          std::complex<float>(4, 14)});
   AddInputFromArray<int32>(TensorShape({}), {3});
+  AddInputFromArray<int32>(TensorShape({}), {0});
   TF_ASSERT_OK(RunOpKernel());
 
   // Check the output.
@@ -80,13 +83,14 @@ TEST_F(GatherOpTest, ScalarIndices_Complex) {
   test::ExpectTensorEqual<std::complex<float>>(expected, *GetOutput(0));
 }
 
-TEST_F(GatherOpTest, Simple_TwoD32) {
+TEST_F(GatherOpTest, Simple_TwoD32_Axis0) {
   MakeOp(DT_FLOAT, DT_INT32);
 
   // Feed and run
   AddInputFromArray<float>(TensorShape({5, 3}),
                            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
   AddInputFromArray<int32>(TensorShape({4}), {0, 4, 0, 2});
+  AddInputFromArray<int32>(TensorShape({}), {0});
   TF_ASSERT_OK(RunOpKernel());
 
   // Check the output.
@@ -95,12 +99,30 @@ TEST_F(GatherOpTest, Simple_TwoD32) {
   test::ExpectTensorEqual<float>(expected, *GetOutput(0));
 }
 
+TEST_F(GatherOpTest, Simple_TwoD32_Axis1) {
+  MakeOp(DT_FLOAT, DT_INT32);
+
+  // Feed and run
+  AddInputFromArray<float>(TensorShape({5, 3}),
+                           {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
+  AddInputFromArray<int32>(TensorShape({4}), {0, 1, 0, 2});
+  AddInputFromArray<int32>(TensorShape({}), {1});
+  TF_ASSERT_OK(RunOpKernel());
+
+  // Check the output.
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 4}));
+  test::FillValues<float>(&expected, {0, 1, 0, 2,  3, 4,  3,  5,  6,  7,
+                                      6, 8, 9, 10, 9, 11, 12, 13, 12, 14});
+  test::ExpectTensorEqual<float>(expected, *GetOutput(0));
+}
+
 TEST_F(GatherOpTest, ZeroSize_TwoD32) {
   MakeOp(DT_FLOAT, DT_INT32);
 
   // Feed and run
   AddInputFromArray<float>(TensorShape({5, 0}), {});
   AddInputFromArray<int32>(TensorShape({4}), {0, 4, 0, 2});
+  AddInputFromArray<int32>(TensorShape({}), {0});
   TF_ASSERT_OK(RunOpKernel());
 
   // Check the output.
@@ -115,6 +137,7 @@ TEST_F(GatherOpTest, Simple_TwoD64) {
   AddInputFromArray<float>(TensorShape({5, 3}),
                            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
   AddInputFromArray<int64>(TensorShape({4}), {0, 4, 0, 2});
+  AddInputFromArray<int64>(TensorShape({}), {0});
   TF_ASSERT_OK(RunOpKernel());
 
   // Check the output.
@@ -129,6 +152,7 @@ TEST_F(GatherOpTest, HighRank) {
   // Feed and run
   AddInputFromArray<float>(TensorShape({4}), {0, 1, 2, 3});
   AddInputFromArray<int32>(TensorShape({2, 3}), {1, 2, 0, 2, 3, 0});
+  AddInputFromArray<int32>(TensorShape({}), {0});
   TF_ASSERT_OK(RunOpKernel());
 
   // Check the output
@@ -144,6 +168,7 @@ TEST_F(GatherOpTest, Error_IndexOutOfRange) {
   AddInputFromArray<float>(TensorShape({5, 3}),
                            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
   AddInputFromArray<int32>(TensorShape({4}), {0, 4, 99, 2});
+  AddInputFromArray<int32>(TensorShape({}), {0});
   Status s = RunOpKernel();
   EXPECT_TRUE(
       StringPiece(s.ToString()).contains("indices[2] = 99 is not in [0, 5)"))
@@ -172,8 +197,12 @@ static Graph* Gather(int dim) {
     indices.flat<Index>()(i) = indices_vec[i];
   }
 
+  Tensor axis(DataTypeToEnum<Index>::value, TensorShape({}));
+  axis.scalar<Index>()() = 0;
+
   test::graph::Gather(g, test::graph::Constant(g, params),
-                      test::graph::Constant(g, indices));
+                      test::graph::Constant(g, indices),
+                      test::graph::HostConstant(g, axis));
   return g;
 }
 
diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index e108a59275..f0fef256d2 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -348,9 +348,14 @@ class ResourceGatherOp : public OpKernel {
     Tensor* out = nullptr;
     OP_REQUIRES_OK(c, c->allocate_output(0, result_shape, &out));
     if (N > 0) {
-      auto params_flat = params.flat_outer_dims<T>();
+      const int64 gather_dim_size = params.dim_size(0);
+      int64 inner_size = 1;
+      for (int i = 1; i < params.dims(); i++) {
+        inner_size *= params.dim_size(i);
+      }
+      auto params_flat = params.shaped<T, 3>({1, gather_dim_size, inner_size});
       auto indices_flat = indices.flat<Index>();
-      auto out_flat = out->shaped<T, 2>({N, out->NumElements() / N});
+      auto out_flat = out->shaped<T, 3>({1, N, out->NumElements() / N});
 
       functor::GatherFunctor<Device, T, Index> functor;
       int64 bad_i = functor(c->eigen_device<Device>(), params_flat,
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 882b4b0cc4..b3b2f1bc80 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1397,6 +1397,105 @@ raising an error.
 </div>
 )doc");
 
+// --------------------------------------------------------------------------
+REGISTER_OP("GatherV2")
+    .Input("params: Tparams")
+    .Input("indices: Tindices")
+    .Input("axis: Taxis")
+    .Output("output: Tparams")
+    .Attr("Tparams: type")
+    .Attr("Tindices: {int32,int64}")
+    .Attr("Taxis: {int32,int64}")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle params_shape;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &params_shape));
+
+      ShapeHandle indices_shape = c->input(1);
+      ShapeHandle unused_axis_shape;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused_axis_shape));
+      const Tensor* axis_t = c->input_tensor(2);
+
+      // If axis is unknown, we can only infer that the result is params_rank +
+      // indices_rank - 1.
+      if (axis_t == nullptr) {
+        if (c->RankKnown(params_shape) && c->RankKnown(indices_shape)) {
+          c->set_output(0, c->UnknownShapeOfRank(c->Rank(params_shape) +
+                                                 c->Rank(indices_shape) - 1));
+        } else {
+          c->set_output(0, c->UnknownShape());
+        }
+        return Status::OK();
+      }
+
+      // Note, axis can be negative.
+      int64 axis = 0;
+      if (axis_t->dtype() == DT_INT32) {
+        axis = axis_t->scalar<int32>()();
+      } else {
+        axis = axis_t->scalar<int64>()();
+      }
+
+      // Check that params has rank of at least axis + 1.
+      ShapeHandle unused;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(
+          params_shape, axis < 0 ? -axis : axis + 1, &unused));
+
+      ShapeHandle params_outer_subshape;
+      TF_RETURN_IF_ERROR(
+          c->Subshape(params_shape, 0, axis, &params_outer_subshape));
+
+      ShapeHandle out;
+      TF_RETURN_IF_ERROR(
+          c->Concatenate(params_outer_subshape, indices_shape, &out));
+
+      // Slice from axis + 1 to the end of params_shape to collect the inner
+      // dimensions of the result. Special case -1 here since -1 + 1 wraps, and
+      // we slice from 0 to the end of shape. Subshape() handles all other
+      // out-of-bounds checking.
+      if (axis != -1) {
+        ShapeHandle params_inner_subshape;
+        TF_RETURN_IF_ERROR(
+            c->Subshape(params_shape, axis + 1, &params_inner_subshape));
+        TF_RETURN_IF_ERROR(c->Concatenate(out, params_inner_subshape, &out));
+      }
+
+      c->set_output(0, out);
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Gather slices from `params` axis `axis` according to `indices`.
+
+`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
+Produces an output tensor with shape `params.shape[:axis] + indices.shape +
+params.shape[axis + 1:]` where:
+
+```python
+    # Scalar indices (output is rank(params) - 1).
+    output[a_0, ..., a_n, b_0, ..., b_n] =
+      params[a_0, ..., a_n, indices, b_0, ..., b_n]
+
+    # Vector indices (output is rank(params)).
+    output[a_0, ..., a_n, i, b_0, ..., b_n] =
+      params[a_0, ..., a_n, indices[i], b_0, ..., b_n]
+
+    # Higher rank indices (output is rank(params) + rank(indices) - 1).
+    output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
+      params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
+```
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
+</div>
+
+params: The tensor from which to gather values. Must be at least rank
+  `axis + 1`.
+indices: Index tensor. Must be in range `[0, params.shape[axis])`.
+axis: The axis in `params` to gather `indices` from. Defaults to the first
+  dimension. Supports negative indexes.
+output: Values from `params` gathered from indices given by `indices`, with
+  shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`.
+)doc");
+
 // --------------------------------------------------------------------------
 REGISTER_OP("GatherNd")
     .Input("params: Tparams")
diff --git a/tensorflow/core/ops/array_ops_test.cc b/tensorflow/core/ops/array_ops_test.cc
index 1351a2f177..a5d7a32e05 100644
--- a/tensorflow/core/ops/array_ops_test.cc
+++ b/tensorflow/core/ops/array_ops_test.cc
@@ -264,6 +264,61 @@ TEST(ArrayOpsTest, Gather_ShapeFn) {
   INFER_ERROR("Shape must be at least rank 1 but is rank 0", op, "[];[1,2,3]");
 }
 
+TEST(ArrayOpsTest, GatherV2_ShapeFn) {
+  ShapeInferenceTestOp op("GatherV2");
+
+  // Tests when axis is unknown.
+  INFER_OK(op, "?;?;?", "?");
+  INFER_OK(op, "[1,2,3];[3];[]", "[?,?,?]");
+  INFER_ERROR("Shape must be at least rank 1 but is rank 0", op,
+              "[];[1,2,3];[]");
+
+  // Non-scalar axis.
+  INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1];[1,2,3];[1]");
+
+  // Test when axis dim is known.
+  Tensor axis_dim_t;
+  op.input_tensors.resize(3);
+  op.input_tensors[2] = &axis_dim_t;
+
+  // Out of range axis.
+  axis_dim_t = test::AsScalar(1);
+  INFER_ERROR("Shape must be at least rank 2 but is rank 1", op,
+              "[1];[1,2];[]");
+
+  // Rank 0 indices.
+  axis_dim_t = test::AsScalar(0);
+  INFER_OK(op, "[1,2,3];[];[]", "[d0_1,d0_2]");
+  axis_dim_t = test::AsScalar(1);
+  INFER_OK(op, "[1,2,3];[];[]", "[d0_0,d0_2]");
+  axis_dim_t = test::AsScalar(2);
+  INFER_OK(op, "[1,2,3];[];[]", "[d0_0,d0_1]");
+
+  // Rank 1 indices.
+  axis_dim_t = test::AsScalar(0);
+  INFER_OK(op, "[1,2,3];[5];[]", "[d1_0,d0_1,d0_2]");
+  axis_dim_t = test::AsScalar(1);
+  INFER_OK(op, "[1,2,3];[5];[]", "[d0_0,d1_0,d0_2]");
+  axis_dim_t = test::AsScalar(2);
+  INFER_OK(op, "[1,2,3];[5];[]", "[d0_0,d0_1,d1_0]");
+
+  // Rank 2 indices.
+  axis_dim_t = test::AsScalar(0);
+  INFER_OK(op, "[1,2,3];[5,6];[]", "[d1_0,d1_1,d0_1,d0_2]");
+  axis_dim_t = test::AsScalar(1);
+  INFER_OK(op, "[1,2,3];[5,6];[]", "[d0_0,d1_0,d1_1,d0_2]");
+  axis_dim_t = test::AsScalar(2);
+  INFER_OK(op, "[1,2,3];[5,6];[]", "[d0_0,d0_1,d1_0,d1_1]");
+
+  // Negative axis.
+  axis_dim_t = test::AsScalar(-3);
+  INFER_OK(op, "[1,2,3];[5,6];[]", "[d1_0,d1_1,d0_1,d0_2]");
+  axis_dim_t = test::AsScalar(-2);
+  INFER_OK(op, "[1,2,3];[5,6];[]", "[d0_0,d1_0,d1_1,d0_2]");
+  axis_dim_t = test::AsScalar(-1);
+  INFER_OK(op, "[1,2,3];[5,6];[]", "[d0_0,d0_1,d1_0,d1_1]");
+}
+
 TEST(ArrayOpsTest, GatherNd_ShapeFn) {
   ShapeInferenceTestOp op("GatherNd");
 
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 17b646d17c..bbb598c978 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1356,7 +1356,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "gather_op_test",
-    size = "small",
+    size = "medium",
     srcs = ["gather_op_test.py"],
     additional_deps = [
         "//third_party/py/numpy",
diff --git a/tensorflow/python/kernel_tests/gather_op_test.py b/tensorflow/python/kernel_tests/gather_op_test.py
index b3ce234d4e..04d65b88a1 100644
--- a/tensorflow/python/kernel_tests/gather_op_test.py
+++ b/tensorflow/python/kernel_tests/gather_op_test.py
@@ -44,70 +44,110 @@ class GatherTest(test.TestCase):
     with self.test_session(use_gpu=True):
       data = np.array([0, 1, 2, 3, 7, 5])
       for dtype in _TEST_TYPES:
-        params_np = self._buildParams(data, dtype)
-        params = constant_op.constant(params_np)
-        indices = constant_op.constant(4)
-        gather_t = array_ops.gather(params, indices)
-        gather_val = gather_t.eval()
-        self.assertAllEqual(params_np[4], gather_val)
-        self.assertEqual([], gather_t.get_shape())
+        for indices in 4, [1, 2, 2, 4, 5]:
+          params_np = self._buildParams(data, dtype)
+          params = constant_op.constant(params_np)
+          indices_tf = constant_op.constant(indices)
+          gather_t = array_ops.gather(params, indices_tf)
+          gather_val = gather_t.eval()
+          np_val = params_np[indices]
+          self.assertAllEqual(np_val, gather_val)
+          self.assertEqual(np_val.shape, gather_t.get_shape())
 
   def testScalar2D(self):
     with self.test_session(use_gpu=True):
       data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8],
                        [9, 10, 11], [12, 13, 14]])
       for dtype in _TEST_TYPES:
-        params_np = self._buildParams(data, dtype)
-        params = constant_op.constant(params_np)
-        indices = constant_op.constant(2)
-        gather_t = array_ops.gather(params, indices)
-        gather_val = gather_t.eval()
-        self.assertAllEqual(params_np[2], gather_val)
-        self.assertEqual([3], gather_t.get_shape())
+        for axis in range(data.ndim):
+          params_np = self._buildParams(data, dtype)
+          params = constant_op.constant(params_np)
+          indices = constant_op.constant(2)
+          gather_t = array_ops.gather(params, indices, axis=axis)
+          gather_val = gather_t.eval()
+          self.assertAllEqual(np.take(params_np, 2, axis=axis), gather_val)
+          expected_shape = data.shape[:axis] + data.shape[axis + 1:]
+          self.assertEqual(expected_shape, gather_t.get_shape())
 
   def testSimpleTwoD32(self):
     with self.test_session(use_gpu=True):
       data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8],
                        [9, 10, 11], [12, 13, 14]])
       for dtype in _TEST_TYPES:
-        params_np = self._buildParams(data, dtype)
-        params = constant_op.constant(params_np)
-        indices = constant_op.constant([0, 4, 0, 2])
-        gather_t = array_ops.gather(params, indices)
-        gather_val = gather_t.eval()
-        self.assertAllEqual(params_np[[0, 4, 0, 2]], gather_val)
-        self.assertEqual([4, 3], gather_t.get_shape())
+        for axis in range(data.ndim):
+          params_np = self._buildParams(data, dtype)
+          params = constant_op.constant(params_np)
+          # The indices must be in bounds for any axis.
+          indices = constant_op.constant([0, 1, 0, 2])
+          gather_t = array_ops.gather(params, indices, axis=axis)
+          gather_val = gather_t.eval()
+          self.assertAllEqual(np.take(params_np, [0, 1, 0, 2], axis=axis),
+                              gather_val)
+          expected_shape = data.shape[:axis] + (4,) + data.shape[axis + 1:]
+          self.assertEqual(expected_shape, gather_t.get_shape())
 
   def testHigherRank(self):
-    np.random.seed(1)
-    # We check that scalar and empty shapes work as well
-    for shape in (7, 0), (4, 3, 2):
-      for indices_shape in (), (0,), (3, 0), (3, 5):
+    # We check that scalar and empty indices shapes work as well
+    for shape in (4, 3, 2), (2, 1, 3, 2):
+      for indices_shape in (), (0,), (3, 0), (3, 5), (5, 2, 3):
         for dtype in _TEST_TYPES:
-          params = self._buildParams(np.random.randn(*shape), dtype)
-          indices = np.random.randint(shape[0], size=indices_shape)
-          with self.test_session(use_gpu=True):
-            tf_params = constant_op.constant(params)
-            tf_indices = constant_op.constant(indices)
-            gather = array_ops.gather(tf_params, tf_indices)
-            self.assertAllEqual(params[indices], gather.eval())
-            self.assertEqual(indices.shape + params.shape[1:],
-                             gather.get_shape())
-            # Test gradients
-            gather_grad = np.random.randn(*gather.get_shape().as_list()).astype(
-                dtype.as_numpy_dtype)
-            if dtype.is_complex:
-              gather_grad -= 1j * gather_grad
-            params_grad, indices_grad = gradients_impl.gradients(
-                gather, [tf_params, tf_indices], gather_grad)
-            self.assertEqual(indices_grad, None)
-            self.assertEqual(type(params_grad), ops.IndexedSlices)
-            params_grad = ops.convert_to_tensor(params_grad)
-            correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
-            for i, g in zip(indices.flat,
-                            gather_grad.reshape((indices.size,) + shape[1:])):
-              correct_params_grad[i] += g
-            self.assertAllClose(correct_params_grad, params_grad.eval())
+          for axis in range(len(shape)):
+            params = self._buildParams(np.random.randn(*shape), dtype)
+            indices = np.random.randint(shape[axis], size=indices_shape)
+            with self.test_session(use_gpu=True) as sess:
+              tf_params = constant_op.constant(params)
+              tf_indices = constant_op.constant(indices)
+              # Check that both positive and negative indices for axis work.
+              tf_axis = constant_op.constant(axis)
+              tf_negative_axis = constant_op.constant(-len(shape) + axis)
+              gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
+              gather_negative_axis = array_ops.gather(
+                  tf_params, tf_indices, axis=tf_negative_axis)
+              gather_value, gather_negative_axis_value = sess.run(
+                  [gather, gather_negative_axis])
+              gather_np = np.take(params, indices, axis)
+              self.assertAllEqual(gather_np, gather_value)
+              self.assertAllEqual(gather_np, gather_negative_axis_value)
+              expected_shape = (params.shape[:axis] + indices.shape +
+                                params.shape[axis + 1:])
+              self.assertEqual(expected_shape, gather.shape)
+              self.assertEqual(expected_shape, gather_negative_axis.shape)
+
+              # Test gradients
+              gather_grad = np.random.randn(
+                  *gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
+              if dtype.is_complex:
+                gather_grad -= 1j * gather_grad
+              params_grad, indices_grad, axis_grad = gradients_impl.gradients(
+                  gather, [tf_params, tf_indices, tf_axis], gather_grad)
+              self.assertEqual(indices_grad, None)
+              self.assertEqual(axis_grad, None)
+              # For axis 0, we are able to create an efficient IndexedSlices for
+              # the gradient.
+              if axis == 0:
+                self.assertEqual(type(params_grad), ops.IndexedSlices)
+                params_grad = ops.convert_to_tensor(params_grad)
+              correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
+              outer_dims = axis
+              inner_dims = len(shape) - axis - 1
+              gather_grad = gather_grad.reshape(
+                  shape[:axis] + (indices.size,) + shape[axis + 1:])
+              for source_index, dest_index in enumerate(indices.flat):
+                dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
+                              (slice(None),) * inner_dims)
+                source_slice = ((slice(None),) * outer_dims + (source_index,) +
+                                (slice(None),) * inner_dims)
+                correct_params_grad[dest_slice] += gather_grad[source_slice]
+              self.assertAllClose(correct_params_grad, params_grad.eval(),
+                                  atol=2e-6, rtol=2e-6)
+
+  def testString(self):
+    params = np.array([[b"asdf", b"zxcv"], [b"qwer", b"uiop"]])
+    with self.test_session():
+      self.assertAllEqual([b"qwer", b"uiop"],
+                          array_ops.gather(params, 1, axis=0).eval())
+      self.assertAllEqual([b"asdf", b"qwer"],
+                          array_ops.gather(params, 0, axis=1).eval())
 
   def testUnknownIndices(self):
     params = constant_op.constant([[0, 1, 2]])
@@ -115,22 +155,62 @@ class GatherTest(test.TestCase):
     gather_t = array_ops.gather(params, indices)
     self.assertEqual(None, gather_t.get_shape())
 
+  def testUnknownAxis(self):
+    params = constant_op.constant([[0, 1, 2]])
+    indices = constant_op.constant([[0, 0], [0, 0]])
+    axis = array_ops.placeholder(dtypes.int32)
+    gather_t = array_ops.gather(params, indices, axis=axis)
+    # Rank 2 params with rank 2 indices results in a rank 3 shape.
+    self.assertEqual([None, None, None], gather_t.shape.as_list())
+
+    # If indices is also unknown the result rank is unknown.
+    indices = array_ops.placeholder(dtypes.int32)
+    gather_t = array_ops.gather(params, indices, axis=axis)
+    self.assertEqual(None, gather_t.shape)
+
   def testBadIndices(self):
     with self.test_session(use_gpu=True):
-      params = [0, 1, 2]
-      indices = [[7]]
-      gather = array_ops.gather(params, indices)
+      params = [[0, 1, 2], [3, 4, 5]]
+      with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"):
+        array_ops.gather(params, [[7]], axis=0).eval()
       with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 3\)"):
-        gather.eval()
+        array_ops.gather(params, [[7]], axis=1).eval()
+
+  def testBadAxis(self):
+    with self.test_session(use_gpu=True):
+      params = [0, 1, 2]
+      params_ph = array_ops.placeholder(dtypes.int32)
+      indices = 0
+      for bad_axis in (1, 2, -2):
+        # Shape inference can validate axis for known params rank.
+        with self.assertRaisesWithPredicateMatch(
+            ValueError, "Shape must be at least rank . but is rank 1"):
+          array_ops.gather(params, indices, axis=bad_axis)
+        # If params rank is unknown, an op error occurs.
+        with self.assertRaisesOpError(
+            r"Expected axis in the range \[-1, 1\), but got %s" % bad_axis):
+          array_ops.gather(params_ph, indices, axis=bad_axis).eval(
+              feed_dict={params_ph: params})
 
   def testEmptySlices(self):
     with self.test_session(use_gpu=True):
       for dtype in _TEST_TYPES:
         for itype in np.int32, np.int64:
-          params = np.zeros((7, 0), dtype=dtype.as_numpy_dtype)
+          # Leading axis gather.
+          params = np.zeros((7, 0, 0), dtype=dtype.as_numpy_dtype)
           indices = np.array([3, 4], dtype=itype)
-          gather = array_ops.gather(params, indices)
-          self.assertAllEqual(gather.eval(), np.zeros((2, 0)))
+          gather = array_ops.gather(params, indices, axis=0)
+          self.assertAllEqual(gather.eval(), np.zeros((2, 0, 0)))
+
+          # Middle axis gather.
+          params = np.zeros((0, 7, 0), dtype=dtype.as_numpy_dtype)
+          gather = array_ops.gather(params, indices, axis=1)
+          self.assertAllEqual(gather.eval(), np.zeros((0, 2, 0)))
+
+          # Trailing axis gather.
+          params = np.zeros((0, 0, 7), dtype=dtype.as_numpy_dtype)
+          gather = array_ops.gather(params, indices, axis=2)
+          self.assertAllEqual(gather.eval(), np.zeros((0, 0, 2)))
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
index 3d10f4d0e9..4cb5e6d208 100644
--- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py
+++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
@@ -163,6 +163,16 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
       v.load(2.0)
       self.assertEqual(2.0, v.value().eval())
 
+  def testSparseRead(self):
+    with self.test_session():
+      init_value = np.reshape(np.arange(np.power(4, 3)), (4, 4, 4))
+      v = resource_variable_ops.ResourceVariable(
+          constant_op.constant(init_value, dtype=dtypes.int32))
+      variables.global_variables_initializer().run()
+
+      value = v.sparse_read([0, 3, 1, 2]).eval()
+      self.assertAllEqual(init_value[[0, 3, 1, 2], ...], value)
+
   def testToFromProto(self):
     with self.test_session():
       v = resource_variable_ops.ResourceVariable(1.0)
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index 73a4b7db9f..2f6e81f322 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -55,7 +55,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index):
     dim_index: An interger index of concat_dim or axis parameter in op.inputs.
 
   Returns:
-    Tensors represending the partial gradients with respect to each input
+    Tensors representing the partial gradients with respect to each input
     of the op.
 
   Raises:
@@ -373,6 +373,67 @@ def _GatherGrad(op, grad):
   return [ops.IndexedSlices(values, indices, params_shape), None]
 
 
+@ops.RegisterGradient("GatherV2")
+def _GatherV2Grad(op, grad):
+  """Gradient for GatherV2 op."""
+  # params can be large, so colocate the shape calculation with it.
+  #
+  # params can be very large for sparse model, array_ops.shape raises
+  # exception on the Windows platform when any dimension is larger than
+  # int32. params_shape is not used in optimizer apply_sparse gradients,
+  # so it's fine to convert it back to int32 regardless of truncation.
+  params = op.inputs[0]
+  with ops.colocate_with(params):
+    params_shape = array_ops.shape(params, out_type=ops.dtypes.int64)
+    params_shape = math_ops.to_int32(params_shape)
+
+  indices = op.inputs[1]
+  indices_size = array_ops.expand_dims(array_ops.size(indices), 0)
+  axis = op.inputs[2]
+  axis_static = tensor_util.constant_value(axis)
+
+  # For axis 0 gathers, build an appropriately shaped IndexedSlices.
+  if axis_static == 0:
+    values_shape = array_ops.concat([indices_size, params_shape[1:]], 0)
+    values = array_ops.reshape(grad, values_shape)
+    indices = array_ops.reshape(indices, indices_size)
+    return [ops.IndexedSlices(values, indices, params_shape), None, None]
+
+  outer_shape = params_shape[:axis]
+  outer_dims = array_ops.size(outer_shape)
+  inner_shape = params_shape[axis:][1:]
+  inner_dims = array_ops.size(inner_shape)
+
+  outer_axes_indices = math_ops.range(outer_dims)
+  inner_axes_indices = math_ops.range(outer_dims + 1,
+                                      outer_dims + 1 + inner_dims)
+
+  values_shape = array_ops.concat([outer_shape, indices_size, inner_shape], 0)
+  values = array_ops.reshape(grad, values_shape)
+  indices = array_ops.reshape(indices, indices_size)
+
+  # We need to sum up every slice `values[..., i, ....]` corresponding to
+  # `params[..., indices[i], ...]`. Since `unsorted_segment_sum` does not
+  # support an axis parameter, we transpose the gather dimension to the front,
+  # then use `unsorted_segment_sum` to build a
+  # [gather_axis, outer_axes, inner_axes] tensor with all the gradients
+  # affecting each index in `gather_axis` summed up.
+  transpose_dims = array_ops.concat(
+      [[outer_dims], outer_axes_indices, inner_axes_indices], 0)
+  values_transpose = array_ops.transpose(values, transpose_dims)
+  num_segments = params_shape[axis]
+
+  params_grad = math_ops.unsorted_segment_sum(
+      values_transpose, indices, num_segments)
+
+  # Inverts the above transpose by moving dimension 0 back to its original
+  # position.
+  invert_transpose_dims = array_ops.concat(
+      [outer_axes_indices + 1, [0], inner_axes_indices], 0)
+  params_grad = array_ops.transpose(params_grad, invert_transpose_dims)
+  return [params_grad, None, None]
+
+
 @ops.RegisterGradient("GatherNd")
 def _GatherNdGrad(op, grad):
   ref = op.inputs[0]
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index be3cadbd21..b42238a935 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -2400,3 +2400,14 @@ reverse_sequence.__doc__ = deprecation.rewrite_argument_docstring(
     deprecation.rewrite_argument_docstring(
         gen_array_ops.reverse_sequence.__doc__, "batch_dim", "batch_axis"),
     "seq_dim", "seq_axis")
+
+
+def gather(params, indices, validate_indices=None, name=None, axis=0):
+  # TODO(rjryan): Remove "Gather" creation in favor of GatherV2 once the forward
+  # compatibility 3 week period has passed.
+  if axis == 0:
+    return gen_array_ops.gather(params, indices,
+                                validate_indices=validate_indices, name=name)
+  else:
+    return gen_array_ops.gather_v2(params, indices, axis, name=name)
+gather.__doc__ = gen_array_ops.gather_v2.__doc__
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 8cf40e3c4b..eb013f3a77 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -994,7 +994,7 @@ tf_module {
   }
   member_method {
     name: "gather"
-    argspec: "args=[\'params\', \'indices\', \'validate_indices\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+    argspec: "args=[\'params\', \'indices\', \'validate_indices\', \'name\', \'axis\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'0\'], "
   }
   member_method {
     name: "gather_nd"
diff --git a/tensorflow/tools/graph_transforms/sparsify_gather.cc b/tensorflow/tools/graph_transforms/sparsify_gather.cc
index c441a089ce..937d8c09ff 100644
--- a/tensorflow/tools/graph_transforms/sparsify_gather.cc
+++ b/tensorflow/tools/graph_transforms/sparsify_gather.cc
@@ -78,13 +78,14 @@ void CreateConstNode(const Tensor& tensor, const string& name,
   node_def->set_name(name);
   SetNodeTensorAttr<float>("value", tensor, node_def);
 }
-}  // namespace
 
-Status SparsifyGather(const GraphDef& input_graph_def,
-                      const TransformFuncContext& context,
-                      GraphDef* output_graph_def) {
+Status SparsifyGatherInternal(const GraphDef& input_graph_def,
+                              const TransformFuncContext& context,
+                              const OpTypePattern& pattern,
+                              GraphDef* output_graph_def) {
   GraphDef current_graph_def = input_graph_def;
   bool any_match_found = false;
+
   // The subgraphs may have overlapping components, therefore GraphMatcher
   // doesn't return all subgraphs in one round -- this has to be multi-round
   // update.
@@ -94,17 +95,7 @@ Status SparsifyGather(const GraphDef& input_graph_def,
     std::vector<string> init_table_node_names;
 
     TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes(
-        current_graph_def,  // clang-format off
-      {"Gather",
-        {
-          {"Identity",
-            {
-              {"Const"}
-            }
-          },
-          {"*"},
-        }
-      },  // clang-format on
+        current_graph_def, pattern,
         [&any_match_found, &init_table_node_names](
             const NodeMatch& match, const std::set<string>& input_nodes,
             const std::set<string>& output_nodes,
@@ -143,6 +134,33 @@ Status SparsifyGather(const GraphDef& input_graph_def,
           //    c. a `default_val` arg, valued at 0
           // clang-format on
           const NodeDef& gather_node = match.node;
+
+          // GatherV2 adds an "axis" parameter. sparsify_gather only supports
+          // axis 0 gathers.
+          if (gather_node.op() == "GatherV2") {
+            // Per the OpTypePattern, the 3rd input to Gather must be a Const.
+            const NodeDef& axis_node = match.inputs[2].node;
+
+            Tensor axis_t;
+            TF_RETURN_IF_ERROR(GetNodeAttr(axis_node, "value", &axis_t));
+            int64 axis = 0;
+            if (axis_t.dtype() == DT_INT32) {
+              axis = axis_t.scalar<int32>()();
+            } else if (axis_t.dtype() == DT_INT64) {
+              axis = axis_t.scalar<int64>()();
+            } else {
+              return tensorflow::errors::FailedPrecondition(
+                  "Gather axis was not int32 or int64.");
+            }
+
+            if (axis != 0) {
+              return tensorflow::errors::FailedPrecondition(
+                  "Transform only applicable to subgraph with GatherV2 over "
+                  "axis 0. Found axis ",
+                  axis, ".");
+            }
+          }
+
           const NodeDef& const_node = match.inputs[0].inputs[0].node;
 
           DataType data_type;
@@ -269,6 +287,45 @@ Status SparsifyGather(const GraphDef& input_graph_def,
   *output_graph_def = current_graph_def;
   return Status::OK();
 }
+}  // namespace
+
+Status SparsifyGather(const GraphDef& input_graph_def,
+                      const TransformFuncContext& context,
+                      GraphDef* output_graph_def) {
+  // clang-format off
+  const OpTypePattern gather_pattern =
+    {"Gather",
+     {
+       {"Identity",
+        {
+          {"Const"}
+        }
+       },
+       {"*"},
+     }
+    };
+  const OpTypePattern gather_v2_pattern =
+    {"GatherV2",
+      {
+        {"Identity",
+          {
+            {"Const"}
+          }
+        },
+        {"*"},
+        // GatherV2's axis must be constant.
+        {"Const"},
+      }
+    };
+  // clang-format on
+
+  GraphDef temp_output;
+  TF_RETURN_IF_ERROR(SparsifyGatherInternal(input_graph_def, context,
+                                            gather_pattern, &temp_output));
+
+  return SparsifyGatherInternal(temp_output, context, gather_v2_pattern,
+                                output_graph_def);
+}
 
 REGISTER_GRAPH_TRANSFORM("sparsify_gather", SparsifyGather);
 
diff --git a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
index 8d353d3476..c999212d69 100644
--- a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
+++ b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/test_benchmark.h"
 #include "tensorflow/core/public/session.h"
@@ -33,19 +34,33 @@ Status SparsifyGather(const GraphDef& input_graph_def,
 
 class SparsifyGatherTest : public ::testing::Test {
  protected:
-  NodeDef* CreateNode(const string& name, const string& op,
+  NodeDef* CreateNode(const StringPiece name, const StringPiece op,
                       const std::vector<NodeDef*>& inputs,
                       GraphDef* graph_def) {
     NodeDef* node_def = graph_def->add_node();
-    node_def->set_name(name);
-    node_def->set_op(op);
+    node_def->set_name(name.ToString());
+    node_def->set_op(op.ToString());
     std::for_each(inputs.begin(), inputs.end(), [&node_def](NodeDef* input) {
       node_def->add_input(input->name());
     });
     return node_def;
   }
 
-  void TestSinglePartitionConst() {
+  void MakeGather(StringPiece name, bool gather_v2, NodeDef* params,
+                  NodeDef* indices, GraphDef* graph_def) {
+    if (gather_v2) {
+      NodeDef* axis_node =
+          CreateNode(strings::StrCat(name, "_axis"), "Const", {}, graph_def);
+      Tensor axis_t(DT_INT32, TensorShape({}));
+      axis_t.scalar<int32>()() = 0;
+      SetNodeTensorAttr<int32>("value", axis_t, axis_node);
+      CreateNode(name, "GatherV2", {params, indices, axis_node}, graph_def);
+    } else {
+      CreateNode(name, "Gather", {params, indices}, graph_def);
+    }
+  }
+
+  void TestSinglePartitionConst(bool gather_v2) {
     GraphDef graph_def;
 
     // Build the graph.
@@ -59,7 +74,7 @@ class SparsifyGatherTest : public ::testing::Test {
 
     NodeDef* identity_node =
         CreateNode("const/read", "Identity", {const_node}, &graph_def);
-    CreateNode("gather", "Gather", {identity_node, input_node}, &graph_def);
+    MakeGather("gather", gather_v2, identity_node, input_node, &graph_def);
     CreateNode("group_deps", "NoOp", {}, &graph_def);
 
     // Run the op.
@@ -151,7 +166,7 @@ class SparsifyGatherTest : public ::testing::Test {
               node_lookup.at("group_deps")->input().end());
   }
 
-  void TestMultiPartitionConst() {
+  void TestMultiPartitionConst(bool gather_v2) {
     // The 'ids' node is served input for two 'Gather's.
     GraphDef graph_def;
 
@@ -177,8 +192,8 @@ class SparsifyGatherTest : public ::testing::Test {
         CreateNode("const1/read", "Identity", {const_node1}, &graph_def);
     NodeDef* identity_node2 =
         CreateNode("const2/read", "Identity", {const_node2}, &graph_def);
-    CreateNode("gather1", "Gather", {identity_node1, input_node}, &graph_def);
-    CreateNode("gather2", "Gather", {identity_node2, input_node}, &graph_def);
+    MakeGather("gather1", gather_v2, identity_node1, input_node, &graph_def);
+    MakeGather("gather2", gather_v2, identity_node2, input_node, &graph_def);
 
     // Run the op.
     GraphDef result;
@@ -341,11 +356,13 @@ class SparsifyGatherTest : public ::testing::Test {
 };
 
 TEST_F(SparsifyGatherTest, TestSinglePartitionConst) {
-  TestSinglePartitionConst();
+  TestSinglePartitionConst(false);
+  TestSinglePartitionConst(true);
 }
 
 TEST_F(SparsifyGatherTest, TestMultiPartitionConst) {
-  TestMultiPartitionConst();
+  TestMultiPartitionConst(false);
+  TestMultiPartitionConst(true);
 }
 
 }  // namespace graph_transforms
-- 
GitLab


From ad814f9fca0cadbe34b587999b9149d30f4275d4 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Tue, 11 Jul 2017 09:57:36 -0700
Subject: [PATCH 0878/1375] Move the tf.contrib.data README to the programmers'
 guide.

PiperOrigin-RevId: 161542200
---
 tensorflow/contrib/data/README.md             | 635 +---------------
 .../docs_src/programmers_guide/datasets.md    | 700 ++++++++++++++++++
 2 files changed, 706 insertions(+), 629 deletions(-)
 create mode 100644 tensorflow/docs_src/programmers_guide/datasets.md

diff --git a/tensorflow/contrib/data/README.md b/tensorflow/contrib/data/README.md
index 6d7d278309..7c59a1ffc3 100644
--- a/tensorflow/contrib/data/README.md
+++ b/tensorflow/contrib/data/README.md
@@ -1,631 +1,8 @@
-# Using the `Dataset` API for TensorFlow Input Pipelines
+`tf.contrib.data` API
+=====================
 
-The `Dataset` API is designed to let you build complex input pipelines from
-simple, reusable pieces. For example, the pipeline for an image model might
-aggregate data from files in a distributed file system, apply random
-perturbations to each image, and merge randomly selected images into a batch
-for training. The pipeline for a text model might involve extracting symbols
-from raw text data, converting them to embedding identifiers with a lookup
-table, and batching together sequences of different lengths. The `Dataset` API
-makes it easy to deal with large amounts of data, different data formats, and
-complicated transformations.
+This directory contains the Python API for the `tf.contrib.data.Dataset` and
+`tf.contrib.data.Iterator` classes, which can be used to build input pipelines.
 
-The `Dataset` API introduces two new abstractions to TensorFlow:
-
-* A `tf.contrib.data.Dataset` represents a sequence of elements, in which
-  each element contains one or more `Tensor` objects. For example, in an image
-  pipeline, an element might be a single training example, with a pair of
-  tensors representing the image data and a label. A `Dataset` can either be a
-  *source* (e.g. `Dataset.from_tensor_slices()` constructs a dataset from one
-  or more `tf.Tensor` objects), or a *transformation* (e.g. `Dataset.batch()`
-  constructs a dataset by stacking consecutive elements of another dataset into
-  a single element).
-
-* A `tf.contrib.data.Iterator` provides the main way to extract elements from a
-  dataset. The `Iterator.get_next()` operation yields the next element of a
-  `Dataset`, and typically acts as the interface between input pipeline code and
-  your model. The simplest iterator is a "one-shot iterator", which is
-  associated with a particular `Dataset` and iterates through it once. For more
-  sophisticated uses, the `Iterator.initializer` operation enables you to
-  reinitialize and parameterize an iterator with different datasets, so that
-  you can, for example, iterate over training and validation data multiple times
-  in the same program.
-
-## Tutorial
-
-This programmers' guide includes step-by-step instructions for a variety of
-input data use cases. Also see the `Dataset` and `Iterator` class references
-for more detailed information about the API.
-
-### Basic mechanics
-
-This section of the guide describes the fundamentals of creating different kinds
-of `Dataset` and `Iterator` objects, and how to extract data from them.
-
-#### Defining a source dataset
-
-You can build a `Dataset` using one of the following *source* dataset
-constructors:
-
-* From in-memory data:
-  * `tf.contrib.data.Dataset.from_tensors()`
-  * `tf.contrib.data.Dataset.from_tensor_slices()`
-
-* From on-disk data:
-  * `tf.contrib.data.FixedLengthRecordDataset()`
-  * `tf.contrib.data.TextLineDataset()`
-  * `tf.contrib.data.TFRecordDataset()`
-
-* From parameters:
-  * `tf.contrib.data.Dataset.range()`
-
-#### Transforming a dataset
-
-The `tf.contrib.data.Dataset` class has many methods that can be chained
-together to *transform* one dataset into another:
-
-* Per-element transformations:
-  * `Dataset.filter()`
-  * `Dataset.flat_map()`
-  * `Dataset.map()`
-  * `Dataset.zip()`
-
-* Multi-element transformations:
-  * `Dataset.batch()`
-  * `Dataset.dense_to_sparse_batch()`
-  * `Dataset.group_by_window()`
-  * `Dataset.padded_batch()`
-  * `Dataset.repeat()`
-  * `Dataset.shuffle()`
-  * `Dataset.skip()`
-  * `Dataset.take()`
-
-The following sections contain examples of how to use these transformations to
-solve common problems.
-
-#### Dataset structure
-
-A dataset comprises elements that each have the same structure. An element
-contains one or more `tf.Tensor` objects, called *components*. Each component
-has a `tf.DType` representing the type of elements in the tensor, and a
-`tf.TensorShape` representing the (possibly partially specified) static shape of
-each element. The `Dataset.output_types` and `Dataset.output_shapes` properties
-allow you to inspect the inferred types and shapes of each component of a
-dataset element. The *nested structure* of these properties map to the structure
-of an element, which may be a single tensor, a tuple of tensors, or a nested
-tuple of tensors. For example:
-
-```python
-dataset1 = tf.contrib.data.Dataset.from_tensor_slices(tf.random_uniform([4, 10]))
-print(dataset1.output_types)  # ==> "tf.float32"
-print(dataset1.output_shapes)  # ==> "(10,)"
-
-dataset2 = tf.contrib.data.Dataset.from_tensor_slices(
-   (tf.random_uniform([4]),
-    tf.random_uniform([4, 100], maxval=100, dtype=tf.int32)))
-print(dataset2.output_types)  # ==> "(tf.float32, tf.int32)"
-print(dataset2.output_shapes)  # ==> "((), (100,))"
-
-dataset3 = tf.contrib.data.Dataset.zip((dataset1, dataset2))
-print(dataset3.output_types)  # ==> (tf.float32, (tf.float32, tf.int32))
-print(dataset3.output_shapes)  # ==> "(10, ((), (100,)))"
-```
-
-The `Dataset` transformations support datasets of any structure. When using the
-`Dataset.map()`, `Dataset.flat_map()` and `Dataset.filter()` transformations,
-which apply a function to each element, the element structure determines the
-arguments of the function:
-
-```python
-dataset1 = dataset1.map(lambda x: ...)
-
-dataset2 = dataset2.flat_map(lambda x, y: ...)
-
-# *N.B.* Lambda argument destructuring is not available in Python 3.
-dataset3 = dataset3.filter(lambda x, (y, z): ...)
-```
-
-#### Creating an iterator
-
-One you have built a `Dataset` to represent your input data, the next step is to
-create an `Iterator` to access elements from that dataset.  The `Dataset` API
-currently supports three kinds of iterator, in increasing level of
-sophistication:
-
-A *one-shot* iterator is the simplest form of iterator, which only supports
-iterating once through a dataset, with no need for explicit initialization.
-One-shot iterators handle almost all of the cases that the existing queue-based
-input pipelines support, but they do not support parameterization. Using the
-example of `Dataset.range()`:
-
-```python
-dataset = tf.contrib.data.Dataset.range(100)
-iterator = dataset.make_one_shot_iterator()
-next_element = iterator.get_next()
-
-for i in range(100):
-  value = sess.run(next_element)
-  assert i == value
-```
-
-An *initializable* iterator requires you to run an explicit
-`iterator.initializer` operation before using it. In exchange for this
-inconvenience, it enables you to *parameterize* the definition of the dataset,
-using one or more `tf.placeholder()` tensors that can be fed when you
-initialize the iterator. Continuing the `Dataset.range()` example:
-
-```python
-max_value = tf.placeholder(tf.int64, shape=[])
-dataset = tf.contrib.data.Dataset.range(max_value)
-iterator = dataset.make_initializable_iterator()
-next_element = iterator.get_next()
-
-# Initialize an iterator over a dataset with 10 elements.
-sess.run(iterator.initializer, feed_dict={max_value: 10})
-for i in range(10):
-  value = sess.run(next_element)
-  assert i == value
-
-# Initialize the same iterator over a dataset with 100 elements.
-sess.run(iterator.initializer, feed_dict={max_value: 100})
-for i in range(100):
-  value = sess.run(next_element)
-  assert i == value
-```
-
-A *reinitializable* iterator can be initialized from multiple different
-`Dataset` objects. For example, you might have a training input pipeline that
-uses random perturbations to the input images to improve generalization, and
-a validation input pipeline that evaluates predictions on unmodified data. These
-pipelines will typically use different `Dataset` objects that have the same
-structure (i.e. the same types and compatible shapes for each component). 
-
-```python
-training_dataset = tf.contrib.data.Dataset.range(100).map(
-    lambda x: x + tf.random_uniform([], -10, 10, tf.int64))
-validation_dataset = tf.contrib.data.Dataset.range(50)
-
-# A reinitializable iterator is defined by its structure. We could use the
-# `output_types` and `output_shapes` properties of either `training_dataset`
-# or `validation_dataset` here, because they are compatible.
-iterator = Iterator.from_structure(training_dataset.output_types,
-                                   training_dataset.output_shapes)
-next_element = iterator.get_next()
-
-training_init_op = iterator.make_initializer(training_dataset)
-validation_init_op = iterator.make_initializer(validation_dataset)
-
-# Run 20 epochs in which the training dataset is traversed, followed by the
-# validation dataset.
-for _ in range(20):
-  # Initialize an iterator over the training dataset.
-  sess.run(training_init_op)
-  for _ in range(100):
-    sess.run(next_element)
-
-  # Initialize an iterator over the validation dataset.
-  sess.run(validation_init_op)
-  for _ in range(50):
-    sess.run(next_element)
-```
-
-#### Consuming values from an iterator
-
-The `Iterator.get_next()` method returns one or more `tf.Tensor` objects that
-correspond to the symbolic next element of an iterator. Each time these tensors
-are evaluated, they take the value of the next element in the underlying
-dataset. (Note that, like other stateful objects in TensorFlow, calling
-`Iterator.get_next()` does not immediately advance the iterator. Instead you
-must use the returned `tf.Tensor` objects in a TensorFlow expression, and pass
-the result of that expression to `tf.Session.run()` to get the next elements and
-advance the iterator.)
-
-If the iterator reaches the end of the dataset, executing
-the `Iterator.get_next()` operation will raise a `tf.errors.OutOfRangeError`.
-After this point the iterator will be in an unusable state, and you must
-initialize it again if you want to use it further.
-
-```python
-dataset = tf.contrib.data.Dataset.range(5)
-iterator = dataset.make_initializable_iterator()
-next_element = iterator.get_next()
-
-# Typically `result` will be the output of a model, or an optimizer's
-# training operation.
-result = tf.add(next_element, next_element)
-
-sess.run(iterator.initializer)
-print(sess.run(result))  # ==> "0"
-print(sess.run(result))  # ==> "2"
-print(sess.run(result))  # ==> "4"
-print(sess.run(result))  # ==> "6"
-print(sess.run(result))  # ==> "8"
-try:
-  sess.run(result)
-except tf.errors.OutOfRangeError:
-  print("End of dataset")  # ==> "End of dataset"
-```
-
-A common pattern is to wrap the "training loop" in a `try`-`except` block:
-
-```python
-sess.run(iterator.initializer)
-while True:
-  try:
-    sess.run(result)
-  except tf.errors.OutOfRangeError:
-    break
-```
-
-If each element of the dataset has a nested structure, the return value of
-`Iterator.get_next()` will be one or more `tf.Tensor` objects in the same
-nested structure:
-
-```python
-dataset1 = tf.contrib.data.Dataset.from_tensor_slices(tf.random_uniform([4, 10]))
-dataset2 = tf.contrib.data.Dataset.from_tensor_slices((tf.random_uniform([4]), tf.random_uniform([4, 100])))
-dataset3 = tf.contrib.data.Dataset.zip((dataset1, dataset2))
-
-iterator = dataset3.make_initializable_iterator()
-
-sess.run(iterator.initializer)
-next1, (next2, next3) = iterator.get_next()
-```
-
-Note that evaluating *any* of `next1`, `next2`, or `next3` will advance the
-iterator for all components. A typical consumer of an iterator will include all
-components in a single expression.
-
-### Reading input data
-
-#### Consuming NumPy arrays
-
-If all of your input data fit in memory, the simplest way to create a `Dataset`
-from them is to convert them to `tf.Tensor` objects and use
-`Dataset.from_tensor_slices()`.
-
-```python
-# Load the training data into two NumPy arrays, for example using `np.load()`.
-with np.load("/var/data/training_data.npy") as data:
-  features = data["features"]
-  labels = data["labels"]
-
-# Assume that each row of `features` corresponds to the same row as `labels`.
-assert features.shape[0] == labels.shape[0]
-
-dataset = tf.contrib.data.Dataset.from_tensor_slices((features, labels))
-```
-
-Note that the above code snippet will embed the `features` and `labels` arrays
-in your TensorFlow graph as constants. This works well for a small dataset, but
-wastes memory, and can run into the 2GB limit for the `tf.GraphDef` protocol
-buffer.
-
-As an alternative, you can define the `Dataset` in terms of `tf.placeholder()`
-tensors, and *feed* the NumPy arrays when you initialize an `Iterator` over the
-dataset.
-
-```python
-# Load the training data into two NumPy arrays, for example using `np.load()`.
-with np.load("/var/data/training_data.npy") as data:
-  features = data["features"]
-  labels = data["labels"]
-
-# Assume that each row of `features` corresponds to the same row as `labels`.
-assert features.shape[0] == labels.shape[0]
-
-features_placeholder = tf.placeholder(features.dtype, features.shape)
-labels_placeholder = tf.placeholder(labels.dtype, labels.shape)
-
-dataset = tf.contrib.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
-# [Other transformations on `dataset`...]
-dataset = ...
-iterator = dataset.make_initializable_iterator()
-
-sess.run(iterator.initializer, feed_dict={features_placeholder: features,
-                                          labels_placeholder: labels})
-```
-
-#### Consuming TFRecord data
-
-The `Dataset` API supports a variety of file formats so that you can process
-large datasets that do not fit in memory. The TFRecord file format is a
-simple record-oriented binary format that many TensorFlow applications use for
-training data. The `tf.contrib.data.TFRecordDataset` class enables you to
-stream over the contents of one or more TFRecord files as part of an input
-pipeline.
-
-```python
-# Creates a dataset that reads all of the examples from two files.
-filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
-dataset = tf.contrib.data.TFRecordDataset(filenames)
-```
-
-The `filenames` argument to the `TFRecordDataset` initializer can be a
-`tf.Tensor` of strings. Therefore if you have two sets of files for training
-and validation purposes, you can use a `tf.placeholder(tf.string)` to represent
-the filenames, and initialize an iterator from the appropriate filenames:
-
-```python
-filenames = tf.placeholder(tf.string, shape=[None])
-dataset = tf.contrib.data.TFRecordDataset(filenames)
-# [Other transformations on `dataset`...]
-dataset = ...
-iterator = dataset.make_initializable_iterator()
-
-# You can feed the initializer with the appropriate filenames for the current
-# phase of execution, e.g. training vs. validation.
-
-# Initialize `iterator` with training data.
-training_filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
-sess.run(iterator.initializer, feed_dict={filenames: training_filenames})
-
-# Initialize `iterator` with validation data.
-validation_filenames = ["/var/data/validation1.tfrecord", ...]
-sess.run(iterator.initializer, feed_dict={filenames: validation_filenames})
-```
-
-#### Consuming text data
-
-Many datasets are distributed as one or more text files. The
-`tf.contrib.data.TextLineDataset` provides an easy way to extract lines from
-one or more text files. Given one or more filenames, a `TextLineDataset` will
-produce one string-valued element per line of those files. Like a
-`TFRecordDataset`, `TextLineDataset` accepts `filenames` as a `tf.Tensor`, so
-you can parameterize it by passing a `tf.placeholder(tf.string)`.
-
-```python
-filenames = ["/var/data/file1.txt", "/var/data/file2.txt"]
-dataset = tf.contrib.data.TextLineDataset(filenames)
-```
-
-By default, a `TextLineDataset` yields *every* line of each file, which may
-not be desirable, for example if the file starts with a header line, or contains
-comments. These lines can be removed using the `Dataset.skip()` and
-`Dataset.filter()` transformations. To apply these transformations to each
-file separately, we use `Dataset.flat_map()` to create a nested `Dataset` for
-each file.
-
-```python
-filenames = ["/var/data/file1.txt", "/var/data/file2.txt"]
-
-dataset = tf.contrib.data.Dataset.from_tensor_slices(filenames)
-
-# Use `Dataset.flat_map()` to transform each file separately.
-# * Skip the first line (header row).
-# * Filter out lines beginning with "#" (comments).
-dataset = dataset.flat_map(
-    lambda filename: (
-        tf.contrib.data.Dataset.TextLineDataset(filename)
-        .skip(1)
-        .filter(lambda line: tf.not_equal(tf.substr(line, 0, 1), "#"))))
-```
-
-<!--
-TODO(mrry): Add these sections.
-
-#### Consuming from a Python generator
-#### Consuming from an index file and images
--->
-
-### Preprocessing data with `Dataset.map()`
-
-The `Dataset.map(f)` transformation produces a new dataset by applying a given
-function `f` to each element of the input dataset. It is based on
-the
-[`map()` function](https://en.wikipedia.org/wiki/Map_(higher-order_function))
-that is commonly applied to lists (and other structures) in functional
-programming languages.  The function `f` takes the `tf.Tensor` objects that
-represent a single element in the input, and returns the `tf.Tensor` objects
-that will represent a single element in the new dataset. Its implementation uses
-standard TensorFlow operations to transform one element into another.
-
-This section covers common examples of how to use `Dataset.map()`.
-
-#### Parsing `tf.Example` protocol buffer messages
-
-Many input pipelines extract `tf.train.Example` protocol buffer messages from a
-TFRecord-format file (written, for example, using
-`tf.python_io.TFRecordWriter`). Each `tf.train.Example` record contains one or
-more "features", and the input pipeline typically converts these features into
-tensors.
-
-```python
-# Transforms a scalar string `example_proto` into a pair of a scalar string and
-# a scalar integer, representing an image and its label, respectively.
-def _parse_function(example_proto):
-  features = {"image": tf.FixedLenFeature((), tf.string, default_value=""),
-              "label": tf.FixedLenFeature((), tf.int32, default_value=0)}
-  parsed_features = tf.parse_single_example(example_proto, features)
-  return parsed_features["image"], parsed_features["label"]
-
-# Creates a dataset that reads all of the examples from two files, and extracts
-# the image and label features.
-filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
-dataset = tf.contrib.data.TFRecordDataset(filenames)
-dataset = dataset.map(_parse_function)
-```
-
-#### Decoding image data and resizing it
-
-When training a neural network on real-world image data, it is often necessary
-to convert images of different sizes to a common size, so that they may be
-batched into a fixed size.
-
-```python
-# Reads an image from a file, decodes it into a dense tensor, and resizes it
-# to a fixed shape.
-def _parse_function(filename, label):
-  image_string = tf.read_file(filename)
-  image_decoded = tf.image.decode_image(image_string)
-  image_resized = tf.image.resize_images(image_decoded, [28, 28])
-  return image_resized, label
-
-filenames = tf.constant(["/var/data/image1.jpg", "/var/data/image2.jpg", ...])
-labels = tf.constant([0, 37, 29, 1, ...])
-
-dataset = tf.contrib.data.Dataset.from_tensor_slices((filenames, labels))
-dataset = dataset.map(_parse_function)
-```
-
-#### Applying arbitrary Python logic with `tf.py_func()`
-
-For performance reasons, we encourage you to use TensorFlow operations for
-preprocessing your data whenever possible. However, it is sometimes useful to
-be able to call upon external Python libraries when parsing your input data,
-and you can do this by invoking the `tf.py_func()` operation in a
-`Dataset.map()` transformation.
-
-```python
-import cv2
-
-# Use a custom OpenCV function to read the image, instead of the standard
-# TensorFlow `tf.read_file()` operation.
-def _read_py_function(filename, label):
-  image_decoded = cv2.imread(image_string, cv2.IMREAD_GRAYSCALE)
-  return image_decoded, label
-
-# Use standard TensorFlow operations to resize the image to a fixed shape.
-def _resize_function(image_decoded, label):
-  image_decoded.set_shape([None, None, None])
-  image_resized = tf.image.resize_images(image_decoded, [28, 28])
-  return image_resized, label
-
-filenames = ["/var/data/image1.jpg", "/var/data/image2.jpg", ...]
-labels = [0, 37, 29, 1, ...]
-
-dataset = tf.contrib.data.Dataset.from_tensor_slices((filenames, labels))
-dataset = dataset.map(
-    lambda filename, label: tf.py_func(
-        _read_py_function, [filename, label], [tf.uint8, label.dtype]))
-dataset = dataset.map(_resize_function)
-```
-
-<!--
-TODO(mrry): Add this section.
-
-#### Handling text data with unusual sizes
--->
-
-### Batching dataset elements
-
-#### Simple batching
-
-The simplest form of batching stacks `n` consecutive elements of a dataset into
-a single element. The `Dataset.batch()` transformation does exactly this, with
-the same constraints as the `tf.stack()` operator, applied to each component
-of the elements: i.e. for each component *i*, all elements must have a tensor
-of the exact same shape.
-
-```python
-inc_dataset = tf.contrib.data.Dataset.range(100)
-dec_dataset = tf.contrib.data.Dataset.range(0, -100, -1)
-dataset = tf.contrib.data.Dataset.zip((inc_dataset, dec_dataset))
-batched_dataset = dataset.batch(4)
-
-iterator = batched_dataset.make_one_shot_iterator()
-next_element = iterator.get_next()
-
-print(sess.run(next_element))  # ==> ([0, 1, 2,   3],   [ 0, -1,  -2,  -3])
-print(sess.run(next_element))  # ==> ([4, 5, 6,   7],   [-4, -5,  -6,  -7])
-print(sess.run(next_element))  # ==> ([8, 9, 10, 11],   [-8, -9, -10, -11])
-```
-
-#### Batching tensors with padding
-
-The above recipe works for tensors that all have the same size. However, many
-models (e.g. sequence models) work with input data that can have varying size
-(e.g. sequences of different lengths). To handle this case, the
-`Dataset.padded_batch()` transformation enables you to batch tensors of
-different shape by specifying one or more dimensions in which they may be
-padded.
-
-```python
-dataset = tf.contrib.data.Dataset.range(100)
-dataset = dataset.map(lambda x: tf.fill([tf.cast(x, tf.int32)], x))
-batched_dataset = dataset.padded_batch(4, padded_shapes=[None])
-
-iterator = batched_dataset.make_one_shot_iterator()
-next_element = iterator.get_next()
-
-print(sess.run(next_element))  # ==> [[0, 0, 0], [1, 0, 0], [2, 2, 0], [3, 3, 3]]
-print(sess.run(next_element))  # ==> [[4, 4, 4, 4, 0, 0, 0],
-                               #      [5, 5, 5, 5, 5, 0, 0],
-                               #      [6, 6, 6, 6, 6, 6, 0],
-                               #      [7, 7, 7, 7, 7, 7, 7]]
-```
-
-The `Dataset.padded_batch()` transformation allows you to set different padding
-for each dimension of each component, and it may be variable-length (signified
-by `None` in the example above) or constant-length. It is also possible to
-override the padding value, which defaults to 0.
-
-<!--
-TODO(mrry): Add this section.
-
-#### Dense ragged -> tf.SparseTensor
--->
-
-### Training workflows
-
-#### Processing multiple epochs
-
-The `Dataset` API offers two main ways to process multiple epochs of the same
-data.
-
-The simplest way to iterate over a dataset in multiple epochs is to use the
-`Dataset.repeat()` transformation. For example, to create a dataset that repeats
-its input for 10 epochs:
-
-```python
-filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
-dataset = tf.contrib.data.TFRecordDataset(filenames)
-dataset = dataset.map(...)
-dataset = dataset.repeat(10)
-dataset = dataset.batch(32)
-```
-
-Applying the `Dataset.repeat()` transformation with no arguments will repeat
-the input indefinitely. The `Dataset.repeat()` transformation concatenates its
-arguments without signaling the end of one epoch and the beginning of the next
-epoch.
-
-If you want to receive a signal at the end of each epoch, you can write a
-training loop that catches the `tf.errors.OutOfRangeError` at the end of a
-dataset. At that point you might collect some statistics (e.g. the validation
-error) for the epoch.
-
-```python
-filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
-dataset = tf.contrib.data.TFRecordDataset(filenames)
-dataset = dataset.map(...)
-dataset = dataset.batch(32)
-iterator = dataset.make_initializable_iterator()
-next_element = iterator.get_next()
-
-# Compute for 100 epochs.
-for _ in range(100):
-  sess.run(iterator.initializer)
-  while True:
-    try:
-      sess.run(next_element)
-    except tf.errors.OutOfRangeError:
-      break
-
-  # [Perform end-of-epoch calculations here.]
-```
-
-#### Randomly shuffling input data
-
-The `Dataset.shuffle()` transformation randomly shuffles the input dataset
-using a similar algorithm to `tf.RandomShuffleQueue`: it maintains a fixed-size
-buffer and chooses the next element uniformly at random from that buffer.
-
-```python
-filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
-dataset = tf.contrib.data.TFRecordDataset(filenames)
-dataset = dataset.map(...)
-dataset = dataset.repeat()
-dataset = dataset.shuffle(buffer_size=10000)
-dataset = dataset.batch(32)
-```
+The documentation for this API has moved to the programmers'
+guide, [here](../../docs_src/programmers_guide/datasets.md).
diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md
new file mode 100644
index 0000000000..435e566e30
--- /dev/null
+++ b/tensorflow/docs_src/programmers_guide/datasets.md
@@ -0,0 +1,700 @@
+# Using the `Dataset` API for TensorFlow Input Pipelines
+
+The `Dataset` API enables you to build complex input pipelines from
+simple, reusable pieces. For example, the pipeline for an image model might
+aggregate data from files in a distributed file system, apply random
+perturbations to each image, and merge randomly selected images into a batch
+for training. The pipeline for a text model might involve extracting symbols
+from raw text data, converting them to embedding identifiers with a lookup
+table, and batching together sequences of different lengths. The `Dataset` API
+makes it easy to deal with large amounts of data, different data formats, and
+complicated transformations.
+
+The `Dataset` API introduces two new abstractions to TensorFlow:
+
+* A `tf.contrib.data.Dataset` represents a sequence of elements, in which
+  each element contains one or more `Tensor` objects. For example, in an image
+  pipeline, an element might be a single training example, with a pair of
+  tensors representing the image data and a label. There are two distinct
+  ways to create a dataset:
+  
+  * Creating a **source* (e.g. `Dataset.from_tensor_slices()`) constructs a
+    dataset from
+    one or more `tf.Tensor` objects.
+  
+  * Applying a **transformation** (e.g. `Dataset.batch()`) constructs a dataset
+    from one or more `tf.contrib.data.Dataset` objects.
+  
+* A `tf.contrib.data.Iterator` provides the main way to extract elements from a
+  dataset. The operation returned by `Iterator.get_next()` yields the next
+  element of a `Dataset` when executed, and typically acts as the interface
+  between input pipeline code and your model. The simplest iterator is a
+  "one-shot iterator", which is associated with a particular `Dataset` and
+  iterates through it once. For more sophisticated uses, the
+  `Iterator.initializer` operation enables you to reinitialize and parameterize
+  an iterator with different datasets, so that you can, for example, iterate
+  over training and validation data multiple times in the same program.
+
+## Basic mechanics
+
+This section of the guide describes the fundamentals of creating different kinds
+of `Dataset` and `Iterator` objects, and how to extract data from them.
+
+To start an input pipeline, you must define a *source*. For example, to
+construct a `Dataset` from some tensors in memory, you can use
+`tf.contrib.data.Dataset.from_tensors()` or
+`tf.contrib.data.Dataset.from_tensor_slices()`. Alternatively, if your input
+data are on disk in the recommend TFRecord format, you can construct a
+`tf.contrib.data.TFRecordDataset`.
+
+Once you have a `Dataset` object, you can *transform* it into a new `Dataset` by
+chaining method calls on the `tf.contrib.data.Dataset` object. For example, you
+c an apply per-element transformations such as `Dataset.map()` (to apply a
+function to each element), and multi-element transformations such as
+`Dataset.batch()`. See the documentation for @{tf.contrib.data.Dataset}
+for a complete list of transformations.
+
+The most common way to consume values from a `Dataset` is to make an
+**iterator** object that provides access to one element of the dataset at a time
+(for example, by calling `Dataset.make_one_shot_iterator()`). A
+`tf.contrib.data.Iterator` provides two operations: `Iterator.initializer`,
+which enables you to (re)initialize the iterator's state; and
+`Iterator.get_next()`, which returns `tf.Tensor` objects that correspond to the
+symbolic next element. Depending on your use case, you might choose a different
+type of iterator, and the options are outlined below.
+
+### Dataset structure
+
+A dataset comprises elements that each have the same structure. An element
+contains one or more `tf.Tensor` objects, called *components*. Each component
+has a `tf.DType` representing the type of elements in the tensor, and a
+`tf.TensorShape` representing the (possibly partially specified) static shape of
+each element. The `Dataset.output_types` and `Dataset.output_shapes` properties
+allow you to inspect the inferred types and shapes of each component of a
+dataset element. The *nested structure* of these properties map to the structure
+of an element, which may be a single tensor, a tuple of tensors, or a nested
+tuple of tensors. For example:
+
+```python
+dataset1 = tf.contrib.data.Dataset.from_tensor_slices(tf.random_uniform([4, 10]))
+print(dataset1.output_types)  # ==> "tf.float32"
+print(dataset1.output_shapes)  # ==> "(10,)"
+
+dataset2 = tf.contrib.data.Dataset.from_tensor_slices(
+   (tf.random_uniform([4]),
+    tf.random_uniform([4, 100], maxval=100, dtype=tf.int32)))
+print(dataset2.output_types)  # ==> "(tf.float32, tf.int32)"
+print(dataset2.output_shapes)  # ==> "((), (100,))"
+
+dataset3 = tf.contrib.data.Dataset.zip((dataset1, dataset2))
+print(dataset3.output_types)  # ==> (tf.float32, (tf.float32, tf.int32))
+print(dataset3.output_shapes)  # ==> "(10, ((), (100,)))"
+```
+
+It is often convenient to give names to each component of an element, for
+example if they represent different features of a training example. In addition
+to tuples, you can use `collections.namedtuple` or a dictionary mapping strings
+to tensors to represent a single element of a `Dataset`.
+
+```python
+dataset = tf.contrib.data.Dataset.from_tensor_slices(
+   {"a": tf.random_uniform([4]),
+    "b": tf.random_uniform([4, 100], maxval=100, dtype=tf.int32)})
+print(dataset.output_types)  # ==> "{'a': tf.float32, 'b': tf.int32}"
+print(dataset.output_shapes)  # ==> "{'a': (), 'b': (100,)}"
+```
+
+The `Dataset` transformations support datasets of any structure. When using the
+`Dataset.map()`, `Dataset.flat_map()`, and `Dataset.filter()` transformations,
+which apply a function to each element, the element structure determines the
+arguments of the function:
+
+```python
+dataset1 = dataset1.map(lambda x: ...)
+
+dataset2 = dataset2.flat_map(lambda x, y: ...)
+
+# Note: Argument destructuring is not available in Python 3.
+dataset3 = dataset3.filter(lambda x, (y, z): ...)
+```
+
+### Creating an iterator
+
+One you have built a `Dataset` to represent your input data, the next step is to
+create an `Iterator` to access elements from that dataset.  The `Dataset` API
+currently supports three kinds of iterator, in increasing level of
+sophistication:
+
+* **one-shot**,
+* **initializable**, and
+* **reinitializable**.
+
+A **one-shot** iterator is the simplest form of iterator, which only supports
+iterating once through a dataset, with no need for explicit initialization.
+One-shot iterators handle almost all of the cases that the existing queue-based
+input pipelines support, but they do not support parameterization. Using the
+example of `Dataset.range()`:
+
+```python
+dataset = tf.contrib.data.Dataset.range(100)
+iterator = dataset.make_one_shot_iterator()
+next_element = iterator.get_next()
+
+for i in range(100):
+  value = sess.run(next_element)
+  assert i == value
+```
+
+An **initializable** iterator requires you to run an explicit
+`iterator.initializer` operation before using it. In exchange for this
+inconvenience, it enables you to *parameterize* the definition of the dataset,
+using one or more `tf.placeholder()` tensors that can be fed when you
+initialize the iterator. Continuing the `Dataset.range()` example:
+
+```python
+max_value = tf.placeholder(tf.int64, shape=[])
+dataset = tf.contrib.data.Dataset.range(max_value)
+iterator = dataset.make_initializable_iterator()
+next_element = iterator.get_next()
+
+# Initialize an iterator over a dataset with 10 elements.
+sess.run(iterator.initializer, feed_dict={max_value: 10})
+for i in range(10):
+  value = sess.run(next_element)
+  assert i == value
+
+# Initialize the same iterator over a dataset with 100 elements.
+sess.run(iterator.initializer, feed_dict={max_value: 100})
+for i in range(100):
+  value = sess.run(next_element)
+  assert i == value
+```
+
+A **reinitializable** iterator can be initialized from multiple different
+`Dataset` objects. For example, you might have a training input pipeline that
+uses random perturbations to the input images to improve generalization, and
+a validation input pipeline that evaluates predictions on unmodified data. These
+pipelines will typically use different `Dataset` objects that have the same
+structure (i.e. the same types and compatible shapes for each component).
+
+```python
+training_dataset = tf.contrib.data.Dataset.range(100).map(
+    lambda x: x + tf.random_uniform([], -10, 10, tf.int64))
+validation_dataset = tf.contrib.data.Dataset.range(50)
+
+# A reinitializable iterator is defined by its structure. We could use the
+# `output_types` and `output_shapes` properties of either `training_dataset`
+# or `validation_dataset` here, because they are compatible.
+iterator = Iterator.from_structure(training_dataset.output_types,
+                                   training_dataset.output_shapes)
+next_element = iterator.get_next()
+
+training_init_op = iterator.make_initializer(training_dataset)
+validation_init_op = iterator.make_initializer(validation_dataset)
+
+# Run 20 epochs in which the training dataset is traversed, followed by the
+# validation dataset.
+for _ in range(20):
+  # Initialize an iterator over the training dataset.
+  sess.run(training_init_op)
+  for _ in range(100):
+    sess.run(next_element)
+
+  # Initialize an iterator over the validation dataset.
+  sess.run(validation_init_op)
+  for _ in range(50):
+    sess.run(next_element)
+```
+
+### Consuming values from an iterator
+
+The `Iterator.get_next()` method returns one or more `tf.Tensor` objects that
+correspond to the symbolic next element of an iterator. Each time these tensors
+are evaluated, they take the value of the next element in the underlying
+dataset. (Note that, like other stateful objects in TensorFlow, calling
+`Iterator.get_next()` does not immediately advance the iterator. Instead you
+must use the returned `tf.Tensor` objects in a TensorFlow expression, and pass
+the result of that expression to `tf.Session.run()` to get the next elements and
+advance the iterator.)
+
+If the iterator reaches the end of the dataset, executing
+the `Iterator.get_next()` operation will raise a `tf.errors.OutOfRangeError`.
+After this point the iterator will be in an unusable state, and you must
+initialize it again if you want to use it further.
+
+```python
+dataset = tf.contrib.data.Dataset.range(5)
+iterator = dataset.make_initializable_iterator()
+next_element = iterator.get_next()
+
+# Typically `result` will be the output of a model, or an optimizer's
+# training operation.
+result = tf.add(next_element, next_element)
+
+sess.run(iterator.initializer)
+print(sess.run(result))  # ==> "0"
+print(sess.run(result))  # ==> "2"
+print(sess.run(result))  # ==> "4"
+print(sess.run(result))  # ==> "6"
+print(sess.run(result))  # ==> "8"
+try:
+  sess.run(result)
+except tf.errors.OutOfRangeError:
+  print("End of dataset")  # ==> "End of dataset"
+```
+
+A common pattern is to wrap the "training loop" in a `try`-`except` block:
+
+```python
+sess.run(iterator.initializer)
+while True:
+  try:
+    sess.run(result)
+  except tf.errors.OutOfRangeError:
+    break
+```
+
+If each element of the dataset has a nested structure, the return value of
+`Iterator.get_next()` will be one or more `tf.Tensor` objects in the same
+nested structure:
+
+```python
+dataset1 = tf.contrib.data.Dataset.from_tensor_slices(tf.random_uniform([4, 10]))
+dataset2 = tf.contrib.data.Dataset.from_tensor_slices((tf.random_uniform([4]), tf.random_uniform([4, 100])))
+dataset3 = tf.contrib.data.Dataset.zip((dataset1, dataset2))
+
+iterator = dataset3.make_initializable_iterator()
+
+sess.run(iterator.initializer)
+next1, (next2, next3) = iterator.get_next()
+```
+
+Note that evaluating *any* of `next1`, `next2`, or `next3` will advance the
+iterator for all components. A typical consumer of an iterator will include all
+components in a single expression.
+
+## Reading input data
+
+### Consuming NumPy arrays
+
+If all of your input data fit in memory, the simplest way to create a `Dataset`
+from them is to convert them to `tf.Tensor` objects and use
+`Dataset.from_tensor_slices()`.
+
+```python
+# Load the training data into two NumPy arrays, for example using `np.load()`.
+with np.load("/var/data/training_data.npy") as data:
+  features = data["features"]
+  labels = data["labels"]
+
+# Assume that each row of `features` corresponds to the same row as `labels`.
+assert features.shape[0] == labels.shape[0]
+
+dataset = tf.contrib.data.Dataset.from_tensor_slices((features, labels))
+```
+
+Note that the above code snippet will embed the `features` and `labels` arrays
+in your TensorFlow graph as `tf.constant()` operations. This works well for a
+small dataset, but wastes memory---because the contents of the array will be
+copied multiple times---and can run into the 2GB limit for the `tf.GraphDef`
+protocol buffer.
+
+As an alternative, you can define the `Dataset` in terms of `tf.placeholder()`
+tensors, and *feed* the NumPy arrays when you initialize an `Iterator` over the
+dataset.
+
+```python
+# Load the training data into two NumPy arrays, for example using `np.load()`.
+with np.load("/var/data/training_data.npy") as data:
+  features = data["features"]
+  labels = data["labels"]
+
+# Assume that each row of `features` corresponds to the same row as `labels`.
+assert features.shape[0] == labels.shape[0]
+
+features_placeholder = tf.placeholder(features.dtype, features.shape)
+labels_placeholder = tf.placeholder(labels.dtype, labels.shape)
+
+dataset = tf.contrib.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
+# [Other transformations on `dataset`...]
+dataset = ...
+iterator = dataset.make_initializable_iterator()
+
+sess.run(iterator.initializer, feed_dict={features_placeholder: features,
+                                          labels_placeholder: labels})
+```
+
+### Consuming TFRecord data
+
+The `Dataset` API supports a variety of file formats so that you can process
+large datasets that do not fit in memory. For example, the TFRecord file format
+is a simple record-oriented binary format that many TensorFlow applications use
+for training data. The `tf.contrib.data.TFRecordDataset` class enables you to
+stream over the contents of one or more TFRecord files as part of an input
+pipeline.
+
+```python
+# Creates a dataset that reads all of the examples from two files.
+filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+dataset = tf.contrib.data.TFRecordDataset(filenames)
+```
+
+The `filenames` argument to the `TFRecordDataset` initializer can either be a
+string, a list of strings, or a `tf.Tensor` of strings. Therefore if you have
+two sets of files for training and validation purposes, you can use a
+`tf.placeholder(tf.string)` to represent the filenames, and initialize an
+iterator from the appropriate filenames:
+
+```python
+filenames = tf.placeholder(tf.string, shape=[None])
+dataset = tf.contrib.data.TFRecordDataset(filenames)
+dataset = dataset.map(...)  # Parse the record into tensors.
+dataset = dataset.repeat()  # Repeat the input indefinitely.
+dataset = dataset.batch(32)
+iterator = dataset.make_initializable_iterator()
+
+# You can feed the initializer with the appropriate filenames for the current
+# phase of execution, e.g. training vs. validation.
+
+# Initialize `iterator` with training data.
+training_filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+sess.run(iterator.initializer, feed_dict={filenames: training_filenames})
+
+# Initialize `iterator` with validation data.
+validation_filenames = ["/var/data/validation1.tfrecord", ...]
+sess.run(iterator.initializer, feed_dict={filenames: validation_filenames})
+```
+
+### Consuming text data
+
+Many datasets are distributed as one or more text files. The
+`tf.contrib.data.TextLineDataset` provides an easy way to extract lines from
+one or more text files. Given one or more filenames, a `TextLineDataset` will
+produce one string-valued element per line of those files. Like a
+`TFRecordDataset`, `TextLineDataset` accepts `filenames` as a `tf.Tensor`, so
+you can parameterize it by passing a `tf.placeholder(tf.string)`.
+
+```python
+filenames = ["/var/data/file1.txt", "/var/data/file2.txt"]
+dataset = tf.contrib.data.TextLineDataset(filenames)
+```
+
+By default, a `TextLineDataset` yields *every* line of each file, which may
+not be desirable, for example if the file starts with a header line, or contains
+comments. These lines can be removed using the `Dataset.skip()` and
+`Dataset.filter()` transformations. To apply these transformations to each
+file separately, we use `Dataset.flat_map()` to create a nested `Dataset` for
+each file.
+
+```python
+filenames = ["/var/data/file1.txt", "/var/data/file2.txt"]
+
+dataset = tf.contrib.data.Dataset.from_tensor_slices(filenames)
+
+# Use `Dataset.flat_map()` to transform each file as a separate nested dataset,
+# and then concatenate their contents sequentially into a single "flat" dataset.
+# * Skip the first line (header row).
+# * Filter out lines beginning with "#" (comments).
+dataset = dataset.flat_map(
+    lambda filename: (
+        tf.contrib.data.Dataset.TextLineDataset(filename)
+        .skip(1)
+        .filter(lambda line: tf.not_equal(tf.substr(line, 0, 1), "#"))))
+```
+
+<!--
+TODO(mrry): Add these sections.
+
+### Consuming from a Python generator
+-->
+
+## Preprocessing data with `Dataset.map()`
+
+The `Dataset.map(f)` transformation produces a new dataset by applying a given
+function `f` to each element of the input dataset. It is based on
+the
+[`map()` function](https://en.wikipedia.org/wiki/Map_(higher-order_function))
+that is commonly applied to lists (and other structures) in functional
+programming languages.  The function `f` takes the `tf.Tensor` objects that
+represent a single element in the input, and returns the `tf.Tensor` objects
+that will represent a single element in the new dataset. Its implementation uses
+standard TensorFlow operations to transform one element into another.
+
+This section covers common examples of how to use `Dataset.map()`.
+
+### Parsing `tf.Example` protocol buffer messages
+
+Many input pipelines extract `tf.train.Example` protocol buffer messages from a
+TFRecord-format file (written, for example, using
+`tf.python_io.TFRecordWriter`). Each `tf.train.Example` record contains one or
+more "features", and the input pipeline typically converts these features into
+tensors.
+
+```python
+# Transforms a scalar string `example_proto` into a pair of a scalar string and
+# a scalar integer, representing an image and its label, respectively.
+def _parse_function(example_proto):
+  features = {"image": tf.FixedLenFeature((), tf.string, default_value=""),
+              "label": tf.FixedLenFeature((), tf.int32, default_value=0)}
+  parsed_features = tf.parse_single_example(example_proto, features)
+  return parsed_features["image"], parsed_features["label"]
+
+# Creates a dataset that reads all of the examples from two files, and extracts
+# the image and label features.
+filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+dataset = tf.contrib.data.TFRecordDataset(filenames)
+dataset = dataset.map(_parse_function)
+```
+
+### Decoding image data and resizing it
+
+When training a neural network on real-world image data, it is often necessary
+to convert images of different sizes to a common size, so that they may be
+batched into a fixed size.
+
+```python
+# Reads an image from a file, decodes it into a dense tensor, and resizes it
+# to a fixed shape.
+def _parse_function(filename, label):
+  image_string = tf.read_file(filename)
+  image_decoded = tf.image.decode_image(image_string)
+  image_resized = tf.image.resize_images(image_decoded, [28, 28])
+  return image_resized, label
+
+# A vector of filenames.
+filenames = tf.constant(["/var/data/image1.jpg", "/var/data/image2.jpg", ...])
+
+# `labels[i]` is the label for the image in `filenames[i].
+labels = tf.constant([0, 37, ...])
+
+dataset = tf.contrib.data.Dataset.from_tensor_slices((filenames, labels))
+dataset = dataset.map(_parse_function)
+```
+
+### Applying arbitrary Python logic with `tf.py_func()`
+
+For performance reasons, we encourage you to use TensorFlow operations for
+preprocessing your data whenever possible. However, it is sometimes useful to
+call upon external Python libraries when parsing your input data. To do so,
+invoke, the `tf.py_func()` operation in a `Dataset.map()` transformation.
+
+```python
+import cv2
+
+# Use a custom OpenCV function to read the image, instead of the standard
+# TensorFlow `tf.read_file()` operation.
+def _read_py_function(filename, label):
+  image_decoded = cv2.imread(image_string, cv2.IMREAD_GRAYSCALE)
+  return image_decoded, label
+
+# Use standard TensorFlow operations to resize the image to a fixed shape.
+def _resize_function(image_decoded, label):
+  image_decoded.set_shape([None, None, None])
+  image_resized = tf.image.resize_images(image_decoded, [28, 28])
+  return image_resized, label
+
+filenames = ["/var/data/image1.jpg", "/var/data/image2.jpg", ...]
+labels = [0, 37, 29, 1, ...]
+
+dataset = tf.contrib.data.Dataset.from_tensor_slices((filenames, labels))
+dataset = dataset.map(
+    lambda filename, label: tf.py_func(
+        _read_py_function, [filename, label], [tf.uint8, label.dtype]))
+dataset = dataset.map(_resize_function)
+```
+
+<!--
+TODO(mrry): Add this section.
+
+### Handling text data with unusual sizes
+-->
+
+## Batching dataset elements
+
+### Simple batching
+
+The simplest form of batching stacks `n` consecutive elements of a dataset into
+a single element. The `Dataset.batch()` transformation does exactly this, with
+the same constraints as the `tf.stack()` operator, applied to each component
+of the elements: i.e. for each component *i*, all elements must have a tensor
+of the exact same shape.
+
+```python
+inc_dataset = tf.contrib.data.Dataset.range(100)
+dec_dataset = tf.contrib.data.Dataset.range(0, -100, -1)
+dataset = tf.contrib.data.Dataset.zip((inc_dataset, dec_dataset))
+batched_dataset = dataset.batch(4)
+
+iterator = batched_dataset.make_one_shot_iterator()
+next_element = iterator.get_next()
+
+print(sess.run(next_element))  # ==> ([0, 1, 2,   3],   [ 0, -1,  -2,  -3])
+print(sess.run(next_element))  # ==> ([4, 5, 6,   7],   [-4, -5,  -6,  -7])
+print(sess.run(next_element))  # ==> ([8, 9, 10, 11],   [-8, -9, -10, -11])
+```
+
+### Batching tensors with padding
+
+The above recipe works for tensors that all have the same size. However, many
+models (e.g. sequence models) work with input data that can have varying size
+(e.g. sequences of different lengths). To handle this case, the
+`Dataset.padded_batch()` transformation enables you to batch tensors of
+different shape by specifying one or more dimensions in which they may be
+padded.
+
+```python
+dataset = tf.contrib.data.Dataset.range(100)
+dataset = dataset.map(lambda x: tf.fill([tf.cast(x, tf.int32)], x))
+dataset = dataset.padded_batch(4, padded_shapes=[None])
+
+iterator = dataset.make_one_shot_iterator()
+next_element = iterator.get_next()
+
+print(sess.run(next_element))  # ==> [[0, 0, 0], [1, 0, 0], [2, 2, 0], [3, 3, 3]]
+print(sess.run(next_element))  # ==> [[4, 4, 4, 4, 0, 0, 0],
+                               #      [5, 5, 5, 5, 5, 0, 0],
+                               #      [6, 6, 6, 6, 6, 6, 0],
+                               #      [7, 7, 7, 7, 7, 7, 7]]
+```
+
+The `Dataset.padded_batch()` transformation allows you to set different padding
+for each dimension of each component, and it may be variable-length (signified
+by `None` in the example above) or constant-length. It is also possible to
+override the padding value, which defaults to 0.
+
+<!--
+TODO(mrry): Add this section.
+
+### Dense ragged -> tf.SparseTensor
+-->
+
+## Training workflows
+
+### Processing multiple epochs
+
+The `Dataset` API offers two main ways to process multiple epochs of the same
+data.
+
+The simplest way to iterate over a dataset in multiple epochs is to use the
+`Dataset.repeat()` transformation. For example, to create a dataset that repeats
+its input for 10 epochs:
+
+```python
+filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+dataset = tf.contrib.data.TFRecordDataset(filenames)
+dataset = dataset.map(...)
+dataset = dataset.repeat(10)
+dataset = dataset.batch(32)
+```
+
+Applying the `Dataset.repeat()` transformation with no arguments will repeat
+the input indefinitely. The `Dataset.repeat()` transformation concatenates its
+arguments without signaling the end of one epoch and the beginning of the next
+epoch.
+
+If you want to receive a signal at the end of each epoch, you can write a
+training loop that catches the `tf.errors.OutOfRangeError` at the end of a
+dataset. At that point you might collect some statistics (e.g. the validation
+error) for the epoch.
+
+```python
+filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+dataset = tf.contrib.data.TFRecordDataset(filenames)
+dataset = dataset.map(...)
+dataset = dataset.batch(32)
+iterator = dataset.make_initializable_iterator()
+next_element = iterator.get_next()
+
+# Compute for 100 epochs.
+for _ in range(100):
+  sess.run(iterator.initializer)
+  while True:
+    try:
+      sess.run(next_element)
+    except tf.errors.OutOfRangeError:
+      break
+
+  # [Perform end-of-epoch calculations here.]
+```
+
+### Randomly shuffling input data
+
+The `Dataset.shuffle()` transformation randomly shuffles the input dataset
+using a similar algorithm to `tf.RandomShuffleQueue`: it maintains a fixed-size
+buffer and chooses the next element uniformly at random from that buffer.
+
+```python
+filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+dataset = tf.contrib.data.TFRecordDataset(filenames)
+dataset = dataset.map(...)
+dataset = dataset.shuffle(buffer_size=10000)
+dataset = dataset.batch(32)
+dataset = dataset.repeat()
+```
+
+### Using high-level APIs
+
+The @{tf.train.MonitoredTrainingSession} API simplifies many aspects of running
+TensorFlow in a distributed setting. `MonitoredTrainingSession` uses the
+@{tf.errors.OutOfRangeError} to signal that training has completed, so to use it
+with the `Dataset` API, we recommend using
+`Dataset.make_one_shot_iterator()`. For example:
+
+```python
+filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+dataset = tf.contrib.data.TFRecordDataset(filenames)
+dataset = dataset.map(...)
+dataset = dataset.shuffle(buffer_size=10000)
+dataset = dataset.batch(32)
+dataset = dataset.repeat(num_epochs)
+iterator = dataset.make_one_shot_iterator()
+
+next_example, next_label = iterator.get_next()
+loss = model_function(next_example, next_label)
+
+training_op = tf.train.AdagradOptimizer(...).minimize(loss)
+
+with tf.train.MonitoredTrainingSession(...) as sess:
+  while not sess.should_stop():
+    sess.run(training_op)
+```
+
+To use a `Dataset` in the `input_fn` of a @{tf.estimator.Estimator}, we also
+recommend using `Dataset.make_one_shot_iterator()`. For example:
+
+```python
+def dataset_input_fn():
+  filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
+  dataset = tf.contrib.data.TFRecordDataset(filenames)
+
+  # Use `tf.parse_single_example()` to extract data from a `tf.Example`
+  # protocol buffer, and perform any additional per-record preprocessing.
+  def parser(record):
+    keys_to_features = {
+        "image_data": tf.FixedLenFeature((), tf.string, default_value=""),
+        "date_time": tf.FixedLenFeature((), tf.int64, default_value=""),
+        "label": tf.FixedLenFeature((), tf.int64,
+                                    default_value=tf.zeros([], dtype=tf.int64)),
+    }
+    parsed = tf.parse_single_example(record, keys_to_features)
+
+    # Perform additional preprocessing on the parsed data.
+    image = tf.decode_jpeg(parsed["image_data"])
+    image = tf.reshape(image, [299, 299, 1])
+    label = tf.cast(parsed["label"], tf.int32)
+    
+    return {"image_data": image, "date_time": parsed["date_time"]}, label
+
+  # Use `Dataset.map()` to build a pair of a feature dictionary and a label 
+  # tensor for each example.
+  dataset = dataset.map(parser)
+  dataset = dataset.shuffle(buffer_size=10000)
+  dataset = dataset.batch(32)
+  dataset = dataset.repeat(num_epochs)
+  iterator = dataset.make_one_shot_iterator()
+
+  # `features` is a dictionary in which each value is a batch of values for
+  # that feature; `labels` is a batch of labels.
+  features, labels = iterator.get_next()
+  return features, labels
+```
-- 
GitLab


From 0478774fcc918cb7a81bfa8832d115b376bfb97b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 10:01:31 -0700
Subject: [PATCH 0879/1375] Slight changes and improvements of style of comment
 to tf.contrib.layers.optimize_loss without changing the actual contents.

PiperOrigin-RevId: 161542768
---
 .../layers/python/layers/optimizers.py        | 29 ++++++++++---------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/optimizers.py b/tensorflow/contrib/layers/python/layers/optimizers.py
index 50c11c696a..ac217f043f 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers.py
@@ -71,28 +71,29 @@ def optimize_loss(loss,
                   increment_global_step=True):
   """Given loss and parameters for optimizer, returns a training op.
 
-  Various ways of passing optimizers, include:
+  Various ways of passing optimizers include:
 
-  - string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES
+  - by string specifying the name of the optimizer. See OPTIMIZER_CLS_NAMES
       for full list. E.g. `optimize_loss(..., optimizer='Adam')`.
-  - function, takes learning rate `Tensor` as argument and must return
+  - by function taking learning rate `Tensor` as argument and returning an
       `Optimizer` instance. E.g. `optimize_loss(...,
       optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
     Alternatively, if `learning_rate` is `None`, the function takes no
     arguments. E.g. `optimize_loss(..., learning_rate=None,
       optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
-  - class, subclass of `Optimizer` that takes only one required argument -
-      learning rate, such as AdamOptimizer, AdagradOptimizer.
-      E.g. `optimize_loss(..., optimizer=tf.train.AdagradOptimizer)`.
-  - object, instance of subclass of `Optimizer`.
-      E.g., `optimizer_loss(..., optimizer=tf.train.AdagradOptimizer(0.5))`.
+  - by a subclass of `Optimizer` having a single-argument constructor
+      (the argument is the learning rate), such as AdamOptimizer or
+      AdagradOptimizer. E.g. `optimize_loss(...,
+      optimizer=tf.train.AdagradOptimizer)`.
+  - by an instance of a subclass of `Optimizer`.
+      E.g., `optimize_loss(..., optimizer=tf.train.AdagradOptimizer(0.5))`.
 
   Args:
     loss: Scalar `Tensor`.
     global_step: Scalar int `Tensor`, step counter to update on each step
                  unless `increment_global_step` is `False`. If not supplied,
                  it will be fetched from the default graph (see
-                 `tf.train.get_global_step` for details). If it's
+                 `tf.train.get_global_step` for details). If it has
                  not been created, no step will be incremented with each weight
                  update. `learning_rate_decay_fn` requires `global_step`.
     learning_rate: float or `Tensor`, magnitude of update per each training
@@ -145,11 +146,11 @@ def optimize_loss(loss,
         * `loss` is an invalid type or shape.
         * `global_step` is an invalid type or shape.
         * `learning_rate` is an invalid type or value.
-        * `optimizer` is wrong type.
-        * `clip_gradients` is not float or callable.
+        * `optimizer` has the wrong type.
+        * `clip_gradients` is neither float nor callable.
         * `learning_rate` and `learning_rate_decay_fn` are supplied, but no
           `global_step` is available.
-        * `gradients` is empty
+        * `gradients` is empty.
   """
   loss = ops.convert_to_tensor(loss)
   contrib_framework.assert_scalar(loss)
@@ -350,8 +351,8 @@ def adaptive_clipping_fn(std_factor=2.,
   https://arxiv.org/abs/1412.1602.
 
   Keeps a moving average of the mean and std of the log(norm) of the gradient.
-  if the norm exceeds `exp(mean + std_factor*std)`, all gradients are rescaled
-  such that the global norm becomes `exp(mean)`.
+  If the norm exceeds `exp(mean + std_factor*std)` then all gradients will be
+  rescaled such that the global norm becomes `exp(mean)`.
 
   Args:
     std_factor: Python scaler (or tensor).
-- 
GitLab


From dbe029ded02962adf0227ae79a1b0f59e9e8d3dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 10:06:30 -0700
Subject: [PATCH 0880/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161543495
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 43 ++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 49 +++++++++++++++++++
 2 files changed, 92 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 0198d4ff22..4fcf7aaa41 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -10073,6 +10073,49 @@ op {
     }
   }
 }
+op {
+  name: "GatherV2"
+  input_arg {
+    name: "params"
+    type_attr: "Tparams"
+  }
+  input_arg {
+    name: "indices"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "axis"
+    type_attr: "Taxis"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "Tparams"
+  }
+  attr {
+    name: "Tparams"
+    type: "type"
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "Taxis"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "GetSessionHandle"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 064b5c62bb..1b20981bb4 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -9306,6 +9306,55 @@ op {
   summary: "Gather slices from `params` into a Tensor with shape specified by `indices`."
   description: "`indices` is an K-dimensional integer tensor, best thought of as a\n(K-1)-dimensional tensor of indices into `params`, where each element defines a\nslice of `params`:\n\n    output[i_0, ..., i_{K-2}] = params[indices[i0, ..., i_{K-2}]]\n\nWhereas in @{tf.gather} `indices` defines slices into the first\ndimension of `params`, in `tf.gather_nd`, `indices` defines slices into the\nfirst `N` dimensions of `params`, where `N = indices.shape[-1]`.\n\nThe last dimension of `indices` can be at most the rank of\n`params`:\n\n    indices.shape[-1] <= params.rank\n\nThe last dimension of `indices` corresponds to elements\n(if `indices.shape[-1] == params.rank`) or slices\n(if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`\nof `params`.  The output tensor has shape\n\n    indices.shape[:-1] + params.shape[indices.shape[-1]:]\n\nSome examples below.\n\nSimple indexing into a matrix:\n\n```python\n    indices = [[0, 0], [1, 1]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [\'a\', \'d\']\n```\n\nSlice indexing into a matrix:\n\n```python\n    indices = [[1], [0]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[\'c\', \'d\'], [\'a\', \'b\']]\n```\n\nIndexing into a 3-tensor:\n\n```python\n    indices = [[1]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n\n\n    indices = [[0, 1], [1, 0]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[\'c0\', \'d0\'], [\'a1\', \'b1\']]\n\n\n    indices = [[0, 0, 1], [1, 0, 1]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [\'b0\', \'b1\']\n```\n\nBatched indexing into a matrix:\n\n```python\n    indices = [[[0, 0]], [[0, 1]]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[\'a\'], [\'b\']]\n```\n\nBatched slice indexing into a matrix:\n\n```python\n    indices = [[[1]], [[0]]]\n    params = [[\'a\', \'b\'], [\'c\', \'d\']]\n    output = [[[\'c\', \'d\']], [[\'a\', \'b\']]]\n```\n\nBatched indexing into a 3-tensor:\n\n```python\n    indices = [[[1]], [[0]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[[\'a1\', \'b1\'], [\'c1\', \'d1\']]],\n              [[[\'a0\', \'b0\'], [\'c0\', \'d0\']]]]\n\n    indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[[\'c0\', \'d0\'], [\'a1\', \'b1\']],\n              [[\'a0\', \'b0\'], [\'c1\', \'d1\']]]\n\n\n    indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]\n    params = [[[\'a0\', \'b0\'], [\'c0\', \'d0\']],\n              [[\'a1\', \'b1\'], [\'c1\', \'d1\']]]\n    output = [[\'b0\', \'b1\'], [\'d0\', \'c1\']]\n```"
 }
+op {
+  name: "GatherV2"
+  input_arg {
+    name: "params"
+    description: "The tensor from which to gather values. Must be at least rank\n`axis + 1`."
+    type_attr: "Tparams"
+  }
+  input_arg {
+    name: "indices"
+    description: "Index tensor. Must be in range `[0, params.shape[axis])`."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "axis"
+    description: "The axis in `params` to gather `indices` from. Defaults to the first\ndimension. Supports negative indexes."
+    type_attr: "Taxis"
+  }
+  output_arg {
+    name: "output"
+    description: "Values from `params` gathered from indices given by `indices`, with\nshape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`."
+    type_attr: "Tparams"
+  }
+  attr {
+    name: "Tparams"
+    type: "type"
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "Taxis"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Gather slices from `params` axis `axis` according to `indices`."
+  description: "`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).\nProduces an output tensor with shape `params.shape[:axis] + indices.shape +\nparams.shape[axis + 1:]` where:\n\n```python\n    # Scalar indices (output is rank(params) - 1).\n    output[a_0, ..., a_n, b_0, ..., b_n] =\n      params[a_0, ..., a_n, indices, b_0, ..., b_n]\n\n    # Vector indices (output is rank(params)).\n    output[a_0, ..., a_n, i, b_0, ..., b_n] =\n      params[a_0, ..., a_n, indices[i], b_0, ..., b_n]\n\n    # Higher rank indices (output is rank(params) + rank(indices) - 1).\n    output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =\n      params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]\n```\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/Gather.png\" alt>\n</div>"
+}
 op {
   name: "GetSessionHandle"
   input_arg {
-- 
GitLab


From e6df395c2f20282ce915ad6fe6fab38ef102961b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 10:13:28 -0700
Subject: [PATCH 0881/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161544617
---
 tensorflow/go/op/wrappers.go | 47 ++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 1fb34bb603..208a02762b 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -21177,6 +21177,53 @@ func Erf(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// Gather slices from `params` axis `axis` according to `indices`.
+//
+// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
+// Produces an output tensor with shape `params.shape[:axis] + indices.shape +
+// params.shape[axis + 1:]` where:
+//
+// ```python
+//     # Scalar indices (output is rank(params) - 1).
+//     output[a_0, ..., a_n, b_0, ..., b_n] =
+//       params[a_0, ..., a_n, indices, b_0, ..., b_n]
+//
+//     # Vector indices (output is rank(params)).
+//     output[a_0, ..., a_n, i, b_0, ..., b_n] =
+//       params[a_0, ..., a_n, indices[i], b_0, ..., b_n]
+//
+//     # Higher rank indices (output is rank(params) + rank(indices) - 1).
+//     output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
+//       params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
+// ```
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
+// </div>
+//
+// Arguments:
+//	params: The tensor from which to gather values. Must be at least rank
+// `axis + 1`.
+//	indices: Index tensor. Must be in range `[0, params.shape[axis])`.
+//	axis: The axis in `params` to gather `indices` from. Defaults to the first
+// dimension. Supports negative indexes.
+//
+// Returns Values from `params` gathered from indices given by `indices`, with
+// shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`.
+func GatherV2(scope *Scope, params tf.Output, indices tf.Output, axis tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "GatherV2",
+		Input: []tf.Input{
+			params, indices, axis,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the complementary error function of `x` element-wise.
 func Erfc(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From 082ede6cec7787dc5e9422a37afdf9ad5d88c5e2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 10:13:43 -0700
Subject: [PATCH 0882/1375] Changed title (first-level header).

PiperOrigin-RevId: 161544671
---
 tensorflow/docs_src/programmers_guide/variables.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index 0ccd2c4899..2f8e7eef5e 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -1,4 +1,4 @@
-# Variables: persistent state in TensorFlow
+# Variables
 
 A TensorFlow **variable** is the best way to represent shared, persistent state
 manipulated by your program.
-- 
GitLab


From c1b6f48808fabc98797d21a78a14dd9ac2ca9d8a Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Tue, 11 Jul 2017 11:10:37 -0700
Subject: [PATCH 0883/1375] Switch FlatMap and FlatSet to use a non-identity
 hasher for pointers.

Previously the default hasher for FlatMap and FlatSet used std::hash by
default.  Most stdlibs' std::hash for pointers is the identity function.
This works for std::unordered_{set,map} because those are prime-sized
hashtables.  But FlatSet and FlatMap are power-of-two-sized, and so the
identity function is bad, bad news.

This change also switches us to use Hash64 for strings, rather than
std::hash.
PiperOrigin-RevId: 161553663
---
 tensorflow/core/lib/gtl/flatmap.h     |  3 ++-
 tensorflow/core/lib/gtl/flatset.h     |  4 ++--
 tensorflow/core/lib/hash/hash.h       | 26 +++++++++++++++++++++-----
 tensorflow/core/lib/hash/hash_test.cc |  5 +++++
 4 files changed, 30 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/lib/gtl/flatmap.h b/tensorflow/core/lib/gtl/flatmap.h
index 19e1eb5c3b..e92083fecf 100644
--- a/tensorflow/core/lib/gtl/flatmap.h
+++ b/tensorflow/core/lib/gtl/flatmap.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include <iterator>
 #include <utility>
 #include "tensorflow/core/lib/gtl/flatrep.h"
+#include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -33,7 +34,7 @@ namespace gtl {
 // The map is implemented using an open-addressed hash table.  A
 // single array holds entire map contents and collisions are resolved
 // by probing at a sequence of locations in the array.
-template <typename Key, typename Val, class Hash = std::hash<Key>,
+template <typename Key, typename Val, class Hash = hash<Key>,
           class Eq = std::equal_to<Key>>
 class FlatMap {
  private:
diff --git a/tensorflow/core/lib/gtl/flatset.h b/tensorflow/core/lib/gtl/flatset.h
index c5dd45457f..74940880da 100644
--- a/tensorflow/core/lib/gtl/flatset.h
+++ b/tensorflow/core/lib/gtl/flatset.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include <iterator>
 #include <utility>
 #include "tensorflow/core/lib/gtl/flatrep.h"
+#include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -33,8 +34,7 @@ namespace gtl {
 // The map is implemented using an open-addressed hash table.  A
 // single array holds entire map contents and collisions are resolved
 // by probing at a sequence of locations in the array.
-template <typename Key, class Hash = std::hash<Key>,
-          class Eq = std::equal_to<Key>>
+template <typename Key, class Hash = hash<Key>, class Eq = std::equal_to<Key>>
 class FlatSet {
  private:
   // Forward declare some internal types needed in public section.
diff --git a/tensorflow/core/lib/hash/hash.h b/tensorflow/core/lib/hash/hash.h
index 6f965928c7..b45391fac5 100644
--- a/tensorflow/core/lib/hash/hash.h
+++ b/tensorflow/core/lib/hash/hash.h
@@ -42,21 +42,37 @@ inline uint64 Hash64Combine(uint64 a, uint64 b) {
   return a ^ (b + 0x9e3779b97f4a7800ULL + (a << 10) + (a >> 4));
 }
 
-// Convenience Hash functors
 struct HashStr {
   size_t operator()(const string& s) const {
     return static_cast<size_t>(Hash64(s));
   }
 };
-template <typename PTR>
-struct HashPtr {
-  size_t operator()(const PTR p) const {
+
+// Hash functor suitable for use with power-of-two sized hashtables.  Use
+// instead of std::hash<T>.
+//
+// In particular, tensorflow::hash is not the identity function for pointers.
+// This is important for power-of-two sized hashtables like FlatMap and FlatSet,
+// because otherwise they waste the majority of their hash buckets.
+template <typename T>
+struct hash {
+  size_t operator()(const T& t) const { return std::hash<T>()(t); }
+};
+
+template <typename T>
+struct hash<T*> {
+  size_t operator()(const T* t) const {
     // Hash pointers as integers, but bring more entropy to the lower bits.
-    size_t k = static_cast<size_t>(reinterpret_cast<uintptr_t>(p));
+    size_t k = static_cast<size_t>(reinterpret_cast<uintptr_t>(t));
     return k + (k >> 6);
   }
 };
 
+template <>
+struct hash<string> {
+  size_t operator()(const string& s) const { return HashStr()(s); }
+};
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_LIB_HASH_HASH_H_
diff --git a/tensorflow/core/lib/hash/hash_test.cc b/tensorflow/core/lib/hash/hash_test.cc
index a335fddc4c..0e5f6c6803 100644
--- a/tensorflow/core/lib/hash/hash_test.cc
+++ b/tensorflow/core/lib/hash/hash_test.cc
@@ -65,6 +65,11 @@ TEST(Hash, SignedUnsignedIssue) {
   }
 }
 
+TEST(Hash, HashPtrIsNotIdentityFunction) {
+  int* ptr = reinterpret_cast<int*>(0xcafe0000);
+  EXPECT_NE(hash<int*>()(ptr), size_t{0xcafe0000});
+}
+
 static void BM_Hash32(int iters, int len) {
   std::string input(len, 'x');
   uint32 h = 0;
-- 
GitLab


From 9508b252b6b9508a6ff42856f562067f7d6c0802 Mon Sep 17 00:00:00 2001
From: TJ Rana <tj.rana@icloud.com>
Date: Tue, 11 Jul 2017 14:27:40 -0400
Subject: [PATCH 0884/1375] Fix secure urls and typos (#11423)

* Fix secure urls

* Fix typos

Correct Python code to conform to Google Python Style Guide
Remove extra period in sentence
---
 README.md                                     |  2 +-
 .../docs_src/community/documentation.md       |  2 +-
 .../docs_src/get_started/get_started.md       | 42 +++++++++----------
 .../examples/image_retraining/retrain.py      |  2 +-
 tensorflow/examples/label_image/README.md     |  4 +-
 5 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/README.md b/README.md
index 90d50f6768..0dec90878e 100644
--- a/README.md
+++ b/README.md
@@ -62,7 +62,7 @@ $ python
 
 ## For more information
 
-* [TensorFlow website](https://tensorflow.org)
+* [TensorFlow website](https://www.tensorflow.org)
 * [TensorFlow whitepaper](http://download.tensorflow.org/paper/whitepaper2015.pdf)
 * [TensorFlow Model Zoo](https://github.com/tensorflow/models)
 * [TensorFlow MOOC on Udacity](https://www.udacity.com/course/deep-learning--ud730)
diff --git a/tensorflow/docs_src/community/documentation.md b/tensorflow/docs_src/community/documentation.md
index a757d8c66d..e8b3787c1f 100644
--- a/tensorflow/docs_src/community/documentation.md
+++ b/tensorflow/docs_src/community/documentation.md
@@ -31,7 +31,7 @@ TensorFlow.
 
 However, most developers will contribute documentation into the master Github
 branch, which is published, occasionally,
-at [tensorflow.org/versions/master](https://tensorflow.org/versions/master).
+at [tensorflow.org/versions/master](https://www.tensorflow.org/versions/master).
 
 If you want documentation changes to appear at root, you will need to also
 contribute that change to the current stable binary branch (and/or
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 17d07cc582..852d41e9ed 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -33,7 +33,7 @@ tensors:
 
 ```python
 3 # a rank 0 tensor; this is a scalar with shape []
-[1. ,2., 3.] # a rank 1 tensor; this is a vector with shape [3]
+[1., 2., 3.] # a rank 1 tensor; this is a vector with shape [3]
 [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
 [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]
 ```
@@ -100,20 +100,20 @@ we see the expected values of 3.0 and 4.0:
 ```
 
 We can build more complicated computations by combining `Tensor` nodes with
-operations (Operations are also nodes.). For example, we can add our two
+operations (Operations are also nodes). For example, we can add our two
 constant nodes and produce a new graph as follows:
 
 ```python
 node3 = tf.add(node1, node2)
-print("node3: ", node3)
-print("sess.run(node3): ",sess.run(node3))
+print("node3:", node3)
+print("sess.run(node3):", sess.run(node3))
 ```
 
 The last two print statements produce
 
 ```
-node3:  Tensor("Add:0", shape=(), dtype=float32)
-sess.run(node3):  7.0
+node3: Tensor("Add:0", shape=(), dtype=float32)
+sess.run(node3): 7.0
 ```
 
 TensorFlow provides a utility called TensorBoard that can display a picture of
@@ -140,8 +140,8 @@ the [run method](https://www.tensorflow.org/api_docs/python/tf/Session#run)
 to feed concrete values to the placeholders:
 
 ```python
-print(sess.run(adder_node, {a: 3, b:4.5}))
-print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
+print(sess.run(adder_node, {a: 3, b: 4.5}))
+print(sess.run(adder_node, {a: [1, 3], b: [2, 4]}))
 ```
 resulting in the output
 
@@ -159,7 +159,7 @@ For example,
 
 ```python
 add_and_triple = adder_node * 3.
-print(sess.run(add_and_triple, {a: 3, b:4.5}))
+print(sess.run(add_and_triple, {a: 3, b: 4.5}))
 ```
 produces the output
 ```
@@ -202,7 +202,7 @@ Since `x` is a placeholder, we can evaluate `linear_model` for several values of
 `x` simultaneously as follows:
 
 ```python
-print(sess.run(linear_model, {x:[1,2,3,4]}))
+print(sess.run(linear_model, {x: [1, 2, 3, 4]}))
 ```
 to produce the output
 ```
@@ -225,7 +225,7 @@ that abstracts the error of all examples using `tf.reduce_sum`:
 y = tf.placeholder(tf.float32)
 squared_deltas = tf.square(linear_model - y)
 loss = tf.reduce_sum(squared_deltas)
-print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
+print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))
 ```
 producing the loss value
 ```
@@ -242,7 +242,7 @@ perfect values of -1 and 1. A variable is initialized to the value provided to
 fixW = tf.assign(W, [-1.])
 fixb = tf.assign(b, [1.])
 sess.run([fixW, fixb])
-print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
+print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))
 ```
 The final print shows the loss now is zero.
 ```
@@ -273,7 +273,7 @@ train = optimizer.minimize(loss)
 ```python
 sess.run(init) # reset values to incorrect defaults.
 for i in range(1000):
-  sess.run(train, {x:[1,2,3,4], y:[0,-1,-2,-3]})
+  sess.run(train, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})
 
 print(sess.run([W, b]))
 ```
@@ -319,10 +319,10 @@ init = tf.global_variables_initializer()
 sess = tf.Session()
 sess.run(init) # reset values to wrong
 for i in range(1000):
-  sess.run(train, {x:x_train, y:y_train})
+  sess.run(train, {x: x_train, y: y_train})
 
 # evaluate training accuracy
-curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
+curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})
 print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
 ```
 When run, it produces
@@ -377,11 +377,11 @@ y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
 input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
 train_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
 eval_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
 
 # We can invoke 1000 training steps by invoking the  method and passing the
 # training data set.
@@ -449,11 +449,11 @@ y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
 input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
 train_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
 eval_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
 
 # train
 estimator.train(input_fn=input_fn, steps=1000)
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index ac39639af1..2e2e578050 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -741,7 +741,7 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
   weights, and then sets up all the gradients for the backward pass.
 
   The set up for the softmax and fully-connected layers is based on:
-  https://tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
+  https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
 
   Args:
     class_count: Integer of how many categories of things we're trying to
diff --git a/tensorflow/examples/label_image/README.md b/tensorflow/examples/label_image/README.md
index c5857f394a..a9e44745e5 100644
--- a/tensorflow/examples/label_image/README.md
+++ b/tensorflow/examples/label_image/README.md
@@ -63,14 +63,14 @@ $ bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png
 ```
 
 For a more detailed look at this code, you can check out the C++ section of the
-[Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+[Inception tutorial](https://www.tensorflow.org/tutorials/image_recognition/).
 
 ## Python implementation
 
 label_image.py is a python implementation that provides code corresponding
 to the C++ code here. This gives more intuitive mapping between C++ and
 Python than the Python code mentioned in the
-[Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+[Inception tutorial](https://www.tensorflow.org/tutorials/image_recognition/).
 and could be easier to add visualization or debug code.
 
 With tensorflow python package installed, you can run it like:
-- 
GitLab


From 66aed1d641083ed77879d2cd13bc55d2744f7605 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carl=20Thom=C3=A9?= <carlthome@gmail.com>
Date: Tue, 11 Jul 2017 20:27:58 +0200
Subject: [PATCH 0885/1375] Update graph_io.py (#11425)

---
 tensorflow/python/framework/graph_io.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/framework/graph_io.py b/tensorflow/python/framework/graph_io.py
index f909bcd62d..a0ea4ad48e 100644
--- a/tensorflow/python/framework/graph_io.py
+++ b/tensorflow/python/framework/graph_io.py
@@ -29,7 +29,7 @@ from tensorflow.python.lib.io import file_io
 def write_graph(graph_or_graph_def, logdir, name, as_text=True):
   """Writes a graph proto to a file.
 
-  The graph is written as a binary proto unless `as_text` is `True`.
+  The graph is written as a text proto unless `as_text` is `False`.
 
   ```python
   v = tf.Variable(0, name='my_variable')
-- 
GitLab


From 78cc877807d42e4fbad379a3655422d2d923c5a9 Mon Sep 17 00:00:00 2001
From: horance <horance-liu@users.noreply.github.com>
Date: Wed, 12 Jul 2017 02:28:15 +0800
Subject: [PATCH 0886/1375] modify SaverDef default version with v2 (#11429)

---
 tensorflow/core/protobuf/saver.proto | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/protobuf/saver.proto b/tensorflow/core/protobuf/saver.proto
index 65fe9c4c98..a757d3f756 100644
--- a/tensorflow/core/protobuf/saver.proto
+++ b/tensorflow/core/protobuf/saver.proto
@@ -37,9 +37,9 @@ message SaverDef {
   enum CheckpointFormatVersion {
     // Internal legacy format.
     LEGACY = 0;
-    // Current format: tf.Saver() which works with tensorflow::table::Table.
+    // Deprecated format: tf.Saver() which works with tensorflow::table::Table.
     V1 = 1;
-    // Experimental format under development.
+    // Current format: more efficient.
     V2 = 2;
   }
   CheckpointFormatVersion version = 7;
-- 
GitLab


From a6bd3ec1064f155ab5cf1aea334b142b0b97a7ad Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Tue, 11 Jul 2017 20:44:13 +0200
Subject: [PATCH 0887/1375] Point protobuf to
 0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66 (#11317)

* Point protobuf to 0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66

* Add #include<numeric>

* Put github url in front of mirror.bazel.build url
---
 tensorflow/compiler/xla/util.cc |  1 +
 tensorflow/workspace.bzl        | 24 ++++++++++++------------
 2 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc
index d467178cb5..f215e93d5e 100644
--- a/tensorflow/compiler/xla/util.cc
+++ b/tensorflow/compiler/xla/util.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/util.h"
 
+#include <numeric>
 #include <stdarg.h>
 
 #include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 20d45a7fdc..1d42c382f1 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -327,11 +327,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   patched_http_archive(
       name = "protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "6d43b9d223ce09e5d4ce8b0060cb8a7513577a35a64c7e3dad10f0703bf3ad93",
+      strip_prefix = "protobuf-0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66",
       # TODO: remove patching when tensorflow stops linking same protos into
       #       multiple shared libraries loaded in runtime by python.
       #       This patch fixes a runtime crash when tensorflow is compiled
@@ -345,21 +345,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.http_archive(
       name = "com_google_protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "6d43b9d223ce09e5d4ce8b0060cb8a7513577a35a64c7e3dad10f0703bf3ad93",
+      strip_prefix = "protobuf-0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66",
   )
 
   native.http_archive(
       name = "com_google_protobuf_cc",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "6d43b9d223ce09e5d4ce8b0060cb8a7513577a35a64c7e3dad10f0703bf3ad93",
+      strip_prefix = "protobuf-0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66",
   )
 
   native.new_http_archive(
-- 
GitLab


From 99a38ffd9d77c55ca6d0c373c6d4b72686284ac5 Mon Sep 17 00:00:00 2001
From: Anthony Platanios <e.a.platanios@gmail.com>
Date: Tue, 11 Jul 2017 14:44:59 -0400
Subject: [PATCH 0888/1375] Third-party Scala API Links in the Documentation
 (#11397)

* Added references to the Scala API in the TensorFlow documentation pages.

* Update index.md

* Update faq.md
---
 tensorflow/docs_src/extend/index.md          | 4 ++--
 tensorflow/docs_src/programmers_guide/faq.md | 2 ++
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/extend/index.md b/tensorflow/docs_src/extend/index.md
index 590023b065..5812caaffc 100644
--- a/tensorflow/docs_src/extend/index.md
+++ b/tensorflow/docs_src/extend/index.md
@@ -20,8 +20,8 @@ TensorFlow:
 
 Python is currently the only language supported by TensorFlow's API stability
 promises.  However, TensorFlow also provides functionality in C++, Java, and Go,
-plus community support for [Haskell](https://github.com/tensorflow/haskell)
-and [Rust](https://github.com/tensorflow/rust).  If you'd like to create or
+plus community support for [Haskell](https://github.com/tensorflow/haskell) and 
+[Rust](https://github.com/tensorflow/rust).  If you'd like to create or
 develop TensorFlow features in a language other than these languages, read the
 following guide:
 
diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index 74407ed315..56486a48b7 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -149,6 +149,8 @@ TensorFlow also has a
 to help build support for more client languages.  We invite contributions of new
 language bindings.
 
+Bindings for various other languages (such as [C#](https://github.com/migueldeicaza/TensorFlowSharp), [Julia](https://github.com/malmaud/TensorFlow.jl), [Ruby](https://github.com/somaticio/tensorflow.rb) and [Scala](https://github.com/eaplatanios/tensorflow_scala)) created and supported by the opensource community build on top of the C API supported by the TensorFlow maintainers.
+
 #### Does TensorFlow make use of all the devices (GPUs and CPUs) available on my machine?
 
 TensorFlow supports multiple GPUs and CPUs. See the how-to documentation on
-- 
GitLab


From c3322543862e14482b3e108fb1b2d466641fd714 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Tue, 11 Jul 2017 11:50:25 -0700
Subject: [PATCH 0889/1375] Remove HashStr and HashStringPiece (a one-off).

Now that we have tensorflow::hash and it's the default hasher for
FlatMap and FlatSet, we can get rid of HashStr and HashStringPiece.

PiperOrigin-RevId: 161560034
---
 tensorflow/core/common_runtime/executor.cc |  7 +++----
 tensorflow/core/framework/function.h       |  4 ++--
 tensorflow/core/lib/gtl/flatmap_test.cc    |  2 +-
 tensorflow/core/lib/gtl/flatset_test.cc    |  2 +-
 tensorflow/core/lib/hash/hash.h            | 18 +++++++++++-------
 5 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/tensorflow/core/common_runtime/executor.cc b/tensorflow/core/common_runtime/executor.cc
index ce6bbb0d65..e8a5b67546 100644
--- a/tensorflow/core/common_runtime/executor.cc
+++ b/tensorflow/core/common_runtime/executor.cc
@@ -363,7 +363,7 @@ class ExecutorImpl : public Executor {
   friend class ExecutorState;
 
   struct ControlFlowInfo {
-    gtl::FlatSet<string, HashStr> unique_frame_names;
+    gtl::FlatSet<string> unique_frame_names;
     std::vector<string> frame_names;
   };
 
@@ -423,7 +423,7 @@ class ExecutorImpl : public Executor {
   // Mapping from frame name to static information about the frame.
   // TODO(yuanbyu): We could cache it along with the graph so to avoid
   // the overhead of constructing it for each executor instance.
-  gtl::FlatMap<string, FrameInfo*, HashStr> frame_info_;
+  gtl::FlatMap<string, FrameInfo*> frame_info_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(ExecutorImpl);
 };
@@ -1209,8 +1209,7 @@ class ExecutorState {
   // child frame is composed of the name of the parent frame, the iteration
   // number at which the parent frame is creating the new frame, and the
   // name of the new frame from nodedef.
-  gtl::FlatMap<string, FrameState*, HashStr> outstanding_frames_
-      GUARDED_BY(mu_);
+  gtl::FlatMap<string, FrameState*> outstanding_frames_ GUARDED_BY(mu_);
 
   // The unique name of a frame.
   inline string MakeFrameName(FrameState* frame, int64 iter_id,
diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h
index 2342e08b38..059f2be629 100644
--- a/tensorflow/core/framework/function.h
+++ b/tensorflow/core/framework/function.h
@@ -354,9 +354,9 @@ class FunctionLibraryDefinition : public OpRegistryInterface {
   };
 
   const OpRegistryInterface* const default_registry_;
-  gtl::FlatMap<string, std::unique_ptr<FunctionDefAndOpRegistration>, HashStr>
+  gtl::FlatMap<string, std::unique_ptr<FunctionDefAndOpRegistration>>
       function_defs_;
-  gtl::FlatMap<string, string, HashStr> func_grad_;
+  gtl::FlatMap<string, string> func_grad_;
 
   // Helper function for GetAttr. Returns the FunctionDef* to get the
   // attr from.
diff --git a/tensorflow/core/lib/gtl/flatmap_test.cc b/tensorflow/core/lib/gtl/flatmap_test.cc
index d8e50830e6..bb65e5357a 100644
--- a/tensorflow/core/lib/gtl/flatmap_test.cc
+++ b/tensorflow/core/lib/gtl/flatmap_test.cc
@@ -588,7 +588,7 @@ TEST(FlatMap, ForwardIterator) {
 // or destructions will show up as errors under a sanitizer or
 // heap checker.
 TEST(FlatMap, ConstructDestruct) {
-  FlatMap<string, string, HashStr> map;
+  FlatMap<string, string> map;
   string k1 = "the quick brown fox jumped over the lazy dog";
   string k2 = k1 + k1;
   string k3 = k1 + k2;
diff --git a/tensorflow/core/lib/gtl/flatset_test.cc b/tensorflow/core/lib/gtl/flatset_test.cc
index 54def603c3..09fbbb1fb6 100644
--- a/tensorflow/core/lib/gtl/flatset_test.cc
+++ b/tensorflow/core/lib/gtl/flatset_test.cc
@@ -485,7 +485,7 @@ TEST(FlatSet, ForwardIterator) {
 // or destructions will show up as errors under a sanitizer or
 // heap checker.
 TEST(FlatSet, ConstructDestruct) {
-  FlatSet<string, HashStr> set;
+  FlatSet<string> set;
   string k1 = "the quick brown fox jumped over the lazy dog";
   string k2 = k1 + k1;
   string k3 = k1 + k2;
diff --git a/tensorflow/core/lib/hash/hash.h b/tensorflow/core/lib/hash/hash.h
index b45391fac5..73b7c94d1f 100644
--- a/tensorflow/core/lib/hash/hash.h
+++ b/tensorflow/core/lib/hash/hash.h
@@ -23,6 +23,7 @@ limitations under the License.
 
 #include <string>
 
+#include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
@@ -42,12 +43,6 @@ inline uint64 Hash64Combine(uint64 a, uint64 b) {
   return a ^ (b + 0x9e3779b97f4a7800ULL + (a << 10) + (a >> 4));
 }
 
-struct HashStr {
-  size_t operator()(const string& s) const {
-    return static_cast<size_t>(Hash64(s));
-  }
-};
-
 // Hash functor suitable for use with power-of-two sized hashtables.  Use
 // instead of std::hash<T>.
 //
@@ -70,7 +65,16 @@ struct hash<T*> {
 
 template <>
 struct hash<string> {
-  size_t operator()(const string& s) const { return HashStr()(s); }
+  size_t operator()(const string& s) const {
+    return static_cast<size_t>(Hash64(s));
+  }
+};
+
+template <>
+struct hash<StringPiece> {
+  size_t operator()(StringPiece sp) const {
+    return static_cast<size_t>(Hash64(sp.data(), sp.size()));
+  }
 };
 
 }  // namespace tensorflow
-- 
GitLab


From 75b936e4c467af836623c7c72ff84fb0d458e5e6 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Tue, 11 Jul 2017 12:28:08 -0700
Subject: [PATCH 0890/1375] Speed up HeapSimulator's
 UniqueOperandSourceBuffers.

Instead of constructing a temporary set, adding all its members to
our big set, then flattening into a vector, add the members to our
vector directly, then sort it and remove duplicates at the end.

PiperOrigin-RevId: 161565289
---
 .../compiler/xla/service/heap_simulator.cc    | 22 +++++++++++++------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc
index c662cec9c7..840be603bf 100644
--- a/tensorflow/compiler/xla/service/heap_simulator.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator.cc
@@ -35,18 +35,26 @@ namespace {
 std::vector<const LogicalBuffer*> UniqueOperandSourceBuffers(
     const HloInstruction* instruction,
     const TuplePointsToAnalysis& points_to_analysis) {
-  FlatSet<const LogicalBuffer*> buffers;
+  std::vector<const LogicalBuffer*> buffers;
   for (const HloInstruction* operand : instruction->operands()) {
-    FlatSet<const LogicalBuffer*> sources =
-        points_to_analysis.GetPointsToSet(operand).CreateFlattenedSet();
-    buffers.insert(sources.begin(), sources.end());
+    points_to_analysis.GetPointsToSet(operand).ForEachElement(
+        [&](const ShapeIndex& /*index*/,
+            const std::vector<const LogicalBuffer*>& points_to) {
+          buffers.insert(buffers.end(), points_to.begin(), points_to.end());
+        });
   }
-  std::vector<const LogicalBuffer*> sorted(buffers.begin(), buffers.end());
-  std::sort(sorted.begin(), sorted.end(),
+
+  // Sort and then remove duplicates from buffers.
+  std::sort(buffers.begin(), buffers.end(),
             [](const LogicalBuffer* a, const LogicalBuffer* b) {
               return a->id() < b->id();
             });
-  return sorted;
+  buffers.erase(std::unique(buffers.begin(), buffers.end(),
+                            [](const LogicalBuffer* a, const LogicalBuffer* b) {
+                              return a->id() == b->id();
+                            }),
+                buffers.end());
+  return buffers;
 }
 
 }  // namespace
-- 
GitLab


From 1de8cce6643bb75edd1441fa01ffb6a0e5258c5f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 12:57:41 -0700
Subject: [PATCH 0891/1375] fix #11372, #11396

PiperOrigin-RevId: 161569039
---
 tensorflow/core/kernels/record_yielder.cc     | 22 +++++++++--
 tensorflow/core/kernels/record_yielder.h      |  1 +
 .../python/kernel_tests/record_input_test.py  | 38 +++++++++++++++++++
 3 files changed, 57 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/kernels/record_yielder.cc b/tensorflow/core/kernels/record_yielder.cc
index 3af555da1a..1370295144 100644
--- a/tensorflow/core/kernels/record_yielder.cc
+++ b/tensorflow/core/kernels/record_yielder.cc
@@ -46,7 +46,7 @@ RecordYielder::~RecordYielder() {
 
 Status RecordYielder::YieldOne(string* value) {
   mutex_lock l(mu_);
-  while (!BufEnough()) {
+  while (!BufEnough() && status_.ok()) {
     buf_enough_.wait(l);
   }
   if (status_.ok()) {
@@ -98,17 +98,22 @@ void RecordYielder::MainLoop() {
   while (true) {
     ++epoch_;
     num_records_yielded_in_epoch_ = 0;
+    num_records_added_in_epoch_ = 0;
 
     // Finds all files.
     std::vector<string> filenames;
     Status s = MatchFiles(opts_.file_pattern, &filenames);
-    if (ShouldFinish(s)) break;
 
     if (filenames.empty()) {
       s = errors::NotFound("Found no files at ", opts_.file_pattern);
-      if (ShouldFinish(s)) break;
+      if (ShouldFinish(s)) {
+        buf_enough_.notify_all();
+        break;
+      }
     }
 
+    if (ShouldFinish(s)) break;
+
     // Shuffles these files according to the epoch # and random seed.
     std::mt19937_64 shuffle_rnd(
         Hash64(reinterpret_cast<char*>(&epoch_), sizeof(epoch_), opts_.seed));
@@ -139,7 +144,15 @@ void RecordYielder::MainLoop() {
       shards[i].done.WaitForNotification();
       s.Update(shards[i].status);
     }
-    if (ShouldFinish(s)) break;
+
+    if (num_records_added_in_epoch_ < opts_.bufsize) {
+      opts_.bufsize = num_records_added_in_epoch_;
+    }
+
+    if (ShouldFinish(s)) {
+      buf_enough_.notify_all();
+      break;
+    }
 
     // Starts the next epoch once all buffered records are consumed.
     {
@@ -173,6 +186,7 @@ bool RecordYielder::Add(std::vector<string>* values) {
       buf_[index] = std::move(values->back());
     }
     values->pop_back();
+    num_records_added_in_epoch_++;
   }
   if (BufEnough()) {
     buf_enough_.notify_all();
diff --git a/tensorflow/core/kernels/record_yielder.h b/tensorflow/core/kernels/record_yielder.h
index 44f7c9511f..d86cb75c15 100644
--- a/tensorflow/core/kernels/record_yielder.h
+++ b/tensorflow/core/kernels/record_yielder.h
@@ -119,6 +119,7 @@ class RecordYielder {
   // True iff we are draining an epoch.
   bool epoch_end_ = false;
 
+  int64 num_records_added_in_epoch_ = 0;
   int64 num_records_yielded_in_epoch_ = 0;
 
   // Trigger when the main loop has exited.
diff --git a/tensorflow/python/kernel_tests/record_input_test.py b/tensorflow/python/kernel_tests/record_input_test.py
index 8fec2affa5..1ec48ac361 100644
--- a/tensorflow/python/kernel_tests/record_input_test.py
+++ b/tensorflow/python/kernel_tests/record_input_test.py
@@ -20,8 +20,10 @@ from __future__ import print_function
 
 import os
 
+from tensorflow.python.framework.errors_impl import NotFoundError
 from tensorflow.python.lib.io import tf_record
 from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 
@@ -96,6 +98,42 @@ class RecordInputOpTest(test.TestCase):
         for _ in range(50):
           sess.run(yield_op)
 
+  def testEmptyGlob(self):
+    with self.test_session() as sess:
+      record_input = data_flow_ops.RecordInput(file_pattern="foo")
+      yield_op = record_input.get_yield_op()
+      sess.run(variables.global_variables_initializer())
+      with self.assertRaises(NotFoundError):
+        sess.run(yield_op)
+
+  def testBufferTooSmall(self):
+    files = 10
+    records_per_file = 10
+    batches = 2
+    with self.test_session() as sess:
+      self.generateTestData("basic", files, records_per_file)
+
+      records = data_flow_ops.RecordInput(
+          file_pattern=os.path.join(self.get_temp_dir(), "basic.*"),
+          parallelism=2,
+          buffer_size=2000,
+          batch_size=1,
+          shift_ratio=0.33,
+          seed=10,
+          name="record_input",
+          batches=batches)
+
+      yield_op = records.get_yield_op()
+
+      # cycle over 3 epochs and make sure we never duplicate
+      for _ in range(3):
+        epoch_set = set()
+        for _ in range(int(files * records_per_file / batches)):
+          op_list = sess.run(yield_op)
+          self.assertTrue(len(op_list) is batches)
+          for r in op_list:
+            self.assertTrue(r[0] not in epoch_set)
+            epoch_set.add(r[0])
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From a80035d761d5563c00624a93a65b4accd7df22d1 Mon Sep 17 00:00:00 2001
From: Adam Roberts <adarob@google.com>
Date: Tue, 11 Jul 2017 14:00:14 -0700
Subject: [PATCH 0892/1375] Make ScheduledEmbeddingTrainingHelper more readable
 and consistent with output ScheduledOutputTrainingHelper helper. Also results
 in minor speedup.

PiperOrigin-RevId: 161577148
---
 .../contrib/seq2seq/python/ops/helper.py      | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/python/ops/helper.py b/tensorflow/contrib/seq2seq/python/ops/helper.py
index 6b8cad7fd7..9d3f8ad441 100644
--- a/tensorflow/contrib/seq2seq/python/ops/helper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/helper.py
@@ -30,8 +30,8 @@ from tensorflow.python.layers import base as layers_base
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import embedding_ops
+from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops.distributions import bernoulli
 from tensorflow.python.ops.distributions import categorical
@@ -258,14 +258,15 @@ class ScheduledEmbeddingTrainingHelper(TrainingHelper):
     with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
                         [time, outputs, state]):
       # Return -1s where we did not sample, and sample_ids elsewhere
-      select_sample_noise = random_ops.random_uniform(
-          [self.batch_size], seed=self._scheduling_seed)
-      select_sample = (self._sampling_probability > select_sample_noise)
+      select_sampler = bernoulli.Bernoulli(
+          probs=self._sampling_probability, dtype=dtypes.bool)
+      select_sample = select_sampler.sample(
+          sample_shape=self.batch_size, seed=self._scheduling_seed)
       sample_id_sampler = categorical.Categorical(logits=outputs)
       return array_ops.where(
           select_sample,
           sample_id_sampler.sample(seed=self._seed),
-          array_ops.tile([-1], [self.batch_size]))
+          gen_array_ops.fill([self.batch_size], -1))
 
   def next_inputs(self, time, outputs, state, sample_ids, name=None):
     with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
@@ -284,11 +285,9 @@ class ScheduledEmbeddingTrainingHelper(TrainingHelper):
             array_ops.where(sample_ids > -1), dtypes.int32)
         where_not_sampling = math_ops.cast(
             array_ops.where(sample_ids <= -1), dtypes.int32)
-        where_sampling_flat = array_ops.reshape(where_sampling, [-1])
-        where_not_sampling_flat = array_ops.reshape(where_not_sampling, [-1])
-        sample_ids_sampling = array_ops.gather(sample_ids, where_sampling_flat)
-        inputs_not_sampling = array_ops.gather(
-            base_next_inputs, where_not_sampling_flat)
+        sample_ids_sampling = array_ops.gather_nd(sample_ids, where_sampling)
+        inputs_not_sampling = array_ops.gather_nd(
+            base_next_inputs, where_not_sampling)
         sampled_next_inputs = self._embedding_fn(sample_ids_sampling)
         base_shape = array_ops.shape(base_next_inputs)
         return (array_ops.scatter_nd(indices=where_sampling,
-- 
GitLab


From b7ed2bcb197dcf43af2646e5f3545e161fbce5fb Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Tue, 11 Jul 2017 17:05:58 -0400
Subject: [PATCH 0893/1375] Temporarily disable barrier_ops_test on Mac
 (#11420)

---
 tensorflow/python/kernel_tests/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 17b646d17c..360e2299b2 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -72,6 +72,7 @@ tf_py_test(
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:string_ops",
     ],
+    tags = ["nomac"],  # b/35468214
 )
 
 tf_py_test(
-- 
GitLab


From cec2d2206ab050de0aa660af14ba237660f10075 Mon Sep 17 00:00:00 2001
From: Vadim Markovtsev <gmarkhor@gmail.com>
Date: Tue, 11 Jul 2017 23:09:04 +0200
Subject: [PATCH 0894/1375] Fix unpickling/copying tf.app.flags.FLAGS (#11437)

pickle.load() and copy.copy() check for the presence of  __setstate__().
The problem is that this check is made in the freshly allocated instance which
has not been __init__()-ed. Thus it's __dict__ is completely empty and
__getattr__() fail with KeyError.

The fix is to check if __parsed is in the __dict__ and raise
AttributeError if it isn't.
---
 tensorflow/python/platform/flags.py      | 7 ++++++-
 tensorflow/python/platform/flags_test.py | 5 +++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/platform/flags.py b/tensorflow/python/platform/flags.py
index f70dbf18d4..60ec4f84c4 100644
--- a/tensorflow/python/platform/flags.py
+++ b/tensorflow/python/platform/flags.py
@@ -44,7 +44,12 @@ class _FlagValues(object):
 
   def __getattr__(self, name):
     """Retrieves the 'value' attribute of the flag --name."""
-    if not self.__dict__['__parsed']:
+    try:
+      parsed = self.__dict__['__parsed']
+    except KeyError:
+      # May happen during pickle.load or copy.copy
+      raise AttributeError(name)
+    if not parsed:
       self._parse_flags()
     if name not in self.__dict__['__flags']:
       raise AttributeError(name)
diff --git a/tensorflow/python/platform/flags_test.py b/tensorflow/python/platform/flags_test.py
index 0dbaafd1fa..8b990975dd 100644
--- a/tensorflow/python/platform/flags_test.py
+++ b/tensorflow/python/platform/flags_test.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import copy
 import sys
 import unittest
 
@@ -79,6 +80,10 @@ class FlagsTest(unittest.TestCase):
     FLAGS.float_foo = -1.0
     self.assertEqual(-1.0, FLAGS.float_foo)
 
+  def test_copy(self):
+    copied = copy.copy(FLAGS)
+    self.assertEqual(copied.__dict__, FLAGS.__dict__)
+
 
 def main(_):
   # unittest.main() tries to interpret the unknown flags, so use the
-- 
GitLab


From ad7fb4d11c8689b04e6fb37ef02af60d8f94b2b5 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Tue, 11 Jul 2017 14:24:51 -0700
Subject: [PATCH 0895/1375] TFTS: Increase test timeouts to avoid ASan failures

PiperOrigin-RevId: 161581264
---
 tensorflow/contrib/timeseries/examples/BUILD     |  3 +++
 .../contrib/timeseries/python/timeseries/BUILD   |  1 +
 .../python/timeseries/state_space_models/BUILD   | 16 +++++++++-------
 3 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD
index c3a8d60484..c6ffc1ac00 100644
--- a/tensorflow/contrib/timeseries/examples/BUILD
+++ b/tensorflow/contrib/timeseries/examples/BUILD
@@ -25,6 +25,7 @@ py_binary(
 
 py_test(
     name = "predict_test",
+    timeout = "long",  # Moderate but for asan
     srcs = [
         "predict_test.py",
     ],
@@ -58,6 +59,7 @@ py_binary(
 
 py_test(
     name = "known_anomaly_test",
+    timeout = "long",  # Moderate but for asan
     srcs = [
         "known_anomaly_test.py",
     ],
@@ -89,6 +91,7 @@ py_binary(
 
 py_test(
     name = "multivariate_test",
+    timeout = "long",  # Moderate but for asan
     srcs = [
         "multivariate_test.py",
     ],
diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index eafaee8ac4..130254f40f 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -264,6 +264,7 @@ py_library(
 
 py_test(
     name = "ar_model_test",
+    timeout = "long",  # Moderate but for asan
     srcs = [
         "ar_model_test.py",
     ],
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
index 7c0c07972f..236797fbf7 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -37,20 +37,24 @@ py_library(
     ],
 )
 
-tf_py_test(
+py_test(
     name = "state_space_model_test",
+    timeout = "long",  # Moderate but for asan
     srcs = [
         "state_space_model_test.py",
     ],
-    additional_deps = [
+    srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
+    deps = [
         ":kalman_filter",
         ":state_space_model",
-        "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
         "//tensorflow/contrib/timeseries/python/timeseries:estimators",
+        "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
         "//tensorflow/contrib/timeseries/python/timeseries:saved_model_utils",
         "//tensorflow/contrib/timeseries/python/timeseries:state_management",
         "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
-        "//third_party/py/numpy",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
@@ -60,9 +64,7 @@ tf_py_test(
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
-    ],
-    tags = [
-        "no_pip_gpu",  # b/63391119
+        "//third_party/py/numpy",
     ],
 )
 
-- 
GitLab


From cbe1ef05fff2d4fbd0ecc8f3a2c3a3a0f0dc312d Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Tue, 11 Jul 2017 14:26:30 -0700
Subject: [PATCH 0896/1375] Add missing deprecation warnings.

Fixes #11422

PiperOrigin-RevId: 161581508
---
 tensorflow/python/ops/math_ops.py | 30 +++++++++++++++++++-----------
 1 file changed, 19 insertions(+), 11 deletions(-)

diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 7edd1c47d2..42839c2b94 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -168,13 +168,27 @@ from tensorflow.python.ops.gen_math_ops import *
 # pylint: enable=wildcard-import
 from tensorflow.python.util import compat
 from tensorflow.python.util.deprecation import deprecated
+from tensorflow.python.util.deprecation import deprecated_args
 
 # Aliases for some automatically-generated names.
 linspace = gen_math_ops.lin_space
 
+arg_max = deprecated(None, "Use `argmax` instead")(arg_max)  # pylint: disable=used-before-assignment
+arg_min = deprecated(None, "Use `argmin` instead")(arg_min)  # pylint: disable=used-before-assignment
+
+
+def _set_doc(doc):
+  def _decorator(func):
+    func.__doc__ = doc
+    return func
+  return _decorator
+
 
 # pylint: disable=redefined-builtin
-# TODO(aselle): deprecate arg_max
+@deprecated_args(None, "Use the `axis` argument instead", "dimension")
+@_set_doc(gen_math_ops.arg_max.__doc__
+          .replace("dimensions", "axes")
+          .replace("dimension", "axis"))
 def argmax(input,
            axis=None,
            name=None,
@@ -189,12 +203,10 @@ def argmax(input,
   return gen_math_ops.arg_max(input, axis, name=name, output_type=output_type)
 
 
-argmax.__doc__ = (gen_math_ops.arg_max.__doc__.replace("dimensions",
-                                                       "axes").replace(
-                                                           "dimension", "axis"))
-
-
-# TODO(aselle:deprecate arg_min)
+@deprecated_args(None, "Use the `axis` argument instead", "dimension")
+@_set_doc(gen_math_ops.arg_min.__doc__
+          .replace("dimensions", "axes")
+          .replace("dimension", "axis"))
 def argmin(input,
            axis=None,
            name=None,
@@ -209,10 +221,6 @@ def argmin(input,
   return gen_math_ops.arg_min(input, axis, name=name, output_type=output_type)
 
 
-argmin.__doc__ = (gen_math_ops.arg_min.__doc__.replace("dimensions",
-                                                       "axes").replace(
-                                                           "dimension", "axis"))
-
 # pylint: enable=redefined-builtin
 
 
-- 
GitLab


From 8e82134acd68c313f0ecadb168a66916006b17a6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 16:10:06 -0700
Subject: [PATCH 0897/1375] Use C API to implement Operation._input_types

This change first converts _input_types into a property and renames the
member to _input_types_val. We keep _input_dtypes as an alias for
_input_types as it was before this change.

Similarly to _output_types, we can't enable normal tests yet. Instead,
we add a simple temporary test for _input_types.

Also, fix two minor typos in doc strings of function.py

PiperOrigin-RevId: 161597185
---
 tensorflow/python/framework/function.py |  4 +--
 tensorflow/python/framework/ops.py      | 35 +++++++++++++++++---
 tensorflow/python/framework/ops_test.py | 43 +++++++++++++++++++++++++
 3 files changed, 76 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index dbd406ebd5..ff47c0dbf8 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -581,10 +581,10 @@ class _OverloadedFunction(object):
 
 
 class _FuncGraph(ops.Graph):
-  """A helper for construction a function.
+  """A helper for constructing a function.
 
   _FuncGraph overrides ops.Graph's create_op() so that we can keep
-  track of every inputs into every op created inside the function.  If
+  track of all inputs into every op created inside the function.  If
   any input is from other graphs, we keep track of it in self.capture
   and substitue the input with a place holder.
 
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 638ed7ef4b..0ab5fdb390 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1183,7 +1183,7 @@ class Operation(object):
                             self.node_def.name,
                             [i.dtype for i in self._inputs],
                             input_types))
-    self._input_types = input_types
+    self._input_types_val = input_types
 
     # Build the list of control inputs.
     self._control_inputs = []
@@ -1416,7 +1416,14 @@ class Operation(object):
     tf_output.index = output_idx
     return tf_output
 
-  def _set_device(self, device):
+  def _tf_input(self, input_idx):
+    """Create and return a new TF_Input for input_idx'th input of this op."""
+    tf_input = c_api.TF_Input()
+    tf_input.oper = self._c_op
+    tf_input.index = input_idx
+    return tf_input
+
+  def _set_device(self, device):  # pylint: disable=redefined-outer-name
     """Set the device of this operation.
 
     Args:
@@ -1438,6 +1445,7 @@ class Operation(object):
         or if input tensor type is not convertible to dtype.
       ValueError: if the Tensor is from a different graph.
     """
+    assert not _USE_C_API, "Operation._add_input doesn't work with C API"
     if not isinstance(tensor, Tensor):
       raise TypeError("tensor must be a Tensor: %s" % tensor)
     _assert_same_graph(self, tensor)
@@ -1450,7 +1458,7 @@ class Operation(object):
             "Cannot convert a tensor of type %s to an input of type %s"
             % (tensor.dtype.name, dtype.name))
     self._inputs.append(tensor)
-    self._input_types.append(dtype)
+    self._input_types_val.append(dtype)
     tensor._add_consumer(self)  # pylint: disable=protected-access
     self._recompute_node_def()
 
@@ -1470,6 +1478,7 @@ class Operation(object):
         or if input tensor type is not convertible to dtype.
       ValueError: if the Tensor is from a different graph.
     """
+    assert not _USE_C_API, "Operation._update_input doesn't work with C API"
     if not isinstance(tensor, Tensor):
       raise TypeError("tensor must be a Tensor: %s" % tensor)
     _assert_same_graph(self, tensor)
@@ -1484,7 +1493,7 @@ class Operation(object):
 
     self._inputs[index].consumers().remove(self)
     self._inputs[index] = tensor
-    self._input_types[index] = dtype
+    self._input_types_val[index] = dtype
     tensor._add_consumer(self)  # pylint: disable=protected-access
     self._recompute_node_def()
 
@@ -1498,6 +1507,8 @@ class Operation(object):
       TypeError: if ops is not a list of Operations.
       ValueError: if any op in ops is from a different graph.
     """
+    assert not _USE_C_API, (
+        "Operation._add_control_inputs doesn't work with C API")
     if ops:
       for op in ops:
         if not isinstance(op, Operation):
@@ -1516,6 +1527,8 @@ class Operation(object):
       TypeError: if op is not an Operation.
       ValueError: if op is from a different graph.
     """
+    assert not _USE_C_API, (
+        "Operation._add_control_input doesn't work with C API")
     self._add_control_inputs([op])
 
   # Methods below are used when building the NodeDef and Graph proto.
@@ -1569,6 +1582,20 @@ class Operation(object):
   def _input_dtypes(self):
     return self._input_types
 
+  @property
+  def _input_types(self):
+    if _USE_C_API:
+      num_inputs = c_api.TF_OperationNumInputs(self._c_op)
+      input_types = [dtypes.as_dtype(
+          c_api.TF_OperationInputType(self._tf_input(i)))
+                     for i in xrange(num_inputs)]
+      # TODO(iga): Remove this assert after converting to C API by default.
+      # Just being a bit paranoid here.
+      assert self._input_types_val == input_types
+      return input_types
+    else:
+      return self._input_types_val
+
   @property
   def control_inputs(self):
     """The `Operation` objects on which this op has a control dependency.
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 01841e6306..0ca9ad1d5f 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -1828,5 +1828,48 @@ class OutputTypesTest(test_util.TensorFlowTestCase):
       # pylint: enable=protected-access
 
 
+class InputTypesTest(test_util.TensorFlowTestCase):
+  """Tests Operation._input_dtypes and Operation._input_types properties.
+
+  This test should not exist as _input_types is a private property.
+  This property is used by many tests that would normally cover its
+  behavior. However, we can't yet run these tests in C
+  API mode because they use _set_device method. This test will be deleted
+  once we port _set_device.
+  """
+  # TODO(iga): Remove this test
+
+  def setUp(self):
+    self.prev_use_c_api = ops._USE_C_API  # pylint: disable=protected-access
+    ops._USE_C_API = True  # pylint: disable=protected-access
+
+  def tearDown(self):
+    ops._USE_C_API = self.prev_use_c_api  # pylint: disable=protected-access
+
+  def testZeroInputs(self):
+    g = ops.Graph()
+    with g.as_default():
+      # Using a constant because creating unregistered ops
+      # doesn't work with the C API.
+      op = constant_op.constant(12, dtype=dtypes.uint16).op
+      # pylint: disable=protected-access
+      self.assertEqual([], op._input_types)
+      self.assertEqual([], op._input_dtypes)
+      # pylint: enable=protected-access
+
+  def testTwoInputs(self):
+    g = ops.Graph()
+    with g.as_default():
+      x = constant_op.constant(1.0, dtype=dtypes.double)
+      y = constant_op.constant(2.0, dtype=dtypes.double)
+      z = math_ops.multiply(x, y)
+      # pylint: disable=protected-access
+      self.assertTrue(isinstance(z.op._input_types[0], dtypes.DType))
+      self.assertTrue(isinstance(z.op._input_types[1], dtypes.DType))
+      self.assertEqual([dtypes.double, dtypes.double], z.op._input_types)
+      self.assertEqual([dtypes.double, dtypes.double], z.op._input_dtypes)
+      # pylint: enable=protected-access
+
+
 if __name__ == "__main__":
   googletest.main()
-- 
GitLab


From 14f7d7f275ad4c3ae015b97b35340826860d980f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 16:16:27 -0700
Subject: [PATCH 0898/1375] Fix lint errors in ops.py and ops_test.py

PiperOrigin-RevId: 161598138
---
 tensorflow/python/framework/ops.py      | 20 +++++++++++++-------
 tensorflow/python/framework/ops_test.py |  1 -
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 0ab5fdb390..c4ace79bb9 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1051,7 +1051,7 @@ def _device_string(dev_spec):
     return dev_spec
 
 
-def _NodeDef(op_type, name, device=None, attrs=None):
+def _NodeDef(op_type, name, device=None, attrs=None):  # pylint: disable=redefined-outer-name
   """Create a NodeDef proto.
 
   Args:
@@ -1203,7 +1203,7 @@ class Operation(object):
     self._op_def = op_def
     self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
     # Add this op to the current control flow context:
-    self._control_flow_context = g._get_control_flow_context()
+    self._control_flow_context = g._get_control_flow_context()  # pylint: disable=protected-access
     if self._control_flow_context is not None:
       self._control_flow_context.AddOp(self)
     # NOTE(keveman): Control flow context's AddOp could be creating new ops and
@@ -1534,7 +1534,9 @@ class Operation(object):
   # Methods below are used when building the NodeDef and Graph proto.
   def _recompute_node_def(self):
     del self._node_def.input[:]
+    # pylint: disable=protected-access
     self._node_def.input.extend([t._as_node_def_input() for t in self._inputs])
+    # pylint: enable=protected-access
     if self._control_inputs:
       self._node_def.input.extend(["^%s" % op.name for op in
                                    self._control_inputs])
@@ -2322,6 +2324,9 @@ class Graph(object):
 
     Note that this is unrelated to the
     @{tf.Graph.graph_def_versions}.
+
+    Returns:
+       An integer version that increases as ops are added to the graph.
     """
     if self._finalized:
       return self._version
@@ -2530,7 +2535,7 @@ class Graph(object):
     return self._building_function
 
   # Helper functions to create operations.
-  def create_op(self, op_type, inputs, dtypes,
+  def create_op(self, op_type, inputs, dtypes,  # pylint: disable=redefined-outer-name
                 input_types=None, name=None, attrs=None, op_def=None,
                 compute_shapes=True, compute_device=True):
     """Creates an `Operation` in this graph.
@@ -2643,7 +2648,7 @@ class Graph(object):
                             name, colocation_op.name,
                             ret.device, colocation_op.device)
           else:
-            ret._set_device(colocation_op.device)
+            ret._set_device(colocation_op.device)  # pylint: disable=protected-access
 
       all_colocation_groups = sorted(set(all_colocation_groups))
       ret.node_def.attr["_class"].CopyFrom(attr_value_pb2.AttrValue(
@@ -3367,7 +3372,7 @@ class Graph(object):
       device_name_or_function: The device name or function to use in
         the context.
 
-    Returns:
+    Yields:
       A context manager that specifies the default device to use for newly
       created ops.
 
@@ -3394,7 +3399,7 @@ class Graph(object):
     for device_function in reversed(self._device_function_stack):
       if device_function is None:
         break
-      op._set_device(device_function(op))
+      op._set_device(device_function(op))  # pylint: disable=protected-access
 
   # pylint: disable=g-doc-return-or-yield
   @tf_contextlib.contextmanager
@@ -3943,7 +3948,7 @@ class _DefaultStack(threading.local):
     self.stack = []
 
   def is_cleared(self):
-    return self.stack == []
+    return not self.stack
 
   @property
   def enforce_nesting(self):
@@ -4566,6 +4571,7 @@ def prepend_name_scope(name, import_scope):
 
 
 # pylint: disable=g-doc-return-or-yield
+# pylint: disable=not-context-manager
 @tf_contextlib.contextmanager
 def op_scope(values, name, default_name=None):
   """DEPRECATED. Same as name_scope above, just different argument order."""
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 0ca9ad1d5f..f495e4e829 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -34,7 +34,6 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_ops
-from tensorflow.python.framework import test_ops_2
 from tensorflow.python.framework import test_util
 from tensorflow.python.framework import versions
 from tensorflow.python.ops import array_ops
-- 
GitLab


From baba8fc6ae2f8a4aaed2b54927131f50d70da2a3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 17:49:40 -0700
Subject: [PATCH 0899/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 161609382
---
 .../contrib/data/python/framework/BUILD       |  12 +-
 .../contrib/data/python/kernel_tests/BUILD    | 114 ++++++++++++------
 tensorflow/contrib/keras/BUILD                |  60 ++++++---
 tensorflow/contrib/session_bundle/BUILD       |  29 +++--
 4 files changed, 146 insertions(+), 69 deletions(-)

diff --git a/tensorflow/contrib/data/python/framework/BUILD b/tensorflow/contrib/data/python/framework/BUILD
index 7b84825bb4..164aa598f6 100644
--- a/tensorflow/contrib/data/python/framework/BUILD
+++ b/tensorflow/contrib/data/python/framework/BUILD
@@ -11,7 +11,11 @@ py_library(
     srcs = ["function.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python:framework",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:function",
+        "//tensorflow/python:util",
+        "//tensorflow/python:variable_scope",
     ],
 )
 
@@ -23,8 +27,10 @@ py_test(
     deps = [
         ":function",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
     ],
 )
 
diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index 655c6f23dd..d5e8eb7fda 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -12,9 +12,19 @@ py_test(
     srcs = ["iterator_ops_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:training",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -24,14 +34,18 @@ py_test(
     srcs = ["batch_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:string_ops",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:util",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -41,9 +55,17 @@ py_test(
     srcs = ["bucketing_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:string_ops",
+        "//tensorflow/python:tensor_shape",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -57,13 +79,15 @@ py_test(
         "nomac",  # b/62040583
     ],
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:util",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -73,13 +97,13 @@ py_test(
     srcs = ["filter_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -89,13 +113,12 @@ py_test(
     srcs = ["flat_map_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
-        "//tensorflow/python:array_ops",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:training",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -120,16 +143,19 @@ py_test(
     srcs = ["map_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
         "//tensorflow/python:data_flow_ops",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
         "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_ops",
         "//tensorflow/python:string_ops",
+        "//tensorflow/python:variable_scope",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -139,12 +165,13 @@ py_test(
     srcs = ["range_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:tensor_shape",
     ],
 )
 
@@ -154,12 +181,17 @@ py_test(
     srcs = ["reader_dataset_ops_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:lib",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -169,14 +201,13 @@ py_test(
     srcs = ["resample_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
-        "//tensorflow/python:array_ops",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:string_ops",
+        "//tensorflow/python:util",
         "//tensorflow/python:variables",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -186,12 +217,12 @@ py_test(
     srcs = ["sequence_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -201,12 +232,13 @@ py_test(
     srcs = ["shuffle_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -216,12 +248,14 @@ py_test(
     srcs = ["cache_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:variables",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -231,12 +265,12 @@ py_test(
     srcs = ["zip_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index 9e92b03399..e48b18ff52 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -115,24 +115,35 @@ py_library(
         "//tensorflow/python:check_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:clip_ops",
+        "//tensorflow/python:constant_op",
         "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:ctc_ops",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:framework",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:functional_ops",
         "//tensorflow/python:gradients",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:layers",
+        "//tensorflow/python:layers_base",
         "//tensorflow/python:logging_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:platform",
+        "//tensorflow/python:random_ops",
         "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:summary",
         "//tensorflow/python:tensor_array_grad",
+        "//tensorflow/python:tensor_array_ops",
+        "//tensorflow/python:tensor_shape",
         "//tensorflow/python:training",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "@six_archive//:six",
     ],
 )
 
@@ -144,8 +155,8 @@ py_test(
     tags = ["notsan"],
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -157,6 +168,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -168,6 +180,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -179,6 +192,8 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:init_ops",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -189,7 +204,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -202,8 +216,8 @@ py_test(
     tags = ["notsan"],
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -215,6 +229,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -226,6 +241,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -302,7 +318,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -314,8 +329,8 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -330,8 +345,8 @@ py_test(
     ],
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -342,7 +357,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -354,8 +368,8 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -366,7 +380,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -378,8 +391,8 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -391,6 +404,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -401,7 +415,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -412,7 +425,7 @@ cuda_py_test(
     srcs = ["python/keras/layers/normalization_test.py"],
     additional_deps = [
         ":keras",
-        ":testing_utils",
+        "//third_party/py/numpy",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -425,8 +438,8 @@ py_test(
     tags = ["notsan"],
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -438,8 +451,8 @@ py_test(
     tags = ["notsan"],  # http://b/62136390
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -451,8 +464,8 @@ py_test(
     tags = ["notsan"],  # http://b/62189182
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -475,6 +488,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -486,8 +500,8 @@ py_test(
     tags = ["notsan"],
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -533,6 +547,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -544,6 +559,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -555,6 +571,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -566,8 +583,8 @@ py_test(
     tags = ["notsan"],
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -579,8 +596,8 @@ py_test(
     tags = ["notsan"],
     deps = [
         ":keras",
-        ":testing_utils",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -591,7 +608,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -603,6 +623,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -614,6 +635,8 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:util",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -625,6 +648,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
+        "//tensorflow/python:util",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/contrib/session_bundle/BUILD b/tensorflow/contrib/session_bundle/BUILD
index fc34d82f25..bd1e100a54 100644
--- a/tensorflow/contrib/session_bundle/BUILD
+++ b/tensorflow/contrib/session_bundle/BUILD
@@ -63,6 +63,7 @@ py_library(
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client",
         "//tensorflow/python:framework",
+        "//tensorflow/python:platform",
         "//tensorflow/python/saved_model:constants",
         "//tensorflow/python/saved_model:loader",
         "//tensorflow/python/saved_model:signature_constants",
@@ -83,13 +84,13 @@ py_test(
     deps = [
         ":bundle_shim_py",
         ":constants",
+        ":manifest_proto_py",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:util",
-        "//tensorflow/python/saved_model:constants",
         "//tensorflow/python/saved_model:signature_constants",
         "//tensorflow/python/saved_model:tag_constants",
     ],
@@ -113,6 +114,7 @@ py_library(
         ":constants",
         ":gc",
         ":manifest_proto_py",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
@@ -125,9 +127,7 @@ py_library(
 py_test(
     name = "exporter_test",
     size = "small",
-    srcs = [
-        "exporter_test.py",
-    ],
+    srcs = ["exporter_test.py"],
     srcs_version = "PY2AND3",
     visibility = ["//visibility:private"],
     deps = [
@@ -157,15 +157,14 @@ py_library(
     deps = [
         "//tensorflow/python:framework",
         "//tensorflow/python:platform",
+        "//tensorflow/python:util",
     ],
 )
 
 py_test(
     name = "gc_test",
     size = "small",
-    srcs = [
-        "gc_test.py",
-    ],
+    srcs = ["gc_test.py"],
     srcs_version = "PY2AND3",
     visibility = ["//visibility:private"],
     deps = [
@@ -199,8 +198,12 @@ cc_library(
     deprecation = "Use SavedModel Loader instead.",
     visibility = ["//visibility:public"],
     deps = [
+        ":manifest_proto_cc",
         ":session_bundle_lite",
         ":signature",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ],
 )
 
@@ -217,6 +220,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         ":signature_lite",
+        "//tensorflow/core:lib_internal",
     ] + if_not_mobile([
         ":manifest_proto_cc",
         "//tensorflow/core:core_cpu",
@@ -238,10 +242,12 @@ cc_test(
     visibility = ["//visibility:private"],
     deps = [
         ":session_bundle",
+        ":signature",
         ":test_util",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
@@ -263,6 +269,7 @@ py_library(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:lib",
         "//tensorflow/python:training",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -281,8 +288,8 @@ py_test(
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:graph_util",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:parsing_ops",
         "//tensorflow/python:training",
@@ -318,7 +325,12 @@ cc_library(
     deprecation = "Use SavedModel instead.",
     visibility = ["//visibility:public"],
     deps = [
+        ":manifest_proto_cc",
         ":signature_lite",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
     ] + if_not_mobile([
         "//tensorflow/core:tensorflow_opensource",
     ]),
@@ -398,6 +410,7 @@ cc_test(
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
-- 
GitLab


From 272dee9d54b95efebc1a8f17aa8eff413dac0db5 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Tue, 11 Jul 2017 18:10:33 -0700
Subject: [PATCH 0900/1375] Java: Make maven release script runnable from any
 directory.

PiperOrigin-RevId: 161611475
---
 tensorflow/java/maven/release.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/java/maven/release.sh b/tensorflow/java/maven/release.sh
index 0bb4efbcc0..b95a4d4674 100755
--- a/tensorflow/java/maven/release.sh
+++ b/tensorflow/java/maven/release.sh
@@ -17,6 +17,7 @@
 # Script to upload release artifacts for the TensorFlow Java library to
 # Maven Central. See README.md for an explanation.
 
+cd $(dirname "$0")
 TF_VERSION="$1"
 SETTINGS_XML="$2"
 shift
-- 
GitLab


From b66ecbe4ef1a8514fe432c1cf52528498d8afade Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 18:23:35 -0700
Subject: [PATCH 0901/1375] Add warning to Experiment so that users know to set
 environment=cloud if they have an explicit cluster and master (i.e. give an
 indication that they intend to be running distributed).

PiperOrigin-RevId: 161612632
---
 tensorflow/contrib/learn/python/learn/experiment.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 475f5beefd..3075d9355c 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -245,6 +245,11 @@ class Experiment(object):
     # Otherwise, the servers will wait to connect to each other before starting
     # to train. We might as well start as soon as we can.
     config = self._estimator.config
+    if (config.cluster_spec and config.master and
+        config.environment == run_config.Environment.LOCAL):
+      logging.warn("ClusterSpec and master are provided, but environment is "
+                   "set to 'local'. Set environment to 'cloud' if you intend "
+                   "to use the distributed runtime.")
     if (config.environment != run_config.Environment.LOCAL and
         config.environment != run_config.Environment.GOOGLE and
         config.cluster_spec and config.master):
-- 
GitLab


From 8d3913a68795b22bb2befad92687d4a56587f509 Mon Sep 17 00:00:00 2001
From: Sukriti Ramesh <sukritiramesh@google.com>
Date: Tue, 11 Jul 2017 19:20:17 -0700
Subject: [PATCH 0902/1375] Mark SessionBundle targets as no longer supported.

PiperOrigin-RevId: 161616162
---
 tensorflow/contrib/session_bundle/BUILD       | 38 +++++++++----------
 tensorflow/contrib/session_bundle/README.md   |  5 ++-
 tensorflow/contrib/session_bundle/exporter.py | 18 ++++++---
 tensorflow/contrib/session_bundle/gc.py       | 18 ++++++---
 .../contrib/session_bundle/session_bundle.py  |  6 ++-
 5 files changed, 50 insertions(+), 35 deletions(-)

diff --git a/tensorflow/contrib/session_bundle/BUILD b/tensorflow/contrib/session_bundle/BUILD
index bd1e100a54..6cd9aee049 100644
--- a/tensorflow/contrib/session_bundle/BUILD
+++ b/tensorflow/contrib/session_bundle/BUILD
@@ -96,19 +96,19 @@ py_test(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 py_library(
     name = "constants",
     srcs = ["constants.py"],
-    deprecation = "Use SavedModel constants instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     srcs_version = "PY2AND3",
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 py_library(
     name = "exporter",
     srcs = ["exporter.py"],
-    deprecation = "Use SavedModel Builder instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     srcs_version = "PY2AND3",
     deps = [
         ":constants",
@@ -148,11 +148,11 @@ py_test(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 py_library(
     name = "gc",
     srcs = ["gc.py"],
-    deprecation = "Use SavedModel instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:framework",
@@ -191,11 +191,11 @@ filegroup(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 cc_library(
     name = "session_bundle",
     hdrs = ["session_bundle.h"],
-    deprecation = "Use SavedModel Loader instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     visibility = ["//visibility:public"],
     deps = [
         ":manifest_proto_cc",
@@ -207,7 +207,7 @@ cc_library(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 # This is a lite version of the session_bundle target that does not link in any
 # Tensorflow ops in order to minimize its size. Clients using this should link
 # any required ops manually.
@@ -216,7 +216,7 @@ cc_library(
     srcs = ["session_bundle.cc"],
     hdrs = ["session_bundle.h"],
     copts = if_ios(["-DGOOGLE_LOGGING"]),
-    deprecation = "Use SavedModel Loader instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     visibility = ["//visibility:public"],
     deps = [
         ":signature_lite",
@@ -254,11 +254,11 @@ cc_test(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 py_library(
     name = "session_bundle_py",
     srcs = ["session_bundle.py"],
-    deprecation = "Use SavedModel Loader instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
     deps = [
@@ -299,7 +299,7 @@ py_test(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 # This is a lite version of the signature target that does not link in any
 # Tensorflow ops in order to minimize its size. Clients using this should
 # link any required ops manually.
@@ -307,7 +307,7 @@ cc_library(
     name = "signature_lite",
     srcs = ["signature.cc"],
     hdrs = ["signature.h"],
-    deprecation = "Use SavedModel instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     visibility = ["//visibility:public"],
     deps = if_not_mobile([
         ":manifest_proto_cc",
@@ -318,11 +318,11 @@ cc_library(
     ]),
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 cc_library(
     name = "signature",
     hdrs = ["signature.h"],
-    deprecation = "Use SavedModel instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     visibility = ["//visibility:public"],
     deps = [
         ":manifest_proto_cc",
@@ -355,13 +355,13 @@ cc_test(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 cc_library(
     name = "test_util",
     testonly = 1,
     srcs = ["test_util.cc"],
     hdrs = ["test_util.h"],
-    deprecation = "Use SavedModel instead.",
+    deprecation = "No longer supported. Switch to SavedModel immediately.",
     visibility = ["//visibility:private"],
     deps = [
         "//tensorflow/core:lib",
@@ -417,7 +417,7 @@ cc_test(
     ],
 )
 
-# DEPRECATED: Use SavedModel instead.
+# DEPRECATED: No longer supported. Switch to SavedModel immediately.
 tf_proto_library(
     name = "manifest_proto",
     srcs = ["manifest.proto"],
diff --git a/tensorflow/contrib/session_bundle/README.md b/tensorflow/contrib/session_bundle/README.md
index 5bcc8fab70..e3a87a0eb9 100644
--- a/tensorflow/contrib/session_bundle/README.md
+++ b/tensorflow/contrib/session_bundle/README.md
@@ -1,7 +1,8 @@
 # TensorFlow Inference Model Format
 
-WARNING: SessionBundle has been deprecated. Please use
-[SavedModel](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) instead.
+WARNING: SessionBundle has been deprecated and is no longer supported. Switch to
+[SavedModel](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md)
+immediately.
 
 [TOC]
 
diff --git a/tensorflow/contrib/session_bundle/exporter.py b/tensorflow/contrib/session_bundle/exporter.py
index efeb808ee7..dcc7fbaa2d 100644
--- a/tensorflow/contrib/session_bundle/exporter.py
+++ b/tensorflow/contrib/session_bundle/exporter.py
@@ -41,7 +41,8 @@ from tensorflow.python.util import compat
 from tensorflow.python.util.deprecation import deprecated
 
 
-@deprecated("2017-06-30", "Please use SavedModel instead.")
+@deprecated("2017-06-30",
+            "No longer supported. Switch to SavedModel immediately.")
 def gfile_copy_callback(files_to_copy, export_dir_path):
   """Callback to copy files using `gfile.Copy` to an export directory.
 
@@ -71,7 +72,8 @@ def gfile_copy_callback(files_to_copy, export_dir_path):
     gfile.Copy(source_filepath, new_path)
 
 
-@deprecated("2017-06-30", "Please use SavedModel instead.")
+@deprecated("2017-06-30",
+            "No longer supported. Switch to SavedModel immediately.")
 def regression_signature(input_tensor, output_tensor):
   """Creates a regression signature.
 
@@ -88,7 +90,8 @@ def regression_signature(input_tensor, output_tensor):
   return signature
 
 
-@deprecated("2017-06-30", "Please use SavedModel instead.")
+@deprecated("2017-06-30",
+            "No longer supported. Switch to SavedModel immediately.")
 def classification_signature(input_tensor,
                              classes_tensor=None,
                              scores_tensor=None):
@@ -111,7 +114,8 @@ def classification_signature(input_tensor,
   return signature
 
 
-@deprecated("2017-06-30", "Please use SavedModel instead.")
+@deprecated("2017-06-30",
+            "No longer supported. Switch to SavedModel immediately.")
 def generic_signature(name_tensor_map):
   """Creates a generic signature of name to Tensor name.
 
@@ -145,7 +149,8 @@ class Exporter(object):
     self._has_init = False
     self._assets_to_copy = {}
 
-  @deprecated("2017-06-30", "Please use SavedModel instead.")
+  @deprecated("2017-06-30",
+              "No longer supported. Switch to SavedModel immediately.")
   def init(self,
            graph_def=None,
            init_op=None,
@@ -227,7 +232,8 @@ class Exporter(object):
 
     self._assets_callback = assets_callback
 
-  @deprecated("2017-06-30", "Please use SavedModel instead.")
+  @deprecated("2017-06-30",
+              "No longer supported. Switch to SavedModel immediately.")
   def export(self,
              export_dir_base,
              global_step_tensor,
diff --git a/tensorflow/contrib/session_bundle/gc.py b/tensorflow/contrib/session_bundle/gc.py
index 885b888e27..fb9e0743a1 100644
--- a/tensorflow/contrib/session_bundle/gc.py
+++ b/tensorflow/contrib/session_bundle/gc.py
@@ -77,7 +77,8 @@ from tensorflow.python.util.deprecation import deprecated
 Path = collections.namedtuple('Path', 'path export_version')
 
 
-@deprecated('2017-06-30', 'Please use SavedModel instead.')
+@deprecated('2017-06-30',
+            'No longer supported. Switch to SavedModel immediately.')
 def largest_export_versions(n):
   """Creates a filter that keeps the largest n export versions.
 
@@ -98,7 +99,8 @@ def largest_export_versions(n):
   return keep
 
 
-@deprecated('2017-06-30', 'Please use SavedModel instead.')
+@deprecated('2017-06-30',
+            'No longer supported. Switch to SavedModel immediately.')
 def one_of_every_n_export_versions(n):
   r"""Creates a filter that keeps one of every n export versions.
 
@@ -128,7 +130,8 @@ def one_of_every_n_export_versions(n):
   return keep
 
 
-@deprecated('2017-06-30', 'Please use SavedModel instead.')
+@deprecated('2017-06-30',
+            'No longer supported. Switch to SavedModel immediately.')
 def mod_export_version(n):
   """Creates a filter that keeps every export that is a multiple of n.
 
@@ -147,7 +150,8 @@ def mod_export_version(n):
   return keep
 
 
-@deprecated('2017-06-30', 'Please use SavedModel instead.')
+@deprecated('2017-06-30',
+            'No longer supported. Switch to SavedModel immediately.')
 def union(lf, rf):
   """Creates a filter that keeps the union of two filters.
 
@@ -165,7 +169,8 @@ def union(lf, rf):
   return keep
 
 
-@deprecated('2017-06-30', 'Please use SavedModel instead.')
+@deprecated('2017-06-30',
+            'No longer supported. Switch to SavedModel immediately.')
 def negation(f):
   """Negate a filter.
 
@@ -182,7 +187,8 @@ def negation(f):
   return keep
 
 
-@deprecated('2017-06-30', 'Please use SavedModel instead.')
+@deprecated('2017-06-30',
+            'No longer supported. Switch to SavedModel immediately.')
 def get_paths(base_dir, parser):
   """Gets a list of Paths in a given directory.
 
diff --git a/tensorflow/contrib/session_bundle/session_bundle.py b/tensorflow/contrib/session_bundle/session_bundle.py
index 37407f9042..66f2e32f58 100644
--- a/tensorflow/contrib/session_bundle/session_bundle.py
+++ b/tensorflow/contrib/session_bundle/session_bundle.py
@@ -34,7 +34,8 @@ from tensorflow.python.training import saver as saver_lib
 from tensorflow.python.util.deprecation import deprecated
 
 
-@deprecated("2017-06-30", "Please use SavedModel instead.")
+@deprecated("2017-06-30",
+            "No longer supported. Switch to SavedModel immediately.")
 def maybe_session_bundle_dir(export_dir):
   """Checks if the model path contains session bundle model.
 
@@ -50,7 +51,8 @@ def maybe_session_bundle_dir(export_dir):
   return file_io.file_exists(meta_graph_filename)
 
 
-@deprecated("2017-06-30", "Please use SavedModel instead.")
+@deprecated("2017-06-30",
+            "No longer supported. Switch to SavedModel immediately.")
 def load_session_bundle_from_path(export_dir,
                                   target="",
                                   config=None,
-- 
GitLab


From 07208911a85f7da4bdc8886fe216544bc6be0454 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 20:49:57 -0700
Subject: [PATCH 0903/1375] Use graph._c_graph instead of global USE_C_API

Looking at graph._c_graph should be safer. The logic would be as
follows. You need to set _USE_C_API global when you create the graph.
Once the graph is created, it is either in the "C API mode" or in the
old mode. All ops created on this graph inherit its mode and don't look
at the global.

PiperOrigin-RevId: 161620767
---
 tensorflow/python/framework/ops.py       | 30 ++++++++++++------------
 tensorflow/python/framework/test_util.py |  3 ++-
 2 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index c4ace79bb9..123a43e5f5 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1214,10 +1214,7 @@ class Operation(object):
     self._id_value = self._graph._next_id()  # pylint: disable=protected-access
     self._recompute_node_def()
 
-    if _USE_C_API:
-      assert self._graph._c_graph, (  # pylint: disable=protected-access
-          "_USE_C_API set to False when creating Graph, you may need to "
-          "manually set 'ops._USE_C_API = True' before creating the Graph")
+    if self._graph._c_graph:  # pylint: disable=protected-access
       if self._op_def:
         # TODO(skyewm): op_def_library.apply_op() flattens the incoming
         # inputs. Refactor so we don't have to do this here.
@@ -1351,7 +1348,7 @@ class Operation(object):
   @property
   def name(self):
     """The full name of this operation."""
-    if _USE_C_API:
+    if self._graph._c_graph:  # pylint: disable=protected-access
       # TODO(iga): Remove this assert after converting to C API by default.
       # Just being a bit paranoid here.
       assert self._node_def.name == c_api.TF_OperationName(self._c_op)
@@ -1373,7 +1370,7 @@ class Operation(object):
       assigned, or an empty string if it has not been assigned to a
       device.
     """
-    if _USE_C_API:
+    if self._graph._c_graph:  # pylint: disable=protected-access
       # TODO(iga): Remove this assert after converting to C API by default.
       # Just being a bit paranoid here.
       assert self._node_def.device == c_api.TF_OperationDevice(self._c_op)
@@ -1392,7 +1389,7 @@ class Operation(object):
       The length of this list indicates the number of output endpoints
       of the operation.
     """
-    if _USE_C_API:
+    if self._graph._c_graph:  # pylint: disable=protected-access
       num_outputs = c_api.TF_OperationNumOutputs(self._c_op)
       output_types = [c_api.TF_OperationOutputType(self._tf_output(i)) for
                       i in xrange(num_outputs)]
@@ -1429,7 +1426,8 @@ class Operation(object):
     Args:
       device: string or device..  The device to set.
     """
-    assert not _USE_C_API, "Operation._set_device doesn't work with C API"
+    assert not self._graph._c_graph, (  # pylint: disable=protected-access
+        "Operation._set_device doesn't work with C API")
     self._node_def.device = _device_string(device)
 
   def _add_input(self, tensor, dtype=None):
@@ -1445,7 +1443,8 @@ class Operation(object):
         or if input tensor type is not convertible to dtype.
       ValueError: if the Tensor is from a different graph.
     """
-    assert not _USE_C_API, "Operation._add_input doesn't work with C API"
+    assert not self._graph._c_graph, (  # pylint: disable=protected-access
+        "Operation._add_input doesn't work with C API")
     if not isinstance(tensor, Tensor):
       raise TypeError("tensor must be a Tensor: %s" % tensor)
     _assert_same_graph(self, tensor)
@@ -1478,7 +1477,8 @@ class Operation(object):
         or if input tensor type is not convertible to dtype.
       ValueError: if the Tensor is from a different graph.
     """
-    assert not _USE_C_API, "Operation._update_input doesn't work with C API"
+    assert not self._graph._c_graph, (  # pylint: disable=protected-access
+        "Operation._update_input doesn't work with C API")
     if not isinstance(tensor, Tensor):
       raise TypeError("tensor must be a Tensor: %s" % tensor)
     _assert_same_graph(self, tensor)
@@ -1507,7 +1507,7 @@ class Operation(object):
       TypeError: if ops is not a list of Operations.
       ValueError: if any op in ops is from a different graph.
     """
-    assert not _USE_C_API, (
+    assert not self._graph._c_graph, (  # pylint: disable=protected-access
         "Operation._add_control_inputs doesn't work with C API")
     if ops:
       for op in ops:
@@ -1527,7 +1527,7 @@ class Operation(object):
       TypeError: if op is not an Operation.
       ValueError: if op is from a different graph.
     """
-    assert not _USE_C_API, (
+    assert not self._graph._c_graph, (  # pylint: disable=protected-access
         "Operation._add_control_input doesn't work with C API")
     self._add_control_inputs([op])
 
@@ -1586,7 +1586,7 @@ class Operation(object):
 
   @property
   def _input_types(self):
-    if _USE_C_API:
+    if self._graph._c_graph:  # pylint: disable=protected-access
       num_inputs = c_api.TF_OperationNumInputs(self._c_op)
       input_types = [dtypes.as_dtype(
           c_api.TF_OperationInputType(self._tf_input(i)))
@@ -1612,7 +1612,7 @@ class Operation(object):
       A list of `Operation` objects.
 
     """
-    if _USE_C_API:
+    if self._graph._c_graph:  # pylint: disable=protected-access
       control_c_ops = c_api.TF_OperationGetControlInputs_wrapper(self._c_op)
       # pylint: disable=protected-access
       return [self.graph._get_operation_by_name_unsafe(
@@ -1624,7 +1624,7 @@ class Operation(object):
   @property
   def type(self):
     """The type of the op (e.g. `"MatMul"`)."""
-    if _USE_C_API:
+    if self._graph._c_graph:  # pylint: disable=protected-access
       op_type = c_api.TF_OperationOpType(self._c_op)
       # TODO(iga): Remove these asserts after converting to C API by default.
       # Just being a bit paranoid here.
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 5ba226ce07..f357d2ef8d 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -245,7 +245,8 @@ def disable_c_api(fn):
     prev_value = ops._USE_C_API
     ops._USE_C_API = False
     try:
-      fn(*args, **kwargs)
+      with ops.Graph().as_default():
+        fn(*args, **kwargs)
     finally:
       ops._USE_C_API = prev_value
   # pylint: disable=protected-access
-- 
GitLab


From 1d1f99ddd3a0efe9f8290c2d4e87d1701f2f19c7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 21:19:13 -0700
Subject: [PATCH 0904/1375] Make ClientLibraryTestBase able to test all layouts
 for all input arguments.

PiperOrigin-RevId: 161622376
---
 .../xla/legacy_flags/debug_options_flags.cc   |  12 +++
 .../xla/tests/client_library_test_base.cc     | 100 +++++++++++++++---
 .../xla/tests/client_library_test_base.h      |  11 +-
 .../compiler/xla/tests/literal_test_util.cc   |  16 ++-
 .../compiler/xla/tests/literal_test_util.h    |   5 +-
 tensorflow/compiler/xla/xla.proto             |   6 ++
 6 files changed, 131 insertions(+), 19 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index e7c037f3f5..5b4fb5d0a7 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -49,6 +49,7 @@ struct DebugOptionsFlags {
   bool xla_gpu_ftz;
 
   bool xla_test_all_output_layouts;
+  bool xla_test_all_input_layouts;
 
   string xla_backend_extra_options;
 };
@@ -85,6 +86,7 @@ void AllocateFlags() {
   flag_values->xla_gpu_ftz = false;
   flag_values->xla_test_all_output_layouts = false;
   flag_values->xla_backend_extra_options = "";
+  flag_values->xla_test_all_input_layouts = false;
 
   flag_objects = new std::vector<tensorflow::Flag>(
       {tensorflow::Flag(
@@ -169,6 +171,13 @@ void AllocateFlags() {
                         "all permutations of output layouts. For example, with "
                         "a 3D shape, all permutations of the set {0, 1, 2} are "
                         "tried."),
+       tensorflow::Flag("xla_test_all_input_layouts",
+                        &flag_values->xla_test_all_input_layouts,
+                        "Let ClientLibraryTestBase::ComputeAndCompare* test "
+                        "all permutations of *input* layouts. For example, for "
+                        "2 input arguments with 2D shape and 4D shape, the "
+                        "computation will run 2! * 4! times for every possible "
+                        "layouts"),
        tensorflow::Flag("xla_backend_extra_options",
                         &flag_values->xla_backend_extra_options,
                         "Extra options to pass to a backend; "
@@ -224,8 +233,11 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
       flag_values->xla_llvm_enable_noalias_metadata);
   options.set_xla_llvm_enable_invariant_load_metadata(
       flag_values->xla_llvm_enable_invariant_load_metadata);
+
   options.set_xla_test_all_output_layouts(
       flag_values->xla_test_all_output_layouts);
+  options.set_xla_test_all_input_layouts(
+      flag_values->xla_test_all_input_layouts);
 
   std::vector<string> extra_options_parts =
       tensorflow::str_util::Split(flag_values->xla_backend_extra_options, ',');
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index 04cc3e310d..42c3f49030 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -155,10 +155,11 @@ tensorflow::Status
 ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
     const xla::Computation& computation, const Literal& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments,
-    const std::function<void(const Literal& actual)>& expect_equal_or_near) {
+    const std::function<void(const Literal& actual,
+                             const string& error_message)>& verify_output) {
   // Try with no layout requirement.
   TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments));
-  expect_equal_or_near(*actual);
+  verify_output(*actual, "");
 
   // Try with all output layouts.
   std::vector<int64> minor_to_major(ShapeUtil::Rank(expected.shape()));
@@ -167,15 +168,80 @@ ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
     auto layout = ShapeUtil::MakeShapeWithLayout(
         expected.shape().element_type(),
         AsInt64Slice(expected.shape().dimensions()), minor_to_major);
-    VLOG(1) << "Test with output layout: "
-            << ShapeUtil::HumanStringWithLayout(layout);
     TF_ASSIGN_OR_RETURN(auto actual,
                         ExecuteAndTransfer(computation, arguments, &layout));
-    expect_equal_or_near(*actual);
+    verify_output(*actual, tensorflow::strings::StrCat(
+                               "Test with output layout: ",
+                               ShapeUtil::HumanStringWithLayout(layout)));
   } while (std::next_permutation(minor_to_major.begin(), minor_to_major.end()));
   return tensorflow::Status::OK();
 }
 
+tensorflow::Status
+ClientLibraryTestBase::ComputeAndCompareLiteralWithAllInputLayouts(
+    const xla::Computation& computation, const Literal& expected,
+    tensorflow::gtl::ArraySlice<GlobalData*> arguments,
+    const std::function<void(const Literal& actual,
+                             const string& error_message)>& verify_output,
+    const Shape* output_with_layout) {
+  std::vector<GlobalData*> arguments_with_layout;
+  std::vector<string> layout_strings;
+  // This is a recursive function. It's an std::function instead of a lambda
+  // because it needs to capture itself. The index is the index of the argument
+  // to try all layouts for.
+  std::function<tensorflow::Status(int64)> choose;
+  choose = [&, this](int64 index) -> tensorflow::Status {
+    if (index < arguments.size()) {
+      // Try out all layouts for the operand.
+      TF_ASSIGN_OR_RETURN(auto literal,
+                          client_->Transfer(*arguments[index], nullptr));
+      // Skip tuples because they don't have a rank.
+      if (ShapeUtil::IsTuple(literal->shape())) {
+        layout_strings.push_back(
+            ShapeUtil::HumanStringWithLayout(literal->shape()));
+        arguments_with_layout.push_back(arguments[index]);
+        TF_RETURN_IF_ERROR(choose(index + 1));
+        arguments_with_layout.pop_back();
+        layout_strings.pop_back();
+        return tensorflow::Status::OK();
+      }
+
+      std::vector<int64> minor_to_major(ShapeUtil::Rank(literal->shape()));
+      std::iota(minor_to_major.begin(), minor_to_major.end(), 0);
+      do {
+        auto literal_relayout =
+            literal->Relayout(LayoutUtil::MakeLayout(minor_to_major));
+        layout_strings.push_back(
+            ShapeUtil::HumanStringWithLayout(literal_relayout->shape()));
+        TF_ASSIGN_OR_RETURN(auto data,
+                            client_->TransferToServer(*literal_relayout));
+        arguments_with_layout.push_back(data.get());
+        TF_RETURN_IF_ERROR(choose(index + 1));
+        arguments_with_layout.pop_back();
+        layout_strings.pop_back();
+      } while (
+          std::next_permutation(minor_to_major.begin(), minor_to_major.end()));
+      return tensorflow::Status::OK();
+    }
+
+    // Every argument has an assigned layout.
+    TF_ASSIGN_OR_RETURN(
+        auto actual,
+        ExecuteAndTransfer(
+            computation,
+            tensorflow::gtl::ArraySlice<GlobalData*>(arguments_with_layout),
+            output_with_layout));
+    string error_message = "Test with input layouts: ";
+    for (const auto& str : layout_strings) {
+      tensorflow::strings::StrAppend(&error_message, str, " ");
+    }
+    verify_output(*actual, error_message);
+    return tensorflow::Status::OK();
+  };
+
+  return choose(0);
+}
+
 tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     ComputationBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice<GlobalData*> arguments,
@@ -187,11 +253,16 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     TF_RET_CHECK(ShapeUtil::ElementIsIntegral(expected.shape()) ||
                  expected.shape().element_type() == PRED);
   }
+  auto expect_equal = [&](const Literal& actual, const string& error_message) {
+    LiteralTestUtil::ExpectEqual(expected, actual, error_message);
+  };
   if (execution_options_.debug_options().xla_test_all_output_layouts()) {
     return ComputeAndCompareLiteralWithAllOutputLayouts(
-        computation, expected, arguments, [&](const Literal& actual) {
-          LiteralTestUtil::ExpectEqual(expected, actual);
-        });
+        computation, expected, arguments, expect_equal);
+  }
+  if (execution_options_.debug_options().xla_test_all_input_layouts()) {
+    return ComputeAndCompareLiteralWithAllInputLayouts(
+        computation, expected, arguments, expect_equal, shape_with_layout);
   }
   TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
                                                       shape_with_layout));
@@ -205,11 +276,16 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     const Shape* shape_with_layout) {
   TF_RET_CHECK(ShapeUtil::ElementIsFloating(expected.shape()));
   TF_ASSIGN_OR_RETURN(auto computation, builder->Build());
+  auto expect_near = [&](const Literal& actual, const string& error_message) {
+    LiteralTestUtil::ExpectNear(expected, actual, error, error_message);
+  };
   if (execution_options_.debug_options().xla_test_all_output_layouts()) {
-    return ComputeAndCompareLiteralWithAllOutputLayouts(
-        computation, expected, arguments, [&](const Literal& actual) {
-          LiteralTestUtil::ExpectNear(expected, actual, error);
-        });
+    return ComputeAndCompareLiteralWithAllOutputLayouts(computation, expected,
+                                                        arguments, expect_near);
+  }
+  if (execution_options_.debug_options().xla_test_all_input_layouts()) {
+    return ComputeAndCompareLiteralWithAllInputLayouts(
+        computation, expected, arguments, expect_near, shape_with_layout);
   }
   TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
                                                       shape_with_layout));
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index 09640672d3..ed63d38f55 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -286,7 +286,16 @@ class ClientLibraryTestBase : public ::testing::Test {
   tensorflow::Status ComputeAndCompareLiteralWithAllOutputLayouts(
       const xla::Computation& computation, const Literal& expected,
       tensorflow::gtl::ArraySlice<GlobalData*> arguments,
-      const std::function<void(const Literal& actual)>& expect_equal_or_near);
+      const std::function<void(const Literal& actual,
+                               const string& error_message)>& verify_output);
+  // Build and run the computation with all permutations of layouts of all input
+  // arguments.
+  tensorflow::Status ComputeAndCompareLiteralWithAllInputLayouts(
+      const xla::Computation& computation, const Literal& expected,
+      tensorflow::gtl::ArraySlice<GlobalData*> arguments,
+      const std::function<void(const Literal& actual,
+                               const string& error_message)>& verify_output,
+      const Shape* output_with_layout = nullptr);
 };
 
 template <typename NativeT>
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index cca04e17ca..0a82083328 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -152,11 +152,15 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
 }  // namespace
 
 /* static */ void LiteralTestUtil::ExpectEqual(const Literal& expected,
-                                               const Literal& actual) {
+                                               const Literal& actual,
+                                               const string& message) {
   EXPECT_TRUE(Equal(expected, actual))
       << "expected:\n"
       << expected.ToString() << "\n\tvs actual:\n"
-      << actual.ToString();
+      << actual.ToString()
+      << (message.empty()
+              ? ""
+              : tensorflow::strings::StrCat("\nmessage: ", message));
 }
 
 /* static */ void LiteralTestUtil::ExpectNotEqual(const Literal& expected,
@@ -435,8 +439,12 @@ class NearComparator {
 
 /* static */ void LiteralTestUtil::ExpectNear(const Literal& expected,
                                               const Literal& actual,
-                                              const ErrorSpec& error) {
-  EXPECT_TRUE(Near(expected, actual, error));
+                                              const ErrorSpec& error,
+                                              const string& message) {
+  EXPECT_TRUE(Near(expected, actual, error))
+      << (message.empty()
+              ? ""
+              : tensorflow::strings::StrCat("\nmessage: ", message));
 }
 
 /* static */ ::testing::AssertionResult LiteralTestUtil::NearTuple(
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h
index 0def25f34e..b7f1e8603f 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.h
+++ b/tensorflow/compiler/xla/tests/literal_test_util.h
@@ -64,7 +64,8 @@ class LiteralTestUtil {
       const Literal& expected, const Literal& actual) TF_MUST_USE_RESULT;
 
   // Expects that expected and actual are Equal.
-  static void ExpectEqual(const Literal& expected, const Literal& actual);
+  static void ExpectEqual(const Literal& expected, const Literal& actual,
+                          const string& message = "");
 
   // Expects that expected and actual are Not Equal.
   static void ExpectNotEqual(const Literal& expected, const Literal& actual);
@@ -110,7 +111,7 @@ class LiteralTestUtil {
 
   // Expects expected and actual to be Near with the given error.
   static void ExpectNear(const Literal& expected, const Literal& actual,
-                         const ErrorSpec& error);
+                         const ErrorSpec& error, const string& message = "");
 
   // Asserts the given literal are within the given error bound of the given
   // expected values. Only supported for floating point values.
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index ea065532d6..9cf3b56c1b 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -105,6 +105,12 @@ message DebugOptions {
   // the set {0, 1, 2} are tried.
   bool xla_test_all_output_layouts = 70;
 
+  // This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the
+  // computation will run for all permunations of layouts of all input
+  // arguments. For example, with 2 input arguments in 2D and 4D shapes, the
+  // computation will run 2! * 4! times.
+  bool xla_test_all_input_layouts = 71;
+
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
   map<string, string> xla_backend_extra_options = 500;
-- 
GitLab


From 54137ffd39bfb86bc6f259ebaa3c85fecfbe7e93 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 21:47:56 -0700
Subject: [PATCH 0905/1375] Emit the correct LLVM opcode for kMinimum and
 kMaximum over unsigned integral types.

PiperOrigin-RevId: 161624003
---
 .../compiler/xla/service/gpu/ir_emitter.cc    | 16 +++++++-----
 tensorflow/compiler/xla/tests/reduce_test.cc  | 26 +++++++++++++++++++
 2 files changed, 36 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc
index 7d5b6ed5cf..80f91e5dae 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc
@@ -202,18 +202,22 @@ bool IrEmitter::MaybeEmitSpecialAtomicOperation(
   // NVPTX supports atomicMax and atomicMin on only integer types.
   if (root_opcode == HloOpcode::kMaximum &&
       primitive_util::IsIntegralType(element_type)) {
-    // min(integral, integral)
-    ir_builder_.CreateAtomicRMW(llvm::AtomicRMWInst::Max, output_address,
-                                source,
+    // max(integral, integral)
+    auto opcode = primitive_util::IsSignedIntegralType(element_type)
+                      ? llvm::AtomicRMWInst::Max
+                      : llvm::AtomicRMWInst::UMax;
+    ir_builder_.CreateAtomicRMW(opcode, output_address, source,
                                 llvm::AtomicOrdering::SequentiallyConsistent);
     return true;
   }
 
   if (root_opcode == HloOpcode::kMinimum &&
       primitive_util::IsIntegralType(element_type)) {
-    // max(integral, integral)
-    ir_builder_.CreateAtomicRMW(llvm::AtomicRMWInst::Min, output_address,
-                                source,
+    // min(integral, integral)
+    auto opcode = primitive_util::IsSignedIntegralType(element_type)
+                      ? llvm::AtomicRMWInst::Min
+                      : llvm::AtomicRMWInst::UMin;
+    ir_builder_.CreateAtomicRMW(opcode, output_address, source,
                                 llvm::AtomicOrdering::SequentiallyConsistent);
     return true;
   }
diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc
index ac65a47afa..a81f9f6ff9 100644
--- a/tensorflow/compiler/xla/tests/reduce_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_test.cc
@@ -457,6 +457,32 @@ XLA_TEST_F(ReduceTest, MinReduce2DToR0) {
   ComputeAndCompareR0<float>(&builder, input_min, {}, ErrorSpec(0.0001));
 }
 
+XLA_TEST_F(ReduceTest, UnsignedInt_MinReduce) {
+  ComputationBuilder builder(client_, TestName());
+  Array2D<uint32> input({{1}, {2}});
+  auto min = CreateScalarMinComputation(U32, &builder);
+  auto input_literal = Literal::CreateR2FromArray2D(input);
+  auto initial_value =
+      builder.ConstantR0<uint32>(std::numeric_limits<uint32>::max());
+
+  builder.Reduce(builder.ConstantLiteral(*input_literal), initial_value, min,
+                 {0, 1});
+  ComputeAndCompareR0<uint32>(&builder, 1, {});
+}
+
+XLA_TEST_F(ReduceTest, UnsignedInt_MaxReduce) {
+  ComputationBuilder builder(client_, TestName());
+  Array2D<uint32> input({{1}, {2}});
+  auto max = CreateScalarMaxComputation(U32, &builder);
+  auto input_literal = Literal::CreateR2FromArray2D(input);
+  auto initial_value =
+      builder.ConstantR0<uint32>(std::numeric_limits<uint32>::min());
+
+  builder.Reduce(builder.ConstantLiteral(*input_literal), initial_value, max,
+                 {0, 1});
+  ComputeAndCompareR0<uint32>(&builder, 2, {});
+}
+
 // Reduces a matrix among dimension 1.
 XLA_TEST_F(ReduceTest, Reduce2DAmong1) {
   ComputationBuilder builder(client_, TestName());
-- 
GitLab


From 2533ada7dd45b84d60677b8735e013d21044651a Mon Sep 17 00:00:00 2001
From: zhongzyd <zhongzyd@qq.com>
Date: Wed, 12 Jul 2017 13:31:37 +0800
Subject: [PATCH 0906/1375] Update initializers.py (#10985)

According to [Understanding the difficulty of training deep feedforward neural networks](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) and the codes, it is sqrt(2. / (in + out))
---
 tensorflow/contrib/layers/python/layers/initializers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/layers/python/layers/initializers.py b/tensorflow/contrib/layers/python/layers/initializers.py
index 271b3c01ff..b12a882d9a 100644
--- a/tensorflow/contrib/layers/python/layers/initializers.py
+++ b/tensorflow/contrib/layers/python/layers/initializers.py
@@ -42,7 +42,7 @@ def xavier_initializer(uniform=True, seed=None, dtype=dtypes.float32):
   This initializer is designed to keep the scale of the gradients roughly the
   same in all layers. In uniform distribution this ends up being the range:
   `x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
-  deviation of `sqrt(3. / (in + out))` is used.
+  deviation of `sqrt(2. / (in + out))` is used.
 
   Args:
     uniform: Whether to use uniform or normal distributed random initialization.
-- 
GitLab


From a05c3ce52ffd4909cb9bf7c155805406b5b1fc06 Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Tue, 11 Jul 2017 22:48:31 -0700
Subject: [PATCH 0907/1375] Add scratch allocator option for 1D, 2D, 3D, and
 batched cufft plan creation.

PiperOrigin-RevId: 161627209
---
 tensorflow/core/kernels/fft_ops.cc            | 108 ++++-
 .../python/kernel_tests/fft_ops_test.py       | 110 +++--
 tensorflow/stream_executor/cuda/cuda_fft.cc   | 393 +++++++++++++++---
 tensorflow/stream_executor/cuda/cuda_fft.h    |  52 ++-
 tensorflow/stream_executor/fft.h              | 121 ++++--
 5 files changed, 622 insertions(+), 162 deletions(-)

diff --git a/tensorflow/core/kernels/fft_ops.cc b/tensorflow/core/kernels/fft_ops.cc
index 35a3f7b189..6924de284b 100644
--- a/tensorflow/core/kernels/fft_ops.cc
+++ b/tensorflow/core/kernels/fft_ops.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 // See docs in ../ops/spectral_ops.cc.
 
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -24,8 +25,8 @@ limitations under the License.
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/env_var.h"
 #include "tensorflow/core/util/work_sharder.h"
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 
 #if GOOGLE_CUDA
 #include "tensorflow/core/platform/stream_executor.h"
@@ -276,22 +277,93 @@ REGISTER_KERNEL_BUILDER(Name("IRFFT3D").Device(DEVICE_CPU).Label(FFT_LABEL),
 #undef FFT_LABEL
 
 #if GOOGLE_CUDA
+namespace gpu = ::perftools::gputools;
 
 namespace {
-// TODO(vrv/zhifengc): Refactor AsDeviceMemory() into GPUUtil.
 template <typename T>
-perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory) {
-  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory));
-  perftools::gputools::DeviceMemory<T> typed(wrapped);
+gpu::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory) {
+  gpu::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory));
+  gpu::DeviceMemory<T> typed(wrapped);
+  return typed;
+}
+
+template <typename T>
+gpu::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory, uint64 size) {
+  gpu::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory), size * sizeof(T));
+  gpu::DeviceMemory<T> typed(wrapped);
   return typed;
 }
+
+// A class to provide scratch-space allocator for Stream-Executor Cufft
+// callback. Tensorflow is responsible for releasing the temporary buffers after
+// the kernel finishes.
+// TODO(yangzihao): Refactor redundant code in subclasses of ScratchAllocator
+// into base class.
+class CufftScratchAllocator : public gpu::ScratchAllocator {
+ public:
+  virtual ~CufftScratchAllocator() {}
+  CufftScratchAllocator(int64 memory_limit, OpKernelContext* context)
+      : memory_limit_(memory_limit), total_byte_size_(0), context_(context) {}
+  int64 GetMemoryLimitInBytes(gpu::Stream* stream) override {
+    return memory_limit_;
+  }
+  gpu::port::StatusOr<gpu::DeviceMemory<uint8>> AllocateBytes(
+      gpu::Stream* stream, int64 byte_size) override {
+    Tensor temporary_memory;
+    if (byte_size > memory_limit_) {
+      return gpu::port::StatusOr<gpu::DeviceMemory<uint8>>();
+    }
+    AllocationAttributes allocation_attr;
+    allocation_attr.no_retry_on_failure = true;
+    Status allocation_status(context_->allocate_temp(
+        DT_UINT8, TensorShape({byte_size}), &temporary_memory,
+        AllocatorAttributes(), allocation_attr));
+    if (!allocation_status.ok()) {
+      return gpu::port::StatusOr<gpu::DeviceMemory<uint8>>();
+    }
+    // Hold the reference of the allocated tensors until the end of the
+    // allocator.
+    allocated_tensors_.push_back(temporary_memory);
+    total_byte_size_ += byte_size;
+    return gpu::port::StatusOr<gpu::DeviceMemory<uint8>>(
+        AsDeviceMemory(temporary_memory.flat<uint8>().data(),
+                       temporary_memory.flat<uint8>().size()));
+  }
+  int64 TotalByteSize() { return total_byte_size_; }
+
+ private:
+  int64 memory_limit_;
+  int64 total_byte_size_;
+  OpKernelContext* context_;
+  std::vector<Tensor> allocated_tensors_;
+};
+
 }  // end namespace
 
+int64 GetCufftWorkspaceLimit(const string& envvar_in_mb,
+                             int64 default_value_in_bytes) {
+  const char* workspace_limit_in_mb_str = getenv(envvar_in_mb.c_str());
+  if (workspace_limit_in_mb_str != nullptr &&
+      strcmp(workspace_limit_in_mb_str, "") != 0) {
+    int64 scratch_limit_in_mb = -1;
+    Status status = ReadInt64FromEnvVar(envvar_in_mb, default_value_in_bytes,
+                                        &scratch_limit_in_mb);
+    if (!status.ok()) {
+      LOG(WARNING) << "Invalid value for env-var " << envvar_in_mb << ": "
+                   << workspace_limit_in_mb_str;
+    } else {
+      return scratch_limit_in_mb * (1 << 20);
+    }
+  }
+  return default_value_in_bytes;
+}
+
 class FFTGPUBase : public FFTBase {
  public:
   using FFTBase::FFTBase;
 
  protected:
+  static int64 CufftScratchSize;
   void DoFFT(OpKernelContext* ctx, const Tensor& in, uint64* fft_shape,
              Tensor* out) override {
     auto* stream = ctx->op_device_context()->stream();
@@ -306,10 +378,10 @@ class FFTGPUBase : public FFTBase {
       batch_size *= input_shape.dim_size(i);
     }
     uint64 input_embed[3];
-    uint64 input_stride = 1;
+    const uint64 input_stride = 1;
     uint64 input_distance = 1;
     uint64 output_embed[3];
-    uint64 output_stride = 1;
+    const uint64 output_stride = 1;
     uint64 output_distance = 1;
 
     for (int i = 0; i < fft_rank; ++i) {
@@ -322,15 +394,16 @@ class FFTGPUBase : public FFTBase {
 
     constexpr bool kInPlaceFft = false;
     const auto kFftType =
-        IsReal() ? (IsForward() ? perftools::gputools::fft::Type::kR2C
-                                : perftools::gputools::fft::Type::kC2R)
-                 : (IsForward() ? perftools::gputools::fft::Type::kC2CForward
-                                : perftools::gputools::fft::Type::kC2CInverse);
+        IsReal() ? (IsForward() ? gpu::fft::Type::kR2C : gpu::fft::Type::kC2R)
+                 : (IsForward() ? gpu::fft::Type::kC2CForward
+                                : gpu::fft::Type::kC2CInverse);
 
-    auto plan = stream->parent()->AsFft()->CreateBatchedPlan(
-        stream, fft_rank, fft_shape, input_embed, input_stride, input_distance,
-        output_embed, output_stride, output_distance, kFftType, kInPlaceFft,
-        batch_size);
+    CufftScratchAllocator scratch_allocator(CufftScratchSize, ctx);
+    auto plan =
+        stream->parent()->AsFft()->CreateBatchedPlanWithScratchAllocator(
+            stream, fft_rank, fft_shape, input_embed, input_stride,
+            input_distance, output_embed, output_stride, output_distance,
+            kFftType, kInPlaceFft, batch_size, &scratch_allocator);
 
     if (IsReal()) {
       if (IsForward()) {
@@ -375,6 +448,11 @@ class FFTGPUBase : public FFTBase {
   }
 };
 
+int64 FFTGPUBase::CufftScratchSize = GetCufftWorkspaceLimit(
+    // default value is in bytes despite the name of the environment variable
+    "TF_CUFFT_WORKSPACE_LIMIT_IN_MB", 1LL << 32  // 4GB
+);
+
 template <bool Forward, bool _Real, int FFTRank>
 class FFTGPU : public FFTGPUBase {
  public:
diff --git a/tensorflow/python/kernel_tests/fft_ops_test.py b/tensorflow/python/kernel_tests/fft_ops_test.py
index 6c575aea12..b6a42731e8 100644
--- a/tensorflow/python/kernel_tests/fft_ops_test.py
+++ b/tensorflow/python/kernel_tests/fft_ops_test.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
@@ -61,11 +62,11 @@ class BaseFFTOpsTest(test.TestCase):
     else:
       return ops.get_default_graph()._kernel_label_map({})
 
-  def _Compare(self, x, rank, fft_length=None, use_placeholder=False):
-    self._CompareForward(x, rank, fft_length, use_placeholder)
-    self._CompareBackward(x, rank, fft_length, use_placeholder)
+  def _compare(self, x, rank, fft_length=None, use_placeholder=False):
+    self._compareForward(x, rank, fft_length, use_placeholder)
+    self._compareBackward(x, rank, fft_length, use_placeholder)
 
-  def _CompareForward(self, x, rank, fft_length=None, use_placeholder=False):
+  def _compareForward(self, x, rank, fft_length=None, use_placeholder=False):
     x_np = self._npFFT(x, rank, fft_length)
     if use_placeholder:
       x_ph = array_ops.placeholder(dtype=dtypes.as_dtype(x.dtype))
@@ -75,7 +76,7 @@ class BaseFFTOpsTest(test.TestCase):
 
     self.assertAllClose(x_np, x_tf, rtol=1e-4, atol=1e-4)
 
-  def _CompareBackward(self, x, rank, fft_length=None, use_placeholder=False):
+  def _compareBackward(self, x, rank, fft_length=None, use_placeholder=False):
     x_np = self._npIFFT(x, rank, fft_length)
     if use_placeholder:
       x_ph = array_ops.placeholder(dtype=dtypes.as_dtype(x.dtype))
@@ -85,6 +86,12 @@ class BaseFFTOpsTest(test.TestCase):
 
     self.assertAllClose(x_np, x_tf, rtol=1e-4, atol=1e-4)
 
+  def _checkMemoryFail(self, x, rank):
+    config = config_pb2.ConfigProto()
+    config.gpu_options.per_process_gpu_memory_fraction = 1e-2
+    with self.test_session(config=config, force_gpu=True):
+      self._tfFFT(x, rank, fft_length=None)
+
   def _checkGradComplex(self, func, x, y, result_is_complex=True):
     with self.test_session(use_gpu=True):
       inx = ops.convert_to_tensor(x)
@@ -182,15 +189,33 @@ class FFTOpsTest(BaseFFTOpsTest):
     with self._fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
-          self._Compare(
+          self._compare(
               np.mod(np.arange(np.power(4, dims)), 10).reshape(
                   (4,) * dims).astype(np.complex64), rank)
 
+  def testLargeBatch(self):
+    if test.is_gpu_available(cuda_only=True):
+      rank = 1
+      for dims in xrange(rank, rank + 3):
+        self._compare(
+            np.mod(np.arange(np.power(128, dims)), 10).reshape(
+                (128,) * dims).astype(np.complex64), rank)
+
+  # TODO(yangzihao): Disable before we can figure out a way to
+  # properly test memory fail for large batch fft.
+  # def testLargeBatchMemoryFail(self):
+  #   if test.is_gpu_available(cuda_only=True):
+  #     rank = 1
+  #     for dims in xrange(rank, rank + 3):
+  #       self._checkMemoryFail(
+  #           np.mod(np.arange(np.power(128, dims)), 64).reshape(
+  #               (128,) * dims).astype(np.complex64), rank)
+
   def testBasicPlaceholder(self):
     with self._fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
-          self._Compare(
+          self._compare(
               np.mod(np.arange(np.power(4, dims)), 10).reshape(
                   (4,) * dims).astype(np.complex64),
               rank,
@@ -208,7 +233,7 @@ class FFTOpsTest(BaseFFTOpsTest):
 
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
-          self._Compare(gen((4,) * dims), rank)
+          self._compare(gen((4,) * dims), rank)
 
   def testError(self):
     for rank in VALID_FFT_RANKS:
@@ -243,8 +268,8 @@ class FFTOpsTest(BaseFFTOpsTest):
 
 class RFFTOpsTest(BaseFFTOpsTest):
 
-  def _CompareBackward(self, x, rank, fft_length=None, use_placeholder=False):
-    super(RFFTOpsTest, self)._CompareBackward(x, rank, fft_length,
+  def _compareBackward(self, x, rank, fft_length=None, use_placeholder=False):
+    super(RFFTOpsTest, self)._compareBackward(x, rank, fft_length,
                                               use_placeholder)
 
   def _tfFFT(self, x, rank, fft_length=None, feed_dict=None):
@@ -312,12 +337,25 @@ class RFFTOpsTest(BaseFFTOpsTest):
             inner_dim = size // 2 + 1
             r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
                 (size,) * dims)
-            self._CompareForward(r2c.astype(np.float32), rank, (size,) * rank)
+            self._compareForward(r2c.astype(np.float32), rank, (size,) * rank)
             c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
                          10).reshape((size,) * (dims - 1) + (inner_dim,))
-            self._CompareBackward(
+            self._compareBackward(
                 c2r.astype(np.complex64), rank, (size,) * rank)
 
+  def testLargeBatch(self):
+    if test.is_gpu_available(cuda_only=True):
+      rank = 1
+      for dims in xrange(rank, rank + 3):
+        for size in (64, 128):
+          inner_dim = size // 2 + 1
+          r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
+              (size,) * dims)
+          self._compareForward(r2c.astype(np.float32), rank, (size,) * rank)
+          c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
+                       10).reshape((size,) * (dims - 1) + (inner_dim,))
+          self._compareBackward(c2r.astype(np.complex64), rank, (size,) * rank)
+
   def testBasicPlaceholder(self):
     with self._fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
@@ -326,13 +364,13 @@ class RFFTOpsTest(BaseFFTOpsTest):
             inner_dim = size // 2 + 1
             r2c = np.mod(np.arange(np.power(size, dims)), 10).reshape(
                 (size,) * dims)
-            self._CompareForward(
+            self._compareForward(
                 r2c.astype(np.float32),
                 rank, (size,) * rank,
                 use_placeholder=True)
             c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
                          10).reshape((size,) * (dims - 1) + (inner_dim,))
-            self._CompareBackward(
+            self._compareBackward(
                 c2r.astype(np.complex64),
                 rank, (size,) * rank,
                 use_placeholder=True)
@@ -348,28 +386,36 @@ class RFFTOpsTest(BaseFFTOpsTest):
                   (size,) * dims)
               c2r = np.mod(np.arange(np.power(size, dims - 1) * inner_dim),
                            10).reshape((size,) * (dims - 1) + (inner_dim,))
-
               # Test truncation (FFT size < dimensions).
               fft_length = (size - 2,) * rank
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
+              self._compareForward(r2c.astype(np.float32), rank, fft_length)
+              self._compareBackward(c2r.astype(np.complex64), rank, fft_length)
               # Confirm it works with unknown shapes as well.
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                   use_placeholder=True)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                    use_placeholder=True)
-
+              self._compareForward(
+                  r2c.astype(np.float32),
+                  rank,
+                  fft_length,
+                  use_placeholder=True)
+              self._compareBackward(
+                  c2r.astype(np.complex64),
+                  rank,
+                  fft_length,
+                  use_placeholder=True)
               # Test padding (FFT size > dimensions).
               fft_length = (size + 2,) * rank
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length)
-
+              self._compareForward(r2c.astype(np.float32), rank, fft_length)
+              self._compareBackward(c2r.astype(np.complex64), rank, fft_length)
               # Confirm it works with unknown shapes as well.
-              self._CompareForward(r2c.astype(np.float32), rank, fft_length,
-                                   use_placeholder=True)
-              self._CompareBackward(c2r.astype(np.complex64), rank, fft_length,
-                                    use_placeholder=True)
+              self._compareForward(
+                  r2c.astype(np.float32),
+                  rank,
+                  fft_length,
+                  use_placeholder=True)
+              self._compareBackward(
+                  c2r.astype(np.complex64),
+                  rank,
+                  fft_length,
+                  use_placeholder=True)
 
   def testRandom(self):
     with self._fft_kernel_label_map():
@@ -392,9 +438,9 @@ class RFFTOpsTest(BaseFFTOpsTest):
         for dims in xrange(rank, rank + 3):
           for size in (5, 6):
             inner_dim = size // 2 + 1
-            self._CompareForward(gen_real((size,) * dims), rank, (size,) * rank)
+            self._compareForward(gen_real((size,) * dims), rank, (size,) * rank)
             complex_dims = (size,) * (dims - 1) + (inner_dim,)
-            self._CompareBackward(
+            self._compareBackward(
                 gen_complex(complex_dims), rank, (size,) * rank)
 
   def testError(self):
diff --git a/tensorflow/stream_executor/cuda/cuda_fft.cc b/tensorflow/stream_executor/cuda/cuda_fft.cc
index 520a3598d7..0f465bea7a 100644
--- a/tensorflow/stream_executor/cuda/cuda_fft.cc
+++ b/tensorflow/stream_executor/cuda/cuda_fft.cc
@@ -53,12 +53,20 @@ namespace wrap {
     }                                                            \
   } __name;
 
-#define CUFFT_ROUTINE_EACH(__macro)                                         \
-  __macro(cufftDestroy) __macro(cufftSetStream) __macro(cufftPlan1d)        \
-      __macro(cufftPlan2d) __macro(cufftPlan3d) __macro(cufftPlanMany)      \
-          __macro(cufftExecD2Z) __macro(cufftExecZ2D) __macro(cufftExecC2C) \
-              __macro(cufftExecC2R) __macro(cufftExecZ2Z)                   \
-                  __macro(cufftExecR2C)
+#define CUFFT_ROUTINE_EACH(__macro)                                            \
+  __macro(cufftDestroy) __macro(cufftSetStream) __macro(cufftPlan1d)           \
+      __macro(cufftPlan2d) __macro(cufftPlan3d) __macro(cufftPlanMany)         \
+          __macro(cufftExecD2Z) __macro(cufftExecZ2D) __macro(cufftExecC2C)    \
+              __macro(cufftExecC2R) __macro(cufftExecZ2Z)                      \
+                  __macro(cufftExecR2C) __macro(cufftCreate)                   \
+                      __macro(cufftSetAutoAllocation)                          \
+                          __macro(cufftSetWorkArea) __macro(cufftGetSize1d)    \
+                              __macro(cufftMakePlan1d) __macro(cufftGetSize2d) \
+                                  __macro(cufftMakePlan2d)                     \
+                                      __macro(cufftGetSize3d)                  \
+                                          __macro(cufftMakePlan3d)             \
+                                              __macro(cufftGetSizeMany)        \
+                                                  __macro(cufftMakePlanMany)
 
 CUFFT_ROUTINE_EACH(PERFTOOLS_GPUTOOLS_CUFFT_WRAP)
 
@@ -100,40 +108,15 @@ bool SetStream(CUDAExecutor *parent, cufftHandle plan, Stream *stream) {
 
 }  // namespace
 
-CUDAFftPlan::CUDAFftPlan(CUDAExecutor *parent, uint64 num_x, fft::Type type)
-    : parent_(parent), fft_type_(type) {
-  auto ret = wrap::cufftPlan1d(parent, &plan_, num_x, CUDAFftType(type),
-                               1 /* = batch */);
-  if (ret != CUFFT_SUCCESS) {
-    LOG(ERROR) << "failed to create cuFFT 1d plan:" << ret;
-  }
-}
-
-CUDAFftPlan::CUDAFftPlan(CUDAExecutor *parent, uint64 num_x, uint64 num_y,
-                         fft::Type type)
-    : parent_(parent), fft_type_(type) {
-  auto ret = wrap::cufftPlan2d(parent, &plan_, num_x, num_y, CUDAFftType(type));
-  if (ret != CUFFT_SUCCESS) {
-    LOG(ERROR) << "failed to create cuFFT 2d plan:" << ret;
+port::Status CUDAFftPlan::Initialize(
+    CUDAExecutor *parent, Stream *stream, int rank, uint64 *elem_count,
+    uint64 *input_embed, uint64 input_stride, uint64 input_distance,
+    uint64 *output_embed, uint64 output_stride, uint64 output_distance,
+    fft::Type type, int batch_count, ScratchAllocator *scratch_allocator) {
+  if (IsInitialized()) {
+    LOG(FATAL) << "Try to repeatedly initialize.";
   }
-}
-
-CUDAFftPlan::CUDAFftPlan(CUDAExecutor *parent, uint64 num_x, uint64 num_y,
-                         uint64 num_z, fft::Type type)
-    : parent_(parent), fft_type_(type) {
-  auto ret =
-      wrap::cufftPlan3d(parent, &plan_, num_x, num_y, num_z, CUDAFftType(type));
-  if (ret != CUFFT_SUCCESS) {
-    LOG(ERROR) << "failed to create cuFFT 3d plan:" << ret;
-  }
-}
-
-CUDAFftPlan::CUDAFftPlan(CUDAExecutor *parent, int rank, uint64 *elem_count,
-                         uint64 *input_embed, uint64 input_stride,
-                         uint64 input_distance, uint64 *output_embed,
-                         uint64 output_stride, uint64 output_distance,
-                         fft::Type type, int batch_count)
-    : parent_(parent), fft_type_(type) {
+  is_initialized_ = true;
   int elem_count_[3], input_embed_[3], output_embed_[3];
   for (int i = 0; i < rank; ++i) {
     elem_count_[i] = elem_count[i];
@@ -144,55 +127,302 @@ CUDAFftPlan::CUDAFftPlan(CUDAExecutor *parent, int rank, uint64 *elem_count,
       output_embed_[i] = output_embed[i];
     }
   }
-  auto ret = wrap::cufftPlanMany(
-      parent, &plan_, rank, elem_count_, input_embed ? input_embed_ : nullptr,
-      input_stride, input_distance, output_embed ? output_embed_ : nullptr,
-      output_stride, output_distance, CUDAFftType(type), batch_count);
-  if (ret != CUFFT_SUCCESS) {
-    LOG(ERROR) << "failed to create cuFFT batched plan:" << ret;
+  parent_ = parent;
+  fft_type_ = type;
+  if (batch_count == 1 && input_embed == nullptr && output_embed == nullptr) {
+    cufftResult_t ret;
+    if (scratch_allocator == nullptr) {
+      switch (rank) {
+        case 1:
+          // cufftPlan1d
+          ret = wrap::cufftPlan1d(parent, &plan_, elem_count_[0],
+                                  CUDAFftType(type), 1 /* = batch */);
+          if (ret != CUFFT_SUCCESS) {
+            LOG(ERROR) << "failed to create cuFFT 1d plan:" << ret;
+            return port::Status{port::error::INTERNAL,
+                                "Failed to create cuFFT 1d plan."};
+          }
+          return port::Status::OK();
+        case 2:
+          // cufftPlan2d
+          ret = wrap::cufftPlan2d(parent, &plan_, elem_count_[0],
+                                  elem_count_[1], CUDAFftType(type));
+          if (ret != CUFFT_SUCCESS) {
+            LOG(ERROR) << "failed to create cuFFT 2d plan:" << ret;
+            return port::Status{port::error::INTERNAL,
+                                "Failed to create cuFFT 2d plan."};
+          }
+          return port::Status::OK();
+        case 3:
+          // cufftPlan3d
+          ret =
+              wrap::cufftPlan3d(parent, &plan_, elem_count_[0], elem_count_[1],
+                                elem_count_[2], CUDAFftType(type));
+          if (ret != CUFFT_SUCCESS) {
+            LOG(ERROR) << "failed to create cuFFT 3d plan:" << ret;
+            return port::Status{port::error::INTERNAL,
+                                "Failed to create cuFFT 3d plan."};
+          }
+          return port::Status::OK();
+        default:
+          LOG(ERROR) << "Invalid rank value for cufftPlan. "
+                        "Requested 1, 2, or 3, given: "
+                     << rank;
+          return port::Status{port::error::INVALID_ARGUMENT,
+                              "cufftPlan only takes rank 1, 2, or 3."};
+      }
+    } else {
+      ret = wrap::cufftCreate(parent, &plan_);
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to create cuFFT plan:" << ret;
+        return port::Status{port::error::INTERNAL,
+                            "Failed to create cuFFT plan."};
+      }
+      ret = wrap::cufftSetAutoAllocation(parent, plan_, 0);
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to set auto allocation for cuFFT plan:" << ret;
+        return port::Status{port::error::INTERNAL,
+                            "Failed to set auto allocation for cuFFT plan."};
+      }
+      size_t size_in_bytes;
+      switch (rank) {
+        case 1:
+          ret = wrap::cufftMakePlan1d(parent, plan_, elem_count_[0],
+                                      CUDAFftType(type), /*batch=*/1,
+                                      &size_in_bytes);
+          if (ret != CUFFT_SUCCESS) {
+            LOG(ERROR) << "failed to make cuFFT 1d plan:" << ret;
+            return port::Status{port::error::INTERNAL,
+                                "Failed to make cuFFT 1d plan."};
+          }
+          break;
+        case 2:
+          ret = wrap::cufftMakePlan2d(parent, plan_, elem_count_[0],
+                                      elem_count_[1], CUDAFftType(type),
+                                      &size_in_bytes);
+          if (ret != CUFFT_SUCCESS) {
+            LOG(ERROR) << "failed to make cuFFT 2d plan:" << ret;
+            return port::Status{port::error::INTERNAL,
+                                "Failed to make cuFFT 2d plan."};
+          }
+          break;
+        case 3:
+          ret = wrap::cufftMakePlan3d(parent, plan_, elem_count_[0],
+                                      elem_count_[1], elem_count_[2],
+                                      CUDAFftType(type), &size_in_bytes);
+          if (ret != CUFFT_SUCCESS) {
+            LOG(ERROR) << "failed to make cuFFT 3d plan:" << ret;
+            return port::Status{port::error::INTERNAL,
+                                "Failed to make cuFFT 3d plan."};
+          }
+          break;
+        default:
+          LOG(ERROR) << "Invalid rank value for cufftPlan. "
+                        "Requested 1, 2, or 3, given: "
+                     << rank;
+          return port::Status{port::error::INVALID_ARGUMENT,
+                              "cufftPlan only takes rank 1, 2, or 3."};
+      }
+      // TODO(yangzihao): refactor this code and the one with the same function
+      // in the batch mode.
+      if (size_in_bytes != 0) {
+        auto allocated =
+            scratch_allocator->AllocateBytes(stream, size_in_bytes);
+        if (!allocated.ok() || (scratch_ = allocated.ValueOrDie()) == nullptr) {
+          LOG(ERROR) << "failed to allocate work area.";
+          return allocated.status();
+        }
+      }
+      // Connect work area with allocated space.
+      ret = wrap::cufftSetWorkArea(parent, plan_, scratch_.opaque());
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to set work area for cuFFT plan:" << ret;
+        return port::Status{port::error::INTERNAL,
+                            "Failed to set work area for cuFFT plan."};
+      }
+      return port::Status::OK();
+    }
+  } else {
+    // For either multiple batches or rank higher than 3, use cufftPlanMany().
+    if (scratch_allocator == nullptr) {
+      auto ret = wrap::cufftPlanMany(
+          parent, &plan_, rank, elem_count_,
+          input_embed ? input_embed_ : nullptr, input_stride, input_distance,
+          output_embed ? output_embed_ : nullptr, output_stride,
+          output_distance, CUDAFftType(type), batch_count);
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to create cuFFT batched plan:" << ret;
+        return port::Status{port::error::INTERNAL,
+                            "Failed to create cuFFT bacthed plan."};
+      }
+    } else {
+      auto ret = wrap::cufftCreate(parent, &plan_);
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to create cuFFT batched plan:" << ret;
+        return port::Status{port::error::INTERNAL,
+                            "Failed to create cuFFT bacthed plan."};
+      }
+      ret = wrap::cufftSetAutoAllocation(parent, plan_, 0);
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to set auto allocation for cuFFT batched plan:"
+                   << ret;
+        return port::Status{
+            port::error::INTERNAL,
+            "Failed to set auto allocation for cuFFT bacthed plan."};
+      }
+      size_t size_in_bytes;
+      ret = wrap::cufftMakePlanMany(
+          parent, plan_, rank, elem_count_,
+          input_embed ? input_embed_ : nullptr, input_stride, input_distance,
+          output_embed ? output_embed_ : nullptr, output_stride,
+          output_distance, CUDAFftType(type), batch_count, &size_in_bytes);
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to make cuFFT batched plan:" << ret;
+        return port::Status{port::error::INTERNAL,
+                            "Failed to make cuFFT bacthed plan."};
+      }
+      if (size_in_bytes != 0) {
+        auto allocated =
+            scratch_allocator->AllocateBytes(stream, size_in_bytes);
+        if (!allocated.ok() || (scratch_ = allocated.ValueOrDie()) == nullptr) {
+          LOG(ERROR) << "failed to allocate work area.";
+          return allocated.status();
+        }
+      }
+      // Connect work area with allocated space.
+      ret = wrap::cufftSetWorkArea(parent, plan_, scratch_.opaque());
+      if (ret != CUFFT_SUCCESS) {
+        LOG(ERROR) << "failed to set work area for cuFFT batched plan:" << ret;
+        return port::Status{port::error::INTERNAL,
+                            "Failed to set work area for cuFFT bacthed plan."};
+      }
+    }
   }
+  return port::Status::OK();
+}
+
+port::Status CUDAFftPlan::Initialize(CUDAExecutor *parent, Stream *stream,
+                                     int rank, uint64 *elem_count,
+                                     fft::Type type,
+                                     ScratchAllocator *scratch_allocator) {
+  return Initialize(parent_, stream, rank, elem_count,
+                    /*input_embed=*/nullptr, /*input_stride=*/0,
+                    /*input_distance=*/0,
+                    /*output_embed=*/nullptr, /*output_stride=*/0,
+                    /*output_distance=*/0, type, 1, scratch_allocator);
 }
 
 CUDAFftPlan::~CUDAFftPlan() { wrap::cufftDestroy(parent_, plan_); }
 
 int CUDAFftPlan::GetFftDirection() const {
-  switch (fft_type_) {
-    case fft::Type::kC2CForward:
-    case fft::Type::kZ2ZForward:
-    case fft::Type::kR2C:
-    case fft::Type::kD2Z:
-      return CUFFT_FORWARD;
-    case fft::Type::kC2CInverse:
-    case fft::Type::kZ2ZInverse:
-    case fft::Type::kC2R:
-    case fft::Type::kZ2D:
-      return CUFFT_INVERSE;
-    default:
-      LOG(FATAL) << "Invalid value of fft::Type.";
+  if (!IsInitialized()) {
+    LOG(FATAL) << "Try to get fft direction before initialization.";
+  } else {
+    switch (fft_type_) {
+      case fft::Type::kC2CForward:
+      case fft::Type::kZ2ZForward:
+      case fft::Type::kR2C:
+      case fft::Type::kD2Z:
+        return CUFFT_FORWARD;
+      case fft::Type::kC2CInverse:
+      case fft::Type::kZ2ZInverse:
+      case fft::Type::kC2R:
+      case fft::Type::kZ2D:
+        return CUFFT_INVERSE;
+      default:
+        LOG(FATAL) << "Invalid value of fft::Type.";
+    }
   }
 }
 
 std::unique_ptr<fft::Plan> CUDAFft::Create1dPlan(Stream *stream, uint64 num_x,
                                                  fft::Type type,
                                                  bool in_place_fft) {
-  std::unique_ptr<fft::Plan> plan{new CUDAFftPlan(parent_, num_x, type)};
-  return plan;
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  uint64 elem_count[1] = {num_x};
+  port::Status status = fft_plan_ptr->Initialize(
+      parent_, stream, 1, elem_count, type, /*scratch_allocator=*/nullptr);
+  // TODO(yangzihao): In the future, send error msg back to TensorFlow
+  // so it can fail gracefully,
+  if (!status.ok()) {
+    LOG(FATAL) << "failed to initialize cufft 1d plan: "
+               << status.error_message();
+  }
+  return std::move(fft_plan_ptr);
+}
+
+std::unique_ptr<fft::Plan> CUDAFft::Create1dPlanWithScratchAllocator(
+    Stream *stream, uint64 num_x, fft::Type type, bool in_place_fft,
+    ScratchAllocator *scratch_allocator) {
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  uint64 elem_count[1] = {num_x};
+  port::Status status = fft_plan_ptr->Initialize(parent_, stream, 1, elem_count,
+                                                 type, scratch_allocator);
+  if (!status.ok()) {
+    LOG(FATAL)
+        << "failed to initialize cufft 1d plan with customized allocator: "
+        << status.error_message();
+  }
+  return std::move(fft_plan_ptr);
 }
 
 std::unique_ptr<fft::Plan> CUDAFft::Create2dPlan(Stream *stream, uint64 num_x,
                                                  uint64 num_y, fft::Type type,
                                                  bool in_place_fft) {
-  std::unique_ptr<fft::Plan> plan{new CUDAFftPlan(parent_, num_x, num_y, type)};
-  return plan;
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  uint64 elem_count[2] = {num_x, num_y};
+  port::Status status = fft_plan_ptr->Initialize(
+      parent_, stream, 1, elem_count, type, /*scratch_allocator=*/nullptr);
+  if (!status.ok()) {
+    LOG(FATAL) << "failed to initialize cufft 2d plan: "
+               << status.error_message();
+  }
+  return std::move(fft_plan_ptr);
+}
+
+std::unique_ptr<fft::Plan> CUDAFft::Create2dPlanWithScratchAllocator(
+    Stream *stream, uint64 num_x, uint64 num_y, fft::Type type,
+    bool in_place_fft, ScratchAllocator *scratch_allocator) {
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  uint64 elem_count[2] = {num_x, num_y};
+  port::Status status = fft_plan_ptr->Initialize(parent_, stream, 2, elem_count,
+                                                 type, scratch_allocator);
+  if (!status.ok()) {
+    LOG(FATAL)
+        << "failed to initialize cufft 2d plan with customized allocator: "
+        << status.error_message();
+  }
+  return std::move(fft_plan_ptr);
 }
 
 std::unique_ptr<fft::Plan> CUDAFft::Create3dPlan(Stream *stream, uint64 num_x,
                                                  uint64 num_y, uint64 num_z,
                                                  fft::Type type,
                                                  bool in_place_fft) {
-  std::unique_ptr<fft::Plan> plan{
-      new CUDAFftPlan(parent_, num_x, num_y, num_z, type)};
-  return plan;
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  uint64 elem_count[3] = {num_x, num_y, num_z};
+  port::Status status = fft_plan_ptr->Initialize(
+      parent_, stream, 3, elem_count, type, /*scratch_allocator=*/nullptr);
+  if (!status.ok()) {
+    LOG(FATAL) << "failed to initialize cufft 3d plan: "
+               << status.error_message();
+  }
+  return std::move(fft_plan_ptr);
+}
+
+std::unique_ptr<fft::Plan> CUDAFft::Create3dPlanWithScratchAllocator(
+    Stream *stream, uint64 num_x, uint64 num_y, uint64 num_z, fft::Type type,
+    bool in_place_fft, ScratchAllocator *scratch_allocator) {
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  uint64 elem_count[3] = {num_x, num_y, num_z};
+  port::Status status = fft_plan_ptr->Initialize(parent_, stream, 3, elem_count,
+                                                 type, scratch_allocator);
+  if (!status.ok()) {
+    LOG(FATAL)
+        << "failed to initialize cufft 3d plan with customized allocator: "
+        << status.error_message();
+  }
+  return std::move(fft_plan_ptr);
 }
 
 std::unique_ptr<fft::Plan> CUDAFft::CreateBatchedPlan(
@@ -200,10 +430,35 @@ std::unique_ptr<fft::Plan> CUDAFft::CreateBatchedPlan(
     uint64 input_stride, uint64 input_distance, uint64 *output_embed,
     uint64 output_stride, uint64 output_distance, fft::Type type,
     bool in_place_fft, int batch_count) {
-  std::unique_ptr<fft::Plan> plan{new CUDAFftPlan(
-      parent_, rank, elem_count, input_embed, input_stride, input_distance,
-      output_embed, output_stride, output_distance, type, batch_count)};
-  return plan;
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  port::Status status = fft_plan_ptr->Initialize(
+      parent_, stream, rank, elem_count, input_embed, input_stride,
+      input_distance, output_embed, output_stride, output_distance, type,
+      batch_count, /*scratch_allocator=*/nullptr);
+  if (!status.ok()) {
+    LOG(FATAL) << "failed to initialize batched cufft plan: "
+               << status.error_message();
+  }
+
+  return std::move(fft_plan_ptr);
+}
+
+std::unique_ptr<fft::Plan> CUDAFft::CreateBatchedPlanWithScratchAllocator(
+    Stream *stream, int rank, uint64 *elem_count, uint64 *input_embed,
+    uint64 input_stride, uint64 input_distance, uint64 *output_embed,
+    uint64 output_stride, uint64 output_distance, fft::Type type,
+    bool in_place_fft, int batch_count, ScratchAllocator *scratch_allocator) {
+  std::unique_ptr<CUDAFftPlan> fft_plan_ptr{new CUDAFftPlan()};
+  port::Status status = fft_plan_ptr->Initialize(
+      parent_, stream, rank, elem_count, input_embed, input_stride,
+      input_distance, output_embed, output_stride, output_distance, type,
+      batch_count, scratch_allocator);
+  if (!status.ok()) {
+    LOG(FATAL)
+        << "failed to initialize batched cufft plan with customized allocator: "
+        << status.error_message();
+  }
+  return std::move(fft_plan_ptr);
 }
 
 template <typename FuncT, typename InputT, typename OutputT>
diff --git a/tensorflow/stream_executor/cuda/cuda_fft.h b/tensorflow/stream_executor/cuda/cuda_fft.h
index 95b3e8de63..16102eb945 100644
--- a/tensorflow/stream_executor/cuda/cuda_fft.h
+++ b/tensorflow/stream_executor/cuda/cuda_fft.h
@@ -20,10 +20,11 @@ limitations under the License.
 #ifndef TENSORFLOW_STREAM_EXECUTOR_CUDA_CUDA_FFT_H_
 #define TENSORFLOW_STREAM_EXECUTOR_CUDA_CUDA_FFT_H_
 
+#include "cuda/include/cufft.h"
 #include "tensorflow/stream_executor/fft.h"
 #include "tensorflow/stream_executor/platform/port.h"
 #include "tensorflow/stream_executor/plugin_registry.h"
-#include "cuda/include/cufft.h"
+#include "tensorflow/stream_executor/scratch_allocator.h"
 
 namespace perftools {
 namespace gputools {
@@ -37,30 +38,53 @@ class CUDAExecutor;
 // Opaque and unique indentifier for the cuFFT plugin.
 extern const PluginId kCuFftPlugin;
 
+// CUDAFftPlan uses deferred initialization. Only a single call of
+// Initialize() is allowed to properly create cufft plan and set member
+// variable is_initialized_ to true. Newly added interface that uses member
+// variables should first check is_initialized_ to make sure that the values of
+// member variables are valid.
 class CUDAFftPlan : public fft::Plan {
  public:
-  // Constructor creating 1d FFT plan.
-  CUDAFftPlan(CUDAExecutor *parent, uint64 num_x, fft::Type type);
-  // Constructor creating 2d FFT plan.
-  CUDAFftPlan(CUDAExecutor *parent, uint64 num_x, uint64 num_y, fft::Type type);
-  // Constructor creating 3d FFT plan.
-  CUDAFftPlan(CUDAExecutor *parent, uint64 num_x, uint64 num_y, uint64 num_z,
-              fft::Type type);
-  // Constructor creating batched FFT plan.
-  CUDAFftPlan(CUDAExecutor *parent, int rank, uint64 *elem_count,
-              uint64 *input_embed, uint64 input_stride, uint64 input_distance,
-              uint64 *output_embed, uint64 output_stride,
-              uint64 output_distance, fft::Type type, int batch_count);
+  CUDAFftPlan()
+      : parent_(nullptr),
+        plan_(-1),
+        fft_type_(fft::Type::kInvalid),
+        scratch_(nullptr),
+        is_initialized_(false) {}
   ~CUDAFftPlan() override;
 
   // Get FFT direction in cuFFT based on FFT type.
   int GetFftDirection() const;
-  cufftHandle GetPlan() const { return plan_; }
+  cufftHandle GetPlan() const {
+    if (IsInitialized()) {
+      return plan_;
+    } else {
+      LOG(FATAL) << "Try to get cufftHandle value before initialization.";
+    }
+  }
+
+  // Initialize function for batched plan
+  port::Status Initialize(CUDAExecutor *parent, Stream *stream, int rank,
+                          uint64 *elem_count, uint64 *input_embed,
+                          uint64 input_stride, uint64 input_distance,
+                          uint64 *output_embed, uint64 output_stride,
+                          uint64 output_distance, fft::Type type,
+                          int batch_count, ScratchAllocator *scratch_allocator);
+
+  // Initialize function for 1d,2d, and 3d plan
+  port::Status Initialize(CUDAExecutor *parent, Stream *stream, int rank,
+                          uint64 *elem_count, fft::Type type,
+                          ScratchAllocator *scratch_allocator);
+
+ protected:
+  bool IsInitialized() const { return is_initialized_; }
 
  private:
   CUDAExecutor *parent_;
   cufftHandle plan_;
   fft::Type fft_type_;
+  DeviceMemory<uint8> scratch_;
+  bool is_initialized_;
 };
 
 // FFT support for CUDA platform via cuFFT library.
diff --git a/tensorflow/stream_executor/fft.h b/tensorflow/stream_executor/fft.h
index 6e921d142b..98cd77e206 100644
--- a/tensorflow/stream_executor/fft.h
+++ b/tensorflow/stream_executor/fft.h
@@ -54,12 +54,14 @@ namespace gputools {
 class Stream;
 template <typename ElemT>
 class DeviceMemory;
+class ScratchAllocator;
 
 namespace fft {
 
 // Specifies FFT input and output types, and the direction.
 // R, D, C, and Z stand for SP real, DP real, SP complex, and DP complex.
 enum class Type {
+  kInvalid,
   kC2CForward,
   kC2CInverse,
   kC2R,
@@ -103,6 +105,21 @@ class FftSupport {
                                              uint64 num_y, uint64 num_z,
                                              Type type, bool in_place_fft) = 0;
 
+  // Creates a 1d FFT plan with scratch allocator.
+  virtual std::unique_ptr<Plan> Create1dPlanWithScratchAllocator(
+      Stream *stream, uint64 num_x, Type type, bool in_place_fft,
+      ScratchAllocator *scratch_allocator) = 0;
+
+  // Creates a 2d FFT plan with scratch allocator.
+  virtual std::unique_ptr<Plan> Create2dPlanWithScratchAllocator(
+      Stream *stream, uint64 num_x, uint64 num_y, Type type, bool in_place_fft,
+      ScratchAllocator *scratch_allocator) = 0;
+
+  // Creates a 3d FFT plan with scratch allocator.
+  virtual std::unique_ptr<Plan> Create3dPlanWithScratchAllocator(
+      Stream *stream, uint64 num_x, uint64 num_y, uint64 num_z, Type type,
+      bool in_place_fft, ScratchAllocator *scratch_allocator) = 0;
+
   // Creates a batched FFT plan.
   //
   // stream:          The GPU stream in which the FFT runs.
@@ -126,6 +143,30 @@ class FftSupport {
       uint64 output_stride, uint64 output_distance, Type type,
       bool in_place_fft, int batch_count) = 0;
 
+  // Creates a batched FFT plan with scratch allocator.
+  //
+  // stream:          The GPU stream in which the FFT runs.
+  // rank:            Dimensionality of the transform (1, 2, or 3).
+  // elem_count:      Array of size rank, describing the size of each dimension.
+  // input_embed, output_embed:
+  //                  Pointer of size rank that indicates the storage dimensions
+  //                  of the input/output data in memory. If set to null_ptr all
+  //                  other advanced data layout parameters are ignored.
+  // input_stride:    Indicates the distance (number of elements; same below)
+  //                  between two successive input elements.
+  // input_distance:  Indicates the distance between the first element of two
+  //                  consecutive signals in a batch of the input data.
+  // output_stride:   Indicates the distance between two successive output
+  //                  elements.
+  // output_distance: Indicates the distance between the first element of two
+  //                  consecutive signals in a batch of the output data.
+  virtual std::unique_ptr<Plan> CreateBatchedPlanWithScratchAllocator(
+      Stream *stream, int rank, uint64 *elem_count, uint64 *input_embed,
+      uint64 input_stride, uint64 input_distance, uint64 *output_embed,
+      uint64 output_stride, uint64 output_distance, Type type,
+      bool in_place_fft, int batch_count,
+      ScratchAllocator *scratch_allocator) = 0;
+
   // Computes complex-to-complex FFT in the transform direction as specified
   // by direction parameter.
   virtual bool DoFft(Stream *stream, Plan *plan,
@@ -161,38 +202,54 @@ class FftSupport {
 // Macro used to quickly declare overrides for abstract virtuals in the
 // fft::FftSupport base class. Assumes that it's emitted somewhere inside the
 // ::perftools::gputools namespace.
-#define TENSORFLOW_STREAM_EXECUTOR_GPU_FFT_SUPPORT_OVERRIDES                \
-  std::unique_ptr<fft::Plan> Create1dPlan(Stream *stream, uint64 num_x,      \
-                                          fft::Type type, bool in_place_fft) \
-      override;                                                              \
-  std::unique_ptr<fft::Plan> Create2dPlan(Stream *stream, uint64 num_x,      \
-                                          uint64 num_y, fft::Type type,      \
-                                          bool in_place_fft) override;       \
-  std::unique_ptr<fft::Plan> Create3dPlan(                                   \
-      Stream *stream, uint64 num_x, uint64 num_y, uint64 num_z,              \
-      fft::Type type, bool in_place_fft) override;                           \
-  std::unique_ptr<fft::Plan> CreateBatchedPlan(                              \
-      Stream *stream, int rank, uint64 *elem_count, uint64 *input_embed,     \
-      uint64 input_stride, uint64 input_distance, uint64 *output_embed,      \
-      uint64 output_stride, uint64 output_distance, fft::Type type,          \
-      bool in_place_fft, int batch_count) override;                          \
-  bool DoFft(Stream *stream, fft::Plan *plan,                                \
-             const DeviceMemory<std::complex<float>> &input,                 \
-             DeviceMemory<std::complex<float>> *output) override;            \
-  bool DoFft(Stream *stream, fft::Plan *plan,                                \
-             const DeviceMemory<std::complex<double>> &input,                \
-             DeviceMemory<std::complex<double>> *output) override;           \
-  bool DoFft(Stream *stream, fft::Plan *plan,                                \
-             const DeviceMemory<float> &input,                               \
-             DeviceMemory<std::complex<float>> *output) override;            \
-  bool DoFft(Stream *stream, fft::Plan *plan,                                \
-             const DeviceMemory<double> &input,                              \
-             DeviceMemory<std::complex<double>> *output) override;           \
-  bool DoFft(Stream *stream, fft::Plan *plan,                                \
-             const DeviceMemory<std::complex<float>> &input,                 \
-             DeviceMemory<float> *output) override;                          \
-  bool DoFft(Stream *stream, fft::Plan *plan,                                \
-             const DeviceMemory<std::complex<double>> &input,                \
+#define TENSORFLOW_STREAM_EXECUTOR_GPU_FFT_SUPPORT_OVERRIDES                   \
+  std::unique_ptr<fft::Plan> Create1dPlan(Stream *stream, uint64 num_x,        \
+                                          fft::Type type, bool in_place_fft)   \
+      override;                                                                \
+  std::unique_ptr<fft::Plan> Create2dPlan(Stream *stream, uint64 num_x,        \
+                                          uint64 num_y, fft::Type type,        \
+                                          bool in_place_fft) override;         \
+  std::unique_ptr<fft::Plan> Create3dPlan(                                     \
+      Stream *stream, uint64 num_x, uint64 num_y, uint64 num_z,                \
+      fft::Type type, bool in_place_fft) override;                             \
+  std::unique_ptr<fft::Plan> Create1dPlanWithScratchAllocator(                 \
+      Stream *stream, uint64 num_x, fft::Type type, bool in_place_fft,         \
+      ScratchAllocator *scratch_allocator) override;                           \
+  std::unique_ptr<fft::Plan> Create2dPlanWithScratchAllocator(                 \
+      Stream *stream, uint64 num_x, uint64 num_y, fft::Type type,              \
+      bool in_place_fft, ScratchAllocator *scratch_allocator) override;        \
+  std::unique_ptr<fft::Plan> Create3dPlanWithScratchAllocator(                 \
+      Stream *stream, uint64 num_x, uint64 num_y, uint64 num_z,                \
+      fft::Type type, bool in_place_fft, ScratchAllocator *scratch_allocator)  \
+      override;                                                                \
+  std::unique_ptr<fft::Plan> CreateBatchedPlan(                                \
+      Stream *stream, int rank, uint64 *elem_count, uint64 *input_embed,       \
+      uint64 input_stride, uint64 input_distance, uint64 *output_embed,        \
+      uint64 output_stride, uint64 output_distance, fft::Type type,            \
+      bool in_place_fft, int batch_count) override;                            \
+  std::unique_ptr<fft::Plan> CreateBatchedPlanWithScratchAllocator(            \
+      Stream *stream, int rank, uint64 *elem_count, uint64 *input_embed,       \
+      uint64 input_stride, uint64 input_distance, uint64 *output_embed,        \
+      uint64 output_stride, uint64 output_distance, fft::Type type,            \
+      bool in_place_fft, int batch_count, ScratchAllocator *scratch_allocator) \
+      override;                                                                \
+  bool DoFft(Stream *stream, fft::Plan *plan,                                  \
+             const DeviceMemory<std::complex<float>> &input,                   \
+             DeviceMemory<std::complex<float>> *output) override;              \
+  bool DoFft(Stream *stream, fft::Plan *plan,                                  \
+             const DeviceMemory<std::complex<double>> &input,                  \
+             DeviceMemory<std::complex<double>> *output) override;             \
+  bool DoFft(Stream *stream, fft::Plan *plan,                                  \
+             const DeviceMemory<float> &input,                                 \
+             DeviceMemory<std::complex<float>> *output) override;              \
+  bool DoFft(Stream *stream, fft::Plan *plan,                                  \
+             const DeviceMemory<double> &input,                                \
+             DeviceMemory<std::complex<double>> *output) override;             \
+  bool DoFft(Stream *stream, fft::Plan *plan,                                  \
+             const DeviceMemory<std::complex<float>> &input,                   \
+             DeviceMemory<float> *output) override;                            \
+  bool DoFft(Stream *stream, fft::Plan *plan,                                  \
+             const DeviceMemory<std::complex<double>> &input,                  \
              DeviceMemory<double> *output) override;
 
 }  // namespace fft
-- 
GitLab


From e8c354f962a46b3ee6395e2f6f9ba48b972033cd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 11 Jul 2017 23:29:02 -0700
Subject: [PATCH 0908/1375] [XLA] Propagate Status from DeviceMemoryAllocator
 on failure.

PiperOrigin-RevId: 161629493
---
 .../compiler/xla/service/device_memory_allocator.cc    | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/device_memory_allocator.cc b/tensorflow/compiler/xla/service/device_memory_allocator.cc
index c13c86741c..2e4b0a5230 100644
--- a/tensorflow/compiler/xla/service/device_memory_allocator.cc
+++ b/tensorflow/compiler/xla/service/device_memory_allocator.cc
@@ -35,7 +35,15 @@ StreamExecutorMemoryAllocator::Allocate(int device_ordinal, uint64 size,
                                         bool retry_on_failure) {
   TF_ASSIGN_OR_RETURN(perftools::gputools::StreamExecutor * stream_executor,
                       GetStreamExecutor(device_ordinal));
-  return stream_executor->AllocateArray<uint8>(size);
+  perftools::gputools::DeviceMemoryBase result =
+      stream_executor->AllocateArray<uint8>(size);
+  if (size > 0 && result == nullptr) {
+    return ResourceExhausted(
+        "Failed to allocate request for %s (%lluB) on device ordinal %d",
+        tensorflow::strings::HumanReadableNumBytes(size).c_str(), size,
+        device_ordinal);
+  }
+  return result;
 }
 
 tensorflow::Status StreamExecutorMemoryAllocator::Deallocate(
-- 
GitLab


From bf1461a9bf6e55d5addfda749834fe57ee0422bc Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Wed, 12 Jul 2017 00:15:22 -0700
Subject: [PATCH 0909/1375] Add platform bridge for grpc response reader.

PiperOrigin-RevId: 161632224
---
 tensorflow/core/BUILD                         |  1 +
 .../rpc/grpc_remote_worker.cc                 |  8 ++--
 .../platform/default/grpc_response_reader.h   | 36 ++++++++++++++
 .../core/platform/grpc_response_reader.h      | 47 +++++++++++++++++++
 4 files changed, 89 insertions(+), 3 deletions(-)
 create mode 100644 tensorflow/core/platform/default/grpc_response_reader.h
 create mode 100644 tensorflow/core/platform/grpc_response_reader.h

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 9411377733..9828c3248f 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -282,6 +282,7 @@ cc_library(
         "platform/env_time.h",
         "platform/file_system.h",
         "platform/fingerprint.h",
+        "platform/grpc_response_reader.h",
         "platform/init_main.h",
         "platform/logging.h",
         "platform/macros.h",
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
index 1e8c30bad5..b25f6b754f 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/grpc_response_reader.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/tracing.h"
 #include "tensorflow/core/protobuf/worker.pb.h"
@@ -195,13 +196,14 @@ class GrpcRemoteWorker : public WorkerInterface {
              const ::grpc::RpcMethod& method, const RequestMessage& request,
              StatusCallback done, CallOptions* call_opts)
         : call_opts_(call_opts),
-          reader_(channel, cq, method, InitContext(call_opts), request),
+          reader_(CreateClientAsyncResponseReader<ResponseMessage>(
+              channel, cq, method, InitContext(call_opts), request)),
           done_(std::move(done)) {}
 
     ~RPCState() override {}
 
     void StartRPC(ResponseMessage* response) {
-      reader_.Finish(response, &status_, this);
+      reader_->Finish(response, &status_, this);
     }
 
     void OnCompleted(bool ok) override {
@@ -219,7 +221,7 @@ class GrpcRemoteWorker : public WorkerInterface {
    private:
     CallOptions* call_opts_;
     ::grpc::ClientContext context_;
-    ::grpc::ClientAsyncResponseReader<ResponseMessage> reader_;
+    std::unique_ptr<::grpc::ClientAsyncResponseReader<ResponseMessage>> reader_;
     ::grpc::Status status_;
     StatusCallback done_;
 
diff --git a/tensorflow/core/platform/default/grpc_response_reader.h b/tensorflow/core/platform/default/grpc_response_reader.h
new file mode 100644
index 0000000000..e2be9d6a65
--- /dev/null
+++ b/tensorflow/core/platform/default/grpc_response_reader.h
@@ -0,0 +1,36 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PLATFORM_DEFAULT_GRPC_RESPONSE_READER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PLATFORM_DEFAULT_GRPC_RESPONSE_READER_H_
+
+#include "grpc++/grpc++.h"
+
+namespace tensorflow {
+
+template <class ResponseMessage, class RequestMessage>
+::grpc::ClientAsyncResponseReader<ResponseMessage>*
+CreateClientAsyncResponseReader(::grpc::ChannelInterface* channel,
+                                ::grpc::CompletionQueue* cq,
+                                const ::grpc::RpcMethod& method,
+                                ::grpc::ClientContext* context,
+                                const RequestMessage& request) {
+  return new ::grpc::ClientAsyncResponseReader<ResponseMessage>(
+      channel, cq, method, context, request);
+}
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PLATFORM_DEFAULT_GRPC_RESPONSE_READER_H_
diff --git a/tensorflow/core/platform/grpc_response_reader.h b/tensorflow/core/platform/grpc_response_reader.h
new file mode 100644
index 0000000000..e36eb698f3
--- /dev/null
+++ b/tensorflow/core/platform/grpc_response_reader.h
@@ -0,0 +1,47 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_PLATFORM_GRPC_RESPONSE_READER_H_
+#define TENSORFLOW_CORE_PLATFORM_GRPC_RESPONSE_READER_H_
+
+#include "grpc++/grpc++.h"
+#include "tensorflow/core/platform/platform.h"
+
+// Include platform-dependent grpc ClientAsyncResponseReader constructors.
+// TODO(b/62910646): Remove this level of indirection once this is resolved.
+#if defined(PLATFORM_GOOGLE)
+#include "tensorflow/core/platform/google/grpc_response_reader.h"
+#else
+#include "tensorflow/core/platform/default/grpc_response_reader.h"
+#endif
+
+namespace tensorflow {
+
+// Start a call and write the request out.
+// The returned pointer is owned by the caller.
+// See
+// https://grpc.io/grpc/cpp/classgrpc_1_1_client_async_response_reader.html#ace2c5bae351f67dd7dd603fc39513e0a
+// for more information.
+template <class ResponseMessage, class RequestMessage>
+::grpc::ClientAsyncResponseReader<ResponseMessage>*
+CreateClientAsyncResponseReader(::grpc::ChannelInterface* channel,
+                                ::grpc::CompletionQueue* cq,
+                                const ::grpc::RpcMethod& method,
+                                ::grpc::ClientContext* context,
+                                const RequestMessage& request);
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_PLATFORM_MUTEX_H_
-- 
GitLab


From 8f66dd24d1355298482afee9b5299f9f0b5b1817 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 06:30:44 -0700
Subject: [PATCH 0910/1375] Add checks to TensorForest that help with debugging
 when labels are wrong.

PiperOrigin-RevId: 161657633
---
 .../tensor_forest/kernels/v4/leaf_model_operators.cc      | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
index 4b9bb0f9c9..49e425642d 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
@@ -53,6 +53,10 @@ void DenseClassificationLeafModelOperator::UpdateModel(
     LeafStat* leaf, const InputTarget* target,
     int example) const {
   const int32 int_label = target->GetTargetAsClassIndex(example, 0);
+  QCHECK_LT(int_label, params_.num_outputs())
+      << "Got label greater than indicated number of classes. Is "
+         "params.num_classes set correctly?";
+  QCHECK_GE(int_label, 0);
   auto* val = leaf->mutable_classification()->mutable_dense_counts()
       ->mutable_value(int_label);
   float weight = target->GetTargetWeight(example);
@@ -87,6 +91,10 @@ void SparseClassificationLeafModelOperator::UpdateModel(
     LeafStat* leaf, const InputTarget* target,
     int example) const {
   const int32 int_label = target->GetTargetAsClassIndex(example, 0);
+  QCHECK_LT(int_label, params_.num_outputs())
+      << "Got label greater than indicated number of classes. Is "
+         "params.num_classes set correctly?";
+  QCHECK_GE(int_label, 0);
   const float weight = target->GetTargetWeight(example);
   leaf->set_weight_sum(leaf->weight_sum() + weight);
   auto value_map = leaf->mutable_classification()->mutable_sparse_counts()
-- 
GitLab


From eb1fe50da445d3880b588215f6fadcc7f48dd3ff Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Wed, 12 Jul 2017 06:58:36 -0700
Subject: [PATCH 0911/1375] [TF:XLA] Add initial implementation of the Stack
 operators to the TF/XLA bridge. Stacks are used when computing loop
 gradients.

PiperOrigin-RevId: 161659980
---
 tensorflow/compiler/tests/BUILD               |  13 +
 tensorflow/compiler/tests/stack_ops_test.py   | 104 +++++++
 tensorflow/compiler/tf2xla/const_analysis.cc  |   1 +
 tensorflow/compiler/tf2xla/kernels/BUILD      |   1 +
 tensorflow/compiler/tf2xla/kernels/arg_op.cc  |   3 +
 .../compiler/tf2xla/kernels/stack_ops.cc      | 259 ++++++++++++++++++
 .../compiler/tf2xla/kernels/while_op.cc       |   4 +
 .../compiler/tf2xla/xla_compilation_device.h  |   1 +
 tensorflow/compiler/tf2xla/xla_compiler.cc    |   1 +
 tensorflow/compiler/tf2xla/xla_compiler.h     |   6 +-
 10 files changed, 392 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/compiler/tests/stack_ops_test.py
 create mode 100644 tensorflow/compiler/tf2xla/kernels/stack_ops.cc

diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 432b24756d..4cd2137eda 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -366,6 +366,19 @@ tf_xla_py_test(
     ],
 )
 
+tf_xla_py_test(
+    name = "stack_ops_test",
+    size = "small",
+    srcs = ["stack_ops_test.py"],
+    deps = [
+        ":xla_test",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:data_flow_ops",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 tf_xla_py_test(
     name = "tensor_array_ops_test",
     size = "small",
diff --git a/tensorflow/compiler/tests/stack_ops_test.py b/tensorflow/compiler/tests/stack_ops_test.py
new file mode 100644
index 0000000000..2b9c227973
--- /dev/null
+++ b/tensorflow/compiler/tests/stack_ops_test.py
@@ -0,0 +1,104 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow.ops.stack_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.compiler.tests.xla_test import XLATestCase
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_data_flow_ops
+from tensorflow.python.platform import test
+
+
+class StackOpTest(XLATestCase):
+
+  def testStackPushPop(self):
+    with self.test_session(), self.test_scope():
+      size = array_ops.placeholder(dtypes.int32)
+      v = array_ops.placeholder(dtypes.float32)
+      h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push_v2(h, v)
+      with ops.control_dependencies([c]):
+        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
+      self.assertAllClose([[4.0, 5.0]], c1.eval({size: 5, v: [[4.0, 5.0]]}))
+
+  def testStackPushPopSwap(self):
+    with self.test_session(), self.test_scope():
+      a = np.arange(2000)
+      x = array_ops.placeholder(dtypes.float32)
+      h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True)
+      with ops.control_dependencies([c]):
+        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
+      self.assertAllClose(a, c1.eval({x: a}))
+
+  def testMultiStack(self):
+    with self.test_session(), self.test_scope():
+      v = array_ops.placeholder(dtypes.float32)
+      h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
+      c1 = gen_data_flow_ops._stack_push_v2(h1, v)
+      with ops.control_dependencies([c1]):
+        c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
+      h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="bar")
+      c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0)
+      with ops.control_dependencies([c2]):
+        c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+      r = c1 + c2
+      self.assertAllClose(9.0, r.eval({v: 4.0}))
+
+  def testSameNameStacks(self):
+    """Different stacks with the same name do not interfere."""
+    with self.test_session() as sess, self.test_scope():
+      v1 = array_ops.placeholder(dtypes.float32)
+      v2 = array_ops.placeholder(dtypes.float32)
+      h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
+      h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
+
+      c1 = gen_data_flow_ops._stack_push_v2(h1, v1)
+      with ops.control_dependencies([c1]):
+        c2 = gen_data_flow_ops._stack_push_v2(h2, v2)
+      with ops.control_dependencies([c2]):
+        pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
+        pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+
+      out1, out2 = sess.run([pop1, pop2], {v1: 4.0, v2: 5.0})
+      self.assertAllClose(out1, 4.0)
+      self.assertAllClose(out2, 5.0)
+
+  def testCloseStack(self):
+    with self.test_session() as sess, self.test_scope():
+      size = array_ops.placeholder(dtypes.int32)
+      h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo")
+      c1 = gen_data_flow_ops._stack_close_v2(h)
+      sess.run(c1, {size: 5})
+
+  def testPushCloseStack(self):
+    with self.test_session() as sess, self.test_scope():
+      v = array_ops.placeholder(dtypes.float32)
+      h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops._stack_push_v2(h, v)
+      with ops.control_dependencies([c]):
+        c1 = gen_data_flow_ops._stack_close_v2(h)
+      sess.run(c1, {v: [[4.0, 5.0]]})
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/compiler/tf2xla/const_analysis.cc b/tensorflow/compiler/tf2xla/const_analysis.cc
index 36a6c90af4..d98cf829bb 100644
--- a/tensorflow/compiler/tf2xla/const_analysis.cc
+++ b/tensorflow/compiler/tf2xla/const_analysis.cc
@@ -81,6 +81,7 @@ Status BackwardsConstAnalysis(const Graph& g,
       {"Split", "split_dim"},
       {"SplitV", "split_dim"},
       {"SplitV", "size_splits"},
+      {"StackV2", "max_size"},
       {"StridedSlice", "begin"},
       {"StridedSlice", "end"},
       {"StridedSlice", "strides"},
diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD
index 96b4fdfec6..35bc6b5a24 100644
--- a/tensorflow/compiler/tf2xla/kernels/BUILD
+++ b/tensorflow/compiler/tf2xla/kernels/BUILD
@@ -54,6 +54,7 @@ tf_kernel_library(
         "softmax_op.cc",
         "spacetobatch_op.cc",
         "split_op.cc",
+        "stack_ops.cc",
         "strided_slice_op.cc",
         "tensor_array_ops.cc",
         "tile_ops.cc",
diff --git a/tensorflow/compiler/tf2xla/kernels/arg_op.cc b/tensorflow/compiler/tf2xla/kernels/arg_op.cc
index 6ad72c6219..1156546512 100644
--- a/tensorflow/compiler/tf2xla/kernels/arg_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/arg_op.cc
@@ -60,6 +60,9 @@ class ArgOp : public XlaOpKernel {
         case XlaCompiler::Argument::kTensorArray:
           kind = XlaResource::kTensorArray;
           break;
+        case XlaCompiler::Argument::kStack:
+          kind = XlaResource::kStack;
+          break;
         default:
           CHECK(false);
       }
diff --git a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc
new file mode 100644
index 0000000000..6a6f65c02c
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc
@@ -0,0 +1,259 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// XLA Stack operators.
+
+#include <limits>
+#include <vector>
+
+#include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/compiler/tf2xla/type_util.h"
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/partial_tensor_shape.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/kernels/concat_lib.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace {
+
+Status GetStackShape(xla::ComputationBuilder* builder, XlaResource* resource,
+                     TensorShape* stack_shape) {
+  auto shape_or_status = builder->GetShape(resource->value);
+  if (!shape_or_status.ok()) {
+    return shape_or_status.status();
+  }
+  xla::Shape shape = *shape_or_status.ValueOrDie();
+  TF_RET_CHECK(xla::ShapeUtil::IsTuple(shape));
+  return XLAShapeToTensorShape(xla::ShapeUtil::GetTupleElementShape(shape, 0),
+                               stack_shape);
+}
+
+// Since the element shape is not provided to the Stack operator,
+// we lazily initialize the Stack at the time of the first write.
+//
+// If a Stack `resource` has not been initialized, constructs storage for the
+// Stack with elements of `elem_shape`. For both initialized and
+// uninitialized Stacks, checks that the tensor has a type compatible with
+// 'dtype' and shape compatible with 'elem_shape'.
+//
+// TODO(phawkins): consider changing the API of the stack operators to
+// allow an optional element shape at stack construction time.
+Status MaybeInitializeStack(xla::ComputationBuilder* builder,
+                            XlaResource* resource, DataType dtype,
+                            const TensorShape& elem_shape) {
+  if (resource->type != dtype) {
+    return errors::InvalidArgument(
+        "Stack dtype is ", DataTypeString(resource->type), " but op has dtype ",
+        DataTypeString(dtype), ".");
+  }
+
+  TensorShape stack_shape;
+  stack_shape.AddDim(resource->tensor_array_size);
+  stack_shape.AppendShape(elem_shape);
+
+  if (resource->value.handle() == 0) {
+    // Stack has not been initialized.
+    xla::ComputationDataHandle zero = XlaHelpers::Zero(builder, resource->type);
+    resource->value =
+        builder->Tuple({builder->Broadcast(zero, stack_shape.dim_sizes()),
+                        builder->ConstantR0<int32>(0)});
+  } else {
+    // Checks the expected shape matches the actual shape.
+    TensorShape actual_shape;
+    TF_RETURN_IF_ERROR(GetStackShape(builder, resource, &actual_shape));
+    if (stack_shape != actual_shape) {
+      return errors::InvalidArgument(
+          "Mismatched Stack shapes: ", stack_shape.DebugString(), " vs ",
+          actual_shape.DebugString());
+    }
+  }
+  return Status::OK();
+}
+
+// Pads 'x' with 'count' zero indices. 'x' must have 1 element.
+xla::ComputationDataHandle PadIndexWithZeros(
+    xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x,
+    int count) {
+  xla::ComputationDataHandle zero = builder->ConstantR1<int32>({0});
+  std::vector<xla::ComputationDataHandle> xs(count + 1, zero);
+  xs[0] = builder->Reshape(x, {1});
+  return builder->ConcatInDim(xs, 0);
+}
+
+class StackOp : public XlaOpKernel {
+ public:
+  explicit StackOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("elem_type", &dtype_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("stack_name", &stack_name_));
+  }
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    int64 size;
+    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(0, &size));
+    OP_REQUIRES(
+        ctx, size >= 0,
+        errors::InvalidArgument(
+            "XLA compilation requires a fixed stack size upper bound."));
+
+    // We defer initializing the Stack resource until we see the first push.
+    // Otherwise we do not know the shape of the stack elements.
+    xla::ComputationDataHandle value;
+    XlaContext& xc = XlaContext::Get(ctx);
+    XlaResource* resource;
+    string name = strings::StrCat("Stack: ", stack_name_);
+    OP_REQUIRES_OK(
+        ctx, xc.CreateResource(XlaResource::kStack, -1, std::move(name), dtype_,
+                               value, &resource));
+    resource->tensor_array_size = size;
+    ctx->SetResourceOutput(0, resource);
+  }
+
+ private:
+  DataType dtype_;
+  string stack_name_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(StackOp);
+};
+
+REGISTER_XLA_OP(Name("StackV2"), StackOp);
+
+class StackPushOp : public XlaOpKernel {
+ public:
+  explicit StackPushOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_));
+  }
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    xla::ComputationBuilder* b = ctx->builder();
+    TensorShape elem_shape = ctx->InputShape(1);
+
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
+
+    // Initializes the Stack, if the element shape was not already known.
+    OP_REQUIRES_OK(ctx, MaybeInitializeStack(b, resource, dtype_, elem_shape));
+
+    xla::ComputationDataHandle ta = b->GetTupleElement(resource->value, 0);
+    xla::ComputationDataHandle index = b->GetTupleElement(resource->value, 1);
+    xla::ComputationDataHandle value = ctx->Input(1);
+
+    // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
+    auto start_indices = PadIndexWithZeros(b, index, elem_shape.dims());
+
+    TensorShape slice_shape = elem_shape;
+    slice_shape.InsertDim(0, 1LL);
+    auto update = b->Reshape(value, slice_shape.dim_sizes());
+
+    // TODO(phawkins): We don't check the index is in bounds --- there is no
+    // error mechanism in XLA.
+    resource->value =
+        b->Tuple({b->DynamicUpdateSlice(ta, update, start_indices),
+                  b->Add(index, b->ConstantR0<int32>(1))});
+
+    ctx->SetOutput(0, value);
+  }
+
+ private:
+  DataType dtype_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(StackPushOp);
+};
+
+REGISTER_XLA_OP(Name("StackPushV2"), StackPushOp);
+
+class StackPopOp : public XlaOpKernel {
+ public:
+  explicit StackPopOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("elem_type", &dtype_));
+  }
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    xla::ComputationBuilder* b = ctx->builder();
+
+    XlaResource* resource;
+    OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource));
+
+    OP_REQUIRES(ctx, resource->type == dtype_,
+                errors::InvalidArgument(
+                    "Stack dtype is ", DataTypeString(resource->type),
+                    " but Op requested dtype ", DataTypeString(dtype_), "."));
+
+    // There is a somewhat subtle issue here: here "uninitialized" means we have
+    // not yet seen a pop in the order that we compile operators, not the order
+    // that we run them. However, in practice the two orders should be the same
+    // for the sole user of the stack operators (loop gradients).
+    OP_REQUIRES(ctx, resource->value.handle() != 0,
+                errors::InvalidArgument("Stack pop on uninitialized stack"));
+
+    TensorShape stack_shape;
+    OP_REQUIRES_OK(ctx, GetStackShape(b, resource, &stack_shape));
+
+    xla::ComputationDataHandle state = resource->value;
+    xla::ComputationDataHandle ta = b->GetTupleElement(state, 0);
+    xla::ComputationDataHandle index = b->GetTupleElement(state, 1);
+
+    index = b->Sub(index, b->ConstantR0<int32>(1));
+    resource->value = b->Tuple({ta, index});
+
+    // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
+    auto start_indices = PadIndexWithZeros(b, index, stack_shape.dims() - 1);
+
+    auto slice_shape = stack_shape.dim_sizes();
+    slice_shape[0] = 1LL;
+
+    // TODO(phawkins): We don't check the index is in bounds --- there is no
+    // error mechanism in XLA.
+    xla::ComputationDataHandle read =
+        b->DynamicSlice(ta, start_indices, slice_shape);
+
+    // Remove the leading '1' dimension.
+    std::vector<int64> value_shape(slice_shape.begin() + 1, slice_shape.end());
+    ctx->SetOutput(0, b->Reshape(read, value_shape));
+  }
+
+ private:
+  DataType dtype_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(StackPopOp);
+};
+
+REGISTER_XLA_OP(Name("StackPopV2"), StackPopOp);
+
+class StackCloseOp : public XlaOpKernel {
+ public:
+  explicit StackCloseOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    // Do nothing.
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(StackCloseOp);
+};
+
+REGISTER_XLA_OP(Name("StackCloseV2"), StackCloseOp);
+
+}  // anonymous namespace
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc
index 6d1af50053..2c2031fc76 100644
--- a/tensorflow/compiler/tf2xla/kernels/while_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/op_kernel.h"
 
@@ -56,6 +57,9 @@ Status MakeXlaCompilerArgumentsFromInputs(
         case XlaResource::kTensorArray:
           arg.kind = XlaCompiler::Argument::kTensorArray;
           break;
+        case XlaResource::kStack:
+          arg.kind = XlaCompiler::Argument::kStack;
+          break;
         case XlaResource::kInvalid:
           CHECK(false);
       }
diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.h b/tensorflow/compiler/tf2xla/xla_compilation_device.h
index e4f43f1950..ec28bdccda 100644
--- a/tensorflow/compiler/tf2xla/xla_compilation_device.h
+++ b/tensorflow/compiler/tf2xla/xla_compilation_device.h
@@ -70,6 +70,7 @@ struct XlaResource {
     kInvalid,
     kVariable,
     kTensorArray,
+    kStack,
   };
 
   Kind kind = kInvalid;
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index d1dc85a0eb..adabb3574c 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -266,6 +266,7 @@ Status BuildArguments(const std::vector<XlaCompiler::Argument>& args,
     switch (args[i].kind) {
       case XlaCompiler::Argument::kVariable:
       case XlaCompiler::Argument::kTensorArray:
+      case XlaCompiler::Argument::kStack:
         context_arg.is_resource = true;
         if (args[i].initialized) {
           resources.push_back(i);
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h
index 968f667bba..429515e2b7 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.h
+++ b/tensorflow/compiler/tf2xla/xla_compiler.h
@@ -85,7 +85,7 @@ class XlaCompiler {
       // Argument is a compile-time constant. No associated runtime parameter.
       kConstant,
 
-      // Argument is a variable resource. Has an associated runtime parameter
+      // Argument is a Variable resource. Has an associated runtime parameter
       // iff `initialized` is true.
       kVariable,
 
@@ -93,6 +93,10 @@ class XlaCompiler {
       // iff `initialized` is true.
       kTensorArray,
 
+      // Argument is a Stack resource. Has an associated runtime parameter
+      // iff `initialized` is true.
+      kStack,
+
       // Argument is a run-time parameter.
       kParameter,
     };
-- 
GitLab


From 786bf6cd656d0d67e56bf50047ff116bae884b9e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 07:37:31 -0700
Subject: [PATCH 0912/1375] Refactor some of TensorForest V4 to make the tree
 model valid during training time, instead of only after FinalizeTreeOp.

PiperOrigin-RevId: 161663317
---
 .../tensor_forest/kernels/model_ops.cc        | 183 ++++++++++++++++--
 .../tensor_forest/kernels/model_ops_test.cc   |  27 +++
 .../tensor_forest/kernels/stats_ops.cc        | 101 ++++------
 .../tensor_forest/kernels/stats_ops_test.cc   |   2 +-
 .../kernels/v4/decision-tree-resource.cc      |   9 +-
 .../kernels/v4/decision-tree-resource.h       |   8 +-
 .../kernels/v4/fertile-stats-resource.cc      |  26 +--
 .../kernels/v4/fertile-stats-resource.h       |  11 +-
 .../kernels/v4/leaf_model_operators.cc        |  33 ++--
 .../kernels/v4/leaf_model_operators.h         |  23 ++-
 .../kernels/v4/leaf_model_operators_test.cc   |  13 +-
 .../kernels/v4/split_collection_operators.cc  |  10 +-
 .../kernels/v4/split_collection_operators.h   |   8 +
 .../contrib/tensor_forest/ops/model_ops.cc    |  52 +++++
 .../contrib/tensor_forest/ops/stats_ops.cc    |   2 +
 .../tensor_forest/python/ops/model_ops.py     |  11 +-
 .../tensor_forest/python/tensor_forest_v4.py  |  27 ++-
 17 files changed, 374 insertions(+), 172 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
index 0f92f05e2c..221f8d969b 100644
--- a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 // =============================================================================
+#include <functional>
 #include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
 #include "tensorflow/contrib/decision_trees/proto/generic_tree_model_extensions.pb.h"
 #include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
@@ -26,6 +27,7 @@
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/thread_annotations.h"
 #include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/work_sharder.h"
 
 namespace tensorflow {
 namespace tensorforest {
@@ -46,7 +48,7 @@ class CreateTreeVariableOp : public OpKernel {
     OP_REQUIRES(context, TensorShapeUtils::IsScalar(tree_config_t->shape()),
                 errors::InvalidArgument("Tree config must be a scalar."));
 
-    auto* result = new DecisionTreeResource();
+    auto* result = new DecisionTreeResource(param_proto_);
     if (!ParseProtoUnlimited(result->mutable_decision_tree(),
                              tree_config_t->scalar<string>()())) {
       result->Unref();
@@ -142,6 +144,16 @@ class TreeSizeOp : public OpKernel {
   }
 };
 
+void TraverseTree(const DecisionTreeResource* tree_resource,
+                  const std::unique_ptr<TensorDataSet>& data, int32 start,
+                  int32 end,
+                  const std::function<void(int32, int32)>& set_leaf_id) {
+  for (int i = start; i < end; ++i) {
+    const int32 id = tree_resource->TraverseTree(data, i, nullptr);
+    set_leaf_id(i, id);
+  }
+}
+
 // Op for tree inference.
 class TreePredictionsV4Op : public OpKernel {
  public:
@@ -176,22 +188,49 @@ class TreePredictionsV4Op : public OpKernel {
     mutex_lock l(*decision_tree_resource->get_mutex());
     core::ScopedUnref unref_me(decision_tree_resource);
 
+    const int num_data = data_set_->NumItems();
+    const int32 num_outputs = param_proto_.num_outputs();
+
     Tensor* output_predictions = nullptr;
     TensorShape output_shape;
-    output_shape.AddDim(data_set_->NumItems());
-    output_shape.AddDim(param_proto_.num_outputs());
+    output_shape.AddDim(num_data);
+    output_shape.AddDim(num_outputs);
     OP_REQUIRES_OK(context, context->allocate_output(0, output_shape,
                                                      &output_predictions));
+    TTypes<float, 2>::Tensor out = output_predictions->tensor<float, 2>();
+
+    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
+    int num_threads = worker_threads->num_threads;
+    const int64 costPerTraverse = 500;
+    auto traverse = [this, &out, decision_tree_resource, num_data](int64 start,
+                                                                   int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      TraverseTree(decision_tree_resource, data_set_, static_cast<int32>(start),
+                   static_cast<int32>(end),
+                   std::bind(&TreePredictionsV4Op::set_output_value, this,
+                             std::placeholders::_1, std::placeholders::_2,
+                             decision_tree_resource, &out));
+    };
+    Shard(num_threads, worker_threads->workers, num_data, costPerTraverse,
+          traverse);
+  }
+
+  void set_output_value(int32 i, int32 id,
+                        DecisionTreeResource* decision_tree_resource,
+                        TTypes<float, 2>::Tensor* out) {
+    const decision_trees::Leaf& leaf = decision_tree_resource->get_leaf(id);
 
-    auto out = output_predictions->tensor<float, 2>();
-    for (int i = 0; i < data_set_->NumItems(); ++i) {
-      const int32 leaf_id =
-          decision_tree_resource->TraverseTree(data_set_, i, nullptr);
-      const decision_trees::Leaf& leaf =
-          decision_tree_resource->get_leaf(leaf_id);
+    float sum = 0;
+    for (int j = 0; j < param_proto_.num_outputs(); ++j) {
+      const float count = model_op_->GetOutputValue(leaf, j);
+      (*out)(i, j) = count;
+      sum += count;
+    }
+
+    if (!param_proto_.is_regression() && sum > 0 && sum != 1) {
       for (int j = 0; j < param_proto_.num_outputs(); ++j) {
-        const float count = model_op_->GetOutputValue(leaf, j);
-        out(i, j) = count;
+        (*out)(i, j) /= sum;
       }
     }
   }
@@ -203,6 +242,122 @@ class TreePredictionsV4Op : public OpKernel {
   TensorForestParams param_proto_;
 };
 
+// Outputs leaf ids for the given examples.
+class TraverseTreeV4Op : public OpKernel {
+ public:
+  explicit TraverseTreeV4Op(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    string serialized_proto;
+    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
+    input_spec_.ParseFromString(serialized_proto);
+
+    data_set_ =
+        std::unique_ptr<TensorDataSet>(new TensorDataSet(input_spec_, 0));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_data = context->input(1);
+    const Tensor& sparse_input_indices = context->input(2);
+    const Tensor& sparse_input_values = context->input(3);
+    const Tensor& sparse_input_shape = context->input(4);
+
+    data_set_->set_input_tensors(input_data, sparse_input_indices,
+                                 sparse_input_values, sparse_input_shape);
+
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    const int num_data = data_set_->NumItems();
+
+    Tensor* output_predictions = nullptr;
+    TensorShape output_shape;
+    output_shape.AddDim(num_data);
+    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape,
+                                                     &output_predictions));
+
+    auto leaf_ids = output_predictions->tensor<int32, 1>();
+
+    auto set_leaf_ids = [&leaf_ids](int32 i, int32 id) { leaf_ids(i) = id; };
+
+    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
+    int num_threads = worker_threads->num_threads;
+    const int64 costPerTraverse = 500;
+    auto traverse = [this, &set_leaf_ids, decision_tree_resource, num_data](
+                        int64 start, int64 end) {
+      CHECK(start <= end);
+      CHECK(end <= num_data);
+      TraverseTree(decision_tree_resource, data_set_, static_cast<int32>(start),
+                   static_cast<int32>(end), set_leaf_ids);
+    };
+    Shard(num_threads, worker_threads->workers, num_data, costPerTraverse,
+          traverse);
+  }
+
+ private:
+  tensorforest::TensorForestDataSpec input_spec_;
+  std::unique_ptr<TensorDataSet> data_set_;
+  TensorForestParams param_proto_;
+};
+
+// Update the given leaf models using the batch of labels.
+class UpdateModelV4Op : public OpKernel {
+ public:
+  explicit UpdateModelV4Op(OpKernelConstruction* context) : OpKernel(context) {
+    string serialized_params;
+    OP_REQUIRES_OK(context, context->GetAttr("params", &serialized_params));
+    ParseProtoUnlimited(&param_proto_, serialized_params);
+
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(param_proto_);
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& leaf_ids = context->input(1);
+    const Tensor& input_labels = context->input(2);
+    const Tensor& input_weights = context->input(3);
+
+    DecisionTreeResource* decision_tree_resource;
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &decision_tree_resource));
+    mutex_lock l(*decision_tree_resource->get_mutex());
+    core::ScopedUnref unref_me(decision_tree_resource);
+
+    const int num_data = input_labels.shape().dim_size(0);
+    const int32 label_dim =
+        input_labels.shape().dims() <= 1
+            ? 0
+            : static_cast<int>(input_labels.shape().dim_size(1));
+    const int32 num_targets =
+        param_proto_.is_regression() ? (std::max(1, label_dim)) : 1;
+
+    TensorInputTarget target(input_labels, input_weights, num_targets);
+
+    // TODO(gilberth): Make this thread safe and multi-thread.
+    UpdateModel(leaf_ids, target, 0, num_data, decision_tree_resource);
+  }
+
+  void UpdateModel(const Tensor& leaf_ids, const TensorInputTarget& target,
+                   int32 start, int32 end,
+                   DecisionTreeResource* decision_tree_resource) {
+    const auto leaves = leaf_ids.unaligned_flat<int32>();
+    for (int i = start; i < end; ++i) {
+      model_op_->UpdateModel(
+          decision_tree_resource->get_mutable_tree_node(leaves(i))
+              ->mutable_leaf(),
+          &target, i);
+    }
+  }
+
+ private:
+  std::unique_ptr<LeafModelOperator> model_op_;
+  TensorForestParams param_proto_;
+};
+
 // Op for getting feature usage counts.
 class FeatureUsageCountsOp : public OpKernel {
  public:
@@ -286,8 +441,14 @@ REGISTER_KERNEL_BUILDER(Name("TreeSize").Device(DEVICE_CPU), TreeSizeOp);
 REGISTER_KERNEL_BUILDER(Name("TreePredictionsV4").Device(DEVICE_CPU),
                         TreePredictionsV4Op);
 
+REGISTER_KERNEL_BUILDER(Name("TraverseTreeV4").Device(DEVICE_CPU),
+                        TraverseTreeV4Op);
+
 REGISTER_KERNEL_BUILDER(Name("FeatureUsageCounts").Device(DEVICE_CPU),
                         FeatureUsageCountsOp);
 
+REGISTER_KERNEL_BUILDER(Name("UpdateModelV4").Device(DEVICE_CPU),
+                        UpdateModelV4Op);
+
 }  // namespace tensorforest
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc
index cece61a54c..0fdab8e6e0 100644
--- a/tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops_test.cc
@@ -64,6 +64,33 @@ TEST(ModelOpsTest, TreePredictionsV4_ShapeFn) {
   INFER_OK(op, "?;?;?;?;[10,11]", "[?,?]");
 }
 
+TEST(ModelOpsTest, TraverseTreeV4_ShapeFn) {
+  ShapeInferenceTestOp op("TraverseTreeV4");
+  TF_ASSERT_OK(NodeDefBuilder("test", "TraverseTreeV4")
+                   .Input("a", 0, DT_RESOURCE)
+                   .Input("b", 1, DT_FLOAT)
+                   .Input("c", 2, DT_INT64)
+                   .Input("d", 3, DT_FLOAT)
+                   .Input("e", 5, DT_INT64)
+                   .Attr("input_spec", "")
+                   .Attr("params", "")
+                   .Finalize(&op.node_def));
+
+  // num_points = 2, sparse shape not known
+  INFER_OK(op, "?;[2,3];?;?;?", "[d1_0]");
+
+  // num_points = 2, sparse and dense shape rank known and > 1
+  INFER_OK(op, "?;[2,3];?;?;[10,11]", "[d1_0]");
+
+  // num_points = 2, sparse shape rank known and > 1
+  INFER_OK(op, "?;?;?;?;[10,11]", "[?]");
+}
+
+TEST(ModelOpsTest, UpdateModelV4_ShapeFn) {
+  ShapeInferenceTestOp op("UpdateModelV4");
+  INFER_OK(op, "[1];?;?;?", "");
+}
+
 TEST(ModelOpsTest, FeatureUsageCounts_ShapeFn) {
   ShapeInferenceTestOp op("FeatureUsageCounts");
   INFER_OK(op, "[1]", "[?]");
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
index 260e03df26..b6d57ef952 100644
--- a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -141,18 +141,6 @@ class FertileStatsDeserializeOp : public OpKernel {
   TensorForestParams param_proto_;
 };
 
-void TraverseTree(const DecisionTreeResource* tree_resource,
-                  const std::unique_ptr<TensorDataSet>& data, int32 start,
-                  int32 end, std::vector<int32>* leaf_ids,
-                  std::vector<int32>* leaf_depths) {
-  for (int i = start; i < end; ++i) {
-    int32 depth;
-    const int32 leaf_id = tree_resource->TraverseTree(data, i, &depth);
-    (*leaf_ids)[i] = leaf_id;
-    (*leaf_depths)[i] = depth;
-  }
-}
-
 // Try to update a leaf's stats by acquiring its lock.  If it can't be
 // acquired, put it in a waiting queue to come back to later and try the next
 // one.  Once all leaf_ids have been visited, cycle through the waiting ids
@@ -160,28 +148,27 @@ void TraverseTree(const DecisionTreeResource* tree_resource,
 void UpdateStats(FertileStatsResource* fertile_stats_resource,
                  const std::unique_ptr<TensorDataSet>& data,
                  const TensorInputTarget& target, int num_targets,
-                 const std::vector<int32>& leaf_ids,
-                 const std::vector<int32>& leaf_depths,
+                 const Tensor& leaf_ids_tensor,
                  std::unordered_map<int32, std::unique_ptr<mutex>>* locks,
                  mutex* set_lock, int32 start, int32 end,
                  std::unordered_set<int32>* ready_to_split) {
+  const auto leaf_ids = leaf_ids_tensor.unaligned_flat<int32>();
+
   // Stores leaf_id, leaf_depth, example_id for examples that are waiting
   // on another to finish.
-  std::queue<std::tuple<int32, int32, int32>> waiting;
+  std::queue<std::tuple<int32, int32>> waiting;
 
   int32 i = start;
   while (i < end || !waiting.empty()) {
     int32 leaf_id;
-    int32 leaf_depth;
     int32 example_id;
     bool was_waiting = false;
     if (i >= end) {
-      std::tie(leaf_id, leaf_depth, example_id) = waiting.front();
+      std::tie(leaf_id, example_id) = waiting.front();
       waiting.pop();
       was_waiting = true;
     } else {
-      leaf_id = leaf_ids[i];
-      leaf_depth = leaf_depths[i];
+      leaf_id = leaf_ids(i);
       example_id = i;
       ++i;
     }
@@ -190,14 +177,14 @@ void UpdateStats(FertileStatsResource* fertile_stats_resource,
       leaf_lock->lock();
     } else {
       if (!leaf_lock->try_lock()) {
-        waiting.emplace(leaf_id, leaf_depth, example_id);
+        waiting.emplace(leaf_id, example_id);
         continue;
       }
     }
 
     bool is_finished;
     fertile_stats_resource->AddExampleToStatsAndInitialize(
-        data, &target, {example_id}, leaf_id, leaf_depth, &is_finished);
+        data, &target, {example_id}, leaf_id, &is_finished);
     leaf_lock->unlock();
     if (is_finished) {
       set_lock->lock();
@@ -214,8 +201,8 @@ void UpdateStatsCollated(
     const std::unique_ptr<TensorDataSet>& data, const TensorInputTarget& target,
     int num_targets,
     const std::unordered_map<int32, std::vector<int>>& leaf_examples,
-    const std::vector<int32>& leaf_depths, mutex* set_lock, int32 start,
-    int32 end, std::unordered_set<int32>* ready_to_split) {
+    mutex* set_lock, int32 start, int32 end,
+    std::unordered_set<int32>* ready_to_split) {
   auto it = leaf_examples.begin();
   std::advance(it, start);
   auto end_it = leaf_examples.begin();
@@ -224,8 +211,7 @@ void UpdateStatsCollated(
     int32 leaf_id = it->first;
     bool is_finished;
     fertile_stats_resource->AddExampleToStatsAndInitialize(
-        data, &target, it->second, leaf_id, leaf_depths[it->second[0]],
-        &is_finished);
+        data, &target, it->second, leaf_id, &is_finished);
     if (is_finished) {
       set_lock->lock();
       ready_to_split->insert(leaf_id);
@@ -261,6 +247,7 @@ class ProcessInputOp : public OpKernel {
     const Tensor& sparse_input_shape = context->input(5);
     const Tensor& input_labels = context->input(6);
     const Tensor& input_weights = context->input(7);
+    const Tensor& leaf_ids_tensor = context->input(8);
 
     data_set_->set_input_tensors(input_data, sparse_input_indices,
                                  sparse_input_values, sparse_input_shape);
@@ -281,22 +268,7 @@ class ProcessInputOp : public OpKernel {
     auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
     int num_threads = worker_threads->num_threads;
 
-    // First find the leaf ids for each example.
-    std::vector<int32> leaf_ids(num_data);
-
-    // The depth of the leaf for example i.
-    std::vector<int32> leaf_depths(num_data);
-
-    const int64 costPerTraverse = 500;
-    auto traverse = [this, &leaf_ids, &leaf_depths, tree_resource, num_data](
-                        int64 start, int64 end) {
-      CHECK(start <= end);
-      CHECK(end <= num_data);
-      TraverseTree(tree_resource, data_set_, static_cast<int32>(start),
-                   static_cast<int32>(end), &leaf_ids, &leaf_depths);
-    };
-    Shard(num_threads, worker_threads->workers, num_data, costPerTraverse,
-          traverse);
+    const auto leaf_ids = leaf_ids_tensor.unaligned_flat<int32>();
 
     // Create one mutex per leaf. We need to protect access to leaf pointers,
     // so instead of grouping examples by leaf, we spread examples out among
@@ -306,10 +278,11 @@ class ProcessInputOp : public OpKernel {
     std::unordered_map<int32, std::vector<int>> leaf_examples;
     if (param_proto_.collate_examples()) {
       for (int i = 0; i < num_data; ++i) {
-        leaf_examples[leaf_ids[i]].push_back(i);
+        leaf_examples[leaf_ids(i)].push_back(i);
       }
     } else {
-      for (const int32 id : leaf_ids) {
+      for (int i = 0; i < num_data; ++i) {
+        const int32 id = leaf_ids(i);
         if (FindOrNull(locks, id) == nullptr) {
           // TODO(gilberth): Consider using a memory pool for these.
           locks[id] = std::unique_ptr<mutex>(new mutex);
@@ -335,27 +308,26 @@ class ProcessInputOp : public OpKernel {
     // from a digits run on local desktop.  Heuristics might be necessary
     // if it really matters that much.
     const int64 costPerUpdate = 1000;
-    auto update = [this, &target, &leaf_ids, &leaf_depths, &num_targets,
+    auto update = [this, &target, &leaf_ids_tensor, &num_targets,
                    fertile_stats_resource, &locks, &set_lock, &ready_to_split,
                    num_data](int64 start, int64 end) {
       CHECK(start <= end);
       CHECK(end <= num_data);
       UpdateStats(fertile_stats_resource, data_set_, target, num_targets,
-                  leaf_ids, leaf_depths, &locks, &set_lock,
-                  static_cast<int32>(start), static_cast<int32>(end),
-                  &ready_to_split);
+                  leaf_ids_tensor, &locks, &set_lock, static_cast<int32>(start),
+                  static_cast<int32>(end), &ready_to_split);
     };
 
-    auto update_collated = [this, &target, &leaf_ids, &num_targets,
-                            &leaf_depths, fertile_stats_resource, tree_resource,
-                            &leaf_examples, &set_lock, &ready_to_split,
+    auto update_collated = [this, &target, &num_targets, fertile_stats_resource,
+                            tree_resource, &leaf_examples, &set_lock,
+                            &ready_to_split,
                             num_leaves](int64 start, int64 end) {
       CHECK(start <= end);
       CHECK(end <= num_leaves);
       UpdateStatsCollated(fertile_stats_resource, tree_resource, data_set_,
-                          target, num_targets, leaf_examples, leaf_depths,
-                          &set_lock, static_cast<int32>(start),
-                          static_cast<int32>(end), &ready_to_split);
+                          target, num_targets, leaf_examples, &set_lock,
+                          static_cast<int32>(start), static_cast<int32>(end),
+                          &ready_to_split);
     };
 
     if (param_proto_.collate_examples()) {
@@ -411,7 +383,8 @@ class GrowTreeOp : public OpKernel {
     const int32 num_nodes =
         static_cast<int32>(finished_nodes.shape().dim_size(0));
 
-    // TODO(gilberth): distribute this work over a number of threads.
+    // This op takes so little of the time for one batch that it isn't worth
+    // threading this.
     for (int i = 0;
          i < num_nodes &&
          tree_resource->decision_tree().decision_tree().nodes_size() <
@@ -420,16 +393,14 @@ class GrowTreeOp : public OpKernel {
       const int32 node = finished(i);
       std::unique_ptr<SplitCandidate> best(new SplitCandidate);
       int32 parent_depth;
+      // TODO(gilberth): Pushing these to an output would allow the complete
+      // decoupling of tree from resource.
       bool found =
           fertile_stats_resource->BestSplit(node, best.get(), &parent_depth);
       if (found) {
         std::vector<int32> new_children;
         tree_resource->SplitNode(node, best.get(), &new_children);
         fertile_stats_resource->Allocate(parent_depth, new_children);
-        fertile_stats_resource->set_leaf_stat(best->left_stats(),
-                                              new_children[0]);
-        fertile_stats_resource->set_leaf_stat(best->right_stats(),
-                                              new_children[1]);
         // We are done with best, so it is now safe to clear node.
         fertile_stats_resource->Clear(node);
         CHECK(tree_resource->get_mutable_tree_node(node)->has_leaf() == false);
@@ -444,20 +415,17 @@ class GrowTreeOp : public OpKernel {
   TensorForestParams param_proto_;
 };
 
-void FinalizeLeaf(const LeafStat& leaf_stats, bool is_regression,
-                  bool drop_final_class,
+void FinalizeLeaf(bool is_regression, bool drop_final_class,
                   const std::unique_ptr<LeafModelOperator>& leaf_op,
                   decision_trees::Leaf* leaf) {
-  leaf_op->ExportModel(leaf_stats, leaf);
-
-  // TODO(thomaswc): Move the rest of this into ExportModel.
-
   // regression models are already stored in leaf in normalized form.
   if (is_regression) {
     return;
   }
 
-  float sum = leaf_stats.weight_sum();
+  // TODO(gilberth): Calculate the leaf's sum.
+  float sum = 0;
+  LOG(FATAL) << "FinalizeTreeOp is disabled for now.";
   if (sum <= 0.0) {
     LOG(WARNING) << "Leaf with sum " << sum << " has stats "
                  << leaf->ShortDebugString();
@@ -517,8 +485,7 @@ class FinalizeTreeOp : public OpKernel {
                        ->mutable_decision_tree()
                        ->mutable_nodes(i);
       if (node->has_leaf()) {
-        const auto& leaf_stats = fertile_stats_resource->leaf_stat(i);
-        FinalizeLeaf(leaf_stats, param_proto_.is_regression(),
+        FinalizeLeaf(param_proto_.is_regression(),
                      param_proto_.drop_final_class(), model_op_,
                      node->mutable_leaf());
       }
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc
index e5b86b0520..b3aa3a96f4 100644
--- a/tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops_test.cc
@@ -45,7 +45,7 @@ TEST(StatsOpsTest, GrowTreeV4_ShapeFn) {
 
 TEST(StatsOpsTest, ProcessInputV4_ShapeFn) {
   ShapeInferenceTestOp op("ProcessInputV4");
-  INFER_OK(op, "[1];[1];?;?;?;?;?;?", "[?]");
+  INFER_OK(op, "[1];[1];?;?;?;?;?;?;?", "[?]");
 }
 
 TEST(StatsOpsTest, FinalizeTree_ShapeFn) {
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc
index 165685ca53..881e4339a7 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.cc
@@ -18,6 +18,7 @@ namespace tensorflow {
 namespace tensorforest {
 
 using decision_trees::DecisionTree;
+using decision_trees::Leaf;
 using decision_trees::TreeNode;
 
 int32 DecisionTreeResource::TraverseTree(
@@ -51,13 +52,15 @@ void DecisionTreeResource::SplitNode(int32 node_id, SplitCandidate* best,
   new_children->push_back(newid);
   TreeNode* new_left = tree->add_nodes();
   new_left->mutable_node_id()->set_value(newid++);
-  new_left->mutable_leaf();
+  Leaf* left_leaf = new_left->mutable_leaf();
+  model_op_->ExportModel(best->left_stats(), left_leaf);
 
   // right
   new_children->push_back(newid);
   TreeNode* new_right = tree->add_nodes();
   new_right->mutable_node_id()->set_value(newid);
-  new_right->mutable_leaf();
+  Leaf* right_leaf = new_right->mutable_leaf();
+  model_op_->ExportModel(best->right_stats(), right_leaf);
 
   node->clear_leaf();
   node->mutable_binary_node()->Swap(best->mutable_split());
@@ -72,7 +75,7 @@ void DecisionTreeResource::SplitNode(int32 node_id, SplitCandidate* best,
 void DecisionTreeResource::MaybeInitialize() {
   DecisionTree* tree = decision_tree_->mutable_decision_tree();
   if (tree->nodes_size() == 0) {
-    tree->add_nodes()->mutable_leaf();
+    model_op_->InitModel(tree->add_nodes()->mutable_leaf());
   } else if (node_evaluators_.empty()) {  // reconstruct evaluators
     for (const auto& node : tree->nodes()) {
       if (node.has_leaf()) {
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h
index c8f09d8e07..438d3d817c 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h
@@ -31,8 +31,10 @@ namespace tensorforest {
 class DecisionTreeResource : public ResourceBase {
  public:
   // Constructor.
-  explicit DecisionTreeResource()
-      : decision_tree_(new decision_trees::Model()) {}
+  explicit DecisionTreeResource(const TensorForestParams& params)
+      : params_(params), decision_tree_(new decision_trees::Model()) {
+    model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(params_);
+  }
 
   string DebugString() override {
     return strings::StrCat("DecisionTree[size=",
@@ -79,7 +81,9 @@ class DecisionTreeResource : public ResourceBase {
 
  private:
   mutex mu_;
+  const TensorForestParams params_;
   std::unique_ptr<decision_trees::Model> decision_tree_;
+  std::shared_ptr<LeafModelOperator> model_op_;
   std::vector<std::unique_ptr<DecisionNodeEvaluator>> node_evaluators_;
 };
 
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
index 5c1b7454ae..7f914aac31 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.cc
@@ -20,14 +20,8 @@ namespace tensorflow {
 namespace tensorforest {
 
 void FertileStatsResource::AddExampleToStatsAndInitialize(
-    const std::unique_ptr<TensorDataSet>& input_data,
-    const InputTarget* target, const std::vector<int>& examples,
-    int32 node_id, int32 node_depth, bool* is_finished) {
-  // Set leaf's counts for calculating probabilities.
-  for (int example : examples) {
-    model_op_->UpdateModel(&leaf_stats_[node_id], target, example);
-  }
-
+    const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target,
+    const std::vector<int>& examples, int32 node_id, bool* is_finished) {
   // Update stats or initialize if needed.
   if (collection_op_->IsInitialized(node_id)) {
     collection_op_->AddExample(input_data, target, examples, node_id);
@@ -47,8 +41,6 @@ void FertileStatsResource::AddExampleToStatsAndInitialize(
 }
 
 void FertileStatsResource::AllocateNode(int32 node_id, int32 depth) {
-  leaf_stats_[node_id] = LeafStat();
-  model_op_->InitModel(&leaf_stats_[node_id]);
   collection_op_->InitializeSlot(node_id, depth);
 }
 
@@ -62,7 +54,6 @@ void FertileStatsResource::Allocate(int32 parent_depth,
 
 void FertileStatsResource::Clear(int32 node) {
   collection_op_->ClearSlot(node);
-  leaf_stats_.erase(node);
 }
 
 bool FertileStatsResource::BestSplit(int32 node_id, SplitCandidate* best,
@@ -71,27 +62,16 @@ bool FertileStatsResource::BestSplit(int32 node_id, SplitCandidate* best,
 }
 
 void FertileStatsResource::MaybeInitialize() {
-  if (leaf_stats_.empty()) {
-    AllocateNode(0, 0);
-  }
+  collection_op_->MaybeInitialize();
 }
 
 void FertileStatsResource::ExtractFromProto(const FertileStats& stats) {
   collection_op_ =
       SplitCollectionOperatorFactory::CreateSplitCollectionOperator(params_);
   collection_op_->ExtractFromProto(stats);
-  for (int i = 0; i < stats.node_to_slot_size(); ++i) {
-    const auto& slot = stats.node_to_slot(i);
-    leaf_stats_[slot.node_id()] = slot.leaf_stats();
-  }
 }
 
 void FertileStatsResource::PackToProto(FertileStats* stats) const {
-  for (const auto& entry : leaf_stats_) {
-    auto* slot = stats->add_node_to_slot();
-    *slot->mutable_leaf_stats() = entry.second;
-    slot->set_node_id(entry.first);
-  }
   collection_op_->PackToProto(stats);
 }
 }  // namespace tensorforest
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h
index 34ec945e84..dacf033d99 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h
@@ -51,7 +51,6 @@ class FertileStatsResource : public ResourceBase {
   // Resets the resource and frees the proto.
   // Caller needs to hold the mutex lock while calling this.
   void Reset() {
-    leaf_stats_.clear();
   }
 
   // Reset the stats for a node, but leave the leaf_stats intact.
@@ -71,7 +70,7 @@ class FertileStatsResource : public ResourceBase {
   void AddExampleToStatsAndInitialize(
       const std::unique_ptr<TensorDataSet>& input_data,
       const InputTarget* target, const std::vector<int>& examples,
-      int32 node_id, int32 node_depth, bool* is_finished);
+      int32 node_id, bool* is_finished);
 
   // Allocate a fertile slot for each ready node, then new children up to
   // max_fertile_nodes_.
@@ -85,19 +84,11 @@ class FertileStatsResource : public ResourceBase {
   // was found.
   bool BestSplit(int32 node_id, SplitCandidate* best, int32* depth);
 
-  const LeafStat& leaf_stat(int32 node_id) {
-    return leaf_stats_[node_id];
-  }
-
-  void set_leaf_stat(const LeafStat& stat, int32 node_id) {
-    leaf_stats_[node_id] = stat;
-  }
 
  private:
   mutex mu_;
   std::shared_ptr<LeafModelOperator> model_op_;
   std::unique_ptr<SplitCollectionOperator> collection_op_;
-  std::unordered_map<int32, LeafStat> leaf_stats_;
   const TensorForestParams params_;
 
   void AllocateNode(int32 node_id, int32 depth);
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
index 49e425642d..d43c068e46 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc
@@ -17,6 +17,8 @@
 namespace tensorflow {
 namespace tensorforest {
 
+using decision_trees::Leaf;
+
 std::unique_ptr<LeafModelOperator>
 LeafModelOperatorFactory::CreateLeafModelOperator(
     const TensorForestParams& params) {
@@ -50,24 +52,21 @@ float DenseClassificationLeafModelOperator::GetOutputValue(
 }
 
 void DenseClassificationLeafModelOperator::UpdateModel(
-    LeafStat* leaf, const InputTarget* target,
-    int example) const {
+    Leaf* leaf, const InputTarget* target, int example) const {
   const int32 int_label = target->GetTargetAsClassIndex(example, 0);
   QCHECK_LT(int_label, params_.num_outputs())
       << "Got label greater than indicated number of classes. Is "
          "params.num_classes set correctly?";
   QCHECK_GE(int_label, 0);
-  auto* val = leaf->mutable_classification()->mutable_dense_counts()
-      ->mutable_value(int_label);
+  auto* val = leaf->mutable_vector()->mutable_value(int_label);
+
   float weight = target->GetTargetWeight(example);
   val->set_float_value(val->float_value() + weight);
-  leaf->set_weight_sum(leaf->weight_sum() + weight);
 }
 
-void DenseClassificationLeafModelOperator::InitModel(
-    LeafStat* leaf) const {
+void DenseClassificationLeafModelOperator::InitModel(Leaf* leaf) const {
   for (int i = 0; i < params_.num_outputs(); ++i) {
-    leaf->mutable_classification()->mutable_dense_counts()->add_value();
+    leaf->mutable_vector()->add_value();
   }
 }
 
@@ -88,17 +87,15 @@ float SparseClassificationLeafModelOperator::GetOutputValue(
 }
 
 void SparseClassificationLeafModelOperator::UpdateModel(
-    LeafStat* leaf, const InputTarget* target,
-    int example) const {
+    Leaf* leaf, const InputTarget* target, int example) const {
   const int32 int_label = target->GetTargetAsClassIndex(example, 0);
   QCHECK_LT(int_label, params_.num_outputs())
       << "Got label greater than indicated number of classes. Is "
          "params.num_classes set correctly?";
   QCHECK_GE(int_label, 0);
   const float weight = target->GetTargetWeight(example);
-  leaf->set_weight_sum(leaf->weight_sum() + weight);
-  auto value_map = leaf->mutable_classification()->mutable_sparse_counts()
-      ->mutable_sparse_value();
+
+  auto value_map = leaf->mutable_sparse_vector()->mutable_sparse_value();
   auto it = value_map->find(int_label);
   if (it == value_map->end()) {
     (*value_map)[int_label].set_float_value(weight);
@@ -123,8 +120,8 @@ float SparseOrDenseClassificationLeafModelOperator::GetOutputValue(
 }
 
 void SparseOrDenseClassificationLeafModelOperator::UpdateModel(
-    LeafStat* leaf, const InputTarget* target, int example) const {
-  if (leaf->classification().has_dense_counts()) {
+    Leaf* leaf, const InputTarget* target, int example) const {
+  if (leaf->has_vector()) {
     return dense_->UpdateModel(leaf, target, example);
   } else {
     return sparse_->UpdateModel(leaf, target, example);
@@ -146,15 +143,15 @@ float RegressionLeafModelOperator::GetOutputValue(
   return leaf.vector().value(o).float_value();
 }
 
-void RegressionLeafModelOperator::InitModel(
-    LeafStat* leaf) const {
+void RegressionLeafModelOperator::InitModel(Leaf* leaf) const {
   for (int i = 0; i < params_.num_outputs(); ++i) {
-    leaf->mutable_regression()->mutable_mean_output()->add_value();
+    leaf->mutable_vector()->add_value();
   }
 }
 
 void RegressionLeafModelOperator::ExportModel(
     const LeafStat& stat, decision_trees::Leaf* leaf) const {
+  leaf->clear_vector();
   for (int i = 0; i < params_.num_outputs(); ++i) {
     const float new_val =
         stat.regression().mean_output().value(i).float_value() /
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h
index 8aadefc403..946a648f22 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h
@@ -42,12 +42,11 @@ class LeafModelOperator {
                                int32 o) const = 0;
 
   // Update the given Leaf's model with the given example.
-  virtual void UpdateModel(LeafStat* leaf,
-                           const InputTarget* target,
-                           int example) const = 0;
+  virtual void UpdateModel(decision_trees::Leaf* leaf,
+                           const InputTarget* target, int example) const = 0;
 
   // Initialize an empty Leaf model.
-  virtual void InitModel(LeafStat* leaf) const = 0;
+  virtual void InitModel(decision_trees::Leaf* leaf) const = 0;
 
   virtual void ExportModel(const LeafStat& stat,
                            decision_trees::Leaf* leaf) const = 0;
@@ -65,10 +64,10 @@ class DenseClassificationLeafModelOperator : public LeafModelOperator {
   float GetOutputValue(const decision_trees::Leaf& leaf,
                        int32 o) const override;
 
-  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+  void UpdateModel(decision_trees::Leaf* leaf, const InputTarget* target,
                    int example) const override;
 
-  void InitModel(LeafStat* leaf) const override;
+  void InitModel(decision_trees::Leaf* leaf) const override;
 
   void ExportModel(const LeafStat& stat,
                    decision_trees::Leaf* leaf) const override;
@@ -84,10 +83,10 @@ class SparseClassificationLeafModelOperator : public LeafModelOperator {
   float GetOutputValue(const decision_trees::Leaf& leaf,
                        int32 o) const override;
 
-  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+  void UpdateModel(decision_trees::Leaf* leaf, const InputTarget* target,
                    int example) const override;
 
-  void InitModel(LeafStat* leaf) const override {}
+  void InitModel(decision_trees::Leaf* leaf) const override {}
 
   void ExportModel(const LeafStat& stat,
                    decision_trees::Leaf* leaf) const override;
@@ -103,10 +102,10 @@ class SparseOrDenseClassificationLeafModelOperator : public LeafModelOperator {
   float GetOutputValue(const decision_trees::Leaf& leaf,
                        int32 o) const override;
 
-  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+  void UpdateModel(decision_trees::Leaf* leaf, const InputTarget* target,
                    int example) const override;
 
-  void InitModel(LeafStat* leaf) const override {}
+  void InitModel(decision_trees::Leaf* leaf) const override {}
 
   void ExportModel(const LeafStat& stat,
                    decision_trees::Leaf* leaf) const override;
@@ -129,10 +128,10 @@ class RegressionLeafModelOperator : public LeafModelOperator {
   // updating model and just using the seeded values.  Can add this in
   // with additional_data, though protobuf::Any is slow.  Maybe make it
   // optional.  Maybe make any update optional.
-  void UpdateModel(LeafStat* leaf, const InputTarget* target,
+  void UpdateModel(decision_trees::Leaf* leaf, const InputTarget* target,
                    int example) const override {}
 
-  void InitModel(LeafStat* leaf) const override;
+  void InitModel(decision_trees::Leaf* leaf) const override;
 
   void ExportModel(const LeafStat& stat,
                    decision_trees::Leaf* leaf) const override;
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
index 35268d15d3..ffd92c01f9 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc
@@ -63,12 +63,8 @@ constexpr char kRegressionStatProto[] =
 "}";
 
 void TestClassificationNormalUse(const std::unique_ptr<LeafModelOperator>& op) {
-  std::unique_ptr<LeafStat> leaf(new LeafStat);
-  op->InitModel(leaf.get());
-
   Leaf l;
-  op->ExportModel(*leaf, &l);
-
+  op->InitModel(&l);
   // Make sure it was initialized correctly.
   for (int i = 0; i < kNumClasses; ++i) {
     EXPECT_EQ(op->GetOutputValue(l, i), 0);
@@ -80,11 +76,10 @@ void TestClassificationNormalUse(const std::unique_ptr<LeafModelOperator>& op) {
       new TestableInputTarget(labels, weights, 1));
 
   // Update and check value.
-  op->UpdateModel(leaf.get(), target.get(), 0);
-  op->UpdateModel(leaf.get(), target.get(), 1);
-  op->UpdateModel(leaf.get(), target.get(), 2);
+  op->UpdateModel(&l, target.get(), 0);
+  op->UpdateModel(&l, target.get(), 1);
+  op->UpdateModel(&l, target.get(), 2);
 
-  op->ExportModel(*leaf, &l);
   EXPECT_FLOAT_EQ(op->GetOutputValue(l, 1), 3.4);
 }
 
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
index 632408fd71..ccc412600c 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc
@@ -71,13 +71,13 @@ void SplitCollectionOperator::ExtractFromProto(
 }
 
 void SplitCollectionOperator::PackToProto(FertileStats* stats_proto) const {
-  for (int i = 0; i < stats_proto->node_to_slot_size(); ++i) {
-    auto* new_slot = stats_proto->mutable_node_to_slot(i);
-    const auto& stats = stats_.at(new_slot->node_id());
+  for (const auto& pair : stats_) {
+    auto* new_slot = stats_proto->add_node_to_slot();
+    new_slot->set_node_id(pair.first);
     if (params_.checkpoint_stats()) {
-      stats->PackToProto(new_slot);
+      pair.second->PackToProto(new_slot);
     }
-    new_slot->set_depth(stats->depth());
+    new_slot->set_depth(pair.second->depth());
   }
 }
 
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
index 6990e82678..6c21c0bd34 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h
@@ -62,6 +62,14 @@ class SplitCollectionOperator {
   // Create a new GrowStats for the given node id and initialize it.
   virtual void InitializeSlot(int32 node_id, int32 depth);
 
+  // Called when the resource is deserialized, possibly needing an
+  // initialization.
+  virtual void MaybeInitialize() {
+    if (stats_.empty()) {
+      InitializeSlot(0, 0);
+    }
+  }
+
   // Perform any necessary cleanup for any tracked state for the slot.
   virtual void ClearSlot(int32 node_id) {
     stats_.erase(node_id);
diff --git a/tensorflow/contrib/tensor_forest/ops/model_ops.cc b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
index 168f079f52..1227a70a2e 100644
--- a/tensorflow/contrib/tensor_forest/ops/model_ops.cc
+++ b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
@@ -115,6 +115,58 @@ sparse_input_shape: The shape tensor from the SparseTensor input.
 predictions: `predictions[i][j]` is the probability that input i is class j.
 )doc");
 
+REGISTER_OP("TraverseTreeV4")
+    .Attr("input_spec: string")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("input_data: float")
+    .Input("sparse_input_indices: int64")
+    .Input("sparse_input_values: float")
+    .Input("sparse_input_shape: int64")
+    .Output("leaf_ids: int32")
+    .SetShapeFn([](InferenceContext* c) {
+      DimensionHandle num_points = c->UnknownDim();
+
+      if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0 &&
+          c->Value(c->Dim(c->input(1), 0)) > 0) {
+        num_points = c->Dim(c->input(1), 0);
+      }
+
+      c->set_output(0, c->Vector(num_points));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs the leaf ids for the given input data.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
+   gives the j-th feature of the i-th input.
+sparse_input_indices: The indices tensor from the SparseTensor input.
+sparse_input_values: The values tensor from the SparseTensor input.
+sparse_input_shape: The shape tensor from the SparseTensor input.
+leaf_ids: `leaf_ids[i]` is the leaf id for input i.
+)doc");
+
+REGISTER_OP("UpdateModelV4")
+    .Attr("params: string")
+    .Input("tree_handle: resource")
+    .Input("leaf_ids: int32")
+    .Input("input_labels: float")
+    .Input("input_weights: float")
+    .SetShapeFn(tensorflow::shape_inference::NoOutputs)
+    .Doc(R"doc(
+Updates the given leaves for each example with the new labels.
+
+params: A serialized TensorForestParams proto.
+tree_handle: The handle to the tree.
+leaf_ids: `leaf_ids[i]` is the leaf id for input i.
+input_labels: The training batch's labels as a 1 or 2-d tensor.
+  'input_labels[i][j]' gives the j-th label/target for the i-th input.
+input_weights: The training batch's eample weights as a 1-d tensor.
+  'input_weights[i]' gives the weight for the i-th input.
+)doc");
+
 REGISTER_OP("FeatureUsageCounts")
     .Attr("params: string")
     .Input("tree_handle: resource")
diff --git a/tensorflow/contrib/tensor_forest/ops/stats_ops.cc b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
index 9652749768..e8b5c5d8a6 100644
--- a/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
+++ b/tensorflow/contrib/tensor_forest/ops/stats_ops.cc
@@ -98,6 +98,7 @@ REGISTER_OP("ProcessInputV4")
     .Input("sparse_input_shape: int64")
     .Input("input_labels: float")
     .Input("input_weights: float")
+    .Input("leaf_ids: int32")
     .Output("finished_nodes: int32")
     .SetShapeFn([](InferenceContext* c) {
       c->set_output(0, c->Vector(c->UnknownDim()));
@@ -122,6 +123,7 @@ input_weights: The training batch's eample weights as a 1-d tensor.
   'input_weights[i]' gives the weight for the i-th input.
 finished_nodes: A 1-d tensor of node ids that have finished and are ready to
   grow.
+leaf_ids: `leaf_ids[i]` is the leaf id for input i.
 )doc");
 
 REGISTER_OP("FinalizeTree")
diff --git a/tensorflow/contrib/tensor_forest/python/ops/model_ops.py b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
index 4c7218305b..d240e2f6de 100644
--- a/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
+++ b/tensorflow/contrib/tensor_forest/python/ops/model_ops.py
@@ -18,12 +18,13 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.tensor_forest.python.ops import gen_model_ops
-from tensorflow.contrib.tensor_forest.python.ops import stats_ops
 
 # pylint: disable=unused-import
 from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import feature_usage_counts
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import traverse_tree_v4
 from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_predictions_v4
 from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import tree_size
+from tensorflow.contrib.tensor_forest.python.ops.gen_model_ops import update_model_v4
 # pylint: enable=unused-import
 
 from tensorflow.contrib.util import loader
@@ -59,13 +60,7 @@ class TreeVariableSavable(saver.BaseSaverBuilder.SaveableObject):
       name: the name to save the tree variable under.
     """
     self.params = params
-    deps = []
-    if stats_handle is not None:
-      deps.append(stats_ops.finalize_tree(
-          tree_handle, stats_handle,
-          params=params.serialized_params_proto))
-    with ops.control_dependencies(deps):
-      tensor = gen_model_ops.tree_serialize(tree_handle)
+    tensor = gen_model_ops.tree_serialize(tree_handle)
     # slice_spec is useful for saving a slice from a variable.
     # It's not meaningful the tree variable. So we just pass an empty value.
     slice_spec = ""
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
index 7e6f00a13d..8198c228dd 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
@@ -27,6 +27,7 @@ from tensorflow.contrib.tensor_forest.python import tensor_forest
 from tensorflow.contrib.tensor_forest.python.ops import model_ops
 from tensorflow.contrib.tensor_forest.python.ops import stats_ops
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.platform import tf_logging as logging
 
@@ -240,6 +241,22 @@ class RandomTreeGraphsV4(tensor_forest.RandomTreeGraphs):
     if input_data is None:
       input_data = []
 
+    leaf_ids = model_ops.traverse_tree_v4(
+        self.variables.tree,
+        input_data,
+        sparse_indices,
+        sparse_values,
+        sparse_shape,
+        input_spec=data_spec.SerializeToString(),
+        params=self.params.serialized_params_proto)
+
+    update_model = model_ops.update_model_v4(
+        self.variables.tree,
+        leaf_ids,
+        input_labels,
+        input_weights,
+        params=self.params.serialized_params_proto)
+
     finished_nodes = stats_ops.process_input_v4(
         self.variables.tree,
         self.variables.stats,
@@ -249,13 +266,17 @@ class RandomTreeGraphsV4(tensor_forest.RandomTreeGraphs):
         sparse_shape,
         input_labels,
         input_weights,
+        leaf_ids,
         input_spec=data_spec.SerializeToString(),
         random_seed=random_seed,
         params=self.params.serialized_params_proto)
 
-    return stats_ops.grow_tree_v4(self.variables.tree, self.variables.stats,
-                                  finished_nodes,
-                                  params=self.params.serialized_params_proto)
+    with ops.control_dependencies([update_model]):
+      return stats_ops.grow_tree_v4(
+          self.variables.tree,
+          self.variables.stats,
+          finished_nodes,
+          params=self.params.serialized_params_proto)
 
   def inference_graph(self, input_data, data_spec, sparse_features=None):
     sparse_indices = []
-- 
GitLab


From 576c7b1ec86408e4aff0b32f63d1d3b306b32e41 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 09:01:22 -0700
Subject: [PATCH 0913/1375] Automated g4 rollback of changelist 161218103

PiperOrigin-RevId: 161671226
---
 tensorflow/contrib/BUILD                      |  1 +
 tensorflow/contrib/__init__.py                |  1 +
 tensorflow/contrib/cmake/tf_python.cmake      |  1 +
 tensorflow/contrib/predictor/BUILD            |  7 ++--
 tensorflow/contrib/predictor/__init__.py      | 16 +++++++---
 .../predictor/saved_model_predictor.py        | 32 ++++++++++++++++---
 6 files changed, 48 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index 7ada956124..c9065b0a89 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -51,6 +51,7 @@ py_library(
         "//tensorflow/contrib/ndlstm",
         "//tensorflow/contrib/nn:nn_py",
         "//tensorflow/contrib/opt:opt_py",
+        "//tensorflow/contrib/predictor",
         "//tensorflow/contrib/quantization:quantization_py",
         "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py",
         "//tensorflow/contrib/resampler:resampler_py",
diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py
index f70b810ec6..513e657a33 100644
--- a/tensorflow/contrib/__init__.py
+++ b/tensorflow/contrib/__init__.py
@@ -50,6 +50,7 @@ from tensorflow.contrib import metrics
 from tensorflow.contrib import nccl
 from tensorflow.contrib import nn
 from tensorflow.contrib import opt
+from tensorflow.contrib import predictor
 from tensorflow.contrib import quantization
 from tensorflow.contrib import resampler
 from tensorflow.contrib import rnn
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 0ffa0200db..1de6559e3c 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -455,6 +455,7 @@ add_python_module("tensorflow/contrib/pi_examples")
 add_python_module("tensorflow/contrib/pi_examples/camera")
 add_python_module("tensorflow/contrib/pi_examples/label_image")
 add_python_module("tensorflow/contrib/pi_examples/label_image/data")
+add_python_module("tensorflow/contrib/predictor")
 add_python_module("tensorflow/contrib/quantization")
 add_python_module("tensorflow/contrib/quantization/python")
 add_python_module("tensorflow/contrib/remote_fused_graph/pylib")
diff --git a/tensorflow/contrib/predictor/BUILD b/tensorflow/contrib/predictor/BUILD
index c4b46551c1..8bd8c5f618 100644
--- a/tensorflow/contrib/predictor/BUILD
+++ b/tensorflow/contrib/predictor/BUILD
@@ -1,6 +1,6 @@
 # `Predictor` classes provide an interface for efficient, repeated inference.
 
-package(default_visibility = ["//third_party/tensroflow/contrib/predictor:__subpackages__"])
+package(default_visibility = ["//tensorflow/contrib/predictor:__subpackages__"])
 
 licenses(["notice"])  # Apache 2.0
 
@@ -62,7 +62,10 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":base_predictor",
-        "//tensorflow/python/tools:saved_model_cli",
+        "//tensorflow/contrib/saved_model:saved_model_py",
+        "//tensorflow/python/saved_model:loader",
+        "//tensorflow/python/saved_model:signature_constants",
+        "//tensorflow/python/saved_model:signature_def_utils",
     ],
 )
 
diff --git a/tensorflow/contrib/predictor/__init__.py b/tensorflow/contrib/predictor/__init__.py
index d270c3f798..68146aea17 100644
--- a/tensorflow/contrib/predictor/__init__.py
+++ b/tensorflow/contrib/predictor/__init__.py
@@ -13,12 +13,20 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Modules for `Predictor`s."""
+"""Modules for `Predictor`s.
+
+@@from_contrib_estimator
+@@from_estimator
+@@from_saved_model
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.predictor import from_contrib_estimator
-from tensorflow.contrib.predictor import from_estimator
-from tensorflow.contrib.predictor import from_saved_model
+from tensorflow.contrib.predictor.predictor_factories import from_contrib_estimator
+from tensorflow.contrib.predictor.predictor_factories import from_estimator
+from tensorflow.contrib.predictor.predictor_factories import from_saved_model
+
+from tensorflow.python.util.all_util import remove_undocumented
+remove_undocumented(__name__)
diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py
index ab2bafa0c8..0dbca0f813 100644
--- a/tensorflow/contrib/predictor/saved_model_predictor.py
+++ b/tensorflow/contrib/predictor/saved_model_predictor.py
@@ -22,12 +22,12 @@ from __future__ import print_function
 import logging
 
 from tensorflow.contrib.predictor import predictor
+from tensorflow.contrib.saved_model.python.saved_model import reader
 from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils
 from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.saved_model import loader
 from tensorflow.python.saved_model import signature_constants
-from tensorflow.python.tools import saved_model_cli
 
 
 DEFAULT_TAGS = 'serve'
@@ -35,13 +35,37 @@ DEFAULT_TAGS = 'serve'
 _DEFAULT_INPUT_ALTERNATIVE_FORMAT = 'default_input_alternative:{}'
 
 
+def get_meta_graph_def(saved_model_dir, tags):
+  """Gets `MetaGraphDef` from a directory containing a `SavedModel`.
+
+  Returns the `MetaGraphDef` for the given tag-set and SavedModel directory.
+
+  Args:
+    saved_model_dir: Directory containing the SavedModel.
+    tags: Comma separated list of tags used to identify the correct
+      `MetaGraphDef`.
+
+  Raises:
+    ValueError: An error when the given tags cannot be found.
+
+  Returns:
+    A `MetaGraphDef` corresponding to the given tags.
+  """
+  saved_model = reader.read_saved_model(saved_model_dir)
+  set_of_tags = set([tag.strip() for tag in tags.split(',')])
+  for meta_graph_def in saved_model.meta_graphs:
+    if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
+      return meta_graph_def
+  raise ValueError('Could not find MetaGraphDef with tags {}'.format(tags))
+
+
 def _get_signature_def(signature_def_key, export_dir, tags):
   """Construct a `SignatureDef` proto."""
   signature_def_key = (
       signature_def_key or
       signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY)
 
-  metagraph_def = saved_model_cli.get_meta_graph_def(export_dir, tags)
+  metagraph_def = get_meta_graph_def(export_dir, tags)
 
   try:
     signature_def = signature_def_utils.get_signature_def_by_key(
@@ -114,8 +138,8 @@ class SavedModelPredictor(predictor.Predictor):
       output_names: A dictionary mapping strings to `Tensor`s in the
         `SavedModel` that represent the output. The keys can be any string of
         the user's choosing.
-      tags: Optional. Tags that will be used to retrieve the correct
-        `SignatureDef`. Defaults to `DEFAULT_TAGS`.
+      tags: Optional. Comma separated list of tags that will be used to retrieve
+        the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`.
       graph: Optional. The Tensorflow `graph` in which prediction should be
         done.
     Raises:
-- 
GitLab


From de546d06692b5c557b8de4528e7458ac8c4f2afa Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 10:10:55 -0700
Subject: [PATCH 0914/1375] BUILD cleanup in tensorflow/compiler/...

PiperOrigin-RevId: 161679855
---
 tensorflow/compiler/tests/BUILD           | 1 +
 tensorflow/compiler/xla/service/BUILD     | 5 -----
 tensorflow/compiler/xla/service/cpu/BUILD | 4 +++-
 tensorflow/compiler/xla/tests/BUILD       | 4 ++++
 4 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 4cd2137eda..4f0137e8d9 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -42,6 +42,7 @@ py_library(
         "//tensorflow/python:platform",
         "//tensorflow/python:random_seed",
         "//tensorflow/python:variables",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index d08bc61e37..b676946934 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -857,7 +857,6 @@ cc_library(
         ":heap_simulator",
         ":hlo",
         ":hlo_ordering",
-        ":hlo_proto",
         ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:shape_util",
@@ -1895,10 +1894,6 @@ cc_library(
         ":buffer_liveness",
         ":hlo",
         ":hlo_pass",
-        "//tensorflow/compiler/xla:status_macros",
-        "//tensorflow/compiler/xla:statusor",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index ddf45f03f0..6c2deb8c9a 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -227,6 +227,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:hlo_module_config",
         "//tensorflow/compiler/xla/service/llvm_ir:ir_array",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
@@ -447,6 +448,7 @@ cc_library(
         ":ir_emission_utils",
         ":shape_partition",
         "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_cost_analysis",
         "//tensorflow/compiler/xla/service:hlo_pass",
@@ -531,7 +533,7 @@ cc_library(
     srcs = ["shape_partition.cc"],
     hdrs = ["shape_partition.h"],
     deps = [
-        "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla:shape_util",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 5298af788e..917e6aa76c 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -726,6 +726,7 @@ xla_test(
     srcs = ["batch_normalization_test.cc"],
     shard_count = 40,
     deps = [
+        ":test_utils",
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla:literal_util",
@@ -733,7 +734,9 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
+        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation",
         "//tensorflow/compiler/xla/client:computation_builder",
@@ -741,6 +744,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
+        "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
-- 
GitLab


From 6e74bd65a19fe80676a45c7f6a7ab3a1cfe8da70 Mon Sep 17 00:00:00 2001
From: Martin Wicke <martin.wicke@gmail.com>
Date: Tue, 11 Jul 2017 15:59:02 +0200
Subject: [PATCH 0915/1375] Add CODEOWNERS

Added what we know about contrib mainly, and some well-separated components.
---
 CODEOWNERS | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 CODEOWNERS

diff --git a/CODEOWNERS b/CODEOWNERS
new file mode 100644
index 0000000000..ef200e2a6a
--- /dev/null
+++ b/CODEOWNERS
@@ -0,0 +1,50 @@
+# Where component owners are known, add them here.
+
+tensorflow/tensorboard/* @jart @dandelionmane
+tensorflow/tools/docs/* @markdaoust
+tensorflow/java/* @asimshankar
+
+# contrib
+
+# NEED OWNER: tensorflow/contrib/avro	
+tensorflow/contrib/batching/* @alextp @chrisolston
+tensorflow/contrib/bayesflow/* @ebrevdo @rsepassi @jvdillon
+tensorflow/contrib/cmake/* @mrry @benoitsteiner
+tensorflow/contrib/copy_graph/* @tucker @poxvoculi
+tensorflow/contrib/crf/* @kentonl
+tensorflow/contrib/data/* @mrry
+tensorflow/contrib/distributions/* @jvdillon @langmore @rsepassi
+tensorflow/contrib/factorization/* @agarwal-ashish @xavigonzalvo
+tensorflow/contrib/ffmpeg/* @fredbertsch
+tensorflow/contrib/framework/*	joel-shor
+tensorflow/contrib/graph_editor/*	@purpledog
+# NEED OWNER: tensorflow/contrib/grid_rnn
+tensorflow/contrib/hvx/* @satok16
+tensorflow/contrib/imperative/* @keveman
+tensorflow/contrib/integrate/* @shoyer
+tensorflow/contrib/kernel_methods/* @petrosmol
+tensorflow/contrib/ios_examples/* @petewarden
+tensorflow/contrib/labeled_tensor/* @shoyer
+tensorflow/contrib/layers/* @fchollet @martinwicke
+tensorflow/contrib/learn/* @martinwicke @ispirmustafa @alextp
+tensorflow/contrib/linalg/* @langmore
+tensorflow/contrib/linear_optimizer/* @petrosmol @andreasst @katsiapis
+tensorflow/contrib/lookup/*	@ysuematsu @andreasst
+tensorflow/contrib/losses/* @alextp @ispirmustafa
+tensorflow/contrib/makefile/* @petewarden @satok16 @wolffg
+tensorflow/contrib/metrics/* @alextp @honkentuber @ispirmustafa
+tensorflow/contrib/nccl/* @cwhipkey @zheng-xq
+tensorflow/contrib/opt/* @strategist333
+tensorflow/contrib/pi_examples/* @maciekcc
+tensorflow/contrib/quantization/* @petewarden @cwhipkey @keveman
+tensorflow/contrib/rnn/* @ebrevdo
+tensorflow/contrib/saved_model/* @nfiedel @sukritiramesh
+tensorflow/contrib/seq2seq/* @lukaszkaiser
+tensorflow/contrib/session_bundle/* @nfiedel @sukritiramesh
+tensorflow/contrib/slim/* @sguada @thenbasilmanran
+tensorflow/contrib/stateless/* @girving
+tensorflow/contrib/tensor_forest/* @gilberthendry @thomascolthurst
+tensorflow/contrib/testing/* @dandelionmane
+tensorflow/contrib/timeseries/* @allenlavoie
+tensorflow/contrib/training/* @joel-shor @ebrevdo
+tensorflow/contrib/util/* @sherrym
-- 
GitLab


From a72fc31bca8540c7092811231e7a73f16e15acfd Mon Sep 17 00:00:00 2001
From: Martin Wicke <martin.wicke@gmail.com>
Date: Wed, 12 Jul 2017 19:54:05 +0200
Subject: [PATCH 0916/1375] Remove tabs. Unassign contrib/framework.

---
 CODEOWNERS | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index ef200e2a6a..2f216e4161 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -6,7 +6,7 @@ tensorflow/java/* @asimshankar
 
 # contrib
 
-# NEED OWNER: tensorflow/contrib/avro	
+# NEED OWNER: tensorflow/contrib/avro/*
 tensorflow/contrib/batching/* @alextp @chrisolston
 tensorflow/contrib/bayesflow/* @ebrevdo @rsepassi @jvdillon
 tensorflow/contrib/cmake/* @mrry @benoitsteiner
@@ -16,9 +16,9 @@ tensorflow/contrib/data/* @mrry
 tensorflow/contrib/distributions/* @jvdillon @langmore @rsepassi
 tensorflow/contrib/factorization/* @agarwal-ashish @xavigonzalvo
 tensorflow/contrib/ffmpeg/* @fredbertsch
-tensorflow/contrib/framework/*	joel-shor
-tensorflow/contrib/graph_editor/*	@purpledog
-# NEED OWNER: tensorflow/contrib/grid_rnn
+# NEED OWNERT: tensorflow/contrib/framework/*
+tensorflow/contrib/graph_editor/* @purpledog
+# NEED OWNER: tensorflow/contrib/grid_rnn/*
 tensorflow/contrib/hvx/* @satok16
 tensorflow/contrib/imperative/* @keveman
 tensorflow/contrib/integrate/* @shoyer
@@ -29,7 +29,7 @@ tensorflow/contrib/layers/* @fchollet @martinwicke
 tensorflow/contrib/learn/* @martinwicke @ispirmustafa @alextp
 tensorflow/contrib/linalg/* @langmore
 tensorflow/contrib/linear_optimizer/* @petrosmol @andreasst @katsiapis
-tensorflow/contrib/lookup/*	@ysuematsu @andreasst
+tensorflow/contrib/lookup/* @ysuematsu @andreasst
 tensorflow/contrib/losses/* @alextp @ispirmustafa
 tensorflow/contrib/makefile/* @petewarden @satok16 @wolffg
 tensorflow/contrib/metrics/* @alextp @honkentuber @ispirmustafa
-- 
GitLab


From 2195db6d8686aabb06233055dcb90190d4ef8fa1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 10:58:16 -0700
Subject: [PATCH 0917/1375] Remove unused flag:
 xla_hlo_graph_for_compute_constant

PiperOrigin-RevId: 161686867
---
 tensorflow/compiler/xla/legacy_flags/service_flags.cc | 6 ------
 tensorflow/compiler/xla/legacy_flags/service_flags.h  | 5 -----
 2 files changed, 11 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.cc b/tensorflow/compiler/xla/legacy_flags/service_flags.cc
index 90d30e7569..e7fa30455c 100644
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/service_flags.cc
@@ -36,18 +36,12 @@ static std::once_flag flags_init;
 static void AllocateFlags() {
   flags = new ServiceFlags;
   flags->xla_hlo_profile = false;
-  flags->xla_hlo_graph_for_compute_constant = false;
   flags->xla_dump_computations_to = "";
   flags->xla_dump_executions_to = "";
   flag_list = new std::vector<tensorflow::Flag>({
       tensorflow::Flag(
           "xla_hlo_profile", &flags->xla_hlo_profile,
           "Instrument the computation to collect per-HLO cycle counts"),
-      tensorflow::Flag(
-          "xla_hlo_graph_for_compute_constant",
-          &flags->xla_hlo_graph_for_compute_constant,
-          "If true, include hlo dumps of graphs from ComputeConstant."
-          "Such graphs still need to be matched via xla_generate_hlo_graph."),
       tensorflow::Flag("xla_dump_computations_to",
                        &flags->xla_dump_computations_to,
                        "Dumps computations that XLA executes into the provided "
diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.h b/tensorflow/compiler/xla/legacy_flags/service_flags.h
index 72d0c52402..a821a94093 100644
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.h
+++ b/tensorflow/compiler/xla/legacy_flags/service_flags.h
@@ -34,11 +34,6 @@ void AppendServiceFlags(std::vector<tensorflow::Flag>* flag_list);
 typedef struct {
   bool xla_hlo_profile;  // Instrument the computation to collect per-HLO cycle
                          // counts
-  bool xla_hlo_graph_for_compute_constant;  // If true, include hlo dumps of
-                                            // graphs from ComputeConstant.
-                                            // Such graphs still need to be
-                                            // matched via
-                                            // xla_generate_hlo_graph.
   string xla_dump_computations_to;  // Dumps computations that XLA executes
                                     // into the provided directory path
   // Dumps parameters and results of computations that XLA executes into
-- 
GitLab


From 7c1fe9068b96448f06a8a1d19d2008ca7e94f863 Mon Sep 17 00:00:00 2001
From: Karl Lessard <karllessard@users.noreply.github.com>
Date: Wed, 12 Jul 2017 14:10:53 -0400
Subject: [PATCH 0918/1375] [Java] Add base classes and utilities for operation
 wrappers. (#11188)

* Add base classes and utilities for operation wrappers.

* Rename Input interface to Operand

* Introduce changes after code review
---
 tensorflow/java/BUILD                         | 26 ++++++++
 .../tensorflow/{Input.java => Operand.java}   | 12 ++--
 .../main/java/org/tensorflow/Operation.java   | 15 +++++
 .../src/main/java/org/tensorflow/Output.java  |  6 +-
 .../src/main/java/org/tensorflow/op/Op.java   | 35 ++++++++++
 .../main/java/org/tensorflow/op/Operands.java | 47 ++++++++++++++
 .../java/org/tensorflow/op/PrimitiveOp.java   | 65 +++++++++++++++++++
 .../java/org/tensorflow/OperationTest.java    | 14 ++++
 .../test/java/org/tensorflow/TestUtil.java    |  8 +++
 .../java/org/tensorflow/op/OperandsTest.java  | 47 ++++++++++++++
 .../org/tensorflow/op/PrimitiveOpTest.java    | 57 ++++++++++++++++
 11 files changed, 323 insertions(+), 9 deletions(-)
 rename tensorflow/java/src/main/java/org/tensorflow/{Input.java => Operand.java} (76%)
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/Op.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/Operands.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java
 create mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java
 create mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java

diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 90372660cd..759f4c1b0e 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -162,6 +162,32 @@ java_test(
     ],
 )
 
+java_test(
+    name = "PrimitiveOpTest",
+    size = "small",
+    srcs = ["src/test/java/org/tensorflow/op/PrimitiveOpTest.java"],
+    javacopts = JAVACOPTS,
+    test_class = "org.tensorflow.op.PrimitiveOpTest",
+    deps = [
+        ":tensorflow",
+        ":testutil",
+        "@junit",
+    ],
+)
+
+java_test(
+    name = "OperandsTest",
+    size = "small",
+    srcs = ["src/test/java/org/tensorflow/op/OperandsTest.java"],
+    javacopts = JAVACOPTS,
+    test_class = "org.tensorflow.op.OperandsTest",
+    deps = [
+        ":tensorflow",
+        ":testutil",
+        "@junit",
+    ],
+)
+
 filegroup(
     name = "libtensorflow_jni",
     srcs = select({
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Operand.java
similarity index 76%
rename from tensorflow/java/src/main/java/org/tensorflow/Input.java
rename to tensorflow/java/src/main/java/org/tensorflow/Operand.java
index 8e6685ee0f..695c4c1060 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Input.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operand.java
@@ -21,20 +21,20 @@ package org.tensorflow;
  * <p>Example usage:
  *
  * <pre>{@code
- * // The "decodeJpeg" operation can be used as input to the "cast" operation
- * Input decodeJpeg = ops.image().decodeJpeg(...);
+ * // The "decodeJpeg" operation can be used as an operand to the "cast" operation
+ * Operand decodeJpeg = ops.image().decodeJpeg(...);
  * ops.math().cast(decodeJpeg, DataType.FLOAT);
  *
- * // The output "y" of the "unique" operation can be used as input to the "cast" operation
+ * // The output "y" of the "unique" operation can be used as an operand to the "cast" operation
  * Output y = ops.array().unique(...).y();
  * ops.math().cast(y, DataType.FLOAT);
  *
- * // The "split" operation can be used as input list to the "concat" operation
- * Iterable<? extends Input> split = ops.array().split(...);
+ * // The "split" operation can be used as operand list to the "concat" operation
+ * Iterable<? extends Operand> split = ops.array().split(...);
  * ops.array().concat(0, split);
  * }</pre>
  */
-public interface Input {
+public interface Operand {
 
   /**
    * Returns the symbolic handle of a tensor.
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index d584085282..ec26309fba 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -91,6 +91,21 @@ public final class Operation {
     }
   }
 
+  /**
+   * Returns symbolic handles to a list of tensors produced by this operation.
+   *
+   * @param idx index of the first tensor of the list
+   * @param length number of tensors in the list
+   * @return array of {@code Output}
+   */
+  public Output[] outputList(int idx, int length) {
+    Output[] outputs = new Output[length];
+    for (int i = 0; i < length; ++i) {
+      outputs[i] = output(idx + i);
+    }
+    return outputs;
+  }
+
   /** Returns a symbolic handle to one of the tensors produced by this operation. */
   public Output output(int idx) {
     return new Output(this, idx);
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index ad28eb2743..f92930619e 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -22,10 +22,10 @@ import java.util.Objects;
  * <p>An Output is a symbolic handle to a tensor. The value of the Tensor is computed by executing
  * the {@link Operation} in a {@link Session}.
  *
- * <p>By implementing the {@link Input} interface, instances of this class could also be passed
- * directly in input to an operation.
+ * <p>By implementing the {@link Operand} interface, instances of this class also act as operands to
+ * {@link org.tensorflow.op.Op Op} instances.
  */
-public final class Output implements Input {
+public final class Output implements Operand {
 
   /** Handle to the idx-th output of the Operation {@code op}. */
   public Output(Operation op, int idx) {
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/Op.java b/tensorflow/java/src/main/java/org/tensorflow/op/Op.java
new file mode 100644
index 0000000000..aa6db40457
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/Op.java
@@ -0,0 +1,35 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+/**
+ * A marker interface for all operation wrappers.
+ *
+ * <p>Operation wrappers provide strongly typed interfaces for building operations and linking them
+ * into a graph without the use of literals and indexes required by the core classes.
+ *
+ * <p>This interface allows keeping references to any operation wrapper using a common type.
+ *
+ * <pre>{@code
+ * // All values returned by an Ops call can be referred as a Op
+ * Op split = ops.array().split(...);
+ * Op shape = ops.array().shape(...);
+ *
+ * // All operations could be added to an Op collection
+ * Collection<Op> allOps = Arrays.asList(split, shape);
+ * }
+ */
+public interface Op {}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/Operands.java b/tensorflow/java/src/main/java/org/tensorflow/op/Operands.java
new file mode 100644
index 0000000000..86e88fea1e
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/Operands.java
@@ -0,0 +1,47 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.tensorflow.Operand;
+import org.tensorflow.OperationBuilder;
+import org.tensorflow.Output;
+
+/** Utilities for manipulating operand related types and lists. */
+public final class Operands {
+
+  /**
+   * Converts a list of {@link Operand} into an array of {@link Output}.
+   *
+   * <p>Operation wrappers need to convert back a list of inputs into an array of outputs in order
+   * to build an operation, see {@link OperationBuilder#addInputList(Output[])}.
+   *
+   * @param inputs an iteration of input operands
+   * @return an array of outputs
+   */
+  public static Output[] asOutputs(Iterable<? extends Operand> inputs) {
+    List<Output> outputList = new ArrayList<>();
+    for (Operand input : inputs) {
+      outputList.add(input.asOutput());
+    }
+    return outputList.toArray(new Output[outputList.size()]);
+  }
+
+  // Disabled constructor
+  private Operands() {}
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java b/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java
new file mode 100644
index 0000000000..8e56f97041
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java
@@ -0,0 +1,65 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import org.tensorflow.Operation;
+
+/**
+ * A base class for {@link Op} implementations that are backed by a single {@link Operation}.
+ *
+ * <p>Each operation registered in the TensorFlow core is a primitive and is provided as a {@code
+ * PrimitiveOp}. Custom operations working with only one primitive may also derive from this class.
+ */
+public abstract class PrimitiveOp implements Op {
+
+  @Override
+  public final int hashCode() {
+    return operation.hashCode();
+  }
+
+  @Override
+  public final boolean equals(Object obj) {
+    if (this == obj) {
+      return true;
+    }
+    // Note: we consider that all objects wrapping the same operation are equal, no matter their
+    // implementation
+    if (!(obj instanceof PrimitiveOp)) {
+      return false;
+    }
+    return operation.equals(((PrimitiveOp) obj).operation);
+  }
+
+  @Override
+  public final String toString() {
+    return String.format("<%s '%s'>", operation.type(), operation.name());
+  }
+
+  /**
+   * Underlying operation. It is deliberately not exposed by a getter method to avoid any name
+   * conflict with generated methods of the subclasses.
+   */
+  protected final Operation operation;
+
+  /**
+   * Constructor.
+   *
+   * @param operation the underlying operation
+   */
+  protected PrimitiveOp(Operation operation) {
+    this.operation = operation;
+  }
+}
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 27afc046ac..aade375db8 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -24,6 +24,7 @@ import static org.junit.Assert.fail;
 import java.util.Arrays;
 import java.util.HashSet;
 import java.util.Set;
+
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.JUnit4;
@@ -153,6 +154,19 @@ public class OperationTest {
     }
   }
 
+  @Test
+  public void outputList() {
+    try (Graph g = new Graph()) {
+      Operation split = TestUtil.split(g, "split", new int[] {0, 1, 2}, 3);
+      Output[] outputs = split.outputList(1, 2);
+      assertNotNull(outputs);
+      assertEquals(2, outputs.length);
+      for (int i = 0; i < outputs.length; ++i) {
+        assertEquals(i + 1, outputs[i].index());
+      }
+    }
+  }
+
   private static int split(int[] values, int num_split) {
     try (Graph g = new Graph()) {
       return g.opBuilder("Split", "Split")
diff --git a/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java b/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java
index 6a3a16c2e1..8d3a5449a6 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java
@@ -48,6 +48,14 @@ public class TestUtil {
         .output(0);
   }
 
+  public static Operation split(Graph g, String name, int[] values, int num_split) {
+    return g.opBuilder("Split", name)
+        .addInput(constant(g, "split_dim", 0))
+        .addInput(constant(g, "values", values))
+        .setAttr("num_split", num_split)
+        .build();
+  }
+
   public static void transpose_A_times_X(Graph g, int[][] a) {
     matmul(g, "Y", constant(g, "A", a), placeholder(g, "X", DataType.INT32), true, false);
   }
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java
new file mode 100644
index 0000000000..44c272708f
--- /dev/null
+++ b/tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java
@@ -0,0 +1,47 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertSame;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.tensorflow.Graph;
+import org.tensorflow.Operation;
+import org.tensorflow.Output;
+import org.tensorflow.TestUtil;
+
+/** Unit tests for {@link org.tensorflow.op.Operands}. */
+@RunWith(JUnit4.class)
+public class OperandsTest {
+
+  @Test
+  public void createOutputArrayFromOperandList() {
+    try (Graph g = new Graph()) {
+      Operation split = TestUtil.split(g, "split", new int[] {0, 1, 2}, 3);
+      List<Output> list = Arrays.asList(split.output(0), split.output(2));
+      Output[] array = Operands.asOutputs(list);
+      assertEquals(list.size(), array.length);
+      assertSame(array[0], list.get(0));
+      assertSame(array[1], list.get(1));
+    }
+  }
+}
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java
new file mode 100644
index 0000000000..aca81d1fac
--- /dev/null
+++ b/tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java
@@ -0,0 +1,57 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertNotEquals;
+import static org.junit.Assert.assertTrue;
+
+import java.util.HashSet;
+import java.util.Set;
+
+import org.junit.Test;
+import org.tensorflow.Graph;
+import org.tensorflow.Output;
+import org.tensorflow.TestUtil;
+
+public class PrimitiveOpTest {
+
+  @Test
+  public void equalsHashcode() {
+    try (Graph g = new Graph()) {
+      Output array = TestUtil.constant(g, "array", new int[2]);
+
+      PrimitiveOp test1 =
+          new PrimitiveOp(g.opBuilder("Shape", "shape1").addInput(array).build()) {};
+      PrimitiveOp test2 =
+          new PrimitiveOp(g.opBuilder("Shape", "shape2").addInput(array).build()) {};
+      PrimitiveOp test3 = new PrimitiveOp(test1.operation) {};
+
+      // equals() tests
+      assertNotEquals(test1, test2);
+      assertEquals(test1, test3);
+      assertEquals(test3, test1);
+      assertNotEquals(test2, test3);
+
+      // hashcode() tests
+      Set<PrimitiveOp> ops = new HashSet<>();
+      assertTrue(ops.add(test1));
+      assertTrue(ops.add(test2));
+      assertFalse(ops.add(test3));
+    }
+  }
+}
-- 
GitLab


From 8605f7ab8e0b0907c881096fad5d6622a4014df7 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Thu, 13 Jul 2017 03:11:51 +0900
Subject: [PATCH 0919/1375] Fix typos (#11444)

---
 tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py        | 2 +-
 tensorflow/contrib/labeled_tensor/python/ops/core.py            | 2 +-
 .../timeseries/python/timeseries/state_space_models/periodic.py | 2 +-
 tensorflow/core/lib/io/snappy/snappy_buffers_test.cc            | 2 +-
 tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java | 2 +-
 tensorflow/python/lib/core/strings.i                            | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index 81ebc821a3..9307fbabac 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -197,7 +197,7 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       prefix = "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell" % i
       w_names.append(prefix + "/kernel")
       # Three transformed bias tensors each layer:
-      # the 1st is for CudnnCompatbleLSTM(Block)Cell restore; the latter two
+      # the 1st is for CudnnCompatibleLSTM(Block)Cell restore; the latter two
       # sum up to the 1st, and are used for cuDNN restore.
       b_names.append(prefix + "/bias")
       b_names.extend([prefix + "/bias_cudnn_%d" % j for j in range(2)])
diff --git a/tensorflow/contrib/labeled_tensor/python/ops/core.py b/tensorflow/contrib/labeled_tensor/python/ops/core.py
index 04bf26a5dd..fc1ea83449 100644
--- a/tensorflow/contrib/labeled_tensor/python/ops/core.py
+++ b/tensorflow/contrib/labeled_tensor/python/ops/core.py
@@ -199,7 +199,7 @@ class Axes(collections.Mapping):
   """Axis names and indices for a tensor.
 
   It is an ordered mapping, with keys given by axis name and values given
-  by Axis objets. Duplicate axis names are not allowed.
+  by Axis objects. Duplicate axis names are not allowed.
   """
 
   @tc.accepts(object, tc.List(AxisLike))
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
index d0770b86fe..e70db93ea1 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
@@ -50,7 +50,7 @@ class CycleStateSpaceModel(state_space_model.StateSpaceModel):
 
   def get_noise_transform(self):
     # transition_power_noise_accumulator makes assumptions about this
-    # transformation. If the noise transform is modified or overriden,
+    # transformation. If the noise transform is modified or overridden,
     # transition_power_noise_accumulator must be modified as well (or discarded,
     # as it is simply an optimization).
     return array_ops.pad(
diff --git a/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc b/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
index a27ab73bf7..e0918c70a7 100644
--- a/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
+++ b/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
@@ -117,7 +117,7 @@ Status TestMultipleWrites(size_t compress_input_buf_size,
   io::SnappyInputBuffer in(file_reader.get(), uncompress_input_buf_size,
                            uncompress_output_buf_size);
 
-  // Run the test twice, reseting the stream after the first attempt.
+  // Run the test twice, resetting the stream after the first attempt.
   for (int attempt = 0; attempt < 2; ++attempt) {
     string actual_result;
     for (int i = 0; i < num_writes; i++) {
diff --git a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
index 15f7b99135..ca06177017 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
@@ -49,7 +49,7 @@ final class NativeLibrary {
       // Either:
       // (1) The native library has already been statically loaded, OR
       // (2) The required native code has been statically linked (through a custom launcher), OR
-      // (3) The native code is part of another library (such as an an application-level libraryh)
+      // (3) The native code is part of another library (such as an application-level library)
       // that has already been loaded. For example, tensorflow/examples/android and
       // tensorflow/contrib/android include the required native code in differently named libraries.
       //
diff --git a/tensorflow/python/lib/core/strings.i b/tensorflow/python/lib/core/strings.i
index b74eb91cd5..938c13e30e 100644
--- a/tensorflow/python/lib/core/strings.i
+++ b/tensorflow/python/lib/core/strings.i
@@ -80,7 +80,7 @@ bool _BytesToStringPiece(PyObject* obj, tensorflow::StringPiece* result) {
   }
 }
 
-// Converts a C++ string vector to a a list of Python bytes objects.
+// Converts a C++ string vector to a list of Python bytes objects.
 %typemap(out) std::vector<string> {
   const int size = $1.size();
   auto temp_string_list = tensorflow::make_safe(PyList_New(size));
-- 
GitLab


From d1e3cadda53f7e3eab4785f6b47257ff044e9afd Mon Sep 17 00:00:00 2001
From: DimanNe <dimanne@gmail.com>
Date: Wed, 12 Jul 2017 21:31:00 +0300
Subject: [PATCH 0920/1375] Fix linking options issued by bazel in oorder to
 make gradients register (#11449)

---
 tensorflow/cc/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index b461a475c1..c65170dfe8 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -248,6 +248,7 @@ cc_library(
         ":gradients",
         "//tensorflow/core:lib_proto_parsing",
     ],
+    alwayslink = 1,
 )
 
 tf_cc_test(
@@ -275,6 +276,7 @@ cc_library(
         ":cc_ops",
         ":grad_op_registry",
     ],
+    alwayslink = 1,
 )
 
 tf_cc_test(
@@ -302,6 +304,7 @@ cc_library(
         ":cc_ops_internal",
         ":grad_op_registry",
     ],
+    alwayslink = 1,
 )
 
 tf_cc_test(
-- 
GitLab


From 9c4da4a249c6b6a7f36c26efb1ada07897fb26b4 Mon Sep 17 00:00:00 2001
From: vhasanov <KyotoSunshine@users.noreply.github.com>
Date: Thu, 13 Jul 2017 03:34:58 +0900
Subject: [PATCH 0921/1375] Deleted unnecessary repetition of the same text.
 (#11459)

The same text was repeated two times. I deleted the repetition.
---
 RELEASE.md | 31 -------------------------------
 1 file changed, 31 deletions(-)

diff --git a/RELEASE.md b/RELEASE.md
index 0cd4eef5d6..33fe88cd54 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -65,37 +65,6 @@
   integration into apps. See
   https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md
   for more details.
-* RNNCells' variable names have been renamed for consistency with Keras layers.
-  Specifically, the previous variable names "weights" and "biases" have
-  been changed to "kernel" and "bias", respectively.
-  This may cause backward incompatibility with regard to your old
-  checkpoints containing such RNN cells, in which case you can use the tool
-  [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
-  to convert the variable names in your old checkpoints.
-* Many of the RNN functions and classes that were in the `tf.nn` namespace
-  before the 1.0 release and which were moved to `tf.contrib.rnn` have now
-  been moved back to the core namespace.  This includes
-  `RNNCell`, `LSTMCell`, `GRUCell`, and a number of other cells.  These
-  now reside in `tf.nn.rnn_cell` (with aliases in `tf.contrib.rnn` for backwards
-  compatibility).  The original `tf.nn.rnn` function is now `tf.nn.static_rnn`,
-  and the bidirectional static and state saving static rnn functions are also
-  now back in the `tf.nn` namespace.
-
-  Notable exceptions are the `EmbeddingWrapper`, `InputProjectionWrapper` and
-  `OutputProjectionWrapper`,  which will slowly be moved to deprecation
-  in `tf.contrib.rnn`.  These are inefficient wrappers that should often
-  be replaced by calling `embedding_lookup` or `layers.dense` as pre- or post-
-  processing of the rnn.  For RNN decoding, this functionality has been replaced
-  with an alternative API in `tf.contrib.seq2seq`.
-* Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of
-  optimized deep learning primitives: In addition to matrix multiplication and
-  convolution, these building blocks include:
-  Direct batched convolution
-  Pooling: maximum, minimum, average
-  Normalization: LRN, batch normalization
-  Activation: rectified linear unit (ReLU)
-  Data manipulation: multi-dimensional transposition (conversion), split,
-  concat, sum and scale.
 
 ## Deprecations
 
-- 
GitLab


From 9aa0dcbf282f119ac5f53bbb71af40f432bc3be9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 12:18:38 -0700
Subject: [PATCH 0922/1375] Add shape check for MakeQuantileSummariesOp.

PiperOrigin-RevId: 161698801
---
 .../boosted_trees/kernels/quantile_ops.cc     |   5 +
 .../contrib/boosted_trees/ops/quantile_ops.cc |  14 +-
 .../python/kernel_tests/quantile_ops_test.py  | 144 ++++++++++--------
 3 files changed, 97 insertions(+), 66 deletions(-)

diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
index 546f6a30b8..ca24582d41 100644
--- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
@@ -382,6 +382,11 @@ class MakeQuantileSummariesOp : public OpKernel {
               sparse_float_feature_values_list[sparse_index].flat<float>();
           const auto sparse_indices =
               sparse_float_feature_indices_list[sparse_index].matrix<int64>();
+          const auto dense_shape =
+              sparse_float_feature_shapes_list[sparse_index].flat<int64>();
+          OP_REQUIRES(context, batch_size == dense_shape(0),
+                      errors::InvalidArgument(
+                          "Sparse column shape doesn't match the batch size."));
           QuantileStream stream(epsilon_, batch_size + 1);
           // Run quantile summary generation.
           const int64 num_sparse_rows =
diff --git a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
index c778a02fb6..f03c50dcc5 100644
--- a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
+++ b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
@@ -20,6 +20,7 @@
 namespace tensorflow {
 namespace gtflow {
 using shape_inference::InferenceContext;
+using shape_inference::DimensionHandle;
 using shape_inference::ShapeHandle;
 
 REGISTER_RESOURCE_HANDLE_OP(QuantileStreamResource);
@@ -172,7 +173,17 @@ REGISTER_OP("MakeQuantileSummaries")
       int num_sparse_features;
       TF_RETURN_IF_ERROR(
           c->GetAttr("num_sparse_features", &num_sparse_features));
+      ShapeHandle example_weights_shape;
+      int example_weights_index = num_dense_features + num_sparse_features * 3;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(example_weights_index), 2,
+                                     &example_weights_shape));
       for (int i = 0; i < num_dense_features; ++i) {
+        ShapeHandle dense_feature_shape;
+        DimensionHandle unused_dim;
+        TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 2, &dense_feature_shape));
+        TF_RETURN_IF_ERROR(c->Merge(c->Dim(dense_feature_shape, 0),
+                                    c->Dim(example_weights_shape, 0),
+                                    &unused_dim));
         c->set_output(i, c->Scalar());
       }
       for (int i = 0; i < num_sparse_features; ++i) {
@@ -193,7 +204,8 @@ sparse_float_feature_values: List of rank 1 tensors containing the sparse float
 feature values.
 sparse_float_feature_shapes: List of rank 1 tensors containing the shape of the
 float feature.
-example_weights: Rank 1 tensor containing the example weight tensor.
+example_weights: Rank 2 (N, 1) tensor of per-example weights. Should match
+    dense and sparse features shape.
 dense_summaries: A list of serialized QuantileSummaryState for dense columns.
 sparse_summaries: A list of serialized QuantileSummaryState for sparse columns.
 )doc");
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
index dc4495fa1f..9ed8b3404f 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
@@ -26,9 +26,12 @@ import numpy as np
 
 from tensorflow.contrib.boosted_trees.proto.quantiles_pb2 import QuantileConfig
 from tensorflow.contrib.boosted_trees.python.ops import quantile_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import resources
 from tensorflow.python.platform import googletest
 from tensorflow.python.training import saver
@@ -56,12 +59,15 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
     | 5        |     1            |   5      |    6
     """
 
-    dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
-    sparse_indices_0 = np.array(
-        [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=np.int64)
-    sparse_values_0 = np.array([2, 3, 4, 5, 6])
-    sparse_shape_0 = np.array([6, 1])
-    example_weights = np.array([10, 1, 1, 1, 1, 1])
+    dense_float_tensor_0 = constant_op.constant(
+        [1, 2, 3, 4, 4, 5], dtype=dtypes.float32)
+    sparse_indices_0 = constant_op.constant(
+        [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=dtypes.int64)
+    sparse_values_0 = constant_op.constant(
+        [2, 3, 4, 5, 6], dtype=dtypes.float32)
+    sparse_shape_0 = constant_op.constant([6, 1], dtype=dtypes.int64)
+    example_weights = constant_op.constant(
+        [10, 1, 1, 1, 1, 1], dtype=dtypes.float32)
 
     with self.test_session():
       config = self._gen_config(0.33, 3)
@@ -78,40 +84,38 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
   def testStreamingQuantileBuckets(self):
     """Sets up the quantile summary op test as follows.
 
-    Create a batch of 6 examples having a dense and sparse features.
-    The data looks like this
-    | Instance | instance weights | Dense 0
-    | 0        |     10           |   1
-    | 1        |     1            |   2
-    | 2        |     1            |   3
-    | 3        |     1            |   4
-    | 4        |     1            |   4
-    | 5        |     1            |   5
+    100 batches of data is added to the accumulator. The batches are in form:
+    [0 1 .. 99]
+    [100 101 .. 200]
+    ...
+    [9900 9901 .. 9999]
+    All the batches have 1 for all the example weights.
     """
-    dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
-    example_weights = np.array([10, 1, 1, 1, 1, 1])
-
     with self.test_session() as sess:
       accumulator = quantile_ops.QuantileAccumulator(
-          init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q1")
-
+          init_stamp_token=0, num_quantiles=3, epsilon=0.01, name="q1")
       resources.initialize_resources(resources.shared_resources()).run()
+    weight_placeholder = array_ops.placeholder(dtypes.float32)
+    dense_placeholder = array_ops.placeholder(dtypes.float32)
+    update = accumulator.add_summary(
+        stamp_token=0,
+        column=dense_placeholder,
+        example_weights=weight_placeholder)
+    with self.test_session() as sess:
+      for i in range(100):
+        dense_float = np.linspace(
+            i * 100, (i + 1) * 100 - 1, num=100).reshape(-1, 1)
+        sess.run(update, {
+            dense_placeholder: dense_float,
+            weight_placeholder: np.ones(shape=(100, 1), dtype=np.float32)
+        })
 
-      are_ready_noflush, _, = (accumulator.get_buckets(stamp_token=0))
-
-      update = accumulator.add_summary(
-          stamp_token=0,
-          column=dense_float_tensor_0,
-          example_weights=example_weights)
-      with ops.control_dependencies([are_ready_noflush, update]):
-        reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
-      with ops.control_dependencies([reset]):
-        are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
-      buckets, are_ready_noflush, are_ready_flush = (sess.run(
-          [buckets, are_ready_noflush, are_ready_flush]))
-      self.assertEqual(False, are_ready_noflush)
+    with self.test_session() as sess:
+      sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1))
+      are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
       self.assertEqual(True, are_ready_flush)
-      self.assertAllEqual([1, 3, 5], buckets)
+      self.assertAllEqual([0, 3335., 6671., 9999.], buckets)
 
   def testSaveRestoreBeforeFlush(self):
     save_dir = os.path.join(self.get_temp_dir(), "save_restore")
@@ -124,11 +128,13 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
       save = saver.Saver()
       resources.initialize_resources(resources.shared_resources()).run()
 
-      sparse_indices_0 = np.array(
-          [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=np.int64)
-      sparse_values_0 = [2.0, 3.0, 4.0, 5.0, 6.0]
-      sparse_shape_0 = np.array([6, 1])
-      example_weights = np.array([10, 1, 1, 1, 1, 1])
+      sparse_indices_0 = constant_op.constant(
+          [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dtype=dtypes.int64)
+      sparse_values_0 = constant_op.constant(
+          [2.0, 3.0, 4.0, 5.0, 6.0], dtype=dtypes.float32)
+      sparse_shape_0 = constant_op.constant([6, 1], dtype=dtypes.int64)
+      example_weights = constant_op.constant(
+          [10, 1, 1, 1, 1, 1], dtype=dtypes.float32, shape=[6, 1])
       update = accumulator.add_summary(
           stamp_token=0,
           column=sparse_tensor.SparseTensor(sparse_indices_0, sparse_values_0,
@@ -173,8 +179,10 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
       save = saver.Saver()
       resources.initialize_resources(resources.shared_resources()).run()
 
-      example_weights = np.array([10, 1, 1, 1, 1, 1])
-      dense_float_tensor_0 = np.array([1, 2, 3, 4, 4, 5])
+      example_weights = constant_op.constant(
+          [10, 1, 1, 1, 1, 1], dtype=dtypes.float32, shape=[6, 1])
+      dense_float_tensor_0 = constant_op.constant(
+          [1, 2, 3, 4, 4, 5], dtype=dtypes.float32, shape=[6, 1])
       update = accumulator.add_summary(
           stamp_token=0,
           column=dense_float_tensor_0,
@@ -206,9 +214,11 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
     Creates array dividing range [0, 1] to 1<<16 elements equally spaced
     with weight of 1.0.
     """
-    dense_float_tensor_0 = np.array([(1.0 * i) / math.pow(
-        2.0, 16) for i in range(0, int(math.pow(2, 16)) + 1)])
-    example_weights = np.array([1] * (int(math.pow(2, 16)) + 1))
+    dense_float_tensor_0 = constant_op.constant(
+        [(1.0 * i) / math.pow(2.0, 16)
+         for i in range(0, int(math.pow(2, 16)) + 1)])
+    example_weights = constant_op.constant(
+        [1] * (int(math.pow(2, 16)) + 1), dtype=dtypes.float32)
     config = self._gen_config(0.1, 10)
 
     with self.test_session():
@@ -228,10 +238,12 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
     Creates array dividing range [0, 1] to 1<<16 elements equally spaced
     with weight same as the value.
     """
-    dense_float_tensor_0 = np.array([(1.0 * i) / math.pow(
-        2.0, 16) for i in range(0, int(math.pow(2, 16)) + 1)])
-    example_weights = np.array([(1.0 * i) / math.pow(2.0, 16)
-                                for i in range(0, int(math.pow(2, 16)) + 1)])
+    dense_float_tensor_0 = constant_op.constant(
+        [(1.0 * i) / math.pow(2.0, 16)
+         for i in range(0, int(math.pow(2, 16)) + 1)])
+    example_weights = constant_op.constant(
+        [(1.0 * i) / math.pow(2.0, 16)
+         for i in range(0, int(math.pow(2, 16)) + 1)])
 
     config = self._gen_config(0.1, 10)
 
@@ -267,28 +279,30 @@ class QuantilesOpTest(test_util.TensorFlowTestCase):
     Sparse 2: (-inf, 100]
     """
     super(QuantilesOpTest, self).setUp()
-    self._dense_float_tensor_0 = np.array([[-0.1], [0.4], [3.2], [190]])
-    self._dense_float_tensor_1 = np.array([[-1], [-15], [18], [1000]])
-
+    self._dense_float_tensor_0 = constant_op.constant(
+        [[-0.1], [0.4], [3.2], [190]], dtype=dtypes.float32)
+    self._dense_float_tensor_1 = constant_op.constant(
+        [[-1], [-15], [18], [1000]], dtype=dtypes.float32)
     # Sparse feature 0
-    self._sparse_indices_0 = np.array([[0, 0], [1, 0], [2, 0], [3, 0]])
-    self._sparse_values_0 = np.array([-2, 5.5, 16, 17.5])
-    self._sparse_shape_0 = np.array([4, 1])
+    self._sparse_indices_0 = constant_op.constant([[0, 0], [1, 0], [2, 0],
+                                                   [3, 0]])
+    self._sparse_values_0 = constant_op.constant([-2, 5.5, 16, 17.5])
+    self._sparse_shape_0 = constant_op.constant([4, 1])
     # Sprase feature 1
-    self._sparse_indices_1 = np.array([[0, 0], [2, 0], [3, 0]])
-    self._sparse_values_1 = np.array([0.1, 3, -3])
-    self._sparse_shape_1 = np.array([4, 1])
+    self._sparse_indices_1 = constant_op.constant([[0, 0], [2, 0], [3, 0]])
+    self._sparse_values_1 = constant_op.constant([0.1, 3, -3])
+    self._sparse_shape_1 = constant_op.constant([4, 1])
     # Sprase feature 2
-    self._sparse_indices_2 = np.array([[1, 0], [3, 0]])
-    self._sparse_values_2 = np.array([2, 4])
-    self._sparse_shape_2 = np.array([4, 1])
+    self._sparse_indices_2 = constant_op.constant([[1, 0], [3, 0]])
+    self._sparse_values_2 = constant_op.constant([2, 4], dtype=dtypes.float32)
+    self._sparse_shape_2 = constant_op.constant([4, 1])
     # Quantiles
-    self._dense_thresholds_0 = np.array([0.4, 5, 190])
-    self._dense_thresholds_1 = np.array([-9, 15, 1000])
+    self._dense_thresholds_0 = [0.4, 5, 190]
+    self._dense_thresholds_1 = [-9, 15, 1000]
 
-    self._sparse_thresholds_0 = np.array([5, 16, 100])
-    self._sparse_thresholds_1 = np.array([2, 5])
-    self._sparse_thresholds_2 = np.array([100])
+    self._sparse_thresholds_0 = [5, 16, 100]
+    self._sparse_thresholds_1 = [2, 5]
+    self._sparse_thresholds_2 = [100]
 
   def testDenseFeaturesOnly(self):
     with self.test_session():
-- 
GitLab


From c9d03a568a221e96c47ee7d5be703984d61b95a4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 12:44:53 -0700
Subject: [PATCH 0923/1375] Add tf.contrib.nn.rank_sampled_softmax_loss, a
 variant of tf.nn.sampled_softmax_loss that has been shown to improve rank
 loss. Paper: https://arxiv.org/abs/1707.03073

PiperOrigin-RevId: 161702455
---
 tensorflow/contrib/nn/BUILD                   |  21 ++
 .../contrib/nn/python/ops/sampling_ops.py     | 243 +++++++++++++
 .../nn/python/ops/sampling_ops_test.py        | 322 ++++++++++++++++++
 3 files changed, 586 insertions(+)
 create mode 100644 tensorflow/contrib/nn/python/ops/sampling_ops.py
 create mode 100644 tensorflow/contrib/nn/python/ops/sampling_ops_test.py

diff --git a/tensorflow/contrib/nn/BUILD b/tensorflow/contrib/nn/BUILD
index dbac049d83..af33496e5d 100644
--- a/tensorflow/contrib/nn/BUILD
+++ b/tensorflow/contrib/nn/BUILD
@@ -7,6 +7,8 @@ exports_files(["LICENSE"])
 
 package(default_visibility = ["//visibility:public"])
 
+load("//tensorflow:tensorflow.bzl", "py_test")
+
 py_library(
     name = "nn_py",
     srcs = [
@@ -14,15 +16,34 @@ py_library(
         "python/__init__.py",
         "python/ops/__init__.py",
         "python/ops/cross_entropy.py",
+        "python/ops/sampling_ops.py",
     ],
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
     deps = [
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:util",
     ],
 )
 
+py_test(
+    name = "sampling_ops_test",
+    size = "small",
+    srcs = ["python/ops/sampling_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":nn_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:nn",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/nn/python/ops/sampling_ops.py b/tensorflow/contrib/nn/python/ops/sampling_ops.py
new file mode 100644
index 0000000000..7a9eed511b
--- /dev/null
+++ b/tensorflow/contrib/nn/python/ops/sampling_ops.py
@@ -0,0 +1,243 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Ops related to candidate sampling."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import embedding_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
+
+
+def _rank_resample(weights, biases, inputs, sampled_values, num_resampled,
+                   resampling_temperature, partition_strategy):
+  """A helper function for rank_sampled_softmax_loss.
+
+  This computes, for each i in `sampled_values`,
+
+      log(sum_j exp((w_i * x_j + b_i) / resampling_temperature))
+
+  where w_i, b_i are the weight and bias of the i-th class, repsectively,
+  and j ranges over the rows of `inputs`. For efficiency, we rearrange the
+  computation to
+
+      log(sum_j exp(w_i * (x_j / resampling_temperature))) +
+          b_i / resampling_temperature.
+
+  This translates to the following batched computation using tensorflow ops:
+
+      reduce_logsumexp(matmul(embeddings,
+                       transpose(inputs / resampling_temperature))) +
+          biases / resampling_temperature
+
+  The computation of the first term is colocated with the embeddings using
+  `transform_fn` in `embedding_ops._embedding_lookup_and_transform`. The second
+  term, not the bottleneck, is computed at the worker.
+
+  Args:
+    weights: From `rank_sampled_softmax_loss`.
+    biases: From `rank_sampled_softmax_loss`.
+    inputs: From `rank_sampled_softmax_loss`.
+    sampled_values: A tuple of (`sampled_candidates`, `true_expected_count`,
+        `sampled_expected_count`) returned by a `*_candidate_sampler` function.
+    num_resampled: An `int`. This many values are selected from
+        `sampled_values` using the adaptive resampling algorithm. The caller
+        must ensure that `num_resampled` is less than the size of
+        `sampled_values`.
+    resampling_temperature: A scalar `Tensor` with the temperature parameter
+        for the adaptive resampling algorithm.
+    partition_strategy: From `rank_sampled_softmax_loss`.
+
+  Returns:
+    A tuple of (`resampled_candidates`, `true_expected_count`,
+        `resampled_expected_count`), similar to `sampled_values` but sampled
+        down to `num_resampled` values.
+  """
+  # This code supports passing a Tensor for num_resampled, but since it is only
+  # called with an int, that's what we specify in the arg list. If this
+  # function is ever externalized, we should change the doc to support Tensor.
+
+  sampled, true_expected_count, sampled_expected_count = sampled_values
+
+  sampled = math_ops.cast(array_ops.stop_gradient(sampled), dtypes.int64)
+  true_expected_count = array_ops.stop_gradient(true_expected_count)
+  sampled_expected_count = array_ops.stop_gradient(sampled_expected_count)
+
+  reweighted_inputs = inputs / resampling_temperature
+
+  def logsumexp_logit(embeddings):
+    return math_ops.reduce_logsumexp(
+        math_ops.matmul(embeddings, reweighted_inputs, transpose_b=True),
+        axis=1,
+        keep_dims=False)
+
+  # Calling this protected form of embedding_lookup allows co-locating
+  # the logsumexp computation with the partitioned weights, which yields
+  # a large speedup in practice.
+  sampled_logits = embedding_ops._embedding_lookup_and_transform(  # pylint: disable=protected-access
+      weights, sampled, partition_strategy, transform_fn=logsumexp_logit)
+  sampled_b = array_ops.reshape(
+      embedding_ops.embedding_lookup(biases, sampled, partition_strategy), [-1])
+  sampled_logits += sampled_b / resampling_temperature
+
+  _, resampled_indices = nn.top_k(sampled_logits, k=num_resampled, sorted=False)
+  resampled = array_ops.gather(sampled, indices=resampled_indices)
+  resampled_expected_count = array_ops.gather(
+      sampled_expected_count, indices=resampled_indices)
+
+  return resampled, true_expected_count, resampled_expected_count
+
+
+# TODO(ccolby): Before checkin, Add reference to TAPAS paper when in arxiv.org.
+def rank_sampled_softmax_loss(weights,
+                              biases,
+                              labels,
+                              inputs,
+                              num_sampled,
+                              num_resampled,
+                              num_classes,
+                              num_true,
+                              sampled_values,
+                              resampling_temperature,
+                              remove_accidental_hits,
+                              partition_strategy,
+                              name=None):
+  """Computes softmax loss using rank-based adaptive resampling.
+
+  This has been shown to improve rank loss after training compared to
+  @{tf.nn.sampled_softmax_loss}. For a description of the algorithm and some
+  experimental results, please see: [TAPAS: Two-pass Approximate Adaptive
+  Sampling for Softmax](https://arxiv.org/abs/1707.03073).
+
+  Sampling follows two phases:
+  * In the first phase, `num_sampled` classes are selected using
+    @{tf.nn.learned_unigram_candidate_sampler} or supplied `sampled_values`.
+    The logits are calculated on those sampled classes. This phases is
+    similar to @{tf.nn.sampled_softmax_loss}.
+  * In the second phase, the `num_resampled` classes with highest predicted
+    probability are kept. Probabilities are
+    `LogSumExp(logits / resampling_temperature)`, where the sum is over
+    `inputs`.
+
+  The `resampling_temperature` parameter controls the "adaptiveness" of the
+  resampling. At lower temperatures, resampling is more adaptive because it
+  picks more candidates close to the predicted classes. A common strategy is
+  to decrease the temperature as training proceeds.
+
+  See @{tf.nn.sampled_softmax_loss} for more documentation on sampling and
+  for typical default values for some of the parameters.
+
+  This operation is for training only. It is generally an underestimate of
+  the full softmax loss.
+
+  A common use case is to use this method for training, and calculate the full
+  softmax loss for evaluation or inference. In this case, you must set
+  `partition_strategy="div"` for the two losses to be consistent, as in the
+  following example:
+
+  ```python
+  if mode == "train":
+    loss = rank_sampled_softmax_loss(
+        weights=weights,
+        biases=biases,
+        labels=labels,
+        inputs=inputs,
+        ...,
+        partition_strategy="div")
+  elif mode == "eval":
+    logits = tf.matmul(inputs, tf.transpose(weights))
+    logits = tf.nn.bias_add(logits, biases)
+    labels_one_hot = tf.one_hot(labels, n_classes)
+    loss = tf.nn.softmax_cross_entropy_with_logits(
+        labels=labels_one_hot,
+        logits=logits)
+  ```
+
+  Args:
+    weights: A `Tensor` or `PartitionedVariable` of shape `[num_classes, dim]`,
+        or a list of `Tensor` objects whose concatenation along dimension 0
+        has shape [num_classes, dim]. The (possibly-sharded) class embeddings.
+    biases: A `Tensor` or `PartitionedVariable` of shape `[num_classes]`.
+        The (possibly-sharded) class biases.
+    labels: A `Tensor` of type `int64` and shape `[batch_size,
+        num_true]`. The target classes. Note that this format differs from
+        the `labels` argument of `nn.softmax_cross_entropy_with_logits`.
+    inputs: A `Tensor` of shape `[batch_size, dim]`. The forward
+        activations of the input network.
+    num_sampled: An `int`. The number of classes to randomly sample per batch.
+    num_resampled: An `int`. The number of classes to select from the
+        `num_sampled` classes using the adaptive resampling algorithm. Must be
+        less than `num_sampled`.
+    num_classes: An `int`. The number of possible classes.
+    num_true: An `int`.  The number of target classes per training example.
+    sampled_values: A tuple of (`sampled_candidates`, `true_expected_count`,
+        `sampled_expected_count`) returned by a `*_candidate_sampler` function.
+        If None, default to `nn.learned_unigram_candidate_sampler`.
+    resampling_temperature: A scalar `Tensor` with the temperature parameter
+        for the adaptive resampling algorithm.
+    remove_accidental_hits: A `bool`. Whether to remove "accidental hits"
+        where a sampled class equals one of the target classes.
+    partition_strategy: A string specifying the partitioning strategy, relevant
+        if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
+        See @{tf.nn.embedding_lookup} for more details.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `batch_size` 1-D tensor of per-example sampled softmax losses.
+
+  Raises:
+    ValueError: If `num_sampled <= num_resampled`.
+  """
+  if num_sampled > num_classes:
+    raise ValueError("num_sampled ({}) cannot be greater than num_classes ({})".
+                     format(num_sampled, num_classes))
+  if num_sampled <= num_resampled:
+    raise ValueError("num_resampled ({}) must be less than num_sampled ({})".
+                     format(num_resampled, num_sampled))
+  if partition_strategy not in ("div", "mod"):
+    raise ValueError(
+        "unsupported partition_strategy ({})".format(partition_strategy))
+  with ops.name_scope(name, "rank_sampled_softmax_loss", [
+      weights, biases, labels, inputs, sampled_values, resampling_temperature
+  ]) as name:
+    if not sampled_values:
+      sampled_values = nn.learned_unigram_candidate_sampler(
+          true_classes=labels,
+          num_true=num_true,
+          num_sampled=num_sampled,
+          unique=True,
+          range_max=num_classes)
+    # From sampled_values, select the top num_resampled values using the
+    # adaptive rank resampling strategy.
+    resampled_values = _rank_resample(weights, biases, inputs, sampled_values,
+                                      num_resampled, resampling_temperature,
+                                      partition_strategy)
+    return nn.sampled_softmax_loss(
+        weights=weights,
+        biases=biases,
+        labels=labels,
+        inputs=inputs,
+        num_sampled=num_resampled,
+        num_classes=num_classes,
+        num_true=num_true,
+        sampled_values=resampled_values,
+        remove_accidental_hits=remove_accidental_hits,
+        partition_strategy=partition_strategy,
+        name=name)
diff --git a/tensorflow/contrib/nn/python/ops/sampling_ops_test.py b/tensorflow/contrib/nn/python/ops/sampling_ops_test.py
new file mode 100644
index 0000000000..1d4fe1321b
--- /dev/null
+++ b/tensorflow/contrib/nn/python/ops/sampling_ops_test.py
@@ -0,0 +1,322 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for sampling_ops.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.nn.python.ops import sampling_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import nn
+from tensorflow.python.platform import test
+
+
+class RankSampledSoftmaxLossTest(test.TestCase):
+
+  def setUp(self):
+    self._sampled = [3, 4, 5, 6, 7]
+    self._num_sampled = len(self._sampled)
+    # Because values of all matrices increase with indices, logits increase with
+    # class id. So, for the above sampled classes, adaptive sampling will select
+    # these resampled classes.
+    self._resampled = [5, 6, 7]
+    self._num_resampled = len(self._resampled)
+    self._num_classes = 10
+    self._num_true = 2
+    self._sampled_values = (self._sampled, [[0.5], [0.5]],
+                            [0.5, 0.5, 0.5, 0.5, 0.5])
+    self._resampled_values = (self._resampled, [[0.5], [0.5]], [0.5, 0.5, 0.5])
+    self._remove_accidental_hits = False
+    self._embed_dim = 5
+    self._batch_size = 2
+
+  def _weights(self):
+    return constant_op.constant([
+        [0.0, 0.1, 0.2, 0.3, 0.4],
+        [1.0, 1.1, 1.2, 1.3, 1.4],
+        [2.0, 2.1, 2.2, 2.3, 2.4],
+        [3.0, 3.1, 3.2, 3.3, 3.4],
+        [4.0, 4.1, 4.2, 4.3, 4.4],
+        [5.0, 5.1, 5.2, 5.3, 5.4],
+        [6.0, 6.1, 6.2, 6.3, 6.4],
+        [7.0, 7.1, 7.2, 7.3, 7.4],
+        [8.0, 8.1, 8.2, 8.3, 8.4],
+        [9.0, 9.1, 9.2, 9.3, 9.4],
+    ])
+
+  def _div_sharded_weights(self):
+    return [
+        constant_op.constant([
+            [0.0, 0.1, 0.2, 0.3, 0.4],
+            [1.0, 1.1, 1.2, 1.3, 1.4],
+        ]),
+        constant_op.constant([
+            [2.0, 2.1, 2.2, 2.3, 2.4],
+            [3.0, 3.1, 3.2, 3.3, 3.4],
+        ]),
+        constant_op.constant([
+            [4.0, 4.1, 4.2, 4.3, 4.4],
+            [5.0, 5.1, 5.2, 5.3, 5.4],
+        ]),
+        constant_op.constant([
+            [6.0, 6.1, 6.2, 6.3, 6.4],
+            [7.0, 7.1, 7.2, 7.3, 7.4],
+        ]),
+        constant_op.constant([
+            [8.0, 8.1, 8.2, 8.3, 8.4],
+            [9.0, 9.1, 9.2, 9.3, 9.4],
+        ]),
+    ]
+
+  def _mod_sharded_weights(self):
+    return [
+        constant_op.constant([
+            [0.0, 0.1, 0.2, 0.3, 0.4],
+            [5.0, 5.1, 5.2, 5.3, 5.4],
+        ]),
+        constant_op.constant([
+            [1.0, 1.1, 1.2, 1.3, 1.4],
+            [6.0, 6.1, 6.2, 6.3, 6.4],
+        ]),
+        constant_op.constant([
+            [2.0, 2.1, 2.2, 2.3, 2.4],
+            [7.0, 7.1, 7.2, 7.3, 7.4],
+        ]),
+        constant_op.constant([
+            [3.0, 3.1, 3.2, 3.3, 3.4],
+            [8.0, 8.1, 8.2, 8.3, 8.4],
+        ]),
+        constant_op.constant([
+            [4.0, 4.1, 4.2, 4.3, 4.4],
+            [9.0, 9.1, 9.2, 9.3, 9.4],
+        ]),
+    ]
+
+  def _biases(self):
+    return constant_op.constant(
+        [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
+
+  def _div_sharded_biases(self):
+    return [
+        constant_op.constant([0.0, 0.1]),
+        constant_op.constant([0.2, 0.3]),
+        constant_op.constant([0.4, 0.5]),
+        constant_op.constant([0.6, 0.7]),
+        constant_op.constant([0.8, 0.9]),
+    ]
+
+  def _mod_sharded_biases(self):
+    return [
+        constant_op.constant([0.0, 0.5]),
+        constant_op.constant([0.1, 0.6]),
+        constant_op.constant([0.2, 0.7]),
+        constant_op.constant([0.3, 0.8]),
+        constant_op.constant([0.4, 0.9]),
+    ]
+
+  def _labels(self):
+    return constant_op.constant(
+        [[0, 1], [1, 2]],
+        shape=(self._batch_size, self._num_true),
+        name='labels',
+        dtype=dtypes.int64)
+
+  def _inputs(self):
+    return constant_op.constant(
+        [
+            [0., 1., 2., 3., 4.],
+            [10., 11., 12., 13., 14.],
+        ],
+        shape=(self._batch_size, self._embed_dim),
+        name='inputs')
+
+  def testInvalidNumSampled0(self):
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(
+          ValueError,
+          r'num_resampled \(3\) must be less than num_sampled \(3\)'):
+        sampling_ops.rank_sampled_softmax_loss(
+            weights=self._weights(),
+            biases=self._biases(),
+            labels=self._labels(),
+            inputs=self._inputs(),
+            num_sampled=3,
+            num_resampled=3,
+            num_classes=self._num_classes,
+            num_true=self._num_true,
+            sampled_values=None,
+            resampling_temperature=1.,
+            remove_accidental_hits=True,
+            partition_strategy='div')
+
+  def testInvalidNumSampled1(self):
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(
+          ValueError,
+          r'num_resampled \(3\) must be less than num_sampled \(2\)'):
+        sampling_ops.rank_sampled_softmax_loss(
+            weights=self._weights(),
+            biases=self._biases(),
+            labels=self._labels(),
+            inputs=self._inputs(),
+            num_sampled=2,
+            num_resampled=3,
+            num_classes=self._num_classes,
+            num_true=self._num_true,
+            sampled_values=None,
+            resampling_temperature=1.,
+            remove_accidental_hits=True,
+            partition_strategy='div')
+
+  def testMissingPartitionStrategy(self):
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(ValueError,
+                                   r'unsupported partition_strategy \(None\)'):
+        sampling_ops.rank_sampled_softmax_loss(
+            weights=self._weights(),
+            biases=self._biases(),
+            labels=self._labels(),
+            inputs=self._inputs(),
+            num_sampled=2,
+            num_resampled=1,
+            num_classes=self._num_classes,
+            num_true=self._num_true,
+            sampled_values=None,
+            resampling_temperature=1.,
+            remove_accidental_hits=True,
+            partition_strategy=None)
+
+  def _testCompareWithNN(self, weights, biases, partition_strategy):
+    with ops.Graph().as_default():
+      loss = sampling_ops.rank_sampled_softmax_loss(
+          weights=weights(),
+          biases=biases(),
+          labels=self._labels(),
+          inputs=self._inputs(),
+          num_sampled=self._num_sampled,
+          num_resampled=self._num_resampled,
+          num_classes=self._num_classes,
+          num_true=self._num_true,
+          sampled_values=self._sampled_values,
+          resampling_temperature=1.,
+          remove_accidental_hits=self._remove_accidental_hits,
+          partition_strategy=partition_strategy)
+      loss_nn = nn.sampled_softmax_loss(
+          weights=weights(),
+          biases=biases(),
+          labels=self._labels(),
+          inputs=self._inputs(),
+          num_sampled=self._num_resampled,
+          num_classes=self._num_classes,
+          num_true=self._num_true,
+          sampled_values=self._resampled_values,
+          remove_accidental_hits=self._remove_accidental_hits,
+          partition_strategy=partition_strategy)
+      with self.test_session() as sess:
+        loss_val = sess.run(loss)
+        loss_nn_val = sess.run(loss_nn)
+
+    self.assertAllClose(loss_val, loss_nn_val)
+
+  def testCompareWithNNUnsharded(self):
+    self._testCompareWithNN(self._weights, self._biases, 'div')
+
+  def testCompareWithNNShardWeightsDiv(self):
+    self._testCompareWithNN(self._div_sharded_weights, self._biases, 'div')
+
+  def testCompareWithNNShardWeightsAndBiasesDiv(self):
+    self._testCompareWithNN(self._div_sharded_weights, self._div_sharded_biases,
+                            'div')
+
+  def testCompareWithNNShardWeightsMod(self):
+    self._testCompareWithNN(self._mod_sharded_weights, self._biases, 'mod')
+
+  def testCompareWithNNShardWeightsAndBiasesMod(self):
+    self._testCompareWithNN(self._mod_sharded_weights, self._mod_sharded_biases,
+                            'mod')
+
+  def _testCompareWithNNTemperature(self, temperature, resampled):
+    weights = [[1., 2.], [3., 4.]]  # two sampled classes
+    inputs = [[6., -5. / 2.], [-11., 21. / 2.]]
+    # Let w0, w1 = weights of sampled classes (biases set to 0 for simplicity)
+    # Let x0, x1 = inputs
+    # logits:
+    #   w0.x0 = 1
+    #   w0.x1 = 10
+    #   w1.x0 = 8
+    #   w1.x1 = 9
+    # Resampling 1 class with temperature = t will pick the larger of:
+    #   exp(1/t) + exp(10/t)  ==> w0, for values of t < 2.12
+    #   exp(8/t) + exp(9/t)   ==> w1, for values of t > 2.13
+    num_sampled = 2
+    num_resampled = 1
+    num_classes = 2
+    num_true = 1
+    sampled_values = [0, 1], [[1.], [1.]], [1., 1.]
+    resampled_values = [resampled], [[1.], [1.]], [1.]
+    remove_accidental_hits = False
+    with ops.Graph().as_default():
+      weights = constant_op.constant(weights)
+      biases = constant_op.constant([0., 0.])
+      labels = constant_op.constant([[0], [1]], dtype=dtypes.int64)
+      inputs = constant_op.constant(inputs)
+      loss = sampling_ops.rank_sampled_softmax_loss(
+          weights=weights,
+          biases=biases,
+          labels=labels,
+          inputs=inputs,
+          num_sampled=num_sampled,
+          num_resampled=num_resampled,
+          num_classes=num_classes,
+          num_true=num_true,
+          sampled_values=sampled_values,
+          resampling_temperature=constant_op.constant(temperature),
+          remove_accidental_hits=remove_accidental_hits,
+          partition_strategy='div')
+      loss_nn = nn.sampled_softmax_loss(
+          weights=weights,
+          biases=biases,
+          labels=labels,
+          inputs=inputs,
+          num_sampled=num_resampled,
+          num_classes=num_classes,
+          num_true=num_true,
+          sampled_values=resampled_values,
+          remove_accidental_hits=remove_accidental_hits,
+          partition_strategy='div')
+      with self.test_session() as sess:
+        loss_val = sess.run(loss)
+        loss_nn_val = sess.run(loss_nn)
+
+    self.assertAllClose(loss_val, loss_nn_val)
+
+  def testCompareWithNNTemperatureLo1(self):
+    self._testCompareWithNNTemperature(1., 0)
+
+  def testCompareWithNNTemperatureLo2(self):
+    self._testCompareWithNNTemperature(2.12, 0)
+
+  def testCompareWithNNTemperatureHi1(self):
+    self._testCompareWithNNTemperature(2.13, 1)
+
+  def testCompareWithNNTemperatureHi2(self):
+    self._testCompareWithNNTemperature(3., 1)
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From e1de7a1b05d1efe81fec93b6128e60bf87784803 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Wed, 12 Jul 2017 21:53:00 +0200
Subject: [PATCH 0924/1375] Windows Bazel Build: Build TensorFlow with
 wrapper-less CROSSTOOL (#11454)

---
 tensorflow/tools/ci_build/windows/bazel/common_env.sh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/tools/ci_build/windows/bazel/common_env.sh b/tensorflow/tools/ci_build/windows/bazel/common_env.sh
index 8853dc53b1..05392c2724 100644
--- a/tensorflow/tools/ci_build/windows/bazel/common_env.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/common_env.sh
@@ -56,3 +56,7 @@ export PATH="/c/tools/cuda/bin:$PATH"
 
 # Set the common build options on Windows
 export BUILD_OPTS='--copt=-w --host_copt=-w --verbose_failures --experimental_ui'
+
+# Build TF with wrapper-less CROSSTOOL
+# TODO(pcloudy): Remove this after wrapper-less CROSSTOOL becomes default
+export NO_MSVC_WRAPPER=1
-- 
GitLab


From 38180d7bbc193d5fe63394ac9e994e29097872e8 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Wed, 12 Jul 2017 21:53:05 +0200
Subject: [PATCH 0925/1375] Disable nn_test on Windows (#11445)

---
 tensorflow/python/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 9a5f059061..61f6048637 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2378,6 +2378,7 @@ cuda_py_test(
         ":variables",
         "//third_party/py/numpy",
     ],
+    tags = ["no_windows"],
 )
 
 cuda_py_test(
-- 
GitLab


From 4f54336348fb40fb5eea2166b949f4457f447ef5 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Wed, 12 Jul 2017 12:59:50 -0700
Subject: [PATCH 0926/1375] Rename TpuEstimator to TPUEstimator and TpuConfig
 to TPUConfig to follow PEP8 naming conventions.

PiperOrigin-RevId: 161704561
---
 .../contrib/tpu/python/tpu/tpu_config.py      |  8 +++---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 26 +++++++++----------
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
index a19d1db831..524e89f563 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_config.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
@@ -24,12 +24,12 @@ import collections
 from tensorflow.contrib.learn.python.learn.estimators import run_config as run_config_lib
 
 
-class TpuConfig(collections.namedtuple(
-    'TpuConfig', ['iterations_per_loop', 'num_shards'])):
+class TPUConfig(collections.namedtuple(
+    'TPUConfig', ['iterations_per_loop', 'num_shards'])):
   """TPU related configuration required by `TPUEstimator`."""
 
   def __new__(cls, iterations_per_loop=2, num_shards=2):
-    return super(TpuConfig, cls).__new__(
+    return super(TPUConfig, cls).__new__(
         cls,
         iterations_per_loop=iterations_per_loop,
         num_shards=num_shards)
@@ -40,7 +40,7 @@ class RunConfig(run_config_lib.RunConfig):
 
   def __init__(self, tpu_config=None, **kwargs):
     super(RunConfig, self).__init__(**kwargs)
-    self._tpu_config = tpu_config or TpuConfig()
+    self._tpu_config = tpu_config or TPUConfig()
 
   @property
   def tpu_config(self):
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 24a3d01272..a68e327082 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ===================================================================
 
-"""TpuEstimator class."""
+"""TPUEstimator class."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -71,15 +71,15 @@ class InfeedThreadController(object):
   stop. It could be the cases that the `max_steps` is reached or some hook
   requests the stop in the monitored_session.
 
-  This controller (with coordination with `TpuInfeedSessionHook`) does the
+  This controller (with coordination with `TPUInfeedSessionHook`) does the
   following:
 
   1) It pre-infeeds one `batch` data for current TPU iterations.
 
-  2) When `before_run` of `TpuInfeedSessionHook` is called, one more `batch`
+  2) When `before_run` of `TPUInfeedSessionHook` is called, one more `batch`
   data will be infed.
 
-  3) When `end` of `TpuInfeedSessionHook` is called, the thread will end
+  3) When `end` of `TPUInfeedSessionHook` is called, the thread will end
   gracefully.
 
   So, we might need to adjust the algorithrm here if the IO is slower than the
@@ -115,7 +115,7 @@ class InfeedThreadController(object):
     self._input_thd.join()
 
 
-class TpuInfeedSessionHook(session_run_hook.SessionRunHook):
+class TPUInfeedSessionHook(session_run_hook.SessionRunHook):
   """A Session hook setting up the TPU initialization and infeed.
 
   This hook does two major things:
@@ -168,15 +168,15 @@ class _PerShardOutput(object):
     return self.output
 
 
-class TpuEstimator(estimator_lib.Estimator):
+class TPUEstimator(estimator_lib.Estimator):
   """Estimator with TPU support.
 
-  TpuEstimator handles many of the details of running on TPU devices, such as
+  TPUEstimator handles many of the details of running on TPU devices, such as
   replicating inputs and models for each core, and returning to host
   periodically to run hooks.
 
   Note: For training (evaluate and predict support on TPU are not yet
-  implemented), TpuEstimator transforms a global batch size in params to a
+  implemented), TPUEstimator transforms a global batch size in params to a
   per-shard batch size when calling the `input_fn` and `model_fn`. Users should
   specify `train_batch_size` in constructor, and then get the batch size for
   each shard in `input_fn` and `model_fn` by `params['batch_size']`.
@@ -189,7 +189,7 @@ class TpuEstimator(estimator_lib.Estimator):
                params=None,
                use_tpu=True,
                train_batch_size=None):
-    """Constructs an `TpuEstimator` instance.
+    """Constructs an `TPUEstimator` instance.
 
     Args:
       model_fn: Model function as required by `Estimator`. For training, the
@@ -208,7 +208,7 @@ class TpuEstimator(estimator_lib.Estimator):
       use_tpu: A bool indicating whether TPU support is enabled. Currently, only
         applied to training. Evaluate and predict still happen on CPU.
       train_batch_size: An int representing the global training batch size.
-        TpuEstimator transforms this global batch size to a per-shard batch
+        TPUEstimator transforms this global batch size to a per-shard batch
         size, as params['batch_size'], when calling `input_fn` and `model_fn`.
         Cannot be `None` if `use_tpu` is `True`. Must be divisible by
         `config.tpu_config.num_shards`.
@@ -251,7 +251,7 @@ class TpuEstimator(estimator_lib.Estimator):
     else:
       model_function = model_fn
 
-    super(TpuEstimator, self).__init__(
+    super(TPUEstimator, self).__init__(
         model_fn=model_function,
         model_dir=model_dir,
         config=config,
@@ -302,7 +302,7 @@ class TpuEstimator(estimator_lib.Estimator):
       ValueError: if input_fn takes invalid arguments or does not have `params`.
     """
     if not self._use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
-      return super(TpuEstimator, self)._call_input_fn(input_fn, mode)
+      return super(TPUEstimator, self)._call_input_fn(input_fn, mode)
 
     input_fn_args = util.fn_args(input_fn)
     config = self.config  # a deep copy.
@@ -518,7 +518,7 @@ def wrapped_model_fn(model_fn, train_batch_size):
     ]
 
     hooks = [
-        TpuInfeedSessionHook(config, enqueue_fn),
+        TPUInfeedSessionHook(config, enqueue_fn),
         training.LoggingTensorHook(
             {'loss': array_ops.identity(loss),
              'step': training.get_global_step()},
-- 
GitLab


From 45a58d378ea081907d1aa7d8963c40e7fb3dcfe8 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Wed, 12 Jul 2017 13:08:51 -0700
Subject: [PATCH 0927/1375] Introduce Python-only extensions to the C API

Implements an incomplete version of Operation._add_control_input()
using a new extension to make sure the plumbing works.

This also adds header guards to c_api_internal.h, which were missing. For some reason the missing guards caused problems in the cmake build even though there doesn't appear to be any #include cycles.

PiperOrigin-RevId: 161705859
---
 tensorflow/c/BUILD                       | 13 ++++++
 tensorflow/c/c_api_internal.h            |  5 +++
 tensorflow/c/python_api.cc               | 28 +++++++++++++
 tensorflow/c/python_api.h                | 30 ++++++++++++++
 tensorflow/contrib/cmake/tf_c.cmake      |  6 +++
 tensorflow/contrib/cmake/tf_python.cmake |  2 +
 tensorflow/python/BUILD                  |  1 +
 tensorflow/python/client/tf_session.i    |  2 +
 tensorflow/python/framework/ops.py       |  7 ++--
 tensorflow/python/framework/ops_test.py  | 50 +++++++++++++-----------
 tensorflow/python/framework/test_util.py | 41 +++++++++++++------
 11 files changed, 149 insertions(+), 36 deletions(-)
 create mode 100644 tensorflow/c/python_api.cc
 create mode 100644 tensorflow/c/python_api.h

diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD
index 9267ef77ef..85429f8c66 100644
--- a/tensorflow/c/BUILD
+++ b/tensorflow/c/BUILD
@@ -144,6 +144,19 @@ tf_custom_op_library(
     srcs = ["test_op.cc"],
 )
 
+# -----------------------------------------------------------------------------
+# Python API target
+
+tf_cuda_library(
+    name = "python_api",
+    srcs = ["python_api.cc"],
+    hdrs = ["python_api.h"],
+    visibility = ["//tensorflow/python:__pkg__"],
+    deps = [
+        ":c_api_internal",
+    ],
+)
+
 # -----------------------------------------------------------------------------
 # Google-internal targets.
 
diff --git a/tensorflow/c/c_api_internal.h b/tensorflow/c/c_api_internal.h
index 7e987a65f7..d077ad264b 100644
--- a/tensorflow/c/c_api_internal.h
+++ b/tensorflow/c/c_api_internal.h
@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#ifndef TENSORFLOW_C_C_API_INTERNAL_H_
+#define TENSORFLOW_C_C_API_INTERNAL_H_
+
 #include "tensorflow/c/c_api.h"
 
 #include <vector>
@@ -125,3 +128,5 @@ class TensorCApi {
 };
 
 }  // end namespace tensorflow
+
+#endif  // TENSORFLOW_C_C_API_INTERNAL_H_
diff --git a/tensorflow/c/python_api.cc b/tensorflow/c/python_api.cc
new file mode 100644
index 0000000000..91e4e3cbb9
--- /dev/null
+++ b/tensorflow/c/python_api.cc
@@ -0,0 +1,28 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/c/python_api.h"
+
+#include "tensorflow/c/c_api_internal.h"
+
+namespace tensorflow {
+
+void AddControlInput(TF_Graph* graph, TF_Operation* op, TF_Operation* input) {
+  // TODO(skyewm): make sure cycles are prevented
+  mutex_lock l(graph->mu);
+  graph->graph.AddControlEdge(&input->node, &op->node);
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/c/python_api.h b/tensorflow/c/python_api.h
new file mode 100644
index 0000000000..bb2b0fd8fd
--- /dev/null
+++ b/tensorflow/c/python_api.h
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_C_PYTHON_API_H_
+#define THIRD_PARTY_TENSORFLOW_C_PYTHON_API_H_
+
+#include "tensorflow/c/c_api.h"
+
+// These functions can be removed without notice. They exist to facilitate some
+// refactoring of graph construction code in the Python API.
+
+namespace tensorflow {
+
+void AddControlInput(TF_Graph* graph, TF_Operation* op, TF_Operation* input);
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_C_PYTHON_API_H_
diff --git a/tensorflow/contrib/cmake/tf_c.cmake b/tensorflow/contrib/cmake/tf_c.cmake
index 069cdfa352..9ea7600a06 100644
--- a/tensorflow/contrib/cmake/tf_c.cmake
+++ b/tensorflow/contrib/cmake/tf_c.cmake
@@ -26,3 +26,9 @@ set(tf_c_srcs
 
 add_library(tf_c OBJECT ${tf_c_srcs})
 add_dependencies(tf_c tf_cc_framework tf_core_lib tf_protos_cc)
+
+add_library(tf_c_python_api OBJECT
+  "${tensorflow_source_dir}/tensorflow/c/python_api.cc"
+  "${tensorflow_source_dir}/tensorflow/c/python_api.h"
+)
+add_dependencies(tf_c_python_api tf_c tf_cc_framework tf_core_lib tf_protos_cc)
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 1de6559e3c..beb630bdfc 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -761,6 +761,7 @@ if(WIN32)
     add_library(pywrap_tensorflow_internal_static STATIC
         ${pywrap_tensorflow_internal_src}
         $<TARGET_OBJECTS:tf_c>
+        $<TARGET_OBJECTS:tf_c_python_api>
         $<TARGET_OBJECTS:tf_core_lib>
         $<TARGET_OBJECTS:tf_core_cpu>
         $<TARGET_OBJECTS:tf_core_framework>
@@ -809,6 +810,7 @@ endif(WIN32)
 add_library(pywrap_tensorflow_internal SHARED
     ${pywrap_tensorflow_internal_src}
     $<TARGET_OBJECTS:tf_c>
+    $<TARGET_OBJECTS:tf_c_python_api>
     $<TARGET_OBJECTS:tf_core_lib>
     $<TARGET_OBJECTS:tf_core_cpu>
     $<TARGET_OBJECTS:tf_core_framework>
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 9a5f059061..65d7a89594 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2787,6 +2787,7 @@ tf_py_wrap_cc(
         ":tf_session_helper",
         "//tensorflow/c:c_api",
         "//tensorflow/c:checkpoint_reader",
+        "//tensorflow/c:python_api",
         "//tensorflow/c:tf_status_helper",
         "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
         "//tensorflow/core/distributed_runtime/rpc:grpc_session",
diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index d3dc8458ce..59d5d8b150 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -17,6 +17,7 @@ limitations under the License.
 
 %{
 
+#include "tensorflow/c/python_api.h"
 #include "tensorflow/python/client/tf_session_helper.h"
 #include "tensorflow/core/framework/session_state.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -269,6 +270,7 @@ bool PyTensorListToVector(PyObject* py_tensor_list,
 %rename("_TF_NewSessionOptions") TF_NewSessionOptions;
 
 %include "tensorflow/c/c_api.h"
+%include "tensorflow/c/python_api.h"
 
 %ignoreall
 %insert("python") %{
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 123a43e5f5..1b258a8b17 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1527,9 +1527,10 @@ class Operation(object):
       TypeError: if op is not an Operation.
       ValueError: if op is from a different graph.
     """
-    assert not self._graph._c_graph, (  # pylint: disable=protected-access
-        "Operation._add_control_input doesn't work with C API")
-    self._add_control_inputs([op])
+    if _USE_C_API:
+      c_api.AddControlInput(self._graph._c_graph, self._c_op, op._c_op)  # pylint: disable=protected-access
+    else:
+      self._add_control_inputs([op])
 
   # Methods below are used when building the NodeDef and Graph proto.
   def _recompute_node_def(self):
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index f495e4e829..c1e2c4e287 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -384,6 +384,15 @@ class OperationTest(test_util.TensorFlowTestCase):
       self.assertIsInstance(x, dtypes.DType)
     self.assertEqual([dtypes.string, dtypes.double], l)
 
+  # TODO(skyewm): test adding cycles, other error cases
+  @test_util.enable_c_api
+  def testAddControlInput(self):
+    with ops.Graph().as_default():
+      x = constant_op.constant(1).op
+      y = constant_op.constant(2).op
+    y._add_control_input(x)  # pylint: disable=protected-access
+    self.assertEqual(y.control_inputs, [x])
+
 
 class CreateOpTest(test_util.TensorFlowTestCase):
 
@@ -1062,29 +1071,26 @@ class ComparisonTest(test_util.TensorFlowTestCase):
 
 class ControlDependenciesTest(test_util.TensorFlowTestCase):
 
+  @test_util.enable_c_api
   def testBasic(self):
-    ops._USE_C_API = True  # pylint: disable=protected-access
-    try:
-      g = ops.Graph()
-      with g.as_default():
-        # Creating unregistered ops with _apply_op() doesn't work with the C API
-        # TODO(skyewm): address this more consistently. Possible solutions are
-        # to use registered ops in all tests, create a way to register ops in
-        # Python tests, or conditionally disable the op registration check in
-        # the C API.
-        a = constant_op.constant(1.0)
-        b = constant_op.constant(1.0)
-        with g.control_dependencies([a]):
-          c = constant_op.constant(1.0)
-          d = array_ops.identity(b)
-          e = array_ops.identity(c)
-
-      self.assertEqual(c.op.control_inputs, [a.op])
-      self.assertEqual(d.op.control_inputs, [a.op])
-      # e should be dominated by c.
-      self.assertEqual(e.op.control_inputs, [])
-    finally:
-      ops._USE_C_API = False  # pylint: disable=protected-access
+    g = ops.Graph()
+    with g.as_default():
+      # Creating unregistered ops with _apply_op() doesn't work with the C API
+      # TODO(skyewm): address this more consistently. Possible solutions are
+      # to use registered ops in all tests, create a way to register ops in
+      # Python tests, or conditionally disable the op registration check in
+      # the C API.
+      a = constant_op.constant(1.0)
+      b = constant_op.constant(1.0)
+      with g.control_dependencies([a]):
+        c = constant_op.constant(1.0)
+        d = array_ops.identity(b)
+        e = array_ops.identity(c)
+
+    self.assertEqual(c.op.control_inputs, [a.op])
+    self.assertEqual(d.op.control_inputs, [a.op])
+    # e should be dominated by c.
+    self.assertEqual(e.op.control_inputs, [])
 
   def testBasicWithConversion(self):
     g = ops.Graph()
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index f357d2ef8d..96a1cd1410 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -227,6 +227,19 @@ def NCHWToNHWC(input_tensor):
     return [input_tensor[a] for a in new_axes[ndims]]
 
 
+# TODO(skyewm): remove this eventually
+# pylint: disable=protected-access
+def _use_c_api_wrapper(fn, use_c_api, *args, **kwargs):
+  prev_value = ops._USE_C_API
+  ops._USE_C_API = use_c_api
+  try:
+    with ops.Graph().as_default():
+      fn(*args, **kwargs)
+  finally:
+    ops._USE_C_API = prev_value
+# pylint: disable=protected-access
+
+
 # TODO(skyewm): remove this eventually
 def disable_c_api(fn):
   """Decorator for disabling the C API on a test.
@@ -240,17 +253,23 @@ def disable_c_api(fn):
   Returns:
     The wrapped function
   """
-  # pylint: disable=protected-access
-  def disable_c_api_wrapper(*args, **kwargs):
-    prev_value = ops._USE_C_API
-    ops._USE_C_API = False
-    try:
-      with ops.Graph().as_default():
-        fn(*args, **kwargs)
-    finally:
-      ops._USE_C_API = prev_value
-  # pylint: disable=protected-access
-  return disable_c_api_wrapper
+  return lambda *args, **kwargs: _use_c_api_wrapper(fn, False, *args, **kwargs)
+
+
+# TODO(skyewm): remove this eventually
+def enable_c_api(fn):
+  """Decorator for enabling the C API on a test.
+
+  Note this enables the C API after running the test class's setup/teardown
+  methods.
+
+  Args:
+    fn: the function to be wrapped
+
+  Returns:
+    The wrapped function
+  """
+  return lambda *args, **kwargs: _use_c_api_wrapper(fn, True, *args, **kwargs)
 
 
 class TensorFlowTestCase(googletest.TestCase):
-- 
GitLab


From e424d209a7b1338b59bc39cd82b03ea9e8c5a646 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Wed, 12 Jul 2017 13:10:45 -0700
Subject: [PATCH 0928/1375] [TF:XLA] Use a more numerically accurate
 formulation of ResourceApplyRMSProp.

PiperOrigin-RevId: 161706120
---
 tensorflow/compiler/tf2xla/kernels/training_ops.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/training_ops.cc b/tensorflow/compiler/tf2xla/kernels/training_ops.cc
index 42104b951a..a2ecbca124 100644
--- a/tensorflow/compiler/tf2xla/kernels/training_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/training_ops.cc
@@ -352,9 +352,9 @@ class ResourceApplyRMSProp : public XlaOpKernel {
                b->Sub(XlaHelpers::FloatLiteral(b, type, 1.0), rho)));
     xla::ComputationDataHandle new_mom =
         b->Add(b->Mul(mom, momentum),
-               b->Div(b->Mul(grad, lr),
+               b->Mul(b->Mul(grad, lr),
                       b->Pow(b->Add(new_ms, epsilon),
-                             XlaHelpers::FloatLiteral(b, type, 0.5))));
+                             XlaHelpers::FloatLiteral(b, type, -0.5))));
     xla::ComputationDataHandle new_var = b->Sub(var, new_mom);
 
     OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, new_var));
-- 
GitLab


From 8e7f5737168853fc28a85dab0d3792520249e541 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 14:18:25 -0700
Subject: [PATCH 0929/1375] Expose tf.contrib.nn.rank_sampled_softmax_loss.

PiperOrigin-RevId: 161716450
---
 tensorflow/contrib/nn/__init__.py            | 3 ++-
 tensorflow/contrib/nn/python/__init__.py     | 2 +-
 tensorflow/contrib/nn/python/ops/__init__.py | 3 +--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/nn/__init__.py b/tensorflow/contrib/nn/__init__.py
index 73757a6696..e7116d78e2 100644
--- a/tensorflow/contrib/nn/__init__.py
+++ b/tensorflow/contrib/nn/__init__.py
@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Module for deprecated ops in tf.nn.
+"""Module for variants of ops in tf.nn.
 
 @@deprecated_flipped_softmax_cross_entropy_with_logits
 @@deprecated_flipped_sparse_softmax_cross_entropy_with_logits
 @@deprecated_flipped_sigmoid_cross_entropy_with_logits
+@@rank_sampled_softmax_loss
 """
 
 from __future__ import absolute_import
diff --git a/tensorflow/contrib/nn/python/__init__.py b/tensorflow/contrib/nn/python/__init__.py
index 285c094ca7..73dd4d4015 100644
--- a/tensorflow/contrib/nn/python/__init__.py
+++ b/tensorflow/contrib/nn/python/__init__.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Module for deprecated ops in tf.nn."""
+"""Module for variants of ops in tf.nn."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/nn/python/ops/__init__.py b/tensorflow/contrib/nn/python/ops/__init__.py
index a945bcdccf..73dd4d4015 100644
--- a/tensorflow/contrib/nn/python/ops/__init__.py
+++ b/tensorflow/contrib/nn/python/ops/__init__.py
@@ -12,9 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Module for deprecated ops in tf.nn."""
+"""Module for variants of ops in tf.nn."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
-- 
GitLab


From 6b28eb084349f90213f6ea7762e69d746731b26f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 14:18:55 -0700
Subject: [PATCH 0930/1375] Rename HloLocation to HloPosition, to avoid
 ambiguity with MemoryLocation.

PiperOrigin-RevId: 161716528
---
 .../xla/service/hlo_alias_analysis.cc         | 10 +--
 .../compiler/xla/service/hlo_alias_analysis.h |  6 +-
 .../xla/service/hlo_alias_analysis_test.cc    | 84 +++++++++----------
 tensorflow/compiler/xla/service/hlo_buffer.cc | 12 +--
 tensorflow/compiler/xla/service/hlo_buffer.h  | 30 +++----
 .../xla/service/hlo_dataflow_analysis.cc      | 16 ++--
 .../xla/service/hlo_dataflow_analysis.h       | 10 +--
 .../xla/service/hlo_dataflow_analysis_test.cc | 48 +++++------
 tensorflow/compiler/xla/service/hlo_value.cc  | 68 +++++++--------
 tensorflow/compiler/xla/service/hlo_value.h   | 40 ++++-----
 10 files changed, 162 insertions(+), 162 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
index d03093f89f..321c48857b 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
@@ -94,13 +94,13 @@ void HloAliasAnalysis::CombineBuffers(
       unified_buffer.AddValue(dataflow_analysis_->GetValue(value_id));
     }
 
-    // Iterate through all locations where the buffer-to-eliminate exists and
+    // Iterate through all positions where the buffer-to-eliminate exists and
     // replace it with the unified buffer.
-    for (const HloLocation& location : buffer.locations()) {
-      VLOG(4) << "Replacing in " << location;
-      GetBufferSet(location.instruction, location.index)
+    for (const HloPosition& position : buffer.positions()) {
+      VLOG(4) << "Replacing in " << position;
+      GetBufferSet(position.instruction, position.index)
           .RemoveBufferOrDie(buffer_id);
-      GetBufferSet(location.instruction, location.index)
+      GetBufferSet(position.instruction, position.index)
           .AddBuffer(unified_buffer.id());
     }
 
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.h b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
index 429cfa0915..101ff63f34 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
@@ -45,7 +45,7 @@ class HloAliasAnalysis {
   InstructionBufferSet& GetInstructionBufferSet(
       const HloInstruction* instruction);
 
-  // Return the HloBufferSet for the given location.
+  // Return the HloBufferSet for the given position.
   const HloBufferSet& GetBufferSet(const HloInstruction* instruction,
                                    const ShapeIndex& index = {}) const;
   HloBufferSet& GetBufferSet(const HloInstruction* instruction,
@@ -59,8 +59,8 @@ class HloAliasAnalysis {
     return buffers_.at(buffer_id);
   }
 
-  // Returns the unique buffer at the given location. CHECK fails if the buffer
-  // set at that location does not contain exactly one buffer.
+  // Returns the unique buffer at the given position. CHECK fails if the buffer
+  // set at that position does not contain exactly one buffer.
   const HloBuffer& GetUniqueBufferAt(const HloInstruction* instruction,
                                      const ShapeIndex& index = {}) const {
     return GetBuffer(GetBufferSet(instruction, index).GetUniqueBufferId());
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
index d67b48dff0..d6023127ad 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
@@ -46,7 +46,7 @@ class HloAliasAnalysisTest : public HloTestBase {
     return *analysis_;
   }
 
-  // Return a vector of the buffers in the buffer set at the current location.
+  // Return a vector of the buffers in the buffer set at the current position.
   std::vector<HloBuffer> GetBuffersAt(const HloInstruction* instruction,
                                       const ShapeIndex& index = {}) const {
     std::vector<HloBuffer> buffers;
@@ -66,7 +66,7 @@ class HloAliasAnalysisTest : public HloTestBase {
     return values;
   }
 
-  // Return the HloValue defined at the given location.
+  // Return the HloValue defined at the given position.
   const HloValue& GetValueDefinedAt(const HloInstruction* instruction,
                                     const ShapeIndex& index = {}) const {
     return analysis_->dataflow_analysis().GetValueDefinedAt(instruction, index);
@@ -174,11 +174,11 @@ TEST_F(HloAliasAnalysisTest, TupleAndGtes) {
   EXPECT_EQ(analysis.GetUniqueBufferAt(param0),
             analysis.GetUniqueBufferAt(gte0));
 
-  // Verify the locations of an aliased buffer.
+  // Verify the positions of an aliased buffer.
   EXPECT_THAT(
-      analysis.GetUniqueBufferAt(param0).locations(),
-      UnorderedElementsAre(HloLocation{param0, {}}, HloLocation{tuple, {0}},
-                           HloLocation{gte0, {}}));
+      analysis.GetUniqueBufferAt(param0).positions(),
+      UnorderedElementsAre(HloPosition{param0, {}}, HloPosition{tuple, {0}},
+                           HloPosition{gte0, {}}));
 
   EXPECT_FALSE(analysis.GetInstructionBufferSet(tuple).IsAmbiguous());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(tuple).IsDistinct());
@@ -201,9 +201,9 @@ TEST_F(HloAliasAnalysisTest, NondistinctTuple) {
   const HloAliasAnalysis& analysis = RunAnalysis();
 
   EXPECT_THAT(
-      analysis.GetUniqueBufferAt(param0).locations(),
-      UnorderedElementsAre(HloLocation{param0, {}}, HloLocation{tuple, {0}},
-                           HloLocation{tuple, {2}}));
+      analysis.GetUniqueBufferAt(param0).positions(),
+      UnorderedElementsAre(HloPosition{param0, {}}, HloPosition{tuple, {0}},
+                           HloPosition{tuple, {2}}));
 
   EXPECT_FALSE(analysis.GetInstructionBufferSet(tuple).IsAmbiguous());
   EXPECT_FALSE(analysis.GetInstructionBufferSet(tuple).IsDistinct());
@@ -236,17 +236,17 @@ TEST_F(HloAliasAnalysisTest, SingleCall) {
   const HloAliasAnalysis& analysis = RunAnalysis();
 
   // Verify aliasing of the kCall operands and the subcomputation parameters.
-  EXPECT_THAT(analysis.GetUniqueBufferAt(constant1).locations(),
-              UnorderedElementsAre(HloLocation{constant1, {}},
-                                   HloLocation{subparam0, {}}));
-  EXPECT_THAT(analysis.GetUniqueBufferAt(constant2).locations(),
-              UnorderedElementsAre(HloLocation{constant2, {}},
-                                   HloLocation{subparam1, {}}));
+  EXPECT_THAT(analysis.GetUniqueBufferAt(constant1).positions(),
+              UnorderedElementsAre(HloPosition{constant1, {}},
+                                   HloPosition{subparam0, {}}));
+  EXPECT_THAT(analysis.GetUniqueBufferAt(constant2).positions(),
+              UnorderedElementsAre(HloPosition{constant2, {}},
+                                   HloPosition{subparam1, {}}));
 
   // The subcomputation root and the kCall itself should alias.
   EXPECT_THAT(
-      analysis.GetUniqueBufferAt(add).locations(),
-      UnorderedElementsAre(HloLocation{add, {}}, HloLocation{call, {}}));
+      analysis.GetUniqueBufferAt(add).positions(),
+      UnorderedElementsAre(HloPosition{add, {}}, HloPosition{call, {}}));
 
   EXPECT_FALSE(AnyValuesInSameBufferInterfere());
 }
@@ -276,20 +276,20 @@ TEST_F(HloAliasAnalysisTest, ComputationCalledTwice) {
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
-  EXPECT_THAT(analysis.GetUniqueBufferAt(constant1).locations(),
-              UnorderedElementsAre(HloLocation{constant1, {}},
-                                   HloLocation{subparam0, {}}));
-  EXPECT_THAT(analysis.GetUniqueBufferAt(constant2).locations(),
-              UnorderedElementsAre(HloLocation{constant2, {}},
-                                   HloLocation{subparam1, {}}));
+  EXPECT_THAT(analysis.GetUniqueBufferAt(constant1).positions(),
+              UnorderedElementsAre(HloPosition{constant1, {}},
+                                   HloPosition{subparam0, {}}));
+  EXPECT_THAT(analysis.GetUniqueBufferAt(constant2).positions(),
+              UnorderedElementsAre(HloPosition{constant2, {}},
+                                   HloPosition{subparam1, {}}));
 
   // The 'add' (root of the subcomputation) aliases the two call instruction,
   // and the first parameter of the subcomputation because 'call1' it is passed
   // as an argument to the subcomputation in 'call2'.
   EXPECT_THAT(
-      analysis.GetUniqueBufferAt(add).locations(),
-      UnorderedElementsAre(HloLocation{add, {}}, HloLocation{call1, {}},
-                           HloLocation{subparam0, {}}, HloLocation{call2, {}}));
+      analysis.GetUniqueBufferAt(add).positions(),
+      UnorderedElementsAre(HloPosition{add, {}}, HloPosition{call1, {}},
+                           HloPosition{subparam0, {}}, HloPosition{call2, {}}));
 
   EXPECT_THAT(GetBuffersAt(subparam0),
               UnorderedElementsAre(analysis.GetUniqueBufferAt(constant1),
@@ -361,24 +361,24 @@ TEST_F(HloAliasAnalysisTest, SingleWhile) {
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
-  // Verify the locations of the aliased while buffers.
-  EXPECT_THAT(analysis.GetUniqueBufferAt(xla_while, /*index=*/{}).locations(),
+  // Verify the positions of the aliased while buffers.
+  EXPECT_THAT(analysis.GetUniqueBufferAt(xla_while, /*index=*/{}).positions(),
               UnorderedElementsAre(
-                  HloLocation{tuple, {}}, HloLocation{xla_while, {}},
-                  HloLocation{body_param, {}}, HloLocation{body_tuple, {}},
-                  HloLocation{cond_param, {}}));
-  EXPECT_THAT(analysis.GetUniqueBufferAt(xla_while, /*index=*/{0}).locations(),
+                  HloPosition{tuple, {}}, HloPosition{xla_while, {}},
+                  HloPosition{body_param, {}}, HloPosition{body_tuple, {}},
+                  HloPosition{cond_param, {}}));
+  EXPECT_THAT(analysis.GetUniqueBufferAt(xla_while, /*index=*/{0}).positions(),
               UnorderedElementsAre(
-                  HloLocation{constant1, {}}, HloLocation{tuple, {0}},
-                  HloLocation{xla_while, {0}}, HloLocation{body_param, {0}},
-                  HloLocation{body_element_0, {}}, HloLocation{body_tuple, {0}},
-                  HloLocation{cond_param, {0}}));
-  EXPECT_THAT(analysis.GetUniqueBufferAt(xla_while, /*index=*/{1}).locations(),
+                  HloPosition{constant1, {}}, HloPosition{tuple, {0}},
+                  HloPosition{xla_while, {0}}, HloPosition{body_param, {0}},
+                  HloPosition{body_element_0, {}}, HloPosition{body_tuple, {0}},
+                  HloPosition{cond_param, {0}}));
+  EXPECT_THAT(analysis.GetUniqueBufferAt(xla_while, /*index=*/{1}).positions(),
               UnorderedElementsAre(
-                  HloLocation{constant2, {}}, HloLocation{tuple, {1}},
-                  HloLocation{xla_while, {1}}, HloLocation{body_param, {1}},
-                  HloLocation{body_element_1, {}}, HloLocation{add, {}},
-                  HloLocation{body_tuple, {1}}, HloLocation{cond_param, {1}}));
+                  HloPosition{constant2, {}}, HloPosition{tuple, {1}},
+                  HloPosition{xla_while, {1}}, HloPosition{body_param, {1}},
+                  HloPosition{body_element_1, {}}, HloPosition{add, {}},
+                  HloPosition{body_tuple, {1}}, HloPosition{cond_param, {1}}));
 
   EXPECT_THAT(
       GetValuesInBuffer(analysis.GetUniqueBufferAt(xla_while, /*index=*/{0})),
@@ -619,7 +619,7 @@ TEST_F(HloAliasAnalysisTest, SwizzlingWhile) {
 
   const HloAliasAnalysis& analysis = RunAnalysis();
 
-  // The swizzling while makes most locations in the module alias leaving only 3
+  // The swizzling while makes most positions in the module alias leaving only 3
   // HloBuffers.
   EXPECT_THAT(
       analysis.buffers(),
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.cc b/tensorflow/compiler/xla/service/hlo_buffer.cc
index f289f56966..516e3c0473 100644
--- a/tensorflow/compiler/xla/service/hlo_buffer.cc
+++ b/tensorflow/compiler/xla/service/hlo_buffer.cc
@@ -46,11 +46,11 @@ void HloBuffer::AddValue(const HloValue& value) {
 
   value_ids_.push_back(value.id());
 
-  // Add all of the locations of the HloValue to this buffer.
-  for (const HloLocation& location : value.locations()) {
-    if (std::find(locations_.begin(), locations_.end(), location) ==
-        locations_.end()) {
-      locations_.push_back(location);
+  // Add all of the positions of the HloValue to this buffer.
+  for (const HloPosition& position : value.positions()) {
+    if (std::find(positions_.begin(), positions_.end(), position) ==
+        positions_.end()) {
+      positions_.push_back(position);
     }
   }
 }
@@ -60,7 +60,7 @@ bool HloBuffer::operator==(const HloBuffer& other) const {
   if (equal) {
     // DCHECK because these comparisons are expensive (linear time).
     DCHECK(value_ids() == other.value_ids());
-    DCHECK(locations() == other.locations());
+    DCHECK(positions() == other.positions());
   }
   return equal;
 }
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.h b/tensorflow/compiler/xla/service/hlo_buffer.h
index e38499d210..cfec31f472 100644
--- a/tensorflow/compiler/xla/service/hlo_buffer.h
+++ b/tensorflow/compiler/xla/service/hlo_buffer.h
@@ -33,7 +33,7 @@ namespace xla {
 // from. Generally there is a one-to-one correspondence between HloBuffers and
 // HloValue where each HloValue in the module is held in a unique HloBuffer. An
 // exception is the while instruction which updates the loop state in-place. In
-// this case, we have a single HloBuffer for each HloLocation in the loop state,
+// this case, we have a single HloBuffer for each HloPosition in the loop state,
 // but multiple HloValues. For example:
 //
 //   %init = ...
@@ -53,7 +53,7 @@ namespace xla {
 // HloValue{%while}, HloValue{%body_param}, HloValue{%body_root}, and
 // HloValue{%cond_param}.
 //
-// HloBuffers may appear at different HloLocations in the module mirroring the
+// HloBuffers may appear at different HloPositions in the module mirroring the
 // same propery of HloValues. For example:
 //
 //   %sub = Sub(...)
@@ -62,11 +62,11 @@ namespace xla {
 //   %gte = GetTupleElement(%tuple, 0)
 //
 // In this case, the HloBuffer containing %add appears at the following
-// locations: HloLocation{%add, {}}, HloLocation{%tuple, {0}}, and
-// HloLocation{%gte, {}}.
+// positions: HloPosition{%add, {}}, HloPosition{%tuple, {0}}, and
+// HloPosition{%gte, {}}.
 //
-// Different HloLocations which share the same HloBuffer indicate mandatory
-// aliasing in the HLO module. These locations must share the same memory
+// Different HloPositions which share the same HloBuffer indicate mandatory
+// aliasing in the HLO module. These positions must share the same memory
 // allocation for correctness (the backends rely on this property). This differs
 // from incidental aliasing introduced by memory reuse in BufferAssignment where
 // different instructions may happen to get the same allocation.
@@ -80,17 +80,17 @@ class HloBuffer {
   Id id() const { return id_; }
 
   // Add a value to the set of values held by this buffer. Also adds the
-  // HloLocations of the value to the locations vector of the buffer. If the
+  // HloPositions of the value to the positions vector of the buffer. If the
   // buffer already contains this value, then this method is a nop.
   void AddValue(const HloValue& value);
 
   // Return the IDs of all values contained in this buffer.
   const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
 
-  // Return the locations (output of which instruction and at what index) where
-  // the buffer is used. This is exactly the union of the locations of the
+  // Return the positions (output of which instruction and at what index) where
+  // the buffer is used. This is exactly the union of the positions of the
   // HloValues contained by the buffer.
-  const std::vector<HloLocation>& locations() const { return locations_; }
+  const std::vector<HloPosition>& positions() const { return positions_; }
 
   string ToString() const;
 
@@ -104,16 +104,16 @@ class HloBuffer {
   // The set of values contained in this buffer.
   std::vector<HloValue::Id> value_ids_;
 
-  // The set of locations where this buffer is used.
-  std::vector<HloLocation> locations_;
+  // The set of positions where this buffer is used.
+  std::vector<HloPosition> positions_;
 };
 
 std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer);
 
 // A class representing the set of possible HloBuffers at a particular
-// HloLocation (shape index in the output of an instruction) in the XLA
+// HloPosition (shape index in the output of an instruction) in the XLA
 // graph. In most cases, the buffer set will have a single HloBuffer indicating
-// that the HloBuffer which appears at that particular location is known
+// that the HloBuffer which appears at that particular position is known
 // unambiguously at compile-time.  However, tuple-shaped Select instructions can
 // introduce ambiguity as the tuple elements of the operands are passed by
 // reference into the output of the Select. For example:
@@ -123,7 +123,7 @@ std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer);
 //   %tuple1 = Tuple(%x, %y)
 //   %select = Select(%pred, %tuple0, %tuple1)
 //
-// In this case the HloBufferSet at HloLocation{%select, {0}} contains the
+// In this case the HloBufferSet at HloPosition{%select, {0}} contains the
 // HloBuffer holding %a and the HloBuffer holding %x.
 class HloBufferSet {
  public:
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
index 91592c1902..2e731da741 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
@@ -247,11 +247,11 @@ InstructionValueSet HloDataflowAnalysis::Phi(
   return new_value_set;
 }
 
-void HloDataflowAnalysis::UpdateLocationsOfValuesAt(
+void HloDataflowAnalysis::UpdatePositionsOfValuesAt(
     HloInstruction* instruction, const InstructionValueSet& new_value_set,
     const InstructionValueSet* prev_value_set) {
   if (prev_value_set != nullptr) {
-    // Remove locations from the old value set.
+    // Remove positions from the old value set.
     prev_value_set->ForEachElement(
         [this, instruction](const ShapeIndex& index,
                             const HloValueSet& value_set) {
@@ -260,17 +260,17 @@ void HloDataflowAnalysis::UpdateLocationsOfValuesAt(
             if (!ContainsKey(values_, value_id)) {
               continue;
             }
-            // Don't remove the defining location of the value.
+            // Don't remove the defining position of the value.
             HloValue& value = GetValue(value_id);
             if (instruction == value.defining_instruction()) {
               CHECK_EQ(index, value.defining_index());
             } else {
-              value.RemoveLocation(instruction, index);
+              value.RemovePosition(instruction, index);
             }
           }
         });
   }
-  // Add locations in the new value set.
+  // Add positions in the new value set.
   new_value_set.ForEachElement(
       [this, instruction](const ShapeIndex& index,
                           const HloValueSet& value_set) {
@@ -279,7 +279,7 @@ void HloDataflowAnalysis::UpdateLocationsOfValuesAt(
           if (instruction == value.defining_instruction()) {
             CHECK_EQ(index, value.defining_index());
           } else {
-            value.AddLocation(instruction, index);
+            value.AddPosition(instruction, index);
           }
         }
       });
@@ -466,7 +466,7 @@ void HloDataflowAnalysis::UpdateInstructionsAndPropagate(
     // Update uses. First clear all of the old uses at the particular
     // operands. Then add the new uses. There may be overlap between the old
     // uses and new uses.
-    UpdateLocationsOfValuesAt(instruction, GetInstructionValueSet(instruction),
+    UpdatePositionsOfValuesAt(instruction, GetInstructionValueSet(instruction),
                               &old_value);
   }
 }
@@ -600,7 +600,7 @@ Status HloDataflowAnalysis::InitializeInstructionValueSets() {
           define_all_values();
           break;
       }
-      UpdateLocationsOfValuesAt(instruction.get(),
+      UpdatePositionsOfValuesAt(instruction.get(),
                                 GetInstructionValueSet(instruction.get()));
     }
   }
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
index d909c5b668..6da956c9bb 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-// Analysis for determining the possible set of values for all locations
+// Analysis for determining the possible set of values for all positions
 // (instructions and ShapeIndexes) in the HLO module. Analysis is module-scoped
 // tracking values across computation boundaries.
 
@@ -170,14 +170,14 @@ class HloDataflowAnalysis {
       tensorflow::gtl::ArraySlice<const InstructionValueSet*> inputs,
       bool skip_top_level = false);
 
-  // Updates the locations of the HloValues in the output of the given
+  // Updates the positions of the HloValues in the output of the given
   // instruction. This should be called after the instruction value set of
   // 'instruction' has been changed. 'prev_value_set' must point to the previous
   // state of the value set prior to the change. 'prev_value_set' may be null if
-  // this is the first time locations are being computed. The previous state is
-  // necessary to efficiently remove locations which have been eliminated due to
+  // this is the first time positions are being computed. The previous state is
+  // necessary to efficiently remove positions which have been eliminated due to
   // changes in the instructions' InstructionValueSet.
-  void UpdateLocationsOfValuesAt(
+  void UpdatePositionsOfValuesAt(
       HloInstruction* instruction, const InstructionValueSet& new_value_set,
       const InstructionValueSet* prev_value_set = nullptr);
 
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
index 79edd0fcb5..af7e22f9e1 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
@@ -51,7 +51,7 @@ class HloDataflowAnalysisTest : public HloTestBase,
     return *analysis_;
   }
 
-  // Return a vector of the HloValues at the given program location.
+  // Return a vector of the HloValues at the given program position.
   std::vector<HloValue> HloValuesAt(const HloInstruction* instruction,
                                     const ShapeIndex& index = {}) {
     CHECK(analysis_ != nullptr);
@@ -101,14 +101,14 @@ TEST_P(HloDataflowAnalysisTest, BinaryOperation) {
   EXPECT_TRUE(analysis.ValueIsDefinedAt(constant2));
   EXPECT_TRUE(analysis.ValueIsDefinedAt(add));
 
-  // Verify the locations of the values. These locations are all trivial because
+  // Verify the positions of the values. These positions are all trivial because
   // there are no instructions which forward values.
-  EXPECT_THAT(analysis.GetValueDefinedAt(constant1).locations(),
-              UnorderedElementsAre(HloLocation{constant1, {}}));
-  EXPECT_THAT(analysis.GetValueDefinedAt(constant2).locations(),
-              UnorderedElementsAre(HloLocation{constant2, {}}));
-  EXPECT_THAT(analysis.GetValueDefinedAt(add).locations(),
-              UnorderedElementsAre(HloLocation{add, {}}));
+  EXPECT_THAT(analysis.GetValueDefinedAt(constant1).positions(),
+              UnorderedElementsAre(HloPosition{constant1, {}}));
+  EXPECT_THAT(analysis.GetValueDefinedAt(constant2).positions(),
+              UnorderedElementsAre(HloPosition{constant2, {}}));
+  EXPECT_THAT(analysis.GetValueDefinedAt(add).positions(),
+              UnorderedElementsAre(HloPosition{add, {}}));
 
   // Verify the uses of the values.
   EXPECT_THAT(analysis.GetValueDefinedAt(constant1).uses(),
@@ -155,17 +155,17 @@ TEST_P(HloDataflowAnalysisTest, TupleAndGtes) {
   EXPECT_FALSE(analysis.ValueIsDefinedAt(gte1));
   EXPECT_TRUE(analysis.ValueIsDefinedAt(add));
 
-  // Verify the locations of the values.
+  // Verify the positions of the values.
   EXPECT_THAT(
-      analysis.GetValueDefinedAt(param0).locations(),
-      UnorderedElementsAre(HloLocation{param0, {}}, HloLocation{tuple, {0}},
-                           HloLocation{gte0, {}}));
+      analysis.GetValueDefinedAt(param0).positions(),
+      UnorderedElementsAre(HloPosition{param0, {}}, HloPosition{tuple, {0}},
+                           HloPosition{gte0, {}}));
   EXPECT_THAT(
-      analysis.GetValueDefinedAt(param1).locations(),
-      UnorderedElementsAre(HloLocation{param1, {}}, HloLocation{tuple, {1}},
-                           HloLocation{gte1, {}}));
-  EXPECT_THAT(analysis.GetValueDefinedAt(tuple).locations(),
-              UnorderedElementsAre(HloLocation{tuple, {}}));
+      analysis.GetValueDefinedAt(param1).positions(),
+      UnorderedElementsAre(HloPosition{param1, {}}, HloPosition{tuple, {1}},
+                           HloPosition{gte1, {}}));
+  EXPECT_THAT(analysis.GetValueDefinedAt(tuple).positions(),
+              UnorderedElementsAre(HloPosition{tuple, {}}));
 
   // Verify uses. Of interest is that a GetTupleElement instruction is only a
   // use of the top-level value in the tuple operand.
@@ -200,15 +200,15 @@ TEST_P(HloDataflowAnalysisTest, NestedTuple) {
 
   EXPECT_EQ(analysis.values().size(), 4);
 
-  // Verify locations and uses.
+  // Verify positions and uses.
   EXPECT_THAT(
-      analysis.GetValueDefinedAt(constant1).locations(),
+      analysis.GetValueDefinedAt(constant1).positions(),
       UnorderedElementsAre(
-          HloLocation{constant1, {}}, HloLocation{tuple, {0}},
-          HloLocation{nested_tuple, {0, 0}}, HloLocation{nested_tuple, {1, 0}},
-          HloLocation{nested_tuple, {2}}, HloLocation{gte_tuple, {0}},
-          HloLocation{gte_out, {}}));
-  // Constant values should have no uses though one is live out. The locations
+          HloPosition{constant1, {}}, HloPosition{tuple, {0}},
+          HloPosition{nested_tuple, {0, 0}}, HloPosition{nested_tuple, {1, 0}},
+          HloPosition{nested_tuple, {2}}, HloPosition{gte_tuple, {0}},
+          HloPosition{gte_out, {}}));
+  // Constant values should have no uses though one is live out. The positions
   // where they appear as operands are on instructions which do not use the
   // values (eg, Tuple).
   EXPECT_TRUE(analysis.GetValueDefinedAt(constant1).uses().empty());
diff --git a/tensorflow/compiler/xla/service/hlo_value.cc b/tensorflow/compiler/xla/service/hlo_value.cc
index a5046a712e..d19cadb854 100644
--- a/tensorflow/compiler/xla/service/hlo_value.cc
+++ b/tensorflow/compiler/xla/service/hlo_value.cc
@@ -38,18 +38,18 @@ namespace xla {
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
-const Shape& HloLocation::shape() const {
+const Shape& HloPosition::shape() const {
   return ShapeUtil::GetSubshape(instruction->shape(), index);
 }
 
-string HloLocation::ToString() const {
+string HloPosition::ToString() const {
   string index_str =
       ShapeUtil::IsTuple(instruction->shape()) ? (" " + index.ToString()) : "";
   return StrCat(instruction->name(), index_str);
 }
 
-std::ostream& operator<<(std::ostream& out, const HloLocation& location) {
-  out << location.ToString();
+std::ostream& operator<<(std::ostream& out, const HloPosition& position) {
+  out << position.ToString();
   return out;
 }
 
@@ -69,8 +69,8 @@ std::ostream& operator<<(std::ostream& out, const HloUse& use) {
 HloValue::HloValue(HloValue::Id id, HloInstruction* instruction,
                    const ShapeIndex& index, bool is_phi)
     : id_(id), is_phi_(is_phi) {
-  // The defining location is always the first element in the locations_ vector.
-  AddLocation(instruction, index);
+  // The defining position is always the first element in the positions_ vector.
+  AddPosition(instruction, index);
 }
 
 bool HloValue::operator==(const HloValue& other) const {
@@ -95,9 +95,9 @@ string HloValue::ToShortString() const {
 
 string HloValue::ToString(int indent) const {
   string indentation(indent, ' ');
-  string out = StrCat(indentation, ToShortString(), ", locations:\n");
-  for (const HloLocation& location : locations()) {
-    StrAppend(&out, indentation, "  ", location.ToString(), "\n");
+  string out = StrCat(indentation, ToShortString(), ", positions:\n");
+  for (const HloPosition& position : positions()) {
+    StrAppend(&out, indentation, "  ", position.ToString(), "\n");
   }
   StrAppend(&out, indentation, " uses:\n");
   for (const HloUse& use : uses()) {
@@ -150,22 +150,22 @@ bool MayUseOperandValue(int64 operand_number, const ShapeIndex& index,
 
 }  // namespace
 
-void HloValue::AddLocation(HloInstruction* instruction,
+void HloValue::AddPosition(HloInstruction* instruction,
                            const ShapeIndex& index) {
-  HloLocation new_location{instruction, index};
+  HloPosition new_position{instruction, index};
 
-  // The new location must not already exist in locations_.
-  for (const HloLocation& location : locations_) {
-    DCHECK_NE(location, new_location);
+  // The new position must not already exist in positions_.
+  for (const HloPosition& position : positions_) {
+    DCHECK_NE(position, new_position);
   }
-  // The shape of the new location must match existing locations.
-  if (!locations_.empty()) {
+  // The shape of the new position must match existing positions.
+  if (!positions_.empty()) {
     CHECK(
-        ShapeUtil::Compatible(locations_.front().shape(), new_location.shape()))
-        << "front: " << locations_.front() << " new: " << new_location;
+        ShapeUtil::Compatible(positions_.front().shape(), new_position.shape()))
+        << "front: " << positions_.front() << " new: " << new_position;
   }
 
-  locations_.push_back(std::move(new_location));
+  positions_.push_back(std::move(new_position));
 
   // Update uses.
   for (HloInstruction* user : instruction->users()) {
@@ -194,23 +194,23 @@ void HloValue::AddLocation(HloInstruction* instruction,
   }
 }
 
-void HloValue::RemoveLocation(HloInstruction* instruction,
+void HloValue::RemovePosition(HloInstruction* instruction,
                               const ShapeIndex& index) {
-  // The defining location cannot be removed.
+  // The defining position cannot be removed.
   CHECK(!(instruction == defining_instruction() && index == defining_index()));
 
-  int64 size_before = locations_.size();
-  locations_.erase(
-      std::remove_if(locations_.begin(), locations_.end(),
-                     [instruction, &index](const HloLocation& location) {
-                       return location.instruction == instruction &&
-                              location.index == index;
+  int64 size_before = positions_.size();
+  positions_.erase(
+      std::remove_if(positions_.begin(), positions_.end(),
+                     [instruction, &index](const HloPosition& position) {
+                       return position.instruction == instruction &&
+                              position.index == index;
                      }),
-      locations_.end());
-  // Only a single location should have been removed.
-  CHECK_EQ(locations_.size(), size_before - 1);
+      positions_.end());
+  // Only a single position should have been removed.
+  CHECK_EQ(positions_.size(), size_before - 1);
 
-  //  Update uses which referred to this location.
+  //  Update uses which referred to this position.
   uses_.erase(std::remove_if(uses_.begin(), uses_.end(),
                              [instruction, &index](const HloUse& use) {
                                return use.instruction->operand(
@@ -221,8 +221,8 @@ void HloValue::RemoveLocation(HloInstruction* instruction,
 
   // Returns whether this value is contained in the given instruction's output.
   auto is_contained_in = [this](const HloInstruction* instruction) {
-    for (const HloLocation& location : locations()) {
-      if (location.instruction == instruction) {
+    for (const HloPosition& position : positions()) {
+      if (position.instruction == instruction) {
         return true;
       }
     }
@@ -231,7 +231,7 @@ void HloValue::RemoveLocation(HloInstruction* instruction,
 
   const HloModule& module = *instruction->parent()->parent();
   if (instruction == module.entry_computation()->root_instruction()) {
-    // Value has been removed from a location in the entry root instruction.
+    // Value has been removed from a position in the entry root instruction.
     live_out_of_module_ =
         is_contained_in(module.entry_computation()->root_instruction());
   }
diff --git a/tensorflow/compiler/xla/service/hlo_value.h b/tensorflow/compiler/xla/service/hlo_value.h
index b0caf24a21..e884fd9418 100644
--- a/tensorflow/compiler/xla/service/hlo_value.h
+++ b/tensorflow/compiler/xla/service/hlo_value.h
@@ -30,24 +30,24 @@ limitations under the License.
 namespace xla {
 
 // Abstraction which identifies a specific point in the XLA graph. An
-// HloLocation specifies a ShapeIndex within the output of a specific
+// HloPosition specifies a ShapeIndex within the output of a specific
 // instruction.
-struct HloLocation {
+struct HloPosition {
   HloInstruction* instruction;
   ShapeIndex index;
 
-  // Returns the shape at this location.
+  // Returns the shape at this position.
   const Shape& shape() const;
 
   string ToString() const;
 
-  bool operator==(const HloLocation& other) const {
+  bool operator==(const HloPosition& other) const {
     return instruction == other.instruction && index == other.index;
   }
-  bool operator!=(const HloLocation& other) const { return !(*this == other); }
+  bool operator!=(const HloPosition& other) const { return !(*this == other); }
 };
 
-std::ostream& operator<<(std::ostream& out, const HloLocation& location);
+std::ostream& operator<<(std::ostream& out, const HloPosition& position);
 
 // Defines a single use of an HLO value.
 struct HloUse {
@@ -111,28 +111,28 @@ class HloValue {
   // Returns whether this value is a phi value.
   bool is_phi() const { return is_phi_; }
 
-  // Return the location where this value is defined.
-  const HloLocation& defining_location() const { return locations_[0]; }
+  // Return the position where this value is defined.
+  const HloPosition& defining_position() const { return positions_[0]; }
 
   // Return the instruction which defines this HloValue.
   HloInstruction* defining_instruction() const {
-    return defining_location().instruction;
+    return defining_position().instruction;
   }
 
   // Return the shape index at which this HloValue is defined in the output of
   // its defining instruction.
-  const ShapeIndex& defining_index() const { return defining_location().index; }
+  const ShapeIndex& defining_index() const { return defining_position().index; }
 
   // Return the shape of this HloValue.
-  const Shape& shape() const { return defining_location().shape(); }
+  const Shape& shape() const { return defining_position().shape(); }
 
-  // Add or remove a location at which the HloValue appears. The definition
-  // location can not be removed. The uses of the HloValue are updated.
-  void AddLocation(HloInstruction* instruction, const ShapeIndex& index);
-  void RemoveLocation(HloInstruction* instruction, const ShapeIndex& index);
+  // Add or remove a position at which the HloValue appears. The definition
+  // position can not be removed. The uses of the HloValue are updated.
+  void AddPosition(HloInstruction* instruction, const ShapeIndex& index);
+  void RemovePosition(HloInstruction* instruction, const ShapeIndex& index);
 
-  // Return all locations of the HloValue in the module.
-  const std::vector<HloLocation>& locations() const { return locations_; }
+  // Return all positions of the HloValue in the module.
+  const std::vector<HloPosition>& positions() const { return positions_; }
 
   // Return all uses of the HloValue.
   const std::vector<HloUse>& uses() const { return uses_; }
@@ -158,9 +158,9 @@ class HloValue {
   // Whether this instruction is a phi value.
   const bool is_phi_;
 
-  // The set of locations of this HloValue. The first element is always the
-  // location of the definition.
-  std::vector<HloLocation> locations_;
+  // The set of positions of this HloValue. The first element is always the
+  // position of the definition.
+  std::vector<HloPosition> positions_;
 
   // The set of uses of this HloValue.
   std::vector<HloUse> uses_;
-- 
GitLab


From 6d6dda807c539efd59f074ef02d52e0140480c1c Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Wed, 12 Jul 2017 14:22:54 -0700
Subject: [PATCH 0931/1375] [TF:XLA] Fix an issue where plugin/Executor backend
 is used by default when TF is built from source with XLA support. See Github
 issue #11122.

The priority of the executor backend is set to be higher than the default (50)
and CPUs (<100), and is therefore selected as the default when tf.device is not
explicitly specified.

PiperOrigin-RevId: 161717173
---
 tensorflow/compiler/plugin/executor/device.cc | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/plugin/executor/device.cc b/tensorflow/compiler/plugin/executor/device.cc
index bbc39dc03f..d902f9df6a 100644
--- a/tensorflow/compiler/plugin/executor/device.cc
+++ b/tensorflow/compiler/plugin/executor/device.cc
@@ -47,7 +47,12 @@ Status XlaExaDeviceFactory::CreateDevices(const SessionOptions& options,
   return Status::OK();
 }
 
-REGISTER_LOCAL_DEVICE_FACTORY(DEVICE_XLA_EXEC, XlaExaDeviceFactory, 110);
+// Set priority to be below the default priority (50), so that Executor is not
+// selected as a high priority device over other default devices.
+// See constructor comments for Registrar in
+// tensorflow/core/common_runtime/device_factory.h for a list of priority for
+// devices.
+REGISTER_LOCAL_DEVICE_FACTORY(DEVICE_XLA_EXEC, XlaExaDeviceFactory, 40);
 
 // Kernel registrations
 
-- 
GitLab


From 71c4ec8ed6cf984650bd21069037ac0ea28728b9 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 12 Jul 2017 14:39:56 -0700
Subject: [PATCH 0932/1375] Add a mechanism for switching between multiple
 iterators by feeding a handle.

With this change, you can do the following:

1. Fetch a string handle for any iterator, by evaluating the result of
   `Iterator.string_handle()`.
2. Define an `Iterator` object based on a `tf.string` placeholder handle.
3. Feed the placeholder using an evaluated string handle to use a particular
   iterator in a particular step.

Concretely, this allows you to define two iterators for a training dataset and
a test dataset, and choose which one to use on a per-run basis:

```python
train_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
train_iterator_handle = sess.run(train_iterator.string_handle())

test_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
test_iterator_handle = sess.run(test_iterator.string_handle())

handle = tf.placeholder(tf.string, shape=[])
iterator = tf.contrib.data.Iterator.from_string_handle(
    handle, train_iterator.output_types)

next_element = iterator.get_next()
loss = f(next_element)

train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
```
PiperOrigin-RevId: 161719836
---
 .../python/kernel_tests/iterator_ops_test.py  | 48 +++++++++++++
 .../contrib/data/python/ops/dataset_ops.py    | 68 +++++++++++++++++++
 tensorflow/core/kernels/iterator_ops.cc       | 67 ++++++++++++++++++
 tensorflow/core/ops/dataset_ops.cc            | 22 ++++++
 4 files changed, 205 insertions(+)

diff --git a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
index b5b115dd70..d12be873f1 100644
--- a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
@@ -328,6 +328,54 @@ class IteratorTest(test.TestCase):
               [1, 2, 3], dtype=dtypes.int64), constant_op.constant(
                   [4., 5., 6., 7.], dtype=dtypes.float64))))
 
+  def testIteratorStringHandle(self):
+    dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
+    dataset_4 = dataset_ops.Dataset.from_tensor_slices([10, 20, 30, 40])
+
+    iterator_3 = dataset_3.make_one_shot_iterator()
+    iterator_4 = dataset_4.make_one_shot_iterator()
+
+    handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
+    feedable_iterator = dataset_ops.Iterator.from_string_handle(
+        handle_placeholder, dataset_3.output_types, dataset_3.output_shapes)
+    next_element = feedable_iterator.get_next()
+
+    self.assertEqual(dataset_3.output_types, feedable_iterator.output_types)
+    self.assertEqual(dataset_4.output_types, feedable_iterator.output_types)
+    self.assertEqual([], feedable_iterator.output_shapes)
+
+    with self.test_session() as sess:
+      iterator_3_handle = sess.run(iterator_3.string_handle())
+      iterator_4_handle = sess.run(iterator_4.string_handle())
+
+      self.assertEqual(
+          10, sess.run(next_element,
+                       feed_dict={handle_placeholder: iterator_4_handle}))
+      self.assertEqual(
+          1, sess.run(next_element,
+                      feed_dict={handle_placeholder: iterator_3_handle}))
+      self.assertEqual(
+          20, sess.run(next_element,
+                       feed_dict={handle_placeholder: iterator_4_handle}))
+      self.assertEqual(
+          2, sess.run(next_element,
+                      feed_dict={handle_placeholder: iterator_3_handle}))
+      self.assertEqual(
+          30, sess.run(next_element,
+                       feed_dict={handle_placeholder: iterator_4_handle}))
+      self.assertEqual(
+          3, sess.run(next_element,
+                      feed_dict={handle_placeholder: iterator_3_handle}))
+      self.assertEqual(
+          40, sess.run(next_element,
+                       feed_dict={handle_placeholder: iterator_4_handle}))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element,
+                 feed_dict={handle_placeholder: iterator_3_handle})
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element,
+                 feed_dict={handle_placeholder: iterator_4_handle})
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index e6b0f25d74..6a461da97f 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -182,6 +182,62 @@ class Iterator(object):
         output_shapes=nest.flatten(output_shapes))
     return Iterator(iterator_resource, None, output_types, output_shapes)
 
+  @staticmethod
+  def from_string_handle(string_handle, output_types, output_shapes=None):
+    """Creates a new, uninitialized `Iterator` based on the given handle.
+
+    This method allows you to define a "feedable" iterator where you can choose
+    between concrete iterators by feeding a value in a @{tf.Session.run} call.
+    In that case, `string_handle` would a @{tf.placeholder}, and you would feed
+    it with the value of @{tf.contrib.data.Iterator.string_handle} in each step.
+
+    For example, if you had two iterators that marked the current position in
+    a training dataset and a test dataset, you could choose which to use in
+    each step as follows:
+
+    ```python
+    train_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
+    train_iterator_handle = sess.run(train_iterator.string_handle())
+
+    test_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
+    test_iterator_handle = sess.run(test_iterator.string_handle())
+
+    handle = tf.placeholder(tf.string, shape=[])
+    iterator = tf.contrib.data.Iterator.from_string_handle(
+        handle, train_iterator.output_types)
+
+    next_element = iterator.get_next()
+    loss = f(next_element)
+
+    train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
+    test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
+    ```
+
+    Args:
+      string_handle: A scalar `tf.Tensor` of type `tf.string` that evaluates
+        to a handle produced by the `Iterator.string_handle()` method.
+      output_types: A nested structure of `tf.DType` objects corresponding to
+        each component of an element of this iterator.
+      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
+        corresponding to each component of an element of this dataset. If
+        omitted, each component will have an unconstrainted shape.
+
+    Returns:
+      An `Iterator`.
+    """
+    output_types = nest.map_structure(dtypes.as_dtype, output_types)
+    if output_shapes is None:
+      output_shapes = nest.map_structure(
+          lambda _: tensor_shape.TensorShape(None), output_types)
+    else:
+      output_shapes = nest.map_structure_up_to(
+          output_types, tensor_shape.as_shape, output_shapes)
+    nest.assert_same_structure(output_types, output_shapes)
+    string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string)
+    iterator_resource = gen_dataset_ops.iterator_from_string_handle(
+        string_handle)
+    return Iterator(iterator_resource, None, output_types, output_shapes)
+
   @property
   def initializer(self):
     """A `tf.Operation` that should be run to initialize this iterator.
@@ -261,6 +317,18 @@ class Iterator(object):
     """
     return gen_dataset_ops.iterator_dispose(self._iterator_resource, name=name)
 
+  def string_handle(self, name=None):
+    """Returns a string-valued `tf.Tensor` that represents this iterator.
+
+    Args:
+      name: (Optional.) A name for the created operation.
+
+    Returns:
+      A scalar `tf.Tensor` of type `tf.string`.
+    """
+    return gen_dataset_ops.iterator_to_string_handle(self._iterator_resource,
+                                                     name=name)
+
   @property
   def output_shapes(self):
     """Returns the shape of each component of an element of this iterator.
diff --git a/tensorflow/core/kernels/iterator_ops.cc b/tensorflow/core/kernels/iterator_ops.cc
index f143d39816..9ed89a12be 100644
--- a/tensorflow/core/kernels/iterator_ops.cc
+++ b/tensorflow/core/kernels/iterator_ops.cc
@@ -415,6 +415,69 @@ class IteratorDisposeOp : public OpKernel {
   }
 };
 
+class IteratorToStringHandleOp : public OpKernel {
+ public:
+  explicit IteratorToStringHandleOp(OpKernelConstruction* ctx)
+      : OpKernel(ctx) {}
+
+  void Compute(OpKernelContext* ctx) override {
+    const Tensor& resource_handle_t = ctx->input(0);
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(resource_handle_t.shape()),
+                errors::InvalidArgument("resource_handle must be a scalar"));
+
+    // Validate that the handle corresponds to a real resource, and
+    // that it is an IteratorResource.
+    IteratorResource* iterator_resource;
+    OP_REQUIRES_OK(
+        ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &iterator_resource));
+    iterator_resource->Unref();
+
+    Tensor* string_handle_t;
+    OP_REQUIRES_OK(ctx,
+                   ctx->allocate_output(0, TensorShape({}), &string_handle_t));
+    string_handle_t->scalar<string>()() =
+        resource_handle_t.scalar<ResourceHandle>()().SerializeAsString();
+  }
+};
+
+class IteratorFromStringHandleOp : public OpKernel {
+ public:
+  explicit IteratorFromStringHandleOp(OpKernelConstruction* ctx)
+      : OpKernel(ctx) {}
+
+  void Compute(OpKernelContext* ctx) override {
+    const Tensor& string_handle_t = ctx->input(0);
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(string_handle_t.shape()),
+                errors::InvalidArgument("string_handle must be a scalar"));
+
+    ResourceHandle resource_handle;
+    OP_REQUIRES(
+        ctx,
+        resource_handle.ParseFromString(string_handle_t.scalar<string>()()),
+        errors::InvalidArgument(
+            "Could not parse string_handle as a valid ResourceHandle"));
+
+    // Validate that the handle corresponds to a real resource, and
+    // that it is an IteratorResource.
+    IteratorResource* iterator_resource;
+    OP_REQUIRES_OK(ctx,
+                   LookupResource(ctx, resource_handle, &iterator_resource));
+    iterator_resource->Unref();
+
+    OP_REQUIRES(
+        ctx, resource_handle.device() == ctx->device()->attributes().name(),
+        errors::InvalidArgument("Attempted create an iterator on device \"",
+                                ctx->device()->attributes().name(),
+                                "\" from handle defined on device \"",
+                                resource_handle.device(), "\""));
+
+    Tensor* resource_handle_t;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output(0, TensorShape({}), &resource_handle_t));
+    resource_handle_t->scalar<ResourceHandle>()() = resource_handle;
+  }
+};
+
 REGISTER_KERNEL_BUILDER(Name("Iterator").Device(DEVICE_CPU), IteratorHandleOp);
 REGISTER_KERNEL_BUILDER(Name("MakeIterator").Device(DEVICE_CPU),
                         MakeIteratorOp);
@@ -424,6 +487,10 @@ REGISTER_KERNEL_BUILDER(Name("IteratorGetNext").Device(DEVICE_CPU),
                         IteratorGetNextOp);
 REGISTER_KERNEL_BUILDER(Name("IteratorDispose").Device(DEVICE_CPU),
                         IteratorDisposeOp);
+REGISTER_KERNEL_BUILDER(Name("IteratorToStringHandle").Device(DEVICE_CPU),
+                        IteratorToStringHandleOp);
+REGISTER_KERNEL_BUILDER(Name("IteratorFromStringHandle").Device(DEVICE_CPU),
+                        IteratorFromStringHandleOp);
 
 }  // namespace
 
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index 789d8a9990..425201529b 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -533,4 +533,26 @@ REGISTER_OP("IteratorDispose")
 Releases any resources used by the given iterator.
 )doc");
 
+REGISTER_OP("IteratorToStringHandle")
+    .Input("resource_handle: resource")
+    .Output("string_handle: string")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Converts the given `resource_handle` representing an iterator to a string.
+
+resource_handle: A handle to an iterator resource.
+string_handle: A string representation of the given handle.
+)doc");
+
+REGISTER_OP("IteratorFromStringHandle")
+    .Input("string_handle: string")
+    .Output("resource_handle: resource")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Converts the given string representing a handle to an iterator to a resource.
+
+string_handle: A string representation of the given handle.
+resource_handle: A handle to an iterator resource.
+)doc");
+
 }  // namespace tensorflow
-- 
GitLab


From fd5530d6ea1515ede0bf503721e9f4c87d710561 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 14:40:19 -0700
Subject: [PATCH 0933/1375] adding bazel-toolchains repo to workspace. This
 repo will be necessary for remote execution (specifically for cross OS
 compilation)

PiperOrigin-RevId: 161719899
---
 tensorflow/workspace.bzl | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 20d45a7fdc..c1832eceb9 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -662,3 +662,13 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       name = "cub",
       actual = "@cub_archive//:cub",
   )
+
+  native.http_archive(
+      name = "bazel_toolchains",
+      urls = [
+          "http://mirror.bazel.build/github.com/bazelbuild/bazel-toolchains/archive/bccee4855c049d34bac481083b4c68e2fab8cc50.tar.gz",
+          "https://github.com/bazelbuild/bazel-toolchains/archive/bccee4855c049d34bac481083b4c68e2fab8cc50.tar.gz",
+      ],
+      sha256 = "3903fd93b96b42067e00b7973a2c16c34e761ad7a0b55e1557d408f352849e41",
+      strip_prefix = "bazel-toolchains-bccee4855c049d34bac481083b4c68e2fab8cc50",
+  )
-- 
GitLab


From 9a172989e0b3fa8de69220caf5279643cd8366d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 15:21:44 -0700
Subject: [PATCH 0934/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161726324
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 24 +++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 30 +++++++++++++++++++
 2 files changed, 54 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 4fcf7aaa41..bfe9214187 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -11204,6 +11204,18 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "IteratorFromStringHandle"
+  input_arg {
+    name: "string_handle"
+    type: DT_STRING
+  }
+  output_arg {
+    name: "resource_handle"
+    type: DT_RESOURCE
+  }
+  is_stateful: true
+}
 op {
   name: "IteratorGetNext"
   input_arg {
@@ -11228,6 +11240,18 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "IteratorToStringHandle"
+  input_arg {
+    name: "resource_handle"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "string_handle"
+    type: DT_STRING
+  }
+  is_stateful: true
+}
 op {
   name: "L2Loss"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 1b20981bb4..11f2220f15 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -10567,6 +10567,21 @@ op {
   summary: "Releases any resources used by the given iterator."
   is_stateful: true
 }
+op {
+  name: "IteratorFromStringHandle"
+  input_arg {
+    name: "string_handle"
+    description: "A string representation of the given handle."
+    type: DT_STRING
+  }
+  output_arg {
+    name: "resource_handle"
+    description: "A handle to an iterator resource."
+    type: DT_RESOURCE
+  }
+  summary: "Converts the given string representing a handle to an iterator to a resource."
+  is_stateful: true
+}
 op {
   name: "IteratorGetNext"
   input_arg {
@@ -10592,6 +10607,21 @@ op {
   summary: "Gets the next output from the given iterator."
   is_stateful: true
 }
+op {
+  name: "IteratorToStringHandle"
+  input_arg {
+    name: "resource_handle"
+    description: "A handle to an iterator resource."
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "string_handle"
+    description: "A string representation of the given handle."
+    type: DT_STRING
+  }
+  summary: "Converts the given `resource_handle` representing an iterator to a string."
+  is_stateful: true
+}
 op {
   name: "L2Loss"
   input_arg {
-- 
GitLab


From c65f691196f4532fd117eacc8e3dd225c4844d3f Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Wed, 12 Jul 2017 15:24:40 -0700
Subject: [PATCH 0935/1375] Factor out DenseUpdate ops into
 dense_update_functor build dep.

Also add support for complex types.

PiperOrigin-RevId: 161726749
---
 tensorflow/core/framework/register_types.h    |  4 +-
 tensorflow/core/kernels/BUILD                 | 34 ++++++---
 .../core/kernels/dense_update_functor.cc      | 73 ++++++++++++++++++
 ...se_update_ops.h => dense_update_functor.h} | 33 +++++----
 ...u.cu.cc => dense_update_functor_gpu.cu.cc} | 30 +++++++-
 tensorflow/core/kernels/dense_update_ops.cc   | 74 ++-----------------
 .../core/kernels/resource_variable_ops.cc     | 27 ++-----
 tensorflow/core/kernels/scatter_nd_op.cc      | 12 +--
 tensorflow/core/kernels/strided_slice_op.cc   |  2 +-
 .../core/kernels/strided_slice_op_impl.h      |  2 +-
 10 files changed, 164 insertions(+), 127 deletions(-)
 create mode 100644 tensorflow/core/kernels/dense_update_functor.cc
 rename tensorflow/core/kernels/{dense_update_ops.h => dense_update_functor.h} (65%)
 rename tensorflow/core/kernels/{dense_update_ops_gpu.cu.cc => dense_update_functor_gpu.cu.cc} (61%)

diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index b62fe647e2..3f91642064 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -172,7 +172,9 @@ limitations under the License.
   TF_CALL_half(m) TF_CALL_float(m) TF_CALL_double(m)
 
 // Call "m" on all types supported on GPU.
-#define TF_CALL_GPU_ALL_TYPES(m) TF_CALL_GPU_NUMBER_TYPES(m) TF_CALL_bool(m)
+#define TF_CALL_GPU_ALL_TYPES(m) \
+  TF_CALL_GPU_NUMBER_TYPES(m)    \
+  TF_CALL_bool(m) TF_CALL_complex64(m) TF_CALL_complex128(m)
 
 #define TF_CALL_GPU_NUMBER_TYPES_NO_HALF(m) TF_CALL_float(m) TF_CALL_double(m)
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 6eee2e16eb..93d1df0f7b 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -19,6 +19,7 @@ package_group(
     name = "friends",
     packages = [
         "//learning/brain/contrib/...",
+        "//learning/brain/research/sparse_matrix/...",
         "//tensorflow/...",
     ],
 )
@@ -94,13 +95,11 @@ tf_kernel_library(
         "strided_slice_op_inst_7.cc",
     ],
     hdrs = [
-        "dense_update_ops.h",
         "slice_op.h",
         "strided_slice_op.h",
         "strided_slice_op_impl.h",
     ],
     gpu_srcs = [
-        "dense_update_ops.h",
         "slice_op.h",
         "strided_slice_op.h",
         "strided_slice_op_impl.h",
@@ -109,6 +108,7 @@ tf_kernel_library(
     ],
     deps = [
         ":bounds_check",
+        ":dense_update_functor",
         ":ops_util",
         ":variable_ops",
         "//tensorflow/core:framework",
@@ -1011,6 +1011,23 @@ tf_kernel_library(
     ],
 )
 
+tf_kernel_library(
+    name = "dense_update_functor",
+    srcs = ["dense_update_functor.cc"],
+    hdrs = ["dense_update_functor.h"],
+    gpu_srcs = [
+        "dense_update_functor.h",
+        "dense_update_functor_gpu.cu.cc",
+    ],
+    visibility = [":friends"],
+    deps = [
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 0,
+)
+
 tf_cuda_cc_test(
     name = "gather_op_test",
     size = "small",
@@ -1606,7 +1623,7 @@ tf_kernel_library(
     srcs = ["resource_variable_ops.cc"],
     deps = [
         ":bounds_check",
-        ":dense_update_ops",
+        ":dense_update_functor",
         ":gather_functor",
         ":scatter_functor",
         ":state",
@@ -2079,7 +2096,7 @@ tf_kernel_library(
         "//tensorflow:darwin": [],
         "//conditions:default": ["-Wl,-z,muldefs"],
     }),
-    visibility = ["//visibility:private"],
+    visibility = [":friends"],
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -3482,7 +3499,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "dense_update_ops",
     prefix = "dense_update_ops",
-    deps = STATE_DEPS,
+    deps = STATE_DEPS + [":dense_update_functor"],
 )
 
 tf_kernel_library(
@@ -3503,16 +3520,14 @@ tf_kernel_library(
         "scatter_nd_op_cpu_impl_5.cc",
     ],
     hdrs = [
-        "dense_update_ops.h",
         "scatter_nd_op.h",
         "scatter_nd_op_cpu_impl.h",
     ],
     gpu_srcs = [
-        "dense_update_ops.h",
         "scatter_nd_op.h",
         "scatter_nd_op_gpu.cu.cc",
     ],
-    deps = STATE_DEPS + [":dense_update_ops"],
+    deps = STATE_DEPS + [":dense_update_functor"],
 )
 
 tf_kernel_library(
@@ -4054,8 +4069,9 @@ filegroup(
         "cwise_ops_common.cc",
         "cwise_ops_common.h",
         "cwise_ops_gradients.h",
+        "dense_update_functor.cc",
+        "dense_update_functor.h",
         "dense_update_ops.cc",
-        "dense_update_ops.h",
         "example_parsing_ops.cc",
         "fill_functor.cc",
         "fill_functor.h",
diff --git a/tensorflow/core/kernels/dense_update_functor.cc b/tensorflow/core/kernels/dense_update_functor.cc
new file mode 100644
index 0000000000..a878fe9a97
--- /dev/null
+++ b/tensorflow/core/kernels/dense_update_functor.cc
@@ -0,0 +1,73 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/kernels/dense_update_functor.h"
+
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace functor {
+
+template <>
+struct DenseUpdate<CPUDevice, string, ASSIGN> {
+  void operator()(const CPUDevice& d, typename TTypes<string>::Flat params,
+                  typename TTypes<string>::ConstFlat update) {
+    if (params.dimension(0) == 1) {
+      params.data()->resize(update.data()->size());
+      auto work = [&params, &update](int64 start, int64 end) {
+        memmove(const_cast<char*>(params.data()->data()) + start,
+                update.data()->data() + start, end - start);
+      };
+      d.parallelFor(update.data()->size(),
+                    Eigen::TensorOpCost(.1,  // chosen to force large chunks
+                                        .1, 0),
+                    work);
+    } else {
+      auto work = [&params, &update](int64 start, int64 end) {
+        for (int i = start; i < end; ++i) {
+          params.data()[i].resize(update.data()[i].size());
+          memmove(const_cast<char*>(params.data()[i].data()),
+                  update.data()[i].data(), update.data()[i].size());
+        }
+      };
+      int64 estimated_string_size;
+      if (update.size() > 0) {
+        // first element of the tensor seems as good a guess as any of the sizes
+        // of the strings contained within...
+        estimated_string_size =
+            std::max(update.data()[0].size(), sizeof(string));
+      } else {
+        estimated_string_size = sizeof(string);
+      }
+      d.parallelFor(
+          params.dimension(0),
+          Eigen::TensorOpCost(estimated_string_size, estimated_string_size, 0),
+          work);
+    }
+  }
+};
+
+}  // namespace functor
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/dense_update_ops.h b/tensorflow/core/kernels/dense_update_functor.h
similarity index 65%
rename from tensorflow/core/kernels/dense_update_ops.h
rename to tensorflow/core/kernels/dense_update_functor.h
index ec7e9dff11..54b080c83b 100644
--- a/tensorflow/core/kernels/dense_update_ops.h
+++ b/tensorflow/core/kernels/dense_update_functor.h
@@ -13,40 +13,47 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
-#define TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
+#ifndef TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
+#define TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
+
+#define EIGEN_USE_THREADS
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor_types.h"
 
 namespace tensorflow {
 
+typedef Eigen::ThreadPoolDevice CPUDevice;
+
 enum DenseUpdateType { ADD, SUB, ASSIGN };
 
 namespace functor {
 
 template <typename Device, typename T, DenseUpdateType OP>
-struct DenseUpdate;
-
-template <typename Device, typename T>
-struct DenseUpdate<Device, T, ADD> {
+struct DenseUpdate {
   void operator()(const Device& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update);
+};
+
+template <typename T>
+struct DenseUpdate<CPUDevice, T, ADD> {
+  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) += update;
   }
 };
 
-template <typename Device, typename T>
-struct DenseUpdate<Device, T, SUB> {
-  void operator()(const Device& d, typename TTypes<T>::Flat params,
+template <typename T>
+struct DenseUpdate<CPUDevice, T, SUB> {
+  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) -= update;
   }
 };
 
-template <typename Device, typename T>
-struct DenseUpdate<Device, T, ASSIGN> {
-  void operator()(const Device& d, typename TTypes<T>::Flat params,
+template <typename T>
+struct DenseUpdate<CPUDevice, T, ASSIGN> {
+  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) = update;
   }
@@ -55,4 +62,4 @@ struct DenseUpdate<Device, T, ASSIGN> {
 }  // end namespace functor
 }  // end namespace tensorflow
 
-#endif  // TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
+#endif  // TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
diff --git a/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
similarity index 61%
rename from tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
rename to tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
index cf2d493061..208401cb24 100644
--- a/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
+++ b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
@@ -17,7 +17,7 @@ limitations under the License.
 
 #define EIGEN_USE_GPU
 
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 
 #include "tensorflow/core/framework/register_types.h"
 
@@ -25,6 +25,34 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
+namespace functor {
+
+template <typename T>
+struct DenseUpdate<GPUDevice, T, ASSIGN> {
+  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) = update;
+  }
+};
+
+template <typename T>
+struct DenseUpdate<GPUDevice, T, ADD> {
+  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) += update;
+  }
+};
+
+template <typename T>
+struct DenseUpdate<GPUDevice, T, SUB> {
+  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) -= update;
+  }
+};
+
+}  // namespace functor
+
 #define DEFINE_GPU_KERNELS(T)                              \
   template struct functor::DenseUpdate<GPUDevice, T, ADD>; \
   template struct functor::DenseUpdate<GPUDevice, T, SUB>;
diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc
index af16c55a5d..6d44a92fa3 100644
--- a/tensorflow/core/kernels/dense_update_ops.cc
+++ b/tensorflow/core/kernels/dense_update_ops.cc
@@ -15,59 +15,20 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif
+
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/assign_op.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
-namespace functor {
-
-template <>
-struct DenseUpdate<Eigen::ThreadPoolDevice, string, ASSIGN> {
-  void operator()(const Eigen::ThreadPoolDevice& d,
-                  typename TTypes<string>::Flat params,
-                  typename TTypes<string>::ConstFlat update) {
-    if (params.dimension(0) == 1) {
-      params.data()->resize(update.data()->size());
-      auto work = [&params, &update](int64 start, int64 end) {
-        memmove(const_cast<char*>(params.data()->data()) + start,
-                update.data()->data() + start, end - start);
-      };
-      d.parallelFor(update.data()->size(),
-                    Eigen::TensorOpCost(.1,  // chosen to force large chunks
-                                        .1, 0),
-                    work);
-    } else {
-      auto work = [&params, &update](int64 start, int64 end) {
-        for (int i = start; i < end; ++i) {
-          params.data()[i].resize(update.data()[i].size());
-          memmove(const_cast<char*>(params.data()[i].data()),
-                  update.data()[i].data(), update.data()[i].size());
-        }
-      };
-      int64 estimated_string_size;
-      if (update.size() > 0) {
-        // first element of the tensor seems as good a guess as any of the sizes
-        // of the strings contained within...
-        estimated_string_size =
-            std::max(update.data()[0].size(), sizeof(string));
-      } else {
-        estimated_string_size = sizeof(string);
-      }
-      d.parallelFor(
-          params.dimension(0),
-          Eigen::TensorOpCost(estimated_string_size, estimated_string_size, 0),
-          work);
-    }
-  }
-};
-
-}  // namespace functor
 
 template <typename Device, typename T>
 class AssignOpT : public AssignOp {
@@ -117,7 +78,7 @@ class DenseUpdateOp : public OpKernel {
         errors::InvalidArgument("Parameters and update must be the same size"));
 
     functor::DenseUpdate<Device, T, OP> update_functor;
-    update_functor(context->eigen_device<Device>(), Tparams.flat<T>(),
+    update_functor(context->template eigen_device<Device>(), Tparams.flat<T>(),
                    Tupdate.flat<T>());
   }
 
@@ -143,13 +104,6 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
 // Only register 'Assign' on GPU for the subset of types also supported by
 // 'Variable' (see variable_ops.cc.)
 #define REGISTER_GPU_KERNELS(type)                                 \
-  namespace functor {                                              \
-  template <>                                                      \
-  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(           \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,         \
-      typename TTypes<type>::ConstFlat rhs);                       \
-  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;     \
-  }                                                                \
   REGISTER_KERNEL_BUILDER(                                         \
       Name("Assign").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
       AssignOpT<GPUDevice, type>);
@@ -180,22 +134,6 @@ TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
 
 #if GOOGLE_CUDA
-// Forward declarations of the functor specializations for GPU.
-namespace functor {
-#define DECLARE_GPU_SPEC_FOR_OP(T, OP)                     \
-  template <>                                              \
-  void DenseUpdate<GPUDevice, T, OP>::operator()(          \
-      const GPUDevice& d, typename TTypes<T>::Flat params, \
-      typename TTypes<T>::ConstFlat update);               \
-  extern template struct DenseUpdate<GPUDevice, T, OP>;
-#define DECLARE_GPU_SPEC(T)                         \
-  DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::ADD); \
-  DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::SUB)
-TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
-#undef DECLARE_GPU_SPEC
-#undef DECLARE_GPU_SPEC_FOR_OP
-}  // namespace functor
-
 #define REGISTER_GPU_KERNELS(type)                                    \
   REGISTER_KERNEL_BUILDER(                                            \
       Name("AssignAdd").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index f0fef256d2..0616bb5a08 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -15,12 +15,16 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif
+
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/gather_functor.h"
 #include "tensorflow/core/kernels/scatter_functor.h"
 #include "tensorflow/core/kernels/variable_ops.h"
@@ -217,13 +221,6 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
 
 #if GOOGLE_CUDA
 #define REGISTER_GPU_KERNELS(type)                             \
-  namespace functor {                                          \
-  template <>                                                  \
-  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(       \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,     \
-      typename TTypes<type>::ConstFlat rhs);                   \
-  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>; \
-  }                                                            \
   REGISTER_KERNEL_BUILDER(Name("AssignVariableOp")             \
                               .Device(DEVICE_GPU)              \
                               .TypeConstraint<type>("dtype")   \
@@ -275,20 +272,6 @@ TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 
 #if GOOGLE_CUDA
 #define REGISTER_GPU_KERNELS(type)                                       \
-  namespace functor {                                                    \
-  template <>                                                            \
-  void DenseUpdate<GPUDevice, type, ADD>::operator()(                    \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,               \
-      typename TTypes<type>::ConstFlat rhs);                             \
-  extern template struct DenseUpdate<GPUDevice, type, ADD>;              \
-  }                                                                      \
-  namespace functor {                                                    \
-  template <>                                                            \
-  void DenseUpdate<GPUDevice, type, SUB>::operator()(                    \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,               \
-      typename TTypes<type>::ConstFlat rhs);                             \
-  extern template struct DenseUpdate<GPUDevice, type, SUB>;              \
-  }                                                                      \
   REGISTER_KERNEL_BUILDER(Name("AssignAddVariableOp")                    \
                               .Device(DEVICE_GPU)                        \
                               .HostMemory("resource")                    \
diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc
index 1428546d52..59f690e7aa 100644
--- a/tensorflow/core/kernels/scatter_nd_op.cc
+++ b/tensorflow/core/kernels/scatter_nd_op.cc
@@ -26,7 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/fill_functor.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -523,16 +523,6 @@ TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DECLARE_GPU_SPECS);
 #undef DECLARE_GPU_SPECS_INDEX
 #undef DECLARE_GPU_SPECS_INDEX_OP
 
-#define REGISTER_GPU_KERNELS(type)                         \
-  template <>                                              \
-  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(   \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs, \
-      typename TTypes<type>::ConstFlat rhs);               \
-  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;
-
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
-#undef REGISTER_GPU_KERNELS
-
 }  // namespace functor
 
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc
index fa518712ce..4655503e26 100644
--- a/tensorflow/core/kernels/strided_slice_op.cc
+++ b/tensorflow/core/kernels/strided_slice_op.cc
@@ -22,7 +22,7 @@ limitations under the License.
 #endif  // GOOGLE_CUDA
 
 #include "tensorflow/core/kernels/strided_slice_op.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/slice_op.h"
 #include "tensorflow/core/kernels/strided_slice_op_impl.h"
 
diff --git a/tensorflow/core/kernels/strided_slice_op_impl.h b/tensorflow/core/kernels/strided_slice_op_impl.h
index d0ccd5c652..de65147572 100644
--- a/tensorflow/core/kernels/strided_slice_op_impl.h
+++ b/tensorflow/core/kernels/strided_slice_op_impl.h
@@ -27,7 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/register_types_traits.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-- 
GitLab


From fe5338177d84d696a02fd5718d649967940855b9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 15:29:07 -0700
Subject: [PATCH 0936/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161727345
---
 tensorflow/go/op/wrappers.go | 154 ++++++++++++++++++++++-------------
 1 file changed, 97 insertions(+), 57 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 208a02762b..efb3b1b07d 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -1514,48 +1514,6 @@ func Slice(scope *Scope, input tf.Output, begin tf.Output, size tf.Output) (outp
 	return op.Output(0)
 }
 
-// ShapeNAttr is an optional argument to ShapeN.
-type ShapeNAttr func(optionalAttr)
-
-// ShapeNOutType sets the optional out_type attribute to value.
-// If not specified, defaults to DT_INT32
-func ShapeNOutType(value tf.DataType) ShapeNAttr {
-	return func(m optionalAttr) {
-		m["out_type"] = value
-	}
-}
-
-// Returns shape of tensors.
-//
-// This operation returns N 1-D integer tensors representing shape of `input[i]s`.
-func ShapeN(scope *Scope, input []tf.Output, optional ...ShapeNAttr) (output []tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "ShapeN",
-		Input: []tf.Input{
-			tf.OutputList(input),
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
-	}
-	var idx int
-	var err error
-	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
-		scope.UpdateErr("ShapeN", err)
-		return
-	}
-	return output
-}
-
 // Checks a tensor for NaN and Inf values.
 //
 // When run, reports an `InvalidArgument` error if `tensor` has any values
@@ -1662,21 +1620,6 @@ func StopGradient(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
-// Return a tensor with the same shape and contents as the input tensor or value.
-func Identity(scope *Scope, input tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Identity",
-		Input: []tf.Input{
-			input,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Gather slices from `params` into a Tensor with shape specified by `indices`.
 //
 // `indices` is an K-dimensional integer tensor, best thought of as a
@@ -5416,6 +5359,103 @@ func DynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged
 	return op.Output(0)
 }
 
+// Return a tensor with the same shape and contents as the input tensor or value.
+func Identity(scope *Scope, input tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Identity",
+		Input: []tf.Input{
+			input,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Converts the given string representing a handle to an iterator to a resource.
+//
+// Arguments:
+//	string_handle: A string representation of the given handle.
+//
+// Returns A handle to an iterator resource.
+func IteratorFromStringHandle(scope *Scope, string_handle tf.Output) (resource_handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "IteratorFromStringHandle",
+		Input: []tf.Input{
+			string_handle,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// ShapeNAttr is an optional argument to ShapeN.
+type ShapeNAttr func(optionalAttr)
+
+// ShapeNOutType sets the optional out_type attribute to value.
+// If not specified, defaults to DT_INT32
+func ShapeNOutType(value tf.DataType) ShapeNAttr {
+	return func(m optionalAttr) {
+		m["out_type"] = value
+	}
+}
+
+// Returns shape of tensors.
+//
+// This operation returns N 1-D integer tensors representing shape of `input[i]s`.
+func ShapeN(scope *Scope, input []tf.Output, optional ...ShapeNAttr) (output []tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ShapeN",
+		Input: []tf.Input{
+			tf.OutputList(input),
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	if scope.Err() != nil {
+		return
+	}
+	var idx int
+	var err error
+	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
+		scope.UpdateErr("ShapeN", err)
+		return
+	}
+	return output
+}
+
+// Converts the given `resource_handle` representing an iterator to a string.
+//
+// Arguments:
+//	resource_handle: A handle to an iterator resource.
+//
+// Returns A string representation of the given handle.
+func IteratorToStringHandle(scope *Scope, resource_handle tf.Output) (string_handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "IteratorToStringHandle",
+		Input: []tf.Input{
+			resource_handle,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // QueueCloseV2Attr is an optional argument to QueueCloseV2.
 type QueueCloseV2Attr func(optionalAttr)
 
-- 
GitLab


From 8f2de3847fdab53926b940fee99d7b95e7dfc6c7 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Wed, 12 Jul 2017 15:45:25 -0700
Subject: [PATCH 0937/1375] TFTS: Re-enable pip GPU tests

I believe these were fixed with cl/161157061

tensorflow-cl-gpu-pip passing: https://ci.tensorflow.org/job/tensorflow-cl-presubmit-multijob/14043/

PiperOrigin-RevId: 161729658
---
 tensorflow/contrib/timeseries/examples/BUILD  |  9 --------
 .../timeseries/python/timeseries/BUILD        |  3 ---
 .../python/timeseries/ar_model_test.py        |  6 +++---
 .../timeseries/state_space_models/BUILD       | 21 -------------------
 4 files changed, 3 insertions(+), 36 deletions(-)

diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD
index c6ffc1ac00..fc18664722 100644
--- a/tensorflow/contrib/timeseries/examples/BUILD
+++ b/tensorflow/contrib/timeseries/examples/BUILD
@@ -33,9 +33,6 @@ py_test(
         "data/period_trend.csv",
     ],
     srcs_version = "PY2AND3",
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
     deps = [
         ":predict",
     ],
@@ -64,9 +61,6 @@ py_test(
         "known_anomaly_test.py",
     ],
     srcs_version = "PY2AND3",
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
     deps = [
         ":known_anomaly",
     ],
@@ -96,9 +90,6 @@ py_test(
         "multivariate_test.py",
     ],
     srcs_version = "PY2AND3",
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
     deps = [
         ":multivariate",
     ],
diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index 130254f40f..e854910f85 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -269,9 +269,6 @@ py_test(
         "ar_model_test.py",
     ],
     srcs_version = "PY2AND3",
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
     deps = [
         ":ar_model",
         ":estimators",
diff --git a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
index f5f86ae1ad..1e1ca4e77f 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
@@ -279,14 +279,14 @@ class ARModelTest(test.TestCase):
         variables.global_variables_initializer().run()
         raw_evaluation_evaled, chunked_evaluation_evaled = sess.run(
             [raw_evaluation, chunked_evaluation])
-        self.assertAllEqual(chunked_evaluation_evaled.loss,
+        self.assertAllClose(chunked_evaluation_evaled.loss,
                             raw_evaluation_evaled.loss)
         last_chunk_evaluation_state = [
             state[-1, None] for state in
             chunked_evaluation_evaled.end_state]
         for last_chunk_state_member, raw_state_member in zip(
             last_chunk_evaluation_state, raw_evaluation_evaled.end_state):
-          self.assertAllEqual(last_chunk_state_member, raw_state_member)
+          self.assertAllClose(last_chunk_state_member, raw_state_member)
         self.assertAllEqual([[5, 7, 11]],
                             raw_evaluation_evaled.prediction_times)
         for feature_name in raw_evaluation.predictions:
@@ -294,7 +294,7 @@ class ARModelTest(test.TestCase):
               [1, 3, 1],  # batch, window, num_features. The window size has 2
                           # cut off for the first input_window.
               raw_evaluation_evaled.predictions[feature_name].shape)
-          self.assertAllEqual(
+          self.assertAllClose(
               np.reshape(chunked_evaluation_evaled.predictions[feature_name],
                          [-1]),
               np.reshape(raw_evaluation_evaled.predictions[feature_name],
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
index 236797fbf7..57ff15e00d 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -44,9 +44,6 @@ py_test(
         "state_space_model_test.py",
     ],
     srcs_version = "PY2AND3",
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
     deps = [
         ":kalman_filter",
         ":state_space_model",
@@ -101,9 +98,6 @@ tf_py_test(
         "//tensorflow/python:dtypes",
         "//tensorflow/python:math_ops",
     ],
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
 )
 
 py_library(
@@ -138,9 +132,6 @@ tf_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
     ],
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
 )
 
 py_library(
@@ -171,9 +162,6 @@ tf_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
     ],
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
 )
 
 py_library(
@@ -198,9 +186,6 @@ py_test(
         "structural_ensemble_test.py",
     ],
     srcs_version = "PY2AND3",
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
     deps = [
         ":state_space_model",
         ":structural_ensemble",
@@ -248,9 +233,6 @@ tf_py_test(
         "//tensorflow/python:variables",
         "//tensorflow/python/estimator:estimator_py",
     ],
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
 )
 
 py_library(
@@ -281,9 +263,6 @@ tf_py_test(
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
     ],
-    tags = [
-        "no_pip_gpu",  # b/63391119
-    ],
 )
 
 py_library(
-- 
GitLab


From 9526b27d7d904acc9e1a7a1990e1320235a8720c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 15:48:31 -0700
Subject: [PATCH 0938/1375] [TF:XLA] Implementing ResourceGather in TF2XLA.

PiperOrigin-RevId: 161730154
---
 .../compiler/tests/variable_ops_test.py       | 48 ++++++++++++++
 .../compiler/tf2xla/kernels/stack_ops.cc      | 15 +----
 .../tf2xla/kernels/tensor_array_ops.cc        | 23 +++----
 .../compiler/tf2xla/kernels/variable_ops.cc   | 65 +++++++++++++++++++
 tensorflow/compiler/tf2xla/xla_helpers.cc     |  9 +++
 tensorflow/compiler/tf2xla/xla_helpers.h      |  5 ++
 6 files changed, 138 insertions(+), 27 deletions(-)

diff --git a/tensorflow/compiler/tests/variable_ops_test.py b/tensorflow/compiler/tests/variable_ops_test.py
index 70dacd9de4..a6b59fc731 100644
--- a/tensorflow/compiler/tests/variable_ops_test.py
+++ b/tensorflow/compiler/tests/variable_ops_test.py
@@ -54,6 +54,54 @@ class VariableOpsTest(XLATestCase):
         self.assertAllClose(np.array([[2, 3], [4, 5]], dtype=dtype),
                             sess.run(y, {p: 1}))
 
+  def testSparseRead0DIndices(self):
+    for dtype in self.numeric_types:
+      init = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype)
+      with self.test_session() as sess, self.test_scope():
+        v = resource_variable_ops.ResourceVariable(init)
+        sess.run(variables.variables_initializer([v]))
+        x = v.sparse_read(2)
+        self.assertAllClose(np.array([8, 9, 10, 11], dtype=dtype), sess.run(x))
+
+  def testSparseRead1DIndices(self):
+    for dtype in self.numeric_types:
+      init = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype)
+      with self.test_session() as sess, self.test_scope():
+        v = resource_variable_ops.ResourceVariable(init)
+        sess.run(variables.variables_initializer([v]))
+        x = v.sparse_read([2, 1])
+        self.assertAllClose(
+            np.array([[8, 9, 10, 11], [4, 5, 6, 7]], dtype=dtype), sess.run(x))
+
+  def testSparseRead2DIndices(self):
+    for dtype in self.numeric_types:
+      init = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype)
+      with self.test_session() as sess, self.test_scope():
+        v = resource_variable_ops.ResourceVariable(init)
+        sess.run(variables.variables_initializer([v]))
+        x = v.sparse_read([[2, 1], [0, 2]])
+        self.assertAllClose(
+            np.array(
+                [[[8, 9, 10, 11], [4, 5, 6, 7]], [[0, 1, 2, 3], [8, 9, 10,
+                                                                 11]]],
+                dtype=dtype), sess.run(x))
+
+  def testSparseRead2DIndices3DTensor(self):
+    for dtype in self.numeric_types:
+      init = np.array(
+          [[[0, 1, 2], [3, 4, 5]], [[10, 11, 12], [13, 14, 15]],
+           [[20, 21, 22], [23, 24, 25]], [[30, 31, 32], [33, 34, 35]]],
+          dtype=dtype)
+      with self.test_session() as sess, self.test_scope():
+        v = resource_variable_ops.ResourceVariable(init)
+        sess.run(variables.variables_initializer([v]))
+        x = v.sparse_read([[2, 1], [3, 0]])
+        self.assertAllClose(
+            np.array(
+                [[[[20, 21, 22], [23, 24, 25]], [[10, 11, 12], [13, 14, 15]]],
+                 [[[30, 31, 32], [33, 34, 35]], [[0, 1, 2], [3, 4, 5]]]],
+                dtype=dtype), sess.run(x))
+
   def testReadWrite(self):
     """Tests initialization, reading, and writing a resource variable."""
     with self.test_session() as session:
diff --git a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc
index 6a6f65c02c..5d1394c280 100644
--- a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc
@@ -92,16 +92,6 @@ Status MaybeInitializeStack(xla::ComputationBuilder* builder,
   return Status::OK();
 }
 
-// Pads 'x' with 'count' zero indices. 'x' must have 1 element.
-xla::ComputationDataHandle PadIndexWithZeros(
-    xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x,
-    int count) {
-  xla::ComputationDataHandle zero = builder->ConstantR1<int32>({0});
-  std::vector<xla::ComputationDataHandle> xs(count + 1, zero);
-  xs[0] = builder->Reshape(x, {1});
-  return builder->ConcatInDim(xs, 0);
-}
-
 class StackOp : public XlaOpKernel {
  public:
   explicit StackOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
@@ -160,7 +150,7 @@ class StackPushOp : public XlaOpKernel {
     xla::ComputationDataHandle value = ctx->Input(1);
 
     // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
-    auto start_indices = PadIndexWithZeros(b, index, elem_shape.dims());
+    auto start_indices = XlaHelpers::PadWithZeros(b, index, elem_shape.dims());
 
     TensorShape slice_shape = elem_shape;
     slice_shape.InsertDim(0, 1LL);
@@ -218,7 +208,8 @@ class StackPopOp : public XlaOpKernel {
     resource->value = b->Tuple({ta, index});
 
     // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
-    auto start_indices = PadIndexWithZeros(b, index, stack_shape.dims() - 1);
+    auto start_indices =
+        XlaHelpers::PadWithZeros(b, index, stack_shape.dims() - 1);
 
     auto slice_shape = stack_shape.dim_sizes();
     slice_shape[0] = 1LL;
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index d720496f74..bdd52b7f8e 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -125,16 +125,6 @@ Status GetTensorArrayShape(const XlaResource* resource,
   return Status::OK();
 }
 
-// Pads 'x' with 'count' zero indices. 'x' must have 1 element.
-xla::ComputationDataHandle PadIndexWithZeros(
-    xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x,
-    int count) {
-  xla::ComputationDataHandle zero = builder->ConstantR1<int32>({0});
-  std::vector<xla::ComputationDataHandle> xs(count + 1, zero);
-  xs[0] = builder->Reshape(x, {1});
-  return builder->ConcatInDim(xs, 0);
-}
-
 // Like ComputationBuilder::DynamicUpdateSlice, but adds 'update' to the
 // relevant slice of 'operand'.
 xla::ComputationDataHandle DynamicAddSlice(
@@ -228,7 +218,7 @@ class TensorArrayWriteOp : public XlaOpKernel {
     xla::ComputationDataHandle value = ctx->Input(2);
 
     // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
-    auto start_indices = PadIndexWithZeros(b, index, elem_shape.dims());
+    auto start_indices = XlaHelpers::PadWithZeros(b, index, elem_shape.dims());
 
     TensorShape slice_shape = elem_shape;
     slice_shape.InsertDim(0, 1LL);
@@ -270,7 +260,8 @@ class TensorArrayReadOp : public XlaOpKernel {
     xla::ComputationDataHandle index = ctx->Input(1);
 
     // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
-    auto start_indices = PadIndexWithZeros(b, index, ta_shape.dims() - 1);
+    auto start_indices =
+        XlaHelpers::PadWithZeros(b, index, ta_shape.dims() - 1);
 
     auto slice_shape = ta_shape.dim_sizes();
     slice_shape[0] = 1LL;
@@ -309,7 +300,7 @@ class TensorArrayGatherOp : public XlaOpKernel {
     OP_REQUIRES_OK(ctx, GetTensorArrayShape(resource, b, &ta_shape));
 
     const TensorShape indices_shape = ctx->InputShape(1);
-    OP_REQUIRES(ctx, indices_shape.dims() >= 1,
+    OP_REQUIRES(ctx, indices_shape.dims() == 1,
                 errors::InvalidArgument("indices must be rank 1"));
     const int num_indices = indices_shape.dim_size(0);
     auto indices = ctx->Input(1);
@@ -324,7 +315,8 @@ class TensorArrayGatherOp : public XlaOpKernel {
       auto index = b->Slice(indices, {i}, {i + 1}, {1});
 
       // start_indices of the DynamicSlice are [index, 0, 0, ..., 0].
-      auto start_indices = PadIndexWithZeros(b, index, ta_shape.dims() - 1);
+      auto start_indices =
+          XlaHelpers::PadWithZeros(b, index, ta_shape.dims() - 1);
 
       auto slice_shape = ta_shape.dim_sizes();
       slice_shape[0] = 1LL;
@@ -396,7 +388,8 @@ class TensorArrayScatterOp : public XlaOpKernel {
 
       // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
       auto index = b->Slice(indices, {i}, {i + 1}, {1});
-      auto start_indices = PadIndexWithZeros(b, index, elem_shape.dims());
+      auto start_indices =
+          XlaHelpers::PadWithZeros(b, index, elem_shape.dims());
       ta = DynamicAddSlice(b, ta, slice, slice_dims, start_indices);
     }
 
diff --git a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc
index 1b04b8b802..0eea81b308 100644
--- a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/tf2xla/xla_helpers.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
@@ -86,5 +87,69 @@ REGISTER_XLA_OP(
     Name("AssignSubVariableOp").TypeConstraint("dtype", kNumericTypes),
     AssignSubVariableOp);
 
+class ResourceGatherOp : public XlaOpKernel {
+ public:
+  explicit ResourceGatherOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+  void Compile(XlaOpKernelContext* ctx) override {
+    xla::ComputationBuilder* builder = ctx->builder();
+
+    // Get the shape of the resource tensor.
+    TensorShape resource_shape;
+    DataType resource_dtype;
+    OP_REQUIRES_OK(
+        ctx, ctx->GetVariableTypeAndShape(0, &resource_dtype, &resource_shape));
+
+    DataType expected_output_dtype = ctx->expected_output_dtype(0);
+    OP_REQUIRES(ctx, resource_dtype == expected_output_dtype,
+                errors::InvalidArgument(
+                    "Variable dtype is ", DataTypeString(resource_dtype),
+                    " but expected output dtype is ",
+                    DataTypeString(expected_output_dtype), "."));
+
+    xla::ComputationDataHandle resource_handle;
+    OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, &resource_handle));
+
+    auto indices = ctx->Input(1);
+    auto indices_shape = ctx->InputShape(1);
+    const int num_indices = indices_shape.num_elements();
+
+    // Flatten the indices into 1-D.
+    auto indices_1d = builder->Reshape(indices, {num_indices});
+
+    // Compute the slice for each of these indices separately.
+    std::vector<xla::ComputationDataHandle> slices(num_indices);
+    for (int i = 0; i < num_indices; ++i) {
+      auto index = builder->Slice(indices_1d, {i}, {i + 1}, {1});
+
+      auto start_indices =
+          XlaHelpers::PadWithZeros(builder, index, resource_shape.dims() - 1);
+
+      auto slice_shape = resource_shape.dim_sizes();
+      slice_shape[0] = 1LL;
+
+      slices[i] =
+          builder->DynamicSlice(resource_handle, start_indices, slice_shape);
+    }
+
+    // Concatenate the slices into one tensor.
+    xla::ComputationDataHandle concat = builder->ConcatInDim(slices, 0);
+
+    // Compute the shape of the result tensor, which is:
+    //    indices.shape + resource.shape[1:]
+    TensorShape gather_shape = indices_shape;
+    gather_shape.AppendShape(resource_shape);
+    gather_shape.RemoveDim(indices_shape.dims());
+
+    // Reshape the concatenated slices into the shape expected of the result
+    // tensor.
+    xla::ComputationDataHandle gather =
+        builder->Reshape(concat, gather_shape.dim_sizes());
+
+    ctx->SetOutput(0, gather);
+  }
+};
+REGISTER_XLA_OP(Name("ResourceGather").TypeConstraint("dtype", kNumericTypes),
+                ResourceGatherOp);
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc
index 2366c02dd2..3af866f9be 100644
--- a/tensorflow/compiler/tf2xla/xla_helpers.cc
+++ b/tensorflow/compiler/tf2xla/xla_helpers.cc
@@ -205,4 +205,13 @@ Status XlaHelpers::OneHot(xla::ComputationBuilder* builder, int64 depth,
   return Status::OK();
 }
 
+xla::ComputationDataHandle XlaHelpers::PadWithZeros(
+    xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x,
+    int count) {
+  xla::ComputationDataHandle zero = builder->ConstantR1<int32>({0});
+  std::vector<xla::ComputationDataHandle> xs(count + 1, zero);
+  xs[0] = builder->Reshape(x, {1});
+  return builder->ConcatInDim(xs, 0);
+}
+
 }  // end namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/xla_helpers.h b/tensorflow/compiler/tf2xla/xla_helpers.h
index a141ee05c1..2166ce3636 100644
--- a/tensorflow/compiler/tf2xla/xla_helpers.h
+++ b/tensorflow/compiler/tf2xla/xla_helpers.h
@@ -77,6 +77,11 @@ class XlaHelpers {
                        const xla::ComputationDataHandle& on_value,
                        const xla::ComputationDataHandle& off_value,
                        xla::ComputationDataHandle* one_hot);
+
+  // Pads 'x' with 'count' zeros. 'x' must have 1 element.
+  static xla::ComputationDataHandle PadWithZeros(
+      xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x,
+      int count);
 };
 
 }  // end namespace tensorflow
-- 
GitLab


From 8337fb50cf36f41fa309833d5b12f3d686c89cb3 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 12 Jul 2017 16:02:29 -0700
Subject: [PATCH 0939/1375] Add a description of "feedable iterators" to the
 Datasets programmers' guide.

This is a potential solution to issue #2514.

PiperOrigin-RevId: 161732107
---
 .../docs_src/programmers_guide/datasets.md    | 66 ++++++++++++++++---
 1 file changed, 58 insertions(+), 8 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md
index 435e566e30..ddf5388ac2 100644
--- a/tensorflow/docs_src/programmers_guide/datasets.md
+++ b/tensorflow/docs_src/programmers_guide/datasets.md
@@ -17,14 +17,14 @@ The `Dataset` API introduces two new abstractions to TensorFlow:
   pipeline, an element might be a single training example, with a pair of
   tensors representing the image data and a label. There are two distinct
   ways to create a dataset:
-  
-  * Creating a **source* (e.g. `Dataset.from_tensor_slices()`) constructs a
+
+  * Creating a **source** (e.g. `Dataset.from_tensor_slices()`) constructs a
     dataset from
     one or more `tf.Tensor` objects.
-  
+
   * Applying a **transformation** (e.g. `Dataset.batch()`) constructs a dataset
     from one or more `tf.contrib.data.Dataset` objects.
-  
+
 * A `tf.contrib.data.Iterator` provides the main way to extract elements from a
   dataset. The operation returned by `Iterator.get_next()` yields the next
   element of a `Dataset` when executed, and typically acts as the interface
@@ -49,7 +49,7 @@ data are on disk in the recommend TFRecord format, you can construct a
 
 Once you have a `Dataset` object, you can *transform* it into a new `Dataset` by
 chaining method calls on the `tf.contrib.data.Dataset` object. For example, you
-c an apply per-element transformations such as `Dataset.map()` (to apply a
+can apply per-element transformations such as `Dataset.map()` (to apply a
 function to each element), and multi-element transformations such as
 `Dataset.batch()`. See the documentation for @{tf.contrib.data.Dataset}
 for a complete list of transformations.
@@ -126,8 +126,9 @@ currently supports three kinds of iterator, in increasing level of
 sophistication:
 
 * **one-shot**,
-* **initializable**, and
-* **reinitializable**.
+* **initializable**,
+* **reinitializable**, and
+* **feedable**.
 
 A **one-shot** iterator is the simplest form of iterator, which only supports
 iterating once through a dataset, with no need for explicit initialization.
@@ -178,6 +179,7 @@ pipelines will typically use different `Dataset` objects that have the same
 structure (i.e. the same types and compatible shapes for each component).
 
 ```python
+# Define training and validation datasets with the same structure.
 training_dataset = tf.contrib.data.Dataset.range(100).map(
     lambda x: x + tf.random_uniform([], -10, 10, tf.int64))
 validation_dataset = tf.contrib.data.Dataset.range(50)
@@ -206,6 +208,54 @@ for _ in range(20):
     sess.run(next_element)
 ```
 
+A **feedable** iterator can be used together with @{tf.placeholder} to select
+what `Iterator` to use in each call to @{tf.Session.run}, via the familiar
+`feed_dict` mechanism. It offers the same functionality as a reinitializable
+iterator, but it does not require you to initialize the iterator from the start
+of a dataset when you switch between iterators. For example, using the same
+training and validation example from above, you can use
+@{tf.contrib.data.Iterator.from_string_handle} to define a feedable iterator
+that allows you to switch between the two datasets:
+
+```python
+# Define training and validation datasets with the same structure.
+training_dataset = tf.contrib.data.Dataset.range(100).map(
+    lambda x: x + tf.random_uniform([], -10, 10, tf.int64)).repeat()
+validation_dataset = tf.contrib.data.Dataset.range(50)
+
+# A feedable iterator is defined by a handle placeholder and its structure. We
+# could use the `output_types` and `output_shapes` properties of either
+# `training_dataset` or `validation_dataset` here, because they have
+# identical structure.
+handle = tf.placeholder(tf.string, shape=[])
+iterator = tf.contrib.data.Iterator.from_string_handle(
+    handle, training_dataset.output_types, training_dataset.output_shapes)
+next_element = iterator.get_next()
+
+# You can use feedable iterators with a variety of different kinds of iterator
+# (such as one-shot and initializable iterators).
+training_iterator = training_dataset.make_one_shot_iterator()
+validation_iterator = validation_dataset.make_initializable_iterator()
+
+# The `Iterator.string_handle()` method returns a tensor that can be evaluated
+# and used to feed the `handle` placeholder.
+training_handle = sess.run(training_iterator.string_handle())
+validation_handle = sess.run(validation_iterator.string_handle())
+
+# Loop forever, alternating between training and validation.
+while True:
+  # Run 200 steps using the training dataset. Note that the training dataset is
+  # infinite, and we resume from where we left off in the previous `while` loop
+  # iteration.
+  for _ in range(200):
+    sess.run(next_element, feed_dict={handle: training_handle})
+
+  # Run one pass over the validation dataset.
+  sess.run(validation_iterator.initializer)
+  for _ in range(50):
+    sess.run(next_element, feed_dict={handle: validation_handle})
+```
+
 ### Consuming values from an iterator
 
 The `Iterator.get_next()` method returns one or more `tf.Tensor` objects that
@@ -682,7 +732,7 @@ def dataset_input_fn():
     image = tf.decode_jpeg(parsed["image_data"])
     image = tf.reshape(image, [299, 299, 1])
     label = tf.cast(parsed["label"], tf.int32)
-    
+
     return {"image_data": image, "date_time": parsed["date_time"]}, label
 
   # Use `Dataset.map()` to build a pair of a feature dictionary and a label 
-- 
GitLab


From bbe22bb053d7fef734e4465f22e8101b0bf5efbc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 16:39:35 -0700
Subject: [PATCH 0940/1375] Change HloBuffer, HloBufferSet and HloValueSet to
 hold pointers rather than ids.

This makes it easier to implement logic like returning the size of an HloBuffer,
which requires knowing the underlying HloValues.

No functional changes; only a change of representation.

PiperOrigin-RevId: 161737042
---
 .../xla/service/hlo_alias_analysis.cc         | 66 +++++++----------
 .../compiler/xla/service/hlo_alias_analysis.h | 12 ++--
 .../xla/service/hlo_alias_analysis_test.cc    | 72 ++++++++-----------
 tensorflow/compiler/xla/service/hlo_buffer.cc | 42 ++++++-----
 tensorflow/compiler/xla/service/hlo_buffer.h  | 38 +++++++---
 .../xla/service/hlo_dataflow_analysis.cc      | 60 ++++++++--------
 .../xla/service/hlo_dataflow_analysis.h       | 15 ++--
 .../xla/service/hlo_dataflow_analysis_test.cc |  6 +-
 tensorflow/compiler/xla/service/hlo_value.cc  | 28 ++++++--
 tensorflow/compiler/xla/service/hlo_value.h   | 40 ++++++++---
 10 files changed, 204 insertions(+), 175 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
index 321c48857b..0c03d72752 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
@@ -41,13 +41,13 @@ using ::tensorflow::strings::StrCat;
 HloAliasAnalysis::HloAliasAnalysis(HloModule* module) : module_(module) {}
 
 void HloAliasAnalysis::InitializeBufferSets() {
-  std::unordered_map<HloValue::Id, HloBuffer::Id> value_to_buffer;
+  std::unordered_map<HloValue::Id, const HloBuffer*> value_to_buffer;
 
   // Initially define a buffer for every HloValue in the module.
   for (const HloValue* value : dataflow_analysis_->values()) {
-    HloBuffer& buffer = NewHloBuffer();
-    buffer.AddValue(*value);
-    value_to_buffer[value->id()] = buffer.id();
+    HloBuffer* buffer = NewHloBuffer();
+    buffer->AddValue(*value);
+    value_to_buffer[value->id()] = buffer;
   }
 
   // Construct the Instruction buffer set to contain the HloBuffers for each
@@ -61,9 +61,9 @@ void HloAliasAnalysis::InitializeBufferSets() {
           .ForEachElement(
               [this, &instruction, &value_to_buffer](
                   const ShapeIndex& index, const HloValueSet& value_set) {
-                for (HloValue::Id value_id : value_set.value_ids()) {
-                  HloBuffer::Id buffer_id = value_to_buffer.at(value_id);
-                  GetBufferSet(instruction.get(), index).AddBuffer(buffer_id);
+                for (const HloValue* value : value_set.values()) {
+                  const HloBuffer* buffer = value_to_buffer.at(value->id());
+                  GetBufferSet(instruction.get(), index).AddBuffer(buffer);
                 }
               });
     }
@@ -90,8 +90,8 @@ void HloAliasAnalysis::CombineBuffers(
     VLOG(4) << "Eliminating buffer: " << buffer_id;
 
     // Add all values held by the buffer-to-eliminate to the unified buffer.
-    for (HloValue::Id value_id : buffer.value_ids()) {
-      unified_buffer.AddValue(dataflow_analysis_->GetValue(value_id));
+    for (const HloValue* value : buffer.values()) {
+      unified_buffer.AddValue(*value);
     }
 
     // Iterate through all positions where the buffer-to-eliminate exists and
@@ -101,7 +101,7 @@ void HloAliasAnalysis::CombineBuffers(
       GetBufferSet(position.instruction, position.index)
           .RemoveBufferOrDie(buffer_id);
       GetBufferSet(position.instruction, position.index)
-          .AddBuffer(unified_buffer.id());
+          .AddBuffer(&unified_buffer);
     }
 
     buffers_.erase(buffer_id);
@@ -120,9 +120,9 @@ Status HloAliasAnalysis::Verify() const {
     TF_RETURN_IF_ERROR(instruction_buffer_set.ForEachElementWithStatus(
         [this, &buffers_in_sets](const ShapeIndex& index,
                                  const HloBufferSet& buffer_set) -> Status {
-          for (HloBuffer::Id buffer_id : buffer_set.buffer_ids()) {
-            TF_RET_CHECK(ContainsKey(buffers_, buffer_id));
-            buffers_in_sets.insert(buffer_id);
+          for (const HloBuffer* buffer : buffer_set.buffers()) {
+            TF_RET_CHECK(ContainsKey(buffers_, buffer->id()));
+            buffers_in_sets.insert(buffer->id());
           }
           return Status::OK();
         }));
@@ -154,10 +154,11 @@ void HloAliasAnalysis::FlattenInstructionBufferSets(
         std::vector<HloBuffer::Id> to_unify;
         for (const HloInstruction* instruction : instructions) {
           const HloBufferSet& buffer_set = GetBufferSet(instruction, index);
-          to_unify.insert(to_unify.end(), buffer_set.buffer_ids().begin(),
-                          buffer_set.buffer_ids().end());
+          for (const HloBuffer* buffer : buffer_set.buffers()) {
+            to_unify.push_back(buffer->id());
+          }
         }
-        // Sort and uniquify buffers to combine.
+        // Sort and uniquify buffer ids to combine.
         std::sort(to_unify.begin(), to_unify.end());
         to_unify.erase(std::unique(to_unify.begin(), to_unify.end()),
                        to_unify.end());
@@ -166,14 +167,13 @@ void HloAliasAnalysis::FlattenInstructionBufferSets(
       });
 }
 
-HloBuffer& HloAliasAnalysis::NewHloBuffer() {
+HloBuffer* HloAliasAnalysis::NewHloBuffer() {
   HloBuffer::Id buffer_id = next_buffer_id_++;
-  auto it_added = buffers_.emplace(std::piecewise_construct,
+  auto emplaced = buffers_.emplace(std::piecewise_construct,
                                    std::forward_as_tuple(buffer_id),
                                    std::forward_as_tuple(buffer_id));
-  CHECK(it_added.second);
-
-  return it_added.first->second;
+  CHECK(emplaced.second);
+  return &emplaced.first->second;
 }
 
 string HloAliasAnalysis::ToString() const {
@@ -184,33 +184,17 @@ string HloAliasAnalysis::ToString() const {
     for (const std::unique_ptr<HloInstruction>& instruction :
          computation->instructions()) {
       StrAppend(&out, "    ", instruction->name(), ":\n");
-      auto buffer_str = [this](const HloBuffer& buffer) {
-        return StrCat(
-            "Buffer ", buffer.id(), ", values: ",
-            Join(buffer.value_ids(), ", ",
-                 [this](string* out, HloValue::Id value_id) {
-                   StrAppend(
-                       out,
-                       dataflow_analysis_->GetValue(value_id).ToShortString());
-                 }));
-      };
       if (ShapeUtil::IsTuple(instruction->shape())) {
         GetInstructionBufferSet(instruction.get())
-            .ForEachElement([this, &out, &buffer_str](
-                                const ShapeIndex& index,
-                                const HloBufferSet& buffer_set) {
+            .ForEachElement([this, &out](const ShapeIndex& index,
+                                         const HloBufferSet& buffer_set) {
               StrAppend(&out, "      tuple index ", index.ToString(), ":\n");
-              for (HloBuffer::Id buffer_id : buffer_set.buffer_ids()) {
-                StrAppend(&out, "        ", buffer_str(GetBuffer(buffer_id)),
-                          "\n");
-              }
+              StrAppend(&out, "        ", buffer_set.ToString(), "\n");
             });
       } else {
         const HloBufferSet top_level_buffer_set =
             GetBufferSet(instruction.get());
-        for (HloBuffer::Id buffer_id : top_level_buffer_set.buffer_ids()) {
-          StrAppend(&out, "      ", buffer_str(GetBuffer(buffer_id)), "\n");
-        }
+        StrAppend(&out, "      ", top_level_buffer_set.ToString(), "\n");
       }
     }
   }
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.h b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
index 101ff63f34..c70ec38c99 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.h
@@ -63,11 +63,11 @@ class HloAliasAnalysis {
   // set at that position does not contain exactly one buffer.
   const HloBuffer& GetUniqueBufferAt(const HloInstruction* instruction,
                                      const ShapeIndex& index = {}) const {
-    return GetBuffer(GetBufferSet(instruction, index).GetUniqueBufferId());
+    return GetBufferSet(instruction, index).GetUniqueBuffer();
   }
   HloBuffer& GetUniqueBufferAt(const HloInstruction* instruction,
                                const ShapeIndex& index = {}) {
-    return GetBuffer(GetBufferSet(instruction, index).GetUniqueBufferId());
+    return GetBuffer(GetBufferSet(instruction, index).GetUniqueBuffer().id());
   }
 
   // Return a vector of all HloBuffers stabily sorted by HloBuffer::Id. This
@@ -83,8 +83,8 @@ class HloAliasAnalysis {
  protected:
   HloAliasAnalysis(HloModule* module);
 
-  // Creates a new HloBuffer and returns a reference to it.
-  HloBuffer& NewHloBuffer();
+  // Returns a new HloBuffer.
+  HloBuffer* NewHloBuffer();
 
   // Construct the initial set of buffer sets where an HloBuffer is created for
   // each HloValue in the module.
@@ -123,7 +123,9 @@ class HloAliasAnalysis {
   // The underlying dataflow analysis used by this alias analysis.
   std::unique_ptr<HloDataflowAnalysis> dataflow_analysis_;
 
-  // The map of all HloBuffers in the module.
+  // The map of all HloBuffers in the module. We pass around pointers to the
+  // mapped HloBuffers, so the underlying container must keep them valid despite
+  // mutations touching other map entries.
   std::unordered_map<HloBuffer::Id, HloBuffer> buffers_;
 
   // A map from instruction to its InstructionBufferSet.
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
index d6023127ad..3c5b2e03b7 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc
@@ -50,9 +50,9 @@ class HloAliasAnalysisTest : public HloTestBase {
   std::vector<HloBuffer> GetBuffersAt(const HloInstruction* instruction,
                                       const ShapeIndex& index = {}) const {
     std::vector<HloBuffer> buffers;
-    for (HloBuffer::Id buffer_id :
-         analysis_->GetBufferSet(instruction, index).buffer_ids()) {
-      buffers.push_back(analysis_->GetBuffer(buffer_id));
+    for (const HloBuffer* buffer :
+         analysis_->GetBufferSet(instruction, index).buffers()) {
+      buffers.push_back(*buffer);
     }
     return buffers;
   }
@@ -60,8 +60,8 @@ class HloAliasAnalysisTest : public HloTestBase {
   // Return a vector containing all of the HloValues in the given buffer.
   std::vector<HloValue> GetValuesInBuffer(const HloBuffer& buffer) {
     std::vector<HloValue> values;
-    for (HloValue::Id value_id : buffer.value_ids()) {
-      values.push_back(analysis_->dataflow_analysis().GetValue(value_id));
+    for (const HloValue* value : buffer.values()) {
+      values.push_back(*value);
     }
     return values;
   }
@@ -72,11 +72,6 @@ class HloAliasAnalysisTest : public HloTestBase {
     return analysis_->dataflow_analysis().GetValueDefinedAt(instruction, index);
   }
 
-  const HloValue& GetUniqueValueInBuffer(const HloBuffer& buffer) const {
-    CHECK_EQ(buffer.value_ids().size(), 1);
-    return analysis_->dataflow_analysis().GetValue(buffer.value_ids()[0]);
-  }
-
   // Returns true if any values held in the same buffer interfere. Generally, in
   // the compiler pipeline copy-insertion will guarantee that this interference
   // never occurs, but HLO graphs with interference can be explicitly
@@ -84,14 +79,12 @@ class HloAliasAnalysisTest : public HloTestBase {
   bool AnyValuesInSameBufferInterfere() {
     DependencyHloOrdering ordering(module_.get());
     for (const HloBuffer* buffer : analysis_->buffers()) {
-      for (HloValue::Id value_id_a : buffer->value_ids()) {
-        for (HloValue::Id value_id_b : buffer->value_ids()) {
-          if (value_id_a != value_id_b &&
-              analysis_->dataflow_analysis().MayInterfere(
-                  value_id_a, value_id_b, ordering)) {
-            VLOG(1) << analysis_->dataflow_analysis().GetValue(value_id_a)
-                    << " interferes with "
-                    << analysis_->dataflow_analysis().GetValue(value_id_b)
+      for (const HloValue* value_a : buffer->values()) {
+        for (const HloValue* value_b : buffer->values()) {
+          if (*value_a != *value_b &&
+              analysis_->dataflow_analysis().MayInterfere(*value_a, *value_b,
+                                                          ordering)) {
+            VLOG(1) << *value_a << " interferes with " << *value_b
                     << " in buffer: " << *buffer;
             return true;
           }
@@ -125,7 +118,7 @@ TEST_F(HloAliasAnalysisTest, BinaryOperation) {
   // All of the buffer sets should trivially contain a single buffer containing
   // a single value.
   for (const HloInstruction* instruction : {constant1, constant2, add}) {
-    EXPECT_EQ(GetUniqueValueInBuffer(analysis.GetUniqueBufferAt(instruction)),
+    EXPECT_EQ(analysis.GetUniqueBufferAt(instruction).GetUniqueValue(),
               GetValueDefinedAt(instruction));
   }
 
@@ -157,15 +150,12 @@ TEST_F(HloAliasAnalysisTest, TupleAndGtes) {
   EXPECT_EQ(analysis.buffers().size(), 4);
 
   // Verify the expected aliasing of the tuple elements.
-  EXPECT_EQ(
-      GetUniqueValueInBuffer(analysis.GetUniqueBufferAt(tuple, /*index=*/{})),
-      GetValueDefinedAt(tuple, /*index=*/{}));
-  EXPECT_EQ(
-      GetUniqueValueInBuffer(analysis.GetUniqueBufferAt(tuple, /*index=*/{0})),
-      GetValueDefinedAt(param0));
-  EXPECT_EQ(
-      GetUniqueValueInBuffer(analysis.GetUniqueBufferAt(tuple, /*index=*/{1})),
-      GetValueDefinedAt(param1));
+  EXPECT_EQ(analysis.GetUniqueBufferAt(tuple, /*index=*/{}).GetUniqueValue(),
+            GetValueDefinedAt(tuple, /*index=*/{}));
+  EXPECT_EQ(analysis.GetUniqueBufferAt(tuple, /*index=*/{0}).GetUniqueValue(),
+            GetValueDefinedAt(param0));
+  EXPECT_EQ(analysis.GetUniqueBufferAt(tuple, /*index=*/{1}).GetUniqueValue(),
+            GetValueDefinedAt(param1));
 
   // The tuple operand, tuple element, and result of the GTE instruction should
   // all be the same buffer.
@@ -682,19 +672,19 @@ TEST_F(HloAliasAnalysisTest, TupleSelect) {
   const HloAliasAnalysis& analysis = RunAnalysis();
 
   // Verify the buffer sets of each select.
-  EXPECT_THAT(analysis.GetBufferSet(select11, /*index=*/{0}).buffer_ids(),
-              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant1).id()));
-  EXPECT_THAT(analysis.GetBufferSet(select12, /*index=*/{0}).buffer_ids(),
-              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant1).id(),
-                                   analysis.GetUniqueBufferAt(constant2).id()));
-  EXPECT_THAT(analysis.GetBufferSet(select34, /*index=*/{0}).buffer_ids(),
-              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant3).id(),
-                                   analysis.GetUniqueBufferAt(constant4).id()));
-  EXPECT_THAT(analysis.GetBufferSet(select1234, /*index=*/{0}).buffer_ids(),
-              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant1).id(),
-                                   analysis.GetUniqueBufferAt(constant2).id(),
-                                   analysis.GetUniqueBufferAt(constant3).id(),
-                                   analysis.GetUniqueBufferAt(constant4).id()));
+  EXPECT_THAT(GetBuffersAt(select11, /*index=*/{0}),
+              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant1)));
+  EXPECT_THAT(GetBuffersAt(select12, /*index=*/{0}),
+              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant1),
+                                   analysis.GetUniqueBufferAt(constant2)));
+  EXPECT_THAT(GetBuffersAt(select34, /*index=*/{0}),
+              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant3),
+                                   analysis.GetUniqueBufferAt(constant4)));
+  EXPECT_THAT(GetBuffersAt(select1234, /*index=*/{0}),
+              UnorderedElementsAre(analysis.GetUniqueBufferAt(constant1),
+                                   analysis.GetUniqueBufferAt(constant2),
+                                   analysis.GetUniqueBufferAt(constant3),
+                                   analysis.GetUniqueBufferAt(constant4)));
 
   EXPECT_FALSE(analysis.GetInstructionBufferSet(select11).IsAmbiguous());
   EXPECT_TRUE(analysis.GetInstructionBufferSet(select12).IsAmbiguous());
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.cc b/tensorflow/compiler/xla/service/hlo_buffer.cc
index 516e3c0473..f1ad5daf79 100644
--- a/tensorflow/compiler/xla/service/hlo_buffer.cc
+++ b/tensorflow/compiler/xla/service/hlo_buffer.cc
@@ -39,13 +39,10 @@ using ::tensorflow::strings::StrCat;
 
 void HloBuffer::AddValue(const HloValue& value) {
   // If the value is already contained in this buffer, just return.
-  if (std::find(value_ids_.begin(), value_ids_.end(), value.id()) !=
-      value_ids_.end()) {
+  if (!values_.AddValue(&value)) {
     return;
   }
 
-  value_ids_.push_back(value.id());
-
   // Add all of the positions of the HloValue to this buffer.
   for (const HloPosition& position : value.positions()) {
     if (std::find(positions_.begin(), positions_.end(), position) ==
@@ -59,14 +56,14 @@ bool HloBuffer::operator==(const HloBuffer& other) const {
   bool equal = id() == other.id();
   if (equal) {
     // DCHECK because these comparisons are expensive (linear time).
-    DCHECK(value_ids() == other.value_ids());
+    DCHECK(values_ == other.values_);
     DCHECK(positions() == other.positions());
   }
   return equal;
 }
 
 string HloBuffer::ToString() const {
-  return StrCat("HloBuffer ", id_, ", values: ", Join(value_ids_, ", "));
+  return StrCat("HloBuffer ", id_, ", values: ", values_.ToString());
 }
 
 std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer) {
@@ -74,21 +71,30 @@ std::ostream& operator<<(std::ostream& out, const HloBuffer& buffer) {
   return out;
 }
 
-void HloBufferSet::AddBuffer(HloBuffer::Id buffer_id) {
-  if (std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id) ==
-      buffer_ids_.end()) {
-    buffer_ids_.push_back(buffer_id);
+void HloBufferSet::AddBuffer(const HloBuffer* buffer) {
+  auto it = std::lower_bound(buffers_.begin(), buffers_.end(), buffer,
+                             HloBuffer::IdLessThan);
+  if (it == buffers_.end() || (*it)->id() != buffer->id()) {
+    buffers_.insert(it, buffer);
   }
 }
 
 void HloBufferSet::RemoveBufferOrDie(HloBuffer::Id buffer_id) {
-  auto it = std::find(buffer_ids_.begin(), buffer_ids_.end(), buffer_id);
-  CHECK(it != buffer_ids_.end());
-  buffer_ids_.erase(it);
+  auto it = std::lower_bound(buffers_.begin(), buffers_.end(), buffer_id,
+                             [](const HloBuffer* buffer, HloBuffer::Id id) {
+                               return buffer->id() < id;
+                             });
+  CHECK(it != buffers_.end() && (*it)->id() == buffer_id)
+      << "HloBuffer " << buffer_id << " doesn't exist in set: " << ToString();
+  buffers_.erase(it);
 }
 
 string HloBufferSet::ToString() const {
-  return StrCat("HloBufferSet, buffers: ", Join(buffer_ids_, ", "));
+  return StrCat(
+      "HloBufferSet, buffers: ",
+      Join(buffers_, ", ", [](string* result, const HloBuffer* buffer) {
+        result->append(buffer->ToString());
+      }));
 }
 
 std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set) {
@@ -100,7 +106,7 @@ bool InstructionBufferSet::IsAmbiguous() const {
   bool is_ambiguous = false;
   ForEachElement(
       [&is_ambiguous](const ShapeIndex& index, const HloBufferSet& buffer_set) {
-        is_ambiguous |= buffer_set.buffer_ids().size() > 1;
+        is_ambiguous |= buffer_set.buffers().size() > 1;
       });
   return is_ambiguous;
 }
@@ -108,10 +114,10 @@ bool InstructionBufferSet::IsAmbiguous() const {
 bool InstructionBufferSet::IsDistinct() const {
   bool is_distinct = true;
   tensorflow::gtl::FlatSet<HloBuffer::Id> seen_ids;
-  ForEachElement([&is_distinct, &seen_ids](const ShapeIndex& index,
+  ForEachElement([&is_distinct, &seen_ids](const ShapeIndex& /*index*/,
                                            const HloBufferSet& buffer_set) {
-    for (HloBuffer::Id buffer_id : buffer_set.buffer_ids()) {
-      auto pair = seen_ids.insert(buffer_id);
+    for (const HloBuffer* buffer : buffer_set.buffers()) {
+      auto pair = seen_ids.insert(buffer->id());
       if (!pair.second) {
         is_distinct = false;
       }
diff --git a/tensorflow/compiler/xla/service/hlo_buffer.h b/tensorflow/compiler/xla/service/hlo_buffer.h
index cfec31f472..f42d2f7720 100644
--- a/tensorflow/compiler/xla/service/hlo_buffer.h
+++ b/tensorflow/compiler/xla/service/hlo_buffer.h
@@ -74,6 +74,16 @@ class HloBuffer {
  public:
   using Id = int64;
 
+  // Predicate comparing HloBuffers by increasing id, useful for std::sort.
+  static bool IdLessThan(const HloBuffer* a, const HloBuffer* b) {
+    return a->id() < b->id();
+  }
+
+  // Predicate comparing HloBuffers by equal id, useful for std::unique.
+  static bool IdEqual(const HloBuffer* a, const HloBuffer* b) {
+    return a->id() == b->id();
+  }
+
   HloBuffer(Id id) : id_(id) {}
 
   // Return the unique identifier for this HloBuffer.
@@ -84,8 +94,14 @@ class HloBuffer {
   // buffer already contains this value, then this method is a nop.
   void AddValue(const HloValue& value);
 
-  // Return the IDs of all values contained in this buffer.
-  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
+  // Return all values contained in this buffer.
+  const std::vector<const HloValue*>& values() const {
+    return values_.values();
+  }
+
+  // Return the unique HLO value in the buffer. CHECK fails if the buffer does
+  // not contain exactly one value.
+  const HloValue& GetUniqueValue() const { return values_.GetUniqueValue(); }
 
   // Return the positions (output of which instruction and at what index) where
   // the buffer is used. This is exactly the union of the positions of the
@@ -102,7 +118,7 @@ class HloBuffer {
   const Id id_;
 
   // The set of values contained in this buffer.
-  std::vector<HloValue::Id> value_ids_;
+  HloValueSet values_;
 
   // The set of positions where this buffer is used.
   std::vector<HloPosition> positions_;
@@ -131,7 +147,7 @@ class HloBufferSet {
 
   // Add the given buffer to this buffer set. If the buffer already exists in
   // the set, then this is a NOP.
-  void AddBuffer(HloBuffer::Id buffer_id);
+  void AddBuffer(const HloBuffer* buffer);
 
   // Removes the given buffer from this buffer set. CHECK fails in the buffer is
   // not contained in this set.
@@ -139,19 +155,19 @@ class HloBufferSet {
 
   // Returns the unique buffer in this set. CHECK fails if the set does not
   // contain exactly one buffer.
-  HloBuffer::Id GetUniqueBufferId() const {
-    CHECK_EQ(buffer_ids().size(), 1);
-    return buffer_ids()[0];
+  const HloBuffer& GetUniqueBuffer() const {
+    CHECK_EQ(buffers_.size(), 1);
+    return *buffers_[0];
   }
 
-  // Returns the IDs of the HloBuffers contained in this buffer set.
-  const std::vector<HloBuffer::Id>& buffer_ids() const { return buffer_ids_; }
+  // Returns the vector of HloBuffers in the set, sorted by HloBuffer::Id.
+  const std::vector<const HloBuffer*>& buffers() const { return buffers_; }
 
   string ToString() const;
 
  private:
-  // The IDs of the HloBuffers containted in this buffer set.
-  std::vector<HloBuffer::Id> buffer_ids_;
+  // HloBuffers sorted by HloBuffer::Id.
+  std::vector<const HloBuffer*> buffers_;
 };
 
 std::ostream& operator<<(std::ostream& out, const HloBufferSet& buffer_set);
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
index 2e731da741..92548dfaf0 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
@@ -49,11 +49,10 @@ HloDataflowAnalysis::HloDataflowAnalysis(HloModule* module, bool ssa_form,
 bool HloDataflowAnalysis::ValueIsDefinedAt(const HloInstruction* instruction,
                                            const ShapeIndex& index) const {
   const HloValueSet& value_set = GetValueSet(instruction, index);
-  if (value_set.value_ids().size() != 1) {
+  if (value_set.values().size() != 1) {
     return false;
   }
-  return GetValue(value_set.GetUniqueValueId()).defining_instruction() ==
-         instruction;
+  return value_set.GetUniqueValue().defining_instruction() == instruction;
 }
 
 const HloValue& HloDataflowAnalysis::GetValueDefinedAt(
@@ -68,20 +67,20 @@ HloValue& HloDataflowAnalysis::GetValueDefinedAt(
   return GetUniqueValueAt(instruction, index);
 }
 
-HloValue::Id HloDataflowAnalysis::NewHloValue(HloInstruction* instruction,
-                                              const ShapeIndex& index,
-                                              bool is_phi) {
-  int64 value_id = next_value_id_++;
-  auto it_added = values_.emplace(
+HloValue* HloDataflowAnalysis::NewHloValue(HloInstruction* instruction,
+                                           const ShapeIndex& index,
+                                           bool is_phi) {
+  const int64 value_id = next_value_id_++;
+  auto emplaced = values_.emplace(
       std::piecewise_construct, std::forward_as_tuple(value_id),
       std::forward_as_tuple(value_id, instruction, index, is_phi));
-  CHECK(it_added.second);
+  CHECK(emplaced.second);
 
   // Clear the vector of values as it is now stale. It will be lazily
   // reconstructed if needed when HloDataflowAnalysis::values() is called.
   values_vector_.clear();
 
-  return value_id;
+  return &emplaced.first->second;
 }
 
 void HloDataflowAnalysis::DeleteHloValue(HloValue::Id value_id) {
@@ -106,9 +105,9 @@ string HloDataflowAnalysis::ToString() const {
                                 const ShapeIndex& index,
                                 const HloValueSet& value_set) {
               StrAppend(&out, "      tuple index ", index.ToString(), ":\n");
-              for (HloValue::Id value_id : value_set.value_ids()) {
+              for (const HloValue* value : value_set.values()) {
                 StrAppend(
-                    &out, "        ", GetValue(value_id).ToShortString(),
+                    &out, "        ", value->ToShortString(),
                     ValueIsDefinedAt(instruction.get(), index) ? " (def)" : "",
                     "\n");
               }
@@ -116,8 +115,8 @@ string HloDataflowAnalysis::ToString() const {
       } else {
         const HloValueSet& top_level_value_set =
             GetValueSet(instruction.get(), /*index=*/{});
-        for (HloValue::Id value_id : top_level_value_set.value_ids()) {
-          StrAppend(&out, "      ", GetValue(value_id).ToShortString(),
+        for (const HloValue* value : top_level_value_set.values()) {
+          StrAppend(&out, "      ", value->ToShortString(),
                     ValueIsDefinedAt(instruction.get()) ? " (def)" : "", "\n");
         }
       }
@@ -155,9 +154,8 @@ const std::vector<const HloValue*>& HloDataflowAnalysis::values() const {
     for (auto& pair : values_) {
       values_vector_.push_back(&pair.second);
     }
-    std::sort(
-        values_vector_.begin(), values_vector_.end(),
-        [](const HloValue* a, const HloValue* b) { return a->id() < b->id(); });
+    std::sort(values_vector_.begin(), values_vector_.end(),
+              HloValue::IdLessThan);
   } else {
     CHECK_EQ(values_vector_.size(), values_.size());
     for (const HloValue* value : values_vector_) {
@@ -199,8 +197,8 @@ InstructionValueSet HloDataflowAnalysis::Phi(
         // Construct a vector of unique value IDs of the inputs.
         std::vector<HloValue::Id> input_value_ids;
         for (const InstructionValueSet* input : inputs) {
-          for (HloValue::Id value_id : input->element(index).value_ids()) {
-            input_value_ids.push_back(value_id);
+          for (const HloValue* value : input->element(index).values()) {
+            input_value_ids.push_back(value->id());
           }
         }
         std::sort(input_value_ids.begin(), input_value_ids.end());
@@ -221,7 +219,7 @@ InstructionValueSet HloDataflowAnalysis::Phi(
 
         if (input_value_ids.size() <= 1) {
           if (input_value_ids.size() == 1) {
-            *value_set = HloValueSet({input_value_ids[0]});
+            *value_set = HloValueSet({&GetValue(input_value_ids[0])});
           }
           if (existing_phi_value) {
             // The merge point does not have multiple distinct inputs (which are
@@ -236,7 +234,7 @@ InstructionValueSet HloDataflowAnalysis::Phi(
           if (existing_phi_value) {
             // A phi value already exists so reuse it in the new
             // InstructionValueSet.
-            *value_set = HloValueSet({existing_phi_value->id()});
+            *value_set = HloValueSet({existing_phi_value});
           } else {
             // Create a new phi value.
             *value_set =
@@ -255,17 +253,16 @@ void HloDataflowAnalysis::UpdatePositionsOfValuesAt(
     prev_value_set->ForEachElement(
         [this, instruction](const ShapeIndex& index,
                             const HloValueSet& value_set) {
-          for (HloValue::Id value_id : value_set.value_ids()) {
+          for (const HloValue* value : value_set.values()) {
             // HloValues in the previous value set may have been deleted.
-            if (!ContainsKey(values_, value_id)) {
+            if (!ContainsKey(values_, value->id())) {
               continue;
             }
             // Don't remove the defining position of the value.
-            HloValue& value = GetValue(value_id);
-            if (instruction == value.defining_instruction()) {
-              CHECK_EQ(index, value.defining_index());
+            if (instruction == value->defining_instruction()) {
+              CHECK_EQ(index, value->defining_index());
             } else {
-              value.RemovePosition(instruction, index);
+              GetValue(value->id()).RemovePosition(instruction, index);
             }
           }
         });
@@ -274,12 +271,11 @@ void HloDataflowAnalysis::UpdatePositionsOfValuesAt(
   new_value_set.ForEachElement(
       [this, instruction](const ShapeIndex& index,
                           const HloValueSet& value_set) {
-        for (HloValue::Id value_id : value_set.value_ids()) {
-          HloValue& value = GetValue(value_id);
-          if (instruction == value.defining_instruction()) {
-            CHECK_EQ(index, value.defining_index());
+        for (const HloValue* value : value_set.values()) {
+          if (instruction == value->defining_instruction()) {
+            CHECK_EQ(index, value->defining_index());
           } else {
-            value.AddPosition(instruction, index);
+            GetValue(value->id()).AddPosition(instruction, index);
           }
         }
       });
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
index 6da956c9bb..4eb4f0bb16 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
@@ -94,11 +94,11 @@ class HloDataflowAnalysis {
   // shape index. CHECKs if the value set does not contain a exactly one value.
   const HloValue& GetUniqueValueAt(const HloInstruction* instruction,
                                    const ShapeIndex& index = {}) const {
-    return GetValue(GetValueSet(instruction, index).GetUniqueValueId());
+    return GetValueSet(instruction, index).GetUniqueValue();
   }
   HloValue& GetUniqueValueAt(const HloInstruction* instruction,
                              const ShapeIndex& index = {}) {
-    return GetValue(GetValueSet(instruction, index).GetUniqueValueId());
+    return GetValue(GetValueSet(instruction, index).GetUniqueValue().id());
   }
 
   // Return the HloValue with the given Id.
@@ -130,10 +130,9 @@ class HloDataflowAnalysis {
   HloDataflowAnalysis(HloModule* module, bool ssa_form,
                       bool bitcast_defines_value = false);
 
-  // Creates a new HloValue defined at the given instruction and shape index and
-  // return its ID.
-  HloValue::Id NewHloValue(HloInstruction* instruction, const ShapeIndex& index,
-                           bool is_phi = false);
+  // Returns a new HloValue defined at the given instruction and shape index.
+  HloValue* NewHloValue(HloInstruction* instruction, const ShapeIndex& index,
+                        bool is_phi = false);
 
   // Delete the HloValue with the given ID.
   void DeleteHloValue(HloValue::Id value_id);
@@ -201,7 +200,9 @@ class HloDataflowAnalysis {
 
   std::unique_ptr<CallGraph> call_graph_;
 
-  // The map of all HloValues in the module.
+  // The map of all HloValues in the module. We pass around pointers to the
+  // mapped HloValues, so the underlying container must keep them valid despite
+  // mutations touching other map entries.
   std::unordered_map<HloValue::Id, HloValue> values_;
 
   // A map from instruction to InstructionValueSet.
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
index af7e22f9e1..2b685e355f 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
@@ -56,9 +56,9 @@ class HloDataflowAnalysisTest : public HloTestBase,
                                     const ShapeIndex& index = {}) {
     CHECK(analysis_ != nullptr);
     std::vector<HloValue> values;
-    for (HloValue::Id value_id :
-         analysis_->GetValueSet(instruction, index).value_ids()) {
-      values.push_back(analysis_->GetValue(value_id));
+    for (const HloValue* value :
+         analysis_->GetValueSet(instruction, index).values()) {
+      values.push_back(*value);
     }
     return values;
   }
diff --git a/tensorflow/compiler/xla/service/hlo_value.cc b/tensorflow/compiler/xla/service/hlo_value.cc
index d19cadb854..221f67b0c1 100644
--- a/tensorflow/compiler/xla/service/hlo_value.cc
+++ b/tensorflow/compiler/xla/service/hlo_value.cc
@@ -35,6 +35,7 @@ limitations under the License.
 
 namespace xla {
 
+using ::tensorflow::str_util::Join;
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
@@ -89,7 +90,7 @@ string HloValue::ToShortString() const {
   string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
                          ? defining_index().ToString()
                          : "";
-  return StrCat(is_phi_ ? "PHI " : "", defining_instruction()->name(),
+  return StrCat(id_, " ", is_phi_ ? "PHI " : "", defining_instruction()->name(),
                 index_str);
 }
 
@@ -249,13 +250,16 @@ std::ostream& operator<<(std::ostream& out, const HloValue& value) {
 }
 
 void HloValueSet::SortAndUniquifyValues() {
-  std::sort(value_ids_.begin(), value_ids_.end());
-  value_ids_.erase(std::unique(value_ids_.begin(), value_ids_.end()),
-                   value_ids_.end());
+  std::sort(values_.begin(), values_.end(), HloValue::IdLessThan);
+  values_.erase(std::unique(values_.begin(), values_.end(), HloValue::IdEqual),
+                values_.end());
 }
 
 string HloValueSet::ToString() const {
-  return StrCat("HloValueSet: ", tensorflow::str_util::Join(value_ids_, ", "));
+  return StrCat("HloValueSet: ",
+                Join(values_, ", ", [](string* result, const HloValue* value) {
+                  result->append(value->ToShortString());
+                }));
 }
 
 /*static */
@@ -263,14 +267,24 @@ HloValueSet HloValueSet::Union(
     tensorflow::gtl::ArraySlice<const HloValueSet*> inputs) {
   HloValueSet union_set;
   for (const HloValueSet* input : inputs) {
-    for (HloValue::Id value_id : input->value_ids()) {
-      union_set.value_ids_.push_back(value_id);
+    for (const HloValue* value : input->values()) {
+      union_set.values_.push_back(value);
     }
   }
   union_set.SortAndUniquifyValues();
   return union_set;
 }
 
+bool HloValueSet::AddValue(const HloValue* value) {
+  auto it = std::lower_bound(values_.begin(), values_.end(), value,
+                             HloValue::IdLessThan);
+  if (it == values_.end() || (*it)->id() != value->id()) {
+    values_.insert(it, value);
+    return true;
+  }
+  return false;  // already exists
+}
+
 std::ostream& operator<<(std::ostream& out, const HloValueSet& value_set) {
   out << value_set.ToString();
   return out;
diff --git a/tensorflow/compiler/xla/service/hlo_value.h b/tensorflow/compiler/xla/service/hlo_value.h
index e884fd9418..a21e348217 100644
--- a/tensorflow/compiler/xla/service/hlo_value.h
+++ b/tensorflow/compiler/xla/service/hlo_value.h
@@ -97,6 +97,16 @@ class HloValue {
  public:
   using Id = int64;
 
+  // Predicate comparing HloValues by increasing id, useful for std::sort.
+  static bool IdLessThan(const HloValue* a, const HloValue* b) {
+    return a->id() < b->id();
+  }
+
+  // Predicate comparing HloValues by equal id, useful for std::unique.
+  static bool IdEqual(const HloValue* a, const HloValue* b) {
+    return a->id() == b->id();
+  }
+
   // Construct an HloValue defined by 'instruction' at shape index 'index'. If
   // is_phi is true, then this value is a phi value, for example, at the
   // parameter of a while body computation. Phi values are only used in the SSA
@@ -186,8 +196,8 @@ class HloValueSet {
  public:
   HloValueSet() = default;
 
-  explicit HloValueSet(tensorflow::gtl::ArraySlice<HloValue::Id> value_ids)
-      : value_ids_(value_ids.begin(), value_ids.end()) {
+  explicit HloValueSet(tensorflow::gtl::ArraySlice<const HloValue*> values)
+      : values_(values.begin(), values.end()) {
     SortAndUniquifyValues();
   }
 
@@ -195,19 +205,29 @@ class HloValueSet {
   static HloValueSet Union(
       tensorflow::gtl::ArraySlice<const HloValueSet*> inputs);
 
-  // Return the vector of the IDs of all HloValues in the set. Values in the
-  // vector are unique and sorted.
-  const std::vector<HloValue::Id>& value_ids() const { return value_ids_; }
+  // Return the vector of HloValues in the set. Values in the vector are unique
+  // and sorted.
+  const std::vector<const HloValue*>& values() const { return values_; }
+
+  // Adds the value to the set.  Returns true iff the value was added and didn't
+  // already exist in the set.
+  bool AddValue(const HloValue* value);
 
   // Return the unique HLO value in the set. CHECKs if the set does not contain
   // exactly one value.
-  HloValue::Id GetUniqueValueId() const {
-    CHECK_EQ(value_ids().size(), 1);
-    return value_ids()[0];
+  const HloValue& GetUniqueValue() const {
+    CHECK_EQ(values_.size(), 1);
+    return *values_[0];
   }
 
   bool operator==(const HloValueSet& other) const {
-    return value_ids() == other.value_ids();
+    if (values_.size() != other.values_.size()) return false;
+    for (size_t i = 0; i < values_.size(); ++i) {
+      if (values_[i]->id() != other.values_[i]->id()) {
+        return false;
+      }
+    }
+    return true;
   }
   bool operator!=(const HloValueSet& other) const { return !(*this == other); }
 
@@ -219,7 +239,7 @@ class HloValueSet {
   void SortAndUniquifyValues();
 
   // HloValues sorted by HloValue::Id.
-  std::vector<HloValue::Id> value_ids_;
+  std::vector<const HloValue*> values_;
 };
 
 std::ostream& operator<<(std::ostream& out, const HloValueSet& hlo_value);
-- 
GitLab


From 67a5805a7fcb58b167122229c9edbe1308ba1481 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 16:46:42 -0700
Subject: [PATCH 0941/1375] Expose colocate_with

PiperOrigin-RevId: 161738084
---
 tensorflow/python/framework/framework_lib.py | 2 ++
 tensorflow/tools/api/golden/tensorflow.pbtxt | 4 ++++
 2 files changed, 6 insertions(+)

diff --git a/tensorflow/python/framework/framework_lib.py b/tensorflow/python/framework/framework_lib.py
index 80f936e8ab..56ac56ba42 100644
--- a/tensorflow/python/framework/framework_lib.py
+++ b/tensorflow/python/framework/framework_lib.py
@@ -32,6 +32,7 @@
 @@device
 @@container
 @@name_scope
+@@colocate_with
 @@control_dependencies
 @@convert_to_tensor
 @@convert_to_tensor_or_indexed_slices
@@ -85,6 +86,7 @@ from tensorflow.python.framework.ops import device
 from tensorflow.python.framework.ops import container
 from tensorflow.python.framework.ops import name_scope
 from tensorflow.python.framework.ops import op_scope
+from tensorflow.python.framework.ops import colocate_with
 from tensorflow.python.framework.ops import control_dependencies
 from tensorflow.python.framework.ops import get_default_graph
 from tensorflow.python.framework.ops import reset_default_graph
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index eb013f3a77..a9f00f63a0 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -740,6 +740,10 @@ tf_module {
     name: "clip_by_value"
     argspec: "args=[\'t\', \'clip_value_min\', \'clip_value_max\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "colocate_with"
+    argspec: "args=[\'op\', \'ignore_existing\'], varargs=None, keywords=None, defaults=[\'False\'], "
+  }
   member_method {
     name: "complex"
     argspec: "args=[\'real\', \'imag\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-- 
GitLab


From d4babb3b087edd4294bb7b144afd6aaae37a0ea3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 16:47:31 -0700
Subject: [PATCH 0942/1375] Allow HashTableOP to map int64 to float.

PiperOrigin-RevId: 161738207
---
 tensorflow/core/kernels/lookup_table_op.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/kernels/lookup_table_op.cc b/tensorflow/core/kernels/lookup_table_op.cc
index 11ce2a71dc..b33b98975d 100644
--- a/tensorflow/core/kernels/lookup_table_op.cc
+++ b/tensorflow/core/kernels/lookup_table_op.cc
@@ -790,6 +790,7 @@ REGISTER_KERNEL(string, int32);
 REGISTER_KERNEL(string, int64);
 REGISTER_KERNEL(int64, string);
 REGISTER_KERNEL(int64, int64);
+REGISTER_KERNEL(int64, float);
 REGISTER_KERNEL(string, string);
 REGISTER_KERNEL(string, bool);
 
-- 
GitLab


From 6743f6574e4deb36adbace7a8ddb775492024c7f Mon Sep 17 00:00:00 2001
From: Gautam <gautamrbharadwaj@gmail.com>
Date: Thu, 13 Jul 2017 05:36:04 +0530
Subject: [PATCH 0943/1375] Update optimize_for_inference_test.py (#11026)

These two should be interchanged "self.assertNotEqual("ResizeBilinear", node.op)" with "self.assertNotEqual("MirrorPad", node.op)"
---
 tensorflow/python/tools/optimize_for_inference_test.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/tools/optimize_for_inference_test.py b/tensorflow/python/tools/optimize_for_inference_test.py
index e547d0e292..447057cfe9 100644
--- a/tensorflow/python/tools/optimize_for_inference_test.py
+++ b/tensorflow/python/tools/optimize_for_inference_test.py
@@ -271,7 +271,8 @@ class OptimizeForInferenceTest(test.TestCase):
 
     for node in optimized_graph_def.node:
       self.assertNotEqual("Conv2D", node.op)
-      self.assertNotEqual("ResizeBilinear", node.op)
+      self.assertNotEqual("MirrorPad", node.op)
+      
 
   def testFusePadAndConv(self):
     with self.test_session() as sess:
@@ -299,7 +300,7 @@ class OptimizeForInferenceTest(test.TestCase):
 
     for node in optimized_graph_def.node:
       self.assertNotEqual("Conv2D", node.op)
-      self.assertNotEqual("MirrorPad", node.op)
+      self.assertNotEqual("ResizeBilinear", node.op)
 
 
 if __name__ == "__main__":
-- 
GitLab


From 57a6f8e0bae1b567a0f67fe1f1bfb7b5d514d714 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carl=20Thom=C3=A9?= <carlthome@gmail.com>
Date: Thu, 13 Jul 2017 02:06:18 +0200
Subject: [PATCH 0944/1375] Return output_graph_def (#11292)

---
 tensorflow/python/tools/freeze_graph.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py
index bd046a7fd0..8069950925 100644
--- a/tensorflow/python/tools/freeze_graph.py
+++ b/tensorflow/python/tools/freeze_graph.py
@@ -115,10 +115,15 @@ def freeze_graph_with_def_protos(
         output_node_names.split(","),
         variable_names_blacklist=variable_names_blacklist)
 
-  with gfile.GFile(output_graph, "wb") as f:
-    f.write(output_graph_def.SerializeToString())
+  # Write GraphDef to file if output path has been given.
+  if output_graph:
+    with gfile.GFile(output_graph, "wb") as f:
+      f.write(output_graph_def.SerializeToString())
+
   print("%d ops in the final graph." % len(output_graph_def.node))
 
+  return output_graph_def
+
 
 def _parse_input_graph_proto(input_graph, input_binary):
   """Parser input tensorflow graph into GraphDef proto."""
-- 
GitLab


From 9384db557e458bce3a5805fb88936e496ad00df6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 18:24:32 -0700
Subject: [PATCH 0945/1375] Backport of https://github.com/grpc/grpc/pull/9421
 in TensorFlow.

PiperOrigin-RevId: 161749205
---
 .../distributed_runtime/rpc/grpc_serialization_traits.h     | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h b/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
index e7eca62fdf..0616b65af0 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
@@ -63,7 +63,11 @@ class GrpcBufferWriter GRPC_FINAL
           &slice_, GPR_SLICE_LENGTH(slice_) - count);
       g_core_codegen_interface->gpr_slice_buffer_add(slice_buffer_, slice_);
     }
-    have_backup_ = true;
+    // It's dangerous to keep an inlined grpc_slice as the backup slice, since
+    // on a following Next() call, a reference will be returned to this slice
+    // via GRPC_SLICE_START_PTR, which will not be an adddress held by
+    // slice_buffer_.
+    have_backup_ = backup_slice_.refcount != NULL;
     byte_count_ -= count;
   }
 
-- 
GitLab


From 690c3b0b56effbd808fec36075bebabcc0362992 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 19:12:18 -0700
Subject: [PATCH 0946/1375] Temporarily disable barrier_ops_test that sometimes
 times out.

PiperOrigin-RevId: 161752846
---
 tensorflow/python/kernel_tests/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index bbb598c978..8ceedd080d 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -57,6 +57,7 @@ tf_py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
     ],
     shard_count = 20,
+    tags = ["manual"],  # b/63628385
 )
 
 tf_py_test(
-- 
GitLab


From c09b99baa624ea6b964df207fc3e239c916627a3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 21:15:48 -0700
Subject: [PATCH 0947/1375] Support 16bit quantized types in tf.bitcast.

PiperOrigin-RevId: 161760434
---
 tensorflow/core/framework/types.cc                |  5 +++++
 tensorflow/core/ops/array_ops.cc                  | 11 +++++++++--
 tensorflow/python/kernel_tests/bitcast_op_test.py |  6 ++++++
 3 files changed, 20 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/framework/types.cc b/tensorflow/core/framework/types.cc
index dc396e468a..28b5e8be11 100644
--- a/tensorflow/core/framework/types.cc
+++ b/tensorflow/core/framework/types.cc
@@ -313,6 +313,11 @@ int DataTypeSize(DataType dt) {
   switch (dt) {
     TF_CALL_POD_TYPES(CASE);
     TF_CALL_QUANTIZED_TYPES(CASE);
+    // TF_CALL_QUANTIZED_TYPES() macro does no cover quint16 and qint16, since
+    // they are not supported widely, but are explicitly listed here for
+    // bitcast.
+    TF_CALL_qint16(CASE);
+    TF_CALL_quint16(CASE);
     default:
       return 0;
   }
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index b3b2f1bc80..10133009b9 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -4354,8 +4354,15 @@ We specify the size-related attributes as:
 REGISTER_OP("Bitcast")
     .Input("input: T")
     .Output("output: type")
-    .Attr("T: numbertype")
-    .Attr("type: numbertype")
+    // All supported dtypes are listed here to include qint16 and quint16.
+    .Attr(
+        "T: {float, double, int64, int32, uint8, uint16, int8, int16,"
+        " complex64, complex128, qint8, quint8, qint16, quint16, qint32,"
+        " half}")
+    .Attr(
+        "type: {float, double, int64, int32, uint8, uint16, int8, int16,"
+        " complex64, complex128, qint8, quint8, qint16, quint16, qint32,"
+        " half}")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle input = c->input(0);
       if (!c->RankKnown(input)) {
diff --git a/tensorflow/python/kernel_tests/bitcast_op_test.py b/tensorflow/python/kernel_tests/bitcast_op_test.py
index 077a4b8e27..a535468b05 100644
--- a/tensorflow/python/kernel_tests/bitcast_op_test.py
+++ b/tensorflow/python/kernel_tests/bitcast_op_test.py
@@ -76,6 +76,12 @@ class BitcastTest(test.TestCase):
     datatype = dtypes.int8
     array_ops.bitcast(x, datatype, None)
 
+  def testQuantizeType(self):
+    shape = [3, 4]
+    x = np.zeros(shape, np.uint16)
+    datatype = dtypes.quint16
+    self._testBitcast(x, datatype, shape)
+
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 79147b44135df9c2e5acbe2aaa106fba65612011 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 12 Jul 2017 21:19:41 -0700
Subject: [PATCH 0948/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161760675
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 59 +++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 |  8 ++-
 2 files changed, 65 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index bfe9214187..9f1ca05baa 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -4025,6 +4025,65 @@ op {
     }
   }
 }
+op {
+  name: "Bitcast"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "type"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT16
+        type: DT_QUINT16
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  attr {
+    name: "type"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT16
+        type: DT_QUINT16
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+}
 op {
   name: "BitwiseAnd"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 11f2220f15..2b9978154c 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -3852,12 +3852,14 @@ op {
         type: DT_INT32
         type: DT_UINT8
         type: DT_UINT16
-        type: DT_INT16
         type: DT_INT8
+        type: DT_INT16
         type: DT_COMPLEX64
         type: DT_COMPLEX128
         type: DT_QINT8
         type: DT_QUINT8
+        type: DT_QINT16
+        type: DT_QUINT16
         type: DT_QINT32
         type: DT_HALF
       }
@@ -3874,12 +3876,14 @@ op {
         type: DT_INT32
         type: DT_UINT8
         type: DT_UINT16
-        type: DT_INT16
         type: DT_INT8
+        type: DT_INT16
         type: DT_COMPLEX64
         type: DT_COMPLEX128
         type: DT_QINT8
         type: DT_QUINT8
+        type: DT_QINT16
+        type: DT_QUINT16
         type: DT_QINT32
         type: DT_HALF
       }
-- 
GitLab


From 896cedfa89b1693a13e5c1b32ac4ae425b3c963e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 02:37:33 -0700
Subject: [PATCH 0949/1375] Automated g4 rollback of changelist 161726749

PiperOrigin-RevId: 161781962
---
 tensorflow/core/framework/register_types.h    |  4 +-
 tensorflow/core/kernels/BUILD                 | 34 +++------
 .../core/kernels/dense_update_functor.cc      | 73 ------------------
 tensorflow/core/kernels/dense_update_ops.cc   | 74 +++++++++++++++++--
 ...se_update_functor.h => dense_update_ops.h} | 33 ++++-----
 ...r_gpu.cu.cc => dense_update_ops_gpu.cu.cc} | 30 +-------
 .../core/kernels/resource_variable_ops.cc     | 27 +++++--
 tensorflow/core/kernels/scatter_nd_op.cc      | 12 ++-
 tensorflow/core/kernels/strided_slice_op.cc   |  2 +-
 .../core/kernels/strided_slice_op_impl.h      |  2 +-
 10 files changed, 127 insertions(+), 164 deletions(-)
 delete mode 100644 tensorflow/core/kernels/dense_update_functor.cc
 rename tensorflow/core/kernels/{dense_update_functor.h => dense_update_ops.h} (65%)
 rename tensorflow/core/kernels/{dense_update_functor_gpu.cu.cc => dense_update_ops_gpu.cu.cc} (61%)

diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index 3f91642064..b62fe647e2 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -172,9 +172,7 @@ limitations under the License.
   TF_CALL_half(m) TF_CALL_float(m) TF_CALL_double(m)
 
 // Call "m" on all types supported on GPU.
-#define TF_CALL_GPU_ALL_TYPES(m) \
-  TF_CALL_GPU_NUMBER_TYPES(m)    \
-  TF_CALL_bool(m) TF_CALL_complex64(m) TF_CALL_complex128(m)
+#define TF_CALL_GPU_ALL_TYPES(m) TF_CALL_GPU_NUMBER_TYPES(m) TF_CALL_bool(m)
 
 #define TF_CALL_GPU_NUMBER_TYPES_NO_HALF(m) TF_CALL_float(m) TF_CALL_double(m)
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 93d1df0f7b..6eee2e16eb 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -19,7 +19,6 @@ package_group(
     name = "friends",
     packages = [
         "//learning/brain/contrib/...",
-        "//learning/brain/research/sparse_matrix/...",
         "//tensorflow/...",
     ],
 )
@@ -95,11 +94,13 @@ tf_kernel_library(
         "strided_slice_op_inst_7.cc",
     ],
     hdrs = [
+        "dense_update_ops.h",
         "slice_op.h",
         "strided_slice_op.h",
         "strided_slice_op_impl.h",
     ],
     gpu_srcs = [
+        "dense_update_ops.h",
         "slice_op.h",
         "strided_slice_op.h",
         "strided_slice_op_impl.h",
@@ -108,7 +109,6 @@ tf_kernel_library(
     ],
     deps = [
         ":bounds_check",
-        ":dense_update_functor",
         ":ops_util",
         ":variable_ops",
         "//tensorflow/core:framework",
@@ -1011,23 +1011,6 @@ tf_kernel_library(
     ],
 )
 
-tf_kernel_library(
-    name = "dense_update_functor",
-    srcs = ["dense_update_functor.cc"],
-    hdrs = ["dense_update_functor.h"],
-    gpu_srcs = [
-        "dense_update_functor.h",
-        "dense_update_functor_gpu.cu.cc",
-    ],
-    visibility = [":friends"],
-    deps = [
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//third_party/eigen3",
-    ],
-    alwayslink = 0,
-)
-
 tf_cuda_cc_test(
     name = "gather_op_test",
     size = "small",
@@ -1623,7 +1606,7 @@ tf_kernel_library(
     srcs = ["resource_variable_ops.cc"],
     deps = [
         ":bounds_check",
-        ":dense_update_functor",
+        ":dense_update_ops",
         ":gather_functor",
         ":scatter_functor",
         ":state",
@@ -2096,7 +2079,7 @@ tf_kernel_library(
         "//tensorflow:darwin": [],
         "//conditions:default": ["-Wl,-z,muldefs"],
     }),
-    visibility = [":friends"],
+    visibility = ["//visibility:private"],
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -3499,7 +3482,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "dense_update_ops",
     prefix = "dense_update_ops",
-    deps = STATE_DEPS + [":dense_update_functor"],
+    deps = STATE_DEPS,
 )
 
 tf_kernel_library(
@@ -3520,14 +3503,16 @@ tf_kernel_library(
         "scatter_nd_op_cpu_impl_5.cc",
     ],
     hdrs = [
+        "dense_update_ops.h",
         "scatter_nd_op.h",
         "scatter_nd_op_cpu_impl.h",
     ],
     gpu_srcs = [
+        "dense_update_ops.h",
         "scatter_nd_op.h",
         "scatter_nd_op_gpu.cu.cc",
     ],
-    deps = STATE_DEPS + [":dense_update_functor"],
+    deps = STATE_DEPS + [":dense_update_ops"],
 )
 
 tf_kernel_library(
@@ -4069,9 +4054,8 @@ filegroup(
         "cwise_ops_common.cc",
         "cwise_ops_common.h",
         "cwise_ops_gradients.h",
-        "dense_update_functor.cc",
-        "dense_update_functor.h",
         "dense_update_ops.cc",
+        "dense_update_ops.h",
         "example_parsing_ops.cc",
         "fill_functor.cc",
         "fill_functor.h",
diff --git a/tensorflow/core/kernels/dense_update_functor.cc b/tensorflow/core/kernels/dense_update_functor.cc
deleted file mode 100644
index a878fe9a97..0000000000
--- a/tensorflow/core/kernels/dense_update_functor.cc
+++ /dev/null
@@ -1,73 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#define EIGEN_USE_THREADS
-
-#include "tensorflow/core/kernels/dense_update_functor.h"
-
-#include "tensorflow/core/framework/register_types.h"
-#include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/platform/mutex.h"
-#include "tensorflow/core/platform/types.h"
-
-namespace tensorflow {
-
-typedef Eigen::ThreadPoolDevice CPUDevice;
-typedef Eigen::GpuDevice GPUDevice;
-
-namespace functor {
-
-template <>
-struct DenseUpdate<CPUDevice, string, ASSIGN> {
-  void operator()(const CPUDevice& d, typename TTypes<string>::Flat params,
-                  typename TTypes<string>::ConstFlat update) {
-    if (params.dimension(0) == 1) {
-      params.data()->resize(update.data()->size());
-      auto work = [&params, &update](int64 start, int64 end) {
-        memmove(const_cast<char*>(params.data()->data()) + start,
-                update.data()->data() + start, end - start);
-      };
-      d.parallelFor(update.data()->size(),
-                    Eigen::TensorOpCost(.1,  // chosen to force large chunks
-                                        .1, 0),
-                    work);
-    } else {
-      auto work = [&params, &update](int64 start, int64 end) {
-        for (int i = start; i < end; ++i) {
-          params.data()[i].resize(update.data()[i].size());
-          memmove(const_cast<char*>(params.data()[i].data()),
-                  update.data()[i].data(), update.data()[i].size());
-        }
-      };
-      int64 estimated_string_size;
-      if (update.size() > 0) {
-        // first element of the tensor seems as good a guess as any of the sizes
-        // of the strings contained within...
-        estimated_string_size =
-            std::max(update.data()[0].size(), sizeof(string));
-      } else {
-        estimated_string_size = sizeof(string);
-      }
-      d.parallelFor(
-          params.dimension(0),
-          Eigen::TensorOpCost(estimated_string_size, estimated_string_size, 0),
-          work);
-    }
-  }
-};
-
-}  // namespace functor
-
-}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc
index 6d44a92fa3..af16c55a5d 100644
--- a/tensorflow/core/kernels/dense_update_ops.cc
+++ b/tensorflow/core/kernels/dense_update_ops.cc
@@ -15,20 +15,59 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
-#if GOOGLE_CUDA
-#define EIGEN_USE_GPU
-#endif
-
+#include "tensorflow/core/kernels/dense_update_ops.h"
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/assign_op.h"
-#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
+namespace functor {
+
+template <>
+struct DenseUpdate<Eigen::ThreadPoolDevice, string, ASSIGN> {
+  void operator()(const Eigen::ThreadPoolDevice& d,
+                  typename TTypes<string>::Flat params,
+                  typename TTypes<string>::ConstFlat update) {
+    if (params.dimension(0) == 1) {
+      params.data()->resize(update.data()->size());
+      auto work = [&params, &update](int64 start, int64 end) {
+        memmove(const_cast<char*>(params.data()->data()) + start,
+                update.data()->data() + start, end - start);
+      };
+      d.parallelFor(update.data()->size(),
+                    Eigen::TensorOpCost(.1,  // chosen to force large chunks
+                                        .1, 0),
+                    work);
+    } else {
+      auto work = [&params, &update](int64 start, int64 end) {
+        for (int i = start; i < end; ++i) {
+          params.data()[i].resize(update.data()[i].size());
+          memmove(const_cast<char*>(params.data()[i].data()),
+                  update.data()[i].data(), update.data()[i].size());
+        }
+      };
+      int64 estimated_string_size;
+      if (update.size() > 0) {
+        // first element of the tensor seems as good a guess as any of the sizes
+        // of the strings contained within...
+        estimated_string_size =
+            std::max(update.data()[0].size(), sizeof(string));
+      } else {
+        estimated_string_size = sizeof(string);
+      }
+      d.parallelFor(
+          params.dimension(0),
+          Eigen::TensorOpCost(estimated_string_size, estimated_string_size, 0),
+          work);
+    }
+  }
+};
+
+}  // namespace functor
 
 template <typename Device, typename T>
 class AssignOpT : public AssignOp {
@@ -78,7 +117,7 @@ class DenseUpdateOp : public OpKernel {
         errors::InvalidArgument("Parameters and update must be the same size"));
 
     functor::DenseUpdate<Device, T, OP> update_functor;
-    update_functor(context->template eigen_device<Device>(), Tparams.flat<T>(),
+    update_functor(context->eigen_device<Device>(), Tparams.flat<T>(),
                    Tupdate.flat<T>());
   }
 
@@ -104,6 +143,13 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
 // Only register 'Assign' on GPU for the subset of types also supported by
 // 'Variable' (see variable_ops.cc.)
 #define REGISTER_GPU_KERNELS(type)                                 \
+  namespace functor {                                              \
+  template <>                                                      \
+  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(           \
+      const GPUDevice& d, typename TTypes<type>::Flat lhs,         \
+      typename TTypes<type>::ConstFlat rhs);                       \
+  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;     \
+  }                                                                \
   REGISTER_KERNEL_BUILDER(                                         \
       Name("Assign").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
       AssignOpT<GPUDevice, type>);
@@ -134,6 +180,22 @@ TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
 
 #if GOOGLE_CUDA
+// Forward declarations of the functor specializations for GPU.
+namespace functor {
+#define DECLARE_GPU_SPEC_FOR_OP(T, OP)                     \
+  template <>                                              \
+  void DenseUpdate<GPUDevice, T, OP>::operator()(          \
+      const GPUDevice& d, typename TTypes<T>::Flat params, \
+      typename TTypes<T>::ConstFlat update);               \
+  extern template struct DenseUpdate<GPUDevice, T, OP>;
+#define DECLARE_GPU_SPEC(T)                         \
+  DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::ADD); \
+  DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::SUB)
+TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
+#undef DECLARE_GPU_SPEC
+#undef DECLARE_GPU_SPEC_FOR_OP
+}  // namespace functor
+
 #define REGISTER_GPU_KERNELS(type)                                    \
   REGISTER_KERNEL_BUILDER(                                            \
       Name("AssignAdd").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
diff --git a/tensorflow/core/kernels/dense_update_functor.h b/tensorflow/core/kernels/dense_update_ops.h
similarity index 65%
rename from tensorflow/core/kernels/dense_update_functor.h
rename to tensorflow/core/kernels/dense_update_ops.h
index 54b080c83b..ec7e9dff11 100644
--- a/tensorflow/core/kernels/dense_update_functor.h
+++ b/tensorflow/core/kernels/dense_update_ops.h
@@ -13,47 +13,40 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
-#define TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
-
-#define EIGEN_USE_THREADS
+#ifndef TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
+#define TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor_types.h"
 
 namespace tensorflow {
 
-typedef Eigen::ThreadPoolDevice CPUDevice;
-
 enum DenseUpdateType { ADD, SUB, ASSIGN };
 
 namespace functor {
 
 template <typename Device, typename T, DenseUpdateType OP>
-struct DenseUpdate {
-  void operator()(const Device& d, typename TTypes<T>::Flat params,
-                  typename TTypes<T>::ConstFlat update);
-};
+struct DenseUpdate;
 
-template <typename T>
-struct DenseUpdate<CPUDevice, T, ADD> {
-  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
+template <typename Device, typename T>
+struct DenseUpdate<Device, T, ADD> {
+  void operator()(const Device& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) += update;
   }
 };
 
-template <typename T>
-struct DenseUpdate<CPUDevice, T, SUB> {
-  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
+template <typename Device, typename T>
+struct DenseUpdate<Device, T, SUB> {
+  void operator()(const Device& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) -= update;
   }
 };
 
-template <typename T>
-struct DenseUpdate<CPUDevice, T, ASSIGN> {
-  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
+template <typename Device, typename T>
+struct DenseUpdate<Device, T, ASSIGN> {
+  void operator()(const Device& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) = update;
   }
@@ -62,4 +55,4 @@ struct DenseUpdate<CPUDevice, T, ASSIGN> {
 }  // end namespace functor
 }  // end namespace tensorflow
 
-#endif  // TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
+#endif  // TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
diff --git a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc b/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
similarity index 61%
rename from tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
rename to tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
index 208401cb24..cf2d493061 100644
--- a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
+++ b/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
@@ -17,7 +17,7 @@ limitations under the License.
 
 #define EIGEN_USE_GPU
 
-#include "tensorflow/core/kernels/dense_update_functor.h"
+#include "tensorflow/core/kernels/dense_update_ops.h"
 
 #include "tensorflow/core/framework/register_types.h"
 
@@ -25,34 +25,6 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-namespace functor {
-
-template <typename T>
-struct DenseUpdate<GPUDevice, T, ASSIGN> {
-  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
-                  typename TTypes<T>::ConstFlat update) {
-    params.device(d) = update;
-  }
-};
-
-template <typename T>
-struct DenseUpdate<GPUDevice, T, ADD> {
-  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
-                  typename TTypes<T>::ConstFlat update) {
-    params.device(d) += update;
-  }
-};
-
-template <typename T>
-struct DenseUpdate<GPUDevice, T, SUB> {
-  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
-                  typename TTypes<T>::ConstFlat update) {
-    params.device(d) -= update;
-  }
-};
-
-}  // namespace functor
-
 #define DEFINE_GPU_KERNELS(T)                              \
   template struct functor::DenseUpdate<GPUDevice, T, ADD>; \
   template struct functor::DenseUpdate<GPUDevice, T, SUB>;
diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index 0616bb5a08..f0fef256d2 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -15,16 +15,12 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
-#if GOOGLE_CUDA
-#define EIGEN_USE_GPU
-#endif
-
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_functor.h"
+#include "tensorflow/core/kernels/dense_update_ops.h"
 #include "tensorflow/core/kernels/gather_functor.h"
 #include "tensorflow/core/kernels/scatter_functor.h"
 #include "tensorflow/core/kernels/variable_ops.h"
@@ -221,6 +217,13 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
 
 #if GOOGLE_CUDA
 #define REGISTER_GPU_KERNELS(type)                             \
+  namespace functor {                                          \
+  template <>                                                  \
+  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(       \
+      const GPUDevice& d, typename TTypes<type>::Flat lhs,     \
+      typename TTypes<type>::ConstFlat rhs);                   \
+  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>; \
+  }                                                            \
   REGISTER_KERNEL_BUILDER(Name("AssignVariableOp")             \
                               .Device(DEVICE_GPU)              \
                               .TypeConstraint<type>("dtype")   \
@@ -272,6 +275,20 @@ TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 
 #if GOOGLE_CUDA
 #define REGISTER_GPU_KERNELS(type)                                       \
+  namespace functor {                                                    \
+  template <>                                                            \
+  void DenseUpdate<GPUDevice, type, ADD>::operator()(                    \
+      const GPUDevice& d, typename TTypes<type>::Flat lhs,               \
+      typename TTypes<type>::ConstFlat rhs);                             \
+  extern template struct DenseUpdate<GPUDevice, type, ADD>;              \
+  }                                                                      \
+  namespace functor {                                                    \
+  template <>                                                            \
+  void DenseUpdate<GPUDevice, type, SUB>::operator()(                    \
+      const GPUDevice& d, typename TTypes<type>::Flat lhs,               \
+      typename TTypes<type>::ConstFlat rhs);                             \
+  extern template struct DenseUpdate<GPUDevice, type, SUB>;              \
+  }                                                                      \
   REGISTER_KERNEL_BUILDER(Name("AssignAddVariableOp")                    \
                               .Device(DEVICE_GPU)                        \
                               .HostMemory("resource")                    \
diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc
index 59f690e7aa..1428546d52 100644
--- a/tensorflow/core/kernels/scatter_nd_op.cc
+++ b/tensorflow/core/kernels/scatter_nd_op.cc
@@ -26,7 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_functor.h"
+#include "tensorflow/core/kernels/dense_update_ops.h"
 #include "tensorflow/core/kernels/fill_functor.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -523,6 +523,16 @@ TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DECLARE_GPU_SPECS);
 #undef DECLARE_GPU_SPECS_INDEX
 #undef DECLARE_GPU_SPECS_INDEX_OP
 
+#define REGISTER_GPU_KERNELS(type)                         \
+  template <>                                              \
+  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(   \
+      const GPUDevice& d, typename TTypes<type>::Flat lhs, \
+      typename TTypes<type>::ConstFlat rhs);               \
+  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
+#undef REGISTER_GPU_KERNELS
+
 }  // namespace functor
 
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc
index 4655503e26..fa518712ce 100644
--- a/tensorflow/core/kernels/strided_slice_op.cc
+++ b/tensorflow/core/kernels/strided_slice_op.cc
@@ -22,7 +22,7 @@ limitations under the License.
 #endif  // GOOGLE_CUDA
 
 #include "tensorflow/core/kernels/strided_slice_op.h"
-#include "tensorflow/core/kernels/dense_update_functor.h"
+#include "tensorflow/core/kernels/dense_update_ops.h"
 #include "tensorflow/core/kernels/slice_op.h"
 #include "tensorflow/core/kernels/strided_slice_op_impl.h"
 
diff --git a/tensorflow/core/kernels/strided_slice_op_impl.h b/tensorflow/core/kernels/strided_slice_op_impl.h
index de65147572..d0ccd5c652 100644
--- a/tensorflow/core/kernels/strided_slice_op_impl.h
+++ b/tensorflow/core/kernels/strided_slice_op_impl.h
@@ -27,7 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/register_types_traits.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_functor.h"
+#include "tensorflow/core/kernels/dense_update_ops.h"
 #include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-- 
GitLab


From 8c30cf6bf58a12c061d263debc35c9946cce172c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 03:24:40 -0700
Subject: [PATCH 0950/1375] Make tf.write_file recursively create the directory
 it is saving to.

PiperOrigin-RevId: 161785793
---
 tensorflow/core/kernels/whole_file_read_ops.cc | 13 +++++++++----
 tensorflow/core/ops/io_ops.cc                  |  3 ++-
 tensorflow/python/kernel_tests/io_ops_test.py  | 15 +++++++++++++++
 3 files changed, 26 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/kernels/whole_file_read_ops.cc b/tensorflow/core/kernels/whole_file_read_ops.cc
index d7ef4bc4c4..17a39ce29b 100644
--- a/tensorflow/core/kernels/whole_file_read_ops.cc
+++ b/tensorflow/core/kernels/whole_file_read_ops.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/io/buffered_inputstream.h"
+#include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/io/random_inputstream.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -132,10 +133,14 @@ class WriteFileOp : public OpKernel {
                 errors::InvalidArgument(
                     "Contents tensor must be scalar, but had shape: ",
                     contents_input->shape().DebugString()));
-    OP_REQUIRES_OK(
-        context,
-        WriteStringToFile(context->env(), filename_input->scalar<string>()(),
-                          contents_input->scalar<string>()()));
+    const string& filename = filename_input->scalar<string>()();
+    const string dir = io::Dirname(filename).ToString();
+    if (!context->env()->FileExists(dir).ok()) {
+      OP_REQUIRES_OK(context, context->env()->RecursivelyCreateDir(dir));
+    }
+    OP_REQUIRES_OK(context,
+                   WriteStringToFile(context->env(), filename,
+                                     contents_input->scalar<string>()()));
   }
 };
 
diff --git a/tensorflow/core/ops/io_ops.cc b/tensorflow/core/ops/io_ops.cc
index 350aefe2c8..de287334b8 100644
--- a/tensorflow/core/ops/io_ops.cc
+++ b/tensorflow/core/ops/io_ops.cc
@@ -839,7 +839,8 @@ REGISTER_OP("WriteFile")
       return Status::OK();
     })
     .Doc(R"doc(
-Writes contents to the file at input filename. Creates file if not existing.
+Writes contents to the file at input filename. Creates file and recursively
+creates directory if not existing.
 
 filename: scalar. The name of the file to which we write the contents.
 contents: scalar. The content to be written to the output file.
diff --git a/tensorflow/python/kernel_tests/io_ops_test.py b/tensorflow/python/kernel_tests/io_ops_test.py
index 472487ccfb..f91875c6f0 100644
--- a/tensorflow/python/kernel_tests/io_ops_test.py
+++ b/tensorflow/python/kernel_tests/io_ops_test.py
@@ -20,6 +20,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+import shutil
 import tempfile
 
 from tensorflow.python.ops import io_ops
@@ -58,6 +59,20 @@ class IoOpsTest(test.TestCase):
         self.assertEqual(file_contents, contents)
       os.remove(temp.name)
 
+  def testWriteFileCreateDir(self):
+    cases = ['', 'Some contents']
+    for contents in cases:
+      contents = compat.as_bytes(contents)
+      subdir = os.path.join(self.get_temp_dir(), 'subdir1')
+      filepath = os.path.join(subdir, 'subdir2', 'filename')
+      with self.test_session() as sess:
+        w = io_ops.write_file(filepath, contents)
+        sess.run(w)
+        with open(filepath, 'rb') as f:
+          file_contents = f.read()
+        self.assertEqual(file_contents, contents)
+      shutil.rmtree(subdir)
+
   def _subset(self, files, indices):
     return set(
         compat.as_bytes(files[i].name) for i in range(len(files))
-- 
GitLab


From 866d3083429ad67932d21261c59c81da0b5ab17a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 03:25:45 -0700
Subject: [PATCH 0951/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161785867
---
 tensorflow/go/op/wrappers.go | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index efb3b1b07d..6d0990cb52 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -10931,7 +10931,9 @@ func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output,
 	return op.Output(0)
 }
 
-// Writes contents to the file at input filename. Creates file if not existing.
+// Writes contents to the file at input filename. Creates file and recursively
+//
+// creates directory if not existing.
 //
 // Arguments:
 //	filename: scalar. The name of the file to which we write the contents.
-- 
GitLab


From 01a38fa3379d0fe444e0fc84c14656d8b06e6ebe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 04:05:40 -0700
Subject: [PATCH 0952/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161788559
---
 tensorflow/core/ops/ops.pbtxt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 2b9978154c..ccae526e5f 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -30695,7 +30695,8 @@ op {
     description: "scalar. The content to be written to the output file."
     type: DT_STRING
   }
-  summary: "Writes contents to the file at input filename. Creates file if not existing."
+  summary: "Writes contents to the file at input filename. Creates file and recursively"
+  description: "creates directory if not existing."
 }
 op {
   name: "ZerosLike"
-- 
GitLab


From 1eccfb6e190cdd594c2cd1d3b27323f87614d8bc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 06:38:53 -0700
Subject: [PATCH 0953/1375] Nest: Raise exceptions with more readable messages

PiperOrigin-RevId: 161800080
---
 tensorflow/python/util/nest.py      |  9 ++++---
 tensorflow/python/util/nest_test.py | 39 ++++++++++++++++++++---------
 2 files changed, 32 insertions(+), 16 deletions(-)

diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py
index b93842b3a7..f3f3887afc 100644
--- a/tensorflow/python/util/nest.py
+++ b/tensorflow/python/util/nest.py
@@ -143,8 +143,8 @@ def _recursive_assert_same_structure(nest1, nest2, check_types):
   is_sequence_nest1 = is_sequence(nest1)
   if is_sequence_nest1 != is_sequence(nest2):
     raise ValueError(
-        "The two structures don't have the same nested structure. "
-        "First structure: %s, second structure: %s." % (nest1, nest2))
+        "The two structures don't have the same nested structure.\n\n"
+        "First structure: %s\n\nSecond structure: %s." % (nest1, nest2))
 
   if not is_sequence_nest1:
     return  # finished checking
@@ -194,8 +194,9 @@ def assert_same_structure(nest1, nest2, check_types=True):
   len_nest2 = len(flatten(nest2)) if is_sequence(nest2) else 1
   if len_nest1 != len_nest2:
     raise ValueError("The two structures don't have the same number of "
-                     "elements. First structure: %s, second structure: %s."
-                     % (nest1, nest2))
+                     "elements.\n\nFirst structure (%i elements): %s\n\n"
+                     "Second structure (%i elements): %s"
+                     % (len_nest1, nest1, len_nest2, nest2))
   _recursive_assert_same_structure(nest1, nest2, check_types)
 
 
diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py
index cb17dfc7eb..375e30e953 100644
--- a/tensorflow/python/util/nest_test.py
+++ b/tensorflow/python/util/nest_test.py
@@ -172,22 +172,33 @@ class NestTest(test.TestCase):
     nest.assert_same_structure("abc", np.array([0, 1]))
     nest.assert_same_structure("abc", constant_op.constant([0, 1]))
 
-    with self.assertRaisesRegexp(ValueError,
-                                 "don't have the same number of elements"):
+    with self.assertRaisesRegexp(
+        ValueError,
+        ("don't have the same number of elements\\.\n\n"
+         "First structure \\(6 elements\\):.*?"
+         "\n\nSecond structure \\(2 elements\\):")):
       nest.assert_same_structure(structure1, structure_different_num_elements)
 
-    with self.assertRaisesRegexp(ValueError,
-                                 "don't have the same number of elements"):
+    with self.assertRaisesRegexp(
+        ValueError,
+        ("don't have the same number of elements\\.\n\n"
+         "First structure \\(2 elements\\):.*?"
+         "\n\nSecond structure \\(1 elements\\):")):
       nest.assert_same_structure([0, 1], np.array([0, 1]))
 
-    with self.assertRaisesRegexp(ValueError,
-                                 "don't have the same number of elements"):
+    with self.assertRaisesRegexp(
+        ValueError,
+        ("don't have the same number of elements\\.\n\n"
+         "First structure \\(1 elements\\):.*"
+         "\n\nSecond structure \\(2 elements\\):")):
       nest.assert_same_structure(0, [0, 1])
 
     self.assertRaises(TypeError, nest.assert_same_structure, (0, 1), [0, 1])
 
-    with self.assertRaisesRegexp(ValueError,
-                                 "don't have the same nested structure"):
+    with self.assertRaisesRegexp(
+        ValueError,
+        ("don't have the same nested structure\\.\n\n"
+         "First structure: .*?\n\nSecond structure: ")):
       nest.assert_same_structure(structure1, structure_different_nesting)
 
     named_type_0 = collections.namedtuple("named_0", ("a", "b"))
@@ -200,12 +211,16 @@ class NestTest(test.TestCase):
     self.assertRaises(TypeError, nest.assert_same_structure,
                       named_type_0(3, 4), named_type_1(3, 4))
 
-    with self.assertRaisesRegexp(ValueError,
-                                 "don't have the same nested structure"):
+    with self.assertRaisesRegexp(
+        ValueError,
+        ("don't have the same nested structure\\.\n\n"
+         "First structure: .*?\n\nSecond structure: ")):
       nest.assert_same_structure(named_type_0(3, 4), named_type_0([3], 4))
 
-    with self.assertRaisesRegexp(ValueError,
-                                 "don't have the same nested structure"):
+    with self.assertRaisesRegexp(
+        ValueError,
+        ("don't have the same nested structure\\.\n\n"
+         "First structure: .*?\n\nSecond structure: ")):
       nest.assert_same_structure([[3], 4], [3, [4]])
 
     structure1_list = [[[1, 2], 3], 4, [5, 6]]
-- 
GitLab


From 31f1375c01782f53cf12ce2aa4efa584483c14ea Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 13 Jul 2017 07:40:00 -0700
Subject: [PATCH 0954/1375] Add GradientsDebugger to tfdbg

to allow retrieval of gradient tensors created by TensorFlow's automatic differentiation algorithm (i.e., tf.gradients and optimizer code that uses it).

PiperOrigin-RevId: 161805516
---
 tensorflow/contrib/cmake/tf_core_ops.cmake    |  12 +
 tensorflow/contrib/cmake/tf_python.cmake      |   2 +
 tensorflow/core/ops/debug_ops.cc              |   3 +
 tensorflow/python/debug/BUILD                 |  40 ++
 tensorflow/python/debug/__init__.py           |   5 +
 .../python/debug/lib/debug_gradients.py       | 417 ++++++++++++++++++
 .../python/debug/lib/debug_gradients_test.py  | 378 ++++++++++++++++
 7 files changed, 857 insertions(+)
 create mode 100644 tensorflow/python/debug/lib/debug_gradients.py
 create mode 100644 tensorflow/python/debug/lib/debug_gradients_test.py

diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index 21d94da6cc..b350b822dd 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -129,3 +129,15 @@ list(REMOVE_ITEM tf_core_ops_srcs ${tf_core_ops_exclude_srcs})
 add_library(tf_core_ops OBJECT ${tf_core_ops_srcs})
 
 add_dependencies(tf_core_ops tf_core_cpu)
+
+########################################################
+# tf_debug_ops library
+########################################################
+
+file(GLOB tf_debug_ops_srcs
+    "${tensorflow_source_dir}/tensorflow/core/ops/debug_ops.cc"
+)
+
+add_library(tf_debug_ops OBJECT ${tf_debug_ops_srcs})
+
+add_dependencies(tf_debug_ops tf_core_framework)
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index beb630bdfc..0024efab38 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -692,6 +692,8 @@ GENERATE_PYTHON_OP_LIB("contrib_bigquery_reader_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/cloud/python/ops/gen_bigquery_reader_ops.py)
 GENERATE_PYTHON_OP_LIB("stateless_random_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/stateless/gen_stateless_random_ops.py)
+GENERATE_PYTHON_OP_LIB("debug_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/debug/ops/gen_debug_ops.py)
 
 add_custom_target(tf_python_ops SOURCES ${tf_python_ops_generated_files} ${PYTHON_PROTO_GENFILES})
 add_dependencies(tf_python_ops tf_python_op_gen_main)
diff --git a/tensorflow/core/ops/debug_ops.cc b/tensorflow/core/ops/debug_ops.cc
index f7a96b58da..bd7f7c2c01 100644
--- a/tensorflow/core/ops/debug_ops.cc
+++ b/tensorflow/core/ops/debug_ops.cc
@@ -14,7 +14,9 @@ limitations under the License.
 ==============================================================================*/
 // This file registers all TensorFlow Debugger (tfdbg) ops.
 
+#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
 
 namespace tensorflow {
 
@@ -88,6 +90,7 @@ REGISTER_OP("DebugIdentity")
     .Attr("debug_urls: list(string) = []")
     .Attr("gated_grpc: bool = false")
     .SetAllowsUninitializedInput()
+    .SetShapeFn(shape_inference::UnchangedShape)
     .Doc(R"doc(
 Debug Identity Op.
 
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 86d7b5f770..d7ca36b12b 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -23,6 +23,7 @@ exports_files(["LICENSE"])
 load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "if_not_windows")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 py_library(
     name = "debug_py",
@@ -31,6 +32,7 @@ py_library(
     visibility = ["//visibility:public"],
     deps = [
         ":debug_data",
+        ":debug_gradients",
         ":debug_utils",
         ":hooks",
         ":local_cli_wrapper",
@@ -60,6 +62,24 @@ py_library(
     ],
 )
 
+tf_gen_op_wrapper_py(
+    name = "debug_ops",
+    deps = ["//tensorflow/core:debug_ops_op_lib"],
+)
+
+py_library(
+    name = "debug_gradients",
+    srcs = ["lib/debug_gradients.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":debug_data",
+        ":debug_ops",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:platform",
+        "@six_archive//:six",
+    ],
+)
+
 py_library(
     name = "debug_utils",
     srcs = ["lib/debug_utils.py"],
@@ -381,6 +401,26 @@ py_test(
     ],
 )
 
+cuda_py_test(
+    name = "debug_gradients_test",
+    size = "small",
+    srcs = [
+        "lib/debug_gradients_test.py",
+    ],
+    additional_deps = [
+        ":debug_data",
+        ":debug_gradients",
+        ":debug_utils",
+        "//tensorflow/python:client",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+    ],
+)
+
 py_test(
     name = "debug_utils_test",
     size = "small",
diff --git a/tensorflow/python/debug/__init__.py b/tensorflow/python/debug/__init__.py
index 147e42d878..e20849bc1c 100644
--- a/tensorflow/python/debug/__init__.py
+++ b/tensorflow/python/debug/__init__.py
@@ -31,6 +31,9 @@ See the @{$python/tfdbg} guide.
 @@LocalCLIDebugHook
 @@LocalCLIDebugWrapperSession
 @@WatchOptions
+
+@@GradientsDebugger
+@@clear_gradient_debuggers
 """
 
 from __future__ import absolute_import
@@ -44,6 +47,8 @@ from tensorflow.python.debug.lib.debug_data import has_inf_or_nan
 from tensorflow.python.debug.lib.debug_data import load_tensor_from_event
 from tensorflow.python.debug.lib.debug_data import load_tensor_from_event_file
 
+from tensorflow.python.debug.lib.debug_gradients import GradientsDebugger
+
 from tensorflow.python.debug.lib.debug_utils import add_debug_tensor_watch
 from tensorflow.python.debug.lib.debug_utils import watch_graph
 from tensorflow.python.debug.lib.debug_utils import watch_graph_with_blacklists
diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py
new file mode 100644
index 0000000000..e5159cb941
--- /dev/null
+++ b/tensorflow/python/debug/lib/debug_gradients.py
@@ -0,0 +1,417 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TensorFlow Debugger: Tools for debugging gradients."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import uuid
+
+import six
+
+from tensorflow.python.debug.lib import debug_data
+from tensorflow.python.debug.ops import gen_debug_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import variables
+
+_GRADIENT_DEBUG_TAG = "gradient_debug_"
+
+_gradient_debuggers = {}
+
+
+def _tensor_to_grad_debug_op_name(tensor, grad_debugger_uuid):
+  op_name, slot = debug_data.parse_node_or_tensor_name(tensor.name)
+  return "%s_%d/%s%s" % (op_name, slot, _GRADIENT_DEBUG_TAG, grad_debugger_uuid)
+
+
+def _parse_grad_debug_op_name(op_name):
+  """Parse the name of a debug gradient op.
+
+  Args:
+    op_name: the name of the debug gradient op.
+
+  Returns:
+    1) The UUID of the GradientsDebugger that created the debug gradient op.
+    2) Name of the original tensor whose gradient is debugged by the debug
+       gradient op.
+  """
+  name_items = op_name.split("/")
+  assert len(name_items) > 1
+  assert name_items[-1].startswith(_GRADIENT_DEBUG_TAG)
+
+  grad_debugger_uuid = name_items[-1][len(_GRADIENT_DEBUG_TAG):]
+  if "_" in grad_debugger_uuid:
+    grad_debugger_uuid = grad_debugger_uuid[:grad_debugger_uuid.index("_")]
+  orig_tensor_slot = int(name_items[-2][name_items[-2].rfind("_") + 1:])
+  orig_base_op_name = name_items[-2][:name_items[-2].rfind("_")]
+  orig_tensor_name = ("/".join(name_items[:-2] + [orig_base_op_name]) +
+                      ":%d" % orig_tensor_slot)
+
+  return grad_debugger_uuid, orig_tensor_name
+
+
+class GradientsDebugger(object):
+  """Gradients Debugger.
+
+  Allows retrieval of gradient tensors created by TensorFlow's automatic
+  differentiation algorithm, i.e., @{tf.gradients} and optimizer classes that
+  use it.
+  """
+  # TODO(cais): Add examples code in the doc string?
+
+  def __init__(self, y_tensor=None):
+    """Constructor of GradientsDebugger.
+
+    Args:
+      y_tensor: optional: the `tf.Tensor` to be differentiated, i.e., the tensor
+        on the numerator of the differentiation.
+    """
+
+    self._uuid = uuid.uuid4().hex
+    _gradient_debuggers[self._uuid] = self
+
+    # A dict mapping x-tensor names to gradient tensor. x-tensor refers to the
+    # independent tf.Tensor, i.e., the tensor on the denominator of the
+    # differentiation.
+    self._gradient_tensors = {}
+    self._y_tensor = y_tensor
+
+    self._graph = None
+    if y_tensor:
+      self._graph = y_tensor.graph
+
+    self._is_active_context = False
+
+  @property
+  def y_tensor(self):
+    return self._y_tensor
+
+  @property
+  def graph(self):
+    return self._graph
+
+  def __enter__(self):
+    self._is_active_context = True
+
+  def __exit__(self, unused_type, unused_value, unused_traceback):
+    self._is_active_context = False
+
+  def identify_gradient(self, input_tensor):
+    """Create a debug identity tensor that registers and forwards gradients.
+
+    The side effect of this method is that when gradient tensor(s) are created
+    with respect to the any paths that include the `input_tensor`, the gradient
+    tensor(s) with repsect to `input_tensor` will be registered with this
+    this `GradientsDebugger` instance and can later be retrieved, with the
+    methods `gradient_tensor` and `gradient_tensors`.
+
+    Example:
+
+    ```python
+    x = tf.Variable(1.0)
+    y = tf.add(x, x)
+
+    grad_debugger = tf_debug.GradientsDebugger()
+    debug_y = grad_debugger.identify_gradient(y)
+    z = tf.square(debug_y)
+
+    # Create a train op under the grad_debugger context.
+    with grad_debugger:
+      train_op = tf.train.GradientDescentOptimizer(z)
+
+    # Now we can reflect through grad_debugger to get the gradient tensor
+    # with respect to y.
+    y_grad = grad_debugger.gradient_tensor(y)
+    ```
+
+    Args:
+      input_tensor: the input `tf.Tensor` object whose related gradient tensors
+        are to be reigstered with this `GradientsDebugger` instance when they
+        are created, e.g., during @{tf.gradients} calls or the construction
+        of optimization (training) op that uses @{tf.gradients}.
+
+    Returns:
+      A forwarded identity of `input_tensor`, as a `tf.Tensor`.
+
+    Raises:
+      ValueError: If an op with name that duplicates the gradient-debugging op
+        already exists in the graph (highly unlikely).
+    """
+    # TODO(cais): Allow overriding gradient.
+    # TODO(cais): Implement value_stack.
+    grad_debug_op_name = _tensor_to_grad_debug_op_name(input_tensor, self._uuid)
+    debug_identity = gen_debug_ops.debug_identity(
+        input_tensor,
+        tensor_name=input_tensor.name,
+        debug_urls=[],
+        name=grad_debug_op_name)
+    if debug_identity.op.name != grad_debug_op_name:
+      raise ValueError(
+          "The graph already contains an op named %s" % grad_debug_op_name)
+    return debug_identity
+
+  def watch_gradients_by_tensors(self, graph, tensors):
+    """Watch gradient tensors by x-tensor(s).
+
+    The side effect of this method is that when gradient tensor(s) are created
+    with respect to the any paths that include the `x_tensor`s, the gradient
+    tensor(s) with repsect to the tensor will be registered with this
+    this `GradientsDebugger` instance and can later be retrieved, with the
+    methods `gradient_tensor` and `gradient_tensors`.
+
+    Unlike the method `identify_gradient`, this method is used to retrieve
+    gradient tensors after the construction of the forward subgraph has
+    completed (but before the construction of the backward subgraph).
+
+    This method is the same as `watch_gradients_by_x_tensor_names` except that
+    the tensors are specified by the Python `tf.Tensor` or `tf.Variable`
+    objects, instead by name patterns.
+
+    Example:
+
+    ```python
+    x = tf.Variable(1.0)
+    y = tf.add(x, x, name="y")
+    z = tf.square(debug_y)
+
+    # Create a train op under the grad_debugger context.
+    grad_debugger = tf_debug.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensors(y):
+      train_op = tf.train.GradientDescentOptimizer(z)
+
+    # Now we can reflect through grad_debugger to get the gradient tensor
+    # with respect to y.
+    y_grad = grad_debugger.gradient_tensor(y)
+    # or
+    y_grad = grad_debugger.gradient_tensor("y:0")
+    ```
+
+    Args:
+      graph: the `tf.Graph` to watch the gradients on.
+      tensors: a `tf.Tensor` or `tf.Variable` object, or a list of such objects.
+
+    Returns:
+      The GradientsDebugger instance itself.
+    """
+
+    if not isinstance(tensors, list):
+      tensors = [tensors]
+
+    tensor_name_regex = []
+    for tensor in tensors:
+      tensor_name_regex.append(re.escape(tensor.name) + "$")
+    tensor_name_regex = "(" + "|".join(tensor_name_regex) + ")"
+    return self.watch_gradients_by_tensor_names(graph, tensor_name_regex)
+
+  def watch_gradients_by_tensor_names(self, graph, tensor_name_regex):
+    """Watch gradient tensors by name(s) of the x-tensor(s).
+
+    The side effect of this method is that when gradient tensor(s) are created
+    with respect to the x-tensors, the gradient tensor(s) will be registered
+    with this `GradientsDebugger` instance and can later be retrieved.
+
+    Unlike the `identify_gradient` method, this method is used after the
+    construction of the forward graph has completed. Unlike the
+    `watch_gradients_by_tensor` method, this method does not use handles to the
+    tensors of interest; it uses their names.
+
+    This method is the same as `watch_gradients_by_tensors` except that the
+    x-tensors are specified by name patterns, instead of `tf.Tensor` or
+    `tf.Variable` objects.
+
+    Example:
+
+    ```python
+    x = tf.Variable(1.0, name="x")
+    y = tf.add(x, x, name="y")
+    z = tf.square(debug_y)
+
+    # Create a train op under the grad_debugger context.
+    grad_debugger = tf_debug.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(r"(x|y):0$"):
+      train_op = tf.train.GradientDescentOptimizer(z)
+
+    # Now we can reflect through grad_debugger to get the gradient tensor
+    # with respect to x and y.
+    x_grad = grad_debugger.gradient_tensor("x:0")
+    y_grad = grad_debugger.gradient_tensor("y:0")
+    ```
+
+    Args:
+      graph: the `tf.Graph` to watch the gradients on.
+      tensor_name_regex: the regular-expression pattern of the name(s) of the
+        x-tensor(s) to watch. x-tensor refers to the tensors on the denominator
+        of the differentiation.
+
+    Returns:
+      The GradientsDebugger instance itself.
+    """
+    tensor_name_pattern = re.compile(tensor_name_regex)
+
+    # pylint: disable=protected-access
+    with graph.as_default():
+      for op in graph.get_operations():
+        for output in op.outputs:
+          if tensor_name_pattern.match(output.name):
+            debug_op = self.identify_gradient(output)
+
+            for consumer in output.consumers():
+              if consumer == debug_op.op:
+                continue
+
+              # Locate the slot index of the original input.
+              input_slots = []
+              for i, consumer_input in enumerate(consumer._inputs):
+                if consumer_input == output:
+                  input_slots.append(i)
+
+              for slot in input_slots:
+                consumer._inputs[slot] = debug_op
+                debug_op._consumers.append(consumer)
+
+            del output._consumers[:]
+            output._consumers.append(debug_op.op)
+    # pylint: enable=protected-access
+
+    return self
+
+  def _check_same_graph(self, tensor):
+    if self._graph is None:
+      self._graph = tensor.graph
+    elif self._graph != tensor.graph:
+      raise ValueError(
+          "The graph of the value (%s) is not the same as the graph %s" %
+          (tensor.graph, self._graph))
+
+  def register_gradient_tensor(self,
+                               x_tensor_name,
+                               gradient_tensor):
+    """Register the gradient tensor for an x-tensor.
+
+    Args:
+      x_tensor_name: (`str`) the name of the the independent `tf.Tensor`, i.e.,
+        the tensor on the denominator of the differentiation.
+      gradient_tensor: the gradient `tf.Tensor`.
+    """
+    if len(_gradient_debuggers) == 1 or self._is_active_context:
+      self._check_same_graph(gradient_tensor)
+      self._gradient_tensors[x_tensor_name] = gradient_tensor
+
+  def gradient_tensor(self, x_tensor):
+    """Get the gradient tensor of an x-tensor.
+
+    Args:
+      x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
+        name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
+        on the denominator of the differentiation.
+
+    Returns:
+      If found, the gradient tensor.
+
+    Raises:
+      TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
+      LookupError: If the `x_tensor` has not been registered with a gradient
+        tensor.
+    """
+    x_tensor_name = self._get_tensor_name(x_tensor)
+    if x_tensor_name not in self._gradient_tensors:
+      raise LookupError(
+          "This GradientsDebugger has not received any gradient tensor for "
+          "x-tensor %s" % x_tensor_name)
+    return self._gradient_tensors[x_tensor_name]
+
+  def gradient_tensors(self):
+    """Get the gradient tensors that this object is aware of.
+
+    Returns:
+      A dict mapping x-tensor names to gradient tensor objects. x-tensor refers
+      to the tensors on the denominator of the differentation.
+    """
+    return self._gradient_tensors
+
+  def _get_tensor_name(self, tensor):
+    if isinstance(tensor, (ops.Tensor, variables.Variable)):
+      return tensor.name
+    elif  isinstance(tensor, six.string_types):
+      return tensor
+    else:
+      raise TypeError(
+          "x_tensor must be a str or tf.Tensor or tf.Variable, "
+          "but instead has type %s" % type(tensor))
+
+
+def clear_gradient_debuggers():
+  """Clear all globally registered gradient debuggers."""
+  _gradient_debuggers.clear()
+
+
+@ops.RegisterGradient("DebugIdentity")
+def _identify_gradient_grad(op, dy):
+  """Gradient function for the DebugIdentity op."""
+  # TODO(cais): Allow overriding gradient.
+  grad_debugger_uuid, orig_tensor_name = _parse_grad_debug_op_name(op.name)
+  grad_debugger = _gradient_debuggers[grad_debugger_uuid]
+  grad_debugger.register_gradient_tensor(orig_tensor_name, dy)
+  return dy
+
+
+def gradient_values_from_dump(grad_debugger, x_tensor, dump):
+  """Find gradient values from a `DebugDumpDir` object.
+
+  Args:
+    grad_debugger: the `tf_debug.GradientsDebugger` instance to be used.
+    x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
+      name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
+      on the denominator of the differentiation.
+    dump: A `tfdbg.DebugDumpDir` object.
+
+  Returns:
+    If this `GradientsDebugger` instance has the gradient tensor of `x_tensor`
+      registered: a list of `numpy.ndarray` representing the value of the
+      gradient tensor from `dump`. The list could be empty, if the gradient
+      tensor is not executed in the `tf.Session.run()` call that generated
+      the `dump`. The list could also contain multiple values of the gradient
+      tensor, e.g., if gradient tensor is computed repeatedly in a
+      `tf.while_loop` during the run that generated the `dump`.
+
+  Raises:
+    LookupError: If this `GradientsDebugger` instance does not have the
+      gradient tensor of `x_tensor` registered.
+    ValueError: If this `GradientsDebugger` has a `tf.Graph` object that
+      does not match the `tf.Graph` object of the `dump`.
+    TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
+  """
+  # TODO(cais): Use this method in LocalCLIDebugWrapperSession to present the
+  # gradient tensors to the TFDBG CLI.
+
+  # If possible, verify that the Python graph of the dump and that of this
+  # GradientsDebugger match.
+  if (dump.python_graph and grad_debugger.graph and
+      dump.python_graph != grad_debugger.graph):
+    raise ValueError(
+        "This GradientsDebugger instance has a graph (%s) that differs from "
+        "the graph of the DebugDumpDir object (%s)." %
+        (grad_debugger.graph, dump.python_graph))
+
+  gradient_tensor = grad_debugger.gradient_tensor(x_tensor)
+  node_name, output_slot = debug_data.parse_node_or_tensor_name(
+      gradient_tensor.name)
+
+  try:
+    return dump.get_tensors(node_name, output_slot, "DebugIdentity")
+  except debug_data.WatchKeyDoesNotExistInDebugDumpDirError:
+    return []
diff --git a/tensorflow/python/debug/lib/debug_gradients_test.py b/tensorflow/python/debug/lib/debug_gradients_test.py
new file mode 100644
index 0000000000..966578320e
--- /dev/null
+++ b/tensorflow/python/debug/lib/debug_gradients_test.py
@@ -0,0 +1,378 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Unit tests for debug_gradients module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import shutil
+import tempfile
+
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.debug.lib import debug_data
+from tensorflow.python.debug.lib import debug_gradients
+from tensorflow.python.debug.lib import debug_utils
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import gradient_descent
+
+
+class IdentifyGradientTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    self.sess = session.Session()
+    with self.sess:
+      self.u = variables.Variable(2.0, name="u")
+      self.v = variables.Variable(3.0, name="v")
+      self.w = math_ops.multiply(self.u.value(), self.v.value(), name="w")
+
+  def tearDown(self):
+    ops.reset_default_graph()
+    debug_gradients.clear_gradient_debuggers()
+
+  def testIdentifyGradientGivesCorrectTensorObjectWithoutContextManager(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    # Fetch the gradient tensor with the x-tensor object.
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor's name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testIdentifyGradientGivesCorrectTensorObjectWithTfGradients(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    with grad_debugger:
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    # Fetch the gradient tensor with the x-tensor object.
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor's name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testCallingIdentifyGradientTwiceWithTheSameGradientsDebuggerErrors(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    grad_debugger.identify_gradient(self.w)
+    with self.assertRaisesRegexp(
+        ValueError, "The graph already contains an op named .*"):
+      grad_debugger.identify_gradient(self.w)
+
+  def testIdentifyGradientWorksOnMultipleLosses(self):
+    grad_debugger_1 = debug_gradients.GradientsDebugger()
+    grad_debugger_2 = debug_gradients.GradientsDebugger()
+
+    y = math_ops.add(self.w, -1.0, name="y")
+    debug_y = grad_debugger_1.identify_gradient(y)
+    z1 = math_ops.square(debug_y, name="z1")
+
+    debug_y = grad_debugger_2.identify_gradient(y)
+    z2 = math_ops.sqrt(debug_y, name="z2")
+
+    with grad_debugger_1:
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
+    with grad_debugger_2:
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
+
+    dz1_dy = grad_debugger_1.gradient_tensor(y)
+    dz2_dy = grad_debugger_2.gradient_tensor(y)
+    self.assertIsInstance(dz1_dy, ops.Tensor)
+    self.assertIsInstance(dz2_dy, ops.Tensor)
+    self.assertIsNot(dz1_dy, dz2_dy)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0 ** 2, self.sess.run(z1))
+    self.assertAllClose(5.0 ** 0.5, self.sess.run(z2))
+    self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
+    self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy))
+
+  def testIdentifyGradientRaisesLookupErrorForUnknownXTensor(self):
+    grad_debugger_1 = debug_gradients.GradientsDebugger()
+    grad_debugger_2 = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger_1.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    # There are >1 gradient debuggers registered, and grad_debugger is not used
+    # as a context manager here, so the gradient w.r.t. self.w will not be
+    # registered.
+    gradients_impl.gradients(y, [self.u, self.v])
+
+    with self.assertRaisesRegexp(
+        LookupError,
+        r"This GradientsDebugger has not received any gradient tensor for "):
+      grad_debugger_1.gradient_tensor(self.w)
+    with self.assertRaisesRegexp(
+        LookupError,
+        r"This GradientsDebugger has not received any gradient tensor for "):
+      grad_debugger_2.gradient_tensor(self.w)
+
+  def testIdentifyGradientRaisesTypeErrorForNonTensorOrTensorNameInput(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with self.assertRaisesRegexp(
+        TypeError,
+        r"x_tensor must be a str or tf\.Tensor or tf\.Variable, but instead "
+        r"has type .*Operation.*"):
+      grad_debugger.gradient_tensor(variables.global_variables_initializer())
+
+  def testIdentifyGradientTensorWorksWithGradientDescentOptimizer(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    with grad_debugger:
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(y)
+
+    self.sess.run(variables.global_variables_initializer())
+
+    # Fetch the gradient tensor with the x-tensor object.
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testWatchGradientsByXTensorNamesWorks(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    # The constructrion of the forward graph has completed.
+    # But we can still get the gradient tensors by using
+    # watch_gradients_by_tensor_names().
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "w:0$"):
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    w_grad = grad_debugger.gradient_tensor("w:0")
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testWatchGradientsByXTensorNamesWorksWithoutContextManager(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    # The constructrion of the forward graph has completed.
+    # But we can still get the gradient tensors by using
+    # watch_gradients_by_tensor_names().
+    grad_debugger = debug_gradients.GradientsDebugger()
+    grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "w:0$")
+    grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    w_grad = grad_debugger.gradient_tensor("w:0")
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testWatchGradientsWorksOnRefTensor(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "u:0$"):
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.assertIs(u_grad, grad_debugger.gradient_tensor("u:0"))
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+    self.assertAllClose(
+        3.0, self.sess.run(grad_debugger.gradient_tensor("u:0")))
+
+  def testWatchGradientsWorksOnMultipleTensors(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph,
+                                                       "(u|w):0$"):
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+
+    self.assertEqual(2, len(grad_debugger.gradient_tensors()))
+    self.assertIs(u_grad, grad_debugger.gradient_tensor("u:0"))
+    self.assertIsInstance(grad_debugger.gradient_tensor("w:0"), ops.Tensor)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(
+        1.0, self.sess.run(grad_debugger.gradient_tensor("w:0")))
+    self.assertAllClose(
+        3.0, self.sess.run(grad_debugger.gradient_tensor("u:0")))
+
+  def testWatchGradientsByXTensorsWorks(self):
+    y = math_ops.add(self.w, -1.0, name="foo/y")
+    z = math_ops.square(y, name="foo/z")
+
+    # The constructrion of the forward graph has completed.
+    # But we can still get the gradient tensors by using
+    # watch_gradients_by_x_tensors().
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensors(
+        self.sess.graph, [self.w, self.u, y]):
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z)
+
+    self.assertEqual(3, len(grad_debugger.gradient_tensors()))
+    u_grad = grad_debugger.gradient_tensor(self.u)
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    y_grad = grad_debugger.gradient_tensor(y)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(10.0, self.sess.run(y_grad))
+    self.assertAllClose(10.0, self.sess.run(w_grad))
+    self.assertAllClose(30.0, self.sess.run(u_grad))
+
+  def testWatchGradientsByTensorCanWorkOnMultipleLosses(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+    z1 = math_ops.square(y, name="z1")
+    z2 = math_ops.sqrt(y, name="z2")
+
+    grad_debugger_1 = debug_gradients.GradientsDebugger()
+    with grad_debugger_1.watch_gradients_by_tensors(self.sess.graph, y):
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
+
+    grad_debugger_2 = debug_gradients.GradientsDebugger()
+    with grad_debugger_2.watch_gradients_by_tensors(self.sess.graph, y):
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
+
+    dz1_dy = grad_debugger_1.gradient_tensor(y)
+    dz2_dy = grad_debugger_2.gradient_tensor(y)
+    self.assertIsInstance(dz1_dy, ops.Tensor)
+    self.assertIsInstance(dz2_dy, ops.Tensor)
+    self.assertIsNot(dz1_dy, dz2_dy)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0 ** 2, self.sess.run(z1))
+    self.assertAllClose(5.0 ** 0.5, self.sess.run(z2))
+    self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
+    self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy))
+
+  def testGradientsValuesFromDumpWorks(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+    z = math_ops.square(y, name="z")
+
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensors(
+        self.sess.graph, [self.w, self.u, y]):
+      train_op = gradient_descent.GradientDescentOptimizer(0.1).minimize(z)
+
+    self.sess.run(variables.global_variables_initializer())
+
+    run_options = config_pb2.RunOptions(output_partition_graphs=True)
+    dump_dir = tempfile.mkdtemp()
+    debug_url = "file://" + dump_dir
+    debug_utils.watch_graph(
+        run_options,
+        self.sess.graph,
+        debug_urls=debug_url)
+    run_metadata = config_pb2.RunMetadata()
+    self.sess.run(train_op, options=run_options, run_metadata=run_metadata)
+
+    dump = debug_data.DebugDumpDir(
+        dump_dir, partition_graphs=run_metadata.partition_graphs)
+    dump.set_python_graph(self.sess.graph)
+
+    y_grad_values = debug_gradients.gradient_values_from_dump(
+        grad_debugger, y, dump)
+    self.assertEqual(1, len(y_grad_values))
+    self.assertAllClose(10.0, y_grad_values[0])
+
+    w_grad_values = debug_gradients.gradient_values_from_dump(
+        grad_debugger, self.w, dump)
+    self.assertEqual(1, len(w_grad_values))
+    self.assertAllClose(10.0, w_grad_values[0])
+
+    u_grad_values = debug_gradients.gradient_values_from_dump(
+        grad_debugger, self.u, dump)
+    self.assertEqual(1, len(u_grad_values))
+    self.assertAllClose(30.0, u_grad_values[0])
+
+    with self.assertRaisesRegexp(
+        LookupError,
+        r"This GradientsDebugger has not received any gradient tensor for "
+        r"x-tensor v:0"):
+      debug_gradients.gradient_values_from_dump(grad_debugger, self.v, dump)
+
+    # Cleanup.
+    shutil.rmtree(dump_dir)
+
+
+if __name__ == "__main__":
+  googletest.main()
-- 
GitLab


From e5d8eb0d2d66b79879eb804ef8317c78e89eb385 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 09:02:28 -0700
Subject: [PATCH 0955/1375] Disable barrier_ops_test that sometimes times out
 take 2

PiperOrigin-RevId: 161814496
---
 tensorflow/python/kernel_tests/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 8ceedd080d..994e3cc1e2 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -57,7 +57,7 @@ tf_py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
     ],
     shard_count = 20,
-    tags = ["manual"],  # b/63628385
+    tags = ["notap"],  # b/63628385
 )
 
 tf_py_test(
-- 
GitLab


From bc79eb57b7302d61a2e5aebbe9ea63f85d2fa952 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 13 Jul 2017 09:48:16 -0700
Subject: [PATCH 0956/1375] barrier_ops_test is size medium.

PiperOrigin-RevId: 161820876
---
 tensorflow/python/kernel_tests/BUILD | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 994e3cc1e2..5d582351fd 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -47,7 +47,7 @@ tf_py_test(
 
 tf_py_test(
     name = "barrier_ops_test",
-    size = "small",
+    size = "medium",  # NOTE(ebrevdo): This test is NOT small.
     srcs = ["barrier_ops_test.py"],
     additional_deps = [
         "//third_party/py/numpy",
@@ -57,7 +57,6 @@ tf_py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
     ],
     shard_count = 20,
-    tags = ["notap"],  # b/63628385
 )
 
 tf_py_test(
-- 
GitLab


From 4ab3342eb5fdae864e0a32a0e460a27527d79997 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 10:00:17 -0700
Subject: [PATCH 0957/1375] Add a test in BatchNormalization tests.

RELNOTES: n/a
PiperOrigin-RevId: 161822988
---
 tensorflow/compiler/xla/tests/batch_normalization_test.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index 98bf2063c2..074e28cec7 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -318,6 +318,7 @@ INSTANTIATE_TEST_CASE_P(
                       BatchNormTestParam{{10, 10, 10, 10}, 2, 666.6f, 777.7f},
                       BatchNormTestParam{{10, 10, 10, 10}, 1, -666.6f, 777.7f},
                       BatchNormTestParam{{10, 10, 10, 10}, 2, 0.f, 777.7f},
+                      BatchNormTestParam{{1, 1, 10, 130}, 2, 0.f, 777.7f},
                       BatchNormTestParam{{1, 1, 10, 1}, 3, 888.8f, 9.9f},
 
                       BatchNormTestParam{{24, 129, 1, 2}, 2, 10000, 10000},
-- 
GitLab


From 0ba21c68508fdda60a778ddad15854e59c1ba86c Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 13 Jul 2017 10:19:45 -0700
Subject: [PATCH 0958/1375] Make the test for "unused inputs" more precise in
 `tf.import_graph_def()`.

Before, `tf.import_graph_def()` would raise a `ValueError` if any of
the tensors named in the `input_map` was not used as an input to
another node. However, the contract for this function states that a
`ValueError` will be raised "if `input_map`... contains names that do
not appear in `graph_def`," so this change expands the valid domain of
`input_map` to include tensors that only appear as unconsumed
operation outputs in the imported graph.

PiperOrigin-RevId: 161826633
---
 tensorflow/python/framework/importer.py      | 18 +++++++++++++-----
 tensorflow/python/framework/importer_test.py | 18 ++++++++++++++++++
 2 files changed, 31 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py
index 5043e7285f..eec7c4a463 100644
--- a/tensorflow/python/framework/importer.py
+++ b/tensorflow/python/framework/importer.py
@@ -495,13 +495,21 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
       if coloc_device:
         op._set_device(coloc_device)  # pylint: disable=protected-access
 
-    # Treat unused input mappings as an error, because they are likely to be
-    # due to a typo.
-    unused_input_keys = frozenset(input_map.keys()).difference(used_input_keys)
-    if unused_input_keys:
+    # Treat input mappings that don't appear in the graph as an error,
+    # because they are likely to be due to a typo.
+    def _IsImportedNodeOutput(tensor_name):
+      operation_name, output_index = _ParseTensorName(tensor_name)
+      try:
+        return output_index < len(name_to_op[operation_name].outputs)
+      except KeyError:
+        return False
+    absent_input_keys = [
+        k for k in frozenset(input_map.keys()).difference(used_input_keys)
+        if not _IsImportedNodeOutput(k)]
+    if absent_input_keys:
       raise ValueError(
           'Attempted to map inputs that were not found in graph_def: [%s]'
-          % ', '.join(unused_input_keys))
+          % ', '.join(absent_input_keys))
 
     if return_elements is None:
       return None
diff --git a/tensorflow/python/framework/importer_test.py b/tensorflow/python/framework/importer_test.py
index 5a683dc733..cfba6af523 100644
--- a/tensorflow/python/framework/importer_test.py
+++ b/tensorflow/python/framework/importer_test.py
@@ -548,6 +548,24 @@ class ImportGraphDefTest(test.TestCase):
             input_map={"B:0": constant_op.constant(5.0)})
       self.assertTrue("not found in graph_def: [B:0]" in str(e.exception))
 
+  def testInputMapUnusedAsInput(self):
+    with ops.Graph().as_default():
+      # Mapping an unused node output should succeed.
+      importer.import_graph_def(
+          self._MakeGraphDef("""
+          node { name: 'A' op: 'Oi' }
+          """),
+          input_map={"A:0": constant_op.constant(5.0)})
+
+      # Mapping a non-existent output of an existing node should fail.
+      with self.assertRaises(ValueError) as e:
+        importer.import_graph_def(
+            self._MakeGraphDef("""
+            node { name: 'A' op: 'Oi' }
+            """),
+            input_map={"A:2": constant_op.constant(5.0)})
+      self.assertTrue("not found in graph_def: [A:2]" in str(e.exception))
+
   def testInputMapTypeMismatch(self):
     with ops.Graph().as_default():
       with self.assertRaises(ValueError) as e:
-- 
GitLab


From d6082e09668a655ef6e4404fdbcad1224f29c2fb Mon Sep 17 00:00:00 2001
From: Vincent Vanhoucke <vanhoucke@google.com>
Date: Thu, 13 Jul 2017 10:27:09 -0700
Subject: [PATCH 0959/1375] Fix TSNE invocation in Udacity word2vec assignment
 (#11482)

Default TSNE method recently changed to the approximate `method='barnes_hut'` which fails to converge per https://github.com/tensorflow/tensorflow/pull/11455
---
 tensorflow/examples/udacity/5_word2vec.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/examples/udacity/5_word2vec.ipynb b/tensorflow/examples/udacity/5_word2vec.ipynb
index 9d4243d7ae..18c456cad7 100644
--- a/tensorflow/examples/udacity/5_word2vec.ipynb
+++ b/tensorflow/examples/udacity/5_word2vec.ipynb
@@ -806,7 +806,7 @@
       "source": [
         "num_points = 400\n",
         "\n",
-        "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
+        "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
         "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
       ],
       "outputs": [],
-- 
GitLab


From 9addec8a0e44d7655f6344f9b28c3ba0c68971b5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 10:59:41 -0700
Subject: [PATCH 0960/1375] [BatchNorm] Use operation expander to rewrite a
 batch norm training op.

- Introdue an operation expander pass which rewrites HLO into smaller ones.
- Support batch norm training rewriting in operation expander.
- Add an option in JF compiler to use operation expander to rewrite batch norm training.

RELNOTES: n/a
PiperOrigin-RevId: 161832778
---
 tensorflow/compiler/xla/service/BUILD         |  40 +++
 .../xla/service/batchnorm_rewriter.cc         | 283 ++++++++++++++++++
 .../compiler/xla/service/batchnorm_rewriter.h |  49 +++
 .../xla/service/batchnorm_rewriter_test.cc    |  78 +++++
 4 files changed, 450 insertions(+)
 create mode 100644 tensorflow/compiler/xla/service/batchnorm_rewriter.cc
 create mode 100644 tensorflow/compiler/xla/service/batchnorm_rewriter.h
 create mode 100644 tensorflow/compiler/xla/service/batchnorm_rewriter_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index b676946934..ad4d23c49b 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -917,6 +917,46 @@ cc_test(
     ],
 )
 
+cc_library(
+    name = "batchnorm_rewriter",
+    srcs = ["batchnorm_rewriter.cc"],
+    hdrs = ["batchnorm_rewriter.h"],
+    deps = [
+        ":hlo",
+        ":hlo_pass",
+        ":hlo_query",
+        ":shape_inference",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:window_util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_test(
+    name = "batchnorm_rewriter_test",
+    size = "small",
+    srcs = ["batchnorm_rewriter_test.cc"],
+    deps = [
+        ":batchnorm_rewriter",
+        ":hlo",
+        ":hlo_matchers",
+        ":hlo_pass",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_library(
     name = "algebraic_simplifier",
     srcs = ["algebraic_simplifier.cc"],
diff --git a/tensorflow/compiler/xla/service/batchnorm_rewriter.cc b/tensorflow/compiler/xla/service/batchnorm_rewriter.cc
new file mode 100644
index 0000000000..5d5d3caa2f
--- /dev/null
+++ b/tensorflow/compiler/xla/service/batchnorm_rewriter.cc
@@ -0,0 +1,283 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/batchnorm_rewriter.h"
+
+#include <algorithm>
+#include <memory>
+#include <numeric>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_query.h"
+#include "tensorflow/compiler/xla/service/shape_inference.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/compiler/xla/window_util.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+
+// BatchNormRewriterVisitor traverses the HLO computation and rewrites BatchNorm
+// operations into smaller operations.
+class BatchNormRewriterVisitor : public DfsHloVisitorWithDefault {
+ public:
+  // Default visitor action is to do nothing and return OK.
+  Status DefaultAction(HloInstruction* /*hlo_instruction*/) override {
+    return Status::OK();
+  }
+
+  Status HandleBatchNormTraining(HloInstruction* batch_norm) override;
+
+  // Runs the visitor on a computation.
+  static bool Run(HloComputation* computation, bool rewrite_training_op,
+                  bool rewrite_grad_op);
+
+  // Returns whether any batch norm ops were rewritten.
+  const bool changed() const { return changed_; }
+
+  ~BatchNormRewriterVisitor() override = default;
+
+ private:
+  explicit BatchNormRewriterVisitor(HloComputation* computation,
+                                    bool rewrite_training_op,
+                                    bool rewrite_grad_op)
+      : computation_(computation),
+        rewrite_training_op_(rewrite_training_op),
+        rewrite_grad_op_(rewrite_grad_op) {}
+
+  HloComputation* GetScalarBinaryComputation(PrimitiveType primitive_type,
+                                             HloOpcode opcode) {
+    HloComputation::Builder b("scalar computation");
+    auto scalar_lhs = b.AddInstruction(HloInstruction::CreateParameter(
+        0, ShapeUtil::MakeShape(F32, {}), "scalar lhs"));
+    auto scalar_rhs = b.AddInstruction(HloInstruction::CreateParameter(
+        1, ShapeUtil::MakeShape(F32, {}), "scalar rhs"));
+    auto scalar_op = b.AddInstruction(
+        HloInstruction::CreateBinary(ShapeUtil::MakeShape(primitive_type, {}),
+                                     opcode, scalar_lhs, scalar_rhs));
+    return computation_->parent()->AddEmbeddedComputation(b.Build(scalar_op));
+  }
+
+  // Current HloComputation instance the BatchNormRewriter is
+  // traversing.
+  HloComputation* computation_;
+
+  bool rewrite_training_op_;
+  bool rewrite_grad_op_;
+
+  // Whether rewrite has occurred.
+  bool changed_ = false;
+
+  // Replaces the existing HLO instruction old_instruction, with
+  // new_instruction, and marks the optimizer status as changed.
+  // Returns the Status representing the result of the replace operation.
+  Status ReplaceWithNewInstruction(
+      HloInstruction* old_instruction,
+      std::unique_ptr<HloInstruction> new_instruction) {
+    TF_RETURN_IF_ERROR(computation_->ReplaceWithNewInstruction(
+        old_instruction, std::move(new_instruction)));
+    changed_ = true;
+    return Status::OK();
+  }
+
+  // Replaces the existing HLO instruction old_instruction, with
+  // new_instruction, and marks the optimizer status as changed.
+  // Returns the Status representing the result of the replace operation.
+  Status ReplaceInstruction(HloInstruction* old_instruction,
+                            HloInstruction* new_instruction) {
+    TF_RETURN_IF_ERROR(
+        computation_->ReplaceInstruction(old_instruction, new_instruction));
+    changed_ = true;
+    return Status::OK();
+  }
+};
+
+bool BatchNormRewriterVisitor::Run(HloComputation* computation,
+                                   bool rewrite_training_op,
+                                   bool rewrite_grad_op) {
+  BatchNormRewriterVisitor visitor(computation,
+                                   /*rewrite_training_op=*/rewrite_training_op,
+                                   /*rewrite_grad_op=*/rewrite_grad_op);
+  TF_CHECK_OK(computation->Accept(&visitor));
+  return visitor.changed_;
+}
+
+Status BatchNormRewriterVisitor::HandleBatchNormTraining(
+    HloInstruction* batch_norm) {
+  if (!rewrite_training_op_) {
+    return Status::OK();
+  }
+  // Expand batch norm training into smaller HLO ops.
+  HloInstruction* operand = batch_norm->mutable_operand(0);
+  const Shape operand_shape = operand->shape();
+  int64 feature_index = batch_norm->feature_index();
+  const int64 feature_count = operand_shape.dimensions(feature_index);
+  const int64 size_in_elements = ShapeUtil::ElementsIn(operand_shape);
+  auto elements_per_feature =
+      computation_->AddInstruction(HloInstruction::CreateConstant(
+          Literal::CreateR0<float>(size_in_elements / feature_count)));
+
+  HloInstruction* scale = batch_norm->mutable_operand(1);
+  HloInstruction* offset = batch_norm->mutable_operand(2);
+  const Shape feature_shape = scale->shape();
+
+  auto zero = computation_->AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0(0.0f)));
+
+  auto epsilon = computation_->AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0(batch_norm->epsilon())));
+
+  std::vector<int64> dimensions_without_feature;
+
+  for (int64 i = 0; i < ShapeUtil::Rank(operand_shape); ++i) {
+    if (i != feature_index) {
+      dimensions_without_feature.push_back(i);
+    }
+  }
+
+  auto scale_broadcasted = computation_->AddInstruction(
+      HloInstruction::CreateBroadcast(operand_shape, scale, {feature_index}));
+
+  auto offset_broadcasted = computation_->AddInstruction(
+      HloInstruction::CreateBroadcast(operand_shape, offset, {feature_index}));
+
+  HloComputation* add_reduce_computation =
+      GetScalarBinaryComputation(F32, HloOpcode::kAdd);
+
+  // X^2.
+  auto operand_squared =
+      computation_->AddInstruction(HloInstruction::CreateBinary(
+          operand_shape, HloOpcode::kMultiply, operand, operand));
+  // Sum[X].
+  auto sum = computation_->AddInstruction(HloInstruction::CreateReduce(
+      feature_shape, operand, zero, dimensions_without_feature,
+      add_reduce_computation));
+
+  // Sum[X^2].
+  auto squared_sum = computation_->AddInstruction(HloInstruction::CreateReduce(
+      feature_shape, operand_squared, zero, dimensions_without_feature,
+      add_reduce_computation));
+
+  // Fuse two parallel reduces together to improve performance.
+  auto tuple = computation_->AddInstruction(
+      HloInstruction::CreateTuple({sum, squared_sum}));
+
+  auto fused = computation_->CreateFusionInstruction(
+      {tuple, sum, squared_sum, operand_squared},
+      HloInstruction::FusionKind::kInput);
+
+  sum = computation_->AddInstruction(
+      HloInstruction::CreateGetTupleElement(feature_shape, fused, 0));
+
+  squared_sum = computation_->AddInstruction(
+      HloInstruction::CreateGetTupleElement(feature_shape, fused, 1));
+
+  // E[X].
+  auto mean = computation_->AddInstruction(HloInstruction::CreateBinary(
+      feature_shape, HloOpcode::kDivide, sum, elements_per_feature));
+
+  auto mean_broadcasted = computation_->AddInstruction(
+      HloInstruction::CreateBroadcast(operand_shape, mean, {feature_index}));
+
+  // E[X^2].
+  auto square_mean = computation_->AddInstruction(HloInstruction::CreateBinary(
+      feature_shape, HloOpcode::kDivide, squared_sum, elements_per_feature));
+
+  // E^2[X].
+  auto mean_square = computation_->AddInstruction(HloInstruction::CreateBinary(
+      feature_shape, HloOpcode::kMultiply, mean, mean));
+
+  // Var[X].
+  auto var = computation_->AddInstruction(HloInstruction::CreateBinary(
+      feature_shape, HloOpcode::kSubtract, square_mean, mean_square));
+
+  auto var_broadcasted = computation_->AddInstruction(
+      HloInstruction::CreateBroadcast(operand_shape, var, {feature_index}));
+
+  // Var[X] + epsilon.
+  auto var_add_epsilon =
+      computation_->AddInstruction(HloInstruction::CreateBinary(
+          operand_shape, HloOpcode::kAdd, var_broadcasted, epsilon));
+
+  auto neg_half = computation_->AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0(-0.5f)));
+
+  // 1 / Sqrt[Var[X] + epsilon].
+  auto rsqrt_var_add_epsilon =
+      computation_->AddInstruction(HloInstruction::CreateBinary(
+          operand_shape, HloOpcode::kPower, var_add_epsilon, neg_half));
+
+  // X - E[X].
+  auto operand_minus_mean =
+      computation_->AddInstruction(HloInstruction::CreateBinary(
+          operand_shape, HloOpcode::kSubtract, operand, mean_broadcasted));
+
+  // (X - E[X]) / Sqrt[Var[X] + epsilon].
+  auto normalized = computation_->AddInstruction(
+      HloInstruction::CreateBinary(operand_shape, HloOpcode::kMultiply,
+                                   operand_minus_mean, rsqrt_var_add_epsilon));
+
+  // (X - E[X]) / Sqrt[Var[X] + epsilon] * scale.
+  auto scaled_normalized =
+      computation_->AddInstruction(HloInstruction::CreateBinary(
+          operand_shape, HloOpcode::kMultiply, normalized, scale_broadcasted));
+
+  // (X - E[X]) / Sqrt[Var[X] + epsilon] * scale + offset.
+  auto shifted_normalized = computation_->AddInstruction(
+      HloInstruction::CreateBinary(operand_shape, HloOpcode::kAdd,
+                                   scaled_normalized, offset_broadcasted));
+
+  TF_CHECK_OK(ReplaceWithNewInstruction(
+      batch_norm,
+      HloInstruction::CreateTuple({shifted_normalized, mean, var})));
+  return Status::OK();
+}
+
+StatusOr<bool> BatchNormRewriter::Run(HloModule* module) {
+  XLA_VLOG_LINES(2, "BatchNormRewriter::Run(), before:\n" + module->ToString());
+  bool changed = false;
+  // Make a copy of the computations because we may add computations to the
+  // module, invalidating iteration.
+  std::vector<HloComputation*> computations;
+  for (auto& comp : module->computations()) {
+    computations.push_back(comp.get());
+  }
+  for (auto& comp : computations) {
+    if (BatchNormRewriterVisitor::Run(comp, rewrite_training_op_,
+                                      rewrite_grad_op_)) {
+      changed = true;
+    }
+  }
+  XLA_VLOG_LINES(2, "BatchNormRewriter::Run(), after:\n" + module->ToString());
+  return changed;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/batchnorm_rewriter.h b/tensorflow/compiler/xla/service/batchnorm_rewriter.h
new file mode 100644
index 0000000000..6d176f4849
--- /dev/null
+++ b/tensorflow/compiler/xla/service/batchnorm_rewriter.h
@@ -0,0 +1,49 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BATCHNORM_REWRITER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_BATCHNORM_REWRITER_H_
+
+#include <utility>
+
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+
+namespace xla {
+
+// A pass which rewrites batch norm operations into more operations. Breaking a
+// big operation into smaller operations helps leverage our generic fusion
+// logic.
+class BatchNormRewriter : public HloPassInterface {
+ public:
+  BatchNormRewriter(bool rewrite_training_op = false,
+                    bool rewrite_grad_op = false)
+      : rewrite_training_op_(rewrite_training_op),
+        rewrite_grad_op_(rewrite_grad_op) {}
+  ~BatchNormRewriter() = default;
+  tensorflow::StringPiece name() const override { return "batchnorm_rewriter"; }
+
+  // Run operation expander on the given computation. Returns whether the
+  // computation was changed.
+  StatusOr<bool> Run(HloModule* module) override;
+
+ private:
+  bool rewrite_training_op_;
+  bool rewrite_grad_op_;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_BATCHNORM_REWRITER_H_
diff --git a/tensorflow/compiler/xla/service/batchnorm_rewriter_test.cc b/tensorflow/compiler/xla/service/batchnorm_rewriter_test.cc
new file mode 100644
index 0000000000..301f31b51c
--- /dev/null
+++ b/tensorflow/compiler/xla/service/batchnorm_rewriter_test.cc
@@ -0,0 +1,78 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/batchnorm_rewriter.h"
+
+#include <memory>
+#include <utility>
+
+#include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_matchers.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+
+namespace xla {
+namespace {
+
+using BatchNormRewriterTest = HloTestBase;
+
+// Test that we expand BatchNormTraining.
+TEST_F(BatchNormRewriterTest, BatchNormTraining) {
+  Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2, 2});
+  Shape scale_shape = ShapeUtil::MakeShape(F32, {2});
+  Shape offset_shape = ShapeUtil::MakeShape(F32, {2});
+
+  HloComputation::Builder builder(TestName());
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_shape, "activiation"));
+
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, scale_shape, "scale"));
+
+  HloInstruction* param2 = builder.AddInstruction(
+      HloInstruction::CreateParameter(2, offset_shape, "offset"));
+
+  builder.AddInstruction(HloInstruction::CreateBatchNormTraining(
+      ShapeUtil::MakeTupleShape({input_shape, scale_shape, offset_shape}),
+      param0, param1, param2,
+      /*epsilon=*/0.001, /*feature_index=*/3));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+  HloInstruction* root = computation->root_instruction();
+  EXPECT_EQ(root->opcode(), HloOpcode::kBatchNormTraining);
+  BatchNormRewriter rewriter(/*rewrite_training_op=*/true);
+  ASSERT_TRUE(rewriter.Run(module.get()).ValueOrDie());
+  root = computation->root_instruction();
+  // Make sure this operation is expanded.
+  EXPECT_EQ(root->opcode(), HloOpcode::kTuple);
+}
+
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  return xla::ParseDebugOptionsFlagsAndRunTests(argc, argv);
+}
-- 
GitLab


From 7d5c426692ba03ebfd67afc03c967c594f036932 Mon Sep 17 00:00:00 2001
From: Shuolongbj <Ru@aijia.io>
Date: Fri, 14 Jul 2017 02:10:32 +0800
Subject: [PATCH 0961/1375] update word2vec_basic.py (#11483)

Fix TSNE invocation in tutorials word2vec assignment
---
 tensorflow/examples/tutorials/word2vec/word2vec_basic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
index aee482fda5..e67442b14b 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -253,7 +253,7 @@ try:
   from sklearn.manifold import TSNE
   import matplotlib.pyplot as plt
 
-  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
+  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
   plot_only = 500
   low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
   labels = [reverse_dictionary[i] for i in xrange(plot_only)]
-- 
GitLab


From 4cb482dc3e0424c3d658ba373a6354dded6a32df Mon Sep 17 00:00:00 2001
From: Chris Antaki <ChrisAntaki@gmail.com>
Date: Thu, 13 Jul 2017 11:11:02 -0700
Subject: [PATCH 0962/1375] Adding a comma to a Python snippet (#11480)

---
 tensorflow/core/profiler/g3doc/python_api.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/profiler/g3doc/python_api.md b/tensorflow/core/profiler/g3doc/python_api.md
index 42a89d22a1..0daac655c4 100644
--- a/tensorflow/core/profiler/g3doc/python_api.md
+++ b/tensorflow/core/profiler/g3doc/python_api.md
@@ -24,7 +24,7 @@ opts = ProfileOptionBuilder(
     ).build()
 param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    cmd='code'
+    cmd='code',
     options=opts)
 
 # param_stats can be tensorflow.tfprof.GraphNodeProto or
-- 
GitLab


From 5b33ae1c383beceb87189469299ce0b76f66bda5 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 13 Jul 2017 11:08:24 -0700
Subject: [PATCH 0963/1375] Initial public release of tf.distributions.

PiperOrigin-RevId: 161834256
---
 tensorflow/python/BUILD                       |   1 +
 tensorflow/python/__init__.py                 |   5 +-
 .../python/ops/distributions/__init__.py      |  18 ---
 .../python/ops/distributions/bijector.py      |   7 +-
 .../python/ops/distributions/bijectors.py     |  31 ++++
 .../python/ops/distributions/distribution.py  |   7 +
 .../python/ops/distributions/distributions.py |  65 ++++++++
 .../ops/distributions/kullback_leibler.py     |   6 +
 .../tensorflow.distributions.-bernoulli.pbtxt | 135 +++++++++++++++++
 .../tensorflow.distributions.-beta.pbtxt      | 139 ++++++++++++++++++
 ...ensorflow.distributions.-categorical.pbtxt | 139 ++++++++++++++++++
 ...distributions.-dirichlet-multinomial.pbtxt | 139 ++++++++++++++++++
 .../tensorflow.distributions.-dirichlet.pbtxt | 135 +++++++++++++++++
 ...nsorflow.distributions.-distribution.pbtxt | 126 ++++++++++++++++
 ...ensorflow.distributions.-exponential.pbtxt | 136 +++++++++++++++++
 .../tensorflow.distributions.-gamma.pbtxt     | 135 +++++++++++++++++
 .../tensorflow.distributions.-laplace.pbtxt   | 135 +++++++++++++++++
 ...ensorflow.distributions.-multinomial.pbtxt | 139 ++++++++++++++++++
 .../tensorflow.distributions.-normal.pbtxt    | 135 +++++++++++++++++
 ...nsorflow.distributions.-register-k-l.pbtxt |   9 ++
 ...stributions.-reparameterization-type.pbtxt |   9 ++
 .../tensorflow.distributions.-student-t.pbtxt | 139 ++++++++++++++++++
 .../tensorflow.distributions.-uniform.pbtxt   | 139 ++++++++++++++++++
 ...ow.distributions.bijectors.-bijector.pbtxt |  65 ++++++++
 ...ow.distributions.bijectors.-identity.pbtxt |  66 +++++++++
 .../tensorflow.distributions.bijectors.pbtxt  |  11 ++
 .../api/golden/tensorflow.distributions.pbtxt |  79 ++++++++++
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   4 +
 28 files changed, 2132 insertions(+), 22 deletions(-)
 create mode 100644 tensorflow/python/ops/distributions/bijectors.py
 create mode 100644 tensorflow/python/ops/distributions/distributions.py
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-bernoulli.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-beta.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-categorical.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet-multinomial.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-distribution.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-exponential.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-gamma.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-laplace.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-multinomial.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-normal.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-register-k-l.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-reparameterization-type.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-student-t.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.-uniform.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-bijector.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-identity.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.bijectors.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.distributions.pbtxt

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 65d7a89594..8d3d81eeba 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2027,6 +2027,7 @@ py_library(
         ":util",
         ":variable_scope",
         ":variables",
+        "//tensorflow/python/ops/distributions",
     ],
 )
 
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index a2c86cbb6c..80878bc976 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -83,6 +83,7 @@ from tensorflow.python.ops import metrics
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import sets
 from tensorflow.python.ops import spectral_ops as spectral
+from tensorflow.python.ops.distributions import distributions
 from tensorflow.python.ops.losses import losses
 from tensorflow.python.profiler import profiler
 from tensorflow.python.user_ops import user_ops
@@ -213,6 +214,7 @@ _allowed_symbols.extend([
     'app',
     'bitwise',
     'compat',
+    'distributions',
     'errors',
     'estimator',
     'feature_column',
@@ -250,7 +252,8 @@ _allowed_symbols.extend([
 # referenced in the whitelist.
 remove_undocumented(__name__, _allowed_symbols, [
     framework_lib, array_ops, check_ops, client_lib, compat, constant_op,
-    control_flow_ops, confusion_matrix_m, functional_ops, histogram_ops, io_ops,
+    control_flow_ops, confusion_matrix_m, distributions,
+    functional_ops, histogram_ops, io_ops,
     losses, math_ops, metrics, nn, resource_loader, sets, script_ops,
     session_ops, sparse_ops, state_ops, string_ops, summary, tensor_array_ops,
     train, layers, profiler
diff --git a/tensorflow/python/ops/distributions/__init__.py b/tensorflow/python/ops/distributions/__init__.py
index 563b189990..e69de29bb2 100644
--- a/tensorflow/python/ops/distributions/__init__.py
+++ b/tensorflow/python/ops/distributions/__init__.py
@@ -1,18 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Core module for TensorFlow distribution objects and helpers."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
diff --git a/tensorflow/python/ops/distributions/bijector.py b/tensorflow/python/ops/distributions/bijector.py
index 70e9fdadd2..84bd0a20da 100644
--- a/tensorflow/python/ops/distributions/bijector.py
+++ b/tensorflow/python/ops/distributions/bijector.py
@@ -19,9 +19,10 @@ from __future__ import division
 from __future__ import print_function
 
 # go/tf-wildcard-import
-# pylint: disable=wildcard-import
-from tensorflow.python.ops.distributions.bijector_impl import *
-# pylint: enable=wildcard-import
+# pylint: disable=wildcard-import,unused-import
+from tensorflow.python.ops.distributions.bijector_impl import Bijector
+
+# pylint: enable=wildcard-import,unused-import
 from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = ["Bijector"]
diff --git a/tensorflow/python/ops/distributions/bijectors.py b/tensorflow/python/ops/distributions/bijectors.py
new file mode 100644
index 0000000000..69c3a5d4c0
--- /dev/null
+++ b/tensorflow/python/ops/distributions/bijectors.py
@@ -0,0 +1,31 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Core module for TensorFlow distribution bijectors."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import,unused-import
+from tensorflow.python.ops.distributions.bijector import Bijector
+from tensorflow.python.ops.distributions.identity_bijector import Identity
+
+# pylint: enable=wildcard-import,unused-import
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = ["Bijector", "Identity"]
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/python/ops/distributions/distribution.py b/tensorflow/python/ops/distributions/distribution.py
index 1684e85cc7..22687a093a 100644
--- a/tensorflow/python/ops/distributions/distribution.py
+++ b/tensorflow/python/ops/distributions/distribution.py
@@ -35,6 +35,13 @@ from tensorflow.python.ops.distributions import util
 from tensorflow.python.util import tf_inspect
 
 
+__all__ = [
+    "ReparameterizationType",
+    "FULLY_REPARAMETERIZED",
+    "NOT_REPARAMETERIZED",
+    "Distribution",
+]
+
 _DISTRIBUTION_PUBLIC_METHOD_WRAPPERS = [
     "batch_shape_tensor", "batch_shape", "event_shape_tensor", "event_shape",
     "sample", "log_prob", "prob", "log_cdf", "cdf", "log_survival_function",
diff --git a/tensorflow/python/ops/distributions/distributions.py b/tensorflow/python/ops/distributions/distributions.py
new file mode 100644
index 0000000000..9df7d148a5
--- /dev/null
+++ b/tensorflow/python/ops/distributions/distributions.py
@@ -0,0 +1,65 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Core module for TensorFlow distribution objects and helpers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+# pylint: disable=wildcard-import,unused-import
+from tensorflow.python.ops.distributions import bijectors
+from tensorflow.python.ops.distributions.bernoulli import Bernoulli
+from tensorflow.python.ops.distributions.beta import Beta
+from tensorflow.python.ops.distributions.categorical import Categorical
+from tensorflow.python.ops.distributions.dirichlet import Dirichlet
+from tensorflow.python.ops.distributions.dirichlet_multinomial import DirichletMultinomial
+from tensorflow.python.ops.distributions.distribution import *
+from tensorflow.python.ops.distributions.exponential import Exponential
+from tensorflow.python.ops.distributions.gamma import Gamma
+from tensorflow.python.ops.distributions.kullback_leibler import *
+from tensorflow.python.ops.distributions.laplace import Laplace
+from tensorflow.python.ops.distributions.multinomial import Multinomial
+from tensorflow.python.ops.distributions.normal import Normal
+from tensorflow.python.ops.distributions.student_t import StudentT
+from tensorflow.python.ops.distributions.uniform import Uniform
+# pylint: enable=wildcard-import,unused-import
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+
+_allowed_symbols = [
+    "bijectors",
+    "Bernoulli",
+    "Beta",
+    "Categorical",
+    "DirichletMultinomial",
+    "Dirichlet",
+    "Distribution",
+    "ReparameterizationType",
+    "FULLY_REPARAMETERIZED",
+    "NOT_REPARAMETERIZED",
+    "Exponential",
+    "Gamma",
+    "RegisterKL",
+    "kl_divergence",
+    "Laplace",
+    "Multinomial",
+    "Normal",
+    "StudentT",
+    "Uniform",
+]
+
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/python/ops/distributions/kullback_leibler.py b/tensorflow/python/ops/distributions/kullback_leibler.py
index 9770d82bd8..a6ab581cc2 100644
--- a/tensorflow/python/ops/distributions/kullback_leibler.py
+++ b/tensorflow/python/ops/distributions/kullback_leibler.py
@@ -28,6 +28,12 @@ from tensorflow.python.util import tf_inspect
 _DIVERGENCES = {}
 
 
+__all__ = [
+    "RegisterKL",
+    "kl_divergence",
+]
+
+
 def _registered_kl(type_a, type_b):
   """Get the KL function registered for classes a and b."""
   hierarchy_a = tf_inspect.getmro(type_a)
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-bernoulli.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-bernoulli.pbtxt
new file mode 100644
index 0000000000..cfe09345ac
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-bernoulli.pbtxt
@@ -0,0 +1,135 @@
+path: "tensorflow.distributions.Bernoulli"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.bernoulli.Bernoulli\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "logits"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "probs"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'logits\', \'probs\', \'dtype\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \"<dtype: \'int32\'>\", \'False\', \'True\', \'Bernoulli\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-beta.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-beta.pbtxt
new file mode 100644
index 0000000000..2e6578bae1
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-beta.pbtxt
@@ -0,0 +1,139 @@
+path: "tensorflow.distributions.Beta"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.beta.Beta\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "concentration0"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "concentration1"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "total_concentration"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'concentration1\', \'concentration0\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'True\', \'Beta\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-categorical.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-categorical.pbtxt
new file mode 100644
index 0000000000..d42b0e82e4
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-categorical.pbtxt
@@ -0,0 +1,139 @@
+path: "tensorflow.distributions.Categorical"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.categorical.Categorical\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "logits"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "probs"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'logits\', \'probs\', \'dtype\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \"<dtype: \'int32\'>\", \'False\', \'True\', \'Categorical\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet-multinomial.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet-multinomial.pbtxt
new file mode 100644
index 0000000000..710164743e
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet-multinomial.pbtxt
@@ -0,0 +1,139 @@
+path: "tensorflow.distributions.DirichletMultinomial"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.dirichlet_multinomial.DirichletMultinomial\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "concentration"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "total_concentration"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "total_count"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'total_count\', \'concentration\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'DirichletMultinomial\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet.pbtxt
new file mode 100644
index 0000000000..6cc361672e
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet.pbtxt
@@ -0,0 +1,135 @@
+path: "tensorflow.distributions.Dirichlet"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.dirichlet.Dirichlet\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "concentration"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "total_concentration"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'concentration\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Dirichlet\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-distribution.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-distribution.pbtxt
new file mode 100644
index 0000000000..40ad07d1be
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-distribution.pbtxt
@@ -0,0 +1,126 @@
+path: "tensorflow.distributions.Distribution"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'dtype\', \'reparameterization_type\', \'validate_args\', \'allow_nan_stats\', \'parameters\', \'graph_parents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-exponential.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-exponential.pbtxt
new file mode 100644
index 0000000000..8f34d25fea
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-exponential.pbtxt
@@ -0,0 +1,136 @@
+path: "tensorflow.distributions.Exponential"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.exponential.Exponential\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.gamma.Gamma\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "concentration"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "rate"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'rate\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Exponential\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-gamma.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-gamma.pbtxt
new file mode 100644
index 0000000000..0ae88fba3b
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-gamma.pbtxt
@@ -0,0 +1,135 @@
+path: "tensorflow.distributions.Gamma"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.gamma.Gamma\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "concentration"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "rate"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'concentration\', \'rate\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Gamma\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-laplace.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-laplace.pbtxt
new file mode 100644
index 0000000000..e7cd595e94
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-laplace.pbtxt
@@ -0,0 +1,135 @@
+path: "tensorflow.distributions.Laplace"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.laplace.Laplace\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "loc"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scale"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'loc\', \'scale\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Laplace\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-multinomial.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-multinomial.pbtxt
new file mode 100644
index 0000000000..7a4a16ff83
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-multinomial.pbtxt
@@ -0,0 +1,139 @@
+path: "tensorflow.distributions.Multinomial"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.multinomial.Multinomial\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "logits"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "probs"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "total_count"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'total_count\', \'logits\', \'probs\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'True\', \'Multinomial\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-normal.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-normal.pbtxt
new file mode 100644
index 0000000000..14c8c34cc2
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-normal.pbtxt
@@ -0,0 +1,135 @@
+path: "tensorflow.distributions.Normal"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.normal.Normal\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "loc"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scale"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'loc\', \'scale\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Normal\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-register-k-l.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-register-k-l.pbtxt
new file mode 100644
index 0000000000..e3db443c2b
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-register-k-l.pbtxt
@@ -0,0 +1,9 @@
+path: "tensorflow.distributions.RegisterKL"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.kullback_leibler.RegisterKL\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'dist_cls_a\', \'dist_cls_b\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-reparameterization-type.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-reparameterization-type.pbtxt
new file mode 100644
index 0000000000..02e8d576dd
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-reparameterization-type.pbtxt
@@ -0,0 +1,9 @@
+path: "tensorflow.distributions.ReparameterizationType"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.ReparameterizationType\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'rep_type\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-student-t.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-student-t.pbtxt
new file mode 100644
index 0000000000..30db6d3f35
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-student-t.pbtxt
@@ -0,0 +1,139 @@
+path: "tensorflow.distributions.StudentT"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.student_t.StudentT\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "df"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "loc"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scale"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'df\', \'loc\', \'scale\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'StudentT\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-uniform.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.-uniform.pbtxt
new file mode 100644
index 0000000000..46cbdf225f
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.-uniform.pbtxt
@@ -0,0 +1,139 @@
+path: "tensorflow.distributions.Uniform"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.uniform.Uniform\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution.Distribution\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.distribution._BaseDistribution\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "allow_nan_stats"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "batch_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "high"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "low"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "parameters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "reparameterization_type"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'low\', \'high\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'False\', \'True\', \'Uniform\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], "
+  }
+  member_method {
+    name: "copy"
+    argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None"
+  }
+  member_method {
+    name: "covariance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
+  }
+  member_method {
+    name: "entropy"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], "
+  }
+  member_method {
+    name: "event_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], "
+  }
+  member_method {
+    name: "is_scalar_batch"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], "
+  }
+  member_method {
+    name: "is_scalar_event"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], "
+  }
+  member_method {
+    name: "log_cdf"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], "
+  }
+  member_method {
+    name: "log_prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], "
+  }
+  member_method {
+    name: "log_survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], "
+  }
+  member_method {
+    name: "mean"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], "
+  }
+  member_method {
+    name: "mode"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], "
+  }
+  member_method {
+    name: "param_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], "
+  }
+  member_method {
+    name: "param_static_shapes"
+    argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "prob"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], "
+  }
+  member_method {
+    name: "quantile"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], "
+  }
+  member_method {
+    name: "range"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range\'], "
+  }
+  member_method {
+    name: "sample"
+    argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], "
+  }
+  member_method {
+    name: "stddev"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], "
+  }
+  member_method {
+    name: "survival_function"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], "
+  }
+  member_method {
+    name: "variance"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-bijector.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-bijector.pbtxt
new file mode 100644
index 0000000000..11565bd3e4
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-bijector.pbtxt
@@ -0,0 +1,65 @@
+path: "tensorflow.distributions.bijectors.Bijector"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.bijector_impl.Bijector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_ndims"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "graph_parents"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "is_constant_jacobian"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'event_ndims\', \'graph_parents\', \'is_constant_jacobian\', \'validate_args\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'False\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "forward"
+    argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'forward\'], "
+  }
+  member_method {
+    name: "forward_event_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "forward_event_shape_tensor"
+    argspec: "args=[\'self\', \'input_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'forward_event_shape_tensor\'], "
+  }
+  member_method {
+    name: "forward_log_det_jacobian"
+    argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'forward_log_det_jacobian\'], "
+  }
+  member_method {
+    name: "inverse"
+    argspec: "args=[\'self\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "
+  }
+  member_method {
+    name: "inverse_event_shape"
+    argspec: "args=[\'self\', \'output_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "inverse_event_shape_tensor"
+    argspec: "args=[\'self\', \'output_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse_event_shape_tensor\'], "
+  }
+  member_method {
+    name: "inverse_log_det_jacobian"
+    argspec: "args=[\'self\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse_log_det_jacobian\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-identity.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-identity.pbtxt
new file mode 100644
index 0000000000..1e5fe624eb
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.-identity.pbtxt
@@ -0,0 +1,66 @@
+path: "tensorflow.distributions.bijectors.Identity"
+tf_class {
+  is_instance: "<class \'tensorflow.python.ops.distributions.identity_bijector.Identity\'>"
+  is_instance: "<class \'tensorflow.python.ops.distributions.bijector_impl.Bijector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "event_ndims"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "graph_parents"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "is_constant_jacobian"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "validate_args"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'validate_args\', \'event_ndims\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'0\', \'identity\'], "
+  }
+  member_method {
+    name: "forward"
+    argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'forward\'], "
+  }
+  member_method {
+    name: "forward_event_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "forward_event_shape_tensor"
+    argspec: "args=[\'self\', \'input_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'forward_event_shape_tensor\'], "
+  }
+  member_method {
+    name: "forward_log_det_jacobian"
+    argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'forward_log_det_jacobian\'], "
+  }
+  member_method {
+    name: "inverse"
+    argspec: "args=[\'self\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "
+  }
+  member_method {
+    name: "inverse_event_shape"
+    argspec: "args=[\'self\', \'output_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "inverse_event_shape_tensor"
+    argspec: "args=[\'self\', \'output_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse_event_shape_tensor\'], "
+  }
+  member_method {
+    name: "inverse_log_det_jacobian"
+    argspec: "args=[\'self\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse_log_det_jacobian\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.pbtxt
new file mode 100644
index 0000000000..1d0144f36e
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.bijectors.pbtxt
@@ -0,0 +1,11 @@
+path: "tensorflow.distributions.bijectors"
+tf_module {
+  member {
+    name: "Bijector"
+    mtype: "<class \'abc.ABCMeta\'>"
+  }
+  member {
+    name: "Identity"
+    mtype: "<class \'abc.ABCMeta\'>"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.pbtxt b/tensorflow/tools/api/golden/tensorflow.distributions.pbtxt
new file mode 100644
index 0000000000..2fba7c506e
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.distributions.pbtxt
@@ -0,0 +1,79 @@
+path: "tensorflow.distributions"
+tf_module {
+  member {
+    name: "Bernoulli"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Beta"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Categorical"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Dirichlet"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "DirichletMultinomial"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Distribution"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Exponential"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "FULLY_REPARAMETERIZED"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution.ReparameterizationType\'>"
+  }
+  member {
+    name: "Gamma"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Laplace"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Multinomial"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "NOT_REPARAMETERIZED"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution.ReparameterizationType\'>"
+  }
+  member {
+    name: "Normal"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "RegisterKL"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "ReparameterizationType"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "StudentT"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "Uniform"
+    mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
+  }
+  member {
+    name: "bijectors"
+    mtype: "<type \'module\'>"
+  }
+  member_method {
+    name: "kl_divergence"
+    argspec: "args=[\'distribution_a\', \'distribution_b\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index a9f00f63a0..91b49e6d04 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -284,6 +284,10 @@ tf_module {
     name: "contrib"
     mtype: "<class \'tensorflow.python.util.lazy_loader.LazyLoader\'>"
   }
+  member {
+    name: "distributions"
+    mtype: "<type \'module\'>"
+  }
   member {
     name: "double"
     mtype: "<class \'tensorflow.python.framework.dtypes.DType\'>"
-- 
GitLab


From 93826fac842e370a3a4689a93fd4dac02cd8cb41 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 12:32:52 -0700
Subject: [PATCH 0964/1375] Adding tree per class multiclass strategy handling.

PiperOrigin-RevId: 161847349
---
 .../boosted_trees/kernels/prediction_ops.cc   |  7 +--
 .../kernels/split_handler_ops.cc              | 60 +++++++++++++------
 .../boosted_trees/ops/prediction_ops.cc       |  7 ++-
 .../boosted_trees/ops/split_handler_ops.cc    |  3 +
 .../python/kernel_tests/model_ops_test.py     | 18 ++++--
 .../kernel_tests/prediction_ops_test.py       | 50 ++++++++++------
 .../kernel_tests/split_handler_ops_test.py    |  8 +++
 7 files changed, 107 insertions(+), 46 deletions(-)

diff --git a/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
index 2e99f42ff0..daca049548 100644
--- a/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc
@@ -88,10 +88,9 @@ class GradientTreesPredictionOp : public OpKernel {
         context, ParseProtoUnlimited(&learner_config, learner_config_str),
         errors::InvalidArgument("Unable to parse learner config config."));
 
-    prediction_vector_size_ =
-        learner_config.multi_class_strategy() == LearnerConfig::TREE_PER_CLASS
-            ? num_classes_ - 1
-            : num_classes_;
+    bool reduce_dim;
+    OP_REQUIRES_OK(context, context->GetAttr("reduce_dim", &reduce_dim));
+    prediction_vector_size_ = reduce_dim ? num_classes_ - 1 : num_classes_;
 
     only_finalized_trees_ =
         learner_config.growing_mode() == learner_config.WHOLE_TREE;
diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
index a2bf374cf5..29635bb3c4 100644
--- a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
@@ -70,6 +70,32 @@ class BaseBuildSplitOp : public OpKernel {
                      multiclass_strategy_, grad_stats);
   }
 
+  void ReadClassId(OpKernelContext* const context, int32* class_id) {
+    const Tensor* class_id_t;
+    OP_REQUIRES_OK(context, context->input("class_id", &class_id_t));
+    OP_REQUIRES(context, TensorShapeUtils::IsScalar(class_id_t->shape()),
+                errors::InvalidArgument("class_id must be a scalar."));
+    *class_id = class_id_t->scalar<int32>()();
+  }
+
+  void FillLeaf(const int class_id, const NodeStats& best_node_stats,
+                boosted_trees::trees::Leaf* leaf) const {
+    if (class_id == -1) {
+      // This would be the case either for TREE_PER_CLASS with only 2 classes,
+      // or for other multiclass strategies.
+      for (float f : best_node_stats.weight_contribution) {
+        leaf->mutable_vector()->add_value(f);
+      }
+    } else {
+      CHECK(best_node_stats.weight_contribution.size() == 1)
+          << "Weight contribution size = "
+          << best_node_stats.weight_contribution.size();
+      leaf->mutable_sparse_vector()->add_index(class_id);
+      leaf->mutable_sparse_vector()->add_value(
+          best_node_stats.weight_contribution[0]);
+    }
+  }
+
  protected:
   LearnerConfig_MultiClassStrategy multiclass_strategy_;
   int32 feature_column_group_id_;
@@ -110,6 +136,9 @@ class BuildDenseInequalitySplitsOp : public BaseBuildSplitOp {
     const Tensor* hessians_t;
     OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
 
+    int class_id;
+    ReadClassId(context, &class_id);
+
     // Find the number of unique partitions before we allocate the output.
     std::vector<int32> partition_boundaries;
     partition_boundaries.push_back(0);
@@ -194,12 +223,9 @@ class BuildDenseInequalitySplitsOp : public BaseBuildSplitOp {
 
       auto* left_child = split_info.mutable_left_child();
       auto* right_child = split_info.mutable_right_child();
-      for (float f : best_left_node_stats.weight_contribution) {
-        left_child->mutable_vector()->add_value(f);
-      }
-      for (float f : best_right_node_stats.weight_contribution) {
-        right_child->mutable_vector()->add_value(f);
-      }
+
+      FillLeaf(class_id, best_left_node_stats, left_child);
+      FillLeaf(class_id, best_right_node_stats, right_child);
       split_info.SerializeToString(&output_splits(root_idx));
       gains(root_idx) =
           best_gain - root_stats.gain - tree_complexity_regularization_;
@@ -244,6 +270,9 @@ class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
     const Tensor* hessians_t;
     OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
 
+    int class_id;
+    ReadClassId(context, &class_id);
+
     // Find the number of unique partitions before we allocate the output.
     std::vector<int32> partition_boundaries;
     std::vector<int32> non_empty_partitions;
@@ -369,12 +398,8 @@ class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
 
       auto* left_child = split_info.mutable_left_child();
       auto* right_child = split_info.mutable_right_child();
-      for (float f : best_left_node_stats.weight_contribution) {
-        left_child->mutable_vector()->add_value(f);
-      }
-      for (float f : best_right_node_stats.weight_contribution) {
-        right_child->mutable_vector()->add_value(f);
-      }
+      FillLeaf(class_id, best_left_node_stats, left_child);
+      FillLeaf(class_id, best_right_node_stats, right_child);
       split_info.SerializeToString(&output_splits(root_idx));
       gains(root_idx) =
           best_gain - root_stats.gain - tree_complexity_regularization_;
@@ -417,6 +442,9 @@ class BuildCategoricalEqualitySplitsOp : public BaseBuildSplitOp {
     const Tensor* hessians_t;
     OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
 
+    int class_id;
+    ReadClassId(context, &class_id);
+
     // Find the number of unique partitions before we allocate the output.
     std::vector<int32> partition_boundaries;
     std::vector<int32> non_empty_partitions;
@@ -494,12 +522,8 @@ class BuildCategoricalEqualitySplitsOp : public BaseBuildSplitOp {
       equality_split->set_feature_id(feature_ids(best_feature_idx));
       auto* left_child = split_info.mutable_left_child();
       auto* right_child = split_info.mutable_right_child();
-      for (float f : best_left_node_stats.weight_contribution) {
-        left_child->mutable_vector()->add_value(f);
-      }
-      for (float f : best_right_node_stats.weight_contribution) {
-        right_child->mutable_vector()->add_value(f);
-      }
+      FillLeaf(class_id, best_left_node_stats, left_child);
+      FillLeaf(class_id, best_right_node_stats, right_child);
       split_info.SerializeToString(&output_splits(root_idx));
       gains(root_idx) =
           best_gain - root_stats.gain - tree_complexity_regularization_;
diff --git a/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc b/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
index 8effb6f98f..3163590624 100644
--- a/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
+++ b/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc
@@ -29,9 +29,10 @@ static Status ApplyGradientTreesPredictionShapeFn(InferenceContext* c) {
   c->GetAttr("learner_config", &learner_config_str).IgnoreError();
   LearnerConfig learner_config;
   ParseProtoUnlimited(&learner_config, learner_config_str);
+
+  bool reduce_dim;
+  c->GetAttr("reduce_dim", &reduce_dim).IgnoreError();
   // Sets the shape of the output as a matrix.
-  const bool reduce_dim =
-      learner_config.multi_class_strategy() == LearnerConfig::TREE_PER_CLASS;
   c->set_output(0, {c->Matrix(InferenceContext::kUnknownDim,
                               reduce_dim ? learner_config.num_classes() - 1
                                          : learner_config.num_classes())});
@@ -51,6 +52,7 @@ REGISTER_OP("GradientTreesPrediction")
     .Attr("apply_dropout: bool")
     .Attr("apply_averaging: bool")
     .Attr("center_bias: bool")
+    .Attr("reduce_dim: bool")
     .Input("tree_ensemble_handle: resource")
     .Input("seed: int64")
     .Input("dense_float_features: num_dense_float_features * float")
@@ -75,6 +77,7 @@ num_sparse_float_features: Number of sparse float features.
 num_sparse_int_features: Number of sparse int features.
 use_locking: Whether to use locking.
 seed: random seed to be used for dropout.
+reduce_dim: whether to reduce the dimension (legacy impl) or not.
 apply_dropout: whether to apply dropout during prediction.
 apply_averaging: whether averaging of tree ensembles should take place. If set
 to true, will be based on AveragingConfig from learner_config.
diff --git a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
index 95ec738796..07cfd413bb 100644
--- a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
+++ b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc
@@ -35,6 +35,7 @@ REGISTER_OP("BuildDenseInequalitySplits")
     .Input("gradients: float32")
     .Input("hessians: float32")
     .Input("bucket_boundaries: float32")
+    .Input("class_id: int32")
     .Output("output_partition_ids: int32")
     .Output("gains: float32")
     .Output("split_infos: string")
@@ -95,6 +96,7 @@ REGISTER_OP("BuildSparseInequalitySplits")
     .Input("gradients: float32")
     .Input("hessians: float32")
     .Input("bucket_boundaries: float32")
+    .Input("class_id: int32")
     .Output("output_partition_ids: int32")
     .Output("gains: float32")
     .Output("split_infos: string")
@@ -154,6 +156,7 @@ REGISTER_OP("BuildCategoricalEqualitySplits")
     .Input("feature_ids: int64")
     .Input("gradients: float32")
     .Input("hessians: float32")
+    .Input("class_id: int32")
     .Output("output_partition_ids: int32")
     .Output("gains: float32")
     .Output("split_infos: string")
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
index 90cc07f661..8e62856854 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
@@ -128,7 +128,8 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
           learner_config=learner_config.SerializeToString(),
           apply_dropout=False,
           apply_averaging=False,
-          center_bias=False)
+          center_bias=False,
+          reduce_dim=True)
       self.assertAllClose(result.eval(), [[-0.4], [-0.4]])
       stamp_token = model_ops.tree_ensemble_stamp_token(tree_ensemble_handle)
       self.assertEqual(stamp_token.eval(), 3)
@@ -154,6 +155,7 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
         _append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
         _append_to_leaf(tree2.nodes.add().leaf, 1, 1.2)
         _append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
+
         tree_ensemble_handle = model_ops.tree_ensemble_variable(
             stamp_token=7,
             tree_ensemble_config=tree_ensemble_config.SerializeToString(),
@@ -187,7 +189,8 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
             learner_config=learner_config.SerializeToString(),
             apply_dropout=False,
             apply_averaging=False,
-            center_bias=False)
+            center_bias=False,
+            reduce_dim=True)
 
         # Re-serialize tree.
         stamp_token2, serialized_config2 = model_ops.tree_ensemble_serialize(
@@ -198,6 +201,8 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
         # the second example will get the same bias class 1 -0.2 and leaf 3
         # payload of class 1 1.2 hence [0.0, 1.0].
         self.assertEqual(stamp_token2.eval(), 9)
+
+        # Class 2 does have scores in the leaf => it gets score 0.
         self.assertEqual(serialized_config2.eval(), serialized_config)
         self.assertAllClose(result.eval(), [[0.5, -0.2], [0, 1.0]])
 
@@ -267,7 +272,8 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False)
+              center_bias=False,
+              reduce_dim=True)
         self.assertAllClose([[-1.1], [-1.1]], result.eval())
         # Save before adding other trees.
         val = my_saver.save(sess, save_path)
@@ -293,7 +299,8 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False)
+              center_bias=False,
+              reduce_dim=True)
         self.assertAllClose(result.eval(), [[-11.1], [-11.1]])
 
     # Start a second session.  In that session the parameter nodes
@@ -315,7 +322,8 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
             learner_config=learner_config.SerializeToString(),
             apply_dropout=False,
             apply_averaging=False,
-            center_bias=False)
+            center_bias=False,
+            reduce_dim=True)
         # Make sure we only have the first and second tree.
         # The third tree was added after the save.
         self.assertAllClose(result.eval(), [[-1.1], [-1.1]])
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
index e5624da965..2b64235bb2 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
@@ -163,7 +163,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
       self.assertAllEqual([[0], [0]], result.eval())
       self.assertAllEqual(result_no_dropout.eval(), result.eval())
       # Empty dropout.
@@ -200,7 +201,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
       self.assertAllClose([[-0.4], [-0.4]], result.eval())
       self.assertAllEqual(result_no_dropout.eval(), result.eval())
 
@@ -240,7 +242,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
       self.assertAllClose([[-0.4, 0.9], [-0.4, 0.9]], result.eval())
       self.assertAllEqual(result_no_dropout.eval(), result.eval())
 
@@ -294,7 +297,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
 
       # The first example will get bias -0.4 from first tree and
       # leaf 4 payload of -0.9 hence -1.3, the second example will
@@ -353,7 +357,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
 
       # All the examples should get only the bias since the second tree is
       # non-finalized
@@ -411,7 +416,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
 
       # The first example will get bias -0.4 from first tree and
       # leaf 4 payload of -0.9 hence -1.3, the second example will
@@ -471,7 +477,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
 
       # The first example will get bias -0.4 from first tree and
       # leaf 4 payload of -0.9 hence -1.3, the second example will
@@ -530,7 +537,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
       # The first example will get bias class 1 -0.2 from first tree and
       # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
       # the second example will get the same bias class 1 -0.2 and leaf 3
@@ -541,7 +549,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
       # Empty dropout.
       self.assertAllEqual([[], []], dropout_info.eval())
 
-  # For all non-tree-per class multiclass handling strategies, predictions vec
+  # For tree-per-class multiclass handling strategies, predictions vec
   # will have the size of the number of classes.
   # This test is when leafs have SPARSE weights stored (class id and
   # contribution).
@@ -591,7 +599,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=False))
       # The first example will get bias class 1 -0.2 from first tree and
       # leaf 2 payload (sparse feature missing) of 0.5 hence [0.5, -0.2],
       # the second example will get the same bias class 1 -0.2 and leaf 3
@@ -651,7 +660,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=False,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=False))
       # The first example will get bias class 1 -0.2  and -2 for class 2 from
       # first tree and leaf 2 payload (sparse feature missing) of 0.5 hence
       # 0.5, -0.2], the second example will get the same bias and leaf 3 payload
@@ -679,7 +689,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
         learner_config=learner_config.SerializeToString(),
         apply_dropout=apply_dropout,
         apply_averaging=apply_averaging,
-        center_bias=center_bias)
+        center_bias=center_bias,
+        reduce_dim=True)
 
   def testDropout(self):
     with self.test_session():
@@ -918,7 +929,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=True,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
 
       _, result_no_dropout_2, dropout_info_2 = (
           prediction_ops.gradient_trees_prediction(
@@ -932,7 +944,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=True,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
 
       # Different seed.
       _, result_no_dropout_3, dropout_info_3 = (
@@ -947,7 +960,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=True,
               apply_averaging=False,
-              center_bias=False))
+              center_bias=False,
+              reduce_dim=True))
 
       # First seed with centering bias.
       _, result_no_dropout_4, dropout_info_4 = (
@@ -962,7 +976,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
               learner_config=learner_config.SerializeToString(),
               apply_dropout=True,
               apply_averaging=False,
-              center_bias=True))
+              center_bias=True,
+              reduce_dim=True))
 
       # The same seed returns the same results.
       self.assertAllEqual(dropout_info_1.eval(), dropout_info_2.eval())
@@ -987,7 +1002,8 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
           learner_config=learner_config.SerializeToString(),
           apply_dropout=False,
           apply_averaging=False,
-          center_bias=False)
+          center_bias=False,
+          reduce_dim=True)
 
       self.assertAllCloseAccordingToType(result.eval(),
                                          result_no_dropout.eval())
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
index aa504d1742..edf088b5fa 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
@@ -54,6 +54,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               l2_regularization=1,
               tree_complexity_regularization=0,
               min_node_weight=0,
+              class_id=-1,
               feature_column_group_id=0,
               multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = sess.run([partitions, gains, splits])
@@ -125,6 +126,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               l2_regularization=1,
               tree_complexity_regularization=0,
               min_node_weight=0,
+              class_id=-1,
               feature_column_group_id=0,
               multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN))
       partitions, gains, splits = sess.run([partitions, gains, splits])
@@ -163,6 +165,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               l2_regularization=1,
               tree_complexity_regularization=0,
               min_node_weight=0,
+              class_id=-1,
               feature_column_group_id=0,
               multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = sess.run([partitions, gains, splits])
@@ -200,6 +203,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               min_node_weight=0,
               feature_column_group_id=0,
               bias_feature_id=-1,
+              class_id=-1,
               multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = (sess.run([partitions, gains, splits]))
     self.assertAllEqual([0, 1], partitions)
@@ -283,6 +287,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
             min_node_weight=0,
             feature_column_group_id=0,
             bias_feature_id=-1,
+            class_id=-1,
             multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN))
     partitions, gains, splits = (sess.run([partitions, gains, splits]))
 
@@ -326,6 +331,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               min_node_weight=0,
               feature_column_group_id=0,
               bias_feature_id=-1,
+              class_id=-1,
               multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = sess.run([partitions, gains, splits])
     self.assertAllEqual([0, 1], partitions)
@@ -420,6 +426,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               min_node_weight=0,
               feature_column_group_id=0,
               bias_feature_id=-1,
+              class_id=-1,
               multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN))
       partitions, gains, splits = sess.run([partitions, gains, splits])
     self.assertAllEqual([0, 1], partitions)
@@ -456,6 +463,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase):
               min_node_weight=0,
               feature_column_group_id=0,
               bias_feature_id=-1,
+              class_id=-1,
               multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
       partitions, gains, splits = (sess.run([partitions, gains, splits]))
     self.assertEqual(0, len(partitions))
-- 
GitLab


From 3ed1893512c73a1878f3a5d6b846a03ed7174cbb Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 13 Jul 2017 13:06:10 -0700
Subject: [PATCH 0965/1375] Automated g4 rollback of changelist 161781962

PiperOrigin-RevId: 161851851
---
 tensorflow/core/framework/register_types.h    |  4 +-
 tensorflow/core/kernels/BUILD                 | 33 ++++++---
 .../core/kernels/dense_update_functor.cc      | 73 ++++++++++++++++++
 ...se_update_ops.h => dense_update_functor.h} | 33 +++++----
 ...u.cu.cc => dense_update_functor_gpu.cu.cc} | 30 +++++++-
 tensorflow/core/kernels/dense_update_ops.cc   | 74 ++-----------------
 .../core/kernels/resource_variable_ops.cc     | 27 ++-----
 tensorflow/core/kernels/scatter_nd_op.cc      | 12 +--
 tensorflow/core/kernels/strided_slice_op.cc   |  2 +-
 .../core/kernels/strided_slice_op_impl.h      |  2 +-
 10 files changed, 163 insertions(+), 127 deletions(-)
 create mode 100644 tensorflow/core/kernels/dense_update_functor.cc
 rename tensorflow/core/kernels/{dense_update_ops.h => dense_update_functor.h} (65%)
 rename tensorflow/core/kernels/{dense_update_ops_gpu.cu.cc => dense_update_functor_gpu.cu.cc} (61%)

diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index b62fe647e2..3f91642064 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -172,7 +172,9 @@ limitations under the License.
   TF_CALL_half(m) TF_CALL_float(m) TF_CALL_double(m)
 
 // Call "m" on all types supported on GPU.
-#define TF_CALL_GPU_ALL_TYPES(m) TF_CALL_GPU_NUMBER_TYPES(m) TF_CALL_bool(m)
+#define TF_CALL_GPU_ALL_TYPES(m) \
+  TF_CALL_GPU_NUMBER_TYPES(m)    \
+  TF_CALL_bool(m) TF_CALL_complex64(m) TF_CALL_complex128(m)
 
 #define TF_CALL_GPU_NUMBER_TYPES_NO_HALF(m) TF_CALL_float(m) TF_CALL_double(m)
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 6eee2e16eb..264d5d5952 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -19,6 +19,7 @@ package_group(
     name = "friends",
     packages = [
         "//learning/brain/contrib/...",
+        "//learning/brain/research/sparse_matrix/...",
         "//tensorflow/...",
     ],
 )
@@ -94,13 +95,11 @@ tf_kernel_library(
         "strided_slice_op_inst_7.cc",
     ],
     hdrs = [
-        "dense_update_ops.h",
         "slice_op.h",
         "strided_slice_op.h",
         "strided_slice_op_impl.h",
     ],
     gpu_srcs = [
-        "dense_update_ops.h",
         "slice_op.h",
         "strided_slice_op.h",
         "strided_slice_op_impl.h",
@@ -109,6 +108,7 @@ tf_kernel_library(
     ],
     deps = [
         ":bounds_check",
+        ":dense_update_functor",
         ":ops_util",
         ":variable_ops",
         "//tensorflow/core:framework",
@@ -1011,6 +1011,22 @@ tf_kernel_library(
     ],
 )
 
+tf_kernel_library(
+    name = "dense_update_functor",
+    srcs = ["dense_update_functor.cc"],
+    hdrs = ["dense_update_functor.h"],
+    gpu_srcs = [
+        "dense_update_functor.h",
+        "dense_update_functor_gpu.cu.cc",
+    ],
+    deps = [
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 0,
+)
+
 tf_cuda_cc_test(
     name = "gather_op_test",
     size = "small",
@@ -1606,7 +1622,7 @@ tf_kernel_library(
     srcs = ["resource_variable_ops.cc"],
     deps = [
         ":bounds_check",
-        ":dense_update_ops",
+        ":dense_update_functor",
         ":gather_functor",
         ":scatter_functor",
         ":state",
@@ -2079,7 +2095,7 @@ tf_kernel_library(
         "//tensorflow:darwin": [],
         "//conditions:default": ["-Wl,-z,muldefs"],
     }),
-    visibility = ["//visibility:private"],
+    visibility = [":friends"],
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -3482,7 +3498,7 @@ tf_kernel_library(
 tf_kernel_library(
     name = "dense_update_ops",
     prefix = "dense_update_ops",
-    deps = STATE_DEPS,
+    deps = STATE_DEPS + [":dense_update_functor"],
 )
 
 tf_kernel_library(
@@ -3503,16 +3519,14 @@ tf_kernel_library(
         "scatter_nd_op_cpu_impl_5.cc",
     ],
     hdrs = [
-        "dense_update_ops.h",
         "scatter_nd_op.h",
         "scatter_nd_op_cpu_impl.h",
     ],
     gpu_srcs = [
-        "dense_update_ops.h",
         "scatter_nd_op.h",
         "scatter_nd_op_gpu.cu.cc",
     ],
-    deps = STATE_DEPS + [":dense_update_ops"],
+    deps = STATE_DEPS + [":dense_update_functor"],
 )
 
 tf_kernel_library(
@@ -4054,8 +4068,9 @@ filegroup(
         "cwise_ops_common.cc",
         "cwise_ops_common.h",
         "cwise_ops_gradients.h",
+        "dense_update_functor.cc",
+        "dense_update_functor.h",
         "dense_update_ops.cc",
-        "dense_update_ops.h",
         "example_parsing_ops.cc",
         "fill_functor.cc",
         "fill_functor.h",
diff --git a/tensorflow/core/kernels/dense_update_functor.cc b/tensorflow/core/kernels/dense_update_functor.cc
new file mode 100644
index 0000000000..a878fe9a97
--- /dev/null
+++ b/tensorflow/core/kernels/dense_update_functor.cc
@@ -0,0 +1,73 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/kernels/dense_update_functor.h"
+
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace functor {
+
+template <>
+struct DenseUpdate<CPUDevice, string, ASSIGN> {
+  void operator()(const CPUDevice& d, typename TTypes<string>::Flat params,
+                  typename TTypes<string>::ConstFlat update) {
+    if (params.dimension(0) == 1) {
+      params.data()->resize(update.data()->size());
+      auto work = [&params, &update](int64 start, int64 end) {
+        memmove(const_cast<char*>(params.data()->data()) + start,
+                update.data()->data() + start, end - start);
+      };
+      d.parallelFor(update.data()->size(),
+                    Eigen::TensorOpCost(.1,  // chosen to force large chunks
+                                        .1, 0),
+                    work);
+    } else {
+      auto work = [&params, &update](int64 start, int64 end) {
+        for (int i = start; i < end; ++i) {
+          params.data()[i].resize(update.data()[i].size());
+          memmove(const_cast<char*>(params.data()[i].data()),
+                  update.data()[i].data(), update.data()[i].size());
+        }
+      };
+      int64 estimated_string_size;
+      if (update.size() > 0) {
+        // first element of the tensor seems as good a guess as any of the sizes
+        // of the strings contained within...
+        estimated_string_size =
+            std::max(update.data()[0].size(), sizeof(string));
+      } else {
+        estimated_string_size = sizeof(string);
+      }
+      d.parallelFor(
+          params.dimension(0),
+          Eigen::TensorOpCost(estimated_string_size, estimated_string_size, 0),
+          work);
+    }
+  }
+};
+
+}  // namespace functor
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/dense_update_ops.h b/tensorflow/core/kernels/dense_update_functor.h
similarity index 65%
rename from tensorflow/core/kernels/dense_update_ops.h
rename to tensorflow/core/kernels/dense_update_functor.h
index ec7e9dff11..54b080c83b 100644
--- a/tensorflow/core/kernels/dense_update_ops.h
+++ b/tensorflow/core/kernels/dense_update_functor.h
@@ -13,40 +13,47 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
-#define TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
+#ifndef TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
+#define TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
+
+#define EIGEN_USE_THREADS
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor_types.h"
 
 namespace tensorflow {
 
+typedef Eigen::ThreadPoolDevice CPUDevice;
+
 enum DenseUpdateType { ADD, SUB, ASSIGN };
 
 namespace functor {
 
 template <typename Device, typename T, DenseUpdateType OP>
-struct DenseUpdate;
-
-template <typename Device, typename T>
-struct DenseUpdate<Device, T, ADD> {
+struct DenseUpdate {
   void operator()(const Device& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update);
+};
+
+template <typename T>
+struct DenseUpdate<CPUDevice, T, ADD> {
+  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) += update;
   }
 };
 
-template <typename Device, typename T>
-struct DenseUpdate<Device, T, SUB> {
-  void operator()(const Device& d, typename TTypes<T>::Flat params,
+template <typename T>
+struct DenseUpdate<CPUDevice, T, SUB> {
+  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) -= update;
   }
 };
 
-template <typename Device, typename T>
-struct DenseUpdate<Device, T, ASSIGN> {
-  void operator()(const Device& d, typename TTypes<T>::Flat params,
+template <typename T>
+struct DenseUpdate<CPUDevice, T, ASSIGN> {
+  void operator()(const CPUDevice& d, typename TTypes<T>::Flat params,
                   typename TTypes<T>::ConstFlat update) {
     params.device(d) = update;
   }
@@ -55,4 +62,4 @@ struct DenseUpdate<Device, T, ASSIGN> {
 }  // end namespace functor
 }  // end namespace tensorflow
 
-#endif  // TENSORFLOW_KERNELS_DENSE_UPDATE_OPS_H_
+#endif  // TENSORFLOW_KERNELS_DENSE_UPDATE_FUNCTOR_H_
diff --git a/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
similarity index 61%
rename from tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
rename to tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
index cf2d493061..208401cb24 100644
--- a/tensorflow/core/kernels/dense_update_ops_gpu.cu.cc
+++ b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc
@@ -17,7 +17,7 @@ limitations under the License.
 
 #define EIGEN_USE_GPU
 
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 
 #include "tensorflow/core/framework/register_types.h"
 
@@ -25,6 +25,34 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
+namespace functor {
+
+template <typename T>
+struct DenseUpdate<GPUDevice, T, ASSIGN> {
+  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) = update;
+  }
+};
+
+template <typename T>
+struct DenseUpdate<GPUDevice, T, ADD> {
+  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) += update;
+  }
+};
+
+template <typename T>
+struct DenseUpdate<GPUDevice, T, SUB> {
+  void operator()(const GPUDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) -= update;
+  }
+};
+
+}  // namespace functor
+
 #define DEFINE_GPU_KERNELS(T)                              \
   template struct functor::DenseUpdate<GPUDevice, T, ADD>; \
   template struct functor::DenseUpdate<GPUDevice, T, SUB>;
diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc
index af16c55a5d..6d44a92fa3 100644
--- a/tensorflow/core/kernels/dense_update_ops.cc
+++ b/tensorflow/core/kernels/dense_update_ops.cc
@@ -15,59 +15,20 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif
+
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/assign_op.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
-namespace functor {
-
-template <>
-struct DenseUpdate<Eigen::ThreadPoolDevice, string, ASSIGN> {
-  void operator()(const Eigen::ThreadPoolDevice& d,
-                  typename TTypes<string>::Flat params,
-                  typename TTypes<string>::ConstFlat update) {
-    if (params.dimension(0) == 1) {
-      params.data()->resize(update.data()->size());
-      auto work = [&params, &update](int64 start, int64 end) {
-        memmove(const_cast<char*>(params.data()->data()) + start,
-                update.data()->data() + start, end - start);
-      };
-      d.parallelFor(update.data()->size(),
-                    Eigen::TensorOpCost(.1,  // chosen to force large chunks
-                                        .1, 0),
-                    work);
-    } else {
-      auto work = [&params, &update](int64 start, int64 end) {
-        for (int i = start; i < end; ++i) {
-          params.data()[i].resize(update.data()[i].size());
-          memmove(const_cast<char*>(params.data()[i].data()),
-                  update.data()[i].data(), update.data()[i].size());
-        }
-      };
-      int64 estimated_string_size;
-      if (update.size() > 0) {
-        // first element of the tensor seems as good a guess as any of the sizes
-        // of the strings contained within...
-        estimated_string_size =
-            std::max(update.data()[0].size(), sizeof(string));
-      } else {
-        estimated_string_size = sizeof(string);
-      }
-      d.parallelFor(
-          params.dimension(0),
-          Eigen::TensorOpCost(estimated_string_size, estimated_string_size, 0),
-          work);
-    }
-  }
-};
-
-}  // namespace functor
 
 template <typename Device, typename T>
 class AssignOpT : public AssignOp {
@@ -117,7 +78,7 @@ class DenseUpdateOp : public OpKernel {
         errors::InvalidArgument("Parameters and update must be the same size"));
 
     functor::DenseUpdate<Device, T, OP> update_functor;
-    update_functor(context->eigen_device<Device>(), Tparams.flat<T>(),
+    update_functor(context->template eigen_device<Device>(), Tparams.flat<T>(),
                    Tupdate.flat<T>());
   }
 
@@ -143,13 +104,6 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
 // Only register 'Assign' on GPU for the subset of types also supported by
 // 'Variable' (see variable_ops.cc.)
 #define REGISTER_GPU_KERNELS(type)                                 \
-  namespace functor {                                              \
-  template <>                                                      \
-  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(           \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,         \
-      typename TTypes<type>::ConstFlat rhs);                       \
-  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;     \
-  }                                                                \
   REGISTER_KERNEL_BUILDER(                                         \
       Name("Assign").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
       AssignOpT<GPUDevice, type>);
@@ -180,22 +134,6 @@ TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 #undef REGISTER_KERNELS
 
 #if GOOGLE_CUDA
-// Forward declarations of the functor specializations for GPU.
-namespace functor {
-#define DECLARE_GPU_SPEC_FOR_OP(T, OP)                     \
-  template <>                                              \
-  void DenseUpdate<GPUDevice, T, OP>::operator()(          \
-      const GPUDevice& d, typename TTypes<T>::Flat params, \
-      typename TTypes<T>::ConstFlat update);               \
-  extern template struct DenseUpdate<GPUDevice, T, OP>;
-#define DECLARE_GPU_SPEC(T)                         \
-  DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::ADD); \
-  DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::SUB)
-TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
-#undef DECLARE_GPU_SPEC
-#undef DECLARE_GPU_SPEC_FOR_OP
-}  // namespace functor
-
 #define REGISTER_GPU_KERNELS(type)                                    \
   REGISTER_KERNEL_BUILDER(                                            \
       Name("AssignAdd").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index f0fef256d2..0616bb5a08 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -15,12 +15,16 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif
+
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/gather_functor.h"
 #include "tensorflow/core/kernels/scatter_functor.h"
 #include "tensorflow/core/kernels/variable_ops.h"
@@ -217,13 +221,6 @@ TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
 
 #if GOOGLE_CUDA
 #define REGISTER_GPU_KERNELS(type)                             \
-  namespace functor {                                          \
-  template <>                                                  \
-  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(       \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,     \
-      typename TTypes<type>::ConstFlat rhs);                   \
-  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>; \
-  }                                                            \
   REGISTER_KERNEL_BUILDER(Name("AssignVariableOp")             \
                               .Device(DEVICE_GPU)              \
                               .TypeConstraint<type>("dtype")   \
@@ -275,20 +272,6 @@ TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
 
 #if GOOGLE_CUDA
 #define REGISTER_GPU_KERNELS(type)                                       \
-  namespace functor {                                                    \
-  template <>                                                            \
-  void DenseUpdate<GPUDevice, type, ADD>::operator()(                    \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,               \
-      typename TTypes<type>::ConstFlat rhs);                             \
-  extern template struct DenseUpdate<GPUDevice, type, ADD>;              \
-  }                                                                      \
-  namespace functor {                                                    \
-  template <>                                                            \
-  void DenseUpdate<GPUDevice, type, SUB>::operator()(                    \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs,               \
-      typename TTypes<type>::ConstFlat rhs);                             \
-  extern template struct DenseUpdate<GPUDevice, type, SUB>;              \
-  }                                                                      \
   REGISTER_KERNEL_BUILDER(Name("AssignAddVariableOp")                    \
                               .Device(DEVICE_GPU)                        \
                               .HostMemory("resource")                    \
diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc
index 1428546d52..59f690e7aa 100644
--- a/tensorflow/core/kernels/scatter_nd_op.cc
+++ b/tensorflow/core/kernels/scatter_nd_op.cc
@@ -26,7 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/fill_functor.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -523,16 +523,6 @@ TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DECLARE_GPU_SPECS);
 #undef DECLARE_GPU_SPECS_INDEX
 #undef DECLARE_GPU_SPECS_INDEX_OP
 
-#define REGISTER_GPU_KERNELS(type)                         \
-  template <>                                              \
-  void DenseUpdate<GPUDevice, type, ASSIGN>::operator()(   \
-      const GPUDevice& d, typename TTypes<type>::Flat lhs, \
-      typename TTypes<type>::ConstFlat rhs);               \
-  extern template struct DenseUpdate<GPUDevice, type, ASSIGN>;
-
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
-#undef REGISTER_GPU_KERNELS
-
 }  // namespace functor
 
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc
index fa518712ce..4655503e26 100644
--- a/tensorflow/core/kernels/strided_slice_op.cc
+++ b/tensorflow/core/kernels/strided_slice_op.cc
@@ -22,7 +22,7 @@ limitations under the License.
 #endif  // GOOGLE_CUDA
 
 #include "tensorflow/core/kernels/strided_slice_op.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/slice_op.h"
 #include "tensorflow/core/kernels/strided_slice_op_impl.h"
 
diff --git a/tensorflow/core/kernels/strided_slice_op_impl.h b/tensorflow/core/kernels/strided_slice_op_impl.h
index d0ccd5c652..de65147572 100644
--- a/tensorflow/core/kernels/strided_slice_op_impl.h
+++ b/tensorflow/core/kernels/strided_slice_op_impl.h
@@ -27,7 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/register_types_traits.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/kernels/dense_update_ops.h"
+#include "tensorflow/core/kernels/dense_update_functor.h"
 #include "tensorflow/core/kernels/ops_util.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-- 
GitLab


From 233c10af2f39b0795ef4ffa09a9b4fa10a55d082 Mon Sep 17 00:00:00 2001
From: Frank Chen <frankchn@gmail.com>
Date: Thu, 13 Jul 2017 13:19:08 -0700
Subject: [PATCH 0966/1375] Update CODEOWNERS, add owners to Windows, TPU
 components (#11485)

---
 CODEOWNERS | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 2f216e4161..69393c3775 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,8 +1,9 @@
 # Where component owners are known, add them here.
 
+tensorflow/core/platform/windows/* @mrry
+tensorflow/java/* @asimshankar
 tensorflow/tensorboard/* @jart @dandelionmane
 tensorflow/tools/docs/* @markdaoust
-tensorflow/java/* @asimshankar
 
 # contrib
 
@@ -46,5 +47,6 @@ tensorflow/contrib/stateless/* @girving
 tensorflow/contrib/tensor_forest/* @gilberthendry @thomascolthurst
 tensorflow/contrib/testing/* @dandelionmane
 tensorflow/contrib/timeseries/* @allenlavoie
+tensorflow/contrib/tpu/* @frankchn @saeta @jhseu
 tensorflow/contrib/training/* @joel-shor @ebrevdo
 tensorflow/contrib/util/* @sherrym
-- 
GitLab


From 37a9f8b879a473109afa3b31dd848149465db35f Mon Sep 17 00:00:00 2001
From: Penghao Cen <scorpiocph@gmail.com>
Date: Fri, 14 Jul 2017 04:19:26 +0800
Subject: [PATCH 0967/1375] Update github link (#11473)

---
 tensorflow/docs_src/about/roadmap.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/about/roadmap.md b/tensorflow/docs_src/about/roadmap.md
index 1789e050fa..3ee825ed40 100644
--- a/tensorflow/docs_src/about/roadmap.md
+++ b/tensorflow/docs_src/about/roadmap.md
@@ -5,7 +5,7 @@ TensorFlow is a fast moving project. In order for the community to better
 understand what the near future will bring, this document shares what we are
 working on internally. Many of these features were requested by the community,
 and we welcome
-[contributions](https://github.com/tensorflow/tensorflow/labels/contributions%20welcome).
+[contributions](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome).
 
 The features on this list are targeted for the next few months. At this point,
 we do not have timelines for these features.
-- 
GitLab


From 8ad81fd88faa3facf206518064d421ad5ece4a5c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 13:39:06 -0700
Subject: [PATCH 0968/1375] This is 1st of 4 CLs to implement BatchNormGrad.
 Various support in user computation is needed to properly have an end-to-end
 flow working for BatchNormGrad.

RELNOTES: n/a
PiperOrigin-RevId: 161856560
---
 .../compiler/xla/service/user_computation.cc  | 50 +++++++++++++++++++
 1 file changed, 50 insertions(+)

diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 2373b5e32e..90122959a6 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -1682,6 +1682,23 @@ void ConstantVisitor(const SessionComputation& session_computation,
       break;
     }
 
+    case OpRequest::kBatchNormGradRequest: {
+      const BatchNormGradRequest& batch_norm_grad_request =
+          request.request().batch_norm_grad_request();
+      ConstantVisitor(session_computation, batch_norm_grad_request.operand(),
+                      visited, is_constant);
+      ConstantVisitor(session_computation, batch_norm_grad_request.scale(),
+                      visited, is_constant);
+      ConstantVisitor(session_computation, batch_norm_grad_request.mean(),
+                      visited, is_constant);
+      ConstantVisitor(session_computation, batch_norm_grad_request.variance(),
+                      visited, is_constant);
+      ConstantVisitor(session_computation,
+                      batch_norm_grad_request.grad_output(), visited,
+                      is_constant);
+      break;
+    }
+
     case OpRequest::kBinaryOpRequest: {
       const BinaryOpRequest& binary_op_request =
           request.request().binary_op_request();
@@ -2106,6 +2123,18 @@ static void ForEachOperand(
       break;
     }
 
+    case OpRequest::kBatchNormGradRequest: {
+      const BatchNormGradRequest& batch_norm_grad_request =
+          request.request().batch_norm_grad_request();
+
+      apply(batch_norm_grad_request.operand());
+      apply(batch_norm_grad_request.scale());
+      apply(batch_norm_grad_request.mean());
+      apply(batch_norm_grad_request.variance());
+      apply(batch_norm_grad_request.grad_output());
+      break;
+    }
+
     case OpRequest::kCrossReplicaSumRequest: {
       const CrossReplicaSumRequest& cross_replica_sum_request =
           request.request().cross_replica_sum_request();
@@ -2622,6 +2651,27 @@ void ComputationLowerer::Visit(
       break;
     }
 
+    case OpRequest::kBatchNormGradRequest: {
+      const BatchNormGradRequest& batch_norm_grad_request =
+          request.request().batch_norm_grad_request();
+
+      HloInstruction* operand =
+          lookup_instruction(batch_norm_grad_request.operand());
+      HloInstruction* scale =
+          lookup_instruction(batch_norm_grad_request.scale());
+      HloInstruction* mean = lookup_instruction(batch_norm_grad_request.mean());
+      HloInstruction* variance =
+          lookup_instruction(batch_norm_grad_request.variance());
+      HloInstruction* grad_output =
+          lookup_instruction(batch_norm_grad_request.grad_output());
+
+      hlo_instruction = add_instruction(HloInstruction::CreateBatchNormGrad(
+          request.output_shape(), operand, scale, mean, variance, grad_output,
+          batch_norm_grad_request.epsilon(),
+          batch_norm_grad_request.feature_index()));
+      break;
+    }
+
     case OpRequest::kBroadcastRequest: {
       const BroadcastRequest& broadcast_request =
           request.request().broadcast_request();
-- 
GitLab


From 0db5e75881a43de504b0a9e79ec4f4e36be36a10 Mon Sep 17 00:00:00 2001
From: Yifei Feng <fengyifei2026@gmail.com>
Date: Thu, 13 Jul 2017 14:23:45 -0700
Subject: [PATCH 0969/1375] Print default when CUDA and CuDNN versions are not
 specified (#11460)

* Print default when CUDA and CuDNN versions are not specified.
Fix #10456.
---
 configure | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/configure b/configure
index 4c6cba2169..46f68f3df5 100755
--- a/configure
+++ b/configure
@@ -492,6 +492,8 @@ while true; do
   if [ -z "$TF_CUDA_VERSION" ]; then
     read -p "Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: " TF_CUDA_VERSION
   fi
+  # Set default CUDA version if not set
+  TF_CUDA_VERSION=${TF_CUDA_VERSION:-8.0}
 
   fromuser=""
   if [ -z "$CUDA_TOOLKIT_PATH" ]; then
@@ -545,11 +547,7 @@ while true; do
   CUDA_TOOLKIT_PATH=""
 done
 
-# Set default CUDA version if not set
-if [ -z "$TF_CUDA_VERSION" ]; then
-  TF_CUDA_VERSION="8.0"
-  export TF_CUDA_VERSION
-fi
+export TF_CUDA_VERSION
 write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION"
 
 # Set up which gcc nvcc should use as the host compiler
@@ -587,6 +585,8 @@ while true; do
   if [ -z "$TF_CUDNN_VERSION" ]; then
     read -p "Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: " TF_CUDNN_VERSION
   fi
+  # Set default CUDNN version if not set
+  TF_CUDNN_VERSION=${TF_CUDNN_VERSION:-6}
 
   fromuser=""
   if [ -z "$CUDNN_INSTALL_PATH" ]; then
@@ -659,11 +659,7 @@ while true; do
   CUDNN_INSTALL_PATH=""
 done
 
-# Set default CUDNN version if not set
-if [ -z "$TF_CUDNN_VERSION" ]; then
-  TF_CUDNN_VERSION="6"
-  export TF_CUDNN_VERSION
-fi
+export TF_CUDNN_VERSION
 write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
 
 # Configure the compute capabilities that TensorFlow builds for.
-- 
GitLab


From a0ffaf3caa0234653035a692858606c7bdacd63b Mon Sep 17 00:00:00 2001
From: Frank Chen <frankchn@google.com>
Date: Thu, 13 Jul 2017 14:51:47 -0700
Subject: [PATCH 0970/1375] Merge changes from github. END_PUBLIC

---
Commit fe5338177 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 161727345

---
Commit c65f69119 authored by Eugene Brevdo<ebrevdo@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Factor out DenseUpdate ops into dense_update_functor build dep.

Also add support for complex types.

PiperOrigin-RevId: 161726749

---
Commit 9a172989e authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Update ops-related pbtxt files.

PiperOrigin-RevId: 161726324

---
Commit fd5530d6e authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
adding bazel-toolchains repo to workspace. This repo will be necessary for remote execution (specifically for cross OS compilation)

PiperOrigin-RevId: 161719899

---
Commit 71c4ec8ed authored by Derek Murray<mrry@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add a mechanism for switching between multiple iterators by feeding a handle.

With this change, you can do the following:

1. Fetch a string handle for any iterator, by evaluating the result of
   `Iterator.string_handle()`.
2. Define an `Iterator` object based on a `tf.string` placeholder handle.
3. Feed the placeholder using an evaluated string handle to use a particular
   iterator in a particular step.

Concretely, this allows you to define two iterators for a training dataset and
a test dataset, and choose which one to use on a per-run basis:

```python
train_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
train_iterator_handle = sess.run(train_iterator.string_handle())

test_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
test_iterator_handle = sess.run(test_iterator.string_handle())

handle = tf.placeholder(tf.string, shape=[])
iterator = tf.contrib.data.Iterator.from_string_handle(
    handle, train_iterator.output_types)

next_element = iterator.get_next()
loss = f(next_element)

train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
```
PiperOrigin-RevId: 161719836

---
Commit 6d6dda807 authored by Kay Zhu<kayzhu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Fix an issue where plugin/Executor backend is used by default when TF
is built from source with XLA support. See Github issue #11122.

The priority of the executor backend is set to be higher than the default (50)
and CPUs (<100), and is therefore selected as the default when tf.device is not
explicitly specified.

PiperOrigin-RevId: 161717173

---
Commit 6b28eb084 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Rename HloLocation to HloPosition, to avoid ambiguity with MemoryLocation.

PiperOrigin-RevId: 161716528

---
Commit 8e7f57371 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Expose tf.contrib.nn.rank_sampled_softmax_loss.

PiperOrigin-RevId: 161716450

---
Commit e424d209a authored by Peter Hawkins<phawkins@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Use a more numerically accurate formulation of ResourceApplyRMSProp.

PiperOrigin-RevId: 161706120

---
Commit 45a58d378 authored by Skye Wanderman-Milne<skyewm@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Introduce Python-only extensions to the C API

Implements an incomplete version of Operation._add_control_input()
using a new extension to make sure the plumbing works.

This also adds header guards to c_api_internal.h, which were missing. For some reason the missing guards caused problems in the cmake build even though there doesn't appear to be any #include cycles.

PiperOrigin-RevId: 161705859

---
Commit 4f5433634 authored by Jonathan Hseu<jhseu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Rename TpuEstimator to TPUEstimator and TpuConfig to TPUConfig to follow PEP8
naming conventions.

PiperOrigin-RevId: 161704561

---
Commit 38180d7bb authored by Yun Peng<pcloudy@google.com>
Committed by gunan<gunan@google.com>:
Disable nn_test on Windows (#11445)

---
Commit e1de7a1b0 authored by Yun Peng<pcloudy@google.com>
Committed by gunan<gunan@google.com>:
Windows Bazel Build: Build TensorFlow with wrapper-less CROSSTOOL (#11454)

---
Commit c9d03a568 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add tf.contrib.nn.rank_sampled_softmax_loss, a variant of tf.nn.sampled_softmax_loss that has been shown to improve rank loss. Paper: https://arxiv.org/abs/1707.03073

PiperOrigin-RevId: 161702455

---
Commit 9aa0dcbf2 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add shape check for MakeQuantileSummariesOp.

PiperOrigin-RevId: 161698801

---
Commit 9c4da4a24 authored by vhasanov<KyotoSunshine@users.noreply.github.com>
Committed by Frank Chen<frankchn@gmail.com>:
Deleted unnecessary repetition of the same text. (#11459)

The same text was repeated two times. I deleted the repetition.
---
Commit d1e3cadda authored by DimanNe<dimanne@gmail.com>
Committed by drpngx<drpngx@users.noreply.github.com>:
Fix linking options issued by bazel in oorder to make gradients register (#11449)

---
Commit 8605f7ab8 authored by Taehoon Lee<me@taehoonlee.com>
Committed by Frank Chen<frankchn@gmail.com>:
Fix typos (#11444)

---
Commit 7c1fe9068 authored by Karl Lessard<karllessard@users.noreply.github.com>
Committed by Frank Chen<frankchn@gmail.com>:
[Java] Add base classes and utilities for operation wrappers. (#11188)

* Add base classes and utilities for operation wrappers.

* Rename Input interface to Operand

* Introduce changes after code review

---
Commit 2195db6d8 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Remove unused flag: xla_hlo_graph_for_compute_constant

PiperOrigin-RevId: 161686867

---
Commit a72fc31bc authored by Martin Wicke<martin.wicke@gmail.com>
Committed by Martin Wicke<martin.wicke@gmail.com>:
Remove tabs. Unassign contrib/framework.
---
Commit 6e74bd65a authored by Martin Wicke<martin.wicke@gmail.com>
Committed by Martin Wicke<martin.wicke@gmail.com>:
Add CODEOWNERS

Added what we know about contrib mainly, and some well-separated components.
---
Commit de546d066 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BUILD cleanup in tensorflow/compiler/...

PiperOrigin-RevId: 161679855

---
Commit 576c7b1ec authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 161218103

PiperOrigin-RevId: 161868747
---
 CODEOWNERS                                    | 50 +++++++++++
 README.md                                     |  2 +-
 RELEASE.md                                    | 31 -------
 tensorflow/cc/BUILD                           |  3 +
 .../compiler/tests/tensor_array_ops_test.py   | 56 ++++++------
 tensorflow/compiler/xla/util.cc               |  1 +
 .../copy_graph/python/util/copy_elements.py   |  4 +-
 .../cudnn_rnn/python/ops/cudnn_rnn_ops.py     |  2 +-
 .../factorization/kernels/wals_solver_ops.cc  |  2 +-
 .../contrib/framework/python/ops/ops.py       |  2 +-
 .../contrib/labeled_tensor/python/ops/core.py |  2 +-
 .../layers/python/layers/initializers.py      |  2 +-
 .../contrib/learn/python/learn/utils/gc.py    |  8 +-
 tensorflow/contrib/lookup/lookup_ops.py       |  4 +-
 tensorflow/contrib/makefile/README.md         |  6 +-
 tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc  | 13 ++-
 tensorflow/contrib/mpi/mpi_rendezvous_mgr.h   | 33 +++----
 tensorflow/contrib/session_bundle/gc.py       |  8 +-
 .../timeseries/python/timeseries/BUILD        | 15 ++++
 .../timeseries/state_space_models/periodic.py |  2 +-
 tensorflow/core/kernels/decode_wav_op_test.cc | 38 ++++++++
 .../core/lib/io/snappy/snappy_buffers_test.cc |  2 +-
 tensorflow/core/lib/wav/wav_io.cc             |  5 +-
 tensorflow/core/lib/wav/wav_io_test.cc        | 58 ++++++++++++
 tensorflow/core/ops/audio_ops.cc              |  4 +-
 tensorflow/core/ops/ops.pbtxt                 | 27 ++++++
 tensorflow/core/protobuf/saver.proto          |  4 +-
 .../docs_src/community/documentation.md       |  2 +-
 tensorflow/docs_src/extend/index.md           |  4 +-
 .../docs_src/get_started/get_started.md       | 42 ++++-----
 tensorflow/docs_src/programmers_guide/faq.md  |  2 +
 .../examples/image_retraining/retrain.py      |  2 +-
 tensorflow/examples/label_image/README.md     |  4 +-
 tensorflow/go/op/wrappers.go                  | 90 +++++++++----------
 tensorflow/java/BUILD                         | 26 ++++++
 .../java/org/tensorflow/NativeLibrary.java    |  2 +-
 .../src/main/java/org/tensorflow/Operand.java | 48 ++++++++++
 .../main/java/org/tensorflow/Operation.java   | 15 ++++
 .../src/main/java/org/tensorflow/Output.java  |  7 +-
 .../src/main/java/org/tensorflow/op/Op.java   | 35 ++++++++
 .../main/java/org/tensorflow/op/Operands.java | 46 ++++++++++
 .../java/org/tensorflow/op/PrimitiveOp.java   | 65 ++++++++++++++
 .../java/org/tensorflow/OperationTest.java    | 13 +++
 .../test/java/org/tensorflow/TestUtil.java    |  8 ++
 .../java/org/tensorflow/op/OperandsTest.java  | 46 ++++++++++
 .../org/tensorflow/op/PrimitiveOpTest.java    | 60 +++++++++++++
 tensorflow/python/BUILD                       |  1 +
 tensorflow/python/framework/graph_io.py       |  2 +-
 tensorflow/python/kernel_tests/BUILD          |  1 +
 .../python/kernel_tests/sparse_ops_test.py    | 13 ++-
 tensorflow/python/lib/core/strings.i          |  2 +-
 tensorflow/python/ops/array_ops.py            |  6 +-
 tensorflow/python/ops/lookup_ops.py           |  4 +-
 tensorflow/python/platform/flags.py           |  7 +-
 tensorflow/python/platform/flags_test.py      |  5 ++
 tensorflow/python/util/tf_should_use.py       |  9 +-
 .../ci_build/windows/bazel/common_env.sh      |  4 +
 tensorflow/tools/docker/Dockerfile            |  5 +-
 tensorflow/tools/docker/Dockerfile.gpu        |  5 +-
 tensorflow/tools/pip_package/setup.py         |  5 +-
 tensorflow/workspace.bzl                      | 24 ++---
 61 files changed, 774 insertions(+), 220 deletions(-)
 create mode 100644 CODEOWNERS
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/Operand.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/Op.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/Operands.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java
 create mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java
 create mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java

diff --git a/CODEOWNERS b/CODEOWNERS
new file mode 100644
index 0000000000..2f216e4161
--- /dev/null
+++ b/CODEOWNERS
@@ -0,0 +1,50 @@
+# Where component owners are known, add them here.
+
+tensorflow/tensorboard/* @jart @dandelionmane
+tensorflow/tools/docs/* @markdaoust
+tensorflow/java/* @asimshankar
+
+# contrib
+
+# NEED OWNER: tensorflow/contrib/avro/*
+tensorflow/contrib/batching/* @alextp @chrisolston
+tensorflow/contrib/bayesflow/* @ebrevdo @rsepassi @jvdillon
+tensorflow/contrib/cmake/* @mrry @benoitsteiner
+tensorflow/contrib/copy_graph/* @tucker @poxvoculi
+tensorflow/contrib/crf/* @kentonl
+tensorflow/contrib/data/* @mrry
+tensorflow/contrib/distributions/* @jvdillon @langmore @rsepassi
+tensorflow/contrib/factorization/* @agarwal-ashish @xavigonzalvo
+tensorflow/contrib/ffmpeg/* @fredbertsch
+# NEED OWNERT: tensorflow/contrib/framework/*
+tensorflow/contrib/graph_editor/* @purpledog
+# NEED OWNER: tensorflow/contrib/grid_rnn/*
+tensorflow/contrib/hvx/* @satok16
+tensorflow/contrib/imperative/* @keveman
+tensorflow/contrib/integrate/* @shoyer
+tensorflow/contrib/kernel_methods/* @petrosmol
+tensorflow/contrib/ios_examples/* @petewarden
+tensorflow/contrib/labeled_tensor/* @shoyer
+tensorflow/contrib/layers/* @fchollet @martinwicke
+tensorflow/contrib/learn/* @martinwicke @ispirmustafa @alextp
+tensorflow/contrib/linalg/* @langmore
+tensorflow/contrib/linear_optimizer/* @petrosmol @andreasst @katsiapis
+tensorflow/contrib/lookup/* @ysuematsu @andreasst
+tensorflow/contrib/losses/* @alextp @ispirmustafa
+tensorflow/contrib/makefile/* @petewarden @satok16 @wolffg
+tensorflow/contrib/metrics/* @alextp @honkentuber @ispirmustafa
+tensorflow/contrib/nccl/* @cwhipkey @zheng-xq
+tensorflow/contrib/opt/* @strategist333
+tensorflow/contrib/pi_examples/* @maciekcc
+tensorflow/contrib/quantization/* @petewarden @cwhipkey @keveman
+tensorflow/contrib/rnn/* @ebrevdo
+tensorflow/contrib/saved_model/* @nfiedel @sukritiramesh
+tensorflow/contrib/seq2seq/* @lukaszkaiser
+tensorflow/contrib/session_bundle/* @nfiedel @sukritiramesh
+tensorflow/contrib/slim/* @sguada @thenbasilmanran
+tensorflow/contrib/stateless/* @girving
+tensorflow/contrib/tensor_forest/* @gilberthendry @thomascolthurst
+tensorflow/contrib/testing/* @dandelionmane
+tensorflow/contrib/timeseries/* @allenlavoie
+tensorflow/contrib/training/* @joel-shor @ebrevdo
+tensorflow/contrib/util/* @sherrym
diff --git a/README.md b/README.md
index 90d50f6768..0dec90878e 100644
--- a/README.md
+++ b/README.md
@@ -62,7 +62,7 @@ $ python
 
 ## For more information
 
-* [TensorFlow website](https://tensorflow.org)
+* [TensorFlow website](https://www.tensorflow.org)
 * [TensorFlow whitepaper](http://download.tensorflow.org/paper/whitepaper2015.pdf)
 * [TensorFlow Model Zoo](https://github.com/tensorflow/models)
 * [TensorFlow MOOC on Udacity](https://www.udacity.com/course/deep-learning--ud730)
diff --git a/RELEASE.md b/RELEASE.md
index 0cd4eef5d6..33fe88cd54 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -65,37 +65,6 @@
   integration into apps. See
   https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md
   for more details.
-* RNNCells' variable names have been renamed for consistency with Keras layers.
-  Specifically, the previous variable names "weights" and "biases" have
-  been changed to "kernel" and "bias", respectively.
-  This may cause backward incompatibility with regard to your old
-  checkpoints containing such RNN cells, in which case you can use the tool
-  [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
-  to convert the variable names in your old checkpoints.
-* Many of the RNN functions and classes that were in the `tf.nn` namespace
-  before the 1.0 release and which were moved to `tf.contrib.rnn` have now
-  been moved back to the core namespace.  This includes
-  `RNNCell`, `LSTMCell`, `GRUCell`, and a number of other cells.  These
-  now reside in `tf.nn.rnn_cell` (with aliases in `tf.contrib.rnn` for backwards
-  compatibility).  The original `tf.nn.rnn` function is now `tf.nn.static_rnn`,
-  and the bidirectional static and state saving static rnn functions are also
-  now back in the `tf.nn` namespace.
-
-  Notable exceptions are the `EmbeddingWrapper`, `InputProjectionWrapper` and
-  `OutputProjectionWrapper`,  which will slowly be moved to deprecation
-  in `tf.contrib.rnn`.  These are inefficient wrappers that should often
-  be replaced by calling `embedding_lookup` or `layers.dense` as pre- or post-
-  processing of the rnn.  For RNN decoding, this functionality has been replaced
-  with an alternative API in `tf.contrib.seq2seq`.
-* Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of
-  optimized deep learning primitives: In addition to matrix multiplication and
-  convolution, these building blocks include:
-  Direct batched convolution
-  Pooling: maximum, minimum, average
-  Normalization: LRN, batch normalization
-  Activation: rectified linear unit (ReLU)
-  Data manipulation: multi-dimensional transposition (conversion), split,
-  concat, sum and scale.
 
 ## Deprecations
 
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index b461a475c1..c65170dfe8 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -248,6 +248,7 @@ cc_library(
         ":gradients",
         "//tensorflow/core:lib_proto_parsing",
     ],
+    alwayslink = 1,
 )
 
 tf_cc_test(
@@ -275,6 +276,7 @@ cc_library(
         ":cc_ops",
         ":grad_op_registry",
     ],
+    alwayslink = 1,
 )
 
 tf_cc_test(
@@ -302,6 +304,7 @@ cc_library(
         ":cc_ops_internal",
         ":grad_op_registry",
     ],
+    alwayslink = 1,
 )
 
 tf_cc_test(
diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index b3067be51d..f277314352 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -139,7 +139,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
 
-      # Unpack a matrix into vectors
+      # Unpack a matrix into vectors.
       w1 = ta.unstack(convert([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1]]))
       r0 = w1.read(0)
       r1 = w1.read(1)
@@ -180,7 +180,7 @@ class TensorArrayTest(xla_test.XLATestCase):
 
       convert = _make_converter(tf_dtype)
 
-      # Split an empty vector
+      # Split an empty vector.
       lengths = constant_op.constant([0, 0, 0])
       w0 = ta.split(convert([]), lengths=lengths)
       r0 = w0.read(0)
@@ -192,7 +192,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       self.assertAllEqual(convert([]), d1)
       self.assertAllEqual(convert([]), d2)
 
-      # Split a vector
+      # Split a vector.
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
       lengths = constant_op.constant([1, 1, 1])
@@ -206,7 +206,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       self.assertAllEqual(convert([2.0]), d1)
       self.assertAllEqual(convert([3.0]), d2)
 
-      # Split a matrix
+      # Split a matrix.
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
       lengths = constant_op.constant([1, 1, 1])
@@ -319,27 +319,31 @@ class TensorArrayTest(xla_test.XLATestCase):
       ta = tensor_array_ops.TensorArray(
           dtype=dtypes.float32, tensor_array_name="foo", size=3)
 
-      # Test writing the wrong datatype
+      # Test writing the wrong datatype.
       with self.assertRaisesOpError(
           "TensorArray dtype is float but op has dtype int32"):
         ta.write(-1, np.int32(7)).flow.eval()
 
   def testTensorArrayReadWrongIndexOrDataTypeFails(self):
-    with self.test_session(), self.test_scope():
-      ta = tensor_array_ops.TensorArray(
-          dtype=dtypes.float32, tensor_array_name="foo", size=3)
-
-      w0 = ta.write(0, [[4.0, 5.0]])
-
-      # Test reading wrong datatype
-      r0_bad = gen_data_flow_ops._tensor_array_read_v3(
-          handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow)
-      with self.assertRaisesOpError(
-          "TensorArray dtype is float but op has dtype double."):
-        r0_bad.eval()
-
-      # Test reading from a different index than the one we wrote to
-      w0.read(1)
+    # Find two different floating point types, create an array of
+    # the first type, but try to read the other type.
+    if len(self.float_types) > 1:
+      dtype1 = self.float_types[0]
+      dtype2 = self.float_types[1]
+      with self.test_session(), self.test_scope():
+        ta = tensor_array_ops.TensorArray(
+            dtype=dtype1, tensor_array_name="foo", size=3)
+
+        w0 = ta.write(0, [[4.0, 5.0]])
+
+        # Test reading wrong datatype.
+        r0_bad = gen_data_flow_ops._tensor_array_read_v3(
+            handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow)
+        with self.assertRaisesOpError("TensorArray dtype is "):
+          r0_bad.eval()
+
+        # Test reading from a different index than the one we wrote to
+        w0.read(1)
 
   def testTensorArraySplitIncompatibleShapesFails(self):
     with self.test_session(), self.test_scope():
@@ -487,7 +491,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w1.read(0)
       s0 = w1.concat()
 
-      # Test gradient accumulation between read(0), pack(), and concat()
+      # Test gradient accumulation between read(0), pack(), and concat().
       with ops.control_dependencies([p0, r0, s0]):
         grad_r = gradients_impl.gradients(
             ys=[p0, r0, s0],
@@ -536,7 +540,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0_1 = w.read(0)
       r1 = w.read(1)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[r0, r0_1, r1],
           xs=[value],
@@ -744,7 +748,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       grad_b_t, = session.run([grad_b])
       self.assertAllEqual(grad_b_t, g0)
 
-      # Test gradients calculated jointly
+      # Test gradients calculated jointly.
       joint_grad_a_t, joint_grad_b_t = session.run([grad_a, grad_b])
       self.assertAllEqual(joint_grad_a_t, g0)
       self.assertAllEqual(joint_grad_b_t, g0)
@@ -877,7 +881,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       x = constant_op.constant([2.0, 3.0])
       w = ta.unstack(x)
       r0 = w.read(0)
-      # calculate (dr0/dx0, dr0/dx1).  since r0 = x0, gradients are (1, 0).
+      # Calculate (dr0/dx0, dr0/dx1).  since r0 = x0, gradients are (1, 0).
       grad_r0 = gradients_impl.gradients(ys=[r0], xs=[x], grad_ys=[1.0])
       grad_r0_vals = session.run(grad_r0)[0]
       self.assertAllEqual(grad_r0_vals, [1.0, 0.0])
@@ -927,7 +931,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w.read(1)
       r1 = w.read(8)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[r0, r1], xs=[value], grad_ys=[[2.0, 3.0], [4.0, 5.0]])
       read_vals, grad_vals = session.run([[r0, r1], grad])
@@ -951,7 +955,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       w = ta.unstack(values)
       g = w.gather(indices)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[g], xs=[values], grad_ys=[[[2.0, 3.0], [4.0, 5.0]]])
       g_vals, grad_vals = session.run([[g], grad])
diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc
index d467178cb5..b58670ecc5 100644
--- a/tensorflow/compiler/xla/util.cc
+++ b/tensorflow/compiler/xla/util.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/util.h"
 
 #include <stdarg.h>
+#include <numeric>
 
 #include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
 #include "tensorflow/compiler/xla/types.h"
diff --git a/tensorflow/contrib/copy_graph/python/util/copy_elements.py b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
index 3f124be771..8c2528f548 100644
--- a/tensorflow/contrib/copy_graph/python/util/copy_elements.py
+++ b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
@@ -100,7 +100,9 @@ def copy_variable_to_graph(org_instance, to_graph, scope=""):
 
 def copy_op_to_graph(org_instance, to_graph, variables,
                      scope=""):
-  """Given an `Operation` 'org_instance` from one `Graph`,
+  """Returns a copy of an operation from another Graph under a specified scope.
+
+  Given an `Operation` `org_instance` from one `Graph`,
   initializes and returns a copy of it from another `Graph`,
   under the specified scope (default `""`).
 
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index 81ebc821a3..9307fbabac 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -197,7 +197,7 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
       prefix = "multi_rnn_cell/cell_%d/cudnn_compatible_lstm_cell" % i
       w_names.append(prefix + "/kernel")
       # Three transformed bias tensors each layer:
-      # the 1st is for CudnnCompatbleLSTM(Block)Cell restore; the latter two
+      # the 1st is for CudnnCompatibleLSTM(Block)Cell restore; the latter two
       # sum up to the 1st, and are used for cuDNN restore.
       b_names.append(prefix + "/bias")
       b_names.extend([prefix + "/bias_cudnn_%d" % j for j in range(2)])
diff --git a/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc b/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc
index cd1f23bba2..bb9b835889 100644
--- a/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc
+++ b/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc
@@ -213,7 +213,7 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel {
       CHECK_LE(shard.second, perm.size());
       CHECK_LE(shard.first, shard.second);
       const int64 input_index = get_input_index(perm[shard.first]);
-      // Acccumulate the rhs and lhs terms in the normal equations
+      // Accumulate the rhs and lhs terms in the normal equations
       // for the non-zero elements in the row or column of the sparse matrix
       // corresponding to input_index.
       int num_batched = 0;
diff --git a/tensorflow/contrib/framework/python/ops/ops.py b/tensorflow/contrib/framework/python/ops/ops.py
index 4fccc2ceac..ac451a974d 100644
--- a/tensorflow/contrib/framework/python/ops/ops.py
+++ b/tensorflow/contrib/framework/python/ops/ops.py
@@ -68,6 +68,6 @@ def get_name_scope():
     would print the string `scope1/scope2`.
 
   Returns:
-    A string represnting the current name scope.
+    A string representing the current name scope.
   """
   return ops.get_default_graph().get_name_scope()
diff --git a/tensorflow/contrib/labeled_tensor/python/ops/core.py b/tensorflow/contrib/labeled_tensor/python/ops/core.py
index 04bf26a5dd..fc1ea83449 100644
--- a/tensorflow/contrib/labeled_tensor/python/ops/core.py
+++ b/tensorflow/contrib/labeled_tensor/python/ops/core.py
@@ -199,7 +199,7 @@ class Axes(collections.Mapping):
   """Axis names and indices for a tensor.
 
   It is an ordered mapping, with keys given by axis name and values given
-  by Axis objets. Duplicate axis names are not allowed.
+  by Axis objects. Duplicate axis names are not allowed.
   """
 
   @tc.accepts(object, tc.List(AxisLike))
diff --git a/tensorflow/contrib/layers/python/layers/initializers.py b/tensorflow/contrib/layers/python/layers/initializers.py
index 271b3c01ff..b12a882d9a 100644
--- a/tensorflow/contrib/layers/python/layers/initializers.py
+++ b/tensorflow/contrib/layers/python/layers/initializers.py
@@ -42,7 +42,7 @@ def xavier_initializer(uniform=True, seed=None, dtype=dtypes.float32):
   This initializer is designed to keep the scale of the gradients roughly the
   same in all layers. In uniform distribution this ends up being the range:
   `x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
-  deviation of `sqrt(3. / (in + out))` is used.
+  deviation of `sqrt(2. / (in + out))` is used.
 
   Args:
     uniform: Whether to use uniform or normal distributed random initialization.
diff --git a/tensorflow/contrib/learn/python/learn/utils/gc.py b/tensorflow/contrib/learn/python/learn/utils/gc.py
index 5af9e8b9e2..b890129064 100644
--- a/tensorflow/contrib/learn/python/learn/utils/gc.py
+++ b/tensorflow/contrib/learn/python/learn/utils/gc.py
@@ -46,14 +46,14 @@ For example,
   path_list = gc.get_paths("/tmp", parser)  # contains all ten Paths
 
   every_fifth = gc.mod_export_version(5)
-  print every_fifth(path_list) # shows ["/tmp/0", "/tmp/5"]
+  print(every_fifth(path_list))  # shows ["/tmp/0", "/tmp/5"]
 
   largest_three = gc.largest_export_versions(3)
-  print largest_three(all_paths)  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
+  print(largest_three(all_paths))  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
 
   both = gc.union(every_fifth, largest_three)
-  print both(all_paths)  # shows ["/tmp/0", "/tmp/5",
-                         #        "/tmp/7", "/tmp/8", "/tmp/9"]
+  print(both(all_paths))  # shows ["/tmp/0", "/tmp/5",
+                          #        "/tmp/7", "/tmp/8", "/tmp/9"]
   # delete everything not in 'both'
   to_delete = gc.negation(both)
   for p in to_delete(all_paths):
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index f53f38f3cf..66caa6a2e5 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -300,7 +300,7 @@ class MutableHashTable(LookupInterface):
                                              default_value=-1)
   table.insert(keys, values)
   out = table.lookup(query_keys)
-  print out.eval()
+  print(out.eval())
   ```
   """
 
@@ -502,7 +502,7 @@ class MutableDenseHashTable(LookupInterface):
                                                   empty_key=0)
   table.insert(keys, values)
   out = table.lookup(query_keys)
-  print out.eval()
+  print(out.eval())
   ```
   """
 
diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md
index 9ba5c035a2..0306ecb214 100644
--- a/tensorflow/contrib/makefile/README.md
+++ b/tensorflow/contrib/makefile/README.md
@@ -130,7 +130,7 @@ For more details, see the [benchmark documentation](../../tools/benchmark).
 ## iOS
 
 _Note: To use this library in an iOS application, see related instructions in
-the [iOS examples](../ios_examples/) directory._
+the [iOS examples](../../examples/ios/) directory._
 
 Install XCode 7.3 or more recent. If you have not already, you will need to
 install the command-line tools using `xcode-select`:
@@ -176,7 +176,7 @@ benchmark program. Although successfully compiling the benchmark program is a
 sign of success, the program is not a complete iOS app.
 
 To see TensorFlow running on iOS, the example Xcode project in
-[tensorflow/contrib/ios_examples](../ios_examples) shows how to use the static
+[tensorflow/examples/ios](../../examples/ios/) shows how to use the static
 library in a simple app.
 
 ### Building by hand
@@ -214,7 +214,7 @@ benchmark program. Although successfully compiling the benchmark program is a
 sign of success, the program is not a complete iOS app. 
 
 To see TensorFlow running on iOS, the example Xcode project in
-[tensorflow/contrib/ios_examples](../ios_examples) shows how to use the static
+[tensorflow/examples/ios](../../examples/ios/) shows how to use the static
 library in a simple app.
 
 #### Universal binaries
diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc
index e97e8d0163..1a2563d20f 100644
--- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc
+++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc
@@ -44,7 +44,8 @@ MPIRendezvousMgr::MPIRendezvousMgr(const WorkerEnv* env)
 
   // extract worker-name
   auto parsed = env->local_devices[0]->parsed_name();
-  const std::string task_id = strings::StrCat(parsed.job, ":", parsed.replica);
+  const std::string task_id =
+      strings::StrCat(parsed.job, ":", parsed.replica, ":", parsed.task);
 
   mpiutils_ = new MPIUtils(task_id);
   background_thread_ =
@@ -66,8 +67,8 @@ void MPIRemoteRendezvous::RecvFromRemoteAsync(
   VLOG(2) << "MPI User requested " << parsed.FullKey()
           << " @ step: " << step_id_;
 
-  std::string src_task =
-      strings::StrCat(parsed.src.job, ":", parsed.src.replica);
+  std::string src_task = strings::StrCat(
+      parsed.src.job, ":", parsed.src.replica, ":", parsed.src.task);
   const int dst = mpiutils_->GetSourceID(src_task);
 
   Device* dst_device;
@@ -138,11 +139,7 @@ void MPIRemoteRendezvous::RecvFromRemoteAsync(
                     std::move(request_call), rendezvous_call);
 }
 
-MPIRemoteRendezvous::~MPIRemoteRendezvous() {
-  MPIRendezvousMgr* mgr =
-      reinterpret_cast<MPIRendezvousMgr*>(this->rendezvous_mgr_);
-  mgr->RemoveStepID(step_id_);
-}
+MPIRemoteRendezvous::~MPIRemoteRendezvous() {}
 
 /*
  * Add the request for one of our Tensors by a remote process
diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
index 50fc380496..24e784df3e 100644
--- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
+++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
@@ -147,15 +147,8 @@ class MPIRendezvousMgr : public BaseRendezvousMgr {
                     MPIRequestTensorCall* rCall) {
     mutex_lock l(mrq_);
     request_queue_.push(RequestQueueEntry(key, std::move(request_call)));
-    recv_tensor_map_[step_id][key] =
-        std::shared_ptr<MPIRequestTensorCall>(rCall);
-  }
-
-  void RemoveStepID(const int64 step_id) {
-    mutex_lock l(mrq_);
-    CHECK(recv_tensor_map_[step_id].size() == 0) << "Removing unfinished step";
-    recv_tensor_map_.erase(step_id);
-    // TODO(jbedorf) Should we verify that the step_id is clear before remove?
+    const std::string key_id = strings::StrCat(key, "_", step_id);
+    recv_tensor_map_[key_id] = std::shared_ptr<MPIRequestTensorCall>(rCall);
   }
 
  protected:
@@ -181,9 +174,8 @@ class MPIRendezvousMgr : public BaseRendezvousMgr {
 
   std::queue<SendQueueEntry> send_queue_ GUARDED_BY(msq_);
   std::queue<RequestQueueEntry> request_queue_ GUARDED_BY(mrq_);
-  std::map<int64, std::unordered_map<std::string,
-                                     std::shared_ptr<MPIRequestTensorCall>>>
-      recv_tensor_map_ GUARDED_BY(mrq_);
+  std::map<std::string, std::shared_ptr<MPIRequestTensorCall>> recv_tensor_map_
+      GUARDED_BY(mrq_);
 
   void AddRequest(RecvTensorRequest, const int);
   void MPIBackgroundThread();
@@ -196,22 +188,19 @@ class MPIRendezvousMgr : public BaseRendezvousMgr {
   void GetRecvCall(const int64 step_id, const std::string& key,
                    std::shared_ptr<MPIRequestTensorCall>* call) {
     mutex_lock l(mrq_);
-    if (recv_tensor_map_.find(step_id) == recv_tensor_map_.end()) {
-      LOG(FATAL) << "Step not found in recv_tensor_map_, step: " << step_id
-                 << " key:  " << key << std::endl;
-    }
-    if (recv_tensor_map_[step_id].find(key) !=
-        recv_tensor_map_[step_id].end()) {
-      *call = recv_tensor_map_[step_id][key];
-    } else {
-      LOG(FATAL) << "Key not found in recv_tensor_map_, step: " << step_id
+
+    const std::string key_id = strings::StrCat(key, "_", step_id);
+    if (recv_tensor_map_.find(key_id) == recv_tensor_map_.end()) {
+      LOG(FATAL) << "Key/step not found in recv_tensor_map_, step: " << step_id
                  << " key:  " << key << std::endl;
     }
+    *call = recv_tensor_map_[key_id];
   }
 
   void RemoveRecvCall(const int64 step_id, const std::string& key) {
     mutex_lock l(mrq_);
-    recv_tensor_map_[step_id].erase(key);
+    const std::string key_id = strings::StrCat(key, "_", step_id);
+    recv_tensor_map_.erase(key_id);
   }
 
   bool GetRequest(RequestQueueEntry* req) {
diff --git a/tensorflow/contrib/session_bundle/gc.py b/tensorflow/contrib/session_bundle/gc.py
index fb9e0743a1..249c23c88f 100644
--- a/tensorflow/contrib/session_bundle/gc.py
+++ b/tensorflow/contrib/session_bundle/gc.py
@@ -46,14 +46,14 @@ For example,
   path_list = gc.get_paths("/tmp", parser)  # contains all ten Paths
 
   every_fifth = gc.mod_export_version(5)
-  print every_fifth(path_list) # shows ["/tmp/0", "/tmp/5"]
+  print(every_fifth(path_list))  # shows ["/tmp/0", "/tmp/5"]
 
   largest_three = gc.largest_export_versions(3)
-  print largest_three(all_paths)  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
+  print(largest_three(all_paths))  # shows ["/tmp/7", "/tmp/8", "/tmp/9"]
 
   both = gc.union(every_fifth, largest_three)
-  print both(all_paths)  # shows ["/tmp/0", "/tmp/5",
-                         #        "/tmp/7", "/tmp/8", "/tmp/9"]
+  print(both(all_paths))  # shows ["/tmp/0", "/tmp/5",
+                          #        "/tmp/7", "/tmp/8", "/tmp/9"]
   # delete everything not in 'both'
   to_delete = gc.negation(both)
   for p in to_delete(all_paths):
diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index e854910f85..d2aa73a178 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -82,6 +82,9 @@ py_test(
         "estimators_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":estimators",
         ":feature_keys",
@@ -122,6 +125,9 @@ py_test(
         "model_utils_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":model_utils",
         ":state_management",
@@ -153,6 +159,9 @@ py_test(
         "state_management_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":input_pipeline",
         ":model",
@@ -203,6 +212,9 @@ py_test(
         "input_pipeline_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":input_pipeline",
         ":test_utils",
@@ -313,6 +325,9 @@ py_test(
         "math_utils_test.py",
     ],
     srcs_version = "PY2AND3",
+    tags = [
+        "no_pip_gpu",  # b/63391119
+    ],
     deps = [
         ":math_utils",
         "//tensorflow/python:array_ops",
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
index d0770b86fe..e70db93ea1 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/periodic.py
@@ -50,7 +50,7 @@ class CycleStateSpaceModel(state_space_model.StateSpaceModel):
 
   def get_noise_transform(self):
     # transition_power_noise_accumulator makes assumptions about this
-    # transformation. If the noise transform is modified or overriden,
+    # transformation. If the noise transform is modified or overridden,
     # transition_power_noise_accumulator must be modified as well (or discarded,
     # as it is simply an optimization).
     return array_ops.pad(
diff --git a/tensorflow/core/kernels/decode_wav_op_test.cc b/tensorflow/core/kernels/decode_wav_op_test.cc
index c282d53a5a..fc323a5e04 100644
--- a/tensorflow/core/kernels/decode_wav_op_test.cc
+++ b/tensorflow/core/kernels/decode_wav_op_test.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/audio_ops.h"
 #include "tensorflow/cc/ops/const_op.h"
 #include "tensorflow/cc/ops/math_ops.h"
+#include "tensorflow/core/framework/shape_inference_testutil.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/types.pb.h"
@@ -83,4 +84,41 @@ TEST(DecodeWavOpTest, DecodeWavTest) {
   EXPECT_EQ(14099, sample_rate);
 }
 
+TEST(DecodeWavOpTest, DecodeWav_ShapeFn) {
+  ShapeInferenceTestOp op("DecodeWav");
+  INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");
+
+  // audio shape is unknown when desired_{samples,channels} are default.
+  TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
+                   .Input({"a", 0, DT_STRING})
+                   .Finalize(&op.node_def));
+  INFER_OK(op, "[]", "[?,?];[]");
+
+  TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
+                   .Input({"a", 0, DT_STRING})
+                   .Attr("desired_samples", 42)
+                   .Finalize(&op.node_def));
+  INFER_OK(op, "[]", "[42,?];[]");
+
+  // Negative sample value is rejected.
+  TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
+                   .Input({"a", 0, DT_STRING})
+                   .Attr("desired_samples", -2)
+                   .Finalize(&op.node_def));
+  INFER_ERROR("samples must be non-negative, got -2", op, "[]");
+
+  TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
+                   .Input({"a", 0, DT_STRING})
+                   .Attr("desired_channels", 2)
+                   .Finalize(&op.node_def));
+  INFER_OK(op, "[]", "[?,2];[]");
+
+  // Negative channel value is rejected.
+  TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
+                   .Input({"a", 0, DT_STRING})
+                   .Attr("desired_channels", -2)
+                   .Finalize(&op.node_def));
+  INFER_ERROR("channels must be non-negative, got -2", op, "[]");
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc b/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
index a27ab73bf7..e0918c70a7 100644
--- a/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
+++ b/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
@@ -117,7 +117,7 @@ Status TestMultipleWrites(size_t compress_input_buf_size,
   io::SnappyInputBuffer in(file_reader.get(), uncompress_input_buf_size,
                            uncompress_output_buf_size);
 
-  // Run the test twice, reseting the stream after the first attempt.
+  // Run the test twice, resetting the stream after the first attempt.
   for (int attempt = 0; attempt < 2; ++attempt) {
     string actual_result;
     for (int i = 0; i < num_writes; i++) {
diff --git a/tensorflow/core/lib/wav/wav_io.cc b/tensorflow/core/lib/wav/wav_io.cc
index 028ff26ffb..79918690db 100644
--- a/tensorflow/core/lib/wav/wav_io.cc
+++ b/tensorflow/core/lib/wav/wav_io.cc
@@ -147,7 +147,7 @@ Status EncodeAudioAsS16LEWav(const float* audio, size_t sample_rate,
     return errors::InvalidArgument("num_frames must be positive.");
   }
 
-  const size_t bytes_per_second = sample_rate * kBytesPerSample;
+  const size_t bytes_per_second = sample_rate * kBytesPerSample * num_channels;
   const size_t num_samples = num_frames * num_channels;
   const size_t data_size = num_samples * kBytesPerSample;
   const size_t file_size = kHeaderSize + num_samples * kBytesPerSample;
@@ -242,8 +242,7 @@ Status DecodeLin16WaveAsFloatVector(const string& wav_string,
         "Bad bytes per sample in WAV header: Expected ",
         expected_bytes_per_sample, " but got ", bytes_per_sample);
   }
-  const uint32 expected_bytes_per_second =
-      (bytes_per_sample * (*sample_rate)) / *channel_count;
+  const uint32 expected_bytes_per_second = bytes_per_sample * *sample_rate;
   if (bytes_per_second != expected_bytes_per_second) {
     return errors::InvalidArgument(
         "Bad bytes per second in WAV header: Expected ",
diff --git a/tensorflow/core/lib/wav/wav_io_test.cc b/tensorflow/core/lib/wav/wav_io_test.cc
index e54b9445ab..40ddd94abe 100644
--- a/tensorflow/core/lib/wav/wav_io_test.cc
+++ b/tensorflow/core/lib/wav/wav_io_test.cc
@@ -97,5 +97,63 @@ TEST(WavIO, EncodeThenDecode) {
   }
 }
 
+TEST(WavIO, BasicMono) {
+  std::vector<uint8> wav_data = {
+      'R', 'I', 'F', 'F',  // ChunkID
+      44, 0, 0, 0,         // ChunkSize: 36 + SubChunk2Size
+      'W', 'A', 'V', 'E',  // Format
+      'f', 'm', 't', ' ',  // Subchunk1ID
+      16, 0, 0, 0,         // Subchunk1Size
+      1, 0,                // AudioFormat: 1=PCM
+      1, 0,                // NumChannels
+      0x44, 0xac, 0, 0,    // SampleRate: 44100
+      0x88, 0x58, 0x1, 0,  // BytesPerSecond: SampleRate * NumChannels *
+                           //                 BitsPerSample/8
+      2, 0,                // BytesPerSample: NumChannels * BitsPerSample/8
+      16, 0,               // BitsPerSample
+      'd', 'a', 't', 'a',  // Subchunk2ID
+      8, 0, 0, 0,          // Subchunk2Size: NumSamples * NumChannels *
+                           //                BitsPerSample/8
+      0, 0,                // Sample 1: 0
+      0xff, 0x7f,          // Sample 2: 32767 (saturated)
+      0, 0,                // Sample 3: 0
+      0x00, 0x80,          // Sample 4: -32768 (saturated)
+  };
+  string expected(wav_data.begin(), wav_data.end());
+  float audio[] = {0.0f, 1.0f, 0.0f, -1.0f};
+  string result;
+  TF_EXPECT_OK(EncodeAudioAsS16LEWav(audio, 44100, 1, 4, &result));
+  EXPECT_EQ(expected, result);
+}
+
+TEST(WavIO, BasicStereo) {
+  std::vector<uint8> wav_data = {
+      'R', 'I', 'F', 'F',  // ChunkID
+      44, 0, 0, 0,         // ChunkSize: 36 + SubChunk2Size
+      'W', 'A', 'V', 'E',  // Format
+      'f', 'm', 't', ' ',  // Subchunk1ID
+      16, 0, 0, 0,         // Subchunk1Size
+      1, 0,                // AudioFormat: 1=PCM
+      2, 0,                // NumChannels
+      0x44, 0xac, 0, 0,    // SampleRate: 44100
+      0x10, 0xb1, 0x2, 0,  // BytesPerSecond: SampleRate * NumChannels *
+                           //                 BitsPerSample/8
+      4, 0,                // BytesPerSample: NumChannels * BitsPerSample/8
+      16, 0,               // BitsPerSample
+      'd', 'a', 't', 'a',  // Subchunk2ID
+      8, 0, 0, 0,          // Subchunk2Size: NumSamples * NumChannels *
+                           //                BitsPerSample/8
+      0, 0,                // Sample 1: 0
+      0xff, 0x7f,          // Sample 2: 32767 (saturated)
+      0, 0,                // Sample 3: 0
+      0x00, 0x80,          // Sample 4: -32768 (saturated)
+  };
+  string expected(wav_data.begin(), wav_data.end());
+  float audio[] = {0.0f, 1.0f, 0.0f, -1.0f};
+  string result;
+  TF_EXPECT_OK(EncodeAudioAsS16LEWav(audio, 44100, 2, 2, &result));
+  EXPECT_EQ(expected, result);
+}
+
 }  // namespace wav
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/audio_ops.cc b/tensorflow/core/ops/audio_ops.cc
index 02b13a455c..91e81f2579 100644
--- a/tensorflow/core/ops/audio_ops.cc
+++ b/tensorflow/core/ops/audio_ops.cc
@@ -33,7 +33,7 @@ Status DecodeWavShapeFn(InferenceContext* c) {
   DimensionHandle channels_dim;
   int32 desired_channels;
   TF_RETURN_IF_ERROR(c->GetAttr("desired_channels", &desired_channels));
-  if (desired_channels == 0) {
+  if (desired_channels == -1) {
     channels_dim = c->UnknownDim();
   } else {
     if (desired_channels < 0) {
@@ -45,7 +45,7 @@ Status DecodeWavShapeFn(InferenceContext* c) {
   DimensionHandle samples_dim;
   int32 desired_samples;
   TF_RETURN_IF_ERROR(c->GetAttr("desired_samples", &desired_samples));
-  if (desired_samples == 0) {
+  if (desired_samples == -1) {
     samples_dim = c->UnknownDim();
   } else {
     if (desired_samples < 0) {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index ccae526e5f..449233e178 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -28467,6 +28467,33 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
+op {
+  name: "LMDBReader"
+  output_arg {
+    name: "reader_handle"
+    description: "The handle to reference the Reader."
+    type: DT_STRING
+    is_ref: true
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
+  }
+  summary: "A Reader that outputs the records from a LMDB database."
+  is_stateful: true
+}
 op {
   name: "TakeDataset"
   input_arg {
diff --git a/tensorflow/core/protobuf/saver.proto b/tensorflow/core/protobuf/saver.proto
index 65fe9c4c98..a757d3f756 100644
--- a/tensorflow/core/protobuf/saver.proto
+++ b/tensorflow/core/protobuf/saver.proto
@@ -37,9 +37,9 @@ message SaverDef {
   enum CheckpointFormatVersion {
     // Internal legacy format.
     LEGACY = 0;
-    // Current format: tf.Saver() which works with tensorflow::table::Table.
+    // Deprecated format: tf.Saver() which works with tensorflow::table::Table.
     V1 = 1;
-    // Experimental format under development.
+    // Current format: more efficient.
     V2 = 2;
   }
   CheckpointFormatVersion version = 7;
diff --git a/tensorflow/docs_src/community/documentation.md b/tensorflow/docs_src/community/documentation.md
index a757d8c66d..e8b3787c1f 100644
--- a/tensorflow/docs_src/community/documentation.md
+++ b/tensorflow/docs_src/community/documentation.md
@@ -31,7 +31,7 @@ TensorFlow.
 
 However, most developers will contribute documentation into the master Github
 branch, which is published, occasionally,
-at [tensorflow.org/versions/master](https://tensorflow.org/versions/master).
+at [tensorflow.org/versions/master](https://www.tensorflow.org/versions/master).
 
 If you want documentation changes to appear at root, you will need to also
 contribute that change to the current stable binary branch (and/or
diff --git a/tensorflow/docs_src/extend/index.md b/tensorflow/docs_src/extend/index.md
index 590023b065..5812caaffc 100644
--- a/tensorflow/docs_src/extend/index.md
+++ b/tensorflow/docs_src/extend/index.md
@@ -20,8 +20,8 @@ TensorFlow:
 
 Python is currently the only language supported by TensorFlow's API stability
 promises.  However, TensorFlow also provides functionality in C++, Java, and Go,
-plus community support for [Haskell](https://github.com/tensorflow/haskell)
-and [Rust](https://github.com/tensorflow/rust).  If you'd like to create or
+plus community support for [Haskell](https://github.com/tensorflow/haskell) and 
+[Rust](https://github.com/tensorflow/rust).  If you'd like to create or
 develop TensorFlow features in a language other than these languages, read the
 following guide:
 
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 17d07cc582..852d41e9ed 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -33,7 +33,7 @@ tensors:
 
 ```python
 3 # a rank 0 tensor; this is a scalar with shape []
-[1. ,2., 3.] # a rank 1 tensor; this is a vector with shape [3]
+[1., 2., 3.] # a rank 1 tensor; this is a vector with shape [3]
 [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
 [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]
 ```
@@ -100,20 +100,20 @@ we see the expected values of 3.0 and 4.0:
 ```
 
 We can build more complicated computations by combining `Tensor` nodes with
-operations (Operations are also nodes.). For example, we can add our two
+operations (Operations are also nodes). For example, we can add our two
 constant nodes and produce a new graph as follows:
 
 ```python
 node3 = tf.add(node1, node2)
-print("node3: ", node3)
-print("sess.run(node3): ",sess.run(node3))
+print("node3:", node3)
+print("sess.run(node3):", sess.run(node3))
 ```
 
 The last two print statements produce
 
 ```
-node3:  Tensor("Add:0", shape=(), dtype=float32)
-sess.run(node3):  7.0
+node3: Tensor("Add:0", shape=(), dtype=float32)
+sess.run(node3): 7.0
 ```
 
 TensorFlow provides a utility called TensorBoard that can display a picture of
@@ -140,8 +140,8 @@ the [run method](https://www.tensorflow.org/api_docs/python/tf/Session#run)
 to feed concrete values to the placeholders:
 
 ```python
-print(sess.run(adder_node, {a: 3, b:4.5}))
-print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
+print(sess.run(adder_node, {a: 3, b: 4.5}))
+print(sess.run(adder_node, {a: [1, 3], b: [2, 4]}))
 ```
 resulting in the output
 
@@ -159,7 +159,7 @@ For example,
 
 ```python
 add_and_triple = adder_node * 3.
-print(sess.run(add_and_triple, {a: 3, b:4.5}))
+print(sess.run(add_and_triple, {a: 3, b: 4.5}))
 ```
 produces the output
 ```
@@ -202,7 +202,7 @@ Since `x` is a placeholder, we can evaluate `linear_model` for several values of
 `x` simultaneously as follows:
 
 ```python
-print(sess.run(linear_model, {x:[1,2,3,4]}))
+print(sess.run(linear_model, {x: [1, 2, 3, 4]}))
 ```
 to produce the output
 ```
@@ -225,7 +225,7 @@ that abstracts the error of all examples using `tf.reduce_sum`:
 y = tf.placeholder(tf.float32)
 squared_deltas = tf.square(linear_model - y)
 loss = tf.reduce_sum(squared_deltas)
-print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
+print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))
 ```
 producing the loss value
 ```
@@ -242,7 +242,7 @@ perfect values of -1 and 1. A variable is initialized to the value provided to
 fixW = tf.assign(W, [-1.])
 fixb = tf.assign(b, [1.])
 sess.run([fixW, fixb])
-print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
+print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))
 ```
 The final print shows the loss now is zero.
 ```
@@ -273,7 +273,7 @@ train = optimizer.minimize(loss)
 ```python
 sess.run(init) # reset values to incorrect defaults.
 for i in range(1000):
-  sess.run(train, {x:[1,2,3,4], y:[0,-1,-2,-3]})
+  sess.run(train, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})
 
 print(sess.run([W, b]))
 ```
@@ -319,10 +319,10 @@ init = tf.global_variables_initializer()
 sess = tf.Session()
 sess.run(init) # reset values to wrong
 for i in range(1000):
-  sess.run(train, {x:x_train, y:y_train})
+  sess.run(train, {x: x_train, y: y_train})
 
 # evaluate training accuracy
-curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
+curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})
 print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
 ```
 When run, it produces
@@ -377,11 +377,11 @@ y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
 input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
 train_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
 eval_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
 
 # We can invoke 1000 training steps by invoking the  method and passing the
 # training data set.
@@ -449,11 +449,11 @@ y_train = np.array([0., -1., -2., -3.])
 x_eval = np.array([2., 5., 8., 1.])
 y_eval = np.array([-1.01, -4.1, -7, 0.])
 input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
 train_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
 eval_input_fn = tf.estimator.inputs.numpy_input_fn(
-    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
+    {"x": x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
 
 # train
 estimator.train(input_fn=input_fn, steps=1000)
diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index 74407ed315..56486a48b7 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -149,6 +149,8 @@ TensorFlow also has a
 to help build support for more client languages.  We invite contributions of new
 language bindings.
 
+Bindings for various other languages (such as [C#](https://github.com/migueldeicaza/TensorFlowSharp), [Julia](https://github.com/malmaud/TensorFlow.jl), [Ruby](https://github.com/somaticio/tensorflow.rb) and [Scala](https://github.com/eaplatanios/tensorflow_scala)) created and supported by the opensource community build on top of the C API supported by the TensorFlow maintainers.
+
 #### Does TensorFlow make use of all the devices (GPUs and CPUs) available on my machine?
 
 TensorFlow supports multiple GPUs and CPUs. See the how-to documentation on
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index ac39639af1..2e2e578050 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -741,7 +741,7 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
   weights, and then sets up all the gradients for the backward pass.
 
   The set up for the softmax and fully-connected layers is based on:
-  https://tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
+  https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
 
   Args:
     class_count: Integer of how many categories of things we're trying to
diff --git a/tensorflow/examples/label_image/README.md b/tensorflow/examples/label_image/README.md
index c5857f394a..a9e44745e5 100644
--- a/tensorflow/examples/label_image/README.md
+++ b/tensorflow/examples/label_image/README.md
@@ -63,14 +63,14 @@ $ bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png
 ```
 
 For a more detailed look at this code, you can check out the C++ section of the
-[Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+[Inception tutorial](https://www.tensorflow.org/tutorials/image_recognition/).
 
 ## Python implementation
 
 label_image.py is a python implementation that provides code corresponding
 to the C++ code here. This gives more intuitive mapping between C++ and
 Python than the Python code mentioned in the
-[Inception tutorial](https://tensorflow.org/tutorials/image_recognition/).
+[Inception tutorial](https://www.tensorflow.org/tutorials/image_recognition/).
 and could be easier to add visualization or debug code.
 
 With tensorflow python package installed, you can run it like:
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 6d0990cb52..5a67cfa441 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14441,6 +14441,51 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// Computes inverse hyperbolic sine of x element-wise.
+func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Asinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes inverse hyperbolic cosine of x element-wise.
+func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Acosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes inverse hyperbolic tangent of x element-wise.
+func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Atanh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
 type TextLineReaderV2Attr func(optionalAttr)
 
@@ -18243,21 +18288,6 @@ func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *
 	return scope.AddOperation(opspec)
 }
 
-// Computes inverse hyperbolic sine of x element-wise.
-func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Asinh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Real-valued fast Fourier transform.
 //
 // Computes the 1-dimensional discrete Fourier transform of a real-valued signal
@@ -21096,21 +21126,6 @@ func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values t
 	return op.Output(0)
 }
 
-// Computes inverse hyperbolic cosine of x element-wise.
-func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Acosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
 //
 // For an explanation see "Differentiation of the Cholesky algorithm" by
@@ -21139,21 +21154,6 @@ func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output)
 	return op.Output(0)
 }
 
-// Computes inverse hyperbolic tangent of x element-wise.
-func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Atanh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes the log of the absolute value of `Gamma(x)` element-wise.
 func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 90372660cd..759f4c1b0e 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -162,6 +162,32 @@ java_test(
     ],
 )
 
+java_test(
+    name = "PrimitiveOpTest",
+    size = "small",
+    srcs = ["src/test/java/org/tensorflow/op/PrimitiveOpTest.java"],
+    javacopts = JAVACOPTS,
+    test_class = "org.tensorflow.op.PrimitiveOpTest",
+    deps = [
+        ":tensorflow",
+        ":testutil",
+        "@junit",
+    ],
+)
+
+java_test(
+    name = "OperandsTest",
+    size = "small",
+    srcs = ["src/test/java/org/tensorflow/op/OperandsTest.java"],
+    javacopts = JAVACOPTS,
+    test_class = "org.tensorflow.op.OperandsTest",
+    deps = [
+        ":tensorflow",
+        ":testutil",
+        "@junit",
+    ],
+)
+
 filegroup(
     name = "libtensorflow_jni",
     srcs = select({
diff --git a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
index 15f7b99135..ca06177017 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
@@ -49,7 +49,7 @@ final class NativeLibrary {
       // Either:
       // (1) The native library has already been statically loaded, OR
       // (2) The required native code has been statically linked (through a custom launcher), OR
-      // (3) The native code is part of another library (such as an an application-level libraryh)
+      // (3) The native code is part of another library (such as an application-level library)
       // that has already been loaded. For example, tensorflow/examples/android and
       // tensorflow/contrib/android include the required native code in differently named libraries.
       //
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operand.java b/tensorflow/java/src/main/java/org/tensorflow/Operand.java
new file mode 100644
index 0000000000..695c4c1060
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operand.java
@@ -0,0 +1,48 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow;
+
+/**
+ * Interface implemented by operands of a TensorFlow operation.
+ *
+ * <p>Example usage:
+ *
+ * <pre>{@code
+ * // The "decodeJpeg" operation can be used as an operand to the "cast" operation
+ * Operand decodeJpeg = ops.image().decodeJpeg(...);
+ * ops.math().cast(decodeJpeg, DataType.FLOAT);
+ *
+ * // The output "y" of the "unique" operation can be used as an operand to the "cast" operation
+ * Output y = ops.array().unique(...).y();
+ * ops.math().cast(y, DataType.FLOAT);
+ *
+ * // The "split" operation can be used as operand list to the "concat" operation
+ * Iterable<? extends Operand> split = ops.array().split(...);
+ * ops.array().concat(0, split);
+ * }</pre>
+ */
+public interface Operand {
+
+  /**
+   * Returns the symbolic handle of a tensor.
+   *
+   * <p>Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
+   * used to obtain a symbolic handle that represents the computation of the input.
+   *
+   * @see OperationBuilder#addInput(Output)
+   */
+  Output asOutput();
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Operation.java b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
index d584085282..ec26309fba 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Operation.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Operation.java
@@ -91,6 +91,21 @@ public final class Operation {
     }
   }
 
+  /**
+   * Returns symbolic handles to a list of tensors produced by this operation.
+   *
+   * @param idx index of the first tensor of the list
+   * @param length number of tensors in the list
+   * @return array of {@code Output}
+   */
+  public Output[] outputList(int idx, int length) {
+    Output[] outputs = new Output[length];
+    for (int i = 0; i < length; ++i) {
+      outputs[i] = output(idx + i);
+    }
+    return outputs;
+  }
+
   /** Returns a symbolic handle to one of the tensors produced by this operation. */
   public Output output(int idx) {
     return new Output(this, idx);
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Output.java b/tensorflow/java/src/main/java/org/tensorflow/Output.java
index ad28eb2743..8dff50fafb 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/Output.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/Output.java
@@ -16,16 +16,17 @@ limitations under the License.
 package org.tensorflow;
 
 import java.util.Objects;
+
 /**
  * A symbolic handle to a tensor produced by an {@link Operation}.
  *
  * <p>An Output is a symbolic handle to a tensor. The value of the Tensor is computed by executing
  * the {@link Operation} in a {@link Session}.
  *
- * <p>By implementing the {@link Input} interface, instances of this class could also be passed
- * directly in input to an operation.
+ * <p>By implementing the {@link Operand} interface, instances of this class also act as operands to
+ * {@link org.tensorflow.op.Op Op} instances.
  */
-public final class Output implements Input {
+public final class Output implements Operand {
 
   /** Handle to the idx-th output of the Operation {@code op}. */
   public Output(Operation op, int idx) {
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/Op.java b/tensorflow/java/src/main/java/org/tensorflow/op/Op.java
new file mode 100644
index 0000000000..aa6db40457
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/Op.java
@@ -0,0 +1,35 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+/**
+ * A marker interface for all operation wrappers.
+ *
+ * <p>Operation wrappers provide strongly typed interfaces for building operations and linking them
+ * into a graph without the use of literals and indexes required by the core classes.
+ *
+ * <p>This interface allows keeping references to any operation wrapper using a common type.
+ *
+ * <pre>{@code
+ * // All values returned by an Ops call can be referred as a Op
+ * Op split = ops.array().split(...);
+ * Op shape = ops.array().shape(...);
+ *
+ * // All operations could be added to an Op collection
+ * Collection<Op> allOps = Arrays.asList(split, shape);
+ * }
+ */
+public interface Op {}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/Operands.java b/tensorflow/java/src/main/java/org/tensorflow/op/Operands.java
new file mode 100644
index 0000000000..5971103d6d
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/Operands.java
@@ -0,0 +1,46 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.tensorflow.Operand;
+import org.tensorflow.OperationBuilder;
+import org.tensorflow.Output;
+
+/** Utilities for manipulating operand related types and lists. */
+public final class Operands {
+
+  /**
+   * Converts a list of {@link Operand} into an array of {@link Output}.
+   *
+   * <p>Operation wrappers need to convert back a list of inputs into an array of outputs in order
+   * to build an operation, see {@link OperationBuilder#addInputList(Output[])}.
+   *
+   * @param inputs an iteration of input operands
+   * @return an array of outputs
+   */
+  public static Output[] asOutputs(Iterable<? extends Operand> inputs) {
+    List<Output> outputList = new ArrayList<>();
+    for (Operand input : inputs) {
+      outputList.add(input.asOutput());
+    }
+    return outputList.toArray(new Output[outputList.size()]);
+  }
+
+  // Disabled constructor
+  private Operands() {}
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java b/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java
new file mode 100644
index 0000000000..8e56f97041
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java
@@ -0,0 +1,65 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import org.tensorflow.Operation;
+
+/**
+ * A base class for {@link Op} implementations that are backed by a single {@link Operation}.
+ *
+ * <p>Each operation registered in the TensorFlow core is a primitive and is provided as a {@code
+ * PrimitiveOp}. Custom operations working with only one primitive may also derive from this class.
+ */
+public abstract class PrimitiveOp implements Op {
+
+  @Override
+  public final int hashCode() {
+    return operation.hashCode();
+  }
+
+  @Override
+  public final boolean equals(Object obj) {
+    if (this == obj) {
+      return true;
+    }
+    // Note: we consider that all objects wrapping the same operation are equal, no matter their
+    // implementation
+    if (!(obj instanceof PrimitiveOp)) {
+      return false;
+    }
+    return operation.equals(((PrimitiveOp) obj).operation);
+  }
+
+  @Override
+  public final String toString() {
+    return String.format("<%s '%s'>", operation.type(), operation.name());
+  }
+
+  /**
+   * Underlying operation. It is deliberately not exposed by a getter method to avoid any name
+   * conflict with generated methods of the subclasses.
+   */
+  protected final Operation operation;
+
+  /**
+   * Constructor.
+   *
+   * @param operation the underlying operation
+   */
+  protected PrimitiveOp(Operation operation) {
+    this.operation = operation;
+  }
+}
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 27afc046ac..23b5abc077 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -153,6 +153,19 @@ public class OperationTest {
     }
   }
 
+  @Test
+  public void outputList() {
+    try (Graph g = new Graph()) {
+      Operation split = TestUtil.split(g, "split", new int[] {0, 1, 2}, 3);
+      Output[] outputs = split.outputList(1, 2);
+      assertNotNull(outputs);
+      assertEquals(2, outputs.length);
+      for (int i = 0; i < outputs.length; ++i) {
+        assertEquals(i + 1, outputs[i].index());
+      }
+    }
+  }
+
   private static int split(int[] values, int num_split) {
     try (Graph g = new Graph()) {
       return g.opBuilder("Split", "Split")
diff --git a/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java b/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java
index 6a3a16c2e1..e3415a696d 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java
@@ -48,6 +48,14 @@ public class TestUtil {
         .output(0);
   }
 
+  public static Operation split(Graph g, String name, int[] values, int numSplit) {
+    return g.opBuilder("Split", name)
+        .addInput(constant(g, "split_dim", 0))
+        .addInput(constant(g, "values", values))
+        .setAttr("num_split", numSplit)
+        .build();
+  }
+
   public static void transpose_A_times_X(Graph g, int[][] a) {
     matmul(g, "Y", constant(g, "A", a), placeholder(g, "X", DataType.INT32), true, false);
   }
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java
new file mode 100644
index 0000000000..4fdd150acc
--- /dev/null
+++ b/tensorflow/java/src/test/java/org/tensorflow/op/OperandsTest.java
@@ -0,0 +1,46 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertSame;
+
+import java.util.Arrays;
+import java.util.List;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.tensorflow.Graph;
+import org.tensorflow.Operation;
+import org.tensorflow.Output;
+import org.tensorflow.TestUtil;
+
+/** Unit tests for {@link org.tensorflow.op.Operands}. */
+@RunWith(JUnit4.class)
+public class OperandsTest {
+
+  @Test
+  public void createOutputArrayFromOperandList() {
+    try (Graph g = new Graph()) {
+      Operation split = TestUtil.split(g, "split", new int[] {0, 1, 2}, 3);
+      List<Output> list = Arrays.asList(split.output(0), split.output(2));
+      Output[] array = Operands.asOutputs(list);
+      assertEquals(list.size(), array.length);
+      assertSame(array[0], list.get(0));
+      assertSame(array[1], list.get(1));
+    }
+  }
+}
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java
new file mode 100644
index 0000000000..b24bf5a476
--- /dev/null
+++ b/tensorflow/java/src/test/java/org/tensorflow/op/PrimitiveOpTest.java
@@ -0,0 +1,60 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertNotEquals;
+import static org.junit.Assert.assertTrue;
+
+import java.util.HashSet;
+import java.util.Set;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.tensorflow.Graph;
+import org.tensorflow.Output;
+import org.tensorflow.TestUtil;
+
+/** Unit tests for {@link org.tensorflow.op.PrimitiveOp} */
+@RunWith(JUnit4.class)
+public class PrimitiveOpTest {
+
+  @Test
+  public void equalsHashcode() {
+    try (Graph g = new Graph()) {
+      Output array = TestUtil.constant(g, "array", new int[2]);
+
+      PrimitiveOp test1 =
+          new PrimitiveOp(g.opBuilder("Shape", "shape1").addInput(array).build()) {};
+      PrimitiveOp test2 =
+          new PrimitiveOp(g.opBuilder("Shape", "shape2").addInput(array).build()) {};
+      PrimitiveOp test3 = new PrimitiveOp(test1.operation) {};
+
+      // equals() tests
+      assertNotEquals(test1, test2);
+      assertEquals(test1, test3);
+      assertEquals(test3, test1);
+      assertNotEquals(test2, test3);
+
+      // hashcode() tests
+      Set<PrimitiveOp> ops = new HashSet<>();
+      assertTrue(ops.add(test1));
+      assertTrue(ops.add(test2));
+      assertFalse(ops.add(test3));
+    }
+  }
+}
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 8d3d81eeba..0e56d6ed19 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2379,6 +2379,7 @@ cuda_py_test(
         ":variables",
         "//third_party/py/numpy",
     ],
+    tags = ["no_windows"],
 )
 
 cuda_py_test(
diff --git a/tensorflow/python/framework/graph_io.py b/tensorflow/python/framework/graph_io.py
index f909bcd62d..a0ea4ad48e 100644
--- a/tensorflow/python/framework/graph_io.py
+++ b/tensorflow/python/framework/graph_io.py
@@ -29,7 +29,7 @@ from tensorflow.python.lib.io import file_io
 def write_graph(graph_or_graph_def, logdir, name, as_text=True):
   """Writes a graph proto to a file.
 
-  The graph is written as a binary proto unless `as_text` is `True`.
+  The graph is written as a text proto unless `as_text` is `False`.
 
   ```python
   v = tf.Variable(0, name='my_variable')
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 5d582351fd..f2cf90f303 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -72,6 +72,7 @@ tf_py_test(
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:string_ops",
     ],
+    tags = ["nomac"],  # b/35468214
 )
 
 tf_py_test(
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 6664362226..d500f94dc2 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -548,7 +548,7 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
 
   # [[1, ?, 2]
   #  [?, 3, ?]]
-  # where ? is implictly-zero.
+  # where ? is implicitly-zero.
   ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64)
   vals = np.array([1, 1, 1]).astype(np.int32)
   dense_shape = np.array([2, 3]).astype(np.int64)
@@ -906,5 +906,16 @@ class SparseTransposeTest(test.TestCase):
           self.assertAllEqual(dn_trans, expected_trans)
 
 
+class SparsePlaceholderTest(test.TestCase):
+
+  def testPlaceholder(self):
+    with self.test_session(use_gpu=False):
+      foo = array_ops.sparse_placeholder(dtypes.float32, shape=(10, 47))
+      self.assertAllEqual([10, 47], foo.get_shape())
+
+      foo = array_ops.sparse_placeholder(dtypes.float32, shape=(None, 47))
+      self.assertAllEqual(None, foo.get_shape())
+
+
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/python/lib/core/strings.i b/tensorflow/python/lib/core/strings.i
index b74eb91cd5..938c13e30e 100644
--- a/tensorflow/python/lib/core/strings.i
+++ b/tensorflow/python/lib/core/strings.i
@@ -80,7 +80,7 @@ bool _BytesToStringPiece(PyObject* obj, tensorflow::StringPiece* result) {
   }
 }
 
-// Converts a C++ string vector to a a list of Python bytes objects.
+// Converts a C++ string vector to a list of Python bytes objects.
 %typemap(out) std::vector<string> {
   const int size = $1.size();
   auto temp_string_list = tensorflow::make_safe(PyList_New(size));
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index b42238a935..0956d914f5 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -721,10 +721,10 @@ def _SliceHelperVar(var, slice_spec):
   A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
   with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
-    print sess.run(A[:2, :2]) # => [[1,2], [4,5]]
+    print(sess.run(A[:2, :2]))  # => [[1,2], [4,5]]
 
     op = A[:2,:2].assign(22. * tf.ones((2, 2)))
-    print sess.run(op) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
+    print(sess.run(op))  # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
   ```
 
   Note that assignments currently do not support NumPy broadcasting
@@ -1556,7 +1556,7 @@ def _normalize_sparse_shape(shape, name):
     for el in shape:
       if el is None:
         return None
-  return ops.convert_to_tensor(shape, name=name)
+  return ops.convert_to_tensor(shape, dtype=dtypes.int64, name=name)
 
 
 def sparse_placeholder(dtype, shape=None, name=None):
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index dade053589..e0cf8bc5e1 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -240,7 +240,7 @@ class HashTable(InitializableLookupTableBase):
       tf.contrib.lookup.KeyValueTensorInitializer(keys, values), -1)
   out = table.lookup(input_tensor).
   table.init.run()
-  print out.eval()
+  print(out.eval())
   ```
   """
 
@@ -709,7 +709,7 @@ class IdTableWithHashBuckets(LookupInterface):
       num_oov_buckets)
   out = table.lookup(input_tensor).
   table.init.run()
-  print out.eval()
+  print(out.eval())
   ```
 
   The hash function used for generating out-of-vocabulary buckets ID is handled
diff --git a/tensorflow/python/platform/flags.py b/tensorflow/python/platform/flags.py
index f70dbf18d4..60ec4f84c4 100644
--- a/tensorflow/python/platform/flags.py
+++ b/tensorflow/python/platform/flags.py
@@ -44,7 +44,12 @@ class _FlagValues(object):
 
   def __getattr__(self, name):
     """Retrieves the 'value' attribute of the flag --name."""
-    if not self.__dict__['__parsed']:
+    try:
+      parsed = self.__dict__['__parsed']
+    except KeyError:
+      # May happen during pickle.load or copy.copy
+      raise AttributeError(name)
+    if not parsed:
       self._parse_flags()
     if name not in self.__dict__['__flags']:
       raise AttributeError(name)
diff --git a/tensorflow/python/platform/flags_test.py b/tensorflow/python/platform/flags_test.py
index 0dbaafd1fa..8b990975dd 100644
--- a/tensorflow/python/platform/flags_test.py
+++ b/tensorflow/python/platform/flags_test.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import copy
 import sys
 import unittest
 
@@ -79,6 +80,10 @@ class FlagsTest(unittest.TestCase):
     FLAGS.float_foo = -1.0
     self.assertEqual(-1.0, FLAGS.float_foo)
 
+  def test_copy(self):
+    copied = copy.copy(FLAGS)
+    self.assertEqual(copied.__dict__, FLAGS.__dict__)
+
 
 def main(_):
   # unittest.main() tries to interpret the unknown flags, so use the
diff --git a/tensorflow/python/util/tf_should_use.py b/tensorflow/python/util/tf_should_use.py
index 05c99856d2..ab9e82a3cc 100644
--- a/tensorflow/python/util/tf_should_use.py
+++ b/tensorflow/python/util/tf_should_use.py
@@ -25,10 +25,15 @@ import types
 
 import six  # pylint: disable=unused-import
 
-from backports import weakref  # pylint: disable=g-bad-import-order
+# pylint: disable=g-bad-import-order,g-import-not-at-top
+try:
+  from weakref import finalize
+except ImportError:
+  from backports.weakref import finalize
 
 from tensorflow.python.platform import tf_logging
 from tensorflow.python.util import tf_decorator
+# pylint: enable=g-bad-import-order,g-import-not-at-top
 
 
 class _RefInfoField(
@@ -107,7 +112,7 @@ def _add_should_use_warning(x, fatal_error=False):
       # garbage collected.  Can't add self as the args because the
       # loop will break garbage collection.  We keep track of
       # ourselves via python ids.
-      weakref.finalize(self, _deleted, self._tf_ref_id, fatal_error)
+      finalize(self, _deleted, self._tf_ref_id, fatal_error)
 
     # Not sure why this pylint warning is being used; this is not an
     # old class form.
diff --git a/tensorflow/tools/ci_build/windows/bazel/common_env.sh b/tensorflow/tools/ci_build/windows/bazel/common_env.sh
index 8853dc53b1..05392c2724 100644
--- a/tensorflow/tools/ci_build/windows/bazel/common_env.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/common_env.sh
@@ -56,3 +56,7 @@ export PATH="/c/tools/cuda/bin:$PATH"
 
 # Set the common build options on Windows
 export BUILD_OPTS='--copt=-w --host_copt=-w --verbose_failures --experimental_ui'
+
+# Build TF with wrapper-less CROSSTOOL
+# TODO(pcloudy): Remove this after wrapper-less CROSSTOOL becomes default
+export NO_MSVC_WRAPPER=1
diff --git a/tensorflow/tools/docker/Dockerfile b/tensorflow/tools/docker/Dockerfile
index 5b3f1f936a..07a972400d 100644
--- a/tensorflow/tools/docker/Dockerfile
+++ b/tensorflow/tools/docker/Dockerfile
@@ -24,14 +24,15 @@ RUN curl -O https://bootstrap.pypa.io/get-pip.py && \
     rm get-pip.py
 
 RUN pip --no-cache-dir install \
+        Pillow \
+        h5py \
         ipykernel \
         jupyter \
         matplotlib \
         numpy \
+        pandas \
         scipy \
         sklearn \
-        pandas \
-        Pillow \
         && \
     python -m ipykernel.kernelspec
 
diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu
index 3ba1e963f9..da83a30058 100644
--- a/tensorflow/tools/docker/Dockerfile.gpu
+++ b/tensorflow/tools/docker/Dockerfile.gpu
@@ -24,14 +24,15 @@ RUN curl -O https://bootstrap.pypa.io/get-pip.py && \
     rm get-pip.py
 
 RUN pip --no-cache-dir install \
+        Pillow \
+        h5py \
         ipykernel \
         jupyter \
         matplotlib \
         numpy \
+        pandas \
         scipy \
         sklearn \
-        pandas \
-        Pillow \
         && \
     python -m ipykernel.kernelspec
 
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 39499e1775..c4284e10de 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -35,7 +35,6 @@ REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
     'six >= 1.10.0',
     'protobuf >= 3.2.0',
-    'backports.weakref == 1.0rc1',
     'tensorflow-tensorboard',
 ]
 
@@ -54,6 +53,10 @@ else:
   # mock comes with unittest.mock for python3, need to install for python2
   REQUIRED_PACKAGES.append('mock >= 2.0.0')
 
+# weakref.finalize was introduced in Python 3.4
+if sys.version_info < (3, 4):
+  REQUIRED_PACKAGES.append('backports.weakref >= 1.0rc1')
+
 # pylint: disable=line-too-long
 CONSOLE_SCRIPTS = [
     'saved_model_cli = tensorflow.python.tools.saved_model_cli:main',
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index c1832eceb9..a5f0440601 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -327,11 +327,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   patched_http_archive(
       name = "protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "6d43b9d223ce09e5d4ce8b0060cb8a7513577a35a64c7e3dad10f0703bf3ad93",
+      strip_prefix = "protobuf-0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66",
       # TODO: remove patching when tensorflow stops linking same protos into
       #       multiple shared libraries loaded in runtime by python.
       #       This patch fixes a runtime crash when tensorflow is compiled
@@ -345,21 +345,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.http_archive(
       name = "com_google_protobuf",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "6d43b9d223ce09e5d4ce8b0060cb8a7513577a35a64c7e3dad10f0703bf3ad93",
+      strip_prefix = "protobuf-0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66",
   )
 
   native.http_archive(
       name = "com_google_protobuf_cc",
       urls = [
-          "http://mirror.bazel.build/github.com/google/protobuf/archive/v3.3.1.tar.gz",
-          "https://github.com/google/protobuf/archive/v3.3.1.tar.gz",
+          "https://github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
+          "http://mirror.bazel.build/github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
       ],
-      sha256 = "30f23a45c6f4515598702a6d19c4295ba92c4a635d7ad8d331a4db9fccff392d",
-      strip_prefix = "protobuf-3.3.1",
+      sha256 = "6d43b9d223ce09e5d4ce8b0060cb8a7513577a35a64c7e3dad10f0703bf3ad93",
+      strip_prefix = "protobuf-0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66",
   )
 
   native.new_http_archive(
-- 
GitLab


From 4ddc7f88d5fa216323dda26d0de0d3409eea2e82 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 14:58:05 -0700
Subject: [PATCH 0971/1375] Make SummaryMetadata available within the tf
 namespace

Previously, SummaryMetadata had been excluded from the namespace because it had been absent from a certain list.

PiperOrigin-RevId: 161869618
---
 tensorflow/python/__init__.py                 |  1 +
 ...rflow.-summary-metadata.-plugin-data.pbtxt | 84 +++++++++++++++++++
 .../golden/tensorflow.-summary-metadata.pbtxt | 84 +++++++++++++++++++
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  4 +
 4 files changed, 173 insertions(+)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt
 create mode 100644 tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt

diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 80878bc976..827e4c53ee 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -154,6 +154,7 @@ _allowed_symbols = [
     'RunMetadata',
     'SessionLog',
     'Summary',
+    'SummaryMetadata',
     'TensorInfo',  # Used for tf.saved_model functionality.
 ]
 
diff --git a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt
new file mode 100644
index 0000000000..067f02ce8c
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt
@@ -0,0 +1,84 @@
+path: "tensorflow.SummaryMetadata.PluginData"
+tf_class {
+  is_instance: "<class \'tensorflow.core.framework.summary_pb2.PluginData\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "CONTENT_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "PLUGIN_NAME_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt
new file mode 100644
index 0000000000..f6ed45003a
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt
@@ -0,0 +1,84 @@
+path: "tensorflow.SummaryMetadata"
+tf_class {
+  is_instance: "<class \'tensorflow.core.framework.summary_pb2.SummaryMetadata\'>"
+  is_instance: "<type \'google.protobuf.pyext._message.CMessage\'>"
+  member {
+    name: "DESCRIPTOR"
+    mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
+  }
+  member {
+    name: "Extensions"
+    mtype: "<type \'getset_descriptor\'>"
+  }
+  member {
+    name: "PLUGIN_DATA_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
+  member {
+    name: "PluginData"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
+  member_method {
+    name: "ByteSize"
+  }
+  member_method {
+    name: "Clear"
+  }
+  member_method {
+    name: "ClearExtension"
+  }
+  member_method {
+    name: "ClearField"
+  }
+  member_method {
+    name: "CopyFrom"
+  }
+  member_method {
+    name: "DiscardUnknownFields"
+  }
+  member_method {
+    name: "FindInitializationErrors"
+  }
+  member_method {
+    name: "FromString"
+  }
+  member_method {
+    name: "HasExtension"
+  }
+  member_method {
+    name: "HasField"
+  }
+  member_method {
+    name: "IsInitialized"
+  }
+  member_method {
+    name: "ListFields"
+  }
+  member_method {
+    name: "MergeFrom"
+  }
+  member_method {
+    name: "MergeFromString"
+  }
+  member_method {
+    name: "ParseFromString"
+  }
+  member_method {
+    name: "RegisterExtension"
+  }
+  member_method {
+    name: "SerializePartialToString"
+  }
+  member_method {
+    name: "SerializeToString"
+  }
+  member_method {
+    name: "SetInParent"
+  }
+  member_method {
+    name: "WhichOneof"
+  }
+  member_method {
+    name: "__init__"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 91b49e6d04..6a035aba63 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -204,6 +204,10 @@ tf_module {
     name: "Summary"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
   }
+  member {
+    name: "SummaryMetadata"
+    mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
+  }
   member {
     name: "TFRecordReader"
     mtype: "<type \'type\'>"
-- 
GitLab


From 201fa224e178b76ece4723c433a17df10005341c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 14:58:13 -0700
Subject: [PATCH 0972/1375] Add tfcompile support for --xla_dump_ir_to, to dump
 the LLVM IR.

Also minor renaming of OptimizationCallback to ModuleHook, because it's shorter
and describes the type slightly better.

Also see #11462

PiperOrigin-RevId: 161869637
---
 .../xla/service/cpu/compiler_functor.cc       |  8 ++---
 .../xla/service/cpu/compiler_functor.h        | 20 ++++++------
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 32 +++++++++++--------
 .../xla/service/cpu/simple_orc_jit.cc         |  8 ++---
 .../compiler/xla/service/cpu/simple_orc_jit.h |  9 +++---
 5 files changed, 40 insertions(+), 37 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index cd6cc09cfb..f693808a10 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -64,8 +64,8 @@ operator()(llvm::Module& module) const {
   VLOG(2) << "IR before optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));
 
-  if (pre_optimization_callback_) {
-    TF_CHECK_OK(pre_optimization_callback_(module));
+  if (pre_optimization_hook_) {
+    TF_CHECK_OK(pre_optimization_hook_(module));
   }
 
   // Build up optimization pipeline.
@@ -91,8 +91,8 @@ operator()(llvm::Module& module) const {
   VLOG(2) << "IR after optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));
 
-  if (post_optimization_callback_) {
-    TF_CHECK_OK(post_optimization_callback_(module));
+  if (post_optimization_hook_) {
+    TF_CHECK_OK(post_optimization_hook_(module));
   }
 
   // Generate code.
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.h b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
index 94611b5814..a5358076b7 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.h
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
@@ -42,19 +42,19 @@ class CompilerFunctor {
   // A callback of this type can be run before and/or after IR-level
   // optimization to e.g. dump out the generated IR to disk or gather some
   // statistics.
-  using OptimizationCallback = std::function<Status(const llvm::Module&)>;
+  using ModuleHook = std::function<Status(const llvm::Module&)>;
 
-  explicit CompilerFunctor(
-      llvm::TargetMachine* target_machine, const Disassembler* disassembler,
-      int opt_level, const VectorIntrinsics& available_intrinsics,
-      OptimizationCallback pre_optimization_callback = nullptr,
-      OptimizationCallback post_optimization_callback = nullptr)
+  explicit CompilerFunctor(llvm::TargetMachine* target_machine,
+                           const Disassembler* disassembler, int opt_level,
+                           const VectorIntrinsics& available_intrinsics,
+                           ModuleHook pre_optimization_hook = nullptr,
+                           ModuleHook post_optimization_hook = nullptr)
       : target_machine_(target_machine),
         disassembler_(CHECK_NOTNULL(disassembler)),
         opt_level_(opt_level),
         available_intrinsics_(available_intrinsics),
-        pre_optimization_callback_(pre_optimization_callback),
-        post_optimization_callback_(post_optimization_callback) {}
+        pre_optimization_hook_(pre_optimization_hook),
+        post_optimization_hook_(post_optimization_hook) {}
 
   // Compile a Module to an ObjectFile.
   llvm::object::OwningBinary<llvm::object::ObjectFile> operator()(
@@ -70,8 +70,8 @@ class CompilerFunctor {
   const Disassembler* disassembler_;
   const unsigned opt_level_;
   const VectorIntrinsics available_intrinsics_;
-  OptimizationCallback pre_optimization_callback_;
-  OptimizationCallback post_optimization_callback_;
+  ModuleHook pre_optimization_hook_;
+  ModuleHook post_optimization_hook_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 4d4cf794cc..46e45b0bbe 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -359,8 +359,8 @@ Status AppendIRToFile(const string& file_name, const string& ir_module_string) {
 
 Status InitializeIRDumpHooks(
     const HloModule& module,
-    SimpleOrcJIT::OptimizationCallback* pre_optimization_ir_dump_hook,
-    SimpleOrcJIT::OptimizationCallback* post_optimization_ir_dump_hook) {
+    CompilerFunctor::ModuleHook* pre_optimization_ir_dump_hook,
+    CompilerFunctor::ModuleHook* post_optimization_ir_dump_hook) {
   const string& dump_ir_to = module.config().debug_options().xla_dump_ir_to();
   if (dump_ir_to.empty()) {
     return Status::OK();
@@ -385,16 +385,14 @@ Status InitializeIRDumpHooks(
 
   *pre_optimization_ir_dump_hook =
       [unoptimized_ir_file_name](const llvm::Module& module) {
-        TF_RETURN_IF_ERROR(AppendIRToFile(unoptimized_ir_file_name,
-                                          llvm_ir::DumpModuleToString(module)));
-        return Status::OK();
+        return AppendIRToFile(unoptimized_ir_file_name,
+                              llvm_ir::DumpModuleToString(module));
       };
 
   *post_optimization_ir_dump_hook =
       [optimized_ir_file_name](const llvm::Module& module) {
-        TF_RETURN_IF_ERROR(AppendIRToFile(optimized_ir_file_name,
-                                          llvm_ir::DumpModuleToString(module)));
-        return Status::OK();
+        return AppendIRToFile(optimized_ir_file_name,
+                              llvm_ir::DumpModuleToString(module));
       };
 
   return Status::OK();
@@ -409,9 +407,8 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
   std::call_once(llvm_command_line_options_initialized,
                  &InitializeLLVMCommandLineOptions, module->config());
 
-  SimpleOrcJIT::OptimizationCallback pre_optimization_ir_dump_hook;
-  SimpleOrcJIT::OptimizationCallback post_optimization_ir_dump_hook;
-
+  CompilerFunctor::ModuleHook pre_optimization_ir_dump_hook;
+  CompilerFunctor::ModuleHook post_optimization_ir_dump_hook;
   TF_RETURN_IF_ERROR(InitializeIRDumpHooks(*module,
                                            &pre_optimization_ir_dump_hook,
                                            &post_optimization_ir_dump_hook));
@@ -757,10 +754,17 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
 
     entry_function->setName(llvm_ir::AsStringRef(entry_point_name));
 
+    CompilerFunctor::ModuleHook pre_optimization_ir_dump_hook;
+    CompilerFunctor::ModuleHook post_optimization_ir_dump_hook;
+    TF_RETURN_IF_ERROR(InitializeIRDumpHooks(*module,
+                                             &pre_optimization_ir_dump_hook,
+                                             &post_optimization_ir_dump_hook));
+
     Disassembler disassembler(*target_machine);
-    CompilerFunctor compiler_functor(target_machine.get(), &disassembler,
-                                     opt_level,
-                                     CompilerFunctor::AllIntrinsics());
+    CompilerFunctor compiler_functor(
+        target_machine.get(), &disassembler, opt_level,
+        CompilerFunctor::AllIntrinsics(), pre_optimization_ir_dump_hook,
+        post_optimization_ir_dump_hook);
     llvm::object::OwningBinary<llvm::object::ObjectFile> object_file =
         compiler_functor(llvm_module);
     llvm::StringRef object_file_data_ref = object_file.getBinary()->getData();
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index b25ecca45c..aaf3fd2f9e 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -143,8 +143,8 @@ CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() {
 
 SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
                            llvm::CodeGenOpt::Level opt_level,
-                           OptimizationCallback pre_optimization_callback,
-                           OptimizationCallback post_optimization_callback)
+                           CompilerFunctor::ModuleHook pre_optimization_hook,
+                           CompilerFunctor::ModuleHook post_optimization_hook)
     : target_machine_(
           CHECK_NOTNULL(llvm::EngineBuilder()
                             .setTargetOptions(target_options)
@@ -160,8 +160,8 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
       compile_layer_(object_layer_,
                      CompilerFunctor(target_machine_.get(), &disassembler_,
                                      opt_level, GetAvailableIntrinsics(),
-                                     std::move(pre_optimization_callback),
-                                     std::move(post_optimization_callback))) {
+                                     std::move(pre_optimization_hook),
+                                     std::move(post_optimization_hook))) {
   VLOG(1) << "CPU target: " << target_machine_->getTargetCPU().str()
           << " features: " << target_machine_->getTargetFeatureString().str();
 }
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index e28717bcd1..86eae3adfd 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -48,7 +48,6 @@ class SimpleOrcJIT {
           llvm::Module&)>;
   using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT, CompileFtor>;
   using ModuleHandleT = CompileLayerT::ModuleHandleT;
-  using OptimizationCallback = CompilerFunctor::OptimizationCallback;
 
   // Create a new JIT, targeting the host architecture.
   // The |target_options| parameter allows customization of certain code
@@ -56,14 +55,14 @@ class SimpleOrcJIT {
   // can be reassociated, etc.).
   // The |opt_level| parameter controls the optimization level of the code
   // generator.
-  // The |pre_optimization_callback| is invoked on the module before any IR
+  // The |pre_optimization_hook| is invoked on the module before any IR
   // level optimizations are applied.
-  // The |post_optimization_callback| is invoked on the module after all IR
+  // The |post_optimization_hook| is invoked on the module after all IR
   // level optimizations are applied.
   SimpleOrcJIT(const llvm::TargetOptions& target_options,
                llvm::CodeGenOpt::Level opt_level,
-               OptimizationCallback pre_optimization_callback,
-               OptimizationCallback post_optimization_callback);
+               CompilerFunctor::ModuleHook pre_optimization_hook,
+               CompilerFunctor::ModuleHook post_optimization_hook);
 
   // Data layout this JIT was created with.
   const llvm::DataLayout& data_layout() const { return data_layout_; }
-- 
GitLab


From cbf4cd3b7409e3ec0135b7a675527117e3bb2ea1 Mon Sep 17 00:00:00 2001
From: Arvinds-ds <as5195@columbia.edu>
Date: Thu, 13 Jul 2017 18:09:57 -0400
Subject: [PATCH 0973/1375] datset.md - Fix minor typo in code (#11488)

Change tf.contrib.data.Dataset.TextLineDataset(filename) to tf.contrib.data.TextLineDataset(filename)
---
 tensorflow/docs_src/programmers_guide/datasets.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md
index 435e566e30..3f9b12045c 100644
--- a/tensorflow/docs_src/programmers_guide/datasets.md
+++ b/tensorflow/docs_src/programmers_guide/datasets.md
@@ -397,7 +397,7 @@ dataset = tf.contrib.data.Dataset.from_tensor_slices(filenames)
 # * Filter out lines beginning with "#" (comments).
 dataset = dataset.flat_map(
     lambda filename: (
-        tf.contrib.data.Dataset.TextLineDataset(filename)
+        tf.contrib.data.TextLineDataset(filename)
         .skip(1)
         .filter(lambda line: tf.not_equal(tf.substr(line, 0, 1), "#"))))
 ```
-- 
GitLab


From 861f15b02058228eac238ac9992c75d481c2d3a4 Mon Sep 17 00:00:00 2001
From: Chris Tava <chris1tava@gmail.com>
Date: Thu, 13 Jul 2017 16:10:38 -0600
Subject: [PATCH 0974/1375] Updating install_golang.sh - bumping to 1.8.3
 (#11448)

---
 tensorflow/tools/ci_build/install/install_golang.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/ci_build/install/install_golang.sh b/tensorflow/tools/ci_build/install/install_golang.sh
index fef203b869..88bc2960e3 100755
--- a/tensorflow/tools/ci_build/install/install_golang.sh
+++ b/tensorflow/tools/ci_build/install/install_golang.sh
@@ -16,7 +16,7 @@
 
 set -ex
 
-GOLANG_URL="https://storage.googleapis.com/golang/go1.7.5.linux-amd64.tar.gz"
+GOLANG_URL="https://storage.googleapis.com/golang/go1.8.3.linux-amd64.tar.gz"
 
 sudo mkdir -p /usr/local
 wget -q -O - "${GOLANG_URL}" | sudo tar -C /usr/local -xz
-- 
GitLab


From 145b0b90dcf10b628e8ad79cfc35d1f1e7613f2e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 15:21:32 -0700
Subject: [PATCH 0975/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161873229
---
 tensorflow/core/ops/ops.pbtxt | 27 ---------------------------
 1 file changed, 27 deletions(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 449233e178..ccae526e5f 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -28467,33 +28467,6 @@ op {
   summary: "A Reader that outputs the records from a TensorFlow Records file."
   is_stateful: true
 }
-op {
-  name: "LMDBReader"
-  output_arg {
-    name: "reader_handle"
-    description: "The handle to reference the Reader."
-    type: DT_STRING
-    is_ref: true
-  }
-  attr {
-    name: "container"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
-  }
-  attr {
-    name: "shared_name"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
-  }
-  summary: "A Reader that outputs the records from a LMDB database."
-  is_stateful: true
-}
 op {
   name: "TakeDataset"
   input_arg {
-- 
GitLab


From ec9adfd1160e7e208f7a984ebcf62c3856bdfa90 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 15:24:55 -0700
Subject: [PATCH 0976/1375] Add equality test function.

To test the results of compilation(aka Executable) are the same, we need a way to tell if they are equal to each other.

RELNOTES: n/a
PiperOrigin-RevId: 161873754
---
 tensorflow/compiler/xla/service/cpu/cpu_executable.h      | 5 +++++
 .../compiler/xla/service/cpu/parallel_cpu_executable.h    | 6 ++++++
 tensorflow/compiler/xla/service/executable.h              | 8 ++++++++
 tensorflow/compiler/xla/service/gpu/gpu_executable.h      | 5 +++++
 4 files changed, 24 insertions(+)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.h b/tensorflow/compiler/xla/service/cpu/cpu_executable.h
index b5746769ba..4b42530c09 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_executable.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.h
@@ -78,6 +78,11 @@ class CpuExecutable : public Executable {
     ir_module_string_ = ir_module_string;
   }
 
+  const Status EqualOrFail(const Executable& executable) {
+    // TODO(b/62952745) Implement equality test on CPU executable.
+    return Unimplemented("Equality test on CPU executable is not implemented.");
+  }
+
   static int64 ShapeSizeBytes(const Shape& shape);
 
  private:
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h
index 6d5f790c39..a3fe265798 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h
+++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h
@@ -89,6 +89,12 @@ class ParallelCpuExecutable : public Executable {
     return ShapeUtil::ByteSizeOf(shape, sizeof(void*));
   }
 
+  const Status EqualOrFail(const Executable& executable) {
+    // TODO(b/62952745) Implement equality test on CPU parallel executable.
+    return Unimplemented(
+        "Equality test on CPU parallel executable is not implemented.");
+  }
+
  private:
   // Allocate buffers required for execution and assign them to the elements of
   // "buffers". "buffers" should be sized to the number of buffers in buffer
diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h
index b36a44e19e..6c86561d52 100644
--- a/tensorflow/compiler/xla/service/executable.h
+++ b/tensorflow/compiler/xla/service/executable.h
@@ -107,6 +107,14 @@ class Executable {
     return execution_profile_;
   }
 
+  // Returns Status::ok() if the two executables are equal to each other.
+  //
+  // An error status is returned otherwise.
+  virtual const Status EqualOrFail(const Executable& executable) {
+    return Unimplemented(
+        "Equality test on this executable is not implemented.");
+  }
+
   // Returns whether this executable was compiled with HLO profilings support
   // enabled. If not, the caller should not expect an hlo_execution_profile
   // passed to ExecuteOnStream above to be populated during execution.
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.h b/tensorflow/compiler/xla/service/gpu/gpu_executable.h
index e1a55118fc..8558e150e0 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_executable.h
+++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.h
@@ -80,6 +80,11 @@ class GpuExecutable : public Executable {
       tensorflow::gtl::ArraySlice<perftools::gputools::DeviceMemoryBase>
           arguments) override;
 
+  const Status EqualOrFail(const Executable& executable) {
+    // TODO(b/62952745) Implement equality test on GPU executable.
+    return Unimplemented("Equality test on GPU executable is not implemented.");
+  }
+
  private:
   // If `block_host_until_done` is false, execution will not block the host
   // until the kernels have completed. This is used as an optimization for
-- 
GitLab


From 6b15b7e997996a6ff5403f8b38871bc28a334066 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 15:32:36 -0700
Subject: [PATCH 0977/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161874836
---
 tensorflow/go/op/wrappers.go | 90 ++++++++++++++++++------------------
 1 file changed, 45 insertions(+), 45 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 5a67cfa441..6d0990cb52 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -14441,51 +14441,6 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
-// Computes inverse hyperbolic sine of x element-wise.
-func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Asinh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes inverse hyperbolic cosine of x element-wise.
-func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Acosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes inverse hyperbolic tangent of x element-wise.
-func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Atanh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
 type TextLineReaderV2Attr func(optionalAttr)
 
@@ -18288,6 +18243,21 @@ func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *
 	return scope.AddOperation(opspec)
 }
 
+// Computes inverse hyperbolic sine of x element-wise.
+func Asinh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Asinh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Real-valued fast Fourier transform.
 //
 // Computes the 1-dimensional discrete Fourier transform of a real-valued signal
@@ -21126,6 +21096,21 @@ func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values t
 	return op.Output(0)
 }
 
+// Computes inverse hyperbolic cosine of x element-wise.
+func Acosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Acosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
 //
 // For an explanation see "Differentiation of the Cholesky algorithm" by
@@ -21154,6 +21139,21 @@ func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output)
 	return op.Output(0)
 }
 
+// Computes inverse hyperbolic tangent of x element-wise.
+func Atanh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Atanh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the log of the absolute value of `Gamma(x)` element-wise.
 func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From 0851dc29389e9c09dd4335a359825fd1894b2842 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Thu, 13 Jul 2017 15:33:36 -0700
Subject: [PATCH 0978/1375] Add c_api dep to python_api target

c_api_internal doesn't actually export c_api.h, which python_api.h depends on.

PiperOrigin-RevId: 161874954
---
 tensorflow/c/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD
index 85429f8c66..af9071238b 100644
--- a/tensorflow/c/BUILD
+++ b/tensorflow/c/BUILD
@@ -153,6 +153,7 @@ tf_cuda_library(
     hdrs = ["python_api.h"],
     visibility = ["//tensorflow/python:__pkg__"],
     deps = [
+        ":c_api",
         ":c_api_internal",
     ],
 )
-- 
GitLab


From a24f96a3f70e0cc0792410673f55c4a898dea604 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 13 Jul 2017 15:44:41 -0700
Subject: [PATCH 0979/1375] Throw a ValueError if the user doesn't pass in a
 dictionary of Tensors for features to canned Estimators. The current
 situation confuses users transitioning from the contrib Estimators because
 those support passing Tensor as features.

Fixes #11252

PiperOrigin-RevId: 161876642
---
 tensorflow/python/estimator/canned/dnn.py     |  8 +++++-
 .../estimator/canned/dnn_linear_combined.py   |  8 ++++--
 .../estimator/canned/dnn_testing_utils.py     | 27 +++++++++++++++++++
 tensorflow/python/estimator/canned/linear.py  |  7 +++--
 4 files changed, 45 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py
index b7a943d2fb..02ceab38b8 100644
--- a/tensorflow/python/estimator/canned/dnn.py
+++ b/tensorflow/python/estimator/canned/dnn.py
@@ -50,7 +50,7 @@ def _dnn_model_fn(
   """Deep Neural Net model_fn.
 
   Args:
-    features: Dict of `Tensor` (depends on data passed to `train`).
+    features: dict of `Tensor`.
     labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
       dtype `int32` or `int64` in the range `[0, n_classes)`.
     mode: Defines whether this is training, evaluation or prediction.
@@ -72,7 +72,13 @@ def _dnn_model_fn(
     predictions: A dict of `Tensor` objects.
     loss: A scalar containing the loss of the step.
     train_op: The op for training.
+
+  Raises:
+    ValueError: If features has the wrong type.
   """
+  if not isinstance(features, dict):
+    raise ValueError('features should be a dictionary of `Tensor`s. '
+                     'Given type: {}'.format(type(features)))
   optimizer = optimizers.get_optimizer_instance(
       optimizer, learning_rate=_LEARNING_RATE)
   num_ps_replicas = config.num_ps_replicas if config else 0
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py
index 03bcf7ae57..cd71c37940 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py
@@ -85,7 +85,7 @@ def _dnn_linear_combined_model_fn(
   """Deep Neural Net and Linear combined model_fn.
 
   Args:
-    features: `Tensor` or dict of `Tensor` (depends on data passed to `fit`).
+    features: dict of `Tensor`.
     labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of dtype
       `int32` or `int64` in the range `[0, n_classes)`.
     mode: Defines whether this is training, evaluation or prediction.
@@ -114,8 +114,12 @@ def _dnn_linear_combined_model_fn(
 
   Raises:
     ValueError: If both `linear_feature_columns` and `dnn_features_columns`
-      are empty at the same time, or `input_layer_partitioner` is missing.
+      are empty at the same time, or `input_layer_partitioner` is missing,
+      or features has the wrong type.
   """
+  if not isinstance(features, dict):
+    raise ValueError('features should be a dictionary of `Tensor`s. '
+                     'Given type: {}'.format(type(features)))
   if not linear_feature_columns and not dnn_feature_columns:
     raise ValueError(
         'Either linear_feature_columns or dnn_feature_columns must be defined.')
diff --git a/tensorflow/python/estimator/canned/dnn_testing_utils.py b/tensorflow/python/estimator/canned/dnn_testing_utils.py
index 269da5246d..0be447604c 100644
--- a/tensorflow/python/estimator/canned/dnn_testing_utils.py
+++ b/tensorflow/python/estimator/canned/dnn_testing_utils.py
@@ -436,6 +436,33 @@ class BaseDNNModelFnTest(object):
           else:
             self.fail('Invalid mode: {}'.format(mode))
 
+  def test_features_tensor_raises_value_error(self):
+    """Tests that passing a Tensor for features raises a ValueError."""
+    hidden_units = (2, 2)
+    logits_dimension = 3
+    inputs = ([[10.]], [[8.]])
+    expected_logits = [[0, 0, 0]]
+
+    with ops.Graph().as_default():
+      training_util.create_global_step()
+      head = mock_head(
+          self,
+          hidden_units=hidden_units,
+          logits_dimension=logits_dimension,
+          expected_logits=expected_logits)
+      with self.assertRaisesRegexp(ValueError, 'features should be a dict'):
+        self._dnn_model_fn(
+            features=constant_op.constant(inputs),
+            labels=constant_op.constant([[1]]),
+            mode=model_fn.ModeKeys.TRAIN,
+            head=head,
+            hidden_units=hidden_units,
+            feature_columns=[
+                feature_column.numeric_column(
+                    'age', shape=np.array(inputs).shape[1:])
+            ],
+            optimizer=mock_optimizer(self, hidden_units))
+
 
 class BaseDNNClassifierEvaluateTest(object):
 
diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py
index 05b1e5b44a..19ae5e79a5 100644
--- a/tensorflow/python/estimator/canned/linear.py
+++ b/tensorflow/python/estimator/canned/linear.py
@@ -47,7 +47,7 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
   """A model_fn for linear models that use a gradient-based optimizer.
 
   Args:
-    features: Dict of `Tensor`.
+    features: dict of `Tensor`.
     labels: `Tensor` of shape `[batch_size, logits_dimension]`.
     mode: Defines whether this is training, evaluation or prediction.
       See `ModeKeys`.
@@ -63,8 +63,11 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
     An `EstimatorSpec` instance.
 
   Raises:
-    ValueError: If mode or params are invalid.
+    ValueError: mode or params are invalid, or features has the wrong type.
   """
+  if not isinstance(features, dict):
+    raise ValueError('features should be a dictionary of `Tensor`s. '
+                     'Given type: {}'.format(type(features)))
   optimizer = optimizers.get_optimizer_instance(
       optimizer or _get_default_optimizer(feature_columns),
       learning_rate=_LEARNING_RATE)
-- 
GitLab


From 0355763a32660b2112f155c752f6bf244fcf7007 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 15:45:44 -0700
Subject: [PATCH 0980/1375] Change ReferenceUtil::Slice{2,3,4}D to accept a
 strides parameter.

This also fixes their ordering in xla/reference_util.h

This also adds a few stride tests to reference_util_test and
adds LiteralTestUtil::ExpectR4Near().

PiperOrigin-RevId: 161876759
---
 tensorflow/compiler/xla/reference_util.h      | 68 +++++++++++-------
 .../compiler/xla/reference_util_test.cc       | 69 +++++++++++++++++++
 .../compiler/xla/tests/literal_test_util.h    | 15 ++++
 tensorflow/compiler/xla/tests/slice_test.cc   |  8 +--
 4 files changed, 133 insertions(+), 27 deletions(-)

diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index 1d326aff5f..41ef1f96ab 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/padding.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/macros.h"
@@ -301,48 +302,56 @@ class ReferenceUtil {
     return result;
   }
 
-  // Slices the input array given starting indices in each dimension and limit
-  // indices in each dimension.
+  // Slices the input array given starting indices, limit indices, and strides
+  // in each dimension.
   template <typename T>
   static std::unique_ptr<Array2D<T>> Slice2D(const Array2D<T>& input,
                                              std::array<int64, 2> starts,
-                                             std::array<int64, 2> limits) {
+                                             std::array<int64, 2> limits,
+                                             std::array<int64, 2> strides) {
     CHECK_LE(starts[0], input.n1());
     CHECK_LE(starts[1], input.n2());
     CHECK_LE(limits[0], input.n1());
     CHECK_LE(limits[1], input.n2());
+    CHECK_GE(strides[0], 1);
+    CHECK_GE(strides[1], 1);
     auto result =
-        MakeUnique<Array2D<T>>(limits[0] - starts[0], limits[1] - starts[1]);
+        MakeUnique<Array2D<T>>(CeilOfRatio(limits[0] - starts[0], strides[0]),
+                               CeilOfRatio(limits[1] - starts[1], strides[1]));
     for (int64 i0 = 0; i0 < result->n1(); ++i0) {
       for (int64 i1 = 0; i1 < result->n2(); ++i1) {
-        (*result)(i0, i1) = input(starts[0] + i0, starts[1] + i1);
+        (*result)(i0, i1) =
+            input(starts[0] + i0 * strides[0], starts[1] + i1 * strides[1]);
       }
     }
     return result;
   }
 
   template <typename T>
-  static std::unique_ptr<Array4D<T>> Slice4D(const Array4D<T>& input,
-                                             std::array<int64, 4> starts,
-                                             std::array<int64, 4> limits) {
+  static std::unique_ptr<Array3D<T>> Slice3D(const Array3D<T>& input,
+                                             std::array<int64, 3> starts,
+                                             std::array<int64, 3> limits,
+                                             std::array<int64, 3> strides) {
     CHECK_LE(starts[0], input.n1());
     CHECK_LE(starts[1], input.n2());
     CHECK_LE(starts[2], input.n3());
-    CHECK_LE(starts[3], input.n4());
     CHECK_LE(limits[0], input.n1());
     CHECK_LE(limits[1], input.n2());
     CHECK_LE(limits[2], input.n3());
-    CHECK_LE(limits[3], input.n4());
+    CHECK_GE(strides[0], 1);
+    CHECK_GE(strides[1], 1);
+    CHECK_GE(strides[2], 1);
     auto result =
-        MakeUnique<Array4D<T>>(limits[0] - starts[0], limits[1] - starts[1],
-                               limits[2] - starts[2], limits[3] - starts[3]);
+        MakeUnique<Array3D<T>>(CeilOfRatio(limits[0] - starts[0], strides[0]),
+                               CeilOfRatio(limits[1] - starts[1], strides[1]),
+                               CeilOfRatio(limits[2] - starts[2], strides[2]));
+
     for (int64 i0 = 0; i0 < result->n1(); ++i0) {
       for (int64 i1 = 0; i1 < result->n2(); ++i1) {
         for (int64 i2 = 0; i2 < result->n3(); ++i2) {
-          for (int64 i3 = 0; i3 < result->n4(); ++i3) {
-            (*result)(i0, i1, i2, i3) = input(starts[0] + i0, starts[1] + i1,
-                                              starts[2] + i2, starts[3] + i3);
-          }
+          (*result)(i0, i1, i2) =
+              input(starts[0] + i0 * strides[0], starts[1] + i1 * strides[1],
+                    starts[2] + i2 * strides[2]);
         }
       }
     }
@@ -350,22 +359,35 @@ class ReferenceUtil {
   }
 
   template <typename T>
-  static std::unique_ptr<Array3D<T>> Slice3D(const Array3D<T>& input,
-                                             std::array<int64, 3> starts,
-                                             std::array<int64, 3> limits) {
+  static std::unique_ptr<Array4D<T>> Slice4D(const Array4D<T>& input,
+                                             std::array<int64, 4> starts,
+                                             std::array<int64, 4> limits,
+                                             std::array<int64, 4> strides) {
     CHECK_LE(starts[0], input.n1());
     CHECK_LE(starts[1], input.n2());
     CHECK_LE(starts[2], input.n3());
+    CHECK_LE(starts[3], input.n4());
     CHECK_LE(limits[0], input.n1());
     CHECK_LE(limits[1], input.n2());
     CHECK_LE(limits[2], input.n3());
-    auto result = MakeUnique<Array3D<T>>(
-        limits[0] - starts[0], limits[1] - starts[1], limits[2] - starts[2]);
+    CHECK_LE(limits[3], input.n4());
+    CHECK_GE(strides[0], 1);
+    CHECK_GE(strides[1], 1);
+    CHECK_GE(strides[2], 1);
+    CHECK_GE(strides[3], 1);
+    auto result =
+        MakeUnique<Array4D<T>>(CeilOfRatio(limits[0] - starts[0], strides[0]),
+                               CeilOfRatio(limits[1] - starts[1], strides[1]),
+                               CeilOfRatio(limits[2] - starts[2], strides[2]),
+                               CeilOfRatio(limits[3] - starts[3], strides[3]));
     for (int64 i0 = 0; i0 < result->n1(); ++i0) {
       for (int64 i1 = 0; i1 < result->n2(); ++i1) {
         for (int64 i2 = 0; i2 < result->n3(); ++i2) {
-          (*result)(i0, i1, i2) =
-              input(starts[0] + i0, starts[1] + i1, starts[2] + i2);
+          for (int64 i3 = 0; i3 < result->n4(); ++i3) {
+            (*result)(i0, i1, i2, i3) =
+                input(starts[0] + i0 * strides[0], starts[1] + i1 * strides[1],
+                      starts[2] + i2 * strides[2], starts[3] + i3 * strides[3]);
+          }
         }
       }
     }
diff --git a/tensorflow/compiler/xla/reference_util_test.cc b/tensorflow/compiler/xla/reference_util_test.cc
index 215f220258..680e908d46 100644
--- a/tensorflow/compiler/xla/reference_util_test.cc
+++ b/tensorflow/compiler/xla/reference_util_test.cc
@@ -132,6 +132,75 @@ TEST_F(ReferenceUtilTest, MapWithIndexArray4D) {
                                        ErrorSpec(0.0001));
 }
 
+TEST_F(ReferenceUtilTest, SliceArray2D) {
+  auto result = ReferenceUtil::Slice2D(*matrix_, {{0, 0}}, {{2, 2}}, {{1, 1}});
+  auto actual_literal = Literal::CreateR2FromArray2D(*result);
+
+  LiteralTestUtil::ExpectR2Near<float>({{1.f, 2.f}, {4.f, 5.f}},
+                                       *actual_literal, ErrorSpec(0.0001));
+}
+
+TEST_F(ReferenceUtilTest, SliceStridedArray2D) {
+  auto result = ReferenceUtil::Slice2D(*matrix_, {{0, 0}}, {{2, 3}}, {{1, 2}});
+  auto actual_literal = Literal::CreateR2FromArray2D(*result);
+
+  LiteralTestUtil::ExpectR2Near<float>({{1.f, 3.f}, {4.f, 6.f}},
+                                       *actual_literal, ErrorSpec(0.0001));
+}
+
+TEST_F(ReferenceUtilTest, SliceArray3D) {
+  Array3D<float> input(2, 3, 4);
+  input.FillIota(0);
+
+  auto result =
+      ReferenceUtil::Slice3D(input, {{0, 0, 0}}, {{2, 2, 2}}, {{1, 1, 1}});
+  auto actual_literal = Literal::CreateR3FromArray3D(*result);
+
+  LiteralTestUtil::ExpectR3Near<float>(
+      {{{0.f, 1.f}, {4.f, 5.f}}, {{12.f, 13.f}, {16.f, 17.f}}}, *actual_literal,
+      ErrorSpec(0.0001));
+}
+
+TEST_F(ReferenceUtilTest, SliceStridedArray3D) {
+  Array3D<float> input(2, 3, 4);
+  input.FillIota(0);
+
+  auto result =
+      ReferenceUtil::Slice3D(input, {{0, 0, 0}}, {{2, 3, 4}}, {{1, 2, 2}});
+  auto actual_literal = Literal::CreateR3FromArray3D(*result);
+
+  LiteralTestUtil::ExpectR3Near<float>(
+      {{{0.f, 2.f}, {8.f, 10.f}}, {{12.f, 14.f}, {20.f, 22.f}}},
+      *actual_literal, ErrorSpec(0.0001));
+}
+
+TEST_F(ReferenceUtilTest, SliceArray4D) {
+  Array4D<float> input(2, 3, 4, 5);
+  input.FillIota(0);
+
+  auto result = ReferenceUtil::Slice4D(input, {{1, 0, 0, 0}}, {{2, 2, 2, 2}},
+                                       {{1, 1, 1, 1}});
+  auto actual_literal = Literal::CreateR4FromArray4D(*result);
+
+  LiteralTestUtil::ExpectR4Near<float>(
+      {{{{60.f, 61.f}, {65.f, 66.f}}, {{80.f, 81.f}, {85.f, 86.f}}}},
+      *actual_literal, ErrorSpec(0.0001));
+}
+
+TEST_F(ReferenceUtilTest, SliceStridedArray4D) {
+  Array4D<float> input(2, 3, 4, 5);
+  input.FillIota(0);
+
+  auto result = ReferenceUtil::Slice4D(input, {{1, 0, 0, 0}}, {{2, 3, 4, 5}},
+                                       {{1, 2, 2, 2}});
+  auto actual_literal = Literal::CreateR4FromArray4D(*result);
+
+  LiteralTestUtil::ExpectR4Near<float>(
+      {{{{60.f, 62.f, 64.f}, {70.f, 72.f, 74.f}},
+        {{100.f, 102.f, 104.f}, {110.f, 112.f, 114.f}}}},
+      *actual_literal, ErrorSpec(0.0001));
+}
+
 TEST_F(ReferenceUtilTest, ConvWithSamePadding) {
   Array4D<float> input(1, 1, 4, 4);
   // clang-format off
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h
index b7f1e8603f..f645c4e8dc 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.h
+++ b/tensorflow/compiler/xla/tests/literal_test_util.h
@@ -131,6 +131,12 @@ class LiteralTestUtil {
           std::initializer_list<std::initializer_list<NativeT>>>
           expected,
       const Literal& actual, const ErrorSpec& error);
+  template <typename NativeT>
+  static void ExpectR4Near(
+      std::initializer_list<std::initializer_list<
+          std::initializer_list<std::initializer_list<NativeT>>>>
+          expected,
+      const Literal& actual, const ErrorSpec& error);
 
   // Asserts the given literal are within the given error bound to the given
   // array. Only supported for floating point values.
@@ -282,6 +288,15 @@ template <typename NativeT>
   ExpectNear(*Literal::CreateR3<NativeT>(expected), actual, error);
 }
 
+template <typename NativeT>
+/* static */ void LiteralTestUtil::ExpectR4Near(
+    std::initializer_list<std::initializer_list<
+        std::initializer_list<std::initializer_list<NativeT>>>>
+        expected,
+    const Literal& actual, const ErrorSpec& error) {
+  ExpectNear(*Literal::CreateR4<NativeT>(expected), actual, error);
+}
+
 template <typename NativeT>
 /* static */ void LiteralTestUtil::ExpectR2NearArray2D(
     const Array2D<NativeT>& expected, const Literal& actual,
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index 5e7d475662..35e2f216d7 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -200,8 +200,8 @@ TEST_F(SliceTest, Slice_16x4_To_16x2) {
 TEST_F(SliceTest, SliceR4ThreeDimsMiddleMinor) {
   Array4D<float> values(2, 2, 24, 256);
   values.FillRandom(3.14f);
-  auto expected =
-      ReferenceUtil::Slice4D(values, {{1, 0, 8, 0}}, {{2, 2, 16, 128}});
+  auto expected = ReferenceUtil::Slice4D(
+      values, {{1, 0, 8, 0}}, {{2, 2, 16, 128}}, /*strides=*/{{1, 1, 1, 1}});
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR4FromArray4D(values);
   builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128}, {1, 1, 1, 1});
@@ -231,8 +231,8 @@ TEST_P(SliceR2Test, DoIt) {
   auto a = builder.ConstantR2FromArray2D<int32>(input);
   builder.Slice(a, spec.slice_starts, spec.slice_limits, spec.slice_strides);
 
-  std::unique_ptr<Array2D<int32>> expected =
-      ReferenceUtil::Slice2D(input, spec.slice_starts, spec.slice_limits);
+  std::unique_ptr<Array2D<int32>> expected = ReferenceUtil::Slice2D(
+      input, spec.slice_starts, spec.slice_limits, spec.slice_strides);
   ComputeAndCompareR2<int32>(&builder, *expected, {});
 }
 
-- 
GitLab


From c959e9b023127d0fb42b8e735301bc70fd20af74 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 15:51:36 -0700
Subject: [PATCH 0981/1375] Add clone support for BatchNormGrad

This is 2nd of 4 CLs that implement BatchNormGrad. The ability to clone gives us PARALLEL_CPU support.

RELNOTES: n/a
PiperOrigin-RevId: 161877575
---
 tensorflow/compiler/xla/service/hlo_instruction.cc | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 73bae5cb31..16440f9a27 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -914,6 +914,10 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       CHECK_EQ(new_operands.size(), 1);
       return CreateOutfeed(shape, new_operands[0], outfeed_config());
     case HloOpcode::kBatchNormGrad:
+      CHECK_EQ(new_operands.size(), 5);
+      return CreateBatchNormGrad(shape, new_operands[0], new_operands[1],
+                                 new_operands[2], new_operands[3],
+                                 new_operands[4], epsilon(), feature_index());
     case HloOpcode::kRecv:
     case HloOpcode::kSend:
     case HloOpcode::kUpdate:
-- 
GitLab


From 9d45160c76bdb2aa7cf3cc5b318512fc7d7337a2 Mon Sep 17 00:00:00 2001
From: Jingyue Wu <jingyue@google.com>
Date: Thu, 13 Jul 2017 16:03:28 -0700
Subject: [PATCH 0982/1375] Extend shape_inference::Conv2DShape to handle
 NCHW_VECT_C format.

Tested Conv2DShape with NCHW_VECT_C format.

PiperOrigin-RevId: 161879362
---
 tensorflow/core/framework/common_shape_fns.cc | 126 +++++++++++++-----
 .../core/framework/common_shape_fns_test.cc   |  21 +++
 tensorflow/core/util/tensor_format.h          |  11 ++
 3 files changed, 127 insertions(+), 31 deletions(-)

diff --git a/tensorflow/core/framework/common_shape_fns.cc b/tensorflow/core/framework/common_shape_fns.cc
index dc2db3d395..e115188cf4 100644
--- a/tensorflow/core/framework/common_shape_fns.cc
+++ b/tensorflow/core/framework/common_shape_fns.cc
@@ -201,32 +201,93 @@ Status BiasAddGradShape(shape_inference::InferenceContext* c) {
   return Status::OK();
 }
 
+Status DimensionsFromShape(ShapeHandle shape, TensorFormat format,
+                           DimensionHandle* batch_dim,
+                           gtl::MutableArraySlice<DimensionHandle> spatial_dims,
+                           DimensionHandle* filter_dim,
+                           InferenceContext* context) {
+  const int32 rank = GetTensorDimsFromSpatialDims(spatial_dims.size(), format);
+  // Batch.
+  *batch_dim = context->Dim(shape, GetTensorBatchDimIndex(rank, format));
+  // Spatial.
+  for (int spatial_dim_index = 0; spatial_dim_index < spatial_dims.size();
+       ++spatial_dim_index) {
+    spatial_dims[spatial_dim_index] = context->Dim(
+        shape, GetTensorSpatialDimIndex(rank, format, spatial_dim_index));
+  }
+  // Channel.
+  *filter_dim = context->Dim(shape, GetTensorFeatureDimIndex(rank, format));
+  if (format == FORMAT_NCHW_VECT_C) {
+    TF_RETURN_IF_ERROR(context->Multiply(
+        *filter_dim,
+        context->Dim(shape, GetTensorInnerFeatureDimIndex(rank, format)),
+        filter_dim));
+  }
+  return Status::OK();
+}
+
+Status ShapeFromDimensions(DimensionHandle batch_dim,
+                           gtl::ArraySlice<DimensionHandle> spatial_dims,
+                           DimensionHandle filter_dim, TensorFormat format,
+                           InferenceContext* context, ShapeHandle* shape) {
+  const int32 rank = GetTensorDimsFromSpatialDims(spatial_dims.size(), format);
+  std::vector<DimensionHandle> out_dims(rank);
+
+  // Batch.
+  out_dims[tensorflow::GetTensorBatchDimIndex(rank, format)] = batch_dim;
+  // Spatial.
+  for (int spatial_dim_index = 0; spatial_dim_index < spatial_dims.size();
+       ++spatial_dim_index) {
+    out_dims[tensorflow::GetTensorSpatialDimIndex(
+        rank, format, spatial_dim_index)] = spatial_dims[spatial_dim_index];
+  }
+  // Channel.
+  if (format == tensorflow::FORMAT_NCHW_VECT_C) {
+    // When format is NCHW_VECT_C, factor the feature map count
+    // into the outer feature count and the inner feature count (=4).
+    TF_RETURN_IF_ERROR(context->Divide(
+        filter_dim, 4, /*evenly_divisible=*/true,
+        &out_dims[tensorflow::GetTensorFeatureDimIndex(rank, format)]));
+    out_dims[GetTensorInnerFeatureDimIndex(rank, format)] = context->MakeDim(4);
+  } else {
+    out_dims[tensorflow::GetTensorFeatureDimIndex(rank, format)] = filter_dim;
+  }
+
+  *shape = context->MakeShape(out_dims);
+  return tensorflow::Status::OK();
+}
+
 Status Conv2DShape(shape_inference::InferenceContext* c) {
+  string data_format_str;
+  Status s = c->GetAttr("data_format", &data_format_str);
+  if (!s.ok()) {
+    data_format_str = "NHWC";
+  }
+
+  TensorFormat data_format;
+  if (!FormatFromString(data_format_str, &data_format)) {
+    return errors::InvalidArgument("Invalid data format string: ",
+                                   data_format_str);
+  }
+
+  const int rank = GetTensorDimsFromSpatialDims(2, data_format);
   ShapeHandle input_shape;
-  TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input_shape));
+  TF_RETURN_IF_ERROR(c->WithRank(c->input(0), rank, &input_shape));
+  // The filter of a 2D convolution is always 4D.
   ShapeHandle filter_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 4, &filter_shape));
 
-  string data_format;
-  Status s = c->GetAttr("data_format", &data_format);
-
   std::vector<int32> strides;
   TF_RETURN_IF_ERROR(c->GetAttr("strides", &strides));
 
-  if (strides.size() != 4) {
-    return errors::InvalidArgument(
-        "Conv2D requires the stride attribute to contain 4 values, but got: ",
-        strides.size());
+  if (strides.size() != rank) {
+    return errors::InvalidArgument("Conv2D on data format ", data_format_str,
+                                   " requires the stride attribute to contain ",
+                                   rank, " values, but got: ", strides.size());
   }
 
   int32 stride_rows, stride_cols;
-
-  if (s.ok() && data_format == "NCHW") {
-    // Convert input shape to default NHWC for inference
-    auto dim = [&](char dimension) {
-      return c->Dim(input_shape, GetTensorDimIndex<2>(FORMAT_NCHW, dimension));
-    };
-    input_shape = c->MakeShape({{dim('N'), dim('0'), dim('1'), dim('C')}});
+  if (data_format == FORMAT_NCHW || data_format == FORMAT_NCHW_VECT_C) {
     stride_rows = strides[2];
     stride_cols = strides[3];
   } else {
@@ -234,35 +295,38 @@ Status Conv2DShape(shape_inference::InferenceContext* c) {
     stride_cols = strides[2];
   }
 
-  DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
-  DimensionHandle in_rows_dim = c->Dim(input_shape, 1);
-  DimensionHandle in_cols_dim = c->Dim(input_shape, 2);
+  DimensionHandle batch_size_dim;
+  DimensionHandle input_depth_dim;
+  gtl::InlinedVector<DimensionHandle, 2> input_spatial_dims(2);
+  TF_RETURN_IF_ERROR(DimensionsFromShape(input_shape, data_format,
+                                         &batch_size_dim, &input_spatial_dims,
+                                         &input_depth_dim, c));
+
   DimensionHandle filter_rows_dim = c->Dim(filter_shape, 0);
   DimensionHandle filter_cols_dim = c->Dim(filter_shape, 1);
   DimensionHandle output_depth_dim = c->Dim(filter_shape, 3);
 
+  // Check that the input tensor and the filter tensor agree on the input
+  // channel count.
   DimensionHandle unused;
   TF_RETURN_IF_ERROR(
-      c->Merge(c->Dim(input_shape, 3), c->Dim(filter_shape, 2), &unused));
+      c->Merge(input_depth_dim, c->Dim(filter_shape, 2), &unused));
 
   Padding padding;
   TF_RETURN_IF_ERROR(c->GetAttr("padding", &padding));
 
   DimensionHandle output_rows, output_cols;
-  TF_RETURN_IF_ERROR(GetWindowedOutputSizeFromDims(
-      c, in_rows_dim, filter_rows_dim, stride_rows, padding, &output_rows));
-  TF_RETURN_IF_ERROR(GetWindowedOutputSizeFromDims(
-      c, in_cols_dim, filter_cols_dim, stride_cols, padding, &output_cols));
+  TF_RETURN_IF_ERROR(GetWindowedOutputSizeFromDims(c, input_spatial_dims[0],
+                                                   filter_rows_dim, stride_rows,
+                                                   padding, &output_rows));
+  TF_RETURN_IF_ERROR(GetWindowedOutputSizeFromDims(c, input_spatial_dims[1],
+                                                   filter_cols_dim, stride_cols,
+                                                   padding, &output_cols));
 
   ShapeHandle output_shape;
-  if (data_format == "NCHW") {
-    output_shape = c->MakeShape(
-        {batch_size_dim, output_depth_dim, output_rows, output_cols});
-  } else {
-    output_shape = c->MakeShape(
-        {batch_size_dim, output_rows, output_cols, output_depth_dim});
-  }
-
+  TF_RETURN_IF_ERROR(
+      ShapeFromDimensions(batch_size_dim, {output_rows, output_cols},
+                          output_depth_dim, data_format, c, &output_shape));
   c->set_output(0, output_shape);
   return Status::OK();
 }
diff --git a/tensorflow/core/framework/common_shape_fns_test.cc b/tensorflow/core/framework/common_shape_fns_test.cc
index 99d3ef3af1..37e211ad68 100644
--- a/tensorflow/core/framework/common_shape_fns_test.cc
+++ b/tensorflow/core/framework/common_shape_fns_test.cc
@@ -479,6 +479,27 @@ TEST(CommonShapeFnsTest, Conv2DShapeTest) {
   set_op({{1, 1, 1, 2}}, "VALID", "NCHW");
   INFER_OK(op, "[1,1,4,4];[2,1,1,1]", "[d0_0,d1_3,3,2]");
 
+  // Tests for NCHW_VECT_C
+  // 1x1 filter
+  set_op({{1, 1, 1, 1, 1}}, "VALID", "NCHW_VECT_C");
+  INFER_OK(op, "[1,1,2,2,4];[1,1,4,4]", "[d0_0,1,2,2,4]");
+
+  // 2x2 filter
+  set_op({{1, 1, 1, 1, 1}}, "VALID", "NCHW_VECT_C");
+  INFER_OK(op, "[1,1,2,2,4];[2,2,4,4]", "[d0_0,1,1,1,4]");
+
+  // 3x3 input, 1x1 filter, 2x2 stride
+  set_op({{1, 1, 2, 2, 1}}, "VALID", "NCHW_VECT_C");
+  INFER_OK(op, "[1,1,3,3,4];[1,1,4,8]", "[d0_0,2,2,2,4]");
+
+  // 3x3 input, 1x1 filter, 2x1 stride
+  set_op({{1, 1, 2, 1, 1}}, "VALID", "NCHW_VECT_C");
+  INFER_OK(op, "[1,1,3,3,4];[1,1,4,4]", "[d0_0,1,2,3,4]");
+
+  // 4x4 input, 2x1 filter, 1x2 stride
+  set_op({{1, 1, 1, 2, 1}}, "VALID", "NCHW_VECT_C");
+  INFER_OK(op, "[1,1,4,4,4];[2,1,4,4]", "[d0_0,1,3,2,4]");
+
   // Some tests for "SAME" padding
 
   // 4x4 input, 1x1 filter, 1x1 stride
diff --git a/tensorflow/core/util/tensor_format.h b/tensorflow/core/util/tensor_format.h
index 9923428a34..83f9500490 100644
--- a/tensorflow/core/util/tensor_format.h
+++ b/tensorflow/core/util/tensor_format.h
@@ -54,6 +54,17 @@ inline int GetTensorSpatialDims(int num_dims, TensorFormat format) {
   }
 }
 
+// Returns the rank of a tensor with 'num_spatial_dims' spatial dimensions and
+// tensor format 'format'. This is the inverse of GetTensorSpatialDims.
+inline int GetTensorDimsFromSpatialDims(int num_spatial_dims,
+                                        TensorFormat format) {
+  if (format == FORMAT_NCHW_VECT_C) {
+    return num_spatial_dims + 3;  // Include N,C,InnerC.
+  } else {
+    return num_spatial_dims + 2;  // Include N,C.
+  }
+}
+
 // Returns the index of the batch dimension.
 inline int GetTensorBatchDimIndex(int num_dims, TensorFormat format) {
   switch (format) {
-- 
GitLab


From 2429ce2c5b28986efe21d4b91d90c8f10088b81d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 16:07:41 -0700
Subject: [PATCH 0983/1375] Expose tf.contrib.nn.rank_sampled_softmax_loss.

PiperOrigin-RevId: 161879977
---
 tensorflow/contrib/nn/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/nn/__init__.py b/tensorflow/contrib/nn/__init__.py
index e7116d78e2..ec832cbd49 100644
--- a/tensorflow/contrib/nn/__init__.py
+++ b/tensorflow/contrib/nn/__init__.py
@@ -26,6 +26,7 @@ from __future__ import print_function
 
 # pylint: disable=unused-import,wildcard-import
 from tensorflow.contrib.nn.python.ops.cross_entropy import *
+from tensorflow.contrib.nn.python.ops.sampling_ops import *
 # pylint: enable=unused-import,wildcard-import
 
 from tensorflow.python.util.all_util import remove_undocumented
-- 
GitLab


From cfd0bf52094e637b516c71164761c7687e0af134 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 16:29:01 -0700
Subject: [PATCH 0984/1375] Adding cautionary comments on the use of
 use_factors_weights_cache in the case where the weights are computed outside
 and set to the WALS object.

PiperOrigin-RevId: 161882927
---
 .../python/ops/factorization_ops.py             | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops.py b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
index fb400dbcea..054888e734 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
@@ -229,7 +229,13 @@ class WALSModel(object):
         a single number or vice versa.
       col_weights: See row_weights.
       use_factors_weights_cache: When True, the factors and weights will be
-        cached on the workers before the updates start. Defaults to True.
+        cached on the workers before the updates start. Defaults to True. Note
+        that the weights cache is initialized through `worker_init`, and the
+        row/col factors cache is initialized through
+        `initialize_{col/row}_update_op`. In the case where the weights are
+        computed outside and set before the training iterations start, it is
+        important to ensure the `worker_init` op is run afterwards for the
+        weights cache to take effect.
       use_gramian_cache: When True, the Gramians will be cached on the workers
         before the updates start. Defaults to True.
     """
@@ -554,7 +560,14 @@ class WALSModel(object):
 
   @property
   def worker_init(self):
-    """Op to initialize worker state once before starting any updates."""
+    """Op to initialize worker state once before starting any updates.
+
+    Note that specifically this initializes the cache of the row and column
+    weights on workers when `use_factors_weights_cache` is True. In this case,
+    if these weights are being calcualted and reset after the object is created,
+    it is important to ensure this ops is run afterwards so the cache reflects
+    the correct values.
+    """
     return self._worker_init
 
   @property
-- 
GitLab


From c311449c76433be7ead189c0acc7202d2e884513 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Thu, 13 Jul 2017 16:40:01 -0700
Subject: [PATCH 0985/1375] Add "gradients" subscope when generating gradients
 in C/++

This makes visualizing the graph easier and is also what Python does.

PiperOrigin-RevId: 161884431
---
 tensorflow/c/c_api.cc                  |  3 ++-
 tensorflow/c/c_api_test.cc             | 10 +++++-----
 tensorflow/core/util/equal_graph_def.h | 12 +++++++-----
 3 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index f620e248c1..3e69134c50 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -2117,7 +2117,8 @@ void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny, TF_Output* x, int nx,
     const int max_node_id_before = g->graph.num_node_ids();
 
     tensorflow::Scope scope =
-        NewInternalScope(&g->graph, &status->status, &g->refiner);
+        NewInternalScope(&g->graph, &status->status, &g->refiner)
+            .NewSubScope("gradients");
 
     if (dx != nullptr) {
       std::vector<tensorflow::Output> dx_arg;
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 736f56837c..123ed7aeca 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -1768,13 +1768,13 @@ class CApiGradientsTest : public ::testing::Test {
       const float const3_val[] = {1.0, 1.0, 1.0, 1.0};
       const3 = FloatConst2x2(expected_graph_, s_, const3_val, "GradInputs");
     } else {
-      const3 = OnesLike(expected_graph_, s_, matmul, "OnesLike");
+      const3 = OnesLike(expected_graph_, s_, matmul, "gradients/OnesLike");
     }
 
-    TF_Operation* matmul1 =
-        MatMul(expected_graph_, s_, const3, const1, "MatMul_1", false, true);
-    TF_Operation* matmul2 =
-        MatMul(expected_graph_, s_, const0, const3, "MatMul_2", true, false);
+    TF_Operation* matmul1 = MatMul(expected_graph_, s_, const3, const1,
+                                   "gradients/MatMul", false, true);
+    TF_Operation* matmul2 = MatMul(expected_graph_, s_, const0, const3,
+                                   "gradients/MatMul_1", true, false);
     expected_grad_outputs[0] = {matmul1, 0};
     expected_grad_outputs[1] = {matmul2, 0};
   }
diff --git a/tensorflow/core/util/equal_graph_def.h b/tensorflow/core/util/equal_graph_def.h
index 14f5bdfda4..0e7f2950cb 100644
--- a/tensorflow/core/util/equal_graph_def.h
+++ b/tensorflow/core/util/equal_graph_def.h
@@ -56,11 +56,13 @@ bool EqualRepeatedNodeDef(const protobuf::RepeatedPtrField<NodeDef>& actual,
                           string* diff,
                           const EqualGraphDefOptions& options = {});
 
-#define TF_EXPECT_GRAPH_EQ(expected, actual)                  \
-  do {                                                        \
-    string diff;                                              \
-    EXPECT_TRUE(EqualGraphDef(actual, expected, &diff))       \
-        << diff << "\nActual: " << SummarizeGraphDef(actual); \
+#define TF_EXPECT_GRAPH_EQ(expected, actual)            \
+  do {                                                  \
+    string diff;                                        \
+    EXPECT_TRUE(EqualGraphDef(actual, expected, &diff)) \
+        << diff << "\nExpected:\n"                      \
+        << SummarizeGraphDef(expected) << "\nActual:\n" \
+        << SummarizeGraphDef(actual);                   \
   } while (false)
 
 }  // namespace tensorflow
-- 
GitLab


From a2df786144cae1ffdf44d051b7fe8a12dabf8ccb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 17:23:32 -0700
Subject: [PATCH 0986/1375] Disable contrib tests on ASAN that are sometimes
 timing out.

PiperOrigin-RevId: 161890056
---
 tensorflow/contrib/distributions/BUILD  | 1 +
 tensorflow/contrib/factorization/BUILD  | 6 +++++-
 tensorflow/contrib/keras/BUILD          | 7 ++++++-
 tensorflow/contrib/learn/BUILD          | 1 +
 tensorflow/contrib/legacy_seq2seq/BUILD | 1 +
 tensorflow/contrib/linalg/BUILD         | 1 +
 tensorflow/contrib/metrics/BUILD        | 1 +
 tensorflow/contrib/rnn/BUILD            | 5 ++++-
 8 files changed, 20 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 993d6d20b1..ef77302bcc 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -693,6 +693,7 @@ cuda_py_test(
         "//tensorflow/python:platform_test",
     ],
     shard_count = 5,
+    tags = ["noasan"],  # times out b/63678675
 )
 
 cuda_py_test(
diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD
index c78bb51c3a..41d0e83720 100644
--- a/tensorflow/contrib/factorization/BUILD
+++ b/tensorflow/contrib/factorization/BUILD
@@ -215,7 +215,11 @@ tf_py_test(
         "//tensorflow/python:state_ops",
         "//tensorflow/python:variables",
     ],
-    tags = ["manual"],
+    tags = [
+        "manual",
+        "noasan",  # times out b/63678675
+        "nomsan",
+    ],
 )
 
 # Kernel tests
diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index e48b18ff52..77358d1253 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -327,6 +327,7 @@ py_test(
     size = "medium",
     srcs = ["python/keras/layers/convolutional_recurrent_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["noasan"],  # times out b/63678675
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
@@ -341,6 +342,7 @@ py_test(
     srcs_version = "PY2AND3",
     tags = [
         "manual",
+        "noasan",  # times out b/63678675
         "notsan",
     ],
     deps = [
@@ -461,7 +463,10 @@ py_test(
     size = "medium",
     srcs = ["python/keras/layers/lstm_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["notsan"],  # http://b/62189182
+    tags = [
+        "noasan",  # times out b/63678675
+        "notsan",  # http://b/62189182
+    ],
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 44c322fba2..9e5282595e 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -588,6 +588,7 @@ py_test(
     size = "medium",
     srcs = ["python/learn/estimators/head_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["noasan"],  # times out b/63678675
     deps = [
         ":learn",
         "//tensorflow/core:protos_all_py",
diff --git a/tensorflow/contrib/legacy_seq2seq/BUILD b/tensorflow/contrib/legacy_seq2seq/BUILD
index 119f0e67be..1fa55132b1 100644
--- a/tensorflow/contrib/legacy_seq2seq/BUILD
+++ b/tensorflow/contrib/legacy_seq2seq/BUILD
@@ -58,6 +58,7 @@ cuda_py_tests(
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
     ],
+    tags = ["noasan"],  # times out b/63678675
 )
 
 filegroup(
diff --git a/tensorflow/contrib/linalg/BUILD b/tensorflow/contrib/linalg/BUILD
index 72d784adb0..810a3d34ee 100644
--- a/tensorflow/contrib/linalg/BUILD
+++ b/tensorflow/contrib/linalg/BUILD
@@ -60,6 +60,7 @@ cuda_py_tests(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform_test",
     ],
+    tags = ["noasan"],  # times out b/63678675
 )
 
 cuda_py_tests(
diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD
index c0741f2997..c17bf60606 100644
--- a/tensorflow/contrib/metrics/BUILD
+++ b/tensorflow/contrib/metrics/BUILD
@@ -78,6 +78,7 @@ py_test(
     srcs = ["python/ops/metric_ops_test.py"],
     shard_count = 3,
     srcs_version = "PY2AND3",
+    tags = ["noasan"],  # times out b/63678675
     deps = [
         ":metrics_py",
         "//tensorflow/python:array_ops",
diff --git a/tensorflow/contrib/rnn/BUILD b/tensorflow/contrib/rnn/BUILD
index 835f7df8b2..784ac96149 100644
--- a/tensorflow/contrib/rnn/BUILD
+++ b/tensorflow/contrib/rnn/BUILD
@@ -83,7 +83,10 @@ cuda_py_tests(
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
     ],
-    tags = ["notsan"],  # http://b/62135981
+    tags = [
+        "noasan",  # times out b/63678675
+        "notsan",  # http://b/62135981
+    ],
     xla_enabled = True,
 )
 
-- 
GitLab


From 3aa588a2b848b1b0961c1eff9304defa65b42f05 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Thu, 13 Jul 2017 17:33:10 -0700
Subject: [PATCH 0987/1375] Add support for generating Hann and Hamming windows
 to tf.contrib.signal.

PiperOrigin-RevId: 161891114
---
 tensorflow/contrib/signal/BUILD               |  17 +++
 tensorflow/contrib/signal/__init__.py         |   4 +
 .../python/kernel_tests/window_ops_test.py    |  96 ++++++++++++++
 .../contrib/signal/python/ops/window_ops.py   | 121 ++++++++++++++++++
 4 files changed, 238 insertions(+)
 create mode 100644 tensorflow/contrib/signal/python/kernel_tests/window_ops_test.py
 create mode 100644 tensorflow/contrib/signal/python/ops/window_ops.py

diff --git a/tensorflow/contrib/signal/BUILD b/tensorflow/contrib/signal/BUILD
index 1d9cce239a..7fa05bf39b 100644
--- a/tensorflow/contrib/signal/BUILD
+++ b/tensorflow/contrib/signal/BUILD
@@ -12,10 +12,12 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:util",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -35,6 +37,21 @@ cuda_py_tests(
     ],
 )
 
+cuda_py_tests(
+    name = "window_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/window_ops_test.py"],
+    additional_deps = [
+        ":signal_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/signal/__init__.py b/tensorflow/contrib/signal/__init__.py
index 9f906dd28e..b4be2f7b4c 100644
--- a/tensorflow/contrib/signal/__init__.py
+++ b/tensorflow/contrib/signal/__init__.py
@@ -15,6 +15,8 @@
 """##Signal ops.
 
 @@frames
+@@hamming_window
+@@hann_window
 """
 
 from __future__ import absolute_import
@@ -22,6 +24,8 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.signal.python.ops.shape_ops import frames
+from tensorflow.contrib.signal.python.ops.window_ops import hamming_window
+from tensorflow.contrib.signal.python.ops.window_ops import hann_window
 
 from tensorflow.python.util.all_util import remove_undocumented
 remove_undocumented(__name__)
diff --git a/tensorflow/contrib/signal/python/kernel_tests/window_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/window_ops_test.py
new file mode 100644
index 0000000000..c3e0464596
--- /dev/null
+++ b/tensorflow/contrib/signal/python/kernel_tests/window_ops_test.py
@@ -0,0 +1,96 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for window_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+
+import numpy as np
+
+from tensorflow.contrib.signal.python.ops import window_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.platform import test
+
+
+def _scipy_raised_cosine(length, symmetric=True, a=0.5, b=0.5):
+  """A simple implementation of a raised cosine window that matches SciPy.
+
+  https://en.wikipedia.org/wiki/Window_function#Hann_window
+  https://github.com/scipy/scipy/blob/v0.14.0/scipy/signal/windows.py#L615
+
+  Args:
+    length: The window length.
+    symmetric: Whether to create a symmetric window.
+    a: The alpha parameter of the raised cosine window.
+    b: The beta parameter of the raised cosine window.
+
+  Returns:
+    A raised cosine window of length `length`.
+  """
+  if length == 1:
+    return np.ones(1)
+  odd = length % 2
+  if not symmetric and not odd:
+    length += 1
+  window = a - b * np.cos(2.0 * np.pi * np.arange(length) / (length - 1))
+  if not symmetric and not odd:
+    window = window[:-1]
+  return window
+
+
+class WindowOpsTest(test.TestCase):
+
+  def setUp(self):
+    self._window_lengths = [1, 2, 3, 4, 5, 31, 64, 128]
+    self._dtypes = [(dtypes.float16, 1e-2),
+                    (dtypes.float32, 1e-6),
+                    (dtypes.float64, 1e-9)]
+
+  def _compare_window_fns(self, np_window_fn, tf_window_fn):
+    with self.test_session(use_gpu=True):
+      for window_length in self._window_lengths:
+        for periodic in [False, True]:
+          for tf_dtype, tol in self._dtypes:
+            np_dtype = tf_dtype.as_numpy_dtype
+            expected = np_window_fn(window_length,
+                                    symmetric=not periodic).astype(np_dtype)
+            actual = tf_window_fn(window_length, periodic=periodic,
+                                  dtype=tf_dtype).eval()
+            self.assertAllClose(expected, actual, tol, tol)
+
+  def test_hann_window(self):
+    """Check that hann_window matches scipy.signal.hann behavior."""
+    # The Hann window is a raised cosine window with parameters alpha=0.5 and
+    # beta=0.5.
+    # https://en.wikipedia.org/wiki/Window_function#Hann_window
+    self._compare_window_fns(
+        functools.partial(_scipy_raised_cosine, a=0.5, b=0.5),
+        window_ops.hann_window)
+
+  def test_hamming_window(self):
+    """Check that hamming_window matches scipy.signal.hamming's behavior."""
+    # The Hamming window is a raised cosine window with parameters alpha=0.54
+    # and beta=0.46.
+    # https://en.wikipedia.org/wiki/Window_function#Hamming_window
+    self._compare_window_fns(
+        functools.partial(_scipy_raised_cosine, a=0.54, b=0.46),
+        window_ops.hamming_window)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/signal/python/ops/window_ops.py b/tensorflow/contrib/signal/python/ops/window_ops.py
new file mode 100644
index 0000000000..2179484ebc
--- /dev/null
+++ b/tensorflow/contrib/signal/python/ops/window_ops.py
@@ -0,0 +1,121 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Ops for computing common window functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+
+
+def hann_window(window_length, periodic=True, dtype=dtypes.float32, name=None):
+  """Generate a Hann window.
+
+  https://en.wikipedia.org/wiki/Window_function#Hann_window
+
+  Args:
+    window_length: A scalar `Tensor` indicating the window length to generate.
+    periodic: A bool `Tensor` indicating whether to generate a periodic or
+      symmetric window. Periodic windows are typically used for spectral
+      analysis while symmetric windows are typically used for digital
+      filter design.
+    dtype: The data type to produce. Must be a floating point type.
+    name: An optional name for the operation.
+
+  Returns:
+    A `Tensor` of shape `[window_length]` of type `dtype`.
+
+  Raises:
+    ValueError: If `dtype` is not a floating point type.
+  """
+  return _raised_cosine_window(name, 'hann_window', window_length, periodic,
+                               dtype, 0.5, 0.5)
+
+
+def hamming_window(window_length, periodic=True, dtype=dtypes.float32,
+                   name=None):
+  """Generate a Hamming window.
+
+  https://en.wikipedia.org/wiki/Window_function#Hamming_window
+
+  Args:
+    window_length: A scalar `Tensor` indicating the window length to generate.
+    periodic: A bool `Tensor` indicating whether to generate a periodic or
+      symmetric window. Periodic windows are typically used for spectral
+      analysis while symmetric windows are typically used for digital
+      filter design.
+    dtype: The data type to produce. Must be a floating point type.
+    name: An optional name for the operation.
+
+  Returns:
+    A `Tensor` of shape `[window_length]` of type `dtype`.
+
+  Raises:
+    ValueError: If `dtype` is not a floating point type.
+  """
+  return _raised_cosine_window(name, 'hamming_window', window_length, periodic,
+                               dtype, 0.54, 0.46)
+
+
+def _raised_cosine_window(name, default_name, window_length, periodic,
+                          dtype, a, b):
+  """Helper function for computing a raised cosine window.
+
+  Args:
+    name: Name to use for the scope.
+    default_name: Default name to use for the scope.
+    window_length: A scalar `Tensor` or integer indicating the window length.
+    periodic: A bool `Tensor` indicating whether to generate a periodic or
+      symmetric window.
+    dtype: A floating point `DType`.
+    a: The alpha parameter to the raised cosine window.
+    b: The beta parameter to the raised cosine window.
+
+  Returns:
+    A `Tensor` of shape `[window_length]` of type `dtype`.
+
+  Raises:
+    ValueError: If `dtype` is not a floating point type or `window_length` is
+      not scalar or `periodic` is not scalar.
+  """
+  if not dtype.is_floating:
+    raise ValueError('dtype must be a floating point type. Found %s' % dtype)
+
+  with ops.name_scope(name, default_name, [window_length, periodic]):
+    window_length = ops.convert_to_tensor(window_length, dtype=dtypes.int32,
+                                          name='window_length')
+    window_length.shape.assert_has_rank(0)
+    periodic = math_ops.cast(
+        ops.convert_to_tensor(periodic, dtype=dtypes.bool, name='periodic'),
+        dtypes.int32)
+    periodic.shape.assert_has_rank(0)
+    even = 1 - math_ops.mod(window_length, 2)
+
+    n = math_ops.cast(window_length + periodic * even - 1, dtype=dtype)
+    count = math_ops.cast(math_ops.range(window_length), dtype)
+    cos_arg = constant_op.constant(2 * np.pi, dtype=dtype) * count / n
+
+    return control_flow_ops.cond(
+        math_ops.equal(window_length, 1),
+        lambda: array_ops.ones([1], dtype=dtype),
+        lambda: math_ops.cast(a - b * math_ops.cos(cos_arg), dtype=dtype))
-- 
GitLab


From 00fda25cf0c51e9e67a93d8ae25f65c363e2a199 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 17:44:44 -0700
Subject: [PATCH 0988/1375] Adds gzip support to TextLineDataset.

- TextLineDataset now accepts the compression_type argument.

PiperOrigin-RevId: 161892408
---
 .../kernel_tests/reader_dataset_ops_test.py   | 52 ++++++++++---
 .../contrib/data/python/ops/dataset_ops.py    | 12 ++-
 tensorflow/core/kernels/reader_dataset_ops.cc | 74 ++++++++++++++++---
 .../core/ops/compat/ops_history.v1.pbtxt      |  5 ++
 tensorflow/core/ops/dataset_ops.cc            |  3 +
 tensorflow/core/ops/ops.pbtxt                 |  5 ++
 6 files changed, 129 insertions(+), 22 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py
index 133165a1c2..fbac4317b1 100644
--- a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py
@@ -40,27 +40,48 @@ class TextLineDatasetTest(test.TestCase):
   def _lineText(self, f, l):
     return compat.as_bytes("%d: %d" % (f, l))
 
-  def _createFiles(self, num_files, num_lines, crlf=False):
+  def _createFiles(self,
+                   num_files,
+                   num_lines,
+                   crlf=False,
+                   compression_type=None):
     filenames = []
     for i in range(num_files):
       fn = os.path.join(self.get_temp_dir(), "text_line.%d.txt" % i)
       filenames.append(fn)
-      with open(fn, "wb") as f:
-        for j in range(num_lines):
-          f.write(self._lineText(i, j))
-          # Always include a newline after the record unless it is
-          # at the end of the file, in which case we include it sometimes.
-          if j + 1 != num_lines or i == 0:
-            f.write(b"\r\n" if crlf else b"\n")
+      contents = []
+      for j in range(num_lines):
+        contents.append(self._lineText(i, j))
+        # Always include a newline after the record unless it is
+        # at the end of the file, in which case we include it sometimes.
+        if j + 1 != num_lines or i == 0:
+          contents.append(b"\r\n" if crlf else b"\n")
+      contents = b"".join(contents)
+
+      if not compression_type:
+        with open(fn, "wb") as f:
+          f.write(contents)
+      elif compression_type == "GZIP":
+        with gzip.GzipFile(fn, "wb") as f:
+          f.write(contents)
+      elif compression_type == "ZLIB":
+        contents = zlib.compress(contents)
+        with open(fn, "wb") as f:
+          f.write(contents)
+      else:
+        raise ValueError("Unsupported compression_type", compression_type)
+
     return filenames
 
-  def testTextLineDataset(self):
-    test_filenames = self._createFiles(2, 5, crlf=True)
+  def _testTextLineDataset(self, compression_type=None):
+    test_filenames = self._createFiles(
+        2, 5, crlf=True, compression_type=compression_type)
     filenames = array_ops.placeholder(dtypes.string, shape=[None])
     num_epochs = array_ops.placeholder(dtypes.int64, shape=[])
     batch_size = array_ops.placeholder(dtypes.int64, shape=[])
 
-    repeat_dataset = dataset_ops.TextLineDataset(filenames).repeat(num_epochs)
+    repeat_dataset = dataset_ops.TextLineDataset(
+        filenames, compression_type=compression_type).repeat(num_epochs)
     batch_dataset = repeat_dataset.batch(batch_size)
 
     iterator = dataset_ops.Iterator.from_structure(batch_dataset.output_types)
@@ -114,6 +135,15 @@ class TextLineDatasetTest(test.TestCase):
         self.assertAllEqual([self._lineText(1, i) for i in range(5)],
                             sess.run(get_next))
 
+  def testTextLineDatasetNoCompression(self):
+    self._testTextLineDataset()
+
+  def testTextLineDatasetGzipCompression(self):
+    self._testTextLineDataset(compression_type="GZIP")
+
+  def testTextLineDatasetZlibCompression(self):
+    self._testTextLineDataset(compression_type="ZLIB")
+
 
 class FixedLengthRecordReaderTest(test.TestCase):
 
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index 6a461da97f..dd7dce9112 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -1931,18 +1931,26 @@ class FilterDataset(Dataset):
 class TextLineDataset(Dataset):
   """A `Dataset` comprising lines from one or more text files."""
 
-  def __init__(self, filenames):
+  def __init__(self, filenames, compression_type=None):
     """Creates a `TextLineDataset`.
 
     Args:
       filenames: A `tf.string` tensor containing one or more filenames.
+      compression_type: A string, one of: `""` (no compression), `"ZLIB"`, or
+        `"GZIP"`.
     """
     super(TextLineDataset, self).__init__()
     self._filenames = ops.convert_to_tensor(
         filenames, dtype=dtypes.string, name="filenames")
+    if compression_type is not None:
+      self._compression_type = ops.convert_to_tensor(
+          compression_type, dtype=dtypes.string, name="compression_type")
+    else:
+      self._compression_type = constant_op.constant("", name="compression_type")
 
   def make_dataset_resource(self):
-    return gen_dataset_ops.text_line_dataset(self._filenames)
+    return gen_dataset_ops.text_line_dataset(self._filenames,
+                                             self._compression_type)
 
   @property
   def output_shapes(self):
diff --git a/tensorflow/core/kernels/reader_dataset_ops.cc b/tensorflow/core/kernels/reader_dataset_ops.cc
index e7f65c39cb..c9da8b149e 100644
--- a/tensorflow/core/kernels/reader_dataset_ops.cc
+++ b/tensorflow/core/kernels/reader_dataset_ops.cc
@@ -16,8 +16,12 @@ limitations under the License.
 
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/io/buffered_inputstream.h"
 #include "tensorflow/core/lib/io/inputbuffer.h"
+#include "tensorflow/core/lib/io/random_inputstream.h"
 #include "tensorflow/core/lib/io/record_reader.h"
+#include "tensorflow/core/lib/io/zlib_compression_options.h"
+#include "tensorflow/core/lib/io/zlib_inputstream.h"
 
 namespace tensorflow {
 
@@ -37,13 +41,39 @@ class TextLineDatasetOp : public OpKernel {
         ctx, filenames_tensor->dims() <= 1,
         errors::InvalidArgument("`filenames` must be a scalar or a vector."));
 
+    const Tensor* compression_type_tensor;
+    OP_REQUIRES_OK(ctx,
+                   ctx->input("compression_type", &compression_type_tensor));
+    OP_REQUIRES(
+        ctx, compression_type_tensor->dims() == 0,
+        errors::InvalidArgument("`compression_type` must be a scalar."));
+    const string& compression_type =
+        compression_type_tensor->scalar<string>()();
+
+    io::ZlibCompressionOptions zlib_compression_options =
+        io::ZlibCompressionOptions::DEFAULT();
+    bool use_compression = false;
+    if (compression_type.empty()) {
+      use_compression = false;
+    } else if (compression_type == "ZLIB") {
+      use_compression = true;
+      zlib_compression_options = io::ZlibCompressionOptions::DEFAULT();
+    } else if (compression_type == "GZIP") {
+      use_compression = true;
+      zlib_compression_options = io::ZlibCompressionOptions::GZIP();
+    } else {
+      OP_REQUIRES(ctx, compression_type.empty(),
+                  errors::InvalidArgument("Unsupported compression_type."));
+    }
+
     std::vector<string> filenames;
     filenames.reserve(filenames_tensor->NumElements());
     for (int i = 0; i < filenames_tensor->NumElements(); ++i) {
       filenames.push_back(filenames_tensor->flat<string>()(i));
     }
 
-    DatasetBase* dataset = new Dataset(std::move(filenames));
+    DatasetBase* dataset = new Dataset(std::move(filenames), use_compression,
+                                       zlib_compression_options);
     Tensor* output = nullptr;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &output));
     ResourceHandle handle = MakeResourceHandle<DatasetBase>(
@@ -55,8 +85,11 @@ class TextLineDatasetOp : public OpKernel {
  private:
   class Dataset : public DatasetBase {
    public:
-    explicit Dataset(std::vector<string> filenames)
-        : filenames_(std::move(filenames)) {}
+    explicit Dataset(std::vector<string> filenames, bool use_compression,
+                     io::ZlibCompressionOptions options)
+        : filenames_(std::move(filenames)),
+          use_compression_(use_compression),
+          options_(options) {}
 
     std::unique_ptr<IteratorBase> MakeIterator() const override {
       return std::unique_ptr<IteratorBase>(new Iterator(this));
@@ -86,9 +119,10 @@ class TextLineDatasetOp : public OpKernel {
         mutex_lock l(mu_);
         do {
           // We are currently processing a file, so try to read the next line.
-          if (input_buffer_) {
+          if (processing_file_) {
             string line_contents;
-            Status s = input_buffer_->ReadLine(&line_contents);
+            Status s = buffered_input_stream_->ReadLine(&line_contents);
+
             if (s.ok()) {
               // Produce the line as output.
               Tensor line_tensor(cpu_allocator(), DT_STRING, {});
@@ -103,7 +137,10 @@ class TextLineDatasetOp : public OpKernel {
 
             // We have reached the end of the current file, so maybe
             // move on to next file.
-            input_buffer_.reset();
+            processing_file_ = false;
+            input_stream_.reset();
+            zlib_input_stream_.reset();
+            buffered_input_stream_.reset();
             file_.reset();
             ++current_file_index_;
           }
@@ -117,7 +154,19 @@ class TextLineDatasetOp : public OpKernel {
           // Actually move on to next file.
           TF_RETURN_IF_ERROR(ctx->env()->NewRandomAccessFile(
               dataset()->filenames_[current_file_index_], &file_));
-          input_buffer_.reset(new io::InputBuffer(file_.get(), kBufferSize));
+          processing_file_ = true;
+          input_stream_.reset(
+              new io::RandomAccessInputStream(file_.get(), false));
+          if (dataset()->use_compression_) {
+            zlib_input_stream_.reset(
+                new io::ZlibInputStream(input_stream_.get(), kBufferSize,
+                                        kBufferSize, dataset()->options_));
+            buffered_input_stream_.reset(new io::BufferedInputStream(
+                zlib_input_stream_.get(), kBufferSize, false));
+          } else {
+            buffered_input_stream_.reset(new io::BufferedInputStream(
+                input_stream_.get(), kBufferSize, false));
+          }
         } while (true);
       }
 
@@ -127,13 +176,20 @@ class TextLineDatasetOp : public OpKernel {
       enum { kBufferSize = 256 << 10 /* 256 kB */ };
 
       mutex mu_;
+      bool processing_file_ GUARDED_BY(mu_) = false;
+      std::unique_ptr<io::RandomAccessInputStream> input_stream_
+          GUARDED_BY(mu_);
+      std::unique_ptr<io::ZlibInputStream> zlib_input_stream_ GUARDED_BY(mu_);
+      std::unique_ptr<io::BufferedInputStream> buffered_input_stream_
+          GUARDED_BY(mu_);
       size_t current_file_index_ GUARDED_BY(mu_) = 0;
       std::unique_ptr<RandomAccessFile> file_
-          GUARDED_BY(mu_);  // must outlive input_buffer_
-      std::unique_ptr<io::InputBuffer> input_buffer_ GUARDED_BY(mu_);
+          GUARDED_BY(mu_);  // must outlive input_stream_
     };
 
     const std::vector<string> filenames_;
+    bool use_compression_;
+    io::ZlibCompressionOptions options_;
   };
 };
 
diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 9f1ca05baa..2087679e2e 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -29353,6 +29353,11 @@ op {
     name: "filenames"
     type: DT_STRING
   }
+  input_arg {
+    name: "compression_type"
+    description: "A scalar containing either (i) the empty string (no\ncompression), (ii) \"ZLIB\", or (iii) \"GZIP\"."
+    type: DT_STRING
+  }
   output_arg {
     name: "handle"
     type: DT_RESOURCE
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index 425201529b..b37b901690 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -393,6 +393,7 @@ filename: A path on the filesystem where we should cache the dataset. Note: this
 
 REGISTER_OP("TextLineDataset")
     .Input("filenames: string")
+    .Input("compression_type: string")
     .Output("handle: resource")
     .SetShapeFn(shape_inference::ScalarShape)  // TODO(mrry): validate
                                                // that `filenames` is
@@ -403,6 +404,8 @@ Creates a dataset that emits the lines of one or more text files.
 
 filenames: A scalar or a vector containing the name(s) of the file(s) to be
   read.
+compression_type: A scalar containing either (i) the empty string (no
+  compression), (ii) "ZLIB", or (iii) "GZIP".
 )doc");
 
 REGISTER_OP("FixedLengthRecordDataset")
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index ccae526e5f..c953f93924 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -29751,6 +29751,11 @@ op {
     description: "A scalar or a vector containing the name(s) of the file(s) to be\nread."
     type: DT_STRING
   }
+  input_arg {
+    name: "compression_type"
+    description: "A scalar containing either (i) the empty string (no\ncompression), (ii) \"ZLIB\", or (iii) \"GZIP\"."
+    type: DT_STRING
+  }
   output_arg {
     name: "handle"
     type: DT_RESOURCE
-- 
GitLab


From ea125c27974135fbad6bcb75b720499c68d52357 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 18:06:50 -0700
Subject: [PATCH 0989/1375] [XLA] Pass shape/layout information in calls to the
 CPU runtime routines.

Previously the CPU runtime wouldn't know how the data that was being outfed was
laid out by the XLA LayoutAssignment pass, which could result in
transposed-value results.

This also allows us to validate the contract between the host program and the
compiled XLA program with (reified) runtime type checks.

PiperOrigin-RevId: 161895093
---
 tensorflow/compiler/xla/service/cpu/BUILD     |  4 ++
 .../compiler/xla/service/cpu/cpu_runtime.cc   | 71 +++++++++++++++----
 .../compiler/xla/service/cpu/cpu_runtime.h    | 19 +++--
 .../compiler/xla/service/cpu/ir_emitter.cc    | 47 +++++++-----
 .../compiler/xla/service/cpu/ir_emitter.h     |  2 +-
 .../compiler/xla/service/cpu/xfeed_manager.cc |  8 ++-
 .../compiler/xla/service/cpu/xfeed_manager.h  | 16 ++++-
 .../xla/service/cpu/xfeed_manager_test.cc     | 48 +++++++++++--
 .../xla/service/cpu_transfer_manager.cc       | 52 ++++++++++----
 .../xla/service/cpu_transfer_manager.h        |  4 +-
 .../xla/service/generic_transfer_manager.cc   |  4 +-
 .../compiler/xla/service/llvm_ir/llvm_util.cc | 19 +++++
 .../compiler/xla/service/llvm_ir/llvm_util.h  | 13 ++++
 13 files changed, 242 insertions(+), 65 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 6c2deb8c9a..7248cb5f4c 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -335,7 +335,11 @@ cc_library(
     ],
     copts = runtime_copts(),
     deps = [
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
index 40cdf079e3..07a9832867 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
@@ -17,8 +17,10 @@ limitations under the License.
 
 #include <functional>
 
+#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/types.h"
 
 namespace xla {
 namespace cpu {
@@ -33,38 +35,79 @@ XfeedManager* GetXfeedManager() {
 }  // namespace cpu
 }  // namespace xla
 
-void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(
-    xla::int32 buffer_length) {
-  VLOG(2) << "AcquireInfeedBufferForDequeue";
+namespace {
+
+tensorflow::string ShapeString(const void* shape_ptr, xla::int32 shape_length) {
+  xla::StatusOr<xla::Shape> shape =
+      xla::llvm_ir::DecodeSelfDescribingShapeConstant(shape_ptr, shape_length);
+  if (shape.ok()) {
+    return xla::ShapeUtil::HumanStringWithLayout(shape.ValueOrDie());
+  }
+  return "<invalid shape>";
+}
+
+}  // namespace
+
+void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(xla::int32 buffer_length,
+                                                      const void* shape,
+                                                      xla::int32 shape_length) {
+  if (VLOG_IS_ON(2)) {
+    LOG(INFO) << "AcquireInfeedBufferForDequeue: "
+              << ShapeString(shape, shape_length);
+  }
   xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
   // Wait until there's a buffer to dequeue.
   xla::cpu::runtime::XfeedBuffer* buffer =
       xfeed->infeed()->BlockingDequeueBuffer();
-  CHECK_EQ(buffer->length(), buffer_length);
+  CHECK_EQ(buffer->length(), buffer_length)
+      << "XLA program infeed request buffer size " << buffer_length
+      << " did not match the runtime's infed buffer length " << buffer->length()
+      << "; program reports desired shape: "
+      << ShapeString(shape, shape_length);
   return buffer->data();
 }
 
-void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(xla::int32 buffer_length,
-                                                       void* buffer_ptr) {
-  VLOG(2) << "ReleaseInfeedBufferAfterDequeue";
+void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
+    xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
+    xla::int32 shape_length) {
+  if (VLOG_IS_ON(2)) {
+    LOG(INFO) << "ReleaseInfeedBufferAfterDeque: "
+              << ShapeString(shape_ptr, shape_length);
+  }
   xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
-  xfeed->infeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr);
+  xla::StatusOr<xla::Shape> shape =
+      xla::llvm_ir::DecodeSelfDescribingShapeConstant(shape_ptr, shape_length);
+  xfeed->infeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr,
+                                        std::move(shape));
 }
 
 void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
-    xla::int32 buffer_length) {
-  VLOG(2) << "AcquireOutfeedBufferForPopulation";
+    xla::int32 buffer_length, const void* shape_ptr, xla::int32 shape_length) {
+  if (VLOG_IS_ON(2)) {
+    LOG(INFO) << "AcquireOutfeedBufferForPopulation: "
+              << ShapeString(shape_ptr, shape_length);
+  }
   xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
   // Wait until there's a buffer to dequeue.
   xla::cpu::runtime::XfeedBuffer* buffer =
       xfeed->outfeed()->BlockingDequeueBuffer();
-  CHECK_EQ(buffer->length(), buffer_length);
+  CHECK_EQ(buffer->length(), buffer_length)
+      << "XLA program outfeed request buffer size " << buffer_length
+      << " did not match the runtime's outfeed buffer length "
+      << buffer->length() << "; program reports outfed shape: "
+      << ShapeString(shape_ptr, shape_length);
   return buffer->data();
 }
 
 void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
-    xla::int32 buffer_length, void* buffer_ptr) {
-  VLOG(2) << "ReleaseOutfeedBufferAfterPopulation";
+    xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
+    xla::int32 shape_length) {
+  if (VLOG_IS_ON(2)) {
+    LOG(INFO) << "ReleaseOutfeedBufferAfterPopulation: "
+              << ShapeString(shape_ptr, shape_length);
+  }
   xla::cpu::runtime::XfeedManager* xfeed = xla::cpu::runtime::GetXfeedManager();
-  xfeed->outfeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr);
+  xla::StatusOr<xla::Shape> shape =
+      xla::llvm_ir::DecodeSelfDescribingShapeConstant(shape_ptr, shape_length);
+  xfeed->outfeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr, shape);
 }
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
index 04126e062e..40a7f548a2 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
@@ -68,13 +68,19 @@ XfeedManager* GetXfeedManager();
 
 extern "C" {
 
+// Note: in the runtime entry points below, the shape pointer and shape_length
+// reflect values that can be deserialized via
+// llvm_ir::DecodeSelfDescribingShapeConstant. This is the way we pass reified
+// type information from the generated program to the runtime, which helps check
+// the type safety and contract for the emitted-code/runtime communication.
+
 // Blocks until the next infeed buffer is ready to be dequeued, then
 // returns it. Fails catastrophically if the next enqueued buffer is
 // not of the correct length in bytes. Checking the shape rather than
 // the length would be more exact, but the length check is chosen as a
 // tradeoff between error checking and speed/simplicity.
 extern void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(
-    xla::int32 buffer_length);
+    xla::int32 buffer_length, const void* shape, xla::int32 shape_length);
 
 // Relinquishes the next infeed buffer that was returned by
 // __xla_cpu_runtime_AcquireInfeedBufferForDequeue. Once this call
@@ -89,12 +95,13 @@ extern void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(
 // implemented we will add support for multiple outstanding buffers
 // that can be returned out of order.
 extern void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
-    xla::int32 buffer_length, void* buffer_ptr);
+    xla::int32 buffer_length, void* buffer_ptr, const void* shape,
+    xla::int32 shape_length);
 
 // Blocks until the next outfeed buffer is available to be populated, then
 // returns it.
 extern void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
-    xla::int32 buffer_length);
+    xla::int32 buffer_length, const void* shape, xla::int32 shape_length);
 
 // Relinquishes the outfeed buffer after it has been populated.
 // buffer_ptr must have been previously returned by
@@ -106,7 +113,9 @@ extern void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
 // acquired, i.e., there may only be one outstanding outfeed buffer in
 // use by the runtime.
 extern void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
-    xla::int32 buffer_length, void* buffer_ptr);
-}
+    xla::int32 buffer_length, void* buffer_ptr, const void* shape,
+    xla::int32 shape_length);
+
+}  // extern "C"
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_H_
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 077d1ee2a1..9db1614cd7 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -403,9 +403,8 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
       llvm::Value* tuple_element_address =
           EmitTempBufferPointer(buffer, tuple_element_shape);
 
-      TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kInfeed,
-                                           ByteSizeOf(tuple_element_shape),
-                                           tuple_element_address));
+      TF_RETURN_IF_ERROR(EmitXfeedTransfer(
+          XfeedKind::kInfeed, tuple_element_shape, tuple_element_address));
 
       tuple_element_addresses.push_back(tuple_element_address);
     }
@@ -413,8 +412,8 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
     llvm_ir::EmitTuple(llvm_ir::IrArray(target_address, shape),
                        tuple_element_addresses, &ir_builder_);
   } else {
-    TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kInfeed, ByteSizeOf(shape),
-                                         target_address));
+    TF_RETURN_IF_ERROR(
+        EmitXfeedTransfer(XfeedKind::kInfeed, shape, target_address));
   }
 
   emitted_value_[infeed] = target_address;
@@ -422,8 +421,9 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) {
   return Status::OK();
 }
 
-Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, int64 length,
+Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape,
                                     llvm::Value* program_buffer_address) {
+  int64 length = ByteSizeOf(shape);
   if (length <= 0 || length > std::numeric_limits<int32>::max()) {
     return InvalidArgument(
         "xfeed (infeed or outfeed) buffer length %lld is outside the valid "
@@ -432,14 +432,19 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, int64 length,
   }
   int32 length_32 = static_cast<int32>(length);
 
+  int32 shape_length;
+  TF_ASSIGN_OR_RETURN(llvm::Value * shape_ptr,
+                      llvm_ir::EncodeSelfDescribingShapeConstant(
+                          shape, &shape_length, &ir_builder_));
+
   // The signature of the acquire infeed buffer function is:
   //
   //   (void*)(int32 length);
-  llvm::Type* i8_ptr_type = llvm::Type::getInt8PtrTy(module_->getContext());
   llvm::Type* int32_type = ir_builder_.getInt32Ty();
-  llvm::FunctionType* acquire_type =
-      llvm::FunctionType::get(i8_ptr_type, {int32_type},
-                              /*isVarArg=*/false);
+  llvm::Type* i8_ptr_type = llvm::Type::getInt8PtrTy(module_->getContext());
+  llvm::FunctionType* acquire_type = llvm::FunctionType::get(
+      i8_ptr_type, {int32_type, i8_ptr_type, int32_type},
+      /*isVarArg=*/false);
 
   llvm::Function* acquire_func;
   if (kind == XfeedKind::kInfeed) {
@@ -455,7 +460,8 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, int64 length,
   //
   //   (void)(int32 length, void* buffer);
   llvm::FunctionType* release_type = llvm::FunctionType::get(
-      ir_builder_.getVoidTy(), {int32_type, i8_ptr_type},
+      ir_builder_.getVoidTy(),
+      {int32_type, i8_ptr_type, i8_ptr_type, int32_type},
       /*isVarArg=*/false);
 
   llvm::Function* release_func;
@@ -468,8 +474,13 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, int64 length,
   }
   release_func->setCallingConv(llvm::CallingConv::C);
 
-  llvm::Value* acquired_pointer =
-      ir_builder_.CreateCall(acquire_func, {ir_builder_.getInt32(length_32)});
+  // Implementation note: this call informs the runtime that it wants a buffer
+  // of size exactly 'length_32', and the runtime is responsible for
+  // check-failing the process if there is a mismatch, versus passing us back a
+  // buffer that we might overrun.
+  llvm::Value* acquired_pointer = ir_builder_.CreateCall(
+      acquire_func, {ir_builder_.getInt32(length_32), shape_ptr,
+                     ir_builder_.getInt32(shape_length)});
 
   if (kind == XfeedKind::kInfeed) {
     // Copy to the program buffer address from the acquired buffer.
@@ -482,7 +493,8 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, int64 length,
   }
 
   ir_builder_.CreateCall(release_func,
-                         {ir_builder_.getInt32(length_32), acquired_pointer});
+                         {ir_builder_.getInt32(length_32), acquired_pointer,
+                          shape_ptr, ir_builder_.getInt32(shape_length)});
 
   return Status::OK();
 }
@@ -493,8 +505,7 @@ Status IrEmitter::HandleOutfeed(HloInstruction* outfeed) {
 
   llvm::Value* value = GetEmittedValueFor(operand);
   if (!ShapeUtil::IsTuple(operand_shape)) {
-    return EmitXfeedTransfer(XfeedKind::kOutfeed, ByteSizeOf(operand_shape),
-                             value);
+    return EmitXfeedTransfer(XfeedKind::kOutfeed, operand_shape, value);
   }
 
   TF_RET_CHECK(!ShapeUtil::IsNestedTuple(operand_shape));
@@ -505,8 +516,8 @@ Status IrEmitter::HandleOutfeed(HloInstruction* outfeed) {
     llvm::Value* tuple_element = llvm_ir::EmitGetTupleElement(
         tuple_element_shape, i, MinimumAlignmentForShape(tuple_element_shape),
         value, &ir_builder_);
-    TF_RETURN_IF_ERROR(EmitXfeedTransfer(
-        XfeedKind::kOutfeed, ByteSizeOf(tuple_element_shape), tuple_element));
+    TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kOutfeed,
+                                         tuple_element_shape, tuple_element));
   }
 
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 4e1308384b..1d9c5a6dd7 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -432,7 +432,7 @@ class IrEmitter : public DfsHloVisitorWithDefault {
 
   // Emit IR to transfer between a {infeed,outfeed} buffer and an in-program
   // address.
-  Status EmitXfeedTransfer(XfeedKind kind, int64 length,
+  Status EmitXfeedTransfer(XfeedKind kind, const Shape& shape,
                            llvm::Value* program_buffer_address);
 
   const HloModuleConfig& hlo_module_config_;
diff --git a/tensorflow/compiler/xla/service/cpu/xfeed_manager.cc b/tensorflow/compiler/xla/service/cpu/xfeed_manager.cc
index 0aa97b7cce..2160c3cd01 100644
--- a/tensorflow/compiler/xla/service/cpu/xfeed_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu/xfeed_manager.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/xfeed_manager.h"
 
+#include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -30,7 +31,7 @@ void XfeedQueueManager::Reset() {
   tensorflow::mutex_lock l(mu_);
   CHECK(current_buffer_ == nullptr);
   for (auto buffer : enqueued_buffers_) {
-    buffer->Done();
+    buffer->Done(ShapeUtil::MakeNil());
   }
   enqueued_buffers_.clear();
 }
@@ -62,12 +63,13 @@ XfeedBuffer* XfeedQueueManager::BlockingDequeueBuffer() {
   return current_buffer_;
 }
 
-void XfeedQueueManager::ReleaseCurrentBuffer(int32 length, void* data) {
+void XfeedQueueManager::ReleaseCurrentBuffer(int32 length, void* data,
+                                             StatusOr<Shape> shape) {
   tensorflow::mutex_lock l(mu_);
   CHECK(current_buffer_ != nullptr);
   CHECK_EQ(length, current_buffer_->length());
   CHECK_EQ(data, current_buffer_->data());
-  current_buffer_->Done();
+  current_buffer_->Done(std::move(shape));
   current_buffer_ = nullptr;
 }
 
diff --git a/tensorflow/compiler/xla/service/cpu/xfeed_manager.h b/tensorflow/compiler/xla/service/cpu/xfeed_manager.h
index efeb5eb980..86af789384 100644
--- a/tensorflow/compiler/xla/service/cpu/xfeed_manager.h
+++ b/tensorflow/compiler/xla/service/cpu/xfeed_manager.h
@@ -22,7 +22,9 @@ limitations under the License.
 
 #include <deque>
 
+#include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/mutex.h"
 
@@ -38,7 +40,11 @@ class XfeedBuffer {
 
   virtual int32 length() = 0;
   virtual void* data() = 0;
-  virtual void Done() = 0;
+
+  // The 'shape' parameter reflects what shape the embedded program was
+  // expecting / producing with respect to this XfeedBuffer. E.g. this will
+  // contain information about the layout of an outfed buffer.
+  virtual void Done(StatusOr<Shape> shape) = 0;
 };
 
 // Reusable component for managing the infeed and outfeed queue state.
@@ -70,7 +76,13 @@ class XfeedQueueManager {
   // BlockingDequeuBuffer and not yet released. length and data must
   // match the buffer->length() and buffer->data() for the current
   // buffer.
-  void ReleaseCurrentBuffer(int32 length, void* data);
+  //
+  // 'shape' communicates the shape of the buffer being released. If the program
+  // passed a value that could not be decoded as a shape, 'shape' will be an
+  // error status. In the case of outfeed, this indicates the layout of the
+  // shape that has been outfed. In the case of infeed, this can be used for
+  // sanity checking purposes.
+  void ReleaseCurrentBuffer(int32 length, void* data, StatusOr<Shape> shape);
 
  private:
   tensorflow::mutex mu_;
diff --git a/tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc b/tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc
index fddb59fc2d..8defd28b01 100644
--- a/tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc
@@ -18,6 +18,8 @@ limitations under the License.
 #include <memory>
 
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/core/threadpool.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/logging.h"
@@ -30,25 +32,53 @@ class InfeedManagerTest : public ::testing::Test {};
 
 class TestInfeedBuffer : public cpu::runtime::XfeedBuffer {
  public:
-  explicit TestInfeedBuffer(int32 length)
-      : done_called_(false), length_(length) {}
+  explicit TestInfeedBuffer(int32 length, bool expect_shape_match = true)
+      : shape_(ShapeUtil::MakeShape(U8, {length})),
+        done_called_(false),
+        length_(length),
+        expect_shape_match_(expect_shape_match) {}
   ~TestInfeedBuffer() override { EXPECT_TRUE(done_called_); }
 
   int32 length() override { return length_; }
   void* data() override { return nullptr; }
-  void Done() override {
+  void Done(StatusOr<Shape> shape) override {
     CHECK(!done_called_);
     done_called_ = true;
+    TF_ASSERT_OK(shape.status());
+    EXPECT_EQ(expect_shape_match_, ShapeUtil::Equal(shape_, shape.ValueOrDie()))
+        << "want " << ShapeUtil::HumanString(shape_) << " "
+        << (expect_shape_match_ ? "==" : "!=") << " "
+        << ShapeUtil::HumanString(shape.ValueOrDie());
   }
 
+  const Shape& shape() const { return shape_; }
+
  private:
+  Shape shape_;
   bool done_called_;
   int32 length_;
+  bool expect_shape_match_;
 };
 
+// Performs the acquire/release sequence on the infeed, as the generated CPU
+// code would in the process of executing the infeed operation.
 void ProcessNextBuffer(int32 length) {
-  void* buffer = __xla_cpu_runtime_AcquireInfeedBufferForDequeue(length);
-  __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(length, buffer);
+  auto shape = ShapeUtil::MakeShape(U8, {length});
+  string bytes = shape.SerializeAsString();
+  void* buffer = __xla_cpu_runtime_AcquireInfeedBufferForDequeue(
+      length, bytes.data(), bytes.size());
+  __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(length, buffer,
+                                                    bytes.data(), bytes.size());
+}
+
+// Performs the acquire/release sequence on the outfeed, as the generated CPU
+// code would in the process of executing the outfeed operation.
+void ProcessNextOutfeedBuffer(int32 length, const Shape& shape) {
+  string bytes = shape.SerializeAsString();
+  void* buffer = __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
+      length, bytes.data(), bytes.size());
+  __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
+      length, buffer, bytes.data(), bytes.size());
 }
 
 TEST_F(InfeedManagerTest, SingleThreadedSequential) {
@@ -98,5 +128,13 @@ TEST_F(InfeedManagerTest, MultiThreaded) {
   ProcessNextBuffer(length);
 }
 
+TEST_F(InfeedManagerTest, OutfeedWrongShape) {
+  TestInfeedBuffer* b = new TestInfeedBuffer(32, /*expect_shape_match=*/false);
+  cpu::runtime::XfeedManager* xfeed = cpu::runtime::GetXfeedManager();
+  xfeed->outfeed()->EnqueueBuffers({b});
+
+  ProcessNextOutfeedBuffer(32, ShapeUtil::MakeShape(U8, {33}));
+}
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
index 80e918970c..d8a76443a6 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.cc
@@ -50,7 +50,7 @@ class CpuInfeedBuffer : public cpu::runtime::XfeedBuffer {
 
   int32 length() override { return length_; }
   void* data() override { return buffer_; }
-  void Done() override { delete this; }
+  void Done(StatusOr<Shape> /*shape*/) override { delete this; }
 
   se::DeviceMemoryBase* device_memory() { return &device_memory_; }
 
@@ -65,15 +65,22 @@ class CpuOutfeedBuffer : public cpu::runtime::XfeedBuffer {
   CpuOutfeedBuffer(void* destination, int32 length)
       : destination_(destination), length_(length) {}
 
-  void WaitForNotification() { return done_.WaitForNotification(); }
+  StatusOr<Shape> WaitForNotification() {
+    done_.WaitForNotification();
+    return status_;
+  }
 
   int32 length() override { return length_; }
   void* data() override { return destination_; }
-  void Done() override { done_.Notify(); }
+  void Done(StatusOr<Shape> shape) override {
+    status_ = std::move(shape);
+    done_.Notify();
+  }
 
  private:
   void* destination_;
   int32 length_;
+  StatusOr<Shape> status_;
   tensorflow::Notification done_;
 };
 
@@ -106,7 +113,7 @@ Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
   buffers.reserve(literal.tuple_literals_size());
   auto cleanup = tensorflow::gtl::MakeCleanup([buffers]() {
     for (cpu::runtime::XfeedBuffer* b : buffers) {
-      b->Done();
+      b->Done(ShapeUtil::MakeNil());
     }
   });
 
@@ -159,7 +166,7 @@ CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor,
                              /*source=*/source, queued_buffer->device_memory());
 
   if (!s.ok()) {
-    queued_buffer->Done();
+    queued_buffer->Done(ShapeUtil::MakeNil());
     return s;
   }
   return queued_buffer;
@@ -178,8 +185,17 @@ Status CpuTransferManager::TransferLiteralFromOutfeed(
     auto empty =
         Literal::CreateFromDimensions(literal_shape.element_type(), dimensions);
     literal->Swap(empty.get());
-    return TransferBufferFromOutfeed(executor, size,
-                                     literal->MutableInternalData());
+    TF_ASSIGN_OR_RETURN(Shape received_shape,
+                        TransferBufferFromOutfeed(
+                            executor, size, literal->MutableInternalData()));
+    TF_RET_CHECK(ShapeUtil::Compatible(received_shape, literal->shape()))
+        << "Shape received from outfeed "
+        << ShapeUtil::HumanString(received_shape)
+        << " did not match the shape that was requested for outfeed: "
+        << ShapeUtil::HumanString(literal_shape);
+    TF_RET_CHECK(size == GetByteSizeRequirement(received_shape));
+    *literal->mutable_shape() = received_shape;
+    return Status::OK();
   }
 
   if (ShapeUtil::IsNestedTuple(literal_shape)) {
@@ -199,9 +215,19 @@ Status CpuTransferManager::TransferLiteralFromOutfeed(
         tuple_element_shape.dimensions().size());
     auto empty = Literal::CreateFromDimensions(
         tuple_element_shape.element_type(), dimensions);
-    TF_RETURN_IF_ERROR(TransferBufferFromOutfeed(
-        executor, GetByteSizeRequirement(tuple_element_shape),
-        empty->MutableInternalData()));
+    TF_ASSIGN_OR_RETURN(
+        Shape received_shape,
+        TransferBufferFromOutfeed(executor,
+                                  GetByteSizeRequirement(tuple_element_shape),
+                                  empty->MutableInternalData()));
+    TF_RET_CHECK(ShapeUtil::Compatible(received_shape, tuple_element_shape))
+        << "Shape received from outfeed "
+        << ShapeUtil::HumanString(received_shape)
+        << " did not match the shape that was requested for outfeed: "
+        << ShapeUtil::HumanString(tuple_element_shape);
+    TF_RET_CHECK(GetByteSizeRequirement(tuple_element_shape) ==
+                 GetByteSizeRequirement(received_shape));
+    *empty->mutable_shape() = received_shape;
     elements.push_back(std::move(empty));
   }
   auto result = Literal::MakeTupleOwned(std::move(elements));
@@ -210,7 +236,7 @@ Status CpuTransferManager::TransferLiteralFromOutfeed(
   return Status::OK();
 }
 
-Status CpuTransferManager::TransferBufferFromOutfeed(
+StatusOr<Shape> CpuTransferManager::TransferBufferFromOutfeed(
     perftools::gputools::StreamExecutor* executor, int64 size,
     void* destination) {
   if (size > std::numeric_limits<int32>::max()) {
@@ -230,9 +256,7 @@ Status CpuTransferManager::TransferBufferFromOutfeed(
           << size_32 << "B";
   xfeed_manager->outfeed()->EnqueueBuffers({&buffer});
   VLOG(2) << "Waiting for buffer to be notified as populated.";
-  buffer.WaitForNotification();
-  VLOG(2) << "Buffer is populated, returning from outfeed buffer request.";
-  return Status::OK();
+  return buffer.WaitForNotification();
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu_transfer_manager.h b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
index f133a0ea49..30dc2d9062 100644
--- a/tensorflow/compiler/xla/service/cpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/cpu_transfer_manager.h
@@ -50,7 +50,9 @@ class CpuTransferManager : public GenericTransferManager {
   StatusOr<cpu::runtime::XfeedBuffer*> TransferBufferToInfeedInternal(
       perftools::gputools::StreamExecutor* executor, int64 size,
       const void* source);
-  Status TransferBufferFromOutfeed(
+
+  // On success, returns the shape that was transferred from the outfeed.
+  StatusOr<Shape> TransferBufferFromOutfeed(
       perftools::gputools::StreamExecutor* executor, int64 size,
       void* destination);
 
diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
index cc7ff83d5e..69195c45ed 100644
--- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
@@ -176,9 +176,9 @@ Status GenericTransferManager::TransferLiteralFromOutfeed(
 
 Status GenericTransferManager::ResetDevices(
     tensorflow::gtl::ArraySlice<perftools::gputools::StreamExecutor*>
-        executors) {
+    /*executors*/) {
   return Unimplemented(
-      "Device reset is not yet supported on CPU and GPU (b/30481585)");
+      "Device reset is not yet supported on this platform (b/30481585)");
 }
 
 int64 GenericTransferManager::GetByteSizeRequirement(const Shape& shape) {
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index e348511c62..a8c17a67f1 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/lib/core/casts.h"
+#include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
@@ -136,6 +137,24 @@ llvm::Type* ShapeToIrType(const Shape& shape, llvm::IRBuilder<>* ir_builder) {
   return result_type;
 }
 
+StatusOr<llvm::Value*> EncodeSelfDescribingShapeConstant(
+    const Shape& shape, int32* shape_size, llvm::IRBuilder<>* ir_builder) {
+  string encoded_shape = shape.SerializeAsString();
+  if (encoded_shape.size() > std::numeric_limits<int32>::max()) {
+    return InternalError("Encoded shape size exceeded int32 size limit.");
+  }
+  *shape_size = static_cast<int32>(encoded_shape.size());
+  return ir_builder->CreateGlobalStringPtr(llvm_ir::AsStringRef(encoded_shape));
+}
+
+StatusOr<Shape> DecodeSelfDescribingShapeConstant(const void* shape_ptr,
+                                                  int32 size_bytes) {
+  Shape shape;
+  TF_RET_CHECK(shape.ParseFromArray(shape_ptr, size_bytes));
+  TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(shape));
+  return shape;
+}
+
 namespace {
 
 // Recursively construct a multidimensional LLVM constant which represents the
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h
index 7b09c1f831..d940c3fcbc 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h
@@ -106,6 +106,19 @@ llvm::Type* PrimitiveTypeToIrType(PrimitiveType element_type,
 // if "shape" is [5 x [10 x f32]], the function returns [5 x [10 x float]].
 llvm::Type* ShapeToIrType(const Shape& shape, llvm::IRBuilder<>* ir_builder);
 
+// Returns a value that represents a pointer to a global string constant that
+// encodes the shape as a serialized protobuf.
+StatusOr<llvm::Value*> EncodeSelfDescribingShapeConstant(
+    const Shape& shape, int32* shape_size, llvm::IRBuilder<>* ir_builder);
+
+// Inverses the encoding of a Shape protobuf into an LLVM global variable.
+//
+// This is intended to be called from the runtime to decode the llvm::Constants
+// that are created via ConvertShapeToSelfDescribingConstant and subsequently
+// embedded into the program.
+StatusOr<Shape> DecodeSelfDescribingShapeConstant(const void* shape_ptr,
+                                                  int32 size_bytes);
+
 // Converts a given literal to an IR Constant. Literals have known constant
 // values at IR emission time.
 llvm::Constant* ConvertLiteralToIrConstant(const Literal& literal,
-- 
GitLab


From 060b61ced1e81f8393ff0f5a66794114abcfd3b9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 18:19:55 -0700
Subject: [PATCH 0990/1375] Fix "Converting to Python 3" error for
 check_load_py_test.

PiperOrigin-RevId: 161896415
---
 tensorflow/tools/pip_package/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index c94d2e4abc..f82640faf4 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -61,6 +61,7 @@ py_binary(
     data = [
         "//tensorflow:all_opensource_files",
     ],
+    srcs_version = "PY2AND3",
 )
 
 # On Windows, python binary is a zip file of runfiles tree.
-- 
GitLab


From 642acfbaefb52da127845825df977b77be34d358 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 18:20:44 -0700
Subject: [PATCH 0991/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 161896495
---
 tensorflow/core/ops/compat/ops_history.v1.pbtxt | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 2087679e2e..51f15ba615 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -29364,6 +29364,22 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "TextLineDataset"
+  input_arg {
+    name: "filenames"
+    type: DT_STRING
+  }
+  input_arg {
+    name: "compression_type"
+    type: DT_STRING
+  }
+  output_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  is_stateful: true
+}
 op {
   name: "TextLineReader"
   output_arg {
-- 
GitLab


From 3f538fadf2dedaeddc295580eaff635c98f1793a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 18:27:01 -0700
Subject: [PATCH 0992/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 161897268
---
 tensorflow/go/op/wrappers.go | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 6d0990cb52..b90c0582d4 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -18768,14 +18768,16 @@ func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output
 // Arguments:
 //	filenames: A scalar or a vector containing the name(s) of the file(s) to be
 // read.
-func TextLineDataset(scope *Scope, filenames tf.Output) (handle tf.Output) {
+//	compression_type: A scalar containing either (i) the empty string (no
+// compression), (ii) "ZLIB", or (iii) "GZIP".
+func TextLineDataset(scope *Scope, filenames tf.Output, compression_type tf.Output) (handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
 		Type: "TextLineDataset",
 		Input: []tf.Input{
-			filenames,
+			filenames, compression_type,
 		},
 	}
 	op := scope.AddOperation(opspec)
-- 
GitLab


From a641129c9b0b801bc14da3d87d28777926b2540c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 18:32:07 -0700
Subject: [PATCH 0993/1375] Adds a file cache to the GCS filesystem, with
 configurable max staleness for file contents. Maximum staleness is specified
 in seconds in the GCS_READ_CACHE_MAX_STALENESS environment variable, and
 defaults to 0 (indicating that staleness is not tolerated in a newly opened
 file). Staleness is measured from the arrival time of the first block in the
 file's block cache.

PiperOrigin-RevId: 161897769
---
 tensorflow/core/platform/cloud/BUILD          |   1 +
 .../core/platform/cloud/file_block_cache.cc   |  25 +-
 .../core/platform/cloud/file_block_cache.h    |  23 +-
 .../platform/cloud/file_block_cache_test.cc   |  47 +++-
 .../core/platform/cloud/gcs_file_system.cc    | 169 ++++++++-----
 .../core/platform/cloud/gcs_file_system.h     |  19 +-
 .../platform/cloud/gcs_file_system_test.cc    | 239 +++++++++++++-----
 7 files changed, 383 insertions(+), 140 deletions(-)

diff --git a/tensorflow/core/platform/cloud/BUILD b/tensorflow/core/platform/cloud/BUILD
index 183a447012..67cd1bb2c6 100644
--- a/tensorflow/core/platform/cloud/BUILD
+++ b/tensorflow/core/platform/cloud/BUILD
@@ -170,6 +170,7 @@ tf_cc_test(
     srcs = ["file_block_cache_test.cc"],
     deps = [
         ":file_block_cache",
+        "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ],
diff --git a/tensorflow/core/platform/cloud/file_block_cache.cc b/tensorflow/core/platform/cloud/file_block_cache.cc
index b794614cd7..84ef0ea1fe 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.cc
+++ b/tensorflow/core/platform/cloud/file_block_cache.cc
@@ -36,9 +36,16 @@ Status FileBlockCache::Read(uint64 offset, size_t n, std::vector<char>* out) {
   if (finish < offset + n) {
     finish += block_size_;
   }
+  mutex_lock lock(mu_);
+  // If the oldest block arrived max_staleness_ in the past, flush the cache.
+  // Note that if max_staleness_ is 0, we don't expire any cached blocks.
+  if (max_staleness_ > 0 && timestamp_ > 0 &&
+      env_->NowSeconds() - timestamp_ > max_staleness_) {
+    TrimCache(0);
+    timestamp_ = 0;
+  }
   // Now iterate through the blocks, reading them one at a time. Reads are
   // locked so that only one block_fetcher call is active at any given time.
-  mutex_lock lock(mu_);
   for (uint64 pos = start; pos < finish; pos += block_size_) {
     auto entry = block_map_.find(pos);
     if (entry == block_map_.end()) {
@@ -47,10 +54,7 @@ Status FileBlockCache::Read(uint64 offset, size_t n, std::vector<char>* out) {
       // time during which the cache size exceeds its desired limit. The
       // tradeoff is that if the fetcher fails, the cache may evict a block
       // prematurely.
-      while (lru_list_.size() >= block_count_) {
-        block_map_.erase(lru_list_.back());
-        lru_list_.pop_back();
-      }
+      TrimCache(block_count_ - 1);
       std::unique_ptr<Block> block(new Block);
       TF_RETURN_IF_ERROR(block_fetcher_(pos, block_size_, &block->data));
       // Sanity check to detect interrupted reads leading to partial blocks: a
@@ -62,6 +66,10 @@ Status FileBlockCache::Read(uint64 offset, size_t n, std::vector<char>* out) {
         return errors::FailedPrecondition("File contents are inconsistent");
       }
       entry = block_map_.emplace(std::make_pair(pos, std::move(block))).first;
+      if (timestamp_ == 0) {
+        // Mark the timestamp of the first block's arrival in the cache.
+        timestamp_ = env_->NowSeconds();
+      }
     } else {
       // Cache hit. Remove the block from the LRU list at its prior location.
       lru_list_.erase(entry->second->lru_iterator);
@@ -94,4 +102,11 @@ Status FileBlockCache::Read(uint64 offset, size_t n, std::vector<char>* out) {
   return Status::OK();
 }
 
+void FileBlockCache::TrimCache(size_t size) {
+  while (lru_list_.size() > size) {
+    block_map_.erase(lru_list_.back());
+    lru_list_.pop_back();
+  }
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/file_block_cache.h b/tensorflow/core/platform/cloud/file_block_cache.h
index 442b1b0319..4acc4c7fca 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.h
+++ b/tensorflow/core/platform/cloud/file_block_cache.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include <vector>
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/thread_annotations.h"
 #include "tensorflow/core/platform/types.h"
@@ -44,11 +45,13 @@ class FileBlockCache {
   typedef std::function<Status(uint64, size_t, std::vector<char>*)>
       BlockFetcher;
 
-  FileBlockCache(uint64 block_size, uint32 block_count,
-                 BlockFetcher block_fetcher)
+  FileBlockCache(uint64 block_size, uint32 block_count, uint64 max_staleness,
+                 BlockFetcher block_fetcher, Env* env = Env::Default())
       : block_size_(block_size),
         block_count_(block_count),
-        block_fetcher_(block_fetcher) {}
+        max_staleness_(max_staleness),
+        block_fetcher_(block_fetcher),
+        env_(env) {}
 
   /// Read `n` bytes starting at `offset` into `out`. This method will return:
   ///
@@ -66,13 +69,20 @@ class FileBlockCache {
   Status Read(uint64 offset, size_t n, std::vector<char>* out);
 
  private:
+  /// Trim the LRU cache until its size is at most `size` blocks.
+  void TrimCache(size_t size) EXCLUSIVE_LOCKS_REQUIRED(mu_);
+
   /// The size of the blocks stored in the LRU cache, as well as the size of the
   /// reads from the underlying filesystem.
   const uint64 block_size_;
   /// The maximum number of blocks allowed in the LRU cache.
   const uint32 block_count_;
+  /// The maximum staleness of any block in the LRU cache, in seconds.
+  const uint64 max_staleness_;
   /// The callback to read a block from the underlying filesystem.
   const BlockFetcher block_fetcher_;
+  /// The Env from which we read timestamps.
+  Env* const env_;  // not owned
 
   /// \brief A block of a file.
   ///
@@ -85,7 +95,7 @@ class FileBlockCache {
     std::list<uint64>::iterator lru_iterator;
   };
 
-  /// Guards access to the block map and LRU list.
+  /// Guards access to the block map, LRU list, and cache timestamp.
   mutex mu_;
 
   /// The block map (map from offset in the file to Block object).
@@ -94,6 +104,11 @@ class FileBlockCache {
   /// The LRU list of offsets in the file. The front of the list is the position
   /// of the most recently accessed block.
   std::list<uint64> lru_list_ GUARDED_BY(mu_);
+
+  /// The most recent timestamp (in seconds since epoch) at which the block map
+  /// transitioned from empty to non-empty.  A value of 0 means the block map is
+  /// currently empty.
+  uint64 timestamp_ GUARDED_BY(mu_) = 0;
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/file_block_cache_test.cc b/tensorflow/core/platform/cloud/file_block_cache_test.cc
index 4f62e4ee6a..8ff02ae00b 100644
--- a/tensorflow/core/platform/cloud/file_block_cache_test.cc
+++ b/tensorflow/core/platform/cloud/file_block_cache_test.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/platform/cloud/file_block_cache.h"
 #include <cstring>
 #include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
@@ -34,9 +35,9 @@ TEST(FileBlockCacheTest, PassThrough) {
     return Status::OK();
   };
   // If block_size, block_count, or both are zero, the cache is a pass-through.
-  FileBlockCache cache1(1, 0, fetcher);
-  FileBlockCache cache2(0, 1, fetcher);
-  FileBlockCache cache3(0, 0, fetcher);
+  FileBlockCache cache1(1, 0, 0, fetcher);
+  FileBlockCache cache2(0, 1, 0, fetcher);
+  FileBlockCache cache3(0, 0, 0, fetcher);
   std::vector<char> out;
   TF_EXPECT_OK(cache1.Read(want_offset, want_n, &out));
   EXPECT_EQ(calls, 1);
@@ -68,7 +69,7 @@ TEST(FileBlockCacheTest, BlockAlignment) {
   for (uint64_t block_size = 2; block_size <= 4; block_size++) {
     // Make a cache of N-byte block size (1 block) and verify that reads of
     // varying offsets and lengths return correct data.
-    FileBlockCache cache(block_size, 1, fetcher);
+    FileBlockCache cache(block_size, 1, 0, fetcher);
     for (uint64_t offset = 0; offset < 10; offset++) {
       for (size_t n = block_size - 2; n <= block_size + 2; n++) {
         std::vector<char> got;
@@ -109,7 +110,7 @@ TEST(FileBlockCacheTest, CacheHits) {
     return Status::OK();
   };
   const uint32 block_count = 256;
-  FileBlockCache cache(block_size, block_count, fetcher);
+  FileBlockCache cache(block_size, block_count, 0, fetcher);
   std::vector<char> out;
   // The cache has space for `block_count` blocks. The loop with i = 0 should
   // fill the cache, and the loop with i = 1 should be all cache hits. The
@@ -143,7 +144,7 @@ TEST(FileBlockCacheTest, OutOfRange) {
     }
     return Status::OK();
   };
-  FileBlockCache cache(block_size, 1, fetcher);
+  FileBlockCache cache(block_size, 1, 0, fetcher);
   std::vector<char> out;
   // Reading the first 16 bytes should be fine.
   TF_EXPECT_OK(cache.Read(0, block_size, &out));
@@ -174,7 +175,7 @@ TEST(FileBlockCacheTest, Inconsistent) {
     out->resize(1, 'x');
     return Status::OK();
   };
-  FileBlockCache cache(block_size, 2, fetcher);
+  FileBlockCache cache(block_size, 2, 0, fetcher);
   std::vector<char> out;
   // Read the second block; this should yield an OK status and a single byte.
   TF_EXPECT_OK(cache.Read(block_size, block_size, &out));
@@ -200,7 +201,7 @@ TEST(FileBlockCacheTest, LRU) {
     return Status::OK();
   };
   const uint32 block_count = 2;
-  FileBlockCache cache(block_size, block_count, fetcher);
+  FileBlockCache cache(block_size, block_count, 0, fetcher);
   std::vector<char> out;
   // Read blocks from the cache, and verify the LRU behavior based on the
   // fetcher calls that the cache makes.
@@ -232,5 +233,35 @@ TEST(FileBlockCacheTest, LRU) {
   TF_EXPECT_OK(cache.Read(0, 1, &out));
 }
 
+TEST(FileBlockCacheTest, MaxStaleness) {
+  // This Env wrapper lets us control the NowSeconds() return value.
+  class FakeEnv : public EnvWrapper {
+   public:
+    FakeEnv() : EnvWrapper(Env::Default()) {}
+    uint64 NowSeconds() override { return now_; };
+    uint64 now_ = 1;
+  };
+  int calls = 0;
+  auto fetcher = [&calls](uint64 offset, size_t n, std::vector<char>* out) {
+    calls++;
+    out->resize(n, 'x');
+    return Status::OK();
+  };
+  std::unique_ptr<FakeEnv> env(new FakeEnv);
+  FileBlockCache cache(8, 1, 2 /* max staleness */, fetcher, env.get());
+  std::vector<char> out;
+  // Execute the first read to load the block.
+  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  EXPECT_EQ(calls, 1);
+  // Now advance the clock one second at a time and redo the read. The call
+  // count should advance every 3 seconds (i.e. every time the staleness is
+  // greater than 2).
+  for (int i = 1; i <= 10; i++) {
+    env->now_ = i + 1;
+    TF_EXPECT_OK(cache.Read(0, 1, &out));
+    EXPECT_EQ(calls, 1 + i / 3);
+  }
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 9f00735304..85def60a1e 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -62,6 +62,10 @@ constexpr char kBlockSize[] = "GCS_READ_CACHE_BLOCK_SIZE_MB";
 // The environment variable that overrides the block count in the LRU cache of
 // blocks read from GCS.
 constexpr char kBlockCount[] = "GCS_READ_CACHE_BLOCK_COUNT";
+// The environment variable that overrides the maximum staleness of cached file
+// contents. Once any block of a file reaches this staleness, all cached blocks
+// will be evicted on the next read.
+constexpr char kMaxStaleness[] = "GCS_READ_CACHE_MAX_STALENESS";
 // The file statistics returned by Stat() for directories.
 const FileStatistics DIRECTORY_STAT(0, 0, true);
 
@@ -213,29 +217,19 @@ Status GetBoolValue(const Json::Value& parent, const string& name,
   return Status::OK();
 }
 
-/// A GCS-based implementation of a random access file with a read-ahead buffer.
+/// A GCS-based implementation of a random access file with an LRU block cache.
 class GcsRandomAccessFile : public RandomAccessFile {
  public:
-  GcsRandomAccessFile(const string& bucket, const string& object,
-                      AuthProvider* auth_provider,
-                      HttpRequest::Factory* http_request_factory,
-                      size_t block_size, uint32 block_count)
-      : bucket_(bucket),
-        object_(object),
-        auth_provider_(auth_provider),
-        http_request_factory_(http_request_factory),
-        file_block_cache_(
-            block_size, block_count,
-            [this](uint64 offset, size_t n, std::vector<char>* out) {
-              return LoadBufferFromGCS(offset, n, out);
-            }) {}
+  explicit GcsRandomAccessFile(
+      const std::shared_ptr<FileBlockCache>& file_block_cache)
+      : file_block_cache_(file_block_cache) {}
 
   /// The implementation of reads with an LRU block cache. Thread safe.
   Status Read(uint64 offset, size_t n, StringPiece* result,
               char* scratch) const override {
     result->clear();
     std::vector<char> out;
-    TF_RETURN_IF_ERROR(file_block_cache_.Read(offset, n, &out));
+    TF_RETURN_IF_ERROR(file_block_cache_->Read(offset, n, &out));
     std::memcpy(scratch, out.data(), std::min(out.size(), n));
     *result = StringPiece(scratch, std::min(out.size(), n));
     if (result->size() < n) {
@@ -249,29 +243,8 @@ class GcsRandomAccessFile : public RandomAccessFile {
   }
 
  private:
-  /// A helper function to actually read the data from GCS.
-  Status LoadBufferFromGCS(uint64_t offset, size_t n, std::vector<char>* out) {
-    string auth_token;
-    TF_RETURN_IF_ERROR(AuthProvider::GetToken(auth_provider_, &auth_token));
-
-    std::unique_ptr<HttpRequest> request(http_request_factory_->Create());
-    TF_RETURN_IF_ERROR(request->Init());
-    TF_RETURN_IF_ERROR(
-        request->SetUri(strings::StrCat("https://", kStorageHost, "/", bucket_,
-                                        "/", request->EscapeString(object_))));
-    TF_RETURN_IF_ERROR(request->AddAuthBearerHeader(auth_token));
-    TF_RETURN_IF_ERROR(request->SetRange(offset, offset + n - 1));
-    TF_RETURN_IF_ERROR(request->SetResultBuffer(out));
-    TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when reading gs://",
-                                    bucket_, "/", object_);
-    return Status::OK();
-  }
-
-  string bucket_;
-  string object_;
-  AuthProvider* auth_provider_;
-  HttpRequest::Factory* http_request_factory_;
-  mutable FileBlockCache file_block_cache_;
+  /// The LRU block cache for this file.
+  mutable std::shared_ptr<FileBlockCache> file_block_cache_;
 };
 
 /// \brief GCS-based implementation of a writeable file.
@@ -283,11 +256,13 @@ class GcsWritableFile : public WritableFile {
   GcsWritableFile(const string& bucket, const string& object,
                   AuthProvider* auth_provider,
                   HttpRequest::Factory* http_request_factory,
+                  std::function<void()> file_cache_erase,
                   int64 initial_retry_delay_usec)
       : bucket_(bucket),
         object_(object),
         auth_provider_(auth_provider),
         http_request_factory_(http_request_factory),
+        file_cache_erase_(std::move(file_cache_erase)),
         sync_needed_(true),
         initial_retry_delay_usec_(initial_retry_delay_usec) {
     if (GetTmpFilename(&tmp_content_filename_).ok()) {
@@ -305,11 +280,13 @@ class GcsWritableFile : public WritableFile {
                   AuthProvider* auth_provider,
                   const string& tmp_content_filename,
                   HttpRequest::Factory* http_request_factory,
+                  std::function<void()> file_cache_erase,
                   int64 initial_retry_delay_usec)
       : bucket_(bucket),
         object_(object),
         auth_provider_(auth_provider),
         http_request_factory_(http_request_factory),
+        file_cache_erase_(std::move(file_cache_erase)),
         sync_needed_(true),
         initial_retry_delay_usec_(initial_retry_delay_usec) {
     tmp_content_filename_ = tmp_content_filename;
@@ -377,6 +354,8 @@ class GcsWritableFile : public WritableFile {
             TF_RETURN_IF_ERROR(RequestUploadSessionStatus(
                 session_uri, &completed, &already_uploaded));
             if (completed) {
+              // Erase the file from the file cache on every successful write.
+              file_cache_erase_();
               // It's unclear why UploadToSession didn't return OK in the
               // previous attempt, but GCS reports that the file is fully
               // uploaded, so succeed.
@@ -529,6 +508,8 @@ class GcsWritableFile : public WritableFile {
         request->SetPutFromFile(tmp_content_filename_, start_offset));
     TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when uploading ",
                                     GetGcsPath());
+    // Erase the file from the file cache on every successful write.
+    file_cache_erase_();
     return Status::OK();
   }
 
@@ -542,6 +523,7 @@ class GcsWritableFile : public WritableFile {
   string tmp_content_filename_;
   std::ofstream outfile_;
   HttpRequest::Factory* http_request_factory_;
+  std::function<void()> file_cache_erase_;
   bool sync_needed_;  // whether there is buffered data that needs to be synced
   int64 initial_retry_delay_usec_;
 };
@@ -557,55 +539,107 @@ class GcsReadOnlyMemoryRegion : public ReadOnlyMemoryRegion {
   std::unique_ptr<char[]> data_;
   uint64 length_;
 };
+
+// Helper function to extract an environment variable and convert it into a
+// value of type T.
+template <typename T>
+bool GetEnvVar(const char* varname, bool (*convert)(StringPiece, T*),
+               T* value) {
+  const char* env_value = std::getenv(varname);
+  if (!env_value) {
+    return false;
+  }
+  return convert(env_value, value);
+}
+
 }  // namespace
 
 GcsFileSystem::GcsFileSystem()
     : auth_provider_(new GoogleAuthProvider()),
       http_request_factory_(new HttpRequest::Factory()) {
   // Apply the sys env override for the readahead buffer size if it's provided.
-  const char* readahead_buffer_size_env = std::getenv(kReadaheadBufferSize);
-  if (readahead_buffer_size_env) {
-    uint64 value;
-    if (strings::safe_strtou64(readahead_buffer_size_env, &value)) {
-      block_size_ = value;
-    }
+  uint64 v64;
+  if (GetEnvVar(kReadaheadBufferSize, strings::safe_strtou64, &v64)) {
+    block_size_ = v64;
   }
   // Apply the override for the block size if provided. This takes precedence
   // over the readahead buffer size.
-  const char* block_size_env = std::getenv(kBlockSize);
-  if (block_size_env) {
-    uint64 value;
-    if (strings::safe_strtou64(block_size_env, &value)) {
-      block_size_ = value * 1024 * 1024;
-    }
+  if (GetEnvVar(kBlockSize, strings::safe_strtou64, &v64)) {
+    block_size_ = v64 * 1024 * 1024;
   }
   // Apply the override for the block count if provided.
-  const char* block_count_env = std::getenv(kBlockCount);
-  if (block_count_env) {
-    uint32 value;
-    if (strings::safe_strtou32(block_count_env, &value)) {
-      block_count_ = value;
-    }
+  uint32 v32;
+  if (GetEnvVar(kBlockCount, strings::safe_strtou32, &v32)) {
+    block_count_ = v32;
+  }
+  // Apply the override for max staleness if provided.
+  if (GetEnvVar(kMaxStaleness, strings::safe_strtou64, &v64)) {
+    max_staleness_ = v64;
   }
 }
 
 GcsFileSystem::GcsFileSystem(
     std::unique_ptr<AuthProvider> auth_provider,
     std::unique_ptr<HttpRequest::Factory> http_request_factory,
-    size_t block_size, uint32 block_count, int64 initial_retry_delay_usec)
+    size_t block_size, uint32 block_count, uint64 max_staleness,
+    int64 initial_retry_delay_usec)
     : auth_provider_(std::move(auth_provider)),
       http_request_factory_(std::move(http_request_factory)),
       block_size_(block_size),
       block_count_(block_count),
+      max_staleness_(max_staleness),
       initial_retry_delay_usec_(initial_retry_delay_usec) {}
 
 Status GcsFileSystem::NewRandomAccessFile(
     const string& fname, std::unique_ptr<RandomAccessFile>* result) {
   string bucket, object;
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, false, &bucket, &object));
-  result->reset(new GcsRandomAccessFile(bucket, object, auth_provider_.get(),
-                                        http_request_factory_.get(),
-                                        block_size_, block_count_));
+  // `file_cache_` is a container of FileBlockCache, keyed by filename. We look
+  // up the filename in this container to see if we have a FileBlockCache for
+  // it. If the FileBlockCache for `fname` exists in file_cache_, we return it.
+  // Otherwise, we create a new FileBlockCache with a block fetcher that calls
+  // GcsFileSystem::LoadBufferFromGCS for the bucket and object derived from
+  // `fname`. If a FileBlockCache is created, it is added to `file_cache_` only
+  // if `max_staleness_` > 0 (indicating that new random acesss files will
+  // tolerate stale reads coming from FileBlockCache instances that persist
+  // across file close/open boundaries).
+  std::shared_ptr<FileBlockCache> file_block_cache;
+  mutex_lock lock(mu_);
+  auto entry = file_cache_.find(fname);
+  if (entry == file_cache_.end()) {
+    file_block_cache.reset(new FileBlockCache(
+        block_size_, block_count_, max_staleness_,
+        [this, bucket, object](uint64 offset, size_t n,
+                               std::vector<char>* out) {
+          return LoadBufferFromGCS(bucket, object, offset, n, out);
+        }));
+    if (max_staleness_ > 0) {
+      file_cache_[fname] = file_block_cache;
+    }
+  } else {
+    file_block_cache = entry->second;
+  }
+  result->reset(new GcsRandomAccessFile(file_block_cache));
+  return Status::OK();
+}
+
+// A helper function to actually read the data from GCS.
+Status GcsFileSystem::LoadBufferFromGCS(const string& bucket,
+                                        const string& object, uint64_t offset,
+                                        size_t n, std::vector<char>* out) {
+  string auth_token;
+  TF_RETURN_IF_ERROR(AuthProvider::GetToken(auth_provider_.get(), &auth_token));
+
+  std::unique_ptr<HttpRequest> request(http_request_factory_->Create());
+  TF_RETURN_IF_ERROR(request->Init());
+  TF_RETURN_IF_ERROR(
+      request->SetUri(strings::StrCat("https://", kStorageHost, "/", bucket,
+                                      "/", request->EscapeString(object))));
+  TF_RETURN_IF_ERROR(request->AddAuthBearerHeader(auth_token));
+  TF_RETURN_IF_ERROR(request->SetRange(offset, offset + n - 1));
+  TF_RETURN_IF_ERROR(request->SetResultBuffer(out));
+  TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when reading gs://",
+                                  bucket, "/", object);
   return Status::OK();
 }
 
@@ -615,6 +649,10 @@ Status GcsFileSystem::NewWritableFile(const string& fname,
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, false, &bucket, &object));
   result->reset(new GcsWritableFile(bucket, object, auth_provider_.get(),
                                     http_request_factory_.get(),
+                                    [this, fname]() {
+                                      mutex_lock lock(mu_);
+                                      file_cache_.erase(fname);
+                                    },
                                     initial_retry_delay_usec_));
   return Status::OK();
 }
@@ -653,9 +691,14 @@ Status GcsFileSystem::NewAppendableFile(const string& fname,
   // Create a writable file and pass the old content to it.
   string bucket, object;
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, false, &bucket, &object));
-  result->reset(new GcsWritableFile(
-      bucket, object, auth_provider_.get(), old_content_filename,
-      http_request_factory_.get(), initial_retry_delay_usec_));
+  result->reset(new GcsWritableFile(bucket, object, auth_provider_.get(),
+                                    old_content_filename,
+                                    http_request_factory_.get(),
+                                    [this, fname]() {
+                                      mutex_lock lock(mu_);
+                                      file_cache_.erase(fname);
+                                    },
+                                    initial_retry_delay_usec_));
   return Status::OK();
 }
 
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.h b/tensorflow/core/platform/cloud/gcs_file_system.h
index ef8899892e..6030b16aa0 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.h
+++ b/tensorflow/core/platform/cloud/gcs_file_system.h
@@ -20,6 +20,7 @@ limitations under the License.
 #include <vector>
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/cloud/auth_provider.h"
+#include "tensorflow/core/platform/cloud/file_block_cache.h"
 #include "tensorflow/core/platform/cloud/http_request.h"
 #include "tensorflow/core/platform/cloud/retrying_file_system.h"
 #include "tensorflow/core/platform/file_system.h"
@@ -35,7 +36,7 @@ class GcsFileSystem : public FileSystem {
   GcsFileSystem();
   GcsFileSystem(std::unique_ptr<AuthProvider> auth_provider,
                 std::unique_ptr<HttpRequest::Factory> http_request_factory,
-                size_t block_size, uint32 block_count,
+                size_t block_size, uint32 block_count, uint64 max_staleness,
                 int64 initial_retry_delay_usec);
 
   Status NewRandomAccessFile(
@@ -77,6 +78,7 @@ class GcsFileSystem : public FileSystem {
                            int64* undeleted_dirs) override;
   size_t block_size() const { return block_size_; }
   uint32 block_count() const { return block_count_; }
+  uint64 max_staleness() const { return max_staleness_; }
 
  private:
   /// \brief Checks if the bucket exists. Returns OK if the check succeeded.
@@ -110,9 +112,19 @@ class GcsFileSystem : public FileSystem {
                        FileStatistics* stat);
   Status RenameObject(const string& src, const string& target);
 
+  /// Loads file contents from GCS for a given bucket and object.
+  Status LoadBufferFromGCS(const string& bucket, const string& object,
+                           uint64_t offset, size_t n, std::vector<char>* out);
+
   std::unique_ptr<AuthProvider> auth_provider_;
   std::unique_ptr<HttpRequest::Factory> http_request_factory_;
 
+  /// Guards access to the file cache.
+  mutex mu_;
+
+  /// Cache from filename to FileBlockCache. Populated iff max_staleness_ > 0.
+  std::map<string, std::shared_ptr<FileBlockCache>> file_cache_ GUARDED_BY(mu_);
+
   /// The block size for block-aligned reads in the RandomAccessFile
   /// implementation. Defaults to 256Mb.
   size_t block_size_ = 256 * 1024 * 1024;
@@ -121,6 +133,11 @@ class GcsFileSystem : public FileSystem {
   /// implementation.  Defaults to 1.
   uint32 block_count_ = 1;
 
+  /// The maximum staleness of cached file blocks across close/open boundaries.
+  /// Defaults to 0, meaning that file blocks do not persist across close/open
+  /// boundaries.
+  uint64 max_staleness_ = 0;
+
   /// The initial delay for exponential backoffs when retrying failed calls.
   const int64 initial_retry_delay_usec_ = 1000000L;
 
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index defedfdb93..915a44ad7f 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -46,7 +46,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoBlockCache) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
@@ -81,7 +81,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoBlockCache_differentN) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
@@ -131,7 +131,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithBlockCache) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    9 /* block size */, 2 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   char scratch[100];
   StringPiece result;
@@ -181,19 +181,134 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithBlockCache) {
   EXPECT_EQ("0123", result);
 }
 
+TEST(GcsFileSystemTest, NewRandomAccessFile_WithBlockCache_MaxStaleness) {
+  // Our underlying file in this test is a 16 byte file with contents
+  // "0123456789abcdef".
+  std::vector<HttpRequest*> requests(
+      {new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
+                           "Auth Token: fake_token\n"
+                           "Range: 0-7\n",
+                           "01234567"),
+       new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
+                           "Auth Token: fake_token\n"
+                           "Range: 8-15\n",
+                           "89abcdef")});
+  GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
+                   std::unique_ptr<HttpRequest::Factory>(
+                       new FakeHttpRequestFactory(&requests)),
+                   8 /* block size */, 2 /* block count */,
+                   3600 /* max staleness */, 0 /* initial retry delay */);
+  char scratch[100];
+  StringPiece result;
+  // There should only be two HTTP requests issued to GCS even though we iterate
+  // this loop 10 times.  This shows that the underlying FileBlockCache persists
+  // across file close/open boundaries.
+  for (int i = 0; i < 10; i++) {
+    // Create two files. Since these files have the same name name and the max
+    // staleness of the filesystem is > 0, they will share the same block cache.
+    std::unique_ptr<RandomAccessFile> file1;
+    std::unique_ptr<RandomAccessFile> file2;
+    TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/object", &file1));
+    TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/object", &file2));
+    // Reading the first block from file1 should load it once.
+    TF_EXPECT_OK(file1->Read(0, 8, &result, scratch));
+    EXPECT_EQ("01234567", result);
+    // Reading the first block from file2 should not trigger a request to load
+    // the first block again, because the FileBlockCache shared by file1 and
+    // file2 already has the first block.
+    TF_EXPECT_OK(file2->Read(0, 8, &result, scratch));
+    EXPECT_EQ("01234567", result);
+    // Reading the second block from file2 should load it once.
+    TF_EXPECT_OK(file2->Read(8, 8, &result, scratch));
+    EXPECT_EQ("89abcdef", result);
+    // Reading the second block from file1 should not trigger a request to load
+    // the second block again, because the FileBlockCache shared by file1 and
+    // file2 already has the second block.
+    TF_EXPECT_OK(file1->Read(8, 8, &result, scratch));
+    EXPECT_EQ("89abcdef", result);
+  }
+}
+
 TEST(GcsFileSystemTest, NewRandomAccessFile_NoObjectName) {
   std::vector<HttpRequest*> requests;
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* read ahead bytes */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
             fs.NewRandomAccessFile("gs://bucket/", &file).code());
 }
 
+TEST(GcsFileSystemTest, NewWritableFile_FlushesFileCache) {
+  // Our underlying file in this test is a 16 byte file with initial contents
+  // "0123456789abcdef", replaced by new contents "08192a3b4c5d6e7f".
+  std::vector<HttpRequest*> requests(
+      {new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
+                           "Auth Token: fake_token\n"
+                           "Range: 0-7\n",
+                           "01234567"),
+       new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
+                           "Auth Token: fake_token\n"
+                           "Range: 8-15\n",
+                           "89abcdef"),
+       new FakeHttpRequest(
+           "Uri: https://www.googleapis.com/upload/storage/v1/b/bucket/o?"
+           "uploadType=resumable&name=object\n"
+           "Auth Token: fake_token\n"
+           "Header X-Upload-Content-Length: 16\n"
+           "Post: yes\n",
+           "", {{"Location", "https://custom/upload/location"}}),
+       new FakeHttpRequest("Uri: https://custom/upload/location\n"
+                           "Auth Token: fake_token\n"
+                           "Header Content-Range: bytes 0-15/16\n"
+                           "Put body: 08192a3b4c5d6e7f\n",
+                           ""),
+       new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
+                           "Auth Token: fake_token\n"
+                           "Range: 0-7\n",
+                           "08192a3b"),
+       new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
+                           "Auth Token: fake_token\n"
+                           "Range: 8-15\n",
+                           "4c5d6e7f")});
+  GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
+                   std::unique_ptr<HttpRequest::Factory>(
+                       new FakeHttpRequestFactory(&requests)),
+                   8 /* block size */, 2 /* block count */,
+                   3600 /* max staleness */, 0 /* initial retry delay */);
+  // First, read both blocks of the file with its initial contents. This should
+  // trigger the first two HTTP requests to GCS, to load each of the blocks in
+  // order.
+  std::unique_ptr<RandomAccessFile> rfile;
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/object", &rfile));
+  char scratch[100];
+  StringPiece result;
+  TF_EXPECT_OK(rfile->Read(0, 16, &result, scratch));
+  EXPECT_EQ("0123456789abcdef", result);
+  // Now open a writable file and write the new file contents.  This should
+  // trigger the next two HTTP requests to GCS.
+  std::unique_ptr<WritableFile> wfile;
+  TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/object", &wfile));
+  TF_EXPECT_OK(wfile->Append("08192a3b4c5d6e7f"));
+  // This rfile read should hit the block cache and not trigger any requests,
+  // because we haven't flushed the write yet.
+  TF_EXPECT_OK(rfile->Read(0, 16, &result, scratch));
+  EXPECT_EQ("0123456789abcdef", result);
+  // Now flush the write. Any opens of gs://bucket/object after this point will
+  // no longer use the previous FileBlockCache for that file.
+  TF_EXPECT_OK(wfile->Flush());
+  // Reopen `rfile` and read its contents again. This will trigger the next two
+  // HTTP requests to GCS, which will return the (updated) file contents. Note
+  // that the contents returned in those HTTP requests are not really relevant;
+  // the main thing is that both blocks of the file are reloaded.
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/object", &rfile));
+  TF_EXPECT_OK(rfile->Read(0, 16, &result, scratch));
+  EXPECT_EQ("08192a3b4c5d6e7f", result);
+}
+
 TEST(GcsFileSystemTest, NewWritableFile) {
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
@@ -212,7 +327,7 @@ TEST(GcsFileSystemTest, NewWritableFile) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -267,7 +382,7 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceeds) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -300,7 +415,7 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceedsOnGetStatus) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -358,7 +473,7 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadAllAttemptsFail) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   2 /* initial retry delay */);
+                   0 /* max staleness */, 2 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -406,7 +521,7 @@ TEST(GcsFileSystemTest, NewWritableFile_UploadReturns410) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -432,7 +547,7 @@ TEST(GcsFileSystemTest, NewWritableFile_NoObjectName) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -462,7 +577,7 @@ TEST(GcsFileSystemTest, NewAppendableFile) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewAppendableFile("gs://bucket/path/appendable.txt", &file));
@@ -477,7 +592,7 @@ TEST(GcsFileSystemTest, NewAppendableFile_NoObjectName) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -504,7 +619,7 @@ TEST(GcsFileSystemTest, NewReadOnlyMemoryRegionFromFile) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<ReadOnlyMemoryRegion> region;
   TF_EXPECT_OK(fs.NewReadOnlyMemoryRegionFromFile(
@@ -520,7 +635,7 @@ TEST(GcsFileSystemTest, NewReadOnlyMemoryRegionFromFile_NoObjectName) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<ReadOnlyMemoryRegion> region;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -538,7 +653,7 @@ TEST(GcsFileSystemTest, FileExists_YesAsObject) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket/path/file1.txt"));
 }
@@ -561,7 +676,7 @@ TEST(GcsFileSystemTest, FileExists_YesAsFolder) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket/path/subfolder"));
 }
@@ -580,7 +695,7 @@ TEST(GcsFileSystemTest, FileExists_YesAsBucket) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket1"));
   TF_EXPECT_OK(fs.FileExists("gs://bucket1/"));
@@ -603,7 +718,7 @@ TEST(GcsFileSystemTest, FileExists_NotAsObjectOrFolder) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   EXPECT_EQ(errors::Code::NOT_FOUND,
             fs.FileExists("gs://bucket/path/file1.txt").code());
@@ -623,7 +738,7 @@ TEST(GcsFileSystemTest, FileExists_NotAsBucket) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
             fs.FileExists("gs://bucket2/").code());
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -641,7 +756,7 @@ TEST(GcsFileSystemTest, GetChildren_NoItems) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -663,7 +778,7 @@ TEST(GcsFileSystemTest, GetChildren_ThreeFiles) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -686,7 +801,7 @@ TEST(GcsFileSystemTest, GetChildren_SelfDirectoryMarker) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -708,7 +823,7 @@ TEST(GcsFileSystemTest, GetChildren_ThreeFiles_NoSlash) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path", &children));
@@ -727,7 +842,7 @@ TEST(GcsFileSystemTest, GetChildren_Root) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket-a-b-c", &children));
@@ -746,7 +861,7 @@ TEST(GcsFileSystemTest, GetChildren_Empty) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -780,7 +895,7 @@ TEST(GcsFileSystemTest, GetChildren_Pagination) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path", &children));
@@ -801,7 +916,7 @@ TEST(GcsFileSystemTest, GetMatchingPaths_NoWildcard) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(
@@ -823,7 +938,7 @@ TEST(GcsFileSystemTest, GetMatchingPaths_BucketAndWildcard) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/*/*", &result));
@@ -846,7 +961,7 @@ TEST(GcsFileSystemTest, GetMatchingPaths_FolderAndWildcard_Matches) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*/file2.txt", &result));
@@ -866,7 +981,7 @@ TEST(GcsFileSystemTest, GetMatchingPaths_SelfDirectoryMarker) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*", &result));
@@ -886,7 +1001,7 @@ TEST(GcsFileSystemTest, GetMatchingPaths_FolderAndWildcard_NoMatches) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*/file3.txt", &result));
@@ -899,7 +1014,7 @@ TEST(GcsFileSystemTest, GetMatchingPaths_OnlyWildcard) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   std::vector<string> result;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -917,7 +1032,7 @@ TEST(GcsFileSystemTest, DeleteFile) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteFile("gs://bucket/path/file1.txt"));
 }
@@ -928,7 +1043,7 @@ TEST(GcsFileSystemTest, DeleteFile_NoObjectName) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
             fs.DeleteFile("gs://bucket/").code());
@@ -944,7 +1059,7 @@ TEST(GcsFileSystemTest, DeleteDir_Empty) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket/path/"));
 }
@@ -966,7 +1081,7 @@ TEST(GcsFileSystemTest, DeleteDir_OnlyDirMarkerLeft) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket/path/"));
 }
@@ -980,7 +1095,7 @@ TEST(GcsFileSystemTest, DeleteDir_BucketOnly) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket"));
 }
@@ -996,7 +1111,7 @@ TEST(GcsFileSystemTest, DeleteDir_NonEmpty) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::FAILED_PRECONDITION,
             fs.DeleteDir("gs://bucket/path/").code());
@@ -1013,7 +1128,7 @@ TEST(GcsFileSystemTest, GetFileSize) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   uint64 size;
   TF_EXPECT_OK(fs.GetFileSize("gs://bucket/file.txt", &size));
@@ -1026,7 +1141,7 @@ TEST(GcsFileSystemTest, GetFileSize_NoObjectName) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   uint64 size;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -1099,7 +1214,7 @@ TEST(GcsFileSystemTest, RenameFile_Folder) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.RenameFile("gs://bucket/path1", "gs://bucket/path2/"));
 }
@@ -1138,7 +1253,7 @@ TEST(GcsFileSystemTest, RenameFile_Object) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(
       fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
@@ -1186,7 +1301,7 @@ TEST(GcsFileSystemTest, RenameFile_Object_DeletionRetried) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(
       fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
@@ -1220,7 +1335,7 @@ TEST(GcsFileSystemTest, RenameFile_Object_Incomplete) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   EXPECT_EQ(
       errors::Code::UNIMPLEMENTED,
@@ -1239,7 +1354,7 @@ TEST(GcsFileSystemTest, Stat_Object) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/file.txt", &stat));
@@ -1266,7 +1381,7 @@ TEST(GcsFileSystemTest, Stat_Folder) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/subfolder", &stat));
@@ -1292,7 +1407,7 @@ TEST(GcsFileSystemTest, Stat_ObjectOrFolderNotFound) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   FileStatistics stat;
   EXPECT_EQ(error::Code::NOT_FOUND, fs.Stat("gs://bucket/path", &stat).code());
@@ -1307,7 +1422,7 @@ TEST(GcsFileSystemTest, Stat_Bucket) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/", &stat));
@@ -1325,7 +1440,7 @@ TEST(GcsFileSystemTest, Stat_BucketNotFound) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   FileStatistics stat;
   EXPECT_EQ(error::Code::NOT_FOUND, fs.Stat("gs://bucket/", &stat).code());
@@ -1348,7 +1463,7 @@ TEST(GcsFileSystemTest, IsDirectory_NotFound) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::NOT_FOUND,
             fs.IsDirectory("gs://bucket/file.txt").code());
@@ -1372,7 +1487,7 @@ TEST(GcsFileSystemTest, IsDirectory_NotDirectoryButObject) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::FAILED_PRECONDITION,
             fs.IsDirectory("gs://bucket/file.txt").code());
@@ -1396,7 +1511,7 @@ TEST(GcsFileSystemTest, IsDirectory_Yes) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/subfolder"));
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/subfolder/"));
@@ -1416,7 +1531,7 @@ TEST(GcsFileSystemTest, IsDirectory_Bucket) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket"));
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/"));
@@ -1431,7 +1546,7 @@ TEST(GcsFileSystemTest, IsDirectory_BucketNotFound) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::NOT_FOUND, fs.IsDirectory("gs://bucket/").code());
 }
@@ -1464,7 +1579,7 @@ TEST(GcsFileSystemTest, CreateDir_Folder) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/subpath"));
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/subpath/"));
@@ -1484,7 +1599,7 @@ TEST(GcsFileSystemTest, CreateDir_Bucket) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/"));
   TF_EXPECT_OK(fs.CreateDir("gs://bucket"));
@@ -1544,7 +1659,7 @@ TEST(GcsFileSystemTest, DeleteRecursively_Ok) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   TF_EXPECT_OK(fs.DeleteRecursively("gs://bucket/path", &undeleted_files,
@@ -1623,7 +1738,7 @@ TEST(GcsFileSystemTest, DeleteRecursively_DeletionErrors) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   TF_EXPECT_OK(fs.DeleteRecursively("gs://bucket/path", &undeleted_files,
@@ -1651,7 +1766,7 @@ TEST(GcsFileSystemTest, DeleteRecursively_NotAFolder) {
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
                    0 /* block size */, 0 /* block count */,
-                   0 /* initial retry delay */);
+                   0 /* max staleness */, 0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   EXPECT_EQ(error::Code::NOT_FOUND,
@@ -1667,6 +1782,7 @@ TEST(GcsFileSystemTest, OverrideCacheParameters) {
   GcsFileSystem fs1;
   EXPECT_EQ(256 * 1024 * 1024, fs1.block_size());
   EXPECT_EQ(1, fs1.block_count());
+  EXPECT_EQ(0, fs1.max_staleness());
 
   // Verify legacy readahead buffer override sets block size.
   setenv("GCS_READAHEAD_BUFFER_SIZE_BYTES", "123456789", 1);
@@ -1682,6 +1798,11 @@ TEST(GcsFileSystemTest, OverrideCacheParameters) {
   setenv("GCS_READ_CACHE_BLOCK_COUNT", "16", 1);
   GcsFileSystem fs4;
   EXPECT_EQ(16, fs4.block_count());
+
+  // Verify max staleness override.
+  setenv("GCS_READ_CACHE_MAX_STALENESS", "60", 1);
+  GcsFileSystem fs5;
+  EXPECT_EQ(60, fs5.max_staleness());
 }
 
 }  // namespace
-- 
GitLab


From ed80b31fdf8c4799a089634b24b2f5e0896f2ad4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 19:51:40 -0700
Subject: [PATCH 0994/1375] No reason to run build_docs_test through the
 sanitizers.

PiperOrigin-RevId: 161903640
---
 tensorflow/tools/docs/BUILD | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tensorflow/tools/docs/BUILD b/tensorflow/tools/docs/BUILD
index 45722ec9eb..8f10bc9e0c 100644
--- a/tensorflow/tools/docs/BUILD
+++ b/tensorflow/tools/docs/BUILD
@@ -102,6 +102,12 @@ py_test(
     srcs = ["build_docs_test.py"],
     data = ["//tensorflow:docs_src"],
     srcs_version = "PY2AND3",
+    tags = [
+        # No reason to run sanitizers for this test.
+        "noasan",
+        "nomsan",
+        "notsan",
+    ],
     deps = [
         ":generate_lib",
         "//tensorflow:tensorflow_py",
-- 
GitLab


From b5cceb367525c85bf8b05fe6aa0d7e1b327c4ce9 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Thu, 13 Jul 2017 20:14:22 -0700
Subject: [PATCH 0995/1375] Refactoring the TPUEstimator.

PiperOrigin-RevId: 161905196
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 569 +++++++++++++-----
 1 file changed, 402 insertions(+), 167 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index a68e327082..712871cc04 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -43,6 +43,7 @@ from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training
 
 
+_INITIAL_LOSS = 1e7
 _BATCH_SIZE_KEY = 'batch_size'
 _RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY]
 
@@ -157,8 +158,8 @@ class TPUInfeedSessionHook(session_run_hook.SessionRunHook):
 class _PerShardOutput(object):
   """Wraps input_fn's outputs into per-shard outputs.
 
-  Used so that the wrapped model_fn can distinguish between sharded input and
-  unsharded inputs (e.g., for export_savedmodel()).
+  Used so that the model_fn can distinguish between sharded input and unsharded
+  inputs (e.g., for export_savedmodel()).
   """
 
   def __init__(self, output):
@@ -168,6 +169,373 @@ class _PerShardOutput(object):
     return self.output
 
 
+class _InputsHolder(object):
+  """A inputs holder holds the `features` and `labels' for all TPU shards.
+
+  Model inputs returned by the `input_fn` can have one of the following forms:
+  1. features
+  2. (features, labels)
+
+  Internally, form 1 is reformed to `(features, None)` as features and labels
+  are passed separatedly to underlying methods. For TPU training, TPUEstimator
+  expects multiple `features` and `labels` tuples one for each shard.
+
+  In addition, TPUEstimator allows various different structures for inputs
+  (namely `features` and `labels`).  `features` can be `Tensor` or dict of
+  string name to `Tensor`, and `labels` could be `None`, `Tensor`, or dict of
+  string name to `Tensor`. TPU infeed/outfeed library expects flattened tensor
+  list. So, `features` and `labels` need to be flattened, before infeed enqueue,
+  and the structure of them needs to be recorded, in order to restore them after
+  infeed dequeue.
+
+  `_InputsHolder` holds the `features` and `labels` tuple for all shards,
+  records the structure details (including presence, dict or single tensor, dict
+  names), validates the structure consistency cross all shards, and encapsulates
+  the flatten/unflatten logic.
+  """
+
+  def __init__(self, sharded_features=None, sharded_labels=None,
+               num_shards=None):
+    """Constructor.
+
+    Args:
+      sharded_features: A list of features one for each shard. Once provided,
+        the corresponding shared_labels should be set also and this
+        `_InputsHolder` is frozen to prevent from future modification. If
+        `None`, it is expected to add features and labels for each shard by
+        calling `append_shard` later.
+      sharded_labels: A list of labels one for each shard.
+      num_shards: Number of shards in the TPU system. Must be provided unless it
+        can be deduced from `sharded_features`.
+
+    Raises:
+      ValueError: If both `sharded_features` and `num_shards` are `None`.
+    """
+    # Holds the features and labels for all shards.
+    self._feature_list = []
+    self._label_list = []
+
+    # Holds the structure of inputs
+    self._feature_names = []
+    self._label_names = []
+    self._has_labels = False
+
+    # Internal state.
+    self._initialized = False
+    self._frozen = False
+
+    if sharded_features is None:
+      if num_shards is None:
+        raise ValueError(
+            '`sharded_features` and `num_shards` cannot be both None')
+      self._num_shards = num_shards
+    else:
+      self._from_sharded_inputs(sharded_features, sharded_labels, num_shards)
+
+  def _from_sharded_inputs(self, sharded_features, sharded_labels, num_shards):
+    """Initializes the inputs with sharded features and labels."""
+    if not isinstance(sharded_features, _PerShardOutput):
+      raise ValueError('`sharded_features` must have type `_PerShardOutput`.')
+    features = sharded_features.as_list()
+
+    if num_shards is not None and num_shards != len(features):
+      raise ValueError(
+          '`num_shards` should be same as the length of sharded_features.')
+
+    self._num_shards = len(features)
+    if not self._num_shards:
+      raise ValueError('`sharded_features` should not be empty.')
+
+    if sharded_labels is not None:
+      if not isinstance(sharded_labels, _PerShardOutput):
+        raise ValueError('sharded_labels` must have type `_PerShardOutput`.')
+
+      self._has_labels = True
+      labels = sharded_labels.as_list()
+      if self._num_shards != len(labels):
+        raise ValueError(
+            'Length of `sharded_features` and `sharded_labels` mismatch.')
+
+    if self._has_labels:
+      for (f, l) in zip(features, labels):
+        self.append_shard((f, l))
+    else:
+      for f in features:
+        self.append_shard(f)
+
+    self._frozen = True
+
+  def _extract_key_names(self, tensor_or_dict):
+    if tensor_or_dict is None:
+      return []
+
+    return tensor_or_dict.keys() if isinstance(tensor_or_dict, dict) else []
+
+  def _validate(self, features, labels):
+    has_labels = labels is not None
+    feature_names = self._extract_key_names(features)
+    label_names = self._extract_key_names(labels)
+
+    if self._initialized:
+      # The following should never happen.
+      assert feature_names == self._feature_names, 'feature keys mismatched'
+      assert label_names == self._label_names, 'label keys mismatched'
+      assert has_labels == self._has_labels, 'label presence mismatched'
+    else:
+      self._initialized = True
+      self._feature_names = feature_names
+      self._label_names = label_names
+      self._has_labels = has_labels
+
+  def append_shard(self, inputs):
+    """Appends `inputs` for one shard into holder.
+
+    Args:
+      inputs: The return from `input_fn`, which could be features or tuple of
+        (features, labels). After the first `inputs` appended into
+        `_InputsHolder`, the structure of `features` and `labels is recorded.
+        Any future invocation should provide the `inputs` with same structure.
+
+    Raises:
+      RuntimeError: If the internal data has been frozen already.
+    """
+    if self._frozen:
+      raise RuntimeError('InputsHolder has frozen, which cannot be mutated.')
+
+    # input_fn may return either features or (features, labels)
+    if isinstance(inputs, tuple):
+      features, labels = inputs
+    else:
+      features, labels = inputs, None
+
+    self._validate(features, labels)
+
+    self._feature_list.append(features)
+    if labels is not None:
+      self._label_list.append(labels)
+
+  def as_features_and_labels_tuple(self):
+    """Returns features and labels as grouped tuple.
+
+    This is intended to be used to pass features and labels for all shards from
+    input_fn to model_fn as the parent class `Estimator` does not have the
+    concept of shards. So, grouped tuple is required.
+
+    Once called, the internal data is frozen and `append_shard` cannot be
+    invoked anymore.
+
+    Returns:
+      A tuple of features and labels. Both have type `_PerShardOutput`, holding
+      the inputs for all shards. `labels` could be `None`.
+
+    Raises:
+      RuntimeError: If the internal data has not been initialized.
+    """
+    self._frozen = True
+    if not self._initialized:
+      raise RuntimeError('InputsHolder has not been initialized.')
+
+    assert len(self._feature_list) == self._num_shards
+    if not self._label_list or all(l is None for l in self._label_list):
+      return _PerShardOutput(self._feature_list), None
+
+    assert len(self._label_list) == self._num_shards
+    return (_PerShardOutput(self._feature_list),
+            _PerShardOutput(self._label_list))
+
+  def as_sharded_flattened_inputs(self):
+    """Flatten the features and label as tensor list for all shards.
+
+    Flattened tensor list contains all tensors in `features` (dict) and `labels`
+    (dict). Conceptually, it has the predicated structure like:
+
+    ```python
+    flatten_list = []
+    for name in features:
+      flatten_list.append(features[name])
+    for name in labels:
+      flatten_list.append(labels[name])
+    ```
+
+    This method handles the label is None case and single tensor case nicely.
+
+    Once called, the internal data is frozen and `append_shard` cannot be
+    invokded anymore.
+
+    Returns:
+      A list of flattened inputs one for each shard.
+
+    Raises:
+      RuntimeError: If the internal data has not been initialized.
+    """
+    self._frozen = True
+    if not self._initialized:
+      raise RuntimeError('InputsHolder has not been initialized.')
+
+    sharded_inputs = []
+
+    for shard in range(self._num_shards):
+      flattened_inputs = []
+      if self._feature_names:
+        # We need a fixed ordering for enqueueing and dequeueing.
+        flattened_inputs.extend([self._feature_list[shard][name] for name in
+                                 self._feature_names])
+      else:
+        flattened_inputs.append(self._feature_list[shard])
+
+      if self._has_labels:
+        if self._label_names:
+          # We need a fixed ordering for enqueueing and dequeueing.
+          flattened_inputs.extend([self._label_list[shard][name] for name in
+                                   self._label_names])
+        else:
+          flattened_inputs.append(self._label_list[shard])
+      sharded_inputs.append(flattened_inputs)
+
+    return sharded_inputs
+
+  def unflatten_features_and_labels(self, flattened_inputs):
+    """Restores the flattened inputs to original features and labels form.
+
+    Once called, the internal data is frozen and `append_shard` cannot be
+    invokded anymore.
+
+    Args:
+      flattened_inputs: Flattened inputs for one each, which should be created
+      by the `as_sharded_flattened_inputs` API.
+
+    Returns:
+      A tuple of (`features`, `labels`), where `labels` could be None.
+      Each one, if present, should have identical structure (single tensor vs
+      dict) as the one returned by input_fn.
+
+    Raises:
+      RuntimeError: If the internal data has not been initialized.
+      ValueError: If the number of expected tensors from `flattened_inputs`
+        mismatches the recorded structure.
+    """
+    self._frozen = True
+    if not self._initialized:
+      raise RuntimeError('InputsHolder has not been initialized.')
+
+    expected_num_features = (len(self._feature_names) if self._feature_names
+                             else 1)
+    if self._has_labels:
+      expected_num_labels = (len(self._label_names) if self._label_names
+                             else 1)
+    else:
+      expected_num_labels = 0
+
+    expected_num_tensors = expected_num_features + expected_num_labels
+
+    if expected_num_tensors != len(flattened_inputs):
+      raise ValueError(
+          'The number of flattened tensors mismatches expected num. '
+          'Expected {}, got {}'.format(expected_num_tensors,
+                                       len(flattened_inputs)))
+    if self._feature_names:
+      unflattened_features = dict(zip(self._feature_names,
+                                      flattened_inputs[:expected_num_features]))
+    else:
+      # Single tensor case
+      unflattened_features = flattened_inputs[0]
+
+    if expected_num_labels == 0:
+      unflattened_label = None
+    elif self._label_names:
+      unflattened_label = dict(zip(self._label_names,
+                                   flattened_inputs[expected_num_features:]))
+    else:
+      # Single tensor case.
+      unflattened_label = flattened_inputs[expected_num_features]
+
+    return unflattened_features, unflattened_label
+
+
+class _ModelFnWrapper(object):
+  """A `model_fn` wrapper.
+
+  This makes calling model_fn on CPU and TPU easier and more consistent and
+  performs necessary check and mutation required by TPU training.
+
+  In addition, this wrapper manages converting the `model_fn` to a single TPU
+  train step.
+  """
+
+  def __init__(self, model_fn, config, params, mode, train_batch_size):
+    self._model_fn = model_fn
+    self._config = config
+    self._params = params
+    self._mode = mode
+    self._train_batch_size = train_batch_size
+
+  def call_without_tpu(self, features, labels):
+    return self._call_model_fn(features, labels)
+
+  def convert_to_single_tpu_train_step(self, dequeue_fn):
+    """Converts the `model_fn` as a single train step on TPU."""
+
+    def train_step(loss):
+      """Training step function for use inside a while loop."""
+      del loss  # unused; required in function signature.
+      features, labels = dequeue_fn()
+
+      # Makes deep copy with `config` and params` in case user mutates them.
+      estimator_spec = self._verify_estimator_spec(self._call_model_fn(
+          features, labels, add_batch_size_in_params=True))
+      loss, train_op = estimator_spec.loss, estimator_spec.train_op
+      with ops.control_dependencies([train_op]):
+        return array_ops.identity(loss)
+    return train_step
+
+  @property
+  def config(self):
+    return self._config
+
+  def _call_model_fn(self, features, labels, add_batch_size_in_params=False):
+    """Calls the model_fn with required parameters."""
+    model_fn_args = util.fn_args(self._model_fn)
+    kwargs = {}
+
+    config = copy.deepcopy(self._config)
+    params = copy.deepcopy(self._params)
+
+    if 'labels' in model_fn_args:
+      kwargs['labels'] = labels
+    else:
+      if labels is not None:
+        raise ValueError(
+            'model_fn does not take labels, but input_fn returns labels.')
+    if 'mode' in model_fn_args:
+      kwargs['mode'] = self._mode
+    if 'config' in model_fn_args:
+      kwargs['config'] = config
+    if 'params' in model_fn_args:
+      kwargs['params'] = params
+
+    if add_batch_size_in_params:
+      if 'params' not in model_fn_args:
+        raise ValueError(
+            'model_fn ({}) does not include params argument, '
+            'required by TPUEstimator to pass batch size as '
+            'params[\'batch_size\']'.format(self._model_fn))
+      if self._mode == model_fn_lib.ModeKeys.TRAIN:
+        # For TPU training. `params` is never `None`.
+        params[_BATCH_SIZE_KEY] = _per_shard_batch_size(self._train_batch_size,
+                                                        config)
+
+    return self._model_fn(features=features, **kwargs)
+
+  def _verify_estimator_spec(self, estimator_spec):
+    """Validates the estimator_spec."""
+    err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
+    if estimator_spec.training_chief_hooks:
+      raise ValueError(err_msg.format('training_chief_hooks'))
+    if estimator_spec.training_hooks:
+      raise ValueError(err_msg.format('training_hooks'))
+    return estimator_spec
+
+
 class TPUEstimator(estimator_lib.Estimator):
   """Estimator with TPU support.
 
@@ -247,7 +615,8 @@ class TPUEstimator(estimator_lib.Estimator):
       # We cannot store config and params in this constructor as parent
       # constructor might change them, such as assigning a temp dir for
       # config.model_dir.
-      model_function = wrapped_model_fn(model_fn, train_batch_size)
+      model_function = augment_model_fn_with_tpu_support(
+          model_fn, train_batch_size)
     else:
       model_function = model_fn
 
@@ -327,110 +696,25 @@ class TPUEstimator(estimator_lib.Estimator):
       else:
         return '/job:%s/replica:0/task:%d/device:CPU:0' % (job, index / 8)
 
-    features = []
-    labels = []
+    num_shards = config.tpu_config.num_shards
+    inputs = _InputsHolder(num_shards=num_shards)
     for i in range(config.tpu_config.num_shards):
       with ops.device(placement_function(i)):
-        result = input_fn(**kwargs)
-        # input_fn may return either features or (features, labels)
-        if isinstance(result, tuple):
-          features.append(result[0])
-          labels.append(result[1])
-        else:
-          features.append(result)
-
-    if not labels or all(l is None for l in labels):
-      return _PerShardOutput(features), None
-
-    return _PerShardOutput(features), _PerShardOutput(labels)
-
+        inputs.append_shard(input_fn(**kwargs))
 
-def _verify_estimator_spec(estimator_spec):
-  """Validates the estimator_spec."""
-  err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
-  if estimator_spec.training_chief_hooks:
-    raise ValueError(err_msg.format('training_chief_hooks'))
-  if estimator_spec.training_hooks:
-    raise ValueError(err_msg.format('training_hooks'))
-  return estimator_spec
+    return inputs.as_features_and_labels_tuple()
 
 
-def _call_model_fn(model_fn, features, labels, mode, config, params,
-                   require_params=False):
-  """Calls the model_fn with required parameters."""
-  model_fn_args = util.fn_args(model_fn)
-  kwargs = {}
-  if 'labels' in model_fn_args:
-    kwargs['labels'] = labels
-  else:
-    if labels is not None:
-      raise ValueError(
-          'model_fn does not take labels, but input_fn returns labels.')
-  if 'mode' in model_fn_args:
-    kwargs['mode'] = mode
-  if 'config' in model_fn_args:
-    kwargs['config'] = config
-  if 'params' in model_fn_args:
-    kwargs['params'] = params
-  elif require_params:
-    raise ValueError(
-        'model_fn ({}) does not include params argument, '
-        'required by TPUEstimator to pass batch size as '
-        'params[\'batch_size\']'.format(model_fn))
-  return model_fn(features=features, **kwargs)
-
-
-def _call_model_fn_with_tpu(model_fn, features, labels, mode, config, params):
-  """Calls user provided `model_fn` and verifies the estimator_spec."""
-  # Makes deep copy with `config` and params` in case user mutates them.
-  config = copy.deepcopy(config)
-  params = copy.deepcopy(params)
-  return _verify_estimator_spec(_call_model_fn(
-      model_fn, features, labels, mode, config, params, require_params=True))
-
-
-def _call_model_fn_without_tpu(
-    model_fn, features, labels, mode, config, params):
-  # Deepcopy of config and params is not required in this branch.
-  return _call_model_fn(model_fn, features, labels, mode, config, params)
-
-
-# TODO(xiejw): Improve the structure of this input_fn to infeed converion.
-# The code now looks not like Estimator style. We need to abstract many
-# details.
-def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
+def _create_infeed_enqueue_ops_and_dequeue_fn(inputs_holder):
   """Utility to convert input_fn to enqueue and dequeue fns for TPU.
 
-  Mainly, three things need to be done here.
-  1. Calls the input_fn many times (`num_shards`) to infeed the data into TPU
-  2. Create a dequeue_fn used by the train_step inside TPU execution to
-  dequeue the tensors.
-  3. Sets up the input thread to infeed.
-
   Args:
-    run_config: run_config
-    features: features
-    labels: labels
+    inputs_holder: An `_InputsHolder` holding features and labels.
 
   Returns:
     A tuple of (dequeue_fn, enqueue_fn)
   """
-  infeed_names = None
-  sharded_inputs = []
-  if isinstance(features[0], dict):
-    # We need a fixed ordering for enqueueing and dequeueing.
-    infeed_names = [name for name in features[0]]
-
-  for shard in range(run_config.tpu_config.num_shards):
-    inputs = []
-    if infeed_names is None:
-      inputs.append(features[shard])
-    else:
-      for name in infeed_names:
-        inputs.append(features[shard][name])
-    if labels is not None:
-      inputs.append(labels[shard])
-    sharded_inputs.append(inputs)
+  sharded_inputs = inputs_holder.as_sharded_flattened_inputs()
 
   infeed_queue = tpu_feed.InfeedQueue(
       number_of_tuple_elements=len(sharded_inputs[0]))
@@ -439,26 +723,7 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
   def dequeue_fn():
     """dequeue_fn is used by the train_step in TPU to retrieve the tensors."""
     values = infeed_queue.generate_dequeue_op()
-
-    expected_num_tensors = 0
-    if labels is not None:
-      expected_num_tensors += 1
-    if infeed_names is None:
-      expected_num_tensors += 1
-    else:
-      expected_num_tensors += len(infeed_names)
-    assert len(values) == expected_num_tensors
-
-    dequeue_label = None
-    if labels is not None:
-      dequeue_label = values[-1]
-    if infeed_names is None:
-      return values[0], dequeue_label
-    # Restore the feature dictionary and label.
-    dequeued_features = {}
-    for i in range(len(infeed_names)):
-      dequeued_features[infeed_names[i]] = values[i]
-    return dequeued_features, dequeue_label
+    return inputs_holder.unflatten_features_and_labels(values)
 
   def tpu_ordinal_function(index):
     """Return the TPU ordinal associated with a shard.
@@ -481,33 +746,23 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(run_config, features, labels):
   return (dequeue_fn, enqueue_fn)
 
 
-def wrapped_model_fn(model_fn, train_batch_size):
+def augment_model_fn_with_tpu_support(model_fn, train_batch_size):
   """Returns a new model_fn, which wraps the TPU support."""
 
   def _model_fn(features, labels, mode, config, params):
-    """model_fn."""
+    """A Estimator `model_fn` for TPUEstimator."""
+    model_fn_wrapper = _ModelFnWrapper(model_fn, config, params, mode,
+                                       train_batch_size)
 
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
     if mode != model_fn_lib.ModeKeys.TRAIN:
-      return _call_model_fn_without_tpu(
-          model_fn, features, labels, mode, config, params)
+      return model_fn_wrapper.call_without_tpu(features, labels)
 
-    # Now for TPU training. `params` is never `None`.
-    params[_BATCH_SIZE_KEY] = _per_shard_batch_size(train_batch_size, config)
-
-    assert isinstance(features, _PerShardOutput)
-    features = features.as_list()
-    if labels is not None:
-      assert isinstance(labels, _PerShardOutput)
-      labels = labels.as_list()
+    inputs = _InputsHolder(sharded_features=features, sharded_labels=labels)
 
-    dequeue_fn, enqueue_fn = (
-        _create_infeed_enqueue_ops_and_dequeue_fn(config, features, labels))
+    dequeue_fn, enqueue_fn = _create_infeed_enqueue_ops_and_dequeue_fn(inputs)
 
-    loss = _train_on_tpu_shards(
-        config,
-        train_step=_convert_model_fn_to_train_step(
-            model_fn, dequeue_fn, mode, config, params))
+    loss = _train_on_tpu_system(model_fn_wrapper, dequeue_fn)
 
     # Gets the variables back from TPU nodes. This means the variables updated
     # by TPU will now be *synced* to host memory.
@@ -533,40 +788,20 @@ def wrapped_model_fn(model_fn, train_batch_size):
   return _model_fn
 
 
-def _convert_model_fn_to_train_step(model_fn, dequeue_fn, mode, run_config,
-                                    params):
-  """Generates a train step based on the model_fn."""
-
-  def train_step(loss):
-    """Training step function for use inside a while loop."""
-    del loss  # unused; required in function signature.
-    features, labels = dequeue_fn()
-
-    # TODO(xiejw): how to do we support hook and savers in the original
-    # model_fn. Realistically, the original
-    # model_fn will be executed on TPU chips in a replica way. The hooks
-    # returned by the model_fn cannot be supported at all. If we have to,
-    # the graph construction part in the model_fn should be separated from the
-    # control part (such as hooks and savers). By that the graph construction
-    # could de defered on TPU chip, while the control logic can stay in host.
-    estimator_spec = _call_model_fn_with_tpu(
-        model_fn, features, labels, mode, run_config, params)
-    loss, train_op = estimator_spec.loss, estimator_spec.train_op
-    with ops.control_dependencies([train_op]):
-      return array_ops.identity(loss)
-  return train_step
-
-
-def _train_on_tpu_shards(run_config, train_step):
-  """Executes the `train_step` on all shards."""
-  def train_shard():
-    return training_loop.repeat(run_config.tpu_config.iterations_per_loop,
-                                train_step,
-                                [1e7],  # initial_loss
-                                name='loop')
-
-  (loss,) = tpu.shard(train_shard,
+def _train_on_tpu_system(model_fn_wrapper, dequeue_fn):
+  """Executes `model_fn_wrapper` multiple times on all TPU shards."""
+  config = model_fn_wrapper.config.tpu_config
+  iterations_per_loop = config.iterations_per_loop
+  num_shards = config.num_shards
+
+  single_tpu_train_step = model_fn_wrapper.convert_to_single_tpu_train_step(
+      dequeue_fn)
+
+  multi_tpu_train_steps_on_single_shard = (lambda: training_loop.repeat(  # pylint: disable=g-long-lambda
+      iterations_per_loop, single_tpu_train_step, [_INITIAL_LOSS], name='loop'))
+
+  (loss,) = tpu.shard(multi_tpu_train_steps_on_single_shard,
                       inputs=[],
-                      num_shards=run_config.tpu_config.num_shards,
+                      num_shards=num_shards,
                       outputs_from_all_shards=False)
   return loss
-- 
GitLab


From db7b812ccbc40be088753e745710c2d87b87a1ee Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 13 Jul 2017 20:33:20 -0700
Subject: [PATCH 0996/1375] Stop running some tests on ASAN that are timing
 out.

PiperOrigin-RevId: 161906344
---
 tensorflow/python/estimator/BUILD    | 5 ++++-
 tensorflow/python/kernel_tests/BUILD | 1 +
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 627fa8b25e..10f1bef060 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -500,7 +500,10 @@ py_test(
     size = "medium",
     srcs = ["canned/linear_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["no_pip"],
+    tags = [
+        "no_pip",
+        "noasan",  # times out b/63680444
+    ],
     deps = [
         ":estimator",
         ":export_export",
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index f2cf90f303..f5771a2870 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2064,6 +2064,7 @@ cuda_py_test(
         "//tensorflow/python:nn_grad",
         "//tensorflow/python:nn_ops",
     ],
+    tags = ["noasan"],  # times out b/63680444
 )
 
 cuda_py_test(
-- 
GitLab


From 00da4348e5bb8f5c4b066ff68d606d1871a8d584 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 13 Jul 2017 23:24:50 -0700
Subject: [PATCH 0997/1375] Add a first draft of the "graphs and sessions"
 section of the programmers' guide.

PiperOrigin-RevId: 161917422
---
 .../docs_src/programmers_guide/graphs.md      | 619 ++++++++++++++++++
 1 file changed, 619 insertions(+)
 create mode 100644 tensorflow/docs_src/programmers_guide/graphs.md

diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/programmers_guide/graphs.md
new file mode 100644
index 0000000000..b2313a4a63
--- /dev/null
+++ b/tensorflow/docs_src/programmers_guide/graphs.md
@@ -0,0 +1,619 @@
+# Graphs and Sessions
+
+TensorFlow uses a **dataflow graph** to represent your computation in terms of
+the dependencies between individual operations. This leads to a low-level
+programming model in which you first define the dataflow graph, then create a
+TensorFlow **session** to run parts of the graph across a set of local and
+remote devices.
+
+This guide will be most useful if you intend to use the low-level programming
+model directly. Higher-level APIs such as @{tf.estimator.Estimator} and Keras
+hide the details of graphs and sessions from the end user, but this guide may
+also be useful if you want to understand how these APIs are implemented.
+
+## Why dataflow graphs?
+
+![](../images/tensors_flowing.gif)
+
+[Dataflow](https://en.wikipedia.org/wiki/Dataflow_programming) is a common
+programming model for parallel computing. In a dataflow graph, the nodes
+represent units of computation, and the edges represent the data consumed or
+produced by a computation. For example, in a TensorFlow graph, the @{tf.matmul}
+operation would correspond to a single node with two incoming edges (the
+matrices to be multiplied) and one outgoing edge (the result of the
+multiplication).
+
+<!-- TODO(barryr): Add a diagram to illustrate the @{tf.matmul} graph. -->
+
+Dataflow has several advantages that TensorFlow leverages when executing your
+programs:
+
+* **Parallelism.** By using explicit edges to represent dependencies between
+  operations, it is easy for the system to identify operations that can execute
+  in parallel.
+
+* **Distributed execution.** By using explicit edges to represent the values
+  that flow between operations, it is possible for TensorFlow to partition your
+  program across multiple devices (CPUs, GPUs, and TPUs) attached to different
+  machines. TensorFlow inserts the necessary communication and coordination
+  between devices.
+
+* **Compilation.** TensorFlow's @{$performance/xla$XLA compiler} can
+  use the information in your dataflow graph to generate faster code, for
+  example, by fusing together adjacent operations.
+
+* **Portability.** The dataflow graph is a language-independent representation
+  of the code in your model. You can build a dataflow graph in Python, store it
+  in a [SavedModel](TODO), and restore it in a C++ program for low-latency
+  inference.
+
+## Building a @{tf.Graph}
+
+Most TensorFlow programs start with a dataflow graph construction phase. In this
+phase, you invoke TensorFlow API functions that construct new @{tf.Operation}
+(node) and @{tf.Tensor} (edge) objects and add them to a @{tf.Graph}
+instance. TensorFlow provides a **default graph** that is an implicit argument
+to all API functions in the same context.  For example:
+
+* Calling `tf.constant(42.0)` creates a single @{tf.Operation} that produces the
+  value `42.0`, adds it to the default graph, and returns a @{tf.Tensor} that
+  represents the value of the constant.
+
+* Calling `tf.matmul(x, y)` creates a single @{tf.Operation} that multiplies
+  the values of @{tf.Tensor} objects `x` and `y`, adds it to the default graph,
+  and returns a @{tf.Tensor} that represents the result of the multiplication.
+
+* Executing `v = tf.Variable(0)` adds to the graph a @{tf.Operation} that will
+  store a writeable tensor value that persists between @{tf.Session.run} calls.
+  The @{tf.Variable} object wraps this operation, and can be used [like a
+  tensor](#tensor-like-objects), which will read the current value of the
+  stored value. The @{tf.Variable} object also has methods such as
+  @{tf.Variable.assign$`assign`} and @{tf.Variable.assign_add$`assign_add`} that
+  create @{tf.Operation} objects that, when executed, update the stored value.
+  (See @{$programmers_guide/variables} for more information about variables.)
+
+* Calling @{tf.train.Optimizer.minimize} will add operations and tensors to the
+  default graph that calculate gradients, and return a @{tf.Operation} that,
+  when run, will apply those gradients to a set of variables.
+
+Most programs rely solely on the default graph. However,
+see [Dealing with multiple graphs](#dealing-with-multiple-graphs) for more
+advanced use cases. High-level APIs such as the @{tf.estimator.Estimator} API
+manage the default graph on your behalf, and--for example--may create different
+graphs for training and evaluation.
+
+Note: Calling most functions in the TensorFlow API merely adds operations
+and tensors to the default graph, but **does not** perform the actual
+computation. Instead, you compose these functions until you have a @{tf.Tensor}
+or @{tf.Operation} that represents the overall computation--such as performing
+one step of gradient descent--and then pass that object to a @{tf.Session} to
+perform the computation. See the section "Executing a graph in a @{tf.Session}"
+for more details.
+
+## Naming operations
+
+A @{tf.Graph} object defines a **namespace** for the @{tf.Operation} objects it
+contains. TensorFlow automatically chooses a unique name for each operation in
+your graph, but giving operations descriptive names can make your program easier
+to read and debug. The TensorFlow API provides two ways to override the name of
+an operation:
+
+* Each API function that creates a new @{tf.Operation} or returns a new
+  @{tf.Tensor} accepts an optional `name` argument. For example,
+  `tf.constant(42.0, name="answer")` creates a new @{tf.Operation} named
+  `"answer"` and returns a @{tf.Tensor} named `"answer:0"`. If the default graph
+  already contained an operation named `"answer"`, the TensorFlow would append
+  `"_1"`, `"_2"`, and so on to the name, in order to make it unique.
+
+* The @{tf.name_scope} function makes it possible to add a **name scope** prefix
+  to all operations created in a particular context. The current name scope
+  prefix is a `"/"`-delimited list of the names of all active @{tf.name_scope}
+  context managers. If a name scope has already been used in the current
+  context, TensorFlow appens `"_1"`, `"_2"`, and so on. For example: 
+
+  ```python
+  c_0 = tf.constant(0, name="c")  # => operation named "c"
+
+  # Already-used names will be "uniquified".
+  c_1 = tf.constant(2, name="c")  # => operation named "c_1"
+
+  # Name scopes add a prefix to all operations created in the same context.
+  with tf.name_scope("outer"):
+    c_2 = tf.constant(2, name="c")  # => operation named "outer/c"
+
+    # Name scopes nest like paths in a hierarchical file system.
+    with tf.name_scope("inner"):
+      c_3 = tf.constant(3, name="c")  # => operation named "outer/inner/c"
+
+    # Exiting a name scope context will return to the previous prefix.
+    c_4 = tf.constant(4, name="c")  # => operation named "outer/c_1"
+
+    # Already-used name scopes will be "uniquified".
+    with tf.name_scope("inner"):
+      c_5 = tf.constant(5, name="c")  # => operation named "outer/inner_1/c"
+  ```
+
+The graph visualizer uses name scopes to group operations and reduce the visual
+complexity of a graph. See [Visualizing your graph](#visualizing-your-graph) for
+more information.
+
+Note that @{tf.Tensor} objects are implicitly named after the @{tf.Operation}
+that produces the tensor as output. A tensor name has the form `"<OP_NAME>:<i>"`
+where:
+
+* `"<OP_NAME>"` is the name of the operation that produces it.
+* `"<i>"` is an integer representing the index of that tensor among the
+  operation's outputs.
+
+## Placing operations on different devices
+
+If you want your TensorFlow program to use multiple different devices, the
+@{tf.device} function provides a convenient way to request that all operations
+created in a particular context are placed on the same device (or type of
+device).
+
+A **device specification** has the following form:
+
+```
+/job:<JOB_NAME>/task:<TASK_INDEX>/device:<DEVICE_TYPE>:<DEVICE_INDEX>
+```
+
+where:
+
+* `<JOB_NAME>` is an alpha-numeric string that does not start with a number.
+* `<DEVICE_TYPE>` is a registered device type (such as `GPU` or `CPU`).
+* `<TASK_INDEX>` is a non-negative integer representing the index of the task
+  in the job named `<JOB_NAME>`. See @{tf.train.ClusterSpec} for an explanation
+  of jobs and tasks.
+* `<DEVICE_INDEX>` is a non-negative integer representing the index of the
+  device, for example, to distinguish between different GPU devices used in the
+  same process.
+
+You do not need to specify every part of a device specification. For example,
+if you are running in a single-machine configuration with a single GPU, you
+might use @{tf.device} to pin some operations to the CPU and GPU:
+
+```python
+# Operations created outside either context will run on the "best possible"
+# device. For example, if you have a GPU and a CPU available, and the operation
+# has a GPU implementation, TensorFlow will choose the GPU.
+weights = tf.random_normal(...)
+
+with tf.device("/device:CPU:0"):
+  # Operations created in this context will be pinned to the CPU.
+  img = tf.decode_jpeg(tf.read_file("img.jpg"))
+
+with tf.device("/device:GPU:0"):
+  # Operations created in this context will be pinned to the GPU.
+  result = tf.matmul(weights, img)
+```
+
+If you are deploying TensorFlow in a @{$deploy/distributed$typical distributed
+configuration}, you might specify the job name and task ID to place variables on
+a task in the parameter server job (`"/job:ps"`), and the other operations on
+task in the worker job (`"/job:worker"`):
+
+```python
+with tf.device("/job:ps/task:0"):
+  weights_1 = tf.Variable(tf.truncated_normal([784, 100]))
+  biases_1 = tf.Variable(tf.zeroes([100]))
+
+with tf.device("/job:ps/task:1"):
+  weights_2 = tf.Variable(tf.truncated_normal([100, 10]))
+  biases_2 = tf.Variable(tf.zeroes([10]))
+
+with tf.device("/job:worker"):
+  layer_1 = tf.matmul(train_batch, weights_1) + biases_1
+  layer_2 = tf.matmul(train_batch, weights_2) + biases_2
+```
+
+@{tf.device} gives you a lot of flexibility to choose placements for individual
+operations or broad regions of a TensorFlow graph. In many cases, there are
+simple heuristics that work well. For example, the
+@{tf.train.replica_device_setter} API can be used with @{tf.device} to place
+operations for **data-parallel distributed training**. For example, the
+following code fragment shows how @{tf.train.replica_device_setter} applies
+different placement policies to @{tf.Variable} objects and other operations:
+
+```python
+with tf.device(tf.train.replica_device_setter(ps_tasks=3)):
+  # tf.Variable objects are, by default, placed on tasks in "/job:ps" in a
+  # round-robin fashion.
+  w_0 = tf.Variable(...)  # placed on "/job:ps/task:0"
+  b_0 = tf.Variable(...)  # placed on "/job:ps/task:1"
+  w_1 = tf.Variable(...)  # placed on "/job:ps/task:2"
+  b_1 = tf.Variable(...)  # placed on "/job:ps/task:0"
+
+  input_data = tf.placeholder(tf.float32)     # placed on "/job:worker"
+  layer_0 = tf.matmul(input_data, w_0) + b_0  # placed on "/job:worker"
+  layer_1 = tf.matmul(layer_0, w_1) + b_1     # placed on "/job:worker"
+```
+
+## Tensor-like objects
+
+Many TensorFlow operations take one or more @{tf.Tensor} objects as arguments.
+For example, @{tf.matmul} takes two @{tf.Tensor} objects, and @{tf.add_n} takes
+a list of `n` @{tf.Tensor} objects. For convenience, these functions will accept
+a **tensor-like object** in place of a @{tf.Tensor}, and implicitly convert it
+to a @{tf.Tensor} using the @{tf.convert_to_tensor} method. Tensor-like objects
+include elements of the following types:
+
+* @{tf.Tensor}
+* @{tf.Variable}
+* [`numpy.ndarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html)
+* `list` (and lists of tensor-like objects)
+* Scalar Python types: `bool`, `float`, `int`, `str`
+
+You can register additional tensor-like types using
+@{tf.register_tensor_conversion_function}.
+
+Note: By default, TensorFlow will create a new @{tf.Tensor} each time you use
+the same tensor-like object. If the tensor-like object is large (e.g. a
+`numpy.ndarray` containing a set of training examples) and you use it multiple
+times, you may run out of memory. To avoid this, manually call
+@{tf.convert_to_tensor} on the tensor-like object once and use the returned
+@{tf.Tensor} instead.
+
+## Executing a graph in a @{tf.Session}
+
+TensorFlow uses the @{tf.Session} class to represent a connection between the
+client program---typically a Python program, although a similar interface is
+available in other languages---and the C++ runtime. A @{tf.Session} object
+provides access to devices in the local machine, and remote devices using the
+distributed TensorFlow runtime. It also caches information about your
+@{tf.Graph} so that you can efficiently run the same computation multiple times.
+
+### Creating a @{tf.Session}
+
+If you are using the low-level TensorFlow API, you can create a @{tf.Session}
+for the current default graph as follows:
+
+```python
+# Create a default in-process session.
+with tf.Session() as sess:
+  # ...
+
+# Create a remote session.
+with tf.Session("grpc://example.org:2222"):
+  # ...
+```
+
+Since a @{tf.Session} owns physical resources (such as GPUs and
+network connections), it is typically used as a context manager (in a `with`
+block) that automatically closes the session when you exit the block. It is
+also possible to create a session without using a `with` block, but you should
+explicitly call @{tf.Session.close} when you are finished with it to free the
+resources.
+
+Note: Higher-level APIs such as @{tf.train.MonitoredTrainingSession} or
+@{tf.estimator.Estimator} will create and manage a @{tf.Session} for you. These
+APIs accept optional `target` and `config` arguments (either directly, or as
+part of a @{tf.estimator.RunConfig} object), with the same meaning as
+described below.
+
+@{tf.Session.__init__} accepts three optional arguments:
+
+* **`target`.** If this argument is left empty (the default), the session will
+  only use devices in the local machine. However, you may also specify a
+  `grpc://` URL to specify the address of a TensorFlow server, which gives the
+  session access to all devices on machines that that server controls. See 
+  @{tf.train.Server} for details of how to create a TensorFlow
+  server. For example, in the common **between-graph replication**
+  configuration, the @{tf.Session} connects to a @{tf.train.Server} in the same
+  process as the client. The [distributed TensorFlow](../deploy/distributed.md)
+  deployment guide describes other common scenarios.
+
+* **`graph`.** By default, a new @{tf.Session} will be bound to---and only able
+  to run operations in---the current default graph. If you are using multiple
+  graphs in your program (see [Programming with multiple
+  graphs](programming-with-multiple-graphs) for more details), you can specify
+  an explicit @{tf.Graph} when you construct the session.
+
+* **`config`.** This argument allows you to specify a @{tf.ConfigProto} that
+  controls the behavior of the session. For example, some of the configuration
+  options include:
+
+  * `allow_soft_placement`. Set this to `True` to enable a "soft" device
+    placement algorithm, which ignores @{tf.device} annotations that attempt
+    to place CPU-only operations on a GPU device, and places them on the CPU
+    instead.
+
+  * `cluster_def`. When using distributed TensorFlow, this option allows you
+    to specify what machines to use in the computation, and provide a mapping
+    between job names, task indices, and network addresses. See
+    @{tf.train.ClusterSpec.as_cluster_def} for details.
+
+  * `graph_options.optimizer_options`. Provides control over the optimizations
+    that TensorFlow performs on your graph before executing it.
+
+  * `gpu_options.allow_growth`. Set this to `True` to change the GPU memory
+    allocator so that it gradually increases the amount of memory allocated,
+    rather than allocating most of the memory at startup.
+
+
+### Using @{tf.Session.run} to execute operations
+
+The @{tf.Session.run} method is the main mechanism for running a @{tf.Operation}
+or evaluating a @{tf.Tensor}. You can pass one or more @{tf.Operation} or
+@{tf.Tensor} objects to @{tf.Session.run}, and TensorFlow will execute the
+operations that are needed to compute the result.
+
+@{tf.Session.run} requires you to specify a list of **fetches**, which determine
+the return values, and may be a @{tf.Operation}, a @{tf.Tensor}, or
+a [tensor-like type](#tensor-like-objects) such as @{tf.Variable}. These fetches
+determine what **subgraph** of the overall @{tf.Graph} must be executed to
+produce the result: this is the subgraph that contains all operations named in
+the fetch list, plus all operations whose outputs are used to compute the value
+of the fetches. For example, the following code fragment shows how different
+arguments to @{tf.Session.run} cause different subgraphs to be executed:
+
+```python
+x = tf.constant([[37.0, -23.0], [1.0, 4.0]])
+w = tf.Variable(tf.random_uniform([2, 2]))
+y = tf.matmul(x, w)
+output = tf.nn.softmax(y)
+init_op = w.initializer
+
+with tf.Session() as sess:
+  # Run the initializer on `w`.
+  sess.run(init_op)
+
+  # Evaluate `output`. `sess.run(output)` will return a NumPy array containing
+  # the result of the computation.
+  print(sess.run(output))
+
+  # Evaluate `y` and `output`. Note that `y` will only be computed once, and its
+  # result used both to return `y_val` and as an input to the `tf.nn.softmax()`
+  # op. Both `y_val` and `output_val` will be NumPy arrays.
+  y_val, output_val = sess.run([y, output])
+```
+
+@{tf.Session.run} also optionally takes a dictionary of **feeds**, which is a
+mapping from @{tf.Tensor} objects (typically @{tf.placeholder} tensors) to
+values (typically Python scalars, lists, or NumPy arrays) that will be
+substituted for those tensors in the execution. For example:
+
+```python
+# Define a placeholder that expects a vector of three floating-point values,
+# and a computation that depends on it.
+x = tf.placeholder(tf.float32, shape=[3])
+y = tf.square(x)
+
+with tf.Session() as sess:
+  # Feeding a value changes the result that is returned when you evaluate `y`.
+  print(sess.run(y, {x: [1.0, 2.0, 3.0]})  # => "[1.0, 4.0, 9.0]"
+  print(sess.run(y, {x: [0.0, 0.0, 5.0]})  # => "[0.0, 0.0, 25.0]"
+
+  # Raises `tf.errors.InvalidArgumentError`, because you must feed a value for
+  # a `tf.placeholder()` when evaluating a tensor that depends on it.
+  sess.run(y)
+
+  # Raises `ValueError`, because the shape of `37.0` does not match the shape
+  # of placeholder `x`.
+  sess.run(y, {x: 37.0})
+```
+
+@{tf.Session.run} also accepts an optional `options` argument that enables you
+to specify options about the call, and an optional `run_metadata` argument that
+enables you to collect metadata about the execution. For example, you can use
+these options together to collect tracing information about the execution:
+
+```
+y = tf.matmul([[37.0, -23.0], [1.0, 4.0]], tf.random_uniform([2, 2]))
+
+with tf.Session() as sess:
+  # Define options for the `sess.run()` call.
+  options = tf.RunOptions()
+  options.output_partition_graphs = True
+  options.trace_level = tf.RunOptions.FULL_TRACE
+
+  # Define a container for the returned metadata.
+  metadata = tf.RunMetadata()
+
+  sess.run(y, options=options, run_metadata=metadata)
+
+  # Print the subgraphs that executed on each device.
+  print(metadata.partition_graphs)
+
+  # Print the timings of each operation that executed.
+  print(metadata.step_stats)
+```
+
+## `GraphDef` and `MetaGraphDef`
+
+TensorFlow uses a dataflow graph as a portable representation for your
+application. A @{tf.Graph} contains two relevant kinds of information:
+
+* **Graph structure.** The nodes and edges of the graph, indicating how
+  individual operations are composed together, but not prescribing how they
+  should be used. The graph structure is like assembly code: inspecting it can
+  convey some useful information, but it does not contain all of the useful
+  context that source code conveys.
+
+* **Graph collections.** TensorFlow provides a general mechanism for storing
+  collections of metadata in a @{tf.Graph}. The @{tf.add_to_collection} function
+  enables you to associate a list of objects with a key (where @{tf.GraphKeys}
+  defines some of the standard keys), and @{tf.get_collection} enables you to
+  look up all objects associated with a key. Many parts of the TensorFlow
+  library use this facility: for example, when you create a @{tf.Variable}, it
+  is added by default to collections representing "global variables" and
+  "trainable variables". When you later come to create a @{tf.train.Saver} or
+  @{tf.train.Optimizer}, the variables in these collections are used as the
+  default arguments.
+
+A @{tf.Graph} can be saved in two forms:
+
+* @{tf.GraphDef}: This is a low-level representation of the graph structure,
+  containing a description of all of its operations (as @{tf.NodeDef} protocol
+  buffers) and the edges between them. The @{tf.GraphDef} representation is
+  primarily used with low-level APIs, such as the `tensorflow::Session` C++
+  API, and it typically requires additional context (such as the names of
+  particular operations) to make use of it. The @{tf.Graph.as_graph_def} method
+  converts a @{tf.Graph} to a @{tf.GraphDef}.
+
+* `tf.train.MetaGraphDef`: This is a higher-level representation of a dataflow
+  graph, which includes a @{tf.GraphDef}, and information that helps to
+  understand the graph (such as the contents of the graph collections). The
+  @{tf.train.export_meta_graph} function converts a @{tf.Graph} to a
+  `tf.train.MetaGraphDef`. The @{tf.train.Saver.save} method also writes a
+  `tf.train.MetaGraphDef` that can be used in conjunction with the saved
+  checkpoint to restore the state of a training process at the point it was
+  saved.
+
+In most cases, we encourage you to use `tf.train.MetaGraphDef` instead of
+@{tf.GraphDef}. There are cases where a @{tf.GraphDef} can be useful---for
+example, when performing low-level graph modifications using functions like
+@{tf.import_graph_def} or
+the
+[Graph Transform](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md) tool---but
+`tf.train.MetaGraphDef` is a better building block for high-level applications.
+For example the [SavedModel library](TODO) uses `tf.train.MetaGraphDef` to
+package up a @{tf.Graph} and a set of trained model parameters so that it can be
+used for serving.
+
+If you have a `tf.train.MetaGraphDef`, the @{tf.train.import_meta_graph}
+function will load it into the default graph. Calling this function has two
+main features:
+
+1. It will restore the contents of the graph collections from the original
+   graph. APIs such as @{tf.global_variables} and the default arguments to
+   APIs like @{tf.train.Optimizer.minimize} will work the same way as they
+   did in the original graph.
+
+2. The function returns a @{tf.train.Saver}, which can be used to restore the
+   state (trained parameters, etc.) associated with the graph from a checkpoint.
+   The @{tf.train.latest_checkpoint} function can help to find the latest
+   checkpoint from a particular checkpoint directory.
+
+If you have a @{tf.GraphDef}, the @{tf.import_graph_def} function enables you
+to load the graph into an existing Python @{tf.Graph} object. To make use of the
+imported graph, you must know the names of operations or tensors in the
+@{tf.GraphDef}. The @{tf.import_graph_def} function has two main features to
+help you use the imported graph:
+
+1. You can **rebind** tensors in the imported graph to @{tf.Tensor} objects in
+   the default graph by passing the optional `input_map` argument. For example,
+   `input_map` enables you to take import a graph fragment defined in a
+   @{tf.GraphDef}, and statically connect tensors in the graph you are
+   building to @{tf.placeholder} tensors in that fragment.
+
+2. You can **return** @{tf.Tensor} or @{tf.Operation} objects from the imported
+   graph by passing their names in the `return_elements` list.
+
+In addition, you can use @{tf.device} and @{tf.name_scope} to control the
+device placement and name of the imported nodes.
+
+## Visualizing your graph
+
+TensorFlow includes tools that can help you to understand the code in a graph.
+The **graph visualizer** is a component of TensorBoard that renders the
+structure of your graph visually in a browser. The easiest way to create a
+visualization is to pass a @{tf.Graph} when creating the
+@{tf.summary.FileWriter}:
+
+```python
+# Build your graph.
+x = tf.constant([[37.0, -23.0], [1.0, 4.0]])
+w = tf.Variable(tf.random_uniform([2, 2]))
+y = tf.matmul(x, w)
+# ...
+loss = ...
+train_op = tf.train.AdagradOptimizer(0.01).minimize(loss)
+
+with tf.Session() as sess:
+  # `sess.graph` provides access to the graph used in a `tf.Session`.
+  writer = tf.summary.FileWriter("/tmp/log/...", sess.graph)
+
+  # Perform your computation...
+  for i in range(1000):
+    sess.run(train_op)
+    # ...
+
+  writer.close()
+```
+
+Note: If you are using a @{tf.estimator.Estimator}, the graph (and any
+summaries) will be logged automatically to the `model_dir` that you specified
+when creating the estimator.
+
+You can then open the log in `tensorboard`, navigate to the "Graph" tab, and
+see a high-level visualization of your graph's structure. Note that a typical
+TensorFlow graph---especially training graphs with automatically computed
+gradients---has too many nodes to visualize at once. The graph visualizer makes
+use of name scopes to group related operations into "super" nodes. You can
+click on the orange "+" button on any of these super nodes to expand the
+subgraph inside.
+
+![](../images/mnist_deep.png)
+
+For more information about visualizing your TensorFlow application with
+TensorBoard, see the [TensorBoard tutorial](TODO).
+
+## Programming with multiple graphs
+
+Note: When training a model, a common way of organizing your code is to use one
+graph for training your model, and a separate graph for evaluating or performing
+inference with a trained model. In many cases, the inference graph will be
+different from the training graph: for example, techniques like dropout and
+batch normalization use different operations in each case. Furthermore, by
+default utilities like @{tf.train.Saver} use the names of @{tf.Variable} objects
+(which have names based on an underlying @{tf.Operation}) to identify each
+variable in a saved checkpoint. When programming this way, you can either use
+completely separate Python processes to build and execute the graphs, or you can
+use multiple graphs in the same process. This section describes how to use
+multiple graphs in the same process.
+
+As noted above, TensorFlow provides a "default graph" that is implicitly passed
+to all API functions in the same context. For many applications, a single graph
+is sufficient. However, TensorFlow also provides methods for manipulating
+the default graph, which can be useful in more advanced used cases. For example:
+
+* A @{tf.Graph} defines the namespace for @{tf.Operation} objects: each
+  operation in a single graph must have a unique name. TensorFlow will
+  "uniquify" the names of operations by appending `"_1"`, `"_2"`, and so on to
+  their names if the requested name is already taken. Using multiple explicitly
+  created graphs gives you more control over what name is given to each
+  operation.
+
+* The default graph stores information about every @{tf.Operation} and
+  @{tf.Tensor} that was ever added to it. If your program creates a large number
+  of unconnected subgraphs, it may be more efficient to use a different 
+  @{tf.Graph} to build each subgraph, so that unrelated state can be garbage
+  collected.
+
+You can install a different @{tf.Graph} as the default graph, using the
+@{tf.Graph.as_default} context manager:
+
+```python
+g_1 = tf.Graph()
+with g_1.as_default():
+  # Operations created in this scope will be added to `g_1`.
+  c = tf.constant("Node in g_1")
+
+  # Sessions created in this scope will run operations from `g_1`.
+  sess_1 = tf.Session()
+
+g_2 = tf.Graph()
+with g_2.as_default():
+  # Operations created in this scope will be added to `g_2`.
+  d = tf.constant("Node in g_2")
+
+# Alternatively, you can pass a graph when constructing a `tf.Session`:
+# `sess_2` will run operations from `g_2`.
+sess_2 = tf.Session(graph=g_2)
+
+assert c.graph is g_1
+assert sess_1.graph is g_1
+
+assert d.graph is g_2
+assert sess_2.graph is g_2
+```
+
+To inspect the current default graph, call @{tf.get_default_graph}, which
+returns a @{tf.Graph} object:
+
+```python
+# Print all of the operations in the default graph.
+g = tf.get_default_graph()
+print(g.get_operations())
+```
-- 
GitLab


From b5473a16a01dfd14884b62fcc412d71b870bf9ce Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 01:01:50 -0700
Subject: [PATCH 0998/1375] Slightly change the test case that tests
 wrap-around for DynamicSlice with R3.

It still tests wrap-around behavior, but in the most minor dimension instead of in the second most minor dimension.

PiperOrigin-RevId: 161924370
---
 tensorflow/compiler/xla/tests/dynamic_ops_test.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
index f653766f39..576c1c703d 100644
--- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
@@ -108,8 +108,8 @@ class DynamicSliceTest : public ClientLibraryTestBase {
     RunR3<IndexT>(
       {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
        {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-        {0, 2, 1}, {2, 2, 1},
-      {{{6.0f}, {2.0f}}, {{12.0f}, {8.0f}}});
+        {0, 2, 1}, {2, 1, 2},
+      {{{6.0f, 5.0f}}, {{12.0f, 11.0f}}});
 
     // clang-format on
   }
-- 
GitLab


From 4048c3a26d7461b9b220117cbd05fcc8650a1872 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 06:40:40 -0700
Subject: [PATCH 0999/1375] Switch TensorForestEstimator to tf.learn heads lib.

PiperOrigin-RevId: 161949540
---
 .../tensor_forest/client/eval_metrics.py      |   4 +-
 .../tensor_forest/client/eval_metrics_test.py |   6 +-
 .../tensor_forest/client/random_forest.py     | 187 +++++++++++-------
 .../client/random_forest_test.py              |  19 +-
 .../kernels/tree_predictions_op.cc            |  10 +
 .../tensor_forest/ops/tensor_forest_ops.cc    |   3 +-
 .../tensor_forest/python/tensor_forest.py     |  45 -----
 7 files changed, 132 insertions(+), 142 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics.py b/tensorflow/contrib/tensor_forest/client/eval_metrics.py
index a0ae083fdb..90033015eb 100644
--- a/tensorflow/contrib/tensor_forest/client/eval_metrics.py
+++ b/tensorflow/contrib/tensor_forest/client/eval_metrics.py
@@ -47,8 +47,6 @@ def _accuracy(predictions, targets, weights=None):
 
 
 def _r2(probabilities, targets, weights=None):
-  if targets.get_shape().ndims == 1:
-    targets = array_ops.expand_dims(targets, -1)
   targets = math_ops.to_float(targets)
   y_mean = math_ops.reduce_mean(targets, 0)
   squares_total = math_ops.reduce_sum(math_ops.square(targets - y_mean), 0)
@@ -142,7 +140,7 @@ _PREDICTION_KEYS = {
     'sigmoid_entropy': INFERENCE_PROB_NAME,
     'softmax_entropy': INFERENCE_PROB_NAME,
     'accuracy': INFERENCE_PRED_NAME,
-    'r2': INFERENCE_PROB_NAME,
+    'r2': prediction_key.PredictionKey.SCORES,
     'predictions': INFERENCE_PRED_NAME,
     'classification_log_loss': INFERENCE_PROB_NAME,
     'precision': INFERENCE_PRED_NAME,
diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics_test.py b/tensorflow/contrib/tensor_forest/client/eval_metrics_test.py
index 7c559cdd85..aa30919167 100644
--- a/tensorflow/contrib/tensor_forest/client/eval_metrics_test.py
+++ b/tensorflow/contrib/tensor_forest/client/eval_metrics_test.py
@@ -69,18 +69,18 @@ class EvalMetricsTest(test_util.TensorFlowTestCase):
       self.assertNear(0.6, accuracy_op.eval(), 0.0001)
 
   def testR2(self):
-    probabilities = constant_op.constant(
+    scores = constant_op.constant(
         [1.2, 3.9, 2.1, 0.9, 2.2, 0.1, 6.0, 4.0, 0.9])
     targets = constant_op.constant(
         [1.0, 4.3, 2.6, 0.5, 1.1, 0.7, 5.1, 3.4, 1.8])
-    r2_op, update_op = eval_metrics._r2(probabilities, targets)
+    r2_op, update_op = eval_metrics._r2(scores, targets)
     with self.test_session():
       # initializes internal accuracy vars
       variables.local_variables_initializer().run()
       # need to call in order to run the r2_op internal operations because
       # it is a streaming function
       update_op.eval()
-      self.assertNear(-19.7729, r2_op.eval(), 0.0001)
+      self.assertNear(0.813583, r2_op.eval(), 0.0001)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index 1221165baa..9200a87fb1 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -19,10 +19,9 @@ from __future__ import print_function
 
 from tensorflow.contrib import framework as contrib_framework
 
-from tensorflow.contrib.learn.python.learn.estimators import constants
 from tensorflow.contrib.learn.python.learn.estimators import estimator
+from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
 from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
-from tensorflow.contrib.learn.python.learn.estimators import prediction_key
 
 from tensorflow.contrib.tensor_forest.client import eval_metrics
 from tensorflow.contrib.tensor_forest.python import tensor_forest
@@ -30,6 +29,7 @@ from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
 
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
@@ -45,6 +45,7 @@ LOSS_NAME = 'rf_training_loss'
 
 VERSION_BUILDERS = {'v2': tensor_forest.RandomForestGraphs,
                     'v4': tensor_forest_v4.RandomForestGraphsV4}
+EPSILON = 0.000001
 
 
 def _assert_float32(tensors):
@@ -79,8 +80,12 @@ class TensorForestRunOpAtEndHook(session_run_hook.SessionRunHook):
 class TensorForestLossHook(session_run_hook.SessionRunHook):
   """Monitor to request stop when loss stops decreasing."""
 
-  def __init__(self, early_stopping_rounds, loss_op=None):
+  def __init__(self,
+               early_stopping_rounds,
+               early_stopping_loss_threshold=None,
+               loss_op=None):
     self.early_stopping_rounds = early_stopping_rounds
+    self.early_stopping_loss_threshold = early_stopping_loss_threshold
     self.loss_op = loss_op
     self.min_loss = None
     self.last_step = -1
@@ -110,7 +115,8 @@ class TensorForestLossHook(session_run_hook.SessionRunHook):
       return
 
     self.last_step = current_step
-    if self.min_loss is None or current_loss < self.min_loss:
+    if (self.min_loss is None or current_loss <
+        (self.min_loss - self.min_loss * self.early_stopping_loss_threshold)):
       self.min_loss = current_loss
       self.steps = 0
     if self.steps > self.early_stopping_rounds:
@@ -134,19 +140,43 @@ class EveryCheckpointPreSaveListener(
     session.run(self._op)
 
 
+def get_default_head(params, weights_name, name=None):
+  if params.regression:
+    return head_lib.regression_head(
+        weight_column_name=weights_name,
+        label_dimension=params.num_outputs,
+        enable_centered_bias=False,
+        head_name=name)
+  else:
+    return head_lib.multi_class_head(
+        params.num_classes,
+        weight_column_name=weights_name,
+        enable_centered_bias=False,
+        head_name=name)
+
+
 def get_model_fn(params,
                  graph_builder_class,
                  device_assigner,
                  weights_name=None,
+                 model_head=None,
                  keys_name=None,
                  early_stopping_rounds=100,
+                 early_stopping_loss_threshold=0.01,
                  num_trainers=1,
                  trainer_id=0,
                  report_feature_importances=False,
-                 local_eval=False):
+                 local_eval=False,
+                 head_scope=None):
   """Return a model function given a way to construct a graph builder."""
+  if model_head is None:
+    model_head = get_default_head(params, weights_name)
+
   def _model_fn(features, labels, mode):
     """Function that returns predictions, training loss, and training op."""
+    if (isinstance(features, ops.Tensor) or
+        isinstance(features, sparse_tensor.SparseTensor)):
+      features = {'features': features}
     weights = None
     if weights_name and weights_name in features:
       weights = features.pop(weights_name)
@@ -164,75 +194,63 @@ def get_model_fn(params,
 
     graph_builder = graph_builder_class(params,
                                         device_assigner=dev_assn)
-    inference = {}
-    output_alternatives = None
-    if (mode == model_fn_lib.ModeKeys.EVAL or
-        mode == model_fn_lib.ModeKeys.INFER):
-      inference[eval_metrics.INFERENCE_PROB_NAME] = (
-          graph_builder.inference_graph(features))
-
-      if params.regression:
-        predictions = {
-            None: inference[eval_metrics.INFERENCE_PROB_NAME]}
-        output_alternatives = {
-            None: (constants.ProblemType.LINEAR_REGRESSION, predictions)}
-      else:
-        inference[eval_metrics.INFERENCE_PRED_NAME] = math_ops.argmax(
-            inference[eval_metrics.INFERENCE_PROB_NAME], 1)
 
-        predictions = {
-            prediction_key.PredictionKey.PROBABILITIES:
-                inference[eval_metrics.INFERENCE_PROB_NAME],
-            prediction_key.PredictionKey.CLASSES:
-                inference[eval_metrics.INFERENCE_PRED_NAME]}
-        output_alternatives = {
-            None: (constants.ProblemType.CLASSIFICATION, predictions)}
-
-      if keys is not None:
-        inference[keys_name] = keys
+    logits = graph_builder.inference_graph(features)
+    # For binary classification problems, convert probabilities to logits.
+    # Includes hack to get around the fact that a probability might be 0 or 1.
+    if not params.regression and params.num_classes == 2:
+      class_1_probs = array_ops.slice(logits, [0, 1], [-1, 1])
+      logits = math_ops.log(
+          math_ops.maximum(class_1_probs / math_ops.maximum(
+              1.0 - class_1_probs, EPSILON), EPSILON))
 
     # labels might be None if we're doing prediction (which brings up the
     # question of why we force everything to adhere to a single model_fn).
-    loss_deps = []
     training_graph = None
     training_hooks = []
-    scaffold = None
     if labels is not None and mode == model_fn_lib.ModeKeys.TRAIN:
-      training_graph = control_flow_ops.group(
-          graph_builder.training_graph(
-              features, labels, input_weights=weights,
-              num_trainers=num_trainers,
-              trainer_id=trainer_id),
-          state_ops.assign_add(contrib_framework.get_global_step(), 1))
-      loss_deps.append(training_graph)
-
-    training_loss = None
-    if (mode == model_fn_lib.ModeKeys.EVAL or
-        mode == model_fn_lib.ModeKeys.TRAIN):
-      with ops.control_dependencies(loss_deps):
-        training_loss = graph_builder.training_loss(
-            features, labels, name=LOSS_NAME)
+      with ops.control_dependencies([logits.op]):
+        training_graph = control_flow_ops.group(
+            graph_builder.training_graph(
+                features, labels, input_weights=weights,
+                num_trainers=num_trainers,
+                trainer_id=trainer_id),
+            state_ops.assign_add(contrib_framework.get_global_step(), 1))
 
     # Put weights back in
     if weights is not None:
       features[weights_name] = weights
 
-    if early_stopping_rounds:
-      training_hooks.append(TensorForestLossHook(early_stopping_rounds,
-                                                 loss_op=training_loss))
+    # TensorForest's training graph isn't calculated directly from the loss
+    # like many other models.
+    def _train_fn(unused_loss):
+      return training_graph
+
+    model_ops = model_head.create_model_fn_ops(
+        features=features,
+        labels=labels,
+        mode=mode,
+        train_op_fn=_train_fn,
+        logits=logits,
+        scope=head_scope)
 
     if report_feature_importances:
       training_hooks.append(TensorForestRunOpAtEndHook(
           {'feature_importances': graph_builder.feature_importances()}))
 
-    return model_fn_lib.ModelFnOps(
-        mode=mode,
-        predictions=inference,
-        loss=training_loss,
-        train_op=training_graph,
-        training_hooks=training_hooks,
-        scaffold=scaffold,
-        output_alternatives=output_alternatives)
+    if early_stopping_rounds:
+      training_hooks.append(
+          TensorForestLossHook(
+              early_stopping_rounds,
+              early_stopping_loss_threshold=early_stopping_loss_threshold,
+              loss_op=model_ops.loss))
+
+    model_ops.training_hooks.extend(training_hooks)
+
+    if keys is not None:
+      model_ops.predictions[keys_name] = keys
+
+    return model_ops
 
   return _model_fn
 
@@ -269,14 +287,23 @@ class TensorForestEstimator(estimator.Estimator):
   ```
   """
 
-  def __init__(self, params, device_assigner=None, model_dir=None,
+  def __init__(self,
+               params,
+               device_assigner=None,
+               model_dir=None,
                graph_builder_class=tensor_forest.RandomForestGraphs,
-               config=None, weights_name=None, keys_name=None,
+               config=None,
+               weights_name=None,
+               keys_name=None,
                feature_engineering_fn=None,
                early_stopping_rounds=100,
-               num_trainers=1, trainer_id=0,
+               early_stopping_loss_threshold=0.01,
+               num_trainers=1,
+               trainer_id=0,
                report_feature_importances=False,
-               local_eval=False, version=None):
+               local_eval=False,
+               version=None,
+               head=None):
     """Initializes a TensorForestEstimator instance.
 
     Args:
@@ -305,6 +332,9 @@ class TensorForestEstimator(estimator.Estimator):
       early_stopping_rounds: Allows training to terminate early if the forest is
         no longer growing. 100 by default.  Set to a Falsy value to disable
         the default training hook.
+      early_stopping_loss_threshold: Percentage (as fraction) that loss must
+        improve by within early_stopping_rounds steps, otherwise training will
+        terminate.
       num_trainers: Number of training jobs, which will partition trees
         among them.
       trainer_id: Which trainer this instance is.
@@ -316,6 +346,8 @@ class TensorForestEstimator(estimator.Estimator):
       version: String indicating TensorForest version to use, for backward
         compatibility. Either 'v2', 'v4', or None to let system pick.
         Overrides graph_builder_class.
+      head: A heads_lib.Head object that calculates losses and such. If None,
+        one will be automatically created based on params.
 
     Returns:
       A `TensorForestEstimator` instance.
@@ -326,14 +358,17 @@ class TensorForestEstimator(estimator.Estimator):
       else:
         logging.info('Setting graph_builder_class to %s', version)
         graph_builder_class = VERSION_BUILDERS[version]
+
     super(TensorForestEstimator, self).__init__(
         model_fn=get_model_fn(
             params.fill(),
             graph_builder_class,
             device_assigner,
+            model_head=head,
             weights_name=weights_name,
             keys_name=keys_name,
             early_stopping_rounds=early_stopping_rounds,
+            early_stopping_loss_threshold=early_stopping_loss_threshold,
             num_trainers=num_trainers,
             trainer_id=trainer_id,
             report_feature_importances=report_feature_importances,
@@ -469,21 +504,27 @@ class MultiForestMultiHeadEstimator(estimator.Estimator):
     Returns:
       A `TensorForestEstimator` instance.
     """
-    model_fns = [get_model_fn(
-        params.fill(),
-        graph_builder_class,
-        device_assigner,
-        weights_name=weights_name,
-        keys_name=keys_name,
-        early_stopping_rounds=early_stopping_rounds,
-        num_trainers=num_trainers,
-        trainer_id=trainer_id,
-        report_feature_importances=report_feature_importances,
-        local_eval=local_eval) for params in params_list]
+    model_fns = []
+    for i in range(len(params_list)):
+      params = params_list[i].fill()
+      model_fns.append(
+          get_model_fn(
+              params,
+              graph_builder_class,
+              device_assigner,
+              model_head=get_default_head(
+                  params, weights_name, name='head{0}'.format(i)),
+              weights_name=weights_name,
+              keys_name=keys_name,
+              early_stopping_rounds=early_stopping_rounds,
+              num_trainers=num_trainers,
+              trainer_id=trainer_id,
+              report_feature_importances=report_feature_importances,
+              local_eval=local_eval,
+              head_scope='output{0}'.format(i)))
 
     super(MultiForestMultiHeadEstimator, self).__init__(
         model_fn=get_combined_model_fn(model_fns),
         model_dir=model_dir,
         config=config,
         feature_engineering_fn=feature_engineering_fn)
-
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest_test.py b/tensorflow/contrib/tensor_forest/client/random_forest_test.py
index e78c772af3..52e41a6fe8 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest_test.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest_test.py
@@ -40,26 +40,11 @@ class TensorForestTrainerTests(test.TestCase):
 
     iris = base.load_iris()
     data = iris.data.astype(np.float32)
-    labels = iris.target.astype(np.float32)
+    labels = iris.target.astype(np.int32)
 
     classifier.fit(x=data, y=labels, steps=100, batch_size=50)
     classifier.evaluate(x=data, y=labels, steps=10)
 
-  def testClassificationTrainingLoss(self):
-    """Tests multi-class classification using matrix data as input."""
-    hparams = tensor_forest.ForestHParams(
-        num_trees=3, max_nodes=1000, num_classes=3, num_features=4)
-    classifier = random_forest.TensorForestEstimator(
-        hparams, graph_builder_class=(tensor_forest.TrainingLossForest))
-
-    iris = base.load_iris()
-    data = iris.data.astype(np.float32)
-    labels = iris.target.astype(np.float32)
-
-    monitors = [random_forest.TensorForestLossHook(10)]
-    classifier.fit(x=data, y=labels, steps=100, monitors=monitors)
-    classifier.evaluate(x=data, y=labels, steps=10)
-
   def testRegression(self):
     """Tests multi-class classification using matrix data as input."""
 
@@ -75,7 +60,7 @@ class TensorForestTrainerTests(test.TestCase):
 
     boston = base.load_boston()
     data = boston.data.astype(np.float32)
-    labels = boston.target.astype(np.float32)
+    labels = boston.target.astype(np.int32)
 
     regressor.fit(x=data, y=labels, steps=100, batch_size=50)
     regressor.evaluate(x=data, y=labels, steps=10)
diff --git a/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc b/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc
index 3fe37d56a8..83bc96be64 100644
--- a/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc
@@ -51,6 +51,16 @@ void Evaluate(OpKernelContext* context,
   const int32 num_classes = static_cast<int32>(weights.shape().dim_size(1));
   const int32 num_nodes = static_cast<int32>(tree_tensor.shape().dim_size(0));
 
+  // Tree has not been grown or initialized.
+  if (tree(0, CHILDREN_INDEX) == FREE_NODE) {
+    for (int i = start; i < end; i++) {
+      for (int c = 1; c < num_classes; c++) {
+        out(i, c - 1) = 1.0 / num_classes;
+      }
+    }
+    return;
+  }
+
   for (int i = start; i < end; i++) {
     int node_index = 0;
     int parent = -1;
diff --git a/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc b/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc
index 79976a8eae..48db5fe625 100644
--- a/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc
+++ b/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc
@@ -528,7 +528,8 @@ REGISTER_OP("TreePredictions")
       DimensionHandle num_classes = c->Dim(c->input(6), 1);
       DimensionHandle num_points = c->UnknownDim();
 
-      if (c->RankKnown(c->input(0)) && c->Rank(c->input(0)) > 0) {
+      if (c->RankKnown(c->input(0)) && c->Rank(c->input(0)) > 0 &&
+          c->Value(c->Dim(c->input(0), 0)) > 0) {
         num_points = c->Dim(c->input(0), 0);
       }
 
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
index bdfe981baf..b756b0129d 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
@@ -19,10 +19,8 @@ from __future__ import print_function
 
 import math
 import random
-import sys
 
 from tensorflow.contrib.framework.python.ops import variables as framework_variables
-from tensorflow.contrib.losses.python.losses import loss_ops
 from tensorflow.contrib.tensor_forest.python import constants
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
 from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
@@ -508,49 +506,6 @@ def one_hot_wrapper(num_classes, loss_fn):
   return _loss
 
 
-class TrainingLossForest(RandomForestGraphs):
-  """Random Forest that uses training loss as the termination criteria."""
-
-  def __init__(self, params, loss_fn=None, **kwargs):
-    """Initialize.
-
-    Args:
-      params: Like RandomForestGraphs, a ForestHParams object.
-      loss_fn: A function that takes probabilities and targets and returns
-        a loss for each example.
-      **kwargs: Keyword args to pass to superclass (RandomForestGraphs).
-    """
-    self.loss_fn = loss_fn or one_hot_wrapper(params.num_classes,
-                                              loss_ops.log_loss)
-    self._loss = None
-    super(TrainingLossForest, self).__init__(params, **kwargs)
-
-  def _get_loss(self, features, labels):
-    """Constructs, caches, and returns the inference-based loss."""
-    if self._loss is not None:
-      return self._loss
-
-    def _average_loss():
-      probs = self.inference_graph(features)
-      return math_ops.reduce_sum(self.loss_fn(
-          probs, labels)) / math_ops.to_float(array_ops.shape(labels)[0])
-
-    self._loss = control_flow_ops.cond(
-        self.average_size() > 0, _average_loss,
-        lambda: constant_op.constant(sys.maxsize, dtype=dtypes.float32))
-
-    return self._loss
-
-  def training_graph(self, input_data, input_labels, **kwargs):
-    loss = self._get_loss(input_data, input_labels)
-    with ops.control_dependencies([loss.op]):
-      return super(TrainingLossForest, self).training_graph(
-          input_data, input_labels, **kwargs)
-
-  def training_loss(self, features, labels, name='training_loss'):
-    return array_ops.identity(self._get_loss(features, labels), name=name)
-
-
 class RandomTreeGraphs(object):
   """Builds TF graphs for random tree training and inference."""
 
-- 
GitLab


From 65e4860155c34d8a017fa474bf7db90994061cc1 Mon Sep 17 00:00:00 2001
From: Lef Ioannidis <rausted@gmail.com>
Date: Fri, 14 Jul 2017 17:30:06 +0300
Subject: [PATCH 1000/1375] Fix on 'Use mcpu instead of march for ppc64le'
 (#11481)

* Fix on 'Use mcpu instead of march for ppc64le'

Use of [] around 'if is_ppc64le; then' makes it always
return true, causing deprecated warnings on every other arch.
This fixes commit 9f57dc8

* Remove CPU variable and inline uname in configure script
---
 configure | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configure b/configure
index 46f68f3df5..93280dc5f5 100755
--- a/configure
+++ b/configure
@@ -26,7 +26,7 @@ function is_windows() {
 }
 
 function is_ppc64le() {
-  [[ "${uname -m}" == "ppc64le" ]]
+  [[ "$(uname -m)" == "ppc64le" ]]
 }
 
 function sed_in_place() {
@@ -298,7 +298,7 @@ fi # TF_NEED_MKL
 
 ## Set up architecture-dependent optimization flags.
 if [ -z "$CC_OPT_FLAGS" ]; then
-  if [ is_ppc64le ]; then
+  if is_ppc64le; then
     # gcc on ppc64le does not support -march, use mcpu instead
     default_cc_opt_flags="-mcpu=native"
   else
-- 
GitLab


From e736b854449ff59d2d586779ce9614ba7567c8f1 Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Fri, 14 Jul 2017 09:36:48 -0700
Subject: [PATCH 1001/1375] Document tf.Learn SavedModel export.

PiperOrigin-RevId: 161967071
---
 tensorflow/docs_src/get_started/export.md | 298 ++++++++++++++++++++++
 tensorflow/docs_src/get_started/index.md  |   2 +
 2 files changed, 300 insertions(+)
 create mode 100644 tensorflow/docs_src/get_started/export.md

diff --git a/tensorflow/docs_src/get_started/export.md b/tensorflow/docs_src/get_started/export.md
new file mode 100644
index 0000000000..8273030271
--- /dev/null
+++ b/tensorflow/docs_src/get_started/export.md
@@ -0,0 +1,298 @@
+# Exporting a Trained Model for Serving
+
+Once you have trained an `Estimator` model, you may want to create a service
+from that model that takes requests and returns a result.  You can run such a
+service locally on your machine or deploy it scalably in the cloud.
+
+To prepare a trained Estimator for serving, you must export it in the standard
+[`SavedModel`](https://www.tensorflow.org/code/tensorflow/python/saved_model/README.md)
+format, which wraps the TensorFlow graph, the trained variable values, any
+required assets, and metadata together in a hermetic package.
+
+In this tutorial, we will discuss how to:
+
+* Add graph nodes that accept and prepare inference requests
+* Specify the output nodes and the corresponding [APIs](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto)
+  that can be served (Classify, Regress, or Predict)
+* Export your model to the `SavedModel` format
+* Deploy the model in Google Cloud ML Engine and request predictions
+* Serve the model from a local server and request predictions
+
+
+## The exported graph and its signatures
+
+The export procedure assembles a new TensorFlow graph from two main components:
+1) a Serving Input Receiver that defines the format of the inputs to be
+   accepted, and
+2) the trained model itself.
+
+An exported `SavedModel` contains that combined graph packaged together with one
+or more *signatures*.  Like a function signature in any programming language, a
+graph signature specifies the required inputs (arguments) and the
+expected outputs (return values) of performing the computation.  In the typical
+case, a single signature is present, corresponding to the predictions that the
+model has learned to make.
+
+The *input* portion of the signature is determined by the Serving Input
+Receiver.  To specify the inputs that your deployed model will accept, you must
+provide a `serving_input_receiver_fn()` to `estimator.export_savedmodel()` (see
+below).
+
+The *output* portion of the signature is determined by the model.  For instance,
+canned Estimators know the nature of the outputs they produce (e.g. whether the
+output is a classification or a regression, and the type and shape of those
+outputs).  Custom Estimators must provide this information via `export_outputs`
+(see [below](#specifying_the_outputs_of_a_custom_model)).
+
+> Note: A *multi-headed model* provides multiple signatures, each corresponding
+> to a different "head", i.e. a set of predictions that can be made from the
+> same inputs by executing a subgraph of the complete trained graph.  The
+> *output* portions of these signatures are determined by the model.
+
+
+![Overview of exporting a SavedModel from Estimator](../images/export_savedmodel_overview.png)
+
+## Preparing serving inputs
+
+During training, an @{$input_fn$`input_fn()`} ingests data and prepares it for
+use by the model.  At serving time, similarly, a `serving_input_receiver_fn()`
+accepts inference requests and prepares them for the model.  The purpose of this
+function is to add placeholders to the graph which the serving system will feed
+with inference requests, as well as to add any additional ops needed to convert
+data from the input format into the feature `Tensor`s expected by the model.
+The function returns a @{tf.estimator.export.ServingInputReceiver} object, which
+packages the placeholders and the resulting feature `Tensor`s together.
+
+A typical pattern is that inference requests arrive in the form of serialized
+`tf.Example`s, so the `serving_input_receiver_fn()` creates a single string
+placeholder to receive them.  The `serving_input_receiver_fn()` is then also
+responsible for parsing the `tf.Example`s by adding a @{tf.parse_example} op to
+the graph.
+
+When writing such a `serving_input_receiver_fn()`, you must pass a parsing
+specification to @{tf.parse_example} to tell the parser what feature names to
+expect and how to map them to `Tensor`s. A parsing specification takes the
+form of a dict from feature names to @{tf.FixedLenFeature}, @{tf.VarLenFeature},
+and @{tf.SparseFeature}.  (Note this parsing specification should not include
+any label or weight columns, since those will not be available at serving
+time&mdash;in contrast to a parsing specification used in the `input_fn()` at
+training time.)
+
+In combination, then:
+
+```py
+feature_spec = {'foo': tf.FixedLenFeature(...),
+                'bar': tf.VarLenFeature(...)}
+
+def serving_input_receiver_fn():
+  """An input receiver that expects a serialized tf.Example."""
+  serialized_tf_example = tf.placeholder(dtype=tf.string,
+                                         shape=[default_batch_size],
+                                         name='input_example_tensor')
+  receiver_tensors = {'examples': serialized_tf_example}
+  features = tf.parse_example(serialized_tf_example, feature_spec)
+  return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
+```
+
+The @{tf.estimator.export.build_parsing_serving_input_receiver_fn} utility
+function provides that input receiver for the common case.
+
+> Note: when training a model to be served using Google Cloud ML Engine (see
+> below), the parsing step is not needed, because the model will receive raw
+> feature data.  This is also true when using the Predict API with a local
+> server.
+
+Even if you require no parsing or other input processing&mdash;i.e., if the
+serving system will feed feature `Tensor`s directly&mdash;you must still provide
+a `serving_input_receiver_fn()` that creates placeholders for the feature
+`Tensor`s and passes them through.  The
+@{tf.estimator.export.build_raw_serving_input_receiver_fn} utility provides for
+this.
+
+If these utilities do not meet your needs, you are free to write your own
+`serving_input_receiver_fn()`.  One case where this may be needed is if your
+training `input_fn()` incorporates some preprocessing logic that must be
+recapitulated at serving time.  To reduce the risk of training-serving skew, we
+recommend encapsulating such processing in a function which is then called
+from both `input_fn()` and `serving_input_receiver_fn()`.
+
+
+## Performing the export
+
+To export your trained Estimator, call
+@{tf.estimator.Estimator.export_savedmodel} with the export base path, together
+with the `serving_input_receiver_fn`.
+
+```py
+estimator.export_savedmodel(export_dir_base, serving_input_receiver_fn)
+```
+
+This method builds a new graph by first calling the
+`serving_input_receiver_fn()` to obtain feature `Tensor`s, and then calling
+this `Estimator`'s `model_fn()` to generate the model graph based on those
+features. It starts a fresh `Session`, and, by default, restores the most recent
+checkpoint into it.  (A different checkpoint may be passed, if needed.)
+Finally it creates a timestamped export directory below the given
+`export_dir_base` (i.e., `export_dir_base/<timestamp>`), and writes a
+`SavedModel` into it containing a single `MetaGraphDef` saved from this
+Session.
+
+> Note: there is currently no built-in mechanism to garbage-collect old exports,
+> so successive exports will accumulate under `export_dir_base` unless deleted
+> by some external means.
+
+## Specifying the outputs of a custom model
+
+When writing a custom `model_fn`, you must populate the `export_outputs` element
+of the @{tf.estimator.EstimatorSpec} return value. This is a dict of
+`{name: output}` describing the output signatures to be exported and used during
+serving.
+
+In the usual case of making a single prediction, this dict contains
+one element, and the `name` is immaterial.  In a multi-headed model, each head
+is represented by an entry in this dict.  In this case the `name` is a string
+of your choice that can be used to request a specific head at serving time.
+
+Each `output` value must be an `ExportOutput` object  such as
+@{tf.estimator.export.ClassificationOutput},
+@{tf.estimator.export.RegressionOutput}, or
+@{tf.estimator.export.PredictOutput}.
+
+These output types map straightforwardly to the
+[TensorFlow Serving APIs](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto),
+and so determine which request types will be honored.
+
+> Note: In the multi-headed case, a `SignatureDef` will be generated for each
+> element of the `export_outputs` dict returned from the model_fn, named using
+> the same keys.  These signatures differ only in their outputs, as provided by
+> the corresponding `ExportOutput` entry.  The inputs are always those provided
+> by the `serving_input_receiver_fn`.
+> An inference request may specify the head by name.  One head must be named
+> using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/saved_model/signature_constants.py)
+> indicating which signature will be served when an inference request does not
+> specify one.
+
+
+## Serving the exported model on Google Cloud ML Engine
+
+[Google Cloud ML Engine](https://cloud.google.com/ml-engine/) provides a fully
+managed, scalable environment for serving your trained SavedModels to make
+online or batch predictions.
+
+Please see [Deploying Models](https://cloud.google.com/ml-engine/docs/how-tos/deploying-models)
+to learn how to deploy your SavedModel on Cloud ML Engine.
+
+> Note: Cloud ML Engine accepts inference requests in JSON, CSV, or TFRecords
+> formats, depending on the circumstance.  Parsing these formats is not the
+> responsibility of the graph.  Cloud ML Engine does the parsing for you, and
+> feeds raw feature data directly into the graph.  Thus, when targeting Cloud ML
+> Engine, you should use a `serving_input_receiver_fn()` of the passthrough form
+> that simply creates placeholders for each feature.
+
+
+## Requesting predictions from Google Cloud ML Engine
+
+To learn how to request predictions from a model deployed in Cloud ML Engine,
+please see:
+
+* [Prediction Basics](https://cloud.google.com/ml-engine/docs/concepts/prediction-overview)
+* [Getting Online Predictions](https://cloud.google.com/ml-engine/docs/how-tos/online-predict)
+* [Getting Batch Predictions](https://cloud.google.com/ml-engine/docs/how-tos/batch-predict)
+
+
+## Serving the exported model locally
+
+For local deployment, you can serve your model using
+@{$deploy/tfserve$Tensorflow Serving}, an open-source project that loads a
+`SavedModel` and exposes it as a [gRPC](http://www.grpc.io/) service.
+
+First, [install TensorFlow Serving](https://tensorflow.github.io/serving/setup#prerequisites).
+
+Then build and run the local model server, substituting `$export_dir_base` with
+the path to the `SavedModel` you exported above:
+
+```sh
+bazel build //tensorflow_serving/model_servers:tensorflow_model_server
+bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_base_path=$export_dir_base
+```
+
+Now you have a server listening for inference requests via gRPC on port 9000!
+
+
+## Requesting predictions from a local server
+
+The server responds to gRPC requests according to the [PredictionService](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto#L15)
+gRPC API service definition.  (The nested protocol buffers are defined in
+various [neighboring files](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis)).
+
+From the API service definition, the gRPC framework generates client libraries
+in various languages providing remote access to the API.  In a project using the
+Bazel build tool, these libraries are built automatically and provided via
+dependencies like these (using Python for example):
+
+```build
+  deps = [
+    "//tensorflow_serving/apis:classification_proto_py_pb2",
+    "//tensorflow_serving/apis:regression_proto_py_pb2",
+    "//tensorflow_serving/apis:predict_proto_py_pb2",
+    "//tensorflow_serving/apis:prediction_service_proto_py_pb2"
+  ]
+```
+
+Python client code can then import the libraries thus:
+
+```py
+from tensorflow_serving.apis import classification_pb2
+from tensorflow_serving.apis import regression_pb2
+from tensorflow_serving.apis import predict_pb2
+from tensorflow_serving.apis import prediction_service_pb2
+```
+
+> Note: `prediction_service_pb2` defines the service as a whole and so
+> is always required.  However a typical client will need only one of
+> `classification_pb2`, `regression_pb2`, and `predict_pb2`, depending on the
+> type of requests being made.
+
+Sending a gRPC request is then accomplished by assembling a protocol buffer
+containing the request data and passing it to the service stub.  Note how the
+request protocol buffer is created empty and then populated via the
+[generated protocol buffer API](https://developers.google.com/protocol-buffers/docs/reference/python-generated).
+
+```py
+from grpc.beta import implementations
+
+channel = implementations.insecure_channel(host, int(port))
+stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
+
+request = classification_pb2.ClassificationRequest()
+example = request.input.example_list.examples.add()
+example.features.feature['x'].float_list.value.extend(image[0].astype(float))
+
+result = stub.Classify(request, 10.0)  # 10 secs timeout
+```
+
+The returned result in this example is a `ClassificationResponse` protocol
+buffer.
+
+This is a skeletal example; please see the @{$deploy$Tensorflow Serving}
+documentation and [examples](https://github.com/tensorflow/serving/tree/master/tensorflow_serving/example)
+for more details.
+
+> Note: `ClassificationRequest` and `RegressionRequest` contain a
+> `tensorflow.serving.Input` protocol buffer, which in turn contains a list of
+> `tensorflow.Example` protocol buffers.  `PredictRequest`, by contrast,
+> contains a mapping from feature names to values encoded via `TensorProto`.
+> Correspondingly: When using the `Classify` and `Regress` APIs, TensorFlow
+> Serving feeds serialized `tf.Example`s to the graph, so your
+> `serving_input_receiver_fn()` should include a `tf.parse_example()` Op.
+> When using the generic `Predict` API, however, TensorFlow Serving feeds raw
+> feature data to the graph, so a passthrough `serving_input_receiver_fn()`
+> should be used.
+
+
+<!-- TODO(soergel): give examples of making requests against this server, using
+the different Tensorflow Serving APIs, selecting the signature by key, etc. -->
+
+<!-- TODO(soergel): document ExportStrategy here once Experiment moves
+from contrib to core. -->
+
diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md
index 0d302ec383..dd69408cba 100644
--- a/tensorflow/docs_src/get_started/index.md
+++ b/tensorflow/docs_src/get_started/index.md
@@ -26,6 +26,8 @@ To learn about the high-level API, read the following guides:
     which takes you into a somewhat more sophisticated use of this API.
   * @{$get_started/monitors$Logging and Monitoring Basics with tf.contrib.learn},
     which explains how to audit the progress of model training.
+  * @{$get_started/export$Exporting a Trained Model for Serving}, which shows
+    how to save a trained model in a form that is ready to deploy.
 
 TensorBoard is a utility to visualize different aspects of machine learning.
 The following guides explain how to use TensorBoard:
-- 
GitLab


From 9961c2225dd5e28f3b3cb151980700d791a74036 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 09:43:41 -0700
Subject: [PATCH 1002/1375] Adds shards in head_test to prevent test timeout.

PiperOrigin-RevId: 161967860
---
 tensorflow/contrib/learn/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 9e5282595e..01d769c80d 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -587,6 +587,7 @@ py_test(
     name = "head_test",
     size = "medium",
     srcs = ["python/learn/estimators/head_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = ["noasan"],  # times out b/63678675
     deps = [
-- 
GitLab


From bd44ec8fe96479561ad538236900e6152f1ebe4a Mon Sep 17 00:00:00 2001
From: Blake Hechtman <blakehechtman@google.com>
Date: Fri, 14 Jul 2017 10:22:39 -0700
Subject: [PATCH 1003/1375] [XLA] Make computation_layouts_ protected.

PiperOrigin-RevId: 161972749
---
 tensorflow/compiler/xla/service/layout_assignment.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h
index ccfc17da4c..256d6aa8aa 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.h
+++ b/tensorflow/compiler/xla/service/layout_assignment.h
@@ -315,6 +315,7 @@ class LayoutAssignment : public HloPassInterface {
 
   ComputationLayout* entry_computation_layout_;
 
+ protected:
   // Map containing the layouts of all computations assigned so
   // far. Computations are handled in a topological sort where computations are
   // handled before their caller instructions so the layouts of caller
-- 
GitLab


From 3a487def43800744d1b83e1f1bac4223b5e88a1c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 11:08:04 -0700
Subject: [PATCH 1004/1375] Remove TODO comment.

PiperOrigin-RevId: 161979883
---
 tensorflow/contrib/nn/python/ops/sampling_ops.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/contrib/nn/python/ops/sampling_ops.py b/tensorflow/contrib/nn/python/ops/sampling_ops.py
index 7a9eed511b..2ae529e015 100644
--- a/tensorflow/contrib/nn/python/ops/sampling_ops.py
+++ b/tensorflow/contrib/nn/python/ops/sampling_ops.py
@@ -105,7 +105,6 @@ def _rank_resample(weights, biases, inputs, sampled_values, num_resampled,
   return resampled, true_expected_count, resampled_expected_count
 
 
-# TODO(ccolby): Before checkin, Add reference to TAPAS paper when in arxiv.org.
 def rank_sampled_softmax_loss(weights,
                               biases,
                               labels,
-- 
GitLab


From 3d72bc69ee838e8c6b0b801e274aac4c31647b22 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 11:20:11 -0700
Subject: [PATCH 1005/1375] Better gating of TensorForest pruning code.

PiperOrigin-RevId: 161981809
---
 tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
index 81b4534f10..63bfc1aef1 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc
@@ -207,7 +207,8 @@ void ClassificationStats::AddExample(
 }
 
 void ClassificationStats::CheckPrune() {
-  if (IsFinished() || weight_sum_ < prune_sample_epoch_ * prune_check_every_) {
+  if (params_.pruning_type().type() == SPLIT_PRUNE_NONE || IsFinished() ||
+      weight_sum_ < prune_sample_epoch_ * prune_check_every_) {
     return;
   }
   ++prune_sample_epoch_;
-- 
GitLab


From d4927c65b5ee4614af301ebcc310ce12720f2454 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 12:17:04 -0700
Subject: [PATCH 1006/1375] Disable contrib/keras:data_utils_test on ASAN since
 it sometimes times out.

PiperOrigin-RevId: 161990256
---
 tensorflow/contrib/keras/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index 77358d1253..4041427dca 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -515,7 +515,10 @@ py_test(
     size = "small",
     srcs = ["python/keras/utils/data_utils_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["notsan"],
+    tags = [
+        "noasan",  # times out
+        "notsan",
+    ],
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
-- 
GitLab


From cf397513c88bd5001d1180f81380523e1cc7d46b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 12:25:54 -0700
Subject: [PATCH 1007/1375] slice_test: Add a few tests for strided slices and
 consolidate R1 tests.

* Fix SliceUnaligned1024In4096Values, which was using
unitialized data.
* Also fix SliceR2Test to respect the layout parameter.

PiperOrigin-RevId: 161991313
---
 tensorflow/compiler/xla/tests/slice_test.cc | 167 +++++++++-----------
 1 file changed, 79 insertions(+), 88 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index 35e2f216d7..091c35df65 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -32,92 +32,7 @@ limitations under the License.
 namespace xla {
 namespace {
 
-class SliceTest : public ClientLibraryTestBase {
- protected:
-  template <typename NativeT>
-  void RunSliceTenToTwo() {
-    std::vector<NativeT> constant;
-    constant.reserve(10);
-    for (int i = 0; i < 10; ++i) {
-      constant.push_back(static_cast<NativeT>(i));
-    }
-
-    ComputationBuilder builder(client_, TestName());
-    auto original = builder.ConstantR1<NativeT>(constant);
-    builder.Slice(original, {2}, {4}, {1});
-
-    const std::vector<NativeT> expected = {static_cast<NativeT>(2),
-                                           static_cast<NativeT>(3)};
-    ComputeAndCompareR1<NativeT>(&builder, expected, {});
-  }
-};
-
-XLA_TEST_F(SliceTest, SliceZeroToZeroF32) {
-  ComputationBuilder builder(client_, TestName());
-  auto original = builder.ConstantR1<float>({});
-  builder.Slice(original, {0}, {0}, {1});
-
-  ComputeAndCompareR1<float>(&builder, {}, {});
-}
-
-XLA_TEST_F(SliceTest, SliceTenToZeroF32) {
-  ComputationBuilder builder(client_, TestName());
-  std::vector<float> constant(10, 0.3);
-  auto original = builder.ConstantR1<float>(constant);
-  builder.Slice(original, {7}, {7}, {1});
-
-  ComputeAndCompareR1<float>(&builder, {}, {});
-}
-
-TEST_F(SliceTest, SliceTenToTwoF32) { RunSliceTenToTwo<float>(); }
-
-XLA_TEST_F(SliceTest, SliceTenToTwoF64) { RunSliceTenToTwo<double>(); }
-
-TEST_F(SliceTest, SliceTenToTwoU32) { RunSliceTenToTwo<uint32>(); }
-
-TEST_F(SliceTest, SliceTenToTwoS32) { RunSliceTenToTwo<int32>(); }
-
-XLA_TEST_F(SliceTest, SliceTenToTwoU64) { RunSliceTenToTwo<uint64>(); }
-
-XLA_TEST_F(SliceTest, SliceTenToTwoS64) { RunSliceTenToTwo<int64>(); }
-
-TEST_F(SliceTest, SliceTenToTen) {
-  const std::vector<float> values = {0.0, 1.0, 2.0, 3.0, 4.0,
-                                     5.0, 6.0, 7.0, 8.0, 9.0};
-
-  ComputationBuilder builder(client_, TestName());
-  auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {0}, {10}, {1});
-
-  ComputeAndCompareR1<float>(&builder, values, {}, ErrorSpec(0.000001));
-}
-
-TEST_F(SliceTest, SliceLastFourOf1024) {
-  std::vector<float> values(1024);
-  std::iota(values.begin(), values.end(), 0.0);
-
-  ComputationBuilder builder(client_, TestName());
-  auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {1024 - 4}, {1024}, {1});
-
-  const std::vector<float> expected = {1020, 1021, 1022, 1023};
-  ComputeAndCompareR1<float>(&builder, expected, {}, ErrorSpec(0.000001));
-}
-
-// TODO(b/28491443): Fix wrong result on CPU and GPU. Failed on
-// 2016-05-01. Also b/28508652
-TEST_F(SliceTest, DISABLED_SliceUnaligned1024In4096Values) {
-  std::vector<float> values(4096);
-  std::iota(values.begin(), values.end(), 0.0);
-
-  ComputationBuilder builder(client_, TestName());
-  auto original = builder.ConstantR1<float>(values);
-  builder.Slice(original, {7}, {7 + 1024}, {1});
-
-  std::vector<float> expected(1024);
-  std::iota(values.begin(), values.end(), 7.0);
-  ComputeAndCompareR1<float>(&builder, expected, {}, ErrorSpec(0.000001));
-}
+class SliceTest : public ClientLibraryTestBase {};
 
 XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
   ComputationBuilder builder(client_, TestName());
@@ -208,6 +123,70 @@ TEST_F(SliceTest, SliceR4ThreeDimsMiddleMinor) {
   ComputeAndCompareR4(&builder, *expected, {}, ErrorSpec(0.000001));
 }
 
+struct R1Spec {
+  int64 input_dim0;
+  int64 slice_start;
+  int64 slice_limit;
+  int64 slice_stride;
+};
+
+// Parameterized test that generates R1 values, slices them according
+// to the R1Spec, and compares the result with a computed version.
+class SliceR1Test : public ClientLibraryTestBase,
+                    public ::testing::WithParamInterface<R1Spec> {
+ protected:
+  template <typename NativeT>
+  void Run(const R1Spec& spec) {
+    std::vector<NativeT> input(spec.input_dim0);
+    std::iota(input.begin(), input.end(), NativeT());
+
+    ComputationBuilder builder(client_, TestName());
+    auto original = builder.ConstantR1<NativeT>(input);
+    builder.Slice(original, {spec.slice_start}, {spec.slice_limit},
+                  {spec.slice_stride});
+
+    std::vector<NativeT> expected;
+    for (int i = spec.slice_start; i < spec.slice_limit;
+         i += spec.slice_stride) {
+      expected.push_back(i);
+    }
+
+    ComputeAndCompareR1<NativeT>(&builder, expected, {});
+  }
+};
+
+XLA_TEST_P(SliceR1Test, DoIt) {
+  Run<float>(GetParam());
+  Run<double>(GetParam());
+  Run<uint32>(GetParam());
+  Run<int32>(GetParam());
+  Run<uint64>(GetParam());
+  Run<int64>(GetParam());
+}
+
+INSTANTIATE_TEST_CASE_P(                  //
+    SliceR1TestInstantiation,             //
+    SliceR1Test,                          //
+    ::testing::Values(                    //
+        R1Spec{10, 0, 0, 1},              //
+        R1Spec{10, 7, 7, 1},              //
+        R1Spec{10, 2, 4, 1},              //
+        R1Spec{10, 2, 4, 1},              //
+        R1Spec{10, 2, 4, 1},              //
+        R1Spec{10, 2, 4, 1},              //
+        R1Spec{10, 2, 4, 1},              //
+        R1Spec{10, 2, 4, 1},              //
+        R1Spec{10, 0, 10, 1},             //
+        R1Spec{1024, 1024 - 4, 1024, 1},  //
+        R1Spec{4096, 7, 7 + 1024, 1},     //
+        R1Spec{10, 0, 10, 2},             //
+        R1Spec{10, 0, 10, 3},             //
+        R1Spec{10, 0, 10, 4},             //
+        R1Spec{10, 0, 10, 5},             //
+        R1Spec{10, 0, 10, 10}             //
+        )                                 //
+);
+
 struct R2Spec {
   int64 input_dim0;
   int64 input_dim1;
@@ -222,13 +201,13 @@ struct R2Spec {
 class SliceR2Test : public ClientLibraryTestBase,
                     public ::testing::WithParamInterface<R2Spec> {};
 
-TEST_P(SliceR2Test, DoIt) {
+XLA_TEST_P(SliceR2Test, DoIt) {
   const R2Spec& spec = GetParam();
   Array2D<int32> input(spec.input_dim0, spec.input_dim1);
   input.FillUnique();
 
   ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR2FromArray2D<int32>(input);
+  auto a = builder.ConstantR2FromArray2DWithLayout<int32>(input, spec.layout);
   builder.Slice(a, spec.slice_starts, spec.slice_limits, spec.slice_strides);
 
   std::unique_ptr<Array2D<int32>> expected = ReferenceUtil::Slice2D(
@@ -257,6 +236,18 @@ INSTANTIATE_TEST_CASE_P(
         R2Spec {384, 512, {{128, 256}}, {{256, 384}}, {{1, 1}},
           LayoutUtil::MakeLayout({1, 0})},
         R2Spec {357, 512, {{111, 256}}, {{301, 384}}, {{1, 1}},
+          LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {10, 10, {{0, 0}}, {{10, 10}}, {{1, 2}},
+          LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {10, 10, {{0, 0}}, {{10, 10}}, {{1, 2}},
+          LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {10, 10, {{0, 0}}, {{10, 10}}, {{2, 1}},
+          LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {10, 10, {{0, 0}}, {{10, 10}}, {{2, 1}},
+          LayoutUtil::MakeLayout({1, 0})},
+        R2Spec {10, 10, {{0, 0}}, {{10, 10}}, {{2, 2}},
+          LayoutUtil::MakeLayout({0, 1})},
+        R2Spec {10, 10, {{0, 0}}, {{10, 10}}, {{2, 2}},
           LayoutUtil::MakeLayout({1, 0})}
     )
 );
-- 
GitLab


From 7798a75cce88e07e66d141c9d3ecccf5ae28a0ef Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 13:12:29 -0700
Subject: [PATCH 1008/1375] Add stats collection for ops used within functions.

PiperOrigin-RevId: 161997142
---
 tensorflow/core/common_runtime/executor.cc | 1 +
 tensorflow/core/common_runtime/function.cc | 3 +++
 tensorflow/core/framework/function.h       | 4 +++-
 tensorflow/core/framework/op_kernel.h      | 5 +++++
 4 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/common_runtime/executor.cc b/tensorflow/core/common_runtime/executor.cc
index e8a5b67546..dae58720ed 100644
--- a/tensorflow/core/common_runtime/executor.cc
+++ b/tensorflow/core/common_runtime/executor.cc
@@ -1514,6 +1514,7 @@ void ExecutorState::Process(TaggedNode tagged_node, int64 scheduled_usec) {
   params.input_device_contexts = &input_device_contexts;
   params.input_alloc_attrs = &input_alloc_attrs;
   params.runner = &runner_;
+  params.stats_collector = stats_collector_;
 
   Status s;
   NodeExecStats* stats = nullptr;
diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index e3cc97c946..94b9d33c0c 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -261,6 +261,7 @@ class CallOp : public AsyncOpKernel {
     FunctionLibraryRuntime::Options opts;
     opts.step_id = ctx->step_id();
     opts.step_container = ctx->step_container();
+    opts.stats_collector = ctx->stats_collector();
     opts.runner = ctx->runner();
     std::vector<Tensor> args;
     args.reserve(ctx->num_inputs());
@@ -545,6 +546,8 @@ void FunctionLibraryRuntimeImpl::Run(const Options& opts, Handle handle,
   // Inherit the step_id from the caller.
   exec_args.step_id = opts.step_id;
   exec_args.step_container = opts.step_container;
+
+  exec_args.stats_collector = opts.stats_collector;
   exec_args.call_frame = frame;
   exec_args.cancellation_manager = opts.cancellation_manager;
   exec_args.runner = *opts.runner;
diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h
index 059f2be629..aeb924a709 100644
--- a/tensorflow/core/framework/function.h
+++ b/tensorflow/core/framework/function.h
@@ -38,6 +38,7 @@ class GraphDef;
 class OpKernel;
 class ResourceMgr;
 class ScopedStepContainer;
+class StepStatsCollector;
 class Node;
 
 // FunctionDefHelper::Create is a convenient helper to construct a
@@ -402,7 +403,8 @@ class FunctionLibraryRuntime {
     int64 step_id = 0;
 
     // Per-step container.
-    ScopedStepContainer* step_container;
+    ScopedStepContainer* step_container = nullptr;
+    StepStatsCollector* stats_collector = nullptr;
 
     std::function<void(std::function<void()>)>* runner = nullptr;
   };
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index bf52c53b88..f8f61df872 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -72,6 +72,7 @@ class OpKernelContext;       // declared below
 class OpRegistryInterface;
 class ResourceMgr;
 class ScopedStepContainer;
+class StepStatsCollector;
 
 class OpKernel {
  public:
@@ -551,6 +552,7 @@ class OpKernelContext {
     FunctionCallFrame* call_frame = nullptr;
     FunctionLibraryRuntime* function_library = nullptr;
     std::function<void(std::function<void()>)>* runner = nullptr;
+    StepStatsCollector* stats_collector = nullptr;
 
     // TensorSliceReaderCache support.
     checkpoint::TensorSliceReaderCacheWrapper* slice_reader_cache = nullptr;
@@ -940,6 +942,9 @@ class OpKernelContext {
   std::function<void(std::function<void()>)>* runner() const {
     return params_->runner;
   }
+  StepStatsCollector* stats_collector() const {
+    return params_->stats_collector;
+  }
 
   // Shared resources accessible to this kernel.
   ResourceMgr* resource_manager() const { return params_->resource_manager; }
-- 
GitLab


From 5051e9743d0482ec6bb0f4681de2c529f99f04e4 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Fri, 14 Jul 2017 13:25:49 -0700
Subject: [PATCH 1009/1375] Add new features to tf.contrib.signal.frames: -
 Support arbitrary rank tensors. - Add an `axis` parameter for framing any
 axis. - Add a `pad_end` and `pad_value` parameter for controlling the signal
 padding. Padding is disabled by default to avoid a copy of a potentially
 large Tensor. - Support shape inference of resulting framed tensor. - Expand
 the tests, including tests for the gradients. - Since `frames` is a noun and
 not a verb, rename `frames` to `frame`.

PiperOrigin-RevId: 161998921
---
 tensorflow/contrib/signal/BUILD               |   1 +
 tensorflow/contrib/signal/__init__.py         |   7 +-
 .../python/kernel_tests/shape_ops_test.py     | 288 +++++++++++++++++-
 .../contrib/signal/python/ops/shape_ops.py    | 189 +++++++++---
 .../contrib/signal/python/ops/util_ops.py     |  57 ++++
 5 files changed, 488 insertions(+), 54 deletions(-)
 create mode 100644 tensorflow/contrib/signal/python/ops/util_ops.py

diff --git a/tensorflow/contrib/signal/BUILD b/tensorflow/contrib/signal/BUILD
index 7fa05bf39b..6ed2313dca 100644
--- a/tensorflow/contrib/signal/BUILD
+++ b/tensorflow/contrib/signal/BUILD
@@ -29,6 +29,7 @@ cuda_py_tests(
         ":signal_py",
         "//third_party/py/numpy",
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
diff --git a/tensorflow/contrib/signal/__init__.py b/tensorflow/contrib/signal/__init__.py
index b4be2f7b4c..780a090971 100644
--- a/tensorflow/contrib/signal/__init__.py
+++ b/tensorflow/contrib/signal/__init__.py
@@ -14,7 +14,7 @@
 # ==============================================================================
 """##Signal ops.
 
-@@frames
+@@frame
 @@hamming_window
 @@hann_window
 """
@@ -23,7 +23,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.signal.python.ops.shape_ops import frames
+from tensorflow.contrib.signal.python.ops.shape_ops import frame
+# `frame` used to be named `frames`, which is a noun and not a verb.
+# Keep an alias to `frames` for backwards compatibility.
+from tensorflow.contrib.signal.python.ops.shape_ops import frame as frames
 from tensorflow.contrib.signal.python.ops.window_ops import hamming_window
 from tensorflow.contrib.signal.python.ops.window_ops import hann_window
 
diff --git a/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py
index e07942875f..8633ced599 100644
--- a/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py
+++ b/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py
@@ -24,18 +24,18 @@ from tensorflow.contrib.signal.python.ops import shape_ops
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
 
-class FramesTest(test.TestCase):
+class FrameTest(test.TestCase):
 
   def test_mapping_of_indices_without_padding(self):
-    with self.test_session():
+    with self.test_session(use_gpu=True):
       tensor = constant_op.constant(np.arange(9152), dtypes.int32)
       tensor = array_ops.expand_dims(tensor, 0)
 
-      result = shape_ops.frames(tensor, 512, 180)
-      result = result.eval()
+      result = shape_ops.frame(tensor, 512, 180, pad_end=False).eval()
 
       expected = np.tile(np.arange(512), (49, 1))
       expected += np.tile(np.arange(49) * 180, (512, 1)).T
@@ -46,15 +46,14 @@ class FramesTest(test.TestCase):
       self.assertAllEqual(expected, result)
 
   def test_mapping_of_indices_with_padding(self):
-    with self.test_session():
+    with self.test_session(use_gpu=True):
       tensor = constant_op.constant(np.arange(10000), dtypes.int32)
       tensor = array_ops.expand_dims(tensor, 0)
 
-      result = shape_ops.frames(tensor, 512, 192)
-      result = result.eval()
+      result = shape_ops.frame(tensor, 512, 192, pad_end=True).eval()
 
-      expected = np.tile(np.arange(512), (51, 1))
-      expected += np.tile(np.arange(51) * 192, (512, 1)).T
+      expected = np.tile(np.arange(512), (53, 1))
+      expected += np.tile(np.arange(53) * 192, (512, 1)).T
 
       expected[expected >= 10000] = 0
 
@@ -63,6 +62,277 @@ class FramesTest(test.TestCase):
 
       self.assertAllEqual(expected, result)
 
+  def test_invalid_inputs(self):
+    # Rank 0 input signal.
+    with self.assertRaises(ValueError):
+      shape_ops.frame(1, 1, 1)
+
+    # If the rank is unknown, do not raise an exception.
+    shape_ops.frame(array_ops.placeholder(dtypes.float32), 1, 1)
+
+    # Non-scalar frame_length.
+    with self.assertRaises(ValueError):
+      shape_ops.frame([1], [1], 1)
+
+    # Non-scalar frame_step.
+    with self.assertRaises(ValueError):
+      shape_ops.frame([1], 1, [1])
+
+    # Non-scalar pad_value.
+    with self.assertRaises(ValueError):
+      shape_ops.frame([1], 1, 1, pad_end=True, pad_value=[1])
+
+  def test_length_zero(self):
+    signal = constant_op.constant([], dtype=dtypes.float32)
+    frame_length = 2
+    frame_step = 1
+
+    with self.test_session(use_gpu=True):
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=True, pad_value=99).eval()
+      self.assertEqual((0, 2), result.shape)
+
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=False).eval()
+      self.assertEqual((0, 2), result.shape)
+
+  def test_shape_inference(self):
+    signal = array_ops.placeholder(dtypes.int32, shape=[1, 1])
+    frame_length = 2
+    frame_step = 1
+    # Shape inference is able to detect the rank and inner-most dimension
+    # if frame_length is known at graph definition time.
+    result = shape_ops.frame(signal, frame_length, frame_step,
+                             pad_end=True, pad_value=99)
+    self.assertEqual([1, 1, 2], result.shape.as_list())
+
+    result = shape_ops.frame(signal, frame_length, frame_step,
+                             pad_end=False)
+    self.assertEqual([1, 0, 2], result.shape.as_list())
+
+    # If frame_length is not known, rank and (known) outer and inner dimensions
+    # are inferred.
+    signal = array_ops.placeholder(dtypes.int32, shape=[1, 2, 3, 4])
+    frame_length = array_ops.placeholder(dtypes.int32, shape=[])
+    frame_step = 1
+    result = shape_ops.frame(signal, frame_length, frame_step,
+                             pad_end=True, pad_value=99, axis=1)
+    self.assertEqual([1, None, None, 3, 4], result.shape.as_list())
+
+    result = shape_ops.frame(signal, frame_length, frame_step,
+                             pad_end=False, axis=1)
+    self.assertEqual([1, None, None, 3, 4], result.shape.as_list())
+
+    # If frame_length and inner-most dimension is known, rank, inner dimensions,
+    # and known outer dimensions are inferred.
+    signal = array_ops.placeholder(dtypes.int32,
+                                   shape=[None, 5, None, 20, 5, 3])
+    frame_length = 4
+    frame_step = 3
+    result = shape_ops.frame(signal, frame_length, frame_step,
+                             pad_end=True, pad_value=99, axis=3)
+    self.assertEqual([None, 5, None, 7, 4, 5, 3], result.shape.as_list())
+
+    result = shape_ops.frame(signal, frame_length, frame_step,
+                             pad_end=False, axis=3)
+    self.assertEqual([None, 5, None, 6, 4, 5, 3], result.shape.as_list())
+
+    # Test that shape inference is consistent with actual returned shapes for
+    # small values of signal_length, frame_length, frame_step, and pad_end in
+    # [True, False].
+    frame_step = 1
+    for signal_length in range(2):
+      signal = [0] * signal_length
+      for frame_length in range(2):
+        for pad_end in [False, True]:
+          op = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=pad_end, pad_value=99)
+          with self.test_session(use_gpu=True):
+            result = op.eval()
+          self.assertEqual(op.shape.as_list(), list(result.shape))
+
+  def test_basic_mono(self):
+    signal = np.arange(6)
+    frame_length = 3
+    frame_step = 2
+
+    with self.test_session(use_gpu=True):
+      for rank in range(5):
+        nd_signal = np.reshape(signal, (1,) * rank + signal.shape)
+
+        # With padding, we pad the last frame with pad_value.
+        result = shape_ops.frame(nd_signal, frame_length, frame_step,
+                                 pad_end=True, pad_value=99).eval()
+        expected_inner_frames = np.array([[0, 1, 2], [2, 3, 4], [4, 5, 99]])
+        expected = np.reshape(
+            expected_inner_frames, (1,) * rank + expected_inner_frames.shape)
+        self.assertAllEqual(expected, result)
+
+        # Without padding, we drop the last frame.
+        expected_inner_frames = np.array([[0, 1, 2], [2, 3, 4]])
+        expected = np.reshape(
+            expected_inner_frames, (1,) * rank + expected_inner_frames.shape)
+        result = shape_ops.frame(nd_signal, frame_length, frame_step,
+                                 pad_end=False).eval()
+        self.assertAllEqual(expected, result)
+
+  def test_basic_stereo(self):
+    signal = np.vstack([np.arange(6),
+                        np.arange(6) + 10])
+    frame_length = 3
+    frame_step = 2
+
+    with self.test_session(use_gpu=True):
+      for rank in range(5):
+        nd_signal = np.reshape(signal, (1,) * rank + signal.shape)
+
+        # With padding, we pad the last frame with pad_value.
+        result = shape_ops.frame(nd_signal, frame_length, frame_step,
+                                 pad_end=True, pad_value=99).eval()
+        expected_inner_frames = np.array([
+            [[0, 1, 2], [2, 3, 4], [4, 5, 99]],
+            [[10, 11, 12], [12, 13, 14], [14, 15, 99]]])
+        expected = np.reshape(
+            expected_inner_frames, (1,) * rank + expected_inner_frames.shape)
+        self.assertAllEqual(expected, result)
+
+        # Without padding, we drop the last frame.
+        expected_inner_frames = np.array([[[0, 1, 2], [2, 3, 4]],
+                                          [[10, 11, 12], [12, 13, 14]]])
+        expected = np.reshape(
+            expected_inner_frames, (1,) * rank + expected_inner_frames.shape)
+        result = shape_ops.frame(nd_signal, frame_length, frame_step,
+                                 pad_end=False).eval()
+        self.assertAllEqual(expected, result)
+
+  def test_complex_shape(self):
+    signal = np.vstack([np.arange(6),
+                        np.arange(6) + 10,
+                        np.arange(6) + 20,
+                        np.arange(6) + 30,
+                        np.arange(6) + 40,
+                        np.arange(6) + 50])
+    signal = np.reshape(signal, (2, 1, 3, 1, 6))
+    frame_length = 3
+    frame_step = 2
+
+    with self.test_session(use_gpu=True):
+      # With padding, we pad the last frame with pad_value.
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=True, pad_value=99).eval()
+      # Resulting shape is (2, 1, 3, 1, 3, 3).
+      expected = [[[[[[0, 1, 2], [2, 3, 4], [4, 5, 99]]],
+                    [[[10, 11, 12], [12, 13, 14], [14, 15, 99]]],
+                    [[[20, 21, 22], [22, 23, 24], [24, 25, 99]]]]],
+                  [[[[[30, 31, 32], [32, 33, 34], [34, 35, 99]]],
+                    [[[40, 41, 42], [42, 43, 44], [44, 45, 99]]],
+                    [[[50, 51, 52], [52, 53, 54], [54, 55, 99]]]]]]
+      self.assertAllEqual(expected, result)
+
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=False).eval()
+      # Resulting shape is (2, 1, 3, 1, 3, 2).
+      expected = [[[[[[0, 1, 2], [2, 3, 4]]],
+                    [[[10, 11, 12], [12, 13, 14]]],
+                    [[[20, 21, 22], [22, 23, 24]]]]],
+                  [[[[[30, 31, 32], [32, 33, 34]]],
+                    [[[40, 41, 42], [42, 43, 44]]],
+                    [[[50, 51, 52], [52, 53, 54]]]]]]
+      self.assertAllEqual(expected, result)
+
+  def test_axis(self):
+    signal = np.reshape(np.arange(16), (2, 4, 2))
+    with self.test_session(use_gpu=True):
+      result = shape_ops.frame(signal, frame_length=2, frame_step=2,
+                               pad_end=True, axis=1)
+      expected = np.reshape(np.arange(16), (2, 2, 2, 2))
+      self.assertAllEqual(expected, result.eval())
+
+      result = shape_ops.frame(signal, frame_length=2, frame_step=1,
+                               pad_end=True, axis=1)
+      expected = [[[[0, 1], [2, 3]],
+                   [[2, 3], [4, 5]],
+                   [[4, 5], [6, 7]],
+                   [[6, 7], [0, 0]]],
+                  [[[8, 9], [10, 11]],
+                   [[10, 11], [12, 13]],
+                   [[12, 13], [14, 15]],
+                   [[14, 15], [0, 0]]]]
+      self.assertAllEqual(expected, result.eval())
+
+      result = shape_ops.frame(signal, frame_length=3, frame_step=1,
+                               pad_end=True, axis=1)
+      expected = [[[[0, 1], [2, 3], [4, 5]],
+                   [[2, 3], [4, 5], [6, 7]],
+                   [[4, 5], [6, 7], [0, 0]],
+                   [[6, 7], [0, 0], [0, 0]]],
+                  [[[8, 9], [10, 11], [12, 13]],
+                   [[10, 11], [12, 13], [14, 15]],
+                   [[12, 13], [14, 15], [0, 0]],
+                   [[14, 15], [0, 0], [0, 0]]]]
+      self.assertAllEqual(expected, result.eval())
+
+  def test_window_larger_than_signal(self):
+    signal = constant_op.constant([[1, 2], [11, 12]], dtype=dtypes.float32)
+    frame_length = 4
+    frame_step = 1
+
+    with self.test_session(use_gpu=True):
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=True, pad_value=99).eval()
+      self.assertAllClose([[[1, 2, 99, 99], [2, 99, 99, 99]],
+                           [[11, 12, 99, 99], [12, 99, 99, 99]]], result)
+
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=False).eval()
+      self.assertEqual((2, 0, 4), result.shape)
+
+      frame_step = 2
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=True, pad_value=99).eval()
+      self.assertAllClose([[[1, 2, 99, 99]], [[11, 12, 99, 99]]], result)
+
+      result = shape_ops.frame(signal, frame_length, frame_step,
+                               pad_end=False).eval()
+      self.assertEqual((2, 0, 4), result.shape)
+
+  def test_preserves_type(self):
+    signal = math_ops.range(10, dtype=dtypes.float64)
+    frame_length = 2
+    frame_step = 3
+
+    with self.test_session(use_gpu=True):
+      result = shape_ops.frame(signal, frame_length, frame_step)
+      self.assertEqual(result.dtype, signal.dtype)
+
+  def test_dynamic_tensor(self):
+    # Show that frame works even when the dimensions of its input are
+    # not known at graph creation time.
+    input_signal = np.vstack([np.arange(4), np.arange(4) + 10,
+                              np.arange(4) + 20])
+    frame_length = 2
+    frame_step = 2
+
+    with self.test_session(use_gpu=True) as sess:
+      signal_placeholder = array_ops.placeholder(shape=(None, None),
+                                                 dtype=dtypes.float32)
+      result = sess.run(shape_ops.frame(
+          signal_placeholder, frame_length, frame_step),
+                        feed_dict={signal_placeholder: input_signal})
+      self.assertAllEqual([[[0, 1], [2, 3]],
+                           [[10, 11], [12, 13]],
+                           [[20, 21], [22, 23]]], result)
+
+  def test_gradient_numerical(self):
+    with self.test_session(use_gpu=True):
+      signal_shape = (2, 128)
+      signal = array_ops.ones(signal_shape)
+      frame_length = 33
+      frame_step = 9
+      frames = shape_ops.frame(signal, frame_length, frame_step)
+      error = test.compute_gradient_error(
+          signal, signal_shape, frames, frames.shape.as_list())
+      self.assertLess(error, 2e-5)
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/signal/python/ops/shape_ops.py b/tensorflow/contrib/signal/python/ops/shape_ops.py
index 4914f19be7..dc7a073242 100644
--- a/tensorflow/contrib/signal/python/ops/shape_ops.py
+++ b/tensorflow/contrib/signal/python/ops/shape_ops.py
@@ -18,70 +18,173 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.framework import dtypes
+
+from tensorflow.contrib.signal.python.ops import util_ops
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
 
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 
 
-def frames(signal, frame_length, frame_step, name=None):
-  """Frame a signal into overlapping frames.
-
-  May be used in front of spectral functions.
+def _infer_frame_shape(signal, frame_length, frame_step, pad_end, axis):
+  """Infers the shape of the return value of `frame`."""
+  frame_length = tensor_util.constant_value(frame_length)
+  frame_step = tensor_util.constant_value(frame_step)
+  axis = tensor_util.constant_value(axis)
+  if signal.shape.ndims is None:
+    return None
+  if axis is None:
+    return [None] * (signal.shape.ndims + 1)
+
+  signal_shape = signal.shape.as_list()
+  num_frames = None
+  frame_axis = signal_shape[axis]
+  outer_dimensions = signal_shape[:axis]
+  inner_dimensions = signal_shape[axis:][1:]
+  if signal_shape and frame_axis is not None:
+    if frame_step and frame_length is not None:
+      if pad_end:
+        # Double negative is so that we round up.
+        num_frames = -(-frame_axis // frame_step)
+      else:
+        num_frames = (frame_axis - frame_length + frame_step) // frame_step
+      num_frames = max(0, num_frames)
+  return outer_dimensions + [num_frames, frame_length] + inner_dimensions
+
+
+def frame(signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=-1,
+          name=None):
+  """Expands `signal`'s `axis` dimension into frames of `frame_length`.
+
+  Slides a window of size `frame_length` over `signal`s `axis` dimension
+  with a stride of `frame_step`, replacing the `axis` dimension with
+  `[frames, frame_length]` frames.
+
+  If `pad_end` is True, window positions that are past the end of the `axis`
+  dimension are padded with `pad_value` until the window moves fully past the
+  end of the dimension. Otherwise, only window positions that fully overlap the
+  `axis` dimension are produced.
 
   For example:
 
   ```python
   pcm = tf.placeholder(tf.float32, [None, 9152])
-  frames = tf.contrib.signal.frames(pcm, 512, 180)
+  frames = tf.contrib.signal.frame(pcm, 512, 180)
   magspec = tf.abs(tf.spectral.rfft(frames, [512]))
   image = tf.expand_dims(magspec, 3)
   ```
 
   Args:
-    signal: A `Tensor` of shape `[batch_size, signal_length]`.
-    frame_length: An `int32` or `int64` `Tensor`. The length of each frame.
-    frame_step: An `int32` or `int64` `Tensor`. The step between frames.
-    name: A name for the operation (optional).
+    signal: A `[..., samples, ...]` `Tensor`. The rank and dimensions
+      may be unknown. Rank must be at least 1.
+    frame_length: The frame length in samples. An integer or scalar `Tensor`.
+    frame_step: The frame hop size in samples. An integer or scalar `Tensor`.
+    pad_end: Whether to pad the end of `signal` with `pad_value`.
+    pad_value: An optional scalar `Tensor` to use where the input signal
+      does not exist when `pad_end` is True.
+    axis: A scalar integer `Tensor` indicating the axis to frame. Defaults to
+      the last axis. Supports negative values for indexing from the end.
+    name: An optional name for the operation.
 
   Returns:
-    A `Tensor` of frames with shape `[batch_size, num_frames, frame_length]`.
+    A `Tensor` of frames with shape `[..., frames, frame_length, ...]`.
 
   Raises:
-    ValueError: if signal does not have rank 2.
+    ValueError: If `frame_length`, `frame_step`, or `pad_value` are not scalar.
   """
-  with ops.name_scope(name, "frames", [signal, frame_length, frame_step]):
+  with ops.name_scope(name, "frame", [signal, frame_length, frame_step,
+                                      pad_value]):
     signal = ops.convert_to_tensor(signal, name="signal")
     frame_length = ops.convert_to_tensor(frame_length, name="frame_length")
     frame_step = ops.convert_to_tensor(frame_step, name="frame_step")
-
-    signal_rank = signal.shape.ndims
-
-    if signal_rank != 2:
-      raise ValueError("expected signal to have rank 2 but was " + signal_rank)
-
-    signal_length = array_ops.shape(signal)[1]
-
-    num_frames = math_ops.ceil((signal_length - frame_length) / frame_step)
-    num_frames = 1 + math_ops.cast(num_frames, dtypes.int32)
-
-    pad_length = (num_frames - 1) * frame_step + frame_length
-    pad_signal = array_ops.pad(signal, [[0, 0], [0,
-                                                 pad_length - signal_length]])
-
-    indices_frame = array_ops.expand_dims(math_ops.range(frame_length), 0)
-    indices_frames = array_ops.tile(indices_frame, [num_frames, 1])
-
-    indices_step = array_ops.expand_dims(
-        math_ops.range(num_frames) * frame_step, 1)
-    indices_steps = array_ops.tile(indices_step, [1, frame_length])
-
-    indices = indices_frames + indices_steps
-
-    # TODO(androbin): remove `transpose` when `gather` gets `axis` support
-    pad_signal = array_ops.transpose(pad_signal)
-    signal_frames = array_ops.gather(pad_signal, indices)
-    signal_frames = array_ops.transpose(signal_frames, perm=[2, 0, 1])
-
-    return signal_frames
+    axis = ops.convert_to_tensor(axis, name="axis")
+
+    signal.shape.with_rank_at_least(1)
+    frame_length.shape.assert_has_rank(0)
+    frame_step.shape.assert_has_rank(0)
+    axis.shape.assert_has_rank(0)
+
+    result_shape = _infer_frame_shape(signal, frame_length, frame_step, pad_end,
+                                      axis)
+
+    # Axis can be negative. Convert it to positive.
+    signal_rank = array_ops.rank(signal)
+    axis = math_ops.range(signal_rank)[axis]
+
+    signal_shape = array_ops.shape(signal)
+    outer_dimensions, length_samples, inner_dimensions = array_ops.split(
+        signal_shape, [axis, 1, signal_rank - 1 - axis])
+    length_samples = array_ops.reshape(length_samples, [])
+    num_outer_dimensions = array_ops.size(outer_dimensions)
+    num_inner_dimensions = array_ops.size(inner_dimensions)
+
+    # If padding is requested, pad the input signal tensor with pad_value.
+    if pad_end:
+      pad_value = ops.convert_to_tensor(pad_value, signal.dtype)
+      pad_value.shape.assert_has_rank(0)
+
+      # Calculate number of frames, using double negatives to round up.
+      num_frames = -(-length_samples // frame_step)
+
+      # Pad the signal by up to frame_length samples based on how many samples
+      # are remaining starting from last_frame_position.
+      pad_samples = math_ops.maximum(
+          0, frame_length + frame_step * (num_frames - 1) - length_samples)
+
+      # Pad the inner dimension of signal by pad_samples.
+      paddings = array_ops.concat(
+          [array_ops.zeros([num_outer_dimensions, 2], dtype=pad_samples.dtype),
+           [[0, pad_samples]],
+           array_ops.zeros([num_inner_dimensions, 2], dtype=pad_samples.dtype)],
+          0)
+      signal = array_ops.pad(signal, paddings, constant_values=pad_value)
+
+      signal_shape = array_ops.shape(signal)
+      length_samples = signal_shape[axis]
+    else:
+      num_frames = math_ops.maximum(
+          0, 1 + (length_samples - frame_length) // frame_step)
+
+    subframe_length = util_ops.gcd(frame_length, frame_step)
+    subframes_per_frame = frame_length // subframe_length
+    subframes_per_hop = frame_step // subframe_length
+    num_subframes = length_samples // subframe_length
+
+    slice_shape = array_ops.concat([outer_dimensions,
+                                    [num_subframes * subframe_length],
+                                    inner_dimensions], 0)
+    subframe_shape = array_ops.concat([outer_dimensions,
+                                       [num_subframes, subframe_length],
+                                       inner_dimensions], 0)
+    subframes = array_ops.reshape(array_ops.strided_slice(
+        signal, array_ops.zeros_like(signal_shape),
+        slice_shape), subframe_shape)
+
+    # frame_selector is a [num_frames, subframes_per_frame] tensor
+    # that indexes into the appropriate frame in subframes. For example:
+    # [[0, 0, 0, 0], [2, 2, 2, 2], [4, 4, 4, 4]]
+    frame_selector = array_ops.reshape(
+        math_ops.range(num_frames) * subframes_per_hop, [num_frames, 1])
+
+    # subframe_selector is a [num_frames, subframes_per_frame] tensor
+    # that indexes into the appropriate subframe within a frame. For example:
+    # [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]
+    subframe_selector = array_ops.reshape(
+        math_ops.range(subframes_per_frame), [1, subframes_per_frame])
+
+    # Adding the 2 selector tensors together produces a [num_frames,
+    # subframes_per_frame] tensor of indices to use with tf.gather to select
+    # subframes from subframes. We then reshape the inner-most
+    # subframes_per_frame dimension to stitch the subframes together into
+    # frames. For example: [[0, 1, 2, 3], [2, 3, 4, 5], [4, 5, 6, 7]].
+    selector = frame_selector + subframe_selector
+
+    frames = array_ops.reshape(
+        array_ops.gather(subframes, selector, axis=axis),
+        array_ops.concat([outer_dimensions, [num_frames, frame_length],
+                          inner_dimensions], 0))
+
+    if result_shape:
+      frames.set_shape(result_shape)
+    return frames
diff --git a/tensorflow/contrib/signal/python/ops/util_ops.py b/tensorflow/contrib/signal/python/ops/util_ops.py
new file mode 100644
index 0000000000..eee829d799
--- /dev/null
+++ b/tensorflow/contrib/signal/python/ops/util_ops.py
@@ -0,0 +1,57 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility ops shared across tf.contrib.signal."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+
+
+def gcd(a, b, name=None):
+  """Returns the greatest common divisor via Euclid's algorithm.
+
+  Args:
+    a: The dividend. A scalar integer `Tensor`.
+    b: The divisor. A scalar integer `Tensor`.
+    name: An optional name for the operation.
+
+  Returns:
+    A scalar `Tensor` representing the greatest common divisor between `a` and
+    `b`.
+
+  Raises:
+    ValueError: If `a` or `b` are not scalar integers.
+  """
+  with ops.name_scope(name, 'gcd', [a, b]):
+    a = ops.convert_to_tensor(a)
+    b = ops.convert_to_tensor(b)
+
+    a.shape.assert_has_rank(0)
+    b.shape.assert_has_rank(0)
+
+    if not a.dtype.is_integer:
+      raise ValueError('a must be an integer type. Got: %s' % a.dtype)
+    if not b.dtype.is_integer:
+      raise ValueError('b must be an integer type. Got: %s' % b.dtype)
+
+    cond = lambda _, b: math_ops.greater(b, array_ops.zeros_like(b))
+    body = lambda a, b: [b, math_ops.mod(a, b)]
+    a, b = control_flow_ops.while_loop(cond, body, [a, b], back_prop=False)
+    return a
-- 
GitLab


From 7269b719af72e55a3fd7e29a2a62ddf72a2752f9 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 14 Jul 2017 13:49:03 -0700
Subject: [PATCH 1010/1375] Add no_oss and nomac filtering to run_pip_test.sh

PiperOrigin-RevId: 162002161
---
 tensorflow/tools/ci_build/builds/run_pip_tests.sh | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/ci_build/builds/run_pip_tests.sh b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
index 8e364f7ffb..87793dabc5 100755
--- a/tensorflow/tools/ci_build/builds/run_pip_tests.sh
+++ b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
@@ -69,10 +69,13 @@ ln -s $(pwd)/tensorflow ${PIP_TEST_ROOT}/tensorflow
 
 # Do not run tests with "no_pip" tag. If running GPU tests, also do not run
 # tests with no_pip_gpu tag.
-PIP_TEST_FILTER_TAG="-no_pip"
+PIP_TEST_FILTER_TAG="-no_oss,-no_pip"
 if [[ ${IS_GPU} == "1" ]]; then
   PIP_TEST_FILTER_TAG="-no_pip_gpu,${PIP_TEST_FILTER_TAG}"
 fi
+if [[ ${IS_MAC} == "1" ]]; then
+  PIP_TEST_FILTER_TAG="-nomac,${PIP_TEST_FILTER_TAG}"
+fi
 
 # Bazel flags we need for all tests:
 #     define=no_tensorflow_py_deps=true, to skip all test dependencies.
-- 
GitLab


From fdf4ad4c35e925bf3d944dac5b91efcc64b2c8b3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 14:28:05 -0700
Subject: [PATCH 1011/1375] Add GPU kernel registrations for int64 tf.concat,
 tf.stack

PiperOrigin-RevId: 162008248
---
 tensorflow/core/kernels/concat_lib_gpu.cc     |  1 +
 .../core/kernels/concat_lib_gpu_impl.cu.cc    |  4 ++
 tensorflow/core/kernels/concat_op.cc          |  1 +
 tensorflow/core/kernels/pack_op.cc            |  1 +
 .../python/kernel_tests/concat_op_test.py     |  1 +
 .../python/kernel_tests/stack_op_test.py      | 66 ++++++++++++-------
 6 files changed, 49 insertions(+), 25 deletions(-)

diff --git a/tensorflow/core/kernels/concat_lib_gpu.cc b/tensorflow/core/kernels/concat_lib_gpu.cc
index cd0414ef40..5159cdaa6e 100644
--- a/tensorflow/core/kernels/concat_lib_gpu.cc
+++ b/tensorflow/core/kernels/concat_lib_gpu.cc
@@ -115,6 +115,7 @@ void ConcatGPU(
 TF_CALL_GPU_NUMBER_TYPES(REGISTER);
 TF_CALL_complex64(REGISTER);
 TF_CALL_complex128(REGISTER);
+TF_CALL_int64(REGISTER);
 REGISTER(bfloat16);
 
 #undef REGISTER
diff --git a/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc b/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
index d120c69c4f..f971637d5d 100644
--- a/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
+++ b/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
@@ -201,21 +201,25 @@ void ConcatGPUImpl(const Eigen::GpuDevice& gpu_device,
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPUCONCAT32);
 TF_CALL_complex64(REGISTER_GPUCONCAT32);
 TF_CALL_complex128(REGISTER_GPUCONCAT32);
+TF_CALL_int64(REGISTER_GPUCONCAT32);
 REGISTER_GPUCONCAT32(bfloat16);
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPUCONCAT64);
 TF_CALL_complex64(REGISTER_GPUCONCAT64);
 TF_CALL_complex128(REGISTER_GPUCONCAT64);
+TF_CALL_int64(REGISTER_GPUCONCAT64);
 REGISTER_GPUCONCAT64(bfloat16);
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU32);
 TF_CALL_complex64(REGISTER_GPU32);
 TF_CALL_complex128(REGISTER_GPU32);
+TF_CALL_int64(REGISTER_GPU32);
 REGISTER_GPU32(bfloat16);
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU64);
 TF_CALL_complex64(REGISTER_GPU64);
 TF_CALL_complex128(REGISTER_GPU64);
+TF_CALL_int64(REGISTER_GPU64);
 REGISTER_GPU64(bfloat16);
 
 #undef REGISTER_GPUCONCAT32
diff --git a/tensorflow/core/kernels/concat_op.cc b/tensorflow/core/kernels/concat_op.cc
index e7848a7e26..01a744dc7e 100644
--- a/tensorflow/core/kernels/concat_op.cc
+++ b/tensorflow/core/kernels/concat_op.cc
@@ -195,6 +195,7 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU);
 REGISTER_GPU(bfloat16);
 TF_CALL_complex64(REGISTER_GPU);
 TF_CALL_complex128(REGISTER_GPU);
+TF_CALL_int64(REGISTER_GPU);
 #undef REGISTER_GPU
 
 // A special GPU kernel for int32.
diff --git a/tensorflow/core/kernels/pack_op.cc b/tensorflow/core/kernels/pack_op.cc
index edaa10761e..75820e3106 100644
--- a/tensorflow/core/kernels/pack_op.cc
+++ b/tensorflow/core/kernels/pack_op.cc
@@ -157,6 +157,7 @@ REGISTER_PACK(string);
       PackOp<GPUDevice, type>)
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU);
+TF_CALL_int64(REGISTER_GPU);
 #undef REGISTER_GPU
 
 // A special GPU kernel for int32.
diff --git a/tensorflow/python/kernel_tests/concat_op_test.py b/tensorflow/python/kernel_tests/concat_op_test.py
index 0bb5b55155..aba4224dc6 100644
--- a/tensorflow/python/kernel_tests/concat_op_test.py
+++ b/tensorflow/python/kernel_tests/concat_op_test.py
@@ -141,6 +141,7 @@ class ConcatOpTest(test.TestCase):
     self._testRandom(dtypes.float32)
     self._testRandom(dtypes.int16)
     self._testRandom(dtypes.int32)
+    self._testRandom(dtypes.int64)
     self._testRandom(dtypes.bfloat16)
     self._testRandom(dtypes.complex64)
     self._testRandom(dtypes.complex128)
diff --git a/tensorflow/python/kernel_tests/stack_op_test.py b/tensorflow/python/kernel_tests/stack_op_test.py
index afc0c38cac..95ea3a9047 100644
--- a/tensorflow/python/kernel_tests/stack_op_test.py
+++ b/tensorflow/python/kernel_tests/stack_op_test.py
@@ -45,45 +45,61 @@ class StackOpTest(test.TestCase):
     np.random.seed(7)
     with self.test_session(use_gpu=True):
       for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
-        data = np.random.randn(*shape)
-        # Convert [data[0], data[1], ...] separately to tensorflow
-        # TODO(irving): Remove list() once we handle maps correctly
+        for dtype in [np.float32, np.int32, np.int64]:
+          data = np.random.randn(*shape).astype(dtype)
+          # Convert [data[0], data[1], ...] separately to tensorflow
+          # TODO(irving): Remove list() once we handle maps correctly
+          xs = list(map(constant_op.constant, data))
+          # Pack back into a single tensorflow tensor
+          c = array_ops.stack(xs)
+          self.assertAllEqual(c.eval(), data)
+
+  def testSimpleParallel(self):
+    np.random.seed(7)
+    with self.test_session(use_gpu=True):
+      for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
+        data = np.random.randn(*shape).astype(np.float32)
         xs = list(map(constant_op.constant, data))
-        # Pack back into a single tensorflow tensor
-        c = array_ops.stack(xs)
-        self.assertAllEqual(c.eval(), data)
-
         c = array_ops.parallel_stack(xs)
         self.assertAllEqual(c.eval(), data)
 
   def testConst(self):
+    np.random.seed(7)
+    with self.test_session(use_gpu=True):
+      for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
+        for dtype in [np.float32, np.int32, np.int64]:
+          data = np.random.randn(*shape).astype(dtype)
+          # Pack back into a single tensorflow tensor directly using np array
+          c = array_ops.stack(data)
+          # This is implemented via a Const:
+          self.assertEqual(c.op.type, "Const")
+          self.assertAllEqual(c.eval(), data)
+
+          # Python lists also work for 1-D case:
+          if len(shape) == 1:
+            data_list = list(data)
+            cl = array_ops.stack(data_list)
+            self.assertEqual(cl.op.type, "Const")
+            self.assertAllEqual(cl.eval(), data)
+
+        # Verify that shape induction works with shapes produced via const stack
+        a = constant_op.constant([1, 2, 3, 4, 5, 6])
+        b = array_ops.reshape(a, array_ops.stack([2, 3]))
+        self.assertAllEqual(b.get_shape(), [2, 3])
+
+  def testConstParallel(self):
     np.random.seed(7)
     with self.test_session(use_gpu=True):
       for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
         data = np.random.randn(*shape).astype(np.float32)
-        # Pack back into a single tensorflow tensor directly using np array
-        c = array_ops.stack(data)
-        # This is implemented via a Const:
-        self.assertEqual(c.op.type, "Const")
-        self.assertAllEqual(c.eval(), data)
-
-        c = array_ops.parallel_stack(data)
-        self.assertAllEqual(c.eval(), data)
-
-        # Python lists also work for 1-D case:
         if len(shape) == 1:
           data_list = list(data)
-          cl = array_ops.stack(data_list)
-          self.assertEqual(cl.op.type, "Const")
-          self.assertAllEqual(cl.eval(), data)
-
           cl = array_ops.parallel_stack(data_list)
           self.assertAllEqual(cl.eval(), data)
 
-      # Verify that shape induction works with shapes produced via const stack
-      a = constant_op.constant([1, 2, 3, 4, 5, 6])
-      b = array_ops.reshape(a, array_ops.stack([2, 3]))
-      self.assertAllEqual(b.get_shape(), [2, 3])
+        data = np.random.randn(*shape).astype(np.float32)
+        c = array_ops.parallel_stack(data)
+        self.assertAllEqual(c.eval(), data)
 
   def testGradientsAxis0(self):
     np.random.seed(7)
-- 
GitLab


From cc741b48e8bba90670fe9db3811d50f283ea8b1b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 14:39:51 -0700
Subject: [PATCH 1012/1375] Fix vector/scalar typos in sequence example parser
 error messages.  Old code erroneously claimed that scalar arguments should be
 vectors.

PiperOrigin-RevId: 162009915
---
 tensorflow/core/kernels/example_parsing_ops.cc | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/kernels/example_parsing_ops.cc b/tensorflow/core/kernels/example_parsing_ops.cc
index e0cc08f101..2db844e410 100644
--- a/tensorflow/core/kernels/example_parsing_ops.cc
+++ b/tensorflow/core/kernels/example_parsing_ops.cc
@@ -216,7 +216,7 @@ class SingleSequenceExampleParserOp : public OpKernel {
                   TensorShapeUtils::IsScalar(context_dense_keys[di].shape()),
                   errors::InvalidArgument(
                       "Expected context_dense_keys[", di,
-                      "] to be a vector, got shape: ",
+                      "] to be a scalar, got shape: ",
                       context_dense_keys[di].shape().DebugString()));
       context_dense_keys_t[di] = context_dense_keys[di].scalar<string>()();
     }
@@ -225,7 +225,7 @@ class SingleSequenceExampleParserOp : public OpKernel {
                   TensorShapeUtils::IsScalar(context_sparse_keys[di].shape()),
                   errors::InvalidArgument(
                       "Expected context_sparse_keys[", di,
-                      "] to be a vector, got shape: ",
+                      "] to be a scalar, got shape: ",
                       context_sparse_keys[di].shape().DebugString()));
       context_sparse_keys_t[di] = context_sparse_keys[di].scalar<string>()();
     }
@@ -234,7 +234,7 @@ class SingleSequenceExampleParserOp : public OpKernel {
           ctx, TensorShapeUtils::IsScalar(feature_list_dense_keys[di].shape()),
           errors::InvalidArgument(
               "Expected feature_list_dense_keys[", di,
-              "] to be a vector, got shape: ",
+              "] to be a scalar, got shape: ",
               feature_list_dense_keys[di].shape().DebugString()));
       feature_list_dense_keys_t[di] =
           feature_list_dense_keys[di].scalar<string>()();
@@ -244,7 +244,7 @@ class SingleSequenceExampleParserOp : public OpKernel {
           ctx, TensorShapeUtils::IsScalar(feature_list_sparse_keys[di].shape()),
           errors::InvalidArgument(
               "Expected feature_list_sparse_keys[", di,
-              "] to be a vector, got shape: ",
+              "] to be a scalar, got shape: ",
               feature_list_sparse_keys[di].shape().DebugString()));
       feature_list_sparse_keys_t[di] =
           feature_list_sparse_keys[di].scalar<string>()();
-- 
GitLab


From 2a8f7eb05e2c61e0fd0d736f093691c0ad6b8658 Mon Sep 17 00:00:00 2001
From: "Yuan (Terry) Tang" <terrytangyuan@users.noreply.github.com>
Date: Fri, 14 Jul 2017 17:57:02 -0400
Subject: [PATCH 1013/1375] Change `as_cluster_spec` to `cluster_spec` (#11506)

---
 .../cluster_resolver/python/training/cluster_resolver.py      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
index 87da24f22d..b04822fa9d 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
@@ -75,8 +75,8 @@ class UnionClusterResolver(ClusterResolver):
 
   This class performs a union given two or more existing ClusterResolvers. It
   merges the underlying ClusterResolvers, and returns one unified ClusterSpec
-  when as_cluster_spec is called. The details of the merge function is
-  documented in the as_cluster_spec function.
+  when cluster_spec is called. The details of the merge function is
+  documented in the cluster_spec function.
   """
 
   def __init__(self, *args):
-- 
GitLab


From da3112ada44c47a12cdace6df7db04cc03c0f43d Mon Sep 17 00:00:00 2001
From: Chris Antaki <ChrisAntaki@gmail.com>
Date: Fri, 14 Jul 2017 14:57:59 -0700
Subject: [PATCH 1014/1375] Adding a link and fixing up two titles (#11491)

This should make it easier to find the newest documentation.
---
 tensorflow/contrib/tfprof/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index eefd88793e..7faf2b9b24 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -1,9 +1,9 @@
 # tfprof: TensorFlow Profiler and Beyond
 
 <h1>Please use `tf.profiler.xxx` instead of `tf.contrib.tfprof.xxx`</h1>
-<h1>Full Document in tensorflow/core/profiler/README.md<h1>
+<h1>Full Document in <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/profiler/README.md">tensorflow/core/profiler/README.md</a><h1>
 
-###Features
+### Features
 
 * Profile model architectures
   * parameters, tensor shapes, float operations, device placement, etc.
@@ -16,7 +16,7 @@
   * operation configuration check
   * distributed runtime check (Not OSS)
 
-###Interfaces
+### Interfaces
 
 * Python API
 * Command Line
-- 
GitLab


From d7fa7ae8ac15118393b6a549eb98ec9ca23497c0 Mon Sep 17 00:00:00 2001
From: Catalin Voss <catalin@stanford.edu>
Date: Fri, 14 Jul 2017 15:17:36 -0700
Subject: [PATCH 1015/1375] Fix syntax error in sample code

This would have otherwise caused a non-keyword arg after keyword arg
---
 tensorflow/contrib/slim/python/slim/evaluation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/slim/python/slim/evaluation.py b/tensorflow/contrib/slim/python/slim/evaluation.py
index 15c9f3d3f4..2d4b08df61 100644
--- a/tensorflow/contrib/slim/python/slim/evaluation.py
+++ b/tensorflow/contrib/slim/python/slim/evaluation.py
@@ -81,7 +81,7 @@ more summaries and call the evaluation_loop method:
 
   # Evaluate every 10 minutes:
   slim.evaluation_loop(
-      master='',
+      '',
       checkpoint_dir,
       logdir,
       num_evals=num_evals,
-- 
GitLab


From ff5b2bf093575b852848b2d9a1fc31684a5800da Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Fri, 14 Jul 2017 15:32:52 -0700
Subject: [PATCH 1016/1375] Add overlap_and_add to tf.contrib.signal.

This is the inverse operation of tf.contrib.signal.frame. This is implemented using GPU-capable ops and supports gradients.

PiperOrigin-RevId: 162017464
---
 tensorflow/contrib/signal/BUILD               |  18 +-
 tensorflow/contrib/signal/__init__.py         |   2 +
 .../kernel_tests/reconstruction_ops_test.py   | 192 ++++++++++++++++++
 .../signal/python/ops/reconstruction_ops.py   | 144 +++++++++++++
 4 files changed, 355 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/signal/python/kernel_tests/reconstruction_ops_test.py
 create mode 100644 tensorflow/contrib/signal/python/ops/reconstruction_ops.py

diff --git a/tensorflow/contrib/signal/BUILD b/tensorflow/contrib/signal/BUILD
index 6ed2313dca..706de58a0a 100644
--- a/tensorflow/contrib/signal/BUILD
+++ b/tensorflow/contrib/signal/BUILD
@@ -21,9 +21,25 @@ py_library(
     ],
 )
 
+cuda_py_tests(
+    name = "reconstruction_ops_test",
+    srcs = ["python/kernel_tests/reconstruction_ops_test.py"],
+    additional_deps = [
+        ":signal_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_tests(
     name = "shape_ops_test",
-    size = "small",
     srcs = ["python/kernel_tests/shape_ops_test.py"],
     additional_deps = [
         ":signal_py",
diff --git a/tensorflow/contrib/signal/__init__.py b/tensorflow/contrib/signal/__init__.py
index 780a090971..d0f6c1d0c6 100644
--- a/tensorflow/contrib/signal/__init__.py
+++ b/tensorflow/contrib/signal/__init__.py
@@ -17,12 +17,14 @@
 @@frame
 @@hamming_window
 @@hann_window
+@@overlap_and_add
 """
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.signal.python.ops.reconstruction_ops import overlap_and_add
 from tensorflow.contrib.signal.python.ops.shape_ops import frame
 # `frame` used to be named `frames`, which is a noun and not a verb.
 # Keep an alias to `frames` for backwards compatibility.
diff --git a/tensorflow/contrib/signal/python/kernel_tests/reconstruction_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/reconstruction_ops_test.py
new file mode 100644
index 0000000000..5c9b2ac518
--- /dev/null
+++ b/tensorflow/contrib/signal/python/kernel_tests/reconstruction_ops_test.py
@@ -0,0 +1,192 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for reconstruction_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.signal.python.ops import reconstruction_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+class ReconstructionOpsTest(test.TestCase):
+
+  def __init__(self, *args, **kwargs):
+    super(ReconstructionOpsTest, self).__init__(*args, **kwargs)
+    self.batch_size = 3
+    self.frames = 3
+    self.samples = 5
+
+    self.bases = np.array(range(2, 5))
+    exponents = np.array(range(self.frames * self.samples))
+    powers = np.power(self.bases[:, np.newaxis], exponents[np.newaxis, :])
+
+    self.powers = np.reshape(powers, [self.batch_size, self.frames,
+                                      self.samples])
+    self.frame_hop = 2
+
+    # Hand computed example using powers of unique numbers: this is easily
+    # verified.
+    self.expected_string = ["1", "10", "100100", "1001000", "10010010000",
+                            "100100000000", "1001000000000", "10000000000000",
+                            "100000000000000"]
+
+  def test_all_ones(self):
+    signal = constant_op.constant(np.ones((3, 5)), dtype=dtypes.int64)
+    reconstruction = reconstruction_ops.overlap_and_add(signal, 2)
+
+    with self.test_session(use_gpu=True) as sess:
+      output = sess.run(reconstruction)
+
+      expected_output = np.array([1, 1, 2, 2, 3, 2, 2, 1, 1])
+
+      self.assertAllClose(output, expected_output)
+
+  def test_simple(self):
+    def make_input(frame_length, num_frames=3):
+      """Generate a tensor of num_frames frames of frame_length."""
+      return np.reshape(np.arange(1, num_frames * frame_length + 1),
+                        (-1, frame_length))
+
+    # List of (signal, expected_result, frame_hop).
+    configurations = [
+        # All hop lengths on a frame length of 2.
+        (make_input(2), [1, 5, 9, 6], 1),
+        (make_input(2), [1, 2, 3, 4, 5, 6], 2),
+
+        # All hop lengths on a frame length of 3.
+        (make_input(3), [1, 6, 15, 14, 9], 1),
+        (make_input(3), [1, 2, 7, 5, 13, 8, 9], 2),
+        (make_input(3), [1, 2, 3, 4, 5, 6, 7, 8, 9], 3),
+
+        # All hop lengths on a frame length of 4.
+        (make_input(4), [1, 7, 18, 21, 19, 12], 1),
+        (make_input(4), [1, 2, 8, 10, 16, 18, 11, 12], 2),
+        (make_input(4), [1, 2, 3, 9, 6, 7, 17, 10, 11, 12], 3),
+        (make_input(4), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 4),
+    ]
+
+    with self.test_session(use_gpu=True):
+      for signal, expected, frame_hop in configurations:
+        reconstruction = reconstruction_ops.overlap_and_add(
+            np.array(signal), frame_hop).eval()
+        expected_output = np.array(expected)
+        self.assertAllClose(reconstruction, expected_output)
+
+  def test_powers(self):
+    signal = constant_op.constant(np.squeeze(self.powers[0, :, :]),
+                                  dtype=dtypes.int64)
+    reconstruction = reconstruction_ops.overlap_and_add(signal, self.frame_hop)
+
+    with self.test_session(use_gpu=True) as sess:
+      output = sess.run(reconstruction)
+      string_output = [np.base_repr(x, self.bases[0]) for x in output]
+
+      self.assertEqual(string_output, self.expected_string)
+
+  def test_batch(self):
+    signal = constant_op.constant(self.powers, dtype=dtypes.int64)
+    reconstruction = reconstruction_ops.overlap_and_add(signal, self.frame_hop)
+
+    with self.test_session(use_gpu=True) as sess:
+      output = sess.run(reconstruction)
+
+      accumulator = True
+      for i in range(self.batch_size):
+        string_output = [np.base_repr(x, self.bases[i]) for x in output[i, :]]
+        accumulator = accumulator and (string_output == self.expected_string)
+
+      self.assertTrue(accumulator)
+
+  def test_one_element_batch(self):
+    input_matrix = np.squeeze(self.powers[0, :, :])
+    input_matrix = input_matrix[np.newaxis, :, :].astype(float)
+    signal = constant_op.constant(input_matrix, dtype=dtypes.float32)
+    reconstruction = reconstruction_ops.overlap_and_add(signal, self.frame_hop)
+
+    with self.test_session(use_gpu=True) as sess:
+      output = sess.run(reconstruction)
+
+      string_output = [np.base_repr(int(x), self.bases[0]) for x in
+                       np.squeeze(output)]
+
+      self.assertEqual(output.shape, (1, 9))
+      self.assertEqual(string_output, self.expected_string)
+
+  def test_gradient(self):
+    configurations = [
+        ((1, 128), 1),
+        ((5, 35), 17),
+        ((10, 128), 128),
+        ((2, 10, 128), 127),
+        ((2, 2, 10, 128), 126),
+        ((2, 2, 2, 10, 128), 125),
+    ]
+
+    for shape, frame_hop in configurations:
+      with self.test_session(use_gpu=True) as sess:
+        signal = array_ops.zeros(shape)
+        reconstruction = reconstruction_ops.overlap_and_add(signal, frame_hop)
+        loss = math_ops.reduce_sum(reconstruction)
+        # Increasing any sample in the input frames by one will increase the sum
+        # of all the samples in the reconstruction by 1, so the gradient should
+        # be all ones, no matter the shape or hop.
+        gradient = sess.run(gradients_impl.gradients([loss], [signal])[0])
+        self.assertTrue((gradient == 1.0).all())
+
+  def test_gradient_batch(self):
+    with self.test_session(use_gpu=True) as sess:
+      signal = array_ops.zeros((2, 10, 10))
+      frame_hop = 10
+      reconstruction = reconstruction_ops.overlap_and_add(signal, frame_hop)
+
+      # Multiply the first batch-item's reconstruction by zeros. This will block
+      # gradient from flowing into the first batch item from the loss. Multiply
+      # the second batch item by the integers from 0 to 99. Since there is zero
+      # overlap, the gradient for this batch item will be 0-99 shaped as (10,
+      # 10).
+      reconstruction *= array_ops.stack(
+          [array_ops.zeros((100,)), math_ops.to_float(math_ops.range(100))])
+      loss = math_ops.reduce_sum(reconstruction)
+
+      # Verify that only the second batch item receives gradient.
+      gradient = sess.run(gradients_impl.gradients([loss], [signal])[0])
+      expected_gradient = np.stack([
+          np.zeros((10, 10)),
+          np.reshape(np.arange(100).astype(np.float32), (10, 10))])
+      self.assertAllEqual(expected_gradient, gradient)
+
+  def test_gradient_numerical(self):
+    with self.test_session(use_gpu=True):
+      shape = (2, 10, 10)
+      framed_signal = array_ops.zeros(shape)
+      frame_hop = 10
+      reconstruction = reconstruction_ops.overlap_and_add(
+          framed_signal, frame_hop)
+      error = test.compute_gradient_error(
+          framed_signal, shape, reconstruction, [2, 100])
+      self.assertLess(error, 2e-5)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/signal/python/ops/reconstruction_ops.py b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
new file mode 100644
index 0000000000..f5f443ad09
--- /dev/null
+++ b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
@@ -0,0 +1,144 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Signal reconstruction via overlapped addition of frames."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.signal.python.ops import shape_ops
+from tensorflow.contrib.signal.python.ops import util_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+
+
+def _shuffle_to_front(input_tensor, k):
+  """Shuffles the last `k` indices of `input_tensor` to the front.
+
+  Transposes `input_tensor` to have the last `k` indices at the front. The input
+  may have arbitrary rank and unknown shape.
+
+  Args:
+    input_tensor: A `Tensor` of arbitrary rank and unknown shape.
+    k: A scalar `Tensor` specifying how many indices to shuffle.
+
+  Returns:
+    A tranposed version of `input_tensor` with `k` indices shuffled to the
+    front.
+
+  Raises:
+    ValueError: If `input_tensor` is not at least rank `k` or `k` is not scalar.
+  """
+  k = ops.convert_to_tensor(k, name="k")
+  k.shape.with_rank(0)
+  k_static = tensor_util.constant_value(k)
+  if k_static is not None:
+    input_tensor.shape.with_rank_at_least(k_static)
+
+  rank = array_ops.rank(input_tensor)
+  outer_indices, inner_indices = array_ops.split(math_ops.range(rank),
+                                                 [rank - k, k])
+  permutation = array_ops.concat([inner_indices, outer_indices], 0)
+
+  return array_ops.transpose(input_tensor, perm=permutation)
+
+
+def overlap_and_add(signal, frame_step, name=None):
+  """Reconstructs a signal from a framed representation.
+
+  Adds potentially overlapping frames of a signal with shape
+  `[..., frames, frame_length]`, offsetting subsequent frames by `frame_step`.
+  The resulting tensor has shape `[..., output_size]` where
+
+      output_size = (frames - 1) * frame_step + frame_length
+
+  Args:
+    signal: A [..., frames, frame_length] `Tensor`. All dimensions may be
+      unknown, and rank must be at least 2.
+    frame_step: An integer or scalar `Tensor` denoting overlap offsets. Must be
+      less than or equal to `frame_length`.
+    name: An optional name for the operation.
+
+  Returns:
+    A `Tensor` with shape `[..., output_size]` containing the overlap-added
+    frames of `signal`'s inner-most two dimensions.
+
+  Raises:
+    ValueError: If `signal`'s rank is less than 2, `frame_step` is not a scalar
+      integer or `frame_step` is greater than `frame_length`.
+  """
+  with ops.name_scope(name, "overlap_and_add", [signal, frame_step]):
+    signal = ops.convert_to_tensor(signal, name="signal")
+    signal.shape.with_rank_at_least(2)
+    frame_step = ops.convert_to_tensor(frame_step, name="frame_step")
+    frame_step.shape.assert_has_rank(0)
+    if not frame_step.dtype.is_integer:
+      raise ValueError("frame_step must be an integer. Got %s" %
+                       frame_step.dtype)
+
+    # If frame_length and frame_step are known at graph construction time, check
+    # frame_step is less than or equal to frame_length.
+    frame_step_static = tensor_util.constant_value(frame_step)
+    if (frame_step_static is not None and signal.shape.ndims is not None and
+        signal.shape[-1].value is not None and
+        frame_step_static > signal.shape[-1].value):
+      raise ValueError(
+          "frame_step (%d) must be less than or equal to frame_length (%d)" % (
+              frame_step_static, signal.shape[-1].value))
+
+    signal_shape = array_ops.shape(signal)
+
+    # All dimensions that are not part of the overlap-and-add. Can be empty for
+    # rank 2 inputs.
+    outer_dimensions = signal_shape[:-2]
+
+    signal_rank = array_ops.rank(signal)
+    frames = signal_shape[-2]
+    frame_length = signal_shape[-1]
+
+    subframe_length = util_ops.gcd(frame_length, frame_step)
+    subframe_step = frame_step // subframe_length
+    subframes_per_frame = frame_length // subframe_length
+    output_size = frame_step * (frames - 1) + frame_length
+    output_subframes = output_size // subframe_length
+
+    # To avoid overlap-adding sample-by-sample, we overlap-add at the "subframe"
+    # level, where a subframe is gcd(frame_length, frame_step). Reshape signal
+    # from [..., frames, frame_length] into [..., subframes, subframe_length].
+    subframe_shape = array_ops.concat(
+        [outer_dimensions, [-1, subframe_length]], 0)
+    subframe_signal = array_ops.reshape(signal, subframe_shape)
+
+    # Now we shuffle the last [subframes, subframe_length] dimensions to the
+    # front.
+    # TODO(rjryan): Add an axis argument to unsorted_segment_sum so we can
+    # avoid this pair of transposes.
+    subframe_signal = _shuffle_to_front(subframe_signal, 2)
+
+    # Use unsorted_segment_sum to add overlapping subframes together.
+    segment_ids = array_ops.reshape(shape_ops.frame(
+        math_ops.range(output_subframes), subframes_per_frame, subframe_step,
+        pad_end=False), [-1])
+    result = math_ops.unsorted_segment_sum(subframe_signal, segment_ids,
+                                           num_segments=output_subframes)
+
+    # result is a [subframes, subframe_length, ...outer_dimensions] tensor. We
+    # return a [...outer_dimensions, output_size] tensor with a transpose and
+    # reshape.
+    result_shape = array_ops.concat([outer_dimensions, [output_size]], 0)
+    return array_ops.reshape(_shuffle_to_front(result, signal_rank - 2),
+                             result_shape)
-- 
GitLab


From 6254eaeb56d02dc520fc4c055da02e16386adc00 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 16:07:21 -0700
Subject: [PATCH 1017/1375] API and an example of two-level quantile
 accumulator.

PiperOrigin-RevId: 162022193
---
 .../boosted_trees/kernels/quantile_ops.cc     | 52 ++++++++++++++++++-
 .../contrib/boosted_trees/ops/quantile_ops.cc | 15 ++++++
 .../python/kernel_tests/quantile_ops_test.py  | 43 +++++++++++++++
 .../boosted_trees/python/ops/quantile_ops.py  | 19 ++++++-
 4 files changed, 126 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
index ca24582d41..df6bf22571 100644
--- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
@@ -547,7 +547,7 @@ REGISTER_KERNEL_BUILDER(
     Name("QuantileAccumulatorDeserialize").Device(DEVICE_CPU),
     QuantileAccumulatorDeserializeOp);
 
-// Adds a summary to the quantile summary stream.
+// Flushes the quantile summary stream resource.
 class QuantileAccumulatorFlushOp : public OpKernel {
  public:
   explicit QuantileAccumulatorFlushOp(OpKernelConstruction* const context)
@@ -586,6 +586,56 @@ class QuantileAccumulatorFlushOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("QuantileAccumulatorFlush").Device(DEVICE_CPU),
                         QuantileAccumulatorFlushOp);
 
+// Flushes the quantile summary stream resource. This version computes the
+// summary.
+class QuantileAccumulatorFlushSummaryOp : public OpKernel {
+ public:
+  explicit QuantileAccumulatorFlushSummaryOp(
+      OpKernelConstruction* const context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    QuantileStreamResource* streams_resource;
+    // Create a reference to the underlying resource using the handle.
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &streams_resource));
+    // Remove the reference at the end of this scope.
+    mutex_lock l(*streams_resource->mutex());
+    core::ScopedUnref unref_me(streams_resource);
+
+    const Tensor* next_stamp_token_t;
+    OP_REQUIRES_OK(context,
+                   context->input(kNextStampTokenName, &next_stamp_token_t));
+    int64 next_stamp_token = next_stamp_token_t->scalar<int64>()();
+
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+    CHECK(streams_resource->is_stamp_valid(stamp_token))
+        << "Invalid stamp token in QuantileAccumulatorFlushSummaryOp. "
+        << "Passed stamp token: " << stamp_token << " "
+        << "Current token: " << streams_resource->stamp();
+    QuantileStream* stream = streams_resource->stream(stamp_token);
+    stream->Finalize();
+    protobuf::Arena arena;
+    ::boosted_trees::QuantileSummaryState* summary_proto =
+        protobuf::Arena::CreateMessage<::boosted_trees::QuantileSummaryState>(
+            &arena);
+    const auto& summary = stream->GetFinalSummary();
+    CopySummaryToProto(summary, summary_proto);
+    // Output to tensor.
+    Tensor* output_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, TensorShape({}), &output_t));
+    summary_proto->SerializeToString(&output_t->scalar<string>()());
+    streams_resource->Reset(next_stamp_token);
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("QuantileAccumulatorFlushSummary").Device(DEVICE_CPU),
+    QuantileAccumulatorFlushSummaryOp);
+
 // Get bucket boundaries from summaries.
 class QuantileAccumulatorGetBucketsOp : public OpKernel {
  public:
diff --git a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
index f03c50dcc5..58d22a4398 100644
--- a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
+++ b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc
@@ -120,6 +120,21 @@ stamp_token: Stamp token for Read/Write operations.
 next_stamp_token: Stamp token to be used for the next iteration.
 )doc");
 
+REGISTER_OP("QuantileAccumulatorFlushSummary")
+    .Input("quantile_accumulator_handle: resource")
+    .Input("stamp_token: int64")
+    .Input("next_stamp_token: int64")
+    .Output("output: string")
+    .Doc(R"doc(
+Resets quantile summary stream and returns the summary.
+
+quantile_accumulator_handle: The handle to the accumulator.
+stamp_token: Stamp token for Read/Write operations.
+             Any operation with a mismatching token will be dropped.
+next_stamp_token: Stamp token to be used for the next iteration.
+output: A scalar string that is the a summary of the accumulator.
+)doc");
+
 REGISTER_OP("QuantileAccumulatorSerialize")
     .Input("quantile_accumulator_handle: resource")
     .Output("stamp_token: int64")
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
index 9ed8b3404f..3871e8d76d 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py
@@ -117,6 +117,49 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase):
       self.assertEqual(True, are_ready_flush)
       self.assertAllEqual([0, 3335., 6671., 9999.], buckets)
 
+  def testStreamingQuantileBucketsTwoLevel(self):
+    """Sets up the quantile summary op test as follows.
+
+    100 batches of data is added to the accumulator. The batches are in form:
+    [0 1 .. 99]
+    [100 101 .. 200]
+    ...
+    [9900 9901 .. 9999]
+    All the batches have 1 for all the example weights.
+    """
+    with self.test_session() as sess:
+      accumulator = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.01, name="q1")
+      accumulator_2 = quantile_ops.QuantileAccumulator(
+          init_stamp_token=0, num_quantiles=3, epsilon=0.01, name="q2")
+      resources.initialize_resources(resources.shared_resources()).run()
+    weight_placeholder = array_ops.placeholder(dtypes.float32)
+    dense_placeholder = array_ops.placeholder(dtypes.float32)
+    update = accumulator.add_summary(
+        stamp_token=0,
+        column=dense_placeholder,
+        example_weights=weight_placeholder)
+    with self.test_session() as sess:
+      for i in range(100):
+        dense_float = np.linspace(
+            i * 100, (i + 1) * 100 - 1, num=100).reshape(-1, 1)
+        sess.run(update, {
+            dense_placeholder: dense_float,
+            weight_placeholder: np.ones(shape=(100, 1), dtype=np.float32)
+        })
+
+    with self.test_session() as sess:
+      summary = sess.run(
+          accumulator.flush_summary(stamp_token=0, next_stamp_token=1))
+      sess.run(
+          accumulator_2.add_prebuilt_summary(
+              stamp_token=0, summary=constant_op.constant(summary)))
+      sess.run(accumulator_2.flush(stamp_token=0, next_stamp_token=1))
+      are_ready_flush, buckets = (accumulator_2.get_buckets(stamp_token=1))
+      buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
+      self.assertEqual(True, are_ready_flush)
+      self.assertAllEqual([0, 3337., 6677., 9999.], buckets)
+
   def testSaveRestoreBeforeFlush(self):
     save_dir = os.path.join(self.get_temp_dir(), "save_restore")
     save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
diff --git a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
index fa62c9428b..fba6720a4c 100644
--- a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
+++ b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
@@ -139,7 +139,7 @@ class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
           sparse_float_feature_values=[column.values],
           sparse_float_feature_shapes=[column.dense_shape],
           example_weights=example_weights,
-          epsilon=self._epsilon)[1][0]
+          epsilon=self._epsilon / 2).sparse_summaries[0]
     else:
       return gen_quantile_ops.make_quantile_summaries(
           dense_float_features=[column],
@@ -147,7 +147,7 @@ class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
           sparse_float_feature_values=[],
           sparse_float_feature_shapes=[],
           example_weights=example_weights,
-          epsilon=self._epsilon)[0][0]
+          epsilon=self._epsilon / 2).dense_summaries[0]
 
   def add_summary(self, stamp_token, column, example_weights):
     """Adds quantile summary to its stream in resource."""
@@ -157,6 +157,13 @@ class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
         stamp_token=stamp_token,
         summaries=[summary])
 
+  def add_prebuilt_summary(self, stamp_token, summary):
+    """Adds quantile summary to its stream in resource."""
+    return gen_quantile_ops.quantile_accumulator_add_summaries(
+        quantile_accumulator_handles=[self._quantile_accumulator_handle],
+        stamp_token=stamp_token,
+        summaries=[summary])
+
   def schedule_add_summary(self, stamp_token, column, example_weights):
     """Schedules to add a quantile summary to its stream in resource."""
     summary = self._make_summary(column, example_weights)
@@ -172,6 +179,14 @@ class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
         stamp_token=stamp_token,
         next_stamp_token=next_stamp_token)
 
+  def flush_summary(self, stamp_token, next_stamp_token):
+    """Finalizes quantile summary stream and resets it for next iteration."""
+    result = gen_quantile_ops.quantile_accumulator_flush_summary(
+        quantile_accumulator_handle=self._quantile_accumulator_handle,
+        stamp_token=stamp_token,
+        next_stamp_token=next_stamp_token)
+    return result
+
 
 # Conditionally load ops, they might already be statically linked in.
 try:
-- 
GitLab


From a55f2481ac4e657b73acd606dbc7f85545ef3b2f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 16:13:52 -0700
Subject: [PATCH 1018/1375] Fix race in record_yielder.

PiperOrigin-RevId: 162022981
---
 tensorflow/core/kernels/record_yielder.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/kernels/record_yielder.cc b/tensorflow/core/kernels/record_yielder.cc
index 1370295144..e4fa0ed322 100644
--- a/tensorflow/core/kernels/record_yielder.cc
+++ b/tensorflow/core/kernels/record_yielder.cc
@@ -146,6 +146,7 @@ void RecordYielder::MainLoop() {
     }
 
     if (num_records_added_in_epoch_ < opts_.bufsize) {
+      mutex_lock l(mu_);
       opts_.bufsize = num_records_added_in_epoch_;
     }
 
-- 
GitLab


From ee8d9f353a29c3b0df8447e94c11f1a61ee75583 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Fri, 14 Jul 2017 16:23:15 -0700
Subject: [PATCH 1019/1375] In colocation op device checking, canonicalize the
 names so /device:GPU:0 compares to /gpu:0 as one would expect.

Adds a test to ensure that the warning message isn't printed
by checking the resulting device name.  No behavior change
is expected here; all this does is change to share
the canonical name and remove the warning.

Fixes #11484.

PiperOrigin-RevId: 162024152
---
 tensorflow/python/framework/ops.py      |  4 +++-
 tensorflow/python/framework/ops_test.py | 15 +++++++++++++++
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 1b258a8b17..c4ff568d49 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -2642,7 +2642,9 @@ class Graph(object):
           # Make this device match the device of the colocated op, to
           # provide consistency between the device and the colocation
           # property.
-          if ret.device and ret.device != colocation_op.device:
+          if (ret.device and
+              pydev.canonical_name(ret.device) !=
+              pydev.canonical_name(colocation_op.device)):
             logging.warning("Tried to colocate %s with an op %s that had "
                             "a different device: %s vs %s. "
                             "Ignoring colocation property.",
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index c1e2c4e287..8fb330100d 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -1562,6 +1562,21 @@ class ColocationGroupTest(test_util.TensorFlowTestCase):
     self.assertEqual([b"loc:@a"], b.op.colocation_groups())
     self.assertEqual(a.op.device, b.op.device)
 
+  def testColocationCanonicalization(self):
+    with ops.device("/gpu:0"):
+      _ = constant_op.constant(2.0)
+    with ops.device(lambda op: "/gpu:0"):
+      b = constant_op.constant(3.0)
+    with ops.get_default_graph().colocate_with(b):
+      with ops.device("/gpu:0"):
+        c = constant_op.constant(4.0)
+
+    # A's device will be /gpu:0
+    # B's device will be /device:GPU:0
+    # C's device will be /device:GPU:0 because it
+    # inherits B's device name, after canonicalizing the names.
+    self.assertEqual(b.op.device, c.op.device)
+
   def testLocationOverrides(self):
     with ops.device("/cpu:0"):
       with ops.device("/gpu:0"):
-- 
GitLab


From 7687debbf63e31375d960d663373da8d469f2d2e Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Fri, 14 Jul 2017 16:46:49 -0700
Subject: [PATCH 1020/1375] Add unit-test for questions:  -
 http://stackoverflow.com/q/45109305  - #10766

PiperOrigin-RevId: 162026912
---
 .../python/kernel_tests/mvn_diag_test.py      | 42 +++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py b/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py
index 3f4582eb7e..e973c056e0 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py
@@ -24,7 +24,12 @@ from tensorflow.contrib import distributions
 from tensorflow.contrib.distributions.python.ops import bijectors
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 
@@ -233,6 +238,43 @@ class MultivariateNormalDiagTest(test.TestCase):
       self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
       self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.1)
 
+  def testMultivariateNormalDiagNegLogLikelihood(self):
+    num_draws = 50
+    dims = 3
+    with self.test_session() as sess:
+      x_pl = array_ops.placeholder(dtype=dtypes.float32,
+                                   shape=[None, dims],
+                                   name="x")
+      mu_var = variable_scope.get_variable(
+          name="mu",
+          shape=[dims],
+          dtype=dtypes.float32,
+          initializer=init_ops.constant_initializer(1.))
+      sess.run([variables.global_variables_initializer()])
+
+      mvn = ds.MultivariateNormalDiag(
+          loc=mu_var,
+          scale_diag=array_ops.ones(shape=[dims], dtype=dtypes.float32))
+
+      # Typically you'd use `mvn.log_prob(x_pl)` which is always at least as
+      # numerically stable as `tf.log(mvn.prob(x_pl))`. However in this test
+      # we're testing a bug specific to `prob` and not `log_prob`;
+      # http://stackoverflow.com/q/45109305. (The underlying issue was not
+      # related to `Distributions` but that `reduce_prod` didn't correctly
+      # handle negative indexes.)
+      neg_log_likelihood = -math_ops.reduce_sum(math_ops.log(mvn.prob(x_pl)))
+      grad_neg_log_likelihood = gradients_impl.gradients(
+          neg_log_likelihood, variables.trainable_variables())
+
+      x = np.zeros([num_draws, dims], dtype=np.float32)
+      grad_neg_log_likelihood_ = sess.run(
+          grad_neg_log_likelihood,
+          feed_dict={x_pl: x})
+      self.assertEqual(1, len(grad_neg_log_likelihood_))
+      self.assertAllClose(grad_neg_log_likelihood_[0],
+                          np.tile(num_draws, dims),
+                          rtol=1e-6, atol=0.)
+
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 8900b54574b0b013154b60b38e310af9668ea94d Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Fri, 14 Jul 2017 17:20:34 -0700
Subject: [PATCH 1021/1375] Update Keras API exports.

PiperOrigin-RevId: 162030530
---
 tensorflow/contrib/cmake/tf_python.cmake      |  1 +
 tensorflow/contrib/keras/BUILD                |  1 +
 .../keras/api/keras/activations/__init__.py   |  1 +
 .../keras/api/keras/applications/__init__.py  |  2 ++
 .../keras/applications/mobilenet/__init__.py  | 27 +++++++++++++++++++
 .../keras/api/keras/callbacks/__init__.py     |  1 +
 .../keras/api/keras/initializers/__init__.py  |  1 +
 .../keras/api/keras/layers/__init__.py        |  3 +++
 .../keras/api/keras/losses/__init__.py        |  2 ++
 .../keras/api/keras/metrics/__init__.py       |  1 +
 .../contrib/keras/api/keras/utils/__init__.py |  3 +++
 .../python/keras/layers/convolutional.py      |  1 +
 12 files changed, 44 insertions(+)
 create mode 100644 tensorflow/contrib/keras/api/keras/applications/mobilenet/__init__.py

diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 0024efab38..75709df55b 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -340,6 +340,7 @@ add_python_module("tensorflow/contrib/keras/api/keras")
 add_python_module("tensorflow/contrib/keras/api/keras/activations")
 add_python_module("tensorflow/contrib/keras/api/keras/applications")
 add_python_module("tensorflow/contrib/keras/api/keras/applications/inception_v3")
+add_python_module("tensorflow/contrib/keras/api/keras/applications/mobilenet")
 add_python_module("tensorflow/contrib/keras/api/keras/applications/resnet50")
 add_python_module("tensorflow/contrib/keras/api/keras/applications/vgg16")
 add_python_module("tensorflow/contrib/keras/api/keras/applications/vgg19")
diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index 4041427dca..a0ffd027a5 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -19,6 +19,7 @@ py_library(
         "api/keras/activations/__init__.py",
         "api/keras/applications/__init__.py",
         "api/keras/applications/inception_v3/__init__.py",
+        "api/keras/applications/mobilenet/__init__.py",
         "api/keras/applications/resnet50/__init__.py",
         "api/keras/applications/vgg16/__init__.py",
         "api/keras/applications/vgg19/__init__.py",
diff --git a/tensorflow/contrib/keras/api/keras/activations/__init__.py b/tensorflow/contrib/keras/api/keras/activations/__init__.py
index e4d4b1e42c..af6f249e71 100644
--- a/tensorflow/contrib/keras/api/keras/activations/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/activations/__init__.py
@@ -23,6 +23,7 @@ from tensorflow.contrib.keras.python.keras.activations import elu
 from tensorflow.contrib.keras.python.keras.activations import hard_sigmoid
 from tensorflow.contrib.keras.python.keras.activations import linear
 from tensorflow.contrib.keras.python.keras.activations import relu
+from tensorflow.contrib.keras.python.keras.activations import selu
 from tensorflow.contrib.keras.python.keras.activations import sigmoid
 from tensorflow.contrib.keras.python.keras.activations import softmax
 from tensorflow.contrib.keras.python.keras.activations import softplus
diff --git a/tensorflow/contrib/keras/api/keras/applications/__init__.py b/tensorflow/contrib/keras/api/keras/applications/__init__.py
index fee5b7103a..f943e84606 100644
--- a/tensorflow/contrib/keras/api/keras/applications/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/applications/__init__.py
@@ -19,11 +19,13 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.keras.api.keras.applications import inception_v3
+from tensorflow.contrib.keras.api.keras.applications import mobilenet
 from tensorflow.contrib.keras.api.keras.applications import resnet50
 from tensorflow.contrib.keras.api.keras.applications import vgg16
 from tensorflow.contrib.keras.api.keras.applications import vgg19
 from tensorflow.contrib.keras.api.keras.applications import xception
 from tensorflow.contrib.keras.api.keras.applications.inception_v3 import InceptionV3
+from tensorflow.contrib.keras.api.keras.applications.mobilenet import MobileNet
 from tensorflow.contrib.keras.api.keras.applications.resnet50 import ResNet50
 from tensorflow.contrib.keras.api.keras.applications.vgg16 import VGG16
 from tensorflow.contrib.keras.api.keras.applications.vgg19 import VGG19
diff --git a/tensorflow/contrib/keras/api/keras/applications/mobilenet/__init__.py b/tensorflow/contrib/keras/api/keras/applications/mobilenet/__init__.py
new file mode 100644
index 0000000000..594861fb51
--- /dev/null
+++ b/tensorflow/contrib/keras/api/keras/applications/mobilenet/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""MobileNet Keras application."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.keras.python.keras.applications.mobilenet import decode_predictions
+from tensorflow.contrib.keras.python.keras.applications.mobilenet import MobileNet
+from tensorflow.contrib.keras.python.keras.applications.mobilenet import preprocess_input
+
+del absolute_import
+del division
+del print_function
diff --git a/tensorflow/contrib/keras/api/keras/callbacks/__init__.py b/tensorflow/contrib/keras/api/keras/callbacks/__init__.py
index 36db34f592..3a97074857 100644
--- a/tensorflow/contrib/keras/api/keras/callbacks/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/callbacks/__init__.py
@@ -30,6 +30,7 @@ from tensorflow.contrib.keras.python.keras.callbacks import ProgbarLogger
 from tensorflow.contrib.keras.python.keras.callbacks import ReduceLROnPlateau
 from tensorflow.contrib.keras.python.keras.callbacks import RemoteMonitor
 from tensorflow.contrib.keras.python.keras.callbacks import TensorBoard
+from tensorflow.contrib.keras.python.keras.callbacks import TerminateOnNaN
 
 del absolute_import
 del division
diff --git a/tensorflow/contrib/keras/api/keras/initializers/__init__.py b/tensorflow/contrib/keras/api/keras/initializers/__init__.py
index f0c1540d9a..9b58723ed5 100644
--- a/tensorflow/contrib/keras/api/keras/initializers/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/initializers/__init__.py
@@ -36,6 +36,7 @@ from tensorflow.contrib.keras.python.keras.initializers import glorot_normal
 from tensorflow.contrib.keras.python.keras.initializers import glorot_uniform
 from tensorflow.contrib.keras.python.keras.initializers import he_normal
 from tensorflow.contrib.keras.python.keras.initializers import he_uniform
+from tensorflow.contrib.keras.python.keras.initializers import lecun_normal
 from tensorflow.contrib.keras.python.keras.initializers import lecun_uniform
 
 # Auxiliary utils.
diff --git a/tensorflow/contrib/keras/api/keras/layers/__init__.py b/tensorflow/contrib/keras/api/keras/layers/__init__.py
index 3c6dce5ee8..aafd189217 100644
--- a/tensorflow/contrib/keras/api/keras/layers/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/layers/__init__.py
@@ -36,6 +36,7 @@ from tensorflow.contrib.keras.python.keras.layers.convolutional import Conv1D
 from tensorflow.contrib.keras.python.keras.layers.convolutional import Conv2D
 from tensorflow.contrib.keras.python.keras.layers.convolutional import Conv3D
 from tensorflow.contrib.keras.python.keras.layers.convolutional import Conv2DTranspose
+from tensorflow.contrib.keras.python.keras.layers.convolutional import Conv3DTranspose
 from tensorflow.contrib.keras.python.keras.layers.convolutional import SeparableConv2D
 
 # Convolution layer aliases.
@@ -43,6 +44,7 @@ from tensorflow.contrib.keras.python.keras.layers.convolutional import Convoluti
 from tensorflow.contrib.keras.python.keras.layers.convolutional import Convolution2D
 from tensorflow.contrib.keras.python.keras.layers.convolutional import Convolution3D
 from tensorflow.contrib.keras.python.keras.layers.convolutional import Convolution2DTranspose
+from tensorflow.contrib.keras.python.keras.layers.convolutional import Convolution3DTranspose
 from tensorflow.contrib.keras.python.keras.layers.convolutional import SeparableConvolution2D
 
 # Image processing layers.
@@ -96,6 +98,7 @@ from tensorflow.contrib.keras.python.keras.layers.merge import concatenate
 from tensorflow.contrib.keras.python.keras.layers.merge import dot
 
 # Noise layers.
+from tensorflow.contrib.keras.python.keras.layers.noise import AlphaDropout
 from tensorflow.contrib.keras.python.keras.layers.noise import GaussianNoise
 from tensorflow.contrib.keras.python.keras.layers.noise import GaussianDropout
 
diff --git a/tensorflow/contrib/keras/api/keras/losses/__init__.py b/tensorflow/contrib/keras/api/keras/losses/__init__.py
index 2d2fee2698..06dd679f9c 100644
--- a/tensorflow/contrib/keras/api/keras/losses/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/losses/__init__.py
@@ -21,9 +21,11 @@ from __future__ import print_function
 # Loss functions.
 from tensorflow.contrib.keras.python.keras.losses import binary_crossentropy
 from tensorflow.contrib.keras.python.keras.losses import categorical_crossentropy
+from tensorflow.contrib.keras.python.keras.losses import categorical_hinge
 from tensorflow.contrib.keras.python.keras.losses import cosine_proximity
 from tensorflow.contrib.keras.python.keras.losses import hinge
 from tensorflow.contrib.keras.python.keras.losses import kullback_leibler_divergence
+from tensorflow.contrib.keras.python.keras.losses import logcosh
 from tensorflow.contrib.keras.python.keras.losses import mean_absolute_error
 from tensorflow.contrib.keras.python.keras.losses import mean_absolute_percentage_error
 from tensorflow.contrib.keras.python.keras.losses import mean_squared_error
diff --git a/tensorflow/contrib/keras/api/keras/metrics/__init__.py b/tensorflow/contrib/keras/api/keras/metrics/__init__.py
index ba43ffece8..99496edde2 100644
--- a/tensorflow/contrib/keras/api/keras/metrics/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/metrics/__init__.py
@@ -32,6 +32,7 @@ from tensorflow.contrib.keras.python.keras.metrics import mean_squared_error
 from tensorflow.contrib.keras.python.keras.metrics import mean_squared_logarithmic_error
 from tensorflow.contrib.keras.python.keras.metrics import poisson
 from tensorflow.contrib.keras.python.keras.metrics import sparse_categorical_crossentropy
+from tensorflow.contrib.keras.python.keras.metrics import sparse_top_k_categorical_accuracy
 from tensorflow.contrib.keras.python.keras.metrics import squared_hinge
 from tensorflow.contrib.keras.python.keras.metrics import top_k_categorical_accuracy
 
diff --git a/tensorflow/contrib/keras/api/keras/utils/__init__.py b/tensorflow/contrib/keras/api/keras/utils/__init__.py
index 7f14fa2065..d6d70f79d5 100644
--- a/tensorflow/contrib/keras/api/keras/utils/__init__.py
+++ b/tensorflow/contrib/keras/api/keras/utils/__init__.py
@@ -18,7 +18,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.keras.python.keras.utils.data_utils import GeneratorEnqueuer
 from tensorflow.contrib.keras.python.keras.utils.data_utils import get_file
+from tensorflow.contrib.keras.python.keras.utils.data_utils import Sequence
+from tensorflow.contrib.keras.python.keras.utils.data_utils import SequenceEnqueuer
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import custom_object_scope
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import CustomObjectScope
 from tensorflow.contrib.keras.python.keras.utils.generic_utils import deserialize_keras_object
diff --git a/tensorflow/contrib/keras/python/keras/layers/convolutional.py b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
index b5e44c89c0..e039959041 100644
--- a/tensorflow/contrib/keras/python/keras/layers/convolutional.py
+++ b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
@@ -1706,5 +1706,6 @@ Convolution2D = Conv2D
 Convolution3D = Conv3D
 SeparableConvolution2D = SeparableConv2D
 Convolution2DTranspose = Conv2DTranspose
+Convolution3DTranspose = Conv3DTranspose
 Deconvolution2D = Deconv2D = Conv2DTranspose
 Deconvolution3D = Deconv3D = Conv3DTranspose
-- 
GitLab


From 9358451d34bab3e93234e5ab22f0bb78374655ce Mon Sep 17 00:00:00 2001
From: Dalmo Cirne <dalmo.cirne@me.com>
Date: Fri, 14 Jul 2017 20:34:37 -0400
Subject: [PATCH 1022/1375] Enable bitcode compilation (#11198)

In order to enable bitcode compilation it is necessary more than adding `-fembed-bitcode`. The `MACOSX_DEPLOYMENT_TARGET` variable must be defined and exported as well.

In this commit the variable is defined grabbing the version of the underlying running macOS.
---
 tensorflow/contrib/makefile/build_all_ios.sh          | 9 +++++----
 tensorflow/contrib/makefile/compile_ios_protobuf.sh   | 5 ++++-
 tensorflow/contrib/makefile/compile_ios_tensorflow.sh | 6 +++++-
 3 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/makefile/build_all_ios.sh b/tensorflow/contrib/makefile/build_all_ios.sh
index 344bf49dcf..a0f5652854 100755
--- a/tensorflow/contrib/makefile/build_all_ios.sh
+++ b/tensorflow/contrib/makefile/build_all_ios.sh
@@ -18,7 +18,7 @@ set -e
 
 # Make sure we're on OS X.
 if [[ $(uname) != "Darwin" ]]; then
-    echo "ERROR: This makefile build requires OS X, which the current system "\
+    echo "ERROR: This makefile build requires macOS, which the current system "\
     "is not."
     exit 1
 fi
@@ -37,7 +37,9 @@ rm -rf tensorflow/contrib/makefile/downloads
 #
 #    ld: -bind_at_load and -bitcode_bundle (Xcode setting ENABLE_BITCODE=YES) cannot be used together
 #
-export MACOSX_DEPLOYMENT_TARGET="10.10"
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
 
 # Pull down the required versions of the frameworks we need.
 tensorflow/contrib/makefile/download_dependencies.sh
@@ -48,6 +50,5 @@ tensorflow/contrib/makefile/compile_ios_protobuf.sh
 # Build the iOS TensorFlow libraries.
 tensorflow/contrib/makefile/compile_ios_tensorflow.sh "-O3"
 
-# Creates a static universal library in 
+# Creates a static universal library in
 # tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a
-
diff --git a/tensorflow/contrib/makefile/compile_ios_protobuf.sh b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
index e8b9454e7e..4056db18a7 100755
--- a/tensorflow/contrib/makefile/compile_ios_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
@@ -15,9 +15,12 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
-set -x
 set -e
 
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
+
 SCRIPT_DIR=$(dirname $0)
 source "${SCRIPT_DIR}/build_helper.subr"
 
diff --git a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
index bcf097b303..5d1cc8b375 100755
--- a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
+++ b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
@@ -31,6 +31,10 @@ function less_than_required_version() {
   )
 }
 
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
+
 ACTUAL_XCODE_VERSION=$(xcodebuild -version | head -n 1 | sed 's/Xcode //')
 REQUIRED_XCODE_VERSION=7.3.0
 if less_than_required_version $ACTUAL_XCODE_VERSION 7 3 0
@@ -44,7 +48,7 @@ LIBDIR=${GENDIR}lib
 LIB_PREFIX=libtensorflow-core
 
 make -j"${JOB_COUNT}" -f tensorflow/contrib/makefile/Makefile \
-TARGET=IOS IOS_ARCH=ARMV7 LIB_NAME=${LIB_PREFIX}-armv7.a OPTFLAGS="$1" 
+TARGET=IOS IOS_ARCH=ARMV7 LIB_NAME=${LIB_PREFIX}-armv7.a OPTFLAGS="$1"
 if [ $? -ne 0 ]
 then
   echo "armv7 compilation failed."
-- 
GitLab


From ac98d1184008e4e6c2e58a3edc503d1c2c7a0e56 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Guo=20Yejun=20=28=E9=83=AD=E5=8F=B6=E5=86=9B=29?=
 <yejun.guo@intel.com>
Date: Sat, 15 Jul 2017 09:05:18 +0800
Subject: [PATCH 1023/1375] add a new config option sycl_nodouble for SYCL
 build (#11234)

* add a new config option sycl_nodouble for SYCL build

When TF is built with SYCL enabled, the SYCL device code is generated
at build time. Currently, all the data types such as float and double
are registered to generate the device code.

The SYCL device code is compiled into SPIR at build time, and then
passed to OpenCL implemenation at runtime. Since double precision is
an optional feature in the OpenCL spec, it is possible that an OpenCL
implemenation does not support double.

To make some platforms without double support work, this new config
option disables double register for SYCL device code.

This patch just changes the cwise_add operation as an example, and
other operations will be changed in future small patches one by one.

* change action_env to cxxopt in tools/bazel.rc to pass the build option

* correct naming and refine #ifdef into a common place

Rename SYCL_NO_DOUBLE to TENSORFLOW_SYCL_NO_DOUBLE.
Refine #ifdef to cwise_ops_common.h, so the enable/disable of double
operation is defined in a single place for all the cwise ops.

* add TF_CALL_SYCL_NUMBER_TYPES to unify the sycl kernel register

* also consider __ANDROID_TYPES_SLIM__

another thing need to mention is that, once all cwise ops finished,
the REGISTER* defined within __ANDROID_TYPES_SLIM__ in file
cwise_ops_common.h will be defined as empty. Anyway, this will be
in another patch.
---
 tensorflow/core/framework/register_types.h | 14 ++++++++++++++
 tensorflow/core/kernels/cwise_op_add_1.cc  |  4 +++-
 tools/bazel.rc                             |  3 +++
 3 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index 3f91642064..973ad4544a 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -183,4 +183,18 @@ limitations under the License.
 #define TF_CALL_QUANTIZED_TYPES(m) \
   TF_CALL_qint8(m) TF_CALL_quint8(m) TF_CALL_qint32(m)
 
+#ifdef TENSORFLOW_SYCL_NO_DOUBLE
+#define TF_CALL_SYCL_double(m)
+#else  // TENSORFLOW_SYCL_NO_DOUBLE
+#define TF_CALL_SYCL_double(m) TF_CALL_double(m)
+#endif // TENSORFLOW_SYCL_NO_DOUBLE
+
+#ifdef __ANDROID_TYPES_SLIM__
+#define TF_CALL_SYCL_NUMBER_TYPES(m)  TF_CALL_float(m)
+#else  // __ANDROID_TYPES_SLIM__
+#define TF_CALL_SYCL_NUMBER_TYPES(m)    \
+    TF_CALL_float(m)                    \
+    TF_CALL_SYCL_double(m)
+#endif // __ANDROID_TYPES_SLIM__
+
 #endif  // TENSORFLOW_FRAMEWORK_REGISTER_TYPES_H_
diff --git a/tensorflow/core/kernels/cwise_op_add_1.cc b/tensorflow/core/kernels/cwise_op_add_1.cc
index acf1f2ad49..c0fe81ef55 100644
--- a/tensorflow/core/kernels/cwise_op_add_1.cc
+++ b/tensorflow/core/kernels/cwise_op_add_1.cc
@@ -36,7 +36,9 @@ REGISTER_KERNEL_BUILDER(Name("Add")
 
 
 #if TENSORFLOW_USE_SYCL
-REGISTER2(BinaryOp, SYCL, "Add", functor::add, float, double);
+#define REGISTER_KERNEL(type) REGISTER(BinaryOp, SYCL, "Add", functor::add, type);
+TF_CALL_SYCL_NUMBER_TYPES(REGISTER_KERNEL);
+
 REGISTER_KERNEL_BUILDER(Name("Add")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tools/bazel.rc b/tools/bazel.rc
index e67a290cf4..414ddf2e47 100644
--- a/tools/bazel.rc
+++ b/tools/bazel.rc
@@ -11,6 +11,9 @@ build:mkl --define=using_mkl=true
 build:sycl --crosstool_top=@local_config_sycl//crosstool:toolchain
 build:sycl --define=using_sycl=true
 
+build:sycl_nodouble --crosstool_top=@local_config_sycl//crosstool:toolchain
+build:sycl_nodouble --define=using_sycl=true --cxxopt -DTENSORFLOW_SYCL_NO_DOUBLE
+
 build:sycl_asan --crosstool_top=@local_config_sycl//crosstool:toolchain
 build:sycl_asan --define=using_sycl=true --copt -fno-omit-frame-pointer --copt -fsanitize-coverage=3 --copt -DGPR_NO_DIRECT_SYSCALLS --linkopt -fPIC --linkopt -fsanitize=address
 
-- 
GitLab


From ade4107640197a33e98b3003409294a13f9bb769 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 18:03:18 -0700
Subject: [PATCH 1024/1375] Change Affine bijector to use LinearOperator
 instead of OperatorPD.

PiperOrigin-RevId: 162034086
---
 tensorflow/contrib/distributions/BUILD        |   2 +-
 .../kernel_tests/bijectors/affine_test.py     |  32 +-
 .../kernel_tests/distribution_util_test.py    | 183 ++++++++
 .../python/ops/bijectors/affine_impl.py       | 427 +++++-------------
 .../python/ops/distribution_util.py           | 157 ++++++-
 .../python/ops/vector_student_t.py            |  81 +---
 6 files changed, 467 insertions(+), 415 deletions(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index ef77302bcc..f3f5b1dcb3 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -677,7 +677,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "affine_test",
-    size = "medium",
+    size = "large",
     srcs = ["python/kernel_tests/bijectors/affine_test.py"],
     additional_deps = [
         ":bijectors_py",
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py
index b8a7d6c0ce..2c4b8277d0 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py
@@ -644,15 +644,17 @@ class AffineBijectorTest(test.TestCase):
           # Has zero on the diagonal.
           scale_diag=[0., 1],
           validate_args=True)
-      with self.assertRaisesOpError("Condition x > 0"):
+      with self.assertRaisesOpError("diagonal part must be non-zero"):
         bijector.forward([1., 1.]).eval()
 
   def testEventNdimsLargerThanOneRaises(self):
     with self.test_session():
       mu = [1., -1]
-      # Scale corresponds to 2x2 identity matrix.
-      bijector = Affine(shift=mu, event_ndims=2, validate_args=True)
-      bijector.forward([1., 1.]).eval()
+      with self.assertRaisesRegexp(
+          ValueError, (r"event_ndims\(2\) was not 0 or 1")):
+        # Scale corresponds to 2x2 identity matrix.
+        bijector = Affine(shift=mu, event_ndims=2, validate_args=True)
+        bijector.forward([1., 1.]).eval()
 
   def testScaleZeroScalarRaises(self):
     with self.test_session():
@@ -660,24 +662,23 @@ class AffineBijectorTest(test.TestCase):
       # Check Identity matrix with zero scaling.
       bijector = Affine(
           shift=mu,
-          scale_identity_multiplier=0.0,
+          scale_identity_multiplier=0.,
           event_ndims=0,
           validate_args=True)
-      with self.assertRaisesOpError("Condition x > 0"):
+      with self.assertRaisesOpError("identity_multiplier should be non-zero"):
         bijector.forward(1.).eval()
 
       # Check Diag matrix with zero scaling.
       bijector = Affine(
           shift=mu, scale_diag=[0.0], event_ndims=0, validate_args=True)
-      with self.assertRaisesOpError("Condition x > 0"):
+      with self.assertRaisesOpError("diagonal part must be non-zero"):
         bijector.forward(1.).eval()
 
   def testScalarCongruency(self):
     with self.test_session():
       bijector = Affine(
           shift=3.6, scale_identity_multiplier=0.42, event_ndims=0)
-      assert_scalar_congruency(
-          bijector, lower_x=-2., upper_x=2.)
+      assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.)
 
   def _makeScale(self,
                  x,
@@ -700,11 +701,7 @@ class AffineBijectorTest(test.TestCase):
     if c is None and d1 is None and tril is None:
       # Special case when no scale args are passed in. This means use an
       # identity matrix.
-      if v is None and d2 is None:
-        c = 1.
-      # No scale.
-      else:
-        return None
+      c = 1.
 
     matrix = np.float32(0.)
     if c is not None:
@@ -833,13 +830,6 @@ class AffineBijectorTest(test.TestCase):
         x=np.array(
             [1., 2], dtype=np.float32))
 
-  def testScalePropertyAssertsCorrectly(self):
-    with self.test_session():
-      with self.assertRaises(NotImplementedError):
-        scale = Affine(  # pylint:disable=unused-variable
-            scale_tril=[[1., 0], [2, 1]],
-            scale_perturb_factor=[2., 1.]).scale
-
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
index 472586987d..6f67f86b9b 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import itertools
+
 import numpy as np
 
 from tensorflow.contrib.distributions.python.ops import distribution_util
@@ -30,6 +32,187 @@ import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
 
 
+def _powerset(x):
+  s = list(x)
+  return itertools.chain.from_iterable(
+      itertools.combinations(s, r) for r in range(len(s) + 1))
+
+
+def _matrix_diag(d):
+  """Batch version of np.diag."""
+  orig_shape = d.shape
+  d = np.reshape(d, (int(np.prod(d.shape[:-1])), d.shape[-1]))
+  diag_list = []
+  for i in range(d.shape[0]):
+    diag_list.append(np.diag(d[i, ...]))
+  return np.reshape(diag_list, orig_shape + (d.shape[-1],))
+
+
+def _make_tril_scale(
+    loc=None,
+    scale_tril=None,
+    scale_diag=None,
+    scale_identity_multiplier=None,
+    shape_hint=None):
+  if scale_tril is not None:
+    scale_tril = np.tril(scale_tril)
+    if scale_diag is not None:
+      scale_tril += _matrix_diag(np.array(scale_diag, dtype=np.float32))
+    if scale_identity_multiplier is not None:
+      scale_tril += (
+          scale_identity_multiplier * _matrix_diag(np.ones(
+              [scale_tril.shape[-1]], dtype=np.float32)))
+    return scale_tril
+  return _make_diag_scale(
+      loc, scale_diag, scale_identity_multiplier, shape_hint)
+
+
+def _make_diag_scale(
+    loc=None,
+    scale_diag=None,
+    scale_identity_multiplier=None,
+    shape_hint=None):
+  if scale_diag is not None:
+    scale_diag = np.asarray(scale_diag)
+    if scale_identity_multiplier is not None:
+      scale_diag += scale_identity_multiplier
+    return _matrix_diag(scale_diag)
+
+  if loc is None and shape_hint is None:
+    return None
+
+  if shape_hint is None:
+    shape_hint = loc.shape[-1]
+  if scale_identity_multiplier is None:
+    scale_identity_multiplier = 1.
+  return scale_identity_multiplier * np.diag(np.ones(shape_hint))
+
+
+class MakeTrilScaleTest(test.TestCase):
+
+  def _testLegalInputs(
+      self, loc=None, shape_hint=None, scale_params=None):
+    for args in _powerset(scale_params.items()):
+      with self.test_session():
+        args = dict(args)
+
+        scale_args = dict({
+            "loc": loc,
+            "shape_hint": shape_hint}, **args)
+        expected_scale = _make_tril_scale(**scale_args)
+        if expected_scale is None:
+          # Not enough shape information was specified.
+          with self.assertRaisesRegexp(ValueError, ("is specified.")):
+            scale = distribution_util.make_tril_scale(**scale_args)
+            scale.to_dense().eval()
+        else:
+          scale = distribution_util.make_tril_scale(**scale_args)
+          self.assertAllClose(expected_scale, scale.to_dense().eval())
+
+  def testLegalInputs(self):
+    self._testLegalInputs(
+        loc=np.array([-1., -1.], dtype=np.float32),
+        shape_hint=2,
+        scale_params={
+            "scale_identity_multiplier": 2.,
+            "scale_diag": [2., 3.],
+            "scale_tril": [[1., 0.],
+                           [-3., 3.]],
+        })
+
+  def testLegalInputsMultidimensional(self):
+    self._testLegalInputs(
+        loc=np.array([[[-1., -1., 2.], [-2., -3., 4.]]], dtype=np.float32),
+        shape_hint=3,
+        scale_params={
+            "scale_identity_multiplier": 2.,
+            "scale_diag": [[[2., 3., 4.], [3., 4., 5.]]],
+            "scale_tril": [[[[1., 0., 0.],
+                             [-3., 3., 0.],
+                             [1., -2., 1.]],
+                            [[2., 1., 0.],
+                             [-4., 7., 0.],
+                             [1., -1., 1.]]]]
+        })
+
+  def testZeroTriU(self):
+    with self.test_session():
+      scale = distribution_util.make_tril_scale(scale_tril=[[1., 1], [1., 1.]])
+      self.assertAllClose([[1., 0], [1., 1.]], scale.to_dense().eval())
+
+  def testValidateArgs(self):
+    with self.test_session():
+      with self.assertRaisesOpError("diagonal part must be non-zero"):
+        scale = distribution_util.make_tril_scale(
+            scale_tril=[[0., 1], [1., 1.]], validate_args=True)
+        scale.to_dense().eval()
+
+  def testAssertPositive(self):
+    with self.test_session():
+      with self.assertRaisesOpError("diagonal part must be positive"):
+        scale = distribution_util.make_tril_scale(
+            scale_tril=[[-1., 1], [1., 1.]],
+            validate_args=True,
+            assert_positive=True)
+        scale.to_dense().eval()
+
+
+class MakeDiagScaleTest(test.TestCase):
+
+  def _testLegalInputs(
+      self, loc=None, shape_hint=None, scale_params=None):
+    for args in _powerset(scale_params.items()):
+      with self.test_session():
+        args = dict(args)
+
+        scale_args = dict({
+            "loc": loc,
+            "shape_hint": shape_hint}, **args)
+        expected_scale = _make_diag_scale(**scale_args)
+        if expected_scale is None:
+          # Not enough shape information was specified.
+          with self.assertRaisesRegexp(ValueError, ("is specified.")):
+            scale = distribution_util.make_diag_scale(**scale_args)
+            scale.to_dense().eval()
+        else:
+          scale = distribution_util.make_diag_scale(**scale_args)
+          self.assertAllClose(expected_scale, scale.to_dense().eval())
+
+  def testLegalInputs(self):
+    self._testLegalInputs(
+        loc=np.array([-1., -1.], dtype=np.float32),
+        shape_hint=2,
+        scale_params={
+            "scale_identity_multiplier": 2.,
+            "scale_diag": [2., 3.]
+        })
+
+  def testLegalInputsMultidimensional(self):
+    self._testLegalInputs(
+        loc=np.array([[[-1., -1., 2.], [-2., -3., 4.]]], dtype=np.float32),
+        shape_hint=3,
+        scale_params={
+            "scale_identity_multiplier": 2.,
+            "scale_diag": [[[2., 3., 4.], [3., 4., 5.]]]
+        })
+
+  def testValidateArgs(self):
+    with self.test_session():
+      with self.assertRaisesOpError("diagonal part must be non-zero"):
+        scale = distribution_util.make_diag_scale(
+            scale_diag=[[0., 1], [1., 1.]], validate_args=True)
+        scale.to_dense().eval()
+
+  def testAssertPositive(self):
+    with self.test_session():
+      with self.assertRaisesOpError("diagonal part must be positive"):
+        scale = distribution_util.make_diag_scale(
+            scale_diag=[[-1., 1], [1., 1.]],
+            validate_args=True,
+            assert_positive=True)
+        scale.to_dense().eval()
+
+
 class ShapesFromLocAndScaleTest(test.TestCase):
 
   def test_static_loc_static_scale_non_matching_event_size_raises(self):
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
index 67d248b977..c3c0413ce2 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
@@ -18,10 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
-from tensorflow.contrib.distributions.python.ops import operator_pd_diag
-from tensorflow.contrib.distributions.python.ops import operator_pd_identity
-from tensorflow.contrib.distributions.python.ops import operator_pd_vdvt_update
+from tensorflow.contrib import linalg
+from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.contrib.distributions.python.ops.shape import _DistributionShape
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -29,7 +27,6 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import bijector
 
@@ -44,143 +41,6 @@ def _as_tensor(x, name):
   return None if x is None else ops.convert_to_tensor(x, name=name)
 
 
-# TODO(srvasude): Deprecate this class with a dedicated Linear Operator
-# corresponding to TriL + V D V.T.
-class _TriLPlusVDVTLightweightOperatorPD(object):
-  """Helper/hidden class fake an OperatorPD for TriL+VDV.T."""
-
-  def __init__(self, tril, v, diag=None, validate_args=False):
-    """Creates an instance of _TriLPlusVDVTLightweightOperatorPD.
-
-    WARNING: This object is not to be used outside of `Affine` where it is
-    currently being temporarily used for refactoring purposes.
-
-    Args:
-      tril: `Tensor` of shape `[B1,..,Bb, d, d]`.
-      v: `Tensor` of shape `[B1,...,Bb, d, k]`.
-      diag: `Tensor` of shape `[B1,...,Bb, k, k]` or None
-      validate_args: Python `bool` indicating whether arguments should be
-        checked for correctness.
-    """
-    self._m = tril
-    self._v = v
-    self._validate_args = validate_args
-    self._inputs = [tril, v]
-    if diag is not None:
-      self._inputs += [diag]
-      self._d = operator_pd_diag.OperatorPDDiag(diag, verify_pd=validate_args)
-      self._d_inv = operator_pd_diag.OperatorPDDiag(1. / diag,
-                                                    verify_pd=validate_args)
-      return
-    if v.get_shape().is_fully_defined():
-      v_shape = v.get_shape().as_list()
-      id_shape = v_shape[:-2] + [v_shape[-1], v_shape[-1]]
-    else:
-      v_shape = array_ops.shape(v)
-      id_shape = array_ops.concat([v_shape[:-2], [v_shape[-1], v_shape[-1]]], 0)
-    self._d = operator_pd_identity.OperatorPDIdentity(
-        id_shape, v.dtype, verify_pd=self.validate_args)
-    self._d_inv = self._d
-
-  @property
-  def inputs(self):
-    return self._inputs
-
-  @property
-  def dtype(self):
-    return self._m.dtype.base_dtype
-
-  @property
-  def validate_args(self):
-    return self._validate_args
-
-  def rank(self):
-    """Returns `rank(self)`."""
-    return array_ops.rank(self._m)
-
-  def sqrt_matmul(self, x):
-    """Computes `matmul(self, x)`.
-
-    Doesn't actually do the sqrt! Named as such to agree with API.
-
-    Args:
-      x: `Tensor`
-
-    Returns:
-      self_times_x: `Tensor`
-    """
-    m_x = math_ops.matmul(self._m, x)
-    vt_x = math_ops.matmul(self._v, x, adjoint_a=True)
-    d_vt_x = self._d.matmul(vt_x)
-    v_d_vt_x = math_ops.matmul(self._v, d_vt_x)
-    return m_x + v_d_vt_x
-
-  def sqrt_solve(self, x):
-    """Computes `solve(self, x)`.
-
-    Doesn't actually do the sqrt! Named as such to agree with API.
-
-    To compute (M + V D V.T), we use the Woodbury matrix identity:
-      inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
-    where,
-      C = inv(D) + V.T inv(M) V.
-    See: https://en.wikipedia.org/wiki/Woodbury_matrix_identity
-
-    Args:
-      x: `Tensor`
-
-    Returns:
-      inv_of_self_times_x: `Tensor`
-    """
-    minv_x = linalg_ops.matrix_triangular_solve(self._m, x)
-    vt_minv_x = math_ops.matmul(self._v, minv_x, transpose_a=True)
-    cinv_vt_minv_x = linalg_ops.matrix_solve(
-        self._woodbury_sandwiched_term(), vt_minv_x)
-    v_cinv_vt_minv_x = math_ops.matmul(self._v, cinv_vt_minv_x)
-    minv_v_cinv_vt_minv_x = linalg_ops.matrix_triangular_solve(
-        self._m, v_cinv_vt_minv_x)
-    return minv_x - minv_v_cinv_vt_minv_x
-
-  def sqrt_log_abs_det(self):
-    """Computes (log o abs o det)(X) for matrix X.
-
-    Doesn't actually do the sqrt! Named as such to agree with API.
-
-    To compute det(M + V D V.T), we use the matrix determinant lemma:
-      det(Tril + V D V.T) = det(C) det(D) det(M)
-    where C is defined as in `_inverse`, ie,
-      C = inv(D) + V.T inv(M) V.
-
-    See: https://en.wikipedia.org/wiki/Matrix_determinant_lemma
-
-    Returns:
-      log_abs_det: `Tensor`.
-    """
-    log_det_c = math_ops.log(math_ops.abs(
-        linalg_ops.matrix_determinant(self._woodbury_sandwiched_term())))
-    # Reduction is ok because we always prepad inputs to this class.
-    log_det_m = math_ops.reduce_sum(math_ops.log(math_ops.abs(
-        array_ops.matrix_diag_part(self._m))), axis=[-1])
-    return log_det_c + 2. * self._d.sqrt_log_abs_det() + log_det_m
-
-  def _woodbury_sandwiched_term(self):
-    """Computes the sandwiched term in the Woodbury identity.
-
-    Computes the "`C`" in the identity:
-       inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
-    where,
-       C = inv(D) + V.T inv(M) V.
-
-    See: https://en.wikipedia.org/wiki/Woodbury_matrix_identity
-
-    Returns:
-      woodbury_sandwich_term: A `Tensor` to be used like `C`, above.
-    """
-    minv_v = linalg_ops.matrix_triangular_solve(self._m, self._v)
-    vt_minv_v = math_ops.matmul(self._v, minv_v, adjoint_a=True)
-    return self._d_inv.add_to_tensor(vt_minv_v)
-
-
 class Affine(bijector.Bijector):
   """Compute `Y = g(X; shift, scale) = scale @ X + shift`.
 
@@ -297,7 +157,7 @@ class Affine(bijector.Bijector):
         matrix. `scale_perturb_diag` has shape [N1, N2, ...  r], which
         represents an `r x r` diagonal matrix. When `None` low rank updates will
         take the form `scale_perturb_factor * scale_perturb_factor.T`.
-      event_ndims: Scalar `int32` `Tensor` indicating the number of dimensions
+      event_ndims: Scalar `int` `Tensor` indicating the number of dimensions
         associated with a particular draw from the distribution. Must be 0 or 1.
       validate_args: Python `bool` indicating whether arguments should be
         checked for correctness.
@@ -310,10 +170,12 @@ class Affine(bijector.Bijector):
     self._graph_parents = []
     self._name = name
     self._validate_args = validate_args
+
     # Ambiguous definition of low rank update.
     if scale_perturb_diag is not None and scale_perturb_factor is None:
       raise ValueError("When scale_perturb_diag is specified, "
                        "scale_perturb_factor must be specified.")
+
     # Special case, only handling a scaled identity matrix. We don't know its
     # dimensions, so this is special cased.
     # We don't check identity_multiplier, since below we set it to 1. if all
@@ -321,43 +183,70 @@ class Affine(bijector.Bijector):
     self._is_only_identity_multiplier = (scale_tril is None and
                                          scale_diag is None and
                                          scale_perturb_factor is None)
-    # When no args are specified, pretend the scale matrix is the identity
-    # matrix.
+
     with self._name_scope("init", values=[
         shift, scale_identity_multiplier, scale_diag, scale_tril,
-        scale_perturb_diag, scale_perturb_factor, event_ndims]):
+        scale_perturb_diag, scale_perturb_factor]):
       event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims")
-      if validate_args:
-        is_less_than_two = check_ops.assert_less(
-            event_ndims, 2,
-            message="event_ndims must be 0 or 1")
-        event_ndims = control_flow_ops.with_dependencies(
-            [is_less_than_two], event_ndims)
-      self._shift = _as_tensor(shift, "shift")
-      if (self._is_only_identity_multiplier
-          and scale_identity_multiplier is None):
-        if self._shift is not None:
-          scale_identity_multiplier = ops.convert_to_tensor(
-              1., dtype=self._shift.dtype)
-        else:
-          scale_identity_multiplier = 1.
-      # self._create_scale_operator returns an OperatorPD in all cases except if
-      # self._is_only_identity_multiplier; in which case it returns a scalar
-      # Tensor.
-      self._scale = self._create_scale_operator(
+      if tensor_util.constant_value(event_ndims) is not None:
+        event_ndims = tensor_util.constant_value(event_ndims)
+        if event_ndims not in (0, 1):
+          raise ValueError("event_ndims({}) was not 0 or 1".format(event_ndims))
+      else:
+        if validate_args:
+          # Shape tool will catch if event_ndims is negative.
+          event_ndims = control_flow_ops.with_dependencies(
+              [check_ops.assert_less(
+                  event_ndims, 2, message="event_ndims must be 0 or 1")],
+              event_ndims)
+
+      # In the absence of `loc` and `scale`, we'll assume `dtype` is `float32`.
+      dtype = dtypes.float32
+
+      if shift is not None:
+        shift = ops.convert_to_tensor(shift, name="shift")
+        dtype = shift.dtype.base_dtype
+      self._shift = shift
+
+      # When no args are specified, pretend the scale matrix is the identity
+      # matrix.
+      if (self._is_only_identity_multiplier and
+          scale_identity_multiplier is None):
+        scale_identity_multiplier = ops.convert_to_tensor(1., dtype=dtype)
+
+      # self._create_scale_operator returns a LinearOperator in all cases
+      # except if self._is_only_identity_multiplier; in which case it
+      # returns a scalar Tensor.
+      scale = self._create_scale_operator(
           identity_multiplier=scale_identity_multiplier,
           diag=scale_diag,
           tril=scale_tril,
           perturb_diag=scale_perturb_diag,
           perturb_factor=scale_perturb_factor,
-          event_ndims=event_ndims,
+          shift=shift,
           validate_args=validate_args)
-      if (self._shift is not None and
-          self._shift.dtype.base_dtype != self._scale.dtype.base_dtype):
-        raise TypeError("shift.dtype({}) does not match scale.dtype({})".format(
-            self._shift.dtype, self._scale.dtype))
+
+      if scale.dtype is not None:
+        dtype = scale.dtype.base_dtype
+
+      if scale is not None and not self._is_only_identity_multiplier:
+        if (shift is not None and
+            shift.dtype.base_dtype != scale.dtype.base_dtype):
+          raise TypeError(
+              "shift.dtype({}) is incompatible with scale.dtype({}).".format(
+                  shift.dtype, scale.dtype))
+
+        if scale.tensor_rank is not None:
+          batch_ndims = scale.tensor_rank - 2
+        else:
+          batch_ndims = scale.tensor_rank_tensor() - 2
+      else:
+        # We won't need shape inference when scale is None or when scale is a
+        # scalar.
+        batch_ndims = 0
+      self._scale = scale
       self._shaper = _DistributionShape(
-          batch_ndims=self._infer_batch_ndims(),
+          batch_ndims=batch_ndims,
           event_ndims=event_ndims,
           validate_args=validate_args)
       super(Affine, self).__init__(
@@ -365,15 +254,15 @@ class Affine(bijector.Bijector):
           graph_parents=(
               [event_ndims] +
               [self._scale] if tensor_util.is_tensor(self._scale)
-              else self._scale.inputs +
+              else self._scale.graph_parents +
               [self._shift] if self._shift is not None else []),
           is_constant_jacobian=True,
-          dtype=self._scale.dtype,
+          dtype=dtype,
           validate_args=validate_args,
           name=name)
 
   def _create_scale_operator(self, identity_multiplier, diag, tril,
-                             perturb_diag, perturb_factor, event_ndims,
+                             perturb_diag, perturb_factor, shift,
                              validate_args):
     """Construct `scale` from various components.
 
@@ -389,14 +278,13 @@ class Affine(bijector.Bijector):
       perturb_diag: Floating-point `Tensor` representing the diagonal matrix of
         the low rank update.
       perturb_factor: Floating-point `Tensor` representing factor matrix.
-      event_ndims: Scalar `int32` `Tensor` indicating the number of dimensions
-        associated with a particular draw from the distribution. Must be 0 or 1
+      shift: Floating-point `Tensor` representing `shift in `scale @ X + shift`.
       validate_args: Python `bool` indicating whether arguments should be
         checked for correctness.
 
     Returns:
       scale. In the case of scaling by a constant, scale is a
-      floating point `Tensor`. Otherwise, scale is an `OperatorPD`.
+      floating point `Tensor`. Otherwise, scale is a `LinearOperator`.
 
     Raises:
       ValueError: if all of `tril`, `diag` and `identity_multiplier` are `None`.
@@ -407,119 +295,44 @@ class Affine(bijector.Bijector):
     perturb_diag = _as_tensor(perturb_diag, "perturb_diag")
     perturb_factor = _as_tensor(perturb_factor, "perturb_factor")
 
-    identity_multiplier = self._maybe_validate_identity_multiplier(
-        identity_multiplier, validate_args)
-
+    # If possible, use the low rank update to infer the shape of
+    # the identity matrix, when scale represents a scaled identity matrix
+    # with a low rank update.
+    shape_hint = None
     if perturb_factor is not None:
-      perturb_factor = self._process_matrix(
-          perturb_factor, min_rank=2, event_ndims=event_ndims)
-
-    if perturb_diag is not None:
-      perturb_diag = self._process_matrix(
-          perturb_diag, min_rank=1, event_ndims=event_ndims)
-
-    # The following if-statments are ordered by increasingly stronger
-    # assumptions in the base matrix, i.e., we process in the order:
-    # TriL, Diag, Identity.
-
-    if tril is not None:
-      tril = self._preprocess_tril(
-          identity_multiplier, diag, tril, event_ndims)
-      if perturb_factor is None:
-        return operator_pd_cholesky.OperatorPDCholesky(
-            tril, verify_pd=validate_args)
-      return _TriLPlusVDVTLightweightOperatorPD(
-          tril=tril, v=perturb_factor, diag=perturb_diag,
-          validate_args=validate_args)
+      shape_hint = distribution_util.dimension_size(perturb_factor, axis=-2)
 
-    if diag is not None:
-      diag = self._preprocess_diag(identity_multiplier, diag, event_ndims)
-      if perturb_factor is None:
-        return operator_pd_diag.OperatorPDSqrtDiag(
-            diag, verify_pd=validate_args)
-      return operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(
-          operator=operator_pd_diag.OperatorPDDiag(
-              diag, verify_pd=validate_args),
-          v=perturb_factor,
-          diag=perturb_diag,
-          verify_pd=validate_args)
-
-    if identity_multiplier is not None:
-      if perturb_factor is None:
-        return identity_multiplier
-      # Infer the shape from the V and D.
-      v_shape = array_ops.shape(perturb_factor)
-      identity_shape = array_ops.concat([v_shape[:-1], [v_shape[-2]]], 0)
-      scaled_identity = operator_pd_identity.OperatorPDIdentity(
-          identity_shape,
-          perturb_factor.dtype.base_dtype,
-          scale=identity_multiplier,
-          verify_pd=validate_args)
-      return operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(
-          operator=scaled_identity,
-          v=perturb_factor,
-          diag=perturb_diag,
-          verify_pd=validate_args)
-
-    raise ValueError("One of tril, diag and/or identity_multiplier must be "
-                     "specified.")
-
-  def _maybe_validate_identity_multiplier(self, identity_multiplier,
-                                          validate_args):
-    """Check that the init arg `identity_multiplier` is valid."""
-    if identity_multiplier is None or not validate_args:
-      return identity_multiplier
-    if validate_args:
-      identity_multiplier = control_flow_ops.with_dependencies(
-          [check_ops.assert_positive(identity_multiplier)],
-          identity_multiplier)
-    return identity_multiplier
-
-  def _preprocess_tril(self, identity_multiplier, diag, tril, event_ndims):
-    """Helper to preprocess a lower triangular matrix."""
-    tril = array_ops.matrix_band_part(tril, -1, 0)  # Zero out TriU.
-    if identity_multiplier is None and diag is None:
-      return self._process_matrix(tril, min_rank=2, event_ndims=event_ndims)
-    new_diag = array_ops.matrix_diag_part(tril)
-    if identity_multiplier is not None:
-      new_diag += identity_multiplier
-    if diag is not None:
-      new_diag += diag
-    tril = array_ops.matrix_set_diag(tril, new_diag)
-    return self._process_matrix(tril, min_rank=2, event_ndims=event_ndims)
-
-  def _preprocess_diag(self, identity_multiplier, diag, event_ndims):
-    """Helper to preprocess a diagonal matrix."""
-    if identity_multiplier is not None:
-      diag += identity_multiplier
-    return self._process_matrix(diag, min_rank=1, event_ndims=event_ndims)
-
-  def _process_matrix(self, matrix, min_rank, event_ndims):
-    """Helper to __init__ which gets matrix in batch-ready form."""
-    # Pad the matrix so that matmul works in the case of a matrix and vector
-    # input. Keep track if the matrix was padded, to distinguish between a
-    # rank 3 tensor and a padded rank 2 tensor.
-    # TODO(srvasude): Remove side-effects from functions. Its currently unbroken
-    # but error-prone since the function call order may change in the future.
-    self._rank_two_event_ndims_one = math_ops.logical_and(
-        math_ops.equal(array_ops.rank(matrix), min_rank),
-        math_ops.equal(event_ndims, 1))
-    left = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
-    pad = array_ops.concat(
-        [array_ops.ones(
-            [left], dtype=dtypes.int32), array_ops.shape(matrix)],
-        0)
-    return array_ops.reshape(matrix, pad)
-
-  def _infer_batch_ndims(self):
-    """Return batch_ndims."""
     if self._is_only_identity_multiplier:
-      return 0
-    # The real batch dims is one less when we pad in the case of event_ndims =
-    # 1, and the rank of the underlying scale being 2. This allows us to have
-    # non-negative sample dims.
-    return (self._scale.rank() - 2 -
-            array_ops.where(self._rank_two_event_ndims_one, 1, 0))
+      if validate_args:
+        return control_flow_ops.with_dependencies(
+            [check_ops.assert_none_equal(
+                identity_multiplier,
+                array_ops.zeros([], identity_multiplier.dtype),
+                ["identity_multiplier should be non-zero."])],
+            identity_multiplier)
+      return identity_multiplier
+
+    scale = distribution_util.make_tril_scale(
+        loc=shift,
+        scale_tril=tril,
+        scale_diag=diag,
+        scale_identity_multiplier=identity_multiplier,
+        validate_args=validate_args,
+        assert_positive=False,
+        shape_hint=shape_hint)
+
+    if perturb_factor is not None:
+      return linalg.LinearOperatorUDVHUpdate(
+          scale,
+          u=perturb_factor,
+          diag_update=perturb_diag,
+          is_diag_update_positive=perturb_diag is None,
+          is_non_singular=True,  # Implied by is_positive_definite=True.
+          is_self_adjoint=True,
+          is_positive_definite=True,
+          is_square=True)
+
+    return scale
 
   @property
   def shift(self):
@@ -529,10 +342,6 @@ class Affine(bijector.Bijector):
   @property
   def scale(self):
     """The `scale` `LinearOperator` in `Y = scale @ X + shift`."""
-    # TODO(srvasude): Remove this exception once TriLPlusVDVT is properly
-    # implemented.
-    if isinstance(self._scale, _TriLPlusVDVTLightweightOperatorPD):
-      raise NotImplementedError("Cannot access scale when Tril+VDV.T.")
     return self._scale
 
   def _forward(self, x):
@@ -542,11 +351,15 @@ class Affine(bijector.Bijector):
       if self.shift is not None:
         return y + self.shift
       return  y
-    y, sample_shape = self._shaper.make_batch_of_event_sample_matrices(y)
-    y = self._scale.sqrt_matmul(y)
-    y = self._shaper.undo_make_batch_of_event_sample_matrices(y, sample_shape)
+    y, sample_shape = self._shaper.make_batch_of_event_sample_matrices(
+        y, expand_batch_dim=False)
+    with ops.control_dependencies(self._maybe_check_scale() if
+                                  self.validate_args else []):
+      y = self.scale.matmul(y)
+    y = self._shaper.undo_make_batch_of_event_sample_matrices(
+        y, sample_shape, expand_batch_dim=False)
     if self.shift is not None:
-      return y + self.shift
+      y += self.shift
     return y
 
   def _inverse(self, y):
@@ -555,9 +368,13 @@ class Affine(bijector.Bijector):
       x -= self.shift
     if self._is_only_identity_multiplier:
       return x / self._scale
-    x, sample_shape = self._shaper.make_batch_of_event_sample_matrices(x)
-    x = self._scale.sqrt_solve(x)
-    x = self._shaper.undo_make_batch_of_event_sample_matrices(x, sample_shape)
+
+    x, sample_shape = self._shaper.make_batch_of_event_sample_matrices(
+        x, expand_batch_dim=False)
+    # Solve fails if the op is singular so we may safely skip this assertion.
+    x = self.scale.solve(x)
+    x = self._shaper.undo_make_batch_of_event_sample_matrices(
+        x, sample_shape, expand_batch_dim=False)
     return x
 
   def _inverse_log_det_jacobian(self, y):
@@ -565,13 +382,17 @@ class Affine(bijector.Bijector):
 
   def _forward_log_det_jacobian(self, x):
     if self._is_only_identity_multiplier:
-      # TODO(jvdillon): We don't pad in this case and instead let the fldj be
-      # applied via broadcast.
+      # We don't pad in this case and instead let the fldj be applied
+      # via broadcast.
       d = math_ops.cast(array_ops.shape(x)[-1], dtype=self._scale.dtype)
       one = ops.convert_to_tensor(1., self._scale.dtype)
       return math_ops.log(math_ops.abs(self._scale)) * array_ops.where(
           math_ops.equal(self._shaper.event_ndims, 0), one, d)
-    fldj = self._scale.sqrt_log_abs_det()
-    # We need to squeeze off the padded dimension.
-    start = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
-    return array_ops.reshape(fldj, array_ops.shape(fldj)[start:])
+    return self.scale.log_abs_determinant()
+
+  def _maybe_check_scale(self):
+    try:
+      return [self.scale.assert_non_singular()]
+    except NotImplementedError:
+      pass
+    return []
diff --git a/tensorflow/contrib/distributions/python/ops/distribution_util.py b/tensorflow/contrib/distributions/python/ops/distribution_util.py
index 5e3b42dd2a..cda5829f45 100644
--- a/tensorflow/contrib/distributions/python/ops/distribution_util.py
+++ b/tensorflow/contrib/distributions/python/ops/distribution_util.py
@@ -25,16 +25,146 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops.distributions import util
 from tensorflow.python.ops.distributions.util import *  # pylint: disable=wildcard-import
 
 
-# TODO(b/35290280): Add unit-tests.
-def make_diag_scale(loc, scale_diag, scale_identity_multiplier,
-                    validate_args, assert_positive, name=None):
-  """Creates a LinOp from `scale_diag`, `scale_identity_multiplier` kwargs."""
-  def _convert_to_tensor(x, name):
-    return None if x is None else ops.convert_to_tensor(x, name=name)
+def _convert_to_tensor(x, name):
+  return None if x is None else ops.convert_to_tensor(x, name=name)
+
+
+def make_tril_scale(
+    loc=None,
+    scale_tril=None,
+    scale_diag=None,
+    scale_identity_multiplier=None,
+    shape_hint=None,
+    validate_args=False,
+    assert_positive=False,
+    name=None):
+  """Creates a LinOp representing a lower triangular matrix.
+
+  Args:
+    loc: Floating-point `Tensor`. This is used for inferring shape in the case
+      where only `scale_identity_multiplier` is set.
+    scale_tril: Floating-point `Tensor` representing the diagonal matrix.
+      `scale_diag` has shape [N1, N2, ...  k, k], which represents a k x k
+      lower triangular matrix.
+      When `None` no `scale_tril` term is added to the LinOp.
+      The upper triangular elements above the diagonal are ignored.
+    scale_diag: Floating-point `Tensor` representing the diagonal matrix.
+      `scale_diag` has shape [N1, N2, ...  k], which represents a k x k
+      diagonal matrix.
+      When `None` no diagonal term is added to the LinOp.
+    scale_identity_multiplier: floating point rank 0 `Tensor` representing a
+      scaling done to the identity matrix.
+      When `scale_identity_multiplier = scale_diag = scale_tril = None` then
+      `scale += IdentityMatrix`. Otherwise no scaled-identity-matrix is added
+      to `scale`.
+    shape_hint: scalar integer `Tensor` representing a hint at the dimension of
+      the identity matrix when only `scale_identity_multiplier` is set.
+    validate_args: Python `bool` indicating whether arguments should be
+      checked for correctness.
+    assert_positive: Python `bool` indicating whether LinOp should be checked
+      for being positive definite.
+    name: Python `str` name given to ops managed by this object.
+
+  Returns:
+    `LinearOperator` representing a lower triangular matrix.
+
+  Raises:
+    ValueError:  If only `scale_identity_multiplier` is set and `loc` and
+      `shape_hint` are both None.
+  """
+
+  def _maybe_attach_assertion(x):
+    if not validate_args:
+      return x
+    if assert_positive:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_positive(
+              array_ops.matrix_diag_part(x),
+              message="diagonal part must be positive"),
+      ], x)
+    return control_flow_ops.with_dependencies([
+        check_ops.assert_none_equal(
+            array_ops.matrix_diag_part(x),
+            array_ops.zeros([], x.dtype),
+            message="diagonal part must be non-zero"),
+    ], x)
+
+  with ops.name_scope(name, "make_tril_scale",
+                      values=[loc, scale_diag, scale_identity_multiplier]):
+
+    loc = _convert_to_tensor(loc, name="loc")
+    scale_tril = _convert_to_tensor(scale_tril, name="scale_tril")
+    scale_diag = _convert_to_tensor(scale_diag, name="scale_diag")
+    scale_identity_multiplier = _convert_to_tensor(
+        scale_identity_multiplier,
+        name="scale_identity_multiplier")
+
+  if scale_tril is not None:
+    scale_tril = array_ops.matrix_band_part(scale_tril, -1, 0)  # Zero out TriU.
+    tril_diag = array_ops.matrix_diag_part(scale_tril)
+    if scale_diag is not None:
+      tril_diag += scale_diag
+    if scale_identity_multiplier is not None:
+      tril_diag += scale_identity_multiplier[..., array_ops.newaxis]
+
+    scale_tril = array_ops.matrix_set_diag(scale_tril, tril_diag)
+
+    return linalg.LinearOperatorTriL(
+        tril=_maybe_attach_assertion(scale_tril),
+        is_non_singular=True,
+        is_self_adjoint=False,
+        is_positive_definite=assert_positive)
+
+  return make_diag_scale(
+      loc=loc,
+      scale_diag=scale_diag,
+      scale_identity_multiplier=scale_identity_multiplier,
+      shape_hint=shape_hint,
+      validate_args=validate_args,
+      assert_positive=assert_positive,
+      name=name)
+
+
+def make_diag_scale(
+    loc=None,
+    scale_diag=None,
+    scale_identity_multiplier=None,
+    shape_hint=None,
+    validate_args=False,
+    assert_positive=False,
+    name=None):
+  """Creates a LinOp representing a diagonal matrix.
+
+  Args:
+    loc: Floating-point `Tensor`. This is used for inferring shape in the case
+      where only `scale_identity_multiplier` is set.
+    scale_diag: Floating-point `Tensor` representing the diagonal matrix.
+      `scale_diag` has shape [N1, N2, ...  k], which represents a k x k
+      diagonal matrix.
+      When `None` no diagonal term is added to the LinOp.
+    scale_identity_multiplier: floating point rank 0 `Tensor` representing a
+      scaling done to the identity matrix.
+      When `scale_identity_multiplier = scale_diag = scale_tril = None` then
+      `scale += IdentityMatrix`. Otherwise no scaled-identity-matrix is added
+      to `scale`.
+    shape_hint: scalar integer `Tensor` representing a hint at the dimension of
+      the identity matrix when only `scale_identity_multiplier` is set.
+    validate_args: Python `bool` indicating whether arguments should be
+      checked for correctness.
+    assert_positive: Python `bool` indicating whether LinOp should be checked
+      for being positive definite.
+    name: Python `str` name given to ops managed by this object.
+
+  Returns:
+    `LinearOperator` representing a lower triangular matrix.
+
+  Raises:
+    ValueError:  If only `scale_identity_multiplier` is set and `loc` and
+      `shape_hint` are both None.
+  """
 
   def _maybe_attach_assertion(x):
     if not validate_args:
@@ -67,23 +197,24 @@ def make_diag_scale(loc, scale_diag, scale_identity_multiplier,
           is_self_adjoint=True,
           is_positive_definite=assert_positive)
 
-    # TODO(b/35290280): Consider inferring shape from scale_perturb_factor.
-    if loc is None:
+    if loc is None and shape_hint is None:
       raise ValueError(
-          "Cannot infer `event_shape` unless `loc` is specified.")
+          "Cannot infer `event_shape` unless `loc` or "
+          "`shape_hint` is specified.")
 
-    num_rows = util.dimension_size(loc, -1)
+    if shape_hint is None:
+      shape_hint = loc.shape[-1]
 
     if scale_identity_multiplier is None:
       return linalg.LinearOperatorIdentity(
-          num_rows=num_rows,
+          num_rows=shape_hint,
           dtype=loc.dtype.base_dtype,
           is_self_adjoint=True,
           is_positive_definite=True,
           assert_proper_shapes=validate_args)
 
     return linalg.LinearOperatorScaledIdentity(
-        num_rows=num_rows,
+        num_rows=shape_hint,
         multiplier=_maybe_attach_assertion(scale_identity_multiplier),
         is_non_singular=True,
         is_self_adjoint=True,
diff --git a/tensorflow/contrib/distributions/python/ops/vector_student_t.py b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
index 507493560b..29d41ab81c 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_student_t.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
@@ -19,87 +19,13 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.distributions.python.ops import bijectors
+from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops.distributions import student_t
 from tensorflow.python.ops.distributions import transformed_distribution
-from tensorflow.python.ops.distributions import util as distribution_util
-
-
-# TODO(jvdillon): Add unittests for this once we know where will put this code
-# and how it will generally be used. In the interim this code is tested via the
-# _VectorStudentT tests.
-def _infer_shapes(scale_oppd, shift):
-  """Helper which returns batch_shape, event_shape from `Affine` properties.
-
-  The `Affine` `Bijector` (roughly) computes `Y = scale @ X.T + shift`. This
-  function infers the `batch_shape` and `event_shape` from the `scale` and
-  `shift` terms.
-
-  Args:
-    scale_oppd: Instance of OperatorPDBase subclass representing the `Affine`
-      `Bijector` scale matrix.
-    shift: `Tensor` representing the `shift` vector.
-
-  Returns:
-    batch_shape: 1D, integer `Tensor` representing the shape of batch
-      dimensions.
-    event_shape: 1D, integer `Tensor` representing the shape of event
-      dimensions.
-
-  Raises:
-    ValueError: if we are not able to infer batch/event shapes from the args.
-  """
-  # Collect known static shape.
-  def _has_static_ndims(x):
-    return x is not None and x.get_shape().ndims is not None
-  if _has_static_ndims(scale_oppd) and _has_static_ndims(shift):
-    batch_shape = scale_oppd.get_batch_shape().merge_with(
-        shift.get_shape()[:-1])
-    event_shape = scale_oppd.get_shape()[-1:].merge_with(
-        shift.get_shape()[-1:])
-  elif _has_static_ndims(scale_oppd):
-    batch_shape = scale_oppd.get_batch_shape()
-    event_shape = scale_oppd.get_shape()[-1:]
-  elif _has_static_ndims(shift):
-    batch_shape = shift.get_shape()[:-1]
-    event_shape = shift.get_shape()[-1:]
-  else:
-    batch_shape = tensor_shape.TensorShape(None)
-    event_shape = tensor_shape.TensorShape(None)
-
-  # Convert TensorShape to Tensors and see if we're done.
-  if batch_shape.is_fully_defined():
-    batch_shape = constant_op.constant(batch_shape.as_list(),
-                                       dtype=dtypes.int32)
-  else:
-    batch_shape = None
-  if event_shape.is_fully_defined():
-    event_shape = constant_op.constant(event_shape.as_list(),
-                                       dtype=dtypes.int32)
-  else:
-    event_shape = None
-  if batch_shape is not None and event_shape is not None:
-    return batch_shape, event_shape
-
-  # Collect known dynamic shape.
-  if scale_oppd is not None:
-    shape = scale_oppd.shape()
-  elif shift is not None:
-    shape = array_ops.shape(shift)
-  else:
-    raise ValueError("unable to infer batch_shape, event_shape")
-
-  # Fill in what we don't know.
-  if batch_shape is None:
-    batch_shape = array_ops.identity(shape[:-1], name="batch_shape")
-  if event_shape is None:
-    event_shape = array_ops.identity(shape[-1:], name="event_shape")
-
-  return batch_shape, event_shape
 
 
 class _VectorStudentT(transformed_distribution.TransformedDistribution):
@@ -282,8 +208,9 @@ class _VectorStudentT(transformed_distribution.TransformedDistribution):
             df=df,
             loc=array_ops.zeros([], dtype=affine.dtype),
             scale=array_ops.ones([], dtype=affine.dtype))
-        batch_shape, override_event_shape = _infer_shapes(
-            affine.scale, affine.shift)
+        batch_shape, override_event_shape = (
+            distribution_util.shapes_from_loc_and_scale(
+                affine.shift, affine.scale))
         override_batch_shape = distribution_util.pick_vector(
             distribution.is_scalar_batch(),
             batch_shape,
-- 
GitLab


From ca5629f77527ad53745db14ea95b64fffb173cb0 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 14 Jul 2017 19:56:12 -0700
Subject: [PATCH 1025/1375] Add missing grpcio install to
 install_python3.5_pip_packages.sh

PiperOrigin-RevId: 162040682
---
 .../tools/ci_build/install/install_python3.5_pip_packages.sh    | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index 084ac49496..706d414746 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -89,3 +89,5 @@ pip3.5 install wheel==0.29.0
 pip3.5 install portpicker
 
 pip3.5 install werkzeug
+
+pip3.5 install grpcio
-- 
GitLab


From 35b606b976de752ee822ace4e290e253bb9482ac Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 14 Jul 2017 20:15:52 -0700
Subject: [PATCH 1026/1375] FileBlockCache is now a filesystem-wide shared
 cache with a configurable maximum size (in total bytes of cached block
 contents). The default maximum cache size is equal to the block size, and can
 be overridden by the environment variable GCS_READ_CACHE_MAX_SIZE_MB. In
 addition, deleting files now flushes them from the block cache, and renaming
 files flushes both the source and destination files from the block cache.

PiperOrigin-RevId: 162041685
---
 .../core/platform/cloud/file_block_cache.cc   |  90 ++--
 .../core/platform/cloud/file_block_cache.h    |  78 ++-
 .../platform/cloud/file_block_cache_test.cc   | 188 +++++--
 .../core/platform/cloud/gcs_file_system.cc    | 129 +++--
 .../core/platform/cloud/gcs_file_system.h     |  38 +-
 .../platform/cloud/gcs_file_system_test.cc    | 500 ++++++++++--------
 6 files changed, 598 insertions(+), 425 deletions(-)

diff --git a/tensorflow/core/platform/cloud/file_block_cache.cc b/tensorflow/core/platform/cloud/file_block_cache.cc
index 84ef0ea1fe..27e023745e 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.cc
+++ b/tensorflow/core/platform/cloud/file_block_cache.cc
@@ -20,62 +20,70 @@ limitations under the License.
 
 namespace tensorflow {
 
-Status FileBlockCache::Read(uint64 offset, size_t n, std::vector<char>* out) {
+Status FileBlockCache::Read(const string& filename, size_t offset, size_t n,
+                            std::vector<char>* out) {
   out->clear();
   if (n == 0) {
     return Status::OK();
   }
-  if (block_size_ == 0 || block_count_ == 0) {
+  if (block_size_ == 0 || max_bytes_ == 0) {
     // The cache is effectively disabled, so we pass the read through to the
     // fetcher without breaking it up into blocks.
-    return block_fetcher_(offset, n, out);
+    return block_fetcher_(filename, offset, n, out);
   }
   // Calculate the block-aligned start and end of the read.
-  uint64 start = block_size_ * (offset / block_size_);
-  uint64 finish = block_size_ * ((offset + n) / block_size_);
+  size_t start = block_size_ * (offset / block_size_);
+  size_t finish = block_size_ * ((offset + n) / block_size_);
   if (finish < offset + n) {
     finish += block_size_;
   }
   mutex_lock lock(mu_);
-  // If the oldest block arrived max_staleness_ in the past, flush the cache.
-  // Note that if max_staleness_ is 0, we don't expire any cached blocks.
-  if (max_staleness_ > 0 && timestamp_ > 0 &&
-      env_->NowSeconds() - timestamp_ > max_staleness_) {
-    TrimCache(0);
-    timestamp_ = 0;
-  }
   // Now iterate through the blocks, reading them one at a time. Reads are
   // locked so that only one block_fetcher call is active at any given time.
-  for (uint64 pos = start; pos < finish; pos += block_size_) {
-    auto entry = block_map_.find(pos);
+  for (size_t pos = start; pos < finish; pos += block_size_) {
+    Key key = std::make_pair(filename, pos);
+    auto entry = block_map_.find(key);
+    // If we're enforcing max staleness and the block is stale, remove all of
+    // the file's cached blocks so we reload them.
+    if (entry != block_map_.end() && max_staleness_ > 0 &&
+        env_->NowSeconds() - entry->second->timestamp > max_staleness_) {
+      RemoveFile_Locked(filename);
+      entry = block_map_.end();
+    }
     if (entry == block_map_.end()) {
       // We need to fetch the block from the remote filesystem. Trim the LRU
       // cache if needed - we do this up front in order to avoid any period of
       // time during which the cache size exceeds its desired limit. The
-      // tradeoff is that if the fetcher fails, the cache may evict a block
+      // tradeoff is that if the fetcher fails, the cache may evict blocks
       // prematurely.
-      TrimCache(block_count_ - 1);
+      while (!lru_list_.empty() && cache_size_ + block_size_ > max_bytes_) {
+        RemoveBlock(block_map_.find(lru_list_.back()));
+      }
       std::unique_ptr<Block> block(new Block);
-      TF_RETURN_IF_ERROR(block_fetcher_(pos, block_size_, &block->data));
+      TF_RETURN_IF_ERROR(
+          block_fetcher_(filename, pos, block_size_, &block->data));
       // Sanity check to detect interrupted reads leading to partial blocks: a
       // partial block must have a higher key than the highest existing key in
-      // the block map.
-      if (block->data.size() < block_size_ && !block_map_.empty() &&
-          pos < block_map_.rbegin()->first) {
-        // We expected to read a full block at this position.
-        return errors::FailedPrecondition("File contents are inconsistent");
-      }
-      entry = block_map_.emplace(std::make_pair(pos, std::move(block))).first;
-      if (timestamp_ == 0) {
-        // Mark the timestamp of the first block's arrival in the cache.
-        timestamp_ = env_->NowSeconds();
+      // the block map for the file.
+      if (block->data.size() < block_size_ && !block_map_.empty()) {
+        Key fmax = std::make_pair(filename, std::numeric_limits<size_t>::max());
+        auto fcmp = block_map_.upper_bound(fmax);
+        if (fcmp != block_map_.begin() && key < (--fcmp)->first) {
+          // We expected to read a full block at this position.
+          return errors::Internal("File contents are inconsistent");
+        }
       }
+      // Record the block timestamp, update the cache size, and add the block to
+      // the cache.
+      block->timestamp = env_->NowSeconds();
+      cache_size_ += block->data.size();
+      entry = block_map_.emplace(std::make_pair(key, std::move(block))).first;
     } else {
       // Cache hit. Remove the block from the LRU list at its prior location.
       lru_list_.erase(entry->second->lru_iterator);
     }
     // Push the block to the front of the LRU list.
-    lru_list_.push_front(pos);
+    lru_list_.push_front(key);
     entry->second->lru_iterator = lru_list_.begin();
     // Copy the relevant portion of the block into the result buffer.
     const auto& data = entry->second->data;
@@ -98,15 +106,33 @@ Status FileBlockCache::Read(uint64 offset, size_t n, std::vector<char>* out) {
     if (begin < end) {
       out->insert(out->end(), begin, end);
     }
+    if (data.size() < block_size_) {
+      // The block was a partial block and thus signals EOF at its upper bound.
+      break;
+    }
   }
   return Status::OK();
 }
 
-void FileBlockCache::TrimCache(size_t size) {
-  while (lru_list_.size() > size) {
-    block_map_.erase(lru_list_.back());
-    lru_list_.pop_back();
+void FileBlockCache::RemoveFile(const string& filename) {
+  mutex_lock lock(mu_);
+  RemoveFile_Locked(filename);
+}
+
+void FileBlockCache::RemoveFile_Locked(const string& filename) {
+  Key begin = std::make_pair(filename, 0);
+  auto it = block_map_.lower_bound(begin);
+  while (it != block_map_.end() && it->first.first == filename) {
+    auto next = std::next(it);
+    RemoveBlock(it);
+    it = next;
   }
 }
 
+void FileBlockCache::RemoveBlock(BlockMap::iterator entry) {
+  lru_list_.erase(entry->second->lru_iterator);
+  cache_size_ -= entry->second->data.size();
+  block_map_.erase(entry);
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/file_block_cache.h b/tensorflow/core/platform/cloud/file_block_cache.h
index 4acc4c7fca..02e00b5bc9 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.h
+++ b/tensorflow/core/platform/cloud/file_block_cache.h
@@ -31,9 +31,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-/// \brief An LRU block cache of file contents.
+/// \brief An LRU block cache of file contents, keyed by {filename, offset}.
 ///
-/// This class should be used by read-only random access files on a remote
+/// This class should be shared by read-only random access files on a remote
 /// filesystem (e.g. GCS).
 class FileBlockCache {
  public:
@@ -42,18 +42,20 @@ class FileBlockCache {
   /// cache is constructed. The returned Status should be OK as long as the
   /// read from the remote filesystem succeeded (similar to the semantics of the
   /// read(2) system call).
-  typedef std::function<Status(uint64, size_t, std::vector<char>*)>
+  typedef std::function<Status(const string&, size_t, size_t,
+                               std::vector<char>*)>
       BlockFetcher;
 
-  FileBlockCache(uint64 block_size, uint32 block_count, uint64 max_staleness,
+  FileBlockCache(size_t block_size, size_t max_bytes, uint64 max_staleness,
                  BlockFetcher block_fetcher, Env* env = Env::Default())
       : block_size_(block_size),
-        block_count_(block_count),
+        max_bytes_(max_bytes),
         max_staleness_(max_staleness),
         block_fetcher_(block_fetcher),
         env_(env) {}
 
-  /// Read `n` bytes starting at `offset` into `out`. This method will return:
+  /// Read `n` bytes from `filename` starting at `offset` into `out`. This
+  /// method will return:
   ///
   /// 1) The error from the remote filesystem, if the read from the remote
   ///    filesystem failed.
@@ -66,17 +68,23 @@ class FileBlockCache {
   ///    placed in `out`.
   /// 4) OK otherwise (i.e. the read succeeded, and at least one byte was placed
   ///    in `out`).
-  Status Read(uint64 offset, size_t n, std::vector<char>* out);
+  Status Read(const string& filename, size_t offset, size_t n,
+              std::vector<char>* out);
 
- private:
-  /// Trim the LRU cache until its size is at most `size` blocks.
-  void TrimCache(size_t size) EXCLUSIVE_LOCKS_REQUIRED(mu_);
+  /// Remove all cached blocks for `filename`.
+  void RemoveFile(const string& filename) LOCKS_EXCLUDED(mu_);
+
+  /// Accessors for cache parameters.
+  size_t block_size() const { return block_size_; }
+  size_t max_bytes() const { return max_bytes_; }
+  uint64 max_staleness() const { return max_staleness_; }
 
+ private:
   /// The size of the blocks stored in the LRU cache, as well as the size of the
   /// reads from the underlying filesystem.
-  const uint64 block_size_;
-  /// The maximum number of blocks allowed in the LRU cache.
-  const uint32 block_count_;
+  const size_t block_size_;
+  /// The maximum number of bytes (sum of block sizes) allowed in the LRU cache.
+  const size_t max_bytes_;
   /// The maximum staleness of any block in the LRU cache, in seconds.
   const uint64 max_staleness_;
   /// The callback to read a block from the underlying filesystem.
@@ -84,31 +92,49 @@ class FileBlockCache {
   /// The Env from which we read timestamps.
   Env* const env_;  // not owned
 
+  /// \brief The key type for the file block cache.
+  ///
+  /// The file block cache key is a {filename, offset} pair.
+  typedef std::pair<string, size_t> Key;
+
   /// \brief A block of a file.
   ///
-  /// A file block consists of the block data and the block's current position
-  /// in the LRU cache.
+  /// A file block consists of the block data, the block's current position in
+  /// the LRU cache, and the timestamp (seconds since epoch) at which the block
+  /// was cached.
   struct Block {
     /// The block data.
     std::vector<char> data;
     /// A list iterator pointing to the block's position in the LRU list.
-    std::list<uint64>::iterator lru_iterator;
+    std::list<Key>::iterator lru_iterator;
+    /// The timestamp (seconds since epoch) at which the block was cached.
+    uint64 timestamp;
   };
 
-  /// Guards access to the block map, LRU list, and cache timestamp.
+  /// \brief The block map type for the file block cache.
+  ///
+  /// The block map is an ordered map from Key to Block.
+  typedef std::map<Key, std::unique_ptr<Block>> BlockMap;
+
+  /// Remove all blocks of a file, with mu_ already held.
+  void RemoveFile_Locked(const string& filename) EXCLUSIVE_LOCKS_REQUIRED(mu_);
+
+  /// Remove the block `entry` from the block map and LRU list, and update the
+  /// cache size accordingly.
+  void RemoveBlock(BlockMap::iterator entry) EXCLUSIVE_LOCKS_REQUIRED(mu_);
+
+  /// Guards access to the block map, LRU list, and cached byte count.
   mutex mu_;
 
-  /// The block map (map from offset in the file to Block object).
-  std::map<uint64, std::unique_ptr<Block>> block_map_ GUARDED_BY(mu_);
+  /// The block map (map from Key to Block).
+  BlockMap block_map_ GUARDED_BY(mu_);
 
-  /// The LRU list of offsets in the file. The front of the list is the position
-  /// of the most recently accessed block.
-  std::list<uint64> lru_list_ GUARDED_BY(mu_);
+  /// The LRU list of block keys. The front of the list identifies the most
+  /// recently accessed block.
+  std::list<Key> lru_list_ GUARDED_BY(mu_);
 
-  /// The most recent timestamp (in seconds since epoch) at which the block map
-  /// transitioned from empty to non-empty.  A value of 0 means the block map is
-  /// currently empty.
-  uint64 timestamp_ GUARDED_BY(mu_) = 0;
+  /// The combined number of bytes in all of the cached blocks.
+  size_t cache_size_ GUARDED_BY(mu_) = 0;
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/file_block_cache_test.cc b/tensorflow/core/platform/cloud/file_block_cache_test.cc
index 8ff02ae00b..3f53cbc6ea 100644
--- a/tensorflow/core/platform/cloud/file_block_cache_test.cc
+++ b/tensorflow/core/platform/cloud/file_block_cache_test.cc
@@ -23,27 +23,30 @@ namespace tensorflow {
 namespace {
 
 TEST(FileBlockCacheTest, PassThrough) {
-  const uint64 want_offset = 42;
+  const string want_filename = "foo/bar";
+  const size_t want_offset = 42;
   const size_t want_n = 1024;
   int calls = 0;
-  auto fetcher = [&calls, want_offset, want_n](uint64 got_offset, size_t got_n,
-                                               std::vector<char>* out) {
+  auto fetcher = [&calls, want_filename, want_offset, want_n](
+                     const string& got_filename, size_t got_offset,
+                     size_t got_n, std::vector<char>* out) {
+    EXPECT_EQ(got_filename, want_filename);
     EXPECT_EQ(got_offset, want_offset);
     EXPECT_EQ(got_n, want_n);
     calls++;
     out->resize(got_n, 'x');
     return Status::OK();
   };
-  // If block_size, block_count, or both are zero, the cache is a pass-through.
+  // If block_size, max_bytes, or both are zero, the cache is a pass-through.
   FileBlockCache cache1(1, 0, 0, fetcher);
   FileBlockCache cache2(0, 1, 0, fetcher);
   FileBlockCache cache3(0, 0, 0, fetcher);
   std::vector<char> out;
-  TF_EXPECT_OK(cache1.Read(want_offset, want_n, &out));
+  TF_EXPECT_OK(cache1.Read(want_filename, want_offset, want_n, &out));
   EXPECT_EQ(calls, 1);
-  TF_EXPECT_OK(cache2.Read(want_offset, want_n, &out));
+  TF_EXPECT_OK(cache2.Read(want_filename, want_offset, want_n, &out));
   EXPECT_EQ(calls, 2);
-  TF_EXPECT_OK(cache3.Read(want_offset, want_n, &out));
+  TF_EXPECT_OK(cache3.Read(want_filename, want_offset, want_n, &out));
   EXPECT_EQ(calls, 3);
 }
 
@@ -56,7 +59,8 @@ TEST(FileBlockCacheTest, BlockAlignment) {
     buf.push_back(i);
   }
   // The fetcher just fetches slices of the buffer.
-  auto fetcher = [&buf](uint64 offset, size_t n, std::vector<char>* out) {
+  auto fetcher = [&buf](const string& filename, size_t offset, size_t n,
+                        std::vector<char>* out) {
     if (offset < buf.size()) {
       if (offset + n > buf.size()) {
         out->insert(out->end(), buf.begin() + offset, buf.end());
@@ -66,14 +70,14 @@ TEST(FileBlockCacheTest, BlockAlignment) {
     }
     return Status::OK();
   };
-  for (uint64_t block_size = 2; block_size <= 4; block_size++) {
+  for (size_t block_size = 2; block_size <= 4; block_size++) {
     // Make a cache of N-byte block size (1 block) and verify that reads of
     // varying offsets and lengths return correct data.
-    FileBlockCache cache(block_size, 1, 0, fetcher);
-    for (uint64_t offset = 0; offset < 10; offset++) {
+    FileBlockCache cache(block_size, block_size, 0, fetcher);
+    for (size_t offset = 0; offset < 10; offset++) {
       for (size_t n = block_size - 2; n <= block_size + 2; n++) {
         std::vector<char> got;
-        TF_EXPECT_OK(cache.Read(offset, n, &got));
+        TF_EXPECT_OK(cache.Read("", offset, n, &got));
         // Verify the size of the read.
         if (offset + n <= size) {
           // Expect a full read.
@@ -98,10 +102,10 @@ TEST(FileBlockCacheTest, BlockAlignment) {
 }
 
 TEST(FileBlockCacheTest, CacheHits) {
-  const uint64 block_size = 16;
-  std::set<uint64_t> calls;
-  auto fetcher = [&calls, block_size](uint64 offset, size_t n,
-                                      std::vector<char>* out) {
+  const size_t block_size = 16;
+  std::set<size_t> calls;
+  auto fetcher = [&calls, block_size](const string& filename, size_t offset,
+                                      size_t n, std::vector<char>* out) {
     EXPECT_EQ(n, block_size);
     EXPECT_EQ(offset % block_size, 0);
     EXPECT_EQ(calls.find(offset), calls.end()) << "at offset " << offset;
@@ -110,7 +114,7 @@ TEST(FileBlockCacheTest, CacheHits) {
     return Status::OK();
   };
   const uint32 block_count = 256;
-  FileBlockCache cache(block_size, block_count, 0, fetcher);
+  FileBlockCache cache(block_size, block_count * block_size, 0, fetcher);
   std::vector<char> out;
   // The cache has space for `block_count` blocks. The loop with i = 0 should
   // fill the cache, and the loop with i = 1 should be all cache hits. The
@@ -118,19 +122,20 @@ TEST(FileBlockCacheTest, CacheHits) {
   // fetch the corresponding block).
   for (int i = 0; i < 2; i++) {
     for (int j = 0; j < block_count; j++) {
-      TF_EXPECT_OK(cache.Read(block_size * j, block_size, &out));
+      TF_EXPECT_OK(cache.Read("", block_size * j, block_size, &out));
     }
   }
 }
 
 TEST(FileBlockCacheTest, OutOfRange) {
   // Tests reads of a 24-byte file with block size 16.
-  const uint64 block_size = 16;
-  const uint64 file_size = 24;
+  const size_t block_size = 16;
+  const size_t file_size = 24;
   bool first_block = false;
   bool second_block = false;
   auto fetcher = [block_size, file_size, &first_block, &second_block](
-                     uint64 offset, size_t n, std::vector<char>* out) {
+                     const string& filename, size_t offset, size_t n,
+                     std::vector<char>* out) {
     EXPECT_EQ(n, block_size);
     EXPECT_EQ(offset % block_size, 0);
     if (offset == 0) {
@@ -144,22 +149,22 @@ TEST(FileBlockCacheTest, OutOfRange) {
     }
     return Status::OK();
   };
-  FileBlockCache cache(block_size, 1, 0, fetcher);
+  FileBlockCache cache(block_size, block_size, 0, fetcher);
   std::vector<char> out;
   // Reading the first 16 bytes should be fine.
-  TF_EXPECT_OK(cache.Read(0, block_size, &out));
+  TF_EXPECT_OK(cache.Read("", 0, block_size, &out));
   EXPECT_TRUE(first_block);
   EXPECT_EQ(out.size(), block_size);
   // Reading at offset file_size + 4 will read the second block (since the read
   // at file_size + 4 = 28 will be aligned to an offset of 16) but will return
   // OutOfRange because the offset is past the end of the 24-byte file.
-  Status status = cache.Read(file_size + 4, 4, &out);
+  Status status = cache.Read("", file_size + 4, 4, &out);
   EXPECT_EQ(status.code(), error::OUT_OF_RANGE);
   EXPECT_TRUE(second_block);
   EXPECT_EQ(out.size(), 0);
   // Reading the second full block will return 8 bytes, from a cache hit.
   second_block = false;
-  TF_EXPECT_OK(cache.Read(block_size, block_size, &out));
+  TF_EXPECT_OK(cache.Read("", block_size, block_size, &out));
   EXPECT_FALSE(second_block);
   EXPECT_EQ(out.size(), file_size - block_size);
 }
@@ -167,30 +172,31 @@ TEST(FileBlockCacheTest, OutOfRange) {
 TEST(FileBlockCacheTest, Inconsistent) {
   // Tests the detection of interrupted reads leading to partially filled blocks
   // where we expected complete blocks.
-  const uint64 block_size = 16;
+  const size_t block_size = 16;
   // This fetcher returns OK but only fills in one byte for any offset.
-  auto fetcher = [block_size](uint64 offset, size_t n, std::vector<char>* out) {
+  auto fetcher = [block_size](const string& filename, size_t offset, size_t n,
+                              std::vector<char>* out) {
     EXPECT_EQ(n, block_size);
     EXPECT_EQ(offset % block_size, 0);
     out->resize(1, 'x');
     return Status::OK();
   };
-  FileBlockCache cache(block_size, 2, 0, fetcher);
+  FileBlockCache cache(block_size, 2 * block_size, 0, fetcher);
   std::vector<char> out;
   // Read the second block; this should yield an OK status and a single byte.
-  TF_EXPECT_OK(cache.Read(block_size, block_size, &out));
+  TF_EXPECT_OK(cache.Read("", block_size, block_size, &out));
   EXPECT_EQ(out.size(), 1);
-  // Now read the first block; this should yield FAILED_PRECONDITION because we
+  // Now read the first block; this should yield an INTERNAL error because we
   // had already cached a partial block at a later position.
-  Status status = cache.Read(0, block_size, &out);
-  EXPECT_EQ(status.code(), error::FAILED_PRECONDITION);
+  Status status = cache.Read("", 0, block_size, &out);
+  EXPECT_EQ(status.code(), error::INTERNAL);
 }
 
 TEST(FileBlockCacheTest, LRU) {
-  const uint64 block_size = 16;
-  std::list<uint64_t> calls;
-  auto fetcher = [&calls, block_size](uint64 offset, size_t n,
-                                      std::vector<char>* out) {
+  const size_t block_size = 16;
+  std::list<size_t> calls;
+  auto fetcher = [&calls, block_size](const string& filename, size_t offset,
+                                      size_t n, std::vector<char>* out) {
     EXPECT_EQ(n, block_size);
     EXPECT_FALSE(calls.empty()) << "at offset = " << offset;
     if (!calls.empty()) {
@@ -201,36 +207,36 @@ TEST(FileBlockCacheTest, LRU) {
     return Status::OK();
   };
   const uint32 block_count = 2;
-  FileBlockCache cache(block_size, block_count, 0, fetcher);
+  FileBlockCache cache(block_size, block_count * block_size, 0, fetcher);
   std::vector<char> out;
   // Read blocks from the cache, and verify the LRU behavior based on the
   // fetcher calls that the cache makes.
   calls.push_back(0);
   // Cache miss - drains an element from `calls`.
-  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  TF_EXPECT_OK(cache.Read("", 0, 1, &out));
   // Cache hit - does not drain an element from `calls`.
-  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  TF_EXPECT_OK(cache.Read("", 0, 1, &out));
   calls.push_back(block_size);
   // Cache miss followed by cache hit.
-  TF_EXPECT_OK(cache.Read(block_size, 1, &out));
-  TF_EXPECT_OK(cache.Read(block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read("", block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read("", block_size, 1, &out));
   calls.push_back(2 * block_size);
   // Cache miss followed by cache hit.  Causes eviction of LRU element.
-  TF_EXPECT_OK(cache.Read(2 * block_size, 1, &out));
-  TF_EXPECT_OK(cache.Read(2 * block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read("", 2 * block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read("", 2 * block_size, 1, &out));
   // LRU element was at offset 0.  Cache miss.
   calls.push_back(0);
-  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  TF_EXPECT_OK(cache.Read("", 0, 1, &out));
   // Element at 2 * block_size is still in cache, and this read should update
   // its position in the LRU list so it doesn't get evicted by the next read.
-  TF_EXPECT_OK(cache.Read(2 * block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read("", 2 * block_size, 1, &out));
   // Element at block_size was evicted.  Reading this element will also cause
   // the LRU element (at 0) to be evicted.
   calls.push_back(block_size);
-  TF_EXPECT_OK(cache.Read(block_size, 1, &out));
+  TF_EXPECT_OK(cache.Read("", block_size, 1, &out));
   // Element at 0 was evicted again.
   calls.push_back(0);
-  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  TF_EXPECT_OK(cache.Read("", 0, 1, &out));
 }
 
 TEST(FileBlockCacheTest, MaxStaleness) {
@@ -242,25 +248,103 @@ TEST(FileBlockCacheTest, MaxStaleness) {
     uint64 now_ = 1;
   };
   int calls = 0;
-  auto fetcher = [&calls](uint64 offset, size_t n, std::vector<char>* out) {
+  auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
+                          std::vector<char>* out) {
     calls++;
     out->resize(n, 'x');
     return Status::OK();
   };
-  std::unique_ptr<FakeEnv> env(new FakeEnv);
-  FileBlockCache cache(8, 1, 2 /* max staleness */, fetcher, env.get());
   std::vector<char> out;
+  std::unique_ptr<FakeEnv> env(new FakeEnv);
+  // Create a cache with max staleness of 2 seconds, and verify that it works as
+  // expected.
+  FileBlockCache cache1(8, 16, 2 /* max staleness */, fetcher, env.get());
   // Execute the first read to load the block.
-  TF_EXPECT_OK(cache.Read(0, 1, &out));
+  TF_EXPECT_OK(cache1.Read("", 0, 1, &out));
   EXPECT_EQ(calls, 1);
   // Now advance the clock one second at a time and redo the read. The call
   // count should advance every 3 seconds (i.e. every time the staleness is
   // greater than 2).
   for (int i = 1; i <= 10; i++) {
     env->now_ = i + 1;
-    TF_EXPECT_OK(cache.Read(0, 1, &out));
+    TF_EXPECT_OK(cache1.Read("", 0, 1, &out));
     EXPECT_EQ(calls, 1 + i / 3);
   }
+  // Now create a cache with max staleness of 0, and verify that it also works
+  // as expected.
+  calls = 0;
+  env->now_ = 0;
+  FileBlockCache cache2(8, 16, 0 /* max staleness */, fetcher, env.get());
+  // Execute the first read to load the block.
+  TF_EXPECT_OK(cache2.Read("", 0, 1, &out));
+  EXPECT_EQ(calls, 1);
+  // Advance the clock by a huge amount and verify that the cached block is
+  // used to satisfy the read.
+  env->now_ = 365 * 24 * 60 * 60;  // ~1 year, just for fun.
+  TF_EXPECT_OK(cache2.Read("", 0, 1, &out));
+  EXPECT_EQ(calls, 1);
+}
+
+TEST(FileBlockCacheTest, RemoveFile) {
+  int calls = 0;
+  auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
+                          std::vector<char>* out) {
+    calls++;
+    char c = (filename == "a") ? 'a' : (filename == "b") ? 'b' : 'x';
+    if (offset > 0) {
+      // The first block is lower case and all subsequent blocks are upper case.
+      c = toupper(c);
+    }
+    out->clear();
+    out->resize(n, c);
+    return Status::OK();
+  };
+  // This cache has space for 4 blocks; we'll read from two files.
+  const size_t n = 3;
+  FileBlockCache cache(8, 32, 0, fetcher);
+  std::vector<char> out;
+  std::vector<char> a(n, 'a');
+  std::vector<char> b(n, 'b');
+  std::vector<char> A(n, 'A');
+  std::vector<char> B(n, 'B');
+  // Fill the cache.
+  TF_EXPECT_OK(cache.Read("a", 0, n, &out));
+  EXPECT_EQ(out, a);
+  EXPECT_EQ(calls, 1);
+  TF_EXPECT_OK(cache.Read("a", 8, n, &out));
+  EXPECT_EQ(out, A);
+  EXPECT_EQ(calls, 2);
+  TF_EXPECT_OK(cache.Read("b", 0, n, &out));
+  EXPECT_EQ(out, b);
+  EXPECT_EQ(calls, 3);
+  TF_EXPECT_OK(cache.Read("b", 8, n, &out));
+  EXPECT_EQ(out, B);
+  EXPECT_EQ(calls, 4);
+  // All four blocks should be in the cache now.
+  TF_EXPECT_OK(cache.Read("a", 0, n, &out));
+  EXPECT_EQ(out, a);
+  TF_EXPECT_OK(cache.Read("a", 8, n, &out));
+  EXPECT_EQ(out, A);
+  TF_EXPECT_OK(cache.Read("b", 0, n, &out));
+  EXPECT_EQ(out, b);
+  TF_EXPECT_OK(cache.Read("b", 8, n, &out));
+  EXPECT_EQ(out, B);
+  EXPECT_EQ(calls, 4);
+  // Remove the blocks from "a".
+  cache.RemoveFile("a");
+  // Both blocks from "b" should still be there.
+  TF_EXPECT_OK(cache.Read("b", 0, n, &out));
+  EXPECT_EQ(out, b);
+  TF_EXPECT_OK(cache.Read("b", 8, n, &out));
+  EXPECT_EQ(out, B);
+  EXPECT_EQ(calls, 4);
+  // The blocks from "a" should not be there.
+  TF_EXPECT_OK(cache.Read("a", 0, n, &out));
+  EXPECT_EQ(out, a);
+  EXPECT_EQ(calls, 5);
+  TF_EXPECT_OK(cache.Read("a", 8, n, &out));
+  EXPECT_EQ(out, A);
+  EXPECT_EQ(calls, 6);
 }
 
 }  // namespace
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 85def60a1e..f1b5431108 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -59,13 +59,16 @@ constexpr char kReadaheadBufferSize[] = "GCS_READAHEAD_BUFFER_SIZE_BYTES";
 // The environment variable that overrides the block size for aligned reads from
 // GCS. Specified in MB (e.g. "16" = 16 x 1024 x 1024 = 16777216 bytes).
 constexpr char kBlockSize[] = "GCS_READ_CACHE_BLOCK_SIZE_MB";
-// The environment variable that overrides the block count in the LRU cache of
-// blocks read from GCS.
-constexpr char kBlockCount[] = "GCS_READ_CACHE_BLOCK_COUNT";
+constexpr size_t kDefaultBlockSize = 256 * 1024 * 1024;
+// The environment variable that overrides the max size of the LRU cache of
+// blocks read from GCS. Specified in MB.
+constexpr char kMaxCacheSize[] = "GCS_READ_CACHE_MAX_SIZE_MB";
+constexpr size_t kDefaultMaxCacheSize = kDefaultBlockSize;
 // The environment variable that overrides the maximum staleness of cached file
 // contents. Once any block of a file reaches this staleness, all cached blocks
 // will be evicted on the next read.
 constexpr char kMaxStaleness[] = "GCS_READ_CACHE_MAX_STALENESS";
+constexpr uint64 kDefaultMaxStaleness = 0;
 // The file statistics returned by Stat() for directories.
 const FileStatistics DIRECTORY_STAT(0, 0, true);
 
@@ -220,16 +223,15 @@ Status GetBoolValue(const Json::Value& parent, const string& name,
 /// A GCS-based implementation of a random access file with an LRU block cache.
 class GcsRandomAccessFile : public RandomAccessFile {
  public:
-  explicit GcsRandomAccessFile(
-      const std::shared_ptr<FileBlockCache>& file_block_cache)
-      : file_block_cache_(file_block_cache) {}
+  GcsRandomAccessFile(const string& filename, FileBlockCache* file_block_cache)
+      : filename_(filename), file_block_cache_(file_block_cache) {}
 
   /// The implementation of reads with an LRU block cache. Thread safe.
   Status Read(uint64 offset, size_t n, StringPiece* result,
               char* scratch) const override {
     result->clear();
     std::vector<char> out;
-    TF_RETURN_IF_ERROR(file_block_cache_->Read(offset, n, &out));
+    TF_RETURN_IF_ERROR(file_block_cache_->Read(filename_, offset, n, &out));
     std::memcpy(scratch, out.data(), std::min(out.size(), n));
     *result = StringPiece(scratch, std::min(out.size(), n));
     if (result->size() < n) {
@@ -243,8 +245,10 @@ class GcsRandomAccessFile : public RandomAccessFile {
   }
 
  private:
+  /// The filename of this file.
+  const string filename_;
   /// The LRU block cache for this file.
-  mutable std::shared_ptr<FileBlockCache> file_block_cache_;
+  mutable FileBlockCache* file_block_cache_;  // not owned
 };
 
 /// \brief GCS-based implementation of a writeable file.
@@ -557,76 +561,64 @@ bool GetEnvVar(const char* varname, bool (*convert)(StringPiece, T*),
 GcsFileSystem::GcsFileSystem()
     : auth_provider_(new GoogleAuthProvider()),
       http_request_factory_(new HttpRequest::Factory()) {
+  uint64 value;
+  size_t block_size = kDefaultBlockSize;
+  size_t max_bytes = kDefaultMaxCacheSize;
+  uint64 max_staleness = kDefaultMaxStaleness;
   // Apply the sys env override for the readahead buffer size if it's provided.
-  uint64 v64;
-  if (GetEnvVar(kReadaheadBufferSize, strings::safe_strtou64, &v64)) {
-    block_size_ = v64;
+  if (GetEnvVar(kReadaheadBufferSize, strings::safe_strtou64, &value)) {
+    block_size = value;
   }
-  // Apply the override for the block size if provided. This takes precedence
-  // over the readahead buffer size.
-  if (GetEnvVar(kBlockSize, strings::safe_strtou64, &v64)) {
-    block_size_ = v64 * 1024 * 1024;
+  // Apply the overrides for the block size (MB), max bytes (MB), and max
+  // staleness (seconds) if provided.
+  if (GetEnvVar(kBlockSize, strings::safe_strtou64, &value)) {
+    block_size = value * 1024 * 1024;
   }
-  // Apply the override for the block count if provided.
-  uint32 v32;
-  if (GetEnvVar(kBlockCount, strings::safe_strtou32, &v32)) {
-    block_count_ = v32;
+  if (GetEnvVar(kMaxCacheSize, strings::safe_strtou64, &value)) {
+    max_bytes = value * 1024 * 1024;
   }
-  // Apply the override for max staleness if provided.
-  if (GetEnvVar(kMaxStaleness, strings::safe_strtou64, &v64)) {
-    max_staleness_ = v64;
+  if (GetEnvVar(kMaxStaleness, strings::safe_strtou64, &value)) {
+    max_staleness = value;
   }
+  file_block_cache_ = MakeFileBlockCache(block_size, max_bytes, max_staleness);
 }
 
 GcsFileSystem::GcsFileSystem(
     std::unique_ptr<AuthProvider> auth_provider,
     std::unique_ptr<HttpRequest::Factory> http_request_factory,
-    size_t block_size, uint32 block_count, uint64 max_staleness,
+    size_t block_size, size_t max_bytes, uint64 max_staleness,
     int64 initial_retry_delay_usec)
     : auth_provider_(std::move(auth_provider)),
       http_request_factory_(std::move(http_request_factory)),
-      block_size_(block_size),
-      block_count_(block_count),
-      max_staleness_(max_staleness),
+      file_block_cache_(
+          MakeFileBlockCache(block_size, max_bytes, max_staleness)),
       initial_retry_delay_usec_(initial_retry_delay_usec) {}
 
 Status GcsFileSystem::NewRandomAccessFile(
     const string& fname, std::unique_ptr<RandomAccessFile>* result) {
   string bucket, object;
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, false, &bucket, &object));
-  // `file_cache_` is a container of FileBlockCache, keyed by filename. We look
-  // up the filename in this container to see if we have a FileBlockCache for
-  // it. If the FileBlockCache for `fname` exists in file_cache_, we return it.
-  // Otherwise, we create a new FileBlockCache with a block fetcher that calls
-  // GcsFileSystem::LoadBufferFromGCS for the bucket and object derived from
-  // `fname`. If a FileBlockCache is created, it is added to `file_cache_` only
-  // if `max_staleness_` > 0 (indicating that new random acesss files will
-  // tolerate stale reads coming from FileBlockCache instances that persist
-  // across file close/open boundaries).
-  std::shared_ptr<FileBlockCache> file_block_cache;
-  mutex_lock lock(mu_);
-  auto entry = file_cache_.find(fname);
-  if (entry == file_cache_.end()) {
-    file_block_cache.reset(new FileBlockCache(
-        block_size_, block_count_, max_staleness_,
-        [this, bucket, object](uint64 offset, size_t n,
-                               std::vector<char>* out) {
-          return LoadBufferFromGCS(bucket, object, offset, n, out);
-        }));
-    if (max_staleness_ > 0) {
-      file_cache_[fname] = file_block_cache;
-    }
-  } else {
-    file_block_cache = entry->second;
-  }
-  result->reset(new GcsRandomAccessFile(file_block_cache));
+  result->reset(new GcsRandomAccessFile(fname, file_block_cache_.get()));
   return Status::OK();
 }
 
+// A helper function to build a FileBlockCache for GcsFileSystem.
+std::unique_ptr<FileBlockCache> GcsFileSystem::MakeFileBlockCache(
+    size_t block_size, size_t max_bytes, uint64 max_staleness) {
+  std::unique_ptr<FileBlockCache> file_block_cache(
+      new FileBlockCache(block_size, max_bytes, max_staleness,
+                         [this](const string& filename, size_t offset, size_t n,
+                                std::vector<char>* out) {
+                           return LoadBufferFromGCS(filename, offset, n, out);
+                         }));
+  return file_block_cache;
+}
+
 // A helper function to actually read the data from GCS.
-Status GcsFileSystem::LoadBufferFromGCS(const string& bucket,
-                                        const string& object, uint64_t offset,
+Status GcsFileSystem::LoadBufferFromGCS(const string& filename, size_t offset,
                                         size_t n, std::vector<char>* out) {
+  string bucket, object;
+  TF_RETURN_IF_ERROR(ParseGcsPath(filename, false, &bucket, &object));
   string auth_token;
   TF_RETURN_IF_ERROR(AuthProvider::GetToken(auth_provider_.get(), &auth_token));
 
@@ -647,13 +639,10 @@ Status GcsFileSystem::NewWritableFile(const string& fname,
                                       std::unique_ptr<WritableFile>* result) {
   string bucket, object;
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, false, &bucket, &object));
-  result->reset(new GcsWritableFile(bucket, object, auth_provider_.get(),
-                                    http_request_factory_.get(),
-                                    [this, fname]() {
-                                      mutex_lock lock(mu_);
-                                      file_cache_.erase(fname);
-                                    },
-                                    initial_retry_delay_usec_));
+  result->reset(new GcsWritableFile(
+      bucket, object, auth_provider_.get(), http_request_factory_.get(),
+      [this, fname]() { file_block_cache_->RemoveFile(fname); },
+      initial_retry_delay_usec_));
   return Status::OK();
 }
 
@@ -691,14 +680,11 @@ Status GcsFileSystem::NewAppendableFile(const string& fname,
   // Create a writable file and pass the old content to it.
   string bucket, object;
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, false, &bucket, &object));
-  result->reset(new GcsWritableFile(bucket, object, auth_provider_.get(),
-                                    old_content_filename,
-                                    http_request_factory_.get(),
-                                    [this, fname]() {
-                                      mutex_lock lock(mu_);
-                                      file_cache_.erase(fname);
-                                    },
-                                    initial_retry_delay_usec_));
+  result->reset(new GcsWritableFile(
+      bucket, object, auth_provider_.get(), old_content_filename,
+      http_request_factory_.get(),
+      [this, fname]() { file_block_cache_->RemoveFile(fname); },
+      initial_retry_delay_usec_));
   return Status::OK();
 }
 
@@ -1040,6 +1026,7 @@ Status GcsFileSystem::DeleteFile(const string& fname) {
   TF_RETURN_IF_ERROR(request->AddAuthBearerHeader(auth_token));
   TF_RETURN_IF_ERROR(request->SetDeleteRequest());
   TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when deleting ", fname);
+  file_block_cache_->RemoveFile(fname);
   return Status::OK();
 }
 
@@ -1135,7 +1122,9 @@ Status GcsFileSystem::RenameObject(const string& src, const string& target) {
   TF_RETURN_IF_ERROR(request->SetResultBuffer(&output_buffer));
   TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when renaming ", src,
                                   " to ", target);
-
+  // Flush the target from the block cache.  The source will be flushed in the
+  // DeleteFile call below.
+  file_block_cache_->RemoveFile(target);
   Json::Value root;
   StringPiece response_piece =
       StringPiece(output_buffer.data(), output_buffer.size());
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.h b/tensorflow/core/platform/cloud/gcs_file_system.h
index 6030b16aa0..9b284722e1 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.h
+++ b/tensorflow/core/platform/cloud/gcs_file_system.h
@@ -36,7 +36,7 @@ class GcsFileSystem : public FileSystem {
   GcsFileSystem();
   GcsFileSystem(std::unique_ptr<AuthProvider> auth_provider,
                 std::unique_ptr<HttpRequest::Factory> http_request_factory,
-                size_t block_size, uint32 block_count, uint64 max_staleness,
+                size_t block_size, size_t max_bytes, uint64 max_staleness,
                 int64 initial_retry_delay_usec);
 
   Status NewRandomAccessFile(
@@ -76,9 +76,9 @@ class GcsFileSystem : public FileSystem {
 
   Status DeleteRecursively(const string& dirname, int64* undeleted_files,
                            int64* undeleted_dirs) override;
-  size_t block_size() const { return block_size_; }
-  uint32 block_count() const { return block_count_; }
-  uint64 max_staleness() const { return max_staleness_; }
+  size_t block_size() const { return file_block_cache_->block_size(); }
+  size_t max_bytes() const { return file_block_cache_->max_bytes(); }
+  uint64 max_staleness() const { return file_block_cache_->max_staleness(); }
 
  private:
   /// \brief Checks if the bucket exists. Returns OK if the check succeeded.
@@ -112,31 +112,17 @@ class GcsFileSystem : public FileSystem {
                        FileStatistics* stat);
   Status RenameObject(const string& src, const string& target);
 
-  /// Loads file contents from GCS for a given bucket and object.
-  Status LoadBufferFromGCS(const string& bucket, const string& object,
-                           uint64_t offset, size_t n, std::vector<char>* out);
+  std::unique_ptr<FileBlockCache> MakeFileBlockCache(size_t block_size,
+                                                     size_t max_bytes,
+                                                     uint64 max_staleness);
+
+  /// Loads file contents from GCS for a given filename, offset, and length.
+  Status LoadBufferFromGCS(const string& filename, size_t offset, size_t n,
+                           std::vector<char>* out);
 
   std::unique_ptr<AuthProvider> auth_provider_;
   std::unique_ptr<HttpRequest::Factory> http_request_factory_;
-
-  /// Guards access to the file cache.
-  mutex mu_;
-
-  /// Cache from filename to FileBlockCache. Populated iff max_staleness_ > 0.
-  std::map<string, std::shared_ptr<FileBlockCache>> file_cache_ GUARDED_BY(mu_);
-
-  /// The block size for block-aligned reads in the RandomAccessFile
-  /// implementation. Defaults to 256Mb.
-  size_t block_size_ = 256 * 1024 * 1024;
-
-  /// The block count for the LRU cache of blocks in the RandomAccessFile
-  /// implementation.  Defaults to 1.
-  uint32 block_count_ = 1;
-
-  /// The maximum staleness of cached file blocks across close/open boundaries.
-  /// Defaults to 0, meaning that file blocks do not persist across close/open
-  /// boundaries.
-  uint64 max_staleness_ = 0;
+  std::unique_ptr<FileBlockCache> file_block_cache_;
 
   /// The initial delay for exponential backoffs when retrying failed calls.
   const int64 initial_retry_delay_usec_ = 1000000L;
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index 915a44ad7f..ba08c7414f 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -45,8 +45,8 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoBlockCache) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
@@ -80,8 +80,8 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoBlockCache_differentN) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
   TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
@@ -130,7 +130,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithBlockCache) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   9 /* block size */, 2 /* block count */,
+                   9 /* block size */, 18 /* max bytes */,
                    0 /* max staleness */, 0 /* initial retry delay */);
 
   char scratch[100];
@@ -196,7 +196,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithBlockCache_MaxStaleness) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   8 /* block size */, 2 /* block count */,
+                   8 /* block size */, 16 /* max bytes */,
                    3600 /* max staleness */, 0 /* initial retry delay */);
   char scratch[100];
   StringPiece result;
@@ -205,7 +205,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithBlockCache_MaxStaleness) {
   // across file close/open boundaries.
   for (int i = 0; i < 10; i++) {
     // Create two files. Since these files have the same name name and the max
-    // staleness of the filesystem is > 0, they will share the same block cache.
+    // staleness of the filesystem is > 0, they will share the same blocks.
     std::unique_ptr<RandomAccessFile> file1;
     std::unique_ptr<RandomAccessFile> file2;
     TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/object", &file1));
@@ -234,7 +234,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* read ahead bytes */, 0 /* block count */,
+                   0 /* read ahead bytes */, 0 /* max bytes */,
                    0 /* max staleness */, 0 /* initial retry delay */);
 
   std::unique_ptr<RandomAccessFile> file;
@@ -242,104 +242,57 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoObjectName) {
             fs.NewRandomAccessFile("gs://bucket/", &file).code());
 }
 
-TEST(GcsFileSystemTest, NewWritableFile_FlushesFileCache) {
-  // Our underlying file in this test is a 16 byte file with initial contents
-  // "0123456789abcdef", replaced by new contents "08192a3b4c5d6e7f".
+TEST(GcsFileSystemTest, NewWritableFile) {
   std::vector<HttpRequest*> requests(
-      {new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
-                           "Auth Token: fake_token\n"
-                           "Range: 0-7\n",
-                           "01234567"),
-       new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
-                           "Auth Token: fake_token\n"
-                           "Range: 8-15\n",
-                           "89abcdef"),
+      {new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fwriteable\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-7\n",
+           "01234567"),
        new FakeHttpRequest(
            "Uri: https://www.googleapis.com/upload/storage/v1/b/bucket/o?"
-           "uploadType=resumable&name=object\n"
+           "uploadType=resumable&name=path%2Fwriteable\n"
            "Auth Token: fake_token\n"
-           "Header X-Upload-Content-Length: 16\n"
+           "Header X-Upload-Content-Length: 17\n"
            "Post: yes\n",
            "", {{"Location", "https://custom/upload/location"}}),
        new FakeHttpRequest("Uri: https://custom/upload/location\n"
                            "Auth Token: fake_token\n"
-                           "Header Content-Range: bytes 0-15/16\n"
-                           "Put body: 08192a3b4c5d6e7f\n",
+                           "Header Content-Range: bytes 0-16/17\n"
+                           "Put body: content1,content2\n",
                            ""),
-       new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
-                           "Auth Token: fake_token\n"
-                           "Range: 0-7\n",
-                           "08192a3b"),
-       new FakeHttpRequest("Uri: https://storage.googleapis.com/bucket/object\n"
-                           "Auth Token: fake_token\n"
-                           "Range: 8-15\n",
-                           "4c5d6e7f")});
+       new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fwriteable\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-7\n",
+           "01234567")});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   8 /* block size */, 2 /* block count */,
-                   3600 /* max staleness */, 0 /* initial retry delay */);
-  // First, read both blocks of the file with its initial contents. This should
-  // trigger the first two HTTP requests to GCS, to load each of the blocks in
-  // order.
+                   8 /* block size */, 8 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
+
+  // Read from the file first, to fill the block cache.
   std::unique_ptr<RandomAccessFile> rfile;
-  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/object", &rfile));
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/path/writeable", &rfile));
   char scratch[100];
   StringPiece result;
-  TF_EXPECT_OK(rfile->Read(0, 16, &result, scratch));
-  EXPECT_EQ("0123456789abcdef", result);
-  // Now open a writable file and write the new file contents.  This should
-  // trigger the next two HTTP requests to GCS.
+  TF_EXPECT_OK(rfile->Read(0, 4, &result, scratch));
+  EXPECT_EQ("0123", result);
+  // Open the writable file.
   std::unique_ptr<WritableFile> wfile;
-  TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/object", &wfile));
-  TF_EXPECT_OK(wfile->Append("08192a3b4c5d6e7f"));
-  // This rfile read should hit the block cache and not trigger any requests,
-  // because we haven't flushed the write yet.
-  TF_EXPECT_OK(rfile->Read(0, 16, &result, scratch));
-  EXPECT_EQ("0123456789abcdef", result);
-  // Now flush the write. Any opens of gs://bucket/object after this point will
-  // no longer use the previous FileBlockCache for that file.
+  TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable", &wfile));
+  TF_EXPECT_OK(wfile->Append("content1,"));
+  TF_EXPECT_OK(wfile->Append("content2"));
   TF_EXPECT_OK(wfile->Flush());
-  // Reopen `rfile` and read its contents again. This will trigger the next two
-  // HTTP requests to GCS, which will return the (updated) file contents. Note
-  // that the contents returned in those HTTP requests are not really relevant;
-  // the main thing is that both blocks of the file are reloaded.
-  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/object", &rfile));
-  TF_EXPECT_OK(rfile->Read(0, 16, &result, scratch));
-  EXPECT_EQ("08192a3b4c5d6e7f", result);
-}
-
-TEST(GcsFileSystemTest, NewWritableFile) {
-  std::vector<HttpRequest*> requests(
-      {new FakeHttpRequest(
-           "Uri: https://www.googleapis.com/upload/storage/v1/b/bucket/o?"
-           "uploadType=resumable&name=path%2Fwriteable.txt\n"
-           "Auth Token: fake_token\n"
-           "Header X-Upload-Content-Length: 17\n"
-           "Post: yes\n",
-           "", {{"Location", "https://custom/upload/location"}}),
-       new FakeHttpRequest("Uri: https://custom/upload/location\n"
-                           "Auth Token: fake_token\n"
-                           "Header Content-Range: bytes 0-16/17\n"
-                           "Put body: content1,content2\n",
-                           "")});
-  GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
-                   std::unique_ptr<HttpRequest::Factory>(
-                       new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
-
-  std::unique_ptr<WritableFile> file;
-  TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
-
-  TF_EXPECT_OK(file->Append("content1,"));
-  TF_EXPECT_OK(file->Append("content2"));
-  TF_EXPECT_OK(file->Flush());
+  // Re-reading the file should trigger another HTTP request to GCS.
+  TF_EXPECT_OK(rfile->Read(0, 4, &result, scratch));
+  EXPECT_EQ("0123", result);
   // The calls to flush, sync, and close below should not cause uploads because
   // the file is not dirty.
-  TF_EXPECT_OK(file->Flush());
-  TF_EXPECT_OK(file->Sync());
-  TF_EXPECT_OK(file->Close());
+  TF_EXPECT_OK(wfile->Flush());
+  TF_EXPECT_OK(wfile->Sync());
+  TF_EXPECT_OK(wfile->Close());
 }
 
 TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceeds) {
@@ -381,8 +334,8 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceeds) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -393,10 +346,18 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceeds) {
 }
 
 TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceedsOnGetStatus) {
+  // This test also verifies that a file's blocks are purged from the cache when
+  // the file is written, even when the write takes the "succeeds on get status"
+  // path.
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fwriteable\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-7\n",
+           "01234567"),
+       new FakeHttpRequest(
            "Uri: https://www.googleapis.com/upload/storage/v1/b/bucket/o?"
-           "uploadType=resumable&name=path%2Fwriteable.txt\n"
+           "uploadType=resumable&name=path%2Fwriteable\n"
            "Auth Token: fake_token\n"
            "Header X-Upload-Content-Length: 17\n"
            "Post: yes\n",
@@ -410,19 +371,41 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceedsOnGetStatus) {
                            "Auth Token: fake_token\n"
                            "Header Content-Range: bytes */17\n"
                            "Put: yes\n",
-                           "", Status::OK(), nullptr, {}, 201)});
+                           "", Status::OK(), nullptr, {}, 201),
+       new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fwriteable\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-7\n",
+           "01234567")});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
-
-  std::unique_ptr<WritableFile> file;
-  TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
-
-  TF_EXPECT_OK(file->Append("content1,"));
-  TF_EXPECT_OK(file->Append("content2"));
-  TF_EXPECT_OK(file->Close());
+                   8 /* block size */, 8 /* max bytes */,
+                   3600 /* max staleness */, 0 /* initial retry delay */);
+  // Pull the file's first block into the cache. This will trigger the first
+  // HTTP request to GCS.
+  std::unique_ptr<RandomAccessFile> rfile;
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/path/writeable", &rfile));
+  char scratch[100];
+  StringPiece result;
+  TF_EXPECT_OK(rfile->Read(0, 4, &result, scratch));
+  EXPECT_EQ("0123", result);
+  // Now write to the same file. Once the write succeeds, the cached block will
+  // be flushed.
+  std::unique_ptr<WritableFile> wfile;
+  TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable", &wfile));
+  TF_EXPECT_OK(wfile->Append("content1,"));
+  TF_EXPECT_OK(wfile->Append("content2"));
+  // Appending doesn't invalidate the read cache - only flushing does. This read
+  // will not trigger an HTTP request to GCS.
+  TF_EXPECT_OK(rfile->Read(4, 4, &result, scratch));
+  EXPECT_EQ("4567", result);
+  // Closing the file triggers HTTP requests to GCS and invalidates the read
+  // cache for the file.
+  TF_EXPECT_OK(wfile->Close());
+  // Reading the first block of the file goes to GCS again.
+  TF_EXPECT_OK(rfile->Read(0, 8, &result, scratch));
+  EXPECT_EQ("01234567", result);
 }
 
 TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadAllAttemptsFail) {
@@ -472,8 +455,8 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadAllAttemptsFail) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 2 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   2 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -520,8 +503,8 @@ TEST(GcsFileSystemTest, NewWritableFile_UploadReturns410) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   TF_EXPECT_OK(fs.NewWritableFile("gs://bucket/path/writeable.txt", &file));
@@ -546,8 +529,8 @@ TEST(GcsFileSystemTest, NewWritableFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -557,13 +540,13 @@ TEST(GcsFileSystemTest, NewWritableFile_NoObjectName) {
 TEST(GcsFileSystemTest, NewAppendableFile) {
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
-           "Uri: https://storage.googleapis.com/bucket/path%2Fappendable.txt\n"
+           "Uri: https://storage.googleapis.com/bucket/path%2Fappendable\n"
            "Auth Token: fake_token\n"
-           "Range: 0-1048575\n",
+           "Range: 0-31\n",
            "content1,"),
        new FakeHttpRequest(
            "Uri: https://www.googleapis.com/upload/storage/v1/b/bucket/o?"
-           "uploadType=resumable&name=path%2Fappendable.txt\n"
+           "uploadType=resumable&name=path%2Fappendable\n"
            "Auth Token: fake_token\n"
            "Header X-Upload-Content-Length: 17\n"
            "Post: yes\n",
@@ -572,18 +555,38 @@ TEST(GcsFileSystemTest, NewAppendableFile) {
                            "Auth Token: fake_token\n"
                            "Header Content-Range: bytes 0-16/17\n"
                            "Put body: content1,content2\n",
-                           "")});
+                           ""),
+       new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fappendable\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-31\n",
+           "01234567")});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
+                   32 /* block size */, 32 /* max bytes */,
                    0 /* max staleness */, 0 /* initial retry delay */);
 
-  std::unique_ptr<WritableFile> file;
-  TF_EXPECT_OK(fs.NewAppendableFile("gs://bucket/path/appendable.txt", &file));
-
-  TF_EXPECT_OK(file->Append("content2"));
-  TF_EXPECT_OK(file->Close());
+  // Create an appendable file. This should read the file from GCS, and pull its
+  // contents into the block cache.
+  std::unique_ptr<WritableFile> wfile;
+  TF_EXPECT_OK(fs.NewAppendableFile("gs://bucket/path/appendable", &wfile));
+  TF_EXPECT_OK(wfile->Append("content2"));
+  // Verify that the file contents are in the block cache. This read should not
+  // trigger an HTTP request to GCS.
+  std::unique_ptr<RandomAccessFile> rfile;
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/path/appendable", &rfile));
+  char scratch[100];
+  StringPiece result;
+  TF_EXPECT_OK(rfile->Read(0, 8, &result, scratch));
+  EXPECT_EQ("content1", result);
+  // Closing the appendable file will flush its contents to GCS, triggering HTTP
+  // requests.
+  TF_EXPECT_OK(wfile->Close());
+  // Redo the read. The block should be reloaded from GCS, causing one more HTTP
+  // request to load it.
+  TF_EXPECT_OK(rfile->Read(0, 4, &result, scratch));
+  EXPECT_EQ("0123", result);
 }
 
 TEST(GcsFileSystemTest, NewAppendableFile_NoObjectName) {
@@ -591,8 +594,8 @@ TEST(GcsFileSystemTest, NewAppendableFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<WritableFile> file;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -618,8 +621,8 @@ TEST(GcsFileSystemTest, NewReadOnlyMemoryRegionFromFile) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<ReadOnlyMemoryRegion> region;
   TF_EXPECT_OK(fs.NewReadOnlyMemoryRegionFromFile(
@@ -634,8 +637,8 @@ TEST(GcsFileSystemTest, NewReadOnlyMemoryRegionFromFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::unique_ptr<ReadOnlyMemoryRegion> region;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -652,8 +655,8 @@ TEST(GcsFileSystemTest, FileExists_YesAsObject) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket/path/file1.txt"));
 }
@@ -675,8 +678,8 @@ TEST(GcsFileSystemTest, FileExists_YesAsFolder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket/path/subfolder"));
 }
@@ -694,8 +697,8 @@ TEST(GcsFileSystemTest, FileExists_YesAsBucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.FileExists("gs://bucket1"));
   TF_EXPECT_OK(fs.FileExists("gs://bucket1/"));
@@ -717,8 +720,8 @@ TEST(GcsFileSystemTest, FileExists_NotAsObjectOrFolder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(errors::Code::NOT_FOUND,
             fs.FileExists("gs://bucket/path/file1.txt").code());
@@ -737,8 +740,8 @@ TEST(GcsFileSystemTest, FileExists_NotAsBucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
             fs.FileExists("gs://bucket2/").code());
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -755,8 +758,8 @@ TEST(GcsFileSystemTest, GetChildren_NoItems) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -777,8 +780,8 @@ TEST(GcsFileSystemTest, GetChildren_ThreeFiles) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -800,8 +803,8 @@ TEST(GcsFileSystemTest, GetChildren_SelfDirectoryMarker) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -822,8 +825,8 @@ TEST(GcsFileSystemTest, GetChildren_ThreeFiles_NoSlash) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path", &children));
@@ -841,8 +844,8 @@ TEST(GcsFileSystemTest, GetChildren_Root) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket-a-b-c", &children));
@@ -860,8 +863,8 @@ TEST(GcsFileSystemTest, GetChildren_Empty) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path/", &children));
@@ -894,8 +897,8 @@ TEST(GcsFileSystemTest, GetChildren_Pagination) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> children;
   TF_EXPECT_OK(fs.GetChildren("gs://bucket/path", &children));
@@ -915,8 +918,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_NoWildcard) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(
@@ -937,8 +940,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_BucketAndWildcard) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/*/*", &result));
@@ -960,8 +963,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_FolderAndWildcard_Matches) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*/file2.txt", &result));
@@ -980,8 +983,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_SelfDirectoryMarker) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*", &result));
@@ -1000,8 +1003,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_FolderAndWildcard_NoMatches) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   TF_EXPECT_OK(fs.GetMatchingPaths("gs://bucket/path/*/file3.txt", &result));
@@ -1013,8 +1016,8 @@ TEST(GcsFileSystemTest, GetMatchingPaths_OnlyWildcard) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   std::vector<string> result;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -1023,18 +1026,40 @@ TEST(GcsFileSystemTest, GetMatchingPaths_OnlyWildcard) {
 
 TEST(GcsFileSystemTest, DeleteFile) {
   std::vector<HttpRequest*> requests(
-      {new FakeHttpRequest("Uri: https://www.googleapis.com/storage/v1/b"
+      {new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Ffile1.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-15\n",
+           "01234567"),
+       new FakeHttpRequest("Uri: https://www.googleapis.com/storage/v1/b"
                            "/bucket/o/path%2Ffile1.txt\n"
                            "Auth Token: fake_token\n"
                            "Delete: yes\n",
-                           "")});
+                           ""),
+       new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Ffile1.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-15\n",
+           "76543210")});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
+                   16 /* block size */, 16 /* max bytes */,
                    0 /* max staleness */, 0 /* initial retry delay */);
 
+  // Do an initial read of the file to load its contents into the block cache.
+  char scratch[100];
+  StringPiece result;
+  std::unique_ptr<RandomAccessFile> file;
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/path/file1.txt", &file));
+  TF_EXPECT_OK(file->Read(0, 8, &result, scratch));
+  EXPECT_EQ("01234567", result);
+  // Deleting the file triggers the next HTTP request to GCS.
   TF_EXPECT_OK(fs.DeleteFile("gs://bucket/path/file1.txt"));
+  // Re-reading the file causes its contents to be reloaded from GCS and not
+  // from the block cache.
+  TF_EXPECT_OK(file->Read(0, 8, &result, scratch));
+  EXPECT_EQ("76543210", result);
 }
 
 TEST(GcsFileSystemTest, DeleteFile_NoObjectName) {
@@ -1042,8 +1067,8 @@ TEST(GcsFileSystemTest, DeleteFile_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
             fs.DeleteFile("gs://bucket/").code());
@@ -1058,8 +1083,8 @@ TEST(GcsFileSystemTest, DeleteDir_Empty) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket/path/"));
 }
@@ -1080,8 +1105,8 @@ TEST(GcsFileSystemTest, DeleteDir_OnlyDirMarkerLeft) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket/path/"));
 }
@@ -1094,8 +1119,8 @@ TEST(GcsFileSystemTest, DeleteDir_BucketOnly) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.DeleteDir("gs://bucket"));
 }
@@ -1110,8 +1135,8 @@ TEST(GcsFileSystemTest, DeleteDir_NonEmpty) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::FAILED_PRECONDITION,
             fs.DeleteDir("gs://bucket/path/").code());
@@ -1127,8 +1152,8 @@ TEST(GcsFileSystemTest, GetFileSize) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   uint64 size;
   TF_EXPECT_OK(fs.GetFileSize("gs://bucket/file.txt", &size));
@@ -1140,8 +1165,8 @@ TEST(GcsFileSystemTest, GetFileSize_NoObjectName) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   uint64 size;
   EXPECT_EQ(errors::Code::INVALID_ARGUMENT,
@@ -1213,15 +1238,25 @@ TEST(GcsFileSystemTest, RenameFile_Folder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.RenameFile("gs://bucket/path1", "gs://bucket/path2/"));
 }
 
 TEST(GcsFileSystemTest, RenameFile_Object) {
   std::vector<HttpRequest*> requests(
-      {// IsDirectory is checking whether there are children objects.
+      {new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fsrc.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-15\n",
+           "01234567"),
+       new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fdst.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-15\n",
+           "76543210"),
+       // IsDirectory is checking whether there are children objects.
        new FakeHttpRequest(
            "Uri: https://www.googleapis.com/storage/v1/b/bucket/o?"
            "fields=items%2Fname%2CnextPageToken&prefix=path%2Fsrc.txt%2F"
@@ -1248,15 +1283,42 @@ TEST(GcsFileSystemTest, RenameFile_Object) {
            "path%2Fsrc.txt\n"
            "Auth Token: fake_token\n"
            "Delete: yes\n",
-           "")});
+           ""),
+       new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fsrc.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-15\n",
+           "89abcdef"),
+       new FakeHttpRequest(
+           "Uri: https://storage.googleapis.com/bucket/path%2Fdst.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-15\n",
+           "fedcba98")});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
+                   16 /* block size */, 64 /* max bytes */,
                    0 /* max staleness */, 0 /* initial retry delay */);
-
+  // Do an initial read of the source and destination files to load their
+  // contents into the block cache.
+  char scratch[100];
+  StringPiece result;
+  std::unique_ptr<RandomAccessFile> src;
+  std::unique_ptr<RandomAccessFile> dst;
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/path/src.txt", &src));
+  TF_EXPECT_OK(src->Read(0, 8, &result, scratch));
+  EXPECT_EQ("01234567", result);
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/path/dst.txt", &dst));
+  TF_EXPECT_OK(dst->Read(0, 8, &result, scratch));
+  EXPECT_EQ("76543210", result);
+  // Now rename src to dst. This should flush the block cache for both files.
   TF_EXPECT_OK(
       fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
+  // Re-read both files. This should reload their contents from GCS.
+  TF_EXPECT_OK(src->Read(0, 8, &result, scratch));
+  EXPECT_EQ("89abcdef", result);
+  TF_EXPECT_OK(dst->Read(0, 8, &result, scratch));
+  EXPECT_EQ("fedcba98", result);
 }
 
 /// Tests the scenario when deletion returns a failure, but actually succeeds.
@@ -1300,8 +1362,8 @@ TEST(GcsFileSystemTest, RenameFile_Object_DeletionRetried) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(
       fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
@@ -1334,8 +1396,8 @@ TEST(GcsFileSystemTest, RenameFile_Object_Incomplete) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(
       errors::Code::UNIMPLEMENTED,
@@ -1353,8 +1415,8 @@ TEST(GcsFileSystemTest, Stat_Object) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/file.txt", &stat));
@@ -1380,8 +1442,8 @@ TEST(GcsFileSystemTest, Stat_Folder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/subfolder", &stat));
@@ -1406,8 +1468,8 @@ TEST(GcsFileSystemTest, Stat_ObjectOrFolderNotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   EXPECT_EQ(error::Code::NOT_FOUND, fs.Stat("gs://bucket/path", &stat).code());
@@ -1421,8 +1483,8 @@ TEST(GcsFileSystemTest, Stat_Bucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   TF_EXPECT_OK(fs.Stat("gs://bucket/", &stat));
@@ -1439,8 +1501,8 @@ TEST(GcsFileSystemTest, Stat_BucketNotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   FileStatistics stat;
   EXPECT_EQ(error::Code::NOT_FOUND, fs.Stat("gs://bucket/", &stat).code());
@@ -1462,8 +1524,8 @@ TEST(GcsFileSystemTest, IsDirectory_NotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::NOT_FOUND,
             fs.IsDirectory("gs://bucket/file.txt").code());
@@ -1486,8 +1548,8 @@ TEST(GcsFileSystemTest, IsDirectory_NotDirectoryButObject) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::FAILED_PRECONDITION,
             fs.IsDirectory("gs://bucket/file.txt").code());
@@ -1510,8 +1572,8 @@ TEST(GcsFileSystemTest, IsDirectory_Yes) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/subfolder"));
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/subfolder/"));
@@ -1530,8 +1592,8 @@ TEST(GcsFileSystemTest, IsDirectory_Bucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket"));
   TF_EXPECT_OK(fs.IsDirectory("gs://bucket/"));
@@ -1545,8 +1607,8 @@ TEST(GcsFileSystemTest, IsDirectory_BucketNotFound) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   EXPECT_EQ(error::Code::NOT_FOUND, fs.IsDirectory("gs://bucket/").code());
 }
@@ -1578,8 +1640,8 @@ TEST(GcsFileSystemTest, CreateDir_Folder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/subpath"));
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/subpath/"));
@@ -1598,8 +1660,8 @@ TEST(GcsFileSystemTest, CreateDir_Bucket) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   TF_EXPECT_OK(fs.CreateDir("gs://bucket/"));
   TF_EXPECT_OK(fs.CreateDir("gs://bucket"));
@@ -1658,8 +1720,8 @@ TEST(GcsFileSystemTest, DeleteRecursively_Ok) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   TF_EXPECT_OK(fs.DeleteRecursively("gs://bucket/path", &undeleted_files,
@@ -1737,8 +1799,8 @@ TEST(GcsFileSystemTest, DeleteRecursively_DeletionErrors) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   TF_EXPECT_OK(fs.DeleteRecursively("gs://bucket/path", &undeleted_files,
@@ -1765,8 +1827,8 @@ TEST(GcsFileSystemTest, DeleteRecursively_NotAFolder) {
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
-                   0 /* block size */, 0 /* block count */,
-                   0 /* max staleness */, 0 /* initial retry delay */);
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* initial retry delay */);
 
   int64 undeleted_files, undeleted_dirs;
   EXPECT_EQ(error::Code::NOT_FOUND,
@@ -1781,7 +1843,7 @@ TEST(GcsFileSystemTest, OverrideCacheParameters) {
   // Verify defaults are propagated correctly.
   GcsFileSystem fs1;
   EXPECT_EQ(256 * 1024 * 1024, fs1.block_size());
-  EXPECT_EQ(1, fs1.block_count());
+  EXPECT_EQ(fs1.block_size(), fs1.max_bytes());
   EXPECT_EQ(0, fs1.max_staleness());
 
   // Verify legacy readahead buffer override sets block size.
@@ -1794,10 +1856,10 @@ TEST(GcsFileSystemTest, OverrideCacheParameters) {
   GcsFileSystem fs3;
   EXPECT_EQ(1048576L, fs3.block_size());
 
-  // Verify block count override.
-  setenv("GCS_READ_CACHE_BLOCK_COUNT", "16", 1);
+  // Verify max size override.
+  setenv("GCS_READ_CACHE_MAX_SIZE_MB", "16", 1);
   GcsFileSystem fs4;
-  EXPECT_EQ(16, fs4.block_count());
+  EXPECT_EQ(16 * 1024 * 1024, fs4.max_bytes());
 
   // Verify max staleness override.
   setenv("GCS_READ_CACHE_MAX_STALENESS", "60", 1);
-- 
GitLab


From 21cef14a63c3d76204a52bba55330bba3b7ab88d Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Sat, 15 Jul 2017 10:22:18 -0700
Subject: [PATCH 1027/1375] Adds evaluation_hooks in EstimatorSpec.

PiperOrigin-RevId: 162078550
---
 tensorflow/python/estimator/estimator.py       |  5 ++++-
 tensorflow/python/estimator/estimator_test.py  | 18 ++++++++++++++++++
 tensorflow/python/estimator/model_fn.py        | 15 ++++++++++-----
 tensorflow/python/estimator/model_fn_test.py   | 18 +++++++++++++++---
 .../tensorflow.estimator.-estimator-spec.pbtxt |  4 ++++
 5 files changed, 51 insertions(+), 9 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 0c1bf7ccf5..a1403367d3 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -729,13 +729,16 @@ class Estimator(object):
             'already defines a default metric with the same name.')
       eval_dict[ops.GraphKeys.GLOBAL_STEP] = global_step_tensor
 
+      all_hooks = list(hooks or [])
+      all_hooks.extend(list(estimator_spec.evaluation_hooks or []))
+
       eval_results = evaluation._evaluate_once(  # pylint: disable=protected-access
           checkpoint_path=checkpoint_path,
           master=self._config.evaluation_master,
           scaffold=estimator_spec.scaffold,
           eval_ops=update_op,
           final_ops=eval_dict,
-          hooks=hooks,
+          hooks=all_hooks,
           config=self._session_config)
 
       _write_dict_to_summary(
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index d0666e4262..caf232b0db 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -1006,6 +1006,24 @@ class EstimatorEvaluateTest(test.TestCase):
     est.train(dummy_input_fn, steps=1)
     est.evaluate(dummy_input_fn, steps=1)
 
+  def test_evaluation_hooks_are_used(self):
+    hook = test.mock.MagicMock(
+        wraps=training.SessionRunHook(), spec=training.SessionRunHook)
+
+    def _model_fn_hooks(features, labels, mode):
+      _, _ = features, labels
+      return model_fn_lib.EstimatorSpec(
+          mode=mode,
+          loss=constant_op.constant(0.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          evaluation_hooks=[hook])
+
+    est = estimator.Estimator(model_fn=_model_fn_hooks)
+    est.train(dummy_input_fn, steps=1)
+    self.assertFalse(hook.begin.called)
+    est.evaluate(dummy_input_fn, steps=1)
+    self.assertTrue(hook.begin.called)
+
 
 class EstimatorPredictTest(test.TestCase):
 
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index 2508a1a3b1..9d0e2c3381 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -56,7 +56,7 @@ class EstimatorSpec(
     collections.namedtuple('EstimatorSpec', [
         'predictions', 'loss', 'train_op', 'eval_metric_ops',
         'export_outputs', 'training_chief_hooks', 'training_hooks',
-        'scaffold'
+        'scaffold', 'evaluation_hooks'
     ])):
   """Ops and objects returned from a `model_fn` and passed to `Estimator`.
 
@@ -72,7 +72,8 @@ class EstimatorSpec(
               export_outputs=None,
               training_chief_hooks=None,
               training_hooks=None,
-              scaffold=None):
+              scaffold=None,
+              evaluation_hooks=None):
     """Creates a validated `EstimatorSpec` instance.
 
     Depending on the value of `mode`, different arguments are required. Namely
@@ -143,10 +144,12 @@ class EstimatorSpec(
         signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY.
       training_chief_hooks: Iterable of `tf.train.SessionRunHook` objects to
         run on the chief worker during training.
-      training_hooks: Iterable of `tf.train.SessionRunHook` objects that to run
+      training_hooks: Iterable of `tf.train.SessionRunHook` objects to run
         on all workers during training.
       scaffold: A `tf.train.Scaffold` object that can be used to set
         initialization, saver, and more to be used in training.
+      evaluation_hooks: Iterable of `tf.train.SessionRunHook` objects to
+        run during evaluation.
 
     Returns:
       A validated `EstimatorSpec` object.
@@ -274,7 +277,8 @@ class EstimatorSpec(
     # Validate hooks.
     training_chief_hooks = tuple(training_chief_hooks or [])
     training_hooks = tuple(training_hooks or [])
-    for hook in training_hooks + training_chief_hooks:
+    evaluation_hooks = tuple(evaluation_hooks or [])
+    for hook in training_hooks + training_chief_hooks + evaluation_hooks:
       if not isinstance(hook, session_run_hook.SessionRunHook):
         raise TypeError(
             'All hooks must be SessionRunHook instances, given: {}'.format(
@@ -295,7 +299,8 @@ class EstimatorSpec(
         export_outputs=export_outputs,
         training_chief_hooks=training_chief_hooks,
         training_hooks=training_hooks,
-        scaffold=scaffold)
+        scaffold=scaffold,
+        evaluation_hooks=evaluation_hooks)
 
 
 def _check_is_tensor_or_operation(x, name):
diff --git a/tensorflow/python/estimator/model_fn_test.py b/tensorflow/python/estimator/model_fn_test.py
index 96c38a987b..c41df41353 100644
--- a/tensorflow/python/estimator/model_fn_test.py
+++ b/tensorflow/python/estimator/model_fn_test.py
@@ -71,7 +71,8 @@ class EstimatorSpecTrainTest(test.TestCase):
           },
           training_chief_hooks=[_FakeHook()],
           training_hooks=[_FakeHook()],
-          scaffold=monitored_session.Scaffold())
+          scaffold=monitored_session.Scaffold(),
+          evaluation_hooks=[_FakeHook()])
 
   def testLossNumber(self):
     """Tests that error is raised when loss is a number (not Tensor)."""
@@ -221,7 +222,17 @@ class EstimatorSpecEvalTest(test.TestCase):
           },
           training_chief_hooks=[_FakeHook()],
           training_hooks=[_FakeHook()],
-          scaffold=monitored_session.Scaffold())
+          scaffold=monitored_session.Scaffold(),
+          evaluation_hooks=[_FakeHook()])
+
+  def testEvaluationHookInvalid(self):
+    with ops.Graph().as_default(), self.test_session():
+      with self.assertRaisesRegexp(
+          TypeError, 'All hooks must be SessionRunHook instances'):
+        model_fn.EstimatorSpec(
+            mode=model_fn.ModeKeys.EVAL,
+            loss=constant_op.constant(1.),
+            evaluation_hooks=[_InvalidHook()])
 
   def testTupleMetric(self):
     """Tests that no errors are raised when a metric is tuple-valued."""
@@ -427,7 +438,8 @@ class EstimatorSpecInferTest(test.TestCase):
           },
           training_chief_hooks=[_FakeHook()],
           training_hooks=[_FakeHook()],
-          scaffold=monitored_session.Scaffold())
+          scaffold=monitored_session.Scaffold(),
+          evaluation_hooks=[_FakeHook()])
 
   def testPredictionsMissing(self):
     with ops.Graph().as_default(), self.test_session():
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt
index 5dbfe21726..6608d21d44 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt
@@ -7,6 +7,10 @@ tf_class {
     name: "eval_metric_ops"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "evaluation_hooks"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "export_outputs"
     mtype: "<type \'property\'>"
-- 
GitLab


From c7d2d6fdaeafca562d65d3aff2bc41dbd31dfa8e Mon Sep 17 00:00:00 2001
From: horance <horance-liu@users.noreply.github.com>
Date: Sun, 16 Jul 2017 07:49:16 +0800
Subject: [PATCH 1028/1375] dequeue or dequeue_many will fail immediately after
 close the queue (#11477)

* dequeue or dequeue_many will fail immediately after close the queue

* append condition for 'queue is closed'
---
 tensorflow/python/ops/data_flow_ops.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 3ff93b61c5..e22cab86c8 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -512,8 +512,9 @@ class QueueBase(object):
     the given queue. Subsequent `enqueue` and `enqueue_many`
     operations will fail. Subsequent `dequeue` and `dequeue_many`
     operations will continue to succeed if sufficient elements remain
-    in the queue. Subsequent `dequeue` and `dequeue_many` operations
-    that would block will fail immediately.
+    in the queue. Subsequently dequeue and dequeue_many operations 
+    that would otherwise block waiting for more elements (if close 
+    hadn't been called) will now fail immediately.
 
     If `cancel_pending_enqueues` is `True`, all pending requests will also
     be canceled.
-- 
GitLab


From 22b1c05bf53f225bb9b97fbbba74861204df994a Mon Sep 17 00:00:00 2001
From: Andrei Costinescu <AndreiCostinescu@users.noreply.github.com>
Date: Sun, 16 Jul 2017 01:51:09 +0200
Subject: [PATCH 1029/1375] Update variables.py (#11513)

Changed ValueError messages to format the passed parameter in the error message.
---
 tensorflow/contrib/framework/python/ops/variables.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py
index bf709f921d..411b4facdb 100644
--- a/tensorflow/contrib/framework/python/ops/variables.py
+++ b/tensorflow/contrib/framework/python/ops/variables.py
@@ -416,7 +416,7 @@ def get_unique_variable(var_op_name):
   for candidate in candidates:
     if candidate.op.name == var_op_name:
       return candidate
-  raise ValueError('Variable %s does not uniquely identify a variable',
+  raise ValueError('Variable %s does not uniquely identify a variable' %
                    var_op_name)
 
 
@@ -444,7 +444,7 @@ def assign_from_values(var_names_to_values):
     var_value = var_names_to_values[var_name]
     var = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, var_name)
     if not var:
-      raise ValueError('Variable %s wasnt found', var_name)
+      raise ValueError('Variable %s wasn\'t found' % var_name)
     elif len(var) > 1:
       # tf.get_collection is just a filter on the prefix: find the exact match:
       found = False
@@ -455,7 +455,7 @@ def assign_from_values(var_names_to_values):
           break
 
       if not found:
-        raise ValueError('Variable %s doesnt uniquely identify a variable',
+        raise ValueError('Variable %s doesn\'t uniquely identify a variable' %
                          var_name)
     else:
       var = var[0]
-- 
GitLab


From f3d5e7e4479eb0d875f61aa13274c86ddca70f86 Mon Sep 17 00:00:00 2001
From: Chris Antaki <ChrisAntaki@gmail.com>
Date: Sat, 15 Jul 2017 17:03:12 -0700
Subject: [PATCH 1030/1375] Fixing a typo (#11518)

---
 tensorflow/contrib/cmake/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/README.md b/tensorflow/contrib/cmake/README.md
index 664d0f4b6b..8ad8527559 100644
--- a/tensorflow/contrib/cmake/README.md
+++ b/tensorflow/contrib/cmake/README.md
@@ -225,7 +225,7 @@ Step-by-step Windows build
 
    * `-Dtensorflow_ENABLE_GPU=(ON|OFF)`. Defaults to `OFF`. Include
      GPU support. If GPU is enabled you need to install the CUDA 8.0 Toolkit and CUDNN 5.1.
-     CMake will expect the location of CUDNN in -DCUDNN_HOME=path_you_unziped_cudnn.
+     CMake will expect the location of CUDNN in -DCUDNN_HOME=path_you_unzipped_cudnn.
 
    * `-Dtensorflow_BUILD_CC_TESTS=(ON|OFF)`. Defaults to `OFF`. This builds cc unit tests.
      There are many of them and building will take a few hours.
-- 
GitLab


From 8d3336dfb55490a4c99926940d2edb2a2b38df38 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Sun, 16 Jul 2017 09:04:21 +0900
Subject: [PATCH 1031/1375] Add white spaces (#11515)

---
 tensorflow/cc/framework/gradients.cc                            | 2 +-
 .../learn/python/learn/dataframe/transforms/boolean_mask.py     | 2 +-
 tensorflow/python/estimator/model_fn.py                         | 2 +-
 tensorflow/python/ops/sparse_ops.py                             | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index 29ad8a934b..d454bc3e4c 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -65,7 +65,7 @@ class SymbolicGradientBuilder {
   // gradients for the node associated with `src`.
   Status BackpropAlongEdge(const Output& dst_grad, const Output& src);
 
-  // Adds a node to the graph (returned in`grad`) that sums the in-bound
+  // Adds a node to the graph (returned in `grad`) that sums the in-bound
   // gradients to `src` (if there are more than one).
   Status SumGradients(const Output& src, Output* grad);
 
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
index 130ac0c90f..874b02c6c9 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
@@ -35,7 +35,7 @@ def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
 
   Args:
     sparse_tensor: a `SparseTensor`.
-    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
+    mask: a 1D boolean dense `Tensor` whose length is equal to the 0th dimension
       of `sparse_tensor`.
     name: optional name for this operation.
   Returns:
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index 2508a1a3b1..e88d2ab83e 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -77,7 +77,7 @@ class EstimatorSpec(
 
     Depending on the value of `mode`, different arguments are required. Namely
     * For `mode == ModeKeys.TRAIN`: required fields are `loss` and `train_op`.
-    * For `mode == ModeKeys.EVAL`: required field is`loss`.
+    * For `mode == ModeKeys.EVAL`: required field is `loss`.
     * For `mode == ModeKeys.PREDICT`: required fields are `predictions`.
 
     model_fn can populate all arguments independent of mode. In this case, some
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index d05fabce86..db33541218 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -1110,7 +1110,7 @@ def sparse_merge(sp_ids, sp_values, vocab_size, name=None,
   Args:
     sp_ids: A single `SparseTensor` with `values` property of type `int32`
       or `int64` or a Python list of such `SparseTensor`s or a list thereof.
-    sp_values: A`SparseTensor` of any type.
+    sp_values: A `SparseTensor` of any type.
     vocab_size: A scalar `int64` Tensor (or Python int) containing the new size
       of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
       Or a list thereof with `all(0 <= sp_ids[i].values < vocab_size[i])` for
-- 
GitLab


From 8fec01948f55453a25d6457d7f97dd60b172f6ff Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Sun, 16 Jul 2017 01:07:42 +0100
Subject: [PATCH 1032/1375] Fixes 'batch_size' may be used uninitialized
 (#11522)

clang++-3.6: warning: 'batch_size' may be used uninitialized in this function
---
 tensorflow/core/kernels/batch_dataset_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/batch_dataset_op.cc b/tensorflow/core/kernels/batch_dataset_op.cc
index 67eff44a5d..64a8e83f84 100644
--- a/tensorflow/core/kernels/batch_dataset_op.cc
+++ b/tensorflow/core/kernels/batch_dataset_op.cc
@@ -31,7 +31,7 @@ class BatchDatasetOp : public UnaryDatasetOpKernel {
 
   void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                    DatasetBase** output) override {
-    int64 batch_size;
+    int64 batch_size = 0;
     OP_REQUIRES_OK(ctx,
                    ParseScalarArgument<int64>(ctx, "batch_size", &batch_size));
     OP_REQUIRES(
-- 
GitLab


From 85d4102862c781af2346b4aa568054522e8946ea Mon Sep 17 00:00:00 2001
From: Andrew Myers <andru@cs.cornell.edu>
Date: Sat, 15 Jul 2017 20:08:11 -0400
Subject: [PATCH 1033/1375] Adding generics to the Java API - Phase 1 (#11251)

* Phase 1 of the proposed generic Java API.

This adds new classes to represent each of the possible tensor types,
and some scripting support for generating those classes. There is
essentially no effect on existing classes, except that DataType is
made slightly more efficient.

All tests pass.

* Addressed Asim's review.

* Hoisted copyright into a separate declaration. Maybe it should go
in a separate file?

* Added private constructors to TF types and shortened their javadoc to be
more standard.

* Added more explanation about the enum relationship.

* Used more-idiomatic import statement.

* Rename zero column.

* Removed the datatype code from tftypes.csv

* Fix the default value for Double, add one for UInt8.

* Got rid of 'boxed type' column in CSV file

* Somehow I did not notice that TFType.java was not checked in.
---
 tensorflow/java/BUILD                         |   5 +-
 .../java/src/gen/perl/tftypes-runall.pl       |  40 +++++
 tensorflow/java/src/gen/perl/tftypes.pl       | 157 ++++++++++++++++++
 tensorflow/java/src/gen/resources/tftypes.csv |  21 +++
 .../main/java/org/tensorflow/DataType.java    |   8 +-
 .../java/org/tensorflow/types/TFBool.java     |  30 ++++
 .../java/org/tensorflow/types/TFDouble.java   |  30 ++++
 .../java/org/tensorflow/types/TFFloat.java    |  30 ++++
 .../java/org/tensorflow/types/TFInt32.java    |  30 ++++
 .../java/org/tensorflow/types/TFInt64.java    |  30 ++++
 .../java/org/tensorflow/types/TFString.java   |  27 +++
 .../java/org/tensorflow/types/TFType.java     |   6 +
 .../java/org/tensorflow/types/TFUInt8.java    |  30 ++++
 .../main/java/org/tensorflow/types/Types.java |  38 +++++
 .../org/tensorflow/types/package-info.java    |  27 +++
 15 files changed, 505 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/java/src/gen/perl/tftypes-runall.pl
 create mode 100644 tensorflow/java/src/gen/perl/tftypes.pl
 create mode 100644 tensorflow/java/src/gen/resources/tftypes.csv
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFString.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFType.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/Types.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/package-info.java

diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 759f4c1b0e..9fb4821cb1 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -22,7 +22,10 @@ java_library(
 # .aar. At some point, might make sense for a .aar rule here instead.
 filegroup(
     name = "java_sources",
-    srcs = glob(["src/main/java/org/tensorflow/*.java"]),
+    srcs = glob([
+        "src/main/java/org/tensorflow/*.java",
+        "src/main/java/org/tensorflow/types/*.java",
+    ]),
     visibility = [
         "//tensorflow/contrib/android:__pkg__",
         "//tensorflow/java:__pkg__",
diff --git a/tensorflow/java/src/gen/perl/tftypes-runall.pl b/tensorflow/java/src/gen/perl/tftypes-runall.pl
new file mode 100644
index 0000000000..258c1ff836
--- /dev/null
+++ b/tensorflow/java/src/gen/perl/tftypes-runall.pl
@@ -0,0 +1,40 @@
+#!/usr/bin/perl
+#
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+my $script = $0;
+my $dir = `dirname $script`;
+chomp $dir;
+my $gen = "$dir/..";
+my $tfjavasrc = "$gen/..";
+my $rsrc = "$gen/resources";
+my $root = "$tfjavasrc/main/java";
+my $pkg = "$root/org/tensorflow";
+
+sub locchk {
+    (my $f) = @_;
+    if (! -r $f) {
+        print STDERR "Script tftypes-runall seems to be located in the wrong place (could not find $f)\n";
+        exit 1;
+    }
+}
+&locchk("$gen");
+&locchk("$tfjavasrc/gen");
+&locchk("$dir/tftypes.pl");
+&locchk("$rsrc/tftypes.csv");
+
+system("perl $dir/tftypes.pl -t $rsrc/tftypes.csv $pkg/types");
+# system("perl $dir/tftypes.pl -c $rsrc/tftypes.csv $rsrc/Tensors.java.tmpl > $pkg/op/Tensors.java");
diff --git a/tensorflow/java/src/gen/perl/tftypes.pl b/tensorflow/java/src/gen/perl/tftypes.pl
new file mode 100644
index 0000000000..86867335cb
--- /dev/null
+++ b/tensorflow/java/src/gen/perl/tftypes.pl
@@ -0,0 +1,157 @@
+#!/usr/bin/perl
+#
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+use strict;
+
+my $copyright =
+'/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+';
+
+my $count;
+
+my $option = '-t', my $template;
+
+sub usage {
+    print "Usage: tftypes [-ctdT] <type desc file> <tmpl file>\n\n"
+         ."This script generates parts of various .java files that depend on which"
+         ."TensorFlow types are supported by the Java API and how much. For each"
+         ."such .java file, there is a .tmpl file in the same source directory in"
+         ."which the strings \@TYPEINFO\@ and \@IMPORTS\@ are replaced with"
+         ."appropriate Java code. Output code is sent to standard output.\n\n";
+
+    print "Modulo putting in the correct directory names, it can be invoked as follows:\n";
+    print "tftypes -c tftypes.csv Tensors.java.tmpl > Tensors.java\n";
+    print "tftypes -t tftypes.csv <dir>                                   [outputs files to dir]\n";
+}
+
+if ($ARGV[0] =~ m/^-/) {
+    $option = shift;
+}
+my $typedesc = shift;
+my $tmpl = shift;
+
+my $dirname;
+
+if ($option eq '-t') {
+    $dirname = $tmpl;
+}
+
+open (TMPL, "<$tmpl") || die "Cannot open $tmpl for reading\n";
+
+my $text = do { local $/; <TMPL> };
+
+my %jtypecount;
+
+my $typeinfo, my $imports;
+
+open (TYPEDESC, $typedesc);
+
+my @info = ([]);
+
+while (<TYPEDESC>) {
+    chomp;
+    my $line = $_;
+    if ($line =~ m/^TF type/) { next }
+    $line =~ s/\r$//;
+    (my $name, my $jtype, my $creat, my $default, my $desc) =
+        split /,/, $line, 5;
+    $desc =~ s/^ *//g;
+    $desc =~ s/ *$//g;
+    $jtypecount{$jtype}++;
+    if ($jtypecount{$jtype} > 1) {
+# currently allowing Java types to stand for more than one TF type, but
+# may want to revisit this.
+#       print STDERR "Ambiguous Java type for $name : $jtype\n";
+#       exit 1
+    }
+
+    push @info, [$name, $jtype, $creat, $default, $desc];
+}
+
+for (my $i = 1; $i <= $#info; $i++) {
+    (my $name, my $jtype, my $creat, my $default, my $desc) =
+        @{$info[$i]};
+    my $tfname = "TF".$name;
+    my $ucname = uc $name;
+
+    if ($option eq '-t') {
+        if ($jtype eq '') { next }
+        # Generate class declarations
+        # print STDERR "Creating $dirname/$tfname.java\n";
+        open (CLASSFILE, ">$dirname/$tfname.java") || die "Can't open $tfname.java";
+        print CLASSFILE $copyright;
+        print CLASSFILE "// GENERATED FILE. To update, edit tftypes.pl instead.\n\n";
+
+        my $fulldesc = $desc;
+        if (substr($desc, 0, 1) =~ m/^[aeoiu8]$/i) {
+            $fulldesc = "an $desc"
+        } else {
+            $fulldesc = "a $desc"
+        }
+        print CLASSFILE  "package org.tensorflow.types;\n\n"
+                        ."import org.tensorflow.DataType;\n\n";
+        print CLASSFILE  "/** Represents $fulldesc. */\n"
+                        ."public class $tfname implements TFType {\n"
+                        ."  private $tfname() {}\n"
+                        ."  static {\n"
+                        ."    Types.typeCodes.put($tfname.class, DataType.$ucname);\n"
+                        ."  }\n";
+        if ($default ne '') {
+            print CLASSFILE
+                         "  static {\n"
+                        ."    Types.scalars.put($tfname.class, $default);\n"
+                        ."  }\n";
+        }
+        print CLASSFILE  "}\n";
+        close(CLASSFILE);
+    } elsif ($option eq '-c') {
+      # Generate creator declarations for Tensors.java
+      if ($jtype ne '' && $creat eq 'y') {
+        for (my $brackets = ''; length $brackets <= 12; $brackets .= '[]') {
+            $typeinfo .=
+                "  public static Tensor<$tfname> create($jtype$brackets data) {\n"
+               ."    return Tensor.create(data, $tfname.class);\n"
+               ."  }\n";
+        }
+      }
+      if ($text =~ m/\b$tfname\b/ || $creat eq 'y') {
+            $imports .= "import org.tensorflow.types.$tfname;\n";
+      }
+    }
+}
+
+if ($option ne '-t') {
+  print "// GENERATED FILE. Edits to this file will be lost -- edit $tmpl instead.\n";
+
+  $text =~ s/\@TYPEINFO\@/$typeinfo/;
+  $text =~ s/\@IMPORTS\@/$imports/;
+
+  print $text;
+}
diff --git a/tensorflow/java/src/gen/resources/tftypes.csv b/tensorflow/java/src/gen/resources/tftypes.csv
new file mode 100644
index 0000000000..19b2b91862
--- /dev/null
+++ b/tensorflow/java/src/gen/resources/tftypes.csv
@@ -0,0 +1,21 @@
+TF type,Java type,Creator?,Zero value,Description
+Float,float,y,0f,32-bit single precision floating point number
+Double,double,y,0.0,64-bit double precision floating point number
+Int32,int,y,0,32-bit signed integer
+UInt8,byte,n,(byte)0,8-bit unsigned integer
+Int16,,n,(short)0,16-bit signed integer
+Int8,,n,(byte)0,8-bit signed integer
+String,byte,n,,arbitrary sequence of bytes
+Complex64,,n,,single-precision complex number
+Int64,long,y,0L,64-bit signed integer
+Bool,boolean,y,false,boolean
+QInt8,,n,,quantized int8
+QUInt8,,n,,quantized uint8
+QInt32,,n,,quantized int32
+BFloat16,,n,,float32 truncated to 16 bits. Only for cast ops.
+QInt16,,n,,quantized int16
+QUInt16,,n,,quantized uint16
+UInt16,,n,,16-bit unsigned integer
+Complex128,,n,,double-precision complex number
+Half,,n,,
+Resource,,n,,
diff --git a/tensorflow/java/src/main/java/org/tensorflow/DataType.java b/tensorflow/java/src/main/java/org/tensorflow/DataType.java
index cafa3ffc7d..e67e266ff7 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/DataType.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/DataType.java
@@ -53,12 +53,14 @@ public enum DataType {
   int c() {
     return value;
   }
+  
+  // Cached to avoid copying it
+  final private static DataType[] values = values();
 
   static DataType fromC(int c) {
-    for (DataType t : DataType.values()) {
-      if (t.c() == c) {
+    for (DataType t : values) {
+      if (t.value == c)
         return t;
-      }
     }
     throw new IllegalArgumentException(
         "DataType " + c + " is not recognized in Java (version " + TensorFlow.version() + ")");
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java
new file mode 100644
index 0000000000..ab34f6aa12
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a boolean. */
+public class TFBool implements TFType {
+  private TFBool() {}
+  static {
+    Types.typeCodes.put(TFBool.class, DataType.BOOL);
+  }
+  static {
+    Types.scalars.put(TFBool.class, false);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java
new file mode 100644
index 0000000000..49e5d9f2f3
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 64-bit double precision floating point number. */
+public class TFDouble implements TFType {
+  private TFDouble() {}
+  static {
+    Types.typeCodes.put(TFDouble.class, DataType.DOUBLE);
+  }
+  static {
+    Types.scalars.put(TFDouble.class, 0.0);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java
new file mode 100644
index 0000000000..8426ee41f0
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 32-bit single precision floating point number. */
+public class TFFloat implements TFType {
+  private TFFloat() {}
+  static {
+    Types.typeCodes.put(TFFloat.class, DataType.FLOAT);
+  }
+  static {
+    Types.scalars.put(TFFloat.class, 0f);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java
new file mode 100644
index 0000000000..3947b6ad09
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 32-bit signed integer. */
+public class TFInt32 implements TFType {
+  private TFInt32() {}
+  static {
+    Types.typeCodes.put(TFInt32.class, DataType.INT32);
+  }
+  static {
+    Types.scalars.put(TFInt32.class, 0);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java
new file mode 100644
index 0000000000..ccdded8693
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 64-bit signed integer. */
+public class TFInt64 implements TFType {
+  private TFInt64() {}
+  static {
+    Types.typeCodes.put(TFInt64.class, DataType.INT64);
+  }
+  static {
+    Types.scalars.put(TFInt64.class, 0L);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFString.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFString.java
new file mode 100644
index 0000000000..e7327e8c57
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFString.java
@@ -0,0 +1,27 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents an arbitrary sequence of bytes. */
+public class TFString implements TFType {
+  private TFString() {}
+  static {
+    Types.typeCodes.put(TFString.class, DataType.STRING);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFType.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFType.java
new file mode 100644
index 0000000000..07516635a0
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFType.java
@@ -0,0 +1,6 @@
+package org.tensorflow.types;
+
+/**
+ * A marker interface for classes representing TensorFlow types.
+ */
+public interface TFType {}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java
new file mode 100644
index 0000000000..d7305ca5a8
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents an 8-bit unsigned integer. */
+public class TFUInt8 implements TFType {
+  private TFUInt8() {}
+  static {
+    Types.typeCodes.put(TFUInt8.class, DataType.UINT8);
+  }
+  static {
+    Types.scalars.put(TFUInt8.class, (byte)0);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/Types.java b/tensorflow/java/src/main/java/org/tensorflow/types/Types.java
new file mode 100644
index 0000000000..42e096e35d
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/Types.java
@@ -0,0 +1,38 @@
+package org.tensorflow.types;
+
+import java.util.HashMap;
+import java.util.Map;
+import org.tensorflow.DataType;
+
+/**
+ * Utility class for managing the representation of TensorFlow types as Java
+ * types. For each TensorFlow type (e.g., int32), there is a corresponding Java
+ * type (e.g., TFInt32) that represents it at compile time and a corresponding
+ * class object (e.g., TFInt32.class) that represents it at run time. There is
+ * also an enumeration value in DataType that can be used to represent the
+ * type, though that should rarely be required.
+ */
+public class Types {
+
+  private Types() {} // not instantiable
+
+  static final Map<Class<?>, DataType> typeCodes = new HashMap<>();
+
+  /** Returns the DataType value corresponding to a TensorFlow type class. */
+  public static DataType dataType(Class<? extends TFType> c) {
+    DataType dtype = typeCodes.get(c);
+    if (dtype == null) {
+      throw new IllegalArgumentException("" + c + " is not a TensorFlow type.");
+    }
+    return dtype;
+  }
+
+  static final Map<Class<?>, Object> scalars = new HashMap<>();
+
+  /** Returns the zero value of type described by {@code c}, or null if
+   *  the type (e.g., string) is not numeric and therefore has no zero value.
+   */
+  public static Object zeroValue(Class<? extends TFType> c) {
+    return scalars.get(c);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/package-info.java b/tensorflow/java/src/main/java/org/tensorflow/types/package-info.java
new file mode 100644
index 0000000000..f1410a760e
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/package-info.java
@@ -0,0 +1,27 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/**
+ * Defines classes that represent TensorFlow data types. For each possible data type
+ * that can be used in a tensor, there is a corresponding class in this package that
+ * is used to represent it. For example, the TensorFlow int32 type is represented by
+ * the type TFInt32 and by the class object TFInt32.class. The former is used to
+ * support compile-time checking of tensor data types and the latter is used for
+ * run-time checking of data types. All such classes implement the TFType interface.
+ * TensorFlow data types are also separately represented by the DataType enum, with
+ * one enum value per data type. The enum representation should rarely be needed, but
+ * the Types class can be used to obtain it from the class object representation.
+ */
+package org.tensorflow.types;
-- 
GitLab


From c0b09babd117fbe9bed9133b0a3f04e9fc2060ec Mon Sep 17 00:00:00 2001
From: "Yuan (Terry) Tang" <terrytangyuan@users.noreply.github.com>
Date: Sun, 16 Jul 2017 16:24:38 -0500
Subject: [PATCH 1034/1375] Added missing `marital_status` assignment (#11537)

---
 tensorflow/docs_src/tutorials/wide_and_deep.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/wide_and_deep.md
index 64b3fa7dbf..e6344405d5 100644
--- a/tensorflow/docs_src/tutorials/wide_and_deep.md
+++ b/tensorflow/docs_src/tutorials/wide_and_deep.md
@@ -87,7 +87,7 @@ education = tf.feature_column.categorical_column_with_vocabulary_list(
         "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
         "Preschool", "12th"
     ])
-tf.feature_column.categorical_column_with_vocabulary_list(
+marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
     "marital_status", [
         "Married-civ-spouse", "Divorced", "Married-spouse-absent",
         "Never-married", "Separated", "Married-AF-spouse", "Widowed"
-- 
GitLab


From ec808c9b75af5dcabcb7233b10b72cfd1366fcde Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Sun, 16 Jul 2017 23:39:15 -0700
Subject: [PATCH 1035/1375] Update head.py (#11526)

* Update head.py

Use tf.losses instead of deprecated contrib

* Update head.py

Corrected incorrect import statement

* Update head.py

Correcting shape

* Update head.py

Entered correct type for reduction

* Update head.py

Improve naming convention
---
 tensorflow/contrib/learn/python/learn/estimators/head.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index fdc266d582..699a92d38a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -25,8 +25,7 @@ import six
 from tensorflow.contrib import framework as framework_lib
 from tensorflow.contrib import layers as layers_lib
 from tensorflow.contrib import lookup as lookup_lib
-# TODO(ptucker): Use tf.losses and tf.metrics.
-from tensorflow.contrib import losses as losses_lib
+# TODO(ptucker): Use tf.metrics.
 from tensorflow.contrib import metrics as metrics_lib
 from tensorflow.contrib.learn.python.learn.estimators import constants
 from tensorflow.contrib.learn.python.learn.estimators import model_fn
@@ -44,6 +43,7 @@ from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import weights_broadcast_ops
+from tensorflow.python.ops.losses import losses as losses_lib
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary
 from tensorflow.python.training import training
@@ -1212,7 +1212,8 @@ class _BinarySvmHead(_SingleHead):
       with ops.name_scope(None, "hinge_loss", (logits, labels)) as name:
         with ops.control_dependencies((_assert_labels_rank(labels),)):
           labels = array_ops.reshape(labels, shape=(-1, 1))
-        loss = losses_lib.hinge_loss(logits=logits, labels=labels, scope=name)
+        loss = losses_lib.hinge_loss(labels=labels, logits=logits, scope=name,
+                                     reduction=losses_lib.Reduction.NONE)
         return _compute_weighted_loss(loss, weights)
 
     super(_BinarySvmHead, self).__init__(
-- 
GitLab


From b97cb57e0f77db05ee005ff8c9cb6fff51ad01ae Mon Sep 17 00:00:00 2001
From: Changming Sun <chasun@microsoft.com>
Date: Mon, 17 Jul 2017 09:21:33 -0500
Subject: [PATCH 1036/1375] Change if_x86 to if_linux_x86_64 (#11467)

---
 tensorflow/core/BUILD     | 4 ++--
 tensorflow/tensorflow.bzl | 6 ++----
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 9828c3248f..fe79c42067 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -65,7 +65,7 @@ load(
     "full_path",
     "if_android",
     "if_ios",
-    "if_x86",
+    "if_linux_x86_64",
     "if_not_mobile",
     "if_not_windows",
     "tf_copts",
@@ -1351,7 +1351,7 @@ cc_library(
     name = "lib_hash_crc32c_accelerate_internal",
     srcs = ["lib/hash/crc32c_accelerate.cc"],
     # -msse4.2 enables the use of crc32c compiler builtins.
-    copts = tf_copts() + if_x86(["-msse4.2"]),
+    copts = tf_copts() + if_linux_x86_64(["-msse4.2"]),
 )
 
 cc_library(
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 83a9313e50..cddf16f147 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -117,11 +117,9 @@ def if_not_windows(a):
   })
 
 
-def if_x86(a):
+def if_linux_x86_64(a):
   return select({
       clean_dep("//tensorflow:linux_x86_64"): a,
-      clean_dep("//tensorflow:windows"): a,
-      clean_dep("//tensorflow:windows_msvc"): a,
       "//conditions:default": [],
   })
 
@@ -152,7 +150,7 @@ def tf_copts():
       "-Wno-sign-compare",
       "-fno-exceptions",
   ]) + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
-      ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
+      ["-mfpu=neon"]) + if_linux_x86_64(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
               "-DTF_LEAN_BINARY",
-- 
GitLab


From dd06643cf098ed362212ce0f76ee746951466e81 Mon Sep 17 00:00:00 2001
From: Changming Sun <chasun@microsoft.com>
Date: Mon, 17 Jul 2017 09:48:19 -0500
Subject: [PATCH 1037/1375] Use Windows Threadpool for SchedClosure (#11465)

---
 tensorflow/core/platform/windows/env.cc | 44 ++++++++++++++++++-------
 1 file changed, 32 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/platform/windows/env.cc b/tensorflow/core/platform/windows/env.cc
index b8e1c91616..fa3d6a8301 100644
--- a/tensorflow/core/platform/windows/env.cc
+++ b/tensorflow/core/platform/windows/env.cc
@@ -82,22 +82,42 @@ class WindowsEnv : public Env {
     return new StdThread(thread_options, name, fn);
   }
 
+  static VOID CALLBACK SchedClosureCallback(PTP_CALLBACK_INSTANCE Instance,
+                                            PVOID Context, PTP_WORK Work) {
+    CloseThreadpoolWork(Work);
+    std::function<void()>* f = (std::function<void()>*)Context;
+    (*f)();
+    delete f;
+  }
   void SchedClosure(std::function<void()> closure) override {
-    // TODO(b/27290852): Spawning a new thread here is wasteful, but
-    // needed to deal with the fact that many `closure` functions are
-    // blocking in the current codebase.
-    std::thread closure_thread(closure);
-    closure_thread.detach();
+    PTP_WORK work = CreateThreadpoolWork(
+        SchedClosureCallback, new std::function<void()>(std::move(closure)),
+        nullptr);
+    SubmitThreadpoolWork(work);
+  }
+
+  static VOID CALLBACK SchedClosureAfterCallback(PTP_CALLBACK_INSTANCE Instance,
+                                                 PVOID Context,
+                                                 PTP_TIMER Timer) {
+    CloseThreadpoolTimer(Timer);
+    std::function<void()>* f = (std::function<void()>*)Context;
+    (*f)();
+    delete f;
   }
 
   void SchedClosureAfter(int64 micros, std::function<void()> closure) override {
-    // TODO(b/27290852): Consuming a thread here is wasteful, but this
-    // code is (currently) only used in the case where a step fails
-    // (AbortStep). This could be replaced by a timer thread
-    SchedClosure([this, micros, closure]() {
-      SleepForMicroseconds(micros);
-      closure();
-    });
+    PTP_TIMER timer = CreateThreadpoolTimer(
+        SchedClosureAfterCallback,
+        new std::function<void()>(std::move(closure)), nullptr);
+    // in 100 nanosecond units
+    FILETIME FileDueTime;
+    ULARGE_INTEGER ulDueTime;
+    // Negative indicates the amount of time to wait is relative to the current
+    // time.
+    ulDueTime.QuadPart = (ULONGLONG) - (10 * micros);
+    FileDueTime.dwHighDateTime = ulDueTime.HighPart;
+    FileDueTime.dwLowDateTime = ulDueTime.LowPart;
+    SetThreadpoolTimer(timer, &FileDueTime, 0, 0);
   }
 
   Status LoadLibrary(const char *library_filename, void** handle) override {
-- 
GitLab


From a925c14596135b19bedb73e0f6ae3cd170180106 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 08:20:39 -0700
Subject: [PATCH 1038/1375] Move cluster initialization to happen during
 training using training hooks instead of depending on variable initialization
 logic.

PiperOrigin-RevId: 162215450
---
 .../contrib/factorization/python/ops/gmm.py   | 47 ++++++++--
 .../factorization/python/ops/gmm_ops.py       | 86 +++++++++++++------
 .../factorization/python/ops/gmm_ops_test.py  | 10 ++-
 .../factorization/python/ops/gmm_test.py      | 26 +++++-
 4 files changed, 132 insertions(+), 37 deletions(-)

diff --git a/tensorflow/contrib/factorization/python/ops/gmm.py b/tensorflow/contrib/factorization/python/ops/gmm.py
index 396dd286b6..0d67e09f81 100644
--- a/tensorflow/contrib/factorization/python/ops/gmm.py
+++ b/tensorflow/contrib/factorization/python/ops/gmm.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import time
 import numpy as np
 
 from tensorflow.contrib import framework
@@ -27,10 +28,13 @@ from tensorflow.contrib.framework.python.ops import variables
 from tensorflow.contrib.learn.python.learn.estimators import estimator
 from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import logging_ops as logging
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops.control_flow_ops import with_dependencies
+from tensorflow.python.training import session_run_hook
 
 
 def _streaming_sum(scalar_tensor):
@@ -40,6 +44,29 @@ def _streaming_sum(scalar_tensor):
   return sum_metric, sum_update
 
 
+class _InitializeClustersHook(session_run_hook.SessionRunHook):
+  """Initializes clusters or waits for cluster initialization."""
+
+  def __init__(self, init_op, is_initialized_op, is_chief):
+    self._init_op = init_op
+    self._is_chief = is_chief
+    self._is_initialized_op = is_initialized_op
+
+  def after_create_session(self, session, _):
+    assert self._init_op.graph == ops.get_default_graph()
+    assert self._is_initialized_op.graph == self._init_op.graph
+    while True:
+      try:
+        if session.run(self._is_initialized_op):
+          break
+        elif self._is_chief:
+          session.run(self._init_op)
+        else:
+          time.sleep(1)
+      except RuntimeError as e:
+        logging.info(e)
+
+
 class GMM(estimator.Estimator):
   """An estimator for GMM clustering."""
   SCORES = 'scores'
@@ -128,16 +155,23 @@ class GMM(estimator.Estimator):
   def _model_builder(self):
     """Creates a model function."""
 
-    def _model_fn(features, labels, mode):
+    def _model_fn(features, labels, mode, config):
       """Model function."""
       assert labels is None, labels
-      (all_scores, model_predictions, losses, training_op) = gmm_ops.gmm(
-          self._parse_tensor_or_dict(features), self._training_initial_clusters,
-          self._num_clusters, self._random_seed, self._covariance_type,
-          self._params)
+      (all_scores,
+       model_predictions,
+       losses, training_op,
+       init_op,
+       is_initialized) = gmm_ops.gmm(self._parse_tensor_or_dict(features),
+                                     self._training_initial_clusters,
+                                     self._num_clusters, self._random_seed,
+                                     self._covariance_type,
+                                     self._params)
       incr_step = state_ops.assign_add(variables.get_global_step(), 1)
       loss = math_ops.reduce_sum(losses)
       training_op = with_dependencies([training_op, incr_step], loss)
+      training_hooks = [_InitializeClustersHook(
+          init_op, is_initialized, config.is_chief)]
       predictions = {
           GMM.ALL_SCORES: all_scores[0],
           GMM.ASSIGNMENTS: model_predictions[0][0],
@@ -147,6 +181,7 @@ class GMM(estimator.Estimator):
       }
       return model_fn_lib.ModelFnOps(mode=mode, predictions=predictions,
                                      eval_metric_ops=eval_metric_ops,
-                                     loss=loss, train_op=training_op)
+                                     loss=loss, train_op=training_op,
+                                     training_hooks=training_hooks)
 
     return _model_fn
diff --git a/tensorflow/contrib/factorization/python/ops/gmm_ops.py b/tensorflow/contrib/factorization/python/ops/gmm_ops.py
index b092eab316..a61681c7f5 100644
--- a/tensorflow/contrib/factorization/python/ops/gmm_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/gmm_ops.py
@@ -34,6 +34,7 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
 from tensorflow.python.ops.embedding_ops import embedding_lookup
 from tensorflow.python.summary import summary
 
@@ -144,15 +145,34 @@ class GmmAlgorithm(object):
     # Small value to guarantee that covariances are invertible.
     self._min_var = array_ops.diag(
         array_ops.ones(array_ops.stack([self._dimensions]))) * 1e-3
-    self._create_variables(data, initial_means)
+    self._create_variables()
+    self._initialize_variables(data, initial_means)
     # Operations of partial statistics for the computation of the means.
     self._w_mul_x = []
     # Operations of partial statistics for the computation of the covariances.
     self._w_mul_x2 = []
     self._define_graph(data)
 
-  def _create_variables(self, data, initial_means=None):
-    """Initializes GMM algorithm.
+  def _create_variables(self):
+    """Initializes GMM algorithm."""
+    init_value = array_ops.constant([], dtype=dtypes.float32)
+    self._means = variables.Variable(init_value,
+                                     name=self.CLUSTERS_VARIABLE,
+                                     validate_shape=False)
+    self._covs = variables.Variable(
+        init_value, name=self.CLUSTERS_COVS_VARIABLE, validate_shape=False)
+    # Mixture weights, representing the probability that a randomly
+    # selected unobservable data (in EM terms) was generated by component k.
+    self._alpha = variable_scope.variable(
+        array_ops.tile([1.0 / self._num_classes], [self._num_classes]),
+        name=self.CLUSTERS_WEIGHT,
+        validate_shape=False)
+    self._cluster_centers_initialized = variables.Variable(False,
+                                                           dtype=dtypes.bool,
+                                                           name='initialized')
+
+  def _initialize_variables(self, data, initial_means=None):
+    """Initializes variables.
 
     Args:
       data: a list of Tensors with data, each row is a new example.
@@ -161,19 +181,11 @@ class GmmAlgorithm(object):
     first_shard = data[0]
     # Initialize means: num_classes X 1 X dimensions.
     if initial_means is not None:
-      self._means = variable_scope.variable(
-          array_ops.expand_dims(initial_means, 1),
-          name=self.CLUSTERS_VARIABLE,
-          validate_shape=False,
-          dtype=dtypes.float32)
+      means = array_ops.expand_dims(initial_means, 1)
     else:
       # Sample data randomly
-      self._means = variable_scope.variable(
-          array_ops.expand_dims(
-              _init_clusters_random(data, self._num_classes, self._random_seed),
-              1),
-          name=self.CLUSTERS_VARIABLE,
-          validate_shape=False)
+      means = array_ops.expand_dims(
+          _init_clusters_random(data, self._num_classes, self._random_seed), 1)
 
     # Initialize covariances.
     if self._covariance_type == FULL_COVARIANCE:
@@ -187,18 +199,40 @@ class GmmAlgorithm(object):
       covs = array_ops.tile(
           array_ops.expand_dims(array_ops.diag_part(cov), 0),
           [self._num_classes, 1])
-    self._covs = variable_scope.variable(
-        covs, name=self.CLUSTERS_COVS_VARIABLE, validate_shape=False)
-    # Mixture weights, representing the probability that a randomly
-    # selected unobservable data (in EM terms) was generated by component k.
-    self._alpha = variable_scope.variable(
-        array_ops.tile([1.0 / self._num_classes], [self._num_classes]),
-        name=self.CLUSTERS_WEIGHT,
-        validate_shape=False)
+
+    with ops.colocate_with(self._cluster_centers_initialized):
+      initialized = control_flow_ops.with_dependencies(
+          [means, covs],
+          array_ops.identity(self._cluster_centers_initialized))
+    self._init_ops = []
+    with ops.colocate_with(self._means):
+      init_means = state_ops.assign(self._means, means, validate_shape=False)
+      init_means = control_flow_ops.with_dependencies(
+          [init_means],
+          state_ops.assign(self._cluster_centers_initialized, True))
+      self._init_ops.append(control_flow_ops.cond(initialized,
+                                                  control_flow_ops.no_op,
+                                                  lambda: init_means).op)
+    with ops.colocate_with(self._covs):
+      init_covs = state_ops.assign(self._covs, covs, validate_shape=False)
+      init_covs = control_flow_ops.with_dependencies(
+          [init_covs],
+          state_ops.assign(self._cluster_centers_initialized, True))
+      self._init_ops.append(control_flow_ops.cond(initialized,
+                                                  control_flow_ops.no_op,
+                                                  lambda: init_covs).op)
+
+  def init_ops(self):
+    """Returns the initialization operation."""
+    return control_flow_ops.group(*self._init_ops)
 
   def training_ops(self):
     """Returns the training operation."""
-    return self._train_ops
+    return control_flow_ops.group(*self._train_ops)
+
+  def is_initialized(self):
+    """Returns a boolean operation for initialized variables."""
+    return self._cluster_centers_initialized
 
   def alphas(self):
     return self._alpha
@@ -486,6 +520,7 @@ def gmm(inp,
     scores: Similar to assignments but specifies the distance to the
       assigned cluster instead.
     training_op: an op that runs an iteration of training.
+    init_op: an op that runs the initialization.
   """
   initial_means = None
   if initial_clusters != 'random' and not isinstance(initial_clusters,
@@ -496,8 +531,7 @@ def gmm(inp,
   inp = inp if isinstance(inp, list) else [inp]
   gmm_tool = GmmAlgorithm(inp, num_clusters, initial_means, params,
                           covariance_type, random_seed)
-  training_ops = gmm_tool.training_ops()
   assignments = gmm_tool.assignments()
   all_scores, scores = gmm_tool.scores()
-  return [all_scores], [assignments], [scores], control_flow_ops.group(
-      *training_ops)
+  return ([all_scores], [assignments], [scores], gmm_tool.training_ops(),
+          gmm_tool.init_ops(), gmm_tool.is_initialized())
diff --git a/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py b/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py
index df8fc40ffa..c50e82db8a 100644
--- a/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py
+++ b/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py
@@ -122,12 +122,11 @@ class GmmOpsTest(test.TestCase):
       g.seed = 5
       with self.test_session() as sess:
         data = constant_op.constant(self.data, dtype=dtypes.float32)
-        _, assignments, _, training_op = gmm_ops.gmm(data,
-                                                     'random',
-                                                     num_classes,
-                                                     random_seed=self.seed)
+        _, assignments, _, training_op, init_op, _ = gmm_ops.gmm(
+            data, 'random', num_classes, random_seed=self.seed)
 
         variables.global_variables_initializer().run()
+        sess.run(init_op)
         for _ in xrange(self.iterations):
           sess.run(training_op)
         assignments = sess.run(assignments)
@@ -146,6 +145,7 @@ class GmmOpsTest(test.TestCase):
                                       [[3.0, 3.0], [0.0, 0.0]], 'w')
       training_ops = gmm_tool.training_ops()
       variables.global_variables_initializer().run()
+      sess.run(gmm_tool.init_ops())
       for _ in xrange(self.iterations):
         sess.run(training_ops)
 
@@ -163,6 +163,7 @@ class GmmOpsTest(test.TestCase):
                                       [[3.0, 3.0], [0.0, 0.0]], 'mc')
       training_ops = gmm_tool.training_ops()
       variables.global_variables_initializer().run()
+      sess.run(gmm_tool.init_ops())
       for _ in xrange(self.iterations):
         sess.run(training_ops)
       alphas = sess.run(gmm_tool.alphas())
@@ -181,6 +182,7 @@ class GmmOpsTest(test.TestCase):
                                       [[-1.0, -1.0], [1.0, 1.0]], 'c')
       training_ops = gmm_tool.training_ops()
       variables.global_variables_initializer().run()
+      sess.run(gmm_tool.init_ops())
       for _ in xrange(self.iterations):
         sess.run(training_ops)
       alphas = sess.run(gmm_tool.alphas())
diff --git a/tensorflow/contrib/factorization/python/ops/gmm_test.py b/tensorflow/contrib/factorization/python/ops/gmm_test.py
index 758c54fbf4..7717b47dae 100644
--- a/tensorflow/contrib/factorization/python/ops/gmm_test.py
+++ b/tensorflow/contrib/factorization/python/ops/gmm_test.py
@@ -28,9 +28,11 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed as random_seed_lib
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.platform import flags
 from tensorflow.python.platform import test
+from tensorflow.python.training import queue_runner
 
 FLAGS = flags.FLAGS
 
@@ -102,7 +104,7 @@ class GMMTest(test.TestCase):
                          np.linalg.inv(covs[assignments[r]])), points[r, :] -
                   means[assignments[r]])))
     return (points, assignments, scores)
-  
+
   def test_weights(self):
     """Tests the shape of the weights."""
     gmm = gmm_lib.GMM(self.num_centers,
@@ -223,5 +225,27 @@ class GMMTest(test.TestCase):
     self.assertFalse(np.isnan(gmm.clusters()).any())
 
 
+class GMMTestQueues(test.TestCase):
+
+  def input_fn(self):
+    def _fn():
+      queue = data_flow_ops.FIFOQueue(capacity=10,
+                                      dtypes=dtypes.float32,
+                                      shapes=[10, 3])
+      enqueue_op = queue.enqueue(array_ops.zeros([10, 3], dtype=dtypes.float32))
+      queue_runner.add_queue_runner(queue_runner.QueueRunner(queue,
+                                                             [enqueue_op]))
+      return queue.dequeue(), None
+    return _fn
+
+  # This test makes sure that there are no deadlocks when using a QueueRunner.
+  # Note that since cluster initialization is dependendent on inputs, if input
+  # is generated using a QueueRunner, one has to make sure that these runners
+  # are started before the initialization.
+  def test_queues(self):
+    gmm = gmm_lib.GMM(2, covariance_type='diag')
+    gmm.fit(input_fn=self.input_fn(), steps=1)
+
+
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From bed7cbe389420abe760d86cd345e88d45eb624ab Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 17 Jul 2017 08:29:52 -0700
Subject: [PATCH 1039/1375] Split out new while_loop_test.cc from c_api_test.cc

This change also separates shared functionality into
c_test_util.h/cc. This brings c_api_test.cc to a mere 1715 LOC
(further splits can be more easily done now too).

PiperOrigin-RevId: 162216399
---
 tensorflow/c/BUILD              |  27 ++
 tensorflow/c/c_api_test.cc      | 604 +-------------------------------
 tensorflow/c/c_test_util.cc     | 304 ++++++++++++++++
 tensorflow/c/c_test_util.h      | 102 ++++++
 tensorflow/c/while_loop_test.cc | 329 +++++++++++++++++
 5 files changed, 763 insertions(+), 603 deletions(-)
 create mode 100644 tensorflow/c/c_test_util.cc
 create mode 100644 tensorflow/c/c_test_util.h
 create mode 100644 tensorflow/c/while_loop_test.cc

diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD
index af9071238b..507b2fe1f1 100644
--- a/tensorflow/c/BUILD
+++ b/tensorflow/c/BUILD
@@ -103,6 +103,19 @@ tf_cuda_library(
 # -----------------------------------------------------------------------------
 # Tests
 
+tf_cuda_library(
+    name = "c_test_util",
+    testonly = 1,
+    srcs = ["c_test_util.cc"],
+    hdrs = ["c_test_util.h"],
+    deps = [
+        ":c_api",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+    ],
+)
+
 tf_cc_test(
     name = "c_api_test",
     size = "small",
@@ -120,6 +133,7 @@ tf_cc_test(
     # linkstatic = tf_kernel_tests_linkstatic(),
     deps = [
         ":c_api",
+        ":c_test_util",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/cc:grad_ops",
         "//tensorflow/cc/saved_model:signature_constants",
@@ -139,6 +153,19 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "while_loop_test",
+    size = "small",
+    srcs = ["while_loop_test.cc"],
+    deps = [
+        ":c_api",
+        ":c_test_util",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
 tf_custom_op_library(
     name = "test_op.so",
     srcs = ["test_op.cc"],
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 123ed7aeca..41f7622668 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include <iterator>
 #include <memory>
 #include <vector>
+#include "tensorflow/c/c_test_util.h"
 #include "tensorflow/cc/saved_model/signature_constants.h"
 #include "tensorflow/cc/saved_model/tag_constants.h"
 #include "tensorflow/core/example/example.pb.h"
@@ -56,9 +57,6 @@ TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src);
 
 namespace {
 
-typedef std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)>
-    unique_tensor_ptr;
-
 TEST(CAPI, Version) { EXPECT_NE("", string(TF_Version())); }
 
 TEST(CAPI, Status) {
@@ -276,194 +274,6 @@ TEST(CAPI, GetAllOpList) {
   TF_DeleteBuffer(buf);
 }
 
-static void Int32Deallocator(void* data, size_t, void* arg) {
-  delete[] static_cast<int32*>(data);
-}
-
-// Create a tensor with values of type TF_INT8 provided by `values`.
-static TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims,
-                             const char* values) {
-  int64_t num_values = 1;
-  for (int i = 0; i < num_dims; ++i) {
-    num_values *= dims[i];
-  }
-  TF_Tensor* t =
-      TF_AllocateTensor(TF_INT8, dims, num_dims, sizeof(char) * num_values);
-  memcpy(TF_TensorData(t), values, sizeof(char) * num_values);
-  return t;
-}
-
-static TF_Tensor* Int32Tensor(int32 v) {
-  const int num_bytes = sizeof(int32);
-  int32* values = new int32[1];
-  values[0] = v;
-  return TF_NewTensor(TF_INT32, nullptr, 0, values, num_bytes,
-                      &Int32Deallocator, nullptr);
-}
-
-TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
-                          const char* name = "feed") {
-  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
-  TF_SetAttrType(desc, "dtype", TF_INT32);
-  return TF_FinishOperation(desc, s);
-}
-
-TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
-                    const char* name = "const") {
-  TF_OperationDescription* desc = TF_NewOperation(graph, "Const", name);
-  TF_SetAttrTensor(desc, "value", t, s);
-  if (TF_GetCode(s) != TF_OK) return nullptr;
-  TF_SetAttrType(desc, "dtype", TF_TensorType(t));
-  return TF_FinishOperation(desc, s);
-}
-
-TF_Operation* ScalarConst(int32 v, TF_Graph* graph, TF_Status* s,
-                          const char* name = "scalar") {
-  unique_tensor_ptr tensor(Int32Tensor(v), TF_DeleteTensor);
-  return Const(tensor.get(), graph, s, name);
-}
-
-TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
-                  TF_Status* s, const char* name = "add") {
-  TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name);
-  TF_Output add_inputs[2] = {{l, 0}, {r, 0}};
-  TF_AddInputList(desc, add_inputs, 2);
-  return TF_FinishOperation(desc, s);
-}
-
-TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,
-                  const char* name = "add") {
-  TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name);
-  TF_Output inputs[2] = {l, r};
-  TF_AddInputList(desc, inputs, 2);
-  return TF_FinishOperation(desc, s);
-}
-
-TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s) {
-  TF_OperationDescription* desc = TF_NewOperation(graph, "Neg", "neg");
-  TF_Output neg_input = {n, 0};
-  TF_AddInput(desc, neg_input);
-  return TF_FinishOperation(desc, s);
-}
-
-TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph,
-                       TF_Status* s) {
-  TF_OperationDescription* desc = TF_NewOperation(graph, "Less", "less_than");
-  TF_AddInput(desc, l);
-  TF_AddInput(desc, r);
-  return TF_FinishOperation(desc, s);
-}
-
-bool IsPlaceholder(const NodeDef& node_def) {
-  if (node_def.op() != "Placeholder" || node_def.name() != "feed") {
-    return false;
-  }
-  bool found_dtype = false;
-  bool found_shape = false;
-  for (const auto& attr : node_def.attr()) {
-    if (attr.first == "dtype") {
-      if (attr.second.type() == tensorflow::DT_INT32) {
-        found_dtype = true;
-      } else {
-        return false;
-      }
-    } else if (attr.first == "shape") {
-      found_shape = true;
-    }
-  }
-  return found_dtype && found_shape;
-}
-
-bool IsScalarConst(const NodeDef& node_def, int v) {
-  if (node_def.op() != "Const" || node_def.name() != "scalar") {
-    return false;
-  }
-  bool found_dtype = false;
-  bool found_value = false;
-  for (const auto& attr : node_def.attr()) {
-    if (attr.first == "dtype") {
-      if (attr.second.type() == tensorflow::DT_INT32) {
-        found_dtype = true;
-      } else {
-        return false;
-      }
-    } else if (attr.first == "value") {
-      if (attr.second.has_tensor() &&
-          attr.second.tensor().int_val_size() == 1 &&
-          attr.second.tensor().int_val(0) == v) {
-        found_value = true;
-      } else {
-        return false;
-      }
-    }
-  }
-  return found_dtype && found_value;
-}
-
-bool IsAddN(const NodeDef& node_def, int n) {
-  if (node_def.op() != "AddN" || node_def.name() != "add" ||
-      node_def.input_size() != n) {
-    return false;
-  }
-  bool found_t = false;
-  bool found_n = false;
-  for (const auto& attr : node_def.attr()) {
-    if (attr.first == "T") {
-      if (attr.second.type() == tensorflow::DT_INT32) {
-        found_t = true;
-      } else {
-        return false;
-      }
-    } else if (attr.first == "N") {
-      if (attr.second.i() == n) {
-        found_n = true;
-      } else {
-        return false;
-      }
-    }
-  }
-  return found_t && found_n;
-}
-
-bool IsNeg(const NodeDef& node_def, const string& input) {
-  return node_def.op() == "Neg" && node_def.name() == "neg" &&
-         node_def.input_size() == 1 && node_def.input(0) == input;
-}
-
-bool GetGraphDef(TF_Graph* graph, GraphDef* graph_def) {
-  TF_Status* s = TF_NewStatus();
-  TF_Buffer* buffer = TF_NewBuffer();
-  TF_GraphToGraphDef(graph, buffer, s);
-  bool ret = TF_GetCode(s) == TF_OK;
-  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
-  if (ret) ret = graph_def->ParseFromArray(buffer->data, buffer->length);
-  TF_DeleteBuffer(buffer);
-  TF_DeleteStatus(s);
-  return ret;
-}
-
-bool GetNodeDef(TF_Operation* oper, NodeDef* node_def) {
-  TF_Status* s = TF_NewStatus();
-  TF_Buffer* buffer = TF_NewBuffer();
-  TF_OperationToNodeDef(oper, buffer, s);
-  bool ret = TF_GetCode(s) == TF_OK;
-  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
-  if (ret) ret = node_def->ParseFromArray(buffer->data, buffer->length);
-  TF_DeleteBuffer(buffer);
-  TF_DeleteStatus(s);
-  return ret;
-}
-
-bool GetAttrValue(TF_Operation* oper, const char* attr_name,
-                  tensorflow::AttrValue* attr_value, TF_Status* s) {
-  TF_Buffer* buffer = TF_NewBuffer();
-  TF_OperationGetAttrValueProto(oper, attr_name, buffer, s);
-  bool ret = TF_GetCode(s) == TF_OK;
-  if (ret) ret = attr_value->ParseFromArray(buffer->data, buffer->length);
-  TF_DeleteBuffer(buffer);
-  return ret;
-}
-
 TEST(CAPI, SetShape) {
   TF_Status* s = TF_NewStatus();
   TF_Graph* graph = TF_NewGraph();
@@ -880,116 +690,6 @@ TEST(CAPI, ImportGraphDef) {
   TF_DeleteStatus(s);
 }
 
-class CSession {
- public:
-  CSession(TF_Graph* graph, TF_Status* s) {
-    TF_SessionOptions* opts = TF_NewSessionOptions();
-    session_ = TF_NewSession(graph, opts, s);
-    TF_DeleteSessionOptions(opts);
-  }
-
-  explicit CSession(TF_Session* session) : session_(session) {}
-
-  ~CSession() {
-    TF_Status* s = TF_NewStatus();
-    CloseAndDelete(s);
-    EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
-    TF_DeleteStatus(s);
-  }
-
-  void SetInputs(std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs) {
-    DeleteInputValues();
-    inputs_.clear();
-    for (const auto& p : inputs) {
-      inputs_.emplace_back(TF_Output{p.first, 0});
-      input_values_.emplace_back(p.second);
-    }
-  }
-
-  void SetOutputs(std::initializer_list<TF_Operation*> outputs) {
-    ResetOutputValues();
-    outputs_.clear();
-    for (TF_Operation* o : outputs) {
-      outputs_.emplace_back(TF_Output{o, 0});
-    }
-    output_values_.resize(outputs_.size());
-  }
-
-  void SetOutputs(const std::vector<TF_Output>& outputs) {
-    ResetOutputValues();
-    outputs_ = outputs;
-    output_values_.resize(outputs_.size());
-  }
-
-  void SetTargets(std::initializer_list<TF_Operation*> targets) {
-    targets_.clear();
-    for (TF_Operation* t : targets) {
-      targets_.emplace_back(t);
-    }
-  }
-
-  void Run(TF_Status* s) {
-    if (inputs_.size() != input_values_.size()) {
-      ADD_FAILURE() << "Call SetInputs() before Run()";
-      return;
-    }
-    ResetOutputValues();
-    output_values_.resize(outputs_.size(), nullptr);
-
-    const TF_Output* inputs_ptr = inputs_.empty() ? nullptr : &inputs_[0];
-    TF_Tensor* const* input_values_ptr =
-        input_values_.empty() ? nullptr : &input_values_[0];
-
-    const TF_Output* outputs_ptr = outputs_.empty() ? nullptr : &outputs_[0];
-    TF_Tensor** output_values_ptr =
-        output_values_.empty() ? nullptr : &output_values_[0];
-
-    TF_Operation* const* targets_ptr =
-        targets_.empty() ? nullptr : &targets_[0];
-
-    TF_SessionRun(session_, nullptr, inputs_ptr, input_values_ptr,
-                  inputs_.size(), outputs_ptr, output_values_ptr,
-                  outputs_.size(), targets_ptr, targets_.size(), nullptr, s);
-
-    DeleteInputValues();
-  }
-
-  void CloseAndDelete(TF_Status* s) {
-    DeleteInputValues();
-    ResetOutputValues();
-    if (session_ != nullptr) {
-      TF_CloseSession(session_, s);
-      EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
-      TF_DeleteSession(session_, s);
-      session_ = nullptr;
-    }
-  }
-
-  TF_Tensor* output_tensor(int i) { return output_values_[i]; }
-
- private:
-  void DeleteInputValues() {
-    for (int i = 0; i < input_values_.size(); ++i) {
-      TF_DeleteTensor(input_values_[i]);
-    }
-    input_values_.clear();
-  }
-
-  void ResetOutputValues() {
-    for (int i = 0; i < output_values_.size(); ++i) {
-      if (output_values_[i] != nullptr) TF_DeleteTensor(output_values_[i]);
-    }
-    output_values_.clear();
-  }
-
-  TF_Session* session_;
-  std::vector<TF_Output> inputs_;
-  std::vector<TF_Tensor*> input_values_;
-  std::vector<TF_Output> outputs_;
-  std::vector<TF_Tensor*> output_values_;
-  std::vector<TF_Operation*> targets_;
-};
-
 TEST(CAPI, Session) {
   TF_Status* s = TF_NewStatus();
   TF_Graph* graph = TF_NewGraph();
@@ -1275,308 +975,6 @@ TEST(CAPI, SavedModelNullArgsAreValid) {
   TF_DeleteStatus(s);
 }
 
-class CApiWhileLoopTest : public ::testing::Test {
- protected:
-  CApiWhileLoopTest() : s_(TF_NewStatus()), graph_(TF_NewGraph()) {}
-
-  ~CApiWhileLoopTest() override {
-    TF_DeleteGraph(graph_);
-    TF_DeleteStatus(s_);
-  }
-
-  void Init(int ninputs) {
-    DCHECK(inputs_.empty());
-    DCHECK_GT(ninputs, 0);
-
-    for (int i = 0; i < ninputs; ++i) {
-      TF_Operation* placeholder = Placeholder(
-          graph_, s_, ::tensorflow::strings::StrCat("p", i).c_str());
-      DCHECK_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-      inputs_.push_back({placeholder, 0});
-    }
-
-    original_graph_description_ = GraphDebugString();
-
-    params_.reset(new TF_WhileParams(
-        TF_NewWhile(graph_, &inputs_[0], inputs_.size(), s_)));
-    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-    ASSERT_EQ(original_graph_description_, GraphDebugString())
-        << "TF_NewWhile() altered graph";
-
-    params_->name = "test_loop";
-
-    // Initialize outputs_ so we can easily detect errors/bugs
-    outputs_.resize(ninputs, {nullptr, -1});
-  }
-
-  void ExpectOK() {
-    TF_FinishWhile(params_.get(), s_, &outputs_[0]);
-    EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  }
-
-  void ExpectError(TF_Code expected_code, const string& expected_msg) {
-    TF_FinishWhile(params_.get(), s_, &outputs_[0]);
-    EXPECT_EQ(expected_code, TF_GetCode(s_));
-    EXPECT_EQ(expected_msg, TF_Message(s_));
-    // TODO(skyewm): this assert is currently broken. Fix or remove guarantee.
-    // ASSERT_EQ(original_graph_description_, GraphDebugString()) <<
-    //     "TF_FinishWhile() altered graph on error";
-  }
-
-  void Run(std::initializer_list<int> input_values) {
-    DCHECK_EQ(inputs_.size(), input_values.size());
-    std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs(inputs_.size());
-    int i = 0;
-    for (int v : input_values) {
-      inputs[i] = {inputs_[i].oper, Int32Tensor(v)};
-      ++i;
-    }
-    csession_.reset(new CSession(graph_, s_));
-    csession_->SetInputs(inputs);
-    csession_->SetOutputs(outputs_);
-    csession_->Run(s_);
-    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  }
-
-  void ExpectOutputValue(int idx, int expected_value) {
-    TF_Tensor* out = csession_->output_tensor(idx);
-    ASSERT_TRUE(out != nullptr);
-    EXPECT_EQ(TF_INT32, TF_TensorType(out));
-    EXPECT_EQ(0, TF_NumDims(out));
-    ASSERT_EQ(sizeof(int32), TF_TensorByteSize(out));
-    int32* data = static_cast<int32*>(TF_TensorData(out));
-    EXPECT_EQ(expected_value, *data);
-  }
-
-  // Create a valid conditional graph. Useful for testing unrelated errors.
-  void CreateCondGraph() {
-    TF_Operation* one = ScalarConst(1, params_->cond_graph, s_);
-    TF_Operation* less_than =
-        LessThan(params_->cond_inputs[0], {one, 0}, params_->cond_graph, s_);
-    DCHECK_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-    params_->cond_output = {less_than, 0};
-  }
-
-  string GraphDebugString() const {
-    TF_Buffer* buf = TF_NewBuffer();
-    TF_GraphToGraphDef(graph_, buf, s_);
-    DCHECK_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-    GraphDef def;
-    bool success = def.ParseFromArray(buf->data, buf->length);
-    DCHECK(success);
-    TF_DeleteBuffer(buf);
-    return def.DebugString();
-  }
-
-  TF_Status* s_;
-  TF_Graph* graph_;
-  std::vector<TF_Output> inputs_;   // The inputs to the while loop
-  std::vector<TF_Output> outputs_;  // The final outputs of the while loop
-  std::unique_ptr<TF_WhileParams> params_;
-  std::unique_ptr<CSession> csession_;
-
- private:
-  // Used to verify that errors don't change graph_
-  string original_graph_description_;
-};
-
-TEST_F(CApiWhileLoopTest, BasicLoop) {
-  Init(2);
-
-  // Validate TF_WhileParams returned by TF_NewWhile()
-  EXPECT_TRUE(params_->body_graph != nullptr);
-  EXPECT_TRUE(params_->cond_graph != nullptr);
-
-  EXPECT_EQ(params_->ninputs, 2);
-
-  ASSERT_TRUE(params_->cond_inputs != nullptr);
-  ASSERT_TRUE(params_->cond_inputs[0].oper != nullptr);
-  EXPECT_TRUE(params_->cond_inputs[1].oper != nullptr);
-
-  ASSERT_TRUE(params_->body_inputs != nullptr);
-  EXPECT_TRUE(params_->body_inputs[0].oper != nullptr);
-  EXPECT_TRUE(params_->body_inputs[1].oper != nullptr);
-
-  ASSERT_TRUE(params_->body_outputs != nullptr);
-
-  // Create loop: while (input1 < input2) input1 += input2 + 1
-  TF_Operation* less_than =
-      LessThan(params_->cond_inputs[0], params_->cond_inputs[1],
-               params_->cond_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  params_->cond_output = {less_than, 0};
-
-  TF_Operation* add1 = Add(params_->body_inputs[0], params_->body_inputs[1],
-                           params_->body_graph, s_, "add1");
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  TF_Operation* one = ScalarConst(1, params_->body_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  TF_Operation* add2 = Add(add1, one, params_->body_graph, s_, "add2");
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  params_->body_outputs[0] = {add2, 0};
-  params_->body_outputs[1] = params_->body_inputs[1];
-
-  // Finalize while loop
-  ExpectOK();
-
-  // Validate while loop outputs returned by TF_FinishWhile()
-  EXPECT_TRUE(outputs_[0].oper != nullptr);
-  EXPECT_GE(outputs_[0].index, 0);
-  EXPECT_TRUE(outputs_[1].oper != nullptr);
-  EXPECT_GE(outputs_[1].index, 0);
-
-  // Run the graph
-  Run({-9, 2});
-  ExpectOutputValue(0, 3);
-  ExpectOutputValue(1, 2);
-}
-
-TEST_F(CApiWhileLoopTest, NestedLoop) {
-  Init(2);
-  // Create nested loop:
-  //  while (input1 < 6) {
-  //    inner_input1 = input1
-  //    while (inner_input1 < 3) {
-  //      input2 += 1
-  //      inner_input1 += 2
-  //    }
-  //    input1 += input2
-  //  }
-  //
-  // Expected execution with initial values input1 = input2 = 0:
-  //
-  // outer inner               inner_
-  // step# step# input1 input2 input1
-  // ------------------------------------
-  //   0     0     0      0      0
-  //   0     1     0      1      2
-  //   0     2     0      2      4
-  //   0     -     2      2      -
-  //   1     0     2      2      2
-  //   1     1     2      3      4
-  //   1     -     5      3      -
-  //   2     0     5      3      5
-  //   2     -     8      3      -
-
-  // Create outer cond graph
-  TF_Operation* six = ScalarConst(6, params_->cond_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  TF_Operation* less_than =
-      LessThan(params_->cond_inputs[0], {six, 0}, params_->cond_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  params_->cond_output = {less_than, 0};
-
-  // Create outer body graph
-  // Init inner graph
-  TF_Output inner_inputs[] = {params_->body_inputs[0], params_->body_inputs[1]};
-  TF_WhileParams inner_params =
-      TF_NewWhile(params_->body_graph, inner_inputs, 2, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  inner_params.name = "inner_loop";
-
-  // Create inner cond graph
-  TF_Operation* three = ScalarConst(3, inner_params.cond_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  TF_Operation* inner_less_than = LessThan(
-      inner_params.cond_inputs[0], {three, 0}, inner_params.cond_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  inner_params.cond_output = {inner_less_than, 0};
-
-  // Create inner body graph
-  TF_Operation* one = ScalarConst(1, inner_params.body_graph, s_, "one");
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  TF_Operation* two = ScalarConst(2, inner_params.body_graph, s_, "two");
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-
-  TF_Operation* input2_add =
-      Add(inner_params.body_inputs[1].oper, one, inner_params.body_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  inner_params.body_outputs[1] = {input2_add, 0};
-
-  TF_Operation* inner_input1_add = Add(inner_params.body_inputs[0].oper, two,
-                                       inner_params.body_graph, s_, "add2");
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  inner_params.body_outputs[0] = {inner_input1_add, 0};
-
-  // Finalize inner graph
-  TF_Output inner_outputs[2] = {{nullptr, -1}};
-  TF_FinishWhile(&inner_params, s_, inner_outputs);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-
-  TF_Operation* input1_add =
-      Add(params_->body_inputs[0], inner_outputs[1], params_->body_graph, s_);
-  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
-  params_->body_outputs[0] = {input1_add, 0};
-
-  params_->body_outputs[1] = inner_outputs[1];
-
-  // Finalize outer graph
-  ExpectOK();
-
-  // Check for a few expected nodes
-  const char* node_name = "test_loop/cond/scalar";
-  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
-  node_name = "test_loop/body/add";
-  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
-  node_name = "test_loop/body/inner_loop/body/one";
-  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
-  node_name = "test_loop/body/inner_loop/cond/less_than";
-  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
-
-  // Run the graph
-  Run({0, 0});
-  ExpectOutputValue(0, 8);
-  ExpectOutputValue(1, 3);
-}
-
-TEST_F(CApiWhileLoopTest, BadCondOutput) {
-  Init(1);
-  params_->body_outputs[0] = params_->body_inputs[0];
-  ExpectError(TF_INVALID_ARGUMENT,
-              "TF_WhileParams `cond_output` field isn't set");
-}
-
-TEST_F(CApiWhileLoopTest, BadBodyOutput) {
-  Init(1);
-  CreateCondGraph();
-  ExpectError(TF_INVALID_ARGUMENT,
-              "TF_WhileParams `body_outputs[0]` field isn't set");
-}
-
-TEST_F(CApiWhileLoopTest, NullName) {
-  Init(1);
-  CreateCondGraph();
-  params_->body_outputs[0] = params_->body_inputs[0];
-  params_->name = nullptr;
-  ExpectError(TF_INVALID_ARGUMENT, "TF_WhileParams `name` field is null");
-}
-
-TEST_F(CApiWhileLoopTest, WrongGraph) {
-  Init(1);
-  CreateCondGraph();
-  // Set body output to output from outer graph
-  params_->body_outputs[0] = inputs_[0];
-  // TODO(skyewm): improve error message
-  ExpectError(TF_INVALID_ARGUMENT,
-              "Requested return node 'p0' not found in graph def");
-}
-
-TEST_F(CApiWhileLoopTest, BadTypes) {
-  Init(1);
-  CreateCondGraph();
-  // Op that has a float input + output
-  TF_OperationDescription* desc = TF_NewOperation(
-      params_->body_graph, "FakeQuantWithMinMaxArgs", "float_op");
-  TF_AddInput(desc, params_->body_inputs[0]);
-  TF_FinishOperation(desc, s_);
-  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
-  string msg(TF_Message(s_));
-  EXPECT_NE(msg.find("Input 'inputs' passed int32 expected float while "
-                     "building NodeDef 'float_op'"),
-            msg.npos);
-  TF_AbortWhile(params_.get());
-}
-
 REGISTER_OP("TestOpWithNoGradient")
     .Input("x: T")
     .Output("y: T")
diff --git a/tensorflow/c/c_test_util.cc b/tensorflow/c/c_test_util.cc
new file mode 100644
index 0000000000..21603c1a07
--- /dev/null
+++ b/tensorflow/c/c_test_util.cc
@@ -0,0 +1,304 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/c/c_test_util.h"
+
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/platform/logging.h"
+
+using tensorflow::GraphDef;
+using tensorflow::NodeDef;
+
+static void Int32Deallocator(void* data, size_t, void* arg) {
+  delete[] static_cast<int32_t*>(data);
+}
+
+TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) {
+  int64_t num_values = 1;
+  for (int i = 0; i < num_dims; ++i) {
+    num_values *= dims[i];
+  }
+  TF_Tensor* t =
+      TF_AllocateTensor(TF_INT8, dims, num_dims, sizeof(char) * num_values);
+  memcpy(TF_TensorData(t), values, sizeof(char) * num_values);
+  return t;
+}
+
+TF_Tensor* Int32Tensor(int32_t v) {
+  const int num_bytes = sizeof(int32_t);
+  int32_t* values = new int32_t[1];
+  values[0] = v;
+  return TF_NewTensor(TF_INT32, nullptr, 0, values, num_bytes,
+                      &Int32Deallocator, nullptr);
+}
+
+TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
+  TF_SetAttrType(desc, "dtype", TF_INT32);
+  return TF_FinishOperation(desc, s);
+}
+
+TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
+                    const char* name) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "Const", name);
+  TF_SetAttrTensor(desc, "value", t, s);
+  if (TF_GetCode(s) != TF_OK) return nullptr;
+  TF_SetAttrType(desc, "dtype", TF_TensorType(t));
+  return TF_FinishOperation(desc, s);
+}
+
+TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
+                          const char* name) {
+  unique_tensor_ptr tensor(Int32Tensor(v), TF_DeleteTensor);
+  return Const(tensor.get(), graph, s, name);
+}
+
+TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
+                  TF_Status* s, const char* name) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name);
+  TF_Output add_inputs[2] = {{l, 0}, {r, 0}};
+  TF_AddInputList(desc, add_inputs, 2);
+  return TF_FinishOperation(desc, s);
+}
+
+TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,
+                  const char* name) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name);
+  TF_Output inputs[2] = {l, r};
+  TF_AddInputList(desc, inputs, 2);
+  return TF_FinishOperation(desc, s);
+}
+
+TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "Neg", "neg");
+  TF_Output neg_input = {n, 0};
+  TF_AddInput(desc, neg_input);
+  return TF_FinishOperation(desc, s);
+}
+
+TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph,
+                       TF_Status* s) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "Less", "less_than");
+  TF_AddInput(desc, l);
+  TF_AddInput(desc, r);
+  return TF_FinishOperation(desc, s);
+}
+
+bool IsPlaceholder(const tensorflow::NodeDef& node_def) {
+  if (node_def.op() != "Placeholder" || node_def.name() != "feed") {
+    return false;
+  }
+  bool found_dtype = false;
+  bool found_shape = false;
+  for (const auto& attr : node_def.attr()) {
+    if (attr.first == "dtype") {
+      if (attr.second.type() == tensorflow::DT_INT32) {
+        found_dtype = true;
+      } else {
+        return false;
+      }
+    } else if (attr.first == "shape") {
+      found_shape = true;
+    }
+  }
+  return found_dtype && found_shape;
+}
+
+bool IsScalarConst(const tensorflow::NodeDef& node_def, int v) {
+  if (node_def.op() != "Const" || node_def.name() != "scalar") {
+    return false;
+  }
+  bool found_dtype = false;
+  bool found_value = false;
+  for (const auto& attr : node_def.attr()) {
+    if (attr.first == "dtype") {
+      if (attr.second.type() == tensorflow::DT_INT32) {
+        found_dtype = true;
+      } else {
+        return false;
+      }
+    } else if (attr.first == "value") {
+      if (attr.second.has_tensor() &&
+          attr.second.tensor().int_val_size() == 1 &&
+          attr.second.tensor().int_val(0) == v) {
+        found_value = true;
+      } else {
+        return false;
+      }
+    }
+  }
+  return found_dtype && found_value;
+}
+
+bool IsAddN(const tensorflow::NodeDef& node_def, int n) {
+  if (node_def.op() != "AddN" || node_def.name() != "add" ||
+      node_def.input_size() != n) {
+    return false;
+  }
+  bool found_t = false;
+  bool found_n = false;
+  for (const auto& attr : node_def.attr()) {
+    if (attr.first == "T") {
+      if (attr.second.type() == tensorflow::DT_INT32) {
+        found_t = true;
+      } else {
+        return false;
+      }
+    } else if (attr.first == "N") {
+      if (attr.second.i() == n) {
+        found_n = true;
+      } else {
+        return false;
+      }
+    }
+  }
+  return found_t && found_n;
+}
+
+bool IsNeg(const tensorflow::NodeDef& node_def, const string& input) {
+  return node_def.op() == "Neg" && node_def.name() == "neg" &&
+         node_def.input_size() == 1 && node_def.input(0) == input;
+}
+
+bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def) {
+  TF_Status* s = TF_NewStatus();
+  TF_Buffer* buffer = TF_NewBuffer();
+  TF_GraphToGraphDef(graph, buffer, s);
+  bool ret = TF_GetCode(s) == TF_OK;
+  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  if (ret) ret = graph_def->ParseFromArray(buffer->data, buffer->length);
+  TF_DeleteBuffer(buffer);
+  TF_DeleteStatus(s);
+  return ret;
+}
+
+bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def) {
+  TF_Status* s = TF_NewStatus();
+  TF_Buffer* buffer = TF_NewBuffer();
+  TF_OperationToNodeDef(oper, buffer, s);
+  bool ret = TF_GetCode(s) == TF_OK;
+  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  if (ret) ret = node_def->ParseFromArray(buffer->data, buffer->length);
+  TF_DeleteBuffer(buffer);
+  TF_DeleteStatus(s);
+  return ret;
+}
+
+bool GetAttrValue(TF_Operation* oper, const char* attr_name,
+                  tensorflow::AttrValue* attr_value, TF_Status* s) {
+  TF_Buffer* buffer = TF_NewBuffer();
+  TF_OperationGetAttrValueProto(oper, attr_name, buffer, s);
+  bool ret = TF_GetCode(s) == TF_OK;
+  if (ret) ret = attr_value->ParseFromArray(buffer->data, buffer->length);
+  TF_DeleteBuffer(buffer);
+  return ret;
+}
+
+CSession::CSession(TF_Graph* graph, TF_Status* s) {
+  TF_SessionOptions* opts = TF_NewSessionOptions();
+  session_ = TF_NewSession(graph, opts, s);
+  TF_DeleteSessionOptions(opts);
+}
+
+CSession::CSession(TF_Session* session) : session_(session) {}
+
+CSession::~CSession() {
+  TF_Status* s = TF_NewStatus();
+  CloseAndDelete(s);
+  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_DeleteStatus(s);
+}
+
+void CSession::SetInputs(
+    std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs) {
+  DeleteInputValues();
+  inputs_.clear();
+  for (const auto& p : inputs) {
+    inputs_.emplace_back(TF_Output{p.first, 0});
+    input_values_.emplace_back(p.second);
+  }
+}
+
+void CSession::SetOutputs(std::initializer_list<TF_Operation*> outputs) {
+  ResetOutputValues();
+  outputs_.clear();
+  for (TF_Operation* o : outputs) {
+    outputs_.emplace_back(TF_Output{o, 0});
+  }
+  output_values_.resize(outputs_.size());
+}
+
+void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
+  ResetOutputValues();
+  outputs_ = outputs;
+  output_values_.resize(outputs_.size());
+}
+
+void CSession::SetTargets(std::initializer_list<TF_Operation*> targets) {
+  targets_.clear();
+  for (TF_Operation* t : targets) {
+    targets_.emplace_back(t);
+  }
+}
+
+void CSession::Run(TF_Status* s) {
+  if (inputs_.size() != input_values_.size()) {
+    ADD_FAILURE() << "Call SetInputs() before Run()";
+    return;
+  }
+  ResetOutputValues();
+  output_values_.resize(outputs_.size(), nullptr);
+
+  const TF_Output* inputs_ptr = inputs_.empty() ? nullptr : &inputs_[0];
+  TF_Tensor* const* input_values_ptr =
+      input_values_.empty() ? nullptr : &input_values_[0];
+
+  const TF_Output* outputs_ptr = outputs_.empty() ? nullptr : &outputs_[0];
+  TF_Tensor** output_values_ptr =
+      output_values_.empty() ? nullptr : &output_values_[0];
+
+  TF_Operation* const* targets_ptr = targets_.empty() ? nullptr : &targets_[0];
+
+  TF_SessionRun(session_, nullptr, inputs_ptr, input_values_ptr, inputs_.size(),
+                outputs_ptr, output_values_ptr, outputs_.size(), targets_ptr,
+                targets_.size(), nullptr, s);
+
+  DeleteInputValues();
+}
+
+void CSession::CloseAndDelete(TF_Status* s) {
+  DeleteInputValues();
+  ResetOutputValues();
+  if (session_ != nullptr) {
+    TF_CloseSession(session_, s);
+    EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+    TF_DeleteSession(session_, s);
+    session_ = nullptr;
+  }
+}
+
+void CSession::DeleteInputValues() {
+  for (size_t i = 0; i < input_values_.size(); ++i) {
+    TF_DeleteTensor(input_values_[i]);
+  }
+  input_values_.clear();
+}
+
+void CSession::ResetOutputValues() {
+  for (size_t i = 0; i < output_values_.size(); ++i) {
+    if (output_values_[i] != nullptr) TF_DeleteTensor(output_values_[i]);
+  }
+  output_values_.clear();
+}
diff --git a/tensorflow/c/c_test_util.h b/tensorflow/c/c_test_util.h
new file mode 100644
index 0000000000..0c0ba667bd
--- /dev/null
+++ b/tensorflow/c/c_test_util.h
@@ -0,0 +1,102 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_C_C_TEST_UTIL_H_
+#define THIRD_PARTY_TENSORFLOW_C_C_TEST_UTIL_H_
+
+#include "tensorflow/c/c_api.h"
+
+#include <vector>
+#include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/types.pb.h"
+#include "tensorflow/core/platform/test.h"
+
+using ::tensorflow::string;
+
+typedef std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)>
+    unique_tensor_ptr;
+
+// Create a tensor with values of type TF_INT8 provided by `values`.
+TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values);
+
+TF_Tensor* Int32Tensor(int32_t v);
+
+TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
+                          const char* name = "feed");
+
+TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
+                    const char* name = "const");
+
+TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
+                          const char* name = "scalar");
+
+TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
+                  TF_Status* s, const char* name = "add");
+
+TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,
+                  const char* name = "add");
+
+TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s);
+
+TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s);
+
+bool IsPlaceholder(const tensorflow::NodeDef& node_def);
+
+bool IsScalarConst(const tensorflow::NodeDef& node_def, int v);
+
+bool IsAddN(const tensorflow::NodeDef& node_def, int n);
+
+bool IsNeg(const tensorflow::NodeDef& node_def, const string& input);
+
+bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def);
+
+bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def);
+
+bool GetAttrValue(TF_Operation* oper, const char* attr_name,
+                  tensorflow::AttrValue* attr_value, TF_Status* s);
+
+class CSession {
+ public:
+  CSession(TF_Graph* graph, TF_Status* s);
+  explicit CSession(TF_Session* session);
+
+  ~CSession();
+
+  void SetInputs(std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs);
+  void SetOutputs(std::initializer_list<TF_Operation*> outputs);
+  void SetOutputs(const std::vector<TF_Output>& outputs);
+  void SetTargets(std::initializer_list<TF_Operation*> targets);
+
+  void Run(TF_Status* s);
+
+  void CloseAndDelete(TF_Status* s);
+
+  TF_Tensor* output_tensor(int i) { return output_values_[i]; }
+
+ private:
+  void DeleteInputValues();
+  void ResetOutputValues();
+
+  TF_Session* session_;
+  std::vector<TF_Output> inputs_;
+  std::vector<TF_Tensor*> input_values_;
+  std::vector<TF_Output> outputs_;
+  std::vector<TF_Tensor*> output_values_;
+  std::vector<TF_Operation*> targets_;
+};
+
+#endif  // THIRD_PARTY_TENSORFLOW_C_C_TEST_UTIL_H_
diff --git a/tensorflow/c/while_loop_test.cc b/tensorflow/c/while_loop_test.cc
new file mode 100644
index 0000000000..7f7e362269
--- /dev/null
+++ b/tensorflow/c/while_loop_test.cc
@@ -0,0 +1,329 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/c/c_api.h"
+
+#include "tensorflow/c/c_test_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/test.h"
+
+using tensorflow::GraphDef;
+
+namespace {
+
+class CApiWhileLoopTest : public ::testing::Test {
+ protected:
+  CApiWhileLoopTest() : s_(TF_NewStatus()), graph_(TF_NewGraph()) {}
+
+  ~CApiWhileLoopTest() override {
+    TF_DeleteGraph(graph_);
+    TF_DeleteStatus(s_);
+  }
+
+  void Init(int ninputs) {
+    DCHECK(inputs_.empty());
+    DCHECK_GT(ninputs, 0);
+
+    for (int i = 0; i < ninputs; ++i) {
+      TF_Operation* placeholder = Placeholder(
+          graph_, s_, ::tensorflow::strings::StrCat("p", i).c_str());
+      DCHECK_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+      inputs_.push_back({placeholder, 0});
+    }
+
+    original_graph_description_ = GraphDebugString();
+
+    params_.reset(new TF_WhileParams(
+        TF_NewWhile(graph_, &inputs_[0], inputs_.size(), s_)));
+    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+    ASSERT_EQ(original_graph_description_, GraphDebugString())
+        << "TF_NewWhile() altered graph";
+
+    params_->name = "test_loop";
+
+    // Initialize outputs_ so we can easily detect errors/bugs
+    outputs_.resize(ninputs, {nullptr, -1});
+  }
+
+  void ExpectOK() {
+    TF_FinishWhile(params_.get(), s_, &outputs_[0]);
+    EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  }
+
+  void ExpectError(TF_Code expected_code, const string& expected_msg) {
+    TF_FinishWhile(params_.get(), s_, &outputs_[0]);
+    EXPECT_EQ(expected_code, TF_GetCode(s_));
+    EXPECT_EQ(expected_msg, TF_Message(s_));
+    // TODO(skyewm): this assert is currently broken. Fix or remove guarantee.
+    // ASSERT_EQ(original_graph_description_, GraphDebugString()) <<
+    //     "TF_FinishWhile() altered graph on error";
+  }
+
+  void Run(std::initializer_list<int> input_values) {
+    DCHECK_EQ(inputs_.size(), input_values.size());
+    std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs(inputs_.size());
+    int i = 0;
+    for (int v : input_values) {
+      inputs[i] = {inputs_[i].oper, Int32Tensor(v)};
+      ++i;
+    }
+    csession_.reset(new CSession(graph_, s_));
+    csession_->SetInputs(inputs);
+    csession_->SetOutputs(outputs_);
+    csession_->Run(s_);
+    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  }
+
+  void ExpectOutputValue(int idx, int expected_value) {
+    TF_Tensor* out = csession_->output_tensor(idx);
+    ASSERT_TRUE(out != nullptr);
+    EXPECT_EQ(TF_INT32, TF_TensorType(out));
+    EXPECT_EQ(0, TF_NumDims(out));
+    ASSERT_EQ(sizeof(int32_t), TF_TensorByteSize(out));
+    int32_t* data = static_cast<int32_t*>(TF_TensorData(out));
+    EXPECT_EQ(expected_value, *data);
+  }
+
+  // Create a valid conditional graph. Useful for testing unrelated errors.
+  void CreateCondGraph() {
+    TF_Operation* one = ScalarConst(1, params_->cond_graph, s_);
+    TF_Operation* less_than =
+        LessThan(params_->cond_inputs[0], {one, 0}, params_->cond_graph, s_);
+    DCHECK_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+    params_->cond_output = {less_than, 0};
+  }
+
+  string GraphDebugString() const {
+    TF_Buffer* buf = TF_NewBuffer();
+    TF_GraphToGraphDef(graph_, buf, s_);
+    DCHECK_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+    GraphDef def;
+    bool success = def.ParseFromArray(buf->data, buf->length);
+    DCHECK(success);
+    TF_DeleteBuffer(buf);
+    return def.DebugString();
+  }
+
+  TF_Status* s_;
+  TF_Graph* graph_;
+  std::vector<TF_Output> inputs_;   // The inputs to the while loop
+  std::vector<TF_Output> outputs_;  // The final outputs of the while loop
+  std::unique_ptr<TF_WhileParams> params_;
+  std::unique_ptr<CSession> csession_;
+
+ private:
+  // Used to verify that errors don't change graph_
+  string original_graph_description_;
+};
+
+TEST_F(CApiWhileLoopTest, BasicLoop) {
+  Init(2);
+
+  // Validate TF_WhileParams returned by TF_NewWhile()
+  EXPECT_TRUE(params_->body_graph != nullptr);
+  EXPECT_TRUE(params_->cond_graph != nullptr);
+
+  EXPECT_EQ(params_->ninputs, 2);
+
+  ASSERT_TRUE(params_->cond_inputs != nullptr);
+  ASSERT_TRUE(params_->cond_inputs[0].oper != nullptr);
+  EXPECT_TRUE(params_->cond_inputs[1].oper != nullptr);
+
+  ASSERT_TRUE(params_->body_inputs != nullptr);
+  EXPECT_TRUE(params_->body_inputs[0].oper != nullptr);
+  EXPECT_TRUE(params_->body_inputs[1].oper != nullptr);
+
+  ASSERT_TRUE(params_->body_outputs != nullptr);
+
+  // Create loop: while (input1 < input2) input1 += input2 + 1
+  TF_Operation* less_than =
+      LessThan(params_->cond_inputs[0], params_->cond_inputs[1],
+               params_->cond_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  params_->cond_output = {less_than, 0};
+
+  TF_Operation* add1 = Add(params_->body_inputs[0], params_->body_inputs[1],
+                           params_->body_graph, s_, "add1");
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  TF_Operation* one = ScalarConst(1, params_->body_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  TF_Operation* add2 = Add(add1, one, params_->body_graph, s_, "add2");
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  params_->body_outputs[0] = {add2, 0};
+  params_->body_outputs[1] = params_->body_inputs[1];
+
+  // Finalize while loop
+  ExpectOK();
+
+  // Validate while loop outputs returned by TF_FinishWhile()
+  EXPECT_TRUE(outputs_[0].oper != nullptr);
+  EXPECT_GE(outputs_[0].index, 0);
+  EXPECT_TRUE(outputs_[1].oper != nullptr);
+  EXPECT_GE(outputs_[1].index, 0);
+
+  // Run the graph
+  Run({-9, 2});
+  ExpectOutputValue(0, 3);
+  ExpectOutputValue(1, 2);
+}
+
+TEST_F(CApiWhileLoopTest, NestedLoop) {
+  Init(2);
+  // Create nested loop:
+  //  while (input1 < 6) {
+  //    inner_input1 = input1
+  //    while (inner_input1 < 3) {
+  //      input2 += 1
+  //      inner_input1 += 2
+  //    }
+  //    input1 += input2
+  //  }
+  //
+  // Expected execution with initial values input1 = input2 = 0:
+  //
+  // outer inner               inner_
+  // step# step# input1 input2 input1
+  // ------------------------------------
+  //   0     0     0      0      0
+  //   0     1     0      1      2
+  //   0     2     0      2      4
+  //   0     -     2      2      -
+  //   1     0     2      2      2
+  //   1     1     2      3      4
+  //   1     -     5      3      -
+  //   2     0     5      3      5
+  //   2     -     8      3      -
+
+  // Create outer cond graph
+  TF_Operation* six = ScalarConst(6, params_->cond_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  TF_Operation* less_than =
+      LessThan(params_->cond_inputs[0], {six, 0}, params_->cond_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  params_->cond_output = {less_than, 0};
+
+  // Create outer body graph
+  // Init inner graph
+  TF_Output inner_inputs[] = {params_->body_inputs[0], params_->body_inputs[1]};
+  TF_WhileParams inner_params =
+      TF_NewWhile(params_->body_graph, inner_inputs, 2, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  inner_params.name = "inner_loop";
+
+  // Create inner cond graph
+  TF_Operation* three = ScalarConst(3, inner_params.cond_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  TF_Operation* inner_less_than = LessThan(
+      inner_params.cond_inputs[0], {three, 0}, inner_params.cond_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  inner_params.cond_output = {inner_less_than, 0};
+
+  // Create inner body graph
+  TF_Operation* one = ScalarConst(1, inner_params.body_graph, s_, "one");
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  TF_Operation* two = ScalarConst(2, inner_params.body_graph, s_, "two");
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+
+  TF_Operation* input2_add =
+      Add(inner_params.body_inputs[1].oper, one, inner_params.body_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  inner_params.body_outputs[1] = {input2_add, 0};
+
+  TF_Operation* inner_input1_add = Add(inner_params.body_inputs[0].oper, two,
+                                       inner_params.body_graph, s_, "add2");
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  inner_params.body_outputs[0] = {inner_input1_add, 0};
+
+  // Finalize inner graph
+  TF_Output inner_outputs[2] = {{nullptr, -1}};
+  TF_FinishWhile(&inner_params, s_, inner_outputs);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+
+  TF_Operation* input1_add =
+      Add(params_->body_inputs[0], inner_outputs[1], params_->body_graph, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  params_->body_outputs[0] = {input1_add, 0};
+
+  params_->body_outputs[1] = inner_outputs[1];
+
+  // Finalize outer graph
+  ExpectOK();
+
+  // Check for a few expected nodes
+  const char* node_name = "test_loop/cond/scalar";
+  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
+  node_name = "test_loop/body/add";
+  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
+  node_name = "test_loop/body/inner_loop/body/one";
+  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
+  node_name = "test_loop/body/inner_loop/cond/less_than";
+  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
+
+  // Run the graph
+  Run({0, 0});
+  ExpectOutputValue(0, 8);
+  ExpectOutputValue(1, 3);
+}
+
+TEST_F(CApiWhileLoopTest, BadCondOutput) {
+  Init(1);
+  params_->body_outputs[0] = params_->body_inputs[0];
+  ExpectError(TF_INVALID_ARGUMENT,
+              "TF_WhileParams `cond_output` field isn't set");
+}
+
+TEST_F(CApiWhileLoopTest, BadBodyOutput) {
+  Init(1);
+  CreateCondGraph();
+  ExpectError(TF_INVALID_ARGUMENT,
+              "TF_WhileParams `body_outputs[0]` field isn't set");
+}
+
+TEST_F(CApiWhileLoopTest, NullName) {
+  Init(1);
+  CreateCondGraph();
+  params_->body_outputs[0] = params_->body_inputs[0];
+  params_->name = nullptr;
+  ExpectError(TF_INVALID_ARGUMENT, "TF_WhileParams `name` field is null");
+}
+
+TEST_F(CApiWhileLoopTest, WrongGraph) {
+  Init(1);
+  CreateCondGraph();
+  // Set body output to output from outer graph
+  params_->body_outputs[0] = inputs_[0];
+  // TODO(skyewm): improve error message
+  ExpectError(TF_INVALID_ARGUMENT,
+              "Requested return node 'p0' not found in graph def");
+}
+
+TEST_F(CApiWhileLoopTest, BadTypes) {
+  Init(1);
+  CreateCondGraph();
+  // Op that has a float input + output
+  TF_OperationDescription* desc = TF_NewOperation(
+      params_->body_graph, "FakeQuantWithMinMaxArgs", "float_op");
+  TF_AddInput(desc, params_->body_inputs[0]);
+  TF_FinishOperation(desc, s_);
+  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
+  string msg(TF_Message(s_));
+  EXPECT_NE(msg.find("Input 'inputs' passed int32 expected float while "
+                     "building NodeDef 'float_op'"),
+            msg.npos);
+  TF_AbortWhile(params_.get());
+}
+
+}  // namespace
-- 
GitLab


From 96af8b0b9738fb147b3b3ffe555f952b2f9e8ccf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 09:16:30 -0700
Subject: [PATCH 1040/1375] Minor cleanup

PiperOrigin-RevId: 162222071
---
 .../compiler/xla/service/algebraic_simplifier_test.cc     | 2 +-
 tensorflow/contrib/tensor_forest/ops/model_ops.cc         | 1 -
 tensorflow/core/graph/graph_partition_test.cc             | 8 +++++---
 tensorflow/core/kernels/fft_ops.cc                        | 2 +-
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index ff119c0098..be71e03e98 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -1392,7 +1392,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) {
           0, ShapeUtil::MakeShape(F32, {dim0, dim1}), "param"));
   builder.AddInstruction(HloInstruction::CreateSlice(
       ShapeUtil::MakeShape(F32, {dim0, dim1}), param, /*start_indices=*/{0, 0},
-      /*limit_indices=*/{dim0, dim1}, /*slices=*/{1, 1}));
+      /*limit_indices=*/{dim0, dim1}, /*strides=*/{1, 1}));
 
   HloModule module(TestName());
   HloComputation* computation = module.AddEntryComputation(builder.Build());
diff --git a/tensorflow/contrib/tensor_forest/ops/model_ops.cc b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
index 1227a70a2e..3dca6913f6 100644
--- a/tensorflow/contrib/tensor_forest/ops/model_ops.cc
+++ b/tensorflow/contrib/tensor_forest/ops/model_ops.cc
@@ -20,7 +20,6 @@
 namespace tensorflow {
 using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::ShapeHandle;
 
 namespace tensorforest {
 
diff --git a/tensorflow/core/graph/graph_partition_test.cc b/tensorflow/core/graph/graph_partition_test.cc
index c54b9592fb..9c49b0b67b 100644
--- a/tensorflow/core/graph/graph_partition_test.cc
+++ b/tensorflow/core/graph/graph_partition_test.cc
@@ -552,6 +552,7 @@ TEST(TopologicalSortNodesWithTimePriority, WhileLoop) {
     placeholders_in_order.back().node()->AddAttr("_start_time", i + 1);
   }
   std::vector<Placeholder> placeholders;
+  placeholders.reserve(indexes.size());
   for (int i : indexes) {
     placeholders.push_back(placeholders_in_order[i]);
   }
@@ -579,15 +580,16 @@ TEST(TopologicalSortNodesWithTimePriority, WhileLoop) {
     scope.graph()->AddEdge(next_iteration.node(), 0, merge.output.node(), 1);
 
     int base_start_time = i * 10 + 100;
-    for (auto op : std::vector<Output>{enter, merge.output, cv, loop_cond,
-                                       switch_node.output_false, identity,
-                                       next_iteration, while_exits.back()}) {
+    for (const auto& op : std::initializer_list<Output>{
+             enter, merge.output, cv, loop_cond, switch_node.output_false,
+             identity, next_iteration, while_exits.back()}) {
       op.node()->AddAttr("_start_time", base_start_time++);
     }
   }
 
   // Create ops that depend on the loop exits.
   std::vector<Square> squares;
+  squares.reserve(indexes.size());
   for (int i : indexes) {
     squares.emplace_back(root.WithOpName(StrCat("s", i)), while_exits[i]);
     squares.back().node()->AddAttr("_start_time", 500 - (i + 1));
diff --git a/tensorflow/core/kernels/fft_ops.cc b/tensorflow/core/kernels/fft_ops.cc
index 6924de284b..ab5af8caad 100644
--- a/tensorflow/core/kernels/fft_ops.cc
+++ b/tensorflow/core/kernels/fft_ops.cc
@@ -301,7 +301,7 @@ gpu::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory, uint64 size) {
 // into base class.
 class CufftScratchAllocator : public gpu::ScratchAllocator {
  public:
-  virtual ~CufftScratchAllocator() {}
+  ~CufftScratchAllocator() override {}
   CufftScratchAllocator(int64 memory_limit, OpKernelContext* context)
       : memory_limit_(memory_limit), total_byte_size_(0), context_(context) {}
   int64 GetMemoryLimitInBytes(gpu::Stream* stream) override {
-- 
GitLab


From e2a8beaaf701c59f34edc3ae5866041f9ae5d72f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 10:11:11 -0700
Subject: [PATCH 1041/1375] Make TensorForest V4 the default implementation.

PiperOrigin-RevId: 162230001
---
 tensorflow/contrib/cmake/tf_tests.cmake                  | 2 ++
 tensorflow/contrib/tensor_forest/BUILD                   | 1 +
 tensorflow/contrib/tensor_forest/client/random_forest.py | 2 +-
 3 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index b7023dbac8..089967ffec 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -176,6 +176,8 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     # flaky tests
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/cwise_ops_test.py"
     "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
+    # Loading resources in contrib doesn't seem to work on Windows
+    "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/random_forest_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 3ef7f30f8a..1f5733b853 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -600,6 +600,7 @@ py_test(
     size = "medium",
     srcs = ["client/random_forest_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["no_windows"],
     deps = [
         ":random_forest",
         ":tensor_forest_py",
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index 9200a87fb1..d067b4b779 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -291,7 +291,7 @@ class TensorForestEstimator(estimator.Estimator):
                params,
                device_assigner=None,
                model_dir=None,
-               graph_builder_class=tensor_forest.RandomForestGraphs,
+               graph_builder_class=tensor_forest_v4.RandomForestGraphsV4,
                config=None,
                weights_name=None,
                keys_name=None,
-- 
GitLab


From ce952bde5ea34708160f3e97d039617a0c11bd5f Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 17 Jul 2017 11:32:32 -0700
Subject: [PATCH 1042/1375] Create ScopedTFStatus to manage TF_Status lifetime.

In addition, this change:
- Adds a SWIG typemap to automatically unwrap ScopedTFStatus's for TF_Status* inputs
- Creates a new c_api_util.py file
- Switches a few TF_Status users to use raise_exception_on_not_ok_status() instead

PiperOrigin-RevId: 162243205
---
 tensorflow/python/BUILD                     | 18 +++++++-
 tensorflow/python/client/session.py         | 16 +++----
 tensorflow/python/client/tf_session.i       | 29 +++++++++++++
 tensorflow/python/framework/c_api_util.py   | 48 +++++++++++++++++++++
 tensorflow/python/framework/errors_impl.py  | 20 ++++-----
 tensorflow/python/framework/load_library.py | 29 ++-----------
 tensorflow/python/framework/ops.py          | 15 +------
 tensorflow/python/lib/io/file_io.py         |  8 ++--
 8 files changed, 116 insertions(+), 67 deletions(-)
 create mode 100644 tensorflow/python/framework/c_api_util.py

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 0e56d6ed19..7b7eb72994 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -431,6 +431,15 @@ py_library(
     ],
 )
 
+py_library(
+    name = "c_api_util",
+    srcs = ["framework/c_api_util.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":pywrap_tensorflow",
+    ],
+)
+
 py_library(
     name = "common_shapes",
     srcs = ["framework/common_shapes.py"],
@@ -480,7 +489,10 @@ py_library(
         "framework/errors_impl.py",
     ],
     srcs_version = "PY2AND3",
-    deps = [":util"],
+    deps = [
+        ":c_api_util",
+        ":util",
+    ],
 )
 
 py_library(
@@ -543,6 +555,7 @@ py_library(
     srcs = ["framework/ops.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":c_api_util",
         ":device",
         ":dtypes",
         ":op_def_registry",
@@ -2489,7 +2502,6 @@ py_library(
     srcs = [
         "client/client_lib.py",
         "client/device_lib.py",
-        "client/session.py",
         "client/timeline.py",
     ],
     srcs_version = "PY2AND3",
@@ -2498,6 +2510,7 @@ py_library(
         ":framework",
         ":framework_for_generated_wrappers",
         ":platform",
+        ":session",
         ":session_ops",
         ":util",
         "//third_party/py/numpy",
@@ -2831,6 +2844,7 @@ py_library(
     srcs = ["client/session.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":c_api_util",
         ":errors",
         ":framework",
         ":framework_for_generated_wrappers",
diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index d8c1489a5e..7d698c2972 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -27,6 +27,7 @@ import numpy as np
 
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python import pywrap_tensorflow as tf_session
+from tensorflow.python.framework import c_api_util
 from tensorflow.python.framework import device
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
@@ -691,24 +692,17 @@ class BaseSession(SessionInterface):
     except Exception:  # pylint: disable=broad-except
       pass
     if self._session is not None:
-      # We create `status` outside the `try` block because at shutdown
-      # `tf_session` may have been garbage collected, and the creation of a
-      # status object may fail. In that case, we prefer to ignore the failure
-      # and silently leak the session object, since the program is about to
-      # terminate.
-      status = None
       try:
-        status = tf_session.TF_NewStatus()
+        status = c_api_util.ScopedTFStatus()
         if self._created_with_new_api:
           tf_session.TF_DeleteSession(self._session, status)
         else:
           tf_session.TF_DeleteDeprecatedSession(self._session, status)
       except AttributeError:
-        # 'NoneType' object has no attribute 'TF_NewStatus'
+        # At shutdown, `c_api_util` or `tf_session` may have been garbage
+        # collected, causing the above method calls to fail. In this case,
+        # silently leak since the program is about to terminate anyway.
         pass
-      finally:
-        if status is not None:
-          tf_session.TF_DeleteStatus(status)
       self._session = None
 
   @property
diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index 59d5d8b150..40bebaa0f9 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -184,6 +184,34 @@ tensorflow::ImportNumpy();
 // END TYPEMAPS FOR tensorflow::TF_Run_wrapper()
 ////////////////////////////////////////////////////////////////////////////////
 
+// Typemap for TF_Status* inputs that automatically unwraps a ScopedTFStatus.
+// This can also handle a wrapped TF_Status* input.
+%typemap(in) (TF_Status*) {
+  PyObject* wrapped_tf_status;
+  if (strcmp(Py_TYPE($input)->tp_name, "ScopedTFStatus") == 0) {
+    DCHECK(PyObject_HasAttrString($input, "status"))
+        << "ScopedTFStatus.status not found! Do you need to modify "
+           "tf_session.i?";
+    wrapped_tf_status = PyObject_GetAttrString($input, "status");
+  } else {
+    // Assume wrapped TF_Status*
+    wrapped_tf_status = $input;
+  }
+  DCHECK_EQ(strcmp(Py_TYPE(wrapped_tf_status)->tp_name, "SwigPyObject"), 0)
+      << Py_TYPE(wrapped_tf_status)->tp_name;
+
+  // The following is the default SWIG code generated for TF_Status*
+  void* tf_status = nullptr;
+  int r = SWIG_ConvertPtr(wrapped_tf_status, &tf_status,
+                          $descriptor(TF_Status*), 0 | 0);
+  if (!SWIG_IsOK(r)) {
+    SWIG_exception_fail(
+        SWIG_ArgError(r),
+        "in method '_TF_DeleteStatus', argument 1 of type 'TF_Status *'");
+  }
+  $1 = reinterpret_cast<TF_Status*>(tf_status);
+}
+
 // Typemap for functions that return a TF_Buffer struct. This typemap creates a
 // Python string from the TF_Buffer and returns it. The TF_Buffer.data string
 // is not expected to be NULL-terminated, and TF_Buffer.length does not count
@@ -272,6 +300,7 @@ bool PyTensorListToVector(PyObject* py_tensor_list,
 %include "tensorflow/c/c_api.h"
 %include "tensorflow/c/python_api.h"
 
+
 %ignoreall
 %insert("python") %{
   def TF_NewSessionOptions(target=None, config=None):
diff --git a/tensorflow/python/framework/c_api_util.py b/tensorflow/python/framework/c_api_util.py
new file mode 100644
index 0000000000..379ba19def
--- /dev/null
+++ b/tensorflow/python/framework/c_api_util.py
@@ -0,0 +1,48 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Utilities for using the TensorFlow C API."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python import pywrap_tensorflow as c_api
+
+
+class ScopedTFStatus(object):
+  """Wrapper around TF_Status that handles deletion."""
+
+  def __init__(self):
+    self.status = c_api.TF_NewStatus()
+
+  def __del__(self):
+    # Note: when we're destructing the global context (i.e when the process is
+    # terminating) we can have already deleted other modules.
+    if c_api.TF_DeleteStatus is not None:
+      c_api.TF_DeleteStatus(self.status)
+
+
+class ScopedTFGraph(object):
+  """Wrapper around TF_Graph that handles deletion."""
+
+  def __init__(self):
+    self.graph = c_api.TF_NewGraph()
+
+  def __del__(self):
+    # Note: when we're destructing the global context (i.e when the process is
+    # terminating) we can have already deleted other modules.
+    if c_api.TF_DeleteGraph is not None:
+      c_api.TF_DeleteGraph(self.graph)
diff --git a/tensorflow/python/framework/errors_impl.py b/tensorflow/python/framework/errors_impl.py
index 32c96ec947..9b1f0a0cfb 100644
--- a/tensorflow/python/framework/errors_impl.py
+++ b/tensorflow/python/framework/errors_impl.py
@@ -23,7 +23,8 @@ import traceback
 import warnings
 
 from tensorflow.core.lib.core import error_codes_pb2
-from tensorflow.python import pywrap_tensorflow
+from tensorflow.python import pywrap_tensorflow as c_api
+from tensorflow.python.framework import c_api_util
 from tensorflow.python.util import compat
 
 
@@ -456,13 +457,10 @@ def _make_specific_exception(node_def, op, message, error_code):
 
 @contextlib.contextmanager
 def raise_exception_on_not_ok_status():
-  status = pywrap_tensorflow.TF_NewStatus()
-  try:
-    yield status
-    if pywrap_tensorflow.TF_GetCode(status) != 0:
-      raise _make_specific_exception(
-          None, None,
-          compat.as_text(pywrap_tensorflow.TF_Message(status)),
-          pywrap_tensorflow.TF_GetCode(status))
-  finally:
-    pywrap_tensorflow.TF_DeleteStatus(status)
+  status = c_api_util.ScopedTFStatus()
+  yield status.status
+  if c_api.TF_GetCode(status) != 0:
+    raise _make_specific_exception(
+        None, None,
+        compat.as_text(c_api.TF_Message(status)),
+        c_api.TF_GetCode(status))
diff --git a/tensorflow/python/framework/load_library.py b/tensorflow/python/framework/load_library.py
index 44ad6a7c3b..909e6d4c7b 100644
--- a/tensorflow/python/framework/load_library.py
+++ b/tensorflow/python/framework/load_library.py
@@ -52,19 +52,8 @@ def load_op_library(library_filename):
   Raises:
     RuntimeError: when unable to load the library or get the python wrappers.
   """
-  status = py_tf.TF_NewStatus()
-
-  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
-  try:
-    error_code = py_tf.TF_GetCode(status)
-    if error_code != 0:
-      error_msg = compat.as_text(py_tf.TF_Message(status))
-      # pylint: disable=protected-access
-      raise errors_impl._make_specific_exception(
-          None, None, error_msg, error_code)
-      # pylint: enable=protected-access
-  finally:
-    py_tf.TF_DeleteStatus(status)
+  with errors_impl.raise_exception_on_not_ok_status() as status:
+    lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
 
   op_list_str = py_tf.TF_GetOpList(lib_handle)
   op_list = op_def_pb2.OpList()
@@ -107,15 +96,5 @@ def load_file_system_library(library_filename):
   Raises:
     RuntimeError: when unable to load the library.
   """
-  status = py_tf.TF_NewStatus()
-  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
-  try:
-    error_code = py_tf.TF_GetCode(status)
-    if error_code != 0:
-      error_msg = compat.as_text(py_tf.TF_Message(status))
-      # pylint: disable=protected-access
-      raise errors_impl._make_specific_exception(
-          None, None, error_msg, error_code)
-      # pylint: enable=protected-access
-  finally:
-    py_tf.TF_DeleteStatus(status)
+  with errors_impl.raise_exception_on_not_ok_status() as status:
+    lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index c4ff568d49..8216b65c02 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -34,6 +34,7 @@ from tensorflow.core.framework import graph_pb2
 from tensorflow.core.framework import node_def_pb2
 from tensorflow.core.framework import versions_pb2
 from tensorflow.python import pywrap_tensorflow as c_api
+from tensorflow.python.framework import c_api_util
 from tensorflow.python.framework import device as pydev
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
@@ -2076,18 +2077,6 @@ def _name_from_scope_name(name):
   return name[:-1] if name[-1] == "/" else name
 
 
-class _ScopedTF_Graph(object):
-
-  def __init__(self):
-    self.graph = c_api.TF_NewGraph()
-
-  def __del__(self):
-    # Note: when we're destructing the global context (i.e when the process is
-    # terminating) we can have already deleted other modules.
-    if c_api.TF_DeleteGraph is not None:
-      c_api.TF_DeleteGraph(self.graph)
-
-
 class Graph(object):
   """A TensorFlow computation, represented as a dataflow graph.
 
@@ -2204,7 +2193,7 @@ class Graph(object):
     # TODO(skyewm): fold as much of the above as possible into the C
     # implementation
     if _USE_C_API:
-      self._scoped_c_graph = _ScopedTF_Graph()
+      self._scoped_c_graph = c_api_util.ScopedTFGraph()
     else:
       self._scoped_c_graph = None
 
diff --git a/tensorflow/python/lib/io/file_io.py b/tensorflow/python/lib/io/file_io.py
index c212d2071f..c4ac97a758 100644
--- a/tensorflow/python/lib/io/file_io.py
+++ b/tensorflow/python/lib/io/file_io.py
@@ -27,6 +27,7 @@ import uuid
 import six
 
 from tensorflow.python import pywrap_tensorflow
+from tensorflow.python.framework import c_api_util
 from tensorflow.python.framework import errors
 from tensorflow.python.util import compat
 from tensorflow.python.util import deprecation
@@ -441,11 +442,8 @@ def is_directory(dirname):
   Returns:
     True, if the path is a directory; False otherwise
   """
-  try:
-    status = pywrap_tensorflow.TF_NewStatus()
-    return pywrap_tensorflow.IsDirectory(compat.as_bytes(dirname), status)
-  finally:
-    pywrap_tensorflow.TF_DeleteStatus(status)
+  status = c_api_util.ScopedTFStatus()
+  return pywrap_tensorflow.IsDirectory(compat.as_bytes(dirname), status)
 
 
 def list_directory(dirname):
-- 
GitLab


From 139a866a82fd232993cb12bb9d054ba5f9ade5ac Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 11:42:05 -0700
Subject: [PATCH 1043/1375] Fix style issue in tfdbg documentation.

PiperOrigin-RevId: 162244864
---
 tensorflow/docs_src/programmers_guide/debugger.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 74b8d4ca61..26128fc2d2 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -346,7 +346,7 @@ line:
 diff = y_ * tf.log(y)
 ```
 
-***tfdbg** has a feature that makes it easy to trace Tensors and ops back to
+**tfdbg** has a feature that makes it easy to trace Tensors and ops back to
 lines in Python source files. It can annotate lines of a Python file with
 the ops or Tensors created by them. To use this feature,
 simply click the underlined line numbers in the stack trace output of the
-- 
GitLab


From 09e9b157787f2d03ae864569de33e62424657450 Mon Sep 17 00:00:00 2001
From: Eric Liu <ioeric@google.com>
Date: Mon, 17 Jul 2017 11:57:56 -0700
Subject: [PATCH 1044/1375] Add a gRPC client for profiling TPU
 (contrib/tpu/profiler/)

This contains a gRPC client that starts/stops tracing and processes/stores the result trace data into a TensorBoard log directory.
This also exposes trace_events proto classes via tf.contrib.tpu.profiler public API so that TensorBoard's profile plugin can process and visualize the profile.

PiperOrigin-RevId: 162247333
---
 tensorflow/contrib/cmake/tf_python.cmake      |   3 +
 tensorflow/contrib/tpu/BUILD                  |  11 ++
 tensorflow/contrib/tpu/__init__.py            |   6 +-
 tensorflow/contrib/tpu/profiler/BUILD         |  36 ++++++
 .../tpu/profiler/capture_tpu_profile.cc       | 105 ++++++++++++++++++
 .../contrib/tpu/profiler/tpu_profiler.proto   |  32 ++++++
 .../contrib/tpu/profiler/trace_events.proto   |  59 ++++++++++
 .../contrib/tpu/python/profiler/__init__.py   |  30 +++++
 8 files changed, 281 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/tpu/profiler/BUILD
 create mode 100644 tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
 create mode 100644 tensorflow/contrib/tpu/profiler/tpu_profiler.proto
 create mode 100644 tensorflow/contrib/tpu/profiler/trace_events.proto
 create mode 100644 tensorflow/contrib/tpu/python/profiler/__init__.py

diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 75709df55b..8701a584e8 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -128,6 +128,7 @@ file(GLOB_RECURSE tf_protos_python_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
+    "${tensorflow_source_dir}/tensorflow/contrib/tpu/profiler/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
 )
 RELATIVE_PROTOBUF_GENERATE_PYTHON(
@@ -541,8 +542,10 @@ add_python_module("tensorflow/contrib/timeseries/python/timeseries")
 add_python_module("tensorflow/contrib/timeseries/python/timeseries/state_space_models")
 add_python_module("tensorflow/contrib/tpu")
 add_python_module("tensorflow/contrib/tpu/ops")
+add_python_module("tensorflow/contrib/tpu/profiler")
 add_python_module("tensorflow/contrib/tpu/python")
 add_python_module("tensorflow/contrib/tpu/python/ops")
+add_python_module("tensorflow/contrib/tpu/python/profiler")
 add_python_module("tensorflow/contrib/tpu/python/tpu")
 add_python_module("tensorflow/contrib/training")
 add_python_module("tensorflow/contrib/training/python")
diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD
index 210bfc766e..c99a4d0475 100644
--- a/tensorflow/contrib/tpu/BUILD
+++ b/tensorflow/contrib/tpu/BUILD
@@ -93,6 +93,15 @@ tf_gen_op_wrapper_py(
     ],
 )
 
+py_library(
+    name = "profiler",
+    srcs = ["python/profiler/__init__.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/tpu/profiler:trace_events_proto_py",
+    ],
+)
+
 tf_custom_op_py_library(
     name = "tpu_py",
     srcs = glob(["python/ops/*.py"]) + ["__init__.py"],
@@ -102,6 +111,7 @@ tf_custom_op_py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":profiler",
         ":tpu_ops",
         "//tensorflow/contrib/util:util_py",
         "//tensorflow/python:client_testlib",
@@ -146,6 +156,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":profiler",
         ":tpu_function",
         ":tpu_py",
         ":training_loop",
diff --git a/tensorflow/contrib/tpu/__init__.py b/tensorflow/contrib/tpu/__init__.py
index bfd7887c51..1abd55b56d 100644
--- a/tensorflow/contrib/tpu/__init__.py
+++ b/tensorflow/contrib/tpu/__init__.py
@@ -20,9 +20,13 @@ from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=wildcard-import,unused-import
+from tensorflow.contrib.tpu.python import profiler
 from tensorflow.contrib.tpu.python.ops.tpu_ops import *
 from tensorflow.contrib.tpu.python.tpu import *
 # pylint: enable=wildcard-import,unused-import
 
 from tensorflow.python.util.all_util import remove_undocumented
-remove_undocumented(__name__)
+
+_allowed_symbols = ['profiler']
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/tpu/profiler/BUILD b/tensorflow/contrib/tpu/profiler/BUILD
new file mode 100644
index 0000000000..b806a94b1b
--- /dev/null
+++ b/tensorflow/contrib/tpu/profiler/BUILD
@@ -0,0 +1,36 @@
+licenses(["notice"])  # Apache 2.0
+
+load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library")
+load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library_cc")
+
+tf_proto_library_cc(
+    name = "tpu_profiler_proto",
+    srcs = ["tpu_profiler.proto"],
+    has_services = 1,
+    cc_api_version = 2,
+    cc_grpc_version = 1,
+    protodeps = [
+        "//tensorflow/core:protos_all",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_binary(
+    name = "capture_tpu_profile",
+    srcs = ["capture_tpu_profile.cc"],
+    visibility = ["//tensorflow/contrib/tpu/profiler:__subpackages__"],
+    deps = [
+        ":tpu_profiler_proto_cc",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
+        "//tensorflow/core/distributed_runtime/rpc:grpc_util",
+        "@grpc//:grpc++_unsecure",
+    ],
+)
+
+tf_proto_library(
+    name = "trace_events_proto",
+    srcs = ["trace_events.proto"],
+    cc_api_version = 2,
+    visibility = ["//visibility:public"],
+)
diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
new file mode 100644
index 0000000000..b575128159
--- /dev/null
+++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
@@ -0,0 +1,105 @@
+/* Copyright 2017 The TensorFlow Authors All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Usage: capture_tpu_profile --service_addr="localhost:8466" --logdir=/tmp/log
+//
+// Initiates a TPU profiling on the TPUProfiler service at service_addr,
+// receives and dumps the profile data to a tensorboard log directory.
+
+#include "grpc++/grpc++.h"
+
+#include <cstdio>
+#include <ctime>
+#include <vector>
+
+#include "tensorflow/contrib/tpu/profiler/tpu_profiler.grpc.pb.h"
+#include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/io/path.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/init_main.h"
+#include "tensorflow/core/util/command_line_flags.h"
+
+namespace tensorflow {
+namespace tpu {
+namespace {
+
+using ::tensorflow::TPUProfiler;
+
+using ::grpc::ClientContext;
+using ::tensorflow::io::JoinPath;
+using ::tensorflow::Env;
+using ::tensorflow::WriteStringToFile;
+
+constexpr char kProfilePluginDirectory[] = "plugins/profile/";
+constexpr char kTraceFileName[] = "trace";
+
+tensorflow::string GetCurrentTimeStampAsString() {
+  char s[128];
+  std::time_t t = std::time(nullptr);
+  CHECK_NE(std::strftime(s, sizeof(s), "%F_%T", std::localtime(&t)), 0);
+  return s;
+}
+
+// The trace will be stored in <logdir>/plugins/profile/<timestamp>/trace.
+void DumpTraceToLogDirectory(const tensorflow::string& logdir,
+                             tensorflow::StringPiece trace) {
+  tensorflow::string run = GetCurrentTimeStampAsString();
+  tensorflow::string run_dir = JoinPath(logdir, kProfilePluginDirectory, run);
+  TF_CHECK_OK(Env::Default()->RecursivelyCreateDir(run_dir));
+  tensorflow::string path = JoinPath(run_dir, kTraceFileName);
+  TF_CHECK_OK(WriteStringToFile(tensorflow::Env::Default(), path, trace));
+  LOG(INFO) << "Dumped trace data to " << path;
+}
+
+ProfileResponse Profile(const tensorflow::string& service_addr) {
+  ProfileRequest request;
+  ProfileResponse response;
+  ClientContext context;
+  std::unique_ptr<TPUProfiler::Stub> stub =
+      TPUProfiler::NewStub(::grpc::CreateChannel(
+          service_addr, ::grpc::InsecureChannelCredentials()));
+  TF_CHECK_OK(FromGrpcStatus(stub->Profile(&context, request, &response)));
+  return response;
+}
+
+}  // namespace
+}  // namespace tpu
+}  // namespace tensorflow
+
+int main(int argc, char** argv) {
+  tensorflow::string FLAGS_service_addr;
+  tensorflow::string FLAGS_logdir;
+  std::vector<tensorflow::Flag> flag_list = {
+      tensorflow::Flag("service_addr", &FLAGS_service_addr,
+                       "Address of TPU profiler service e.g. localhost:8466"),
+      tensorflow::Flag("logdir", &FLAGS_logdir,
+                       "Path of TensorBoard log directory e.g. /tmp/tb_log"),
+  };
+  tensorflow::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  bool parse_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_ok || FLAGS_service_addr.empty() || FLAGS_logdir.empty()) {
+    std::printf("%s", usage.c_str());
+    return 2;
+  }
+  tensorflow::port::InitMain(argv[0], &argc, &argv);
+  tensorflow::ProfileResponse response =
+      tensorflow::tpu::Profile(FLAGS_service_addr);
+  // Ignore computation_graph for now.
+  tensorflow::tpu::DumpTraceToLogDirectory(FLAGS_logdir,
+                                           response.encoded_trace());
+}
diff --git a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto
new file mode 100644
index 0000000000..d12eccbf4b
--- /dev/null
+++ b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto
@@ -0,0 +1,32 @@
+syntax = "proto3";
+package tensorflow;
+
+import "tensorflow/core/framework/graph.proto";
+
+// The TPUProfiler service retrieves performance information about
+// the programs running on connected TPUs over a period of time.
+service TPUProfiler {
+  // Starts a profiling session, blocks until it completes, and returns data.
+  rpc Profile(ProfileRequest) returns (ProfileResponse) {
+  }
+}
+
+message ProfileRequest {
+  // In future, the caller will be able to customize when profiling starts and
+  // stops. For now, it always collects 10 seconds worth of data.
+
+  // In future, the caller will indicate which TF session is being profiled, and
+  // only data relating to that program will be returned. For now, we assume
+  // all activity during the profiling period is relevant.
+}
+
+message ProfileResponse {
+  uint64 xprof_response_size = 1;  // Placeholder: return something meaningful.
+  // Graphs of programs executed on TPUs during the profiling period.
+  repeated GraphDef computation_graph = 2;
+
+  // Encoded Trace proto message that contains metadata about the trace captured
+  // during the profiling period. Describes the devices and resources that
+  // 'trace_events' refers to.
+  bytes encoded_trace = 3;
+}
diff --git a/tensorflow/contrib/tpu/profiler/trace_events.proto b/tensorflow/contrib/tpu/profiler/trace_events.proto
new file mode 100644
index 0000000000..0ab553ca96
--- /dev/null
+++ b/tensorflow/contrib/tpu/profiler/trace_events.proto
@@ -0,0 +1,59 @@
+syntax = "proto3";
+
+package tensorflow.tpu;
+
+// A 'Trace' contains metadata for the individual traces of a system.
+message Trace {
+  // The devices that this trace has information about. Maps from device_id to
+  // more data about the specific device.
+  map<uint64, Device> devices = 1;
+
+  // All trace events capturing in the profiling period.
+  repeated TraceEvent trace_events = 4;
+}
+
+// A 'device' is a physical entity in the system and is comprised of several
+// resources.
+message Device {
+  // The name of the device.
+  string name = 1;
+
+  // The id of this device, unique in a single trace.
+  uint64 device_id = 2;
+
+  // The resources on this device, keyed by resource_id;
+  map<uint64, Resource> resources = 3;
+}
+
+// A 'resource' generally is a specific computation component on a device. These
+// can range from threads on CPUs to specific arithmetic units on hardware
+// devices.
+message Resource {
+  // The name of the resource.
+  string name = 1;
+
+  // The id of the resource. Unique within a device.
+  uint64 resource_id = 2;
+}
+
+message TraceEvent {
+  // The id of the device that this event occurred on. The full dataset should
+  // have this device present in the Trace object.
+  uint64 device_id = 1;
+
+  // The id of the resource that this event occurred on. The full dataset should
+  // have this resource present in the Device object of the Trace object. A
+  // resource_id is unique on a specific device, but not necessarily within the
+  // trace.
+  uint64 resource_id = 2;
+
+  // The name of this trace event.
+  string name = 3;
+
+  // The timestamp that this event occurred at (in picos since tracing started).
+  uint64 timestamp_ps = 9;
+
+  // The duration of the event in picoseconds if applicable.
+  // Events without duration are called instant events.
+  uint64 duration_ps = 10;
+}
diff --git a/tensorflow/contrib/tpu/python/profiler/__init__.py b/tensorflow/contrib/tpu/python/profiler/__init__.py
new file mode 100644
index 0000000000..bde13f0527
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/profiler/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Classes for TPU trace events."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import,unused-import
+from tensorflow.contrib.tpu.profiler.trace_events_pb2 import *
+# pylint: enable=wildcard-import,unused-import
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = ['Trace', 'Resource', 'Device', 'TraceEvent']
+
+remove_undocumented(__name__, _allowed_symbols)
-- 
GitLab


From 0c144afecef6800589d255dd990a9a88e9f94b23 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 12:11:12 -0700
Subject: [PATCH 1045/1375] CPU IR Emitter: Lower slice operations with memcpy.

This changes the way the CPU backend emits slice operations
so that the maximal contiguous blocks get copied with memcpy.

Without this change, LLVM's optimizers prefer to unroll the
elementwise loops emitted for doing slice. This leads to increased
code size. With this change, the inliner only inlines the memcpy
when it is small enough.

This change also adds a test that exercises the logic to find
maximal contiguous blocks.

PiperOrigin-RevId: 162249449
---
 .../compiler/xla/service/cpu/ir_emitter.cc    | 137 +++++++++++++++++-
 tensorflow/compiler/xla/tests/slice_test.cc   |  39 +++++
 2 files changed, 170 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 9db1614cd7..5780653d46 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -53,6 +53,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/window_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 
@@ -1484,13 +1485,137 @@ Status IrEmitter::HandleSend(HloInstruction* send) {
 }
 
 Status IrEmitter::HandleSlice(HloInstruction* slice, HloInstruction* operand) {
-  if (ShapeUtil::IsScalar(slice->shape())) {
-    TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
-                        EmitTargetAddressForOp(slice));
-    emitted_value_[slice] = target_address;
-    return EmitMemcpy(*operand, *slice);
+  VLOG(2) << "HandleSlice: " << slice->ToString();
+
+  TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                      EmitTargetAddressForOp(slice));
+  emitted_value_[slice] = target_address;
+
+  // The code below assumes the layouts are equal.
+  if (!LayoutUtil::Equal(operand->shape().layout(), slice->shape().layout())) {
+    return DefaultAction(slice);
+  }
+
+  if (ShapeUtil::HasZeroElements(slice->shape())) {
+    return Status::OK();
+  }
+
+  const Layout& layout = operand->shape().layout();
+  const int64 num_dims = operand->shape().dimensions_size();
+
+  // The slice lowering finds maximal contiguous blocks of memory that can be
+  // copied from the source to the target. This is done by looking at the
+  // source/target layout in minor to major order and do the following:
+  //
+  // * Find an initial segment of dimensions along which the slice uses the
+  //   whole dimension. These are the "inner" dimensions and can be folded into
+  //   the memcpy.
+  //
+  // * Of the remaining dimensions decide which ones require loops.
+  //
+  // * Implement the memcpy within the innermost loop.
+
+  tensorflow::gtl::FlatSet<int64> inner_dims;
+  for (int64 dim : layout.minor_to_major()) {
+    if (operand->shape().dimensions(dim) != slice->shape().dimensions(dim)) {
+      break;
+    }
+    inner_dims.insert(dim);
+  }
+
+  const bool is_trivial_copy = (inner_dims.size() == num_dims);
+  if (is_trivial_copy) {
+    if (ShapeUtil::IsEffectiveScalar(slice->shape())) {
+      return DefaultAction(slice);
+    } else {
+      return EmitMemcpy(*slice, *operand);
+    }
+  }
+
+  // The memcpy will copy elements that are logically this shape (allowed to be
+  // scalar).
+  const Shape element_shape = ShapeUtil::FilterDimensions(
+      [&inner_dims](int64 dim) -> bool { return inner_dims.count(dim); },
+      operand->shape());
+
+  // memcpy_dim is the innermost (in terms of layout) dimension for which the
+  // slice does *not* just copy all the elements along the dimension.
+  const int64 memcpy_dim = layout.minor_to_major(inner_dims.size());
+
+  const bool memcpy_is_contiguous = slice->slice_strides(memcpy_dim) == 1;
+  // The number of logical elements that can be copied in a single call
+  // to memcpy. We can only copy 1 element at a time if there is a non-trivial
+  // stride.
+  const int64 memcpy_elements =
+      memcpy_is_contiguous
+          ? slice->slice_limits(memcpy_dim) - slice->slice_starts(memcpy_dim)
+          : 1;
+
+  if (memcpy_elements == 1 && ShapeUtil::IsEffectiveScalar(element_shape)) {
+    // Avoid using memcpy for copying element by element at a time. This does
+    // not buy us anything and may actually cause LLVM's load/store optimization
+    // to be less effective.
+    return DefaultAction(slice);
+  }
+
+  // Determine the dimensions that get lowered as loops.
+  std::vector<int64> outer_dims;
+  for (int64 i = 0; i < num_dims - inner_dims.size() - 1; ++i) {
+    outer_dims.push_back(LayoutUtil::Major(layout, i));
+  }
+
+  // Is the slice along the memcpy dimension contiguous? If not, then memcpy_dim
+  // needs to be wrapped around a loop as well.
+  if (!memcpy_is_contiguous) {
+    outer_dims.push_back(memcpy_dim);
+  }
+
+  llvm_ir::IrArray target_array(target_address, slice->shape());
+  AddAliasingInformationToIrArray(*slice, &target_array);
+
+  const int64 num_outer_loops = outer_dims.size();
+  llvm_ir::ForLoopNest loops(&ir_builder_);
+  llvm_ir::IrArray::Index target_index =
+      loops.AddLoopsForShapeOnDimensions(slice->shape(), outer_dims, "slice");
+
+  // Only the indices for the outer dimensions have been initialized in
+  // target_index. The rest of the indices should get initialized to 0, since
+  // for the rest of the dimensions the copy writes to the full dimension.
+  for (llvm::Value*& index : target_index) {
+    if (index == nullptr) {
+      index = ir_builder_.getInt64(0);
+    }
   }
-  return DefaultAction(slice);
+
+  if (num_outer_loops > 0) {
+    SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_);
+  }
+
+  llvm_ir::IrArray source_array(GetEmittedValueFor(operand), operand->shape());
+
+  const llvm_ir::IrArray::Index source_index = target_index.SourceIndexOfSlice(
+      /*shape=*/slice->shape(), /*starts=*/slice->slice_starts(),
+      /*strides=*/slice->slice_strides(), /*builder=*/&ir_builder_);
+
+  llvm::Value* memcpy_dest = target_array.EmitArrayElementAddress(
+      target_index, &ir_builder_, "slice.dest");
+  llvm::Value* memcpy_source = source_array.EmitArrayElementAddress(
+      source_index, &ir_builder_, "slice.source");
+  const int64 memcpy_bytes =
+      ShapeUtil::ByteSizeOf(element_shape) * memcpy_elements;
+  // TODO(b/63762267): Be more aggressive with `align` by using the GCD of the
+  // element size and buffer alignment.
+  ir_builder_.CreateMemCpy(memcpy_dest, memcpy_source, memcpy_bytes,
+                           /*align=*/1);
+
+  VLOG(2) << "  emitted memcpy of " << memcpy_bytes << " bytes inside "
+          << num_outer_loops << " loops";
+
+  if (num_outer_loops > 0) {
+    SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_);
+  }
+
+  return Status::OK();
 }
 
 Status IrEmitter::HandleDynamicSlice(HloInstruction* dynamic_slice,
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index 091c35df65..c77e892665 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -34,6 +34,45 @@ namespace {
 
 class SliceTest : public ClientLibraryTestBase {};
 
+TEST_F(SliceTest, Slice3x3x3_To_3x3x1_F32) {
+  Array3D<float> values(3, 3, 3);
+  values.FillIota(0);
+
+  ComputationBuilder builder(client_, TestName());
+  auto original = builder.ConstantR3FromArray3D<float>(values);
+  builder.Slice(original, {0, 0, 0}, {3, 3, 1}, {1, 1, 1});
+
+  Array3D<float> expected{
+      {{0.0}, {3.0}, {6.0}}, {{9.0}, {12.0}, {15.0}}, {{18.0}, {21.0}, {24.0}}};
+  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
+}
+
+TEST_F(SliceTest, Slice3x3x3_To_3x1x3_F32) {
+  Array3D<float> values(3, 3, 3);
+  values.FillIota(0);
+
+  ComputationBuilder builder(client_, TestName());
+  auto original = builder.ConstantR3FromArray3D<float>(values);
+  builder.Slice(original, {0, 0, 0}, {3, 1, 3}, {1, 1, 1});
+
+  Array3D<float> expected{
+      {{0.0, 1.0, 2.0}}, {{9.0, 10.0, 11.0}}, {{18.0, 19.0, 20.0}}};
+  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
+}
+
+TEST_F(SliceTest, Slice3x3x3_To_1x3x3_F32) {
+  Array3D<float> values(3, 3, 3);
+  values.FillIota(0);
+
+  ComputationBuilder builder(client_, TestName());
+  auto original = builder.ConstantR3FromArray3D<float>(values);
+  builder.Slice(original, {0, 0, 0}, {1, 3, 3}, {1, 1, 1});
+
+  Array3D<float> expected{
+      {{{0.0, 1.0, 2.0}, {3.0, 4.0, 5.0}, {6.0, 7.0, 8.0}}}};
+  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
+}
+
 XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 0));
-- 
GitLab


From 7bf4e6cbaae9ca930aa17d058c94aa11119fc0c3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 14:34:18 -0700
Subject: [PATCH 1046/1375] Avoid the duplication in debug_options_flags.cc by
 generalizing tensorflow::Flag.

PiperOrigin-RevId: 162271241
---
 .../xla/legacy_flags/debug_options_flags.cc   | 335 +++++++++---------
 tensorflow/core/util/command_line_flags.cc    | 147 ++++++--
 tensorflow/core/util/command_line_flags.h     |  51 ++-
 .../core/util/command_line_flags_test.cc      | 163 +++++++--
 4 files changed, 457 insertions(+), 239 deletions(-)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 5b4fb5d0a7..8bd3d0f040 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -23,167 +23,215 @@ limitations under the License.
 namespace xla {
 namespace legacy_flags {
 
-struct DebugOptionsFlags {
-  string xla_generate_hlo_graph;
-  bool xla_hlo_graph_addresses;
-  bool xla_hlo_graph_layout;
-  string xla_hlo_graph_path;
-  bool xla_hlo_dump_as_graphdef;
-  string xla_log_hlo_text;
-  string xla_generate_hlo_text_to;
-
-  string xla_disable_hlo_passes;
-  bool xla_enable_fast_math;
-  bool xla_llvm_enable_alias_scope_metadata;
-  bool xla_llvm_enable_noalias_metadata;
-  bool xla_llvm_enable_invariant_load_metadata;
-  int32 xla_backend_optimization_level;
-  bool xla_embed_ir_in_executable;
-  string xla_dump_ir_to;
-  string xla_dump_debug_json_to;
-  bool xla_eliminate_hlo_implicit_broadcast;
-
-  bool xla_cpu_multi_thread_eigen;
-
-  string xla_gpu_cuda_data_dir;
-  bool xla_gpu_ftz;
-
-  bool xla_test_all_output_layouts;
-  bool xla_test_all_input_layouts;
-
-  string xla_backend_extra_options;
-};
-
 namespace {
 
-DebugOptionsFlags* flag_values;
+DebugOptions* flag_values;
 std::vector<tensorflow::Flag>* flag_objects;
 std::once_flag flags_init;
 
+namespace {
+void SetDebugOptionsDefaults(DebugOptions* flags) {
+  flags->set_xla_hlo_graph_path("/tmp/");
+  flags->set_xla_enable_fast_math(true);
+  flags->set_xla_llvm_enable_alias_scope_metadata(true);
+  flags->set_xla_llvm_enable_noalias_metadata(true);
+  flags->set_xla_llvm_enable_invariant_load_metadata(true);
+  flags->set_xla_backend_optimization_level(3);
+  flags->set_xla_cpu_multi_thread_eigen(true);
+  flags->set_xla_gpu_cuda_data_dir("./cuda_sdk_lib");
+}
+}  // namespace
+
 // Allocates flag_values and flag_objects; this function must not be called more
 // than once - its call done via call_once.
 void AllocateFlags() {
-  flag_values = new DebugOptionsFlags;
-  flag_values->xla_generate_hlo_graph = "";
-  flag_values->xla_hlo_graph_addresses = false;
-  flag_values->xla_hlo_graph_layout = false;
-  flag_values->xla_hlo_graph_path = "/tmp/";
-  flag_values->xla_hlo_dump_as_graphdef = false;
-  flag_values->xla_log_hlo_text = "";
-  flag_values->xla_generate_hlo_text_to = "";
-  flag_values->xla_disable_hlo_passes = "";
-  flag_values->xla_enable_fast_math = true;
-  flag_values->xla_llvm_enable_alias_scope_metadata = true;
-  flag_values->xla_llvm_enable_noalias_metadata = true;
-  flag_values->xla_llvm_enable_invariant_load_metadata = true;
-  flag_values->xla_backend_optimization_level = 3;
-  flag_values->xla_embed_ir_in_executable = false;
-  flag_values->xla_dump_ir_to = "";
-  flag_values->xla_dump_debug_json_to = "";
-  flag_values->xla_eliminate_hlo_implicit_broadcast = false;
-  flag_values->xla_cpu_multi_thread_eigen = true;
-  flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
-  flag_values->xla_gpu_ftz = false;
-  flag_values->xla_test_all_output_layouts = false;
-  flag_values->xla_backend_extra_options = "";
-  flag_values->xla_test_all_input_layouts = false;
+  flag_values = new DebugOptions;
+
+  SetDebugOptionsDefaults(flag_values);
+
+  // Returns a lambda that calls "member_setter" on "flag_values" with the
+  // argument passed in to the lambda.
+  auto bool_setter_for = [](void (DebugOptions::*member_setter)(bool)) {
+    return [member_setter](bool value) {
+      (flag_values->*member_setter)(value);
+      return true;
+    };
+  };
+
+  // Returns a lambda that calls "member_setter" on "flag_values" with the
+  // argument passed in to the lambda.
+  auto int32_setter_for = [](void (DebugOptions::*member_setter)(int32)) {
+    return [member_setter](int32 value) {
+      (flag_values->*member_setter)(value);
+      return true;
+    };
+  };
+
+  // Returns a lambda that is a custom "sub-parser" for xla_disable_hlo_passes.
+  auto setter_for_xla_disable_hlo_passes = [](string comma_separated_values) {
+    std::vector<string> disabled_passes =
+        tensorflow::str_util::Split(comma_separated_values, ',');
+    for (const auto& passname : disabled_passes) {
+      flag_values->add_xla_disable_hlo_passes(passname);
+    }
+    return true;
+  };
+
+  // Returns a lambda that is a custom "sub-parser" for
+  // xla_backend_extra_options.
+  auto setter_for_xla_backend_extra_options =
+      [](string comma_separated_values) {
+        std::vector<string> extra_options_parts =
+            tensorflow::str_util::Split(comma_separated_values, ',');
+        auto* extra_options_map =
+            flag_values->mutable_xla_backend_extra_options();
+
+        // The flag contains a comma-separated list of options; some options
+        // have arguments following "=", some don't.
+        for (const auto& part : extra_options_parts) {
+          size_t eq_pos = part.find_first_of('=');
+          if (eq_pos == string::npos) {
+            (*extra_options_map)[part] = "";
+          } else {
+            string value = "";
+            if (eq_pos + 1 < part.size()) {
+              value = part.substr(eq_pos + 1);
+            }
+            (*extra_options_map)[part.substr(0, eq_pos)] = value;
+          }
+        }
+
+        return true;
+      };
 
   flag_objects = new std::vector<tensorflow::Flag>(
       {tensorflow::Flag(
-           "xla_generate_hlo_graph", &flag_values->xla_generate_hlo_graph,
+           "xla_generate_hlo_graph",
+           flag_values->mutable_xla_generate_hlo_graph(),
            "HLO modules matching this regex will be dumped to a .dot file "
            "throughout various stages in compilation."),
        tensorflow::Flag(
-           "xla_hlo_graph_addresses", &flag_values->xla_hlo_graph_addresses,
+           "xla_hlo_graph_addresses",
+           bool_setter_for(&DebugOptions::set_xla_hlo_graph_addresses),
+           flag_values->xla_hlo_graph_addresses(),
            "With xla_generate_hlo_graph, show addresses of HLO ops in "
            "graph dump."),
        tensorflow::Flag(
-           "xla_hlo_graph_layout", &flag_values->xla_hlo_graph_layout,
+           "xla_hlo_graph_layout",
+           bool_setter_for(&DebugOptions::set_xla_hlo_graph_layout),
+           flag_values->xla_hlo_graph_layout(),
            "With xla_generate_hlo_graph, show layout of HLO ops in "
            "graph dump."),
        tensorflow::Flag(
-           "xla_hlo_graph_path", &flag_values->xla_hlo_graph_path,
+           "xla_hlo_graph_path", flag_values->mutable_xla_hlo_graph_path(),
            "With xla_generate_hlo_graph, dump the graphs into this path."),
-       tensorflow::Flag("xla_hlo_dump_as_graphdef",
-                        &flag_values->xla_hlo_dump_as_graphdef,
-                        "Dump HLO graphs as TensorFlow GraphDefs."),
        tensorflow::Flag(
-           "xla_log_hlo_text", &flag_values->xla_log_hlo_text,
+           "xla_hlo_dump_as_graphdef",
+           bool_setter_for(&DebugOptions::set_xla_hlo_dump_as_graphdef),
+           flag_values->xla_hlo_dump_as_graphdef(),
+           "Dump HLO graphs as TensorFlow GraphDefs."),
+       tensorflow::Flag(
+           "xla_log_hlo_text", flag_values->mutable_xla_log_hlo_text(),
            "HLO modules matching this regex will be dumped to LOG(INFO). "),
        tensorflow::Flag(
-           "xla_generate_hlo_text_to", &flag_values->xla_generate_hlo_text_to,
+           "xla_generate_hlo_text_to",
+           flag_values->mutable_xla_generate_hlo_text_to(),
            "Dump all HLO modules as text into the provided directory path."),
        tensorflow::Flag(
-           "xla_enable_fast_math", &flag_values->xla_enable_fast_math,
+           "xla_enable_fast_math",
+           bool_setter_for(&DebugOptions::set_xla_enable_fast_math),
+           flag_values->xla_enable_fast_math(),
            "Enable unsafe fast-math optimizations in the compiler; "
            "this may produce faster code at the expense of some accuracy."),
-       tensorflow::Flag("xla_llvm_enable_alias_scope_metadata",
-                        &flag_values->xla_llvm_enable_alias_scope_metadata,
-                        "In LLVM-based backends, enable the emission of "
-                        "!alias.scope metadata in the generated IR."),
-       tensorflow::Flag("xla_llvm_enable_noalias_metadata",
-                        &flag_values->xla_llvm_enable_noalias_metadata,
-                        "In LLVM-based backends, enable the emission of "
-                        "!noalias metadata in the generated IR."),
-       tensorflow::Flag("xla_llvm_enable_invariant_load_metadata",
-                        &flag_values->xla_llvm_enable_invariant_load_metadata,
-                        "In LLVM-based backends, enable the emission of "
-                        "!invariant.load metadata in "
-                        "the generated IR."),
+       tensorflow::Flag(
+           "xla_llvm_enable_alias_scope_metadata",
+           bool_setter_for(
+               &DebugOptions::set_xla_llvm_enable_alias_scope_metadata),
+           flag_values->xla_llvm_enable_alias_scope_metadata(),
+           "In LLVM-based backends, enable the emission of "
+           "!alias.scope metadata in the generated IR."),
+       tensorflow::Flag(
+           "xla_llvm_enable_noalias_metadata",
+           bool_setter_for(&DebugOptions::set_xla_llvm_enable_noalias_metadata),
+           flag_values->xla_llvm_enable_noalias_metadata(),
+           "In LLVM-based backends, enable the emission of "
+           "!noalias metadata in the generated IR."),
+       tensorflow::Flag(
+           "xla_llvm_enable_invariant_load_metadata",
+           bool_setter_for(
+               &DebugOptions::set_xla_llvm_enable_invariant_load_metadata),
+           flag_values->xla_llvm_enable_invariant_load_metadata(),
+           "In LLVM-based backends, enable the emission of "
+           "!invariant.load metadata in "
+           "the generated IR."),
        tensorflow::Flag(
            "xla_backend_optimization_level",
-           &flag_values->xla_backend_optimization_level,
+           int32_setter_for(&DebugOptions::set_xla_backend_optimization_level),
+           flag_values->xla_backend_optimization_level(),
            "Numerical optimization level for the XLA compiler backend."),
        tensorflow::Flag(
-           "xla_disable_hlo_passes", &flag_values->xla_disable_hlo_passes,
+           "xla_disable_hlo_passes", setter_for_xla_disable_hlo_passes, "",
            "Comma-separated list of hlo passes to be disabled. These names "
            "must exactly match the passes' names; no whitespace around "
            "commas."),
-       tensorflow::Flag("xla_embed_ir_in_executable",
-                        &flag_values->xla_embed_ir_in_executable,
-                        "Embed the compiler IR as a string in the executable."),
        tensorflow::Flag(
-           "xla_dump_ir_to", &flag_values->xla_dump_ir_to,
+           "xla_embed_ir_in_executable",
+           bool_setter_for(&DebugOptions::set_xla_embed_ir_in_executable),
+           flag_values->xla_embed_ir_in_executable(),
+           "Embed the compiler IR as a string in the executable."),
+       tensorflow::Flag(
+           "xla_dump_ir_to", flag_values->mutable_xla_dump_ir_to(),
            "Dump the compiler IR into this directory as individual files."),
-       tensorflow::Flag("xla_eliminate_hlo_implicit_broadcast",
-                        &flag_values->xla_eliminate_hlo_implicit_broadcast,
-                        "Eliminate implicit broadcasts when lowering user "
-                        "computations to HLO instructions; use explicit "
-                        "broadcast instead."),
-       tensorflow::Flag("xla_cpu_multi_thread_eigen",
-                        &flag_values->xla_cpu_multi_thread_eigen,
-                        "When generating calls to Eigen in the CPU backend, "
-                        "use multi-threaded Eigen mode."),
+       tensorflow::Flag(
+           "xla_eliminate_hlo_implicit_broadcast",
+           bool_setter_for(
+               &DebugOptions::set_xla_eliminate_hlo_implicit_broadcast),
+           flag_values->xla_eliminate_hlo_implicit_broadcast(),
+           "Eliminate implicit broadcasts when lowering user "
+           "computations to HLO instructions; use explicit "
+           "broadcast instead."),
+       tensorflow::Flag(
+           "xla_cpu_multi_thread_eigen",
+           bool_setter_for(&DebugOptions::set_xla_cpu_multi_thread_eigen),
+           flag_values->xla_cpu_multi_thread_eigen(),
+           "When generating calls to Eigen in the CPU backend, "
+           "use multi-threaded Eigen mode."),
        tensorflow::Flag("xla_gpu_cuda_data_dir",
-                        &flag_values->xla_gpu_cuda_data_dir,
+                        flag_values->mutable_xla_gpu_cuda_data_dir(),
                         "If non-empty, speficies a local directory containing "
                         "ptxas and nvvm libdevice files; otherwise we use "
                         "those from runfile directories."),
-       tensorflow::Flag("xla_gpu_ftz", &flag_values->xla_gpu_ftz,
+       tensorflow::Flag("xla_gpu_ftz",
+                        bool_setter_for(&DebugOptions::set_xla_gpu_ftz),
+                        flag_values->xla_gpu_ftz(),
                         "If true, flush-to-zero semantics are enabled in the "
                         "code generated for GPUs."),
        tensorflow::Flag(
-           "xla_dump_debug_json_to", &flag_values->xla_dump_debug_json_to,
+           "xla_dump_debug_json_to",
+           flag_values->mutable_xla_dump_debug_json_to(),
            "Dump compilation artifacts as JSON into this directory."),
-       tensorflow::Flag("xla_test_all_output_layouts",
-                        &flag_values->xla_test_all_output_layouts,
-                        "Let ClientLibraryTestBase::ComputeAndCompare* test "
-                        "all permutations of output layouts. For example, with "
-                        "a 3D shape, all permutations of the set {0, 1, 2} are "
-                        "tried."),
-       tensorflow::Flag("xla_test_all_input_layouts",
-                        &flag_values->xla_test_all_input_layouts,
-                        "Let ClientLibraryTestBase::ComputeAndCompare* test "
-                        "all permutations of *input* layouts. For example, for "
-                        "2 input arguments with 2D shape and 4D shape, the "
-                        "computation will run 2! * 4! times for every possible "
-                        "layouts"),
+       tensorflow::Flag(
+           "xla_test_all_output_layouts",
+           bool_setter_for(&DebugOptions::set_xla_test_all_output_layouts),
+           flag_values->xla_test_all_output_layouts(),
+           "Let ClientLibraryTestBase::ComputeAndCompare* test "
+           "all permutations of output layouts. For example, with "
+           "a 3D shape, all permutations of the set {0, 1, 2} are "
+           "tried."),
+       tensorflow::Flag(
+           "xla_test_all_input_layouts",
+           bool_setter_for(&DebugOptions::set_xla_test_all_input_layouts),
+           flag_values->xla_test_all_input_layouts(),
+           "Let ClientLibraryTestBase::ComputeAndCompare* test "
+           "all permutations of *input* layouts. For example, for "
+           "2 input arguments with 2D shape and 4D shape, the "
+           "computation will run 2! * 4! times for every possible "
+           "layouts"),
        tensorflow::Flag("xla_backend_extra_options",
-                        &flag_values->xla_backend_extra_options,
+                        setter_for_xla_backend_extra_options, "",
                         "Extra options to pass to a backend; "
                         "comma-separated list of 'key=val' strings (=val "
                         "may be omitted); no whitespace around commas.")});
-
   ParseFlagsFromEnv(*flag_objects);
 }
 
@@ -197,68 +245,7 @@ void AppendDebugOptionsFlags(std::vector<tensorflow::Flag>* flag_list) {
 
 xla::DebugOptions GetDebugOptionsFromFlags() {
   std::call_once(flags_init, &AllocateFlags);
-
-  DebugOptions options;
-  options.set_xla_generate_hlo_graph(flag_values->xla_generate_hlo_graph);
-  options.set_xla_hlo_graph_addresses(flag_values->xla_hlo_graph_addresses);
-  options.set_xla_hlo_graph_layout(flag_values->xla_hlo_graph_layout);
-  options.set_xla_hlo_graph_path(flag_values->xla_hlo_graph_path);
-  options.set_xla_hlo_dump_as_graphdef(flag_values->xla_hlo_dump_as_graphdef);
-  options.set_xla_log_hlo_text(flag_values->xla_log_hlo_text);
-  options.set_xla_generate_hlo_text_to(flag_values->xla_generate_hlo_text_to);
-
-  std::vector<string> disabled_passes =
-      tensorflow::str_util::Split(flag_values->xla_disable_hlo_passes, ',');
-  for (const auto& passname : disabled_passes) {
-    options.add_xla_disable_hlo_passes(passname);
-  }
-
-  options.set_xla_enable_fast_math(flag_values->xla_enable_fast_math);
-  options.set_xla_backend_optimization_level(
-      flag_values->xla_backend_optimization_level);
-  options.set_xla_embed_ir_in_executable(
-      flag_values->xla_embed_ir_in_executable);
-  options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
-  options.set_xla_eliminate_hlo_implicit_broadcast(
-      flag_values->xla_eliminate_hlo_implicit_broadcast);
-  options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
-
-  options.set_xla_cpu_multi_thread_eigen(
-      flag_values->xla_cpu_multi_thread_eigen);
-  options.set_xla_gpu_cuda_data_dir(flag_values->xla_gpu_cuda_data_dir);
-  options.set_xla_gpu_ftz(flag_values->xla_gpu_ftz);
-  options.set_xla_llvm_enable_alias_scope_metadata(
-      flag_values->xla_llvm_enable_alias_scope_metadata);
-  options.set_xla_llvm_enable_noalias_metadata(
-      flag_values->xla_llvm_enable_noalias_metadata);
-  options.set_xla_llvm_enable_invariant_load_metadata(
-      flag_values->xla_llvm_enable_invariant_load_metadata);
-
-  options.set_xla_test_all_output_layouts(
-      flag_values->xla_test_all_output_layouts);
-  options.set_xla_test_all_input_layouts(
-      flag_values->xla_test_all_input_layouts);
-
-  std::vector<string> extra_options_parts =
-      tensorflow::str_util::Split(flag_values->xla_backend_extra_options, ',');
-  auto* extra_options_map = options.mutable_xla_backend_extra_options();
-
-  // The flag contains a comma-separated list of options; some options have
-  // arguments following "=", some don't.
-  for (const auto& part : extra_options_parts) {
-    size_t eq_pos = part.find_first_of('=');
-    if (eq_pos == string::npos) {
-      (*extra_options_map)[part] = "";
-    } else {
-      string value = "";
-      if (eq_pos + 1 < part.size()) {
-        value = part.substr(eq_pos + 1);
-      }
-      (*extra_options_map)[part.substr(0, eq_pos)] = value;
-    }
-  }
-
-  return options;
+  return *flag_values;
 }
 
 }  // namespace legacy_flags
diff --git a/tensorflow/core/util/command_line_flags.cc b/tensorflow/core/util/command_line_flags.cc
index 8373eb1f9e..3efc703faf 100644
--- a/tensorflow/core/util/command_line_flags.cc
+++ b/tensorflow/core/util/command_line_flags.cc
@@ -25,10 +25,11 @@ namespace tensorflow {
 namespace {
 
 bool ParseStringFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
-                     string* dst, bool* value_parsing_ok) {
+                     const std::function<bool(string)>& hook,
+                     bool* value_parsing_ok) {
   *value_parsing_ok = true;
   if (arg.Consume("--") && arg.Consume(flag) && arg.Consume("=")) {
-    *dst = arg.ToString();
+    *value_parsing_ok = hook(arg.ToString());
     return true;
   }
 
@@ -36,14 +37,18 @@ bool ParseStringFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
 }
 
 bool ParseInt32Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
-                    tensorflow::int32* dst, bool* value_parsing_ok) {
+                    const std::function<bool(int32)>& hook,
+                    bool* value_parsing_ok) {
   *value_parsing_ok = true;
   if (arg.Consume("--") && arg.Consume(flag) && arg.Consume("=")) {
     char extra;
-    if (sscanf(arg.data(), "%d%c", dst, &extra) != 1) {
+    int32 parsed_int32;
+    if (sscanf(arg.data(), "%d%c", &parsed_int32, &extra) != 1) {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
                  << ".";
       *value_parsing_ok = false;
+    } else {
+      *value_parsing_ok = hook(parsed_int32);
     }
     return true;
   }
@@ -52,14 +57,18 @@ bool ParseInt32Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
 }
 
 bool ParseInt64Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
-                    tensorflow::int64* dst, bool* value_parsing_ok) {
+                    const std::function<bool(int64)>& hook,
+                    bool* value_parsing_ok) {
   *value_parsing_ok = true;
   if (arg.Consume("--") && arg.Consume(flag) && arg.Consume("=")) {
     char extra;
-    if (sscanf(arg.data(), "%lld%c", dst, &extra) != 1) {
+    int64 parsed_int64;
+    if (sscanf(arg.data(), "%lld%c", &parsed_int64, &extra) != 1) {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
                  << ".";
       *value_parsing_ok = false;
+    } else {
+      *value_parsing_ok = hook(parsed_int64);
     }
     return true;
   }
@@ -68,19 +77,20 @@ bool ParseInt64Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
 }
 
 bool ParseBoolFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
-                   bool* dst, bool* value_parsing_ok) {
+                   const std::function<bool(bool)>& hook,
+                   bool* value_parsing_ok) {
   *value_parsing_ok = true;
   if (arg.Consume("--") && arg.Consume(flag)) {
     if (arg.empty()) {
-      *dst = true;
+      *value_parsing_ok = hook(true);
       return true;
     }
 
     if (arg == "=true") {
-      *dst = true;
+      *value_parsing_ok = hook(true);
       return true;
     } else if (arg == "=false") {
-      *dst = false;
+      *value_parsing_ok = hook(false);
       return true;
     } else {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
@@ -94,14 +104,18 @@ bool ParseBoolFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
 }
 
 bool ParseFloatFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
-                    float* dst, bool* value_parsing_ok) {
+                    const std::function<bool(float)>& hook,
+                    bool* value_parsing_ok) {
   *value_parsing_ok = true;
   if (arg.Consume("--") && arg.Consume(flag) && arg.Consume("=")) {
     char extra;
-    if (sscanf(arg.data(), "%f%c", dst, &extra) != 1) {
+    float parsed_float;
+    if (sscanf(arg.data(), "%f%c", &parsed_float, &extra) != 1) {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
                  << ".";
       *value_parsing_ok = false;
+    } else {
+      *value_parsing_ok = hook(parsed_float);
     }
     return true;
   }
@@ -112,44 +126,107 @@ bool ParseFloatFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
 }  // namespace
 
 Flag::Flag(const char* name, tensorflow::int32* dst, const string& usage_text)
-    : name_(name), type_(TYPE_INT), int_value_(dst), usage_text_(usage_text) {}
+    : name_(name),
+      type_(TYPE_INT32),
+      int32_hook_([dst](int32 value) {
+        *dst = value;
+        return true;
+      }),
+      int32_default_for_display_(*dst),
+      usage_text_(usage_text) {}
 
 Flag::Flag(const char* name, tensorflow::int64* dst, const string& usage_text)
     : name_(name),
       type_(TYPE_INT64),
-      int64_value_(dst),
+      int64_hook_([dst](int64 value) {
+        *dst = value;
+        return true;
+      }),
+      int64_default_for_display_(*dst),
+      usage_text_(usage_text) {}
+
+Flag::Flag(const char* name, float* dst, const string& usage_text)
+    : name_(name),
+      type_(TYPE_FLOAT),
+      float_hook_([dst](float value) {
+        *dst = value;
+        return true;
+      }),
+      float_default_for_display_(*dst),
       usage_text_(usage_text) {}
 
 Flag::Flag(const char* name, bool* dst, const string& usage_text)
     : name_(name),
       type_(TYPE_BOOL),
-      bool_value_(dst),
+      bool_hook_([dst](bool value) {
+        *dst = value;
+        return true;
+      }),
+      bool_default_for_display_(*dst),
       usage_text_(usage_text) {}
 
 Flag::Flag(const char* name, string* dst, const string& usage_text)
     : name_(name),
       type_(TYPE_STRING),
-      string_value_(dst),
+      string_hook_([dst](string value) {
+        *dst = std::move(value);
+        return true;
+      }),
+      string_default_for_display_(*dst),
       usage_text_(usage_text) {}
 
-Flag::Flag(const char* name, float* dst, const string& usage_text)
+Flag::Flag(const char* name, std::function<bool(int32)> int32_hook,
+           int32 default_value_for_display, const string& usage_text)
+    : name_(name),
+      type_(TYPE_INT32),
+      int32_hook_(std::move(int32_hook)),
+      int32_default_for_display_(default_value_for_display),
+      usage_text_(usage_text) {}
+
+Flag::Flag(const char* name, std::function<bool(int64)> int64_hook,
+           int64 default_value_for_display, const string& usage_text)
+    : name_(name),
+      type_(TYPE_INT64),
+      int64_hook_(std::move(int64_hook)),
+      int64_default_for_display_(default_value_for_display),
+      usage_text_(usage_text) {}
+
+Flag::Flag(const char* name, std::function<bool(float)> float_hook,
+           float default_value_for_display, const string& usage_text)
     : name_(name),
       type_(TYPE_FLOAT),
-      float_value_(dst),
+      float_hook_(std::move(float_hook)),
+      float_default_for_display_(default_value_for_display),
+      usage_text_(usage_text) {}
+
+Flag::Flag(const char* name, std::function<bool(bool)> bool_hook,
+           bool default_value_for_display, const string& usage_text)
+    : name_(name),
+      type_(TYPE_BOOL),
+      bool_hook_(std::move(bool_hook)),
+      bool_default_for_display_(default_value_for_display),
+      usage_text_(usage_text) {}
+
+Flag::Flag(const char* name, std::function<bool(string)> string_hook,
+           string default_value_for_display, const string& usage_text)
+    : name_(name),
+      type_(TYPE_STRING),
+      string_hook_(std::move(string_hook)),
+      string_default_for_display_(std::move(default_value_for_display)),
       usage_text_(usage_text) {}
 
 bool Flag::Parse(string arg, bool* value_parsing_ok) const {
   bool result = false;
-  if (type_ == TYPE_INT) {
-    result = ParseInt32Flag(arg, name_, int_value_, value_parsing_ok);
+  if (type_ == TYPE_INT32) {
+    result = ParseInt32Flag(arg, name_, int32_hook_, value_parsing_ok);
   } else if (type_ == TYPE_INT64) {
-    result = ParseInt64Flag(arg, name_, int64_value_, value_parsing_ok);
+    result = ParseInt64Flag(arg, name_, int64_hook_, value_parsing_ok);
   } else if (type_ == TYPE_BOOL) {
-    result = ParseBoolFlag(arg, name_, bool_value_, value_parsing_ok);
+    result = ParseBoolFlag(arg, name_, bool_hook_, value_parsing_ok);
   } else if (type_ == TYPE_STRING) {
-    result = ParseStringFlag(arg, name_, string_value_, value_parsing_ok);
+    result = ParseStringFlag(arg, name_, string_hook_, value_parsing_ok);
   } else if (type_ == TYPE_FLOAT) {
-    result = ParseFloatFlag(arg, name_, float_value_, value_parsing_ok);
+    result = ParseFloatFlag(arg, name_, float_hook_, value_parsing_ok);
   }
   return result;
 }
@@ -203,26 +280,28 @@ bool Flag::Parse(string arg, bool* value_parsing_ok) const {
   for (const Flag& flag : flag_list) {
     const char* type_name = "";
     string flag_string;
-    if (flag.type_ == Flag::TYPE_INT) {
+    if (flag.type_ == Flag::TYPE_INT32) {
       type_name = "int32";
-      flag_string =
-          strings::Printf("--%s=%d", flag.name_.c_str(), *flag.int_value_);
+      flag_string = strings::Printf("--%s=%d", flag.name_.c_str(),
+                                    flag.int32_default_for_display_);
     } else if (flag.type_ == Flag::TYPE_INT64) {
       type_name = "int64";
-      flag_string = strings::Printf("--%s=%lld", flag.name_.c_str(),
-                                    static_cast<long long>(*flag.int64_value_));
+      flag_string = strings::Printf(
+          "--%s=%lld", flag.name_.c_str(),
+          static_cast<long long>(flag.int64_default_for_display_));
     } else if (flag.type_ == Flag::TYPE_BOOL) {
       type_name = "bool";
-      flag_string = strings::Printf("--%s=%s", flag.name_.c_str(),
-                                    *flag.bool_value_ ? "true" : "false");
+      flag_string =
+          strings::Printf("--%s=%s", flag.name_.c_str(),
+                          flag.bool_default_for_display_ ? "true" : "false");
     } else if (flag.type_ == Flag::TYPE_STRING) {
       type_name = "string";
       flag_string = strings::Printf("--%s=\"%s\"", flag.name_.c_str(),
-                                    flag.string_value_->c_str());
+                                    flag.string_default_for_display_.c_str());
     } else if (flag.type_ == Flag::TYPE_FLOAT) {
       type_name = "float";
-      flag_string =
-          strings::Printf("--%s=%f", flag.name_.c_str(), *flag.float_value_);
+      flag_string = strings::Printf("--%s=%f", flag.name_.c_str(),
+                                    flag.float_default_for_display_);
     }
     strings::Appendf(&usage_text, "\t%-33s\t%s\t%s\n", flag_string.c_str(),
                      type_name, flag.usage_text_.c_str());
diff --git a/tensorflow/core/util/command_line_flags.h b/tensorflow/core/util/command_line_flags.h
index f349df16fd..121c7063c9 100644
--- a/tensorflow/core/util/command_line_flags.h
+++ b/tensorflow/core/util/command_line_flags.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef THIRD_PARTY_TENSORFLOW_CORE_UTIL_COMMAND_LINE_FLAGS_H
 #define THIRD_PARTY_TENSORFLOW_CORE_UTIL_COMMAND_LINE_FLAGS_H
 
+#include <functional>
 #include <string>
 #include <vector>
 #include "tensorflow/core/platform/types.h"
@@ -61,24 +62,58 @@ namespace tensorflow {
 // text, and a pointer to the corresponding variable.
 class Flag {
  public:
-  Flag(const char* name, int32* dst1, const string& usage_text);
-  Flag(const char* name, int64* dst1, const string& usage_text);
+  Flag(const char* name, int32* dst, const string& usage_text);
+  Flag(const char* name, int64* dst, const string& usage_text);
   Flag(const char* name, bool* dst, const string& usage_text);
   Flag(const char* name, string* dst, const string& usage_text);
   Flag(const char* name, float* dst, const string& usage_text);
 
+  // These constructors invoke a hook on a match instead of writing to a
+  // specific memory location.  The hook may return false to signal a malformed
+  // or illegal value, which will then fail the command line parse.
+  //
+  // "default_value_for_display" is shown as the default value of this flag in
+  // Flags::Usage().
+  Flag(const char* name, std::function<bool(int32)> int32_hook,
+       int32 default_value_for_display, const string& usage_text);
+  Flag(const char* name, std::function<bool(int64)> int64_hook,
+       int64 default_value_for_display, const string& usage_text);
+  Flag(const char* name, std::function<bool(float)> float_hook,
+       float default_value_for_display, const string& usage_text);
+  Flag(const char* name, std::function<bool(bool)> bool_hook,
+       bool default_value_for_display, const string& usage_text);
+  Flag(const char* name, std::function<bool(string)> string_hook,
+       string default_value_for_display, const string& usage_text);
+
  private:
   friend class Flags;
 
   bool Parse(string arg, bool* value_parsing_ok) const;
 
   string name_;
-  enum { TYPE_INT, TYPE_INT64, TYPE_BOOL, TYPE_STRING, TYPE_FLOAT } type_;
-  int* int_value_;
-  int64* int64_value_;
-  bool* bool_value_;
-  string* string_value_;
-  float* float_value_;
+  enum {
+    TYPE_INT32,
+    TYPE_INT64,
+    TYPE_BOOL,
+    TYPE_STRING,
+    TYPE_FLOAT,
+  } type_;
+
+  std::function<bool(int32)> int32_hook_;
+  int32 int32_default_for_display_;
+
+  std::function<bool(int64)> int64_hook_;
+  int64 int64_default_for_display_;
+
+  std::function<bool(float)> float_hook_;
+  float float_default_for_display_;
+
+  std::function<bool(bool)> bool_hook_;
+  bool bool_default_for_display_;
+
+  std::function<bool(string)> string_hook_;
+  string string_default_for_display_;
+
   string usage_text_;
 };
 
diff --git a/tensorflow/core/util/command_line_flags_test.cc b/tensorflow/core/util/command_line_flags_test.cc
index c86a70ec9d..6139c8e7bc 100644
--- a/tensorflow/core/util/command_line_flags_test.cc
+++ b/tensorflow/core/util/command_line_flags_test.cc
@@ -36,32 +36,85 @@ std::vector<char *> CharPointerVectorFromStrings(
 }  // namespace
 
 TEST(CommandLineFlagsTest, BasicUsage) {
-  int some_int = 10;
-  int64 some_int64 = 21474836470;  // max int32 is 2147483647
-  bool some_switch = false;
-  string some_name = "something";
-  float some_float = -23.23f;
-  int argc = 6;
+  int some_int32_set_directly = 10;
+  int some_int32_set_via_hook = 20;
+  int64 some_int64_set_directly = 21474836470;  // max int32 is 2147483647
+  int64 some_int64_set_via_hook = 21474836479;  // max int32 is 2147483647
+  bool some_switch_set_directly = false;
+  bool some_switch_set_via_hook = true;
+  string some_name_set_directly = "something_a";
+  string some_name_set_via_hook = "something_b";
+  float some_float_set_directly = -23.23f;
+  float some_float_set_via_hook = -25.23f;
   std::vector<string> argv_strings = {"program_name",
-                                      "--some_int=20",
-                                      "--some_int64=214748364700",
-                                      "--some_switch",
-                                      "--some_name=somethingelse",
-                                      "--some_float=42.0"};
+                                      "--some_int32_set_directly=20",
+                                      "--some_int32_set_via_hook=50",
+                                      "--some_int64_set_directly=214748364700",
+                                      "--some_int64_set_via_hook=214748364710",
+                                      "--some_switch_set_directly",
+                                      "--some_switch_set_via_hook=false",
+                                      "--some_name_set_directly=somethingelse",
+                                      "--some_name_set_via_hook=anythingelse",
+                                      "--some_float_set_directly=42.0",
+                                      "--some_float_set_via_hook=43.0"};
+  int argc = argv_strings.size();
   std::vector<char *> argv_array = CharPointerVectorFromStrings(argv_strings);
-  bool parsed_ok =
-      Flags::Parse(&argc, argv_array.data(),
-                   {Flag("some_int", &some_int, "some int"),
-                    Flag("some_int64", &some_int64, "some int64"),
-                    Flag("some_switch", &some_switch, "some switch"),
-                    Flag("some_name", &some_name, "some name"),
-                    Flag("some_float", &some_float, "some float")});
+  bool parsed_ok = Flags::Parse(
+      &argc, argv_array.data(),
+      {
+          Flag("some_int32_set_directly", &some_int32_set_directly,
+               "some int32 set directly"),
+          Flag("some_int32_set_via_hook",
+               [&](int32 value) {
+                 some_int32_set_via_hook = value;
+                 return true;
+               },
+               some_int32_set_via_hook, "some int32 set via hook"),
+          Flag("some_int64_set_directly", &some_int64_set_directly,
+               "some int64 set directly"),
+          Flag("some_int64_set_via_hook",
+               [&](int64 value) {
+                 some_int64_set_via_hook = value;
+                 return true;
+               },
+               some_int64_set_via_hook, "some int64 set via hook"),
+          Flag("some_switch_set_directly", &some_switch_set_directly,
+               "some switch set directly"),
+          Flag("some_switch_set_via_hook",
+               [&](bool value) {
+                 some_switch_set_via_hook = value;
+                 return true;
+               },
+               some_switch_set_via_hook, "some switch set via hook"),
+          Flag("some_name_set_directly", &some_name_set_directly,
+               "some name set directly"),
+          Flag("some_name_set_via_hook",
+               [&](string value) {
+                 some_name_set_via_hook = std::move(value);
+                 return true;
+               },
+               some_name_set_via_hook, "some name set via hook"),
+          Flag("some_float_set_directly", &some_float_set_directly,
+               "some float set directly"),
+          Flag("some_float_set_via_hook",
+               [&](float value) {
+                 some_float_set_via_hook = value;
+                 return true;
+               },
+               some_float_set_via_hook, "some float set via hook"),
+      });
+
   EXPECT_EQ(true, parsed_ok);
-  EXPECT_EQ(20, some_int);
-  EXPECT_EQ(214748364700, some_int64);
-  EXPECT_EQ(true, some_switch);
-  EXPECT_EQ("somethingelse", some_name);
-  EXPECT_NEAR(42.0f, some_float, 1e-5f);
+  EXPECT_EQ(20, some_int32_set_directly);
+  EXPECT_EQ(50, some_int32_set_via_hook);
+  EXPECT_EQ(214748364700, some_int64_set_directly);
+  EXPECT_EQ(214748364710, some_int64_set_via_hook);
+  EXPECT_EQ(true, some_switch_set_directly);
+  EXPECT_EQ(false, some_switch_set_via_hook);
+  EXPECT_EQ("somethingelse", some_name_set_directly);
+  EXPECT_EQ("anythingelse", some_name_set_via_hook);
+  EXPECT_NEAR(42.0f, some_float_set_directly, 1e-5f);
+  EXPECT_NEAR(43.0f, some_float_set_via_hook, 1e-5f);
   EXPECT_EQ(argc, 1);
 }
 
@@ -107,6 +160,70 @@ TEST(CommandLineFlagsTest, BadFloatValue) {
   EXPECT_EQ(argc, 1);
 }
 
+TEST(CommandLineFlagsTest, FailedInt32Hook) {
+  int argc = 2;
+  std::vector<string> argv_strings = {"program_name", "--some_int32=200"};
+  std::vector<char *> argv_array = CharPointerVectorFromStrings(argv_strings);
+  bool parsed_ok =
+      Flags::Parse(&argc, argv_array.data(),
+                   {Flag("some_int32", [](int32 value) { return false; }, 30,
+                         "some int32")});
+
+  EXPECT_EQ(false, parsed_ok);
+  EXPECT_EQ(argc, 1);
+}
+
+TEST(CommandLineFlagsTest, FailedInt64Hook) {
+  int argc = 2;
+  std::vector<string> argv_strings = {"program_name", "--some_int64=200"};
+  std::vector<char *> argv_array = CharPointerVectorFromStrings(argv_strings);
+  bool parsed_ok =
+      Flags::Parse(&argc, argv_array.data(),
+                   {Flag("some_int64", [](int64 value) { return false; }, 30,
+                         "some int64")});
+
+  EXPECT_EQ(false, parsed_ok);
+  EXPECT_EQ(argc, 1);
+}
+
+TEST(CommandLineFlagsTest, FailedFloatHook) {
+  int argc = 2;
+  std::vector<string> argv_strings = {"program_name", "--some_float=200.0"};
+  std::vector<char *> argv_array = CharPointerVectorFromStrings(argv_strings);
+  bool parsed_ok =
+      Flags::Parse(&argc, argv_array.data(),
+                   {Flag("some_float", [](float value) { return false; }, 30.0f,
+                         "some float")});
+
+  EXPECT_EQ(false, parsed_ok);
+  EXPECT_EQ(argc, 1);
+}
+
+TEST(CommandLineFlagsTest, FailedBoolHook) {
+  int argc = 2;
+  std::vector<string> argv_strings = {"program_name", "--some_switch=true"};
+  std::vector<char *> argv_array = CharPointerVectorFromStrings(argv_strings);
+  bool parsed_ok =
+      Flags::Parse(&argc, argv_array.data(),
+                   {Flag("some_switch", [](bool value) { return false; }, false,
+                         "some switch")});
+
+  EXPECT_EQ(false, parsed_ok);
+  EXPECT_EQ(argc, 1);
+}
+
+TEST(CommandLineFlagsTest, FailedStringHook) {
+  int argc = 2;
+  std::vector<string> argv_strings = {"program_name", "--some_name=true"};
+  std::vector<char *> argv_array = CharPointerVectorFromStrings(argv_strings);
+  bool parsed_ok = Flags::Parse(
+      &argc, argv_array.data(),
+      {Flag("some_name", [](string value) { return false; }, "", "some name")});
+
+  EXPECT_EQ(false, parsed_ok);
+  EXPECT_EQ(argc, 1);
+}
+
 // Return whether str==pat, but allowing any whitespace in pat
 // to match zero or more whitespace characters in str.
 static bool MatchWithAnyWhitespace(const string &str, const string &pat) {
-- 
GitLab


From 1754a2c0c425006324eac85645beb8599366a4f5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 14:51:12 -0700
Subject: [PATCH 1047/1375] TensorForest v4 kernel tests for proto-based model.
  Also some stuff to include decision_trees in pip package properly.

PiperOrigin-RevId: 162273792
---
 tensorflow/BUILD                              |   1 +
 tensorflow/contrib/cmake/tf_tests.cmake       |   1 +
 tensorflow/contrib/decision_trees/BUILD       |  37 ++
 tensorflow/contrib/tensor_forest/BUILD        |  18 +
 .../kernel_tests/model_ops_kernel_test.py     | 527 ++++++++++++++++++
 tensorflow/tools/pip_package/BUILD            |   1 +
 6 files changed, 585 insertions(+)
 create mode 100644 tensorflow/contrib/decision_trees/BUILD
 create mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 779dd1e8ed..8b1dfddd2a 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -251,6 +251,7 @@ filegroup(
         "//tensorflow/contrib/data/python/kernel_tests:all_files",
         "//tensorflow/contrib/data/python/ops:all_files",
         "//tensorflow/contrib/data/python/util:all_files",
+        "//tensorflow/contrib/decision_trees:all_files",
         "//tensorflow/contrib/decision_trees/proto:all_files",
         "//tensorflow/contrib/distributions:all_files",
         "//tensorflow/contrib/factorization:all_files",
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 089967ffec..6ad7480965 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -178,6 +178,7 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
     # Loading resources in contrib doesn't seem to work on Windows
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/random_forest_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
diff --git a/tensorflow/contrib/decision_trees/BUILD b/tensorflow/contrib/decision_trees/BUILD
new file mode 100644
index 0000000000..6be69a6f94
--- /dev/null
+++ b/tensorflow/contrib/decision_trees/BUILD
@@ -0,0 +1,37 @@
+# TensorFlow shared library for training decision trees.
+package(default_visibility = [
+    "//visibility:public",
+])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["*"],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+py_library(
+    name = "init_py",
+    srcs = [
+        "__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
+    ],
+)
+
+# Pip
+
+py_library(
+    name = "decision_trees_pip",
+    deps = [
+        ":init_py",
+    ],
+)
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 1f5733b853..6b20bb48c4 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -526,6 +526,24 @@ py_test(
     ],
 )
 
+py_test(
+    name = "model_ops_kernel_test",
+    size = "small",
+    srcs = ["python/kernel_tests/model_ops_kernel_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_windows"],
+    deps = [
+        ":model_ops_py",
+        ":tensor_forest_py",
+        ":tensor_forest_v4_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:resources",
+    ],
+)
+
 py_library(
     name = "tensor_forest_py",
     srcs = ["python/tensor_forest.py"],
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py
new file mode 100644
index 0000000000..296ac7220d
--- /dev/null
+++ b/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py
@@ -0,0 +1,527 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tf.contrib.tensor_forest.ops.tree_predictions_op."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from google.protobuf import descriptor_pool
+from google.protobuf import text_format
+
+from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2
+from tensorflow.contrib.tensor_forest.python import tensor_forest
+from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
+from tensorflow.contrib.tensor_forest.python.ops import data_ops
+from tensorflow.contrib.tensor_forest.python.ops import model_ops
+
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+
+
+TREE_PROTO_DENSE = """
+decision_tree {
+  nodes {
+    binary_node {
+      left_child_id {
+        value: 1
+      }
+      right_child_id {
+        value: 2
+      }
+      inequality_left_child_test {
+        feature_id {
+          id {
+            value: "0"
+          }
+        }
+        threshold {
+          float_value: 0
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 1
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 10
+        }
+        value {
+          float_value: 10
+        }
+        value {
+          float_value: 80
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 2
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 50
+        }
+        value {
+          float_value: 25
+        }
+        value {
+          float_value: 25
+        }
+      }
+    }
+  }
+}
+"""
+
+# TODO(gilberth): This currently has to load inequality_left_child_test
+# in the tree because of a proto parsing error (MatchingValuesTest not
+# found in the descriptor pool).
+TREE_PROTO_SPARSE = """
+decision_tree {
+  nodes {
+    binary_node {
+      left_child_id {
+        value: 1
+      }
+      right_child_id {
+        value: 2
+      }
+      inequality_left_child_test {
+        feature_id {
+          id {
+            value: "1"
+          }
+        }
+        threshold {
+          float_value: 10
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 1
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 10
+        }
+        value {
+          float_value: 10
+        }
+        value {
+          float_value: 80
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 2
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 50
+        }
+        value {
+          float_value: 25
+        }
+        value {
+          float_value: 25
+        }
+      }
+    }
+  }
+}
+"""
+
+FEATURE_COUNT_TREE = """
+decision_tree {
+  nodes {
+    binary_node {
+      left_child_id {
+        value: 1
+      }
+      right_child_id {
+        value: 2
+      }
+      inequality_left_child_test {
+        feature_id {
+          id {
+            value: "0"
+          }
+        }
+        threshold {
+          float_value: 0
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 1
+    }
+    binary_node {
+      left_child_id {
+        value: 3
+      }
+      right_child_id {
+        value: 4
+      }
+      inequality_left_child_test {
+        feature_id {
+          id {
+            value: "1"
+          }
+        }
+        threshold {
+          float_value: 0
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 2
+    }
+    binary_node {
+      left_child_id {
+        value: 5
+      }
+      right_child_id {
+        value: 6
+      }
+      inequality_left_child_test {
+        feature_id {
+          id {
+            value: "0"
+          }
+        }
+        threshold {
+          float_value: 10
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 3
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 10
+        }
+        value {
+          float_value: 10
+        }
+        value {
+          float_value: 80
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 4
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 50
+        }
+        value {
+          float_value: 25
+        }
+        value {
+          float_value: 25
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 5
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 50
+        }
+        value {
+          float_value: 25
+        }
+        value {
+          float_value: 25
+        }
+      }
+    }
+  }
+  nodes {
+    node_id {
+      value: 6
+    }
+    leaf {
+      vector {
+        value {
+          float_value: 50
+        }
+        value {
+          float_value: 25
+        }
+        value {
+          float_value: 25
+        }
+      }
+    }
+  }
+}
+"""
+
+
+def get_v4_params(num_classes, num_features, regression):
+  params = tensor_forest.ForestHParams()
+  params.num_classes = num_classes
+  params.num_features = num_features
+  params.regression = regression
+  v4_params = tensor_forest_v4.V4ForestHParams(params)
+  v4_params.params_proto = tensor_forest_v4.build_params_proto(v4_params)
+  v4_params.serialized_params_proto = (
+      v4_params.params_proto.SerializeToString())
+  return v4_params
+
+
+def get_dense_data_spec():
+  spec_proto = data_ops.TensorForestDataSpec()
+  f1 = spec_proto.dense.add()
+  f1.name = 'f1'
+  f1.original_type = data_ops.DATA_FLOAT
+  f1.size = 1
+
+  f2 = spec_proto.dense.add()
+  f2.name = 'f2'
+  f2.original_type = data_ops.DATA_FLOAT
+  f2.size = 1
+  spec_proto.dense_features_size = 2
+  return spec_proto.SerializeToString()
+
+
+def get_sparse_data_spec():
+  spec_proto = data_ops.TensorForestDataSpec()
+  f1 = spec_proto.dense.add()
+  f1.name = 'f1'
+  f1.original_type = data_ops.DATA_CATEGORICAL
+  f1.size = 10
+  spec_proto.dense_features_size = 0
+
+  return spec_proto.SerializeToString()
+
+
+class ModelOpsPredictionsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    self.nothing = []
+
+  def testDense(self):
+    input_data = [[-1., 0.], [-1., 2.],  # node 1
+                  [1., 0.], [1., -2.]]  # node 2
+
+    v4_params = get_v4_params(3, 2, False)
+
+    tree_proto = generic_tree_model_pb2.Model()
+    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
+                      descriptor_pool=descriptor_pool.Default())
+
+    with self.test_session():
+      tree = model_ops.tree_variable(
+          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      predictions = model_ops.tree_predictions_v4(
+          tree,
+          input_data,
+          self.nothing,
+          self.nothing,
+          self.nothing,
+          input_spec=get_dense_data_spec(),
+          params=v4_params.serialized_params_proto)
+
+      leaves = model_ops.traverse_tree_v4(
+          tree,
+          input_data,
+          self.nothing,
+          self.nothing,
+          self.nothing,
+          input_spec=get_dense_data_spec(),
+          params=v4_params.serialized_params_proto)
+
+      self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
+                           [0.5, 0.25, 0.25], [0.5, 0.25, 0.25]],
+                          predictions.eval())
+
+      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
+
+  def testSparseInput(self):
+    sparse_shape = [4, 10]
+    sparse_indices = [[0, 0], [0, 1], [0, 9],
+                      [1, 0], [1, 1],
+                      [2, 1],
+                      [3, 6]]
+    sparse_values = [3.0, 5.0, 0.5,
+                     15.0, 10.0,
+                     20.0,
+                     20.0]
+
+    v4_params = get_v4_params(3, 10, False)
+
+    tree_proto = generic_tree_model_pb2.Model()
+    text_format.Merge(TREE_PROTO_SPARSE, tree_proto,
+                      descriptor_pool=descriptor_pool.Default())
+
+    with self.test_session():
+      tree = model_ops.tree_variable(
+          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      predictions = model_ops.tree_predictions_v4(
+          tree,
+          self.nothing,
+          sparse_indices,
+          sparse_values,
+          sparse_shape,
+          input_spec=get_sparse_data_spec(),
+          params=v4_params.serialized_params_proto)
+
+      leaves = model_ops.traverse_tree_v4(
+          tree,
+          self.nothing,
+          sparse_indices,
+          sparse_values,
+          sparse_shape,
+          input_spec=get_sparse_data_spec(),
+          params=v4_params.serialized_params_proto)
+
+      self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
+                           [0.5, 0.25, 0.25], [0.1, 0.1, 0.8]],
+                          predictions.eval())
+
+      self.assertAllEqual([1, 1, 2, 1], leaves.eval())
+
+  def testNoInput(self):
+    v4_params = get_v4_params(3, 4, False)
+
+    tree_proto = generic_tree_model_pb2.Model()
+    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
+                      descriptor_pool=descriptor_pool.Default())
+
+    with self.test_session():
+      tree = model_ops.tree_variable(
+          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      predictions = model_ops.tree_predictions_v4(
+          tree,
+          self.nothing,
+          self.nothing,
+          self.nothing,
+          self.nothing,
+          input_spec=get_dense_data_spec(),
+          params=v4_params.serialized_params_proto)
+
+      self.assertEqual((0, 3), predictions.eval().shape)
+
+
+class ModelOpsUpdateTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    self.nothing = []
+
+  def testBasic(self):
+    input_labels = [0, 1, 2, 1]
+    leaves = [1, 2, 1, 2]
+    weights = [10, 10, 10, 10]
+
+    # For checking the resulting leaves after update.
+    input_data = [[-1., 0.],  # node 1
+                  [1., 0.]]   # node 2
+
+    v4_params = get_v4_params(3, 2, False)
+
+    tree_proto = generic_tree_model_pb2.Model()
+    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
+                      descriptor_pool=descriptor_pool.Default())
+
+    with self.test_session():
+      tree = model_ops.tree_variable(
+          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      model_ops.update_model_v4(
+          tree,
+          leaves,
+          input_labels,
+          weights,
+          params=v4_params.serialized_params_proto).run()
+
+      predictions = model_ops.tree_predictions_v4(
+          tree,
+          input_data,
+          self.nothing,
+          self.nothing,
+          self.nothing,
+          input_spec=get_dense_data_spec(),
+          params=v4_params.serialized_params_proto)
+
+      self.assertAllClose([[0.16666, 0.08333, 0.75],
+                           [0.416666, 0.375, 0.208333]],
+                          predictions.eval(),
+                          rtol=0.0001, atol=0.0001)
+
+
+class ModelOpsFeatureCountsTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    self.nothing = []
+
+  def testBasic(self):
+    v4_params = get_v4_params(3, 4, False)
+
+    tree_proto = generic_tree_model_pb2.Model()
+    text_format.Merge(FEATURE_COUNT_TREE, tree_proto,
+                      descriptor_pool=descriptor_pool.Default())
+
+    with self.test_session():
+      tree = model_ops.tree_variable(
+          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      feature_counts = model_ops.feature_usage_counts(
+          tree,
+          params=v4_params.serialized_params_proto)
+
+      self.assertAllEqual([2, 1, 0, 0], feature_counts.eval())
+
+
+if __name__ == '__main__':
+  googletest.main()
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index f82640faf4..faee0f5386 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -146,6 +146,7 @@ sh_binary(
             "//tensorflow:tensorflow_py",
             "//tensorflow/contrib/boosted_trees:boosted_trees_pip",
             "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip",
+            "//tensorflow/contrib/decision_trees:decision_trees_pip",
             "//tensorflow/contrib/graph_editor:graph_editor_pip",
             "//tensorflow/contrib/keras:keras",
             "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip",
-- 
GitLab


From eb16993d2b3c57678d5a7a6634d20c7b7283d964 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 14:59:39 -0700
Subject: [PATCH 1048/1375] [XLA] Allow logical buffer coloring to use liveness
 information.

PiperOrigin-RevId: 162275084
---
 .../compiler/xla/service/buffer_assignment.cc    |  4 ++--
 .../compiler/xla/service/buffer_assignment.h     | 11 +++--------
 .../xla/service/buffer_assignment_test.cc        | 16 +++++++++-------
 .../compiler/xla/service/buffer_liveness.h       | 15 ++++++++++++---
 .../xla/service/tuple_points_to_analysis.h       | 12 ------------
 5 files changed, 26 insertions(+), 32 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index f372b18f7e..ddc3d11b7c 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -585,7 +585,7 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::Run(
     const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
     LogicalBuffer::SizeFunction buffer_size,
     LogicalBuffer::AlignmentFunction color_alignment,
-    bool allow_input_output_aliasing, TuplePointsToAnalysis::Colorer colorer) {
+    bool allow_input_output_aliasing, BufferLiveness::Colorer colorer) {
   BufferAssigner assigner(allow_input_output_aliasing, std::move(colorer));
   return assigner.CreateAssignment(module, std::move(hlo_ordering),
                                    std::move(buffer_size),
@@ -1351,7 +1351,7 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
   std::vector<ColocatedBufferSet> colocated_buffer_sets;
   BuildColocatedBufferSets(module, assignment->liveness(),
                            assignment->buffer_size_, &colocated_buffer_sets);
-  TF_RETURN_IF_ERROR(colorer_(&assignment->mutable_points_to_analysis()));
+  TF_RETURN_IF_ERROR(colorer_(assignment->liveness()));
   VLOG(3) << "After coloring:";
   XLA_VLOG_LINES(3, assignment->points_to_analysis().ToString());
 
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h
index e0b49505d2..35c904df13 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.h
+++ b/tensorflow/compiler/xla/service/buffer_assignment.h
@@ -320,10 +320,6 @@ class BufferAssignment {
     return liveness_->points_to_analysis();
   }
 
-  TuplePointsToAnalysis& mutable_points_to_analysis() const {
-    return liveness_->mutable_points_to_analysis();
-  }
-
   // Returns the BufferLiveness object used to construct this assignment.
   const BufferLiveness& liveness() const { return *liveness_; }
 
@@ -432,12 +428,11 @@ class BufferAssigner {
       LogicalBuffer::SizeFunction buffer_size,
       LogicalBuffer::AlignmentFunction color_alignment,
       bool allow_input_output_aliasing = false,
-      TuplePointsToAnalysis::Colorer colorer =
-          TuplePointsToAnalysis::DefaultColorer());
+      BufferLiveness::Colorer colorer = BufferLiveness::DefaultColorer());
 
  private:
   BufferAssigner(bool allow_input_output_aliasing,
-                 TuplePointsToAnalysis::Colorer colorer)
+                 BufferLiveness::Colorer colorer)
       : allow_input_output_aliasing_(allow_input_output_aliasing),
         colorer_(colorer) {}
   virtual ~BufferAssigner() = default;
@@ -538,7 +533,7 @@ class BufferAssigner {
   bool allow_input_output_aliasing_;
 
   // Functor used to assign colors to newly allocated logical buffers.
-  TuplePointsToAnalysis::Colorer colorer_;
+  BufferLiveness::Colorer colorer_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(BufferAssigner);
 };
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 21ba083dd9..e27575db43 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -92,8 +92,7 @@ class BufferAssignmentTest : public HloTestBase {
   }
 
   std::unique_ptr<BufferAssignment> RunColoredBufferAssignment(
-      HloModule* module, TuplePointsToAnalysis::Colorer colorer,
-      int64 alignment = 1) {
+      HloModule* module, BufferLiveness::Colorer colorer, int64 alignment = 1) {
     return BufferAssigner::Run(
                module, MakeUnique<DependencyHloOrdering>(module),
                backend_->compiler()->BufferSizeBytesFunction(),
@@ -381,9 +380,10 @@ TEST_F(BufferAssignmentTest, BasicUniquelyColored) {
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
-  auto colorer = [](TuplePointsToAnalysis* points_to_analysis) {
+  auto colorer = [](const BufferLiveness& buffer_liveness) {
     int color = 0;
-    for (auto& buffer : points_to_analysis->logical_buffers()) {
+    for (auto& buffer :
+         buffer_liveness.points_to_analysis().logical_buffers()) {
       buffer->set_color(LogicalBuffer::Color(color++));
     }
     return Status::OK();
@@ -436,9 +436,11 @@ TEST_F(BufferAssignmentTest, BasicPartiallyColored) {
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
-  auto colorer = [](TuplePointsToAnalysis* points_to_analysis) {
-    for (auto& buffer : points_to_analysis->logical_buffers()) {
-      const auto& aliases = points_to_analysis->GetBufferAliases(*buffer);
+  auto colorer = [](const BufferLiveness& buffer_liveness) {
+    for (auto& buffer :
+         buffer_liveness.points_to_analysis().logical_buffers()) {
+      const auto& aliases =
+          buffer_liveness.points_to_analysis().GetBufferAliases(*buffer);
       for (const auto& alias : aliases) {
         if (alias.instruction()->opcode() == HloOpcode::kAdd ||
             alias.instruction()->opcode() == HloOpcode::kMultiply) {
diff --git a/tensorflow/compiler/xla/service/buffer_liveness.h b/tensorflow/compiler/xla/service/buffer_liveness.h
index 7e484430ea..24807551f1 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.h
+++ b/tensorflow/compiler/xla/service/buffer_liveness.h
@@ -36,6 +36,8 @@ namespace xla {
 // interference.
 class BufferLiveness {
  public:
+  using Colorer = std::function<Status(const BufferLiveness& buffer_liveness)>;
+
   // Constructs a buffer liveness object for the given module assuming the given
   // HLO instruction ordering.
   static StatusOr<std::unique_ptr<BufferLiveness>> Run(
@@ -61,15 +63,22 @@ class BufferLiveness {
   const TuplePointsToAnalysis& points_to_analysis() const {
     return *points_to_analysis_;
   }
-  TuplePointsToAnalysis& mutable_points_to_analysis() const {
-    return *points_to_analysis_;
-  }
 
   // Returns the underlying hlo ordering used for this liveness analysis.
   const HloOrdering& hlo_ordering() const { return *hlo_ordering_; }
 
   string ToString() const;
 
+  static Colorer DefaultColorer() {
+    return [](const BufferLiveness& buffer_liveness) {
+      for (auto& buffer :
+           buffer_liveness.points_to_analysis().logical_buffers()) {
+        buffer->set_color(LogicalBuffer::Color(0));
+      }
+      return Status::OK();
+    };
+  }
+
  private:
   explicit BufferLiveness(const HloModule* module,
                           std::unique_ptr<HloOrdering> hlo_ordering)
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
index be821d5154..099713d671 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.h
@@ -145,9 +145,6 @@ std::ostream& operator<<(std::ostream& out, const BufferAlias& buffer_alias);
 // the potential sources of each buffer in each instruction's output.
 class TuplePointsToAnalysis : public DfsHloVisitorWithDefault {
  public:
-  using Colorer =
-      std::function<Status(TuplePointsToAnalysis* points_to_analysis)>;
-
   // Runs points-to analysis on 'module'.
   static StatusOr<std::unique_ptr<TuplePointsToAnalysis>> Run(
       const HloModule* module);
@@ -213,15 +210,6 @@ class TuplePointsToAnalysis : public DfsHloVisitorWithDefault {
 
   string ToString() const;
 
-  static Colorer DefaultColorer() {
-    return [](TuplePointsToAnalysis* points_to_analysis) {
-      for (auto& buffer : points_to_analysis->logical_buffers()) {
-        buffer->set_color(LogicalBuffer::Color(0));
-      }
-      return Status::OK();
-    };
-  }
-
  private:
   explicit TuplePointsToAnalysis(const HloModule* module) : module_(module) {}
 
-- 
GitLab


From 625ddbaf2d64d5b53551480eb51d2d1c02775d2d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 15:06:39 -0700
Subject: [PATCH 1049/1375] Restore previous IrEmitter::HandleSlice() behavior.
 END_PUBLIC

BEGIN_PUBLIC
Automated g4 rollback of changelist 162249449

PiperOrigin-RevId: 162276286
---
 .../compiler/xla/service/cpu/ir_emitter.cc    | 137 +-----------------
 tensorflow/compiler/xla/tests/slice_test.cc   |  39 -----
 2 files changed, 6 insertions(+), 170 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 5780653d46..9db1614cd7 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -53,7 +53,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/window_util.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 
@@ -1485,137 +1484,13 @@ Status IrEmitter::HandleSend(HloInstruction* send) {
 }
 
 Status IrEmitter::HandleSlice(HloInstruction* slice, HloInstruction* operand) {
-  VLOG(2) << "HandleSlice: " << slice->ToString();
-
-  TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
-                      EmitTargetAddressForOp(slice));
-  emitted_value_[slice] = target_address;
-
-  // The code below assumes the layouts are equal.
-  if (!LayoutUtil::Equal(operand->shape().layout(), slice->shape().layout())) {
-    return DefaultAction(slice);
-  }
-
-  if (ShapeUtil::HasZeroElements(slice->shape())) {
-    return Status::OK();
-  }
-
-  const Layout& layout = operand->shape().layout();
-  const int64 num_dims = operand->shape().dimensions_size();
-
-  // The slice lowering finds maximal contiguous blocks of memory that can be
-  // copied from the source to the target. This is done by looking at the
-  // source/target layout in minor to major order and do the following:
-  //
-  // * Find an initial segment of dimensions along which the slice uses the
-  //   whole dimension. These are the "inner" dimensions and can be folded into
-  //   the memcpy.
-  //
-  // * Of the remaining dimensions decide which ones require loops.
-  //
-  // * Implement the memcpy within the innermost loop.
-
-  tensorflow::gtl::FlatSet<int64> inner_dims;
-  for (int64 dim : layout.minor_to_major()) {
-    if (operand->shape().dimensions(dim) != slice->shape().dimensions(dim)) {
-      break;
-    }
-    inner_dims.insert(dim);
-  }
-
-  const bool is_trivial_copy = (inner_dims.size() == num_dims);
-  if (is_trivial_copy) {
-    if (ShapeUtil::IsEffectiveScalar(slice->shape())) {
-      return DefaultAction(slice);
-    } else {
-      return EmitMemcpy(*slice, *operand);
-    }
-  }
-
-  // The memcpy will copy elements that are logically this shape (allowed to be
-  // scalar).
-  const Shape element_shape = ShapeUtil::FilterDimensions(
-      [&inner_dims](int64 dim) -> bool { return inner_dims.count(dim); },
-      operand->shape());
-
-  // memcpy_dim is the innermost (in terms of layout) dimension for which the
-  // slice does *not* just copy all the elements along the dimension.
-  const int64 memcpy_dim = layout.minor_to_major(inner_dims.size());
-
-  const bool memcpy_is_contiguous = slice->slice_strides(memcpy_dim) == 1;
-  // The number of logical elements that can be copied in a single call
-  // to memcpy. We can only copy 1 element at a time if there is a non-trivial
-  // stride.
-  const int64 memcpy_elements =
-      memcpy_is_contiguous
-          ? slice->slice_limits(memcpy_dim) - slice->slice_starts(memcpy_dim)
-          : 1;
-
-  if (memcpy_elements == 1 && ShapeUtil::IsEffectiveScalar(element_shape)) {
-    // Avoid using memcpy for copying element by element at a time. This does
-    // not buy us anything and may actually cause LLVM's load/store optimization
-    // to be less effective.
-    return DefaultAction(slice);
-  }
-
-  // Determine the dimensions that get lowered as loops.
-  std::vector<int64> outer_dims;
-  for (int64 i = 0; i < num_dims - inner_dims.size() - 1; ++i) {
-    outer_dims.push_back(LayoutUtil::Major(layout, i));
-  }
-
-  // Is the slice along the memcpy dimension contiguous? If not, then memcpy_dim
-  // needs to be wrapped around a loop as well.
-  if (!memcpy_is_contiguous) {
-    outer_dims.push_back(memcpy_dim);
-  }
-
-  llvm_ir::IrArray target_array(target_address, slice->shape());
-  AddAliasingInformationToIrArray(*slice, &target_array);
-
-  const int64 num_outer_loops = outer_dims.size();
-  llvm_ir::ForLoopNest loops(&ir_builder_);
-  llvm_ir::IrArray::Index target_index =
-      loops.AddLoopsForShapeOnDimensions(slice->shape(), outer_dims, "slice");
-
-  // Only the indices for the outer dimensions have been initialized in
-  // target_index. The rest of the indices should get initialized to 0, since
-  // for the rest of the dimensions the copy writes to the full dimension.
-  for (llvm::Value*& index : target_index) {
-    if (index == nullptr) {
-      index = ir_builder_.getInt64(0);
-    }
-  }
-
-  if (num_outer_loops > 0) {
-    SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_);
-  }
-
-  llvm_ir::IrArray source_array(GetEmittedValueFor(operand), operand->shape());
-
-  const llvm_ir::IrArray::Index source_index = target_index.SourceIndexOfSlice(
-      /*shape=*/slice->shape(), /*starts=*/slice->slice_starts(),
-      /*strides=*/slice->slice_strides(), /*builder=*/&ir_builder_);
-
-  llvm::Value* memcpy_dest = target_array.EmitArrayElementAddress(
-      target_index, &ir_builder_, "slice.dest");
-  llvm::Value* memcpy_source = source_array.EmitArrayElementAddress(
-      source_index, &ir_builder_, "slice.source");
-  const int64 memcpy_bytes =
-      ShapeUtil::ByteSizeOf(element_shape) * memcpy_elements;
-  // TODO(b/63762267): Be more aggressive with `align` by using the GCD of the
-  // element size and buffer alignment.
-  ir_builder_.CreateMemCpy(memcpy_dest, memcpy_source, memcpy_bytes,
-                           /*align=*/1);
-
-  VLOG(2) << "  emitted memcpy of " << memcpy_bytes << " bytes inside "
-          << num_outer_loops << " loops";
-
-  if (num_outer_loops > 0) {
-    SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_);
+  if (ShapeUtil::IsScalar(slice->shape())) {
+    TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                        EmitTargetAddressForOp(slice));
+    emitted_value_[slice] = target_address;
+    return EmitMemcpy(*operand, *slice);
   }
-
-  return Status::OK();
+  return DefaultAction(slice);
 }
 
 Status IrEmitter::HandleDynamicSlice(HloInstruction* dynamic_slice,
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index c77e892665..091c35df65 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -34,45 +34,6 @@ namespace {
 
 class SliceTest : public ClientLibraryTestBase {};
 
-TEST_F(SliceTest, Slice3x3x3_To_3x3x1_F32) {
-  Array3D<float> values(3, 3, 3);
-  values.FillIota(0);
-
-  ComputationBuilder builder(client_, TestName());
-  auto original = builder.ConstantR3FromArray3D<float>(values);
-  builder.Slice(original, {0, 0, 0}, {3, 3, 1}, {1, 1, 1});
-
-  Array3D<float> expected{
-      {{0.0}, {3.0}, {6.0}}, {{9.0}, {12.0}, {15.0}}, {{18.0}, {21.0}, {24.0}}};
-  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
-}
-
-TEST_F(SliceTest, Slice3x3x3_To_3x1x3_F32) {
-  Array3D<float> values(3, 3, 3);
-  values.FillIota(0);
-
-  ComputationBuilder builder(client_, TestName());
-  auto original = builder.ConstantR3FromArray3D<float>(values);
-  builder.Slice(original, {0, 0, 0}, {3, 1, 3}, {1, 1, 1});
-
-  Array3D<float> expected{
-      {{0.0, 1.0, 2.0}}, {{9.0, 10.0, 11.0}}, {{18.0, 19.0, 20.0}}};
-  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
-}
-
-TEST_F(SliceTest, Slice3x3x3_To_1x3x3_F32) {
-  Array3D<float> values(3, 3, 3);
-  values.FillIota(0);
-
-  ComputationBuilder builder(client_, TestName());
-  auto original = builder.ConstantR3FromArray3D<float>(values);
-  builder.Slice(original, {0, 0, 0}, {1, 3, 3}, {1, 1, 1});
-
-  Array3D<float> expected{
-      {{{0.0, 1.0, 2.0}, {3.0, 4.0, 5.0}, {6.0, 7.0, 8.0}}}};
-  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
-}
-
 XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 0));
-- 
GitLab


From 5799c01281ff607214f755693b98d03dd0847e18 Mon Sep 17 00:00:00 2001
From: Petros Mol <pmol@google.com>
Date: Mon, 17 Jul 2017 15:11:33 -0700
Subject: [PATCH 1050/1375] Adds multi-class (Crammer-Singer) hinge loss to
 tf.contrib.kernel_methods

PiperOrigin-RevId: 162277054
---
 tensorflow/contrib/kernel_methods/BUILD       |  17 ++
 tensorflow/contrib/kernel_methods/__init__.py |   2 +
 .../contrib/kernel_methods/python/losses.py   | 135 ++++++++++++
 .../kernel_methods/python/losses_test.py      | 206 ++++++++++++++++++
 4 files changed, 360 insertions(+)
 create mode 100644 tensorflow/contrib/kernel_methods/python/losses.py
 create mode 100644 tensorflow/contrib/kernel_methods/python/losses_test.py

diff --git a/tensorflow/contrib/kernel_methods/BUILD b/tensorflow/contrib/kernel_methods/BUILD
index fccaa3abd4..ae1402b0e6 100644
--- a/tensorflow/contrib/kernel_methods/BUILD
+++ b/tensorflow/contrib/kernel_methods/BUILD
@@ -14,6 +14,7 @@ py_library(
     srcs = [
         "__init__.py",
         "python/kernel_estimators.py",
+        "python/losses.py",
         "python/mappers/random_fourier_features.py",
     ],
     srcs_version = "PY2AND3",
@@ -22,11 +23,15 @@ py_library(
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/learn",
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:util",
+        "//tensorflow/python/ops/losses",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
@@ -71,6 +76,18 @@ py_test(
     ],
 )
 
+py_test(
+    name = "losses_test",
+    srcs = ["python/losses_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":kernel_methods",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/kernel_methods/__init__.py b/tensorflow/contrib/kernel_methods/__init__.py
index 7272e59516..0f3827d187 100644
--- a/tensorflow/contrib/kernel_methods/__init__.py
+++ b/tensorflow/contrib/kernel_methods/__init__.py
@@ -16,12 +16,14 @@
 
 @@KernelLinearClassifier
 @@RandomFourierFeatureMapper
+@@sparse_multiclass_hinge_loss
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.kernel_methods.python.kernel_estimators import KernelLinearClassifier
+from tensorflow.contrib.kernel_methods.python.losses import sparse_multiclass_hinge_loss
 from tensorflow.contrib.kernel_methods.python.mappers.random_fourier_features import RandomFourierFeatureMapper
 
 from tensorflow.python.util.all_util import remove_undocumented
diff --git a/tensorflow/contrib/kernel_methods/python/losses.py b/tensorflow/contrib/kernel_methods/python/losses.py
new file mode 100644
index 0000000000..208b0e1c9d
--- /dev/null
+++ b/tensorflow/contrib/kernel_methods/python/losses.py
@@ -0,0 +1,135 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implementation of kernel-methods-related loss operations."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops.losses import losses
+
+
+def sparse_multiclass_hinge_loss(
+    labels,
+    logits,
+    weights=1.0,
+    scope=None,
+    loss_collection=ops.GraphKeys.LOSSES,
+    reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS):
+  """Adds Ops for computing the multiclass hinge loss.
+
+  The implementation is based on the following paper:
+  On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
+  by Crammer and Singer.
+  link: http://jmlr.csail.mit.edu/papers/volume2/crammer01a/crammer01a.pdf
+
+  This is a generalization of standard (binary) hinge loss. For a given instance
+  with correct label c*, the loss is given by:
+    loss = max_{c != c*} logits_c - logits_{c*} + 1.
+  or equivalently
+    loss = max_c { logits_c - logits_{c*} + I_{c != c*} }
+  where I_{c != c*} = 1 if c != c* and 0 otherwise.
+
+  Args:
+    labels: `Tensor` of shape [batch_size] or [batch_size, 1]. Corresponds to
+      the ground truth. Each entry must be an index in `[0, num_classes)`.
+    logits: `Tensor` of shape [batch_size, num_classes] corresponding to the
+      unscaled logits. Its dtype should be either `float32` or `float64`.
+    weights: Optional (python) scalar or `Tensor`. If a non-scalar `Tensor`, its
+      rank should be either 1 ([batch_size]) or 2 ([batch_size, 1]).
+    scope: The scope for the operations performed in computing the loss.
+    loss_collection: collection to which the loss will be added.
+    reduction: Type of reduction to apply to loss.
+
+  Returns:
+    Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same
+    shape as `labels`; otherwise, it is a scalar.
+
+  Raises:
+    ValueError: If `logits`, `labels` or `weights` have invalid or inconsistent
+      shapes.
+    ValueError: If `labels` tensor has invalid dtype.
+  """
+
+  with ops.name_scope(scope, 'sparse_multiclass_hinge_loss', (logits,
+                                                              labels)) as scope:
+
+    # Check logits Tensor has valid rank.
+    logits_shape = logits.get_shape()
+    logits_rank = logits_shape.ndims
+    if logits_rank != 2:
+      raise ValueError(
+          'logits should have rank 2 ([batch_size, num_classes]). Given rank is'
+          ' {}'.format(logits_rank))
+    batch_size, num_classes = logits_shape[0].value, logits_shape[1].value
+    logits = math_ops.to_float(logits)
+
+    # Check labels have valid type.
+    if labels.dtype != dtypes.int32 and labels.dtype != dtypes.int64:
+      raise ValueError(
+          'Invalid dtype for labels: {}. Acceptable dtypes: int32 and int64'.
+          format(labels.dtype))
+
+    # Check labels and weights have valid ranks and are consistent.
+    labels_rank = labels.get_shape().ndims
+    if labels_rank not in [1, 2]:
+      raise ValueError(
+          'labels should have rank 1 ([batch_size]) or 2 ([batch_size, 1]). '
+          'Given rank is {}'.format(labels_rank))
+    with ops.control_dependencies([
+        check_ops.assert_less(labels, math_ops.cast(num_classes, labels.dtype))
+    ]):
+      labels = array_ops.reshape(labels, shape=[-1])
+
+    weights = ops.convert_to_tensor(weights)
+    weights_rank = weights.get_shape().ndims
+    if weights_rank not in [0, 1, 2]:
+      raise ValueError(
+          'non-scalar weights should have rank 1 ([batch_size]) or 2 '
+          '([batch_size, 1]). Given rank is {}'.format(labels_rank))
+
+    if weights_rank > 0:
+      weights = array_ops.reshape(weights, shape=[-1])
+      # Check weights and labels have the same number of elements.
+      weights.get_shape().assert_is_compatible_with(labels.get_shape())
+
+    # Compute the logits tensor corresponding to the correct class per instance.
+    example_indices = array_ops.reshape(
+        math_ops.range(batch_size), shape=[batch_size, 1])
+    indices = array_ops.concat(
+        [
+            example_indices,
+            array_ops.reshape(
+                math_ops.cast(labels, example_indices.dtype),
+                shape=[batch_size, 1])
+        ],
+        axis=1)
+    label_logits = array_ops.reshape(
+        array_ops.gather_nd(params=logits, indices=indices),
+        shape=[batch_size, 1])
+
+    one_cold_labels = array_ops.one_hot(
+        indices=labels, depth=num_classes, on_value=0.0, off_value=1.0)
+    margin = logits - label_logits + one_cold_labels
+    margin = nn_ops.relu(margin)
+    loss = math_ops.reduce_max(margin, axis=1)
+    return losses.compute_weighted_loss(
+        loss, weights, scope, loss_collection, reduction=reduction)
diff --git a/tensorflow/contrib/kernel_methods/python/losses_test.py b/tensorflow/contrib/kernel_methods/python/losses_test.py
new file mode 100644
index 0000000000..8a1a5ffe56
--- /dev/null
+++ b/tensorflow/contrib/kernel_methods/python/losses_test.py
@@ -0,0 +1,206 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for third_party.tensorflow.contrib.kernel_methods.python.losses."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.kernel_methods.python import losses
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.platform import test
+
+
+class SparseMulticlassHingeLossTest(test.TestCase):
+
+  def testInvalidLogitsShape(self):
+    """An error is raised when logits have invalid shape."""
+    with self.test_session():
+      logits = constant_op.constant([-1.0, 2.1], shape=(2,))
+      labels = constant_op.constant([0, 1])
+      with self.assertRaises(ValueError):
+        _ = losses.sparse_multiclass_hinge_loss(labels, logits)
+
+  def testInvalidLabelsShape(self):
+    """An error is raised when labels have invalid shape."""
+    with self.test_session():
+      logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
+      labels = constant_op.constant([1, 0], shape=(1, 1, 2))
+      with self.assertRaises(ValueError):
+        _ = losses.sparse_multiclass_hinge_loss(labels, logits)
+
+  def testInvalidWeightsShape(self):
+    """An error is raised when weights have invalid shape."""
+    with self.test_session():
+      logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
+      labels = constant_op.constant([1, 0], shape=(2,))
+      weights = constant_op.constant([1.5, 0.2], shape=(2, 1, 1))
+      with self.assertRaises(ValueError):
+        _ = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+
+  def testInvalidLabelsDtype(self):
+    """An error is raised when labels have invalid shape."""
+    with self.test_session():
+      logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
+      labels = constant_op.constant([1, 0], dtype=dtypes.float32)
+      with self.assertRaises(ValueError):
+        _ = losses.sparse_multiclass_hinge_loss(labels, logits)
+
+  def testNoneWeightRaisesValueError(self):
+    """An error is raised when weights are None."""
+    with self.test_session():
+      logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
+      labels = constant_op.constant([1, 0])
+      with self.assertRaises(ValueError):
+        _ = losses.sparse_multiclass_hinge_loss(labels, logits, weights=None)
+
+  def testInconsistentLabelsAndWeightsShapesSameRank(self):
+    """Error raised when weights and labels have same ranks, different sizes."""
+    with self.test_session():
+      logits = constant_op.constant([-1.0, 2.1, 4.1], shape=(3, 1))
+      labels = constant_op.constant([1, 0, 2], shape=(3, 1))
+      weights = constant_op.constant([1.1, 2.0], shape=(2, 1))
+      with self.assertRaises(ValueError):
+        _ = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+
+  def testInconsistentLabelsAndWeightsShapesDifferentRank(self):
+    """Error raised when weights and labels have different ranks and sizes."""
+    with self.test_session():
+      logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
+      labels = constant_op.constant([1, 0], shape=(2, 1))
+      weights = constant_op.constant([1.1, 2.0, 2.8], shape=(3,))
+      with self.assertRaises(ValueError):
+        _ = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+
+  def testOutOfRangeLabels(self):
+    """An error is raised when labels are not in [0, num_classes)."""
+    with self.test_session():
+      logits = constant_op.constant([[1.2, -1.4, -1.0], [1.4, 1.8, 4.0],
+                                     [0.5, 1.8, -1.0]])
+      labels = constant_op.constant([1, 0, 4])
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits)
+      with self.assertRaises(errors.InvalidArgumentError):
+        loss.eval()
+
+  def testZeroLossInt32Labels(self):
+    """Loss is 0 if true class logits sufficiently higher than other classes."""
+    with self.test_session():
+      logits = constant_op.constant([[1.2, -1.4, -1.0], [1.4, 1.8, 4.0],
+                                     [0.5, 1.8, -1.0]])
+      labels = constant_op.constant([0, 2, 1], dtype=dtypes.int32)
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits)
+      self.assertAlmostEqual(loss.eval(), 0.0, 3)
+
+  def testZeroLossInt64Labels(self):
+    """Loss is 0 if true class logits sufficiently higher than other classes."""
+    with self.test_session():
+      logits = constant_op.constant([[2.1, -0.4, -1.0], [1.4, 2.8, 4.0],
+                                     [-0.5, 0.8, -1.0]])
+      labels = constant_op.constant([0, 2, 1], dtype=dtypes.int64)
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits)
+      self.assertAlmostEqual(loss.eval(), 0.0, 3)
+
+  def testCorrectPredictionsSomeClassesInsideMargin(self):
+    """Loss is > 0 even if true class logits are higher than other classes."""
+    with self.test_session():
+      logits = constant_op.constant([[1.2, -1.4, 0.8], [1.4, 1.8, 4.0],
+                                     [1.5, 1.8, -1.0]])
+      labels = constant_op.constant([0, 2, 1])
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits)
+      # The first and third samples incur some loss (0.6 and 0.7 respectively).
+      self.assertAlmostEqual(loss.eval(), 0.4333, 3)
+
+  def testIncorrectPredictions(self):
+    """Loss is >0 when an incorrect class has higher logits than true class."""
+    with self.test_session():
+      logits = constant_op.constant([[2.6, 0.4, 0.8], [1.4, 0.8, -1.0],
+                                     [0.5, -1.8, 2.0]])
+      labels = constant_op.constant([1, 0, 2])
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits)
+      # The first examples incurs a high loss (3.2) since the logits of an
+      # incorrect class (0) are higher than the logits of the ground truth. The
+      # second example also incures a (smaller) loss (0.4).
+      self.assertAlmostEqual(loss.eval(), 1.2, 3)
+
+  def testIncorrectPredictionsColumnLabels(self):
+    """Same as above but labels is a rank-2 tensor."""
+    with self.test_session():
+      logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
+                                     [0.2, -1.8, 4.0]])
+      labels = constant_op.constant([1, 0, 2], shape=(3, 1))
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits)
+      # The first examples incurs a high loss (3.0) since the logits of an
+      # incorrect class (0) are higher than the logits of the ground truth. The
+      # second example also incures a (smaller) loss (0.3).
+      self.assertAlmostEqual(loss.eval(), 1.1, 3)
+
+  def testIncorrectPredictionsZeroWeights(self):
+    """Loss is 0 when all weights are missing even if predictions are wrong."""
+    with self.test_session():
+      logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
+                                     [0.2, -1.8, 4.0]])
+      labels = constant_op.constant([1, 0, 2], shape=(3, 1))
+      weights = constant_op.constant([0.0, 0.0, 0.0], shape=(3, 1))
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+      # No overall loss since all weights are 0.
+      self.assertAlmostEqual(loss.eval(), 0.0, 3)
+
+  def testNonZeroLossWithPythonScalarWeights(self):
+    """Weighted loss is correctly computed when weights is a python scalar."""
+    with self.test_session():
+      logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
+                                     [0.2, -1.8, 4.0]])
+      labels = constant_op.constant([1, 0, 2], shape=(3, 1))
+      weights = 10.0
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+      self.assertAlmostEqual(loss.eval(), 11.0, 3)
+
+  def testNonZeroLossWithScalarTensorWeights(self):
+    """Weighted loss is correctly computed when weights is a rank-0 tensor."""
+    with self.test_session():
+      logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
+                                     [0.2, -1.8, 4.0]])
+      labels = constant_op.constant([1, 0, 2], shape=(3, 1))
+      weights = constant_op.constant(5.0)
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+      self.assertAlmostEqual(loss.eval(), 5.5, 3)
+
+  def testNonZeroLossWith1DTensorWeightsColumnLabels(self):
+    """Weighted loss is correctly computed when weights is a rank-0 tensor."""
+    with self.test_session():
+      logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
+                                     [0.2, -1.8, 4.0]])
+      labels = constant_op.constant([1, 0, 2], shape=(3, 1))
+      weights = constant_op.constant([1.0, 0.5, 2.0], shape=(3,))
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+      # The overall loss is 1/3 *(3.0*1.0 + 0.5*0.3+ 2.0*0.0) = 1.05
+      self.assertAlmostEqual(loss.eval(), 1.05, 3)
+
+  def testNonZeroLossWith2DTensorWeights1DLabelsSomeWeightsMissing(self):
+    """Weighted loss is correctly computed when weights is a rank-0 tensor."""
+    with self.test_session():
+      logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
+                                     [0.2, -1.8, 4.0], [1.6, 1.8, -4.0]])
+      labels = constant_op.constant([1, 0, 2, 1])
+      weights = constant_op.constant([[1.0], [0.0], [2.0], [4.0]])
+      loss = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
+      # The overall loss is 1/3 *(3.0*1.0 + 0.0*0.3+ 2.0*0.0 + 4.0*0.8) = 6.2/3.
+      self.assertAlmostEqual(loss.eval(), 2.06666, 3)
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 2d039643a9240494715063763df5300466bdc3d5 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Mon, 17 Jul 2017 15:50:10 -0700
Subject: [PATCH 1051/1375] TFTS: LSTM model-building example

PiperOrigin-RevId: 162282906
---
 tensorflow/contrib/timeseries/BUILD           |   1 +
 tensorflow/contrib/timeseries/README.md       |   7 +-
 tensorflow/contrib/timeseries/examples/BUILD  |  28 +++
 .../examples/data/multivariate_periods.csv    | 100 ++++++++
 .../contrib/timeseries/examples/lstm.py       | 219 ++++++++++++++++++
 .../contrib/timeseries/examples/lstm_test.py  |  42 ++++
 .../python/timeseries/estimators.py           |  10 +-
 .../timeseries/python/timeseries/model.py     |   3 +-
 .../python/timeseries/test_utils.py           |   2 +-
 9 files changed, 401 insertions(+), 11 deletions(-)
 create mode 100644 tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
 create mode 100644 tensorflow/contrib/timeseries/examples/lstm.py
 create mode 100644 tensorflow/contrib/timeseries/examples/lstm_test.py

diff --git a/tensorflow/contrib/timeseries/BUILD b/tensorflow/contrib/timeseries/BUILD
index 40aadb02a5..b4ecb61a42 100644
--- a/tensorflow/contrib/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/BUILD
@@ -27,6 +27,7 @@ py_library(
     deps = [
         ":timeseries",
         "//tensorflow/contrib/timeseries/examples:known_anomaly",
+        "//tensorflow/contrib/timeseries/examples:lstm",
         "//tensorflow/contrib/timeseries/examples:multivariate",
         "//tensorflow/contrib/timeseries/examples:predict",
         "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
diff --git a/tensorflow/contrib/timeseries/README.md b/tensorflow/contrib/timeseries/README.md
index ddc77ca54b..2b36ade986 100644
--- a/tensorflow/contrib/timeseries/README.md
+++ b/tensorflow/contrib/timeseries/README.md
@@ -9,10 +9,11 @@ series (using truncated backpropagation).
 
 To get started, take a look at the `examples/` directory, which includes:
 
- - Making probabilistic forecasts
- - Using exogenous features to train on data with known anomalies/changepoints
+ - Making probabilistic forecasts (`examples/predict.py`)
+ - Using exogenous features to train on data with known anomalies/changepoints (`examples/known_anomaly.py`)
  - Learning correlations between series (multivariate forecasting/anomaly
-   detection)
+   detection; `examples/multivariate.py`)
+ - More advanced custom model building (`examples/lstm.py`)
 
 TFTS includes many other modeling tools, including non-linear autoregression
 (see the `hidden_layer_sizes` argument to `ARRegressor` in `estimators.py`) and
diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD
index fc18664722..14ebe5d08f 100644
--- a/tensorflow/contrib/timeseries/examples/BUILD
+++ b/tensorflow/contrib/timeseries/examples/BUILD
@@ -95,6 +95,34 @@ py_test(
     ],
 )
 
+py_binary(
+    name = "lstm",
+    srcs = [
+        "lstm.py",
+    ],
+    data = [
+        "data/multivariate_periods.csv",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "lstm_test",
+    timeout = "long",  # Moderate but for asan
+    srcs = [
+        "lstm_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":lstm",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
new file mode 100644
index 0000000000..02a60d1cf6
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
@@ -0,0 +1,100 @@
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+73,-0.912801257303,1.60791309476,1.8729264524,4.03037260012,4.36072588913
+74,-0.0894895640338,2.02535207407,1.93484909619,4.09557485132,4.35327025188
+75,0.978646999652,2.20085086625,2.09003440427,4.27542353033,4.1805058388
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+79,1.0823738177,1.12211589848,3.27079386925,4.94288270502,4.01851068083
+80,0.124370187893,0.616474412808,3.4284236674,4.76942168327,3.9749536483
+81,-0.929423379352,0.290977090976,3.34131726136,4.78590392707,4.10190661656
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+90,0.264820472091,1.86981946157,1.50048403865,3.43619796921,5.05651761669
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+93,-0.724285777937,0.553052311957,1.48573560173,2.7365973598,5.72549174225
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+95,1.0323195039,0.260873217055,1.81913034804,2.83951143848,5.90325028086
+96,-0.53285682538,0.387695521405,1.70935609313,2.57977050631,5.79579213161
+97,-0.975127997215,0.920948771589,2.51292643636,2.71004616612,5.87016469227
+98,0.540246804099,1.36445470181,2.61949412896,2.98482553485,6.02447664937
+99,0.987764008058,1.85581989607,2.84685706149,2.94760204892,6.0212151724
diff --git a/tensorflow/contrib/timeseries/examples/lstm.py b/tensorflow/contrib/timeseries/examples/lstm.py
new file mode 100644
index 0000000000..6bab06f56c
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/lstm.py
@@ -0,0 +1,219 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A more advanced example, of building an RNN-based time series model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from os import path
+
+import numpy
+import tensorflow as tf
+
+from tensorflow.contrib.timeseries.python.timeseries import estimators as ts_estimators
+from tensorflow.contrib.timeseries.python.timeseries import model as ts_model
+
+try:
+  import matplotlib  # pylint: disable=g-import-not-at-top
+  matplotlib.use("TkAgg")  # Need Tk for interactive plots.
+  from matplotlib import pyplot  # pylint: disable=g-import-not-at-top
+  HAS_MATPLOTLIB = True
+except ImportError:
+  # Plotting requires matplotlib, but the unit test running this code may
+  # execute in an environment without it (i.e. matplotlib is not a build
+  # dependency). We'd still like to test the TensorFlow-dependent parts of this
+  # example.
+  HAS_MATPLOTLIB = False
+
+_MODULE_PATH = path.dirname(__file__)
+_DATA_FILE = path.join(_MODULE_PATH, "data/multivariate_periods.csv")
+
+
+class _LSTMModel(ts_model.SequentialTimeSeriesModel):
+  """A time series model-building example using an RNNCell."""
+
+  def __init__(self, num_units, num_features, dtype=tf.float32):
+    """Initialize/configure the model object.
+
+    Note that we do not start graph building here. Rather, this object is a
+    configurable factory for TensorFlow graphs which are run by an Estimator.
+
+    Args:
+      num_units: The number of units in the model's LSTMCell.
+      num_features: The dimensionality of the time series (features per
+        timestep).
+      dtype: The floating point data type to use.
+    """
+    super(_LSTMModel, self).__init__(
+        # Pre-register the metrics we'll be outputting (just a mean here).
+        train_output_names=["mean"],
+        predict_output_names=["mean"],
+        num_features=num_features,
+        dtype=dtype)
+    self._num_units = num_units
+    # Filled in by initialize_graph()
+    self._lstm_cell = None
+    self._lstm_cell_run = None
+    self._predict_from_lstm_output = None
+
+  def initialize_graph(self, input_statistics):
+    """Save templates for components, which can then be used repeatedly.
+
+    This method is called every time a new graph is created. It's safe to start
+    adding ops to the current default graph here, but the graph should be
+    constructed from scratch.
+
+    Args:
+      input_statistics: A math_utils.InputStatistics object.
+    """
+    super(_LSTMModel, self).initialize_graph(input_statistics=input_statistics)
+    self._lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=self._num_units)
+    # Create templates so we don't have to worry about variable reuse.
+    self._lstm_cell_run = tf.make_template(
+        name_="lstm_cell",
+        func_=self._lstm_cell,
+        create_scope_now_=True)
+    # Transforms LSTM output into mean predictions.
+    self._predict_from_lstm_output = tf.make_template(
+        name_="predict_from_lstm_output",
+        func_=
+        lambda inputs: tf.layers.dense(inputs=inputs, units=self.num_features),
+        create_scope_now_=True)
+
+  def get_start_state(self):
+    """Return initial state for the time series model."""
+    return (
+        # Keeps track of the time associated with this state for error checking.
+        tf.zeros([], dtype=tf.int64),
+        # The previous observation or prediction.
+        tf.zeros([self.num_features], dtype=self.dtype),
+        # The state of the RNNCell (batch dimension removed since this parent
+        # class will broadcast).
+        [tf.squeeze(state_element, axis=0)
+         for state_element
+         in self._lstm_cell.zero_state(batch_size=1, dtype=self.dtype)])
+
+  def _transform(self, data):
+    """Normalize data based on input statistics to encourage stable training."""
+    mean, variance = self._input_statistics.overall_feature_moments
+    return (data - mean) / variance
+
+  def _de_transform(self, data):
+    """Transform data back to the input scale."""
+    mean, variance = self._input_statistics.overall_feature_moments
+    return data * variance + mean
+
+  def _filtering_step(self, current_times, current_values, state, predictions):
+    """Update model state based on observations.
+
+    Note that we don't do much here aside from computing a loss. In this case
+    it's easier to update the RNN state in _prediction_step, since that covers
+    running the RNN both on observations (from this method) and our own
+    predictions. This distinction can be important for probabilistic models,
+    where repeatedly predicting without filtering should lead to low-confidence
+    predictions.
+
+    Args:
+      current_times: A [batch size] integer Tensor.
+      current_values: A [batch size, self.num_features] floating point Tensor
+        with new observations.
+      state: The model's state tuple.
+      predictions: The output of the previous `_prediction_step`.
+    Returns:
+      A tuple of new state and a predictions dictionary updated to include a
+      loss (note that we could also return other measures of goodness of fit,
+      although only "loss" will be optimized).
+    """
+    state_from_time, prediction, lstm_state = state
+    with tf.control_dependencies(
+        [tf.assert_equal(current_times, state_from_time)]):
+      transformed_values = self._transform(current_values)
+      # Use mean squared error across features for the loss.
+      predictions["loss"] = tf.reduce_mean(
+          (prediction - transformed_values) ** 2, axis=-1)
+      # Keep track of the new observation in model state. It won't be run
+      # through the LSTM until the next _imputation_step.
+      new_state_tuple = (current_times, transformed_values, lstm_state)
+    return (new_state_tuple, predictions)
+
+  def _prediction_step(self, current_times, state):
+    """Advance the RNN state using a previous observation or prediction."""
+    _, previous_observation_or_prediction, lstm_state = state
+    lstm_output, new_lstm_state = self._lstm_cell_run(
+        inputs=previous_observation_or_prediction, state=lstm_state)
+    next_prediction = self._predict_from_lstm_output(lstm_output)
+    new_state_tuple = (current_times, next_prediction, new_lstm_state)
+    return new_state_tuple, {"mean": self._de_transform(next_prediction)}
+
+  def _imputation_step(self, current_times, state):
+    """Advance model state across a gap."""
+    # Does not do anything special if we're jumping across a gap. More advanced
+    # models, especially probabilistic ones, would want a special case that
+    # depends on the gap size.
+    return state
+
+  def _exogenous_input_step(
+      self, current_times, current_exogenous_regressors, state):
+    """Update model state based on exogenous regressors."""
+    raise NotImplementedError(
+        "Exogenous inputs are not implemented for this example.")
+
+
+def train_and_predict(csv_file_name=_DATA_FILE, training_steps=200):
+  """Train and predict using a custom time series model."""
+  # Construct an Estimator from our LSTM model.
+  estimator = ts_estimators.TimeSeriesRegressor(
+      model=_LSTMModel(num_features=5, num_units=128),
+      optimizer=tf.train.AdamOptimizer(0.001))
+  reader = tf.contrib.timeseries.CSVReader(
+      csv_file_name,
+      column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
+                    + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5))
+  train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
+      reader, batch_size=4, window_size=32)
+  estimator.train(input_fn=train_input_fn, steps=training_steps)
+  evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
+  evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)
+  # Predict starting after the evaluation
+  (predictions,) = tuple(estimator.predict(
+      input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
+          evaluation, steps=100)))
+  times = evaluation["times"][0]
+  observed = evaluation["observed"][0, :, :]
+  predicted_mean = numpy.squeeze(numpy.concatenate(
+      [evaluation["mean"][0], predictions["mean"]], axis=0))
+  all_times = numpy.concatenate([times, predictions["times"]], axis=0)
+  return times, observed, all_times, predicted_mean
+
+
+def main(unused_argv):
+  if not HAS_MATPLOTLIB:
+    raise ImportError(
+        "Please install matplotlib to generate a plot from this example.")
+  (observed_times, observations,
+   all_times, predictions) = train_and_predict()
+  pyplot.axvline(99, linestyle="dotted")
+  observed_lines = pyplot.plot(
+      observed_times, observations, label="Observed", color="k")
+  predicted_lines = pyplot.plot(
+      all_times, predictions, label="Predicted", color="b")
+  pyplot.legend(handles=[observed_lines[0], predicted_lines[0]],
+                loc="upper left")
+  pyplot.show()
+
+
+if __name__ == "__main__":
+  tf.app.run(main=main)
diff --git a/tensorflow/contrib/timeseries/examples/lstm_test.py b/tensorflow/contrib/timeseries/examples/lstm_test.py
new file mode 100644
index 0000000000..138e64dbd8
--- /dev/null
+++ b/tensorflow/contrib/timeseries/examples/lstm_test.py
@@ -0,0 +1,42 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests that the TensorFlow parts of the LSTM example run."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.timeseries.examples import lstm
+
+from tensorflow.python.platform import test
+
+
+class LSTMExampleTest(test.TestCase):
+
+  def test_periodicity_learned(self):
+    (observed_times, observed_values,
+     all_times, predicted_values) = lstm.train_and_predict(training_steps=100)
+    self.assertAllEqual([100], observed_times.shape)
+    self.assertAllEqual([100, 5], observed_values.shape)
+    self.assertAllEqual([200], all_times.shape)
+    self.assertAllEqual([200, 5], predicted_values.shape)
+    self.assertGreater(
+        predicted_values[100, 4]
+        - predicted_values[115, 4],  # Amplitude of fifth component
+        0.4)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
index 50c7779243..4025a8f014 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/estimators.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
@@ -36,7 +36,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.training import training as train
 
 
-class _TimeSeriesRegressor(estimator_lib.Estimator):
+class TimeSeriesRegressor(estimator_lib.Estimator):
   """An Estimator to fit and evaluate a time series model."""
 
   def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
@@ -62,7 +62,7 @@ class _TimeSeriesRegressor(estimator_lib.Estimator):
     model_fn = model_utils.make_model_fn(
         model, state_manager, optimizer,
         input_statistics_generator=input_statistics_generator)
-    super(_TimeSeriesRegressor, self).__init__(
+    super(TimeSeriesRegressor, self).__init__(
         model_fn=model_fn,
         model_dir=model_dir,
         config=config)
@@ -145,7 +145,7 @@ class _TimeSeriesRegressor(estimator_lib.Estimator):
     return _serving_input_receiver_fn
 
 
-class ARRegressor(_TimeSeriesRegressor):
+class ARRegressor(TimeSeriesRegressor):
   """An Estimator for an (optionally non-linear) autoregressive model.
 
   ARRegressor is a window-based model, inputting fixed windows of length
@@ -236,12 +236,12 @@ class ARRegressor(_TimeSeriesRegressor):
         config=config)
 
 
-class StateSpaceRegressor(_TimeSeriesRegressor):
+class StateSpaceRegressor(TimeSeriesRegressor):
   """An Estimator for general state space models."""
 
   def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
                config=None):
-    """See _TimeSeriesRegressor. Uses the ChainingStateManager by default."""
+    """See TimeSeriesRegressor. Uses the ChainingStateManager by default."""
     if not isinstance(model, state_space_model.StateSpaceModel):
       raise ValueError(
           "StateSpaceRegressor only supports state space models (children of "
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py
index 016db33e69..e6c60fdf0c 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/model.py
@@ -84,7 +84,6 @@ class TimeSeriesModel(object):
   # TODO(allenl): Move more of the generic machinery for generating and
   # predicting into TimeSeriesModel, and possibly share it between generate()
   # and predict()
-  @abc.abstractmethod
   def generate(self, number_of_series, series_length,
                model_parameters=None, seed=None):
     """Sample synthetic data from model parameters, with optional substitutions.
@@ -109,7 +108,7 @@ class TimeSeriesModel(object):
       (mapping to an array with shape [number_of_series, series_length,
       num_features]).
     """
-    pass
+    raise NotImplementedError("This model does not support generation.")
 
   def initialize_graph(self, input_statistics=None):
     """Define ops for the model, not depending on any previously defined ops.
diff --git a/tensorflow/contrib/timeseries/python/timeseries/test_utils.py b/tensorflow/contrib/timeseries/python/timeseries/test_utils.py
index 72afae14dc..ed02960fae 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/test_utils.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/test_utils.py
@@ -149,7 +149,7 @@ def _train_on_generated_data(
     def tf_random_seed(self):
       return seed
 
-  estimator = estimators._TimeSeriesRegressor(  # pylint: disable=protected-access
+  estimator = estimators.TimeSeriesRegressor(
       model=generative_model,
       config=_RunConfig(),
       state_manager=train_state_manager,
-- 
GitLab


From 1c69ffc73b7bd3f90daaef1fc6f67253d53082ad Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Mon, 17 Jul 2017 15:58:37 -0700
Subject: [PATCH 1052/1375] Add support for cublas<t>geam to cuda_solvers
 kernel helpers.

PiperOrigin-RevId: 162284275
---
 tensorflow/core/kernels/cuda_solvers.cc | 31 +++++++++++++++++++++++++
 tensorflow/core/kernels/cuda_solvers.h  | 21 +++++++++++++----
 2 files changed, 48 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/kernels/cuda_solvers.cc b/tensorflow/core/kernels/cuda_solvers.cc
index 914627992b..358ff7d0e0 100644
--- a/tensorflow/core/kernels/cuda_solvers.cc
+++ b/tensorflow/core/kernels/cuda_solvers.cc
@@ -252,6 +252,37 @@ static inline Status PotrfImpl(BufSizeFnT bufsize, SolverFnT solver,
 
 TF_CALL_LAPACK_TYPES(POTRF_INSTANCE);
 
+template <typename Scalar, typename SolverFnT>
+static inline Status GeamImpl(SolverFnT solver, cublasHandle_t cublas_handle,
+                              cublasOperation_t transa,
+                              cublasOperation_t transb, int m, int n,
+                              const Scalar* alpha, /* host or device pointer */
+                              const Scalar* A, int lda,
+                              const Scalar* beta, /* host or device pointer */
+                              const Scalar* B, int ldb, Scalar* C, int ldc) {
+  using CudaScalar = typename CUDAComplexT<Scalar>::type;
+  TF_RETURN_IF_CUBLAS_ERROR(
+      solver(cublas_handle, transa, transb, m, n, (const CudaScalar*)alpha,
+             (const CudaScalar*)A, lda, (const CudaScalar*)beta,
+             (const CudaScalar*)B, ldb, (CudaScalar*)C, ldc));
+  return Status::OK();
+}
+
+#define GEAM_INSTANCE(Scalar, lapack_prefix)                              \
+  template <>                                                             \
+  Status CudaSolver::Geam<Scalar>(                                        \
+      cublasOperation_t transa, cublasOperation_t transb, int m, int n,   \
+      const Scalar* alpha, /* host or device pointer */                   \
+      const Scalar* A, int lda,                                           \
+      const Scalar* beta, /* host or device pointer */                    \
+      const Scalar* B, int ldb, Scalar* C, int ldc) const {               \
+    return GeamImpl(BLAS_SOLVER_FN(geam, lapack_prefix), cublas_handle_,  \
+                    transa, transb, m, n, alpha, A, lda, beta, B, ldb, C, \
+                    ldc);                                                 \
+  }
+
+TF_CALL_LAPACK_TYPES(GEAM_INSTANCE);
+
 //=============================================================================
 // Wrappers of cuBlas computational methods begin here.
 //
diff --git a/tensorflow/core/kernels/cuda_solvers.h b/tensorflow/core/kernels/cuda_solvers.h
index 5d1c807e66..26a169b93b 100644
--- a/tensorflow/core/kernels/cuda_solvers.h
+++ b/tensorflow/core/kernels/cuda_solvers.h
@@ -116,9 +116,10 @@ class CudaSolver {
   // Launches a memcpy of solver status data specified by dev_lapack_info from
   // device to the host, and asynchronously invokes the given callback when the
   // copy is complete. The first Status argument to the callback will be
-  // Status::OK if all lapack infos retrieved are zero, otherwise an error status
-  // is given. The second argument contains a host-side copy of the entire set
-  // of infos retrieved, and can be used for generating detailed error messages.
+  // Status::OK if all lapack infos retrieved are zero, otherwise an error
+  // status is given. The second argument contains a host-side copy of the
+  // entire set of infos retrieved, and can be used for generating detailed
+  // error messages.
   Status CopyLapackInfoToHostAsync(
       const std::vector<DeviceLapackInfo>& dev_lapack_info,
       std::function<void(const Status&, const std::vector<HostLapackInfo>&)>
@@ -131,8 +132,20 @@ class CudaSolver {
   // to those in cuSolverDN and cuBlas, which follow the naming convention in
   // LAPACK see, e.g., http://docs.nvidia.com/cuda/cusolver/#naming-convention
 
+  // This function performs the matrix-matrix addition/transposition
+  //   C = alpha * op(A) + beta * op(B).
+  // Returns Status::OK() if the kernel was launched successfully.  See:
+  // http://docs.nvidia.com/cuda/cublas/index.html#cublas-lt-t-gt-geam
+  // NOTE(ebrevdo): Does not support in-place transpose of non-square matrices.
+  template <typename Scalar>
+  Status Geam(cublasOperation_t transa, cublasOperation_t transb, int m, int n,
+              const Scalar* alpha, /* host or device pointer */
+              const Scalar* A, int lda,
+              const Scalar* beta, /* host or device pointer */
+              const Scalar* B, int ldb, Scalar* C, int ldc) const;
+
   // Computes the Cholesky factorization A = L * L^T for a single matrix.
-  // Returns Status::OK(), if the kernel was launched successfully. See:
+  // Returns Status::OK() if the kernel was launched successfully. See:
   // http://docs.nvidia.com/cuda/cusolver/#cuds-lt-t-gt-potrf
   template <typename Scalar>
   Status Potrf(cublasFillMode_t uplo, int n, Scalar* dev_A, int lda,
-- 
GitLab


From 23a3e222562cba97b4b03ccf8d4027a91d179051 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 17 Jul 2017 16:38:11 -0700
Subject: [PATCH 1053/1375] GPU registration for resource scatter add

PiperOrigin-RevId: 162289810
---
 tensorflow/core/kernels/resource_variable_ops.cc      | 11 +++++++++++
 .../python/kernel_tests/resource_variable_ops_test.py |  2 +-
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index 0616bb5a08..e731971bea 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -445,6 +445,17 @@ class ResourceScatterUpdateOp : public OpKernel {
 
 TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ARITHEMTIC_CPU);
 
+// Registers GPU kernels.
+#if GOOGLE_CUDA
+#define REGISTER_SCATTER_ARITHEMTIC_GPU(type) \
+  REGISTER_SCATTER_ARITHEMTIC(type, GPU);
+
+#define REGISTER_SCATTER_UPDATE_GPU(type) REGISTER_SCATTER_UPDATE(type, GPU);
+
+TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ARITHEMTIC_GPU);
+
+#endif  // GOOGLE_CUDA
+
 #undef REGISTER_SCATTER_ARITHEMTIC
 #undef REGISTER_SCATTER_ARITHEMTIC_CPU
 #undef REGISTER_SCATTER_KERNEL
diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
index 4cb5e6d208..4dec0e8380 100644
--- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py
+++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
@@ -93,7 +93,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
       self.assertEqual(read.eval(), 2)
 
   def testScatterAdd(self):
-    with self.test_session():
+    with self.test_session(use_gpu=True):
       handle = resource_variable_ops.var_handle_op(
           dtype=dtypes.int32, shape=[1, 1])
       resource_variable_ops.assign_variable_op(
-- 
GitLab


From bdd3189e5ce308cf27abeadd5e98b1bc987d160c Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Mon, 17 Jul 2017 17:35:28 -0700
Subject: [PATCH 1054/1375] Automated g4 rollback of changelist 162273792

PiperOrigin-RevId: 162296574
---
 tensorflow/BUILD                              |   1 -
 tensorflow/contrib/cmake/tf_tests.cmake       |   1 -
 tensorflow/contrib/decision_trees/BUILD       |  37 --
 tensorflow/contrib/tensor_forest/BUILD        |  18 -
 .../kernel_tests/model_ops_kernel_test.py     | 527 ------------------
 tensorflow/tools/pip_package/BUILD            |   1 -
 6 files changed, 585 deletions(-)
 delete mode 100644 tensorflow/contrib/decision_trees/BUILD
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 8b1dfddd2a..779dd1e8ed 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -251,7 +251,6 @@ filegroup(
         "//tensorflow/contrib/data/python/kernel_tests:all_files",
         "//tensorflow/contrib/data/python/ops:all_files",
         "//tensorflow/contrib/data/python/util:all_files",
-        "//tensorflow/contrib/decision_trees:all_files",
         "//tensorflow/contrib/decision_trees/proto:all_files",
         "//tensorflow/contrib/distributions:all_files",
         "//tensorflow/contrib/factorization:all_files",
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 6ad7480965..089967ffec 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -178,7 +178,6 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
     # Loading resources in contrib doesn't seem to work on Windows
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/random_forest_test.py"
-    "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
diff --git a/tensorflow/contrib/decision_trees/BUILD b/tensorflow/contrib/decision_trees/BUILD
deleted file mode 100644
index 6be69a6f94..0000000000
--- a/tensorflow/contrib/decision_trees/BUILD
+++ /dev/null
@@ -1,37 +0,0 @@
-# TensorFlow shared library for training decision trees.
-package(default_visibility = [
-    "//visibility:public",
-])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-filegroup(
-    name = "all_files",
-    srcs = glob(
-        ["*"],
-    ),
-    visibility = ["//tensorflow:__subpackages__"],
-)
-
-py_library(
-    name = "init_py",
-    srcs = [
-        "__init__.py",
-    ],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_py",
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
-    ],
-)
-
-# Pip
-
-py_library(
-    name = "decision_trees_pip",
-    deps = [
-        ":init_py",
-    ],
-)
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 6b20bb48c4..1f5733b853 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -526,24 +526,6 @@ py_test(
     ],
 )
 
-py_test(
-    name = "model_ops_kernel_test",
-    size = "small",
-    srcs = ["python/kernel_tests/model_ops_kernel_test.py"],
-    srcs_version = "PY2AND3",
-    tags = ["no_windows"],
-    deps = [
-        ":model_ops_py",
-        ":tensor_forest_py",
-        ":tensor_forest_v4_py",
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_py",
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-        "//tensorflow/python:resources",
-    ],
-)
-
 py_library(
     name = "tensor_forest_py",
     srcs = ["python/tensor_forest.py"],
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py
deleted file mode 100644
index 296ac7220d..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.tree_predictions_op."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from google.protobuf import descriptor_pool
-from google.protobuf import text_format
-
-from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2
-from tensorflow.contrib.tensor_forest.python import tensor_forest
-from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
-from tensorflow.contrib.tensor_forest.python.ops import data_ops
-from tensorflow.contrib.tensor_forest.python.ops import model_ops
-
-from tensorflow.python.framework import test_util
-from tensorflow.python.ops import resources
-from tensorflow.python.platform import googletest
-
-
-TREE_PROTO_DENSE = """
-decision_tree {
-  nodes {
-    binary_node {
-      left_child_id {
-        value: 1
-      }
-      right_child_id {
-        value: 2
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "0"
-          }
-        }
-        threshold {
-          float_value: 0
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 1
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 80
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 2
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-}
-"""
-
-# TODO(gilberth): This currently has to load inequality_left_child_test
-# in the tree because of a proto parsing error (MatchingValuesTest not
-# found in the descriptor pool).
-TREE_PROTO_SPARSE = """
-decision_tree {
-  nodes {
-    binary_node {
-      left_child_id {
-        value: 1
-      }
-      right_child_id {
-        value: 2
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "1"
-          }
-        }
-        threshold {
-          float_value: 10
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 1
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 80
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 2
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-}
-"""
-
-FEATURE_COUNT_TREE = """
-decision_tree {
-  nodes {
-    binary_node {
-      left_child_id {
-        value: 1
-      }
-      right_child_id {
-        value: 2
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "0"
-          }
-        }
-        threshold {
-          float_value: 0
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 1
-    }
-    binary_node {
-      left_child_id {
-        value: 3
-      }
-      right_child_id {
-        value: 4
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "1"
-          }
-        }
-        threshold {
-          float_value: 0
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 2
-    }
-    binary_node {
-      left_child_id {
-        value: 5
-      }
-      right_child_id {
-        value: 6
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "0"
-          }
-        }
-        threshold {
-          float_value: 10
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 3
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 80
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 4
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 5
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 6
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-}
-"""
-
-
-def get_v4_params(num_classes, num_features, regression):
-  params = tensor_forest.ForestHParams()
-  params.num_classes = num_classes
-  params.num_features = num_features
-  params.regression = regression
-  v4_params = tensor_forest_v4.V4ForestHParams(params)
-  v4_params.params_proto = tensor_forest_v4.build_params_proto(v4_params)
-  v4_params.serialized_params_proto = (
-      v4_params.params_proto.SerializeToString())
-  return v4_params
-
-
-def get_dense_data_spec():
-  spec_proto = data_ops.TensorForestDataSpec()
-  f1 = spec_proto.dense.add()
-  f1.name = 'f1'
-  f1.original_type = data_ops.DATA_FLOAT
-  f1.size = 1
-
-  f2 = spec_proto.dense.add()
-  f2.name = 'f2'
-  f2.original_type = data_ops.DATA_FLOAT
-  f2.size = 1
-  spec_proto.dense_features_size = 2
-  return spec_proto.SerializeToString()
-
-
-def get_sparse_data_spec():
-  spec_proto = data_ops.TensorForestDataSpec()
-  f1 = spec_proto.dense.add()
-  f1.name = 'f1'
-  f1.original_type = data_ops.DATA_CATEGORICAL
-  f1.size = 10
-  spec_proto.dense_features_size = 0
-
-  return spec_proto.SerializeToString()
-
-
-class ModelOpsPredictionsTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-
-  def testDense(self):
-    input_data = [[-1., 0.], [-1., 2.],  # node 1
-                  [1., 0.], [1., -2.]]  # node 2
-
-    v4_params = get_v4_params(3, 2, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      leaves = model_ops.traverse_tree_v4(
-          tree,
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
-                           [0.5, 0.25, 0.25], [0.5, 0.25, 0.25]],
-                          predictions.eval())
-
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testSparseInput(self):
-    sparse_shape = [4, 10]
-    sparse_indices = [[0, 0], [0, 1], [0, 9],
-                      [1, 0], [1, 1],
-                      [2, 1],
-                      [3, 6]]
-    sparse_values = [3.0, 5.0, 0.5,
-                     15.0, 10.0,
-                     20.0,
-                     20.0]
-
-    v4_params = get_v4_params(3, 10, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_SPARSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          self.nothing,
-          sparse_indices,
-          sparse_values,
-          sparse_shape,
-          input_spec=get_sparse_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      leaves = model_ops.traverse_tree_v4(
-          tree,
-          self.nothing,
-          sparse_indices,
-          sparse_values,
-          sparse_shape,
-          input_spec=get_sparse_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
-                           [0.5, 0.25, 0.25], [0.1, 0.1, 0.8]],
-                          predictions.eval())
-
-      self.assertAllEqual([1, 1, 2, 1], leaves.eval())
-
-  def testNoInput(self):
-    v4_params = get_v4_params(3, 4, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertEqual((0, 3), predictions.eval().shape)
-
-
-class ModelOpsUpdateTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-
-  def testBasic(self):
-    input_labels = [0, 1, 2, 1]
-    leaves = [1, 2, 1, 2]
-    weights = [10, 10, 10, 10]
-
-    # For checking the resulting leaves after update.
-    input_data = [[-1., 0.],  # node 1
-                  [1., 0.]]   # node 2
-
-    v4_params = get_v4_params(3, 2, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      model_ops.update_model_v4(
-          tree,
-          leaves,
-          input_labels,
-          weights,
-          params=v4_params.serialized_params_proto).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllClose([[0.16666, 0.08333, 0.75],
-                           [0.416666, 0.375, 0.208333]],
-                          predictions.eval(),
-                          rtol=0.0001, atol=0.0001)
-
-
-class ModelOpsFeatureCountsTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-
-  def testBasic(self):
-    v4_params = get_v4_params(3, 4, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(FEATURE_COUNT_TREE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      feature_counts = model_ops.feature_usage_counts(
-          tree,
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllEqual([2, 1, 0, 0], feature_counts.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index faee0f5386..f82640faf4 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -146,7 +146,6 @@ sh_binary(
             "//tensorflow:tensorflow_py",
             "//tensorflow/contrib/boosted_trees:boosted_trees_pip",
             "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip",
-            "//tensorflow/contrib/decision_trees:decision_trees_pip",
             "//tensorflow/contrib/graph_editor:graph_editor_pip",
             "//tensorflow/contrib/keras:keras",
             "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip",
-- 
GitLab


From 28d9223a55d3c02e91781e02ff8b3f6a31bdd66a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 17 Jul 2017 19:37:12 -0700
Subject: [PATCH 1055/1375] [XLA:CPU] Vectorize reduction operations

This change teaches XLA to generate vectorized code sequences for reduction
operations.  This is still a work in progress, but I wanted to get it out for
discussion for early feedback.

PiperOrigin-RevId: 162305323
---
 .../compiler/xla/client/lib/arithmetic.cc     |   8 +
 .../compiler/xla/client/lib/arithmetic.h      |   4 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   8 +-
 .../compiler/xla/service/cpu/ir_emitter.cc    | 488 +++++++++++++++++-
 .../compiler/xla/service/cpu/ir_emitter.h     | 113 +++-
 .../compiler/xla/service/cpu/simple_orc_jit.h |   2 +
 .../compiler/xla/service/llvm_ir/ir_array.cc  |  21 +-
 .../compiler/xla/service/llvm_ir/ir_array.h   |   4 +
 .../compiler/xla/service/llvm_ir/llvm_loop.cc |  20 +-
 .../compiler/xla/service/llvm_ir/llvm_loop.h  |  10 +
 tensorflow/compiler/xla/tests/reduce_test.cc  | 148 ++++++
 11 files changed, 810 insertions(+), 16 deletions(-)

diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc
index 3ad769a040..969b0eee1d 100644
--- a/tensorflow/compiler/xla/client/lib/arithmetic.cc
+++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc
@@ -57,6 +57,14 @@ Computation CreateScalarAddComputation(PrimitiveType type,
          const ComputationDataHandle& rhs) { return b->Add(lhs, rhs); });
 }
 
+Computation CreateScalarMultiplyComputation(PrimitiveType type,
+                                            ComputationBuilder* builder) {
+  return CreateScalarComputation(
+      "add", type, builder,
+      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
+         const ComputationDataHandle& rhs) { return b->Mul(lhs, rhs); });
+}
+
 Computation CreateScalarGeComputation(PrimitiveType type,
                                       ComputationBuilder* builder) {
   return CreateScalarComputation(
diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h
index 633086a2e7..f43d35fe4a 100644
--- a/tensorflow/compiler/xla/client/lib/arithmetic.h
+++ b/tensorflow/compiler/xla/client/lib/arithmetic.h
@@ -28,6 +28,10 @@ namespace xla {
 Computation CreateScalarAddComputation(PrimitiveType type,
                                        ComputationBuilder* builder);
 
+// Creates a scalar multiply computation and returns it.
+Computation CreateScalarMultiplyComputation(PrimitiveType type,
+                                            ComputationBuilder* builder);
+
 // Creates a scalar ge computation and returns it.
 Computation CreateScalarGeComputation(PrimitiveType type,
                                       ComputationBuilder* builder);
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 46e45b0bbe..6d819355c4 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -169,7 +169,7 @@ void InitializeLLVMCommandLineOptions(const HloModuleConfig& config) {
     fake_argv_storage.push_back("");
     for (const auto& it : options) {
       // Skip options the XLA backend itself consumes.
-      if (it.first != kXlaParallelCpuOption) {
+      if (!tensorflow::StringPiece(it.first).starts_with("xla_")) {
         if (it.second.empty()) {
           fake_argv_storage.push_back(it.first);
         } else {
@@ -495,7 +495,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     }
 
     IrEmitter ir_emitter(*module, *assignment, llvm_module.get(),
-                         &hlo_to_profile_idx);
+                         &hlo_to_profile_idx, jit->target_machine());
 
     std::unique_ptr<std::map<HloInstruction*, string>> function_names(
         new std::map<HloInstruction*, string>());
@@ -569,7 +569,7 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
     // GetEmbeddedComputations guarantees that a called computation occurs
     // before a caller computation.
     IrEmitter ir_emitter(*module, *assignment, llvm_module.get(),
-                         &hlo_to_profile_idx);
+                         &hlo_to_profile_idx, jit->target_machine());
 
     for (auto embedded_computation :
          computation->MakeEmbeddedComputationsList()) {
@@ -733,7 +733,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
     }
 
     IrEmitter ir_emitter(*module, *assignment, &llvm_module,
-                         /*hlo_to_profile_idx=*/nullptr);
+                         /*hlo_to_profile_idx=*/nullptr, target_machine.get());
     HloComputation* computation = module->entry_computation();
     for (auto embedded_computation :
          computation->MakeEmbeddedComputationsList()) {
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 9db1614cd7..674697a97c 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -32,6 +32,8 @@ limitations under the License.
 #include "external/llvm/include/llvm/IR/Instructions.h"
 #include "external/llvm/include/llvm/IR/Intrinsics.h"
 #include "external/llvm/include/llvm/IR/LLVMContext.h"
+#include "external/llvm/include/llvm/Target/TargetRegisterInfo.h"
+#include "external/llvm/include/llvm/Target/TargetSubtargetInfo.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
@@ -51,11 +53,20 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/window_util.h"
+#include "tensorflow/core/lib/core/bits.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 
+namespace {
+const char* kXlaDisableVectorizedReduce = "xla_disable_vectorized_reduce";
+bool VectorizedReduceDisabled(const xla::HloModuleConfig& config) {
+  return config.debug_options().xla_backend_extra_options().count(
+      kXlaDisableVectorizedReduce);
+}
+}  // namespace
+
 namespace xla {
 
 using llvm_ir::SetToFirstInsertPoint;
@@ -65,14 +76,16 @@ namespace cpu {
 IrEmitter::IrEmitter(
     const HloModule& hlo_module, const BufferAssignment& assignment,
     llvm::Module* llvm_module,
-    const std::unordered_map<const HloInstruction*, size_t>* hlo_to_profile_idx)
+    const std::unordered_map<const HloInstruction*, size_t>* hlo_to_profile_idx,
+    llvm::TargetMachine* target_machine)
     : assignment_(assignment),
       module_(llvm_module),
       arch_type_(llvm::Triple(llvm_module->getTargetTriple()).getArch()),
       ir_builder_(llvm_module->getContext()),
       hlo_to_profile_idx_(hlo_to_profile_idx),
       alias_analysis_(hlo_module, assignment, &llvm_module->getContext()),
-      hlo_module_config_(hlo_module.config()) {
+      hlo_module_config_(hlo_module.config()),
+      target_machine_features_(target_machine) {
   ir_builder_.setFastMathFlags(llvm_ir::GetFastMathFlags(
       /*fast_math_enabled=*/hlo_module_config_.debug_options()
           .xla_enable_fast_math()));
@@ -1417,10 +1430,450 @@ Status IrEmitter::HandleParameter(HloInstruction* parameter) {
   return Status::OK();
 }
 
+IrEmitter::ReductionGenerator IrEmitter::MatchReductionGenerator(
+    HloComputation* function, string* failure_reason) const {
+  CHECK_EQ(function->num_parameters(), 2);
+
+  auto root_instruction = function->root_instruction();
+  CHECK(ShapeUtil::IsScalar(root_instruction->shape()));
+
+  if (root_instruction->operand_count() != 2) {
+    *failure_reason = "root instruction is not a binary operation";
+    return nullptr;
+  }
+
+  const Shape& root_shape = root_instruction->shape();
+  bool root_is_floating_point = ShapeUtil::ElementIsFloating(root_shape);
+  bool root_is_integral = ShapeUtil::ElementIsIntegral(root_shape);
+  bool root_is_signed = ShapeUtil::ElementIsSigned(root_shape);
+
+  auto lhs = root_instruction->operand(0);
+  auto rhs = root_instruction->operand(1);
+
+  auto param_0 = function->parameter_instruction(0);
+  auto param_1 = function->parameter_instruction(1);
+  if (!(lhs == param_0 && rhs == param_1) &&
+      !(rhs == param_0 && lhs == param_1)) {
+    *failure_reason =
+        "root instruction is not a binary operation on the incoming arguments";
+    return nullptr;
+  }
+
+  CHECK(ShapeUtil::IsScalar(lhs->shape()) && ShapeUtil::IsScalar(rhs->shape()));
+
+  // This is visually similar to ElementalIrEmitter, though conceptually we're
+  // doing something different here.  ElementalIrEmitter emits scalar operations
+  // while these emit scalar or vector operations depending on the type of the
+  // operands.
+  switch (root_instruction->opcode()) {
+    default:
+      *failure_reason = "did not recognize root instruction opcode";
+      return nullptr;
+
+    case HloOpcode::kAdd:
+      return [root_is_integral](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs,
+                                llvm::Value* rhs) {
+        return root_is_integral ? ir_builder->CreateAdd(lhs, rhs)
+                                : ir_builder->CreateFAdd(lhs, rhs);
+      };
+
+    case HloOpcode::kMultiply:
+      return [root_is_integral](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs,
+                                llvm::Value* rhs) {
+        return root_is_integral ? ir_builder->CreateMul(lhs, rhs)
+                                : ir_builder->CreateFMul(lhs, rhs);
+      };
+
+    case HloOpcode::kLogicalAnd:
+      return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs,
+                llvm::Value* rhs) { return ir_builder->CreateAnd(lhs, rhs); };
+
+    case HloOpcode::kLogicalOr:
+      return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs,
+                llvm::Value* rhs) { return ir_builder->CreateOr(lhs, rhs); };
+
+    case HloOpcode::kMaximum:
+      return [root_is_floating_point, root_is_signed](
+                 llvm::IRBuilder<>* ir_builder, llvm::Value* lhs,
+                 llvm::Value* rhs) {
+        if (root_is_floating_point) {
+          return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::maxnum,
+                                              {lhs, rhs}, {lhs->getType()},
+                                              ir_builder);
+        }
+
+        return ir_builder->CreateSelect(
+            ir_builder->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SGE
+                                                  : llvm::ICmpInst::ICMP_UGE,
+                                   lhs, rhs),
+            lhs, rhs);
+      };
+
+    case HloOpcode::kMinimum:
+      return [root_is_floating_point, root_is_signed](
+                 llvm::IRBuilder<>* ir_builder, llvm::Value* lhs,
+                 llvm::Value* rhs) {
+        if (root_is_floating_point) {
+          return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::minnum,
+                                              {lhs, rhs}, {lhs->getType()},
+                                              ir_builder);
+        }
+
+        return ir_builder->CreateSelect(
+            ir_builder->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SLE
+                                                  : llvm::ICmpInst::ICMP_ULE,
+                                   lhs, rhs),
+            lhs, rhs);
+      };
+  }
+}
+
+IrEmitter::ShardedVectorType IrEmitter::CreateShardedVectorType(
+    PrimitiveType element_type, unsigned element_count) {
+  // Here we assume that the largest register is a vector register.
+  int max_vector_register_size_in_bytes =
+      target_machine_features_.largest_register_size_in_bytes(
+          compute_function_);
+
+  int vector_register_size_in_elements =
+      max_vector_register_size_in_bytes /
+      ShapeUtil::ByteSizeOfPrimitiveType(element_type);
+
+  ShardedVectorType sharded_vector_type;
+  llvm::Type* element_ir_type =
+      llvm_ir::PrimitiveTypeToIrType(element_type, &ir_builder_);
+
+  for (int i = 0, e = 1 + tensorflow::Log2Ceiling(element_count); i < e; i++) {
+    // For every power of two present in element_count, we generate one or more
+    // vector or scalar types.
+    const unsigned current_size_fragment = 1u << i;
+    if (!(element_count & current_size_fragment)) {
+      // Power of two not present in element_count.
+      continue;
+    }
+
+    if (current_size_fragment == 1) {
+      // Single element, use a scalar type.
+      sharded_vector_type.push_back(element_ir_type);
+      continue;
+    }
+
+    // Lower "current_size_fragment" number of elements using (as few as
+    // possible) vector registers.
+
+    if (current_size_fragment >= vector_register_size_in_elements) {
+      auto vector_type = llvm::VectorType::get(
+          element_ir_type, vector_register_size_in_elements);
+      sharded_vector_type.insert(
+          sharded_vector_type.end(),
+          current_size_fragment / vector_register_size_in_elements,
+          vector_type);
+
+      // Both current_size_fragment and vector_register_size_in_elements are
+      // powers of two.
+      CHECK_EQ(current_size_fragment % vector_register_size_in_elements, 0);
+      continue;
+    }
+
+    // For now we assume that vector_register_size_in_elements and lower powers
+    // of two are all legal vector sizes (or at least can be lowered easily by
+    // LLVM).
+    sharded_vector_type.push_back(
+        llvm::VectorType::get(element_ir_type, current_size_fragment));
+  }
+  return sharded_vector_type;
+}
+
+StatusOr<IrEmitter::ShardedVector>
+IrEmitter::EmitInnerLoopForVectorizedReduction(
+    const ReductionGenerator& reduction_generator,
+    const llvm_ir::IrArray::Index& output_index,
+    const ShardedVectorType& accumulator_type, HloInstruction* init_value,
+    HloInstruction* arg, tensorflow::gtl::ArraySlice<int64> dimensions,
+    unsigned element_alignment) {
+  ShardedVector accumulator;
+  accumulator.reserve(accumulator_type.size());
+  for (auto accumulator_shard_type : accumulator_type) {
+    accumulator.push_back(llvm_ir::EmitAllocaAtFunctionEntry(
+        accumulator_shard_type, "accumulator", &ir_builder_, 0));
+  }
+
+  llvm::Value* init_value_ssa =
+      ir_builder_.CreateLoad(GetEmittedValueFor(init_value));
+
+  for (llvm::Value* accumulator_shard : accumulator) {
+    llvm::Value* initial_value;
+    auto shard_type = accumulator_shard->getType()->getPointerElementType();
+    if (auto vector_type = llvm::dyn_cast<llvm::VectorType>(shard_type)) {
+      initial_value = ir_builder_.CreateVectorSplat(
+          vector_type->getNumElements(), init_value_ssa);
+    } else {
+      initial_value = init_value_ssa;
+    }
+
+    ir_builder_.CreateAlignedStore(initial_value, accumulator_shard,
+                                   element_alignment);
+  }
+
+  llvm_ir::ForLoopNest reduction_loop_nest(&ir_builder_);
+  llvm_ir::IrArray::Index reduced_dims_index =
+      reduction_loop_nest.AddLoopsForShapeOnDimensions(arg->shape(), dimensions,
+                                                       "reduction_dim");
+
+  SetToFirstInsertPoint(reduction_loop_nest.GetInnerLoopBodyBasicBlock(),
+                        &ir_builder_);
+
+  llvm_ir::IrArray arg_array(GetIrArrayForOp(arg));
+  llvm_ir::IrArray::Index input_index = reduced_dims_index;
+  llvm_ir::IrArray::Index::const_iterator it = output_index.begin();
+
+  for (size_t i = 0; i < input_index.size(); ++i) {
+    if (input_index[i] == nullptr) {
+      input_index[i] = *it++;
+    }
+  }
+  CHECK(output_index.end() == it);
+
+  llvm::Value* input_address = ir_builder_.CreateBitCast(
+      arg_array.EmitArrayElementAddress(input_index, &ir_builder_),
+      ir_builder_.getInt8PtrTy());
+
+  for (int i = 0; i < accumulator.size(); i++) {
+    auto input_address_typed =
+        ir_builder_.CreateBitCast(input_address, accumulator[i]->getType());
+    auto current_accumulator_value =
+        ir_builder_.CreateAlignedLoad(accumulator[i], element_alignment);
+    auto addend =
+        ir_builder_.CreateAlignedLoad(input_address_typed, element_alignment);
+    arg_array.AnnotateLoadStoreInstructionWithMetadata(addend);
+
+    auto reduced_result =
+        reduction_generator(&ir_builder_, current_accumulator_value, addend);
+    ir_builder_.CreateAlignedStore(reduced_result, accumulator[i],
+                                   element_alignment);
+
+    if (i != (accumulator.size() - 1)) {
+      input_address = ir_builder_.CreateConstInBoundsGEP1_32(
+          reduced_result->getType(), input_address_typed, 1);
+    }
+  }
+
+  SetToFirstInsertPoint(reduction_loop_nest.GetOuterLoopExitBasicBlock(),
+                        &ir_builder_);
+
+  ShardedVector result_ssa;
+  result_ssa.reserve(accumulator.size());
+  for (auto accumulator_shard : accumulator) {
+    result_ssa.push_back(
+        ir_builder_.CreateAlignedLoad(accumulator_shard, element_alignment));
+  }
+  return result_ssa;
+}
+
+void IrEmitter::EmitShardedVectorStore(
+    llvm::Value* store_address, const std::vector<llvm::Value*>& value_to_store,
+    const int alignment, const llvm_ir::IrArray& containing_array) {
+  for (int i = 0; i < value_to_store.size(); i++) {
+    auto store_address_typed = ir_builder_.CreateBitCast(
+        store_address,
+        llvm::PointerType::getUnqual(value_to_store[i]->getType()));
+
+    auto store_instruction = ir_builder_.CreateAlignedStore(
+        value_to_store[i], store_address_typed, alignment);
+    containing_array.AnnotateLoadStoreInstructionWithMetadata(
+        store_instruction);
+
+    if (i != (value_to_store.size() - 1)) {
+      store_address = ir_builder_.CreateConstInBoundsGEP1_32(
+          value_to_store[i]->getType(), store_address_typed, 1);
+    }
+  }
+}
+
+namespace {
+// TODO(sanjoy): This is duplicated in tensorflow/core/lib/core/arena.cc.
+// Extract out a common implementation to tensorflow/core/lib/math/math_util.h
+uint32 GCD(uint32 x, uint32 y) {
+  while (y != 0) {
+    uint32 r = x % y;
+    x = y;
+    y = r;
+  }
+  return x;
+}
+}  // namespace
+
+StatusOr<bool> IrEmitter::EmitVectorizedReduce(
+    HloInstruction* reduce, HloInstruction* arg, HloInstruction* init_value,
+    tensorflow::gtl::ArraySlice<int64> dimensions, HloComputation* function,
+    string* failure_reason) {
+  ReductionGenerator reduction_generator =
+      MatchReductionGenerator(function, failure_reason);
+  if (!reduction_generator) {
+    return false;
+  }
+
+  int vectorization_factor_in_bytes =
+      target_machine_features_.vectorization_factor_in_bytes();
+
+  // We try to process vectorization_factor elements at the same time.
+  const int vectorization_factor =
+      vectorization_factor_in_bytes /
+      ShapeUtil::ByteSizeOfPrimitiveType(reduce->shape().element_type());
+
+  bool is_reduction_over_minor_dimension =
+      std::find(dimensions.begin(), dimensions.end(),
+                arg->shape().layout().minor_to_major(0)) != dimensions.end();
+
+  unsigned element_alignment =
+      GCD(ShapeUtil::ByteSizeOfPrimitiveType(reduce->shape().element_type()),
+          MinimumAlignmentForPrimitiveType(reduce->shape().element_type()));
+
+  if (is_reduction_over_minor_dimension) {
+    // TODO(sanjoy): Implement vectorized reduction over the minor dimension.
+    *failure_reason = "reduction over minor dimension not implemented";
+    return false;
+  }
+
+  CHECK(!ShapeUtil::IsTuple(reduce->shape()));
+
+  // We know we're not reducing over the most minor dimension, which means we
+  // can lower the reduction loop as:
+  //
+  //  1. We're reducing over dimensions R0, R1.
+  //  2. D0 is the most minor dimension.
+  //  3. VS is the vectorization stride (we want to reduce this many elements at
+  //     once)
+  //
+  //  for (d1 in D1) {
+  //    for (d0 in D0 with stride VS) {
+  //      vector_acc = init
+  //      for (r1 in R1) {
+  //        for (r0 in R0) {
+  //          vector_acc = elementwise_reduce(vector_acc, input[d1, d0, r1, r0]
+  //        }
+  //      }
+  //      output[d1, d0] = vector_acc
+  //    }
+  //  }
+
+  llvm_ir::ForLoopNest loop_nest(&ir_builder_);
+  llvm_ir::IrArray::Index array_index(reduce->shape().dimensions_size());
+  for (int i = reduce->shape().layout().minor_to_major_size() - 1; i > 0; --i) {
+    int64 dimension = reduce->shape().layout().minor_to_major(i);
+    int64 start_index = 0;
+    int64 end_index = reduce->shape().dimensions(dimension);
+    std::unique_ptr<llvm_ir::ForLoop> loop =
+        loop_nest.AddLoop(start_index, end_index,
+                          tensorflow::strings::Printf("dim.%lld", dimension));
+    array_index[dimension] = loop->GetIndVarValue();
+  }
+
+  int64 innermost_dimension = reduce->shape().layout().minor_to_major(0);
+  int64 innermost_dimension_size =
+      reduce->shape().dimensions(innermost_dimension);
+
+  if (llvm::BasicBlock* innermost_body_bb =
+          loop_nest.GetInnerLoopBodyBasicBlock()) {
+    SetToFirstInsertPoint(innermost_body_bb, &ir_builder_);
+  }
+
+  auto outermost_loop_exit_block = loop_nest.GetOuterLoopExitBasicBlock();
+
+  if (innermost_dimension_size >= vectorization_factor) {
+    int64 start_index = 0;
+    int64 end_index = (innermost_dimension_size / vectorization_factor) *
+                      vectorization_factor;
+    std::unique_ptr<llvm_ir::ForLoop> loop = loop_nest.AddLoop(
+        start_index, end_index, vectorization_factor,
+        tensorflow::strings::Printf("dim.%lld", innermost_dimension));
+    array_index[innermost_dimension] = loop->GetIndVarValue();
+
+    SetToFirstInsertPoint(loop->GetBodyBasicBlock(), &ir_builder_);
+
+    ShardedVectorType vector_type = CreateShardedVectorType(
+        reduce->shape().element_type(), vectorization_factor);
+    TF_ASSIGN_OR_RETURN(std::vector<llvm::Value*> accumulator,
+                        EmitInnerLoopForVectorizedReduction(
+                            reduction_generator, array_index, vector_type,
+                            init_value, arg, dimensions, element_alignment));
+
+    TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                        EmitTargetAddressForOp(reduce));
+    llvm_ir::IrArray target_array(target_address, reduce->shape());
+    AddAliasingInformationToIrArray(*reduce, &target_array);
+    llvm::Value* output_address =
+        target_array.EmitArrayElementAddress(array_index, &ir_builder_);
+    EmitShardedVectorStore(output_address, accumulator, element_alignment,
+                           target_array);
+
+    if (auto exit_terminator = loop->GetExitBasicBlock()->getTerminator()) {
+      CHECK_GT(reduce->shape().layout().minor_to_major_size(), 1);
+      ir_builder_.SetInsertPoint(exit_terminator);
+    } else {
+      CHECK_EQ(reduce->shape().layout().minor_to_major_size(), 1);
+      ir_builder_.SetInsertPoint(loop->GetExitBasicBlock());
+    }
+  }
+
+  // Since we increment the stride for the inner dimension by more than 1, we
+  // may need to peel out an "epilogue" iteration to get the remaining elements
+  // in the following case:
+  if (innermost_dimension_size % vectorization_factor) {
+    // TODO(b/63775531): Consider using a scalar loop here to save on code size.
+    array_index[innermost_dimension] =
+        ir_builder_.getInt64(innermost_dimension_size -
+                             (innermost_dimension_size % vectorization_factor));
+
+    ShardedVectorType vector_type = CreateShardedVectorType(
+        reduce->shape().element_type(),
+        innermost_dimension_size % vectorization_factor);
+    TF_ASSIGN_OR_RETURN(std::vector<llvm::Value*> accumulator,
+                        EmitInnerLoopForVectorizedReduction(
+                            reduction_generator, array_index, vector_type,
+                            init_value, arg, dimensions, element_alignment));
+
+    TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                        EmitTargetAddressForOp(reduce));
+    llvm_ir::IrArray target_array(target_address, reduce->shape());
+    AddAliasingInformationToIrArray(*reduce, &target_array);
+    llvm::Value* output_address =
+        target_array.EmitArrayElementAddress(array_index, &ir_builder_);
+    EmitShardedVectorStore(output_address, accumulator, element_alignment,
+                           target_array);
+  }
+
+  if (outermost_loop_exit_block) {
+    ir_builder_.SetInsertPoint(outermost_loop_exit_block);
+  }
+
+  TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                      EmitTargetAddressForOp(reduce));
+
+  emitted_value_[reduce] = target_address;
+  return true;
+}
+
 Status IrEmitter::HandleReduce(HloInstruction* reduce, HloInstruction* arg,
                                HloInstruction* init_value,
                                tensorflow::gtl::ArraySlice<int64> dimensions,
                                HloComputation* function) {
+  if (!VectorizedReduceDisabled(hlo_module_config_)) {
+    string vectorization_failure_reason;
+    TF_ASSIGN_OR_RETURN(
+        bool vectorization_successful,
+        EmitVectorizedReduce(reduce, arg, init_value, dimensions, function,
+                             &vectorization_failure_reason));
+    if (vectorization_successful) {
+      VLOG(1) << "Successfully vectorized reduction " << reduce->ToString()
+              << "\n";
+      return Status::OK();
+    } else {
+      VLOG(1) << "Could not vectorize reduction " << reduce->ToString() << ": "
+              << vectorization_failure_reason;
+    }
+  }
+
   // The called computation should have been emitted previously.
   llvm::Function* reducer_function = FindOrDie(emitted_functions_, function);
   return EmitTargetElementLoop(
@@ -2274,5 +2727,36 @@ Status IrEmitter::DefaultAction(HloInstruction* hlo) {
       hlo, elemental_emitter.MakeElementGenerator(hlo, operand_to_generator));
 }
 
+unsigned TargetMachineFeatures::largest_register_size_in_bytes(
+    llvm::Function* function) {
+  auto itr = largest_register_size_in_bytes_.find(function);
+  if (itr != largest_register_size_in_bytes_.end()) {
+    return itr->second;
+  }
+
+  int result = largest_register_size_in_bytes_impl(function);
+
+  InsertOrDie(&largest_register_size_in_bytes_, function, result);
+  DCHECK_EQ(result, largest_register_size_in_bytes_.begin()->second);
+  return result;
+}
+
+unsigned TargetMachineFeatures::largest_register_size_in_bytes_impl(
+    llvm::Function* function) const {
+  auto register_info =
+      target_machine_->getSubtargetImpl(*function)->getRegisterInfo();
+
+  unsigned largest_register_size = 0;
+  for (const llvm::TargetRegisterClass* register_class :
+       register_info->regclasses()) {
+    if (register_class->isAllocatable()) {
+      largest_register_size =
+          std::max(largest_register_size,
+                   register_info->getRegSizeInBits(*register_class));
+    }
+  }
+
+  return largest_register_size / 8;
+}
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 1d9c5a6dd7..4d98528284 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -41,12 +41,55 @@ limitations under the License.
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace xla {
 namespace cpu {
 
+// Wraps an llvm::TargetMachine and parses out some information that feeds into
+// code LLVM IR generation decisions.
+//
+// Ideally we'd be able to use llvm::TargetTransformInfo here (since its
+// interface is pretty much a perfect fit for our use case), but obtaining an
+// instance of llvm::TargetTransformInfo outside an LLVM pass pipeline without
+// super-ugly hacks is difficult.
+//
+// TODO(b/27457097): See if the LLVM community will be receptive to exposing an
+// API that lets us directly create and use llvm::TargetTransformInfo instances
+// outside of a pass manager.
+class TargetMachineFeatures {
+ public:
+  TargetMachineFeatures(llvm::TargetMachine* target_machine)
+      : target_machine_(target_machine) {}
+
+  // Return the vectorization factor, which is the number of bytes of data
+  // explicitly vectorized routines will try to process at once.
+  int vectorization_factor_in_bytes() const {
+    // Ideally this should be a function of the cache line size (which we can
+    // get from llvm::TargetTransformInfo::getCacheLineSize) of the target
+    // machine.  Guess a value of 128 bytes for now.
+    return 128;
+  }
+
+  // Return the size of the largest register size in bytes.  We need to pass in
+  // "function" since llvm functions can contain annotations for specializing
+  // them to specific micro-architectures (though currently XLA does not use
+  // this functionality).
+  //
+  // Ideally we should have been able to use
+  // llvm::TargetTransformInfo::getRegisterBitWidth(true) here.
+  unsigned largest_register_size_in_bytes(llvm::Function* function);
+
+ private:
+  unsigned largest_register_size_in_bytes_impl(llvm::Function* function) const;
+
+  tensorflow::gtl::FlatMap<llvm::Function*, int>
+      largest_register_size_in_bytes_;
+  llvm::TargetMachine* target_machine_;
+};
+
 // This class is the top-level API for the XLA HLO --> LLVM IR compiler.  It
 // implements the DfsHloVisitor interface and emits HLO computations as LLVM IR
 // functions.
@@ -63,7 +106,8 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   IrEmitter(const HloModule& hlo_module, const BufferAssignment& assignment,
             llvm::Module* llvm_module,
             const std::unordered_map<const HloInstruction*, size_t>*
-                hlo_to_profile_idx);
+                hlo_to_profile_idx,
+            llvm::TargetMachine* target_machine);
   ~IrEmitter() override;
 
   // Emit and return the given HLO computation as an LLVM IR
@@ -298,6 +342,71 @@ class IrEmitter : public DfsHloVisitorWithDefault {
       const std::vector<llvm::Constant*>& array_elements, const Shape& shape,
       int64 dimension_index);
 
+  // Tries to codegen a reduction operation using vectorized instructions.
+  // Returns true if successful, and false on failure.  On failure, sets
+  // "failure_reason" to a string describing why it could not vectorize the
+  // reduction.
+  //
+  // TODO(sanjoy): Some of the things we do here can be abstracted out into
+  // concepts that generalize over other vectorizable operations.  We should
+  // consider pulling out these abstractions into a VectorizingIrEmitter or
+  // something similar.
+  StatusOr<bool> EmitVectorizedReduce(
+      HloInstruction* reduce, HloInstruction* arg, HloInstruction* init_value,
+      tensorflow::gtl::ArraySlice<int64> dimensions, HloComputation* function,
+      string* failure_reason);
+
+  // We'd like to keep one or two one cache-line's worth of data in registers
+  // without generating IR with illegal (e.g. excessively large or
+  // non-power-of-two) vector types.  We do this by introducing a layer of
+  // abstraction: we introduce a high level vector-like concept called a
+  // "sharded vector" that models data paralleism, and is mapped to a sequence
+  // scalar and vector llvm::Value s.
+  //
+  // For example, we can represent 29 f32 elements by a sharded vector mapped to
+  // a sequence of LLVM values of types [<16 x f32>, <8 x f32>, <4 x f32>, f32].
+  // Note that the last element is scalar.
+  //
+  // There is no requirement on the ordering or the uniqueness of the elements
+  // mapped to sharded vectors -- we allow repeated elements, and we allow
+  // elements to appear in any order.
+  using ShardedVector = std::vector<llvm::Value*>;
+
+  // A sharded vector type is the element-wise llvm::Type's of some
+  // ShardedVector.
+  using ShardedVectorType = std::vector<llvm::Type*>;
+
+  // Create a sharded vector type corresponding to a "element_count" long
+  // sequence of "element_type" values.
+  ShardedVectorType CreateShardedVectorType(PrimitiveType element_type,
+                                            unsigned element_count);
+
+  // Emit LLVM IR to store the sharded vector "value_to_store" to
+  // "store_address".
+  void EmitShardedVectorStore(llvm::Value* store_address,
+                              const ShardedVector& value_to_store,
+                              const int alignment,
+                              const llvm_ir::IrArray& containing_array);
+
+  using ReductionGenerator = std ::function<llvm::Value*(
+      llvm::IRBuilder<>*, llvm::Value*, llvm::Value*)>;
+
+  // Tries to match the reduction function "function" to a known reduction
+  // pattern.  Returns a non-null ReductionGenerator on a successful match,
+  // which can be used to generate the LLVM IR corresponding to said reduction.
+  // On failure, this stores a reason string into "failure_reason".
+  ReductionGenerator MatchReductionGenerator(HloComputation* function,
+                                             string* failure_reason) const;
+
+  // Emits the inner loop nest that runs the reduction.  Helper function for
+  // EmitVectorizedReduce.
+  StatusOr<ShardedVector> EmitInnerLoopForVectorizedReduction(
+      const ReductionGenerator& reduction_generator,
+      const llvm_ir::IrArray::Index& output_index,
+      const ShardedVectorType& accumulator_type, HloInstruction* init_value,
+      HloInstruction* arg, tensorflow::gtl::ArraySlice<int64> dimensions,
+      unsigned element_alignment);
+
   // Name of the computation entry function. This function serves as the
   // top-level "main" of the computation and will be invoked by the JIT.
   string entry_function_name_;
@@ -437,6 +546,8 @@ class IrEmitter : public DfsHloVisitorWithDefault {
 
   const HloModuleConfig& hlo_module_config_;
 
+  TargetMachineFeatures target_machine_features_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(IrEmitter);
 };
 
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index 86eae3adfd..f57049c9dd 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -83,6 +83,8 @@ class SimpleOrcJIT {
   // nullptr if the symbol cannot be found.
   llvm::JITSymbol FindSymbol(const std::string& name);
 
+  llvm::TargetMachine* target_machine() const { return target_machine_.get(); }
+
  private:
   std::vector<ModuleHandleT> module_handles_;
   std::unique_ptr<llvm::TargetMachine> target_machine_;
diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index dd13a39232..75b7856800 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -250,6 +250,18 @@ llvm::Value* IrArray::EmitArrayElementAddress(
                                        llvm_ir::AsStringRef(name));
 }
 
+void IrArray::AnnotateLoadStoreInstructionWithMetadata(
+    llvm::Instruction* instruction) const {
+  CHECK(llvm::isa<llvm::LoadInst>(instruction) ||
+        llvm::isa<llvm::StoreInst>(instruction));
+
+  for (const auto& kind_md_pair : metadata_) {
+    CHECK(kind_md_pair.first != llvm::LLVMContext::MD_invariant_load ||
+          llvm::isa<llvm::LoadInst>(instruction));
+    instruction->setMetadata(kind_md_pair.first, kind_md_pair.second);
+  }
+}
+
 llvm::Value* IrArray::EmitReadArrayElement(const Index& index,
                                            llvm::IRBuilder<>* ir_builder,
                                            tensorflow::StringPiece name) const {
@@ -258,9 +270,7 @@ llvm::Value* IrArray::EmitReadArrayElement(const Index& index,
   llvm::LoadInst* load = ir_builder->CreateLoad(element_address);
   llvm_ir::SetTbaaForInstruction(load, GetShape(),
                                  /*is_pointer_to=*/false);
-  for (const auto& kind_md_pair : metadata_) {
-    load->setMetadata(kind_md_pair.first, kind_md_pair.second);
-  }
+  AnnotateLoadStoreInstructionWithMetadata(load);
   return load;
 }
 
@@ -270,10 +280,7 @@ void IrArray::EmitWriteArrayElement(const Index& index, llvm::Value* value,
   llvm::StoreInst* store = ir_builder->CreateStore(value, element_address);
   llvm_ir::SetTbaaForInstruction(store, GetShape(),
                                  /*is_pointer_to=*/false);
-  for (const auto& kind_md_pair : metadata_) {
-    CHECK_NE(kind_md_pair.first, llvm::LLVMContext::MD_invariant_load);
-    store->setMetadata(kind_md_pair.first, kind_md_pair.second);
-  }
+  AnnotateLoadStoreInstructionWithMetadata(store);
 }
 
 IrArray IrArray::CastToShape(const Shape& new_shape,
diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h
index e72f651872..5fabb1e243 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h
@@ -193,6 +193,10 @@ class IrArray {
                                        llvm::IRBuilder<>* ir_builder,
                                        tensorflow::StringPiece name = "") const;
 
+  // Attach metadata this IrArray instance knows about to "instruction".
+  void AnnotateLoadStoreInstructionWithMetadata(
+      llvm::Instruction* instruction) const;
+
   // Emit IR to read an array element at the given index. Returns the read
   // result (effectively, a Value loaded from memory). This method seamlessly
   // handles scalar shapes by broadcasting their value to all indices (index is
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc
index 97f1b8ce30..0995ed6ff5 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc
@@ -144,12 +144,19 @@ llvm::BasicBlock* ForLoop::CreateBasicBlockWithSuffix(
 std::unique_ptr<ForLoop> ForLoopNest::AddLoop(tensorflow::StringPiece suffix,
                                               llvm::Value* start_index,
                                               llvm::Value* end_index) {
+  return AddLoop(suffix, start_index, end_index, ir_builder_->getInt64(1));
+}
+
+std::unique_ptr<ForLoop> ForLoopNest::AddLoop(tensorflow::StringPiece suffix,
+                                              llvm::Value* start_index,
+                                              llvm::Value* end_index,
+                                              llvm::Value* stride) {
   if (inner_loop_body_bb_ != nullptr) {
     // Create this loop inside the previous one.
     ir_builder_->SetInsertPoint(&*inner_loop_body_bb_->getFirstInsertionPt());
   }
-  std::unique_ptr<ForLoop> loop = ForLoop::EmitForLoop(
-      suffix, start_index, end_index, ir_builder_->getInt64(1), ir_builder_);
+  std::unique_ptr<ForLoop> loop =
+      ForLoop::EmitForLoop(suffix, start_index, end_index, stride, ir_builder_);
 
   if (outer_loop_preheader_bb_ == nullptr) {
     outer_loop_preheader_bb_ = loop->GetPreheaderBasicBlock();
@@ -172,6 +179,15 @@ std::unique_ptr<ForLoop> ForLoopNest::AddLoop(int64 start_index,
                  ir_builder_->getInt64(end_index));
 }
 
+std::unique_ptr<ForLoop> ForLoopNest::AddLoop(int64 start_index,
+                                              int64 end_index, int64 stride,
+                                              tensorflow::StringPiece suffix) {
+  CHECK_LE(start_index, end_index);
+  return AddLoop(suffix, ir_builder_->getInt64(start_index),
+                 ir_builder_->getInt64(end_index),
+                 ir_builder_->getInt64(stride));
+}
+
 IrArray::Index ForLoopNest::AddLoopsForShape(const Shape& shape,
                                              tensorflow::StringPiece suffix) {
   std::vector<int64> dimensions(ShapeUtil::Rank(shape));
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h
index 60ac0444bc..a66bf80959 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h
@@ -167,12 +167,22 @@ class ForLoopNest {
   // Adds a loop to the nest. If no loop has been added yet then emit a loop at
   // the current insert point of the given builder. If one or more loops have
   // been added then emit loop inside the body of the last added loop.
+  std::unique_ptr<ForLoop> AddLoop(tensorflow::StringPiece suffix,
+                                   llvm::Value* start_index,
+                                   llvm::Value* end_index, llvm::Value* stride);
+
+  // Like the above, except that it defaults to a stride of one.
   std::unique_ptr<ForLoop> AddLoop(tensorflow::StringPiece suffix,
                                    llvm::Value* start_index,
                                    llvm::Value* end_index);
 
   // A convenient wrapper of the other flavor of AddLoop. The given start and
   // end index are constant.
+  std::unique_ptr<ForLoop> AddLoop(int64 start_index, int64 end_index,
+                                   int64 stride,
+                                   tensorflow::StringPiece suffix);
+
+  // Like the above, except that it defaults to a stride of one.
   std::unique_ptr<ForLoop> AddLoop(int64 start_index, int64 end_index,
                                    tensorflow::StringPiece suffix);
 
diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc
index a81f9f6ff9..b22866fc84 100644
--- a/tensorflow/compiler/xla/tests/reduce_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_test.cc
@@ -202,6 +202,102 @@ class ReduceTest : public ClientLibraryTestBase {
                                ErrorSpec(0.01, 1e-4));
   }
 
+  template <typename NativeT>
+  void ComputeAndCompareGeneric(
+      typename std::enable_if<std::is_floating_point<NativeT>::value,
+                              ComputationBuilder>::type* builder,
+      tensorflow::gtl::ArraySlice<NativeT> expected,
+      tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
+    ComputeAndCompareR1<NativeT>(builder, expected, arguments,
+                                 ErrorSpec(0.01, 1e-4));
+  }
+
+  template <typename NativeT>
+  void ComputeAndCompareGeneric(
+      typename std::enable_if<std::is_integral<NativeT>::value,
+                              ComputationBuilder>::type* builder,
+      tensorflow::gtl::ArraySlice<NativeT> expected,
+      tensorflow::gtl::ArraySlice<GlobalData*> arguments) {
+    ComputeAndCompareR1<NativeT>(builder, expected, arguments);
+  }
+
+  template <typename NativeT>
+  void RunVectorizedReduceTestForType(
+      const std::function<Computation(ComputationBuilder*)>&
+          reduction_function_generator,
+      const std::function<NativeT(NativeT, NativeT)>&
+          reference_reduction_function,
+      const NativeT& initial_value) {
+    const int rows = 64, cols = 128;
+    const int minor = 1, major = 0;
+    ComputationBuilder builder(client_, TestName());
+    Computation reduction_function = reduction_function_generator(&builder);
+    const Shape input_shape = ShapeUtil::MakeShape(
+        xla::primitive_util::NativeToPrimitiveType<NativeT>(), {rows, cols});
+    auto input = builder.Parameter(0, input_shape, "input");
+    auto zero = builder.ConstantR0<NativeT>(initial_value);
+    builder.Reduce(input, zero, reduction_function,
+                   /*dimensions_to_reduce=*/{0});
+
+    Array2D<NativeT> input_data(rows, cols);
+    input_data.FillUnique(initial_value);
+    std::unique_ptr<Literal> input_literal =
+        Literal::CreateR2FromArray2D(input_data);
+    input_literal =
+        input_literal->Relayout(LayoutUtil::MakeLayout({minor, major}));
+    std::unique_ptr<GlobalData> input_global_data =
+        client_->TransferToServer(*input_literal).ConsumeValueOrDie();
+
+    // NativeT can be bool, and std::vector<bool> does not convert to
+    // ArraySlice.
+    std::unique_ptr<NativeT[]> expected(new NativeT[cols]);
+    for (int64 colno = 0; colno < cols; ++colno) {
+      NativeT column_result = initial_value;
+      for (int64 rowno = 0; rowno < rows; ++rowno) {
+        column_result = reference_reduction_function(column_result,
+                                                     input_data(rowno, colno));
+      }
+      expected[colno] = column_result;
+    }
+
+    ComputeAndCompareGeneric<NativeT>(
+        &builder, tensorflow::gtl::ArraySlice<NativeT>(expected.get(), cols),
+        {input_global_data.get()});
+  }
+
+  void RunVectorizedReduceTest(
+      const std::function<Computation(PrimitiveType, ComputationBuilder*)>&
+          reduction_function_generator_for_type,
+      const std::function<float(float, float)>&
+          reference_reduction_function_for_floats,
+      const std::function<int32(int32, int32)>&
+          reference_reduction_function_for_ints,
+      const std::function<uint32(uint32, uint32)>&
+          reference_reduction_function_for_uints,
+      float floating_point_identity, int32 signed_int_identity,
+      uint32 unsigned_int_identity) {
+    // Float version
+    RunVectorizedReduceTestForType<float>(
+        [&](ComputationBuilder* builder) {
+          return reduction_function_generator_for_type(F32, builder);
+        },
+        reference_reduction_function_for_floats, floating_point_identity);
+
+    // Signed int version
+    RunVectorizedReduceTestForType<int32>(
+        [&](ComputationBuilder* builder) {
+          return reduction_function_generator_for_type(S32, builder);
+        },
+        reference_reduction_function_for_ints, signed_int_identity);
+
+    // Unsigned int version
+    RunVectorizedReduceTestForType<uint32>(
+        [&](ComputationBuilder* builder) {
+          return reduction_function_generator_for_type(U32, builder);
+        },
+        reference_reduction_function_for_uints, unsigned_int_identity);
+  }
+
   std::unique_ptr<Literal> literal_2d_;
   std::unique_ptr<Literal> literal_3d_;
   uint32 seed_ = 0xdeadbeef;
@@ -594,6 +690,58 @@ XLA_TEST_F(ReduceTest, ReduceR3AmongDim2) {
   ComputeAndCompareR2<float>(&builder, expected, {}, ErrorSpec(0.0001));
 }
 
+XLA_TEST_F(ReduceTest, VectorizedReduce_Add) {
+  RunVectorizedReduceTest(CreateScalarAddComputation,
+                          [](float a, float b) { return a + b; },
+                          [](int32 a, int32 b) {
+                            return static_cast<int32>(static_cast<uint32>(a) +
+                                                      static_cast<uint32>(b));
+                          },
+                          [](uint32 a, uint32 b) { return a + b; }, 0.0, 0, 0);
+}
+
+XLA_TEST_F(ReduceTest, VectorizedReduce_Multiply) {
+  RunVectorizedReduceTest(CreateScalarMultiplyComputation,
+                          [](float a, float b) { return a * b; },
+                          [](int32 a, int32 b) {
+                            return static_cast<int32>(static_cast<uint32>(a) *
+                                                      static_cast<uint32>(b));
+                          },
+                          [](uint32 a, uint32 b) { return a * b; }, 1.0, 1, 1);
+}
+
+XLA_TEST_F(ReduceTest, VectorizedReduce_Max) {
+  RunVectorizedReduceTest(CreateScalarMaxComputation,
+                          [](float a, float b) { return std::max(a, b); },
+                          [](int32 a, int32 b) { return std::max(a, b); },
+                          [](uint32 a, uint32 b) { return std::max(a, b); },
+                          std::numeric_limits<float>::min(),
+                          std::numeric_limits<int32>::min(),
+                          std::numeric_limits<uint32>::min());
+}
+
+XLA_TEST_F(ReduceTest, VectorizedReduce_Min) {
+  RunVectorizedReduceTest(CreateScalarMinComputation,
+                          [](float a, float b) { return std::min(a, b); },
+                          [](int32 a, int32 b) { return std::min(a, b); },
+                          [](uint32 a, uint32 b) { return std::min(a, b); },
+                          std::numeric_limits<float>::max(),
+                          std::numeric_limits<int32>::max(),
+                          std::numeric_limits<uint32>::max());
+}
+
+XLA_TEST_F(ReduceTest, VectorizedReduce_LogicalAnd) {
+  RunVectorizedReduceTestForType<bool>(CreateScalarLogicalAndComputation,
+                                       [](bool a, bool b) { return a && b; },
+                                       true);
+}
+
+XLA_TEST_F(ReduceTest, VectorizedReduce_LogicalOr) {
+  RunVectorizedReduceTestForType<bool>(CreateScalarLogicalOrComputation,
+                                       [](bool a, bool b) { return a || b; },
+                                       false);
+}
+
 class ReduceR3ToR2Test : public ReduceTest,
                          public ::testing::WithParamInterface<BoundsLayout> {};
 
-- 
GitLab


From 7d37fe01e54bedeff5e0943ec6bfec07c8841f04 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <taehoonlee@snu.ac.kr>
Date: Tue, 18 Jul 2017 16:10:25 +0900
Subject: [PATCH 1056/1375] Fix typos

---
 tensorflow/contrib/signal/python/ops/reconstruction_ops.py      | 2 +-
 tensorflow/contrib/timeseries/python/timeseries/model.py        | 2 +-
 .../python/timeseries/state_space_models/state_space_model.py   | 2 +-
 tensorflow/python/debug/lib/debug_gradients.py                  | 2 +-
 tensorflow/python/framework/common_shapes.py                    | 2 +-
 tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh   | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/signal/python/ops/reconstruction_ops.py b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
index f5f443ad09..653c030a04 100644
--- a/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
+++ b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
@@ -37,7 +37,7 @@ def _shuffle_to_front(input_tensor, k):
     k: A scalar `Tensor` specifying how many indices to shuffle.
 
   Returns:
-    A tranposed version of `input_tensor` with `k` indices shuffled to the
+    A transposed version of `input_tensor` with `k` indices shuffled to the
     front.
 
   Raises:
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py
index 016db33e69..7c9795d721 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/model.py
@@ -354,7 +354,7 @@ class SequentialTimeSeriesModel(TimeSeriesModel):
 
     Args:
       current_times: A [batch size] Tensor of times for each observation.
-      current_values: A [batch size] Tensor of values for each observaiton.
+      current_values: A [batch size] Tensor of values for each observation.
       state: Model state, updated to current_times.
       predictions: The outputs of _prediction_step
     Returns:
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
index 3193d5607c..6a9660b400 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
@@ -391,7 +391,7 @@ class StateSpaceModel(model.SequentialTimeSeriesModel):
 
     Args:
       current_times: A [batch size] Tensor for times for each observation.
-      current_values: A [batch size] Tensor of values for each observaiton.
+      current_values: A [batch size] Tensor of values for each observation.
       state: A tuple of (mean, covariance, previous_times) having shapes
           mean; [batch size x state dimension]
           covariance; [batch size x state dimension x state dimension]
diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py
index e5159cb941..6655f85d3a 100644
--- a/tensorflow/python/debug/lib/debug_gradients.py
+++ b/tensorflow/python/debug/lib/debug_gradients.py
@@ -303,7 +303,7 @@ class GradientsDebugger(object):
     """Register the gradient tensor for an x-tensor.
 
     Args:
-      x_tensor_name: (`str`) the name of the the independent `tf.Tensor`, i.e.,
+      x_tensor_name: (`str`) the name of the independent `tf.Tensor`, i.e.,
         the tensor on the denominator of the differentiation.
       gradient_tensor: the gradient `tf.Tensor`.
     """
diff --git a/tensorflow/python/framework/common_shapes.py b/tensorflow/python/framework/common_shapes.py
index 79bf0879d7..52a0b9baa6 100644
--- a/tensorflow/python/framework/common_shapes.py
+++ b/tensorflow/python/framework/common_shapes.py
@@ -617,7 +617,7 @@ def call_cpp_shape_fn(op, require_shape_fn=True):
 
 def _call_cpp_shape_fn_impl(
     op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn):
-  """Core implementaton of call_cpp_shape_fn."""
+  """Core implementation of call_cpp_shape_fn."""
   graph_def_version = op.graph.graph_def_versions.producer
   node_def_str = op.node_def.SerializeToString()
 
diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
index cc157c33f5..7cb81c20f0 100644
--- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
+++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
@@ -60,7 +60,7 @@ reinstall_tensorflow_pip ${PIP_NAME}
 
 # Define no_tensorflow_py_deps=true so that every py_test has no deps anymore,
 # which will result testing system installed tensorflow
-# GPU tests are very flaky when running concurently, so set local_test_jobs=1
+# GPU tests are very flaky when running concurrently, so set local_test_jobs=1
 bazel test -c opt --config=win-cuda $BUILD_OPTS -k --test_output=errors \
   --define=no_tensorflow_py_deps=true --test_lang_filters=py \
   --test_tag_filters=-no_pip,-no_windows,-no_windows_gpu \
-- 
GitLab


From 8a90e8487d39cedc8fdd2cbab5a6c237c2f3a1cb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 01:44:40 -0700
Subject: [PATCH 1057/1375] Fixes in control_flow_ops.

PiperOrigin-RevId: 162325768
---
 tensorflow/python/ops/control_flow_ops.py      | 12 +++++++-----
 tensorflow/python/ops/control_flow_ops_test.py |  7 +++----
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index f2c3dae03c..44d6c7e275 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1372,8 +1372,10 @@ class ControlFlowContext(object):
       k = ops.prepend_name_scope(k, import_scope)
       self._external_values[k] = g.as_graph_element(
           ops.prepend_name_scope(v, import_scope))
-    op_names = set([op.split(":")[0]
-                    for op in self._values - set(self._external_values)])
+    op_names = set([
+        op.split(":")[0]
+        for op in self._values - set(self._external_values.keys())
+    ])
     for op in op_names:
       # pylint: disable=protected-access
       g.as_graph_element(op)._set_control_flow_context(self)
@@ -1801,7 +1803,7 @@ def cond(pred, true_fn=None, false_fn=None, strict=False, name=None,
   if not callable(false_fn):
     raise TypeError("false_fn must be callable.")
 
-  with ops.name_scope(name, "cond", [pred]) as name:
+  with ops.name_scope(name, "cond", [pred]):
     # Add the Switch to the graph.
     if isinstance(pred, bool):
       raise TypeError("pred must not be a Python bool")
@@ -2757,7 +2759,7 @@ def while_loop(cond, body, loop_vars, shape_invariants=None,
   ```
 
   """
-  with ops.name_scope(name, "while", loop_vars) as name:
+  with ops.name_scope(name, "while", loop_vars):
     if not loop_vars:
       raise ValueError("No loop variables provided")
     if not callable(cond):
@@ -2770,7 +2772,7 @@ def while_loop(cond, body, loop_vars, shape_invariants=None,
     if shape_invariants is not None:
       nest.assert_same_structure(loop_vars, shape_invariants)
 
-    context = WhileContext(parallel_iterations, back_prop, swap_memory, name)
+    context = WhileContext(parallel_iterations, back_prop, swap_memory)
     ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, context)
     result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
     return result
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index 07c47e05d2..c23957443f 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -43,7 +43,6 @@ import tensorflow.python.ops.tensor_array_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import googletest
 from tensorflow.python.training import momentum
 from tensorflow.python.util import nest
-from tensorflow.python.util.protobuf import compare
 
 
 TestTuple = collections.namedtuple("TestTuple", "a b")
@@ -399,7 +398,7 @@ class ContextTest(TensorFlowTestCase):
       for op in sess.graph.get_operations():
         c = op._get_control_flow_context()
         if c:
-          compare.ProtoEq(
+          self.assertProtoEquals(
               c.to_proto(),
               control_flow_ops.CondContext.from_proto(c.to_proto()).to_proto())
 
@@ -412,7 +411,7 @@ class ContextTest(TensorFlowTestCase):
       for op in sess.graph.get_operations():
         c = op._get_control_flow_context()
         if c:
-          compare.ProtoEq(
+          self.assertProtoEquals(
               c.to_proto(),
               control_flow_ops.WhileContext.from_proto(c.to_proto()).to_proto())
 
@@ -437,7 +436,7 @@ class ContextTest(TensorFlowTestCase):
           c_with_scope._external_values, {"test_scope/a": b2})
 
       # Calling _to_proto() with export_scope should remove "test_scope".
-      compare.ProtoEq(
+      self.assertProtoEquals(
           c._to_proto(),
           c_with_scope._to_proto(export_scope="test_scope"))
 
-- 
GitLab


From 2fd237c164a2f2133dbba6a93671a9de633ab7fe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 02:12:15 -0700
Subject: [PATCH 1058/1375] Fix sample usage of learn.utils.gc: re.escape() the
 base_dir before matching.

PiperOrigin-RevId: 162327608
---
 tensorflow/contrib/learn/python/learn/utils/gc.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/utils/gc.py b/tensorflow/contrib/learn/python/learn/utils/gc.py
index b890129064..226915987a 100644
--- a/tensorflow/contrib/learn/python/learn/utils/gc.py
+++ b/tensorflow/contrib/learn/python/learn/utils/gc.py
@@ -32,13 +32,14 @@ Note that functions should always return a sorted list.
 
 For example,
   base_dir = "/tmp"
-  # create the directories
+  # Create the directories.
   for e in xrange(10):
     os.mkdir("%s/%d" % (base_dir, e), 0o755)
 
-  # create a simple parser that pulls the export_version from the directory
+  # Create a simple parser that pulls the export_version from the directory.
+  path_regex = "^" + re.escape(base_dir) + "/(\\d+)$"
   def parser(path):
-    match = re.match("^" + base_dir + "/(\\d+)$", path.path)
+    match = re.match(path_regex, path.path)
     if not match:
       return None
     return path._replace(export_version=int(match.group(1)))
@@ -54,7 +55,7 @@ For example,
   both = gc.union(every_fifth, largest_three)
   print(both(all_paths))  # shows ["/tmp/0", "/tmp/5",
                           #        "/tmp/7", "/tmp/8", "/tmp/9"]
-  # delete everything not in 'both'
+  # Delete everything not in 'both'.
   to_delete = gc.negation(both)
   for p in to_delete(all_paths):
     gfile.DeleteRecursively(p.path)  # deletes:  "/tmp/1", "/tmp/2",
-- 
GitLab


From 30d7ee5e73e238db0bf43a6af2ac813ade346b2e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 02:22:48 -0700
Subject: [PATCH 1059/1375] Add option to fold_constants to exclude specified
 ops.

PiperOrigin-RevId: 162328390
---
 .../graph_transforms/fold_constants_lib.cc    | 16 +++++--
 .../graph_transforms/fold_constants_test.cc   | 42 ++++++++++++++++++-
 2 files changed, 54 insertions(+), 4 deletions(-)

diff --git a/tensorflow/tools/graph_transforms/fold_constants_lib.cc b/tensorflow/tools/graph_transforms/fold_constants_lib.cc
index 79472ae554..f97e485418 100644
--- a/tensorflow/tools/graph_transforms/fold_constants_lib.cc
+++ b/tensorflow/tools/graph_transforms/fold_constants_lib.cc
@@ -152,9 +152,19 @@ Status FoldConstants(const GraphDef& input_graph_def,
       &input_graph, context.input_names, context.output_names, {},
       device_attributes, false /* use_function_convention */, &metadata));
   bool was_mutated;
-  TF_RETURN_IF_ERROR(ConstantFold(ConstantFoldingOptions(), nullptr,
-                                  Env::Default(), nullptr, &input_graph,
-                                  &was_mutated));
+  // Exclude specified nodes from constant folding.
+  ConstantFoldingOptions cf_opts;
+  if (context.params.count("exclude_op") > 0) {
+    const auto& excluded_nodes = context.params.at("exclude_op");
+    const std::set<string> excluded_nodes_set(excluded_nodes.begin(),
+                                              excluded_nodes.end());
+    cf_opts.consider = [excluded_nodes_set](const Node* n) {
+      return excluded_nodes_set.find(n->op_def().name()) ==
+             excluded_nodes_set.end();
+    };
+  }
+  TF_RETURN_IF_ERROR(ConstantFold(cf_opts, nullptr, Env::Default(), nullptr,
+                                  &input_graph, &was_mutated));
   GraphDef folded_graph_def;
   input_graph.ToGraphDef(&folded_graph_def);
   GraphDef send_recvs_replaced;
diff --git a/tensorflow/tools/graph_transforms/fold_constants_test.cc b/tensorflow/tools/graph_transforms/fold_constants_test.cc
index 902f92952a..14e2c01c7c 100644
--- a/tensorflow/tools/graph_transforms/fold_constants_test.cc
+++ b/tensorflow/tools/graph_transforms/fold_constants_test.cc
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include <utility>
+
 #include "tensorflow/cc/ops/const_op.h"
 #include "tensorflow/cc/ops/image_ops.h"
 #include "tensorflow/cc/ops/nn_ops.h"
@@ -69,11 +71,46 @@ class ConstantFoldingTest : public ::testing::Test {
     test::FillIota<float>(&placeholder_tensor, 1.0f);
     TestConstantFolding(graph_def,
                         {{"placeholder_expect_remains", placeholder_tensor}},
-                        {"output_expect_remains"});
+                        {}, {"output_expect_remains"});
+  }
+
+  void TestOpExclusionAdd() {
+    auto root = tensorflow::Scope::NewRootScope();
+    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+
+    const int width = 100;
+
+    Tensor a_data(DT_FLOAT, TensorShape({width}));
+    test::FillIota<float>(&a_data, 1.0f);
+    Output a_const =
+        Const(root.WithOpName("a_expect_remains"), Input::Initializer(a_data));
+
+    Tensor b_data(DT_FLOAT, TensorShape({width}));
+    test::FillIota<float>(&b_data, 1.0f);
+    Output b_const =
+        Const(root.WithOpName("b_expect_remains"), Input::Initializer(b_data));
+
+    Output add = Add(root.WithOpName("add_expect_remains"), a_const, b_const);
+
+    Output placeholder =
+        Placeholder(root.WithOpName("placeholder_expect_remains"), DT_FLOAT);
+
+    Output mul =
+        Mul(root.WithOpName("output_expect_remains"), add, placeholder);
+
+    GraphDef graph_def;
+    TF_ASSERT_OK(root.ToGraphDef(&graph_def));
+
+    Tensor placeholder_tensor(DT_FLOAT, TensorShape({width}));
+    test::FillIota<float>(&placeholder_tensor, 1.0f);
+    TestConstantFolding(graph_def,
+                        {{"placeholder_expect_remains", placeholder_tensor}},
+                        {"Add"}, {"output_expect_remains"});
   }
 
   void TestConstantFolding(const GraphDef& graph_def,
                            std::vector<std::pair<string, Tensor> > inputs,
+                           std::vector<string> excluded_ops,
                            const std::vector<string>& outputs) {
     std::unique_ptr<tensorflow::Session> unfolded_session(
         tensorflow::NewSession(tensorflow::SessionOptions()));
@@ -87,6 +124,7 @@ class ConstantFoldingTest : public ::testing::Test {
       context.input_names.push_back(input.first);
     }
     context.output_names = outputs;
+    context.params["exclude_op"] = std::move(excluded_ops);
     TF_ASSERT_OK(
         graph_transforms::FoldConstants(graph_def, context, &folded_graph_def));
 
@@ -203,6 +241,8 @@ class ConstantFoldingTest : public ::testing::Test {
 
 TEST_F(ConstantFoldingTest, TestSimpleAdd) { TestSimpleAdd(); }
 
+TEST_F(ConstantFoldingTest, TestOpExclusionAdd) { TestOpExclusionAdd(); }
+
 TEST_F(ConstantFoldingTest, TestReplaceSendRecvs) { TestReplaceSendRecvs(); }
 
 TEST_F(ConstantFoldingTest, TestRemoveUnusedNodes) { TestRemoveUnusedNodes(); }
-- 
GitLab


From 5acc81f15d5713eafa0da0ee33c6179ff8cb67d0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 03:00:37 -0700
Subject: [PATCH 1060/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 162330841
---
 tensorflow/contrib/distributions/BUILD         | 1 +
 tensorflow/contrib/factorization/kernels/BUILD | 1 +
 tensorflow/contrib/graph_editor/BUILD          | 1 +
 tensorflow/contrib/integrate/BUILD             | 2 ++
 tensorflow/contrib/linear_optimizer/BUILD      | 1 +
 tensorflow/contrib/opt/BUILD                   | 1 +
 tensorflow/contrib/specs/BUILD                 | 1 +
 7 files changed, 8 insertions(+)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index f3f5b1dcb3..bb2d34c447 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -40,6 +40,7 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:clip_ops",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
diff --git a/tensorflow/contrib/factorization/kernels/BUILD b/tensorflow/contrib/factorization/kernels/BUILD
index 1884ef8c93..15c527b0e8 100644
--- a/tensorflow/contrib/factorization/kernels/BUILD
+++ b/tensorflow/contrib/factorization/kernels/BUILD
@@ -43,6 +43,7 @@ cc_library(
     srcs = ["masked_matmul_ops.cc"],
     deps = [
         "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core/kernels:bounds_check",
         "//third_party/eigen3",
         "@protobuf//:protobuf_headers",
     ],
diff --git a/tensorflow/contrib/graph_editor/BUILD b/tensorflow/contrib/graph_editor/BUILD
index bdb58fb543..c6023c25b1 100644
--- a/tensorflow/contrib/graph_editor/BUILD
+++ b/tensorflow/contrib/graph_editor/BUILD
@@ -155,6 +155,7 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:gradients",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
diff --git a/tensorflow/contrib/integrate/BUILD b/tensorflow/contrib/integrate/BUILD
index d70be594b2..66948c1ea1 100644
--- a/tensorflow/contrib/integrate/BUILD
+++ b/tensorflow/contrib/integrate/BUILD
@@ -20,9 +20,11 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:functional_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:tensor_array_ops",
         "//tensorflow/python:util",
+        "@six_archive//:six",
     ],
 )
 
diff --git a/tensorflow/contrib/linear_optimizer/BUILD b/tensorflow/contrib/linear_optimizer/BUILD
index 1fde6e5c6c..22398d2255 100644
--- a/tensorflow/contrib/linear_optimizer/BUILD
+++ b/tensorflow/contrib/linear_optimizer/BUILD
@@ -131,6 +131,7 @@ py_test(
     deps = [
         ":sdca_estimator_py",
         "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:sparse_tensor",
diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index f0f2ae5711..01a5573a1f 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -50,6 +50,7 @@ py_test(
     tags = ["manual"],
     deps = [
         ":opt_py",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:extra_py_tests_deps",
         "//tensorflow/python:framework_for_generated_wrappers",
diff --git a/tensorflow/contrib/specs/BUILD b/tensorflow/contrib/specs/BUILD
index b60a1ef61e..808709f83f 100644
--- a/tensorflow/contrib/specs/BUILD
+++ b/tensorflow/contrib/specs/BUILD
@@ -32,6 +32,7 @@ py_library(
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
+        "@six_archive//:six",
     ],
 )
 
-- 
GitLab


From 0a9ce966098fd2e1039daefd2960a9135a567cf9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 03:44:46 -0700
Subject: [PATCH 1061/1375] Improve BUILD correctness tensorflow/contrib/keras

PiperOrigin-RevId: 162334011
---
 tensorflow/contrib/keras/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index a0ffd027a5..78e86542e7 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -218,6 +218,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
 )
@@ -523,6 +524,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -545,6 +547,7 @@ py_test(
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
-- 
GitLab


From 3d91dc4b3af462207fe357f2f5ac271327998855 Mon Sep 17 00:00:00 2001
From: Hans Gaiser <j.c.gaiser@delftrobotics.com>
Date: Tue, 18 Jul 2017 14:48:51 +0200
Subject: [PATCH 1062/1375] Fix typo in exception.

---
 tensorflow/python/layers/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index ebf108d8ca..9e5772b891 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -537,7 +537,7 @@ class Layer(object):
         if x.get_shape().ndims is None:
           raise ValueError('Input ' + str(input_index) + ' of layer ' +
                            self.name + ' is incompatible with the layer: '
-                           'its rank is undefined, by the layer requires a '
+                           'its rank is undefined, but the layer requires a '
                            'defined rank.')
 
       # Check ndim.
-- 
GitLab


From bdb2967a298236e24011405907cd19737386934e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 08:47:06 -0700
Subject: [PATCH 1063/1375] Fix metagemm calls in quantized ops. Only use
 metagemm multiplication for k <= 2048.

PiperOrigin-RevId: 162356878
---
 tensorflow/core/kernels/quantized_conv_ops.cc  | 2 +-
 tensorflow/core/kernels/quantized_matmul_op.cc | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/quantized_conv_ops.cc b/tensorflow/core/kernels/quantized_conv_ops.cc
index 5658dcc069..3b0764bb9b 100644
--- a/tensorflow/core/kernels/quantized_conv_ops.cc
+++ b/tensorflow/core/kernels/quantized_conv_ops.cc
@@ -381,7 +381,7 @@ class Im2ColConvFunctor {
       if (meta::IsSupportedAndEnabled() && std::is_same<T1, quint8>() &&
           std::is_same<T2, quint8>() && std::is_same<T3, qint32>() &&
           (output_offset == 0) && (output_mult == 1) && (output_shift == 0) &&
-          (transpose_c == false)) {
+          (transpose_c == false) && (k <= 2048)) {
         meta::QuantizedGemm(context, transpose_a, transpose_b, im2col_buffer,
                             filter_data, chunk_output_data, m, n, k,
                             -input_offset, -filter_offset, lda, ldb, ldc);
diff --git a/tensorflow/core/kernels/quantized_matmul_op.cc b/tensorflow/core/kernels/quantized_matmul_op.cc
index 86c722e587..afb30d5f62 100644
--- a/tensorflow/core/kernels/quantized_matmul_op.cc
+++ b/tensorflow/core/kernels/quantized_matmul_op.cc
@@ -135,7 +135,7 @@ class QuantizedMatMulOp : public OpKernel {
     if (meta::IsSupportedAndEnabled() && std::is_same<T1, quint8>() &&
         std::is_same<T2, quint8>() && std::is_same<Toutput, qint32>() &&
         (offset_c == 0) && (mult_c == 1) && (shift_c == 0) &&
-        (transpose_c == false)) {
+        (transpose_c == false) && (k <= 2048)) {
       // Gemmlowp/meta code path works on 32 & 64 bit Arm with NEON Simd and
       // allows optimized quantized 8bit to 32bit gemm.
       meta::QuantizedGemm(context, transpose_a_, transpose_b_, a_data, b_data,
-- 
GitLab


From 235060d7e721cf2c0cd2bb7b1fd5927261115021 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 09:23:39 -0700
Subject: [PATCH 1064/1375] [BatchNormGrad] Add shape inference support for
 BatchNormGrad

This is 3rd of 4 CLs to implement BatchNormGrad. Adding shape inference support for BatchNormGrad
helps us detect imcompatible shapes and inference the output shape.

RELNOTES: n/a
PiperOrigin-RevId: 162361218
---
 .../compiler/xla/service/shape_inference.cc   | 155 +++++++++++++++++-
 1 file changed, 150 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 7024948cee..69fee6a621 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -803,7 +803,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
         "Expected feature_index of batch-norm-training to be "
         "smaller than the rank of operand_shape; "
         "got feature_index %lld, and rank %lld",
-        feature_index, ShapeUtil::Rank(offset_shape));
+        feature_index, ShapeUtil::Rank(operand_shape));
   }
 
   if (feature_index < 0) {
@@ -817,7 +817,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "Expected the rank of operand to "
         "batch-norm-training to be at least 1; got %lld",
-        ShapeUtil::Rank(offset_shape));
+        ShapeUtil::Rank(operand_shape));
   }
 
   if (ShapeUtil::Rank(offset_shape) != 1) {
@@ -888,9 +888,154 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const Shape& operand_shape, const Shape& scale_shape,
     const Shape& mean_shape, const Shape& var_shape,
     const Shape& output_grad_shape, int64 feature_index) {
-  // TODO(b/62843645) Implement shape inference.
-  return Unimplemented(
-      "Shape inference on BatchNormGrad is not implemented yet.");
+  TF_RETURN_IF_ERROR(
+      ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm grad"));
+  TF_RETURN_IF_ERROR(
+      ExpectNotTupleOrOpaque(scale_shape, "scale input of batch norm grad"));
+  TF_RETURN_IF_ERROR(
+      ExpectNotTupleOrOpaque(mean_shape, "mean input of batch norm grad"));
+  TF_RETURN_IF_ERROR(
+      ExpectNotTupleOrOpaque(var_shape, "var input of batch norm grad"));
+  TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(
+      output_grad_shape, "output_grad input of batch norm grad"));
+
+  TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(operand_shape));
+  TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(mean_shape));
+  TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(scale_shape));
+  TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(var_shape));
+  TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(output_grad_shape));
+
+  if (feature_index >= ShapeUtil::Rank(operand_shape)) {
+    return InvalidArgument(
+        "Expected feature_index of batch-norm-grad to be "
+        "smaller than the rank of operand_shape; "
+        "got feature_index %lld, and rank %lld",
+        feature_index, ShapeUtil::Rank(operand_shape));
+  }
+
+  if (ShapeUtil::Rank(operand_shape) != ShapeUtil::Rank(output_grad_shape)) {
+    return InvalidArgument(
+        "Expected operand_shape of batch-norm-grad to have the same rank as"
+        " output_grad_shape; got rank(oprand_shape) %lld, and"
+        " rank(output_grad_shape) %lld",
+        ShapeUtil::Rank(operand_shape), ShapeUtil::Rank(output_grad_shape));
+  }
+
+  if (ShapeUtil::Rank(mean_shape) != 1) {
+    return InvalidArgument(
+        "Mean input of batch-norm-grad must have"
+        " rank 1, but has rank %lld.",
+        ShapeUtil::Rank(mean_shape));
+  }
+
+  if (ShapeUtil::Rank(scale_shape) != 1) {
+    return InvalidArgument(
+        "Scale input of batch-norm-grad must have"
+        " rank 1, but has rank %lld.",
+        ShapeUtil::Rank(scale_shape));
+  }
+
+  if (ShapeUtil::Rank(var_shape) != 1) {
+    return InvalidArgument(
+        "Var input of batch-norm-grad must have"
+        " rank 1, but has rank %lld.",
+        ShapeUtil::Rank(var_shape));
+  }
+
+  if (!ShapeUtil::ElementIsFloating(operand_shape)) {
+    return InvalidArgument(
+        "The operand to batch-norm-grad must have a floating point "
+        "element type, but the shape is %s",
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  if (!ShapeUtil::ElementIsFloating(output_grad_shape)) {
+    return InvalidArgument(
+        "The output_grad to batch-norm-grad must have a floating point "
+        "element type, but the shape is %s",
+        PrimitiveType_Name(output_grad_shape.element_type()).c_str());
+  }
+
+  if (!ShapeUtil::SameElementType(output_grad_shape, operand_shape)) {
+    return InvalidArgument(
+        "The inputs should have the same element type for batch-norm-grad, "
+        "but the element type of output_grad is %s "
+        "and the element type of operand is %s",
+        PrimitiveType_Name(output_grad_shape.element_type()).c_str(),
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  if (!ShapeUtil::SameElementType(scale_shape, operand_shape)) {
+    return InvalidArgument(
+        "The inputs should have the same element type for batch-norm-grad, "
+        "but the element type of scale factor is %s "
+        "and the element type of operand is %s",
+        PrimitiveType_Name(scale_shape.element_type()).c_str(),
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  if (!ShapeUtil::SameElementType(mean_shape, operand_shape)) {
+    return InvalidArgument(
+        "The inputs should have the same element type for batch-norm-grad, "
+        "but the element type of mean is %s "
+        "and the element type of operand is %s",
+        PrimitiveType_Name(mean_shape.element_type()).c_str(),
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  if (!ShapeUtil::SameElementType(var_shape, operand_shape)) {
+    return InvalidArgument(
+        "The inputs should have the same element type for batch-norm-grad, "
+        "but the element type of mean is %s "
+        "and the element type of operand is %s",
+        PrimitiveType_Name(mean_shape.element_type()).c_str(),
+        PrimitiveType_Name(operand_shape.element_type()).c_str());
+  }
+
+  const int64 feature_count = operand_shape.dimensions(feature_index);
+
+  Shape feature_shape =
+      ShapeUtil::MakeShape(operand_shape.element_type(), {feature_count});
+
+  if (ShapeUtil::GetDimension(mean_shape, 0) != feature_count) {
+    return InvalidArgument(
+        "The size of mean should be the same as feature count,"
+        "but the size of offset factor is %lld "
+        "and the feature count is %lld",
+        ShapeUtil::GetDimension(mean_shape, 0), feature_count);
+  }
+
+  if (ShapeUtil::GetDimension(scale_shape, 0) != feature_count) {
+    return InvalidArgument(
+        "The size of scale factor should be the same as feature count,"
+        "but the size of scale factor is %lld "
+        "and the feature count is %lld",
+        ShapeUtil::GetDimension(scale_shape, 0), feature_count);
+  }
+
+  if (ShapeUtil::GetDimension(var_shape, 0) != feature_count) {
+    return InvalidArgument(
+        "The size of variance should be the same as feature count,"
+        "but the size of variance is %lld "
+        "and the feature count is %lld",
+        ShapeUtil::GetDimension(var_shape, 0), feature_count);
+  }
+
+  // Verify operand_shape and output_grad_shape have same bounds.
+  for (int64 i = 0; i < ShapeUtil::Rank(operand_shape); ++i) {
+    if (ShapeUtil::GetDimension(operand_shape, i) !=
+        ShapeUtil::GetDimension(output_grad_shape, i)) {
+      return InvalidArgument(
+          "The bounds of operand shape should be the same as output_grad's,"
+          "but the bound of operand_shape at dimension %lld is %lld "
+          "and the bound of output_grad_shape is %lld",
+          i, ShapeUtil::GetDimension(operand_shape, i),
+          ShapeUtil::GetDimension(output_grad_shape, i));
+    }
+  }
+
+  return ShapeUtil::MakeTupleShape(
+      {operand_shape, feature_shape, feature_shape});
 }
 
 /* static */ StatusOr<Shape> ShapeInference::InferConvolveShape(
-- 
GitLab


From a3d3ce160368efe4b0b3f7f8df23cc8423680364 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 18 Jul 2017 09:30:16 -0700
Subject: [PATCH 1065/1375] [tf contrib seq2seq] Provide informative error
 messages in AttentionWrapper.call.

Related issues: #11077, #11540.

PiperOrigin-RevId: 162362081
---
 tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index cd65a54b83..9c6939bb46 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -1137,7 +1137,14 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
       - `attention_or_cell_output` depending on `output_attention`.
       - `next_state` is an instance of `AttentionWrapperState`
          containing the state calculated at this time step.
+
+    Raises:
+      TypeError: If `state` is not an instance of `AttentionWrapperState`.
     """
+    if not isinstance(state, AttentionWrapperState):
+      raise TypeError("Expected state to be instance of AttentionWrapperState. "
+                      "Received type %s instead."  % type(state))
+
     # Step 1: Calculate the true inputs to the cell based on the
     # previous attention value.
     cell_inputs = self._cell_input_fn(inputs, state.attention)
-- 
GitLab


From 7e08b5c7ae5a10810948095b96d2da53e816e446 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Tue, 18 Jul 2017 09:59:09 -0700
Subject: [PATCH 1066/1375] [XLA] Clean up clang-tidy warnings in
 reference_util

By marking std::function parameters as const& where applicable; this also makes it more consistent with the other utils that use std::function parameters.

PiperOrigin-RevId: 162365501
---
 tensorflow/compiler/xla/reference_util.cc |  8 ++++----
 tensorflow/compiler/xla/reference_util.h  | 10 +++++-----
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index 138e360c29..1495790870 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -578,7 +578,7 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
 /* static */ std::unique_ptr<std::vector<float>>
 ReferenceUtil::ReduceToColArray2D(
     const Array2D<float>& matrix, float init,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
   auto result = MakeUnique<std::vector<float>>();
@@ -595,7 +595,7 @@ ReferenceUtil::ReduceToColArray2D(
 /* static */ std::unique_ptr<std::vector<float>>
 ReferenceUtil::ReduceToRowArray2D(
     const Array2D<float>& matrix, float init,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
   auto result = MakeUnique<std::vector<float>>();
@@ -612,7 +612,7 @@ ReferenceUtil::ReduceToRowArray2D(
 /*static*/ std::vector<float> ReferenceUtil::Reduce4DTo1D(
     const Array4D<float>& array, float init,
     tensorflow::gtl::ArraySlice<int64> dims,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   std::vector<float> result;
   CHECK_EQ(dims.size(), 3);
   const std::set<int64> dim_set(dims.begin(), dims.end());
@@ -682,7 +682,7 @@ ReferenceUtil::ReduceToRowArray2D(
 /* static */ std::unique_ptr<Array2D<float>> ReferenceUtil::Reduce3DTo2D(
     const Array3D<float>& array, float init,
     tensorflow::gtl::ArraySlice<int64> dims,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   CHECK_EQ(dims.size(), 1);
   int64 rows = dims[0] == 0 ? array.n2() : array.n1();
   int64 cols = dims[0] == 2 ? array.n2() : array.n3();
diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index 41ef1f96ab..5b2ddf4d7f 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -88,21 +88,21 @@ class ReferenceUtil {
   // to apply for each reduction step.
   static std::unique_ptr<std::vector<float>> ReduceToColArray2D(
       const Array2D<float>& matrix, float init,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Returns the result of reducing a matrix to a row vector. init is the
   // initial value for the reduce operation, and reduce_function is the function
   // to apply for each reduction step.
   static std::unique_ptr<std::vector<float>> ReduceToRowArray2D(
       const Array2D<float>& matrix, float init,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Performs a R2=>R1 reduction by reducing away the dimension specified in
   // 'dimension_to_reduce'.
   template <typename T>
   static std::vector<T> ReduceR2ToR1(const Array2D<T>& input,
                                      int dimension_to_reduce, T init,
-                                     std::function<T(T, T)> freduce) {
+                                     const std::function<T(T, T)>& freduce) {
     std::vector<T> result(dimension_to_reduce == 0 ? input.n2() : input.n1(),
                           init);
     for (int i0 = 0; i0 < input.n1(); ++i0) {
@@ -119,7 +119,7 @@ class ReferenceUtil {
   static std::vector<float> Reduce4DTo1D(
       const Array4D<float>& array, float init,
       tensorflow::gtl::ArraySlice<int64> dims,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Broadcast 1D dimension to 4D, from the dimension `broadcast_from_dim`.
   static std::unique_ptr<Array4D<float>> Broadcast1DTo4D(
@@ -131,7 +131,7 @@ class ReferenceUtil {
   static std::unique_ptr<Array2D<float>> Reduce3DTo2D(
       const Array3D<float>& array, float init,
       tensorflow::gtl::ArraySlice<int64> dims,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Applies map_function to each element in the input (2D array) and returns
   // the result.
-- 
GitLab


From 44894caad3a10e4c67e241ea26cc4ce8b71e887e Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Tue, 18 Jul 2017 10:27:25 -0700
Subject: [PATCH 1067/1375] BUGFIX:  Affine bijector was putting non-tensor
 value into graph parents, which caused issues downstream, e.g. when
 transformed distribution tried to init with these.

PiperOrigin-RevId: 162369143
---
 .../distributions/python/ops/bijectors/affine_impl.py      | 7 +++----
 tensorflow/python/ops/distributions/bijector_impl.py       | 7 +++++++
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
index c3c0413ce2..6a5c37da16 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py
@@ -188,10 +188,9 @@ class Affine(bijector.Bijector):
         shift, scale_identity_multiplier, scale_diag, scale_tril,
         scale_perturb_diag, scale_perturb_factor]):
       event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims")
-      if tensor_util.constant_value(event_ndims) is not None:
-        event_ndims = tensor_util.constant_value(event_ndims)
-        if event_ndims not in (0, 1):
-          raise ValueError("event_ndims({}) was not 0 or 1".format(event_ndims))
+      event_ndims_const = tensor_util.constant_value(event_ndims)
+      if event_ndims_const is not None and event_ndims_const not in (0, 1):
+        raise ValueError("event_ndims(%s) was not 0 or 1" % event_ndims_const)
       else:
         if validate_args:
           # Shape tool will catch if event_ndims is negative.
diff --git a/tensorflow/python/ops/distributions/bijector_impl.py b/tensorflow/python/ops/distributions/bijector_impl.py
index 7be7c27ae9..49a8938f01 100644
--- a/tensorflow/python/ops/distributions/bijector_impl.py
+++ b/tensorflow/python/ops/distributions/bijector_impl.py
@@ -356,6 +356,9 @@ class Bijector(object):
       dtype: `tf.dtype` supported by this `Bijector`. `None` means dtype is not
         enforced.
       name: The name to give Ops created by the initializer.
+
+    Raises:
+      ValueError:  If a member of `graph_parents` is not a `Tensor`.
     """
     self._event_ndims = (
         ops.convert_to_tensor(event_ndims, dtype=dtypes.int32)
@@ -379,6 +382,10 @@ class Bijector(object):
         return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower()
       self._name = camel_to_snake(type(self).__name__.lstrip("_"))
 
+    for i, t in enumerate(self._graph_parents):
+      if t is None or not tensor_util.is_tensor(t):
+        raise ValueError("Graph parent item %d is not a Tensor; %s." % (i, t))
+
   @property
   def event_ndims(self):
     """Returns then number of event dimensions this bijector operates on."""
-- 
GitLab


From 1558c10c8d07c78a58d74d0fcf53c1d2e2505d5e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 11:32:17 -0700
Subject: [PATCH 1068/1375] Python code for running gradient boosted trees.

PiperOrigin-RevId: 162379380
---
 tensorflow/BUILD                              |    1 +
 tensorflow/contrib/boosted_trees/BUILD        |   60 +
 .../boosted_trees/estimator_batch/BUILD       |  108 ++
 .../boosted_trees/estimator_batch/__init__.py |   22 +
 .../estimator_batch/custom_export_strategy.py |  159 +++
 .../custom_export_strategy_test.py            |  278 ++++
 .../estimator_batch/custom_loss_head.py       |   73 ++
 .../estimator_batch/estimator.py              |  193 +++
 .../boosted_trees/estimator_batch/model.py    |  117 ++
 .../estimator_batch/trainer_hooks.py          |  177 +++
 .../estimator_batch/trainer_hooks_test.py     |   76 ++
 tensorflow/contrib/boosted_trees/lib/BUILD    |   61 +
 .../lib/learner/batch/base_split_handler.py   |  147 +++
 .../batch/categorical_split_handler.py        |  189 +++
 .../batch/categorical_split_handler_test.py   |  338 +++++
 .../learner/batch/ordinal_split_handler.py    |  518 ++++++++
 .../batch/ordinal_split_handler_test.py       | 1126 +++++++++++++++++
 .../python/training/functions/gbdt_batch.py   | 1012 +++++++++++++++
 .../training/functions/gbdt_batch_test.py     |  917 ++++++++++++++
 .../boosted_trees/python/utils/losses.py      |  210 +++
 .../boosted_trees/python/utils/losses_test.py |  106 ++
 21 files changed, 5888 insertions(+)
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/BUILD
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/__init__.py
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/custom_loss_head.py
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/estimator.py
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/model.py
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks.py
 create mode 100644 tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py
 create mode 100644 tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/utils/losses.py
 create mode 100644 tensorflow/contrib/boosted_trees/python/utils/losses_test.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 779dd1e8ed..827807816c 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -236,6 +236,7 @@ filegroup(
         "//tensorflow/contrib/batching/util:all_files",
         "//tensorflow/contrib/bayesflow:all_files",
         "//tensorflow/contrib/boosted_trees:all_files",
+        "//tensorflow/contrib/boosted_trees/estimator_batch:all_files",
         "//tensorflow/contrib/boosted_trees/lib:all_files",
         "//tensorflow/contrib/boosted_trees/proto:all_files",
         "//tensorflow/contrib/boosted_trees/resources:all_files",
diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 5f5a9488e6..2e53030666 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -63,6 +63,60 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":boosted_trees_ops_py",
+        ":losses",
+    ],
+)
+
+py_library(
+    name = "losses",
+    srcs = ["python/utils/losses.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:nn",
+    ],
+)
+
+py_test(
+    name = "losses_test",
+    size = "small",
+    srcs = ["python/utils/losses_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":losses",
+        "//tensorflow/python:framework_test_lib",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_library(
+    name = "gbdt_batch",
+    srcs = [
+        "python/training/functions/gbdt_batch.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/boosted_trees:batch_ops_utils_py",
+        "//tensorflow/contrib/boosted_trees:boosted_trees_ops_py",
+        "//tensorflow/contrib/boosted_trees/lib:categorical_split_handler",
+        "//tensorflow/contrib/boosted_trees/lib:ordinal_split_handler",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/learn",
+        "//tensorflow/contrib/stateless",
+    ],
+)
+
+py_test(
+    name = "gbdt_batch_test",
+    size = "small",
+    srcs = ["python/training/functions/gbdt_batch_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":gbdt_batch",
+        ":losses",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:framework_test_lib",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -253,6 +307,7 @@ tf_custom_op_py_library(
         "//tensorflow/contrib/util:util_py",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:resources",
     ],
 )
 
@@ -500,6 +555,8 @@ tf_custom_op_py_library(
         "//tensorflow/contrib/util:util_py",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
     ],
 )
 
@@ -621,6 +678,8 @@ tf_custom_op_py_library(
         "//tensorflow/contrib/util:util_py",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:resources",
+        "//tensorflow/python:training",
     ],
 )
 
@@ -656,6 +715,7 @@ py_library(
     name = "boosted_trees_pip",
     deps = [
         ":init_py",
+        "//tensorflow/contrib/boosted_trees/estimator_batch:init_py",
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/BUILD b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD
new file mode 100644
index 0000000000..f9e186788f
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD
@@ -0,0 +1,108 @@
+# This directory contains estimators to train and run inference on
+# gradient boosted trees on top of TensorFlow.
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+package(
+    default_visibility = ["//visibility:public"],
+)
+
+load("//tensorflow:tensorflow.bzl", "py_test")
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+py_library(
+    name = "init_py",
+    srcs = [
+        "__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "custom_export_strategy",
+        ":custom_loss_head",
+        ":estimator",
+        ":model",
+        ":trainer_hooks",
+    ],
+)
+
+py_library(
+    name = "model",
+    srcs = ["model.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/boosted_trees:gbdt_batch",
+    ],
+)
+
+py_library(
+    name = "trainer_hooks",
+    srcs = ["trainer_hooks.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_test(
+    name = "trainer_hooks_test",
+    size = "small",
+    srcs = ["trainer_hooks_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":trainer_hooks",
+    ],
+)
+
+py_library(
+    name = "custom_loss_head",
+    srcs = ["custom_loss_head.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+    ],
+)
+
+py_library(
+    name = "custom_export_strategy",
+    srcs = ["custom_export_strategy.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/boosted_trees:gbdt_batch",
+        "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_test(
+    name = "custom_export_strategy_test",
+    size = "small",
+    srcs = ["custom_export_strategy_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":custom_export_strategy",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_py",
+        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
+    ],
+)
+
+py_library(
+    name = "estimator",
+    srcs = ["estimator.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model",
+        ":trainer_hooks",
+    ],
+)
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/__init__.py b/tensorflow/contrib/boosted_trees/estimator_batch/__init__.py
new file mode 100644
index 0000000000..02af4adbd9
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Gradient boosted trees implementation in tensorflow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.boosted_trees.estimator_batch import *
+# pylint: enable=unused-import,wildcard-import
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py
new file mode 100644
index 0000000000..923d27c160
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py
@@ -0,0 +1,159 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Strategy to export custom proto formats."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.training.functions import gbdt_batch
+from tensorflow.contrib.decision_trees.proto import generic_tree_model_extensions_pb2
+from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2
+from tensorflow.contrib.learn.python.learn import export_strategy
+from tensorflow.contrib.learn.python.learn.utils import saved_model_export_utils
+from tensorflow.python.client import session as tf_session
+from tensorflow.python.framework import ops
+from tensorflow.python.saved_model import loader as saved_model_loader
+from tensorflow.python.saved_model import tag_constants
+
+
+def make_custom_export_strategy(name, convert_fn, feature_columns,
+                                export_input_fn):
+  """Makes custom exporter of GTFlow tree format.
+
+  Args:
+    name: A string, for the name of the export strategy.
+    convert_fn: A function that converts the tree proto to desired format and
+      saves it to the desired location.
+    feature_columns: A list of feature columns.
+    export_input_fn: A function that takes no arguments and returns an
+      `InputFnOps`.
+
+  Returns:
+    An `ExportStrategy`.
+  """
+  base_strategy = saved_model_export_utils.make_export_strategy(
+      serving_input_fn=export_input_fn)
+  input_fn = export_input_fn()
+  (sorted_feature_names, dense_floats, sparse_float_indices, _, _,
+   sparse_int_indices, _, _) = gbdt_batch.extract_features(
+       input_fn.features, feature_columns)
+
+  def export_fn(estimator, export_dir, checkpoint_path, eval_result=None):
+    """A wrapper to export to SavedModel, and convert it to other formats."""
+    result_dir = base_strategy.export(estimator, export_dir,
+                                      checkpoint_path,
+                                      eval_result)
+    with ops.Graph().as_default() as graph:
+      with tf_session.Session(graph=graph) as sess:
+        saved_model_loader.load(
+            sess, [tag_constants.SERVING], result_dir)
+        # Note: This is GTFlow internal API and might change.
+        ensemble_model = graph.get_operation_by_name(
+            "ensemble_model/TreeEnsembleSerialize")
+        _, dfec_str = sess.run(ensemble_model.outputs)
+        dtec = tree_config_pb2.DecisionTreeEnsembleConfig()
+        dtec.ParseFromString(dfec_str)
+        # Export the result in the same folder as the saved model.
+        convert_fn(dtec, sorted_feature_names, len(dense_floats),
+                   len(sparse_float_indices), len(sparse_int_indices),
+                   result_dir, eval_result)
+    return result_dir
+  return export_strategy.ExportStrategy(name, export_fn)
+
+
+def convert_to_universal_format(dtec, sorted_feature_names,
+                                num_dense, num_sparse_float,
+                                num_sparse_int):
+  """Convert GTFlow trees to universal format."""
+  del num_sparse_int  # unused.
+  model_and_features = generic_tree_model_pb2.ModelAndFeatures()
+  # TODO(jonasz): Feature descriptions should contain information about how each
+  # feature is processed before it's fed to the model (e.g. bucketing
+  # information). As of now, this serves as a list of features the model uses.
+  for feature_name in sorted_feature_names:
+    model_and_features.features[feature_name].SetInParent()
+  model = model_and_features.model
+  model.ensemble.summation_combination_technique.SetInParent()
+  for tree_idx in range(len(dtec.trees)):
+    gtflow_tree = dtec.trees[tree_idx]
+    tree_weight = dtec.tree_weights[tree_idx]
+    member = model.ensemble.members.add()
+    member.submodel_id.value = tree_idx
+    tree = member.submodel.decision_tree
+    for node_idx in range(len(gtflow_tree.nodes)):
+      gtflow_node = gtflow_tree.nodes[node_idx]
+      node = tree.nodes.add()
+      node_type = gtflow_node.WhichOneof("node")
+      node.node_id.value = node_idx
+      if node_type == "leaf":
+        leaf = gtflow_node.leaf
+        if leaf.HasField("vector"):
+          for weight in leaf.vector.value:
+            new_value = node.leaf.vector.value.add()
+            new_value.float_value = weight * tree_weight
+        else:
+          for index, weight in zip(
+              leaf.sparse_vector.index, leaf.sparse_vector.value):
+            new_value = node.leaf.sparse_vector.sparse_value[index]
+            new_value.float_value = weight * tree_weight
+      else:
+        node = node.binary_node
+        # Binary nodes here.
+        if node_type == "dense_float_binary_split":
+          split = gtflow_node.dense_float_binary_split
+          feature_id = split.feature_column
+          inequality_test = node.inequality_left_child_test
+          inequality_test.feature_id.id.value = sorted_feature_names[feature_id]
+          inequality_test.type = (
+              generic_tree_model_pb2.InequalityTest.LESS_OR_EQUAL)
+          inequality_test.threshold.float_value = split.threshold
+        elif node_type == "sparse_float_binary_split_default_left":
+          split = gtflow_node.sparse_float_binary_split_default_left.split
+          node.default_direction = (
+              generic_tree_model_pb2.BinaryNode.LEFT)
+          feature_id = split.feature_column + num_dense
+          inequality_test = node.inequality_left_child_test
+          inequality_test.feature_id.id.value = sorted_feature_names[feature_id]
+          inequality_test.type = (
+              generic_tree_model_pb2.InequalityTest.LESS_OR_EQUAL)
+          inequality_test.threshold.float_value = split.threshold
+        elif node_type == "sparse_float_binary_split_default_right":
+          split = gtflow_node.sparse_float_binary_split_default_right
+          node.default_direction = (
+              generic_tree_model_pb2.BinaryNode.RIGHT)
+          feature_id = split.feature_column + num_dense
+          inequality_test = node.inequality_left_child_test
+          inequality_test.feature_id.id.value = sorted_feature_names[feature_id]
+          inequality_test.type = (
+              generic_tree_model_pb2.InequalityTest.LESS_OR_EQUAL)
+          inequality_test.threshold.float_value = split.threshold
+        elif node_type == "categorical_id_binary_split":
+          split = gtflow_node.categorical_id_binary_split
+          node.default_direction = generic_tree_model_pb2.BinaryNode.RIGHT
+          feature_id = split.feature_column + num_dense + num_sparse_float
+          categorical_test = (
+              generic_tree_model_extensions_pb2.MatchingValuesTest())
+          categorical_test.feature_id.id.value = sorted_feature_names[
+              feature_id]
+          matching_id = categorical_test.value.add()
+          matching_id.int64_value = split.feature_id
+          node.custom_left_child_test.Pack(categorical_test)
+        else:
+          raise ValueError("Unexpected node type %s", node_type)
+        node.left_child_id.value = split.left_id
+        node.right_child_id.value = split.right_id
+  return model_and_features
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy_test.py
new file mode 100644
index 0000000000..8d801fa1f3
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy_test.py
@@ -0,0 +1,278 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the conversion code from GTFlow format to Chauffeur."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from google.protobuf import text_format
+from tensorflow.contrib.boosted_trees.estimator_batch import custom_export_strategy
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import googletest
+
+
+class ConvertModelTest(test_util.TensorFlowTestCase):
+
+  def testConvertModel(self):
+    dtec_str = """
+    trees {
+      nodes {
+        leaf {
+          vector {
+            value: -1
+          }
+        }
+      }
+    }
+    trees {
+      nodes {
+        dense_float_binary_split {
+          feature_column: 0
+          threshold: 1740.0
+          left_id: 1
+          right_id: 2
+        }
+        node_metadata {
+          gain: 500
+        }
+      }
+      nodes {
+        leaf {
+          vector {
+            value: 0.6
+          }
+        }
+      }
+      nodes {
+        sparse_float_binary_split_default_left {
+          split {
+            feature_column: 0
+            threshold: 1500.0
+            left_id: 3
+            right_id: 4
+          }
+        }
+        node_metadata {
+          gain: 500
+        }
+      }
+      nodes {
+        categorical_id_binary_split {
+          feature_column: 0
+          feature_id: 5
+          left_id: 5
+          right_id: 6
+        }
+        node_metadata {
+          gain: 500
+        }
+      }
+      nodes {
+        leaf {
+          vector {
+            value: 0.8
+          }
+        }
+      }
+      nodes {
+        leaf {
+          vector {
+            value: 0.5
+          }
+        }
+      }
+      nodes {
+        leaf {
+          vector {
+            value: 0.3
+          }
+        }
+      }
+    }
+    tree_weights: 1.0
+    tree_weights: 0.1
+    """
+    dtec = tree_config_pb2.DecisionTreeEnsembleConfig()
+    text_format.Merge(dtec_str, dtec)
+    # The feature columns in the order they were added.
+    feature_columns = ["feature_b", "feature_a", "feature_d"]
+    out = custom_export_strategy.convert_to_universal_format(
+        dtec, feature_columns, 1, 1,
+        1)
+    expected_tree = """
+    features { key: "feature_a" }
+    features { key: "feature_b" }
+    features { key: "feature_d" }
+    model {
+      ensemble {
+        summation_combination_technique {
+        }
+        members {
+          submodel {
+            decision_tree {
+              nodes {
+                node_id {
+                }
+                leaf {
+                  vector {
+                    value {
+                      float_value: -1.0
+                    }
+                  }
+                }
+              }
+            }
+          }
+          submodel_id {
+          }
+        }
+        members {
+          submodel {
+            decision_tree {
+              nodes {
+                node_id {
+                }
+                binary_node {
+                  left_child_id {
+                    value: 1
+                  }
+                  right_child_id {
+                    value: 2
+                  }
+                  inequality_left_child_test {
+                    feature_id {
+                      id {
+                        value: "feature_b"
+                      }
+                    }
+                    threshold {
+                      float_value: 1740.0
+                    }
+                  }
+                }
+              }
+
+              nodes {
+                node_id {
+                  value: 1
+                }
+                leaf {
+                  vector {
+                    value {
+                      float_value: 0.06
+                    }
+                  }
+                }
+              }
+              nodes {
+                node_id {
+                  value: 2
+                }
+                binary_node {
+                  left_child_id {
+                    value: 3
+                  }
+                  right_child_id {
+                    value: 4
+                  }
+                  inequality_left_child_test {
+                    feature_id {
+                      id {
+                        value: "feature_a"
+                      }
+                    }
+                    threshold {
+                      float_value: 1500.0
+                    }
+                  }
+                }
+              }
+              nodes {
+                node_id {
+                  value: 3
+                }
+                binary_node {
+                  left_child_id {
+                    value: 5
+                  }
+                  right_child_id {
+                    value: 6
+                  }
+                  default_direction: RIGHT
+                  custom_left_child_test {
+                    [type.googleapis.com/tensorflow.decision_trees.MatchingValuesTest] {
+                      feature_id {
+                        id {
+                          value: "feature_d"
+                        }
+                      }
+                      value {
+                        int64_value: 5
+                      }
+                    }
+                  }
+                }
+              }
+              nodes {
+                node_id {
+                  value: 4
+                }
+                leaf {
+                  vector {
+                    value {
+                      float_value: 0.08
+                    }
+                  }
+                }
+              }
+              nodes {
+                node_id {
+                  value: 5
+                }
+                leaf {
+                  vector {
+                    value {
+                      float_value: 0.05
+                    }
+                  }
+                }
+              }
+              nodes {
+                node_id {
+                  value: 6
+                }
+                leaf {
+                  vector {
+                    value {
+                      float_value: 0.03
+                    }
+                  }
+                }
+              }
+            }
+          }
+          submodel_id {
+            value: 1
+          }
+        }
+      }
+    }"""
+    self.assertProtoEquals(expected_tree, out)
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/custom_loss_head.py b/tensorflow/contrib/boosted_trees/estimator_batch/custom_loss_head.py
new file mode 100644
index 0000000000..1f5c6040e5
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/custom_loss_head.py
@@ -0,0 +1,73 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implementation of `head.Head` with custom loss and link function."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+
+
+class CustomLossHead(head_lib._RegressionHead):  # pylint: disable=protected-access
+  """A Head object with custom loss function and link function."""
+
+  def __init__(self,
+               loss_fn,
+               link_fn,
+               logit_dimension,
+               head_name=None,
+               weight_column_name=None,
+               metrics_fn=None):
+    """`Head` for specifying arbitrary loss function.
+
+    Args:
+      loss_fn: Loss function.
+      link_fn: Function that converts logits to prediction.
+      logit_dimension: Number of dimensions for the logits.
+      head_name: name of the head. Predictions, summary, metrics keys are
+        suffixed by `"/" + head_name` and the default variable scope is
+        `head_name`.
+      weight_column_name: A string defining feature column name representing
+        weights. It is used to down weight or boost examples during training. It
+        will be multiplied by the loss of the example.
+      metrics_fn: a function that takes predictions dict, labels and weights and
+        returns a dictionary of metrics to be calculated.
+    """
+
+    def loss_wrapper(labels, logits, weight_tensor):
+      if weight_tensor is None:
+        weight_tensor = array_ops.ones(
+            shape=[array_ops.shape(labels)[0], 1], dtype=dtypes.float32)
+      weighted_loss, _ = loss_fn(labels, weight_tensor, logits)
+      average_loss = math_ops.reduce_mean(weighted_loss)
+      return average_loss, average_loss / math_ops.reduce_mean(weight_tensor)
+
+    super(CustomLossHead, self).__init__(
+        loss_fn=loss_wrapper,
+        link_fn=link_fn,
+        head_name=head_name,
+        weight_column_name=weight_column_name,
+        enable_centered_bias=False,
+        label_dimension=logit_dimension)
+
+    self._metrics_fn = metrics_fn
+
+  def _metrics(self, eval_loss, predictions, labels, weights):
+    if self._metrics_fn is not None:
+      return self._metrics_fn(predictions, labels, weights)
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py
new file mode 100644
index 0000000000..e2e0b071b7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py
@@ -0,0 +1,193 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""GTFlow Estimator definition."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.estimator_batch import model
+from tensorflow.contrib.learn.python.learn.estimators import estimator
+from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
+
+
+class GradientBoostedDecisionTreeClassifier(estimator.Estimator):
+  """An estimator using gradient boosted decision trees."""
+
+  def __init__(self,
+               learner_config,
+               examples_per_layer,
+               n_classes=2,
+               num_trees=None,
+               feature_columns=None,
+               weight_column_name=None,
+               model_dir=None,
+               config=None,
+               label_keys=None,
+               feature_engineering_fn=None,
+               center_bias=True):
+    """Initializes a GradientBoostedDecisionTreeClassifier estimator instance.
+
+    Args:
+      learner_config: A config for the learner.
+      examples_per_layer: Number of examples to accumulate before growing a
+        layer. It can also be a function that computes the number of examples
+        based on the depth of the layer that's being built.
+      n_classes: Number of classes in the classification.
+      num_trees: An int, number of trees to build.
+      feature_columns: A list of feature columns.
+      weight_column_name: Name of the column for weights, or None if not
+        weighted.
+      model_dir: Directory for model exports, etc.
+      config: `RunConfig` object to configure the runtime settings.
+      label_keys: Optional list of strings with size `[n_classes]` defining the
+        label vocabulary. Only supported for `n_classes` > 2.
+      feature_engineering_fn: Feature engineering function. Takes features and
+        labels which are the output of `input_fn` and returns features and
+        labels which will be fed into the model.
+      center_bias: Whether a separate tree should be created for first fitting
+        the bias.
+    """
+    head = head_lib.multi_class_head(
+        n_classes=n_classes,
+        weight_column_name=weight_column_name,
+        enable_centered_bias=False)
+    super(GradientBoostedDecisionTreeClassifier, self).__init__(
+        model_fn=model.model_builder,
+        params={
+            'head': head,
+            'feature_columns': feature_columns,
+            'learner_config': learner_config,
+            'num_trees': num_trees,
+            'weight_column_name': weight_column_name,
+            'examples_per_layer': examples_per_layer,
+            'center_bias': center_bias
+        },
+        model_dir=model_dir,
+        config=config,
+        feature_engineering_fn=feature_engineering_fn)
+
+
+class GradientBoostedDecisionTreeRegressor(estimator.Estimator):
+  """An estimator using gradient boosted decision trees."""
+
+  def __init__(self,
+               learner_config,
+               examples_per_layer,
+               label_dimension=1,
+               num_trees=None,
+               feature_columns=None,
+               label_name=None,
+               weight_column_name=None,
+               model_dir=None,
+               config=None,
+               feature_engineering_fn=None,
+               center_bias=True):
+    """Initializes a GradientBoostedDecisionTreeRegressor estimator instance.
+
+    Args:
+      learner_config: A config for the learner.
+      examples_per_layer: Number of examples to accumulate before growing a
+        layer. It can also be a function that computes the number of examples
+        based on the depth of the layer that's being built.
+      label_dimension: Number of regression labels per example. This is the size
+        of the last dimension of the labels `Tensor` (typically, this has shape
+        `[batch_size, label_dimension]`).
+      num_trees: An int, number of trees to build.
+      feature_columns: A list of feature columns.
+      label_name: String, name of the key in label dict. Can be null if label
+          is a tensor (single headed models).
+      weight_column_name: Name of the column for weights, or None if not
+        weighted.
+      model_dir: Directory for model exports, etc.
+      config: `RunConfig` object to configure the runtime settings.
+      feature_engineering_fn: Feature engineering function. Takes features and
+        labels which are the output of `input_fn` and returns features and
+        labels which will be fed into the model.
+      center_bias: Whether a separate tree should be created for first fitting
+        the bias.
+    """
+    head = head_lib.regression_head(
+        label_name=label_name,
+        label_dimension=label_dimension,
+        weight_column_name=weight_column_name,
+        enable_centered_bias=False)
+    super(GradientBoostedDecisionTreeRegressor, self).__init__(
+        model_fn=model.model_builder,
+        params={
+            'head': head,
+            'feature_columns': feature_columns,
+            'learner_config': learner_config,
+            'num_trees': num_trees,
+            'weight_column_name': weight_column_name,
+            'examples_per_layer': examples_per_layer,
+            'center_bias': center_bias
+        },
+        model_dir=model_dir,
+        config=config,
+        feature_engineering_fn=feature_engineering_fn)
+
+
+class GradientBoostedDecisionTreeEstimator(estimator.Estimator):
+  """An estimator using gradient boosted decision trees.
+
+  Useful for training with user specified `Head`.
+  """
+
+  def __init__(self,
+               learner_config,
+               examples_per_layer,
+               head,
+               num_trees=None,
+               feature_columns=None,
+               weight_column_name=None,
+               model_dir=None,
+               config=None,
+               feature_engineering_fn=None,
+               center_bias=True):
+    """Initializes a GradientBoostedDecisionTreeEstimator estimator instance.
+
+    Args:
+      learner_config: A config for the learner.
+      examples_per_layer: Number of examples to accumulate before growing a
+        layer. It can also be a function that computes the number of examples
+        based on the depth of the layer that's being built.
+      head: `Head` instance.
+      num_trees: An int, number of trees to build.
+      feature_columns: A list of feature columns.
+      weight_column_name: Name of the column for weights, or None if not
+        weighted.
+      model_dir: Directory for model exports, etc.
+      config: `RunConfig` object to configure the runtime settings.
+      feature_engineering_fn: Feature engineering function. Takes features and
+        labels which are the output of `input_fn` and returns features and
+        labels which will be fed into the model.
+      center_bias: Whether a separate tree should be created for first fitting
+        the bias.
+    """
+    super(GradientBoostedDecisionTreeEstimator, self).__init__(
+        model_fn=model.model_builder,
+        params={
+            'head': head,
+            'feature_columns': feature_columns,
+            'learner_config': learner_config,
+            'num_trees': num_trees,
+            'weight_column_name': weight_column_name,
+            'examples_per_layer': examples_per_layer,
+            'center_bias': center_bias
+        },
+        model_dir=model_dir,
+        config=config,
+        feature_engineering_fn=feature_engineering_fn)
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/model.py b/tensorflow/contrib/boosted_trees/estimator_batch/model.py
new file mode 100644
index 0000000000..9e9fd64cbf
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/model.py
@@ -0,0 +1,117 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""GTFlow Model definitions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+from tensorflow.contrib.boosted_trees.estimator_batch import trainer_hooks
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.training.functions import gbdt_batch
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.training import training_util
+
+
+def model_builder(features, labels, mode, params, config):
+  """Multi-machine batch gradient descent tree model.
+
+  Args:
+    features: `Tensor` or `dict` of `Tensor` objects.
+    labels: Labels used to train on.
+    mode: Mode we are in. (TRAIN/EVAL/INFER)
+    params: A dict of hyperparameters.
+      The following hyperparameters are expected:
+      * head: A `Head` instance.
+      * learner_config: A config for the learner.
+      * feature_columns: An iterable containing all the feature columns used by
+          the model.
+      * examples_per_layer: Number of examples to accumulate before growing a
+          layer. It can also be a function that computes the number of examples
+          based on the depth of the layer that's being built.
+      * weight_column_name: The name of weight column.
+      * center_bias: Whether a separate tree should be created for first fitting
+          the bias.
+    config: `RunConfig` of the estimator.
+
+  Returns:
+    A `ModelFnOps` object.
+  Raises:
+    ValueError: if inputs are not valid.
+  """
+  head = params["head"]
+  learner_config = params["learner_config"]
+  examples_per_layer = params["examples_per_layer"]
+  feature_columns = params["feature_columns"]
+  weight_column_name = params["weight_column_name"]
+  num_trees = params["num_trees"]
+  if features is None:
+    raise ValueError("At least one feature must be specified.")
+
+  if config is None:
+    raise ValueError("Missing estimator RunConfig.")
+
+  center_bias = params["center_bias"]
+
+  # Make a shallow copy of features to ensure downstream usage
+  # is unaffected by modifications in the model function.
+  training_features = copy.copy(features)
+  training_features.pop(weight_column_name, None)
+  global_step = training_util.get_global_step()
+  with ops.device(global_step.device):
+    ensemble_handle = model_ops.tree_ensemble_variable(
+        stamp_token=0,
+        tree_ensemble_config="",  # Initialize an empty ensemble.
+        name="ensemble_model")
+
+  # Create GBDT model.
+  gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+      is_chief=config.is_chief,
+      num_ps_replicas=config.num_ps_replicas,
+      ensemble_handle=ensemble_handle,
+      center_bias=center_bias,
+      examples_per_layer=examples_per_layer,
+      learner_config=learner_config,
+      feature_columns=feature_columns,
+      features=features)
+  with ops.name_scope("gbdt", "gbdt_optimizer"):
+    predictions_dict = gbdt_model.predict(mode)
+    logits = predictions_dict["predictions"]
+
+    def _train_op_fn(loss):
+      """Returns the op to optimize the loss."""
+      update_op = gbdt_model.train(loss, predictions_dict, labels)
+      with ops.control_dependencies(
+          [update_op]), (ops.colocate_with(global_step)):
+        update_op = state_ops.assign_add(global_step, 1).op
+        return update_op
+
+  model_fn_ops = head.create_model_fn_ops(
+      features=features,
+      mode=mode,
+      labels=labels,
+      train_op_fn=_train_op_fn,
+      logits=logits)
+  if num_trees:
+    if center_bias:
+      num_trees += 1
+    finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor()
+    model_fn_ops.training_hooks.append(
+        trainer_hooks.StopAfterNTrees(num_trees, attempted_trees,
+                                      finalized_trees))
+  return model_fn_ops
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks.py b/tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks.py
new file mode 100644
index 0000000000..222d066f7b
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks.py
@@ -0,0 +1,177 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Hooks for use with GTFlow Estimator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from tensorflow.contrib.learn.python.learn import session_run_hook
+from tensorflow.contrib.learn.python.learn.session_run_hook import SessionRunArgs
+from tensorflow.core.framework.summary_pb2 import Summary
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import training_util
+from tensorflow.python.training.summary_io import SummaryWriterCache
+
+
+class FeatureImportanceSummarySaver(session_run_hook.SessionRunHook):
+  """Hook to save feature importance summaries."""
+
+  def __init__(self, model_dir, every_n_steps=1):
+    """Create a FeatureImportanceSummarySaver Hook.
+
+    This hook creates scalar summaries representing feature importance
+    for each feature column during training.
+
+    Args:
+      model_dir: model base output directory.
+      every_n_steps: frequency, in number of steps, for logging summaries.
+
+    Raises:
+      ValueError: If one of the arguments is invalid.
+    """
+    if model_dir is None:
+      raise ValueError("model dir must be specified.")
+    self._model_dir = model_dir
+    self._every_n_steps = every_n_steps
+    self._last_triggered_step = None
+
+  def begin(self):
+    self._global_step_tensor = training_util.get_global_step()
+    if self._global_step_tensor is None:
+      raise RuntimeError(
+          "Global step should be created to use FeatureImportanceSummarySaver.")
+    graph = ops.get_default_graph()
+    self._feature_names_tensor = graph.get_tensor_by_name(
+        "gbdt/feature_names:0")
+    self._feature_usage_counts_tensor = graph.get_tensor_by_name(
+        "gbdt/feature_usage_counts:0")
+    self._feature_gains_tensor = graph.get_tensor_by_name(
+        "gbdt/feature_gains:0")
+
+  def before_run(self, run_context):
+    del run_context  # Unused by feature importance summary saver hook.
+    requests = {
+        "global_step": self._global_step_tensor,
+        "feature_names": self._feature_names_tensor,
+        "feature_usage_counts": self._feature_usage_counts_tensor,
+        "feature_gains": self._feature_gains_tensor
+    }
+    return SessionRunArgs(requests)
+
+  def after_run(self, run_context, run_values):
+    del run_context  # Unused by feature importance summary saver hook.
+
+    # Read result tensors.
+    global_step = run_values.results["global_step"]
+    feature_names = run_values.results["feature_names"]
+    feature_usage_counts = run_values.results["feature_usage_counts"]
+    feature_gains = run_values.results["feature_gains"]
+
+    # Ensure summaries are logged at desired frequency
+    if (self._last_triggered_step is not None and
+        global_step < self._last_triggered_step + self._every_n_steps):
+      return
+
+    # Validate tensors.
+    if (len(feature_names) != len(feature_usage_counts) or
+        len(feature_names) != len(feature_gains)):
+      raise RuntimeError(
+          "Feature names and importance measures have inconsistent lengths.")
+
+    # Compute total usage.
+    total_usage_count = 0.0
+    for usage_count in feature_usage_counts:
+      total_usage_count += usage_count
+    usage_count_norm = 1.0 / total_usage_count if total_usage_count else 1.0
+
+    # Compute total gain.
+    total_gain = 0.0
+    for gain in feature_gains:
+      total_gain += gain
+    gain_norm = 1.0 / total_gain if total_gain else 1.0
+
+    # Output summary for each feature.
+    self._last_triggered_step = global_step
+    for (name, usage_count, gain) in zip(feature_names, feature_usage_counts,
+                                         feature_gains):
+      output_dir = os.path.join(self._model_dir, name.decode("utf-8"))
+      summary_writer = SummaryWriterCache.get(output_dir)
+      usage_count_summary = Summary(value=[
+          Summary.Value(
+              tag="feature_importance/usage_counts",
+              simple_value=usage_count)
+      ])
+      usage_fraction_summary = Summary(value=[
+          Summary.Value(
+              tag="feature_importance/usage_fraction",
+              simple_value=usage_count * usage_count_norm)
+      ])
+      summary_writer.add_summary(usage_count_summary, global_step)
+      summary_writer.add_summary(usage_fraction_summary, global_step)
+      gains_summary = Summary(
+          value=[Summary.Value(
+              tag="feature_importance/gains",
+              simple_value=gain)])
+      gains_fraction_summary = Summary(
+          value=[Summary.Value(
+              tag="feature_importance/gains_fraction",
+              simple_value=gain * gain_norm)])
+      summary_writer.add_summary(gains_summary, global_step)
+      summary_writer.add_summary(gains_fraction_summary, global_step)
+
+
+class FeedFnHook(session_run_hook.SessionRunHook):
+  """Runs feed_fn and sets the feed_dict accordingly."""
+
+  def __init__(self, feed_fn):
+    self.feed_fn = feed_fn
+
+  def before_run(self, run_context):
+    del run_context  # unused by FeedFnHook.
+    return session_run_hook.SessionRunArgs(
+        fetches=None, feed_dict=self.feed_fn)
+
+
+class StopAfterNTrees(session_run_hook.SessionRunHook):
+  """Stop training after building N full trees."""
+
+  def __init__(self, n, num_attempted_trees_tensor, num_finalized_trees_tensor):
+    self._num_trees = n
+    # num_attempted_trees_tensor and num_finalized_trees_tensor are both
+    # tensors.
+    self._num_attempted_trees_tensor = num_attempted_trees_tensor
+    self._num_finalized_trees_tensor = num_finalized_trees_tensor
+
+  def before_run(self, run_context):
+    del run_context  # unused by StopTrainingAfterNTrees.
+    return session_run_hook.SessionRunArgs({
+        "num_attempted_trees": self._num_attempted_trees_tensor,
+        "num_finalized_trees": self._num_finalized_trees_tensor,
+    })
+
+  def after_run(self, run_context, run_values):
+    num_attempted_trees = run_values.results["num_attempted_trees"]
+    num_finalized_trees = run_values.results["num_finalized_trees"]
+    assert num_attempted_trees is not None
+    assert num_finalized_trees is not None
+    if (num_finalized_trees >= self._num_trees or
+        num_attempted_trees > self._num_trees):
+      logging.info("Requesting stop since we have reached %d trees.",
+                   num_finalized_trees)
+      run_context.request_stop()
diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks_test.py
new file mode 100644
index 0000000000..47e0eb6d97
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks_test.py
@@ -0,0 +1,76 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for trainer hooks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import tempfile
+
+from tensorflow.contrib.boosted_trees.estimator_batch import trainer_hooks
+from tensorflow.contrib.framework.python.ops import variables
+from tensorflow.python.client import session as tf_session
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variables as tf_variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import monitored_session
+
+
+class FeatureImportanceSummarySaverTest(test_util.TensorFlowTestCase):
+
+  def test_invalid_input(self):
+    with self.assertRaises(ValueError):
+      trainer_hooks.FeatureImportanceSummarySaver(model_dir=None)
+
+  def test_invalid_graph(self):
+    # Create inputs.
+    model_dir = tempfile.mkdtemp()
+    hook = trainer_hooks.FeatureImportanceSummarySaver(model_dir)
+    with ops.Graph().as_default():
+      # Begin won't be able to find the required tensors in the graph.
+      _ = variables.get_or_create_global_step()
+      with self.assertRaises(KeyError):
+        hook.begin()
+
+  def test_run(self):
+    # Create inputs.
+    model_dir = tempfile.mkdtemp()
+    hook = trainer_hooks.FeatureImportanceSummarySaver(model_dir)
+    with ops.Graph().as_default(), tf_session.Session() as sess:
+      global_step = variables.get_or_create_global_step()
+      with ops.name_scope("gbdt"):
+        constant_op.constant(["featA", "featB"], name="feature_names")
+        constant_op.constant([0, 2], name="feature_usage_counts")
+        constant_op.constant([0, 0.8], name="feature_gains")
+      # Begin finds tensors in the graph.
+      hook.begin()
+      sess.run(tf_variables.global_variables_initializer())
+      # Run hook in a monitored session.
+      train_op = state_ops.assign_add(global_step, 1)
+      mon_sess = monitored_session._HookedSession(sess, [hook])
+      mon_sess.run(train_op)
+      hook.end(sess)
+      # Ensure output summary dirs are created.
+      self.assertTrue(os.path.exists(os.path.join(model_dir, "featA")))
+      self.assertTrue(os.path.exists(os.path.join(model_dir, "featB")))
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/lib/BUILD b/tensorflow/contrib/boosted_trees/lib/BUILD
index 6de0b4515a..6f9443cf94 100644
--- a/tensorflow/contrib/boosted_trees/lib/BUILD
+++ b/tensorflow/contrib/boosted_trees/lib/BUILD
@@ -11,6 +11,8 @@ package(
     ],
 )
 
+load("//tensorflow:tensorflow.bzl", "py_test")
+
 filegroup(
     name = "all_files",
     srcs = glob(
@@ -254,6 +256,65 @@ cc_test(
     ],
 )
 
+# Learner/batch
+
+py_library(
+    name = "base_split_handler",
+    srcs = ["learner/batch/base_split_handler.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+    ],
+)
+
+py_library(
+    name = "categorical_split_handler",
+    srcs = ["learner/batch/categorical_split_handler.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_split_handler",
+        "//tensorflow/contrib/boosted_trees:quantile_ops_py",
+        "//tensorflow/contrib/boosted_trees:split_handler_ops_py",
+        "//tensorflow/contrib/boosted_trees:stats_accumulator_ops_py",
+    ],
+)
+
+py_test(
+    name = "categorical_split_handler_test",
+    srcs = ["learner/batch/categorical_split_handler_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":categorical_split_handler",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/python:framework_test_lib",
+    ],
+)
+
+py_library(
+    name = "ordinal_split_handler",
+    srcs = ["learner/batch/ordinal_split_handler.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_split_handler",
+        "//tensorflow/contrib/boosted_trees:quantile_ops_py",
+        "//tensorflow/contrib/boosted_trees:split_handler_ops_py",
+        "//tensorflow/contrib/boosted_trees:stats_accumulator_ops_py",
+        "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
+    ],
+)
+
+py_test(
+    name = "ordinal_split_handler_test",
+    srcs = ["learner/batch/ordinal_split_handler_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":ordinal_split_handler",
+        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
+        "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/python:framework_test_lib",
+    ],
+)
+
 # Learner/Common
 
 cc_library(
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py
new file mode 100644
index 0000000000..56ff00b390
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py
@@ -0,0 +1,147 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Base class for creating split nodes using one or more features."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
+from tensorflow.python.ops import control_flow_ops
+
+
+class BaseSplitHandler(object):
+  """Abstract Base class defining split handlers interface."""
+
+  __metaclass__ = abc.ABCMeta
+
+  def __init__(self,
+               l1_regularization,
+               l2_regularization,
+               tree_complexity_regularization,
+               min_node_weight,
+               feature_column_group_id,
+               gradient_shape,
+               hessian_shape,
+               multiclass_strategy,
+               name=None):
+    """Constructor for BaseSplitHandler.
+
+    Args:
+      l1_regularization: L1 regularization applied for this split handler.
+      l2_regularization: L2 regularization applied for this split handler.
+      tree_complexity_regularization: Tree complexity regularization applied
+          for this split handler.
+      min_node_weight: Minimum sum of weights of examples in each partition to
+          be considered for splitting.
+      feature_column_group_id: Feature column group index.
+      gradient_shape: A TensorShape, containing shape of gradients.
+      hessian_shape: A TensorShape, containing shape of hessians.
+      multiclass_strategy: Strategy describing how to treat multiclass problems.
+      name: An optional handler name.
+    """
+    self._l1_regularization = l1_regularization
+    self._l2_regularization = l2_regularization
+    self._tree_complexity_regularization = tree_complexity_regularization
+    self._min_node_weight = min_node_weight
+    self._feature_column_group_id = feature_column_group_id
+    self._name = name or ""
+    self._multiclass_strategy = multiclass_strategy
+    self._hessian_shape = hessian_shape
+    self._gradient_shape = gradient_shape
+
+  def scheduled_reads(self):
+    """Returns the list of `ScheduledOp`s required for update_stats."""
+    return []
+
+  @abc.abstractmethod
+  def update_stats(self, stamp_token, example_partition_ids, gradients,
+                   hessians, empty_gradients, empty_hessians, weights,
+                   is_active, scheduled_reads):
+    """Updates the state for this split handler.
+
+    Args:
+      stamp_token: An int32 scalar tensor containing the current stamp token.
+      example_partition_ids: A dense tensor, containing an int32 for each
+        example which is the partition id that the example ends up in.
+      gradients: A dense tensor of gradients.
+      hessians: A dense tensor of hessians.
+      empty_gradients: A dense empty tensor of the same shape (for dimensions >
+        0) as gradients.
+      empty_hessians: A dense empty tensor of the same shape (for dimensions >
+        0) as hessians.
+      weights: A dense float32 tensor with a weight for each example.
+      is_active: A boolean tensor that says if this handler is active or not.
+          One value for the current layer and one value for the next layer.
+      scheduled_reads: List of results from the scheduled reads.
+
+    Returns:
+      A tuple of the op that updates the stats for this handler and a list of
+      `ScheduledOp`s.
+    """
+
+  def update_stats_sync(self, stamp_token, example_partition_ids, gradients,
+                        hessians, empty_gradients, empty_hessians, weights,
+                        is_active):
+    """Updates the state for this split handler running the scheduled I/O.
+
+    Args:
+      stamp_token: An int32 scalar tensor containing the current stamp token.
+      example_partition_ids: A dense tensor, containing an int32 for each
+        example which is the partition id that the example ends up in.
+      gradients: A dense tensor of gradients.
+      hessians: A dense tensor of hessians.
+      empty_gradients: A dense empty tensor of the same shape (for dimensions >
+        0) as gradients.
+      empty_hessians: A dense empty tensor of the same shape (for dimensions >
+        0) as hessians.
+      weights: A dense float32 tensor with a weight for each example.
+      is_active: A boolean tensor that says if this handler is active or not.
+          One value for the current layer and one value for the next layer.
+
+    Returns:
+      Op that updates the stats for this handler.
+    """
+    handler_reads = {self: self.scheduled_reads()}
+    handler_results = batch_ops_utils.run_handler_scheduled_ops(
+        handler_reads, stamp_token, None)
+    update_1, scheduled_updates = self.update_stats(
+        stamp_token, example_partition_ids, gradients, hessians,
+        empty_gradients, empty_hessians, weights, is_active,
+        handler_results[self])
+    update_2 = batch_ops_utils.run_handler_scheduled_ops({
+        self: scheduled_updates
+    }, stamp_token, None)
+    return control_flow_ops.group(update_1, *update_2[self])
+
+  @abc.abstractmethod
+  def make_splits(self, stamp_token, next_stamp_token, class_id):
+    """Create the best split using the accumulated stats and flush the state.
+
+    This should only be called by the master.
+
+    Args:
+      stamp_token: An int32 scalar tensor containing the current stamp token.
+      next_stamp_token: An int32 scalar tensor containing the stamp token for
+        the next iteration.
+      class_id: what class id the handler gathers stats for (for tree per class
+        strategy). When class_id=-1, the strategy is not tree per class.
+    Returns:
+      A tuple (are_splits_ready, partition_id, gain, split_info) where
+      are_splits_ready is a scalar boolean tensor, partition_id is a rank 1,
+      int32 tensor, gain is a rank 1 float32 tensor and split_info is a rank 1
+      string tensor containing serialized SplitInfo protos.
+    """
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py
new file mode 100644
index 0000000000..83dad7e4b3
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py
@@ -0,0 +1,189 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implementation of handler for split nodes for categorical columns."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.lib.learner.batch import base_split_handler
+from tensorflow.contrib.boosted_trees.python.ops import split_handler_ops
+from tensorflow.contrib.boosted_trees.python.ops import stats_accumulator_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+
+_BIAS_FEATURE_ID = -1
+
+
+class EqualitySplitHandler(base_split_handler.BaseSplitHandler):
+  """Creates equality split type for categorical features."""
+
+  def __init__(self,
+               sparse_int_column,
+               l1_regularization,
+               l2_regularization,
+               tree_complexity_regularization,
+               min_node_weight,
+               feature_column_group_id,
+               gradient_shape,
+               hessian_shape,
+               multiclass_strategy,
+               init_stamp_token=0,
+               name=None):
+    """Initialize the internal state for this split handler.
+
+    Args:
+      sparse_int_column: A `SparseTensor` column with int64 values associated
+        with this handler.
+      l1_regularization: L1 regularization applied for this split handler.
+      l2_regularization: L2 regularization applied for this split handler.
+      tree_complexity_regularization: Tree complexity regularization applied
+          for this split handler.
+      min_node_weight: Minimum sum of weights of examples in each partition to
+          be considered for splitting.
+      feature_column_group_id: Feature column group index.
+      gradient_shape: A TensorShape, containing shape of gradients.
+      hessian_shape: A TensorShape, containing shape of hessians.
+      multiclass_strategy: Strategy describing how to treat multiclass problems.
+      init_stamp_token: A tensor containing an scalar for initial stamp of the
+         stamped objects.
+      name: An optional handler name.
+    """
+    super(EqualitySplitHandler, self).__init__(
+        l1_regularization=l1_regularization,
+        l2_regularization=l2_regularization,
+        tree_complexity_regularization=tree_complexity_regularization,
+        min_node_weight=min_node_weight,
+        feature_column_group_id=feature_column_group_id,
+        gradient_shape=gradient_shape,
+        hessian_shape=hessian_shape,
+        multiclass_strategy=multiclass_strategy,
+        name=name)
+    self._stats_accumulator = stats_accumulator_ops.StatsAccumulator(
+        init_stamp_token,
+        gradient_shape,
+        hessian_shape,
+        name="StatsAccumulator/{}".format(self._name))
+    self._sparse_int_column = sparse_int_column
+
+  def update_stats(self, stamp_token, example_partition_ids, gradients,
+                   hessians, empty_gradients, empty_hessians, weights,
+                   is_active, scheduled_reads):
+    """Updates the state for equality split handler.
+
+    Args:
+      stamp_token: An int32 scalar tensor containing the current stamp token.
+      example_partition_ids: A dense tensor, containing an int32 for each
+        example which is the partition id that the example ends up in.
+      gradients: A dense tensor of gradients.
+      hessians: A dense tensor of hessians.
+      empty_gradients: A dense empty tensor of the same shape (for dimensions >
+        0) as gradients.
+      empty_hessians: A dense empty tensor of the same shape (for dimensions >
+        0) as hessians.
+      weights: A dense float32 tensor with a weight for each example.
+      is_active: A boolean tensor that says if this handler is active or not.
+          One value for the current layer and one value for the next layer.
+      scheduled_reads: List of results from the scheduled reads.
+    Returns:
+      The op that updates the stats for this handler.
+    Raises:
+      ValueError: If example_columns is not a single sparse column.
+
+    """
+    del scheduled_reads  # Unused by the categorical split handler.
+
+    def not_active_inputs():
+      return (constant_op.constant([], dtype=dtypes.int32),
+              constant_op.constant([], dtype=dtypes.int64), empty_gradients,
+              empty_hessians)
+
+    def active_inputs():
+      """The normal flow when the handler is active."""
+      # Remove the second column of example indices matrix since it is not
+      # useful.
+      example_indices, _ = array_ops.split(
+          self._sparse_int_column.indices, num_or_size_splits=2, axis=1)
+      example_indices = array_ops.squeeze(example_indices, [1])
+
+      filtered_gradients = array_ops.gather(gradients, example_indices)
+      filtered_hessians = array_ops.gather(hessians, example_indices)
+      filtered_partition_ids = array_ops.gather(example_partition_ids,
+                                                example_indices)
+      unique_partitions, mapped_partitions = array_ops.unique(
+          example_partition_ids)
+
+      # Compute aggregate stats for each partition.
+      # The bias is computed on gradients and hessians (and not
+      # filtered_gradients) which have exactly one value per example, so we
+      # don't double count a gradient in multivalent columns.
+      per_partition_gradients = math_ops.unsorted_segment_sum(
+          gradients, mapped_partitions, array_ops.size(unique_partitions))
+      per_partition_hessians = math_ops.unsorted_segment_sum(
+          hessians, mapped_partitions, array_ops.size(unique_partitions))
+
+      # Prepend a bias feature per partition that accumulates the stats for all
+      # examples in that partition.
+      # Bias is added to the stats even if there are no examples with values in
+      # the current sparse column. The reason is that the other example batches
+      # might have values in these partitions so we have to keep the bias
+      # updated.
+      bias_feature_ids = array_ops.fill(
+          array_ops.shape(unique_partitions), _BIAS_FEATURE_ID)
+      bias_feature_ids = math_ops.cast(bias_feature_ids, dtypes.int64)
+      partition_ids = array_ops.concat(
+          [unique_partitions, filtered_partition_ids], 0)
+      filtered_gradients = array_ops.concat(
+          [per_partition_gradients, filtered_gradients], 0)
+      filtered_hessians = array_ops.concat(
+          [per_partition_hessians, filtered_hessians], 0)
+      feature_ids = array_ops.concat(
+          [bias_feature_ids, self._sparse_int_column.values], 0)
+      return partition_ids, feature_ids, filtered_gradients, filtered_hessians
+
+    partition_ids, feature_ids, gradients_out, hessians_out = (
+        control_flow_ops.cond(is_active[0], active_inputs, not_active_inputs))
+    result = self._stats_accumulator.schedule_add(partition_ids, feature_ids,
+                                                  gradients_out, hessians_out)
+    return (control_flow_ops.no_op(), [result])
+
+  def make_splits(self, stamp_token, next_stamp_token, class_id):
+    """Create the best split using the accumulated stats and flush the state."""
+    # Get the aggregated gradients and hessians per <partition_id, feature_id>
+    # pair.
+    num_minibatches, partition_ids, feature_ids, gradients, hessians = (
+        self._stats_accumulator.flush(stamp_token, next_stamp_token))
+    partition_ids, gains, split_infos = (
+        split_handler_ops.build_categorical_equality_splits(
+            num_minibatches=num_minibatches,
+            partition_ids=partition_ids,
+            feature_ids=feature_ids,
+            gradients=gradients,
+            hessians=hessians,
+            class_id=class_id,
+            feature_column_group_id=self._feature_column_group_id,
+            l1_regularization=self._l1_regularization,
+            l2_regularization=self._l2_regularization,
+            tree_complexity_regularization=self._tree_complexity_regularization,
+            min_node_weight=self._min_node_weight,
+            bias_feature_id=_BIAS_FEATURE_ID,
+            multiclass_strategy=self._multiclass_strategy,))
+    # There are no warm-up rounds needed in the equality column handler. So we
+    # always return ready.
+    are_splits_ready = constant_op.constant(True)
+    return (are_splits_ready, partition_ids, gains, split_infos)
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py
new file mode 100644
index 0000000000..0b65eba2a7
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py
@@ -0,0 +1,338 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for checking stats accumulator related ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.lib.learner.batch import categorical_split_handler
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import split_info_pb2
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+
+
+def get_empty_tensors(gradient_shape, hessian_shape):
+  empty_hess_shape = [1] + hessian_shape.as_list()
+  empty_grad_shape = [1] + gradient_shape.as_list()
+
+  empty_gradients = constant_op.constant(
+      [], dtype=dtypes.float32, shape=empty_grad_shape)
+  empty_hessians = constant_op.constant(
+      [], dtype=dtypes.float32, shape=empty_hess_shape)
+
+  return empty_gradients, empty_hessians
+
+
+class EqualitySplitHandlerTest(test_util.TensorFlowTestCase):
+
+  def testGenerateFeatureSplitCandidates(self):
+    with self.test_session() as sess:
+      # The data looks like the following:
+      # Example |  Gradients    | Partition | Feature ID     |
+      # i0      |  (0.2, 0.12)  | 0         | 1,2            |
+      # i1      |  (-0.5, 0.07) | 0         |                |
+      # i2      |  (1.2, 0.2)   | 0         | 2              |
+      # i3      |  (4.0, 0.13)  | 1         | 1              |
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      partition_ids = [0, 0, 0, 1]
+      indices = [[0, 0], [0, 1], [2, 0], [3, 0]]
+      values = array_ops.constant([1, 2, 2, 1], dtype=dtypes.int64)
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = categorical_split_handler.EqualitySplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          sparse_int_column=sparse_tensor.SparseTensor(indices, values, [4, 1]),
+          feature_column_group_id=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS,
+          init_stamp_token=0)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready, partitions, gains, splits = (
+            split_handler.make_splits(0, 1, class_id))
+        are_splits_ready, partitions, gains, splits = (sess.run(
+            [are_splits_ready, partitions, gains, splits]))
+    self.assertTrue(are_splits_ready)
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the split on partition 0.
+    # -(0.2 + 1.2 - 0.1) / (0.12 + 0.2 + 1)
+    expected_left_weight = -0.9848484848484846
+
+    # (0.2 + 1.2 - 0.1) ** 2 / (0.12 + 0.2 + 1)
+    expected_left_gain = 1.2803030303030298
+
+    # -(-0.5 + 0.1) / (0.07 + 1)
+    expected_right_weight = 0.37383177570093457
+
+    # (-0.5 + 0.1) ** 2 / (0.07 + 1)
+    expected_right_gain = 0.14953271028037385
+
+    # (0.2 + -0.5 + 1.2 - 0.1) ** 2 / (0.12 + 0.07 + 0.2 + 1)
+    expected_bias_gain = 0.46043165467625885
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertEqual(2, split_node.feature_id)
+
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0],
+        0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (0.13 + 1)
+    expected_left_weight = -3.4513274336283186
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_left_gain = 13.460176991150442
+    expected_right_weight = 0
+    expected_right_gain = 0
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_bias_gain = 13.460176991150442
+
+    # Verify candidate for partition 1, there's only one active feature here
+    # so zero gain is expected.
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+    self.assertAllClose(0.0, gains[1], 0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertEqual(1, split_node.feature_id)
+
+  def testGenerateFeatureSplitCandidatesMulticlass(self):
+    with self.test_session() as sess:
+      # Batch size is 4, 2 gradients per each instance.
+      gradients = array_ops.constant(
+          [[0.2, 0.1], [-0.5, 0.2], [1.2, 3.4], [4.0, -3.5]], shape=[4, 2])
+      # 2x2 matrix for each instance
+      hessian_0 = [[0.12, 0.02], [0.3, 0.11]]
+      hessian_1 = [[0.07, -0.2], [-0.5, 0.2]]
+      hessian_2 = [[0.2, -0.23], [-0.8, 0.9]]
+      hessian_3 = [[0.13, -0.3], [-1.5, 2.2]]
+      hessians = array_ops.constant(
+          [hessian_0, hessian_1, hessian_2, hessian_3])
+
+      partition_ids = [0, 0, 0, 1]
+      indices = [[0, 0], [0, 1], [2, 0], [3, 0]]
+      values = array_ops.constant([1, 2, 2, 1], dtype=dtypes.int64)
+
+      hessians = array_ops.constant(
+          [hessian_0, hessian_1, hessian_2, hessian_3])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+
+      gradient_shape = tensor_shape.TensorShape([2])
+      hessian_shape = tensor_shape.TensorShape([2, 2])
+      class_id = -1
+
+      split_handler = categorical_split_handler.EqualitySplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          sparse_int_column=sparse_tensor.SparseTensor(indices, values, [4, 1]),
+          feature_column_group_id=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN,
+          init_stamp_token=0)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready, partitions, gains, splits = (
+            split_handler.make_splits(0, 1, class_id))
+        are_splits_ready, partitions, gains, splits = (sess.run(
+            [are_splits_ready, partitions, gains, splits]))
+    self.assertTrue(are_splits_ready)
+    self.assertAllEqual([0, 1], partitions)
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+    self.assertEqual(1, split_node.feature_id)
+
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.categorical_id_binary_split
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(0, len(right_child.value))
+    self.assertEqual(1, split_node.feature_id)
+
+  def testEmpty(self):
+    with self.test_session() as sess:
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      partition_ids = [0, 0, 0, 1]
+      indices = array_ops.constant([], dtype=dtypes.int64, shape=[0, 2])
+      values = array_ops.constant([], dtype=dtypes.int64)
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = categorical_split_handler.EqualitySplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          sparse_int_column=sparse_tensor.SparseTensor(indices, values, [4, 1]),
+          feature_column_group_id=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS,
+          init_stamp_token=0)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready, partitions, gains, splits = (
+            split_handler.make_splits(0, 1, class_id))
+        are_splits_ready, partitions, gains, splits = (sess.run(
+            [are_splits_ready, partitions, gains, splits]))
+    self.assertTrue(are_splits_ready)
+    self.assertEqual(len(partitions), 0)
+    self.assertEqual(len(gains), 0)
+    self.assertEqual(len(splits), 0)
+
+  def testInactive(self):
+    with self.test_session() as sess:
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      partition_ids = [0, 0, 0, 1]
+      indices = [[0, 0], [0, 1], [2, 0], [3, 0]]
+      values = array_ops.constant([1, 2, 2, 1], dtype=dtypes.int64)
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = categorical_split_handler.EqualitySplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          sparse_int_column=sparse_tensor.SparseTensor(indices, values, [4, 1]),
+          feature_column_group_id=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS,
+          init_stamp_token=0)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([False, False]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready, partitions, gains, splits = (
+            split_handler.make_splits(0, 1, class_id))
+        are_splits_ready, partitions, gains, splits = (sess.run(
+            [are_splits_ready, partitions, gains, splits]))
+    self.assertTrue(are_splits_ready)
+    self.assertEqual(len(partitions), 0)
+    self.assertEqual(len(gains), 0)
+    self.assertEqual(len(splits), 0)
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py
new file mode 100644
index 0000000000..8c0a3f0d91
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py
@@ -0,0 +1,518 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implementation of handler for split nodes for float columns.
+
+The general idea in batch split finding is that each handler will accumulate its
+own statistics on multiple workers. After some steps, the master runs
+make_splits() sub-graph of each handler and each handler returns its best split
+per partition.
+
+The way we ensure consistency of statistics is by using stamp_tokens for read
+and write operations. During each update of the model, a new stamp token is
+created. This stamp token makes sure that updates from the previous iterations
+are not included in the statistics for this iteration.
+
+Inequality splits for float features are created similar to the method described
+in Approximate Algorithm described in https://arxiv.org/pdf/1603.02754v3.pdf.
+Weighted quantiles of the feature columns are computed in a distributed fashion
+using quantile_ops.quantile_accumulator.
+After certain number of steps of parallel accumulation of quantile statistics,
+we decide on bucket boundaries. These bucket boundaries are then used for the
+next N steps to accumulate gradients and hessians per bucket.
+
+In this implementation, we gather quantile statistics and gradient statistics
+concurrently. That means that we don't wait until we have enough quantile
+statistics for bucketization before we start gathering gradient stats. Instead
+during each step we create quantile stats for the next iteration and use the
+previous quantile buckets for gradient stats accumulation.
+In make_splits, we do these steps:
+1) Get the buckets that were used creating for the gradient stats.
+2) Create bucket boundaries for the next N iterations and clear the accumulated
+   quantile stats.
+n3) Get the accumulated gradient stats and clear the accumulator. This step can
+   run in parallel to step 2.
+4) For each leaf node in the current tree (partition):
+   4.1) Get the overall gain computed with gradients and hessians of all
+        examples that end up in this partition.
+   4.2) Compute tensors of left and right cumulative sum of gradients, hessians
+        and gain. The first dimension of these tensors are the bucket
+        boundaries.
+   4.3) Find the gains for all bucket boundaries:
+        split_gains = left_gain + right_gain - overall_gain.
+   4.4) Find the bucket boundary that has the best gain (argmax(split_gains))
+   4.5) For Sparse handler, we also consider the gain for when the examples go
+        the left child and when the examples go to the right child and pick the
+        default direction that yields the most gain.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+
+from tensorflow.contrib.boosted_trees.lib.learner.batch import base_split_handler
+from tensorflow.contrib.boosted_trees.python.ops import quantile_ops
+from tensorflow.contrib.boosted_trees.python.ops import split_handler_ops
+from tensorflow.contrib.boosted_trees.python.ops import stats_accumulator_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import function
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+_BIAS_FEATURE_ID = -1
+# Pattern to remove all non alpha numeric from a string.
+_PATTERN = re.compile(r"[\W_]+")
+
+
+class InequalitySplitHandler(base_split_handler.BaseSplitHandler):
+  """Base class for handlers of inequality splits."""
+
+  def __init__(self,
+               l1_regularization,
+               l2_regularization,
+               tree_complexity_regularization,
+               min_node_weight,
+               feature_column_group_id,
+               epsilon,
+               num_quantiles,
+               gradient_shape,
+               hessian_shape,
+               multiclass_strategy,
+               init_stamp_token=0,
+               name=None):
+    """Initialize the internal state for this split handler.
+
+    Args:
+      l1_regularization: L1 regularization applied for this split handler.
+      l2_regularization: L2 regularization applied for this split handler.
+      tree_complexity_regularization: Tree complexity regularization applied
+          for this split handler.
+      min_node_weight: Minimum sum of weights of examples in each partition to
+          be considered for splitting.
+      feature_column_group_id: Feature column group index.
+      epsilon: A float, the error bound for quantile computation.
+      num_quantiles: An int, the number of buckets to create from the histogram.
+      gradient_shape: A TensorShape, containing shape of gradients.
+      hessian_shape: A TensorShape, containing shape of hessians.
+      multiclass_strategy: Strategy describing how to treat multiclass problems.
+      init_stamp_token: A tensor containing an scalar for initial stamp of the
+         stamped objects.
+      name: An optional handler name.
+    """
+    super(InequalitySplitHandler, self).__init__(
+        name=name,
+        l1_regularization=l1_regularization,
+        l2_regularization=l2_regularization,
+        tree_complexity_regularization=tree_complexity_regularization,
+        min_node_weight=min_node_weight,
+        feature_column_group_id=feature_column_group_id,
+        gradient_shape=gradient_shape,
+        hessian_shape=hessian_shape,
+        multiclass_strategy=multiclass_strategy)
+    self._stats_accumulator = stats_accumulator_ops.StatsAccumulator(
+        init_stamp_token,
+        gradient_shape,
+        hessian_shape,
+        name="StatsAccumulator/{}".format(self._name))
+    self._quantile_accumulator = quantile_ops.QuantileAccumulator(
+        init_stamp_token,
+        epsilon=epsilon,
+        num_quantiles=num_quantiles,
+        name="QuantileAccumulator/{}".format(self._name))
+
+
+class DenseSplitHandler(InequalitySplitHandler):
+  """Computes stats and finds the best inequality splits on dense columns."""
+
+  def __init__(self,
+               dense_float_column,
+               l1_regularization,
+               l2_regularization,
+               tree_complexity_regularization,
+               min_node_weight,
+               feature_column_group_id,
+               epsilon,
+               num_quantiles,
+               gradient_shape,
+               hessian_shape,
+               multiclass_strategy,
+               init_stamp_token=0,
+               name=None):
+    """Initialize the internal state for this split handler.
+
+    Args:
+      dense_float_column: A `Tensor` column associated with this handler.
+      l1_regularization: L1 regularization applied for this split handler.
+      l2_regularization: L2 regularization applied for this split handler.
+      tree_complexity_regularization: Tree complexity regularization applied
+          for this split handler.
+      min_node_weight: Minimum sum of weights of examples in each partition to
+          be considered for splitting.
+      feature_column_group_id: Feature column group index.
+      epsilon: A float, the error bound for quantile computation.
+      num_quantiles: An int, the number of buckets to create from the histogram.
+      gradient_shape: A TensorShape, containing shape of gradients.
+      hessian_shape: A TensorShape, containing shape of hessians.
+      multiclass_strategy: Strategy describing how to treat multiclass problems.
+      init_stamp_token: A tensor containing an scalar for initial stamp of the
+         stamped objects.
+      name: An optional handler name.
+    """
+    super(DenseSplitHandler, self).__init__(
+        l1_regularization=l1_regularization,
+        l2_regularization=l2_regularization,
+        tree_complexity_regularization=tree_complexity_regularization,
+        min_node_weight=min_node_weight,
+        feature_column_group_id=feature_column_group_id,
+        epsilon=epsilon,
+        num_quantiles=num_quantiles,
+        init_stamp_token=init_stamp_token,
+        name=name,
+        gradient_shape=gradient_shape,
+        hessian_shape=hessian_shape,
+        multiclass_strategy=multiclass_strategy)
+    self._dense_float_column = dense_float_column
+    # Register dense_make_stats_update function as an Op to the graph.
+    g = ops.get_default_graph()
+    dense_make_stats_update.add_to_graph(g)
+
+  def scheduled_reads(self):
+    return [self._quantile_accumulator.schedule_get_buckets()]
+
+  def update_stats(self, stamp_token, example_partition_ids, gradients,
+                   hessians, empty_gradients, empty_hessians, weights,
+                   is_active, scheduled_reads):
+    """Updates the state for dense split handler.
+
+    Args:
+      stamp_token: An int32 scalar tensor containing the current stamp token.
+      example_partition_ids: A dense tensor, containing an int32 for each
+        example which is the partition id that the example ends up in.
+      gradients: A dense tensor of gradients.
+      hessians: A dense tensor of hessians.
+      empty_gradients: A dense empty tensor of the same shape (for dimensions >
+        0) as gradients.
+      empty_hessians: A dense empty tensor of the same shape (for dimensions >
+        0) as hessians.
+      weights: A dense float32 tensor with a weight for each example.
+      is_active: A boolean tensor that says if this handler is active or not.
+          One value for the current layer and one value for the next layer.
+      scheduled_reads: List of scheduled reads for this handler.
+
+    Returns:
+      The op that updates the stats for this handler.
+    """
+    name = _PATTERN.sub("", self._name)
+    with ops.name_scope(name, "DenseSplitHandler"):
+      are_buckets_ready, buckets = scheduled_reads[0]
+      (quantile_values, quantile_weights, example_partition_ids,
+       feature_ids, gradients, hessians) = dense_make_stats_update(
+           is_active, are_buckets_ready, self._dense_float_column, buckets,
+           example_partition_ids, gradients, hessians, weights, empty_gradients,
+           empty_hessians)
+      update_quantiles = self._quantile_accumulator.schedule_add_summary(
+          stamp_token=stamp_token,
+          column=quantile_values,
+          example_weights=quantile_weights)
+      update_stats = self._stats_accumulator.schedule_add(
+          example_partition_ids, feature_ids, gradients, hessians)
+      return control_flow_ops.no_op(), [update_quantiles, update_stats]
+
+  def make_splits(self, stamp_token, next_stamp_token, class_id):
+    """Create the best split using the accumulated stats and flush the state."""
+    # Get the bucket boundaries
+    are_splits_ready, buckets = (
+        self._quantile_accumulator.get_buckets(stamp_token))
+    # After we receive the boundaries from previous iteration we can flush
+    # the quantile accumulator.
+    with ops.control_dependencies([buckets]):
+      flush_quantiles = self._quantile_accumulator.flush(
+          stamp_token=stamp_token, next_stamp_token=next_stamp_token)
+
+    # Get the aggregated gradients and hessians per <partition_id, feature_id>
+    # pair.
+    # In order to distribute the computation on all the PSs we use the PS that
+    # had the stats accumulator on.
+    with ops.device(None):
+      with ops.device(self._stats_accumulator.resource().device):
+        num_minibatches, partition_ids, bucket_ids, gradients, hessians = (
+            self._stats_accumulator.flush(stamp_token, next_stamp_token))
+
+        # Put quantile and stats accumulator flushing in the dependency path.
+        are_splits_ready = control_flow_ops.with_dependencies(
+            [flush_quantiles, partition_ids], are_splits_ready)
+        partition_ids, gains, split_infos = (
+            split_handler_ops.build_dense_inequality_splits(
+                num_minibatches=num_minibatches,
+                bucket_boundaries=buckets,
+                partition_ids=partition_ids,
+                bucket_ids=bucket_ids,
+                gradients=gradients,
+                hessians=hessians,
+                class_id=class_id,
+                feature_column_group_id=self._feature_column_group_id,
+                l1_regularization=self._l1_regularization,
+                l2_regularization=self._l2_regularization,
+                tree_complexity_regularization=self.
+                _tree_complexity_regularization,
+                min_node_weight=self._min_node_weight,
+                multiclass_strategy=self._multiclass_strategy))
+    return (are_splits_ready, partition_ids, gains, split_infos)
+
+
+class SparseSplitHandler(InequalitySplitHandler):
+  """Computes stats and finds the best inequality splits on sparse columns."""
+
+  def __init__(self,
+               sparse_float_column,
+               l1_regularization,
+               l2_regularization,
+               tree_complexity_regularization,
+               min_node_weight,
+               feature_column_group_id,
+               epsilon,
+               num_quantiles,
+               gradient_shape,
+               hessian_shape,
+               multiclass_strategy,
+               init_stamp_token=0,
+               name=None):
+    """Initialize the internal state for this split handler.
+
+    Args:
+      sparse_float_column: A `SparseTensor` column associated with this handler.
+      l1_regularization: L1 regularization applied for this split handler.
+      l2_regularization: L2 regularization applied for this split handler.
+      tree_complexity_regularization: Tree complexity regularization applied
+          for this split handler.
+      min_node_weight: Minimum sum of weights of examples in each partition to
+          be considered for splitting.
+      feature_column_group_id: Feature column group index.
+      epsilon: A float, the error bound for quantile computation.
+      num_quantiles: An int, the number of buckets to create from the histogram.
+      gradient_shape: A TensorShape, containing shape of gradients.
+      hessian_shape: A TensorShape, containing shape of hessians.
+      multiclass_strategy: Strategy describing how to treat multiclass problems.
+      init_stamp_token: A tensor containing an scalar for initial stamp of the
+         stamped objects.
+      name: An optional handler name.
+    """
+    super(SparseSplitHandler, self).__init__(
+        l1_regularization=l1_regularization,
+        l2_regularization=l2_regularization,
+        tree_complexity_regularization=tree_complexity_regularization,
+        min_node_weight=min_node_weight,
+        feature_column_group_id=feature_column_group_id,
+        epsilon=epsilon,
+        num_quantiles=num_quantiles,
+        gradient_shape=gradient_shape,
+        hessian_shape=hessian_shape,
+        multiclass_strategy=multiclass_strategy,
+        init_stamp_token=init_stamp_token,
+        name=name)
+    # Register sparse_make_stats_update function as an Op to the graph.
+    g = ops.get_default_graph()
+    sparse_make_stats_update.add_to_graph(g)
+    self._sparse_float_column = sparse_float_column
+
+  def scheduled_reads(self):
+    return [self._quantile_accumulator.schedule_get_buckets()]
+
+  def update_stats(self, stamp_token, example_partition_ids, gradients,
+                   hessians, empty_gradients, empty_hessians, weights,
+                   is_active, scheduled_reads):
+    """Updates the state for dense split handler.
+
+    Args:
+      stamp_token: An int32 scalar tensor containing the current stamp token.
+      example_partition_ids: A dense tensor, containing an int32 for each
+        example which is the partition id that the example ends up in.
+      gradients: A dense tensor of gradients.
+      hessians: A dense tensor of hessians.
+      empty_gradients: A dense empty tensor of the same shape (for dimensions >
+        0) as gradients.
+      empty_hessians: A dense empty tensor of the same shape (for dimensions >
+        0) as hessians.
+      weights: A dense float32 tensor with a weight for each example.
+      is_active: A boolean tensor that says if this handler is active or not.
+          One value for the current layer and one value for the next layer.
+      scheduled_reads: List of results from the scheduled reads.
+
+    Returns:
+      The op that updates the stats for this handler.
+    """
+    are_buckets_ready, buckets = scheduled_reads[0]
+    with ops.name_scope(self._name, "SparseSplitHandler"):
+      (quantile_indices, quantile_values, quantile_shapes, quantile_weights,
+       example_partition_ids,
+       feature_ids, gradients, hessians) = sparse_make_stats_update(
+           is_active, are_buckets_ready, self._sparse_float_column.indices,
+           self._sparse_float_column.values,
+           self._sparse_float_column.dense_shape, buckets,
+           example_partition_ids, gradients, hessians, weights, empty_gradients,
+           empty_hessians)
+      update_quantiles = self._quantile_accumulator.schedule_add_summary(
+          stamp_token=stamp_token,
+          column=sparse_tensor.SparseTensor(quantile_indices, quantile_values,
+                                            quantile_shapes),
+          example_weights=quantile_weights)
+      update_stats = self._stats_accumulator.schedule_add(
+          example_partition_ids, feature_ids, gradients, hessians)
+      return (control_flow_ops.no_op(), [update_quantiles, update_stats])
+
+  def make_splits(self, stamp_token, next_stamp_token, class_id):
+    """Create the best split using the accumulated stats and flush the state."""
+    # Get the bucket boundaries
+    are_splits_ready, buckets = (
+        self._quantile_accumulator.get_buckets(stamp_token))
+
+    # After we receive the boundaries from previous iteration we can flush
+    # the quantile accumulator.
+    with ops.control_dependencies([buckets]):
+      flush_quantiles = self._quantile_accumulator.flush(
+          stamp_token=stamp_token, next_stamp_token=next_stamp_token)
+
+    with ops.device(None):
+      with ops.device(self._stats_accumulator.resource().device):
+        num_minibatches, partition_ids, bucket_ids, gradients, hessians = (
+            self._stats_accumulator.flush(stamp_token, next_stamp_token))
+
+        # Put quantile and stats accumulator flushing in the dependency path.
+        are_splits_ready = control_flow_ops.with_dependencies(
+            [flush_quantiles, partition_ids], are_splits_ready)
+        partition_ids, gains, split_infos = (
+            split_handler_ops.build_sparse_inequality_splits(
+                num_minibatches=num_minibatches,
+                bucket_boundaries=buckets,
+                partition_ids=partition_ids,
+                bucket_ids=bucket_ids,
+                gradients=gradients,
+                hessians=hessians,
+                class_id=class_id,
+                feature_column_group_id=self._feature_column_group_id,
+                l1_regularization=self._l1_regularization,
+                l2_regularization=self._l2_regularization,
+                tree_complexity_regularization=self.
+                _tree_complexity_regularization,
+                min_node_weight=self._min_node_weight,
+                bias_feature_id=_BIAS_FEATURE_ID,
+                multiclass_strategy=self._multiclass_strategy))
+    return (are_splits_ready, partition_ids, gains, split_infos)
+
+
+@function.Defun(dtypes.bool, dtypes.bool, dtypes.float32, dtypes.float32,
+                dtypes.int32, dtypes.float32, dtypes.float32, dtypes.float32,
+                dtypes.float32, dtypes.float32)
+def dense_make_stats_update(is_active, are_buckets_ready, float_column,
+                            quantile_buckets, example_partition_ids, gradients,
+                            hessians, weights, empty_gradients, empty_hessians):
+  """Updates the state for dense split handler."""
+  empty_float = constant_op.constant([], dtype=dtypes.float32)
+
+  quantile_values, quantile_weights = control_flow_ops.cond(
+      is_active[1],  # For the next layer, this handler is inactive.
+      lambda: (float_column, weights),
+      lambda: (empty_float, empty_float))
+
+  def ready_inputs_fn():
+    """Branch to execute when quantiles are ready."""
+    quantized_feature = quantile_ops.quantiles([float_column], [],
+                                               [quantile_buckets], [])
+    quantized_feature = math_ops.cast(quantized_feature[0], dtypes.int64)
+    quantized_feature = array_ops.reshape(quantized_feature, [-1])
+    return (example_partition_ids, quantized_feature, gradients, hessians)
+
+  def not_ready_inputs_fn():
+    return (constant_op.constant([], dtype=dtypes.int32), constant_op.constant(
+        [], dtype=dtypes.int64), empty_gradients, empty_hessians)
+
+  example_partition_ids, feature_ids, gradients, hessians = (
+      control_flow_ops.cond(
+          math_ops.logical_and(are_buckets_ready, is_active[0]),
+          ready_inputs_fn, not_ready_inputs_fn))
+  return (quantile_values, quantile_weights, example_partition_ids, feature_ids,
+          gradients, hessians)
+
+
+@function.Defun(dtypes.bool, dtypes.bool, dtypes.int64, dtypes.float32,
+                dtypes.int64, dtypes.float32, dtypes.int32, dtypes.float32,
+                dtypes.float32, dtypes.float32, dtypes.float32, dtypes.float32)
+def sparse_make_stats_update(
+    is_active, are_buckets_ready, sparse_column_indices, sparse_column_values,
+    sparse_column_shape, quantile_buckets, example_partition_ids, gradients,
+    hessians, weights, empty_gradients, empty_hessians):
+  """Updates the state for this split handler."""
+
+  def quantiles_ready():
+    """The subgraph for when the quantiles are ready."""
+    quantized_feature = quantile_ops.quantiles([sparse_column_values], [],
+                                               [quantile_buckets], [])
+    quantized_feature = math_ops.cast(quantized_feature[0], dtypes.int64)
+    quantized_feature = array_ops.reshape(quantized_feature, [-1])
+    example_indices, _ = array_ops.split(
+        sparse_column_indices, num_or_size_splits=2, axis=1)
+    example_indices = array_ops.squeeze(example_indices, [1])
+    filtered_gradients = array_ops.gather(gradients, example_indices)
+    filtered_hessians = array_ops.gather(hessians, example_indices)
+    filtered_partition_ids = array_ops.gather(example_partition_ids,
+                                              example_indices)
+    unique_partitions, mapped_partitions = array_ops.unique(
+        example_partition_ids)
+
+    # Compute aggregate stats for each partition.
+    per_partition_gradients = math_ops.unsorted_segment_sum(
+        gradients, mapped_partitions, array_ops.size(unique_partitions))
+    per_partition_hessians = math_ops.unsorted_segment_sum(
+        hessians, mapped_partitions, array_ops.size(unique_partitions))
+
+    # Prepend a bias feature per partition that accumulates the stats for all
+    # examples in that partition.
+    bias_feature_ids = array_ops.fill(
+        array_ops.shape(unique_partitions), _BIAS_FEATURE_ID)
+    bias_feature_ids = math_ops.cast(bias_feature_ids, dtypes.int64)
+    partition_ids = array_ops.concat(
+        [unique_partitions, filtered_partition_ids], 0)
+    filtered_gradients = array_ops.concat(
+        [per_partition_gradients, filtered_gradients], 0)
+    filtered_hessians = array_ops.concat(
+        [per_partition_hessians, filtered_hessians], 0)
+    bucket_ids = array_ops.concat([bias_feature_ids, quantized_feature], 0)
+    return partition_ids, bucket_ids, filtered_gradients, filtered_hessians
+
+  def quantiles_not_ready():
+    """The subgraph for when the quantiles are not ready."""
+    return (constant_op.constant([], dtype=dtypes.int32), constant_op.constant(
+        [], dtype=dtypes.int64), empty_gradients, empty_hessians)
+
+  empty_float = constant_op.constant([], dtype=dtypes.float32)
+  handler_not_active = (constant_op.constant(
+      [], dtype=dtypes.int64, shape=[0, 2]), empty_float, constant_op.constant(
+          [0, 1], dtype=dtypes.int64), empty_float)
+  handler_active = (sparse_column_indices, sparse_column_values,
+                    sparse_column_shape, weights)
+  quantile_indices, quantile_values, quantile_shape, quantile_weights = (
+      control_flow_ops.cond(is_active[1], lambda: handler_active,
+                            lambda: handler_not_active))
+
+  example_partition_ids, feature_ids, gradients, hessians = (
+      control_flow_ops.cond(are_buckets_ready, quantiles_ready,
+                            quantiles_not_ready))
+
+  return (quantile_indices, quantile_values, quantile_shape, quantile_weights,
+          example_partition_ids, feature_ids, gradients, hessians)
diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py
new file mode 100644
index 0000000000..ee16a5f838
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py
@@ -0,0 +1,1126 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for checking stats accumulator related ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.boosted_trees.lib.learner.batch import ordinal_split_handler
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import split_info_pb2
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.platform import googletest
+
+
+def get_empty_tensors(gradient_shape, hessian_shape):
+  empty_hess_shape = [1] + hessian_shape.as_list()
+  empty_grad_shape = [1] + gradient_shape.as_list()
+
+  empty_gradients = constant_op.constant(
+      [], dtype=dtypes.float32, shape=empty_grad_shape)
+  empty_hessians = constant_op.constant(
+      [], dtype=dtypes.float32, shape=empty_hess_shape)
+
+  return empty_gradients, empty_hessians
+
+
+class DenseSplitHandlerTest(test_util.TensorFlowTestCase):
+
+  def testGenerateFeatureSplitCandidates(self):
+    with self.test_session() as sess:
+      # The data looks like the following:
+      # Example |  Gradients    | Partition | Dense Quantile |
+      # i0      |  (0.2, 0.12)  | 0         | 1              |
+      # i1      |  (-0.5, 0.07) | 0         | 1              |
+      # i2      |  (1.2, 0.2)   | 0         | 0              |
+      # i3      |  (4.0, 0.13)  | 1         | 1              |
+      dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+      class_id = -1
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      split_handler = ordinal_split_handler.DenseSplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.001,
+          num_quantiles=10,
+          feature_column_group_id=0,
+          dense_float_column=dense_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            partition_ids,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the split on partition 0.
+    # -(1.2 - 0.1) / (0.2 + 1)
+    expected_left_weight = -0.91666
+
+    # expected_left_weight * -(1.2 - 0.1)
+    expected_left_gain = 1.0083333333333331
+
+    # (-0.5 + 0.2 + 0.1) / (0.19 + 1)
+    expected_right_weight = 0.1680672
+
+    # expected_right_weight * -(-0.5 + 0.2 + 0.1))
+    expected_right_gain = 0.033613445378151252
+
+    # (0.2 + -0.5 + 1.2 - 0.1) ** 2 / (0.12 + 0.07 + 0.2 + 1)
+    expected_bias_gain = 0.46043165467625885
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0],
+        0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertAllClose(0.3, split_node.threshold, 0.00001)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (0.13 + 1)
+    expected_left_weight = -3.4513274336283186
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_left_gain = 13.460176991150442
+    expected_right_weight = 0
+    expected_right_gain = 0
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_bias_gain = 13.460176991150442
+
+    # Verify candidate for partition 1, there's only one active bucket here
+    # so zero gain is expected.
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    self.assertAllClose(0.0, gains[1], 0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertAllClose(0.52, split_node.threshold, 0.00001)
+
+  def testGenerateFeatureSplitCandidatesMulticlassFullHessian(self):
+    with self.test_session() as sess:
+      dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
+      # Batch size is 4, 2 gradients per each instance.
+      gradients = array_ops.constant(
+          [[0.2, 0.1], [-0.5, 0.2], [1.2, 3.4], [4.0, -3.5]], shape=[4, 2])
+      # 2x2 matrix for each instance
+      hessian_0 = [[0.12, 0.02], [0.3, 0.11]]
+      hessian_1 = [[0.07, -0.2], [-0.5, 0.2]]
+      hessian_2 = [[0.2, -0.23], [-0.8, 0.9]]
+      hessian_3 = [[0.13, -0.3], [-1.5, 2.2]]
+
+      hessians = array_ops.constant(
+          [hessian_0, hessian_1, hessian_2, hessian_3])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+      class_id = -1
+
+      gradient_shape = tensor_shape.TensorShape([2])
+      hessian_shape = tensor_shape.TensorShape([2, 2])
+
+      split_handler = ordinal_split_handler.DenseSplitHandler(
+          l1_regularization=0,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.001,
+          num_quantiles=3,
+          feature_column_group_id=0,
+          dense_float_column=dense_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            partition_ids,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+    self.assertEqual(0, split_node.feature_column)
+    self.assertAllClose(0.3, split_node.threshold, 1e-6)
+
+  def testGenerateFeatureSplitCandidatesMulticlassDiagonalHessian(self):
+    with self.test_session() as sess:
+      dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
+      # Batch size is 4, 2 gradients per each instance.
+      gradients = array_ops.constant(
+          [[0.2, 0.1], [-0.5, 0.2], [1.2, 3.4], [4.0, -3.5]], shape=[4, 2])
+      # Each hessian is a diagonal of a full hessian matrix.
+      hessian_0 = [0.12, 0.11]
+      hessian_1 = [0.07, 0.2]
+      hessian_2 = [0.2, 0.9]
+      hessian_3 = [0.13, 2.2]
+
+      hessians = array_ops.constant(
+          [hessian_0, hessian_1, hessian_2, hessian_3])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+      class_id = -1
+
+      gradient_shape = tensor_shape.TensorShape([2])
+      hessian_shape = tensor_shape.TensorShape([2])
+
+      split_handler = ordinal_split_handler.DenseSplitHandler(
+          l1_regularization=0,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.001,
+          num_quantiles=3,
+          feature_column_group_id=0,
+          dense_float_column=dense_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.DIAGONAL_HESSIAN)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            partition_ids,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+    self.assertEqual(0, split_node.feature_column)
+    self.assertAllClose(0.3, split_node.threshold, 1e-6)
+
+  def testGenerateFeatureSplitCandidatesInactive(self):
+    with self.test_session() as sess:
+      # The data looks like the following:
+      # Example |  Gradients    | Partition | Dense Quantile |
+      # i0      |  (0.2, 0.12)  | 0         | 1              |
+      # i1      |  (-0.5, 0.07) | 0         | 1              |
+      # i2      |  (1.2, 0.2)   | 0         | 0              |
+      # i3      |  (4.0, 0.13)  | 1         | 1              |
+      dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = ordinal_split_handler.DenseSplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.001,
+          num_quantiles=10,
+          feature_column_group_id=0,
+          dense_float_column=dense_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, False]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            partition_ids,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([False, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+    # The handler was inactive, so it shouldn't return any splits.
+    self.assertEqual(len(partitions), 0)
+    self.assertEqual(len(gains), 0)
+    self.assertEqual(len(splits), 0)
+
+  def testGenerateFeatureSplitCandidatesWithTreeComplexity(self):
+    with self.test_session() as sess:
+      # The data looks like the following:
+      # Example |  Gradients    | Partition | Dense Quantile |
+      # i0      |  (0.2, 0.12)  | 0         | 1              |
+      # i1      |  (-0.5, 0.07) | 0         | 1              |
+      # i2      |  (1.2, 0.2)   | 0         | 0              |
+      # i3      |  (4.0, 0.13)  | 1         | 1              |
+      dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = ordinal_split_handler.DenseSplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0.5,
+          min_node_weight=0,
+          epsilon=0.001,
+          num_quantiles=10,
+          feature_column_group_id=0,
+          dense_float_column=dense_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            partition_ids,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the split on partition 0.
+    # -(1.2 - 0.1) / (0.2 + 1)
+    expected_left_weight = -0.91666
+
+    # expected_left_weight * -(1.2 - 0.1)
+    expected_left_gain = 1.0083333333333331
+
+    # (-0.5 + 0.2 + 0.1) / (0.19 + 1)
+    expected_right_weight = 0.1680672
+
+    # expected_right_weight * -(-0.5 + 0.2 + 0.1))
+    expected_right_gain = 0.033613445378151252
+
+    # (0.2 + -0.5 + 1.2 - 0.1) ** 2 / (0.12 + 0.07 + 0.2 + 1)
+    expected_bias_gain = 0.46043165467625885
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    # Make sure the gain is subtracted by the tree complexity regularization.
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain - 0.5,
+        gains[0], 0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertAllClose(0.3, split_node.threshold, 0.00001)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (0.13 + 1)
+    expected_left_weight = -3.4513274336283186
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_left_gain = 13.460176991150442
+    expected_right_weight = 0
+    expected_right_gain = 0
+    # (-4 + 0.1) ** 2 / (0.13 + 1)
+    expected_bias_gain = 13.460176991150442
+
+    # Verify candidate for partition 1, there's only one active bucket here
+    # so -0.5 gain is expected (because of tree complexity.
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    self.assertAllClose(-0.5, gains[1], 0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertAllClose(0.52, split_node.threshold, 0.00001)
+
+  def testGenerateFeatureSplitCandidatesWithMinNodeWeight(self):
+    with self.test_session() as sess:
+      # The data looks like the following:
+      # Example |  Gradients    | Partition | Dense Quantile |
+      # i0      |  (0.2, 0.12)  | 0         | 1              |
+      # i1      |  (-0.5, 0.07) | 0         | 1              |
+      # i2      |  (1.2, 0.2)   | 0         | 0              |
+      # i3      |  (4.0, 2.0)   | 1         | 1              |
+      dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 2])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = ordinal_split_handler.DenseSplitHandler(
+          l1_regularization=0.1,
+          l2_regularization=1,
+          tree_complexity_regularization=0.5,
+          min_node_weight=1.5,
+          epsilon=0.001,
+          num_quantiles=10,
+          feature_column_group_id=0,
+          dense_float_column=dense_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            partition_ids,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    self.assertAllEqual([0, 1], partitions)
+
+    # Check the gain on partition 0 to be -0.5.
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    # Make sure the gain is subtracted by the tree complexity regularization.
+    self.assertAllClose(-0.5, gains[0], 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    # Check the split on partition 1.
+    # (-4 + 0.1) / (2 + 1)
+    expected_left_weight = -1.3
+    expected_right_weight = 0
+
+    # Verify candidate for partition 1, there's only one active bucket here
+    # so -0.5 gain is expected (because of tree complexity.
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.dense_float_binary_split
+    self.assertAllClose(-0.5, gains[1], 0.00001)
+
+    self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
+
+    self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
+
+    self.assertEqual(0, split_node.feature_column)
+
+    self.assertAllClose(0.52, split_node.threshold, 0.00001)
+
+
+class SparseSplitHandlerTest(test_util.TensorFlowTestCase):
+
+  def testGenerateFeatureSplitCandidates(self):
+    with self.test_session() as sess:
+      # The data looks like the following:
+      # Example |  Gradients    | Partition | Sparse Quantile |
+      # i0      |  (0.2, 0.12)  | 0         | 1               |
+      # i1      |  (-0.5, 0.07) | 0         | N/A             |
+      # i2      |  (1.2, 0.2)   | 0         | 0               |
+      # i3      |  (4.0, 0.13)  | 1         | 1               |
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      example_partitions = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+      indices = array_ops.constant([[0, 0], [2, 0], [3, 0]], dtype=dtypes.int64)
+      values = array_ops.constant([0.52, 0.3, 0.52])
+      sparse_column = sparse_tensor.SparseTensor(indices, values, [4, 1])
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = ordinal_split_handler.SparseSplitHandler(
+          l1_regularization=0,
+          l2_regularization=2,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.01,
+          num_quantiles=2,
+          feature_column_group_id=0,
+          sparse_float_column=sparse_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          example_partitions,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            example_partitions,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    self.assertAllEqual([0, 1], partitions)
+    # Check the split on partition 0.
+    # -(0.2 + 1.2) / (0.12 + 0.2 + 2)
+    expected_left_weight = -0.603448275862069
+    # (0.2 + 1.2) ** 2 / (0.12 + 0.2 + 2)
+    expected_left_gain = 0.8448275862068965
+    # 0.5 / (0.07 + 2)
+    expected_right_weight = 0.24154589371980678
+    # 0.5 ** 2 / (0.07 + 2)
+    expected_right_gain = 0.12077294685990339
+    # (0.2 + 1.2 - 0.5) ** 2 /  (0.12 + 0.2 + 0.07 + 2)
+    expected_bias_gain = 0.3389121338912133
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_right
+    self.assertAllClose(
+        expected_left_gain + expected_right_gain - expected_bias_gain, gains[0])
+
+    self.assertAllClose([expected_left_weight], left_child.value)
+
+    self.assertAllClose([expected_right_weight], right_child.value)
+
+    self.assertEqual(0, split_node.split.feature_column)
+
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+    # Check the split on partition 1.
+    expected_left_weight = -1.8779342723004695
+    expected_right_weight = 0
+
+    # Verify candidate for partition 1, there's only one active bucket here
+    # so zero gain is expected.
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_left
+
+    self.assertAllClose(0.0, gains[1])
+
+    self.assertAllClose([expected_left_weight], left_child.value)
+
+    self.assertAllClose([expected_right_weight], right_child.value)
+
+    self.assertEqual(0, split_node.split.feature_column)
+
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+  def testGenerateFeatureSplitCandidatesMulticlassFullHessian(self):
+    with self.test_session() as sess:
+      # Batch is 4, 2 classes
+      gradients = array_ops.constant(
+          [[0.2, 1.4], [-0.5, 0.1], [1.2, 3], [4.0, -3]])
+      # 2x2 matrix for each instance
+      hessian_0 = [[0.12, 0.02], [0.3, 0.11]]
+      hessian_1 = [[0.07, -0.2], [-0.5, 0.2]]
+      hessian_2 = [[0.2, -0.23], [-0.8, 0.9]]
+      hessian_3 = [[0.13, -0.3], [-1.5, 2.2]]
+      hessians = array_ops.constant(
+          [hessian_0, hessian_1, hessian_2, hessian_3])
+
+      example_partitions = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+      indices = array_ops.constant([[0, 0], [2, 0], [3, 0]], dtype=dtypes.int64)
+      values = array_ops.constant([0.52, 0.3, 0.52])
+      sparse_column = sparse_tensor.SparseTensor(indices, values, [4, 1])
+
+      gradient_shape = tensor_shape.TensorShape([2])
+      hessian_shape = tensor_shape.TensorShape([2, 2])
+      class_id = -1
+
+      split_handler = ordinal_split_handler.SparseSplitHandler(
+          l1_regularization=0,
+          l2_regularization=2,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.01,
+          num_quantiles=2,
+          feature_column_group_id=0,
+          sparse_float_column=sparse_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.FULL_HESSIAN)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          example_partitions,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            example_partitions,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_right
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+    self.assertEqual(0, split_node.split.feature_column)
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_left
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(0, split_node.split.feature_column)
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+  def testGenerateFeatureSplitCandidatesMulticlassDiagonalHessian(self):
+    with self.test_session() as sess:
+      # Batch is 4, 2 classes
+      gradients = array_ops.constant(
+          [[0.2, 1.4], [-0.5, 0.1], [1.2, 3], [4.0, -3]])
+      # Each hessian is a diagonal from a full hessian matrix.
+      hessian_0 = [0.12, 0.11]
+      hessian_1 = [0.07, 0.2]
+      hessian_2 = [0.2, 0.9]
+      hessian_3 = [0.13, 2.2]
+      hessians = array_ops.constant(
+          [hessian_0, hessian_1, hessian_2, hessian_3])
+
+      example_partitions = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+      indices = array_ops.constant([[0, 0], [2, 0], [3, 0]], dtype=dtypes.int64)
+      values = array_ops.constant([0.52, 0.3, 0.52])
+      sparse_column = sparse_tensor.SparseTensor(indices, values, [4, 1])
+
+      gradient_shape = tensor_shape.TensorShape([2])
+      hessian_shape = tensor_shape.TensorShape([2])
+      class_id = -1
+
+      split_handler = ordinal_split_handler.SparseSplitHandler(
+          l1_regularization=0,
+          l2_regularization=2,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.01,
+          num_quantiles=2,
+          feature_column_group_id=0,
+          sparse_float_column=sparse_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.DIAGONAL_HESSIAN)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          example_partitions,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            example_partitions,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+
+    split_info = split_info_pb2.SplitInfo()
+    split_info.ParseFromString(splits[0])
+
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_right
+    # Each leaf has 2 element vector.
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(2, len(right_child.value))
+    self.assertEqual(0, split_node.split.feature_column)
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+    split_info.ParseFromString(splits[1])
+    left_child = split_info.left_child.vector
+    right_child = split_info.right_child.vector
+    split_node = split_info.split_node.sparse_float_binary_split_default_left
+    self.assertEqual(2, len(left_child.value))
+    self.assertEqual(0, split_node.split.feature_column)
+    self.assertAllClose(0.52, split_node.split.threshold)
+
+  def testGenerateFeatureSplitCandidatesInactive(self):
+    with self.test_session() as sess:
+      # The data looks like the following:
+      # Example |  Gradients    | Partition | Sparse Quantile |
+      # i0      |  (0.2, 0.12)  | 0         | 1               |
+      # i1      |  (-0.5, 0.07) | 0         | N/A             |
+      # i2      |  (1.2, 0.2)   | 0         | 0               |
+      # i3      |  (4.0, 0.13)  | 1         | 1               |
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      example_partitions = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+      indices = array_ops.constant([[0, 0], [2, 0], [3, 0]], dtype=dtypes.int64)
+      values = array_ops.constant([0.52, 0.3, 0.52])
+      sparse_column = sparse_tensor.SparseTensor(indices, values, [4, 1])
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = ordinal_split_handler.SparseSplitHandler(
+          l1_regularization=0,
+          l2_regularization=2,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.01,
+          num_quantiles=2,
+          feature_column_group_id=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          sparse_float_column=sparse_column,
+          init_stamp_token=0,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          example_partitions,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, False]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            example_partitions,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([False, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+
+    # During the first iteration, inequality split handlers are not going to
+    # have any splits. Make sure that we return not_ready in that case.
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+    # The handler was inactive so it shouldn't any splits.
+    self.assertEqual(len(partitions), 0)
+    self.assertEqual(len(gains), 0)
+    self.assertEqual(len(splits), 0)
+
+  def testEmpty(self):
+    with self.test_session() as sess:
+      indices = array_ops.constant([], dtype=dtypes.int64, shape=[0, 2])
+      # No values in this feature column in this mini-batch.
+      values = array_ops.constant([], dtype=dtypes.float32)
+      sparse_column = sparse_tensor.SparseTensor(indices, values, [4, 1])
+
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+      class_id = -1
+
+      split_handler = ordinal_split_handler.SparseSplitHandler(
+          l1_regularization=0,
+          l2_regularization=2,
+          tree_complexity_regularization=0,
+          min_node_weight=0,
+          epsilon=0.01,
+          num_quantiles=2,
+          feature_column_group_id=0,
+          sparse_float_column=sparse_column,
+          init_stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      resources.initialize_resources(resources.shared_resources()).run()
+      gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
+      hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
+      partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
+
+      empty_gradients, empty_hessians = get_empty_tensors(
+          gradient_shape, hessian_shape)
+      example_weights = array_ops.ones([4, 1], dtypes.float32)
+
+      update_1 = split_handler.update_stats_sync(
+          0,
+          partition_ids,
+          gradients,
+          hessians,
+          empty_gradients,
+          empty_hessians,
+          example_weights,
+          is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_1]):
+        are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
+
+      with ops.control_dependencies([are_splits_ready]):
+        update_2 = split_handler.update_stats_sync(
+            1,
+            partition_ids,
+            gradients,
+            hessians,
+            empty_gradients,
+            empty_hessians,
+            example_weights,
+            is_active=array_ops.constant([True, True]))
+      with ops.control_dependencies([update_2]):
+        are_splits_ready2, partitions, gains, splits = (
+            split_handler.make_splits(1, 2, class_id))
+        are_splits_ready, are_splits_ready2, partitions, gains, splits = (
+            sess.run([
+                are_splits_ready, are_splits_ready2, partitions, gains, splits
+            ]))
+    self.assertFalse(are_splits_ready)
+    self.assertTrue(are_splits_ready2)
+    self.assertEqual(len(partitions), 0)
+    self.assertEqual(len(gains), 0)
+    self.assertEqual(len(splits), 0)
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
new file mode 100644
index 0000000000..0775716291
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
@@ -0,0 +1,1012 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Training functions for Gradient boosted decision trees."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+from tensorflow.contrib import learn
+from tensorflow.contrib import stateless
+
+from tensorflow.contrib.boosted_trees.lib.learner.batch import categorical_split_handler
+from tensorflow.contrib.boosted_trees.lib.learner.batch import ordinal_split_handler
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
+from tensorflow.contrib.boosted_trees.python.ops import gen_model_ops
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.ops import prediction_ops
+from tensorflow.contrib.boosted_trees.python.ops import stats_accumulator_ops
+from tensorflow.contrib.boosted_trees.python.ops import training_ops
+from tensorflow.contrib.layers.python.layers import feature_column as feature_column_lib
+from tensorflow.contrib.layers.python.layers import feature_column_ops
+from tensorflow.python.feature_column import feature_column as fc_core
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.summary import summary
+from tensorflow.python.training import device_setter
+
+# Key names for prediction dict.
+ENSEMBLE_STAMP = "ensemble_stamp"
+PREDICTIONS = "predictions"
+PARTITION_IDS = "partition_ids"
+NUM_LAYERS_ATTEMPTED = "num_layers"
+NUM_TREES_ATTEMPTED = "num_trees"
+PREDICTIONS_NO_DROPOUT = "predictions_no_dropout"
+_FEATURE_NAME_TEMPLATE = "%s_%d"
+
+
+def _get_column_by_index(tensor, indices):
+  """Returns columns from a 2-D tensor by index."""
+  shape = array_ops.shape(tensor)
+  p_flat = array_ops.reshape(tensor, [-1])
+  i_flat = array_ops.reshape(
+      array_ops.reshape(math_ops.range(0, shape[0]) * shape[1], [-1, 1]) +
+      indices, [-1])
+  return array_ops.reshape(array_ops.gather(p_flat, i_flat), [shape[0], -1])
+
+
+def _make_predictions_dict(stamp, logits, logits_no_dropout, partition_ids,
+                           ensemble_stats):
+  """Returns predictions for the given logits and n_classes.
+
+  Args:
+    stamp: The ensemble stamp.
+    logits: A rank 2 `Tensor` with shape [batch_size, n_classes - 1].
+    logits_no_dropout: A rank 2 `Tensor` with shape [batch_size, n_classes - 1]
+    that contains predictions when no dropout was applied.
+    partition_ids: A rank 1 `Tensor` with shape [batch_size].
+    ensemble_stats: A TreeEnsembleStatsOp result tuple.
+
+  Returns:
+    A dict of predictions.
+  """
+  result = {}
+  result[ENSEMBLE_STAMP] = stamp
+  result[PREDICTIONS] = logits
+  result[PREDICTIONS_NO_DROPOUT] = logits_no_dropout
+  result[PARTITION_IDS] = partition_ids
+
+  result[NUM_LAYERS_ATTEMPTED] = ensemble_stats.attempted_layers
+  result[NUM_TREES_ATTEMPTED] = ensemble_stats.attempted_trees
+  return result
+
+
+class _OpRoundRobinStrategy(object):
+  """Returns the next ps task index for placement via per-Op round-robin order.
+
+  This strategy works slightly better for the GBDT graph because of using
+  custom resources which vary significantly in compute cost.
+  """
+
+  def __init__(self, ps_ops, num_tasks):
+    """Create a new `_RoundRobinStrategy`.
+
+    Args:
+      ps_ops: List of Op types to place on PS.
+      num_tasks: Number of ps tasks to cycle among.
+    """
+    next_task = 0
+    self._next_task_per_op = {}
+    for op in ps_ops:
+      self._next_task_per_op[op] = next_task
+      next_task = (next_task + 1) % num_tasks if num_tasks else 0
+    self._num_tasks = num_tasks
+
+  def __call__(self, op):
+    """Choose a ps task index for the given `Operation`.
+
+    Args:
+      op: An `Operation` to be placed on ps.
+
+    Returns:
+      The next ps task index to use for the `Operation`. Returns the next
+      index, in the range `[offset, offset + num_tasks)`.
+
+    Raises:
+      ValueError: If attempting to place non-PS Op.
+    """
+    if op.type not in self._next_task_per_op:
+      raise ValueError("Unknown op type '%s' for placement:" % op.type)
+    task = self._next_task_per_op[op.type]
+    self._next_task_per_op[op.type] = ((task + 1) % self._num_tasks
+                                       if self._num_tasks else 0)
+    return task
+
+
+def extract_features(features, feature_columns):
+  """Extracts columns from a dictionary of features.
+
+  Args:
+    features: `Tensor` or `dict` of `Tensor` objects.
+    feature_columns: A list of feature_columns.
+
+  Returns:
+    Seven values:
+      - A list of all feature column names.
+      - A list of dense floats.
+      - A list of sparse float feature indices.
+      - A list of sparse float feature values.
+      - A list of sparse float feature shapes.
+      - A list of sparse int feature indices.
+      - A list of sparse int feature values.
+      - A list of sparse int feature shapes.
+  Raises:
+    ValueError: if features is not valid.
+  """
+  if not features:
+    raise ValueError("Features dictionary must be specified.")
+
+  if isinstance(features, ops.Tensor):
+    features = {features.name, features}
+
+  # Make a shallow copy of features to ensure downstream usage
+  # is unaffected by modifications in the model function.
+  features = copy.copy(features)
+  if feature_columns:
+    scope = "gbdt"
+    with variable_scope.variable_scope(scope):
+      feature_columns = list(feature_columns)
+      transformed_features = {}
+      for fc in feature_columns:
+        # pylint: disable=protected-access
+        if isinstance(fc, feature_column_lib._EmbeddingColumn):
+          # pylint: enable=protected-access
+          transformed_features[fc.name] = fc_core.input_layer(
+              features, [fc],
+              weight_collections=[scope])
+        else:
+          result = feature_column_ops.transform_features(features, [fc])
+          if len(result) > 1:
+            raise ValueError("Unexpected number of output features")
+          transformed_features[fc.name] = result[list(result.keys())[0]]
+    features = transformed_features
+
+  dense_float_names = []
+  dense_floats = []
+  sparse_float_names = []
+  sparse_float_indices = []
+  sparse_float_values = []
+  sparse_float_shapes = []
+  sparse_int_names = []
+  sparse_int_indices = []
+  sparse_int_values = []
+  sparse_int_shapes = []
+  for key in sorted(features.keys()):
+    tensor = features[key]
+    if isinstance(tensor, sparse_tensor.SparseTensor):
+      if tensor.values.dtype == dtypes.float32:
+        sparse_float_names.append(key)
+        sparse_float_indices.append(tensor.indices)
+        sparse_float_values.append(tensor.values)
+        sparse_float_shapes.append(tensor.dense_shape)
+      elif tensor.values.dtype == dtypes.int64:
+        sparse_int_names.append(key)
+        sparse_int_indices.append(tensor.indices)
+        sparse_int_values.append(tensor.values)
+        sparse_int_shapes.append(tensor.dense_shape)
+      else:
+        raise ValueError("Unsupported sparse feature %s with dtype %s." %
+                         (tensor.indices.name, tensor.dtype))
+    else:
+      if tensor.dtype == dtypes.float32:
+        if len(tensor.shape) > 1 and tensor.shape[1] > 1:
+          unstacked = array_ops.unstack(tensor, axis=1)
+          for i in xrange(len(unstacked)):
+            dense_float_names.append(_FEATURE_NAME_TEMPLATE % (key, i))
+            dense_floats.append(array_ops.reshape(unstacked[i], [-1, 1]))
+        else:
+          dense_float_names.append(key)
+          dense_floats.append(tensor)
+      else:
+        raise ValueError("Unsupported dense feature %s with dtype %s." %
+                         (tensor.name, tensor.dtype))
+  # Feature columns are logically organized into incrementing slots starting
+  # from dense floats, then sparse floats then sparse ints.
+  fc_names = (dense_float_names + sparse_float_names + sparse_int_names)
+  return (fc_names, dense_floats, sparse_float_indices, sparse_float_values,
+          sparse_float_shapes, sparse_int_indices, sparse_int_values,
+          sparse_int_shapes)
+
+
+def _dropout_params(mode, ensemble_stats):
+  """Returns parameters relevant for dropout.
+
+  Args:
+    mode: Train/Eval/Infer
+    ensemble_stats: A TreeEnsembleStatsOp result tuple.
+
+  Returns:
+    Whether to apply dropout and a dropout seed.
+  """
+  if mode == learn.ModeKeys.TRAIN:
+    # Do dropout only during training.
+    apply_dropout = True
+    seed = ensemble_stats.attempted_trees
+  else:
+    seed = -1
+    apply_dropout = False
+  return apply_dropout, seed
+
+
+class GradientBoostedDecisionTreeModel(object):
+  """A GBDT model function."""
+
+  def __init__(self,
+               is_chief,
+               num_ps_replicas,
+               ensemble_handle,
+               center_bias,
+               examples_per_layer,
+               learner_config,
+               features,
+               feature_columns=None):
+    """Construct a new GradientBoostedDecisionTreeModel function.
+
+    Args:
+      is_chief: Whether to build the chief graph.
+      num_ps_replicas: Number of parameter server replicas, can be 0.
+      ensemble_handle: A handle to the ensemble variable.
+      center_bias: Whether to center the bias before growing trees.
+      examples_per_layer: Number of examples to accumulate before growing
+        a tree layer. It can also be a function that computes the number of
+        examples based on the depth of the layer that's being built.
+      learner_config: A learner config.
+          print split, sorted_feature_names[split.feature_column]
+      features: `Tensor` or `dict` of `Tensor` objects.
+      feature_columns: A list of feature columns.
+
+    Raises:
+      ValueError: if inputs are not valid.
+    """
+    if ensemble_handle is None:
+      raise ValueError("ensemble_handle must be specified.")
+
+    if learner_config is None:
+      raise ValueError("learner_config must be specified.")
+
+    if learner_config.num_classes < 2:
+      raise ValueError("Number of classes must be >=2")
+
+    self._is_chief = is_chief
+    self._num_ps_replicas = num_ps_replicas
+    self._ensemble_handle = ensemble_handle
+    self._center_bias = center_bias
+    self._examples_per_layer = examples_per_layer
+    self._learner_config = learner_config
+    self._feature_columns = feature_columns
+    self._learner_config_serialized = learner_config.SerializeToString()
+    self._attempted_trees = variables.Variable(
+        initial_value=array_ops.zeros([], dtypes.int64), trainable=False)
+    self._finalized_trees = variables.Variable(
+        initial_value=array_ops.zeros([], dtypes.int64), trainable=False)
+    if not features:
+      raise ValueError("Features dictionary must be specified.")
+    (fc_names, dense_floats, sparse_float_indices, sparse_float_values,
+     sparse_float_shapes, sparse_int_indices, sparse_int_values,
+     sparse_int_shapes) = extract_features(features, self._feature_columns)
+    logging.info("Active Feature Columns: " + str(fc_names))
+    self._fc_names = fc_names
+    self._dense_floats = dense_floats
+    self._sparse_float_indices = sparse_float_indices
+    self._sparse_float_values = sparse_float_values
+    self._sparse_float_shapes = sparse_float_shapes
+    self._sparse_int_indices = sparse_int_indices
+    self._sparse_int_values = sparse_int_values
+    self._sparse_int_shapes = sparse_int_shapes
+    self._reduce_dim = (self._learner_config.multi_class_strategy ==
+                        learner_pb2.LearnerConfig.TREE_PER_CLASS and
+                        learner_config.num_classes == 2)
+
+  def predict(self, mode):
+    """Returns predictions given the features and mode.
+
+    Args:
+      mode: Mode the graph is running in (train|predict|eval).
+
+    Returns:
+      A dict of predictions tensors.
+
+    Raises:
+      ValueError: if features is not valid.
+    """
+    apply_averaging = mode != learn.ModeKeys.TRAIN
+
+    # Use the current ensemble to predict on the current batch of input.
+    # For faster prediction we check if the inputs are on the same device
+    # as the model. If not, we create a copy of the model on the worker.
+    input_deps = (self._dense_floats + self._sparse_float_indices +
+                  self._sparse_int_indices)
+    if not input_deps:
+      raise ValueError("No input tensors for prediction.")
+
+    if any(i.device != input_deps[0].device for i in input_deps):
+      raise ValueError("All input tensors should be on the same device.")
+
+    # Get most current model stamp.
+    ensemble_stamp = model_ops.tree_ensemble_stamp_token(self._ensemble_handle)
+
+    # Determine if ensemble is colocated with the inputs.
+    if self._ensemble_handle.device != input_deps[0].device:
+      # Create a local ensemble and get its local stamp.
+      with ops.name_scope("local_ensemble", "TreeEnsembleVariable") as name:
+        local_ensemble_handle = (
+            gen_model_ops.decision_tree_ensemble_resource_handle_op(name=name))
+        create_op = gen_model_ops.create_tree_ensemble_variable(
+            local_ensemble_handle, stamp_token=-1, tree_ensemble_config="")
+        with ops.control_dependencies([create_op]):
+          local_stamp = model_ops.tree_ensemble_stamp_token(
+              local_ensemble_handle)
+
+      # Determine whether the local ensemble is stale and update it if needed.
+      def _refresh_local_ensemble_fn():
+        # Serialize the model from parameter server after reading all inputs.
+        with ops.control_dependencies(input_deps):
+          (ensemble_stamp, serialized_model) = (
+              model_ops.tree_ensemble_serialize(self._ensemble_handle))
+
+        # Update local ensemble with the serialized model from parameter server.
+        with ops.control_dependencies([create_op]):
+          return model_ops.tree_ensemble_deserialize(
+              local_ensemble_handle,
+              stamp_token=ensemble_stamp,
+              tree_ensemble_config=serialized_model), ensemble_stamp
+
+      refresh_local_ensemble, ensemble_stamp = control_flow_ops.cond(
+          math_ops.not_equal(ensemble_stamp,
+                             local_stamp), _refresh_local_ensemble_fn,
+          lambda: (control_flow_ops.no_op(), ensemble_stamp))
+
+      # Once updated, Use the the local model for prediction.
+      with ops.control_dependencies([refresh_local_ensemble]):
+        ensemble_stats = training_ops.tree_ensemble_stats(
+            local_ensemble_handle, ensemble_stamp)
+        apply_dropout, seed = _dropout_params(mode, ensemble_stats)
+        # We don't need dropout info - we can always restore it based on the
+        # seed.
+        predictions, predictions_no_dropout, _ = (
+            prediction_ops.gradient_trees_prediction(
+                local_ensemble_handle,
+                seed,
+                self._dense_floats,
+                self._sparse_float_indices,
+                self._sparse_float_values,
+                self._sparse_float_shapes,
+                self._sparse_int_indices,
+                self._sparse_int_values,
+                self._sparse_int_shapes,
+                learner_config=self._learner_config_serialized,
+                apply_dropout=apply_dropout,
+                apply_averaging=apply_averaging,
+                use_locking=False,
+                center_bias=self._center_bias,
+                reduce_dim=self._reduce_dim))
+        partition_ids = prediction_ops.gradient_trees_partition_examples(
+            local_ensemble_handle,
+            self._dense_floats,
+            self._sparse_float_indices,
+            self._sparse_float_values,
+            self._sparse_float_shapes,
+            self._sparse_int_indices,
+            self._sparse_int_values,
+            self._sparse_int_shapes,
+            use_locking=False)
+
+    else:
+      with ops.device(self._ensemble_handle.device):
+        ensemble_stats = training_ops.tree_ensemble_stats(
+            self._ensemble_handle, ensemble_stamp)
+        apply_dropout, seed = _dropout_params(mode, ensemble_stats)
+        # We don't need dropout info - we can always restore it based on the
+        # seed.
+        predictions, predictions_no_dropout, _ = (
+            prediction_ops.gradient_trees_prediction(
+                self._ensemble_handle,
+                seed,
+                self._dense_floats,
+                self._sparse_float_indices,
+                self._sparse_float_values,
+                self._sparse_float_shapes,
+                self._sparse_int_indices,
+                self._sparse_int_values,
+                self._sparse_int_shapes,
+                learner_config=self._learner_config_serialized,
+                apply_dropout=apply_dropout,
+                apply_averaging=apply_averaging,
+                use_locking=False,
+                center_bias=self._center_bias,
+                reduce_dim=self._reduce_dim))
+        partition_ids = prediction_ops.gradient_trees_partition_examples(
+            self._ensemble_handle,
+            self._dense_floats,
+            self._sparse_float_indices,
+            self._sparse_float_values,
+            self._sparse_float_shapes,
+            self._sparse_int_indices,
+            self._sparse_int_values,
+            self._sparse_int_shapes,
+            use_locking=False)
+
+    return _make_predictions_dict(ensemble_stamp, predictions,
+                                  predictions_no_dropout, partition_ids,
+                                  ensemble_stats)
+
+  def train(self, loss, predictions_dict, labels):
+    """Grows a new tree and adds it to the ensemble.
+
+    Args:
+      loss: A scalar tensor representing average loss of examples.
+      predictions_dict: Dictionary of Rank 2 `Tensor` representing information
+          about predictions per example.
+      labels: Rank 2 `Tensor` representing labels per example.
+
+    Returns:
+      An op that adds a new tree to the ensemble.
+
+    Raises:
+      ValueError: if inputs are not valid.
+    """
+    # Get tensors relevant for training and form the loss.
+    predictions = predictions_dict[PREDICTIONS]
+    partition_ids = predictions_dict[PARTITION_IDS]
+    ensemble_stamp = predictions_dict[ENSEMBLE_STAMP]
+    gradients = gradients_impl.gradients(
+        loss,
+        predictions,
+        name="Gradients",
+        colocate_gradients_with_ops=False,
+        gate_gradients=0,
+        aggregation_method=None)[0]
+
+    strategy = self._learner_config.multi_class_strategy
+    num_classes = self._learner_config.num_classes
+
+    class_id = -1
+    # Handle different multiclass strategies.
+    if strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS:
+      # We build one vs rest trees.
+      gradient_shape = tensor_shape.scalar()
+      hessian_shape = tensor_shape.scalar()
+
+      if num_classes == 2:
+        # We have only 1 score, gradients is of shape [batch, 1].
+        hessians = gradients_impl.gradients(
+            gradients,
+            predictions,
+            name="Hessian",
+            colocate_gradients_with_ops=False,
+            gate_gradients=0,
+            aggregation_method=None)[0]
+
+        squeezed_gradients = array_ops.squeeze(gradients, axis=[1])
+        squeezed_hessians = array_ops.squeeze(hessians, axis=[1])
+      else:
+        hessian_list = self._diagonal_hessian(gradients, predictions)
+        # Assemble hessian list into a tensor.
+        hessians = array_ops.stack(hessian_list, axis=1)
+
+        # Choose the class for which the tree is built (one vs rest).
+        class_id = predictions_dict[NUM_TREES_ATTEMPTED] % num_classes
+        class_id = math_ops.to_int32(class_id)
+
+        # Use class id tensor to get the column with that index from gradients
+        # and hessians.
+        squeezed_gradients = array_ops.squeeze(
+            _get_column_by_index(gradients, class_id))
+        squeezed_hessians = array_ops.squeeze(
+            _get_column_by_index(hessians, class_id))
+    else:
+      # Other multiclass strategies.
+      gradient_shape = tensor_shape.TensorShape([num_classes])
+
+      if strategy == learner_pb2.LearnerConfig.FULL_HESSIAN:
+        hessian_shape = tensor_shape.TensorShape(([num_classes, num_classes]))
+        hessian_list = self._full_hessian(gradients, predictions)
+      else:
+        # Diagonal hessian strategy.
+        hessian_shape = tensor_shape.TensorShape(([num_classes]))
+        hessian_list = self._diagonal_hessian(gradients, predictions)
+
+      squeezed_gradients = gradients
+      hessians = array_ops.stack(hessian_list, axis=1)
+      squeezed_hessians = hessians
+
+    # Get the weights for each example for quantiles calculation,
+    weights = self._get_weights(hessian_shape, squeezed_hessians)
+
+    regularization_config = self._learner_config.regularization
+    min_node_weight = self._learner_config.constraints.min_node_weight
+    # Create all handlers ensuring resources are evenly allocated across PS.
+    fc_name_idx = 0
+    handlers = []
+    init_stamp_token = constant_op.constant(0, dtype=dtypes.int64)
+    with ops.device(self._get_replica_device_setter()):
+      # Create handlers for dense float columns
+      for dense_float_column_idx in range(len(self._dense_floats)):
+        fc_name = self._fc_names[fc_name_idx]
+        handlers.append(
+            ordinal_split_handler.DenseSplitHandler(
+                l1_regularization=regularization_config.l1,
+                l2_regularization=regularization_config.l2,
+                tree_complexity_regularization=(
+                    regularization_config.tree_complexity),
+                min_node_weight=min_node_weight,
+                feature_column_group_id=dense_float_column_idx,
+                epsilon=0.01,
+                num_quantiles=100,
+                dense_float_column=self._dense_floats[dense_float_column_idx],
+                name=fc_name,
+                gradient_shape=gradient_shape,
+                hessian_shape=hessian_shape,
+                multiclass_strategy=strategy,
+                init_stamp_token=init_stamp_token))
+        fc_name_idx += 1
+
+      # Create handlers for sparse float columns.
+      for sparse_float_column_idx in range(len(self._sparse_float_indices)):
+        fc_name = self._fc_names[fc_name_idx]
+        handlers.append(
+            ordinal_split_handler.SparseSplitHandler(
+                l1_regularization=regularization_config.l1,
+                l2_regularization=regularization_config.l2,
+                tree_complexity_regularization=(
+                    regularization_config.tree_complexity),
+                min_node_weight=min_node_weight,
+                feature_column_group_id=sparse_float_column_idx,
+                epsilon=0.01,
+                num_quantiles=100,
+                sparse_float_column=sparse_tensor.SparseTensor(
+                    self._sparse_float_indices[sparse_float_column_idx],
+                    self._sparse_float_values[sparse_float_column_idx],
+                    self._sparse_float_shapes[sparse_float_column_idx]),
+                name=fc_name,
+                gradient_shape=gradient_shape,
+                hessian_shape=hessian_shape,
+                multiclass_strategy=strategy,
+                init_stamp_token=init_stamp_token))
+        fc_name_idx += 1
+
+      # Create handlers for sparse int columns.
+      for sparse_int_column_idx in range(len(self._sparse_int_indices)):
+        fc_name = self._fc_names[fc_name_idx]
+        handlers.append(
+            categorical_split_handler.EqualitySplitHandler(
+                l1_regularization=regularization_config.l1,
+                l2_regularization=regularization_config.l2,
+                tree_complexity_regularization=(
+                    regularization_config.tree_complexity),
+                min_node_weight=min_node_weight,
+                feature_column_group_id=sparse_int_column_idx,
+                sparse_int_column=sparse_tensor.SparseTensor(
+                    self._sparse_int_indices[sparse_int_column_idx],
+                    self._sparse_int_values[sparse_int_column_idx],
+                    self._sparse_int_shapes[sparse_int_column_idx]),
+                name=fc_name,
+                gradient_shape=gradient_shape,
+                hessian_shape=hessian_shape,
+                multiclass_strategy=strategy,
+                init_stamp_token=init_stamp_token))
+        fc_name_idx += 1
+
+      # Create steps accumulator.
+      steps_accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=tensor_shape.scalar(),
+          hessian_shape=tensor_shape.scalar(),
+          name="StepsAccumulator")
+
+      # Create bias stats accumulator.
+      bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator(
+          stamp_token=0,
+          gradient_shape=gradient_shape,
+          hessian_shape=hessian_shape,
+          name="BiasAccumulator")
+
+      # Create ensemble stats variables.
+      num_layer_examples = variables.Variable(
+          initial_value=array_ops.zeros([], dtypes.int64),
+          name="num_layer_examples",
+          trainable=False)
+      num_layer_steps = variables.Variable(
+          initial_value=array_ops.zeros([], dtypes.int64),
+          name="num_layer_steps",
+          trainable=False)
+      num_layers = variables.Variable(
+          initial_value=array_ops.zeros([], dtypes.int64),
+          name="num_layers",
+          trainable=False)
+      active_tree = variables.Variable(
+          initial_value=array_ops.zeros([], dtypes.int64),
+          name="active_tree",
+          trainable=False)
+      active_layer = variables.Variable(
+          initial_value=array_ops.zeros([], dtypes.int64),
+          name="active_layer",
+          trainable=False)
+
+    # Create ensemble stats summaries.
+    summary.scalar("layer_stats/num_examples", num_layer_examples)
+    summary.scalar("layer_stats/num_steps", num_layer_steps)
+    summary.scalar("ensemble_stats/active_tree", active_tree)
+    summary.scalar("ensemble_stats/active_layer", active_layer)
+
+    # Update bias stats.
+    stats_update_ops = []
+    continue_centering = variables.Variable(
+        initial_value=self._center_bias,
+        name="continue_centering",
+        trainable=False)
+    stats_update_ops.append(
+        control_flow_ops.cond(continue_centering,
+                              self._make_update_bias_stats_fn(
+                                  ensemble_stamp, predictions, gradients,
+                                  bias_stats_accumulator),
+                              control_flow_ops.no_op))
+
+    # Update handler stats.
+    handler_reads = {}
+
+    input_deps = (self._dense_floats + self._sparse_float_indices +
+                  self._sparse_int_indices)
+    worker_device = input_deps[0].device
+    for handler in handlers:
+      handler_reads[handler] = handler.scheduled_reads()
+
+    handler_results = batch_ops_utils.run_handler_scheduled_ops(
+        handler_reads, ensemble_stamp, worker_device)
+    per_handler_updates = {}
+    # Two values per handler. First one is if the the handler is active for the
+    # current layer. The second one is if the handler is going to be active
+    # for the next layer.
+    subsampling_type = self._learner_config.WhichOneof("feature_fraction")
+    if subsampling_type == "feature_fraction_per_level":
+      seed = predictions_dict[NUM_LAYERS_ATTEMPTED]
+      active_handlers_current_layer = stateless.stateless_random_uniform(
+          shape=[len(handlers)], seed=[seed, 1])
+      active_handlers_next_layer = stateless.stateless_random_uniform(
+          shape=[len(handlers)], seed=[seed + 1, 1])
+      active_handlers = array_ops.stack(
+          [active_handlers_current_layer, active_handlers_next_layer], axis=1)
+      active_handlers = (active_handlers <
+                         self._learner_config.feature_fraction_per_level)
+    elif subsampling_type == "feature_fraction_per_tree":
+      seed = predictions_dict[NUM_TREES_ATTEMPTED]
+      active_handlers_current_layer = stateless.stateless_random_uniform(
+          shape=[len(handlers)], seed=[seed, 2])
+      active_handlers_current_layer = (
+          active_handlers_current_layer <
+          self._learner_config.feature_fraction_per_tree)
+      active_handlers = array_ops.stack(active_handlers_current_layer,
+                                        array_ops.ones(
+                                            [len(handlers)], dtype=dtypes.bool))
+    else:
+      active_handlers = array_ops.ones([len(handlers), 2], dtype=dtypes.bool)
+
+    # Prepare empty gradients and hessians when handlers are not ready.
+    empty_hess_shape = [1] + hessian_shape.as_list()
+    empty_grad_shape = [1] + gradient_shape.as_list()
+
+    empty_gradients = constant_op.constant(
+        [], dtype=dtypes.float32, shape=empty_grad_shape)
+    empty_hessians = constant_op.constant(
+        [], dtype=dtypes.float32, shape=empty_hess_shape)
+
+    for handler_idx in range(len(handlers)):
+      handler = handlers[handler_idx]
+      is_active = active_handlers[handler_idx]
+      updates, scheduled_updates = handler.update_stats(
+          ensemble_stamp, partition_ids, squeezed_gradients, squeezed_hessians,
+          empty_gradients, empty_hessians, weights, is_active,
+          handler_results[handler])
+      stats_update_ops.append(updates)
+      per_handler_updates[handler] = scheduled_updates
+
+    update_results = batch_ops_utils.run_handler_scheduled_ops(
+        per_handler_updates, ensemble_stamp, worker_device)
+    for update in update_results.values():
+      stats_update_ops += update
+    # Accumulate a step after updating stats.
+    batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32)
+    with ops.control_dependencies(stats_update_ops):
+      add_step_op = steps_accumulator.add(ensemble_stamp, [0], [0],
+                                          [batch_size], [1.0])
+
+    # Determine learning rate.
+    learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof(
+        "tuner")
+    if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout":
+      tuner = getattr(self._learner_config.learning_rate_tuner,
+                      learning_rate_tuner)
+      learning_rate = tuner.learning_rate
+    else:
+      # TODO(nponomareva, soroush) do the line search.
+      raise ValueError("Line search learning rate is not yet supported.")
+
+    # After adding the step, decide if further processing is needed.
+    ensemble_update_ops = [add_step_op]
+    with ops.control_dependencies([add_step_op]):
+      if self._is_chief:
+        dropout_seed = predictions_dict[NUM_TREES_ATTEMPTED]
+
+        # Get accumulated steps and examples for the current layer.
+        _, _, _, _, acc_examples, acc_steps = steps_accumulator.serialize()
+        acc_examples = math_ops.cast(acc_examples[0], dtypes.int64)
+        acc_steps = math_ops.cast(acc_steps[0], dtypes.int64)
+        ensemble_update_ops.append(num_layer_examples.assign(acc_examples))
+        ensemble_update_ops.append(num_layer_steps.assign(acc_steps))
+        # Determine whether we need to update tree ensemble.
+        examples_per_layer = self._examples_per_layer
+        if callable(examples_per_layer):
+          examples_per_layer = examples_per_layer(active_layer)
+        ensemble_update_ops.append(
+            control_flow_ops.cond(
+                acc_examples >= examples_per_layer,
+                self._make_update_ensemble_fn(
+                    ensemble_stamp, steps_accumulator, bias_stats_accumulator,
+                    continue_centering, learning_rate, handlers, num_layers,
+                    active_tree, active_layer, dropout_seed, class_id),
+                control_flow_ops.no_op))
+
+    # Calculate the loss to be reported - use the predictions without dropout.
+    return control_flow_ops.group(*ensemble_update_ops)
+
+  def _get_weights(self, hessian_shape, hessians):
+    """Derives weights to be used based on hessians and multiclass strategy."""
+    if hessian_shape == tensor_shape.scalar():
+      # This is tree per class.
+      weights = hessians
+    elif len(hessian_shape.dims) == 1:
+      # This is diagonal hessian.
+      weights = math_ops.reduce_sum(hessians, axis=1)
+    else:
+      # This is full hessian.
+      weights = math_ops.trace(hessians)
+    return weights
+
+  def _full_hessian(self, grads, predictions):
+    """Prepares hessians for full-hessian multiclass strategy."""
+    # Because of
+    # https://github.com/tensorflow/tensorflow/issues/675, we can't just
+    # compute the full hessian with a single call to gradients, but instead
+    # must compute it row-by-row.
+    gradients_list = array_ops.unstack(
+        grads, num=self._learner_config.num_classes, axis=1)
+    hessian_rows = []
+
+    for row in range(self._learner_config.num_classes):
+      # If current row is i, K is number of classes,each row returns a tensor of
+      # size batch_size x K representing for each example dx_i dx_1, dx_i dx_2
+      # etc dx_i dx_K
+      hessian_row = gradients_impl.gradients(
+          gradients_list[row],
+          predictions,
+          name="Hessian_%d" % row,
+          colocate_gradients_with_ops=False,
+          gate_gradients=0,
+          aggregation_method=None)
+
+      # hessian_row is of dimension 1, batch_size, K, => trim first dimension
+      # to get batch_size x K
+      hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0])
+      hessian_rows.append(hessian_row)
+    return hessian_rows
+
+  def _diagonal_hessian(self, grads, predictions):
+    """Prepares hessians for diagonal-hessian multiclass mode."""
+    diag_hessian_list = []
+
+    gradients_list = array_ops.unstack(
+        grads, num=self._learner_config.num_classes, axis=1)
+
+    for row, row_grads in enumerate(gradients_list):
+      # If current row is i, K is number of classes,each row returns a tensor of
+      # size batch_size x K representing for each example dx_i dx_1, dx_1 dx_2
+      # etc dx_i dx_K
+      hessian_row = gradients_impl.gradients(
+          row_grads,
+          predictions,
+          name="Hessian_%d" % row,
+          colocate_gradients_with_ops=False,
+          gate_gradients=0,
+          aggregation_method=None)
+
+      # hessian_row is of dimension 1, batch_size, K, => trim first dimension
+      # to get batch_size x K
+      hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0])
+
+      # Get dx_i^2 for the whole batch.
+      elem = array_ops.transpose(hessian_row)[row]
+      diag_hessian_list.append(elem)
+
+    return diag_hessian_list
+
+  def _get_replica_device_setter(self):
+    """Creates a replica device setter."""
+    ps_tasks = self._num_ps_replicas
+    ps_ops = [
+        "Variable",
+        "VariableV2",
+        "DecisionTreeEnsembleResourceHandleOp",
+        "StatsAccumulatorScalarResourceHandleOp",
+        "StatsAccumulatorTensorResourceHandleOp",
+        "QuantileStreamResourceHandleOp",
+    ]
+    ps_strategy = _OpRoundRobinStrategy(ps_ops, ps_tasks)
+    return device_setter.replica_device_setter(
+        ps_tasks=ps_tasks,
+        merge_devices=True,
+        ps_ops=ps_ops,
+        ps_strategy=ps_strategy)
+
+  def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients,
+                                 bias_stats_accumulator):
+    """A method to create the function which updates the bias stats."""
+
+    def _update_bias_stats():
+      """A method to update the bias stats."""
+      # Get reduced gradients and hessians.
+      grads_sum = math_ops.reduce_sum(gradients, 0)
+      hess = gradients_impl.gradients(
+          grads_sum,
+          predictions,
+          name="Hessians",
+          colocate_gradients_with_ops=False,
+          gate_gradients=0,
+          aggregation_method=None)[0]
+      hess_sum = math_ops.reduce_sum(hess, 0)
+
+      # Accumulate gradients and hessians.
+      partition_ids = math_ops.range(predictions.get_shape()[1])
+      feature_ids = array_ops.zeros_like(partition_ids, dtype=dtypes.int64)
+      add_stats_op = bias_stats_accumulator.add(
+          ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum)
+      return control_flow_ops.group(*[add_stats_op], name="update_bias_stats")
+
+    return _update_bias_stats
+
+  def _make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator,
+                               bias_stats_accumulator, continue_centering,
+                               learning_rate, handlers, num_layers, active_tree,
+                               active_layer, dropout_seed, class_id):
+    """A method to create the function which updates the tree ensemble."""
+
+    def _update_ensemble():
+      """A method to update the tree ensemble."""
+      # Get next stamp token.
+      next_ensemble_stamp = ensemble_stamp + 1
+      # Finalize bias stats.
+      _, _, _, bias_grads, bias_hess = bias_stats_accumulator.flush(
+          ensemble_stamp, next_ensemble_stamp)
+
+      # Finalize handler splits.
+      are_splits_ready_list = []
+      partition_ids_list = []
+      gains_list = []
+      split_info_list = []
+
+      for handler in handlers:
+        (are_splits_ready,
+         partition_ids, gains, split_info) = handler.make_splits(
+             ensemble_stamp, next_ensemble_stamp, class_id)
+        are_splits_ready_list.append(are_splits_ready)
+        partition_ids_list.append(partition_ids)
+        gains_list.append(gains)
+        split_info_list.append(split_info)
+      # Stack all the inputs to one tensor per type.
+      # This is a workaround for the slowness of graph building in tf.cond.
+      # See (b/36554864).
+      split_sizes = array_ops.stack([
+          array_ops.shape(partition_id)[0]
+          for partition_id in partition_ids_list
+      ])
+      partition_ids = array_ops.concat(partition_ids_list, axis=0)
+      gains = array_ops.concat(gains_list, axis=0)
+      split_infos = array_ops.concat(split_info_list, axis=0)
+
+      # Determine if all splits are ready.
+      are_all_splits_ready = math_ops.reduce_all(
+          array_ops.stack(
+              are_splits_ready_list, axis=0, name="stack_handler_readiness"))
+
+      # Define bias centering update operation.
+      def _center_bias_fn():
+        # Center tree ensemble bias.
+        delta_updates = array_ops.where(bias_hess > 0, -bias_grads / bias_hess,
+                                        array_ops.zeros_like(bias_grads))
+        center_bias = training_ops.center_tree_ensemble_bias(
+            tree_ensemble_handle=self._ensemble_handle,
+            stamp_token=ensemble_stamp,
+            next_stamp_token=next_ensemble_stamp,
+            delta_updates=delta_updates,
+            learner_config=self._learner_config_serialized)
+        return continue_centering.assign(center_bias)
+
+      # Define ensemble growing operations.
+      def _grow_ensemble_ready_fn():
+        # Grow the ensemble given the current candidates.
+        sizes = array_ops.unstack(split_sizes)
+        partition_ids_list = list(array_ops.split(partition_ids, sizes, axis=0))
+        gains_list = list(array_ops.split(gains, sizes, axis=0))
+        split_info_list = list(array_ops.split(split_infos, sizes, axis=0))
+        return training_ops.grow_tree_ensemble(
+            tree_ensemble_handle=self._ensemble_handle,
+            stamp_token=ensemble_stamp,
+            next_stamp_token=next_ensemble_stamp,
+            learning_rate=learning_rate,
+            partition_ids=partition_ids_list,
+            gains=gains_list,
+            splits=split_info_list,
+            learner_config=self._learner_config_serialized,
+            dropout_seed=dropout_seed,
+            center_bias=self._center_bias)
+
+      def _grow_ensemble_not_ready_fn():
+        # Don't grow the ensemble, just update the stamp.
+        return training_ops.grow_tree_ensemble(
+            tree_ensemble_handle=self._ensemble_handle,
+            stamp_token=ensemble_stamp,
+            next_stamp_token=next_ensemble_stamp,
+            learning_rate=0,
+            partition_ids=[],
+            gains=[],
+            splits=[],
+            learner_config=self._learner_config_serialized,
+            dropout_seed=dropout_seed,
+            center_bias=self._center_bias)
+
+      def _grow_ensemble_fn():
+        # Conditionally grow an ensemble depending on whether the splits
+        # from all the handlers are ready.
+        return control_flow_ops.cond(are_all_splits_ready,
+                                     _grow_ensemble_ready_fn,
+                                     _grow_ensemble_not_ready_fn)
+
+      # Update ensemble.
+      update_ops = [are_all_splits_ready]
+      update_model = control_flow_ops.cond(continue_centering, _center_bias_fn,
+                                           _grow_ensemble_fn)
+      update_ops.append(update_model)
+
+      # Update ensemble stats.
+      with ops.control_dependencies([update_model]):
+        stats = training_ops.tree_ensemble_stats(
+            self._ensemble_handle, stamp_token=next_ensemble_stamp)
+        update_ops.append(self._finalized_trees.assign(stats.num_trees))
+        update_ops.append(self._attempted_trees.assign(stats.attempted_trees))
+        update_ops.append(num_layers.assign(stats.num_layers))
+        update_ops.append(active_tree.assign(stats.active_tree))
+        update_ops.append(active_layer.assign(stats.active_layer))
+
+      # Flush step stats.
+      update_ops.extend(
+          steps_accumulator.flush(ensemble_stamp, next_ensemble_stamp))
+      return control_flow_ops.group(*update_ops, name="update_ensemble")
+
+    return _update_ensemble
+
+  def get_number_of_trees_tensor(self):
+    return self._finalized_trees, self._attempted_trees
diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py
new file mode 100644
index 0000000000..74f9a909df
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py
@@ -0,0 +1,917 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for GBDT train function."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from google.protobuf import text_format
+
+from tensorflow.contrib import layers
+from tensorflow.contrib.boosted_trees.proto import learner_pb2
+from tensorflow.contrib.boosted_trees.proto import tree_config_pb2
+from tensorflow.contrib.boosted_trees.python.ops import model_ops
+from tensorflow.contrib.boosted_trees.python.training.functions import gbdt_batch
+from tensorflow.contrib.boosted_trees.python.utils import losses
+
+from tensorflow.contrib.layers.python.layers import feature_column as feature_column_lib
+from tensorflow.contrib.learn.python.learn.estimators import model_fn
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import resources
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+
+
+def _squared_loss(label, unused_weights, predictions):
+  """Unweighted loss implementation."""
+  loss = math_ops.reduce_sum(
+      math_ops.square(predictions - label), 1, keep_dims=True)
+  return loss
+
+
+class GbdtTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    super(GbdtTest, self).setUp()
+
+  def testExtractFeatures(self):
+    """Tests feature extraction."""
+    with self.test_session():
+      features = {}
+      features["dense_float"] = array_ops.zeros([2, 1], dtypes.float32)
+      features["sparse_float"] = sparse_tensor.SparseTensor(
+          array_ops.zeros([2, 2], dtypes.int64),
+          array_ops.zeros([2], dtypes.float32),
+          array_ops.zeros([2], dtypes.int64))
+      features["sparse_int"] = sparse_tensor.SparseTensor(
+          array_ops.zeros([2, 2], dtypes.int64),
+          array_ops.zeros([2], dtypes.int64),
+          array_ops.zeros([2], dtypes.int64))
+      (fc_names, dense_floats, sparse_float_indices, sparse_float_values,
+       sparse_float_shapes, sparse_int_indices, sparse_int_values,
+       sparse_int_shapes) = (gbdt_batch.extract_features(features, None))
+      self.assertEqual(len(fc_names), 3)
+      self.assertAllEqual(fc_names,
+                          ["dense_float", "sparse_float", "sparse_int"])
+      self.assertEqual(len(dense_floats), 1)
+      self.assertEqual(len(sparse_float_indices), 1)
+      self.assertEqual(len(sparse_float_values), 1)
+      self.assertEqual(len(sparse_float_shapes), 1)
+      self.assertEqual(len(sparse_int_indices), 1)
+      self.assertEqual(len(sparse_int_values), 1)
+      self.assertEqual(len(sparse_int_shapes), 1)
+      self.assertAllEqual(dense_floats[0].eval(),
+                          features["dense_float"].eval())
+      self.assertAllEqual(sparse_float_indices[0].eval(),
+                          features["sparse_float"].indices.eval())
+      self.assertAllEqual(sparse_float_values[0].eval(),
+                          features["sparse_float"].values.eval())
+      self.assertAllEqual(sparse_float_shapes[0].eval(),
+                          features["sparse_float"].dense_shape.eval())
+      self.assertAllEqual(sparse_int_indices[0].eval(),
+                          features["sparse_int"].indices.eval())
+      self.assertAllEqual(sparse_int_values[0].eval(),
+                          features["sparse_int"].values.eval())
+      self.assertAllEqual(sparse_int_shapes[0].eval(),
+                          features["sparse_int"].dense_shape.eval())
+
+  def testExtractFeaturesWithTransformation(self):
+    """Tests feature extraction."""
+    with self.test_session():
+      features = {}
+      features["dense_float"] = array_ops.zeros([2, 1], dtypes.float32)
+      features["sparse_float"] = sparse_tensor.SparseTensor(
+          array_ops.zeros([2, 2], dtypes.int64),
+          array_ops.zeros([2], dtypes.float32),
+          array_ops.zeros([2], dtypes.int64))
+      features["sparse_categorical"] = sparse_tensor.SparseTensor(
+          array_ops.zeros([2, 2], dtypes.int64),
+          array_ops.zeros(
+              [2], dtypes.string), array_ops.zeros([2], dtypes.int64))
+      feature_columns = set()
+      feature_columns.add(layers.real_valued_column("dense_float"))
+      feature_columns.add(
+          layers.feature_column._real_valued_var_len_column(
+              "sparse_float", is_sparse=True))
+      feature_columns.add(
+          feature_column_lib.sparse_column_with_hash_bucket(
+              "sparse_categorical", hash_bucket_size=1000000))
+      (fc_names, dense_floats, sparse_float_indices, sparse_float_values,
+       sparse_float_shapes, sparse_int_indices, sparse_int_values,
+       sparse_int_shapes) = (gbdt_batch.extract_features(
+           features, feature_columns))
+      self.assertEqual(len(fc_names), 3)
+      self.assertAllEqual(fc_names,
+                          ["dense_float", "sparse_float", "sparse_categorical"])
+      self.assertEqual(len(dense_floats), 1)
+      self.assertEqual(len(sparse_float_indices), 1)
+      self.assertEqual(len(sparse_float_values), 1)
+      self.assertEqual(len(sparse_float_shapes), 1)
+      self.assertEqual(len(sparse_int_indices), 1)
+      self.assertEqual(len(sparse_int_values), 1)
+      self.assertEqual(len(sparse_int_shapes), 1)
+      self.assertAllEqual(dense_floats[0].eval(),
+                          features["dense_float"].eval())
+      self.assertAllEqual(sparse_float_indices[0].eval(),
+                          features["sparse_float"].indices.eval())
+      self.assertAllEqual(sparse_float_values[0].eval(),
+                          features["sparse_float"].values.eval())
+      self.assertAllEqual(sparse_float_shapes[0].eval(),
+                          features["sparse_float"].dense_shape.eval())
+      self.assertAllEqual(sparse_int_indices[0].eval(),
+                          features["sparse_categorical"].indices.eval())
+      self.assertAllEqual(sparse_int_values[0].eval(), [397263, 397263])
+      self.assertAllEqual(sparse_int_shapes[0].eval(),
+                          features["sparse_categorical"].dense_shape.eval())
+
+  def testTrainFnChiefNoBiasCentering(self):
+    """Tests the train function running on chief without bias centering."""
+    with self.test_session() as sess:
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp,
+          "num_trees": 12,
+      }
+
+      labels = array_ops.ones([4, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 0)
+      self.assertEquals(len(output.tree_weights), 0)
+      self.assertEquals(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+
+      # On second run, expect a trivial split to be chosen to basically
+      # predict the average.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 1)
+      self.assertAllClose(output.tree_weights, [0.1])
+      self.assertEquals(stamp_token.eval(), 2)
+      expected_tree = """
+          nodes {
+            dense_float_binary_split {
+              threshold: 1.0
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 0
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: 0.25
+              }
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: 0.0
+              }
+            }
+          }"""
+      self.assertProtoEquals(expected_tree, output.trees[0])
+
+  def testTrainFnChiefScalingNumberOfExamples(self):
+    """Tests the train function running on chief without bias centering."""
+    with self.test_session() as sess:
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      num_examples_fn = (
+          lambda layer: math_ops.pow(math_ops.cast(2, dtypes.int64), layer) * 1)
+      features = {}
+      features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=num_examples_fn,
+          learner_config=learner_config,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp,
+          "num_trees": 12,
+      }
+
+      labels = array_ops.ones([4, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 0)
+      self.assertEquals(len(output.tree_weights), 0)
+      self.assertEquals(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+
+      # On second run, expect a trivial split to be chosen to basically
+      # predict the average.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 1)
+      self.assertAllClose(output.tree_weights, [0.1])
+      self.assertEquals(stamp_token.eval(), 2)
+      expected_tree = """
+          nodes {
+            dense_float_binary_split {
+              threshold: 1.0
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 0
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: 0.25
+              }
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: 0.0
+              }
+            }
+          }"""
+      self.assertProtoEquals(expected_tree, output.trees[0])
+
+  def testTrainFnChiefWithBiasCentering(self):
+    """Tests the train function running on chief with bias centering."""
+    with self.test_session():
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=True,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp,
+          "num_trees": 12,
+      }
+
+      labels = array_ops.ones([4, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect bias to be centered.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      expected_tree = """
+          nodes {
+            leaf {
+              vector {
+                value: 0.25
+              }
+            }
+          }"""
+      self.assertEquals(len(output.trees), 1)
+      self.assertAllEqual(output.tree_weights, [1.0])
+      self.assertProtoEquals(expected_tree, output.trees[0])
+      self.assertEquals(stamp_token.eval(), 1)
+
+  def testTrainFnNonChiefNoBiasCentering(self):
+    """Tests the train function running on worker without bias centering."""
+    with self.test_session():
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=False,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp
+      }
+
+      labels = array_ops.ones([4, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Regardless of how many times the train op is run, a non-chief worker
+      # can only accumulate stats so the tree ensemble never changes.
+      for _ in range(5):
+        train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 0)
+      self.assertEquals(len(output.tree_weights), 0)
+      self.assertEquals(stamp_token.eval(), 0)
+
+  def testTrainFnNonChiefWithCentering(self):
+    """Tests the train function running on worker with bias centering."""
+    with self.test_session():
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=False,
+          num_ps_replicas=0,
+          center_bias=True,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp
+      }
+
+      labels = array_ops.ones([4, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Regardless of how many times the train op is run, a non-chief worker
+      # can only accumulate stats so the tree ensemble never changes.
+      for _ in range(5):
+        train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 0)
+      self.assertEquals(len(output.tree_weights), 0)
+      self.assertEquals(stamp_token.eval(), 0)
+
+  def testPredictFn(self):
+    """Tests the predict function."""
+    with self.test_session() as sess:
+      # Create ensemble with one bias node.
+      ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      text_format.Merge("""
+          trees {
+            nodes {
+              leaf {
+                vector {
+                  value: 0.25
+                }
+              }
+            }
+          }
+          tree_weights: 1.0
+          tree_metadata {
+            num_tree_weight_updates: 1
+            num_layers_grown: 1
+            is_finalized: true
+          }""", ensemble_config)
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=3,
+          tree_ensemble_config=ensemble_config.SerializeToString(),
+          name="tree_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=False,
+          num_ps_replicas=0,
+          center_bias=True,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      # Create predict op.
+      mode = model_fn.ModeKeys.EVAL
+      predictions_dict = sess.run(gbdt_model.predict(mode))
+      self.assertEquals(predictions_dict["ensemble_stamp"], 3)
+      self.assertAllClose(predictions_dict["predictions"], [[0.25], [0.25],
+                                                            [0.25], [0.25]])
+      self.assertAllClose(predictions_dict["partition_ids"], [0, 0, 0, 0])
+
+  def testTrainFnMulticlassFullHessian(self):
+    """Tests the GBDT train for multiclass full hessian."""
+    with self.test_session() as sess:
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 1
+      # Use full hessian multiclass strategy.
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.FULL_HESSIAN)
+      learner_config.num_classes = 5
+      learner_config.regularization.l1 = 0
+      # To make matrix inversible.
+      learner_config.regularization.l2 = 1e-5
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      batch_size = 3
+      features["dense_float"] = array_ops.ones([batch_size, 1], dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0, -1.0, 0.5, 1.2, 3.1], [1.0, 0.0, 0.8, 0.3, 1.0],
+           [0.0, 0.0, 0.0, 0.0, 1.2]],
+          dtype=dtypes.float32)
+
+      labels = array_ops.constant([[2], [2], [3]], dtype=dtypes.float32)
+      weights = array_ops.ones([batch_size, 1], dtypes.float32)
+
+      partition_ids = array_ops.zeros([batch_size], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp,
+          "num_trees": 0,
+      }
+
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              losses.per_example_maxent_loss(
+                  labels,
+                  weights,
+                  predictions,
+                  num_classes=learner_config.num_classes)[0]),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 0)
+      self.assertEquals(len(output.tree_weights), 0)
+      self.assertEquals(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+      # On second run, expect a trivial split to be chosen to basically
+      # predict the average.
+      train_op.run()
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 1)
+      self.assertAllClose(output.tree_weights, [1])
+      self.assertEquals(stamp_token.eval(), 2)
+
+      # Leaf should have a dense vector of size 5.
+      expected = [
+          -1.29972088337, -1.38769364357, 3.42812108994, 1.02690505981,
+          -1.75405228138
+      ]
+      for i in range(learner_config.num_classes):
+        self.assertAlmostEqual(expected[i],
+                               output.trees[0].nodes[1].leaf.vector.value[i],
+                               places=2)
+
+  def testTrainFnMulticlassDiagonalHessian(self):
+    """Tests the GBDT train for multiclass diagonal hessian."""
+    with self.test_session() as sess:
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 1
+      # Use full hessian multiclass strategy.
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.DIAGONAL_HESSIAN)
+      learner_config.num_classes = 5
+      learner_config.regularization.l1 = 0
+      # To make matrix inversible.
+      learner_config.regularization.l2 = 1e-5
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      batch_size = 3
+      features = {}
+      features["dense_float"] = array_ops.ones([batch_size, 1], dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0, -1.0, 0.5, 1.2, 3.1], [1.0, 0.0, 0.8, 0.3, 1.0],
+           [0.0, 0.0, 0.0, 0.0, 1.2]],
+          dtype=dtypes.float32)
+
+      labels = array_ops.constant([[2], [2], [3]], dtype=dtypes.float32)
+      weights = array_ops.ones([batch_size, 1], dtypes.float32)
+
+      partition_ids = array_ops.zeros([batch_size], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp,
+          "num_trees": 0,
+      }
+
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              losses.per_example_maxent_loss(
+                  labels,
+                  weights,
+                  predictions,
+                  num_classes=learner_config.num_classes)[0]),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEqual(len(output.trees), 0)
+      self.assertEqual(len(output.tree_weights), 0)
+      self.assertEqual(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+      # On second run, expect a trivial split to be chosen to basically
+      # predict the average.
+      train_op.run()
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output.ParseFromString(serialized.eval())
+      self.assertEqual(len(output.trees), 1)
+      self.assertAllClose(output.tree_weights, [1])
+      self.assertEqual(stamp_token.eval(), 2)
+
+      # Leaf should have a dense vector of size 5.
+      expected = [
+          -1.26767396927, -1.13043296337, 4.58542203903, 1.81428349018,
+          -2.43038392067
+      ]
+      for i in range(learner_config.num_classes):
+        self.assertAlmostEqual(expected[i],
+                               output.trees[0].nodes[1].leaf.vector.value[i])
+
+  def testTrainFnMulticlassTreePerClass(self):
+    """Tests the GBDT train for multiclass tree per class strategy."""
+    with self.test_session() as sess:
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 1
+      # Use full hessian multiclass strategy.
+      learner_config.multi_class_strategy = (
+          learner_pb2.LearnerConfig.TREE_PER_CLASS)
+      learner_config.num_classes = 5
+      learner_config.regularization.l1 = 0
+      # To make matrix inversible.
+      learner_config.regularization.l2 = 1e-5
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {
+          "dense_float": array_ops.constant(
+              [[1.0], [1.5], [2.0]], dtypes.float32),
+      }
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          features=features)
+
+      batch_size = 3
+      predictions = array_ops.constant(
+          [[0.0, -1.0, 0.5, 1.2, 3.1], [1.0, 0.0, 0.8, 0.3, 1.0],
+           [0.0, 0.0, 0.0, 2.0, 1.2]],
+          dtype=dtypes.float32)
+
+      labels = array_ops.constant([[2], [2], [3]], dtype=dtypes.float32)
+      weights = array_ops.ones([batch_size, 1], dtypes.float32)
+
+      partition_ids = array_ops.zeros([batch_size], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions": predictions,
+          "predictions_no_dropout": predictions,
+          "partition_ids": partition_ids,
+          "ensemble_stamp": ensemble_stamp,
+          # This should result in a tree built for a class 2.
+          "num_trees": 13,
+      }
+
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              losses.per_example_maxent_loss(
+                  labels,
+                  weights,
+                  predictions,
+                  num_classes=learner_config.num_classes)[0]),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEqual(len(output.trees), 0)
+      self.assertEqual(len(output.tree_weights), 0)
+      self.assertEqual(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+      # On second run, expect a trivial split to be chosen to basically
+      # predict the average.
+      train_op.run()
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output.ParseFromString(serialized.eval())
+      self.assertEqual(len(output.trees), 1)
+      self.assertAllClose(output.tree_weights, [1])
+      self.assertEqual(stamp_token.eval(), 2)
+
+      # One node for a split, two children nodes.
+      self.assertEqual(3, len(output.trees[0].nodes))
+
+      # Leafs will have a sparse vector for class 3.
+      self.assertEqual(1,
+                       len(output.trees[0].nodes[1].leaf.sparse_vector.index))
+      self.assertEqual(3, output.trees[0].nodes[1].leaf.sparse_vector.index[0])
+      self.assertAlmostEqual(
+          -1.13134455681, output.trees[0].nodes[1].leaf.sparse_vector.value[0])
+
+      self.assertEqual(1,
+                       len(output.trees[0].nodes[2].leaf.sparse_vector.index))
+      self.assertEqual(3, output.trees[0].nodes[2].leaf.sparse_vector.index[0])
+      self.assertAlmostEqual(
+          0.893284678459, output.trees[0].nodes[2].leaf.sparse_vector.value[0])
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/utils/losses.py b/tensorflow/contrib/boosted_trees/python/utils/losses.py
new file mode 100644
index 0000000000..4f128b2301
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/utils/losses.py
@@ -0,0 +1,210 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Losses for Gtflow Estimator and Batch Estimator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
+
+
+def per_example_logistic_loss(labels, weights, predictions):
+  """Logistic loss given labels, example weights and predictions.
+
+  Args:
+    labels: Rank 2 (N, 1) tensor of per-example labels.
+    weights: Rank 2 (N, 1) tensor of per-example weights.
+    predictions: Rank 2 (N, 1) tensor of per-example predictions.
+
+  Returns:
+    loss: A Rank 2 (N, 1) tensor of per-example logistic loss.
+    update_op: An update operation to update the loss's internal state.
+  """
+  labels = math_ops.to_float(labels)
+  unweighted_loss = nn.sigmoid_cross_entropy_with_logits(
+      labels=labels, logits=predictions)
+  return unweighted_loss * weights, control_flow_ops.no_op()
+
+
+# This is classical form of Maximum entropy loss, that is twice differentiable
+# (sparse_softmax_cross_entropy which is what we go for is not twice
+# differentiable).
+def per_example_maxent_loss(labels, weights, logits, num_classes, eps=1e-15):
+  """Maximum entropy loss for multiclass problems.
+
+  Maximum entropy is a generalization of logistic loss for the case when more
+  than 2 classes are present.
+
+  Args:
+    labels: Rank 2 (N, 1) or Rank 1 (N) tensor of per-example labels.
+    weights: Rank 2 (N, 1) tensor of per-example weights.
+    logits: Rank 2 (N, K) tensor of per-example predictions, K - num of
+    classes.
+    num_classes: number of classes in classification task. Used to expand label
+    indices into one-hot encodings.
+    eps: tolerance, used as a minimum possible value.
+
+  Returns:
+    loss: A Rank 2 (N, 1) tensor of per-example maxent loss
+    update_op: An update operation to update the loss's internal state.
+  """
+  labels = math_ops.to_int64(labels)
+  # If labels are of rank 1, make them rank 2.
+  labels_shape = labels.get_shape()
+  if len(labels_shape) != 2:
+    labels = array_ops.expand_dims(labels, 1)
+  # Labels are indices of classes, convert them to one hot encodings.
+  target_one_hot = array_ops.one_hot(indices=labels, depth=num_classes)
+  labels = math_ops.reduce_sum(
+      input_tensor=target_one_hot, reduction_indices=[1])
+  labels = math_ops.to_float(labels)
+
+  # Calculate softmax probabilities for each class.
+  unnormalized_probs = math_ops.exp(logits)
+  normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keep_dims=True)
+  softmax_predictions = math_ops.divide(unnormalized_probs,
+                                        math_ops.add(normalizers, eps))
+
+  # Pull out the probabilities for real label.
+  probs_for_real_class = math_ops.reduce_sum(labels * softmax_predictions, 1)
+
+  # Add handling for values near 0 and 1.
+  zeros = array_ops.zeros_like(probs_for_real_class, dtype=logits.dtype) + eps
+  one_minus_eps = array_ops.ones_like(
+      probs_for_real_class, dtype=logits.dtype) - eps
+
+  # Take maximum(eps, pred)
+  cond = (probs_for_real_class >= eps)
+  probs_for_real_class = array_ops.where(cond, probs_for_real_class, zeros)
+
+  # Take minimum(1-eps, pred)
+  cond = (probs_for_real_class <= 1 - eps)
+  probs_for_real_class = array_ops.where(cond, probs_for_real_class,
+                                         one_minus_eps)
+
+  unweighted_loss = array_ops.expand_dims(-math_ops.log(probs_for_real_class),
+                                          1)
+  return unweighted_loss * weights, control_flow_ops.no_op()
+
+
+def per_example_squared_loss(labels, weights, predictions):
+  """Squared loss given labels, example weights and predictions.
+
+  Args:
+    labels: Rank 2 (N, D) tensor of per-example labels.
+    weights: Rank 2 (N, 1) tensor of per-example weights.
+    predictions: Rank 2 (N, D) tensor of per-example predictions.
+
+  Returns:
+    loss: A Rank 2 (N, 1) tensor of per-example squared loss.
+    update_op: An update operation to update the loss's internal state.
+  """
+  unweighted_loss = math_ops.reduce_sum(
+      math_ops.square(predictions - labels), 1, keep_dims=True)
+
+  return unweighted_loss * weights, control_flow_ops.no_op()
+
+
+def per_example_exp_loss(labels, weights, predictions, name=None, eps=0.1):
+  """Exponential loss given labels, example weights and predictions.
+
+  Note that this is only for binary classification.
+  If logistic loss tries to make sure that the classifier is certain of its
+  predictions, exp loss says: "as long as it got it correct, even barely, i
+  don't care". Can be used on noisy data, or when you don't care about getting
+  the actual probabilities from the model, just the correct label.
+
+  The loss returns is exp(-targets*modified_predictions), where
+  modified_predictions are 1 if sigmoid is >= 0.5+eps (eg we predict positive
+  class), -1 if sigmoid < 0.5-eps (e.g. we predict negative class) and ax+b in
+  the interval 0.5-eps, 0.5+eps, where a = 1/eps, b=1/(2eps).
+
+  Args:
+    labels: Rank 2 (N, D) tensor of per-example labels.
+    weights: Rank 2 (N, 1) tensor of per-example weights.
+    predictions: Rank 2 (N, D) tensor of per-example predictions.
+    name: A name for the operation (optional).
+    eps: For the range (0.5-eps, 0.5+eps) we set the predictions to be ax+b.
+
+  Returns:
+    loss: A Rank 2 (N, 1) tensor of per-example exp loss
+    update_op: An update operation to update the loss's internal state.
+  """
+
+  def exp_with_logits(name, eps, labels=None, logits=None):
+    """Computes exponential loss given `logits`.
+
+    The loss returns is exp(-targets*modified_predictions), where
+    modified_predictions are 1 if sigmoid is >= 0.5+eps (eg we predict positive
+    class), -1 if sigmoid < 0.5-eps (e.g. we predict negative class) and ax+b in
+    the interval 0.5-eps, 0.5+eps, where a = 1/eps, b=1/(2eps).
+
+    Args:
+      name: A name for the operation (optional).
+      eps: For the range (0.5-eps, 0.5+eps) we set the predictions to be ax+b.
+      labels: A `Tensor` of the same type and shape as `logits`.
+      logits: A `Tensor` of type `float32` or `float64`.
+
+    Returns:
+      A `Tensor` of the same shape as `logits` with the componentwise
+      exponential losses.
+
+    Raises:
+      ValueError: If `logits` and `labels` do not have the same shape.
+    """
+    with ops.name_scope(name, "exp_loss", [logits, labels]) as name:
+      logits = ops.convert_to_tensor(logits, name="logits")
+      labels = ops.convert_to_tensor(labels, name="labels")
+      try:
+        labels.get_shape().merge_with(logits.get_shape())
+      except ValueError:
+        raise ValueError("logits and labels must have the same shape (%s vs %s)"
+                         % (logits.get_shape(), labels.get_shape()))
+
+    # Default threshold to switch between classes
+    zeros = array_ops.zeros_like(logits, dtype=logits.dtype)
+    ones = array_ops.ones_like(logits, dtype=logits.dtype)
+    neg_ones = -array_ops.ones_like(logits, dtype=logits.dtype)
+
+    # Convert labels to 1 and -1
+    cond_labels = (labels > zeros)
+    labels_converted = array_ops.where(cond_labels, ones, neg_ones)
+
+    # Convert predictions to 1 and -1
+    # The loss we build is min(1, max(-1,ax+b))
+    # where a=1/eps, b=-1/2eps.
+
+    a = 1.0 / eps
+    b = -1.0 / 2 / eps
+    probs = math_ops.sigmoid(logits)
+    y = a * probs + b
+    # Build max(-1, ax+b)
+    cond = (y < -1)
+    max_res = array_ops.where(cond, neg_ones, y)
+    # Build min part
+    cond = (max_res > 1)
+    min_res = array_ops.where(cond, ones, max_res)
+    preds_converted = min_res
+    return math_ops.exp(-preds_converted * labels_converted)
+
+  labels = math_ops.to_float(labels)
+  unweighted_loss = exp_with_logits(
+      name=name, eps=eps, labels=labels, logits=predictions)
+  return unweighted_loss * weights, control_flow_ops.no_op()
diff --git a/tensorflow/contrib/boosted_trees/python/utils/losses_test.py b/tensorflow/contrib/boosted_trees/python/utils/losses_test.py
new file mode 100644
index 0000000000..1b8d8ac43e
--- /dev/null
+++ b/tensorflow/contrib/boosted_trees/python/utils/losses_test.py
@@ -0,0 +1,106 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for trainer hooks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import numpy as np
+
+from tensorflow.contrib.boosted_trees.python.utils import losses
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import googletest
+
+
+class LossesTest(test_util.TensorFlowTestCase):
+
+  def test_per_example_exp_loss(self):
+
+    def _logit(p):
+      return np.log(p) - np.log(1 - p)
+
+    labels_positive = array_ops.ones([10, 1], dtypes.float32)
+    weights = array_ops.ones([10, 1], dtypes.float32)
+    labels_negative = array_ops.zeros([10, 1], dtypes.float32)
+    predictions_probs = np.array(
+        [[0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [0.99]],
+        dtype=np.float32)
+    prediction_logits = _logit(predictions_probs)
+
+    eps = 0.2
+
+    with self.test_session():
+      predictions_tensor = constant_op.constant(
+          prediction_logits, dtype=dtypes.float32)
+      loss_for_positives, _ = losses.per_example_exp_loss(
+          labels_positive, weights, predictions_tensor, eps=eps)
+
+      loss_for_negatives, _ = losses.per_example_exp_loss(
+          labels_negative, weights, predictions_tensor, eps=eps)
+
+      pos_loss = loss_for_positives.eval()
+      neg_loss = loss_for_negatives.eval()
+      # For positive labels, points <= 0.3 get max loss of e.
+      # For negative labels, these points have minimum loss  of 1/e.
+      for i in range(2):
+        self.assertEqual(math.exp(1), pos_loss[i])
+        self.assertEqual(math.exp(-1), neg_loss[i])
+
+      # For positive lables, p oints with predictions 0.7 and larger get minimum
+      # loss value of 1/e. For negative labels, these points are wrongly
+      # classified and get loss e.
+      for i in range(6, 10):
+        self.assertEqual(math.exp(-1), pos_loss[i])
+        self.assertEqual(math.exp(1), neg_loss[i])
+
+      # Points in between 0.5-eps, 0..5+eps get loss exp(-label_m*y), where
+      # y = 1/eps *x -1/(2eps), where x is the probability and label_m is either
+      # 1 or -1 (for label of 0).
+      for i in range(2, 6):
+        self.assertAlmostEqual(
+            math.exp(-1.0 * (predictions_probs[i] * 1.0 / eps - 0.5 / eps)),
+            pos_loss[i])
+        self.assertAlmostEqual(
+            math.exp(1.0 * (predictions_probs[i] * 1.0 / eps - 0.5 / eps)),
+            neg_loss[i])
+
+  def test_per_example_squared_loss(self):
+
+    def _squared_loss(p, y):
+      return np.mean(1.0 * (p - y) * (p - y))
+
+    labels = np.array([[0.123], [224.2], [-3], [2], [.3]], dtype=np.float32)
+    weights = array_ops.ones([5, 1], dtypes.float32)
+    predictions = np.array(
+        [[0.123], [23.2], [233], [52], [3]], dtype=np.float32)
+
+    with self.test_session():
+      loss_tensor, _ = losses.per_example_squared_loss(labels, weights,
+                                                       predictions)
+
+      loss = loss_tensor.eval()
+      for i in range(5):
+        self.assertAlmostEqual(
+            _squared_loss(labels[i], predictions[i]), loss[i])
+
+
+if __name__ == "__main__":
+  googletest.main()
-- 
GitLab


From 9867119831afa821aa53206cc5b7ae773db4284d Mon Sep 17 00:00:00 2001
From: Olivia Nordquist <nolivia@google.com>
Date: Tue, 18 Jul 2017 11:34:24 -0700
Subject: [PATCH 1069/1375] Implementing set_device for the C API

PiperOrigin-RevId: 162379684
---
 tensorflow/c/python_api.cc                          |  5 +++++
 tensorflow/c/python_api.h                           |  2 ++
 tensorflow/core/graph/graph.cc                      |  5 +++++
 tensorflow/core/graph/graph.h                       |  4 ++++
 .../python/client/session_clusterspec_prop_test.py  |  8 --------
 tensorflow/python/client/session_test.py            | 13 -------------
 tensorflow/python/framework/ops.py                  |  8 +++++---
 tensorflow/python/ops/math_ops_test.py              | 11 -----------
 8 files changed, 21 insertions(+), 35 deletions(-)

diff --git a/tensorflow/c/python_api.cc b/tensorflow/c/python_api.cc
index 91e4e3cbb9..adca6c7625 100644
--- a/tensorflow/c/python_api.cc
+++ b/tensorflow/c/python_api.cc
@@ -25,4 +25,9 @@ void AddControlInput(TF_Graph* graph, TF_Operation* op, TF_Operation* input) {
   graph->graph.AddControlEdge(&input->node, &op->node);
 }
 
+void SetRequestedDevice(TF_Graph* graph, TF_Operation* op, const char* device) {
+  mutex_lock l(graph->mu);
+  op->node.set_requested_device(device);
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/c/python_api.h b/tensorflow/c/python_api.h
index bb2b0fd8fd..e1a55d7755 100644
--- a/tensorflow/c/python_api.h
+++ b/tensorflow/c/python_api.h
@@ -25,6 +25,8 @@ namespace tensorflow {
 
 void AddControlInput(TF_Graph* graph, TF_Operation* op, TF_Operation* input);
 
+void SetRequestedDevice(TF_Graph* graph, TF_Operation* op, const char* device);
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_C_PYTHON_API_H_
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index a840ef39d2..f6586f0519 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -184,6 +184,11 @@ void Node::ClearAttr(const string& name) {
   (*props_->node_def.mutable_attr()).erase(name);
 }
 
+void Node::set_requested_device(const string& device) {
+  MaybeCopyOnWrite();
+  props_->node_def.set_device(device);
+}
+
 Status Node::input_edge(int idx, const Edge** e) const {
   if (idx < 0 || idx >= num_inputs()) {
     return errors::InvalidArgument("Invalid input_edge index: ", idx, ", Node ",
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 565f430455..78a0e8fd79 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -101,6 +101,10 @@ class Node {
   // use assigned_device_name() below.
   const string& requested_device() const;
 
+  // This changes the user requested device but not necessarily the device that
+  // on which the operation will run.
+  void set_requested_device(const string& device);
+
   // This gives the device the runtime has assigned this node to.  If
   // you want the device the user requested, use def().device() instead.
   // TODO(josh11b): Validate that the assigned_device, if not empty:
diff --git a/tensorflow/python/client/session_clusterspec_prop_test.py b/tensorflow/python/client/session_clusterspec_prop_test.py
index 37cbf8062c..f40d3f1872 100644
--- a/tensorflow/python/client/session_clusterspec_prop_test.py
+++ b/tensorflow/python/client/session_clusterspec_prop_test.py
@@ -49,7 +49,6 @@ ops.RegisterShape('ConstructionFails')(common_shapes.unknown_shape)
 
 class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationSimple(self):
     server1 = server_lib.Server.create_local_server()
     server2 = server_lib.Server.create_local_server()
@@ -65,7 +64,6 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
     output = sess.run(const)
     self.assertEqual(17, output)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationWorker2Placement(self):
     server1 = server_lib.Server.create_local_server()
     server2 = server_lib.Server.create_local_server()
@@ -93,7 +91,6 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
                          dev_stats.device and 'Const' == node_stats.node_name
                      ]))
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationWorker1Placement(self):
     server1 = server_lib.Server.create_local_server()
     server2 = server_lib.Server.create_local_server()
@@ -110,7 +107,6 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
     output = sess.run(const)
     self.assertEqual(17, output)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testCanonicalDeviceNames(self):
     server1 = server_lib.Server.create_local_server()
     server2 = server_lib.Server.create_local_server()
@@ -139,7 +135,6 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
                          dev_stats.device and 'Const' == node_stats.node_name
                      ]))
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testLegacyDeviceNames(self):
     server1 = server_lib.Server.create_local_server()
     server2 = server_lib.Server.create_local_server()
@@ -167,7 +162,6 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
                          dev_stats.device and 'Const' == node_stats.node_name
                      ]))
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationThreeServers2Graphs(self):
     """Boots 3 servers, creates 2 sessions, ensures appropriate operations.
 
@@ -229,7 +223,6 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
     self.assertAllEqual(expected_ones, sess2.run(var2))
     self.assertAllEqual(expected_ones + expected_ones, sess1.run(var1))
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationThreeServers(self):
     """Boots 3 servers, creates 2 sessions, ensures appropriate operations.
 
@@ -284,7 +277,6 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
     self.assertAllEqual(expected_ones, sess2.run(var))
     self.assertAllEqual(expected_ones + expected_ones, sess1.run(var))
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testClusterSpecPropagationThreeServersOneCluster(self):
     """Boots 3 servers, ensures appropriate communication across workers.
 
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index 83c52b7cf7..61d411b6f9 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -64,7 +64,6 @@ ops.RegisterShape('ConstructionFails')(common_shapes.unknown_shape)
 
 class SessionTest(test_util.TensorFlowTestCase):
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testUseExistingGraph(self):
     with ops.Graph().as_default() as g, ops.device('/cpu:0'):
       a = constant_op.constant(6.0, shape=[1, 1])
@@ -74,7 +73,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       result = c.eval()
       self.assertAllEqual(result, [[42.0]])
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testUseDefaultGraph(self):
     with ops.Graph().as_default(), ops.device('/cpu:0'):
       a = constant_op.constant(6.0, shape=[1, 1])
@@ -879,7 +877,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       v_val = v.eval()
       self.assertAllEqual([[6.0, 6.0, 6.0]], v_val)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testExtendWithGroupBy(self):
     with session.Session() as s:
       a = constant_op.constant(1.0, shape=[1, 2])
@@ -1091,7 +1088,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       with self.assertRaisesRegexp(RuntimeError, 'The Session graph is empty.'):
         sess.run({})
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testNotEntered(self):
     # pylint: disable=protected-access
     self.assertEqual(ops._default_session_stack.get_default(), None)
@@ -1107,7 +1103,6 @@ class SessionTest(test_util.TensorFlowTestCase):
           ValueError, lambda e: 'No default session is registered.' in str(e)):
         c_2.eval()
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testInteractive(self):
     with ops.device('/cpu:0'):
       sess = session.InteractiveSession()
@@ -1120,7 +1115,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       self.assertAllEqual([[24.0]], e.eval())
       sess.close()
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testInteractivePlacePrunedGraph(self):
     sess = session.InteractiveSession()
 
@@ -1142,7 +1136,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       a.eval()
     sess.close()
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testDefaultSessionPlacePrunedGraph(self):
     sess = session.Session()
 
@@ -1164,7 +1157,6 @@ class SessionTest(test_util.TensorFlowTestCase):
 
     sess.close()
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testSharedGraph(self):
     with ops.Graph().as_default() as g, ops.device('/cpu:0'):
       a = constant_op.constant(1.0, shape=[1, 2])
@@ -1423,7 +1415,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       with self.assertRaisesRegexp(TypeError, 'Cannot interpret feed_dict'):
         sess.run(a, feed_dict={'a': [2.0]})
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testPerStepTrace(self):
     run_options = config_pb2.RunOptions(
         trace_level=config_pb2.RunOptions.FULL_TRACE)
@@ -1444,7 +1435,6 @@ class SessionTest(test_util.TensorFlowTestCase):
         self.assertTrue(run_metadata.HasField('step_stats'))
         self.assertEquals(len(run_metadata.step_stats.dev_stats), 1)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testRunOptionsRunMetadata(self):
     run_options = config_pb2.RunOptions(
         trace_level=config_pb2.RunOptions.FULL_TRACE)
@@ -1480,7 +1470,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       with self.assertRaisesRegexp(ValueError, 'may not be fed'):
         sess.run(reshaped_tensor, feed_dict={new_shape: [3, 7]})
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testInferShapesFalse(self):
     with ops.Graph().as_default(), ops.device('/cpu:0'):
       a = constant_op.constant([[1, 2]])
@@ -1489,7 +1478,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       # Avoid lint error regarding 'unused' var a.
       self.assertTrue(a == a)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testInferShapesTrue(self):
     config = config_pb2.ConfigProto(
         graph_options=config_pb2.GraphOptions(infer_shapes=True))
@@ -1500,7 +1488,6 @@ class SessionTest(test_util.TensorFlowTestCase):
       # Avoid lint error regarding 'unused' var a.
       self.assertTrue(a == a)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testBuildCostModel(self):
     run_options = config_pb2.RunOptions()
     config = config_pb2.ConfigProto(
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 8216b65c02..e70716d316 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1373,7 +1373,7 @@ class Operation(object):
     """
     if self._graph._c_graph:  # pylint: disable=protected-access
       # TODO(iga): Remove this assert after converting to C API by default.
-      # Just being a bit paranoid here.
+      # Just being a bit paranoid here
       assert self._node_def.device == c_api.TF_OperationDevice(self._c_op)
       return c_api.TF_OperationDevice(self._c_op)
     else:
@@ -1427,8 +1427,10 @@ class Operation(object):
     Args:
       device: string or device..  The device to set.
     """
-    assert not self._graph._c_graph, (  # pylint: disable=protected-access
-        "Operation._set_device doesn't work with C API")
+    if _USE_C_API:
+      c_api.SetRequestedDevice(
+          self._graph._c_graph, self._c_op, _device_string(device))  # pylint: disable=protected-access
+    # TODO(nolivia): remove this line when switch to C api
     self._node_def.device = _device_string(device)
 
   def _add_input(self, tensor, dtype=None):
diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py
index 9683603785..617d2305bd 100644
--- a/tensorflow/python/ops/math_ops_test.py
+++ b/tensorflow/python/ops/math_ops_test.py
@@ -224,7 +224,6 @@ class ApproximateEqualTest(test_util.TensorFlowTestCase):
 
 class ScalarMulTest(test_util.TensorFlowTestCase):
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testAcceptsRefs(self):
     var = variables.Variable(10)
     result = math_ops.scalar_mul(3, var)
@@ -327,7 +326,6 @@ class DivAndModTest(test_util.TensorFlowTestCase):
     divs = np.arange(-3, 0, .25).reshape(1, 12)
     return nums, divs
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testFloorModInt(self):
     nums, divs = self.intTestData()
     with self.test_session():
@@ -337,7 +335,6 @@ class DivAndModTest(test_util.TensorFlowTestCase):
       np_result = nums % divs
       self.assertAllEqual(tf_result, np_result)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testFloorModFloat(self):
     nums, divs = self.floatTestData()
     with self.test_session():
@@ -349,7 +346,6 @@ class DivAndModTest(test_util.TensorFlowTestCase):
       #               % array_ops.constant(divs)).eval()
       # self.assertAllEqual(tf2_result, tf_result)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testTruncateModInt(self):
     nums, divs = self.intTestData()
     with self.test_session():
@@ -357,7 +353,6 @@ class DivAndModTest(test_util.TensorFlowTestCase):
       np_result = np.fmod(nums, divs)
       self.assertAllEqual(tf_result, np_result)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testTruncateModFloat(self):
     nums, divs = self.floatTestData()
     with self.test_session():
@@ -365,7 +360,6 @@ class DivAndModTest(test_util.TensorFlowTestCase):
       np_result = np.fmod(nums, divs)
       self.assertAllEqual(tf_result, np_result)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testDivideInt(self):
     nums, divs = self.intTestData()
     with self.test_session():
@@ -377,14 +371,12 @@ class DivAndModTest(test_util.TensorFlowTestCase):
       #               // array_ops.constant(divs)).eval()
       # self.assertAllEqual(tf2_result, tf_result)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testDivideName(self):
     with self.test_session():
       op = math_ops.divide(
           array_ops.constant(3), array_ops.constant(4), name="my_cool_divide")
       self.assertEqual(op.name, "my_cool_divide:0")
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testRealDiv(self):
     nums, divs = self.floatTestData()
     with self.test_session():
@@ -392,14 +384,12 @@ class DivAndModTest(test_util.TensorFlowTestCase):
       np_result = np.divide(nums, divs)
       self.assertAllEqual(tf_result, np_result)
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testComplexDiv(self):
     foo = array_ops.constant([1. + 3.j])
     with self.test_session():
       _ = math_ops.divide(foo, 1.).eval()
       _ = math_ops.div(foo, 2.).eval()
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testFloorDivGrad(self):
     with self.test_session():
       a = variables.Variable(2.)
@@ -414,7 +404,6 @@ class DivAndModTest(test_util.TensorFlowTestCase):
         self.assertAllEqual([None if x is None else x.eval()
                              for x in c_grad], [None, None])
 
-  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testConsistent(self):
     nums, divs = self.intTestData()
     with self.test_session():
-- 
GitLab


From 5bdfcfbcbab3580f51531c9ac832302016775f63 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 11:54:04 -0700
Subject: [PATCH 1070/1375] Remove extra Ref in resource handles when using
 IsResourceInitialized.

PiperOrigin-RevId: 162382430
---
 tensorflow/core/framework/resource_mgr.h | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/framework/resource_mgr.h b/tensorflow/core/framework/resource_mgr.h
index c92c0a36ba..c0128cc77d 100644
--- a/tensorflow/core/framework/resource_mgr.h
+++ b/tensorflow/core/framework/resource_mgr.h
@@ -492,9 +492,16 @@ template <typename T>
 void IsResourceInitialized<T>::Compute(OpKernelContext* ctx) {
   Tensor* output;
   OP_REQUIRES_OK(ctx, ctx->allocate_output(0, {}, &output));
-  T* unused;
-  output->flat<bool>()(0) =
-      LookupResource(ctx, HandleFromInput(ctx, 0), &unused).ok();
+  T* object;
+  bool found;
+  if (LookupResource(ctx, HandleFromInput(ctx, 0), &object).ok()) {
+    found = true;
+    object->Unref();
+  } else {
+    found = false;
+  }
+
+  output->flat<bool>()(0) = found;
 }
 
 template <typename T>
-- 
GitLab


From e87e8199a88938da2084796c29e66a89e8708f2d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 12:02:27 -0700
Subject: [PATCH 1071/1375] Destroy resources at end of session in
 TensorForestEstimator to avoid memory leak.

PiperOrigin-RevId: 162383623
---
 .../tensor_forest/client/random_forest.py     | 19 +++++++++++++++----
 .../tensor_forest/python/tensor_forest.py     |  3 +++
 .../tensor_forest/python/tensor_forest_v4.py  |  5 +++++
 3 files changed, 23 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index d067b4b779..2eb37d3e53 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -33,6 +33,7 @@ from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging as logging
@@ -73,8 +74,8 @@ class TensorForestRunOpAtEndHook(session_run_hook.SessionRunHook):
     self._ops = op_dict
 
   def end(self, session):
-    for name, op in self._ops.iteritems():
-      logging.info('{0}: {1}'.format(name, session.run(op)))
+    for name in sorted(self._ops.keys()):
+      logging.info('{0}: {1}'.format(name, session.run(self._ops[name])))
 
 
 class TensorForestLossHook(session_run_hook.SessionRunHook):
@@ -234,9 +235,19 @@ def get_model_fn(params,
         logits=logits,
         scope=head_scope)
 
+    # Ops are run in lexigraphical order of their keys. Run the resource
+    # clean-up op last.
+    all_handles = graph_builder.get_all_resource_handles()
+    ops_at_end = {
+        '9: clean up resources': control_flow_ops.group(
+            *[resource_variable_ops.destroy_resource_op(handle)
+              for handle in all_handles])}
+
     if report_feature_importances:
-      training_hooks.append(TensorForestRunOpAtEndHook(
-          {'feature_importances': graph_builder.feature_importances()}))
+      ops_at_end['1: feature_importances'] = (
+          graph_builder.feature_importances())
+
+    training_hooks.append(TensorForestRunOpAtEndHook(ops_at_end))
 
     if early_stopping_rounds:
       training_hooks.append(
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
index b756b0129d..844ac00b0a 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
@@ -328,6 +328,9 @@ class RandomForestGraphs(object):
     return array_ops.concat(
         [split_data[ind] for ind in self.params.bagged_features[tree_num]], 1)
 
+  def get_all_resource_handles(self):
+    return []
+
   def training_graph(self,
                      input_data,
                      input_labels,
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
index 8198c228dd..fefc19d3bd 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
@@ -315,6 +315,7 @@ class RandomTreeGraphsV4(tensor_forest.RandomTreeGraphs):
 
 
 class RandomForestGraphsV4(tensor_forest.RandomForestGraphs):
+  """Constructs training/inference graphs for TensorForest v4."""
 
   def __init__(self, params, tree_graphs=None, tree_variables_class=None,
                **kwargs):
@@ -324,3 +325,7 @@ class RandomForestGraphsV4(tensor_forest.RandomForestGraphs):
         params, tree_graphs=tree_graphs or RandomTreeGraphsV4,
         tree_variables_class=(tree_variables_class or TreeTrainingVariablesV4),
         **kwargs)
+
+  def get_all_resource_handles(self):
+    return ([self.variables[i].tree for i in range(len(self.trees))] +
+            [self.variables[i].stats for i in range(len(self.trees))])
-- 
GitLab


From f0923262ed4d89f2a201f6e7ca02e5d118c1b4d3 Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Tue, 18 Jul 2017 12:38:51 -0700
Subject: [PATCH 1072/1375] Update get_started.md (#11539)

* Support tensors as dropout rates again, by removing the min(max(..)..) clipping
that throws TypeError for tensors.

PiperOrigin-RevId: 157000504

* Patch release PR. Updating versions to 1.2.1. Updating the release notes. Updating markdown version. Patching Github issue 11005.

* Fix for contrib.layers test that was raising an IndexError (#11069)

* variable name change and documentation clarification

* Updated a test to eliminate an error it raises

* Updating release.md.

* Allowing the PIP_TEST_ROOT variable to be set.

PiperOrigin-RevId: 161087696

* Update get_started.md

Fix various errors in print out statements and unnecessary lines of code
---
 tensorflow/docs_src/get_started/get_started.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 852d41e9ed..815b83e5fb 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -32,8 +32,8 @@ tensor's **rank** is its number of dimensions. Here are some examples of
 tensors:
 
 ```python
-3 # a rank 0 tensor; this is a scalar with shape []
-[1., 2., 3.] # a rank 1 tensor; this is a vector with shape [3]
+3 # a rank 0 tensor; a scalar with shape []
+[1., 2., 3.] # a rank 1 tensor; a vector with shape [3]
 [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
 [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]
 ```
@@ -181,7 +181,7 @@ initial value:
 W = tf.Variable([.3], dtype=tf.float32)
 b = tf.Variable([-.3], dtype=tf.float32)
 x = tf.placeholder(tf.float32)
-linear_model = W * x + b
+linear_model = W*x + b
 ```
 
 Constants are initialized when you call `tf.constant`, and their value can never
@@ -302,7 +302,7 @@ W = tf.Variable([.3], dtype=tf.float32)
 b = tf.Variable([-.3], dtype=tf.float32)
 # Model input and output
 x = tf.placeholder(tf.float32)
-linear_model = W * x + b
+linear_model = W*x + b
 y = tf.placeholder(tf.float32)
 
 # loss
@@ -330,9 +330,9 @@ When run, it produces
 W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11
 ```
 
-Notice that the loss is a very small number (very close to zero). If you run this
-program, your loss may not be the exact same because the model is initialized
-with pseudorandom values.
+Notice that the loss is a very small number (very close to zero). If you run 
+this program, your loss may not be exactly the same as the aforementioned loss 
+because the model is initialized with pseudorandom values.
 
 This more complicated program can still be visualized in TensorBoard
 ![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png)
@@ -426,7 +426,7 @@ def model_fn(features, labels, mode):
   # Build a linear model and predict values
   W = tf.get_variable("W", [1], dtype=tf.float64)
   b = tf.get_variable("b", [1], dtype=tf.float64)
-  y = W * features['x'] + b
+  y = W*features['x'] + b
   # Loss sub-graph
   loss = tf.reduce_sum(tf.square(y - labels))
   # Training sub-graph
-- 
GitLab


From 5beccaa020f1c499262d6b4100e3f1a222ed9b95 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 18 Jul 2017 13:12:14 -0700
Subject: [PATCH 1073/1375] Make non_max_suppression_v2 private because it
 still needs API review.

PiperOrigin-RevId: 162392717
---
 tensorflow/python/ops/image_ops.py                 | 1 -
 tensorflow/tools/api/golden/tensorflow.image.pbtxt | 4 ----
 2 files changed, 5 deletions(-)

diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py
index 51d0276140..75c67dcb3c 100644
--- a/tensorflow/python/ops/image_ops.py
+++ b/tensorflow/python/ops/image_ops.py
@@ -60,7 +60,6 @@ See the @{$python/image} guide.
 @@per_image_standardization
 @@draw_bounding_boxes
 @@non_max_suppression
-@@non_max_suppression_v2
 @@sample_distorted_bounding_box
 @@total_variation
 """
diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
index 93257c84a1..8f7790f299 100644
--- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
@@ -96,10 +96,6 @@ tf_module {
     name: "non_max_suppression"
     argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
-  member_method {
-    name: "non_max_suppression_v2"
-    argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "pad_to_bounding_box"
     argspec: "args=[\'image\', \'offset_height\', \'offset_width\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None"
-- 
GitLab


From 39f9cd47fde2816387c5a2d16d1be730fff8a7d7 Mon Sep 17 00:00:00 2001
From: David Norman <DavidNorman@users.noreply.github.com>
Date: Tue, 18 Jul 2017 21:32:39 +0100
Subject: [PATCH 1074/1375] [XLA] Add DT_HALF to the list of floating point
 types (#11386)

* Add DT_HALF to the list of floating point types

* Add DT_HALF to the list of generic numerical types too
---
 tensorflow/compiler/tf2xla/xla_op_registry.h | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.h b/tensorflow/compiler/tf2xla/xla_op_registry.h
index 9a39cc9675..47d61a21a1 100644
--- a/tensorflow/compiler/tf2xla/xla_op_registry.h
+++ b/tensorflow/compiler/tf2xla/xla_op_registry.h
@@ -45,9 +45,10 @@ extern const char* const DEVICE_XLA_CPU;
 extern const char* const DEVICE_XLA_GPU;
 
 constexpr std::array<DataType, 2> kIntTypes = {{DT_INT32, DT_INT64}};
-constexpr std::array<DataType, 2> kFloatTypes = {{DT_FLOAT, DT_DOUBLE}};
-constexpr std::array<DataType, 4> kNumericTypes = {
-    {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE}};
+constexpr std::array<DataType, 3> kFloatTypes = {
+    {DT_HALF, DT_FLOAT, DT_DOUBLE}};
+constexpr std::array<DataType, 5> kNumericTypes = {
+    {DT_INT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE}};
 
 constexpr std::array<DataType, 5> kCpuAllTypes = {
     {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE, DT_BOOL}};
-- 
GitLab


From e64baeafce818c600cb70e2fe71997c3b4f24297 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Tue, 18 Jul 2017 14:03:12 -0700
Subject: [PATCH 1075/1375] [tf.contrib.data] Improve error checking for
 feedable iterators.

The IteratorFromStringHandle op now takes optional type and shape constraints,
and will raise an error if the iterator to be returned is not compatible with
those constraints.

PiperOrigin-RevId: 162400114
---
 .../python/kernel_tests/iterator_ops_test.py  | 40 +++++++++++++++++++
 .../contrib/data/python/ops/dataset_ops.py    |  4 +-
 tensorflow/core/kernels/iterator_ops.cc       | 38 ++++++++++++++----
 tensorflow/core/ops/dataset_ops.cc            |  6 +++
 4 files changed, 79 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
index d12be873f1..30f685842b 100644
--- a/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py
@@ -376,6 +376,46 @@ class IteratorTest(test.TestCase):
         sess.run(next_element,
                  feed_dict={handle_placeholder: iterator_4_handle})
 
+  def testIteratorStringHandleError(self):
+    dataset_int_scalar = (dataset_ops.Dataset.from_tensor_slices([1, 2,
+                                                                  3]).repeat())
+    dataset_float_vector = (dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0]))
+
+    handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
+
+    feedable_int_scalar = dataset_ops.Iterator.from_string_handle(
+        handle_placeholder, dtypes.int32, [])
+    feedable_int_vector = dataset_ops.Iterator.from_string_handle(
+        handle_placeholder, dtypes.int32, [None])
+    feedable_int_any = dataset_ops.Iterator.from_string_handle(
+        handle_placeholder, dtypes.int32)
+
+    with self.test_session() as sess:
+      handle_int_scalar = sess.run(
+          dataset_int_scalar.make_one_shot_iterator().string_handle())
+      handle_float_vector = sess.run(
+          dataset_float_vector.make_one_shot_iterator().string_handle())
+
+      self.assertEqual(1,
+                       sess.run(
+                           feedable_int_scalar.get_next(),
+                           feed_dict={handle_placeholder: handle_int_scalar}))
+
+      self.assertEqual(2,
+                       sess.run(
+                           feedable_int_any.get_next(),
+                           feed_dict={handle_placeholder: handle_int_scalar}))
+
+      with self.assertRaises(errors.InvalidArgumentError):
+        print(sess.run(
+            feedable_int_vector.get_next(),
+            feed_dict={handle_placeholder: handle_int_scalar}))
+
+      with self.assertRaises(errors.InvalidArgumentError):
+        print(sess.run(
+            feedable_int_vector.get_next(),
+            feed_dict={handle_placeholder: handle_float_vector}))
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index dd7dce9112..81da8b050a 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -235,7 +235,9 @@ class Iterator(object):
     nest.assert_same_structure(output_types, output_shapes)
     string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string)
     iterator_resource = gen_dataset_ops.iterator_from_string_handle(
-        string_handle)
+        string_handle,
+        output_types=nest.flatten(output_types),
+        output_shapes=nest.flatten(output_shapes))
     return Iterator(iterator_resource, None, output_types, output_shapes)
 
   @property
diff --git a/tensorflow/core/kernels/iterator_ops.cc b/tensorflow/core/kernels/iterator_ops.cc
index 9ed89a12be..a340aacf14 100644
--- a/tensorflow/core/kernels/iterator_ops.cc
+++ b/tensorflow/core/kernels/iterator_ops.cc
@@ -443,7 +443,16 @@ class IteratorToStringHandleOp : public OpKernel {
 class IteratorFromStringHandleOp : public OpKernel {
  public:
   explicit IteratorFromStringHandleOp(OpKernelConstruction* ctx)
-      : OpKernel(ctx) {}
+      : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_dtypes_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_));
+    OP_REQUIRES(
+        ctx,
+        output_dtypes_.empty() || output_shapes_.empty() ||
+            output_dtypes_.size() == output_shapes_.size(),
+        errors::InvalidArgument("If both 'output_types' and 'output_shapes' "
+                                "are set, they must have the same length."));
+  }
 
   void Compute(OpKernelContext* ctx) override {
     const Tensor& string_handle_t = ctx->input(0);
@@ -457,13 +466,6 @@ class IteratorFromStringHandleOp : public OpKernel {
         errors::InvalidArgument(
             "Could not parse string_handle as a valid ResourceHandle"));
 
-    // Validate that the handle corresponds to a real resource, and
-    // that it is an IteratorResource.
-    IteratorResource* iterator_resource;
-    OP_REQUIRES_OK(ctx,
-                   LookupResource(ctx, resource_handle, &iterator_resource));
-    iterator_resource->Unref();
-
     OP_REQUIRES(
         ctx, resource_handle.device() == ctx->device()->attributes().name(),
         errors::InvalidArgument("Attempted create an iterator on device \"",
@@ -471,11 +473,31 @@ class IteratorFromStringHandleOp : public OpKernel {
                                 "\" from handle defined on device \"",
                                 resource_handle.device(), "\""));
 
+    // Validate that the handle corresponds to a real resource, and
+    // that it is an IteratorResource.
+    IteratorResource* iterator_resource;
+    OP_REQUIRES_OK(ctx,
+                   LookupResource(ctx, resource_handle, &iterator_resource));
+    core::ScopedUnref unref_iterator(iterator_resource);
+    if (!output_dtypes_.empty()) {
+      OP_REQUIRES_OK(ctx, VerifyTypesMatch(output_dtypes_,
+                                           iterator_resource->output_dtypes()));
+    }
+    if (!output_shapes_.empty()) {
+      OP_REQUIRES_OK(
+          ctx, VerifyShapesCompatible(output_shapes_,
+                                      iterator_resource->output_shapes()));
+    }
+
     Tensor* resource_handle_t;
     OP_REQUIRES_OK(
         ctx, ctx->allocate_output(0, TensorShape({}), &resource_handle_t));
     resource_handle_t->scalar<ResourceHandle>()() = resource_handle;
   }
+
+ private:
+  DataTypeVector output_dtypes_;
+  std::vector<PartialTensorShape> output_shapes_;
 };
 
 REGISTER_KERNEL_BUILDER(Name("Iterator").Device(DEVICE_CPU), IteratorHandleOp);
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index b37b901690..3f2de6f066 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -550,12 +550,18 @@ string_handle: A string representation of the given handle.
 REGISTER_OP("IteratorFromStringHandle")
     .Input("string_handle: string")
     .Output("resource_handle: resource")
+    .Attr("output_types: list(type) >= 0 = []")
+    .Attr("output_shapes: list(shape) >= 0 = []")
     .SetShapeFn(shape_inference::ScalarShape)
     .Doc(R"doc(
 Converts the given string representing a handle to an iterator to a resource.
 
 string_handle: A string representation of the given handle.
 resource_handle: A handle to an iterator resource.
+output_types: If specified, defines the type of each tuple component in an
+  element produced by the resulting iterator.
+output_shapes: If specified, defines the shape of each tuple component in an
+  element produced by the resulting iterator.
 )doc");
 
 }  // namespace tensorflow
-- 
GitLab


From 083f5433f65b92a406f41806e7bc8c5d0ae679ac Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 14:03:23 -0700
Subject: [PATCH 1076/1375] Allow for platform specific implementations of
 gpu_tracer.

PiperOrigin-RevId: 162400146
---
 tensorflow/contrib/cmake/tf_core_cpu.cmake    |  6 ------
 tensorflow/contrib/makefile/Makefile          |  2 +-
 tensorflow/core/BUILD                         | 19 ++++++++++---------
 .../core/common_runtime/direct_session.cc     |  4 ++--
 .../core/platform/default/build_config.bzl    |  9 +++++++++
 .../gpu => platform/default}/gpu_tracer.cc    | 12 ++++++++----
 .../gpu => platform}/gpu_tracer.h             | 10 ++++++----
 .../gpu => platform}/gpu_tracer_test.cc       |  2 +-
 .../profiler/internal/run_metadata_test.py    | 13 +++++++------
 9 files changed, 44 insertions(+), 33 deletions(-)
 rename tensorflow/core/{common_runtime/gpu => platform/default}/gpu_tracer.cc (98%)
 rename tensorflow/core/{common_runtime/gpu => platform}/gpu_tracer.h (91%)
 rename tensorflow/core/{common_runtime/gpu => platform}/gpu_tracer_test.cc (99%)

diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake
index c390136d46..0db6607985 100644
--- a/tensorflow/contrib/cmake/tf_core_cpu.cmake
+++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake
@@ -57,12 +57,6 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs
 )
 list(REMOVE_ITEM tf_core_cpu_srcs ${tf_core_cpu_exclude_srcs})
 
-# We need to include stubs for the GPU tracer, which are in the exclude glob.
-list(APPEND tf_core_cpu_srcs
-     "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_tracer.cc"
-     "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_tracer.h"
-)
-
 if (tensorflow_ENABLE_GPU)
   file(GLOB_RECURSE tf_core_gpu_srcs
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/*.cc"
diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index 2150cfe9ea..fc465ab380 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -500,7 +500,7 @@ $(wildcard tensorflow/core/grappler/clusters/single_machine.*)
 # Filter out all the excluded files.
 TF_CC_SRCS := $(filter-out $(CORE_CC_EXCLUDE_SRCS), $(CORE_CC_ALL_SRCS))
 # Add in any extra files that don't fit the patterns easily
-TF_CC_SRCS += tensorflow/core/common_runtime/gpu/gpu_tracer.cc
+TF_CC_SRCS += tensorflow/core/platform/default/gpu_tracer.cc
 # Also include the op and kernel definitions.
 TF_CC_SRCS += $(shell cat $(MAKEFILE_DIR)/tf_op_files.txt)
 PBT_CC_SRCS := $(shell cat $(MAKEFILE_DIR)/tf_pb_text_files.txt)
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 9828c3248f..bb17d8832f 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -112,6 +112,9 @@ load(
     "tf_lib_proto_parsing_deps",
     "tf_additional_verbs_lib_defines",
     "tf_additional_mpi_lib_defines",
+    "tf_additional_gpu_tracer_srcs",
+    "tf_additional_gpu_tracer_deps",
+    "tf_additional_gpu_tracer_cuda_deps",
 )
 load(
     "//tensorflow/core:platform/default/build_config_root.bzl",
@@ -868,8 +871,6 @@ filegroup(
         "//tensorflow/core/platform/default/build_config:android_srcs",
         "//tensorflow/core/util/ctc:android_srcs",
         "//tensorflow/core/util/tensor_bundle:android_srcs",
-        "common_runtime/gpu/gpu_tracer.cc",
-        "common_runtime/gpu/gpu_tracer.h",
     ] + glob(
         [
             "client/**/*.cc",
@@ -1268,6 +1269,7 @@ cc_library(
                 "platform/**/cuda.h",
                 "platform/**/cuda_libdevice_path.cc",
                 "platform/**/stream_executor.h",
+                "platform/**/gpu_tracer.cc",
             ],
         ),
     }) + tf_additional_lib_srcs(
@@ -1277,6 +1279,7 @@ cc_library(
             "platform/**/cuda_libdevice_path.cc",
             "platform/**/stream_executor.h",
             "platform/**/env_time.cc",
+            "platform/**/gpu_tracer.cc",
         ] +
         # Protobuf deps already included through the ":lib_proto_parsing"
         # dependency.
@@ -1769,19 +1772,17 @@ cc_library(
 
 tf_cuda_library(
     name = "gpu_tracer",
-    srcs = [
-        "common_runtime/gpu/gpu_tracer.cc",
-    ],
+    srcs = tf_additional_gpu_tracer_srcs(),
     hdrs = [
-        "common_runtime/gpu/gpu_tracer.h",
+        "platform/gpu_tracer.h",
     ],
     copts = tf_copts(),
-    cuda_deps = tf_additional_cupti_wrapper_deps(),
+    cuda_deps = tf_additional_cupti_wrapper_deps() + tf_additional_gpu_tracer_cuda_deps(),
     deps = [
         ":core_cpu_internal",
         ":lib",
         ":protos_all_cc",
-    ],
+    ] + tf_additional_gpu_tracer_deps(),
 )
 
 tf_cuda_library(
@@ -2878,7 +2879,7 @@ cc_test(
 tf_cc_test_gpu(
     name = "gpu_tracer_test",
     size = "small",
-    srcs = ["common_runtime/gpu/gpu_tracer_test.cc"],
+    srcs = ["platform/gpu_tracer_test.cc"],
     args = ["--heap_check=local"],
     linkstatic = tf_kernel_tests_linkstatic(),
     tags = tf_cuda_tests_tags() + ["nomac"],
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index ada2e32f38..dc2731c0da 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -62,7 +62,7 @@ limitations under the License.
 #include "tensorflow/core/util/env_var.h"
 
 #if GOOGLE_CUDA
-#include "tensorflow/core/common_runtime/gpu/gpu_tracer.h"
+#include "tensorflow/core/platform/gpu_tracer.h"
 #endif  // GOOGLE_CUDA
 
 namespace tensorflow {
@@ -558,7 +558,7 @@ Status DirectSession::Run(const RunOptions& run_options,
 #if GOOGLE_CUDA
   std::unique_ptr<GPUTracer> tracer;
   if (run_options.trace_level() >= RunOptions::HARDWARE_TRACE) {
-    tracer.reset(CreateGPUTracer());
+    tracer = CreateGPUTracer();
     // tracer will be NULL on non-GPU platforms.
     // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
     if (tracer) tracer->Start().IgnoreError();
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index f9c876cc21..4696e1c1d2 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -172,6 +172,15 @@ def tf_additional_stream_executor_srcs():
 def tf_additional_cupti_wrapper_deps():
   return ["//tensorflow/core/platform/default/gpu:cupti_wrapper"]
 
+def tf_additional_gpu_tracer_srcs():
+  return ["platform/default/gpu_tracer.cc"]
+
+def tf_additional_gpu_tracer_cuda_deps():
+  return []
+
+def tf_additional_gpu_tracer_deps():
+  return []
+
 def tf_additional_libdevice_data():
   return []
 
diff --git a/tensorflow/core/common_runtime/gpu/gpu_tracer.cc b/tensorflow/core/platform/default/gpu_tracer.cc
similarity index 98%
rename from tensorflow/core/common_runtime/gpu/gpu_tracer.cc
rename to tensorflow/core/platform/default/gpu_tracer.cc
index fb76348ddd..7375287e1b 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_tracer.cc
+++ b/tensorflow/core/platform/default/gpu_tracer.cc
@@ -13,11 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/core/common_runtime/gpu/gpu_tracer.h"
+#include "tensorflow/core/platform/gpu_tracer.h"
 
 #if GOOGLE_CUDA
 
 #include <stdlib.h>
+#include <memory>
 
 #include "tensorflow/core/common_runtime/step_stats_collector.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
@@ -255,7 +256,7 @@ CUPTIManager *GetCUPTIManager() {
 }
 
 #ifdef _MSC_VER
-#define __thread __declspec(thread) 
+#define __thread __declspec(thread)
 #endif
 
 // TODO(pbar) Move this to platform specific header file?
@@ -619,7 +620,10 @@ Status GPUTracerImpl::Collect(StepStatsCollector *collector) {
 
 }  // namespace gputracer
 
-GPUTracer *CreateGPUTracer() { return new gputracer::GPUTracerImpl(); }
+std::unique_ptr<GPUTracer> CreateGPUTracer() {
+  std::unique_ptr<GPUTracer> tracer(new gputracer::GPUTracerImpl());
+  return tracer;
+}
 
 }  // namespace tensorflow
 
@@ -627,7 +631,7 @@ GPUTracer *CreateGPUTracer() { return new gputracer::GPUTracerImpl(); }
 
 namespace tensorflow {
 
-GPUTracer *CreateGPUTracer() { return nullptr; }
+std::unique_ptr<GPUTracer> CreateGPUTracer() { return nullptr; }
 
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/common_runtime/gpu/gpu_tracer.h b/tensorflow/core/platform/gpu_tracer.h
similarity index 91%
rename from tensorflow/core/common_runtime/gpu/gpu_tracer.h
rename to tensorflow/core/platform/gpu_tracer.h
index d83508191b..3373d974e3 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_tracer.h
+++ b/tensorflow/core/platform/gpu_tracer.h
@@ -13,8 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_COMMON_RUNTIME_GPU_GPU_TRACER_H_
-#define TENSORFLOW_COMMON_RUNTIME_GPU_GPU_TRACER_H_
+#ifndef TENSORFLOW_CORE_PLATFORM_GPU_TRACER_H_
+#define TENSORFLOW_CORE_PLATFORM_GPU_TRACER_H_
+
+#include <memory>
 
 #include "tensorflow/core/lib/core/status.h"
 
@@ -70,8 +72,8 @@ class GPUTracer {
 
 // Creates a platform-specific GPUTracer.
 // Returns 'nullptr' on platforms where tracing is not supported.
-GPUTracer* CreateGPUTracer();
+std::unique_ptr<GPUTracer> CreateGPUTracer();
 
 }  // namespace tensorflow
 
-#endif  // TENSORFLOW_COMMON_RUNTIME_GPU_GPU_TRACER_H_
+#endif  // TENSORFLOW_CORE_PLATFORM_GPU_TRACER_H_
diff --git a/tensorflow/core/common_runtime/gpu/gpu_tracer_test.cc b/tensorflow/core/platform/gpu_tracer_test.cc
similarity index 99%
rename from tensorflow/core/common_runtime/gpu/gpu_tracer_test.cc
rename to tensorflow/core/platform/gpu_tracer_test.cc
index 7ca5d4aa5b..713282c1fd 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_tracer_test.cc
+++ b/tensorflow/core/platform/gpu_tracer_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/core/common_runtime/gpu/gpu_tracer.h"
+#include "tensorflow/core/platform/gpu_tracer.h"
 
 #include <map>
 #include <memory>
diff --git a/tensorflow/python/profiler/internal/run_metadata_test.py b/tensorflow/python/profiler/internal/run_metadata_test.py
index f2d1967981..62b2314aea 100644
--- a/tensorflow/python/profiler/internal/run_metadata_test.py
+++ b/tensorflow/python/profiler/internal/run_metadata_test.py
@@ -40,12 +40,14 @@ SIZE = 1300
 builder = option_builder.ProfileOptionBuilder
 
 
-def _extract_node(run_meta, node_name):
+def _extract_node(run_meta, node_names):
+  if not isinstance(node_names, list):
+    node_names = [node_names]
   ret = defaultdict(list)
   for dev_stat in run_meta.step_stats.dev_stats:
     dev = dev_stat.device
     for node_stat in dev_stat.node_stats:
-      if node_stat.node_name == node_name:
+      if node_stat.node_name in node_names:
         ret[dev].append(node_stat)
   return ret
 
@@ -101,12 +103,11 @@ class RunMetadataTest(test.TestCase):
       self.assertEqual(tfprof_node.children[0].name, 'MatMul')
       self.assertGreater(tfprof_node.children[0].exec_micros, 10)
 
-    ret = _extract_node(run_meta, 'MatMul')
-    self.assertEqual(len(ret), 1)
+    ret = _extract_node(run_meta, ['MatMul', 'MatMul:MatMul'])
+    self.assertEqual(len(ret), 3)
     self.assertTrue('/job:localhost/replica:0/task:0/gpu:0' in ret)
+    del ret['/job:localhost/replica:0/task:0/gpu:0']
 
-    ret = _extract_node(run_meta, 'MatMul:MatMul')
-    self.assertEqual(len(ret), 2)
     has_all_stream = False
     for k, _ in six.iteritems(ret):
       self.assertTrue('gpu:0/stream' in k)
-- 
GitLab


From 7afa1a0f5e67228ffba07d81ffac8cde8e466da4 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 18 Jul 2017 14:23:01 -0700
Subject: [PATCH 1077/1375] Disable tests that don't work on macs.

PiperOrigin-RevId: 162403380
---
 tensorflow/contrib/boosted_trees/BUILD | 6 ++++++
 tensorflow/contrib/tensor_forest/BUILD | 5 ++++-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 2e53030666..269e314940 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -81,6 +81,9 @@ py_test(
     size = "small",
     srcs = ["python/utils/losses_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":losses",
         "//tensorflow/python:framework_test_lib",
@@ -110,6 +113,9 @@ py_test(
     size = "small",
     srcs = ["python/training/functions/gbdt_batch_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":gbdt_batch",
         ":losses",
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 1f5733b853..b4a8ca4320 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -600,7 +600,10 @@ py_test(
     size = "medium",
     srcs = ["client/random_forest_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["no_windows"],
+    tags = [
+        "no_windows",
+        "nomac",  # b/63258195
+    ],
     deps = [
         ":random_forest",
         ":tensor_forest_py",
-- 
GitLab


From 3b67fe7e59885ece03c14e09f76dd39f6f9f4a39 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Tue, 18 Jul 2017 14:25:08 -0700
Subject: [PATCH 1078/1375] Add VectorDiffeomixture.

PiperOrigin-RevId: 162403713
---
 tensorflow/contrib/distributions/BUILD        |  18 +
 tensorflow/contrib/distributions/__init__.py  |   1 +
 .../kernel_tests/vector_diffeomixture_test.py | 384 ++++++++
 .../python/ops/distribution_util.py           |  34 +-
 .../python/ops/vector_diffeomixture.py        | 836 ++++++++++++++++++
 5 files changed, 1267 insertions(+), 6 deletions(-)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/vector_diffeomixture_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index bb2d34c447..838214bc85 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -572,6 +572,24 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "vector_diffeomixture_test",
+    size = "large",
+    srcs = ["python/kernel_tests/vector_diffeomixture_test.py"],
+    additional_deps = [
+        ":bijectors_py",
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/contrib/linalg:linalg_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "conditional_transformed_distribution_test",
     size = "medium",
diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py
index 6b693300a2..5386271f77 100644
--- a/tensorflow/contrib/distributions/__init__.py
+++ b/tensorflow/contrib/distributions/__init__.py
@@ -126,6 +126,7 @@ _allowed_symbols = [
     'Dirichlet',
     'DirichletMultinomial',
     'Multinomial',
+    'VectorDiffeomixture',
     'VectorLaplaceDiag',
     'WishartCholesky',
     'WishartFull',
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/vector_diffeomixture_test.py b/tensorflow/contrib/distributions/python/kernel_tests/vector_diffeomixture_test.py
new file mode 100644
index 0000000000..62ffbea1b5
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/vector_diffeomixture_test.py
@@ -0,0 +1,384 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for VectorDiffeomixture."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.distributions.python.ops import vector_diffeomixture as vector_diffeomixture_lib
+from tensorflow.contrib.linalg.python.ops import linear_operator_diag as linop_diag_lib
+from tensorflow.contrib.linalg.python.ops import linear_operator_identity as linop_identity_lib
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import normal as normal_lib
+from tensorflow.python.platform import test
+
+
+class VectorDistributionTestHelpers(object):
+  """VectorDistributionTestHelpers helps test vector-event distributions."""
+
+  def linop(self, num_rows=None, multiplier=None, diag=None):
+    """Helper to create non-singular, symmetric, positive definite matrices."""
+    if num_rows is not None and multiplier is not None:
+      if any(p is not None for p in [diag]):
+        raise ValueError("Found extra args for scaled identity.")
+      return linop_identity_lib.LinearOperatorScaledIdentity(
+          num_rows=num_rows,
+          multiplier=multiplier,
+          is_positive_definite=True)
+    elif num_rows is not None:
+      if any(p is not None for p in [multiplier, diag]):
+        raise ValueError("Found extra args for identity.")
+      return linop_identity_lib.LinearOperatorIdentity(
+          num_rows=num_rows,
+          is_positive_definite=True)
+    elif diag is not None:
+      if any(p is not None for p in [num_rows, multiplier]):
+        raise ValueError("Found extra args for diag.")
+      return linop_diag_lib.LinearOperatorDiag(
+          diag=diag,
+          is_positive_definite=True)
+    else:
+      raise ValueError("Must specify at least one arg.")
+
+  def run_test_sample_consistent_log_prob(
+      self,
+      sess,
+      dist,
+      num_samples=int(1e5),
+      radius=1.,
+      center=0.,
+      seed=42,
+      rtol=1e-2,
+      atol=0.):
+    """Tests that sample/log_prob are mutually consistent.
+
+    "Consistency" means that `sample` and `log_prob` correspond to the same
+    distribution.
+
+    The idea of this test is to compute the Monte-Carlo estimate of the volume
+    enclosed by a hypersphere, i.e., the volume of an `n`-ball. While we could
+    choose an arbitrary function to integrate, the hypersphere's volume is nice
+    because it is intuitive, has an easy analytical expression, and works for
+    `dimensions > 1`.
+
+    Technical Details:
+
+    Observe that:
+
+    ```none
+    int_{R**d} dx [x in Ball(radius=r, center=c)]
+    = E_{p(X)}[ [X in Ball(r, c)] / p(X) ]
+    = lim_{m->infty} m**-1 sum_j^m [x[j] in Ball(r, c)] / p(x[j]),
+        where x[j] ~iid p(X)
+    ```
+
+    Thus, for fixed `m`, the above is approximately true when `sample` and
+    `log_prob` are mutually consistent.
+
+    Furthermore, the above calculation has the analytical result:
+    `pi**(d/2) r**d / Gamma(1 + d/2)`.
+
+    Note: this test only verifies a necessary condition for consistency--it does
+    does not verify sufficiency hence does not prove `sample`, `log_prob` truly
+    are consistent. For this reason we recommend testing several different
+    hyperspheres (assuming the hypersphere is supported by the distribution).
+    Furthermore, we gain additional trust in this test when also tested `sample`
+    against the first, second moments
+    (`run_test_sample_consistent_mean_covariance`); it is probably unlikely that
+    a "best-effort" implementation of `log_prob` would incorrectly pass both
+    tests and for different hyperspheres.
+
+    For a discussion on the analytical result (second-line) see:
+      https://en.wikipedia.org/wiki/Volume_of_an_n-ball.
+
+    For a discussion of importance sampling (fourth-line) see:
+      https://en.wikipedia.org/wiki/Importance_sampling.
+
+    Args:
+      sess: Tensorflow session.
+      dist: Distribution instance or object which implements `sample`,
+        `log_prob`, `event_shape_tensor` and `batch_shape_tensor`. The
+        distribution must have non-zero probability of sampling every point
+        enclosed by the hypersphere.
+      num_samples: Python `int` scalar indicating the number of Monte-Carlo
+        samples to draw from `dist`.
+      radius: Python `float`-type indicating the radius of the `n`-ball which
+        we're computing the volume.
+      center: Python floating-type vector (or scalar) indicating the center of
+        the `n`-ball which we're computing the volume. When scalar, the value is
+        broadcast to all event dims.
+      seed: Python `int` indicating the seed to use when sampling from `dist`.
+        In general it is not recommended to use `None` during a test as this
+        increases the likelihood of spurious test failure.
+      rtol: Python `float`-type indicating the admissible relative error between
+        actual- and approximate-volumes.
+      atol: Python `float`-type indicating the admissible absolute error between
+        actual- and approximate-volumes. In general this should be zero since
+        a typical radius implies a non-zero volume.
+    """
+
+    def actual_hypersphere_volume(dims, radius):
+      # https://en.wikipedia.org/wiki/Volume_of_an_n-ball
+      # Using tf.lgamma because we'd have to otherwise use SciPy which is not
+      # a required dependency of core.
+      radius = np.asarray(radius)
+      dims = math_ops.cast(dims, dtype=radius.dtype)
+      return math_ops.exp(
+          (dims / 2.) * np.log(np.pi)
+          - math_ops.lgamma(1. + dims / 2.)
+          + dims * math_ops.log(radius))
+
+    def is_in_ball(x, radius, center):
+      return math_ops.cast(linalg_ops.norm(x - center, axis=-1) <= radius,
+                           dtype=x.dtype)
+
+    def monte_carlo_hypersphere_volume(dist, num_samples, radius, center):
+      # https://en.wikipedia.org/wiki/Importance_sampling
+      x = dist.sample(num_samples, seed=seed)
+      return math_ops.reduce_mean(
+          math_ops.exp(-dist.log_prob(x)) * is_in_ball(x, radius, center),
+          axis=0)
+
+    [
+        batch_shape_,
+        actual_volume_,
+        sample_volume_,
+    ] = sess.run([
+        dist.batch_shape_tensor(),
+        actual_hypersphere_volume(
+            dims=dist.event_shape_tensor()[0],
+            radius=radius),
+        monte_carlo_hypersphere_volume(
+            dist,
+            num_samples=num_samples,
+            radius=radius,
+            center=center),
+    ])
+
+    self.assertAllClose(np.tile(actual_volume_, reps=batch_shape_),
+                        sample_volume_,
+                        rtol=rtol, atol=atol)
+
+  def run_test_sample_consistent_mean_covariance(
+      self,
+      sess,
+      dist,
+      num_samples=int(1e5),
+      seed=24,
+      rtol=1e-2,
+      atol=0.,
+      cov_rtol=None,
+      cov_atol=None):
+    """Tests that sample/mean/covariance are consistent with each other.
+
+    "Consistency" means that `sample`, `mean`, `covariance`, etc all correspond
+    to the same distribution.
+
+    Args:
+      sess: Tensorflow session.
+      dist: Distribution instance or object which implements `sample`,
+        `log_prob`, `event_shape_tensor` and `batch_shape_tensor`.
+      num_samples: Python `int` scalar indicating the number of Monte-Carlo
+        samples to draw from `dist`.
+      seed: Python `int` indicating the seed to use when sampling from `dist`.
+        In general it is not recommended to use `None` during a test as this
+        increases the likelihood of spurious test failure.
+      rtol: Python `float`-type indicating the admissible relative error between
+        analytical and sample statistics.
+      atol: Python `float`-type indicating the admissible absolute error between
+        analytical and sample statistics.
+      cov_rtol: Python `float`-type indicating the admissible relative error
+        between analytical and sample covariance. Default: rtol.
+      cov_atol: Python `float`-type indicating the admissible absolute error
+        between analytical and sample covariance. Default: atol.
+    """
+
+    def vec_osquare(x):
+      """Computes the outer-product of a vector, i.e., x.T x."""
+      return x[..., :, array_ops.newaxis] * x[..., array_ops.newaxis, :]
+
+    x = dist.sample(num_samples, seed=seed)
+    sample_mean = math_ops.reduce_mean(x, axis=0)
+    sample_covariance = math_ops.reduce_mean(
+        vec_osquare(x - sample_mean), axis=0)
+    sample_variance = array_ops.matrix_diag_part(sample_covariance)
+    sample_stddev = math_ops.sqrt(sample_variance)
+
+    [
+        sample_mean_,
+        sample_covariance_,
+        sample_variance_,
+        sample_stddev_,
+        mean_,
+        covariance_,
+        variance_,
+        stddev_
+    ] = sess.run([
+        sample_mean,
+        sample_covariance,
+        sample_variance,
+        sample_stddev,
+        dist.mean(),
+        dist.covariance(),
+        dist.variance(),
+        dist.stddev(),
+    ])
+
+    self.assertAllClose(mean_, sample_mean_, rtol=rtol, atol=atol)
+    self.assertAllClose(covariance_, sample_covariance_,
+                        rtol=cov_rtol or rtol,
+                        atol=cov_atol or atol)
+    self.assertAllClose(variance_, sample_variance_, rtol=rtol, atol=atol)
+    self.assertAllClose(stddev_, sample_stddev_, rtol=rtol, atol=atol)
+
+
+class VectorDiffeomixtureTest(VectorDistributionTestHelpers, test.TestCase):
+  """Tests the VectorDiffeomixture distribution."""
+
+  def testSampleProbConsistentBroadcastMix(self):
+    with self.test_session() as sess:
+      dims = 4
+      vdm = vector_diffeomixture_lib.VectorDiffeomixture(
+          mix_loc=[[0.], [1.]],
+          mix_scale=[1.],
+          distribution=normal_lib.Normal(0., 1.),
+          loc=[
+              None,
+              np.float32([2.]*dims),
+          ],
+          scale=[
+              linop_identity_lib.LinearOperatorScaledIdentity(
+                  num_rows=dims,
+                  multiplier=np.float32(1.1),
+                  is_positive_definite=True),
+              linop_diag_lib.LinearOperatorDiag(
+                  diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
+                  is_positive_definite=True),
+          ],
+          validate_args=True)
+      # Ball centered at component0's mean.
+      self.run_test_sample_consistent_log_prob(
+          sess, vdm, radius=2., center=0., rtol=0.005)
+      # Larger ball centered at component1's mean.
+      self.run_test_sample_consistent_log_prob(
+          sess, vdm, radius=4., center=2., rtol=0.005)
+
+  def testSampleProbConsistentBroadcastMixNonStandardBase(self):
+    with self.test_session() as sess:
+      dims = 4
+      vdm = vector_diffeomixture_lib.VectorDiffeomixture(
+          mix_loc=[[0.], [1.]],
+          mix_scale=[1.],
+          distribution=normal_lib.Normal(1., 1.5),
+          loc=[
+              None,
+              np.float32([2.]*dims),
+          ],
+          scale=[
+              linop_identity_lib.LinearOperatorScaledIdentity(
+                  num_rows=dims,
+                  multiplier=np.float32(1.1),
+                  is_positive_definite=True),
+              linop_diag_lib.LinearOperatorDiag(
+                  diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
+                  is_positive_definite=True),
+          ],
+          validate_args=True)
+      # Ball centered at component0's mean.
+      self.run_test_sample_consistent_log_prob(
+          sess, vdm, radius=2., center=1., rtol=0.006)
+      # Larger ball centered at component1's mean.
+      self.run_test_sample_consistent_log_prob(
+          sess, vdm, radius=4., center=3., rtol=0.009)
+
+  def testMeanCovariance(self):
+    with self.test_session() as sess:
+      dims = 3
+      vdm = vector_diffeomixture_lib.VectorDiffeomixture(
+          mix_loc=[[0.], [4.]],
+          mix_scale=[10.],
+          distribution=normal_lib.Normal(0., 1.),
+          loc=[
+              np.float32([-2.]),
+              None,
+          ],
+          scale=[
+              linop_identity_lib.LinearOperatorScaledIdentity(
+                  num_rows=dims,
+                  multiplier=np.float32(1.5),
+                  is_positive_definite=True),
+              linop_diag_lib.LinearOperatorDiag(
+                  diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
+                  is_positive_definite=True),
+          ],
+          validate_args=True)
+      self.run_test_sample_consistent_mean_covariance(
+          sess, vdm, rtol=0.02, cov_rtol=0.06)
+
+  def testMeanCovarianceUncenteredNonStandardBase(self):
+    with self.test_session() as sess:
+      dims = 3
+      vdm = vector_diffeomixture_lib.VectorDiffeomixture(
+          mix_loc=[[0.], [4.]],
+          mix_scale=[10.],
+          distribution=normal_lib.Normal(-1., 1.5),
+          loc=[
+              np.float32([-2.]),
+              np.float32([0.]),
+          ],
+          scale=[
+              linop_identity_lib.LinearOperatorScaledIdentity(
+                  num_rows=dims,
+                  multiplier=np.float32(1.5),
+                  is_positive_definite=True),
+              linop_diag_lib.LinearOperatorDiag(
+                  diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
+                  is_positive_definite=True),
+          ],
+          validate_args=True)
+      self.run_test_sample_consistent_mean_covariance(
+          sess, vdm, num_samples=int(1e6), rtol=0.01, cov_atol=0.025)
+
+  # TODO(jvdillon): We've tested that (i) .sample and .log_prob are consistent,
+  # (ii) .mean, .stddev etc... and .sample are consistent. However, we haven't
+  # tested that the quadrature approach well-approximates the integral.
+  #
+  # To that end, consider adding these tests:
+  #
+  # Test1: In the limit of high mix_scale, this approximates a discrete mixture,
+  # and there are many discrete mixtures where we can explicitly compute
+  # mean/var, etc... So test1 would choose one of those discrete mixtures and
+  # show our mean/var/etc... is close to that.
+  #
+  # Test2:  In the limit of low mix_scale, the a diffeomixture of Normal(-5, 1),
+  # Normal(5, 1) should (I believe...must check) should look almost like
+  # Uniform(-5, 5), and thus (i) .prob(x) should be about 1/10 for x in (-5, 5),
+  # and (ii) the first few moments should approximately match that of
+  # Uniform(-5, 5)
+  #
+  # Test3:  If mix_loc is symmetric, then for any mix_scale, our
+  # quadrature-based diffeomixture of Normal(-1, 1), Normal(1, 1) should have
+  # mean zero, exactly.
+
+  # TODO(jvdillon): Add more tests which verify broadcasting.
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/distribution_util.py b/tensorflow/contrib/distributions/python/ops/distribution_util.py
index cda5829f45..a0ff2778aa 100644
--- a/tensorflow/contrib/distributions/python/ops/distribution_util.py
+++ b/tensorflow/contrib/distributions/python/ops/distribution_util.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib import linalg
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
@@ -301,12 +302,33 @@ def prefer_static_broadcast_shape(
       statically), or as a `Tensor`.
   """
   with ops.name_scope(name, values=[shape1, shape2]):
-    if (tensor_util.constant_value(shape1) is not None and
-        tensor_util.constant_value(shape2) is not None):
-      return array_ops.broadcast_static_shape(
-          tensor_shape.TensorShape(tensor_util.constant_value(shape1)),
-          tensor_shape.TensorShape(tensor_util.constant_value(shape2)))
-    return array_ops.broadcast_dynamic_shape(shape1, shape2)
+    def make_shape_tensor(x):
+      return ops.convert_to_tensor(x, name="shape", dtype=dtypes.int32)
+
+    def get_tensor_shape(s):
+      if isinstance(s, tensor_shape.TensorShape):
+        return s
+      s_ = tensor_util.constant_value(make_shape_tensor(s))
+      if s_ is not None:
+        return tensor_shape.TensorShape(s_)
+      return None
+
+    def get_shape_tensor(s):
+      if not isinstance(s, tensor_shape.TensorShape):
+        return make_shape_tensor(s)
+      if s.is_fully_defined():
+        return make_shape_tensor(s.as_list())
+      raise ValueError("Cannot broadcast from partially "
+                       "defined `TensorShape`.")
+
+    shape1_ = get_tensor_shape(shape1)
+    shape2_ = get_tensor_shape(shape2)
+    if shape1_ is not None and shape2_ is not None:
+      return array_ops.broadcast_static_shape(shape1_, shape2_)
+
+    shape1_ = get_shape_tensor(shape1)
+    shape2_ = get_shape_tensor(shape2)
+    return array_ops.broadcast_dynamic_shape(shape1_, shape2_)
 
 
 def is_diagonal_scale(scale):
diff --git a/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
new file mode 100644
index 0000000000..282683ef39
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
@@ -0,0 +1,836 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""The VectorDiffeomixture distribution class."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.distributions.python.ops.bijectors.affine_linear_operator import AffineLinearOperator
+from tensorflow.contrib.linalg.python.ops import linear_operator_addition as linop_add_lib
+from tensorflow.contrib.linalg.python.ops import linear_operator_composition as linop_composition_lib
+from tensorflow.contrib.linalg.python.ops import linear_operator_diag as linop_diag_lib
+from tensorflow.contrib.linalg.python.ops import linear_operator_full_matrix as linop_full_lib
+from tensorflow.contrib.linalg.python.ops import linear_operator_identity as linop_identity_lib
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import categorical as categorical_lib
+from tensorflow.python.ops.distributions import distribution as distribution_lib
+
+
+__all__ = [
+    "VectorDiffeomixture",
+]
+
+
+class VectorDiffeomixture(distribution_lib.Distribution):
+  """VectorDiffeomixture distribution.
+
+  The VectorDiffeomixture is an approximation to a [compound distribution](
+  https://en.wikipedia.org/wiki/Compound_probability_distribution), i.e.,
+
+  ```none
+  p(x) = int_{X} q(x | v) p(v) dv
+       = lim_{Q->infty} sum{ prob[i] q(x | loc=sum_k^K lambda[k;i] loc[k],
+                                            scale=sum_k^K lambda[k;i] scale[k])
+                            : i=0, ..., Q-1 }
+  ```
+
+  where `q(x | v)` is a vector version of the `distribution` argument and `p(v)`
+  is a SoftmaxNormal parameterized by `mix_loc` and `mix_scale`. The
+  vector-ization of `distribution` entails an affine transformation of iid
+  samples from `distribution`.  The `prob` term is from quadrature and
+  `lambda[k] = sigmoid(mix_loc[k] + sqrt(2) mix_scale[k] grid[k])` where the
+  `grid` points correspond to the `prob`s.
+
+  In the non-approximation case, a draw from the mixture distribution (the
+  "prior") represents the convex weights for different affine transformations.
+  I.e., draw a mixing vector `v` (from the `K-1`-simplex) and let the final
+  sample be: `y = (sum_k^K v[k] scale[k]) @ x + (sum_k^K v[k] loc[k])` where `@`
+  denotes matrix multiplication.  However, the non-approximate distribution does
+  not have an analytical probability density function (pdf). Therefore the
+  `VectorDiffeomixture` class implements an approximation based on
+  [Gauss-Hermite quadrature](
+  https://en.wikipedia.org/wiki/Gauss%E2%80%93Hermite_quadrature). I.e., in
+  Note: although the `VectorDiffeomixture` is approximately the
+  `SoftmaxNormal-Distribution` compound distribution, it is itself a valid
+  distribution. It possesses a `sample`, `log_prob`, `mean`, `covariance` which
+  are all mutually consistent.
+
+  #### Intended Use
+
+  This distribution is noteworthy because it implements a mixture of
+  `Vector`-ized distributions yet has samples differentiable in the
+  distribution's parameters (aka "reparameterized"). It has an analytical
+  density function with `O(dKQ)` complexity. `d` is the vector dimensionality,
+  `K` is the number of components, and `Q` is the number of quadrature points.
+  These properties make it well-suited for Bayesian Variational Inference, i.e.,
+  as a surrogate family for the posterior.
+
+  For large values of `mix_scale`, the `VectorDistribution` behaves increasingly
+  like a discrete mixture. (In most cases this limit is only achievable by also
+  increasing the quadrature polynomial degree, `Q`.)
+
+  The term `Vector` is consistent with similar named Tensorflow `Distribution`s.
+  For more details, see the "About `Vector` distributions in Tensorflow."
+  section.
+
+  The term `Diffeomixture` is a portmanteau of
+  [diffeomorphism](https://en.wikipedia.org/wiki/Diffeomorphism) and [compound
+  mixture](https://en.wikipedia.org/wiki/Compound_probability_distribution). For
+  more details, see the "About `Diffeomixture`s and reparametrization.`"
+  section.
+
+  #### Mathematical Details
+
+  The `VectorDiffeomixture` approximates a SoftmaxNormal-mixed ("prior")
+  [compound distribution](
+  https://en.wikipedia.org/wiki/Compound_probability_distribution).
+  Using variable-substitution and [Gauss-Hermite quadrature](
+  https://en.wikipedia.org/wiki/Gauss%E2%80%93Hermite_quadrature) we can
+  redefine the distribution to be a parameter-less convex combination of `K`
+  different affine combinations of a `d` iid samples from `distribution`.
+
+  That is, defined over `R**d` this distribution is parameterized by a
+  (batch of) length-`K` `mix_loc` and `mix_scale` vectors, a length-`K` list of
+  (a batch of) length-`d` `loc` vectors, and a length-`K` list of `scale`
+  `LinearOperator`s each operating on a (batch of) length-`d` vector space.
+  Finally, a `distribution` parameter specifies the underlying base distribution
+  which is "lifted" to become multivariate ("lifting" is the same concept as in
+  `TransformedDistribution`).
+
+  The probability density function (pdf) is,
+
+  ```none
+  pdf(y; mix_loc, mix_scale, loc, scale, phi)
+    = sum{ prob[i] phi(f_inverse(x; i)) / abs(det(interp_scale[i]))
+          : i=0, ..., Q-1 }
+  ```
+
+  where, `phi` is the base distribution pdf, and,
+
+  ```none
+  f_inverse(x; i) = inv(interp_scale[i]) @ (x - interp_loc[i])
+  interp_loc[i]   = sum{ lambda[k; i] loc[k]   : k=0, ..., K-1 }
+  interp_scale[i] = sum{ lambda[k; i] scale[k] : k=0, ..., K-1 }
+  ```
+
+  and,
+
+  ```none
+  grid, weight = np.polynomial.hermite.hermgauss(quadrature_polynomial_degree)
+  prob[k]   = weight[k] / sqrt(pi)
+  lambda[k; i] = sigmoid(mix_loc[k] + sqrt(2) mix_scale[k] grid[i])
+  ```
+
+  The distribution corresponding to `phi` must be a scalar-batch, scalar-event
+  distribution. Typically it is reparameterized. If not, it must be a function
+  of non-trainable parameters.
+
+  WARNING: If you backprop through a VectorDiffeomixture sample and the "base"
+  distribution is both: not `FULLY_REPARAMETERIZED` and a function of trainable
+  variables, then the gradient is not guaranteed correct!
+
+  #### About `Vector` distributions in TensorFlow.
+
+  The `VectorDiffeomixture` is a non-standard distribution that has properties
+  particularly useful in [variational Bayesian
+  methods](https://en.wikipedia.org/wiki/Variational_Bayesian_methods).
+
+  Conditioned on a draw from the SoftmaxNormal, `Y|v` is a vector whose
+  components are linear combinations of affine transformations, thus is itself
+  an affine transformation. Therefore `Y|v` lives in the vector space generated
+  by vectors of affine-transformed distributions.
+
+  Note: The marginals `Y_1|v, ..., Y_d|v` are *not* generally identical to some
+  parameterization of `distribution`.  This is due to the fact that the sum of
+  draws from `distribution` are not generally itself the same `distribution`.
+
+  #### About `Diffeomixture`s and reparameterization.
+
+  The `VectorDiffeomixture` is designed to be reparameterized, i.e., its
+  parameters are only used to transform samples from a distribution which has no
+  trainable parameters. This property is important because backprop stops at
+  sources of stochasticity. That is, as long as the parameters are used *after*
+  the underlying source of stochasticity, the computed gradient is accurate.
+
+  Reparametrization means that we can use gradient-descent (via backprop) to
+  optimize Monte-Carlo objectives. Such objectives are a finite-sample
+  approximation of an expectation and arise throughout scientific computing.
+
+  #### Examples
+
+  ```python
+  ds = tf.contrib.distributions
+  la = tf.contrib.linalg
+
+  # Create two batches of VectorDiffeomixtures, one with mix_loc=[0.] and
+  # another with mix_loc=[1]. In both cases, `K=2` and the affine
+  # transformations involve:
+  # k=0: loc=zeros(dims)  scale=LinearOperatorScaledIdentity
+  # k=1: loc=[2.]*dims    scale=LinOpDiag
+  dims = 5
+  vdm = ds.VectorDiffeomixture(
+      mix_loc=[[0.], [1]],
+      mix_scale=[1.],
+      distribution=ds.Normal(loc=0., scale=1.),
+      loc=[
+          None,  # Equivalent to `np.zeros(dims, dtype=np.float32)`.
+          np.float32([2.]*dims),
+      ],
+      scale=[
+          la.LinearOperatorScaledIdentity(
+            num_rows=dims,
+            multiplier=np.float32(1.1),
+            is_positive_definite=True),
+          la.LinearOperatorDiag(
+            diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
+            is_positive_definite=True),
+      ],
+      validate_args=True)
+  """
+
+  def __init__(self,
+               mix_loc,
+               mix_scale,
+               distribution,
+               loc=None,
+               scale=None,
+               quadrature_polynomial_degree=8,
+               validate_args=False,
+               allow_nan_stats=True,
+               name="VectorLocationScaleDiffeomixture"):
+    """Constructs the VectorDiffeomixture on `R**k`.
+
+    Args:
+      mix_loc: `float`-like `Tensor`. Represents the `location` parameter of the
+        SoftmaxNormal used for selecting one of the `K` affine transformations.
+      mix_scale: `float`-like `Tensor`. Represents the `scale` parameter of the
+        SoftmaxNormal used for selecting one of the `K` affine transformations.
+      distribution: `tf.Distribution`-like instance. Distribution from which `d`
+        iid samples are used as input to the selected affine transformation.
+        Must be a scalar batch, scalar event distribution.  Typically
+        `distribution.reparameterization_type = FULLY_REPARAMETERIZED` or it is
+        a function of non-trainable parameters. WARNING: If you backprop through
+        a VectorDiffeomixture sample and the `distribution` is not
+        `FULLY_REPARAMETERIZED` yet is a function of trainable variables, then
+        the gradient will be incorrect!
+      loc: Length-`K` list of `float`-type `Tensor`s. The `k`-th element
+        represents the `shift` used for the `k`-th affine transformation.  If
+        the `k`-th item is `None`, `loc` is implicitly `0`.  When specified,
+        must have shape `[B1, ..., Bb, d]` where `b >= 0` and `d` is the event
+        size.
+      scale: Length-`K` list of `LinearOperator`s. Each should be
+        positive-definite and operate on a `d`-dimensional vector space. The
+        `k`-th element represents the `scale` used for the `k`-th affine
+        transformation. `LinearOperator`s must have shape `[B1, ..., Bb, d, d]`,
+        `b >= 0`, i.e., characterizes `b`-batches of `d x d` matrices
+      quadrature_polynomial_degree: Python `int`-like scalar.
+      validate_args: Python `bool`, default `False`. When `True` distribution
+        parameters are checked for validity despite possibly degrading runtime
+        performance. When `False` invalid inputs may silently render incorrect
+        outputs.
+      allow_nan_stats: Python `bool`, default `True`. When `True`,
+        statistics (e.g., mean, mode, variance) use the value "`NaN`" to
+        indicate the result is undefined. When `False`, an exception is raised
+        if one or more of the statistic's batch members are undefined.
+      name: Python `str` name prefixed to Ops created by this class.
+
+    Raises:
+      ValueError: if `not scale or len(scale) < 2`.
+      ValueError: if `len(loc) != len(scale)`
+      ValueError: if `quadrature_polynomial_degree < 1`.
+      ValueError: if `validate_args` and any not scale.is_positive_definite.
+      TypeError: if any scale.dtype != scale[0].dtype.
+      TypeError: if any loc.dtype != scale[0].dtype.
+      NotImplementedError: if `len(scale) != 2`.
+    """
+    parameters = locals()
+    with ops.name_scope(name, values=[mix_loc, mix_scale]):
+      if not scale or len(scale) < 2:
+        raise ValueError("Must specify list (or list-like object) of scale "
+                         "LinearOperators, one for each component with "
+                         "num_component >= 2.")
+
+      if loc is None:
+        loc = [None]*len(scale)
+
+      if len(loc) != len(scale):
+        raise ValueError("loc/scale must be same-length lists "
+                         "(or same-length list-like objects).")
+
+      dtype = scale[0].dtype.base_dtype
+
+      loc = [ops.convert_to_tensor(loc_, dtype=dtype, name="loc{}".format(k))
+             if loc_ is not None else None
+             for k, loc_ in enumerate(loc)]
+
+      for k, scale_ in enumerate(scale):
+        if validate_args and not scale_.is_positive_definite:
+          raise ValueError("scale[{}].is_positive_definite = {} != True".format(
+              k, scale_.is_positive_definite))
+        if scale_.dtype.base_dtype != dtype:
+          raise TypeError(
+              "dtype mismatch; scale[{}].base_dtype=\"{}\" != \"{}\"".format(
+                  k, scale_.dtype.base_dtype.name, dtype.name))
+
+      self._endpoint_affine = [
+          AffineLinearOperator(shift=loc_,
+                               scale=scale_,
+                               event_ndims=1,
+                               validate_args=validate_args,
+                               name="endpoint_affine_{}".format(k))
+          for k, (loc_, scale_) in enumerate(zip(loc, scale))]
+
+      if quadrature_polynomial_degree < 1:
+        raise ValueError("quadrature_polynomial_degree={} "
+                         "is not at least 1".format(
+                             quadrature_polynomial_degree))
+      self._degree = quadrature_polynomial_degree
+
+      # TODO(jvdillon): Remove once we support k-mixtures.
+      # We make this assertion here because otherwise `grid` would need to be a
+      # vector not a scalar.
+      if len(scale) != 2:
+        raise NotImplementedError("Currently only bimixtures are supported; "
+                                  "len(scale)={} is not 2.".format(len(scale)))
+
+      grid, prob = np.polynomial.hermite.hermgauss(
+          deg=quadrature_polynomial_degree)
+      grid = grid.astype(dtype.as_numpy_dtype)
+      prob = prob.astype(dtype.as_numpy_dtype)
+      prob /= np.linalg.norm(prob, ord=1)
+
+      self._mixture_distribution = categorical_lib.Categorical(
+          logits=np.log(prob),
+          validate_args=validate_args,
+          allow_nan_stats=allow_nan_stats)
+
+      mix_loc = maybe_check_mix_param(
+          mix_loc, "mix_loc", dtype, validate_args)
+      mix_scale = maybe_check_mix_param(
+          mix_scale, "mix_scale", dtype, validate_args)
+
+      distribution_assertions = maybe_check_distribution(
+          distribution, dtype, validate_args)
+      if distribution_assertions:
+        mix_loc = control_flow_ops.with_dependencies(
+            distribution_assertions, mix_loc)
+      self._distribution = distribution
+
+      # shape: [B, deg]
+      self._interpolate_weight = math_ops.sigmoid(
+          mix_loc
+          + np.sqrt(2.) * mix_scale * grid)
+
+      self._interpolated_affine = [
+          AffineLinearOperator(shift=loc_,
+                               scale=scale_,
+                               event_ndims=1,
+                               validate_args=validate_args,
+                               name="interpolated_affine_{}".format(k))
+          for k, (loc_, scale_) in enumerate(zip(
+              interpolate_loc(quadrature_polynomial_degree,
+                              self._interpolate_weight,
+                              loc),
+              interpolate_scale(quadrature_polynomial_degree,
+                                self._interpolate_weight,
+                                scale)))]
+
+      self._batch_shape_, self._event_shape_ = determine_batch_event_shapes(
+          mix_loc, mix_scale, self._endpoint_affine)
+
+      super(VectorDiffeomixture, self).__init__(
+          dtype=dtype,
+          # We hard-code `FULLY_REPARAMETERIZED` because when
+          # `validate_args=True` we verify that indeed
+          # `distribution.reparameterization_type == FULLY_REPARAMETERIZED`. A
+          # distribution which is a function of only non-trainable parameters
+          # also implies we can use `FULLY_REPARAMETERIZED`. However, we cannot
+          # easily test for that possibility thus we use `validate_args=False`
+          # as a "back-door" to allow users a way to use non
+          # `FULLY_REPARAMETERIZED` distribution. In such cases IT IS THE USERS
+          # RESPONSIBILITY to verify that the base distribution is a function of
+          # non-trainable parameters.
+          reparameterization_type=distribution_lib.FULLY_REPARAMETERIZED,
+          validate_args=validate_args,
+          allow_nan_stats=allow_nan_stats,
+          parameters=parameters,
+          graph_parents=(
+              [mix_loc, mix_scale]
+              + distribution._graph_parents  # pylint: disable=protected-access
+              + [loc_ for loc_ in loc if loc_ is not None]
+              + [p for scale_ in scale for p in scale_.graph_parents]),
+          name=name)
+
+  @property
+  def mixture_distribution(self):
+    return self._mixture_distribution
+
+  @property
+  def distribution(self):
+    return self._distribution
+
+  @property
+  def interpolate_weight(self):
+    return self._interpolate_weight
+
+  @property
+  def endpoint_affine(self):
+    return self._endpoint_affine
+
+  @property
+  def interpolated_affine(self):
+    return self._interpolated_affine
+
+  def _batch_shape_tensor(self):
+    return self._batch_shape_
+
+  def _batch_shape(self):
+    return tensor_shape.TensorShape(static_value(self._batch_shape_))
+
+  def _event_shape_tensor(self):
+    return self._event_shape_
+
+  def _event_shape(self):
+    return tensor_shape.TensorShape(static_value(self._event_shape_))
+
+  def _sample_n(self, n, seed=None):
+    batch_size = reduce_prod(self.batch_shape_tensor())
+    x = self.distribution.sample(
+        sample_shape=concat_vectors(
+            [n * batch_size],
+            self.event_shape_tensor()),
+        seed=seed)
+    x = [array_ops.reshape(
+        aff.forward(x),
+        shape=concat_vectors(
+            [-1],
+            self.batch_shape_tensor(),
+            self.event_shape_tensor()))
+         for aff in self.endpoint_affine]
+
+    # Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
+    # ids as a [n]-shaped vector.
+    ids = self._mixture_distribution.sample(
+        sample_shape=concat_vectors(
+            [n],
+            distribution_util.pick_vector(
+                self.is_scalar_batch(),
+                np.int32([]),
+                [batch_size])),
+        seed=distribution_util.gen_new_seed(
+            seed, "vector_diffeomixture"))
+
+    # Stride `self._degree` for `batch_size` number of times.
+    offset = math_ops.range(start=0,
+                            limit=batch_size * self._degree,
+                            delta=self._degree,
+                            dtype=ids.dtype)
+
+    weight = array_ops.gather(
+        array_ops.reshape(self.interpolate_weight, shape=[-1]),
+        ids + offset)
+    weight = weight[..., array_ops.newaxis]
+
+    # Alternatively:
+    # x = weight * x[0] + (1. - weight) * x[1]
+    x = weight * (x[0] - x[1]) + array_ops.ones_like(x[0]) * x[1]
+
+    return x
+
+  def _log_prob(self, x):
+    # By convention, we always put the the grid points right-most.
+    y = array_ops.stack(
+        [aff.inverse(x) for aff in self.interpolated_affine],
+        axis=-1)
+    log_prob = math_ops.reduce_sum(self.distribution.log_prob(y), axis=-2)
+    # Because the affine transformation has a constant Jacobian, it is the case
+    # that `affine.fldj(x) = -affine.ildj(x)`. This is not true in general.
+    fldj = array_ops.stack(
+        [aff.forward_log_det_jacobian(x) for aff in self.interpolated_affine],
+        axis=-1)
+    return math_ops.reduce_logsumexp(
+        self.mixture_distribution.logits - fldj + log_prob, axis=-1)
+
+  def _mean(self):
+    # Since we created logits to already be scaled, we can use exp which is
+    # slightly cheaper than `self.mixture_distribution.probs`.
+    p = math_ops.exp(self.mixture_distribution.logits)
+
+    m = array_ops.tile(self.distribution.mean()[..., array_ops.newaxis],
+                       multiples=self.event_shape_tensor())
+    m = m[..., array_ops.newaxis, :]
+    mean = None
+    for k, aff in enumerate(self.interpolated_affine):
+      # aff.forward is going to do this:
+      # y = array_ops.squeeze(aff.scale.matmul(m), axis=[-1])
+      # if aff.shift is not None:
+      #   y += aff.shift
+      mean = add(mean, p[..., k] * aff.forward(m))
+    return mean
+
+  def _covariance(self):
+    # Law of total variance:
+    #
+    # Cov[Z] = E[Cov[Z | V]] + Cov[E[Z | V]]
+    #
+    # where,
+    #
+    # E[Cov[Z | V]] = sum_i mix_prob[i] Scale[i]
+    # Cov[E[Z | V]] = sum_i mix_prob[i] osquare(loc[i])
+    #                  - osquare(sum_i mix_prob[i] loc[i])
+    #
+    # osquare(x) = x.transpose @ x
+    return add(
+        self._mean_of_covariance_given_quadrature_component(diag_only=False),
+        self._covariance_of_mean_given_quadrature_component(diag_only=False))
+
+  def _variance(self):
+    # Equivalent to: tf.diag_part(self._covariance()),
+    return add(
+        self._mean_of_covariance_given_quadrature_component(diag_only=True),
+        self._covariance_of_mean_given_quadrature_component(diag_only=True))
+
+  def _mean_of_covariance_given_quadrature_component(self, diag_only):
+    # Since we created logits to already be scaled, we can use exp which is
+    # slightly cheaper than `self.mixture_distribution.probs`.
+    p = math_ops.exp(self.mixture_distribution.logits)
+
+    # To compute E[Cov(Z|V)], we'll add matrices within three categories:
+    # scaled-identity, diagonal, and full. Then we'll combine these at the end.
+    scaled_identity = None
+    diag = None
+    full = None
+
+    for k, aff in enumerate(self.interpolated_affine):
+      s = aff.scale  # Just in case aff.scale has side-effects, we'll call once.
+      if (s is None
+          or isinstance(s, linop_identity_lib.LinearOperatorIdentity)):
+        scaled_identity = add(scaled_identity, p[..., k, array_ops.newaxis])
+      elif isinstance(s, linop_identity_lib.LinearOperatorScaledIdentity):
+        scaled_identity = add(scaled_identity, (p[..., k, array_ops.newaxis] *
+                                                math_ops.square(s.multiplier)))
+      elif isinstance(s, linop_diag_lib.LinearOperatorDiag):
+        diag = add(diag, (p[..., k, array_ops.newaxis] *
+                          math_ops.square(s.diag_part())))
+      else:
+        x = (p[..., k, array_ops.newaxis, array_ops.newaxis] *
+             s.matmul(s.to_dense(), adjoint_arg=True))
+        if diag_only:
+          x = array_ops.matrix_diag_part(x)
+        full = add(full, x)
+
+    # We must now account for the fact that the base distribution might have a
+    # non-unity variance. Recall that `Cov(SX+m) = S.T Cov(X) S = S.T S Var(X)`.
+    # We can scale by `Var(X)` (vs `Cov(X)`) since X corresponds to `d` iid
+    # samples from a scalar-event distribution.
+    v = self.distribution.variance()
+    if scaled_identity is not None:
+      scaled_identity *= v
+    if diag is not None:
+      diag *= v[..., array_ops.newaxis]
+    if full is not None:
+      full *= v[..., array_ops.newaxis]
+
+    if diag_only:
+      # Apparently we don't need the full matrix, just the diagonal.
+      r = add(diag, full)
+      if r is None and scaled_identity is not None:
+        ones = array_ops.ones(self.event_shape_tensor(), dtype=self.dtype)
+        return scaled_identity * ones
+      return add(r, scaled_identity)
+
+    # `None` indicates we don't know if the result is positive-definite.
+    is_positive_definite = (True if all(aff.scale.is_positive_definite
+                                        for aff in self.endpoint_affine)
+                            else None)
+
+    to_add = []
+    if diag is not None:
+      to_add.append(linop_diag_lib.LinearOperatorDiag(
+          diag=diag,
+          is_positive_definite=is_positive_definite))
+    if full is not None:
+      to_add.append(linop_full_lib.LinearOperatorFullMatrix(
+          matrix=full,
+          is_positive_definite=is_positive_definite))
+    if scaled_identity is not None:
+      to_add.append(linop_identity_lib.LinearOperatorScaledIdentity(
+          num_rows=self.event_shape_tensor()[0],
+          multiplier=scaled_identity,
+          is_positive_definite=is_positive_definite))
+
+    return (linop_add_lib.add_operators(to_add)[0].to_dense()
+            if to_add else None)
+
+  def _covariance_of_mean_given_quadrature_component(self, diag_only):
+    square = math_ops.square if diag_only else vec_osquare
+
+    # Since we created logits to already be scaled, we can use exp which is
+    # slightly cheaper than `self.mixture_distribution.probs`.
+    p = math_ops.exp(self.mixture_distribution.logits)
+
+    m = array_ops.tile(self.distribution.mean()[..., array_ops.newaxis],
+                       multiples=self.event_shape_tensor())
+    m = m[..., array_ops.newaxis, :]
+
+    cov_e_z_given_v = None
+    e_z_given_v = self._mean()
+    for k, aff in enumerate(self.interpolated_affine):
+      y = aff.forward(m)
+      cov_e_z_given_v = add(cov_e_z_given_v,
+                            p[..., k] * square(y - e_z_given_v))
+
+    return cov_e_z_given_v
+
+
+def maybe_check_mix_param(param, name, expected_base_dtype, validate_args):
+  """Helper which checks validity of `mix_loc` and `mix_scale` init args."""
+  with ops.name_scope(name="check_" + name, values=[param]):
+    param = ops.convert_to_tensor(param, dtype=expected_base_dtype, name=name)
+
+    if param.dtype.base_dtype != expected_base_dtype:
+      raise TypeError(
+          "dtype mismatch; {}.base_dtype=\"{}\" is not \"{}\".".format(
+              name, param.dtype.base_dtype.name, expected_base_dtype.name))
+
+    assertions = []
+    if param.shape.ndims is not None:
+      if param.shape.ndims == 0:
+        raise ValueError("Mixing params must be a (batch of) vector; "
+                         "{}.rank={} is not at least one.".format(
+                             name, param.shape.ndims))
+    elif validate_args:
+      assertions.append(check_ops.assert_rank_at_least(
+          param, 1,
+          message=("Mixing params must be a (batch of) vector; "
+                   "{}.rank is not at least one.".format(
+                       name))))
+
+    # TODO(jvdillon): Remove once we support k-mixtures.
+    if param.shape.with_rank_at_least(1)[-1] is not None:
+      if param.shape[-1].value != 1:
+        raise NotImplementedError("Currently only bimixtures are supported; "
+                                  "{}.shape[-1]={} is not 1.".format(
+                                      name, param.shape[-1].value))
+    elif validate_args:
+      assertions.append(check_ops.assert_equal(
+          array_ops.shape(param)[-1], 1,
+          message=("Currently only bimixtures are supported; "
+                   "{}.shape[-1] is not 1.".format(name))))
+
+    if assertions:
+      return control_flow_ops.with_dependencies(assertions, param)
+    return param
+
+
+def maybe_check_distribution(distribution, expected_base_dtype, validate_args):
+  """Helper which checks validity of `distribution` init arg."""
+  if distribution.dtype != expected_base_dtype:
+    raise TypeError("dtype mismatch; "
+                    "distribution.dtype=\"{}\" is not \"{}\"".format(
+                        distribution.dtype.name, expected_base_dtype.name))
+
+  # Although `reparameterization_type` is a static property, we guard it by
+  # `validate_args`. This allows users to use a `distribution` which is not
+  # reparameterized itself. However, we tacitly assume that although the
+  # distribution is not reparameterized, it only depends on non-trainable
+  # variables.
+  if validate_args and (distribution.reparameterization_type
+                        != distribution_lib.FULLY_REPARAMETERIZED):
+    raise ValueError("Base distribution should be reparameterized or be "
+                     "a function of non-trainable variables; "
+                     "distribution.reparameterization_type = \"{}\" "
+                     "!= \"FULLY_REPARAMETERIZED\".".format(
+                         distribution.reparameterization_type))
+  with ops.name_scope(name="check_distribution"):
+    assertions = []
+    def check_is_scalar(is_scalar, name):
+      is_scalar_ = static_value(is_scalar)
+      if is_scalar_ is not None:
+        if not is_scalar_:
+          raise ValueError("distribution must be scalar; "
+                           "distribution.{}=False is not True".format(name))
+      elif validate_args:
+        assertions.append(check_ops.assert_equal(
+            is_scalar, True,
+            message=("distribution must be scalar; "
+                     "distribution.{}=False is not True".format(name))))
+    check_is_scalar(distribution.is_scalar_event(), "is_scalar_event")
+    check_is_scalar(distribution.is_scalar_batch(), "is_scalar_batch")
+    return assertions
+
+
+def determine_batch_event_shapes(mix_loc, mix_scale, endpoint_affine):
+  """Helper to infer batch_shape and event_shape."""
+  with ops.name_scope(name="determine_batch_event_shapes"):
+    mix_batch_shape = distribution_util.prefer_static_broadcast_shape(
+        array_ops.shape(mix_loc, name="mix_loc_shape"),
+        array_ops.shape(mix_scale, name="mix_scale_shape"))
+    if isinstance(mix_batch_shape, tensor_shape.TensorShape):
+      mix_batch_shape = mix_batch_shape.with_rank_at_least(1)[:-1]
+    else:
+      s = static_value(mix_batch_shape)
+      if s is not None:
+        mix_batch_shape = ops.convert_to_tensor(
+            s[:-1], dtype=dtypes.int32, name="mix_batch_shape")
+      else:
+        mix_batch_shape = mix_batch_shape[:-1]
+
+    # We broadcast with a 1D constant to automatically make the result a
+    # TensorShape if possible.
+    batch_shape = distribution_util.prefer_static_broadcast_shape(
+        mix_batch_shape,
+        constant_op.constant([], dtype=dtypes.int32, name="batch_shape"))
+    event_shape = constant_op.constant(
+        [], dtype=dtypes.int32, name="event_shape")
+    for aff in endpoint_affine:
+      b, e = distribution_util.shapes_from_loc_and_scale(aff.shift, aff.scale)
+      if batch_shape is None:
+        batch_shape = distribution_util.prefer_static_broadcast_shape(
+            mix_batch_shape, b)
+      else:
+        batch_shape = distribution_util.prefer_static_broadcast_shape(
+            batch_shape, b)
+      event_shape = distribution_util.prefer_static_broadcast_shape(
+          event_shape, e)
+    if isinstance(batch_shape, tensor_shape.TensorShape):
+      batch_shape = ops.convert_to_tensor(
+          batch_shape.as_list(), dtype=dtypes.int32, name="batch_shape")
+    if isinstance(event_shape, tensor_shape.TensorShape):
+      event_shape = ops.convert_to_tensor(
+          event_shape.as_list(), dtype=dtypes.int32, name="event_shape")
+    return batch_shape, event_shape
+
+
+def interpolate_loc(deg, interpolate_weight, loc):
+  """Helper which interpolates between two locs."""
+  if len(loc) != 2:
+    raise NotImplementedError("Currently only bimixtures are supported; "
+                              "len(scale)={} is not 2.".format(len(loc)))
+  with ops.name_scope("interpolate_loc", values=[interpolate_weight, loc]):
+    if loc is None or loc[0] is None and loc[1] is None:
+      return [None]*deg
+    if loc[0] is None:
+      x = (1. - interpolate_weight[..., array_ops.newaxis]) * loc[1]
+    elif loc[1] is None:
+      x = interpolate_weight[..., array_ops.newaxis] * loc[0]
+    else:
+      delta = loc[0] - loc[1]
+      offset = array_ops.ones_like(loc[0]) * loc[1]
+      x = interpolate_weight[..., array_ops.newaxis] * delta + offset
+    return [x[..., k, :] for k in range(deg)]
+
+
+def interpolate_scale(deg, interpolate_weight, scale):
+  """Helper which interpolates between two scales."""
+  if len(scale) != 2:
+    raise NotImplementedError("Currently only bimixtures are supported; "
+                              "len(scale)={} is not 2.".format(len(scale)))
+  with ops.name_scope("interpolate_scale", values=[interpolate_weight]):
+    return [linop_add_lib.add_operators([
+        linop_scale(interpolate_weight[..., k], scale[0]),
+        linop_scale(1. - interpolate_weight[..., k], scale[1]),
+    ])[0] for k in range(deg)]
+
+
+def linop_scale(w, op):
+  # We assume w > 0. (This assumption only relates to the is_* attributes.)
+  with ops.name_scope("linop_scale", values=[w]):
+    def scaled_identity(w):
+      return linop_identity_lib.LinearOperatorScaledIdentity(
+          num_rows=op.range_dimension_tensor(),
+          multiplier=w,
+          is_non_singular=op.is_non_singular,
+          is_self_adjoint=op.is_self_adjoint,
+          is_positive_definite=op.is_positive_definite)
+    if isinstance(op, linop_identity_lib.LinearOperatorIdentity):
+      return scaled_identity(w)
+    elif isinstance(op, linop_identity_lib.LinearOperatorScaledIdentity):
+      return scaled_identity(w * op.multiplier)
+    elif isinstance(op, linop_diag_lib.LinearOperatorDiag):
+      return linop_diag_lib.LinearOperatorDiag(
+          diag=w[..., array_ops.newaxis] * op.diag_part(),
+          is_non_singular=op.is_non_singular,
+          is_self_adjoint=op.is_self_adjoint,
+          is_positive_definite=op.is_positive_definite)
+    else:
+      return linop_composition_lib.LinearOperatorComposition([
+          scaled_identity(w), op])
+
+
+def static_value(x):
+  """Returns the static value of a `Tensor` or `None`."""
+  return tensor_util.constant_value(ops.convert_to_tensor(x))
+
+
+def concat_vectors(*args):
+  """Concatenates input vectors, statically if possible."""
+  args_ = [static_value(x) for x in args]
+  if any(vec is None for vec in args_):
+    return array_ops.concat(args, axis=0)
+  return [val for vec in args_ for val in vec]
+
+
+def reduce_prod(x):
+  """Same as `math_ops.reduce_prod` but statically if possible."""
+  x_ = static_value(x)
+  if x_ is not None:
+    return np.prod(x_, dtype=x.dtype.as_numpy_dtype)
+  return array_ops.reduce_prod(x)
+
+
+def ndims_from_shape(shape):
+  """Returns `Tensor`'s `rank` implied by a `Tensor` shape."""
+  if shape.shape.ndims not in (None, 1):
+    raise ValueError("input is not a valid shape: not 1D")
+  if not shape.dtype.is_integer:
+    raise TypeError("input is not a valid shape: wrong dtype")
+  if shape.shape.is_fully_defined():
+    return shape.shape.as_list()[0]
+  return array_ops.shape(shape)[0]
+
+
+def ndims(x):
+  """Returns rank, statically if possible."""
+  x = ops.convert_to_tensor(x)
+  if x.shape.ndims is not None:
+    return x.shape.ndims
+  return array_ops.rank(x)
+
+
+def add(x, y):
+  """Adds inputs; interprets `None` as zero."""
+  if x is None:
+    return y
+  if y is None:
+    return x
+  return x + y
+
+
+def vec_osquare(x):
+  """Computes the outer-product of a (batch of) vector, i.e., x.T x."""
+  return x[..., :, array_ops.newaxis] * x[..., array_ops.newaxis, :]
-- 
GitLab


From 3acdb7dae41cc54119398dcf8891a2691aa2dc45 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 14:31:42 -0700
Subject: [PATCH 1079/1375] [XLA:CPU] Sync function definition argument names
 with function declaration argument names; NFC.

PiperOrigin-RevId: 162404670
---
 tensorflow/compiler/xla/service/cpu/cpu_runtime.h | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
index 40a7f548a2..46cf64966a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
@@ -95,13 +95,13 @@ extern void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(
 // implemented we will add support for multiple outstanding buffers
 // that can be returned out of order.
 extern void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
-    xla::int32 buffer_length, void* buffer_ptr, const void* shape,
+    xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
     xla::int32 shape_length);
 
 // Blocks until the next outfeed buffer is available to be populated, then
 // returns it.
 extern void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
-    xla::int32 buffer_length, const void* shape, xla::int32 shape_length);
+    xla::int32 buffer_length, const void* shape_ptr, xla::int32 shape_length);
 
 // Relinquishes the outfeed buffer after it has been populated.
 // buffer_ptr must have been previously returned by
@@ -113,7 +113,7 @@ extern void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
 // acquired, i.e., there may only be one outstanding outfeed buffer in
 // use by the runtime.
 extern void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
-    xla::int32 buffer_length, void* buffer_ptr, const void* shape,
+    xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
     xla::int32 shape_length);
 
 }  // extern "C"
-- 
GitLab


From 5e79b58d6028f284c82082d2077b06d5629cec57 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 14:36:47 -0700
Subject: [PATCH 1080/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 162405396
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 30 +++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 20 +++++++++++++
 2 files changed, 50 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 51f15ba615..409c0023a9 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -11275,6 +11275,36 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "IteratorFromStringHandle"
+  input_arg {
+    name: "string_handle"
+    type: DT_STRING
+  }
+  output_arg {
+    name: "resource_handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    default_value {
+      list {
+      }
+    }
+    has_minimum: true
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    default_value {
+      list {
+      }
+    }
+    has_minimum: true
+  }
+  is_stateful: true
+}
 op {
   name: "IteratorGetNext"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index c953f93924..bb5419b7c0 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -10583,6 +10583,26 @@ op {
     description: "A handle to an iterator resource."
     type: DT_RESOURCE
   }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    default_value {
+      list {
+      }
+    }
+    description: "If specified, defines the type of each tuple component in an\nelement produced by the resulting iterator."
+    has_minimum: true
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    default_value {
+      list {
+      }
+    }
+    description: "If specified, defines the shape of each tuple component in an\nelement produced by the resulting iterator."
+    has_minimum: true
+  }
   summary: "Converts the given string representing a handle to an iterator to a resource."
   is_stateful: true
 }
-- 
GitLab


From 0c967255448215e1e76aab76c135a174f2f0c0cf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 14:41:19 -0700
Subject: [PATCH 1081/1375] Make sparse_placeholder maintain rank of partial
 shapes.

PiperOrigin-RevId: 162406054
---
 .../python/kernel_tests/sparse_ops_test.py      | 14 +++++++++-----
 tensorflow/python/ops/array_ops.py              | 17 ++++++++---------
 2 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index d500f94dc2..c70f152af8 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -909,12 +909,16 @@ class SparseTransposeTest(test.TestCase):
 class SparsePlaceholderTest(test.TestCase):
 
   def testPlaceholder(self):
-    with self.test_session(use_gpu=False):
-      foo = array_ops.sparse_placeholder(dtypes.float32, shape=(10, 47))
-      self.assertAllEqual([10, 47], foo.get_shape())
+    foo = array_ops.sparse_placeholder(dtypes.float32, shape=(10, 47))
+    self.assertAllEqual([10, 47], foo.get_shape())
+
+  def testPartialShapePlaceholder(self):
+    foo = array_ops.sparse_placeholder(dtypes.float32, shape=(None, 47))
+    self.assertAllEqual([None, None], foo.get_shape().as_list())
 
-      foo = array_ops.sparse_placeholder(dtypes.float32, shape=(None, 47))
-      self.assertAllEqual(None, foo.get_shape())
+  def testNoShapePlaceholder(self):
+    foo = array_ops.sparse_placeholder(dtypes.float32, shape=None)
+    self.assertAllEqual(None, foo.get_shape())
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 0956d914f5..89e01c2230 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1550,13 +1550,12 @@ def placeholder(dtype, shape=None, name=None):
 
 # pylint: disable=redefined-outer-name
 def _normalize_sparse_shape(shape, name):
-  """Takes numpy array or Tensor or None and returns either None or Tensor."""
-  if shape is None: return None
-  if not isinstance(shape, ops.Tensor):
-    for el in shape:
-      if el is None:
-        return None
-  return ops.convert_to_tensor(shape, dtype=dtypes.int64, name=name)
+  """Returns a tuple of (Tensor or None, rank or None)."""
+  if shape is None: return (None, None)
+  rank = shape.get_shape()[0] if isinstance(shape, ops.Tensor) else len(shape)
+  if not isinstance(shape, ops.Tensor) and None in shape:
+    return (None, rank)
+  return (ops.convert_to_tensor(shape, dtype=dtypes.int64, name=name), rank)
 
 
 def sparse_placeholder(dtype, shape=None, name=None):
@@ -1599,9 +1598,9 @@ def sparse_placeholder(dtype, shape=None, name=None):
     evaluated directly.
   """
   shape_name = (name + "/shape") if name is not None else None
-  shape = _normalize_sparse_shape(shape, shape_name)
+  shape, rank = _normalize_sparse_shape(shape, shape_name)
   if shape is None:
-    shape = placeholder(dtypes.int64, shape=[None], name=shape_name)
+    shape = placeholder(dtypes.int64, shape=[rank], name=shape_name)
   return sparse_tensor.SparseTensor(
       values=placeholder(
           dtype, shape=[None],
-- 
GitLab


From bda2712d0322ef45bb7a779afa2b0d8ae0e6f588 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 14:51:35 -0700
Subject: [PATCH 1082/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 162407635
---
 tensorflow/go/op/wrappers.go | 36 +++++++++++++++++++++++++++++++++++-
 1 file changed, 35 insertions(+), 1 deletion(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index b90c0582d4..3b33706dc5 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -5374,21 +5374,55 @@ func Identity(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
+// IteratorFromStringHandleAttr is an optional argument to IteratorFromStringHandle.
+type IteratorFromStringHandleAttr func(optionalAttr)
+
+// IteratorFromStringHandleOutputTypes sets the optional output_types attribute to value.
+//
+// value: If specified, defines the type of each tuple component in an
+// element produced by the resulting iterator.
+// If not specified, defaults to <>
+//
+// REQUIRES: len(value) >= 0
+func IteratorFromStringHandleOutputTypes(value []tf.DataType) IteratorFromStringHandleAttr {
+	return func(m optionalAttr) {
+		m["output_types"] = value
+	}
+}
+
+// IteratorFromStringHandleOutputShapes sets the optional output_shapes attribute to value.
+//
+// value: If specified, defines the shape of each tuple component in an
+// element produced by the resulting iterator.
+// If not specified, defaults to <>
+//
+// REQUIRES: len(value) >= 0
+func IteratorFromStringHandleOutputShapes(value []tf.Shape) IteratorFromStringHandleAttr {
+	return func(m optionalAttr) {
+		m["output_shapes"] = value
+	}
+}
+
 // Converts the given string representing a handle to an iterator to a resource.
 //
 // Arguments:
 //	string_handle: A string representation of the given handle.
 //
 // Returns A handle to an iterator resource.
-func IteratorFromStringHandle(scope *Scope, string_handle tf.Output) (resource_handle tf.Output) {
+func IteratorFromStringHandle(scope *Scope, string_handle tf.Output, optional ...IteratorFromStringHandleAttr) (resource_handle tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
 		Type: "IteratorFromStringHandle",
 		Input: []tf.Input{
 			string_handle,
 		},
+		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
-- 
GitLab


From d8672f1839df67f765baaa34a4c806ee1d433842 Mon Sep 17 00:00:00 2001
From: Toby Boyd <tobyboyd@google.com>
Date: Tue, 18 Jul 2017 15:10:01 -0700
Subject: [PATCH 1083/1375] Fix image to be Max

PiperOrigin-RevId: 162410385
---
 tensorflow/core/ops/math_ops.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 89818bc210..fc02732af4 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -1658,7 +1658,7 @@ If the maximum is empty for a given segment ID `i`, it outputs the smallest poss
  `output[i] = numeric_limits<T>::min()`.
 
 <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentSum.png" alt>
+<img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentMax.png" alt>
 </div>
 
 segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-- 
GitLab


From c0463c080789854a3cc30e9dbbb27c31f3250b02 Mon Sep 17 00:00:00 2001
From: Dan J <daj@users.noreply.github.com>
Date: Tue, 18 Jul 2017 18:17:16 -0400
Subject: [PATCH 1084/1375] Show usage when no arguments passed to
 import_pb_to_tensorboard.py (#11520)

It seems like --model_dir and --log_dir should be required arguments.
---
 tensorflow/python/tools/import_pb_to_tensorboard.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/python/tools/import_pb_to_tensorboard.py b/tensorflow/python/tools/import_pb_to_tensorboard.py
index 2cab409cfb..a8712fc37e 100644
--- a/tensorflow/python/tools/import_pb_to_tensorboard.py
+++ b/tensorflow/python/tools/import_pb_to_tensorboard.py
@@ -64,11 +64,13 @@ if __name__ == "__main__":
       "--model_dir",
       type=str,
       default="",
+      required=True,
       help="The location of the protobuf (\'pb\') model to visualize.")
   parser.add_argument(
       "--log_dir",
       type=str,
       default="",
+      required=True,
       help="The location for the Tensorboard log to begin visualization from.")
   FLAGS, unparsed = parser.parse_known_args()
   app.run(main=main, argv=[sys.argv[0]] + unparsed)
-- 
GitLab


From d5f4d9bbac520ad9eae6614fe678e9d1568435a4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 15:20:03 -0700
Subject: [PATCH 1085/1375] Add a way to fuse a graph by remote graph executor
 so that users don't need to be aware of supported op types, node names,
 subgraph stracture etc.

PiperOrigin-RevId: 162411763
---
 .../hvx_ops_support_checker_main.cc           |   5 +-
 .../contrib/makefile/build_all_android.sh     |   2 +-
 .../sub_makefiles/quantization/Makefile.in    |   1 -
 tensorflow/core/kernels/BUILD                 |   7 +-
 tensorflow/core/kernels/hexagon/BUILD         |  24 +---
 .../kernels/hexagon/graph_transfer_utils.cc   |   2 +-
 .../kernels/hexagon/graph_transfer_utils.h    |   2 +-
 .../core/kernels/hexagon/graph_transferer.cc  |  28 ++--
 .../core/kernels/hexagon/graph_transferer.h   |  37 +++--
 .../kernels/hexagon/graph_transferer_test.cc  |  14 +-
 .../hexagon/hexagon_control_wrapper.cc        |  70 ++++-----
 .../kernels/hexagon/hexagon_control_wrapper.h |   7 +
 .../hexagon/hexagon_graph_execution_test.cc   |  13 +-
 .../hexagon/hexagon_ops_definitions.cc        |  18 +--
 .../kernels/hexagon/hexagon_ops_definitions.h |   7 +-
 ...xagon_remote_fused_graph_executor_build.cc |   2 +-
 .../quantized_matmul_op_for_hexagon_test.cc   | 136 ------------------
 .../core/kernels/hexagon/soc_interface.cc     |  83 +++++++++++
 .../hexagon/soc_interface.h                   |   1 +
 .../kernels/i_remote_fused_graph_executor.h   |   7 +
 ...> i_remote_fused_graph_ops_definitions.cc} |   7 +-
 ...=> i_remote_fused_graph_ops_definitions.h} |  25 ++--
 .../kernels/remote_fused_graph_execute_op.cc  |   3 +-
 .../remote_fused_graph_execute_op_test.cc     |  16 ++-
 ...emote_fused_graph_execute_op_test_utils.cc |  33 +++++
 ...remote_fused_graph_execute_op_test_utils.h |  25 ++++
 .../remote_fused_graph_execute_utils.cc       |  44 ++++++
 .../remote_fused_graph_execute_utils.h        |  13 ++
 .../remote_fused_graph_execute_utils_test.cc  |  63 +++++++-
 .../remote_fused_graph_rewriter_transform.cc  |  19 ++-
 ...ote_fused_graph_rewriter_transform_test.cc |  59 +++++++-
 31 files changed, 474 insertions(+), 299 deletions(-)
 delete mode 100644 tensorflow/core/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc
 create mode 100644 tensorflow/core/kernels/hexagon/soc_interface.cc
 rename tensorflow/core/{platform => kernels}/hexagon/soc_interface.h (99%)
 rename tensorflow/core/kernels/{hexagon/i_graph_transfer_ops_definitions.cc => i_remote_fused_graph_ops_definitions.cc} (74%)
 rename tensorflow/core/kernels/{hexagon/i_graph_transfer_ops_definitions.h => i_remote_fused_graph_ops_definitions.h} (56%)

diff --git a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
index 6af608396a..8fc3f35f41 100644
--- a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
+++ b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/remote_fused_graph_execute_info.pb.h"
 #include "tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h"
 #include "tensorflow/core/kernels/remote_fused_graph_execute_utils.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -110,7 +111,7 @@ static void DumpRemoteFusedGraph(const NodeDef& node_def) {
 static void CheckOpsSupport(const GraphDef& graph_def,
                             const bool dump_all_nodes,
                             const bool dump_shape_and_type) {
-  const IGraphTransferOpsDefinitions& ops_definition =
+  const IRemoteFusedGraphOpsDefinitions& ops_definition =
       HexagonOpsDefinitions::getInstance();
   LOG(INFO) << "Checking " << graph_def.node_size() << " nodes";
   LOG(INFO) << "dump_all_nodes = " << dump_all_nodes
@@ -127,7 +128,7 @@ static void CheckOpsSupport(const GraphDef& graph_def,
     }
     // TODO(satok): Set correct data type if it's given.
     const int op_id = ops_definition.GetOpIdFor(node.op(), {});
-    if (op_id == IGraphTransferOpsDefinitions::INVALID_OP_ID) {
+    if (op_id == IRemoteFusedGraphOpsDefinitions::INVALID_OP_ID) {
       all_supported = false;
       LOG(ERROR) << "OP type: " << node.op() << " is not supported on hvx. "
                  << "Name = " << node.name();
diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh
index 161f2df5b2..7f0c3f38c2 100755
--- a/tensorflow/contrib/makefile/build_all_android.sh
+++ b/tensorflow/contrib/makefile/build_all_android.sh
@@ -82,7 +82,7 @@ fi
 if [[ "${USE_HEXAGON}" == "true" ]]; then
     HEXAGON_PARENT_DIR=$(cd "${HEXAGON_DOWNLOAD_PATH}" >/dev/null && pwd)
     HEXAGON_LIBS="${HEXAGON_PARENT_DIR}/libs"
-    HEXAGON_INCLUDE=$(cd "tensorflow/core/platform/hexagon" >/dev/null && pwd)
+    HEXAGON_INCLUDE=$(cd "tensorflow/core/kernels/hexagon" >/dev/null && pwd)
 fi
 
 if [[ "${ENABLE_EXPERIMENTAL_HEXNN_OPS}" == "true" ]]; then
diff --git a/tensorflow/contrib/makefile/sub_makefiles/quantization/Makefile.in b/tensorflow/contrib/makefile/sub_makefiles/quantization/Makefile.in
index 6ba41d5d12..362ccedfc2 100644
--- a/tensorflow/contrib/makefile/sub_makefiles/quantization/Makefile.in
+++ b/tensorflow/contrib/makefile/sub_makefiles/quantization/Makefile.in
@@ -56,7 +56,6 @@ tensorflow/core/platform/posix/test.cc
 
 QUANTIZATION_TEST_SRCS := \
 $(GRAPH_TRANSFER_SRCS) \
-tensorflow/core/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc \
 tensorflow/core/kernels/hexagon/graph_transferer_test.cc \
 tensorflow/contrib/makefile/test/test_main.cc
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 264d5d5952..21194e80c3 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -5057,9 +5057,13 @@ tf_kernel_library(
 
 cc_library(
     name = "remote_fused_graph_execute_utils",
-    srcs = ["remote_fused_graph_execute_utils.cc"],
+    srcs = [
+        "i_remote_fused_graph_ops_definitions.cc",
+        "remote_fused_graph_execute_utils.cc",
+    ],
     hdrs = [
         "i_remote_fused_graph_executor.h",
+        "i_remote_fused_graph_ops_definitions.h",
         "remote_fused_graph_execute_utils.h",
     ],
     deps = [
@@ -5078,6 +5082,7 @@ cc_library(
     srcs = ["remote_fused_graph_execute_op_test_utils.cc"],
     hdrs = ["remote_fused_graph_execute_op_test_utils.h"],
     deps = [
+        ":remote_fused_graph_execute_utils",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/cc:ops",
         "//tensorflow/cc:scope",
diff --git a/tensorflow/core/kernels/hexagon/BUILD b/tensorflow/core/kernels/hexagon/BUILD
index e9391c8954..ce83d7c0fc 100644
--- a/tensorflow/core/kernels/hexagon/BUILD
+++ b/tensorflow/core/kernels/hexagon/BUILD
@@ -26,23 +26,6 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
-tf_cc_test(
-    name = "quantized_matmul_op_for_hexagon_test",
-    size = "small",
-    srcs = ["quantized_matmul_op_for_hexagon_test.cc"],
-    tags = ["nomsan"],  # http://b/32242946
-    deps = [
-        "//tensorflow/core:framework",
-        "//tensorflow/core:protos_all_cc",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-        "//tensorflow/core:testlib",
-        "//tensorflow/core/kernels:ops_testutil",
-        "//tensorflow/core/kernels:ops_util",
-        "//tensorflow/core/kernels:quantized_ops",
-    ],
-)
-
 tf_cc_test(
     name = "graph_transferer_test",
     size = "small",
@@ -79,14 +62,14 @@ tf_kernel_library(
         "graph_transferer.cc",
         "hexagon_control_wrapper.cc",
         "hexagon_ops_definitions.cc",
-        "i_graph_transfer_ops_definitions.cc",
+        "soc_interface.cc",
     ],
     hdrs = [
         "graph_transfer_utils.h",
         "graph_transferer.h",
         "hexagon_control_wrapper.h",
         "hexagon_ops_definitions.h",
-        "i_graph_transfer_ops_definitions.h",
+        "soc_interface.h",
     ],
     deps = [
         "//tensorflow/cc:cc_ops",
@@ -111,6 +94,7 @@ cc_library(
         ":graph_transferer",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/cc:remote_fused_graph_ops",
+        "//tensorflow/core/kernels:remote_fused_graph_execute_utils",
         "//tensorflow/tools/graph_transforms:transform_utils",
     ],
     alwayslink = 1,
@@ -121,6 +105,7 @@ tf_cc_test(
     size = "small",
     srcs = ["hexagon_rewriter_transform_test.cc"],
     deps = [
+        ":graph_transferer",
         ":hexagon_rewriter_transform",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/core:core_cpu",
@@ -129,6 +114,7 @@ tf_cc_test(
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
+        "//tensorflow/core/kernels:remote_fused_graph_execute_utils",
         "//tensorflow/tools/graph_transforms:transform_utils",
     ],
 )
diff --git a/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc b/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc
index 8538ebc5c6..f0d7c670a6 100644
--- a/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc
@@ -96,7 +96,7 @@ GraphTransferUtils::BuildRemoteFusedGraphExecuteInfo(
 }
 
 /* static */ GraphDef GraphTransferUtils::BuildFusedGraphDef(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const string& remote_graph_execute_name,
     const std::vector<std::pair<string, Tensor>>& inputs,
     const std::vector<string>& outputs, GraphDef* original_def) {
diff --git a/tensorflow/core/kernels/hexagon/graph_transfer_utils.h b/tensorflow/core/kernels/hexagon/graph_transfer_utils.h
index a11e2e6eb1..352d548bd3 100644
--- a/tensorflow/core/kernels/hexagon/graph_transfer_utils.h
+++ b/tensorflow/core/kernels/hexagon/graph_transfer_utils.h
@@ -39,7 +39,7 @@ class GraphTransferUtils {
                                    const int element_count, const int top_n);
 
   static GraphDef BuildFusedGraphDef(
-      const IGraphTransferOpsDefinitions& ops_definitions,
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
       const string& remote_graph_execute_name,
       const std::vector<std::pair<string, Tensor>>& inputs,
       const std::vector<string>& outputs, GraphDef* original_def);
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.cc b/tensorflow/core/kernels/hexagon/graph_transferer.cc
index 65677cc3de..7768acc771 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transferer.cc
@@ -81,7 +81,7 @@ static Node* FindMutableNodeByName(const string& name, Graph* graph) {
  * of node to transfer the graph to SOC.
  */
 Status GraphTransferer::LoadGraphFromProto(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const GraphDef& graph_def,
     const std::vector<std::pair<string, Tensor>>& input_node_info_list,
     const std::vector<string>& output_node_names,
@@ -177,9 +177,6 @@ Status GraphTransferer::LoadGraphFromProto(
     }
   }
 
-  graph_transfer_info_.set_destination(
-      ops_definitions.GetTransferDestination());
-
   ClearCache();
   if (DBG_DUMP_PARAMS) {
     DumpNodeTransferParams();
@@ -191,7 +188,7 @@ Status GraphTransferer::LoadGraphFromProto(
 }
 
 Status GraphTransferer::LoadGraphFromProtoFile(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const string& graph_def_path,
     const std::vector<std::pair<string, Tensor>>& input_node_info_list,
     const std::vector<string>& output_node_names, const bool is_text_proto,
@@ -415,7 +412,7 @@ Status GraphTransferer::TransformGraphToAddAggregatedInputNode(
 }
 
 Status GraphTransferer::RegisterNode(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node,
     const std::vector<std::pair<string, Tensor>>& input_node_info_list,
     const std::vector<string>& output_node_names) {
@@ -438,7 +435,7 @@ Status GraphTransferer::RegisterNode(
   } else if (IsNodeFlattenReshape(node, shape_refiner)) {
     RegisterFlattenNode(ops_definitions, shape_refiner, node);
   } else if (ops_definitions.GetOpIdFor(node.type_string(), {}) !=
-             IGraphTransferOpsDefinitions::INVALID_OP_ID) {
+             IRemoteFusedGraphOpsDefinitions::INVALID_OP_ID) {
     // TODO(satok): Set correct data type if it's given.
     RegisterGenericNode(ops_definitions, shape_refiner, node);
   } else {
@@ -637,7 +634,7 @@ bool GraphTransferer::IsNodeFlattenReshape(const Node& node,
 }
 
 void GraphTransferer::RegisterNodeWithPaddingAndStrides(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node) {
   CHECK_EQ(node_name_to_id_cache_map_.count(node.name()), 1);
   const int id = node_name_to_id_cache_map_[node.name()];
@@ -671,7 +668,7 @@ void GraphTransferer::RegisterNodeWithPaddingAndStrides(
 }
 
 void GraphTransferer::RegisterNodeWithRank(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node) {
   CHECK_EQ(node_name_to_id_cache_map_.count(node.name()), 1);
   const int id = node_name_to_id_cache_map_[node.name()];
@@ -704,7 +701,7 @@ void GraphTransferer::RegisterNodeWithRank(
 }
 
 void GraphTransferer::RegisterPadNode(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node) {
   static constexpr int PAD_WIDTH = 4;
   static constexpr int PAD_HEIGHT = 2;
@@ -779,7 +776,7 @@ void GraphTransferer::RegisterPadNode(
 }
 
 void GraphTransferer::RegisterInputNode(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node) {
   const string op_type = node.type_string();
   VLOG(1) << "Register input node: " << node.name() << ", " << op_type;
@@ -797,12 +794,13 @@ void GraphTransferer::RegisterInputNode(
 }
 
 void GraphTransferer::RegisterFlattenNode(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node) {
   VLOG(1) << "Register flatten node: " << node.name();
   CHECK_EQ(node_name_to_id_cache_map_.count(node.name()), 1);
   const int id = node_name_to_id_cache_map_[node.name()];
-  const string op_type = IGraphTransferOpsDefinitions::FLATTEN_OP_NAME;
+  // TODO(satok): Remove dependency to specific type
+  const string op_type = "FLATTEN";
   // TODO(satok): Set correct data type if it's given.
   const int op_type_id = ops_definitions.GetOpIdFor(op_type, {});
   CHECK(op_type_id >= 0 && op_type_id < ops_definitions.GetTotalOpsCount());
@@ -814,7 +812,7 @@ void GraphTransferer::RegisterFlattenNode(
 }
 
 void GraphTransferer::RegisterGenericNode(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node) {
   VLOG(1) << "Register generic node: " << node.name();
   CHECK_EQ(node_name_to_id_cache_map_.count(node.name()), 1);
@@ -832,7 +830,7 @@ void GraphTransferer::RegisterGenericNode(
 // TODO(satok): Remove this function.
 // TODO(satok): Remove only_register_const_node.
 Status GraphTransferer::RegisterNodeIfAllInputsAreCached(
-    const IGraphTransferOpsDefinitions& ops_definitions,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions,
     const ShapeRefiner& shape_refiner, const Node& node,
     const bool only_register_const_node,
     const std::vector<std::pair<string, Tensor>>& input_node_info_list,
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.h b/tensorflow/core/kernels/hexagon/graph_transferer.h
index 64c60b87c6..125d1fd200 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer.h
+++ b/tensorflow/core/kernels/hexagon/graph_transferer.h
@@ -26,7 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/graph_transfer_info.pb.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/graph/graph.h"
-#include "tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h"
 #include "tensorflow/core/kernels/remote_fused_graph_execute_utils.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/protobuf.h"
@@ -53,7 +53,7 @@ class GraphTransferer {
   // TODO(satok): Pass a pair of TensorShape and DataType instead of
   // Tensor as input_node_info_list.
   Status LoadGraphFromProto(
-      const IGraphTransferOpsDefinitions& ops_definitions,
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
       const GraphDef& graph_def,
       const std::vector<std::pair<string, Tensor>>& input_node_info_list,
       const std::vector<string>& output_node_names,
@@ -63,7 +63,7 @@ class GraphTransferer {
   // TODO(satok): Pass a pair of TensorShape and DataType instead of
   // Tensor as input_node_info_list.
   Status LoadGraphFromProtoFile(
-      const IGraphTransferOpsDefinitions& ops_definitions,
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
       const string& graph_def_path,
       const std::vector<std::pair<string, Tensor>>& input_node_info_list,
       const std::vector<string>& output_node_names, const bool is_text_proto,
@@ -112,7 +112,7 @@ class GraphTransferer {
       Graph* graph, ShapeRefiner* shape_refiner);
 
   Status RegisterNode(
-      const IGraphTransferOpsDefinitions& ops_definitions,
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
       const ShapeRefiner& shape_refiner, const Node& node,
       const std::vector<std::pair<string, Tensor>>& input_node_info_list,
       const std::vector<string>& output_node_names);
@@ -140,30 +140,29 @@ class GraphTransferer {
                             const ShapeRefiner& shape_refiner);
 
   void RegisterNodeWithPaddingAndStrides(
-      const IGraphTransferOpsDefinitions& ops_definitions,
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
       const ShapeRefiner& shape_refiner, const Node& node);
 
-  void RegisterNodeWithRank(const IGraphTransferOpsDefinitions& ops_definitions,
-                            const ShapeRefiner& shape_refiner,
-                            const Node& node);
+  void RegisterNodeWithRank(
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
+      const ShapeRefiner& shape_refiner, const Node& node);
 
-  void RegisterPadNode(const IGraphTransferOpsDefinitions& ops_definitions,
+  void RegisterPadNode(const IRemoteFusedGraphOpsDefinitions& ops_definitions,
                        const ShapeRefiner& shape_refiner, const Node& node);
 
-  void RegisterInputNode(const IGraphTransferOpsDefinitions& ops_definitions,
-                         const ShapeRefiner& shape_refiner,
-                         const Node& node);
+  void RegisterInputNode(const IRemoteFusedGraphOpsDefinitions& ops_definitions,
+                         const ShapeRefiner& shape_refiner, const Node& node);
 
-  void RegisterFlattenNode(const IGraphTransferOpsDefinitions& ops_definitions,
-                           const ShapeRefiner& shape_refiner,
-                           const Node& node);
+  void RegisterFlattenNode(
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
+      const ShapeRefiner& shape_refiner, const Node& node);
 
-  void RegisterGenericNode(const IGraphTransferOpsDefinitions& ops_definitions,
-                           const ShapeRefiner& shape_refiner,
-                           const Node& node);
+  void RegisterGenericNode(
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
+      const ShapeRefiner& shape_refiner, const Node& node);
 
   Status RegisterNodeIfAllInputsAreCached(
-      const IGraphTransferOpsDefinitions& ops_definitions,
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions,
       const ShapeRefiner& shape_refiner, const Node& node,
       const bool only_register_const_node,
       const std::vector<std::pair<string, Tensor>>& input_node_info_list,
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer_test.cc b/tensorflow/core/kernels/hexagon/graph_transferer_test.cc
index 74ffc026f7..536d295506 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer_test.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transferer_test.cc
@@ -22,8 +22,8 @@ limitations under the License.
 #include "tensorflow/core/kernels/hexagon/graph_transfer_utils.h"
 #include "tensorflow/core/kernels/hexagon/graph_transferer.h"
 #include "tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h"
-#include "tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h"
 #include "tensorflow/core/kernels/i_remote_fused_graph_executor.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/io/path.h"
@@ -50,7 +50,7 @@ class GraphTransfererTest : public ::testing::Test {
 
 const RemoteFusedGraphExecuteUtils::TensorShapeMap EMPTY_OUTPUT_TENSOR_MAP;
 
-class TestGraphTransferOpsDefinitions : public IGraphTransferOpsDefinitions {
+class TestGraphTransferOpsDefinitions : public IRemoteFusedGraphOpsDefinitions {
  public:
   int GetTotalOpsCount() const final { return op_types_.size(); }
 
@@ -63,10 +63,6 @@ class TestGraphTransferOpsDefinitions : public IGraphTransferOpsDefinitions {
     return -1;
 }
 
-GraphTransferInfo::Destination GetTransferDestination() const final {
-  return GraphTransferInfo::NOP;
-  }
-
  private:
   const std::vector<string> op_types_{"INPUT",   "OUTPUT",  "Conv2D",
                                       "MaxPool", "NoOp",    "Add",
@@ -371,14 +367,14 @@ TEST_F(GraphTransfererTest, LoadMaxPoolGraph) {
 }
 
 TEST(HexagonOpsDefinitions, CheckOpsDefinitions) {
-  const IGraphTransferOpsDefinitions& ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions& ops_definitions =
       HexagonOpsDefinitions::getInstance();
   const int total_ops_count = ops_definitions.GetTotalOpsCount();
   EXPECT_GT(total_ops_count, 0);
 }
 
 TEST(GraphTransferer, LoadGraphFromProtoFile) {
-  const IGraphTransferOpsDefinitions* ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions* ops_definitions =
       &TEST_GRAPH_TRANSFER_OPS_DEFINITIONS;
   string filename =
       io::JoinPath(testing::TensorFlowSrcRoot(),
@@ -441,7 +437,7 @@ void CompareGraphTransferInfo(const GraphTransferInfo& a,
 }  // anonymous namespace
 
 TEST(GraphTransferer, LoadGraphFromProtoFileShapeInferenceSimple) {
-  const IGraphTransferOpsDefinitions* ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions* ops_definitions =
       &TEST_GRAPH_TRANSFER_OPS_DEFINITIONS;
   string filename =
       io::JoinPath(testing::TensorFlowSrcRoot(),
diff --git a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
index 660ffd268d..ab791d66f6 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
@@ -16,18 +16,19 @@ limitations under the License.
 #include "tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h"
 
 #include "tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h"
-
-#ifdef USE_HEXAGON_LIBS
-#include "tensorflow/core/platform/hexagon/soc_interface.h"
+#include "tensorflow/core/kernels/hexagon/soc_interface.h"
 #include "tensorflow/core/platform/profile_utils/cpu_utils.h"
-#endif
 
 namespace tensorflow {
 
-constexpr const char* const INPUT_OP_NAME = "INPUT";
 constexpr const char* const OUTPUT_OP_NAME = "OUTPUT";
+constexpr const char* const REMOTE_FUSED_GRAPH_NODE_NAME_PREFIX =
+    "hexagon_remote_fused_graph";
+/* static */ constexpr const char* const
+    HexagonControlWrapper::REMOTE_FUSED_GRAPH_EXECUTOR_NAME;
 
 constexpr int ALIGNMENT_BYTES = 16;
+constexpr int MAX_IN_OUT_COUNT = 128;
 
 const bool DBG_DUMP_VERIFICATION_STRING = false;
 const int DBG_LEVEL = 0;  // -2: verbose, -1: debug, 0: info
@@ -63,7 +64,6 @@ static uint8* FindAlignedPointer(uint8* ptr) {
   return nullptr;
 }
 
-#ifdef USE_HEXAGON_LIBS
 int HexagonControlWrapper::GetVersion() {
   return soc_interface_GetSocControllerVersion();
 }
@@ -95,7 +95,6 @@ bool HexagonControlWrapper::Init(const RemoteFusedGraphExecuteInfo& info) {
     LOG(ERROR) << "Hexagon initialization was failed.  See log output.";
     return false;
   }
-  const GraphTransferInfo& gt_info = graph_transferer_.GetGraphTransferInfo();
   std::vector<int> input_sizes;
   std::vector<int> output_sizes;
   CHECK_NOTNULL(execute_info_);
@@ -207,8 +206,9 @@ bool HexagonControlWrapper::SetupGraph() {
   for (const GraphTransferInfo::NodeInputInfo& input_params :
        graph_transfer_info.node_input_info()) {
     const int count = input_params.node_input_size();
-    int node_ids[count];
-    int ports[count];
+    CHECK(count <= MAX_IN_OUT_COUNT);
+    int node_ids[MAX_IN_OUT_COUNT];
+    int ports[MAX_IN_OUT_COUNT];
     for (int i = 0; i < count; ++i) {
       const GraphTransferInfo::NodeInput& node_input =
           input_params.node_input(i);
@@ -226,7 +226,8 @@ bool HexagonControlWrapper::SetupGraph() {
   for (const GraphTransferInfo::NodeOutputInfo& output_params :
        graph_transfer_info.node_output_info()) {
     const int count = output_params.max_byte_size_size();
-    int sizes[count];
+    CHECK(count <= MAX_IN_OUT_COUNT);
+    int sizes[MAX_IN_OUT_COUNT];
     for (int i = 0; i < count; ++i) {
       const int size = output_params.max_byte_size(i);
       sizes[i] = size;
@@ -373,6 +374,7 @@ bool HexagonControlWrapper::ReadOutputNode(
       << output_tensor->TotalBytes() << ", " << std::get<1>(output);
   TF_CHECK_OK(RemoteFusedGraphExecuteUtils::CopyByteArrayToTensor(
       std::get<0>(output), std::get<1>(output), output_tensor));
+  return true;
 }
 
 bool HexagonControlWrapper::ReadOutputNode(
@@ -382,14 +384,30 @@ bool HexagonControlWrapper::ReadOutputNode(
   const string tensor_name = AddPort(node_name);
   CHECK(output_port_map_.count(tensor_name) > 0);
   const int port = output_port_map_.at(tensor_name);
-  soc_interface_ReadOutputNodeWithPort(port, &std::get<0>(output),
-                                       &std::get<1>(output));
+  soc_interface_ReadOutputNodeWithPort(
+      port, &std::get<0>(output),
+      reinterpret_cast<uint64_t*>(&std::get<1>(output)));
   // TODO: Accept all results
   // std::get<2>(output) = DT_FLOAT;
   outputs->emplace_back(output);
   return true;
 }
 
+Status HexagonControlWrapper::FuseRemoteGraph(
+    const GraphDef& original_graph_def, const std::vector<string>& inputs,
+    const std::vector<string>& outputs, GraphDef* fused_graph_def) {
+  const std::unordered_set<string> fused_node_names =
+      RemoteFusedGraphExecuteUtils::BuildNodeMapFromOpsDefinitions(
+          original_graph_def, HexagonOpsDefinitions::getInstance());
+  // TODO(satok): We may want to place shape and type inside this function
+  // if they are not placed in the given graph.
+  TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::FuseRemoteGraphByNodeNames(
+      original_graph_def, inputs, outputs, REMOTE_FUSED_GRAPH_NODE_NAME_PREFIX,
+      fused_node_names, REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
+      /*require_shape_type=*/true, fused_graph_def));
+  return Status::OK();
+}
+
 bool HexagonControlWrapper::FillInputNode(const string& node_name,
                                           const Tensor& tensor) {
   StringPiece tensor_data = tensor.tensor_data();
@@ -415,31 +433,5 @@ bool HexagonControlWrapper::FillInputNode(const string& node_name,
   return true;
 }
 
-#else
-int HexagonControlWrapper::GetVersion() { return -1; }
-bool HexagonControlWrapper::Init(const RemoteFusedGraphExecuteInfo&) {
-  return false;
-}
-bool HexagonControlWrapper::Finalize() { return false; }
-bool HexagonControlWrapper::SetupGraph() { return false; }
-bool HexagonControlWrapper::ExecuteGraph() { return false; }
-bool HexagonControlWrapper::TeardownGraph() { return false; }
-bool HexagonControlWrapper::FillInputNode(
-    const string&, const std::array<int64, GraphTransferer::SHAPE_ARRAY_SIZE>&,
-    const ConstByteArray) {
-  return false;
-}
-bool HexagonControlWrapper::FillInputNode(const string&, const Tensor&) {
-  return false;
-}
-bool HexagonControlWrapper::ReadOutputNode(
-    const string& node_name, TensorAllocatorFunc tensor_allocator) {
-  return false;
-}
-bool HexagonControlWrapper::ReadOutputNode(const string&,
-                                           std::vector<ByteArray>* const) {
-  return false;
-}
-#endif
-
+bool HexagonControlWrapper::IsEnabled() const { return true; };
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h
index 209ac9dbf4..8eb3995fc4 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h
+++ b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h
@@ -35,6 +35,8 @@ class HexagonControlWrapper final : public IRemoteFusedGraphExecutor {
  public:
   using ByteArray =
       std::tuple<uint8* /* data */, uint64 /* size */, DataType /* type */>;
+  static constexpr const char* const REMOTE_FUSED_GRAPH_EXECUTOR_NAME =
+      "build_hexagon_remote_fused_graph_executor";
 
   HexagonControlWrapper() = default;
   int GetVersion() final;
@@ -46,6 +48,11 @@ class HexagonControlWrapper final : public IRemoteFusedGraphExecutor {
   bool FillInputNode(const string& node_name, const Tensor& tensor) final;
   bool ReadOutputNode(const string& node_name,
                       TensorAllocatorFunc tensor_allocator) final;
+  Status FuseRemoteGraph(const GraphDef& original_graph_def,
+                         const std::vector<string>& inputs,
+                         const std::vector<string>& outputs,
+                         GraphDef* fused_graph_def) final;
+  bool IsEnabled() const final;
   bool ReadOutputNode(const string& node_name, std::vector<ByteArray>* outputs);
 
  private:
diff --git a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
index 218cdff0c6..49a8d095d6 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
@@ -35,8 +35,8 @@ adb push /tmp/imagenet_comp_graph_label_strings.txt /data/local/tmp
 #include "tensorflow/core/kernels/hexagon/graph_transferer.h"
 #include "tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h"
 #include "tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h"
-#include "tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h"
 #include "tensorflow/core/kernels/i_remote_fused_graph_executor.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h"
 #include "tensorflow/core/kernels/quantization_utils.h"
 #include "tensorflow/core/lib/core/casts.h"
 #include "tensorflow/core/lib/core/status.h"
@@ -389,9 +389,6 @@ static void CompareGraphTransferInfo(const GraphTransferInfo& gfi0,
     EXPECT_EQ(goni0.ByteSizeLong(), goni1.ByteSizeLong());
     EXPECT_EQ(goni0.DebugString(), goni1.DebugString());
   }
-
-  // 7. check destination
-  EXPECT_EQ(gfi0.destination(), gfi1.destination());
 }
 
 // CAVEAT: This test only runs when you specify hexagon library using
@@ -407,7 +404,7 @@ TEST(GraphTransferer,
   LOG(INFO) << "Run inception v3 on hexagon with hexagon controller";
   CheckHexagonControllerVersion();
 
-  const IGraphTransferOpsDefinitions* ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions* ops_definitions =
       &HexagonOpsDefinitions::getInstance();
   std::vector<std::pair<string, Tensor>> inputs;
   inputs.emplace_back("Mul", Tensor(DT_FLOAT, {1, WIDTH, HEIGHT, DEPTH}));
@@ -474,7 +471,7 @@ TEST(GraphTransferer,
   LOG(INFO) << "Run inception v3 on hexagon with hexagon controller";
   CheckHexagonControllerVersion();
 
-  const IGraphTransferOpsDefinitions* ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions* ops_definitions =
       &HexagonOpsDefinitions::getInstance();
   std::vector<std::pair<string, Tensor>> inputs;
   inputs.emplace_back("Mul", Tensor(DT_FLOAT, {1, WIDTH, HEIGHT, DEPTH}));
@@ -505,7 +502,7 @@ TEST(GraphTransferer, RunInceptionV3OnHexagonExampleWithTfRuntime) {
   LOG(INFO) << "Fuse and run inception v3 on hexagon with tf runtime";
   CheckHexagonControllerVersion();
 
-  const IGraphTransferOpsDefinitions* ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions* ops_definitions =
       &HexagonOpsDefinitions::getInstance();
   std::vector<std::pair<string, Tensor>> inputs;
   inputs.emplace_back("Mul", Tensor(DT_FLOAT, {1, WIDTH, HEIGHT, DEPTH}));
@@ -543,7 +540,7 @@ TEST(GraphTransferer, DISABLED_CheckShapeInferencePerformance) {
   CheckHexagonControllerVersion();
   profile_utils::CpuUtils::EnableClockCycleProfiling(true);
 
-  const IGraphTransferOpsDefinitions* ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions* ops_definitions =
       &HexagonOpsDefinitions::getInstance();
   std::vector<std::pair<string, Tensor>> inputs;
   inputs.emplace_back("Mul", Tensor(DT_FLOAT, {1, WIDTH, HEIGHT, DEPTH}));
diff --git a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
index 2b7585aed1..cdae8d449a 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
@@ -304,8 +304,8 @@ HexagonOpsDefinitions::BuildOpNameToSocOpTypeMap() {
   EmplaceOpType("INPUT", {}, SupportedOpType::INPUT, &op_map);
   EmplaceOpType("OUTPUT", {}, SupportedOpType::OUTPUT, &op_map);
   EmplaceOpType("NoOp", {}, SupportedOpType::NOP, &op_map);
-  EmplaceOpType(IGraphTransferOpsDefinitions::FLATTEN_OP_NAME, {},
-                SupportedOpType::FLATTEN, &op_map);
+  // Special op type for hexagon
+  EmplaceOpType("FLATTEN", {}, SupportedOpType::FLATTEN, &op_map);
   // Tensorflow op name
   // CAVEAT: Keep order of SupportedOpType
   EmplaceOpType("Identity", {}, SupportedOpType::NOP, &op_map);
@@ -373,7 +373,7 @@ HexagonOpsDefinitions::BuildOpNameToSocOpTypeMap() {
 HexagonOpsDefinitions::HexagonOpsDefinitions()
     : op_name_to_soc_op_type_map_(BuildOpNameToSocOpTypeMap()) {}
 
-/* static */ const IGraphTransferOpsDefinitions&
+/* static */ const IRemoteFusedGraphOpsDefinitions&
 HexagonOpsDefinitions::getInstance() {
   const static HexagonOpsDefinitions instance{};
   return instance;
@@ -393,17 +393,17 @@ int HexagonOpsDefinitions::GetOpIdFor(const string& op_type,
     if (dt_vec.empty()) {
       return static_cast<int>(std::get<1>(dt_to_op_vec.front()));
     }
+    // If there is only one op_id registered for empty op_vec, we assume
+    // that the op supports any data types.
+    if (dt_to_op_vec.size() == 1 && std::get<0>(dt_to_op_vec.front()).empty()) {
+      return static_cast<int>(std::get<1>(dt_to_op_vec.front()));
+    }
     for (const DataTypeToOp& data_type_to_op : dt_to_op_vec) {
       if (std::get<0>(data_type_to_op) == dt_vec) {
         return static_cast<int>(std::get<1>(data_type_to_op));
       }
     }
   }
-  return IGraphTransferOpsDefinitions::INVALID_OP_ID;
-}
-
-GraphTransferInfo::Destination HexagonOpsDefinitions::GetTransferDestination()
-    const {
-  return GraphTransferInfo::HEXAGON;
+  return IRemoteFusedGraphOpsDefinitions::INVALID_OP_ID;
 }
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
index d0689428dd..993a5f9a3a 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
+++ b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h
@@ -18,21 +18,20 @@ limitations under the License.
 
 #include <unordered_map>
 
-#include "i_graph_transfer_ops_definitions.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h"
 #include "tensorflow/core/platform/macros.h"
 
 namespace tensorflow {
 
 // HexagonOpsDefinitions provides ops definitions supported in hexagon library
 // TODO(satok): add a functionality to call functions in hexagon library
-class HexagonOpsDefinitions final : public IGraphTransferOpsDefinitions {
+class HexagonOpsDefinitions final : public IRemoteFusedGraphOpsDefinitions {
  public:
-  static const IGraphTransferOpsDefinitions& getInstance();
+  static const IRemoteFusedGraphOpsDefinitions& getInstance();
 
   int GetTotalOpsCount() const final;
   int GetOpIdFor(const string& op_type, const DataTypeVector& dt) const final;
-  GraphTransferInfo::Destination GetTransferDestination() const final;
 
  private:
   enum class SupportedOpType;
diff --git a/tensorflow/core/kernels/hexagon/hexagon_remote_fused_graph_executor_build.cc b/tensorflow/core/kernels/hexagon/hexagon_remote_fused_graph_executor_build.cc
index d22bfa1be2..595baaa5d0 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_remote_fused_graph_executor_build.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_remote_fused_graph_executor_build.cc
@@ -27,7 +27,7 @@ Status BuildRemoteFusedGraphExecutor(
 
 static RemoteFusedGraphExecuteUtils::ExecutorBuildRegistrar
     k_hexagon_remote_fused_graph_executor_build(
-        "build_hexagon_remote_fused_graph_executor",
+        HexagonControlWrapper::REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
         BuildRemoteFusedGraphExecutor);
 
 }  // namespace hexagon_remote_fused_graph_executor_build
diff --git a/tensorflow/core/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc b/tensorflow/core/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc
deleted file mode 100644
index 285993aaaa..0000000000
--- a/tensorflow/core/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc
+++ /dev/null
@@ -1,136 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-// Tests in this file are designed to evaluate hexagon DSP operations.
-
-#define EIGEN_USE_THREADS
-
-#include "tensorflow/core/framework/allocator.h"
-#include "tensorflow/core/framework/fake_input.h"
-#include "tensorflow/core/framework/node_def_builder.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_testutil.h"
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/framework/types.pb.h"
-#include "tensorflow/core/kernels/ops_testutil.h"
-#include "tensorflow/core/kernels/ops_util.h"
-#include "tensorflow/core/kernels/quantization_utils.h"
-#include "tensorflow/core/lib/core/status_test_util.h"
-#include "tensorflow/core/platform/test.h"
-
-#ifdef USE_HEXAGON_LIBS
-#include "tensorflow/core/platform/hexagon/soc_interface.h"
-#include "tensorflow/core/platform/profile_utils/cpu_utils.h"
-#endif
-
-namespace tensorflow {
-
-class QuantizedMatMulOpForHexagonTest : public OpsTestBase {
- protected:
-  void SetUp() final {
-#ifdef USE_HEXAGON_LIBS
-    profile_utils::CpuUtils::EnableClockCycleProfiling(true);
-    LOG(INFO) << "Hexagon libs are linked (wrapper version = "
-              << soc_interface_GetWrapperVersion()
-              << ", hexagon binary version = "
-              << soc_interface_GetSocControllerVersion() << ")";
-    LOG(INFO) << "Cpu frequency = "
-              << profile_utils::CpuUtils::GetCycleCounterFrequency();
-#else
-    LOG(WARNING) << "Hexagon libs are not linked.";
-#endif
-  }
-};
-
-// Shows some statistics of hexagon dsp using hexagon specific APIs
-#ifdef USE_HEXAGON_LIBS
-TEST_F(QuantizedMatMulOpForHexagonTest, EvaluateSharedLibOverhead) {
-  const uint64 overhead_shared_lib_start =
-      profile_utils::CpuUtils::GetCurrentClockCycle();
-  const int wrapper_version = soc_interface_GetWrapperVersion();
-  const uint64 overhead_shared_lib_end =
-      profile_utils::CpuUtils::GetCurrentClockCycle();
-  const uint64 overhead_shared_lib_diff =
-      (overhead_shared_lib_end - overhead_shared_lib_start);
-  const uint64 overhead_hexagon_rpc_start =
-      profile_utils::CpuUtils::GetCurrentClockCycle();
-  const int hexagon_binary_version = soc_interface_GetSocControllerVersion();
-  const uint64 overhead_hexagon_rpc_end =
-      profile_utils::CpuUtils::GetCurrentClockCycle();
-  const uint64 overhead_hexagon_rpc_diff =
-      (overhead_hexagon_rpc_end - overhead_hexagon_rpc_start);
-  LOG(INFO) << "Shared lib (ver = " << wrapper_version << ") overhead is "
-            << overhead_shared_lib_diff << " cycles, time = "
-            << std::chrono::duration_cast<std::chrono::microseconds>(
-                   profile_utils::CpuUtils::ConvertClockCycleToTime(
-                       overhead_shared_lib_diff))
-                   .count()
-            << " usec";
-  LOG(INFO) << "hexagon rpc (ver = " << hexagon_binary_version
-            << ") overhead is " << overhead_hexagon_rpc_diff
-            << " cycles, time = "
-            << std::chrono::duration_cast<std::chrono::microseconds>(
-                   profile_utils::CpuUtils::ConvertClockCycleToTime(
-                       overhead_hexagon_rpc_diff))
-                   .count()
-            << " usec";
-}
-#endif
-
-// Runs two small matrices through the operator, and leaves all the parameters
-// at their default values.
-// This test is a sample to execute matmul on hexagon.
-TEST_F(QuantizedMatMulOpForHexagonTest, Small_NoParams) {
-  TF_ASSERT_OK(NodeDefBuilder("quantized_mat_mul_op", "QuantizedMatMul")
-                   .Input(FakeInput(DT_QUINT8))
-                   .Input(FakeInput(DT_QUINT8))
-                   .Input(FakeInput(DT_FLOAT))
-                   .Input(FakeInput(DT_FLOAT))
-                   .Input(FakeInput(DT_FLOAT))
-                   .Input(FakeInput(DT_FLOAT))
-                   .Attr("Toutput", DataTypeToEnum<qint32>::v())
-                   .Finalize(node_def()));
-  TF_ASSERT_OK(InitOp());
-  // A matrix is:
-  // |  1 |  2 |  3 |
-  // |  4 |  5 |  6 |
-  AddInputFromArray<quint8>(TensorShape({2, 3}), {1, 2, 3, 4, 5, 6});
-  // B matrix is:
-  // |  7 |  8 |  9 | 10 |
-  // | 11 | 12 | 13 | 14 |
-  // | 15 | 16 | 17 | 18 |
-  AddInputFromArray<quint8>(TensorShape({3, 4}),
-                            {7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18});
-  AddInputFromArray<float>(TensorShape({1}), {0});
-  AddInputFromArray<float>(TensorShape({1}), {255.0f});
-  AddInputFromArray<float>(TensorShape({1}), {0});
-  AddInputFromArray<float>(TensorShape({1}), {255.0f});
-
-  TF_ASSERT_OK(RunOpKernel());
-  // Here are the results we expect, from hand calculations:
-  // (1 * 7) + (2 * 11) + (3 * 15) = 74
-  // (1 * 8) + (2 * 12) + (3 * 16) = 80
-  // (1 * 9) + (2 * 13) + (3 * 17) = 86
-  // (1 * 10) + (2 * 14) + (3 * 18) = 92
-  // (4 * 7) + (5 * 11) + (6 * 15) = 173
-  // (4 * 8) + (5 * 12) + (6 * 16) = 188
-  // (4 * 9) + (5 * 13) + (6 * 17) = 203
-  // (4 * 10) + (5 * 14) + (6 * 18) = 218
-  Tensor expected(allocator(), DT_QINT32, TensorShape({2, 4}));
-  test::FillValues<qint32>(&expected, {74, 80, 86, 92, 173, 188, 203, 218});
-  test::ExpectTensorEqual<qint32>(expected, *GetOutput(0));
-}
-
-}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/hexagon/soc_interface.cc b/tensorflow/core/kernels/hexagon/soc_interface.cc
new file mode 100644
index 0000000000..a37571a36e
--- /dev/null
+++ b/tensorflow/core/kernels/hexagon/soc_interface.cc
@@ -0,0 +1,83 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+vcyou may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/hexagon/soc_interface.h"
+
+// Dummy implementation of soc_interface.
+
+int soc_interface_GetWrapperVersion() { return -1; }
+int soc_interface_GetSocControllerVersion() { return -1; }
+bool soc_interface_Init() { return false; }
+bool soc_interface_Finalize() { return false; }
+bool soc_interface_ExecuteGraph() { return false; }
+bool soc_interface_TeardownGraph() { return false; }
+bool soc_interface_AllocateInOutNodeBuffers(int /*input_count*/,
+                                            int* /*input_sizes*/,
+                                            int /*output_count*/,
+                                            int* /*output_sizes*/) {
+  return false;
+}
+bool soc_interface_FillInputNodeWithPort(int /*port*/, int /*x*/, int /*y*/,
+                                         int /*z*/, int /*d*/,
+                                         const uint8_t* const /*buf*/,
+                                         uint64_t /*buf_byte_size*/) {
+  return false;
+}
+bool soc_interface_FillInputNodeFloat(int /*x*/, int /*y*/, int /*z*/,
+                                      int /*d*/, const uint8_t* const /*buf*/,
+                                      uint64_t /*buf_byte_size*/) {
+  return false;
+}
+bool soc_interface_ReadOutputNodeWithPort(int /*port*/, uint8_t** /*buf*/,
+                                          uint64_t* /*buf_byte_size*/) {
+  return false;
+}
+bool soc_interface_ReadOutputNodeFloat(const char* const /*node_name*/,
+                                       uint8_t** /*buf*/,
+                                       uint64_t* /*buf_byte_size*/) {
+  return false;
+}
+bool soc_interface_setupDummyGraph(int /*version*/) { return false; }
+bool soc_interface_AllocateNodeInputAndNodeOutputArray(
+    int /*total_input_count*/, int /*total_output_count*/) {
+  return false;
+}
+bool soc_interface_ReleaseNodeInputAndNodeOutputArray() { return false; }
+void* soc_interface_SetOneNodeInputs(int /*input_count*/,
+                                     const int* const /*node_id*/,
+                                     const int* const /*port*/) {
+  return nullptr;
+}
+void* soc_interface_SetOneNodeOutputs(int /*output_count*/, int* /*max_size*/) {
+  return nullptr;
+}
+bool soc_interface_AppendConstNode(const char* const /*name*/, int /*node_id*/,
+                                   int /*batch*/, int /*height*/, int /*width*/,
+                                   int /*depth*/, const uint8_t* const /*data*/,
+                                   int /*data_length*/) {
+  return false;
+}
+bool soc_interface_AppendNode(const char* const /*name*/, int /*node_id*/,
+                              int /*op_id*/, int /*padding_id*/,
+                              const void* const /*inputs*/,
+                              int /*inputs_count*/,
+                              const void* const /*outputs*/,
+                              int /*outputs_count*/) {
+  return false;
+}
+bool soc_interface_InstantiateGraph() { return false; }
+bool soc_interface_ConstructGraph() { return false; }
+void soc_interface_SetLogLevel(int /*log_level*/) {}
+void soc_interface_SetDebugFlag(uint64_t /*flag*/) {}
diff --git a/tensorflow/core/platform/hexagon/soc_interface.h b/tensorflow/core/kernels/hexagon/soc_interface.h
similarity index 99%
rename from tensorflow/core/platform/hexagon/soc_interface.h
rename to tensorflow/core/kernels/hexagon/soc_interface.h
index ca37b63e2b..062103ed98 100644
--- a/tensorflow/core/platform/hexagon/soc_interface.h
+++ b/tensorflow/core/kernels/hexagon/soc_interface.h
@@ -21,6 +21,7 @@ limitations under the License.
 // All functions defined here must have prefix "soc_interface" to avoid
 // naming conflicts.
 #ifdef __cplusplus
+#include <cstdint>
 extern "C" {
 #else
 #include <stdbool.h>
diff --git a/tensorflow/core/kernels/i_remote_fused_graph_executor.h b/tensorflow/core/kernels/i_remote_fused_graph_executor.h
index 09d1f43ff1..05b76172b2 100644
--- a/tensorflow/core/kernels/i_remote_fused_graph_executor.h
+++ b/tensorflow/core/kernels/i_remote_fused_graph_executor.h
@@ -59,6 +59,13 @@ class IRemoteFusedGraphExecutor {
   virtual bool ReadOutputNode(const string& node_name,
                               TensorAllocatorFunc tensor_allocator) = 0;
 
+  virtual Status FuseRemoteGraph(const GraphDef& original_graph_def,
+                                 const std::vector<string>& inputs,
+                                 const std::vector<string>& outputs,
+                                 GraphDef* fused_graph_def) = 0;
+
+  virtual bool IsEnabled() const = 0;
+
  private:
   TF_DISALLOW_COPY_AND_ASSIGN(IRemoteFusedGraphExecutor);
 };
diff --git a/tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.cc b/tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.cc
similarity index 74%
rename from tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.cc
rename to tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.cc
index 36d48a46cc..335b912d1f 100644
--- a/tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.cc
+++ b/tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.cc
@@ -13,11 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "i_graph_transfer_ops_definitions.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h"
 
 namespace tensorflow {
-/* static */ constexpr int IGraphTransferOpsDefinitions::INVALID_OP_ID;
-// TODO(satok): Remove
-/* static */ constexpr const char* const
-    IGraphTransferOpsDefinitions::FLATTEN_OP_NAME;
+/* static */ constexpr int IRemoteFusedGraphOpsDefinitions::INVALID_OP_ID;
 }
diff --git a/tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h b/tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h
similarity index 56%
rename from tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h
rename to tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h
index d5b4cf7451..7d3329f490 100644
--- a/tensorflow/core/kernels/hexagon/i_graph_transfer_ops_definitions.h
+++ b/tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h
@@ -13,39 +13,34 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_HEXAGON_I_GRAPH_TRANSFER_OPS_DEFINITIONS_H_
-#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_HEXAGON_I_GRAPH_TRANSFER_OPS_DEFINITIONS_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_I_REMOTE_FUSED_GRAPH_OPS_DEFINITIONS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_I_REMOTE_FUSED_GRAPH_OPS_DEFINITIONS_H_
 
-#include "tensorflow/core/framework/graph_transfer_info.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/platform/macros.h"
 
 namespace tensorflow {
 
-// IGraphTransferOpsDefinitions is an interface class which provides interfaces
-// about ops supported by SOC.
+// IRemoteFusedGraphOpsDefinitions is an interface class which provides
+// APIs to provide information about op types supported by SOC.
 // TODO(satok): Provide ways to transfer graph definitions into SOC
-class IGraphTransferOpsDefinitions {
+class IRemoteFusedGraphOpsDefinitions {
  public:
   // op id which is not supported by SOC
   static constexpr int INVALID_OP_ID = -1;
-  // Custom op name for flatten node
-  static constexpr const char* const FLATTEN_OP_NAME = "FLATTEN";
 
-  IGraphTransferOpsDefinitions() = default;
-  virtual ~IGraphTransferOpsDefinitions() = default;
+  IRemoteFusedGraphOpsDefinitions() = default;
+  virtual ~IRemoteFusedGraphOpsDefinitions() = default;
   // Return total ops count supported by SOC
   virtual int GetTotalOpsCount() const = 0;
   // Return op id for given string op name
-  virtual int GetOpIdFor(const string& op_name,
+  virtual int GetOpIdFor(const string& op_type,
                          const DataTypeVector& dt) const = 0;
-  // Return destination of transfer
-  virtual GraphTransferInfo::Destination GetTransferDestination() const = 0;
 
  private:
-  TF_DISALLOW_COPY_AND_ASSIGN(IGraphTransferOpsDefinitions);
+  TF_DISALLOW_COPY_AND_ASSIGN(IRemoteFusedGraphOpsDefinitions);
 };
 
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_HEXAGON_I_GRAPH_TRANSFER_OPS_DEFINITIONS_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_I_REMOTE_FUSED_GRAPH_OPS_DEFINITIONS_H_
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_op.cc b/tensorflow/core/kernels/remote_fused_graph_execute_op.cc
index f0b2aa6e1f..8a25f58329 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_op.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_op.cc
@@ -41,7 +41,8 @@ class RemoteFusedGraphExecuteOp : public OpKernel {
           RemoteFusedGraphExecuteUtils::GetExecutorBuildFunc(
               execute_info_.executor_name());
       if (build_func != nullptr) {
-        Status status = (*build_func)(&remote_fused_graph_executor_);
+        TF_CHECK_OK((*build_func)(&remote_fused_graph_executor_));
+        CHECK(remote_fused_graph_executor_->IsEnabled());
       } else {
         LOG(ERROR) << "Executor not found for "
                    << execute_info_.executor_name();
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_op_test.cc b/tensorflow/core/kernels/remote_fused_graph_execute_op_test.cc
index 655de2f98f..ec769d41f9 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_op_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_op_test.cc
@@ -159,8 +159,8 @@ static RemoteFusedGraphExecuteInfo BuildRemoteFusedGraphExecuteInfo(
   return execute_info;
 }
 
-// 1. Create TestRemoteFusedGraphExecutor to execute your fused graph
-class TestRemoteFusedGraphExecutor final : public IRemoteFusedGraphExecutor {
+// 1. Create SampleRemoteFusedGraphExecutor to execute your fused graph
+class SampleRemoteFusedGraphExecutor final : public IRemoteFusedGraphExecutor {
  public:
   int GetVersion() final { return 1; }
   bool Init(const RemoteFusedGraphExecuteInfo& info) final {
@@ -214,6 +214,16 @@ class TestRemoteFusedGraphExecutor final : public IRemoteFusedGraphExecutor {
     return true;
   }
 
+  Status FuseRemoteGraph(const GraphDef& original_graph_def,
+                         const std::vector<string>& /*inputs*/,
+                         const std::vector<string>& /*outputs*/,
+                         GraphDef* fused_graph_def) final {
+    *fused_graph_def = original_graph_def;
+    return Status::OK();
+  }
+
+  bool IsEnabled() const final { return true; }
+
  private:
   const RemoteFusedGraphExecuteInfo* info_;
   std::unordered_map<string, Tensor> input_tensor_cache_;
@@ -225,7 +235,7 @@ class TestRemoteFusedGraphExecutor final : public IRemoteFusedGraphExecutor {
 namespace remote_fused_graph_execute_op {
 Status BuildRemoteFusedGraphExecutor(
     std::unique_ptr<IRemoteFusedGraphExecutor>* executor) {
-  executor->reset(new TestRemoteFusedGraphExecutor());
+  executor->reset(new SampleRemoteFusedGraphExecutor());
   return Status::OK();
 }
 
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.cc
index 31c48082dd..cca77adcff 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include "tensorflow/cc/ops/const_op.h"
 #include "tensorflow/cc/ops/math_ops.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/kernels/remote_fused_graph_execute_utils.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/logging.h"
 
@@ -92,4 +93,36 @@ namespace tensorflow {
   return Status::OK();
 }
 
+TestRemoteFusedGraphExecutor::TestRemoteFusedGraphExecutor(
+    const std::unordered_set<string>& fused_op_types,
+    const string& executor_name)
+    : fused_op_types_(fused_op_types), executor_name_(executor_name) {}
+
+int TestRemoteFusedGraphExecutor::GetVersion() { return 0; }
+bool TestRemoteFusedGraphExecutor::Init(const RemoteFusedGraphExecuteInfo&) {
+  return true;
+}
+bool TestRemoteFusedGraphExecutor::Finalize() { return true; }
+bool TestRemoteFusedGraphExecutor::SetupGraph() { return true; }
+bool TestRemoteFusedGraphExecutor::ExecuteGraph() { return true; }
+bool TestRemoteFusedGraphExecutor::TeardownGraph() { return true; }
+bool TestRemoteFusedGraphExecutor::FillInputNode(const string&, const Tensor&) {
+  return true;
+}
+bool TestRemoteFusedGraphExecutor::ReadOutputNode(const string&,
+                                                  TensorAllocatorFunc) {
+  return true;
+}
+Status TestRemoteFusedGraphExecutor::FuseRemoteGraph(
+    const GraphDef& original_graph_def, const std::vector<string>& inputs,
+    const std::vector<string>& outputs, GraphDef* fused_graph_def) {
+  return RemoteFusedGraphExecuteUtils::FuseRemoteGraphByOpTypes(
+      original_graph_def, inputs, outputs, "remote_fused_graph_node_names",
+      fused_op_types_, executor_name_,
+      /*require_shape_type=*/false, fused_graph_def);
+  return Status::OK();
+}
+
+bool TestRemoteFusedGraphExecutor::IsEnabled() const { return true; }
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.h b/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.h
index a0df50162b..3fa052108e 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.h
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include "tensorflow/cc/framework/ops.h"
 #include "tensorflow/cc/framework/scope.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_executor.h"
 #include "tensorflow/core/platform/macros.h"
 
 namespace tensorflow {
@@ -59,6 +60,30 @@ class RemoteFusedGraphExecuteOpTestUtils {
   TF_DISALLOW_COPY_AND_ASSIGN(RemoteFusedGraphExecuteOpTestUtils);
 };
 
+class TestRemoteFusedGraphExecutor final : public IRemoteFusedGraphExecutor {
+ public:
+  TestRemoteFusedGraphExecutor(const std::unordered_set<string>& fused_op_types,
+                               const string& executor_name);
+
+  int GetVersion() final;
+  bool Init(const RemoteFusedGraphExecuteInfo&) final;
+  bool Finalize() final;
+  bool SetupGraph() final;
+  bool ExecuteGraph() final;
+  bool TeardownGraph() final;
+  bool FillInputNode(const string&, const Tensor&) final;
+  bool ReadOutputNode(const string&, TensorAllocatorFunc) final;
+  Status FuseRemoteGraph(const GraphDef& original_graph_def,
+                         const std::vector<string>& inputs,
+                         const std::vector<string>& outputs,
+                         GraphDef* fused_graph_def) final;
+  bool IsEnabled() const final;
+
+ private:
+  const std::unordered_set<string> fused_op_types_;
+  const string executor_name_;
+};
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_REMOTE_FUSED_GRAPH_EXECUTE_OP_TEST_UTILS_H_
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index a79092e8a8..aba755b5c8 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -161,6 +161,8 @@ string DumpCluster(const RemoteFusedGraphExecuteUtils::ClusterInfo& cluster) {
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_BORDER_OUTPUTS;
 /* static */ constexpr const char* const
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES;
+/* static */ constexpr const char* const
+    RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSE_BY_EXECUTOR;
 /* static */ constexpr const char* const
     RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_INPUT_TYPES;
 /* static */ constexpr const char* const
@@ -1084,6 +1086,26 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
       require_shape_type, output_graph_def);
 }
 
+/* static */ Status RemoteFusedGraphExecuteUtils::FuseRemoteGraphByExecutor(
+    const GraphDef& input_graph_def, const std::vector<string>& inputs,
+    const std::vector<string>& outputs, const string& executor_name,
+    GraphDef* output_graph_def) {
+  const ExecutorBuildFunc* build_func = GetExecutorBuildFunc(executor_name);
+  if (build_func == nullptr) {
+    return errors::InvalidArgument("Unknown executor name: " + executor_name);
+  }
+  std::unique_ptr<IRemoteFusedGraphExecutor> executor;
+  TF_RETURN_IF_ERROR((*build_func)(&executor));
+  CHECK_NOTNULL(executor.get());
+  if (!executor->IsEnabled()) {
+    // As this executor is not enabled, just return original graph as is.
+    *output_graph_def = input_graph_def;
+    return Status::OK();
+  }
+  return executor->FuseRemoteGraph(input_graph_def, inputs, outputs,
+                                   output_graph_def);
+}
+
 /* static */ Status RemoteFusedGraphExecuteUtils::PlaceRemoteGraphArguments(
     const std::vector<string>& inputs, const std::vector<string>& outputs,
     const std::unordered_set<string>& fused_node_names,
@@ -1387,6 +1409,28 @@ RemoteFusedGraphExecuteUtils::BuildNodeMapFromOpTypes(
   return retval;
 }
 
+/* static */ std::unordered_set<string>
+RemoteFusedGraphExecuteUtils::BuildNodeMapFromOpsDefinitions(
+    const GraphDef& graph_def,
+    const IRemoteFusedGraphOpsDefinitions& ops_definitions) {
+  std::unordered_set<string> retval;
+  for (const NodeDef& node_def : graph_def.node()) {
+    std::vector<DataType> dt_vec;
+    std::vector<TensorShape> shape_vec;
+    const Status status =
+        GetOutputTensorShapeType(node_def, &dt_vec, &shape_vec);
+    if (!status.ok()) {
+      shape_vec.clear();
+    }
+    if (ops_definitions.GetOpIdFor(
+            node_def.op(), DataTypeVector(dt_vec.begin(), dt_vec.end())) !=
+        IRemoteFusedGraphOpsDefinitions::INVALID_OP_ID) {
+      retval.emplace(node_def.name());
+    }
+  }
+  return retval;
+}
+
 /* static */ Status RemoteFusedGraphExecuteUtils::ReplaceInputNodeByPlaceHolder(
     const string& input, const DataType type, const TensorShape& shape,
     GraphDef* graph_def) {
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
index 28d170d982..541c26baaf 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/kernels/i_remote_fused_graph_executor.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_ops_definitions.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/macros.h"
 
@@ -59,6 +60,8 @@ class RemoteFusedGraphExecuteUtils {
       "border_outputs";
   static constexpr const char* const TRANSFORM_ARG_FUSED_OP_TYPES =
       "fused_op_types";
+  static constexpr const char* const TRANSFORM_ARG_FUSE_BY_EXECUTOR =
+      "fuse_by_executor";
   static constexpr const char* const TRANSFORM_ARG_INPUT_TYPES = "input_types";
   static constexpr const char* const TRANSFORM_ARG_INPUT_SHAPES =
       "input_shapes";
@@ -257,6 +260,12 @@ class RemoteFusedGraphExecuteUtils {
       const std::vector<std::pair<string, Tensor>>& input_tensors,
       GraphDef* output_graph_def);
 
+  static Status FuseRemoteGraphByExecutor(const GraphDef& input_graph_def,
+                                          const std::vector<string>& inputs,
+                                          const std::vector<string>& outputs,
+                                          const string& executor_name,
+                                          GraphDef* output_graph_def);
+
   static bool IsFuseReady(
       const GraphDef& input_graph_def,
       const std::vector<std::pair<string, Tensor>>& input_tensors);
@@ -273,6 +282,10 @@ class RemoteFusedGraphExecuteUtils {
   static std::unordered_set<string> BuildNodeMapFromOpTypes(
       const GraphDef& graph_def, const std::unordered_set<string>& op_types);
 
+  static std::unordered_set<string> BuildNodeMapFromOpsDefinitions(
+      const GraphDef& graph_def,
+      const IRemoteFusedGraphOpsDefinitions& ops_definitions);
+
  private:
   static void EmplaceTensorShapeType(const string& name, const Tensor& tensor,
                                      TensorShapeMap* tensor_shape_map);
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
index e7a2da4c53..aca8ddfae9 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils_test.cc
@@ -33,6 +33,10 @@ constexpr const char* const NAME_A_PLUS_B = "A_PLUS_B";
 constexpr float NODE_A_VAL = 2.0f;
 constexpr float NODE_B_VAL = 3.0f;
 constexpr float VALUE_TOLERANCE_FLOAT = 1e-8f;
+constexpr const char* const REMOTE_FUSED_EXECUTOR_NAME0 =
+    "fuse_test_remote_fused_graph_executor0";
+constexpr const char* const REMOTE_FUSED_EXECUTOR_NAME1 =
+    "fuse_test_remote_fused_graph_executor1";
 
 static NodeDef* GetNodeDef(const string& name, GraphDef* def) {
   CHECK_NE(def, nullptr);
@@ -44,17 +48,38 @@ static NodeDef* GetNodeDef(const string& name, GraphDef* def) {
   return nullptr;
 }
 
+Status BuildRemoteFusedGraphExecutor0(
+    std::unique_ptr<IRemoteFusedGraphExecutor>* executor) {
+  executor->reset(
+      new TestRemoteFusedGraphExecutor({"Mul"}, REMOTE_FUSED_EXECUTOR_NAME0));
+  return Status::OK();
+}
+
+Status BuildRemoteFusedGraphExecutor1(
+    std::unique_ptr<IRemoteFusedGraphExecutor>* executor) {
+  executor->reset(new TestRemoteFusedGraphExecutor(
+      {"Const", "Mul"}, REMOTE_FUSED_EXECUTOR_NAME1));
+  return Status::OK();
+}
+
 class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
  protected:
   void SetUp() final {
     TF_ASSERT_OK(
         RemoteFusedGraphExecuteOpTestUtils::BuildMultipleAddGraph(&graph_def_));
     RemoteFusedGraphExecuteUtils::ExecutorBuildRegistrar
-        k_hexagon_remote_fused_graph_executor_build(
+        hexagon_remote_fused_graph_executor_build(
             "remote_graph_executor_name",
             [](std::unique_ptr<IRemoteFusedGraphExecutor>* executor) -> Status {
               return Status::OK();
             });
+    RemoteFusedGraphExecuteUtils::ExecutorBuildRegistrar
+        test_remote_fused_graph_executor_build0(REMOTE_FUSED_EXECUTOR_NAME0,
+                                                BuildRemoteFusedGraphExecutor0);
+
+    RemoteFusedGraphExecuteUtils::ExecutorBuildRegistrar
+        test_remote_fused_graph_executor_build1(REMOTE_FUSED_EXECUTOR_NAME1,
+                                                BuildRemoteFusedGraphExecutor1);
   }
 
   void TearDown() final {}
@@ -87,6 +112,18 @@ class FuseRemoteGraphMultipleAddOpsTest : public ::testing::Test {
         /*require_shape_type=*/false, &result_graph_def_);
   }
 
+  Status FuseByExecutor0() {
+    return RemoteFusedGraphExecuteUtils::FuseRemoteGraphByExecutor(
+        graph_def_, inputs_, outputs_, REMOTE_FUSED_EXECUTOR_NAME0,
+        &result_graph_def_);
+  }
+
+  Status FuseByExecutor1() {
+    return RemoteFusedGraphExecuteUtils::FuseRemoteGraphByExecutor(
+        graph_def_, inputs_, outputs_, REMOTE_FUSED_EXECUTOR_NAME1,
+        &result_graph_def_);
+  }
+
   Status BuildAndAddTensorShape() {
     return RemoteFusedGraphExecuteUtils::BuildAndAddTensorShapes(
         input_tensors_, /*dry_run_inference=*/true, &graph_def_);
@@ -694,6 +731,30 @@ TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByOpTypes_FGHIJ) {
       << SummarizeGraphDef(result_graph_def_);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByExecutor_HIJ) {
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+
+  TF_ASSERT_OK(FuseByExecutor0());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+  EXPECT_EQ(9, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsTest, FuseSubgraphByExecutor_FGHIJ) {
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+
+  TF_ASSERT_OK(FuseByExecutor1());
+
+  EXPECT_EQ(11, graph_def_.node_size());
+  EXPECT_EQ(3, result_graph_def_.node_size())
+      << "=== Before: \n"
+      << SummarizeGraphDef(graph_def_) << "\n\n\n=== After: \n"
+      << SummarizeGraphDef(result_graph_def_);
+}
+
 TEST_F(FuseRemoteGraphMultipleAddOpsTest, PlaceAndFuse_H) {
   subgraph_node_names_ = {"H"};
 
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
index 11670e80c7..0822061b14 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform.cc
@@ -66,7 +66,7 @@ static Status ParseArguments(const TransformFuncContext& context,
                              string* input_types_str, string* input_shapes_str,
                              string* fused_nodes_str, string* border_inputs_str,
                              string* border_outputs_str,
-                             string* fused_op_types_str,
+                             string* fused_op_types_str, bool* fuse_by_executor,
                              string* remote_fused_graph_node_name,
                              string* remote_graph_executor_name) {
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
@@ -87,6 +87,9 @@ static Status ParseArguments(const TransformFuncContext& context,
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
       RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSED_OP_TYPES, "",
       fused_op_types_str));
+  TF_RETURN_IF_ERROR(context.GetOneBoolParameter(
+      RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSE_BY_EXECUTOR, false,
+      fuse_by_executor));
   TF_RETURN_IF_ERROR(context.GetOneStringParameter(
       RemoteFusedGraphExecuteUtils::
           TRANSFORM_ARG_REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
@@ -140,12 +143,14 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
   string border_inputs_str;
   string border_outputs_str;
   string fused_op_types_str;
+  bool fuse_by_executor = false;
   string remote_fused_graph_node_name;
   string remote_graph_executor_name;
   TF_RETURN_IF_ERROR(ParseArguments(
       context, &input_types_str, &input_shapes_str, &fused_nodes_str,
       &border_inputs_str, &border_outputs_str, &fused_op_types_str,
-      &remote_fused_graph_node_name, &remote_graph_executor_name));
+      &fuse_by_executor, &remote_fused_graph_node_name,
+      &remote_graph_executor_name));
 
   if (!input_types_str.empty()) {
     TF_RETURN_IF_ERROR(PlaceShapeType(inputs, outputs, input_types_str,
@@ -187,6 +192,10 @@ Status FuseRemoteGraph(const GraphDef& input_graph_def,
         mutable_input_graph_def, inputs, outputs, remote_fused_graph_node_name,
         fused_op_types, remote_graph_executor_name, require_shape_type,
         output_graph_def));
+  } else if (fuse_by_executor) {
+    TF_RETURN_IF_ERROR(RemoteFusedGraphExecuteUtils::FuseRemoteGraphByExecutor(
+        mutable_input_graph_def, inputs, outputs, remote_graph_executor_name,
+        output_graph_def));
   } else {
     CHECK(false) << "Fuse targets are not specified.";
   }
@@ -205,15 +214,17 @@ Status PlaceRemoteGraphArguments(const GraphDef& input_graph_def,
   string input_types_str;
   string input_shapes_str;
   string fused_nodes_str;
-  string fused_op_types_str;
   string border_inputs_str;
   string border_outputs_str;
+  string fused_op_types_str;
+  bool fuse_by_executor = false;
   string remote_fused_graph_node_name;
   string remote_graph_executor_name;
   TF_RETURN_IF_ERROR(ParseArguments(
       context, &input_types_str, &input_shapes_str, &fused_nodes_str,
       &border_inputs_str, &border_outputs_str, &fused_op_types_str,
-      &remote_fused_graph_node_name, &remote_graph_executor_name));
+      &fuse_by_executor, &remote_fused_graph_node_name,
+      &remote_graph_executor_name));
 
   if (!input_types_str.empty()) {
     TF_RETURN_IF_ERROR(PlaceShapeType(inputs, outputs, input_types_str,
diff --git a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
index 113f5c55ea..d5b37b1ce1 100644
--- a/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_rewriter_transform_test.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/core/graph/default_device.h"
 #include "tensorflow/core/graph/node_builder.h"
 #include "tensorflow/core/graph/testlib.h"
+#include "tensorflow/core/kernels/i_remote_fused_graph_executor.h"
 #include "tensorflow/core/kernels/remote_fused_graph_execute_op_test_utils.h"
 #include "tensorflow/core/kernels/remote_fused_graph_execute_utils.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -43,11 +44,28 @@ Status PlaceRemoteGraphArguments(const GraphDef& input_graph_def,
                                  GraphDef* output_graph_def);
 
 namespace {
-
 constexpr const char* const REMOTE_FUSED_GRAPH_EXECUTOR_NAME =
     "remote_fused_graph_executor_name";
 constexpr const char* const REMOTE_FUSED_GRAPH_NODE_NAME =
     "remote_fused_graph_node_name";
+constexpr const char* const REMOTE_FUSED_EXECUTOR_NAME0 =
+    "fuse_test_remote_fused_graph_executor0";
+constexpr const char* const REMOTE_FUSED_EXECUTOR_NAME1 =
+    "fuse_test_remote_fused_graph_executor1";
+
+Status BuildRemoteFusedGraphExecutor0(
+    std::unique_ptr<IRemoteFusedGraphExecutor>* executor) {
+  executor->reset(
+      new TestRemoteFusedGraphExecutor({"Mul"}, REMOTE_FUSED_EXECUTOR_NAME0));
+  return Status::OK();
+}
+
+Status BuildRemoteFusedGraphExecutor1(
+    std::unique_ptr<IRemoteFusedGraphExecutor>* executor) {
+  executor->reset(new TestRemoteFusedGraphExecutor(
+      {"Const", "Mul"}, REMOTE_FUSED_EXECUTOR_NAME1));
+  return Status::OK();
+}
 
 class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
  protected:
@@ -55,11 +73,18 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
     TF_ASSERT_OK(RemoteFusedGraphExecuteOpTestUtils::BuildMultipleAddGraph(
         &input_graph_def_));
     RemoteFusedGraphExecuteUtils::ExecutorBuildRegistrar
-        k_hexagon_remote_fused_graph_executor_build(
+        hexagon_remote_fused_graph_executor_build(
             REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
             [](std::unique_ptr<IRemoteFusedGraphExecutor>* executor) -> Status {
               return Status::OK();
             });
+    RemoteFusedGraphExecuteUtils::ExecutorBuildRegistrar
+        test_remote_fused_graph_executor_build0(REMOTE_FUSED_EXECUTOR_NAME0,
+                                                BuildRemoteFusedGraphExecutor0);
+
+    RemoteFusedGraphExecuteUtils::ExecutorBuildRegistrar
+        test_remote_fused_graph_executor_build1(REMOTE_FUSED_EXECUTOR_NAME1,
+                                                BuildRemoteFusedGraphExecutor1);
   }
 
   void TearDown() final {}
@@ -113,10 +138,16 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
            {fused_op_types_str_}}));
     }
 
+    if (fuse_by_executor_) {
+      context.params.insert(std::pair<string, std::vector<string>>(
+          {RemoteFusedGraphExecuteUtils::TRANSFORM_ARG_FUSE_BY_EXECUTOR,
+           {"true"}}));
+    }
+
     context.params.insert(std::pair<string, std::vector<string>>(
         {RemoteFusedGraphExecuteUtils::
              TRANSFORM_ARG_REMOTE_FUSED_GRAPH_EXECUTOR_NAME,
-         {REMOTE_FUSED_GRAPH_EXECUTOR_NAME}}));
+         {remote_fused_graph_executor_name_}}));
     context.params.insert(std::pair<string, std::vector<string>>(
         {RemoteFusedGraphExecuteUtils::
              TRANSFORM_ARG_REMOTE_FUSED_GRAPH_NODE_NAME,
@@ -160,7 +191,7 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
                             ATTR_SERIALIZED_REMOTE_FUSED_GRAPH_EXECUTE_INFO,
                         &serialized_proto));
         info.ParseFromString(serialized_proto);
-        CHECK_EQ(REMOTE_FUSED_GRAPH_EXECUTOR_NAME, info.executor_name());
+        CHECK_EQ(remote_fused_graph_executor_name_, info.executor_name());
       }
     }
     EXPECT_EQ(expected_cluster_count, cluster_count);
@@ -178,6 +209,8 @@ class FuseRemoteGraphMultipleAddOpsRewriterTest : public ::testing::Test {
   string border_inputs_str_;
   string border_outputs_str_;
   string fused_op_types_str_;
+  string remote_fused_graph_executor_name_{REMOTE_FUSED_GRAPH_EXECUTOR_NAME};
+  bool fuse_by_executor_{false};
 };
 
 TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
@@ -260,6 +293,24 @@ TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
   CheckGraph(3, 1);
 }
 
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       FuseRemoteGraphByExecutor_HIJ) {
+  ReplaceOpType({"H", "I", "J"}, "Mul");
+  remote_fused_graph_executor_name_ = REMOTE_FUSED_EXECUTOR_NAME0;
+  fuse_by_executor_ = true;
+  TF_ASSERT_OK(Fuse());
+  CheckGraph(9, 1);
+}
+
+TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest,
+       FuseRemoteGraphByExecutor_FGHIJ) {
+  ReplaceOpType({"F", "G", "H", "I", "J"}, "Mul");
+  remote_fused_graph_executor_name_ = REMOTE_FUSED_EXECUTOR_NAME1;
+  fuse_by_executor_ = true;
+  TF_ASSERT_OK(Fuse());
+  CheckGraph(3, 1);
+}
+
 TEST_F(FuseRemoteGraphMultipleAddOpsRewriterTest, PlaceAndFuse_HIJ) {
   fused_node_names_str_ = "H,I,J";
   TF_ASSERT_OK(PlaceFuseArgs());
-- 
GitLab


From 7a7a4f8412e2c16cffdc4c4d199dcadcec1129a4 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Tue, 18 Jul 2017 15:20:26 -0700
Subject: [PATCH 1086/1375] No-op change to python op bindings.

PiperOrigin-RevId: 162411818
---
 tensorflow/python/framework/python_op_gen.cc      | 15 +++++++++------
 .../python/framework/python_op_gen_internal.h     |  1 +
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc
index 142bd53573..090436aebf 100644
--- a/tensorflow/python/framework/python_op_gen.cc
+++ b/tensorflow/python/framework/python_op_gen.cc
@@ -682,8 +682,13 @@ void GenPythonOp::AddDocStringOutputs() {
 }
 
 void GenPythonOp::AddBody(const string& prefix) {
+  AddBodyNoReturn(prefix);
+  strings::StrAppend(&result_, prefix, "return _result\n");
+}
+
+void GenPythonOp::AddBodyNoReturn(const string& prefix) {
   string return_prefix =
-      strings::StrCat(prefix, "result = _op_def_lib.apply_op(");
+      strings::StrCat(prefix, "_result = _op_def_lib.apply_op(");
   string return_args = strings::StrCat("\"", op_def_.name(), "\", ");
   for (size_t i = 0; i < param_names_.size(); ++i) {
     strings::StrAppend(&return_args, param_names_[i], "=", param_names_[i],
@@ -695,11 +700,9 @@ void GenPythonOp::AddBody(const string& prefix) {
                      // Wrap the arguments, and indent to the (.
                      WordWrap(return_prefix, return_args, kRightMargin), "\n");
 
-  if (num_outs_ <= 1) {
-    strings::StrAppend(&result_, prefix, "return result\n");
-  } else {
-    strings::StrAppend(&result_, prefix, "return _", op_def_.name(),
-                       "Output._make(result)\n");
+  if (num_outs_ > 1) {
+    strings::StrAppend(&result_, prefix, "_result = _", op_def_.name(),
+                       "Output._make(_result)\n");
   }
 }
 
diff --git a/tensorflow/python/framework/python_op_gen_internal.h b/tensorflow/python/framework/python_op_gen_internal.h
index 44b1aed71f..d588f362d8 100644
--- a/tensorflow/python/framework/python_op_gen_internal.h
+++ b/tensorflow/python/framework/python_op_gen_internal.h
@@ -59,6 +59,7 @@ class GenPythonOp {
   void AddOutputGlobals();
   void AddDocStringOutputs();
   void AddBody(const string& prefix);
+  void AddBodyNoReturn(const string& prefix);
 
   // From constructor arguments
   const OpDef& op_def_;
-- 
GitLab


From 79e85fa6efcaea3fc26fa914119883bcb7a99ecd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 15:21:01 -0700
Subject: [PATCH 1087/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 162411912
---
 tensorflow/core/ops/ops.pbtxt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index bb5419b7c0..8bbf2d7989 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -30467,7 +30467,7 @@ op {
     }
   }
   summary: "Computes the Max along segments of a tensor."
-  description: "Read @{$math_ops#segmentation$the section on segmentation} for an explanation of\nsegments.\n\nThis operator is similar to the [unsorted segment sum operator](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).\nInstead of computing the sum over segments, it computes the maximum\nsuch that:\n\n\\\\(output_i = \\max_j data_j\\\\) where max is over `j` such\nthat `segment_ids[j] == i`.\n\nIf the maximum is empty for a given segment ID `i`, it outputs the smallest possible value for specific numeric type,\n `output[i] = numeric_limits<T>::min()`.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/UnsortedSegmentSum.png\" alt>\n</div>"
+  description: "Read @{$math_ops#segmentation$the section on segmentation} for an explanation of\nsegments.\n\nThis operator is similar to the [unsorted segment sum operator](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).\nInstead of computing the sum over segments, it computes the maximum\nsuch that:\n\n\\\\(output_i = \\max_j data_j\\\\) where max is over `j` such\nthat `segment_ids[j] == i`.\n\nIf the maximum is empty for a given segment ID `i`, it outputs the smallest possible value for specific numeric type,\n `output[i] = numeric_limits<T>::min()`.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/UnsortedSegmentMax.png\" alt>\n</div>"
 }
 op {
   name: "UnsortedSegmentSum"
-- 
GitLab


From ed03ee3fc74c09b64b6703337a5d0ed5d25b8b43 Mon Sep 17 00:00:00 2001
From: James Qin <jamesqin@google.com>
Date: Tue, 18 Jul 2017 15:27:37 -0700
Subject: [PATCH 1088/1375] Support float64 CuDNN RNN

PiperOrigin-RevId: 162412879
---
 .../cudnn_rnn/kernels/cudnn_rnn_ops.cc        |  81 +++++----
 .../contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc    |  10 +-
 .../python/kernel_tests/cudnn_rnn_ops_test.py | 154 ++++++++++++++----
 .../cudnn_rnn/python/ops/cudnn_rnn_ops.py     |  17 +-
 tensorflow/stream_executor/cuda/cuda_dnn.cc   | 110 ++++++++++++-
 tensorflow/stream_executor/cuda/cuda_dnn.h    |  55 ++++++-
 tensorflow/stream_executor/dnn.h              |  46 ++++++
 tensorflow/stream_executor/stream.cc          |  75 +++++++++
 tensorflow/stream_executor/stream.h           |  41 +++++
 9 files changed, 505 insertions(+), 84 deletions(-)

diff --git a/tensorflow/contrib/cudnn_rnn/kernels/cudnn_rnn_ops.cc b/tensorflow/contrib/cudnn_rnn/kernels/cudnn_rnn_ops.cc
index 7c3ca46e10..50a90e753d 100644
--- a/tensorflow/contrib/cudnn_rnn/kernels/cudnn_rnn_ops.cc
+++ b/tensorflow/contrib/cudnn_rnn/kernels/cudnn_rnn_ops.cc
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def_builder.h"
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
@@ -565,15 +566,20 @@ class CudnnRNNParamsSizeOp<GPUDevice, T, Index> : public CudnnRNNKernelCommon {
   }
 };
 
-REGISTER_KERNEL_BUILDER(Name("CudnnRNNParamsSize")
-                            .Device(DEVICE_GPU)
-                            .HostMemory("num_layers")
-                            .HostMemory("num_units")
-                            .HostMemory("input_size")
-                            .HostMemory("params_size")
-                            .TypeConstraint<float>("T")
-                            .TypeConstraint<int32>("S"),
-                        CudnnRNNParamsSizeOp<GPUDevice, float, int32>);
+#define REGISTER_GPU(T)                                    \
+  REGISTER_KERNEL_BUILDER(Name("CudnnRNNParamsSize")       \
+                              .Device(DEVICE_GPU)          \
+                              .HostMemory("num_layers")    \
+                              .HostMemory("num_units")     \
+                              .HostMemory("input_size")    \
+                              .HostMemory("params_size")   \
+                              .TypeConstraint<T>("T")      \
+                              .TypeConstraint<int32>("S"), \
+                          CudnnRNNParamsSizeOp<GPUDevice, T, int32>);
+
+TF_CALL_float(REGISTER_GPU);
+TF_CALL_double(REGISTER_GPU);
+#undef REGISTER_GPU
 
 // Convert weight and bias params from a platform-specific layout to the
 // canonical form.
@@ -683,13 +689,17 @@ class CudnnRNNParamsToCanonical<GPUDevice, T> : public CudnnRNNKernelCommon {
   int num_params_;
 };
 
-REGISTER_KERNEL_BUILDER(Name("CudnnRNNParamsToCanonical")
-                            .Device(DEVICE_GPU)
-                            .HostMemory("num_layers")
-                            .HostMemory("num_units")
-                            .HostMemory("input_size")
-                            .TypeConstraint<float>("T"),
-                        CudnnRNNParamsToCanonical<GPUDevice, float>);
+#define REGISTER_GPU(T)                                     \
+  REGISTER_KERNEL_BUILDER(Name("CudnnRNNParamsToCanonical") \
+                              .Device(DEVICE_GPU)           \
+                              .HostMemory("num_layers")     \
+                              .HostMemory("num_units")      \
+                              .HostMemory("input_size")     \
+                              .TypeConstraint<T>("T"),      \
+                          CudnnRNNParamsToCanonical<GPUDevice, T>);
+TF_CALL_float(REGISTER_GPU);
+TF_CALL_double(REGISTER_GPU);
+#undef REGISTER_GPU
 
 // Convert weight and bias params from the canonical form to a
 // platform-specific layout.
@@ -725,13 +735,16 @@ class CudnnRNNCanonicalToParams<GPUDevice, T> : public CudnnRNNKernelCommon {
   }
 };
 
-REGISTER_KERNEL_BUILDER(Name("CudnnRNNCanonicalToParams")
-                            .Device(DEVICE_GPU)
-                            .HostMemory("num_layers")
-                            .HostMemory("num_units")
-                            .HostMemory("input_size")
-                            .TypeConstraint<float>("T"),
-                        CudnnRNNCanonicalToParams<GPUDevice, float>);
+#define REGISTER_GPU(T)                                     \
+  REGISTER_KERNEL_BUILDER(Name("CudnnRNNCanonicalToParams") \
+                              .Device(DEVICE_GPU)           \
+                              .HostMemory("num_layers")     \
+                              .HostMemory("num_units")      \
+                              .HostMemory("input_size")     \
+                              .TypeConstraint<T>("T"),      \
+                          CudnnRNNCanonicalToParams<GPUDevice, T>);
+TF_CALL_float(REGISTER_GPU) TF_CALL_double(REGISTER_GPU);
+#undef REGISTER_GPU
 
 // Run the forward operation of the RNN model.
 template <typename T>
@@ -874,9 +887,14 @@ class CudnnRNNForwardOp<GPUDevice, T> : public CudnnRNNKernelCommon {
       GUARDED_BY(mu_);
 };
 
-REGISTER_KERNEL_BUILDER(
-    Name("CudnnRNN").Device(DEVICE_GPU).TypeConstraint<float>("T"),
-    CudnnRNNForwardOp<GPUDevice, float>);
+#define REGISTER_GPU(T)                                           \
+  REGISTER_KERNEL_BUILDER(                                        \
+      Name("CudnnRNN").Device(DEVICE_GPU).TypeConstraint<T>("T"), \
+      CudnnRNNForwardOp<GPUDevice, T>);
+
+TF_CALL_float(REGISTER_GPU);
+TF_CALL_double(REGISTER_GPU);
+#undef REGISTER_GPU
 
 // Run the backward operation of the RNN model.
 template <typename T>
@@ -1088,9 +1106,14 @@ class CudnnRNNBackwardOp<GPUDevice, T> : public CudnnRNNKernelCommon {
       GUARDED_BY(mu_);
 };
 
-REGISTER_KERNEL_BUILDER(
-    Name("CudnnRNNBackprop").Device(DEVICE_GPU).TypeConstraint<float>("T"),
-    CudnnRNNBackwardOp<GPUDevice, float>);
+#define REGISTER_GPU(T)                                                   \
+  REGISTER_KERNEL_BUILDER(                                                \
+      Name("CudnnRNNBackprop").Device(DEVICE_GPU).TypeConstraint<T>("T"), \
+      CudnnRNNBackwardOp<GPUDevice, T>);
+
+TF_CALL_float(REGISTER_GPU);
+TF_CALL_double(REGISTER_GPU);
+#undef REGISTER_GPU
 
 // TODO(zhengxq): Add the conversion of Cudnn RNN Params from and to
 // its canonical form.
diff --git a/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc b/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc
index 2c631b064b..151dc10c26 100644
--- a/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc
+++ b/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc
@@ -75,7 +75,7 @@ REGISTER_OP("CudnnRNNParamsSize")
     .Input("num_layers: int32")
     .Input("num_units: int32")
     .Input("input_size: int32")
-    .Attr("T: {float}")
+    .Attr("T: {float32, float64}")
     .Attr("S: {int32, int64}")
     .Attr(kRNNModeAttrs)
     .Attr(kRNNInputModeAttrs)
@@ -130,7 +130,7 @@ REGISTER_OP("CudnnRNN")
     .Output("output_h: T")
     .Output("output_c: T")
     .Output("reserve_space: T")
-    .Attr("T: {float}")
+    .Attr("T: {float32, float64}")
     .Attr(kRNNModeAttrs)
     .Attr(kRNNInputModeAttrs)
     .Attr(kRNNDirectionAttrs)
@@ -190,7 +190,7 @@ REGISTER_OP("CudnnRNNBackprop")
     .Output("input_h_backprop: T")
     .Output("input_c_backprop: T")
     .Output("params_backprop: T")
-    .Attr("T: {float}")
+    .Attr("T: {float32, float64}")
     .Attr(kRNNModeAttrs)
     .Attr(kRNNInputModeAttrs)
     .Attr(kRNNDirectionAttrs)
@@ -236,7 +236,7 @@ REGISTER_OP("CudnnRNNParamsToCanonical")
     .Input("params: T")
     .Output("weights: num_params * T")
     .Output("biases: num_params * T")
-    .Attr("T: {float}")
+    .Attr("T: {float32, float64}")
     .Attr("num_params: int")
     .Attr(kRNNModeAttrs)
     .Attr(kRNNInputModeAttrs)
@@ -279,7 +279,7 @@ REGISTER_OP("CudnnRNNCanonicalToParams")
     .Input("weights: num_params * T")
     .Input("biases: num_params * T")
     .Output("params: T")
-    .Attr("T: {float}")
+    .Attr("T: {float32, float64}")
     .Attr("num_params: int")
     .Attr(kRNNModeAttrs)
     .Attr(kRNNInputModeAttrs)
diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
index 5a48b535ff..11a97b6626 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
@@ -78,19 +78,20 @@ class CudnnRNNTest(TensorFlowTestCase):
                    num_units,
                    input_size,
                    input_mode="linear_input",
+                   dtype=dtypes.float32,
                    dropout=0.):
     if rnn_mode == cudnn_rnn_ops.CUDNN_LSTM:
       model = cudnn_rnn_ops.CudnnLSTM(
-          num_layers, num_units, input_size, dropout=dropout)
+          num_layers, num_units, input_size, dtype=dtype, dropout=dropout)
     elif rnn_mode == cudnn_rnn_ops.CUDNN_GRU:
       model = cudnn_rnn_ops.CudnnGRU(
-          num_layers, num_units, input_size, dropout=dropout)
+          num_layers, num_units, input_size, dtype=dtype, dropout=dropout)
     elif rnn_mode == cudnn_rnn_ops.CUDNN_RNN_TANH:
       model = cudnn_rnn_ops.CudnnRNNTanh(
-          num_layers, num_units, input_size, dropout=dropout)
+          num_layers, num_units, input_size, dtype=dtype, dropout=dropout)
     elif rnn_mode == cudnn_rnn_ops.CUDNN_RNN_RELU:
       model = cudnn_rnn_ops.CudnnRNNRelu(
-          num_layers, num_units, input_size, dropout=dropout)
+          num_layers, num_units, input_size, dtype=dtype, dropout=dropout)
     else:
       raise ValueError("Invalid rnn_mode: %s" % rnn_mode)
     return model
@@ -107,12 +108,15 @@ class CudnnRNNTest(TensorFlowTestCase):
         "rnn")
     ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, params_saveable)
 
-  def _testSaveRestoreVariable(self, rnn_mode):
-    model = self._CreateModel(rnn_mode, num_layers=2, num_units=7, input_size=3)
+  def _testSaveRestoreVariable(self, rnn_mode, dtype):
+    model = self._CreateModel(
+        rnn_mode, num_layers=2, num_units=7, input_size=3, dtype=dtype)
     random_seed.set_random_seed(1234)
     params_size_t = model.params_size()
     params = variables.Variable(
-        random_ops.random_uniform([params_size_t]), validate_shape=False)
+        random_ops.random_uniform([params_size_t], dtype=dtype),
+        dtype=dtype,
+        validate_shape=False)
     self._create_params_savable(params, model)
     save_path = os.path.join(self.get_temp_dir(), "save-restore-variable-test")
     saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
@@ -122,7 +126,9 @@ class CudnnRNNTest(TensorFlowTestCase):
       val = saver.save(sess, save_path)
       self.assertEqual(save_path, val)
     with self.test_session(use_gpu=True) as sess:
-      reset_params = state_ops.assign(params, array_ops.zeros([params_size_t]))
+      reset_params = state_ops.assign(params,
+                                      array_ops.zeros(
+                                          [params_size_t], dtype=dtype))
       sess.run(reset_params)
       saver.restore(sess, save_path)
       params_v_restored = sess.run(params)
@@ -308,26 +314,32 @@ class CudnnRNNTest(TensorFlowTestCase):
             self.assertAllClose(
                 cudnn_output[1][i, :], states_v[i], atol=1e-6, rtol=1e-6)
 
-  def _testSaveRestoreOutput(self, rnn_mode):
+  def _testSaveRestoreOutput(self, rnn_mode, dtype):
     num_layers = 2
     num_units = 7
     input_size = 7
     seq_length = 10
     batch_size = 5
     dir_count = 1
-    model = self._CreateModel(rnn_mode, num_layers, num_units, input_size)
+    model = self._CreateModel(
+        rnn_mode, num_layers, num_units, input_size, dtype=dtype)
     params_size_t = model.params_size()
     params = variables.Variable(
-        array_ops.ones([params_size_t]), validate_shape=False)
+        array_ops.ones([params_size_t], dtype=dtype),
+        validate_shape=False,
+        dtype=dtype)
     self._create_params_savable(params, model)
     save_path = os.path.join(self.get_temp_dir(), "save-restore-output-test")
     saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
 
     has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
-    input_data = array_ops.ones([seq_length, batch_size, input_size])
-    input_h = array_ops.ones([num_layers * dir_count, batch_size, num_units])
+    input_data = array_ops.ones(
+        [seq_length, batch_size, input_size], dtype=dtype)
+    input_h = array_ops.ones(
+        [num_layers * dir_count, batch_size, num_units], dtype=dtype)
     if has_input_c:
-      input_c = array_ops.ones([num_layers * dir_count, batch_size, num_units])
+      input_c = array_ops.ones(
+          [num_layers * dir_count, batch_size, num_units], dtype=dtype)
       outputs = model(
           input_data=input_data,
           input_h=input_h,
@@ -347,7 +359,9 @@ class CudnnRNNTest(TensorFlowTestCase):
       val = saver.save(sess, save_path)
       self.assertEqual(save_path, val)
     with self.test_session(use_gpu=True) as sess:
-      reset_params = state_ops.assign(params, array_ops.zeros([params_size_t]))
+      reset_params = state_ops.assign(params,
+                                      array_ops.zeros(
+                                          [params_size_t], dtype=dtype))
       sess.run(reset_params)
       saver.restore(sess, save_path)
       total_sum_v_restored = sess.run(total_sum)
@@ -360,9 +374,10 @@ class CudnnRNNTest(TensorFlowTestCase):
         cudnn_rnn_ops.CUDNN_LSTM, cudnn_rnn_ops.CUDNN_GRU,
         cudnn_rnn_ops.CUDNN_RNN_TANH, cudnn_rnn_ops.CUDNN_RNN_RELU
     ]
-    for rnn_mode in rnn_modes:
-      self._testSaveRestoreVariable(rnn_mode)
-      self._testSaveRestoreOutput(rnn_mode)
+    dtype_list = [dtypes.float32, dtypes.float64]
+    for rnn_mode, dtype in itertools.product(rnn_modes, dtype_list):
+      self._testSaveRestoreVariable(rnn_mode, dtype)
+      self._testSaveRestoreOutput(rnn_mode, dtype)
 
   def _MinLSTMParamSize(self,
                         num_layers,
@@ -525,8 +540,8 @@ class CudnnRNNTest(TensorFlowTestCase):
               shape["dir_count"], dropout, expected, tolerance)
 
   def _testOneSimpleTraining(self, rnn_mode, num_layers, num_units, input_size,
-                             batch_size, seq_length, dir_count, dropout,
-                             tolerance):
+                             batch_size, seq_length, dir_count, dropout, dtype,
+                             delta, tolerance):
     # Gradient checking runs two forward ops with almost the same input. Need to
     # make sure the drop patterns across the two runs are the same.
     old_env_state = os.environ.get("TF_CUDNN_RESET_RND_GEN_STATE", str(False))
@@ -534,19 +549,30 @@ class CudnnRNNTest(TensorFlowTestCase):
     has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
     random_seed.set_random_seed(1234)
     model = self._CreateModel(
-        rnn_mode, num_layers, num_units, input_size, dropout=dropout)
+        rnn_mode,
+        num_layers,
+        num_units,
+        input_size,
+        dtype=dtype,
+        dropout=dropout)
     params_size_t = model.params_size()
     input_data = variables.Variable(
-        random_ops.random_uniform([seq_length, batch_size, input_size]))
+        random_ops.random_uniform(
+            [seq_length, batch_size, input_size], dtype=dtype),
+        dtype=dtype)
     input_h = variables.Variable(
         random_ops.random_uniform(
-            [num_layers * dir_count, batch_size, num_units]))
+            [num_layers * dir_count, batch_size, num_units], dtype=dtype),
+        dtype=dtype)
     params = variables.Variable(
-        random_ops.random_uniform([params_size_t]), validate_shape=False)
+        random_ops.random_uniform([params_size_t], dtype=dtype),
+        validate_shape=False,
+        dtype=dtype)
     if has_input_c:
       input_c = variables.Variable(
           random_ops.random_uniform(
-              [num_layers * dir_count, batch_size, num_units]))
+              [num_layers * dir_count, batch_size, num_units], dtype=dtype),
+          dtype=dtype)
 
       output, output_h, output_c = model(
           input_data=input_data,
@@ -577,8 +603,8 @@ class CudnnRNNTest(TensorFlowTestCase):
       all_inputs = [entry[0] for entry in inputs_and_shapes]
       all_shapes = [entry[1] for entry in inputs_and_shapes]
 
-      err = gradient_checker.compute_gradient_error(all_inputs, all_shapes,
-                                                    total_sum, [1])
+      err = gradient_checker.compute_gradient_error(
+          all_inputs, all_shapes, total_sum, [1], delta=delta)
 
       self.assertLess(err, tolerance)
       os.environ["TF_CUDNN_RESET_RND_GEN_STATE"] = old_env_state
@@ -590,6 +616,67 @@ class CudnnRNNTest(TensorFlowTestCase):
         {
             "rnn_mode": cudnn_rnn_ops.CUDNN_LSTM,
             "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float64,
+            "delta": 1e-4,
+            "tolerance": 5e-6,
+            "shape": {
+                "num_layers": 2,
+                "num_units": 3,
+                "input_size": 4,
+                "batch_size": 3,
+                "seq_length": 4,
+                "dir_count": 1,
+            },
+        },
+        {
+            "rnn_mode": cudnn_rnn_ops.CUDNN_GRU,
+            "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float64,
+            "delta": 1e-4,
+            "tolerance": 5e-6,
+            "shape": {
+                "num_layers": 2,
+                "num_units": 3,
+                "input_size": 4,
+                "batch_size": 3,
+                "seq_length": 4,
+                "dir_count": 1,
+            },
+        },
+        {
+            "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_TANH,
+            "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float64,
+            "delta": 1e-4,
+            "tolerance": 5e-6,
+            "shape": {
+                "num_layers": 2,
+                "num_units": 3,
+                "input_size": 4,
+                "batch_size": 3,
+                "seq_length": 4,
+                "dir_count": 1,
+            },
+        },
+        {
+            "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_RELU,
+            "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float64,
+            "delta": 1e-4,
+            "tolerance": 5e-6,
+            "shape": {
+                "num_layers": 2,
+                "num_units": 3,
+                "input_size": 4,
+                "batch_size": 3,
+                "seq_length": 4,
+                "dir_count": 1,
+            },
+        },
+        {
+            "rnn_mode": cudnn_rnn_ops.CUDNN_LSTM,
+            "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float32,
             "tolerance": 1e-2,
             "shape": {
                 "num_layers": 2,
@@ -603,6 +690,7 @@ class CudnnRNNTest(TensorFlowTestCase):
         {
             "rnn_mode": cudnn_rnn_ops.CUDNN_GRU,
             "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float32,
             "tolerance": 4e-3,
             "shape": {
                 "num_layers": 2,
@@ -616,6 +704,7 @@ class CudnnRNNTest(TensorFlowTestCase):
         {
             "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_TANH,
             "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float32,
             "tolerance": 5e-3,
             "shape": {
                 "num_layers": 2,
@@ -629,6 +718,7 @@ class CudnnRNNTest(TensorFlowTestCase):
         {
             "rnn_mode": cudnn_rnn_ops.CUDNN_RNN_RELU,
             "dropout": [0., 0.5, 1.],
+            "dtype": dtypes.float32,
             "tolerance": 4e-1,
             "shape": {
                 "num_layers": 2,
@@ -645,13 +735,15 @@ class CudnnRNNTest(TensorFlowTestCase):
       for config in test_configs:
         rnn_mode = config["rnn_mode"]
         dropout_list = config.get("dropout", [0.])
+        dtype = config.get("dtype", dtypes.float32)
+        delta = config.get("delta", 1e-3)
         tolerance = config["tolerance"]
         shape = config["shape"]
         for dropout in dropout_list:
-          self._testOneSimpleTraining(rnn_mode, shape["num_layers"],
-                                      shape["num_units"], shape["input_size"],
-                                      shape["batch_size"], shape["seq_length"],
-                                      shape["dir_count"], dropout, tolerance)
+          self._testOneSimpleTraining(
+              rnn_mode, shape["num_layers"], shape["num_units"],
+              shape["input_size"], shape["batch_size"], shape["seq_length"],
+              shape["dir_count"], dropout, dtype, delta, tolerance)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index 9307fbabac..dbe495fd00 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -513,6 +513,7 @@ class _CudnnRNN(object):
   """
   __doc__ += _cudnn_rnn_common_doc_string
 
+  # TODO(jamesqin): support float16 CuDNN RNN
   def __init__(self,
                rnn_mode,
                num_layers,
@@ -520,6 +521,7 @@ class _CudnnRNN(object):
                input_size,
                input_mode="linear_input",
                direction=CUDNN_RNN_UNIDIRECTION,
+               dtype=dtypes.float32,
                dropout=0.,
                seed=0):
     """Creates a CudnnRNN model from model spec.
@@ -541,6 +543,7 @@ class _CudnnRNN(object):
           otherwise, it implies 'linear_input'.
       direction: the direction model that the model operates. Could be either
           'unidirectional' or 'bidirectional'
+      dtype: dtype of params, tf.float32 or tf.float64.
       dropout: whether to enable dropout. With it is 0, dropout is disabled.
       seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
           for behavior.
@@ -551,6 +554,7 @@ class _CudnnRNN(object):
     self._rnn_mode = rnn_mode
     self._input_mode = input_mode
     self._direction = direction
+    self._dtype = dtype
     self._dropout = dropout
     # get graph and op seed.
     self._seed, self._seed2 = random_seed.get_seed(seed)
@@ -587,7 +591,7 @@ class _CudnnRNN(object):
         num_layers=self._num_layers,
         num_units=self._num_units,
         input_size=self._input_size,
-        T=dtypes.float32,
+        T=self._dtype,
         S=dtypes.int32,
         dropout=self._dropout,
         seed=self._seed,
@@ -608,7 +612,6 @@ class _CudnnRNN(object):
         A Tensor of the same shape as input_h.
       params: the parameter buffer created for this model.
       is_training: whether this operation will be used in training or inference.
-
     Returns:
       output: the output sequuence.
       output_h: the final state for h.
@@ -616,7 +619,7 @@ class _CudnnRNN(object):
     """
     if self._rnn_mode != CUDNN_LSTM:
       # For model that doesn't take input_c, replace with a dummy tensor.
-      input_c = array_ops.constant([], dtype=dtypes.float32)
+      input_c = array_ops.constant([], dtype=self._dtype)
     output, output_h, output_c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
         input=input_data,
         input_h=input_h,
@@ -691,6 +694,7 @@ class CudnnLSTM(_CudnnRNN):
                input_size,
                input_mode="linear_input",
                direction=CUDNN_RNN_UNIDIRECTION,
+               dtype=dtypes.float32,
                dropout=0.,
                seed=0):
     """Creates a Cudnn LSTM model from model spec.
@@ -708,6 +712,7 @@ class CudnnLSTM(_CudnnRNN):
           otherwise, it implies 'linear_input'.
       direction: the direction model that the model operates. Could be either
           'unidirectional' or 'bidirectional'
+      dtype: dtype of params, tf.float32 or tf.float64.
       dropout: whether to enable dropout. With it is 0, dropout is disabled.
       seed: the seed used for initializing dropout.
     """
@@ -718,6 +723,7 @@ class CudnnLSTM(_CudnnRNN):
         input_size,
         input_mode=input_mode,
         direction=direction,
+        dtype=dtype,
         dropout=dropout,
         seed=seed)
 
@@ -733,7 +739,6 @@ class CudnnLSTM(_CudnnRNN):
         input_h.
       params: the parameter buffer created for this model.
       is_training: whether this operation will be used in training or inference.
-
     Returns:
       output: the output sequuence.
       output_h: the final state for h.
@@ -754,6 +759,7 @@ class _CudnnRNNNoInputC(_CudnnRNN):
                input_size,
                input_mode="linear_input",
                direction=CUDNN_RNN_UNIDIRECTION,
+               dtype=dtypes.float32,
                dropout=0.,
                seed=0):
     """Creates a Cudnn RNN model from model without hidden-state C.
@@ -771,6 +777,7 @@ class _CudnnRNNNoInputC(_CudnnRNN):
           otherwise, it implies 'linear_input'.
       direction: the direction model that the model operates. Could be either
           'unidirectional' or 'bidirectional'
+      dtype: dtype of params, tf.float32 or tf.float64.
       dropout: whether to enable dropout. With it is 0, dropout is disabled.
       seed: the seed used for initializing dropout.
 
@@ -788,6 +795,7 @@ class _CudnnRNNNoInputC(_CudnnRNN):
         input_size,
         input_mode=input_mode,
         direction=direction,
+        dtype=dtype,
         dropout=dropout,
         seed=seed)
 
@@ -801,7 +809,6 @@ class _CudnnRNNNoInputC(_CudnnRNN):
         batch_size, num_units].
       params: the parameter buffer created for this model.
       is_training: whether this operation will be used in training or inference.
-
     Returns:
       output: the output sequuence.
       output_h: the final state for h.
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index af8a1826a9..dbe02cc521 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -1563,13 +1563,13 @@ bool CudnnSupport::DoRnnBackwardImpl(
     const DeviceMemory<T>& output_h_data,
     const CudnnRnnStateTensorDescriptor& output_c_desc,
     const DeviceMemory<T>& output_c_data,
-    const DeviceMemory<float>& output_backprop_data,
-    const DeviceMemory<float>& output_h_backprop_data,
-    const DeviceMemory<float>& output_c_backprop_data,
-    DeviceMemory<float>* input_backprop_data,
-    DeviceMemory<float>* input_h_backprop_data,
-    DeviceMemory<float>* input_c_backprop_data,
-    DeviceMemory<float>* params_backprop_data,
+    const DeviceMemory<T>& output_backprop_data,
+    const DeviceMemory<T>& output_h_backprop_data,
+    const DeviceMemory<T>& output_c_backprop_data,
+    DeviceMemory<T>* input_backprop_data,
+    DeviceMemory<T>* input_h_backprop_data,
+    DeviceMemory<T>* input_c_backprop_data,
+    DeviceMemory<T>* params_backprop_data,
     DeviceMemory<uint8>* reserve_space_data,
     ScratchAllocator* workspace_allocator) {
   // extract model parameters
@@ -1769,6 +1769,49 @@ bool CudnnSupport::DoRnnForward(
 #endif  // CUDNN_VERSION
 }
 
+bool CudnnSupport::DoRnnForward(
+    Stream* stream, const dnn::RnnDescriptor& rnn_desc,
+    const dnn::RnnSequenceTensorDescriptor& input_desc,
+    const DeviceMemory<double>& input_data,
+    const dnn::RnnStateTensorDescriptor& input_h_desc,
+    const DeviceMemory<double>& input_h_data,
+    const dnn::RnnStateTensorDescriptor& input_c_desc,
+    const DeviceMemory<double>& input_c_data,
+    const DeviceMemory<double>& params,
+    const dnn::RnnSequenceTensorDescriptor& output_desc,
+    DeviceMemory<double>* output_data,
+    const dnn::RnnStateTensorDescriptor& output_h_desc,
+    DeviceMemory<double>* output_h_data,
+    const dnn::RnnStateTensorDescriptor& output_c_desc,
+    DeviceMemory<double>* output_c_data, bool is_training,
+    ScratchAllocator* reserve_space_allocator,
+    ScratchAllocator* workspace_allocator) {
+#if CUDNN_VERSION >= 5000
+  const CudnnRnnDescriptor& cudnn_rnn_desc =
+      static_cast<const CudnnRnnDescriptor&>(rnn_desc);
+  const CudnnRnnSequenceTensorDescriptor& cudnn_input_desc =
+      static_cast<const CudnnRnnSequenceTensorDescriptor&>(input_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_input_h_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(input_h_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_input_c_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(input_c_desc);
+  const CudnnRnnSequenceTensorDescriptor& cudnn_output_desc =
+      static_cast<const CudnnRnnSequenceTensorDescriptor&>(output_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_output_h_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(output_h_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_output_c_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(output_c_desc);
+
+  return DoRnnForwardImpl<double>(
+      stream, cudnn_rnn_desc, cudnn_input_desc, input_data, cudnn_input_h_desc,
+      input_h_data, cudnn_input_c_desc, input_c_data, params, cudnn_output_desc,
+      output_data, cudnn_output_h_desc, output_h_data, cudnn_output_c_desc,
+      output_c_data, is_training, reserve_space_allocator, workspace_allocator);
+#else
+  return false;
+#endif  // CUDNN_VERSION
+}
+
 bool CudnnSupport::DoRnnBackward(
     Stream* stream, const dnn::RnnDescriptor& rnn_desc,
     const dnn::RnnSequenceTensorDescriptor& input_desc,
@@ -1821,6 +1864,59 @@ bool CudnnSupport::DoRnnBackward(
 #endif  // CUDNN_VERSION
 }
 
+bool CudnnSupport::DoRnnBackward(
+    Stream* stream, const dnn::RnnDescriptor& rnn_desc,
+    const dnn::RnnSequenceTensorDescriptor& input_desc,
+    const DeviceMemory<double>& input_data,
+    const dnn::RnnStateTensorDescriptor& input_h_desc,
+    const DeviceMemory<double>& input_h_data,
+    const dnn::RnnStateTensorDescriptor& input_c_desc,
+    const DeviceMemory<double>& input_c_data,
+    const DeviceMemory<double>& params,
+    const dnn::RnnSequenceTensorDescriptor& output_desc,
+    const DeviceMemory<double>& output_data,
+    const dnn::RnnStateTensorDescriptor& output_h_desc,
+    const DeviceMemory<double>& output_h_data,
+    const dnn::RnnStateTensorDescriptor& output_c_desc,
+    const DeviceMemory<double>& output_c_data,
+    const DeviceMemory<double>& output_backprop_data,
+    const DeviceMemory<double>& output_h_backprop_data,
+    const DeviceMemory<double>& output_c_backprop_data,
+    DeviceMemory<double>* input_backprop_data,
+    DeviceMemory<double>* input_h_backprop_data,
+    DeviceMemory<double>* input_c_backprop_data,
+    DeviceMemory<double>* params_backprop_data,
+    DeviceMemory<uint8>* reserve_space_data,
+    ScratchAllocator* workspace_allocator) {
+#if CUDNN_VERSION >= 5000
+  const CudnnRnnDescriptor& cudnn_rnn_desc =
+      static_cast<const CudnnRnnDescriptor&>(rnn_desc);
+  const CudnnRnnSequenceTensorDescriptor& cudnn_input_desc =
+      static_cast<const CudnnRnnSequenceTensorDescriptor&>(input_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_input_h_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(input_h_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_input_c_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(input_c_desc);
+  const CudnnRnnSequenceTensorDescriptor& cudnn_output_desc =
+      static_cast<const CudnnRnnSequenceTensorDescriptor&>(output_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_output_h_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(output_h_desc);
+  const CudnnRnnStateTensorDescriptor& cudnn_output_c_desc =
+      static_cast<const CudnnRnnStateTensorDescriptor&>(output_c_desc);
+
+  return DoRnnBackwardImpl<double>(
+      stream, cudnn_rnn_desc, cudnn_input_desc, input_data, cudnn_input_h_desc,
+      input_h_data, cudnn_input_c_desc, input_c_data, params, cudnn_output_desc,
+      output_data, cudnn_output_h_desc, output_h_data, cudnn_output_c_desc,
+      output_c_data, output_backprop_data, output_h_backprop_data,
+      output_c_backprop_data, input_backprop_data, input_h_backprop_data,
+      input_c_backprop_data, params_backprop_data, reserve_space_data,
+      workspace_allocator);
+#else
+  return false;
+#endif  // CUDNN_VERSION
+}
+
 template <class T>
 bool CudnnSupport::DoConvolveImpl(
     Stream* stream, int cudnn_type,  // Actually cudnnDataType_t.
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h
index 16fa656ef6..b094cf76e9 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.h
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.h
@@ -79,6 +79,23 @@ class CudnnSupport : public dnn::DnnSupport {
                     ScratchAllocator* reserve_space_allocator,
                     ScratchAllocator* workspace_allocator) override;
 
+  bool DoRnnForward(Stream* stream, const dnn::RnnDescriptor& rnn_desc,
+                    const dnn::RnnSequenceTensorDescriptor& input_desc,
+                    const DeviceMemory<double>& input_data,
+                    const dnn::RnnStateTensorDescriptor& input_h_desc,
+                    const DeviceMemory<double>& input_h_data,
+                    const dnn::RnnStateTensorDescriptor& input_c_desc,
+                    const DeviceMemory<double>& input_c_data,
+                    const DeviceMemory<double>& params,
+                    const dnn::RnnSequenceTensorDescriptor& output_desc,
+                    DeviceMemory<double>* output_data,
+                    const dnn::RnnStateTensorDescriptor& output_h_desc,
+                    DeviceMemory<double>* output_h_data,
+                    const dnn::RnnStateTensorDescriptor& output_c_desc,
+                    DeviceMemory<double>* output_c_data, bool is_training,
+                    ScratchAllocator* reserve_space_allocator,
+                    ScratchAllocator* workspace_allocator) override;
+
   bool DoRnnBackward(Stream* stream, const dnn::RnnDescriptor& rnn_desc,
                      const dnn::RnnSequenceTensorDescriptor& input_desc,
                      const DeviceMemory<float>& input_data,
@@ -103,6 +120,30 @@ class CudnnSupport : public dnn::DnnSupport {
                      DeviceMemory<uint8>* reserve_space_data,
                      ScratchAllocator* workspace_allocator) override;
 
+  bool DoRnnBackward(Stream* stream, const dnn::RnnDescriptor& rnn_desc,
+                     const dnn::RnnSequenceTensorDescriptor& input_desc,
+                     const DeviceMemory<double>& input_data,
+                     const dnn::RnnStateTensorDescriptor& input_h_desc,
+                     const DeviceMemory<double>& input_h_data,
+                     const dnn::RnnStateTensorDescriptor& input_c_desc,
+                     const DeviceMemory<double>& input_c_data,
+                     const DeviceMemory<double>& params,
+                     const dnn::RnnSequenceTensorDescriptor& output_desc,
+                     const DeviceMemory<double>& output_data,
+                     const dnn::RnnStateTensorDescriptor& output_h_desc,
+                     const DeviceMemory<double>& output_h_data,
+                     const dnn::RnnStateTensorDescriptor& output_c_desc,
+                     const DeviceMemory<double>& output_c_data,
+                     const DeviceMemory<double>& output_backprop_data,
+                     const DeviceMemory<double>& output_h_backprop_data,
+                     const DeviceMemory<double>& output_c_backprop_data,
+                     DeviceMemory<double>* input_backprop_data,
+                     DeviceMemory<double>* input_h_backprop_data,
+                     DeviceMemory<double>* input_c_backprop_data,
+                     DeviceMemory<double>* params_backprop_data,
+                     DeviceMemory<uint8>* reserve_space_data,
+                     ScratchAllocator* workspace_allocator) override;
+
   bool GetConvolveAlgorithms(
       bool with_winograd_nonfused,
       std::vector<dnn::AlgorithmType>* out_algorithms) override;
@@ -597,13 +638,13 @@ class CudnnSupport : public dnn::DnnSupport {
                          const DeviceMemory<T>& output_h_data,
                          const CudnnRnnStateTensorDescriptor& output_c_desc,
                          const DeviceMemory<T>& output_c_data,
-                         const DeviceMemory<float>& output_backprop_data,
-                         const DeviceMemory<float>& output_h_backprop_data,
-                         const DeviceMemory<float>& output_c_backprop_data,
-                         DeviceMemory<float>* input_backprop_data,
-                         DeviceMemory<float>* input_h_backprop_data,
-                         DeviceMemory<float>* input_c_backprop_data,
-                         DeviceMemory<float>* params_backprop_data,
+                         const DeviceMemory<T>& output_backprop_data,
+                         const DeviceMemory<T>& output_h_backprop_data,
+                         const DeviceMemory<T>& output_c_backprop_data,
+                         DeviceMemory<T>* input_backprop_data,
+                         DeviceMemory<T>* input_h_backprop_data,
+                         DeviceMemory<T>* input_c_backprop_data,
+                         DeviceMemory<T>* params_backprop_data,
                          DeviceMemory<uint8>* reserve_space_data,
                          ScratchAllocator* workspace_allocator);
 
diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h
index f12aa6d38b..f97deb7222 100644
--- a/tensorflow/stream_executor/dnn.h
+++ b/tensorflow/stream_executor/dnn.h
@@ -1902,6 +1902,25 @@ class DnnSupport {
     return false;
   }
 
+  virtual bool DoRnnForward(Stream* stream, const dnn::RnnDescriptor& rnn_desc,
+                            const dnn::RnnSequenceTensorDescriptor& input_desc,
+                            const DeviceMemory<double>& input_data,
+                            const dnn::RnnStateTensorDescriptor& input_h_desc,
+                            const DeviceMemory<double>& input_h_data,
+                            const dnn::RnnStateTensorDescriptor& input_c_desc,
+                            const DeviceMemory<double>& input_c_data,
+                            const DeviceMemory<double>& params,
+                            const dnn::RnnSequenceTensorDescriptor& output_desc,
+                            DeviceMemory<double>* output_data,
+                            const dnn::RnnStateTensorDescriptor& output_h_desc,
+                            DeviceMemory<double>* output_h_data,
+                            const dnn::RnnStateTensorDescriptor& output_c_desc,
+                            DeviceMemory<double>* output_c_data,
+                            bool is_training,
+                            ScratchAllocator* reserve_space_allocator,
+                            ScratchAllocator* workspace_allocator) {
+    return false;
+  }
   // Enqueue a backward operation of the RNN model onto the stream.
   //
   // Arguments:
@@ -1970,6 +1989,33 @@ class DnnSupport {
     return false;
   }
 
+  virtual bool DoRnnBackward(
+      Stream* stream, const dnn::RnnDescriptor& rnn_desc,
+      const dnn::RnnSequenceTensorDescriptor& input_desc,
+      const DeviceMemory<double>& input_data,
+      const dnn::RnnStateTensorDescriptor& input_h_desc,
+      const DeviceMemory<double>& input_h_data,
+      const dnn::RnnStateTensorDescriptor& input_c_desc,
+      const DeviceMemory<double>& input_c_data,
+      const DeviceMemory<double>& params,
+      const dnn::RnnSequenceTensorDescriptor& output_desc,
+      const DeviceMemory<double>& output_data,
+      const dnn::RnnStateTensorDescriptor& output_h_desc,
+      const DeviceMemory<double>& output_h_data,
+      const dnn::RnnStateTensorDescriptor& output_c_desc,
+      const DeviceMemory<double>& output_c_data,
+      const DeviceMemory<double>& output_backprop_data,
+      const DeviceMemory<double>& output_h_backprop_data,
+      const DeviceMemory<double>& output_c_backprop_data,
+      DeviceMemory<double>* input_backprop_data,
+      DeviceMemory<double>* input_h_backprop_data,
+      DeviceMemory<double>* input_c_backprop_data,
+      DeviceMemory<double>* params_backprop_data,
+      DeviceMemory<uint8>* reserve_space_data,
+      ScratchAllocator* workspace_allocator) {
+    return false;
+  }
+
   // Transforms a tensor into another tensor with a different layout and/or data
   // type.
   //
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index 9b4a4c4fb1..5996195173 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -4388,6 +4388,39 @@ Stream &Stream::ThenRnnForward(
   return *this;
 }
 
+Stream &Stream::ThenRnnForward(
+    const dnn::RnnDescriptor &rnn_desc,
+    const dnn::RnnSequenceTensorDescriptor &input_desc,
+    const DeviceMemory<double> &input_data,
+    const dnn::RnnStateTensorDescriptor &input_h_desc,
+    const DeviceMemory<double> &input_h_data,
+    const dnn::RnnStateTensorDescriptor &input_c_desc,
+    const DeviceMemory<double> &input_c_data,
+    const DeviceMemory<double> &params,
+    const dnn::RnnSequenceTensorDescriptor &output_desc,
+    DeviceMemory<double> *output_data,
+    const dnn::RnnStateTensorDescriptor &output_h_desc,
+    DeviceMemory<double> *output_h_data,
+    const dnn::RnnStateTensorDescriptor &output_c_desc,
+    DeviceMemory<double> *output_c_data, bool is_training,
+    ScratchAllocator *reserve_space_allocator,
+    ScratchAllocator *workspace_allocator) {
+  // TODO(zhengxq): add VLOG PARAM calls.
+  if (ok()) {
+    if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
+      CheckError(dnn->DoRnnForward(
+          this, rnn_desc, input_desc, input_data, input_h_desc, input_h_data,
+          input_c_desc, input_c_data, params, output_desc, output_data,
+          output_h_desc, output_h_data, output_c_desc, output_c_data,
+          is_training, reserve_space_allocator, workspace_allocator));
+    } else {
+      SetError();
+      LOG(WARNING) << "Attempting to call ThenRnnForward without DNN support";
+    }
+  }
+  return *this;
+}
+
 Stream &Stream::ThenRnnBackward(
     const dnn::RnnDescriptor &rnn_desc,
     const dnn::RnnSequenceTensorDescriptor &input_desc,
@@ -4429,6 +4462,48 @@ Stream &Stream::ThenRnnBackward(
   return *this;
 }
 
+Stream &Stream::ThenRnnBackward(
+    const dnn::RnnDescriptor &rnn_desc,
+    const dnn::RnnSequenceTensorDescriptor &input_desc,
+    const DeviceMemory<double> &input_data,
+    const dnn::RnnStateTensorDescriptor &input_h_desc,
+    const DeviceMemory<double> &input_h_data,
+    const dnn::RnnStateTensorDescriptor &input_c_desc,
+    const DeviceMemory<double> &input_c_data,
+    const DeviceMemory<double> &params,
+    const dnn::RnnSequenceTensorDescriptor &output_desc,
+    const DeviceMemory<double> &output_data,
+    const dnn::RnnStateTensorDescriptor &output_h_desc,
+    const DeviceMemory<double> &output_h_data,
+    const dnn::RnnStateTensorDescriptor &output_c_desc,
+    const DeviceMemory<double> &output_c_data,
+    const DeviceMemory<double> &output_backprop_data,
+    const DeviceMemory<double> &output_h_backprop_data,
+    const DeviceMemory<double> &output_c_backprop_data,
+    DeviceMemory<double> *input_backprop_data,
+    DeviceMemory<double> *input_h_backprop_data,
+    DeviceMemory<double> *input_c_backprop_data,
+    DeviceMemory<double> *params_backprop_data,
+    DeviceMemory<uint8> *reserve_space_data,
+    ScratchAllocator *workspace_allocator) {
+  // TODO(zhengxq): add VLOG PARAM calls.
+  if (ok()) {
+    if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
+      CheckError(dnn->DoRnnBackward(
+          this, rnn_desc, input_desc, input_data, input_h_desc, input_h_data,
+          input_c_desc, input_c_data, params, output_desc, output_data,
+          output_h_desc, output_h_data, output_c_desc, output_c_data,
+          output_backprop_data, output_h_backprop_data, output_c_backprop_data,
+          input_backprop_data, input_h_backprop_data, input_c_backprop_data,
+          params_backprop_data, reserve_space_data, workspace_allocator));
+    } else {
+      SetError();
+      LOG(WARNING) << "Attempting to call ThenRnnBackward without DNN support";
+    }
+  }
+  return *this;
+}
+
 Stream &Stream::ThenTransformTensor(const dnn::BatchDescriptor &input_desc,
                                     dnn::DataType input_type,
                                     const DeviceMemoryBase &input_data,
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index ab6b866744..3c8b7ee894 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -1632,6 +1632,23 @@ class Stream {
                          ScratchAllocator *reserve_space_allocator,
                          ScratchAllocator *workspace_allocator);
 
+  Stream &ThenRnnForward(const dnn::RnnDescriptor &rnn_desc,
+                         const dnn::RnnSequenceTensorDescriptor &input_desc,
+                         const DeviceMemory<double> &input_data,
+                         const dnn::RnnStateTensorDescriptor &input_h_desc,
+                         const DeviceMemory<double> &input_h_data,
+                         const dnn::RnnStateTensorDescriptor &input_c_desc,
+                         const DeviceMemory<double> &input_c_data,
+                         const DeviceMemory<double> &params,
+                         const dnn::RnnSequenceTensorDescriptor &output_desc,
+                         DeviceMemory<double> *output_data,
+                         const dnn::RnnStateTensorDescriptor &output_h_desc,
+                         DeviceMemory<double> *output_h_data,
+                         const dnn::RnnStateTensorDescriptor &output_c_desc,
+                         DeviceMemory<double> *output_c_data, bool is_training,
+                         ScratchAllocator *reserve_space_allocator,
+                         ScratchAllocator *workspace_allocator);
+
   // Enqueue a backward operation of the RNN model onto the stream.
   // See DnnSupport::DoRnnBackward for more details.
   Stream &ThenRnnBackward(const dnn::RnnDescriptor &rnn_desc,
@@ -1658,6 +1675,30 @@ class Stream {
                           DeviceMemory<uint8> *reserve_space_data,
                           ScratchAllocator *workspace_allocator);
 
+  Stream &ThenRnnBackward(const dnn::RnnDescriptor &rnn_desc,
+                          const dnn::RnnSequenceTensorDescriptor &input_desc,
+                          const DeviceMemory<double> &input_data,
+                          const dnn::RnnStateTensorDescriptor &input_h_desc,
+                          const DeviceMemory<double> &input_h_data,
+                          const dnn::RnnStateTensorDescriptor &input_c_desc,
+                          const DeviceMemory<double> &input_c_data,
+                          const DeviceMemory<double> &params,
+                          const dnn::RnnSequenceTensorDescriptor &output_desc,
+                          const DeviceMemory<double> &output_data,
+                          const dnn::RnnStateTensorDescriptor &output_h_desc,
+                          const DeviceMemory<double> &output_h_data,
+                          const dnn::RnnStateTensorDescriptor &output_c_desc,
+                          const DeviceMemory<double> &output_c_data,
+                          const DeviceMemory<double> &output_backprop_data,
+                          const DeviceMemory<double> &output_h_backprop_data,
+                          const DeviceMemory<double> &output_c_backprop_data,
+                          DeviceMemory<double> *input_backprop_data,
+                          DeviceMemory<double> *input_h_backprop_data,
+                          DeviceMemory<double> *input_c_backprop_data,
+                          DeviceMemory<double> *params_backprop_data,
+                          DeviceMemory<uint8> *reserve_space_data,
+                          ScratchAllocator *workspace_allocator);
+
   // Enqueue onto the stream a operation that transforms a tensor.
   // See DnnSupport::DoTransformTensor for more details.
   Stream &ThenTransformTensor(const dnn::BatchDescriptor &input_desc,
-- 
GitLab


From 75855ab22749c9dd2ff930e1dadd4a1754db4f41 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 15:31:50 -0700
Subject: [PATCH 1089/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 162413543
---
 tensorflow/go/op/wrappers.go | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 3b33706dc5..3c13ca4f13 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -22045,7 +22045,7 @@ func Exp(scope *Scope, x tf.Output) (y tf.Output) {
 //  `output[i] = numeric_limits<T>::min()`.
 //
 // <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentSum.png" alt>
+// <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentMax.png" alt>
 // </div>
 //
 // Arguments:
-- 
GitLab


From afe603348babf6f055d635e230e1494dd138df21 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 15:42:11 -0700
Subject: [PATCH 1090/1375] Fix crash when OOM error occurs with verbose
 logging enabled

PiperOrigin-RevId: 162414962
---
 tensorflow/core/framework/tensor.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 980703f190..80b98fb9c6 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -376,10 +376,10 @@ Buffer<T>::Buffer(Allocator* a, int64 n,
 
 template <typename T>
 Buffer<T>::~Buffer() {
-  if (LogMemory::IsEnabled()) {
-    RecordDeallocation();
-  }
   if (data_) {
+    if (LogMemory::IsEnabled()) {
+      RecordDeallocation();
+    }
     alloc_->Deallocate<T>(data_, elem_);
   }
 }
-- 
GitLab


From ed3b2bc8274046ad71630a060e615ad4f78becb9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 15:54:54 -0700
Subject: [PATCH 1091/1375] FileBlockCache will now run a thread to prune files
 with expired blocks, if configured with a nonzero max staleness.

PiperOrigin-RevId: 162416777
---
 .../core/platform/cloud/file_block_cache.cc   | 25 +++++
 .../core/platform/cloud/file_block_cache.h    | 37 +++++++-
 .../platform/cloud/file_block_cache_test.cc   | 91 +++++++++++++++++--
 3 files changed, 141 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/platform/cloud/file_block_cache.cc b/tensorflow/core/platform/cloud/file_block_cache.cc
index 27e023745e..e4970a4188 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.cc
+++ b/tensorflow/core/platform/cloud/file_block_cache.cc
@@ -76,6 +76,8 @@ Status FileBlockCache::Read(const string& filename, size_t offset, size_t n,
       // Record the block timestamp, update the cache size, and add the block to
       // the cache.
       block->timestamp = env_->NowSeconds();
+      lra_list_.push_front(key);
+      block->lra_iterator = lra_list_.begin();
       cache_size_ += block->data.size();
       entry = block_map_.emplace(std::make_pair(key, std::move(block))).first;
     } else {
@@ -114,6 +116,28 @@ Status FileBlockCache::Read(const string& filename, size_t offset, size_t n,
   return Status::OK();
 }
 
+size_t FileBlockCache::CacheSize() const {
+  mutex_lock lock(mu_);
+  return cache_size_;
+}
+
+void FileBlockCache::Prune() {
+  while (!WaitForNotificationWithTimeout(&stop_pruning_thread_, 1000000)) {
+    mutex_lock lock(mu_);
+    uint64 now = env_->NowSeconds();
+    while (!lra_list_.empty()) {
+      auto it = block_map_.find(lra_list_.back());
+      if (now - it->second->timestamp <= max_staleness_) {
+        // The oldest block is not yet expired. Come back later.
+        break;
+      }
+      // We need to make a copy of the filename here, since it could otherwise
+      // be used within RemoveFile_Locked after `it` is deleted.
+      RemoveFile_Locked(std::string(it->first.first));
+    }
+  }
+}
+
 void FileBlockCache::RemoveFile(const string& filename) {
   mutex_lock lock(mu_);
   RemoveFile_Locked(filename);
@@ -131,6 +155,7 @@ void FileBlockCache::RemoveFile_Locked(const string& filename) {
 
 void FileBlockCache::RemoveBlock(BlockMap::iterator entry) {
   lru_list_.erase(entry->second->lru_iterator);
+  lra_list_.erase(entry->second->lra_iterator);
   cache_size_ -= entry->second->data.size();
   block_map_.erase(entry);
 }
diff --git a/tensorflow/core/platform/cloud/file_block_cache.h b/tensorflow/core/platform/cloud/file_block_cache.h
index 02e00b5bc9..0429228a2b 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.h
+++ b/tensorflow/core/platform/cloud/file_block_cache.h
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/notification.h"
 #include "tensorflow/core/platform/thread_annotations.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -52,7 +53,21 @@ class FileBlockCache {
         max_bytes_(max_bytes),
         max_staleness_(max_staleness),
         block_fetcher_(block_fetcher),
-        env_(env) {}
+        env_(env) {
+    if (max_staleness_ > 0) {
+      pruning_thread_.reset(env_->StartThread(ThreadOptions(), "TF_prune_FBC",
+                                              [this] { Prune(); }));
+    }
+  }
+
+  ~FileBlockCache() {
+    if (pruning_thread_) {
+      stop_pruning_thread_.Notify();
+      // Destroying pruning_thread_ will block until Prune() receives the above
+      // notification and returns.
+      pruning_thread_.reset();
+    }
+  }
 
   /// Read `n` bytes from `filename` starting at `offset` into `out`. This
   /// method will return:
@@ -79,6 +94,9 @@ class FileBlockCache {
   size_t max_bytes() const { return max_bytes_; }
   uint64 max_staleness() const { return max_staleness_; }
 
+  /// The current size (in bytes) of the cache.
+  size_t CacheSize() const LOCKS_EXCLUDED(mu_);
+
  private:
   /// The size of the blocks stored in the LRU cache, as well as the size of the
   /// reads from the underlying filesystem.
@@ -107,6 +125,8 @@ class FileBlockCache {
     std::vector<char> data;
     /// A list iterator pointing to the block's position in the LRU list.
     std::list<Key>::iterator lru_iterator;
+    /// A list iterator pointing to the block's position in the LRA list.
+    std::list<Key>::iterator lra_iterator;
     /// The timestamp (seconds since epoch) at which the block was cached.
     uint64 timestamp;
   };
@@ -116,6 +136,9 @@ class FileBlockCache {
   /// The block map is an ordered map from Key to Block.
   typedef std::map<Key, std::unique_ptr<Block>> BlockMap;
 
+  /// Prune the cache by removing files with expired blocks.
+  void Prune() LOCKS_EXCLUDED(mu_);
+
   /// Remove all blocks of a file, with mu_ already held.
   void RemoveFile_Locked(const string& filename) EXCLUSIVE_LOCKS_REQUIRED(mu_);
 
@@ -123,8 +146,14 @@ class FileBlockCache {
   /// cache size accordingly.
   void RemoveBlock(BlockMap::iterator entry) EXCLUSIVE_LOCKS_REQUIRED(mu_);
 
+  /// The cache pruning thread that removes files with expired blocks.
+  std::unique_ptr<Thread> pruning_thread_;
+
+  /// Notification for stopping the cache pruning thread.
+  Notification stop_pruning_thread_;
+
   /// Guards access to the block map, LRU list, and cached byte count.
-  mutex mu_;
+  mutable mutex mu_;
 
   /// The block map (map from Key to Block).
   BlockMap block_map_ GUARDED_BY(mu_);
@@ -133,6 +162,10 @@ class FileBlockCache {
   /// recently accessed block.
   std::list<Key> lru_list_ GUARDED_BY(mu_);
 
+  /// The LRA (least recently added) list of block keys. The front of the list
+  /// identifies the most recently added block.
+  std::list<Key> lra_list_ GUARDED_BY(mu_);
+
   /// The combined number of bytes in all of the cached blocks.
   size_t cache_size_ GUARDED_BY(mu_) = 0;
 };
diff --git a/tensorflow/core/platform/cloud/file_block_cache_test.cc b/tensorflow/core/platform/cloud/file_block_cache_test.cc
index 3f53cbc6ea..d01181daeb 100644
--- a/tensorflow/core/platform/cloud/file_block_cache_test.cc
+++ b/tensorflow/core/platform/cloud/file_block_cache_test.cc
@@ -22,6 +22,25 @@ limitations under the License.
 namespace tensorflow {
 namespace {
 
+// This Env wrapper lets us control the NowSeconds() return value.
+class FakeEnv : public EnvWrapper {
+ public:
+  FakeEnv() : EnvWrapper(Env::Default()) {}
+
+  uint64 NowSeconds() override {
+    mutex_lock lock(mu_);
+    return now_;
+  }
+
+  void SetNowSeconds(uint64 now) {
+    mutex_lock lock(mu_);
+    now_ = now;
+  }
+
+  mutex mu_;
+  uint64 now_ = 1;
+};
+
 TEST(FileBlockCacheTest, PassThrough) {
   const string want_filename = "foo/bar";
   const size_t want_offset = 42;
@@ -240,13 +259,6 @@ TEST(FileBlockCacheTest, LRU) {
 }
 
 TEST(FileBlockCacheTest, MaxStaleness) {
-  // This Env wrapper lets us control the NowSeconds() return value.
-  class FakeEnv : public EnvWrapper {
-   public:
-    FakeEnv() : EnvWrapper(Env::Default()) {}
-    uint64 NowSeconds() override { return now_; };
-    uint64 now_ = 1;
-  };
   int calls = 0;
   auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
                           std::vector<char>* out) {
@@ -266,21 +278,21 @@ TEST(FileBlockCacheTest, MaxStaleness) {
   // count should advance every 3 seconds (i.e. every time the staleness is
   // greater than 2).
   for (int i = 1; i <= 10; i++) {
-    env->now_ = i + 1;
+    env->SetNowSeconds(i + 1);
     TF_EXPECT_OK(cache1.Read("", 0, 1, &out));
     EXPECT_EQ(calls, 1 + i / 3);
   }
   // Now create a cache with max staleness of 0, and verify that it also works
   // as expected.
   calls = 0;
-  env->now_ = 0;
+  env->SetNowSeconds(0);
   FileBlockCache cache2(8, 16, 0 /* max staleness */, fetcher, env.get());
   // Execute the first read to load the block.
   TF_EXPECT_OK(cache2.Read("", 0, 1, &out));
   EXPECT_EQ(calls, 1);
   // Advance the clock by a huge amount and verify that the cached block is
   // used to satisfy the read.
-  env->now_ = 365 * 24 * 60 * 60;  // ~1 year, just for fun.
+  env->SetNowSeconds(365 * 24 * 60 * 60);  // ~1 year, just for fun.
   TF_EXPECT_OK(cache2.Read("", 0, 1, &out));
   EXPECT_EQ(calls, 1);
 }
@@ -347,5 +359,64 @@ TEST(FileBlockCacheTest, RemoveFile) {
   EXPECT_EQ(calls, 6);
 }
 
+TEST(FileBlockCacheTest, Prune) {
+  int calls = 0;
+  auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
+                          std::vector<char>* out) {
+    calls++;
+    out->clear();
+    out->resize(n, 'x');
+    return Status::OK();
+  };
+  std::vector<char> out;
+  // Our fake environment is initialized with the current timestamp.
+  std::unique_ptr<FakeEnv> env(new FakeEnv);
+  uint64 now = Env::Default()->NowSeconds();
+  env->SetNowSeconds(now);
+  FileBlockCache cache(8, 32, 1 /* max staleness */, fetcher, env.get());
+  // Read three blocks into the cache, and advance the timestamp by one second
+  // with each read. Start with a block of "a" at the current timestamp `now`.
+  TF_EXPECT_OK(cache.Read("a", 0, 1, &out));
+  // Now load a block of a different file "b" at timestamp `now` + 1
+  env->SetNowSeconds(now + 1);
+  TF_EXPECT_OK(cache.Read("b", 0, 1, &out));
+  // Now load a different block of file "a" at timestamp `now` + 1. When the
+  // first block of "a" expires, this block should also be removed because it
+  // also belongs to file "a".
+  TF_EXPECT_OK(cache.Read("a", 8, 1, &out));
+  // Ensure that all blocks are in the cache (i.e. reads are cache hits).
+  EXPECT_EQ(cache.CacheSize(), 24);
+  EXPECT_EQ(calls, 3);
+  TF_EXPECT_OK(cache.Read("a", 0, 1, &out));
+  TF_EXPECT_OK(cache.Read("b", 0, 1, &out));
+  TF_EXPECT_OK(cache.Read("a", 8, 1, &out));
+  EXPECT_EQ(calls, 3);
+  // Advance the fake timestamp so that "a" becomes stale via its first block.
+  env->SetNowSeconds(now + 2);
+  // The pruning thread periodically compares env->NowSeconds() with the oldest
+  // block's timestamp to see if it should evict any files. At the current fake
+  // timestamp of `now` + 2, file "a" is stale because its first block is stale,
+  // but file "b" is not stale yet. Thus, once the pruning thread wakes up (in
+  // one second of wall time), it should remove "a" and leave "b" alone.
+  uint64 start = Env::Default()->NowSeconds();
+  do {
+    Env::Default()->SleepForMicroseconds(100000);
+  } while (cache.CacheSize() == 24 && Env::Default()->NowSeconds() - start < 3);
+  // There should be one block left in the cache, and it should be the first
+  // block of "b".
+  EXPECT_EQ(cache.CacheSize(), 8);
+  TF_EXPECT_OK(cache.Read("b", 0, 1, &out));
+  EXPECT_EQ(calls, 3);
+  // Advance the fake time to `now` + 3, at which point "b" becomes stale.
+  env->SetNowSeconds(now + 3);
+  // Wait for the pruner to remove "b".
+  start = Env::Default()->NowSeconds();
+  do {
+    Env::Default()->SleepForMicroseconds(100000);
+  } while (cache.CacheSize() == 8 && Env::Default()->NowSeconds() - start < 3);
+  // The cache should now be empty.
+  EXPECT_EQ(cache.CacheSize(), 0);
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From ec76799118faffd8d04f4084db2bcabc6f18e102 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Tue, 18 Jul 2017 16:09:01 -0700
Subject: [PATCH 1092/1375] fix leftnav files for programmer's guide

PiperOrigin-RevId: 162418535
---
 tensorflow/docs_src/programmers_guide/leftnav_files | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/programmers_guide/leftnav_files
index 322e11cbd6..7f0b9b8db9 100644
--- a/tensorflow/docs_src/programmers_guide/leftnav_files
+++ b/tensorflow/docs_src/programmers_guide/leftnav_files
@@ -1,14 +1,15 @@
 index.md
+tensors.md
 variables.md
 dims_types.md
-variable_scope.md
+graphs.md
+datasets.md
 threading_and_queues.md
 reading_data.md
-supervisor.md
+embedding.md
 debugger.md
-tfdbg-tflearn.md
-meta_graph.md
+supervisor.md
 saved_model_cli.md
-version_semantics.md
-data_versions.md
+meta_graph.md
+version_compat.md
 faq.md
-- 
GitLab


From 74ebe50402f9088060b9973e95fede585d872f4d Mon Sep 17 00:00:00 2001
From: Nayana Thorat <nayana.thorat@gmail.com>
Date: Wed, 19 Jul 2017 04:58:10 +0530
Subject: [PATCH 1093/1375] Update workspace.bzl to use latest farmhash commit
 to support s390x (#11579)

---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index a5f0440601..4760673058 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -205,11 +205,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.new_http_archive(
       name = "farmhash_archive",
       urls = [
-          "http://mirror.bazel.build/github.com/google/farmhash/archive/92e897b282426729f4724d91a637596c7e2fe28f.zip",
-          "https://github.com/google/farmhash/archive/92e897b282426729f4724d91a637596c7e2fe28f.zip",
+          "http://mirror.bazel.build/github.com/google/farmhash/archive/23eecfbe7e84ebf2e229bd02248f431c36e12f1a.zip",
+          "https://github.com/google/farmhash/archive/23eecfbe7e84ebf2e229bd02248f431c36e12f1a.zip",
       ],
-      sha256 = "4c626d1f306bda2c6804ab955892f803f5245f4dcaecb4979dc08b091256da54",
-      strip_prefix = "farmhash-92e897b282426729f4724d91a637596c7e2fe28f",
+      sha256 = "55215f8cd3ddbe9781f6fe5cc228731d6dcc8301b6191c6d420034c3fff1cb8d",
+      strip_prefix = "farmhash-23eecfbe7e84ebf2e229bd02248f431c36e12f1a",
       build_file = str(Label("//third_party:farmhash.BUILD")),
   )
 
-- 
GitLab


From f6a2bcbeed43744aede1d312dacfa252ce63eaf2 Mon Sep 17 00:00:00 2001
From: John Lawson <john@codeplay.com>
Date: Wed, 19 Jul 2017 00:28:49 +0100
Subject: [PATCH 1094/1375] Fixes ArgMax deprecation warnings in test (#11551)

The recent commit cbe1ef0 marks the `dimension` argument of argmax and
argmin as deprecated, which are used in the corresponding tests and so
this brings up warnings when the tests are run. The tests should be
using the `axis` argument instead.
---
 .../python/kernel_tests/argmax_op_test.py     | 25 +++++++++----------
 1 file changed, 12 insertions(+), 13 deletions(-)

diff --git a/tensorflow/python/kernel_tests/argmax_op_test.py b/tensorflow/python/kernel_tests/argmax_op_test.py
index ae171dec55..ce06769902 100644
--- a/tensorflow/python/kernel_tests/argmax_op_test.py
+++ b/tensorflow/python/kernel_tests/argmax_op_test.py
@@ -29,12 +29,12 @@ class ArgMaxTest(test.TestCase):
   def _testArg(self,
                method,
                x,
-               dimension,
+               axis,
                expected_values,
                use_gpu=False,
                expected_err_re=None):
     with self.test_session(use_gpu=use_gpu):
-      ans = method(x, dimension=dimension)
+      ans = method(x, axis=axis)
       if expected_err_re is None:
         tf_ans = ans.eval()
         # Defaults to int64 output.
@@ -48,27 +48,26 @@ class ArgMaxTest(test.TestCase):
   def _testBothArg(self,
                    method,
                    x,
-                   dimension,
+                   axis,
                    expected_values,
                    expected_err_re=None):
-    self._testArg(method, x, dimension, expected_values, True, expected_err_re)
-    self._testArg(method, x, dimension, expected_values, False, expected_err_re)
+    self._testArg(method, x, axis, expected_values, True, expected_err_re)
+    self._testArg(method, x, axis, expected_values, False, expected_err_re)
 
   def _testBasic(self, dtype):
     x = np.asarray(100 * np.random.randn(200), dtype=dtype)
 
-    # Check that argmin and argmax match numpy along the primary
-    # dimension
+    # Check that argmin and argmax match numpy along the primary axis
     self._testBothArg(math_ops.argmax, x, 0, x.argmax())
     self._testBothArg(math_ops.argmin, x, 0, x.argmin())
 
   def _testDim(self, dtype):
     x = np.asarray(100 * np.random.randn(3, 2, 4, 5, 6), dtype=dtype)
 
-    # Check that argmin and argmax match numpy along all dimensions
-    for dim in range(-5, 5):
-      self._testBothArg(math_ops.argmax, x, dim, x.argmax(dim))
-      self._testBothArg(math_ops.argmin, x, dim, x.argmin(dim))
+    # Check that argmin and argmax match numpy along all axes
+    for axis in range(-5, 5):
+      self._testBothArg(math_ops.argmax, x, axis, x.argmax(axis))
+      self._testBothArg(math_ops.argmin, x, axis, x.argmin(axis))
 
   def testFloat(self):
     self._testBasic(np.float32)
@@ -78,7 +77,7 @@ class ArgMaxTest(test.TestCase):
     x = np.asarray(100 * np.random.randn(200), dtype=np.float32)
     expected_values = x.argmax()
     with self.test_session(use_gpu=True):
-      ans = math_ops.argmax(x, dimension=0, output_type=dtypes.int32)
+      ans = math_ops.argmax(x, axis=0, output_type=dtypes.int32)
       tf_ans = ans.eval()
       self.assertEqual(np.int32, tf_ans.dtype)
       # The values are equal when comparing int32 to int64 because
@@ -86,7 +85,7 @@ class ArgMaxTest(test.TestCase):
       self.assertAllEqual(tf_ans, expected_values)
     expected_values = x.argmin()
     with self.test_session(use_gpu=True):
-      ans = math_ops.argmin(x, dimension=0, output_type=dtypes.int32)
+      ans = math_ops.argmin(x, axis=0, output_type=dtypes.int32)
       tf_ans = ans.eval()
       self.assertEqual(np.int32, tf_ans.dtype)
       self.assertAllEqual(tf_ans, expected_values)
-- 
GitLab


From 988bed7e66462367bb63fda2dcbf31a0f14e7fe9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 16:28:48 -0700
Subject: [PATCH 1095/1375] Add a utility function to build and write a
 SavedModel, setting reasonable defaults for the required arguments.

PiperOrigin-RevId: 162420810
---
 tensorflow/contrib/saved_model/BUILD          |  15 +++
 .../saved_model/python/saved_model/utils.py   |  81 ++++++++++++++
 .../python/saved_model/utils_test.py          | 102 ++++++++++++++++++
 3 files changed, 198 insertions(+)
 create mode 100644 tensorflow/contrib/saved_model/python/saved_model/utils.py
 create mode 100644 tensorflow/contrib/saved_model/python/saved_model/utils_test.py

diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD
index e86fd318bd..a82ee6ac41 100644
--- a/tensorflow/contrib/saved_model/BUILD
+++ b/tensorflow/contrib/saved_model/BUILD
@@ -77,6 +77,21 @@ py_test(
     ],
 )
 
+py_test(
+    name = "utils_test",
+    size = "small",
+    srcs = ["python/saved_model/utils_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":saved_model_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python/saved_model",
+        "//tensorflow/python/saved_model:signature_constants",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/saved_model/python/saved_model/utils.py b/tensorflow/contrib/saved_model/python/saved_model/utils.py
new file mode 100644
index 0000000000..9f34af64a6
--- /dev/null
+++ b/tensorflow/contrib/saved_model/python/saved_model/utils.py
@@ -0,0 +1,81 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SavedModel utility functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.saved_model import builder
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import signature_def_utils
+from tensorflow.python.saved_model import tag_constants
+
+
+def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None):
+  """Convenience function to build a SavedModel suitable for serving.
+
+  In many common cases, saving models for serving will be as simple as:
+
+      simple_save(session,
+                  export_dir,
+                  inputs={"x": x, "y": y},
+                  outputs={"z": z})
+
+  Although in many cases it's not necessary to understand all of the many ways
+      to configure a SavedModel, this method has a few practical implications:
+    - It will be treated as a graph for inference / serving (i.e. uses the tag
+      `tag_constants.SERVING`)
+    - The saved model will load in TensorFlow Serving and supports the
+      [Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).
+      To use the Classify, Regress, or MultiInference APIs, please
+      use either
+      [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)
+      or the lower level
+      [SavedModel APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md).
+    - Some TensorFlow ops depend on information on disk or other information
+      called "assets". These are generally handled automatically by adding the
+      assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that
+      collection are exported; if you need more custom behavior, you'll need to
+      use the [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py).
+
+  More information about SavedModel and signatures can be found here:
+  https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md.
+
+  Args:
+    session: The TensorFlow session from which to save the meta graph and
+        variables.
+    export_dir: The path to which the SavedModel will be stored.
+    inputs: dict mapping string input names to tensors. These are added
+        to the SignatureDef as the inputs.
+    outputs:  dict mapping string output names to tensors. These are added
+        to the SignatureDef as the outputs.
+    legacy_init_op: Legacy support for op or group of ops to execute after the
+        restore op upon a load.
+  """
+  signature_def_map = {
+      signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+          signature_def_utils.predict_signature_def(inputs, outputs)
+  }
+  b = builder.SavedModelBuilder(export_dir)
+  b.add_meta_graph_and_variables(
+      session,
+      tags=[tag_constants.SERVING],
+      signature_def_map=signature_def_map,
+      assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS),
+      legacy_init_op=legacy_init_op,
+      clear_devices=True)
+  b.save()
diff --git a/tensorflow/contrib/saved_model/python/saved_model/utils_test.py b/tensorflow/contrib/saved_model/python/saved_model/utils_test.py
new file mode 100644
index 0000000000..36dfb88871
--- /dev/null
+++ b/tensorflow/contrib/saved_model/python/saved_model/utils_test.py
@@ -0,0 +1,102 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for saved_model utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from tensorflow.contrib.saved_model.python.saved_model import utils
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.saved_model import loader
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import tag_constants
+
+
+class UtilsTest(test.TestCase):
+
+  def _init_and_validate_variable(self, sess, variable_name, variable_value):
+    v = variables.Variable(variable_value, name=variable_name)
+    sess.run(variables.global_variables_initializer())
+    self.assertEqual(variable_value, v.eval())
+    return v
+
+  def _check_variable_info(self, actual_variable, expected_variable):
+    self.assertEqual(actual_variable.name, expected_variable.name)
+    self.assertEqual(actual_variable.dtype, expected_variable.dtype)
+    self.assertEqual(len(actual_variable.shape), len(expected_variable.shape))
+    for i in range(len(actual_variable.shape)):
+      self.assertEqual(actual_variable.shape[i], expected_variable.shape[i])
+
+  def _check_tensor_info(self, actual_tensor_info, expected_tensor):
+    self.assertEqual(actual_tensor_info.name, expected_tensor.name)
+    self.assertEqual(actual_tensor_info.dtype, expected_tensor.dtype)
+    self.assertEqual(
+        len(actual_tensor_info.tensor_shape.dim), len(expected_tensor.shape))
+    for i in range(len(actual_tensor_info.tensor_shape.dim)):
+      self.assertEqual(actual_tensor_info.tensor_shape.dim[i].size,
+                       expected_tensor.shape[i])
+
+  def testSimpleSave(self):
+    """Test simple_save that uses the default parameters."""
+    export_dir = os.path.join(test.get_temp_dir(),
+                              "test_simple_save")
+
+    # Initialize input and output variables and save a prediction graph using
+    # the default parameters.
+    with self.test_session(graph=ops.Graph()) as sess:
+      var_x = self._init_and_validate_variable(sess, "var_x", 1)
+      var_y = self._init_and_validate_variable(sess, "var_y", 2)
+      inputs = {"x": var_x}
+      outputs = {"y": var_y}
+      utils.simple_save(sess, export_dir, inputs, outputs)
+
+    # Restore the graph with a valid tag and check the global variables and
+    # signature def map.
+    with self.test_session(graph=ops.Graph()) as sess:
+      graph = loader.load(sess, [tag_constants.SERVING], export_dir)
+      collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+
+      # Check value and metadata of the saved variables.
+      self.assertEqual(len(collection_vars), 2)
+      self.assertEqual(1, collection_vars[0].eval())
+      self.assertEqual(2, collection_vars[1].eval())
+      self._check_variable_info(collection_vars[0], var_x)
+      self._check_variable_info(collection_vars[1], var_y)
+
+      # Check that the appropriate signature_def_map is created with the
+      # default key and method name, and the specified inputs and outputs.
+      signature_def_map = graph.signature_def
+      self.assertEqual(1, len(signature_def_map))
+      self.assertEqual(signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
+                       list(signature_def_map.keys())[0])
+
+      signature_def = signature_def_map[
+          signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
+      self.assertEqual(signature_constants.PREDICT_METHOD_NAME,
+                       signature_def.method_name)
+
+      self.assertEqual(1, len(signature_def.inputs))
+      self._check_tensor_info(signature_def.inputs["x"], var_x)
+      self.assertEqual(1, len(signature_def.outputs))
+      self._check_tensor_info(signature_def.outputs["y"], var_y)
+
+
+if __name__ == "__main__":
+  test.main()
-- 
GitLab


From 11dff5b05b3488520d3a415173d73ae91fded092 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 16:30:45 -0700
Subject: [PATCH 1096/1375] Remove all references to OperatorPD and replace
 with LinearOperator.   - Only dependent is Wishart, which is updated to use
 LinearOperator.

PiperOrigin-RevId: 162421033
---
 tensorflow/contrib/distributions/BUILD        |  96 --
 .../kernel_tests/operator_pd_cholesky_test.py | 290 ------
 .../kernel_tests/operator_pd_diag_test.py     | 104 ---
 .../kernel_tests/operator_pd_full_test.py     |  63 --
 .../kernel_tests/operator_pd_identity_test.py | 120 ---
 .../python/kernel_tests/operator_pd_test.py   | 375 --------
 .../operator_pd_vdvt_update_test.py           | 277 ------
 .../python/kernel_tests/wishart_test.py       |  18 +-
 .../distributions/python/ops/operator_pd.py   | 850 ------------------
 .../python/ops/operator_pd_cholesky.py        | 185 ----
 .../python/ops/operator_pd_diag.py            | 306 -------
 .../python/ops/operator_pd_full.py            |  91 --
 .../python/ops/operator_pd_identity.py        | 244 -----
 .../python/ops/operator_pd_vdvt_update.py     | 480 ----------
 .../python/ops/operator_test_util.py          | 185 ----
 .../distributions/python/ops/wishart.py       | 165 ++--
 16 files changed, 115 insertions(+), 3734 deletions(-)
 delete mode 100644 tensorflow/contrib/distributions/python/kernel_tests/operator_pd_cholesky_test.py
 delete mode 100644 tensorflow/contrib/distributions/python/kernel_tests/operator_pd_diag_test.py
 delete mode 100644 tensorflow/contrib/distributions/python/kernel_tests/operator_pd_full_test.py
 delete mode 100644 tensorflow/contrib/distributions/python/kernel_tests/operator_pd_identity_test.py
 delete mode 100644 tensorflow/contrib/distributions/python/kernel_tests/operator_pd_test.py
 delete mode 100644 tensorflow/contrib/distributions/python/kernel_tests/operator_pd_vdvt_update_test.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/operator_pd.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/operator_pd_cholesky.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/operator_pd_diag.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/operator_pd_full.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/operator_pd_identity.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/operator_pd_vdvt_update.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/operator_test_util.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 838214bc85..d8278ca90b 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -93,102 +93,6 @@ cuda_py_test(
     tags = ["no_pip"],
 )
 
-cuda_py_test(
-    name = "operator_pd_test",
-    size = "small",
-    srcs = ["python/kernel_tests/operator_pd_test.py"],
-    additional_deps = [
-        ":distributions_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:linalg_ops",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:nn_ops",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-cuda_py_test(
-    name = "operator_pd_cholesky_test",
-    size = "medium",
-    srcs = ["python/kernel_tests/operator_pd_cholesky_test.py"],
-    additional_deps = [
-        ":distributions_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:nn_ops",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-cuda_py_test(
-    name = "operator_pd_diag_test",
-    size = "medium",
-    srcs = ["python/kernel_tests/operator_pd_diag_test.py"],
-    additional_deps = [
-        ":distributions_py",
-        "//third_party/py/numpy",
-        "@six_archive//:six",
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-cuda_py_test(
-    name = "operator_pd_full_test",
-    size = "medium",
-    srcs = ["python/kernel_tests/operator_pd_full_test.py"],
-    additional_deps = [
-        ":distributions_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-cuda_py_test(
-    name = "operator_pd_identity_test",
-    size = "medium",
-    srcs = ["python/kernel_tests/operator_pd_identity_test.py"],
-    additional_deps = [
-        ":distributions_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-cuda_py_test(
-    name = "operator_pd_vdvt_update_test",
-    size = "large",
-    srcs = ["python/kernel_tests/operator_pd_vdvt_update_test.py"],
-    additional_deps = [
-        ":distributions_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
-    ],
-    shard_count = 5,
-    tags = ["notap"],  # http://b/30441813
-)
-
 cuda_py_test(
     name = "binomial_test",
     size = "small",
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_cholesky_test.py b/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_cholesky_test.py
deleted file mode 100644
index 6549992633..0000000000
--- a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_cholesky_test.py
+++ /dev/null
@@ -1,290 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.contrib import distributions
-from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
-from tensorflow.python.ops.distributions import util as distribution_util
-from tensorflow.python.platform import test
-
-
-def softplus(x):
-  return np.log(1 + np.exp(x))
-
-
-class OperatorPDCholeskyTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _random_cholesky_array(self, shape):
-    mat = self._rng.rand(*shape)
-    chol = distribution_util.matrix_diag_transform(
-        mat, transform=nn_ops.softplus)
-    # Zero the upper triangle because we're using this as a true Cholesky factor
-    # in our tests.
-    return array_ops.matrix_band_part(chol, -1, 0).eval()
-
-  def testLogDet(self):
-    with self.test_session():
-      batch_shape = ()
-      for k in [1, 4]:
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-        log_det = operator.log_det()
-        expected_log_det = np.log(np.prod(np.diag(chol))**2)
-
-        self.assertEqual(batch_shape, log_det.get_shape())
-        self.assertAllClose(expected_log_det, log_det.eval())
-
-  def testLogDetBatchMatrix(self):
-    with self.test_session():
-      batch_shape = (2, 3)
-      for k in [1, 4]:
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-        log_det = operator.log_det()
-
-        self.assertEqual(batch_shape, log_det.get_shape())
-
-        # Test the log-determinant of the [1, 1] matrix.
-        chol_11 = chol[1, 1, :, :]
-        expected_log_det = np.log(np.prod(np.diag(chol_11))**2)
-        self.assertAllClose(expected_log_det, log_det.eval()[1, 1])
-
-  def testSqrtMatmulSingleMatrix(self):
-    with self.test_session():
-      batch_shape = ()
-      for k in [1, 4]:
-        x_shape = batch_shape + (k, 3)
-        x = self._rng.rand(*x_shape)
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-
-        sqrt_operator_times_x = operator.sqrt_matmul(x)
-        expected = math_ops.matmul(chol, x)
-
-        self.assertEqual(expected.get_shape(),
-                         sqrt_operator_times_x.get_shape())
-        self.assertAllClose(expected.eval(), sqrt_operator_times_x.eval())
-
-  def testSqrtMatmulBatchMatrix(self):
-    with self.test_session():
-      batch_shape = (2, 3)
-      for k in [1, 4]:
-        x_shape = batch_shape + (k, 5)
-        x = self._rng.rand(*x_shape)
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-
-        sqrt_operator_times_x = operator.sqrt_matmul(x)
-        expected = math_ops.matmul(chol, x)
-
-        self.assertEqual(expected.get_shape(),
-                         sqrt_operator_times_x.get_shape())
-        self.assertAllClose(expected.eval(), sqrt_operator_times_x.eval())
-
-  def testSqrtMatmulBatchMatrixWithTranspose(self):
-    with self.test_session():
-      batch_shape = (2, 3)
-      for k in [1, 4]:
-        x_shape = batch_shape + (5, k)
-        x = self._rng.rand(*x_shape)
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-
-        sqrt_operator_times_x = operator.sqrt_matmul(x, transpose_x=True)
-        # tf.batch_matmul is defined x * y, so "y" is on the right, not "x".
-        expected = math_ops.matmul(chol, x, adjoint_b=True)
-
-        self.assertEqual(expected.get_shape(),
-                         sqrt_operator_times_x.get_shape())
-        self.assertAllClose(expected.eval(), sqrt_operator_times_x.eval())
-
-  def testMatmulSingleMatrix(self):
-    with self.test_session():
-      batch_shape = ()
-      for k in [1, 4]:
-        x_shape = batch_shape + (k, 5)
-        x = self._rng.rand(*x_shape)
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-        matrix = math_ops.matmul(chol, chol, adjoint_b=True)
-
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-
-        expected = math_ops.matmul(matrix, x)
-
-        self.assertEqual(expected.get_shape(), operator.matmul(x).get_shape())
-        self.assertAllClose(expected.eval(), operator.matmul(x).eval())
-
-  def testMatmulBatchMatrix(self):
-    with self.test_session():
-      batch_shape = (2, 3)
-      for k in [1, 4]:
-        x_shape = batch_shape + (k, 5)
-        x = self._rng.rand(*x_shape)
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-        matrix = math_ops.matmul(chol, chol, adjoint_b=True)
-
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-
-        expected = math_ops.matmul(matrix, x)
-
-        self.assertEqual(expected.get_shape(), operator.matmul(x).get_shape())
-        self.assertAllClose(expected.eval(), operator.matmul(x).eval())
-
-  def testMatmulBatchMatrixWithTranspose(self):
-    with self.test_session():
-      batch_shape = (2, 3)
-      for k in [1, 4]:
-        x_shape = batch_shape + (5, k)
-        x = self._rng.rand(*x_shape)
-        chol_shape = batch_shape + (k, k)
-        chol = self._random_cholesky_array(chol_shape)
-        matrix = math_ops.matmul(chol, chol, adjoint_b=True)
-
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-        operator_times_x = operator.matmul(x, transpose_x=True)
-
-        # tf.batch_matmul is defined x * y, so "y" is on the right, not "x".
-        expected = math_ops.matmul(matrix, x, adjoint_b=True)
-
-        self.assertEqual(expected.get_shape(), operator_times_x.get_shape())
-        self.assertAllClose(expected.eval(), operator_times_x.eval())
-
-  def testShape(self):
-    # All other shapes are defined by the abstractmethod shape, so we only need
-    # to test this.
-    with self.test_session():
-      for shape in [(3, 3), (2, 3, 3), (1, 2, 3, 3)]:
-        chol = self._random_cholesky_array(shape)
-        operator = operator_pd_cholesky.OperatorPDCholesky(chol)
-        self.assertAllEqual(shape, operator.shape().eval())
-
-  def testToDense(self):
-    with self.test_session():
-      chol = self._random_cholesky_array((3, 3))
-      chol_2 = chol.copy()
-      chol_2[0, 2] = 1000  # Make sure upper triangular part makes no diff.
-      operator = operator_pd_cholesky.OperatorPDCholesky(chol_2)
-      self.assertAllClose(chol.dot(chol.T), operator.to_dense().eval())
-
-  def testSqrtToDense(self):
-    with self.test_session():
-      chol = self._random_cholesky_array((2, 3, 3))
-      chol_2 = chol.copy()
-      chol_2[0, 0, 2] = 1000  # Make sure upper triangular part makes no diff.
-      operator = operator_pd_cholesky.OperatorPDCholesky(chol_2)
-      self.assertAllClose(chol, operator.sqrt_to_dense().eval())
-
-  def testNonPositiveDefiniteMatrixRaises(self):
-    # Singlular matrix with one positive eigenvalue and one zero eigenvalue.
-    with self.test_session():
-      lower_mat = [[1.0, 0.0], [2.0, 0.0]]
-      operator = operator_pd_cholesky.OperatorPDCholesky(lower_mat)
-      with self.assertRaisesOpError("x > 0 did not hold"):
-        operator.to_dense().eval()
-
-  def testNonPositiveDefiniteMatrixDoesNotRaiseIfNotVerifyPd(self):
-    # Singlular matrix with one positive eigenvalue and one zero eigenvalue.
-    with self.test_session():
-      lower_mat = [[1.0, 0.0], [2.0, 0.0]]
-      operator = operator_pd_cholesky.OperatorPDCholesky(
-          lower_mat, verify_pd=False)
-      operator.to_dense().eval()  # Should not raise.
-
-  def testNotHavingTwoIdenticalLastDimsRaises(self):
-    # Unless the last two dims are equal, this cannot represent a matrix, and it
-    # should raise.
-    with self.test_session():
-      batch_vec = [[1.0], [2.0]]  # shape 2 x 1
-      with self.assertRaisesOpError("x == y did not hold"):
-        operator = operator_pd_cholesky.OperatorPDCholesky(batch_vec)
-        operator.to_dense().eval()
-
-
-class MatrixDiagTransformTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState(0)
-
-  def check_off_diagonal_same(self, m1, m2):
-    """Check the lower triangular part, not upper or diag."""
-    self.assertAllClose(np.tril(m1, k=-1), np.tril(m2, k=-1))
-    self.assertAllClose(np.triu(m1, k=1), np.triu(m2, k=1))
-
-  def testNonBatchMatrixWithTransform(self):
-    mat = self._rng.rand(4, 4)
-    with self.test_session():
-      chol = distributions.matrix_diag_transform(mat, transform=nn_ops.softplus)
-      self.assertEqual((4, 4), chol.get_shape())
-
-      self.check_off_diagonal_same(mat, chol.eval())
-      self.assertAllClose(softplus(np.diag(mat)), np.diag(chol.eval()))
-
-  def testNonBatchMatrixNoTransform(self):
-    mat = self._rng.rand(4, 4)
-    with self.test_session():
-      # Default is no transform.
-      chol = distributions.matrix_diag_transform(mat)
-      self.assertEqual((4, 4), chol.get_shape())
-      self.assertAllClose(mat, chol.eval())
-
-  def testBatchMatrixWithTransform(self):
-    mat = self._rng.rand(2, 4, 4)
-    mat_0 = mat[0, :, :]
-    with self.test_session():
-      chol = distributions.matrix_diag_transform(mat, transform=nn_ops.softplus)
-
-      self.assertEqual((2, 4, 4), chol.get_shape())
-
-      chol_0 = chol.eval()[0, :, :]
-
-      self.check_off_diagonal_same(mat_0, chol_0)
-      self.assertAllClose(softplus(np.diag(mat_0)), np.diag(chol_0))
-
-      self.check_off_diagonal_same(mat_0, chol_0)
-      self.assertAllClose(softplus(np.diag(mat_0)), np.diag(chol_0))
-
-  def testBatchMatrixNoTransform(self):
-    mat = self._rng.rand(2, 4, 4)
-    with self.test_session():
-      # Default is no transform.
-      chol = distributions.matrix_diag_transform(mat)
-
-      self.assertEqual((2, 4, 4), chol.get_shape())
-      self.assertAllClose(mat, chol.eval())
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_diag_test.py b/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_diag_test.py
deleted file mode 100644
index 0a8f9640c4..0000000000
--- a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_diag_test.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import abc
-import numpy as np
-import six
-
-from tensorflow.contrib.distributions.python.ops import operator_pd_diag
-from tensorflow.contrib.distributions.python.ops import operator_test_util
-from tensorflow.python.ops import array_ops
-from tensorflow.python.platform import test
-
-
-@six.add_metaclass(abc.ABCMeta)
-class OperatorPDDiagBaseTest(object):
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _random_pd_diag(self, diag_shape):
-    return self._rng.rand(*diag_shape) + 0.1
-
-  @abc.abstractmethod
-  def _diag_to_matrix(self, diag):
-    pass
-
-  @abc.abstractproperty
-  def operator_class(self):
-    # Return the operator class that this tests.
-    pass
-
-  def _build_operator_and_mat(self, batch_shape, k, dtype=np.float64):
-    # Create a diagonal matrix explicitly.
-    # Create an OperatorPDSqrtDiag using the same diagonal.
-    # The operator should have the same behavior.
-    #
-    batch_shape = list(batch_shape)
-    diag_shape = batch_shape + [k]
-
-    # The diag is the square root.
-    diag = self._random_pd_diag(diag_shape).astype(dtype)
-    mat = self._diag_to_matrix(diag).astype(dtype)
-    operator = self.operator_class(diag)
-
-    return operator, mat
-
-  def testNonPositiveDefiniteMatrixRaises(self):
-    # Singlular matrix with one positive eigenvalue and one zero eigenvalue.
-    with self.test_session():
-      diag = [1.0, 0.0]
-      operator = operator_pd_diag.OperatorPDSqrtDiag(diag)
-      with self.assertRaisesOpError("assert_positive"):
-        operator.to_dense().eval()
-
-  def testNonPositiveDefiniteMatrixDoesNotRaiseIfNotVerifyPd(self):
-    # Singlular matrix with one positive eigenvalue and one zero eigenvalue.
-    with self.test_session():
-      diag = [1.0, 0.0]
-      operator = operator_pd_diag.OperatorPDSqrtDiag(diag, verify_pd=False)
-      operator.to_dense().eval()  # Should not raise
-
-
-class OperatorPDDiagTest(OperatorPDDiagBaseTest,
-                         operator_test_util.OperatorPDDerivedClassTest):
-  """Most tests done in the base classes."""
-
-  def _diag_to_matrix(self, diag):
-    return array_ops.matrix_diag(diag).eval()
-
-  @property
-  def operator_class(self):
-    return operator_pd_diag.OperatorPDDiag
-
-
-class OperatorPDSqrtDiagTest(OperatorPDDiagBaseTest,
-                             operator_test_util.OperatorPDDerivedClassTest):
-  """Most tests done in the base classes."""
-
-  def _diag_to_matrix(self, diag):
-    return array_ops.matrix_diag(diag**2).eval()
-
-  @property
-  def operator_class(self):
-    return operator_pd_diag.OperatorPDSqrtDiag
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_full_test.py b/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_full_test.py
deleted file mode 100644
index 35a7c7e603..0000000000
--- a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_full_test.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.contrib.distributions.python.ops import operator_pd_full
-from tensorflow.python.ops import math_ops
-from tensorflow.python.platform import test
-
-
-class OperatorPDFullTest(test.TestCase):
-  # The only method needing checked (because it isn't part of the parent class)
-  # is the check for symmetry.
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _random_positive_def_array(self, *shape):
-    matrix = self._rng.rand(*shape)
-    return math_ops.matmul(matrix, matrix, adjoint_b=True).eval()
-
-  def testPositiveDefiniteMatrixDoesntRaise(self):
-    with self.test_session():
-      matrix = self._random_positive_def_array(2, 3, 3)
-      operator = operator_pd_full.OperatorPDFull(matrix, verify_pd=True)
-      operator.to_dense().eval()  # Should not raise
-
-  def testNegativeDefiniteMatrixRaises(self):
-    with self.test_session():
-      matrix = -1 * self._random_positive_def_array(3, 2, 2)
-      operator = operator_pd_full.OperatorPDFull(matrix, verify_pd=True)
-      # Could fail inside Cholesky decomposition, or later when we test the
-      # diag.
-      with self.assertRaisesOpError("x > 0|Cholesky"):
-        operator.to_dense().eval()
-
-  def testNonSymmetricMatrixRaises(self):
-    with self.test_session():
-      matrix = self._random_positive_def_array(3, 2, 2)
-      matrix[0, 0, 1] += 0.001
-      operator = operator_pd_full.OperatorPDFull(matrix, verify_pd=True)
-      with self.assertRaisesOpError("x == y"):
-        operator.to_dense().eval()
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_identity_test.py b/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_identity_test.py
deleted file mode 100644
index 2b1e4c912b..0000000000
--- a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_identity_test.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.contrib import distributions as distributions_lib
-from tensorflow.contrib.distributions.python.ops import operator_pd_identity
-from tensorflow.contrib.distributions.python.ops import operator_test_util
-from tensorflow.python.framework import constant_op
-from tensorflow.python.ops import array_ops
-from tensorflow.python.platform import test
-
-distributions = distributions_lib
-
-
-class OperatorPDIdentityTest(operator_test_util.OperatorPDDerivedClassTest):
-  """Most tests done in the base class."""
-
-  def _build_operator_and_mat(self, batch_shape, k, dtype=np.float64):
-    # Build an identity matrix with right shape and dtype.
-    # Build an operator that should act the same way.
-    batch_shape = list(batch_shape)
-    diag_shape = batch_shape + [k]
-    matrix_shape = batch_shape + [k, k]
-    diag = array_ops.ones(diag_shape, dtype=dtype)
-    scale = constant_op.constant(2.0, dtype=dtype)
-    scaled_identity_matrix = scale * array_ops.matrix_diag(diag)
-    operator = operator_pd_identity.OperatorPDIdentity(
-        matrix_shape, dtype, scale=scale)
-    return operator, scaled_identity_matrix.eval()
-
-  def testBadDtypeArgsRaise(self):
-    dtype = np.float32
-    batch_shape = [2, 3]
-    k = 4
-    with self.test_session():
-      operator, _ = self._build_operator_and_mat(batch_shape, k, dtype=dtype)
-
-      x_good_shape = batch_shape + [k, 5]
-      x_good = self._rng.randn(*x_good_shape).astype(dtype)
-      x_bad = x_good.astype(np.float64)
-
-      operator.matmul(x_good).eval()  # Should not raise.
-
-      with self.assertRaisesRegexp(TypeError, "dtype"):
-        operator.matmul(x_bad)
-
-      with self.assertRaisesRegexp(TypeError, "dtype"):
-        operator.solve(x_bad)
-
-      with self.assertRaisesRegexp(TypeError, "dtype"):
-        operator.sqrt_solve(x_bad)
-
-  def testBadRankArgsRaise(self):
-    # Prepend a singleton dimension, changing the rank of "x", but not the size.
-    dtype = np.float32
-    batch_shape = [2, 3]
-    k = 4
-    with self.test_session():
-      operator, _ = self._build_operator_and_mat(batch_shape, k, dtype=dtype)
-
-      x_good_shape = batch_shape + [k, 5]
-      x_good = self._rng.randn(*x_good_shape).astype(dtype)
-      x_bad = x_good.reshape(1, 2, 3, 4, 5)
-
-      operator.matmul(x_good).eval()  # Should not raise.
-
-      with self.assertRaisesRegexp(ValueError, "tensor rank"):
-        operator.matmul(x_bad)
-
-      with self.assertRaisesRegexp(ValueError, "tensor rank"):
-        operator.solve(x_bad)
-
-      with self.assertRaisesRegexp(ValueError, "tensor rank"):
-        operator.sqrt_solve(x_bad)
-
-  def testIncompatibleShapeArgsRaise(self):
-    # Test shapes that are the same rank but incompatible for matrix
-    # multiplication.
-    dtype = np.float32
-    batch_shape = [2, 3]
-    k = 4
-    with self.test_session():
-      operator, _ = self._build_operator_and_mat(batch_shape, k, dtype=dtype)
-
-      x_good_shape = batch_shape + [k, 5]
-      x_good = self._rng.randn(*x_good_shape).astype(dtype)
-      x_bad_shape = batch_shape + [5, k]
-      x_bad = x_good.reshape(*x_bad_shape)
-
-      operator.matmul(x_good).eval()  # Should not raise.
-
-      with self.assertRaisesRegexp(ValueError, "Incompatible"):
-        operator.matmul(x_bad)
-
-      with self.assertRaisesRegexp(ValueError, "Incompatible"):
-        operator.solve(x_bad)
-
-      with self.assertRaisesRegexp(ValueError, "Incompatible"):
-        operator.sqrt_solve(x_bad)
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_test.py b/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_test.py
deleted file mode 100644
index a2a1b54be6..0000000000
--- a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_test.py
+++ /dev/null
@@ -1,375 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-# For private members.
-from tensorflow.contrib import distributions as distributions_lib
-from tensorflow.contrib.distributions.python.ops import operator_pd
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import linalg_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
-from tensorflow.python.platform import test
-
-distributions = distributions_lib
-
-
-class OperatorShape(operator_pd.OperatorPDBase):
-  """Operator implements the ABC method ._shape."""
-
-  def __init__(self, shape):
-    self._stored_shape = shape
-
-  @property
-  def verify_pd(self):
-    return True
-
-  def get_shape(self):
-    return tensor_shape.TensorShape(self._stored_shape)
-
-  def _shape(self):
-    return array_ops.shape(np.random.rand(*self._stored_shape))
-
-  @property
-  def name(self):
-    return "OperatorShape"
-
-  def dtype(self):
-    return dtypes.int32
-
-  @property
-  def inputs(self):
-    return []
-
-
-class OperatorSqrtSolve(OperatorShape):
-  """Operator implements .sqrt_solve."""
-
-  def __init__(self, chol_array):
-    self._chol = ops.convert_to_tensor(chol_array)
-    super(OperatorSqrtSolve, self).__init__(chol_array.shape)
-
-  def _sqrt_solve(self, rhs):
-    return linalg_ops.matrix_triangular_solve(self._chol, rhs, lower=True)
-
-  def _batch_sqrt_solve(self, rhs):
-    return linalg_ops.matrix_triangular_solve(self._chol, rhs, lower=True)
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    return self._iqfov_via_sqrt_solve(x)
-
-
-class OperatorSolve(OperatorShape):
-  """Operator implements .solve."""
-
-  def __init__(self, chol):
-    self._pos_def_matrix = math_ops.matmul(chol, chol, adjoint_b=True)
-    super(OperatorSolve, self).__init__(chol.shape)
-
-  def _solve(self, rhs):
-    return linalg_ops.matrix_solve(self._pos_def_matrix, rhs)
-
-  def _batch_solve(self, rhs):
-    return linalg_ops.matrix_solve(self._pos_def_matrix, rhs)
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    return self._iqfov_via_solve(x)
-
-
-class OperatorPDBaseTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _random_cholesky_array(self, shape):
-    mat = self._rng.rand(*shape)
-    chol = distributions.matrix_diag_transform(mat, transform=nn_ops.softplus)
-    # Zero the upper triangle because we're using this as a true Cholesky factor
-    # in our tests.
-    return array_ops.matrix_band_part(chol, -1, 0).eval()
-
-  def _numpy_inv_quadratic_form_on_vectors(self, chol, x):
-    # Numpy works with batches now (calls them "stacks").
-    x_expanded = np.expand_dims(x, -1)
-    whitened = np.linalg.solve(chol, x_expanded)
-    return (whitened**2).sum(axis=-1).sum(axis=-1)
-
-  def testAllShapesMethodsDefinedByTheOneAbstractpropertyShape(self):
-
-    shape = (1, 2, 3, 3)
-    with self.test_session():
-      operator = OperatorShape(shape)
-
-      self.assertAllEqual(shape, operator.shape().eval())
-      self.assertAllEqual(4, operator.rank().eval())
-      self.assertAllEqual((1, 2), operator.batch_shape().eval())
-      self.assertAllEqual((1, 2, 3), operator.vector_shape().eval())
-      self.assertAllEqual(3, operator.vector_space_dimension().eval())
-
-      self.assertEqual(shape, operator.get_shape())
-      self.assertEqual((1, 2), operator.get_batch_shape())
-      self.assertEqual((1, 2, 3), operator.get_vector_shape())
-
-  def testIqfovXRankSameAsBroadcastRankUsingSqrtSolve(self):
-    with self.test_session():
-      for batch_shape in [(), (2,)]:
-        for k in [1, 3]:
-
-          x_shape = batch_shape + (k,)
-          x = self._rng.randn(*x_shape)
-
-          chol_shape = batch_shape + (k, k)
-          chol = self._random_cholesky_array(chol_shape)
-          operator = OperatorSqrtSolve(chol)
-          qf = operator.inv_quadratic_form_on_vectors(x)
-
-          self.assertEqual(batch_shape, qf.get_shape())
-
-          numpy_qf = self._numpy_inv_quadratic_form_on_vectors(chol, x)
-          self.assertAllClose(numpy_qf, qf.eval())
-
-  def testIqfovXRankGreaterThanBroadcastRankUsingSqrtSolve(self):
-    with self.test_session():
-      for batch_shape in [(), (2,), (2, 3)]:
-        for k in [1, 4]:
-
-          x_shape = batch_shape + (k,)
-          x = self._rng.randn(*x_shape)
-
-          # chol will not have the leading dimension.
-          chol_shape = batch_shape[1:] + (k, k)
-          chol = self._random_cholesky_array(chol_shape)
-          operator = OperatorSqrtSolve(chol)
-          qf = operator.inv_quadratic_form_on_vectors(x)
-          numpy_qf = self._numpy_inv_quadratic_form_on_vectors(chol, x)
-
-          self.assertEqual(batch_shape, qf.get_shape())
-          self.assertAllClose(numpy_qf, qf.eval())
-
-  def testIqfovXRankTwoGreaterThanBroadcastRankUsingSqrtSolve(self):
-    with self.test_session():
-      for batch_shape in [(2, 3), (2, 3, 4), (2, 3, 4, 5)]:
-        for k in [1, 4]:
-
-          x_shape = batch_shape + (k,)
-          x = self._rng.randn(*x_shape)
-
-          # chol will not have the leading two dimensions.
-          chol_shape = batch_shape[2:] + (k, k)
-          chol = self._random_cholesky_array(chol_shape)
-          operator = OperatorSqrtSolve(chol)
-          qf = operator.inv_quadratic_form_on_vectors(x)
-          numpy_qf = self._numpy_inv_quadratic_form_on_vectors(chol, x)
-
-          self.assertEqual(batch_shape, qf.get_shape())
-          self.assertAllClose(numpy_qf, qf.eval())
-
-  def testIqfovXRankSameAsBroadcastRankUsingSolve(self):
-    with self.test_session():
-      for batch_shape in [(), (2,)]:
-        for k in [1, 3]:
-
-          x_shape = batch_shape + (k,)
-          x = self._rng.randn(*x_shape)
-
-          chol_shape = batch_shape + (k, k)
-          chol = self._random_cholesky_array(chol_shape)
-          operator = OperatorSolve(chol)
-          qf = operator.inv_quadratic_form_on_vectors(x)
-
-          self.assertEqual(batch_shape, qf.get_shape())
-
-          numpy_qf = self._numpy_inv_quadratic_form_on_vectors(chol, x)
-          self.assertAllClose(numpy_qf, qf.eval())
-
-  def testIqfovXRankGreaterThanBroadcastRankUsingSolve(self):
-    with self.test_session():
-      for batch_shape in [(2,), (2, 3)]:
-        for k in [1, 4]:
-
-          x_shape = batch_shape + (k,)
-          x = self._rng.randn(*x_shape)
-
-          # chol will not have the leading dimension.
-          chol_shape = batch_shape[1:] + (k, k)
-          chol = self._random_cholesky_array(chol_shape)
-          operator = OperatorSolve(chol)
-          qf = operator.inv_quadratic_form_on_vectors(x)
-          numpy_qf = self._numpy_inv_quadratic_form_on_vectors(chol, x)
-
-          self.assertEqual(batch_shape, qf.get_shape())
-          self.assertAllClose(numpy_qf, qf.eval())
-
-  def testIqfovXRankTwoGreaterThanBroadcastRankUsingSolve(self):
-    with self.test_session():
-      for batch_shape in [(2, 3), (2, 3, 4), (2, 3, 4, 5)]:
-        for k in [1, 4]:
-
-          x_shape = batch_shape + (k,)
-          x = self._rng.randn(*x_shape)
-
-          # chol will not have the leading two dimensions.
-          chol_shape = batch_shape[2:] + (k, k)
-          chol = self._random_cholesky_array(chol_shape)
-          operator = OperatorSolve(chol)
-          qf = operator.inv_quadratic_form_on_vectors(x)
-          numpy_qf = self._numpy_inv_quadratic_form_on_vectors(chol, x)
-
-          self.assertEqual(batch_shape, qf.get_shape())
-          self.assertAllClose(numpy_qf, qf.eval())
-
-
-class FlipMatrixToVectorTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState()
-
-  def testMatrixAndVectorBatchShapesTheSame(self):
-    batch_shape = [6, 2, 3]
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = self._rng.rand(2, 3, 4, 6)
-        vec = operator_pd.flip_matrix_to_vector(mat, batch_shape,
-                                                static_batch_shape)
-        vec_v = vec.eval()
-        self.assertAllEqual((6, 2, 3, 4), vec_v.shape)
-        self.assertAllEqual(mat[1, 2, 3, 4], vec_v[4, 1, 2, 3])
-
-  def testMatrixAndVectorBatchShapesSameRankButPermuted(self):
-    batch_shape = [6, 3, 2]
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = self._rng.rand(2, 3, 4, 6)
-        vec = operator_pd.flip_matrix_to_vector(mat, batch_shape,
-                                                static_batch_shape)
-        vec_v = vec.eval()
-        self.assertAllEqual((6, 3, 2, 4), vec_v.shape)
-
-  def testVectorBatchShapeLongerThanMatrixBatchShape(self):
-    batch_shape = [2, 3, 2, 3]
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = self._rng.rand(2, 3, 4, 6)
-        vec = operator_pd.flip_matrix_to_vector(mat, batch_shape,
-                                                static_batch_shape)
-        vec_v = vec.eval()
-        self.assertAllEqual((2, 3, 2, 3, 4), vec_v.shape)
-
-  def testMatrixBatchShapeHasASingletonThatVecBatchShapeDoesnt(self):
-    batch_shape = [6, 3]
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = self._rng.rand(1, 3, 4, 6)
-        vec = operator_pd.flip_matrix_to_vector(mat, batch_shape,
-                                                static_batch_shape)
-        vec_v = vec.eval()
-        self.assertAllEqual((6, 3, 4), vec_v.shape)
-        self.assertAllEqual(mat[0, 2, 3, 4], vec_v[4, 2, 3])
-
-
-class FlipVectorToMatrixTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState()
-
-  def testWhenXBatchRankIsSameAsBatchRankArg(self):
-    batch_shape = [4, 5]
-    x = self._rng.rand(4, 5, 6)
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = operator_pd.flip_vector_to_matrix(x, batch_shape,
-                                                static_batch_shape)
-        mat_v = mat.eval()
-        expected_mat_v = x.reshape(x.shape + (1,))
-        self.assertAllEqual(expected_mat_v, mat_v)
-
-  def testWhenXHasOneLargerLargerBatchRankThanBatchRankArg(self):
-    batch_shape = [4, 5]
-    x = self._rng.rand(3, 4, 5, 6)
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = operator_pd.flip_vector_to_matrix(x, batch_shape,
-                                                static_batch_shape)
-        mat_v = mat.eval()
-        self.assertAllEqual((4, 5, 6, 3), mat_v.shape)
-        self.assertAllEqual(x[2, 2, 2, 1], mat_v[2, 2, 1, 2])
-
-  def testWhenBatchShapeRequiresReshapeOfVectorBatchShape(self):
-    batch_shape = [5, 4]
-    x = self._rng.rand(3, 4, 5, 6)  # Note x has (4,5) and batch_shape is (5, 4)
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = operator_pd.flip_vector_to_matrix(x, batch_shape,
-                                                static_batch_shape)
-        mat_v = mat.eval()
-        self.assertAllEqual((5, 4, 6, 3), mat_v.shape)
-
-  def testWhenXHasTwoLargerLargerBatchRankThanBatchRankArg(self):
-    batch_shape = [4, 5]
-    x = self._rng.rand(2, 3, 4, 5, 6)
-    for static_batch_shape in [
-        tensor_shape.TensorShape(batch_shape), tensor_shape.TensorShape(None)
-    ]:
-      with self.test_session():
-        mat = operator_pd.flip_vector_to_matrix(x, batch_shape,
-                                                static_batch_shape)
-        mat_v = mat.eval()
-        self.assertAllEqual((4, 5, 6, 2 * 3), mat_v.shape)
-
-
-class ExtractBatchShapeTest(test.TestCase):
-
-  def setUp(self):
-    self._rng = np.random.RandomState()
-
-  def testXHasEmptyBatchShape(self):
-    with self.test_session():
-      x = self._rng.rand(2, 3)
-      num_event_dims = 2
-      batch_shape = operator_pd.extract_batch_shape(x, num_event_dims)
-      self.assertAllEqual([], batch_shape.eval())
-
-  def testXHasNonEmptyBatchShape(self):
-    with self.test_session():
-      x = self._rng.rand(2, 3, 4, 5)
-      num_event_dims = 2
-      batch_shape = operator_pd.extract_batch_shape(x, num_event_dims)
-      self.assertAllEqual([2, 3], batch_shape.eval())
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_vdvt_update_test.py b/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_vdvt_update_test.py
deleted file mode 100644
index e2fc081b35..0000000000
--- a/tensorflow/contrib/distributions/python/kernel_tests/operator_pd_vdvt_update_test.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.contrib import distributions as distributions_lib
-from tensorflow.contrib.distributions.python.ops import operator_pd_full
-from tensorflow.contrib.distributions.python.ops import operator_pd_vdvt_update
-from tensorflow.contrib.distributions.python.ops import operator_test_util
-from tensorflow.python.framework import dtypes
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.platform import test
-
-distributions = distributions_lib
-
-
-class OperatorPDSqrtVDVTUpdateTest(
-    operator_test_util.OperatorPDDerivedClassTest):
-  """Most tests done in the base class."""
-  _diag_is_none = False
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _random_pd_matrix(self, shape):
-    # With probability 1 this is positive definite.
-    sqrt = self._rng.randn(*shape)
-    mat = math_ops.matmul(sqrt, sqrt, adjoint_b=True)
-    return mat.eval()
-
-  def _random_v_and_diag(self, mat_shape, v_matrix_rank):
-    # Get the necessary elements to make the sqrt update.
-    mat_shape = list(mat_shape)
-    batch_shape = mat_shape[:-2]
-    diag_shape = mat_shape[:-2] + [v_matrix_rank]
-    k = mat_shape[-1]
-    assert k == mat_shape[-2], "Must be a square matrix"
-    v_shape = batch_shape + [k, v_matrix_rank]
-    v = self._rng.randn(*v_shape)  # anything goes with "v"!
-
-    if self._diag_is_none:
-      diag = None
-    else:
-      diag = self._rng.rand(*diag_shape) + 0.1  # Positive diag!
-    return v, diag
-
-  def _updated_mat(self, mat, v, diag):
-    # Get dense matrix defined by its square root, which is an update of `mat`:
-    # A = (mat + v D v^T) (mat + v D v^T)^T
-    # D is the diagonal matrix with `diag` on the diagonal.
-
-    # If diag is None, then it defaults to the identity matrix, so DV^T = V^T
-    if diag is None:
-      diag_vt = array_ops.matrix_transpose(v)
-    else:
-      diag_mat = array_ops.matrix_diag(diag)
-      diag_vt = math_ops.matmul(diag_mat, v, adjoint_b=True)
-
-    v_diag_vt = math_ops.matmul(v, diag_vt)
-    sqrt = mat + v_diag_vt
-    a = math_ops.matmul(sqrt, sqrt, adjoint_b=True)
-    return a.eval()
-
-  def _build_operator_and_mat(self, batch_shape, k, dtype=np.float64):
-    """This method is called by base class, enabling many standard tests."""
-    # Create a matrix then explicitly update it with v and diag.
-    # Create an OperatorPDSqrtVDVTUpdate from the matrix and v and diag
-    # The operator should have the same behavior.
-    #
-    # The low-rank matrix V will have rank 1/2 of k, unless k is 1, in which
-    # case it will be 1 as well.
-    if k == 1:
-      v_matrix_rank = k
-    else:
-      v_matrix_rank = k // 2
-    mat_shape = list(batch_shape) + [k, k]
-    mat = self._random_pd_matrix(mat_shape)
-    v, diag = self._random_v_and_diag(mat_shape, v_matrix_rank)
-
-    # Set dtypes
-    mat = mat.astype(dtype)
-    v = v.astype(dtype)
-    if diag is not None:
-      diag = diag.astype(dtype)
-
-    # The matrix: (mat + v*diag*v^T) * (mat + v*diag*v^T)^T
-    # Our final updated operator should behave like this.
-    updated_mat = self._updated_mat(mat, v, diag)
-
-    # Represents the matrix: `mat`, before updating.
-    # This is the Operator that we will update.
-    o_made_with_mat = operator_pd_full.OperatorPDFull(mat)
-
-    # Represents the matrix: (mat + v*diag*v^T) * (mat + v*diag*v^T)^T,
-    # achieved by updating the operator "o_made_with_mat".
-    # This is the operator we're testing.
-    operator = operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(o_made_with_mat,
-                                                                v, diag)
-
-    return operator, updated_mat
-
-  def testToDensePlaceholder(self):
-    # Test simple functionality when the inputs are placeholders.
-    mat_shape = [3, 3]
-    v_matrix_rank = 2
-    with self.test_session():
-      # Make an OperatorPDFull with a matrix placeholder.
-      mat_ph = array_ops.placeholder(dtypes.float64, name="mat_ph")
-      mat = self._random_pd_matrix(mat_shape)
-      o_made_with_mat = operator_pd_full.OperatorPDFull(mat_ph)
-
-      # Make the placeholders and arrays for the updated operator.
-      v_ph = array_ops.placeholder(dtypes.float64, name="v_ph")
-      v, diag = self._random_v_and_diag(mat_shape, v_matrix_rank)
-      if self._diag_is_none:
-        diag_ph = None
-        feed_dict = {v_ph: v, mat_ph: mat}
-      else:
-        diag_ph = array_ops.placeholder(dtypes.float64, name="diag_ph")
-        feed_dict = {v_ph: v, diag_ph: diag, mat_ph: mat}
-
-      # Make the OperatorPDSqrtVDVTUpdate with v and diag placeholders.
-      operator = operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(
-          o_made_with_mat, v_ph, diag=diag_ph)
-
-      # Should not fail
-      operator.to_dense().eval(feed_dict=feed_dict)
-      operator.log_det().eval(feed_dict=feed_dict)
-
-  def testOperatorNotSubclassOfOperatorPdRaises(self):
-    # We enforce that `operator` is an `OperatorPDBase`.
-    with self.test_session():
-      v, diag = self._random_v_and_diag((3, 3), 2)
-      operator_m = "I am not a subclass of OperatorPDBase"
-
-      with self.assertRaisesRegexp(TypeError, "not instance"):
-        operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m, v, diag)
-
-  def testNonPosDefDiagRaises(self):
-    if self._diag_is_none:
-      return
-    # We enforce that the diag is positive definite.
-    with self.test_session():
-      matrix_shape = (3, 3)
-      v_rank = 2
-      v, diag = self._random_v_and_diag(matrix_shape, v_rank)
-      mat = self._random_pd_matrix(matrix_shape)
-      diag[0] = 0.0
-
-      operator_m = operator_pd_full.OperatorPDFull(mat)
-      operator = operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m, v,
-                                                                  diag)
-
-      with self.assertRaisesOpError("positive"):
-        operator.to_dense().eval()
-
-  def testNonPosDefDiagDoesntRaiseIfVerifyPd_false(self):
-    # We enforce that the diag is positive definite.
-    if self._diag_is_none:
-      return
-    with self.test_session():
-      matrix_shape = (3, 3)
-      v_rank = 2
-      v, diag = self._random_v_and_diag(matrix_shape, v_rank)
-      mat = self._random_pd_matrix(matrix_shape)
-      diag[0] = 0.0
-
-      operator_m = operator_pd_full.OperatorPDFull(mat)
-      operator = operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(
-          operator_m, v, diag, verify_pd=False)
-
-      operator.to_dense().eval()  # Should not raise.
-
-  def testEventShapeMismatchVAndDiagRaisesStatic(self):
-    v = self._rng.rand(4, 3, 2)
-    diag = self._rng.rand(4, 1)  # Should be shape (4, 2,) to match v.
-    with self.test_session():
-
-      mat = self._random_pd_matrix((4, 3, 3))  # mat and v match
-      operator_m = operator_pd_full.OperatorPDFull(mat)
-      with self.assertRaisesRegexp(ValueError, "diag.*v.*last dimension"):
-        operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m, v, diag)
-
-  def testBatchShapeMismatchVAndDiagRaisesStatic(self):
-    v = self._rng.rand(4, 3, 2)
-    diag = self._rng.rand(5, 1)  # Should be shape (4, 2,) to match v.
-    with self.test_session():
-
-      mat = self._random_pd_matrix((4, 3, 3))  # mat and v match
-      operator_m = operator_pd_full.OperatorPDFull(mat)
-      with self.assertRaisesRegexp(ValueError, "diag.*batch shape"):
-        operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m, v, diag)
-
-  def testTensorRankShapeMismatchVAndDiagRaisesStatic(self):
-    v = self._rng.rand(1, 2, 2, 2)
-    diag = self._rng.rand(5, 1)  # Should have rank 1 less than v.
-    with self.test_session():
-
-      mat = self._random_pd_matrix((1, 2, 2, 2))  # mat and v match
-      operator_m = operator_pd_full.OperatorPDFull(mat)
-      with self.assertRaisesRegexp(ValueError, "diag.*rank"):
-        operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m, v, diag)
-
-  def testEventShapeMismatchVAndDiagRaisesDynamic(self):
-    with self.test_session():
-
-      v = self._rng.rand(4, 3, 2)
-      diag = self._rng.rand(4, 1)  # Should be shape (4, 2,) to match v.
-      mat = self._random_pd_matrix((4, 3, 3))  # mat and v match
-
-      v_ph = array_ops.placeholder(dtypes.float32, name="v_ph")
-      diag_ph = array_ops.placeholder(dtypes.float32, name="diag_ph")
-      mat_ph = array_ops.placeholder(dtypes.float32, name="mat_ph")
-
-      operator_m = operator_pd_full.OperatorPDFull(mat_ph)
-      updated = operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m,
-                                                                 v_ph, diag_ph)
-      with self.assertRaisesOpError("x == y"):
-        updated.to_dense().eval(feed_dict={v_ph: v, diag_ph: diag, mat_ph: mat})
-
-  def testBatchShapeMismatchVAndDiagRaisesDynamic(self):
-    with self.test_session():
-      v = self._rng.rand(4, 3, 2)
-      diag = self._rng.rand(5, 1)  # Should be shape (4, 2,) to match v.
-      mat = self._random_pd_matrix((4, 3, 3))  # mat and v match
-
-      v_ph = array_ops.placeholder(dtypes.float32, name="v_ph")
-      diag_ph = array_ops.placeholder(dtypes.float32, name="diag_ph")
-      mat_ph = array_ops.placeholder(dtypes.float32, name="mat_ph")
-
-      operator_m = operator_pd_full.OperatorPDFull(mat_ph)
-      updated = operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m,
-                                                                 v_ph, diag_ph)
-      with self.assertRaisesOpError("x == y"):
-        updated.to_dense().eval(feed_dict={v_ph: v, diag_ph: diag, mat_ph: mat})
-
-  def testTensorRankShapeMismatchVAndDiagRaisesDynamic(self):
-    with self.test_session():
-
-      v = self._rng.rand(2, 2, 2, 2)
-      diag = self._rng.rand(2, 2)  # Should have rank 1 less than v.
-      mat = self._random_pd_matrix((2, 2, 2, 2))  # mat and v match
-
-      v_ph = array_ops.placeholder(dtypes.float32, name="v_ph")
-      diag_ph = array_ops.placeholder(dtypes.float32, name="diag_ph")
-      mat_ph = array_ops.placeholder(dtypes.float32, name="mat_ph")
-
-      operator_m = operator_pd_full.OperatorPDFull(mat_ph)
-      updated = operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(operator_m,
-                                                                 v_ph, diag_ph)
-      with self.assertRaisesOpError("rank"):
-        updated.to_dense().eval(feed_dict={v_ph: v, diag_ph: diag, mat_ph: mat})
-
-
-class OperatorPDSqrtVDVTUpdateNoneDiagTest(OperatorPDSqrtVDVTUpdateTest):
-  _diag_is_none = True
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/wishart_test.py b/tensorflow/contrib/distributions/python/kernel_tests/wishart_test.py
index d9dc978f23..9044aa2850 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/wishart_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/wishart_test.py
@@ -351,6 +351,13 @@ class WishartCholeskyTest(test.TestCase):
                          (3, 3), dtype=np.float32)
                  })
 
+      with self.assertRaisesOpError("scale must be square"):
+        chol_w = distributions.WishartCholesky(
+            df=4.,
+            scale=np.array([[2., 3., 4.], [1., 2., 3.]], dtype=np.float32),
+            validate_args=True)
+        sess.run(chol_w.scale().eval())
+
       # Ensure no assertions.
       chol_w = distributions.WishartCholesky(
           df=df_deferred,
@@ -366,13 +373,18 @@ class WishartCholeskyTest(test.TestCase):
                feed_dict={df_deferred: 4,
                           chol_scale_deferred: np.ones((3, 3))})
 
+  def testStaticAsserts(self):
+    with self.test_session():
+      x = make_pd(1., 3)
+      chol_scale = chol(x)
+
       # Still has these assertions because they're resolveable at graph
       # construction
       with self.assertRaisesRegexp(ValueError, "cannot be less than"):
-        chol_w = distributions.WishartCholesky(
+        distributions.WishartCholesky(
             df=2, scale=chol_scale, validate_args=False)
-      with self.assertRaisesRegexp(TypeError, "not a floating-point type"):
-        chol_w = distributions.WishartCholesky(
+      with self.assertRaisesRegexp(TypeError, "Argument tril must have dtype"):
+        distributions.WishartCholesky(
             df=4.,
             scale=np.asarray(
                 chol_scale, dtype=np.int32),
diff --git a/tensorflow/contrib/distributions/python/ops/operator_pd.py b/tensorflow/contrib/distributions/python/ops/operator_pd.py
deleted file mode 100644
index 5471db21ed..0000000000
--- a/tensorflow/contrib/distributions/python/ops/operator_pd.py
+++ /dev/null
@@ -1,850 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Base class for symmetric positive definite operator."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import abc
-import numpy as np
-import six
-
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import math_ops
-
-
-@six.add_metaclass(abc.ABCMeta)
-class OperatorPDBase(object):
-  """Class representing a (batch) of positive definite matrices `A`.
-
-  This class provides access to functions of a (batch) symmetric positive
-  definite (PD) matrix, without the need to materialize them.  In other words,
-  this provides means to do "matrix free" computations.
-
-  ### Basics
-
-  For example, `my_operator.matmul(x)` computes the result of matrix
-  multiplication, and this class is free to do this computation with or without
-  ever materializing a matrix.
-
-  In practice, this operator represents a (batch) matrix `A` with shape
-  `[N1,...,Nn, k, k]` for some `n >= 0`.  The first `n` indices index a
-  batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,in, : :]` is
-  a `k x k` matrix.  Again, this matrix `A` may not be materialized, but for
-  purposes of broadcasting this shape will be relevant.
-
-  Since `A` is (batch) positive definite, it has a (or several) square roots `S`
-  such that `A = SS^T`.
-
-  For example, if `MyOperator` inherits from `OperatorPDBase`, the user can do
-
-  ```python
-  operator = MyOperator(...)  # Initialize with some tensors.
-  operator.log_det()
-
-  # Compute the quadratic form x^T A^{-1} x for vector x.
-  x = ... # some shape [M1,...,Mm, N1,...,Nn, k] tensor
-  operator.inv_quadratic_form_on_vectors(x)
-
-  # Matrix multiplication by the square root, S w.
-  # If w is iid normal, S w has covariance A.
-  w = ... # some shape [N1,...,Nn, k, r] tensor, r >= 1
-  operator.sqrt_matmul(w)
-  ```
-
-  The above three methods, `log_det`, `inv_quadratic_form_on_vectors`, and
-  `sqrt_matmul` provide "all" that is necessary to use a covariance matrix
-  in a multi-variate normal distribution.  See the class `MVNOperatorPD`.
-
-  ### Details about shape requirements
-
-  The `Operator` classes operate on batch vectors and batch matrices with
-  compatible shapes.  `matrix` is a batch matrix with compatible shape if
-
-  ```
-  operator.shape = [N1,...,Nn] + [j, k]
-  matrix.shape =   [N1,...,Nn] + [k, r]
-  ```
-
-  This is the same requirement as `tf.matmul`.  `vec` is a batch vector with
-  compatible shape if
-
-  ```
-  operator.shape = [N1,...,Nn] + [j, k]
-  vec.shape =   [N1,...,Nn] + [k]
-  OR
-  vec.shape = [M1,...,Mm] + [N1,...,Nn] + [k]
-  ```
-
-  We are strict with the matrix shape requirements since we do not want to
-  require `Operator` broadcasting.  The `Operator` may be defined by large
-  tensors (thus broadcasting is expensive), or the `Operator` may be matrix
-  free, in which case there is no guarantee that the underlying implementation
-  will broadcast.
-
-  We are more flexible with vector shapes since extra leading dimensions can
-  be "flipped" to the end to change the vector to a compatible matrix.
-
-  """
-
-  @abc.abstractproperty
-  def name(self):
-    """String name identifying this `Operator`."""
-    return self._name
-
-  @abc.abstractproperty
-  def verify_pd(self):
-    """Whether to verify that this `Operator` is positive definite."""
-    # return self._verify_pd
-    pass
-
-  @abc.abstractproperty
-  def dtype(self):
-    """Data type of matrix elements of `A`."""
-    pass
-
-  def add_to_tensor(self, mat, name="add_to_tensor"):
-    """Add matrix represented by this operator to `mat`.  Equiv to `A + mat`.
-
-    Args:
-      mat:  `Tensor` with same `dtype` and shape broadcastable to `self`.
-      name:  A name to give this `Op`.
-
-    Returns:
-      A `Tensor` with broadcast shape and same `dtype` as `self`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs + [mat]):
-        mat = ops.convert_to_tensor(mat, name="mat")
-        return self._add_to_tensor(mat)
-
-  def _add_to_tensor(self, mat):
-    # Re-implement in derived class if a more efficient method is available.
-    return self.to_dense() + mat
-
-  def _dispatch_based_on_batch(self, batch_method, singleton_method, **args):
-    """Helper to automatically call batch or singleton operation."""
-    if self.get_shape().ndims is not None:
-      is_batch = self.get_shape().ndims > 2
-      if is_batch:
-        return batch_method(**args)
-      else:
-        return singleton_method(**args)
-    else:
-      is_batch = self.rank() > 2
-      return control_flow_ops.cond(
-          is_batch,
-          lambda: batch_method(**args),
-          lambda: singleton_method(**args)
-      )
-
-  def inv_quadratic_form_on_vectors(
-      self, x, name="inv_quadratic_form_on_vectors"):
-    """Compute the quadratic form: `x^T A^{-1} x` where `x` is a batch vector.
-
-    `x` is a batch vector with compatible shape if
-
-    ```
-    self.shape = [N1,...,Nn] + [k, k]
-    x.shape = [M1,...,Mm] + [N1,...,Nn] + [k]
-    ```
-
-    Args:
-      x: `Tensor` with compatible batch vector shape and same `dtype` as self.
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `Tensor` with shape `[M1,...,Mm] + [N1,...,Nn]` and same `dtype`
-        as `self`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[x] + self.inputs):
-        x = ops.convert_to_tensor(x, name="x")
-        return self._inv_quadratic_form_on_vectors(x)
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    # Implement in derived class to enable self.inv_quadratic_form_on_vectors().
-    #
-    # To implement,
-    # Depending on which is more efficient, derived class should be a one-liner
-    # calling either
-    # return self._iqfov_via_sqrt_solve(x)
-    # OR
-    # return self._iqfov_via_solve(x)
-    # both of which are written in this base class.
-    raise NotImplementedError(
-        "inv_quadratic_form_on_vectors not implemented")
-
-  def _iqfov_via_sqrt_solve(self, x):
-    """Get the inverse quadratic form on vectors via a sqrt_solve."""
-    # x^{-1} A^{-1} x = || S^{-1}x ||^2,
-    # where S is a square root of A (A = SS^T).
-    # Steps:
-    # 1. Convert x to a matrix, flipping all extra dimensions in `x` to the
-    #    final dimension of x_matrix.
-    x_matrix = flip_vector_to_matrix(
-        x, self.batch_shape(), self.get_batch_shape())
-    # 2. Get soln_matrix = S^{-1} x_matrix
-    soln_matrix = self.sqrt_solve(x_matrix)
-    # 3. Reshape back to a vector.
-    soln = flip_matrix_to_vector(
-        soln_matrix, extract_batch_shape(x, 1), x.get_shape()[:-1])
-    # 4. L2 (batch) vector norm squared.
-    result = math_ops.reduce_sum(
-        math_ops.square(soln), reduction_indices=[-1])
-    result.set_shape(x.get_shape()[:-1])
-    return result
-
-  def _iqfov_via_solve(self, x):
-    """Get the inverse quadratic form on vectors via a solve."""
-    # x^{-1} A^{-1} x
-    # 1. Convert x to a matrix, flipping all extra dimensions in `x` to the
-    #    final dimension of x_matrix.
-    x_matrix = flip_vector_to_matrix(
-        x, self.batch_shape(), self.get_batch_shape())
-    # 2. Get x_whitened_matrix = A^{-1} x_matrix
-    soln_matrix = self.solve(x_matrix)
-    # 3. Reshape back to a vector.
-    soln = flip_matrix_to_vector(
-        soln_matrix, extract_batch_shape(x, 1), x.get_shape()[:-1])
-    # 4. Compute the dot product: x^T soln
-    result = math_ops.reduce_sum(x * soln, reduction_indices=[-1])
-    result.set_shape(x.get_shape()[:-1])
-    return result
-
-  def det(self, name="det"):
-    """Determinant for every batch member.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      Determinant for every batch member.
-    """
-    # Derived classes are encouraged to implement log_det() (since it is
-    # usually more stable), and then det() comes for free.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return self._det()
-
-  def _det(self):
-    return math_ops.exp(self.log_det())
-
-  def log_det(self, name="log_det"):
-    """Log of the determinant for every batch member.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      Logarithm of determinant for every batch member.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return self._dispatch_based_on_batch(self._batch_log_det, self._log_det)
-
-  def _batch_log_det(self):
-    # Implement in derived class to enable self.log_det(x).
-    raise NotImplementedError("Log determinant (log_det) not implemented.")
-
-  def _log_det(self):
-    # As implemented here, this just calls the batch version.  If a more
-    # efficient non-batch version is available, override in the derived class.
-    return self._batch_log_det()
-
-  def sqrt_log_abs_det(self, name="sqrt_log_det"):
-    """Log absolute value determinant of the sqrt `S` for every batch member.
-
-    In most cases, this will be the same as `sqrt_log_det`, but for certain
-    operators defined by a square root, this might be implemented slightly
-    differently.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      Logarithm of absolute value determinant of the square root `S` for
-      every batch member.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return self._dispatch_based_on_batch(
-            self._batch_sqrt_log_abs_det, self._sqrt_log_abs_det)
-
-  def sqrt_log_det(self, name="sqrt_log_det"):
-    """Log of the determinant of the sqrt `S` for every batch member.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      Logarithm of determinant of the square root `S` for every batch member.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return self._dispatch_based_on_batch(
-            self._batch_sqrt_log_det, self._sqrt_log_det)
-
-  def _batch_sqrt_log_det(self):
-    # Over-ride in derived class if it can be done more efficiently.
-    return 0.5 * self._log_det()
-
-  def _sqrt_log_det(self):
-    # As implemented here, this just calls the batch version.  If a more
-    # efficient non-batch version is available, override in the derived class.
-    return self._batch_sqrt_log_det()
-
-  def _batch_sqrt_log_abs_det(self):
-    # Over-ride in derived class if it can be done more efficiently.
-    return self._sqrt_log_det()
-
-  def _sqrt_log_abs_det(self):
-    # As implemented here, this just calls the batch version.  If a more
-    # efficient non-batch version is available, override in the derived class.
-    return self._batch_sqrt_log_abs_det()
-
-  @abc.abstractproperty
-  def inputs(self):
-    """List of tensors that were provided as initialization inputs."""
-    pass
-
-  @abc.abstractmethod
-  def get_shape(self):
-    """Static `TensorShape` of entire operator.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, then this returns
-    `TensorShape([N1,...,Nn, k, k])`
-
-    Returns:
-      `TensorShape`, statically determined, may be undefined.
-    """
-    pass
-
-  def get_batch_shape(self):
-    """`TensorShape` with batch shape.  Statically determined if possible.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, then this returns `TensorShape([N1,...,Nn])`
-
-    Returns:
-      `TensorShape`, statically determined, may be undefined.
-    """
-    # Derived classes get this "for free" once .get_shape() is implemented.
-    return self.get_shape()[:-2]
-
-  def get_vector_shape(self):
-    """`TensorShape` of vectors this operator will work with.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, then this returns
-    `TensorShape([N1,...,Nn, k])`
-
-    Returns:
-      `TensorShape`, statically determined, may be undefined.
-    """
-    # Derived classes get this "for free" once .get_shape() is implemented.
-    return self.get_shape()[:-1]
-
-  def shape(self, name="shape"):
-    """Equivalent to `tf.shape(A).`  Equal to `[N1,...,Nn, k, k]`, `n >= 0`.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `int32` `Tensor`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return self._shape()
-
-  @abc.abstractmethod
-  def _shape(self):
-    # Implement in derived class to enable .shape().
-    pass
-
-  def rank(self, name="rank"):
-    """Tensor rank.  Equivalent to `tf.rank(A)`.  Will equal `n + 2`.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, the `rank` is `n + 2`.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `int32` `Tensor`
-    """
-    # Derived classes get this "for free" once .shape() is implemented.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return array_ops.size(self.shape())
-
-  def batch_shape(self, name="batch_shape"):
-    """Shape of batches associated with this operator.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, the `batch_shape` is `[N1,...,Nn]`.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `int32` `Tensor`
-    """
-    # Derived classes get this "for free" once .shape() is implemented.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return array_ops.strided_slice(self.shape(), [0], [self.rank() - 2])
-
-  def vector_shape(self, name="vector_shape"):
-    """Shape of (batch) vectors that this (batch) matrix will multiply.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, the `vector_shape` is `[N1,...,Nn, k]`.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `int32` `Tensor`
-    """
-    # Derived classes get this "for free" once .shape() is implemented.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return array_ops.concat(
-            (self.batch_shape(), [self.vector_space_dimension()]), 0)
-
-  def vector_space_dimension(self, name="vector_space_dimension"):
-    """Dimension of vector space on which this acts.  The `k` in `R^k`.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, the `vector_space_dimension` is `k`.
-
-    Args:
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `int32` `Tensor`
-    """
-    # Derived classes get this "for free" once .shape() is implemented.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return array_ops.gather(self.shape(), self.rank() - 1)
-
-  def matmul(self, x, transpose_x=False, name="matmul"):
-    """Left (batch) matmul `x` by this matrix:  `Ax`.
-
-    `x` is a batch matrix with compatible shape if
-
-    ```
-    self.shape = [N1,...,Nn] + [k, k]
-    x.shape = [N1,...,Nn] + [k, r]
-    ```
-
-    Args:
-      x: `Tensor` with shape `self.batch_shape + [k, r]` and same `dtype` as
-        this `Operator`.
-      transpose_x: If `True`, `x` is transposed before multiplication.
-      name:  A name to give this `Op`.
-
-    Returns:
-      A result equivalent to `tf.matmul(self.to_dense(), x)`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[x] + self.inputs):
-        x = ops.convert_to_tensor(x, name="x")
-        return self._dispatch_based_on_batch(
-            self._batch_matmul, self._matmul, x=x, transpose_x=transpose_x)
-
-  def _batch_matmul(self, x, transpose_x=False):
-    # Implement in derived class to enable self.matmul(x).
-    raise NotImplementedError("This operator has no batch matmul Op.")
-
-  def _matmul(self, x, transpose_x=False):
-    # As implemented here, this just calls the batch version.  If a more
-    # efficient non-batch version is available, override in the derived class.
-    return self._batch_matmul(x, transpose_x=transpose_x)
-
-  def sqrt_matmul(self, x, transpose_x=False, name="sqrt_matmul"):
-    """Left (batch) matmul `x` by a sqrt of this matrix: `Sx` where `A = S S^T`.
-
-    `x` is a batch matrix with compatible shape if
-
-    ```
-    self.shape = [N1,...,Nn] + [k, k]
-    x.shape = [N1,...,Nn] + [k, r]
-    ```
-
-    Args:
-      x: `Tensor` with shape `self.batch_shape + [k, r]` and same `dtype` as
-        this `Operator`.
-      transpose_x: If `True`, `x` is transposed before multiplication.
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      A result equivalent to `tf.matmul(self.sqrt_to_dense(), x)`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[x] + self.inputs):
-        x = ops.convert_to_tensor(x, name="x")
-        return self._dispatch_based_on_batch(
-            self._batch_sqrt_matmul, self._sqrt_matmul, x=x,
-            transpose_x=transpose_x)
-
-  def _batch_sqrt_matmul(self, x, transpose_x=False):
-    # Implement in derived class to enable self.sqrt_matmul(x).
-    raise NotImplementedError("This operator has no batch_sqrt_matmul Op.")
-
-  def _sqrt_matmul(self, x, transpose_x=False):
-    # As implemented here, this just calls the batch version.  If a more
-    # efficient non-batch version is available, override in the derived class.
-    return self._batch_sqrt_matmul(x, transpose_x=transpose_x)
-
-  def solve(self, rhs, name="solve"):
-    """Solve `r` batch systems: `A X = rhs`.
-
-    `rhs` is a batch matrix with compatible shape if
-
-    ```python
-    self.shape = [N1,...,Nn] + [k, k]
-    rhs.shape = [N1,...,Nn] + [k, r]
-    ```
-
-    For every batch member, this is done in `O(r*k^2)` complexity using back
-    substitution.
-
-    ```python
-    # Solve one linear system (r = 1) for every member of the length 10 batch.
-    A = ... # shape 10 x 2 x 2
-    RHS = ... # shape 10 x 2 x 1
-    operator.shape # = 10 x 2 x 2
-    X = operator.squrt_solve(RHS)  # shape 10 x 2 x 1
-    # operator.squrt_matmul(X) ~ RHS
-    X[3, :, 0]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 0]
-
-    # Solve five linear systems (r = 5) for every member of the length 10 batch.
-    operator.shape # = 10 x 2 x 2
-    RHS = ... # shape 10 x 2 x 5
-    ...
-    X[3, :, 2]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 2]
-    ```
-
-    Args:
-      rhs: `Tensor` with same `dtype` as this operator and compatible shape,
-        `rhs.shape = self.shape[:-1] + [r]` for `r >= 1`.
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `Tensor` with same `dtype` and shape as `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[rhs] + self.inputs):
-        rhs = ops.convert_to_tensor(rhs, name="rhs")
-        return self._dispatch_based_on_batch(
-            self._batch_solve, self._solve, rhs=rhs)
-
-  def _solve(self, rhs):
-    # As implemented here, this just calls the batch version.  If a more
-    # efficient non-batch version is available, override in the derived class.
-    return self._batch_solve(rhs)
-
-  def _batch_solve(self, rhs):
-    # Implement in derived class to enable self.solve().
-    raise NotImplementedError("batch_solve not implemented for this Operator.")
-
-  def sqrt_solve(self, rhs, name="sqrt_solve"):
-    """Solve `r` batch systems involving sqrt: `S X = rhs` where `A = SS^T`.
-
-    `rhs` is a batch matrix with compatible shape if
-
-    ```python
-    self.shape = [N1,...,Nn] + [k, k]
-    rhs.shape = [N1,...,Nn] + [k, r]
-    ```
-
-    For every batch member, this is done in `O(r*k^2)` complexity using back
-    substitution.
-
-    ```python
-    # Solve one linear system (r = 1) for every member of the length 10 batch.
-    A = ... # shape 10 x 2 x 2
-    RHS = ... # shape 10 x 2 x 1
-    operator.shape # = 10 x 2 x 2
-    X = operator.squrt_solve(RHS)  # shape 10 x 2 x 1
-    # operator.squrt_matmul(X) ~ RHS
-    X[3, :, 0]  # Solution to the linear system S[3, :, :] x = RHS[3, :, 0]
-
-    # Solve five linear systems (r = 5) for every member of the length 10 batch.
-    operator.shape # = 10 x 2 x 2
-    RHS = ... # shape 10 x 2 x 5
-    ...
-    X[3, :, 2]  # Solution to the linear system S[3, :, :] x = RHS[3, :, 2]
-    ```
-
-    Args:
-      rhs: `Tensor` with same `dtype` as this operator and compatible shape,
-        `rhs.shape = self.shape[:-1] + [r]` for `r >= 1`.
-      name:  A name scope to use for ops added by this method.
-
-    Returns:
-      `Tensor` with same `dtype` and shape as `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[rhs] + self.inputs):
-        rhs = ops.convert_to_tensor(rhs, name="rhs")
-        return self._dispatch_based_on_batch(
-            self._batch_sqrt_solve, self._sqrt_solve, rhs=rhs)
-
-  def _sqrt_solve(self, rhs):
-    # As implemented here, this just calls the batch version.  If a more
-    # efficient non-batch version is available, override in the derived class.
-    return self._batch_sqrt_solve(rhs)
-
-  def _batch_sqrt_solve(self, rhs):
-    # Implement in derived class to enable self.sqrt_solve()
-    raise NotImplementedError(
-        "batch sqrt_solve not implemented for this Operator.")
-
-  def to_dense(self, name="to_dense"):
-    """Return a dense (batch) matrix representing this operator."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return self._to_dense()
-
-  def _to_dense(self):
-    # Implement in derived class to enable self.to_dense().
-    raise NotImplementedError("This operator has no dense representation.")
-
-  def sqrt_to_dense(self, name="sqrt_to_dense"):
-    """Return a dense (batch) matrix representing sqrt of this operator."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self.inputs):
-        return self._sqrt_to_dense()
-
-  def _sqrt_to_dense(self):
-    # Implement in derived class to enable self.sqrt_to_dense().
-    raise NotImplementedError("This operator has no dense sqrt representation.")
-
-
-def flip_matrix_to_vector(mat, batch_shape, static_batch_shape):
-  """Flip dims to reshape batch matrix `mat` to a vector with given batch shape.
-
-  ```python
-  mat = tf.random_normal(2, 3, 4, 6)
-
-  # Flip the trailing dimension around to the front.
-  flip_matrix_to_vector(mat, [6, 2, 3], [6, 3, 2])  # Shape [6, 2, 3, 4]
-
-  # Flip the trailing dimension around then reshape batch indices to batch_shape
-  flip_matrix_to_vector(mat, [6, 3, 2], [6, 3, 2])  # Shape [6, 3, 2, 4]
-  flip_matrix_to_vector(mat, [2, 3, 2, 3], [2,3,2,3])  # Shape [2, 3, 2, 3, 4]
-  ```
-
-  Assume `mat.shape = matrix_batch_shape + [k, M]`.  The returned vector is
-  generated in two steps:
-
-  1. Flip the final dimension to the front, giving a shape
-    `[M] + matrix_batch_shape + [k]`.
-  2. Reshape the leading dimensions, giving final shape = `batch_shape + [k]`.
-
-  The reshape in step 2 will fail if the number of elements is not equal, i.e.
-  `M*prod(matrix_batch_shape) != prod(batch_shape)`.
-
-  See also:  flip_vector_to_matrix.
-
-  Args:
-    mat:  `Tensor` with rank `>= 2`.
-    batch_shape:  `int32` `Tensor` giving leading "batch" shape of result.
-    static_batch_shape:  `TensorShape` object giving batch shape of result.
-
-  Returns:
-    `Tensor` with same elements as `mat` but with shape `batch_shape + [k]`.
-  """
-  mat = ops.convert_to_tensor(mat, name="mat")
-  if (static_batch_shape.is_fully_defined()
-      and mat.get_shape().is_fully_defined()):
-    return _flip_matrix_to_vector_static(mat, static_batch_shape)
-  else:
-    return _flip_matrix_to_vector_dynamic(mat, batch_shape)
-
-
-def _flip_matrix_to_vector_static(mat, static_batch_shape):
-  """Flip matrix to vector with static shapes."""
-  mat_rank = mat.get_shape().ndims
-  k = mat.get_shape()[-2]
-  final_shape = static_batch_shape.concatenate(k)
-
-  # mat.shape = matrix_batch_shape + [k, M]
-  # Permutation corresponding to [M] + matrix_batch_shape + [k]
-  perm = [mat_rank - 1] + list(range(0, mat_rank - 1))
-  mat_with_end_at_beginning = array_ops.transpose(mat, perm=perm)
-  vector = array_ops.reshape(mat_with_end_at_beginning, final_shape)
-  return vector
-
-
-def _flip_matrix_to_vector_dynamic(mat, batch_shape):
-  """Flip matrix to vector with dynamic shapes."""
-  mat_rank = array_ops.rank(mat)
-  k = array_ops.gather(array_ops.shape(mat), mat_rank - 2)
-  final_shape = array_ops.concat((batch_shape, [k]), 0)
-
-  # mat.shape = matrix_batch_shape + [k, M]
-  # Permutation corresponding to [M] + matrix_batch_shape + [k]
-  perm = array_ops.concat(([mat_rank - 1], math_ops.range(0, mat_rank - 1)), 0)
-  mat_with_end_at_beginning = array_ops.transpose(mat, perm=perm)
-  vector = array_ops.reshape(mat_with_end_at_beginning, final_shape)
-  return vector
-
-
-def flip_vector_to_matrix(vec, batch_shape, static_batch_shape):
-  """Flip dims to reshape batch vector `x` to a matrix with given batch shape.
-
-  ```python
-  vec = tf.random_normal(2, 3, 4, 5)
-
-  # Flip the leading dimension to the end.
-  flip_vector_to_matrix(vec, [3, 4], [3, 4])  # Shape [3, 4, 5, 2]
-
-  # Flip nothing, just extend with a singleton dimension.
-  flip_vector_to_matrix(vec, [2, 3, 4], [2, 3, 4])  # Shape [2, 3, 4, 5, 1]
-
-  # Flip leading dimension to the end and reshape the batch indices to
-  # batch_shape.
-  flip_vector_to_matrix(vec, [4, 3], [4, 3])  # Shape [4, 3, 5, 2]
-  ```
-
-  Suppose `batch_shape` is length `n`.  Then...
-
-  Given `vec.shape = [M1,...,Mm] + [N1,...,Nn] + [k]`, for some
-  `m > 0` we reshape to a batch matrix with shape `batch_shape + [k, M]`
-  where `M = M1*...*Mm`.  This is done by "flipping" the leading dimensions to
-  the end and possibly reshaping `[N1,...,Nn]` to `batch_shape`.
-
-  In the case `vec.shape = [N1,...,Nn] + [k]`, we reshape to
-  `batch_shape + [k, 1]` by extending the tensor with a singleton dimension and
-  possibly reshaping `[N1,...,Nn]` to `batch_shape`.
-
-  See also: flip_matrix_to_vector.
-
-  Args:
-    vec:  `Tensor` with shape `[M1,...,Mm] + [N1,...,Nn] + [k]`
-    batch_shape:  `int32` `Tensor`.
-    static_batch_shape:  `TensorShape` with statically determined batch shape.
-
-  Returns:
-    `Tensor` with same `dtype` as `vec` and new shape.
-  """
-  vec = ops.convert_to_tensor(vec, name="vec")
-  if (
-      vec.get_shape().is_fully_defined()
-      and static_batch_shape.is_fully_defined()):
-    return _flip_vector_to_matrix_static(vec, static_batch_shape)
-  else:
-    return _flip_vector_to_matrix_dynamic(vec, batch_shape)
-
-
-def _flip_vector_to_matrix_dynamic(vec, batch_shape):
-  """flip_vector_to_matrix with dynamic shapes."""
-  # Shapes associated with batch_shape
-  batch_rank = array_ops.size(batch_shape)
-
-  # Shapes associated with vec.
-  vec = ops.convert_to_tensor(vec, name="vec")
-  vec_shape = array_ops.shape(vec)
-  vec_rank = array_ops.rank(vec)
-  vec_batch_rank = vec_rank - 1
-
-  m = vec_batch_rank - batch_rank
-  # vec_shape_left = [M1,...,Mm] or [].
-  vec_shape_left = array_ops.strided_slice(vec_shape, [0], [m])
-  # If vec_shape_left = [], then condensed_shape = [1] since reduce_prod([]) = 1
-  # If vec_shape_left = [M1,...,Mm], condensed_shape = [M1*...*Mm]
-  condensed_shape = [math_ops.reduce_prod(vec_shape_left)]
-  k = array_ops.gather(vec_shape, vec_rank - 1)
-  new_shape = array_ops.concat((batch_shape, [k], condensed_shape), 0)
-
-  def _flip_front_dims_to_back():
-    # Permutation corresponding to [N1,...,Nn] + [k, M1,...,Mm]
-    perm = array_ops.concat((math_ops.range(m, vec_rank), math_ops.range(0, m)),
-                            0)
-    return array_ops.transpose(vec, perm=perm)
-
-  x_flipped = control_flow_ops.cond(
-      math_ops.less(0, m),
-      _flip_front_dims_to_back,
-      lambda: array_ops.expand_dims(vec, -1))
-
-  return array_ops.reshape(x_flipped, new_shape)
-
-
-def _flip_vector_to_matrix_static(vec, batch_shape):
-  """flip_vector_to_matrix with static shapes."""
-  # Shapes associated with batch_shape
-  batch_rank = batch_shape.ndims
-
-  # Shapes associated with vec.
-  vec = ops.convert_to_tensor(vec, name="vec")
-  vec_shape = vec.get_shape()
-  vec_rank = len(vec_shape)
-  vec_batch_rank = vec_rank - 1
-
-  m = vec_batch_rank - batch_rank
-  # vec_shape_left = [M1,...,Mm] or [].
-  vec_shape_left = vec_shape[:m]
-  # If vec_shape_left = [], then condensed_shape = [1] since reduce_prod([]) = 1
-  # If vec_shape_left = [M1,...,Mm], condensed_shape = [M1*...*Mm]
-  condensed_shape = [np.prod(vec_shape_left)]
-  k = vec_shape[-1]
-  new_shape = batch_shape.concatenate(k).concatenate(condensed_shape)
-
-  def _flip_front_dims_to_back():
-    # Permutation corresponding to [N1,...,Nn] + [k, M1,...,Mm]
-    perm = array_ops.concat((math_ops.range(m, vec_rank), math_ops.range(0, m)),
-                            0)
-    return array_ops.transpose(vec, perm=perm)
-
-  if 0 < m:
-    x_flipped = _flip_front_dims_to_back()
-  else:
-    x_flipped = array_ops.expand_dims(vec, -1)
-
-  return array_ops.reshape(x_flipped, new_shape)
-
-
-def extract_batch_shape(x, num_event_dims, name="extract_batch_shape"):
-  """Extract the batch shape from `x`.
-
-  Assuming `x.shape = batch_shape + event_shape`, when `event_shape` has
-  `num_event_dims` dimensions.  This `Op` returns the batch shape `Tensor`.
-
-  Args:
-    x: `Tensor` with rank at least `num_event_dims`.  If rank is not high enough
-      this `Op` will fail.
-    num_event_dims:  `int32` scalar `Tensor`.  The number of trailing dimensions
-      in `x` to be considered as part of `event_shape`.
-    name:  A name to prepend to created `Ops`.
-
-  Returns:
-    batch_shape:  `1-D` `int32` `Tensor`
-  """
-  with ops.name_scope(name, values=[x]):
-    x = ops.convert_to_tensor(x, name="x")
-    return array_ops.strided_slice(
-        array_ops.shape(x), [0], [array_ops.rank(x) - num_event_dims])
diff --git a/tensorflow/contrib/distributions/python/ops/operator_pd_cholesky.py b/tensorflow/contrib/distributions/python/ops/operator_pd_cholesky.py
deleted file mode 100644
index 09712f6860..0000000000
--- a/tensorflow/contrib/distributions/python/ops/operator_pd_cholesky.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Symmetric positive definite (PD) Operator defined by a Cholesky factor."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.distributions.python.ops import operator_pd
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import linalg_ops
-from tensorflow.python.ops import math_ops
-
-
-class OperatorPDCholesky(operator_pd.OperatorPDBase):
-  """Class representing a (batch) of positive definite matrices `A`.
-
-  This class provides access to functions of a batch of symmetric positive
-  definite (PD) matrices `A` in `R^{k x k}` defined by Cholesky factor(s).
-  Determinants and solves are `O(k^2)`.
-
-  In practice, this operator represents a (batch) matrix `A` with shape
-  `[N1,...,Nn, k, k]` for some `n >= 0`.  The first `n` indices designate a
-  batch member.  For every batch member `(i1,...,in)`, `A[i1,...,ib, : :]` is
-  a `k x k` matrix.
-
-  Since `A` is (batch) positive definite, it has a (or several) square roots `S`
-  such that `A = SS^T`.
-
-  For example,
-
-  ```python
-  distributions = tf.contrib.distributions
-  chol = [[1.0, 0.0], [1.0, 2.0]]
-  operator = OperatorPDCholesky(chol)
-  operator.log_det()
-
-  # Compute the quadratic form x^T A^{-1} x for vector x.
-  x = [1.0, 2.0]
-  operator.inv_quadratic_form_on_vectors(x)
-
-  # Matrix multiplication by the square root, S w.
-  # If w is iid normal, S w has covariance A.
-  w = [[1.0], [2.0]]
-  operator.sqrt_matmul(w)
-  ```
-
-  The above three methods, `log_det`, `inv_quadratic_form_on_vectors`, and
-  `sqrt_matmul` provide "all" that is necessary to use a covariance matrix
-  in a multi-variate normal distribution.  See the class
-  `MultivariateNormalCholesky`.
-  """
-
-  def __init__(self, chol, verify_pd=True, name="OperatorPDCholesky"):
-    """Initialize an OperatorPDCholesky.
-
-    Args:
-      chol:  Shape `[N1,...,Nn, k, k]` tensor with `n >= 0`, `k >= 1`, and
-        positive diagonal elements.  The strict upper triangle of `chol` is
-        never used, and the user may set these elements to zero, or ignore them.
-      verify_pd: Whether to check that `chol` has positive diagonal (this is
-        equivalent to it being a Cholesky factor of a symmetric positive
-        definite matrix.  If `verify_pd` is `False`, correct behavior is not
-        guaranteed.
-      name:  A name to prepend to all ops created by this class.
-    """
-    self._verify_pd = verify_pd
-    self._name = name
-    with ops.name_scope(name):
-      with ops.name_scope("init", values=[chol]):
-        self._chol = self._check_chol(chol)
-
-  @property
-  def verify_pd(self):
-    """Whether to verify that this `Operator` is positive definite."""
-    return self._verify_pd
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._chol.dtype
-
-  def _batch_log_det(self):
-    """Log determinant of every batch member."""
-    # Note that array_ops.diag_part does not seem more efficient for non-batch,
-    # and would give a bad result for a batch matrix, so aways use
-    # matrix_diag_part.
-    diag = array_ops.matrix_diag_part(self._chol)
-    det = 2.0 * math_ops.reduce_sum(math_ops.log(math_ops.abs(diag)),
-                                    reduction_indices=[-1])
-    det.set_shape(self.get_shape()[:-2])
-    return det
-
-  @property
-  def inputs(self):
-    """List of tensors that were provided as initialization inputs."""
-    return [self._chol]
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    # This Operator is defined in terms of the square root, which is easy to
-    # solve with (backsubstitution), so this is the preferred way to do
-    # inv_quadratic_form_on_vectors().
-    return self._iqfov_via_sqrt_solve(x)
-
-  def _matmul(self, x, transpose_x=False):
-    # tf.matmul is defined a * b.
-    chol = array_ops.matrix_band_part(self._chol, -1, 0)
-    chol_times_x = math_ops.matmul(
-        chol, x, transpose_a=True, transpose_b=transpose_x)
-    return math_ops.matmul(chol, chol_times_x)
-
-  def _batch_matmul(self, x, transpose_x=False):
-    # tf.matmul is defined x * y, so "y" is on the right, not "x".
-    chol = array_ops.matrix_band_part(self._chol, -1, 0)
-    chol_times_x = math_ops.matmul(
-        chol, x, adjoint_a=True, adjoint_b=transpose_x)
-    return math_ops.matmul(chol, chol_times_x)
-
-  def _sqrt_matmul(self, x, transpose_x=False):
-    chol = array_ops.matrix_band_part(self._chol, -1, 0)
-    # tf.matmul is defined a * b
-    return math_ops.matmul(chol, x, adjoint_b=transpose_x)
-
-  def _batch_sqrt_matmul(self, x, transpose_x=False):
-    chol = array_ops.matrix_band_part(self._chol, -1, 0)
-    # tf.batch_matmul is defined x * y, so "y" is on the right, not "x".
-    return math_ops.matmul(chol, x, adjoint_b=transpose_x)
-
-  def _batch_solve(self, rhs):
-    return linalg_ops.cholesky_solve(self._chol, rhs)
-
-  def _batch_sqrt_solve(self, rhs):
-    return linalg_ops.matrix_triangular_solve(self._chol, rhs, lower=True)
-
-  def get_shape(self):
-    """`TensorShape` giving static shape."""
-    return self._chol.get_shape()
-
-  def _shape(self):
-    return array_ops.shape(self._chol)
-
-  def _check_chol(self, chol):
-    """Verify that `chol` is proper."""
-    chol = ops.convert_to_tensor(chol, name="chol")
-    if not self.verify_pd:
-      return chol
-
-    shape = array_ops.shape(chol)
-    rank = array_ops.rank(chol)
-
-    is_matrix = check_ops.assert_rank_at_least(chol, 2)
-    is_square = check_ops.assert_equal(
-        array_ops.gather(shape, rank - 2), array_ops.gather(shape, rank - 1))
-
-    deps = [is_matrix, is_square]
-    diag = array_ops.matrix_diag_part(chol)
-    deps.append(check_ops.assert_positive(diag))
-
-    return control_flow_ops.with_dependencies(deps, chol)
-
-  def _sqrt_to_dense(self):
-    chol = array_ops.matrix_band_part(self._chol, -1, 0)
-    return array_ops.identity(chol)
-
-  def _to_dense(self):
-    chol = array_ops.matrix_band_part(self._chol, -1, 0)
-    return math_ops.matmul(chol, chol, adjoint_b=True)
diff --git a/tensorflow/contrib/distributions/python/ops/operator_pd_diag.py b/tensorflow/contrib/distributions/python/ops/operator_pd_diag.py
deleted file mode 100644
index 9d7d2a3621..0000000000
--- a/tensorflow/contrib/distributions/python/ops/operator_pd_diag.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Positive definite Operator defined with diagonal covariance."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import abc
-import six
-
-from tensorflow.contrib.distributions.python.ops import operator_pd
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import math_ops
-
-__all__ = [
-    "OperatorPDDiag",
-    "OperatorPDSqrtDiag",
-]
-
-
-@six.add_metaclass(abc.ABCMeta)
-class OperatorPDDiagBase(operator_pd.OperatorPDBase):
-  """Base class for diagonal operators."""
-
-  def __init__(self, diag, verify_pd=True, name="OperatorPDDiagBase"):
-    self._verify_pd = verify_pd
-    self._name = name
-    with ops.name_scope(name):
-      with ops.name_scope("init", values=[diag]):
-        self._diag = self._check_diag(diag)
-
-  def _check_diag(self, diag):
-    """Verify that `diag` is positive."""
-    diag = ops.convert_to_tensor(diag, name="diag")
-    if not self.verify_pd:
-      return diag
-    deps = [check_ops.assert_positive(diag)]
-    return control_flow_ops.with_dependencies(deps, diag)
-
-  @property
-  def name(self):
-    """String name identifying this `Operator`."""
-    return self._name
-
-  @property
-  def verify_pd(self):
-    """Whether to verify that this `Operator` is positive definite."""
-    return self._verify_pd
-
-  @property
-  def dtype(self):
-    """Data type of matrix elements of `A`."""
-    return self._diag.dtype
-
-  @property
-  def inputs(self):
-    """Initialization arguments."""
-    return [self._diag]
-
-  def get_shape(self):
-    """`TensorShape` giving static shape."""
-    # If d_shape = [5, 3], we return [5, 3, 3].
-    d_shape = self._diag.get_shape()
-    return d_shape.concatenate(d_shape[-1:])
-
-  def _shape(self):
-    d_shape = array_ops.shape(self._diag)
-    k = array_ops.gather(d_shape, array_ops.size(d_shape) - 1)
-    return array_ops.concat((d_shape, [k]), 0)
-
-  @abc.abstractmethod
-  def _batch_log_det(self):
-    pass
-
-  @abc.abstractmethod
-  def _inv_quadratic_form_on_vectors(self, x):
-    pass
-
-  @abc.abstractmethod
-  def _batch_matmul(self, x, transpose_x=False):
-    pass
-
-  @abc.abstractmethod
-  def _batch_sqrt_matmul(self, x, transpose_x=False):
-    pass
-
-  @abc.abstractmethod
-  def _batch_solve(self, rhs):
-    pass
-
-  @abc.abstractmethod
-  def _batch_sqrt_solve(self, rhs):
-    pass
-
-  @abc.abstractmethod
-  def _to_dense(self):
-    pass
-
-  @abc.abstractmethod
-  def _sqrt_to_dense(self):
-    pass
-
-  @abc.abstractmethod
-  def _add_to_tensor(self, mat):
-    pass
-
-
-class OperatorPDDiag(OperatorPDDiagBase):
-  """Class representing a (batch) of positive definite matrices `A`.
-
-  This class provides access to functions of a batch of symmetric positive
-  definite (PD) matrices `A` in `R^{k x k}`.
-
-  In this case, `A` is diagonal and is defined by a provided tensor `diag`,
-  `A_{ii} = diag[i]`.
-
-  Determinants, solves, and storage are `O(k)`.
-
-  In practice, this operator represents a (batch) matrix `A` with shape
-  `[N1,...,Nn, k, k]` for some `n >= 0`.  The first `n` indices designate a
-  batch member.  For every batch member `(i1,...,ib)`, `A[i1,...,ib, : :]` is
-  a `k x k` matrix.
-
-  For example,
-
-  ```python
-  distributions = tf.contrib.distributions
-  diag = [1.0, 2.0]
-  operator = OperatorPDDiag(diag)
-  operator.det()  # ==> (1 * 2)
-
-  # Compute the quadratic form x^T A^{-1} x for vector x.
-  x = [1.0, 2.0]
-  operator.inv_quadratic_form_on_vectors(x)
-
-  # Matrix multiplication by the square root, S w, with A = S S^T.
-  # Recall A is diagonal, and so then is S, with  S_{ij} = sqrt(A_{ij}).
-  # If w is iid normal, S w has covariance A.
-  w = [[1.0],
-       [2.0]]
-  operator.sqrt_matmul(w)
-  ```
-
-  The above three methods, `log_det`, `inv_quadratic_form_on_vectors`, and
-  `sqrt_matmul` provide "all" that is necessary to use a covariance matrix
-  in a multi-variate normal distribution.  See the class
-  `MultivariateNormalDiag`.
-  """
-
-  def __init__(self, diag, verify_pd=True, name="OperatorPDDiag"):
-    """Initialize an OperatorPDDiag.
-
-    Args:
-      diag:  Shape `[N1,...,Nn, k]` positive tensor with `n >= 0`, `k >= 1`.
-      verify_pd: Whether to check `diag` is positive.
-      name:  A name to prepend to all ops created by this class.
-    """
-    super(OperatorPDDiag, self).__init__(
-        diag, verify_pd=verify_pd, name=name)
-
-  def _batch_log_det(self):
-    return math_ops.reduce_sum(
-        math_ops.log(math_ops.abs(self._diag)), reduction_indices=[-1])
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    return self._iqfov_via_solve(x)
-
-  def _batch_matmul(self, x, transpose_x=False):
-    if transpose_x:
-      x = array_ops.matrix_transpose(x)
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return diag_mat * x
-
-  def _batch_sqrt_matmul(self, x, transpose_x=False):
-    if transpose_x:
-      x = array_ops.matrix_transpose(x)
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return math_ops.sqrt(diag_mat) * x
-
-  def _batch_solve(self, rhs):
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return rhs / diag_mat
-
-  def _batch_sqrt_solve(self, rhs):
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return rhs / math_ops.sqrt(diag_mat)
-
-  def _to_dense(self):
-    return array_ops.matrix_diag(self._diag)
-
-  def _sqrt_to_dense(self):
-    return array_ops.matrix_diag(math_ops.sqrt(self._diag))
-
-  def _add_to_tensor(self, mat):
-    mat_diag = array_ops.matrix_diag_part(mat)
-    new_diag = self._diag + mat_diag
-    return array_ops.matrix_set_diag(mat, new_diag)
-
-
-class OperatorPDSqrtDiag(OperatorPDDiagBase):
-  """Class representing a (batch) of positive definite matrices `A`.
-
-  This class provides access to functions of a batch of symmetric positive
-  definite (PD) matrices `A` in `R^{k x k}` defined by their square root,
-  `S`, such that `A = SS^T`.
-
-  In this case, `S` is diagonal and is defined by a provided tensor `diag`,
-  `S_{ii} = diag[i]`.  As a result, `A` is diagonal with `A_{ii} = diag[i]**2`.
-
-  Determinants, solves, and storage are `O(k)`.
-
-  In practice, this operator represents a (batch) matrix `A` with shape
-  `[N1,...,Nn, k, k]` for some `n >= 0`.  The first `n` indices designate a
-  batch member.  For every batch member `(i1,...,ib)`, `A[i1,...,ib, : :]` is
-  a `k x k` matrix.
-
-  For example,
-
-  ```python
-  distributions = tf.contrib.distributions
-  diag = [1.0, 2.0]
-  operator = OperatorPDSqrtDiag(diag)
-  operator.det()  # ==> (1 * 2)**2
-
-  # Compute the quadratic form x^T A^{-1} x for vector x.
-  x = [1.0, 2.0]
-  operator.inv_quadratic_form_on_vectors(x)
-
-  # Matrix multiplication by the square root, S w.
-  # If w is iid normal, S w has covariance A.
-  w = [[1.0], [2.0]]
-  operator.sqrt_matmul(w)
-  ```
-
-  The above three methods, `log_det`, `inv_quadratic_form_on_vectors`, and
-  `sqrt_matmul` provide "all" that is necessary to use a covariance matrix
-  in a multi-variate normal distribution.  See the class
-  `MultivariateNormalDiag`.
-  """
-
-  def __init__(self, diag, verify_pd=True, name="OperatorPDSqrtDiag"):
-    """Initialize an OperatorPDSqrtDiag.
-
-    Args:
-      diag:  Shape `[N1,...,Nn, k]` positive tensor with `n >= 0`, `k >= 1`.
-      verify_pd: Whether to check `diag` is positive.
-      name:  A name to prepend to all ops created by this class.
-    """
-    super(OperatorPDSqrtDiag, self).__init__(
-        diag, verify_pd=verify_pd, name=name)
-
-  def _batch_log_det(self):
-    return 2 * math_ops.reduce_sum(
-        math_ops.log(math_ops.abs(self._diag)),
-        reduction_indices=[-1])
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    # This Operator is defined in terms of diagonal entries of the sqrt.
-    return self._iqfov_via_sqrt_solve(x)
-
-  def _batch_matmul(self, x, transpose_x=False):
-    if transpose_x:
-      x = array_ops.matrix_transpose(x)
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return math_ops.square(diag_mat) * x
-
-  def _batch_sqrt_matmul(self, x, transpose_x=False):
-    if transpose_x:
-      x = array_ops.matrix_transpose(x)
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return diag_mat * x
-
-  def _batch_solve(self, rhs):
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return rhs / math_ops.square(diag_mat)
-
-  def _batch_sqrt_solve(self, rhs):
-    diag_mat = array_ops.expand_dims(self._diag, -1)
-    return rhs / diag_mat
-
-  def _to_dense(self):
-    return array_ops.matrix_diag(math_ops.square(self._diag))
-
-  def _sqrt_to_dense(self):
-    return array_ops.matrix_diag(self._diag)
-
-  def _add_to_tensor(self, mat):
-    mat_diag = array_ops.matrix_diag_part(mat)
-    new_diag = math_ops.square(self._diag) + mat_diag
-    return array_ops.matrix_set_diag(mat, new_diag)
diff --git a/tensorflow/contrib/distributions/python/ops/operator_pd_full.py b/tensorflow/contrib/distributions/python/ops/operator_pd_full.py
deleted file mode 100644
index 3ca341bb83..0000000000
--- a/tensorflow/contrib/distributions/python/ops/operator_pd_full.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Symmetric positive definite (PD) Operator defined by a full matrix."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import linalg_ops
-from tensorflow.python.ops.distributions import util as distribution_util
-
-
-__all__ = [
-    "OperatorPDFull",
-]
-
-
-class OperatorPDFull(operator_pd_cholesky.OperatorPDCholesky):
-  """Class representing a (batch) of positive definite matrices `A`.
-
-  This class provides access to functions of a batch of symmetric positive
-  definite (PD) matrices `A` in `R^{k x k}` defined by dense matrices.
-  Determinants and solves are `O(k^3)`.
-
-  In practice, this operator represents a (batch) matrix `A` with shape
-  `[N1,...,Nb, k, k]` for some `b >= 0`.  The first `b` indices designate a
-  batch member.  For every batch member `(n1,...,nb)`, `A[n1,...,nb, : :]` is
-  a `k x k` matrix.
-
-  Since `A` is (batch) positive definite, it has a (or several) square roots `S`
-  such that `A = SS^T`.
-
-  For example,
-
-  ```python
-  distributions = tf.contrib.distributions
-  matrix = [[1.0, 0.5], [1.0, 2.0]]
-  operator = OperatorPDFull(matrix)
-  operator.log_det()
-
-  # Compute the quadratic form x^T A^{-1} x for vector x.
-  x = [1.0, 2.0]
-  operator.inv_quadratic_form(x)
-
-  # Matrix multiplication by the square root, S w.
-  # If w is iid normal, S w has covariance A.
-  w = [[1.0], [2.0]]
-  operator.sqrt_matmul(w)
-  ```
-
-  The above three methods, `log_det`, `inv_quadratic_form`, and
-  `sqrt_matmul` provide "all" that is necessary to use a covariance matrix
-  in a multi-variate normal distribution.  See the class `MVNOperatorPD`.
-  """
-
-  def __init__(self, matrix, verify_pd=True, name="OperatorPDFull"):
-    """Initialize an OperatorPDFull.
-
-    Args:
-      matrix:  Shape `[N1,...,Nb, k, k]` tensor with `b >= 0`, `k >= 1`.  The
-        last two dimensions should be `k x k` symmetric positive definite
-        matrices.
-      verify_pd: Whether to check that `matrix` is symmetric positive definite.
-        If `verify_pd` is `False`, correct behavior is not guaranteed.
-      name:  A name to prepend to all ops created by this class.
-    """
-    with ops.name_scope(name):
-      with ops.name_scope("init", values=[matrix]):
-        matrix = ops.convert_to_tensor(matrix)
-        # Check symmetric here.  Positivity will be verified by checking the
-        # diagonal of the Cholesky factor inside the parent class.  The Cholesky
-        # factorization linalg_ops.cholesky() does not always fail for non PSD
-        # matrices, so don't rely on that.
-        if verify_pd:
-          matrix = distribution_util.assert_symmetric(matrix)
-        chol = linalg_ops.cholesky(matrix)
-        super(OperatorPDFull, self).__init__(chol, verify_pd=verify_pd)
diff --git a/tensorflow/contrib/distributions/python/ops/operator_pd_identity.py b/tensorflow/contrib/distributions/python/ops/operator_pd_identity.py
deleted file mode 100644
index 4cee299790..0000000000
--- a/tensorflow/contrib/distributions/python/ops/operator_pd_identity.py
+++ /dev/null
@@ -1,244 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Identity operator in `R^k`."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from tensorflow.contrib.distributions.python.ops import operator_pd
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import math_ops
-
-
-class OperatorPDIdentity(operator_pd.OperatorPDBase):
-  """Identity operator in `R^k`:  `Ax = x`.
-
-  This provides an efficient implementation of the identity as an `OperatorPD`.
-  Storage, solves, and matmul are all `O(1)`, independent of batch size.
-
-  In order to be a drop-in replacement for other operators, shape and dtype
-  of arguments (e.g. to `matmul`) are checked statically as though this operator
-  was an instantiated matrix.
-
-  Dynamic shape checks of arguments are not done since that could impede
-  performance.
-  """
-
-  def __init__(self, shape, dtype, scale=None,
-               verify_pd=True, name="OperatorPDIdentity"):
-    """Initialize an `OperatorPDIdentity`.
-
-    Args:
-      shape:  `int32` rank 1 `Tensor` of length at least 2, and with the last
-        two entries equal (since this is a square matrix).
-      dtype:  Data type of the matrix that this operator represents.
-      scale: floating point rank 0 `Tensor` representing a scalar to
-        multiply the identity matrix by. This will default to a scale of 1.
-        This will be converted to the dtype `dtype`.
-      verify_pd:  `Boolean`, if `True`, asserts are added to the initialization
-        args to ensure they define this operator as a square (batch) matrix.
-      name:  Name to prepend to `Ops`.
-    """
-
-    # Grab static shape if available now.
-    with ops.name_scope(name):
-      with ops.name_scope("init", values=[shape, scale]):
-        self._dtype = dtypes.as_dtype(dtype)
-        self._verify_pd = verify_pd
-        self._name = name
-
-        # Store the static shape (if possible) right now before adding the
-        # asserts, since the asserts prevent .constant_value from working.
-        shape = ops.convert_to_tensor(shape, name="shape")
-        self._get_shape = tensor_shape.TensorShape(
-            tensor_util.constant_value(shape))
-        self._shape_arg = self._check_shape(shape)
-        self._scale = self._check_scale(scale, self._dtype)
-
-  def _check_shape(self, shape):
-    """Check that the init arg `shape` defines a valid operator."""
-    shape = ops.convert_to_tensor(shape, name="shape")
-    if not self._verify_pd:
-      return shape
-
-    # Further checks are equivalent to verification that this is positive
-    # definite.  Why?  Because the further checks simply check that this is a
-    # square matrix, and combining the fact that this is square (and thus maps
-    # a vector space R^k onto itself), with the behavior of .matmul(), this must
-    # be the identity operator.
-    rank = array_ops.size(shape)
-    assert_matrix = check_ops.assert_less_equal(2, rank)
-    with ops.control_dependencies([assert_matrix]):
-      last_dim = array_ops.gather(shape, rank - 1)
-      second_to_last_dim = array_ops.gather(shape, rank - 2)
-      assert_square = check_ops.assert_equal(last_dim, second_to_last_dim)
-      return control_flow_ops.with_dependencies([assert_matrix, assert_square],
-                                                shape)
-
-  def _check_scale(self, scale, dtype):
-    """Check that the init arg `scale` defines a valid operator."""
-    if scale is None:
-      return constant_op.constant(1.0, dtype=dtype)
-
-    scale = ops.convert_to_tensor(scale, dtype=dtype, name="scale")
-
-    if not self._verify_pd:
-      return scale
-
-    # Further check that this is a rank 0, positive tensor.
-    scale = check_ops.assert_scalar(scale)
-    return control_flow_ops.with_dependencies(
-        [check_ops.assert_positive(scale)], scale)
-
-  def _check_x(self, x):
-    """Static check that the argument `x` is proper `shape`, `dtype`."""
-    # x is a typical argument e.g. to matmul or solve.  In both cases, x should
-    # have the same type/shape since this is a square matrix.  These checks are
-    # usually not needed since we usually have some tensor backing this
-    # distribution, and the calls to tf.matmul do a shape/type check.
-    #
-    # Static checks only for efficiency, the identity should be fast.
-    #
-    # Why check at all?  Because we want this operator to be swappable for a
-    # real Operator.
-    if self.dtype != x.dtype:
-      raise TypeError(
-          "Expected argument \"x\" to have same dtype as this operator (%s).  "
-          "Found: %s" % (self.dtype, x.dtype))
-
-    x_shape = x.get_shape()
-    self_shape = self.get_shape()
-    found_msg = (
-        "Found: operator.shape = %s,  x.shape = %s" % (self_shape, x_shape))
-    if x_shape.ndims is not None and self_shape.ndims is not None:
-      if x_shape.ndims != self_shape.ndims:
-        raise ValueError(
-            "Expected argument \"x\" to have same tensor rank as this "
-            "operator. " + found_msg)
-      if x_shape.is_fully_defined() and self_shape.is_fully_defined():
-        if x_shape[-2] != self_shape[-1]:
-          raise ValueError(
-              "Incompatible shapes for matrix-matrix operation.  " + found_msg)
-
-  @property
-  def name(self):
-    """String name identifying this `Operator`."""
-    return self._name
-
-  @property
-  def verify_pd(self):
-    """Whether to verify that this `Operator` is positive definite."""
-    return self._verify_pd
-
-  @property
-  def dtype(self):
-    """Data type of matrix elements of `A`."""
-    return self._dtype
-
-  def _add_to_tensor(self, mat):
-    # Add to a tensor in O(k) time!
-    mat_diag = array_ops.matrix_diag_part(mat)
-    new_diag = self._scale + mat_diag
-    return array_ops.matrix_set_diag(mat, new_diag)
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    self._check_x(x)
-    return self._iqfov_via_sqrt_solve(x)
-
-  @property
-  def inputs(self):
-    """List of tensors that were provided as initialization inputs."""
-    return [self._shape_arg, self._scale]
-
-  def get_shape(self):
-    """Static `TensorShape` of entire operator.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, then this returns
-    `TensorShape([N1,...,Nn, k, k])`
-
-    Returns:
-      `TensorShape`, statically determined, may be undefined.
-    """
-    return self._get_shape
-
-  def _shape(self):
-    return self._shape_arg
-
-  def _det(self):
-    return math_ops.exp(self._batch_log_det())
-
-  def _batch_log_det(self):
-    rank = array_ops.size(self._shape_arg)
-    last_dim = math_ops.cast(
-        array_ops.gather(self._shape_arg, rank - 1), dtype=self.dtype)
-    log_det = (last_dim * math_ops.log(math_ops.abs(self._scale)) *
-               array_ops.ones(self.batch_shape(), dtype=self.dtype))
-    log_det.set_shape(self.get_batch_shape())
-    return log_det
-
-  def _batch_sqrt_log_det(self):
-    return 0.5 * self._batch_log_det()
-
-  def _batch_sqrt_log_abs_det(self):
-    rank = array_ops.size(self._shape_arg)
-    last_dim = math_ops.cast(
-        array_ops.gather(self._shape_arg, rank - 1), dtype=self.dtype)
-    sqrt_log_abs_det = 0.5 * last_dim * math_ops.log(
-        math_ops.abs(self._scale)) * array_ops.ones(
-            self.batch_shape(), dtype=self.dtype)
-    sqrt_log_abs_det.set_shape(self.get_batch_shape())
-    return sqrt_log_abs_det
-
-  def _batch_matmul(self, x, transpose_x=False):
-    if transpose_x:
-      x = array_ops.matrix_transpose(x)
-    self._check_x(x)
-    return self._scale * x
-
-  def _batch_sqrt_matmul(self, x, transpose_x=False):
-    if transpose_x:
-      x = array_ops.matrix_transpose(x)
-    self._check_x(x)
-    return math_ops.sqrt(self._scale) * x
-
-  def _batch_solve(self, rhs):
-    self._check_x(rhs)
-    return rhs / self._scale
-
-  def _batch_sqrt_solve(self, rhs):
-    self._check_x(rhs)
-    return rhs / math_ops.sqrt(self._scale)
-
-  def _to_dense(self):
-    diag = array_ops.ones(self.vector_shape(), dtype=self.dtype)
-    dense = array_ops.matrix_diag(diag)
-    dense.set_shape(self.get_shape())
-    return self._scale * dense
-
-  def _sqrt_to_dense(self):
-    diag = array_ops.ones(self.vector_shape(), dtype=self.dtype)
-    dense = array_ops.matrix_diag(diag)
-    dense.set_shape(self.get_shape())
-    return math_ops.sqrt(self._scale) * dense
diff --git a/tensorflow/contrib/distributions/python/ops/operator_pd_vdvt_update.py b/tensorflow/contrib/distributions/python/ops/operator_pd_vdvt_update.py
deleted file mode 100644
index 9f494e9e3d..0000000000
--- a/tensorflow/contrib/distributions/python/ops/operator_pd_vdvt_update.py
+++ /dev/null
@@ -1,480 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Operator defined: `A = SS^T` where `S = M + VDV^T`, for `OperatorPD` `M`."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.distributions.python.ops import operator_pd
-from tensorflow.contrib.distributions.python.ops import operator_pd_diag
-from tensorflow.contrib.distributions.python.ops import operator_pd_identity
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import linalg_ops
-from tensorflow.python.ops import math_ops
-
-
-class OperatorPDSqrtVDVTUpdate(operator_pd.OperatorPDBase):
-  r"""Operator defined by `A=SS^T`, where `S = M + VDV^T` for `OperatorPD` `M`.
-
-  This provides efficient low-rank updates of arbitrary `OperatorPD`.
-
-  Some math:
-
-  Given positive definite operator representing positive definite (batch) matrix
-  `M` in `R^{k x k}`, diagonal matrix `D` in `R^{r x r}`, and low rank `V` in
-  `R^{k x r}` this class represents the batch matrix `A`, defined by its square
-  root `S` as follows:
-
-  ```
-  A = SS^T, where
-  S := M + VDV^T
-  ```
-
-  Defining an operator in terms of its square root means that
-  `A_{ij} = S_i S_j^T`, where `S_i` is the ith row of `S`.  The update
-  `VDV^T` has `ij` coordinate equal to `sum_k V_{ik} D_{kk} V_{jk}`.
-
-  Computational efficiency:
-
-  Defining `A` via its square root eliminates the need to compute the square
-  root.
-
-  Performance depends on the operator representing `M`, the batch size `B`, and
-  the width of the matrix being multiplied, or systems being solved `L`.
-
-  Since `V` is rank `r`, the update adds
-
-  * `O(B L k r)` to matmul, which requires a call to `M.matmul`.
-  * `O(B L r^3)` to solves, which require a call to `M.solve` as well as the
-    solution to a batch of rank `r` systems.
-  * `O(B r^3)` to determinants, which require a call to `M.solve` as well as the
-    solution to a batch of rank `r` systems.
-
-  The rank `r` solve and determinant are both done through a Cholesky
-  factorization, thus some computation is shared.
-
-  See
-    https://en.wikipedia.org/wiki/Woodbury_matrix_identity
-    https://en.wikipedia.org/wiki/Matrix_determinant_lemma
-  """
-
-  # Note that diag must be nonsingular to use Woodbury lemma, and must be
-  # positive def to use a Cholesky factorization, so we enforce that here.
-  def __init__(self,
-               operator,
-               v,
-               diag=None,
-               verify_pd=True,
-               verify_shapes=True,
-               name="OperatorPDSqrtVDVTUpdate"):
-    """Initialize an `OperatorPDSqrtVDVTUpdate`.
-
-    Args:
-      operator:  Subclass of `OperatorPDBase`.  Represents the (batch) positive
-        definite matrix `M` in `R^{k x k}`.
-      v: `Tensor` defining batch matrix of same `dtype` and `batch_shape` as
-        `operator`, and last two dimensions of shape `(k, r)`.
-      diag:  Optional `Tensor` defining batch vector of same `dtype` and
-        `batch_shape` as `operator`, and last dimension of size `r`.  If `None`,
-        the update becomes `VV^T` rather than `VDV^T`.
-      verify_pd:  `Boolean`.  If `True`, add asserts that `diag > 0`, which,
-        along with the positive definiteness of `operator`, is sufficient to
-        make the resulting operator positive definite.
-      verify_shapes:  `Boolean`.  If `True`, check that `operator`, `v`, and
-        `diag` have compatible shapes.
-      name:  A name to prepend to `Op` names.
-    """
-
-    if not isinstance(operator, operator_pd.OperatorPDBase):
-      raise TypeError("operator was not instance of OperatorPDBase.")
-
-    with ops.name_scope(name):
-      with ops.name_scope("init", values=operator.inputs + [v, diag]):
-        self._operator = operator
-        self._v = ops.convert_to_tensor(v, name="v")
-        self._verify_pd = verify_pd
-        self._verify_shapes = verify_shapes
-        self._name = name
-
-        # This operator will be PD so long as the diag is PSD, but Woodbury
-        # and determinant lemmas require diag to be PD.  So require diag PD
-        # whenever we ask to "verify_pd".
-        if diag is not None:
-          self._diag = ops.convert_to_tensor(diag, name="diag")
-          self._diag_operator = operator_pd_diag.OperatorPDDiag(
-              diag, verify_pd=self.verify_pd)
-          # No need to verify that the inverse of a PD is PD.
-          self._diag_inv_operator = operator_pd_diag.OperatorPDDiag(
-              1 / self._diag, verify_pd=False)
-        else:
-          self._diag = None
-          self._diag_operator = self._get_identity_operator(self._v)
-          self._diag_inv_operator = self._diag_operator
-
-        self._check_types(operator, self._v, self._diag)
-        # Always check static.
-        checked = self._check_shapes_static(operator, self._v, self._diag)
-        if not checked and self._verify_shapes:
-          self._v, self._diag = self._check_shapes_dynamic(
-              operator, self._v, self._diag)
-
-  def _get_identity_operator(self, v):
-    """Get an `OperatorPDIdentity` to play the role of `D` in `VDV^T`."""
-    with ops.name_scope("get_identity_operator", values=[v]):
-      if v.get_shape().is_fully_defined():
-        v_shape = v.get_shape().as_list()
-        v_batch_shape = v_shape[:-2]
-        r = v_shape[-1]
-        id_shape = v_batch_shape + [r, r]
-      else:
-        v_shape = array_ops.shape(v)
-        v_rank = array_ops.rank(v)
-        v_batch_shape = array_ops.strided_slice(v_shape, [0], [v_rank - 2])
-        r = array_ops.gather(v_shape, v_rank - 1)  # Last dim of v
-        id_shape = array_ops.concat((v_batch_shape, [r, r]), 0)
-      return operator_pd_identity.OperatorPDIdentity(
-          id_shape, v.dtype, verify_pd=self._verify_pd)
-
-  def _check_types(self, operator, v, diag):
-    def msg():
-      string = (
-          "dtypes must match:  Found operator.dtype = %s, v.dtype = %s"
-          % (operator.dtype, v.dtype))
-      return string
-
-    if operator.dtype != v.dtype:
-      raise TypeError(msg())
-    if diag is not None:
-      if diag.dtype != v.dtype:
-        raise TypeError("%s, diag.dtype = %s" % (msg(), diag.dtype))
-
-  def _check_shapes_static(self, operator, v, diag):
-    """True if they are compatible. Raise if not. False if could not check."""
-    def msg():
-      # Error message when shapes don't match.
-      string = "  Found: operator.shape = %s, v.shape = %s" % (s_op, s_v)
-      if diag is not None:
-        string += ", diag.shape = " % s_d
-      return string
-
-    s_op = operator.get_shape()
-    s_v = v.get_shape()
-
-    # If everything is not fully defined, return False because we couldn"t check
-    if not (s_op.is_fully_defined() and s_v.is_fully_defined()):
-      return False
-    if diag is not None:
-      s_d = diag.get_shape()
-      if not s_d.is_fully_defined():
-        return False
-
-    # Now perform the checks, raising ValueError if they fail.
-
-    # Check tensor rank.
-    if s_v.ndims != s_op.ndims:
-      raise ValueError("v should have same rank as operator" + msg())
-    if diag is not None:
-      if s_d.ndims != s_op.ndims - 1:
-        raise ValueError("diag should have rank 1 less than operator" + msg())
-
-    # Check batch shape
-    if s_v[:-2] != s_op[:-2]:
-      raise ValueError("v and operator should have same batch shape" + msg())
-    if diag is not None:
-      if s_d[:-1] != s_op[:-2]:
-        raise ValueError(
-            "diag and operator should have same batch shape" + msg())
-
-    # Check event shape
-    if s_v[-2] != s_op[-1]:
-      raise ValueError(
-          "v and operator should be compatible for matmul" + msg())
-    if diag is not None:
-      if s_d[-1] != s_v[-1]:
-        raise ValueError("diag and v should have same last dimension" + msg())
-
-    return True
-
-  def _check_shapes_dynamic(self, operator, v, diag):
-    """Return (v, diag) with Assert dependencies, which check shape."""
-    checks = []
-    with ops.name_scope("check_shapes", values=[operator, v, diag]):
-      s_v = array_ops.shape(v)
-      r_op = operator.rank()
-      r_v = array_ops.rank(v)
-      if diag is not None:
-        s_d = array_ops.shape(diag)
-        r_d = array_ops.rank(diag)
-
-      # Check tensor rank.
-      checks.append(check_ops.assert_rank(
-          v, r_op, message="v is not the same rank as operator."))
-      if diag is not None:
-        checks.append(check_ops.assert_rank(
-            diag, r_op - 1, message="diag is not the same rank as operator."))
-
-      # Check batch shape
-      checks.append(check_ops.assert_equal(
-          operator.batch_shape(), array_ops.strided_slice(s_v, [0], [r_v - 2]),
-          message="v does not have same batch shape as operator."))
-      if diag is not None:
-        checks.append(check_ops.assert_equal(
-            operator.batch_shape(), array_ops.strided_slice(
-                s_d, [0], [r_d - 1]),
-            message="diag does not have same batch shape as operator."))
-
-      # Check event shape
-      checks.append(check_ops.assert_equal(
-          operator.vector_space_dimension(), array_ops.gather(s_v, r_v - 2),
-          message="v does not have same event shape as operator."))
-      if diag is not None:
-        checks.append(check_ops.assert_equal(
-            array_ops.gather(s_v, r_v - 1), array_ops.gather(s_d, r_d - 1),
-            message="diag does not have same event shape as v."))
-
-      v = control_flow_ops.with_dependencies(checks, v)
-      if diag is not None:
-        diag = control_flow_ops.with_dependencies(checks, diag)
-      return v, diag
-
-  @property
-  def name(self):
-    """String name identifying this `Operator`."""
-    return self._name
-
-  @property
-  def verify_pd(self):
-    """Whether to verify that this `Operator` is positive definite."""
-    return self._verify_pd
-
-  @property
-  def dtype(self):
-    """Data type of matrix elements of `A`."""
-    return self._v.dtype
-
-  def _inv_quadratic_form_on_vectors(self, x):
-    return self._iqfov_via_sqrt_solve(x)
-
-  @property
-  def inputs(self):
-    """List of tensors that were provided as initialization inputs."""
-    return self._operator.inputs + self._diag_operator.inputs + [self._v]
-
-  def get_shape(self):
-    """Static `TensorShape` of entire operator.
-
-    If this operator represents the batch matrix `A` with
-    `A.shape = [N1,...,Nn, k, k]`, then this returns
-    `TensorShape([N1,...,Nn, k, k])`
-
-    Returns:
-      `TensorShape`, statically determined, may be undefined.
-    """
-    return self._operator.get_shape()
-
-  def _shape(self):
-    return self._operator.shape()
-
-  def _det(self):
-    return math_ops.exp(self.log_det())
-
-  def _batch_log_det(self):
-    return 2 * self._batch_sqrt_log_det()
-
-  def _log_det(self):
-    return 2 * self._sqrt_log_det()
-
-  def _sqrt_log_det(self):
-    # The matrix determinant lemma states:
-    # det(M + VDV^T) = det(D^{-1} + V^T M^{-1} V) * det(D) * det(M)
-    #                = det(C) * det(D) * det(M)
-    #
-    # Here we compute the Cholesky factor of "C", then pass the result on.
-    abs_diag_chol_c = math_ops.abs(array_ops.matrix_diag_part(
-        self._chol_capacitance(batch_mode=False)))
-    return self._sqrt_log_det_core(abs_diag_chol_c)
-
-  def _batch_sqrt_log_det(self):
-    # Here we compute the Cholesky factor of "C", then pass the result on.
-    abs_diag_chol_c = math_ops.abs(array_ops.matrix_diag_part(
-        self._chol_capacitance(batch_mode=True)))
-    return self._sqrt_log_det_core(abs_diag_chol_c)
-
-  def _chol_capacitance(self, batch_mode):
-    """Cholesky factorization of the capacitance term."""
-    # Cholesky factor for (D^{-1} + V^T M^{-1} V), which is sometimes
-    # known as the "capacitance" matrix.
-    # We can do a Cholesky decomposition, since a priori M is a
-    # positive-definite Hermitian matrix, which causes the "capacitance" to
-    # also be positive-definite Hermitian, and thus have a Cholesky
-    # decomposition.
-
-    # self._operator will use batch if need be. Automatically.  We cannot force
-    # that here.
-    # M^{-1} V
-    minv_v = self._operator.solve(self._v)
-    # V^T M^{-1} V
-    vt_minv_v = math_ops.matmul(self._v, minv_v, adjoint_a=True)
-
-    # D^{-1} + V^T M^{-1} V
-    capacitance = self._diag_inv_operator.add_to_tensor(vt_minv_v)
-    # Cholesky[D^{-1} + V^T M^{-1} V]
-    return linalg_ops.cholesky(capacitance)
-
-  def _sqrt_log_det_core(self, diag_chol_c):
-    """Finish computation of Sqrt[Log[Det]]."""
-    # Complete computation of ._log_det and ._batch_log_det, after the initial
-    # Cholesky factor has been taken with the appropriate batch/non-batch method
-
-    # det(M + VDV^T) = det(D^{-1} + V^T M^{-1} V) * det(D) * det(M)
-    #                = det(C) * det(D) * det(M)
-    # Multiply by 2 here because this is the log-det of the Cholesky factor of C
-    log_det_c = 2 * math_ops.reduce_sum(
-        math_ops.log(math_ops.abs(diag_chol_c)),
-        reduction_indices=[-1])
-    # Add together to get Log[det(M + VDV^T)], the Log-det of the updated square
-    # root.
-    log_det_updated_sqrt = (
-        log_det_c + self._diag_operator.log_det() + self._operator.log_det())
-    return log_det_updated_sqrt
-
-  def _batch_matmul(self, x, transpose_x=False):
-    # Since the square root is PD, it is symmetric, and so A = SS^T = SS.
-    s_x = self._batch_sqrt_matmul(x, transpose_x=transpose_x)
-    return self._batch_sqrt_matmul(s_x)
-
-  def _matmul(self, x, transpose_x=False):
-    # Since the square root is PD, it is symmetric, and so A = SS^T = SS.
-    s_x = self._sqrt_matmul(x, transpose_x=transpose_x)
-    return self._sqrt_matmul(s_x)
-
-  def _batch_sqrt_matmul(self, x, transpose_x=False):
-    v = self._v
-    m = self._operator
-    d = self._diag_operator
-    # The operators call the appropriate matmul/batch_matmul automatically.
-    # We cannot override.
-    # batch_matmul is defined as:  x * y, so adjoint_a and adjoint_b are the
-    # ways to transpose the left and right.
-    mx = m.matmul(x, transpose_x=transpose_x)
-    vt_x = math_ops.matmul(v, x, adjoint_a=True, adjoint_b=transpose_x)
-    d_vt_x = d.matmul(vt_x)
-    v_d_vt_x = math_ops.matmul(v, d_vt_x)
-
-    return mx + v_d_vt_x
-
-  def _sqrt_matmul(self, x, transpose_x=False):
-    v = self._v
-    m = self._operator
-    d = self._diag_operator
-    # The operators call the appropriate matmul/batch_matmul automatically.  We
-    # cannot override.
-    # matmul is defined as:  a * b, so transpose_a, transpose_b are used.
-    # transpose the left and right.
-    mx = m.matmul(x, transpose_x=transpose_x)
-    vt_x = math_ops.matmul(v, x, transpose_a=True, transpose_b=transpose_x)
-    d_vt_x = d.matmul(vt_x)
-    v_d_vt_x = math_ops.matmul(v, d_vt_x)
-
-    return mx + v_d_vt_x
-
-  def _solve(self, rhs):
-    # This operator represents A = SS^T, but S is symmetric, so A = SS,
-    # which means A^{-1} = S^{-1}S^{-2}
-    # S^{-1} rhs
-    sqrtinv_rhs = self._sqrt_solve(rhs)
-    return self._sqrt_solve(sqrtinv_rhs)
-
-  def _batch_solve(self, rhs):
-    sqrtinv_rhs = self._batch_sqrt_solve(rhs)
-    return self._batch_sqrt_solve(sqrtinv_rhs)
-
-  def _sqrt_solve(self, rhs):
-    # Recall the square root of this operator is M + VDV^T.
-    # The Woodbury formula gives:
-    # (M + VDV^T)^{-1}
-    # = M^{-1} - M^{-1} V (D^{-1} + V^T M^{-1} V)^{-1} V^T M^{-1}
-    # = M^{-1} - M^{-1} V C^{-1} V^T M^{-1}
-    # where C is the capacitance matrix.
-    # TODO(jvdillon) Determine if recursively applying rank-1 updates is more
-    # efficient.  May not be possible because a general n x n matrix can be
-    # represeneted as n rank-1 updates, and solving with this matrix is always
-    # done in O(n^3) time.
-    m = self._operator
-    v = self._v
-    cchol = self._chol_capacitance(batch_mode=False)
-
-    # The operators will use batch/singleton mode automatically.  We don't
-    # override.
-    # M^{-1} rhs
-    minv_rhs = m.solve(rhs)
-    # V^T M^{-1} rhs
-    vt_minv_rhs = math_ops.matmul(v, minv_rhs, transpose_a=True)
-    # C^{-1} V^T M^{-1} rhs
-    cinv_vt_minv_rhs = linalg_ops.cholesky_solve(cchol, vt_minv_rhs)
-    # V C^{-1} V^T M^{-1} rhs
-    v_cinv_vt_minv_rhs = math_ops.matmul(v, cinv_vt_minv_rhs)
-    # M^{-1} V C^{-1} V^T M^{-1} rhs
-    minv_v_cinv_vt_minv_rhs = m.solve(v_cinv_vt_minv_rhs)
-
-    # M^{-1} - M^{-1} V C^{-1} V^T M^{-1}
-    return minv_rhs - minv_v_cinv_vt_minv_rhs
-
-  def _batch_sqrt_solve(self, rhs):
-    # Recall the square root of this operator is M + VDV^T.
-    # The Woodbury formula gives:
-    # (M + VDV^T)^{-1}
-    # = M^{-1} - M^{-1} V (D^{-1} + V^T M^{-1} V)^{-1} V^T M^{-1}
-    # = M^{-1} - M^{-1} V C^{-1} V^T M^{-1}
-    # where C is the capacitance matrix.
-    m = self._operator
-    v = self._v
-    cchol = self._chol_capacitance(batch_mode=True)
-
-    # The operators will use batch/singleton mode automatically.  We don't
-    # override.
-    # M^{-1} rhs
-    minv_rhs = m.solve(rhs)
-    # V^T M^{-1} rhs
-    vt_minv_rhs = math_ops.matmul(v, minv_rhs, adjoint_a=True)
-    # C^{-1} V^T M^{-1} rhs
-    cinv_vt_minv_rhs = linalg_ops.cholesky_solve(cchol, vt_minv_rhs)
-    # V C^{-1} V^T M^{-1} rhs
-    v_cinv_vt_minv_rhs = math_ops.matmul(v, cinv_vt_minv_rhs)
-    # M^{-1} V C^{-1} V^T M^{-1} rhs
-    minv_v_cinv_vt_minv_rhs = m.solve(v_cinv_vt_minv_rhs)
-
-    # M^{-1} - M^{-1} V C^{-1} V^T M^{-1}
-    return minv_rhs - minv_v_cinv_vt_minv_rhs
-
-  def _to_dense(self):
-    sqrt = self.sqrt_to_dense()
-    return math_ops.matmul(sqrt, sqrt, adjoint_b=True)
-
-  def _sqrt_to_dense(self):
-    v = self._v
-    d = self._diag_operator
-    m = self._operator
-
-    d_vt = d.matmul(v, transpose_x=True)
-    # Batch op won't be efficient for singletons.  Currently we don't break
-    # to_dense into batch/singleton methods.
-    v_d_vt = math_ops.matmul(v, d_vt)
-    m_plus_v_d_vt = m.to_dense() + v_d_vt
-    return m_plus_v_d_vt
diff --git a/tensorflow/contrib/distributions/python/ops/operator_test_util.py b/tensorflow/contrib/distributions/python/ops/operator_test_util.py
deleted file mode 100644
index bc78340e5a..0000000000
--- a/tensorflow/contrib/distributions/python/ops/operator_test_util.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Utilities for testing `OperatorPDBase` and related classes."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import abc
-import numpy as np
-import six
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import linalg_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.platform import test
-
-
-@six.add_metaclass(abc.ABCMeta)  # pylint: disable=no-init
-class OperatorPDDerivedClassTest(test.TestCase):
-  """Tests for derived classes.
-
-  Subclasses should implement every abstractmethod, and this will enable all
-  test methods to work.
-  """
-
-  def setUp(self):
-    self._rng = np.random.RandomState(42)
-
-  def _compare_results(self, expected, actual, static_shapes=True, atol=1e-5):
-    """Compare expected value (array) to the actual value (Tensor)."""
-    if static_shapes:
-      self.assertEqual(expected.shape, actual.get_shape())
-    self.assertAllClose(expected, actual.eval(), atol=atol)
-
-  @abc.abstractmethod
-  def _build_operator_and_mat(self, batch_shape, k, dtype=np.float64):
-    """Build a batch matrix and an Operator that should have similar behavior.
-
-    Every operator represents a (batch) matrix.  This method returns both
-    together, and is used e.g. by tests.
-
-    Args:
-      batch_shape:  List-like of Python integers giving batch shape of operator.
-      k: Python integer, the event size.
-      dtype:  Numpy dtype.  Data type of returned array/operator.
-
-    Returns:
-      operator:  `OperatorPDBase` subclass.
-      mat:  numpy array representing a (batch) matrix.
-    """
-    # Create a matrix as a numpy array.  Shape = batch_shape + [k, k].
-    # Create an OperatorPDDiag that should have the same behavior as the matrix.
-    # All arguments are convertable to numpy arrays.
-    #
-    batch_shape = list(batch_shape)
-    mat_shape = batch_shape + [k, k]
-    # return operator, mat
-    raise NotImplementedError("Not implemented yet.")
-
-  def testToDense(self):
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          for dtype in [np.float32, np.float64]:
-            operator, mat = self._build_operator_and_mat(
-                batch_shape, k, dtype=dtype)
-            self._compare_results(expected=mat, actual=operator.to_dense())
-
-  def testSqrtToDense(self):
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          operator, mat = self._build_operator_and_mat(batch_shape, k)
-          sqrt = operator.sqrt_to_dense()
-          self.assertEqual(mat.shape, sqrt.get_shape())
-          # Square roots are not unique, but SS^T should equal mat.  In this
-          # case however, we should have S = S^T.
-          self._compare_results(
-              expected=mat, actual=math_ops.matmul(sqrt, sqrt))
-
-  def testDeterminants(self):
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          operator, mat = self._build_operator_and_mat(batch_shape, k)
-          expected_det = linalg_ops.matrix_determinant(mat).eval()
-
-          self._compare_results(expected_det, operator.det())
-          self._compare_results(np.log(expected_det), operator.log_det())
-
-  def testMatmul(self):
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          operator, mat = self._build_operator_and_mat(batch_shape, k)
-
-          # Work with 5 simultaneous systems.  5 is arbitrary.
-          x = self._rng.randn(*(batch_shape + (k, 5)))
-
-          self._compare_results(
-              expected=math_ops.matmul(mat, x).eval(),
-              actual=operator.matmul(x))
-
-  def testSqrtMatmul(self):
-    # Square roots are not unique, but we should have SS^T x = Ax, and in our
-    # case, we should have S = S^T, so SSx = Ax.
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          operator, mat = self._build_operator_and_mat(batch_shape, k)
-
-          # Work with 5 simultaneous systems.  5 is arbitrary.
-          x = self._rng.randn(*(batch_shape + (k, 5)))
-
-          self._compare_results(
-              expected=math_ops.matmul(mat, x).eval(),
-              actual=operator.sqrt_matmul(operator.sqrt_matmul(x)))
-
-  def testSolve(self):
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          operator, mat = self._build_operator_and_mat(batch_shape, k)
-
-          # Work with 5 simultaneous systems.  5 is arbitrary.
-          x = self._rng.randn(*(batch_shape + (k, 5)))
-
-          self._compare_results(
-              expected=linalg_ops.matrix_solve(mat, x).eval(),
-              actual=operator.solve(x))
-
-  def testSqrtSolve(self):
-    # Square roots are not unique, but we should still have
-    # S^{-T} S^{-1} x = A^{-1} x.
-    # In our case, we should have S = S^T, so then S^{-1} S^{-1} x = A^{-1} x.
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          operator, mat = self._build_operator_and_mat(batch_shape, k)
-
-          # Work with 5 simultaneous systems.  5 is arbitrary.
-          x = self._rng.randn(*(batch_shape + (k, 5)))
-
-          self._compare_results(
-              expected=linalg_ops.matrix_solve(mat, x).eval(),
-              actual=operator.sqrt_solve(operator.sqrt_solve(x)))
-
-  def testAddToTensor(self):
-    with self.test_session():
-      for batch_shape in [(), (
-          2,
-          3,)]:
-        for k in [1, 4]:
-          operator, mat = self._build_operator_and_mat(batch_shape, k)
-          tensor = array_ops.ones_like(mat)
-
-          self._compare_results(
-              expected=(mat + tensor).eval(),
-              actual=operator.add_to_tensor(tensor))
diff --git a/tensorflow/contrib/distributions/python/ops/wishart.py b/tensorflow/contrib/distributions/python/ops/wishart.py
index e162a79610..9d30ce6719 100644
--- a/tensorflow/contrib/distributions/python/ops/wishart.py
+++ b/tensorflow/contrib/distributions/python/ops/wishart.py
@@ -21,11 +21,13 @@ from __future__ import print_function
 import math
 import numpy as np
 
-from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
-from tensorflow.contrib.distributions.python.ops import operator_pd_full
+from tensorflow.contrib import linalg
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
@@ -34,8 +36,6 @@ from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
-from tensorflow.python.ops.distributions import util as distribution_util
-
 
 __all__ = [
     "WishartCholesky",
@@ -43,11 +43,11 @@ __all__ = [
 ]
 
 
-class _WishartOperatorPD(distribution.Distribution):
+class _WishartLinearOperator(distribution.Distribution):
   """The matrix Wishart distribution on positive definite matrices.
 
   This distribution is defined by a scalar number of degrees of freedom `df` and
-  an instance of `OperatorPDBase`, which provides matrix-free access to a
+  an instance of `LinearOperator`, which provides matrix-free access to a
   symmetric positive definite operator, which defines the scale matrix.
 
   #### Mathematical Details
@@ -75,7 +75,7 @@ class _WishartOperatorPD(distribution.Distribution):
 
   def __init__(self,
                df,
-               scale_operator_pd,
+               scale_operator,
                cholesky_input_output_matrices=False,
                validate_args=False,
                allow_nan_stats=True,
@@ -85,7 +85,7 @@ class _WishartOperatorPD(distribution.Distribution):
     Args:
       df: `float` or `double` tensor, the degrees of freedom of the
         distribution(s). `df` must be greater than or equal to `k`.
-      scale_operator_pd: `float` or `double` instance of `OperatorPDBase`.
+      scale_operator: `float` or `double` instance of `LinearOperator`.
       cholesky_input_output_matrices: Python `bool`. Any function which whose
         input or output is a matrix assumes the input is Cholesky and returns a
         Cholesky factored matrix. Example `log_prob` input takes a Cholesky and
@@ -109,28 +109,32 @@ class _WishartOperatorPD(distribution.Distribution):
     """
     parameters = locals()
     self._cholesky_input_output_matrices = cholesky_input_output_matrices
-    with ops.name_scope(name):
-      with ops.name_scope("init", values=[df, scale_operator_pd]):
-        if not scale_operator_pd.dtype.is_floating:
+    with ops.name_scope(name) as ns:
+      with ops.name_scope("init", values=[df, scale_operator]):
+        if not scale_operator.dtype.is_floating:
           raise TypeError(
-              "scale_operator_pd.dtype=%s is not a floating-point type" %
-              scale_operator_pd.dtype)
-        self._scale_operator_pd = scale_operator_pd
+              "scale_operator.dtype=%s is not a floating-point type" %
+              scale_operator.dtype)
+        if not scale_operator.is_square:
+          print(scale_operator.to_dense().eval())
+          raise ValueError("scale_operator must be square.")
+
+        self._scale_operator = scale_operator
         self._df = ops.convert_to_tensor(
             df,
-            dtype=scale_operator_pd.dtype,
+            dtype=scale_operator.dtype,
             name="df")
-        check_ops.assert_same_float_dtype(
-            (self._df, self._scale_operator_pd))
-        if (self._scale_operator_pd.get_shape().ndims is None or
-            self._scale_operator_pd.get_shape()[-1].value is None):
+        contrib_tensor_util.assert_same_float_dtype(
+            (self._df, self._scale_operator))
+        if (self._scale_operator.shape.ndims is None or
+            self._scale_operator.shape[-1].value is None):
           self._dimension = math_ops.cast(
-              self._scale_operator_pd.vector_space_dimension(),
-              dtype=self._scale_operator_pd.dtype, name="dimension")
+              self._scale_operator.domain_dimension_tensor(),
+              dtype=self._scale_operator.dtype, name="dimension")
         else:
           self._dimension = ops.convert_to_tensor(
-              self._scale_operator_pd.get_shape()[-1].value,
-              dtype=self._scale_operator_pd.dtype, name="dimension")
+              self._scale_operator.shape[-1].value,
+              dtype=self._scale_operator.dtype, name="dimension")
         df_val = tensor_util.constant_value(self._df)
         dim_val = tensor_util.constant_value(self._dimension)
         if df_val is not None and dim_val is not None:
@@ -151,32 +155,36 @@ class _WishartOperatorPD(distribution.Distribution):
                        (self._dimension, self._df)))
           self._df = control_flow_ops.with_dependencies(
               [assertions], self._df)
-    super(_WishartOperatorPD, self).__init__(
-        dtype=self._scale_operator_pd.dtype,
+    super(_WishartLinearOperator, self).__init__(
+        dtype=self._scale_operator.dtype,
         validate_args=validate_args,
         allow_nan_stats=allow_nan_stats,
         reparameterization_type=distribution.FULLY_REPARAMETERIZED,
         parameters=parameters,
         graph_parents=([self._df, self._dimension] +
-                       self._scale_operator_pd.inputs),
-        name=name)
+                       self._scale_operator.graph_parents),
+        name=ns)
 
   @property
   def df(self):
     """Wishart distribution degree(s) of freedom."""
     return self._df
 
+  def _square_scale_operator(self):
+    return self.scale_operator.matmul(
+        self.scale_operator.to_dense(), adjoint_arg=True)
+
   def scale(self):
     """Wishart distribution scale matrix."""
     if self._cholesky_input_output_matrices:
-      return self.scale_operator_pd.sqrt_to_dense()
+      return self.scale_operator.to_dense()
     else:
-      return self.scale_operator_pd.to_dense()
+      return self._square_scale_operator()
 
   @property
-  def scale_operator_pd(self):
-    """Wishart distribution scale matrix as an OperatorPD."""
-    return self._scale_operator_pd
+  def scale_operator(self):
+    """Wishart distribution scale matrix as an Linear Operator."""
+    return self._scale_operator
 
   @property
   def cholesky_input_output_matrices(self):
@@ -189,18 +197,18 @@ class _WishartOperatorPD(distribution.Distribution):
     return self._dimension
 
   def _event_shape_tensor(self):
-    s = self.scale_operator_pd.shape()
-    return array_ops.strided_slice(s, array_ops.shape(s) - 2,
-                                   array_ops.shape(s))
+    dimension = self.scale_operator.domain_dimension_tensor()
+    return array_ops.stack([dimension, dimension])
 
   def _event_shape(self):
-    return self.scale_operator_pd.get_shape()[-2:]
+    dimension = self.scale_operator.domain_dimension
+    return tensor_shape.TensorShape([dimension, dimension])
 
   def _batch_shape_tensor(self):
-    return self.scale_operator_pd.batch_shape()
+    return self.scale_operator.batch_shape_tensor()
 
   def _batch_shape(self):
-    return self.scale_operator_pd.get_batch_shape()
+    return self.scale_operator.batch_shape
 
   def _sample_n(self, n, seed):
     batch_shape = self.batch_shape_tensor()
@@ -242,10 +250,10 @@ class _WishartOperatorPD(distribution.Distribution):
     x = array_ops.reshape(x, shape)
 
     # Complexity: O(nbM) where M is the complexity of the operator solving a
-    # vector system. E.g., for OperatorPDDiag, each matmul is O(k**2), so
-    # this complexity is O(nbk**2). For OperatorPDCholesky, each matmul is
+    # vector system. E.g., for LinearOperatorDiag, each matmul is O(k**2), so
+    # this complexity is O(nbk**2). For LinearOperatorTriL, each matmul is
     # O(k^3) so this step has complexity O(nbk^3).
-    x = self.scale_operator_pd.sqrt_matmul(x)
+    x = self.scale_operator.matmul(x)
 
     # Undo make batch-op ready.
     # Complexity: O(nbk**2)
@@ -298,10 +306,10 @@ class _WishartOperatorPD(distribution.Distribution):
     scale_sqrt_inv_x_sqrt = array_ops.reshape(scale_sqrt_inv_x_sqrt, shape)
 
     # Complexity: O(nbM*k) where M is the complexity of the operator solving
-    # a vector system. E.g., for OperatorPDDiag, each solve is O(k), so
-    # this complexity is O(nbk**2). For OperatorPDCholesky, each solve is
+    # a vector system. E.g., for LinearOperatorDiag, each solve is O(k), so
+    # this complexity is O(nbk**2). For LinearOperatorTriL, each solve is
     # O(k**2) so this step has complexity O(nbk^3).
-    scale_sqrt_inv_x_sqrt = self.scale_operator_pd.sqrt_solve(
+    scale_sqrt_inv_x_sqrt = self.scale_operator.solve(
         scale_sqrt_inv_x_sqrt)
 
     # Undo make batch-op ready.
@@ -353,18 +361,18 @@ class _WishartOperatorPD(distribution.Distribution):
     half_dp1 = 0.5 * self.dimension + 0.5
     half_df = 0.5 * self.df
     return (self.dimension * (half_df + half_dp1 * math.log(2.)) +
-            half_dp1 * self.scale_operator_pd.log_det() +
+            2 * half_dp1 * self.scale_operator.log_abs_determinant() +
             self._multi_lgamma(half_df, self.dimension) +
             (half_dp1 - half_df) * self._multi_digamma(half_df, self.dimension))
 
   def _mean(self):
     if self.cholesky_input_output_matrices:
       return (math_ops.sqrt(self.df)
-              * self.scale_operator_pd.sqrt_to_dense())
-    return self.df * self.scale_operator_pd.to_dense()
+              * self.scale_operator.to_dense())
+    return self.df * self._square_scale_operator()
 
   def _variance(self):
-    x = math_ops.sqrt(self.df) * self.scale_operator_pd.to_dense()
+    x = math_ops.sqrt(self.df) * self._square_scale_operator()
     d = array_ops.expand_dims(array_ops.matrix_diag_part(x), -1)
     v = math_ops.square(x) + math_ops.matmul(d, d, adjoint_b=True)
     if self.cholesky_input_output_matrices:
@@ -385,20 +393,20 @@ class _WishartOperatorPD(distribution.Distribution):
         constant_op.constant(float("NaN"), dtype=self.dtype, name="nan"),
         s)
     if self.cholesky_input_output_matrices:
-      return math_ops.sqrt(s) * self.scale_operator_pd.sqrt_to_dense()
-    return s * self.scale_operator_pd.to_dense()
+      return math_ops.sqrt(s) * self.scale_operator.to_dense()
+    return s * self._square_scale_operator()
 
   def mean_log_det(self, name="mean_log_det"):
     """Computes E[log(det(X))] under this Wishart distribution."""
     with self._name_scope(name):
       return (self._multi_digamma(0.5 * self.df, self.dimension) +
               self.dimension * math.log(2.) +
-              self.scale_operator_pd.log_det())
+              2 * self.scale_operator.log_abs_determinant())
 
   def log_normalization(self, name="log_normalization"):
     """Computes the log normalizing constant, log(Z)."""
     with self._name_scope(name):
-      return (self.df * self.scale_operator_pd.sqrt_log_det() +
+      return (self.df * self.scale_operator.log_abs_determinant() +
               0.5 * self.df * self.dimension * math.log(2.) +
               self._multi_lgamma(0.5 * self.df, self.dimension))
 
@@ -428,7 +436,7 @@ class _WishartOperatorPD(distribution.Distribution):
                                  axis=[-1])
 
 
-class WishartCholesky(_WishartOperatorPD):
+class WishartCholesky(_WishartLinearOperator):
   """The matrix Wishart distribution on positive definite matrices.
 
   This distribution is defined by a scalar degrees of freedom `df` and a
@@ -521,10 +529,26 @@ class WishartCholesky(_WishartOperatorPD):
     """
     parameters = locals()
     with ops.name_scope(name, values=[scale]):
+      with ops.name_scope("init", values=[scale]):
+        scale = ops.convert_to_tensor(scale)
+        if validate_args:
+          scale = control_flow_ops.with_dependencies([
+              check_ops.assert_positive(
+                  array_ops.matrix_diag_part(scale),
+                  message="scale must be positive definite"),
+              check_ops.assert_equal(
+                  array_ops.shape(scale)[-1],
+                  array_ops.shape(scale)[-2],
+                  message="scale must be square")
+          ] if validate_args else [], scale)
+
       super(WishartCholesky, self).__init__(
           df=df,
-          scale_operator_pd=operator_pd_cholesky.OperatorPDCholesky(
-              scale, verify_pd=validate_args),
+          scale_operator=linalg.LinearOperatorTriL(
+              tril=scale,
+              is_non_singular=True,
+              is_positive_definite=True,
+              is_square=True),
           cholesky_input_output_matrices=cholesky_input_output_matrices,
           validate_args=validate_args,
           allow_nan_stats=allow_nan_stats,
@@ -532,7 +556,7 @@ class WishartCholesky(_WishartOperatorPD):
     self._parameters = parameters
 
 
-class WishartFull(_WishartOperatorPD):
+class WishartFull(_WishartLinearOperator):
   """The matrix Wishart distribution on positive definite matrices.
 
   This distribution is defined by a scalar degrees of freedom `df` and a
@@ -620,13 +644,24 @@ class WishartFull(_WishartOperatorPD):
       name: Python `str` name prefixed to Ops created by this class.
     """
     parameters = locals()
-    with ops.name_scope(name, values=[scale]) as ns:
-      super(WishartFull, self).__init__(
-          df=df,
-          scale_operator_pd=operator_pd_full.OperatorPDFull(
-              scale, verify_pd=validate_args),
-          cholesky_input_output_matrices=cholesky_input_output_matrices,
-          validate_args=validate_args,
-          allow_nan_stats=allow_nan_stats,
-          name=ns)
+    with ops.name_scope(name) as ns:
+      with ops.name_scope("init", values=[scale]):
+        scale = ops.convert_to_tensor(scale)
+        if validate_args:
+          scale = distribution_util.assert_symmetric(scale)
+        chol = linalg_ops.cholesky(scale)
+        chol = control_flow_ops.with_dependencies([
+            check_ops.assert_positive(array_ops.matrix_diag_part(chol))
+        ] if validate_args else [], chol)
+    super(WishartFull, self).__init__(
+        df=df,
+        scale_operator=linalg.LinearOperatorTriL(
+            tril=chol,
+            is_non_singular=True,
+            is_positive_definite=True,
+            is_square=True),
+        cholesky_input_output_matrices=cholesky_input_output_matrices,
+        validate_args=validate_args,
+        allow_nan_stats=allow_nan_stats,
+        name=ns)
     self._parameters = parameters
-- 
GitLab


From 06acccabcb41513c76bbfffcd17817a7b136494b Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Tue, 18 Jul 2017 16:48:30 -0700
Subject: [PATCH 1097/1375] Add autotuning code for matmul operator. Currently
 it is turned off by default.

PiperOrigin-RevId: 162423171
---
 tensorflow/core/BUILD                         |   1 +
 tensorflow/core/kernels/BUILD                 |  14 +-
 tensorflow/core/kernels/conv_ops_gpu.h        | 129 +-------
 tensorflow/core/kernels/gpu_utils.h           | 166 ++++++++++
 tensorflow/core/kernels/matmul_op.cc          | 312 ++++++++++++++----
 tensorflow/core/kernels/matmul_op.h           |  64 ++++
 tensorflow/core/util/matmul_autotune.cc       |  51 +++
 tensorflow/core/util/matmul_autotune.h        |  28 ++
 tensorflow/python/BUILD                       |  42 +++
 .../python/kernel_tests/matmul_op_test.py     |   3 +
 tensorflow/python/ops/matmul_benchmark.py     | 143 ++++++++
 .../python/ops/matmul_benchmark_test.py       | 122 +++++++
 tensorflow/stream_executor/blas.cc            |   4 +
 tensorflow/stream_executor/blas.h             | 138 +++++++-
 tensorflow/stream_executor/cuda/cuda_blas.cc  | 181 +++++++++-
 tensorflow/stream_executor/cuda/cuda_blas.h   |  17 +
 tensorflow/stream_executor/stream.cc          | 178 ++++++++++
 tensorflow/stream_executor/stream.h           |  63 ++++
 18 files changed, 1460 insertions(+), 196 deletions(-)
 create mode 100644 tensorflow/core/kernels/gpu_utils.h
 create mode 100644 tensorflow/core/util/matmul_autotune.cc
 create mode 100644 tensorflow/core/util/matmul_autotune.h
 create mode 100644 tensorflow/python/ops/matmul_benchmark.py
 create mode 100644 tensorflow/python/ops/matmul_benchmark_test.py

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index bb17d8832f..1dbb90d263 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -405,6 +405,7 @@ tf_cuda_library(
         "util/tensor_slice_reader_cache.h",
         "util/tensor_slice_writer.h",
         "util/use_cudnn.h",
+        "util/matmul_autotune.h",
         "util/util.h",
         "util/work_sharder.h",
     ] + select({
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 21194e80c3..6e983a9ea7 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -157,6 +157,7 @@ cc_library(
     hdrs = ["conv_2d.h"],
     deps = [
         ":eigen_helpers",
+        ":gpu_util_hdrs",
         "//tensorflow/core:framework",
         "//third_party/eigen3",
     ],
@@ -248,6 +249,15 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "gpu_util_hdrs",
+    hdrs = ["gpu_utils.h"],
+    deps = [
+        ":eigen_helpers",
+        "//third_party/eigen3",
+    ],
+)
+
 tf_cc_test(
     name = "ops_util_test",
     size = "small",
@@ -2406,7 +2416,9 @@ tf_kernel_library(
         ],
         "//conditions:default": [],
     }),
-    deps = MATH_DEPS + select({
+    deps = MATH_DEPS + [
+        ":gpu_util_hdrs",
+    ] + select({
         ":xsmm": [
             "@libxsmm_archive//:xsmm_avx",
         ],
diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h
index b268f8dbd2..d2f07c2207 100644
--- a/tensorflow/core/kernels/conv_ops_gpu.h
+++ b/tensorflow/core/kernels/conv_ops_gpu.h
@@ -21,27 +21,12 @@ limitations under the License.
 #include <tuple>
 #include <unordered_map>
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/kernels/gpu_utils.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/hash/hash.h"
-#include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/platform/stream_executor.h"
 
 namespace tensorflow {
 
-// TODO(zhengxq): move this to gpu_util.h. The use of such wrappers is wide
-// spread.
-template <typename T>
-inline perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory,
-                                                           uint64 size) {
-  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory),
-                                                size * sizeof(T));
-  perftools::gputools::DeviceMemory<T> typed(wrapped);
-  return typed;
-}
-
 // Get the Cudnn workspace limit from the environment variable, which is in MB.
 // Return the workspace memory limit in bytes. If no value is set, return the
 // default value.
@@ -56,12 +41,10 @@ class CudnnScratchAllocator : public perftools::gputools::ScratchAllocator {
   virtual ~CudnnScratchAllocator() {}
   CudnnScratchAllocator(int64 memory_limit, OpKernelContext* context)
       : memory_limit_(memory_limit), total_byte_size_(0), context_(context) {}
-  virtual int64 GetMemoryLimitInBytes(
-      perftools::gputools::Stream* stream) override {
+  int64 GetMemoryLimitInBytes(perftools::gputools::Stream* stream) override {
     return memory_limit_;
   }
-  virtual perftools::gputools::port::StatusOr<
-      perftools::gputools::DeviceMemory<uint8>>
+  perftools::gputools::port::StatusOr<perftools::gputools::DeviceMemory<uint8>>
   AllocateBytes(perftools::gputools::Stream* stream, int64 byte_size) override {
     Tensor temporary_memory;
     if (byte_size > memory_limit_) {
@@ -185,112 +168,6 @@ class ConvParameters {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-// A helper class that looks up the best autotuned config from parameters.
-// Due to the noisy nature of autotune, especially with multiple devices, it
-// only accepts a config if its margin exceeds a threshold.
-// For the same shape configs, if a new best config matches the previous best,
-// they get promoted; otherwise, the winner gets demoted. This process stops
-// when the winner's score exceeds the threshold.
-// In a bad case when two configs are very close to each other and flips
-// back and forth randomly, the expected number of experiments before autotune
-// settles is O(threshold ^ 2). So we recommend that number of warmup runs
-// for any benchmarks.
-template <typename Parameters, typename Config>
-class AutoTuneMap {
- public:
-  bool Find(const Parameters& params, Config* config) const {
-    mutex_lock lock(mu_);
-    auto iter = params_config_map_.find(params);
-    if (iter == params_config_map_.end() ||
-        iter->second.score < min_score_threshold_) {
-      return false;
-    }
-    *config = iter->second.config;
-    return true;
-  }
-  void Insert(const ConvParameters& params, const Config& config) {
-    mutex_lock lock(mu_);
-    auto iter = params_config_map_.find(params);
-    int new_score = 0;
-    if (iter == params_config_map_.end()) {
-      // Create a new entry if params is new.
-      VLOG(1) << GetActionSummary("creates", params, config);
-      params_config_map_.insert(std::make_pair(params, ValueType{config, 1}));
-      new_score = 1;
-    } else if (iter->second.score < min_score_threshold_) {
-      DCHECK(iter->second.score > 0);
-      if (iter->second.config != config) {
-        // If it is different from the current winner, demotes the winner.
-        VLOG(1) << GetActionSummary("demotes", params, config);
-        new_score = --iter->second.score;
-        if (new_score <= 0) {
-          VLOG(1) << GetActionSummary("erases", params, config);
-          params_config_map_.erase(iter);
-        }
-      } else {
-        // If it is the same as the current winner, promotes the winner.
-        VLOG(1) << GetActionSummary("promotes", params, config);
-        new_score = ++iter->second.score;
-      }
-    }
-    if (new_score >= min_score_threshold_) {
-      VLOG(1) << GetActionSummary("accepts", params, config);
-    }
-  }
-
- private:
-  AutoTuneMap(const string& name) : name_(name) {
-    min_score_threshold_ = 1;
-    const char* threshold_str = getenv("TF_AUTOTUNE_THRESHOLD");
-    if (threshold_str != nullptr) {
-      strings::safe_strto32(threshold_str, &min_score_threshold_);
-    }
-    min_score_threshold_ = std::max(min_score_threshold_, 1);
-  }
-
-  template <class Group, class Params, class Cfg>
-  friend class AutoTuneSingleton;
-
-  struct Hasher {
-    std::size_t operator()(const Parameters& parameter) const {
-      return parameter.hash();
-    }
-  };
-
-  string GetActionSummary(StringPiece action, const Parameters& params,
-                          const Config& config) {
-    return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
-                           action.ToString().c_str(), params.ToString().c_str(),
-                           config.ToString().c_str());
-  }
-
-  mutable mutex mu_;
-  struct ValueType {
-    Config config;
-    int32 score;
-  };
-  std::unordered_map<Parameters, ValueType, Hasher> params_config_map_
-      GUARDED_BY(mu_);
-  string name_;
-  int32 min_score_threshold_;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(AutoTuneMap);
-};
-
-// A Singleton helper that manages the global autotune results by groups.
-// The caller specified arbitrary Group type that can distinguish between
-// different autotune results, even if their Parameters and Configs are the
-// same.
-template <class Group, typename Parameters, typename Config>
-class AutoTuneSingleton {
- public:
-  typedef AutoTuneMap<Parameters, Config> AutoTuneType;
-  static AutoTuneType* GetInstance() {
-    static AutoTuneType* instance = new AutoTuneType(Group::name());
-    return instance;
-  }
-};
-
 }  // namespace tensorflow
 
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/gpu_utils.h b/tensorflow/core/kernels/gpu_utils.h
new file mode 100644
index 0000000000..18bfb8ce8e
--- /dev/null
+++ b/tensorflow/core/kernels/gpu_utils.h
@@ -0,0 +1,166 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
+
+#if GOOGLE_CUDA
+
+#include <unordered_map>
+
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor.h"
+
+namespace tensorflow {
+
+template <typename T>
+inline perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory,
+                                                           uint64 size) {
+  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory),
+                                                size * sizeof(T));
+  perftools::gputools::DeviceMemory<T> typed(wrapped);
+  return typed;
+}
+
+
+// A helper class that looks up the best autotuned config from parameters.
+// Due to the noisy nature of autotune, especially with multiple devices, it
+// only accepts a config if its margin exceeds a threshold.
+// For the same shape configs, if a new best config matches the previous best,
+// they get promoted; otherwise, the winner gets demoted. This process stops
+// when the winner's score exceeds the threshold.
+// In a bad case when two configs are very close to each other and flips
+// back and forth randomly, the expected number of experiments before autotune
+// settles is O(threshold ^ 2). So we recommend that number of warmup runs
+// for any benchmarks.
+template <typename Parameters, typename Config>
+class AutoTuneMap {
+ public:
+  bool Find(const Parameters& params, Config* config) const {
+    mutex_lock lock(mu_);
+    auto iter = params_config_map_.find(params);
+    if (iter == params_config_map_.end() ||
+        (iter->second.score < min_score_threshold_ &&
+         iter->second.count <= max_autotune_count_)) {
+      return false;
+    }
+    *config = iter->second.config;
+    return true;
+  }
+  void Insert(const Parameters& params, const Config& config) {
+    mutex_lock lock(mu_);
+    auto iter = params_config_map_.find(params);
+    int new_score = 0;
+    if (iter == params_config_map_.end()) {
+      // Create a new entry if params is new.
+      VLOG(1) << GetActionSummary("creates", params, config);
+      params_config_map_.insert(
+          std::make_pair(params, ValueType{config, 1, 1}));
+      new_score = 1;
+    } else if (iter->second.score < min_score_threshold_ &&
+               iter->second.count <= max_autotune_count_) {
+      DCHECK_GT(iter->second.score, 0);
+      if (iter->second.config != config) {
+        // If it is different from the current winner, demotes the winner.
+        VLOG(1) << GetActionSummary("demotes", params, config);
+        new_score = --iter->second.score;
+        ++iter->second.count;
+        if (new_score <= 0) {
+          VLOG(1) << GetActionSummary("erases", params, config);
+          params_config_map_.erase(iter);
+        }
+      } else {
+        // If it is the same as the current winner, promotes the winner.
+        VLOG(1) << GetActionSummary("promotes", params, config);
+        new_score = ++iter->second.score;
+        ++iter->second.count;
+      }
+    }
+    if (new_score >= min_score_threshold_) {
+      VLOG(1) << GetActionSummary("accepts", params, config);
+    }
+  }
+
+ private:
+  AutoTuneMap(const string& name) : name_(name) {
+    min_score_threshold_ = 1;
+    int min_warmup_iterations = 10;
+    const char* threshold_str = getenv("TF_AUTOTUNE_THRESHOLD");
+    if (threshold_str != nullptr) {
+      strings::safe_strto32(threshold_str, &min_score_threshold_);
+    }
+    const char* min_warmup_iteration_str =
+        getenv("TF_AUTOTUNE_MIN_WARMUP_ITERATIONS");
+    if (min_warmup_iteration_str != nullptr) {
+      strings::safe_strto32(min_warmup_iteration_str, &min_warmup_iterations);
+    }
+    min_score_threshold_ = std::max(min_score_threshold_, 1);
+    max_autotune_count_ = std::max(
+        5 * min_score_threshold_ * min_score_threshold_, min_warmup_iterations);
+  }
+
+  template <class Group, class Params, class Cfg>
+  friend class AutoTuneSingleton;
+
+  struct Hasher {
+    std::size_t operator()(const Parameters& parameter) const {
+      return parameter.hash();
+    }
+  };
+
+  string GetActionSummary(StringPiece action, const Parameters& params,
+                          const Config& config) {
+    return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
+                           action.ToString().c_str(), params.ToString().c_str(),
+                           config.ToString().c_str());
+  }
+
+  mutable mutex mu_;
+  struct ValueType {
+    Config config;
+    int32 score;
+    int32 count;
+  };
+  std::unordered_map<Parameters, ValueType, Hasher> params_config_map_
+      GUARDED_BY(mu_);
+  string name_;
+  int32 min_score_threshold_;
+  int32 max_autotune_count_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(AutoTuneMap);
+};
+
+// A Singleton helper that manages the global autotune results by groups.
+// The caller specified arbitrary Group type that can distinguish between
+// different autotune results, even if their Parameters and Configs are the
+// same.
+template <class Group, typename Parameters, typename Config>
+class AutoTuneSingleton {
+ public:
+  typedef AutoTuneMap<Parameters, Config> AutoTuneType;
+  static AutoTuneType* GetInstance() {
+    static AutoTuneType* instance = new AutoTuneType(Group::name());
+    return instance;
+  }
+};
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc
index 8003f7ff67..62c5ecfe81 100644
--- a/tensorflow/core/kernels/matmul_op.cc
+++ b/tensorflow/core/kernels/matmul_op.cc
@@ -23,27 +23,15 @@ limitations under the License.
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/fill_functor.h"
-
+#include "tensorflow/core/util/matmul_autotune.h"
 #if GOOGLE_CUDA
 #include "cuda/include/cuda.h"
+#include "tensorflow/core/kernels/gpu_utils.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #endif  // GOOGLE_CUDA
 
 namespace tensorflow {
 
-#if GOOGLE_CUDA
-
-namespace {
-template <typename T>
-perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory) {
-  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory));
-  perftools::gputools::DeviceMemory<T> typed(wrapped);
-  return typed;
-}
-}  // namespace
-
-#endif  // GOOGLE_CUDA
-
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 #ifdef TENSORFLOW_USE_SYCL
@@ -123,10 +111,16 @@ bool ExplicitVectorMatrixOptimization<Eigen::half>(
 
 template <typename Device, typename T>
 struct LaunchMatMulBase {
+#if GOOGLE_CUDA
+  typedef perftools::gputools::blas::AlgorithmType AlgorithmType;
+#else
+  typedef int64 AlgorithmType;
+#endif  // GOOGLE_CUDA
+
   static void launch(
-      OpKernelContext* ctx, OpKernel* kernel, const Tensor& a, const Tensor& b,
+      OpKernelContext* ctx, const Tensor& a, const Tensor& b,
       const Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1>& dim_pair,
-      Tensor* out) {
+      std::vector<AlgorithmType>* algorithms, bool use_aututone, Tensor* out) {
 #ifndef TENSORFLOW_USE_SYCL
     // An explicit vector-matrix multiply is much better optimized than an
     // implicit one and this is a bottleneck during non-batched inference.
@@ -140,6 +134,10 @@ struct LaunchMatMulBase {
     }
 #endif  // TENSORFLOW_USE_SYCL
   }
+
+  static void GetBlasGemmAlgorithm(OpKernelConstruction* ctx,
+                                   std::vector<int64>* algorithms,
+                                   bool* algorithm_set_flag) {}
 };
 // On CPUs, we ignore USE_CUBLAS
 template <typename T>
@@ -159,24 +157,39 @@ struct LaunchMatMul<SYCLDevice, T, USE_CUBLAS> : public LaunchMatMulSYCL<T> {};
 #if GOOGLE_CUDA
 
 namespace {
+
 template <typename T>
 struct LaunchBlasGemv {
-  static void Compute(OpKernelContext* ctx, perftools::gputools::Stream* stream,
-                      bool trans, uint64 m, uint64 n,
-                      const perftools::gputools::DeviceMemory<T>& a,
-                      const perftools::gputools::DeviceMemory<T>& b,
-                      perftools::gputools::DeviceMemory<T>* c) {
+  static void Compute(
+      OpKernelContext* ctx, perftools::gputools::Stream* stream, bool trans,
+      uint64 m, uint64 n, const perftools::gputools::DeviceMemory<T>& a,
+      const perftools::gputools::DeviceMemory<T>& b,
+      perftools::gputools::DeviceMemory<T>* c,
+      perftools::gputools::blas::ProfileResult* output_profile) {
     const auto blas_trans =
         trans ? perftools::gputools::blas::Transpose::kTranspose
               : perftools::gputools::blas::Transpose::kNoTranspose;
-    bool blas_launch_status =
-        stream
-            ->ThenBlasGemv(blas_trans, m, n, static_cast<T>(1.0), a, m, b, 1,
-                           static_cast<T>(0.0), c, 1)
-            .ok();
-    if (!blas_launch_status) {
-      ctx->SetStatus(
-          errors::Internal("Blas GEMV launch failed:  m=", m, ", n=", n));
+    if (output_profile == nullptr) {
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemv(blas_trans, m, n, static_cast<T>(1.0), a, m, b, 1,
+                             static_cast<T>(0.0), c, 1)
+              .ok();
+      if (!blas_launch_status) {
+        ctx->SetStatus(
+            errors::Internal("Blas GEMV launch failed:  m=", m, ", n=", n));
+      }
+    } else {
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemvWithProfiling(blas_trans, m, n, static_cast<T>(1.0),
+                                          a, m, b, 1, static_cast<T>(0.0), c, 1,
+                                          output_profile)
+              .ok();
+      if (!blas_launch_status) {
+        ctx->SetStatus(errors::Internal(
+            "Blas GEMV with profiling launch failed:  m=", m, ", n=", n));
+      }
     }
   }
 
@@ -188,7 +201,8 @@ void LaunchBlasGemv<Eigen::half>::Compute(
     OpKernelContext* ctx, perftools::gputools::Stream* stream, bool trans,
     uint64 m, uint64 n, const perftools::gputools::DeviceMemory<Eigen::half>& a,
     const perftools::gputools::DeviceMemory<Eigen::half>& b,
-    perftools::gputools::DeviceMemory<Eigen::half>* c) {
+    perftools::gputools::DeviceMemory<Eigen::half>* c,
+    perftools::gputools::blas::ProfileResult* output_profile) {
   ctx->SetStatus(errors::Internal(
       "Blas GEMV launch failed: GEMV is not implemented for float16."));
 }
@@ -200,15 +214,55 @@ bool LaunchBlasGemv<Eigen::half>::IsSupported() {
 
 }  // namespace
 
+bool GetCublasAutotuneComputationType(
+    const DataType& dtype,
+    perftools::gputools::blas::ComputationType* compute_type) {
+  using perftools::gputools::blas::ComputationType;
+  bool use_f32_for_f16_computation = MatmulDoFP32ComputationFP16Input();
+  switch (dtype) {
+    case DT_HALF:
+    case DT_BFLOAT16:
+      if (use_f32_for_f16_computation) {
+        *compute_type = ComputationType::kF32;
+      } else {
+        *compute_type = ComputationType::kF16;
+      }
+      return false;
+    case DT_FLOAT:
+      *compute_type = ComputationType::kF32;
+      return true;
+    case DT_DOUBLE:
+      *compute_type = ComputationType::kF64;
+      return true;
+    default:
+      // Unsupported compute_type, return false.
+      return false;
+  }
+}
+
+// A dummy type to group matmul autotune results together.
+struct MatmulAutoTuneGroup {
+  static string name() { return "Matmul"; }
+};
+typedef AutoTuneSingleton<MatmulAutoTuneGroup, MatmulParameters,
+                          perftools::gputools::blas::AlgorithmConfig>
+    AutoTuneMatmul;
+
 template <typename T>
 struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
   static void launch(
-      OpKernelContext* ctx, OpKernel* kernel, const Tensor& a, const Tensor& b,
+      OpKernelContext* ctx, const Tensor& a, const Tensor& b,
       const Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1>& dim_pair,
-      Tensor* out) {
-    perftools::gputools::blas::Transpose trans[] = {
-        perftools::gputools::blas::Transpose::kNoTranspose,
-        perftools::gputools::blas::Transpose::kTranspose};
+      std::vector<int64>* algorithms, bool use_autotune, Tensor* out) {
+    using perftools::gputools::blas::AlgorithmConfig;
+    using perftools::gputools::blas::ComputationType;
+    using perftools::gputools::blas::ProfileResult;
+    using perftools::gputools::blas::Transpose;
+    using perftools::gputools::blas::kDefaultAlgorithm;
+    using perftools::gputools::blas::kDefaultBlasGemm;
+    using perftools::gputools::blas::kDefaultBlasGemv;
+    using perftools::gputools::blas::kNoAlgorithm;
+    Transpose trans[] = {Transpose::kNoTranspose, Transpose::kTranspose};
     const uint64 m = a.dim_size(1 - dim_pair[0].first);
     const uint64 k = a.dim_size(dim_pair[0].first);
     const uint64 n = b.dim_size(1 - dim_pair[0].second);
@@ -220,35 +274,156 @@ struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
     auto* stream = ctx->op_device_context()->stream();
     OP_REQUIRES(ctx, stream, errors::Internal("No GPU stream available."));
 
-    auto a_ptr = AsDeviceMemory(a.template flat<T>().data());
-    auto b_ptr = AsDeviceMemory(b.template flat<T>().data());
-    auto c_ptr = AsDeviceMemory(out->template flat<T>().data());
-    // Cublas does
-    // C = A x B
-    // where A, B and C are assumed to be in column major.
-    // We want the output to be in row-major, so we can compute
-    // C' = B' x A' (' stands for transpose)
-    if (LaunchBlasGemv<T>::IsSupported() && n == 1) {
-      // This is a matrix*vector multiply so use GEMV to compute A * b.
-      // Here we are multiplying in the natural order, so we have to flip
-      // the transposition flag to compensate for the tensor being stored
-      // row-major.
-      LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a, transpose_a ? m : k,
-                                 transpose_a ? k : m, a_ptr, b_ptr, &c_ptr);
-    } else {
-      bool blas_launch_status =
-          stream
-              ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k, 1.0f,
-                             b_ptr, transpose_b ? k : n, a_ptr,
-                             transpose_a ? m : k, 0.0f, &c_ptr, n)
-              .ok();
-      if (!blas_launch_status) {
-        ctx->SetStatus(errors::Internal(
-            "Blas GEMM launch failed : a.shape=(", a.dim_size(0), ", ",
-            a.dim_size(1), "), b.shape=(", b.dim_size(0), ", ", b.dim_size(1),
-            "), m=", m, ", n=", n, ", k=", k));
+    auto a_ptr = AsDeviceMemory(a.template flat<T>().data(),
+                                a.template flat<T>().size());
+    auto b_ptr = AsDeviceMemory(b.template flat<T>().data(),
+                                b.template flat<T>().size());
+    auto c_ptr = AsDeviceMemory(out->template flat<T>().data(),
+                                out->template flat<T>().size());
+    auto alpha = static_cast<T>(1.0);
+    auto beta = static_cast<T>(0.0);
+
+    int device_id = stream->parent()->device_ordinal();
+    DataType dtype = a.dtype();
+    MatmulParameters matmul_parameters = {
+        transpose_a, transpose_b, m, n, k, dtype, device_id,
+    };
+    AlgorithmConfig algorithm_config(kNoAlgorithm);
+
+    ComputationType computation_type;
+    bool compute_type_supported =
+        GetCublasAutotuneComputationType(dtype, &computation_type);
+    if (use_autotune && compute_type_supported && !algorithms->empty()) {
+      ProfileResult best_result;
+      // TODO(yangzihao): Unify this code with conv autotuning.
+      if (!AutoTuneMatmul::GetInstance()->Find(matmul_parameters,
+                                               &algorithm_config)) {
+        ProfileResult profile_result;
+        for (auto profile_algorithm : (*algorithms)) {
+          // Cublas does
+          // C = A x B
+          // where A, B and C are assumed to be in column major.
+          // We want the output to be in row-major, so we can compute
+          // C' = B' x A' (' stands for transpose)
+          bool cublas_launch_status =
+              stream
+                  ->ThenBlasGemmWithAlgorithm(
+                      blas_transpose_b, blas_transpose_a, n, m, k, alpha, b_ptr,
+                      transpose_b ? k : n, a_ptr, transpose_a ? m : k, beta,
+                      &c_ptr, n, computation_type, profile_algorithm,
+                      &profile_result)
+                  .ok();
+          if (cublas_launch_status) {
+            if (profile_result.is_valid()) {
+              if (profile_result.elapsed_time_in_ms() <
+                  best_result.elapsed_time_in_ms()) {
+                best_result = profile_result;
+              }
+            }
+          }
+        }
+        // Try BlasGemmWithProfiling
+        bool cublas_launch_status =
+            stream
+                ->ThenBlasGemmWithProfiling(
+                    blas_transpose_b, blas_transpose_a, n, m, k, 1.0, b_ptr,
+                    transpose_b ? k : n, a_ptr, transpose_a ? m : k, 0.0,
+                    &c_ptr, n, &profile_result)
+                .ok();
+        if (cublas_launch_status) {
+          if (profile_result.is_valid()) {
+            if (profile_result.elapsed_time_in_ms() <
+                best_result.elapsed_time_in_ms()) {
+              best_result = profile_result;
+            }
+          }
+        }
+        // Try BlasGemvWithProfiling
+        if (LaunchBlasGemv<T>::IsSupported() && n == 1) {
+          LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a,
+                                     transpose_a ? m : k, transpose_a ? k : m,
+                                     a_ptr, b_ptr, &c_ptr, &profile_result);
+          if (profile_result.is_valid()) {
+            if (profile_result.elapsed_time_in_ms() <
+                best_result.elapsed_time_in_ms()) {
+              best_result = profile_result;
+            }
+          }
+        }
+      }
+      // We make sure that each matmul parameter set only gets one pass of
+      // autotune. If the best result is found, assign it to algorithm_type
+      // and insert it to autotune map. If all internal kernels of
+      // cublasGemmEx() returns invalid results, we add kNoAlgorithm to the
+      // autotune map.
+      if (best_result.is_valid()) {
+        algorithm_config.set_algorithm(best_result.algorithm());
+      }
+      AutoTuneMatmul::GetInstance()->Insert(matmul_parameters,
+                                            algorithm_config);
+      if (algorithm_config.algorithm() != kNoAlgorithm &&
+          algorithm_config.algorithm() != kDefaultBlasGemm &&
+          algorithm_config.algorithm() != kDefaultBlasGemv) {
+        bool cublas_launch_status =
+            stream
+                ->ThenBlasGemmWithAlgorithm(
+                    blas_transpose_b, blas_transpose_a, n, m, k, alpha, b_ptr,
+                    transpose_b ? k : n, a_ptr, transpose_a ? m : k, beta,
+                    &c_ptr, n, computation_type, algorithm_config.algorithm(),
+                    nullptr)
+                .ok();
+        if (!cublas_launch_status) {
+          ctx->SetStatus(errors::Internal(
+              "Blas GEMM with algorithm launch failed : a.shape=(",
+              a.dim_size(0), ", ", a.dim_size(1), "), b.shape=(", b.dim_size(0),
+              ", ", b.dim_size(1), "), m=", m, ", n=", n, ", k=", k));
+        }
       }
     }
+    // For the following case, we use normal BlasGemm():
+    //  1) We didn't set the use_autotune flag;
+    //  2) compute type does not support autotune;
+    //  3) no algorithm is found;
+    //  4) all internal kernels in autotune return invalid results.
+    if (!use_autotune || !compute_type_supported || algorithms->empty() ||
+        algorithm_config.algorithm() == kNoAlgorithm ||
+        algorithm_config.algorithm() == kDefaultBlasGemm ||
+        algorithm_config.algorithm() == kDefaultBlasGemv) {
+      if (algorithm_config.algorithm() == kDefaultBlasGemv) {
+        // This is a matrix*vector multiply so use GEMV to compute A * b.
+        // Here we are multiplying in the natural order, so we have to flip
+        // the transposition flag to compensate for the tensor being stored
+        // row-major.
+        // TODO(yangzihao): Add Gemv as an autotuning option too.
+        LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a,
+                                   transpose_a ? m : k, transpose_a ? k : m,
+                                   a_ptr, b_ptr, &c_ptr, nullptr);
+      } else {
+        // Use C' = B' x A' (' stands for transpose)
+        bool blas_launch_status =
+            stream
+                ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k,
+                               1.0f, b_ptr, transpose_b ? k : n, a_ptr,
+                               transpose_a ? m : k, 0.0f, &c_ptr, n)
+                .ok();
+        if (!blas_launch_status) {
+          ctx->SetStatus(errors::Internal(
+              "Blas GEMM launch failed : a.shape=(", a.dim_size(0), ", ",
+              a.dim_size(1), "), b.shape=(", b.dim_size(0), ", ", b.dim_size(1),
+              "), m=", m, ", n=", n, ", k=", k));
+        }
+      }
+    }
+  }
+
+  static void GetBlasGemmAlgorithm(OpKernelConstruction* ctx,
+                                   std::vector<int64>* algorithms,
+                                   bool* algorithm_set_flag) {
+    if (*algorithm_set_flag == false) {
+      auto* stream = ctx->device()->tensorflow_gpu_device_info()->stream;
+      stream->parent()->GetBlasGemmAlgorithms(algorithms);
+      *algorithm_set_flag = true;
+    }
   }
 };
 
@@ -257,9 +432,14 @@ struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
 template <typename Device, typename T, bool USE_CUBLAS>
 class MatMulOp : public OpKernel {
  public:
-  explicit MatMulOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
+  explicit MatMulOp(OpKernelConstruction* ctx)
+      : OpKernel(ctx), algorithms_set_already_(false) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_a", &transpose_a_));
     OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_b", &transpose_b_));
+
+    LaunchMatMul<Device, T, USE_CUBLAS>::GetBlasGemmAlgorithm(
+        ctx, &algorithms_, &algorithms_set_already_);
+    use_autotune_ = MatmulAutotuneEnable();
   }
 
   void Compute(OpKernelContext* ctx) override {
@@ -302,10 +482,14 @@ class MatMulOp : public OpKernel {
       return;
     }
 
-    LaunchMatMul<Device, T, USE_CUBLAS>::launch(ctx, this, a, b, dim_pair, out);
+    LaunchMatMul<Device, T, USE_CUBLAS>::launch(
+        ctx, a, b, dim_pair, &algorithms_, use_autotune_, out);
   }
 
  private:
+  std::vector<int64> algorithms_;
+  bool algorithms_set_already_;
+  bool use_autotune_;
   bool transpose_a_;
   bool transpose_b_;
 };
diff --git a/tensorflow/core/kernels/matmul_op.h b/tensorflow/core/kernels/matmul_op.h
index 5a8db6da19..6398da2fb9 100644
--- a/tensorflow/core/kernels/matmul_op.h
+++ b/tensorflow/core/kernels/matmul_op.h
@@ -17,7 +17,9 @@ limitations under the License.
 #define TENSORFLOW_KERNELS_MATMUL_OP_H_
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/hash/hash.h"
 
 namespace tensorflow {
 namespace functor {
@@ -50,6 +52,68 @@ struct MatMulFunctor {
 };
 
 }  // end namespace functor
+
+#if GOOGLE_CUDA
+// Encapsulate all the shape information that is used in matmul operations.
+class MatmulParameters {
+ public:
+  MatmulParameters(bool transa, bool transb, uint64 m, uint64 n, uint64 k,
+                   DataType dtype, int device_id)
+      : transa_(transa),
+        transb_(transb),
+        m_(m),
+        n_(n),
+        k_(k),
+        dtype_(dtype),
+        device_id_(device_id) {
+    hash_code_ = transa;
+    hash_code_ = Hash64Combine(hash_code_, transb);
+    hash_code_ = Hash64Combine(hash_code_, m);
+    hash_code_ = Hash64Combine(hash_code_, n);
+    hash_code_ = Hash64Combine(hash_code_, k);
+    hash_code_ = Hash64Combine(hash_code_, dtype);
+    hash_code_ = Hash64Combine(hash_code_, device_id);
+  }
+  bool operator==(const MatmulParameters& other) const {
+    return this->get_data_as_tuple() == other.get_data_as_tuple();
+  }
+
+  bool operator!=(const MatmulParameters& other) const {
+    return !(*this == other);
+  }
+  uint64 hash() const { return hash_code_; }
+
+  string ToString() const {
+    // clang-format off
+    return strings::StrCat(
+        transa_, ", ", transb_, ", ",
+        m_, ", ", n_, ", ", k_,
+        dtype_, ", ", device_id_);
+    // clang-format on
+  }
+
+ private:
+  typedef std::tuple<bool, bool, int64, int64, int64, DataType, int>
+      ParameterDataType;
+
+  ParameterDataType get_data_as_tuple() const {
+    return std::make_tuple(transa_, transb_, m_, n_, k_, dtype_, device_id_);
+  }
+
+  bool transa_;
+  bool transb_;
+  uint64 m_;
+  uint64 n_;
+  uint64 k_;
+  DataType dtype_;
+  int device_id_;
+  uint64 hash_code_;
+};
+
+typedef Eigen::GpuDevice GPUDevice;
+
+#endif  // GOOGLE_CUDA
+
 }  // end namespace tensorflow
 
 #endif  // TENSORFLOW_KERNELS_MATMUL_OP_H_
diff --git a/tensorflow/core/util/matmul_autotune.cc b/tensorflow/core/util/matmul_autotune.cc
new file mode 100644
index 0000000000..741a78a193
--- /dev/null
+++ b/tensorflow/core/util/matmul_autotune.cc
@@ -0,0 +1,51 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/util/matmul_autotune.h"
+
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/util/env_var.h"
+
+namespace tensorflow {
+bool MatmulAutotuneEnable() {
+  bool value;
+  Status status =
+      ReadBoolFromEnvVar("TF_MATMUL_AUTOTUNE_ENABLE", false, &value);
+  if (!status.ok()) {
+    LOG(ERROR) << status.error_message();
+  }
+  return value;
+}
+
+bool MatmulDoFP32ComputationFP16Input() {
+  bool value;
+  // Feedback from NVIDIA: the "true floating point 16" compute capability is
+  // absent from compute capability SM 5.2. The native 16 bit floating point
+  // computation was introduced in SM 5.3 and higher compute capability. So
+  // for compatibility, set this to be true by default for now.
+  // TODO(yangzihao): In the future, we need to return three possibilities:
+  // user-set-true, user-set-false, user-no-setting. In the calling sites,
+  // check the compatibilities. Note that user-set-false with compute
+  // capability <= 5.2 will cause an error in the later cublasGemmEx() call.
+  Status status =
+      ReadBoolFromEnvVar("TF_FP16_MATMUL_USE_FP32_COMPUTE", true, &value);
+  if (!status.ok()) {
+    LOG(ERROR) << status.error_message();
+  }
+  return value;
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/util/matmul_autotune.h b/tensorflow/core/util/matmul_autotune.h
new file mode 100644
index 0000000000..5366623883
--- /dev/null
+++ b/tensorflow/core/util/matmul_autotune.h
@@ -0,0 +1,28 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// The utility to check matmul autotune related flags.
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
+
+namespace tensorflow {
+
+bool MatmulAutotuneEnable();
+bool MatmulDoFP32ComputationFP16Input();
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 7b7eb72994..29d351304c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3850,6 +3850,48 @@ cuda_py_test(
     main = "ops/transpose_benchmark.py",
 )
 
+cuda_py_test(
+    name = "matmul_benchmark",
+    size = "medium",
+    srcs = ["ops/matmul_benchmark.py"],
+    additional_deps = [
+        ":math_ops",
+        ":random_ops",
+        ":client",
+        ":client_testlib",
+        ":control_flow_ops",
+        ":framework_for_generated_wrappers",
+        ":framework_test_lib",
+        ":platform",
+        ":platform_benchmark",
+        ":variables",
+        "//third_party/py/numpy",
+        "//tensorflow/core:protos_all_py",
+    ],
+    main = "ops/matmul_benchmark.py",
+)
+
+cuda_py_test(
+    name = "matmul_benchmark_test",
+    size = "medium",
+    srcs = ["ops/matmul_benchmark_test.py"],
+    additional_deps = [
+        ":math_ops",
+        ":random_ops",
+        ":client",
+        ":client_testlib",
+        ":control_flow_ops",
+        ":framework_for_generated_wrappers",
+        ":platform",
+        ":platform_benchmark",
+        ":matmul_benchmark",
+        ":variables",
+        "//third_party/py/numpy",
+        "//tensorflow/core:protos_all_py",
+    ],
+    main = "ops/matmul_benchmark_test.py",
+)
+
 cuda_py_test(
     name = "session_benchmark",
     srcs = ["client/session_benchmark.py"],
diff --git a/tensorflow/python/kernel_tests/matmul_op_test.py b/tensorflow/python/kernel_tests/matmul_op_test.py
index 042f462357..b167278984 100644
--- a/tensorflow/python/kernel_tests/matmul_op_test.py
+++ b/tensorflow/python/kernel_tests/matmul_op_test.py
@@ -31,6 +31,9 @@ from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test as test_lib
 
+# TODO(yangzihao): Currently matmul autotuning is disabled by default. Use
+# os.environ["TF_MATMUL_AUTOTUNE_ENABLE"] = "1" to enable it.
+
 
 def _AddTest(test, op_name, testcase_name, fn):
   test_name = "_".join(["test", op_name, testcase_name])
diff --git a/tensorflow/python/ops/matmul_benchmark.py b/tensorflow/python/ops/matmul_benchmark.py
new file mode 100644
index 0000000000..55c575162a
--- /dev/null
+++ b/tensorflow/python/ops/matmul_benchmark.py
@@ -0,0 +1,143 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Benchmark for Matmul operator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+import time
+
+import numpy as np
+
+from tensorflow.python.client import session as session_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+def build_graph(device, n, m, k, transpose_a, transpose_b, dtype):
+  """Build a graph containing a sequence of matmul operations.
+
+  Args:
+    device: String, the device to run on.
+    n: tensor A's first dimension size.
+    m: tensor A's second dimension size.
+    k: tensor B's second dimension size.
+    transpose_a: boolean value to show if tensor A is transposed.
+    transpose_b: boolean value to show if tensor B is transposed.
+    dtype: numpy data type of the input tensor.
+
+  Returns:
+    A matmul operation to run()
+  """
+  with ops.device('/%s:0' % device):
+    if not transpose_a:
+      x = variables.Variable(random_ops.random_uniform([n, m], dtype=dtype))
+    else:
+      x = variables.Variable(random_ops.random_uniform([m, n], dtype=dtype))
+    if not transpose_b:
+      y = variables.Variable(random_ops.random_uniform([m, k], dtype=dtype))
+    else:
+      y = variables.Variable(random_ops.random_uniform([k, m], dtype=dtype))
+
+    z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b)
+    return control_flow_ops.group(z)
+
+
+class MatmulBenchmark(test.Benchmark):
+  """Benchmark matmul!"""
+
+  def run_graph(self, device, n, m, k, transpose_a, transpose_b, num_iters,
+                dtype):
+    """Run the graph and print its execution time.
+
+    Args:
+      device: String, the device to run on.
+      n: tensor A's first dimension size.
+      m: tensor A's second dimension size.
+      k: tensor B's second dimension size.
+      transpose_a: boolean value to show if tensor A is transposed.
+      transpose_b: boolean value to show if tensor B is transposed.
+      num_iters: number of iterations to run the benchmark.
+      dtype: numpy data type of the input tensor.
+
+    Returns:
+      The duration of the run in seconds.
+    """
+    graph = ops.Graph()
+    with graph.as_default():
+      output = build_graph(device, n, m, k, transpose_a, transpose_b, dtype)
+      with session_lib.Session(graph=graph) as session:
+        variables.global_variables_initializer().run()
+        for _ in range(500):
+          session.run(output)
+        start_time = time.time()
+        for _ in range(num_iters):
+          session.run(output)
+        duration = (time.time() - start_time)
+        num_items = n * m * k * 2
+        throughput = num_items * num_iters / duration / 1e9
+        print('%s %s input_info:%s %d %.4fsec, %.4fGitems/s.' %
+              (device, str(dtype), str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:'
+               + str(transpose_a) + '.tb:' + str(transpose_b), num_iters,
+               duration, throughput))
+
+    name_template = ('matmul_{device}_{dtype}_input_info_{inputinfo}')
+
+    self.report_benchmark(
+        name=name_template.format(
+            device=device,
+            dtype=str(dtype).replace(' ', ''),
+            inputinfo=str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:' +
+            str(transpose_a) + '.tb:' + str(transpose_b)).replace(' ', ''),
+        iters=num_iters,
+        wall_time=duration)
+    return duration
+
+  def run_test_gpu(self, n, m, k, transpose_a, transpose_b, dtype, num_iters):
+    self.run_graph('gpu', n, m, k, transpose_a, transpose_b, num_iters, dtype)
+
+  def test_round(self, num_iters):
+    dtypes = [np.float32, np.float64]
+    for dtype in dtypes:
+      for n, m, (transpose_a, transpose_b) in itertools.product(
+          [512, 1024], [1, 8, 16, 128], [(False, False), (True, False),
+                                         (False, True)]):
+        k = n
+        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
+
+      for n, m, k, (transpose_a, transpose_b) in itertools.product(
+          [200], [1, 8, 20], [10000], [(False, False), (True, False), (False,
+                                                                       True)]):
+        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
+
+      for (n, m, k), (transpose_a, transpose_b) in itertools.product(
+          [(200, 20, 20000), (1, 10000, 200)], [(False, False), (True, False),
+                                                (False, True)]):
+        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
+
+  def benchmark_matmul(self):
+    num_iters = 200
+    for _ in range(10):
+      self.test_round(num_iters)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/ops/matmul_benchmark_test.py b/tensorflow/python/ops/matmul_benchmark_test.py
new file mode 100644
index 0000000000..a7914dba78
--- /dev/null
+++ b/tensorflow/python/ops/matmul_benchmark_test.py
@@ -0,0 +1,122 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for matmul_benchmark.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+import numpy as np
+
+from tensorflow.core.framework import graph_pb2
+from tensorflow.core.framework import node_def_pb2
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import matmul_benchmark
+from tensorflow.python.platform import test as googletest
+from tensorflow.python.platform import tf_logging
+
+
+def BuildGraphTest(n, m, k, transpose_a, transpose_b, dtype):
+
+  def Test(self):
+    if not googletest.is_gpu_available():
+      tf_logging.info("Skipping BuildGraphTest %s", (n, m, k, transpose_a,
+                                                     transpose_b))
+      return
+    tf_logging.info("Testing BuildGraphTest %s", (n, m, k, transpose_a,
+                                                  transpose_b))
+    self._VerifyBuildGraph(n, m, k, transpose_a, transpose_b, dtype)
+
+  return Test
+
+
+def RunGraphTest(n, m, k, transpose_a, transpose_b, dtype):
+
+  def Test(self):
+    if not googletest.is_gpu_available():
+      tf_logging.info("Skipping RunGraphTest %s", (n, m, k, transpose_a,
+                                                   transpose_b))
+      return
+    tf_logging.info("Testing RunGraphTest %s", (n, m, k, transpose_a,
+                                                transpose_b))
+    self._VerifyRunGraph(n, m, k, transpose_a, transpose_b, dtype)
+
+  return Test
+
+
+class MatmulBenchmarkTest(googletest.TestCase):
+
+  def _StripNode(self, nd):
+    snode = node_def_pb2.NodeDef(name=nd.name, op=nd.op, input=nd.input)
+    if nd.device:
+      snode.device = nd.device
+    return snode
+
+  def _StripGraph(self, gd):
+    return graph_pb2.GraphDef(node=[self._StripNode(nd) for nd in gd.node])
+
+  def _VerifyBuildGraph(self, n, m, k, transpose_a, transpose_b, dtype):
+    graph = ops.Graph()
+    with graph.as_default():
+      matmul_benchmark.build_graph("gpu", n, m, k, transpose_a, transpose_b,
+                                   dtype)
+      gd = graph.as_graph_def()
+      self.assertProtoEquals("""
+      node { name: "random_uniform/shape" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform/min" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform/max" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform/RandomUniform" op: "RandomUniform" input: "random_uniform/shape" device: "/device:GPU:0" }
+      node { name: "random_uniform/sub" op: "Sub" input: "random_uniform/max" input: "random_uniform/min" device: "/device:GPU:0" }
+      node { name: "random_uniform/mul" op: "Mul" input: "random_uniform/RandomUniform" input: "random_uniform/sub" device: "/device:GPU:0" }
+      node { name: "random_uniform" op: "Add" input: "random_uniform/mul" input: "random_uniform/min" device: "/device:GPU:0" }
+      node { name: "Variable" op: "VariableV2" device: "/device:GPU:0" }
+      node { name: "Variable/Assign" op: "Assign" input: "Variable" input: "random_uniform" device: "/device:GPU:0" }
+      node { name: "Variable/read" op: "Identity" input: "Variable" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/shape" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/min" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/max" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/RandomUniform" op: "RandomUniform" input: "random_uniform_1/shape" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/sub" op: "Sub" input: "random_uniform_1/max" input: "random_uniform_1/min" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: "/device:GPU:0" }
+      node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: "/device:GPU:0" }
+      node { name: "Variable_1" op: "VariableV2" device: "/device:GPU:0" }
+      node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: "/device:GPU:0" }
+      node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: "/device:GPU:0" }
+      node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: "/device:GPU:0" }
+      node { name: "group_deps" op: "NoOp" input: "^MatMul" device: "/device:GPU:0" }
+                             """, self._StripGraph(gd))
+
+  def _VerifyRunGraph(self, n, m, k, transpose_a, transpose_b, dtype):
+    benchmark_instance = matmul_benchmark.MatmulBenchmark()
+    duration = benchmark_instance.run_graph("gpu", n, m, k, transpose_a,
+                                            transpose_b, 1, dtype)
+    self.assertTrue(duration > 1e-6)
+
+
+if __name__ == "__main__":
+  dtypes = [np.float32, np.float64]
+  index = 0
+  for _dtype in dtypes:
+    for _n, _m, (_transpose_a, _transpose_b) in itertools.product(
+        [512, 1024], [1, 8, 16, 128], [(False, False), (True, False), (False,
+                                                                       True)]):
+      _k = _n
+      setattr(MatmulBenchmarkTest, "testBuildGraph_" + str(index),
+              BuildGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
+      setattr(MatmulBenchmarkTest, "testRunGraph_" + str(index),
+              RunGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
+      index += 1
+  googletest.main()
diff --git a/tensorflow/stream_executor/blas.cc b/tensorflow/stream_executor/blas.cc
index a59a1dda71..da09d84921 100644
--- a/tensorflow/stream_executor/blas.cc
+++ b/tensorflow/stream_executor/blas.cc
@@ -67,6 +67,10 @@ string SideString(Side s) {
   }
 }
 
+// -- AlgorithmConfig
+
+string AlgorithmConfig::ToString() const { return port::StrCat(algorithm_); }
+
 string ComputationTypeString(ComputationType ty) {
   switch (ty) {
     case ComputationType::kF16:
diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h
index cfff3649c8..eb1b19c5d9 100644
--- a/tensorflow/stream_executor/blas.h
+++ b/tensorflow/stream_executor/blas.h
@@ -44,7 +44,6 @@ limitations under the License.
 #include "tensorflow/stream_executor/platform/port.h"
 
 #include "tensorflow/stream_executor/lib/array_slice.h"
-#include "tensorflow/stream_executor/platform/port.h"
 
 namespace Eigen {
 struct half;
@@ -108,6 +107,10 @@ string ComputationTypeString(ComputationType ty);
 // Opaque identifier for an "algorithm" used by a blas routine.  This functions
 // as a hint to the blas library.
 typedef int64 AlgorithmType;
+constexpr AlgorithmType kDefaultAlgorithm = -1;
+constexpr AlgorithmType kDefaultBlasGemm = -2;
+constexpr AlgorithmType kDefaultBlasGemv = -3;
+constexpr AlgorithmType kNoAlgorithm = -4;
 
 // blas uses -1 to represent the default algorithm. This happens to match up
 // with the CUBLAS_GEMM_DFALT constant, so cuda_blas.cc is using static_cast
@@ -134,10 +137,28 @@ class ProfileResult {
 
  private:
   bool is_valid_ = false;
-  AlgorithmType algorithm_ = 0;
+  AlgorithmType algorithm_ = kDefaultAlgorithm;
   float elapsed_time_in_ms_ = std::numeric_limits<float>::max();
 };
 
+class AlgorithmConfig {
+ public:
+  AlgorithmConfig() : algorithm_(kDefaultAlgorithm) {}
+  explicit AlgorithmConfig(AlgorithmType algorithm) : algorithm_(algorithm) {}
+  AlgorithmType algorithm() const { return algorithm_; }
+  void set_algorithm(AlgorithmType val) { algorithm_ = val; }
+  bool operator==(const AlgorithmConfig &other) const {
+    return this->algorithm_ == other.algorithm_;
+  }
+  bool operator!=(const AlgorithmConfig &other) const {
+    return !(*this == other);
+  }
+  string ToString() const;
+
+ private:
+  AlgorithmType algorithm_;
+};
+
 // BLAS support interface -- this can be derived from a GPU executor when the
 // underlying platform has an BLAS library implementation available. See
 // StreamExecutor::AsBlas().
@@ -453,6 +474,29 @@ class BlasSupport {
                           std::complex<double> beta,
                           DeviceMemory<std::complex<double>> *y, int incy) = 0;
 
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,
+      const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
+      int incx, float beta, DeviceMemory<float> *y, int incy,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha,
+      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
+      int incx, double beta, DeviceMemory<double> *y, int incy,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+      std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,
+      int lda, const DeviceMemory<std::complex<float>> &x, int incx,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+      std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a,
+      int lda, const DeviceMemory<std::complex<double>> &x, int incx,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,
+      int incy, ProfileResult *output_profile_result) = 0;
+
   // Performs a rank-1 update of a general matrix.
   //
   //     a <- alpha * x * y' + a,
@@ -935,8 +979,39 @@ class BlasSupport {
                           std::complex<double> beta,
                           DeviceMemory<std::complex<double>> *c, int ldc) = 0;
 
-  // Gets a list of supported algorithms for DoBlasGemmWithAlgorithm.  Note that
-  // any or all of these algorithms may still be
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, float alpha, const DeviceMemory<Eigen::half> &a,
+      int lda, const DeviceMemory<Eigen::half> &b, int ldb, float beta,
+      DeviceMemory<Eigen::half> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
+      const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
+      int ldc, ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
+      const DeviceMemory<double> &b, int ldb, double beta,
+      DeviceMemory<double> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, std::complex<float> alpha,
+      const DeviceMemory<std::complex<float>> &a, int lda,
+      const DeviceMemory<std::complex<float>> &b, int ldb,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, std::complex<double> alpha,
+      const DeviceMemory<std::complex<double>> &a, int lda,
+      const DeviceMemory<std::complex<double>> &b, int ldb,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+
+  // Gets a list of supported algorithms for DoBlasGemmWithAlgorithm.
   virtual bool GetBlasGemmAlgorithms(
       std::vector<AlgorithmType> *out_algorithms) = 0;
 
@@ -1473,6 +1548,28 @@ class BlasSupport {
                   const DeviceMemory<std::complex<double>> &x, int incx,       \
                   std::complex<double> beta,                                   \
                   DeviceMemory<std::complex<double>> *y, int incy) override;   \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,  \
+      const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,     \
+      int incx, float beta, DeviceMemory<float> *y, int incy,                  \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha, \
+      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,   \
+      int incx, double beta, DeviceMemory<double> *y, int incy,                \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,               \
+      std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,   \
+      int lda, const DeviceMemory<std::complex<float>> &x, int incx,           \
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *y,          \
+      int incy, blas::ProfileResult *output_profile_result) override;          \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,               \
+      std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a, \
+      int lda, const DeviceMemory<std::complex<double>> &x, int incx,          \
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,        \
+      int incy, blas::ProfileResult *output_profile_result) override;          \
   bool DoBlasGer(Stream *stream, uint64 m, uint64 n, float alpha,              \
                  const DeviceMemory<float> &x, int incx,                       \
                  const DeviceMemory<float> &y, int incy,                       \
@@ -1751,6 +1848,39 @@ class BlasSupport {
                   const DeviceMemory<std::complex<double>> &b, int ldb,        \
                   std::complex<double> beta,                                   \
                   DeviceMemory<std::complex<double>> *c, int ldc) override;    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, float alpha,                               \
+      const DeviceMemory<Eigen::half> &a, int lda,                             \
+      const DeviceMemory<Eigen::half> &b, int ldb, float beta,                 \
+      DeviceMemory<Eigen::half> *c, int ldc,                                   \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, \
+      int lda, const DeviceMemory<float> &b, int ldb, float beta,              \
+      DeviceMemory<float> *c, int ldc,                                         \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, double alpha,                              \
+      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &b,   \
+      int ldb, double beta, DeviceMemory<double> *c, int ldc,                  \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, std::complex<float> alpha,                 \
+      const DeviceMemory<std::complex<float>> &a, int lda,                     \
+      const DeviceMemory<std::complex<float>> &b, int ldb,                     \
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc, \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, std::complex<double> alpha,                \
+      const DeviceMemory<std::complex<double>> &a, int lda,                    \
+      const DeviceMemory<std::complex<double>> &b, int ldb,                    \
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *c,        \
+      int ldc, blas::ProfileResult *output_profile_result) override;           \
   bool GetBlasGemmAlgorithms(std::vector<blas::AlgorithmType> *out_algorithms) \
       override;                                                                \
   bool DoBlasGemmWithAlgorithm(                                                \
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 2817364e97..9070766166 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -1857,6 +1857,180 @@ bool CUDABlas::DoBlasGemm(Stream *stream, blas::Transpose transa,
       CUDAComplex(CUDAMemoryMutable(c)), ldc);
 }
 
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,
+    const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
+    int incx, float beta, DeviceMemory<float> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha,
+    const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
+    int incx, double beta, DeviceMemory<double> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+    std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,
+    int lda, const DeviceMemory<std::complex<float>> &x, int incx,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+    std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a,
+    int lda, const DeviceMemory<std::complex<double>> &x, int incx,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, float alpha, const DeviceMemory<Eigen::half> &a,
+    int lda, const DeviceMemory<Eigen::half> &b, int ldb, float beta,
+    DeviceMemory<Eigen::half> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
+    const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
+    int ldc, blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
+    const DeviceMemory<double> &b, int ldb, double beta,
+    DeviceMemory<double> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, std::complex<float> alpha,
+    const DeviceMemory<std::complex<float>> &a, int lda,
+    const DeviceMemory<std::complex<float>> &b, int ldb,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, std::complex<double> alpha,
+    const DeviceMemory<std::complex<double>> &a, int lda,
+    const DeviceMemory<std::complex<double>> &b, int ldb,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+template <typename T>
+bool CUDABlas::DoBlasGemvWithProfilingImpl(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, const T &alpha,
+    const DeviceMemory<T> &a, int lda, const DeviceMemory<T> &x, int incx,
+    const T &beta, DeviceMemory<T> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  struct TimerDeleter {
+    void operator()(CUDATimer *t) {
+      t->Destroy();
+      delete t;
+    }
+  };
+  std::unique_ptr<CUDATimer, TimerDeleter> timer;
+  if (output_profile_result != nullptr) {
+    timer.reset(new CUDATimer(parent_));
+    if (!timer->Init() || !timer->Start(AsCUDAStream(stream))) {
+      return false;
+    }
+  }
+
+  // Call blasGemm
+  bool result =
+      DoBlasGemv(stream, trans, m, n, alpha, a, lda, x, incx, beta, y, incy);
+
+  if (timer != nullptr && result) {
+    // CUDATimer will CHECK-fail if we Stop() it while the stream is in an error
+    // state.
+    if (!timer->Stop(AsCUDAStream(stream))) {
+      return false;
+    }
+    output_profile_result->set_is_valid(true);
+    output_profile_result->set_algorithm(blas::kDefaultBlasGemv);
+    output_profile_result->set_elapsed_time_in_ms(
+        timer->GetElapsedMilliseconds());
+  }
+  return result;
+}
+
+template <typename T, typename ParamType>
+bool CUDABlas::DoBlasGemmWithProfilingImpl(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, const ParamType &alpha, const DeviceMemory<T> &a,
+    int lda, const DeviceMemory<T> &b, int ldb, const ParamType &beta,
+    DeviceMemory<T> *c, int ldc, blas::ProfileResult *output_profile_result) {
+  struct TimerDeleter {
+    void operator()(CUDATimer *t) {
+      t->Destroy();
+      delete t;
+    }
+  };
+  std::unique_ptr<CUDATimer, TimerDeleter> timer;
+  if (output_profile_result != nullptr) {
+    timer.reset(new CUDATimer(parent_));
+    if (!timer->Init() || !timer->Start(AsCUDAStream(stream))) {
+      return false;
+    }
+  }
+
+  // Call blasGemm
+  bool result = DoBlasGemm(stream, transa, transb, m, n, k, alpha, a, lda, b,
+                           ldb, beta, c, ldc);
+
+  if (timer != nullptr && result) {
+    // CUDATimer will CHECK-fail if we Stop() it while the stream is in an error
+    // state.
+    if (!timer->Stop(AsCUDAStream(stream))) {
+      return false;
+    }
+    output_profile_result->set_is_valid(true);
+    output_profile_result->set_algorithm(blas::kDefaultBlasGemm);
+    output_profile_result->set_elapsed_time_in_ms(
+        timer->GetElapsedMilliseconds());
+  }
+  return result;
+}
+
 template <typename InT, typename OutT, typename CompT>
 bool CUDABlas::DoBlasGemmWithAlgorithmImpl(
     Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
@@ -1920,6 +2094,9 @@ bool CUDABlas::GetBlasGemmAlgorithms(
     std::vector<blas::AlgorithmType> *out_algorithms) {
 // cublasGemmAlgo_t (and the function that accepts this type, cublasGemmEx)
 // were first introduced in CUDA 8.
+// Note that when CUDA version and compute capability is not sufficient, we
+// still return the out_algorithms. Caller needs to make sure that in this case,
+// the returned vector is empty.
 #if CUDA_VERSION >= 8000
   for (cublasGemmAlgo_t algo :
        {CUBLAS_GEMM_DFALT, CUBLAS_GEMM_ALGO0, CUBLAS_GEMM_ALGO1,
@@ -1927,8 +2104,10 @@ bool CUDABlas::GetBlasGemmAlgorithms(
         CUBLAS_GEMM_ALGO5, CUBLAS_GEMM_ALGO6, CUBLAS_GEMM_ALGO7}) {
     out_algorithms->push_back(algo);
   }
-#endif
   return true;
+#else
+  return false;
+#endif
 }
 
 bool CUDABlas::DoBlasGemmWithAlgorithm(
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.h b/tensorflow/stream_executor/cuda/cuda_blas.h
index 4a8641b300..80cda97117 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.h
+++ b/tensorflow/stream_executor/cuda/cuda_blas.h
@@ -127,6 +127,23 @@ class CUDABlas : public blas::BlasSupport {
       blas::AlgorithmType algorithm,
       blas::ProfileResult *output_profile_result);
 
+  // Helper function for implementing DoBlasGemmWithProfiling.
+  template <typename T, typename ParamType>
+  bool DoBlasGemmWithProfilingImpl(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, const ParamType &alpha, const DeviceMemory<T> &a,
+      int lda, const DeviceMemory<T> &b, int ldb, const ParamType &beta,
+      DeviceMemory<T> *c, int ldc, blas::ProfileResult *output_profile_result);
+
+  // Helper function for implementing DoBlasGemvWithProfiling.
+  template <typename T>
+  bool DoBlasGemvWithProfilingImpl(Stream *stream, blas::Transpose trans,
+                                   uint64 m, uint64 n, const T &alpha,
+                                   const DeviceMemory<T> &a, int lda,
+                                   const DeviceMemory<T> &x, int incx,
+                                   const T &beta, DeviceMemory<T> *y, int incy,
+                                   blas::ProfileResult *output_profile_result);
+
   // mutex that guards the cuBLAS handle for this device.
   mutex mu_;
 
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index 5996195173..c9b36ba7ab 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -3458,6 +3458,184 @@ struct ThenBlasWithProfileImpl {
 };
 }  // anonymous namespace
 
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, float alpha,
+    const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
+    int incx, float beta, DeviceMemory<float> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<
+      blas::Transpose, uint64, uint64, float, const DeviceMemory<float> &, int,
+      const DeviceMemory<float> &, int, float, DeviceMemory<float> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, double alpha,
+    const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
+    int incx, double beta, DeviceMemory<double> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, double,
+                          const DeviceMemory<double> &, int,
+                          const DeviceMemory<double> &, int, double,
+                          DeviceMemory<double> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, std::complex<float> alpha,
+    const DeviceMemory<std::complex<float>> &a, int lda,
+    const DeviceMemory<std::complex<float>> &x, int incx,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, std::complex<float>,
+                          const DeviceMemory<std::complex<float>> &, int,
+                          const DeviceMemory<std::complex<float>> &, int,
+                          std::complex<float>,
+                          DeviceMemory<std::complex<float>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, std::complex<double> alpha,
+    const DeviceMemory<std::complex<double>> &a, int lda,
+    const DeviceMemory<std::complex<double>> &x, int incx,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, std::complex<double>,
+                          const DeviceMemory<std::complex<double>> &, int,
+                          const DeviceMemory<std::complex<double>> &, int,
+                          std::complex<double>,
+                          DeviceMemory<std::complex<double>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, float alpha, const DeviceMemory<Eigen::half> &a, int lda,
+    const DeviceMemory<Eigen::half> &b, int ldb, float beta,
+    DeviceMemory<Eigen::half> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
+                          uint64, float, const DeviceMemory<Eigen::half> &, int,
+                          const DeviceMemory<Eigen::half> &, int, float,
+                          DeviceMemory<Eigen::half> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
+    const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
+    int ldc, blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
+                          uint64, float, const DeviceMemory<float> &, int,
+                          const DeviceMemory<float> &, int, float,
+                          DeviceMemory<float> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
+    const DeviceMemory<double> &b, int ldb, double beta,
+    DeviceMemory<double> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
+                          uint64, double, const DeviceMemory<double> &, int,
+                          const DeviceMemory<double> &, int, double,
+                          DeviceMemory<double> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, std::complex<float> alpha,
+    const DeviceMemory<std::complex<float>> &a, int lda,
+    const DeviceMemory<std::complex<float>> &b, int ldb,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<
+      blas::Transpose, blas::Transpose, uint64, uint64, uint64,
+      std::complex<float>, const DeviceMemory<std::complex<float>> &, int,
+      const DeviceMemory<std::complex<float>> &, int, std::complex<float>,
+      DeviceMemory<std::complex<float>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, std::complex<double> alpha,
+    const DeviceMemory<std::complex<double>> &a, int lda,
+    const DeviceMemory<std::complex<double>> &b, int ldb,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<
+      blas::Transpose, blas::Transpose, uint64, uint64, uint64,
+      std::complex<double>, const DeviceMemory<std::complex<double>> &, int,
+      const DeviceMemory<std::complex<double>> &, int, std::complex<double>,
+      DeviceMemory<std::complex<double>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
 Stream &Stream::ThenBlasGemmWithAlgorithm(
     blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
     uint64 k, const Eigen::half &alpha, const DeviceMemory<Eigen::half> &a,
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index 3c8b7ee894..e218873839 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -934,6 +934,31 @@ class Stream {
                        std::complex<double> beta,
                        DeviceMemory<std::complex<double>> *y, int incy);
 
+  Stream &ThenBlasGemvWithProfiling(blas::Transpose trans, uint64 m, uint64 n,
+                                    float alpha, const DeviceMemory<float> &a,
+                                    int lda, const DeviceMemory<float> &x,
+                                    int incx, float beta,
+                                    DeviceMemory<float> *y, int incy,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemvWithProfiling(blas::Transpose trans, uint64 m, uint64 n,
+                                    double alpha, const DeviceMemory<double> &a,
+                                    int lda, const DeviceMemory<double> &x,
+                                    int incx, double beta,
+                                    DeviceMemory<double> *y, int incy,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemvWithProfiling(
+      blas::Transpose trans, uint64 m, uint64 n, std::complex<float> alpha,
+      const DeviceMemory<std::complex<float>> &a, int lda,
+      const DeviceMemory<std::complex<float>> &x, int incx,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+      blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemvWithProfiling(
+      blas::Transpose trans, uint64 m, uint64 n, std::complex<double> alpha,
+      const DeviceMemory<std::complex<double>> &a, int lda,
+      const DeviceMemory<std::complex<double>> &x, int incx,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,
+      int incy, blas::ProfileResult *output_profile_result);
+
   // See BlasSupport::DoBlasGer.
   Stream &ThenBlasGer(uint64 m, uint64 n, float alpha,
                       const DeviceMemory<float> &x, int incx,
@@ -1249,6 +1274,44 @@ class Stream {
                        std::complex<double> beta,
                        DeviceMemory<std::complex<double>> *c, int ldc);
 
+  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
+                                    blas::Transpose transb, uint64 m, uint64 n,
+                                    uint64 k, float alpha,
+                                    const DeviceMemory<Eigen::half> &a, int lda,
+                                    const DeviceMemory<Eigen::half> &b, int ldb,
+                                    float beta, DeviceMemory<Eigen::half> *c,
+                                    int ldc,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
+                                    blas::Transpose transb, uint64 m, uint64 n,
+                                    uint64 k, float alpha,
+                                    const DeviceMemory<float> &a, int lda,
+                                    const DeviceMemory<float> &b, int ldb,
+                                    float beta, DeviceMemory<float> *c, int ldc,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
+                                    blas::Transpose transb, uint64 m, uint64 n,
+                                    uint64 k, double alpha,
+                                    const DeviceMemory<double> &a, int lda,
+                                    const DeviceMemory<double> &b, int ldb,
+                                    double beta, DeviceMemory<double> *c,
+                                    int ldc,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(
+      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+      uint64 k, std::complex<float> alpha,
+      const DeviceMemory<std::complex<float>> &a, int lda,
+      const DeviceMemory<std::complex<float>> &b, int ldb,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+      blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(
+      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+      uint64 k, std::complex<double> alpha,
+      const DeviceMemory<std::complex<double>> &a, int lda,
+      const DeviceMemory<std::complex<double>> &b, int ldb,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+      blas::ProfileResult *output_profile_result);
+
   // See BlasSupport::DoBlasGemmWithAlgorithm.
   Stream &ThenBlasGemmWithAlgorithm(
       blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-- 
GitLab


From afb6962992b8e9e14e27989772519ee4fe38e851 Mon Sep 17 00:00:00 2001
From: Frank Chen <frankchn@google.com>
Date: Tue, 18 Jul 2017 17:04:44 -0700
Subject: [PATCH 1098/1375] Introduces fuzzers for some complex operations and
 kernels in TensorFlow in preparation for eventaul fuzzing using Google
 OSS-Fuzz.

PiperOrigin-RevId: 162425120
---
 tensorflow/BUILD                              |   1 +
 tensorflow/core/kernels/fuzzing/BUILD         |  55 ++++++
 .../kernels/fuzzing/decode_base64_fuzz.cc     |  29 ++++
 .../core/kernels/fuzzing/decode_jpeg_fuzz.cc  |  29 ++++
 .../fuzzing/decode_json_example_fuzz.cc       |  29 ++++
 .../core/kernels/fuzzing/decode_png_fuzz.cc   |  29 ++++
 .../kernels/fuzzing/encode_base64_fuzz.cc     |  29 ++++
 .../core/kernels/fuzzing/encode_jpeg_fuzz.cc  |  63 +++++++
 .../example_proto_fast_parsing_fuzz.cc        |  64 +++++++
 .../core/kernels/fuzzing/fuzz_session.h       | 156 ++++++++++++++++++
 .../core/kernels/fuzzing/identity_fuzz.cc     |  45 +++++
 .../kernels/fuzzing/parse_tensor_op_fuzz.cc   |  45 +++++
 .../core/kernels/fuzzing/string_split_fuzz.cc |  60 +++++++
 .../kernels/fuzzing/string_to_number_fuzz.cc  |  32 ++++
 .../fuzzing/tf_ops_fuzz_target_lib.bzl        |  13 ++
 15 files changed, 679 insertions(+)
 create mode 100644 tensorflow/core/kernels/fuzzing/BUILD
 create mode 100644 tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/fuzz_session.h
 create mode 100644 tensorflow/core/kernels/fuzzing/identity_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/string_split_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc
 create mode 100644 tensorflow/core/kernels/fuzzing/tf_ops_fuzz_target_lib.bzl

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 827807816c..afa1cc7605 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -330,6 +330,7 @@ filegroup(
         "//tensorflow/core/grappler/optimizers:all_files",
         "//tensorflow/core/grappler/utils:all_files",
         "//tensorflow/core/kernels:all_files",
+        "//tensorflow/core/kernels/fuzzing:all_files",
         "//tensorflow/core/kernels/hexagon:all_files",
         "//tensorflow/core/kernels/neon:all_files",
         "//tensorflow/core/ops/compat:all_files",
diff --git a/tensorflow/core/kernels/fuzzing/BUILD b/tensorflow/core/kernels/fuzzing/BUILD
new file mode 100644
index 0000000000..3e7f9bdb44
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/BUILD
@@ -0,0 +1,55 @@
+licenses(["notice"])  # Apache 2.0
+
+package(
+    default_visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "fuzz_session",
+    hdrs = ["fuzz_session.h"],
+    deps = [
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:scope",
+        "//tensorflow/core:all_kernels",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:direct_session",
+        "//tensorflow/core:ops",
+        "//tensorflow/core:tensorflow",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+load("//tensorflow/core/kernels/fuzzing:tf_ops_fuzz_target_lib.bzl", "tf_ops_fuzz_target_lib")
+
+tf_ops_fuzz_target_lib("identity")
+
+tf_ops_fuzz_target_lib("string_to_number")
+
+tf_ops_fuzz_target_lib("string_split")
+
+tf_ops_fuzz_target_lib("encode_base64")
+
+tf_ops_fuzz_target_lib("decode_base64")
+
+tf_ops_fuzz_target_lib("encode_jpeg")
+
+tf_ops_fuzz_target_lib("decode_png")
+
+tf_ops_fuzz_target_lib("decode_jpeg")
+
+tf_ops_fuzz_target_lib("example_proto_fast_parsing")
+
+tf_ops_fuzz_target_lib("parse_tensor_op")
+
+tf_ops_fuzz_target_lib("decode_json_example")
diff --git a/tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc
new file mode 100644
index 0000000000..6d4a9dfdef
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc
@@ -0,0 +1,29 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzDecodeBase64 : public FuzzStringInputOp {
+  SINGLE_INPUT_OP_BUILDER(DT_STRING, DecodeBase64);
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzDecodeBase64);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc
new file mode 100644
index 0000000000..b084a97204
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc
@@ -0,0 +1,29 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzDecodeJpeg : public FuzzStringInputOp {
+  SINGLE_INPUT_OP_BUILDER(DT_STRING, DecodeJpeg);
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzDecodeJpeg);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc
new file mode 100644
index 0000000000..9dd795b94e
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc
@@ -0,0 +1,29 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzDecodeJSONExample : public FuzzStringInputOp {
+  SINGLE_INPUT_OP_BUILDER(DT_STRING, DecodeJSONExample);
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzDecodeJSONExample);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc
new file mode 100644
index 0000000000..4a68a5b580
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc
@@ -0,0 +1,29 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzDecodePng : public FuzzStringInputOp {
+  SINGLE_INPUT_OP_BUILDER(DT_STRING, DecodePng);
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzDecodePng);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc b/tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc
new file mode 100644
index 0000000000..2d6c82826c
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc
@@ -0,0 +1,29 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzEncodeBase64 : public FuzzSession {
+  SINGLE_INPUT_OP_BUILDER(DT_STRING, EncodeBase64);
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzEncodeBase64);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc b/tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc
new file mode 100644
index 0000000000..81b6e49124
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc
@@ -0,0 +1,63 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzEncodeJpeg : public FuzzSession {
+  SINGLE_INPUT_OP_BUILDER(DT_UINT8, EncodeJpeg);
+
+  void FuzzImpl(const uint8_t* data, size_t size) final {
+    if (size < 6) return;
+
+    // Pick random channels and aspect ratio, and then set the
+    // input based upon the aspect ratio and size.
+    int64 channels = (data[0] % 2) * 2 + 1;  // 1, 3
+    int64 height = data[1] + (data[2] << 8);
+    int64 width = data[2] + (data[3] << 8);
+    if (width == 0) return;
+
+    // TODO(dga): kcc@ notes: better to use actual supplied h, w and then
+    // trim them if needed to ensure w*h <= size-4.
+    double hw_ratio = height / width;
+    int64 remaining_bytes = size - 5;
+    int64 pixels = remaining_bytes / channels;
+    height = static_cast<int64>(floor(sqrt(hw_ratio * pixels)));
+    if (height == 0) return;
+    width = static_cast<int64>(floor(pixels / height));
+    if (width == 0) return;
+    size_t actual_pixels = height * width * channels;
+    if (actual_pixels == 0) return;
+
+    // TODO(dga):  Generalize this by borrowing the AsTensor logic
+    // from tf testing, once we have a few more fuzzers written.
+    Tensor input_tensor(tensorflow::DT_UINT8,
+                        TensorShape({height, width, channels}));
+    auto flat_tensor = input_tensor.flat<uint8>();
+    for (size_t i = 0; i < actual_pixels; i++) {
+      flat_tensor(i) = data[i];
+    }
+    // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
+    RunOneInput(input_tensor).IgnoreError();
+  }
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzEncodeJpeg);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc b/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc
new file mode 100644
index 0000000000..d91a351c59
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc
@@ -0,0 +1,64 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+// Fuzz inputs to the example proto decoder.
+// TODO(dga):  Make this more comprehensive.
+// Right now, it's just a quick PoC to show how to attach the
+// plumbing, but it needs some real protos to chew on as a
+// corpus, and the sparse/dense parts should be made more rich
+// to achieve higher code coverage.
+
+class FuzzExampleProtoFastParsing : public FuzzSession {
+  void BuildGraph(const Scope& scope) final {
+    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+    // The serialized proto.
+    auto input = Placeholder(scope.WithOpName("input1"), DT_STRING);
+
+    auto in_expanded = ExpandDims(scope, input, Const<int>(scope, 0));
+
+    auto names = Const(scope, {"noname"});
+    std::vector<Output> dense_keys = {Const(scope, {"a"})};
+    std::vector<Output> sparse_keys;  // Empty.
+    std::vector<Output> dense_defaults = {Const(scope, {1.0f})};
+
+    DataTypeSlice sparse_types = {};
+    std::vector<PartialTensorShape> dense_shapes;
+    dense_shapes.push_back(PartialTensorShape());
+
+    std::ignore = ParseExample(scope.WithOpName("output"), in_expanded, names,
+                               sparse_keys, dense_keys, dense_defaults,
+                               sparse_types, dense_shapes);
+  }
+
+  void FuzzImpl(const uint8_t* data, size_t size) final {
+    // TODO(dga):  Test the batch case also.
+    Tensor input_tensor(tensorflow::DT_STRING, TensorShape({}));
+    input_tensor.scalar<string>()() =
+        string(reinterpret_cast<const char*>(data), size);
+    // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
+    RunOneInput(input_tensor).IgnoreError();
+  }
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzExampleProtoFastParsing);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/fuzz_session.h b/tensorflow/core/kernels/fuzzing/fuzz_session.h
new file mode 100644
index 0000000000..fb518798b2
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/fuzz_session.h
@@ -0,0 +1,156 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef LEARNING_BRAIN_KERNELS_FUZZING_FUZZ_SESSION_H_
+#define LEARNING_BRAIN_KERNELS_FUZZING_FUZZ_SESSION_H_
+
+#include "tensorflow/cc/framework/scope.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/public/session.h"
+
+// Standard invoking function macro to dispatch to a fuzzer class.
+#ifndef PLATFORM_WINDOWS
+#define STANDARD_TF_FUZZ_FUNCTION(FuzzerClass)                              \
+  extern "C" int LLVMFuzzerTestOneInput(const uint8_t* data, size_t size) { \
+    static FuzzerClass* fuzzer = new FuzzerClass();                         \
+    return fuzzer->Fuzz(data, size);                                        \
+  }
+#else
+// We don't compile this for Windows, MSVC doesn't like it as pywrap in Windows
+// links all the code into one big object file and there are conflicting
+// function names.
+#define STANDARD_TF_FUZZ_FUNCTION(FuzzerClass)
+#endif
+
+// Standard builder for hooking one placeholder to one op.
+#define SINGLE_INPUT_OP_BUILDER(dtype, opName)                           \
+  void BuildGraph(const Scope& scope) override {                         \
+    auto op_node =                                                       \
+        tensorflow::ops::Placeholder(scope.WithOpName("input1"), dtype); \
+    std::ignore =                                                        \
+        tensorflow::ops::opName(scope.WithOpName("output"), op_node);    \
+  }
+
+namespace tensorflow {
+namespace fuzzing {
+
+// Create a TensorFlow session using a specific GraphDef created
+// by BuildGraph(), and make it available for fuzzing.
+// Users must override BuildGraph and FuzzImpl to specify
+// (1) which operations are being fuzzed; and
+// (2) How to translate the uint8_t* buffer from the fuzzer
+//     to a Tensor or Tensors that are semantically appropriate
+//     for the op under test.
+// For the simple cases of testing a single op that takes a single
+// input Tensor, use the SINGLE_INPUT_OP_BUILDER(dtype, opName) macro in place
+// of defining BuildGraphDef.
+//
+// Typical use:
+// class FooFuzzer : public FuzzSession {
+//   SINGLE_INPUT_OP_BUILDER(DT_INT8, Identity);
+//   void FuzzImpl(const uint8_t* data, size_t size) {
+//      ... convert data and size to a Tensor, pass it to:
+//      RunOneInput(input_tensor);
+//
+class FuzzSession {
+ public:
+  FuzzSession() : initialized_(false) {}
+  virtual ~FuzzSession() {}
+
+  // Constructs a Graph using the supplied Scope.
+  // By convention, the graph should have inputs named "input1", ...
+  // "inputN", and one output node, named "output".
+  // Users of FuzzSession should override this method to create their graph.
+  virtual void BuildGraph(const Scope& scope) {}
+
+  // Implements the logic that converts an opaque byte buffer
+  // from the fuzzer to Tensor inputs to the graph.  Users must override.
+  virtual void FuzzImpl(const uint8_t* data, size_t size) {}
+
+  // Initializes the FuzzSession.  Not safe for multithreading.
+  // Separate init function because the call to virtual BuildGraphDef
+  // can't be put into the constructor.
+  Status InitIfNeeded() {
+    if (initialized_) {
+      return Status::OK();
+    }
+    initialized_ = true;
+
+    Scope root = Scope::NewRootScope().ExitOnError();
+    SessionOptions options;
+    session_ = std::unique_ptr<Session>(NewSession(options));
+
+    BuildGraph(root);
+
+    GraphDef graph_def;
+    TF_CHECK_OK(root.ToGraphDef(&graph_def));
+
+    Status status = session_->Create(graph_def);
+    if (!status.ok()) {
+      // This is FATAL, because this code is designed to fuzz an op
+      // within a session.  Failure to create the session means we
+      // can't send any data to the op.
+      LOG(FATAL) << "Could not create session: " << status.error_message();
+    }
+    return status;
+  }
+
+  // Runs the TF session by pulling on the "output" node, attaching
+  // the supplied input_tensor to the "input1" node, and discarding
+  // any returned output.
+  Status RunOneInput(const Tensor& input_tensor) {
+    return session_->Run({{"input1", input_tensor}}, {}, {"output"}, nullptr);
+  }
+
+  Status RunTwoInputs(const Tensor& input1, const Tensor& input2) {
+    return session_->Run({{"input1", input1}, {"input2", input2}}, {},
+                         {"output"}, nullptr);
+  }
+
+  // Dispatches to FuzzImpl;  small amount of sugar to keep the code
+  // of the per-op fuzzers tiny.
+  int Fuzz(const uint8_t* data, size_t size) {
+    Status status = InitIfNeeded();
+    TF_CHECK_OK(status) << "Fuzzer graph initialization failed: "
+                        << status.error_message();
+    // No return value from fuzzing:  Success is defined as "did not
+    // crash".  The actual application results are irrelevant.
+    FuzzImpl(data, size);
+    return 0;
+  }
+
+ private:
+  bool initialized_;
+  std::unique_ptr<Session> session_;
+};
+
+// A specialized fuzz implementation for ops that take
+// a single string.  Caller must still define the op
+// to plumb by overriding BuildGraph or using
+// a plumbing macro.
+class FuzzStringInputOp : public FuzzSession {
+  void FuzzImpl(const uint8_t* data, size_t size) final {
+    Tensor input_tensor(tensorflow::DT_STRING, TensorShape({}));
+    input_tensor.scalar<string>()() =
+        string(reinterpret_cast<const char*>(data), size);
+    // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
+    RunOneInput(input_tensor).IgnoreError();
+  }
+};
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
+
+#endif  // LEARNING_BRAIN_KERNELS_FUZZING_FUZZ_SESSION_H_
diff --git a/tensorflow/core/kernels/fuzzing/identity_fuzz.cc b/tensorflow/core/kernels/fuzzing/identity_fuzz.cc
new file mode 100644
index 0000000000..ac3a12aa39
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/identity_fuzz.cc
@@ -0,0 +1,45 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzIdentity : public FuzzSession {
+  SINGLE_INPUT_OP_BUILDER(DT_INT8, Identity);
+
+  void FuzzImpl(const uint8_t* data, size_t size) final {
+    Tensor input_tensor(tensorflow::DT_INT8,
+                        TensorShape({static_cast<int64>(size)}));
+    auto flat_tensor = input_tensor.flat<int8>();
+    for (size_t i = 0; i < size; i++) {
+      flat_tensor(i) = data[i];
+    }
+
+    Status s = RunOneInput(input_tensor);
+    // Note:  For many ops, we don't care about this success -- but when
+    // testing to make sure the harness actually works, it's useful.
+    if (!s.ok()) {
+      LOG(ERROR) << "Execution failed: " << s.error_message();
+    }
+  }
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzIdentity);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc b/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc
new file mode 100644
index 0000000000..978fcd1028
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc
@@ -0,0 +1,45 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+// Fuzz inputs to the serialized Tensor decoder.
+
+class FuzzParseTensor : public FuzzSession {
+  void BuildGraph(const Scope& scope) final {
+    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+    // The serialized proto.
+    auto input = Placeholder(scope.WithOpName("input1"), DT_STRING);
+
+    std::ignore = ParseTensor(scope.WithOpName("output"), input, DT_FLOAT);
+  }
+
+  void FuzzImpl(const uint8_t* data, size_t size) final {
+    Tensor input_tensor(tensorflow::DT_STRING, TensorShape({}));
+    input_tensor.scalar<string>()() =
+        string(reinterpret_cast<const char*>(data), size);
+    // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
+    RunOneInput(input_tensor).IgnoreError();
+  }
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzParseTensor);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc b/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc
new file mode 100644
index 0000000000..7d1aa1fbf3
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc
@@ -0,0 +1,60 @@
+/* Copyright 2017 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzStringSplit : public FuzzSession {
+  void BuildGraph(const Scope& scope) override {
+    auto input =
+        tensorflow::ops::Placeholder(scope.WithOpName("input1"), DT_STRING);
+    auto delimeter =
+        tensorflow::ops::Placeholder(scope.WithOpName("input2"), DT_STRING);
+    std::ignore = tensorflow::ops::StringSplit(scope.WithOpName("output"),
+                                               input, delimeter);
+  }
+
+  void FuzzImpl(const uint8_t* data, size_t size) final {
+    Tensor input_tensor(tensorflow::DT_STRING, TensorShape({}));
+    Tensor delimeter_tensor(tensorflow::DT_STRING, TensorShape({}));
+
+    if (size > 0) {
+      // The spec for split is that the delimeter should be 0 or 1 characters.
+      // Naturally, fuzz it with something larger.  (This omits the possibility
+      // of handing it a > int32_max size string, which should be tested for in
+      // an
+      // explicit test).
+      size_t delim_len = static_cast<size_t>(data[0]);
+      if (delim_len > size) {
+        delim_len = size - 1;
+      }
+      delimeter_tensor.scalar<string>()() =
+          string(reinterpret_cast<const char*>(data), delim_len);
+      input_tensor.scalar<string>()() = string(
+          reinterpret_cast<const char*>(data + delim_len), size - delim_len);
+    }
+
+    // TODO(b/32704451): Don't just ignore the ::tensorflow::Status object!
+    RunTwoInputs(input_tensor, delimeter_tensor).IgnoreError();
+  }
+};
+
+STANDARD_TF_FUZZ_FUNCTION(FuzzStringSplit);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc b/tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc
new file mode 100644
index 0000000000..94255d215e
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc
@@ -0,0 +1,32 @@
+/* Copyright 2016 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/kernels/fuzzing/fuzz_session.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+
+namespace tensorflow {
+namespace fuzzing {
+
+class FuzzStringToNumber : public FuzzStringInputOp {
+  SINGLE_INPUT_OP_BUILDER(DT_STRING, StringToNumber);
+};
+
+// TODO(dga):  Generalize this to hit both the float and int
+// variants of StringToNumber - requires an update to the
+// plumbing code to specify the output dtype.
+STANDARD_TF_FUZZ_FUNCTION(FuzzStringToNumber);
+
+}  // end namespace fuzzing
+}  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/fuzzing/tf_ops_fuzz_target_lib.bzl b/tensorflow/core/kernels/fuzzing/tf_ops_fuzz_target_lib.bzl
new file mode 100644
index 0000000000..f752b59568
--- /dev/null
+++ b/tensorflow/core/kernels/fuzzing/tf_ops_fuzz_target_lib.bzl
@@ -0,0 +1,13 @@
+"""Fuzzing template for TensorFlow ops."""
+
+def tf_ops_fuzz_target_lib(name):
+  native.cc_library(
+      name = name + "_fuzz_lib",
+      srcs = [name + "_fuzz.cc"],
+      deps = [
+          "//tensorflow/core/kernels/fuzzing:fuzz_session",
+          "//tensorflow/cc:cc_ops",
+      ],
+      tags = ["no_windows"],
+      alwayslink = 1,
+  )
-- 
GitLab


From 2aaa620f8cfcf9df22d4be3bd85954a217faa818 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 18:08:59 -0700
Subject: [PATCH 1099/1375] adds missing "pass" to empty method in
 tf.contrib.learn.DNNClassifier doc

PiperOrigin-RevId: 162431577
---
 tensorflow/contrib/learn/python/learn/estimators/dnn.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn.py b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
index 04607dcdb1..cb15ef23e9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
@@ -245,7 +245,9 @@ class DNNClassifier(estimator.Estimator):
   def input_fn_eval: # returns x, y (where y represents label's class index).
     pass
   estimator.evaluate(input_fn=input_fn_eval)
+
   def input_fn_predict: # returns x, None
+    pass
   # predict_classes returns class indices.
   estimator.predict_classes(input_fn=input_fn_predict)
   ```
-- 
GitLab


From 53d4c4b80f465e4d7ab267f4d48c6c29bbbd24f3 Mon Sep 17 00:00:00 2001
From: "Gao, Xiang" <qasdfgtyuiop@gmail.com>
Date: Tue, 18 Jul 2017 21:14:35 -0400
Subject: [PATCH 1100/1375] fix broken links, add links check to sanity
 (#11394)

* fix broken links, add links check to sanity

* fix broken link in export.md
---
 tensorflow/docs_src/extend/adding_an_op.md    |  2 +-
 tensorflow/docs_src/get_started/export.md     |  3 +-
 .../get_started/summaries_and_tensorboard.md  |  5 ++--
 .../docs_src/tutorials/image_retraining.md    |  4 +--
 tensorflow/tools/ci_build/ci_sanity.sh        |  8 ++++--
 tensorflow/tools/ci_build/code_link_check.sh  | 28 +++++++++++++++++++
 6 files changed, 40 insertions(+), 10 deletions(-)
 create mode 100755 tensorflow/tools/ci_build/code_link_check.sh

diff --git a/tensorflow/docs_src/extend/adding_an_op.md b/tensorflow/docs_src/extend/adding_an_op.md
index e8441d51c2..4b1d1b6e34 100644
--- a/tensorflow/docs_src/extend/adding_an_op.md
+++ b/tensorflow/docs_src/extend/adding_an_op.md
@@ -155,7 +155,7 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
 ### Multi-threaded CPU kernels
 
 To write a multi-threaded CPU kernel, the Shard function in
-[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/framework/work_sharder.h)
+[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
 can be used. This function shards a computation function across the
 threads configured to be used for intra-op threading (see
 intra_op_parallelism_threads in
diff --git a/tensorflow/docs_src/get_started/export.md b/tensorflow/docs_src/get_started/export.md
index 8273030271..77e628699e 100644
--- a/tensorflow/docs_src/get_started/export.md
+++ b/tensorflow/docs_src/get_started/export.md
@@ -168,7 +168,7 @@ and so determine which request types will be honored.
 > the corresponding `ExportOutput` entry.  The inputs are always those provided
 > by the `serving_input_receiver_fn`.
 > An inference request may specify the head by name.  One head must be named
-> using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/saved_model/signature_constants.py)
+> using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/tensorflow/python/saved_model/signature_constants.py)
 > indicating which signature will be served when an inference request does not
 > specify one.
 
@@ -295,4 +295,3 @@ the different Tensorflow Serving APIs, selecting the signature by key, etc. -->
 
 <!-- TODO(soergel): document ExportStrategy here once Experiment moves
 from contrib to core. -->
-
diff --git a/tensorflow/docs_src/get_started/summaries_and_tensorboard.md b/tensorflow/docs_src/get_started/summaries_and_tensorboard.md
index 45d43e7a6e..ece8fbf43c 100644
--- a/tensorflow/docs_src/get_started/summaries_and_tensorboard.md
+++ b/tensorflow/docs_src/get_started/summaries_and_tensorboard.md
@@ -17,7 +17,7 @@ TensorBoard is fully configured, it looks like this:
 </div>
 
 This tutorial is intended to get you started with simple TensorBoard usage.
-There are other resources available as well! The [TensorBoard README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md)
+There are other resources available as well! The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard)
 has a lot more information on TensorBoard usage, including tips & tricks, and
 debugging information.
 
@@ -216,5 +216,4 @@ corner. Each tab represents a set of serialized data that can be visualized.
 For in depth information on how to use the *graph* tab to visualize your graph,
 see @{$graph_viz$TensorBoard: Graph Visualization}.
 
-For more usage information on TensorBoard in general, see the [TensorBoard
-README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md).
+For more usage information on TensorBoard in general, see the [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard).
diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md
index 898db0e73c..b0e715edcb 100644
--- a/tensorflow/docs_src/tutorials/image_retraining.md
+++ b/tensorflow/docs_src/tutorials/image_retraining.md
@@ -137,7 +137,7 @@ Once TensorBoard is running, navigate your web browser to `localhost:6006` to vi
 
 The script will log TensorBoard summaries to `/tmp/retrain_logs` by default. You can change the directory with the `--summaries_dir` flag.
 
-The [TensorBoard README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md) has a lot more information on TensorBoard usage, including tips & tricks, and debugging information.
+The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard) has a lot more information on TensorBoard usage, including tips & tricks, and debugging information.
 
 ## Using the Retrained Model
 
@@ -337,7 +337,7 @@ the (much larger) training set.
 
 By default the script uses a pretrained version of the Inception v3 model
 architecture. This is a good place to start because it provides high accuracy
-results, but if you intend to deploy your model on mobile devices or other 
+results, but if you intend to deploy your model on mobile devices or other
 resource-constrained environments you may want to trade off a little accuracy
 for much smaller file sizes or faster speeds. To help with that, the
 [retrain.py script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py)
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index 5bea031b68..82b41d4f9d 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -418,6 +418,10 @@ do_pip_smoke_test() {
     "The pip smoke test failed."
 }
 
+do_code_link_check() {
+  tensorflow/tools/ci_build/code_link_check.sh
+}
+
 do_check_load_py_test() {
   BUILD_CMD="bazel build //tensorflow/tools/pip_package:check_load_py_test"
   ${BUILD_CMD}
@@ -431,8 +435,8 @@ do_check_load_py_test() {
 }
 
 # Supply all sanity step commands and descriptions
-SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test")
-SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test")
+SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check")
+SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test" "Code Link Check: Check there are no broken links")
 
 INCREMENTAL_FLAG=""
 
diff --git a/tensorflow/tools/ci_build/code_link_check.sh b/tensorflow/tools/ci_build/code_link_check.sh
new file mode 100755
index 0000000000..e7eaa49d08
--- /dev/null
+++ b/tensorflow/tools/ci_build/code_link_check.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+
+# please run this at root directory of tensorflow
+success=1
+
+for i in `grep -onI https://www.tensorflow.org/code/\[a-zA-Z0-9/._-\]\* -r tensorflow`
+do
+  filename=`echo $i|awk -F: '{print $1}'`
+  linenumber=`echo $i|awk -F: '{print $2}'`
+  target=`echo $i|awk -F: '{print $4}'|tail -c +27`
+
+  # skip files in tensorflow/models
+  if [[ $target == tensorflow_models/* ]] ; then
+    continue
+  fi
+
+  if [ ! -f $target ] && [ ! -d $target ]; then
+    success=0
+    echo Broken link $target at line $linenumber of file $filename
+  fi
+done
+
+if [ $success == 0 ]; then
+  echo Code link check fails.
+  exit 1
+fi
+
+echo Code link check success.
-- 
GitLab


From 9293c557bd2df05658727418067ccee7a77a4be3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 19:15:53 -0700
Subject: [PATCH 1101/1375] [XLA] Get rid of ServiceFlags by absorbing it into
 DebugOptions.

After this change HloModuleConfig::hlo_profiling_enabled_ is redundant.  I'll
remove it in a future change.

PiperOrigin-RevId: 162436163
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ----
 .../xla/legacy_flags/debug_options_flags.cc   | 13 ++++
 .../xla/legacy_flags/service_flags.cc         | 71 -------------------
 .../compiler/xla/legacy_flags/service_flags.h | 52 --------------
 tensorflow/compiler/xla/service/BUILD         |  5 +-
 .../xla/service/compile_only_service.cc       | 11 +--
 tensorflow/compiler/xla/service/executable.cc |  6 +-
 tensorflow/compiler/xla/service/executable.h  |  9 +--
 .../compiler/xla/service/local_service.cc     |  4 +-
 tensorflow/compiler/xla/service/service.cc    | 52 +++++++-------
 tensorflow/compiler/xla/service/service.h     |  2 +-
 tensorflow/compiler/xla/xla.proto             | 10 +++
 14 files changed, 68 insertions(+), 182 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/service_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/service_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index b12b5318ec..1ee0859030 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -128,7 +128,6 @@ cc_library(
         ":tfcompile_lib",
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/compiler/xla/legacy_flags:util_flags",
         "//tensorflow/compiler/xla/service:compiler",
         "//tensorflow/core:core_cpu",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 12825344d5..4f7fb55014 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -24,7 +24,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/core/framework/function.h"
@@ -131,7 +130,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendServiceFlags(&flag_list);
   xla::legacy_flags::AppendUtilFlags(&flag_list);
 
   tensorflow::string usage = tensorflow::tfcompile::kUsageHeader;
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 1fdb6d59cf..0ab5fac962 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -90,18 +90,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "service_flags",
-    srcs = ["service_flags.cc"],
-    hdrs = ["service_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "backend_flags",
     srcs = ["backend_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 8bd3d0f040..476fa5f171 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -227,6 +227,19 @@ void AllocateFlags() {
            "2 input arguments with 2D shape and 4D shape, the "
            "computation will run 2! * 4! times for every possible "
            "layouts"),
+       tensorflow::Flag(
+           "xla_hlo_profile",
+           bool_setter_for(&DebugOptions::set_xla_hlo_profile),
+           flag_values->xla_hlo_profile(),
+           "Instrument the computation to collect per-HLO cycle counts"),
+       tensorflow::Flag("xla_dump_computations_to",
+                        flag_values->mutable_xla_dump_computations_to(),
+                        "Dump computations that XLA executes into the provided "
+                        "directory path"),
+       tensorflow::Flag("xla_dump_executions_to",
+                        flag_values->mutable_xla_dump_executions_to(),
+                        "Dump parameters and results of computations that XLA "
+                        "executes into the provided directory path"),
        tensorflow::Flag("xla_backend_extra_options",
                         setter_for_xla_backend_extra_options, "",
                         "Extra options to pass to a backend; "
diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.cc b/tensorflow/compiler/xla/legacy_flags/service_flags.cc
deleted file mode 100644
index e7fa30455c..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.cc
+++ /dev/null
@@ -1,71 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's service module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static ServiceFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new ServiceFlags;
-  flags->xla_hlo_profile = false;
-  flags->xla_dump_computations_to = "";
-  flags->xla_dump_executions_to = "";
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_hlo_profile", &flags->xla_hlo_profile,
-          "Instrument the computation to collect per-HLO cycle counts"),
-      tensorflow::Flag("xla_dump_computations_to",
-                       &flags->xla_dump_computations_to,
-                       "Dumps computations that XLA executes into the provided "
-                       "directory path"),
-      tensorflow::Flag("xla_dump_executions_to", &flags->xla_dump_executions_to,
-                       "Dumps parameters and results of computations that XLA "
-                       "executes into the provided directory path"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's service module.
-void AppendServiceFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the ServiceFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-ServiceFlags* GetServiceFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/service_flags.h b/tensorflow/compiler/xla/legacy_flags/service_flags.h
deleted file mode 100644
index a821a94093..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/service_flags.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_SERVICE_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_SERVICE_FLAGS_H_
-
-// Legacy flags for XLA's service module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's service module.
-void AppendServiceFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's service module.
-typedef struct {
-  bool xla_hlo_profile;  // Instrument the computation to collect per-HLO cycle
-                         // counts
-  string xla_dump_computations_to;  // Dumps computations that XLA executes
-                                    // into the provided directory path
-  // Dumps parameters and results of computations that XLA executes into
-  // the provided directory path
-  string xla_dump_executions_to;
-} ServiceFlags;
-
-// Return a pointer to the ServiceFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-ServiceFlags* GetServiceFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_SERVICE_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index ad4d23c49b..ada130aa84 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -415,7 +415,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
         "//tensorflow/compiler/xla/legacy_flags:backend_flags",
-        "//tensorflow/compiler/xla/legacy_flags:service_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
@@ -450,7 +450,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:service_flags",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
     ],
@@ -540,7 +539,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:service_flags",
+        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
         "//tensorflow/stream_executor",
diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index dc81323c99..d43dc5b214 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -75,9 +75,11 @@ CompileOnlyService::CompileAheadOfTime(
     VersionedComputationHandle versioned_handle =
         user_computation->GetVersionedHandle();
 
+    // TODO(b/63773457): Track DebugOptions in AotCompilationOptions.
+    DebugOptions debug_options = legacy_flags::GetDebugOptionsFromFlags();
+
     // Dump computation proto state if flag is set.
-    legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-    const string& directory_path = flags->xla_dump_computations_to;
+    const string& directory_path = debug_options.xla_dump_computations_to();
     if (!directory_path.empty()) {
       TF_ASSIGN_OR_RETURN(
           std::unique_ptr<SessionModule> session_module,
@@ -95,11 +97,10 @@ CompileOnlyService::CompileAheadOfTime(
         user_computation->ComputeProgramShape(versioned_handle.version));
 
     HloModuleConfig hlo_module_config(*program_shape);
-    hlo_module_config.set_debug_options(
-        legacy_flags::GetDebugOptionsFromFlags());
+    hlo_module_config.set_debug_options(debug_options);
     auto* computation_layout =
         hlo_module_config.mutable_entry_computation_layout();
-    if (flags->xla_hlo_profile) {
+    if (debug_options.xla_hlo_profile()) {
       hlo_module_config.enable_hlo_profiling(true);
     }
     for (int i = 0; i < instance.argument_layouts.size(); ++i) {
diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc
index 20eb1aea37..cbc02b8462 100644
--- a/tensorflow/compiler/xla/service/executable.cc
+++ b/tensorflow/compiler/xla/service/executable.cc
@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/executable.h"
 
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/core/lib/io/path.h"
@@ -57,8 +57,8 @@ Executable::ExecuteOnStreams(
 
 Status Executable::DumpSessionModule() {
   TF_RET_CHECK(dumping());
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-  const string& directory_path = flags->xla_dump_executions_to;
+  const string& directory_path =
+      module_config().debug_options().xla_dump_executions_to();
   VersionedComputationHandle versioned_handle = entry_computation_handle();
   // This filename does not include the version number because the computation
   // is only ever executed at one version.
diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h
index 6c86561d52..5388c9efa4 100644
--- a/tensorflow/compiler/xla/service/executable.h
+++ b/tensorflow/compiler/xla/service/executable.h
@@ -19,7 +19,7 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/service/computation_layout.h"
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
@@ -196,10 +196,11 @@ StatusOr<ReturnT> Executable::ExecuteOnStreamWrapper(
   // If the profiling flag isn't enabled, we pass nullptr as the profile to
   // indicate profiling is not requested.
   HloExecutionProfile hlo_execution_profile;
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
   HloExecutionProfile* profile_ptr =
-      flags->xla_hlo_profile && hlo_profiling_enabled() ? &hlo_execution_profile
-                                                        : nullptr;
+      module_config().debug_options().xla_hlo_profile() &&
+              hlo_profiling_enabled()
+          ? &hlo_execution_profile
+          : nullptr;
 
   auto return_value = ExecuteOnStream(run_options, arguments, profile_ptr);
 
diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc
index 25588a6fb8..45e37c6f65 100644
--- a/tensorflow/compiler/xla/service/local_service.cc
+++ b/tensorflow/compiler/xla/service/local_service.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/computation_layout.h"
@@ -151,8 +150,7 @@ StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable(
     module_config->set_intra_op_parallelism_threads(
         execute_backend_->eigen_intra_op_thread_pool()->NumThreads());
   }
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-  if (flags->xla_hlo_profile) {
+  if (module_config->debug_options().xla_hlo_profile()) {
     module_config->enable_hlo_profiling(true);
   }
   auto* computation_layout = module_config->mutable_entry_computation_layout();
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 1eaa56867f..37458988e0 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -22,7 +22,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/backend_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/computation_layout.h"
@@ -328,8 +328,7 @@ StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig(
             shape_with_output_layout));
   }
 
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-  if (flags->xla_hlo_profile) {
+  if (module_config->debug_options().xla_hlo_profile()) {
     module_config->enable_hlo_profiling(true);
   }
 
@@ -349,23 +348,25 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> Service::BuildExecutables(
 
   // Dump computation proto state if flag is set.
   std::vector<std::unique_ptr<SessionModule>> session_modules;
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-  const string& directory_path = flags->xla_dump_computations_to;
-  const string& other_directory_path = flags->xla_dump_executions_to;
-  if ((!directory_path.empty() || !other_directory_path.empty())) {
-    for (int64 i = 0; i < versioned_handles.size(); ++i) {
-      TF_ASSIGN_OR_RETURN(std::unique_ptr<SessionModule> session_module,
-                          computation_tracker_.SnapshotComputation(
-                              versioned_handles[i].handle));
-      if (!directory_path.empty()) {
-        string filename = Printf("computation_%lld__%s__version_%lld",
-                                 versioned_handles[i].handle.handle(),
-                                 session_module->entry().name().c_str(),
-                                 versioned_handles[i].version);
-        TF_RETURN_IF_ERROR(Executable::DumpToDirectory(directory_path, filename,
-                                                       *session_module));
-        session_modules.push_back(std::move(session_module));
-      }
+  for (int64 i = 0; i < versioned_handles.size(); ++i) {
+    const string& directory_path =
+        module_configs[i]->debug_options().xla_dump_computations_to();
+    const string& other_directory_path =
+        module_configs[i]->debug_options().xla_dump_executions_to();
+    if (directory_path.empty() && other_directory_path.empty()) {
+      continue;
+    }
+    TF_ASSIGN_OR_RETURN(
+        std::unique_ptr<SessionModule> session_module,
+        computation_tracker_.SnapshotComputation(versioned_handles[i].handle));
+    if (!directory_path.empty()) {
+      string filename = Printf("computation_%lld__%s__version_%lld",
+                               versioned_handles[i].handle.handle(),
+                               session_module->entry().name().c_str(),
+                               versioned_handles[i].version);
+      TF_RETURN_IF_ERROR(Executable::DumpToDirectory(directory_path, filename,
+                                                     *session_module));
+      session_modules.push_back(std::move(session_module));
     }
   }
 
@@ -390,8 +391,8 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> Service::BuildExecutables(
       std::vector<std::unique_ptr<Executable>> executables,
       backend->compiler()->Compile(std::move(modules), std::move(executors)));
 
-  if (!other_directory_path.empty()) {
-    for (size_t i = 0; i < versioned_handles.size(); ++i) {
+  for (size_t i = 0; i < versioned_handles.size(); ++i) {
+    if (!module_configs[i]->debug_options().xla_dump_executions_to().empty()) {
       executables[i]->set_session_module(std::move(session_modules[i]));
     }
   }
@@ -411,9 +412,10 @@ StatusOr<std::unique_ptr<Executable>> Service::BuildExecutable(
 
   // Dump computation proto state if flag is set.
   std::unique_ptr<SessionModule> session_module;
-  legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-  const string& directory_path = flags->xla_dump_computations_to;
-  const string& other_directory_path = flags->xla_dump_executions_to;
+  const string& directory_path =
+      module_config->debug_options().xla_dump_computations_to();
+  const string& other_directory_path =
+      module_config->debug_options().xla_dump_executions_to();
   if (!executable_for_compute_constant &&
       (!directory_path.empty() || !other_directory_path.empty())) {
     TF_ASSIGN_OR_RETURN(
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 034604dfd1..9f216aad87 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -22,7 +22,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/executable_run_options.h"
-#include "tensorflow/compiler/xla/legacy_flags/service_flags.h"
+#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/service/allocation_tracker.h"
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/channel_tracker.h"
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 9cf3b56c1b..fd6131da96 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -50,6 +50,16 @@ message DebugOptions {
   // Dump compilation artifacts as JSON into this directory.
   string xla_dump_debug_json_to = 8;
 
+  // Instrument the computation to collect per-HLO cycle counts.
+  bool xla_hlo_profile = 9;
+
+  // Dumps computations that XLA executes into the provided directory path.
+  string xla_dump_computations_to = 10;
+
+  // Dumps parameters and results of computations that XLA executes into the
+  // provided directory path.
+  string xla_dump_executions_to = 11;
+
   // List of HLO passes to disable. These names must exactly match the pass
   // names as specified by the HloPassInterface::name() method.
   repeated string xla_disable_hlo_passes = 30;
-- 
GitLab


From 491beb74cc5a08693d0b884d10532514ac1aef19 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 19:36:18 -0700
Subject: [PATCH 1102/1375] Automated g4 rollback of changelist 162423171

PiperOrigin-RevId: 162437318
---
 tensorflow/core/BUILD                         |   1 -
 tensorflow/core/kernels/BUILD                 |  14 +-
 tensorflow/core/kernels/conv_ops_gpu.h        | 129 +++++++-
 tensorflow/core/kernels/gpu_utils.h           | 166 ----------
 tensorflow/core/kernels/matmul_op.cc          | 312 ++++--------------
 tensorflow/core/kernels/matmul_op.h           |  64 ----
 tensorflow/core/util/matmul_autotune.cc       |  51 ---
 tensorflow/core/util/matmul_autotune.h        |  28 --
 tensorflow/python/BUILD                       |  42 ---
 .../python/kernel_tests/matmul_op_test.py     |   3 -
 tensorflow/python/ops/matmul_benchmark.py     | 143 --------
 .../python/ops/matmul_benchmark_test.py       | 122 -------
 tensorflow/stream_executor/blas.cc            |   4 -
 tensorflow/stream_executor/blas.h             | 138 +-------
 tensorflow/stream_executor/cuda/cuda_blas.cc  | 181 +---------
 tensorflow/stream_executor/cuda/cuda_blas.h   |  17 -
 tensorflow/stream_executor/stream.cc          | 178 ----------
 tensorflow/stream_executor/stream.h           |  63 ----
 18 files changed, 196 insertions(+), 1460 deletions(-)
 delete mode 100644 tensorflow/core/kernels/gpu_utils.h
 delete mode 100644 tensorflow/core/util/matmul_autotune.cc
 delete mode 100644 tensorflow/core/util/matmul_autotune.h
 delete mode 100644 tensorflow/python/ops/matmul_benchmark.py
 delete mode 100644 tensorflow/python/ops/matmul_benchmark_test.py

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 1dbb90d263..bb17d8832f 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -405,7 +405,6 @@ tf_cuda_library(
         "util/tensor_slice_reader_cache.h",
         "util/tensor_slice_writer.h",
         "util/use_cudnn.h",
-        "util/matmul_autotune.h",
         "util/util.h",
         "util/work_sharder.h",
     ] + select({
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 6e983a9ea7..21194e80c3 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -157,7 +157,6 @@ cc_library(
     hdrs = ["conv_2d.h"],
     deps = [
         ":eigen_helpers",
-        ":gpu_util_hdrs",
         "//tensorflow/core:framework",
         "//third_party/eigen3",
     ],
@@ -249,15 +248,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "gpu_util_hdrs",
-    hdrs = ["gpu_utils.h"],
-    deps = [
-        ":eigen_helpers",
-        "//third_party/eigen3",
-    ],
-)
-
 tf_cc_test(
     name = "ops_util_test",
     size = "small",
@@ -2416,9 +2406,7 @@ tf_kernel_library(
         ],
         "//conditions:default": [],
     }),
-    deps = MATH_DEPS + [
-        ":gpu_util_hdrs",
-    ] + select({
+    deps = MATH_DEPS + select({
         ":xsmm": [
             "@libxsmm_archive//:xsmm_avx",
         ],
diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h
index d2f07c2207..b268f8dbd2 100644
--- a/tensorflow/core/kernels/conv_ops_gpu.h
+++ b/tensorflow/core/kernels/conv_ops_gpu.h
@@ -21,12 +21,27 @@ limitations under the License.
 #include <tuple>
 #include <unordered_map>
 #include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/kernels/gpu_utils.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/hash/hash.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor.h"
 
 namespace tensorflow {
 
+// TODO(zhengxq): move this to gpu_util.h. The use of such wrappers is wide
+// spread.
+template <typename T>
+inline perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory,
+                                                           uint64 size) {
+  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory),
+                                                size * sizeof(T));
+  perftools::gputools::DeviceMemory<T> typed(wrapped);
+  return typed;
+}
+
 // Get the Cudnn workspace limit from the environment variable, which is in MB.
 // Return the workspace memory limit in bytes. If no value is set, return the
 // default value.
@@ -41,10 +56,12 @@ class CudnnScratchAllocator : public perftools::gputools::ScratchAllocator {
   virtual ~CudnnScratchAllocator() {}
   CudnnScratchAllocator(int64 memory_limit, OpKernelContext* context)
       : memory_limit_(memory_limit), total_byte_size_(0), context_(context) {}
-  int64 GetMemoryLimitInBytes(perftools::gputools::Stream* stream) override {
+  virtual int64 GetMemoryLimitInBytes(
+      perftools::gputools::Stream* stream) override {
     return memory_limit_;
   }
-  perftools::gputools::port::StatusOr<perftools::gputools::DeviceMemory<uint8>>
+  virtual perftools::gputools::port::StatusOr<
+      perftools::gputools::DeviceMemory<uint8>>
   AllocateBytes(perftools::gputools::Stream* stream, int64 byte_size) override {
     Tensor temporary_memory;
     if (byte_size > memory_limit_) {
@@ -168,6 +185,112 @@ class ConvParameters {
 
 typedef Eigen::GpuDevice GPUDevice;
 
+// A helper class that looks up the best autotuned config from parameters.
+// Due to the noisy nature of autotune, especially with multiple devices, it
+// only accepts a config if its margin exceeds a threshold.
+// For the same shape configs, if a new best config matches the previous best,
+// they get promoted; otherwise, the winner gets demoted. This process stops
+// when the winner's score exceeds the threshold.
+// In a bad case when two configs are very close to each other and flips
+// back and forth randomly, the expected number of experiments before autotune
+// settles is O(threshold ^ 2). So we recommend that number of warmup runs
+// for any benchmarks.
+template <typename Parameters, typename Config>
+class AutoTuneMap {
+ public:
+  bool Find(const Parameters& params, Config* config) const {
+    mutex_lock lock(mu_);
+    auto iter = params_config_map_.find(params);
+    if (iter == params_config_map_.end() ||
+        iter->second.score < min_score_threshold_) {
+      return false;
+    }
+    *config = iter->second.config;
+    return true;
+  }
+  void Insert(const ConvParameters& params, const Config& config) {
+    mutex_lock lock(mu_);
+    auto iter = params_config_map_.find(params);
+    int new_score = 0;
+    if (iter == params_config_map_.end()) {
+      // Create a new entry if params is new.
+      VLOG(1) << GetActionSummary("creates", params, config);
+      params_config_map_.insert(std::make_pair(params, ValueType{config, 1}));
+      new_score = 1;
+    } else if (iter->second.score < min_score_threshold_) {
+      DCHECK(iter->second.score > 0);
+      if (iter->second.config != config) {
+        // If it is different from the current winner, demotes the winner.
+        VLOG(1) << GetActionSummary("demotes", params, config);
+        new_score = --iter->second.score;
+        if (new_score <= 0) {
+          VLOG(1) << GetActionSummary("erases", params, config);
+          params_config_map_.erase(iter);
+        }
+      } else {
+        // If it is the same as the current winner, promotes the winner.
+        VLOG(1) << GetActionSummary("promotes", params, config);
+        new_score = ++iter->second.score;
+      }
+    }
+    if (new_score >= min_score_threshold_) {
+      VLOG(1) << GetActionSummary("accepts", params, config);
+    }
+  }
+
+ private:
+  AutoTuneMap(const string& name) : name_(name) {
+    min_score_threshold_ = 1;
+    const char* threshold_str = getenv("TF_AUTOTUNE_THRESHOLD");
+    if (threshold_str != nullptr) {
+      strings::safe_strto32(threshold_str, &min_score_threshold_);
+    }
+    min_score_threshold_ = std::max(min_score_threshold_, 1);
+  }
+
+  template <class Group, class Params, class Cfg>
+  friend class AutoTuneSingleton;
+
+  struct Hasher {
+    std::size_t operator()(const Parameters& parameter) const {
+      return parameter.hash();
+    }
+  };
+
+  string GetActionSummary(StringPiece action, const Parameters& params,
+                          const Config& config) {
+    return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
+                           action.ToString().c_str(), params.ToString().c_str(),
+                           config.ToString().c_str());
+  }
+
+  mutable mutex mu_;
+  struct ValueType {
+    Config config;
+    int32 score;
+  };
+  std::unordered_map<Parameters, ValueType, Hasher> params_config_map_
+      GUARDED_BY(mu_);
+  string name_;
+  int32 min_score_threshold_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(AutoTuneMap);
+};
+
+// A Singleton helper that manages the global autotune results by groups.
+// The caller specified arbitrary Group type that can distinguish between
+// different autotune results, even if their Parameters and Configs are the
+// same.
+template <class Group, typename Parameters, typename Config>
+class AutoTuneSingleton {
+ public:
+  typedef AutoTuneMap<Parameters, Config> AutoTuneType;
+  static AutoTuneType* GetInstance() {
+    static AutoTuneType* instance = new AutoTuneType(Group::name());
+    return instance;
+  }
+};
+
 }  // namespace tensorflow
 
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/gpu_utils.h b/tensorflow/core/kernels/gpu_utils.h
deleted file mode 100644
index 18bfb8ce8e..0000000000
--- a/tensorflow/core/kernels/gpu_utils.h
+++ /dev/null
@@ -1,166 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
-#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
-
-#if GOOGLE_CUDA
-
-#include <unordered_map>
-
-#include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/platform/stream_executor.h"
-
-namespace tensorflow {
-
-template <typename T>
-inline perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory,
-                                                           uint64 size) {
-  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory),
-                                                size * sizeof(T));
-  perftools::gputools::DeviceMemory<T> typed(wrapped);
-  return typed;
-}
-
-
-// A helper class that looks up the best autotuned config from parameters.
-// Due to the noisy nature of autotune, especially with multiple devices, it
-// only accepts a config if its margin exceeds a threshold.
-// For the same shape configs, if a new best config matches the previous best,
-// they get promoted; otherwise, the winner gets demoted. This process stops
-// when the winner's score exceeds the threshold.
-// In a bad case when two configs are very close to each other and flips
-// back and forth randomly, the expected number of experiments before autotune
-// settles is O(threshold ^ 2). So we recommend that number of warmup runs
-// for any benchmarks.
-template <typename Parameters, typename Config>
-class AutoTuneMap {
- public:
-  bool Find(const Parameters& params, Config* config) const {
-    mutex_lock lock(mu_);
-    auto iter = params_config_map_.find(params);
-    if (iter == params_config_map_.end() ||
-        (iter->second.score < min_score_threshold_ &&
-         iter->second.count <= max_autotune_count_)) {
-      return false;
-    }
-    *config = iter->second.config;
-    return true;
-  }
-  void Insert(const Parameters& params, const Config& config) {
-    mutex_lock lock(mu_);
-    auto iter = params_config_map_.find(params);
-    int new_score = 0;
-    if (iter == params_config_map_.end()) {
-      // Create a new entry if params is new.
-      VLOG(1) << GetActionSummary("creates", params, config);
-      params_config_map_.insert(
-          std::make_pair(params, ValueType{config, 1, 1}));
-      new_score = 1;
-    } else if (iter->second.score < min_score_threshold_ &&
-               iter->second.count <= max_autotune_count_) {
-      DCHECK_GT(iter->second.score, 0);
-      if (iter->second.config != config) {
-        // If it is different from the current winner, demotes the winner.
-        VLOG(1) << GetActionSummary("demotes", params, config);
-        new_score = --iter->second.score;
-        ++iter->second.count;
-        if (new_score <= 0) {
-          VLOG(1) << GetActionSummary("erases", params, config);
-          params_config_map_.erase(iter);
-        }
-      } else {
-        // If it is the same as the current winner, promotes the winner.
-        VLOG(1) << GetActionSummary("promotes", params, config);
-        new_score = ++iter->second.score;
-        ++iter->second.count;
-      }
-    }
-    if (new_score >= min_score_threshold_) {
-      VLOG(1) << GetActionSummary("accepts", params, config);
-    }
-  }
-
- private:
-  AutoTuneMap(const string& name) : name_(name) {
-    min_score_threshold_ = 1;
-    int min_warmup_iterations = 10;
-    const char* threshold_str = getenv("TF_AUTOTUNE_THRESHOLD");
-    if (threshold_str != nullptr) {
-      strings::safe_strto32(threshold_str, &min_score_threshold_);
-    }
-    const char* min_warmup_iteration_str =
-        getenv("TF_AUTOTUNE_MIN_WARMUP_ITERATIONS");
-    if (min_warmup_iteration_str != nullptr) {
-      strings::safe_strto32(min_warmup_iteration_str, &min_warmup_iterations);
-    }
-    min_score_threshold_ = std::max(min_score_threshold_, 1);
-    max_autotune_count_ = std::max(
-        5 * min_score_threshold_ * min_score_threshold_, min_warmup_iterations);
-  }
-
-  template <class Group, class Params, class Cfg>
-  friend class AutoTuneSingleton;
-
-  struct Hasher {
-    std::size_t operator()(const Parameters& parameter) const {
-      return parameter.hash();
-    }
-  };
-
-  string GetActionSummary(StringPiece action, const Parameters& params,
-                          const Config& config) {
-    return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
-                           action.ToString().c_str(), params.ToString().c_str(),
-                           config.ToString().c_str());
-  }
-
-  mutable mutex mu_;
-  struct ValueType {
-    Config config;
-    int32 score;
-    int32 count;
-  };
-  std::unordered_map<Parameters, ValueType, Hasher> params_config_map_
-      GUARDED_BY(mu_);
-  string name_;
-  int32 min_score_threshold_;
-  int32 max_autotune_count_;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(AutoTuneMap);
-};
-
-// A Singleton helper that manages the global autotune results by groups.
-// The caller specified arbitrary Group type that can distinguish between
-// different autotune results, even if their Parameters and Configs are the
-// same.
-template <class Group, typename Parameters, typename Config>
-class AutoTuneSingleton {
- public:
-  typedef AutoTuneMap<Parameters, Config> AutoTuneType;
-  static AutoTuneType* GetInstance() {
-    static AutoTuneType* instance = new AutoTuneType(Group::name());
-    return instance;
-  }
-};
-
-}  // namespace tensorflow
-
-#endif  // GOOGLE_CUDA
-
-#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc
index 62c5ecfe81..8003f7ff67 100644
--- a/tensorflow/core/kernels/matmul_op.cc
+++ b/tensorflow/core/kernels/matmul_op.cc
@@ -23,15 +23,27 @@ limitations under the License.
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/fill_functor.h"
-#include "tensorflow/core/util/matmul_autotune.h"
+
 #if GOOGLE_CUDA
 #include "cuda/include/cuda.h"
-#include "tensorflow/core/kernels/gpu_utils.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #endif  // GOOGLE_CUDA
 
 namespace tensorflow {
 
+#if GOOGLE_CUDA
+
+namespace {
+template <typename T>
+perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory) {
+  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory));
+  perftools::gputools::DeviceMemory<T> typed(wrapped);
+  return typed;
+}
+}  // namespace
+
+#endif  // GOOGLE_CUDA
+
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 #ifdef TENSORFLOW_USE_SYCL
@@ -111,16 +123,10 @@ bool ExplicitVectorMatrixOptimization<Eigen::half>(
 
 template <typename Device, typename T>
 struct LaunchMatMulBase {
-#if GOOGLE_CUDA
-  typedef perftools::gputools::blas::AlgorithmType AlgorithmType;
-#else
-  typedef int64 AlgorithmType;
-#endif  // GOOGLE_CUDA
-
   static void launch(
-      OpKernelContext* ctx, const Tensor& a, const Tensor& b,
+      OpKernelContext* ctx, OpKernel* kernel, const Tensor& a, const Tensor& b,
       const Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1>& dim_pair,
-      std::vector<AlgorithmType>* algorithms, bool use_aututone, Tensor* out) {
+      Tensor* out) {
 #ifndef TENSORFLOW_USE_SYCL
     // An explicit vector-matrix multiply is much better optimized than an
     // implicit one and this is a bottleneck during non-batched inference.
@@ -134,10 +140,6 @@ struct LaunchMatMulBase {
     }
 #endif  // TENSORFLOW_USE_SYCL
   }
-
-  static void GetBlasGemmAlgorithm(OpKernelConstruction* ctx,
-                                   std::vector<int64>* algorithms,
-                                   bool* algorithm_set_flag) {}
 };
 // On CPUs, we ignore USE_CUBLAS
 template <typename T>
@@ -157,39 +159,24 @@ struct LaunchMatMul<SYCLDevice, T, USE_CUBLAS> : public LaunchMatMulSYCL<T> {};
 #if GOOGLE_CUDA
 
 namespace {
-
 template <typename T>
 struct LaunchBlasGemv {
-  static void Compute(
-      OpKernelContext* ctx, perftools::gputools::Stream* stream, bool trans,
-      uint64 m, uint64 n, const perftools::gputools::DeviceMemory<T>& a,
-      const perftools::gputools::DeviceMemory<T>& b,
-      perftools::gputools::DeviceMemory<T>* c,
-      perftools::gputools::blas::ProfileResult* output_profile) {
+  static void Compute(OpKernelContext* ctx, perftools::gputools::Stream* stream,
+                      bool trans, uint64 m, uint64 n,
+                      const perftools::gputools::DeviceMemory<T>& a,
+                      const perftools::gputools::DeviceMemory<T>& b,
+                      perftools::gputools::DeviceMemory<T>* c) {
     const auto blas_trans =
         trans ? perftools::gputools::blas::Transpose::kTranspose
               : perftools::gputools::blas::Transpose::kNoTranspose;
-    if (output_profile == nullptr) {
-      bool blas_launch_status =
-          stream
-              ->ThenBlasGemv(blas_trans, m, n, static_cast<T>(1.0), a, m, b, 1,
-                             static_cast<T>(0.0), c, 1)
-              .ok();
-      if (!blas_launch_status) {
-        ctx->SetStatus(
-            errors::Internal("Blas GEMV launch failed:  m=", m, ", n=", n));
-      }
-    } else {
-      bool blas_launch_status =
-          stream
-              ->ThenBlasGemvWithProfiling(blas_trans, m, n, static_cast<T>(1.0),
-                                          a, m, b, 1, static_cast<T>(0.0), c, 1,
-                                          output_profile)
-              .ok();
-      if (!blas_launch_status) {
-        ctx->SetStatus(errors::Internal(
-            "Blas GEMV with profiling launch failed:  m=", m, ", n=", n));
-      }
+    bool blas_launch_status =
+        stream
+            ->ThenBlasGemv(blas_trans, m, n, static_cast<T>(1.0), a, m, b, 1,
+                           static_cast<T>(0.0), c, 1)
+            .ok();
+    if (!blas_launch_status) {
+      ctx->SetStatus(
+          errors::Internal("Blas GEMV launch failed:  m=", m, ", n=", n));
     }
   }
 
@@ -201,8 +188,7 @@ void LaunchBlasGemv<Eigen::half>::Compute(
     OpKernelContext* ctx, perftools::gputools::Stream* stream, bool trans,
     uint64 m, uint64 n, const perftools::gputools::DeviceMemory<Eigen::half>& a,
     const perftools::gputools::DeviceMemory<Eigen::half>& b,
-    perftools::gputools::DeviceMemory<Eigen::half>* c,
-    perftools::gputools::blas::ProfileResult* output_profile) {
+    perftools::gputools::DeviceMemory<Eigen::half>* c) {
   ctx->SetStatus(errors::Internal(
       "Blas GEMV launch failed: GEMV is not implemented for float16."));
 }
@@ -214,55 +200,15 @@ bool LaunchBlasGemv<Eigen::half>::IsSupported() {
 
 }  // namespace
 
-bool GetCublasAutotuneComputationType(
-    const DataType& dtype,
-    perftools::gputools::blas::ComputationType* compute_type) {
-  using perftools::gputools::blas::ComputationType;
-  bool use_f32_for_f16_computation = MatmulDoFP32ComputationFP16Input();
-  switch (dtype) {
-    case DT_HALF:
-    case DT_BFLOAT16:
-      if (use_f32_for_f16_computation) {
-        *compute_type = ComputationType::kF32;
-      } else {
-        *compute_type = ComputationType::kF16;
-      }
-      return false;
-    case DT_FLOAT:
-      *compute_type = ComputationType::kF32;
-      return true;
-    case DT_DOUBLE:
-      *compute_type = ComputationType::kF64;
-      return true;
-    default:
-      // Unsupported compute_type, return false.
-      return false;
-  }
-}
-
-// A dummy type to group matmul autotune results together.
-struct MatmulAutoTuneGroup {
-  static string name() { return "Matmul"; }
-};
-typedef AutoTuneSingleton<MatmulAutoTuneGroup, MatmulParameters,
-                          perftools::gputools::blas::AlgorithmConfig>
-    AutoTuneMatmul;
-
 template <typename T>
 struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
   static void launch(
-      OpKernelContext* ctx, const Tensor& a, const Tensor& b,
+      OpKernelContext* ctx, OpKernel* kernel, const Tensor& a, const Tensor& b,
       const Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1>& dim_pair,
-      std::vector<int64>* algorithms, bool use_autotune, Tensor* out) {
-    using perftools::gputools::blas::AlgorithmConfig;
-    using perftools::gputools::blas::ComputationType;
-    using perftools::gputools::blas::ProfileResult;
-    using perftools::gputools::blas::Transpose;
-    using perftools::gputools::blas::kDefaultAlgorithm;
-    using perftools::gputools::blas::kDefaultBlasGemm;
-    using perftools::gputools::blas::kDefaultBlasGemv;
-    using perftools::gputools::blas::kNoAlgorithm;
-    Transpose trans[] = {Transpose::kNoTranspose, Transpose::kTranspose};
+      Tensor* out) {
+    perftools::gputools::blas::Transpose trans[] = {
+        perftools::gputools::blas::Transpose::kNoTranspose,
+        perftools::gputools::blas::Transpose::kTranspose};
     const uint64 m = a.dim_size(1 - dim_pair[0].first);
     const uint64 k = a.dim_size(dim_pair[0].first);
     const uint64 n = b.dim_size(1 - dim_pair[0].second);
@@ -274,157 +220,36 @@ struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
     auto* stream = ctx->op_device_context()->stream();
     OP_REQUIRES(ctx, stream, errors::Internal("No GPU stream available."));
 
-    auto a_ptr = AsDeviceMemory(a.template flat<T>().data(),
-                                a.template flat<T>().size());
-    auto b_ptr = AsDeviceMemory(b.template flat<T>().data(),
-                                b.template flat<T>().size());
-    auto c_ptr = AsDeviceMemory(out->template flat<T>().data(),
-                                out->template flat<T>().size());
-    auto alpha = static_cast<T>(1.0);
-    auto beta = static_cast<T>(0.0);
-
-    int device_id = stream->parent()->device_ordinal();
-    DataType dtype = a.dtype();
-    MatmulParameters matmul_parameters = {
-        transpose_a, transpose_b, m, n, k, dtype, device_id,
-    };
-    AlgorithmConfig algorithm_config(kNoAlgorithm);
-
-    ComputationType computation_type;
-    bool compute_type_supported =
-        GetCublasAutotuneComputationType(dtype, &computation_type);
-    if (use_autotune && compute_type_supported && !algorithms->empty()) {
-      ProfileResult best_result;
-      // TODO(yangzihao): Unify this code with conv autotuning.
-      if (!AutoTuneMatmul::GetInstance()->Find(matmul_parameters,
-                                               &algorithm_config)) {
-        ProfileResult profile_result;
-        for (auto profile_algorithm : (*algorithms)) {
-          // Cublas does
-          // C = A x B
-          // where A, B and C are assumed to be in column major.
-          // We want the output to be in row-major, so we can compute
-          // C' = B' x A' (' stands for transpose)
-          bool cublas_launch_status =
-              stream
-                  ->ThenBlasGemmWithAlgorithm(
-                      blas_transpose_b, blas_transpose_a, n, m, k, alpha, b_ptr,
-                      transpose_b ? k : n, a_ptr, transpose_a ? m : k, beta,
-                      &c_ptr, n, computation_type, profile_algorithm,
-                      &profile_result)
-                  .ok();
-          if (cublas_launch_status) {
-            if (profile_result.is_valid()) {
-              if (profile_result.elapsed_time_in_ms() <
-                  best_result.elapsed_time_in_ms()) {
-                best_result = profile_result;
-              }
-            }
-          }
-        }
-        // Try BlasGemmWithProfiling
-        bool cublas_launch_status =
-            stream
-                ->ThenBlasGemmWithProfiling(
-                    blas_transpose_b, blas_transpose_a, n, m, k, 1.0, b_ptr,
-                    transpose_b ? k : n, a_ptr, transpose_a ? m : k, 0.0,
-                    &c_ptr, n, &profile_result)
-                .ok();
-        if (cublas_launch_status) {
-          if (profile_result.is_valid()) {
-            if (profile_result.elapsed_time_in_ms() <
-                best_result.elapsed_time_in_ms()) {
-              best_result = profile_result;
-            }
-          }
-        }
-        // Try BlasGemvWithProfiling
-        if (LaunchBlasGemv<T>::IsSupported() && n == 1) {
-          LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a,
-                                     transpose_a ? m : k, transpose_a ? k : m,
-                                     a_ptr, b_ptr, &c_ptr, &profile_result);
-          if (profile_result.is_valid()) {
-            if (profile_result.elapsed_time_in_ms() <
-                best_result.elapsed_time_in_ms()) {
-              best_result = profile_result;
-            }
-          }
-        }
-      }
-      // We make sure that each matmul parameter set only gets one pass of
-      // autotune. If the best result is found, assign it to algorithm_type
-      // and insert it to autotune map. If all internal kernels of
-      // cublasGemmEx() returns invalid results, we add kNoAlgorithm to the
-      // autotune map.
-      if (best_result.is_valid()) {
-        algorithm_config.set_algorithm(best_result.algorithm());
-      }
-      AutoTuneMatmul::GetInstance()->Insert(matmul_parameters,
-                                            algorithm_config);
-      if (algorithm_config.algorithm() != kNoAlgorithm &&
-          algorithm_config.algorithm() != kDefaultBlasGemm &&
-          algorithm_config.algorithm() != kDefaultBlasGemv) {
-        bool cublas_launch_status =
-            stream
-                ->ThenBlasGemmWithAlgorithm(
-                    blas_transpose_b, blas_transpose_a, n, m, k, alpha, b_ptr,
-                    transpose_b ? k : n, a_ptr, transpose_a ? m : k, beta,
-                    &c_ptr, n, computation_type, algorithm_config.algorithm(),
-                    nullptr)
-                .ok();
-        if (!cublas_launch_status) {
-          ctx->SetStatus(errors::Internal(
-              "Blas GEMM with algorithm launch failed : a.shape=(",
-              a.dim_size(0), ", ", a.dim_size(1), "), b.shape=(", b.dim_size(0),
-              ", ", b.dim_size(1), "), m=", m, ", n=", n, ", k=", k));
-        }
-      }
-    }
-    // For the following case, we use normal BlasGemm():
-    //  1) We didn't set the use_autotune flag;
-    //  2) compute type does not support autotune;
-    //  3) no algorithm is found;
-    //  4) all internal kernels in autotune return invalid results.
-    if (!use_autotune || !compute_type_supported || algorithms->empty() ||
-        algorithm_config.algorithm() == kNoAlgorithm ||
-        algorithm_config.algorithm() == kDefaultBlasGemm ||
-        algorithm_config.algorithm() == kDefaultBlasGemv) {
-      if (algorithm_config.algorithm() == kDefaultBlasGemv) {
-        // This is a matrix*vector multiply so use GEMV to compute A * b.
-        // Here we are multiplying in the natural order, so we have to flip
-        // the transposition flag to compensate for the tensor being stored
-        // row-major.
-        // TODO(yangzihao): Add Gemv as an autotuning option too.
-        LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a,
-                                   transpose_a ? m : k, transpose_a ? k : m,
-                                   a_ptr, b_ptr, &c_ptr, nullptr);
-      } else {
-        // Use C' = B' x A' (' stands for transpose)
-        bool blas_launch_status =
-            stream
-                ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k,
-                               1.0f, b_ptr, transpose_b ? k : n, a_ptr,
-                               transpose_a ? m : k, 0.0f, &c_ptr, n)
-                .ok();
-        if (!blas_launch_status) {
-          ctx->SetStatus(errors::Internal(
-              "Blas GEMM launch failed : a.shape=(", a.dim_size(0), ", ",
-              a.dim_size(1), "), b.shape=(", b.dim_size(0), ", ", b.dim_size(1),
-              "), m=", m, ", n=", n, ", k=", k));
-        }
+    auto a_ptr = AsDeviceMemory(a.template flat<T>().data());
+    auto b_ptr = AsDeviceMemory(b.template flat<T>().data());
+    auto c_ptr = AsDeviceMemory(out->template flat<T>().data());
+    // Cublas does
+    // C = A x B
+    // where A, B and C are assumed to be in column major.
+    // We want the output to be in row-major, so we can compute
+    // C' = B' x A' (' stands for transpose)
+    if (LaunchBlasGemv<T>::IsSupported() && n == 1) {
+      // This is a matrix*vector multiply so use GEMV to compute A * b.
+      // Here we are multiplying in the natural order, so we have to flip
+      // the transposition flag to compensate for the tensor being stored
+      // row-major.
+      LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a, transpose_a ? m : k,
+                                 transpose_a ? k : m, a_ptr, b_ptr, &c_ptr);
+    } else {
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k, 1.0f,
+                             b_ptr, transpose_b ? k : n, a_ptr,
+                             transpose_a ? m : k, 0.0f, &c_ptr, n)
+              .ok();
+      if (!blas_launch_status) {
+        ctx->SetStatus(errors::Internal(
+            "Blas GEMM launch failed : a.shape=(", a.dim_size(0), ", ",
+            a.dim_size(1), "), b.shape=(", b.dim_size(0), ", ", b.dim_size(1),
+            "), m=", m, ", n=", n, ", k=", k));
       }
     }
   }
-
-  static void GetBlasGemmAlgorithm(OpKernelConstruction* ctx,
-                                   std::vector<int64>* algorithms,
-                                   bool* algorithm_set_flag) {
-    if (*algorithm_set_flag == false) {
-      auto* stream = ctx->device()->tensorflow_gpu_device_info()->stream;
-      stream->parent()->GetBlasGemmAlgorithms(algorithms);
-      *algorithm_set_flag = true;
-    }
-  }
 };
 
 #endif  // GOOGLE_CUDA
@@ -432,14 +257,9 @@ struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
 template <typename Device, typename T, bool USE_CUBLAS>
 class MatMulOp : public OpKernel {
  public:
-  explicit MatMulOp(OpKernelConstruction* ctx)
-      : OpKernel(ctx), algorithms_set_already_(false) {
+  explicit MatMulOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_a", &transpose_a_));
     OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_b", &transpose_b_));
-
-    LaunchMatMul<Device, T, USE_CUBLAS>::GetBlasGemmAlgorithm(
-        ctx, &algorithms_, &algorithms_set_already_);
-    use_autotune_ = MatmulAutotuneEnable();
   }
 
   void Compute(OpKernelContext* ctx) override {
@@ -482,14 +302,10 @@ class MatMulOp : public OpKernel {
       return;
     }
 
-    LaunchMatMul<Device, T, USE_CUBLAS>::launch(
-        ctx, a, b, dim_pair, &algorithms_, use_autotune_, out);
+    LaunchMatMul<Device, T, USE_CUBLAS>::launch(ctx, this, a, b, dim_pair, out);
   }
 
  private:
-  std::vector<int64> algorithms_;
-  bool algorithms_set_already_;
-  bool use_autotune_;
   bool transpose_a_;
   bool transpose_b_;
 };
diff --git a/tensorflow/core/kernels/matmul_op.h b/tensorflow/core/kernels/matmul_op.h
index 6398da2fb9..5a8db6da19 100644
--- a/tensorflow/core/kernels/matmul_op.h
+++ b/tensorflow/core/kernels/matmul_op.h
@@ -17,9 +17,7 @@ limitations under the License.
 #define TENSORFLOW_KERNELS_MATMUL_OP_H_
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_types.h"
-#include "tensorflow/core/lib/hash/hash.h"
 
 namespace tensorflow {
 namespace functor {
@@ -52,68 +50,6 @@ struct MatMulFunctor {
 };
 
 }  // end namespace functor
-
-#if GOOGLE_CUDA
-// Encapsulate all the shape information that is used in matmul operations.
-class MatmulParameters {
- public:
-  MatmulParameters(bool transa, bool transb, uint64 m, uint64 n, uint64 k,
-                   DataType dtype, int device_id)
-      : transa_(transa),
-        transb_(transb),
-        m_(m),
-        n_(n),
-        k_(k),
-        dtype_(dtype),
-        device_id_(device_id) {
-    hash_code_ = transa;
-    hash_code_ = Hash64Combine(hash_code_, transb);
-    hash_code_ = Hash64Combine(hash_code_, m);
-    hash_code_ = Hash64Combine(hash_code_, n);
-    hash_code_ = Hash64Combine(hash_code_, k);
-    hash_code_ = Hash64Combine(hash_code_, dtype);
-    hash_code_ = Hash64Combine(hash_code_, device_id);
-  }
-  bool operator==(const MatmulParameters& other) const {
-    return this->get_data_as_tuple() == other.get_data_as_tuple();
-  }
-
-  bool operator!=(const MatmulParameters& other) const {
-    return !(*this == other);
-  }
-  uint64 hash() const { return hash_code_; }
-
-  string ToString() const {
-    // clang-format off
-    return strings::StrCat(
-        transa_, ", ", transb_, ", ",
-        m_, ", ", n_, ", ", k_,
-        dtype_, ", ", device_id_);
-    // clang-format on
-  }
-
- private:
-  typedef std::tuple<bool, bool, int64, int64, int64, DataType, int>
-      ParameterDataType;
-
-  ParameterDataType get_data_as_tuple() const {
-    return std::make_tuple(transa_, transb_, m_, n_, k_, dtype_, device_id_);
-  }
-
-  bool transa_;
-  bool transb_;
-  uint64 m_;
-  uint64 n_;
-  uint64 k_;
-  DataType dtype_;
-  int device_id_;
-  uint64 hash_code_;
-};
-
-typedef Eigen::GpuDevice GPUDevice;
-
-#endif  // GOOGLE_CUDA
-
 }  // end namespace tensorflow
 
 #endif  // TENSORFLOW_KERNELS_MATMUL_OP_H_
diff --git a/tensorflow/core/util/matmul_autotune.cc b/tensorflow/core/util/matmul_autotune.cc
deleted file mode 100644
index 741a78a193..0000000000
--- a/tensorflow/core/util/matmul_autotune.cc
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/core/util/matmul_autotune.h"
-
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/lib/core/stringpiece.h"
-#include "tensorflow/core/util/env_var.h"
-
-namespace tensorflow {
-bool MatmulAutotuneEnable() {
-  bool value;
-  Status status =
-      ReadBoolFromEnvVar("TF_MATMUL_AUTOTUNE_ENABLE", false, &value);
-  if (!status.ok()) {
-    LOG(ERROR) << status.error_message();
-  }
-  return value;
-}
-
-bool MatmulDoFP32ComputationFP16Input() {
-  bool value;
-  // Feedback from NVIDIA: the "true floating point 16" compute capability is
-  // absent from compute capability SM 5.2. The native 16 bit floating point
-  // computation was introduced in SM 5.3 and higher compute capability. So
-  // for compatibility, set this to be true by default for now.
-  // TODO(yangzihao): In the future, we need to return three possibilities:
-  // user-set-true, user-set-false, user-no-setting. In the calling sites,
-  // check the compatibilities. Note that user-set-false with compute
-  // capability <= 5.2 will cause an error in the later cublasGemmEx() call.
-  Status status =
-      ReadBoolFromEnvVar("TF_FP16_MATMUL_USE_FP32_COMPUTE", true, &value);
-  if (!status.ok()) {
-    LOG(ERROR) << status.error_message();
-  }
-  return value;
-}
-
-}  // namespace tensorflow
diff --git a/tensorflow/core/util/matmul_autotune.h b/tensorflow/core/util/matmul_autotune.h
deleted file mode 100644
index 5366623883..0000000000
--- a/tensorflow/core/util/matmul_autotune.h
+++ /dev/null
@@ -1,28 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// The utility to check matmul autotune related flags.
-
-#ifndef THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
-#define THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
-
-namespace tensorflow {
-
-bool MatmulAutotuneEnable();
-bool MatmulDoFP32ComputationFP16Input();
-
-}  // namespace tensorflow
-
-#endif  // THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 29d351304c..7b7eb72994 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3850,48 +3850,6 @@ cuda_py_test(
     main = "ops/transpose_benchmark.py",
 )
 
-cuda_py_test(
-    name = "matmul_benchmark",
-    size = "medium",
-    srcs = ["ops/matmul_benchmark.py"],
-    additional_deps = [
-        ":math_ops",
-        ":random_ops",
-        ":client",
-        ":client_testlib",
-        ":control_flow_ops",
-        ":framework_for_generated_wrappers",
-        ":framework_test_lib",
-        ":platform",
-        ":platform_benchmark",
-        ":variables",
-        "//third_party/py/numpy",
-        "//tensorflow/core:protos_all_py",
-    ],
-    main = "ops/matmul_benchmark.py",
-)
-
-cuda_py_test(
-    name = "matmul_benchmark_test",
-    size = "medium",
-    srcs = ["ops/matmul_benchmark_test.py"],
-    additional_deps = [
-        ":math_ops",
-        ":random_ops",
-        ":client",
-        ":client_testlib",
-        ":control_flow_ops",
-        ":framework_for_generated_wrappers",
-        ":platform",
-        ":platform_benchmark",
-        ":matmul_benchmark",
-        ":variables",
-        "//third_party/py/numpy",
-        "//tensorflow/core:protos_all_py",
-    ],
-    main = "ops/matmul_benchmark_test.py",
-)
-
 cuda_py_test(
     name = "session_benchmark",
     srcs = ["client/session_benchmark.py"],
diff --git a/tensorflow/python/kernel_tests/matmul_op_test.py b/tensorflow/python/kernel_tests/matmul_op_test.py
index b167278984..042f462357 100644
--- a/tensorflow/python/kernel_tests/matmul_op_test.py
+++ b/tensorflow/python/kernel_tests/matmul_op_test.py
@@ -31,9 +31,6 @@ from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test as test_lib
 
-# TODO(yangzihao): Currently matmul autotuning is disabled by default. Use
-# os.environ["TF_MATMUL_AUTOTUNE_ENABLE"] = "1" to enable it.
-
 
 def _AddTest(test, op_name, testcase_name, fn):
   test_name = "_".join(["test", op_name, testcase_name])
diff --git a/tensorflow/python/ops/matmul_benchmark.py b/tensorflow/python/ops/matmul_benchmark.py
deleted file mode 100644
index 55c575162a..0000000000
--- a/tensorflow/python/ops/matmul_benchmark.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Benchmark for Matmul operator."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import itertools
-import time
-
-import numpy as np
-
-from tensorflow.python.client import session as session_lib
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import variables
-from tensorflow.python.platform import test
-
-
-def build_graph(device, n, m, k, transpose_a, transpose_b, dtype):
-  """Build a graph containing a sequence of matmul operations.
-
-  Args:
-    device: String, the device to run on.
-    n: tensor A's first dimension size.
-    m: tensor A's second dimension size.
-    k: tensor B's second dimension size.
-    transpose_a: boolean value to show if tensor A is transposed.
-    transpose_b: boolean value to show if tensor B is transposed.
-    dtype: numpy data type of the input tensor.
-
-  Returns:
-    A matmul operation to run()
-  """
-  with ops.device('/%s:0' % device):
-    if not transpose_a:
-      x = variables.Variable(random_ops.random_uniform([n, m], dtype=dtype))
-    else:
-      x = variables.Variable(random_ops.random_uniform([m, n], dtype=dtype))
-    if not transpose_b:
-      y = variables.Variable(random_ops.random_uniform([m, k], dtype=dtype))
-    else:
-      y = variables.Variable(random_ops.random_uniform([k, m], dtype=dtype))
-
-    z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b)
-    return control_flow_ops.group(z)
-
-
-class MatmulBenchmark(test.Benchmark):
-  """Benchmark matmul!"""
-
-  def run_graph(self, device, n, m, k, transpose_a, transpose_b, num_iters,
-                dtype):
-    """Run the graph and print its execution time.
-
-    Args:
-      device: String, the device to run on.
-      n: tensor A's first dimension size.
-      m: tensor A's second dimension size.
-      k: tensor B's second dimension size.
-      transpose_a: boolean value to show if tensor A is transposed.
-      transpose_b: boolean value to show if tensor B is transposed.
-      num_iters: number of iterations to run the benchmark.
-      dtype: numpy data type of the input tensor.
-
-    Returns:
-      The duration of the run in seconds.
-    """
-    graph = ops.Graph()
-    with graph.as_default():
-      output = build_graph(device, n, m, k, transpose_a, transpose_b, dtype)
-      with session_lib.Session(graph=graph) as session:
-        variables.global_variables_initializer().run()
-        for _ in range(500):
-          session.run(output)
-        start_time = time.time()
-        for _ in range(num_iters):
-          session.run(output)
-        duration = (time.time() - start_time)
-        num_items = n * m * k * 2
-        throughput = num_items * num_iters / duration / 1e9
-        print('%s %s input_info:%s %d %.4fsec, %.4fGitems/s.' %
-              (device, str(dtype), str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:'
-               + str(transpose_a) + '.tb:' + str(transpose_b), num_iters,
-               duration, throughput))
-
-    name_template = ('matmul_{device}_{dtype}_input_info_{inputinfo}')
-
-    self.report_benchmark(
-        name=name_template.format(
-            device=device,
-            dtype=str(dtype).replace(' ', ''),
-            inputinfo=str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:' +
-            str(transpose_a) + '.tb:' + str(transpose_b)).replace(' ', ''),
-        iters=num_iters,
-        wall_time=duration)
-    return duration
-
-  def run_test_gpu(self, n, m, k, transpose_a, transpose_b, dtype, num_iters):
-    self.run_graph('gpu', n, m, k, transpose_a, transpose_b, num_iters, dtype)
-
-  def test_round(self, num_iters):
-    dtypes = [np.float32, np.float64]
-    for dtype in dtypes:
-      for n, m, (transpose_a, transpose_b) in itertools.product(
-          [512, 1024], [1, 8, 16, 128], [(False, False), (True, False),
-                                         (False, True)]):
-        k = n
-        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
-
-      for n, m, k, (transpose_a, transpose_b) in itertools.product(
-          [200], [1, 8, 20], [10000], [(False, False), (True, False), (False,
-                                                                       True)]):
-        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
-
-      for (n, m, k), (transpose_a, transpose_b) in itertools.product(
-          [(200, 20, 20000), (1, 10000, 200)], [(False, False), (True, False),
-                                                (False, True)]):
-        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
-
-  def benchmark_matmul(self):
-    num_iters = 200
-    for _ in range(10):
-      self.test_round(num_iters)
-
-
-if __name__ == '__main__':
-  test.main()
diff --git a/tensorflow/python/ops/matmul_benchmark_test.py b/tensorflow/python/ops/matmul_benchmark_test.py
deleted file mode 100644
index a7914dba78..0000000000
--- a/tensorflow/python/ops/matmul_benchmark_test.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for matmul_benchmark.py."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import itertools
-import numpy as np
-
-from tensorflow.core.framework import graph_pb2
-from tensorflow.core.framework import node_def_pb2
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import matmul_benchmark
-from tensorflow.python.platform import test as googletest
-from tensorflow.python.platform import tf_logging
-
-
-def BuildGraphTest(n, m, k, transpose_a, transpose_b, dtype):
-
-  def Test(self):
-    if not googletest.is_gpu_available():
-      tf_logging.info("Skipping BuildGraphTest %s", (n, m, k, transpose_a,
-                                                     transpose_b))
-      return
-    tf_logging.info("Testing BuildGraphTest %s", (n, m, k, transpose_a,
-                                                  transpose_b))
-    self._VerifyBuildGraph(n, m, k, transpose_a, transpose_b, dtype)
-
-  return Test
-
-
-def RunGraphTest(n, m, k, transpose_a, transpose_b, dtype):
-
-  def Test(self):
-    if not googletest.is_gpu_available():
-      tf_logging.info("Skipping RunGraphTest %s", (n, m, k, transpose_a,
-                                                   transpose_b))
-      return
-    tf_logging.info("Testing RunGraphTest %s", (n, m, k, transpose_a,
-                                                transpose_b))
-    self._VerifyRunGraph(n, m, k, transpose_a, transpose_b, dtype)
-
-  return Test
-
-
-class MatmulBenchmarkTest(googletest.TestCase):
-
-  def _StripNode(self, nd):
-    snode = node_def_pb2.NodeDef(name=nd.name, op=nd.op, input=nd.input)
-    if nd.device:
-      snode.device = nd.device
-    return snode
-
-  def _StripGraph(self, gd):
-    return graph_pb2.GraphDef(node=[self._StripNode(nd) for nd in gd.node])
-
-  def _VerifyBuildGraph(self, n, m, k, transpose_a, transpose_b, dtype):
-    graph = ops.Graph()
-    with graph.as_default():
-      matmul_benchmark.build_graph("gpu", n, m, k, transpose_a, transpose_b,
-                                   dtype)
-      gd = graph.as_graph_def()
-      self.assertProtoEquals("""
-      node { name: "random_uniform/shape" op: "Const" device: "/device:GPU:0" }
-      node { name: "random_uniform/min" op: "Const" device: "/device:GPU:0" }
-      node { name: "random_uniform/max" op: "Const" device: "/device:GPU:0" }
-      node { name: "random_uniform/RandomUniform" op: "RandomUniform" input: "random_uniform/shape" device: "/device:GPU:0" }
-      node { name: "random_uniform/sub" op: "Sub" input: "random_uniform/max" input: "random_uniform/min" device: "/device:GPU:0" }
-      node { name: "random_uniform/mul" op: "Mul" input: "random_uniform/RandomUniform" input: "random_uniform/sub" device: "/device:GPU:0" }
-      node { name: "random_uniform" op: "Add" input: "random_uniform/mul" input: "random_uniform/min" device: "/device:GPU:0" }
-      node { name: "Variable" op: "VariableV2" device: "/device:GPU:0" }
-      node { name: "Variable/Assign" op: "Assign" input: "Variable" input: "random_uniform" device: "/device:GPU:0" }
-      node { name: "Variable/read" op: "Identity" input: "Variable" device: "/device:GPU:0" }
-      node { name: "random_uniform_1/shape" op: "Const" device: "/device:GPU:0" }
-      node { name: "random_uniform_1/min" op: "Const" device: "/device:GPU:0" }
-      node { name: "random_uniform_1/max" op: "Const" device: "/device:GPU:0" }
-      node { name: "random_uniform_1/RandomUniform" op: "RandomUniform" input: "random_uniform_1/shape" device: "/device:GPU:0" }
-      node { name: "random_uniform_1/sub" op: "Sub" input: "random_uniform_1/max" input: "random_uniform_1/min" device: "/device:GPU:0" }
-      node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: "/device:GPU:0" }
-      node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: "/device:GPU:0" }
-      node { name: "Variable_1" op: "VariableV2" device: "/device:GPU:0" }
-      node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: "/device:GPU:0" }
-      node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: "/device:GPU:0" }
-      node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: "/device:GPU:0" }
-      node { name: "group_deps" op: "NoOp" input: "^MatMul" device: "/device:GPU:0" }
-                             """, self._StripGraph(gd))
-
-  def _VerifyRunGraph(self, n, m, k, transpose_a, transpose_b, dtype):
-    benchmark_instance = matmul_benchmark.MatmulBenchmark()
-    duration = benchmark_instance.run_graph("gpu", n, m, k, transpose_a,
-                                            transpose_b, 1, dtype)
-    self.assertTrue(duration > 1e-6)
-
-
-if __name__ == "__main__":
-  dtypes = [np.float32, np.float64]
-  index = 0
-  for _dtype in dtypes:
-    for _n, _m, (_transpose_a, _transpose_b) in itertools.product(
-        [512, 1024], [1, 8, 16, 128], [(False, False), (True, False), (False,
-                                                                       True)]):
-      _k = _n
-      setattr(MatmulBenchmarkTest, "testBuildGraph_" + str(index),
-              BuildGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
-      setattr(MatmulBenchmarkTest, "testRunGraph_" + str(index),
-              RunGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
-      index += 1
-  googletest.main()
diff --git a/tensorflow/stream_executor/blas.cc b/tensorflow/stream_executor/blas.cc
index da09d84921..a59a1dda71 100644
--- a/tensorflow/stream_executor/blas.cc
+++ b/tensorflow/stream_executor/blas.cc
@@ -67,10 +67,6 @@ string SideString(Side s) {
   }
 }
 
-// -- AlgorithmConfig
-
-string AlgorithmConfig::ToString() const { return port::StrCat(algorithm_); }
-
 string ComputationTypeString(ComputationType ty) {
   switch (ty) {
     case ComputationType::kF16:
diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h
index eb1b19c5d9..cfff3649c8 100644
--- a/tensorflow/stream_executor/blas.h
+++ b/tensorflow/stream_executor/blas.h
@@ -44,6 +44,7 @@ limitations under the License.
 #include "tensorflow/stream_executor/platform/port.h"
 
 #include "tensorflow/stream_executor/lib/array_slice.h"
+#include "tensorflow/stream_executor/platform/port.h"
 
 namespace Eigen {
 struct half;
@@ -107,10 +108,6 @@ string ComputationTypeString(ComputationType ty);
 // Opaque identifier for an "algorithm" used by a blas routine.  This functions
 // as a hint to the blas library.
 typedef int64 AlgorithmType;
-constexpr AlgorithmType kDefaultAlgorithm = -1;
-constexpr AlgorithmType kDefaultBlasGemm = -2;
-constexpr AlgorithmType kDefaultBlasGemv = -3;
-constexpr AlgorithmType kNoAlgorithm = -4;
 
 // blas uses -1 to represent the default algorithm. This happens to match up
 // with the CUBLAS_GEMM_DFALT constant, so cuda_blas.cc is using static_cast
@@ -137,28 +134,10 @@ class ProfileResult {
 
  private:
   bool is_valid_ = false;
-  AlgorithmType algorithm_ = kDefaultAlgorithm;
+  AlgorithmType algorithm_ = 0;
   float elapsed_time_in_ms_ = std::numeric_limits<float>::max();
 };
 
-class AlgorithmConfig {
- public:
-  AlgorithmConfig() : algorithm_(kDefaultAlgorithm) {}
-  explicit AlgorithmConfig(AlgorithmType algorithm) : algorithm_(algorithm) {}
-  AlgorithmType algorithm() const { return algorithm_; }
-  void set_algorithm(AlgorithmType val) { algorithm_ = val; }
-  bool operator==(const AlgorithmConfig &other) const {
-    return this->algorithm_ == other.algorithm_;
-  }
-  bool operator!=(const AlgorithmConfig &other) const {
-    return !(*this == other);
-  }
-  string ToString() const;
-
- private:
-  AlgorithmType algorithm_;
-};
-
 // BLAS support interface -- this can be derived from a GPU executor when the
 // underlying platform has an BLAS library implementation available. See
 // StreamExecutor::AsBlas().
@@ -474,29 +453,6 @@ class BlasSupport {
                           std::complex<double> beta,
                           DeviceMemory<std::complex<double>> *y, int incy) = 0;
 
-  virtual bool DoBlasGemvWithProfiling(
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,
-      const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
-      int incx, float beta, DeviceMemory<float> *y, int incy,
-      ProfileResult *output_profile_result) = 0;
-  virtual bool DoBlasGemvWithProfiling(
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha,
-      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
-      int incx, double beta, DeviceMemory<double> *y, int incy,
-      ProfileResult *output_profile_result) = 0;
-  virtual bool DoBlasGemvWithProfiling(
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
-      std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,
-      int lda, const DeviceMemory<std::complex<float>> &x, int incx,
-      std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
-      ProfileResult *output_profile_result) = 0;
-  virtual bool DoBlasGemvWithProfiling(
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
-      std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a,
-      int lda, const DeviceMemory<std::complex<double>> &x, int incx,
-      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,
-      int incy, ProfileResult *output_profile_result) = 0;
-
   // Performs a rank-1 update of a general matrix.
   //
   //     a <- alpha * x * y' + a,
@@ -979,39 +935,8 @@ class BlasSupport {
                           std::complex<double> beta,
                           DeviceMemory<std::complex<double>> *c, int ldc) = 0;
 
-  virtual bool DoBlasGemmWithProfiling(
-      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-      uint64 n, uint64 k, float alpha, const DeviceMemory<Eigen::half> &a,
-      int lda, const DeviceMemory<Eigen::half> &b, int ldb, float beta,
-      DeviceMemory<Eigen::half> *c, int ldc,
-      ProfileResult *output_profile_result) = 0;
-  virtual bool DoBlasGemmWithProfiling(
-      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-      uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
-      const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
-      int ldc, ProfileResult *output_profile_result) = 0;
-  virtual bool DoBlasGemmWithProfiling(
-      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-      uint64 n, uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
-      const DeviceMemory<double> &b, int ldb, double beta,
-      DeviceMemory<double> *c, int ldc,
-      ProfileResult *output_profile_result) = 0;
-  virtual bool DoBlasGemmWithProfiling(
-      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-      uint64 n, uint64 k, std::complex<float> alpha,
-      const DeviceMemory<std::complex<float>> &a, int lda,
-      const DeviceMemory<std::complex<float>> &b, int ldb,
-      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
-      ProfileResult *output_profile_result) = 0;
-  virtual bool DoBlasGemmWithProfiling(
-      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-      uint64 n, uint64 k, std::complex<double> alpha,
-      const DeviceMemory<std::complex<double>> &a, int lda,
-      const DeviceMemory<std::complex<double>> &b, int ldb,
-      std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
-      ProfileResult *output_profile_result) = 0;
-
-  // Gets a list of supported algorithms for DoBlasGemmWithAlgorithm.
+  // Gets a list of supported algorithms for DoBlasGemmWithAlgorithm.  Note that
+  // any or all of these algorithms may still be
   virtual bool GetBlasGemmAlgorithms(
       std::vector<AlgorithmType> *out_algorithms) = 0;
 
@@ -1548,28 +1473,6 @@ class BlasSupport {
                   const DeviceMemory<std::complex<double>> &x, int incx,       \
                   std::complex<double> beta,                                   \
                   DeviceMemory<std::complex<double>> *y, int incy) override;   \
-  bool DoBlasGemvWithProfiling(                                                \
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,  \
-      const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,     \
-      int incx, float beta, DeviceMemory<float> *y, int incy,                  \
-      blas::ProfileResult *output_profile_result) override;                    \
-  bool DoBlasGemvWithProfiling(                                                \
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha, \
-      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,   \
-      int incx, double beta, DeviceMemory<double> *y, int incy,                \
-      blas::ProfileResult *output_profile_result) override;                    \
-  bool DoBlasGemvWithProfiling(                                                \
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,               \
-      std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,   \
-      int lda, const DeviceMemory<std::complex<float>> &x, int incx,           \
-      std::complex<float> beta, DeviceMemory<std::complex<float>> *y,          \
-      int incy, blas::ProfileResult *output_profile_result) override;          \
-  bool DoBlasGemvWithProfiling(                                                \
-      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,               \
-      std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a, \
-      int lda, const DeviceMemory<std::complex<double>> &x, int incx,          \
-      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,        \
-      int incy, blas::ProfileResult *output_profile_result) override;          \
   bool DoBlasGer(Stream *stream, uint64 m, uint64 n, float alpha,              \
                  const DeviceMemory<float> &x, int incx,                       \
                  const DeviceMemory<float> &y, int incy,                       \
@@ -1848,39 +1751,6 @@ class BlasSupport {
                   const DeviceMemory<std::complex<double>> &b, int ldb,        \
                   std::complex<double> beta,                                   \
                   DeviceMemory<std::complex<double>> *c, int ldc) override;    \
-  bool DoBlasGemmWithProfiling(                                                \
-      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
-      uint64 m, uint64 n, uint64 k, float alpha,                               \
-      const DeviceMemory<Eigen::half> &a, int lda,                             \
-      const DeviceMemory<Eigen::half> &b, int ldb, float beta,                 \
-      DeviceMemory<Eigen::half> *c, int ldc,                                   \
-      blas::ProfileResult *output_profile_result) override;                    \
-  bool DoBlasGemmWithProfiling(                                                \
-      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
-      uint64 m, uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, \
-      int lda, const DeviceMemory<float> &b, int ldb, float beta,              \
-      DeviceMemory<float> *c, int ldc,                                         \
-      blas::ProfileResult *output_profile_result) override;                    \
-  bool DoBlasGemmWithProfiling(                                                \
-      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
-      uint64 m, uint64 n, uint64 k, double alpha,                              \
-      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &b,   \
-      int ldb, double beta, DeviceMemory<double> *c, int ldc,                  \
-      blas::ProfileResult *output_profile_result) override;                    \
-  bool DoBlasGemmWithProfiling(                                                \
-      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
-      uint64 m, uint64 n, uint64 k, std::complex<float> alpha,                 \
-      const DeviceMemory<std::complex<float>> &a, int lda,                     \
-      const DeviceMemory<std::complex<float>> &b, int ldb,                     \
-      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc, \
-      blas::ProfileResult *output_profile_result) override;                    \
-  bool DoBlasGemmWithProfiling(                                                \
-      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
-      uint64 m, uint64 n, uint64 k, std::complex<double> alpha,                \
-      const DeviceMemory<std::complex<double>> &a, int lda,                    \
-      const DeviceMemory<std::complex<double>> &b, int ldb,                    \
-      std::complex<double> beta, DeviceMemory<std::complex<double>> *c,        \
-      int ldc, blas::ProfileResult *output_profile_result) override;           \
   bool GetBlasGemmAlgorithms(std::vector<blas::AlgorithmType> *out_algorithms) \
       override;                                                                \
   bool DoBlasGemmWithAlgorithm(                                                \
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 9070766166..2817364e97 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -1857,180 +1857,6 @@ bool CUDABlas::DoBlasGemm(Stream *stream, blas::Transpose transa,
       CUDAComplex(CUDAMemoryMutable(c)), ldc);
 }
 
-bool CUDABlas::DoBlasGemvWithProfiling(
-    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,
-    const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
-    int incx, float beta, DeviceMemory<float> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
-                                     incx, beta, y, incy,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemvWithProfiling(
-    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha,
-    const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
-    int incx, double beta, DeviceMemory<double> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
-                                     incx, beta, y, incy,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemvWithProfiling(
-    Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
-    std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,
-    int lda, const DeviceMemory<std::complex<float>> &x, int incx,
-    std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
-                                     incx, beta, y, incy,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemvWithProfiling(
-    Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
-    std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a,
-    int lda, const DeviceMemory<std::complex<double>> &x, int incx,
-    std::complex<double> beta, DeviceMemory<std::complex<double>> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
-                                     incx, beta, y, incy,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemmWithProfiling(
-    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-    uint64 n, uint64 k, float alpha, const DeviceMemory<Eigen::half> &a,
-    int lda, const DeviceMemory<Eigen::half> &b, int ldb, float beta,
-    DeviceMemory<Eigen::half> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
-                                     lda, b, ldb, beta, c, ldc,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemmWithProfiling(
-    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-    uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
-    const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
-    int ldc, blas::ProfileResult *output_profile_result) {
-  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
-                                     lda, b, ldb, beta, c, ldc,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemmWithProfiling(
-    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-    uint64 n, uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
-    const DeviceMemory<double> &b, int ldb, double beta,
-    DeviceMemory<double> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
-                                     lda, b, ldb, beta, c, ldc,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemmWithProfiling(
-    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-    uint64 n, uint64 k, std::complex<float> alpha,
-    const DeviceMemory<std::complex<float>> &a, int lda,
-    const DeviceMemory<std::complex<float>> &b, int ldb,
-    std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
-                                     lda, b, ldb, beta, c, ldc,
-                                     output_profile_result);
-}
-
-bool CUDABlas::DoBlasGemmWithProfiling(
-    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-    uint64 n, uint64 k, std::complex<double> alpha,
-    const DeviceMemory<std::complex<double>> &a, int lda,
-    const DeviceMemory<std::complex<double>> &b, int ldb,
-    std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
-                                     lda, b, ldb, beta, c, ldc,
-                                     output_profile_result);
-}
-
-template <typename T>
-bool CUDABlas::DoBlasGemvWithProfilingImpl(
-    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, const T &alpha,
-    const DeviceMemory<T> &a, int lda, const DeviceMemory<T> &x, int incx,
-    const T &beta, DeviceMemory<T> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  struct TimerDeleter {
-    void operator()(CUDATimer *t) {
-      t->Destroy();
-      delete t;
-    }
-  };
-  std::unique_ptr<CUDATimer, TimerDeleter> timer;
-  if (output_profile_result != nullptr) {
-    timer.reset(new CUDATimer(parent_));
-    if (!timer->Init() || !timer->Start(AsCUDAStream(stream))) {
-      return false;
-    }
-  }
-
-  // Call blasGemm
-  bool result =
-      DoBlasGemv(stream, trans, m, n, alpha, a, lda, x, incx, beta, y, incy);
-
-  if (timer != nullptr && result) {
-    // CUDATimer will CHECK-fail if we Stop() it while the stream is in an error
-    // state.
-    if (!timer->Stop(AsCUDAStream(stream))) {
-      return false;
-    }
-    output_profile_result->set_is_valid(true);
-    output_profile_result->set_algorithm(blas::kDefaultBlasGemv);
-    output_profile_result->set_elapsed_time_in_ms(
-        timer->GetElapsedMilliseconds());
-  }
-  return result;
-}
-
-template <typename T, typename ParamType>
-bool CUDABlas::DoBlasGemmWithProfilingImpl(
-    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-    uint64 n, uint64 k, const ParamType &alpha, const DeviceMemory<T> &a,
-    int lda, const DeviceMemory<T> &b, int ldb, const ParamType &beta,
-    DeviceMemory<T> *c, int ldc, blas::ProfileResult *output_profile_result) {
-  struct TimerDeleter {
-    void operator()(CUDATimer *t) {
-      t->Destroy();
-      delete t;
-    }
-  };
-  std::unique_ptr<CUDATimer, TimerDeleter> timer;
-  if (output_profile_result != nullptr) {
-    timer.reset(new CUDATimer(parent_));
-    if (!timer->Init() || !timer->Start(AsCUDAStream(stream))) {
-      return false;
-    }
-  }
-
-  // Call blasGemm
-  bool result = DoBlasGemm(stream, transa, transb, m, n, k, alpha, a, lda, b,
-                           ldb, beta, c, ldc);
-
-  if (timer != nullptr && result) {
-    // CUDATimer will CHECK-fail if we Stop() it while the stream is in an error
-    // state.
-    if (!timer->Stop(AsCUDAStream(stream))) {
-      return false;
-    }
-    output_profile_result->set_is_valid(true);
-    output_profile_result->set_algorithm(blas::kDefaultBlasGemm);
-    output_profile_result->set_elapsed_time_in_ms(
-        timer->GetElapsedMilliseconds());
-  }
-  return result;
-}
-
 template <typename InT, typename OutT, typename CompT>
 bool CUDABlas::DoBlasGemmWithAlgorithmImpl(
     Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
@@ -2094,9 +1920,6 @@ bool CUDABlas::GetBlasGemmAlgorithms(
     std::vector<blas::AlgorithmType> *out_algorithms) {
 // cublasGemmAlgo_t (and the function that accepts this type, cublasGemmEx)
 // were first introduced in CUDA 8.
-// Note that when CUDA version and compute capability is not sufficient, we
-// still return the out_algorithms. Caller needs to make sure that in this case,
-// the returned vector is empty.
 #if CUDA_VERSION >= 8000
   for (cublasGemmAlgo_t algo :
        {CUBLAS_GEMM_DFALT, CUBLAS_GEMM_ALGO0, CUBLAS_GEMM_ALGO1,
@@ -2104,10 +1927,8 @@ bool CUDABlas::GetBlasGemmAlgorithms(
         CUBLAS_GEMM_ALGO5, CUBLAS_GEMM_ALGO6, CUBLAS_GEMM_ALGO7}) {
     out_algorithms->push_back(algo);
   }
-  return true;
-#else
-  return false;
 #endif
+  return true;
 }
 
 bool CUDABlas::DoBlasGemmWithAlgorithm(
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.h b/tensorflow/stream_executor/cuda/cuda_blas.h
index 80cda97117..4a8641b300 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.h
+++ b/tensorflow/stream_executor/cuda/cuda_blas.h
@@ -127,23 +127,6 @@ class CUDABlas : public blas::BlasSupport {
       blas::AlgorithmType algorithm,
       blas::ProfileResult *output_profile_result);
 
-  // Helper function for implementing DoBlasGemmWithProfiling.
-  template <typename T, typename ParamType>
-  bool DoBlasGemmWithProfilingImpl(
-      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
-      uint64 n, uint64 k, const ParamType &alpha, const DeviceMemory<T> &a,
-      int lda, const DeviceMemory<T> &b, int ldb, const ParamType &beta,
-      DeviceMemory<T> *c, int ldc, blas::ProfileResult *output_profile_result);
-
-  // Helper function for implementing DoBlasGemvWithProfiling.
-  template <typename T>
-  bool DoBlasGemvWithProfilingImpl(Stream *stream, blas::Transpose trans,
-                                   uint64 m, uint64 n, const T &alpha,
-                                   const DeviceMemory<T> &a, int lda,
-                                   const DeviceMemory<T> &x, int incx,
-                                   const T &beta, DeviceMemory<T> *y, int incy,
-                                   blas::ProfileResult *output_profile_result);
-
   // mutex that guards the cuBLAS handle for this device.
   mutex mu_;
 
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index c9b36ba7ab..5996195173 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -3458,184 +3458,6 @@ struct ThenBlasWithProfileImpl {
 };
 }  // anonymous namespace
 
-Stream &Stream::ThenBlasGemvWithProfiling(
-    blas::Transpose trans, uint64 m, uint64 n, float alpha,
-    const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
-    int incx, float beta, DeviceMemory<float> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
-            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
-            PARAM(incy));
-
-  ThenBlasWithProfileImpl<
-      blas::Transpose, uint64, uint64, float, const DeviceMemory<float> &, int,
-      const DeviceMemory<float> &, int, float, DeviceMemory<float> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
-              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemvWithProfiling(
-    blas::Transpose trans, uint64 m, uint64 n, double alpha,
-    const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
-    int incx, double beta, DeviceMemory<double> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
-            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
-            PARAM(incy));
-
-  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, double,
-                          const DeviceMemory<double> &, int,
-                          const DeviceMemory<double> &, int, double,
-                          DeviceMemory<double> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
-              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemvWithProfiling(
-    blas::Transpose trans, uint64 m, uint64 n, std::complex<float> alpha,
-    const DeviceMemory<std::complex<float>> &a, int lda,
-    const DeviceMemory<std::complex<float>> &x, int incx,
-    std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
-            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
-            PARAM(incy));
-
-  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, std::complex<float>,
-                          const DeviceMemory<std::complex<float>> &, int,
-                          const DeviceMemory<std::complex<float>> &, int,
-                          std::complex<float>,
-                          DeviceMemory<std::complex<float>> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
-              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemvWithProfiling(
-    blas::Transpose trans, uint64 m, uint64 n, std::complex<double> alpha,
-    const DeviceMemory<std::complex<double>> &a, int lda,
-    const DeviceMemory<std::complex<double>> &x, int incx,
-    std::complex<double> beta, DeviceMemory<std::complex<double>> *y, int incy,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
-            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
-            PARAM(incy));
-
-  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, std::complex<double>,
-                          const DeviceMemory<std::complex<double>> &, int,
-                          const DeviceMemory<std::complex<double>> &, int,
-                          std::complex<double>,
-                          DeviceMemory<std::complex<double>> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
-              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemmWithProfiling(
-    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-    uint64 k, float alpha, const DeviceMemory<Eigen::half> &a, int lda,
-    const DeviceMemory<Eigen::half> &b, int ldb, float beta,
-    DeviceMemory<Eigen::half> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
-            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
-            PARAM(beta), PARAM(c), PARAM(ldc));
-
-  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
-                          uint64, float, const DeviceMemory<Eigen::half> &, int,
-                          const DeviceMemory<Eigen::half> &, int, float,
-                          DeviceMemory<Eigen::half> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
-              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-              output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemmWithProfiling(
-    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-    uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
-    const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
-    int ldc, blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
-            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
-            PARAM(beta), PARAM(c), PARAM(ldc));
-
-  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
-                          uint64, float, const DeviceMemory<float> &, int,
-                          const DeviceMemory<float> &, int, float,
-                          DeviceMemory<float> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
-              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-              output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemmWithProfiling(
-    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-    uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
-    const DeviceMemory<double> &b, int ldb, double beta,
-    DeviceMemory<double> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
-            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
-            PARAM(beta), PARAM(c), PARAM(ldc));
-
-  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
-                          uint64, double, const DeviceMemory<double> &, int,
-                          const DeviceMemory<double> &, int, double,
-                          DeviceMemory<double> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
-              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-              output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemmWithProfiling(
-    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-    uint64 k, std::complex<float> alpha,
-    const DeviceMemory<std::complex<float>> &a, int lda,
-    const DeviceMemory<std::complex<float>> &b, int ldb,
-    std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
-            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
-            PARAM(beta), PARAM(c), PARAM(ldc));
-
-  ThenBlasWithProfileImpl<
-      blas::Transpose, blas::Transpose, uint64, uint64, uint64,
-      std::complex<float>, const DeviceMemory<std::complex<float>> &, int,
-      const DeviceMemory<std::complex<float>> &, int, std::complex<float>,
-      DeviceMemory<std::complex<float>> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
-              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-              output_profile_result);
-}
-
-Stream &Stream::ThenBlasGemmWithProfiling(
-    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-    uint64 k, std::complex<double> alpha,
-    const DeviceMemory<std::complex<double>> &a, int lda,
-    const DeviceMemory<std::complex<double>> &b, int ldb,
-    std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
-    blas::ProfileResult *output_profile_result) {
-  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
-            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
-            PARAM(beta), PARAM(c), PARAM(ldc));
-
-  ThenBlasWithProfileImpl<
-      blas::Transpose, blas::Transpose, uint64, uint64, uint64,
-      std::complex<double>, const DeviceMemory<std::complex<double>> &, int,
-      const DeviceMemory<std::complex<double>> &, int, std::complex<double>,
-      DeviceMemory<std::complex<double>> *, int>
-      impl;
-  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
-              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-              output_profile_result);
-}
-
 Stream &Stream::ThenBlasGemmWithAlgorithm(
     blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
     uint64 k, const Eigen::half &alpha, const DeviceMemory<Eigen::half> &a,
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index e218873839..3c8b7ee894 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -934,31 +934,6 @@ class Stream {
                        std::complex<double> beta,
                        DeviceMemory<std::complex<double>> *y, int incy);
 
-  Stream &ThenBlasGemvWithProfiling(blas::Transpose trans, uint64 m, uint64 n,
-                                    float alpha, const DeviceMemory<float> &a,
-                                    int lda, const DeviceMemory<float> &x,
-                                    int incx, float beta,
-                                    DeviceMemory<float> *y, int incy,
-                                    blas::ProfileResult *output_profile_result);
-  Stream &ThenBlasGemvWithProfiling(blas::Transpose trans, uint64 m, uint64 n,
-                                    double alpha, const DeviceMemory<double> &a,
-                                    int lda, const DeviceMemory<double> &x,
-                                    int incx, double beta,
-                                    DeviceMemory<double> *y, int incy,
-                                    blas::ProfileResult *output_profile_result);
-  Stream &ThenBlasGemvWithProfiling(
-      blas::Transpose trans, uint64 m, uint64 n, std::complex<float> alpha,
-      const DeviceMemory<std::complex<float>> &a, int lda,
-      const DeviceMemory<std::complex<float>> &x, int incx,
-      std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
-      blas::ProfileResult *output_profile_result);
-  Stream &ThenBlasGemvWithProfiling(
-      blas::Transpose trans, uint64 m, uint64 n, std::complex<double> alpha,
-      const DeviceMemory<std::complex<double>> &a, int lda,
-      const DeviceMemory<std::complex<double>> &x, int incx,
-      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,
-      int incy, blas::ProfileResult *output_profile_result);
-
   // See BlasSupport::DoBlasGer.
   Stream &ThenBlasGer(uint64 m, uint64 n, float alpha,
                       const DeviceMemory<float> &x, int incx,
@@ -1274,44 +1249,6 @@ class Stream {
                        std::complex<double> beta,
                        DeviceMemory<std::complex<double>> *c, int ldc);
 
-  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
-                                    blas::Transpose transb, uint64 m, uint64 n,
-                                    uint64 k, float alpha,
-                                    const DeviceMemory<Eigen::half> &a, int lda,
-                                    const DeviceMemory<Eigen::half> &b, int ldb,
-                                    float beta, DeviceMemory<Eigen::half> *c,
-                                    int ldc,
-                                    blas::ProfileResult *output_profile_result);
-  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
-                                    blas::Transpose transb, uint64 m, uint64 n,
-                                    uint64 k, float alpha,
-                                    const DeviceMemory<float> &a, int lda,
-                                    const DeviceMemory<float> &b, int ldb,
-                                    float beta, DeviceMemory<float> *c, int ldc,
-                                    blas::ProfileResult *output_profile_result);
-  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
-                                    blas::Transpose transb, uint64 m, uint64 n,
-                                    uint64 k, double alpha,
-                                    const DeviceMemory<double> &a, int lda,
-                                    const DeviceMemory<double> &b, int ldb,
-                                    double beta, DeviceMemory<double> *c,
-                                    int ldc,
-                                    blas::ProfileResult *output_profile_result);
-  Stream &ThenBlasGemmWithProfiling(
-      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-      uint64 k, std::complex<float> alpha,
-      const DeviceMemory<std::complex<float>> &a, int lda,
-      const DeviceMemory<std::complex<float>> &b, int ldb,
-      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
-      blas::ProfileResult *output_profile_result);
-  Stream &ThenBlasGemmWithProfiling(
-      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-      uint64 k, std::complex<double> alpha,
-      const DeviceMemory<std::complex<double>> &a, int lda,
-      const DeviceMemory<std::complex<double>> &b, int ldb,
-      std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
-      blas::ProfileResult *output_profile_result);
-
   // See BlasSupport::DoBlasGemmWithAlgorithm.
   Stream &ThenBlasGemmWithAlgorithm(
       blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-- 
GitLab


From daa67ad176013aa31b30a40d30dc92efe9270b20 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <vomjom@vomjom.net>
Date: Tue, 18 Jul 2017 19:40:38 -0700
Subject: [PATCH 1103/1375] Remove unittest import (#11596)

---
 tensorflow/python/kernel_tests/sparse_ops_test.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index a57071e158..d500f94dc2 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -19,7 +19,6 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-import unittest
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -606,7 +605,6 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
     self._compare(sp_t, reduction_axes, ndims, True, False)
     self._compare(sp_t, reduction_axes, ndims, True, True)
 
-  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
@@ -646,7 +644,6 @@ class SparseReduceTest(test_util.TensorFlowTestCase):
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_max(sp_t, 2).eval()
 
-  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
     if np.__version__ == "1.13.0":
       self.skipTest("numpy 1.13.0 bug")
-- 
GitLab


From a1f497d05d0819a007d16d9c27ae8af8bab8a8b6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 20:50:19 -0700
Subject: [PATCH 1104/1375] No public change

PiperOrigin-RevId: 162441153
---
 tensorflow/core/BUILD                         | 25 +++++++++++++++++++
 .../core/platform/default/build_config.bzl    |  4 +++
 2 files changed, 29 insertions(+)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index bb17d8832f..3d367b1b9b 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -115,6 +115,7 @@ load(
     "tf_additional_gpu_tracer_srcs",
     "tf_additional_gpu_tracer_deps",
     "tf_additional_gpu_tracer_cuda_deps",
+    "tf_pyclif_proto_library",
 )
 load(
     "//tensorflow/core:platform/default/build_config_root.bzl",
@@ -1168,6 +1169,30 @@ cc_library(
     ],
 )
 
+# -----------------------------------------------------------------------------
+# Clif-related proto libraries.
+
+tf_pyclif_proto_library(
+    name = "example/example_pyclif",
+    proto_lib = ":protos_all",
+    proto_srcfile = "example/example.proto",
+    visibility = ["//visibility:public"],
+)
+
+tf_pyclif_proto_library(
+    name = "example/feature_pyclif",
+    proto_lib = ":protos_all",
+    proto_srcfile = "example/feature.proto",
+    visibility = ["//visibility:public"],
+)
+
+tf_pyclif_proto_library(
+    name = "framework/tensor_pyclif",
+    proto_lib = ":protos_all",
+    proto_srcfile = "framework/tensor.proto",
+    visibility = ["//visibility:public"],
+)
+
 # -----------------------------------------------------------------------------
 # Internal targets
 
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 4696e1c1d2..1e2d026a79 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -276,3 +276,7 @@ def tf_additional_mpi_lib_defines():
       "//tensorflow:with_mpi_support": ["TENSORFLOW_USE_MPI"],
       "//conditions:default": [],
   })
+
+def tf_pyclif_proto_library(name, proto_lib, proto_srcfile="", visibility=None,
+                            **kwargs):
+  pass
-- 
GitLab


From 28832c2f63774b77e15690bebef5ea47992f420f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 18 Jul 2017 21:00:42 -0700
Subject: [PATCH 1105/1375] Fix is_floating for tf.qint*, tf.quint*.

is_floating fails for tf.qint* because issubclass is called on tf.qint*, which is not a class.  Use numpy.issubdtype to perform the check instead.

PiperOrigin-RevId: 162441672
---
 tensorflow/python/framework/dtypes.py      |  6 +++---
 tensorflow/python/framework/dtypes_test.py | 24 ++++++++++++++++++++--
 2 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py
index 3e6c04982b..00bfae213a 100644
--- a/tensorflow/python/framework/dtypes.py
+++ b/tensorflow/python/framework/dtypes.py
@@ -135,13 +135,13 @@ class DType(object):
   def is_integer(self):
     """Returns whether this is a (non-quantized) integer type."""
     return (self.is_numpy_compatible and not self.is_quantized and
-            issubclass(self.as_numpy_dtype, np.integer))
+            np.issubdtype(self.as_numpy_dtype, np.integer))
 
   @property
   def is_floating(self):
     """Returns whether this is a (non-quantized, real) floating point type."""
-    return self.is_numpy_compatible and issubclass(self.as_numpy_dtype,
-                                                   np.floating)
+    return self.is_numpy_compatible and np.issubdtype(self.as_numpy_dtype,
+                                                      np.floating)
 
   @property
   def is_complex(self):
diff --git a/tensorflow/python/framework/dtypes_test.py b/tensorflow/python/framework/dtypes_test.py
index 5bb60763b6..d5a1e61a6f 100644
--- a/tensorflow/python/framework/dtypes_test.py
+++ b/tensorflow/python/framework/dtypes_test.py
@@ -154,6 +154,11 @@ class TypesTest(test_util.TensorFlowTestCase):
     self.assertEqual(dtypes.as_dtype("string").is_integer, False)
     self.assertEqual(dtypes.as_dtype("bool").is_integer, False)
     self.assertEqual(dtypes.as_dtype("bfloat16").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("qint8").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("qint16").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("qint32").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("quint8").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("quint16").is_integer, False)
 
   def testIsFloating(self):
     self.assertEqual(dtypes.as_dtype("int8").is_floating, False)
@@ -169,6 +174,11 @@ class TypesTest(test_util.TensorFlowTestCase):
     self.assertEqual(dtypes.as_dtype("string").is_floating, False)
     self.assertEqual(dtypes.as_dtype("bool").is_floating, False)
     self.assertEqual(dtypes.as_dtype("bfloat16").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("qint8").is_floating, False)
+    self.assertEqual(dtypes.as_dtype("qint16").is_floating, False)
+    self.assertEqual(dtypes.as_dtype("qint32").is_floating, False)
+    self.assertEqual(dtypes.as_dtype("quint8").is_floating, False)
+    self.assertEqual(dtypes.as_dtype("quint16").is_floating, False)
 
   def testIsComplex(self):
     self.assertEqual(dtypes.as_dtype("int8").is_complex, False)
@@ -183,7 +193,12 @@ class TypesTest(test_util.TensorFlowTestCase):
     self.assertEqual(dtypes.as_dtype("float64").is_complex, False)
     self.assertEqual(dtypes.as_dtype("string").is_complex, False)
     self.assertEqual(dtypes.as_dtype("bool").is_complex, False)
-    self.assertEqual(dtypes.as_dtype("bfloat16").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("bfloat16").is_complex, False)
+    self.assertEqual(dtypes.as_dtype("qint8").is_complex, False)
+    self.assertEqual(dtypes.as_dtype("qint16").is_complex, False)
+    self.assertEqual(dtypes.as_dtype("qint32").is_complex, False)
+    self.assertEqual(dtypes.as_dtype("quint8").is_complex, False)
+    self.assertEqual(dtypes.as_dtype("quint16").is_complex, False)
 
   def testIsUnsigned(self):
     self.assertEqual(dtypes.as_dtype("int8").is_unsigned, False)
@@ -198,7 +213,12 @@ class TypesTest(test_util.TensorFlowTestCase):
     self.assertEqual(dtypes.as_dtype("string").is_unsigned, False)
     self.assertEqual(dtypes.as_dtype("complex64").is_unsigned, False)
     self.assertEqual(dtypes.as_dtype("complex128").is_unsigned, False)
-    self.assertEqual(dtypes.as_dtype("bfloat16").is_integer, False)
+    self.assertEqual(dtypes.as_dtype("bfloat16").is_unsigned, False)
+    self.assertEqual(dtypes.as_dtype("qint8").is_unsigned, False)
+    self.assertEqual(dtypes.as_dtype("qint16").is_unsigned, False)
+    self.assertEqual(dtypes.as_dtype("qint32").is_unsigned, False)
+    self.assertEqual(dtypes.as_dtype("quint8").is_unsigned, False)
+    self.assertEqual(dtypes.as_dtype("quint16").is_unsigned, False)
 
   def testMinMax(self):
     # make sure min/max evaluates for all data types that have min/max
-- 
GitLab


From 9b28e435c1507be52129ec22d9d006d12b1e79f3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 00:37:13 -0700
Subject: [PATCH 1106/1375] Added support for testing convolution of 3D arrays
 (i.e. 1d-convolution).

PiperOrigin-RevId: 162453537
---
 tensorflow/compiler/xla/BUILD                 |   1 +
 tensorflow/compiler/xla/reference_util.cc     | 102 ++++++++++++++++++
 tensorflow/compiler/xla/reference_util.h      |  19 ++++
 .../compiler/xla/reference_util_test.cc       |  27 +++++
 4 files changed, 149 insertions(+)

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index c508071f8c..39489a2ad1 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -574,6 +574,7 @@ cc_test(
     srcs = ["reference_util_test.cc"],
     deps = [
         ":array2d",
+        ":array3d",
         ":array4d",
         ":literal_util",
         ":reference_util",
diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index 1495790870..7ef5c6d916 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -86,6 +86,53 @@ namespace xla {
   return result;
 }
 
+/*  static */ std::unique_ptr<Array3D<float>> ReferenceUtil::ConvArray3D(
+    const Array3D<float>& lhs, const Array3D<float>& rhs, int64 kernel_stride,
+    Padding padding) {
+  return ConvArray3DGeneralDimensionsDilated(
+      lhs, rhs, kernel_stride, padding, 1, 1,
+      ComputationBuilder::CreateDefaultConvDimensionNumbers(1));
+}
+
+/*static*/ std::unique_ptr<Array3D<float>>
+ReferenceUtil::ConvArray3DGeneralDimensionsDilated(
+    const Array3D<float>& lhs, const Array3D<float>& rhs, int64 kernel_stride,
+    Padding padding, int64 lhs_dilation, int64 rhs_dilation,
+    const ConvolutionDimensionNumbers& dnums) {
+  CHECK_EQ(dnums.spatial_dimensions_size(), 1);
+  // Reuse the code for Array4D-convolution by extending the 3D input into a 4D
+  // array by adding a fourth dummy dimension of size 1 without stride, padding
+  // and dilation.
+  Array4D<float> a4dlhs(lhs.n1(), lhs.n2(), lhs.n3(), 1);
+  a4dlhs.Each(
+      [&](tensorflow::gtl::ArraySlice<int64> indices, float* value_ptr) {
+        CHECK_EQ(indices[3], 0);
+        *value_ptr = lhs.operator()(indices[0], indices[1], indices[2]);
+      });
+  Array4D<float> a4drhs(rhs.n1(), rhs.n2(), rhs.n3(), 1);
+  a4drhs.Each(
+      [&](tensorflow::gtl::ArraySlice<int64> indices, float* value_ptr) {
+        CHECK_EQ(indices[3], 0);
+        *value_ptr = rhs.operator()(indices[0], indices[1], indices[2]);
+      });
+  // Add a second dummy spatial dimensions.
+  ConvolutionDimensionNumbers dnums2d = dnums;
+  dnums2d.add_spatial_dimensions(3);
+  dnums2d.add_kernel_spatial_dimensions(3);
+  std::unique_ptr<Array4D<float>> convr4 = ConvArray4DGeneralDimensionsDilated(
+      a4dlhs, a4drhs, {kernel_stride, 1}, padding, {lhs_dilation, 1},
+      {rhs_dilation, 1}, dnums2d);
+
+  auto convr3 = MakeUnique<Array3D<float>>(convr4->planes(), convr4->depth(),
+                                           convr4->height());
+  convr4->Each(
+      [&](tensorflow::gtl::ArraySlice<int64> indices, float* value_ptr) {
+        CHECK_EQ(indices[3], 0);
+        convr3->operator()(indices[0], indices[1], indices[2]) = *value_ptr;
+      });
+  return convr3;
+}
+
 /* static */ std::unique_ptr<Array4D<float>> ReferenceUtil::ConvArray4D(
     const Array4D<float>& lhs, const Array4D<float>& rhs,
     std::pair<int64, int64> kernel_stride, Padding padding) {
@@ -778,6 +825,61 @@ ReferenceUtil::ReduceToRowArray2D(
   return result;
 }
 
+/* static */ Array3D<float> ReferenceUtil::PadArray3D(
+    const Array3D<float>& operand, const PaddingConfig& padding,
+    const float pad) {
+  CHECK_EQ(padding.dimensions_size(), 3);
+
+  const std::vector<int64> input_bounds = {operand.n1(), operand.n2(),
+                                           operand.n3()};
+  std::vector<int64> pad_low(3);
+  std::vector<int64> pad_high(3);
+  std::vector<int64> pad_interior(3);
+  std::vector<int64> output_bounds(3);
+  for (int64 i = 0; i < 3; ++i) {
+    pad_low[i] = padding.dimensions(i).edge_padding_low();
+    pad_high[i] = padding.dimensions(i).edge_padding_high();
+    CHECK_LE(0, pad_low[i]);
+    CHECK_LE(0, pad_high[i]);
+    CHECK_LE(0, padding.dimensions(i).interior_padding()) << "not implemented";
+    pad_interior[i] = padding.dimensions(i).interior_padding();
+
+    output_bounds[i] = pad_low[i] + input_bounds[i] + pad_high[i] +
+                       (input_bounds[i] - 1) * pad_interior[i];
+  }
+
+  Array3D<float> result(output_bounds[0], output_bounds[1], output_bounds[2]);
+  std::vector<int> indices = {0, 0, 0};
+  for (indices[0] = 0; indices[0] < output_bounds[0]; ++indices[0]) {
+    for (indices[1] = 0; indices[1] < output_bounds[1]; ++indices[1]) {
+      for (indices[2] = 0; indices[2] < output_bounds[2]; ++indices[2]) {
+        float* value = &result(indices[0], indices[1], indices[2]);
+        bool value_padded = false;
+        for (int i = 0; i < 3; ++i) {
+          bool in_low_padding = indices[i] < pad_low[i];
+          bool in_high_padding = indices[i] >= output_bounds[i] - pad_high[i];
+          if (in_low_padding || in_high_padding) {
+            *value = pad;
+            value_padded = true;
+          }
+          if (pad_interior[i] &&
+              (indices[i] - pad_low[i]) % (pad_interior[i] + 1)) {
+            *value = pad;
+            value_padded = true;
+          }
+        }
+        if (value_padded) {
+          continue;
+        }
+        *value = operand((indices[0] - pad_low[0]) / (pad_interior[0] + 1),
+                         (indices[1] - pad_low[1]) / (pad_interior[1] + 1),
+                         (indices[2] - pad_low[2]) / (pad_interior[2] + 1));
+      }
+    }
+  }
+  return result;
+}
+
 /* static */ Array4D<float> ReferenceUtil::PadArray4D(
     const Array4D<float>& operand, const PaddingConfig& padding,
     const float pad) {
diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index 5b2ddf4d7f..2da1730781 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -74,6 +74,20 @@ class ReferenceUtil {
       std::pair<int64, int64> lhs_dilation,
       std::pair<int64, int64> rhs_dilation, ConvolutionDimensionNumbers dnums);
 
+  // Returns the result of a convolution `lhs <conv> rhs`, with the default
+  // convolution dimension numbers returned from
+  // ComputationBuilder::CreateDefaultConvDimensionNumbers().
+  static std::unique_ptr<Array3D<float>> ConvArray3D(const Array3D<float>& lhs,
+                                                     const Array3D<float>& rhs,
+                                                     int64 kernel_stride,
+                                                     Padding padding);
+
+  // Returns the result of a convolution `lhs <conv> rhs`.
+  static std::unique_ptr<Array3D<float>> ConvArray3DGeneralDimensionsDilated(
+      const Array3D<float>& lhs, const Array3D<float>& rhs, int64 kernel_stride,
+      Padding padding, int64 lhs_dilation, int64 rhs_dilation,
+      const ConvolutionDimensionNumbers& dnums);
+
   // Returns the result of a separable  convolution with the given parameters.
   // kernel_stride and padding applies to the depthwise convolution during
   // the separable convolution. pointwise_weights.depth() must be equal to
@@ -476,6 +490,11 @@ class ReferenceUtil {
       const Array2D<float>& operand, const PaddingConfig& padding,
       const float pad);
 
+  // Returns the result of a 3D pad on an input matrix.
+  static Array3D<float> PadArray3D(const Array3D<float>& operand,
+                                   const PaddingConfig& padding,
+                                   const float pad);
+
   // Returns the result of a 4D pad on an input array.
   static Array4D<float> PadArray4D(const Array4D<float>& operand,
                                    const PaddingConfig& padding,
diff --git a/tensorflow/compiler/xla/reference_util_test.cc b/tensorflow/compiler/xla/reference_util_test.cc
index 680e908d46..35b5e8cd52 100644
--- a/tensorflow/compiler/xla/reference_util_test.cc
+++ b/tensorflow/compiler/xla/reference_util_test.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include <memory>
 
 #include "tensorflow/compiler/xla/array2d.h"
+#include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/padding.h"
 #include "tensorflow/compiler/xla/literal_util.h"
@@ -201,6 +202,32 @@ TEST_F(ReferenceUtilTest, SliceStridedArray4D) {
       *actual_literal, ErrorSpec(0.0001));
 }
 
+TEST_F(ReferenceUtilTest, ConvArray3DWithSamePadding) {
+  Array3D<float> input = {{{1, 2, 3, 4}}};
+  Array3D<float> weights = {{{5, 6}}};
+  std::unique_ptr<Array3D<float>> actual =
+      ReferenceUtil::ConvArray3D(input, weights, 1, Padding::kSame);
+  Array3D<float> expected = {{{17, 28, 39, 20}}};
+
+  auto actual_literal = Literal::CreateR3FromArray3D(*actual);
+
+  LiteralTestUtil::ExpectR3NearArray3D<float>(expected, *actual_literal,
+                                              ErrorSpec(0.0001));
+}
+
+TEST_F(ReferenceUtilTest, ConvArray3DWithValidPadding) {
+  Array3D<float> input = {{{1, 2, 3, 4}}};
+  Array3D<float> weights = {{{5, 6}}};
+  std::unique_ptr<Array3D<float>> actual =
+      ReferenceUtil::ConvArray3D(input, weights, 1, Padding::kValid);
+  Array3D<float> expected = {{{17, 28, 39}}};
+
+  auto actual_literal = Literal::CreateR3FromArray3D(*actual);
+
+  LiteralTestUtil::ExpectR3NearArray3D<float>(expected, *actual_literal,
+                                              ErrorSpec(0.0001));
+}
+
 TEST_F(ReferenceUtilTest, ConvWithSamePadding) {
   Array4D<float> input(1, 1, 4, 4);
   // clang-format off
-- 
GitLab


From a555b786bb5ca046ffe2bbb2b1615ab24851e954 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Wed, 19 Jul 2017 01:17:42 -0700
Subject: [PATCH 1107/1375] Add output_partitions support in distributed
 runtime.

PiperOrigin-RevId: 162456565
---
 tensorflow/core/distributed_runtime/BUILD     |  1 +
 .../core/distributed_runtime/graph_mgr.cc     | 17 ++++++--
 .../core/distributed_runtime/graph_mgr.h      |  5 ++-
 .../distributed_runtime/master_session.cc     | 28 +++++++++----
 .../core/distributed_runtime/master_session.h |  1 +
 .../distributed_runtime/message_wrappers.cc   | 41 +++++++++++++++++++
 .../distributed_runtime/message_wrappers.h    | 14 +++++++
 .../message_wrappers_test.cc                  | 15 +++++++
 tensorflow/core/distributed_runtime/worker.cc |  5 +--
 tensorflow/core/protobuf/worker.proto         |  7 +++-
 tensorflow/python/client/session_test.py      | 16 ++++++++
 11 files changed, 134 insertions(+), 16 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD
index 89a5f433bd..d9ed40c50a 100644
--- a/tensorflow/core/distributed_runtime/BUILD
+++ b/tensorflow/core/distributed_runtime/BUILD
@@ -359,6 +359,7 @@ cc_library(
     srcs = ["graph_mgr.cc"],
     hdrs = ["graph_mgr.h"],
     deps = [
+        ":message_wrappers",
         ":rendezvous_mgr_interface",
         ":worker_env",
         "//tensorflow/core:core_cpu_internal",
diff --git a/tensorflow/core/distributed_runtime/graph_mgr.cc b/tensorflow/core/distributed_runtime/graph_mgr.cc
index 205843d342..7f77bf8b4e 100644
--- a/tensorflow/core/distributed_runtime/graph_mgr.cc
+++ b/tensorflow/core/distributed_runtime/graph_mgr.cc
@@ -442,10 +442,9 @@ void GraphMgr::RecvOutputsAsync(const int64 step_id, NamedTensors* out,
 }
 
 void GraphMgr::ExecuteAsync(const string& handle, const int64 step_id,
-                            WorkerSession* session,
-                            const ExecutorOpts& /*opts*/,
+                            WorkerSession* session, const ExecutorOpts& opts,
                             StepStatsCollector* collector,
-                            CostGraphDef* cost_graph,
+                            MutableRunGraphResponseWrapper* response,
                             CancellationManager* cancellation_manager,
                             const NamedTensors& in, StatusCallback done) {
   // Lookup an item. Holds one ref while executing.
@@ -464,6 +463,18 @@ void GraphMgr::ExecuteAsync(const string& handle, const int64 step_id,
     return;
   }
 
+  CostGraphDef* cost_graph = nullptr;
+  if (response != nullptr) {
+    cost_graph = response->mutable_cost_graph();
+    if (opts.record_partition_graphs()) {
+      for (const ExecutionUnit& unit : item->units) {
+        GraphDef graph_def;
+        unit.graph->ToGraphDef(&graph_def);
+        response->AddPartitionGraph(graph_def);
+      }
+    }
+  }
+
   RemoteRendezvous* rendezvous = worker_env_->rendezvous_mgr->Find(step_id);
   Status s = rendezvous->Initialize(session);
 
diff --git a/tensorflow/core/distributed_runtime/graph_mgr.h b/tensorflow/core/distributed_runtime/graph_mgr.h
index 4ee3711d02..fb83720d2a 100644
--- a/tensorflow/core/distributed_runtime/graph_mgr.h
+++ b/tensorflow/core/distributed_runtime/graph_mgr.h
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include "tensorflow/core/common_runtime/costmodel_manager.h"
 #include "tensorflow/core/common_runtime/executor.h"
+#include "tensorflow/core/distributed_runtime/message_wrappers.h"
 #include "tensorflow/core/distributed_runtime/worker_env.h"
 #include "tensorflow/core/framework/cancellation.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
@@ -31,6 +32,7 @@ limitations under the License.
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/protobuf/config.pb.h"
 #include "tensorflow/core/protobuf/debug.pb.h"
+#include "tensorflow/core/protobuf/worker.pb.h"
 
 namespace tensorflow {
 
@@ -80,7 +82,8 @@ class GraphMgr {
   typedef std::function<void(const Status&)> StatusCallback;
   void ExecuteAsync(const string& handle, const int64 step_id,
                     WorkerSession* session, const ExecutorOpts& opts,
-                    StepStatsCollector* collector, CostGraphDef* cost_graph,
+                    StepStatsCollector* collector,
+                    MutableRunGraphResponseWrapper* response,
                     CancellationManager* cancellation_manager,
                     const NamedTensors& in, StatusCallback done);
 
diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc
index 2b6e0c5268..361e89290d 100644
--- a/tensorflow/core/distributed_runtime/master_session.cc
+++ b/tensorflow/core/distributed_runtime/master_session.cc
@@ -513,6 +513,9 @@ Status MasterSession::ReffedClientGraph::RunPartitions(
   if (pss->collect_rpcs) {
     SetRPCLogging(true);
   }
+  if (pss->collect_partition_graphs) {
+    exec_opts.set_record_partition_graphs(true);
+  }
   if (pss->collect_costs || pss->collect_timeline) {
     pss->step_stats.resize(partitions_.size());
   }
@@ -615,30 +618,39 @@ Status MasterSession::ReffedClientGraph::RunPartitions(
   if (status.ok()) {
     for (int i = 0; i < num; ++i) {
       const Part& part = partitions_[i];
-      for (size_t j = 0; j < calls.get(i)->resp->num_recvs(); ++j) {
-        auto iter = part.key_fetch.find(calls.get(i)->resp->recv_key(j));
+      MutableRunGraphResponseWrapper* run_graph_resp = calls.get(i)->resp.get();
+      for (size_t j = 0; j < run_graph_resp->num_recvs(); ++j) {
+        auto iter = part.key_fetch.find(run_graph_resp->recv_key(j));
         if (iter == part.key_fetch.end()) {
           status.Update(errors::Internal("Unexpected fetch key: ",
-                                         calls.get(i)->resp->recv_key(j)));
+                                         run_graph_resp->recv_key(j)));
           break;
         }
         const string& fetch = iter->second;
-        status.Update(resp->AddTensorFromRunGraphResponse(
-            fetch, calls.get(i)->resp.get(), j));
+        status.Update(
+            resp->AddTensorFromRunGraphResponse(fetch, run_graph_resp, j));
         if (!status.ok()) {
           break;
         }
       }
       if (pss->collect_timeline) {
-        pss->step_stats[i].Swap(calls.get(i)->resp->mutable_step_stats());
+        pss->step_stats[i].Swap(run_graph_resp->mutable_step_stats());
       }
       if (pss->collect_costs) {
-        CostGraphDef* cost_graph = calls.get(i)->resp->mutable_cost_graph();
+        CostGraphDef* cost_graph = run_graph_resp->mutable_cost_graph();
         for (int j = 0; j < cost_graph->node_size(); ++j) {
           resp->mutable_metadata()->mutable_cost_graph()->add_node()->Swap(
               cost_graph->mutable_node(j));
         }
       }
+      if (pss->collect_partition_graphs) {
+        protobuf::RepeatedPtrField<GraphDef>* partition_graph_defs =
+            resp->mutable_metadata()->mutable_partition_graphs();
+        for (size_t i = 0; i < run_graph_resp->num_partition_graphs(); i++) {
+          partition_graph_defs->Add()->Swap(
+              run_graph_resp->mutable_partition_graph(i));
+        }
+      }
     }
   }
   return status;
@@ -1361,6 +1373,7 @@ Status MasterSession::DoPartialRun(CallOptions* opts,
     pss.collect_costs =
         build_cost_model_every > 0 &&
         ((count + 1 - build_cost_model_after) % build_cost_model_every == 0);
+    pss.collect_partition_graphs = req.options().output_partition_graphs();
 
     std::unique_ptr<ProfileHandler> ph = run_state->rcg->GetProfileHandler(
         run_state->step_id, count, req.options());
@@ -1517,6 +1530,7 @@ Status MasterSession::DoRunWithLocalExecution(
   pss.collect_costs =
       build_cost_model_every > 0 &&
       ((count + 1 - build_cost_model_after) % build_cost_model_every == 0);
+  pss.collect_partition_graphs = req.options().output_partition_graphs();
 
   std::unique_ptr<ProfileHandler> ph =
       rcg->GetProfileHandler(step_id, count, req.options());
diff --git a/tensorflow/core/distributed_runtime/master_session.h b/tensorflow/core/distributed_runtime/master_session.h
index 10fc4868ca..33b9bfe631 100644
--- a/tensorflow/core/distributed_runtime/master_session.h
+++ b/tensorflow/core/distributed_runtime/master_session.h
@@ -145,6 +145,7 @@ class MasterSession : public core::RefCounted {
     bool collect_costs = false;
     bool collect_timeline = false;
     bool collect_rpcs = false;
+    bool collect_partition_graphs = false;
     Microseconds start_micros = Microseconds(0);
     Microseconds end_micros = Microseconds(0);
     std::vector<StepStats> step_stats;  // per partition
diff --git a/tensorflow/core/distributed_runtime/message_wrappers.cc b/tensorflow/core/distributed_runtime/message_wrappers.cc
index b5b564375d..a4a88e6e3b 100644
--- a/tensorflow/core/distributed_runtime/message_wrappers.cc
+++ b/tensorflow/core/distributed_runtime/message_wrappers.cc
@@ -523,6 +523,19 @@ RunGraphResponse* InMemoryRunGraphResponse::get_proto() {
   return nullptr;
 }
 
+size_t InMemoryRunGraphResponse::num_partition_graphs() const {
+  return partition_graphs_.size();
+}
+
+GraphDef* InMemoryRunGraphResponse::mutable_partition_graph(size_t i) {
+  return &partition_graphs_[i];
+}
+
+void InMemoryRunGraphResponse::AddPartitionGraph(
+    const GraphDef& partition_graph) {
+  partition_graphs_.push_back(partition_graph);
+}
+
 size_t OwnedProtoRunGraphResponse::num_recvs() const {
   return response_.recv_size();
 }
@@ -563,6 +576,20 @@ CostGraphDef* OwnedProtoRunGraphResponse::mutable_cost_graph() {
 
 RunGraphResponse* OwnedProtoRunGraphResponse::get_proto() { return &response_; }
 
+size_t OwnedProtoRunGraphResponse::num_partition_graphs() const {
+  return response_.partition_graph_size();
+}
+
+GraphDef* OwnedProtoRunGraphResponse::mutable_partition_graph(size_t i) {
+  return response_.mutable_partition_graph(i);
+}
+
+void OwnedProtoRunGraphResponse::AddPartitionGraph(
+    const GraphDef& partition_graph) {
+  GraphDef* graph_def = response_.mutable_partition_graph()->Add();
+  *graph_def = partition_graph;
+}
+
 NonOwnedProtoRunGraphResponse::NonOwnedProtoRunGraphResponse(
     RunGraphResponse* response)
     : response_(response) {}
@@ -609,6 +636,20 @@ RunGraphResponse* NonOwnedProtoRunGraphResponse::get_proto() {
   return response_;
 }
 
+size_t NonOwnedProtoRunGraphResponse::num_partition_graphs() const {
+  return response_->partition_graph_size();
+}
+
+GraphDef* NonOwnedProtoRunGraphResponse::mutable_partition_graph(size_t i) {
+  return response_->mutable_partition_graph(i);
+}
+
+void NonOwnedProtoRunGraphResponse::AddPartitionGraph(
+    const GraphDef& partition_graph) {
+  GraphDef* graph_def = response_->add_partition_graph();
+  *graph_def = partition_graph;
+}
+
 MutableRunStepResponseWrapper::~MutableRunStepResponseWrapper() {}
 
 size_t InMemoryRunStepResponse::num_tensors() const { return tensors_.size(); }
diff --git a/tensorflow/core/distributed_runtime/message_wrappers.h b/tensorflow/core/distributed_runtime/message_wrappers.h
index f247b50dd5..0e3f5b98cb 100644
--- a/tensorflow/core/distributed_runtime/message_wrappers.h
+++ b/tensorflow/core/distributed_runtime/message_wrappers.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
+#include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor.pb_text.h"
@@ -424,6 +425,9 @@ class MutableRunGraphResponseWrapper {
   // execution, if necessary.
   virtual StepStats* mutable_step_stats() = 0;
   virtual CostGraphDef* mutable_cost_graph() = 0;
+  virtual size_t num_partition_graphs() const = 0;
+  virtual GraphDef* mutable_partition_graph(size_t i) = 0;
+  virtual void AddPartitionGraph(const GraphDef& partition_graph) = 0;
 
  protected:
   // Returns a mutable protobuf message that represents the contents of
@@ -451,6 +455,9 @@ class InMemoryRunGraphResponse : public MutableRunGraphResponseWrapper {
   void AddRecv(const string& key, const Tensor& value) override;
   StepStats* mutable_step_stats() override;
   CostGraphDef* mutable_cost_graph() override;
+  size_t num_partition_graphs() const override;
+  GraphDef* mutable_partition_graph(size_t i) override;
+  void AddPartitionGraph(const GraphDef& partition_graph) override;
 
  protected:
   // NOTE: This method is not implemented. See
@@ -461,6 +468,7 @@ class InMemoryRunGraphResponse : public MutableRunGraphResponseWrapper {
   gtl::InlinedVector<std::pair<string, Tensor>, 4> recvs_;
   StepStats step_stats_;
   CostGraphDef cost_graph_;
+  std::vector<GraphDef> partition_graphs_;
 };
 
 // Proto-based message wrapper for use on the client side of the RunGraph RPC.
@@ -474,6 +482,9 @@ class OwnedProtoRunGraphResponse : public MutableRunGraphResponseWrapper {
   void AddRecv(const string& key, const Tensor& value) override;
   StepStats* mutable_step_stats() override;
   CostGraphDef* mutable_cost_graph() override;
+  size_t num_partition_graphs() const override;
+  GraphDef* mutable_partition_graph(size_t i) override;
+  void AddPartitionGraph(const GraphDef& partition_graph) override;
 
  protected:
   RunGraphResponse* get_proto() override;
@@ -495,6 +506,9 @@ class NonOwnedProtoRunGraphResponse : public MutableRunGraphResponseWrapper {
   void AddRecv(const string& key, const Tensor& value) override;
   StepStats* mutable_step_stats() override;
   CostGraphDef* mutable_cost_graph() override;
+  size_t num_partition_graphs() const override;
+  GraphDef* mutable_partition_graph(size_t i) override;
+  void AddPartitionGraph(const GraphDef& partition_graph) override;
 
  protected:
   RunGraphResponse* get_proto() override;
diff --git a/tensorflow/core/distributed_runtime/message_wrappers_test.cc b/tensorflow/core/distributed_runtime/message_wrappers_test.cc
index 5b0c2945b9..e87fff151e 100644
--- a/tensorflow/core/distributed_runtime/message_wrappers_test.cc
+++ b/tensorflow/core/distributed_runtime/message_wrappers_test.cc
@@ -88,6 +88,7 @@ static void CheckRunGraphRequest(const RunGraphRequestWrapper& request) {
   EXPECT_EQ(13, request.step_id());
   EXPECT_FALSE(request.exec_opts().record_costs());
   EXPECT_TRUE(request.exec_opts().record_timeline());
+  EXPECT_FALSE(request.exec_opts().record_partition_graphs());
   EXPECT_EQ(2, request.num_sends());
   Tensor val;
   TF_EXPECT_OK(request.SendValue(0, &val));
@@ -105,6 +106,9 @@ static void BuildRunGraphResponse(
   run_graph_response->mutable_step_stats()->add_dev_stats()->set_device(
       "/cpu:0");
   run_graph_response->mutable_cost_graph()->add_node()->set_name("cost_node");
+  GraphDef graph_def;
+  graph_def.set_version(1234);
+  run_graph_response->AddPartitionGraph(graph_def);
 }
 
 static void CheckRunGraphResponse(MutableRunGraphResponseWrapper* response) {
@@ -120,6 +124,9 @@ static void CheckRunGraphResponse(MutableRunGraphResponseWrapper* response) {
   EXPECT_EQ("/cpu:0", response->mutable_step_stats()->dev_stats(0).device());
   EXPECT_EQ(1, response->mutable_cost_graph()->node_size());
   EXPECT_EQ("cost_node", response->mutable_cost_graph()->node(0).name());
+  EXPECT_EQ(1, response->num_partition_graphs());
+  GraphDef graph_def;
+  EXPECT_EQ(1234, response->mutable_partition_graph(0)->version());
 }
 
 static void BuildRunStepResponse(
@@ -131,6 +138,12 @@ static void BuildRunStepResponse(
       "fetch_y:0", run_graph_response, 1));
   *run_step_response->mutable_metadata()->mutable_step_stats() =
       *run_graph_response->mutable_step_stats();
+  protobuf::RepeatedPtrField<GraphDef>* partition_graph_defs =
+      run_step_response->mutable_metadata()->mutable_partition_graphs();
+  for (size_t i = 0; i < run_graph_response->num_partition_graphs(); i++) {
+    partition_graph_defs->Add()->Swap(
+        run_graph_response->mutable_partition_graph(i));
+  }
 }
 
 static void CheckRunStepResponse(
@@ -145,6 +158,8 @@ static void CheckRunStepResponse(
   test::ExpectTensorEqual<int32>(TensorB(), val);
   EXPECT_EQ(1, response.metadata().step_stats().dev_stats_size());
   EXPECT_EQ("/cpu:0", response.metadata().step_stats().dev_stats(0).device());
+  EXPECT_EQ(1, response.metadata().partition_graphs_size());
+  EXPECT_EQ(1234, response.metadata().partition_graphs(0).version());
 }
 
 TEST(MessageWrappers, RunStepRequest_Basic) {
diff --git a/tensorflow/core/distributed_runtime/worker.cc b/tensorflow/core/distributed_runtime/worker.cc
index 16e450abb0..34b53e965f 100644
--- a/tensorflow/core/distributed_runtime/worker.cc
+++ b/tensorflow/core/distributed_runtime/worker.cc
@@ -156,10 +156,9 @@ void Worker::DoRunGraph(CallOptions* opts, RunGraphRequestWrapper* request,
       return;
     }
   }
-  CostGraphDef* cost_graph = response->mutable_cost_graph();
   session->graph_mgr->ExecuteAsync(
       request->graph_handle(), step_id, session, request->exec_opts(),
-      collector, cost_graph, cm, in,
+      collector, response, cm, in,
       [this, step_id, response, session, cm, out, token, collector, opts,
        done](Status s) {
         if (s.ok()) {
@@ -230,7 +229,7 @@ void Worker::DoPartialRunGraph(CallOptions* opts,
     }
     session->graph_mgr->ExecuteAsync(
         graph_handle, step_id, session, request->exec_opts(),
-        nullptr /* collector */, nullptr /* cost_graph */, cm, in,
+        nullptr /* collector */, nullptr /* response */, cm, in,
         [this, token, step_id, cm](Status s) {
           {
             mutex_lock l(mu_);
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index 9d4a417aa3..137f9bc216 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -168,6 +168,7 @@ message CleanupAllResponse {
 message ExecutorOpts {
   bool record_costs = 1;
   bool record_timeline = 3;
+  bool record_partition_graphs = 4;
 };
 
 message RunGraphRequest {
@@ -212,10 +213,12 @@ message RunGraphResponse {
   // `RunGraphRequest.recv_key`.
   repeated NamedTensorProto recv = 1;
 
-  // If the request asked for execution stats or cost graph, these are returned
-  // here.
+  // If the request asked for execution stats, the cost graph, or the partition
+  // graphs, these are returned here.
+  // TODO(suharshs): Package these in a RunMetadata instead.
   StepStats step_stats = 2;
   CostGraphDef cost_graph = 3;
+  repeated GraphDef partition_graph = 4;
 }
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index 61d411b6f9..0cec75cf99 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -1508,6 +1508,22 @@ class SessionTest(test_util.TensorFlowTestCase):
         else:
           self.assertFalse(run_metadata.HasField('cost_graph'))
 
+  def runTestOutputPartitionGraphs(self, sess):
+    run_options = config_pb2.RunOptions(output_partition_graphs=True)
+    a = constant_op.constant(1)
+    run_metadata = config_pb2.RunMetadata()
+    sess.run(a, options=run_options, run_metadata=run_metadata)
+    self.assertGreater(len(run_metadata.partition_graphs), 0)
+    sess.run(a, run_metadata=run_metadata)
+    self.assertEqual(len(run_metadata.partition_graphs), 0)
+
+  def testOutputPartitionGraphsDirect(self):
+    self.runTestOutputPartitionGraphs(session.Session())
+
+  def testOutputPartitionGraphsDistributed(self):
+    server = server_lib.Server.create_local_server()
+    self.runTestOutputPartitionGraphs(session.Session(server.target))
+
   def testNonInteractiveSessionNesting(self):
     sess1 = session.Session()
     sess1_controller = sess1.as_default()
-- 
GitLab


From ac7530e54ddaa17c17e070e5a141002c43b86275 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 03:19:57 -0700
Subject: [PATCH 1108/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 162466482
---
 tensorflow/contrib/bayesflow/BUILD         |  2 ++
 tensorflow/contrib/boosted_trees/BUILD     | 33 ++++++++++++++--------
 tensorflow/contrib/boosted_trees/lib/BUILD |  5 ----
 tensorflow/contrib/cluster_resolver/BUILD  |  5 +++-
 tensorflow/contrib/copy_graph/BUILD        |  4 +--
 5 files changed, 29 insertions(+), 20 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index ab090aaa88..71828f084d 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -16,11 +16,13 @@ py_library(
     srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
     srcs_version = "PY2AND3",
     deps = [
+        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
+        "//tensorflow/python:nn_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:training",
         "//tensorflow/python:util",
diff --git a/tensorflow/contrib/boosted_trees/BUILD b/tensorflow/contrib/boosted_trees/BUILD
index 269e314940..b6c4db1416 100644
--- a/tensorflow/contrib/boosted_trees/BUILD
+++ b/tensorflow/contrib/boosted_trees/BUILD
@@ -18,9 +18,7 @@ filegroup(
     name = "all_files",
     srcs = glob(
         ["**/*"],
-        exclude = [
-            "**/OWNERS",
-        ],
+        exclude = ["**/OWNERS"],
     ),
     visibility = ["//tensorflow:__subpackages__"],
 )
@@ -139,12 +137,14 @@ py_test(
     deps = [
         ":ensemble_optimizer_ops_py",
         ":model_ops_py",
-        "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:resources",
-        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
 )
@@ -164,10 +164,13 @@ py_test(
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:resources",
         "//tensorflow/python:training",
+        "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
 )
@@ -188,7 +191,6 @@ py_test(
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:resources",
-        "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
 )
@@ -204,9 +206,11 @@ py_test(
     deps = [
         ":quantile_ops_py",
         "//tensorflow/contrib/boosted_trees/proto:quantiles_proto_py",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:resources",
+        "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
     ],
@@ -221,9 +225,10 @@ py_test(
         ":split_handler_ops_py",
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
-        "//tensorflow/python:training",
     ],
 )
 
@@ -237,11 +242,10 @@ py_test(
     ],
     deps = [
         ":stats_accumulator_ops_py",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
-        "//tensorflow/python:resources",
-        "//tensorflow/python:training",
-        "//third_party/py/numpy",
+        "//tensorflow/python:tensor_shape",
     ],
 )
 
@@ -259,10 +263,12 @@ py_test(
         "//tensorflow/contrib/boosted_trees/proto:learner_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:split_info_proto_py",
         "//tensorflow/contrib/boosted_trees/proto:tree_config_proto_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:resources",
-        "//tensorflow/python:training",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -272,6 +278,11 @@ py_library(
     name = "batch_ops_utils_py",
     srcs = ["python/ops/batch_ops_utils.py"],
     srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:tensor_shape",
+    ],
 )
 
 py_library(
diff --git a/tensorflow/contrib/boosted_trees/lib/BUILD b/tensorflow/contrib/boosted_trees/lib/BUILD
index 6f9443cf94..fad56cfeaf 100644
--- a/tensorflow/contrib/boosted_trees/lib/BUILD
+++ b/tensorflow/contrib/boosted_trees/lib/BUILD
@@ -196,7 +196,6 @@ cc_test(
     deps = [
         ":weighted_quantiles",
         "//tensorflow/core",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -210,7 +209,6 @@ cc_test(
     deps = [
         ":weighted_quantiles",
         "//tensorflow/core",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -223,7 +221,6 @@ cc_test(
     srcs = ["quantiles/weighted_quantiles_stream_test.cc"],
     deps = [
         ":weighted_quantiles",
-        "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -363,7 +360,6 @@ cc_test(
     srcs = ["learner/common/partitioners/example_partitioner_test.cc"],
     deps = [
         ":example_partitioner",
-        "//tensorflow/core:lib",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -406,7 +402,6 @@ cc_test(
     ],
     deps = [
         ":feature-column-handlers",
-        "//tensorflow/core:lib",
         "//tensorflow/core:tensor_testutil",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD
index 1e2c17eb52..ece5b9c04c 100644
--- a/tensorflow/contrib/cluster_resolver/BUILD
+++ b/tensorflow/contrib/cluster_resolver/BUILD
@@ -48,7 +48,10 @@ py_library(
         "python/training/gce_cluster_resolver.py",
     ],
     srcs_version = "PY2AND3",
-    deps = [":cluster_resolver_py"],
+    deps = [
+        ":cluster_resolver_py",
+        "//tensorflow/python:training",
+    ],
 )
 
 tf_py_test(
diff --git a/tensorflow/contrib/copy_graph/BUILD b/tensorflow/contrib/copy_graph/BUILD
index f47a084db1..e683f6229f 100644
--- a/tensorflow/contrib/copy_graph/BUILD
+++ b/tensorflow/contrib/copy_graph/BUILD
@@ -20,7 +20,7 @@ py_library(
     deps = [
         "//tensorflow/python:client",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:session",
+        "//tensorflow/python:util",
         "//tensorflow/python:variables",
     ],
 )
@@ -37,9 +37,7 @@ py_test(
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
-- 
GitLab


From 41803db36d4f4a3239bd81e5d460eb0e6e2eea88 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Wed, 19 Jul 2017 07:23:53 -0700
Subject: [PATCH 1109/1375] tfdbg: open-source C++ and Python libraries of gRPC
 debugger mode

with the exception of Windows

the session_debug_grpc_test is temporarily disabled on mac pending pip install of futures and grpcio on all slaves.

PiperOrigin-RevId: 162482541
---
 tensorflow/contrib/cmake/tf_tests.cmake       |   3 +
 tensorflow/core/debug/BUILD                   |  42 +-
 .../core/debug/debug_grpc_io_utils_test.cc    | 432 ++++++++++++
 tensorflow/core/debug/debug_io_utils.cc       |  45 +-
 tensorflow/core/debug/debug_io_utils.h        |   4 +-
 .../core/platform/default/build_config.bzl    |   4 +-
 tensorflow/python/debug/BUILD                 |  91 ++-
 tensorflow/python/debug/lib/debug_data.py     |   3 +-
 .../debug/lib/debug_service_pb2_grpc.py       |  76 +++
 .../debug/lib/dist_session_debug_grpc_test.py | 230 +++++++
 .../python/debug/lib/grpc_debug_server.py     | 395 +++++++++++
 .../debug/lib/grpc_debug_test_server.py       | 336 ++++++++++
 .../debug/lib/session_debug_grpc_test.py      | 622 ++++++++++++++++++
 tensorflow/tools/ci_build/builds/pip.sh       | 118 +++-
 tensorflow/tools/pip_package/BUILD            |   1 +
 15 files changed, 2330 insertions(+), 72 deletions(-)
 create mode 100644 tensorflow/core/debug/debug_grpc_io_utils_test.cc
 create mode 100755 tensorflow/python/debug/lib/debug_service_pb2_grpc.py
 create mode 100644 tensorflow/python/debug/lib/dist_session_debug_grpc_test.py
 create mode 100644 tensorflow/python/debug/lib/grpc_debug_server.py
 create mode 100644 tensorflow/python/debug/lib/grpc_debug_test_server.py
 create mode 100644 tensorflow/python/debug/lib/session_debug_grpc_test.py

diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 089967ffec..0b73f8bb7f 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -162,6 +162,9 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/python/debug/cli/profile_analyzer_cli_test.py"
     # Windows does not have the curses library and uses readline.
     "${tensorflow_source_dir}/tensorflow/python/debug/cli/curses_ui_test.py"
+    # TFDBG grpc:// mode is not yet available on Windows.
+    "${tensorflow_source_dir}/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/debug/lib/session_debug_grpc_test.py"
     # generally not working
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/__init__.py"
     "${tensorflow_source_dir}/tensorflow/python/kernel_tests/benchmark_test.py"
diff --git a/tensorflow/core/debug/BUILD b/tensorflow/core/debug/BUILD
index dd26177d25..971576698e 100644
--- a/tensorflow/core/debug/BUILD
+++ b/tensorflow/core/debug/BUILD
@@ -15,7 +15,6 @@ package(
 
 licenses(["notice"])  # Apache 2.0
 
-# Google-internal rules omitted.
 load(
     "//tensorflow:tensorflow.bzl",
     "check_deps",
@@ -28,6 +27,7 @@ load(
 load(
     "//tensorflow/core:platform/default/build_config.bzl",
     "tf_kernel_tests_linkstatic",
+    "tf_proto_library",
     "tf_proto_library_cc",
 )
 load(
@@ -42,13 +42,25 @@ check_deps(
     deps = ["//tensorflow/core:tensorflow"],
 )
 
-tf_proto_library_cc(
+tf_proto_library(
     name = "debug_service_proto",
-    srcs = ["debug_service.proto"],
+    srcs = [
+        "debug_service.proto",
+    ],
     has_services = 1,
     cc_api_version = 2,
     cc_grpc_version = 1,
-    protodeps = ["//tensorflow/core:protos_all"],
+    protodeps = [
+        ":debugger_event_metadata_proto",
+        "//tensorflow/core:protos_all",
+    ],
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+tf_proto_library(
+    name = "debugger_event_metadata_proto",
+    srcs = ["debugger_event_metadata.proto"],
+    cc_api_version = 2,
 )
 
 cc_library(
@@ -196,6 +208,7 @@ tf_cc_test(
     deps = [
         ":debug_grpc_testlib",
         ":debug_io_utils",
+        ":debug_service_proto_cc",
         ":debugger_event_metadata_proto_cc",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
@@ -254,10 +267,23 @@ tf_cc_test(
     ],
 )
 
-tf_proto_library_cc(
-    name = "debugger_event_metadata_proto",
-    srcs = ["debugger_event_metadata.proto"],
-    cc_api_version = 2,
+tf_cc_test(
+    name = "debug_grpc_io_utils_test",
+    size = "small",
+    srcs = ["debug_grpc_io_utils_test.cc"],
+    linkstatic = tf_kernel_tests_linkstatic(),
+    deps = [
+        ":debug_graph_utils",
+        ":debug_grpc_testlib",
+        ":debug_io_utils",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
 )
 
 # TODO(cais): Add the following back in when tfdbg is supported on Android.
diff --git a/tensorflow/core/debug/debug_grpc_io_utils_test.cc b/tensorflow/core/debug/debug_grpc_io_utils_test.cc
new file mode 100644
index 0000000000..6510424182
--- /dev/null
+++ b/tensorflow/core/debug/debug_grpc_io_utils_test.cc
@@ -0,0 +1,432 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/debug/debug_graph_utils.h"
+#include "tensorflow/core/debug/debug_grpc_testlib.h"
+#include "tensorflow/core/debug/debug_io_utils.h"
+#include "tensorflow/core/lib/core/notification.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+#include "tensorflow/core/lib/io/path.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/tracing.h"
+
+namespace tensorflow {
+
+class GrpcDebugTest : public ::testing::Test {
+ protected:
+  struct ServerData {
+    int port;
+    string url;
+    std::unique_ptr<test::TestEventListenerImpl> server;
+    std::unique_ptr<thread::ThreadPool> thread_pool;
+  };
+
+  void SetUp() override {
+    ClearEnabledWatchKeys();
+    SetUpInProcessServer(&server_data_, 0);
+  }
+
+  void TearDown() override { TearDownInProcessServer(&server_data_); }
+
+  void SetUpInProcessServer(ServerData* server_data,
+                            int64 server_start_delay_micros) {
+    server_data->port = testing::PickUnusedPortOrDie();
+    server_data->url = strings::StrCat("grpc://localhost:", server_data->port);
+    server_data->server.reset(new test::TestEventListenerImpl());
+
+    server_data->thread_pool.reset(
+        new thread::ThreadPool(Env::Default(), "test_server", 1));
+    server_data->thread_pool->Schedule(
+        [server_data, server_start_delay_micros]() {
+          Env::Default()->SleepForMicroseconds(server_start_delay_micros);
+          server_data->server->RunServer(server_data->port);
+        });
+  }
+
+  void TearDownInProcessServer(ServerData* server_data) {
+    server_data->server->StopServer();
+    server_data->thread_pool.reset();
+  }
+
+  void ClearEnabledWatchKeys() { DebugGrpcIO::ClearEnabledWatchKeys(); }
+
+  void CreateEmptyEnabledSet(const string& grpc_debug_url) {
+    DebugGrpcIO::CreateEmptyEnabledSet(grpc_debug_url);
+  }
+
+  const int64 GetChannelConnectionTimeoutMicros() {
+    return DebugGrpcIO::channel_connection_timeout_micros;
+  }
+
+  void SetChannelConnectionTimeoutMicros(const int64 timeout) {
+    DebugGrpcIO::channel_connection_timeout_micros = timeout;
+  }
+
+  ServerData server_data_;
+};
+
+TEST_F(GrpcDebugTest, ConnectionTimeoutWorks) {
+  // Use a short timeout so the test won't take too long.
+  const int64 kOriginalTimeoutMicros = GetChannelConnectionTimeoutMicros();
+  const int64 kShortTimeoutMicros = 500 * 1000;
+  SetChannelConnectionTimeoutMicros(kShortTimeoutMicros);
+  ASSERT_EQ(kShortTimeoutMicros, GetChannelConnectionTimeoutMicros());
+
+  const string& kInvalidGrpcUrl =
+      strings::StrCat("grpc://localhost:", testing::PickUnusedPortOrDie());
+  Tensor tensor(DT_FLOAT, TensorShape({1, 1}));
+  tensor.flat<float>()(0) = 42.0;
+  Status publish_status = DebugIO::PublishDebugTensor(
+      DebugNodeKey("/job:localhost/replica:0/task:0/cpu:0", "foo_tensor", 0,
+                   "DebugIdentity"),
+      tensor, Env::Default()->NowMicros(), {kInvalidGrpcUrl});
+  SetChannelConnectionTimeoutMicros(kOriginalTimeoutMicros);
+  TF_ASSERT_OK(DebugIO::CloseDebugURL(kInvalidGrpcUrl));
+
+  ASSERT_FALSE(publish_status.ok());
+  const string expected_error_msg = strings::StrCat(
+      "Failed to connect to gRPC channel at ", kInvalidGrpcUrl.substr(7),
+      " within a timeout of ", kShortTimeoutMicros / 1e6, " s");
+  ASSERT_NE(string::npos,
+            publish_status.error_message().find(expected_error_msg));
+}
+
+TEST_F(GrpcDebugTest, ConnectionToDelayedStartingServerWorks) {
+  ServerData server_data;
+  // Server start will be delayed for 1 second.
+  SetUpInProcessServer(&server_data, 1 * 1000 * 1000);
+
+  Tensor tensor(DT_FLOAT, TensorShape({1, 1}));
+  tensor.flat<float>()(0) = 42.0;
+  const DebugNodeKey kDebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
+                                   "foo_tensor", 0, "DebugIdentity");
+  Status publish_status = DebugIO::PublishDebugTensor(
+      kDebugNodeKey, tensor, Env::Default()->NowMicros(), {server_data.url});
+  ASSERT_TRUE(publish_status.ok());
+  TF_ASSERT_OK(DebugIO::CloseDebugURL(server_data.url));
+
+  ASSERT_EQ(1, server_data.server->node_names.size());
+  ASSERT_EQ(1, server_data.server->output_slots.size());
+  ASSERT_EQ(1, server_data.server->debug_ops.size());
+  EXPECT_EQ(kDebugNodeKey.device_name, server_data.server->device_names[0]);
+  EXPECT_EQ(kDebugNodeKey.node_name, server_data.server->node_names[0]);
+  EXPECT_EQ(kDebugNodeKey.output_slot, server_data.server->output_slots[0]);
+  EXPECT_EQ(kDebugNodeKey.debug_op, server_data.server->debug_ops[0]);
+  TearDownInProcessServer(&server_data);
+}
+
+TEST_F(GrpcDebugTest, SendSingleDebugTensorViaGrpcTest) {
+  Tensor tensor(DT_FLOAT, TensorShape({1, 1}));
+  tensor.flat<float>()(0) = 42.0;
+  const DebugNodeKey kDebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
+                                   "foo_tensor", 0, "DebugIdentity");
+  TF_ASSERT_OK(DebugIO::PublishDebugTensor(
+      kDebugNodeKey, tensor, Env::Default()->NowMicros(), {server_data_.url}));
+  TF_ASSERT_OK(DebugIO::CloseDebugURL(server_data_.url));
+
+  // Verify that the expected debug tensor sending happened.
+  ASSERT_EQ(1, server_data_.server->node_names.size());
+  ASSERT_EQ(1, server_data_.server->output_slots.size());
+  ASSERT_EQ(1, server_data_.server->debug_ops.size());
+  EXPECT_EQ(kDebugNodeKey.device_name, server_data_.server->device_names[0]);
+  EXPECT_EQ(kDebugNodeKey.node_name, server_data_.server->node_names[0]);
+  EXPECT_EQ(kDebugNodeKey.output_slot, server_data_.server->output_slots[0]);
+  EXPECT_EQ(kDebugNodeKey.debug_op, server_data_.server->debug_ops[0]);
+}
+
+TEST_F(GrpcDebugTest, SendDebugTensorWithLargeStringAtIndex0ViaGrpcTest) {
+  Tensor tensor(DT_STRING, TensorShape({1, 1}));
+  tensor.flat<string>()(0) = string(5000 * 1024, 'A');
+  const DebugNodeKey kDebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
+                                   "foo_tensor", 0, "DebugIdentity");
+  const Status status = DebugIO::PublishDebugTensor(
+      kDebugNodeKey, tensor, Env::Default()->NowMicros(), {server_data_.url});
+  ASSERT_FALSE(status.ok());
+  ASSERT_NE(status.error_message().find("string value at index 0 from debug "
+                                        "node foo_tensor:0:DebugIdentity does "
+                                        "not fit gRPC message size limit"),
+            string::npos);
+  TF_ASSERT_OK(DebugIO::CloseDebugURL(server_data_.url));
+}
+
+TEST_F(GrpcDebugTest, SendDebugTensorWithLargeStringAtIndex1ViaGrpcTest) {
+  Tensor tensor(DT_STRING, TensorShape({1, 2}));
+  tensor.flat<string>()(0) = "A";
+  tensor.flat<string>()(1) = string(5000 * 1024, 'A');
+  const DebugNodeKey kDebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
+                                   "foo_tensor", 0, "DebugIdentity");
+  const Status status = DebugIO::PublishDebugTensor(
+      kDebugNodeKey, tensor, Env::Default()->NowMicros(), {server_data_.url});
+  ASSERT_FALSE(status.ok());
+  ASSERT_NE(status.error_message().find("string value at index 1 from debug "
+                                        "node foo_tensor:0:DebugIdentity does "
+                                        "not fit gRPC message size limit"),
+            string::npos);
+  TF_ASSERT_OK(DebugIO::CloseDebugURL(server_data_.url));
+}
+
+TEST_F(GrpcDebugTest, SendMultipleDebugTensorsSynchronizedViaGrpcTest) {
+  const int32 kSends = 4;
+
+  // Prepare the tensors to sent.
+  std::vector<Tensor> tensors;
+  for (int i = 0; i < kSends; ++i) {
+    Tensor tensor(DT_INT32, TensorShape({1, 1}));
+    tensor.flat<int>()(0) = i * i;
+    tensors.push_back(tensor);
+  }
+
+  thread::ThreadPool* tp =
+      new thread::ThreadPool(Env::Default(), "grpc_debug_test", kSends);
+
+  mutex mu;
+  Notification all_done;
+  int tensor_count GUARDED_BY(mu) = 0;
+  std::vector<Status> statuses GUARDED_BY(mu);
+
+  const std::vector<string> urls({server_data_.url});
+
+  // Set up the concurrent tasks of sending Tensors via an Event stream to the
+  // server.
+  auto fn = [this, &mu, &tensor_count, &tensors, &statuses, &all_done,
+             &urls]() {
+    int this_count;
+    {
+      mutex_lock l(mu);
+      this_count = tensor_count++;
+    }
+
+    // Different concurrent tasks will send different tensors.
+    const uint64 wall_time = Env::Default()->NowMicros();
+    Status publish_status = DebugIO::PublishDebugTensor(
+        DebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
+                     strings::StrCat("synchronized_node_", this_count), 0,
+                     "DebugIdentity"),
+        tensors[this_count], wall_time, urls);
+
+    {
+      mutex_lock l(mu);
+      statuses.push_back(publish_status);
+      if (this_count == kSends - 1 && !all_done.HasBeenNotified()) {
+        all_done.Notify();
+      }
+    }
+  };
+
+  // Schedule the concurrent tasks.
+  for (int i = 0; i < kSends; ++i) {
+    tp->Schedule(fn);
+  }
+
+  // Wait for all client tasks to finish.
+  all_done.WaitForNotification();
+  delete tp;
+
+  // Close the debug gRPC stream.
+  Status close_status = DebugIO::CloseDebugURL(server_data_.url);
+  ASSERT_TRUE(close_status.ok());
+
+  // Check all statuses from the PublishDebugTensor calls().
+  for (const Status& status : statuses) {
+    TF_ASSERT_OK(status);
+  }
+
+  // One prep tensor plus kSends concurrent tensors are expected.
+  ASSERT_EQ(kSends, server_data_.server->node_names.size());
+  for (size_t i = 0; i < server_data_.server->node_names.size(); ++i) {
+    std::vector<string> items =
+        str_util::Split(server_data_.server->node_names[i], '_');
+    int tensor_index;
+    strings::safe_strto32(items[2], &tensor_index);
+
+    ASSERT_EQ(TensorShape({1, 1}),
+              server_data_.server->debug_tensors[i].shape());
+    ASSERT_EQ(tensor_index * tensor_index,
+              server_data_.server->debug_tensors[i].flat<int>()(0));
+  }
+}
+
+TEST_F(GrpcDebugTest, SendeDebugTensorsThroughMultipleRoundsUsingGrpcGating) {
+  // Prepare the tensor to send.
+  const DebugNodeKey kDebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
+                                   "test_namescope/test_node", 0,
+                                   "DebugIdentity");
+  Tensor tensor(DT_INT32, TensorShape({1, 1}));
+  tensor.flat<int>()(0) = 42;
+
+  const std::vector<string> urls({server_data_.url});
+  for (int i = 0; i < 3; ++i) {
+    server_data_.server->ClearReceivedDebugData();
+    const uint64 wall_time = Env::Default()->NowMicros();
+
+    // On the 1st send (i == 0), gating is disabled, so data should be sent.
+    // On the 2nd send (i == 1), gating is enabled, and the server has enabled
+    //   the watch key in the previous send, so data should be sent.
+    // On the 3rd send (i == 2), gating is enabled, but the server has disabled
+    //   the watch key in the previous send, so data should not be sent.
+    const bool enable_gated_grpc = (i != 0);
+    TF_ASSERT_OK(DebugIO::PublishDebugTensor(kDebugNodeKey, tensor, wall_time,
+                                             urls, enable_gated_grpc));
+
+    server_data_.server->RequestDebugOpStateChangeAtNextStream(i == 0,
+                                                               kDebugNodeKey);
+
+    // Close the debug gRPC stream.
+    Status close_status = DebugIO::CloseDebugURL(server_data_.url);
+    ASSERT_TRUE(close_status.ok());
+
+    // Check dumped files according to the expected gating results.
+    if (i < 2) {
+      ASSERT_EQ(1, server_data_.server->node_names.size());
+      ASSERT_EQ(1, server_data_.server->output_slots.size());
+      ASSERT_EQ(1, server_data_.server->debug_ops.size());
+      EXPECT_EQ(kDebugNodeKey.device_name,
+                server_data_.server->device_names[0]);
+      EXPECT_EQ(kDebugNodeKey.node_name, server_data_.server->node_names[0]);
+      EXPECT_EQ(kDebugNodeKey.output_slot,
+                server_data_.server->output_slots[0]);
+      EXPECT_EQ(kDebugNodeKey.debug_op, server_data_.server->debug_ops[0]);
+    } else {
+      ASSERT_EQ(0, server_data_.server->node_names.size());
+    }
+  }
+}
+
+TEST_F(GrpcDebugTest, TestGateDebugNodeOnEmptyEnabledSet) {
+  CreateEmptyEnabledSet("grpc://localhost:3333");
+
+  ASSERT_FALSE(DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity",
+                                            {"grpc://localhost:3333"}));
+
+  // file:// debug URLs are not subject to grpc gating.
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen(
+      "foo:0:DebugIdentity", {"grpc://localhost:3333", "file:///tmp/tfdbg_1"}));
+}
+
+TEST_F(GrpcDebugTest, TestGateDebugNodeOnNonEmptyEnabledSet) {
+  const string kGrpcUrl1 = "grpc://localhost:3333";
+  const string kGrpcUrl2 = "grpc://localhost:3334";
+
+  DebugGrpcIO::EnableWatchKey(kGrpcUrl1, "foo:0:DebugIdentity");
+  DebugGrpcIO::EnableWatchKey(kGrpcUrl1, "bar:0:DebugIdentity");
+
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("foo:1:DebugIdentity", {kGrpcUrl1}));
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("foo:1:DebugNumericSummary", {kGrpcUrl1}));
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("qux:0:DebugIdentity", {kGrpcUrl1}));
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity", {kGrpcUrl1}));
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("bar:0:DebugIdentity", {kGrpcUrl1}));
+
+  // Wrong grpc:// debug URLs.
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity", {kGrpcUrl2}));
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("bar:0:DebugIdentity", {kGrpcUrl2}));
+
+  // file:// debug URLs are not subject to grpc gating.
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("qux:0:DebugIdentity",
+                                           {"file:///tmp/tfdbg_1", kGrpcUrl1}));
+}
+
+TEST_F(GrpcDebugTest, TestGateDebugNodeOnMultipleEmptyEnabledSets) {
+  const string kGrpcUrl1 = "grpc://localhost:3333";
+  const string kGrpcUrl2 = "grpc://localhost:3334";
+  const string kGrpcUrl3 = "grpc://localhost:3335";
+
+  DebugGrpcIO::EnableWatchKey(kGrpcUrl1, "foo:0:DebugIdentity");
+  DebugGrpcIO::EnableWatchKey(kGrpcUrl2, "bar:0:DebugIdentity");
+  CreateEmptyEnabledSet(kGrpcUrl3);
+
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity", {kGrpcUrl1}));
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("bar:0:DebugIdentity", {kGrpcUrl2}));
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity", {kGrpcUrl2}));
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("bar:0:DebugIdentity", {kGrpcUrl1}));
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity", {kGrpcUrl3}));
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("bar:0:DebugIdentity", {kGrpcUrl3}));
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity",
+                                           {kGrpcUrl1, kGrpcUrl2}));
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("bar:0:DebugIdentity",
+                                           {kGrpcUrl1, kGrpcUrl2}));
+  ASSERT_TRUE(DebugIO::IsDebugNodeGateOpen("foo:0:DebugIdentity",
+                                           {kGrpcUrl1, kGrpcUrl3}));
+  ASSERT_FALSE(DebugIO::IsDebugNodeGateOpen("bar:0:DebugIdentity",
+                                            {kGrpcUrl1, kGrpcUrl3}));
+}
+
+TEST_F(GrpcDebugTest, TestGateDebugNodeOnNonEmptyEnabledSetAndEmptyURLs) {
+  DebugGrpcIO::EnableWatchKey("grpc://localhost:3333", "foo:0:DebugIdentity");
+
+  std::vector<string> debug_urls_1;
+  ASSERT_FALSE(
+      DebugIO::IsDebugNodeGateOpen("foo:1:DebugIdentity", debug_urls_1));
+}
+
+TEST_F(GrpcDebugTest, TestGateCopyNodeOnEmptyEnabledSet) {
+  const string kGrpcUrl1 = "grpc://localhost:3333";
+  const string kWatch1 = "foo:0:DebugIdentity";
+  CreateEmptyEnabledSet(kGrpcUrl1);
+
+  ASSERT_FALSE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch1, kGrpcUrl1, true)}));
+  ASSERT_TRUE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch1, kGrpcUrl1, false)}));
+
+  // file:// debug URLs are not subject to grpc gating.
+  ASSERT_TRUE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec("foo:0:DebugIdentity", kGrpcUrl1, true),
+       DebugWatchAndURLSpec("foo:0:DebugIdentity", "file:///tmp/tfdbg_1",
+                            false)}));
+}
+
+TEST_F(GrpcDebugTest, TestGateCopyNodeOnNonEmptyEnabledSet) {
+  const string kGrpcUrl1 = "grpc://localhost:3333";
+  const string kGrpcUrl2 = "grpc://localhost:3334";
+  const string kWatch1 = "foo:0:DebugIdentity";
+  const string kWatch2 = "foo:1:DebugIdentity";
+  CreateEmptyEnabledSet(kGrpcUrl1);
+  CreateEmptyEnabledSet(kGrpcUrl2);
+  DebugGrpcIO::EnableWatchKey(kGrpcUrl1, kWatch1);
+
+  ASSERT_TRUE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch1, kGrpcUrl1, true)}));
+
+  ASSERT_FALSE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch1, kGrpcUrl2, true)}));
+  ASSERT_TRUE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch1, kGrpcUrl2, false)}));
+
+  ASSERT_FALSE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch2, kGrpcUrl1, true)}));
+  ASSERT_TRUE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch2, kGrpcUrl1, false)}));
+
+  ASSERT_TRUE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch1, kGrpcUrl1, true),
+       DebugWatchAndURLSpec(kWatch1, kGrpcUrl2, true)}));
+  ASSERT_TRUE(DebugIO::IsCopyNodeGateOpen(
+      {DebugWatchAndURLSpec(kWatch1, kGrpcUrl1, true),
+       DebugWatchAndURLSpec(kWatch2, kGrpcUrl2, true)}));
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index 4e185b37a8..f4208a0bbc 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -17,14 +17,12 @@ limitations under the License.
 
 #include <vector>
 
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
 #include "grpc++/create_channel.h"
-#endif
-
-#if defined(PLATFORM_WINDOWS)
+#else
 // winsock2.h is used in grpc, so Ws2_32.lib is needed
 #pragma comment(lib,"Ws2_32.lib")
-#endif
+#endif  // #ifndef PLATFORM_WINDOWS
 
 #include "tensorflow/core/debug/debugger_event_metadata.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
@@ -37,10 +35,9 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/util/event.pb.h"
 
-#define GRPC_OSS_UNIMPLEMENTED_ERROR \
-  return errors::Unimplemented(      \
-      kGrpcURLScheme,                \
-      " debug URL scheme is not implemented in open source yet.")
+#define GRPC_OSS_WINDOWS_UNIMPLEMENTED_ERROR \
+  return errors::Unimplemented(              \
+      kGrpcURLScheme, " debug URL scheme is not implemented on Windows yet.")
 
 namespace tensorflow {
 
@@ -234,7 +231,7 @@ string AppendTimestampToFilePath(const string& in, const uint64 timestamp) {
   return out;
 }
 
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
 // Publishes encoded GraphDef through a gRPC debugger stream, in chunks,
 // conforming to the gRPC message size limit.
 Status PublishEncodedGraphDefInChunks(const string& encoded_graph_def,
@@ -268,7 +265,7 @@ Status PublishEncodedGraphDefInChunks(const string& encoded_graph_def,
   }
   return Status::OK();
 }
-#endif
+#endif  // #ifndef PLATFORM_WINDOWS
 
 }  // namespace
 
@@ -393,7 +390,7 @@ Status DebugIO::PublishDebugMetadata(
   Status status;
   for (const string& url : debug_urls) {
     if (str_util::Lowercase(url).find(kGrpcURLScheme) == 0) {
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
       Event grpc_event;
 
       // Determine the path (if any) in the grpc:// URL, and add it as a field
@@ -411,7 +408,7 @@ Status DebugIO::PublishDebugMetadata(
       status.Update(
           DebugGrpcIO::SendEventProtoThroughGrpcStream(grpc_event, url));
 #else
-      GRPC_OSS_UNIMPLEMENTED_ERROR;
+      GRPC_OSS_WINDOWS_UNIMPLEMENTED_ERROR;
 #endif
     } else if (str_util::Lowercase(url).find(kFileURLScheme) == 0) {
       const string dump_root_dir = url.substr(strlen(kFileURLScheme));
@@ -450,7 +447,7 @@ Status DebugIO::PublishDebugTensor(const DebugNodeKey& debug_node_key,
         fail_statuses.push_back(s);
       }
     } else if (str_util::Lowercase(url).find(kGrpcURLScheme) == 0) {
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
       Status s = DebugGrpcIO::SendTensorThroughGrpcStream(
           debug_node_key, tensor, wall_time_us, url, gated_grpc);
 
@@ -459,7 +456,7 @@ Status DebugIO::PublishDebugTensor(const DebugNodeKey& debug_node_key,
         fail_statuses.push_back(s);
       }
 #else
-      GRPC_OSS_UNIMPLEMENTED_ERROR;
+      GRPC_OSS_WINDOWS_UNIMPLEMENTED_ERROR;
 #endif
     } else {
       return Status(error::UNAVAILABLE,
@@ -519,11 +516,11 @@ Status DebugIO::PublishGraph(const Graph& graph, const string& device_name,
       status.Update(
           DebugFileIO::DumpEventProtoToFile(event, dump_root_dir, file_name));
     } else if (debug_url.find(kGrpcURLScheme) == 0) {
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
       status.Update(PublishEncodedGraphDefInChunks(buf, device_name, now_micros,
                                                    debug_url));
 #else
-      GRPC_OSS_UNIMPLEMENTED_ERROR;
+      GRPC_OSS_WINDOWS_UNIMPLEMENTED_ERROR;
 #endif
     }
   }
@@ -534,7 +531,7 @@ Status DebugIO::PublishGraph(const Graph& graph, const string& device_name,
 // static
 bool DebugIO::IsCopyNodeGateOpen(
     const std::vector<DebugWatchAndURLSpec>& specs) {
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
   for (const DebugWatchAndURLSpec& spec : specs) {
     if (!spec.gated_grpc || spec.url.compare(0, strlen(DebugIO::kGrpcURLScheme),
                                              DebugIO::kGrpcURLScheme)) {
@@ -554,7 +551,7 @@ bool DebugIO::IsCopyNodeGateOpen(
 // static
 bool DebugIO::IsDebugNodeGateOpen(const string& watch_key,
                                   const std::vector<string>& debug_urls) {
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
   for (const string& debug_url : debug_urls) {
     if (debug_url.compare(0, strlen(DebugIO::kGrpcURLScheme),
                           DebugIO::kGrpcURLScheme)) {
@@ -574,7 +571,7 @@ bool DebugIO::IsDebugNodeGateOpen(const string& watch_key,
 // static
 bool DebugIO::IsDebugURLGateOpen(const string& watch_key,
                                  const string& debug_url) {
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
   if (debug_url.find(kGrpcURLScheme) != 0) {
     return true;
   } else {
@@ -588,10 +585,10 @@ bool DebugIO::IsDebugURLGateOpen(const string& watch_key,
 // static
 Status DebugIO::CloseDebugURL(const string& debug_url) {
   if (debug_url.find(DebugIO::kGrpcURLScheme) == 0) {
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
     return DebugGrpcIO::CloseGrpcStream(debug_url);
 #else
-    GRPC_OSS_UNIMPLEMENTED_ERROR;
+    GRPC_OSS_WINDOWS_UNIMPLEMENTED_ERROR;
 #endif
   } else {
     // No-op for non-gRPC URLs.
@@ -703,7 +700,7 @@ Status DebugFileIO::RecursiveCreateDir(Env* env, const string& dir) {
   }
 }
 
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
 DebugGrpcChannel::DebugGrpcChannel(const string& server_stream_addr)
     : server_stream_addr_(server_stream_addr),
       url_(strings::StrCat(DebugIO::kGrpcURLScheme, server_stream_addr)) {}
@@ -926,6 +923,6 @@ void DebugGrpcIO::CreateEmptyEnabledSet(const string& grpc_debug_url) {
   }
 }
 
-#endif  // #if defined(PLATFORM_GOOGLE)
+#endif  // #ifndef PLATFORM_WINDOWS
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/debug/debug_io_utils.h b/tensorflow/core/debug/debug_io_utils.h
index 98effed425..caf9f5341d 100644
--- a/tensorflow/core/debug/debug_io_utils.h
+++ b/tensorflow/core/debug/debug_io_utils.h
@@ -221,7 +221,7 @@ class DebugFileIO {
 
 // TODO(cais): Support grpc:// debug URLs in open source once Python grpc
 //   genrule becomes available. See b/23796275.
-#if defined(PLATFORM_GOOGLE)
+#ifndef PLATFORM_WINDOWS
 #include "tensorflow/core/debug/debug_service.grpc.pb.h"
 
 namespace tensorflow {
@@ -345,6 +345,6 @@ class DebugGrpcIO {
 };
 
 }  // namespace tensorflow
-#endif  // #if defined(PLATFORM_GOOGLE)
+#endif  // #ifndef(PLATFORM_WINDOWS)
 
 #endif  // TENSORFLOW_DEBUG_IO_UTILS_H_
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 1e2d026a79..d12ad8a04f 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -75,7 +75,8 @@ def tf_proto_library_py(name, srcs=[], protodeps=[], deps=[], visibility=[],
 def tf_proto_library(name, srcs = [], has_services = None,
                      protodeps = [], visibility = [], testonly = 0,
                      cc_libs = [],
-                     cc_api_version = 2, go_api_version = 2,
+                     cc_api_version = 2, cc_grpc_version = None,
+                     go_api_version = 2,
                      j2objc_api_version = 1,
                      java_api_version = 2, py_api_version = 2,
                      js_api_version = 2, js_codegen = "jspb"):
@@ -84,6 +85,7 @@ def tf_proto_library(name, srcs = [], has_services = None,
       name = name,
       srcs = srcs,
       protodeps = protodeps,
+      cc_grpc_version = cc_grpc_version,
       cc_libs = cc_libs,
       testonly = testonly,
       visibility = visibility,
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index d7ca36b12b..1c66413c05 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -6,7 +6,7 @@
 # ":debug_py": Public Python methods and classes of tfdbg.
 #   For API documentation, see https://www.tensorflow.org/api_docs/python/tfdbg
 #   For a user interface walkthrough, see https://www.tensorflow.org/programmers_guide/debugger
-# (Omitted internal-only public target)
+# ":grpc_debug_server": Server interface for grpc:// debug URLs.
 
 package(
     default_visibility = ["//tensorflow:internal"],
@@ -34,6 +34,7 @@ py_library(
         ":debug_data",
         ":debug_gradients",
         ":debug_utils",
+        ":grpc_debug_server",
         ":hooks",
         ":local_cli_wrapper",
     ],
@@ -44,6 +45,7 @@ py_library(
     name = "debug_pip",
     deps = [
         ":debug_py",
+        ":grpc_debug_test_server",
         ":offline_analyzer",
         ":session_debug_testlib",
     ] + if_not_windows([
@@ -577,6 +579,41 @@ py_library(
     ],
 )
 
+py_library(
+    name = "debug_service_pb2_grpc",
+    srcs = ["lib/debug_service_pb2_grpc.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/core/debug:debug_service_proto_py",
+    ],
+)
+
+py_library(
+    name = "grpc_debug_server",
+    srcs = ["lib/grpc_debug_server.py"],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":debug_data",
+        ":debug_service_pb2_grpc",
+        "@six_archive//:six",
+    ],
+)
+
+py_library(
+    name = "grpc_debug_test_server",
+    srcs = ["lib/grpc_debug_test_server.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":debug_data",
+        ":debug_utils",
+        ":grpc_debug_server",
+        "//tensorflow/python:client",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:variables",
+    ],
+)
+
 cuda_py_test(
     name = "session_debug_file_test",
     size = "small",
@@ -743,6 +780,58 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "session_debug_grpc_test",
+    size = "medium",
+    srcs = ["lib/session_debug_grpc_test.py"],
+    additional_deps = [
+        ":debug_data",
+        ":debug_utils",
+        ":dumping_wrapper",
+        ":grpc_debug_test_server",
+        ":grpc_wrapper",
+        ":hooks",
+        ":session_debug_testlib",
+        "//third_party/py/numpy",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:variables",
+    ],
+    tags = [
+        "nomac",  # TODO(cais): Install of futures and grpcio on all macs.
+        "notsan",
+    ],
+)
+
+# TODO(cais): Run the test in OSS, perhaps through a sh_test.
+cuda_py_test(
+    name = "dist_session_debug_grpc_test",
+    size = "medium",
+    srcs = ["lib/dist_session_debug_grpc_test.py"],
+    additional_deps = [
+        ":debug_data",
+        ":debug_utils",
+        ":dumping_wrapper",
+        ":grpc_debug_test_server",
+        ":grpc_wrapper",
+        ":hooks",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:variables",
+    ],
+    data = ["//tensorflow/tools/dist_test/server:grpc_tensorflow_server"],
+    tags = [
+        "no_oss",  # b/62956105: port conflicts + Incompatible with bazel_pip.
+        "notsan",
+    ],
+)
+
 py_test(
     name = "dumping_wrapper_test",
     size = "small",
diff --git a/tensorflow/python/debug/lib/debug_data.py b/tensorflow/python/debug/lib/debug_data.py
index 2da386bb0f..3335657a61 100644
--- a/tensorflow/python/debug/lib/debug_data.py
+++ b/tensorflow/python/debug/lib/debug_data.py
@@ -34,6 +34,7 @@ from tensorflow.core.util import event_pb2
 from tensorflow.python.framework import op_def_registry
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.platform import gfile
+from tensorflow.python.util import compat
 
 
 # TODO(cais): Tie these string constants in with C++?
@@ -362,7 +363,7 @@ def extract_core_metadata_from_event_proto(event):
 
 def device_name_to_device_path(device_name):
   """Convert device name to device path."""
-  device_name_items = device_name.split("/")
+  device_name_items = compat.as_text(device_name).split("/")
   device_name_items = [item.replace(":", "_") for item in device_name_items]
   return METADATA_FILE_PREFIX + DEVICE_TAG + ",".join(device_name_items)
 
diff --git a/tensorflow/python/debug/lib/debug_service_pb2_grpc.py b/tensorflow/python/debug/lib/debug_service_pb2_grpc.py
new file mode 100755
index 0000000000..98adc3284b
--- /dev/null
+++ b/tensorflow/python/debug/lib/debug_service_pb2_grpc.py
@@ -0,0 +1,76 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+#
+# Do not use pylint on generated code.
+# pylint: disable=missing-docstring,g-short-docstring-punctuation,g-no-space-after-docstring-summary,invalid-name,line-too-long,unused-argument,g-doc-args
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import grpc
+
+from tensorflow.core.debug import debug_service_pb2 as tensorflow_dot_core_dot_debug_dot_debug__service__pb2
+from tensorflow.core.util import event_pb2 as tensorflow_dot_core_dot_util_dot_event__pb2
+
+
+class EventListenerStub(object):
+  """EventListener: Receives Event protos, e.g., from debugged TensorFlow
+  runtime(s).
+  """
+
+  def __init__(self, channel):
+    """Constructor.
+
+    Args:
+      channel: A grpc.Channel.
+    """
+    self.SendEvents = channel.stream_stream(
+        '/tensorflow.EventListener/SendEvents',
+        request_serializer=tensorflow_dot_core_dot_util_dot_event__pb2.Event.SerializeToString,
+        response_deserializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.FromString,
+        )
+
+
+class EventListenerServicer(object):
+  """EventListener: Receives Event protos, e.g., from debugged TensorFlow
+  runtime(s).
+  """
+
+  def SendEvents(self, request_iterator, context):
+    """Client(s) can use this RPC method to send the EventListener Event protos.
+    The Event protos can hold information such as:
+    1) intermediate tensors from a debugged graph being executed, which can
+    be sent from DebugIdentity ops configured with grpc URLs.
+    2) GraphDefs of partition graphs, which can be sent from special debug
+    ops that get executed immediately after the beginning of the graph
+    execution.
+    """
+    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+    context.set_details('Method not implemented!')
+    raise NotImplementedError('Method not implemented!')
+
+
+def add_EventListenerServicer_to_server(servicer, server):
+  rpc_method_handlers = {
+      'SendEvents': grpc.stream_stream_rpc_method_handler(
+          servicer.SendEvents,
+          request_deserializer=tensorflow_dot_core_dot_util_dot_event__pb2.Event.FromString,
+          response_serializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.SerializeToString,
+      ),
+  }
+  generic_handler = grpc.method_handlers_generic_handler(
+      'tensorflow.EventListener', rpc_method_handlers)
+  server.add_generic_rpc_handlers((generic_handler,))
diff --git a/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py b/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py
new file mode 100644
index 0000000000..1e1fbf39d4
--- /dev/null
+++ b/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py
@@ -0,0 +1,230 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for debugger functionalities in tf.Session with grpc:// URLs.
+
+This test focus on grpc:// debugging of distributed (gRPC) sessions.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+import subprocess
+import sys
+import time
+
+import portpicker
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.debug.lib import debug_utils
+from tensorflow.python.debug.lib import grpc_debug_test_server
+from tensorflow.python.debug.wrappers import framework
+from tensorflow.python.debug.wrappers import grpc_wrapper
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging
+
+
+class DistributedSessionDebugTest(test_util.TensorFlowTestCase):
+  """Test the debugging of distributed sessions."""
+
+  PER_PROC_GPU_MEMORY_FRACTION = 0.1
+  POLLING_INTERVAL_SEC = 0.025
+
+  @classmethod
+  def setUpClass(cls):
+    gpu_memory_fraction_opt = (
+        "--gpu_memory_fraction=%f" % cls.PER_PROC_GPU_MEMORY_FRACTION)
+
+    worker_port = portpicker.pick_unused_port()
+    cluster_spec = "worker|localhost:%d" % worker_port
+    tf_logging.info("cluster_spec: %s", cluster_spec)
+
+    server_bin = test.test_src_dir_path(
+        "tools/dist_test/server/grpc_tensorflow_server")
+
+    cls.server_target = "grpc://localhost:%d" % worker_port
+
+    cls.server_procs = {}
+    cls.server_procs["worker"] = subprocess.Popen(
+        [
+            server_bin,
+            "--cluster_spec=%s" % cluster_spec,
+            "--job_name=worker",
+            "--task_id=0",
+            gpu_memory_fraction_opt,
+        ],
+        stdout=sys.stdout,
+        stderr=sys.stderr)
+
+    # Start debug server in-process, on separate thread.
+    (cls.debug_server_port, cls.debug_server_url, _, cls.debug_server_thread,
+     cls.debug_server
+    ) = grpc_debug_test_server.start_server_on_separate_thread(
+        dump_to_filesystem=False)
+    tf_logging.info("debug server url: %s", cls.debug_server_url)
+
+    cls.session_config = config_pb2.ConfigProto(
+        gpu_options=config_pb2.GPUOptions(
+            per_process_gpu_memory_fraction=cls.PER_PROC_GPU_MEMORY_FRACTION))
+
+  @classmethod
+  def tearDownClass(cls):
+    for key in cls.server_procs:
+      cls.server_procs[key].terminate()
+    cls.debug_server.stop_server().wait()
+    cls.debug_server_thread.join()
+
+  def setUp(self):
+    pass
+
+  def tearDown(self):
+    self.debug_server.clear_data()
+
+  def _pollingAssertDebugTensorValuesAllClose(self, expected_values,
+                                              debug_tensor_name):
+    """Poll debug_server till tensor appears and matches expected values."""
+    while (debug_tensor_name not in self.debug_server.debug_tensor_values or
+           len(self.debug_server.debug_tensor_values) < len(expected_values)):
+      time.sleep(self.POLLING_INTERVAL_SEC)
+    self.assertAllClose(
+        expected_values,
+        self.debug_server.debug_tensor_values[debug_tensor_name])
+
+  def _createGraph(self):
+    """Create graph for testing.
+
+    Returns:
+      Python Graph object.
+    """
+    with ops.Graph().as_default() as graph:
+      with ops.device("/job:worker/task:0/cpu:0"):
+        self.a = variables.Variable(10.0, name="a")
+        self.b = variables.Variable(100.0, name="b")
+        self.inc_a = state_ops.assign_add(self.a, 2.0, name="inc_a")
+        self.dec_b = state_ops.assign_add(self.b, -5.0, name="dec_b")
+        self.p = math_ops.multiply(self.inc_a, self.dec_b, name="p")
+        self.q = math_ops.negative(self.p, name="q")
+    return graph
+
+  def testDistributedRunWithGatedGrpcCommunicatesWithDebugServerCorrectly(self):
+    graph = self._createGraph()
+    with session.Session(
+        config=self.session_config, graph=graph,
+        target=self.server_target) as sess:
+      sess.run(self.a.initializer)
+      sess.run(self.b.initializer)
+
+      run_options = config_pb2.RunOptions()
+      debug_utils.watch_graph(
+          run_options,
+          sess.graph,
+          node_name_regex_whitelist=r"a",
+          debug_ops=["DebugIdentity"],
+          debug_urls=[self.debug_server_url])
+
+      # Test gated_grpc for an op located on the worker, i.e., on the same
+      # host as where MasterSession is.
+      # TODO(cais): gRPC gating of debug ops does not work on partition graphs
+      # not located on MasterSession hosts (e.g., parameter servers) yet. Make
+      # it work.
+      debug_utils.watch_graph(
+          run_options,
+          sess.graph,
+          node_name_regex_whitelist=r"p",
+          debug_ops=["DebugIdentity(gated_grpc=True)"],
+          debug_urls=[self.debug_server_url])
+
+      for i in xrange(4):
+        # N.B.: These requests will be fulfilled not in this debugged
+        # Session.run() invocation, but in the next one.
+        if i % 2 == 0:
+          self.debug_server.request_watch("p", 0, "DebugIdentity")
+        else:
+          self.debug_server.request_unwatch("p", 0, "DebugIdentity")
+
+        expected_p = (10.0 + 2.0 * (i + 1)) * (100.0 - 5.0 * (i + 1))
+        self.assertAllClose(-expected_p, sess.run(self.q, options=run_options))
+
+        self.assertEqual(1, len(self.debug_server.core_metadata_json_strings))
+        core_metadata = json.loads(
+            self.debug_server.core_metadata_json_strings[0])
+        self.assertEqual([], core_metadata["input_names"])
+        self.assertEqual(["q:0"], core_metadata["output_names"])
+        self.assertEqual(i, core_metadata["executor_step_index"])
+
+        if i == 0:
+          self.assertEqual(1, len(self.debug_server.partition_graph_defs))
+
+        # Tensor "a" is from a PS. It may take longer to arrive due to the fact
+        # that the stream connection between the PS and the debug server is
+        # persistent and not torn down at the end of each Session.run()
+        self._pollingAssertDebugTensorValuesAllClose([10.0 + 2.0 * i],
+                                                     "a:0:DebugIdentity")
+
+        # Due to the gRPC gating of the debug op for "p", the debug tensor
+        # should be available on odd-indexed runs.
+        if i % 2 == 0:
+          self.assertNotIn("p:0:DebugIdentity",
+                           self.debug_server.debug_tensor_values)
+        else:
+          self.assertAllClose(
+              [expected_p],
+              self.debug_server.debug_tensor_values["p:0:DebugIdentity"])
+
+        self.assertNotIn("b:0:DebugIdentity",
+                         self.debug_server.debug_tensor_values)
+        self.debug_server.clear_data()
+
+  def testDistributedRunWithGrpcDebugWrapperWorks(self):
+    graph = self._createGraph()
+    with session.Session(
+        config=self.session_config, graph=graph,
+        target=self.server_target) as sess:
+      sess.run(self.a.initializer)
+      sess.run(self.b.initializer)
+
+      def watch_fn(feeds, fetch_keys):
+        del feeds, fetch_keys
+        return framework.WatchOptions(
+            debug_ops=["DebugIdentity"],
+            node_name_regex_whitelist=r"p")
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn)
+
+      for i in xrange(4):
+        expected_p = (10.0 + 2.0 * (i + 1)) * (100.0 - 5.0 * (i + 1))
+        self.assertAllClose(-expected_p, sess.run(self.q))
+
+        if i == 0:
+          self.assertEqual(1, len(self.debug_server.partition_graph_defs))
+
+        self.assertAllClose(
+            [expected_p],
+            self.debug_server.debug_tensor_values["p:0:DebugIdentity"])
+        self.assertNotIn("b:0:DebugIdentity",
+                         self.debug_server.debug_tensor_values)
+        self.debug_server.clear_data()
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/python/debug/lib/grpc_debug_server.py b/tensorflow/python/debug/lib/grpc_debug_server.py
new file mode 100644
index 0000000000..181b437695
--- /dev/null
+++ b/tensorflow/python/debug/lib/grpc_debug_server.py
@@ -0,0 +1,395 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""gRPC debug server in Python."""
+# pylint: disable=g-bad-import-order
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import json
+import threading
+import time
+
+from concurrent import futures
+import grpc
+from six.moves import queue
+
+from tensorflow.core.debug import debug_service_pb2
+from tensorflow.core.framework import graph_pb2
+from tensorflow.python.debug.lib import debug_data
+from tensorflow.python.debug.lib import debug_service_pb2_grpc
+
+DebugWatch = collections.namedtuple("DebugWatch",
+                                    ["node_name", "output_slot", "debug_op"])
+
+
+def _watch_key_event_reply(to_enable, node_name, output_slot, debug_op):
+  """Make EventReply proto to represent a request to watch/unwatch a debug op.
+
+  Args:
+    to_enable: (`bool`) whether the request is to enable the watch key.
+    node_name: (`str`) name of the node.
+    output_slot: (`int`) output slot of the tensor.
+    debug_op: (`str`) the debug op attached to node_name:output_slot tensor to
+      watch or unwatch.
+
+  Returns:
+    An EventReply proto.
+  """
+  event_reply = debug_service_pb2.EventReply()
+  state_change = event_reply.debug_op_state_changes.add()
+  state_change.change = (
+      debug_service_pb2.EventReply.DebugOpStateChange.ENABLE
+      if to_enable else debug_service_pb2.EventReply.DebugOpStateChange.DISABLE)
+  state_change.node_name = node_name
+  state_change.output_slot = output_slot
+  state_change.debug_op = debug_op
+  return event_reply
+
+
+class EventListenerBaseStreamHandler(object):
+  """Per-stream handler of EventListener gRPC streams."""
+
+  def __init__(self):
+    """Constructor of EventListenerStreamHandler."""
+    raise NotImplementedError(
+        "__init__() is not implemented in the base stream handler class")
+
+  def on_core_metadata_event(self, event):
+    """Callback for core metadata.
+
+    Args:
+      event: The Event proto that carries a JSON string in its
+        `log_message.message` field.
+    """
+    raise NotImplementedError(
+        "on_core_metadata_event() is not implemented in the base servicer "
+        "class")
+
+  def on_graph_def(self, graph_def, device_name, wall_time):
+    """Callback for Event proto received through the gRPC stream.
+
+    This Event proto carries a GraphDef, encoded as bytes, in its graph_def
+    field.
+
+    Args:
+      graph_def: A GraphDef object.
+      device_name: Name of the device on which the graph was created.
+      wall_time: An epoch timestamp (in microseconds) for the graph.
+    """
+    raise NotImplementedError(
+        "on_graph_def() is not implemented in the base servicer class")
+
+  def on_value_event(self, event):
+    """Callback for Event proto received through the gRPC stream.
+
+    This Event proto carries a Tensor in its summary.value[0] field.
+
+    Args:
+      event: The Event proto from the stream to be processed.
+    """
+    raise NotImplementedError(
+        "on_value_event() is not implemented in the base servicer class")
+
+
+class EventListenerBaseServicer(debug_service_pb2_grpc.EventListenerServicer):
+  """Base Python class for gRPC debug server."""
+
+  def __init__(self, server_port, stream_handler_class):
+    """Constructor.
+
+    Args:
+      server_port: (int) Port number to bind to.
+      stream_handler_class: A class of the base class
+        `EventListenerBaseStreamHandler` that will be used to constructor
+        stream handler objects during `SendEvents` calls.
+    """
+
+    self._server_port = server_port
+    self._stream_handler_class = stream_handler_class
+
+    self._server_lock = threading.Lock()
+    self._server_started = False
+    self._stop_requested = False
+
+    self._event_reply_queue = queue.Queue()
+    self._gated_grpc_debug_watches = set()
+
+  def SendEvents(self, request_iterator, context):
+    """Implementation of the SendEvents service method.
+
+    This method receives streams of Event protos from the client, and processes
+    them in ways specified in the on_event() callback. The stream is
+    bi-directional, but currently only the client-to-server stream (i.e., the
+    stream from the debug ops to the server) is used.
+
+    Args:
+      request_iterator: The incoming stream of Event protos.
+      context: Server context.
+
+    Raises:
+      ValueError: If there are more than one core metadata events.
+
+    Yields:
+      An empty stream of responses.
+    """
+    core_metadata_count = 0
+
+    # A map from GraphDef hash to a list of received chunks.
+    graph_def_chunks = {}
+    tensor_chunks = {}
+
+    stream_handler = None
+    for event in request_iterator:
+      if not stream_handler:
+        stream_handler = self._stream_handler_class()
+
+      if event.graph_def:
+        maybe_graph_def, maybe_device_name, maybe_wall_time = (
+            self._process_encoded_graph_def_in_chunks(event, graph_def_chunks))
+        if maybe_graph_def:
+          stream_handler.on_graph_def(
+              maybe_graph_def, maybe_device_name, maybe_wall_time)
+      elif event.log_message.message:
+        core_metadata_count += 1
+        if core_metadata_count > 1:
+          raise ValueError(
+              "Expected one core metadata event; received multiple")
+        stream_handler.on_core_metadata_event(event)
+      elif event.summary and event.summary.value:
+        maybe_tensor_event = self._process_tensor_event_in_chunks(
+            event, tensor_chunks)
+        if maybe_tensor_event:
+          stream_handler.on_value_event(maybe_tensor_event)
+
+    # The server writes EventReply messages, if any.
+    while not self._event_reply_queue.empty():
+      yield self._event_reply_queue.get()
+
+  def _process_tensor_event_in_chunks(self, event, tensor_chunks):
+    """Possibly reassemble event chunks.
+
+    Due to gRPC's message size limit, a large tensor can be encapsulated in
+    multiple Event proto chunks to be sent through the debugger stream. This
+    method keeps track of the chunks that have arrived, reassemble all chunks
+    corresponding to a tensor when they have arrived and return the reassembled
+    Event proto.
+
+    Args:
+      event: The single Event proto that has arrived.
+      tensor_chunks: A dict used to keep track of the Event protos that have
+        arrived but haven't been reassembled.
+
+    Returns:
+      If all Event protos corresponding to a tensor have arrived, returns the
+      reassembled Event proto. Otherwise, return None.
+    """
+
+    value = event.summary.value[0]
+    debugger_plugin_metadata = json.loads(
+        value.metadata.plugin_data[0].content)
+    device_name = debugger_plugin_metadata["device"]
+    num_chunks = debugger_plugin_metadata["numChunks"]
+    chunk_index = debugger_plugin_metadata["chunkIndex"]
+
+    if num_chunks <= 1:
+      return event
+
+    debug_node_name = value.node_name
+    timestamp = int(event.wall_time)
+    tensor_key = "%s_%s_%d" % (device_name, debug_node_name, timestamp)
+
+    if tensor_key not in tensor_chunks:
+      tensor_chunks[tensor_key] = [None] * num_chunks
+
+    chunks = tensor_chunks[tensor_key]
+    if value.tensor.tensor_content:
+      chunks[chunk_index] = value.tensor
+    elif value.tensor.string_val:
+      chunks[chunk_index] = event
+
+    if None not in chunks:
+      if value.tensor.tensor_content:
+        event.summary.value[0].tensor.tensor_content = b"".join(
+            chunk.tensor_content for chunk in chunks)
+        del tensor_chunks[tensor_key]
+        return event
+      elif value.tensor.string_val:
+        merged_event = chunks[0]
+        for chunk in chunks[1:]:
+          merged_event.summary.value[0].tensor.string_val.extend(
+              list(chunk.summary.value[0].tensor.string_val))
+        return merged_event
+
+  def _process_encoded_graph_def_in_chunks(self,
+                                           event,
+                                           graph_def_chunks):
+    """Process an Event proto containing a chunk of encoded GraphDef.
+
+    Args:
+      event: the Event proto containing the chunk of encoded GraphDef.
+      graph_def_chunks: A dict mapping keys for GraphDefs (i.e.,
+      "<graph_def_hash>,<device_name>,<wall_time>") to a list of chunks of
+      encoded GraphDefs.
+
+    Returns:
+      If all chunks of the GraphDef have arrived,
+        return decoded GraphDef proto, device name, wall_time.
+      Otherwise,
+        return None, None, None.
+    """
+    graph_def = graph_pb2.GraphDef()
+    index_bar_0 = event.graph_def.find(b"|")
+    index_bar_1 = event.graph_def.find(b"|", index_bar_0 + 1)
+    index_bar_2 = event.graph_def.find(b"|", index_bar_1 + 1)
+    graph_def_hash_device_timestamp = event.graph_def[:index_bar_0]
+    chunk_index = int(event.graph_def[index_bar_0 + 1 : index_bar_1])
+    num_chunks = int(event.graph_def[index_bar_1 + 1 : index_bar_2])
+    if graph_def_hash_device_timestamp not in graph_def_chunks:
+      graph_def_chunks[graph_def_hash_device_timestamp] = [None] * num_chunks
+    graph_def_chunks[graph_def_hash_device_timestamp][
+        chunk_index] = event.graph_def[index_bar_2 + 1:]
+    if all(graph_def_chunks[graph_def_hash_device_timestamp]):
+      device_name = graph_def_hash_device_timestamp.split(b",")[1]
+      wall_time = int(graph_def_hash_device_timestamp.split(b",")[2])
+      graph_def.ParseFromString(
+          b"".join(graph_def_chunks[graph_def_hash_device_timestamp]))
+      del graph_def_chunks[graph_def_hash_device_timestamp]
+      self._process_graph_def(graph_def)
+      return graph_def, device_name, wall_time
+    else:
+      return None, None, None
+
+  def _process_graph_def(self, graph_def):
+    for node_def in graph_def.node:
+      if (debug_data.is_debug_node(node_def.name) and
+          node_def.attr["gated_grpc"].b):
+        node_name, output_slot, _, debug_op = (
+            debug_data.parse_debug_node_name(node_def.name))
+        self._gated_grpc_debug_watches.add(
+            DebugWatch(node_name, output_slot, debug_op))
+
+  def run_server(self):
+    """Start running the server.
+
+    Blocks until `stop_server` is invoked.
+
+    Raises:
+      ValueError: If server stop has already been requested, or if the server
+        has already started running.
+    """
+    self._server_lock.acquire()
+    try:
+      if self._stop_requested:
+        raise ValueError("Server has already stopped")
+      if self._server_started:
+        raise ValueError("Server has already started running")
+
+      self.server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+      debug_service_pb2_grpc.add_EventListenerServicer_to_server(self,
+                                                                 self.server)
+      self.server.add_insecure_port("[::]:%d" % self._server_port)
+      self.server.start()
+      self._server_started = True
+    finally:
+      self._server_lock.release()
+
+    while not self._stop_requested:
+      time.sleep(1.0)
+
+  def stop_server(self, grace=1.0):
+    """Request server stopping.
+
+    Once stopped, server cannot be stopped or started again. This method is
+    non-blocking. Call `wait()` on the returned event to block until the server
+    has completely stopped.
+
+    Args:
+      grace: Grace period in seconds to be used when calling `server.stop()`.
+
+    Raises:
+      ValueError: If server stop has already been requested, or if the server
+        has not started running yet.
+
+    Returns:
+      A threading.Event that will be set when the server has completely stopped.
+    """
+    self._server_lock.acquire()
+    try:
+      if not self._server_started:
+        raise ValueError("Server has not started running")
+      if self._stop_requested:
+        raise ValueError("Server has already stopped")
+
+      self._stop_requested = True
+      return self.server.stop(grace=grace)
+    finally:
+      self._server_lock.release()
+
+  def request_watch(self, node_name, output_slot, debug_op):
+    """Request enabling a debug tensor watch.
+
+    This will let the server send a EventReply to the client side
+    (i.e., the debugged TensorFlow runtime process) to request adding a watch
+    key (i.e., <node_name>:<output_slot>:<debug_op>) to the list of enabled
+    watch keys. The list applies only to debug ops with the attribute
+    gated_grpc=True.
+
+    The request will take effect on the next debugged `Session.run()` call.
+
+    To disable the watch, use `request_unwatch()`.
+
+    Args:
+      node_name: (`str`) name of the node that the to-be-watched tensor belongs
+        to, e.g., "hidden/Weights".
+      output_slot: (`int`) output slot index of the tensor to watch.
+      debug_op: (`str`) name of the debug op to enable. This should not include
+        any attribute substrings.
+    """
+    self._event_reply_queue.put(
+        _watch_key_event_reply(True, node_name, output_slot, debug_op))
+
+  def request_unwatch(self, node_name, output_slot, debug_op):
+    """Request disabling a debug tensor watch.
+
+    The request will take effect on the next debugged `Session.run()` call.
+
+    This is the opposite of `request_watch()`.
+
+    Args:
+      node_name: (`str`) name of the node that the to-be-watched tensor belongs
+        to, e.g., "hidden/Weights".
+      output_slot: (`int`) output slot index of the tensor to watch.
+      debug_op: (`str`) name of the debug op to enable. This should not include
+        any attribute substrings.
+    """
+    self._event_reply_queue.put(
+        _watch_key_event_reply(False, node_name, output_slot, debug_op))
+
+  def gated_grpc_debug_watches(self):
+    """Get the list of debug watches with attribute gated_grpc=True.
+
+    Since the server receives `GraphDef` from the debugged runtime, it can only
+    return such debug watches that it has received so far.
+
+    Returns:
+      A `list` of `DebugWatch` `namedtuples` representing the debug watches with
+      gated_grpc=True. Each `namedtuple` element has the attributes:
+        `node_name` as a `str`,
+        `output_slot` as an `int`,
+        `debug_op` as a `str`.
+    """
+    return list(self._gated_grpc_debug_watches)
diff --git a/tensorflow/python/debug/lib/grpc_debug_test_server.py b/tensorflow/python/debug/lib/grpc_debug_test_server.py
new file mode 100644
index 0000000000..32751e0f29
--- /dev/null
+++ b/tensorflow/python/debug/lib/grpc_debug_test_server.py
@@ -0,0 +1,336 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""GRPC debug server for testing."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import errno
+import functools
+import hashlib
+import json
+import os
+import re
+import shutil
+import tempfile
+import threading
+import time
+
+import portpicker
+
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.util import event_pb2
+from tensorflow.python.client import session
+from tensorflow.python.debug.lib import debug_data
+from tensorflow.python.debug.lib import debug_utils
+from tensorflow.python.debug.lib import grpc_debug_server
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import errors
+from tensorflow.python.ops import variables
+
+
+def _get_dump_file_path(dump_root, device_name, debug_node_name):
+  """Get the file path of the dump file for a debug node.
+
+  Args:
+    dump_root: (str) Root dump directory.
+    device_name: (str) Name of the device that the debug node resides on.
+    debug_node_name: (str) Name of the debug node, e.g.,
+      cross_entropy/Log:0:DebugIdentity.
+
+  Returns:
+    (str) Full path of the dump file.
+  """
+
+  dump_root = os.path.join(
+      dump_root, debug_data.device_name_to_device_path(device_name))
+  if "/" in debug_node_name:
+    dump_dir = os.path.join(dump_root, os.path.dirname(debug_node_name))
+    dump_file_name = re.sub(":", "_", os.path.basename(debug_node_name))
+  else:
+    dump_dir = dump_root
+    dump_file_name = re.sub(":", "_", debug_node_name)
+
+  now_microsec = int(round(time.time() * 1000 * 1000))
+  dump_file_name += "_%d" % now_microsec
+
+  return os.path.join(dump_dir, dump_file_name)
+
+
+class EventListenerTestStreamHandler(
+    grpc_debug_server.EventListenerBaseStreamHandler):
+  """Implementation of EventListenerBaseStreamHandler that dumps to file."""
+
+  def __init__(self, dump_dir, event_listener_servicer):
+    self._dump_dir = dump_dir
+    self._event_listener_servicer = event_listener_servicer
+    if self._dump_dir:
+      self._try_makedirs(self._dump_dir)
+
+    self._grpc_path = None
+    self._cached_graph_defs = []
+    self._cached_graph_def_device_names = []
+    self._cached_graph_def_wall_times = []
+
+  def on_core_metadata_event(self, event):
+    core_metadata = json.loads(event.log_message.message)
+
+    if not self._grpc_path:
+      grpc_path = core_metadata["grpc_path"]
+      if grpc_path:
+        if grpc_path.startswith("/"):
+          grpc_path = grpc_path[1:]
+      if self._dump_dir:
+        self._dump_dir = os.path.join(self._dump_dir, grpc_path)
+
+        # Write cached graph defs to filesystem.
+        for graph_def, device_name, wall_time in zip(
+            self._cached_graph_defs,
+            self._cached_graph_def_device_names,
+            self._cached_graph_def_wall_times):
+          self._write_graph_def(graph_def, device_name, wall_time)
+
+    if self._dump_dir:
+      self._write_core_metadata_event(event)
+    else:
+      self._event_listener_servicer.core_metadata_json_strings.append(
+          event.log_message.message)
+
+  def on_graph_def(self, graph_def, device_name, wall_time):
+    """Implementation of the tensor value-carrying Event proto callback.
+
+    Args:
+      graph_def: A GraphDef object.
+      device_name: Name of the device on which the graph was created.
+      wall_time: An epoch timestamp (in microseconds) for the graph.
+    """
+    if self._dump_dir:
+      if self._grpc_path:
+        self._write_graph_def(graph_def, device_name, wall_time)
+      else:
+        self._cached_graph_defs.append(graph_def)
+        self._cached_graph_def_device_names.append(device_name)
+        self._cached_graph_def_wall_times.append(wall_time)
+    else:
+      self._event_listener_servicer.partition_graph_defs.append(graph_def)
+
+  def on_value_event(self, event):
+    """Implementation of the tensor value-carrying Event proto callback.
+
+    Writes the Event proto to the file system for testing. The path written to
+    follows the same pattern as the file:// debug URLs of tfdbg, i.e., the
+    name scope of the op becomes the directory structure under the dump root
+    directory.
+
+    Args:
+      event: The Event proto carrying a tensor value.
+    """
+    if self._dump_dir:
+      self._write_value_event(event)
+    else:
+      value = event.summary.value[0]
+      self._event_listener_servicer.debug_tensor_values[value.node_name].append(
+          debug_data.load_tensor_from_event(event))
+
+  def _try_makedirs(self, dir_path):
+    if not os.path.isdir(dir_path):
+      try:
+        os.makedirs(dir_path)
+      except OSError as error:
+        if error.errno != errno.EEXIST:
+          raise
+
+  def _write_core_metadata_event(self, event):
+    core_metadata_path = os.path.join(
+        self._dump_dir,
+        debug_data.METADATA_FILE_PREFIX + debug_data.CORE_METADATA_TAG +
+        "_%d" % event.wall_time)
+    self._try_makedirs(self._dump_dir)
+    with open(core_metadata_path, "wb") as f:
+      f.write(event.SerializeToString())
+
+  def _write_graph_def(self, graph_def, device_name, wall_time):
+    encoded_graph_def = graph_def.SerializeToString()
+    graph_hash = int(hashlib.md5(encoded_graph_def).hexdigest(), 16)
+    event = event_pb2.Event(graph_def=encoded_graph_def, wall_time=wall_time)
+    graph_file_path = os.path.join(
+        self._dump_dir,
+        debug_data.device_name_to_device_path(device_name),
+        debug_data.METADATA_FILE_PREFIX + debug_data.GRAPH_FILE_TAG +
+        debug_data.HASH_TAG + "%d_%d" % (graph_hash, wall_time))
+    self._try_makedirs(os.path.dirname(graph_file_path))
+    with open(graph_file_path, "wb") as f:
+      f.write(event.SerializeToString())
+
+  def _write_value_event(self, event):
+    value = event.summary.value[0]
+
+    # Obtain the device name from the metadata.
+    summary_metadata = event.summary.value[0].metadata
+    if not summary_metadata.plugin_data:
+      raise ValueError("The value lacks plugin data.")
+    try:
+      content = json.loads(summary_metadata.plugin_data[0].content)
+    except ValueError as err:
+      raise ValueError("Could not parse content into JSON: %r, %r" % (content,
+                                                                      err))
+    device_name = content["device"]
+
+    dump_full_path = _get_dump_file_path(
+        self._dump_dir, device_name, value.node_name)
+    self._try_makedirs(os.path.dirname(dump_full_path))
+    with open(dump_full_path, "wb") as f:
+      f.write(event.SerializeToString())
+
+
+class EventListenerTestServicer(grpc_debug_server.EventListenerBaseServicer):
+  """An implementation of EventListenerBaseServicer for testing."""
+
+  def __init__(self, server_port, dump_dir):
+    """Constructor of EventListenerTestServicer.
+
+    Args:
+      server_port: (int) The server port number.
+      dump_dir: (str) The root directory to which the data files will be
+        dumped. If empty or None, the received debug data will not be dumped
+        to the file system: they will be stored in memory instead.
+    """
+    self.core_metadata_json_strings = []
+    self.partition_graph_defs = []
+    self.debug_tensor_values = collections.defaultdict(list)
+
+    grpc_debug_server.EventListenerBaseServicer.__init__(
+        self, server_port,
+        functools.partial(EventListenerTestStreamHandler, dump_dir, self))
+
+  def clear_data(self):
+    self.core_metadata_json_strings = []
+    self.partition_graph_defs = []
+    self.debug_tensor_values = collections.defaultdict(list)
+
+
+def start_server_on_separate_thread(dump_to_filesystem=True,
+                                    server_start_delay_sec=0.0,
+                                    poll_server=False):
+  """Create a test gRPC debug server and run on a separate thread.
+
+  Args:
+    dump_to_filesystem: (bool) whether the debug server will dump debug data
+      to the filesystem.
+    server_start_delay_sec: (float) amount of time (in sec) to delay the server
+      start up for.
+    poll_server: (bool) whether the server will be polled till success on
+      startup.
+
+  Returns:
+    server_port: (int) Port on which the server runs.
+    debug_server_url: (str) grpc:// URL to the server.
+    server_dump_dir: (str) The debug server's dump directory.
+    server_thread: The server Thread object.
+    server: The `EventListenerTestServicer` object.
+
+  Raises:
+    ValueError: If polling the server process for ready state is not successful
+      within maximum polling count.
+  """
+  server_port = portpicker.pick_unused_port()
+  debug_server_url = "grpc://localhost:%d" % server_port
+
+  server_dump_dir = tempfile.mkdtemp() if dump_to_filesystem else None
+  server = EventListenerTestServicer(server_port=server_port,
+                                     dump_dir=server_dump_dir)
+
+  def delay_then_run_server():
+    time.sleep(server_start_delay_sec)
+    server.run_server()
+  server_thread = threading.Thread(target=delay_then_run_server)
+  server_thread.start()
+
+  if poll_server:
+    if not _poll_server_till_success(
+        50,
+        0.2,
+        debug_server_url,
+        server_dump_dir,
+        server,
+        gpu_memory_fraction=0.1):
+      raise ValueError(
+          "Failed to start test gRPC debug server at port %d" % server_port)
+    server.clear_data()
+  return server_port, debug_server_url, server_dump_dir, server_thread, server
+
+
+def _poll_server_till_success(max_attempts,
+                              sleep_per_poll_sec,
+                              debug_server_url,
+                              dump_dir,
+                              server,
+                              gpu_memory_fraction=1.0):
+  """Poll server until success or exceeding max polling count.
+
+  Args:
+    max_attempts: (int) How many times to poll at maximum
+    sleep_per_poll_sec: (float) How many seconds to sleep for after each
+      unsuccessful poll.
+    debug_server_url: (str) gRPC URL to the debug server.
+    dump_dir: (str) Dump directory to look for files in. If None, will directly
+      check data from the server object.
+    server: The server object.
+    gpu_memory_fraction: (float) Fraction of GPU memory to be
+      allocated for the Session used in server polling.
+
+  Returns:
+    (bool) Whether the polling succeeded within max_polls attempts.
+  """
+  poll_count = 0
+
+  config = config_pb2.ConfigProto(gpu_options=config_pb2.GPUOptions(
+      per_process_gpu_memory_fraction=gpu_memory_fraction))
+  with session.Session(config=config) as sess:
+    for poll_count in range(max_attempts):
+      server.clear_data()
+      print("Polling: poll_count = %d" % poll_count)
+
+      x_init_name = "x_init_%d" % poll_count
+      x_init = constant_op.constant([42.0], shape=[1], name=x_init_name)
+      x = variables.Variable(x_init, name=x_init_name)
+
+      run_options = config_pb2.RunOptions()
+      debug_utils.add_debug_tensor_watch(
+          run_options, x_init_name, 0, debug_urls=[debug_server_url])
+      try:
+        sess.run(x.initializer, options=run_options)
+      except errors.FailedPreconditionError:
+        pass
+
+      if dump_dir:
+        if os.path.isdir(
+            dump_dir) and debug_data.DebugDumpDir(dump_dir).size > 0:
+          shutil.rmtree(dump_dir)
+          print("Poll succeeded.")
+          return True
+        else:
+          print("Poll failed. Sleeping for %f s" % sleep_per_poll_sec)
+          time.sleep(sleep_per_poll_sec)
+      else:
+        if server.debug_tensor_values:
+          print("Poll succeeded.")
+          return True
+        else:
+          print("Poll failed. Sleeping for %f s" % sleep_per_poll_sec)
+          time.sleep(sleep_per_poll_sec)
+
+    return False
diff --git a/tensorflow/python/debug/lib/session_debug_grpc_test.py b/tensorflow/python/debug/lib/session_debug_grpc_test.py
new file mode 100644
index 0000000000..f97b4debd3
--- /dev/null
+++ b/tensorflow/python/debug/lib/session_debug_grpc_test.py
@@ -0,0 +1,622 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for debugger functionalities in tf.Session with grpc:// URLs.
+
+This test file focuses on the grpc:// debugging of local (non-distributed)
+tf.Sessions.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import shutil
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.debug.lib import debug_data
+from tensorflow.python.debug.lib import debug_utils
+from tensorflow.python.debug.lib import grpc_debug_test_server
+from tensorflow.python.debug.lib import session_debug_testlib
+from tensorflow.python.debug.wrappers import framework
+from tensorflow.python.debug.wrappers import grpc_wrapper
+from tensorflow.python.debug.wrappers import hooks
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging
+from tensorflow.python.training import monitored_session
+
+
+def no_rewrite_session_config():
+  rewriter_config = rewriter_config_pb2.RewriterConfig(
+      disable_model_pruning=True)
+  graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
+  return config_pb2.ConfigProto(graph_options=graph_options)
+
+
+class GrpcDebugServerTest(test_util.TensorFlowTestCase):
+
+  def testRepeatedRunServerRaisesException(self):
+    (_, _, _, server_thread,
+     server) = grpc_debug_test_server.start_server_on_separate_thread(
+         poll_server=True)
+    # The server is started asynchronously. It needs to be polled till its state
+    # has become started.
+
+    with self.assertRaisesRegexp(
+        ValueError, "Server has already started running"):
+      server.run_server()
+
+    server.stop_server().wait()
+    server_thread.join()
+
+  def testRepeatedStopServerRaisesException(self):
+    (_, _, _, server_thread,
+     server) = grpc_debug_test_server.start_server_on_separate_thread(
+         poll_server=True)
+    server.stop_server().wait()
+    server_thread.join()
+
+    with self.assertRaisesRegexp(ValueError, "Server has already stopped"):
+      server.stop_server().wait()
+
+  def testRunServerAfterStopRaisesException(self):
+    (_, _, _, server_thread,
+     server) = grpc_debug_test_server.start_server_on_separate_thread(
+         poll_server=True)
+    server.stop_server().wait()
+    server_thread.join()
+
+    with self.assertRaisesRegexp(ValueError, "Server has already stopped"):
+      server.run_server()
+
+
+class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase):
+
+  @classmethod
+  def setUpClass(cls):
+    session_debug_testlib.SessionDebugTestBase.setUpClass()
+    (cls._server_port, cls._debug_server_url, cls._server_dump_dir,
+     cls._server_thread,
+     cls._server) = grpc_debug_test_server.start_server_on_separate_thread()
+
+  @classmethod
+  def tearDownClass(cls):
+    # Stop the test server and join the thread.
+    cls._server.stop_server().wait()
+    cls._server_thread.join()
+
+    session_debug_testlib.SessionDebugTestBase.tearDownClass()
+
+  def setUp(self):
+    # Override the dump root as the test server's dump directory.
+    self._dump_root = self._server_dump_dir
+
+  def tearDown(self):
+    if os.path.isdir(self._server_dump_dir):
+      shutil.rmtree(self._server_dump_dir)
+    session_debug_testlib.SessionDebugTestBase.tearDown(self)
+
+  def _debug_urls(self, run_number=None):
+    return ["grpc://localhost:%d" % self._server_port]
+
+  def _debug_dump_dir(self, run_number=None):
+    if run_number is None:
+      return self._dump_root
+    else:
+      return os.path.join(self._dump_root, "run_%d" % run_number)
+
+  def testConstructGrpcDebugWrapperSessionWithInvalidTypeRaisesException(self):
+    sess = session.Session(config=no_rewrite_session_config())
+    with self.assertRaisesRegexp(
+        TypeError, "Expected type str or list in grpc_debug_server_addresses"):
+      grpc_wrapper.GrpcDebugWrapperSession(sess, 1337)
+
+  def testConstructGrpcDebugWrapperSessionWithInvalidTypeRaisesException2(self):
+    sess = session.Session(config=no_rewrite_session_config())
+    with self.assertRaisesRegexp(
+        TypeError, "Expected type str in list grpc_debug_server_addresses"):
+      grpc_wrapper.GrpcDebugWrapperSession(sess, ["localhost:1337", 1338])
+
+  def testUseInvalidWatchFnTypeWithGrpcDebugWrapperSessionRaisesException(self):
+    sess = session.Session(config=no_rewrite_session_config())
+    with self.assertRaises(TypeError):
+      grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self._server_port, watch_fn="foo")
+
+  def testGrpcDebugWrapperSessionWithoutWatchFnWorks(self):
+    u = variables.Variable(2.1, name="u")
+    v = variables.Variable(20.0, name="v")
+    w = math_ops.multiply(u, v, name="w")
+
+    sess = session.Session(config=no_rewrite_session_config())
+    sess.run(u.initializer)
+    sess.run(v.initializer)
+
+    sess = grpc_wrapper.GrpcDebugWrapperSession(
+        sess, "localhost:%d" % self._server_port)
+    w_result = sess.run(w)
+    self.assertAllClose(42.0, w_result)
+
+    dump = debug_data.DebugDumpDir(self._dump_root)
+    self.assertEqual(5, dump.size)
+    self.assertAllClose([2.1], dump.get_tensors("u", 0, "DebugIdentity"))
+    self.assertAllClose([2.1], dump.get_tensors("u/read", 0, "DebugIdentity"))
+    self.assertAllClose([20.0], dump.get_tensors("v", 0, "DebugIdentity"))
+    self.assertAllClose([20.0], dump.get_tensors("v/read", 0, "DebugIdentity"))
+    self.assertAllClose([42.0], dump.get_tensors("w", 0, "DebugIdentity"))
+
+  def testGrpcDebugWrapperSessionWithWatchFnWorks(self):
+    def watch_fn(feeds, fetch_keys):
+      del feeds, fetch_keys
+      return ["DebugIdentity", "DebugNumericSummary"], r".*/read", None
+
+    u = variables.Variable(2.1, name="u")
+    v = variables.Variable(20.0, name="v")
+    w = math_ops.multiply(u, v, name="w")
+
+    sess = session.Session(config=no_rewrite_session_config())
+    sess.run(u.initializer)
+    sess.run(v.initializer)
+
+    sess = grpc_wrapper.GrpcDebugWrapperSession(
+        sess, "localhost:%d" % self._server_port, watch_fn=watch_fn)
+    w_result = sess.run(w)
+    self.assertAllClose(42.0, w_result)
+
+    dump = debug_data.DebugDumpDir(self._dump_root)
+    self.assertEqual(4, dump.size)
+    self.assertAllClose([2.1], dump.get_tensors("u/read", 0, "DebugIdentity"))
+    self.assertEqual(
+        14, len(dump.get_tensors("u/read", 0, "DebugNumericSummary")[0]))
+    self.assertAllClose([20.0], dump.get_tensors("v/read", 0, "DebugIdentity"))
+    self.assertEqual(
+        14, len(dump.get_tensors("v/read", 0, "DebugNumericSummary")[0]))
+
+  def testGrpcDebugHookWithStatelessWatchFnWorks(self):
+    # Perform some set up. Specifically, construct a simple TensorFlow graph and
+    # create a watch function for certain ops.
+    def watch_fn(feeds, fetch_keys):
+      del feeds, fetch_keys
+      return framework.WatchOptions(
+          debug_ops=["DebugIdentity", "DebugNumericSummary"],
+          node_name_regex_whitelist=r".*/read",
+          op_type_regex_whitelist=None,
+          tolerate_debug_op_creation_failures=True)
+
+    u = variables.Variable(2.1, name="u")
+    v = variables.Variable(20.0, name="v")
+    w = math_ops.multiply(u, v, name="w")
+
+    sess = session.Session(config=no_rewrite_session_config())
+    sess.run(u.initializer)
+    sess.run(v.initializer)
+
+    # Create a hook. One could use this hook with say a tflearn Estimator.
+    # However, we use a HookedSession in this test to avoid depending on the
+    # internal implementation of Estimators.
+    grpc_debug_hook = hooks.GrpcDebugHook(
+        ["localhost:%d" % self._server_port], watch_fn=watch_fn)
+    sess = monitored_session._HookedSession(sess, [grpc_debug_hook])
+
+    # Run the hooked session. This should stream tensor data to the GRPC
+    # endpoints.
+    w_result = sess.run(w)
+
+    # Verify that the hook monitored the correct tensors.
+    self.assertAllClose(42.0, w_result)
+    dump = debug_data.DebugDumpDir(self._dump_root)
+    self.assertEqual(4, dump.size)
+    self.assertAllClose([2.1], dump.get_tensors("u/read", 0, "DebugIdentity"))
+    self.assertEqual(
+        14, len(dump.get_tensors("u/read", 0, "DebugNumericSummary")[0]))
+    self.assertAllClose([20.0], dump.get_tensors("v/read", 0, "DebugIdentity"))
+    self.assertEqual(
+        14, len(dump.get_tensors("v/read", 0, "DebugNumericSummary")[0]))
+
+  def testConstructGrpcDebugHookWithGrpcInUrlRaisesValueError(self):
+    """Tests that the hook raises an error if the URL starts with grpc://."""
+    with self.assertRaises(ValueError):
+      hooks.GrpcDebugHook(["grpc://foo:42"])
+
+
+class LargeGraphAndLargeTensorsDebugTest(test_util.TensorFlowTestCase):
+
+  @classmethod
+  def setUpClass(cls):
+    (cls.debug_server_port, cls.debug_server_url, _, cls.debug_server_thread,
+     cls.debug_server
+    ) = grpc_debug_test_server.start_server_on_separate_thread(
+        dump_to_filesystem=False)
+    tf_logging.info("debug server url: %s", cls.debug_server_url)
+
+  @classmethod
+  def tearDownClass(cls):
+    cls.debug_server.stop_server().wait()
+    cls.debug_server_thread.join()
+
+  def tearDown(self):
+    ops.reset_default_graph()
+    self.debug_server.clear_data()
+
+  def testSendingLargeGraphDefsWorks(self):
+    with self.test_session(
+        use_gpu=True, config=no_rewrite_session_config()) as sess:
+      u = variables.Variable(42.0, name="original_u")
+      for _ in xrange(50 * 1000):
+        u = array_ops.identity(u)
+      sess.run(variables.global_variables_initializer())
+
+      def watch_fn(fetches, feeds):
+        del fetches, feeds
+        return framework.WatchOptions(
+            debug_ops=["DebugIdentity"],
+            node_name_regex_whitelist=r"original_u")
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn)
+      self.assertAllClose(42.0, sess.run(u))
+
+      self.assertAllClose(
+          [42.0],
+          self.debug_server.debug_tensor_values["original_u:0:DebugIdentity"])
+      self.assertEqual(2 if test.is_gpu_available() else 1,
+                       len(self.debug_server.partition_graph_defs))
+      max_graph_def_size = max([
+          len(graph_def.SerializeToString())
+          for graph_def in self.debug_server.partition_graph_defs])
+      self.assertGreater(max_graph_def_size, 4 * 1024 * 1024)
+
+  def testSendingLargeFloatTensorWorks(self):
+    with self.test_session(
+        use_gpu=True, config=no_rewrite_session_config()) as sess:
+      u_init_val_array = list(xrange(1200 * 1024))
+      # Size: 4 * 1200 * 1024 = 4800k > 4M
+
+      u_init = constant_op.constant(
+          u_init_val_array, dtype=dtypes.float32, name="u_init")
+      u = variables.Variable(u_init, name="u")
+
+      def watch_fn(fetches, feeds):
+        del fetches, feeds  # Unused by this watch_fn.
+        return framework.WatchOptions(
+            debug_ops=["DebugIdentity"],
+            node_name_regex_whitelist=r"u_init")
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn)
+      sess.run(u.initializer)
+
+      self.assertAllEqual(
+          u_init_val_array,
+          self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0])
+
+  def testSendingStringTensorWithAlmostTooLargeStringsWorks(self):
+    with self.test_session(
+        use_gpu=True, config=no_rewrite_session_config()) as sess:
+      u_init_val = [
+          b"", b"spam", b"A" * 2500 * 1024, b"B" * 2500 * 1024, b"egg", b""]
+      u_init = constant_op.constant(
+          u_init_val, dtype=dtypes.string, name="u_init")
+      u = variables.Variable(u_init, name="u")
+
+      def watch_fn(fetches, feeds):
+        del fetches, feeds
+        return framework.WatchOptions(
+            debug_ops=["DebugIdentity"],
+            node_name_regex_whitelist=r"u_init")
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn)
+      sess.run(u.initializer)
+
+      self.assertAllEqual(
+          u_init_val,
+          self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0])
+
+  def testSendingLargeStringTensorWorks(self):
+    with self.test_session(
+        use_gpu=True, config=no_rewrite_session_config()) as sess:
+      strs_total_size_threshold = 5000 * 1024
+      cum_size = 0
+      u_init_val_array = []
+      while cum_size < strs_total_size_threshold:
+        strlen = np.random.randint(200)
+        u_init_val_array.append(b"A" * strlen)
+        cum_size += strlen
+
+      u_init = constant_op.constant(
+          u_init_val_array, dtype=dtypes.string, name="u_init")
+      u = variables.Variable(u_init, name="u")
+
+      def watch_fn(fetches, feeds):
+        del fetches, feeds
+        return framework.WatchOptions(
+            debug_ops=["DebugIdentity"],
+            node_name_regex_whitelist=r"u_init")
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn)
+      sess.run(u.initializer)
+
+      self.assertAllEqual(
+          u_init_val_array,
+          self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0])
+
+  def testSendingEmptyFloatTensorWorks(self):
+    with self.test_session(
+        use_gpu=True, config=no_rewrite_session_config()) as sess:
+      u_init = constant_op.constant(
+          [], dtype=dtypes.float32, shape=[0], name="u_init")
+      u = variables.Variable(u_init, name="u")
+
+      def watch_fn(fetches, feeds):
+        del fetches, feeds
+        return framework.WatchOptions(
+            debug_ops=["DebugIdentity"],
+            node_name_regex_whitelist=r"u_init")
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn)
+      sess.run(u.initializer)
+
+      u_init_value = self.debug_server.debug_tensor_values[
+          "u_init:0:DebugIdentity"][0]
+      self.assertEqual(np.float32, u_init_value.dtype)
+      self.assertEqual(0, len(u_init_value))
+
+  def testSendingEmptyStringTensorWorks(self):
+    with self.test_session(
+        use_gpu=True, config=no_rewrite_session_config()) as sess:
+      u_init = constant_op.constant(
+          [], dtype=dtypes.string, shape=[0], name="u_init")
+      u = variables.Variable(u_init, name="u")
+
+      def watch_fn(fetches, feeds):
+        del fetches, feeds
+        return framework.WatchOptions(
+            debug_ops=["DebugIdentity"],
+            node_name_regex_whitelist=r"u_init")
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn)
+      sess.run(u.initializer)
+
+      u_init_value = self.debug_server.debug_tensor_values[
+          "u_init:0:DebugIdentity"][0]
+      self.assertEqual(np.object, u_init_value.dtype)
+      self.assertEqual(0, len(u_init_value))
+
+
+class SessionDebugConcurrentTest(
+    session_debug_testlib.DebugConcurrentRunCallsTest):
+
+  @classmethod
+  def setUpClass(cls):
+    session_debug_testlib.SessionDebugTestBase.setUpClass()
+    (cls._server_port, cls._debug_server_url, cls._server_dump_dir,
+     cls._server_thread,
+     cls._server) = grpc_debug_test_server.start_server_on_separate_thread()
+
+  @classmethod
+  def tearDownClass(cls):
+    # Stop the test server and join the thread.
+    cls._server.stop_server().wait()
+    cls._server_thread.join()
+    session_debug_testlib.SessionDebugTestBase.tearDownClass()
+
+  def setUp(self):
+    self._num_concurrent_runs = 3
+    self._dump_roots = []
+    for i in range(self._num_concurrent_runs):
+      self._dump_roots.append(
+          os.path.join(self._server_dump_dir, "thread%d" % i))
+
+  def tearDown(self):
+    ops.reset_default_graph()
+    if os.path.isdir(self._server_dump_dir):
+      shutil.rmtree(self._server_dump_dir)
+
+  def _get_concurrent_debug_urls(self):
+    urls = []
+    for i in range(self._num_concurrent_runs):
+      urls.append(self._debug_server_url + "/thread%d" % i)
+    return urls
+
+
+class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase):
+  """Test server gating of debug ops."""
+
+  @classmethod
+  def setUpClass(cls):
+    (cls._server_port_1, cls._debug_server_url_1, _, cls._server_thread_1,
+     cls._server_1) = grpc_debug_test_server.start_server_on_separate_thread(
+         dump_to_filesystem=False)
+    (cls._server_port_2, cls._debug_server_url_2, _, cls._server_thread_2,
+     cls._server_2) = grpc_debug_test_server.start_server_on_separate_thread(
+         dump_to_filesystem=False)
+
+  @classmethod
+  def tearDownClass(cls):
+    cls._server_1.stop_server().wait()
+    cls._server_thread_1.join()
+    cls._server_2.stop_server().wait()
+    cls._server_thread_2.join()
+
+  def tearDown(self):
+    ops.reset_default_graph()
+    self._server_1.clear_data()
+    self._server_2.clear_data()
+
+  def testToggleEnableTwoDebugWatchesNoCrosstalkBetweenDebugNodes(self):
+    with session.Session(config=no_rewrite_session_config()) as sess:
+      v = variables.Variable(50.0, name="v")
+      delta = constant_op.constant(5.0, name="delta")
+      inc_v = state_ops.assign_add(v, delta, name="inc_v")
+
+      sess.run(v.initializer)
+
+      run_metadata = config_pb2.RunMetadata()
+      run_options = config_pb2.RunOptions(output_partition_graphs=True)
+      debug_utils.watch_graph(
+          run_options,
+          sess.graph,
+          debug_ops=["DebugIdentity(gated_grpc=true)",
+                     "DebugNumericSummary(gated_grpc=true)"],
+          debug_urls=[self._debug_server_url_1])
+
+      for i in xrange(4):
+        self._server_1.clear_data()
+
+        # N.B.: These requests will be fulfilled not in this debugged
+        # Session.run() invocation, but in the next one.
+        if i % 2 == 0:
+          self._server_1.request_watch("delta", 0, "DebugIdentity")
+          self._server_1.request_unwatch("delta", 0, "DebugNumericSummary")
+        else:
+          self._server_1.request_unwatch("delta", 0, "DebugIdentity")
+          self._server_1.request_watch("delta", 0, "DebugNumericSummary")
+
+        sess.run(inc_v, options=run_options, run_metadata=run_metadata)
+
+        if i == 0:
+          self.assertEqual(0, len(self._server_1.debug_tensor_values))
+        else:
+          self.assertEqual(1, len(self._server_1.debug_tensor_values))
+          if i % 2 == 1:
+            self.assertAllClose(
+                [5.0],
+                self._server_1.debug_tensor_values["delta:0:DebugIdentity"])
+          else:
+            self.assertAllClose(
+                [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 5.0, 5.0, 5.0,
+                  0.0, 1.0, 0.0]],
+                self._server_1.debug_tensor_values[
+                    "delta:0:DebugNumericSummary"])
+
+  def testToggleEnableTwoDebugWatchesNoCrosstalkBetweenServers(self):
+    with session.Session(config=no_rewrite_session_config()) as sess:
+      v = variables.Variable(50.0, name="v")
+      delta = constant_op.constant(5.0, name="delta")
+      inc_v = state_ops.assign_add(v, delta, name="inc_v")
+
+      sess.run(v.initializer)
+
+      run_metadata = config_pb2.RunMetadata()
+      run_options = config_pb2.RunOptions(output_partition_graphs=True)
+      debug_utils.watch_graph(
+          run_options,
+          sess.graph,
+          debug_ops=["DebugIdentity(gated_grpc=true)"],
+          debug_urls=[self._debug_server_url_1, self._debug_server_url_2])
+
+      for i in xrange(4):
+        self._server_1.clear_data()
+        self._server_2.clear_data()
+
+        # N.B.: These requests will be fulfilled not in this debugged
+        # Session.run() invocation, but in the next one.
+        if i % 2 == 0:
+          self._server_1.request_watch("delta", 0, "DebugIdentity")
+          self._server_2.request_watch("v", 0, "DebugIdentity")
+        else:
+          self._server_1.request_unwatch("delta", 0, "DebugIdentity")
+          self._server_2.request_unwatch("v", 0, "DebugIdentity")
+
+        sess.run(inc_v, options=run_options, run_metadata=run_metadata)
+
+        if i % 2 == 0:
+          self.assertEqual(0, len(self._server_1.debug_tensor_values))
+          self.assertEqual(0, len(self._server_2.debug_tensor_values))
+        else:
+          self.assertEqual(1, len(self._server_1.debug_tensor_values))
+          self.assertEqual(1, len(self._server_2.debug_tensor_values))
+          self.assertAllClose(
+              [5.0],
+              self._server_1.debug_tensor_values["delta:0:DebugIdentity"])
+          self.assertAllClose(
+              [50 + 5.0 * i],
+              self._server_2.debug_tensor_values["v:0:DebugIdentity"])
+
+  def testGetGrpcDebugWatchesReturnsCorrectAnswer(self):
+    with session.Session() as sess:
+      v = variables.Variable(50.0, name="v")
+      delta = constant_op.constant(5.0, name="delta")
+      inc_v = state_ops.assign_add(v, delta, name="inc_v")
+
+      sess.run(v.initializer)
+
+      # Before any debugged runs, the server should be aware of no debug
+      # watches.
+      self.assertEqual([], self._server_1.gated_grpc_debug_watches())
+
+      run_metadata = config_pb2.RunMetadata()
+      run_options = config_pb2.RunOptions(output_partition_graphs=True)
+      debug_utils.add_debug_tensor_watch(
+          run_options, "delta", output_slot=0,
+          debug_ops=["DebugNumericSummary(gated_grpc=true)"],
+          debug_urls=[self._debug_server_url_1])
+      debug_utils.add_debug_tensor_watch(
+          run_options, "v", output_slot=0,
+          debug_ops=["DebugIdentity"],
+          debug_urls=[self._debug_server_url_1])
+      sess.run(inc_v, options=run_options, run_metadata=run_metadata)
+
+      # After the first run, the server should have noted the debug watches
+      # for which gated_grpc == True, but not the ones with gated_grpc == False.
+      self.assertEqual(1, len(self._server_1.gated_grpc_debug_watches()))
+      debug_watch = self._server_1.gated_grpc_debug_watches()[0]
+      self.assertEqual("delta", debug_watch.node_name)
+      self.assertEqual(0, debug_watch.output_slot)
+      self.assertEqual("DebugNumericSummary", debug_watch.debug_op)
+
+
+class DelayedDebugServerTest(test_util.TensorFlowTestCase):
+
+  def testDebuggedSessionRunWorksWithDelayedDebugServerStartup(self):
+    """Test debugged Session.run() tolerates delayed debug server startup."""
+    ops.reset_default_graph()
+
+    # Start a debug server asynchronously, with a certain amount of delay.
+    (debug_server_port, _, _, server_thread,
+     debug_server) = grpc_debug_test_server.start_server_on_separate_thread(
+         server_start_delay_sec=2.0, dump_to_filesystem=False)
+
+    with self.test_session() as sess:
+      a_init = constant_op.constant(42.0, name="a_init")
+      a = variables.Variable(a_init, name="a")
+
+      def watch_fn(fetches, feeds):
+        del fetches, feeds
+        return framework.WatchOptions(debug_ops=["DebugIdentity"])
+
+      sess = grpc_wrapper.GrpcDebugWrapperSession(
+          sess, "localhost:%d" % debug_server_port, watch_fn=watch_fn)
+      sess.run(a.initializer)
+      self.assertAllClose(
+          [42.0], debug_server.debug_tensor_values["a_init:0:DebugIdentity"])
+
+    debug_server.stop_server().wait()
+    server_thread.join()
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index 7cb93c1774..295601c4b8 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -242,52 +242,101 @@ if [[ $(uname) == "Linux" ]]; then
   fi
 fi
 
-# Perform installation
-echo "Installing pip whl file: ${WHL_PATH}"
 
-# Create virtualenv directory for install test
-VENV_DIR="${PIP_TEST_ROOT}/venv"
+create_activate_virtualenv_and_install_tensorflow() {
+  # Create and activate a virtualenv; then install tensorflow pip package in it.
+  #
+  # Usage:
+  #   create_activate_virtualenv_and_install_tensorflow [--clean] \
+  #       <VIRTUALENV_DIR> <TF_WHEEL_PATH>
+  #
+  # Arguments:
+  #   --clean: Create a clean virtualenv, i.e., without --system-site-packages.
+  #   VIRTUALENV_DIR: virtualenv directory to be created.
+  #   TF_WHEEL_PATH: Path to the tensorflow wheel file to be installed in the
+  #     virtualenv.
+
+  VIRTUALENV_FLAGS="--system-site-packages"
+  if [[ "$1" == "--clean" ]]; then
+    VIRTUALENV_FLAGS=""
+    shift
+  fi
+
+  VIRTUALENV_DIR="$1"
+  TF_WHEEL_PATH="$2"
+  if [[ -d "${VIRTUALENV_DIR}" ]]; then
+    if rm -rf "${VIRTUALENV_DIR}"
+    then
+      echo "Removed existing virtualenv directory: ${VIRTUALENV_DIR}"
+    else
+      die "Failed to remove existing virtualenv directory: ${VIRTUALENV_DIR}"
+    fi
+  fi
 
-if [[ -d "${VENV_DIR}" ]]; then
-  if rm -rf "${VENV_DIR}"
+  if mkdir -p "${VIRTUALENV_DIR}"
   then
-    echo "Removed existing virtualenv directory: ${VENV_DIR}"
+    echo "Created virtualenv directory: ${VIRTUALENV_DIR}"
   else
-    die "Failed to remove existing virtualenv directory: ${VENV_DIR}"
+    die "FAILED to create virtualenv directory: ${VIRTUALENV_DIR}"
   fi
-fi
-
-if mkdir -p ${VENV_DIR}
-then
-  echo "Created virtualenv directory: ${VENV_DIR}"
-else
-  die "FAILED to create virtualenv directory: ${VENV_DIR}"
-fi
 
-# Verify that virtualenv exists
-if [[ -z $(which virtualenv) ]]; then
-  die "FAILED: virtualenv not available on path"
-fi
+  # Verify that virtualenv exists
+  if [[ -z $(which virtualenv) ]]; then
+    die "FAILED: virtualenv not available on path"
+  fi
 
-virtualenv --system-site-packages -p "${PYTHON_BIN_PATH}" "${VENV_DIR}" || \
+  virtualenv ${VIRTUALENV_FLAGS} \
+    -p "${PYTHON_BIN_PATH}" "${VIRTUALENV_DIR}" || \
     die "FAILED: Unable to create virtualenv"
 
-source "${VENV_DIR}/bin/activate" || \
-    die "FAILED: Unable to activate virtualenv"
-
+  source "${VIRTUALENV_DIR}/bin/activate" || \
+    die "FAILED: Unable to activate virtualenv in ${VIRTUALENV_DIR}"
 
-# Install the pip file in virtual env (plus missing dependencies)
+  # Install the pip file in virtual env.
 
-# Upgrade pip so it supports tags such as cp27mu, manylinux1 etc.
-echo "Upgrade pip in virtualenv"
-pip install --upgrade pip==8.1.2
+  # Upgrade pip so it supports tags such as cp27mu, manylinux1 etc.
+  echo "Upgrade pip in virtualenv"
+  pip install --upgrade pip==8.1.2
 
-# Force tensorflow reinstallation. Otherwise it may not get installed from
-# last build if it had the same version number as previous build.
-PIP_FLAGS="--upgrade --force-reinstall"
-pip install -v ${PIP_FLAGS} ${WHL_PATH} || \
+  # Force tensorflow reinstallation. Otherwise it may not get installed from
+  # last build if it had the same version number as previous build.
+  PIP_FLAGS="--upgrade --force-reinstall"
+  pip install -v ${PIP_FLAGS} ${WHL_PATH} || \
     die "pip install (forcing to reinstall tensorflow) FAILED"
-echo "Successfully installed pip package ${WHL_PATH}"
+  echo "Successfully installed pip package ${TF_WHEEL_PATH}"
+}
+
+
+# 1. Smoke test of tensorflow install in clean virtualenv
+echo
+echo "Installing and smoke-testing pip wheel in clean virtualenv: ${WHL_PATH}"
+echo
+
+CLEAN_VENV_DIR="${PIP_TEST_ROOT}/venv_clean"
+create_activate_virtualenv_and_install_tensorflow --clean \
+  "${CLEAN_VENV_DIR}" "${WHL_PATH}"
+# cd to a temporary directory to avoid picking up Python files in the source
+# tree.
+TMP_DIR=$(mktemp -d)
+pushd "${TMP_DIR}"
+[[ $(python -c "import tensorflow as tf; print(tf.Session().run(tf.constant(42)))") == 42 ]] \
+  && echo "Smoke test of tensorflow install in clean virtualenv PASSED." \
+  || die "Smoke test of tensroflow install in clean virtualenv FAILED."
+deactivate || \
+  die "FAILED: Unable to deactivate virtualenv from ${CLEAN_VENV_DIR}"
+popd
+rm -rf "${TMP_DIR}" "${CLEAN_VENV_DIR}"
+
+# 2. Perform installation of tensorflow in "non-clean" virtualenv and tests
+# against the install.
+echo
+echo "Installing and testing pip wheel in virtualenv: ${WHL_PATH}"
+echo
+
+# Create virtualenv directory for install test
+VENV_DIR="${PIP_TEST_ROOT}/venv"
+create_activate_virtualenv_and_install_tensorflow \
+  "${CLEAN_VENV_DIR}" "${WHL_PATH}"
 
 # Install extra pip packages required by the test-on-install
 for PACKAGE in ${INSTALL_EXTRA_PIP_PACKAGES}; do
@@ -343,5 +392,4 @@ if [[ "${DO_INTEGRATION_TESTS}" == "1" ]]; then
       die "Integration tests on install FAILED"
 fi
 
-deactivate || \
-    die "FAILED: Unable to deactivate virtualenv"
+deactivate || die "FAILED: Unable to deactivate virtualenv from ${VENV_DIR}"
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index f82640faf4..644cab95f8 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -171,6 +171,7 @@ sh_binary(
             "//tensorflow/python/debug:debug_pip",
             "//tensorflow/python/saved_model:saved_model",
             "//tensorflow/python/tools:tools_pip",
+            "//tensorflow/tools/dist_test/server:grpc_tensorflow_server",
         ],
     }) + if_mkl(["//third_party/mkl:intel_binary_blob"]),
 )
-- 
GitLab


From 80d4b2bccc1e632d4ae4679091b8af2c0c521fb3 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Wed, 19 Jul 2017 09:01:15 -0700
Subject: [PATCH 1110/1375] Adds a warning if the evaluation steps are 0

PiperOrigin-RevId: 162491844
---
 tensorflow/contrib/learn/python/learn/estimators/estimator.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index c3cf5662c2..b9201cc805 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -838,6 +838,10 @@ class BaseEstimator(
       hooks = hooks[:] if hooks else []
       if feed_fn:
         hooks.append(basic_session_run_hooks.FeedFnHook(feed_fn))
+      if steps == 0:
+        logging.warning('evaluation steps are 0. If `input_fn` does not raise'
+                        'OutOfRangeError`, the evaluation will never stop.'
+                        'Use steps=None if intended.')
       if steps:
         hooks.append(
             evaluation.StopAfterNEvalsHook(
-- 
GitLab


From a18ffba66c6c79508c63c23de093122511a35d6b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 09:27:37 -0700
Subject: [PATCH 1111/1375] Seals tf.contrib.factorization

PiperOrigin-RevId: 162494862
---
 tensorflow/contrib/factorization/BUILD       |  1 +
 tensorflow/contrib/factorization/__init__.py | 17 +++++++++++++++++
 2 files changed, 18 insertions(+)

diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD
index 41d0e83720..8b0f63f29e 100644
--- a/tensorflow/contrib/factorization/BUILD
+++ b/tensorflow/contrib/factorization/BUILD
@@ -55,6 +55,7 @@ tf_custom_op_py_library(
         "//tensorflow/python:sparse_ops",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:summary",
+        "//tensorflow/python:util",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
diff --git a/tensorflow/contrib/factorization/__init__.py b/tensorflow/contrib/factorization/__init__.py
index f0ca879259..486c2ea933 100644
--- a/tensorflow/contrib/factorization/__init__.py
+++ b/tensorflow/contrib/factorization/__init__.py
@@ -25,3 +25,20 @@ from tensorflow.contrib.factorization.python.ops.gmm import *
 from tensorflow.contrib.factorization.python.ops.gmm_ops import *
 from tensorflow.contrib.factorization.python.ops.wals import *
 # pylint: enable=wildcard-import
+
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = [
+    'KMeans',
+    'COSINE_DISTANCE',
+    'KMEANS_PLUS_PLUS_INIT',
+    'RANDOM_INIT',
+    'SQUARED_EUCLIDEAN_DISTANCE',
+    'WALSModel',
+    'GMM',
+    'gmm',
+    'GmmAlgorithm',
+    'WALSMatrixFactorization',
+]
+
+remove_undocumented(__name__, _allowed_symbols)
-- 
GitLab


From c6ec24290259c09099de22eca7ed5351a9fde811 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 10:29:20 -0700
Subject: [PATCH 1112/1375] Internal change.

PiperOrigin-RevId: 162502710
---
 tensorflow/compiler/xla/service/user_computation.cc | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 90122959a6..8c29945d0b 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -1036,9 +1036,6 @@ Status UserComputation::AddOutfeedInstruction(
   tensorflow::mutex_lock lock(mutex_);
 
   const Shape& shape = outfeed_request.shape();
-  if (ShapeUtil::IsNestedTuple(shape)) {
-    return InvalidArgument("Outfeed does not support nested tuple shapes");
-  }
   if (!LayoutUtil::HasLayout(shape)) {
     return InvalidArgument("Given shape to Outfeed must have a layout");
   }
-- 
GitLab


From fbd7059017d24b8c185ef7e6ad04857bc0616731 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Wed, 19 Jul 2017 10:47:49 -0700
Subject: [PATCH 1113/1375] [TF:XLA] Relax numeric tolerances of some tests.

PiperOrigin-RevId: 162505321
---
 tensorflow/compiler/tests/adagrad_test.py     | 18 +++++++----
 tensorflow/compiler/tests/binary_ops_test.py  | 11 +++----
 tensorflow/compiler/tests/ftrl_test.py        | 30 +++++++++++--------
 tensorflow/compiler/tests/pooling_ops_test.py |  2 +-
 4 files changed, 37 insertions(+), 24 deletions(-)

diff --git a/tensorflow/compiler/tests/adagrad_test.py b/tensorflow/compiler/tests/adagrad_test.py
index a5c5885b42..9a93b32164 100644
--- a/tensorflow/compiler/tests/adagrad_test.py
+++ b/tensorflow/compiler/tests/adagrad_test.py
@@ -49,9 +49,11 @@ class AdagradOptimizerTest(XLATestCase):
           ada_update.run()
         # Validate updated params
         self.assertAllCloseAccordingToType(
-            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval())
+            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval(),
+            float_rtol=1e-5)
         self.assertAllCloseAccordingToType(
-            np.array([2.715679168701172, 3.715679168701172]), var1.eval())
+            np.array([2.715679168701172, 3.715679168701172]), var1.eval(),
+            float_rtol=1e-5)
 
   def testTensorLearningRate(self):
     for dtype in self.float_types:
@@ -73,9 +75,11 @@ class AdagradOptimizerTest(XLATestCase):
           ada_update.run()
         # Validate updated params
         self.assertAllCloseAccordingToType(
-            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval())
+            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval(),
+            float_rtol=1e-5)
         self.assertAllCloseAccordingToType(
-            np.array([2.715679168701172, 3.715679168701172]), var1.eval())
+            np.array([2.715679168701172, 3.715679168701172]), var1.eval(),
+            float_rtol=1e-5)
 
   def testSharing(self):
     for dtype in self.float_types:
@@ -107,9 +111,11 @@ class AdagradOptimizerTest(XLATestCase):
         ada_update1.run()
         # Validate updated params (the same as with only 1 Adagrad).
         self.assertAllCloseAccordingToType(
-            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval())
+            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval(),
+            float_rtol=1e-5)
         self.assertAllCloseAccordingToType(
-            np.array([2.715679168701172, 3.715679168701172]), var1.eval())
+            np.array([2.715679168701172, 3.715679168701172]), var1.eval(),
+            float_rtol=1e-5)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py
index 7221a0a3c7..9eaede7f40 100644
--- a/tensorflow/compiler/tests/binary_ops_test.py
+++ b/tensorflow/compiler/tests/binary_ops_test.py
@@ -555,17 +555,18 @@ class BinaryOpsTest(XLATestCase):
     self._testBinary(
         math_ops.matmul,
         np.array(
-            [[[[1000, 100], [10, 1]], [[2000, 200], [20, 2]]],
-             [[[3000, 300], [30, 3]], [[4000, 400], [40, 4]]]],
+            [[[[7, 13], [10, 1]], [[2, 0.25], [20, 2]]],
+             [[[3, 5], [30, 3]], [[0.75, 1], [40, 4]]]],
             dtype=np.float32),
         np.array(
             [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[11, 22], [33, 44]],
                                                     [[55, 66], [77, 88]]]],
             dtype=np.float32),
         expected=np.array(
-            [[[[1300, 2400], [13, 24]], [[11400, 13600], [114, 136]]],
-             [[[42900, 79200], [429, 792]], [[250800, 299200], [2508, 2992]]]],
+            [[[[46, 66], [13, 24]], [[11.75, 14], [114, 136]]],
+             [[[198, 286], [429, 792]], [[118.25, 137.5], [2508, 2992]]]],
             dtype=np.float32))
+
     self._testBinary(
         math_ops.matmul,
         np.array([], dtype=np.float32).reshape((2, 2, 0)),
@@ -581,7 +582,7 @@ class BinaryOpsTest(XLATestCase):
 
     # Regression test for b/31472796.
     if hasattr(np, "matmul"):
-      x = np.arange(0, 3 * 5 * 16 * 7, dtype=np.float32).reshape((3, 5, 16, 7))
+      x = np.arange(0, 3 * 5 * 2 * 7, dtype=np.float32).reshape((3, 5, 2, 7))
       self._testBinary(
           lambda x, y: math_ops.matmul(x, y, adjoint_b=True),
           x, x,
diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py
index 7918276849..7e3871312c 100644
--- a/tensorflow/compiler/tests/ftrl_test.py
+++ b/tensorflow/compiler/tests/ftrl_test.py
@@ -134,9 +134,9 @@ class FtrlOptimizerTest(XLATestCase):
 
         # Validate updated params
         self.assertAllCloseAccordingToType(
-            np.array([-2.60260963, -4.29698515]), var0.eval())
+            np.array([-2.60260963, -4.29698515]), var0.eval(), float_rtol=1e-5)
         self.assertAllCloseAccordingToType(
-            np.array([-0.28432083, -0.56694895]), var1.eval())
+            np.array([-0.28432083, -0.56694895]), var1.eval(), float_rtol=1e-5)
 
   def testFtrlwithoutRegularization2(self):
     for dtype in self.float_types:
@@ -189,8 +189,10 @@ class FtrlOptimizerTest(XLATestCase):
           ftrl_update.run()
 
         # Validate updated params
-        self.assertAllClose(np.array([-7.66718769, -10.91273689]), var0.eval())
-        self.assertAllClose(np.array([-0.93460727, -1.86147261]), var1.eval())
+        self.assertAllClose(np.array([-7.66718769, -10.91273689]), var0.eval(),
+                            rtol=1e-4)
+        self.assertAllClose(np.array([-0.93460727, -1.86147261]), var1.eval(),
+                            rtol=1e-4)
 
   def testFtrlWithL1_L2(self):
     for dtype in self.float_types:
@@ -215,8 +217,10 @@ class FtrlOptimizerTest(XLATestCase):
           ftrl_update.run()
 
         # Validate updated params
-        self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval())
-        self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval())
+        self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval(),
+                            rtol=1e-5)
+        self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval(),
+                            rtol=1e-5)
 
   def testFtrlWithL1_L2_L2Shrinkage(self):
     """Test the new FTRL op with support for l2 shrinkage.
@@ -248,8 +252,10 @@ class FtrlOptimizerTest(XLATestCase):
           ftrl_update.run()
 
         # Validate updated params
-        self.assertAllClose(np.array([-0.21931979, -0.40642974]), var0.eval())
-        self.assertAllClose(np.array([-0.0282721, -0.07188385]), var1.eval())
+        self.assertAllClose(np.array([-0.21931979, -0.40642974]), var0.eval(),
+                            rtol=1e-4)
+        self.assertAllClose(np.array([-0.0282721, -0.07188385]), var1.eval(),
+                            rtol=1e-4)
 
   # When variables are initialized with Zero, FTRL-Proximal has two properties:
   # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
@@ -266,8 +272,8 @@ class FtrlOptimizerTest(XLATestCase):
       with self.test_session(), self.test_scope():
         val2, val3 = self.equivAdagradTest_AdagradPart(steps, dtype)
 
-    self.assertAllClose(val0, val2)
-    self.assertAllClose(val1, val3)
+    self.assertAllClose(val0, val2, rtol=1e-4)
+    self.assertAllClose(val1, val3, rtol=1e-4)
 
   def testEquivGradientDescentwithoutRegularization(self):
     steps = 5
@@ -278,8 +284,8 @@ class FtrlOptimizerTest(XLATestCase):
         val2, val3 = self.equivGradientDescentTest_GradientDescentPart(
             steps, dtype)
 
-    self.assertAllClose(val0, val2)
-    self.assertAllClose(val1, val3)
+    self.assertAllClose(val0, val2, rtol=1e-5)
+    self.assertAllClose(val1, val3, rtol=1e-5)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/compiler/tests/pooling_ops_test.py b/tensorflow/compiler/tests/pooling_ops_test.py
index 52290e6354..7c19a99c4e 100644
--- a/tensorflow/compiler/tests/pooling_ops_test.py
+++ b/tensorflow/compiler/tests/pooling_ops_test.py
@@ -376,7 +376,7 @@ class PoolGradTest(XLATestCase):
       self.assertAllClose(
           expected_input_gradient_vals.flatten(),
           actual.flatten(),
-          rtol=1e-5,
+          rtol=1e-4,
           atol=1e-6)
       self.assertShapeEqual(actual, inputs)
 
-- 
GitLab


From ba875d1d80c4e9e2b8f8b6f2d556e5037d9db246 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 10:49:23 -0700
Subject: [PATCH 1114/1375] Minor improvements in TensorForestEstimator: reduce
 default  stopping loss threshold, which makes it train a little longer and
 results in better models.  Also add average tree size to summary for handy
 viewing in TensorBoard.

PiperOrigin-RevId: 162505559
---
 tensorflow/contrib/tensor_forest/client/random_forest.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index 2eb37d3e53..edfc67ce58 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -37,6 +37,7 @@ from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.summary import summary
 from tensorflow.python.training import basic_session_run_hooks
 from tensorflow.python.training import session_run_hook
 
@@ -163,7 +164,7 @@ def get_model_fn(params,
                  model_head=None,
                  keys_name=None,
                  early_stopping_rounds=100,
-                 early_stopping_loss_threshold=0.01,
+                 early_stopping_loss_threshold=0.001,
                  num_trainers=1,
                  trainer_id=0,
                  report_feature_importances=False,
@@ -197,6 +198,8 @@ def get_model_fn(params,
                                         device_assigner=dev_assn)
 
     logits = graph_builder.inference_graph(features)
+
+    summary.scalar('average_tree_size', graph_builder.average_size())
     # For binary classification problems, convert probabilities to logits.
     # Includes hack to get around the fact that a probability might be 0 or 1.
     if not params.regression and params.num_classes == 2:
@@ -308,7 +311,7 @@ class TensorForestEstimator(estimator.Estimator):
                keys_name=None,
                feature_engineering_fn=None,
                early_stopping_rounds=100,
-               early_stopping_loss_threshold=0.01,
+               early_stopping_loss_threshold=0.001,
                num_trainers=1,
                trainer_id=0,
                report_feature_importances=False,
-- 
GitLab


From cae9d0f3fe6b1bc37f0e5a69765239f89a05ce6e Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Wed, 19 Jul 2017 10:55:21 -0700
Subject: [PATCH 1115/1375] [TF:XLA] Use a separate function library for
 functions generated by control flow functionalization pass. Avoids copying
 the main function library for each compilation.

PiperOrigin-RevId: 162506388
---
 tensorflow/compiler/tf2xla/xla_compiler.cc | 34 ++++++++++++++++------
 tensorflow/compiler/tf2xla/xla_compiler.h  |  7 ++++-
 2 files changed, 31 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index adabb3574c..11a62b23ae 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -88,10 +88,15 @@ XlaCompiler::XlaCompiler(XlaCompiler::Options options)
         (*options_.populate_resource_manager)(device_->resource_manager());
   }
 
-  flib_def_.reset(new FunctionLibraryDefinition(*options.flib_def));
+  local_flib_def_.reset(new FunctionLibraryDefinition(OpRegistry::Global(),
+                                                      FunctionDefLibrary{}));
+  local_flib_runtime_.reset(NewFunctionLibraryRuntime(
+      &device_mgr_, Env::Default(), device_, options.graph_def_version,
+      local_flib_def_.get(), OptimizerOptions(),
+      nullptr /* custom_kernel_creator */));
   flib_runtime_.reset(NewFunctionLibraryRuntime(
       &device_mgr_, Env::Default(), device_, options.graph_def_version,
-      flib_def_.get(), OptimizerOptions(),
+      options.flib_def, OptimizerOptions(),
       nullptr /* custom_kernel_creator */));
 }
 
@@ -107,6 +112,18 @@ uint64 XlaCompiler::SignatureHash::operator()(
   return std::hash<string>()(signature.first);
 }
 
+static Status GetFunctionBody(const NameAttrList& function,
+                              FunctionLibraryRuntime* flib_runtime,
+                              const FunctionBody** fbody) {
+  FunctionLibraryRuntime::Handle handle;
+  TF_RETURN_IF_ERROR(flib_runtime->Instantiate(
+      function.name(), AttrSlice(&function.attr()), &handle));
+
+  *fbody = flib_runtime->GetFunctionBody(handle);
+  TF_RET_CHECK(*fbody);
+  return Status::OK();
+}
+
 Status XlaCompiler::CompileFunction(
     const XlaCompiler::CompileOptions& options, const NameAttrList& function,
     const std::vector<XlaCompiler::Argument>& args,
@@ -121,12 +138,10 @@ Status XlaCompiler::CompileFunction(
     return Status::OK();
   }
 
-  FunctionLibraryRuntime::Handle handle;
-  TF_RETURN_IF_ERROR(flib_runtime_->Instantiate(
-      function.name(), AttrSlice(&function.attr()), &handle));
-
-  const FunctionBody* fbody = flib_runtime_->GetFunctionBody(handle);
-  CHECK(fbody);
+  const FunctionBody* fbody;
+  if (!GetFunctionBody(function, local_flib_runtime_.get(), &fbody).ok()) {
+    TF_RETURN_IF_ERROR(GetFunctionBody(function, flib_runtime_.get(), &fbody));
+  }
 
   TF_RETURN_IF_ERROR(CheckSignature(fbody->arg_types, args));
 
@@ -433,7 +448,8 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options,
 
   // Converts Tensorflow's graph control-flow constructs into functional
   // control-flow that can be compiled into XLA code.
-  TF_RETURN_IF_ERROR(FunctionalizeControlFlow(graph.get(), flib_def_.get()));
+  TF_RETURN_IF_ERROR(
+      FunctionalizeControlFlow(graph.get(), local_flib_def_.get()));
 
   xla::ComputationBuilder builder(client(), name);
   XlaContext* context =
diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h
index 429515e2b7..7251c92edb 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.h
+++ b/tensorflow/compiler/tf2xla/xla_compiler.h
@@ -301,7 +301,12 @@ class XlaCompiler {
   XlaCompilationDevice* device_;  // Owned by device_mgr_
   DeviceMgr device_mgr_;
 
-  std::unique_ptr<FunctionLibraryDefinition> flib_def_;
+  // To avoid copying the client's function library, use a local function
+  // library and runtime for functions created as part of the functionalize
+  // control flow transformation.
+  std::unique_ptr<FunctionLibraryDefinition> local_flib_def_;
+  std::unique_ptr<FunctionLibraryRuntime> local_flib_runtime_;
+
   std::unique_ptr<FunctionLibraryRuntime> flib_runtime_;
 
   struct SignatureHash {
-- 
GitLab


From c2182e05ebb1a2dea8a554eb1644dea7e593a8b1 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 19 Jul 2017 10:59:15 -0700
Subject: [PATCH 1116/1375] [XLA] Move the flag from stream_assignment_flags
 into DebugOptions

PiperOrigin-RevId: 162506964
---
 tensorflow/compiler/xla/legacy_flags/BUILD    | 11 ----
 .../xla/legacy_flags/debug_options_flags.cc   |  5 ++
 .../legacy_flags/stream_assignment_flags.cc   | 63 -------------------
 .../legacy_flags/stream_assignment_flags.h    | 47 --------------
 tensorflow/compiler/xla/service/gpu/BUILD     |  1 -
 .../xla/service/gpu/stream_assignment.cc      |  8 +--
 tensorflow/compiler/xla/xla.proto             | 13 ++--
 7 files changed, 17 insertions(+), 131 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.h

diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 0ab5fac962..786093034d 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -79,17 +79,6 @@ cc_library(
         ],
 )
 
-cc_library(
-    name = "stream_assignment_flags",
-    srcs = ["stream_assignment_flags.cc"],
-    hdrs = ["stream_assignment_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "backend_flags",
     srcs = ["backend_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index 476fa5f171..87c6215e6b 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -206,6 +206,11 @@ void AllocateFlags() {
                         flag_values->xla_gpu_ftz(),
                         "If true, flush-to-zero semantics are enabled in the "
                         "code generated for GPUs."),
+       tensorflow::Flag(
+           "xla_gpu_disable_multi_streaming",
+           bool_setter_for(&DebugOptions::set_xla_gpu_disable_multi_streaming),
+           flag_values->xla_gpu_disable_multi_streaming(),
+           "If true, multi-streaming in the GPU backend is disabled."),
        tensorflow::Flag(
            "xla_dump_debug_json_to",
            flag_values->mutable_xla_dump_debug_json_to(),
diff --git a/tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.cc b/tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.cc
deleted file mode 100644
index 6506175777..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's stream_assignment module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static StreamAssignmentFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new StreamAssignmentFlags;
-  flags->xla_gpu_disable_multi_streaming = false;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_gpu_disable_multi_streaming",
-                       &flags->xla_gpu_disable_multi_streaming,
-                       "Disable multi-streaming in XLA's GPU backend"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's stream_assignment
-// module.
-void AppendStreamAssignmentFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the StreamAssignmentFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-StreamAssignmentFlags* GetStreamAssignmentFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.h b/tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.h
deleted file mode 100644
index a98f9b3458..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_STREAM_ASSIGNMENT_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_STREAM_ASSIGNMENT_FLAGS_H_
-
-// Legacy flags for XLA's stream_assignment module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's stream_assignment
-// module.
-void AppendStreamAssignmentFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's stream_assignment module.
-typedef struct {
-  bool xla_gpu_disable_multi_streaming;  // Disable multi-streaming in XLA's GPU
-                                         // backend
-} StreamAssignmentFlags;
-
-// Return a pointer to the StreamAssignmentFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-StreamAssignmentFlags* GetStreamAssignmentFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_STREAM_ASSIGNMENT_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index 6809099695..fa95e23499 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -68,7 +68,6 @@ cc_library(
     deps = [
         ":ir_emission_utils",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/legacy_flags:stream_assignment_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_reachability",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
index a304decc49..e4cfc6999f 100644
--- a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
+++ b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h"
 
-#include "tensorflow/compiler/xla/legacy_flags/stream_assignment_flags.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h"
@@ -66,9 +65,10 @@ int ComputeStreamToAssign(
     return -1;
   }
 
-  legacy_flags::StreamAssignmentFlags* flags =
-      legacy_flags::GetStreamAssignmentFlags();
-  if (flags->xla_gpu_disable_multi_streaming) {
+  if (hlo.GetModule()
+          ->config()
+          .debug_options()
+          .xla_gpu_disable_multi_streaming()) {
     return 0;
   }
 
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index fd6131da96..00fb7f12b8 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -97,29 +97,32 @@ message DebugOptions {
   // Enable flush-to-zero semantics in the GPU backend.
   bool xla_gpu_ftz = 62;
 
+  // Disable multi-streaming in the GPU backend.
+  bool xla_gpu_disable_multi_streaming = 63;
+
   // If true, in LLVM-based backends, emit !alias.scope metadata in
   // generated IR.
-  bool xla_llvm_enable_alias_scope_metadata = 63;
+  bool xla_llvm_enable_alias_scope_metadata = 70;
 
   // If true, in LLVM-based backends, emit !noalias metadata in the
   // generated IR.
-  bool xla_llvm_enable_noalias_metadata = 64;
+  bool xla_llvm_enable_noalias_metadata = 71;
 
   // If true, in LLVM-based backends, emit !invariant.load metadata in
   // the generated IR.
-  bool xla_llvm_enable_invariant_load_metadata = 65;
+  bool xla_llvm_enable_invariant_load_metadata = 72;
 
   // This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the
   // computation will run n! times with all permunations of layouts for the
   // output shape in rank n. For example, with a 3D shape, all permutations of
   // the set {0, 1, 2} are tried.
-  bool xla_test_all_output_layouts = 70;
+  bool xla_test_all_output_layouts = 90;
 
   // This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the
   // computation will run for all permunations of layouts of all input
   // arguments. For example, with 2 input arguments in 2D and 4D shapes, the
   // computation will run 2! * 4! times.
-  bool xla_test_all_input_layouts = 71;
+  bool xla_test_all_input_layouts = 91;
 
   // Extra options to pass to the compilation backend; specific interpretation
   // of these values is left to the backend.
-- 
GitLab


From b9ea027b991a5d60b3581f2b981357276b8993b6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 11:17:32 -0700
Subject: [PATCH 1117/1375] Remove class BoolVector.  Use uint8 in its place.

Both preds() and uint8() use the same storage.  In a subsequent CL, I will
unify all the different storage types and make Literal a template class.  It's
now replicating storage that is never used.

PiperOrigin-RevId: 162509649
---
 tensorflow/compiler/xla/literal_util.cc |  23 ++---
 tensorflow/compiler/xla/literal_util.h  | 106 +++---------------------
 2 files changed, 17 insertions(+), 112 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 0c38b6d3e7..ef21148b8b 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -653,7 +653,6 @@ void* Literal::MutableInternalData() {
   // created by the accessor functions.
   switch (shape().element_type()) {
     case PRED:
-      return reinterpret_cast<void*>(preds_.data());
     case U8:
       return reinterpret_cast<void*>(u8s_.data());
     case S32:
@@ -721,8 +720,6 @@ tensorflow::Status Literal::ValidateLiteral() const {
   int64 actual = -1;
   switch (shape().element_type()) {
     case PRED:
-      actual = preds_size();
-      break;
     case U8:
       actual = u8s_size();
       break;
@@ -928,8 +925,8 @@ bool Literal::Equal(const Literal& literal2) const {
 template <>
 tensorflow::gtl::MutableArraySlice<bool> Literal::GetMutableArraySlice() {
   auto values = mutable_preds();
-  return tensorflow::gtl::MutableArraySlice<bool>(values->data(),
-                                                  values->size());
+  return tensorflow::gtl::MutableArraySlice<bool>(
+      reinterpret_cast<bool*>(&(*values)[0]), values->size());
 }
 
 template <>
@@ -1021,7 +1018,8 @@ tensorflow::gtl::MutableArraySlice<half> Literal::GetMutableArraySlice<half>() {
 template <>
 tensorflow::gtl::ArraySlice<bool> Literal::GetArraySlice<bool>() const {
   CHECK_EQ(shape().element_type(), PRED);
-  return tensorflow::gtl::ArraySlice<bool>(preds().data(), preds().size());
+  return tensorflow::gtl::ArraySlice<bool>(
+      reinterpret_cast<const bool*>(preds().data()), preds().size());
 }
 
 template <>
@@ -1236,21 +1234,13 @@ static void CopyToRepeatedField(RepeatedFieldT* dest,
   *dest = RepeatedFieldT(src.begin(), src.end());
 }
 
-template <typename RepeatedFieldT>
-static void CopyToRepeatedBoolField(RepeatedFieldT* dest,
-                                    const BoolVector& src) {
-  *dest = RepeatedFieldT(src.begin(), src.end());
-}
-
 LiteralProto Literal::ToProto() const {
   LiteralProto proto;
   proto.Clear();
   *proto.mutable_shape() = shape();
   switch (shape().element_type()) {
     case PRED:
-      if (preds().begin()) {
-        CopyToRepeatedBoolField(proto.mutable_preds(), preds());
-      }
+      CopyToRepeatedField(proto.mutable_preds(), preds());
       break;
     case U8:
       *proto.mutable_u8s() = u8s_string();
@@ -1304,8 +1294,7 @@ void Literal::CopyFromProto(const LiteralProto& literal_proto) {
   *mutable_shape() = literal_proto.shape();
   switch (shape().element_type()) {
     case PRED:
-      *mutable_preds() = BoolVector(literal_proto.preds().begin(),
-                                    literal_proto.preds().end());
+      CopyFromRepeatedField(mutable_preds(), literal_proto.preds());
       break;
     case U8:
       set_u8s(literal_proto.u8s());
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 4b1464a132..125c268573 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -49,96 +49,6 @@ limitations under the License.
 
 namespace xla {
 
-// This class is a simple vector of boolean values. It's used to workaround some
-// implementations of std::vector<bool> that use a bitset which does not have
-// the semantics expected by Literal::preds().
-class BoolVector {
- public:
-  typedef bool* iterator;
-  typedef const bool* const_iterator;
-
-  BoolVector() : bits_(nullptr), size_(0), capacity_(0) {}
-
-  BoolVector(const_iterator other_begin, const_iterator other_end)
-      : bits_(nullptr), size_(0), capacity_(0) {
-    if (other_begin && other_end) {
-      resize(other_end - other_begin);
-      memcpy(begin(), other_begin, size());
-    }
-  }
-
-  BoolVector(const BoolVector& other) { CopyFrom(other); }
-  BoolVector(BoolVector&&) = default;
-
-  BoolVector& operator=(const BoolVector& other) {
-    CopyFrom(other);
-    return *this;
-  }
-  BoolVector& operator=(BoolVector&&) = default;
-
-  void push_back(const bool& value) {
-    resize(size_ + 1);
-    bits_[size_ - 1] = value;
-  }
-
-  bool* data() const { return bits_.get(); }
-
-  size_t size() const { return size_; }
-
-  size_t capacity() const { return capacity_; }
-
-  void resize(size_t new_size, bool val = false) {
-    if (new_size == 0) {
-      bits_.reset(nullptr);
-      size_ = 0;
-      capacity_ = 0;
-    } else {
-      size_t old_size = size();
-      if (new_size > old_size) {
-        grow(new_size);
-      }
-      if (old_size < new_size) {
-        memset(&bits_[old_size], val, new_size - old_size);
-      }
-      size_ = new_size;
-    }
-  }
-
-  void clear() {
-    bits_.reset(nullptr);
-    size_ = 0;
-    capacity_ = 0;
-  }
-
-  iterator begin() { return &bits_[0]; }
-  iterator end() { return &bits_[size()]; }
-  const_iterator begin() const { return &bits_[0]; }
-  const_iterator end() const { return &bits_[size()]; }
-
- private:
-  void grow(size_t n) {
-    if (capacity_ < n) {
-      capacity_ = 2 * n;
-      bool* new_bits = new bool[capacity_]();
-      if (size_ > 0) {
-        memcpy(new_bits, bits_.get(), size_);
-      }
-      bits_.reset(new_bits);
-    }
-  }
-
-  void CopyFrom(const BoolVector& other) {
-    bits_ = MakeUnique<bool[]>(other.capacity());
-    memcpy(begin(), other.begin(), other.size());
-    size_ = other.size();
-    capacity_ = other.capacity();
-  }
-
-  std::unique_ptr<bool[]> bits_;
-  size_t size_;
-  size_t capacity_;
-};
-
 // Utility class for dealing with XLA literal values.  Most methods are
 // templated by native (host) type which corresponds to a unique XLA
 // PrimitiveType. See ComputationBuilder for details.  Not all primitive types
@@ -169,7 +79,6 @@ class Literal {
 
   void Clear() {
     shape_.Clear();
-    preds_.clear();
     u8s_.clear();
     s32s_.clear();
     s64s_.clear();
@@ -181,9 +90,17 @@ class Literal {
     tuple_literals_.clear();
   }
 
-  int preds_size() const { return preds().size(); }
-  const BoolVector& preds() const { return preds_; }
-  BoolVector* mutable_preds() { return &preds_; }
+  int preds_size() const { return u8s().size(); }
+  const std::vector<uint8>& preds() const {
+    static_assert(sizeof(uint8) == sizeof(bool),
+                  "The uint8 and bool types should be the same size");
+    return u8s_;
+  }
+  std::vector<uint8>* mutable_preds() {
+    static_assert(sizeof(uint8) == sizeof(bool),
+                  "The uint8 and bool types should be the same size");
+    return &u8s_;
+  }
 
   int s32s_size() const { return s32s().size(); }
   int32 s32s(int i) const { return s32s_[i]; }
@@ -648,7 +565,6 @@ class Literal {
   };
 
   Shape shape_;
-  BoolVector preds_;
   std::vector<uint8> u8s_;
   std::vector<int32> s32s_;
   std::vector<int64> s64s_;
-- 
GitLab


From d70b39b5bd6eb60fcf0df7420969474c29fdaad7 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Wed, 19 Jul 2017 11:28:53 -0700
Subject: [PATCH 1118/1375] Add Short-time Fourier Transform (STFT) and inverse
 STFT support to tf.contrib.signal.

These are implemented using GPU-capable ops and have gradient support.

PiperOrigin-RevId: 162511440
---
 tensorflow/contrib/signal/BUILD               |  21 ++
 tensorflow/contrib/signal/__init__.py         |   4 +
 .../python/kernel_tests/spectral_ops_test.py  | 250 ++++++++++++++++++
 .../contrib/signal/python/ops/spectral_ops.py | 165 ++++++++++++
 tensorflow/python/BUILD                       |  10 +
 tensorflow/python/kernel_tests/BUILD          |   1 +
 .../python/kernel_tests/fft_ops_test.py       |  54 ++--
 .../python/ops/spectral_ops_test_util.py      |  50 ++++
 tensorflow/tools/pip_package/BUILD            |   2 +
 9 files changed, 518 insertions(+), 39 deletions(-)
 create mode 100644 tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
 create mode 100644 tensorflow/contrib/signal/python/ops/spectral_ops.py
 create mode 100644 tensorflow/python/ops/spectral_ops_test_util.py

diff --git a/tensorflow/contrib/signal/BUILD b/tensorflow/contrib/signal/BUILD
index 706de58a0a..52813b76fb 100644
--- a/tensorflow/contrib/signal/BUILD
+++ b/tensorflow/contrib/signal/BUILD
@@ -16,6 +16,7 @@ py_library(
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:spectral_ops",
         "//tensorflow/python:util",
         "//third_party/py/numpy",
     ],
@@ -54,6 +55,26 @@ cuda_py_tests(
     ],
 )
 
+cuda_py_tests(
+    name = "spectral_ops_test",
+    size = "large",
+    srcs = ["python/kernel_tests/spectral_ops_test.py"],
+    additional_deps = [
+        ":signal_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:spectral_ops_test_util",
+    ],
+)
+
 cuda_py_tests(
     name = "window_ops_test",
     size = "small",
diff --git a/tensorflow/contrib/signal/__init__.py b/tensorflow/contrib/signal/__init__.py
index d0f6c1d0c6..c0136346b7 100644
--- a/tensorflow/contrib/signal/__init__.py
+++ b/tensorflow/contrib/signal/__init__.py
@@ -17,7 +17,9 @@
 @@frame
 @@hamming_window
 @@hann_window
+@@inverse_stft
 @@overlap_and_add
+@@stft
 """
 
 from __future__ import absolute_import
@@ -29,6 +31,8 @@ from tensorflow.contrib.signal.python.ops.shape_ops import frame
 # `frame` used to be named `frames`, which is a noun and not a verb.
 # Keep an alias to `frames` for backwards compatibility.
 from tensorflow.contrib.signal.python.ops.shape_ops import frame as frames
+from tensorflow.contrib.signal.python.ops.spectral_ops import inverse_stft
+from tensorflow.contrib.signal.python.ops.spectral_ops import stft
 from tensorflow.contrib.signal.python.ops.window_ops import hamming_window
 from tensorflow.contrib.signal.python.ops.window_ops import hann_window
 
diff --git a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
new file mode 100644
index 0000000000..904924b5e4
--- /dev/null
+++ b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
@@ -0,0 +1,250 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for spectral_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.signal.python.ops import spectral_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import spectral_ops_test_util
+from tensorflow.python.platform import test
+
+
+class SpectralOpsTest(test.TestCase):
+
+  @staticmethod
+  def _np_hann_periodic_window(length):
+    if length == 1:
+      return np.ones(1)
+    odd = length % 2
+    if not odd:
+      length += 1
+    window = 0.5 - 0.5 * np.cos(2.0 * np.pi * np.arange(length) / (length - 1))
+    if not odd:
+      window = window[:-1]
+    return window
+
+  @staticmethod
+  def _np_frame(data, window_length, hop_length):
+    num_frames = 1 + int(np.floor((len(data) - window_length) // hop_length))
+    shape = (num_frames, window_length)
+    strides = (data.strides[0] * hop_length, data.strides[0])
+    return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
+
+  @staticmethod
+  def _np_stft(data, fft_length, hop_length, window_length):
+    frames = SpectralOpsTest._np_frame(data, window_length, hop_length)
+    window = SpectralOpsTest._np_hann_periodic_window(window_length)
+    return np.fft.rfft(frames * window, fft_length)
+
+  @staticmethod
+  def _np_inverse_stft(stft, fft_length, hop_length, window_length):
+    frames = np.fft.irfft(stft, fft_length)[..., :window_length]
+    window = SpectralOpsTest._np_hann_periodic_window(window_length)
+    return SpectralOpsTest._np_overlap_add(frames * window, hop_length)
+
+  @staticmethod
+  def _np_overlap_add(stft, hop_length):
+    num_frames, window_length = np.shape(stft)
+    # Output length will be one complete window, plus another hop_length's
+    # worth of points for each additional window.
+    output_length = window_length + (num_frames - 1) * hop_length
+    output = np.zeros(output_length)
+    for i in range(num_frames):
+      output[i * hop_length:i * hop_length + window_length] += stft[i,]
+    return output
+
+  def _compare(self, signal, frame_length, frame_step, fft_length):
+    with spectral_ops_test_util.fft_kernel_label_map(), (
+        self.test_session(use_gpu=True)) as sess:
+      actual_stft = spectral_ops.stft(
+          signal, frame_length, frame_step, fft_length, pad_end=False)
+
+      actual_inverse_stft = spectral_ops.inverse_stft(
+          actual_stft, frame_length, frame_step, fft_length)
+
+      actual_stft, actual_inverse_stft = sess.run(
+          [actual_stft, actual_inverse_stft])
+
+      expected_stft = SpectralOpsTest._np_stft(
+          signal, fft_length, frame_step, frame_length)
+      self.assertAllClose(expected_stft, actual_stft, 1e-4, 1e-4)
+
+      expected_inverse_stft = SpectralOpsTest._np_inverse_stft(
+          expected_stft, fft_length, frame_step, frame_length)
+      self.assertAllClose(
+          expected_inverse_stft, actual_inverse_stft, 1e-4, 1e-4)
+
+  def _compare_round_trip(self, signal, frame_length, frame_step, fft_length):
+    with spectral_ops_test_util.fft_kernel_label_map(), (
+        self.test_session(use_gpu=True)) as sess:
+      stft = spectral_ops.stft(signal, frame_length, frame_step, fft_length,
+                               pad_end=False)
+      inverse_stft = spectral_ops.inverse_stft(stft, frame_length, frame_step,
+                                               fft_length)
+      signal, inverse_stft = sess.run([signal, inverse_stft])
+
+      # Since the shapes can differ due to padding, pad both signals to the max
+      # of their lengths.
+      max_length = max(signal.shape[0], inverse_stft.shape[0])
+      signal = np.pad(signal, (0, max_length - signal.shape[0]), "constant")
+      inverse_stft = np.pad(inverse_stft,
+                            (0, max_length - inverse_stft.shape[0]), "constant")
+
+      # Ignore the frame_length samples at either edge.
+      start = frame_length
+      end = signal.shape[0] - frame_length
+      ratio = signal[start:end] / inverse_stft[start:end]
+
+      # Check that the inverse and original signal are equal up to a constant
+      # factor.
+      self.assertLess(np.var(ratio), 2e-5)
+
+  def test_shapes(self):
+    with spectral_ops_test_util.fft_kernel_label_map(), (
+        self.test_session(use_gpu=True)):
+      signal = np.zeros((512,)).astype(np.float32)
+
+      # If fft_length is not provided, the smallest enclosing power of 2 of
+      # frame_length (8) is used.
+      stft = spectral_ops.stft(signal, frame_length=7, frame_step=8,
+                               pad_end=True)
+      self.assertAllEqual([64, 5], stft.shape.as_list())
+      self.assertAllEqual([64, 5], stft.eval().shape)
+
+      stft = spectral_ops.stft(signal, frame_length=8, frame_step=8,
+                               pad_end=True)
+      self.assertAllEqual([64, 5], stft.shape.as_list())
+      self.assertAllEqual([64, 5], stft.eval().shape)
+
+      stft = spectral_ops.stft(signal, frame_length=8, frame_step=8,
+                               fft_length=16, pad_end=True)
+      self.assertAllEqual([64, 9], stft.shape.as_list())
+      self.assertAllEqual([64, 9], stft.eval().shape)
+
+      stft = np.zeros((32, 9)).astype(np.complex64)
+
+      inverse_stft = spectral_ops.inverse_stft(stft, frame_length=8,
+                                               fft_length=16, frame_step=8)
+      expected_length = (stft.shape[0] - 1) * 8 + 8
+      self.assertAllEqual([None], inverse_stft.shape.as_list())
+      self.assertAllEqual([expected_length], inverse_stft.eval().shape)
+
+  def test_stft_and_inverse_stft(self):
+    """Test that spectral_ops.stft/inverse_stft match a NumPy implementation."""
+    # Tuples of (signal_length, frame_length, frame_step, fft_length).
+    test_configs = [
+        (512, 64, 32, 64),
+        (512, 64, 64, 64),
+        (512, 64, 25, 64),
+        (512, 25, 15, 36),
+        (123, 23, 5, 42),
+    ]
+
+    for signal_length, frame_length, frame_step, fft_length in test_configs:
+      signal = np.random.random(signal_length).astype(np.float32)
+      self._compare(signal, frame_length, frame_step, fft_length)
+
+  def test_stft_round_trip(self):
+    # Tuples of (signal_length, frame_length, frame_step, fft_length).
+    test_configs = [
+        # 87.5% overlap.
+        (4096, 256, 32, 256),
+        # 75% overlap.
+        (4096, 256, 64, 256),
+        # Odd frame hop.
+        (4096, 128, 25, 128),
+        # Odd frame length.
+        (4096, 127, 32, 128),
+    ]
+
+    for signal_length, frame_length, frame_step, fft_length in test_configs:
+      # Generate a 440Hz signal at 8kHz sample rate.
+      signal = math_ops.sin(2 * np.pi * 440 / 8000 *
+                            math_ops.to_float(math_ops.range(signal_length)))
+      self._compare_round_trip(signal, frame_length, frame_step, fft_length)
+
+  @staticmethod
+  def _compute_stft_gradient(signal, frame_length=32, frame_step=16,
+                             fft_length=32):
+    """Computes the gradient of the STFT with respect to `signal`."""
+    stft = spectral_ops.stft(signal, frame_length, frame_step, fft_length)
+    magnitude_stft = math_ops.abs(stft)
+    loss = math_ops.reduce_sum(magnitude_stft)
+    return gradients_impl.gradients([loss], [signal])[0]
+
+  def test_gradients(self):
+    """Test that spectral_ops.stft has a working gradient."""
+    with spectral_ops_test_util.fft_kernel_label_map(), (
+        self.test_session(use_gpu=True)) as sess:
+      signal_length = 512
+
+      # An all-zero signal has all zero gradients with respect to the sum of the
+      # magnitude STFT.
+      empty_signal = array_ops.zeros([signal_length], dtype=dtypes.float32)
+      empty_signal_gradient = sess.run(
+          self._compute_stft_gradient(empty_signal))
+      self.assertTrue((empty_signal_gradient == 0.0).all())
+
+      # A sinusoid will have non-zero components of its gradient with respect to
+      # the sum of the magnitude STFT.
+      sinusoid = math_ops.sin(
+          2 * np.pi * math_ops.linspace(0.0, 1.0, signal_length))
+      sinusoid_gradient = sess.run(self._compute_stft_gradient(sinusoid))
+      self.assertFalse((sinusoid_gradient == 0.0).all())
+
+  def test_gradients_numerical(self):
+    with spectral_ops_test_util.fft_kernel_label_map(), (
+        self.test_session(use_gpu=True)):
+      # Tuples of (signal_length, frame_length, frame_step, fft_length,
+      # stft_bound, inverse_stft_bound).
+      # TODO(rjryan): Investigate why STFT gradient error is so high.
+      test_configs = [
+          (512, 64, 32, 64, 2e-3, 3e-5),
+          (512, 64, 64, 64, 2e-3, 3e-5),
+          (512, 64, 25, 64, 2e-3, 3e-5),
+          (512, 25, 15, 36, 8e-4, 3e-5),
+          (123, 23, 5, 42, 8e-4, 4e-5),
+      ]
+
+      for (signal_length, frame_length, frame_step, fft_length,
+           stft_bound, inverse_stft_bound) in test_configs:
+        signal_shape = [signal_length]
+        signal = random_ops.random_uniform(signal_shape)
+        stft_shape = [max(0, 1 + (signal_length - frame_length) // frame_step),
+                      fft_length // 2 + 1]
+        stft = spectral_ops.stft(signal, frame_length, frame_step, fft_length,
+                                 pad_end=False)
+        inverse_stft_shape = [(stft_shape[0] - 1) * frame_step + frame_length]
+        inverse_stft = spectral_ops.inverse_stft(stft, frame_length, frame_step,
+                                                 fft_length)
+        stft_error = test.compute_gradient_error(signal, [signal_length],
+                                                 stft, stft_shape)
+        inverse_stft_error = test.compute_gradient_error(
+            stft, stft_shape, inverse_stft, inverse_stft_shape)
+        self.assertLess(stft_error, stft_bound)
+        self.assertLess(inverse_stft_error, inverse_stft_bound)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/signal/python/ops/spectral_ops.py b/tensorflow/contrib/signal/python/ops/spectral_ops.py
new file mode 100644
index 0000000000..1457f13604
--- /dev/null
+++ b/tensorflow/contrib/signal/python/ops/spectral_ops.py
@@ -0,0 +1,165 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Spectral operations (e.g. Short-time Fourier Transform)."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+
+import numpy as np
+
+from tensorflow.contrib.signal.python.ops import reconstruction_ops
+from tensorflow.contrib.signal.python.ops import shape_ops
+from tensorflow.contrib.signal.python.ops import window_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import spectral_ops
+
+
+def stft(signal, frame_length, frame_step, fft_length=None,
+         window_fn=functools.partial(window_ops.hann_window, periodic=True),
+         pad_end=True, name=None):
+  """Computes the Short-time Fourier Transform of a batch of real signals.
+
+  https://en.wikipedia.org/wiki/Short-time_Fourier_transform
+
+  Implemented with GPU-compatible ops and supports gradients.
+
+  Args:
+    signal: A `[..., samples]` `float32` `Tensor` of real-valued signals.
+    frame_length: An integer scalar `Tensor`. The window length in samples.
+    frame_step: An integer scalar `Tensor`. The number of samples to step.
+    fft_length: An integer scalar `Tensor`. The size of the FFT to apply.
+      If not provided, uses the smallest power of 2 enclosing `frame_length`.
+    window_fn: A callable that takes a window length and a `dtype` keyword
+      argument and returns a `[window_length]` `Tensor` of samples in the
+      provided datatype. If set to `None`, no windowing is used.
+    pad_end: Whether to pad the end of signal with zeros when the provided
+      window length and hop produces a window that lies partially past the end
+      of `signal`.
+    name: An optional name for the operation.
+
+  Returns:
+    A `[..., frames, fft_unique_bins]` `Tensor` of `complex64` STFT values where
+    `fft_unique_bins` is `fft_length / 2 + 1` (the unique components of the
+    FFT).
+
+  Raises:
+    ValueError: If `signal` is not at least rank 1, `frame_length` is
+      not scalar, `frame_step` is not scalar, or `frame_length`
+      is greater than `fft_length`.
+  """
+  with ops.name_scope(name, 'stft', [signal, frame_length,
+                                     frame_step]):
+    signal = ops.convert_to_tensor(signal, name='signal')
+    signal.shape.with_rank_at_least(1)
+    frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
+    frame_length.shape.assert_has_rank(0)
+    frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
+    frame_step.shape.assert_has_rank(0)
+
+    if fft_length is None:
+      fft_length = _enclosing_power_of_two(frame_length)
+    else:
+      fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
+
+    frame_length_static = tensor_util.constant_value(
+        frame_length)
+    fft_length_static = tensor_util.constant_value(fft_length)
+    if (frame_length_static is not None and fft_length_static is not None and
+        frame_length_static > fft_length_static):
+      raise ValueError('frame_length (%d) may not be larger than '
+                       'fft_length (%d)' % (frame_length_static,
+                                            fft_length_static))
+
+    framed_signal = shape_ops.frame(
+        signal, frame_length, frame_step, pad_end=pad_end)
+
+    # Optionally window the framed signal.
+    if window_fn is not None:
+      window = window_fn(frame_length, dtype=framed_signal.dtype)
+      framed_signal *= window
+
+    # spectral_ops.rfft produces the (fft_length/2 + 1) unique components of the
+    # FFT of the real windowed signals in framed_signal.
+    return spectral_ops.rfft(framed_signal, [fft_length])
+
+
+def inverse_stft(stfts,
+                 frame_length,
+                 frame_step,
+                 fft_length,
+                 window_fn=functools.partial(window_ops.hann_window,
+                                             periodic=True),
+                 name=None):
+  """Computes the inverse Short-time Fourier Transform of a batch of STFTs.
+
+  https://en.wikipedia.org/wiki/Short-time_Fourier_transform
+
+  Implemented with GPU-compatible ops and supports gradients.
+
+  Args:
+    stfts: A `complex64` `[..., frames, fft_unique_bins]` `Tensor` of STFT bins
+      representing a batch of `fft_length`-point STFTs.
+    frame_length: An integer scalar `Tensor`. The window length in samples.
+    frame_step: An integer scalar `Tensor`. The number of samples to step.
+    fft_length: An integer scalar `Tensor`. The size of the FFT that produced
+      `stfts`.
+    window_fn: A callable that takes a window length and a `dtype` keyword
+      argument and returns a `[window_length]` `Tensor` of samples in the
+      provided datatype. If set to `None`, no windowing is used.
+    name: An optional name for the operation.
+
+  Returns:
+    A `[..., samples]` `Tensor` of `float32` signals representing the inverse
+    STFT for each input STFT in `stfts`.
+
+  Raises:
+    ValueError: If `stfts` is not at least rank 2, `frame_length` is not scalar,
+      `frame_step` is not scalar, or `fft_length` is not scalar.
+  """
+  with ops.name_scope(name, 'inverse_stft', [stfts]):
+    stfts = ops.convert_to_tensor(stfts, name='stfts')
+    stfts.shape.with_rank_at_least(2)
+    frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
+    frame_length.shape.assert_has_rank(0)
+    frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
+    frame_step.shape.assert_has_rank(0)
+    fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
+    fft_length.shape.assert_has_rank(0)
+    real_frames = spectral_ops.irfft(stfts, [fft_length])[..., :frame_length]
+
+    # Optionally window and overlap-add the inner 2 dimensions of real_frames
+    # into a single [samples] dimension.
+    if window_fn is not None:
+      window = window_fn(frame_length, dtype=stfts.dtype.real_dtype)
+      real_frames *= window
+    return reconstruction_ops.overlap_and_add(real_frames, frame_step)
+
+
+def _enclosing_power_of_two(value):
+  """Return 2**N for integer N such that 2**N >= value."""
+  value_static = tensor_util.constant_value(value)
+  if value_static is not None:
+    return constant_op.constant(
+        int(2**np.ceil(np.log(value_static) / np.log(2.0))), value.dtype)
+  return math_ops.cast(
+      math_ops.pow(2.0, math_ops.ceil(
+          math_ops.log(math_ops.to_float(value)) / math_ops.log(2.0))),
+      value.dtype)
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 7b7eb72994..aa66f25391 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -1927,6 +1927,16 @@ py_library(
     ],
 )
 
+py_library(
+    name = "spectral_ops_test_util",
+    srcs = ["ops/spectral_ops_test_util.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":client_testlib",
+        ":framework_ops",
+    ],
+)
+
 py_library(
     name = "confusion_matrix",
     srcs = ["ops/confusion_matrix.py"],
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index f5771a2870..7363279a49 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2198,6 +2198,7 @@ cuda_py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:spectral_ops",
+        "//tensorflow/python:spectral_ops_test_util",
     ],
     shard_count = 3,
 )
diff --git a/tensorflow/python/kernel_tests/fft_ops_test.py b/tensorflow/python/kernel_tests/fft_ops_test.py
index b6a42731e8..b9e2aa1f3a 100644
--- a/tensorflow/python/kernel_tests/fft_ops_test.py
+++ b/tensorflow/python/kernel_tests/fft_ops_test.py
@@ -30,6 +30,7 @@ from tensorflow.python.ops import gen_spectral_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import spectral_ops
+from tensorflow.python.ops import spectral_ops_test_util
 from tensorflow.python.platform import test
 
 VALID_FFT_RANKS = (1, 2, 3)
@@ -37,31 +38,6 @@ VALID_FFT_RANKS = (1, 2, 3)
 
 class BaseFFTOpsTest(test.TestCase):
 
-  def _use_eigen_kernels(self):
-    use_eigen_kernels = False  # Eigen kernels are default
-    if test.is_gpu_available(cuda_only=True):
-      use_eigen_kernels = False
-    return use_eigen_kernels
-
-  def _fft_kernel_label_map(self):
-    """Returns a generator overriding kernel selection.
-
-    This is used to force testing of the eigen kernels, even
-    when they are not the default registered kernels.
-
-    Returns:
-      A generator in which to wrap every test.
-    """
-    if self._use_eigen_kernels():
-      d = dict([(op, "eigen")
-                for op in [
-                    "FFT", "FFT2D", "FFT3D", "IFFT", "IFFT2D", "IFFT3D",
-                    "IRFFT", "IRFFT2D", "IRFFT3D", "RFFT", "RFFT2D", "RFFT3D"
-                ]])
-      return ops.get_default_graph()._kernel_label_map(d)
-    else:
-      return ops.get_default_graph()._kernel_label_map({})
-
   def _compare(self, x, rank, fft_length=None, use_placeholder=False):
     self._compareForward(x, rank, fft_length, use_placeholder)
     self._compareBackward(x, rank, fft_length, use_placeholder)
@@ -178,7 +154,7 @@ class FFTOpsTest(BaseFFTOpsTest):
       raise ValueError("invalid rank")
 
   def testEmpty(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
           x = np.zeros((0,) * dims).astype(np.complex64)
@@ -186,7 +162,7 @@ class FFTOpsTest(BaseFFTOpsTest):
           self.assertEqual(x.shape, self._tfIFFT(x, rank).shape)
 
   def testBasic(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
           self._compare(
@@ -212,7 +188,7 @@ class FFTOpsTest(BaseFFTOpsTest):
   #               (128,) * dims).astype(np.complex64), rank)
 
   def testBasicPlaceholder(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
           self._compare(
@@ -222,7 +198,7 @@ class FFTOpsTest(BaseFFTOpsTest):
               use_placeholder=True)
 
   def testRandom(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       np.random.seed(12345)
 
       def gen(shape):
@@ -247,7 +223,7 @@ class FFTOpsTest(BaseFFTOpsTest):
           self._tfIFFT(x, rank)
 
   def testGrad_Simple(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 2):
           re = np.ones(shape=(4,) * dims, dtype=np.float32) / 10.0
@@ -256,7 +232,7 @@ class FFTOpsTest(BaseFFTOpsTest):
           self._checkGradComplex(self._tfIFFTForRank(rank), re, im)
 
   def testGrad_Random(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       np.random.seed(54321)
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 2):
@@ -321,7 +297,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
       raise ValueError("invalid rank")
 
   def testEmpty(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
           x = np.zeros((0,) * dims).astype(np.float32)
@@ -330,7 +306,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
           self.assertEqual(x.shape, self._tfIFFT(x, rank).shape)
 
   def testBasic(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
           for size in (5, 6):
@@ -357,7 +333,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
           self._compareBackward(c2r.astype(np.complex64), rank, (size,) * rank)
 
   def testBasicPlaceholder(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(rank, rank + 3):
           for size in (5, 6):
@@ -377,7 +353,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
 
   def testFftLength(self):
     if test.is_gpu_available(cuda_only=True):
-      with self._fft_kernel_label_map():
+      with spectral_ops_test_util.fft_kernel_label_map():
         for rank in VALID_FFT_RANKS:
           for dims in xrange(rank, rank + 3):
             for size in (5, 6):
@@ -418,7 +394,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
                   use_placeholder=True)
 
   def testRandom(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       np.random.seed(12345)
 
       def gen_real(shape):
@@ -444,7 +420,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
                 gen_complex(complex_dims), rank, (size,) * rank)
 
   def testError(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         for dims in xrange(0, rank):
           x = np.zeros((1,) * dims).astype(np.complex64)
@@ -500,7 +476,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
             irfft_fn(x, fft_length).eval()
 
   def testGrad_Simple(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       for rank in VALID_FFT_RANKS:
         # rfft3d/irfft3d do not have gradients yet.
         if rank == 3:
@@ -514,7 +490,7 @@ class RFFTOpsTest(BaseFFTOpsTest):
                 self._tfIFFTForRank(rank), re, im, result_is_complex=False)
 
   def testGrad_Random(self):
-    with self._fft_kernel_label_map():
+    with spectral_ops_test_util.fft_kernel_label_map():
       np.random.seed(54321)
       for rank in VALID_FFT_RANKS:
         # rfft3d/irfft3d do not have gradients yet.
diff --git a/tensorflow/python/ops/spectral_ops_test_util.py b/tensorflow/python/ops/spectral_ops_test_util.py
new file mode 100644
index 0000000000..1f2e730edc
--- /dev/null
+++ b/tensorflow/python/ops/spectral_ops_test_util.py
@@ -0,0 +1,50 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for writing test involving spectral_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import test
+
+
+def _use_eigen_kernels():
+  use_eigen_kernels = False  # Eigen kernels are default
+  if test.is_gpu_available(cuda_only=True):
+    use_eigen_kernels = False
+  return use_eigen_kernels
+
+
+def fft_kernel_label_map():
+  """Returns a generator overriding kernel selection.
+
+  This is used to force testing of the eigen kernels, even
+  when they are not the default registered kernels.
+
+  Returns:
+    A generator in which to wrap every test.
+  """
+  if _use_eigen_kernels():
+    d = dict([(op, "eigen")
+              for op in [
+                  "FFT", "FFT2D", "FFT3D", "IFFT", "IFFT2D", "IFFT3D",
+                  "IRFFT", "IRFFT2D", "IRFFT3D", "RFFT", "RFFT2D", "RFFT3D"
+              ]])
+    return ops.get_default_graph()._kernel_label_map(d)  # pylint: disable=protected-access
+  else:
+    return ops.get_default_graph()._kernel_label_map({})  # pylint: disable=protected-access
+
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 644cab95f8..442b335be5 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -82,6 +82,7 @@ py_binary(
         "//tensorflow/python:util_example_parser_configuration",
         "//tensorflow/python/debug:debug_pip",
         "//tensorflow/python/saved_model",
+        "//tensorflow/python:spectral_ops_test_util",
         "//tensorflow/python/tools:tools_pip",
         # These targets don't build on Windows yet. Exclude them for now.
         # "//tensorflow/contrib/ndlstm",
@@ -170,6 +171,7 @@ sh_binary(
             "//tensorflow/python:util_example_parser_configuration",
             "//tensorflow/python/debug:debug_pip",
             "//tensorflow/python/saved_model:saved_model",
+            "//tensorflow/python:spectral_ops_test_util",
             "//tensorflow/python/tools:tools_pip",
             "//tensorflow/tools/dist_test/server:grpc_tensorflow_server",
         ],
-- 
GitLab


From bad0e44c1722fd230968f04f99562fa9f4a289dd Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Wed, 19 Jul 2017 12:05:57 -0700
Subject: [PATCH 1119/1375] Update README.md (#11358)

* Update README.md

Update readability in example, replaced HTTP link to HTTPS links, and added a link

* Update README.md

-Changed link as suggested
-Added links and replaced some more
-Few other minor edits

* Update README.md

Applying requested changes

* Update README.md

* Update README.md

Update link

* Update README.md

Remove extra line of white space
---
 README.md | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 0dec90878e..f86e747978 100644
--- a/README.md
+++ b/README.md
@@ -25,12 +25,12 @@ variety of other domains, as well.
 guidelines](CONTRIBUTING.md).**
 
 **We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for
-tracking requests and bugs, but please see
-[Community](https://www.tensorflow.org/community/) for general questions
-and discussion.**
+tracking requests and bugs. So please see 
+[TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) for general questions
+and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).**
 
 ## Installation
-*See [Installing TensorFlow](https://www.tensorflow.org/install/) for instructions on how to install our release binaries or how to build from source.*
+*See [Installing TensorFlow](https://www.tensorflow.org/install) for instructions on how to install our release binaries or how to build from source.*
 
 People who are a little more adventurous can also try our nightly binaries:
 
@@ -40,7 +40,7 @@ People who are a little more adventurous can also try our nightly binaries:
 * Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
 * Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
 * Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
@@ -55,7 +55,7 @@ $ python
 'Hello, TensorFlow!'
 >>> a = tf.constant(10)
 >>> b = tf.constant(32)
->>> sess.run(a+b)
+>>> sess.run(a + b)
 42
 >>>
 ```
@@ -63,8 +63,9 @@ $ python
 ## For more information
 
 * [TensorFlow website](https://www.tensorflow.org)
-* [TensorFlow whitepaper](http://download.tensorflow.org/paper/whitepaper2015.pdf)
+* [TensorFlow White Papers](https://www.tensorflow.org/about/bib)
 * [TensorFlow Model Zoo](https://github.com/tensorflow/models)
 * [TensorFlow MOOC on Udacity](https://www.udacity.com/course/deep-learning--ud730)
+* [TensorFlow course at Stanford](https://web.stanford.edu/class/cs20si)
 
-The TensorFlow community has created amazing things with TensorFlow, please see the [resources section of tensorflow.org](https://www.tensorflow.org/about/#community) for an incomplete list.
+Learn more about the TensorFlow community at the [community page of tensorflow.org](https://www.tensorflow.org/community) for a few ways to participate.
-- 
GitLab


From a9ec017cc5f70ca0c2ee6372052d7010b3568137 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 12:30:31 -0700
Subject: [PATCH 1120/1375] Use math_ops.{sinh,asin,cosh} instead of
 trig.{sinh,arc_sinh) in Sinh bijector.

PiperOrigin-RevId: 162519993
---
 .../python/ops/bijectors/sinh_arcsinh_impl.py         | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
index 27359bad32..41ee47636d 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
@@ -25,9 +25,6 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import bijector
 
-sinh = trig.sinh
-arcsinh = trig.arcsinh
-cosh = trig.cosh
 log_cosh = trig.log_cosh
 
 __all__ = [
@@ -114,10 +111,10 @@ class SinhArcsinh(bijector.Bijector):
     return self._tailweight
 
   def _forward(self, x):
-    return sinh((arcsinh(x) + self.skewness) * self.tailweight)
+    return math_ops.sinh((math_ops.asinh(x) + self.skewness) * self.tailweight)
 
   def _inverse(self, y):
-    return sinh(arcsinh(y) / self.tailweight - self.skewness)
+    return math_ops.sinh(math_ops.asinh(y) / self.tailweight - self.skewness)
 
   def _inverse_log_det_jacobian(self, y):
     # x = sinh(arcsinh(y) / tailweight - skewness)
@@ -127,7 +124,7 @@ class SinhArcsinh(bijector.Bijector):
     #     / (tailweight * sqrt(y**2 + 1))
     event_dims = self._event_dims_tensor(y)
     return math_ops.reduce_sum(
-        log_cosh(arcsinh(y) / self.tailweight - self.skewness) -
+        log_cosh(math_ops.asinh(y) / self.tailweight - self.skewness) -
         math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(y**2),
         axis=event_dims)
 
@@ -138,6 +135,6 @@ class SinhArcsinh(bijector.Bijector):
     # = cosh((arcsinh(x) + skewness) * tailweight) * tailweight / sqrt(x**2 + 1)
     event_dims = self._event_dims_tensor(x)
     return math_ops.reduce_sum(
-        log_cosh((arcsinh(x) + self.skewness) * self.tailweight) +
+        log_cosh((math_ops.asinh(x) + self.skewness) * self.tailweight) +
         math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(x**2),
         axis=event_dims)
-- 
GitLab


From 00331578f746797989803a22a112e2046649dfbb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 12:51:01 -0700
Subject: [PATCH 1121/1375] Internal change.

PiperOrigin-RevId: 162522519
---
 tensorflow/compiler/xla/service/user_computation.cc | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 8c29945d0b..d9079d70df 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -1010,9 +1010,6 @@ StatusOr<ComputationDataHandle> UserComputation::AddInfeedInstruction(
   tensorflow::mutex_lock lock(mutex_);
 
   const Shape& shape = infeed_request.shape();
-  if (ShapeUtil::IsNestedTuple(shape)) {
-    return InvalidArgument("Infeed does not support nested tuple shapes");
-  }
   if (!LayoutUtil::HasLayout(shape)) {
     return InvalidArgument("Given shape to Infeed must have a layout");
   }
-- 
GitLab


From 03a790d5bff835365a54a534fc1658dc287c0f63 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Wed, 19 Jul 2017 13:00:45 -0700
Subject: [PATCH 1122/1375] Disable padding by default in
 tf.contrib.signal.stft to match tf.contrib.signal.frame.

PiperOrigin-RevId: 162523648
---
 tensorflow/contrib/signal/python/ops/spectral_ops.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/signal/python/ops/spectral_ops.py b/tensorflow/contrib/signal/python/ops/spectral_ops.py
index 1457f13604..0d1ef0d3d1 100644
--- a/tensorflow/contrib/signal/python/ops/spectral_ops.py
+++ b/tensorflow/contrib/signal/python/ops/spectral_ops.py
@@ -34,7 +34,7 @@ from tensorflow.python.ops import spectral_ops
 
 def stft(signal, frame_length, frame_step, fft_length=None,
          window_fn=functools.partial(window_ops.hann_window, periodic=True),
-         pad_end=True, name=None):
+         pad_end=False, name=None):
   """Computes the Short-time Fourier Transform of a batch of real signals.
 
   https://en.wikipedia.org/wiki/Short-time_Fourier_transform
@@ -51,7 +51,7 @@ def stft(signal, frame_length, frame_step, fft_length=None,
       argument and returns a `[window_length]` `Tensor` of samples in the
       provided datatype. If set to `None`, no windowing is used.
     pad_end: Whether to pad the end of signal with zeros when the provided
-      window length and hop produces a window that lies partially past the end
+      frame length and step produces a frame that lies partially past the end
       of `signal`.
     name: An optional name for the operation.
 
-- 
GitLab


From 831a3a46d34e9bd305555889392c016690ff9bc4 Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Wed, 19 Jul 2017 13:15:03 -0700
Subject: [PATCH 1123/1375] Automated g4 rollback of changelist 160314706

PiperOrigin-RevId: 162525519
---
 tensorflow/core/kernels/conv_ops_gpu_3.cu.cc  | 127 +++++++++++++++-
 tensorflow/python/BUILD                       |  20 +++
 tensorflow/python/kernel_tests/BUILD          |   2 +-
 .../python/kernel_tests/transpose_op_test.py  |  20 +++
 tensorflow/python/ops/conv2d_benchmark.py     | 141 ++++++++++++++++++
 tensorflow/python/ops/transpose_benchmark.py  |  64 +++++---
 6 files changed, 348 insertions(+), 26 deletions(-)
 create mode 100644 tensorflow/python/ops/conv2d_benchmark.py

diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
index 2307c2de0e..bcabda848c 100644
--- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
+++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
@@ -272,6 +272,87 @@ __global__ void SwapDimension1And2InTensor3UsingTiles(const T* input,
   }
 }
 
+// Use shared memory tiles to swap dimension-1 and dimension-2 of a 3D tensor
+// when only one of the dimension sizes is smaller than 16,
+// where dimensions are zero-based: output[i][j][k] = input[i][k][j].
+//
+// small_dim = the_smaller_dimension_size
+// large_dim = the_larger_dimension_size
+// tile_num_per_block = blockDim.x
+// kTileLength = small_dim
+//
+// Each thread block operates on a single rectangle tile, where its width is
+// kTileLength (we currently set it to 64) and its height is small_dim,
+// We set the thread block's X dimension to be tile_num_per_block, and its Y
+// and Z to be one.
+template <typename T, bool SmallDim2>
+__global__ void SwapDimension1And2InTensor3SmallDim(const T* input,
+                                                    int batch_per_block,
+                                                    Dimension<3> input_dims,
+                                                    T* output) {
+  // TODO(yangzihao) avoid share memory bank conflict.
+  extern __shared__ __align__(sizeof(T)) unsigned char shmem[];
+  T* shared_memory_tile = reinterpret_cast<T*>(shmem);
+
+  eigen_assert(blockDim.y == 1);
+  eigen_assert(blockDim.z == 1);
+  eigen_assert(gridDim.z == 1);
+
+  int block_offset = blockIdx.x * blockDim.x * batch_per_block;
+
+  int x = threadIdx.x;
+  int tile_height = blockDim.x;
+
+  // Get tile height, width, and thread/block origin indices.
+  int small_dim = SmallDim2 ? input_dims[2] : input_dims[1];
+  int large_dim = SmallDim2 ? input_dims[1] : input_dims[2];
+
+  int global_offset = small_dim * large_dim * (blockIdx.y * batch_per_block) +
+                      (SmallDim2 ? block_offset * small_dim : block_offset) + x;
+  if (global_offset > (input_dims[0] * input_dims[1] * input_dims[2])) return;
+
+  for (int batch = 0; batch < batch_per_block; ++batch) {
+    int block_origin_idx =
+        small_dim * large_dim * (blockIdx.y * batch_per_block + batch);
+    int thread_origin_idx =
+        block_origin_idx +
+        (SmallDim2 ? block_offset * small_dim : block_offset) + x;
+
+    if (block_offset + blockDim.x > large_dim) {
+      tile_height = large_dim - block_offset;
+    }
+
+    // Load a continuous memory region to shared memory tile.
+    if (x < tile_height) {
+      for (int y = 0; y < small_dim; y++) {
+        int shmem_index =
+            SmallDim2 ? (x + y * tile_height) : (x * small_dim + y);
+        shared_memory_tile[shmem_index] =
+            ldg(input + thread_origin_idx +
+                y * (SmallDim2 ? tile_height : large_dim));
+      }
+    }
+
+    __syncthreads();
+
+    // Get block origin index for output array.
+    int output_block_offset = block_origin_idx;
+    int output_block_idx = SmallDim2 ? block_offset : block_offset * small_dim;
+    int output_block_origin_idx = output_block_offset + output_block_idx;
+
+    // Store the tranposed memory region in shared memory to device.
+    if (x < tile_height) {
+      for (int y = 0; y < small_dim; y++) {
+        int output_idx = output_block_origin_idx + x +
+                         y * (SmallDim2 ? large_dim : tile_height);
+        int shmem_index =
+            SmallDim2 ? (x * small_dim + y) : (x + y * tile_height);
+        output[output_idx] = shared_memory_tile[shmem_index];
+      }
+    }
+  }
+}
+
 // A Cuda custom kernel that convert input to output, given proper padding on
 // the left and the top. The padded value is zero.
 template <typename T, int NDIMS>
@@ -420,25 +501,63 @@ template <typename T>
 void RunSwapDimension1And2InTensor3(const GPUDevice& d, const T* input,
                                     const Dimension<3>& input_dims, T* output) {
   // If both dimensions are not trivial, use tiles for the actual swapping.
+  // If one dimension is trivial, use SmallDim kernel for swapping.
   // Otherwise, the trivial swapping relying on the ldg cache is more efficient.
   static const int kMinDimensionToUseTiles = 16;
   bool use_tiles = (input_dims[1] >= kMinDimensionToUseTiles &&
                     input_dims[2] >= kMinDimensionToUseTiles);
+  bool use_small_dim = ((input_dims[1] >= kMinDimensionToUseTiles &&
+                         input_dims[2] < kMinDimensionToUseTiles)) ||
+                       ((input_dims[1] < kMinDimensionToUseTiles &&
+                         input_dims[2] >= kMinDimensionToUseTiles));
+  static const int NumSubTiles = 8;
+
   if (use_tiles) {
-    // We get best performance when TileSize is the number of threads in a warp
-    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
-    // threads.
     static const int TileSize = 32;
-    static const int NumSubTiles = 8;
     Dimension<3> input_dims_in_tiles = {
         input_dims[0], (input_dims[1] + TileSize - 1) / TileSize,
         (input_dims[2] + TileSize - 1) / TileSize,
     };
     int total_tiles_count = input_dims_in_tiles[0] * input_dims_in_tiles[1] *
                             input_dims_in_tiles[2];
+    // We get best performance when TileSize is the number of threads in a warp
+    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
+    // threads.
     SwapDimension1And2InTensor3UsingTiles<T, TileSize, NumSubTiles><<<
         total_tiles_count, dim3(TileSize, NumSubTiles), 0, d.stream()>>>(
         input, input_dims, output);
+  } else if (use_small_dim) {
+    // When only one of the dimensions is smaller than kMinDimensionToUseTiles,
+    // we use one block to process a rectangle region with the size of
+    // kTileLength * small_dim. We found that when set kTileLength to 64 on
+    // TitanX Maxwell GPU, it achieves the best performance.
+    //              large_dim
+    //            +---------------...--------+
+    //            |            |        |    |
+    // small_dim  |            |  ...   |    |
+    //            |            |        |    |
+    //            +--------------...---------+
+    //            \----- ------/         \- -/
+    //                  V                  V
+    //    kTileLength(tile_height)    tile_height
+    static const int kTileLength = 64;
+    static const int kGridDimY = 2048;
+    int small_dim = std::min(input_dims[2], input_dims[1]);
+    int large_dim = std::max(input_dims[2], input_dims[1]);
+    int tile_num_per_block = (large_dim + kTileLength - 1) / kTileLength;
+    int grid_dim_y = input_dims[0] < kGridDimY ? input_dims[0] : kGridDimY;
+    int batch_per_block = (input_dims[0] + grid_dim_y - 1) / grid_dim_y;
+    if (input_dims[2] < input_dims[1]) {
+      SwapDimension1And2InTensor3SmallDim<T, true>
+          <<<dim3(tile_num_per_block, grid_dim_y), kTileLength,
+             kTileLength * small_dim * sizeof(T), d.stream()>>>(
+              input, batch_per_block, input_dims, output);
+    } else {
+      SwapDimension1And2InTensor3SmallDim<T, false>
+          <<<dim3(tile_num_per_block, grid_dim_y), kTileLength,
+             kTileLength * small_dim * sizeof(T), d.stream()>>>(
+              input, batch_per_block, input_dims, output);
+    }
   } else {
     int total_element_count = input_dims[0] * input_dims[1] * input_dims[2];
     CudaLaunchConfig config = GetCudaLaunchConfig(total_element_count, d);
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index aa66f25391..b7063d59ba 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3823,6 +3823,26 @@ cuda_py_test(
     main = "ops/concat_benchmark.py",
 )
 
+cuda_py_test(
+    name = "conv2d_benchmark",
+    size = "large",
+    srcs = ["ops/conv2d_benchmark.py"],
+    additional_deps = [
+        ":client",
+        ":client_testlib",
+        ":control_flow_ops",
+        ":framework_for_generated_wrappers",
+        ":nn_ops",
+        ":platform",
+        ":platform_benchmark",
+        ":random_ops",
+        ":variables",
+        "//third_party/py/numpy",
+        "//tensorflow/core:protos_all_py",
+    ],
+    main = "ops/conv2d_benchmark.py",
+)
+
 cuda_py_test(
     name = "split_benchmark",
     srcs = ["ops/split_benchmark.py"],
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 7363279a49..442b4b2df5 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1985,7 +1985,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "transpose_op_test",
-    size = "large",
+    size = "small",
     srcs = ["transpose_op_test.py"],
     additional_deps = [
         "//third_party/py/numpy",
diff --git a/tensorflow/python/kernel_tests/transpose_op_test.py b/tensorflow/python/kernel_tests/transpose_op_test.py
index 570fa79944..67bdb4237d 100644
--- a/tensorflow/python/kernel_tests/transpose_op_test.py
+++ b/tensorflow/python/kernel_tests/transpose_op_test.py
@@ -229,6 +229,26 @@ class TransposeTest(test.TestCase):
         self.assertAllEqual(np_ans, tf_ans)
         self.assertShapeEqual(np_ans, y)
 
+  def testLargeSizeGPU(self):
+    # If no GPU available, skip the test
+    if not test.is_gpu_available(cuda_only=True):
+      return
+
+    large_shapes = [[1000000, 31, 3], [3, 1000000, 31], [3, 31, 1000000],
+                    [2, 1000, 1000], [1000, 2, 1000], [1000, 1000, 2]]
+    perms = [[0, 2, 1]] * 6
+
+    for input_shape, perm in zip(large_shapes, perms):
+      total_size = np.prod(input_shape)
+      inp = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_shape)
+      np_ans = self._np_transpose(inp, perm)
+      with self.test_session(use_gpu=True):
+        inx = ops.convert_to_tensor(inp)
+        y = array_ops.transpose(inx, perm)
+        tf_ans = y.eval()
+      self.assertAllEqual(np_ans, tf_ans)
+      self.assertShapeEqual(np_ans, y)
+
   def testNop(self):
     self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
 
diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py
new file mode 100644
index 0000000000..6992fa57ea
--- /dev/null
+++ b/tensorflow/python/ops/conv2d_benchmark.py
@@ -0,0 +1,141 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Benchmark for Conv2D op."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+import time
+
+from tensorflow.python.client import session as session_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+def build_graph(device, input_shape, filter_shape, strides, padding, num_iters):
+  """builds a graph containing a sequence of conv2d operations.
+
+  Args:
+    device: String, the device to run on.
+    input_shape: Shape of the input tensor.
+    filter_shape: Shape of the filter tensor.
+    strides: A list of ints. 1-D of length 4. The stride of sliding
+             window for each dimension of input.
+    padding: A string from: "SAME", "VALID". The type of padding
+             algorithm to use.
+    num_iters: number of iterations to run conv2d.
+
+  Returns:
+    An array of tensors to run()
+  """
+  with ops.device("/%s:0" % device):
+    inp = variables.Variable(random_ops.truncated_normal(input_shape))
+    filt = variables.Variable(random_ops.truncated_normal(filter_shape))
+
+    outputs = []
+    conv2d_op = nn_ops.conv2d(inp, filt, strides, padding, data_format="NHWC")
+    outputs.append(conv2d_op)
+    for _ in range(1, num_iters):
+      with ops.control_dependencies([conv2d_op]):
+        conv2d_op = nn_ops.conv2d(
+            inp, filt, strides, padding, data_format="NHWC")
+        outputs.append(conv2d_op)
+    return control_flow_ops.group(*outputs)
+
+
+class Conv2DBenchmark(test.Benchmark):
+  """Benchmark conv2d!"""
+
+  def _run_graph(self, device, input_shape, filter_shape, strides, padding,
+                 num_iters):
+    """runs the graph and print its execution time.
+
+    Args:
+      device: String, the device to run on.
+      input_shape: Shape of the input tensor.
+      filter_shape: Shape of the filter tensor.
+      strides: A list of ints. 1-D of length 4. The stride of sliding
+               window for each dimension of input.
+      padding: A string from: "SAME", "VALID". The type of padding
+               algorithm to use.  num_iters: Number of iterations to run the
+                 benchmark.
+      num_iters: number of iterations to run conv2d.
+
+    Returns:
+      The duration of the run in seconds.
+    """
+    graph = ops.Graph()
+    with graph.as_default():
+      outputs = build_graph(device, input_shape, filter_shape, strides, padding,
+                            num_iters)
+      with session_lib.Session(graph=graph) as session:
+        variables.global_variables_initializer().run()
+        # warmup runs
+        session.run(outputs)
+
+        start_time = time.time()
+        session.run(outputs)
+        duration = (time.time() - start_time) / num_iters
+
+        print("%s inputshape:%s filtershape:%s strides:%s padding:%s "
+              "%d iters: %.8f sec" %
+              (device, str(input_shape).replace(" ", ""),
+               str(filter_shape).replace(" ", ""),
+               str(strides).replace(" ", ""), padding, num_iters, duration))
+
+    name_template = (
+        "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_"
+        "strides_{strides}_padding_{padding}")
+
+    self.report_benchmark(
+        name=name_template.format(
+            device=device,
+            inputshape=str(input_shape).replace(" ", ""),
+            filtershape=str(filter_shape).replace(" ", ""),
+            strides=str(strides).replace(" ", ""),
+            padding=padding).replace(" ", ""),
+        iters=num_iters,
+        wall_time=duration / num_iters)
+
+    return duration
+
+  def benchmark_conv2d(self):
+    print("conv2d benchmark:")
+
+    h = 500
+    w = 500
+    fh = 3
+    fw = 3
+    input_shapes = []
+    filter_shapes = []
+    for b, c in itertools.product([4, 16, 32], [i for i in range(3, 16)]):
+      input_shapes += [[b, h, w, c]]
+      filter_shapes += [[fh, fw, c, b]]
+    strides = [[1, 2, 2, 1]]
+    paddings = ["VALID", "SAME"]
+    for ishape, fshape in zip(input_shapes, filter_shapes):
+      for stride in strides:
+        for padding in paddings:
+          self._run_graph("gpu", ishape, fshape, stride, padding, 80)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/ops/transpose_benchmark.py b/tensorflow/python/ops/transpose_benchmark.py
index 6bd3fe5e5a..cddefacf2e 100644
--- a/tensorflow/python/ops/transpose_benchmark.py
+++ b/tensorflow/python/ops/transpose_benchmark.py
@@ -1,4 +1,4 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -32,7 +32,7 @@ from tensorflow.python.platform import test
 
 
 def build_graph(device, input_shape, perm, datatype, num_iters):
-  """Build a graph containing a sequence of conv2d operations.
+  """builds a graph containing a sequence of conv2d operations.
 
   Args:
     device: String, the device to run on.
@@ -50,10 +50,12 @@ def build_graph(device, input_shape, perm, datatype, num_iters):
     t = constant_op.constant(inp, shape=input_shape)
 
     outputs = []
-    outputs.append(array_ops.transpose(t, perm))
-    for i in range(1, num_iters):
-      with ops.control_dependencies([outputs[i - 1]]):
-        outputs.append(array_ops.transpose(t, perm))
+    transpose_op = array_ops.transpose(t, perm)
+    outputs.append(transpose_op)
+    for _ in range(1, num_iters):
+      with ops.control_dependencies([transpose_op]):
+        transpose_op = array_ops.transpose(t, perm)
+        outputs.append(transpose_op)
     return control_flow_ops.group(*outputs)
 
 
@@ -61,7 +63,7 @@ class TransposeBenchmark(test.Benchmark):
   """Benchmark transpose!"""
 
   def _run_graph(self, device, input_shape, perm, num_iters, datatype):
-    """Run the graph and print its execution time.
+    """runs the graph and print its execution time.
 
     Args:
       device: String, the device to run on.
@@ -82,9 +84,11 @@ class TransposeBenchmark(test.Benchmark):
         session.run(outputs)
         start_time = time.time()
         session.run(outputs)
+
         duration = (time.time() - start_time) / num_iters
-        throughput = np.prod(np.array(
-            input_shape)) * datatype().itemsize * 2 / duration / 1e9
+        throughput = np.prod(
+            np.array(input_shape)) * datatype().itemsize * 2 / duration / 1e9
+
         print("%s %s inputshape:%s perm:%s %d %.6fsec, %.4fGB/s." %
               (device, str(datatype), str(input_shape).replace(" ", ""),
                str(perm).replace(" ", ""), num_iters, duration, throughput))
@@ -108,19 +112,19 @@ class TransposeBenchmark(test.Benchmark):
 
     datatypes = [np.complex128, np.float64, np.float32, np.float16, np.int8]
 
-    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2 + [[
-        2, 100, 100, 16
-    ], [2, 16, 100, 100]] * 2 + [[2, 5000, 16], [2, 16, 5000]] * 2
-    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
-        [0, 3, 1, 2], [0, 2, 3, 1]
-    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2
+    small_shapes += [[2, 100, 100, 16], [2, 16, 100, 100]] * 2
+    small_shapes += [[2, 5000, 16], [2, 16, 5000]] * 2
+    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
+    small_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
+    small_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
-    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2 + [[
-        2, 1000, 1000, 32
-    ], [2, 1000, 1000, 64]] * 2 + [[2, 1000000, 32], [2, 1000000, 64]] * 2
-    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
-        [0, 3, 1, 2], [0, 2, 3, 1]
-    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2
+    large_shapes += [[2, 1000, 1000, 32], [2, 1000, 1000, 64]] * 2
+    large_shapes += [[2, 1000000, 32], [2, 1000000, 64]] * 2
+    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
+    large_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
+    large_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
     huge_shapes = [[2, 100, 100, 100, 128], [2, 1000, 1000, 128],
                    [2, 1000000, 128]] * 2
@@ -143,5 +147,23 @@ class TransposeBenchmark(test.Benchmark):
             for ishape, perm in zip(huge_shapes, huge_perms):
               self._run_graph("gpu", ishape, perm, num_iters, datatype)
 
+    small_dim_large_shapes = [[2, 1000000, 3], [2, 3, 1000000], [2, 1000000, 8],
+                              [2, 8, 1000000]]
+    small_dim_small_shapes = [[2, 5000, 3], [2, 3, 5000], [2, 5000, 8],
+                              [2, 8, 5000]]
+    small_dim_perms = [[0, 2, 1]] * 4
+
+    num_iters = 320
+    small_dim_large_shape_datatypes = [np.float64, np.float32, np.int8]
+    for datatype in small_dim_large_shape_datatypes:
+      for ishape, perm in zip(small_dim_large_shapes, small_dim_perms):
+        self._run_graph("gpu", ishape, perm, num_iters, datatype)
+
+    small_dim_small_shape_datatypes = [np.complex128, np.float16]
+    for datatype in small_dim_small_shape_datatypes:
+      for ishape, perm in zip(small_dim_small_shapes, small_dim_perms):
+        self._run_graph("gpu", ishape, perm, num_iters, datatype)
+
+
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From b977998827180e3e3ba872db28ff211715cfe73a Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Wed, 19 Jul 2017 13:32:35 -0700
Subject: [PATCH 1124/1375] Add functions for specifying a custom gradient.

PiperOrigin-RevId: 162527721
---
 tensorflow/contrib/bayesflow/BUILD            |  27 +++
 tensorflow/contrib/bayesflow/__init__.py      |  10 +-
 .../python/kernel_tests/custom_grad_test.py   | 157 ++++++++++++++++++
 .../bayesflow/python/ops/custom_grad.py       |  34 ++++
 .../bayesflow/python/ops/custom_grad_impl.py  | 110 ++++++++++++
 5 files changed, 334 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/custom_grad.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 71828f084d..1cd6e64b32 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -42,15 +42,39 @@ cuda_py_test(
         "//third_party/py/numpy",
         "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python/ops/distributions",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:platform_test",
     ],
 )
 
+cuda_py_test(
+    name = "custom_grad_test",
+    size = "small",
+    srcs = ["python/kernel_tests/custom_grad_test.py"],
+    additional_deps = [
+        ":bayesflow_py",
+        "//third_party/py/numpy",
+        "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+    ],
+)
+
 cuda_py_test(
     name = "entropy_test",
     size = "medium",
@@ -99,12 +123,15 @@ cuda_py_test(
         "//third_party/py/numpy",
         "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python/ops/distributions",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_seed",
     ],
 )
 
diff --git a/tensorflow/contrib/bayesflow/__init__.py b/tensorflow/contrib/bayesflow/__init__.py
index 65a17d742c..15c1614a67 100644
--- a/tensorflow/contrib/bayesflow/__init__.py
+++ b/tensorflow/contrib/bayesflow/__init__.py
@@ -22,6 +22,7 @@ from __future__ import print_function
 
 # pylint: disable=unused-import,line-too-long
 from tensorflow.contrib.bayesflow.python.ops import csiszar_divergence
+from tensorflow.contrib.bayesflow.python.ops import custom_grad
 from tensorflow.contrib.bayesflow.python.ops import entropy
 from tensorflow.contrib.bayesflow.python.ops import monte_carlo
 from tensorflow.contrib.bayesflow.python.ops import stochastic_gradient_estimators
@@ -34,9 +35,10 @@ from tensorflow.contrib.bayesflow.python.ops import variational_inference
 from tensorflow.python.util.all_util import remove_undocumented
 
 
-_allowed_symbols = ['csiszar_divergence', 'entropy', 'monte_carlo',
-                    'special_math', 'stochastic_gradient_estimators',
-                    'stochastic_graph', 'stochastic_tensor',
-                    'stochastic_variables', 'variational_inference']
+_allowed_symbols = ['csiszar_divergence', 'custom_grad', 'entropy',
+                    'monte_carlo', 'special_math',
+                    'stochastic_gradient_estimators', 'stochastic_graph',
+                    'stochastic_tensor', 'stochastic_variables',
+                    'variational_inference']
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py
new file mode 100644
index 0000000000..5f8f7f692c
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py
@@ -0,0 +1,157 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Custom Gradient Ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.bayesflow.python.ops import custom_grad_impl
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+cg = custom_grad_impl
+
+
+class CustomGradientTest(test.TestCase):
+
+  def test_works_correctly(self):
+    with self.test_session() as sess:
+      f = lambda x: x**2 / 2
+      g = lambda x: (x - 1)**3 / 3
+      x_ = np.linspace(-100, 100, int(1e4)) + [0.]
+
+      x = constant_op.constant(x_)
+      fx = cg.custom_gradient(f(x), g(x), x)
+      gx = gradients_impl.gradients(fx, x)[0]
+      [fx_, gx_] = sess.run([fx, gx])
+
+      self.assertAllEqual(f(x_), fx_)
+      self.assertAllEqual(g(x_), gx_)
+
+  def test_works_correctly_both_f_g_zero(self):
+    with self.test_session() as sess:
+      f = lambda x: x**2 / 2
+      g = lambda x: x**3 / 3
+      x_ = np.linspace(-100, 100, int(1e4)) + [0.]
+
+      x = constant_op.constant(x_)
+      fx = cg.custom_gradient(f(x), g(x), x)
+      gx = gradients_impl.gradients(fx, x)[0]
+      [fx_, gx_] = sess.run([fx, gx])
+
+      self.assertAllEqual(f(x_), fx_)
+      self.assertAllEqual(g(x_), gx_)
+
+  def test_works_correctly_vector_of_vars(self):
+    with self.test_session() as sess:
+      x = variable_scope.get_variable(
+          name="x",
+          shape=[],
+          dtype=dtypes.float32,
+          initializer=init_ops.constant_initializer(2))
+      y = variable_scope.get_variable(
+          name="y",
+          shape=[],
+          dtype=dtypes.float32,
+          initializer=init_ops.constant_initializer(3))
+      sess.run([variables.global_variables_initializer()])
+
+      f = lambda z: z[0] * z[1]
+      g = lambda z: z[0]**2 * z[1]**2 / 2
+
+      z = array_ops.stack([x, y])
+      fz = cg.custom_gradient(f(z), g(z), z, axis=0)
+      gz = gradients_impl.gradients(fz, variables.trainable_variables())
+      [z_, fz_, gx_, gy_] = sess.run([z, fz, gz[0], gz[1]])
+
+      self.assertEqual(f(z_), fz_)
+      self.assertEqual(g(z_), gx_)
+      self.assertEqual(g(z_), gy_)
+
+  def test_works_correctly_side_vars(self):
+    with self.test_session() as sess:
+      x_ = np.float32(2.1)  # Adding extra tenth to force imprecision.
+      y_ = np.float32(3.1)
+      x = variable_scope.get_variable(
+          name="x",
+          shape=[],
+          dtype=dtypes.float32,
+          initializer=init_ops.constant_initializer(x_))
+      y = variable_scope.get_variable(
+          name="y",
+          shape=[],
+          dtype=dtypes.float32,
+          initializer=init_ops.constant_initializer(y_))
+      sess.run([variables.global_variables_initializer()])
+
+      f = lambda x: x * y
+      g = lambda z: math_ops.square(x) * y
+
+      fx = cg.custom_gradient(f(x), g(x), x)
+      gx = gradients_impl.gradients(fx, variables.trainable_variables())
+      [x_, fx_, gx_] = sess.run([x, fx, gx[0]])
+      gy_ = gx[1]
+
+      self.assertEqual(x_ * y_, fx_)
+      self.assertEqual(np.square(x_) * y_, gx_)
+      self.assertEqual(None, gy_)
+
+  def test_works_correctly_fx_gx_manually_stopped(self):
+    with self.test_session() as sess:
+      x_ = np.float32(2.1)  # Adding extra tenth to force imprecision.
+      y_ = np.float32(3.1)
+      x = variable_scope.get_variable(
+          name="x",
+          shape=[],
+          dtype=dtypes.float32,
+          initializer=init_ops.constant_initializer(x_))
+      y = variable_scope.get_variable(
+          name="y",
+          shape=[],
+          dtype=dtypes.float32,
+          initializer=init_ops.constant_initializer(y_))
+      sess.run([variables.global_variables_initializer()])
+
+      stop = array_ops.stop_gradient  # For readability.
+
+      # Basically we need to stop the `x` portion of `f`. And when we supply the
+      # arg to `custom_gradient` we need to stop the complement, i.e., the `y`
+      # part.
+      f = lambda x: stop(x) * y
+      g = lambda x: stop(math_ops.square(x)) * y
+      fx = cg.custom_gradient(f(x), g(x), x + stop(y),
+                              fx_gx_manually_stopped=True)
+
+      gx = gradients_impl.gradients(fx, variables.trainable_variables())
+      [x_, fx_, gx_, gy_] = sess.run([x, fx, gx[0], gx[1]])
+
+      self.assertEqual(x_ * y_, fx_)
+      self.assertEqual(np.square(x_) * y_, gx_)
+      self.assertEqual(x_, gy_)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/custom_grad.py b/tensorflow/contrib/bayesflow/python/ops/custom_grad.py
new file mode 100644
index 0000000000..ca1ecb9c40
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/custom_grad.py
@@ -0,0 +1,34 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for specifying custom gradients.
+
+See ${python/contrib.bayesflow.custom_gradient}.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import
+from tensorflow.contrib.bayesflow.python.ops.custom_grad_impl import *
+# pylint: enable=wildcard-import
+from tensorflow.python.util.all_util import remove_undocumented
+
+_allowed_symbols = [
+    'custom_gradient',
+]
+
+remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py b/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py
new file mode 100644
index 0000000000..ee3719232d
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py
@@ -0,0 +1,110 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for specifying custom gradients.
+
+@@custom_gradient
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+
+__all__ = [
+    "custom_gradient",
+]
+
+
+def custom_gradient(fx, gx, x, axis=(),
+                    fx_gx_manually_stopped=False,
+                    name=None):
+  """Enables specifying a custom gradient.
+
+  This function works by clever application of `stop_gradient`. I.e., observe
+  that:
+
+  ```none
+  h(x) = x * stop_gradient(g(x)) + stop_gradient(f(x) - x * g(x))
+  ```
+
+  is such that `h(x) = stop(f(x))` and `grad[h(x), x] = stop_gradient(g(x)).`
+
+  In addition to scalar-domain/scalar-range functions, this function also
+  supports tensor-domain/scalar-range functions. However, in the latter case it
+  is necessary to reduce `x` to a scalar. This can be done by indicating the
+  `axis` over which `f` operates or by appropriately `reduce_sum`-ing `x`, prior
+  to calling this function.
+
+  Partial Custom Gradient:
+
+  Suppose `h(x) = htilde(x, y)`. Note that `dh/dx = stop(g(x))` but `dh/dy =
+  None`. This is because a `Tensor` cannot have only a portion of its gradient
+  stopped. To circumvent this issue, one must manually `stop_gradient` the
+  relevant portions of `f`, `g`. For example see the unit-test,
+  `test_works_correctly_fx_gx_manually_stopped`.
+
+  Args:
+    fx: `Tensor`. Output of function evaluated at `x`.
+    gx: `Tensor`. Gradient of function evaluated at `x`.
+    x: `Tensor`. Point of evaluation for `f, g`.
+    axis: 1D `int` `Tensor` representing dimensions of `x` which are the domain
+      of `f`. If `()` (the default), `f` is assumed scalar-domain/scalar-range.
+      If `None` `f` is assumed to render one scalar given all of `x`. Otherwise
+      `f` is assumed to output one scalar for each of `axis` dimensions of `x`.
+    fx_gx_manually_stopped: Python `bool` indicating that `fx`, `gx` manually
+      have `stop_gradient` applied.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    fx: Floating-type `Tensor` equal to `f(x)` but which has gradient
+      `stop_gradient(g(x))`.
+  """
+  with ops.name_scope(name, "custom_gradient", [fx, gx, x]):
+    fx = ops.convert_to_tensor(fx, name="fx")
+    # We don't want to bother eagerly computing `gx` since we may not even need
+    # it.
+    with ops.control_dependencies([fx]):
+      gx = ops.convert_to_tensor(gx, dtype=fx.dtype, name="gx")
+      gx = array_ops.identity(gx, name="gx")
+    # Proof of correctness:
+    #
+    #  f(x) = x * stop[gx] + stop[fx - x * gx]
+    #       = stop[fx]
+    #
+    #  g(x) = grad[fx]
+    #       = stop[gx] + grad[stop[fx - x * gx]]
+    #       = stop[gx] + 0
+    #
+    # Notice that when x is zero it still works:
+    # grad[x * stop(gx) + stop(fx - x * gx)] = 1 * stop[gx] + 0 = stop[gx]
+    #
+    # The proof is similar for the tensor-domain case, except that `x` is
+    # replaced by `reduce_sum(x)`.
+    sum_x = math_ops.reduce_sum(x, axis=axis, name="sum_x")
+    if not fx_gx_manually_stopped:
+      fx = array_ops.stop_gradient(fx)
+      gx = array_ops.stop_gradient(gx)
+    # IEEE754 ensures `(x-x)==0.` and that `0.*x==0.` so we make sure to write
+    # the code this way, rather than, e.g.,
+    # `sum_x * stop(gx) + stop(fx - sum_x * gx)`.
+    # For more discussion regarding the relevant portions of the IEEE754
+    # standard, see the StackOverflow question,
+    # "Is there a floating point value of x, for which x-x == 0 is false?"
+    # http://stackoverflow.com/q/2686644
+    return (sum_x - array_ops.stop_gradient(sum_x)) * gx + fx
-- 
GitLab


From 4d5ff2ee958d789b45535ba5dad0ae837b730d13 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Wed, 19 Jul 2017 14:34:03 -0700
Subject: [PATCH 1125/1375] Support placeholder for parameter k in
 tf.nn.in_top_k (#11197)

* Support placeholder for parameter k in tf.nn.in_top_k

This fix tries to address the issue raised in #9717 where
it was not possible to have tensor for k in nn.in_top_k.

This fix adds the implementation of InTopKV2Op, adds addition test
cases, and following similiar workflow in #10840:
1. Register new kennel InTopKV2Op
2. Hide InTopK and InTopKV2 in python (tensorflow/python/ops/hidden_ops.txt)
3. Add a wrapper in_top_k (in tensorflow/python/ops/nn_ops.py) pointing to gen_nn_ops._in_top_k

Another PR will be created after 3 weeks once this PR is merged:
1. Change the implementation of the wrapper in_top_k (in tensorflow/python/ops/nn_ops.py)
pointing to gen_nn_ops._in_top_kv2

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Address review comments

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Add HostMemory to InTopK kernel

Add HostMemory to InTopK kernel based on the review feedback

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/core/kernels/in_topk_op.cc         | 52 +++++++++++++++++--
 tensorflow/core/ops/nn_ops.cc                 | 43 +++++++++++++++
 .../python/kernel_tests/in_topk_op_test.py    | 14 +++++
 tensorflow/python/ops/hidden_ops.txt          |  2 +
 tensorflow/python/ops/nn_ops.py               | 33 ++++++++++++
 5 files changed, 139 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/kernels/in_topk_op.cc b/tensorflow/core/kernels/in_topk_op.cc
index 13890e5b7f..e2861ae090 100644
--- a/tensorflow/core/kernels/in_topk_op.cc
+++ b/tensorflow/core/kernels/in_topk_op.cc
@@ -17,11 +17,11 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/kernels/bounds_check.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 
 namespace tensorflow {
 
@@ -29,12 +29,29 @@ template <typename T, typename TARGET_T>
 class InTopK : public OpKernel {
  public:
   explicit InTopK(OpKernelConstruction* context) : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr("k", &k_));
+    if (context->num_inputs() == 2) {
+      OP_REQUIRES_OK(context, context->GetAttr("k", &k_));
+    }
   }
 
   void Compute(OpKernelContext* context) override {
     const auto& predictions_in = context->input(0);
     const auto& targets_in = context->input(1);
+    int64 k_val = k_;
+    if (context->num_inputs() == 3) {
+      const auto& k_in = context->input(2);
+
+      OP_REQUIRES(context, TensorShapeUtils::IsScalar(k_in.shape()),
+                  errors::InvalidArgument("k must be 0-D, got shape ",
+                                          k_in.shape().DebugString()));
+
+      if (k_in.dtype() == DT_INT32) {
+        k_val = k_in.scalar<int32>()();
+      } else {
+        k_val = k_in.scalar<int64>()();
+      }
+    }
+
     OP_REQUIRES(context, predictions_in.dims() == 2,
                 errors::InvalidArgument("predictions must be 2-dimensional"));
     OP_REQUIRES(context, targets_in.dims() == 1,
@@ -73,7 +90,7 @@ class InTopK : public OpKernel {
           }
         }
       }
-      out(b) = cannot_say ? false : (more_probable_classes < k_);
+      out(b) = cannot_say ? false : (more_probable_classes < k_val);
     }
   }
 
@@ -82,10 +99,35 @@ class InTopK : public OpKernel {
 };
 
 REGISTER_KERNEL_BUILDER(
-    Name("InTopK").Device(DEVICE_CPU).TypeConstraint<int32>("T"),
+    Name("InTopK").Device(DEVICE_CPU)
+    .HostMemory("predictions")
+    .HostMemory("targets")
+    .HostMemory("precision")
+    .TypeConstraint<int32>("T"),
+    InTopK<float, int32>);
+REGISTER_KERNEL_BUILDER(
+    Name("InTopK").Device(DEVICE_CPU)
+    .HostMemory("predictions")
+    .HostMemory("targets")
+    .HostMemory("precision")
+    .TypeConstraint<int64>("T"),
+    InTopK<float, int64>);
+
+REGISTER_KERNEL_BUILDER(
+    Name("InTopKV2").Device(DEVICE_CPU)
+    .HostMemory("predictions")
+    .HostMemory("targets")
+    .HostMemory("k")
+    .HostMemory("precision")
+    .TypeConstraint<int32>("T"),
     InTopK<float, int32>);
 REGISTER_KERNEL_BUILDER(
-    Name("InTopK").Device(DEVICE_CPU).TypeConstraint<int64>("T"),
+    Name("InTopKV2").Device(DEVICE_CPU)
+    .HostMemory("predictions")
+    .HostMemory("targets")
+    .HostMemory("k")
+    .HostMemory("precision")
+    .TypeConstraint<int64>("T"),
     InTopK<float, int64>);
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 3a25fd15da..555b97f53b 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -1979,6 +1979,49 @@ precision: Computed Precision at `k` as a `bool Tensor`.
 
 )doc");
 
+// This is the same as `InTopK`, but takes `k` as in input rather than an attr.
+REGISTER_OP("InTopKV2")
+    .Input("predictions: float")
+    .Input("targets: T")
+    .Input("k: T")
+    .Output("precision: bool")
+    .Attr("T: {int32, int64} = DT_INT32")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle predictions;
+      ShapeHandle targets;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &predictions));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &targets));
+      DimensionHandle batch_size;
+      TF_RETURN_IF_ERROR(
+          c->Merge(c->Dim(predictions, 0), c->Dim(targets, 0), &batch_size));
+      c->set_output(0, c->Vector(batch_size));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Says whether the targets are in the top `K` predictions.
+
+This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
+prediction for the target class is among the top `k` predictions among
+all predictions for example `i`. Note that the behavior of `InTopK` differs
+from the `TopK` op in its handling of ties; if multiple classes have the
+same prediction value and straddle the top-`k` boundary, all of those
+classes are considered to be in the top `k`.
+
+More formally, let
+
+  \\(predictions_i\\) be the predictions for all classes for example `i`,
+  \\(targets_i\\) be the target class for example `i`,
+  \\(out_i\\) be the output for example `i`,
+
+$$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+
+predictions: A `batch_size` x `classes` tensor.
+targets: A `batch_size` vector of class ids.
+k: Number of top elements to look at for computing precision.
+precision: Computed precision at `k` as a `bool Tensor`.
+
+)doc");
+
 namespace {
 
 Status TopKShapeFn(InferenceContext* c) {
diff --git a/tensorflow/python/kernel_tests/in_topk_op_test.py b/tensorflow/python/kernel_tests/in_topk_op_test.py
index 4a4686d1b9..37e9a8e3d1 100644
--- a/tensorflow/python/kernel_tests/in_topk_op_test.py
+++ b/tensorflow/python/kernel_tests/in_topk_op_test.py
@@ -20,7 +20,9 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import errors_impl
+from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.platform import test
 
@@ -69,6 +71,18 @@ class InTopKTest(test.TestCase):
                                    "target.*out of range"):
         nn_ops.in_top_k(predictions, target, 2).eval()
 
+  def testTensorK(self):
+    predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
+    target = [0, 2]
+    k = constant_op.constant(3)
+    np_ans = np.array([False, True])
+    with self.test_session():
+      # TODO (yongtang): The test will be switch to nn_ops.in_top
+      # once nn_ops.in_top points to _in_top_kv2 later
+      precision = gen_nn_ops._in_top_kv2(predictions, target, k)
+      out = precision.eval()
+      self.assertAllClose(np_ans, out)
+      self.assertShapeEqual(np_ans, precision)
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index 28817e99e8..81e0852e62 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -302,6 +302,8 @@ Softmax
 LogSoftmax
 FractionalAvgPoolGrad
 FractionalMaxPoolGrad
+InTopK
+InTopKV2
 
 # parsing_ops
 ParseExample
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index 44f98d64cd..1ce7ea179f 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -2085,3 +2085,36 @@ def erosion2d(value, kernel, strides, rates, padding, name=None):
                               rates=rates,
                               padding=padding,
                               name=name))
+
+def in_top_k(predictions, targets, k, name=None):
+  r"""Says whether the targets are in the top `K` predictions.
+
+  This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
+  prediction for the target class is among the top `k` predictions among
+  all predictions for example `i`. Note that the behavior of `InTopK` differs
+  from the `TopK` op in its handling of ties; if multiple classes have the
+  same prediction value and straddle the top-`k` boundary, all of those
+  classes are considered to be in the top `k`.
+
+  More formally, let
+
+    \\(predictions_i\\) be the predictions for all classes for example `i`,
+    \\(targets_i\\) be the target class for example `i`,
+    \\(out_i\\) be the output for example `i`,
+
+  $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+
+  Args:
+    predictions: A `Tensor` of type `float32`.
+      A `batch_size` x `classes` tensor.
+    targets: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      A `batch_size` vector of class ids.
+    k: An `int`. Number of top elements to look at for computing precision.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`.
+  """
+  with ops.name_scope(name, 'in_top_k'):
+    # TODO (yongtang): Need to switch to v2 after 3 weeks.
+    return gen_nn_ops._in_top_kv2(predictions, targets, k, name=name)
-- 
GitLab


From 1fefe92f47ff4149428409847592ec443f1279dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 14:49:00 -0700
Subject: [PATCH 1126/1375] PiperOrigin-RevId: 162539241

---
 tensorflow/core/kernels/BUILD | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 21194e80c3..96cb94cfb0 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -65,7 +65,6 @@ config_setting(
     # will also need appropriate -mavx*, as required by specific op you use.
     name = "xsmm_backward",
     values = {
-        "define": "tensorflow_xsmm=1",
         "define": "tensorflow_xsmm_backward=1",
     },
 )
-- 
GitLab


From 5a44a711bdbd72c6e0e6a12125bffc1fd5db72b9 Mon Sep 17 00:00:00 2001
From: horance <horance-liu@users.noreply.github.com>
Date: Thu, 20 Jul 2017 06:08:06 +0800
Subject: [PATCH 1127/1375] mod _CoordinatedSessionCreator (#11609)

---
 tensorflow/python/training/monitored_session.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index 2feb6bd3c3..383a9d1e30 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -535,7 +535,7 @@ class _MonitoredSession(object):
     # __exit__ should return True to suppress an exception.
     return exception_type is None
 
-  class _CoordinatedSessionCreator(object):
+  class _CoordinatedSessionCreator(SessionCreator):
     """Factory for the _RecoverableSession."""
 
     def __init__(self, session_creator, hooks, stop_grace_period_secs):
-- 
GitLab


From ad46b6f9de15d8b4755f9a558a271e254c71924b Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Wed, 19 Jul 2017 15:08:44 -0700
Subject: [PATCH 1128/1375] Adding documentation about contrib.losses (#11600)

* Update contrib.lossses.md

Adding note that this module is deprecated

* Update __init__.py

Add spaces so that this shows up well on the website

* Update __init__.py

Alignment does not comply with PEP8, minor cleanup of comments

* Update README.md

Notify readers that this module is deprecated

* Update losses_op.py

-Clarify deprecated information
-Correct minor error
-Minor grammar fixes

* Grammar fix
---
 tensorflow/contrib/deprecated/__init__.py     | 29 +++++++++++--------
 tensorflow/contrib/losses/README.md           |  6 ++++
 .../contrib/losses/python/losses/loss_ops.py  | 13 +++++----
 .../api_guides/python/contrib.losses.md       |  6 +++-
 4 files changed, 35 insertions(+), 19 deletions(-)

diff --git a/tensorflow/contrib/deprecated/__init__.py b/tensorflow/contrib/deprecated/__init__.py
index befb8e6198..0bbca8d8ed 100644
--- a/tensorflow/contrib/deprecated/__init__.py
+++ b/tensorflow/contrib/deprecated/__init__.py
@@ -4,7 +4,7 @@
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#     https://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
@@ -14,15 +14,20 @@
 # ==============================================================================
 """Non-core alias for the deprecated tf.X_summary ops.
 
-For TensorFlow 1.0, we have re-organized the TensorFlow summary ops into a
+For TensorFlow 1.0, we have reorganized the TensorFlow summary ops into a
 submodule, and made some semantic tweaks. The first thing to note is that we
 moved the APIs around as follows:
 
-tf.scalar_summary      -> tf.summary.scalar
-tf.histogram_summary   -> tf.summary.histogram
-tf.audio_summary       -> tf.summary.audio
-tf.image_summary       -> tf.summary.image
-tf.merge_summary       -> tf.summary.merge
+tf.scalar_summary -> tf.summary.scalar
+
+tf.histogram_summary -> tf.summary.histogram
+
+tf.audio_summary -> tf.summary.audio
+
+tf.image_summary -> tf.summary.image
+
+tf.merge_summary -> tf.summary.merge
+
 tf.merge_all_summaries -> tf.summary.merge_all
 
 We think this is a cleaner API and will improve long-term discoverability and
@@ -35,14 +40,14 @@ Previously, the tag was allowed to be any unique string, and had no relation
 to the summary op generating it, and no relation to the TensorFlow name system.
 This made it very difficult to write re-usable code that would add summary
 ops to the graph. If you had a function that would add summary ops, you would
-need to manually pass in a name scope to that function to create de-duplicated
+need to manually pass in a name scope to that function to create deduplicated
 tags, otherwise your program would fail with a runtime error due to tag
 collision.
 
 The new summary APIs under tf.summary throw away the "tag" as an independent
-concept; instead, the first argument is the node name. This means that summary
-tags now automatically inherit the surrounding TF name scope, and automatically
-are deduplicated if there is a conflict. However, now the only allowed
+concept; instead, the first argument is the node name. So summary tags now 
+automatically inherit the surrounding TF name scope, and automatically
+are deduplicated if there is a conflict. Now however, the only allowed
 characters are alphanumerics, underscores, and forward slashes. To make
 migration easier, the new APIs automatically convert illegal characters to
 underscores.
@@ -75,7 +80,7 @@ to the new summary ops:
   tf.summary.scalar requires a single scalar name and scalar value. In most
   cases, you can create tf.summary.scalars in a loop to get the same behavior
 
-As before, TensorBoard will group charts by the top-level name scope. This may
+As before, TensorBoard groups charts by the top-level name scope. This may
 be inconvenient, since in the new summary ops the summary will inherit that
 name scope without user control. We plan to add more grouping mechanisms to
 TensorBoard, so it will be possible to specify the TensorBoard group for
diff --git a/tensorflow/contrib/losses/README.md b/tensorflow/contrib/losses/README.md
index 7b73c4483a..dcda898ed8 100644
--- a/tensorflow/contrib/losses/README.md
+++ b/tensorflow/contrib/losses/README.md
@@ -1,7 +1,13 @@
 # TensorFlow contrib losses.
 
+## Deprecated
+
+This module is deprecated. Instructions for updating: Use tf.losses instead.
+
 ## losses
 
+Note: By default all the losses are collected into the GraphKeys.LOSSES collection.
+
 Loss operations for use in training models, typically with signature like the
 following:
 
diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py
index f6d3601c7d..1d2477b8b7 100644
--- a/tensorflow/contrib/losses/python/losses/loss_ops.py
+++ b/tensorflow/contrib/losses/python/losses/loss_ops.py
@@ -301,7 +301,7 @@ def absolute_difference(predictions, labels=None, weights=1.0, scope=None):
 
 @deprecated("2016-12-30",
             "Use tf.losses.sigmoid_cross_entropy instead. Note that the order "
-            "of the predictions and labels arguments was changed.")
+            "of the predictions and labels arguments has been changed.")
 def sigmoid_cross_entropy(
     logits, multi_class_labels, weights=1.0, label_smoothing=0, scope=None):
   """Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
@@ -436,7 +436,7 @@ def sparse_softmax_cross_entropy(logits, labels, weights=1.0, scope=None):
 
 @deprecated("2016-12-30",
             "Use tf.losses.log_loss instead. Note that the order of the "
-            "predictions and labels arguments was changed.")
+            "predictions and labels arguments has been changed.")
 def log_loss(predictions, labels=None, weights=1.0, epsilon=1e-7, scope=None):
   """Adds a Log Loss term to the training procedure.
 
@@ -477,7 +477,8 @@ def log_loss(predictions, labels=None, weights=1.0, epsilon=1e-7, scope=None):
 
 @deprecated("2016-12-30",
             "Use tf.losses.hinge_loss instead. Note that the order of the "
-            "predictions and labels arguments were changed.")
+            "logits and labels arguments has been changed, and to stay "
+            "unweighted, reduction=Reduction.NONE")
 def hinge_loss(logits, labels=None, scope=None):
   """Method that returns the loss tensor for hinge loss.
 
@@ -488,8 +489,8 @@ def hinge_loss(logits, labels=None, scope=None):
     scope: The scope for the operations performed in computing the loss.
 
   Returns:
-    A `Tensor` of same shape as `logits` and `labels` representing the loss
-      values across the batch.
+    An unweighted `Tensor` of same shape as `logits` and `labels` representing the
+      loss values across the batch.
 
   Raises:
     ValueError: If the shapes of `logits` and `labels` don't match.
@@ -541,7 +542,7 @@ def mean_squared_error(predictions, labels=None, weights=1.0, scope=None):
 
 @deprecated("2016-12-30",
             "Use tf.losses.mean_pairwise_squared_error instead. Note that the "
-            "order of the predictions and labels arguments was changed.")
+            "order of the predictions and labels arguments has been changed.")
 def mean_pairwise_squared_error(
     predictions, labels=None, weights=1.0, scope=None):
   """Adds a pairwise-errors-squared loss to the training procedure.
diff --git a/tensorflow/docs_src/api_guides/python/contrib.losses.md b/tensorflow/docs_src/api_guides/python/contrib.losses.md
index 30123e367f..d7f862625e 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.losses.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.losses.md
@@ -1,8 +1,12 @@
 # Losses (contrib)
 
+## Deprecated
+
+This module is deprecated. Instructions for updating: Use @{tf.losses} instead.
+
 ## Loss operations for use in neural networks.
 
-Note: By default all the losses are collected into the `GraphKeys.LOSSES`
+Note: By default, all the losses are collected into the `GraphKeys.LOSSES`
 collection.
 
 All of the loss functions take a pair of predictions and ground truth labels,
-- 
GitLab


From 08d79310f971677bd4bfe50da023a45465ecf10f Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Wed, 19 Jul 2017 23:08:59 +0100
Subject: [PATCH 1129/1375] Fixes 'window_size' may be used uninitialized
 (#11560)

---
 tensorflow/core/kernels/group_by_window_dataset_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/group_by_window_dataset_op.cc b/tensorflow/core/kernels/group_by_window_dataset_op.cc
index 948e83390e..94591a26af 100644
--- a/tensorflow/core/kernels/group_by_window_dataset_op.cc
+++ b/tensorflow/core/kernels/group_by_window_dataset_op.cc
@@ -42,7 +42,7 @@ class GroupByWindowDatasetOp : public UnaryDatasetOpKernel {
 
   void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                    DatasetBase** output) override {
-    int64 window_size;
+    int64 window_size = 0;
     OP_REQUIRES_OK(
         ctx, ParseScalarArgument<int64>(ctx, "window_size", &window_size));
     OP_REQUIRES(
-- 
GitLab


From 49961e588d41d81716c3a976a2420b647bc54076 Mon Sep 17 00:00:00 2001
From: Martin Wicke <martin.wicke@gmail.com>
Date: Thu, 20 Jul 2017 00:10:33 +0200
Subject: [PATCH 1130/1375] Add a code of conduct. (#11458)

---
 CODE_OF_CONDUCT.md | 70 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 CODE_OF_CONDUCT.md

diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000..10fd595fec
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,70 @@
+# TensorFlow Code of Conduct
+
+In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic address, without explicit permission
+* Conduct which could reasonably be considered inappropriate for the forum in which it occurs. 
+
+All TensorFlow forums and spaces are meant for professional interactions, and any behavior which could reasonably be considered inappropriate in a professional setting is unacceptable.
+
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
+
+
+## Scope
+
+This Code of Conduct applies to all content on tensorflow.org, TensorFlow’s GitHub organization, or any other official TensorFlow web presence allowing for community interactions, as well as at all official TensorFlow events, whether offline or online.
+
+The Code of Conduct also applies within project spaces and in public spaces whenever an individual is representing TensorFlow or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed or de facto representative at an online or offline event. 
+
+
+## Conflict Resolution
+
+Conflicts in an open source project can take many forms, from someone having a bad day and using harsh and hurtful language in the issue queue, to more serious instances such as sexist/racist statements or threats of violence, and everything in between.
+
+If the behaviour is threatening or harassing, or for other reasons requires immediate escalation, please see below.
+
+However, for the vast majority of issues, we aim to empower individuals to first resolve conflicts themselves, asking for help when needed, and only after that fails to escalate further. This approach gives people more control over the outcome of their dispute. 
+
+If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:
+
+1. Address the perceived conflict directly with those involved, preferably in a real-time medium. 
+2. If this fails, get a third party (e.g. a mutual friend, and/or someone with background on the issue, but not involved in conflict) to intercede.
+3. If you are still unable to resolve the conflict, and you believe it rises to harassment or another code of conduct violation, report it.
+
+
+## Reporting Violations
+
+Violations of the Code of Conduct can be reported to TensorFlow’s Project Steward at conduct@tensorflow.org. The Project Steward will determine whether the Code of Conduct was violated, and will issue an appropriate sanction, possibly including a written warning or expulsion from the project, project sponsored spaces, or project forums. We ask that you make a good-faith effort to resolve your conflict via the conflict resolution policy before submitting a report.
+
+Violations of the Code of Conduct can occur in any setting, even those unrelated to the project. We will only consider complaints about conduct that has occurred within one year of the report.
+
+
+## Enforcement
+
+If the Project Steward receives a report alleging a violation of the Code of Conduct, the Project Steward will notify the accused of the report, and provide them an opportunity to discuss the report before a sanction is issued. The Project Steward will do their utmost to keep the reporter anonymous. If the act is ongoing (such as someone engaging in harassment), or involves a threat to anyone's safety (e.g. threats of violence), the Project Steward may issue sanctions without notice.
+
+
+## Attribution
+
+This Code of Conduct is adapted from the Contributor Covenant, version 1.4, available at http://contributor-covenant.org/version/1/4, and includes some aspects of the Geek Feminism Code of Conduct and the Drupal Code of Conduct.
-- 
GitLab


From 9cc871e81c04ed11829c3364546b4500742140eb Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Wed, 19 Jul 2017 15:04:52 -0700
Subject: [PATCH 1131/1375] Merge changes from github. END_PUBLIC

---
Commit daa67ad17 authored by Jonathan Hseu<vomjom@vomjom.net>
Committed by Frank Chen<frankchn@gmail.com>:
Remove unittest import (#11596)

---
Commit 491beb74c authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 162423171

PiperOrigin-RevId: 162541442
---
 CODEOWNERS                                    |   4 +-
 configure                                     |  20 +--
 tensorflow/cc/framework/gradients.cc          |   2 +-
 tensorflow/compiler/tf2xla/xla_op_registry.h  |   7 +-
 tensorflow/compiler/xla/util.cc               |   1 +
 .../python/training/cluster_resolver.py       |   4 +-
 tensorflow/contrib/cmake/README.md            |   2 +-
 .../contrib/framework/python/ops/variables.py |   6 +-
 .../dataframe/transforms/boolean_mask.py      |   2 +-
 .../learn/python/learn/estimators/head.py     |   7 +-
 tensorflow/contrib/makefile/build_all_ios.sh  |   9 +-
 .../contrib/makefile/compile_ios_protobuf.sh  |   5 +-
 .../makefile/compile_ios_tensorflow.sh        |   6 +-
 .../signal/python/ops/reconstruction_ops.py   |   2 +-
 .../contrib/slim/python/slim/evaluation.py    |   2 +-
 tensorflow/contrib/tfprof/README.md           |   6 +-
 .../timeseries/python/timeseries/model.py     |   2 +-
 .../state_space_models/state_space_model.py   |   2 +-
 tensorflow/core/BUILD                         |   4 +-
 tensorflow/core/framework/register_types.h    |  14 ++
 tensorflow/core/kernels/batch_dataset_op.cc   |   2 +-
 tensorflow/core/kernels/cwise_op_add_1.cc     |   4 +-
 tensorflow/core/platform/windows/env.cc       |  44 +++--
 tensorflow/core/profiler/g3doc/python_api.md  |   2 +-
 tensorflow/docs_src/about/roadmap.md          |   2 +-
 tensorflow/docs_src/extend/adding_an_op.md    |   2 +-
 tensorflow/docs_src/get_started/export.md     |   3 +-
 .../docs_src/get_started/get_started.md       |  16 +-
 .../get_started/summaries_and_tensorboard.md  |   5 +-
 .../docs_src/programmers_guide/datasets.md    |   2 +-
 .../docs_src/tutorials/image_retraining.md    |   4 +-
 .../docs_src/tutorials/wide_and_deep.md       |   2 +-
 .../tutorials/word2vec/word2vec_basic.py      |   2 +-
 tensorflow/examples/udacity/5_word2vec.ipynb  |   2 +-
 tensorflow/java/BUILD                         |   5 +-
 .../java/src/gen/perl/tftypes-runall.pl       |  40 +++++
 tensorflow/java/src/gen/perl/tftypes.pl       | 157 ++++++++++++++++++
 tensorflow/java/src/gen/resources/tftypes.csv |  21 +++
 .../main/java/org/tensorflow/DataType.java    |   8 +-
 .../java/org/tensorflow/types/TFBool.java     |  30 ++++
 .../java/org/tensorflow/types/TFDouble.java   |  30 ++++
 .../java/org/tensorflow/types/TFFloat.java    |  30 ++++
 .../java/org/tensorflow/types/TFInt32.java    |  30 ++++
 .../java/org/tensorflow/types/TFInt64.java    |  30 ++++
 .../java/org/tensorflow/types/TFString.java   |  27 +++
 .../java/org/tensorflow/types/TFType.java     |  20 +++
 .../java/org/tensorflow/types/TFUInt8.java    |  30 ++++
 .../main/java/org/tensorflow/types/Types.java |  52 ++++++
 .../org/tensorflow/types/package-info.java    |  27 +++
 .../java/org/tensorflow/OperationTest.java    |   1 +
 .../python/debug/lib/debug_gradients.py       |   2 +-
 tensorflow/python/estimator/model_fn.py       |   2 +-
 tensorflow/python/framework/common_shapes.py  |   2 +-
 .../python/kernel_tests/argmax_op_test.py     |  25 ++-
 tensorflow/python/layers/base.py              |   2 +-
 tensorflow/python/ops/data_flow_ops.py        |   5 +-
 tensorflow/python/ops/sparse_ops.py           |   2 +-
 tensorflow/python/tools/freeze_graph.py       |   9 +-
 .../python/tools/import_pb_to_tensorboard.py  |   2 +
 .../tools/optimize_for_inference_test.py      |   5 +-
 tensorflow/tensorflow.bzl                     |   6 +-
 tensorflow/tools/ci_build/ci_sanity.sh        |   8 +-
 tensorflow/tools/ci_build/code_link_check.sh  |  42 +++++
 .../tools/ci_build/install/install_golang.sh  |   2 +-
 .../windows/gpu/pip/build_tf_windows.sh       |   2 +-
 tensorflow/workspace.bzl                      |   8 +-
 tools/bazel.rc                                |   3 +
 67 files changed, 746 insertions(+), 118 deletions(-)
 create mode 100644 tensorflow/java/src/gen/perl/tftypes-runall.pl
 create mode 100644 tensorflow/java/src/gen/perl/tftypes.pl
 create mode 100644 tensorflow/java/src/gen/resources/tftypes.csv
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFString.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFType.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/Types.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/types/package-info.java
 create mode 100755 tensorflow/tools/ci_build/code_link_check.sh

diff --git a/CODEOWNERS b/CODEOWNERS
index 2f216e4161..69393c3775 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,8 +1,9 @@
 # Where component owners are known, add them here.
 
+tensorflow/core/platform/windows/* @mrry
+tensorflow/java/* @asimshankar
 tensorflow/tensorboard/* @jart @dandelionmane
 tensorflow/tools/docs/* @markdaoust
-tensorflow/java/* @asimshankar
 
 # contrib
 
@@ -46,5 +47,6 @@ tensorflow/contrib/stateless/* @girving
 tensorflow/contrib/tensor_forest/* @gilberthendry @thomascolthurst
 tensorflow/contrib/testing/* @dandelionmane
 tensorflow/contrib/timeseries/* @allenlavoie
+tensorflow/contrib/tpu/* @frankchn @saeta @jhseu
 tensorflow/contrib/training/* @joel-shor @ebrevdo
 tensorflow/contrib/util/* @sherrym
diff --git a/configure b/configure
index 4c6cba2169..93280dc5f5 100755
--- a/configure
+++ b/configure
@@ -26,7 +26,7 @@ function is_windows() {
 }
 
 function is_ppc64le() {
-  [[ "${uname -m}" == "ppc64le" ]]
+  [[ "$(uname -m)" == "ppc64le" ]]
 }
 
 function sed_in_place() {
@@ -298,7 +298,7 @@ fi # TF_NEED_MKL
 
 ## Set up architecture-dependent optimization flags.
 if [ -z "$CC_OPT_FLAGS" ]; then
-  if [ is_ppc64le ]; then
+  if is_ppc64le; then
     # gcc on ppc64le does not support -march, use mcpu instead
     default_cc_opt_flags="-mcpu=native"
   else
@@ -492,6 +492,8 @@ while true; do
   if [ -z "$TF_CUDA_VERSION" ]; then
     read -p "Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: " TF_CUDA_VERSION
   fi
+  # Set default CUDA version if not set
+  TF_CUDA_VERSION=${TF_CUDA_VERSION:-8.0}
 
   fromuser=""
   if [ -z "$CUDA_TOOLKIT_PATH" ]; then
@@ -545,11 +547,7 @@ while true; do
   CUDA_TOOLKIT_PATH=""
 done
 
-# Set default CUDA version if not set
-if [ -z "$TF_CUDA_VERSION" ]; then
-  TF_CUDA_VERSION="8.0"
-  export TF_CUDA_VERSION
-fi
+export TF_CUDA_VERSION
 write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION"
 
 # Set up which gcc nvcc should use as the host compiler
@@ -587,6 +585,8 @@ while true; do
   if [ -z "$TF_CUDNN_VERSION" ]; then
     read -p "Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: " TF_CUDNN_VERSION
   fi
+  # Set default CUDNN version if not set
+  TF_CUDNN_VERSION=${TF_CUDNN_VERSION:-6}
 
   fromuser=""
   if [ -z "$CUDNN_INSTALL_PATH" ]; then
@@ -659,11 +659,7 @@ while true; do
   CUDNN_INSTALL_PATH=""
 done
 
-# Set default CUDNN version if not set
-if [ -z "$TF_CUDNN_VERSION" ]; then
-  TF_CUDNN_VERSION="6"
-  export TF_CUDNN_VERSION
-fi
+export TF_CUDNN_VERSION
 write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
 
 # Configure the compute capabilities that TensorFlow builds for.
diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index 29ad8a934b..d454bc3e4c 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -65,7 +65,7 @@ class SymbolicGradientBuilder {
   // gradients for the node associated with `src`.
   Status BackpropAlongEdge(const Output& dst_grad, const Output& src);
 
-  // Adds a node to the graph (returned in`grad`) that sums the in-bound
+  // Adds a node to the graph (returned in `grad`) that sums the in-bound
   // gradients to `src` (if there are more than one).
   Status SumGradients(const Output& src, Output* grad);
 
diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.h b/tensorflow/compiler/tf2xla/xla_op_registry.h
index 9a39cc9675..47d61a21a1 100644
--- a/tensorflow/compiler/tf2xla/xla_op_registry.h
+++ b/tensorflow/compiler/tf2xla/xla_op_registry.h
@@ -45,9 +45,10 @@ extern const char* const DEVICE_XLA_CPU;
 extern const char* const DEVICE_XLA_GPU;
 
 constexpr std::array<DataType, 2> kIntTypes = {{DT_INT32, DT_INT64}};
-constexpr std::array<DataType, 2> kFloatTypes = {{DT_FLOAT, DT_DOUBLE}};
-constexpr std::array<DataType, 4> kNumericTypes = {
-    {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE}};
+constexpr std::array<DataType, 3> kFloatTypes = {
+    {DT_HALF, DT_FLOAT, DT_DOUBLE}};
+constexpr std::array<DataType, 5> kNumericTypes = {
+    {DT_INT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE}};
 
 constexpr std::array<DataType, 5> kCpuAllTypes = {
     {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE, DT_BOOL}};
diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc
index b58670ecc5..3e80394fe1 100644
--- a/tensorflow/compiler/xla/util.cc
+++ b/tensorflow/compiler/xla/util.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/util.h"
 
+#include <numeric>
 #include <stdarg.h>
 #include <numeric>
 
diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
index 87da24f22d..b04822fa9d 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
@@ -75,8 +75,8 @@ class UnionClusterResolver(ClusterResolver):
 
   This class performs a union given two or more existing ClusterResolvers. It
   merges the underlying ClusterResolvers, and returns one unified ClusterSpec
-  when as_cluster_spec is called. The details of the merge function is
-  documented in the as_cluster_spec function.
+  when cluster_spec is called. The details of the merge function is
+  documented in the cluster_spec function.
   """
 
   def __init__(self, *args):
diff --git a/tensorflow/contrib/cmake/README.md b/tensorflow/contrib/cmake/README.md
index 664d0f4b6b..8ad8527559 100644
--- a/tensorflow/contrib/cmake/README.md
+++ b/tensorflow/contrib/cmake/README.md
@@ -225,7 +225,7 @@ Step-by-step Windows build
 
    * `-Dtensorflow_ENABLE_GPU=(ON|OFF)`. Defaults to `OFF`. Include
      GPU support. If GPU is enabled you need to install the CUDA 8.0 Toolkit and CUDNN 5.1.
-     CMake will expect the location of CUDNN in -DCUDNN_HOME=path_you_unziped_cudnn.
+     CMake will expect the location of CUDNN in -DCUDNN_HOME=path_you_unzipped_cudnn.
 
    * `-Dtensorflow_BUILD_CC_TESTS=(ON|OFF)`. Defaults to `OFF`. This builds cc unit tests.
      There are many of them and building will take a few hours.
diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py
index bf709f921d..411b4facdb 100644
--- a/tensorflow/contrib/framework/python/ops/variables.py
+++ b/tensorflow/contrib/framework/python/ops/variables.py
@@ -416,7 +416,7 @@ def get_unique_variable(var_op_name):
   for candidate in candidates:
     if candidate.op.name == var_op_name:
       return candidate
-  raise ValueError('Variable %s does not uniquely identify a variable',
+  raise ValueError('Variable %s does not uniquely identify a variable' %
                    var_op_name)
 
 
@@ -444,7 +444,7 @@ def assign_from_values(var_names_to_values):
     var_value = var_names_to_values[var_name]
     var = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, var_name)
     if not var:
-      raise ValueError('Variable %s wasnt found', var_name)
+      raise ValueError('Variable %s wasn\'t found' % var_name)
     elif len(var) > 1:
       # tf.get_collection is just a filter on the prefix: find the exact match:
       found = False
@@ -455,7 +455,7 @@ def assign_from_values(var_names_to_values):
           break
 
       if not found:
-        raise ValueError('Variable %s doesnt uniquely identify a variable',
+        raise ValueError('Variable %s doesn\'t uniquely identify a variable' %
                          var_name)
     else:
       var = var[0]
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
index 130ac0c90f..874b02c6c9 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
@@ -35,7 +35,7 @@ def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
 
   Args:
     sparse_tensor: a `SparseTensor`.
-    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
+    mask: a 1D boolean dense `Tensor` whose length is equal to the 0th dimension
       of `sparse_tensor`.
     name: optional name for this operation.
   Returns:
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index fdc266d582..699a92d38a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -25,8 +25,7 @@ import six
 from tensorflow.contrib import framework as framework_lib
 from tensorflow.contrib import layers as layers_lib
 from tensorflow.contrib import lookup as lookup_lib
-# TODO(ptucker): Use tf.losses and tf.metrics.
-from tensorflow.contrib import losses as losses_lib
+# TODO(ptucker): Use tf.metrics.
 from tensorflow.contrib import metrics as metrics_lib
 from tensorflow.contrib.learn.python.learn.estimators import constants
 from tensorflow.contrib.learn.python.learn.estimators import model_fn
@@ -44,6 +43,7 @@ from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import weights_broadcast_ops
+from tensorflow.python.ops.losses import losses as losses_lib
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary
 from tensorflow.python.training import training
@@ -1212,7 +1212,8 @@ class _BinarySvmHead(_SingleHead):
       with ops.name_scope(None, "hinge_loss", (logits, labels)) as name:
         with ops.control_dependencies((_assert_labels_rank(labels),)):
           labels = array_ops.reshape(labels, shape=(-1, 1))
-        loss = losses_lib.hinge_loss(logits=logits, labels=labels, scope=name)
+        loss = losses_lib.hinge_loss(labels=labels, logits=logits, scope=name,
+                                     reduction=losses_lib.Reduction.NONE)
         return _compute_weighted_loss(loss, weights)
 
     super(_BinarySvmHead, self).__init__(
diff --git a/tensorflow/contrib/makefile/build_all_ios.sh b/tensorflow/contrib/makefile/build_all_ios.sh
index 344bf49dcf..a0f5652854 100755
--- a/tensorflow/contrib/makefile/build_all_ios.sh
+++ b/tensorflow/contrib/makefile/build_all_ios.sh
@@ -18,7 +18,7 @@ set -e
 
 # Make sure we're on OS X.
 if [[ $(uname) != "Darwin" ]]; then
-    echo "ERROR: This makefile build requires OS X, which the current system "\
+    echo "ERROR: This makefile build requires macOS, which the current system "\
     "is not."
     exit 1
 fi
@@ -37,7 +37,9 @@ rm -rf tensorflow/contrib/makefile/downloads
 #
 #    ld: -bind_at_load and -bitcode_bundle (Xcode setting ENABLE_BITCODE=YES) cannot be used together
 #
-export MACOSX_DEPLOYMENT_TARGET="10.10"
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
 
 # Pull down the required versions of the frameworks we need.
 tensorflow/contrib/makefile/download_dependencies.sh
@@ -48,6 +50,5 @@ tensorflow/contrib/makefile/compile_ios_protobuf.sh
 # Build the iOS TensorFlow libraries.
 tensorflow/contrib/makefile/compile_ios_tensorflow.sh "-O3"
 
-# Creates a static universal library in 
+# Creates a static universal library in
 # tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a
-
diff --git a/tensorflow/contrib/makefile/compile_ios_protobuf.sh b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
index e8b9454e7e..4056db18a7 100755
--- a/tensorflow/contrib/makefile/compile_ios_protobuf.sh
+++ b/tensorflow/contrib/makefile/compile_ios_protobuf.sh
@@ -15,9 +15,12 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
-set -x
 set -e
 
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
+
 SCRIPT_DIR=$(dirname $0)
 source "${SCRIPT_DIR}/build_helper.subr"
 
diff --git a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
index bcf097b303..5d1cc8b375 100755
--- a/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
+++ b/tensorflow/contrib/makefile/compile_ios_tensorflow.sh
@@ -31,6 +31,10 @@ function less_than_required_version() {
   )
 }
 
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
+
 ACTUAL_XCODE_VERSION=$(xcodebuild -version | head -n 1 | sed 's/Xcode //')
 REQUIRED_XCODE_VERSION=7.3.0
 if less_than_required_version $ACTUAL_XCODE_VERSION 7 3 0
@@ -44,7 +48,7 @@ LIBDIR=${GENDIR}lib
 LIB_PREFIX=libtensorflow-core
 
 make -j"${JOB_COUNT}" -f tensorflow/contrib/makefile/Makefile \
-TARGET=IOS IOS_ARCH=ARMV7 LIB_NAME=${LIB_PREFIX}-armv7.a OPTFLAGS="$1" 
+TARGET=IOS IOS_ARCH=ARMV7 LIB_NAME=${LIB_PREFIX}-armv7.a OPTFLAGS="$1"
 if [ $? -ne 0 ]
 then
   echo "armv7 compilation failed."
diff --git a/tensorflow/contrib/signal/python/ops/reconstruction_ops.py b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
index f5f443ad09..653c030a04 100644
--- a/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
+++ b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
@@ -37,7 +37,7 @@ def _shuffle_to_front(input_tensor, k):
     k: A scalar `Tensor` specifying how many indices to shuffle.
 
   Returns:
-    A tranposed version of `input_tensor` with `k` indices shuffled to the
+    A transposed version of `input_tensor` with `k` indices shuffled to the
     front.
 
   Raises:
diff --git a/tensorflow/contrib/slim/python/slim/evaluation.py b/tensorflow/contrib/slim/python/slim/evaluation.py
index 15c9f3d3f4..2d4b08df61 100644
--- a/tensorflow/contrib/slim/python/slim/evaluation.py
+++ b/tensorflow/contrib/slim/python/slim/evaluation.py
@@ -81,7 +81,7 @@ more summaries and call the evaluation_loop method:
 
   # Evaluate every 10 minutes:
   slim.evaluation_loop(
-      master='',
+      '',
       checkpoint_dir,
       logdir,
       num_evals=num_evals,
diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index eefd88793e..7faf2b9b24 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -1,9 +1,9 @@
 # tfprof: TensorFlow Profiler and Beyond
 
 <h1>Please use `tf.profiler.xxx` instead of `tf.contrib.tfprof.xxx`</h1>
-<h1>Full Document in tensorflow/core/profiler/README.md<h1>
+<h1>Full Document in <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/profiler/README.md">tensorflow/core/profiler/README.md</a><h1>
 
-###Features
+### Features
 
 * Profile model architectures
   * parameters, tensor shapes, float operations, device placement, etc.
@@ -16,7 +16,7 @@
   * operation configuration check
   * distributed runtime check (Not OSS)
 
-###Interfaces
+### Interfaces
 
 * Python API
 * Command Line
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py
index e6c60fdf0c..f2ef8d2211 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/model.py
@@ -353,7 +353,7 @@ class SequentialTimeSeriesModel(TimeSeriesModel):
 
     Args:
       current_times: A [batch size] Tensor of times for each observation.
-      current_values: A [batch size] Tensor of values for each observaiton.
+      current_values: A [batch size] Tensor of values for each observation.
       state: Model state, updated to current_times.
       predictions: The outputs of _prediction_step
     Returns:
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
index 3193d5607c..6a9660b400 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
@@ -391,7 +391,7 @@ class StateSpaceModel(model.SequentialTimeSeriesModel):
 
     Args:
       current_times: A [batch size] Tensor for times for each observation.
-      current_values: A [batch size] Tensor of values for each observaiton.
+      current_values: A [batch size] Tensor of values for each observation.
       state: A tuple of (mean, covariance, previous_times) having shapes
           mean; [batch size x state dimension]
           covariance; [batch size x state dimension x state dimension]
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 3d367b1b9b..870efab858 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -65,7 +65,7 @@ load(
     "full_path",
     "if_android",
     "if_ios",
-    "if_x86",
+    "if_linux_x86_64",
     "if_not_mobile",
     "if_not_windows",
     "tf_copts",
@@ -1379,7 +1379,7 @@ cc_library(
     name = "lib_hash_crc32c_accelerate_internal",
     srcs = ["lib/hash/crc32c_accelerate.cc"],
     # -msse4.2 enables the use of crc32c compiler builtins.
-    copts = tf_copts() + if_x86(["-msse4.2"]),
+    copts = tf_copts() + if_linux_x86_64(["-msse4.2"]),
 )
 
 cc_library(
diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h
index 3f91642064..973ad4544a 100644
--- a/tensorflow/core/framework/register_types.h
+++ b/tensorflow/core/framework/register_types.h
@@ -183,4 +183,18 @@ limitations under the License.
 #define TF_CALL_QUANTIZED_TYPES(m) \
   TF_CALL_qint8(m) TF_CALL_quint8(m) TF_CALL_qint32(m)
 
+#ifdef TENSORFLOW_SYCL_NO_DOUBLE
+#define TF_CALL_SYCL_double(m)
+#else  // TENSORFLOW_SYCL_NO_DOUBLE
+#define TF_CALL_SYCL_double(m) TF_CALL_double(m)
+#endif // TENSORFLOW_SYCL_NO_DOUBLE
+
+#ifdef __ANDROID_TYPES_SLIM__
+#define TF_CALL_SYCL_NUMBER_TYPES(m)  TF_CALL_float(m)
+#else  // __ANDROID_TYPES_SLIM__
+#define TF_CALL_SYCL_NUMBER_TYPES(m)    \
+    TF_CALL_float(m)                    \
+    TF_CALL_SYCL_double(m)
+#endif // __ANDROID_TYPES_SLIM__
+
 #endif  // TENSORFLOW_FRAMEWORK_REGISTER_TYPES_H_
diff --git a/tensorflow/core/kernels/batch_dataset_op.cc b/tensorflow/core/kernels/batch_dataset_op.cc
index 67eff44a5d..64a8e83f84 100644
--- a/tensorflow/core/kernels/batch_dataset_op.cc
+++ b/tensorflow/core/kernels/batch_dataset_op.cc
@@ -31,7 +31,7 @@ class BatchDatasetOp : public UnaryDatasetOpKernel {
 
   void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                    DatasetBase** output) override {
-    int64 batch_size;
+    int64 batch_size = 0;
     OP_REQUIRES_OK(ctx,
                    ParseScalarArgument<int64>(ctx, "batch_size", &batch_size));
     OP_REQUIRES(
diff --git a/tensorflow/core/kernels/cwise_op_add_1.cc b/tensorflow/core/kernels/cwise_op_add_1.cc
index acf1f2ad49..c0fe81ef55 100644
--- a/tensorflow/core/kernels/cwise_op_add_1.cc
+++ b/tensorflow/core/kernels/cwise_op_add_1.cc
@@ -36,7 +36,9 @@ REGISTER_KERNEL_BUILDER(Name("Add")
 
 
 #if TENSORFLOW_USE_SYCL
-REGISTER2(BinaryOp, SYCL, "Add", functor::add, float, double);
+#define REGISTER_KERNEL(type) REGISTER(BinaryOp, SYCL, "Add", functor::add, type);
+TF_CALL_SYCL_NUMBER_TYPES(REGISTER_KERNEL);
+
 REGISTER_KERNEL_BUILDER(Name("Add")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")
diff --git a/tensorflow/core/platform/windows/env.cc b/tensorflow/core/platform/windows/env.cc
index b8e1c91616..fa3d6a8301 100644
--- a/tensorflow/core/platform/windows/env.cc
+++ b/tensorflow/core/platform/windows/env.cc
@@ -82,22 +82,42 @@ class WindowsEnv : public Env {
     return new StdThread(thread_options, name, fn);
   }
 
+  static VOID CALLBACK SchedClosureCallback(PTP_CALLBACK_INSTANCE Instance,
+                                            PVOID Context, PTP_WORK Work) {
+    CloseThreadpoolWork(Work);
+    std::function<void()>* f = (std::function<void()>*)Context;
+    (*f)();
+    delete f;
+  }
   void SchedClosure(std::function<void()> closure) override {
-    // TODO(b/27290852): Spawning a new thread here is wasteful, but
-    // needed to deal with the fact that many `closure` functions are
-    // blocking in the current codebase.
-    std::thread closure_thread(closure);
-    closure_thread.detach();
+    PTP_WORK work = CreateThreadpoolWork(
+        SchedClosureCallback, new std::function<void()>(std::move(closure)),
+        nullptr);
+    SubmitThreadpoolWork(work);
+  }
+
+  static VOID CALLBACK SchedClosureAfterCallback(PTP_CALLBACK_INSTANCE Instance,
+                                                 PVOID Context,
+                                                 PTP_TIMER Timer) {
+    CloseThreadpoolTimer(Timer);
+    std::function<void()>* f = (std::function<void()>*)Context;
+    (*f)();
+    delete f;
   }
 
   void SchedClosureAfter(int64 micros, std::function<void()> closure) override {
-    // TODO(b/27290852): Consuming a thread here is wasteful, but this
-    // code is (currently) only used in the case where a step fails
-    // (AbortStep). This could be replaced by a timer thread
-    SchedClosure([this, micros, closure]() {
-      SleepForMicroseconds(micros);
-      closure();
-    });
+    PTP_TIMER timer = CreateThreadpoolTimer(
+        SchedClosureAfterCallback,
+        new std::function<void()>(std::move(closure)), nullptr);
+    // in 100 nanosecond units
+    FILETIME FileDueTime;
+    ULARGE_INTEGER ulDueTime;
+    // Negative indicates the amount of time to wait is relative to the current
+    // time.
+    ulDueTime.QuadPart = (ULONGLONG) - (10 * micros);
+    FileDueTime.dwHighDateTime = ulDueTime.HighPart;
+    FileDueTime.dwLowDateTime = ulDueTime.LowPart;
+    SetThreadpoolTimer(timer, &FileDueTime, 0, 0);
   }
 
   Status LoadLibrary(const char *library_filename, void** handle) override {
diff --git a/tensorflow/core/profiler/g3doc/python_api.md b/tensorflow/core/profiler/g3doc/python_api.md
index 42a89d22a1..0daac655c4 100644
--- a/tensorflow/core/profiler/g3doc/python_api.md
+++ b/tensorflow/core/profiler/g3doc/python_api.md
@@ -24,7 +24,7 @@ opts = ProfileOptionBuilder(
     ).build()
 param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    cmd='code'
+    cmd='code',
     options=opts)
 
 # param_stats can be tensorflow.tfprof.GraphNodeProto or
diff --git a/tensorflow/docs_src/about/roadmap.md b/tensorflow/docs_src/about/roadmap.md
index 1789e050fa..3ee825ed40 100644
--- a/tensorflow/docs_src/about/roadmap.md
+++ b/tensorflow/docs_src/about/roadmap.md
@@ -5,7 +5,7 @@ TensorFlow is a fast moving project. In order for the community to better
 understand what the near future will bring, this document shares what we are
 working on internally. Many of these features were requested by the community,
 and we welcome
-[contributions](https://github.com/tensorflow/tensorflow/labels/contributions%20welcome).
+[contributions](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome).
 
 The features on this list are targeted for the next few months. At this point,
 we do not have timelines for these features.
diff --git a/tensorflow/docs_src/extend/adding_an_op.md b/tensorflow/docs_src/extend/adding_an_op.md
index e8441d51c2..4b1d1b6e34 100644
--- a/tensorflow/docs_src/extend/adding_an_op.md
+++ b/tensorflow/docs_src/extend/adding_an_op.md
@@ -155,7 +155,7 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
 ### Multi-threaded CPU kernels
 
 To write a multi-threaded CPU kernel, the Shard function in
-[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/framework/work_sharder.h)
+[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
 can be used. This function shards a computation function across the
 threads configured to be used for intra-op threading (see
 intra_op_parallelism_threads in
diff --git a/tensorflow/docs_src/get_started/export.md b/tensorflow/docs_src/get_started/export.md
index 8273030271..77e628699e 100644
--- a/tensorflow/docs_src/get_started/export.md
+++ b/tensorflow/docs_src/get_started/export.md
@@ -168,7 +168,7 @@ and so determine which request types will be honored.
 > the corresponding `ExportOutput` entry.  The inputs are always those provided
 > by the `serving_input_receiver_fn`.
 > An inference request may specify the head by name.  One head must be named
-> using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/saved_model/signature_constants.py)
+> using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/tensorflow/python/saved_model/signature_constants.py)
 > indicating which signature will be served when an inference request does not
 > specify one.
 
@@ -295,4 +295,3 @@ the different Tensorflow Serving APIs, selecting the signature by key, etc. -->
 
 <!-- TODO(soergel): document ExportStrategy here once Experiment moves
 from contrib to core. -->
-
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 852d41e9ed..815b83e5fb 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -32,8 +32,8 @@ tensor's **rank** is its number of dimensions. Here are some examples of
 tensors:
 
 ```python
-3 # a rank 0 tensor; this is a scalar with shape []
-[1., 2., 3.] # a rank 1 tensor; this is a vector with shape [3]
+3 # a rank 0 tensor; a scalar with shape []
+[1., 2., 3.] # a rank 1 tensor; a vector with shape [3]
 [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
 [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]
 ```
@@ -181,7 +181,7 @@ initial value:
 W = tf.Variable([.3], dtype=tf.float32)
 b = tf.Variable([-.3], dtype=tf.float32)
 x = tf.placeholder(tf.float32)
-linear_model = W * x + b
+linear_model = W*x + b
 ```
 
 Constants are initialized when you call `tf.constant`, and their value can never
@@ -302,7 +302,7 @@ W = tf.Variable([.3], dtype=tf.float32)
 b = tf.Variable([-.3], dtype=tf.float32)
 # Model input and output
 x = tf.placeholder(tf.float32)
-linear_model = W * x + b
+linear_model = W*x + b
 y = tf.placeholder(tf.float32)
 
 # loss
@@ -330,9 +330,9 @@ When run, it produces
 W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11
 ```
 
-Notice that the loss is a very small number (very close to zero). If you run this
-program, your loss may not be the exact same because the model is initialized
-with pseudorandom values.
+Notice that the loss is a very small number (very close to zero). If you run 
+this program, your loss may not be exactly the same as the aforementioned loss 
+because the model is initialized with pseudorandom values.
 
 This more complicated program can still be visualized in TensorBoard
 ![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png)
@@ -426,7 +426,7 @@ def model_fn(features, labels, mode):
   # Build a linear model and predict values
   W = tf.get_variable("W", [1], dtype=tf.float64)
   b = tf.get_variable("b", [1], dtype=tf.float64)
-  y = W * features['x'] + b
+  y = W*features['x'] + b
   # Loss sub-graph
   loss = tf.reduce_sum(tf.square(y - labels))
   # Training sub-graph
diff --git a/tensorflow/docs_src/get_started/summaries_and_tensorboard.md b/tensorflow/docs_src/get_started/summaries_and_tensorboard.md
index 45d43e7a6e..ece8fbf43c 100644
--- a/tensorflow/docs_src/get_started/summaries_and_tensorboard.md
+++ b/tensorflow/docs_src/get_started/summaries_and_tensorboard.md
@@ -17,7 +17,7 @@ TensorBoard is fully configured, it looks like this:
 </div>
 
 This tutorial is intended to get you started with simple TensorBoard usage.
-There are other resources available as well! The [TensorBoard README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md)
+There are other resources available as well! The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard)
 has a lot more information on TensorBoard usage, including tips & tricks, and
 debugging information.
 
@@ -216,5 +216,4 @@ corner. Each tab represents a set of serialized data that can be visualized.
 For in depth information on how to use the *graph* tab to visualize your graph,
 see @{$graph_viz$TensorBoard: Graph Visualization}.
 
-For more usage information on TensorBoard in general, see the [TensorBoard
-README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md).
+For more usage information on TensorBoard in general, see the [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard).
diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md
index ddf5388ac2..68ed4bcd47 100644
--- a/tensorflow/docs_src/programmers_guide/datasets.md
+++ b/tensorflow/docs_src/programmers_guide/datasets.md
@@ -447,7 +447,7 @@ dataset = tf.contrib.data.Dataset.from_tensor_slices(filenames)
 # * Filter out lines beginning with "#" (comments).
 dataset = dataset.flat_map(
     lambda filename: (
-        tf.contrib.data.Dataset.TextLineDataset(filename)
+        tf.contrib.data.TextLineDataset(filename)
         .skip(1)
         .filter(lambda line: tf.not_equal(tf.substr(line, 0, 1), "#"))))
 ```
diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md
index 898db0e73c..b0e715edcb 100644
--- a/tensorflow/docs_src/tutorials/image_retraining.md
+++ b/tensorflow/docs_src/tutorials/image_retraining.md
@@ -137,7 +137,7 @@ Once TensorBoard is running, navigate your web browser to `localhost:6006` to vi
 
 The script will log TensorBoard summaries to `/tmp/retrain_logs` by default. You can change the directory with the `--summaries_dir` flag.
 
-The [TensorBoard README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md) has a lot more information on TensorBoard usage, including tips & tricks, and debugging information.
+The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard) has a lot more information on TensorBoard usage, including tips & tricks, and debugging information.
 
 ## Using the Retrained Model
 
@@ -337,7 +337,7 @@ the (much larger) training set.
 
 By default the script uses a pretrained version of the Inception v3 model
 architecture. This is a good place to start because it provides high accuracy
-results, but if you intend to deploy your model on mobile devices or other 
+results, but if you intend to deploy your model on mobile devices or other
 resource-constrained environments you may want to trade off a little accuracy
 for much smaller file sizes or faster speeds. To help with that, the
 [retrain.py script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py)
diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/wide_and_deep.md
index 64b3fa7dbf..e6344405d5 100644
--- a/tensorflow/docs_src/tutorials/wide_and_deep.md
+++ b/tensorflow/docs_src/tutorials/wide_and_deep.md
@@ -87,7 +87,7 @@ education = tf.feature_column.categorical_column_with_vocabulary_list(
         "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
         "Preschool", "12th"
     ])
-tf.feature_column.categorical_column_with_vocabulary_list(
+marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
     "marital_status", [
         "Married-civ-spouse", "Divorced", "Married-spouse-absent",
         "Never-married", "Separated", "Married-AF-spouse", "Widowed"
diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
index aee482fda5..e67442b14b 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -253,7 +253,7 @@ try:
   from sklearn.manifold import TSNE
   import matplotlib.pyplot as plt
 
-  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
+  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
   plot_only = 500
   low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
   labels = [reverse_dictionary[i] for i in xrange(plot_only)]
diff --git a/tensorflow/examples/udacity/5_word2vec.ipynb b/tensorflow/examples/udacity/5_word2vec.ipynb
index 9d4243d7ae..18c456cad7 100644
--- a/tensorflow/examples/udacity/5_word2vec.ipynb
+++ b/tensorflow/examples/udacity/5_word2vec.ipynb
@@ -806,7 +806,7 @@
       "source": [
         "num_points = 400\n",
         "\n",
-        "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
+        "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
         "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
       ],
       "outputs": [],
diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 759f4c1b0e..9fb4821cb1 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -22,7 +22,10 @@ java_library(
 # .aar. At some point, might make sense for a .aar rule here instead.
 filegroup(
     name = "java_sources",
-    srcs = glob(["src/main/java/org/tensorflow/*.java"]),
+    srcs = glob([
+        "src/main/java/org/tensorflow/*.java",
+        "src/main/java/org/tensorflow/types/*.java",
+    ]),
     visibility = [
         "//tensorflow/contrib/android:__pkg__",
         "//tensorflow/java:__pkg__",
diff --git a/tensorflow/java/src/gen/perl/tftypes-runall.pl b/tensorflow/java/src/gen/perl/tftypes-runall.pl
new file mode 100644
index 0000000000..258c1ff836
--- /dev/null
+++ b/tensorflow/java/src/gen/perl/tftypes-runall.pl
@@ -0,0 +1,40 @@
+#!/usr/bin/perl
+#
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+my $script = $0;
+my $dir = `dirname $script`;
+chomp $dir;
+my $gen = "$dir/..";
+my $tfjavasrc = "$gen/..";
+my $rsrc = "$gen/resources";
+my $root = "$tfjavasrc/main/java";
+my $pkg = "$root/org/tensorflow";
+
+sub locchk {
+    (my $f) = @_;
+    if (! -r $f) {
+        print STDERR "Script tftypes-runall seems to be located in the wrong place (could not find $f)\n";
+        exit 1;
+    }
+}
+&locchk("$gen");
+&locchk("$tfjavasrc/gen");
+&locchk("$dir/tftypes.pl");
+&locchk("$rsrc/tftypes.csv");
+
+system("perl $dir/tftypes.pl -t $rsrc/tftypes.csv $pkg/types");
+# system("perl $dir/tftypes.pl -c $rsrc/tftypes.csv $rsrc/Tensors.java.tmpl > $pkg/op/Tensors.java");
diff --git a/tensorflow/java/src/gen/perl/tftypes.pl b/tensorflow/java/src/gen/perl/tftypes.pl
new file mode 100644
index 0000000000..86867335cb
--- /dev/null
+++ b/tensorflow/java/src/gen/perl/tftypes.pl
@@ -0,0 +1,157 @@
+#!/usr/bin/perl
+#
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+use strict;
+
+my $copyright =
+'/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+';
+
+my $count;
+
+my $option = '-t', my $template;
+
+sub usage {
+    print "Usage: tftypes [-ctdT] <type desc file> <tmpl file>\n\n"
+         ."This script generates parts of various .java files that depend on which"
+         ."TensorFlow types are supported by the Java API and how much. For each"
+         ."such .java file, there is a .tmpl file in the same source directory in"
+         ."which the strings \@TYPEINFO\@ and \@IMPORTS\@ are replaced with"
+         ."appropriate Java code. Output code is sent to standard output.\n\n";
+
+    print "Modulo putting in the correct directory names, it can be invoked as follows:\n";
+    print "tftypes -c tftypes.csv Tensors.java.tmpl > Tensors.java\n";
+    print "tftypes -t tftypes.csv <dir>                                   [outputs files to dir]\n";
+}
+
+if ($ARGV[0] =~ m/^-/) {
+    $option = shift;
+}
+my $typedesc = shift;
+my $tmpl = shift;
+
+my $dirname;
+
+if ($option eq '-t') {
+    $dirname = $tmpl;
+}
+
+open (TMPL, "<$tmpl") || die "Cannot open $tmpl for reading\n";
+
+my $text = do { local $/; <TMPL> };
+
+my %jtypecount;
+
+my $typeinfo, my $imports;
+
+open (TYPEDESC, $typedesc);
+
+my @info = ([]);
+
+while (<TYPEDESC>) {
+    chomp;
+    my $line = $_;
+    if ($line =~ m/^TF type/) { next }
+    $line =~ s/\r$//;
+    (my $name, my $jtype, my $creat, my $default, my $desc) =
+        split /,/, $line, 5;
+    $desc =~ s/^ *//g;
+    $desc =~ s/ *$//g;
+    $jtypecount{$jtype}++;
+    if ($jtypecount{$jtype} > 1) {
+# currently allowing Java types to stand for more than one TF type, but
+# may want to revisit this.
+#       print STDERR "Ambiguous Java type for $name : $jtype\n";
+#       exit 1
+    }
+
+    push @info, [$name, $jtype, $creat, $default, $desc];
+}
+
+for (my $i = 1; $i <= $#info; $i++) {
+    (my $name, my $jtype, my $creat, my $default, my $desc) =
+        @{$info[$i]};
+    my $tfname = "TF".$name;
+    my $ucname = uc $name;
+
+    if ($option eq '-t') {
+        if ($jtype eq '') { next }
+        # Generate class declarations
+        # print STDERR "Creating $dirname/$tfname.java\n";
+        open (CLASSFILE, ">$dirname/$tfname.java") || die "Can't open $tfname.java";
+        print CLASSFILE $copyright;
+        print CLASSFILE "// GENERATED FILE. To update, edit tftypes.pl instead.\n\n";
+
+        my $fulldesc = $desc;
+        if (substr($desc, 0, 1) =~ m/^[aeoiu8]$/i) {
+            $fulldesc = "an $desc"
+        } else {
+            $fulldesc = "a $desc"
+        }
+        print CLASSFILE  "package org.tensorflow.types;\n\n"
+                        ."import org.tensorflow.DataType;\n\n";
+        print CLASSFILE  "/** Represents $fulldesc. */\n"
+                        ."public class $tfname implements TFType {\n"
+                        ."  private $tfname() {}\n"
+                        ."  static {\n"
+                        ."    Types.typeCodes.put($tfname.class, DataType.$ucname);\n"
+                        ."  }\n";
+        if ($default ne '') {
+            print CLASSFILE
+                         "  static {\n"
+                        ."    Types.scalars.put($tfname.class, $default);\n"
+                        ."  }\n";
+        }
+        print CLASSFILE  "}\n";
+        close(CLASSFILE);
+    } elsif ($option eq '-c') {
+      # Generate creator declarations for Tensors.java
+      if ($jtype ne '' && $creat eq 'y') {
+        for (my $brackets = ''; length $brackets <= 12; $brackets .= '[]') {
+            $typeinfo .=
+                "  public static Tensor<$tfname> create($jtype$brackets data) {\n"
+               ."    return Tensor.create(data, $tfname.class);\n"
+               ."  }\n";
+        }
+      }
+      if ($text =~ m/\b$tfname\b/ || $creat eq 'y') {
+            $imports .= "import org.tensorflow.types.$tfname;\n";
+      }
+    }
+}
+
+if ($option ne '-t') {
+  print "// GENERATED FILE. Edits to this file will be lost -- edit $tmpl instead.\n";
+
+  $text =~ s/\@TYPEINFO\@/$typeinfo/;
+  $text =~ s/\@IMPORTS\@/$imports/;
+
+  print $text;
+}
diff --git a/tensorflow/java/src/gen/resources/tftypes.csv b/tensorflow/java/src/gen/resources/tftypes.csv
new file mode 100644
index 0000000000..88acaafd3c
--- /dev/null
+++ b/tensorflow/java/src/gen/resources/tftypes.csv
@@ -0,0 +1,21 @@
+TF type,Java type,Creator?,Zero value,Description
+Float,float,y,0f,32-bit single precision floating point number
+Double,double,y,0.0,64-bit double precision floating point number
+Int32,int,y,0,32-bit signed integer
+UInt8,byte,n,(byte)0,8-bit unsigned integer
+Int16,,n,(short)0,16-bit signed integer
+Int8,,n,(byte)0,8-bit signed integer
+String,byte,n,,arbitrary sequence of bytes
+Complex64,,n,,single-precision complex number
+Int64,long,y,0L,64-bit signed integer
+Bool,boolean,y,false,boolean
+QInt8,,n,,quantized int8
+QUInt8,,n,,quantized uint8
+QInt32,,n,,quantized int32
+BFloat16,,n,,float32 truncated to 16 bits. Only for cast ops.
+QInt16,,n,,quantized int16
+QUInt16,,n,,quantized uint16
+UInt16,,n,,16-bit unsigned integer
+Complex128,,n,,double-precision complex number
+Half,,n,,
+Resource,,n,,
diff --git a/tensorflow/java/src/main/java/org/tensorflow/DataType.java b/tensorflow/java/src/main/java/org/tensorflow/DataType.java
index cafa3ffc7d..e67e266ff7 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/DataType.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/DataType.java
@@ -53,12 +53,14 @@ public enum DataType {
   int c() {
     return value;
   }
+  
+  // Cached to avoid copying it
+  final private static DataType[] values = values();
 
   static DataType fromC(int c) {
-    for (DataType t : DataType.values()) {
-      if (t.c() == c) {
+    for (DataType t : values) {
+      if (t.value == c)
         return t;
-      }
     }
     throw new IllegalArgumentException(
         "DataType " + c + " is not recognized in Java (version " + TensorFlow.version() + ")");
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java
new file mode 100644
index 0000000000..ab34f6aa12
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a boolean. */
+public class TFBool implements TFType {
+  private TFBool() {}
+  static {
+    Types.typeCodes.put(TFBool.class, DataType.BOOL);
+  }
+  static {
+    Types.scalars.put(TFBool.class, false);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java
new file mode 100644
index 0000000000..49e5d9f2f3
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 64-bit double precision floating point number. */
+public class TFDouble implements TFType {
+  private TFDouble() {}
+  static {
+    Types.typeCodes.put(TFDouble.class, DataType.DOUBLE);
+  }
+  static {
+    Types.scalars.put(TFDouble.class, 0.0);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java
new file mode 100644
index 0000000000..8426ee41f0
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 32-bit single precision floating point number. */
+public class TFFloat implements TFType {
+  private TFFloat() {}
+  static {
+    Types.typeCodes.put(TFFloat.class, DataType.FLOAT);
+  }
+  static {
+    Types.scalars.put(TFFloat.class, 0f);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java
new file mode 100644
index 0000000000..3947b6ad09
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 32-bit signed integer. */
+public class TFInt32 implements TFType {
+  private TFInt32() {}
+  static {
+    Types.typeCodes.put(TFInt32.class, DataType.INT32);
+  }
+  static {
+    Types.scalars.put(TFInt32.class, 0);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java
new file mode 100644
index 0000000000..ccdded8693
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 64-bit signed integer. */
+public class TFInt64 implements TFType {
+  private TFInt64() {}
+  static {
+    Types.typeCodes.put(TFInt64.class, DataType.INT64);
+  }
+  static {
+    Types.scalars.put(TFInt64.class, 0L);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFString.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFString.java
new file mode 100644
index 0000000000..e7327e8c57
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFString.java
@@ -0,0 +1,27 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents an arbitrary sequence of bytes. */
+public class TFString implements TFType {
+  private TFString() {}
+  static {
+    Types.typeCodes.put(TFString.class, DataType.STRING);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFType.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFType.java
new file mode 100644
index 0000000000..562953ac9d
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFType.java
@@ -0,0 +1,20 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+package org.tensorflow.types;
+
+/**
+ * A marker interface for classes representing TensorFlow types.
+ */
+public interface TFType {}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java b/tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java
new file mode 100644
index 0000000000..d7305ca5a8
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java
@@ -0,0 +1,30 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents an 8-bit unsigned integer. */
+public class TFUInt8 implements TFType {
+  private TFUInt8() {}
+  static {
+    Types.typeCodes.put(TFUInt8.class, DataType.UINT8);
+  }
+  static {
+    Types.scalars.put(TFUInt8.class, (byte)0);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/Types.java b/tensorflow/java/src/main/java/org/tensorflow/types/Types.java
new file mode 100644
index 0000000000..976cd9fd34
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/Types.java
@@ -0,0 +1,52 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+package org.tensorflow.types;
+
+import java.util.HashMap;
+import java.util.Map;
+import org.tensorflow.DataType;
+
+/**
+ * Utility class for managing the representation of TensorFlow types as Java
+ * types. For each TensorFlow type (e.g., int32), there is a corresponding Java
+ * type (e.g., TFInt32) that represents it at compile time and a corresponding
+ * class object (e.g., TFInt32.class) that represents it at run time. There is
+ * also an enumeration value in DataType that can be used to represent the
+ * type, though that should rarely be required.
+ */
+public class Types {
+
+  private Types() {} // not instantiable
+
+  static final Map<Class<?>, DataType> typeCodes = new HashMap<>();
+
+  /** Returns the DataType value corresponding to a TensorFlow type class. */
+  public static DataType dataType(Class<? extends TFType> c) {
+    DataType dtype = typeCodes.get(c);
+    if (dtype == null) {
+      throw new IllegalArgumentException("" + c + " is not a TensorFlow type.");
+    }
+    return dtype;
+  }
+
+  static final Map<Class<?>, Object> scalars = new HashMap<>();
+
+  /** Returns the zero value of type described by {@code c}, or null if
+   *  the type (e.g., string) is not numeric and therefore has no zero value.
+   */
+  public static Object zeroValue(Class<? extends TFType> c) {
+    return scalars.get(c);
+  }
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/types/package-info.java b/tensorflow/java/src/main/java/org/tensorflow/types/package-info.java
new file mode 100644
index 0000000000..f1410a760e
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/types/package-info.java
@@ -0,0 +1,27 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/**
+ * Defines classes that represent TensorFlow data types. For each possible data type
+ * that can be used in a tensor, there is a corresponding class in this package that
+ * is used to represent it. For example, the TensorFlow int32 type is represented by
+ * the type TFInt32 and by the class object TFInt32.class. The former is used to
+ * support compile-time checking of tensor data types and the latter is used for
+ * run-time checking of data types. All such classes implement the TFType interface.
+ * TensorFlow data types are also separately represented by the DataType enum, with
+ * one enum value per data type. The enum representation should rarely be needed, but
+ * the Types class can be used to obtain it from the class object representation.
+ */
+package org.tensorflow.types;
diff --git a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
index 23b5abc077..aade375db8 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/OperationTest.java
@@ -24,6 +24,7 @@ import static org.junit.Assert.fail;
 import java.util.Arrays;
 import java.util.HashSet;
 import java.util.Set;
+
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.JUnit4;
diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py
index e5159cb941..6655f85d3a 100644
--- a/tensorflow/python/debug/lib/debug_gradients.py
+++ b/tensorflow/python/debug/lib/debug_gradients.py
@@ -303,7 +303,7 @@ class GradientsDebugger(object):
     """Register the gradient tensor for an x-tensor.
 
     Args:
-      x_tensor_name: (`str`) the name of the the independent `tf.Tensor`, i.e.,
+      x_tensor_name: (`str`) the name of the independent `tf.Tensor`, i.e.,
         the tensor on the denominator of the differentiation.
       gradient_tensor: the gradient `tf.Tensor`.
     """
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index 9d0e2c3381..1a023c971f 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -78,7 +78,7 @@ class EstimatorSpec(
 
     Depending on the value of `mode`, different arguments are required. Namely
     * For `mode == ModeKeys.TRAIN`: required fields are `loss` and `train_op`.
-    * For `mode == ModeKeys.EVAL`: required field is`loss`.
+    * For `mode == ModeKeys.EVAL`: required field is `loss`.
     * For `mode == ModeKeys.PREDICT`: required fields are `predictions`.
 
     model_fn can populate all arguments independent of mode. In this case, some
diff --git a/tensorflow/python/framework/common_shapes.py b/tensorflow/python/framework/common_shapes.py
index 79bf0879d7..52a0b9baa6 100644
--- a/tensorflow/python/framework/common_shapes.py
+++ b/tensorflow/python/framework/common_shapes.py
@@ -617,7 +617,7 @@ def call_cpp_shape_fn(op, require_shape_fn=True):
 
 def _call_cpp_shape_fn_impl(
     op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn):
-  """Core implementaton of call_cpp_shape_fn."""
+  """Core implementation of call_cpp_shape_fn."""
   graph_def_version = op.graph.graph_def_versions.producer
   node_def_str = op.node_def.SerializeToString()
 
diff --git a/tensorflow/python/kernel_tests/argmax_op_test.py b/tensorflow/python/kernel_tests/argmax_op_test.py
index ae171dec55..ce06769902 100644
--- a/tensorflow/python/kernel_tests/argmax_op_test.py
+++ b/tensorflow/python/kernel_tests/argmax_op_test.py
@@ -29,12 +29,12 @@ class ArgMaxTest(test.TestCase):
   def _testArg(self,
                method,
                x,
-               dimension,
+               axis,
                expected_values,
                use_gpu=False,
                expected_err_re=None):
     with self.test_session(use_gpu=use_gpu):
-      ans = method(x, dimension=dimension)
+      ans = method(x, axis=axis)
       if expected_err_re is None:
         tf_ans = ans.eval()
         # Defaults to int64 output.
@@ -48,27 +48,26 @@ class ArgMaxTest(test.TestCase):
   def _testBothArg(self,
                    method,
                    x,
-                   dimension,
+                   axis,
                    expected_values,
                    expected_err_re=None):
-    self._testArg(method, x, dimension, expected_values, True, expected_err_re)
-    self._testArg(method, x, dimension, expected_values, False, expected_err_re)
+    self._testArg(method, x, axis, expected_values, True, expected_err_re)
+    self._testArg(method, x, axis, expected_values, False, expected_err_re)
 
   def _testBasic(self, dtype):
     x = np.asarray(100 * np.random.randn(200), dtype=dtype)
 
-    # Check that argmin and argmax match numpy along the primary
-    # dimension
+    # Check that argmin and argmax match numpy along the primary axis
     self._testBothArg(math_ops.argmax, x, 0, x.argmax())
     self._testBothArg(math_ops.argmin, x, 0, x.argmin())
 
   def _testDim(self, dtype):
     x = np.asarray(100 * np.random.randn(3, 2, 4, 5, 6), dtype=dtype)
 
-    # Check that argmin and argmax match numpy along all dimensions
-    for dim in range(-5, 5):
-      self._testBothArg(math_ops.argmax, x, dim, x.argmax(dim))
-      self._testBothArg(math_ops.argmin, x, dim, x.argmin(dim))
+    # Check that argmin and argmax match numpy along all axes
+    for axis in range(-5, 5):
+      self._testBothArg(math_ops.argmax, x, axis, x.argmax(axis))
+      self._testBothArg(math_ops.argmin, x, axis, x.argmin(axis))
 
   def testFloat(self):
     self._testBasic(np.float32)
@@ -78,7 +77,7 @@ class ArgMaxTest(test.TestCase):
     x = np.asarray(100 * np.random.randn(200), dtype=np.float32)
     expected_values = x.argmax()
     with self.test_session(use_gpu=True):
-      ans = math_ops.argmax(x, dimension=0, output_type=dtypes.int32)
+      ans = math_ops.argmax(x, axis=0, output_type=dtypes.int32)
       tf_ans = ans.eval()
       self.assertEqual(np.int32, tf_ans.dtype)
       # The values are equal when comparing int32 to int64 because
@@ -86,7 +85,7 @@ class ArgMaxTest(test.TestCase):
       self.assertAllEqual(tf_ans, expected_values)
     expected_values = x.argmin()
     with self.test_session(use_gpu=True):
-      ans = math_ops.argmin(x, dimension=0, output_type=dtypes.int32)
+      ans = math_ops.argmin(x, axis=0, output_type=dtypes.int32)
       tf_ans = ans.eval()
       self.assertEqual(np.int32, tf_ans.dtype)
       self.assertAllEqual(tf_ans, expected_values)
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index ebf108d8ca..9e5772b891 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -537,7 +537,7 @@ class Layer(object):
         if x.get_shape().ndims is None:
           raise ValueError('Input ' + str(input_index) + ' of layer ' +
                            self.name + ' is incompatible with the layer: '
-                           'its rank is undefined, by the layer requires a '
+                           'its rank is undefined, but the layer requires a '
                            'defined rank.')
 
       # Check ndim.
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 3ff93b61c5..e22cab86c8 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -512,8 +512,9 @@ class QueueBase(object):
     the given queue. Subsequent `enqueue` and `enqueue_many`
     operations will fail. Subsequent `dequeue` and `dequeue_many`
     operations will continue to succeed if sufficient elements remain
-    in the queue. Subsequent `dequeue` and `dequeue_many` operations
-    that would block will fail immediately.
+    in the queue. Subsequently dequeue and dequeue_many operations 
+    that would otherwise block waiting for more elements (if close 
+    hadn't been called) will now fail immediately.
 
     If `cancel_pending_enqueues` is `True`, all pending requests will also
     be canceled.
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index d05fabce86..db33541218 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -1110,7 +1110,7 @@ def sparse_merge(sp_ids, sp_values, vocab_size, name=None,
   Args:
     sp_ids: A single `SparseTensor` with `values` property of type `int32`
       or `int64` or a Python list of such `SparseTensor`s or a list thereof.
-    sp_values: A`SparseTensor` of any type.
+    sp_values: A `SparseTensor` of any type.
     vocab_size: A scalar `int64` Tensor (or Python int) containing the new size
       of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
       Or a list thereof with `all(0 <= sp_ids[i].values < vocab_size[i])` for
diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py
index bd046a7fd0..8069950925 100644
--- a/tensorflow/python/tools/freeze_graph.py
+++ b/tensorflow/python/tools/freeze_graph.py
@@ -115,10 +115,15 @@ def freeze_graph_with_def_protos(
         output_node_names.split(","),
         variable_names_blacklist=variable_names_blacklist)
 
-  with gfile.GFile(output_graph, "wb") as f:
-    f.write(output_graph_def.SerializeToString())
+  # Write GraphDef to file if output path has been given.
+  if output_graph:
+    with gfile.GFile(output_graph, "wb") as f:
+      f.write(output_graph_def.SerializeToString())
+
   print("%d ops in the final graph." % len(output_graph_def.node))
 
+  return output_graph_def
+
 
 def _parse_input_graph_proto(input_graph, input_binary):
   """Parser input tensorflow graph into GraphDef proto."""
diff --git a/tensorflow/python/tools/import_pb_to_tensorboard.py b/tensorflow/python/tools/import_pb_to_tensorboard.py
index 2cab409cfb..a8712fc37e 100644
--- a/tensorflow/python/tools/import_pb_to_tensorboard.py
+++ b/tensorflow/python/tools/import_pb_to_tensorboard.py
@@ -64,11 +64,13 @@ if __name__ == "__main__":
       "--model_dir",
       type=str,
       default="",
+      required=True,
       help="The location of the protobuf (\'pb\') model to visualize.")
   parser.add_argument(
       "--log_dir",
       type=str,
       default="",
+      required=True,
       help="The location for the Tensorboard log to begin visualization from.")
   FLAGS, unparsed = parser.parse_known_args()
   app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/python/tools/optimize_for_inference_test.py b/tensorflow/python/tools/optimize_for_inference_test.py
index e547d0e292..447057cfe9 100644
--- a/tensorflow/python/tools/optimize_for_inference_test.py
+++ b/tensorflow/python/tools/optimize_for_inference_test.py
@@ -271,7 +271,8 @@ class OptimizeForInferenceTest(test.TestCase):
 
     for node in optimized_graph_def.node:
       self.assertNotEqual("Conv2D", node.op)
-      self.assertNotEqual("ResizeBilinear", node.op)
+      self.assertNotEqual("MirrorPad", node.op)
+      
 
   def testFusePadAndConv(self):
     with self.test_session() as sess:
@@ -299,7 +300,7 @@ class OptimizeForInferenceTest(test.TestCase):
 
     for node in optimized_graph_def.node:
       self.assertNotEqual("Conv2D", node.op)
-      self.assertNotEqual("MirrorPad", node.op)
+      self.assertNotEqual("ResizeBilinear", node.op)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 83a9313e50..cddf16f147 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -117,11 +117,9 @@ def if_not_windows(a):
   })
 
 
-def if_x86(a):
+def if_linux_x86_64(a):
   return select({
       clean_dep("//tensorflow:linux_x86_64"): a,
-      clean_dep("//tensorflow:windows"): a,
-      clean_dep("//tensorflow:windows_msvc"): a,
       "//conditions:default": [],
   })
 
@@ -152,7 +150,7 @@ def tf_copts():
       "-Wno-sign-compare",
       "-fno-exceptions",
   ]) + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
-      ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
+      ["-mfpu=neon"]) + if_linux_x86_64(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
               "-DTF_LEAN_BINARY",
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index 5bea031b68..82b41d4f9d 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -418,6 +418,10 @@ do_pip_smoke_test() {
     "The pip smoke test failed."
 }
 
+do_code_link_check() {
+  tensorflow/tools/ci_build/code_link_check.sh
+}
+
 do_check_load_py_test() {
   BUILD_CMD="bazel build //tensorflow/tools/pip_package:check_load_py_test"
   ${BUILD_CMD}
@@ -431,8 +435,8 @@ do_check_load_py_test() {
 }
 
 # Supply all sanity step commands and descriptions
-SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test")
-SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test")
+SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check")
+SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test" "Code Link Check: Check there are no broken links")
 
 INCREMENTAL_FLAG=""
 
diff --git a/tensorflow/tools/ci_build/code_link_check.sh b/tensorflow/tools/ci_build/code_link_check.sh
new file mode 100755
index 0000000000..09130482cc
--- /dev/null
+++ b/tensorflow/tools/ci_build/code_link_check.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# please run this at root directory of tensorflow
+success=1
+
+for i in `grep -onI https://www.tensorflow.org/code/\[a-zA-Z0-9/._-\]\* -r tensorflow`
+do
+  filename=`echo $i|awk -F: '{print $1}'`
+  linenumber=`echo $i|awk -F: '{print $2}'`
+  target=`echo $i|awk -F: '{print $4}'|tail -c +27`
+
+  # skip files in tensorflow/models
+  if [[ $target == tensorflow_models/* ]] ; then
+    continue
+  fi
+
+  if [ ! -f $target ] && [ ! -d $target ]; then
+    success=0
+    echo Broken link $target at line $linenumber of file $filename
+  fi
+done
+
+if [ $success == 0 ]; then
+  echo Code link check fails.
+  exit 1
+fi
+
+echo Code link check success.
diff --git a/tensorflow/tools/ci_build/install/install_golang.sh b/tensorflow/tools/ci_build/install/install_golang.sh
index fef203b869..88bc2960e3 100755
--- a/tensorflow/tools/ci_build/install/install_golang.sh
+++ b/tensorflow/tools/ci_build/install/install_golang.sh
@@ -16,7 +16,7 @@
 
 set -ex
 
-GOLANG_URL="https://storage.googleapis.com/golang/go1.7.5.linux-amd64.tar.gz"
+GOLANG_URL="https://storage.googleapis.com/golang/go1.8.3.linux-amd64.tar.gz"
 
 sudo mkdir -p /usr/local
 wget -q -O - "${GOLANG_URL}" | sudo tar -C /usr/local -xz
diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
index cc157c33f5..7cb81c20f0 100644
--- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
+++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
@@ -60,7 +60,7 @@ reinstall_tensorflow_pip ${PIP_NAME}
 
 # Define no_tensorflow_py_deps=true so that every py_test has no deps anymore,
 # which will result testing system installed tensorflow
-# GPU tests are very flaky when running concurently, so set local_test_jobs=1
+# GPU tests are very flaky when running concurrently, so set local_test_jobs=1
 bazel test -c opt --config=win-cuda $BUILD_OPTS -k --test_output=errors \
   --define=no_tensorflow_py_deps=true --test_lang_filters=py \
   --test_tag_filters=-no_pip,-no_windows,-no_windows_gpu \
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index a5f0440601..4760673058 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -205,11 +205,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.new_http_archive(
       name = "farmhash_archive",
       urls = [
-          "http://mirror.bazel.build/github.com/google/farmhash/archive/92e897b282426729f4724d91a637596c7e2fe28f.zip",
-          "https://github.com/google/farmhash/archive/92e897b282426729f4724d91a637596c7e2fe28f.zip",
+          "http://mirror.bazel.build/github.com/google/farmhash/archive/23eecfbe7e84ebf2e229bd02248f431c36e12f1a.zip",
+          "https://github.com/google/farmhash/archive/23eecfbe7e84ebf2e229bd02248f431c36e12f1a.zip",
       ],
-      sha256 = "4c626d1f306bda2c6804ab955892f803f5245f4dcaecb4979dc08b091256da54",
-      strip_prefix = "farmhash-92e897b282426729f4724d91a637596c7e2fe28f",
+      sha256 = "55215f8cd3ddbe9781f6fe5cc228731d6dcc8301b6191c6d420034c3fff1cb8d",
+      strip_prefix = "farmhash-23eecfbe7e84ebf2e229bd02248f431c36e12f1a",
       build_file = str(Label("//third_party:farmhash.BUILD")),
   )
 
diff --git a/tools/bazel.rc b/tools/bazel.rc
index e67a290cf4..414ddf2e47 100644
--- a/tools/bazel.rc
+++ b/tools/bazel.rc
@@ -11,6 +11,9 @@ build:mkl --define=using_mkl=true
 build:sycl --crosstool_top=@local_config_sycl//crosstool:toolchain
 build:sycl --define=using_sycl=true
 
+build:sycl_nodouble --crosstool_top=@local_config_sycl//crosstool:toolchain
+build:sycl_nodouble --define=using_sycl=true --cxxopt -DTENSORFLOW_SYCL_NO_DOUBLE
+
 build:sycl_asan --crosstool_top=@local_config_sycl//crosstool:toolchain
 build:sycl_asan --define=using_sycl=true --copt -fno-omit-frame-pointer --copt -fsanitize-coverage=3 --copt -DGPR_NO_DIRECT_SYSCALLS --linkopt -fPIC --linkopt -fsanitize=address
 
-- 
GitLab


From 4c849137952b649785d8a8ed591fbb77b1f49498 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Wed, 19 Jul 2017 15:07:08 -0700
Subject: [PATCH 1132/1375] Split HLO profile display logic out of
 hlo_execution_profile.cc, moving it into execution_profile_builder.cc.

PiperOrigin-RevId: 162541782
---
 tensorflow/compiler/xla/service/BUILD         |  13 +++
 .../xla/service/hlo_execution_profile.cc      | 102 ++----------------
 .../service/human_readable_profile_builder.cc |  96 +++++++++++++++++
 .../service/human_readable_profile_builder.h  |  82 ++++++++++++++
 4 files changed, 202 insertions(+), 91 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/human_readable_profile_builder.cc
 create mode 100644 tensorflow/compiler/xla/service/human_readable_profile_builder.h

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index ada130aa84..9c94091412 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1083,6 +1083,18 @@ cc_library(
     alwayslink = True,  # Contains per-platform computation placer registration
 )
 
+cc_library(
+    name = "human_readable_profile_builder",
+    srcs = ["human_readable_profile_builder.cc"],
+    hdrs = ["human_readable_profile_builder.h"],
+    deps = [
+        "//tensorflow/compiler/xla:metric_table_report",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_library(
     name = "generic_transfer_manager",
     srcs = ["generic_transfer_manager.cc"],
@@ -1211,6 +1223,7 @@ cc_library(
     deps = [
         ":hlo",
         ":hlo_cost_analysis",
+        ":human_readable_profile_builder",
         "//tensorflow/compiler/xla:metric_table_report",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
diff --git a/tensorflow/compiler/xla/service/hlo_execution_profile.cc b/tensorflow/compiler/xla/service/hlo_execution_profile.cc
index 9e25f1aceb..7a83a92404 100644
--- a/tensorflow/compiler/xla/service/hlo_execution_profile.cc
+++ b/tensorflow/compiler/xla/service/hlo_execution_profile.cc
@@ -19,14 +19,11 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
-#include "tensorflow/compiler/xla/metric_table_report.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/human_readable_profile_builder.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
-#include "tensorflow/core/lib/strings/numbers.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
 
 namespace xla {
 
@@ -55,96 +52,19 @@ string HloExecutionProfile::ToString(
     return "";
   }
 
-  using Item = std::pair<const HloInstruction*, uint64>;
-  std::vector<Item> items;
-  for (Item item : hlo_to_cycles_taken_) {
-    // Only include the HLOs which are part of the desired computation.
-    if (item.first->parent() == &computation) {
-      items.push_back(item);
-    }
-  }
-  auto custom_less = [](const Item& lhs, const Item& rhs) {
-    return lhs.second > rhs.second;
-  };
-  std::sort(items.begin(), items.end(), custom_less);
-  string result;
-  const int64 total_cycles = total_cycles_executed(computation);
-  double clock_rate_ghz = device_description.clock_rate_ghz();
-  CHECK_GE(clock_rate_ghz, 1e-9);
-
-  const auto cycles_to_microseconds = [&](double cycles) {
-    return cycles / clock_rate_ghz / 1000.0;
-  };
-
-  auto append_item = [&](int64 cycles, int64 flops, int64 bytes_accessed,
-                         const string& name) {
-    double nsecs = cycles / clock_rate_ghz;
-    string bytes_per_sec;
-    string bytes_per_cycle;
-    if (cycles <= 0 || bytes_accessed < 0) {
-      bytes_per_sec = "<unknown>";
-      bytes_per_cycle = "<unknown>";
-    } else {
-      bytes_per_sec = tensorflow::strings::HumanReadableNumBytes(
-          bytes_accessed / (nsecs / 1e9));
-      bytes_per_cycle =
-          tensorflow::strings::HumanReadableNumBytes(bytes_accessed / cycles);
-    }
-
-    double cycles_percent = 0;
-    if (total_cycles > 0) {
-      cycles_percent = cycles / static_cast<double>(total_cycles) * 100;
-    }
-
-    tensorflow::strings::StrAppend(
-        &result,
-        tensorflow::strings::Printf(
-            "%15lld cycles (%6.2f%%) :: %12.1f usec @ f_nom :: %18s :: %12s/s "
-            ":: "
-            "%12s/cycle :: "
-            "%s",
-            cycles, cycles_percent, cycles_to_microseconds(cycles),
-            flops <= 0 ? "<none>" : HumanReadableNumFlops(flops, nsecs).c_str(),
-            bytes_per_sec.c_str(), bytes_per_cycle.c_str(), name.c_str()));
-  };
-  tensorflow::strings::StrAppend(
-      &result, tensorflow::strings::Printf(
-                   "HLO execution profile for %s: (%s @ f_nom)\n\t",
-                   computation.name().c_str(),
-                   tensorflow::strings::HumanReadableElapsedTime(
-                       total_cycles / clock_rate_ghz / 1e9)
-                       .c_str()));
-
-  append_item(total_cycles, -1, -1, "[total]");
-  for (const auto& item : items) {
+  HumanReadableProfileBuilder builder(computation.name(),
+                                      total_cycles_executed(computation),
+                                      device_description.clock_rate_ghz());
+  for (const auto& item : hlo_to_cycles_taken_) {
     const HloInstruction* hlo = item.first;
-    tensorflow::strings::StrAppend(&result, "\n\t");
-    const int64 flops = (hlo == nullptr) ? -1 : cost_analysis.flop_count(*hlo);
-    const int64 bytes_accessed =
-        (hlo == nullptr) ? -1 : cost_analysis.bytes_accessed(*hlo);
-    const string display = (hlo == nullptr) ? "<none>" : hlo->ToString();
-    append_item(item.second, flops, bytes_accessed, display);
-  }
+    int64 cycles = item.second;
 
-  if (total_cycles <= 0) {
-    result += "****** 0 total cycles ******\n";
-  } else {
-    MetricTableReport table;
-    table.SetMetricName("microseconds");
-    table.SetEntryName("ops");
-    table.SetShowCategoryTable();
-    for (const auto& item : items) {
-      MetricTableReport::Entry entry;
-      entry.text = item.first->ToString();
-      entry.short_text = item.first->ToString(/*compact_operands=*/true);
-      entry.category_text = item.first->ToCategory();
-      entry.metric = cycles_to_microseconds(item.second);
-      table.AddEntry(std::move(entry));
-    }
-    result += table.MakeReport(cycles_to_microseconds(total_cycles));
+    builder.AddOp(/*op_name=*/hlo->ToString(),
+                  /*short_name=*/hlo->ToString(/*compact_operands=*/true),
+                  hlo->ToCategory(), cycles, cost_analysis.flop_count(*hlo),
+                  cost_analysis.bytes_accessed(*hlo));
   }
-
-  return result;
+  return builder.ToString();
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/human_readable_profile_builder.cc b/tensorflow/compiler/xla/service/human_readable_profile_builder.cc
new file mode 100644
index 0000000000..1b9a7a297f
--- /dev/null
+++ b/tensorflow/compiler/xla/service/human_readable_profile_builder.cc
@@ -0,0 +1,96 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/human_readable_profile_builder.h"
+#include "tensorflow/compiler/xla/metric_table_report.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/strings/numbers.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+
+namespace xla {
+
+using tensorflow::strings::Appendf;
+using tensorflow::strings::HumanReadableElapsedTime;
+using tensorflow::strings::HumanReadableNumBytes;
+using tensorflow::strings::StrAppend;
+
+string HumanReadableProfileBuilder::ToString() const {
+  string s;
+
+  Appendf(&s, "Execution profile for %s: (%s @ f_nom)\n",
+          computation_name_.c_str(),
+          HumanReadableElapsedTime(CyclesToSeconds(total_cycles_)).c_str());
+
+  auto append_op = [&](const OpInfo& op) {
+    string bytes_per_sec;
+    string bytes_per_cycle;
+    if (op.cycles <= 0 || op.bytes_accessed < 0) {
+      bytes_per_sec = "<unknown>";
+      bytes_per_cycle = "<unknown>";
+    } else {
+      bytes_per_sec =
+          HumanReadableNumBytes(op.bytes_accessed / CyclesToSeconds(op.cycles));
+      bytes_per_cycle = HumanReadableNumBytes(op.bytes_accessed / op.cycles);
+    }
+
+    double cycles_percent = 0;
+    if (total_cycles_ > 0) {
+      cycles_percent = op.cycles / static_cast<double>(total_cycles_) * 100;
+    }
+
+    double nsecs = op.cycles / clock_rate_ghz_;
+    Appendf(&s,
+            "\t%15lld cycles (%6.2f%%) :: %12.1f usec @ f_nom :: %18s "
+            ":: %12s/s :: %12s/cycle :: %s\n",
+            op.cycles, cycles_percent, CyclesToMicroseconds(op.cycles),
+            op.flop_count <= 0
+                ? "<none>"
+                : HumanReadableNumFlops(op.flop_count, nsecs).c_str(),
+            bytes_per_sec.c_str(), bytes_per_cycle.c_str(), op.name.c_str());
+  };
+
+  append_op({"[total]", "[total]", /*category=*/"", total_cycles_, -1, -1});
+
+  // Sort ops in decreasing order of cycles.
+  std::vector<OpInfo> sorted_ops(op_infos_);
+  std::sort(
+      sorted_ops.begin(), sorted_ops.end(),
+      [](const OpInfo& a, const OpInfo& b) { return a.cycles > b.cycles; });
+  for (const auto& op : sorted_ops) {
+    append_op(op);
+  }
+
+  if (total_cycles_ <= 0) {
+    StrAppend(&s, "****** 0 total cycles ******\n");
+  } else {
+    MetricTableReport table;
+    table.SetMetricName("microseconds");
+    table.SetEntryName("ops");
+    table.SetShowCategoryTable();
+    for (const auto& op : sorted_ops) {
+      MetricTableReport::Entry entry;
+      entry.text = op.name;
+      entry.short_text = op.short_name;
+      entry.category_text = op.category;
+      entry.metric = CyclesToMicroseconds(op.cycles);
+      table.AddEntry(std::move(entry));
+    }
+    StrAppend(&s, table.MakeReport(CyclesToMicroseconds(total_cycles_)));
+  }
+  return s;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/human_readable_profile_builder.h b/tensorflow/compiler/xla/service/human_readable_profile_builder.h
new file mode 100644
index 0000000000..1a69cbf8bf
--- /dev/null
+++ b/tensorflow/compiler/xla/service/human_readable_profile_builder.h
@@ -0,0 +1,82 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HUMAN_READABLE_PROFILE_BUILDER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HUMAN_READABLE_PROFILE_BUILDER_H_
+
+#include <vector>
+
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+
+// HumanReadableProfileBuilder helps you create a textual profile of a
+// computation, suitable for consumption by humans.
+class HumanReadableProfileBuilder {
+ public:
+  explicit HumanReadableProfileBuilder(tensorflow::StringPiece computation_name,
+                                       int64 total_cycles,
+                                       double clock_rate_ghz)
+      : computation_name_(computation_name.ToString()),
+        total_cycles_(total_cycles),
+        clock_rate_ghz_(clock_rate_ghz) {
+    CHECK_GE(clock_rate_ghz, 1e-9);
+  }
+
+  int64 total_cycles() const { return total_cycles_; }
+
+  // Adds an operation to the profile.  If you don't know the number of
+  // floating-point ops or bytes touched by the op, pass -1 for that param.
+  void AddOp(tensorflow::StringPiece op_name,
+             tensorflow::StringPiece short_name,
+             tensorflow::StringPiece category, int64 cycles, int64 flop_count,
+             int64 bytes_accessed) {
+    op_infos_.push_back({op_name.ToString(), short_name.ToString(),
+                         category.ToString(), cycles, flop_count,
+                         bytes_accessed});
+  }
+
+  // Gets the human-readable profile.
+  string ToString() const;
+
+ private:
+  struct OpInfo {
+    string name;
+    string short_name;
+    string category;
+    int64 cycles;
+    int64 flop_count;
+    int64 bytes_accessed;
+  };
+
+  double CyclesToSeconds(int64 cycles) const {
+    return cycles / clock_rate_ghz_ / 1e9;
+  }
+  double CyclesToMicroseconds(int64 cycles) const {
+    return cycles / clock_rate_ghz_ / 1000.0;
+  }
+
+  string computation_name_;
+  int64 total_cycles_;
+  double clock_rate_ghz_;
+  std::vector<OpInfo> op_infos_;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HUMAN_READABLE_PROFILE_BUILDER_H_
-- 
GitLab


From f7877726a5d9404bc75c5c48f1fd9c43657595dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 15:28:48 -0700
Subject: [PATCH 1133/1375] Fix comment: OperatorPDCholesky ->
 LinearOperatorTriL

PiperOrigin-RevId: 162544918
---
 tensorflow/python/ops/distributions/util.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index c808f1fa7a..63fb87e93c 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -525,8 +525,8 @@ def matrix_diag_transform(matrix, transform=None, name=None):
   # valid Cholesky factor.
   chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
 
-  # OperatorPDCholesky ignores the upper triangle.
-  operator = OperatorPDCholesky(chol)
+  # LinearOperatorTriL ignores the upper triangle.
+  operator = LinearOperatorTriL(chol)
   ```
 
   Example of heteroskedastic 2-D linear regression.
-- 
GitLab


From 1ee98618dd74404e26ee0202f27e05cb3dcef5c3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 16:58:33 -0700
Subject: [PATCH 1134/1375] Allow kernel op tests to add resource inputs

PiperOrigin-RevId: 162556398
---
 tensorflow/core/kernels/BUILD                |  1 +
 tensorflow/core/kernels/ops_testutil.h       | 26 ++++++++++++++++++++
 tensorflow/core/kernels/ops_testutil_test.cc | 14 +++++++++++
 3 files changed, 41 insertions(+)

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 96cb94cfb0..3044c7700f 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -795,6 +795,7 @@ tf_cc_test(
         ":identity_op",
         ":ops_testutil",
         ":ops_util",
+        ":variable_ops",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
diff --git a/tensorflow/core/kernels/ops_testutil.h b/tensorflow/core/kernels/ops_testutil.h
index 2554359d13..4795cba98f 100644
--- a/tensorflow/core/kernels/ops_testutil.h
+++ b/tensorflow/core/kernels/ops_testutil.h
@@ -170,6 +170,32 @@ class OpsTestBase : public ::testing::Test {
     }
   }
 
+  // Adds a Resource type as input. If <container> is empty, uses the default
+  // container name.
+  template <typename T>
+  void AddResourceInput(const string& container, const string& name,
+                        T* resource) {
+    CHECK_GT(input_types_.size(), inputs_.size())
+        << "Adding more inputs than types; perhaps you need to call MakeOp";
+    ResourceMgr* rm = device_->resource_manager();
+    EXPECT_TRUE(
+        rm->Create(container == "" ? rm->default_container() : container, name,
+                   resource)
+            .ok());
+    TypeIndex type_index = MakeTypeIndex<T>();
+    ResourceHandle handle;
+    handle.set_device(device_->name());
+    handle.set_container(container);
+    handle.set_name(name);
+    handle.set_hash_code(type_index.hash_code());
+    handle.set_maybe_type_name(type_index.name());
+    Tensor* input = new Tensor(device_->GetAllocator(AllocatorAttributes()),
+                               DT_RESOURCE, TensorShape({}));
+    input->scalar<ResourceHandle>()() = handle;
+    tensors_.push_back(input);
+    inputs_.push_back({nullptr, input});
+  }
+
   // Runs an operation producing 'num_outputs' outputs.
   //
   // Returns the context's status after running the operation.
diff --git a/tensorflow/core/kernels/ops_testutil_test.cc b/tensorflow/core/kernels/ops_testutil_test.cc
index 906fd890e3..239e460825 100644
--- a/tensorflow/core/kernels/ops_testutil_test.cc
+++ b/tensorflow/core/kernels/ops_testutil_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/kernels/variable_ops.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/test_benchmark.h"
 
@@ -33,4 +34,17 @@ TEST_F(OpsTestBase, ScopedStepContainer) {
   EXPECT_TRUE(step_container_ != nullptr);
 }
 
+// Verify that a Resource input can be added to the test kernel.
+TEST_F(OpsTestBase, ResourceVariableInput) {
+  TF_EXPECT_OK(NodeDefBuilder("identity", "Identity")
+                   .Input(FakeInput(DT_RESOURCE))
+                   .Finalize(node_def()));
+  TF_ASSERT_OK(InitOp());
+  Var* var = new Var(DT_STRING);
+  AddResourceInput("" /* container */, "Test" /* name */, var);
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor* output = GetOutput(0);
+  EXPECT_EQ(output->dtype(), DT_RESOURCE);
+}
+
 }  // namespace tensorflow
-- 
GitLab


From 965ae3e34e5e5dd33a4910fdf3a31d0f7a250ac1 Mon Sep 17 00:00:00 2001
From: Adam Roberts <adarob@google.com>
Date: Wed, 19 Jul 2017 17:08:07 -0700
Subject: [PATCH 1135/1375] Add multi-head attention capabilities to
 AttentionWrapper via the specification of multiple AttentionMechanisms.

PiperOrigin-RevId: 162557562
---
 .../kernel_tests/attention_wrapper_test.py    | 172 +++++++++++++-
 .../seq2seq/python/ops/attention_wrapper.py   | 216 ++++++++++++------
 2 files changed, 307 insertions(+), 81 deletions(-)

diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
index 8401918128..91493302b1 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
@@ -88,6 +88,30 @@ class AttentionWrapperTest(test.TestCase):
                          expected_final_alignment_history=None,
                          attention_layer_size=6,
                          name=''):
+    self._testWithMaybeMultiAttention(
+        is_multi=False,
+        create_attention_mechanisms=[create_attention_mechanism],
+        expected_final_output=expected_final_output,
+        expected_final_state=expected_final_state,
+        attention_mechanism_depths=[attention_mechanism_depth],
+        alignment_history=alignment_history,
+        expected_final_alignment_history=expected_final_alignment_history,
+        attention_layer_sizes=[attention_layer_size],
+        name=name)
+
+  def _testWithMaybeMultiAttention(self,
+                                   is_multi,
+                                   create_attention_mechanisms,
+                                   expected_final_output,
+                                   expected_final_state,
+                                   attention_mechanism_depths,
+                                   alignment_history=False,
+                                   expected_final_alignment_history=None,
+                                   attention_layer_sizes=None,
+                                   name=''):
+    # Allow is_multi to be True with a single mechanism to enable test for
+    # passing in a single mechanism in a list.
+    assert len(create_attention_mechanisms) == 1 or is_multi
     encoder_sequence_length = [3, 2, 3, 1, 1]
     decoder_sequence_length = [2, 0, 1, 2, 3]
     batch_size = 5
@@ -97,10 +121,12 @@ class AttentionWrapperTest(test.TestCase):
     encoder_output_depth = 10
     cell_depth = 9
 
-    if attention_layer_size is not None:
-      attention_depth = attention_layer_size
+    if attention_layer_sizes is None:
+      attention_depth = encoder_output_depth * len(create_attention_mechanisms)
     else:
-      attention_depth = encoder_output_depth
+      # Compute sum of attention_layer_sizes. Use encoder_output_depth if None.
+      attention_depth = sum([attention_layer_size or encoder_output_depth
+                             for attention_layer_size in attention_layer_sizes])
 
     decoder_inputs = array_ops.placeholder_with_default(
         np.random.randn(batch_size, decoder_max_time,
@@ -111,10 +137,12 @@ class AttentionWrapperTest(test.TestCase):
                         encoder_output_depth).astype(np.float32),
         shape=(None, None, encoder_output_depth))
 
-    attention_mechanism = create_attention_mechanism(
-        num_units=attention_mechanism_depth,
-        memory=encoder_outputs,
-        memory_sequence_length=encoder_sequence_length)
+    attention_mechanisms = [
+        creator(num_units=depth,
+                memory=encoder_outputs,
+                memory_sequence_length=encoder_sequence_length)
+        for creator, depth in zip(create_attention_mechanisms,
+                                  attention_mechanism_depths)]
 
     with self.test_session(use_gpu=True) as sess:
       with vs.variable_scope(
@@ -123,8 +151,9 @@ class AttentionWrapperTest(test.TestCase):
         cell = rnn_cell.LSTMCell(cell_depth)
         cell = wrapper.AttentionWrapper(
             cell,
-            attention_mechanism,
-            attention_layer_size=attention_layer_size,
+            attention_mechanisms if is_multi else attention_mechanisms[0],
+            attention_layer_size=(attention_layer_sizes if is_multi
+                                  else attention_layer_sizes[0]),
             alignment_history=alignment_history)
         helper = helper_py.TrainingHelper(decoder_inputs,
                                           decoder_sequence_length)
@@ -156,12 +185,23 @@ class AttentionWrapperTest(test.TestCase):
                        tuple(final_state.cell_state.h.get_shape().as_list()))
 
       if alignment_history:
-        state_alignment_history = final_state.alignment_history.stack()
+        if is_multi:
+          state_alignment_history = []
+          for history_array in final_state.alignment_history:
+            history = history_array.stack()
+            self.assertEqual(
+                (None, batch_size, None),
+                tuple(history.get_shape().as_list()))
+            state_alignment_history.append(history)
+          state_alignment_history = tuple(state_alignment_history)
+        else:
+          state_alignment_history = final_state.alignment_history.stack()
+          self.assertEqual(
+              (None, batch_size, None),
+              tuple(state_alignment_history.get_shape().as_list()))
         # Remove the history from final_state for purposes of the
         # remainder of the tests.
         final_state = final_state._replace(alignment_history=())  # pylint: disable=protected-access
-        self.assertEqual((None, batch_size, None),
-                         tuple(state_alignment_history.get_shape().as_list()))
       else:
         state_alignment_history = ()
 
@@ -617,6 +657,114 @@ class AttentionWrapperTest(test.TestCase):
         expected_final_alignment_history=expected_final_alignment_history,
         name='testLuongMonotonicScaled')
 
+  def testMultiAttention(self):
+    create_attention_mechanisms = (
+        wrapper.BahdanauAttention, wrapper.LuongAttention)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 7), dtype=dtype('float32'), mean=0.0011709079),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=3.2000000000000002))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0038725811),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019329828)),
+        attention=ResultSummary(
+            shape=(5, 7), dtype=dtype('float32'), mean=0.001174294),
+        time=3,
+        alignments=(
+            ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125),
+            ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125)),
+        alignment_history=())
+
+    expected_final_alignment_history = (
+        ResultSummary(shape=(3, 5, 8), dtype=dtype('float32'), mean=0.125),
+        ResultSummary(shape=(3, 5, 8), dtype=dtype('float32'), mean=0.125))
+
+    self._testWithMaybeMultiAttention(
+        True,
+        create_attention_mechanisms,
+        expected_final_output,
+        expected_final_state,
+        attention_mechanism_depths=[9, 9],
+        attention_layer_sizes=[3, 4],
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testMultiAttention')
+
+  def testMultiAttentionNoAttentionLayer(self):
+    create_attention_mechanisms = (
+        wrapper.BahdanauAttention, wrapper.LuongAttention)
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 20), dtype=dtype('float32'), mean=0.11691988),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=7.2666666666666666))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0036486709),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0018835809)),
+        attention=ResultSummary(
+            shape=(5, 20), dtype=dtype('float32'), mean=0.11680689),
+        time=3,
+        alignments=(
+            ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125),
+            ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125)),
+        alignment_history=())
+    expected_final_alignment_history = (
+        ResultSummary(shape=(3, 5, 8), dtype=dtype('float32'), mean=0.125),
+        ResultSummary(shape=(3, 5, 8), dtype=dtype('float32'), mean=0.125))
+
+    self._testWithMaybeMultiAttention(
+        is_multi=True,
+        create_attention_mechanisms=create_attention_mechanisms,
+        expected_final_output=expected_final_output,
+        expected_final_state=expected_final_state,
+        attention_mechanism_depths=[9, 9],
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testMultiAttention')
+
+  def testSingleAttentionAsList(self):
+    create_attention_mechanisms = [wrapper.BahdanauAttention]
+
+    expected_final_output = BasicDecoderOutput(
+        rnn_output=ResultSummary(
+            shape=(5, 3, 3), dtype=dtype('float32'), mean=-0.0098485695),
+        sample_id=ResultSummary(
+            shape=(5, 3), dtype=dtype('int32'), mean=1.8))
+    expected_final_state = AttentionWrapperState(
+        cell_state=LSTMStateTuple(
+            c=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0040023471),
+            h=ResultSummary(
+                shape=(5, 9), dtype=dtype('float32'), mean=-0.0019979973)),
+        attention=ResultSummary(
+            shape=(5, 3), dtype=dtype('float32'), mean=-0.0098808752),
+        time=3,
+        alignments=(
+            ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125),),
+        alignment_history=())
+
+    expected_final_alignment_history = (
+        ResultSummary(shape=(3, 5, 8), dtype=dtype('float32'), mean=0.125),)
+
+    self._testWithMaybeMultiAttention(
+        is_multi=True,  # pass the AttentionMechanism wrapped in a list
+        create_attention_mechanisms=create_attention_mechanisms,
+        expected_final_output=expected_final_output,
+        expected_final_state=expected_final_state,
+        attention_mechanism_depths=[9],
+        attention_layer_sizes=[3],
+        alignment_history=True,
+        expected_final_alignment_history=expected_final_alignment_history,
+        name='testMultiAttention')
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index 9c6939bb46..a162a919cf 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -917,9 +917,11 @@ class AttentionWrapperState(
       step.
     - `attention`: The attention emitted at the previous time step.
     - `time`: int32 scalar containing the current time step.
-    - `alignments`: The alignment emitted at the previous time step.
-    - `alignment_history`: (if enabled) a `TensorArray` containing alignment
-       matrices from all time steps.  Call `stack()` to convert to a `Tensor`.
+    - `alignments`: A single or tuple of `Tensor`(s) containing the alignments
+       emitted at the previous time step for each attention mechanism.
+    - `alignment_history`: (if enabled) a single or tuple of `TensorArray`(s)
+       containing alignment matrices from all time steps for each attention
+       mechanism. Call `stack()` on each to convert to a `Tensor`.
   """
 
   def clone(self, **kwargs):
@@ -964,6 +966,34 @@ def hardmax(logits, name=None):
         math_ops.argmax(logits, -1), depth, dtype=logits.dtype)
 
 
+def _compute_attention(attention_mechanism, cell_output, previous_alignments,
+                       attention_layer):
+  """Computes the attention and alignments for a given attention_mechanism."""
+  alignments = attention_mechanism(
+      cell_output, previous_alignments=previous_alignments)
+
+  # Reshape from [batch_size, memory_time] to [batch_size, 1, memory_time]
+  expanded_alignments = array_ops.expand_dims(alignments, 1)
+  # Context is the inner product of alignments and values along the
+  # memory time dimension.
+  # alignments shape is
+  #   [batch_size, 1, memory_time]
+  # attention_mechanism.values shape is
+  #   [batch_size, memory_time, attention_mechanism.num_units]
+  # the batched matmul is over memory_time, so the output shape is
+  #   [batch_size, 1, attention_mechanism.num_units].
+  # we then squeeze out the singleton dim.
+  context = math_ops.matmul(expanded_alignments, attention_mechanism.values)
+  context = array_ops.squeeze(context, [1])
+
+  if attention_layer is not None:
+    attention = attention_layer(array_ops.concat([cell_output, context], 1))
+  else:
+    attention = context
+
+  return attention, alignments
+
+
 class AttentionWrapper(rnn_cell_impl.RNNCell):
   """Wraps another `RNNCell` with attention.
   """
@@ -981,11 +1011,14 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
 
     Args:
       cell: An instance of `RNNCell`.
-      attention_mechanism: An instance of `AttentionMechanism`.
-      attention_layer_size: Python integer, the depth of the attention (output)
-        layer. If None (default), use the context as attention at each time
-        step. Otherwise, feed the context and cell output into the attention
-        layer to generate attention at each time step.
+      attention_mechanism: A list of `AttentionMechanism` instances or a single
+        instance.
+      attention_layer_size: A list of Python integers or a single Python
+        integer, the depth of the attention (output) layer(s). If None
+        (default), use the context as attention at each time step. Otherwise,
+        feed the context and cell output into the attention layer to generate
+        attention at each time step. If attention_mechanism is a list,
+        attention_layer_size must be a list of the same length.
       alignment_history: Python boolean, whether to store alignment history
         from all time steps in the final output state (currently stored as a
         time major `TensorArray` on which you must call `stack()`).
@@ -1005,15 +1038,35 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
         does not match the batch size of `initial_cell_state`, proper
         behavior is not guaranteed.
       name: Name to use when creating ops.
+
+    Raises:
+      TypeError: `attention_layer_size` is not None and (`attention_mechanism`
+        is a list but `attention_layer_size` is not; or vice versa).
+      ValueError: if `attention_layer_size` is not None, `attention_mechanism`
+        is a list, and its length does not match that of `attention_layer_size`.
     """
     super(AttentionWrapper, self).__init__(name=name)
     if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
       raise TypeError(
           "cell must be an RNNCell, saw type: %s" % type(cell).__name__)
-    if not isinstance(attention_mechanism, AttentionMechanism):
-      raise TypeError(
-          "attention_mechanism must be a AttentionMechanism, saw type: %s"
-          % type(attention_mechanism).__name__)
+    if isinstance(attention_mechanism, (list, tuple)):
+      self._is_multi = True
+      attention_mechanisms = attention_mechanism
+      for attention_mechanism in attention_mechanisms:
+        if not isinstance(attention_mechanism, AttentionMechanism):
+          raise TypeError(
+              "attention_mechanism must contain only instances of "
+              "AttentionMechanism, saw type: %s"
+              % type(attention_mechanism).__name__)
+    else:
+      self._is_multi = False
+      if not isinstance(attention_mechanism, AttentionMechanism):
+        raise TypeError(
+            "attention_mechanism must be an AttentionMechanism or list of "
+            "multiple AttentionMechanism instances, saw type: %s"
+            % type(attention_mechanism).__name__)
+      attention_mechanisms = (attention_mechanism,)
+
     if cell_input_fn is None:
       cell_input_fn = (
           lambda inputs, attention: array_ops.concat([inputs, attention], -1))
@@ -1024,16 +1077,28 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
             % type(cell_input_fn).__name__)
 
     if attention_layer_size is not None:
-      self._attention_layer = layers_core.Dense(
-          attention_layer_size, name="attention_layer", use_bias=False)
-      self._attention_layer_size = attention_layer_size
+      attention_layer_sizes = tuple(
+          attention_layer_size
+          if isinstance(attention_layer_size, (list, tuple))
+          else (attention_layer_size,))
+      if len(attention_layer_sizes) != len(attention_mechanisms):
+        raise ValueError(
+            "If provided, attention_layer_size must contain exactly one "
+            "integer per attention_mechanism, saw: %d vs %d"
+            % (len(attention_layer_sizes), len(attention_mechanisms)))
+      self._attention_layers = tuple(
+          layers_core.Dense(
+              attention_layer_size, name="attention_layer", use_bias=False)
+          for attention_layer_size in attention_layer_sizes)
+      self._attention_layer_size = sum(attention_layer_sizes)
     else:
-      self._attention_layer = None
-      self._attention_layer_size = attention_mechanism.values.get_shape()[
-          -1].value
+      self._attention_layers = None
+      self._attention_layer_size = sum(
+          attention_mechanism.values.get_shape()[-1].value
+          for attention_mechanism in attention_mechanisms)
 
     self._cell = cell
-    self._attention_mechanism = attention_mechanism
+    self._attention_mechanisms = attention_mechanisms
     self._cell_input_fn = cell_input_fn
     self._output_attention = output_attention
     self._alignment_history = alignment_history
@@ -1053,13 +1118,36 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
             "via the tf.contrib.seq2seq.tile_batch function with argument "
             "multiple=beam_width.")
         with ops.control_dependencies(
-            [check_ops.assert_equal(state_batch_size,
-                                    self._attention_mechanism.batch_size,
-                                    message=error_message)]):
+            self._batch_size_checks(state_batch_size, error_message)):
           self._initial_cell_state = nest.map_structure(
               lambda s: array_ops.identity(s, name="check_initial_cell_state"),
               initial_cell_state)
 
+  def _batch_size_checks(self, batch_size, error_message):
+    return [check_ops.assert_equal(batch_size,
+                                   attention_mechanism.batch_size,
+                                   message=error_message)
+            for attention_mechanism in self._attention_mechanisms]
+
+  def _item_or_tuple(self, seq):
+    """Returns `seq` as tuple or the singular element.
+
+    Which is returned is determined by how the AttentionMechanism(s) were passed
+    to the constructor.
+
+    Args:
+      seq: A non-empty sequence of items or generator.
+
+    Returns:
+       Either the values in the sequence as a tuple if AttentionMechanism(s)
+       were passed to the constructor as a sequence or the singular element.
+    """
+    t = tuple(seq)
+    if self._is_multi:
+      return t
+    else:
+      return t[0]
+
   @property
   def output_size(self):
     if self._output_attention:
@@ -1073,8 +1161,10 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
         cell_state=self._cell.state_size,
         time=tensor_shape.TensorShape([]),
         attention=self._attention_layer_size,
-        alignments=self._attention_mechanism.alignments_size,
-        alignment_history=())  # alignment_history is sometimes a TensorArray
+        alignments=self._item_or_tuple(
+            a.alignments_size for a in self._attention_mechanisms),
+        alignment_history=self._item_or_tuple(
+            () for _ in self._attention_mechanisms))  # sometimes a TensorArray
 
   def zero_state(self, batch_size, dtype):
     with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
@@ -1091,25 +1181,23 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
           "the batch_size= argument passed to zero_state is "
           "batch_size * beam_width.")
       with ops.control_dependencies(
-          [check_ops.assert_equal(batch_size,
-                                  self._attention_mechanism.batch_size,
-                                  message=error_message)]):
+          self._batch_size_checks(batch_size, error_message)):
         cell_state = nest.map_structure(
             lambda s: array_ops.identity(s, name="checked_cell_state"),
             cell_state)
-      if self._alignment_history:
-        alignment_history = tensor_array_ops.TensorArray(
-            dtype=dtype, size=0, dynamic_size=True)
-      else:
-        alignment_history = ()
       return AttentionWrapperState(
           cell_state=cell_state,
           time=array_ops.zeros([], dtype=dtypes.int32),
           attention=_zero_state_tensors(self._attention_layer_size, batch_size,
                                         dtype),
-          alignments=self._attention_mechanism.initial_alignments(
-              batch_size, dtype),
-          alignment_history=alignment_history)
+          alignments=self._item_or_tuple(
+              attention_mechanism.initial_alignments(batch_size, dtype)
+              for attention_mechanism in self._attention_mechanisms),
+          alignment_history=self._item_or_tuple(
+              tensor_array_ops.TensorArray(dtype=dtype, size=0,
+                                           dynamic_size=True)
+              if self._alignment_history else ()
+              for _ in self._attention_mechanisms))
 
   def call(self, inputs, state):
     """Perform a step of attention-wrapped RNN.
@@ -1161,48 +1249,38 @@ class AttentionWrapper(rnn_cell_impl.RNNCell):
         "the tf.contrib.seq2seq.tile_batch function with argument "
         "multiple=beam_width.")
     with ops.control_dependencies(
-        [check_ops.assert_equal(cell_batch_size,
-                                self._attention_mechanism.batch_size,
-                                message=error_message)]):
+        self._batch_size_checks(cell_batch_size, error_message)):
       cell_output = array_ops.identity(
           cell_output, name="checked_cell_output")
 
-    alignments = self._attention_mechanism(
-        cell_output, previous_alignments=state.alignments)
-
-    # Reshape from [batch_size, memory_time] to [batch_size, 1, memory_time]
-    expanded_alignments = array_ops.expand_dims(alignments, 1)
-    # Context is the inner product of alignments and values along the
-    # memory time dimension.
-    # alignments shape is
-    #   [batch_size, 1, memory_time]
-    # attention_mechanism.values shape is
-    #   [batch_size, memory_time, attention_mechanism.num_units]
-    # the batched matmul is over memory_time, so the output shape is
-    #   [batch_size, 1, attention_mechanism.num_units].
-    # we then squeeze out the singleton dim.
-    attention_mechanism_values = self._attention_mechanism.values
-    context = math_ops.matmul(expanded_alignments, attention_mechanism_values)
-    context = array_ops.squeeze(context, [1])
-
-    if self._attention_layer is not None:
-      attention = self._attention_layer(
-          array_ops.concat([cell_output, context], 1))
+    if self._is_multi:
+      previous_alignments = state.alignments
+      previous_alignment_history = state.alignment_history
     else:
-      attention = context
-
-    if self._alignment_history:
-      alignment_history = state.alignment_history.write(
-          state.time, alignments)
-    else:
-      alignment_history = ()
-
+      previous_alignments = [state.alignments]
+      previous_alignment_history = [state.alignment_history]
+
+    all_alignments = []
+    all_attentions = []
+    all_histories = []
+    for i, attention_mechanism in enumerate(self._attention_mechanisms):
+      attention, alignments = _compute_attention(
+          attention_mechanism, cell_output, previous_alignments[i],
+          self._attention_layers[i] if self._attention_layers else None)
+      alignment_history = previous_alignment_history[i].write(
+          state.time, alignments) if self._alignment_history else ()
+
+      all_alignments.append(alignments)
+      all_histories.append(alignment_history)
+      all_attentions.append(attention)
+
+    attention = array_ops.concat(all_attentions, 1)
     next_state = AttentionWrapperState(
         time=state.time + 1,
         cell_state=next_cell_state,
         attention=attention,
-        alignments=alignments,
-        alignment_history=alignment_history)
+        alignments=self._item_or_tuple(all_alignments),
+        alignment_history=self._item_or_tuple(all_histories))
 
     if self._output_attention:
       return attention, next_state
-- 
GitLab


From 71f266cc2678afc0a62540b6c65d6f2c80a9a1c3 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Wed, 19 Jul 2017 17:19:08 -0700
Subject: [PATCH 1136/1375] Fully qualify namespaces in XLA macros.

No functional change.

PiperOrigin-RevId: 162558977
---
 tensorflow/compiler/xla/util.h | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 31f0c3147e..00151e5da6 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -344,18 +344,19 @@ std::vector<std::pair<int64, int64>> CommonFactors(
 
 }  // namespace xla
 
-#define XLA_LOG_LINES(SEV, STRING) LogLines(SEV, STRING, __FILE__, __LINE__)
+#define XLA_LOG_LINES(SEV, STRING) \
+  ::xla::LogLines(SEV, STRING, __FILE__, __LINE__)
 
-#define XLA_VLOG_LINES(LEVEL, STRING)                               \
-  do {                                                              \
-    if (VLOG_IS_ON(LEVEL)) XLA_LOG_LINES(tensorflow::INFO, STRING); \
+#define XLA_VLOG_LINES(LEVEL, STRING)                                 \
+  do {                                                                \
+    if (VLOG_IS_ON(LEVEL)) XLA_LOG_LINES(::tensorflow::INFO, STRING); \
   } while (false);
 
 // Utility macro that performs the equivalent of what one would expect
 // LOG_LINES(FATAL, X) to do but can be used at the end of a function that
 // returns a value without getting a compiler warning that no value is returned.
-#define XLA_FATAL_LOG(X)               \
-  XLA_LOG_LINES(tensorflow::ERROR, X); \
+#define XLA_FATAL_LOG(X)                 \
+  XLA_LOG_LINES(::tensorflow::ERROR, X); \
   LOG(FATAL) << "Aborting in " << __FUNCTION__ << " due to previous errors.";
 
 #endif  // TENSORFLOW_COMPILER_XLA_UTIL_H_
-- 
GitLab


From 8a121b1eaa61be7f25e6484e2d633e652707eb08 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 17:33:03 -0700
Subject: [PATCH 1137/1375] Disable a test temporarily while we fix it to
 account for fastmath-caused floating point imprecision.

PiperOrigin-RevId: 162560345
---
 tensorflow/compiler/xla/tests/reduce_window_test.cc | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index 4b2fa683d8..fab1046504 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -676,7 +676,9 @@ INSTANTIATE_TEST_CASE_P(R4ReduceWindowTestInstantiation, R4ReduceWindowTest,
 
 class R4ReduceWindowLargeTest : public R4ReduceWindowTest {};
 
-XLA_TEST_P(R4ReduceWindowLargeTest, DoIt) { DoIt(); }
+// TODO(b/63838758): This test fails due to floating point imprecision justified
+// by fast-math.  We need to change it to use ErrorSpec.
+XLA_TEST_P(R4ReduceWindowLargeTest, DISABLED_DoIt) { DoIt(); }
 
 // Test cases that are large/slow/failed.
 const R4ReduceWindowTestData kR4ReduceWindowLargeTestValues[] = {
-- 
GitLab


From ca8d25815ea5df2198947618806c7a6e0a055ba1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 17:44:32 -0700
Subject: [PATCH 1138/1375] Minor cleanup

PiperOrigin-RevId: 162561496
---
 tensorflow/c/c_api_test.cc                    | 24 ++++++++-----------
 .../hexagon/hexagon_ops_definitions.cc        |  1 -
 2 files changed, 10 insertions(+), 15 deletions(-)

diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 41f7622668..d6debe3b99 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -16,9 +16,11 @@ limitations under the License.
 #include "tensorflow/c/c_api.h"
 
 #include <algorithm>
+#include <cstddef>
 #include <iterator>
 #include <memory>
 #include <vector>
+
 #include "tensorflow/c/c_test_util.h"
 #include "tensorflow/cc/saved_model/signature_constants.h"
 #include "tensorflow/cc/saved_model/tag_constants.h"
@@ -31,7 +33,6 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/graph/tensor_id.h"
@@ -43,21 +44,14 @@ limitations under the License.
 #include "tensorflow/core/protobuf/meta_graph.pb.h"
 #include "tensorflow/core/util/equal_graph_def.h"
 
-using tensorflow::int32;
-using tensorflow::string;
-using tensorflow::GraphDef;
-using tensorflow::NodeDef;
-using tensorflow::Tensor;
-using tensorflow::TensorShape;
-
 namespace tensorflow {
+
 bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status);
 TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src);
-}  // namespace tensorflow
 
 namespace {
 
-TEST(CAPI, Version) { EXPECT_NE("", string(TF_Version())); }
+TEST(CAPI, Version) { EXPECT_STRNE("", TF_Version()); }
 
 TEST(CAPI, Status) {
   TF_Status* s = TF_NewStatus();
@@ -69,7 +63,7 @@ TEST(CAPI, Status) {
   TF_DeleteStatus(s);
 }
 
-static void Deallocator(void* data, size_t, void* arg) {
+void Deallocator(void* data, size_t, void* arg) {
   tensorflow::cpu_allocator()->DeallocateRaw(data);
   *reinterpret_cast<bool*>(arg) = true;
 }
@@ -142,7 +136,7 @@ TEST(CAPI, LibraryLoadFunctions) {
   TF_DeleteLibraryHandle(lib);
 }
 
-static void TestEncodeDecode(int line, const std::vector<string>& data) {
+void TestEncodeDecode(int line, const std::vector<string>& data) {
   const tensorflow::int64 n = data.size();
   for (const std::vector<tensorflow::int64>& dims :
        std::vector<std::vector<tensorflow::int64>>{
@@ -1705,12 +1699,14 @@ TEST_F(CApiAttributesTest, Errors) {
   TF_OperationGetAttrString(oper, "v", nullptr, 0, s_);
   EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_)) << TF_Message(s_);
 }
+
 #undef EXPECT_TF_META
 
+}  // namespace
+}  // namespace tensorflow
+
 // TODO(josh11b): Test:
 // * TF_SetDevice(desc, "/job:worker");
 // * control inputs / outputs
 // * targets
 // * TF_DeleteGraph() before TF_DeleteSession()
-
-}  // namespace
diff --git a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
index cdae8d449a..8e517dc93c 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_ops_definitions.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/core/kernels/hexagon/hexagon_ops_definitions.h"
 
-#include "tensorflow/core/framework/graph_transfer_info.pb.h"
 #include "tensorflow/core/framework/types.h"
 
 // CAVEAT: Comment-out the following macro if you want to use experimental
-- 
GitLab


From a257874457de16e104c385c3cdf612ceee8f1725 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 17:45:06 -0700
Subject: [PATCH 1139/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 162561536
---
 .../contrib/data/python/kernel_tests/BUILD    | 18 +++--
 tensorflow/contrib/predictor/BUILD            | 73 ++++++++++---------
 tensorflow/contrib/tensor_forest/BUILD        |  6 +-
 tensorflow/contrib/tpu/BUILD                  |  2 +
 tensorflow/contrib/verbs/BUILD                |  1 +
 5 files changed, 56 insertions(+), 44 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index d5e8eb7fda..8e9f1527ef 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -114,8 +114,10 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
         "//tensorflow/python:training",
         "//third_party/py/numpy",
@@ -128,12 +130,12 @@ py_test(
     srcs = ["list_files_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -150,10 +152,12 @@ py_test(
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
+        "//tensorflow/python:io_ops",
         "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
         "//tensorflow/python:string_ops",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//third_party/py/numpy",
     ],
@@ -280,12 +284,12 @@ py_test(
     srcs = ["concatenate_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/data",
-        "//tensorflow/python:array_ops",
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/contrib/data/python/util:nest",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:errors",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform_test",
+        "//tensorflow/python:tensor_shape",
+        "//third_party/py/numpy",
     ],
 )
 
diff --git a/tensorflow/contrib/predictor/BUILD b/tensorflow/contrib/predictor/BUILD
index 8bd8c5f618..745dc2f836 100644
--- a/tensorflow/contrib/predictor/BUILD
+++ b/tensorflow/contrib/predictor/BUILD
@@ -22,47 +22,40 @@ filegroup(
 
 py_library(
     name = "predictor",
-    srcs = [
-        "__init__.py",
-    ],
+    srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
-    deps = [
-        ":predictor_factories",
-    ],
+    deps = [":predictor_factories"],
 )
 
 py_library(
     name = "predictor_factories",
-    srcs = [
-        "predictor_factories.py",
-    ],
+    srcs = ["predictor_factories.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":contrib_estimator_predictor",
         ":core_estimator_predictor",
         ":saved_model_predictor",
-        "//tensorflow/contrib/learn",
+        "//tensorflow/python/estimator",
     ],
 )
 
 py_library(
     name = "base_predictor",
-    srcs = [
-        "predictor.py",
-    ],
+    srcs = ["predictor.py"],
     srcs_version = "PY2AND3",
+    deps = ["@six_archive//:six"],
 )
 
 py_library(
     name = "saved_model_predictor",
-    srcs = [
-        "saved_model_predictor.py",
-    ],
+    srcs = ["saved_model_predictor.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":base_predictor",
         "//tensorflow/contrib/saved_model:saved_model_py",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:session",
         "//tensorflow/python/saved_model:loader",
         "//tensorflow/python/saved_model:signature_constants",
         "//tensorflow/python/saved_model:signature_def_utils",
@@ -71,36 +64,46 @@ py_library(
 
 py_library(
     name = "core_estimator_predictor",
-    srcs = [
-        "core_estimator_predictor.py",
-    ],
+    srcs = ["core_estimator_predictor.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":base_predictor",
-        "//tensorflow/contrib/learn",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python/estimator:model_fn",
+        "//tensorflow/python/saved_model:signature_constants",
     ],
 )
 
 py_library(
     name = "contrib_estimator_predictor",
-    srcs = [
-        "contrib_estimator_predictor.py",
-    ],
+    srcs = ["contrib_estimator_predictor.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":base_predictor",
         "//tensorflow/contrib/learn",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:training",
     ],
 )
 
 py_library(
     name = "testing_common",
-    srcs = [
-        "testing_common.py",
-    ],
+    srcs = ["testing_common.py"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
+        "//tensorflow/contrib/learn",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:export",
+        "//tensorflow/python/estimator:export_output",
+        "//tensorflow/python/estimator:model_fn",
+        "//tensorflow/python/saved_model:signature_constants",
     ],
 )
 
@@ -117,38 +120,36 @@ py_library(
 
 py_test(
     name = "saved_model_predictor_test",
-    srcs = [
-        "saved_model_predictor_test.py",
-    ],
+    srcs = ["saved_model_predictor_test.py"],
     data = [":test_export_dir"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
         ":saved_model_predictor",
-        ":testing_common",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python/saved_model:signature_def_utils",
+        "//third_party/py/numpy",
     ],
 )
 
 py_test(
     name = "core_estimator_predictor_test",
-    srcs = [
-        "core_estimator_predictor_test.py",
-    ],
+    srcs = ["core_estimator_predictor_test.py"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
         ":core_estimator_predictor",
         ":testing_common",
         "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
     ],
 )
 
 py_test(
     name = "contrib_estimator_predictor_test",
-    srcs = [
-        "contrib_estimator_predictor_test.py",
-    ],
+    srcs = ["contrib_estimator_predictor_test.py"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index b4a8ca4320..5ac2979faf 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -122,6 +122,7 @@ py_library(
         ":stats_ops_py",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
+        ":tensor_forest_v4_py",
     ],
 )
 
@@ -554,13 +555,14 @@ py_library(
     srcs = ["python/tensor_forest_v4.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":tensor_forest_py",
         ":tensor_forest_v4_ops_py",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_py",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "@six_archive//:six",
     ],
 )
 
@@ -586,12 +588,14 @@ py_library(
         ":client_lib",
         "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/learn",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
     ],
 )
 
diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD
index c99a4d0475..69e91d05b3 100644
--- a/tensorflow/contrib/tpu/BUILD
+++ b/tensorflow/contrib/tpu/BUILD
@@ -39,6 +39,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":tpu",
+        ":tpu_feed",
         ":tpu_py",
         ":training_loop",
         "//tensorflow/contrib/learn",
@@ -52,6 +53,7 @@ py_library(
         "//tensorflow/python:variables",
         "//tensorflow/python/estimator",
         "//tensorflow/python/estimator:model_fn",
+        "//tensorflow/python/estimator:util",
     ],
 )
 
diff --git a/tensorflow/contrib/verbs/BUILD b/tensorflow/contrib/verbs/BUILD
index 5f062cde89..173a65a7eb 100644
--- a/tensorflow/contrib/verbs/BUILD
+++ b/tensorflow/contrib/verbs/BUILD
@@ -108,6 +108,7 @@ cc_library(
         ":rdma_mgr",
         ":verbs_util",
         "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:gpu_runtime",
         "//tensorflow/core:lib",
         "//tensorflow/core/distributed_runtime:base_rendezvous_mgr",
         "//tensorflow/core/distributed_runtime:worker_env",
-- 
GitLab


From 0f10de76dea754de155fb3f6e1c024fe9fc9ee1f Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Wed, 19 Jul 2017 17:56:29 -0700
Subject: [PATCH 1140/1375] Internal change.

PiperOrigin-RevId: 162562631
---
 tensorflow/python/kernel_tests/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 442b4b2df5..3e4a6a3666 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1985,7 +1985,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "transpose_op_test",
-    size = "small",
+    size = "medium",
     srcs = ["transpose_op_test.py"],
     additional_deps = [
         "//third_party/py/numpy",
-- 
GitLab


From 7caedc30d47623d329c359343029913929e893ca Mon Sep 17 00:00:00 2001
From: Dandelion Man? <dandelion@google.com>
Date: Wed, 19 Jul 2017 18:39:47 -0700
Subject: [PATCH 1141/1375] Add support for display_name and
 summary_description to the tf.summary.tensor_summary op.

The display_name will be used to display the series in TensorBoard, in lieu of the tag, assuming that it is specified. (When it is not specified, behavior will stay the same.)
The summary_description will allow the user to write a longform readable description of the summary series for display in TensorBoard. Markdown will be supported.

This will make it possible for TensorBoard-2 summary ops to give the user direct control over the display name, solving https://github.com/tensorflow/tensorboard/issues/59.

PiperOrigin-RevId: 162566261
---
 tensorflow/core/framework/summary.proto       |  6 +++
 tensorflow/python/BUILD                       |  1 +
 .../kernel_tests/summary_tensor_op_test.py    | 50 +++++++++++++++++++
 tensorflow/python/ops/summary_ops.py          | 46 ++++++++++-------
 .../golden/tensorflow.-summary-metadata.pbtxt |  8 +++
 .../tools/api/golden/tensorflow.summary.pbtxt |  2 +-
 6 files changed, 94 insertions(+), 19 deletions(-)

diff --git a/tensorflow/core/framework/summary.proto b/tensorflow/core/framework/summary.proto
index 12274d5e13..b6101fda3d 100644
--- a/tensorflow/core/framework/summary.proto
+++ b/tensorflow/core/framework/summary.proto
@@ -51,6 +51,12 @@ message SummaryMetadata {
   // A list of plugin data. A single summary value instance may be used by more
   // than 1 plugin.
   repeated PluginData plugin_data = 1;
+
+  // Display name for viewing in TensorBoard.
+  string display_name = 2;
+
+  // Longform readable description of the summary sequence. Markdown supported.
+  string summary_description = 3;
 };
 
 // A Summary is a set of named values to be displayed by the
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index b7063d59ba..8833ebb3bd 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2090,6 +2090,7 @@ py_library(
     srcs = ["ops/summary_ops.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":framework",
         ":framework_for_generated_wrappers",
         ":logging_ops_gen",
         ":summary_op_util",
diff --git a/tensorflow/python/kernel_tests/summary_tensor_op_test.py b/tensorflow/python/kernel_tests/summary_tensor_op_test.py
index 48489433a0..3584637865 100644
--- a/tensorflow/python/kernel_tests/summary_tensor_op_test.py
+++ b/tensorflow/python/kernel_tests/summary_tensor_op_test.py
@@ -115,6 +115,56 @@ class SummaryOpsTest(test.TestCase):
     n = tensor_util.MakeNdarray(value.tensor)
     self._AssertNumpyEq(n, bools)
 
+  def testSummaryDescriptionAndDisplayName(self):
+    with self.test_session() as sess:
+
+      def get_description(summary_op):
+        summ_str = sess.run(summary_op)
+        summ = summary_pb2.Summary()
+        summ.ParseFromString(summ_str)
+        return summ.value[0].metadata
+
+      const = constant_op.constant(1)
+      # Default case; no description or display name
+      simple_summary = summary_ops.tensor_summary("simple", const)
+
+      descr = get_description(simple_summary)
+      self.assertEqual(descr.display_name, "")
+      self.assertEqual(descr.summary_description, "")
+
+      # Values are provided via function args
+      with_values = summary_ops.tensor_summary(
+          "simple",
+          const,
+          display_name="my name",
+          summary_description="my description")
+
+      descr = get_description(with_values)
+      self.assertEqual(descr.display_name, "my name")
+      self.assertEqual(descr.summary_description, "my description")
+
+      # Values are provided via the SummaryMetadata arg
+      metadata = summary_pb2.SummaryMetadata()
+      metadata.display_name = "my name"
+      metadata.summary_description = "my description"
+
+      with_metadata = summary_ops.tensor_summary(
+          "simple", const, summary_metadata=metadata)
+      descr = get_description(with_metadata)
+      self.assertEqual(descr.display_name, "my name")
+      self.assertEqual(descr.summary_description, "my description")
+
+      # If both SummmaryMetadata and explicit args are provided, the args win
+      overwrite = summary_ops.tensor_summary(
+          "simple",
+          const,
+          summary_metadata=metadata,
+          display_name="overwritten",
+          summary_description="overwritten")
+      descr = get_description(overwrite)
+      self.assertEqual(descr.display_name, "overwritten")
+      self.assertEqual(descr.summary_description, "overwritten")
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/summary_ops.py b/tensorflow/python/ops/summary_ops.py
index 02bb394822..2cf2eda16e 100644
--- a/tensorflow/python/ops/summary_ops.py
+++ b/tensorflow/python/ops/summary_ops.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.core.framework import summary_pb2
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import gen_logging_ops
 from tensorflow.python.ops import summary_op_util
@@ -27,38 +28,47 @@ from tensorflow.python.ops.gen_logging_ops import *
 # pylint: enable=wildcard-import
 
 
-def tensor_summary(
-    name,
-    tensor,
-    summary_description=None,
-    collections=None,
-    summary_metadata=None,
-    family=None):
+def tensor_summary(name,
+                   tensor,
+                   summary_description=None,
+                   collections=None,
+                   summary_metadata=None,
+                   family=None,
+                   display_name=None):
   """Outputs a `Summary` protocol buffer with a serialized tensor.proto.
 
   Args:
-    name: A name for the generated node. Will also serve as the series name in
-      TensorBoard.
+    name: A name for the generated node. If display_name is not set, it will
+      also serve as the tag name in TensorBoard. (In that case, the tag
+      name will inherit tf name scopes.)
     tensor: A tensor of any type and shape to serialize.
-    summary_description: This is currently un-used but must be kept for
-      backwards compatibility.
+    summary_description: A long description of the summary sequence. Markdown
+      is supported.
     collections: Optional list of graph collections keys. The new summary op is
       added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
     summary_metadata: Optional SummaryMetadata proto (which describes which
       plugins may use the summary value).
-    family: Optional; if provided, used as the prefix of the summary tag name,
-      which controls the tab name used for display on Tensorboard.
+    family: Optional; if provided, used as the prefix of the summary tag,
+      which controls the name used for display on TensorBoard when
+      display_name is not set.
+    display_name: A string used to name this data in TensorBoard. If this is
+      not set, then the node name will be used instead.
 
   Returns:
     A scalar `Tensor` of type `string`. The serialized `Summary` protocol
     buffer.
   """
-  # The summary description is unused now.
-  del summary_description
 
-  serialized_summary_metadata = ""
-  if summary_metadata:
-    serialized_summary_metadata = summary_metadata.SerializeToString()
+  if summary_metadata is None:
+    summary_metadata = summary_pb2.SummaryMetadata()
+
+  if summary_description is not None:
+    summary_metadata.summary_description = summary_description
+
+  if display_name is not None:
+    summary_metadata.display_name = display_name
+
+  serialized_summary_metadata = summary_metadata.SerializeToString()
 
   with summary_op_util.summary_scope(
       name, family, values=[tensor]) as (tag, scope):
diff --git a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt
index f6ed45003a..b9156521cc 100644
--- a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt
@@ -6,6 +6,10 @@ tf_class {
     name: "DESCRIPTOR"
     mtype: "<type \'google.protobuf.pyext._message.MessageDescriptor\'>"
   }
+  member {
+    name: "DISPLAY_NAME_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
   member {
     name: "Extensions"
     mtype: "<type \'getset_descriptor\'>"
@@ -18,6 +22,10 @@ tf_class {
     name: "PluginData"
     mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>"
   }
+  member {
+    name: "SUMMARY_DESCRIPTION_FIELD_NUMBER"
+    mtype: "<type \'int\'>"
+  }
   member_method {
     name: "ByteSize"
   }
diff --git a/tensorflow/tools/api/golden/tensorflow.summary.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.pbtxt
index b3748d2c6f..326e077d39 100644
--- a/tensorflow/tools/api/golden/tensorflow.summary.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.summary.pbtxt
@@ -58,7 +58,7 @@ tf_module {
   }
   member_method {
     name: "tensor_summary"
-    argspec: "args=[\'name\', \'tensor\', \'summary_description\', \'collections\', \'summary_metadata\', \'family\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'name\', \'tensor\', \'summary_description\', \'collections\', \'summary_metadata\', \'family\', \'display_name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "text"
-- 
GitLab


From 22bbd23a4215995724e0a95090f8c7035307164e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 18:46:53 -0700
Subject: [PATCH 1142/1375] Remove invalid usage of DISABLED_ON_(C|G)PU --
 these don't work on reduce_window_test.cc

PiperOrigin-RevId: 162566699
---
 tensorflow/compiler/xla/tests/reduce_window_test.cc | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index fab1046504..f4010aef44 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -36,6 +36,10 @@ limitations under the License.
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/types.h"
 
+// These don't work on reduce_window_test.cc, please see the BUILD file.
+#undef DISABLED_ON_CPU
+#undef DISABLED_ON_GPU
+
 namespace xla {
 namespace {
 
@@ -74,7 +78,7 @@ class ReduceWindowTest : public ClientLibraryTestBase {
   ComputationBuilder builder_;
 };
 
-TEST_F(ReduceWindowTest, DISABLED_ON_CPU(DISABLED_ON_GPU(Min3In5Stride2))) {
+TEST_F(ReduceWindowTest, Min3In5Stride2) {
   const auto input = builder_.ConstantR1<float>({10000, 1000, 100, 10, 1});
   ReduceWindowMin(input, {3}, {2}, Padding::kValid);
   ComputeAndCompareR1<float>(&builder_, {100, 1}, {}, ErrorSpec(0.0001));
-- 
GitLab


From f783f5c193a2216619af5b41c6503e52c0796db5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 19:06:12 -0700
Subject: [PATCH 1143/1375] Fix missing spaces for several errors.

PiperOrigin-RevId: 162567926
---
 tensorflow/contrib/layers/python/layers/feature_column.py   | 4 ++--
 tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py     | 4 ++--
 tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py | 2 +-
 .../timeseries/python/timeseries/state_management.py        | 6 +++---
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index 4cbd198c02..0ba9873f3a 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -1352,8 +1352,8 @@ def shared_embedding_columns(sparse_id_columns,
                      "element.")
   for sparse_id_column in sparse_id_columns:
     if not isinstance(sparse_id_column, _SparseColumn):
-      raise TypeError("Elements of sparse_id_columns must be _SparseColumn, but"
-                      "{} is not.".format(sparse_id_column))
+      raise TypeError("Elements of sparse_id_columns must be _SparseColumn, "
+                      "but {} is not.".format(sparse_id_column))
 
   if len(sparse_id_columns) == 1:
     return [
diff --git a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
index 23b4a73b23..d4de638338 100644
--- a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
+++ b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
@@ -1189,10 +1189,10 @@ def model_with_buckets(encoder_inputs,
     raise ValueError("Length of encoder_inputs (%d) must be at least that of la"
                      "st bucket (%d)." % (len(encoder_inputs), buckets[-1][0]))
   if len(targets) < buckets[-1][1]:
-    raise ValueError("Length of targets (%d) must be at least that of last"
+    raise ValueError("Length of targets (%d) must be at least that of last "
                      "bucket (%d)." % (len(targets), buckets[-1][1]))
   if len(weights) < buckets[-1][1]:
-    raise ValueError("Length of weights (%d) must be at least that of last"
+    raise ValueError("Length of weights (%d) must be at least that of last "
                      "bucket (%d)." % (len(weights), buckets[-1][1]))
 
   all_inputs = encoder_inputs + decoder_inputs + targets + weights
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
index 09aa30a20b..701590a8fe 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
@@ -74,7 +74,7 @@ class DummyMultiDimensionalLSTM(rnn_lib.RNNCell):
       without including 'Time' or 'Batch' dimensions.
     """
     if not isinstance(dims, tuple):
-      raise TypeError("The dimensions passed to DummyMultiDimensionalLSTM"
+      raise TypeError("The dimensions passed to DummyMultiDimensionalLSTM "
                       "should be a tuple of ints.")
     self._dims = dims
     self._output_size = tensor_shape.TensorShape(self._dims)
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_management.py b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
index dc8f8fdb92..13eecd4d82 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_management.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_management.py
@@ -80,9 +80,9 @@ class _OverridableStateManager(PassthroughStateManager):
       # Explicit start state has been provided, so we should use that.
       if mode == estimator_lib.ModeKeys.TRAIN:
         raise ValueError(
-            ("Overriding saved state for training is not supported (but a value"
-             "for feature {} was specified)."
-            ).format(feature_keys.FilteringFeatures.STATE_TUPLE))
+            "Overriding saved state for training is not supported (but a value "
+            "for feature {} was specified).".format(
+                feature_keys.FilteringFeatures.STATE_TUPLE))
       start_state = features[feature_keys.FilteringFeatures.STATE_TUPLE]
       del features[feature_keys.FilteringFeatures.STATE_TUPLE]
       return model.get_batch_loss(
-- 
GitLab


From 679c30e10ded33fa98ae8aab8e439f02df4ad4d6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 19 Jul 2017 19:48:16 -0700
Subject: [PATCH 1144/1375] Use an ErrorSpec in R4ReduceWindowTest::DoIt and
 re-enable a disabled test.

PiperOrigin-RevId: 162570059
---
 tensorflow/compiler/xla/tests/reduce_window_test.cc | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index f4010aef44..ccdd91473c 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -545,7 +545,8 @@ class R4ReduceWindowTest
             /*window=*/param.window_bounds,
             /*stride=*/param.strides,
             /*padding=*/padding);
-    ComputeAndCompareR4<float>(&b, *expected, {input_arg.get()});
+    ComputeAndCompareR4<float>(&b, *expected, {input_arg.get()},
+                               ErrorSpec(1e-3, 1e-3));
   }
 };
 
@@ -680,9 +681,7 @@ INSTANTIATE_TEST_CASE_P(R4ReduceWindowTestInstantiation, R4ReduceWindowTest,
 
 class R4ReduceWindowLargeTest : public R4ReduceWindowTest {};
 
-// TODO(b/63838758): This test fails due to floating point imprecision justified
-// by fast-math.  We need to change it to use ErrorSpec.
-XLA_TEST_P(R4ReduceWindowLargeTest, DISABLED_DoIt) { DoIt(); }
+XLA_TEST_P(R4ReduceWindowLargeTest, DoIt) { DoIt(); }
 
 // Test cases that are large/slow/failed.
 const R4ReduceWindowTestData kR4ReduceWindowLargeTestValues[] = {
-- 
GitLab


From 7bab2180c239171a3d0d3017e3f140c027171459 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 04:52:20 -0700
Subject: [PATCH 1145/1375] Generalize categorical CDF broadcasting over
 argument.

PiperOrigin-RevId: 162601793
---
 .../distributions/categorical_test.py         | 96 +++++++++++++++++++
 .../python/ops/distributions/categorical.py   | 70 ++++++++------
 2 files changed, 138 insertions(+), 28 deletions(-)

diff --git a/tensorflow/python/kernel_tests/distributions/categorical_test.py b/tensorflow/python/kernel_tests/distributions/categorical_test.py
index 33db933e82..019c1bc353 100644
--- a/tensorflow/python/kernel_tests/distributions/categorical_test.py
+++ b/tensorflow/python/kernel_tests/distributions/categorical_test.py
@@ -30,6 +30,7 @@ from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import categorical
 from tensorflow.python.ops.distributions import kullback_leibler
+from tensorflow.python.ops.distributions import normal
 from tensorflow.python.platform import test
 
 
@@ -183,11 +184,105 @@ class CategoricalTest(test.TestCase):
     with self.test_session():
       self.assertAlmostEqual(cdf_op.eval(), expected_cdf)
 
+  def testCDFBroadcasting(self):
+    # shape: [batch=2, n_bins=3]
+    histograms = [[0.2, 0.1, 0.7],
+                  [0.3, 0.45, 0.25]]
+
+    # shape: [batch=3, batch=2]
+    devent = [
+        [0, 0],
+        [1, 1],
+        [2, 2]
+    ]
+    dist = categorical.Categorical(probs=histograms)
+
+    # We test that the probabilities are correctly broadcasted over the
+    # additional leading batch dimension of size 3.
+    expected_cdf_result = np.zeros((3, 2))
+    expected_cdf_result[0, 0] = 0
+    expected_cdf_result[0, 1] = 0
+    expected_cdf_result[1, 0] = 0.2
+    expected_cdf_result[1, 1] = 0.3
+    expected_cdf_result[2, 0] = 0.3
+    expected_cdf_result[2, 1] = 0.75
+
+    with self.test_session():
+      self.assertAllClose(dist.cdf(devent).eval(), expected_cdf_result)
+
+  def testBroadcastWithBatchParamsAndBiggerEvent(self):
+    ## The parameters have a single batch dimension, and the event has two.
+
+    # param shape is [3 x 4], where 4 is the number of bins (non-batch dim).
+    cat_params_py = [
+        [0.2, 0.15, 0.35, 0.3],
+        [0.1, 0.05, 0.68, 0.17],
+        [0.1, 0.05, 0.68, 0.17]
+    ]
+
+    # event shape = [5, 3], both are "batch" dimensions.
+    disc_event_py = [
+        [0, 1, 2],
+        [1, 2, 3],
+        [0, 0, 0],
+        [1, 1, 1],
+        [2, 1, 0]
+    ]
+
+    # shape is [3]
+    normal_params_py = [
+        -10.0,
+        120.0,
+        50.0
+    ]
+
+    # shape is [5, 3]
+    real_event_py = [
+        [-1.0, 0.0, 1.0],
+        [100.0, 101, -50],
+        [90, 90, 90],
+        [-4, -400, 20.0],
+        [0.0, 0.0, 0.0]
+    ]
+
+    cat_params_tf = array_ops.constant(cat_params_py)
+    disc_event_tf = array_ops.constant(disc_event_py)
+    cat = categorical.Categorical(probs=cat_params_tf)
+
+    normal_params_tf = array_ops.constant(normal_params_py)
+    real_event_tf = array_ops.constant(real_event_py)
+    norm = normal.Normal(loc=normal_params_tf, scale=1.0)
+
+    # Check that normal and categorical have the same broadcasting behaviour.
+    to_run = {
+        "cat_prob": cat.prob(disc_event_tf),
+        "cat_log_prob": cat.log_prob(disc_event_tf),
+        "cat_cdf": cat.cdf(disc_event_tf),
+        "cat_log_cdf": cat.log_cdf(disc_event_tf),
+        "norm_prob": norm.prob(real_event_tf),
+        "norm_log_prob": norm.log_prob(real_event_tf),
+        "norm_cdf": norm.cdf(real_event_tf),
+        "norm_log_cdf": norm.log_cdf(real_event_tf),
+    }
+
+    with self.test_session() as sess:
+      run_result = sess.run(to_run)
+
+    self.assertAllEqual(run_result["cat_prob"].shape,
+                        run_result["norm_prob"].shape)
+    self.assertAllEqual(run_result["cat_log_prob"].shape,
+                        run_result["norm_log_prob"].shape)
+    self.assertAllEqual(run_result["cat_cdf"].shape,
+                        run_result["norm_cdf"].shape)
+    self.assertAllEqual(run_result["cat_log_cdf"].shape,
+                        run_result["norm_log_cdf"].shape)
+
   def testLogPMF(self):
     logits = np.log([[0.2, 0.8], [0.6, 0.4]]) - 50.
     dist = categorical.Categorical(logits)
     with self.test_session():
       self.assertAllClose(dist.log_prob([0, 1]).eval(), np.log([0.2, 0.4]))
+      self.assertAllClose(dist.log_prob([0.0, 1.0]).eval(), np.log([0.2, 0.4]))
 
   def testEntropyNoBatch(self):
     logits = np.log([0.2, 0.8]) - 50.
@@ -263,6 +358,7 @@ class CategoricalTest(test.TestCase):
 
   def testLogPMFBroadcasting(self):
     with self.test_session():
+      # 1 x 2 x 2
       histograms = [[[0.2, 0.8], [0.4, 0.6]]]
       dist = categorical.Categorical(math_ops.log(histograms) - 50.)
 
diff --git a/tensorflow/python/ops/distributions/categorical.py b/tensorflow/python/ops/distributions/categorical.py
index d72f6a1fe5..84ca6db4c4 100644
--- a/tensorflow/python/ops/distributions/categorical.py
+++ b/tensorflow/python/ops/distributions/categorical.py
@@ -31,6 +31,33 @@ from tensorflow.python.ops.distributions import kullback_leibler
 from tensorflow.python.ops.distributions import util as distribution_util
 
 
+def _broadcast_cat_event_and_params(event, params, base_dtype=dtypes.int32):
+  """Broadcasts the event or distribution parameters."""
+  if event.shape.ndims is None:
+    raise NotImplementedError(
+        "Cannot broadcast with an event tensor of unknown rank.")
+
+  if event.dtype.is_integer:
+    pass
+  elif event.dtype.is_floating:
+    # When `validate_args=True` we've already ensured int/float casting
+    # is closed.
+    event = math_ops.cast(event, dtype=dtypes.int32)
+  else:
+    raise TypeError("`value` should have integer `dtype` or "
+                    "`self.dtype` ({})".format(base_dtype))
+
+  if params.get_shape()[:-1] == event.get_shape():
+    params = params
+  else:
+    params *= array_ops.ones_like(
+        array_ops.expand_dims(event, -1), dtype=params.dtype)
+    params_shape = array_ops.shape(params)[:-1]
+    event *= array_ops.ones(params_shape, dtype=event.dtype)
+    event.set_shape(tensor_shape.TensorShape(params.get_shape()[:-1]))
+  return event, params
+
+
 class Categorical(distribution.Distribution):
   """Categorical distribution.
 
@@ -248,43 +275,30 @@ class Categorical(distribution.Distribution):
       k = distribution_util.embed_check_integer_casting_closed(
           k, target_dtype=dtypes.int32)
 
-    # If there are multiple batch dimension, flatten them into one.
-    batch_flattened_probs = array_ops.reshape(self._probs,
-                                              [-1, self._event_size])
+    k, probs = _broadcast_cat_event_and_params(
+        k, self.probs, base_dtype=self.dtype.base_dtype)
+
+    # batch-flatten everything in order to use `sequence_mask()`.
+    batch_flattened_probs = array_ops.reshape(probs,
+                                              (-1, self._event_size))
     batch_flattened_k = array_ops.reshape(k, [-1])
 
-    # Form a tensor to sum over.
-    # We don't need to cast k to integer since `sequence_mask` does this for us.
-    mask_tensor = array_ops.sequence_mask(batch_flattened_k, self._event_size)
-    to_sum_over = array_ops.where(mask_tensor,
-                                  batch_flattened_probs,
-                                  array_ops.zeros_like(batch_flattened_probs))
-    batch_flat_cdf = math_ops.reduce_sum(to_sum_over, axis=-1)
-    return array_ops.reshape(batch_flat_cdf, self._batch_shape())
+    to_sum_over = array_ops.where(
+        array_ops.sequence_mask(batch_flattened_k, self._event_size),
+        batch_flattened_probs,
+        array_ops.zeros_like(batch_flattened_probs))
+    batch_flattened_cdf = math_ops.reduce_sum(to_sum_over, axis=-1)
+    # Reshape back to the shape of the argument.
+    return array_ops.reshape(batch_flattened_cdf, array_ops.shape(k))
 
   def _log_prob(self, k):
     k = ops.convert_to_tensor(k, name="k")
     if self.validate_args:
       k = distribution_util.embed_check_integer_casting_closed(
           k, target_dtype=dtypes.int32)
+    k, logits = _broadcast_cat_event_and_params(
+        k, self.logits, base_dtype=self.dtype.base_dtype)
 
-    if self.logits.get_shape()[:-1] == k.get_shape():
-      logits = self.logits
-    else:
-      logits = self.logits * array_ops.ones_like(
-          array_ops.expand_dims(k, -1), dtype=self.logits.dtype)
-      logits_shape = array_ops.shape(logits)[:-1]
-      k *= array_ops.ones(logits_shape, dtype=k.dtype)
-      k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
-      if k.dtype.is_integer:
-        pass
-      elif k.dtype.is_floating:
-        # When `validate_args=True` we've already ensured int/float casting
-        # is closed.
-        return ops.cast(k, dtype=dtypes.int32)
-      else:
-        raise TypeError("`value` should have integer `dtype` or "
-                        "`self.dtype` ({})".format(self.dtype.base_dtype))
     return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
                                                             logits=logits)
 
-- 
GitLab


From 540958dc02b5abf6f5dde016f63b384ec32ef191 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 08:44:11 -0700
Subject: [PATCH 1146/1375] Modify batch_shape and event_shape for transformed
 distributions to handle dynamic shapes.

PiperOrigin-RevId: 162619535
---
 .../python/kernel_tests/mvn_diag_test.py           | 14 ++++++++++++++
 .../ops/distributions/transformed_distribution.py  | 11 ++++++-----
 2 files changed, 20 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py b/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py
index e973c056e0..43e302475b 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/mvn_diag_test.py
@@ -275,6 +275,20 @@ class MultivariateNormalDiagTest(test.TestCase):
                           np.tile(num_draws, dims),
                           rtol=1e-6, atol=0.)
 
+  def testDynamicBatchShape(self):
+    mvn = ds.MultivariateNormalDiag(
+        loc=array_ops.placeholder(dtypes.float32, shape=[None, None, 2]),
+        scale_diag=array_ops.placeholder(dtypes.float32, shape=[None, None, 2]))
+    self.assertListEqual(mvn.batch_shape.as_list(), [None, None])
+    self.assertListEqual(mvn.event_shape.as_list(), [2])
+
+  def testDynamicEventShape(self):
+    mvn = ds.MultivariateNormalDiag(
+        loc=array_ops.placeholder(dtypes.float32, shape=[2, 3, None]),
+        scale_diag=array_ops.placeholder(dtypes.float32, shape=[2, 3, None]))
+    self.assertListEqual(mvn.batch_shape.as_list(), [2, 3])
+    self.assertListEqual(mvn.event_shape.as_list(), [None])
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/distributions/transformed_distribution.py b/tensorflow/python/ops/distributions/transformed_distribution.py
index 1be3819569..d72e07a867 100644
--- a/tensorflow/python/ops/distributions/transformed_distribution.py
+++ b/tensorflow/python/ops/distributions/transformed_distribution.py
@@ -24,7 +24,6 @@ import numpy as np
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
@@ -347,9 +346,10 @@ class TransformedDistribution(distribution_lib.Distribution):
     # Since the `bijector` may change the `event_shape`, we then forward what we
     # know to the bijector. This allows the `bijector` to have final say in the
     # `event_shape`.
-    static_override = tensor_util.constant_value(self._override_event_shape)
+    static_override = tensor_util.constant_value_as_shape(
+        self._override_event_shape)
     return self.bijector.forward_event_shape(
-        tensor_shape.TensorShape(static_override)
+        static_override
         if self._is_maybe_event_override
         else self.distribution.event_shape)
 
@@ -369,8 +369,9 @@ class TransformedDistribution(distribution_lib.Distribution):
     # the `bijector` doesn't get to alter the `batch_shape`. Recall that
     # `batch_shape` is a property of a distribution while `event_shape` is
     # shared between both the `distribution` instance and the `bijector`.
-    static_override = tensor_util.constant_value(self._override_batch_shape)
-    return (tensor_shape.TensorShape(static_override)
+    static_override = tensor_util.constant_value_as_shape(
+        self._override_batch_shape)
+    return (static_override
             if self._is_maybe_batch_override
             else self.distribution.batch_shape)
 
-- 
GitLab


From b3451058a25201c50573f68556812e51cff56edb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 09:31:17 -0700
Subject: [PATCH 1147/1375] Adds RELU6 to the list of cheap operations for
 potential recompute in memory_optimizer.

PiperOrigin-RevId: 162624766
---
 .../core/grappler/optimizers/memory_optimizer.cc    | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 23479d84b0..31e525fb4a 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -48,13 +48,12 @@ const char* kRecomputationTargetNamePrefix = "gradients/";
 // TODO(allenl): Replace this list with a cost model.
 std::unordered_set<string> GetCheapToRecomputeOps() {
   std::unordered_set<string> cheap_ops = {
-      "Add",      "AddN",           "BiasAdd",
-      "Cast",     "Fill",           "FloorDiv",
-      "FloorMod", "FusedBatchNorm", "Mul",
-      "Neg",      "RealDiv",        "Reciprocal",
-      "Relu",     "Reshape",        "Rsqrt",
-      "Sqrt",     "Square",         "SquaredDifference",
-      "Sub",      "Tile",           "Transpose"};
+      "Add",  "AddN",     "BiasAdd",           "Cast",
+      "Fill", "FloorDiv", "FloorMod",          "FusedBatchNorm",
+      "Mul",  "Neg",      "RealDiv",           "Reciprocal",
+      "Relu", "Relu6",    "Reshape",           "Rsqrt",
+      "Sqrt", "Square",   "SquaredDifference", "Sub",
+      "Tile", "Transpose"};
   return cheap_ops;
 }
 
-- 
GitLab


From 4bbd9bd11fb52ebe0e3de6f8553a2372c13146bb Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Thu, 20 Jul 2017 09:32:48 -0700
Subject: [PATCH 1148/1375] Add fused_conv2d_bias_activation operator for the
 forward phase.

PiperOrigin-RevId: 162624917
---
 tensorflow/BUILD                              |   1 +
 tensorflow/contrib/BUILD                      |   1 +
 tensorflow/contrib/fused_conv/BUILD           | 168 +++++
 tensorflow/contrib/fused_conv/__init__.py     |  25 +
 .../fused_conv2d_bias_activation_op.cc        | 497 +++++++++++++++
 .../kernels/fused_conv2d_bias_activation_op.h |  62 ++
 .../ops/fused_conv2d_bias_activation_op.cc    |  72 +++
 .../contrib/fused_conv/python/__init__.py     |  19 +
 .../fused_conv2d_bias_activation_benchmark.py | 243 ++++++++
 .../ops/fused_conv2d_bias_activation_op.py    |  87 +++
 .../fused_conv2d_bias_activation_op_test.py   | 573 ++++++++++++++++++
 tensorflow/core/BUILD                         |   1 +
 tensorflow/core/framework/common_shape_fns.cc |  33 +-
 tensorflow/core/framework/common_shape_fns.h  |   3 +
 tensorflow/core/kernels/BUILD                 |  18 +
 tensorflow/core/kernels/conv_ops_gpu.h        |  10 +-
 tensorflow/core/util/activation_mode.cc       |  43 ++
 tensorflow/core/util/activation_mode.h        |  45 ++
 tensorflow/stream_executor/cuda/cuda_dnn.cc   |  22 +-
 19 files changed, 1905 insertions(+), 18 deletions(-)
 create mode 100644 tensorflow/contrib/fused_conv/BUILD
 create mode 100644 tensorflow/contrib/fused_conv/__init__.py
 create mode 100644 tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc
 create mode 100644 tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.h
 create mode 100644 tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc
 create mode 100644 tensorflow/contrib/fused_conv/python/__init__.py
 create mode 100644 tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py
 create mode 100644 tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py
 create mode 100644 tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py
 create mode 100644 tensorflow/core/util/activation_mode.cc
 create mode 100644 tensorflow/core/util/activation_mode.h

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index afa1cc7605..c61775e146 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -259,6 +259,7 @@ filegroup(
         "//tensorflow/contrib/ffmpeg:all_files",
         "//tensorflow/contrib/ffmpeg/default:all_files",
         "//tensorflow/contrib/framework:all_files",
+        "//tensorflow/contrib/fused_conv:all_files",
         "//tensorflow/contrib/graph_editor:all_files",
         "//tensorflow/contrib/grid_rnn:all_files",
         "//tensorflow/contrib/hooks:all_files",
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index c9065b0a89..5cd4753664 100755
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -26,6 +26,7 @@ py_library(
         "//tensorflow/contrib/factorization:factorization_py",
         "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py",
         "//tensorflow/contrib/framework:framework_py",
+        "//tensorflow/contrib/fused_conv:fused_conv_py",
         "//tensorflow/contrib/graph_editor:graph_editor_py",
         "//tensorflow/contrib/grid_rnn:grid_rnn_py",
         "//tensorflow/contrib/hooks",
diff --git a/tensorflow/contrib/fused_conv/BUILD b/tensorflow/contrib/fused_conv/BUILD
new file mode 100644
index 0000000000..026ee3df07
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/BUILD
@@ -0,0 +1,168 @@
+# Description:
+#   A Fused Conv Bias Activation operator wrapper.
+#   APIs are meant to change over time.
+package(
+    default_visibility = ["//visibility:private"],
+    features = ["-parse_headers"],
+)
+
+package_group(
+    name = "friends",
+    packages = [
+        "//tensorflow/...",
+    ],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
+
+tf_custom_op_py_library(
+    name = "fused_conv_py",
+    srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
+    dso = [":python/ops/_fused_conv2d_bias_activation_op.so"],
+    kernels = [
+        ":fused_conv2d_bias_activation_op_kernels",
+        ":fused_conv2d_bias_activation_op_op_lib",
+    ],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":fused_conv2d_bias_activation_op",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:util",
+        "//tensorflow/python:variables",
+        "//third_party/py/numpy",
+    ],
+)
+
+tf_kernel_library(
+    name = "fused_conv2d_bias_activation_op_kernels",
+    srcs = [
+        "kernels/fused_conv2d_bias_activation_op.cc",
+        "kernels/fused_conv2d_bias_activation_op.h",
+    ],
+    prefix = "fused_conv2d_bias_activation_op",
+    deps = [
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib_proto_parsing",
+        "//tensorflow/core/kernels:bounds_check_lib",
+        "//tensorflow/core/kernels:conv_2d_hdrs",
+        "//tensorflow/core/kernels:conv_ops_gpu_hdrs",
+        "//tensorflow/core/kernels:ops_util_hdrs",
+        "//third_party/eigen3",
+    ],
+    alwayslink = 1,
+)
+
+tf_custom_op_library(
+    name = "python/ops/_fused_conv2d_bias_activation_op.so",
+    srcs = [
+        "kernels/fused_conv2d_bias_activation_op.cc",
+        "kernels/fused_conv2d_bias_activation_op.h",
+        "ops/fused_conv2d_bias_activation_op.cc",
+    ],
+    deps = [
+        "//tensorflow/core:lib_proto_parsing",
+        "//tensorflow/core/kernels:bounds_check_lib",
+        "//tensorflow/core/kernels:conv_2d_hdrs",
+        "//tensorflow/core/kernels:conv_ops_gpu_hdrs",
+        "//tensorflow/core/kernels:ops_util_hdrs",
+    ],
+)
+
+tf_gen_op_libs(
+    op_lib_names = [
+        "fused_conv2d_bias_activation_op",
+    ],
+    deps = [
+        "//tensorflow/core:lib_proto_parsing",
+    ],
+)
+
+tf_gen_op_wrapper_py(
+    name = "fused_conv2d_bias_activation_op",
+    deps = [":fused_conv2d_bias_activation_op_op_lib"],
+)
+
+cuda_py_test(
+    name = "fused_conv2d_bias_activation_op_test",
+    size = "small",
+    srcs = ["python/ops/fused_conv2d_bias_activation_op_test.py"],
+    additional_deps = [
+        ":fused_conv_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:nn",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+    ],
+    tags = [
+        "manual",
+        "requires_cudnn6",
+    ],
+)
+
+cuda_py_test(
+    name = "fused_conv2d_bias_activation_benchmark",
+    size = "large",
+    srcs = ["python/ops/fused_conv2d_bias_activation_benchmark.py"],
+    additional_deps = [
+        ":fused_conv_py",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:platform_benchmark",
+        "//tensorflow/python:random_ops",
+        "//tensorflow/python:variables",
+        "//third_party/py/numpy",
+        "//tensorflow/core:protos_all_py",
+    ],
+    main = "python/ops/fused_conv2d_bias_activation_benchmark.py",
+    tags = [
+        "manual",
+        "requires_cudnn6",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/contrib/fused_conv/__init__.py b/tensorflow/contrib/fused_conv/__init__.py
new file mode 100644
index 0000000000..dd4d3fc707
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/__init__.py
@@ -0,0 +1,25 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Ops and modules related to fused_conv2d_bias_activation."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=wildcard-import
+from tensorflow.contrib.fused_conv.python.ops.fused_conv2d_bias_activation_op import *
+from tensorflow.python.util.all_util import remove_undocumented
+
+remove_undocumented(__name__, ['fused_conv2d_bias_activation'])
diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc
new file mode 100644
index 0000000000..d553d5a0a6
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc
@@ -0,0 +1,497 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+#endif  // GOOGLE_CUDA
+
+#include "tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.h"
+
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_slice.h"
+#include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/kernels/conv_2d.h"
+#include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/util/padding.h"
+#include "tensorflow/core/util/tensor_format.h"
+#include "tensorflow/core/util/use_cudnn.h"
+
+#if GOOGLE_CUDA
+#include "tensorflow/core/kernels/conv_ops_gpu.h"
+#include "tensorflow/core/platform/stream_executor.h"
+#include "tensorflow/core/util/activation_mode.h"
+#endif  // GOOGLE_CUDA
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+struct LaunchConvOp;
+
+template <typename Device, typename T>
+class FusedConv2DBiasActivationOp : public OpKernel {
+ public:
+  explicit FusedConv2DBiasActivationOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    string data_format;
+    OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
+    OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
+                errors::InvalidArgument("Invalid data format"));
+    OP_REQUIRES(context,
+                (data_format_ == FORMAT_NHWC || data_format_ == FORMAT_NCHW),
+                errors::InvalidArgument("Current implementation only supports "
+                                        "NHWC and NCHW data formats."));
+    OP_REQUIRES_OK(context, context->GetAttr("strides", &strides_));
+    OP_REQUIRES(context, strides_.size() == 4,
+                errors::InvalidArgument("Sliding window strides field must "
+                                        "specify 4 dimensions"));
+    OP_REQUIRES(
+        context,
+        (GetTensorDim(strides_, data_format_, 'N') == 1 &&
+         GetTensorDim(strides_, data_format_, 'C') == 1),
+        errors::InvalidArgument("Current implementation does not yet support "
+                                "strides in the batch and depth dimensions."));
+    OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
+    string activation_mode_str;
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("activation_mode", &activation_mode_str));
+    OP_REQUIRES_OK(context, GetActivationModeFromString(activation_mode_str,
+                                                        &activation_mode_));
+    OP_REQUIRES(context, activation_mode_ == ActivationMode::RELU,
+                errors::InvalidArgument("Current implementation only supports "
+                                        "relu as the activation mode."));
+    cudnn_use_autotune_ = CudnnUseAutotune();
+  }
+
+  void Compute(OpKernelContext* context) override {
+    // Input tensor is one of the following shapes:
+    // [ batch, in_rows, in_cols, in_depth ] (for NHWC data format)
+    // [ batch, in_depth, in_rows, in_cols ] (for NCHW data format)
+    const Tensor& input = context->input(0);
+
+    // Input filter is of the following dimensions:
+    // [ filter_rows, filter_cols, in_depth, out_depth ]
+    const Tensor& filter = context->input(1);
+
+    // Input bias is a 1-D tensor the size of the last
+    // dimension of Output tensor
+    const Tensor& bias = context->input(2);
+
+    // For 2D convolution, there should be 4 dimensions.
+    OP_REQUIRES(context, input.dims() == 4,
+                errors::InvalidArgument("input must be 4-dimensional",
+                                        input.shape().DebugString()));
+    OP_REQUIRES(context, filter.dims() == 4,
+                errors::InvalidArgument("filter must be 4-dimensional: ",
+                                        filter.shape().DebugString()));
+
+    // Bias should be a 1-D tensor.
+    OP_REQUIRES(context, bias.dims() == 1,
+                errors::InvalidArgument("bias must be 1-dimensional: ",
+                                        bias.shape().DebugString()));
+
+    for (int i = 0; i < 4; i++) {
+      OP_REQUIRES(context,
+                  FastBoundsCheck(filter.dim_size(i),
+                                  std::numeric_limits<int32>::max()),
+                  errors::InvalidArgument("filter dimension too large"));
+      OP_REQUIRES(
+          context,
+          FastBoundsCheck(input.dim_size(i), std::numeric_limits<int32>::max()),
+          errors::InvalidArgument("input dimension too large"));
+    }
+
+    // The last dimension for input is in_depth. It must be the same as the
+    // filter's in_depth.
+    const int64 in_depth = GetTensorDim(input, data_format_, 'C');
+    OP_REQUIRES(context, in_depth == filter.dim_size(2),
+                errors::InvalidArgument(
+                    "input and filter must have the same depth: ", in_depth,
+                    " vs ", filter.dim_size(2)));
+
+    // The last dimension for filter is out_depth.
+    const int32 out_depth = static_cast<int32>(filter.dim_size(3));
+
+    // The second dimension for input is rows/height.
+    // The first dimension for filter is rows/height.
+    const int64 input_rows_raw = GetTensorDim(input, data_format_, 'H');
+    const int32 input_rows = static_cast<int32>(input_rows_raw);
+    const int32 filter_rows = static_cast<int32>(filter.dim_size(0));
+
+    // The third dimension for input is columns/width.
+    // The second dimension for filter is columns/width.
+    const int64 input_cols_raw = GetTensorDim(input, data_format_, 'W');
+    const int32 input_cols = static_cast<int32>(input_cols_raw);
+    const int32 filter_cols = static_cast<int32>(filter.dim_size(1));
+
+    // The first dimension for input is batch.
+    const int64 batch_raw = GetTensorDim(input, data_format_, 'N');
+    const int32 batch = static_cast<int32>(batch_raw);
+
+    // For now we take the stride from the second and third dimensions only (we
+    // do not support striding on the batch or depth dimension).
+    const int32 stride_rows =
+        static_cast<int32>(GetTensorDim(strides_, data_format_, 'H'));
+    const int32 stride_cols =
+        static_cast<int32>(GetTensorDim(strides_, data_format_, 'W'));
+    const int32 bias_size = static_cast<int32>(bias.dim_size(0));
+
+    int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0;
+    OP_REQUIRES_OK(context,
+                   GetWindowedOutputSize(input_rows, filter_rows, stride_rows,
+                                         padding_, &out_rows, &pad_rows));
+    OP_REQUIRES_OK(context,
+                   GetWindowedOutputSize(input_cols, filter_cols, stride_cols,
+                                         padding_, &out_cols, &pad_cols));
+    // Output tensor is of the following dimensions:
+    // [ in_batch, out_rows, out_cols, out_depth ]
+    TensorShape out_shape =
+        ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth);
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
+
+    // Bias size should be the same as the size of the channel dimension of
+    // output.
+    OP_REQUIRES(context, bias_size == out_depth,
+                errors::InvalidArgument(
+                    "bias size should equal the channel "
+                    "dimension size of output. bias shape: ",
+                    bias.shape().DebugString() +
+                        ", output shape: " + output->shape().DebugString()));
+
+    VLOG(2) << "FusedConv2DBiasActivation: in_depth = " << in_depth
+            << ", input_cols = " << input_cols
+            << ", filter_cols = " << filter_cols
+            << ", input_rows = " << input_rows
+            << ", filter_rows = " << filter_rows
+            << ", stride_rows = " << stride_rows
+            << ", stride_cols = " << stride_cols
+            << ", bias_size = " << bias_size << ", out_depth = " << out_depth;
+
+    // If there is nothing to compute, return.
+    if (out_shape.num_elements() == 0) {
+      return;
+    }
+    launcher_.launch(context, cudnn_use_autotune_, input, filter, stride_rows,
+                     stride_cols, bias, activation_mode_,
+                     BrainPadding2EigenPadding(padding_), data_format_, output);
+  }
+
+ private:
+  std::vector<int32> strides_;
+  Padding padding_;
+  ActivationMode activation_mode_;
+  TensorFormat data_format_;
+  LaunchFusedConv2DBiasActivationOp<Device, T> launcher_;
+  bool cudnn_use_autotune_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(FusedConv2DBiasActivationOp);
+};
+
+#if GOOGLE_CUDA
+namespace dnn = ::perftools::gputools::dnn;
+
+dnn::ActivationMode BrainActivationMode2CudnnActivationMode(
+    ActivationMode activation_mode) {
+  switch (activation_mode) {
+    case ActivationMode::SIGMOID:
+      return dnn::ActivationMode::kSigmoid;
+    case ActivationMode::RELU:
+      return dnn::ActivationMode::kRelu;
+    case ActivationMode::RELUX:
+      return dnn::ActivationMode::kReluX;
+    case ActivationMode::RELU6:
+      return dnn::ActivationMode::kRelu6;
+    case ActivationMode::TANH:
+      return dnn::ActivationMode::kTanh;
+    case ActivationMode::BANDPASS:
+      return dnn::ActivationMode::kBandPass;
+  }
+  // Prevent compiler warning about missing return
+  return dnn::ActivationMode::kRelu;
+}
+
+// A dummy type to group forward convolution autotune results together.
+struct ConvBiasActivationAutoTuneGroup {
+  static string name() { return "ConvBiasActivation"; }
+};
+typedef AutoTuneSingleton<ConvBiasActivationAutoTuneGroup, ConvParameters,
+                          perftools::gputools::dnn::AlgorithmConfig>
+    AutoTuneConvBiasActivation;
+
+template <typename T>
+void LaunchFusedConv2DBiasActivationOp<GPUDevice, T>::launch(
+    OpKernelContext* ctx, bool cudnn_use_autotune, const Tensor& input_param,
+    const Tensor& filter, int32 row_stride, int32 col_stride,
+    const Tensor& bias, const ActivationMode& activation_mode,
+    const Eigen::PaddingType& padding, TensorFormat data_format,
+    Tensor* output) {
+  using perftools::gputools::dnn::AlgorithmConfig;
+  using perftools::gputools::dnn::AlgorithmType;
+  using perftools::gputools::dnn::ProfileResult;
+  using perftools::gputools::dnn::kDefaultAlgorithm;
+  auto* stream = ctx->op_device_context()->stream();
+  OP_REQUIRES(ctx, stream, errors::Internal("No GPU stream available."));
+
+  Tensor input = input_param;
+
+  perftools::gputools::dnn::ActivationMode cudnn_activation_mode =
+      BrainActivationMode2CudnnActivationMode(activation_mode);
+
+  // TODO(yangzihao): refactor all the complicated/duplicated code in regular
+  // conv ops to a shared conv utility.
+  int32 padding_rows = 0;
+  int32 padding_cols = 0;
+  const int64 in_batch = GetTensorDim(input, data_format, 'N');
+  int64 in_rows = GetTensorDim(input, data_format, 'H');
+  int64 in_cols = GetTensorDim(input, data_format, 'W');
+  const int64 in_depths = GetTensorDim(input, data_format, 'C');
+  const int64 out_batch = GetTensorDim(*output, data_format, 'N');
+  const int64 out_rows = GetTensorDim(*output, data_format, 'H');
+  const int64 out_cols = GetTensorDim(*output, data_format, 'W');
+  const int64 out_depths = GetTensorDim(*output, data_format, 'C');
+  const int64 patch_rows = filter.dim_size(0);
+  const int64 patch_cols = filter.dim_size(1);
+  if (padding == Eigen::PADDING_SAME) {
+    // Total padding on rows and cols is
+    // Pr = (R' - 1) * S + Kr - R
+    // Pc = (C' - 1) * S + Kc - C
+    // where (R', C') are output dimensions, (R, C) are input dimensions, S
+    // is stride, (Kr, Kc) are filter dimensions.
+    // We pad Pr/2 on the left and Pr - Pr/2 on the right, Pc/2 on the top
+    // and Pc - Pc/2 on the bottom.  When Pr or Pc is odd, this means
+    // we pad more on the right and bottom than on the top and left.
+    padding_rows =
+        std::max<int32>(0, (out_rows - 1) * row_stride + patch_rows - in_rows);
+    padding_cols =
+        std::max<int32>(0, (out_cols - 1) * col_stride + patch_cols - in_cols);
+    const int rows_parity = padding_rows & 1;
+    const int cols_parity = padding_cols & 1;
+    if ((rows_parity | cols_parity) != 0) {
+      Tensor transformed_input;
+      int64 new_in_rows = in_rows + rows_parity;
+      int64 new_in_cols = in_cols + cols_parity;
+      OP_REQUIRES_OK(
+          ctx,
+          ctx->allocate_temp(DataTypeToEnum<T>::value,
+                             ShapeFromFormat(data_format, in_batch, new_in_rows,
+                                             new_in_cols, in_depths),
+                             &transformed_input));
+
+      functor::PadInput<GPUDevice, T, int, 4>()(
+          ctx->eigen_device<GPUDevice>(), To32Bit(input_param.tensor<T, 4>()),
+          {{0, 0}}, {{rows_parity, cols_parity}},
+          To32Bit(transformed_input.tensor<T, 4>()), data_format);
+
+      input = transformed_input;
+      in_rows = new_in_rows;
+      in_cols = new_in_cols;
+    }
+  }
+
+  if (data_format == FORMAT_NHWC) {
+    // Convert the input tensor from NHWC to NCHW.
+    TensorShape nchw_shape =
+        ShapeFromFormat(FORMAT_NCHW, in_batch, in_rows, in_cols, in_depths);
+    if (in_depths > 1) {
+      Tensor transformed_input;
+      OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                                             nchw_shape, &transformed_input));
+      functor::NHWCToNCHW<GPUDevice, T, 4>()(
+          ctx->eigen_device<GPUDevice>(),
+          const_cast<const Tensor&>(input).tensor<T, 4>(),
+          transformed_input.tensor<T, 4>());
+      input = transformed_input;
+    } else {
+      // If depth <= 1, then just reshape.
+      CHECK(input.CopyFrom(input, nchw_shape));
+    }
+  }
+
+  CHECK(padding_rows >= 0 && padding_cols >= 0)
+      << "Negative row or col paddings: (" << padding_rows << ", "
+      << padding_cols << ")";
+  perftools::gputools::dnn::BatchDescriptor input_desc;
+  input_desc.set_count(in_batch)
+      .set_feature_map_count(in_depths)
+      .set_height(in_rows)
+      .set_width(in_cols)
+      .set_layout(perftools::gputools::dnn::DataLayout::kBatchDepthYX);
+  perftools::gputools::dnn::BatchDescriptor output_desc;
+  output_desc.set_count(out_batch)
+      .set_height(out_rows)
+      .set_width(out_cols)
+      .set_feature_map_count(out_depths)
+      .set_layout(perftools::gputools::dnn::DataLayout::kBatchDepthYX);
+  perftools::gputools::dnn::FilterDescriptor filter_desc;
+  filter_desc.set_input_filter_height(filter.dim_size(0))
+      .set_input_filter_width(filter.dim_size(1))
+      .set_input_feature_map_count(filter.dim_size(2))
+      .set_output_feature_map_count(filter.dim_size(3));
+  perftools::gputools::dnn::ConvolutionDescriptor conv_desc;
+  conv_desc.set_vertical_filter_stride(row_stride)
+      .set_horizontal_filter_stride(col_stride)
+      .set_zero_padding_height(padding_rows / 2)
+      .set_zero_padding_width(padding_cols / 2);
+
+  // Shuffles a filter tensor from:
+  //   [<spatial_dims>, in, out]
+  // to:
+  //   [out, in, <spatial_dims>]
+  // TODO(yangzihao): Support a data layout tag for the filter weights, and only
+  // do the transform if the weights are not already in the correct layout.
+  Tensor transformed_filter;
+  OP_REQUIRES_OK(ctx, ctx->allocate_temp(
+                          DataTypeToEnum<T>::value,
+                          TensorShape({filter.dim_size(3), filter.dim_size(2),
+                                       filter.dim_size(0), filter.dim_size(1)}),
+                          &transformed_filter));
+
+  functor::TransformFilter<GPUDevice, T, int, 4>()(
+      ctx->eigen_device<GPUDevice>(), To32Bit(filter.tensor<T, 4>()),
+      To32Bit(transformed_filter.tensor<T, 4>()));
+
+  Tensor transformed_output;
+  OP_REQUIRES_OK(
+      ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+                              ShapeFromFormat(FORMAT_NCHW, out_batch, out_rows,
+                                              out_cols, out_depths),
+                              &transformed_output));
+
+  auto input_ptr = AsDeviceMemory(input.template flat<T>().data(),
+                                  input.template flat<T>().size());
+  auto filter_ptr =
+      AsDeviceMemory(transformed_filter.template flat<T>().data(),
+                     transformed_filter.template flat<T>().size());
+  auto output_ptr =
+      AsDeviceMemory(transformed_output.template flat<T>().data(),
+                     transformed_output.template flat<T>().size());
+
+  auto bias_ptr = AsDeviceMemory(bias.template flat<T>().data(),
+                                 bias.template flat<T>().size());
+
+  static int64 ConvolveScratchSize = GetCudnnWorkspaceLimit(
+      // default value is in bytes despite the name of the environment variable
+      "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32  // 4GB
+  );
+
+  int device_id = stream->parent()->device_ordinal();
+  DataType dtype = input.dtype();
+  ConvParameters conv_parameters = {
+      in_batch,
+      in_depths,
+      {{in_rows, in_cols}},
+      out_depths,
+      {{patch_rows, patch_cols}},
+      {{row_stride, col_stride}},
+      {{padding_rows, padding_cols}},
+      dtype,
+      device_id,
+  };
+
+  AlgorithmConfig algorithm_config;
+  if (cudnn_use_autotune && !AutoTuneConvBiasActivation::GetInstance()->Find(
+                                conv_parameters, &algorithm_config)) {
+    std::vector<AlgorithmType> algorithms;
+    CHECK(stream->parent()->GetConvolveAlgorithms(
+        conv_parameters.ShouldIncludeWinogradNonfusedAlgo<T>(), &algorithms));
+    ProfileResult best_result;
+    ProfileResult best_result_no_scratch;
+    for (auto profile_algorithm : algorithms) {
+      // TODO(zhengxq): profile each algorithm multiple times to better
+      // accuracy.
+      CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx);
+      ProfileResult profile_result;
+      bool cudnn_launch_status =
+          stream
+              ->ThenConvolveWithAlgorithm(
+                  input_desc, input_ptr, filter_desc, filter_ptr, conv_desc,
+                  bias_ptr, cudnn_activation_mode, output_desc, &output_ptr,
+                  &scratch_allocator, AlgorithmConfig(profile_algorithm),
+                  &profile_result)
+              .ok();
+      if (cudnn_launch_status) {
+        if (profile_result.is_valid()) {
+          if (profile_result.elapsed_time_in_ms() <
+              best_result.elapsed_time_in_ms()) {
+            best_result = profile_result;
+          }
+          if (scratch_allocator.TotalByteSize() == 0 &&
+              profile_result.elapsed_time_in_ms() <
+                  best_result_no_scratch.elapsed_time_in_ms()) {
+            best_result_no_scratch = profile_result;
+          }
+        }
+      }
+    }
+    OP_REQUIRES(
+        ctx,
+        best_result.is_valid() && best_result.algorithm() != kDefaultAlgorithm,
+        errors::NotFound("No algorithm worked!"));
+    OP_REQUIRES(ctx,
+                best_result_no_scratch.is_valid() &&
+                    best_result_no_scratch.algorithm() != kDefaultAlgorithm,
+                errors::NotFound("No algorithm without scratch worked!"));
+    algorithm_config.set_algorithm(best_result.algorithm());
+    algorithm_config.set_algorithm_no_scratch(
+        best_result_no_scratch.algorithm());
+    AutoTuneConvBiasActivation::GetInstance()->Insert(conv_parameters,
+                                                      algorithm_config);
+  }
+
+  CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx);
+  bool cudnn_launch_status =
+      stream
+          ->ThenConvolveWithAlgorithm(
+              input_desc, input_ptr, filter_desc, filter_ptr, conv_desc,
+              bias_ptr, cudnn_activation_mode, output_desc, &output_ptr,
+              &scratch_allocator, algorithm_config,
+              /*output_profile_result=*/nullptr)
+          .ok();
+
+  if (!cudnn_launch_status) {
+    ctx->SetStatus(errors::Internal(
+        "cuDNN launch failure : input shape(", input.shape().DebugString(),
+        ") filter shape(", filter.shape().DebugString(), ")"));
+  }
+
+  // Convert the output tensor back from NCHW to NHWC.
+  if (data_format == FORMAT_NHWC) {
+    functor::NCHWToNHWC<GPUDevice, T, 4>()(
+        ctx->eigen_device<GPUDevice>(),
+        const_cast<const Tensor&>(transformed_output).tensor<T, 4>(),
+        output->tensor<T, 4>());
+  } else {
+    *output = transformed_output;
+  }
+}
+
+// Registration of the GPU implementations.
+REGISTER_KERNEL_BUILDER(Name("FusedConv2DBiasActivation")
+                            .Device(DEVICE_GPU)
+                            .TypeConstraint<float>("T"),
+                        FusedConv2DBiasActivationOp<GPUDevice, float>);
+
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.h b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.h
new file mode 100644
index 0000000000..d71b26cf1d
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.h
@@ -0,0 +1,62 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRDPARTY_TENSORFLOW_CONTRIB_KERNELS_FUSED_CONV2D_BIAS_ACTIVATION_OP_H_
+#define THIRDPARTY_TENSORFLOW_CONTRIB_KERNELS_FUSED_CONV2D_BIAS_ACTIVATION_OP_H_
+
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/mem.h"
+#include "tensorflow/core/util/activation_mode.h"
+#include "tensorflow/core/util/tensor_format.h"
+
+#if GOOGLE_CUDA
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/kernels/conv_ops_gpu.h"
+#include "tensorflow/core/platform/stream_executor.h"
+#endif  // GOOGLE_CUDA
+
+namespace tensorflow {
+
+// Forward declaration.
+class OpKernelContext;
+
+template <typename Device, typename T>
+class LaunchFusedConv2DBiasActivationOp {
+ public:
+  void launch(OpKernelContext* ctx, bool cudnn_use_autotune,
+              const Tensor& input, const Tensor& filter, int row_stride,
+              int col_stride, const Tensor& bias,
+              const ActivationMode& activation_mode,
+              const Eigen::PaddingType& padding, TensorFormat data_format,
+              Tensor* output);
+};
+
+#ifdef GOOGLE_CUDA
+template <typename T>
+class LaunchFusedConv2DBiasActivationOp<Eigen::GpuDevice, T> {
+ public:
+  void launch(OpKernelContext* ctx, bool cudnn_use_autotune,
+              const Tensor& input, const Tensor& filter, int32 row_stride,
+              int32 col_stride, const Tensor& bias,
+              const ActivationMode& activation_mode,
+              const Eigen::PaddingType& padding, TensorFormat data_format,
+              Tensor* output);
+};
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
+
+#endif
diff --git a/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc
new file mode 100644
index 0000000000..6134c5c699
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc
@@ -0,0 +1,72 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <string>
+
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/activation_mode.h"
+#include "tensorflow/core/util/padding.h"
+#include "tensorflow/core/util/tensor_format.h"
+
+namespace tensorflow {
+
+namespace {
+// Return the string containing the list of valid activation modes, that can be
+// used as an Attr() in REGISTER_OP.
+string GetAllActivationModeAttrString() { return "activation_mode: {'Relu'}"; }
+
+}  // namespace
+
+// --------------------------------------------------------------------------
+REGISTER_OP("FusedConv2DBiasActivation")
+    .Input("input: T")
+    .Input("filter: T")
+    .Input("bias: T")
+    .Output("output: T")
+    .Attr("T: {float}")
+    .Attr("strides: list(int)")
+    .Attr(GetPaddingAttrString())
+    .Attr(GetConvnetDataFormatAttrString())
+    .Attr(GetAllActivationModeAttrString())
+    .SetShapeFn(shape_inference::FusedConvBiasActivationShape)
+    .Doc(R"doc(
+    Computes a fused 2-D convolution, adds bias, and applies an activation function
+    on the output given 4-D `input`, 4-D `filter`, 1-D `bias` tensors and an activation mode.
+
+    input: A 4-D tensor. The dimension order is interpreted according to the value
+        of `data_format`, see below for details.
+    filter: A 4-D tensor of shape
+        `[filter_height, filter_width, in_channels, out_channels]`
+    bias: 1-D with size of the `out_channels` dimension in filter.
+    output: A 4-D tensor. The dimension order is determined by the value of
+        `data_format`, see below for details.
+    T: The data type for the elements of input, filter, bias, and output Tensors.
+    strides: 1-D tensor of length 4.  The stride of the sliding window for each
+        dimension of `input`. The dimension order is determined by the value of
+        `data_format`, see below for details.
+    padding: The type of padding algorithm to use.
+    data_format: Specify the data format of the input and output data. With the
+        default format "NHWC", the data is stored in the order of:
+        [batch, height, width, channels].
+        Alternatively, the format could be "NCHW", the data storage order of:
+        [batch, channels, height, width].
+    activation_mode: Specify the activation function to apply to the output tensor
+        of bias add. Currently only supports "Relu".
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/fused_conv/python/__init__.py b/tensorflow/contrib/fused_conv/python/__init__.py
new file mode 100644
index 0000000000..23d817cefb
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/python/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""ops module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py
new file mode 100644
index 0000000000..a65d4bc50f
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py
@@ -0,0 +1,243 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Benchmark for fused conv2d bias and activation op."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import time
+
+from tensorflow.contrib.fused_conv.python.ops import fused_conv2d_bias_activation_op
+from tensorflow.python.client import session as session_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+def build_conv_bias_relu_graph(device, input_shape, filter_shape, strides,
+                               padding, num_iters, data_format):
+  """builds a graph containing a sequence of conv2d operations.
+
+  Args:
+    device: String, the device to run on.
+    input_shape: Shape of the input tensor.
+    filter_shape: Shape of the filter tensor.
+    strides: A list of ints. 1-D of length 4. The stride of sliding
+             window for each dimension of input.
+    padding: A string from: "SAME", "VALID". The type of padding
+             algorithm to use.
+    num_iters: number of iterations to run conv2d.
+    data_format: data format string of input, 'NHWC' and 'NCHW' are
+    supported.
+
+  Returns:
+    An array of tensors to run()
+  """
+  if data_format == "NCHW":
+    input_shape = [
+        input_shape[0], input_shape[3], input_shape[1], input_shape[2]
+    ]
+  with ops.device("/%s:0" % device):
+    inp = variables.Variable(random_ops.truncated_normal(input_shape))
+    filt = variables.Variable(random_ops.truncated_normal(filter_shape))
+    bias_shape = [filter_shape[-1]]
+    bias = variables.Variable(random_ops.truncated_normal(bias_shape))
+
+    outputs = []
+    conv2d_out = nn_ops.conv2d(
+        inp, filt, strides, padding, data_format=data_format)
+    bias_out = nn_ops.bias_add(conv2d_out, bias, data_format=data_format)
+    relu_out = nn_ops.relu(bias_out)
+    outputs.append(relu_out)
+    for _ in range(1, num_iters):
+      with ops.control_dependencies([relu_out]):
+        conv2d_out = nn_ops.conv2d(
+            inp, filt, strides, padding, data_format=data_format)
+        bias_out = nn_ops.bias_add(conv2d_out, bias, data_format=data_format)
+        relu_out = nn_ops.relu(bias_out)
+        outputs.append(relu_out)
+    return control_flow_ops.group(*outputs)
+
+
+def build_fused_conv_bias_relu_graph(device, input_shape, filter_shape, strides,
+                                     padding, num_iters, data_format):
+  """builds a graph containing a sequence of conv2d operations.
+
+  Args:
+    device: String, the device to run on.
+    input_shape: Shape of the input tensor.
+    filter_shape: Shape of the filter tensor.
+    strides: A list of ints. 1-D of length 4. The stride of sliding
+             window for each dimension of input.
+    padding: A string from: "SAME", "VALID". The type of padding
+             algorithm to use.
+    num_iters: number of iterations to run conv2d.
+    data_format: data format string of input, 'NHWC' and 'NCHW' are
+    supported.
+
+  Returns:
+    An array of tensors to run()
+  """
+  if data_format == "NCHW":
+    input_shape = [
+        input_shape[0], input_shape[3], input_shape[1], input_shape[2]
+    ]
+  with ops.device("/%s:0" % device):
+    inp = variables.Variable(random_ops.truncated_normal(input_shape))
+    filt = variables.Variable(random_ops.truncated_normal(filter_shape))
+    bias_shape = [filter_shape[-1]]
+    bias = variables.Variable(random_ops.truncated_normal(bias_shape))
+
+    outputs = []
+    fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+        inp,
+        filt,
+        bias,
+        strides,
+        padding,
+        data_format=data_format,
+        activation_mode="Relu")
+    outputs.append(fused_out)
+    for _ in range(1, num_iters):
+      with ops.control_dependencies([fused_out]):
+        # pylint: disable=g-line-too-long
+        fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+            inp,
+            filt,
+            bias,
+            strides,
+            padding,
+            data_format=data_format,
+            activation_mode="Relu")
+        outputs.append(fused_out)
+    return control_flow_ops.group(*outputs)
+
+
+class FusedConv2DBiasActivationBenchmark(test.Benchmark):
+  """Benchmark conv2d!"""
+
+  def _run_graph(self, device, input_shape, filter_shape, strides, padding,
+                 num_iters, data_format):
+    """runs the graph and print its execution time.
+
+    Args:
+      device: String, the device to run on.
+      input_shape: Shape of the input tensor.
+      filter_shape: Shape of the filter tensor.
+      strides: A list of ints. 1-D of length 4. The stride of sliding
+               window for each dimension of input.
+      padding: A string from: "SAME", "VALID". The type of padding
+               algorithm to use.  num_iters: Number of iterations to run the
+                 benchmark.
+      num_iters: number of iterations to run conv2d.
+      data_format: data format string of input, 'NHWC' and 'NCHW' are
+      supported.
+
+    Returns:
+      The duration of the run in seconds.
+    """
+    graph = ops.Graph()
+    with graph.as_default():
+      outputs = build_fused_conv_bias_relu_graph(device, input_shape,
+                                                 filter_shape, strides, padding,
+                                                 num_iters, data_format)
+      with session_lib.Session(graph=graph) as session:
+        variables.global_variables_initializer().run()
+        # warmup runs
+        session.run(outputs)
+
+        start_time = time.time()
+        session.run(outputs)
+        duration = (time.time() - start_time) / num_iters
+
+        print("%s inputshape:%s filtershape:%s strides:%s padding:%s "
+              "%d iters: %.8f sec" %
+              (device, str(input_shape).replace(" ", ""),
+               str(filter_shape).replace(" ", ""),
+               str(strides).replace(" ", ""), padding, num_iters, duration))
+    name_template = (
+        "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_"
+        "strides_{strides}_padding_{padding}")
+
+    self.report_benchmark(
+        name=name_template.format(
+            device=device,
+            inputshape=str(input_shape).replace(" ", ""),
+            filtershape=str(filter_shape).replace(" ", ""),
+            strides=str(strides).replace(" ", ""),
+            padding=padding).replace(" ", ""),
+        iters=num_iters,
+        wall_time=duration)
+
+    return duration
+
+  def benchmark_fused_conv2d_bias_activation(self):
+
+    stride = [1, 1, 1, 1]
+    paddings = ["VALID", "SAME"]
+    data_formats = ["NHWC", "NCHW"]
+
+    resnet50_input_shapes = [[64, 14, 14, 256], [64, 14, 14, 256], [
+        64, 14, 14, 1024
+    ], [64, 55, 55, 64], [64, 28, 28, 128], [64, 28, 28, 128], [64, 55, 55, 64],
+                             [64, 7, 7, 512], [64, 7, 7, 512],
+                             [64, 28, 28, 512], [64, 55, 55,
+                                                 256], [64, 7, 7, 2048]]
+
+    resnet50_filter_shapes = [[1, 1, 256, 1024], [3, 3, 256, 256], [
+        1, 1, 1024, 256
+    ], [1, 1, 64, 256], [1, 1, 128, 512], [3, 3, 128, 128], [3, 3, 64, 64], [
+        3, 3, 512, 512
+    ], [1, 1, 512, 2048], [1, 1, 512, 128], [1, 1, 256, 64], [1, 1, 2048, 512]]
+
+    inception3_input_shapes = [[64, 17, 17, 768], [64, 35, 35, 96], [
+        64, 35, 35, 288
+    ], [64, 8, 8, 384], [64, 8, 8, 384], [64, 17, 17, 192], [64, 35, 35, 64], [
+        64, 17, 17, 192
+    ], [64, 17, 17, 160], [64, 17, 17, 160], [64, 17, 17, 768], [
+        64, 35, 35, 256
+    ], [64, 35, 35, 48], [64, 35, 35, 192], [64, 17, 17, 128], [
+        64, 17, 17, 160
+    ], [64, 8, 8, 448], [64, 17, 17, 128], [64, 17, 17, 768], [64, 17, 17, 160]]
+    inception3_filter_shapes = [[1, 1, 768, 192], [3, 3, 96, 96], [
+        1, 1, 288, 64
+    ], [1, 3, 384, 384], [3, 1, 384, 384], [7, 1, 192, 192], [3, 3, 64, 96], [
+        1, 7, 192, 192
+    ], [7, 1, 160, 160], [1, 7, 160, 160], [1, 1, 768, 160], [1, 1, 256, 64], [
+        5, 5, 48, 64
+    ], [1, 1, 192, 64], [1, 7, 128, 128], [1, 7, 160, 192], [3, 3, 448, 384],
+                                [7, 1, 128, 128], [1, 1, 768,
+                                                   128], [7, 1, 160, 192]]
+
+    print("fused conv2d bias activation benchmark using resnet50's shapes:")
+    for ishape, fshape in zip(resnet50_input_shapes, resnet50_filter_shapes):
+      for padding in paddings:
+        for data_format in data_formats:
+          self._run_graph("gpu", ishape, fshape, stride, padding, 80,
+                          data_format)
+    print("fused conv2d bias activation benchmark using inception3's shapes:")
+    for ishape, fshape in zip(inception3_input_shapes,
+                              inception3_filter_shapes):
+      for padding in paddings:
+        for data_format in data_formats:
+          self._run_graph("gpu", ishape, fshape, stride, padding, 80,
+                          data_format)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py
new file mode 100644
index 0000000000..41fd114f0f
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py
@@ -0,0 +1,87 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Tensorflow op performing fused conv2d bias_add and relu."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.fused_conv.ops import gen_fused_conv2d_bias_activation_op
+from tensorflow.contrib.util import loader
+from tensorflow.python.platform import resource_loader
+
+_fused_conv2d_bias_activation_op_so = loader.load_op_library(
+    resource_loader.get_path_to_datafile("_fused_conv2d_bias_activation_op.so"))
+
+
+def fused_conv2d_bias_activation(input_tensor,
+                                 filter_tensor,
+                                 bias,
+                                 strides,
+                                 padding,
+                                 activation_mode,
+                                 data_format=None,
+                                 name=None):
+  """Computes a fused 2-D convolution, adds bias, and applies relu.
+
+      input_tensor: A 4-D tensor. The dimension order is interpreted
+      according to the value of `data_format`, see below for details.
+      filter_tensor: A 4-D tensor of shape
+          `[filter_height, filter_width, in_channels, out_channels]`
+      bias: 1-D with size of the `out_channels` dimension in filter.
+      output: A 4-D tensor. The dimension order is determined by the value of
+          `data_format`, see below for details.
+      T: The data type for the elements of input, filter, bias, and output
+      Tensors.
+      strides: 1-D tensor of length 4.  The stride of the sliding window for
+      each
+          dimension of `input`. The dimension order is determined by the value
+          of
+          `data_format`, see below for details.
+      padding: The type of padding algorithm to use.
+      data_format: Specify the data format of the input and output data. With
+      the
+          default format "NHWC", the data is stored in the order of:
+          [batch, height, width, channels].
+          Alternatively, the format could be "NCHW", the data storage order of:
+          [batch, channels, height, width].
+      activation_mode: Specify the activation function to apply to the output
+      tensor
+          of bias add. Currently only supports "Relu".
+
+  Args:
+    input_tensor: A `Tensor`. Must be one of the following types: `float32`.
+    filter_tensor: A `Tensor`. Must have the same type as `input`.
+    bias: A `Tensor`. Must have the same type as `input`.
+    strides: A list of `ints`.
+    padding: A `string` from: `"SAME", "VALID"`.
+    activation_mode: A `string` from: `"Sigmoid", "Relu", "Relu6", "ReluX",
+      "Tanh", "BandPass"`.
+    data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to
+      `"NHWC"`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  return gen_fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+      input=input_tensor,
+      filter=filter_tensor,
+      bias=bias,
+      strides=strides,
+      padding=padding,
+      activation_mode=activation_mode,
+      data_format=data_format,
+      name=name)
diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py
new file mode 100644
index 0000000000..5d6a2fa3b8
--- /dev/null
+++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py
@@ -0,0 +1,573 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functional tests for fused conv2d bias and activation operation."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from tensorflow.contrib.fused_conv.python.ops import fused_conv2d_bias_activation_op
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors_impl
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging
+
+
+def GetShrunkInceptionShapes(shrink=10):
+  """Iterator for smaller versions of convolution shapes in 2015 Inception.
+
+  Relative to inception, each depth value is `depth // shrink`.
+
+  Args:
+    shrink: Factor to shrink each depth value by relative to Inception.
+
+  Yields:
+    Tuple (input_size, filter_size, out_size, stride, padding), the convolution
+    parameters of Inception layers.
+  """
+  input_sizes = [[4, 5, 5, 1248], [4, 8, 8, 384], [4, 8, 8, 384], [
+      4, 8, 8, 2048
+  ], [4, 8, 8, 448], [4, 8, 8, 2048], [4, 8, 8, 2048], [4, 8, 8, 2048], [
+      4, 8, 8, 1760
+  ], [4, 8, 8, 1760], [4, 8, 8, 1760], [4, 8, 8, 1760], [4, 17, 17, 192], [
+      4, 17, 17, 192
+  ], [4, 17, 17, 1248], [4, 17, 17, 128], [4, 17, 17, 1248], [4, 17, 17, 224], [
+      4, 17, 17, 192
+  ], [4, 17, 17, 192], [4, 17, 17, 1216], [4, 17, 17, 1216], [4, 17, 17, 224], [
+      4, 17, 17, 192
+  ], [4, 17, 17, 192], [4, 17, 17, 1152], [4, 17, 17, 1152], [4, 17, 17, 192], [
+      4, 17, 17, 160
+  ], [4, 17, 17, 1152], [4, 17, 17, 1024], [4, 17, 17, 128], [4, 17, 17, 1024],
+                 [4, 17, 17, 128], [4, 17, 17, 1024], [4, 17, 17, 128], [
+                     4, 17, 17, 768
+                 ], [4, 17, 17, 128], [4, 17, 17, 128], [4, 17, 17, 768],
+                 [4, 17, 17, 768], [4, 35, 35, 96], [4, 35, 35, 288], [
+                     4, 35, 35, 64
+                 ], [4, 35, 35, 288], [4, 35, 35, 256], [4, 35, 35, 48], [
+                     4, 35, 35, 256
+                 ], [4, 35, 35, 96], [4, 35, 35, 192], [4, 35, 35, 192], [
+                     4, 35, 35, 192
+                 ], [4, 73, 73, 64], [4, 73, 73, 64], [4, 147, 147, 24]]
+  filter_sizes = [[1, 1, 1248, 128], [1, 3, 384, 384], [3, 1, 384, 384], [
+      1, 1, 2048, 192
+  ], [3, 3, 448, 384], [1, 1, 2048, 320], [1, 1, 2048, 448], [1, 1, 2048, 384],
+                  [1, 1, 1760, 384], [1, 1, 1760, 192], [1, 1, 1760, 448], [
+                      1, 1, 1760, 320
+                  ], [3, 3, 192, 192], [3, 3, 192, 192], [1, 1, 1248, 192], [
+                      3, 3, 128, 320
+                  ], [1, 1, 1248, 128], [1, 3, 224, 224], [3, 1, 192, 256], [
+                      1, 3, 192, 256
+                  ], [1, 1, 1216, 192], [1, 1, 1216, 96], [3, 1, 224, 224], [
+                      3, 3, 192, 224
+                  ], [1, 3, 192, 192], [1, 1, 1152, 192], [1, 1, 1152, 128], [
+                      3, 1, 192, 192
+                  ], [3, 3, 160, 192], [1, 1, 1152, 160], [1, 1, 1024, 128], [
+                      1, 3, 128, 192
+                  ], [1, 1, 1024, 160], [3, 1, 128, 192], [1, 1, 1024, 256], [
+                      3, 1, 128, 128
+                  ], [1, 1, 768, 192], [1, 3, 128, 128], [3, 3, 128, 128], [
+                      1, 1, 768, 128
+                  ], [1, 1, 768, 320], [3, 3, 96, 96], [3, 3, 288, 384], [
+                      3, 3, 64, 96
+                  ], [1, 1, 288, 64], [1, 1, 256, 64], [5, 5, 48, 64],
+                  [1, 1, 256, 48], [3, 3, 96, 96], [1, 1, 192, 32], [
+                      1, 1, 192, 64
+                  ], [1, 1, 192, 48], [3, 3, 64, 192], [1, 1, 64,
+                                                        64], [1, 1, 24, 64]]
+  out_sizes = [[4, 5, 5, 128], [4, 8, 8, 384], [4, 8, 8, 384], [4, 8, 8, 192], [
+      4, 8, 8, 384
+  ], [4, 8, 8, 320], [4, 8, 8, 448], [4, 8, 8, 384], [4, 8, 8, 384], [
+      4, 8, 8, 192
+  ], [4, 8, 8, 448], [4, 8, 8, 320], [4, 8, 8, 192], [4, 17, 17, 192], [
+      4, 17, 17, 192
+  ], [4, 8, 8, 320], [4, 17, 17, 128], [4, 17, 17, 224], [4, 17, 17, 256], [
+      4, 17, 17, 256
+  ], [4, 17, 17, 192], [4, 17, 17, 96], [4, 17, 17, 224], [4, 17, 17, 224], [
+      4, 17, 17, 192
+  ], [4, 17, 17, 192], [4, 17, 17, 128], [4, 17, 17, 192], [4, 17, 17, 192], [
+      4, 17, 17, 160
+  ], [4, 17, 17, 128], [4, 17, 17, 192], [4, 17, 17, 160], [4, 17, 17, 192], [
+      4, 17, 17, 256
+  ], [4, 17, 17, 128], [4, 17, 17, 192], [4, 17, 17, 128], [4, 17, 17, 128], [
+      4, 17, 17, 128
+  ], [4, 17, 17, 320], [4, 17, 17, 96], [4, 17, 17, 384], [4, 35, 35, 96], [
+      4, 35, 35, 64
+  ], [4, 35, 35, 64], [4, 35, 35, 64], [4, 35, 35, 48], [4, 35, 35, 96],
+               [4, 35, 35, 32], [4, 35, 35, 64], [4, 35, 35, 48],
+               [4, 71, 71, 192], [4, 73, 73, 64], [4, 147, 147, 64]]
+  strides = [
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1,
+      1, 1, 1, 1, 1
+  ]
+  # Shrink sizes to make the test faster
+  for i in input_sizes:
+    i[3] //= shrink
+  for f in filter_sizes:
+    f[2] //= shrink
+    f[3] //= shrink
+  for o in out_sizes:
+    o[3] //= shrink
+  # pylint: disable=invalid-name
+  VALID = "VALID"
+  SAME = "SAME"
+  # pylint: enable=invalid-name
+  paddings = [
+      SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
+      VALID, SAME, SAME, VALID, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
+      SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
+      SAME, SAME, SAME, SAME, SAME, VALID, VALID, SAME, SAME, SAME, SAME, SAME,
+      SAME, SAME, SAME, SAME, VALID, VALID, VALID
+  ]
+  for i, f, o, s, p in zip(input_sizes, filter_sizes, out_sizes, strides,
+                           paddings):
+    yield i, f, o, s, p
+
+
+def GetTestConfigs():
+  """Get all the valid tests configs to run.
+
+  Returns:
+    all the valid test configs as tuples of data_format and use_gpu.
+  """
+  test_configs = [("NCHW", True), ("NHWC", True)]
+  return test_configs
+
+
+class FusedConv2DBiasActivationTest(test.TestCase):
+
+  def _DtypesToTest(self, use_gpu):
+    return [dtypes.float32]
+
+  def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, bias,
+                            strides, padding, activation_mode, data_format,
+                            dtype):
+    """Verifies the output values of the convolution function.
+
+    Args:
+      tensor_in_sizes: Input tensor dimensions in
+        [batch, input_rows, input_cols, input_depth].
+      filter_in_sizes: Filter tensor dimensions in
+        [kernel_rows, kernel_cols, input_depth, output_depth].
+      bias: 1-D bias tensor of length output_depth.
+      strides: Stride: [col_stride, row_stride]
+      padding: Padding type.
+      activation_mode: Activation mode.
+      data_format: Format of the data tensors.
+      dtype: Data type for inputs and outputs.
+    Returns:
+      Symbolic tensor value and reference value that can be used to
+      execute the computation and verify the results.
+    """
+    input_size = np.prod(tensor_in_sizes)
+    filter_size = np.prod(filter_in_sizes)
+    bias_size = filter_in_sizes[-1]  # equals to output depth
+    # Initializes the input tensor with array containing incrementing
+    # numbers from 1.
+    x1 = [f * 1.0 for f in range(1, input_size + 1)]
+    x2 = [f * 1.0 for f in range(1, filter_size + 1)]
+    # This is to guarantee that there is always negative values after
+    # bias add so that we can test whether relu works correctly.
+    x3 = bias
+    with self.test_session(use_gpu=True):
+      t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype)
+      t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype)
+      t3 = constant_op.constant(x3, shape=[bias_size], dtype=dtype)
+      strides = [1] + strides + [1]
+      if data_format == "NCHW":
+        t1 = test_util.NHWCToNCHW(t1)
+        strides = test_util.NHWCToNCHW(strides)
+      output = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+          t1,
+          t2,
+          t3,
+          strides=strides,
+          padding=padding,
+          data_format=data_format,
+          activation_mode=activation_mode)
+      ref_conv_output = nn_ops.conv2d(
+          t1, t2, strides=strides, padding=padding, data_format=data_format)
+      ref_bias_output = nn_ops.bias_add(
+          ref_conv_output, t3, data_format=data_format)
+      ref_output = nn_ops.relu(ref_bias_output)
+      if data_format == "NCHW":
+        output = test_util.NCHWToNHWC(output)
+        ref_output = test_util.NCHWToNHWC(ref_output)
+
+      return output, ref_output
+
+  def _CompareFwdValues(self, tensor_in_sizes, filter_in_sizes, conv_strides,
+                        padding):
+    """Verifies that CPU and GPU produce the same values.
+
+    Args:
+      tensor_in_sizes: Input tensor dimensions in
+        [batch, input_rows, input_cols, input_depth].
+      filter_in_sizes: Filter tensor dimensions in
+        [kernel_rows, kernel_cols, input_depth, output_depth].
+      conv_strides: [row_stride, col_stride] for the convolution;
+      padding: Padding type.
+    """
+    x1 = np.random.rand(*tensor_in_sizes).astype(np.float32)
+    x2 = np.random.rand(*filter_in_sizes).astype(np.float32)
+    x3 = np.random.rand(*[filter_in_sizes[-1]]).astype(np.float32)
+
+    def _SetupVal(data_format, use_gpu):
+      with self.test_session(use_gpu=use_gpu):
+        t1 = constant_op.constant(x1, shape=tensor_in_sizes)
+        t2 = constant_op.constant(x2, shape=filter_in_sizes)
+        t3 = constant_op.constant(x3, shape=[filter_in_sizes[-1]])
+        strides = [1] + conv_strides + [1]
+        if data_format == "NCHW":
+          t1 = test_util.NHWCToNCHW(t1)
+          strides = test_util.NHWCToNCHW(strides)
+        output = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+            t1,
+            t2,
+            t3,
+            strides=strides,
+            padding=padding,
+            data_format=data_format,
+            activation_mode="Relu")
+
+        if data_format == "NCHW":
+          output = test_util.NCHWToNHWC(output)
+        return output
+
+    tensors = []
+    for (data_format, use_gpu) in GetTestConfigs():
+      tensors.append(_SetupVal(data_format, use_gpu))
+    with self.test_session() as sess:
+      values = sess.run(tensors)
+      for i in range(1, len(values)):
+        self.assertAllClose(values[0], values[i], rtol=1e-5, atol=1e-5)
+
+  def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, bias, strides,
+                    padding):
+    tensors = []
+    ref_tensors = []
+    for (data_format, use_gpu) in GetTestConfigs():
+      for dtype in self._DtypesToTest(use_gpu):
+        result, expected = self._SetupValuesForDevice(
+            tensor_in_sizes, filter_in_sizes, bias, strides, padding, "Relu",
+            data_format, dtype)
+        tensors.append(result)
+        ref_tensors.append(expected)
+      with self.test_session() as sess:
+        values = sess.run(tensors)
+        ref_values = sess.run(ref_tensors)
+        for i in range(len(tensors)):
+          conv = tensors[i]
+          value = values[i]
+          ref_value = ref_values[i]
+          print("expected = ", ref_value)
+          print("actual = ", value)
+          tol = 1e-5
+          if value.dtype == np.float16:
+            tol = 1e-3
+          self.assertAllClose(
+              np.ravel(ref_value), np.ravel(value), atol=tol, rtol=tol)
+          self.assertShapeEqual(value, conv)
+
+  def testConv2D1x1Filter(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2D1x1Filter test.")
+      return
+    # expected_output = [
+    #    0.0, 0.0, 0.0, 21.0, 0.0, 0.0, 57.0, 0.0, 0.0, 93.0, 41.0, 0.0, 129.0,
+    #    86.0, 43.0, 165.0, 131.0, 97.0
+    # ]
+    medians = [-45.0, -130.0, -215.0]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 2, 3, 3],
+        filter_in_sizes=[1, 1, 3, 3],
+        bias=medians,
+        strides=[1, 1],
+        padding="VALID")
+
+  def testConv2DEmpty(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2DEmpty test.")
+      return
+    # expected_output = []
+    self._VerifyValues(
+        tensor_in_sizes=[0, 2, 3, 3],
+        filter_in_sizes=[1, 1, 3, 3],
+        bias=[0.0, 0.0, 0.0],
+        strides=[1, 1],
+        padding="VALID")
+
+  def testConv2D2x2Filter(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2D2x2Filter test.")
+      return
+    # expected_output = [0.0, 0.0, 0.0, 401.0, 533.0, 665.0]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 2, 3, 3],
+        filter_in_sizes=[2, 2, 3, 3],
+        bias=[-2500.0, -2500.0, -2500.0],
+        strides=[1, 1],
+        padding="VALID")
+
+  def testConv2D1x2Filter(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2D1x2Filter test.")
+      return
+    # expected_output = [
+    #    0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 190.0, 265.0, 340.0, 343.0, 436.0, 529.0
+    # ]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 2, 3, 3],
+        filter_in_sizes=[1, 2, 3, 3],
+        bias=[-500.0, -500.0, -500.0],
+        strides=[1, 1],
+        padding="VALID")
+
+  def testConv2D2x2FilterStride2(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2D2x2FilterStride2 test.")
+      return
+    # expected_output = [0.0, 67.0, 163.0]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 2, 3, 3],
+        filter_in_sizes=[2, 2, 3, 3],
+        bias=[-2300.0, -2300.0, -2300.0],
+        strides=[2, 2],
+        padding="VALID")
+
+  def testConv2D2x2FilterStride2Same(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2D2x2FilterStride2Same test.")
+      return
+    # expected_output = [0.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 2, 3, 3],
+        filter_in_sizes=[2, 2, 3, 3],
+        bias=[-2300.0, -1000.0, -1000.0],
+        strides=[2, 2],
+        padding="SAME")
+
+  def testConv2D2x2FilterStride1x2(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2D2x2FilterStride1x2 test.")
+      return
+    # expected_output = [0.0, 0.0, 8.0, 28.0, 48.0, 68.0]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 3, 6, 1],
+        filter_in_sizes=[2, 2, 1, 1],
+        bias=[-90.0],
+        strides=[1, 2],
+        padding="VALID")
+
+  def testConv2DKernelSmallerThanStrideValid(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2DKernelSmallerThanStrideValid test.")
+      return
+    # expected_output = [0, 0, 175, 205]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 7, 7, 1],
+        filter_in_sizes=[2, 2, 1, 1],
+        bias=[-100.0],
+        strides=[3, 3],
+        padding="VALID")
+
+  def testConv2DKernelSmallerThanStrideSame(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2DKernelSmallerThanStrideSame test.")
+      return
+    # expected = [0, 0, 2, 4]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 3, 3, 1],
+        filter_in_sizes=[1, 1, 1, 1],
+        bias=[-5.0],
+        strides=[2, 2],
+        padding="SAME")
+
+    # expected = [0, 0, 4, 6]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 4, 4, 1],
+        filter_in_sizes=[1, 1, 1, 1],
+        bias=[-5.0],
+        strides=[2, 2],
+        padding="SAME")
+
+    # expected = [4, 0, 1, 0]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 4, 4, 1],
+        filter_in_sizes=[2, 2, 1, 1],
+        bias=[-40.0],
+        strides=[3, 3],
+        padding="SAME")
+
+  def testConv2DKernelSizeMatchesInputSize(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping Conv2DKernelSizeMatchesInputSize test.")
+      return
+    # expected = [0, 5]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 2, 2, 1],
+        filter_in_sizes=[2, 2, 1, 2],
+        bias=[-50.0, -55.0],
+        strides=[1, 1],
+        padding="VALID")
+
+    # expected = [0, 2, 282, 322]
+    self._VerifyValues(
+        tensor_in_sizes=[1, 8, 8, 1],
+        filter_in_sizes=[2, 2, 1, 1],
+        bias=[-200.0],
+        strides=[4, 4],
+        padding="SAME")
+
+  def testShapeFunctionEdgeCases(self):
+    # All shapes unknown.
+    c1 = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+        array_ops.placeholder(dtypes.float32),
+        array_ops.placeholder(dtypes.float32),
+        array_ops.placeholder(dtypes.float32),
+        strides=[1, 1, 1, 1],
+        padding="SAME",
+        activation_mode="Relu")
+    self.assertEqual([None, None, None, None], c1.get_shape().as_list())
+
+    # Incorrect input shape.
+    with self.assertRaises(ValueError):
+      fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+          array_ops.placeholder(dtypes.float32, shape=[1, 3]),
+          array_ops.placeholder(dtypes.float32),
+          array_ops.placeholder(dtypes.float32),
+          strides=[1, 1, 1, 1],
+          padding="SAME",
+          activation_mode="Relu")
+
+    # Incorrect filter shape.
+    with self.assertRaises(ValueError):
+      fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+          array_ops.placeholder(dtypes.float32),
+          array_ops.placeholder(dtypes.float32, shape=[1, 3]),
+          array_ops.placeholder(dtypes.float32),
+          strides=[1, 1, 1, 1],
+          padding="SAME",
+          activation_mode="Relu")
+
+    # Depth mismatch.
+    with self.assertRaises(ValueError):
+      fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+          array_ops.placeholder(dtypes.float32, shape=[32, 20, 20, 3]),
+          array_ops.placeholder(dtypes.float32, shape=[4, 4, 2, 2]),
+          array_ops.placeholder(dtypes.float32),
+          strides=[1, 1, 1, 1],
+          padding="SAME",
+          activation_mode="Relu")
+
+  def testOpEdgeCases(self, gpu_only=True):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping OpEdgeCases tests.")
+      return
+    with self.test_session() as sess:
+      # Illegal strides.
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   "strides in the batch and depth"):
+        sess.run(
+            fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+                array_ops.placeholder(dtypes.float32),
+                array_ops.placeholder(dtypes.float32),
+                array_ops.placeholder(dtypes.float32),
+                strides=[2, 1, 1, 1],
+                padding="SAME",
+                activation_mode="Relu"))
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   "strides in the batch and depth"):
+        sess.run(
+            fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+                array_ops.placeholder(dtypes.float32),
+                array_ops.placeholder(dtypes.float32),
+                array_ops.placeholder(dtypes.float32),
+                strides=[1, 1, 1, 2],
+                padding="SAME",
+                activation_mode="Relu"))
+
+      # Illegal activation mode.
+      with self.assertRaisesRegexp(ValueError,
+                                   "Op passed string 'Tanh' not in:"):
+        sess.run(
+            fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+                array_ops.placeholder(dtypes.float32),
+                array_ops.placeholder(dtypes.float32),
+                array_ops.placeholder(dtypes.float32),
+                strides=[1, 1, 1, 1],
+                padding="SAME",
+                activation_mode="Tanh"))
+
+      # Filter larger than input.
+      with self.assertRaisesRegexp(ValueError, "Negative dimension size"):
+        sess.run(
+            fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+                array_ops.placeholder(dtypes.float32, shape=[32, 20, 20, 3]),
+                array_ops.placeholder(dtypes.float32, shape=[20, 21, 3, 2]),
+                array_ops.placeholder(dtypes.float32, shape=[2]),
+                strides=[1, 1, 1, 1],
+                padding="VALID",
+                activation_mode="Relu"))
+      with self.assertRaisesRegexp(ValueError, "Negative dimension size"):
+        sess.run(
+            fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
+                array_ops.placeholder(dtypes.float32, shape=[32, 20, 20, 3]),
+                array_ops.placeholder(dtypes.float32, shape=[21, 20, 3, 2]),
+                array_ops.placeholder(dtypes.float32, shape=[2]),
+                strides=[1, 1, 1, 1],
+                padding="VALID",
+                activation_mode="Relu"))
+
+
+def GetInceptionFwdTest(input_size, filter_size, stride, padding,
+                        gpu_only=True):
+
+  def Test(self):
+    if gpu_only and not test.is_gpu_available():
+      tf_logging.info("Skipping InceptionFwd %s", (input_size, filter_size,
+                                                   stride, padding))
+      return
+    tf_logging.info("Testing InceptionFwd %s", (input_size, filter_size, stride,
+                                                padding))
+    self._CompareFwdValues(input_size, filter_size, [stride, stride], padding)
+
+  return Test
+
+
+if __name__ == "__main__":
+  for index, (input_size_, filter_size_, output_size_, stride_,
+              padding_) in enumerate(GetShrunkInceptionShapes()):
+    setattr(FusedConv2DBiasActivationTest, "testInceptionFwd_" + str(index),
+            GetInceptionFwdTest(input_size_, filter_size_, stride_, padding_))
+
+  # TODO(b/35359731)
+  # Fwd, BckInput, and BackFilter to test that for certain input parameter
+  # set, winograd nonfused algorithm will be excluded from conv autotune. If
+  # in such case, winograd nonfused algorithm is added as one option of the
+  # conv autotune, and cuDNN version is smaller than 7, the following tests
+  # will fail.
+  ishape = [1, 400, 400, 1]
+  fshape = [1, 1, 1, 256]
+  oshape = [1, 400, 400, 256]
+  setattr(FusedConv2DBiasActivationTest,
+          "testInceptionFwd_No_Winograd_Nonfused",
+          GetInceptionFwdTest(ishape, fshape, 1, "SAME", gpu_only=True))
+  test.main()
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 870efab858..5885d4ed52 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -384,6 +384,7 @@ tf_cuda_library(
         "framework/type_traits.h",
         "framework/types.h",
         "public/version.h",
+        "util/activation_mode.h",
         "util/bcast.h",
         "util/cuda_kernel_helper.h",
         "util/device_name_utils.h",
diff --git a/tensorflow/core/framework/common_shape_fns.cc b/tensorflow/core/framework/common_shape_fns.cc
index e115188cf4..38024fcf68 100644
--- a/tensorflow/core/framework/common_shape_fns.cc
+++ b/tensorflow/core/framework/common_shape_fns.cc
@@ -201,6 +201,30 @@ Status BiasAddGradShape(shape_inference::InferenceContext* c) {
   return Status::OK();
 }
 
+// input, filter, bias, output
+Status FusedConvBiasActivationShape(shape_inference::InferenceContext* c) {
+  TF_RETURN_IF_ERROR(Conv2DShape(c));
+
+  ShapeHandle bias_shape;
+  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(2), 1, &bias_shape));
+  DimensionHandle bias_dim = c->Dim(bias_shape, 0);
+
+  ShapeHandle filter_shape;
+  TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 4, &filter_shape));
+  DimensionHandle output_depth_dim = c->Dim(filter_shape, 3);
+
+  int64 output_depth_dim_val = c->Value(output_depth_dim);
+  int64 bias_dim_val = c->Value(bias_dim);
+
+  if (output_depth_dim_val != bias_dim_val) {
+    return errors::InvalidArgument(
+        "Output depth dimension (", output_depth_dim_val,
+        ") and bias dimension (", bias_dim_val, ") do not match.");
+  }
+
+  return Status::OK();
+}
+
 Status DimensionsFromShape(ShapeHandle shape, TensorFormat format,
                            DimensionHandle* batch_dim,
                            gtl::MutableArraySlice<DimensionHandle> spatial_dims,
@@ -425,7 +449,8 @@ Status DepthwiseConv2DNativeShape(shape_inference::InferenceContext* c) {
   int32 stride_rows;
   int32 stride_cols;
   if (s.ok() && data_format == "NCHW") {
-    // Convert input shape to default NHWC for inference
+    // Canonicalize input shape to NHWC so the shape inference code below can
+    // process it.
     input_shape =
         c->MakeShape({{c->Dim(input_shape, 0), c->Dim(input_shape, 2),
                        c->Dim(input_shape, 3), c->Dim(input_shape, 1)}});
@@ -505,7 +530,8 @@ Status AvgPoolShape(shape_inference::InferenceContext* c) {
   int32 kernel_rows, kernel_cols;
 
   if (s.ok() && data_format == "NCHW") {
-    // Convert input shape to default NHWC for inference.
+    // Canonicalize input shape to NHWC so the shape inference code below can
+    // process it.
     auto dim = [&](char dimension) {
       return c->Dim(input_shape, GetTensorDimIndex<2>(FORMAT_NCHW, dimension));
     };
@@ -580,7 +606,8 @@ Status MaxPoolShape(shape_inference::InferenceContext* c) {
   int32 kernel_rows, kernel_cols, kernel_depth;
 
   if (s.ok() && data_format == "NCHW") {
-    // Convert input shape to default NHWC for inference.
+    // Canonicalize input shape to NHWC so the shape inference code below can
+    // process it.
     auto dim = [&](char dimension) {
       return c->Dim(input_shape, GetTensorDimIndex<2>(FORMAT_NCHW, dimension));
     };
diff --git a/tensorflow/core/framework/common_shape_fns.h b/tensorflow/core/framework/common_shape_fns.h
index aafd9eff27..73b915652f 100644
--- a/tensorflow/core/framework/common_shape_fns.h
+++ b/tensorflow/core/framework/common_shape_fns.h
@@ -167,6 +167,9 @@ Status Conv2DShape(shape_inference::InferenceContext* c);
 // Shape function for Conv3D-like operations.
 Status Conv3DShape(shape_inference::InferenceContext* c);
 
+// Shape function for FusedConvBiasActivation operation.
+Status FusedConvBiasActivationShape(shape_inference::InferenceContext* c);
+
 // Shape function for DepthwiseConv2D-like operations.
 Status DepthwiseConv2DNativeShape(shape_inference::InferenceContext* c);
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 3044c7700f..49aa3a11ba 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -161,6 +161,15 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "conv_2d_hdrs",
+    hdrs = ["conv_2d.h"],
+    deps = [
+        ":eigen_helpers",
+        "//third_party/eigen3",
+    ],
+)
+
 tf_kernel_library(
     name = "extract_image_patches_op",
     prefix = "extract_image_patches_op",
@@ -247,6 +256,15 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "conv_ops_gpu_hdrs",
+    hdrs = ["conv_ops_gpu.h"],
+    deps = [
+        ":eigen_helpers",
+        "//third_party/eigen3",
+    ],
+)
+
 tf_cc_test(
     name = "ops_util_test",
     size = "small",
diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h
index b268f8dbd2..34d50fdc27 100644
--- a/tensorflow/core/kernels/conv_ops_gpu.h
+++ b/tensorflow/core/kernels/conv_ops_gpu.h
@@ -31,6 +31,8 @@ limitations under the License.
 
 namespace tensorflow {
 
+namespace dnn = ::perftools::gputools::dnn;
+
 // TODO(zhengxq): move this to gpu_util.h. The use of such wrappers is wide
 // spread.
 template <typename T>
@@ -56,12 +58,10 @@ class CudnnScratchAllocator : public perftools::gputools::ScratchAllocator {
   virtual ~CudnnScratchAllocator() {}
   CudnnScratchAllocator(int64 memory_limit, OpKernelContext* context)
       : memory_limit_(memory_limit), total_byte_size_(0), context_(context) {}
-  virtual int64 GetMemoryLimitInBytes(
-      perftools::gputools::Stream* stream) override {
+  int64 GetMemoryLimitInBytes(perftools::gputools::Stream* stream) override {
     return memory_limit_;
   }
-  virtual perftools::gputools::port::StatusOr<
-      perftools::gputools::DeviceMemory<uint8>>
+  perftools::gputools::port::StatusOr<perftools::gputools::DeviceMemory<uint8>>
   AllocateBytes(perftools::gputools::Stream* stream, int64 byte_size) override {
     Tensor temporary_memory;
     if (byte_size > memory_limit_) {
@@ -218,7 +218,7 @@ class AutoTuneMap {
       params_config_map_.insert(std::make_pair(params, ValueType{config, 1}));
       new_score = 1;
     } else if (iter->second.score < min_score_threshold_) {
-      DCHECK(iter->second.score > 0);
+      DCHECK_GT(iter->second.score, 0);
       if (iter->second.config != config) {
         // If it is different from the current winner, demotes the winner.
         VLOG(1) << GetActionSummary("demotes", params, config);
diff --git a/tensorflow/core/util/activation_mode.cc b/tensorflow/core/util/activation_mode.cc
new file mode 100644
index 0000000000..4bf947a0a9
--- /dev/null
+++ b/tensorflow/core/util/activation_mode.cc
@@ -0,0 +1,43 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/util/activation_mode.h"
+
+#include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/lib/core/errors.h"
+
+namespace tensorflow {
+
+Status GetActivationModeFromString(const string& str_value,
+                                   ActivationMode* value) {
+  if (str_value == "Sigmoid") {
+    *value = SIGMOID;
+  } else if (str_value == "Relu") {
+    *value = RELU;
+  } else if (str_value == "Relu6") {
+    *value = RELU6;
+  } else if (str_value == "ReluX") {
+    *value = RELUX;
+  } else if (str_value == "Tanh") {
+    *value = TANH;
+  } else if (str_value == "BandPass") {
+    *value = BANDPASS;
+  } else {
+    return errors::NotFound(str_value, " is not an allowed activation mode");
+  }
+  return Status::OK();
+}
+
+}  // end namespace tensorflow
diff --git a/tensorflow/core/util/activation_mode.h b/tensorflow/core/util/activation_mode.h
new file mode 100644
index 0000000000..2a8564847d
--- /dev/null
+++ b/tensorflow/core/util/activation_mode.h
@@ -0,0 +1,45 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_UTIL_ACTIVATION_MODE_H_
+#define TENSORFLOW_UTIL_ACTIVATION_MODE_H_
+
+// This file contains helper routines to deal with activation mode in various
+// ops and kernels.
+
+#include <string>
+
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+// ActivationMode: the activation function we apply to the input tensor:
+enum ActivationMode {
+  SIGMOID = 1,
+  RELU = 2,
+  RELU6 = 3,
+  RELUX = 4,
+  TANH = 5,
+  BANDPASS = 6,
+};
+
+// Specialization to parse an attribute directly into a ActivationMode enum.
+Status GetActivationModeFromString(const string& str_value,
+                                   ActivationMode* value);
+
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_UTIL_ACTIVATION_MODE_H_
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index dbe02cc521..b4d2589c7e 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -728,6 +728,7 @@ class ScopedActivationDescriptor {
  public:
   ScopedActivationDescriptor(CUDAExecutor* parent,
                              dnn::ActivationMode activation_mode,
+                             cudnnNanPropagation_t nan_propagation,
                              double value_max)
       : parent_(parent), handle_(nullptr) {
     cudnnStatus_t status =
@@ -762,8 +763,6 @@ class ScopedActivationDescriptor {
                    << static_cast<int>(activation_mode);
     }
 
-    // Always propagate nans.
-    cudnnNanPropagation_t nan_propagation = CUDNN_PROPAGATE_NAN;
     status = wrap::cudnnSetActivationDescriptor(parent_, handle_, mode,
                                                 nan_propagation, relu_ceiling);
     if (status != CUDNN_STATUS_SUCCESS) {
@@ -2050,9 +2049,7 @@ bool CudnnSupport::DoConvolveImpl(
   }
   const bool has_biases = (biases != nullptr);
   const bool supported_activation_mode =
-      (activation_mode == dnn::ActivationMode::kRelu6 ||
-       activation_mode == dnn::ActivationMode::kReluX ||
-       activation_mode == dnn::ActivationMode::kRelu);
+      (activation_mode == dnn::ActivationMode::kRelu);
 
   if (has_biases && !supported_activation_mode) {
     LOG(ERROR) << "cudnnConvolutionBiasActivationForward() only "
@@ -2060,11 +2057,11 @@ bool CudnnSupport::DoConvolveImpl(
     return false;
   }
 
-  if (has_biases && activation_mode != dnn::ActivationMode::kNone) {
+  if (has_biases && activation_mode == dnn::ActivationMode::kNone) {
     LOG(ERROR) << "To use cudnnConvolutionBiasActivationForward() "
                   "with a valid biases tensor, need to also provide "
                   "a valid activation mode (currently only supports "
-                  "kRelu6, kReluX, and kRelu).";
+                  "kRelu).";
     return false;
   }
 
@@ -2097,7 +2094,12 @@ bool CudnnSupport::DoConvolveImpl(
         .set_layout(dnn::DataLayout::kBatchYXDepth);
     ScopedTensorDescriptor bias_descriptor{
         parent_, bias_dimensions, static_cast<cudnnDataType_t>(cudnn_type)};
+    // CUDNN v6 only supports CUDNN_NOT_PROPAGATE_NAN as the reluNanOpt for
+    // activation descriptor. Note that this will change the nan propagation
+    // behavior from separate conv, bias, and relu (which by default is
+    // CUDNN_PROPAGATE_NAN.
     ScopedActivationDescriptor activation_desc{parent_, activation_mode,
+                                               CUDNN_NOT_PROPAGATE_NAN,
                                                output_descriptor.value_max()};
     status = wrap::cudnnConvolutionBiasActivationForward(
         parent_, ToHandle(dnn_handle_),
@@ -2106,7 +2108,7 @@ bool CudnnSupport::DoConvolveImpl(
         /*filterData=*/filter_data.opaque(), /*convDesc=*/conv.handle(),
         /*algo=*/algo, /*workSpace=*/scratch.opaque(),
         /*workSpaceSizeInBytes=*/scratch.size(), /*alpha2=*/&beta,
-        /*zDesc=*/output_nd.handle(), /*z=*/nullptr,
+        /*zDesc=*/output_nd.handle(), /*z=*/input_data.opaque(),
         /*biasDesc=*/bias_descriptor.handle(),
         /*bias=*/biases.opaque(), /*activationDesc=*/activation_desc.handle(),
         /*destDesc=*/output_nd.handle(), /*destData=*/output_data->opaque());
@@ -3288,8 +3290,8 @@ bool CudnnSupport::DoActivate(Stream* stream,
   }
 
 #if CUDNN_VERSION >= 5000
-  ScopedActivationDescriptor activation_desc{parent_, activation_mode,
-                                             dimensions.value_max()};
+  ScopedActivationDescriptor activation_desc{
+      parent_, activation_mode, CUDNN_PROPAGATE_NAN, dimensions.value_max()};
 #else
   cudnnActivationMode_t mode;
   switch (activation_mode) {
-- 
GitLab


From b4056fcc2c1a7003c190e1345b8b534d8353e21a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 09:46:10 -0700
Subject: [PATCH 1149/1375] Make boosted_trees work on windows.

PiperOrigin-RevId: 162626331
---
 .../contrib/cmake/tf_core_kernels.cmake       | 27 +++++++++++++++++++
 tensorflow/contrib/cmake/tf_core_ops.cmake    |  7 +++++
 tensorflow/contrib/cmake/tf_python.cmake      | 21 +++++++++++++++
 3 files changed, 55 insertions(+)

diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 57df33c13e..ca4c9f0026 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -40,6 +40,33 @@ endif(tensorflow_BUILD_ALL_KERNELS)
 
 if(tensorflow_BUILD_CONTRIB_KERNELS)
   set(tf_contrib_kernels_srcs
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/kernels/ensemble_optimizer_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/kernels/model_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/kernels/training_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/utils/batch_features.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/utils/examples_iterable.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/utils/parallel_for.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/utils/sparse_column_iterable.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/example_partitioner.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/bias-feature-column-handler.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/categorical-feature-column-handler.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/dense-quantized-feature-column-handler.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/models/multiple_additive_trees.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/lib/trees/decision_tree.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/model_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/training_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/clustering_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/masked_matmul_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc"
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index b350b822dd..e04972b024 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -71,6 +71,13 @@ file(GLOB_RECURSE tpu_ops_srcs
      "${tensorflow_source_dir}/tensorflow/contrib/tpu/ops/*.cc"
 )
 
+GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_model "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/model_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_split_handler "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_training "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/training_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_prediction "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/prediction_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_quantiles "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_ensemble_optimzier "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/ensemble_optimizer_ops.cc")
+GENERATE_CONTRIB_OP_LIBRARY(boosted_trees_stats_accumulator "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(cudnn_rnn "${tensorflow_source_dir}/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(factorization_clustering "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/clustering_ops.cc")
 GENERATE_CONTRIB_OP_LIBRARY(factorization_factorization "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/factorization_ops.cc")
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 8701a584e8..f32e7adaec 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -124,6 +124,7 @@ file(GLOB_RECURSE tf_protos_python_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/core/*.proto"
     "${tensorflow_source_dir}/tensorflow/core/profiler/*.proto"
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
+    "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/decision_trees/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/proto/*.proto"
@@ -141,6 +142,7 @@ RELATIVE_PROTOBUF_GENERATE_PYTHON(
 file(GLOB_RECURSE tf_python_protos_cc_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/core/profiler/*.proto"
     "${tensorflow_source_dir}/tensorflow/python/*.proto"
+    "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto"
@@ -253,7 +255,12 @@ add_python_module("tensorflow/contrib/bayesflow/python")
 add_python_module("tensorflow/contrib/bayesflow/python/kernel_tests")
 add_python_module("tensorflow/contrib/bayesflow/python/ops")
 add_python_module("tensorflow/contrib/boosted_trees")
+add_python_module("tensorflow/contrib/boosted_trees/estimator_batch")
+add_python_module("tensorflow/contrib/boosted_trees/ops")
 add_python_module("tensorflow/contrib/boosted_trees/proto")
+add_python_module("tensorflow/contrib/boosted_trees/python")
+add_python_module("tensorflow/contrib/boosted_trees/python/kernel_tests")
+add_python_module("tensorflow/contrib/boosted_trees/python/ops")
 add_python_module("tensorflow/contrib/cloud")
 add_python_module("tensorflow/contrib/cloud/kernels")
 add_python_module("tensorflow/contrib/cloud/ops")
@@ -654,6 +661,20 @@ GENERATE_PYTHON_OP_LIB("user_ops")
 GENERATE_PYTHON_OP_LIB("training_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/training/gen_training_ops.py)
 
+GENERATE_PYTHON_OP_LIB("contrib_boosted_trees_model_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/boosted_trees/python/ops/gen_model_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_boosted_trees_split_handler_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/boosted_trees/python/ops/gen_split_handler_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_boosted_trees_training_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/boosted_trees/python/ops/gen_training_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_boosted_trees_prediction_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/boosted_trees/python/ops/gen_prediction_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_boosted_trees_quantiles_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/boosted_trees/python/ops/gen_quantile_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_boosted_trees_ensemble_optimzier_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/boosted_trees/python/ops/gen_ensemble_optimizer_ops.py)
+GENERATE_PYTHON_OP_LIB("contrib_boosted_trees_stats_accumulator_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/boosted_trees/python/ops/gen_stats_accumulator_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_cudnn_rnn_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/cudnn_rnn/ops/gen_cudnn_rnn_ops.py)
 GENERATE_PYTHON_OP_LIB("contrib_factorization_clustering_ops"
-- 
GitLab


From 7f6e728e11d41f4ce0d374f4ef692ae82dd4eb7b Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Thu, 20 Jul 2017 10:22:08 -0700
Subject: [PATCH 1150/1375] Java: Release 1.3.0-rc0

PiperOrigin-RevId: 162631285
---
 tensorflow/java/maven/libtensorflow/pom.xml     | 2 +-
 tensorflow/java/maven/libtensorflow_jni/pom.xml | 2 +-
 tensorflow/java/maven/pom.xml                   | 2 +-
 tensorflow/java/maven/proto/pom.xml             | 2 +-
 tensorflow/java/maven/tensorflow/pom.xml        | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml
index 462e38d332..a955f14275 100644
--- a/tensorflow/java/maven/libtensorflow/pom.xml
+++ b/tensorflow/java/maven/libtensorflow/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0</version>
+    <version>1.3.0-rc0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>libtensorflow</artifactId>
diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml
index 4002de97bd..8f1062ff3b 100644
--- a/tensorflow/java/maven/libtensorflow_jni/pom.xml
+++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0</version>
+    <version>1.3.0-rc0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>libtensorflow_jni</artifactId>
diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml
index f1d8aa1715..6c378f07de 100644
--- a/tensorflow/java/maven/pom.xml
+++ b/tensorflow/java/maven/pom.xml
@@ -6,7 +6,7 @@
   <modelVersion>4.0.0</modelVersion>
   <groupId>org.tensorflow</groupId>
   <artifactId>parentpom</artifactId>
-  <version>1.2.0</version>
+  <version>1.3.0-rc0</version>
   <packaging>pom</packaging>
 
   <url>https://www.tensorflow.org</url>
diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index f2974220d9..72984b740e 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0</version>
+    <version>1.3.0-rc0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>proto</artifactId>
diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml
index 21c2207404..89c67d6b60 100644
--- a/tensorflow/java/maven/tensorflow/pom.xml
+++ b/tensorflow/java/maven/tensorflow/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.2.0</version>
+    <version>1.3.0-rc0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>tensorflow</artifactId>
-- 
GitLab


From e925629edb5b72247c9105d8eac941c9c2aad565 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 10:28:07 -0700
Subject: [PATCH 1151/1375] fix map_func in `read_batch_features`.

PiperOrigin-RevId: 162632092
---
 tensorflow/contrib/data/python/ops/dataset_ops.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index 81da8b050a..0a6933274c 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -688,6 +688,11 @@ class Dataset(object):
     else:
       dataset = reader(filenames)
     dataset = dataset.repeat(num_epochs)
+    if dataset.output_types == (dtypes.string, dtypes.string):
+      dataset = dataset.map(lambda unused_k, v: v)
+    elif dataset.output_types != dtypes.string:
+      raise TypeError("`reader` must be a dataset of `tf.string` values, "
+                      "or `(tf.string, tf.string)` key-value pairs.")
     if randomize_input:
       dataset = dataset.shuffle(capacity)
     dataset = dataset.map(lambda x: _parse_example(nest.flatten(x), features))
-- 
GitLab


From 3a593f7555309982b19d50e7436fb6d320384b50 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 10:34:29 -0700
Subject: [PATCH 1152/1375] Simplify BUILD by changing the default of
 require_shape_functions in tf_gen_op_wrapper_private_py rule.

PiperOrigin-RevId: 162632990
---
 tensorflow/python/BUILD          | 26 +-------------------------
 tensorflow/python/build_defs.bzl |  4 ++--
 2 files changed, 3 insertions(+), 27 deletions(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 8833ebb3bd..922f9baf78 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -885,7 +885,6 @@ cc_library(
 tf_gen_op_wrapper_py(
     name = "test_ops_2",
     out = "framework/test_ops_2.py",
-    require_shape_functions = True,
     deps = [":test_ops_2_kernels"],
 )
 
@@ -1075,7 +1074,6 @@ py_test(
 
 tf_gen_op_wrapper_private_py(
     name = "array_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow/compiler/tests:__pkg__",
@@ -1097,13 +1095,11 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "candidate_sampling_ops_gen",
-    require_shape_functions = True,
     visibility = ["//learning/brain/python/ops:__pkg__"],
 )
 
 tf_gen_op_wrapper_private_py(
     name = "control_flow_ops_gen",
-    require_shape_functions = True,
     visibility = ["//learning/brain/python/ops:__pkg__"],
     deps = [
         "//tensorflow/core:control_flow_ops_op_lib",
@@ -1113,12 +1109,10 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "ctc_ops_gen",
-    require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "data_flow_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow/python/kernel_tests:__pkg__",
@@ -1127,7 +1121,6 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "dataset_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow:__subpackages__",
@@ -1137,13 +1130,11 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "image_ops_gen",
-    require_shape_functions = True,
     visibility = ["//learning/brain/python/ops:__pkg__"],
 )
 
 tf_gen_op_wrapper_private_py(
     name = "io_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow/python/kernel_tests:__pkg__",
@@ -1152,13 +1143,11 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "linalg_ops_gen",
-    require_shape_functions = True,
     visibility = ["//learning/brain/python/ops:__pkg__"],
 )
 
 tf_gen_op_wrapper_private_py(
     name = "logging_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow/python/kernel_tests:__pkg__",
@@ -1167,7 +1156,6 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "lookup_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow/contrib/lookup:__pkg__",
@@ -1177,7 +1165,6 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "math_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/google/python/ops:__pkg__",
         "//learning/brain/python/ops:__pkg__",
@@ -1189,7 +1176,6 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "nn_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow/compiler/tests:__pkg__",
@@ -1201,7 +1187,6 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "parsing_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
     ],
@@ -1209,34 +1194,28 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "random_ops_gen",
-    require_shape_functions = True,
     visibility = ["//learning/brain/python/ops:__pkg__"],
 )
 
 tf_gen_op_wrapper_private_py(
     name = "resource_variable_ops_gen",
-    require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "script_ops_gen",
-    require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "sdca_ops_gen",
-    require_shape_functions = True,
     visibility = ["//tensorflow/contrib/linear_optimizer:__pkg__"],
 )
 
 tf_gen_op_wrapper_private_py(
     name = "set_ops_gen",
-    require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "state_ops_gen",
-    require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
         "//tensorflow/contrib/framework:__pkg__",
@@ -1246,27 +1225,24 @@ tf_gen_op_wrapper_private_py(
 
 tf_gen_op_wrapper_private_py(
     name = "sparse_ops_gen",
-    require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "spectral_ops_gen",
-    require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "string_ops_gen",
-    require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "user_ops_gen",
+    require_shape_functions = False,
 )
 
 tf_gen_op_wrapper_private_py(
     name = "training_ops_gen",
     out = "training/gen_training_ops.py",
-    require_shape_functions = True,
 )
 
 py_library(
diff --git a/tensorflow/python/build_defs.bzl b/tensorflow/python/build_defs.bzl
index 1efac5738c..2d8625933f 100644
--- a/tensorflow/python/build_defs.bzl
+++ b/tensorflow/python/build_defs.bzl
@@ -13,13 +13,13 @@ load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 # hard code the ops/ directory.
 
 def tf_gen_op_wrapper_private_py(name, out=None, deps=[],
-                                 require_shape_functions=False,
+                                 require_shape_functions=True,
                                  visibility=[]):
   if not name.endswith("_gen"):
     fail("name must end in _gen")
   if not visibility:
     visibility = ["//visibility:private"]
-  bare_op_name = name[:-4] # Strip of the _gen
+  bare_op_name = name[:-4] # Strip off the _gen
   tf_gen_op_wrapper_py(name=bare_op_name,
     out=out,
     hidden_file="ops/hidden_ops.txt",
-- 
GitLab


From 28c642d73446057dd19bed86d9749a0c1b27a201 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 10:45:08 -0700
Subject: [PATCH 1153/1375] Remove trig.py in favor of
 math_ops.{sinh,asinh,cosh,acosh}.

The math_ops functions seems to be numerically stable enough for our purposes, as will be faster.

PiperOrigin-RevId: 162634739
---
 tensorflow/contrib/distributions/BUILD        |  17 --
 .../python/kernel_tests/trig_test.py          | 262 ------------------
 .../python/ops/bijectors/sinh_arcsinh_impl.py |  23 +-
 .../contrib/distributions/python/ops/trig.py  | 173 ------------
 4 files changed, 18 insertions(+), 457 deletions(-)
 delete mode 100644 tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
 delete mode 100644 tensorflow/contrib/distributions/python/ops/trig.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index d8278ca90b..239cb5ae7c 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -548,23 +548,6 @@ cuda_py_test(
     ],
 )
 
-cuda_py_test(
-    name = "trig_test",
-    size = "small",
-    srcs = ["python/kernel_tests/trig_test.py"],
-    additional_deps = [
-        ":distributions_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:nn_ops",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py b/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
deleted file mode 100644
index 8c856abf8b..0000000000
--- a/tensorflow/contrib/distributions/python/kernel_tests/trig_test.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for Trigonometric functions."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.contrib.distributions.python.ops import trig
-from tensorflow.python.framework import constant_op
-from tensorflow.python.ops import gradients_impl
-from tensorflow.python.platform import test
-
-
-class SinhTest(test.TestCase):
-
-  def test_versus_numpy_scalar_positive_x(self):
-    x = 1.23
-    with self.test_session():
-      self.assertAllClose(np.sinh(x), trig.sinh(x).eval())
-
-  def test_versus_numpy_multidim_x(self):
-    x = [[0., -1.], [0.5, -2.]]
-    with self.test_session():
-      self.assertAllClose(np.sinh(x), trig.sinh(x).eval())
-
-
-class ArcSinhTest(test.TestCase):
-  """Test arcsinh.
-
-  Note that many tests below were run over a range of values using np.logspace.
-  The accuracy was highly dependent on the exact values used within that range,
-  and whether our approximation matched with numpy.  For that reason, we used
-  1000 values over every range, which ensures we hit most of the "tricky" values
-  """
-
-  def _assert_all_finite(self, values):
-    self.assertAllEqual(np.ones_like(values).astype(np.bool),
-                        np.isfinite(values))
-
-  def test_versus_numpy_scalar_positive_x(self):
-    x = 1.23
-    with self.test_session():
-      self.assertAllClose(np.arcsinh(x), trig.arcsinh(x).eval())
-
-  def test_versus_numpy_at_zero(self):
-    # Zero is especially difficult.
-    with self.test_session() as sess:
-      x = constant_op.constant(0.)
-      y = trig.arcsinh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      x_, y_, grad_ = sess.run([x, y, grad])
-      self.assertAllClose(np.arcsinh(x_), y_)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-  def test_versus_numpy_multidim_x(self):
-    with self.test_session() as sess:
-      x = constant_op.constant([[0., -1.], [0.5, -2.]])
-      y = trig.arcsinh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      x_, y_, grad_ = sess.run([x, y, grad])
-      self.assertAllClose(np.arcsinh(x_), y_)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-  def test_versus_numpy_positive_values_32_bit(self):
-    with self.test_session() as sess:
-      # Larger than 38 is Inf in float32.
-      x = constant_op.constant(np.logspace(0, 38, num=2000).astype(np.float32))
-      y = trig.arcsinh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      x_, y_, grad_ = sess.run([x, y, grad])
-      self.assertAllClose(
-          np.arcsinh(x_),  # numpy does this in 64bit
-          y_,
-          rtol=1e-6)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-  def test_versus_numpy_moderate_negative_values_32_bit(self):
-    # The moderate negative values were the most difficult to get close to
-    # numpy.
-    with self.test_session() as sess:
-      x = constant_op.constant(-np.logspace(0, 10, num=1000).astype(np.float32))
-      y = trig.arcsinh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      x_, y_, grad_ = sess.run([x, y, grad])
-      self.assertAllClose(
-          np.arcsinh(x_),  # numpy does this in 64bit
-          y_,
-          rtol=1e-4)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-  def test_versus_numpy_extreme_negative_values_32_bit(self):
-    # For these extreme values arcsinh uses the approximation 1 / (2 * x), and
-    # 1 / 10^38 = 0 in 32bit...so stop at 10^37.
-    with self.test_session() as sess:
-      x = constant_op.constant(
-          -np.logspace(10, 37, num=1000).astype(np.float32))
-      y = trig.arcsinh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      x_, y_, grad_ = sess.run([x, y, grad])
-      self.assertAllClose(
-          np.arcsinh(x_),  # numpy does this in 64bit
-          y_,
-          rtol=1e-6)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-  def test_versus_numpy_positive_values_64_bit(self):
-    with self.test_session() as sess:
-      x = constant_op.constant(np.logspace(0, 200, num=1000))
-      y = trig.arcsinh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      x_, y_, grad_ = sess.run([x, y, grad])
-      self.assertAllClose(
-          np.arcsinh(x_),
-          y_,
-          rtol=1e-6)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-  def test_versus_numpy_negative_values_64_bit(self):
-    with self.test_session() as sess:
-      x = constant_op.constant(-np.logspace(0, 200, num=1000))
-      y = trig.arcsinh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      x_, y_, grad_ = sess.run([x, y, grad])
-      self.assertAllClose(
-          np.arcsinh(x_),
-          y_,
-          rtol=1e-5)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-  def test_arcsinh_is_inverse_to_sinh_near_zero(self):
-    sinh = trig.sinh
-    arcsinh = trig.arcsinh
-    with self.test_session() as sess:
-      x = np.linspace(-1.1, 1.1, num=1000).astype(np.float32)
-      arcsinh_x = arcsinh(x)
-      sinh_arcsinh_x = sinh(arcsinh_x)
-      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
-
-      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
-          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
-
-    self.assertAllClose(x, sinh_arcsinh_x_)
-    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
-
-  def test_arcsinh_is_inverse_to_sinh_where_x_is_very_small(self):
-    sinh = trig.sinh
-    arcsinh = trig.arcsinh
-    # Exact same cutoff as is in the code.
-    very_small_cutoff = -0.01 / np.sqrt(np.finfo(np.float32).eps)
-    with self.test_session() as sess:
-      x = -np.logspace(
-          np.log(-very_small_cutoff),
-          np.log(-1000 * very_small_cutoff),
-          num=1000).astype(np.float32)
-      arcsinh_x = arcsinh(x)
-      sinh_arcsinh_x = sinh(arcsinh_x)
-      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
-
-      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
-          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
-
-    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
-    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
-
-  def test_arcsinh_is_inverse_to_sinh_where_x_is_moderate_or_big(self):
-    sinh = trig.sinh
-    arcsinh = trig.arcsinh
-    very_big_cutoff = np.sqrt(np.finfo(np.float32).max)
-    with self.test_session() as sess:
-      x = np.linspace(1., very_big_cutoff, num=1000).astype(np.float32)
-      arcsinh_x = arcsinh(x)
-      sinh_arcsinh_x = sinh(arcsinh_x)
-      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
-
-      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
-          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
-
-    self._assert_all_finite(arcsinh_x_)
-    self._assert_all_finite(sinh_arcsinh_x_)
-    self._assert_all_finite(arcsinh_sinh_arcsinh_x_)
-    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
-    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
-
-  def test_arcsinh_is_inverse_to_sinh_where_x_is_very_big(self):
-    sinh = trig.sinh
-    arcsinh = trig.arcsinh
-    maxfloat = np.finfo(np.float32).max  # Maximum representable float.
-    with self.test_session() as sess:
-      x = np.logspace(
-          0.5 * np.log(maxfloat),
-          0.95 * np.log(maxfloat), num=1000, base=np.e).astype(np.float32)
-      arcsinh_x = arcsinh(x)
-      sinh_arcsinh_x = sinh(arcsinh_x)
-      arcsinh_sinh_arcsinh_x = arcsinh(sinh_arcsinh_x)
-
-      arcsinh_x_, sinh_arcsinh_x_, arcsinh_sinh_arcsinh_x_ = sess.run(
-          [arcsinh_x, sinh_arcsinh_x, arcsinh_sinh_arcsinh_x])
-
-    self._assert_all_finite(arcsinh_x_)
-    self._assert_all_finite(sinh_arcsinh_x_)
-    self._assert_all_finite(arcsinh_sinh_arcsinh_x_)
-    self.assertAllClose(x, sinh_arcsinh_x_, rtol=1e-5)
-    self.assertAllClose(arcsinh_x_, arcsinh_sinh_arcsinh_x_)
-
-
-class CoshTest(test.TestCase):
-
-  def test_versus_numpy_scalar_positive_x(self):
-    x = 1.23
-    with self.test_session():
-      self.assertAllClose(np.cosh(x), trig.cosh(x).eval())
-
-  def test_versus_numpy_multidim_x(self):
-    x = [[0., -1.], [0.5, -2.]]
-    with self.test_session():
-      self.assertAllClose(np.cosh(x), trig.cosh(x).eval())
-
-
-class LogCoshTest(test.TestCase):
-
-  def _assert_all_finite(self, values):
-    self.assertAllEqual(np.ones_like(values).astype(np.bool),
-                        np.isfinite(values))
-
-  def test_versus_numpy_scalar_32bit(self):
-    with self.test_session() as sess:
-      x_64 = np.linspace(-200, 200, 2000)
-      x = constant_op.constant(x_64.astype(np.float32))
-
-      y = trig.log_cosh(x)
-      grad = gradients_impl.gradients(y, x)[0]
-      y_, grad_ = sess.run([y, grad])
-
-      self.assertAllClose(np.log(np.cosh(x_64)), y_)
-      self._assert_all_finite(y_)
-      self._assert_all_finite(grad_)
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
index 41ee47636d..4007e605b6 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh_impl.py
@@ -18,15 +18,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.distributions.python.ops import trig
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import bijector
 
-log_cosh = trig.log_cosh
-
 __all__ = [
     "SinhArcsinh",
 ]
@@ -122,9 +119,17 @@ class SinhArcsinh(bijector.Bijector):
     # dx/dy
     # = cosh(arcsinh(y) / tailweight - skewness)
     #     / (tailweight * sqrt(y**2 + 1))
+    # Note that this could potentially return a NaN due to the log1p(x**2)
+    # term since, for instance, this will only be valid for float32 til ~1.7e19.
+    # This is in contrast with the forward/inverse passes since an arcsinh
+    # transformation is done first, which is valid until the maximum float
+    # value.
+    # TODO(srvasude): It might be possible to extend the range of validity to
+    # match that of forward/inverse by approximating log1p(y**2) by 2 * log(y).
     event_dims = self._event_dims_tensor(y)
     return math_ops.reduce_sum(
-        log_cosh(math_ops.asinh(y) / self.tailweight - self.skewness) -
+        math_ops.log(math_ops.cosh(
+            math_ops.asinh(y) / self.tailweight - self.skewness)) -
         math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(y**2),
         axis=event_dims)
 
@@ -133,8 +138,16 @@ class SinhArcsinh(bijector.Bijector):
     # Using sinh' = cosh, arcsinh'(x) = 1 / sqrt(x**2 + 1),
     # dy/dx
     # = cosh((arcsinh(x) + skewness) * tailweight) * tailweight / sqrt(x**2 + 1)
+    # Note that this could potentially return a NaN due to the log1p(x**2)
+    # term since, for instance, this will only be valid for float32 til ~1.7e19.
+    # This is in contrast with the forward/inverse passes since an arcsinh
+    # transformation is done first, which is valid until the maximum float
+    # value.
+    # TODO(srvasude): It might be possible to extend the range of validity to
+    # match that of forward/inverse by approximating log1p(y**2) by 2 * log(y).
     event_dims = self._event_dims_tensor(x)
     return math_ops.reduce_sum(
-        log_cosh((math_ops.asinh(x) + self.skewness) * self.tailweight) +
+        math_ops.log(math_ops.cosh(
+            (math_ops.asinh(x) + self.skewness) * self.tailweight)) +
         math_ops.log(self.tailweight) - 0.5 * math_ops.log1p(x**2),
         axis=event_dims)
diff --git a/tensorflow/contrib/distributions/python/ops/trig.py b/tensorflow/contrib/distributions/python/ops/trig.py
deleted file mode 100644
index e49288e5b1..0000000000
--- a/tensorflow/contrib/distributions/python/ops/trig.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Trigonometric functions."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import clip_ops
-from tensorflow.python.ops import math_ops
-
-__all__ = [
-    "sinh",
-    "arcsinh",
-    "cosh",
-    "log_cosh",
-]
-
-
-def sinh(x, name="sinh"):
-  """Hyperbolic sin:  `sinh(x) = (e**x - e**-x) / 2`.
-
-  For `x in (-inf, inf)`, `arcsinh(sinh(x)) = sinh(arcsinh(x)) = x.`
-
-  Args:
-    x:  Numeric `Tensor`.
-    name:  A string name to prepend to created Ops.
-
-  Returns:
-    Numeric `Tensor` of same `shape` and `dtype` as `x`.
-  """
-  with ops.name_scope(name):
-    # For x near zero, this could be replaced with the identity x --> x as a
-    # Taylor approximation.  The Taylor approximation would be more accurate,
-    # and the current implementation will equal zero "too often", specifically
-    # for |x| small enough so that exp(x) = 1 (up to floating point precision).
-    # Although less accurate, the present implementation is still safe for
-    # antipated use.  Why?  No one would use this function and expect that zero
-    # was not a possible output value.
-    x = ops.convert_to_tensor(x, name="x")
-    return 0.5 * (math_ops.exp(x) - math_ops.exp(-x))
-
-
-def arcsinh(x, name="arcsinh"):
-  """Inverse hyperbolic sin:  `arcsinh(x) = log(x + sqrt(x**2 + 1))`.
-
-  For `x in (-inf, inf)`, `arcsinh(sinh(x)) = sinh(arcsinh(x)) = x.`
-
-  Args:
-    x:  Numeric `Tensor`.
-    name:  A string name to prepend to created Ops.
-
-  Returns:
-    Numeric `Tensor` of same `shape` and `dtype` as `x`.
-  """
-  with ops.name_scope(name):
-    x = ops.convert_to_tensor(x, name="x")
-    finfo = np.finfo(x.dtype.as_numpy_dtype)
-
-    # To compute stable arcsinh(x), we will compute various approximations of
-    # z := x + sqrt(x**2 + 1), and then arcsinh(x) = log(z).
-    # Different approximations are used over different values of x, then the
-    # result is pieced together using tf.where.  Since NaN propagate through the
-    # unselected branch during grdients of where, care is taken to ensure that
-    # every approximation is finite for all input values.
-
-    # For x near zero, the straightforward formula for z is fine.
-    # This formula will have trouble once x < 0 and x**2 + 1 = x**2, since then
-    # z = 0 (numerically), and then we have log(z) = log(0) = -inf
-    # This formula also has trouble once x > sqrt(finfo.max), since then
-    # x**2 = inf, and thus z = inf.  Therefore we clip.
-    x_near_zero = clip_ops.clip_by_value(x, -1., 1.)
-    x_is_near_zero = math_ops.abs(x) < 1.
-    z_for_x_near_zero = x + math_ops.sqrt(x_near_zero**2 + 1)
-
-    # Some cutoffs.
-    # Important!  Keep these cutoffs in sync with the tests, which use cutoffs
-    # of the exact same name.
-    # very_big_cutoff**2 = finfo.max, the maximum representable value.
-    # very_small_cutoff could have been defined as 1 / sqrt(eps), so that for
-    # x < very_small_cutoff, 1 / x**2 + 1 = 1, which causes trouble.
-    # The 0.01 was added in order to make this match numpy in 32bit
-    # as much as possible.  Anything < 1 should be stable.
-    very_small_cutoff = -0.01 / np.sqrt(finfo.eps)
-    very_big_cutoff = np.sqrt(finfo.max)
-
-    # For very_small_cutoff < x < -1, and 1 < x < very_big_cutoff, and
-    # x != 0, we can use
-    # z = sqrt(x**2 + 1) = |x| * sqrt(1 + 1 / x**2).
-    # This formula has trouble if x < -sqrt(eps) since then 1 + 1 / x**2 = 1,
-    # and then we get z = x + |x| = 0, thus returning log(0) = -inf.
-    # This formula also has trouble if x**2 = Inf.  Therefore we clip.
-    # This formula also has trouble if x = 0, since then we have 1 / 0**2 = inf.
-    x_not_near_zero = array_ops.where(
-        x >= 0.,
-        math_ops.maximum(x, 1.),
-        math_ops.minimum(x, -1.))
-    x_clipped_moderate_or_big = clip_ops.clip_by_value(
-        x_not_near_zero, very_small_cutoff, very_big_cutoff)
-    z_for_moderate_or_big_x = x + math_ops.abs(x) * math_ops.sqrt(
-        1. + 1. / x_clipped_moderate_or_big**2)
-
-    # For x < very_small_cutoff, we use the first order Taylor series,
-    # sqrt(1 + 1 / x**2) approx 1 + 1 / (2 * x**2)
-    # This formula has trouble for x = 0.
-    x_is_very_small = x < very_small_cutoff
-    z_for_very_small_x = 1 / (2. * math_ops.abs(x_not_near_zero))
-
-    z = array_ops.where(
-        x_is_near_zero,
-        z_for_x_near_zero,
-        array_ops.where(
-            x_is_very_small,
-            z_for_very_small_x,
-            z_for_moderate_or_big_x))
-
-    return math_ops.log(z)
-
-
-def cosh(x, name="cosh"):
-  """Hyperbolic cosine:  `cosh(x) = (e**x + e**-x) / 2`.
-
-  For `x in (-inf, inf)`, `arccosh(cosh(x)) = cosh(arccosh(x)) = x.`
-
-  Args:
-    x:  Numeric `Tensor`.
-    name:  A string name to prepend to created Ops.
-
-  Returns:
-    Numeric `Tensor` of same `shape` and `dtype` as `x`.
-  """
-  with ops.name_scope(name):
-    x = ops.convert_to_tensor(x, name="x")
-    return 0.5 * (math_ops.exp(x) + math_ops.exp(-x))
-
-
-def log_cosh(x, name="log_cosh"):
-  """Logarithm of hyperbolic cosine:  `log_cosh(x) = Log[(e**x + e**-x) / 2]`.
-
-  Args:
-    x:  Numeric `Tensor`.
-    name:  A string name to prepend to created Ops.
-
-  Returns:
-    Numeric `Tensor` of same `shape` and `dtype` as `x`.
-  """
-  # For large |x| >> 1, e**x will become Inf.  So we need to approximate
-  # Log[e**x + e**-x] approx |x|.
-  # We also need to ensure that large |x| is never fed to the exponential func.
-  with ops.name_scope(name):
-    x = ops.convert_to_tensor(x, name="x")
-    large_x_value = 0.9 * np.log(np.finfo(x.dtype.as_numpy_dtype).max)
-    x_capped = clip_ops.clip_by_value(x, -large_x_value, large_x_value)
-    return array_ops.where(
-        math_ops.abs(x) > large_x_value,
-        math_ops.abs(x) - np.log(2).astype(x.dtype.as_numpy_dtype),
-        math_ops.log(cosh(x_capped)))
-- 
GitLab


From e31a4d340b6188f4a77fbfb16476c22774739053 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 11:57:29 -0700
Subject: [PATCH 1154/1375] Read xla_hlo_profile() from the right DebugOptions
 object when assigning HloModuleConfing::_hlo_profiling.

PiperOrigin-RevId: 162646184
---
 tensorflow/compiler/xla/service/service.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 37458988e0..d4cdc6df30 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -328,7 +328,7 @@ StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig(
             shape_with_output_layout));
   }
 
-  if (module_config->debug_options().xla_hlo_profile()) {
+  if (execution_options.debug_options().xla_hlo_profile()) {
     module_config->enable_hlo_profiling(true);
   }
 
-- 
GitLab


From 7eb0fac662369728bb98174b91bd327dd06905cc Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 20 Jul 2017 12:13:33 -0700
Subject: [PATCH 1155/1375] tfdbg: allow CLI to display feed values

Also updates programmer_guide/debugger.md

RELNOTES: TensorFlow Debugger (tfdbg) CLI: added ability to display feed_dict values to Session.run through the "pf" / "print_feed" command and clickable links in the curses UI.
PiperOrigin-RevId: 162648456
---
 .../docs_src/programmers_guide/debugger.md    |   2 +
 tensorflow/python/debug/BUILD                 |   3 +
 tensorflow/python/debug/cli/analyzer_cli.py   |  48 +----
 tensorflow/python/debug/cli/cli_shared.py     |  52 +++---
 tensorflow/python/debug/cli/command_parser.py |  53 ++++++
 .../debug/wrappers/local_cli_wrapper.py       |  55 ++++++
 .../debug/wrappers/local_cli_wrapper_test.py  | 164 +++++++++++-------
 7 files changed, 253 insertions(+), 124 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 26128fc2d2..14b8143d26 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -153,6 +153,8 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at
 | **`@[coordinates]`** | | Navigate to specified element in `pt` output. | `@[10,0]` or `@10,0` |
 | **`/regex`** | |  [less](https://linux.die.net/man/1/less)-style search for given regular expression. | `/inf` |
 | **`/`** | | Scroll to the next line with matches to the searched regex (if any). | `/` |
+| **`pf`** | | **Print a value in the feed_dict to `Session.run` ** | |
+| | `pf <feed_tensor_name>` | Print the value of the feed. Also note that the `pf` command has the `-a`, `-r` and `-s` flags (not listed below), which have the same syntax and semantics as the identically-named flags of `pt`. | `pf input_xs:0` |
 | **`ni`** | | **Display node information.** | |
 | | `-a` | Include node attributes in the output. | `ni -a hidden/Relu` |
 | | `-d` | List the debug dumps available from the node. | `ni -d hidden/Relu` |
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 1c66413c05..d4df789f01 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -295,12 +295,15 @@ py_library(
     deps = [
         ":analyzer_cli",
         ":cli_shared",
+        ":command_parser",
         ":debug_data",
         ":debugger_cli_common",
         ":framework",
         ":profile_analyzer_cli",
         ":stepper_cli",
+        ":tensor_format",
         ":ui_factory",
+        "@six_archive//:six",
     ],
 )
 
diff --git a/tensorflow/python/debug/cli/analyzer_cli.py b/tensorflow/python/debug/cli/analyzer_cli.py
index 45154bee58..2bb8a7f6ce 100644
--- a/tensorflow/python/debug/cli/analyzer_cli.py
+++ b/tensorflow/python/debug/cli/analyzer_cli.py
@@ -279,45 +279,9 @@ class DebugAnalyzer(object):
     self._arg_parsers["list_outputs"] = ap
 
     # Parser for print_tensor.
-    ap = argparse.ArgumentParser(
-        description="Print the value of a dumped tensor.",
-        usage=argparse.SUPPRESS)
-    ap.add_argument(
-        "tensor_name",
-        type=str,
-        help="Name of the tensor, followed by any slicing indices, "
-        "e.g., hidden1/Wx_plus_b/MatMul:0, "
-        "hidden1/Wx_plus_b/MatMul:0[1, :]")
-    ap.add_argument(
-        "-n",
-        "--number",
-        dest="number",
-        type=int,
-        default=-1,
-        help="0-based dump number for the specified tensor. "
-        "Required for tensor with multiple dumps.")
-    ap.add_argument(
-        "-r",
-        "--ranges",
-        dest="ranges",
-        type=str,
-        default="",
-        help="Numerical ranges to highlight tensor elements in. "
-        "Examples: -r 0,1e-8, -r [-0.1,0.1], "
-        "-r \"[[-inf, -0.1], [0.1, inf]]\"")
-    ap.add_argument(
-        "-a",
-        "--all",
-        dest="print_all",
-        action="store_true",
-        help="Print the tensor in its entirety, i.e., do not use ellipses.")
-    ap.add_argument(
-        "-s",
-        "--numeric_summary",
-        action="store_true",
-        help="Include summary for non-empty tensors of numeric (int*, float*, "
-        "complex*) and Boolean types.")
-    self._arg_parsers["print_tensor"] = ap
+    self._arg_parsers["print_tensor"] = (
+        command_parser.get_print_tensor_argparser(
+            "Print the value of a dumped tensor."))
 
     # Parser for print_source.
     ap = argparse.ArgumentParser(
@@ -843,10 +807,8 @@ class DebugAnalyzer(object):
 
     parsed = self._arg_parsers["print_tensor"].parse_args(args)
 
-    if screen_info and "cols" in screen_info:
-      np_printoptions = {"linewidth": screen_info["cols"]}
-    else:
-      np_printoptions = {}
+    np_printoptions = cli_shared.numpy_printoptions_from_screen_info(
+        screen_info)
 
     # Determine if any range-highlighting is required.
     highlight_options = cli_shared.parse_ranges_highlight(parsed.ranges)
diff --git a/tensorflow/python/debug/cli/cli_shared.py b/tensorflow/python/debug/cli/cli_shared.py
index 888080c634..34d942fdf1 100644
--- a/tensorflow/python/debug/cli/cli_shared.py
+++ b/tensorflow/python/debug/cli/cli_shared.py
@@ -138,6 +138,13 @@ def parse_ranges_highlight(ranges_string):
     return None
 
 
+def numpy_printoptions_from_screen_info(screen_info):
+  if screen_info and "cols" in screen_info:
+    return {"linewidth": screen_info["cols"]}
+  else:
+    return {}
+
+
 def format_tensor(tensor,
                   tensor_name,
                   np_printoptions,
@@ -318,32 +325,35 @@ def get_run_start_intro(run_call_count,
   fetch_lines = _get_fetch_names(fetches)
 
   if not feed_dict:
-    feed_dict_lines = ["(Empty)"]
+    feed_dict_lines = [debugger_cli_common.RichLine("  (Empty)")]
   else:
     feed_dict_lines = []
     for feed_key in feed_dict:
-      if isinstance(feed_key, six.string_types):
-        feed_dict_lines.append(feed_key)
-      else:
-        feed_dict_lines.append(feed_key.name)
-
-  intro_lines = [_HORIZONTAL_BAR]
+      feed_key_name = (feed_key if isinstance(feed_key, six.string_types)
+                       else feed_key.name)
+      feed_dict_line = debugger_cli_common.RichLine("  ")
+      feed_dict_line += debugger_cli_common.RichLine(
+          feed_key_name,
+          debugger_cli_common.MenuItem(None, "pf %s" % feed_key_name))
+      feed_dict_lines.append(feed_dict_line)
+  feed_dict_lines = debugger_cli_common.rich_text_lines_from_rich_line_list(
+      feed_dict_lines)
+
+  out = debugger_cli_common.RichTextLines(_HORIZONTAL_BAR)
   if is_callable_runner:
-    intro_lines.append("Running a runner returned by Session.make_callabe()")
+    out.append("Running a runner returned by Session.make_callabe()")
   else:
-    intro_lines.extend([
-        "Session.run() call #%d:" % run_call_count,
-        "", "Fetch(es):"
-    ])
-    intro_lines.extend(["  " + line for line in fetch_lines])
-    intro_lines.extend(["", "Feed dict(s):"])
-    intro_lines.extend(["  " + line for line in feed_dict_lines])
-  intro_lines.extend([
-      _HORIZONTAL_BAR, "",
-      "Select one of the following commands to proceed ---->"
-  ])
-
-  out = debugger_cli_common.RichTextLines(intro_lines)
+    out.append("Session.run() call #%d:" % run_call_count)
+    out.append("")
+    out.append("Fetch(es):")
+    out.extend(debugger_cli_common.RichTextLines(
+        ["  " + line for line in fetch_lines]))
+    out.append("")
+    out.append("Feed dict:")
+    out.extend(feed_dict_lines)
+  out.append(_HORIZONTAL_BAR)
+  out.append("")
+  out.append("Select one of the following commands to proceed ---->")
 
   out.extend(
       _recommend_command(
diff --git a/tensorflow/python/debug/cli/command_parser.py b/tensorflow/python/debug/cli/command_parser.py
index 143c104519..d78dc1649a 100644
--- a/tensorflow/python/debug/cli/command_parser.py
+++ b/tensorflow/python/debug/cli/command_parser.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import argparse
 import ast
 import re
 import sys
@@ -487,3 +488,55 @@ def evaluate_tensor_slice(tensor, tensor_slicing):
     raise ValueError("Invalid tensor-slicing string.")
 
   return tensor[_parse_slices(tensor_slicing)]
+
+
+def get_print_tensor_argparser(description):
+  """Get an ArgumentParser for a command that prints tensor values.
+
+  Examples of such commands include print_tensor and print_feed.
+
+  Args:
+    description: Description of the ArgumentParser.
+
+  Returns:
+    An instance of argparse.ArgumentParser.
+  """
+
+  ap = argparse.ArgumentParser(
+      description=description, usage=argparse.SUPPRESS)
+  ap.add_argument(
+      "tensor_name",
+      type=str,
+      help="Name of the tensor, followed by any slicing indices, "
+      "e.g., hidden1/Wx_plus_b/MatMul:0, "
+      "hidden1/Wx_plus_b/MatMul:0[1, :]")
+  ap.add_argument(
+      "-n",
+      "--number",
+      dest="number",
+      type=int,
+      default=-1,
+      help="0-based dump number for the specified tensor. "
+      "Required for tensor with multiple dumps.")
+  ap.add_argument(
+      "-r",
+      "--ranges",
+      dest="ranges",
+      type=str,
+      default="",
+      help="Numerical ranges to highlight tensor elements in. "
+      "Examples: -r 0,1e-8, -r [-0.1,0.1], "
+      "-r \"[[-inf, -0.1], [0.1, inf]]\"")
+  ap.add_argument(
+      "-a",
+      "--all",
+      dest="print_all",
+      action="store_true",
+      help="Print the tensor in its entirety, i.e., do not use ellipses.")
+  ap.add_argument(
+      "-s",
+      "--numeric_summary",
+      action="store_true",
+      help="Include summary for non-empty tensors of numeric (int*, float*, "
+      "complex*) and Boolean types.")
+  return ap
diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper.py b/tensorflow/python/debug/wrappers/local_cli_wrapper.py
index 9c409168a6..3dddc43d82 100644
--- a/tensorflow/python/debug/wrappers/local_cli_wrapper.py
+++ b/tensorflow/python/debug/wrappers/local_cli_wrapper.py
@@ -23,9 +23,12 @@ import shutil
 import sys
 import tempfile
 
+import six
+
 # Google-internal import(s).
 from tensorflow.python.debug.cli import analyzer_cli
 from tensorflow.python.debug.cli import cli_shared
+from tensorflow.python.debug.cli import command_parser
 from tensorflow.python.debug.cli import debugger_cli_common
 from tensorflow.python.debug.cli import profile_analyzer_cli
 from tensorflow.python.debug.cli import stepper_cli
@@ -181,6 +184,9 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
         usage=argparse.SUPPRESS)
     self._argparsers["run_info"] = ap
 
+    self._argparsers["print_feed"] = command_parser.get_print_tensor_argparser(
+        "Print the value of a feed in feed_dict.")
+
   def add_tensor_filter(self, filter_name, tensor_filter):
     """Add a tensor filter.
 
@@ -440,6 +446,44 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
 
     return output
 
+  def _print_feed_handler(self, args, screen_info=None):
+    np_printoptions = cli_shared.numpy_printoptions_from_screen_info(
+        screen_info)
+
+    if not self._feed_dict:
+      return cli_shared.error(
+          "The feed_dict of the current run is None or empty.")
+
+    parsed = self._argparsers["print_feed"].parse_args(args)
+    tensor_name, tensor_slicing = (
+        command_parser.parse_tensor_name_with_slicing(parsed.tensor_name))
+
+    feed_key = None
+    feed_value = None
+    for key in self._feed_dict:
+      if isinstance(key, six.string_types):
+        if key == tensor_name:
+          feed_key = key
+      elif key.name == tensor_name:
+        feed_key = key.name
+      if feed_key is not None:
+        feed_value = self._feed_dict[key]
+        break
+
+    if feed_key is None:
+      return cli_shared.error(
+          "The feed_dict of the current run does not contain the key %s" %
+          tensor_name)
+    else:
+      return cli_shared.format_tensor(
+          feed_value,
+          feed_key + " (feed)",
+          np_printoptions,
+          print_all=parsed.print_all,
+          tensor_slicing=tensor_slicing,
+          highlight_options=cli_shared.parse_ranges_highlight(parsed.ranges),
+          include_numeric_summary=parsed.numeric_summary)
+
   def _run_handler(self, args, screen_info=None):
     """Command handler for "run" command during on-run-start."""
 
@@ -504,11 +548,21 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
         self._run_info_handler,
         self._argparsers["run_info"].format_help(),
         prefix_aliases=["ri"])
+    curses_cli.register_command_handler(
+        "print_feed",
+        self._print_feed_handler,
+        self._argparsers["print_feed"].format_help(),
+        prefix_aliases=["pf"])
 
     if self._tensor_filters:
       # Register tab completion for the filter names.
       curses_cli.register_tab_comp_context(["run", "r"],
                                            list(self._tensor_filters.keys()))
+    if self._feed_dict:
+      # Register tab completion for feed_dict keys.
+      feed_keys = [(key if isinstance(key, six.string_types) else key.name)
+                   for key in self._feed_dict.keys()]
+      curses_cli.register_tab_comp_context(["print_feed", "pf"], feed_keys)
 
   def _on_run_start_step_handler(self, args, screen_info=None):
     """Command handler for "invoke_stepper" command during on-run-start."""
@@ -552,6 +606,7 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
     """
 
     self._run_call_count = run_call_count
+    self._feed_dict = feed_dict
     self._run_description = cli_shared.get_run_short_description(
         run_call_count,
         fetches,
diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py
index 3a2d0fc422..cde161469a 100644
--- a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py
+++ b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py
@@ -50,14 +50,16 @@ class LocalCLIDebuggerWrapperSessionForTest(
   """
 
   def __init__(self,
-               command_args_sequence,
+               command_sequence,
                sess,
                dump_root=None):
     """Constructor of the for-test subclass.
 
     Args:
-      command_args_sequence: (list of list of str) A list of arguments for the
-        "run" command.
+      command_sequence: (list of list of str) A list of command arguments,
+        including the command prefix, each element of the list is such as:
+        ["run", "-n"],
+        ["print_feed", "input:0"].
       sess: See the doc string of LocalCLIDebugWrapperSession.__init__.
       dump_root: See the doc string of LocalCLIDebugWrapperSession.__init__.
     """
@@ -65,8 +67,8 @@ class LocalCLIDebuggerWrapperSessionForTest(
     local_cli_wrapper.LocalCLIDebugWrapperSession.__init__(
         self, sess, dump_root=dump_root, log_usage=False)
 
-    self._command_args_sequence = command_args_sequence
-    self._response_pointer = 0
+    self._command_sequence = command_sequence
+    self._command_pointer = 0
 
     # Observer variables.
     self.observers = {
@@ -74,6 +76,7 @@ class LocalCLIDebuggerWrapperSessionForTest(
         "tf_errors": [],
         "run_start_cli_run_numbers": [],
         "run_end_cli_run_numbers": [],
+        "print_feed_responses": [],
         "profiler_py_graphs": [],
         "profiler_run_metadata": [],
     }
@@ -95,15 +98,20 @@ class LocalCLIDebuggerWrapperSessionForTest(
     else:
       self.observers["run_end_cli_run_numbers"].append(self._run_call_count)
 
-    command_args = self._command_args_sequence[self._response_pointer]
-    self._response_pointer += 1
+    while True:
+      command = self._command_sequence[self._command_pointer]
+      self._command_pointer += 1
 
-    try:
-      self._run_handler(command_args)
-    except debugger_cli_common.CommandLineExit as e:
-      response = e.exit_token
-
-    return response
+      try:
+        if command[0] == "run":
+          self._run_handler(command[1:])
+        elif command[0] == "print_feed":
+          self.observers["print_feed_responses"].append(
+              self._print_feed_handler(command[1:]))
+        else:
+          raise ValueError("Unrecognized command prefix: %s" % command[0])
+      except debugger_cli_common.CommandLineExit as e:
+        return e.exit_token
 
 
 class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
@@ -169,9 +177,8 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
           session.Session(), dump_root=file_path, log_usage=False)
 
   def testRunsUnderDebugMode(self):
-    # Test command sequence: run; run; run;
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], [], []], self.sess, dump_root=self._tmp_dir)
+        [["run"], ["run"], ["run"]], self.sess, dump_root=self._tmp_dir)
 
     # run under debug mode twice.
     wrapped_sess.run(self.inc_v)
@@ -193,9 +200,8 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual([None, None], wrapped_sess.observers["tf_errors"])
 
   def testRunsWithEmptyStringDumpRootWorks(self):
-    # Test command sequence: run, run
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], []], self.sess, dump_root="")
+        [["run"], ["run"]], self.sess, dump_root="")
 
     # run under debug mode.
     wrapped_sess.run(self.inc_v)
@@ -204,7 +210,7 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
   def testRunInfoOutputAtRunEndIsCorrect(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], [], []], self.sess, dump_root=self._tmp_dir)
+        [["run"], ["run"], ["run"]], self.sess, dump_root=self._tmp_dir)
 
     wrapped_sess.run(self.inc_v)
     run_info_output = wrapped_sess._run_info_handler([])
@@ -227,9 +233,9 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertIn("list_tensors", menu.captions())
 
   def testRunsUnderNonDebugMode(self):
-    # Test command sequence: run -n; run -n; run -n;
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["-n"], ["-n"], ["-n"]], self.sess, dump_root=self._tmp_dir)
+        [["run", "-n"], ["run", "-n"], ["run", "-n"]],
+        self.sess, dump_root=self._tmp_dir)
 
     # run three times.
     wrapped_sess.run(self.inc_v)
@@ -243,10 +249,10 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual([], wrapped_sess.observers["run_end_cli_run_numbers"])
 
   def testRunsUnderNonDebugThenDebugMode(self):
-    # Test command sequence: run -n; run -n; run; run;
     # Do two NON_DEBUG_RUNs, followed by DEBUG_RUNs.
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["-n"], ["-n"], [], []], self.sess, dump_root=self._tmp_dir)
+        [["run", "-n"], ["run", "-n"], ["run"], ["run"]],
+        self.sess, dump_root=self._tmp_dir)
 
     # run three times.
     wrapped_sess.run(self.inc_v)
@@ -265,9 +271,9 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual([None], wrapped_sess.observers["tf_errors"])
 
   def testRunMultipleTimesWithinLimit(self):
-    # Test command sequence: run -t 3; run;
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["-t", "3"], []], self.sess, dump_root=self._tmp_dir)
+        [["run", "-t", "3"], ["run"]],
+        self.sess, dump_root=self._tmp_dir)
 
     # run three times.
     wrapped_sess.run(self.inc_v)
@@ -282,9 +288,8 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual([None], wrapped_sess.observers["tf_errors"])
 
   def testRunMultipleTimesOverLimit(self):
-    # Test command sequence: run -t 3;
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["-t", "3"]], self.sess, dump_root=self._tmp_dir)
+        [["run", "-t", "3"]], self.sess, dump_root=self._tmp_dir)
 
     # run twice, which is less than the number of times specified by the
     # command.
@@ -299,9 +304,9 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual([], wrapped_sess.observers["tf_errors"])
 
   def testRunMixingDebugModeAndMultpleTimes(self):
-    # Test command sequence: run -n; run -t 2; run; run;
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["-n"], ["-t", "2"], [], []], self.sess, dump_root=self._tmp_dir)
+        [["run", "-n"], ["run", "-t", "2"], ["run"], ["run"]],
+        self.sess, dump_root=self._tmp_dir)
 
     # run four times.
     wrapped_sess.run(self.inc_v)
@@ -319,7 +324,7 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
   def testDebuggingMakeCallableTensorRunnerWorks(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], []], self.sess, dump_root=self._tmp_dir)
+        [["run"], ["run"]], self.sess, dump_root=self._tmp_dir)
     v = variables.Variable(42)
     tensor_runner = wrapped_sess.make_callable(v)
     self.sess.run(v.initializer)
@@ -329,7 +334,7 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
   def testDebuggingMakeCallableTensorRunnerWithCustomRunOptionsWorks(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], []], self.sess, dump_root=self._tmp_dir)
+        [["run"], ["run"]], self.sess, dump_root=self._tmp_dir)
     a = constant_op.constant(42)
     tensor_runner = wrapped_sess.make_callable(a)
 
@@ -343,7 +348,7 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
   def testDebuggingMakeCallableOperationRunnerWorks(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], []], self.sess, dump_root=self._tmp_dir)
+        [["run"], ["run"]], self.sess, dump_root=self._tmp_dir)
     v = variables.Variable(10.0)
     inc_v = state_ops.assign_add(v, 1.0)
     op_runner = wrapped_sess.make_callable(inc_v.op)
@@ -355,7 +360,7 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
   def testDebuggingMakeCallableRunnerWithFeedListWorks(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], []], self.sess, dump_root=self._tmp_dir)
+        [["run"], ["run"]], self.sess, dump_root=self._tmp_dir)
     ph1 = array_ops.placeholder(dtypes.float32)
     ph2 = array_ops.placeholder(dtypes.float32)
     a = math_ops.add(ph1, ph2)
@@ -366,7 +371,7 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
 
   def testRuntimeErrorShouldBeCaught(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[], []], self.sess, dump_root=self._tmp_dir)
+        [["run"], ["run"]], self.sess, dump_root=self._tmp_dir)
 
     # Do a run that should lead to an TensorFlow runtime error.
     wrapped_sess.run(self.y, feed_dict={self.ph: [[0.0], [1.0], [2.0]]})
@@ -386,15 +391,15 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
       w = variables.Variable([1.0] * 10, name="w")
 
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [[]], self.sess, dump_root=self._tmp_dir)
+        [["run"]], self.sess, dump_root=self._tmp_dir)
     with self.assertRaisesRegexp(errors.OpError, r".*[Dd]evice.*1337.*"):
       wrapped_sess.run(w)
 
   def testRunTillFilterPassesShouldLaunchCLIAtCorrectRun(self):
-    # Test command sequence:
-    #   run -f greater_than_twelve; run -f greater_than_twelve; run;
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["-f", "v_greater_than_twelve"], ["-f", "v_greater_than_twelve"], []],
+        [["run", "-f", "v_greater_than_twelve"],
+         ["run", "-f", "v_greater_than_twelve"],
+         ["run"]],
         self.sess,
         dump_root=self._tmp_dir)
 
@@ -423,12 +428,10 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual([None, None], wrapped_sess.observers["tf_errors"])
 
   def testRunsUnderDebugModeWithWatchFnFilteringNodeNames(self):
-    # Test command sequence:
-    #   run --node_name_filter inc.*
-    #   run --node_name_filter delta
-    #   run
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["--node_name_filter", "inc.*"], ["--node_name_filter", "delta"], []],
+        [["run", "--node_name_filter", "inc.*"],
+         ["run", "--node_name_filter", "delta"],
+         ["run"]],
         self.sess, dump_root=self._tmp_dir)
 
     # run under debug mode twice.
@@ -450,14 +453,10 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual("delta", dumps.dumped_tensor_data[0].node_name)
 
   def testRunsUnderDebugModeWithWatchFnFilteringOpTypes(self):
-    # Test command sequence:
-    #   run --node_name_filter delta
-    #   run --op_type_filter AssignAdd
-    #   run
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["--node_name_filter", "delta"],
-         ["--op_type_filter", "AssignAdd"],
-         []],
+        [["run", "--node_name_filter", "delta"],
+         ["run", "--op_type_filter", "AssignAdd"],
+         ["run"]],
         self.sess, dump_root=self._tmp_dir)
 
     # run under debug mode twice.
@@ -479,13 +478,10 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual("inc_v", dumps.dumped_tensor_data[0].node_name)
 
   def testRunsUnderDebugModeWithWatchFnFilteringTensorDTypes(self):
-    # Test command sequence:
-    #   run --op_type_filter Variable.*
-    #   run --dtype_filter int32
-    #   run
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["--op_type_filter", "Variable.*"],
-         ["--tensor_dtype_filter", "int32"], []],
+        [["run", "--op_type_filter", "Variable.*"],
+         ["run", "--tensor_dtype_filter", "int32"],
+         ["run"]],
         self.sess, dump_root=self._tmp_dir)
 
     # run under debug mode twice.
@@ -507,11 +503,9 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
         [dumps.dumped_tensor_data[i].node_name for i in [0, 1]])
 
   def testRunsUnderDebugModeWithWatchFnFilteringOpTypesAndTensorDTypes(self):
-    # Test command sequence:
-    #   run --op_type_filter Cast --dtype_filter int32
-    #   run
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["--op_type_filter", "Cast", "--tensor_dtype_filter", "int32"], []],
+        [["run", "--op_type_filter", "Cast", "--tensor_dtype_filter", "int32"],
+         ["run"]],
         self.sess, dump_root=self._tmp_dir)
 
     # run under debug mode twice.
@@ -524,9 +518,59 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual(1, dumps.size)
     self.assertEqual("w_int_inner", dumps.dumped_tensor_data[0].node_name)
 
+  def testPrintFeedPrintsFeedValueForTensorFeedKey(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [["print_feed", "ph:0"], ["run"], ["run"]], self.sess)
+
+    self.assertAllClose(
+        [[5.0], [-1.0]],
+        wrapped_sess.run(self.y, feed_dict={self.ph: [[0.0, 1.0, 2.0]]}))
+    print_feed_responses = wrapped_sess.observers["print_feed_responses"]
+    self.assertEqual(1, len(print_feed_responses))
+    self.assertEqual(
+        ["Tensor \"ph:0 (feed)\":", "", "[[0.0, 1.0, 2.0]]"],
+        print_feed_responses[0].lines)
+
+  def testPrintFeedPrintsFeedValueForTensorNameFeedKey(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [["print_feed", "ph:0"], ["run"], ["run"]], self.sess)
+
+    self.assertAllClose(
+        [[5.0], [-1.0]],
+        wrapped_sess.run(self.y, feed_dict={"ph:0": [[0.0, 1.0, 2.0]]}))
+    print_feed_responses = wrapped_sess.observers["print_feed_responses"]
+    self.assertEqual(1, len(print_feed_responses))
+    self.assertEqual(
+        ["Tensor \"ph:0 (feed)\":", "", "[[0.0, 1.0, 2.0]]"],
+        print_feed_responses[0].lines)
+
+  def testPrintFeedPrintsErrorForInvalidFeedKey(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [["print_feed", "spam"], ["run"], ["run"]], self.sess)
+
+    self.assertAllClose(
+        [[5.0], [-1.0]],
+        wrapped_sess.run(self.y, feed_dict={"ph:0": [[0.0, 1.0, 2.0]]}))
+    print_feed_responses = wrapped_sess.observers["print_feed_responses"]
+    self.assertEqual(1, len(print_feed_responses))
+    self.assertEqual(
+        ["ERROR: The feed_dict of the current run does not contain the key "
+         "spam"], print_feed_responses[0].lines)
+
+  def testPrintFeedPrintsErrorWhenFeedDictIsNone(self):
+    wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
+        [["print_feed", "spam"], ["run"], ["run"]], self.sess)
+
+    wrapped_sess.run(self.w_int)
+    print_feed_responses = wrapped_sess.observers["print_feed_responses"]
+    self.assertEqual(1, len(print_feed_responses))
+    self.assertEqual(
+        ["ERROR: The feed_dict of the current run is None or empty."],
+        print_feed_responses[0].lines)
+
   def testRunUnderProfilerModeWorks(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
-        [["-p"], []], self.sess)
+        [["run", "-p"], ["run"]], self.sess)
 
     wrapped_sess.run(self.w_int)
 
-- 
GitLab


From 9daa29c95e4b1e83ac20a29b6b1df3b9a6277759 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 20 Jul 2017 12:43:09 -0700
Subject: [PATCH 1156/1375] Deprecate old `monte_carlo.expectation` in favor of
 `monte_carlo.expectation_v2`.

PiperOrigin-RevId: 162651911
---
 .../python/kernel_tests/monte_carlo_test.py   | 31 ++-------
 .../python/ops/csiszar_divergence_impl.py     |  2 +-
 .../bayesflow/python/ops/entropy_impl.py      |  5 +-
 .../bayesflow/python/ops/monte_carlo.py       |  1 -
 .../bayesflow/python/ops/monte_carlo_impl.py  | 66 ++-----------------
 5 files changed, 12 insertions(+), 93 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
index cf520f73ad..38426ebf1f 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
@@ -26,7 +26,6 @@ from tensorflow.contrib.bayesflow.python.ops.monte_carlo_impl import _get_sample
 from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import normal as normal_lib
@@ -122,28 +121,6 @@ class ExpectationImportanceSampleLogspaceTest(test.TestCase):
       self.assertAllClose([1., (2 / 3.)**2], e_x2.eval(), rtol=0.02)
 
 
-class ExpectationTest(test.TestCase):
-
-  def test_mc_estimate_of_normal_mean_and_variance_is_correct_vs_analytic(self):
-    random_seed.set_random_seed(0)
-    n = 20000
-    with self.test_session():
-      p = normal_lib.Normal(loc=[1.0, -1.0], scale=[0.3, 0.5])
-      # Compute E_p[X] and E_p[X^2].
-      z = p.sample(n, seed=42)
-      e_x = mc.expectation(lambda x: x, p, z=z, seed=42)
-      e_x2 = mc.expectation(math_ops.square, p, z=z, seed=0)
-      var = e_x2 - math_ops.square(e_x)
-
-      self.assertEqual(p.batch_shape, e_x.get_shape())
-      self.assertEqual(p.batch_shape, e_x2.get_shape())
-
-      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
-      # pass.
-      self.assertAllClose(p.mean().eval(), e_x.eval(), rtol=0.01)
-      self.assertAllClose(p.variance().eval(), var.eval(), rtol=0.02)
-
-
 class GetSamplesTest(test.TestCase):
   """Test the private method 'get_samples'."""
 
@@ -184,7 +161,7 @@ class GetSamplesTest(test.TestCase):
       self.assertEqual((10,), z.get_shape())
 
 
-class Expectationv2Test(test.TestCase):
+class ExpectationTest(test.TestCase):
 
   def test_works_correctly(self):
     with self.test_session() as sess:
@@ -195,9 +172,9 @@ class Expectationv2Test(test.TestCase):
       f = lambda u: u
       efx_true = x
       samples = p.sample(int(1e5), seed=1)
-      efx_reparam = mc.expectation_v2(f, samples, p.log_prob)
-      efx_score = mc.expectation_v2(f, samples, p.log_prob,
-                                    use_reparametrization=False)
+      efx_reparam = mc.expectation(f, samples, p.log_prob)
+      efx_score = mc.expectation(f, samples, p.log_prob,
+                                 use_reparametrization=False)
 
       [
           efx_true_,
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index e6e82f3b6f..68f0f05b99 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -831,7 +831,7 @@ def monte_carlo_csiszar_f_divergence(
           "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
           "transformation of a parameterless distribution. (Otherwise this "
           "function has a biased gradient.)")
-    return monte_carlo.expectation_v2(
+    return monte_carlo.expectation(
         f=lambda x: f(p.log_prob(x) - q.log_prob(x)),
         samples=q.sample(num_draws, seed=seed),
         log_prob=q.log_prob,
diff --git a/tensorflow/contrib/bayesflow/python/ops/entropy_impl.py b/tensorflow/contrib/bayesflow/python/ops/entropy_impl.py
index f155de5032..4a7679fb43 100644
--- a/tensorflow/contrib/bayesflow/python/ops/entropy_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/entropy_impl.py
@@ -195,8 +195,9 @@ def entropy_shannon(p,
     # Sample path
     if entropy is None:
       logging.info('Using sampled entropy(p:%s)', p)
-      entropy = -1. * monte_carlo.expectation(
-          p.log_prob, p, z=z, n=n, seed=seed)
+      if z is None:
+        z = p.sample(n, seed=seed)
+      entropy = -monte_carlo.expectation(p.log_prob, z)
 
     return entropy
 
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
index 848a01421f..5770bcdd70 100644
--- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
@@ -29,7 +29,6 @@ from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = [
     'expectation',
-    'expectation_v2',
     'expectation_importance_sampler',
     'expectation_importance_sampler_logspace',
 ]
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
index 3f836e7149..0dae74d31f 100644
--- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
@@ -32,7 +32,6 @@ from tensorflow.python.ops import nn
 
 __all__ = [
     'expectation',
-    'expectation_v2',
     'expectation_importance_sampler',
     'expectation_importance_sampler_logspace',
 ]
@@ -195,65 +194,8 @@ def _logspace_mean(log_values):
   return log_mean_of_values
 
 
-def expectation(f, p, z=None, n=None, seed=None, name='expectation'):
-  r"""Monte Carlo estimate of an expectation:  `E_p[f(Z)]` with sample mean.
-
-  This `Op` returns
-
-  ```
-  n^{-1} sum_{i=1}^n f(z_i),  where z_i ~ p
-  \approx E_p[f(Z)]
-  ```
-
-  User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
-
-  Args:
-    f: Callable mapping samples from `p` to `Tensors`.
-    p:  `tf.contrib.distributions.Distribution`.
-    z:  `Tensor` of samples from `p`, produced by `p.sample` for some `n`.
-    n:  Integer `Tensor`.  Number of samples to generate if `z` is not provided.
-    seed:  Python integer to seed the random number generator.
-    name:  A name to give this `Op`.
-
-  Returns:
-    A `Tensor` with the same `dtype` as `p`.
-
-  Example:
-
-  ```python
-  N_samples = 10000
-
-  distributions = tf.contrib.distributions
-
-  dist = distributions.Uniform([0.0, 0.0], [1.0, 2.0])
-  elementwise_mean = lambda x: x
-  mean_sum = lambda x: tf.reduce_sum(x, 1)
-
-  estimate_elementwise_mean_tf = monte_carlo.expectation(elementwise_mean,
-                                                         dist,
-                                                         n=N_samples)
-  estimate_mean_sum_tf = monte_carlo.expectation(mean_sum,
-                                                 dist,
-                                                 n=N_samples)
-
-  with tf.Session() as sess:
-    estimate_elementwise_mean, estimate_mean_sum = (
-        sess.run([estimate_elementwise_mean_tf, estimate_mean_sum_tf]))
-  print estimate_elementwise_mean
-  >>> np.array([ 0.50018013  1.00097895], dtype=np.float32)
-  print estimate_mean_sum
-  >>> 1.49571
-
-  ```
-
-  """
-  with ops.name_scope(name, values=[n, z]):
-    z = _get_samples(p, z, n, seed)
-    return _sample_mean(f(z))
-
-
-def expectation_v2(f, samples, log_prob=None, use_reparametrization=True,
-                   axis=0, keep_dims=False, name=None):
+def expectation(f, samples, log_prob=None, use_reparametrization=True,
+                axis=0, keep_dims=False, name=None):
   """Computes the Monte-Carlo approximation of `E_p[f(X)]`.
 
   This function computes the Monte-Carlo approximation of an expectation, i.e.,
@@ -311,7 +253,7 @@ def expectation_v2(f, samples, log_prob=None, use_reparametrization=True,
       dimensions.
     keep_dims: If true, retains averaged dimensions with length 1.
     name: A `name_scope` for operations created by this function (optional).
-      Default value: "expectation_v2".
+      Default value: "expectation".
 
   Returns:
     approx_expectation: `Tensor` corresponding to the Monte-Carlo approximation
@@ -323,7 +265,7 @@ def expectation_v2(f, samples, log_prob=None, use_reparametrization=True,
       `callable`.
   """
 
-  with ops.name_scope(name, 'expectation_v2', [samples]):
+  with ops.name_scope(name, 'expectation', [samples]):
     if not callable(f):
       raise ValueError('`f` must be a callable function.')
     if use_reparametrization:
-- 
GitLab


From 90802c44a293199b193a1d9e57747cf3e8bb70cd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 12:55:22 -0700
Subject: [PATCH 1157/1375] Make the logic around linking to CPU runtime
 functions less error-prone.

The way we link to CPU runtime functions from code generated by LLVM is problematic in a few ways:

 1. Forgetting to add lookup code in the ORC JIT's resolver for a new builtin does not break JIT tests in obvious ways.  JIT tests will usually continue to work because of the way we build our unit tests.
 2. There are ways to break the AOT link in ways that don't show up in when JITing by naming the extern "C" symbols incorrectly.

This change addresses the above by implementing the following:

  1. For every builtin, we have to manually declare and define `__xla_cpu_runtime_MyBuiltin` and `xla::cpu::runtime::kMyBuiltinSymbolName`.
  2. A XLA_REGISTER_CPU_RUNTIME_BUILTIN(MyBuiltin) macro populates a map with `xla::cpu::runtime::kMyBuiltinSymbolName` -> `&__xla_cpu_runtime_MyBuiltin`.
  3. The ORC JIT does not dlsym symbols that start with "__xla_cpu_runtime", it only uses the registry above for these special symbols.

PiperOrigin-RevId: 162653398
---
 tensorflow/compiler/xla/service/cpu/BUILD     | 10 ++
 .../xla/service/cpu/compiler_functor.cc       | 24 ++---
 .../compiler/xla/service/cpu/cpu_runtime.cc   | 64 +++++++++++++
 .../compiler/xla/service/cpu/cpu_runtime.h    | 91 +++++++++++++++----
 .../xla/service/cpu/cpu_runtime_avx.cc        | 17 +++-
 .../xla/service/cpu/cpu_runtime_avx.h         | 15 +--
 .../xla/service/cpu/cpu_runtime_sse4_1.cc     | 17 +++-
 .../xla/service/cpu/cpu_runtime_sse4_1.h      | 15 +--
 .../xla/service/cpu/dot_op_emitter.cc         |  8 +-
 .../xla/service/cpu/runtime_conv2d.cc         |  3 +
 .../xla/service/cpu/runtime_matmul.cc         |  5 +
 .../cpu/runtime_single_threaded_conv2d.cc     |  3 +
 .../cpu/runtime_single_threaded_matmul.cc     |  5 +
 .../xla/service/cpu/simple_orc_jit.cc         | 49 ++--------
 14 files changed, 235 insertions(+), 91 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 7248cb5f4c..0440c5b5d9 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -305,6 +305,7 @@ cc_library(
     hdrs = ["cpu_runtime_sse4_1.h"],
     copts = ["-DEIGEN_AVOID_STL_ARRAY"],
     deps = [
+        ":cpu_runtime",
         "//tensorflow/core:lib",
         "//third_party/eigen3",
     ],
@@ -317,6 +318,7 @@ cc_library(
     hdrs = ["cpu_runtime_avx.h"],
     copts = ["-DEIGEN_AVOID_STL_ARRAY"],
     deps = [
+        ":cpu_runtime",
         "//tensorflow/core:lib",
         "//third_party/eigen3",
     ],
@@ -354,11 +356,13 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
+        ":cpu_runtime",
         "//tensorflow/compiler/xla:executable_run_options",
         "//tensorflow/core:framework_lite",
         "//tensorflow/core/kernels:eigen_helpers",
         "//third_party/eigen3",
     ],
+    alwayslink = True,
 )
 
 cc_library(
@@ -368,10 +372,12 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
+        ":cpu_runtime",
         "//tensorflow/compiler/xla:executable_run_options",
         "//tensorflow/core:framework_lite",
         "//third_party/eigen3",
     ],
+    alwayslink = True,
 )
 
 cc_library(
@@ -384,10 +390,12 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
+        ":cpu_runtime",
         "//tensorflow/core:framework_lite",
         "//tensorflow/core/kernels:eigen_helpers",
         "//third_party/eigen3",
     ],
+    alwayslink = True,
 )
 
 cc_library(
@@ -397,9 +405,11 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
+        ":cpu_runtime",
         "//tensorflow/core:framework_lite",
         "//third_party/eigen3",
     ],
+    alwayslink = True,
 )
 
 cc_test(
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index f693808a10..d86881c282 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -131,25 +131,25 @@ std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl(
   std::vector<llvm::VecDesc> vector_functions;
 
   const llvm::VecDesc four_wide_vector_functions[] = {
-      {"expf", runtime::kExpV4F32, 4},
-      {"llvm.exp.f32", runtime::kExpV4F32, 4},
+      {"expf", runtime::kExpV4F32SymbolName, 4},
+      {"llvm.exp.f32", runtime::kExpV4F32SymbolName, 4},
 
-      {"logf", runtime::kLogV4F32, 4},
-      {"llvm.log.f32", runtime::kLogV4F32, 4},
+      {"logf", runtime::kLogV4F32SymbolName, 4},
+      {"llvm.log.f32", runtime::kLogV4F32SymbolName, 4},
 
-      {"tanhf", runtime::kTanhV4F32, 4},
-      {"llvm.tanh.f32", runtime::kTanhV4F32, 4},
+      {"tanhf", runtime::kTanhV4F32SymbolName, 4},
+      {"llvm.tanh.f32", runtime::kTanhV4F32SymbolName, 4},
   };
 
   const llvm::VecDesc eight_wide_vector_functions[] = {
-      {"expf", runtime::kExpV8F32, 8},
-      {"llvm.exp.f32", runtime::kExpV8F32, 8},
+      {"expf", runtime::kExpV8F32SymbolName, 8},
+      {"llvm.exp.f32", runtime::kExpV8F32SymbolName, 8},
 
-      {"logf", runtime::kLogV8F32, 8},
-      {"llvm.log.f32", runtime::kLogV8F32, 8},
+      {"logf", runtime::kLogV8F32SymbolName, 8},
+      {"llvm.log.f32", runtime::kLogV8F32SymbolName, 8},
 
-      {"tanhf", runtime::kTanhV8F32, 8},
-      {"llvm.tanh.f32", runtime::kTanhV8F32, 8},
+      {"tanhf", runtime::kTanhV8F32SymbolName, 8},
+      {"llvm.tanh.f32", runtime::kTanhV8F32SymbolName, 8},
   };
 
   if (arch == llvm::Triple::x86 || llvm::Triple::x86_64) {
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
index 07a9832867..fc85172772 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
@@ -17,20 +17,76 @@ limitations under the License.
 
 #include <functional>
 
+#include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
 
+namespace {
+tensorflow::gtl::FlatMap<tensorflow::StringPiece, void*>* BuiltinMap() {
+  static auto* builtin_map =
+      new tensorflow::gtl::FlatMap<tensorflow::StringPiece, void*>;
+  return builtin_map;
+}
+
+tensorflow::mutex builtin_map_mutex(tensorflow::LINKER_INITIALIZED);
+}  // namespace
+
 namespace xla {
 namespace cpu {
 namespace runtime {
 
+const char* const kEigenMatMulF32SymbolName =
+    "__xla_cpu_runtime_EigenMatMulF32";
+const char* const kEigenMatMulF64SymbolName =
+    "__xla_cpu_runtime_EigenMatMulF64";
+const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32";
+const char* const kEigenSingleThreadedMatMulF32SymbolName =
+    "__xla_cpu_runtime_EigenSingleThreadedMatMulF32";
+const char* const kEigenSingleThreadedMatMulF64SymbolName =
+    "__xla_cpu_runtime_EigenSingleThreadedMatMulF64";
+const char* const kEigenSingleThreadedConvF32SymbolName =
+    "__xla_cpu_runtime_EigenSingleThreadedConvF32";
+const char* const kAcquireInfeedBufferForDequeueSymbolName =
+    "__xla_cpu_runtime_AcquireInfeedBufferForDequeue";
+const char* const kReleaseInfeedBufferAfterDequeueSymbolName =
+    "__xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue";
+const char* const kAcquireOutfeedBufferForPopulationSymbolName =
+    "__xla_cpu_runtime_AcquireOutfeedBufferForPopulation";
+const char* const kReleaseOutfeedBufferAfterPopulationSymbolName =
+    "__xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation";
+
+const char* const kXlaCpuRuntimeSymbolPrefix = "__xla_cpu_runtime_";
+
 XfeedManager* GetXfeedManager() {
   static XfeedManager* manager = new XfeedManager;
   return manager;
 }
 
+namespace internal {
+Registrar::Registrar(tensorflow::StringPiece name, void* function_pointer,
+                     tensorflow::StringPiece base_name) {
+  CHECK(function_pointer);
+  CHECK_EQ(tensorflow::strings::StrCat(kXlaCpuRuntimeSymbolPrefix, base_name),
+           name);
+
+  tensorflow::mutex_lock lock(builtin_map_mutex);
+  InsertOrDie(BuiltinMap(), name, function_pointer);
+}
+}  // namespace internal
+
+void* ResolveSymbol(tensorflow::StringPiece name) {
+  CHECK(name.starts_with(kXlaCpuRuntimeSymbolPrefix));
+
+  tensorflow::mutex_lock lock(builtin_map_mutex);
+  const auto& builtin_map = *BuiltinMap();
+  auto lookup_iterator = builtin_map.find(name);
+  return lookup_iterator == builtin_map.end() ? nullptr
+                                              : lookup_iterator->second;
+}
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
@@ -67,6 +123,8 @@ void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(xla::int32 buffer_length,
   return buffer->data();
 }
 
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(AcquireInfeedBufferForDequeue);
+
 void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
     xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
     xla::int32 shape_length) {
@@ -81,6 +139,8 @@ void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
                                         std::move(shape));
 }
 
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(ReleaseInfeedBufferAfterDequeue);
+
 void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
     xla::int32 buffer_length, const void* shape_ptr, xla::int32 shape_length) {
   if (VLOG_IS_ON(2)) {
@@ -99,6 +159,8 @@ void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
   return buffer->data();
 }
 
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(AcquireOutfeedBufferForPopulation);
+
 void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
     xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
     xla::int32 shape_length) {
@@ -111,3 +173,5 @@ void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
       xla::llvm_ir::DecodeSelfDescribingShapeConstant(shape_ptr, shape_length);
   xfeed->outfeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr, shape);
 }
+
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(ReleaseOutfeedBufferAfterPopulation);
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
index 46cf64966a..4935b0b35e 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
@@ -32,7 +32,6 @@ limitations under the License.
 namespace xla {
 namespace cpu {
 namespace runtime {
-
 // Names of runtime functions. These get resolved from the generated code to the
 // right symbol at link time in one of two ways:
 // 1. When using the JIT, the symbol resolver (SimpleResolver in
@@ -41,27 +40,83 @@ namespace runtime {
 //    the actual symbol.
 // 2. When using ahead-of-time compilation, the linker can resolve the name
 //    because it is a symbol in the cpu_runtime library.
-constexpr char kEigenMatmulF32SymbolName[] = "__xla_cpu_runtime_EigenMatMulF32";
-constexpr char kEigenMatmulF64SymbolName[] = "__xla_cpu_runtime_EigenMatMulF64";
-constexpr char kEigenConvF32SymbolName[] = "__xla_cpu_runtime_EigenConvF32";
-constexpr char kEigenSingleThreadedMatmulF32SymbolName[] =
-    "__xla_cpu_runtime_EigenSingleThreadedMatMulF32";
-constexpr char kEigenSingleThreadedMatmulF64SymbolName[] =
-    "__xla_cpu_runtime_EigenSingleThreadedMatMulF64";
-constexpr char kEigenSingleThreadedConvF32SymbolName[] =
-    "__xla_cpu_runtime_EigenSingleThreadedConvF32";
-constexpr char kAcquireInfeedBufferForDequeueSymbolName[] =
-    "__xla_cpu_runtime_AcquireInfeedBufferForDequeue";
-constexpr char kReleaseInfeedBufferAfterDequeueSymbolName[] =
-    "__xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue";
-constexpr char kAcquireOutfeedBufferForPopulationSymbolName[] =
-    "__xla_cpu_runtime_AcquireOutfeedBufferForPopulation";
-constexpr char kReleaseOutfeedBufferAfterPopulationSymbolName[] =
-    "__xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation";
+extern const char* const kEigenMatMulF32SymbolName;
+extern const char* const kEigenMatMulF64SymbolName;
+extern const char* const kEigenConvF32SymbolName;
+extern const char* const kEigenSingleThreadedMatMulF32SymbolName;
+extern const char* const kEigenSingleThreadedMatMulF64SymbolName;
+extern const char* const kEigenSingleThreadedConvF32SymbolName;
+extern const char* const kAcquireInfeedBufferForDequeueSymbolName;
+extern const char* const kReleaseInfeedBufferAfterDequeueSymbolName;
+extern const char* const kAcquireOutfeedBufferForPopulationSymbolName;
+extern const char* const kReleaseOutfeedBufferAfterPopulationSymbolName;
+
+// Names for all of the XLA CPU runtime functions start have
+// kXlaCpuRuntimeSymbolPrefix as their prefix.
+extern const char* const kXlaCpuRuntimeSymbolPrefix;
 
 // Returns the infeed manager used by the CPU runtime.
 XfeedManager* GetXfeedManager();
 
+// This macro registers a cpu runtime function that can be invoked from
+// generated LLVM IR.  These registered functions can be looked up using
+// ResolveSymbol.  For instance, to register a CPU builtin named MyBuiltin, you
+// need to have:
+//
+// my_builtin.h:
+//
+// namespace xla { namespace cpu { namespace runtime {
+// extern const char* const kMyBuiltinSymbolName;
+// extern "C" void __xla_cpu_runtime_MyBuiltin(int a, int* b);
+// } } }
+//
+// my_builtin.cc:
+//
+// namespace xla { namespace cpu { namespace runtime {
+// const char* const kMyBuiltinSymbolName = "__xla_cpu_runtime_MyBuiltin";
+// extern "C" void __xla_cpu_runtime_MyBuiltin(int a, int* b) {
+//   // Implementation.
+// }
+//
+// REGISTER_XLA_CPU_RUNTIME_BUILTIN(MyBuiltin);
+// } } }
+#define REGISTER_XLA_CPU_RUNTIME_BUILTIN(base_name) \
+  REGISTER_XLA_CPU_RUNTIME_BUILTIN_IMPL(base_name, __COUNTER__)
+
+// Returns the address corresponding to the CPU runtime function called "name".
+// Returns nullptr if no such runtime function was registered.
+void* ResolveSymbol(tensorflow::StringPiece name);
+
+// Internal implementation details below this point.
+
+namespace internal {
+// This class is used by the REGISTER_XLA_CPU_RUNTIME_BUILTIN macro to register
+// addresses of __xla_cpu_runtime_* functions into a map.  This map can be
+// queried using the ResolveSymbol method.
+struct Registrar {
+  // The constructor adds an entry to the cpu runtime builtin registry.
+  //
+  // "name" is the name of the symbol, "function_pointer" is the address of
+  // runtime function corresponding to the symbol, and "base_name" is the
+  // stringification of the argument passed to the
+  // REGISTER_XLA_CPU_RUNTIME_BUILTIN ("base_name" is used to sanity check
+  // "name").
+  explicit Registrar(tensorflow::StringPiece name, void* function_pointer,
+                     tensorflow::StringPiece base_name);
+};
+}  // namespace internal
+
+#define REGISTER_XLA_CPU_RUNTIME_BUILTIN_IMPL(base_name, ctr) \
+                                                              \
+  static ::xla::cpu::runtime::internal::Registrar             \
+      REGISTER_XLA_CPU_RUNTIME_BUILTIN_NAME(ctr)(             \
+          ::xla::cpu::runtime::k##base_name##SymbolName,      \
+          reinterpret_cast<void*>(__xla_cpu_runtime_##base_name), #base_name)
+
+// __COUNTER__ must go through another macro to be properly expanded
+#define REGISTER_XLA_CPU_RUNTIME_BUILTIN_NAME(ctr) \
+  __xla_cpu_runtime_registrar_##ctr##__object_
+
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
index 646254887c..c23c146119 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
+#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -24,13 +25,23 @@ namespace cpu {
 namespace runtime {
 
 #ifdef __AVX__
-V8F32 ExpV8F32(V8F32 x) { return Eigen::internal::pexp(x); }
+V8F32 __xla_cpu_runtime_ExpV8F32(V8F32 x) { return Eigen::internal::pexp(x); }
 
-V8F32 LogV8F32(V8F32 x) { return Eigen::internal::plog(x); }
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(ExpV8F32);
 
-V8F32 TanhV8F32(V8F32 x) { return Eigen::internal::ptanh(x); }
+V8F32 __xla_cpu_runtime_LogV8F32(V8F32 x) { return Eigen::internal::plog(x); }
+
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(LogV8F32);
+
+V8F32 __xla_cpu_runtime_TanhV8F32(V8F32 x) { return Eigen::internal::ptanh(x); }
+
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(TanhV8F32);
 #endif  // __AVX__
 
+const char *const kExpV8F32SymbolName = "__xla_cpu_runtime_ExpV8F32";
+const char *const kLogV8F32SymbolName = "__xla_cpu_runtime_LogV8F32";
+const char *const kTanhV8F32SymbolName = "__xla_cpu_runtime_TanhV8F32";
+
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
index 89721aaf83..baee321c24 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
@@ -28,20 +28,23 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-constexpr char kExpV8F32[] = "__xla_cpu_runtime_ExpV8F32";
-constexpr char kLogV8F32[] = "__xla_cpu_runtime_LogV8F32";
-constexpr char kTanhV8F32[] = "__xla_cpu_runtime_TanhV8F32";
+extern const char *const kExpV8F32SymbolName;
+extern const char *const kLogV8F32SymbolName;
+extern const char *const kTanhV8F32SymbolName;
 
 typedef float V8F32 __attribute__((__vector_size__(32)));
 
+extern "C" {
+
 // The following functions are vectorized versions of a selection of libm
 // library functions.
 // References to these functions are created by the LLVM vectorizer.
-V8F32 ExpV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+V8F32 __xla_cpu_runtime_ExpV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
 
-V8F32 LogV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+V8F32 __xla_cpu_runtime_LogV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
 
-V8F32 TanhV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+V8F32 __xla_cpu_runtime_TanhV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+}
 
 }  // namespace runtime
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
index 69d04427c6..ecb4de9f43 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h"
+#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -25,23 +26,33 @@ namespace runtime {
 
 #ifdef __SSE4_1__
 
-V4F32 ExpV4F32(V4F32 x) {
+V4F32 __xla_cpu_runtime_ExpV4F32(V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::pexp(p);
 }
 
-V4F32 LogV4F32(V4F32 x) {
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(ExpV4F32);
+
+V4F32 __xla_cpu_runtime_LogV4F32(V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::plog(p);
 }
 
-V4F32 TanhV4F32(V4F32 x) {
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(LogV4F32);
+
+V4F32 __xla_cpu_runtime_TanhV4F32(V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::ptanh(p);
 }
 
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(TanhV4F32);
+
 #endif  // __SSE4_1__
 
+const char *const kExpV4F32SymbolName = "__xla_cpu_runtime_ExpV4F32";
+const char *const kLogV4F32SymbolName = "__xla_cpu_runtime_LogV4F32";
+const char *const kTanhV4F32SymbolName = "__xla_cpu_runtime_TanhV4F32";
+
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
index ded206f90a..413a595e90 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
@@ -28,20 +28,23 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-constexpr char kExpV4F32[] = "__xla_cpu_runtime_ExpV4F32";
-constexpr char kLogV4F32[] = "__xla_cpu_runtime_LogV4F32";
-constexpr char kTanhV4F32[] = "__xla_cpu_runtime_TanhV4F32";
+extern const char *const kExpV4F32SymbolName;
+extern const char *const kLogV4F32SymbolName;
+extern const char *const kTanhV4F32SymbolName;
 
 typedef float V4F32 __attribute__((__vector_size__(16)));
 
+extern "C" {
+
 // The following functions are vectorized versions of a selection of libm
 // library functions.
 // References to these functions are created by the LLVM vectorizer.
-V4F32 ExpV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+V4F32 __xla_cpu_runtime_ExpV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
 
-V4F32 LogV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+V4F32 __xla_cpu_runtime_LogV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
 
-V4F32 TanhV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+V4F32 __xla_cpu_runtime_TanhV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+}
 
 }  // namespace runtime
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index c21b8a9add..f45c28ef74 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -244,14 +244,14 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   switch (type) {
     case F32:
       fn_name = multi_threaded_eigen
-                    ? runtime::kEigenMatmulF32SymbolName
-                    : runtime::kEigenSingleThreadedMatmulF32SymbolName;
+                    ? runtime::kEigenMatMulF32SymbolName
+                    : runtime::kEigenSingleThreadedMatMulF32SymbolName;
       float_type = ir_builder_->getFloatTy();
       break;
     case F64:
       fn_name = multi_threaded_eigen
-                    ? runtime::kEigenMatmulF64SymbolName
-                    : runtime::kEigenSingleThreadedMatmulF64SymbolName;
+                    ? runtime::kEigenMatMulF64SymbolName
+                    : runtime::kEigenSingleThreadedMatMulF64SymbolName;
       float_type = ir_builder_->getDoubleTy();
       break;
     default:
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc
index c2f64eb27a..fa8b31f0a0 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/runtime_conv2d.h"
+#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -41,3 +42,5 @@ TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConvF32(
       col_stride, padding_top, padding_bottom, padding_left, padding_right,
       lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation);
 }
+
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenConvF32);
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
index ee772f5c39..35ace1e5ba 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h"
+#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -72,6 +73,8 @@ void __xla_cpu_runtime_EigenMatMulF32(const void* run_options_ptr, float* out,
                 transpose_rhs);
 }
 
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenMatMulF32);
+
 void __xla_cpu_runtime_EigenMatMulF64(const void* run_options_ptr, double* out,
                                       double* lhs, double* rhs, int64 m,
                                       int64 n, int64 k, int32 transpose_lhs,
@@ -79,3 +82,5 @@ void __xla_cpu_runtime_EigenMatMulF64(const void* run_options_ptr, double* out,
   MatMul<double>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
                  transpose_rhs);
 }
+
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenMatMulF64);
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc
index d0b0e11ac0..feaff63b1c 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h"
 
+#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_conv2d_impl.h"
 #include "tensorflow/core/platform/dynamic_annotations.h"
 #include "tensorflow/core/platform/types.h"
@@ -37,3 +38,5 @@ __xla_cpu_runtime_EigenSingleThreadedConvF32(
       padding_top, padding_bottom, padding_left, padding_right,
       lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation);
 }
+
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenSingleThreadedConvF32);
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
index 6f1c97a233..ed8079c3dd 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/core/platform/types.h"
 
 using tensorflow::int32;
@@ -65,9 +66,13 @@ void __xla_cpu_runtime_EigenSingleThreadedMatMulF32(
                 transpose_rhs);
 }
 
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenSingleThreadedMatMulF32);
+
 void __xla_cpu_runtime_EigenSingleThreadedMatMulF64(
     const void* run_options_ptr, double* out, double* lhs, double* rhs, int64 m,
     int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) {
   MatMul<double>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
                  transpose_rhs);
 }
+
+REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenSingleThreadedMatMulF64);
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index aaf3fd2f9e..f7aa4b2719 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -56,44 +56,15 @@ std::string CanonicalizeSymbol(const std::string &name) {
 // A simple SymbolResolver that delegates to the host dynamic linker.
 struct SimpleResolver : public llvm::JITSymbolResolver {
   llvm::JITSymbol findSymbol(const std::string &name) override {
-    void *func_addr = nullptr;
-
     std::string canonical_name = CanonicalizeSymbol(name);
-    if (canonical_name == runtime::kEigenMatmulF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(__xla_cpu_runtime_EigenMatMulF32);
-    } else if (canonical_name ==
-               runtime::kEigenSingleThreadedMatmulF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_EigenSingleThreadedMatMulF32);
-    } else if (canonical_name == runtime::kEigenConvF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(__xla_cpu_runtime_EigenConvF32);
-    } else if (canonical_name ==
-               runtime::kEigenSingleThreadedConvF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_EigenSingleThreadedConvF32);
-    } else if (canonical_name ==
-               runtime::kAcquireInfeedBufferForDequeueSymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_AcquireInfeedBufferForDequeue);
-    } else if (canonical_name ==
-               runtime::kReleaseInfeedBufferAfterDequeueSymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue);
-    } else if (canonical_name == runtime::kExpV4F32) {
-      func_addr = reinterpret_cast<void *>(runtime::ExpV4F32);
-    } else if (canonical_name == runtime::kExpV8F32) {
-      func_addr = reinterpret_cast<void *>(runtime::ExpV8F32);
-    } else if (canonical_name == runtime::kLogV4F32) {
-      func_addr = reinterpret_cast<void *>(runtime::LogV4F32);
-    } else if (canonical_name == runtime::kLogV8F32) {
-      func_addr = reinterpret_cast<void *>(runtime::LogV8F32);
-    } else if (canonical_name == runtime::kTanhV4F32) {
-      func_addr = reinterpret_cast<void *>(runtime::TanhV4F32);
-    } else if (canonical_name == runtime::kTanhV8F32) {
-      func_addr = reinterpret_cast<void *>(runtime::TanhV8F32);
-    } else {
-      func_addr = dlsym(RTLD_DEFAULT, canonical_name.c_str());
-    }
+    bool must_be_builtin =
+        tensorflow::StringPiece(canonical_name.c_str(), canonical_name.size())
+            .starts_with(runtime::kXlaCpuRuntimeSymbolPrefix);
+
+    void *func_addr = must_be_builtin
+                          ? runtime::ResolveSymbol(
+                                {canonical_name.c_str(), canonical_name.size()})
+                          : dlsym(RTLD_DEFAULT, canonical_name.c_str());
 
     if (func_addr == nullptr) {
       return nullptr;
@@ -134,8 +105,8 @@ llvm::StringRef GetHostCpuName() {
 
 CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() {
   CompilerFunctor::VectorIntrinsics intrinsics;
-  intrinsics.sse_intrinsics = (&runtime::ExpV4F32 != nullptr);
-  intrinsics.avx_intrinsics = (&runtime::ExpV8F32 != nullptr);
+  intrinsics.sse_intrinsics = (&runtime::__xla_cpu_runtime_ExpV4F32 != nullptr);
+  intrinsics.avx_intrinsics = (&runtime::__xla_cpu_runtime_ExpV8F32 != nullptr);
   return intrinsics;
 }
 
-- 
GitLab


From a2ad9123aad76329cadb35bc6a816e53700eebe4 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Thu, 20 Jul 2017 12:59:39 -0700
Subject: [PATCH 1158/1375] Fix bug in C++ gradient computation where
 multi-output nodes may not have been processed.

If a subset of the outputs of a multi-output node were included in the
'inputs' argument to AddSymbolicGradients(), they would not be
populated in 'grad_outputs'. This is because the pending count of the
multi-output node would never go to zero (unless the backprop
traversal happened to visit all the output edges, even though not all
were explicitly requested).

This change fixes the bug by processing any remaining requested edges
after finishing the traversal.

PiperOrigin-RevId: 162653892
---
 tensorflow/cc/framework/gradients.cc      | 17 +++++++++++
 tensorflow/cc/framework/gradients_test.cc | 36 +++++++++++++++++++++++
 2 files changed, 53 insertions(+)

diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index d454bc3e4c..cec3ebc0ad 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -352,6 +352,23 @@ Status SymbolicGradientBuilder::AddGradients() {
           BackpropAlongEdge(dx[dx_index++], {e->src(), e->src_output()}));
     }
   }
+
+  // Check if any input nodes still have pending gradients and have not been
+  // processed yet. This happens if not all outputs of a node are in 'inputs_'.
+  std::unordered_map<Node*, int> requested_grads;
+  for (Output nout : inputs_) {
+    if (pending_[nout.node()->id()] > 0) {
+      DCHECK_GT(nout.node()->num_outputs(), 1);
+      int idx = input_nodes_[nout];
+      DCHECK(((*grad_outputs_)[idx].node() == nullptr));
+      TF_RETURN_IF_ERROR(SumGradients(nout, &(*grad_outputs_)[idx]));
+      ++requested_grads[nout.node()];
+    }
+  }
+  for (auto& p : requested_grads) {
+    int num_requested_inputs = p.first->num_outputs() - pending_[p.first->id()];
+    CHECK_EQ(num_requested_inputs, p.second);
+  }
   return Status::OK();
 }
 
diff --git a/tensorflow/cc/framework/gradients_test.cc b/tensorflow/cc/framework/gradients_test.cc
index 2aad978480..24af7d567b 100644
--- a/tensorflow/cc/framework/gradients_test.cc
+++ b/tensorflow/cc/framework/gradients_test.cc
@@ -260,6 +260,42 @@ TEST_F(GradientsTest, StackUnstack_StopBackprop) {
   CompareTestAndExpectedGraphs();
 }
 
+TEST_F(GradientsTest, StackUnstack_SubsetOfUnstackOutputs) {
+  // Constructs an unstack with three outputs, and takes the gradient with
+  // respect to only two of the outputs. Tests that the output gradients are
+  // computed.
+  for (const bool expected : {false, true}) {
+    const Scope& scope = expected ? scope_expected_ : scope_test_;
+    // Construct forward graph.
+    auto c = Const(scope, 1, {3, 4, 2});
+    auto unpack = Unstack(scope, c, 3);
+    auto x = Identity(scope, unpack.output[0]);
+    auto y = Identity(scope, unpack.output[1]);
+    auto z = Identity(scope, unpack.output[2]);
+    TF_ASSERT_OK(scope.status());
+
+    // Construct grad inputs.
+    auto dy = Const(scope, 4, {4, 2});
+    auto dz = Const(scope, 5, {4, 2});
+
+    if (expected) {
+      // Construct backward graph.
+      auto g1 = Identity(scope, dy);
+      auto g2 = Identity(scope, dz);
+    } else {
+      // Call AddSymbolicGradients.
+      std::vector<Output> grad_outputs;
+      TF_ASSERT_OK(AddSymbolicGradients(scope, {y, z},
+                                        {unpack.output[1], unpack.output[2]},
+                                        {dy, dz}, &grad_outputs));
+      ASSERT_EQ(grad_outputs.size(), 2);
+      EXPECT_TRUE(grad_outputs[0].node() != nullptr);
+      EXPECT_TRUE(grad_outputs[1].node() != nullptr);
+    }
+  }
+  CompareTestAndExpectedGraphs();
+}
+
 TEST_F(GradientsTest, DependentGradOutputs) {
   // Tests that dependent gradients (in this case the gradients w.r.t to the
   // output and one input of MatMul) are computed properly.
-- 
GitLab


From 65f9f9b94fc905ee9bfd1785fdf7fd2b366e4b10 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 13:20:18 -0700
Subject: [PATCH 1159/1375] Update docstring for compression_type argument of
 TextLineDataset. compression_type will accept a scalar String tensor, not
 just a Python string.

PiperOrigin-RevId: 162656520
---
 tensorflow/contrib/data/python/ops/dataset_ops.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index 0a6933274c..bf60efbff9 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -1943,8 +1943,8 @@ class TextLineDataset(Dataset):
 
     Args:
       filenames: A `tf.string` tensor containing one or more filenames.
-      compression_type: A string, one of: `""` (no compression), `"ZLIB"`, or
-        `"GZIP"`.
+      compression_type: A `tf.string` scalar evaluating to one of `""` (no
+        compression), `"ZLIB"`, or `"GZIP"`.
     """
     super(TextLineDataset, self).__init__()
     self._filenames = ops.convert_to_tensor(
-- 
GitLab


From 9f4ec7e8492ae4527315a7ef2df4beb5cc8b9ab8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 13:34:06 -0700
Subject: [PATCH 1160/1375] Update the version check function for bazel.

PiperOrigin-RevId: 162658391
---
 configure | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configure b/configure
index 93280dc5f5..b7bc87dcb7 100755
--- a/configure
+++ b/configure
@@ -163,7 +163,7 @@ function version {
 
 
 bazel version > bazel.version
-curr_bazel_version=$(head -n 1 bazel.version | cut -d ' ' -f3)
+curr_bazel_version=$(grep -m 1 'Build label:' bazel.version | cut -d ' ' -f3)
 rm -f bazel.version
 
 
-- 
GitLab


From 97cf16bb937e0cea7c024b2e689b198795682101 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Thu, 20 Jul 2017 13:36:21 -0700
Subject: [PATCH 1161/1375] Documentation fixes and polishing for
 tf.contrib.signal.

PiperOrigin-RevId: 162658696
---
 tensorflow/contrib/signal/__init__.py         |  6 ++-
 .../contrib/signal/python/ops/shape_ops.py    |  5 +-
 .../contrib/signal/python/ops/spectral_ops.py | 48 +++++++++----------
 .../contrib/signal/python/ops/window_ops.py   | 12 ++---
 4 files changed, 38 insertions(+), 33 deletions(-)

diff --git a/tensorflow/contrib/signal/__init__.py b/tensorflow/contrib/signal/__init__.py
index c0136346b7..6cc51d6fb0 100644
--- a/tensorflow/contrib/signal/__init__.py
+++ b/tensorflow/contrib/signal/__init__.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""##Signal ops.
+"""Signal processing operations.
 
 @@frame
 @@hamming_window
@@ -20,6 +20,10 @@
 @@inverse_stft
 @@overlap_and_add
 @@stft
+
+[hamming]: https://en.wikipedia.org/wiki/Window_function#Hamming_window
+[hann]: https://en.wikipedia.org/wiki/Window_function#Hann_window
+[stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
 """
 
 from __future__ import absolute_import
diff --git a/tensorflow/contrib/signal/python/ops/shape_ops.py b/tensorflow/contrib/signal/python/ops/shape_ops.py
index dc7a073242..1ddc2941ec 100644
--- a/tensorflow/contrib/signal/python/ops/shape_ops.py
+++ b/tensorflow/contrib/signal/python/ops/shape_ops.py
@@ -57,7 +57,7 @@ def frame(signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=-1,
           name=None):
   """Expands `signal`'s `axis` dimension into frames of `frame_length`.
 
-  Slides a window of size `frame_length` over `signal`s `axis` dimension
+  Slides a window of size `frame_length` over `signal`'s `axis` dimension
   with a stride of `frame_step`, replacing the `axis` dimension with
   `[frames, frame_length]` frames.
 
@@ -91,7 +91,8 @@ def frame(signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=-1,
     A `Tensor` of frames with shape `[..., frames, frame_length, ...]`.
 
   Raises:
-    ValueError: If `frame_length`, `frame_step`, or `pad_value` are not scalar.
+    ValueError: If `frame_length`, `frame_step`, `pad_value`, or `axis` are not
+      scalar.
   """
   with ops.name_scope(name, "frame", [signal, frame_length, frame_step,
                                       pad_value]):
diff --git a/tensorflow/contrib/signal/python/ops/spectral_ops.py b/tensorflow/contrib/signal/python/ops/spectral_ops.py
index 0d1ef0d3d1..75bc0bd21d 100644
--- a/tensorflow/contrib/signal/python/ops/spectral_ops.py
+++ b/tensorflow/contrib/signal/python/ops/spectral_ops.py
@@ -32,17 +32,15 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import spectral_ops
 
 
-def stft(signal, frame_length, frame_step, fft_length=None,
+def stft(signals, frame_length, frame_step, fft_length=None,
          window_fn=functools.partial(window_ops.hann_window, periodic=True),
          pad_end=False, name=None):
-  """Computes the Short-time Fourier Transform of a batch of real signals.
-
-  https://en.wikipedia.org/wiki/Short-time_Fourier_transform
+  """Computes the [Short-time Fourier Transform][stft] of `signals`.
 
   Implemented with GPU-compatible ops and supports gradients.
 
   Args:
-    signal: A `[..., samples]` `float32` `Tensor` of real-valued signals.
+    signals: A `[..., samples]` `float32` `Tensor` of real-valued signals.
     frame_length: An integer scalar `Tensor`. The window length in samples.
     frame_step: An integer scalar `Tensor`. The number of samples to step.
     fft_length: An integer scalar `Tensor`. The size of the FFT to apply.
@@ -50,25 +48,26 @@ def stft(signal, frame_length, frame_step, fft_length=None,
     window_fn: A callable that takes a window length and a `dtype` keyword
       argument and returns a `[window_length]` `Tensor` of samples in the
       provided datatype. If set to `None`, no windowing is used.
-    pad_end: Whether to pad the end of signal with zeros when the provided
-      frame length and step produces a frame that lies partially past the end
-      of `signal`.
+    pad_end: Whether to pad the end of `signals` with zeros when the provided
+      frame length and step produces a frame that lies partially past its end.
     name: An optional name for the operation.
 
   Returns:
     A `[..., frames, fft_unique_bins]` `Tensor` of `complex64` STFT values where
-    `fft_unique_bins` is `fft_length / 2 + 1` (the unique components of the
+    `fft_unique_bins` is `fft_length // 2 + 1` (the unique components of the
     FFT).
 
   Raises:
-    ValueError: If `signal` is not at least rank 1, `frame_length` is
+    ValueError: If `signals` is not at least rank 1, `frame_length` is
       not scalar, `frame_step` is not scalar, or `frame_length`
       is greater than `fft_length`.
+
+  [stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
   """
-  with ops.name_scope(name, 'stft', [signal, frame_length,
+  with ops.name_scope(name, 'stft', [signals, frame_length,
                                      frame_step]):
-    signal = ops.convert_to_tensor(signal, name='signal')
-    signal.shape.with_rank_at_least(1)
+    signals = ops.convert_to_tensor(signals, name='signals')
+    signals.shape.with_rank_at_least(1)
     frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
     frame_length.shape.assert_has_rank(0)
     frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
@@ -88,17 +87,17 @@ def stft(signal, frame_length, frame_step, fft_length=None,
                        'fft_length (%d)' % (frame_length_static,
                                             fft_length_static))
 
-    framed_signal = shape_ops.frame(
-        signal, frame_length, frame_step, pad_end=pad_end)
+    framed_signals = shape_ops.frame(
+        signals, frame_length, frame_step, pad_end=pad_end)
 
-    # Optionally window the framed signal.
+    # Optionally window the framed signals.
     if window_fn is not None:
-      window = window_fn(frame_length, dtype=framed_signal.dtype)
-      framed_signal *= window
+      window = window_fn(frame_length, dtype=framed_signals.dtype)
+      framed_signals *= window
 
     # spectral_ops.rfft produces the (fft_length/2 + 1) unique components of the
-    # FFT of the real windowed signals in framed_signal.
-    return spectral_ops.rfft(framed_signal, [fft_length])
+    # FFT of the real windowed signals in framed_signals.
+    return spectral_ops.rfft(framed_signals, [fft_length])
 
 
 def inverse_stft(stfts,
@@ -108,15 +107,14 @@ def inverse_stft(stfts,
                  window_fn=functools.partial(window_ops.hann_window,
                                              periodic=True),
                  name=None):
-  """Computes the inverse Short-time Fourier Transform of a batch of STFTs.
-
-  https://en.wikipedia.org/wiki/Short-time_Fourier_transform
+  """Computes the inverse [Short-time Fourier Transform][stft] of `stfts`.
 
   Implemented with GPU-compatible ops and supports gradients.
 
   Args:
     stfts: A `complex64` `[..., frames, fft_unique_bins]` `Tensor` of STFT bins
-      representing a batch of `fft_length`-point STFTs.
+      representing a batch of `fft_length`-point STFTs where `fft_unique_bins`
+      is `fft_length // 2 + 1`
     frame_length: An integer scalar `Tensor`. The window length in samples.
     frame_step: An integer scalar `Tensor`. The number of samples to step.
     fft_length: An integer scalar `Tensor`. The size of the FFT that produced
@@ -133,6 +131,8 @@ def inverse_stft(stfts,
   Raises:
     ValueError: If `stfts` is not at least rank 2, `frame_length` is not scalar,
       `frame_step` is not scalar, or `fft_length` is not scalar.
+
+  [stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
   """
   with ops.name_scope(name, 'inverse_stft', [stfts]):
     stfts = ops.convert_to_tensor(stfts, name='stfts')
diff --git a/tensorflow/contrib/signal/python/ops/window_ops.py b/tensorflow/contrib/signal/python/ops/window_ops.py
index 2179484ebc..07a847dd2a 100644
--- a/tensorflow/contrib/signal/python/ops/window_ops.py
+++ b/tensorflow/contrib/signal/python/ops/window_ops.py
@@ -29,9 +29,7 @@ from tensorflow.python.ops import math_ops
 
 
 def hann_window(window_length, periodic=True, dtype=dtypes.float32, name=None):
-  """Generate a Hann window.
-
-  https://en.wikipedia.org/wiki/Window_function#Hann_window
+  """Generate a [Hann window][hann].
 
   Args:
     window_length: A scalar `Tensor` indicating the window length to generate.
@@ -47,6 +45,8 @@ def hann_window(window_length, periodic=True, dtype=dtypes.float32, name=None):
 
   Raises:
     ValueError: If `dtype` is not a floating point type.
+
+  [hann]: https://en.wikipedia.org/wiki/Window_function#Hann_window
   """
   return _raised_cosine_window(name, 'hann_window', window_length, periodic,
                                dtype, 0.5, 0.5)
@@ -54,9 +54,7 @@ def hann_window(window_length, periodic=True, dtype=dtypes.float32, name=None):
 
 def hamming_window(window_length, periodic=True, dtype=dtypes.float32,
                    name=None):
-  """Generate a Hamming window.
-
-  https://en.wikipedia.org/wiki/Window_function#Hamming_window
+  """Generate a [Hamming][hamming] window.
 
   Args:
     window_length: A scalar `Tensor` indicating the window length to generate.
@@ -72,6 +70,8 @@ def hamming_window(window_length, periodic=True, dtype=dtypes.float32,
 
   Raises:
     ValueError: If `dtype` is not a floating point type.
+
+  [hamming]: https://en.wikipedia.org/wiki/Window_function#Hamming_window
   """
   return _raised_cosine_window(name, 'hamming_window', window_length, periodic,
                                dtype, 0.54, 0.46)
-- 
GitLab


From 306c585a179d9983afa3f6d3f54d4e4a532cb479 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 13:54:23 -0700
Subject: [PATCH 1162/1375] Make DISABLED_ON_${X}PU work again. Before this
 change, when a test target refers to sources by depending on the cc_library
 that contains the sources, the backend specific macros won't work because the
 sources only get compiled once. This change introduces a new rule that
 compile the sources to different cc_library targets for different backends.

PiperOrigin-RevId: 162661336
---
 tensorflow/compiler/xla/tests/BUILD           | 14 ++--
 tensorflow/compiler/xla/tests/build_defs.bzl  | 70 +++++++++++++++++++
 .../compiler/xla/tests/reduce_window_test.cc  |  4 --
 3 files changed, 77 insertions(+), 11 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 917e6aa76c..da173d2ee6 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -26,6 +26,7 @@ filegroup(
 
 load("//tensorflow/compiler/xla:xla.bzl", "export_dynamic_linkopts")
 load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test")
+load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test_library")
 load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_suites")
 load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_test_macros")
 
@@ -876,11 +877,11 @@ xla_test(
     ],
 )
 
-# Note that the backend-specific macros (e.g. DISABLED_ON_CPU) would not work here, because the
-# library does not get recompiled for each backend.
-cc_library(
+# External xla_test targets can add "reduce_window_test_library" to xla_test_library_deps, in order
+# to refer to the cc_library compiled with the correct backend macros. The following test target
+# "reduce_window_test" is an example.
+xla_test_library(
     name = "reduce_window_test_library",
-    testonly = 1,
     srcs = ["reduce_window_test.cc"],
     deps = [
         ":test_macros_header",
@@ -906,9 +907,8 @@ xla_test(
     name = "reduce_window_test",
     timeout = "long",
     srcs = [],
-    deps = [
-        ":reduce_window_test_library",
-    ],
+    xla_test_library_deps = [":reduce_window_test_library"],
+    deps = [],
 )
 
 xla_test(
diff --git a/tensorflow/compiler/xla/tests/build_defs.bzl b/tensorflow/compiler/xla/tests/build_defs.bzl
index a297132dd3..dae0956f0a 100644
--- a/tensorflow/compiler/xla/tests/build_defs.bzl
+++ b/tensorflow/compiler/xla/tests/build_defs.bzl
@@ -26,6 +26,7 @@ def filter_backends(backends):
 def xla_test(name,
              srcs,
              deps,
+             xla_test_library_deps=[],
              backends=[],
              args=[],
              tags=[],
@@ -84,6 +85,8 @@ def xla_test(name,
     name: Name of the target.
     srcs: Sources for the target.
     deps: Dependencies of the target.
+    xla_test_library_deps: If set, the generated test targets will depend on the
+      respective cc_libraries generated by the xla_test_library rule.
     backends: A list of backends to generate tests for. Supported
       values: "cpu", "cpu_parallel", "gpu". If this list is empty, the test will
       be generated for all supported backends.
@@ -130,6 +133,10 @@ def xla_test(name,
     else:
       fail("Unknown backend %s" % backend)
 
+    if xla_test_library_deps:
+      for lib_dep in xla_test_library_deps:
+        backend_deps += ["%s_%s" % (lib_dep, backend)]
+
     native.cc_test(
         name=test_name,
         srcs=srcs,
@@ -144,6 +151,69 @@ def xla_test(name,
 
   native.test_suite(name=name, tests=test_names)
 
+def xla_test_library(name,
+                     srcs,
+                     hdrs=[],
+                     deps=[],
+                     backends=[]):
+  """Generates cc_library targets for the given XLA backends.
+
+  This rule forces the sources to be compiled for each backend so that the
+  backend specific macros could expand correctly. It's useful when test targets
+  in different directories referring to the same sources but test with different
+  arguments.
+
+  Examples:
+
+    # Generates the targets: foo_test_library_cpu and foo_test_gpu.
+    xla_test_library(
+        name = "foo_test_library",
+        srcs = ["foo_test.cc"],
+        backends = ["cpu", "gpu"],
+        deps = [...],
+    )
+    # Then use the xla_test rule to generate test targets:
+    xla_test(
+        name = "foo_test",
+        srcs = [],
+        backends = ["cpu", "gpu"],
+        deps = [...],
+        xla_test_library_deps = [":foo_test_library"],
+    )
+
+  Args:
+    name: Name of the target.
+    srcs: Sources for the target.
+    hdrs: Headers for the target.
+    deps: Dependencies of the target.
+    backends: A list of backends to generate libraries for.
+      Supported values: "cpu", "cpu_parallel", "gpu". If this list is empty, the
+      library will be generated for all supported backends.
+  """
+
+  if not backends:
+    backends = all_backends
+
+  for backend in filter_backends(backends):
+    this_backend_copts = []
+    if backend in ["cpu", "cpu_parallel", "gpu"]:
+      backend_deps = ["//tensorflow/compiler/xla/tests:test_macros_%s" % backend]
+    elif backend in plugins:
+      backend_deps = plugins[backend]["deps"]
+      this_backend_copts += plugins[backend]["copts"]
+    else:
+      fail("Unknown backend %s" % backend)
+
+    native.cc_library(
+        name = "%s_%s" % (name, backend),
+        srcs = srcs,
+        testonly = True,
+        hdrs = hdrs,
+        copts = ["-DXLA_TEST_BACKEND_%s=1" % backend.upper()]
+        + this_backend_copts,
+        deps = deps + backend_deps,
+    )
+
 
 def generate_backend_suites(backends=[]):
   if not backends:
diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index ccdd91473c..a438236c45 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -36,10 +36,6 @@ limitations under the License.
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/types.h"
 
-// These don't work on reduce_window_test.cc, please see the BUILD file.
-#undef DISABLED_ON_CPU
-#undef DISABLED_ON_GPU
-
 namespace xla {
 namespace {
 
-- 
GitLab


From 5dbae025a1b61155883c2f3c868bed9e53000e09 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 13:58:27 -0700
Subject: [PATCH 1163/1375] Simplify type casts in conversion to array slices.

This change introduces a slight semantics change. Empty vectors are guaranteed to not assert, because v.data() is allowed on empty vectors.

PiperOrigin-RevId: 162661944
---
 tensorflow/compiler/xla/literal_util.cc | 23 ++++++++---------------
 1 file changed, 8 insertions(+), 15 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index ef21148b8b..0db9bd757d 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -926,25 +926,21 @@ template <>
 tensorflow::gtl::MutableArraySlice<bool> Literal::GetMutableArraySlice() {
   auto values = mutable_preds();
   return tensorflow::gtl::MutableArraySlice<bool>(
-      reinterpret_cast<bool*>(&(*values)[0]), values->size());
+      reinterpret_cast<bool*>(values->data()), values->size());
 }
 
 template <>
 tensorflow::gtl::MutableArraySlice<int8> Literal::GetMutableArraySlice() {
-  // C++11 standard, basic_string 21.4.1.5, values should be stored
-  // contiguously. From C++17 a mutable data() member will be provided.
   auto values = mutable_u8s();
   return tensorflow::gtl::MutableArraySlice<int8>(
-      reinterpret_cast<int8*>(&(*values)[0]), values->size());
+      reinterpret_cast<int8*>(values->data()), values->size());
 }
 
 template <>
 tensorflow::gtl::MutableArraySlice<uint8> Literal::GetMutableArraySlice() {
-  // C++11 standard, basic_string 21.4.1.5, values should be stored
-  // contiguously. From C++17 a mutable data() member will be provided.
   auto values = mutable_u8s();
-  return tensorflow::gtl::MutableArraySlice<uint8>(
-      reinterpret_cast<uint8*>(&(*values)[0]), values->size());
+  return tensorflow::gtl::MutableArraySlice<uint8>(values->data(),
+                                                   values->size());
 }
 
 template <>
@@ -1006,13 +1002,11 @@ tensorflow::gtl::MutableArraySlice<double> Literal::GetMutableArraySlice() {
 
 template <>
 tensorflow::gtl::MutableArraySlice<half> Literal::GetMutableArraySlice<half>() {
-  // C++11 standard, basic_string 21.4.1.5, values should be stored
-  // contiguously. From C++17 a mutable data() member will be provided.
   // TODO - there is an endianess problem here. fix it, or wait for uint16
   //        support in protobuf
   auto values = mutable_f16s();
-  return tensorflow::gtl::MutableArraySlice<half>(
-      reinterpret_cast<half*>(&(*values)[0]), values->size());
+  return tensorflow::gtl::MutableArraySlice<half>(values->data(),
+                                                  values->size());
 }
 
 template <>
@@ -1069,9 +1063,8 @@ tensorflow::gtl::ArraySlice<double> Literal::GetArraySlice<double>() const {
 template <>
 tensorflow::gtl::ArraySlice<half> Literal::GetArraySlice<half>() const {
   CHECK_EQ(shape().element_type(), F16);
-  return tensorflow::gtl::ArraySlice<half>(
-      reinterpret_cast<const half*>(f16s().data()),
-      f16s().size() / sizeof(half));
+  return tensorflow::gtl::ArraySlice<half>(f16s().data(),
+                                           f16s().size() / sizeof(half));
 }
 
 template <typename NativeT>
-- 
GitLab


From c2baef7a6c3d47dec2b123f4d24939abefc530ba Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Thu, 20 Jul 2017 14:05:37 -0700
Subject: [PATCH 1164/1375] Merging 1.3.0rc0 back to master.

---
 README.md                                     |  15 +-
 RELEASE.md                                    | 103 +++++
 tensorflow/contrib/cmake/tf_core_ops.cmake    |  12 -
 tensorflow/contrib/cmake/tf_python.cmake      |   2 -
 tensorflow/core/ops/debug_ops.cc              |   3 -
 tensorflow/core/public/version.h              |   6 +-
 tensorflow/docs_src/install/install_c.md      |   2 +-
 tensorflow/docs_src/install/install_go.md     |   2 +-
 tensorflow/docs_src/install/install_java.md   |  18 +-
 tensorflow/docs_src/install/install_linux.md  |  22 +-
 tensorflow/docs_src/install/install_mac.md    |  10 +-
 .../docs_src/install/install_sources.md       |   4 +-
 .../docs_src/install/install_windows.md       |   4 +-
 tensorflow/python/debug/BUILD                 |  40 --
 tensorflow/python/debug/__init__.py           |   5 -
 .../python/debug/lib/debug_gradients.py       | 417 ------------------
 .../python/debug/lib/debug_gradients_test.py  | 378 ----------------
 tensorflow/tools/docker/Dockerfile.devel      |   2 +-
 tensorflow/tools/docker/Dockerfile.devel-gpu  |   2 +-
 tensorflow/tools/pip_package/setup.py         |   2 +-
 20 files changed, 148 insertions(+), 901 deletions(-)
 delete mode 100644 tensorflow/python/debug/lib/debug_gradients.py
 delete mode 100644 tensorflow/python/debug/lib/debug_gradients_test.py

diff --git a/README.md b/README.md
index f86e747978..bb87656ce9 100644
--- a/README.md
+++ b/README.md
@@ -34,13 +34,14 @@ and discussion, and please direct specific questions to [Stack Overflow](https:/
 
 People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.2.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.2.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.2.1-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
+
+* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.3.0rc0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.3.0rc0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/))
+* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.3.0rc0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.3.0rc0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/))
+* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
 ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
 
 #### *Try your first TensorFlow program*
diff --git a/RELEASE.md b/RELEASE.md
index 33fe88cd54..e7c086164a 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,3 +1,106 @@
+# Release 1.3.0
+
+## Major Features and Improvements
+* Added canned estimators to Tensorflow library. List of added estimators: `DNNClassifier`, `DNNRegressor`, `LinearClassifer`, `LinearRegressor`, `DNNLinearCombinedClassifier`, `DNNLinearCombinedRegressor`.
+* All our prebuilt binaries have been built with cuDNN 6.
+* Adds a file cache to the GCS filesystem with configurable max staleness for file contents. This permits caching of file contents across close/open boundaries.
+* Added an axis parameter to `tf.gather`.
+* Added a `constant_values` keyword argument to `tf.pad`.
+* Adds `Dataset.interleave` transformation.
+* Add `ConcatenateDataset` to concatenate two datasets.
+* Added Mobilenet support to TensorFlow for Poets training script.
+* Adds a block cache to the GCS filesystem with configurable block size and count.
+* SinhArcSinh bijector added.
+* Added `Dataset.list_files` API.
+* Introduces new operations and Python bindings for the Cloud TPU.
+* Adding TensorFlow-iOS CocoaPod for symmetry with tensorflow-android.
+* Introduces base implementations of ClusterResolvers.
+* Unify memory representations of TensorShape and PartialTensorShape. As a consequence, tensors now have a maximum of 254 dimensions, not 255.
+* Changed references to LIBXSMM to use version 1.8.1.
+* TensorFlow Debugger (tfdbg): Display summaries of numeric tensor values with the `-s` flag to command `print_tensor` or `pt`.
+* Initial release of the statistical distribution library `tf.distributions`.
+* GPU kernels and speed improvements for for unary `tf.where` and `tf.nn.top_k`.
+* Monotonic Attention wrappers added to `tf.contrib.seq2seq`.
+
+## Breaking Changes to the API
+* `tf.RewriterConfig` was removed from the Python API after being available in 1.2 release candidates (it was never in an actual release). Graph rewriting is still available, just not as `tf.RewriterConfig`. Instead add an explicit import.
+* Breaking change to `tf.contrib.data.Dataset` APIs that expect a nested structure. Lists are now converted to `tf.Tensor` implicitly. You may need to change uses of lists to tuples in existing code. In addition, dicts are now supported as a nested structure.
+
+## Changes to contrib APIs
+* Adds tf.contrib.nn.rank_sampled_softmax_loss, a sampled-softmax variant that can improve rank loss.
+* `tf.contrib.metrics`.{streaming_covariance,streaming_pearson_correlation} modified to return nan when they have seen less or equal to 1 unit of weight.
+* Adds time series models to contrib. See contrib/timeseries/README.md for details.
+* Adds FULLY_CONNECTED Op to tensorflow/contrib/lite/schema.fbs
+
+## Bug Fixes and Other Changes
+* Fixes 'strides' and 'begin' dtype mismatch when slicing using int64 Tensor index in python.
+* Improved convolution padding documentation.
+* Add a tag constant, gpu, to present graph with GPU support.
+* `saved_model.utils` now support SparseTensors transparently.
+* A more efficient implementation of non-max suppression.
+* Add support for the shrinkage-type L2 to FtrlOptimizer in addition to the online L2 it already supports.
+* Fix negative variance in moments calculation.
+* Expand UniqueOp Benchmark Tests to cover more collision cases.
+* Improves stability of GCS filesystem on Mac.
+* Add time estimation to HloCostAnalysis.
+* Fixed the bug in Estimator that params in constructor was not a deepcopy of the user provided one. This bugs inadvertently enabled user to mutate the params after the creation of Estimator, leading to potentially undefined behavior.
+* Added None check for save_path in `saver.restore`.
+* Register devices under their legacy names in device_mgr to ease the transition to clusterspec-propagated configurations.
+* VectorExponential added to distributions.
+* Add a bitwise module with bitwise_and, bitwise_or, bitwise_xor, and invert functions.
+* Add fixed-grid ODE integration routines.
+* Allow passing bounds to ScipyOptimizerInterface.
+* Correctness fixes for fft_length parameter to `tf.spectral.rfft` & `tf.spectral.irfft`.
+* Exported model signatures using the 'predict' method will no longer have their input and output keys silently ignored and rewritten to 'inputs' and 'outputs'. If a model was exported with different names before 1.2, and is now served with tensorflow/serving, it will accept requests using 'inputs' and 'outputs'. Starting at 1.2, such a model will accept the keys specified during export. Therefore, inference requests using 'inputs' and 'outputs' may start to fail. To fix this, either update any inference clients to send requests with the actual input and output keys used by the trainer code, or conversely, update the trainer code to name the input and output Tensors 'inputs' and 'outputs', respectively. Signatures using the 'classify' and 'regress' methods are not affected by this change; they will continue to standardize their input and output keys as before.
+* Add in-memory caching to the Dataset API.
+* Set default end_of_sequence variable in datasets iterators to false.
+* [Performance] Increase performance of `tf.layers.con2d` when setting use_bias=True by 2x by using nn.bias_add.
+* Update iOS examples to use CocoaPods, and moved to tensorflow/examples/ios.
+* Adds a family= attribute in `tf.summary` ops to allow controlling the tab name used in Tensorboard for organizing summaries.
+* When GPU is configured, do not require --config=cuda, instead, automatically build for GPU if this is requested in the configure script.
+* Fix incorrect sampling of small probabilities in CPU/GPU multinomial.
+* Add a list_devices() API on sessions to list devices within a cluster. Additionally, this change augment the ListDevices master API to support specifying a session.
+* Allow uses of over-parameterized separable convolution.
+* TensorForest multi-regression bug fix.
+* Framework now supports armv7, cocoapods.org now displays correct page.
+* Script to create iOS framework for CocoaPods.
+* Android releases of TensorFlow are now pushed to jcenter for easier integration into apps. See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md for more details.
+* Fixed a bug that prevented tfdbg from functioning with multi-GPU setups.
+* Fixed a bug that prevented tfdbg from working with `tf.Session.make_callable`.
+
+## Thanks to our Contributors
+
+This release contains contributions from many people at Google, as well as:
+
+4F2E4A2E, Adriano Carmezim, Adrià Arrufat, Alan Yee, Alex Lattas, Alex Rothberg,
+Alexandr Baranezky, Ali Siddiqui, Andreas Solleder, Andrei Costinescu, Andrew Hundt,
+Androbin, Andy Kernahan, Anish Shah, Anthony Platanios, Arvinds-Ds, b1rd, Baptiste
+Arnaud, Ben Mabey, Benedikt Linse, Beomsu Kim, Bo Wang, Boyuan Deng, Brett Koonce,
+Bruno Rosa, Carl Thomé, Changming Sun, Chase Roberts, Chirag Bhatia, Chris Antaki,
+Chris Hoyean Song, Chris Tava, Christos Nikolaou, Croath Liu, cxx, Czxck001, Daniel
+Ylitalo, Danny Goodman, Darren Garvey, David Brailovsky, David Norman, DavidNorman,
+davidpham87, ddurham2, Dhruv, DimanNe, Drew Hintz, Dustin Tran, Earthson Lu, ethiraj,
+Fabian Winnen, Fei Sun, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Gautam,
+Guenther Schmuelling, Gyu-Ho Lee, Hauke Brammer, horance, Humanity123, J Alammar,
+Jayeol Chun, Jeroen BéDorf, Jianfei Wang, jiefangxuanyan, Jing Jun Yin, Joan Puigcerver,
+Joel Hestness, Johannes Mayer, John Lawson, Johnson145, Jon Malmaud, Jonathan Alvarez-Gutierrez,
+Juang, Yi-Lin, Julian Viereck, Kaarthik Sivashanmugam, Karl Lessard, karl@kubx.ca, Kevin
+Carbone, Kevin Van Der Burgt, Kongsea, ksellesk, lanhin, Lef Ioannidis, Liangliang He,
+Louis Tiao, Luke Iwanski, LáSzló Csomor, magixsno, Mahmoud Abuzaina, Marcel Hlopko, Mark
+Neumann, Maxwell Paul Brickner, mdfaijul, MichaëL Defferrard, Michał JastrzęBski, Michele
+Colombo, Mike Brodie, Mosnoi Ion, mouradmourafiq, myPrecious, Nayana Thorat,
+Neeraj Kashyap, Nelson Liu, Niranjan Hasabnis, Olivier Moindrot, orome, Pankaj Gupta, Paul
+Van Eck, peeyush18, Peng Yu, Pierre, preciousdp11, qjivy, Raingo, raoqiyu, ribx, Richard S.
+Imaoka, Rishabh Patel, Robert Walecki, Rockford Wei, Ryan Kung, Sahil Dua, Sandip Giri, Sayed
+Hadi Hashemi, sgt101, Shitian Ni, Shuolongbj, Siim PõDer, Simon Perkins, sj6077, SOLARIS,
+Spotlight0xff, Steffen Eberbach, Stephen Fox, superryanguo, Sven Mayer, Tapan Prakash,
+Tiago Morais Morgado, Till Hoffmann, Tj Rana, Vadim Markovtsev, vhasanov, Wei Wu,
+windead, Yan (Asta) Li, Yan Chen, Yann Henon, Yi Wang, Yong Tang, yorkie, Yuan (Terry)
+Tang, Yuxin Wu, zhengjiajin, zhongzyd, 黄璞
+
+We are also grateful to all who filed issues or helped resolve them, asked and
+answered questions, and were part of inspiring discussions.
+
 # Release 1.2.1
 
 ## Bug Fixes and Other Changes
diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index b350b822dd..21d94da6cc 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -129,15 +129,3 @@ list(REMOVE_ITEM tf_core_ops_srcs ${tf_core_ops_exclude_srcs})
 add_library(tf_core_ops OBJECT ${tf_core_ops_srcs})
 
 add_dependencies(tf_core_ops tf_core_cpu)
-
-########################################################
-# tf_debug_ops library
-########################################################
-
-file(GLOB tf_debug_ops_srcs
-    "${tensorflow_source_dir}/tensorflow/core/ops/debug_ops.cc"
-)
-
-add_library(tf_debug_ops OBJECT ${tf_debug_ops_srcs})
-
-add_dependencies(tf_debug_ops tf_core_framework)
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 8701a584e8..206ba96747 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -696,8 +696,6 @@ GENERATE_PYTHON_OP_LIB("contrib_bigquery_reader_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/cloud/python/ops/gen_bigquery_reader_ops.py)
 GENERATE_PYTHON_OP_LIB("stateless_random_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/stateless/gen_stateless_random_ops.py)
-GENERATE_PYTHON_OP_LIB("debug_ops"
-  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/debug/ops/gen_debug_ops.py)
 
 add_custom_target(tf_python_ops SOURCES ${tf_python_ops_generated_files} ${PYTHON_PROTO_GENFILES})
 add_dependencies(tf_python_ops tf_python_op_gen_main)
diff --git a/tensorflow/core/ops/debug_ops.cc b/tensorflow/core/ops/debug_ops.cc
index bd7f7c2c01..f7a96b58da 100644
--- a/tensorflow/core/ops/debug_ops.cc
+++ b/tensorflow/core/ops/debug_ops.cc
@@ -14,9 +14,7 @@ limitations under the License.
 ==============================================================================*/
 // This file registers all TensorFlow Debugger (tfdbg) ops.
 
-#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/shape_inference.h"
 
 namespace tensorflow {
 
@@ -90,7 +88,6 @@ REGISTER_OP("DebugIdentity")
     .Attr("debug_urls: list(string) = []")
     .Attr("gated_grpc: bool = false")
     .SetAllowsUninitializedInput()
-    .SetShapeFn(shape_inference::UnchangedShape)
     .Doc(R"doc(
 Debug Identity Op.
 
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 8fc10111a6..9ba6b0ed5a 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -19,12 +19,12 @@ limitations under the License.
 // TensorFlow uses semantic versioning, see http://semver.org/.
 
 #define TF_MAJOR_VERSION 1
-#define TF_MINOR_VERSION 2
-#define TF_PATCH_VERSION 1
+#define TF_MINOR_VERSION 3
+#define TF_PATCH_VERSION 0
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX ""
+#define TF_VERSION_SUFFIX "-rc0"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index e971a03d36..b83113438c 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.3.0-rc0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 6fb8f7eb55..068a42d16b 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.3.0-rc0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index a8e6356f9d..bf0d03903d 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.1</version>
+  <version>1.3.0-rc0</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.1</version>
+                 <version>1.3.0-rc0</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.3.0-rc0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.3.0-rc0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.3.0-rc0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.1.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.3.0-rc0.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.1.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.3.0-rc0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.3.0-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.1.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.3.0-rc0.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index c371712f57..d9089c7269 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -172,7 +172,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -277,7 +277,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc0-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -632,14 +632,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -651,14 +651,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -670,14 +670,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -689,14 +689,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index c8a9c49559..0a17b6bf67 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -109,7 +109,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc0-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -230,7 +230,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc0-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc0-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc0-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py2-none-any.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc0-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 37ef3816af..63c8c625b0 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -342,10 +342,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.1 on Linux:
+for TensorFlow 1.3.0rc0 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.1-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.3.0rc0-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 221e6d4ff1..58749a53f1 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -115,12 +115,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.3.0rc0-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.3.0rc0-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index d7ca36b12b..86d7b5f770 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -23,7 +23,6 @@ exports_files(["LICENSE"])
 load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "if_not_windows")
-load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 py_library(
     name = "debug_py",
@@ -32,7 +31,6 @@ py_library(
     visibility = ["//visibility:public"],
     deps = [
         ":debug_data",
-        ":debug_gradients",
         ":debug_utils",
         ":hooks",
         ":local_cli_wrapper",
@@ -62,24 +60,6 @@ py_library(
     ],
 )
 
-tf_gen_op_wrapper_py(
-    name = "debug_ops",
-    deps = ["//tensorflow/core:debug_ops_op_lib"],
-)
-
-py_library(
-    name = "debug_gradients",
-    srcs = ["lib/debug_gradients.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":debug_data",
-        ":debug_ops",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:platform",
-        "@six_archive//:six",
-    ],
-)
-
 py_library(
     name = "debug_utils",
     srcs = ["lib/debug_utils.py"],
@@ -401,26 +381,6 @@ py_test(
     ],
 )
 
-cuda_py_test(
-    name = "debug_gradients_test",
-    size = "small",
-    srcs = [
-        "lib/debug_gradients_test.py",
-    ],
-    additional_deps = [
-        ":debug_data",
-        ":debug_gradients",
-        ":debug_utils",
-        "//tensorflow/python:client",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:gradients",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
-        "//tensorflow/python:training",
-        "//tensorflow/python:variables",
-    ],
-)
-
 py_test(
     name = "debug_utils_test",
     size = "small",
diff --git a/tensorflow/python/debug/__init__.py b/tensorflow/python/debug/__init__.py
index e20849bc1c..147e42d878 100644
--- a/tensorflow/python/debug/__init__.py
+++ b/tensorflow/python/debug/__init__.py
@@ -31,9 +31,6 @@ See the @{$python/tfdbg} guide.
 @@LocalCLIDebugHook
 @@LocalCLIDebugWrapperSession
 @@WatchOptions
-
-@@GradientsDebugger
-@@clear_gradient_debuggers
 """
 
 from __future__ import absolute_import
@@ -47,8 +44,6 @@ from tensorflow.python.debug.lib.debug_data import has_inf_or_nan
 from tensorflow.python.debug.lib.debug_data import load_tensor_from_event
 from tensorflow.python.debug.lib.debug_data import load_tensor_from_event_file
 
-from tensorflow.python.debug.lib.debug_gradients import GradientsDebugger
-
 from tensorflow.python.debug.lib.debug_utils import add_debug_tensor_watch
 from tensorflow.python.debug.lib.debug_utils import watch_graph
 from tensorflow.python.debug.lib.debug_utils import watch_graph_with_blacklists
diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py
deleted file mode 100644
index 6655f85d3a..0000000000
--- a/tensorflow/python/debug/lib/debug_gradients.py
+++ /dev/null
@@ -1,417 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""TensorFlow Debugger: Tools for debugging gradients."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import re
-import uuid
-
-import six
-
-from tensorflow.python.debug.lib import debug_data
-from tensorflow.python.debug.ops import gen_debug_ops
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import variables
-
-_GRADIENT_DEBUG_TAG = "gradient_debug_"
-
-_gradient_debuggers = {}
-
-
-def _tensor_to_grad_debug_op_name(tensor, grad_debugger_uuid):
-  op_name, slot = debug_data.parse_node_or_tensor_name(tensor.name)
-  return "%s_%d/%s%s" % (op_name, slot, _GRADIENT_DEBUG_TAG, grad_debugger_uuid)
-
-
-def _parse_grad_debug_op_name(op_name):
-  """Parse the name of a debug gradient op.
-
-  Args:
-    op_name: the name of the debug gradient op.
-
-  Returns:
-    1) The UUID of the GradientsDebugger that created the debug gradient op.
-    2) Name of the original tensor whose gradient is debugged by the debug
-       gradient op.
-  """
-  name_items = op_name.split("/")
-  assert len(name_items) > 1
-  assert name_items[-1].startswith(_GRADIENT_DEBUG_TAG)
-
-  grad_debugger_uuid = name_items[-1][len(_GRADIENT_DEBUG_TAG):]
-  if "_" in grad_debugger_uuid:
-    grad_debugger_uuid = grad_debugger_uuid[:grad_debugger_uuid.index("_")]
-  orig_tensor_slot = int(name_items[-2][name_items[-2].rfind("_") + 1:])
-  orig_base_op_name = name_items[-2][:name_items[-2].rfind("_")]
-  orig_tensor_name = ("/".join(name_items[:-2] + [orig_base_op_name]) +
-                      ":%d" % orig_tensor_slot)
-
-  return grad_debugger_uuid, orig_tensor_name
-
-
-class GradientsDebugger(object):
-  """Gradients Debugger.
-
-  Allows retrieval of gradient tensors created by TensorFlow's automatic
-  differentiation algorithm, i.e., @{tf.gradients} and optimizer classes that
-  use it.
-  """
-  # TODO(cais): Add examples code in the doc string?
-
-  def __init__(self, y_tensor=None):
-    """Constructor of GradientsDebugger.
-
-    Args:
-      y_tensor: optional: the `tf.Tensor` to be differentiated, i.e., the tensor
-        on the numerator of the differentiation.
-    """
-
-    self._uuid = uuid.uuid4().hex
-    _gradient_debuggers[self._uuid] = self
-
-    # A dict mapping x-tensor names to gradient tensor. x-tensor refers to the
-    # independent tf.Tensor, i.e., the tensor on the denominator of the
-    # differentiation.
-    self._gradient_tensors = {}
-    self._y_tensor = y_tensor
-
-    self._graph = None
-    if y_tensor:
-      self._graph = y_tensor.graph
-
-    self._is_active_context = False
-
-  @property
-  def y_tensor(self):
-    return self._y_tensor
-
-  @property
-  def graph(self):
-    return self._graph
-
-  def __enter__(self):
-    self._is_active_context = True
-
-  def __exit__(self, unused_type, unused_value, unused_traceback):
-    self._is_active_context = False
-
-  def identify_gradient(self, input_tensor):
-    """Create a debug identity tensor that registers and forwards gradients.
-
-    The side effect of this method is that when gradient tensor(s) are created
-    with respect to the any paths that include the `input_tensor`, the gradient
-    tensor(s) with repsect to `input_tensor` will be registered with this
-    this `GradientsDebugger` instance and can later be retrieved, with the
-    methods `gradient_tensor` and `gradient_tensors`.
-
-    Example:
-
-    ```python
-    x = tf.Variable(1.0)
-    y = tf.add(x, x)
-
-    grad_debugger = tf_debug.GradientsDebugger()
-    debug_y = grad_debugger.identify_gradient(y)
-    z = tf.square(debug_y)
-
-    # Create a train op under the grad_debugger context.
-    with grad_debugger:
-      train_op = tf.train.GradientDescentOptimizer(z)
-
-    # Now we can reflect through grad_debugger to get the gradient tensor
-    # with respect to y.
-    y_grad = grad_debugger.gradient_tensor(y)
-    ```
-
-    Args:
-      input_tensor: the input `tf.Tensor` object whose related gradient tensors
-        are to be reigstered with this `GradientsDebugger` instance when they
-        are created, e.g., during @{tf.gradients} calls or the construction
-        of optimization (training) op that uses @{tf.gradients}.
-
-    Returns:
-      A forwarded identity of `input_tensor`, as a `tf.Tensor`.
-
-    Raises:
-      ValueError: If an op with name that duplicates the gradient-debugging op
-        already exists in the graph (highly unlikely).
-    """
-    # TODO(cais): Allow overriding gradient.
-    # TODO(cais): Implement value_stack.
-    grad_debug_op_name = _tensor_to_grad_debug_op_name(input_tensor, self._uuid)
-    debug_identity = gen_debug_ops.debug_identity(
-        input_tensor,
-        tensor_name=input_tensor.name,
-        debug_urls=[],
-        name=grad_debug_op_name)
-    if debug_identity.op.name != grad_debug_op_name:
-      raise ValueError(
-          "The graph already contains an op named %s" % grad_debug_op_name)
-    return debug_identity
-
-  def watch_gradients_by_tensors(self, graph, tensors):
-    """Watch gradient tensors by x-tensor(s).
-
-    The side effect of this method is that when gradient tensor(s) are created
-    with respect to the any paths that include the `x_tensor`s, the gradient
-    tensor(s) with repsect to the tensor will be registered with this
-    this `GradientsDebugger` instance and can later be retrieved, with the
-    methods `gradient_tensor` and `gradient_tensors`.
-
-    Unlike the method `identify_gradient`, this method is used to retrieve
-    gradient tensors after the construction of the forward subgraph has
-    completed (but before the construction of the backward subgraph).
-
-    This method is the same as `watch_gradients_by_x_tensor_names` except that
-    the tensors are specified by the Python `tf.Tensor` or `tf.Variable`
-    objects, instead by name patterns.
-
-    Example:
-
-    ```python
-    x = tf.Variable(1.0)
-    y = tf.add(x, x, name="y")
-    z = tf.square(debug_y)
-
-    # Create a train op under the grad_debugger context.
-    grad_debugger = tf_debug.GradientsDebugger()
-    with grad_debugger.watch_gradients_by_tensors(y):
-      train_op = tf.train.GradientDescentOptimizer(z)
-
-    # Now we can reflect through grad_debugger to get the gradient tensor
-    # with respect to y.
-    y_grad = grad_debugger.gradient_tensor(y)
-    # or
-    y_grad = grad_debugger.gradient_tensor("y:0")
-    ```
-
-    Args:
-      graph: the `tf.Graph` to watch the gradients on.
-      tensors: a `tf.Tensor` or `tf.Variable` object, or a list of such objects.
-
-    Returns:
-      The GradientsDebugger instance itself.
-    """
-
-    if not isinstance(tensors, list):
-      tensors = [tensors]
-
-    tensor_name_regex = []
-    for tensor in tensors:
-      tensor_name_regex.append(re.escape(tensor.name) + "$")
-    tensor_name_regex = "(" + "|".join(tensor_name_regex) + ")"
-    return self.watch_gradients_by_tensor_names(graph, tensor_name_regex)
-
-  def watch_gradients_by_tensor_names(self, graph, tensor_name_regex):
-    """Watch gradient tensors by name(s) of the x-tensor(s).
-
-    The side effect of this method is that when gradient tensor(s) are created
-    with respect to the x-tensors, the gradient tensor(s) will be registered
-    with this `GradientsDebugger` instance and can later be retrieved.
-
-    Unlike the `identify_gradient` method, this method is used after the
-    construction of the forward graph has completed. Unlike the
-    `watch_gradients_by_tensor` method, this method does not use handles to the
-    tensors of interest; it uses their names.
-
-    This method is the same as `watch_gradients_by_tensors` except that the
-    x-tensors are specified by name patterns, instead of `tf.Tensor` or
-    `tf.Variable` objects.
-
-    Example:
-
-    ```python
-    x = tf.Variable(1.0, name="x")
-    y = tf.add(x, x, name="y")
-    z = tf.square(debug_y)
-
-    # Create a train op under the grad_debugger context.
-    grad_debugger = tf_debug.GradientsDebugger()
-    with grad_debugger.watch_gradients_by_tensor_names(r"(x|y):0$"):
-      train_op = tf.train.GradientDescentOptimizer(z)
-
-    # Now we can reflect through grad_debugger to get the gradient tensor
-    # with respect to x and y.
-    x_grad = grad_debugger.gradient_tensor("x:0")
-    y_grad = grad_debugger.gradient_tensor("y:0")
-    ```
-
-    Args:
-      graph: the `tf.Graph` to watch the gradients on.
-      tensor_name_regex: the regular-expression pattern of the name(s) of the
-        x-tensor(s) to watch. x-tensor refers to the tensors on the denominator
-        of the differentiation.
-
-    Returns:
-      The GradientsDebugger instance itself.
-    """
-    tensor_name_pattern = re.compile(tensor_name_regex)
-
-    # pylint: disable=protected-access
-    with graph.as_default():
-      for op in graph.get_operations():
-        for output in op.outputs:
-          if tensor_name_pattern.match(output.name):
-            debug_op = self.identify_gradient(output)
-
-            for consumer in output.consumers():
-              if consumer == debug_op.op:
-                continue
-
-              # Locate the slot index of the original input.
-              input_slots = []
-              for i, consumer_input in enumerate(consumer._inputs):
-                if consumer_input == output:
-                  input_slots.append(i)
-
-              for slot in input_slots:
-                consumer._inputs[slot] = debug_op
-                debug_op._consumers.append(consumer)
-
-            del output._consumers[:]
-            output._consumers.append(debug_op.op)
-    # pylint: enable=protected-access
-
-    return self
-
-  def _check_same_graph(self, tensor):
-    if self._graph is None:
-      self._graph = tensor.graph
-    elif self._graph != tensor.graph:
-      raise ValueError(
-          "The graph of the value (%s) is not the same as the graph %s" %
-          (tensor.graph, self._graph))
-
-  def register_gradient_tensor(self,
-                               x_tensor_name,
-                               gradient_tensor):
-    """Register the gradient tensor for an x-tensor.
-
-    Args:
-      x_tensor_name: (`str`) the name of the independent `tf.Tensor`, i.e.,
-        the tensor on the denominator of the differentiation.
-      gradient_tensor: the gradient `tf.Tensor`.
-    """
-    if len(_gradient_debuggers) == 1 or self._is_active_context:
-      self._check_same_graph(gradient_tensor)
-      self._gradient_tensors[x_tensor_name] = gradient_tensor
-
-  def gradient_tensor(self, x_tensor):
-    """Get the gradient tensor of an x-tensor.
-
-    Args:
-      x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
-        name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
-        on the denominator of the differentiation.
-
-    Returns:
-      If found, the gradient tensor.
-
-    Raises:
-      TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
-      LookupError: If the `x_tensor` has not been registered with a gradient
-        tensor.
-    """
-    x_tensor_name = self._get_tensor_name(x_tensor)
-    if x_tensor_name not in self._gradient_tensors:
-      raise LookupError(
-          "This GradientsDebugger has not received any gradient tensor for "
-          "x-tensor %s" % x_tensor_name)
-    return self._gradient_tensors[x_tensor_name]
-
-  def gradient_tensors(self):
-    """Get the gradient tensors that this object is aware of.
-
-    Returns:
-      A dict mapping x-tensor names to gradient tensor objects. x-tensor refers
-      to the tensors on the denominator of the differentation.
-    """
-    return self._gradient_tensors
-
-  def _get_tensor_name(self, tensor):
-    if isinstance(tensor, (ops.Tensor, variables.Variable)):
-      return tensor.name
-    elif  isinstance(tensor, six.string_types):
-      return tensor
-    else:
-      raise TypeError(
-          "x_tensor must be a str or tf.Tensor or tf.Variable, "
-          "but instead has type %s" % type(tensor))
-
-
-def clear_gradient_debuggers():
-  """Clear all globally registered gradient debuggers."""
-  _gradient_debuggers.clear()
-
-
-@ops.RegisterGradient("DebugIdentity")
-def _identify_gradient_grad(op, dy):
-  """Gradient function for the DebugIdentity op."""
-  # TODO(cais): Allow overriding gradient.
-  grad_debugger_uuid, orig_tensor_name = _parse_grad_debug_op_name(op.name)
-  grad_debugger = _gradient_debuggers[grad_debugger_uuid]
-  grad_debugger.register_gradient_tensor(orig_tensor_name, dy)
-  return dy
-
-
-def gradient_values_from_dump(grad_debugger, x_tensor, dump):
-  """Find gradient values from a `DebugDumpDir` object.
-
-  Args:
-    grad_debugger: the `tf_debug.GradientsDebugger` instance to be used.
-    x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
-      name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
-      on the denominator of the differentiation.
-    dump: A `tfdbg.DebugDumpDir` object.
-
-  Returns:
-    If this `GradientsDebugger` instance has the gradient tensor of `x_tensor`
-      registered: a list of `numpy.ndarray` representing the value of the
-      gradient tensor from `dump`. The list could be empty, if the gradient
-      tensor is not executed in the `tf.Session.run()` call that generated
-      the `dump`. The list could also contain multiple values of the gradient
-      tensor, e.g., if gradient tensor is computed repeatedly in a
-      `tf.while_loop` during the run that generated the `dump`.
-
-  Raises:
-    LookupError: If this `GradientsDebugger` instance does not have the
-      gradient tensor of `x_tensor` registered.
-    ValueError: If this `GradientsDebugger` has a `tf.Graph` object that
-      does not match the `tf.Graph` object of the `dump`.
-    TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
-  """
-  # TODO(cais): Use this method in LocalCLIDebugWrapperSession to present the
-  # gradient tensors to the TFDBG CLI.
-
-  # If possible, verify that the Python graph of the dump and that of this
-  # GradientsDebugger match.
-  if (dump.python_graph and grad_debugger.graph and
-      dump.python_graph != grad_debugger.graph):
-    raise ValueError(
-        "This GradientsDebugger instance has a graph (%s) that differs from "
-        "the graph of the DebugDumpDir object (%s)." %
-        (grad_debugger.graph, dump.python_graph))
-
-  gradient_tensor = grad_debugger.gradient_tensor(x_tensor)
-  node_name, output_slot = debug_data.parse_node_or_tensor_name(
-      gradient_tensor.name)
-
-  try:
-    return dump.get_tensors(node_name, output_slot, "DebugIdentity")
-  except debug_data.WatchKeyDoesNotExistInDebugDumpDirError:
-    return []
diff --git a/tensorflow/python/debug/lib/debug_gradients_test.py b/tensorflow/python/debug/lib/debug_gradients_test.py
deleted file mode 100644
index 966578320e..0000000000
--- a/tensorflow/python/debug/lib/debug_gradients_test.py
+++ /dev/null
@@ -1,378 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for debug_gradients module."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import shutil
-import tempfile
-
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.python.client import session
-from tensorflow.python.debug.lib import debug_data
-from tensorflow.python.debug.lib import debug_gradients
-from tensorflow.python.debug.lib import debug_utils
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import test_util
-from tensorflow.python.ops import gradients_impl
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import variables
-from tensorflow.python.platform import googletest
-from tensorflow.python.training import gradient_descent
-
-
-class IdentifyGradientTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.sess = session.Session()
-    with self.sess:
-      self.u = variables.Variable(2.0, name="u")
-      self.v = variables.Variable(3.0, name="v")
-      self.w = math_ops.multiply(self.u.value(), self.v.value(), name="w")
-
-  def tearDown(self):
-    ops.reset_default_graph()
-    debug_gradients.clear_gradient_debuggers()
-
-  def testIdentifyGradientGivesCorrectTensorObjectWithoutContextManager(self):
-    grad_debugger = debug_gradients.GradientsDebugger()
-    id_grad_w = grad_debugger.identify_gradient(self.w)
-    y = math_ops.add(id_grad_w, -1.0, name="y")
-
-    grads = gradients_impl.gradients(y, [self.u, self.v])
-    self.assertEqual(2, len(grads))
-    u_grad = grads[0]
-    v_grad = grads[1]
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(5.0, self.sess.run(y))
-    self.assertAllClose(3.0, self.sess.run(u_grad))
-    self.assertAllClose(2.0, self.sess.run(v_grad))
-
-    # Fetch the gradient tensor with the x-tensor object.
-    w_grad = grad_debugger.gradient_tensor(self.w)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-    # Fetch the gradient tensor with the x-tensor's name.
-    w_grad = grad_debugger.gradient_tensor(self.w.name)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-    # Fetch the gradient tensor with the x-tensor name.
-    w_grad = grad_debugger.gradient_tensor(self.w.name)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-  def testIdentifyGradientGivesCorrectTensorObjectWithTfGradients(self):
-    grad_debugger = debug_gradients.GradientsDebugger()
-    id_grad_w = grad_debugger.identify_gradient(self.w)
-    y = math_ops.add(id_grad_w, -1.0, name="y")
-
-    with grad_debugger:
-      grads = gradients_impl.gradients(y, [self.u, self.v])
-    self.assertEqual(2, len(grads))
-    u_grad = grads[0]
-    v_grad = grads[1]
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(5.0, self.sess.run(y))
-    self.assertAllClose(3.0, self.sess.run(u_grad))
-    self.assertAllClose(2.0, self.sess.run(v_grad))
-
-    # Fetch the gradient tensor with the x-tensor object.
-    w_grad = grad_debugger.gradient_tensor(self.w)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-    # Fetch the gradient tensor with the x-tensor's name.
-    w_grad = grad_debugger.gradient_tensor(self.w.name)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-    # Fetch the gradient tensor with the x-tensor name.
-    w_grad = grad_debugger.gradient_tensor(self.w.name)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-  def testCallingIdentifyGradientTwiceWithTheSameGradientsDebuggerErrors(self):
-    grad_debugger = debug_gradients.GradientsDebugger()
-    grad_debugger.identify_gradient(self.w)
-    with self.assertRaisesRegexp(
-        ValueError, "The graph already contains an op named .*"):
-      grad_debugger.identify_gradient(self.w)
-
-  def testIdentifyGradientWorksOnMultipleLosses(self):
-    grad_debugger_1 = debug_gradients.GradientsDebugger()
-    grad_debugger_2 = debug_gradients.GradientsDebugger()
-
-    y = math_ops.add(self.w, -1.0, name="y")
-    debug_y = grad_debugger_1.identify_gradient(y)
-    z1 = math_ops.square(debug_y, name="z1")
-
-    debug_y = grad_debugger_2.identify_gradient(y)
-    z2 = math_ops.sqrt(debug_y, name="z2")
-
-    with grad_debugger_1:
-      gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
-    with grad_debugger_2:
-      gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
-
-    dz1_dy = grad_debugger_1.gradient_tensor(y)
-    dz2_dy = grad_debugger_2.gradient_tensor(y)
-    self.assertIsInstance(dz1_dy, ops.Tensor)
-    self.assertIsInstance(dz2_dy, ops.Tensor)
-    self.assertIsNot(dz1_dy, dz2_dy)
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(5.0 ** 2, self.sess.run(z1))
-    self.assertAllClose(5.0 ** 0.5, self.sess.run(z2))
-    self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
-    self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy))
-
-  def testIdentifyGradientRaisesLookupErrorForUnknownXTensor(self):
-    grad_debugger_1 = debug_gradients.GradientsDebugger()
-    grad_debugger_2 = debug_gradients.GradientsDebugger()
-    id_grad_w = grad_debugger_1.identify_gradient(self.w)
-    y = math_ops.add(id_grad_w, -1.0, name="y")
-
-    # There are >1 gradient debuggers registered, and grad_debugger is not used
-    # as a context manager here, so the gradient w.r.t. self.w will not be
-    # registered.
-    gradients_impl.gradients(y, [self.u, self.v])
-
-    with self.assertRaisesRegexp(
-        LookupError,
-        r"This GradientsDebugger has not received any gradient tensor for "):
-      grad_debugger_1.gradient_tensor(self.w)
-    with self.assertRaisesRegexp(
-        LookupError,
-        r"This GradientsDebugger has not received any gradient tensor for "):
-      grad_debugger_2.gradient_tensor(self.w)
-
-  def testIdentifyGradientRaisesTypeErrorForNonTensorOrTensorNameInput(self):
-    grad_debugger = debug_gradients.GradientsDebugger()
-    with self.assertRaisesRegexp(
-        TypeError,
-        r"x_tensor must be a str or tf\.Tensor or tf\.Variable, but instead "
-        r"has type .*Operation.*"):
-      grad_debugger.gradient_tensor(variables.global_variables_initializer())
-
-  def testIdentifyGradientTensorWorksWithGradientDescentOptimizer(self):
-    grad_debugger = debug_gradients.GradientsDebugger()
-    id_grad_w = grad_debugger.identify_gradient(self.w)
-    y = math_ops.add(id_grad_w, -1.0, name="y")
-
-    with grad_debugger:
-      gradient_descent.GradientDescentOptimizer(0.1).minimize(y)
-
-    self.sess.run(variables.global_variables_initializer())
-
-    # Fetch the gradient tensor with the x-tensor object.
-    w_grad = grad_debugger.gradient_tensor(self.w)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-  def testWatchGradientsByXTensorNamesWorks(self):
-    y = math_ops.add(self.w, -1.0, name="y")
-
-    # The constructrion of the forward graph has completed.
-    # But we can still get the gradient tensors by using
-    # watch_gradients_by_tensor_names().
-    grad_debugger = debug_gradients.GradientsDebugger()
-    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "w:0$"):
-      grads = gradients_impl.gradients(y, [self.u, self.v])
-    self.assertEqual(2, len(grads))
-    u_grad = grads[0]
-    v_grad = grads[1]
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(5.0, self.sess.run(y))
-    self.assertAllClose(3.0, self.sess.run(u_grad))
-    self.assertAllClose(2.0, self.sess.run(v_grad))
-
-    w_grad = grad_debugger.gradient_tensor(self.w)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-    w_grad = grad_debugger.gradient_tensor("w:0")
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-  def testWatchGradientsByXTensorNamesWorksWithoutContextManager(self):
-    y = math_ops.add(self.w, -1.0, name="y")
-
-    # The constructrion of the forward graph has completed.
-    # But we can still get the gradient tensors by using
-    # watch_gradients_by_tensor_names().
-    grad_debugger = debug_gradients.GradientsDebugger()
-    grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "w:0$")
-    grads = gradients_impl.gradients(y, [self.u, self.v])
-    self.assertEqual(2, len(grads))
-    u_grad = grads[0]
-    v_grad = grads[1]
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(5.0, self.sess.run(y))
-    self.assertAllClose(3.0, self.sess.run(u_grad))
-    self.assertAllClose(2.0, self.sess.run(v_grad))
-
-    w_grad = grad_debugger.gradient_tensor(self.w)
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-    w_grad = grad_debugger.gradient_tensor("w:0")
-    self.assertIsInstance(w_grad, ops.Tensor)
-    self.assertAllClose(1.0, self.sess.run(w_grad))
-
-  def testWatchGradientsWorksOnRefTensor(self):
-    y = math_ops.add(self.w, -1.0, name="y")
-
-    grad_debugger = debug_gradients.GradientsDebugger()
-    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "u:0$"):
-      grads = gradients_impl.gradients(y, [self.u, self.v])
-    self.assertEqual(2, len(grads))
-    u_grad = grads[0]
-    v_grad = grads[1]
-
-    self.assertIs(u_grad, grad_debugger.gradient_tensor("u:0"))
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(3.0, self.sess.run(u_grad))
-    self.assertAllClose(2.0, self.sess.run(v_grad))
-    self.assertAllClose(
-        3.0, self.sess.run(grad_debugger.gradient_tensor("u:0")))
-
-  def testWatchGradientsWorksOnMultipleTensors(self):
-    y = math_ops.add(self.w, -1.0, name="y")
-
-    grad_debugger = debug_gradients.GradientsDebugger()
-    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph,
-                                                       "(u|w):0$"):
-      grads = gradients_impl.gradients(y, [self.u, self.v])
-    self.assertEqual(2, len(grads))
-    u_grad = grads[0]
-
-    self.assertEqual(2, len(grad_debugger.gradient_tensors()))
-    self.assertIs(u_grad, grad_debugger.gradient_tensor("u:0"))
-    self.assertIsInstance(grad_debugger.gradient_tensor("w:0"), ops.Tensor)
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(
-        1.0, self.sess.run(grad_debugger.gradient_tensor("w:0")))
-    self.assertAllClose(
-        3.0, self.sess.run(grad_debugger.gradient_tensor("u:0")))
-
-  def testWatchGradientsByXTensorsWorks(self):
-    y = math_ops.add(self.w, -1.0, name="foo/y")
-    z = math_ops.square(y, name="foo/z")
-
-    # The constructrion of the forward graph has completed.
-    # But we can still get the gradient tensors by using
-    # watch_gradients_by_x_tensors().
-    grad_debugger = debug_gradients.GradientsDebugger()
-    with grad_debugger.watch_gradients_by_tensors(
-        self.sess.graph, [self.w, self.u, y]):
-      gradient_descent.GradientDescentOptimizer(0.1).minimize(z)
-
-    self.assertEqual(3, len(grad_debugger.gradient_tensors()))
-    u_grad = grad_debugger.gradient_tensor(self.u)
-    w_grad = grad_debugger.gradient_tensor(self.w)
-    y_grad = grad_debugger.gradient_tensor(y)
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(10.0, self.sess.run(y_grad))
-    self.assertAllClose(10.0, self.sess.run(w_grad))
-    self.assertAllClose(30.0, self.sess.run(u_grad))
-
-  def testWatchGradientsByTensorCanWorkOnMultipleLosses(self):
-    y = math_ops.add(self.w, -1.0, name="y")
-    z1 = math_ops.square(y, name="z1")
-    z2 = math_ops.sqrt(y, name="z2")
-
-    grad_debugger_1 = debug_gradients.GradientsDebugger()
-    with grad_debugger_1.watch_gradients_by_tensors(self.sess.graph, y):
-      gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
-
-    grad_debugger_2 = debug_gradients.GradientsDebugger()
-    with grad_debugger_2.watch_gradients_by_tensors(self.sess.graph, y):
-      gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
-
-    dz1_dy = grad_debugger_1.gradient_tensor(y)
-    dz2_dy = grad_debugger_2.gradient_tensor(y)
-    self.assertIsInstance(dz1_dy, ops.Tensor)
-    self.assertIsInstance(dz2_dy, ops.Tensor)
-    self.assertIsNot(dz1_dy, dz2_dy)
-
-    self.sess.run(variables.global_variables_initializer())
-    self.assertAllClose(5.0 ** 2, self.sess.run(z1))
-    self.assertAllClose(5.0 ** 0.5, self.sess.run(z2))
-    self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
-    self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy))
-
-  def testGradientsValuesFromDumpWorks(self):
-    y = math_ops.add(self.w, -1.0, name="y")
-    z = math_ops.square(y, name="z")
-
-    grad_debugger = debug_gradients.GradientsDebugger()
-    with grad_debugger.watch_gradients_by_tensors(
-        self.sess.graph, [self.w, self.u, y]):
-      train_op = gradient_descent.GradientDescentOptimizer(0.1).minimize(z)
-
-    self.sess.run(variables.global_variables_initializer())
-
-    run_options = config_pb2.RunOptions(output_partition_graphs=True)
-    dump_dir = tempfile.mkdtemp()
-    debug_url = "file://" + dump_dir
-    debug_utils.watch_graph(
-        run_options,
-        self.sess.graph,
-        debug_urls=debug_url)
-    run_metadata = config_pb2.RunMetadata()
-    self.sess.run(train_op, options=run_options, run_metadata=run_metadata)
-
-    dump = debug_data.DebugDumpDir(
-        dump_dir, partition_graphs=run_metadata.partition_graphs)
-    dump.set_python_graph(self.sess.graph)
-
-    y_grad_values = debug_gradients.gradient_values_from_dump(
-        grad_debugger, y, dump)
-    self.assertEqual(1, len(y_grad_values))
-    self.assertAllClose(10.0, y_grad_values[0])
-
-    w_grad_values = debug_gradients.gradient_values_from_dump(
-        grad_debugger, self.w, dump)
-    self.assertEqual(1, len(w_grad_values))
-    self.assertAllClose(10.0, w_grad_values[0])
-
-    u_grad_values = debug_gradients.gradient_values_from_dump(
-        grad_debugger, self.u, dump)
-    self.assertEqual(1, len(u_grad_values))
-    self.assertAllClose(30.0, u_grad_values[0])
-
-    with self.assertRaisesRegexp(
-        LookupError,
-        r"This GradientsDebugger has not received any gradient tensor for "
-        r"x-tensor v:0"):
-      debug_gradients.gradient_values_from_dump(grad_debugger, self.v, dump)
-
-    # Cleanup.
-    shutil.rmtree(dump_dir)
-
-
-if __name__ == "__main__":
-  googletest.main()
diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel
index 38a67f80aa..1b97c0d108 100644
--- a/tensorflow/tools/docker/Dockerfile.devel
+++ b/tensorflow/tools/docker/Dockerfile.devel
@@ -72,7 +72,7 @@ RUN mkdir /bazel && \
 
 RUN git clone https://github.com/tensorflow/tensorflow.git && \
     cd tensorflow && \
-    git checkout r1.2
+    git checkout r1.3
 WORKDIR /tensorflow
 
 # TODO(craigcitro): Don't install the pip package, since it makes it
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index e10565a064..80b45ae704 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -73,7 +73,7 @@ RUN mkdir /bazel && \
 
 RUN git clone https://github.com/tensorflow/tensorflow.git && \
     cd tensorflow && \
-    git checkout r1.2
+    git checkout r1.3
 WORKDIR /tensorflow
 
 # Configure the build for our CUDA configuration.
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index c4284e10de..0726a28cc9 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.2.1'
+_VERSION = '1.3.0-rc0'
 
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
-- 
GitLab


From 9504e952ce07180200bb0a30150d5ce2a0440b9f Mon Sep 17 00:00:00 2001
From: Amit Patankar <amitpatankar@google.com>
Date: Thu, 20 Jul 2017 14:05:56 -0700
Subject: [PATCH 1165/1375] Revert "Revert "Add GradientsDebugger to tfdbg""

This reverts commit 71f8402dc33b44c4f2ec9bf9a5070ce2d383cac1.
---
 tensorflow/contrib/cmake/tf_core_ops.cmake    |  12 +
 tensorflow/contrib/cmake/tf_python.cmake      |   2 +
 tensorflow/core/ops/debug_ops.cc              |   3 +
 tensorflow/python/debug/BUILD                 |  40 ++
 tensorflow/python/debug/__init__.py           |   5 +
 .../python/debug/lib/debug_gradients.py       | 417 ++++++++++++++++++
 .../python/debug/lib/debug_gradients_test.py  | 378 ++++++++++++++++
 7 files changed, 857 insertions(+)
 create mode 100644 tensorflow/python/debug/lib/debug_gradients.py
 create mode 100644 tensorflow/python/debug/lib/debug_gradients_test.py

diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake
index 21d94da6cc..b350b822dd 100644
--- a/tensorflow/contrib/cmake/tf_core_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_core_ops.cmake
@@ -129,3 +129,15 @@ list(REMOVE_ITEM tf_core_ops_srcs ${tf_core_ops_exclude_srcs})
 add_library(tf_core_ops OBJECT ${tf_core_ops_srcs})
 
 add_dependencies(tf_core_ops tf_core_cpu)
+
+########################################################
+# tf_debug_ops library
+########################################################
+
+file(GLOB tf_debug_ops_srcs
+    "${tensorflow_source_dir}/tensorflow/core/ops/debug_ops.cc"
+)
+
+add_library(tf_debug_ops OBJECT ${tf_debug_ops_srcs})
+
+add_dependencies(tf_debug_ops tf_core_framework)
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index 206ba96747..8701a584e8 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -696,6 +696,8 @@ GENERATE_PYTHON_OP_LIB("contrib_bigquery_reader_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/cloud/python/ops/gen_bigquery_reader_ops.py)
 GENERATE_PYTHON_OP_LIB("stateless_random_ops"
   DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/stateless/gen_stateless_random_ops.py)
+GENERATE_PYTHON_OP_LIB("debug_ops"
+  DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/debug/ops/gen_debug_ops.py)
 
 add_custom_target(tf_python_ops SOURCES ${tf_python_ops_generated_files} ${PYTHON_PROTO_GENFILES})
 add_dependencies(tf_python_ops tf_python_op_gen_main)
diff --git a/tensorflow/core/ops/debug_ops.cc b/tensorflow/core/ops/debug_ops.cc
index f7a96b58da..bd7f7c2c01 100644
--- a/tensorflow/core/ops/debug_ops.cc
+++ b/tensorflow/core/ops/debug_ops.cc
@@ -14,7 +14,9 @@ limitations under the License.
 ==============================================================================*/
 // This file registers all TensorFlow Debugger (tfdbg) ops.
 
+#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
 
 namespace tensorflow {
 
@@ -88,6 +90,7 @@ REGISTER_OP("DebugIdentity")
     .Attr("debug_urls: list(string) = []")
     .Attr("gated_grpc: bool = false")
     .SetAllowsUninitializedInput()
+    .SetShapeFn(shape_inference::UnchangedShape)
     .Doc(R"doc(
 Debug Identity Op.
 
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 86d7b5f770..d7ca36b12b 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -23,6 +23,7 @@ exports_files(["LICENSE"])
 load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "if_not_windows")
+load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 py_library(
     name = "debug_py",
@@ -31,6 +32,7 @@ py_library(
     visibility = ["//visibility:public"],
     deps = [
         ":debug_data",
+        ":debug_gradients",
         ":debug_utils",
         ":hooks",
         ":local_cli_wrapper",
@@ -60,6 +62,24 @@ py_library(
     ],
 )
 
+tf_gen_op_wrapper_py(
+    name = "debug_ops",
+    deps = ["//tensorflow/core:debug_ops_op_lib"],
+)
+
+py_library(
+    name = "debug_gradients",
+    srcs = ["lib/debug_gradients.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":debug_data",
+        ":debug_ops",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:platform",
+        "@six_archive//:six",
+    ],
+)
+
 py_library(
     name = "debug_utils",
     srcs = ["lib/debug_utils.py"],
@@ -381,6 +401,26 @@ py_test(
     ],
 )
 
+cuda_py_test(
+    name = "debug_gradients_test",
+    size = "small",
+    srcs = [
+        "lib/debug_gradients_test.py",
+    ],
+    additional_deps = [
+        ":debug_data",
+        ":debug_gradients",
+        ":debug_utils",
+        "//tensorflow/python:client",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+    ],
+)
+
 py_test(
     name = "debug_utils_test",
     size = "small",
diff --git a/tensorflow/python/debug/__init__.py b/tensorflow/python/debug/__init__.py
index 147e42d878..e20849bc1c 100644
--- a/tensorflow/python/debug/__init__.py
+++ b/tensorflow/python/debug/__init__.py
@@ -31,6 +31,9 @@ See the @{$python/tfdbg} guide.
 @@LocalCLIDebugHook
 @@LocalCLIDebugWrapperSession
 @@WatchOptions
+
+@@GradientsDebugger
+@@clear_gradient_debuggers
 """
 
 from __future__ import absolute_import
@@ -44,6 +47,8 @@ from tensorflow.python.debug.lib.debug_data import has_inf_or_nan
 from tensorflow.python.debug.lib.debug_data import load_tensor_from_event
 from tensorflow.python.debug.lib.debug_data import load_tensor_from_event_file
 
+from tensorflow.python.debug.lib.debug_gradients import GradientsDebugger
+
 from tensorflow.python.debug.lib.debug_utils import add_debug_tensor_watch
 from tensorflow.python.debug.lib.debug_utils import watch_graph
 from tensorflow.python.debug.lib.debug_utils import watch_graph_with_blacklists
diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py
new file mode 100644
index 0000000000..e5159cb941
--- /dev/null
+++ b/tensorflow/python/debug/lib/debug_gradients.py
@@ -0,0 +1,417 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TensorFlow Debugger: Tools for debugging gradients."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import uuid
+
+import six
+
+from tensorflow.python.debug.lib import debug_data
+from tensorflow.python.debug.ops import gen_debug_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import variables
+
+_GRADIENT_DEBUG_TAG = "gradient_debug_"
+
+_gradient_debuggers = {}
+
+
+def _tensor_to_grad_debug_op_name(tensor, grad_debugger_uuid):
+  op_name, slot = debug_data.parse_node_or_tensor_name(tensor.name)
+  return "%s_%d/%s%s" % (op_name, slot, _GRADIENT_DEBUG_TAG, grad_debugger_uuid)
+
+
+def _parse_grad_debug_op_name(op_name):
+  """Parse the name of a debug gradient op.
+
+  Args:
+    op_name: the name of the debug gradient op.
+
+  Returns:
+    1) The UUID of the GradientsDebugger that created the debug gradient op.
+    2) Name of the original tensor whose gradient is debugged by the debug
+       gradient op.
+  """
+  name_items = op_name.split("/")
+  assert len(name_items) > 1
+  assert name_items[-1].startswith(_GRADIENT_DEBUG_TAG)
+
+  grad_debugger_uuid = name_items[-1][len(_GRADIENT_DEBUG_TAG):]
+  if "_" in grad_debugger_uuid:
+    grad_debugger_uuid = grad_debugger_uuid[:grad_debugger_uuid.index("_")]
+  orig_tensor_slot = int(name_items[-2][name_items[-2].rfind("_") + 1:])
+  orig_base_op_name = name_items[-2][:name_items[-2].rfind("_")]
+  orig_tensor_name = ("/".join(name_items[:-2] + [orig_base_op_name]) +
+                      ":%d" % orig_tensor_slot)
+
+  return grad_debugger_uuid, orig_tensor_name
+
+
+class GradientsDebugger(object):
+  """Gradients Debugger.
+
+  Allows retrieval of gradient tensors created by TensorFlow's automatic
+  differentiation algorithm, i.e., @{tf.gradients} and optimizer classes that
+  use it.
+  """
+  # TODO(cais): Add examples code in the doc string?
+
+  def __init__(self, y_tensor=None):
+    """Constructor of GradientsDebugger.
+
+    Args:
+      y_tensor: optional: the `tf.Tensor` to be differentiated, i.e., the tensor
+        on the numerator of the differentiation.
+    """
+
+    self._uuid = uuid.uuid4().hex
+    _gradient_debuggers[self._uuid] = self
+
+    # A dict mapping x-tensor names to gradient tensor. x-tensor refers to the
+    # independent tf.Tensor, i.e., the tensor on the denominator of the
+    # differentiation.
+    self._gradient_tensors = {}
+    self._y_tensor = y_tensor
+
+    self._graph = None
+    if y_tensor:
+      self._graph = y_tensor.graph
+
+    self._is_active_context = False
+
+  @property
+  def y_tensor(self):
+    return self._y_tensor
+
+  @property
+  def graph(self):
+    return self._graph
+
+  def __enter__(self):
+    self._is_active_context = True
+
+  def __exit__(self, unused_type, unused_value, unused_traceback):
+    self._is_active_context = False
+
+  def identify_gradient(self, input_tensor):
+    """Create a debug identity tensor that registers and forwards gradients.
+
+    The side effect of this method is that when gradient tensor(s) are created
+    with respect to the any paths that include the `input_tensor`, the gradient
+    tensor(s) with repsect to `input_tensor` will be registered with this
+    this `GradientsDebugger` instance and can later be retrieved, with the
+    methods `gradient_tensor` and `gradient_tensors`.
+
+    Example:
+
+    ```python
+    x = tf.Variable(1.0)
+    y = tf.add(x, x)
+
+    grad_debugger = tf_debug.GradientsDebugger()
+    debug_y = grad_debugger.identify_gradient(y)
+    z = tf.square(debug_y)
+
+    # Create a train op under the grad_debugger context.
+    with grad_debugger:
+      train_op = tf.train.GradientDescentOptimizer(z)
+
+    # Now we can reflect through grad_debugger to get the gradient tensor
+    # with respect to y.
+    y_grad = grad_debugger.gradient_tensor(y)
+    ```
+
+    Args:
+      input_tensor: the input `tf.Tensor` object whose related gradient tensors
+        are to be reigstered with this `GradientsDebugger` instance when they
+        are created, e.g., during @{tf.gradients} calls or the construction
+        of optimization (training) op that uses @{tf.gradients}.
+
+    Returns:
+      A forwarded identity of `input_tensor`, as a `tf.Tensor`.
+
+    Raises:
+      ValueError: If an op with name that duplicates the gradient-debugging op
+        already exists in the graph (highly unlikely).
+    """
+    # TODO(cais): Allow overriding gradient.
+    # TODO(cais): Implement value_stack.
+    grad_debug_op_name = _tensor_to_grad_debug_op_name(input_tensor, self._uuid)
+    debug_identity = gen_debug_ops.debug_identity(
+        input_tensor,
+        tensor_name=input_tensor.name,
+        debug_urls=[],
+        name=grad_debug_op_name)
+    if debug_identity.op.name != grad_debug_op_name:
+      raise ValueError(
+          "The graph already contains an op named %s" % grad_debug_op_name)
+    return debug_identity
+
+  def watch_gradients_by_tensors(self, graph, tensors):
+    """Watch gradient tensors by x-tensor(s).
+
+    The side effect of this method is that when gradient tensor(s) are created
+    with respect to the any paths that include the `x_tensor`s, the gradient
+    tensor(s) with repsect to the tensor will be registered with this
+    this `GradientsDebugger` instance and can later be retrieved, with the
+    methods `gradient_tensor` and `gradient_tensors`.
+
+    Unlike the method `identify_gradient`, this method is used to retrieve
+    gradient tensors after the construction of the forward subgraph has
+    completed (but before the construction of the backward subgraph).
+
+    This method is the same as `watch_gradients_by_x_tensor_names` except that
+    the tensors are specified by the Python `tf.Tensor` or `tf.Variable`
+    objects, instead by name patterns.
+
+    Example:
+
+    ```python
+    x = tf.Variable(1.0)
+    y = tf.add(x, x, name="y")
+    z = tf.square(debug_y)
+
+    # Create a train op under the grad_debugger context.
+    grad_debugger = tf_debug.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensors(y):
+      train_op = tf.train.GradientDescentOptimizer(z)
+
+    # Now we can reflect through grad_debugger to get the gradient tensor
+    # with respect to y.
+    y_grad = grad_debugger.gradient_tensor(y)
+    # or
+    y_grad = grad_debugger.gradient_tensor("y:0")
+    ```
+
+    Args:
+      graph: the `tf.Graph` to watch the gradients on.
+      tensors: a `tf.Tensor` or `tf.Variable` object, or a list of such objects.
+
+    Returns:
+      The GradientsDebugger instance itself.
+    """
+
+    if not isinstance(tensors, list):
+      tensors = [tensors]
+
+    tensor_name_regex = []
+    for tensor in tensors:
+      tensor_name_regex.append(re.escape(tensor.name) + "$")
+    tensor_name_regex = "(" + "|".join(tensor_name_regex) + ")"
+    return self.watch_gradients_by_tensor_names(graph, tensor_name_regex)
+
+  def watch_gradients_by_tensor_names(self, graph, tensor_name_regex):
+    """Watch gradient tensors by name(s) of the x-tensor(s).
+
+    The side effect of this method is that when gradient tensor(s) are created
+    with respect to the x-tensors, the gradient tensor(s) will be registered
+    with this `GradientsDebugger` instance and can later be retrieved.
+
+    Unlike the `identify_gradient` method, this method is used after the
+    construction of the forward graph has completed. Unlike the
+    `watch_gradients_by_tensor` method, this method does not use handles to the
+    tensors of interest; it uses their names.
+
+    This method is the same as `watch_gradients_by_tensors` except that the
+    x-tensors are specified by name patterns, instead of `tf.Tensor` or
+    `tf.Variable` objects.
+
+    Example:
+
+    ```python
+    x = tf.Variable(1.0, name="x")
+    y = tf.add(x, x, name="y")
+    z = tf.square(debug_y)
+
+    # Create a train op under the grad_debugger context.
+    grad_debugger = tf_debug.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(r"(x|y):0$"):
+      train_op = tf.train.GradientDescentOptimizer(z)
+
+    # Now we can reflect through grad_debugger to get the gradient tensor
+    # with respect to x and y.
+    x_grad = grad_debugger.gradient_tensor("x:0")
+    y_grad = grad_debugger.gradient_tensor("y:0")
+    ```
+
+    Args:
+      graph: the `tf.Graph` to watch the gradients on.
+      tensor_name_regex: the regular-expression pattern of the name(s) of the
+        x-tensor(s) to watch. x-tensor refers to the tensors on the denominator
+        of the differentiation.
+
+    Returns:
+      The GradientsDebugger instance itself.
+    """
+    tensor_name_pattern = re.compile(tensor_name_regex)
+
+    # pylint: disable=protected-access
+    with graph.as_default():
+      for op in graph.get_operations():
+        for output in op.outputs:
+          if tensor_name_pattern.match(output.name):
+            debug_op = self.identify_gradient(output)
+
+            for consumer in output.consumers():
+              if consumer == debug_op.op:
+                continue
+
+              # Locate the slot index of the original input.
+              input_slots = []
+              for i, consumer_input in enumerate(consumer._inputs):
+                if consumer_input == output:
+                  input_slots.append(i)
+
+              for slot in input_slots:
+                consumer._inputs[slot] = debug_op
+                debug_op._consumers.append(consumer)
+
+            del output._consumers[:]
+            output._consumers.append(debug_op.op)
+    # pylint: enable=protected-access
+
+    return self
+
+  def _check_same_graph(self, tensor):
+    if self._graph is None:
+      self._graph = tensor.graph
+    elif self._graph != tensor.graph:
+      raise ValueError(
+          "The graph of the value (%s) is not the same as the graph %s" %
+          (tensor.graph, self._graph))
+
+  def register_gradient_tensor(self,
+                               x_tensor_name,
+                               gradient_tensor):
+    """Register the gradient tensor for an x-tensor.
+
+    Args:
+      x_tensor_name: (`str`) the name of the the independent `tf.Tensor`, i.e.,
+        the tensor on the denominator of the differentiation.
+      gradient_tensor: the gradient `tf.Tensor`.
+    """
+    if len(_gradient_debuggers) == 1 or self._is_active_context:
+      self._check_same_graph(gradient_tensor)
+      self._gradient_tensors[x_tensor_name] = gradient_tensor
+
+  def gradient_tensor(self, x_tensor):
+    """Get the gradient tensor of an x-tensor.
+
+    Args:
+      x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
+        name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
+        on the denominator of the differentiation.
+
+    Returns:
+      If found, the gradient tensor.
+
+    Raises:
+      TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
+      LookupError: If the `x_tensor` has not been registered with a gradient
+        tensor.
+    """
+    x_tensor_name = self._get_tensor_name(x_tensor)
+    if x_tensor_name not in self._gradient_tensors:
+      raise LookupError(
+          "This GradientsDebugger has not received any gradient tensor for "
+          "x-tensor %s" % x_tensor_name)
+    return self._gradient_tensors[x_tensor_name]
+
+  def gradient_tensors(self):
+    """Get the gradient tensors that this object is aware of.
+
+    Returns:
+      A dict mapping x-tensor names to gradient tensor objects. x-tensor refers
+      to the tensors on the denominator of the differentation.
+    """
+    return self._gradient_tensors
+
+  def _get_tensor_name(self, tensor):
+    if isinstance(tensor, (ops.Tensor, variables.Variable)):
+      return tensor.name
+    elif  isinstance(tensor, six.string_types):
+      return tensor
+    else:
+      raise TypeError(
+          "x_tensor must be a str or tf.Tensor or tf.Variable, "
+          "but instead has type %s" % type(tensor))
+
+
+def clear_gradient_debuggers():
+  """Clear all globally registered gradient debuggers."""
+  _gradient_debuggers.clear()
+
+
+@ops.RegisterGradient("DebugIdentity")
+def _identify_gradient_grad(op, dy):
+  """Gradient function for the DebugIdentity op."""
+  # TODO(cais): Allow overriding gradient.
+  grad_debugger_uuid, orig_tensor_name = _parse_grad_debug_op_name(op.name)
+  grad_debugger = _gradient_debuggers[grad_debugger_uuid]
+  grad_debugger.register_gradient_tensor(orig_tensor_name, dy)
+  return dy
+
+
+def gradient_values_from_dump(grad_debugger, x_tensor, dump):
+  """Find gradient values from a `DebugDumpDir` object.
+
+  Args:
+    grad_debugger: the `tf_debug.GradientsDebugger` instance to be used.
+    x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
+      name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
+      on the denominator of the differentiation.
+    dump: A `tfdbg.DebugDumpDir` object.
+
+  Returns:
+    If this `GradientsDebugger` instance has the gradient tensor of `x_tensor`
+      registered: a list of `numpy.ndarray` representing the value of the
+      gradient tensor from `dump`. The list could be empty, if the gradient
+      tensor is not executed in the `tf.Session.run()` call that generated
+      the `dump`. The list could also contain multiple values of the gradient
+      tensor, e.g., if gradient tensor is computed repeatedly in a
+      `tf.while_loop` during the run that generated the `dump`.
+
+  Raises:
+    LookupError: If this `GradientsDebugger` instance does not have the
+      gradient tensor of `x_tensor` registered.
+    ValueError: If this `GradientsDebugger` has a `tf.Graph` object that
+      does not match the `tf.Graph` object of the `dump`.
+    TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
+  """
+  # TODO(cais): Use this method in LocalCLIDebugWrapperSession to present the
+  # gradient tensors to the TFDBG CLI.
+
+  # If possible, verify that the Python graph of the dump and that of this
+  # GradientsDebugger match.
+  if (dump.python_graph and grad_debugger.graph and
+      dump.python_graph != grad_debugger.graph):
+    raise ValueError(
+        "This GradientsDebugger instance has a graph (%s) that differs from "
+        "the graph of the DebugDumpDir object (%s)." %
+        (grad_debugger.graph, dump.python_graph))
+
+  gradient_tensor = grad_debugger.gradient_tensor(x_tensor)
+  node_name, output_slot = debug_data.parse_node_or_tensor_name(
+      gradient_tensor.name)
+
+  try:
+    return dump.get_tensors(node_name, output_slot, "DebugIdentity")
+  except debug_data.WatchKeyDoesNotExistInDebugDumpDirError:
+    return []
diff --git a/tensorflow/python/debug/lib/debug_gradients_test.py b/tensorflow/python/debug/lib/debug_gradients_test.py
new file mode 100644
index 0000000000..966578320e
--- /dev/null
+++ b/tensorflow/python/debug/lib/debug_gradients_test.py
@@ -0,0 +1,378 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Unit tests for debug_gradients module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import shutil
+import tempfile
+
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.debug.lib import debug_data
+from tensorflow.python.debug.lib import debug_gradients
+from tensorflow.python.debug.lib import debug_utils
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import gradient_descent
+
+
+class IdentifyGradientTest(test_util.TensorFlowTestCase):
+
+  def setUp(self):
+    self.sess = session.Session()
+    with self.sess:
+      self.u = variables.Variable(2.0, name="u")
+      self.v = variables.Variable(3.0, name="v")
+      self.w = math_ops.multiply(self.u.value(), self.v.value(), name="w")
+
+  def tearDown(self):
+    ops.reset_default_graph()
+    debug_gradients.clear_gradient_debuggers()
+
+  def testIdentifyGradientGivesCorrectTensorObjectWithoutContextManager(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    # Fetch the gradient tensor with the x-tensor object.
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor's name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testIdentifyGradientGivesCorrectTensorObjectWithTfGradients(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    with grad_debugger:
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    # Fetch the gradient tensor with the x-tensor object.
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor's name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    # Fetch the gradient tensor with the x-tensor name.
+    w_grad = grad_debugger.gradient_tensor(self.w.name)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testCallingIdentifyGradientTwiceWithTheSameGradientsDebuggerErrors(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    grad_debugger.identify_gradient(self.w)
+    with self.assertRaisesRegexp(
+        ValueError, "The graph already contains an op named .*"):
+      grad_debugger.identify_gradient(self.w)
+
+  def testIdentifyGradientWorksOnMultipleLosses(self):
+    grad_debugger_1 = debug_gradients.GradientsDebugger()
+    grad_debugger_2 = debug_gradients.GradientsDebugger()
+
+    y = math_ops.add(self.w, -1.0, name="y")
+    debug_y = grad_debugger_1.identify_gradient(y)
+    z1 = math_ops.square(debug_y, name="z1")
+
+    debug_y = grad_debugger_2.identify_gradient(y)
+    z2 = math_ops.sqrt(debug_y, name="z2")
+
+    with grad_debugger_1:
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
+    with grad_debugger_2:
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
+
+    dz1_dy = grad_debugger_1.gradient_tensor(y)
+    dz2_dy = grad_debugger_2.gradient_tensor(y)
+    self.assertIsInstance(dz1_dy, ops.Tensor)
+    self.assertIsInstance(dz2_dy, ops.Tensor)
+    self.assertIsNot(dz1_dy, dz2_dy)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0 ** 2, self.sess.run(z1))
+    self.assertAllClose(5.0 ** 0.5, self.sess.run(z2))
+    self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
+    self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy))
+
+  def testIdentifyGradientRaisesLookupErrorForUnknownXTensor(self):
+    grad_debugger_1 = debug_gradients.GradientsDebugger()
+    grad_debugger_2 = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger_1.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    # There are >1 gradient debuggers registered, and grad_debugger is not used
+    # as a context manager here, so the gradient w.r.t. self.w will not be
+    # registered.
+    gradients_impl.gradients(y, [self.u, self.v])
+
+    with self.assertRaisesRegexp(
+        LookupError,
+        r"This GradientsDebugger has not received any gradient tensor for "):
+      grad_debugger_1.gradient_tensor(self.w)
+    with self.assertRaisesRegexp(
+        LookupError,
+        r"This GradientsDebugger has not received any gradient tensor for "):
+      grad_debugger_2.gradient_tensor(self.w)
+
+  def testIdentifyGradientRaisesTypeErrorForNonTensorOrTensorNameInput(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with self.assertRaisesRegexp(
+        TypeError,
+        r"x_tensor must be a str or tf\.Tensor or tf\.Variable, but instead "
+        r"has type .*Operation.*"):
+      grad_debugger.gradient_tensor(variables.global_variables_initializer())
+
+  def testIdentifyGradientTensorWorksWithGradientDescentOptimizer(self):
+    grad_debugger = debug_gradients.GradientsDebugger()
+    id_grad_w = grad_debugger.identify_gradient(self.w)
+    y = math_ops.add(id_grad_w, -1.0, name="y")
+
+    with grad_debugger:
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(y)
+
+    self.sess.run(variables.global_variables_initializer())
+
+    # Fetch the gradient tensor with the x-tensor object.
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testWatchGradientsByXTensorNamesWorks(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    # The constructrion of the forward graph has completed.
+    # But we can still get the gradient tensors by using
+    # watch_gradients_by_tensor_names().
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "w:0$"):
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    w_grad = grad_debugger.gradient_tensor("w:0")
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testWatchGradientsByXTensorNamesWorksWithoutContextManager(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    # The constructrion of the forward graph has completed.
+    # But we can still get the gradient tensors by using
+    # watch_gradients_by_tensor_names().
+    grad_debugger = debug_gradients.GradientsDebugger()
+    grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "w:0$")
+    grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0, self.sess.run(y))
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+    w_grad = grad_debugger.gradient_tensor("w:0")
+    self.assertIsInstance(w_grad, ops.Tensor)
+    self.assertAllClose(1.0, self.sess.run(w_grad))
+
+  def testWatchGradientsWorksOnRefTensor(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "u:0$"):
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+    v_grad = grads[1]
+
+    self.assertIs(u_grad, grad_debugger.gradient_tensor("u:0"))
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(3.0, self.sess.run(u_grad))
+    self.assertAllClose(2.0, self.sess.run(v_grad))
+    self.assertAllClose(
+        3.0, self.sess.run(grad_debugger.gradient_tensor("u:0")))
+
+  def testWatchGradientsWorksOnMultipleTensors(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph,
+                                                       "(u|w):0$"):
+      grads = gradients_impl.gradients(y, [self.u, self.v])
+    self.assertEqual(2, len(grads))
+    u_grad = grads[0]
+
+    self.assertEqual(2, len(grad_debugger.gradient_tensors()))
+    self.assertIs(u_grad, grad_debugger.gradient_tensor("u:0"))
+    self.assertIsInstance(grad_debugger.gradient_tensor("w:0"), ops.Tensor)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(
+        1.0, self.sess.run(grad_debugger.gradient_tensor("w:0")))
+    self.assertAllClose(
+        3.0, self.sess.run(grad_debugger.gradient_tensor("u:0")))
+
+  def testWatchGradientsByXTensorsWorks(self):
+    y = math_ops.add(self.w, -1.0, name="foo/y")
+    z = math_ops.square(y, name="foo/z")
+
+    # The constructrion of the forward graph has completed.
+    # But we can still get the gradient tensors by using
+    # watch_gradients_by_x_tensors().
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensors(
+        self.sess.graph, [self.w, self.u, y]):
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z)
+
+    self.assertEqual(3, len(grad_debugger.gradient_tensors()))
+    u_grad = grad_debugger.gradient_tensor(self.u)
+    w_grad = grad_debugger.gradient_tensor(self.w)
+    y_grad = grad_debugger.gradient_tensor(y)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(10.0, self.sess.run(y_grad))
+    self.assertAllClose(10.0, self.sess.run(w_grad))
+    self.assertAllClose(30.0, self.sess.run(u_grad))
+
+  def testWatchGradientsByTensorCanWorkOnMultipleLosses(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+    z1 = math_ops.square(y, name="z1")
+    z2 = math_ops.sqrt(y, name="z2")
+
+    grad_debugger_1 = debug_gradients.GradientsDebugger()
+    with grad_debugger_1.watch_gradients_by_tensors(self.sess.graph, y):
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
+
+    grad_debugger_2 = debug_gradients.GradientsDebugger()
+    with grad_debugger_2.watch_gradients_by_tensors(self.sess.graph, y):
+      gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
+
+    dz1_dy = grad_debugger_1.gradient_tensor(y)
+    dz2_dy = grad_debugger_2.gradient_tensor(y)
+    self.assertIsInstance(dz1_dy, ops.Tensor)
+    self.assertIsInstance(dz2_dy, ops.Tensor)
+    self.assertIsNot(dz1_dy, dz2_dy)
+
+    self.sess.run(variables.global_variables_initializer())
+    self.assertAllClose(5.0 ** 2, self.sess.run(z1))
+    self.assertAllClose(5.0 ** 0.5, self.sess.run(z2))
+    self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
+    self.assertAllClose(0.5 * (5.0 ** -0.5), self.sess.run(dz2_dy))
+
+  def testGradientsValuesFromDumpWorks(self):
+    y = math_ops.add(self.w, -1.0, name="y")
+    z = math_ops.square(y, name="z")
+
+    grad_debugger = debug_gradients.GradientsDebugger()
+    with grad_debugger.watch_gradients_by_tensors(
+        self.sess.graph, [self.w, self.u, y]):
+      train_op = gradient_descent.GradientDescentOptimizer(0.1).minimize(z)
+
+    self.sess.run(variables.global_variables_initializer())
+
+    run_options = config_pb2.RunOptions(output_partition_graphs=True)
+    dump_dir = tempfile.mkdtemp()
+    debug_url = "file://" + dump_dir
+    debug_utils.watch_graph(
+        run_options,
+        self.sess.graph,
+        debug_urls=debug_url)
+    run_metadata = config_pb2.RunMetadata()
+    self.sess.run(train_op, options=run_options, run_metadata=run_metadata)
+
+    dump = debug_data.DebugDumpDir(
+        dump_dir, partition_graphs=run_metadata.partition_graphs)
+    dump.set_python_graph(self.sess.graph)
+
+    y_grad_values = debug_gradients.gradient_values_from_dump(
+        grad_debugger, y, dump)
+    self.assertEqual(1, len(y_grad_values))
+    self.assertAllClose(10.0, y_grad_values[0])
+
+    w_grad_values = debug_gradients.gradient_values_from_dump(
+        grad_debugger, self.w, dump)
+    self.assertEqual(1, len(w_grad_values))
+    self.assertAllClose(10.0, w_grad_values[0])
+
+    u_grad_values = debug_gradients.gradient_values_from_dump(
+        grad_debugger, self.u, dump)
+    self.assertEqual(1, len(u_grad_values))
+    self.assertAllClose(30.0, u_grad_values[0])
+
+    with self.assertRaisesRegexp(
+        LookupError,
+        r"This GradientsDebugger has not received any gradient tensor for "
+        r"x-tensor v:0"):
+      debug_gradients.gradient_values_from_dump(grad_debugger, self.v, dump)
+
+    # Cleanup.
+    shutil.rmtree(dump_dir)
+
+
+if __name__ == "__main__":
+  googletest.main()
-- 
GitLab


From 19cc9a796d86b5cf2193e7648ac9ae05c5efb53f Mon Sep 17 00:00:00 2001
From: Manjunath Kudlur <keveman@google.com>
Date: Thu, 20 Jul 2017 14:44:20 -0700
Subject: [PATCH 1166/1375] Added DT_VARIANT type.

A tensor with DT_VARIANT type can store arbitrary C++ data structures.
DT_VARIANT is implemented using a type-erased data structure similar to
std::any, but with extensions to make it compatible with tensorflow::Tensor.
In particular, Encode and Decode methods need to be provided by C++ classes
whose objects are stored in Variant.
PiperOrigin-RevId: 162668355
---
 tensorflow/c/c_api.h                          |   1 +
 .../contrib/cmake/tf_core_framework.cmake     |   7 +
 tensorflow/core/BUILD                         |  22 +-
 tensorflow/core/framework/allocator.h         |  19 ++
 tensorflow/core/framework/tensor.cc           |  78 +++++
 tensorflow/core/framework/tensor.proto        |  13 +
 tensorflow/core/framework/tensor_test.cc      |  98 ++++++
 tensorflow/core/framework/types.cc            |   7 +-
 tensorflow/core/framework/types.h             |   2 +
 tensorflow/core/framework/types.proto         |   2 +
 tensorflow/core/framework/variant.cc          |  87 ++++++
 tensorflow/core/framework/variant.h           | 287 ++++++++++++++++++
 .../core/framework/variant_encode_decode.h    | 220 ++++++++++++++
 tensorflow/core/framework/variant_test.cc     | 249 +++++++++++++++
 .../core/platform/default/build_config.bzl    |   8 +
 tensorflow/core/platform/variant_coding.cc    |  63 ++++
 tensorflow/core/platform/variant_coding.h     |  39 +++
 tensorflow/go/tensor.go                       |   1 +
 tensorflow/python/__init__.py                 |   1 +
 tensorflow/python/framework/dtypes.py         |  17 +-
 tensorflow/python/framework/dtypes_test.py    |   9 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   4 +
 22 files changed, 1225 insertions(+), 9 deletions(-)
 create mode 100644 tensorflow/core/framework/variant.cc
 create mode 100644 tensorflow/core/framework/variant.h
 create mode 100644 tensorflow/core/framework/variant_encode_decode.h
 create mode 100644 tensorflow/core/framework/variant_test.cc
 create mode 100644 tensorflow/core/platform/variant_coding.cc
 create mode 100644 tensorflow/core/platform/variant_coding.h

diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h
index 3aeafb4685..46758408c4 100644
--- a/tensorflow/c/c_api.h
+++ b/tensorflow/c/c_api.h
@@ -117,6 +117,7 @@ typedef enum TF_DataType {
   TF_COMPLEX128 = 18,  // Double-precision complex
   TF_HALF = 19,
   TF_RESOURCE = 20,
+  TF_VARIANT = 21,
 } TF_DataType;
 
 // TF_DataTypeSize returns the sizeof() for the underlying type corresponding
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index a15621ba6d..b66eb35339 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -156,6 +156,11 @@ if (NOT tensorflow_ENABLE_GPU)
       "${tensorflow_source_dir}/tensorflow/core/platform/default/cuda_libdevice_path.*")
   list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_gpu_srcs})
 endif()
+
+file(GLOB tf_core_platform_exclude_srcs
+  "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.cc")
+list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_exclude_srcs})
+
 list(APPEND tf_core_lib_srcs ${tf_core_platform_srcs})
 
 if(UNIX)
@@ -225,6 +230,8 @@ set(tf_version_srcs ${tensorflow_source_dir}/tensorflow/core/util/version_info.c
 file(GLOB_RECURSE tf_core_framework_srcs
     "${tensorflow_source_dir}/tensorflow/core/framework/*.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.h"
+    "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.cc"
     "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.h"
     "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc"
     "${tensorflow_source_dir}/tensorflow/core/graph/graph.h"
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 5885d4ed52..271b2492fe 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -94,6 +94,8 @@ load(
     "tf_additional_lib_deps",
     "tf_additional_lib_hdrs",
     "tf_additional_lib_srcs",
+    "tf_additional_framework_hdrs",
+    "tf_additional_framework_srcs",
     "tf_additional_minimal_lib_srcs",
     "tf_additional_proto_hdrs",
     "tf_additional_proto_srcs",
@@ -343,6 +345,8 @@ tf_cuda_library(
     hdrs = [
         "example/feature_util.h",
         "framework/allocator.h",
+        "framework/variant.h",
+        "framework/variant_encode_decode.h",
         "framework/allocator_registry.h",
         "framework/attr_value_util.h",
         "framework/bfloat16.h",
@@ -1265,6 +1269,8 @@ LIB_INTERNAL_WINDOWS_DEPS = glob(
         "platform/**/cuda_libdevice_path.cc",
         "platform/**/stream_executor.h",
         "platform/load_library.cc",
+        "platform/variant_coding.cc",
+        "platform/**/variant_cord_coding.cc",
     ],
 )
 
@@ -1286,6 +1292,8 @@ cc_library(
             ],
             exclude = [
                 "**/*test*",
+                "framework/variant.cc",
+                "platform/variant_coding.cc",
                 "lib/hash/crc32c_accelerate.cc",
                 "lib/gif/**/*",
                 "lib/jpeg/**/*",
@@ -1296,6 +1304,8 @@ cc_library(
                 "platform/**/cuda_libdevice_path.cc",
                 "platform/**/stream_executor.h",
                 "platform/**/gpu_tracer.cc",
+                "platform/variant_coding.cc",
+                "platform/**/variant_cord_coding.cc",
             ],
         ),
     }) + tf_additional_lib_srcs(
@@ -1306,6 +1316,8 @@ cc_library(
             "platform/**/stream_executor.h",
             "platform/**/env_time.cc",
             "platform/**/gpu_tracer.cc",
+            "platform/variant_coding.cc",
+            "platform/**/variant_cord_coding.cc",
         ] +
         # Protobuf deps already included through the ":lib_proto_parsing"
         # dependency.
@@ -1462,6 +1474,8 @@ tf_cuda_library(
             "util/**/*.h",
             "util/**/*.cc",
         ] + [
+            "platform/variant_coding.cc",
+            "platform/variant_coding.h",
             "graph/edgeset.h",
             "graph/edgeset.cc",
             "graph/graph.h",
@@ -1490,20 +1504,22 @@ tf_cuda_library(
             "util/memmapped_file_system_writer.h",
             "util/memmapped_file_system_writer.cc",
         ],
-    }),
+    }) + tf_additional_framework_srcs(),
     hdrs = [
+        "framework/variant.h",
         "framework/op_segment.h",
         "framework/rendezvous.h",  # only needed for tests
         "framework/tensor_reference.h",
         "framework/tracking_allocator.h",  # only needed for tests
         "framework/unique_tensor_references.h",
+        "platform/variant_coding.h",
         "util/command_line_flags.h",
         "util/env_var.h",
         "util/equal_graph_def.h",
         "util/presized_cuckoo_map.h",
         "util/tensor_slice_set.h",
         "util/tensor_slice_util.h",
-    ],
+    ] + tf_additional_framework_hdrs(),
     copts = tf_copts(),
     linkopts = select({
         "//tensorflow:freebsd": [],
@@ -1517,6 +1533,7 @@ tf_cuda_library(
         ":proto_text",
         ":protos_all_cc",
         ":version_lib",
+        "//tensorflow/core/platform/default/build_config:platformlib",
         "//tensorflow/core/kernels:bounds_check",
         "//third_party/eigen3",
     ] + if_mkl(["//third_party/mkl:intel_binary_blob"]),
@@ -2183,6 +2200,7 @@ tf_cc_tests(
         "common_runtime/simple_placer_test.cc",
         "example/feature_util_test.cc",
         "framework/allocator_test.cc",
+        "framework/variant_test.cc",
         "framework/attr_value_util_test.cc",
         "framework/bfloat16_test.cc",
         "framework/cancellation_test.cc",
diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h
index 1cb183d81e..868335b072 100644
--- a/tensorflow/core/framework/allocator.h
+++ b/tensorflow/core/framework/allocator.h
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/type_traits.h"
+#include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -229,6 +230,14 @@ class Allocator {
     for (size_t i = 0; i < n; ++p, ++i) p->~ResourceHandle();
   }
 
+  virtual void RunVariantCtor(Variant* p, size_t n) {
+    for (size_t i = 0; i < n; ++p, ++i) new (p) Variant();
+  }
+
+  virtual void RunVariantDtor(Variant* p, size_t n) {
+    for (size_t i = 0; i < n; ++p, ++i) p->~Variant();
+  }
+
   // TODO(jeff): Maybe provide some interface to give info about
   // current allocation state (total number of bytes available for
   // allocation, number of bytes free on device, etc.)
@@ -256,6 +265,16 @@ inline void Allocator::RunDtor(ResourceHandle* p, size_t n) {
   RunResourceDtor(p, n);
 }
 
+template <>
+inline void Allocator::RunCtor(Variant* p, size_t n) {
+  RunVariantCtor(p, n);
+}
+
+template <>
+inline void Allocator::RunDtor(Variant* p, size_t n) {
+  RunVariantDtor(p, n);
+}
+
 // An implementation of Allocator that delegates all calls to another Allocator.
 //
 // Useful to clients who want to override part of the functionality of another
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 80b98fb9c6..0a85894071 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -36,6 +36,8 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_description.pb.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/variant.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
@@ -47,6 +49,7 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/tensor_coding.h"
 #include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/platform/variant_coding.h"
 
 namespace tensorflow {
 namespace {
@@ -220,6 +223,36 @@ struct Helper<ResourceHandle> {
   }
 };
 
+template <>
+struct Helper<Variant> {
+  // Encodes "n" elements of type Variant stored in "in" into destination
+  // "out", which is usually the TensorProto::tensor_content.
+  template <typename Destination>
+  static void Encode(TensorBuffer* in, int64 n, Destination* out) {
+    port::EncodeVariantList(in->base<const Variant>(), n, out);
+  }
+
+  // Decodes "n" elements of type Variant from "in" and constructs a
+  // buffer out of it. Returns nullptr if the decoding fails. "in" is
+  // usually the TensorProto::tensor_content.
+  template <typename Source>
+  static TensorBuffer* Decode(Allocator* a, const Source& in, int64 n) {
+    auto* buf = new Buffer<Variant>(a, n);
+    Variant* ps = buf->template base<Variant>();
+    if (ps == nullptr || !port::DecodeVariantList(in, ps, n)) {
+      buf->Unref();
+      return nullptr;
+    }
+    return buf;
+  }
+
+  // Returns the estimated memory usage of "n" elements of type T
+  // stored in buffer "in".
+  static int64 TotalBytes(TensorBuffer* in, int n) {
+    return n * sizeof(Variant);
+  }
+};
+
 template <typename T>
 struct ProtoHelper {};
 
@@ -289,6 +322,26 @@ struct ProtoHelper<ResourceHandle> {
   }
 };
 
+template <>
+struct ProtoHelper<Variant> {
+  static protobuf::RepeatedPtrField<VariantTensorDataProto>::const_iterator
+  Begin(const TensorProto& proto) {
+    return proto.variant_val().begin();
+  }
+  static size_t NumElements(const TensorProto& proto) {
+    return proto.variant_val().size();
+  }
+  static void Fill(const Variant* data, size_t n, TensorProto* proto) {
+    auto* variant_values = proto->mutable_variant_val();
+    variant_values->Clear();
+    for (size_t i = 0; i < n; ++i) {
+      VariantTensorData tmp;
+      data[i].Encode(&tmp);
+      tmp.ToProto(variant_values->Add());
+    }
+  }
+};
+
 template <>
 struct ProtoHelper<complex64> {
   typedef Helper<float>::RepeatedFieldType FieldType;
@@ -421,6 +474,30 @@ TensorBuffer* FromProtoField(Allocator* a, const TensorProto& in, int64 n) {
   return buf;
 }
 
+template <>
+TensorBuffer* FromProtoField<Variant>(Allocator* a, const TensorProto& in,
+                                      int64 n) {
+  CHECK_GT(n, 0);
+  Buffer<Variant>* buf = new Buffer<Variant>(a, n);
+  Variant* data = buf->template base<Variant>();
+  if (data == nullptr) {
+    buf->Unref();
+    return nullptr;
+  }
+  const int64 in_n = ProtoHelper<Variant>::NumElements(in);
+  if (in_n <= 0) {
+    std::fill_n(data, n, Variant());
+  } else {
+    for (int64 i = 0; i < in_n; ++i) {
+      data[i] = in.variant_val(i);
+    }
+    for (int64 i = in_n; i < n; ++i) {
+      data[i] = Variant();
+    }
+  }
+  return buf;
+}
+
 // fp16 is opaque to the protobuf, so we deserialize these identical to uint16
 // but with data stored in half_val instead of int_val (ie., we don't use
 // ProtoHelper<uint16>).
@@ -571,6 +648,7 @@ bool Tensor::RefCountIsOne() const {
     CASE(bfloat16, SINGLE_ARG(STMTS))                          \
     CASE(Eigen::half, SINGLE_ARG(STMTS))                       \
     CASE(ResourceHandle, SINGLE_ARG(STMTS))                    \
+    CASE(Variant, SINGLE_ARG(STMTS))                           \
     case DT_INVALID:                                           \
       INVALID;                                                 \
       break;                                                   \
diff --git a/tensorflow/core/framework/tensor.proto b/tensorflow/core/framework/tensor.proto
index 98ecef225a..7e4af7a645 100644
--- a/tensorflow/core/framework/tensor.proto
+++ b/tensorflow/core/framework/tensor.proto
@@ -72,4 +72,17 @@ message TensorProto {
 
   // DT_RESOURCE
   repeated ResourceHandleProto resource_handle_val = 14;
+
+  // DT_VARIANT
+  repeated VariantTensorDataProto variant_val = 15;
 };
+
+// Protocol buffer representing the serialization format of DT_VARIANT tensors.
+message VariantTensorDataProto {
+  // Name of the type of objects being serialized.
+  string type_name = 1;
+  // Portions of the object that are not Tensors.
+  bytes metadata = 2;
+  // Tensors contained within objects being serialized.
+  repeated TensorProto tensors = 3;
+}
diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc
index 6c9c803af6..9aaf00853d 100644
--- a/tensorflow/core/framework/tensor_test.cc
+++ b/tensorflow/core/framework/tensor_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/test.h"
@@ -37,6 +38,35 @@ inline bool operator==(const ResourceHandle& a, const ResourceHandle& b) {
          a.maybe_type_name() == b.maybe_type_name();
 }
 
+inline bool operator==(const Variant& a, const Variant& b) {
+  if (a.is_empty()) {
+    return b.is_empty();
+  }
+
+  if (a.TypeId() != b.TypeId()) return false;
+  if (a.TypeName() != b.TypeName()) return false;
+
+  VariantTensorData a_data, b_data;
+  a.Encode(&a_data);
+  b.Encode(&b_data);
+
+  if (a_data.metadata != b_data.metadata) return false;
+
+  if (a_data.tensors.size() != b_data.tensors.size()) return false;
+
+  for (int i = 0; i < a_data.tensors.size(); ++i) {
+    TensorProto a_proto, b_proto;
+    a_data.tensors[i].AsProtoTensorContent(&a_proto);
+    b_data.tensors[i].AsProtoTensorContent(&b_proto);
+    string a_str, b_str;
+    a_proto.SerializeToString(&a_str);
+    b_proto.SerializeToString(&b_str);
+    if (a_str != b_str) return false;
+  }
+
+  return true;
+}
+
 TEST(TensorTest, Default) {
   Tensor t;
   EXPECT_EQ(t.dtype(), DT_FLOAT);
@@ -159,6 +189,74 @@ TEST(Tensor_ResourceHandle, Simple) {
   TestCopies<ResourceHandle>(t);
 }
 
+TEST(Tensor_Variant, Simple) {
+  Tensor t(DT_VARIANT, TensorShape({}));
+  Tensor value(DT_FLOAT, TensorShape({}));
+  value.flat<float>()(0) = 42.0f;
+  t.flat<Variant>()(0) = value;
+  // All the tests in TestCopies except the ones that serialize and deserialize
+  // the tensor. The consumer of a serialized Variant Tensor should know what
+  // type is stored in the Tensor, so not testing the generic
+  // serialize/deserialize case here.
+  {
+    LOG(INFO) << "CopyFrom()";
+    Tensor t2(t.dtype());
+    EXPECT_TRUE(t2.CopyFrom(t, t.shape()));
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "operator=()";
+    Tensor t2(t.dtype());
+    t2 = t;
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "deep copy";
+    Tensor t2(t.dtype(), t.shape());
+    t2.flat<Variant>() = t.flat<Variant>();
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "AsTensor";
+    gtl::ArraySlice<Variant> values(t.flat<Variant>().data(), t.NumElements());
+    Tensor t2 = test::AsTensor(values, t.shape());
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "Move constructor";
+    Tensor t2 = t;
+    Tensor t3(std::move(t2));
+    test::ExpectTensorEqual<Variant>(t, t3);
+    EXPECT_TRUE(t3.IsInitialized());
+    EXPECT_FALSE(t2.IsInitialized());
+  }
+  {
+    LOG(INFO) << "Move assignment";
+    Tensor t2 = t;
+    Tensor t3 = std::move(t2);
+    Tensor* t4 = &t3;
+    *t4 = std::move(t3);
+    test::ExpectTensorEqual<Variant>(t, t3);
+    EXPECT_TRUE(t3.IsInitialized());
+    EXPECT_FALSE(t2.IsInitialized());
+  }
+}
+
+TEST(Tensor_Variant, Marshal) {
+  Tensor t(DT_VARIANT, TensorShape({}));
+
+  Tensor internal(DT_FLOAT, TensorShape({}));
+  internal.flat<float>()(0) = 42.0f;
+  t.flat<Variant>()(0) = internal;
+
+  LOG(INFO) << "AsProtoField()";
+  TensorProto proto;
+  t.AsProtoField(&proto);
+
+  Tensor t2(t.dtype());
+  EXPECT_TRUE(t2.FromProto(proto));
+}
+
 TEST(Tensor_UInt16, Simple) {
   Tensor t(DT_UINT16, TensorShape({2, 2}));
   EXPECT_TRUE(t.shape().IsSameSize(TensorShape({2, 2})));
diff --git a/tensorflow/core/framework/types.cc b/tensorflow/core/framework/types.cc
index 28b5e8be11..39dd5b435e 100644
--- a/tensorflow/core/framework/types.cc
+++ b/tensorflow/core/framework/types.cc
@@ -87,6 +87,8 @@ string DataTypeString(DataType dtype) {
       return "half";
     case DT_RESOURCE:
       return "resource";
+    case DT_VARIANT:
+      return "variant";
     default:
       LOG(ERROR) << "Unrecognized DataType enum value " << dtype;
       return strings::StrCat("unknown dtype enum (", dtype, ")");
@@ -165,6 +167,9 @@ bool DataTypeFromString(StringPiece sp, DataType* dt) {
   } else if (sp == "resource") {
     *dt = DT_RESOURCE;
     return true;
+  } else if (sp == "variant") {
+    *dt = DT_VARIANT;
+    return true;
   }
   return false;
 }
@@ -186,7 +191,7 @@ DataTypeVector AllTypes() {
   return {DT_FLOAT,   DT_DOUBLE, DT_INT32,  DT_UINT8,     DT_INT16,
           DT_UINT16,  DT_INT8,   DT_STRING, DT_COMPLEX64, DT_COMPLEX128,
           DT_INT64,   DT_BOOL,   DT_QINT8,  DT_QUINT8,    DT_QINT16,
-          DT_QUINT16, DT_QINT32, DT_HALF,   DT_RESOURCE};
+          DT_QUINT16, DT_QINT32, DT_HALF,   DT_RESOURCE,  DT_VARIANT};
 }
 
 #if !defined(IS_MOBILE_PLATFORM) || defined(SUPPORT_SELECTIVE_REGISTRATION)
diff --git a/tensorflow/core/framework/types.h b/tensorflow/core/framework/types.h
index 889056647c..9127750d68 100644
--- a/tensorflow/core/framework/types.h
+++ b/tensorflow/core/framework/types.h
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/types.pb.h"
+#include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
@@ -181,6 +182,7 @@ MATCH_TYPE_AND_ENUM(qint32, DT_QINT32);
 MATCH_TYPE_AND_ENUM(bfloat16, DT_BFLOAT16);
 MATCH_TYPE_AND_ENUM(Eigen::half, DT_HALF);
 MATCH_TYPE_AND_ENUM(ResourceHandle, DT_RESOURCE);
+MATCH_TYPE_AND_ENUM(Variant, DT_VARIANT);
 
 #undef MATCH_TYPE_AND_ENUM
 
diff --git a/tensorflow/core/framework/types.proto b/tensorflow/core/framework/types.proto
index b80e2b31dc..1beb2a1aa2 100644
--- a/tensorflow/core/framework/types.proto
+++ b/tensorflow/core/framework/types.proto
@@ -34,6 +34,7 @@ enum DataType {
   DT_COMPLEX128 = 18;  // Double-precision complex
   DT_HALF = 19;
   DT_RESOURCE = 20;
+  DT_VARIANT = 21;  // Arbitrary C++ data types
 
   // TODO(josh11b): DT_GENERIC_PROTO = ??;
   // TODO(jeff,josh11b): DT_UINT64?  DT_UINT32?
@@ -60,5 +61,6 @@ enum DataType {
   DT_COMPLEX128_REF = 118;
   DT_HALF_REF = 119;
   DT_RESOURCE_REF = 120;
+  DT_VARIANT_REF = 121;
 }
 // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.h,https://www.tensorflow.org/code/tensorflow/go/tensor.go)
diff --git a/tensorflow/core/framework/variant.cc b/tensorflow/core/framework/variant.cc
new file mode 100644
index 0000000000..6a11f86c23
--- /dev/null
+++ b/tensorflow/core/framework/variant.cc
@@ -0,0 +1,87 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/variant.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/map_util.h"
+
+namespace tensorflow {
+
+template <>
+void* Variant::get() {
+  if (is_empty()) {
+    return nullptr;
+  }
+  return value_->RawPtr();
+}
+
+template <>
+const void* Variant::get() const {
+  if (is_empty()) {
+    return nullptr;
+  }
+  return value_->RawPtr();
+}
+
+void VariantTensorData::ToProto(VariantTensorDataProto* proto) const {
+  proto->set_type_name(type_name);
+  proto->set_metadata(metadata);
+  proto->clear_tensors();
+  for (int i = 0; i < tensors.size(); ++i) {
+    tensors[i].AsProtoField(proto->mutable_tensors()->Add());
+  }
+}
+
+bool VariantTensorData::FromProto(const VariantTensorDataProto& proto) {
+  type_name = proto.type_name();
+  metadata = proto.metadata();
+  tensors.clear();
+  for (int i = 0; i < proto.tensors_size(); ++i) {
+    Tensor tmp;
+    if (!tmp.FromProto(proto.tensors(i))) return false;
+    tensors.push_back(tmp);
+  }
+  return true;
+}
+
+template <>
+string TypeNameVariant(VariantTensorDataProto&& value) {
+  return value.GetTypeName();
+}
+
+template <>
+void EncodeVariant(VariantTensorDataProto&& value, VariantTensorData* data) {
+  data->FromProto(value);
+}
+
+template <>
+bool DecodeVariant(const VariantTensorData& data,
+                   VariantTensorDataProto* value) {
+  data.ToProto(value);
+  return true;
+}
+
+template <>
+void EncodeVariant(VariantTensorDataProto&& value, string* buf) {
+  value.SerializeToString(buf);
+}
+
+template <>
+bool DecodeVariant(const string& buf, VariantTensorDataProto* value) {
+  return value->ParseFromString(buf);
+}
+
+}  // end namespace tensorflow
diff --git a/tensorflow/core/framework/variant.h b/tensorflow/core/framework/variant.h
new file mode 100644
index 0000000000..df6d903143
--- /dev/null
+++ b/tensorflow/core/framework/variant.h
@@ -0,0 +1,287 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_FRAMEWORK_VARIANT_H_
+#define TENSORFLOW_FRAMEWORK_VARIANT_H_
+
+#include <functional>
+#include <iostream>
+#include <memory>
+#include <type_traits>
+#include <unordered_map>
+#include <utility>
+
+#include "tensorflow/core/framework/type_index.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/mutex.h"
+
+namespace tensorflow {
+
+struct VariantTensorData;
+
+template <typename T>
+string TypeNameVariant(T&& value);
+
+template <typename T>
+void EncodeVariant(T&& value, VariantTensorData* data);
+
+template <typename T>
+bool DecodeVariant(const VariantTensorData& data, T* value);
+
+template <typename T>
+void EncodeVariant(T&& value, string* buf);
+
+template <typename T>
+bool DecodeVariant(const string& buf, T* value);
+
+// This is an implementation of a type-erased container that can store an
+// object of any type. The implementation is very similar to std::any, but has
+// restrictions on the types of objects that can be stored, and eschews some of
+// the fancier constructors available for std::any. An object of
+// tensorflow::Variant is intended to be used as the value that will be stored
+// in a tensorflow::Tensor object when its type is DT_VARIANT.
+//
+// tensorflow::Variant can store an object of a class that satisfies the
+// following constraints:
+//
+// * The class is CopyConstructible.
+// * The class has a default constructor.
+// * It's either a protocol buffer, a tensorflow::Tensor, or defines the
+// following functions:
+//
+//   string TypeName() const;
+//   void Encode(VariantTensorData* data) const;
+//   void Decode(const VariantTensorData& data);
+//
+// Simple POD types can elide the Encode/Decode functions, they are provided by
+// helper methods.
+// Here are some typical usage patterns:
+//
+//   Variant x = 10;
+//   EXPECT_EQ(*x.get<int>(), 10);
+//
+//   Tensor t(DT_FLOAT, TensorShape({}));
+//   t.flat<float>()(0) = 42.0f;
+//   Variant x = t;
+//   EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 42.0f);
+//
+// Accessing the stored object:
+//
+// The get<T> function is the main mechanism to access the object stored in the
+// contained. It is type-safe, that is, calling get<T> when the stored object's
+// type is not T, returns a nullptr. A raw pointer to the stored object can be
+// obtained by calling get<void>().
+//
+// Serializing/deserializing Variant object:
+//
+// The Variant class delegates serializing and deserializing operations to the
+// contained object. Helper functions to do these operations are provided for
+// POD data types, tensorflow::Tensor, and protocol buffer objects. However,
+// other classes have to provide Encode/Decode functions to do handle
+// serialization.
+//
+// Objects stored in a Variant object often contain references to other
+// tensorflow::Tensors of primitive types (Eg., a list of tensorflow::Tensors).
+// To efficiently support those use cases, a structure is imposed on the
+// serialization format. Namely, classes should serialize their contents in to a
+// VariantTensorData object:
+//
+// struct VariantTensorData {
+//   string type_name;
+//   string metadata;
+//   std::vector<Tensor> tensors;
+// };
+//
+// Objects with references to other Tensors can simply store those tensors in
+// the `tensors` field, and serialize other metadata content in to the
+// `metadata` field.
+//
+// Serialization example:
+//
+// Foo f = Foo {...};
+// Variant x = f;
+// string serialized_f;
+// x.Encode(&serialized_f);
+//
+// Variant y = Foo(); // default constructed Foo.
+// y.Decode(&serialized_f);
+// EXPECT_EQ(*x.get<Foo>(), *y.get<Foo>());
+//
+class Variant {
+ public:
+  constexpr Variant() noexcept = default;
+
+  Variant(const Variant& other)
+      : value_(other.is_empty() ? std::unique_ptr<ValueInterface>()
+                                : other.value_->Clone()) {}
+
+  Variant(Variant&& other) noexcept = default;
+
+  // Make sure that the type is CopyConstructible and not a tensorflow::Variant
+  // object itself. We want the copy constructor to be chosen for the
+  // tensorflow::Variant case.
+  template <typename T, typename VT = typename std::decay<T>::type,
+            typename std::enable_if<!std::is_same<Variant, VT>::value &&
+                                        std::is_copy_constructible<VT>::value,
+                                    void>::type* = nullptr>
+  Variant(T&& value)  // NOLINT
+      : value_(new Value<VT>(in_place, std::forward<T>(value))) {}
+
+  Variant& operator=(const Variant& rhs) {
+    Variant(rhs).swap(*this);
+    return *this;
+  }
+
+  Variant& operator=(Variant&& rhs) noexcept {
+    Variant(std::move(rhs)).swap(*this);
+    return *this;
+  }
+
+  bool is_empty() const { return value_ == nullptr; }
+
+  void clear() noexcept { value_.reset(); }
+
+  void swap(Variant& other) noexcept { value_.swap(other.value_); }
+
+  TypeIndex TypeId() const {
+    const TypeIndex VoidTypeIndex = MakeTypeIndex<void>();
+    if (is_empty()) {
+      return VoidTypeIndex;
+    }
+    return value_->TypeId();
+  }
+
+  template <typename T>
+  T* get() {
+    const TypeIndex TTypeIndex = MakeTypeIndex<T>();
+    if (is_empty() || (TTypeIndex != TypeId())) {
+      return nullptr;
+    }
+    return std::addressof(static_cast<Variant::Value<T>*>(value_.get())->value);
+  }
+
+  template <typename T>
+  const T* get() const {
+    const TypeIndex TTypeIndex = MakeTypeIndex<T>();
+    if (is_empty() || (TTypeIndex != TypeId())) {
+      return nullptr;
+    }
+    return std::addressof(
+        static_cast<const Variant::Value<T>*>(value_.get())->value);
+  }
+
+  string TypeName() const {
+    if (is_empty()) {
+      return "";
+    }
+    return value_->TypeName();
+  }
+
+  // Serialize the contents of the stored object into `data`.
+  void Encode(VariantTensorData* data) const {
+    if (!is_empty()) {
+      value_->Encode(data);
+    }
+  }
+
+  // Deserialize `data` and update the stored object.
+  bool Decode(const VariantTensorData& data) {
+    if (!is_empty()) {
+      return value_->Decode(data);
+    }
+    return true;
+  }
+
+  // Helper methods to directly serialize/deserialize from strings.
+  void Encode(string* buf) const {
+    if (!is_empty()) {
+      value_->Encode(buf);
+    }
+  }
+  bool Decode(const string& buf) {
+    if (!is_empty()) {
+      return value_->Decode(buf);
+    }
+    return true;
+  }
+
+ private:
+  struct in_place_t {};
+  static constexpr in_place_t in_place{};
+
+  struct ValueInterface {
+    virtual ~ValueInterface() = default;
+    virtual TypeIndex TypeId() const = 0;
+    virtual void* RawPtr() = 0;
+    virtual const void* RawPtr() const = 0;
+    virtual std::unique_ptr<ValueInterface> Clone() const = 0;
+    virtual string TypeName() const = 0;
+    virtual void Encode(VariantTensorData* data) const = 0;
+    virtual bool Decode(const VariantTensorData& data) = 0;
+    virtual void Encode(string* buf) const = 0;
+    virtual bool Decode(const string& data) = 0;
+  };
+
+  template <typename T>
+  struct Value : ValueInterface {
+    template <class... Args>
+    explicit Value(in_place_t /*tag*/, Args&&... args)
+        : value(std::forward<Args>(args)...) {}
+
+    TypeIndex TypeId() const override {
+      const TypeIndex value_type_index =
+          MakeTypeIndex<typename std::decay<T>::type>();
+      return value_type_index;
+    }
+
+    void* RawPtr() override { return &value; }
+
+    const void* RawPtr() const override { return &value; }
+
+    std::unique_ptr<ValueInterface> Clone() const override {
+      return std::unique_ptr<ValueInterface>(new Value(in_place, value));
+    }
+
+    string TypeName() const override { return TypeNameVariant(value); }
+
+    void Encode(VariantTensorData* data) const override {
+      EncodeVariant(value, data);
+    }
+
+    bool Decode(const VariantTensorData& data) override {
+      return DecodeVariant(data, &value);
+    }
+
+    void Encode(string* buf) const override { EncodeVariant(value, buf); }
+
+    bool Decode(const string& buf) override {
+      return DecodeVariant(buf, &value);
+    }
+
+    T value;
+  };
+
+  std::unique_ptr<ValueInterface> value_;
+};
+
+template <>
+void* Variant::get();
+
+template <>
+const void* Variant::get() const;
+
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_FRAMEWORK_VARIANT_H_
diff --git a/tensorflow/core/framework/variant_encode_decode.h b/tensorflow/core/framework/variant_encode_decode.h
new file mode 100644
index 0000000000..09309aa7e5
--- /dev/null
+++ b/tensorflow/core/framework/variant_encode_decode.h
@@ -0,0 +1,220 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
+#define TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
+
+#include <iostream>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/platform/protobuf.h"
+
+namespace tensorflow {
+
+// The serialization format for Variant objects. Objects with references to
+// other Tensors can simply store those tensors in the `tensors` field, and
+// serialize other metadata content in to the `metadata` field. Objects can
+// optionally set the `type_name` for type-checking before deserializing an
+// object.
+struct VariantTensorData {
+  string type_name;
+  string metadata;
+  std::vector<Tensor> tensors;
+  void ToProto(VariantTensorDataProto* proto) const;
+  bool FromProto(const VariantTensorDataProto& proto);
+};
+
+// Type used for tag-dispatch of the Encode/Decode Variant implementations. This
+// template can determine whether the first type parameter `T` is one of the
+// following:
+//
+// * A POD type (TypeResolver<T, true>)
+// * A tensorflow::Tensor (TypeResolver<T, false, true>)
+// * A protocol buffer (TypeResolver<T, false, false, true>)
+// * None of the above (TypeResolver<T, false, false, false>)
+//
+template <typename T, bool = std::is_pod<typename std::decay<T>::type>::value,
+          bool = std::is_same<typename std::decay<T>::type,
+                              ::tensorflow::Tensor>::value,
+          bool = std::is_base_of<protobuf::MessageLite,
+                                 typename std::decay<T>::type>::value>
+struct TypeResolver {};
+
+// Specialization for POD type
+template <typename T>
+void EncodeVariantImpl(T&& value, TypeResolver<T, true /* is_pod */>,
+                       VariantTensorData* data) {
+  data->metadata.assign(reinterpret_cast<const char*>(&value), sizeof(value));
+}
+
+// Specialization for tensorflow::Tensor
+template <typename T>
+void EncodeVariantImpl(T&& value,
+                       TypeResolver<T, false /* is_pod */, true /* Tensor */>,
+                       VariantTensorData* data) {
+  data->tensors.clear();
+  data->tensors.push_back(value);
+}
+
+// Specialization for protobuf
+template <typename T>
+void EncodeVariantImpl(T&& value,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    true /* protobuf */>,
+                       VariantTensorData* data) {
+  value.SerializeToString(&data->metadata);
+}
+
+// Specialization for other types
+template <typename T>
+void EncodeVariantImpl(T&& value,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    false /* protobuf */>,
+                       VariantTensorData* data) {
+  value.Encode(data);
+}
+
+// Specialization for POD type
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, true /* is_pod */>, T* value) {
+  std::copy_n(data.metadata.data(), sizeof(*value),
+              reinterpret_cast<char*>(value));
+  return true;
+}
+
+// Specialization for tensorflow::Tensor
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, false /* is_pod */, true /* Tensor */>,
+                       T* value) {
+  *value = data.tensors[0];
+  return true;
+}
+
+// Specialization for protobuf
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    true /* protobuf */>,
+                       T* value) {
+  return value->ParseFromString(data.metadata);
+}
+
+// Specialization for other types
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    false /* protobuf */>,
+                       T* value) {
+  return value->Decode(data);
+}
+
+template <typename C, typename = void>
+struct has_type_name : std::false_type {};
+
+template <typename C>
+struct has_type_name<
+    C, typename std::enable_if<std::is_same<
+           decltype(std::declval<C>().TypeName()), string>::value>::type>
+    : std::true_type {};
+
+template <typename T, bool = has_type_name<typename std::decay<T>::type>::value,
+          bool = std::is_same<typename std::decay<T>::type,
+                              ::tensorflow::Tensor>::value,
+          bool = std::is_base_of<protobuf::MessageLite,
+                                 typename std::decay<T>::type>::value>
+struct TypeNameResolver {};
+
+template <typename T>
+string TypeNameVariantImpl(T&& value,
+                           TypeNameResolver<T, true /* has_type_name */>) {
+  return value.TypeName();
+}
+
+template <typename T>
+string TypeNameVariantImpl(
+    T&& value,
+    TypeNameResolver<T, false /* has_type_name */, true /* Tensor */>) {
+  return "tensorflow::Tensor";
+}
+
+template <typename T>
+string TypeNameVariantImpl(
+    T&& value, TypeNameResolver<T, false /* has_type_name */,
+                                false /* Tensor */, true /* protobuf */>) {
+  return value.GetTypeName();
+}
+
+template <typename T>
+string TypeNameVariantImpl(
+    T&& value, TypeNameResolver<T, false /* has_type_name */,
+                                false /* Tensor */, false /* protobuf */>) {
+  return value.TypeName();
+}
+
+template <typename T>
+string TypeNameVariant(T&& value) {
+  return TypeNameVariantImpl(std::forward<T>(value), TypeNameResolver<T>());
+}
+
+template <typename T>
+void EncodeVariant(T&& value, VariantTensorData* data) {
+  EncodeVariantImpl(std::forward<T>(value), TypeResolver<T>(), data);
+}
+
+template <typename T>
+bool DecodeVariant(const VariantTensorData& data, T* value) {
+  return DecodeVariantImpl(data, TypeResolver<T>(), value);
+}
+
+template <typename T>
+void EncodeVariant(T&& value, string* buf) {
+  VariantTensorData data;
+  EncodeVariantImpl(std::forward<T>(value), TypeResolver<T>(), &data);
+  VariantTensorDataProto proto;
+  data.ToProto(&proto);
+  proto.SerializeToString(buf);
+}
+
+template <typename T>
+bool DecodeVariant(const string& buf, T* value) {
+  VariantTensorDataProto proto;
+  if (!proto.ParseFromString(buf)) return false;
+  VariantTensorData data;
+  if (!data.FromProto(proto)) return false;
+  if (!DecodeVariantImpl(data, TypeResolver<T>(), value)) return false;
+  return true;
+}
+
+// Specializations for VariantTensorDataProto
+template <>
+string TypeNameVariant(VariantTensorDataProto&& value);
+template <>
+void EncodeVariant(VariantTensorDataProto&& value, VariantTensorData* data);
+template <>
+bool DecodeVariant(const VariantTensorData& data,
+                   VariantTensorDataProto* value);
+template <>
+void EncodeVariant(VariantTensorDataProto&& value, string* buf);
+template <>
+bool DecodeVariant(const string& buf, VariantTensorDataProto* value);
+
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
diff --git a/tensorflow/core/framework/variant_test.cc b/tensorflow/core/framework/variant_test.cc
new file mode 100644
index 0000000000..c7ffdd28f4
--- /dev/null
+++ b/tensorflow/core/framework/variant_test.cc
@@ -0,0 +1,249 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <vector>
+
+#include "tensorflow/core/framework/variant.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
+
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/core/coding.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+
+namespace {
+
+template <typename T>
+struct Wrapper {
+  T value;
+  string TypeName() const { return "POD"; }
+};
+
+using Int = Wrapper<int>;
+using Float = Wrapper<float>;
+
+}  // end namespace
+
+TEST(VariantTest, Basic) {
+  Variant x;
+  EXPECT_EQ(x.get<void>(), nullptr);
+
+  x = Int{42};
+
+  EXPECT_NE(x.get<void>(), nullptr);
+  EXPECT_NE(x.get<Int>(), nullptr);
+  EXPECT_EQ(x.get<Int>()->value, 42);
+  EXPECT_EQ(x.TypeName(), "POD");
+}
+
+TEST(VariantTest, ConstGet) {
+  Variant x;
+  EXPECT_EQ(x.get<void>(), nullptr);
+
+  x = Int{42};
+
+  const Variant y = x;
+
+  EXPECT_NE(y.get<void>(), nullptr);
+  EXPECT_NE(y.get<Int>(), nullptr);
+  EXPECT_EQ(y.get<Int>()->value, 42);
+}
+
+TEST(VariantTest, Clear) {
+  Variant x;
+  EXPECT_EQ(x.get<void>(), nullptr);
+
+  x = Int{42};
+
+  EXPECT_NE(x.get<void>(), nullptr);
+  EXPECT_NE(x.get<Int>(), nullptr);
+  EXPECT_EQ(x.get<Int>()->value, 42);
+
+  x.clear();
+  EXPECT_EQ(x.get<void>(), nullptr);
+}
+
+TEST(VariantTest, Tensor) {
+  Variant x;
+  Tensor t(DT_FLOAT, {});
+  t.flat<float>()(0) = 42.0f;
+  x = t;
+
+  EXPECT_NE(x.get<Tensor>(), nullptr);
+  EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 42.0f);
+  x.get<Tensor>()->flat<float>()(0) += 1.0f;
+  EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 43.0f);
+  EXPECT_EQ(x.TypeName(), "tensorflow::Tensor");
+}
+
+TEST(VariantTest, TensorProto) {
+  Variant x;
+  TensorProto t;
+  t.set_dtype(DT_FLOAT);
+  t.mutable_tensor_shape()->set_unknown_rank(true);
+  x = t;
+
+  EXPECT_EQ(x.TypeName(), "tensorflow.TensorProto");
+  EXPECT_NE(x.get<TensorProto>(), nullptr);
+  EXPECT_EQ(x.get<TensorProto>()->dtype(), DT_FLOAT);
+  EXPECT_EQ(x.get<TensorProto>()->tensor_shape().unknown_rank(), true);
+}
+
+TEST(VariantTest, CopyValue) {
+  Variant x, y;
+  x = Int{10};
+  y = x;
+
+  EXPECT_EQ(x.get<Int>()->value, 10);
+  EXPECT_EQ(x.get<Int>()->value, y.get<Int>()->value);
+}
+
+TEST(VariantTest, MoveValue) {
+  Variant x;
+  x = []() -> Variant {
+    Variant y;
+    y = Int{10};
+    return y;
+  }();
+  EXPECT_EQ(x.get<Int>()->value, 10);
+}
+
+TEST(VariantTest, TypeMismatch) {
+  Variant x;
+  x = Int{10};
+  EXPECT_EQ(x.get<float>(), nullptr);
+  EXPECT_EQ(x.get<int>(), nullptr);
+  EXPECT_NE(x.get<Int>(), nullptr);
+}
+
+struct TensorList {
+  void Encode(VariantTensorData* data) const { data->tensors = vec; }
+
+  bool Decode(const VariantTensorData& data) {
+    vec = data.tensors;
+    return true;
+  }
+
+  string TypeName() const { return "TensorList"; }
+
+  std::vector<Tensor> vec;
+};
+
+TEST(VariantTest, TensorListTest) {
+  Variant x;
+
+  TensorList vec;
+  for (int i = 0; i < 4; ++i) {
+    Tensor elem(DT_INT32, {1});
+    elem.flat<int>()(0) = i;
+    vec.vec.push_back(elem);
+  }
+
+  for (int i = 0; i < 4; ++i) {
+    Tensor elem(DT_FLOAT, {1});
+    elem.flat<float>()(0) = 2 * i;
+    vec.vec.push_back(elem);
+  }
+
+  x = vec;
+
+  EXPECT_EQ(x.TypeName(), "TensorList");
+  const TensorList& stored_vec = *x.get<TensorList>();
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(stored_vec.vec[i].flat<int>()(0), i);
+  }
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(stored_vec.vec[i + 4].flat<float>()(0), 2 * i);
+  }
+
+  VariantTensorData serialized;
+  x.Encode(&serialized);
+
+  Variant y = TensorList();
+  y.Decode(serialized);
+
+  const TensorList& decoded_vec = *x.get<TensorList>();
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(decoded_vec.vec[i].flat<int>()(0), i);
+  }
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(decoded_vec.vec[i + 4].flat<float>()(0), 2 * i);
+  }
+}
+
+TEST(VariantTest, VariantArray) {
+  Variant x[2];
+  x[0] = Int{2};
+  x[1] = Float{2.0f};
+
+  EXPECT_EQ(x[0].get<Int>()->value, 2);
+  EXPECT_EQ(x[1].get<Float>()->value, 2.0f);
+}
+
+TEST(VariantTest, PodUpdate) {
+  struct Pod {
+    int x;
+    float y;
+
+    string TypeName() const { return "POD"; }
+  };
+
+  Variant x = Pod{10, 20.f};
+  EXPECT_NE(x.get<Pod>(), nullptr);
+  EXPECT_EQ(x.TypeName(), "POD");
+
+  x.get<Pod>()->x += x.get<Pod>()->y;
+  EXPECT_EQ(x.get<Pod>()->x, 30);
+}
+
+TEST(VariantTest, EncodeDecodePod) {
+  struct Pod {
+    int x;
+    float y;
+
+    string TypeName() const { return "POD"; }
+  };
+
+  Variant x;
+  Pod p{10, 20.0f};
+  x = p;
+
+  VariantTensorData serialized;
+  x.Encode(&serialized);
+
+  Variant y;
+  y = Pod();
+  y.Decode(serialized);
+
+  EXPECT_EQ(p.x, y.get<Pod>()->x);
+  EXPECT_EQ(p.y, y.get<Pod>()->y);
+}
+
+TEST(VariantTest, EncodeDecodeTensor) {
+  Variant x;
+  Tensor t(DT_INT32, {});
+  t.flat<int>()(0) = 42;
+  x = t;
+
+  VariantTensorData serialized;
+  x.Encode(&serialized);
+
+  Variant y = Tensor();
+  y.Decode(serialized);
+  EXPECT_EQ(x.get<Tensor>()->flat<int>()(0), y.get<Tensor>()->flat<int>()(0));
+}
+
+}  // end namespace tensorflow
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index d12ad8a04f..5db0fe423c 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -130,6 +130,14 @@ def tf_additional_lib_srcs(exclude = []):
       ], exclude = exclude),
   })
 
+# pylint: disable=unused-argument
+def tf_additional_framework_hdrs(exclude = []):
+  return []
+
+def tf_additional_framework_srcs(exclude = []):
+  return []
+# pylint: enable=unused-argument
+
 def tf_additional_minimal_lib_srcs():
   return [
       "platform/default/integral_types.h",
diff --git a/tensorflow/core/platform/variant_coding.cc b/tensorflow/core/platform/variant_coding.cc
new file mode 100644
index 0000000000..4bcde4f581
--- /dev/null
+++ b/tensorflow/core/platform/variant_coding.cc
@@ -0,0 +1,63 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/platform/variant_coding.h"
+
+#include <vector>
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/lib/core/coding.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+
+namespace tensorflow {
+namespace port {
+
+void EncodeVariantList(const Variant* variant_array, int64 n, string* out) {
+  out->clear();
+  string rest;
+  for (int i = 0; i < n; ++i) {
+    string s;
+    variant_array[i].Encode(&s);
+    core::PutVarint32(out, s.length());
+    strings::StrAppend(&rest, s);
+  }
+  strings::StrAppend(out, rest);
+}
+
+bool DecodeVariantList(const string& in, Variant* variant_array, int64 n) {
+  std::vector<uint32> sizes(n);
+  StringPiece reader(in);
+  int64 total = 0;
+  for (auto& size : sizes) {
+    if (!core::GetVarint32(&reader, &size)) return false;
+    total += size;
+  }
+  if (total != static_cast<int64>(reader.size())) {
+    return false;
+  }
+
+  for (int i = 0; i < n; ++i) {
+    if (variant_array[i].is_empty()) {
+      variant_array[i] = VariantTensorDataProto();
+    }
+    string str(reader.data(), sizes[i]);
+    if (!variant_array[i].Decode(str)) return false;
+    reader.remove_prefix(sizes[i]);
+  }
+  return true;
+}
+
+}  // end namespace port
+}  // end namespace tensorflow
diff --git a/tensorflow/core/platform/variant_coding.h b/tensorflow/core/platform/variant_coding.h
new file mode 100644
index 0000000000..34c2481149
--- /dev/null
+++ b/tensorflow/core/platform/variant_coding.h
@@ -0,0 +1,39 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_PLATFORM_VARIANT_CODING_H_
+#define TENSORFLOW_PLATFORM_VARIANT_CODING_H_
+
+#include "tensorflow/core/framework/variant.h"
+
+#ifdef PLATFORM_GOOGLE
+#include "tensorflow/core/platform/google/variant_cord_coding.h"
+#endif
+
+namespace tensorflow {
+namespace port {
+
+// Encodes an array of Variant objects in to the given string.
+// `variant_array` is assumed to point to an array of `n` Variant objects.
+void EncodeVariantList(const Variant* variant_array, int64 n, string* out);
+
+// Decodes an array of Variant objects from the given string.
+// `variant_array` is assumed to point to an array of `n` Variant objects.
+bool DecodeVariantList(const string& in, Variant* variant_array, int64 n);
+
+}  // end namespace port
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_PLATFORM_VARIANT_CODING_H_
diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
index 4a60c736b5..8b8909a6f8 100644
--- a/tensorflow/go/tensor.go
+++ b/tensorflow/go/tensor.go
@@ -227,6 +227,7 @@ var types = []struct {
 	{reflect.TypeOf(uint16(0)), C.TF_UINT16},
 	{reflect.TypeOf(complex(float64(0), float64(0))), C.TF_COMPLEX128},
 	// TODO(apassos): support DT_RESOURCE representation in go.
+	// TODO(keveman): support DT_VARIANT representation in go.
 }
 
 // shapeAndDataTypeOf returns the data type and shape of the Tensor
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 827e4c53ee..2fd8fc8688 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -208,6 +208,7 @@ _allowed_symbols.extend([
     'uint16',
     'uint8',
     'resource',
+    'variant',
 ])
 
 # Export modules and constants.
diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py
index 00bfae213a..43535a593e 100644
--- a/tensorflow/python/framework/dtypes.py
+++ b/tensorflow/python/framework/dtypes.py
@@ -48,6 +48,7 @@ class DType(object):
   * `tf.quint16`: Quantized 16-bit unsigned integer.
   * `tf.qint32`: Quantized 32-bit signed integer.
   * `tf.resource`: Handle to a mutable resource.
+  * `tf.variant`: Values of arbitrary types.
 
   In addition, variants of these types with the `_ref` suffix are
   defined for reference-typed tensors.
@@ -113,8 +114,11 @@ class DType(object):
 
   @property
   def is_numpy_compatible(self):
-    return (self._type_enum != types_pb2.DT_RESOURCE and
-            self._type_enum != types_pb2.DT_RESOURCE_REF)
+    numpy_incompatible = [types_pb2.DT_VARIANT,
+                          types_pb2.DT_VARIANT_REF,
+                          types_pb2.DT_RESOURCE,
+                          types_pb2.DT_RESOURCE_REF]
+    return self._type_enum not in numpy_incompatible
 
   @property
   def as_numpy_dtype(self):
@@ -284,7 +288,8 @@ class DType(object):
 
   @property
   def size(self):
-    if self._type_enum == types_pb2.DT_RESOURCE:
+    if (self._type_enum == types_pb2.DT_VARIANT or
+        self._type_enum == types_pb2.DT_RESOURCE):
       return 1
     return np.dtype(self.as_numpy_dtype).itemsize
 
@@ -304,6 +309,7 @@ dtype_range = {np.bool_: (False, True),
 
 # Define standard wrappers for the types_pb2.DataType enum.
 resource = DType(types_pb2.DT_RESOURCE)
+variant = DType(types_pb2.DT_VARIANT)
 float16 = DType(types_pb2.DT_HALF)
 half = float16
 float32 = DType(types_pb2.DT_FLOAT)
@@ -325,6 +331,7 @@ qint16 = DType(types_pb2.DT_QINT16)
 quint16 = DType(types_pb2.DT_QUINT16)
 qint32 = DType(types_pb2.DT_QINT32)
 resource_ref = DType(types_pb2.DT_RESOURCE_REF)
+variant_ref = DType(types_pb2.DT_VARIANT_REF)
 bfloat16 = DType(types_pb2.DT_BFLOAT16)
 float16_ref = DType(types_pb2.DT_HALF_REF)
 half_ref = float16_ref
@@ -372,6 +379,7 @@ _INTERN_TABLE = {
     types_pb2.DT_QINT32: qint32,
     types_pb2.DT_BFLOAT16: bfloat16,
     types_pb2.DT_RESOURCE: resource,
+    types_pb2.DT_VARIANT: variant,
     types_pb2.DT_HALF_REF: float16_ref,
     types_pb2.DT_FLOAT_REF: float32_ref,
     types_pb2.DT_DOUBLE_REF: float64_ref,
@@ -392,6 +400,7 @@ _INTERN_TABLE = {
     types_pb2.DT_QINT32_REF: qint32_ref,
     types_pb2.DT_BFLOAT16_REF: bfloat16_ref,
     types_pb2.DT_RESOURCE_REF: resource_ref,
+    types_pb2.DT_VARIANT_REF: variant_ref,
 }
 
 
@@ -417,6 +426,7 @@ _TYPE_TO_STRING = {
     types_pb2.DT_QINT32: "qint32",
     types_pb2.DT_BFLOAT16: "bfloat16",
     types_pb2.DT_RESOURCE: "resource",
+    types_pb2.DT_VARIANT: "variant",
     types_pb2.DT_HALF_REF: "float16_ref",
     types_pb2.DT_FLOAT_REF: "float32_ref",
     types_pb2.DT_DOUBLE_REF: "float64_ref",
@@ -437,6 +447,7 @@ _TYPE_TO_STRING = {
     types_pb2.DT_QINT32_REF: "qint32_ref",
     types_pb2.DT_BFLOAT16_REF: "bfloat16_ref",
     types_pb2.DT_RESOURCE_REF: "resource_ref",
+    types_pb2.DT_VARIANT_REF: "variant_ref",
 }
 _STRING_TO_TF = {value: _INTERN_TABLE[key]
                  for key, value in _TYPE_TO_STRING.items()}
diff --git a/tensorflow/python/framework/dtypes_test.py b/tensorflow/python/framework/dtypes_test.py
index d5a1e61a6f..1e84f1b656 100644
--- a/tensorflow/python/framework/dtypes_test.py
+++ b/tensorflow/python/framework/dtypes_test.py
@@ -27,9 +27,12 @@ from tensorflow.python.platform import googletest
 
 
 def _is_numeric_dtype_enum(datatype_enum):
-  return (datatype_enum != types_pb2.DT_INVALID and
-          datatype_enum != types_pb2.DT_RESOURCE and
-          datatype_enum != types_pb2.DT_RESOURCE_REF)
+  non_numeric_dtypes = [types_pb2.DT_VARIANT,
+                        types_pb2.DT_VARIANT_REF,
+                        types_pb2.DT_INVALID,
+                        types_pb2.DT_RESOURCE,
+                        types_pb2.DT_RESOURCE_REF]
+  return datatype_enum not in non_numeric_dtypes
 
 
 class TypesTest(test_util.TensorFlowTestCase):
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 6a035aba63..314449bb73 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -492,6 +492,10 @@ tf_module {
     name: "variance_scaling_initializer"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "variant"
+    mtype: "<class \'tensorflow.python.framework.dtypes.DType\'>"
+  }
   member {
     name: "zeros_initializer"
     mtype: "<type \'type\'>"
-- 
GitLab


From 387bf2d7ccb2ec98f3346b9d7b187395b742e277 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Thu, 20 Jul 2017 15:06:16 -0700
Subject: [PATCH 1167/1375] fix link

PiperOrigin-RevId: 162671439
---
 .../contrib/learn/python/learn/estimators/run_config.py    | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index 9235a3ce00..0642c5900a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -211,12 +211,7 @@ class ClusterConfig(object):
 class RunConfig(ClusterConfig, core_run_config.RunConfig):
   """This class specifies the configurations for an `Estimator` run.
 
-  This class is the implementation of ${tf.estimator.RunConfig} interface.
-
-  If you're a Google-internal user using command line flags with
-  `learn_runner.py` (for instance, to do distributed training or to use
-  parameter servers), you probably want to use `learn_runner.EstimatorConfig`
-  instead.
+  This class is the implementation of @{tf.estimator.RunConfig} interface.
   """
   _USE_DEFAULT = 0
 
-- 
GitLab


From 2690081406b39d6ecb883edbb24237f0f14b0255 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 15:27:58 -0700
Subject: [PATCH 1168/1375] CPU IR Emitter: Lower slice operations with memcpy.

This changes the way the CPU backend emits slice operations
so that the maximal contiguous blocks get copied with memcpy.

Without this change, LLVM's optimizers prefer to unroll the
elementwise loops emitted for doing slice. This leads to increased
code size. With this change, the inliner only inlines the memcpy
when it is small enough.

This change also adds a test that exercises the logic to find
maximal contiguous blocks.

PiperOrigin-RevId: 162674439
---
 .../compiler/xla/service/cpu/ir_emitter.cc    | 148 ++++++++++++++++--
 .../compiler/xla/service/cpu/ir_emitter.h     |   8 +
 tensorflow/compiler/xla/tests/slice_test.cc   |  39 +++++
 3 files changed, 185 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 674697a97c..3ee417191d 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -56,6 +56,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/bits.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 
@@ -1937,13 +1938,143 @@ Status IrEmitter::HandleSend(HloInstruction* send) {
 }
 
 Status IrEmitter::HandleSlice(HloInstruction* slice, HloInstruction* operand) {
-  if (ShapeUtil::IsScalar(slice->shape())) {
-    TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
-                        EmitTargetAddressForOp(slice));
-    emitted_value_[slice] = target_address;
-    return EmitMemcpy(*operand, *slice);
+  VLOG(2) << "HandleSlice: " << slice->ToString();
+
+  // The code below emits a sequential loop nest. For the parallel backend, use
+  // EmitParallelTargetElementLoop() which respects dynamic loop bounds.
+  if (ShouldEmitParallelLoopFor(*slice)) {
+    return DefaultAction(slice);
+  }
+
+  // The code below assumes the layouts are equal.
+  if (!LayoutUtil::Equal(operand->shape().layout(), slice->shape().layout())) {
+    return DefaultAction(slice);
+  }
+
+  TF_ASSIGN_OR_RETURN(llvm::Value * target_address,
+                      EmitTargetAddressForOp(slice));
+  emitted_value_[slice] = target_address;
+
+  if (ShapeUtil::HasZeroElements(slice->shape())) {
+    return Status::OK();
   }
-  return DefaultAction(slice);
+
+  const Layout& layout = operand->shape().layout();
+  const int64 num_dims = operand->shape().dimensions_size();
+
+  // The slice lowering finds maximal contiguous blocks of memory that can be
+  // copied from the source to the target. This is done by looking at the
+  // source/target layout in minor to major order and do the following:
+  //
+  // * Find an initial segment of dimensions along which the slice uses the
+  //   whole dimension. These are the "inner" dimensions and can be folded into
+  //   the memcpy.
+  //
+  // * Of the remaining dimensions decide which ones require loops.
+  //
+  // * Implement the memcpy within the innermost loop.
+
+  tensorflow::gtl::FlatSet<int64> inner_dims;
+  for (int64 dim : layout.minor_to_major()) {
+    if (operand->shape().dimensions(dim) != slice->shape().dimensions(dim)) {
+      break;
+    }
+    inner_dims.insert(dim);
+  }
+
+  const bool is_trivial_copy = (inner_dims.size() == num_dims);
+  if (is_trivial_copy) {
+    if (ShapeUtil::IsEffectiveScalar(slice->shape())) {
+      return DefaultAction(slice);
+    } else {
+      return EmitMemcpy(*slice, *operand);
+    }
+  }
+
+  // The memcpy will copy elements that are logically this shape (allowed to be
+  // scalar).
+  const Shape element_shape = ShapeUtil::FilterDimensions(
+      [&inner_dims](int64 dim) -> bool { return inner_dims.count(dim); },
+      operand->shape());
+
+  // memcpy_dim is the innermost (in terms of layout) dimension for which the
+  // slice does *not* just copy all the elements along the dimension.
+  const int64 memcpy_dim = layout.minor_to_major(inner_dims.size());
+
+  const bool memcpy_is_contiguous = slice->slice_strides(memcpy_dim) == 1;
+  // The number of logical elements that can be copied in a single call
+  // to memcpy. We can only copy 1 element at a time if there is a non-trivial
+  // stride.
+  const int64 memcpy_elements =
+      memcpy_is_contiguous
+          ? slice->slice_limits(memcpy_dim) - slice->slice_starts(memcpy_dim)
+          : 1;
+
+  if (memcpy_elements == 1 && ShapeUtil::IsEffectiveScalar(element_shape)) {
+    // Avoid using memcpy for copying element by element at a time. This does
+    // not buy us anything and may actually cause LLVM's load/store optimization
+    // to be less effective.
+    return DefaultAction(slice);
+  }
+
+  // Determine the dimensions that get lowered as loops.
+  std::vector<int64> outer_dims;
+  for (int64 i = 0; i < num_dims - inner_dims.size() - 1; ++i) {
+    outer_dims.push_back(LayoutUtil::Major(layout, i));
+  }
+
+  // Is the slice along the memcpy dimension contiguous? If not, then memcpy_dim
+  // needs to be wrapped around a loop as well.
+  if (!memcpy_is_contiguous) {
+    outer_dims.push_back(memcpy_dim);
+  }
+
+  llvm_ir::IrArray target_array(target_address, slice->shape());
+  AddAliasingInformationToIrArray(*slice, &target_array);
+
+  const int64 num_outer_loops = outer_dims.size();
+  llvm_ir::ForLoopNest loops(&ir_builder_);
+  llvm_ir::IrArray::Index target_index =
+      loops.AddLoopsForShapeOnDimensions(slice->shape(), outer_dims, "slice");
+
+  // Only the indices for the outer dimensions have been initialized in
+  // target_index. The rest of the indices should get initialized to 0, since
+  // for the rest of the dimensions the copy writes to the full dimension.
+  for (llvm::Value*& index : target_index) {
+    if (index == nullptr) {
+      index = ir_builder_.getInt64(0);
+    }
+  }
+
+  if (num_outer_loops > 0) {
+    SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_);
+  }
+
+  llvm_ir::IrArray source_array(GetEmittedValueFor(operand), operand->shape());
+
+  const llvm_ir::IrArray::Index source_index = target_index.SourceIndexOfSlice(
+      /*shape=*/slice->shape(), /*starts=*/slice->slice_starts(),
+      /*strides=*/slice->slice_strides(), /*builder=*/&ir_builder_);
+
+  llvm::Value* memcpy_dest = target_array.EmitArrayElementAddress(
+      target_index, &ir_builder_, "slice.dest");
+  llvm::Value* memcpy_source = source_array.EmitArrayElementAddress(
+      source_index, &ir_builder_, "slice.source");
+  const int64 memcpy_bytes =
+      ShapeUtil::ByteSizeOf(element_shape) * memcpy_elements;
+  // TODO(b/63762267): Be more aggressive with `align` by using the GCD of the
+  // element size and buffer alignment.
+  ir_builder_.CreateMemCpy(memcpy_dest, memcpy_source, memcpy_bytes,
+                           /*align=*/1);
+
+  VLOG(2) << "  emitted memcpy of " << memcpy_bytes << " bytes inside "
+          << num_outer_loops << " loops";
+
+  if (num_outer_loops > 0) {
+    SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_);
+  }
+
+  return Status::OK();
 }
 
 Status IrEmitter::HandleDynamicSlice(HloInstruction* dynamic_slice,
@@ -2614,10 +2745,7 @@ Status IrEmitter::EmitTargetElementLoop(
     llvm_ir::IrArray target_array(target_address, target_shape);
     AddAliasingInformationToIrArray(*target_op, &target_array);
 
-    if (num_dynamic_loop_bounds_ > 0 &&
-        target_op == target_op->parent()->root_instruction()) {
-      // Emit parallel loop for root instruction if dynamic outer-dimension loop
-      // bounds were specified.
+    if (ShouldEmitParallelLoopFor(*target_op)) {
       TF_RETURN_IF_ERROR(EmitParallelTargetElementLoop(
           target_shape, element_generator, &target_array));
     } else {
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 4d98528284..1a77f69580 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -449,6 +449,14 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   // emission (EmitParallelTargetElementLoop).
   int64 num_dynamic_loop_bounds_ = 0;
 
+  // Returns whether the given instruction should be emitted as a parallel loop.
+  bool ShouldEmitParallelLoopFor(const HloInstruction& op) const {
+    // Emit parallel loop for root instruction if dynamic outer-dimension loop
+    // bounds were specified.
+    return num_dynamic_loop_bounds_ > 0 &&
+           op.parent()->root_instruction() == &op;
+  }
+
   // This struct contains all the state needed to emit instructions for
   // profiling a computation.
   class ProfilingState {
diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index 091c35df65..c77e892665 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -34,6 +34,45 @@ namespace {
 
 class SliceTest : public ClientLibraryTestBase {};
 
+TEST_F(SliceTest, Slice3x3x3_To_3x3x1_F32) {
+  Array3D<float> values(3, 3, 3);
+  values.FillIota(0);
+
+  ComputationBuilder builder(client_, TestName());
+  auto original = builder.ConstantR3FromArray3D<float>(values);
+  builder.Slice(original, {0, 0, 0}, {3, 3, 1}, {1, 1, 1});
+
+  Array3D<float> expected{
+      {{0.0}, {3.0}, {6.0}}, {{9.0}, {12.0}, {15.0}}, {{18.0}, {21.0}, {24.0}}};
+  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
+}
+
+TEST_F(SliceTest, Slice3x3x3_To_3x1x3_F32) {
+  Array3D<float> values(3, 3, 3);
+  values.FillIota(0);
+
+  ComputationBuilder builder(client_, TestName());
+  auto original = builder.ConstantR3FromArray3D<float>(values);
+  builder.Slice(original, {0, 0, 0}, {3, 1, 3}, {1, 1, 1});
+
+  Array3D<float> expected{
+      {{0.0, 1.0, 2.0}}, {{9.0, 10.0, 11.0}}, {{18.0, 19.0, 20.0}}};
+  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
+}
+
+TEST_F(SliceTest, Slice3x3x3_To_1x3x3_F32) {
+  Array3D<float> values(3, 3, 3);
+  values.FillIota(0);
+
+  ComputationBuilder builder(client_, TestName());
+  auto original = builder.ConstantR3FromArray3D<float>(values);
+  builder.Slice(original, {0, 0, 0}, {1, 3, 3}, {1, 1, 1});
+
+  Array3D<float> expected{
+      {{{0.0, 1.0, 2.0}, {3.0, 4.0, 5.0}, {6.0, 7.0, 8.0}}}};
+  ComputeAndCompareR3<float>(&builder, expected, {}, ErrorSpec(0.000001));
+}
+
 XLA_TEST_F(SliceTest, Slice0x0to0x0F32) {
   ComputationBuilder builder(client_, TestName());
   auto original = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 0));
-- 
GitLab


From 99e439297174c6c109d5eaa97bc727dd5188e481 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 20 Jul 2017 15:32:14 -0700
Subject: [PATCH 1169/1375] Add op which computes exponential-moving-mean and
 exponential-moving-variance.

PiperOrigin-RevId: 162675008
---
 tensorflow/contrib/distributions/BUILD        |  18 ++
 tensorflow/contrib/distributions/__init__.py  |   5 +-
 .../python/kernel_tests/moving_stats_test.py  | 108 ++++++++++++
 .../distributions/python/ops/moving_stats.py  | 166 ++++++++++++++++++
 4 files changed, 296 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/moving_stats_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/moving_stats.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 239cb5ae7c..e29456f4e6 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -168,6 +168,24 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "moving_stats_test",
+    size = "small",
+    srcs = ["python/kernel_tests/moving_stats_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform_test",
+    ],
+    tags = ["nomsan"],  # disable to avoid false positives from scipy.
+)
+
 cuda_py_test(
     name = "mvn_diag_test",
     size = "small",
diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py
index 5386271f77..f4ebb3cb59 100644
--- a/tensorflow/contrib/distributions/__init__.py
+++ b/tensorflow/contrib/distributions/__init__.py
@@ -34,6 +34,7 @@ from tensorflow.contrib.distributions.python.ops.geometric import *
 from tensorflow.contrib.distributions.python.ops.inverse_gamma import *
 from tensorflow.contrib.distributions.python.ops.logistic import *
 from tensorflow.contrib.distributions.python.ops.mixture import *
+from tensorflow.contrib.distributions.python.ops.moving_stats import *
 from tensorflow.contrib.distributions.python.ops.mvn_diag import *
 from tensorflow.contrib.distributions.python.ops.mvn_diag_plus_low_rank import *
 from tensorflow.contrib.distributions.python.ops.mvn_full_covariance import *
@@ -143,7 +144,9 @@ _allowed_symbols = [
     'normal_conjugates_known_scale_posterior',
     'normal_conjugates_known_scale_predictive',
     'softplus_inverse',
-    'percentile'
+    'percentile',
+    'assign_exponential_moving_mean_variance',
+    'exponential_moving_mean_variance',
 ]
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/moving_stats_test.py b/tensorflow/contrib/distributions/python/kernel_tests/moving_stats_test.py
new file mode 100644
index 0000000000..329f168f42
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/moving_stats_test.py
@@ -0,0 +1,108 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for computing moving-average statistics."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.distributions.python.ops import moving_stats
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+rng = np.random.RandomState(0)
+
+
+class MovingReduceMeanVarianceTest(test.TestCase):
+
+  def test_assign_exponential_moving_mean_variance(self):
+    shape = [1, 2]
+    true_mean = np.array([[0., 3.]])
+    true_stddev = np.array([[1.1, 0.5]])
+    with self.test_session() as sess:
+      # Start "x" out with this mean.
+      mean_var = variables.Variable(array_ops.zeros_like(true_mean))
+      variance_var = variables.Variable(array_ops.ones_like(true_stddev))
+      x = random_ops.random_normal(shape, dtype=np.float64, seed=0)
+      x = true_stddev * x + true_mean
+      ema, emv = moving_stats.assign_exponential_moving_mean_variance(
+          mean_var, variance_var, x, decay=0.99)
+
+      self.assertEqual(ema.dtype.base_dtype, dtypes.float64)
+      self.assertEqual(emv.dtype.base_dtype, dtypes.float64)
+
+      # Run 1000 updates; moving averages should be near the true values.
+      variables.global_variables_initializer().run()
+      for _ in range(2000):
+        sess.run([ema, emv])
+
+      [mean_var_, variance_var_, ema_, emv_] = sess.run([
+          mean_var, variance_var, ema, emv])
+      # Test that variables are passed-through.
+      self.assertAllEqual(mean_var_, ema_)
+      self.assertAllEqual(variance_var_, emv_)
+      # Test that values are as expected.
+      self.assertAllClose(true_mean, ema_, rtol=0.005, atol=0.015)
+      self.assertAllClose(true_stddev**2., emv_, rtol=0.06, atol=0.)
+
+      # Change the mean, var then update some more. Moving averages should
+      # re-converge.
+      sess.run([
+          mean_var.assign(np.array([[-1., 2.]])),
+          variance_var.assign(np.array([[2., 1.]])),
+      ])
+      for _ in range(2000):
+        sess.run([ema, emv])
+
+      [mean_var_, variance_var_, ema_, emv_] = sess.run([
+          mean_var, variance_var, ema, emv])
+      # Test that variables are passed-through.
+      self.assertAllEqual(mean_var_, ema_)
+      self.assertAllEqual(variance_var_, emv_)
+      # Test that values are as expected.
+      self.assertAllClose(true_mean, ema_, rtol=0.005, atol=0.015)
+      self.assertAllClose(true_stddev**2., emv_, rtol=0.1, atol=0.)
+
+  def test_exponential_moving_mean_variance(self):
+    shape = [1, 2]
+    true_mean = np.array([[0., 3.]])
+    true_stddev = np.array([[1.1, 0.5]])
+    with self.test_session() as sess:
+      # Start "x" out with this mean.
+      x = random_ops.random_normal(shape, dtype=np.float64, seed=0)
+      x = true_stddev * x + true_mean
+      ema, emv = moving_stats.exponential_moving_mean_variance(
+          x, decay=0.99)
+
+      self.assertEqual(ema.dtype.base_dtype, dtypes.float64)
+      self.assertEqual(emv.dtype.base_dtype, dtypes.float64)
+
+      # Run 1000 updates; moving averages should be near the true values.
+      variables.global_variables_initializer().run()
+      for _ in range(2000):
+        sess.run([ema, emv])
+
+      [ema_, emv_] = sess.run([ema, emv])
+      self.assertAllClose(true_mean, ema_, rtol=0.005, atol=0.015)
+      self.assertAllClose(true_stddev**2., emv_, rtol=0.06, atol=0.)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/moving_stats.py b/tensorflow/contrib/distributions/python/ops/moving_stats.py
new file mode 100644
index 0000000000..ba816d55ef
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/moving_stats.py
@@ -0,0 +1,166 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for computing moving-average statistics."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope
+
+
+__all__ = [
+    "assign_exponential_moving_mean_variance",
+    "exponential_moving_mean_variance",
+]
+
+
+def assign_exponential_moving_mean_variance(
+    mean_var, variance_var, value, decay, name=None):
+  """Compute the exponential-moving mean, variance of a streaming value.
+
+  The exponential moving `mean_var`, `variance_var` updated by `value` is
+  given by the following recurrence relations,
+
+  ```python
+  variance_var = decay * (variance_var + (1-decay) * (value - mean_var)**2)
+  mean_var     = decay * mean_var + (1 - decay) * value
+  ```
+
+  Note: `mean_var` is updated *after* `variance_var`, i.e., `variance_var` uses
+  the lag-1 mean.
+
+  For derivation justification, see equation 143 of:
+    T. Finch, Feb 2009. "Incremental calculation of weighted mean and variance".
+    http://people.ds.cam.ac.uk/fanf2/hermes/doc/antiforgery/stats.pdf
+
+  Args:
+    mean_var: `float`-like `Variable` representing the exponential-moving
+      mean. Same shape as `variance_var` and `value`.
+    variance_var: `float`-like `Variable` representing the exponential-moving
+      variance. Same shape as `mean_var` and `value`.
+    value: `float`-like `Tensor`. Same shape as `mean_var` and `variance_var`.
+    decay: A `float`-like `Tensor`. The moving average decay. Typically close to
+      `1.`, e.g., `0.999`.
+    name: Optional name of the returned operation.
+
+  Returns:
+    mean_var: `Variable` representing the `value`-updated exponential-moving
+      mean.
+    variance_var: `Variable` representing the `value`-updated exponential-moving
+      variance.
+
+  Raises:
+    TypeError: if `mean_var` is not a floating type.
+    TypeError: if `mean_var`, `variance_var`, `value`, `decay` have different
+      `base_dtype`.
+  """
+  with ops.name_scope(name, "assign_exponential_moving_mean_variance",
+                      [variance_var, mean_var, value, decay]):
+    with ops.colocate_with(variance_var):
+      with ops.colocate_with(mean_var):
+        base_dtype = mean_var.dtype.base_dtype
+        if not base_dtype.is_floating:
+          raise TypeError(
+              "mean_var.base_dtype({}) is not a floating-type.".format(
+                  base_dtype.name))
+        if base_dtype != variance_var.dtype.base_dtype:
+          raise TypeError(
+              "mean_var.base_dtype({}) != variance_var.base_dtype({})".format(
+                  base_dtype.name,
+                  variance_var.dtype.base_dtype.name))
+        value = ops.convert_to_tensor(value, dtype=base_dtype, name="value")
+        decay = ops.convert_to_tensor(decay, dtype=base_dtype, name="decay")
+        delta = value - mean_var
+        with ops.control_dependencies([delta]):
+          mean_var = state_ops.assign_add(
+              mean_var,
+              (1. - decay) * delta)
+          variance_var = state_ops.assign_sub(
+              variance_var,
+              (1. - decay) * (variance_var - decay * math_ops.square(delta)))
+        return mean_var, variance_var
+
+
+def exponential_moving_mean_variance(value, decay, collections=None, name=None):
+  """Compute the exponential-moving mean, variance of a streaming value.
+
+  The exponential moving `mean_var`, `variance_var` updated by `value` is
+  given by the following recurrence relations,
+
+  ```python
+  variance_var = decay * (variance_var + (1-decay) * (value - mean_var)**2)
+  mean_var     = decay * mean_var + (1 - decay) * value
+  ```
+
+  Note: `mean_var` is updated *after* `variance_var`, i.e., `variance_var` uses
+  the lag-`1` mean.
+
+  For derivation justification, see equation 143 of:
+    T. Finch, Feb 2009. "Incremental calculation of weighted mean and variance".
+    http://people.ds.cam.ac.uk/fanf2/hermes/doc/antiforgery/stats.pdf
+
+  Unlike `assign_exponential_moving_mean_variance`, this function handles
+  variable creation.
+
+  Args:
+    value: `float`-like `Tensor`. Same shape as `mean_var` and `variance_var`.
+    decay: A `float`-like `Tensor`. The moving average decay. Typically close to
+      `1.`, e.g., `0.999`.
+    collections: Python list of graph-collections keys to which the the internal
+      variables `mean_var` and `variance_var` are added.
+      Default value is `[GraphKeys.GLOBAL_VARIABLES]`.
+    name: Optional name of the returned operation.
+
+  Returns:
+    mean_var: `Variable` representing the `value`-updated exponential-moving
+      mean.
+    variance_var: `Variable` representing the `value`-updated exponential-moving
+      variance.
+
+  Raises:
+    TypeError: if `value_var` is not a floating type.
+    TypeError: if `value`, `decay` have different `base_dtype`.
+  """
+  if collections is None:
+    collections = [ops.GraphKeys.GLOBAL_VARIABLES]
+  with variable_scope.variable_scope(
+      name, "exponential_moving_mean_variance", [value, decay]):
+    value = ops.convert_to_tensor(value, name="value")
+    base_dtype = value.dtype.base_dtype
+    if not base_dtype.is_floating:
+      raise TypeError(
+          "value.base_dtype({}) is not a floating-type.".format(
+              base_dtype.name))
+    decay = ops.convert_to_tensor(decay, dtype=base_dtype, name="decay")
+    variance_var = variable_scope.get_variable(
+        "exponential_moving_variance",
+        shape=value.shape,
+        dtype=value.dtype,
+        initializer=init_ops.zeros_initializer(),
+        trainable=False,
+        collections=collections)
+    mean_var = variable_scope.get_variable(
+        "exponential_moving_mean",
+        shape=value.shape,
+        dtype=value.dtype,
+        initializer=init_ops.zeros_initializer(),
+        trainable=False,
+        collections=collections)
+    return assign_exponential_moving_mean_variance(
+        mean_var, variance_var, value, decay)
-- 
GitLab


From 856f2a5c1296a4256053553b6fd7f9d087f7d7ed Mon Sep 17 00:00:00 2001
From: Sukriti Ramesh <sukritiramesh@google.com>
Date: Thu, 20 Jul 2017 15:40:58 -0700
Subject: [PATCH 1170/1375] Update example to use tf.saved_model API.

PiperOrigin-RevId: 162676113
---
 tensorflow/examples/saved_model/saved_model_half_plus_two.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/examples/saved_model/saved_model_half_plus_two.py b/tensorflow/examples/saved_model/saved_model_half_plus_two.py
index 02afa0b0fc..0d6f1ef655 100644
--- a/tensorflow/examples/saved_model/saved_model_half_plus_two.py
+++ b/tensorflow/examples/saved_model/saved_model_half_plus_two.py
@@ -46,7 +46,6 @@ import sys
 import tensorflow as tf
 
 from tensorflow.python.lib.io import file_io
-from tensorflow.python.saved_model import main_op
 
 FLAGS = None
 
@@ -205,7 +204,8 @@ def _generate_saved_model_for_half_plus_two(export_dir,
           sess, [tf.saved_model.tag_constants.SERVING],
           signature_def_map=signature_def_map,
           assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS),
-          main_op=tf.group(main_op.main_op(), assign_filename_op))
+          main_op=tf.group(tf.saved_model.main_op.main_op(),
+                           assign_filename_op))
     else:
       builder.add_meta_graph_and_variables(
           sess, [tf.saved_model.tag_constants.SERVING],
-- 
GitLab


From 0264e10b784a187662a2d3367f2eea75b55b07e1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 15:52:20 -0700
Subject: [PATCH 1171/1375] Suppress errors during the check of Bazel version.

PiperOrigin-RevId: 162677739
---
 configure | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/configure b/configure
index b7bc87dcb7..288674753b 100755
--- a/configure
+++ b/configure
@@ -163,7 +163,9 @@ function version {
 
 
 bazel version > bazel.version
+set +e
 curr_bazel_version=$(grep -m 1 'Build label:' bazel.version | cut -d ' ' -f3)
+set -e
 rm -f bazel.version
 
 
-- 
GitLab


From f199febfa8e5e7a6bbb93cb74725c8bca99f3c8d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 15:55:52 -0700
Subject: [PATCH 1172/1375] Avoid using a static mutex to prevent
 initialization-order-fiasco.

PiperOrigin-RevId: 162678155
---
 tensorflow/compiler/xla/service/cpu/cpu_runtime.cc | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
index fc85172772..48a30c8078 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
@@ -32,7 +32,10 @@ tensorflow::gtl::FlatMap<tensorflow::StringPiece, void*>* BuiltinMap() {
   return builtin_map;
 }
 
-tensorflow::mutex builtin_map_mutex(tensorflow::LINKER_INITIALIZED);
+tensorflow::mutex* BuiltinMapMutex() {
+  static tensorflow::mutex* builtin_map_mutex = new tensorflow::mutex;
+  return builtin_map_mutex;
+}
 }  // namespace
 
 namespace xla {
@@ -73,7 +76,7 @@ Registrar::Registrar(tensorflow::StringPiece name, void* function_pointer,
   CHECK_EQ(tensorflow::strings::StrCat(kXlaCpuRuntimeSymbolPrefix, base_name),
            name);
 
-  tensorflow::mutex_lock lock(builtin_map_mutex);
+  tensorflow::mutex_lock lock(*BuiltinMapMutex());
   InsertOrDie(BuiltinMap(), name, function_pointer);
 }
 }  // namespace internal
@@ -81,7 +84,7 @@ Registrar::Registrar(tensorflow::StringPiece name, void* function_pointer,
 void* ResolveSymbol(tensorflow::StringPiece name) {
   CHECK(name.starts_with(kXlaCpuRuntimeSymbolPrefix));
 
-  tensorflow::mutex_lock lock(builtin_map_mutex);
+  tensorflow::mutex_lock lock(*BuiltinMapMutex());
   const auto& builtin_map = *BuiltinMap();
   auto lookup_iterator = builtin_map.find(name);
   return lookup_iterator == builtin_map.end() ? nullptr
-- 
GitLab


From d2f8e98650f553009b53d814814fb2d9ead66711 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Thu, 20 Jul 2017 16:11:17 -0700
Subject: [PATCH 1173/1375] Bugfix: Never use env_->device_mgr

The base_rendezvous_mgr handles transferring a tensor using DMAs to non-"host"
devices such as GPUs in the SameWorkerRecvDone function. This function would
use the worker_env's device_mgr to obtain a pointer to the relevant Device
(using the LookupDevice call). In the ClusterSpec-propagation world, using the
environment's device_set means that the devices are not renamed, often
resulting in devices not being found.

This change updates BaseRemoteRendezvous to use the WorkerSession stored when
the BaseRemoteRendezvous is initialized. The WorkerSession has a pointer to a
DeviceMgr that contains the appropriately renamed devices for the given
session the Rendezvous is associated with.

Note: because we have a fast-path host-device-only copy, the original bug does
not show up when using 2 CPU devices. I have added a test to ensure that
transferring between 2 CPU devices works in a ClusterSpec propagation session,
but note that this test does not actually reproduce the motivating bug.

In the process of writing a test for the original bug, I discovered another
latent bug in ClusterSpec propagation where if there were 2 CPU devices
(i.e. due to explicit server configuration to have 2 CPU devices), a DCHECK
could be triggered. The Master::CreateSession would call
`device_set->set_client_device` multiple times (once for each CPU device).

PiperOrigin-RevId: 162680163
---
 .../base_rendezvous_mgr.cc                    |  5 +-
 tensorflow/core/distributed_runtime/master.cc |  5 +-
 .../core/distributed_runtime/worker_env.h     |  4 +
 tensorflow/python/BUILD                       |  1 +
 .../client/session_clusterspec_prop_test.py   | 77 +++++++++++++++++++
 5 files changed, 88 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc b/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc
index e68aea46ec..b0979d4c8d 100644
--- a/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc
+++ b/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc
@@ -248,14 +248,15 @@ void BaseRemoteRendezvous::SameWorkerRecvDone(
     return;
   }
 
+  WorkerSession* sess = session();
   Device* src_device;
-  Status s = env_->device_mgr->LookupDevice(parsed.src_device, &src_device);
+  Status s = sess->device_mgr->LookupDevice(parsed.src_device, &src_device);
   if (!s.ok()) {
     done(s);
     return;
   }
   Device* dst_device;
-  s = env_->device_mgr->LookupDevice(parsed.dst_device, &dst_device);
+  s = sess->device_mgr->LookupDevice(parsed.dst_device, &dst_device);
   if (!s.ok()) {
     done(s);
     return;
diff --git a/tensorflow/core/distributed_runtime/master.cc b/tensorflow/core/distributed_runtime/master.cc
index e3f23ef0dd..4ff2d0f5e3 100644
--- a/tensorflow/core/distributed_runtime/master.cc
+++ b/tensorflow/core/distributed_runtime/master.cc
@@ -372,7 +372,7 @@ void Master::CreateSession(const CreateSessionRequest* req,
         DeviceNameUtils::ParsedName name = d->parsed_name();
         if (name.job == *worker_cache_factory_options.job_name &&
             name.task == worker_cache_factory_options.task_index &&
-            name.type == "CPU") {
+            name.type == "CPU" && name.id == 0) {
           device_set->set_client_device(d.get());
         }
       }
@@ -399,7 +399,8 @@ void Master::CreateSession(const CreateSessionRequest* req,
       }
     }
 
-    CHECK(device_set->client_device());
+    CHECK(device_set->client_device()) << "No client device found. Missing "
+                                       << "CPU:0 device?";
 
     SessionOptions options;
     options.config = req->config();
diff --git a/tensorflow/core/distributed_runtime/worker_env.h b/tensorflow/core/distributed_runtime/worker_env.h
index f09bea328f..793d58c8a1 100644
--- a/tensorflow/core/distributed_runtime/worker_env.h
+++ b/tensorflow/core/distributed_runtime/worker_env.h
@@ -48,6 +48,10 @@ struct WorkerEnv {
 
   // device_mgr manages local devices (cpu and gpu). The WorkerService
   // is the network interface for managed devices.
+  //
+  // Note: Please use the device_mgr associated with your session if appropriate
+  // instead of this one. Using this device_mgr does not support ClusterSpec
+  // propagated sessions.
   DeviceMgr* device_mgr = nullptr;
 
   // A set of rendezvous keyed by step ids.
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 922f9baf78..7882e088d0 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3074,6 +3074,7 @@ py_test(
     deps = [
         ":array_ops",
         ":client",
+        ":client_testlib",
         ":framework",
         ":framework_for_generated_wrappers",
         ":framework_test_lib",
diff --git a/tensorflow/python/client/session_clusterspec_prop_test.py b/tensorflow/python/client/session_clusterspec_prop_test.py
index f40d3f1872..1ce2b7d7c3 100644
--- a/tensorflow/python/client/session_clusterspec_prop_test.py
+++ b/tensorflow/python/client/session_clusterspec_prop_test.py
@@ -38,6 +38,7 @@ from tensorflow.python.ops import resource_variable_ops  # pylint: disable=unuse
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import googletest
+from tensorflow.python.platform import test
 from tensorflow.python.training import server_lib
 
 ops._USE_C_API = True
@@ -135,6 +136,82 @@ class SessionClusterSpecPropagationTest(test_util.TensorFlowTestCase):
                          dev_stats.device and 'Const' == node_stats.node_name
                      ]))
 
+  def testFullDeviceNames(self):
+    server1 = server_lib.Server.create_local_server()
+    server2 = server_lib.Server.create_local_server()
+    cluster_def = cluster_pb2.ClusterDef()
+    job = cluster_def.job.add()
+    job.name = 'renamed_worker'
+    job.tasks[0] = server1.target[len('grpc://'):]
+    job.tasks[1] = server2.target[len('grpc://'):]
+    config = config_pb2.ConfigProto(cluster_def=cluster_def)
+
+    with ops.Graph().as_default() as g, ops.device(
+        '/job:renamed_worker/replica:0/task:1/device:CPU:0'):
+      const = constant_op.constant(17)
+    sess = session.Session(server1.target, config=config, graph=g)
+    run_options = config_pb2.RunOptions(
+        trace_level=config_pb2.RunOptions.FULL_TRACE)
+    run_metadata = config_pb2.RunMetadata()
+    output = sess.run(const, options=run_options, run_metadata=run_metadata)
+    self.assertEqual(17, output)
+    self.assertEqual(1,
+                     len([
+                         node_stats
+                         for dev_stats in run_metadata.step_stats.dev_stats
+                         for node_stats in dev_stats.node_stats
+                         if '/job:renamed_worker/replica:0/task:1/device:CPU:0'
+                         == dev_stats.device and 'Const' == node_stats.node_name
+                     ]))
+
+  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
+  def testMultipleLocalDevices(self):
+    # Note: CPU->CPU transfers have a fast-path in
+    # BaseRemoteRendezvous::SameWorkerRecvDone that means the test doesn't
+    # actually capture the motivating bug unless run on a GPU machine.
+    #
+    # Example error message (before bugfix -- linebreaks added because  lint):
+    #
+    # W0718 17:14:41.521534  190121 device_mgr.cc:107] Unknown device:
+    #     /job:worker/replica:0/task:0/device:CPU:0 all devices:
+    #     /job:local/replica:0/task:0/gpu:0,
+    #     /job:local/replica:0/task:0/device:GPU:0,
+    #     /job:local/replica:0/task:0/cpu:1, CPU:0, GPU:0,
+    #     /job:local/replica:0/task:0/device:CPU:1,
+    #     /job:local/replica:0/task:0/device:CPU:0, CPU:1,
+    #     /job:local/replica:0/task:0/cpu:0
+    server_config = config_pb2.ConfigProto(device_count={'CPU': 2})
+    server1 = server_lib.Server.create_local_server(config=server_config)
+    server2 = server_lib.Server.create_local_server(config=server_config)
+    cluster_def = cluster_pb2.ClusterDef()
+    job = cluster_def.job.add()
+    job.name = 'worker'
+    job.tasks[0] = server1.target[len('grpc://'):]
+    job.tasks[1] = server2.target[len('grpc://'):]
+    config = config_pb2.ConfigProto(cluster_def=cluster_def)
+
+    with ops.Graph().as_default() as g:
+      with ops.device('/job:worker/task:1/cpu:1'):
+        input1 = constant_op.constant(17, dtypes.float32)
+      with ops.device('/job:worker/task:0/cpu:1'):
+        input2 = constant_op.constant(3, dtypes.float32)
+      with ops.device('/job:worker/task:1/cpu:0'):
+        sum1 = input1 + input2
+
+      if test.is_gpu_available():
+        device_str = '/job:worker/task:0/gpu:0'
+      else:
+        device_str = '/job:worker/task:0/cpu:1'
+      with ops.device(device_str):
+        sum2 = input2 + input1
+
+      with ops.device('/job:worker/task:0/cpu:0'):
+        sum3 = sum1 + sum2
+    sess = session.Session(server1.target, config=config, graph=g)
+    output = sess.run(sum3)
+    self.assertEqual(40, output)
+
+  @test_util.disable_c_api  # Operation._set_device doesn't work with C API
   def testLegacyDeviceNames(self):
     server1 = server_lib.Server.create_local_server()
     server2 = server_lib.Server.create_local_server()
-- 
GitLab


From e4d6cb9744d94d9a84369f4b8afda6ce89f1d1d3 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 20 Jul 2017 16:19:22 -0700
Subject: [PATCH 1174/1375] Change contrib/bayesflow/custom_grad tests to
 assertAllClose.

PiperOrigin-RevId: 162681035
---
 .../bayesflow/python/kernel_tests/custom_grad_test.py     | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py
index 5f8f7f692c..a95df31ac1 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py
@@ -48,8 +48,8 @@ class CustomGradientTest(test.TestCase):
       gx = gradients_impl.gradients(fx, x)[0]
       [fx_, gx_] = sess.run([fx, gx])
 
-      self.assertAllEqual(f(x_), fx_)
-      self.assertAllEqual(g(x_), gx_)
+      self.assertAllClose(f(x_), fx_)
+      self.assertAllClose(g(x_), gx_)
 
   def test_works_correctly_both_f_g_zero(self):
     with self.test_session() as sess:
@@ -62,8 +62,8 @@ class CustomGradientTest(test.TestCase):
       gx = gradients_impl.gradients(fx, x)[0]
       [fx_, gx_] = sess.run([fx, gx])
 
-      self.assertAllEqual(f(x_), fx_)
-      self.assertAllEqual(g(x_), gx_)
+      self.assertAllClose(f(x_), fx_)
+      self.assertAllClose(g(x_), gx_)
 
   def test_works_correctly_vector_of_vars(self):
     with self.test_session() as sess:
-- 
GitLab


From 41ae3a8c0f64cb316a64df21a2ac789f3e5bb008 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 16:25:58 -0700
Subject: [PATCH 1175/1375] Updating the regex match to catch p100 for run and
 gather logs."

PiperOrigin-RevId: 162681705
---
 tensorflow/tools/test/run_and_gather_logs_lib.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/test/run_and_gather_logs_lib.py b/tensorflow/tools/test/run_and_gather_logs_lib.py
index 570e09f165..c798dd5de7 100644
--- a/tensorflow/tools/test/run_and_gather_logs_lib.py
+++ b/tensorflow/tools/test/run_and_gather_logs_lib.py
@@ -135,7 +135,7 @@ def run_and_gather_logs(name, test_name, test_args,
   gpu_config = gpu_info_lib.gather_gpu_devices()
   if gpu_config:
     gpu_name = gpu_config[0].model
-    gpu_short_name_match = re.search(r"Tesla [KP][4,8]0", gpu_name)
+    gpu_short_name_match = re.search(r"Tesla (K40|K80|P100)", gpu_name)
     if gpu_short_name_match:
       gpu_short_name = gpu_short_name_match.group(0)
       test_adjusted_name = name + "|" + gpu_short_name.replace(" ", "_")
-- 
GitLab


From 9cf6fe1ad3843383b527ee718203763036c6e396 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 20 Jul 2017 17:43:18 -0700
Subject: [PATCH 1176/1375] Allow access to the module BufferLiveness applies
 to.

PiperOrigin-RevId: 162690202
---
 tensorflow/compiler/xla/service/buffer_liveness.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/compiler/xla/service/buffer_liveness.h b/tensorflow/compiler/xla/service/buffer_liveness.h
index 24807551f1..70d642b40c 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.h
+++ b/tensorflow/compiler/xla/service/buffer_liveness.h
@@ -67,6 +67,8 @@ class BufferLiveness {
   // Returns the underlying hlo ordering used for this liveness analysis.
   const HloOrdering& hlo_ordering() const { return *hlo_ordering_; }
 
+  const HloModule& module() const { return *module_; }
+
   string ToString() const;
 
   static Colorer DefaultColorer() {
-- 
GitLab


From 386f4aef0d05489cc3a4cdc01470533849569dba Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Thu, 20 Jul 2017 18:09:16 -0700
Subject: [PATCH 1177/1375] Add
 hlo_graph_dumper::GetInstructionsInNeighborhood, which lets you graph the
 nodes that are "near" a particular node.

PiperOrigin-RevId: 162692461
---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 163 ++++++++++++++++--
 .../compiler/xla/service/hlo_graph_dumper.h   |   7 +
 2 files changed, 157 insertions(+), 13 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 6138496fe5..a712dc3b33 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -64,6 +64,32 @@ enum ColorScheme {
   kYellow,
 };
 
+// Used to indicate how we should treat a given HLOInstruction in the graph --
+// should we treat it like normal, hide it, or highlight it?
+enum NodeFilterResult { kNormalNode, kHideNode, kHighlightNode };
+
+// NodeFilter is essentially a map from HloInstruction*s to NodeFilterResult.
+// It lets callers tell the graph-drawing routines which nodes they want to be
+// shown, hidden, or highlighted.
+class NodeFilter {
+ public:
+  NodeFilter() : filter_([](const HloInstruction*) { return kNormalNode; }) {}
+
+  explicit NodeFilter(
+      std::function<NodeFilterResult(const HloInstruction* instr)> filter)
+      : filter_(std::move(filter)) {}
+
+  bool Show(const HloInstruction* instr) const {
+    return filter_(instr) != kHideNode;
+  }
+  bool Highlight(const HloInstruction* instr) const {
+    return filter_(instr) == kHighlightNode;
+  }
+
+ private:
+  std::function<NodeFilterResult(const HloInstruction* instr)> filter_;
+};
+
 // Given a ColorScheme, returns an attribute string for a node of that color.
 // Sets the node's fill, stroke, and text colors.
 //
@@ -134,7 +160,8 @@ string InstructionSequenceGraph(
     const std::list<std::unique_ptr<HloInstruction>>& instructions,
     bool show_addresses, bool show_layouts,
     std::vector<string>* intercomputation_edges,
-    const HloExecutionProfile* hlo_execution_profile) {
+    const HloExecutionProfile* hlo_execution_profile,
+    const NodeFilter& filter) {
   string graph_body;
 
   // Create a single "record" node for the parameters. This node is a
@@ -157,6 +184,9 @@ string InstructionSequenceGraph(
     param_node_name =
         StrCat("parameters_", InstructionId(param_instructions[0]));
     for (auto& param : param_instructions) {
+      if (!filter.Show(param)) {
+        continue;
+      }
       string label = StrCat(param->parameter_name(), "\\n",
                             ShapeUtil::HumanString(param->shape()));
       if (show_addresses) {
@@ -178,6 +208,9 @@ string InstructionSequenceGraph(
   }
 
   for (auto& instruction : instructions) {
+    if (!filter.Show(instruction.get())) {
+      continue;
+    }
     ColorScheme color = kYellow;
     string shape = "box";
     string name =
@@ -325,7 +358,7 @@ string InstructionSequenceGraph(
       case HloOpcode::kReducePrecision:
         // Make ReducePrecision ops a bit more visible, since typically they
         // will be inserted as modifications to an existing graph.
-        color = kDarkRed;
+        color = kRed;
         break;
     }
 
@@ -371,6 +404,12 @@ string InstructionSequenceGraph(
       }
     }
 
+    // If this node is highlighted, override its formatting.
+    if (filter.Highlight(instruction.get())) {
+      shape = "diamond";
+      color = kDarkRed;
+    }
+
     Appendf(&graph_body, "%s [label=<%s>, shape=%s, %s];\n",
             InstructionId(instruction.get()).c_str(), label.c_str(),
             shape.c_str(), NodeColorAttributes(color).c_str());
@@ -378,6 +417,10 @@ string InstructionSequenceGraph(
     // Create edges from the instruction's operands to the instruction.
     int64 operand_number = 0;
     for (auto* operand : instruction->operands()) {
+      if (!filter.Show(operand)) {
+        ++operand_number;
+        continue;
+      }
       string src;
       if (operand->opcode() == HloOpcode::kParameter) {
         // If operand is a parameter, then select the proper partition (port) in
@@ -405,10 +448,11 @@ string InstructionSequenceGraph(
       StrAppend(&graph_body,
                 "label=<<b>fused expression</b>>;\nstyle=\"rounded,filled\";\n"
                 "color=lightgrey;\n");
-      StrAppend(&graph_body, InstructionSequenceGraph(
-                                 instruction->fused_instructions(),
-                                 show_addresses, show_layouts,
-                                 intercomputation_edges, hlo_execution_profile),
+      StrAppend(&graph_body,
+                InstructionSequenceGraph(instruction->fused_instructions(),
+                                         show_addresses, show_layouts,
+                                         intercomputation_edges,
+                                         hlo_execution_profile, NodeFilter()),
                 "}\n");
       string fusion_edge =
           StrCat(InstructionId(instruction->fused_expression_root()), " -> ",
@@ -450,7 +494,8 @@ svg text {
 string ComputationToDotGraph(const HloComputation& computation,
                              const string& label, bool show_addresses,
                              bool show_layouts,
-                             const HloExecutionProfile* hlo_execution_profile) {
+                             const HloExecutionProfile* hlo_execution_profile,
+                             const NodeFilter& filter) {
   string graph_label = StrCat(label, "<br/>", computation.name());
   if (hlo_execution_profile != nullptr) {
     auto cycles = hlo_execution_profile->total_cycles_executed(computation);
@@ -467,12 +512,31 @@ stylesheet="%s"
 )",
       graph_label.c_str(), dot_stylesheet);
 
+  std::unordered_set<const HloComputation*> computations_to_dump;
+  for (const auto& instr : computation.instructions()) {
+    if (!filter.Show(instr.get())) {
+      continue;
+    }
+    if (instr->opcode() == HloOpcode::kFusion) {
+      computations_to_dump.insert(instr->fused_instructions_computation());
+    }
+    for (const HloComputation* computation : instr->called_computations()) {
+      computations_to_dump.insert(computation);
+    }
+  }
+
   // Emit embedded computations as subgraph clusters.
   std::vector<string> intercomputation_edges;
-  for (auto embedded : computation.MakeEmbeddedComputationsList()) {
+  for (const HloComputation* embedded :
+       computation.MakeEmbeddedComputationsList()) {
+    if (!computations_to_dump.count(embedded)) {
+      continue;
+    }
+    // Don't pass our filter down into the subcomputation -- always render the
+    // whole thing.
     string graph_body = InstructionSequenceGraph(
         embedded->instructions(), show_addresses, show_layouts,
-        &intercomputation_edges, hlo_execution_profile);
+        &intercomputation_edges, hlo_execution_profile, NodeFilter());
     Appendf(&graph,
             "subgraph cluster_%s "
             "{\nstyle=rounded;label=<<b>%s</b>>;labelloc=t;\n%s}\n",
@@ -482,7 +546,7 @@ stylesheet="%s"
   StrAppend(&graph,
             InstructionSequenceGraph(computation.instructions(), show_addresses,
                                      show_layouts, &intercomputation_edges,
-                                     hlo_execution_profile));
+                                     hlo_execution_profile, filter));
 
   // Edges between computations (subgraph clusters) must be emitted last for the
   // graph to be rendered properly for some reason.
@@ -555,6 +619,51 @@ class FileGraphRenderer : public GraphRendererInterface {
   }
 };
 
+// Gets roughly all instructions whose distance from root is <= radius.
+std::unordered_set<const HloInstruction*> GetInstructionsInNeighborhood(
+    const HloInstruction& root, int64 radius) {
+  std::unordered_set<const HloInstruction*> ret;
+
+  std::deque<std::pair<const HloInstruction*, /*depth*/ int64>> worklist;
+  worklist.push_back({&root, 0});
+
+  while (!worklist.empty()) {
+    const HloInstruction* instr;
+    int64 depth;
+    std::tie(instr, depth) = worklist.front();
+    worklist.pop_front();
+
+    ret.insert(instr);
+    if (depth == radius) {
+      continue;
+    }
+
+    // Don't traverse into tuples' operands unless the tuple is the root.
+    // Usually a tuple is the bottommost node in the graph, and so its operands
+    // are not interesting to the graph at hand.
+    if (instr == &root || instr->opcode() != HloOpcode::kTuple) {
+      for (const HloInstruction* operand : instr->operands()) {
+        if (ret.find(operand) == ret.end()) {
+          worklist.push_back({operand, depth + 1});
+        }
+      }
+    }
+
+    // If you're looking at node X, it's probably not interesting that node Y
+    // also happens to use the same constant, so we don't traverse into
+    // constants' users.
+    if (instr->opcode() != HloOpcode::kConstant) {
+      for (const HloInstruction* user : instr->users()) {
+        if (ret.find(user) == ret.end()) {
+          worklist.push_back({user, depth + 1});
+        }
+      }
+    }
+  }
+
+  return ret;
+}
+
 XLA_REGISTER_GRAPH_RENDERER(FileGraphRenderer, 0);
 
 }  // namespace
@@ -575,9 +684,10 @@ string DumpGraph(const HloComputation& computation, const string& label,
     graph_url = FileGraphRenderer().RenderGraph(
         graph, GraphRendererInterface::TF_GRAPHDEF, debug_options);
   } else {
-    graph = ComputationToDotGraph(
-        computation, label, debug_options.xla_hlo_graph_addresses(),
-        debug_options.xla_hlo_graph_layout(), hlo_execution_profile);
+    graph = ComputationToDotGraph(computation, label,
+                                  debug_options.xla_hlo_graph_addresses(),
+                                  debug_options.xla_hlo_graph_layout(),
+                                  hlo_execution_profile, NodeFilter());
     graph_url = GetGraphRenderer()->RenderGraph(
         graph, GraphRendererInterface::DOT_GRAPH, debug_options);
   }
@@ -586,6 +696,33 @@ string DumpGraph(const HloComputation& computation, const string& label,
   return graph_url;
 }
 
+string DumpNeighborhoodAround(const HloInstruction& node, int radius) {
+  auto debug_options = node.GetModule()->config().debug_options();
+
+  std::unordered_set<const HloInstruction*> neighborhood =
+      GetInstructionsInNeighborhood(node, radius);
+
+  NodeFilter filter([&](const HloInstruction* instr) {
+    if (instr == &node) {
+      return kHighlightNode;
+    }
+    if (neighborhood.find(instr) != neighborhood.end()) {
+      return kNormalNode;
+    }
+    return kHideNode;
+  });
+
+  string label =
+      StrCat("Neighborhood of ", radius, " nodes around ", node.name());
+  string graph = ComputationToDotGraph(
+      *node.parent(), label,
+      /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
+      /*show_layouts=*/debug_options.xla_hlo_graph_layout(),
+      /*hlo_execution_profile=*/nullptr, filter);
+  return GetGraphRenderer()->RenderGraph(
+      graph, GraphRendererInterface::DOT_GRAPH, debug_options);
+}
+
 void DumpText(const HloModule& module, const string& label,
               const string& directory_path, bool do_prefix) {
   Env* env = Env::Default();
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.h b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
index bc404a7a37..0100d50c05 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.h
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
@@ -57,6 +57,13 @@ string DumpGraph(const HloComputation& computation, const string& label,
                  const DebugOptions& debug_options,
                  const HloExecutionProfile* hlo_execution_profile = nullptr);
 
+// Like DumpGraph, but renders only nodes "near" the given node in the graph.
+//
+// The number of nodes dumped is controlled by the radius parameter, which
+// (roughly) corresponds to the max distance a node may be from the primary node
+// before it's omitted from the graph.
+string DumpNeighborhoodAround(const HloInstruction& node, int radius);
+
 // Dumps the HloModule::ToString() as a file into the provided directory path
 // suffixed with the provided label.
 //
-- 
GitLab


From a9e5591dbf3ee499f1df6ba0770600ef76d8d27b Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 20 Jul 2017 19:38:40 -0700
Subject: [PATCH 1178/1375] Preserve identity nodes on the receiver side of a
 cross device link instead of preserving them when they're on the sender side.

PiperOrigin-RevId: 162698209
---
 .../grappler/optimizers/graph_rewriter.cc     | 11 +++--
 .../core/grappler/optimizers/graph_rewriter.h |  5 +++
 .../core/grappler/optimizers/model_pruner.cc  |  6 ++-
 .../grappler/optimizers/model_pruner_test.cc  | 42 +++++++++++++++++--
 4 files changed, 56 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.cc b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
index 2eba753fa4..5273f11ca0 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.cc
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.cc
@@ -69,6 +69,10 @@ bool GraphRewriter::IsConnectedToFunction(const NodeDef& node) const {
   return function_neighbors_.find(&node) != function_neighbors_.end();
 }
 
+bool GraphRewriter::IsDrivenByAnotherDevice(const NodeDef& node) const {
+  return cross_device_receivers_.find(&node) != cross_device_receivers_.end();
+}
+
 void GraphRewriter::RecordConnectivity(
     const NodeDef& node, const std::unordered_set<string>& function_names) {
   const bool is_function =
@@ -94,6 +98,9 @@ void GraphRewriter::RecordConnectivity(
         function_neighbors_.insert(fanin);
       }
     }
+    if (fanin->device() != node.device()) {
+      cross_device_receivers_.insert(&node);
+    }
   }
 }
 
@@ -119,9 +126,7 @@ void GraphRewriter::ForwardInputsInternal(
       continue;
     }
     const NodeDef* input_node = itr->second;
-    if ((input_node->device().empty() || node.device().empty() ||
-         input_node->device() == node.device()) &&
-        nodes_to_delete.find(input_node) != nodes_to_delete.end()) {
+    if (nodes_to_delete.find(input_node) != nodes_to_delete.end()) {
       ForwardInputsInternal(*input_node, nodes_to_delete, new_node);
     } else {
       *new_node->add_input() = input;
diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.h b/tensorflow/core/grappler/optimizers/graph_rewriter.h
index 9a61d768b2..4bdb063d58 100644
--- a/tensorflow/core/grappler/optimizers/graph_rewriter.h
+++ b/tensorflow/core/grappler/optimizers/graph_rewriter.h
@@ -51,6 +51,10 @@ class GraphRewriter {
   // fanout (excluding control dependencies) of 'node' is a function.
   bool IsConnectedToFunction(const NodeDef& node) const;
 
+  // Returns true if the node is driven by at least one node placed on another
+  // device.
+  bool IsDrivenByAnotherDevice(const NodeDef& node) const;
+
  private:
   void RecordConnectivity(const NodeDef& node,
                           const std::unordered_set<string>& function_names);
@@ -63,6 +67,7 @@ class GraphRewriter {
   std::unordered_map<string, const NodeDef*> optimized_nodes_;
   std::unordered_set<const NodeDef*> control_dependency_drivers_;
   std::unordered_set<const NodeDef*> function_neighbors_;
+  std::unordered_set<const NodeDef*> cross_device_receivers_;
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index 5abada3d6a..676b41e53d 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -51,6 +51,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     if (nodes_to_preserve.find(node.name()) != nodes_to_preserve.end()) {
       continue;
     }
+
     // Don't remove nodes that drive control dependencies.
     // Don't remove nodes that are driven by control dependencies either since
     // we can't ensure (yet) that we won't increase the number of control
@@ -59,9 +60,12 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     // creation of 100 edges).
     // Don't modify nodes that are connected to functions since that can result
     // in inlining failures later on.
+    // Don't prune nodes that are driven by another device since these could be
+    // used to reduce cross device communication.
     if (!rewriter.DrivesControlDependency(node) &&
         !rewriter.IsDrivenByControlDependency(node) &&
-        !rewriter.IsConnectedToFunction(node)) {
+        !rewriter.IsConnectedToFunction(node) &&
+        !rewriter.IsDrivenByAnotherDevice(node)) {
       nodes_to_delete.insert(&node);
     }
   }
diff --git a/tensorflow/core/grappler/optimizers/model_pruner_test.cc b/tensorflow/core/grappler/optimizers/model_pruner_test.cc
index 3118f3a360..fdfb3f41cf 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner_test.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner_test.cc
@@ -94,8 +94,8 @@ TEST_F(ModelPrunerTest, IdentityPruning) {
 
   Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
   Output b = ops::AddN(s.WithOpName("b"), {a});
-  Output c = ops::Identity(s.WithOpName("c").WithDevice("CPU:0"), b);
-  Output d = ops::Identity(s.WithOpName("d").WithDevice("GPU:0"), c);
+  Output c = ops::Identity(s.WithOpName("c"), b);
+  Output d = ops::Identity(s.WithOpName("d"), c);
   Output e = ops::AddN(s.WithOpName("e"), {d});
 
   GrapplerItem item;
@@ -119,9 +119,11 @@ TEST_F(ModelPrunerTest, IdentityPruning) {
   EXPECT_EQ(NodeName(e.name()), new_e.name());
 
   EXPECT_EQ(1, new_e.input_size());
-  EXPECT_EQ(NodeName(c.name()), new_e.input(0));
+  EXPECT_EQ(NodeName(b.name()), new_e.input(0));
   EXPECT_EQ(1, new_d.input_size());
-  EXPECT_EQ(NodeName(c.name()), new_d.input(0));
+  EXPECT_EQ(NodeName(b.name()), new_d.input(0));
+  EXPECT_EQ(1, new_c.input_size());
+  EXPECT_EQ(NodeName(b.name()), new_c.input(0));
 }
 
 TEST_F(ModelPrunerTest, PruningSkipsCtrlDependencies) {
@@ -235,6 +237,38 @@ TEST_F(ModelPrunerTest, PruningPerservesFetch) {
   EXPECT_EQ(NodeName(c.name()), new_c.name());
 }
 
+TEST_F(ModelPrunerTest, PruningPerservesCrossDeviceIdentity) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output c = ops::Const(s.WithOpName("c").WithDevice("/cpu:0"), 0.0f, {10, 10});
+
+  // Node i1 should be preserved.
+  Output i1 = ops::Identity(s.WithOpName("i1").WithDevice("/gpu:0"), c);
+  Output a1 = ops::AddN(s.WithOpName("a1").WithDevice("/gpu:0"), {i1});
+  Output a2 = ops::AddN(s.WithOpName("a2").WithDevice("/gpu:0"), {i1});
+
+  // Node i2 should be pruned since it resides on the sender's device.
+  Output i2 = ops::Identity(s.WithOpName("i2").WithDevice("/cpu:0"), c);
+  Output a3 = ops::AddN(s.WithOpName("a3").WithDevice("/gpu:0"), {i2});
+  Output a4 = ops::AddN(s.WithOpName("a4").WithDevice("/gpu:0"), {i2});
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+  item.fetch = {"a1", "a2", "a3", "a4"};
+
+  ModelPruner pruner;
+  GraphDef output;
+  Status status = pruner.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  for (const auto& node : output.node()) {
+    if (node.name() == "a1" || node.name() == "a2") {
+      EXPECT_EQ("i1", node.input(0));
+    } else if (node.name() == "a3" || node.name() == "a4") {
+      EXPECT_EQ("c", node.input(0));
+    }
+  }
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From 78e7cffa71df4d5057b8a87f78f2d91e421a36f7 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 20 Jul 2017 20:53:28 -0700
Subject: [PATCH 1179/1375] Fix open-source build breakage related to
 std::deque

PiperOrigin-RevId: 162701622
---
 tensorflow/compiler/xla/service/hlo_graph_dumper.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index a712dc3b33..2455925b96 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 
 #include <unistd.h>
+#include <deque>
 #include <string>
 
 #include "tensorflow/compiler/xla/layout_util.h"
-- 
GitLab


From 602632b5bce191ba9b6a8ba339599168802827ac Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Thu, 20 Jul 2017 22:13:58 -0700
Subject: [PATCH 1180/1375] Introduce a fake_quantize_training graph transform.

PiperOrigin-RevId: 162705909
---
 tensorflow/core/graph/quantize_training.cc    | 34 ++++++----
 tensorflow/core/graph/quantize_training.h     | 11 +++-
 .../core/graph/quantize_training_test.cc      | 36 ++++++++++-
 tensorflow/tools/graph_transforms/BUILD       |  2 +
 .../fake_quantize_training.cc                 | 50 +++++++++++++++
 .../fake_quantize_training_test.cc            | 63 +++++++++++++++++++
 6 files changed, 180 insertions(+), 16 deletions(-)
 create mode 100644 tensorflow/tools/graph_transforms/fake_quantize_training.cc
 create mode 100644 tensorflow/tools/graph_transforms/fake_quantize_training_test.cc

diff --git a/tensorflow/core/graph/quantize_training.cc b/tensorflow/core/graph/quantize_training.cc
index 48b6b2a497..b74fa2127e 100644
--- a/tensorflow/core/graph/quantize_training.cc
+++ b/tensorflow/core/graph/quantize_training.cc
@@ -653,28 +653,38 @@ Status DoQuantizeTraining(int32 num_bits, const string& quant_op_type,
   return Status::OK();
 }
 
-Status DoQuantizeTrainingOnSerializedGraphDef(const string& input_graph,
-                                              int32 num_bits,
-                                              const string& quant_op_type,
-                                              string* result_graph) {
-  // First create the graph from the GraphDef.
+Status DoQuantizeTrainingOnGraphDef(const GraphDef& input_graphdef,
+                                    int32 num_bits, const string& quant_op_type,
+                                    GraphDef* result_graphdef) {
   Graph graph(OpRegistry::Global());
   GraphConstructorOptions opts;
-  GraphDef input_graphdef;
-  if (!ParseProtoUnlimited(&input_graphdef, input_graph)) {
-    return errors::InvalidArgument("Invalid input graph");
-  }
   TF_RETURN_IF_ERROR(ConvertGraphDefToGraph(opts, input_graphdef, &graph));
 
   // Call the rewriter on the graph.
   TF_RETURN_IF_ERROR(DoQuantizeTraining(num_bits, quant_op_type, &graph));
 
   // Convert the result graph back to a GraphDef.
+  graph.ToGraphDef(result_graphdef);
+  return Status::OK();
+}
+
+Status DoQuantizeTrainingOnSerializedGraphDef(const string& input_graph_string,
+                                              int32 num_bits,
+                                              const string& quant_op_type,
+                                              string* result_graph_string) {
+  // First create the graph from the GraphDef.
+  GraphDef input_graphdef;
+  if (!ParseProtoUnlimited(&input_graphdef, input_graph_string)) {
+    return errors::InvalidArgument(
+        "input_graph_string is not a serialized GraphDef protocol buffer");
+  }
   GraphDef output_graphdef;
-  graph.ToGraphDef(&output_graphdef);
+  TF_RETURN_IF_ERROR(DoQuantizeTrainingOnGraphDef(
+      input_graphdef, num_bits, quant_op_type, &output_graphdef));
 
-  if (!output_graphdef.SerializeToString(result_graph)) {
-    return errors::InvalidArgument("Invalid output graph");
+  if (!output_graphdef.SerializeToString(result_graph_string)) {
+    return errors::Internal(
+        "quantize training transformation resulted in invalid GraphDef");
   }
   return Status::OK();
 }
diff --git a/tensorflow/core/graph/quantize_training.h b/tensorflow/core/graph/quantize_training.h
index 2c1a7e6ae3..2bb4ee1cf0 100644
--- a/tensorflow/core/graph/quantize_training.h
+++ b/tensorflow/core/graph/quantize_training.h
@@ -38,12 +38,19 @@ namespace tensorflow {
 Status DoQuantizeTraining(int32 num_bits, const string& quant_op_type,
                           Graph* g);
 
-// Converts a input GraphDef and returns a rewritten GraphDef with the
-// quantized training.
+// Converts the input serialized GraphDef and returns a rewritten serialized
+// GraphDef for quantized training.
 Status DoQuantizeTrainingOnSerializedGraphDef(const string& input_graph,
                                               int32 num_bits,
                                               const string& quant_op_type,
                                               string* result_graph);
+
+// Converts the input GraphDef and returns a rewritten GraphDef for quantized
+// training.
+Status DoQuantizeTrainingOnGraphDef(const GraphDef& input_graphdef,
+                                    int32 num_bits, const string& quant_op_type,
+                                    GraphDef* result_graphdef);
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_GRAPH_QUANTIZE_TRAINING_H_
diff --git a/tensorflow/core/graph/quantize_training_test.cc b/tensorflow/core/graph/quantize_training_test.cc
index d817d980de..2ad69dbd0c 100644
--- a/tensorflow/core/graph/quantize_training_test.cc
+++ b/tensorflow/core/graph/quantize_training_test.cc
@@ -282,7 +282,7 @@ TEST_F(QuantizeTrainingTest, WithBackwardNodes_FakeQuant) {
       g, strings::StrCat(c->name(), "/FakeQuantWithMinMaxVars"), &found_node));
 }
 
-TEST_F(QuantizeTrainingTest, QuantizeGraphDef) {
+TEST_F(QuantizeTrainingTest, QuantizeSerializedGraphDef) {
   // Construct a simple graph with 5 nodes.
   Reset();
   Graph* graph = g_.get();
@@ -310,8 +310,40 @@ TEST_F(QuantizeTrainingTest, QuantizeGraphDef) {
   GraphDef result_graphdef;
   EXPECT_TRUE(ParseProtoUnlimited(&result_graphdef, result_string));
 
+  // Ensure that quantizing the serialized graph_def results in a graph with the
+  // same number of nodes as quantizing the graph.
+  GraphConstructorOptions opts;
+  Graph result_graph(OpRegistry::Global());
+  TF_ASSERT_OK(ConvertGraphDefToGraph(opts, result_graphdef, &result_graph));
+  TF_ASSERT_OK(DoQuantizeTraining(num_bits, "QuantizeAndDequantizeV2", graph));
+  EXPECT_EQ(graph->num_nodes(), result_graph.num_nodes());
+}
+
+TEST_F(QuantizeTrainingTest, QuantizeGraphDef) {
+  // Construct a simple graph with 5 nodes.
+  Reset();
+  Graph* graph = g_.get();
+  Node* const_a = Constant<float>({1.0, 2.0, 3.0, 4.0}, {2, 2});
+  Node* const_b = Constant<float>({1.0, 2.0, 3.0, 4.0}, {2, 2});
+  graph->AddControlEdge(graph->source_node(), const_a);
+  graph->AddControlEdge(graph->source_node(), const_b);
+  Node* relu = test::graph::Relu(graph, const_a);
+  Node* identity = test::graph::Identity(graph, const_b);
+  Node* matmul = test::graph::Matmul(graph, relu, identity, false, false);
+  graph->AddControlEdge(matmul, graph->sink_node());
+
+  int num_bits = 8;
+
+  // Convert the graph to the graphdef string.
+  GraphDef input_graphdef;
+  graph->ToGraphDef(&input_graphdef);
+
+  GraphDef result_graphdef;
+  TF_ASSERT_OK(DoQuantizeTrainingOnGraphDef(
+      input_graphdef, num_bits, "QuantizeAndDequantizeV2", &result_graphdef));
+
   // Ensure that quantizing the graph_def results in a graph with the same
-  // number of nodes.
+  // number of nodes as the graph_def.
   GraphConstructorOptions opts;
   Graph result_graph(OpRegistry::Global());
   TF_ASSERT_OK(ConvertGraphDefToGraph(opts, result_graphdef, &result_graph));
diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD
index d9ec8e8e9b..ec582739b7 100644
--- a/tensorflow/tools/graph_transforms/BUILD
+++ b/tensorflow/tools/graph_transforms/BUILD
@@ -60,6 +60,7 @@ cc_library(
     srcs = [
         "add_default_attributes.cc",
         "backports.cc",
+        "fake_quantize_training.cc",
         "fold_batch_norms.cc",
         "fold_constants_lib.cc",
         "fold_old_batch_norms.cc",
@@ -109,6 +110,7 @@ tf_cc_test(
     srcs = [
         "add_default_attributes_test.cc",
         "backports_test.cc",
+        "fake_quantize_training_test.cc",
         "fold_batch_norms_test.cc",
         "fold_constants_test.cc",
         "fold_old_batch_norms_test.cc",
diff --git a/tensorflow/tools/graph_transforms/fake_quantize_training.cc b/tensorflow/tools/graph_transforms/fake_quantize_training.cc
new file mode 100644
index 0000000000..321de47db1
--- /dev/null
+++ b/tensorflow/tools/graph_transforms/fake_quantize_training.cc
@@ -0,0 +1,50 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/graph/quantize_training.h"
+#include "tensorflow/tools/graph_transforms/transform_utils.h"
+
+namespace tensorflow {
+namespace graph_transforms {
+
+// Rewrites the GraphDef for quantized training.
+// Rewrites the forward pass to include the precision loss with quantization so
+// the model can learn to deal with such loss and achieve better accuracy when
+// it is quantized later for inference.
+// Quantization range information is collected in FakeQuantizeWithMinMaxVars
+// ops.
+//
+// TODO(suharshs): Provide instructions on converting the resulting graph for
+// inference.
+// TODO(suharshs): Implement this using the GTT rather than calling the old
+// prototype function.
+Status FakeQuantizeTraining(const GraphDef& input_graph_def,
+                            const TransformFuncContext& context,
+                            GraphDef* output_graph_def) {
+  // TODO(suharshs): Make num_bits a parameter.
+  const int32 num_bits = 8;
+  // TODO(suharshs): Make quantization op a parameter?
+  const string quant_op_type = "FakeQuantWithMinMaxVars";
+
+  return DoQuantizeTrainingOnGraphDef(input_graph_def, num_bits, quant_op_type,
+                                      output_graph_def);
+}
+
+REGISTER_GRAPH_TRANSFORM("fake_quantize_training", FakeQuantizeTraining);
+
+}  // namespace graph_transforms
+}  // namespace tensorflow
diff --git a/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc b/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc
new file mode 100644
index 0000000000..3ea7f512c6
--- /dev/null
+++ b/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc
@@ -0,0 +1,63 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/cc/ops/const_op.h"
+#include "tensorflow/cc/ops/math_ops.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/tools/graph_transforms/transform_utils.h"
+
+namespace tensorflow {
+namespace graph_transforms {
+
+// Declare here, so we don't need a public header.
+Status FakeQuantizeTraining(const GraphDef& input_graph_def,
+                            const TransformFuncContext& context,
+                            GraphDef* output_graph_def);
+
+class FakeQuantizeTrainingTest : public ::testing::Test {};
+
+// For now, since the fake_quantize_training transform just calls the
+// quantize_training rewrite from tensorflow/core/graph/quantize_training.h,
+// we just test that the graph has been changed by the transform.
+// TODO(suharshs): Once we implement the fake_quantize_training transform
+// using the GTT, write proper tests of the transform here.
+TEST_F(FakeQuantizeTrainingTest, TransformOccurred) {
+  auto root = tensorflow::Scope::NewRootScope();
+  using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+
+  Tensor a_data(DT_FLOAT, TensorShape());
+  test::FillIota<float>(&a_data, 1.0f);
+  Output a_const = Const(root.WithOpName("a"), Input::Initializer(a_data));
+
+  Tensor b_data(DT_FLOAT, TensorShape());
+  test::FillIota<float>(&b_data, 1.0f);
+  Output b_const = Const(root.WithOpName("b"), Input::Initializer(b_data));
+
+  Output matmul = MatMul(root.WithOpName("matmul"), a_const, b_const);
+  GraphDef graph_def;
+  TF_ASSERT_OK(root.ToGraphDef(&graph_def));
+
+  GraphDef result;
+  TransformFuncContext context;
+  TF_ASSERT_OK(FakeQuantizeTraining(graph_def, context, &result));
+
+  // Test that the transformation resulted in a graph with more nodes.
+  EXPECT_GT(result.node_size(), graph_def.node_size());
+}
+
+}  // namespace graph_transforms
+}  // namespace tensorflow
-- 
GitLab


From d3edb8c60ed4fd831d62833ed22f5c23486c561c Mon Sep 17 00:00:00 2001
From: Guenther Schmuelling <guschmue@microsoft.com>
Date: Thu, 20 Jul 2017 23:19:37 -0700
Subject: [PATCH 1181/1375] fix gpu build (#11647)

---
 tensorflow/contrib/cmake/tf_core_cpu.cmake       | 1 +
 tensorflow/contrib/cmake/tf_core_framework.cmake | 4 ++++
 2 files changed, 5 insertions(+)

diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake
index 0db6607985..c76f124892 100644
--- a/tensorflow/contrib/cmake/tf_core_cpu.cmake
+++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake
@@ -61,6 +61,7 @@ if (tensorflow_ENABLE_GPU)
   file(GLOB_RECURSE tf_core_gpu_srcs
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/*.cc"
     "${tensorflow_source_dir}/tensorflow/core/platform/default/gpu/cupti_wrapper.cc"
+    "${tensorflow_source_dir}/tensorflow/core/platform/default/gpu_tracer.cc"
     "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu_device_factory.cc"
     "${tensorflow_source_dir}/tensorflow/core/grappler/devices.h"
     "${tensorflow_source_dir}/tensorflow/core/grappler/devices.cc"
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index a15621ba6d..e3876bb87d 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -155,6 +155,10 @@ if (NOT tensorflow_ENABLE_GPU)
       "${tensorflow_source_dir}/tensorflow/core/platform/cuda_libdevice_path.*"
       "${tensorflow_source_dir}/tensorflow/core/platform/default/cuda_libdevice_path.*")
   list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_gpu_srcs})
+else()
+  file(GLOB tf_core_platform_srcs_exclude
+      "${tensorflow_source_dir}/tensorflow/core/platform/default/gpu_tracer.cc")
+  list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_srcs_exclude})
 endif()
 list(APPEND tf_core_lib_srcs ${tf_core_platform_srcs})
 
-- 
GitLab


From 133705c3ce9526bf696cb11a0d64f942de894c11 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Fri, 21 Jul 2017 00:39:34 -0700
Subject: [PATCH 1182/1375] Tidy up opensouce mkl build. Closes #11212

PiperOrigin-RevId: 162713588
---
 .gitignore                                    |   2 -
 configure                                     |  77 +------
 .../core/common_runtime/mkl_cpu_allocator.h   |   2 +-
 tensorflow/core/kernels/mkl_concat_op.cc      |   4 +-
 .../core/kernels/mkl_conv_grad_bias_ops.cc    |   4 +-
 .../core/kernels/mkl_conv_grad_filter_ops.cc  |   4 +-
 .../core/kernels/mkl_conv_grad_input_ops.cc   |   4 +-
 tensorflow/core/kernels/mkl_conv_ops.cc       |   4 +-
 .../core/kernels/mkl_fused_batch_norm_op.cc   |   4 +-
 tensorflow/core/kernels/mkl_identity_op.cc    |   4 +-
 tensorflow/core/kernels/mkl_lrn_op.cc         |   4 +-
 tensorflow/core/kernels/mkl_matmul_op.cc      |   2 +-
 tensorflow/core/kernels/mkl_relu_op.cc        |   4 +-
 tensorflow/core/kernels/mkl_reshape_op.cc     |   4 +-
 tensorflow/core/kernels/mkl_tfconv_op.cc      |   4 +-
 tensorflow/core/kernels/mkl_transpose_op.cc   |   2 +-
 tensorflow/core/util/mkl_util.h               |   8 +-
 tensorflow/tools/lib_package/BUILD            |  11 +-
 tensorflow/tools/pip_package/BUILD            |   5 +-
 .../tools/pip_package/build_pip_package.sh    |  41 ++--
 tensorflow/workspace.bzl                      |  11 +
 third_party/mkl/BUILD                         |  11 +-
 third_party/mkl/LICENSE                       | 201 ++++++++++++++++++
 third_party/mkl/mkl.BUILD                     |  22 ++
 24 files changed, 300 insertions(+), 139 deletions(-)
 create mode 100644 third_party/mkl/LICENSE
 create mode 100644 third_party/mkl/mkl.BUILD

diff --git a/.gitignore b/.gitignore
index bdcb067fc2..fdc61ee825 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,8 +5,6 @@ node_modules
 /.tf_configure.bazelrc
 /bazel-*
 /bazel_pip
-/third_party/eigen3/mkl_include
-/third_party/mkl/*
 /tools/python_bin_path.sh
 /tools/git/gen
 /pip_test
diff --git a/configure b/configure
index 288674753b..cd957a06f8 100755
--- a/configure
+++ b/configure
@@ -217,84 +217,15 @@ done
 OSNAME=`uname -s`
 
 if [ "$TF_NEED_MKL" == "1" ]; then # TF_NEED_MKL
-  while [ "$TF_DOWNLOAD_MKL" == "" ]; do
-    fromuser=""
-    read -p "Do you wish to download MKL LIB from the web? [Y/n] " INPUT
-    fromuser="1"
-    case $INPUT in
-      [Yy]* ) TF_DOWNLOAD_MKL=1;;
-      [Nn]* ) TF_DOWNLOAD_MKL=0;;
-      "" )    TF_DOWNLOAD_MKL=1;;
-      * )     echo "Invalid selection: " $INPUT; exit 1;;
-    esac
-  done
-
-  if [[ "$TF_DOWNLOAD_MKL" == "1" ]]; then
-    DST=`dirname $0`
-    ARCHIVE_BASENAME=mklml_lnx_2018.0.20170425.tgz
-    GITHUB_RELEASE_TAG=v0.7
-    MKLURL="https://github.com/01org/mkl-dnn/releases/download/$GITHUB_RELEASE_TAG/$ARCHIVE_BASENAME"
-    if ! [ -e "${DST}/third_party/mkl/${ARCHIVE_BASENAME}" ]; then
-      curl -fSsL -o "${DST}/third_party/mkl/${ARCHIVE_BASENAME}" "${MKLURL}"
-    fi
-    tar -xzf $DST/third_party/mkl/$ARCHIVE_BASENAME -C $DST/third_party/mkl/
-    extracted_dir_name="${ARCHIVE_BASENAME%.*}"
-    MKL_INSTALL_PATH=$DST/third_party/mkl/$extracted_dir_name
-    MKL_INSTALL_PATH=`${PYTHON_BIN_PATH} -c "import os; print(os.path.realpath(os.path.expanduser('${MKL_INSTALL_PATH}')))"`
-
-  else
-    default_mkl_path=/opt/intel/mklml
-    fromuser=""
-    if [ -z "$MKL_INSTALL_PATH" ]; then
-      read -p "Please specify the location where MKL is installed. [Default is $default_mkl_path]: " MKL_INSTALL_PATH
-      fromuser="1"
-    fi
-    if [ -z "$MKL_INSTALL_PATH" ]; then
-      MKL_INSTALL_PATH=$default_mkl_path
-    fi
-    # Result returned from "read" will be used unexpanded. That make "~" unusable.
-    # Going through one more level of expansion to handle that.
-    MKL_INSTALL_PATH=`${PYTHON_BIN_PATH} -c "import os; print(os.path.realpath(os.path.expanduser('${MKL_INSTALL_PATH}')))"`
-  fi
-
   if [ "$OSNAME" == "Linux" ]; then
-      # Full MKL configuration
-      MKL_RT_LIB_PATH="lib/intel64/libmkl_rt.so" #${TF_MKL_EXT}#TODO version?
-      MKL_RT_OMP_LIB_PATH="../compiler/lib/intel64/libiomp5.so" #TODO VERSION?
-
-      # MKL-ML configuration
-      MKL_ML_LIB_PATH="lib/libmklml_intel.so" #${TF_MKL_EXT}#TODO version?
-      MKL_ML_OMP_LIB_PATH="lib/libiomp5.so" #TODO VERSION?
+    write_to_bazelrc 'build:mkl --define with_mkl_support=true'
+    write_to_bazelrc 'build:mkl -c opt'
+    write_to_bazelrc 'build:mkl --copt="-DEIGEN_USE_VML"'
+    echo "Add \"--config=mkl\" to your bazel command to build with MKL support."
   elif [ "$OSNAME" == "Darwin" ]; then
       echo "Darwin is unsupported yet";
       exit 1
   fi
-
-  if [ -e "$MKL_INSTALL_PATH/${MKL_ML_LIB_PATH}" ]; then
-      ln -sf $MKL_INSTALL_PATH/${MKL_ML_LIB_PATH} third_party/mkl/
-      ln -sf $MKL_INSTALL_PATH/${MKL_ML_OMP_LIB_PATH} third_party/mkl/
-      ln -sf $MKL_INSTALL_PATH/include third_party/mkl/
-      ln -sf $MKL_INSTALL_PATH/include third_party/eigen3/mkl_include
-      loc=$(locate -e libdl.so.2 | sed -n 1p)
-      ln -sf $loc third_party/mkl/libdl.so.2
-  elif [ -e "$MKL_INSTALL_PATH/${MKL_RT_LIB_PATH}" ]; then
-      ln -sf $MKL_INSTALL_PATH/${MKL_RT_LIB_PATH} third_party/mkl/
-      ln -sf $MKL_INSTALL_PATH/${MKL_RT_OMP_LIB_PATH} third_party/mkl/
-      ln -sf $MKL_INSTALL_PATH/include third_party/mkl/
-      ln -sf $MKL_INSTALL_PATH/include third_party/eigen3/mkl_include
-      loc=$(locate -e libdl.so.2 | sed -n 1p)
-      ln -sf $loc third_party/mkl/libdl.so.2
-  else
-      echo "ERROR: $MKL_INSTALL_PATH/${MKL_ML_LIB_PATH} nor $MKL_INSTALL_PATH/${MKL_RT_LIB_PATH} exists";
-      exit 1
-  fi
-
-cat > third_party/mkl/mkl.config <<EOF
-# MKL_INSTALL_PATH refers to the location of MKL root folder. The MKL header and library
-# files can be either in this directory, or under include/ and lib64/
-MKL_INSTALL_PATH=$MKL_INSTALL_PATH
-EOF
-
 fi # TF_NEED_MKL
 ## End MKL setup
 
diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator.h b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
index 41bf23be27..005aabf9b8 100644
--- a/tensorflow/core/common_runtime/mkl_cpu_allocator.h
+++ b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
@@ -26,7 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/platform/mem.h"
 
-#include "third_party/mkl/include/i_malloc.h"
+#include "i_malloc.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc
index d8a8cc74bf..e6673b2ffb 100644
--- a/tensorflow/core/kernels/mkl_concat_op.cc
+++ b/tensorflow/core/kernels/mkl_concat_op.cc
@@ -29,8 +29,8 @@ limitations under the License.
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/types.h"
 
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 #include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc
index d4364d31e4..0f1a218fe6 100644
--- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc
@@ -39,8 +39,8 @@ limitations under the License.
 #include "tensorflow/core/util/work_sharder.h"
 
 #include "tensorflow/core/util/mkl_util.h"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
index ddcf241277..3b23c72f0f 100644
--- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc
@@ -38,8 +38,8 @@ limitations under the License.
 #include "tensorflow/core/util/work_sharder.h"
 
 #include "tensorflow/core/util/mkl_util.h"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc
index 23827ceea5..ef7338e0e0 100644
--- a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc
@@ -40,8 +40,8 @@ limitations under the License.
 #include "tensorflow/core/util/tensor_format.h"
 #include "tensorflow/core/util/use_cudnn.h"
 #include "tensorflow/core/util/work_sharder.h"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc
index df49e03f31..135dd254a4 100644
--- a/tensorflow/core/kernels/mkl_conv_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_ops.cc
@@ -37,8 +37,8 @@ limitations under the License.
 #include "tensorflow/core/util/tensor_format.h"
 
 #include "tensorflow/core/util/mkl_util.h"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
index d533536802..bc9e906c39 100644
--- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
+++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
@@ -21,8 +21,8 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/util/tensor_format.h"
 
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 #include "tensorflow/core/util/mkl_util.h"
 
 // TODO(inteltf) Address comments from PR 8968.
diff --git a/tensorflow/core/kernels/mkl_identity_op.cc b/tensorflow/core/kernels/mkl_identity_op.cc
index cb7ea7e7f9..ca20294a26 100644
--- a/tensorflow/core/kernels/mkl_identity_op.cc
+++ b/tensorflow/core/kernels/mkl_identity_op.cc
@@ -24,8 +24,8 @@ limitations under the License.
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/logging.h"
 
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 #include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc
index 07a7e6b5da..aa08e93924 100644
--- a/tensorflow/core/kernels/mkl_lrn_op.cc
+++ b/tensorflow/core/kernels/mkl_lrn_op.cc
@@ -31,8 +31,8 @@ limitations under the License.
 #include "tensorflow/core/util/mkl_util.h"
 #include "tensorflow/core/util/tensor_format.h"
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 
 #if !defined(IS_MOBILE_PLATFORM)
 #include "tensorflow/core/util/work_sharder.h"
diff --git a/tensorflow/core/kernels/mkl_matmul_op.cc b/tensorflow/core/kernels/mkl_matmul_op.cc
index 16143191a3..47598f443f 100644
--- a/tensorflow/core/kernels/mkl_matmul_op.cc
+++ b/tensorflow/core/kernels/mkl_matmul_op.cc
@@ -25,7 +25,7 @@ limitations under the License.
 
 #if defined(INTEL_MKL)
 
-#include "third_party/mkl/include/mkl_cblas.h"
+#include "mkl_cblas.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc
index fabecc39a8..86a77d769a 100644
--- a/tensorflow/core/kernels/mkl_relu_op.cc
+++ b/tensorflow/core/kernels/mkl_relu_op.cc
@@ -25,8 +25,8 @@ limitations under the License.
 
 #include "tensorflow/core/platform/default/logging.h"
 #include "tensorflow/core/util/mkl_util.h"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/mkl_reshape_op.cc b/tensorflow/core/kernels/mkl_reshape_op.cc
index 593aa3a2fd..b3763f17bc 100644
--- a/tensorflow/core/kernels/mkl_reshape_op.cc
+++ b/tensorflow/core/kernels/mkl_reshape_op.cc
@@ -24,8 +24,8 @@ limitations under the License.
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/logging.h"
 
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 #include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc
index b4aae67ca6..c8e5df32ce 100644
--- a/tensorflow/core/kernels/mkl_tfconv_op.cc
+++ b/tensorflow/core/kernels/mkl_tfconv_op.cc
@@ -29,8 +29,8 @@ limitations under the License.
 #include "tensorflow/core/util/tensor_format.h"
 
 #include "tensorflow/core/util/mkl_util.h"
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
 
 namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
diff --git a/tensorflow/core/kernels/mkl_transpose_op.cc b/tensorflow/core/kernels/mkl_transpose_op.cc
index fcbf105f8f..50d25ac511 100644
--- a/tensorflow/core/kernels/mkl_transpose_op.cc
+++ b/tensorflow/core/kernels/mkl_transpose_op.cc
@@ -18,7 +18,7 @@ limitations under the License.
 #ifdef INTEL_MKL
 #define EIGEN_USE_THREADS
 
-#include "third_party/mkl/include/mkl_trans.h"
+#include "mkl_trans.h"
 #include "tensorflow/core/kernels/transpose_functor.h"
 #include "tensorflow/core/kernels/transpose_op.h"
 
diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h
index 67468bdc3f..35aca709d9 100644
--- a/tensorflow/core/util/mkl_util.h
+++ b/tensorflow/core/util/mkl_util.h
@@ -20,10 +20,10 @@ limitations under the License.
 #include <string>
 #include <vector>
 
-#include "third_party/mkl/include/mkl_dnn.h"
-#include "third_party/mkl/include/mkl_dnn_types.h"
-#include "third_party/mkl/include/mkl_service.h"
-#include "third_party/mkl/include/mkl_trans.h"
+#include "mkl_dnn.h"
+#include "mkl_dnn_types.h"
+#include "mkl_service.h"
+#include "mkl_trans.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/util/tensor_format.h"
diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD
index 9da5d5cb5b..6b2523d52b 100644
--- a/tensorflow/tools/lib_package/BUILD
+++ b/tensorflow/tools/lib_package/BUILD
@@ -4,6 +4,7 @@
 package(default_visibility = ["//visibility:private"])
 
 load("@bazel_tools//tools/build_defs/pkg:pkg.bzl", "pkg_tar")
+load("//third_party/mkl:build_defs.bzl", "if_mkl")
 
 genrule(
     name = "libtensorflow_proto",
@@ -104,7 +105,10 @@ genrule(
         "@protobuf//:LICENSE",
         "@snappy//:COPYING",
         "@zlib_archive//:zlib.h",
-    ],
+    ] + if_mkl([
+        "//third_party/mkl:LICENSE",
+        "@mkl//:LICENSE",
+    ]),
     outs = ["include/tensorflow/c/LICENSE"],
     cmd = "$(location :concat_licenses.sh) $(SRCS) >$@",
     tools = [":concat_licenses.sh"],
@@ -135,7 +139,10 @@ genrule(
         "@protobuf//:LICENSE",
         "@snappy//:COPYING",
         "@zlib_archive//:zlib.h",
-    ],
+    ] + if_mkl([
+        "//third_party/mkl:LICENSE",
+        "@mkl//:LICENSE",
+    ]),
     outs = ["include/tensorflow/jni/LICENSE"],
     cmd = "$(location :concat_licenses.sh) $(SRCS) >$@",
     tools = [":concat_licenses.sh"],
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 442b335be5..bffa870d32 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -126,7 +126,10 @@ filegroup(
         "@snappy//:COPYING",
         "@zlib_archive//:zlib.h",
         "@org_python_pypi_backports_weakref//:LICENSE",
-    ] + if_not_windows([
+    ] + if_mkl([
+        "//third_party/mkl:LICENSE",
+        "@mkl//:LICENSE",
+    ]) + if_not_windows([
         "@nccl_archive//:LICENSE.txt",
     ]) + tf_additional_license_deps(),
 )
diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh
index 8b9a6b3de0..a158fd7345 100755
--- a/tensorflow/tools/pip_package/build_pip_package.sh
+++ b/tensorflow/tools/pip_package/build_pip_package.sh
@@ -79,23 +79,6 @@ function main() {
       bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles \
       "${TMPDIR}/external"
     RUNFILES=bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles/org_tensorflow
-  elif [ ! -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow ]; then
-    # Really old (0.2.1-) runfiles, without workspace name.
-    cp -R \
-      bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/tensorflow \
-      "${TMPDIR}"
-    mkdir "${TMPDIR}/external"
-    cp_external \
-      bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/external \
-      "${TMPDIR}/external"
-    RUNFILES=bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles
-    # Copy MKL libs over so they can be loaded at runtime
-    if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl ]; then
-      mkdir "${TMPDIR}/_solib_k8"
-  		cp -R \
-  			bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl \
-        "${TMPDIR}/_solib_k8"
-    fi
   else
     if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/external ]; then
       # Old-style runfiles structure (--legacy_external_runfiles).
@@ -107,11 +90,13 @@ function main() {
         bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/external \
         "${TMPDIR}/external"
       # Copy MKL libs over so they can be loaded at runtime
-      if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl ]; then
-        mkdir "${TMPDIR}/_solib_k8"
-        cp -R \
-          bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl \
-          "${TMPDIR}/_solib_k8"
+      so_lib_dir="bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8"
+      if [ -d ${so_lib_dir} ]; then
+        mkl_so_dir=$(ls ${so_lib_dir} | grep mkl)
+        if [ $? -eq 0 ]; then
+          mkdir "${TMPDIR}/_solib_k8"
+          cp -R ${so_lib_dir}/${mkl_so_dir} "${TMPDIR}/_solib_k8"
+        fi
       fi
     else
       # New-style runfiles structure (--nolegacy_external_runfiles).
@@ -124,11 +109,13 @@ function main() {
         bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles \
         "${TMPDIR}/external"
       # Copy MKL libs over so they can be loaded at runtime
-      if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl ]; then
-        mkdir "${TMPDIR}/_solib_k8"
-    		cp -R \
-    			bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl \
-          "${TMPDIR}/_solib_k8"
+      so_lib_dir="bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8"
+      if [ -d ${so_lib_dir} ]; then
+        mkl_so_dir=$(ls ${so_lib_dir} | grep mkl)
+        if [ $? -eq 0 ]; then
+          mkdir "${TMPDIR}/_solib_k8"
+          cp -R ${so_lib_dir}/${mkl_so_dir} "${TMPDIR}/_solib_k8"
+        fi
       fi
     fi
     RUNFILES=bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 4760673058..42d2ebc3fe 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -171,6 +171,17 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       actual = "@libxsmm_archive//third_party:xsmm_avx",
   )
 
+  native.new_http_archive(
+      name = "mkl",
+      urls = [
+          "http://mirror.bazel.build/github.com/01org/mkl-dnn/releases/download/v0.7/mklml_lnx_2018.0.20170425.tgz",
+          "https://github.com/01org/mkl-dnn/releases/download/v0.7/mklml_lnx_2018.0.20170425.tgz",
+      ],
+      sha256 = "3cc2501fb209e1fd0960a5f61c919438f9619c68a644dcebf0fdf69b07460c57",
+      strip_prefix = "mklml_lnx_2018.0.20170425",
+      build_file = str(Label("//third_party/mkl:mkl.BUILD")),
+  )
+
   native.new_http_archive(
       name = "ortools_archive",
       urls = [
diff --git a/third_party/mkl/BUILD b/third_party/mkl/BUILD
index 8c86766eff..b27d341404 100644
--- a/third_party/mkl/BUILD
+++ b/third_party/mkl/BUILD
@@ -1,4 +1,6 @@
-licenses(["restricted"])  # MPL2, portions GPL v3, LGPL v3, BSD-like     TODO
+licenses(["notice"])  # 3-Clause BSD
+
+exports_files(["LICENSE"])
 
 config_setting(
     name = "using_mkl",
@@ -16,10 +18,9 @@ load(
 cc_library(
     name = "intel_binary_blob",
     srcs = if_mkl([
-        "libdl.so.2",
-        "libmklml_intel.so",
-        "libiomp5.so",
+        "@mkl//:libmklml_intel.so",
+        "@mkl//:libiomp5.so",
     ]),
-    includes = ["."],
     visibility = ["//visibility:public"],
+    deps = ["@mkl//:mkl_headers"],
 )
diff --git a/third_party/mkl/LICENSE b/third_party/mkl/LICENSE
new file mode 100644
index 0000000000..9c8f3ea087
--- /dev/null
+++ b/third_party/mkl/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
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+
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+
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+
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+
+   END OF TERMS AND CONDITIONS
+
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+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
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+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
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+
+   Copyright {yyyy} {name of copyright owner}
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
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+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/third_party/mkl/mkl.BUILD b/third_party/mkl/mkl.BUILD
new file mode 100644
index 0000000000..37daa52419
--- /dev/null
+++ b/third_party/mkl/mkl.BUILD
@@ -0,0 +1,22 @@
+licenses(["notice"])  # 3-Clause BSD
+
+exports_files(["LICENSE"])
+
+cc_library(
+    name = "mkl_headers",
+    srcs = glob(["include/*"]),
+    includes = ["include"],
+    visibility = ["//visibility:public"],
+)
+
+filegroup(
+    name = "libmklml_intel.so",
+    srcs = ["lib/libmklml_intel.so"],
+    visibility = ["//visibility:public"],
+)
+
+filegroup(
+    name = "libiomp5.so",
+    srcs = ["lib/libiomp5.so"],
+    visibility = ["//visibility:public"],
+)
-- 
GitLab


From ab1956d36644f3e3c3d54add8572d2c59da7e306 Mon Sep 17 00:00:00 2001
From: Eric Liu <ioeric@google.com>
Date: Fri, 21 Jul 2017 01:14:02 -0700
Subject: [PATCH 1183/1375] [tpu-profiler] Set gRPC max message length to max
 int.

PiperOrigin-RevId: 162715887
---
 tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
index b575128159..bda049f3f4 100644
--- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
+++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
@@ -70,9 +70,13 @@ ProfileResponse Profile(const tensorflow::string& service_addr) {
   ProfileRequest request;
   ProfileResponse response;
   ClientContext context;
+  ::grpc::ChannelArguments channel_args;
+  // TODO(ioeric): use `SetMaxReceiveMessageSize` instead once it's available.
+  channel_args.SetInt(GRPC_ARG_MAX_MESSAGE_LENGTH,
+                      std::numeric_limits<int32>::max());
   std::unique_ptr<TPUProfiler::Stub> stub =
-      TPUProfiler::NewStub(::grpc::CreateChannel(
-          service_addr, ::grpc::InsecureChannelCredentials()));
+      TPUProfiler::NewStub(::grpc::CreateCustomChannel(
+          service_addr, ::grpc::InsecureChannelCredentials(), channel_args));
   TF_CHECK_OK(FromGrpcStatus(stub->Profile(&context, request, &response)));
   return response;
 }
-- 
GitLab


From 4ffa226db076c838bb37846a84a2910883e6d888 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 04:40:28 -0700
Subject: [PATCH 1184/1375] Allow for specifying a checkpoint path when
 creating SingularMonitoredSession.

PiperOrigin-RevId: 162728280
---
 tensorflow/python/training/monitored_session.py           | 8 ++++++--
 .../tensorflow.train.-singular-monitored-session.pbtxt    | 2 +-
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index 2feb6bd3c3..245891c95e 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -726,7 +726,8 @@ class SingularMonitoredSession(_MonitoredSession):
                master='',
                config=None,
                checkpoint_dir=None,
-               stop_grace_period_secs=120):
+               stop_grace_period_secs=120,
+               checkpoint_filename_with_path=None):
     """Creates a SingularMonitoredSession.
 
     Args:
@@ -739,12 +740,15 @@ class SingularMonitoredSession(_MonitoredSession):
         variables.
       stop_grace_period_secs: Number of seconds given to threads to stop after
         `close()` has been called.
+      checkpoint_filename_with_path: A string. Optional path to a checkpoint
+        file from which to restore variables.
     """
     session_creator = ChiefSessionCreator(
         scaffold=scaffold,
         master=master,
         config=config,
-        checkpoint_dir=checkpoint_dir)
+        checkpoint_dir=checkpoint_dir,
+        checkpoint_filename_with_path=checkpoint_filename_with_path)
     super(SingularMonitoredSession, self).__init__(
         session_creator, hooks, should_recover=False,
         stop_grace_period_secs=stop_grace_period_secs)
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-singular-monitored-session.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-singular-monitored-session.pbtxt
index 62bfdab40b..7caf837cc3 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-singular-monitored-session.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-singular-monitored-session.pbtxt
@@ -9,7 +9,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'hooks\', \'scaffold\', \'master\', \'config\', \'checkpoint_dir\', \'stop_grace_period_secs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\', \'None\', \'None\', \'120\'], "
+    argspec: "args=[\'self\', \'hooks\', \'scaffold\', \'master\', \'config\', \'checkpoint_dir\', \'stop_grace_period_secs\', \'checkpoint_filename_with_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\', \'None\', \'None\', \'120\', \'None\'], "
   }
   member_method {
     name: "close"
-- 
GitLab


From 9c7972ec559dd63f3b792a09fadd93f2fbf31a34 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 04:40:56 -0700
Subject: [PATCH 1185/1375] Update datalayout constant copied in XLA to match
 LLVM r308675 This may break the CUDA backend until the LLVM revision is
 updated.

PiperOrigin-RevId: 162728312
---
 tensorflow/compiler/xla/service/gpu/gpu_compiler.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 47fba36822..d60c45a5c3 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -81,7 +81,7 @@ const char* kTargetTriple = "nvptx64-nvidia-cuda";
 
 // The data layout of the emitted module. Copied from computeDataLayout in
 // NVPTXTargetMachine.cpp.
-const char* kDataLayout = "e-i64:64-v16:16-v32:32-n16:32:64";
+const char* kDataLayout = "e-i64:64-i128:128-v16:16-v32:32-n16:32:64";
 
 // Any address of a variable residing in global memory or returned by one of the
 // memory allocation routines from the driver or runtime API is always aligned
-- 
GitLab


From 83faa857765a4c9846447866ee99aeecfafb9b59 Mon Sep 17 00:00:00 2001
From: Manjunath Kudlur <keveman@google.com>
Date: Fri, 21 Jul 2017 06:06:40 -0700
Subject: [PATCH 1186/1375] Automated g4 rollback of changelist 162668355

PiperOrigin-RevId: 162733043
---
 tensorflow/c/c_api.h                          |   1 -
 .../contrib/cmake/tf_core_framework.cmake     |   7 -
 tensorflow/core/BUILD                         |  22 +-
 tensorflow/core/framework/allocator.h         |  19 --
 tensorflow/core/framework/tensor.cc           |  78 -----
 tensorflow/core/framework/tensor.proto        |  13 -
 tensorflow/core/framework/tensor_test.cc      |  98 ------
 tensorflow/core/framework/types.cc            |   7 +-
 tensorflow/core/framework/types.h             |   2 -
 tensorflow/core/framework/types.proto         |   2 -
 tensorflow/core/framework/variant.cc          |  87 ------
 tensorflow/core/framework/variant.h           | 287 ------------------
 .../core/framework/variant_encode_decode.h    | 220 --------------
 tensorflow/core/framework/variant_test.cc     | 249 ---------------
 .../core/platform/default/build_config.bzl    |   8 -
 tensorflow/core/platform/variant_coding.cc    |  63 ----
 tensorflow/core/platform/variant_coding.h     |  39 ---
 tensorflow/go/tensor.go                       |   1 -
 tensorflow/python/__init__.py                 |   1 -
 tensorflow/python/framework/dtypes.py         |  17 +-
 tensorflow/python/framework/dtypes_test.py    |   9 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   4 -
 22 files changed, 9 insertions(+), 1225 deletions(-)
 delete mode 100644 tensorflow/core/framework/variant.cc
 delete mode 100644 tensorflow/core/framework/variant.h
 delete mode 100644 tensorflow/core/framework/variant_encode_decode.h
 delete mode 100644 tensorflow/core/framework/variant_test.cc
 delete mode 100644 tensorflow/core/platform/variant_coding.cc
 delete mode 100644 tensorflow/core/platform/variant_coding.h

diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h
index 46758408c4..3aeafb4685 100644
--- a/tensorflow/c/c_api.h
+++ b/tensorflow/c/c_api.h
@@ -117,7 +117,6 @@ typedef enum TF_DataType {
   TF_COMPLEX128 = 18,  // Double-precision complex
   TF_HALF = 19,
   TF_RESOURCE = 20,
-  TF_VARIANT = 21,
 } TF_DataType;
 
 // TF_DataTypeSize returns the sizeof() for the underlying type corresponding
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index b66eb35339..a15621ba6d 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -156,11 +156,6 @@ if (NOT tensorflow_ENABLE_GPU)
       "${tensorflow_source_dir}/tensorflow/core/platform/default/cuda_libdevice_path.*")
   list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_gpu_srcs})
 endif()
-
-file(GLOB tf_core_platform_exclude_srcs
-  "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.cc")
-list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_exclude_srcs})
-
 list(APPEND tf_core_lib_srcs ${tf_core_platform_srcs})
 
 if(UNIX)
@@ -230,8 +225,6 @@ set(tf_version_srcs ${tensorflow_source_dir}/tensorflow/core/util/version_info.c
 file(GLOB_RECURSE tf_core_framework_srcs
     "${tensorflow_source_dir}/tensorflow/core/framework/*.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*.cc"
-    "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.h"
-    "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.cc"
     "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.h"
     "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc"
     "${tensorflow_source_dir}/tensorflow/core/graph/graph.h"
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 271b2492fe..5885d4ed52 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -94,8 +94,6 @@ load(
     "tf_additional_lib_deps",
     "tf_additional_lib_hdrs",
     "tf_additional_lib_srcs",
-    "tf_additional_framework_hdrs",
-    "tf_additional_framework_srcs",
     "tf_additional_minimal_lib_srcs",
     "tf_additional_proto_hdrs",
     "tf_additional_proto_srcs",
@@ -345,8 +343,6 @@ tf_cuda_library(
     hdrs = [
         "example/feature_util.h",
         "framework/allocator.h",
-        "framework/variant.h",
-        "framework/variant_encode_decode.h",
         "framework/allocator_registry.h",
         "framework/attr_value_util.h",
         "framework/bfloat16.h",
@@ -1269,8 +1265,6 @@ LIB_INTERNAL_WINDOWS_DEPS = glob(
         "platform/**/cuda_libdevice_path.cc",
         "platform/**/stream_executor.h",
         "platform/load_library.cc",
-        "platform/variant_coding.cc",
-        "platform/**/variant_cord_coding.cc",
     ],
 )
 
@@ -1292,8 +1286,6 @@ cc_library(
             ],
             exclude = [
                 "**/*test*",
-                "framework/variant.cc",
-                "platform/variant_coding.cc",
                 "lib/hash/crc32c_accelerate.cc",
                 "lib/gif/**/*",
                 "lib/jpeg/**/*",
@@ -1304,8 +1296,6 @@ cc_library(
                 "platform/**/cuda_libdevice_path.cc",
                 "platform/**/stream_executor.h",
                 "platform/**/gpu_tracer.cc",
-                "platform/variant_coding.cc",
-                "platform/**/variant_cord_coding.cc",
             ],
         ),
     }) + tf_additional_lib_srcs(
@@ -1316,8 +1306,6 @@ cc_library(
             "platform/**/stream_executor.h",
             "platform/**/env_time.cc",
             "platform/**/gpu_tracer.cc",
-            "platform/variant_coding.cc",
-            "platform/**/variant_cord_coding.cc",
         ] +
         # Protobuf deps already included through the ":lib_proto_parsing"
         # dependency.
@@ -1474,8 +1462,6 @@ tf_cuda_library(
             "util/**/*.h",
             "util/**/*.cc",
         ] + [
-            "platform/variant_coding.cc",
-            "platform/variant_coding.h",
             "graph/edgeset.h",
             "graph/edgeset.cc",
             "graph/graph.h",
@@ -1504,22 +1490,20 @@ tf_cuda_library(
             "util/memmapped_file_system_writer.h",
             "util/memmapped_file_system_writer.cc",
         ],
-    }) + tf_additional_framework_srcs(),
+    }),
     hdrs = [
-        "framework/variant.h",
         "framework/op_segment.h",
         "framework/rendezvous.h",  # only needed for tests
         "framework/tensor_reference.h",
         "framework/tracking_allocator.h",  # only needed for tests
         "framework/unique_tensor_references.h",
-        "platform/variant_coding.h",
         "util/command_line_flags.h",
         "util/env_var.h",
         "util/equal_graph_def.h",
         "util/presized_cuckoo_map.h",
         "util/tensor_slice_set.h",
         "util/tensor_slice_util.h",
-    ] + tf_additional_framework_hdrs(),
+    ],
     copts = tf_copts(),
     linkopts = select({
         "//tensorflow:freebsd": [],
@@ -1533,7 +1517,6 @@ tf_cuda_library(
         ":proto_text",
         ":protos_all_cc",
         ":version_lib",
-        "//tensorflow/core/platform/default/build_config:platformlib",
         "//tensorflow/core/kernels:bounds_check",
         "//third_party/eigen3",
     ] + if_mkl(["//third_party/mkl:intel_binary_blob"]),
@@ -2200,7 +2183,6 @@ tf_cc_tests(
         "common_runtime/simple_placer_test.cc",
         "example/feature_util_test.cc",
         "framework/allocator_test.cc",
-        "framework/variant_test.cc",
         "framework/attr_value_util_test.cc",
         "framework/bfloat16_test.cc",
         "framework/cancellation_test.cc",
diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h
index 868335b072..1cb183d81e 100644
--- a/tensorflow/core/framework/allocator.h
+++ b/tensorflow/core/framework/allocator.h
@@ -23,7 +23,6 @@ limitations under the License.
 #include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/type_traits.h"
-#include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -230,14 +229,6 @@ class Allocator {
     for (size_t i = 0; i < n; ++p, ++i) p->~ResourceHandle();
   }
 
-  virtual void RunVariantCtor(Variant* p, size_t n) {
-    for (size_t i = 0; i < n; ++p, ++i) new (p) Variant();
-  }
-
-  virtual void RunVariantDtor(Variant* p, size_t n) {
-    for (size_t i = 0; i < n; ++p, ++i) p->~Variant();
-  }
-
   // TODO(jeff): Maybe provide some interface to give info about
   // current allocation state (total number of bytes available for
   // allocation, number of bytes free on device, etc.)
@@ -265,16 +256,6 @@ inline void Allocator::RunDtor(ResourceHandle* p, size_t n) {
   RunResourceDtor(p, n);
 }
 
-template <>
-inline void Allocator::RunCtor(Variant* p, size_t n) {
-  RunVariantCtor(p, n);
-}
-
-template <>
-inline void Allocator::RunDtor(Variant* p, size_t n) {
-  RunVariantDtor(p, n);
-}
-
 // An implementation of Allocator that delegates all calls to another Allocator.
 //
 // Useful to clients who want to override part of the functionality of another
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 0a85894071..80b98fb9c6 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -36,8 +36,6 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_description.pb.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/framework/variant.h"
-#include "tensorflow/core/framework/variant_encode_decode.h"
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
@@ -49,7 +47,6 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/tensor_coding.h"
 #include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/platform/variant_coding.h"
 
 namespace tensorflow {
 namespace {
@@ -223,36 +220,6 @@ struct Helper<ResourceHandle> {
   }
 };
 
-template <>
-struct Helper<Variant> {
-  // Encodes "n" elements of type Variant stored in "in" into destination
-  // "out", which is usually the TensorProto::tensor_content.
-  template <typename Destination>
-  static void Encode(TensorBuffer* in, int64 n, Destination* out) {
-    port::EncodeVariantList(in->base<const Variant>(), n, out);
-  }
-
-  // Decodes "n" elements of type Variant from "in" and constructs a
-  // buffer out of it. Returns nullptr if the decoding fails. "in" is
-  // usually the TensorProto::tensor_content.
-  template <typename Source>
-  static TensorBuffer* Decode(Allocator* a, const Source& in, int64 n) {
-    auto* buf = new Buffer<Variant>(a, n);
-    Variant* ps = buf->template base<Variant>();
-    if (ps == nullptr || !port::DecodeVariantList(in, ps, n)) {
-      buf->Unref();
-      return nullptr;
-    }
-    return buf;
-  }
-
-  // Returns the estimated memory usage of "n" elements of type T
-  // stored in buffer "in".
-  static int64 TotalBytes(TensorBuffer* in, int n) {
-    return n * sizeof(Variant);
-  }
-};
-
 template <typename T>
 struct ProtoHelper {};
 
@@ -322,26 +289,6 @@ struct ProtoHelper<ResourceHandle> {
   }
 };
 
-template <>
-struct ProtoHelper<Variant> {
-  static protobuf::RepeatedPtrField<VariantTensorDataProto>::const_iterator
-  Begin(const TensorProto& proto) {
-    return proto.variant_val().begin();
-  }
-  static size_t NumElements(const TensorProto& proto) {
-    return proto.variant_val().size();
-  }
-  static void Fill(const Variant* data, size_t n, TensorProto* proto) {
-    auto* variant_values = proto->mutable_variant_val();
-    variant_values->Clear();
-    for (size_t i = 0; i < n; ++i) {
-      VariantTensorData tmp;
-      data[i].Encode(&tmp);
-      tmp.ToProto(variant_values->Add());
-    }
-  }
-};
-
 template <>
 struct ProtoHelper<complex64> {
   typedef Helper<float>::RepeatedFieldType FieldType;
@@ -474,30 +421,6 @@ TensorBuffer* FromProtoField(Allocator* a, const TensorProto& in, int64 n) {
   return buf;
 }
 
-template <>
-TensorBuffer* FromProtoField<Variant>(Allocator* a, const TensorProto& in,
-                                      int64 n) {
-  CHECK_GT(n, 0);
-  Buffer<Variant>* buf = new Buffer<Variant>(a, n);
-  Variant* data = buf->template base<Variant>();
-  if (data == nullptr) {
-    buf->Unref();
-    return nullptr;
-  }
-  const int64 in_n = ProtoHelper<Variant>::NumElements(in);
-  if (in_n <= 0) {
-    std::fill_n(data, n, Variant());
-  } else {
-    for (int64 i = 0; i < in_n; ++i) {
-      data[i] = in.variant_val(i);
-    }
-    for (int64 i = in_n; i < n; ++i) {
-      data[i] = Variant();
-    }
-  }
-  return buf;
-}
-
 // fp16 is opaque to the protobuf, so we deserialize these identical to uint16
 // but with data stored in half_val instead of int_val (ie., we don't use
 // ProtoHelper<uint16>).
@@ -648,7 +571,6 @@ bool Tensor::RefCountIsOne() const {
     CASE(bfloat16, SINGLE_ARG(STMTS))                          \
     CASE(Eigen::half, SINGLE_ARG(STMTS))                       \
     CASE(ResourceHandle, SINGLE_ARG(STMTS))                    \
-    CASE(Variant, SINGLE_ARG(STMTS))                           \
     case DT_INVALID:                                           \
       INVALID;                                                 \
       break;                                                   \
diff --git a/tensorflow/core/framework/tensor.proto b/tensorflow/core/framework/tensor.proto
index 7e4af7a645..98ecef225a 100644
--- a/tensorflow/core/framework/tensor.proto
+++ b/tensorflow/core/framework/tensor.proto
@@ -72,17 +72,4 @@ message TensorProto {
 
   // DT_RESOURCE
   repeated ResourceHandleProto resource_handle_val = 14;
-
-  // DT_VARIANT
-  repeated VariantTensorDataProto variant_val = 15;
 };
-
-// Protocol buffer representing the serialization format of DT_VARIANT tensors.
-message VariantTensorDataProto {
-  // Name of the type of objects being serialized.
-  string type_name = 1;
-  // Portions of the object that are not Tensors.
-  bytes metadata = 2;
-  // Tensors contained within objects being serialized.
-  repeated TensorProto tensors = 3;
-}
diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc
index 9aaf00853d..6c9c803af6 100644
--- a/tensorflow/core/framework/tensor_test.cc
+++ b/tensorflow/core/framework/tensor_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/framework/variant_encode_decode.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/test.h"
@@ -38,35 +37,6 @@ inline bool operator==(const ResourceHandle& a, const ResourceHandle& b) {
          a.maybe_type_name() == b.maybe_type_name();
 }
 
-inline bool operator==(const Variant& a, const Variant& b) {
-  if (a.is_empty()) {
-    return b.is_empty();
-  }
-
-  if (a.TypeId() != b.TypeId()) return false;
-  if (a.TypeName() != b.TypeName()) return false;
-
-  VariantTensorData a_data, b_data;
-  a.Encode(&a_data);
-  b.Encode(&b_data);
-
-  if (a_data.metadata != b_data.metadata) return false;
-
-  if (a_data.tensors.size() != b_data.tensors.size()) return false;
-
-  for (int i = 0; i < a_data.tensors.size(); ++i) {
-    TensorProto a_proto, b_proto;
-    a_data.tensors[i].AsProtoTensorContent(&a_proto);
-    b_data.tensors[i].AsProtoTensorContent(&b_proto);
-    string a_str, b_str;
-    a_proto.SerializeToString(&a_str);
-    b_proto.SerializeToString(&b_str);
-    if (a_str != b_str) return false;
-  }
-
-  return true;
-}
-
 TEST(TensorTest, Default) {
   Tensor t;
   EXPECT_EQ(t.dtype(), DT_FLOAT);
@@ -189,74 +159,6 @@ TEST(Tensor_ResourceHandle, Simple) {
   TestCopies<ResourceHandle>(t);
 }
 
-TEST(Tensor_Variant, Simple) {
-  Tensor t(DT_VARIANT, TensorShape({}));
-  Tensor value(DT_FLOAT, TensorShape({}));
-  value.flat<float>()(0) = 42.0f;
-  t.flat<Variant>()(0) = value;
-  // All the tests in TestCopies except the ones that serialize and deserialize
-  // the tensor. The consumer of a serialized Variant Tensor should know what
-  // type is stored in the Tensor, so not testing the generic
-  // serialize/deserialize case here.
-  {
-    LOG(INFO) << "CopyFrom()";
-    Tensor t2(t.dtype());
-    EXPECT_TRUE(t2.CopyFrom(t, t.shape()));
-    test::ExpectTensorEqual<Variant>(t, t2);
-  }
-  {
-    LOG(INFO) << "operator=()";
-    Tensor t2(t.dtype());
-    t2 = t;
-    test::ExpectTensorEqual<Variant>(t, t2);
-  }
-  {
-    LOG(INFO) << "deep copy";
-    Tensor t2(t.dtype(), t.shape());
-    t2.flat<Variant>() = t.flat<Variant>();
-    test::ExpectTensorEqual<Variant>(t, t2);
-  }
-  {
-    LOG(INFO) << "AsTensor";
-    gtl::ArraySlice<Variant> values(t.flat<Variant>().data(), t.NumElements());
-    Tensor t2 = test::AsTensor(values, t.shape());
-    test::ExpectTensorEqual<Variant>(t, t2);
-  }
-  {
-    LOG(INFO) << "Move constructor";
-    Tensor t2 = t;
-    Tensor t3(std::move(t2));
-    test::ExpectTensorEqual<Variant>(t, t3);
-    EXPECT_TRUE(t3.IsInitialized());
-    EXPECT_FALSE(t2.IsInitialized());
-  }
-  {
-    LOG(INFO) << "Move assignment";
-    Tensor t2 = t;
-    Tensor t3 = std::move(t2);
-    Tensor* t4 = &t3;
-    *t4 = std::move(t3);
-    test::ExpectTensorEqual<Variant>(t, t3);
-    EXPECT_TRUE(t3.IsInitialized());
-    EXPECT_FALSE(t2.IsInitialized());
-  }
-}
-
-TEST(Tensor_Variant, Marshal) {
-  Tensor t(DT_VARIANT, TensorShape({}));
-
-  Tensor internal(DT_FLOAT, TensorShape({}));
-  internal.flat<float>()(0) = 42.0f;
-  t.flat<Variant>()(0) = internal;
-
-  LOG(INFO) << "AsProtoField()";
-  TensorProto proto;
-  t.AsProtoField(&proto);
-
-  Tensor t2(t.dtype());
-  EXPECT_TRUE(t2.FromProto(proto));
-}
-
 TEST(Tensor_UInt16, Simple) {
   Tensor t(DT_UINT16, TensorShape({2, 2}));
   EXPECT_TRUE(t.shape().IsSameSize(TensorShape({2, 2})));
diff --git a/tensorflow/core/framework/types.cc b/tensorflow/core/framework/types.cc
index 39dd5b435e..28b5e8be11 100644
--- a/tensorflow/core/framework/types.cc
+++ b/tensorflow/core/framework/types.cc
@@ -87,8 +87,6 @@ string DataTypeString(DataType dtype) {
       return "half";
     case DT_RESOURCE:
       return "resource";
-    case DT_VARIANT:
-      return "variant";
     default:
       LOG(ERROR) << "Unrecognized DataType enum value " << dtype;
       return strings::StrCat("unknown dtype enum (", dtype, ")");
@@ -167,9 +165,6 @@ bool DataTypeFromString(StringPiece sp, DataType* dt) {
   } else if (sp == "resource") {
     *dt = DT_RESOURCE;
     return true;
-  } else if (sp == "variant") {
-    *dt = DT_VARIANT;
-    return true;
   }
   return false;
 }
@@ -191,7 +186,7 @@ DataTypeVector AllTypes() {
   return {DT_FLOAT,   DT_DOUBLE, DT_INT32,  DT_UINT8,     DT_INT16,
           DT_UINT16,  DT_INT8,   DT_STRING, DT_COMPLEX64, DT_COMPLEX128,
           DT_INT64,   DT_BOOL,   DT_QINT8,  DT_QUINT8,    DT_QINT16,
-          DT_QUINT16, DT_QINT32, DT_HALF,   DT_RESOURCE,  DT_VARIANT};
+          DT_QUINT16, DT_QINT32, DT_HALF,   DT_RESOURCE};
 }
 
 #if !defined(IS_MOBILE_PLATFORM) || defined(SUPPORT_SELECTIVE_REGISTRATION)
diff --git a/tensorflow/core/framework/types.h b/tensorflow/core/framework/types.h
index 9127750d68..889056647c 100644
--- a/tensorflow/core/framework/types.h
+++ b/tensorflow/core/framework/types.h
@@ -30,7 +30,6 @@ limitations under the License.
 #include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/types.pb.h"
-#include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
@@ -182,7 +181,6 @@ MATCH_TYPE_AND_ENUM(qint32, DT_QINT32);
 MATCH_TYPE_AND_ENUM(bfloat16, DT_BFLOAT16);
 MATCH_TYPE_AND_ENUM(Eigen::half, DT_HALF);
 MATCH_TYPE_AND_ENUM(ResourceHandle, DT_RESOURCE);
-MATCH_TYPE_AND_ENUM(Variant, DT_VARIANT);
 
 #undef MATCH_TYPE_AND_ENUM
 
diff --git a/tensorflow/core/framework/types.proto b/tensorflow/core/framework/types.proto
index 1beb2a1aa2..b80e2b31dc 100644
--- a/tensorflow/core/framework/types.proto
+++ b/tensorflow/core/framework/types.proto
@@ -34,7 +34,6 @@ enum DataType {
   DT_COMPLEX128 = 18;  // Double-precision complex
   DT_HALF = 19;
   DT_RESOURCE = 20;
-  DT_VARIANT = 21;  // Arbitrary C++ data types
 
   // TODO(josh11b): DT_GENERIC_PROTO = ??;
   // TODO(jeff,josh11b): DT_UINT64?  DT_UINT32?
@@ -61,6 +60,5 @@ enum DataType {
   DT_COMPLEX128_REF = 118;
   DT_HALF_REF = 119;
   DT_RESOURCE_REF = 120;
-  DT_VARIANT_REF = 121;
 }
 // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.h,https://www.tensorflow.org/code/tensorflow/go/tensor.go)
diff --git a/tensorflow/core/framework/variant.cc b/tensorflow/core/framework/variant.cc
deleted file mode 100644
index 6a11f86c23..0000000000
--- a/tensorflow/core/framework/variant.cc
+++ /dev/null
@@ -1,87 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/core/framework/variant.h"
-#include "tensorflow/core/framework/variant_encode_decode.h"
-#include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/lib/gtl/map_util.h"
-
-namespace tensorflow {
-
-template <>
-void* Variant::get() {
-  if (is_empty()) {
-    return nullptr;
-  }
-  return value_->RawPtr();
-}
-
-template <>
-const void* Variant::get() const {
-  if (is_empty()) {
-    return nullptr;
-  }
-  return value_->RawPtr();
-}
-
-void VariantTensorData::ToProto(VariantTensorDataProto* proto) const {
-  proto->set_type_name(type_name);
-  proto->set_metadata(metadata);
-  proto->clear_tensors();
-  for (int i = 0; i < tensors.size(); ++i) {
-    tensors[i].AsProtoField(proto->mutable_tensors()->Add());
-  }
-}
-
-bool VariantTensorData::FromProto(const VariantTensorDataProto& proto) {
-  type_name = proto.type_name();
-  metadata = proto.metadata();
-  tensors.clear();
-  for (int i = 0; i < proto.tensors_size(); ++i) {
-    Tensor tmp;
-    if (!tmp.FromProto(proto.tensors(i))) return false;
-    tensors.push_back(tmp);
-  }
-  return true;
-}
-
-template <>
-string TypeNameVariant(VariantTensorDataProto&& value) {
-  return value.GetTypeName();
-}
-
-template <>
-void EncodeVariant(VariantTensorDataProto&& value, VariantTensorData* data) {
-  data->FromProto(value);
-}
-
-template <>
-bool DecodeVariant(const VariantTensorData& data,
-                   VariantTensorDataProto* value) {
-  data.ToProto(value);
-  return true;
-}
-
-template <>
-void EncodeVariant(VariantTensorDataProto&& value, string* buf) {
-  value.SerializeToString(buf);
-}
-
-template <>
-bool DecodeVariant(const string& buf, VariantTensorDataProto* value) {
-  return value->ParseFromString(buf);
-}
-
-}  // end namespace tensorflow
diff --git a/tensorflow/core/framework/variant.h b/tensorflow/core/framework/variant.h
deleted file mode 100644
index df6d903143..0000000000
--- a/tensorflow/core/framework/variant.h
+++ /dev/null
@@ -1,287 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_FRAMEWORK_VARIANT_H_
-#define TENSORFLOW_FRAMEWORK_VARIANT_H_
-
-#include <functional>
-#include <iostream>
-#include <memory>
-#include <type_traits>
-#include <unordered_map>
-#include <utility>
-
-#include "tensorflow/core/framework/type_index.h"
-#include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/platform/mutex.h"
-
-namespace tensorflow {
-
-struct VariantTensorData;
-
-template <typename T>
-string TypeNameVariant(T&& value);
-
-template <typename T>
-void EncodeVariant(T&& value, VariantTensorData* data);
-
-template <typename T>
-bool DecodeVariant(const VariantTensorData& data, T* value);
-
-template <typename T>
-void EncodeVariant(T&& value, string* buf);
-
-template <typename T>
-bool DecodeVariant(const string& buf, T* value);
-
-// This is an implementation of a type-erased container that can store an
-// object of any type. The implementation is very similar to std::any, but has
-// restrictions on the types of objects that can be stored, and eschews some of
-// the fancier constructors available for std::any. An object of
-// tensorflow::Variant is intended to be used as the value that will be stored
-// in a tensorflow::Tensor object when its type is DT_VARIANT.
-//
-// tensorflow::Variant can store an object of a class that satisfies the
-// following constraints:
-//
-// * The class is CopyConstructible.
-// * The class has a default constructor.
-// * It's either a protocol buffer, a tensorflow::Tensor, or defines the
-// following functions:
-//
-//   string TypeName() const;
-//   void Encode(VariantTensorData* data) const;
-//   void Decode(const VariantTensorData& data);
-//
-// Simple POD types can elide the Encode/Decode functions, they are provided by
-// helper methods.
-// Here are some typical usage patterns:
-//
-//   Variant x = 10;
-//   EXPECT_EQ(*x.get<int>(), 10);
-//
-//   Tensor t(DT_FLOAT, TensorShape({}));
-//   t.flat<float>()(0) = 42.0f;
-//   Variant x = t;
-//   EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 42.0f);
-//
-// Accessing the stored object:
-//
-// The get<T> function is the main mechanism to access the object stored in the
-// contained. It is type-safe, that is, calling get<T> when the stored object's
-// type is not T, returns a nullptr. A raw pointer to the stored object can be
-// obtained by calling get<void>().
-//
-// Serializing/deserializing Variant object:
-//
-// The Variant class delegates serializing and deserializing operations to the
-// contained object. Helper functions to do these operations are provided for
-// POD data types, tensorflow::Tensor, and protocol buffer objects. However,
-// other classes have to provide Encode/Decode functions to do handle
-// serialization.
-//
-// Objects stored in a Variant object often contain references to other
-// tensorflow::Tensors of primitive types (Eg., a list of tensorflow::Tensors).
-// To efficiently support those use cases, a structure is imposed on the
-// serialization format. Namely, classes should serialize their contents in to a
-// VariantTensorData object:
-//
-// struct VariantTensorData {
-//   string type_name;
-//   string metadata;
-//   std::vector<Tensor> tensors;
-// };
-//
-// Objects with references to other Tensors can simply store those tensors in
-// the `tensors` field, and serialize other metadata content in to the
-// `metadata` field.
-//
-// Serialization example:
-//
-// Foo f = Foo {...};
-// Variant x = f;
-// string serialized_f;
-// x.Encode(&serialized_f);
-//
-// Variant y = Foo(); // default constructed Foo.
-// y.Decode(&serialized_f);
-// EXPECT_EQ(*x.get<Foo>(), *y.get<Foo>());
-//
-class Variant {
- public:
-  constexpr Variant() noexcept = default;
-
-  Variant(const Variant& other)
-      : value_(other.is_empty() ? std::unique_ptr<ValueInterface>()
-                                : other.value_->Clone()) {}
-
-  Variant(Variant&& other) noexcept = default;
-
-  // Make sure that the type is CopyConstructible and not a tensorflow::Variant
-  // object itself. We want the copy constructor to be chosen for the
-  // tensorflow::Variant case.
-  template <typename T, typename VT = typename std::decay<T>::type,
-            typename std::enable_if<!std::is_same<Variant, VT>::value &&
-                                        std::is_copy_constructible<VT>::value,
-                                    void>::type* = nullptr>
-  Variant(T&& value)  // NOLINT
-      : value_(new Value<VT>(in_place, std::forward<T>(value))) {}
-
-  Variant& operator=(const Variant& rhs) {
-    Variant(rhs).swap(*this);
-    return *this;
-  }
-
-  Variant& operator=(Variant&& rhs) noexcept {
-    Variant(std::move(rhs)).swap(*this);
-    return *this;
-  }
-
-  bool is_empty() const { return value_ == nullptr; }
-
-  void clear() noexcept { value_.reset(); }
-
-  void swap(Variant& other) noexcept { value_.swap(other.value_); }
-
-  TypeIndex TypeId() const {
-    const TypeIndex VoidTypeIndex = MakeTypeIndex<void>();
-    if (is_empty()) {
-      return VoidTypeIndex;
-    }
-    return value_->TypeId();
-  }
-
-  template <typename T>
-  T* get() {
-    const TypeIndex TTypeIndex = MakeTypeIndex<T>();
-    if (is_empty() || (TTypeIndex != TypeId())) {
-      return nullptr;
-    }
-    return std::addressof(static_cast<Variant::Value<T>*>(value_.get())->value);
-  }
-
-  template <typename T>
-  const T* get() const {
-    const TypeIndex TTypeIndex = MakeTypeIndex<T>();
-    if (is_empty() || (TTypeIndex != TypeId())) {
-      return nullptr;
-    }
-    return std::addressof(
-        static_cast<const Variant::Value<T>*>(value_.get())->value);
-  }
-
-  string TypeName() const {
-    if (is_empty()) {
-      return "";
-    }
-    return value_->TypeName();
-  }
-
-  // Serialize the contents of the stored object into `data`.
-  void Encode(VariantTensorData* data) const {
-    if (!is_empty()) {
-      value_->Encode(data);
-    }
-  }
-
-  // Deserialize `data` and update the stored object.
-  bool Decode(const VariantTensorData& data) {
-    if (!is_empty()) {
-      return value_->Decode(data);
-    }
-    return true;
-  }
-
-  // Helper methods to directly serialize/deserialize from strings.
-  void Encode(string* buf) const {
-    if (!is_empty()) {
-      value_->Encode(buf);
-    }
-  }
-  bool Decode(const string& buf) {
-    if (!is_empty()) {
-      return value_->Decode(buf);
-    }
-    return true;
-  }
-
- private:
-  struct in_place_t {};
-  static constexpr in_place_t in_place{};
-
-  struct ValueInterface {
-    virtual ~ValueInterface() = default;
-    virtual TypeIndex TypeId() const = 0;
-    virtual void* RawPtr() = 0;
-    virtual const void* RawPtr() const = 0;
-    virtual std::unique_ptr<ValueInterface> Clone() const = 0;
-    virtual string TypeName() const = 0;
-    virtual void Encode(VariantTensorData* data) const = 0;
-    virtual bool Decode(const VariantTensorData& data) = 0;
-    virtual void Encode(string* buf) const = 0;
-    virtual bool Decode(const string& data) = 0;
-  };
-
-  template <typename T>
-  struct Value : ValueInterface {
-    template <class... Args>
-    explicit Value(in_place_t /*tag*/, Args&&... args)
-        : value(std::forward<Args>(args)...) {}
-
-    TypeIndex TypeId() const override {
-      const TypeIndex value_type_index =
-          MakeTypeIndex<typename std::decay<T>::type>();
-      return value_type_index;
-    }
-
-    void* RawPtr() override { return &value; }
-
-    const void* RawPtr() const override { return &value; }
-
-    std::unique_ptr<ValueInterface> Clone() const override {
-      return std::unique_ptr<ValueInterface>(new Value(in_place, value));
-    }
-
-    string TypeName() const override { return TypeNameVariant(value); }
-
-    void Encode(VariantTensorData* data) const override {
-      EncodeVariant(value, data);
-    }
-
-    bool Decode(const VariantTensorData& data) override {
-      return DecodeVariant(data, &value);
-    }
-
-    void Encode(string* buf) const override { EncodeVariant(value, buf); }
-
-    bool Decode(const string& buf) override {
-      return DecodeVariant(buf, &value);
-    }
-
-    T value;
-  };
-
-  std::unique_ptr<ValueInterface> value_;
-};
-
-template <>
-void* Variant::get();
-
-template <>
-const void* Variant::get() const;
-
-}  // end namespace tensorflow
-
-#endif  // TENSORFLOW_FRAMEWORK_VARIANT_H_
diff --git a/tensorflow/core/framework/variant_encode_decode.h b/tensorflow/core/framework/variant_encode_decode.h
deleted file mode 100644
index 09309aa7e5..0000000000
--- a/tensorflow/core/framework/variant_encode_decode.h
+++ /dev/null
@@ -1,220 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
-#define TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
-
-#include <iostream>
-#include <type_traits>
-#include <utility>
-#include <vector>
-
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/platform/protobuf.h"
-
-namespace tensorflow {
-
-// The serialization format for Variant objects. Objects with references to
-// other Tensors can simply store those tensors in the `tensors` field, and
-// serialize other metadata content in to the `metadata` field. Objects can
-// optionally set the `type_name` for type-checking before deserializing an
-// object.
-struct VariantTensorData {
-  string type_name;
-  string metadata;
-  std::vector<Tensor> tensors;
-  void ToProto(VariantTensorDataProto* proto) const;
-  bool FromProto(const VariantTensorDataProto& proto);
-};
-
-// Type used for tag-dispatch of the Encode/Decode Variant implementations. This
-// template can determine whether the first type parameter `T` is one of the
-// following:
-//
-// * A POD type (TypeResolver<T, true>)
-// * A tensorflow::Tensor (TypeResolver<T, false, true>)
-// * A protocol buffer (TypeResolver<T, false, false, true>)
-// * None of the above (TypeResolver<T, false, false, false>)
-//
-template <typename T, bool = std::is_pod<typename std::decay<T>::type>::value,
-          bool = std::is_same<typename std::decay<T>::type,
-                              ::tensorflow::Tensor>::value,
-          bool = std::is_base_of<protobuf::MessageLite,
-                                 typename std::decay<T>::type>::value>
-struct TypeResolver {};
-
-// Specialization for POD type
-template <typename T>
-void EncodeVariantImpl(T&& value, TypeResolver<T, true /* is_pod */>,
-                       VariantTensorData* data) {
-  data->metadata.assign(reinterpret_cast<const char*>(&value), sizeof(value));
-}
-
-// Specialization for tensorflow::Tensor
-template <typename T>
-void EncodeVariantImpl(T&& value,
-                       TypeResolver<T, false /* is_pod */, true /* Tensor */>,
-                       VariantTensorData* data) {
-  data->tensors.clear();
-  data->tensors.push_back(value);
-}
-
-// Specialization for protobuf
-template <typename T>
-void EncodeVariantImpl(T&& value,
-                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
-                                    true /* protobuf */>,
-                       VariantTensorData* data) {
-  value.SerializeToString(&data->metadata);
-}
-
-// Specialization for other types
-template <typename T>
-void EncodeVariantImpl(T&& value,
-                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
-                                    false /* protobuf */>,
-                       VariantTensorData* data) {
-  value.Encode(data);
-}
-
-// Specialization for POD type
-template <typename T>
-bool DecodeVariantImpl(const VariantTensorData& data,
-                       TypeResolver<T, true /* is_pod */>, T* value) {
-  std::copy_n(data.metadata.data(), sizeof(*value),
-              reinterpret_cast<char*>(value));
-  return true;
-}
-
-// Specialization for tensorflow::Tensor
-template <typename T>
-bool DecodeVariantImpl(const VariantTensorData& data,
-                       TypeResolver<T, false /* is_pod */, true /* Tensor */>,
-                       T* value) {
-  *value = data.tensors[0];
-  return true;
-}
-
-// Specialization for protobuf
-template <typename T>
-bool DecodeVariantImpl(const VariantTensorData& data,
-                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
-                                    true /* protobuf */>,
-                       T* value) {
-  return value->ParseFromString(data.metadata);
-}
-
-// Specialization for other types
-template <typename T>
-bool DecodeVariantImpl(const VariantTensorData& data,
-                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
-                                    false /* protobuf */>,
-                       T* value) {
-  return value->Decode(data);
-}
-
-template <typename C, typename = void>
-struct has_type_name : std::false_type {};
-
-template <typename C>
-struct has_type_name<
-    C, typename std::enable_if<std::is_same<
-           decltype(std::declval<C>().TypeName()), string>::value>::type>
-    : std::true_type {};
-
-template <typename T, bool = has_type_name<typename std::decay<T>::type>::value,
-          bool = std::is_same<typename std::decay<T>::type,
-                              ::tensorflow::Tensor>::value,
-          bool = std::is_base_of<protobuf::MessageLite,
-                                 typename std::decay<T>::type>::value>
-struct TypeNameResolver {};
-
-template <typename T>
-string TypeNameVariantImpl(T&& value,
-                           TypeNameResolver<T, true /* has_type_name */>) {
-  return value.TypeName();
-}
-
-template <typename T>
-string TypeNameVariantImpl(
-    T&& value,
-    TypeNameResolver<T, false /* has_type_name */, true /* Tensor */>) {
-  return "tensorflow::Tensor";
-}
-
-template <typename T>
-string TypeNameVariantImpl(
-    T&& value, TypeNameResolver<T, false /* has_type_name */,
-                                false /* Tensor */, true /* protobuf */>) {
-  return value.GetTypeName();
-}
-
-template <typename T>
-string TypeNameVariantImpl(
-    T&& value, TypeNameResolver<T, false /* has_type_name */,
-                                false /* Tensor */, false /* protobuf */>) {
-  return value.TypeName();
-}
-
-template <typename T>
-string TypeNameVariant(T&& value) {
-  return TypeNameVariantImpl(std::forward<T>(value), TypeNameResolver<T>());
-}
-
-template <typename T>
-void EncodeVariant(T&& value, VariantTensorData* data) {
-  EncodeVariantImpl(std::forward<T>(value), TypeResolver<T>(), data);
-}
-
-template <typename T>
-bool DecodeVariant(const VariantTensorData& data, T* value) {
-  return DecodeVariantImpl(data, TypeResolver<T>(), value);
-}
-
-template <typename T>
-void EncodeVariant(T&& value, string* buf) {
-  VariantTensorData data;
-  EncodeVariantImpl(std::forward<T>(value), TypeResolver<T>(), &data);
-  VariantTensorDataProto proto;
-  data.ToProto(&proto);
-  proto.SerializeToString(buf);
-}
-
-template <typename T>
-bool DecodeVariant(const string& buf, T* value) {
-  VariantTensorDataProto proto;
-  if (!proto.ParseFromString(buf)) return false;
-  VariantTensorData data;
-  if (!data.FromProto(proto)) return false;
-  if (!DecodeVariantImpl(data, TypeResolver<T>(), value)) return false;
-  return true;
-}
-
-// Specializations for VariantTensorDataProto
-template <>
-string TypeNameVariant(VariantTensorDataProto&& value);
-template <>
-void EncodeVariant(VariantTensorDataProto&& value, VariantTensorData* data);
-template <>
-bool DecodeVariant(const VariantTensorData& data,
-                   VariantTensorDataProto* value);
-template <>
-void EncodeVariant(VariantTensorDataProto&& value, string* buf);
-template <>
-bool DecodeVariant(const string& buf, VariantTensorDataProto* value);
-
-}  // end namespace tensorflow
-
-#endif  // TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
diff --git a/tensorflow/core/framework/variant_test.cc b/tensorflow/core/framework/variant_test.cc
deleted file mode 100644
index c7ffdd28f4..0000000000
--- a/tensorflow/core/framework/variant_test.cc
+++ /dev/null
@@ -1,249 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include <vector>
-
-#include "tensorflow/core/framework/variant.h"
-#include "tensorflow/core/framework/variant_encode_decode.h"
-
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/lib/core/coding.h"
-#include "tensorflow/core/platform/test.h"
-
-namespace tensorflow {
-
-namespace {
-
-template <typename T>
-struct Wrapper {
-  T value;
-  string TypeName() const { return "POD"; }
-};
-
-using Int = Wrapper<int>;
-using Float = Wrapper<float>;
-
-}  // end namespace
-
-TEST(VariantTest, Basic) {
-  Variant x;
-  EXPECT_EQ(x.get<void>(), nullptr);
-
-  x = Int{42};
-
-  EXPECT_NE(x.get<void>(), nullptr);
-  EXPECT_NE(x.get<Int>(), nullptr);
-  EXPECT_EQ(x.get<Int>()->value, 42);
-  EXPECT_EQ(x.TypeName(), "POD");
-}
-
-TEST(VariantTest, ConstGet) {
-  Variant x;
-  EXPECT_EQ(x.get<void>(), nullptr);
-
-  x = Int{42};
-
-  const Variant y = x;
-
-  EXPECT_NE(y.get<void>(), nullptr);
-  EXPECT_NE(y.get<Int>(), nullptr);
-  EXPECT_EQ(y.get<Int>()->value, 42);
-}
-
-TEST(VariantTest, Clear) {
-  Variant x;
-  EXPECT_EQ(x.get<void>(), nullptr);
-
-  x = Int{42};
-
-  EXPECT_NE(x.get<void>(), nullptr);
-  EXPECT_NE(x.get<Int>(), nullptr);
-  EXPECT_EQ(x.get<Int>()->value, 42);
-
-  x.clear();
-  EXPECT_EQ(x.get<void>(), nullptr);
-}
-
-TEST(VariantTest, Tensor) {
-  Variant x;
-  Tensor t(DT_FLOAT, {});
-  t.flat<float>()(0) = 42.0f;
-  x = t;
-
-  EXPECT_NE(x.get<Tensor>(), nullptr);
-  EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 42.0f);
-  x.get<Tensor>()->flat<float>()(0) += 1.0f;
-  EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 43.0f);
-  EXPECT_EQ(x.TypeName(), "tensorflow::Tensor");
-}
-
-TEST(VariantTest, TensorProto) {
-  Variant x;
-  TensorProto t;
-  t.set_dtype(DT_FLOAT);
-  t.mutable_tensor_shape()->set_unknown_rank(true);
-  x = t;
-
-  EXPECT_EQ(x.TypeName(), "tensorflow.TensorProto");
-  EXPECT_NE(x.get<TensorProto>(), nullptr);
-  EXPECT_EQ(x.get<TensorProto>()->dtype(), DT_FLOAT);
-  EXPECT_EQ(x.get<TensorProto>()->tensor_shape().unknown_rank(), true);
-}
-
-TEST(VariantTest, CopyValue) {
-  Variant x, y;
-  x = Int{10};
-  y = x;
-
-  EXPECT_EQ(x.get<Int>()->value, 10);
-  EXPECT_EQ(x.get<Int>()->value, y.get<Int>()->value);
-}
-
-TEST(VariantTest, MoveValue) {
-  Variant x;
-  x = []() -> Variant {
-    Variant y;
-    y = Int{10};
-    return y;
-  }();
-  EXPECT_EQ(x.get<Int>()->value, 10);
-}
-
-TEST(VariantTest, TypeMismatch) {
-  Variant x;
-  x = Int{10};
-  EXPECT_EQ(x.get<float>(), nullptr);
-  EXPECT_EQ(x.get<int>(), nullptr);
-  EXPECT_NE(x.get<Int>(), nullptr);
-}
-
-struct TensorList {
-  void Encode(VariantTensorData* data) const { data->tensors = vec; }
-
-  bool Decode(const VariantTensorData& data) {
-    vec = data.tensors;
-    return true;
-  }
-
-  string TypeName() const { return "TensorList"; }
-
-  std::vector<Tensor> vec;
-};
-
-TEST(VariantTest, TensorListTest) {
-  Variant x;
-
-  TensorList vec;
-  for (int i = 0; i < 4; ++i) {
-    Tensor elem(DT_INT32, {1});
-    elem.flat<int>()(0) = i;
-    vec.vec.push_back(elem);
-  }
-
-  for (int i = 0; i < 4; ++i) {
-    Tensor elem(DT_FLOAT, {1});
-    elem.flat<float>()(0) = 2 * i;
-    vec.vec.push_back(elem);
-  }
-
-  x = vec;
-
-  EXPECT_EQ(x.TypeName(), "TensorList");
-  const TensorList& stored_vec = *x.get<TensorList>();
-  for (int i = 0; i < 4; ++i) {
-    EXPECT_EQ(stored_vec.vec[i].flat<int>()(0), i);
-  }
-  for (int i = 0; i < 4; ++i) {
-    EXPECT_EQ(stored_vec.vec[i + 4].flat<float>()(0), 2 * i);
-  }
-
-  VariantTensorData serialized;
-  x.Encode(&serialized);
-
-  Variant y = TensorList();
-  y.Decode(serialized);
-
-  const TensorList& decoded_vec = *x.get<TensorList>();
-  for (int i = 0; i < 4; ++i) {
-    EXPECT_EQ(decoded_vec.vec[i].flat<int>()(0), i);
-  }
-  for (int i = 0; i < 4; ++i) {
-    EXPECT_EQ(decoded_vec.vec[i + 4].flat<float>()(0), 2 * i);
-  }
-}
-
-TEST(VariantTest, VariantArray) {
-  Variant x[2];
-  x[0] = Int{2};
-  x[1] = Float{2.0f};
-
-  EXPECT_EQ(x[0].get<Int>()->value, 2);
-  EXPECT_EQ(x[1].get<Float>()->value, 2.0f);
-}
-
-TEST(VariantTest, PodUpdate) {
-  struct Pod {
-    int x;
-    float y;
-
-    string TypeName() const { return "POD"; }
-  };
-
-  Variant x = Pod{10, 20.f};
-  EXPECT_NE(x.get<Pod>(), nullptr);
-  EXPECT_EQ(x.TypeName(), "POD");
-
-  x.get<Pod>()->x += x.get<Pod>()->y;
-  EXPECT_EQ(x.get<Pod>()->x, 30);
-}
-
-TEST(VariantTest, EncodeDecodePod) {
-  struct Pod {
-    int x;
-    float y;
-
-    string TypeName() const { return "POD"; }
-  };
-
-  Variant x;
-  Pod p{10, 20.0f};
-  x = p;
-
-  VariantTensorData serialized;
-  x.Encode(&serialized);
-
-  Variant y;
-  y = Pod();
-  y.Decode(serialized);
-
-  EXPECT_EQ(p.x, y.get<Pod>()->x);
-  EXPECT_EQ(p.y, y.get<Pod>()->y);
-}
-
-TEST(VariantTest, EncodeDecodeTensor) {
-  Variant x;
-  Tensor t(DT_INT32, {});
-  t.flat<int>()(0) = 42;
-  x = t;
-
-  VariantTensorData serialized;
-  x.Encode(&serialized);
-
-  Variant y = Tensor();
-  y.Decode(serialized);
-  EXPECT_EQ(x.get<Tensor>()->flat<int>()(0), y.get<Tensor>()->flat<int>()(0));
-}
-
-}  // end namespace tensorflow
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 5db0fe423c..d12ad8a04f 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -130,14 +130,6 @@ def tf_additional_lib_srcs(exclude = []):
       ], exclude = exclude),
   })
 
-# pylint: disable=unused-argument
-def tf_additional_framework_hdrs(exclude = []):
-  return []
-
-def tf_additional_framework_srcs(exclude = []):
-  return []
-# pylint: enable=unused-argument
-
 def tf_additional_minimal_lib_srcs():
   return [
       "platform/default/integral_types.h",
diff --git a/tensorflow/core/platform/variant_coding.cc b/tensorflow/core/platform/variant_coding.cc
deleted file mode 100644
index 4bcde4f581..0000000000
--- a/tensorflow/core/platform/variant_coding.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/core/platform/variant_coding.h"
-
-#include <vector>
-#include "tensorflow/core/framework/tensor.pb.h"
-#include "tensorflow/core/lib/core/coding.h"
-#include "tensorflow/core/lib/core/stringpiece.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-
-namespace tensorflow {
-namespace port {
-
-void EncodeVariantList(const Variant* variant_array, int64 n, string* out) {
-  out->clear();
-  string rest;
-  for (int i = 0; i < n; ++i) {
-    string s;
-    variant_array[i].Encode(&s);
-    core::PutVarint32(out, s.length());
-    strings::StrAppend(&rest, s);
-  }
-  strings::StrAppend(out, rest);
-}
-
-bool DecodeVariantList(const string& in, Variant* variant_array, int64 n) {
-  std::vector<uint32> sizes(n);
-  StringPiece reader(in);
-  int64 total = 0;
-  for (auto& size : sizes) {
-    if (!core::GetVarint32(&reader, &size)) return false;
-    total += size;
-  }
-  if (total != static_cast<int64>(reader.size())) {
-    return false;
-  }
-
-  for (int i = 0; i < n; ++i) {
-    if (variant_array[i].is_empty()) {
-      variant_array[i] = VariantTensorDataProto();
-    }
-    string str(reader.data(), sizes[i]);
-    if (!variant_array[i].Decode(str)) return false;
-    reader.remove_prefix(sizes[i]);
-  }
-  return true;
-}
-
-}  // end namespace port
-}  // end namespace tensorflow
diff --git a/tensorflow/core/platform/variant_coding.h b/tensorflow/core/platform/variant_coding.h
deleted file mode 100644
index 34c2481149..0000000000
--- a/tensorflow/core/platform/variant_coding.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_PLATFORM_VARIANT_CODING_H_
-#define TENSORFLOW_PLATFORM_VARIANT_CODING_H_
-
-#include "tensorflow/core/framework/variant.h"
-
-#ifdef PLATFORM_GOOGLE
-#include "tensorflow/core/platform/google/variant_cord_coding.h"
-#endif
-
-namespace tensorflow {
-namespace port {
-
-// Encodes an array of Variant objects in to the given string.
-// `variant_array` is assumed to point to an array of `n` Variant objects.
-void EncodeVariantList(const Variant* variant_array, int64 n, string* out);
-
-// Decodes an array of Variant objects from the given string.
-// `variant_array` is assumed to point to an array of `n` Variant objects.
-bool DecodeVariantList(const string& in, Variant* variant_array, int64 n);
-
-}  // end namespace port
-}  // end namespace tensorflow
-
-#endif  // TENSORFLOW_PLATFORM_VARIANT_CODING_H_
diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
index 8b8909a6f8..4a60c736b5 100644
--- a/tensorflow/go/tensor.go
+++ b/tensorflow/go/tensor.go
@@ -227,7 +227,6 @@ var types = []struct {
 	{reflect.TypeOf(uint16(0)), C.TF_UINT16},
 	{reflect.TypeOf(complex(float64(0), float64(0))), C.TF_COMPLEX128},
 	// TODO(apassos): support DT_RESOURCE representation in go.
-	// TODO(keveman): support DT_VARIANT representation in go.
 }
 
 // shapeAndDataTypeOf returns the data type and shape of the Tensor
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 2fd8fc8688..827e4c53ee 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -208,7 +208,6 @@ _allowed_symbols.extend([
     'uint16',
     'uint8',
     'resource',
-    'variant',
 ])
 
 # Export modules and constants.
diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py
index 43535a593e..00bfae213a 100644
--- a/tensorflow/python/framework/dtypes.py
+++ b/tensorflow/python/framework/dtypes.py
@@ -48,7 +48,6 @@ class DType(object):
   * `tf.quint16`: Quantized 16-bit unsigned integer.
   * `tf.qint32`: Quantized 32-bit signed integer.
   * `tf.resource`: Handle to a mutable resource.
-  * `tf.variant`: Values of arbitrary types.
 
   In addition, variants of these types with the `_ref` suffix are
   defined for reference-typed tensors.
@@ -114,11 +113,8 @@ class DType(object):
 
   @property
   def is_numpy_compatible(self):
-    numpy_incompatible = [types_pb2.DT_VARIANT,
-                          types_pb2.DT_VARIANT_REF,
-                          types_pb2.DT_RESOURCE,
-                          types_pb2.DT_RESOURCE_REF]
-    return self._type_enum not in numpy_incompatible
+    return (self._type_enum != types_pb2.DT_RESOURCE and
+            self._type_enum != types_pb2.DT_RESOURCE_REF)
 
   @property
   def as_numpy_dtype(self):
@@ -288,8 +284,7 @@ class DType(object):
 
   @property
   def size(self):
-    if (self._type_enum == types_pb2.DT_VARIANT or
-        self._type_enum == types_pb2.DT_RESOURCE):
+    if self._type_enum == types_pb2.DT_RESOURCE:
       return 1
     return np.dtype(self.as_numpy_dtype).itemsize
 
@@ -309,7 +304,6 @@ dtype_range = {np.bool_: (False, True),
 
 # Define standard wrappers for the types_pb2.DataType enum.
 resource = DType(types_pb2.DT_RESOURCE)
-variant = DType(types_pb2.DT_VARIANT)
 float16 = DType(types_pb2.DT_HALF)
 half = float16
 float32 = DType(types_pb2.DT_FLOAT)
@@ -331,7 +325,6 @@ qint16 = DType(types_pb2.DT_QINT16)
 quint16 = DType(types_pb2.DT_QUINT16)
 qint32 = DType(types_pb2.DT_QINT32)
 resource_ref = DType(types_pb2.DT_RESOURCE_REF)
-variant_ref = DType(types_pb2.DT_VARIANT_REF)
 bfloat16 = DType(types_pb2.DT_BFLOAT16)
 float16_ref = DType(types_pb2.DT_HALF_REF)
 half_ref = float16_ref
@@ -379,7 +372,6 @@ _INTERN_TABLE = {
     types_pb2.DT_QINT32: qint32,
     types_pb2.DT_BFLOAT16: bfloat16,
     types_pb2.DT_RESOURCE: resource,
-    types_pb2.DT_VARIANT: variant,
     types_pb2.DT_HALF_REF: float16_ref,
     types_pb2.DT_FLOAT_REF: float32_ref,
     types_pb2.DT_DOUBLE_REF: float64_ref,
@@ -400,7 +392,6 @@ _INTERN_TABLE = {
     types_pb2.DT_QINT32_REF: qint32_ref,
     types_pb2.DT_BFLOAT16_REF: bfloat16_ref,
     types_pb2.DT_RESOURCE_REF: resource_ref,
-    types_pb2.DT_VARIANT_REF: variant_ref,
 }
 
 
@@ -426,7 +417,6 @@ _TYPE_TO_STRING = {
     types_pb2.DT_QINT32: "qint32",
     types_pb2.DT_BFLOAT16: "bfloat16",
     types_pb2.DT_RESOURCE: "resource",
-    types_pb2.DT_VARIANT: "variant",
     types_pb2.DT_HALF_REF: "float16_ref",
     types_pb2.DT_FLOAT_REF: "float32_ref",
     types_pb2.DT_DOUBLE_REF: "float64_ref",
@@ -447,7 +437,6 @@ _TYPE_TO_STRING = {
     types_pb2.DT_QINT32_REF: "qint32_ref",
     types_pb2.DT_BFLOAT16_REF: "bfloat16_ref",
     types_pb2.DT_RESOURCE_REF: "resource_ref",
-    types_pb2.DT_VARIANT_REF: "variant_ref",
 }
 _STRING_TO_TF = {value: _INTERN_TABLE[key]
                  for key, value in _TYPE_TO_STRING.items()}
diff --git a/tensorflow/python/framework/dtypes_test.py b/tensorflow/python/framework/dtypes_test.py
index 1e84f1b656..d5a1e61a6f 100644
--- a/tensorflow/python/framework/dtypes_test.py
+++ b/tensorflow/python/framework/dtypes_test.py
@@ -27,12 +27,9 @@ from tensorflow.python.platform import googletest
 
 
 def _is_numeric_dtype_enum(datatype_enum):
-  non_numeric_dtypes = [types_pb2.DT_VARIANT,
-                        types_pb2.DT_VARIANT_REF,
-                        types_pb2.DT_INVALID,
-                        types_pb2.DT_RESOURCE,
-                        types_pb2.DT_RESOURCE_REF]
-  return datatype_enum not in non_numeric_dtypes
+  return (datatype_enum != types_pb2.DT_INVALID and
+          datatype_enum != types_pb2.DT_RESOURCE and
+          datatype_enum != types_pb2.DT_RESOURCE_REF)
 
 
 class TypesTest(test_util.TensorFlowTestCase):
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 314449bb73..6a035aba63 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -492,10 +492,6 @@ tf_module {
     name: "variance_scaling_initializer"
     mtype: "<type \'type\'>"
   }
-  member {
-    name: "variant"
-    mtype: "<class \'tensorflow.python.framework.dtypes.DType\'>"
-  }
   member {
     name: "zeros_initializer"
     mtype: "<type \'type\'>"
-- 
GitLab


From 1483328a27822dd370f384ff69b144bca3c1d2e3 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 21 Jul 2017 08:09:30 -0700
Subject: [PATCH 1187/1375] Disable session_debug_grpc_test in OSS

PiperOrigin-RevId: 162742059
---
 tensorflow/python/debug/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index d4df789f01..1f70dc2467 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -804,6 +804,7 @@ cuda_py_test(
         "//tensorflow/python:variables",
     ],
     tags = [
+        "no_oss",  # b/62956105: port conflicts.
         "nomac",  # TODO(cais): Install of futures and grpcio on all macs.
         "notsan",
     ],
-- 
GitLab


From 1afeafe016959fc72902aa04f156ec3775c9d9d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 08:23:40 -0700
Subject: [PATCH 1188/1375] Update XLA LLVM to r308712

PiperOrigin-RevId: 162743186
---
 tensorflow/workspace.bzl    |  8 ++++----
 third_party/llvm/llvm.BUILD | 31 ++++++++++++++++++++++++++++++-
 2 files changed, 34 insertions(+), 5 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 42d2ebc3fe..25a2b7e84f 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -495,11 +495,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   temp_workaround_http_archive(
       name = "llvm",
       urls = [
-          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/9889fe2290766430b99a2d4fadbc5ba92f8004b6.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/9889fe2290766430b99a2d4fadbc5ba92f8004b6.tar.gz",
+          "http://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/4d98985c94c36b9eb4396c91fe0a72a0c5f707b2.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/4d98985c94c36b9eb4396c91fe0a72a0c5f707b2.tar.gz",
       ],
-      sha256 = "00fb4a83a4dd1c046b19730a80e2183acc647715b7a8dcc8e808d49ea5530ca8",
-      strip_prefix = "llvm-9889fe2290766430b99a2d4fadbc5ba92f8004b6",
+      sha256 = "1a085c995522fa19900568c03eb595b425df53842c7f281e3ab79aaa04affffa",
+      strip_prefix = "llvm-4d98985c94c36b9eb4396c91fe0a72a0c5f707b2",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
       repository = tf_repo_name,
   )
diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD
index 3b13b297f8..12bacf3c27 100644
--- a/third_party/llvm/llvm.BUILD
+++ b/third_party/llvm/llvm.BUILD
@@ -386,6 +386,7 @@ llvm_target_list = [
         "tbl_outs": [
             ("-gen-register-bank", "lib/Target/ARM/ARMGenRegisterBank.inc"),
             ("-gen-register-info", "lib/Target/ARM/ARMGenRegisterInfo.inc"),
+            ("-gen-searchable-tables", "lib/Target/ARM/ARMGenSystemRegister.inc"),
             ("-gen-instr-info", "lib/Target/ARM/ARMGenInstrInfo.inc"),
             ("-gen-emitter", "lib/Target/ARM/ARMGenMCCodeEmitter.inc"),
             ("-gen-pseudo-lowering", "lib/Target/ARM/ARMGenMCPseudoLowering.inc"),
@@ -879,6 +880,7 @@ cc_library(
     deps = [
         ":arm_desc",
         ":arm_info",
+        ":arm_utils",
         ":config",
         ":mc",
         ":mc_parser",
@@ -903,6 +905,7 @@ cc_library(
     deps = [
         ":arm_info",
         ":arm_target_gen",
+        ":arm_utils",
         ":config",
         ":mc",
         ":support",
@@ -928,6 +931,7 @@ cc_library(
         ":arm_asm_printer",
         ":arm_desc",
         ":arm_info",
+        ":arm_utils",
         ":asm_printer",
         ":code_gen",
         ":config",
@@ -1016,6 +1020,29 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "arm_utils",
+    srcs = glob([
+        "lib/Target/ARM/Utils/*.c",
+        "lib/Target/ARM/Utils/*.cpp",
+        "lib/Target/ARM/Utils/*.inc",
+        "lib/Target/ARM/MCTargetDesc/*.h",
+    ]),
+    hdrs = glob([
+        "include/llvm/Target/ARM/Utils/*.h",
+        "include/llvm/Target/ARM/Utils/*.def",
+        "include/llvm/Target/ARM/Utils/*.inc",
+        "lib/Target/ARM/Utils/*.h",
+    ]),
+    copts = ["-Iexternal/llvm/lib/Target/ARM"],
+    deps = [
+        ":arm_target_gen",
+        ":config",
+        ":mc",
+        ":support",
+    ],
+)
+
 cc_library(
     name = "asm_parser",
     srcs = glob([
@@ -1129,6 +1156,7 @@ cc_library(
         ":config",
         ":core",
         ":mc",
+        ":object",
         ":support",
     ],
 )
@@ -1182,7 +1210,7 @@ cc_library(
         "include/llvm/IR/*.def",
         "include/llvm/IR/*.inc",
         "include/llvm/*.h",
-    ]) + ["include/llvm/Support/VCSRevision.h"],
+    ]),
     deps = [
         ":attributes_compat_gen",
         ":attributes_gen",
@@ -1950,6 +1978,7 @@ cc_library(
     ]) + [
         "include/llvm/BinaryFormat/MachO.def",
         "include/llvm/Support/DataTypes.h",
+        "include/llvm/Support/VCSRevision.h",
         "include/llvm/ExecutionEngine/ObjectMemoryBuffer.h",
     ],
     deps = [
-- 
GitLab


From eed99068ecb860a6e23b697d99aae2dcd5743d71 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 09:02:25 -0700
Subject: [PATCH 1189/1375] Update deprecated GraphDef.version use in test,
 plus minor cleanup

PiperOrigin-RevId: 162746576
---
 .../message_wrappers_test.cc                  | 57 ++++++++++---------
 1 file changed, 30 insertions(+), 27 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/message_wrappers_test.cc b/tensorflow/core/distributed_runtime/message_wrappers_test.cc
index e87fff151e..f64d476d6d 100644
--- a/tensorflow/core/distributed_runtime/message_wrappers_test.cc
+++ b/tensorflow/core/distributed_runtime/message_wrappers_test.cc
@@ -23,20 +23,21 @@ limitations under the License.
 #include "tensorflow/core/protobuf/config.pb.h"
 
 namespace tensorflow {
+namespace {
 
-static Tensor TensorA() {
+Tensor TensorA() {
   Tensor a_tensor(DT_INT32, TensorShape({2, 2}));
   test::FillValues<int32>(&a_tensor, {3, 2, -1, 0});
   return a_tensor;
 }
 
-static Tensor TensorB() {
+Tensor TensorB() {
   Tensor b_tensor(DT_INT32, TensorShape({1, 2}));
   test::FillValues<int32>(&b_tensor, {1, 2});
   return b_tensor;
 }
 
-static void BuildRunStepRequest(MutableRunStepRequestWrapper* request) {
+void BuildRunStepRequest(MutableRunStepRequestWrapper* request) {
   request->set_session_handle("handle");
   request->set_partial_run_handle("partial_handle");
   request->add_feed("feed_a:0", TensorA());
@@ -48,7 +49,7 @@ static void BuildRunStepRequest(MutableRunStepRequestWrapper* request) {
   request->mutable_options()->set_timeout_in_ms(37);
 }
 
-static void CheckRunStepRequest(const RunStepRequestWrapper& request) {
+void CheckRunStepRequest(const RunStepRequestWrapper& request) {
   EXPECT_EQ("handle", request.session_handle());
   EXPECT_EQ("partial_handle", request.partial_run_handle());
   EXPECT_EQ(2, request.num_feeds());
@@ -68,9 +69,8 @@ static void CheckRunStepRequest(const RunStepRequestWrapper& request) {
   EXPECT_EQ(37, request.options().timeout_in_ms());
 }
 
-static void BuildRunGraphRequest(
-    const RunStepRequestWrapper& run_step_request,
-    MutableRunGraphRequestWrapper* run_graph_request) {
+void BuildRunGraphRequest(const RunStepRequestWrapper& run_step_request,
+                          MutableRunGraphRequestWrapper* run_graph_request) {
   run_graph_request->set_graph_handle("graph_handle");
   run_graph_request->set_step_id(13);
   run_graph_request->mutable_exec_opts()->set_record_timeline(true);
@@ -83,7 +83,7 @@ static void BuildRunGraphRequest(
   run_graph_request->set_is_partial(true);
 }
 
-static void CheckRunGraphRequest(const RunGraphRequestWrapper& request) {
+void CheckRunGraphRequest(const RunGraphRequestWrapper& request) {
   EXPECT_EQ("graph_handle", request.graph_handle());
   EXPECT_EQ(13, request.step_id());
   EXPECT_FALSE(request.exec_opts().record_costs());
@@ -99,20 +99,20 @@ static void CheckRunGraphRequest(const RunGraphRequestWrapper& request) {
   EXPECT_FALSE(request.is_last_partial_run());
 }
 
-static void BuildRunGraphResponse(
-    MutableRunGraphResponseWrapper* run_graph_response) {
+void BuildRunGraphResponse(MutableRunGraphResponseWrapper* run_graph_response) {
   run_graph_response->AddRecv("recv_2", TensorA());
   run_graph_response->AddRecv("recv_3", TensorB());
   run_graph_response->mutable_step_stats()->add_dev_stats()->set_device(
       "/cpu:0");
   run_graph_response->mutable_cost_graph()->add_node()->set_name("cost_node");
   GraphDef graph_def;
-  graph_def.set_version(1234);
+  graph_def.mutable_versions()->set_producer(1234);
+  graph_def.mutable_versions()->set_min_consumer(1234);
   run_graph_response->AddPartitionGraph(graph_def);
 }
 
-static void CheckRunGraphResponse(MutableRunGraphResponseWrapper* response) {
-  EXPECT_EQ(2, response->num_recvs());
+void CheckRunGraphResponse(MutableRunGraphResponseWrapper* response) {
+  ASSERT_EQ(2, response->num_recvs());
   EXPECT_EQ("recv_2", response->recv_key(0));
   EXPECT_EQ("recv_3", response->recv_key(1));
   Tensor val;
@@ -120,18 +120,18 @@ static void CheckRunGraphResponse(MutableRunGraphResponseWrapper* response) {
   test::ExpectTensorEqual<int32>(TensorA(), val);
   TF_EXPECT_OK(response->RecvValue(1, &val));
   test::ExpectTensorEqual<int32>(TensorB(), val);
-  EXPECT_EQ(1, response->mutable_step_stats()->dev_stats_size());
+  ASSERT_EQ(1, response->mutable_step_stats()->dev_stats_size());
   EXPECT_EQ("/cpu:0", response->mutable_step_stats()->dev_stats(0).device());
-  EXPECT_EQ(1, response->mutable_cost_graph()->node_size());
+  ASSERT_EQ(1, response->mutable_cost_graph()->node_size());
   EXPECT_EQ("cost_node", response->mutable_cost_graph()->node(0).name());
-  EXPECT_EQ(1, response->num_partition_graphs());
-  GraphDef graph_def;
-  EXPECT_EQ(1234, response->mutable_partition_graph(0)->version());
+  ASSERT_EQ(1, response->num_partition_graphs());
+  EXPECT_EQ(1234, response->mutable_partition_graph(0)->versions().producer());
+  EXPECT_EQ(1234,
+            response->mutable_partition_graph(0)->versions().min_consumer());
 }
 
-static void BuildRunStepResponse(
-    MutableRunGraphResponseWrapper* run_graph_response,
-    MutableRunStepResponseWrapper* run_step_response) {
+void BuildRunStepResponse(MutableRunGraphResponseWrapper* run_graph_response,
+                          MutableRunStepResponseWrapper* run_step_response) {
   TF_EXPECT_OK(run_step_response->AddTensorFromRunGraphResponse(
       "fetch_x:0", run_graph_response, 0));
   TF_EXPECT_OK(run_step_response->AddTensorFromRunGraphResponse(
@@ -146,9 +146,8 @@ static void BuildRunStepResponse(
   }
 }
 
-static void CheckRunStepResponse(
-    const MutableRunStepResponseWrapper& response) {
-  EXPECT_EQ(2, response.num_tensors());
+void CheckRunStepResponse(const MutableRunStepResponseWrapper& response) {
+  ASSERT_EQ(2, response.num_tensors());
   EXPECT_EQ("fetch_x:0", response.tensor_name(0));
   EXPECT_EQ("fetch_y:0", response.tensor_name(1));
   Tensor val;
@@ -156,10 +155,13 @@ static void CheckRunStepResponse(
   test::ExpectTensorEqual<int32>(TensorA(), val);
   TF_EXPECT_OK(response.TensorValue(1, &val));
   test::ExpectTensorEqual<int32>(TensorB(), val);
-  EXPECT_EQ(1, response.metadata().step_stats().dev_stats_size());
+  ASSERT_EQ(1, response.metadata().step_stats().dev_stats_size());
   EXPECT_EQ("/cpu:0", response.metadata().step_stats().dev_stats(0).device());
-  EXPECT_EQ(1, response.metadata().partition_graphs_size());
-  EXPECT_EQ(1234, response.metadata().partition_graphs(0).version());
+  ASSERT_EQ(1, response.metadata().partition_graphs_size());
+  EXPECT_EQ(1234,
+            response.metadata().partition_graphs(0).versions().producer());
+  EXPECT_EQ(1234,
+            response.metadata().partition_graphs(0).versions().min_consumer());
 }
 
 TEST(MessageWrappers, RunStepRequest_Basic) {
@@ -338,4 +340,5 @@ TEST(MessageWrappers, RunStepResponse_Basic) {
   }
 }
 
+}  // namespace
 }  // namespace tensorflow
-- 
GitLab


From 116ae02048b15276eb402c439fbdea5a6ab4c779 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 09:02:29 -0700
Subject: [PATCH 1190/1375] BUILD cleanup in
 tensorflow/contrib/timeseries/python/timeseries/...

PiperOrigin-RevId: 162746583
---
 .../timeseries/python/timeseries/BUILD        | 89 ++++++++++++++++---
 .../timeseries/state_space_models/BUILD       | 89 ++++++++-----------
 2 files changed, 111 insertions(+), 67 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index d2aa73a178..3726e4b889 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -16,6 +16,13 @@ py_library(
         "__init__.py",
     ],
     srcs_version = "PY2AND3",
+    deps = [
+        ":ar_model",
+        ":estimators",
+        ":feature_keys",
+        ":input_pipeline",
+        ":saved_model_utils",
+    ],
 )
 
 py_library(
@@ -24,6 +31,7 @@ py_library(
         "feature_keys.py",
     ],
     srcs_version = "PY2AND3",
+    deps = ["//tensorflow/python/saved_model:signature_constants"],
 )
 
 py_library(
@@ -33,7 +41,10 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/python/saved_model",
+        ":feature_keys",
+        ":input_pipeline",
+        ":model_utils",
+        "//tensorflow/python:util",
     ],
 )
 
@@ -50,9 +61,11 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:tensor_array_ops",
-        "//third_party/py/numpy",
+        "//tensorflow/python:util",
+        "//tensorflow/python:variable_scope",
     ],
 )
 
@@ -64,14 +77,20 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":ar_model",
+        ":feature_keys",
+        ":math_utils",
         ":model_utils",
         ":state_management",
         "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:filtering_postprocessor",
+        "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:state_space_model",
         "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:structural_ensemble",
-        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:dtypes",
-        "//tensorflow/python:math_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:training",
         "//tensorflow/python/estimator:estimator_py",
+        "//tensorflow/python/estimator:export",
     ],
 )
 
@@ -86,13 +105,19 @@ py_test(
         "no_pip_gpu",  # b/63391119
     ],
     deps = [
+        ":ar_model",
         ":estimators",
         ":feature_keys",
         ":input_pipeline",
         ":saved_model_utils",
-        ":state_management",
-        "//tensorflow/contrib/timeseries/python/timeseries/state_space_models:structural_ensemble",
-        "//tensorflow/python:platform",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:session",
+        "//tensorflow/python/estimator:estimator_py",
+        "//tensorflow/python/saved_model:loader",
+        "//tensorflow/python/saved_model:tag_constants",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -114,8 +139,11 @@ py_library(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:state_ops",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python/estimator:estimator_py",
+        "//tensorflow/python/estimator:export",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -129,10 +157,19 @@ py_test(
         "no_pip_gpu",  # b/63391119
     ],
     deps = [
+        ":feature_keys",
+        ":model",
         ":model_utils",
         ":state_management",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:metrics",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/estimator:estimator_py",
     ],
 )
 
@@ -143,13 +180,15 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":feature_keys",
         ":math_utils",
         ":model",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
-        "//third_party/py/numpy",
+        "//tensorflow/python:util",
+        "//tensorflow/python/estimator:estimator_py",
     ],
 )
 
@@ -163,7 +202,9 @@ py_test(
         "no_pip_gpu",  # b/63391119
     ],
     deps = [
+        ":feature_keys",
         ":input_pipeline",
+        ":math_utils",
         ":model",
         ":state_management",
         ":test_utils",
@@ -171,10 +212,14 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/estimator:estimator_py",
         "//third_party/py/numpy",
     ],
 )
@@ -191,6 +236,7 @@ py_library(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:io_ops",
         "//tensorflow/python:math_ops",
@@ -199,7 +245,9 @@ py_library(
         "//tensorflow/python:random_ops",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:tensor_array_ops",
+        "//tensorflow/python:tensor_shape",
         "//tensorflow/python:training",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python/estimator:estimator_py",
         "//third_party/py/numpy",
@@ -216,11 +264,11 @@ py_test(
         "no_pip_gpu",  # b/63391119
     ],
     deps = [
+        ":feature_keys",
         ":input_pipeline",
         ":test_utils",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:dtypes",
-        "//tensorflow/python:platform",
+        "//tensorflow/python:errors",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
@@ -236,14 +284,17 @@ py_library(
     tags = ["no_pip"],
     deps = [
         ":estimators",
+        ":feature_keys",
         ":input_pipeline",
         ":state_management",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform",
         "//tensorflow/python:random_seed",
         "//tensorflow/python:training",
+        "//tensorflow/python:util",
         "//tensorflow/python:variables",
         "//tensorflow/python/estimator:estimator_py",
     ],
@@ -256,7 +307,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        ":math_utils",
+        ":feature_keys",
         ":model",
         ":model_utils",
         "//tensorflow/contrib/distributions:distributions_py",
@@ -266,6 +317,7 @@ py_library(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
+        "//tensorflow/python:init_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:tensor_array_ops",
@@ -284,14 +336,18 @@ py_test(
     deps = [
         ":ar_model",
         ":estimators",
+        ":feature_keys",
         ":input_pipeline",
-        ":model_utils",
         ":test_utils",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/estimator:estimator_py",
         "//third_party/py/numpy",
     ],
 )
@@ -303,18 +359,22 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        ":input_pipeline",
+        ":feature_keys",
         "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/contrib/lookup:lookup_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
         "//tensorflow/python:functional_ops",
         "//tensorflow/python:init_ops",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:state_ops",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
     ],
 )
@@ -329,12 +389,15 @@ py_test(
         "no_pip_gpu",  # b/63391119
     ],
     deps = [
+        ":feature_keys",
+        ":input_pipeline",
         ":math_utils",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
+        "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//third_party/py/numpy",
     ],
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
index 57ff15e00d..d0deedc50f 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -1,11 +1,10 @@
 # State space components and ensembles
+
 load("//tensorflow:tensorflow.bzl", "tf_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 
 package(
-    default_visibility = [
-        "//tensorflow:internal",
-    ],
+    default_visibility = ["//tensorflow:internal"],
 )
 
 licenses(["notice"])  # Apache 2.0
@@ -14,9 +13,7 @@ exports_files(["LICENSE"])
 
 py_library(
     name = "state_space_model",
-    srcs = [
-        "state_space_model.py",
-    ],
+    srcs = ["state_space_model.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":kalman_filter",
@@ -33,6 +30,7 @@ py_library(
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:variable_scope",
+        "//tensorflow/python/estimator:estimator_py",
         "//third_party/py/numpy",
     ],
 )
@@ -40,15 +38,15 @@ py_library(
 py_test(
     name = "state_space_model_test",
     timeout = "long",  # Moderate but for asan
-    srcs = [
-        "state_space_model_test.py",
-    ],
+    srcs = ["state_space_model_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":kalman_filter",
         ":state_space_model",
+        "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/timeseries/python/timeseries:estimators",
+        "//tensorflow/contrib/timeseries/python/timeseries:feature_keys",
         "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
+        "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
         "//tensorflow/contrib/timeseries/python/timeseries:saved_model_utils",
         "//tensorflow/contrib/timeseries/python/timeseries:state_management",
         "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
@@ -56,20 +54,24 @@ py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_seed",
+        "//tensorflow/python:tensor_shape",
         "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/estimator:estimator_py",
+        "//tensorflow/python/saved_model:loader",
+        "//tensorflow/python/saved_model:tag_constants",
         "//third_party/py/numpy",
     ],
 )
 
 py_library(
     name = "kalman_filter",
-    srcs = [
-        "kalman_filter.py",
-    ],
+    srcs = ["kalman_filter.py"],
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/contrib/distributions:distributions_py",
@@ -86,9 +88,7 @@ py_library(
 
 tf_py_test(
     name = "kalman_filter_test",
-    srcs = [
-        "kalman_filter_test.py",
-    ],
+    srcs = ["kalman_filter_test.py"],
     additional_deps = [
         ":kalman_filter",
         "//third_party/py/numpy",
@@ -102,9 +102,7 @@ tf_py_test(
 
 py_library(
     name = "level_trend",
-    srcs = [
-        "level_trend.py",
-    ],
+    srcs = ["level_trend.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":state_space_model",
@@ -121,9 +119,7 @@ py_library(
 
 tf_py_test(
     name = "level_trend_test",
-    srcs = [
-        "level_trend_test.py",
-    ],
+    srcs = ["level_trend_test.py"],
     additional_deps = [
         ":level_trend",
         ":test_utils",
@@ -136,9 +132,7 @@ tf_py_test(
 
 py_library(
     name = "periodic",
-    srcs = [
-        "periodic.py",
-    ],
+    srcs = ["periodic.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":state_space_model",
@@ -147,14 +141,13 @@ py_library(
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
+        "//third_party/py/numpy",
     ],
 )
 
 tf_py_test(
     name = "periodic_test",
-    srcs = [
-        "periodic_test.py",
-    ],
+    srcs = ["periodic_test.py"],
     additional_deps = [
         ":periodic",
         ":test_utils",
@@ -166,15 +159,14 @@ tf_py_test(
 
 py_library(
     name = "structural_ensemble",
-    srcs = [
-        "structural_ensemble.py",
-    ],
+    srcs = ["structural_ensemble.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":level_trend",
         ":periodic",
         ":state_space_model",
         ":varma",
+        "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
     ],
 )
@@ -182,36 +174,33 @@ py_library(
 py_test(
     name = "structural_ensemble_test",
     timeout = "long",  # Moderate but for asan/tsan timeouts
-    srcs = [
-        "structural_ensemble_test.py",
-    ],
+    srcs = ["structural_ensemble_test.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":state_space_model",
         ":structural_ensemble",
         "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/contrib/timeseries/python/timeseries:estimators",
+        "//tensorflow/contrib/timeseries/python/timeseries:feature_keys",
         "//tensorflow/contrib/timeseries/python/timeseries:input_pipeline",
-        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
-        "//tensorflow/python:framework_ops",
+        "//tensorflow/python/estimator:estimator_py",
         "//third_party/py/numpy",
     ],
 )
 
 py_library(
     name = "varma",
-    srcs = [
-        "varma.py",
-    ],
+    srcs = ["varma.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":state_space_model",
         "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_ops",
-        "//tensorflow/python:functional_ops",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:variable_scope",
     ],
@@ -219,9 +208,7 @@ py_library(
 
 tf_py_test(
     name = "varma_test",
-    srcs = [
-        "varma_test.py",
-    ],
+    srcs = ["varma_test.py"],
     additional_deps = [
         ":varma",
         "//tensorflow/contrib/timeseries/python/timeseries:feature_keys",
@@ -237,9 +224,7 @@ tf_py_test(
 
 py_library(
     name = "filtering_postprocessor",
-    srcs = [
-        "filtering_postprocessor.py",
-    ],
+    srcs = ["filtering_postprocessor.py"],
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/contrib/distributions:distributions_py",
@@ -249,14 +234,13 @@ py_library(
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:util",
     ],
 )
 
 tf_py_test(
     name = "filtering_postprocessor_test",
-    srcs = [
-        "filtering_postprocessor_test.py",
-    ],
+    srcs = ["filtering_postprocessor_test.py"],
     additional_deps = [
         ":filtering_postprocessor",
         "//tensorflow/python:client_testlib",
@@ -267,18 +251,15 @@ tf_py_test(
 
 py_library(
     name = "test_utils",
-    srcs = [
-        "test_utils.py",
-    ],
+    srcs = ["test_utils.py"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
         "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
-        "//tensorflow/contrib/timeseries/python/timeseries:model_utils",
-        "//tensorflow/contrib/timeseries/python/timeseries:test_utils",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
+        "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
         "//third_party/py/numpy",
-- 
GitLab


From 4729180d24af3126d736a7045c43fcbf031b5bef Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Fri, 21 Jul 2017 09:20:19 -0700
Subject: [PATCH 1191/1375] Fix various mac flakes.

PiperOrigin-RevId: 162748291
---
 .../contrib/signal/python/kernel_tests/spectral_ops_test.py   | 4 ++--
 tensorflow/contrib/xla_tf_graph/BUILD                         | 1 +
 tensorflow/python/debug/BUILD                                 | 1 +
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
index 904924b5e4..be28184ae6 100644
--- a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
+++ b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
@@ -223,8 +223,8 @@ class SpectralOpsTest(test.TestCase):
           (512, 64, 32, 64, 2e-3, 3e-5),
           (512, 64, 64, 64, 2e-3, 3e-5),
           (512, 64, 25, 64, 2e-3, 3e-5),
-          (512, 25, 15, 36, 8e-4, 3e-5),
-          (123, 23, 5, 42, 8e-4, 4e-5),
+          (512, 25, 15, 36, 2e-3, 3e-5),
+          (123, 23, 5, 42, 2e-3, 4e-5),
       ]
 
       for (signal_length, frame_length, frame_step, fft_length,
diff --git a/tensorflow/contrib/xla_tf_graph/BUILD b/tensorflow/contrib/xla_tf_graph/BUILD
index 33373e0eb4..fa917ea849 100644
--- a/tensorflow/contrib/xla_tf_graph/BUILD
+++ b/tensorflow/contrib/xla_tf_graph/BUILD
@@ -44,6 +44,7 @@ cc_test(
     size = "small",
     srcs = ["xla_tf_graph_util_test.cc"],
     linkstatic = 1,
+    tags = ["nomac"],  # b/63908145
     deps = [
         ":xla_tf_graph_util",
         "//tensorflow/cc:cc_ops",
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 1f70dc2467..e48bcc8788 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -832,6 +832,7 @@ cuda_py_test(
     data = ["//tensorflow/tools/dist_test/server:grpc_tensorflow_server"],
     tags = [
         "no_oss",  # b/62956105: port conflicts + Incompatible with bazel_pip.
+        "nomac",  # TODO(cais): Install of futures and grpcio on all macs.
         "notsan",
     ],
 )
-- 
GitLab


From 3e3306ef0009b5b21050139f9b8e5f4868c4c0c7 Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Fri, 21 Jul 2017 09:22:31 -0700
Subject: [PATCH 1192/1375] Let GetBlasGemmAlgorithms() always return true.

PiperOrigin-RevId: 162748507
---
 tensorflow/contrib/fused_conv/BUILD           |   2 +
 tensorflow/core/BUILD                         |   1 +
 tensorflow/core/kernels/BUILD                 |  14 +-
 tensorflow/core/kernels/conv_ops_gpu.h        | 124 +------
 tensorflow/core/kernels/gpu_utils.h           | 165 +++++++++
 tensorflow/core/kernels/matmul_op.cc          | 312 ++++++++++++++----
 tensorflow/core/kernels/matmul_op.h           |  64 ++++
 tensorflow/core/util/matmul_autotune.cc       |  51 +++
 tensorflow/core/util/matmul_autotune.h        |  28 ++
 tensorflow/python/BUILD                       |  42 +++
 .../python/kernel_tests/matmul_op_test.py     |   3 +
 tensorflow/python/ops/matmul_benchmark.py     | 143 ++++++++
 .../python/ops/matmul_benchmark_test.py       | 122 +++++++
 tensorflow/stream_executor/blas.cc            |   4 +
 tensorflow/stream_executor/blas.h             | 138 +++++++-
 tensorflow/stream_executor/cuda/cuda_blas.cc  | 177 ++++++++++
 tensorflow/stream_executor/cuda/cuda_blas.h   |  17 +
 tensorflow/stream_executor/stream.cc          | 178 ++++++++++
 tensorflow/stream_executor/stream.h           |  63 ++++
 19 files changed, 1456 insertions(+), 192 deletions(-)
 create mode 100644 tensorflow/core/kernels/gpu_utils.h
 create mode 100644 tensorflow/core/util/matmul_autotune.cc
 create mode 100644 tensorflow/core/util/matmul_autotune.h
 create mode 100644 tensorflow/python/ops/matmul_benchmark.py
 create mode 100644 tensorflow/python/ops/matmul_benchmark_test.py

diff --git a/tensorflow/contrib/fused_conv/BUILD b/tensorflow/contrib/fused_conv/BUILD
index 026ee3df07..5a9eeea70e 100644
--- a/tensorflow/contrib/fused_conv/BUILD
+++ b/tensorflow/contrib/fused_conv/BUILD
@@ -68,6 +68,7 @@ tf_kernel_library(
         "//tensorflow/core/kernels:bounds_check_lib",
         "//tensorflow/core/kernels:conv_2d_hdrs",
         "//tensorflow/core/kernels:conv_ops_gpu_hdrs",
+        "//tensorflow/core/kernels:gpu_util_hdrs",
         "//tensorflow/core/kernels:ops_util_hdrs",
         "//third_party/eigen3",
     ],
@@ -86,6 +87,7 @@ tf_custom_op_library(
         "//tensorflow/core/kernels:bounds_check_lib",
         "//tensorflow/core/kernels:conv_2d_hdrs",
         "//tensorflow/core/kernels:conv_ops_gpu_hdrs",
+        "//tensorflow/core/kernels:gpu_util_hdrs",
         "//tensorflow/core/kernels:ops_util_hdrs",
     ],
 )
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 5885d4ed52..eea59e8e0e 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -407,6 +407,7 @@ tf_cuda_library(
         "util/tensor_slice_reader_cache.h",
         "util/tensor_slice_writer.h",
         "util/use_cudnn.h",
+        "util/matmul_autotune.h",
         "util/util.h",
         "util/work_sharder.h",
     ] + select({
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 49aa3a11ba..e676d5a367 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -156,6 +156,7 @@ cc_library(
     hdrs = ["conv_2d.h"],
     deps = [
         ":eigen_helpers",
+        ":gpu_util_hdrs",
         "//tensorflow/core:framework",
         "//third_party/eigen3",
     ],
@@ -265,6 +266,15 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "gpu_util_hdrs",
+    hdrs = ["gpu_utils.h"],
+    deps = [
+        ":eigen_helpers",
+        "//third_party/eigen3",
+    ],
+)
+
 tf_cc_test(
     name = "ops_util_test",
     size = "small",
@@ -2424,7 +2434,9 @@ tf_kernel_library(
         ],
         "//conditions:default": [],
     }),
-    deps = MATH_DEPS + select({
+    deps = MATH_DEPS + [
+        ":gpu_util_hdrs",
+    ] + select({
         ":xsmm": [
             "@libxsmm_archive//:xsmm_avx",
         ],
diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h
index 34d50fdc27..83ef4f01ca 100644
--- a/tensorflow/core/kernels/conv_ops_gpu.h
+++ b/tensorflow/core/kernels/conv_ops_gpu.h
@@ -21,28 +21,12 @@ limitations under the License.
 #include <tuple>
 #include <unordered_map>
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/kernels/gpu_utils.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/hash/hash.h"
-#include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/platform/stream_executor.h"
 
 namespace tensorflow {
 
-namespace dnn = ::perftools::gputools::dnn;
-
-// TODO(zhengxq): move this to gpu_util.h. The use of such wrappers is wide
-// spread.
-template <typename T>
-inline perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory,
-                                                           uint64 size) {
-  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory),
-                                                size * sizeof(T));
-  perftools::gputools::DeviceMemory<T> typed(wrapped);
-  return typed;
-}
 
 // Get the Cudnn workspace limit from the environment variable, which is in MB.
 // Return the workspace memory limit in bytes. If no value is set, return the
@@ -185,112 +169,6 @@ class ConvParameters {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-// A helper class that looks up the best autotuned config from parameters.
-// Due to the noisy nature of autotune, especially with multiple devices, it
-// only accepts a config if its margin exceeds a threshold.
-// For the same shape configs, if a new best config matches the previous best,
-// they get promoted; otherwise, the winner gets demoted. This process stops
-// when the winner's score exceeds the threshold.
-// In a bad case when two configs are very close to each other and flips
-// back and forth randomly, the expected number of experiments before autotune
-// settles is O(threshold ^ 2). So we recommend that number of warmup runs
-// for any benchmarks.
-template <typename Parameters, typename Config>
-class AutoTuneMap {
- public:
-  bool Find(const Parameters& params, Config* config) const {
-    mutex_lock lock(mu_);
-    auto iter = params_config_map_.find(params);
-    if (iter == params_config_map_.end() ||
-        iter->second.score < min_score_threshold_) {
-      return false;
-    }
-    *config = iter->second.config;
-    return true;
-  }
-  void Insert(const ConvParameters& params, const Config& config) {
-    mutex_lock lock(mu_);
-    auto iter = params_config_map_.find(params);
-    int new_score = 0;
-    if (iter == params_config_map_.end()) {
-      // Create a new entry if params is new.
-      VLOG(1) << GetActionSummary("creates", params, config);
-      params_config_map_.insert(std::make_pair(params, ValueType{config, 1}));
-      new_score = 1;
-    } else if (iter->second.score < min_score_threshold_) {
-      DCHECK_GT(iter->second.score, 0);
-      if (iter->second.config != config) {
-        // If it is different from the current winner, demotes the winner.
-        VLOG(1) << GetActionSummary("demotes", params, config);
-        new_score = --iter->second.score;
-        if (new_score <= 0) {
-          VLOG(1) << GetActionSummary("erases", params, config);
-          params_config_map_.erase(iter);
-        }
-      } else {
-        // If it is the same as the current winner, promotes the winner.
-        VLOG(1) << GetActionSummary("promotes", params, config);
-        new_score = ++iter->second.score;
-      }
-    }
-    if (new_score >= min_score_threshold_) {
-      VLOG(1) << GetActionSummary("accepts", params, config);
-    }
-  }
-
- private:
-  AutoTuneMap(const string& name) : name_(name) {
-    min_score_threshold_ = 1;
-    const char* threshold_str = getenv("TF_AUTOTUNE_THRESHOLD");
-    if (threshold_str != nullptr) {
-      strings::safe_strto32(threshold_str, &min_score_threshold_);
-    }
-    min_score_threshold_ = std::max(min_score_threshold_, 1);
-  }
-
-  template <class Group, class Params, class Cfg>
-  friend class AutoTuneSingleton;
-
-  struct Hasher {
-    std::size_t operator()(const Parameters& parameter) const {
-      return parameter.hash();
-    }
-  };
-
-  string GetActionSummary(StringPiece action, const Parameters& params,
-                          const Config& config) {
-    return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
-                           action.ToString().c_str(), params.ToString().c_str(),
-                           config.ToString().c_str());
-  }
-
-  mutable mutex mu_;
-  struct ValueType {
-    Config config;
-    int32 score;
-  };
-  std::unordered_map<Parameters, ValueType, Hasher> params_config_map_
-      GUARDED_BY(mu_);
-  string name_;
-  int32 min_score_threshold_;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(AutoTuneMap);
-};
-
-// A Singleton helper that manages the global autotune results by groups.
-// The caller specified arbitrary Group type that can distinguish between
-// different autotune results, even if their Parameters and Configs are the
-// same.
-template <class Group, typename Parameters, typename Config>
-class AutoTuneSingleton {
- public:
-  typedef AutoTuneMap<Parameters, Config> AutoTuneType;
-  static AutoTuneType* GetInstance() {
-    static AutoTuneType* instance = new AutoTuneType(Group::name());
-    return instance;
-  }
-};
-
 }  // namespace tensorflow
 
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/gpu_utils.h b/tensorflow/core/kernels/gpu_utils.h
new file mode 100644
index 0000000000..366877bcf5
--- /dev/null
+++ b/tensorflow/core/kernels/gpu_utils.h
@@ -0,0 +1,165 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
+
+#if GOOGLE_CUDA
+
+#include <unordered_map>
+
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor.h"
+
+namespace tensorflow {
+
+template <typename T>
+inline perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory,
+                                                           uint64 size) {
+  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory),
+                                                size * sizeof(T));
+  perftools::gputools::DeviceMemory<T> typed(wrapped);
+  return typed;
+}
+
+// A helper class that looks up the best autotuned config from parameters.
+// Due to the noisy nature of autotune, especially with multiple devices, it
+// only accepts a config if its margin exceeds a threshold.
+// For the same shape configs, if a new best config matches the previous best,
+// they get promoted; otherwise, the winner gets demoted. This process stops
+// when the winner's score exceeds the threshold.
+// In a bad case when two configs are very close to each other and flips
+// back and forth randomly, the expected number of experiments before autotune
+// settles is O(threshold ^ 2). So we recommend that number of warmup runs
+// for any benchmarks.
+template <typename Parameters, typename Config>
+class AutoTuneMap {
+ public:
+  bool Find(const Parameters& params, Config* config) const {
+    mutex_lock lock(mu_);
+    auto iter = params_config_map_.find(params);
+    if (iter == params_config_map_.end() ||
+        (iter->second.score < min_score_threshold_ &&
+         iter->second.count <= max_autotune_count_)) {
+      return false;
+    }
+    *config = iter->second.config;
+    return true;
+  }
+  void Insert(const Parameters& params, const Config& config) {
+    mutex_lock lock(mu_);
+    auto iter = params_config_map_.find(params);
+    int new_score = 0;
+    if (iter == params_config_map_.end()) {
+      // Create a new entry if params is new.
+      VLOG(1) << GetActionSummary("creates", params, config);
+      params_config_map_.insert(
+          std::make_pair(params, ValueType{config, 1, 1}));
+      new_score = 1;
+    } else if (iter->second.score < min_score_threshold_ &&
+               iter->second.count <= max_autotune_count_) {
+      DCHECK_GT(iter->second.score, 0);
+      if (iter->second.config != config) {
+        // If it is different from the current winner, demotes the winner.
+        VLOG(1) << GetActionSummary("demotes", params, config);
+        new_score = --iter->second.score;
+        ++iter->second.count;
+        if (new_score <= 0) {
+          VLOG(1) << GetActionSummary("erases", params, config);
+          params_config_map_.erase(iter);
+        }
+      } else {
+        // If it is the same as the current winner, promotes the winner.
+        VLOG(1) << GetActionSummary("promotes", params, config);
+        new_score = ++iter->second.score;
+        ++iter->second.count;
+      }
+    }
+    if (new_score >= min_score_threshold_) {
+      VLOG(1) << GetActionSummary("accepts", params, config);
+    }
+  }
+
+ private:
+  AutoTuneMap(const string& name) : name_(name) {
+    min_score_threshold_ = 1;
+    int min_warmup_iterations = 10;
+    const char* threshold_str = getenv("TF_AUTOTUNE_THRESHOLD");
+    if (threshold_str != nullptr) {
+      strings::safe_strto32(threshold_str, &min_score_threshold_);
+    }
+    const char* min_warmup_iteration_str =
+        getenv("TF_AUTOTUNE_MIN_WARMUP_ITERATIONS");
+    if (min_warmup_iteration_str != nullptr) {
+      strings::safe_strto32(min_warmup_iteration_str, &min_warmup_iterations);
+    }
+    min_score_threshold_ = std::max(min_score_threshold_, 1);
+    max_autotune_count_ = std::max(
+        5 * min_score_threshold_ * min_score_threshold_, min_warmup_iterations);
+  }
+
+  template <class Group, class Params, class Cfg>
+  friend class AutoTuneSingleton;
+
+  struct Hasher {
+    std::size_t operator()(const Parameters& parameter) const {
+      return parameter.hash();
+    }
+  };
+
+  string GetActionSummary(StringPiece action, const Parameters& params,
+                          const Config& config) {
+    return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
+                           action.ToString().c_str(), params.ToString().c_str(),
+                           config.ToString().c_str());
+  }
+
+  mutable mutex mu_;
+  struct ValueType {
+    Config config;
+    int32 score;
+    int32 count;
+  };
+  std::unordered_map<Parameters, ValueType, Hasher> params_config_map_
+      GUARDED_BY(mu_);
+  string name_;
+  int32 min_score_threshold_;
+  int32 max_autotune_count_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(AutoTuneMap);
+};
+
+// A Singleton helper that manages the global autotune results by groups.
+// The caller specified arbitrary Group type that can distinguish between
+// different autotune results, even if their Parameters and Configs are the
+// same.
+template <class Group, typename Parameters, typename Config>
+class AutoTuneSingleton {
+ public:
+  typedef AutoTuneMap<Parameters, Config> AutoTuneType;
+  static AutoTuneType* GetInstance() {
+    static AutoTuneType* instance = new AutoTuneType(Group::name());
+    return instance;
+  }
+};
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc
index 8003f7ff67..62c5ecfe81 100644
--- a/tensorflow/core/kernels/matmul_op.cc
+++ b/tensorflow/core/kernels/matmul_op.cc
@@ -23,27 +23,15 @@ limitations under the License.
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/fill_functor.h"
-
+#include "tensorflow/core/util/matmul_autotune.h"
 #if GOOGLE_CUDA
 #include "cuda/include/cuda.h"
+#include "tensorflow/core/kernels/gpu_utils.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #endif  // GOOGLE_CUDA
 
 namespace tensorflow {
 
-#if GOOGLE_CUDA
-
-namespace {
-template <typename T>
-perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory) {
-  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory));
-  perftools::gputools::DeviceMemory<T> typed(wrapped);
-  return typed;
-}
-}  // namespace
-
-#endif  // GOOGLE_CUDA
-
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 #ifdef TENSORFLOW_USE_SYCL
@@ -123,10 +111,16 @@ bool ExplicitVectorMatrixOptimization<Eigen::half>(
 
 template <typename Device, typename T>
 struct LaunchMatMulBase {
+#if GOOGLE_CUDA
+  typedef perftools::gputools::blas::AlgorithmType AlgorithmType;
+#else
+  typedef int64 AlgorithmType;
+#endif  // GOOGLE_CUDA
+
   static void launch(
-      OpKernelContext* ctx, OpKernel* kernel, const Tensor& a, const Tensor& b,
+      OpKernelContext* ctx, const Tensor& a, const Tensor& b,
       const Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1>& dim_pair,
-      Tensor* out) {
+      std::vector<AlgorithmType>* algorithms, bool use_aututone, Tensor* out) {
 #ifndef TENSORFLOW_USE_SYCL
     // An explicit vector-matrix multiply is much better optimized than an
     // implicit one and this is a bottleneck during non-batched inference.
@@ -140,6 +134,10 @@ struct LaunchMatMulBase {
     }
 #endif  // TENSORFLOW_USE_SYCL
   }
+
+  static void GetBlasGemmAlgorithm(OpKernelConstruction* ctx,
+                                   std::vector<int64>* algorithms,
+                                   bool* algorithm_set_flag) {}
 };
 // On CPUs, we ignore USE_CUBLAS
 template <typename T>
@@ -159,24 +157,39 @@ struct LaunchMatMul<SYCLDevice, T, USE_CUBLAS> : public LaunchMatMulSYCL<T> {};
 #if GOOGLE_CUDA
 
 namespace {
+
 template <typename T>
 struct LaunchBlasGemv {
-  static void Compute(OpKernelContext* ctx, perftools::gputools::Stream* stream,
-                      bool trans, uint64 m, uint64 n,
-                      const perftools::gputools::DeviceMemory<T>& a,
-                      const perftools::gputools::DeviceMemory<T>& b,
-                      perftools::gputools::DeviceMemory<T>* c) {
+  static void Compute(
+      OpKernelContext* ctx, perftools::gputools::Stream* stream, bool trans,
+      uint64 m, uint64 n, const perftools::gputools::DeviceMemory<T>& a,
+      const perftools::gputools::DeviceMemory<T>& b,
+      perftools::gputools::DeviceMemory<T>* c,
+      perftools::gputools::blas::ProfileResult* output_profile) {
     const auto blas_trans =
         trans ? perftools::gputools::blas::Transpose::kTranspose
               : perftools::gputools::blas::Transpose::kNoTranspose;
-    bool blas_launch_status =
-        stream
-            ->ThenBlasGemv(blas_trans, m, n, static_cast<T>(1.0), a, m, b, 1,
-                           static_cast<T>(0.0), c, 1)
-            .ok();
-    if (!blas_launch_status) {
-      ctx->SetStatus(
-          errors::Internal("Blas GEMV launch failed:  m=", m, ", n=", n));
+    if (output_profile == nullptr) {
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemv(blas_trans, m, n, static_cast<T>(1.0), a, m, b, 1,
+                             static_cast<T>(0.0), c, 1)
+              .ok();
+      if (!blas_launch_status) {
+        ctx->SetStatus(
+            errors::Internal("Blas GEMV launch failed:  m=", m, ", n=", n));
+      }
+    } else {
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemvWithProfiling(blas_trans, m, n, static_cast<T>(1.0),
+                                          a, m, b, 1, static_cast<T>(0.0), c, 1,
+                                          output_profile)
+              .ok();
+      if (!blas_launch_status) {
+        ctx->SetStatus(errors::Internal(
+            "Blas GEMV with profiling launch failed:  m=", m, ", n=", n));
+      }
     }
   }
 
@@ -188,7 +201,8 @@ void LaunchBlasGemv<Eigen::half>::Compute(
     OpKernelContext* ctx, perftools::gputools::Stream* stream, bool trans,
     uint64 m, uint64 n, const perftools::gputools::DeviceMemory<Eigen::half>& a,
     const perftools::gputools::DeviceMemory<Eigen::half>& b,
-    perftools::gputools::DeviceMemory<Eigen::half>* c) {
+    perftools::gputools::DeviceMemory<Eigen::half>* c,
+    perftools::gputools::blas::ProfileResult* output_profile) {
   ctx->SetStatus(errors::Internal(
       "Blas GEMV launch failed: GEMV is not implemented for float16."));
 }
@@ -200,15 +214,55 @@ bool LaunchBlasGemv<Eigen::half>::IsSupported() {
 
 }  // namespace
 
+bool GetCublasAutotuneComputationType(
+    const DataType& dtype,
+    perftools::gputools::blas::ComputationType* compute_type) {
+  using perftools::gputools::blas::ComputationType;
+  bool use_f32_for_f16_computation = MatmulDoFP32ComputationFP16Input();
+  switch (dtype) {
+    case DT_HALF:
+    case DT_BFLOAT16:
+      if (use_f32_for_f16_computation) {
+        *compute_type = ComputationType::kF32;
+      } else {
+        *compute_type = ComputationType::kF16;
+      }
+      return false;
+    case DT_FLOAT:
+      *compute_type = ComputationType::kF32;
+      return true;
+    case DT_DOUBLE:
+      *compute_type = ComputationType::kF64;
+      return true;
+    default:
+      // Unsupported compute_type, return false.
+      return false;
+  }
+}
+
+// A dummy type to group matmul autotune results together.
+struct MatmulAutoTuneGroup {
+  static string name() { return "Matmul"; }
+};
+typedef AutoTuneSingleton<MatmulAutoTuneGroup, MatmulParameters,
+                          perftools::gputools::blas::AlgorithmConfig>
+    AutoTuneMatmul;
+
 template <typename T>
 struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
   static void launch(
-      OpKernelContext* ctx, OpKernel* kernel, const Tensor& a, const Tensor& b,
+      OpKernelContext* ctx, const Tensor& a, const Tensor& b,
       const Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1>& dim_pair,
-      Tensor* out) {
-    perftools::gputools::blas::Transpose trans[] = {
-        perftools::gputools::blas::Transpose::kNoTranspose,
-        perftools::gputools::blas::Transpose::kTranspose};
+      std::vector<int64>* algorithms, bool use_autotune, Tensor* out) {
+    using perftools::gputools::blas::AlgorithmConfig;
+    using perftools::gputools::blas::ComputationType;
+    using perftools::gputools::blas::ProfileResult;
+    using perftools::gputools::blas::Transpose;
+    using perftools::gputools::blas::kDefaultAlgorithm;
+    using perftools::gputools::blas::kDefaultBlasGemm;
+    using perftools::gputools::blas::kDefaultBlasGemv;
+    using perftools::gputools::blas::kNoAlgorithm;
+    Transpose trans[] = {Transpose::kNoTranspose, Transpose::kTranspose};
     const uint64 m = a.dim_size(1 - dim_pair[0].first);
     const uint64 k = a.dim_size(dim_pair[0].first);
     const uint64 n = b.dim_size(1 - dim_pair[0].second);
@@ -220,35 +274,156 @@ struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
     auto* stream = ctx->op_device_context()->stream();
     OP_REQUIRES(ctx, stream, errors::Internal("No GPU stream available."));
 
-    auto a_ptr = AsDeviceMemory(a.template flat<T>().data());
-    auto b_ptr = AsDeviceMemory(b.template flat<T>().data());
-    auto c_ptr = AsDeviceMemory(out->template flat<T>().data());
-    // Cublas does
-    // C = A x B
-    // where A, B and C are assumed to be in column major.
-    // We want the output to be in row-major, so we can compute
-    // C' = B' x A' (' stands for transpose)
-    if (LaunchBlasGemv<T>::IsSupported() && n == 1) {
-      // This is a matrix*vector multiply so use GEMV to compute A * b.
-      // Here we are multiplying in the natural order, so we have to flip
-      // the transposition flag to compensate for the tensor being stored
-      // row-major.
-      LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a, transpose_a ? m : k,
-                                 transpose_a ? k : m, a_ptr, b_ptr, &c_ptr);
-    } else {
-      bool blas_launch_status =
-          stream
-              ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k, 1.0f,
-                             b_ptr, transpose_b ? k : n, a_ptr,
-                             transpose_a ? m : k, 0.0f, &c_ptr, n)
-              .ok();
-      if (!blas_launch_status) {
-        ctx->SetStatus(errors::Internal(
-            "Blas GEMM launch failed : a.shape=(", a.dim_size(0), ", ",
-            a.dim_size(1), "), b.shape=(", b.dim_size(0), ", ", b.dim_size(1),
-            "), m=", m, ", n=", n, ", k=", k));
+    auto a_ptr = AsDeviceMemory(a.template flat<T>().data(),
+                                a.template flat<T>().size());
+    auto b_ptr = AsDeviceMemory(b.template flat<T>().data(),
+                                b.template flat<T>().size());
+    auto c_ptr = AsDeviceMemory(out->template flat<T>().data(),
+                                out->template flat<T>().size());
+    auto alpha = static_cast<T>(1.0);
+    auto beta = static_cast<T>(0.0);
+
+    int device_id = stream->parent()->device_ordinal();
+    DataType dtype = a.dtype();
+    MatmulParameters matmul_parameters = {
+        transpose_a, transpose_b, m, n, k, dtype, device_id,
+    };
+    AlgorithmConfig algorithm_config(kNoAlgorithm);
+
+    ComputationType computation_type;
+    bool compute_type_supported =
+        GetCublasAutotuneComputationType(dtype, &computation_type);
+    if (use_autotune && compute_type_supported && !algorithms->empty()) {
+      ProfileResult best_result;
+      // TODO(yangzihao): Unify this code with conv autotuning.
+      if (!AutoTuneMatmul::GetInstance()->Find(matmul_parameters,
+                                               &algorithm_config)) {
+        ProfileResult profile_result;
+        for (auto profile_algorithm : (*algorithms)) {
+          // Cublas does
+          // C = A x B
+          // where A, B and C are assumed to be in column major.
+          // We want the output to be in row-major, so we can compute
+          // C' = B' x A' (' stands for transpose)
+          bool cublas_launch_status =
+              stream
+                  ->ThenBlasGemmWithAlgorithm(
+                      blas_transpose_b, blas_transpose_a, n, m, k, alpha, b_ptr,
+                      transpose_b ? k : n, a_ptr, transpose_a ? m : k, beta,
+                      &c_ptr, n, computation_type, profile_algorithm,
+                      &profile_result)
+                  .ok();
+          if (cublas_launch_status) {
+            if (profile_result.is_valid()) {
+              if (profile_result.elapsed_time_in_ms() <
+                  best_result.elapsed_time_in_ms()) {
+                best_result = profile_result;
+              }
+            }
+          }
+        }
+        // Try BlasGemmWithProfiling
+        bool cublas_launch_status =
+            stream
+                ->ThenBlasGemmWithProfiling(
+                    blas_transpose_b, blas_transpose_a, n, m, k, 1.0, b_ptr,
+                    transpose_b ? k : n, a_ptr, transpose_a ? m : k, 0.0,
+                    &c_ptr, n, &profile_result)
+                .ok();
+        if (cublas_launch_status) {
+          if (profile_result.is_valid()) {
+            if (profile_result.elapsed_time_in_ms() <
+                best_result.elapsed_time_in_ms()) {
+              best_result = profile_result;
+            }
+          }
+        }
+        // Try BlasGemvWithProfiling
+        if (LaunchBlasGemv<T>::IsSupported() && n == 1) {
+          LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a,
+                                     transpose_a ? m : k, transpose_a ? k : m,
+                                     a_ptr, b_ptr, &c_ptr, &profile_result);
+          if (profile_result.is_valid()) {
+            if (profile_result.elapsed_time_in_ms() <
+                best_result.elapsed_time_in_ms()) {
+              best_result = profile_result;
+            }
+          }
+        }
+      }
+      // We make sure that each matmul parameter set only gets one pass of
+      // autotune. If the best result is found, assign it to algorithm_type
+      // and insert it to autotune map. If all internal kernels of
+      // cublasGemmEx() returns invalid results, we add kNoAlgorithm to the
+      // autotune map.
+      if (best_result.is_valid()) {
+        algorithm_config.set_algorithm(best_result.algorithm());
+      }
+      AutoTuneMatmul::GetInstance()->Insert(matmul_parameters,
+                                            algorithm_config);
+      if (algorithm_config.algorithm() != kNoAlgorithm &&
+          algorithm_config.algorithm() != kDefaultBlasGemm &&
+          algorithm_config.algorithm() != kDefaultBlasGemv) {
+        bool cublas_launch_status =
+            stream
+                ->ThenBlasGemmWithAlgorithm(
+                    blas_transpose_b, blas_transpose_a, n, m, k, alpha, b_ptr,
+                    transpose_b ? k : n, a_ptr, transpose_a ? m : k, beta,
+                    &c_ptr, n, computation_type, algorithm_config.algorithm(),
+                    nullptr)
+                .ok();
+        if (!cublas_launch_status) {
+          ctx->SetStatus(errors::Internal(
+              "Blas GEMM with algorithm launch failed : a.shape=(",
+              a.dim_size(0), ", ", a.dim_size(1), "), b.shape=(", b.dim_size(0),
+              ", ", b.dim_size(1), "), m=", m, ", n=", n, ", k=", k));
+        }
       }
     }
+    // For the following case, we use normal BlasGemm():
+    //  1) We didn't set the use_autotune flag;
+    //  2) compute type does not support autotune;
+    //  3) no algorithm is found;
+    //  4) all internal kernels in autotune return invalid results.
+    if (!use_autotune || !compute_type_supported || algorithms->empty() ||
+        algorithm_config.algorithm() == kNoAlgorithm ||
+        algorithm_config.algorithm() == kDefaultBlasGemm ||
+        algorithm_config.algorithm() == kDefaultBlasGemv) {
+      if (algorithm_config.algorithm() == kDefaultBlasGemv) {
+        // This is a matrix*vector multiply so use GEMV to compute A * b.
+        // Here we are multiplying in the natural order, so we have to flip
+        // the transposition flag to compensate for the tensor being stored
+        // row-major.
+        // TODO(yangzihao): Add Gemv as an autotuning option too.
+        LaunchBlasGemv<T>::Compute(ctx, stream, !transpose_a,
+                                   transpose_a ? m : k, transpose_a ? k : m,
+                                   a_ptr, b_ptr, &c_ptr, nullptr);
+      } else {
+        // Use C' = B' x A' (' stands for transpose)
+        bool blas_launch_status =
+            stream
+                ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k,
+                               1.0f, b_ptr, transpose_b ? k : n, a_ptr,
+                               transpose_a ? m : k, 0.0f, &c_ptr, n)
+                .ok();
+        if (!blas_launch_status) {
+          ctx->SetStatus(errors::Internal(
+              "Blas GEMM launch failed : a.shape=(", a.dim_size(0), ", ",
+              a.dim_size(1), "), b.shape=(", b.dim_size(0), ", ", b.dim_size(1),
+              "), m=", m, ", n=", n, ", k=", k));
+        }
+      }
+    }
+  }
+
+  static void GetBlasGemmAlgorithm(OpKernelConstruction* ctx,
+                                   std::vector<int64>* algorithms,
+                                   bool* algorithm_set_flag) {
+    if (*algorithm_set_flag == false) {
+      auto* stream = ctx->device()->tensorflow_gpu_device_info()->stream;
+      stream->parent()->GetBlasGemmAlgorithms(algorithms);
+      *algorithm_set_flag = true;
+    }
   }
 };
 
@@ -257,9 +432,14 @@ struct LaunchMatMul<GPUDevice, T, true /* USE_CUBLAS */> {
 template <typename Device, typename T, bool USE_CUBLAS>
 class MatMulOp : public OpKernel {
  public:
-  explicit MatMulOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
+  explicit MatMulOp(OpKernelConstruction* ctx)
+      : OpKernel(ctx), algorithms_set_already_(false) {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_a", &transpose_a_));
     OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_b", &transpose_b_));
+
+    LaunchMatMul<Device, T, USE_CUBLAS>::GetBlasGemmAlgorithm(
+        ctx, &algorithms_, &algorithms_set_already_);
+    use_autotune_ = MatmulAutotuneEnable();
   }
 
   void Compute(OpKernelContext* ctx) override {
@@ -302,10 +482,14 @@ class MatMulOp : public OpKernel {
       return;
     }
 
-    LaunchMatMul<Device, T, USE_CUBLAS>::launch(ctx, this, a, b, dim_pair, out);
+    LaunchMatMul<Device, T, USE_CUBLAS>::launch(
+        ctx, a, b, dim_pair, &algorithms_, use_autotune_, out);
   }
 
  private:
+  std::vector<int64> algorithms_;
+  bool algorithms_set_already_;
+  bool use_autotune_;
   bool transpose_a_;
   bool transpose_b_;
 };
diff --git a/tensorflow/core/kernels/matmul_op.h b/tensorflow/core/kernels/matmul_op.h
index 5a8db6da19..6398da2fb9 100644
--- a/tensorflow/core/kernels/matmul_op.h
+++ b/tensorflow/core/kernels/matmul_op.h
@@ -17,7 +17,9 @@ limitations under the License.
 #define TENSORFLOW_KERNELS_MATMUL_OP_H_
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/lib/hash/hash.h"
 
 namespace tensorflow {
 namespace functor {
@@ -50,6 +52,68 @@ struct MatMulFunctor {
 };
 
 }  // end namespace functor
+
+#if GOOGLE_CUDA
+// Encapsulate all the shape information that is used in matmul operations.
+class MatmulParameters {
+ public:
+  MatmulParameters(bool transa, bool transb, uint64 m, uint64 n, uint64 k,
+                   DataType dtype, int device_id)
+      : transa_(transa),
+        transb_(transb),
+        m_(m),
+        n_(n),
+        k_(k),
+        dtype_(dtype),
+        device_id_(device_id) {
+    hash_code_ = transa;
+    hash_code_ = Hash64Combine(hash_code_, transb);
+    hash_code_ = Hash64Combine(hash_code_, m);
+    hash_code_ = Hash64Combine(hash_code_, n);
+    hash_code_ = Hash64Combine(hash_code_, k);
+    hash_code_ = Hash64Combine(hash_code_, dtype);
+    hash_code_ = Hash64Combine(hash_code_, device_id);
+  }
+  bool operator==(const MatmulParameters& other) const {
+    return this->get_data_as_tuple() == other.get_data_as_tuple();
+  }
+
+  bool operator!=(const MatmulParameters& other) const {
+    return !(*this == other);
+  }
+  uint64 hash() const { return hash_code_; }
+
+  string ToString() const {
+    // clang-format off
+    return strings::StrCat(
+        transa_, ", ", transb_, ", ",
+        m_, ", ", n_, ", ", k_,
+        dtype_, ", ", device_id_);
+    // clang-format on
+  }
+
+ private:
+  typedef std::tuple<bool, bool, int64, int64, int64, DataType, int>
+      ParameterDataType;
+
+  ParameterDataType get_data_as_tuple() const {
+    return std::make_tuple(transa_, transb_, m_, n_, k_, dtype_, device_id_);
+  }
+
+  bool transa_;
+  bool transb_;
+  uint64 m_;
+  uint64 n_;
+  uint64 k_;
+  DataType dtype_;
+  int device_id_;
+  uint64 hash_code_;
+};
+
+typedef Eigen::GpuDevice GPUDevice;
+
+#endif  // GOOGLE_CUDA
+
 }  // end namespace tensorflow
 
 #endif  // TENSORFLOW_KERNELS_MATMUL_OP_H_
diff --git a/tensorflow/core/util/matmul_autotune.cc b/tensorflow/core/util/matmul_autotune.cc
new file mode 100644
index 0000000000..741a78a193
--- /dev/null
+++ b/tensorflow/core/util/matmul_autotune.cc
@@ -0,0 +1,51 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/util/matmul_autotune.h"
+
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/util/env_var.h"
+
+namespace tensorflow {
+bool MatmulAutotuneEnable() {
+  bool value;
+  Status status =
+      ReadBoolFromEnvVar("TF_MATMUL_AUTOTUNE_ENABLE", false, &value);
+  if (!status.ok()) {
+    LOG(ERROR) << status.error_message();
+  }
+  return value;
+}
+
+bool MatmulDoFP32ComputationFP16Input() {
+  bool value;
+  // Feedback from NVIDIA: the "true floating point 16" compute capability is
+  // absent from compute capability SM 5.2. The native 16 bit floating point
+  // computation was introduced in SM 5.3 and higher compute capability. So
+  // for compatibility, set this to be true by default for now.
+  // TODO(yangzihao): In the future, we need to return three possibilities:
+  // user-set-true, user-set-false, user-no-setting. In the calling sites,
+  // check the compatibilities. Note that user-set-false with compute
+  // capability <= 5.2 will cause an error in the later cublasGemmEx() call.
+  Status status =
+      ReadBoolFromEnvVar("TF_FP16_MATMUL_USE_FP32_COMPUTE", true, &value);
+  if (!status.ok()) {
+    LOG(ERROR) << status.error_message();
+  }
+  return value;
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/util/matmul_autotune.h b/tensorflow/core/util/matmul_autotune.h
new file mode 100644
index 0000000000..5366623883
--- /dev/null
+++ b/tensorflow/core/util/matmul_autotune.h
@@ -0,0 +1,28 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// The utility to check matmul autotune related flags.
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
+
+namespace tensorflow {
+
+bool MatmulAutotuneEnable();
+bool MatmulDoFP32ComputationFP16Input();
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_UTIL_MATMUL_AUTOTUNE_H_
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 7882e088d0..65ca4be547 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3858,6 +3858,48 @@ cuda_py_test(
     main = "ops/transpose_benchmark.py",
 )
 
+cuda_py_test(
+    name = "matmul_benchmark",
+    size = "medium",
+    srcs = ["ops/matmul_benchmark.py"],
+    additional_deps = [
+        ":math_ops",
+        ":random_ops",
+        ":client",
+        ":client_testlib",
+        ":control_flow_ops",
+        ":framework_for_generated_wrappers",
+        ":framework_test_lib",
+        ":platform",
+        ":platform_benchmark",
+        ":variables",
+        "//third_party/py/numpy",
+        "//tensorflow/core:protos_all_py",
+    ],
+    main = "ops/matmul_benchmark.py",
+)
+
+cuda_py_test(
+    name = "matmul_benchmark_test",
+    size = "medium",
+    srcs = ["ops/matmul_benchmark_test.py"],
+    additional_deps = [
+        ":math_ops",
+        ":random_ops",
+        ":client",
+        ":client_testlib",
+        ":control_flow_ops",
+        ":framework_for_generated_wrappers",
+        ":platform",
+        ":platform_benchmark",
+        ":matmul_benchmark",
+        ":variables",
+        "//third_party/py/numpy",
+        "//tensorflow/core:protos_all_py",
+    ],
+    main = "ops/matmul_benchmark_test.py",
+)
+
 cuda_py_test(
     name = "session_benchmark",
     srcs = ["client/session_benchmark.py"],
diff --git a/tensorflow/python/kernel_tests/matmul_op_test.py b/tensorflow/python/kernel_tests/matmul_op_test.py
index 042f462357..b167278984 100644
--- a/tensorflow/python/kernel_tests/matmul_op_test.py
+++ b/tensorflow/python/kernel_tests/matmul_op_test.py
@@ -31,6 +31,9 @@ from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test as test_lib
 
+# TODO(yangzihao): Currently matmul autotuning is disabled by default. Use
+# os.environ["TF_MATMUL_AUTOTUNE_ENABLE"] = "1" to enable it.
+
 
 def _AddTest(test, op_name, testcase_name, fn):
   test_name = "_".join(["test", op_name, testcase_name])
diff --git a/tensorflow/python/ops/matmul_benchmark.py b/tensorflow/python/ops/matmul_benchmark.py
new file mode 100644
index 0000000000..55c575162a
--- /dev/null
+++ b/tensorflow/python/ops/matmul_benchmark.py
@@ -0,0 +1,143 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Benchmark for Matmul operator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+import time
+
+import numpy as np
+
+from tensorflow.python.client import session as session_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+def build_graph(device, n, m, k, transpose_a, transpose_b, dtype):
+  """Build a graph containing a sequence of matmul operations.
+
+  Args:
+    device: String, the device to run on.
+    n: tensor A's first dimension size.
+    m: tensor A's second dimension size.
+    k: tensor B's second dimension size.
+    transpose_a: boolean value to show if tensor A is transposed.
+    transpose_b: boolean value to show if tensor B is transposed.
+    dtype: numpy data type of the input tensor.
+
+  Returns:
+    A matmul operation to run()
+  """
+  with ops.device('/%s:0' % device):
+    if not transpose_a:
+      x = variables.Variable(random_ops.random_uniform([n, m], dtype=dtype))
+    else:
+      x = variables.Variable(random_ops.random_uniform([m, n], dtype=dtype))
+    if not transpose_b:
+      y = variables.Variable(random_ops.random_uniform([m, k], dtype=dtype))
+    else:
+      y = variables.Variable(random_ops.random_uniform([k, m], dtype=dtype))
+
+    z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b)
+    return control_flow_ops.group(z)
+
+
+class MatmulBenchmark(test.Benchmark):
+  """Benchmark matmul!"""
+
+  def run_graph(self, device, n, m, k, transpose_a, transpose_b, num_iters,
+                dtype):
+    """Run the graph and print its execution time.
+
+    Args:
+      device: String, the device to run on.
+      n: tensor A's first dimension size.
+      m: tensor A's second dimension size.
+      k: tensor B's second dimension size.
+      transpose_a: boolean value to show if tensor A is transposed.
+      transpose_b: boolean value to show if tensor B is transposed.
+      num_iters: number of iterations to run the benchmark.
+      dtype: numpy data type of the input tensor.
+
+    Returns:
+      The duration of the run in seconds.
+    """
+    graph = ops.Graph()
+    with graph.as_default():
+      output = build_graph(device, n, m, k, transpose_a, transpose_b, dtype)
+      with session_lib.Session(graph=graph) as session:
+        variables.global_variables_initializer().run()
+        for _ in range(500):
+          session.run(output)
+        start_time = time.time()
+        for _ in range(num_iters):
+          session.run(output)
+        duration = (time.time() - start_time)
+        num_items = n * m * k * 2
+        throughput = num_items * num_iters / duration / 1e9
+        print('%s %s input_info:%s %d %.4fsec, %.4fGitems/s.' %
+              (device, str(dtype), str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:'
+               + str(transpose_a) + '.tb:' + str(transpose_b), num_iters,
+               duration, throughput))
+
+    name_template = ('matmul_{device}_{dtype}_input_info_{inputinfo}')
+
+    self.report_benchmark(
+        name=name_template.format(
+            device=device,
+            dtype=str(dtype).replace(' ', ''),
+            inputinfo=str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:' +
+            str(transpose_a) + '.tb:' + str(transpose_b)).replace(' ', ''),
+        iters=num_iters,
+        wall_time=duration)
+    return duration
+
+  def run_test_gpu(self, n, m, k, transpose_a, transpose_b, dtype, num_iters):
+    self.run_graph('gpu', n, m, k, transpose_a, transpose_b, num_iters, dtype)
+
+  def test_round(self, num_iters):
+    dtypes = [np.float32, np.float64]
+    for dtype in dtypes:
+      for n, m, (transpose_a, transpose_b) in itertools.product(
+          [512, 1024], [1, 8, 16, 128], [(False, False), (True, False),
+                                         (False, True)]):
+        k = n
+        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
+
+      for n, m, k, (transpose_a, transpose_b) in itertools.product(
+          [200], [1, 8, 20], [10000], [(False, False), (True, False), (False,
+                                                                       True)]):
+        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
+
+      for (n, m, k), (transpose_a, transpose_b) in itertools.product(
+          [(200, 20, 20000), (1, 10000, 200)], [(False, False), (True, False),
+                                                (False, True)]):
+        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
+
+  def benchmark_matmul(self):
+    num_iters = 200
+    for _ in range(10):
+      self.test_round(num_iters)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/ops/matmul_benchmark_test.py b/tensorflow/python/ops/matmul_benchmark_test.py
new file mode 100644
index 0000000000..a7914dba78
--- /dev/null
+++ b/tensorflow/python/ops/matmul_benchmark_test.py
@@ -0,0 +1,122 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for matmul_benchmark.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+import numpy as np
+
+from tensorflow.core.framework import graph_pb2
+from tensorflow.core.framework import node_def_pb2
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import matmul_benchmark
+from tensorflow.python.platform import test as googletest
+from tensorflow.python.platform import tf_logging
+
+
+def BuildGraphTest(n, m, k, transpose_a, transpose_b, dtype):
+
+  def Test(self):
+    if not googletest.is_gpu_available():
+      tf_logging.info("Skipping BuildGraphTest %s", (n, m, k, transpose_a,
+                                                     transpose_b))
+      return
+    tf_logging.info("Testing BuildGraphTest %s", (n, m, k, transpose_a,
+                                                  transpose_b))
+    self._VerifyBuildGraph(n, m, k, transpose_a, transpose_b, dtype)
+
+  return Test
+
+
+def RunGraphTest(n, m, k, transpose_a, transpose_b, dtype):
+
+  def Test(self):
+    if not googletest.is_gpu_available():
+      tf_logging.info("Skipping RunGraphTest %s", (n, m, k, transpose_a,
+                                                   transpose_b))
+      return
+    tf_logging.info("Testing RunGraphTest %s", (n, m, k, transpose_a,
+                                                transpose_b))
+    self._VerifyRunGraph(n, m, k, transpose_a, transpose_b, dtype)
+
+  return Test
+
+
+class MatmulBenchmarkTest(googletest.TestCase):
+
+  def _StripNode(self, nd):
+    snode = node_def_pb2.NodeDef(name=nd.name, op=nd.op, input=nd.input)
+    if nd.device:
+      snode.device = nd.device
+    return snode
+
+  def _StripGraph(self, gd):
+    return graph_pb2.GraphDef(node=[self._StripNode(nd) for nd in gd.node])
+
+  def _VerifyBuildGraph(self, n, m, k, transpose_a, transpose_b, dtype):
+    graph = ops.Graph()
+    with graph.as_default():
+      matmul_benchmark.build_graph("gpu", n, m, k, transpose_a, transpose_b,
+                                   dtype)
+      gd = graph.as_graph_def()
+      self.assertProtoEquals("""
+      node { name: "random_uniform/shape" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform/min" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform/max" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform/RandomUniform" op: "RandomUniform" input: "random_uniform/shape" device: "/device:GPU:0" }
+      node { name: "random_uniform/sub" op: "Sub" input: "random_uniform/max" input: "random_uniform/min" device: "/device:GPU:0" }
+      node { name: "random_uniform/mul" op: "Mul" input: "random_uniform/RandomUniform" input: "random_uniform/sub" device: "/device:GPU:0" }
+      node { name: "random_uniform" op: "Add" input: "random_uniform/mul" input: "random_uniform/min" device: "/device:GPU:0" }
+      node { name: "Variable" op: "VariableV2" device: "/device:GPU:0" }
+      node { name: "Variable/Assign" op: "Assign" input: "Variable" input: "random_uniform" device: "/device:GPU:0" }
+      node { name: "Variable/read" op: "Identity" input: "Variable" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/shape" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/min" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/max" op: "Const" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/RandomUniform" op: "RandomUniform" input: "random_uniform_1/shape" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/sub" op: "Sub" input: "random_uniform_1/max" input: "random_uniform_1/min" device: "/device:GPU:0" }
+      node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: "/device:GPU:0" }
+      node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: "/device:GPU:0" }
+      node { name: "Variable_1" op: "VariableV2" device: "/device:GPU:0" }
+      node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: "/device:GPU:0" }
+      node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: "/device:GPU:0" }
+      node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: "/device:GPU:0" }
+      node { name: "group_deps" op: "NoOp" input: "^MatMul" device: "/device:GPU:0" }
+                             """, self._StripGraph(gd))
+
+  def _VerifyRunGraph(self, n, m, k, transpose_a, transpose_b, dtype):
+    benchmark_instance = matmul_benchmark.MatmulBenchmark()
+    duration = benchmark_instance.run_graph("gpu", n, m, k, transpose_a,
+                                            transpose_b, 1, dtype)
+    self.assertTrue(duration > 1e-6)
+
+
+if __name__ == "__main__":
+  dtypes = [np.float32, np.float64]
+  index = 0
+  for _dtype in dtypes:
+    for _n, _m, (_transpose_a, _transpose_b) in itertools.product(
+        [512, 1024], [1, 8, 16, 128], [(False, False), (True, False), (False,
+                                                                       True)]):
+      _k = _n
+      setattr(MatmulBenchmarkTest, "testBuildGraph_" + str(index),
+              BuildGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
+      setattr(MatmulBenchmarkTest, "testRunGraph_" + str(index),
+              RunGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
+      index += 1
+  googletest.main()
diff --git a/tensorflow/stream_executor/blas.cc b/tensorflow/stream_executor/blas.cc
index a59a1dda71..da09d84921 100644
--- a/tensorflow/stream_executor/blas.cc
+++ b/tensorflow/stream_executor/blas.cc
@@ -67,6 +67,10 @@ string SideString(Side s) {
   }
 }
 
+// -- AlgorithmConfig
+
+string AlgorithmConfig::ToString() const { return port::StrCat(algorithm_); }
+
 string ComputationTypeString(ComputationType ty) {
   switch (ty) {
     case ComputationType::kF16:
diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h
index cfff3649c8..eb1b19c5d9 100644
--- a/tensorflow/stream_executor/blas.h
+++ b/tensorflow/stream_executor/blas.h
@@ -44,7 +44,6 @@ limitations under the License.
 #include "tensorflow/stream_executor/platform/port.h"
 
 #include "tensorflow/stream_executor/lib/array_slice.h"
-#include "tensorflow/stream_executor/platform/port.h"
 
 namespace Eigen {
 struct half;
@@ -108,6 +107,10 @@ string ComputationTypeString(ComputationType ty);
 // Opaque identifier for an "algorithm" used by a blas routine.  This functions
 // as a hint to the blas library.
 typedef int64 AlgorithmType;
+constexpr AlgorithmType kDefaultAlgorithm = -1;
+constexpr AlgorithmType kDefaultBlasGemm = -2;
+constexpr AlgorithmType kDefaultBlasGemv = -3;
+constexpr AlgorithmType kNoAlgorithm = -4;
 
 // blas uses -1 to represent the default algorithm. This happens to match up
 // with the CUBLAS_GEMM_DFALT constant, so cuda_blas.cc is using static_cast
@@ -134,10 +137,28 @@ class ProfileResult {
 
  private:
   bool is_valid_ = false;
-  AlgorithmType algorithm_ = 0;
+  AlgorithmType algorithm_ = kDefaultAlgorithm;
   float elapsed_time_in_ms_ = std::numeric_limits<float>::max();
 };
 
+class AlgorithmConfig {
+ public:
+  AlgorithmConfig() : algorithm_(kDefaultAlgorithm) {}
+  explicit AlgorithmConfig(AlgorithmType algorithm) : algorithm_(algorithm) {}
+  AlgorithmType algorithm() const { return algorithm_; }
+  void set_algorithm(AlgorithmType val) { algorithm_ = val; }
+  bool operator==(const AlgorithmConfig &other) const {
+    return this->algorithm_ == other.algorithm_;
+  }
+  bool operator!=(const AlgorithmConfig &other) const {
+    return !(*this == other);
+  }
+  string ToString() const;
+
+ private:
+  AlgorithmType algorithm_;
+};
+
 // BLAS support interface -- this can be derived from a GPU executor when the
 // underlying platform has an BLAS library implementation available. See
 // StreamExecutor::AsBlas().
@@ -453,6 +474,29 @@ class BlasSupport {
                           std::complex<double> beta,
                           DeviceMemory<std::complex<double>> *y, int incy) = 0;
 
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,
+      const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
+      int incx, float beta, DeviceMemory<float> *y, int incy,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha,
+      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
+      int incx, double beta, DeviceMemory<double> *y, int incy,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+      std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,
+      int lda, const DeviceMemory<std::complex<float>> &x, int incx,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemvWithProfiling(
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+      std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a,
+      int lda, const DeviceMemory<std::complex<double>> &x, int incx,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,
+      int incy, ProfileResult *output_profile_result) = 0;
+
   // Performs a rank-1 update of a general matrix.
   //
   //     a <- alpha * x * y' + a,
@@ -935,8 +979,39 @@ class BlasSupport {
                           std::complex<double> beta,
                           DeviceMemory<std::complex<double>> *c, int ldc) = 0;
 
-  // Gets a list of supported algorithms for DoBlasGemmWithAlgorithm.  Note that
-  // any or all of these algorithms may still be
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, float alpha, const DeviceMemory<Eigen::half> &a,
+      int lda, const DeviceMemory<Eigen::half> &b, int ldb, float beta,
+      DeviceMemory<Eigen::half> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
+      const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
+      int ldc, ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
+      const DeviceMemory<double> &b, int ldb, double beta,
+      DeviceMemory<double> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, std::complex<float> alpha,
+      const DeviceMemory<std::complex<float>> &a, int lda,
+      const DeviceMemory<std::complex<float>> &b, int ldb,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+  virtual bool DoBlasGemmWithProfiling(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, std::complex<double> alpha,
+      const DeviceMemory<std::complex<double>> &a, int lda,
+      const DeviceMemory<std::complex<double>> &b, int ldb,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+      ProfileResult *output_profile_result) = 0;
+
+  // Gets a list of supported algorithms for DoBlasGemmWithAlgorithm.
   virtual bool GetBlasGemmAlgorithms(
       std::vector<AlgorithmType> *out_algorithms) = 0;
 
@@ -1473,6 +1548,28 @@ class BlasSupport {
                   const DeviceMemory<std::complex<double>> &x, int incx,       \
                   std::complex<double> beta,                                   \
                   DeviceMemory<std::complex<double>> *y, int incy) override;   \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,  \
+      const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,     \
+      int incx, float beta, DeviceMemory<float> *y, int incy,                  \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha, \
+      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,   \
+      int incx, double beta, DeviceMemory<double> *y, int incy,                \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,               \
+      std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,   \
+      int lda, const DeviceMemory<std::complex<float>> &x, int incx,           \
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *y,          \
+      int incy, blas::ProfileResult *output_profile_result) override;          \
+  bool DoBlasGemvWithProfiling(                                                \
+      Stream *stream, blas::Transpose trans, uint64 m, uint64 n,               \
+      std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a, \
+      int lda, const DeviceMemory<std::complex<double>> &x, int incx,          \
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,        \
+      int incy, blas::ProfileResult *output_profile_result) override;          \
   bool DoBlasGer(Stream *stream, uint64 m, uint64 n, float alpha,              \
                  const DeviceMemory<float> &x, int incx,                       \
                  const DeviceMemory<float> &y, int incy,                       \
@@ -1751,6 +1848,39 @@ class BlasSupport {
                   const DeviceMemory<std::complex<double>> &b, int ldb,        \
                   std::complex<double> beta,                                   \
                   DeviceMemory<std::complex<double>> *c, int ldc) override;    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, float alpha,                               \
+      const DeviceMemory<Eigen::half> &a, int lda,                             \
+      const DeviceMemory<Eigen::half> &b, int ldb, float beta,                 \
+      DeviceMemory<Eigen::half> *c, int ldc,                                   \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, \
+      int lda, const DeviceMemory<float> &b, int ldb, float beta,              \
+      DeviceMemory<float> *c, int ldc,                                         \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, double alpha,                              \
+      const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &b,   \
+      int ldb, double beta, DeviceMemory<double> *c, int ldc,                  \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, std::complex<float> alpha,                 \
+      const DeviceMemory<std::complex<float>> &a, int lda,                     \
+      const DeviceMemory<std::complex<float>> &b, int ldb,                     \
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc, \
+      blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmWithProfiling(                                                \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, std::complex<double> alpha,                \
+      const DeviceMemory<std::complex<double>> &a, int lda,                    \
+      const DeviceMemory<std::complex<double>> &b, int ldb,                    \
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *c,        \
+      int ldc, blas::ProfileResult *output_profile_result) override;           \
   bool GetBlasGemmAlgorithms(std::vector<blas::AlgorithmType> *out_algorithms) \
       override;                                                                \
   bool DoBlasGemmWithAlgorithm(                                                \
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 2817364e97..cb2b06d47c 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -1857,6 +1857,180 @@ bool CUDABlas::DoBlasGemm(Stream *stream, blas::Transpose transa,
       CUDAComplex(CUDAMemoryMutable(c)), ldc);
 }
 
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, float alpha,
+    const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
+    int incx, float beta, DeviceMemory<float> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, double alpha,
+    const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
+    int incx, double beta, DeviceMemory<double> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+    std::complex<float> alpha, const DeviceMemory<std::complex<float>> &a,
+    int lda, const DeviceMemory<std::complex<float>> &x, int incx,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemvWithProfiling(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n,
+    std::complex<double> alpha, const DeviceMemory<std::complex<double>> &a,
+    int lda, const DeviceMemory<std::complex<double>> &x, int incx,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemvWithProfilingImpl(stream, trans, m, n, alpha, a, lda, x,
+                                     incx, beta, y, incy,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, float alpha, const DeviceMemory<Eigen::half> &a,
+    int lda, const DeviceMemory<Eigen::half> &b, int ldb, float beta,
+    DeviceMemory<Eigen::half> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
+    const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
+    int ldc, blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
+    const DeviceMemory<double> &b, int ldb, double beta,
+    DeviceMemory<double> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, std::complex<float> alpha,
+    const DeviceMemory<std::complex<float>> &a, int lda,
+    const DeviceMemory<std::complex<float>> &b, int ldb,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+bool CUDABlas::DoBlasGemmWithProfiling(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, std::complex<double> alpha,
+    const DeviceMemory<std::complex<double>> &a, int lda,
+    const DeviceMemory<std::complex<double>> &b, int ldb,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  return DoBlasGemmWithProfilingImpl(stream, transa, transb, m, n, k, alpha, a,
+                                     lda, b, ldb, beta, c, ldc,
+                                     output_profile_result);
+}
+
+template <typename T>
+bool CUDABlas::DoBlasGemvWithProfilingImpl(
+    Stream *stream, blas::Transpose trans, uint64 m, uint64 n, const T &alpha,
+    const DeviceMemory<T> &a, int lda, const DeviceMemory<T> &x, int incx,
+    const T &beta, DeviceMemory<T> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  struct TimerDeleter {
+    void operator()(CUDATimer *t) {
+      t->Destroy();
+      delete t;
+    }
+  };
+  std::unique_ptr<CUDATimer, TimerDeleter> timer;
+  if (output_profile_result != nullptr) {
+    timer.reset(new CUDATimer(parent_));
+    if (!timer->Init() || !timer->Start(AsCUDAStream(stream))) {
+      return false;
+    }
+  }
+
+  // Call blasGemm
+  bool result =
+      DoBlasGemv(stream, trans, m, n, alpha, a, lda, x, incx, beta, y, incy);
+
+  if (timer != nullptr && result) {
+    // CUDATimer will CHECK-fail if we Stop() it while the stream is in an error
+    // state.
+    if (!timer->Stop(AsCUDAStream(stream))) {
+      return false;
+    }
+    output_profile_result->set_is_valid(true);
+    output_profile_result->set_algorithm(blas::kDefaultBlasGemv);
+    output_profile_result->set_elapsed_time_in_ms(
+        timer->GetElapsedMilliseconds());
+  }
+  return result;
+}
+
+template <typename T, typename ParamType>
+bool CUDABlas::DoBlasGemmWithProfilingImpl(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, const ParamType &alpha, const DeviceMemory<T> &a,
+    int lda, const DeviceMemory<T> &b, int ldb, const ParamType &beta,
+    DeviceMemory<T> *c, int ldc, blas::ProfileResult *output_profile_result) {
+  struct TimerDeleter {
+    void operator()(CUDATimer *t) {
+      t->Destroy();
+      delete t;
+    }
+  };
+  std::unique_ptr<CUDATimer, TimerDeleter> timer;
+  if (output_profile_result != nullptr) {
+    timer.reset(new CUDATimer(parent_));
+    if (!timer->Init() || !timer->Start(AsCUDAStream(stream))) {
+      return false;
+    }
+  }
+
+  // Call blasGemm
+  bool result = DoBlasGemm(stream, transa, transb, m, n, k, alpha, a, lda, b,
+                           ldb, beta, c, ldc);
+
+  if (timer != nullptr && result) {
+    // CUDATimer will CHECK-fail if we Stop() it while the stream is in an error
+    // state.
+    if (!timer->Stop(AsCUDAStream(stream))) {
+      return false;
+    }
+    output_profile_result->set_is_valid(true);
+    output_profile_result->set_algorithm(blas::kDefaultBlasGemm);
+    output_profile_result->set_elapsed_time_in_ms(
+        timer->GetElapsedMilliseconds());
+  }
+  return result;
+}
+
 template <typename InT, typename OutT, typename CompT>
 bool CUDABlas::DoBlasGemmWithAlgorithmImpl(
     Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
@@ -1920,6 +2094,9 @@ bool CUDABlas::GetBlasGemmAlgorithms(
     std::vector<blas::AlgorithmType> *out_algorithms) {
 // cublasGemmAlgo_t (and the function that accepts this type, cublasGemmEx)
 // were first introduced in CUDA 8.
+// Note that when CUDA version and compute capability is not sufficient, we
+// still return the out_algorithms. Caller needs to make sure that in this case,
+// the returned vector is empty.
 #if CUDA_VERSION >= 8000
   for (cublasGemmAlgo_t algo :
        {CUBLAS_GEMM_DFALT, CUBLAS_GEMM_ALGO0, CUBLAS_GEMM_ALGO1,
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.h b/tensorflow/stream_executor/cuda/cuda_blas.h
index 4a8641b300..80cda97117 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.h
+++ b/tensorflow/stream_executor/cuda/cuda_blas.h
@@ -127,6 +127,23 @@ class CUDABlas : public blas::BlasSupport {
       blas::AlgorithmType algorithm,
       blas::ProfileResult *output_profile_result);
 
+  // Helper function for implementing DoBlasGemmWithProfiling.
+  template <typename T, typename ParamType>
+  bool DoBlasGemmWithProfilingImpl(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, const ParamType &alpha, const DeviceMemory<T> &a,
+      int lda, const DeviceMemory<T> &b, int ldb, const ParamType &beta,
+      DeviceMemory<T> *c, int ldc, blas::ProfileResult *output_profile_result);
+
+  // Helper function for implementing DoBlasGemvWithProfiling.
+  template <typename T>
+  bool DoBlasGemvWithProfilingImpl(Stream *stream, blas::Transpose trans,
+                                   uint64 m, uint64 n, const T &alpha,
+                                   const DeviceMemory<T> &a, int lda,
+                                   const DeviceMemory<T> &x, int incx,
+                                   const T &beta, DeviceMemory<T> *y, int incy,
+                                   blas::ProfileResult *output_profile_result);
+
   // mutex that guards the cuBLAS handle for this device.
   mutex mu_;
 
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index 5996195173..c9b36ba7ab 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -3458,6 +3458,184 @@ struct ThenBlasWithProfileImpl {
 };
 }  // anonymous namespace
 
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, float alpha,
+    const DeviceMemory<float> &a, int lda, const DeviceMemory<float> &x,
+    int incx, float beta, DeviceMemory<float> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<
+      blas::Transpose, uint64, uint64, float, const DeviceMemory<float> &, int,
+      const DeviceMemory<float> &, int, float, DeviceMemory<float> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, double alpha,
+    const DeviceMemory<double> &a, int lda, const DeviceMemory<double> &x,
+    int incx, double beta, DeviceMemory<double> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, double,
+                          const DeviceMemory<double> &, int,
+                          const DeviceMemory<double> &, int, double,
+                          DeviceMemory<double> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, std::complex<float> alpha,
+    const DeviceMemory<std::complex<float>> &a, int lda,
+    const DeviceMemory<std::complex<float>> &x, int incx,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, std::complex<float>,
+                          const DeviceMemory<std::complex<float>> &, int,
+                          const DeviceMemory<std::complex<float>> &, int,
+                          std::complex<float>,
+                          DeviceMemory<std::complex<float>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemvWithProfiling(
+    blas::Transpose trans, uint64 m, uint64 n, std::complex<double> alpha,
+    const DeviceMemory<std::complex<double>> &a, int lda,
+    const DeviceMemory<std::complex<double>> &x, int incx,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *y, int incy,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(trans), PARAM(m), PARAM(n), PARAM(alpha), PARAM(a),
+            PARAM(lda), PARAM(x), PARAM(incx), PARAM(beta), PARAM(y),
+            PARAM(incy));
+
+  ThenBlasWithProfileImpl<blas::Transpose, uint64, uint64, std::complex<double>,
+                          const DeviceMemory<std::complex<double>> &, int,
+                          const DeviceMemory<std::complex<double>> &, int,
+                          std::complex<double>,
+                          DeviceMemory<std::complex<double>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemvWithProfiling, trans, m, n,
+              alpha, a, lda, x, incx, beta, y, incy, output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, float alpha, const DeviceMemory<Eigen::half> &a, int lda,
+    const DeviceMemory<Eigen::half> &b, int ldb, float beta,
+    DeviceMemory<Eigen::half> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
+                          uint64, float, const DeviceMemory<Eigen::half> &, int,
+                          const DeviceMemory<Eigen::half> &, int, float,
+                          DeviceMemory<Eigen::half> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, float alpha, const DeviceMemory<float> &a, int lda,
+    const DeviceMemory<float> &b, int ldb, float beta, DeviceMemory<float> *c,
+    int ldc, blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
+                          uint64, float, const DeviceMemory<float> &, int,
+                          const DeviceMemory<float> &, int, float,
+                          DeviceMemory<float> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, double alpha, const DeviceMemory<double> &a, int lda,
+    const DeviceMemory<double> &b, int ldb, double beta,
+    DeviceMemory<double> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<blas::Transpose, blas::Transpose, uint64, uint64,
+                          uint64, double, const DeviceMemory<double> &, int,
+                          const DeviceMemory<double> &, int, double,
+                          DeviceMemory<double> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, std::complex<float> alpha,
+    const DeviceMemory<std::complex<float>> &a, int lda,
+    const DeviceMemory<std::complex<float>> &b, int ldb,
+    std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<
+      blas::Transpose, blas::Transpose, uint64, uint64, uint64,
+      std::complex<float>, const DeviceMemory<std::complex<float>> &, int,
+      const DeviceMemory<std::complex<float>> &, int, std::complex<float>,
+      DeviceMemory<std::complex<float>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
+Stream &Stream::ThenBlasGemmWithProfiling(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, std::complex<double> alpha,
+    const DeviceMemory<std::complex<double>> &a, int lda,
+    const DeviceMemory<std::complex<double>> &b, int ldb,
+    std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+    blas::ProfileResult *output_profile_result) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc));
+
+  ThenBlasWithProfileImpl<
+      blas::Transpose, blas::Transpose, uint64, uint64, uint64,
+      std::complex<double>, const DeviceMemory<std::complex<double>> &, int,
+      const DeviceMemory<std::complex<double>> &, int, std::complex<double>,
+      DeviceMemory<std::complex<double>> *, int>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmWithProfiling, transa, transb,
+              m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+              output_profile_result);
+}
+
 Stream &Stream::ThenBlasGemmWithAlgorithm(
     blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
     uint64 k, const Eigen::half &alpha, const DeviceMemory<Eigen::half> &a,
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index 3c8b7ee894..e218873839 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -934,6 +934,31 @@ class Stream {
                        std::complex<double> beta,
                        DeviceMemory<std::complex<double>> *y, int incy);
 
+  Stream &ThenBlasGemvWithProfiling(blas::Transpose trans, uint64 m, uint64 n,
+                                    float alpha, const DeviceMemory<float> &a,
+                                    int lda, const DeviceMemory<float> &x,
+                                    int incx, float beta,
+                                    DeviceMemory<float> *y, int incy,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemvWithProfiling(blas::Transpose trans, uint64 m, uint64 n,
+                                    double alpha, const DeviceMemory<double> &a,
+                                    int lda, const DeviceMemory<double> &x,
+                                    int incx, double beta,
+                                    DeviceMemory<double> *y, int incy,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemvWithProfiling(
+      blas::Transpose trans, uint64 m, uint64 n, std::complex<float> alpha,
+      const DeviceMemory<std::complex<float>> &a, int lda,
+      const DeviceMemory<std::complex<float>> &x, int incx,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *y, int incy,
+      blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemvWithProfiling(
+      blas::Transpose trans, uint64 m, uint64 n, std::complex<double> alpha,
+      const DeviceMemory<std::complex<double>> &a, int lda,
+      const DeviceMemory<std::complex<double>> &x, int incx,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *y,
+      int incy, blas::ProfileResult *output_profile_result);
+
   // See BlasSupport::DoBlasGer.
   Stream &ThenBlasGer(uint64 m, uint64 n, float alpha,
                       const DeviceMemory<float> &x, int incx,
@@ -1249,6 +1274,44 @@ class Stream {
                        std::complex<double> beta,
                        DeviceMemory<std::complex<double>> *c, int ldc);
 
+  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
+                                    blas::Transpose transb, uint64 m, uint64 n,
+                                    uint64 k, float alpha,
+                                    const DeviceMemory<Eigen::half> &a, int lda,
+                                    const DeviceMemory<Eigen::half> &b, int ldb,
+                                    float beta, DeviceMemory<Eigen::half> *c,
+                                    int ldc,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
+                                    blas::Transpose transb, uint64 m, uint64 n,
+                                    uint64 k, float alpha,
+                                    const DeviceMemory<float> &a, int lda,
+                                    const DeviceMemory<float> &b, int ldb,
+                                    float beta, DeviceMemory<float> *c, int ldc,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(blas::Transpose transa,
+                                    blas::Transpose transb, uint64 m, uint64 n,
+                                    uint64 k, double alpha,
+                                    const DeviceMemory<double> &a, int lda,
+                                    const DeviceMemory<double> &b, int ldb,
+                                    double beta, DeviceMemory<double> *c,
+                                    int ldc,
+                                    blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(
+      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+      uint64 k, std::complex<float> alpha,
+      const DeviceMemory<std::complex<float>> &a, int lda,
+      const DeviceMemory<std::complex<float>> &b, int ldb,
+      std::complex<float> beta, DeviceMemory<std::complex<float>> *c, int ldc,
+      blas::ProfileResult *output_profile_result);
+  Stream &ThenBlasGemmWithProfiling(
+      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+      uint64 k, std::complex<double> alpha,
+      const DeviceMemory<std::complex<double>> &a, int lda,
+      const DeviceMemory<std::complex<double>> &b, int ldb,
+      std::complex<double> beta, DeviceMemory<std::complex<double>> *c, int ldc,
+      blas::ProfileResult *output_profile_result);
+
   // See BlasSupport::DoBlasGemmWithAlgorithm.
   Stream &ThenBlasGemmWithAlgorithm(
       blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
-- 
GitLab


From a36a83c9b0766c109df8445cae8b5cf9075e4dd8 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Fri, 21 Jul 2017 09:29:48 -0700
Subject: [PATCH 1193/1375] [XLA] Remove replica count setting flag.

PiperOrigin-RevId: 162749134
---
 .../compiler/xla/client/client_library.cc     |  7 +++
 .../compiler/xla/client/client_library.h      | 13 ++--
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ----
 .../xla/legacy_flags/backend_flags.cc         | 63 -------------------
 .../compiler/xla/legacy_flags/backend_flags.h | 46 --------------
 tensorflow/compiler/xla/service/BUILD         |  1 -
 tensorflow/compiler/xla/service/service.cc    |  8 +--
 tensorflow/compiler/xla/service/service.h     |  5 +-
 .../xla/tests/client_library_test_base.cc     | 21 +++++--
 .../xla/tests/client_library_test_base.h      |  5 ++
 10 files changed, 40 insertions(+), 141 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/backend_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/backend_flags.h

diff --git a/tensorflow/compiler/xla/client/client_library.cc b/tensorflow/compiler/xla/client/client_library.cc
index 8238261e1c..801596c0ad 100644
--- a/tensorflow/compiler/xla/client/client_library.cc
+++ b/tensorflow/compiler/xla/client/client_library.cc
@@ -23,6 +23,13 @@ limitations under the License.
 
 namespace xla {
 
+LocalClientOptions::LocalClientOptions(perftools::gputools::Platform* platform,
+                                       int number_of_replicas,
+                                       int intra_op_parallelism_threads)
+    : platform_(platform),
+      number_of_replicas_(number_of_replicas),
+      intra_op_parallelism_threads_(intra_op_parallelism_threads) {}
+
 LocalClientOptions& LocalClientOptions::set_platform(
     perftools::gputools::Platform* platform) {
   platform_ = platform;
diff --git a/tensorflow/compiler/xla/client/client_library.h b/tensorflow/compiler/xla/client/client_library.h
index 3ddd235d0e..cff2b5124e 100644
--- a/tensorflow/compiler/xla/client/client_library.h
+++ b/tensorflow/compiler/xla/client/client_library.h
@@ -43,13 +43,16 @@ namespace xla {
 // Options to configure the local client when it is created.
 class LocalClientOptions {
  public:
+  LocalClientOptions(perftools::gputools::Platform* platform = nullptr,
+                     int number_of_replicas = 1,
+                     int intra_op_parallelism_threads = -1);
+
   // Set the platform backing the service, or nullptr for the default platform.
   LocalClientOptions& set_platform(perftools::gputools::Platform* platform);
   perftools::gputools::Platform* platform() const;
 
   // Set the number of replicas to use when compiling replicated
-  // programs. The default is -1 meaning that the value is read from
-  // the xla_replicas flag.
+  // programs.
   LocalClientOptions& set_number_of_replicas(int number_of_replicas);
   int number_of_replicas() const;
 
@@ -58,9 +61,9 @@ class LocalClientOptions {
   int intra_op_parallelism_threads() const;
 
  private:
-  perftools::gputools::Platform* platform_ = nullptr;
-  int number_of_replicas_ = -1;
-  int intra_op_parallelism_threads_ = -1;
+  perftools::gputools::Platform* platform_;
+  int number_of_replicas_;
+  int intra_op_parallelism_threads_;
 };
 
 class ClientLibrary {
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 786093034d..cddcc40840 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -79,18 +79,6 @@ cc_library(
         ],
 )
 
-cc_library(
-    name = "backend_flags",
-    srcs = ["backend_flags.cc"],
-    hdrs = ["backend_flags.h"],
-    deps = [
-        ":parse_flags_from_env",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-    ],
-)
-
 # -----------------------------------------------------------------------------
 
 filegroup(
diff --git a/tensorflow/compiler/xla/legacy_flags/backend_flags.cc b/tensorflow/compiler/xla/legacy_flags/backend_flags.cc
deleted file mode 100644
index 7c007f4435..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/backend_flags.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's backend module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/backend_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static BackendFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new BackendFlags;
-  // TODO(b/32648682): Decide if this should continue to be a flag longer term.
-  flags->xla_replicas = 1;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_replicas", &flags->xla_replicas,
-          "The number of replicas to use. 1 means no replication."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's backend module.
-void AppendBackendFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the BackendFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-BackendFlags* GetBackendFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/backend_flags.h b/tensorflow/compiler/xla/legacy_flags/backend_flags.h
deleted file mode 100644
index 061238b7e6..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/backend_flags.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_BACKEND_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_BACKEND_FLAGS_H_
-
-// Legacy flags for XLA's backend module.
-
-#include <vector>
-
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's backend module.
-void AppendBackendFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's backend module.
-typedef struct {
-  int64 xla_replicas;  // The number of replicas to use.  1 means no
-                       // replication.
-} BackendFlags;
-
-// Return a pointer to the BackendFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-BackendFlags* GetBackendFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_BACKEND_FLAGS_H_
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 9c94091412..696dc28564 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -414,7 +414,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
-        "//tensorflow/compiler/xla/legacy_flags:backend_flags",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index d4cdc6df30..25e3f57dfb 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/backend_flags.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
@@ -172,12 +171,7 @@ Service::Service(const ServiceOptions& options,
     : options_(options),
       execute_backend_(std::move(execute_backend)),
       compute_constant_backend_(std::move(compute_constant_backend)) {
-  // TODO(b/32648682): this flag / options update dance will go away once we
-  // pass the replica count explicitly to the service.
-  if (options_.number_of_replicas() < 0) {
-    legacy_flags::BackendFlags* flags = legacy_flags::GetBackendFlags();
-    options_.set_number_of_replicas(flags->xla_replicas);
-  }
+  CHECK(options_.number_of_replicas() > 0);
 
   if (execute_backend_) {
     if (execute_backend_->device_count() > 0) {
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 9f216aad87..ccd699516e 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -57,8 +57,7 @@ class ServiceOptions {
   perftools::gputools::Platform* platform() const;
 
   // Set the number of replicas to use when compiling replicated
-  // programs. The default is -1 meaning that the value is read from
-  // the xla_replicas flag.
+  // programs.
   ServiceOptions& set_number_of_replicas(int number_of_replicas);
   int number_of_replicas() const;
 
@@ -68,7 +67,7 @@ class ServiceOptions {
 
  private:
   perftools::gputools::Platform* platform_ = nullptr;
-  int number_of_replicas_ = -1;
+  int number_of_replicas_ = 1;
   int intra_op_parallelism_threads_ = -1;
 };
 
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index 42c3f49030..3082630505 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -37,16 +37,20 @@ namespace xla {
 namespace {
 // Wrapper function that creates a nicer error message (than a bare
 // ValueOrDie()) if the platform we intend to test is not available.
-Client* GetOrCreateLocalClientOrDie(se::Platform* platform) {
-  StatusOr<Client*> result = ClientLibrary::GetOrCreateLocalClient(platform);
+Client* GetOrCreateLocalClientOrDie(const LocalClientOptions& client_options) {
+  StatusOr<Client*> result =
+      ClientLibrary::GetOrCreateLocalClient(client_options);
   TF_CHECK_OK(result.status()) << "could not create local client for testing";
   return result.ValueOrDie();
 }
 }  // namespace
 
-ClientLibraryTestBase::ClientLibraryTestBase(se::Platform* platform)
-    : client_(GetOrCreateLocalClientOrDie(platform)),
+ClientLibraryTestBase::ClientLibraryTestBase(
+    perftools::gputools::Platform* platform,
+    const LocalClientOptions& client_options)
+    : client_(GetOrCreateLocalClientOrDie(client_options)),
       execution_options_(CreateDefaultExecutionOptions()) {
+  CHECK_EQ(platform, client_options.platform());
   // Disabling constant_folding so that tests (usually written using Constants)
   // will exercise the intended code paths, instead of being constant folded.
   //
@@ -57,6 +61,15 @@ ClientLibraryTestBase::ClientLibraryTestBase(se::Platform* platform)
       "constant_folding");
 }
 
+ClientLibraryTestBase::ClientLibraryTestBase(se::Platform* platform)
+    : execution_options_(CreateDefaultExecutionOptions()) {
+  LocalClientOptions default_options;
+  default_options.set_platform(platform);
+  client_ = GetOrCreateLocalClientOrDie(default_options);
+  execution_options_.mutable_debug_options()->add_xla_disable_hlo_passes(
+      "constant_folding");
+}
+
 string ClientLibraryTestBase::TestName() const {
   return ::testing::UnitTest::GetInstance()->current_test_info()->name();
 }
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index ed63d38f55..19c179c4ba 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
+#include "tensorflow/compiler/xla/client/client_library.h"
 #include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
@@ -48,6 +49,10 @@ class ClientLibraryTestBase : public ::testing::Test {
   explicit ClientLibraryTestBase(
       perftools::gputools::Platform* platform = nullptr);
 
+  // Creates a new ClientLibraryTestBase with custom client options.
+  ClientLibraryTestBase(perftools::gputools::Platform* platform,
+                        const LocalClientOptions& client_options);
+
   // Returns the name of the test currently being run.
   string TestName() const;
 
-- 
GitLab


From 8e059376b5d9bbce159d6de2af5307210209ff5c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 10:09:10 -0700
Subject: [PATCH 1194/1375] Convert contrib ModeKeys correctly to align with
 core ModeKeys in ModelFnOps -> EstimatorSpec.

PiperOrigin-RevId: 162753780
---
 .../contrib/learn/python/learn/estimators/model_fn.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
index 8a327ab01f..8be9c72adf 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
@@ -270,8 +270,17 @@ class ModelFnOps(
           result[key] = value
       return result
 
+    # Convert the contrib mode enum to the core mode enum.
+    # Note: mode already validated in __new__().
+    if self.mode == ModeKeys.TRAIN:
+      core_mode = core_model_fn_lib.ModeKeys.TRAIN
+    elif self.mode == ModeKeys.EVAL:
+      core_mode = core_model_fn_lib.ModeKeys.EVAL
+    elif self.mode == ModeKeys.INFER:
+      core_mode = core_model_fn_lib.ModeKeys.PREDICT
+
     return core_model_fn_lib.EstimatorSpec(
-        mode=self.mode,
+        mode=core_mode,
         predictions=self.predictions,
         loss=self.loss,
         train_op=self.train_op,
-- 
GitLab


From fce5222dcf4d563a23055efeee89599dc36539c0 Mon Sep 17 00:00:00 2001
From: Manjunath Kudlur <keveman@google.com>
Date: Fri, 21 Jul 2017 10:18:08 -0700
Subject: [PATCH 1195/1375] Added DT_VARIANT type.

A tensor with DT_VARIANT type can store arbitrary C++ data structures.
DT_VARIANT is implemented using a type-erased data structure similar to
std::any, but with extensions to make it compatible with tensorflow::Tensor.
In particular, Encode and Decode methods need to be provided by C++ classes
whose objects are stored in Variant.
PiperOrigin-RevId: 162754827
---
 tensorflow/c/c_api.h                          |   1 +
 .../contrib/cmake/tf_core_framework.cmake     |   7 +
 tensorflow/core/BUILD                         |  22 +-
 tensorflow/core/framework/allocator.h         |  19 ++
 tensorflow/core/framework/tensor.cc           |  78 +++++
 tensorflow/core/framework/tensor.proto        |  13 +
 tensorflow/core/framework/tensor_test.cc      |  98 ++++++
 tensorflow/core/framework/types.cc            |   7 +-
 tensorflow/core/framework/types.h             |   2 +
 tensorflow/core/framework/types.proto         |   2 +
 tensorflow/core/framework/variant.cc          |  87 ++++++
 tensorflow/core/framework/variant.h           | 287 ++++++++++++++++++
 .../core/framework/variant_encode_decode.h    | 222 ++++++++++++++
 tensorflow/core/framework/variant_test.cc     | 249 +++++++++++++++
 .../core/platform/default/build_config.bzl    |   8 +
 tensorflow/core/platform/variant_coding.cc    |  63 ++++
 tensorflow/core/platform/variant_coding.h     |  39 +++
 tensorflow/go/tensor.go                       |   1 +
 tensorflow/python/__init__.py                 |   1 +
 tensorflow/python/framework/dtypes.py         |  17 +-
 tensorflow/python/framework/dtypes_test.py    |   9 +-
 tensorflow/tools/api/golden/tensorflow.pbtxt  |   4 +
 22 files changed, 1227 insertions(+), 9 deletions(-)
 create mode 100644 tensorflow/core/framework/variant.cc
 create mode 100644 tensorflow/core/framework/variant.h
 create mode 100644 tensorflow/core/framework/variant_encode_decode.h
 create mode 100644 tensorflow/core/framework/variant_test.cc
 create mode 100644 tensorflow/core/platform/variant_coding.cc
 create mode 100644 tensorflow/core/platform/variant_coding.h

diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h
index 3aeafb4685..46758408c4 100644
--- a/tensorflow/c/c_api.h
+++ b/tensorflow/c/c_api.h
@@ -117,6 +117,7 @@ typedef enum TF_DataType {
   TF_COMPLEX128 = 18,  // Double-precision complex
   TF_HALF = 19,
   TF_RESOURCE = 20,
+  TF_VARIANT = 21,
 } TF_DataType;
 
 // TF_DataTypeSize returns the sizeof() for the underlying type corresponding
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index a15621ba6d..b66eb35339 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -156,6 +156,11 @@ if (NOT tensorflow_ENABLE_GPU)
       "${tensorflow_source_dir}/tensorflow/core/platform/default/cuda_libdevice_path.*")
   list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_gpu_srcs})
 endif()
+
+file(GLOB tf_core_platform_exclude_srcs
+  "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.cc")
+list(REMOVE_ITEM tf_core_platform_srcs ${tf_core_platform_exclude_srcs})
+
 list(APPEND tf_core_lib_srcs ${tf_core_platform_srcs})
 
 if(UNIX)
@@ -225,6 +230,8 @@ set(tf_version_srcs ${tensorflow_source_dir}/tensorflow/core/util/version_info.c
 file(GLOB_RECURSE tf_core_framework_srcs
     "${tensorflow_source_dir}/tensorflow/core/framework/*.h"
     "${tensorflow_source_dir}/tensorflow/core/framework/*.cc"
+    "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.h"
+    "${tensorflow_source_dir}/tensorflow/core/platform/variant_coding.cc"
     "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.h"
     "${tensorflow_source_dir}/tensorflow/core/graph/edgeset.cc"
     "${tensorflow_source_dir}/tensorflow/core/graph/graph.h"
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index eea59e8e0e..926bfa2278 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -94,6 +94,8 @@ load(
     "tf_additional_lib_deps",
     "tf_additional_lib_hdrs",
     "tf_additional_lib_srcs",
+    "tf_additional_framework_hdrs",
+    "tf_additional_framework_srcs",
     "tf_additional_minimal_lib_srcs",
     "tf_additional_proto_hdrs",
     "tf_additional_proto_srcs",
@@ -343,6 +345,8 @@ tf_cuda_library(
     hdrs = [
         "example/feature_util.h",
         "framework/allocator.h",
+        "framework/variant.h",
+        "framework/variant_encode_decode.h",
         "framework/allocator_registry.h",
         "framework/attr_value_util.h",
         "framework/bfloat16.h",
@@ -1266,6 +1270,8 @@ LIB_INTERNAL_WINDOWS_DEPS = glob(
         "platform/**/cuda_libdevice_path.cc",
         "platform/**/stream_executor.h",
         "platform/load_library.cc",
+        "platform/variant_coding.cc",
+        "platform/**/variant_cord_coding.cc",
     ],
 )
 
@@ -1287,6 +1293,8 @@ cc_library(
             ],
             exclude = [
                 "**/*test*",
+                "framework/variant.cc",
+                "platform/variant_coding.cc",
                 "lib/hash/crc32c_accelerate.cc",
                 "lib/gif/**/*",
                 "lib/jpeg/**/*",
@@ -1297,6 +1305,8 @@ cc_library(
                 "platform/**/cuda_libdevice_path.cc",
                 "platform/**/stream_executor.h",
                 "platform/**/gpu_tracer.cc",
+                "platform/variant_coding.cc",
+                "platform/**/variant_cord_coding.cc",
             ],
         ),
     }) + tf_additional_lib_srcs(
@@ -1307,6 +1317,8 @@ cc_library(
             "platform/**/stream_executor.h",
             "platform/**/env_time.cc",
             "platform/**/gpu_tracer.cc",
+            "platform/variant_coding.cc",
+            "platform/**/variant_cord_coding.cc",
         ] +
         # Protobuf deps already included through the ":lib_proto_parsing"
         # dependency.
@@ -1463,6 +1475,8 @@ tf_cuda_library(
             "util/**/*.h",
             "util/**/*.cc",
         ] + [
+            "platform/variant_coding.cc",
+            "platform/variant_coding.h",
             "graph/edgeset.h",
             "graph/edgeset.cc",
             "graph/graph.h",
@@ -1491,20 +1505,22 @@ tf_cuda_library(
             "util/memmapped_file_system_writer.h",
             "util/memmapped_file_system_writer.cc",
         ],
-    }),
+    }) + tf_additional_framework_srcs(),
     hdrs = [
+        "framework/variant.h",
         "framework/op_segment.h",
         "framework/rendezvous.h",  # only needed for tests
         "framework/tensor_reference.h",
         "framework/tracking_allocator.h",  # only needed for tests
         "framework/unique_tensor_references.h",
+        "platform/variant_coding.h",
         "util/command_line_flags.h",
         "util/env_var.h",
         "util/equal_graph_def.h",
         "util/presized_cuckoo_map.h",
         "util/tensor_slice_set.h",
         "util/tensor_slice_util.h",
-    ],
+    ] + tf_additional_framework_hdrs(),
     copts = tf_copts(),
     linkopts = select({
         "//tensorflow:freebsd": [],
@@ -1518,6 +1534,7 @@ tf_cuda_library(
         ":proto_text",
         ":protos_all_cc",
         ":version_lib",
+        "//tensorflow/core/platform/default/build_config:platformlib",
         "//tensorflow/core/kernels:bounds_check",
         "//third_party/eigen3",
     ] + if_mkl(["//third_party/mkl:intel_binary_blob"]),
@@ -2184,6 +2201,7 @@ tf_cc_tests(
         "common_runtime/simple_placer_test.cc",
         "example/feature_util_test.cc",
         "framework/allocator_test.cc",
+        "framework/variant_test.cc",
         "framework/attr_value_util_test.cc",
         "framework/bfloat16_test.cc",
         "framework/cancellation_test.cc",
diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h
index 1cb183d81e..868335b072 100644
--- a/tensorflow/core/framework/allocator.h
+++ b/tensorflow/core/framework/allocator.h
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/type_traits.h"
+#include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -229,6 +230,14 @@ class Allocator {
     for (size_t i = 0; i < n; ++p, ++i) p->~ResourceHandle();
   }
 
+  virtual void RunVariantCtor(Variant* p, size_t n) {
+    for (size_t i = 0; i < n; ++p, ++i) new (p) Variant();
+  }
+
+  virtual void RunVariantDtor(Variant* p, size_t n) {
+    for (size_t i = 0; i < n; ++p, ++i) p->~Variant();
+  }
+
   // TODO(jeff): Maybe provide some interface to give info about
   // current allocation state (total number of bytes available for
   // allocation, number of bytes free on device, etc.)
@@ -256,6 +265,16 @@ inline void Allocator::RunDtor(ResourceHandle* p, size_t n) {
   RunResourceDtor(p, n);
 }
 
+template <>
+inline void Allocator::RunCtor(Variant* p, size_t n) {
+  RunVariantCtor(p, n);
+}
+
+template <>
+inline void Allocator::RunDtor(Variant* p, size_t n) {
+  RunVariantDtor(p, n);
+}
+
 // An implementation of Allocator that delegates all calls to another Allocator.
 //
 // Useful to clients who want to override part of the functionality of another
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 80b98fb9c6..0a85894071 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -36,6 +36,8 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_description.pb.h"
 #include "tensorflow/core/framework/type_traits.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/variant.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
@@ -47,6 +49,7 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/tensor_coding.h"
 #include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/platform/variant_coding.h"
 
 namespace tensorflow {
 namespace {
@@ -220,6 +223,36 @@ struct Helper<ResourceHandle> {
   }
 };
 
+template <>
+struct Helper<Variant> {
+  // Encodes "n" elements of type Variant stored in "in" into destination
+  // "out", which is usually the TensorProto::tensor_content.
+  template <typename Destination>
+  static void Encode(TensorBuffer* in, int64 n, Destination* out) {
+    port::EncodeVariantList(in->base<const Variant>(), n, out);
+  }
+
+  // Decodes "n" elements of type Variant from "in" and constructs a
+  // buffer out of it. Returns nullptr if the decoding fails. "in" is
+  // usually the TensorProto::tensor_content.
+  template <typename Source>
+  static TensorBuffer* Decode(Allocator* a, const Source& in, int64 n) {
+    auto* buf = new Buffer<Variant>(a, n);
+    Variant* ps = buf->template base<Variant>();
+    if (ps == nullptr || !port::DecodeVariantList(in, ps, n)) {
+      buf->Unref();
+      return nullptr;
+    }
+    return buf;
+  }
+
+  // Returns the estimated memory usage of "n" elements of type T
+  // stored in buffer "in".
+  static int64 TotalBytes(TensorBuffer* in, int n) {
+    return n * sizeof(Variant);
+  }
+};
+
 template <typename T>
 struct ProtoHelper {};
 
@@ -289,6 +322,26 @@ struct ProtoHelper<ResourceHandle> {
   }
 };
 
+template <>
+struct ProtoHelper<Variant> {
+  static protobuf::RepeatedPtrField<VariantTensorDataProto>::const_iterator
+  Begin(const TensorProto& proto) {
+    return proto.variant_val().begin();
+  }
+  static size_t NumElements(const TensorProto& proto) {
+    return proto.variant_val().size();
+  }
+  static void Fill(const Variant* data, size_t n, TensorProto* proto) {
+    auto* variant_values = proto->mutable_variant_val();
+    variant_values->Clear();
+    for (size_t i = 0; i < n; ++i) {
+      VariantTensorData tmp;
+      data[i].Encode(&tmp);
+      tmp.ToProto(variant_values->Add());
+    }
+  }
+};
+
 template <>
 struct ProtoHelper<complex64> {
   typedef Helper<float>::RepeatedFieldType FieldType;
@@ -421,6 +474,30 @@ TensorBuffer* FromProtoField(Allocator* a, const TensorProto& in, int64 n) {
   return buf;
 }
 
+template <>
+TensorBuffer* FromProtoField<Variant>(Allocator* a, const TensorProto& in,
+                                      int64 n) {
+  CHECK_GT(n, 0);
+  Buffer<Variant>* buf = new Buffer<Variant>(a, n);
+  Variant* data = buf->template base<Variant>();
+  if (data == nullptr) {
+    buf->Unref();
+    return nullptr;
+  }
+  const int64 in_n = ProtoHelper<Variant>::NumElements(in);
+  if (in_n <= 0) {
+    std::fill_n(data, n, Variant());
+  } else {
+    for (int64 i = 0; i < in_n; ++i) {
+      data[i] = in.variant_val(i);
+    }
+    for (int64 i = in_n; i < n; ++i) {
+      data[i] = Variant();
+    }
+  }
+  return buf;
+}
+
 // fp16 is opaque to the protobuf, so we deserialize these identical to uint16
 // but with data stored in half_val instead of int_val (ie., we don't use
 // ProtoHelper<uint16>).
@@ -571,6 +648,7 @@ bool Tensor::RefCountIsOne() const {
     CASE(bfloat16, SINGLE_ARG(STMTS))                          \
     CASE(Eigen::half, SINGLE_ARG(STMTS))                       \
     CASE(ResourceHandle, SINGLE_ARG(STMTS))                    \
+    CASE(Variant, SINGLE_ARG(STMTS))                           \
     case DT_INVALID:                                           \
       INVALID;                                                 \
       break;                                                   \
diff --git a/tensorflow/core/framework/tensor.proto b/tensorflow/core/framework/tensor.proto
index 98ecef225a..7e4af7a645 100644
--- a/tensorflow/core/framework/tensor.proto
+++ b/tensorflow/core/framework/tensor.proto
@@ -72,4 +72,17 @@ message TensorProto {
 
   // DT_RESOURCE
   repeated ResourceHandleProto resource_handle_val = 14;
+
+  // DT_VARIANT
+  repeated VariantTensorDataProto variant_val = 15;
 };
+
+// Protocol buffer representing the serialization format of DT_VARIANT tensors.
+message VariantTensorDataProto {
+  // Name of the type of objects being serialized.
+  string type_name = 1;
+  // Portions of the object that are not Tensors.
+  bytes metadata = 2;
+  // Tensors contained within objects being serialized.
+  repeated TensorProto tensors = 3;
+}
diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc
index 6c9c803af6..9aaf00853d 100644
--- a/tensorflow/core/framework/tensor_test.cc
+++ b/tensorflow/core/framework/tensor_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/test.h"
@@ -37,6 +38,35 @@ inline bool operator==(const ResourceHandle& a, const ResourceHandle& b) {
          a.maybe_type_name() == b.maybe_type_name();
 }
 
+inline bool operator==(const Variant& a, const Variant& b) {
+  if (a.is_empty()) {
+    return b.is_empty();
+  }
+
+  if (a.TypeId() != b.TypeId()) return false;
+  if (a.TypeName() != b.TypeName()) return false;
+
+  VariantTensorData a_data, b_data;
+  a.Encode(&a_data);
+  b.Encode(&b_data);
+
+  if (a_data.metadata != b_data.metadata) return false;
+
+  if (a_data.tensors.size() != b_data.tensors.size()) return false;
+
+  for (int i = 0; i < a_data.tensors.size(); ++i) {
+    TensorProto a_proto, b_proto;
+    a_data.tensors[i].AsProtoTensorContent(&a_proto);
+    b_data.tensors[i].AsProtoTensorContent(&b_proto);
+    string a_str, b_str;
+    a_proto.SerializeToString(&a_str);
+    b_proto.SerializeToString(&b_str);
+    if (a_str != b_str) return false;
+  }
+
+  return true;
+}
+
 TEST(TensorTest, Default) {
   Tensor t;
   EXPECT_EQ(t.dtype(), DT_FLOAT);
@@ -159,6 +189,74 @@ TEST(Tensor_ResourceHandle, Simple) {
   TestCopies<ResourceHandle>(t);
 }
 
+TEST(Tensor_Variant, Simple) {
+  Tensor t(DT_VARIANT, TensorShape({}));
+  Tensor value(DT_FLOAT, TensorShape({}));
+  value.flat<float>()(0) = 42.0f;
+  t.flat<Variant>()(0) = value;
+  // All the tests in TestCopies except the ones that serialize and deserialize
+  // the tensor. The consumer of a serialized Variant Tensor should know what
+  // type is stored in the Tensor, so not testing the generic
+  // serialize/deserialize case here.
+  {
+    LOG(INFO) << "CopyFrom()";
+    Tensor t2(t.dtype());
+    EXPECT_TRUE(t2.CopyFrom(t, t.shape()));
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "operator=()";
+    Tensor t2(t.dtype());
+    t2 = t;
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "deep copy";
+    Tensor t2(t.dtype(), t.shape());
+    t2.flat<Variant>() = t.flat<Variant>();
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "AsTensor";
+    gtl::ArraySlice<Variant> values(t.flat<Variant>().data(), t.NumElements());
+    Tensor t2 = test::AsTensor(values, t.shape());
+    test::ExpectTensorEqual<Variant>(t, t2);
+  }
+  {
+    LOG(INFO) << "Move constructor";
+    Tensor t2 = t;
+    Tensor t3(std::move(t2));
+    test::ExpectTensorEqual<Variant>(t, t3);
+    EXPECT_TRUE(t3.IsInitialized());
+    EXPECT_FALSE(t2.IsInitialized());
+  }
+  {
+    LOG(INFO) << "Move assignment";
+    Tensor t2 = t;
+    Tensor t3 = std::move(t2);
+    Tensor* t4 = &t3;
+    *t4 = std::move(t3);
+    test::ExpectTensorEqual<Variant>(t, t3);
+    EXPECT_TRUE(t3.IsInitialized());
+    EXPECT_FALSE(t2.IsInitialized());
+  }
+}
+
+TEST(Tensor_Variant, Marshal) {
+  Tensor t(DT_VARIANT, TensorShape({}));
+
+  Tensor internal(DT_FLOAT, TensorShape({}));
+  internal.flat<float>()(0) = 42.0f;
+  t.flat<Variant>()(0) = internal;
+
+  LOG(INFO) << "AsProtoField()";
+  TensorProto proto;
+  t.AsProtoField(&proto);
+
+  Tensor t2(t.dtype());
+  EXPECT_TRUE(t2.FromProto(proto));
+}
+
 TEST(Tensor_UInt16, Simple) {
   Tensor t(DT_UINT16, TensorShape({2, 2}));
   EXPECT_TRUE(t.shape().IsSameSize(TensorShape({2, 2})));
diff --git a/tensorflow/core/framework/types.cc b/tensorflow/core/framework/types.cc
index 28b5e8be11..39dd5b435e 100644
--- a/tensorflow/core/framework/types.cc
+++ b/tensorflow/core/framework/types.cc
@@ -87,6 +87,8 @@ string DataTypeString(DataType dtype) {
       return "half";
     case DT_RESOURCE:
       return "resource";
+    case DT_VARIANT:
+      return "variant";
     default:
       LOG(ERROR) << "Unrecognized DataType enum value " << dtype;
       return strings::StrCat("unknown dtype enum (", dtype, ")");
@@ -165,6 +167,9 @@ bool DataTypeFromString(StringPiece sp, DataType* dt) {
   } else if (sp == "resource") {
     *dt = DT_RESOURCE;
     return true;
+  } else if (sp == "variant") {
+    *dt = DT_VARIANT;
+    return true;
   }
   return false;
 }
@@ -186,7 +191,7 @@ DataTypeVector AllTypes() {
   return {DT_FLOAT,   DT_DOUBLE, DT_INT32,  DT_UINT8,     DT_INT16,
           DT_UINT16,  DT_INT8,   DT_STRING, DT_COMPLEX64, DT_COMPLEX128,
           DT_INT64,   DT_BOOL,   DT_QINT8,  DT_QUINT8,    DT_QINT16,
-          DT_QUINT16, DT_QINT32, DT_HALF,   DT_RESOURCE};
+          DT_QUINT16, DT_QINT32, DT_HALF,   DT_RESOURCE,  DT_VARIANT};
 }
 
 #if !defined(IS_MOBILE_PLATFORM) || defined(SUPPORT_SELECTIVE_REGISTRATION)
diff --git a/tensorflow/core/framework/types.h b/tensorflow/core/framework/types.h
index 889056647c..9127750d68 100644
--- a/tensorflow/core/framework/types.h
+++ b/tensorflow/core/framework/types.h
@@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/resource_handle.h"
 #include "tensorflow/core/framework/types.pb.h"
+#include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
@@ -181,6 +182,7 @@ MATCH_TYPE_AND_ENUM(qint32, DT_QINT32);
 MATCH_TYPE_AND_ENUM(bfloat16, DT_BFLOAT16);
 MATCH_TYPE_AND_ENUM(Eigen::half, DT_HALF);
 MATCH_TYPE_AND_ENUM(ResourceHandle, DT_RESOURCE);
+MATCH_TYPE_AND_ENUM(Variant, DT_VARIANT);
 
 #undef MATCH_TYPE_AND_ENUM
 
diff --git a/tensorflow/core/framework/types.proto b/tensorflow/core/framework/types.proto
index b80e2b31dc..1beb2a1aa2 100644
--- a/tensorflow/core/framework/types.proto
+++ b/tensorflow/core/framework/types.proto
@@ -34,6 +34,7 @@ enum DataType {
   DT_COMPLEX128 = 18;  // Double-precision complex
   DT_HALF = 19;
   DT_RESOURCE = 20;
+  DT_VARIANT = 21;  // Arbitrary C++ data types
 
   // TODO(josh11b): DT_GENERIC_PROTO = ??;
   // TODO(jeff,josh11b): DT_UINT64?  DT_UINT32?
@@ -60,5 +61,6 @@ enum DataType {
   DT_COMPLEX128_REF = 118;
   DT_HALF_REF = 119;
   DT_RESOURCE_REF = 120;
+  DT_VARIANT_REF = 121;
 }
 // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.h,https://www.tensorflow.org/code/tensorflow/go/tensor.go)
diff --git a/tensorflow/core/framework/variant.cc b/tensorflow/core/framework/variant.cc
new file mode 100644
index 0000000000..9f03a60892
--- /dev/null
+++ b/tensorflow/core/framework/variant.cc
@@ -0,0 +1,87 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/variant.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/map_util.h"
+
+namespace tensorflow {
+
+template <>
+void* Variant::get() {
+  if (is_empty()) {
+    return nullptr;
+  }
+  return value_->RawPtr();
+}
+
+template <>
+const void* Variant::get() const {
+  if (is_empty()) {
+    return nullptr;
+  }
+  return value_->RawPtr();
+}
+
+void VariantTensorData::ToProto(VariantTensorDataProto* proto) const {
+  proto->set_type_name(type_name);
+  proto->set_metadata(metadata);
+  proto->clear_tensors();
+  for (int i = 0; i < tensors.size(); ++i) {
+    tensors[i].AsProtoField(proto->mutable_tensors()->Add());
+  }
+}
+
+bool VariantTensorData::FromProto(const VariantTensorDataProto& proto) {
+  type_name = proto.type_name();
+  metadata = proto.metadata();
+  tensors.clear();
+  for (int i = 0; i < proto.tensors_size(); ++i) {
+    Tensor tmp;
+    if (!tmp.FromProto(proto.tensors(i))) return false;
+    tensors.push_back(tmp);
+  }
+  return true;
+}
+
+template <>
+string TypeNameVariant(const VariantTensorDataProto& value) {
+  return value.GetTypeName();
+}
+
+template <>
+void EncodeVariant(const VariantTensorDataProto& value, VariantTensorData* data) {
+  data->FromProto(value);
+}
+
+template <>
+bool DecodeVariant(const VariantTensorData& data,
+                   VariantTensorDataProto* value) {
+  data.ToProto(value);
+  return true;
+}
+
+template <>
+void EncodeVariant(const VariantTensorDataProto& value, string* buf) {
+  value.SerializeToString(buf);
+}
+
+template <>
+bool DecodeVariant(const string& buf, VariantTensorDataProto* value) {
+  return value->ParseFromString(buf);
+}
+
+}  // end namespace tensorflow
diff --git a/tensorflow/core/framework/variant.h b/tensorflow/core/framework/variant.h
new file mode 100644
index 0000000000..f5c64aea0a
--- /dev/null
+++ b/tensorflow/core/framework/variant.h
@@ -0,0 +1,287 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_FRAMEWORK_VARIANT_H_
+#define TENSORFLOW_FRAMEWORK_VARIANT_H_
+
+#include <functional>
+#include <iostream>
+#include <memory>
+#include <type_traits>
+#include <unordered_map>
+#include <utility>
+
+#include "tensorflow/core/framework/type_index.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/mutex.h"
+
+namespace tensorflow {
+
+struct VariantTensorData;
+
+template <typename T>
+string TypeNameVariant(const T& value);
+
+template <typename T>
+void EncodeVariant(const T& value, VariantTensorData* data);
+
+template <typename T>
+bool DecodeVariant(const VariantTensorData& data, T* value);
+
+template <typename T>
+void EncodeVariant(const T& value, string* buf);
+
+template <typename T>
+bool DecodeVariant(const string& buf, T* value);
+
+// This is an implementation of a type-erased container that can store an
+// object of any type. The implementation is very similar to std::any, but has
+// restrictions on the types of objects that can be stored, and eschews some of
+// the fancier constructors available for std::any. An object of
+// tensorflow::Variant is intended to be used as the value that will be stored
+// in a tensorflow::Tensor object when its type is DT_VARIANT.
+//
+// tensorflow::Variant can store an object of a class that satisfies the
+// following constraints:
+//
+// * The class is CopyConstructible.
+// * The class has a default constructor.
+// * It's either a protocol buffer, a tensorflow::Tensor, or defines the
+// following functions:
+//
+//   string TypeName() const;
+//   void Encode(VariantTensorData* data) const;
+//   void Decode(const VariantTensorData& data);
+//
+// Simple POD types can elide the Encode/Decode functions, they are provided by
+// helper methods.
+// Here are some typical usage patterns:
+//
+//   Variant x = 10;
+//   EXPECT_EQ(*x.get<int>(), 10);
+//
+//   Tensor t(DT_FLOAT, TensorShape({}));
+//   t.flat<float>()(0) = 42.0f;
+//   Variant x = t;
+//   EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 42.0f);
+//
+// Accessing the stored object:
+//
+// The get<T> function is the main mechanism to access the object stored in the
+// contained. It is type-safe, that is, calling get<T> when the stored object's
+// type is not T, returns a nullptr. A raw pointer to the stored object can be
+// obtained by calling get<void>().
+//
+// Serializing/deserializing Variant object:
+//
+// The Variant class delegates serializing and deserializing operations to the
+// contained object. Helper functions to do these operations are provided for
+// POD data types, tensorflow::Tensor, and protocol buffer objects. However,
+// other classes have to provide Encode/Decode functions to do handle
+// serialization.
+//
+// Objects stored in a Variant object often contain references to other
+// tensorflow::Tensors of primitive types (Eg., a list of tensorflow::Tensors).
+// To efficiently support those use cases, a structure is imposed on the
+// serialization format. Namely, classes should serialize their contents in to a
+// VariantTensorData object:
+//
+// struct VariantTensorData {
+//   string type_name;
+//   string metadata;
+//   std::vector<Tensor> tensors;
+// };
+//
+// Objects with references to other Tensors can simply store those tensors in
+// the `tensors` field, and serialize other metadata content in to the
+// `metadata` field.
+//
+// Serialization example:
+//
+// Foo f = Foo {...};
+// Variant x = f;
+// string serialized_f;
+// x.Encode(&serialized_f);
+//
+// Variant y = Foo(); // default constructed Foo.
+// y.Decode(&serialized_f);
+// EXPECT_EQ(*x.get<Foo>(), *y.get<Foo>());
+//
+class Variant {
+ public:
+  constexpr Variant() noexcept = default;
+
+  Variant(const Variant& other)
+      : value_(other.is_empty() ? std::unique_ptr<ValueInterface>()
+                                : other.value_->Clone()) {}
+
+  Variant(Variant&& other) noexcept = default;
+
+  // Make sure that the type is CopyConstructible and not a tensorflow::Variant
+  // object itself. We want the copy constructor to be chosen for the
+  // tensorflow::Variant case.
+  template <typename T, typename VT = typename std::decay<T>::type,
+            typename std::enable_if<!std::is_same<Variant, VT>::value &&
+                                        std::is_copy_constructible<VT>::value,
+                                    void>::type* = nullptr>
+  Variant(T&& value)  // NOLINT
+      : value_(new Value<VT>(in_place, std::forward<T>(value))) {}
+
+  Variant& operator=(const Variant& rhs) {
+    Variant(rhs).swap(*this);
+    return *this;
+  }
+
+  Variant& operator=(Variant&& rhs) noexcept {
+    Variant(std::move(rhs)).swap(*this);
+    return *this;
+  }
+
+  bool is_empty() const { return value_ == nullptr; }
+
+  void clear() noexcept { value_.reset(); }
+
+  void swap(Variant& other) noexcept { value_.swap(other.value_); }
+
+  TypeIndex TypeId() const {
+    const TypeIndex VoidTypeIndex = MakeTypeIndex<void>();
+    if (is_empty()) {
+      return VoidTypeIndex;
+    }
+    return value_->TypeId();
+  }
+
+  template <typename T>
+  T* get() {
+    const TypeIndex TTypeIndex = MakeTypeIndex<T>();
+    if (is_empty() || (TTypeIndex != TypeId())) {
+      return nullptr;
+    }
+    return std::addressof(static_cast<Variant::Value<T>*>(value_.get())->value);
+  }
+
+  template <typename T>
+  const T* get() const {
+    const TypeIndex TTypeIndex = MakeTypeIndex<T>();
+    if (is_empty() || (TTypeIndex != TypeId())) {
+      return nullptr;
+    }
+    return std::addressof(
+        static_cast<const Variant::Value<T>*>(value_.get())->value);
+  }
+
+  string TypeName() const {
+    if (is_empty()) {
+      return "";
+    }
+    return value_->TypeName();
+  }
+
+  // Serialize the contents of the stored object into `data`.
+  void Encode(VariantTensorData* data) const {
+    if (!is_empty()) {
+      value_->Encode(data);
+    }
+  }
+
+  // Deserialize `data` and update the stored object.
+  bool Decode(const VariantTensorData& data) {
+    if (!is_empty()) {
+      return value_->Decode(data);
+    }
+    return true;
+  }
+
+  // Helper methods to directly serialize/deserialize from strings.
+  void Encode(string* buf) const {
+    if (!is_empty()) {
+      value_->Encode(buf);
+    }
+  }
+  bool Decode(const string& buf) {
+    if (!is_empty()) {
+      return value_->Decode(buf);
+    }
+    return true;
+  }
+
+ private:
+  struct in_place_t {};
+  static constexpr in_place_t in_place{};
+
+  struct ValueInterface {
+    virtual ~ValueInterface() = default;
+    virtual TypeIndex TypeId() const = 0;
+    virtual void* RawPtr() = 0;
+    virtual const void* RawPtr() const = 0;
+    virtual std::unique_ptr<ValueInterface> Clone() const = 0;
+    virtual string TypeName() const = 0;
+    virtual void Encode(VariantTensorData* data) const = 0;
+    virtual bool Decode(const VariantTensorData& data) = 0;
+    virtual void Encode(string* buf) const = 0;
+    virtual bool Decode(const string& data) = 0;
+  };
+
+  template <typename T>
+  struct Value : ValueInterface {
+    template <class... Args>
+    explicit Value(in_place_t /*tag*/, Args&&... args)
+        : value(std::forward<Args>(args)...) {}
+
+    TypeIndex TypeId() const override {
+      const TypeIndex value_type_index =
+          MakeTypeIndex<typename std::decay<T>::type>();
+      return value_type_index;
+    }
+
+    void* RawPtr() override { return &value; }
+
+    const void* RawPtr() const override { return &value; }
+
+    std::unique_ptr<ValueInterface> Clone() const override {
+      return std::unique_ptr<ValueInterface>(new Value(in_place, value));
+    }
+
+    string TypeName() const override { return TypeNameVariant(value); }
+
+    void Encode(VariantTensorData* data) const override {
+      EncodeVariant(value, data);
+    }
+
+    bool Decode(const VariantTensorData& data) override {
+      return DecodeVariant(data, &value);
+    }
+
+    void Encode(string* buf) const override { EncodeVariant(value, buf); }
+
+    bool Decode(const string& buf) override {
+      return DecodeVariant(buf, &value);
+    }
+
+    T value;
+  };
+
+  std::unique_ptr<ValueInterface> value_;
+};
+
+template <>
+void* Variant::get();
+
+template <>
+const void* Variant::get() const;
+
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_FRAMEWORK_VARIANT_H_
diff --git a/tensorflow/core/framework/variant_encode_decode.h b/tensorflow/core/framework/variant_encode_decode.h
new file mode 100644
index 0000000000..6ec64448eb
--- /dev/null
+++ b/tensorflow/core/framework/variant_encode_decode.h
@@ -0,0 +1,222 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
+#define TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
+
+#include <iostream>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/platform/protobuf.h"
+
+namespace tensorflow {
+
+// The serialization format for Variant objects. Objects with references to
+// other Tensors can simply store those tensors in the `tensors` field, and
+// serialize other metadata content in to the `metadata` field. Objects can
+// optionally set the `type_name` for type-checking before deserializing an
+// object.
+struct VariantTensorData {
+  string type_name;
+  string metadata;
+  std::vector<Tensor> tensors;
+  void ToProto(VariantTensorDataProto* proto) const;
+  bool FromProto(const VariantTensorDataProto& proto);
+};
+
+// Type used for tag-dispatch of the Encode/Decode Variant implementations. This
+// template can determine whether the first type parameter `T` is one of the
+// following:
+//
+// * A POD type (TypeResolver<T, true>)
+// * A tensorflow::Tensor (TypeResolver<T, false, true>)
+// * A protocol buffer (TypeResolver<T, false, false, true>)
+// * None of the above (TypeResolver<T, false, false, false>)
+//
+template <typename T, bool = std::is_pod<typename std::decay<T>::type>::value,
+          bool = std::is_same<typename std::decay<T>::type,
+                              ::tensorflow::Tensor>::value,
+          bool = std::is_base_of<protobuf::MessageLite,
+                                 typename std::decay<T>::type>::value>
+struct TypeResolver {};
+
+// Specialization for POD type
+template <typename T>
+void EncodeVariantImpl(const T& value, TypeResolver<T, true /* is_pod */>,
+                       VariantTensorData* data) {
+  data->metadata.assign(reinterpret_cast<const char*>(&value), sizeof(value));
+}
+
+// Specialization for tensorflow::Tensor
+template <typename T>
+void EncodeVariantImpl(const T& value,
+                       TypeResolver<T, false /* is_pod */, true /* Tensor */>,
+                       VariantTensorData* data) {
+  data->tensors.clear();
+  data->tensors.push_back(value);
+}
+
+// Specialization for protobuf
+template <typename T>
+void EncodeVariantImpl(const T& value,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    true /* protobuf */>,
+                       VariantTensorData* data) {
+  value.SerializeToString(&data->metadata);
+}
+
+// Specialization for other types
+template <typename T>
+void EncodeVariantImpl(const T& value,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    false /* protobuf */>,
+                       VariantTensorData* data) {
+  value.Encode(data);
+}
+
+// Specialization for POD type
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, true /* is_pod */>, T* value) {
+  std::copy_n(data.metadata.data(), sizeof(*value),
+              reinterpret_cast<char*>(value));
+  return true;
+}
+
+// Specialization for tensorflow::Tensor
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, false /* is_pod */, true /* Tensor */>,
+                       T* value) {
+  *value = data.tensors[0];
+  return true;
+}
+
+// Specialization for protobuf
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    true /* protobuf */>,
+                       T* value) {
+  return value->ParseFromString(data.metadata);
+}
+
+// Specialization for other types
+template <typename T>
+bool DecodeVariantImpl(const VariantTensorData& data,
+                       TypeResolver<T, false /* is_pod */, false /* Tensor */,
+                                    false /* protobuf */>,
+                       T* value) {
+  return value->Decode(data);
+}
+
+template <typename C, typename = void>
+struct has_type_name : std::false_type {};
+
+template <typename C>
+struct has_type_name<
+    C, typename std::enable_if<std::is_same<
+           decltype(std::declval<C>().TypeName()), string>::value>::type>
+    : std::true_type {};
+
+template <typename T, bool = has_type_name<typename std::decay<T>::type>::value,
+          bool = std::is_same<typename std::decay<T>::type,
+                              ::tensorflow::Tensor>::value,
+          bool = std::is_base_of<protobuf::MessageLite,
+                                 typename std::decay<T>::type>::value>
+struct TypeNameResolver {};
+
+template <typename T>
+string TypeNameVariantImpl(const T& value,
+                           TypeNameResolver<T, true /* has_type_name */>) {
+  return value.TypeName();
+}
+
+template <typename T>
+string TypeNameVariantImpl(
+    const T& value,
+    TypeNameResolver<T, false /* has_type_name */, true /* Tensor */>) {
+  return "tensorflow::Tensor";
+}
+
+template <typename T>
+string TypeNameVariantImpl(
+    const T& value, TypeNameResolver<T, false /* has_type_name */,
+                                     false /* Tensor */, true /* protobuf */>) {
+  return value.GetTypeName();
+}
+
+template <typename T>
+string TypeNameVariantImpl(
+    const T& value,
+    TypeNameResolver<T, false /* has_type_name */, false /* Tensor */,
+                     false /* protobuf */>) {
+  return value.TypeName();
+}
+
+template <typename T>
+string TypeNameVariant(const T& value) {
+  return TypeNameVariantImpl(value, TypeNameResolver<T>());
+}
+
+template <typename T>
+void EncodeVariant(const T& value, VariantTensorData* data) {
+  EncodeVariantImpl(value, TypeResolver<T>(), data);
+}
+
+template <typename T>
+bool DecodeVariant(const VariantTensorData& data, T* value) {
+  return DecodeVariantImpl(data, TypeResolver<T>(), value);
+}
+
+template <typename T>
+void EncodeVariant(const T& value, string* buf) {
+  VariantTensorData data;
+  EncodeVariantImpl(value, TypeResolver<T>(), &data);
+  VariantTensorDataProto proto;
+  data.ToProto(&proto);
+  proto.SerializeToString(buf);
+}
+
+template <typename T>
+bool DecodeVariant(const string& buf, T* value) {
+  VariantTensorDataProto proto;
+  if (!proto.ParseFromString(buf)) return false;
+  VariantTensorData data;
+  if (!data.FromProto(proto)) return false;
+  if (!DecodeVariantImpl(data, TypeResolver<T>(), value)) return false;
+  return true;
+}
+
+// Specializations for VariantTensorDataProto
+template <>
+string TypeNameVariant(const VariantTensorDataProto& value);
+template <>
+void EncodeVariant(const VariantTensorDataProto& value,
+                   VariantTensorData* data);
+template <>
+bool DecodeVariant(const VariantTensorData& data,
+                   VariantTensorDataProto* value);
+template <>
+void EncodeVariant(const VariantTensorDataProto& value, string* buf);
+template <>
+bool DecodeVariant(const string& buf, VariantTensorDataProto* value);
+
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_FRAMEWORK_VARIANT_ENCODE_DECODE_H_
diff --git a/tensorflow/core/framework/variant_test.cc b/tensorflow/core/framework/variant_test.cc
new file mode 100644
index 0000000000..c7ffdd28f4
--- /dev/null
+++ b/tensorflow/core/framework/variant_test.cc
@@ -0,0 +1,249 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <vector>
+
+#include "tensorflow/core/framework/variant.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
+
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/core/coding.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+
+namespace {
+
+template <typename T>
+struct Wrapper {
+  T value;
+  string TypeName() const { return "POD"; }
+};
+
+using Int = Wrapper<int>;
+using Float = Wrapper<float>;
+
+}  // end namespace
+
+TEST(VariantTest, Basic) {
+  Variant x;
+  EXPECT_EQ(x.get<void>(), nullptr);
+
+  x = Int{42};
+
+  EXPECT_NE(x.get<void>(), nullptr);
+  EXPECT_NE(x.get<Int>(), nullptr);
+  EXPECT_EQ(x.get<Int>()->value, 42);
+  EXPECT_EQ(x.TypeName(), "POD");
+}
+
+TEST(VariantTest, ConstGet) {
+  Variant x;
+  EXPECT_EQ(x.get<void>(), nullptr);
+
+  x = Int{42};
+
+  const Variant y = x;
+
+  EXPECT_NE(y.get<void>(), nullptr);
+  EXPECT_NE(y.get<Int>(), nullptr);
+  EXPECT_EQ(y.get<Int>()->value, 42);
+}
+
+TEST(VariantTest, Clear) {
+  Variant x;
+  EXPECT_EQ(x.get<void>(), nullptr);
+
+  x = Int{42};
+
+  EXPECT_NE(x.get<void>(), nullptr);
+  EXPECT_NE(x.get<Int>(), nullptr);
+  EXPECT_EQ(x.get<Int>()->value, 42);
+
+  x.clear();
+  EXPECT_EQ(x.get<void>(), nullptr);
+}
+
+TEST(VariantTest, Tensor) {
+  Variant x;
+  Tensor t(DT_FLOAT, {});
+  t.flat<float>()(0) = 42.0f;
+  x = t;
+
+  EXPECT_NE(x.get<Tensor>(), nullptr);
+  EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 42.0f);
+  x.get<Tensor>()->flat<float>()(0) += 1.0f;
+  EXPECT_EQ(x.get<Tensor>()->flat<float>()(0), 43.0f);
+  EXPECT_EQ(x.TypeName(), "tensorflow::Tensor");
+}
+
+TEST(VariantTest, TensorProto) {
+  Variant x;
+  TensorProto t;
+  t.set_dtype(DT_FLOAT);
+  t.mutable_tensor_shape()->set_unknown_rank(true);
+  x = t;
+
+  EXPECT_EQ(x.TypeName(), "tensorflow.TensorProto");
+  EXPECT_NE(x.get<TensorProto>(), nullptr);
+  EXPECT_EQ(x.get<TensorProto>()->dtype(), DT_FLOAT);
+  EXPECT_EQ(x.get<TensorProto>()->tensor_shape().unknown_rank(), true);
+}
+
+TEST(VariantTest, CopyValue) {
+  Variant x, y;
+  x = Int{10};
+  y = x;
+
+  EXPECT_EQ(x.get<Int>()->value, 10);
+  EXPECT_EQ(x.get<Int>()->value, y.get<Int>()->value);
+}
+
+TEST(VariantTest, MoveValue) {
+  Variant x;
+  x = []() -> Variant {
+    Variant y;
+    y = Int{10};
+    return y;
+  }();
+  EXPECT_EQ(x.get<Int>()->value, 10);
+}
+
+TEST(VariantTest, TypeMismatch) {
+  Variant x;
+  x = Int{10};
+  EXPECT_EQ(x.get<float>(), nullptr);
+  EXPECT_EQ(x.get<int>(), nullptr);
+  EXPECT_NE(x.get<Int>(), nullptr);
+}
+
+struct TensorList {
+  void Encode(VariantTensorData* data) const { data->tensors = vec; }
+
+  bool Decode(const VariantTensorData& data) {
+    vec = data.tensors;
+    return true;
+  }
+
+  string TypeName() const { return "TensorList"; }
+
+  std::vector<Tensor> vec;
+};
+
+TEST(VariantTest, TensorListTest) {
+  Variant x;
+
+  TensorList vec;
+  for (int i = 0; i < 4; ++i) {
+    Tensor elem(DT_INT32, {1});
+    elem.flat<int>()(0) = i;
+    vec.vec.push_back(elem);
+  }
+
+  for (int i = 0; i < 4; ++i) {
+    Tensor elem(DT_FLOAT, {1});
+    elem.flat<float>()(0) = 2 * i;
+    vec.vec.push_back(elem);
+  }
+
+  x = vec;
+
+  EXPECT_EQ(x.TypeName(), "TensorList");
+  const TensorList& stored_vec = *x.get<TensorList>();
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(stored_vec.vec[i].flat<int>()(0), i);
+  }
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(stored_vec.vec[i + 4].flat<float>()(0), 2 * i);
+  }
+
+  VariantTensorData serialized;
+  x.Encode(&serialized);
+
+  Variant y = TensorList();
+  y.Decode(serialized);
+
+  const TensorList& decoded_vec = *x.get<TensorList>();
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(decoded_vec.vec[i].flat<int>()(0), i);
+  }
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_EQ(decoded_vec.vec[i + 4].flat<float>()(0), 2 * i);
+  }
+}
+
+TEST(VariantTest, VariantArray) {
+  Variant x[2];
+  x[0] = Int{2};
+  x[1] = Float{2.0f};
+
+  EXPECT_EQ(x[0].get<Int>()->value, 2);
+  EXPECT_EQ(x[1].get<Float>()->value, 2.0f);
+}
+
+TEST(VariantTest, PodUpdate) {
+  struct Pod {
+    int x;
+    float y;
+
+    string TypeName() const { return "POD"; }
+  };
+
+  Variant x = Pod{10, 20.f};
+  EXPECT_NE(x.get<Pod>(), nullptr);
+  EXPECT_EQ(x.TypeName(), "POD");
+
+  x.get<Pod>()->x += x.get<Pod>()->y;
+  EXPECT_EQ(x.get<Pod>()->x, 30);
+}
+
+TEST(VariantTest, EncodeDecodePod) {
+  struct Pod {
+    int x;
+    float y;
+
+    string TypeName() const { return "POD"; }
+  };
+
+  Variant x;
+  Pod p{10, 20.0f};
+  x = p;
+
+  VariantTensorData serialized;
+  x.Encode(&serialized);
+
+  Variant y;
+  y = Pod();
+  y.Decode(serialized);
+
+  EXPECT_EQ(p.x, y.get<Pod>()->x);
+  EXPECT_EQ(p.y, y.get<Pod>()->y);
+}
+
+TEST(VariantTest, EncodeDecodeTensor) {
+  Variant x;
+  Tensor t(DT_INT32, {});
+  t.flat<int>()(0) = 42;
+  x = t;
+
+  VariantTensorData serialized;
+  x.Encode(&serialized);
+
+  Variant y = Tensor();
+  y.Decode(serialized);
+  EXPECT_EQ(x.get<Tensor>()->flat<int>()(0), y.get<Tensor>()->flat<int>()(0));
+}
+
+}  // end namespace tensorflow
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index d12ad8a04f..5db0fe423c 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -130,6 +130,14 @@ def tf_additional_lib_srcs(exclude = []):
       ], exclude = exclude),
   })
 
+# pylint: disable=unused-argument
+def tf_additional_framework_hdrs(exclude = []):
+  return []
+
+def tf_additional_framework_srcs(exclude = []):
+  return []
+# pylint: enable=unused-argument
+
 def tf_additional_minimal_lib_srcs():
   return [
       "platform/default/integral_types.h",
diff --git a/tensorflow/core/platform/variant_coding.cc b/tensorflow/core/platform/variant_coding.cc
new file mode 100644
index 0000000000..4bcde4f581
--- /dev/null
+++ b/tensorflow/core/platform/variant_coding.cc
@@ -0,0 +1,63 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/platform/variant_coding.h"
+
+#include <vector>
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/lib/core/coding.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+
+namespace tensorflow {
+namespace port {
+
+void EncodeVariantList(const Variant* variant_array, int64 n, string* out) {
+  out->clear();
+  string rest;
+  for (int i = 0; i < n; ++i) {
+    string s;
+    variant_array[i].Encode(&s);
+    core::PutVarint32(out, s.length());
+    strings::StrAppend(&rest, s);
+  }
+  strings::StrAppend(out, rest);
+}
+
+bool DecodeVariantList(const string& in, Variant* variant_array, int64 n) {
+  std::vector<uint32> sizes(n);
+  StringPiece reader(in);
+  int64 total = 0;
+  for (auto& size : sizes) {
+    if (!core::GetVarint32(&reader, &size)) return false;
+    total += size;
+  }
+  if (total != static_cast<int64>(reader.size())) {
+    return false;
+  }
+
+  for (int i = 0; i < n; ++i) {
+    if (variant_array[i].is_empty()) {
+      variant_array[i] = VariantTensorDataProto();
+    }
+    string str(reader.data(), sizes[i]);
+    if (!variant_array[i].Decode(str)) return false;
+    reader.remove_prefix(sizes[i]);
+  }
+  return true;
+}
+
+}  // end namespace port
+}  // end namespace tensorflow
diff --git a/tensorflow/core/platform/variant_coding.h b/tensorflow/core/platform/variant_coding.h
new file mode 100644
index 0000000000..34c2481149
--- /dev/null
+++ b/tensorflow/core/platform/variant_coding.h
@@ -0,0 +1,39 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_PLATFORM_VARIANT_CODING_H_
+#define TENSORFLOW_PLATFORM_VARIANT_CODING_H_
+
+#include "tensorflow/core/framework/variant.h"
+
+#ifdef PLATFORM_GOOGLE
+#include "tensorflow/core/platform/google/variant_cord_coding.h"
+#endif
+
+namespace tensorflow {
+namespace port {
+
+// Encodes an array of Variant objects in to the given string.
+// `variant_array` is assumed to point to an array of `n` Variant objects.
+void EncodeVariantList(const Variant* variant_array, int64 n, string* out);
+
+// Decodes an array of Variant objects from the given string.
+// `variant_array` is assumed to point to an array of `n` Variant objects.
+bool DecodeVariantList(const string& in, Variant* variant_array, int64 n);
+
+}  // end namespace port
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_PLATFORM_VARIANT_CODING_H_
diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
index 4a60c736b5..8b8909a6f8 100644
--- a/tensorflow/go/tensor.go
+++ b/tensorflow/go/tensor.go
@@ -227,6 +227,7 @@ var types = []struct {
 	{reflect.TypeOf(uint16(0)), C.TF_UINT16},
 	{reflect.TypeOf(complex(float64(0), float64(0))), C.TF_COMPLEX128},
 	// TODO(apassos): support DT_RESOURCE representation in go.
+	// TODO(keveman): support DT_VARIANT representation in go.
 }
 
 // shapeAndDataTypeOf returns the data type and shape of the Tensor
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 827e4c53ee..2fd8fc8688 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -208,6 +208,7 @@ _allowed_symbols.extend([
     'uint16',
     'uint8',
     'resource',
+    'variant',
 ])
 
 # Export modules and constants.
diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py
index 00bfae213a..43535a593e 100644
--- a/tensorflow/python/framework/dtypes.py
+++ b/tensorflow/python/framework/dtypes.py
@@ -48,6 +48,7 @@ class DType(object):
   * `tf.quint16`: Quantized 16-bit unsigned integer.
   * `tf.qint32`: Quantized 32-bit signed integer.
   * `tf.resource`: Handle to a mutable resource.
+  * `tf.variant`: Values of arbitrary types.
 
   In addition, variants of these types with the `_ref` suffix are
   defined for reference-typed tensors.
@@ -113,8 +114,11 @@ class DType(object):
 
   @property
   def is_numpy_compatible(self):
-    return (self._type_enum != types_pb2.DT_RESOURCE and
-            self._type_enum != types_pb2.DT_RESOURCE_REF)
+    numpy_incompatible = [types_pb2.DT_VARIANT,
+                          types_pb2.DT_VARIANT_REF,
+                          types_pb2.DT_RESOURCE,
+                          types_pb2.DT_RESOURCE_REF]
+    return self._type_enum not in numpy_incompatible
 
   @property
   def as_numpy_dtype(self):
@@ -284,7 +288,8 @@ class DType(object):
 
   @property
   def size(self):
-    if self._type_enum == types_pb2.DT_RESOURCE:
+    if (self._type_enum == types_pb2.DT_VARIANT or
+        self._type_enum == types_pb2.DT_RESOURCE):
       return 1
     return np.dtype(self.as_numpy_dtype).itemsize
 
@@ -304,6 +309,7 @@ dtype_range = {np.bool_: (False, True),
 
 # Define standard wrappers for the types_pb2.DataType enum.
 resource = DType(types_pb2.DT_RESOURCE)
+variant = DType(types_pb2.DT_VARIANT)
 float16 = DType(types_pb2.DT_HALF)
 half = float16
 float32 = DType(types_pb2.DT_FLOAT)
@@ -325,6 +331,7 @@ qint16 = DType(types_pb2.DT_QINT16)
 quint16 = DType(types_pb2.DT_QUINT16)
 qint32 = DType(types_pb2.DT_QINT32)
 resource_ref = DType(types_pb2.DT_RESOURCE_REF)
+variant_ref = DType(types_pb2.DT_VARIANT_REF)
 bfloat16 = DType(types_pb2.DT_BFLOAT16)
 float16_ref = DType(types_pb2.DT_HALF_REF)
 half_ref = float16_ref
@@ -372,6 +379,7 @@ _INTERN_TABLE = {
     types_pb2.DT_QINT32: qint32,
     types_pb2.DT_BFLOAT16: bfloat16,
     types_pb2.DT_RESOURCE: resource,
+    types_pb2.DT_VARIANT: variant,
     types_pb2.DT_HALF_REF: float16_ref,
     types_pb2.DT_FLOAT_REF: float32_ref,
     types_pb2.DT_DOUBLE_REF: float64_ref,
@@ -392,6 +400,7 @@ _INTERN_TABLE = {
     types_pb2.DT_QINT32_REF: qint32_ref,
     types_pb2.DT_BFLOAT16_REF: bfloat16_ref,
     types_pb2.DT_RESOURCE_REF: resource_ref,
+    types_pb2.DT_VARIANT_REF: variant_ref,
 }
 
 
@@ -417,6 +426,7 @@ _TYPE_TO_STRING = {
     types_pb2.DT_QINT32: "qint32",
     types_pb2.DT_BFLOAT16: "bfloat16",
     types_pb2.DT_RESOURCE: "resource",
+    types_pb2.DT_VARIANT: "variant",
     types_pb2.DT_HALF_REF: "float16_ref",
     types_pb2.DT_FLOAT_REF: "float32_ref",
     types_pb2.DT_DOUBLE_REF: "float64_ref",
@@ -437,6 +447,7 @@ _TYPE_TO_STRING = {
     types_pb2.DT_QINT32_REF: "qint32_ref",
     types_pb2.DT_BFLOAT16_REF: "bfloat16_ref",
     types_pb2.DT_RESOURCE_REF: "resource_ref",
+    types_pb2.DT_VARIANT_REF: "variant_ref",
 }
 _STRING_TO_TF = {value: _INTERN_TABLE[key]
                  for key, value in _TYPE_TO_STRING.items()}
diff --git a/tensorflow/python/framework/dtypes_test.py b/tensorflow/python/framework/dtypes_test.py
index d5a1e61a6f..1e84f1b656 100644
--- a/tensorflow/python/framework/dtypes_test.py
+++ b/tensorflow/python/framework/dtypes_test.py
@@ -27,9 +27,12 @@ from tensorflow.python.platform import googletest
 
 
 def _is_numeric_dtype_enum(datatype_enum):
-  return (datatype_enum != types_pb2.DT_INVALID and
-          datatype_enum != types_pb2.DT_RESOURCE and
-          datatype_enum != types_pb2.DT_RESOURCE_REF)
+  non_numeric_dtypes = [types_pb2.DT_VARIANT,
+                        types_pb2.DT_VARIANT_REF,
+                        types_pb2.DT_INVALID,
+                        types_pb2.DT_RESOURCE,
+                        types_pb2.DT_RESOURCE_REF]
+  return datatype_enum not in non_numeric_dtypes
 
 
 class TypesTest(test_util.TensorFlowTestCase):
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 6a035aba63..314449bb73 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -492,6 +492,10 @@ tf_module {
     name: "variance_scaling_initializer"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "variant"
+    mtype: "<class \'tensorflow.python.framework.dtypes.DType\'>"
+  }
   member {
     name: "zeros_initializer"
     mtype: "<type \'type\'>"
-- 
GitLab


From ccdd327749c220f5df2a5858815b0cf2e3c7e8fc Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Fri, 21 Jul 2017 11:02:23 -0700
Subject: [PATCH 1196/1375] Add no_pip to matmul_benchmark_test

---
 tensorflow/python/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 65ca4be547..4625ee9a1c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3898,6 +3898,7 @@ cuda_py_test(
         "//tensorflow/core:protos_all_py",
     ],
     main = "ops/matmul_benchmark_test.py",
+    tags = ["no_pip"],
 )
 
 cuda_py_test(
-- 
GitLab


From 6943870f81c376d6f7634393f786bccefe720d5b Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 21 Jul 2017 10:54:51 -0700
Subject: [PATCH 1197/1375] Apply no_pip tag to matmul_benchmark_test

PiperOrigin-RevId: 162759608
---
 tensorflow/python/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 65ca4be547..4625ee9a1c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3898,6 +3898,7 @@ cuda_py_test(
         "//tensorflow/core:protos_all_py",
     ],
     main = "ops/matmul_benchmark_test.py",
+    tags = ["no_pip"],
 )
 
 cuda_py_test(
-- 
GitLab


From 40bf259e711b84744177e89574c88b59cc1f3030 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 10:55:16 -0700
Subject: [PATCH 1198/1375] Revert "Make the logic around linking to CPU
 runtime functions less error-prone."  It breaks tf_library. END_PUBLIC
 BEGIN_PUBLIC Automated g4 rollback of changelist 162653398

PiperOrigin-RevId: 162759671
---
 tensorflow/compiler/xla/service/cpu/BUILD     | 10 --
 .../xla/service/cpu/compiler_functor.cc       | 24 ++---
 .../compiler/xla/service/cpu/cpu_runtime.cc   | 67 --------------
 .../compiler/xla/service/cpu/cpu_runtime.h    | 91 ++++---------------
 .../xla/service/cpu/cpu_runtime_avx.cc        | 17 +---
 .../xla/service/cpu/cpu_runtime_avx.h         | 15 ++-
 .../xla/service/cpu/cpu_runtime_sse4_1.cc     | 17 +---
 .../xla/service/cpu/cpu_runtime_sse4_1.h      | 15 ++-
 .../xla/service/cpu/dot_op_emitter.cc         |  8 +-
 .../xla/service/cpu/runtime_conv2d.cc         |  3 -
 .../xla/service/cpu/runtime_matmul.cc         |  5 -
 .../cpu/runtime_single_threaded_conv2d.cc     |  3 -
 .../cpu/runtime_single_threaded_matmul.cc     |  5 -
 .../xla/service/cpu/simple_orc_jit.cc         | 49 ++++++++--
 14 files changed, 91 insertions(+), 238 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 0440c5b5d9..7248cb5f4c 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -305,7 +305,6 @@ cc_library(
     hdrs = ["cpu_runtime_sse4_1.h"],
     copts = ["-DEIGEN_AVOID_STL_ARRAY"],
     deps = [
-        ":cpu_runtime",
         "//tensorflow/core:lib",
         "//third_party/eigen3",
     ],
@@ -318,7 +317,6 @@ cc_library(
     hdrs = ["cpu_runtime_avx.h"],
     copts = ["-DEIGEN_AVOID_STL_ARRAY"],
     deps = [
-        ":cpu_runtime",
         "//tensorflow/core:lib",
         "//third_party/eigen3",
     ],
@@ -356,13 +354,11 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
-        ":cpu_runtime",
         "//tensorflow/compiler/xla:executable_run_options",
         "//tensorflow/core:framework_lite",
         "//tensorflow/core/kernels:eigen_helpers",
         "//third_party/eigen3",
     ],
-    alwayslink = True,
 )
 
 cc_library(
@@ -372,12 +368,10 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
-        ":cpu_runtime",
         "//tensorflow/compiler/xla:executable_run_options",
         "//tensorflow/core:framework_lite",
         "//third_party/eigen3",
     ],
-    alwayslink = True,
 )
 
 cc_library(
@@ -390,12 +384,10 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
-        ":cpu_runtime",
         "//tensorflow/core:framework_lite",
         "//tensorflow/core/kernels:eigen_helpers",
         "//third_party/eigen3",
     ],
-    alwayslink = True,
 )
 
 cc_library(
@@ -405,11 +397,9 @@ cc_library(
     copts = runtime_copts(),
     visibility = ["//visibility:public"],
     deps = [
-        ":cpu_runtime",
         "//tensorflow/core:framework_lite",
         "//third_party/eigen3",
     ],
-    alwayslink = True,
 )
 
 cc_test(
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index d86881c282..f693808a10 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -131,25 +131,25 @@ std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl(
   std::vector<llvm::VecDesc> vector_functions;
 
   const llvm::VecDesc four_wide_vector_functions[] = {
-      {"expf", runtime::kExpV4F32SymbolName, 4},
-      {"llvm.exp.f32", runtime::kExpV4F32SymbolName, 4},
+      {"expf", runtime::kExpV4F32, 4},
+      {"llvm.exp.f32", runtime::kExpV4F32, 4},
 
-      {"logf", runtime::kLogV4F32SymbolName, 4},
-      {"llvm.log.f32", runtime::kLogV4F32SymbolName, 4},
+      {"logf", runtime::kLogV4F32, 4},
+      {"llvm.log.f32", runtime::kLogV4F32, 4},
 
-      {"tanhf", runtime::kTanhV4F32SymbolName, 4},
-      {"llvm.tanh.f32", runtime::kTanhV4F32SymbolName, 4},
+      {"tanhf", runtime::kTanhV4F32, 4},
+      {"llvm.tanh.f32", runtime::kTanhV4F32, 4},
   };
 
   const llvm::VecDesc eight_wide_vector_functions[] = {
-      {"expf", runtime::kExpV8F32SymbolName, 8},
-      {"llvm.exp.f32", runtime::kExpV8F32SymbolName, 8},
+      {"expf", runtime::kExpV8F32, 8},
+      {"llvm.exp.f32", runtime::kExpV8F32, 8},
 
-      {"logf", runtime::kLogV8F32SymbolName, 8},
-      {"llvm.log.f32", runtime::kLogV8F32SymbolName, 8},
+      {"logf", runtime::kLogV8F32, 8},
+      {"llvm.log.f32", runtime::kLogV8F32, 8},
 
-      {"tanhf", runtime::kTanhV8F32SymbolName, 8},
-      {"llvm.tanh.f32", runtime::kTanhV8F32SymbolName, 8},
+      {"tanhf", runtime::kTanhV8F32, 8},
+      {"llvm.tanh.f32", runtime::kTanhV8F32, 8},
   };
 
   if (arch == llvm::Triple::x86 || llvm::Triple::x86_64) {
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
index 48a30c8078..07a9832867 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
@@ -17,79 +17,20 @@ limitations under the License.
 
 #include <functional>
 
-#include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
-#include "tensorflow/core/lib/gtl/flatmap.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
 
-namespace {
-tensorflow::gtl::FlatMap<tensorflow::StringPiece, void*>* BuiltinMap() {
-  static auto* builtin_map =
-      new tensorflow::gtl::FlatMap<tensorflow::StringPiece, void*>;
-  return builtin_map;
-}
-
-tensorflow::mutex* BuiltinMapMutex() {
-  static tensorflow::mutex* builtin_map_mutex = new tensorflow::mutex;
-  return builtin_map_mutex;
-}
-}  // namespace
-
 namespace xla {
 namespace cpu {
 namespace runtime {
 
-const char* const kEigenMatMulF32SymbolName =
-    "__xla_cpu_runtime_EigenMatMulF32";
-const char* const kEigenMatMulF64SymbolName =
-    "__xla_cpu_runtime_EigenMatMulF64";
-const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32";
-const char* const kEigenSingleThreadedMatMulF32SymbolName =
-    "__xla_cpu_runtime_EigenSingleThreadedMatMulF32";
-const char* const kEigenSingleThreadedMatMulF64SymbolName =
-    "__xla_cpu_runtime_EigenSingleThreadedMatMulF64";
-const char* const kEigenSingleThreadedConvF32SymbolName =
-    "__xla_cpu_runtime_EigenSingleThreadedConvF32";
-const char* const kAcquireInfeedBufferForDequeueSymbolName =
-    "__xla_cpu_runtime_AcquireInfeedBufferForDequeue";
-const char* const kReleaseInfeedBufferAfterDequeueSymbolName =
-    "__xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue";
-const char* const kAcquireOutfeedBufferForPopulationSymbolName =
-    "__xla_cpu_runtime_AcquireOutfeedBufferForPopulation";
-const char* const kReleaseOutfeedBufferAfterPopulationSymbolName =
-    "__xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation";
-
-const char* const kXlaCpuRuntimeSymbolPrefix = "__xla_cpu_runtime_";
-
 XfeedManager* GetXfeedManager() {
   static XfeedManager* manager = new XfeedManager;
   return manager;
 }
 
-namespace internal {
-Registrar::Registrar(tensorflow::StringPiece name, void* function_pointer,
-                     tensorflow::StringPiece base_name) {
-  CHECK(function_pointer);
-  CHECK_EQ(tensorflow::strings::StrCat(kXlaCpuRuntimeSymbolPrefix, base_name),
-           name);
-
-  tensorflow::mutex_lock lock(*BuiltinMapMutex());
-  InsertOrDie(BuiltinMap(), name, function_pointer);
-}
-}  // namespace internal
-
-void* ResolveSymbol(tensorflow::StringPiece name) {
-  CHECK(name.starts_with(kXlaCpuRuntimeSymbolPrefix));
-
-  tensorflow::mutex_lock lock(*BuiltinMapMutex());
-  const auto& builtin_map = *BuiltinMap();
-  auto lookup_iterator = builtin_map.find(name);
-  return lookup_iterator == builtin_map.end() ? nullptr
-                                              : lookup_iterator->second;
-}
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
@@ -126,8 +67,6 @@ void* __xla_cpu_runtime_AcquireInfeedBufferForDequeue(xla::int32 buffer_length,
   return buffer->data();
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(AcquireInfeedBufferForDequeue);
-
 void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
     xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
     xla::int32 shape_length) {
@@ -142,8 +81,6 @@ void __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue(
                                         std::move(shape));
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(ReleaseInfeedBufferAfterDequeue);
-
 void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
     xla::int32 buffer_length, const void* shape_ptr, xla::int32 shape_length) {
   if (VLOG_IS_ON(2)) {
@@ -162,8 +99,6 @@ void* __xla_cpu_runtime_AcquireOutfeedBufferForPopulation(
   return buffer->data();
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(AcquireOutfeedBufferForPopulation);
-
 void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
     xla::int32 buffer_length, void* buffer_ptr, const void* shape_ptr,
     xla::int32 shape_length) {
@@ -176,5 +111,3 @@ void __xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation(
       xla::llvm_ir::DecodeSelfDescribingShapeConstant(shape_ptr, shape_length);
   xfeed->outfeed()->ReleaseCurrentBuffer(buffer_length, buffer_ptr, shape);
 }
-
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(ReleaseOutfeedBufferAfterPopulation);
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
index 4935b0b35e..46cf64966a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
@@ -32,6 +32,7 @@ limitations under the License.
 namespace xla {
 namespace cpu {
 namespace runtime {
+
 // Names of runtime functions. These get resolved from the generated code to the
 // right symbol at link time in one of two ways:
 // 1. When using the JIT, the symbol resolver (SimpleResolver in
@@ -40,83 +41,27 @@ namespace runtime {
 //    the actual symbol.
 // 2. When using ahead-of-time compilation, the linker can resolve the name
 //    because it is a symbol in the cpu_runtime library.
-extern const char* const kEigenMatMulF32SymbolName;
-extern const char* const kEigenMatMulF64SymbolName;
-extern const char* const kEigenConvF32SymbolName;
-extern const char* const kEigenSingleThreadedMatMulF32SymbolName;
-extern const char* const kEigenSingleThreadedMatMulF64SymbolName;
-extern const char* const kEigenSingleThreadedConvF32SymbolName;
-extern const char* const kAcquireInfeedBufferForDequeueSymbolName;
-extern const char* const kReleaseInfeedBufferAfterDequeueSymbolName;
-extern const char* const kAcquireOutfeedBufferForPopulationSymbolName;
-extern const char* const kReleaseOutfeedBufferAfterPopulationSymbolName;
-
-// Names for all of the XLA CPU runtime functions start have
-// kXlaCpuRuntimeSymbolPrefix as their prefix.
-extern const char* const kXlaCpuRuntimeSymbolPrefix;
+constexpr char kEigenMatmulF32SymbolName[] = "__xla_cpu_runtime_EigenMatMulF32";
+constexpr char kEigenMatmulF64SymbolName[] = "__xla_cpu_runtime_EigenMatMulF64";
+constexpr char kEigenConvF32SymbolName[] = "__xla_cpu_runtime_EigenConvF32";
+constexpr char kEigenSingleThreadedMatmulF32SymbolName[] =
+    "__xla_cpu_runtime_EigenSingleThreadedMatMulF32";
+constexpr char kEigenSingleThreadedMatmulF64SymbolName[] =
+    "__xla_cpu_runtime_EigenSingleThreadedMatMulF64";
+constexpr char kEigenSingleThreadedConvF32SymbolName[] =
+    "__xla_cpu_runtime_EigenSingleThreadedConvF32";
+constexpr char kAcquireInfeedBufferForDequeueSymbolName[] =
+    "__xla_cpu_runtime_AcquireInfeedBufferForDequeue";
+constexpr char kReleaseInfeedBufferAfterDequeueSymbolName[] =
+    "__xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue";
+constexpr char kAcquireOutfeedBufferForPopulationSymbolName[] =
+    "__xla_cpu_runtime_AcquireOutfeedBufferForPopulation";
+constexpr char kReleaseOutfeedBufferAfterPopulationSymbolName[] =
+    "__xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation";
 
 // Returns the infeed manager used by the CPU runtime.
 XfeedManager* GetXfeedManager();
 
-// This macro registers a cpu runtime function that can be invoked from
-// generated LLVM IR.  These registered functions can be looked up using
-// ResolveSymbol.  For instance, to register a CPU builtin named MyBuiltin, you
-// need to have:
-//
-// my_builtin.h:
-//
-// namespace xla { namespace cpu { namespace runtime {
-// extern const char* const kMyBuiltinSymbolName;
-// extern "C" void __xla_cpu_runtime_MyBuiltin(int a, int* b);
-// } } }
-//
-// my_builtin.cc:
-//
-// namespace xla { namespace cpu { namespace runtime {
-// const char* const kMyBuiltinSymbolName = "__xla_cpu_runtime_MyBuiltin";
-// extern "C" void __xla_cpu_runtime_MyBuiltin(int a, int* b) {
-//   // Implementation.
-// }
-//
-// REGISTER_XLA_CPU_RUNTIME_BUILTIN(MyBuiltin);
-// } } }
-#define REGISTER_XLA_CPU_RUNTIME_BUILTIN(base_name) \
-  REGISTER_XLA_CPU_RUNTIME_BUILTIN_IMPL(base_name, __COUNTER__)
-
-// Returns the address corresponding to the CPU runtime function called "name".
-// Returns nullptr if no such runtime function was registered.
-void* ResolveSymbol(tensorflow::StringPiece name);
-
-// Internal implementation details below this point.
-
-namespace internal {
-// This class is used by the REGISTER_XLA_CPU_RUNTIME_BUILTIN macro to register
-// addresses of __xla_cpu_runtime_* functions into a map.  This map can be
-// queried using the ResolveSymbol method.
-struct Registrar {
-  // The constructor adds an entry to the cpu runtime builtin registry.
-  //
-  // "name" is the name of the symbol, "function_pointer" is the address of
-  // runtime function corresponding to the symbol, and "base_name" is the
-  // stringification of the argument passed to the
-  // REGISTER_XLA_CPU_RUNTIME_BUILTIN ("base_name" is used to sanity check
-  // "name").
-  explicit Registrar(tensorflow::StringPiece name, void* function_pointer,
-                     tensorflow::StringPiece base_name);
-};
-}  // namespace internal
-
-#define REGISTER_XLA_CPU_RUNTIME_BUILTIN_IMPL(base_name, ctr) \
-                                                              \
-  static ::xla::cpu::runtime::internal::Registrar             \
-      REGISTER_XLA_CPU_RUNTIME_BUILTIN_NAME(ctr)(             \
-          ::xla::cpu::runtime::k##base_name##SymbolName,      \
-          reinterpret_cast<void*>(__xla_cpu_runtime_##base_name), #base_name)
-
-// __COUNTER__ must go through another macro to be properly expanded
-#define REGISTER_XLA_CPU_RUNTIME_BUILTIN_NAME(ctr) \
-  __xla_cpu_runtime_registrar_##ctr##__object_
-
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
index c23c146119..646254887c 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h"
-#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -25,23 +24,13 @@ namespace cpu {
 namespace runtime {
 
 #ifdef __AVX__
-V8F32 __xla_cpu_runtime_ExpV8F32(V8F32 x) { return Eigen::internal::pexp(x); }
+V8F32 ExpV8F32(V8F32 x) { return Eigen::internal::pexp(x); }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(ExpV8F32);
+V8F32 LogV8F32(V8F32 x) { return Eigen::internal::plog(x); }
 
-V8F32 __xla_cpu_runtime_LogV8F32(V8F32 x) { return Eigen::internal::plog(x); }
-
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(LogV8F32);
-
-V8F32 __xla_cpu_runtime_TanhV8F32(V8F32 x) { return Eigen::internal::ptanh(x); }
-
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(TanhV8F32);
+V8F32 TanhV8F32(V8F32 x) { return Eigen::internal::ptanh(x); }
 #endif  // __AVX__
 
-const char *const kExpV8F32SymbolName = "__xla_cpu_runtime_ExpV8F32";
-const char *const kLogV8F32SymbolName = "__xla_cpu_runtime_LogV8F32";
-const char *const kTanhV8F32SymbolName = "__xla_cpu_runtime_TanhV8F32";
-
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
index baee321c24..89721aaf83 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
@@ -28,23 +28,20 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-extern const char *const kExpV8F32SymbolName;
-extern const char *const kLogV8F32SymbolName;
-extern const char *const kTanhV8F32SymbolName;
+constexpr char kExpV8F32[] = "__xla_cpu_runtime_ExpV8F32";
+constexpr char kLogV8F32[] = "__xla_cpu_runtime_LogV8F32";
+constexpr char kTanhV8F32[] = "__xla_cpu_runtime_TanhV8F32";
 
 typedef float V8F32 __attribute__((__vector_size__(32)));
 
-extern "C" {
-
 // The following functions are vectorized versions of a selection of libm
 // library functions.
 // References to these functions are created by the LLVM vectorizer.
-V8F32 __xla_cpu_runtime_ExpV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+V8F32 ExpV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
 
-V8F32 __xla_cpu_runtime_LogV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+V8F32 LogV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
 
-V8F32 __xla_cpu_runtime_TanhV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
-}
+V8F32 TanhV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
 
 }  // namespace runtime
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
index ecb4de9f43..69d04427c6 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h"
-#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -26,33 +25,23 @@ namespace runtime {
 
 #ifdef __SSE4_1__
 
-V4F32 __xla_cpu_runtime_ExpV4F32(V4F32 x) {
+V4F32 ExpV4F32(V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::pexp(p);
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(ExpV4F32);
-
-V4F32 __xla_cpu_runtime_LogV4F32(V4F32 x) {
+V4F32 LogV4F32(V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::plog(p);
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(LogV4F32);
-
-V4F32 __xla_cpu_runtime_TanhV4F32(V4F32 x) {
+V4F32 TanhV4F32(V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::ptanh(p);
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(TanhV4F32);
-
 #endif  // __SSE4_1__
 
-const char *const kExpV4F32SymbolName = "__xla_cpu_runtime_ExpV4F32";
-const char *const kLogV4F32SymbolName = "__xla_cpu_runtime_LogV4F32";
-const char *const kTanhV4F32SymbolName = "__xla_cpu_runtime_TanhV4F32";
-
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
index 413a595e90..ded206f90a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
@@ -28,23 +28,20 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-extern const char *const kExpV4F32SymbolName;
-extern const char *const kLogV4F32SymbolName;
-extern const char *const kTanhV4F32SymbolName;
+constexpr char kExpV4F32[] = "__xla_cpu_runtime_ExpV4F32";
+constexpr char kLogV4F32[] = "__xla_cpu_runtime_LogV4F32";
+constexpr char kTanhV4F32[] = "__xla_cpu_runtime_TanhV4F32";
 
 typedef float V4F32 __attribute__((__vector_size__(16)));
 
-extern "C" {
-
 // The following functions are vectorized versions of a selection of libm
 // library functions.
 // References to these functions are created by the LLVM vectorizer.
-V4F32 __xla_cpu_runtime_ExpV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+V4F32 ExpV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
 
-V4F32 __xla_cpu_runtime_LogV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+V4F32 LogV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
 
-V4F32 __xla_cpu_runtime_TanhV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
-}
+V4F32 TanhV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
 
 }  // namespace runtime
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index f45c28ef74..c21b8a9add 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -244,14 +244,14 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   switch (type) {
     case F32:
       fn_name = multi_threaded_eigen
-                    ? runtime::kEigenMatMulF32SymbolName
-                    : runtime::kEigenSingleThreadedMatMulF32SymbolName;
+                    ? runtime::kEigenMatmulF32SymbolName
+                    : runtime::kEigenSingleThreadedMatmulF32SymbolName;
       float_type = ir_builder_->getFloatTy();
       break;
     case F64:
       fn_name = multi_threaded_eigen
-                    ? runtime::kEigenMatMulF64SymbolName
-                    : runtime::kEigenSingleThreadedMatMulF64SymbolName;
+                    ? runtime::kEigenMatmulF64SymbolName
+                    : runtime::kEigenSingleThreadedMatmulF64SymbolName;
       float_type = ir_builder_->getDoubleTy();
       break;
     default:
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc
index fa8b31f0a0..c2f64eb27a 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/runtime_conv2d.h"
-#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -42,5 +41,3 @@ TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConvF32(
       col_stride, padding_top, padding_bottom, padding_left, padding_right,
       lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation);
 }
-
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenConvF32);
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
index 35ace1e5ba..ee772f5c39 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h"
-#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 
 #define EIGEN_USE_THREADS
 
@@ -73,8 +72,6 @@ void __xla_cpu_runtime_EigenMatMulF32(const void* run_options_ptr, float* out,
                 transpose_rhs);
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenMatMulF32);
-
 void __xla_cpu_runtime_EigenMatMulF64(const void* run_options_ptr, double* out,
                                       double* lhs, double* rhs, int64 m,
                                       int64 n, int64 k, int32 transpose_lhs,
@@ -82,5 +79,3 @@ void __xla_cpu_runtime_EigenMatMulF64(const void* run_options_ptr, double* out,
   MatMul<double>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
                  transpose_rhs);
 }
-
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenMatMulF64);
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc
index feaff63b1c..d0b0e11ac0 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h"
 
-#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_conv2d_impl.h"
 #include "tensorflow/core/platform/dynamic_annotations.h"
 #include "tensorflow/core/platform/types.h"
@@ -38,5 +37,3 @@ __xla_cpu_runtime_EigenSingleThreadedConvF32(
       padding_top, padding_bottom, padding_left, padding_right,
       lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation);
 }
-
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenSingleThreadedConvF32);
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
index ed8079c3dd..6f1c97a233 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/core/platform/types.h"
 
 using tensorflow::int32;
@@ -66,13 +65,9 @@ void __xla_cpu_runtime_EigenSingleThreadedMatMulF32(
                 transpose_rhs);
 }
 
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenSingleThreadedMatMulF32);
-
 void __xla_cpu_runtime_EigenSingleThreadedMatMulF64(
     const void* run_options_ptr, double* out, double* lhs, double* rhs, int64 m,
     int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) {
   MatMul<double>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
                  transpose_rhs);
 }
-
-REGISTER_XLA_CPU_RUNTIME_BUILTIN(EigenSingleThreadedMatMulF64);
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index f7aa4b2719..aaf3fd2f9e 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -56,15 +56,44 @@ std::string CanonicalizeSymbol(const std::string &name) {
 // A simple SymbolResolver that delegates to the host dynamic linker.
 struct SimpleResolver : public llvm::JITSymbolResolver {
   llvm::JITSymbol findSymbol(const std::string &name) override {
-    std::string canonical_name = CanonicalizeSymbol(name);
-    bool must_be_builtin =
-        tensorflow::StringPiece(canonical_name.c_str(), canonical_name.size())
-            .starts_with(runtime::kXlaCpuRuntimeSymbolPrefix);
+    void *func_addr = nullptr;
 
-    void *func_addr = must_be_builtin
-                          ? runtime::ResolveSymbol(
-                                {canonical_name.c_str(), canonical_name.size()})
-                          : dlsym(RTLD_DEFAULT, canonical_name.c_str());
+    std::string canonical_name = CanonicalizeSymbol(name);
+    if (canonical_name == runtime::kEigenMatmulF32SymbolName) {
+      func_addr = reinterpret_cast<void *>(__xla_cpu_runtime_EigenMatMulF32);
+    } else if (canonical_name ==
+               runtime::kEigenSingleThreadedMatmulF32SymbolName) {
+      func_addr = reinterpret_cast<void *>(
+          __xla_cpu_runtime_EigenSingleThreadedMatMulF32);
+    } else if (canonical_name == runtime::kEigenConvF32SymbolName) {
+      func_addr = reinterpret_cast<void *>(__xla_cpu_runtime_EigenConvF32);
+    } else if (canonical_name ==
+               runtime::kEigenSingleThreadedConvF32SymbolName) {
+      func_addr = reinterpret_cast<void *>(
+          __xla_cpu_runtime_EigenSingleThreadedConvF32);
+    } else if (canonical_name ==
+               runtime::kAcquireInfeedBufferForDequeueSymbolName) {
+      func_addr = reinterpret_cast<void *>(
+          __xla_cpu_runtime_AcquireInfeedBufferForDequeue);
+    } else if (canonical_name ==
+               runtime::kReleaseInfeedBufferAfterDequeueSymbolName) {
+      func_addr = reinterpret_cast<void *>(
+          __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue);
+    } else if (canonical_name == runtime::kExpV4F32) {
+      func_addr = reinterpret_cast<void *>(runtime::ExpV4F32);
+    } else if (canonical_name == runtime::kExpV8F32) {
+      func_addr = reinterpret_cast<void *>(runtime::ExpV8F32);
+    } else if (canonical_name == runtime::kLogV4F32) {
+      func_addr = reinterpret_cast<void *>(runtime::LogV4F32);
+    } else if (canonical_name == runtime::kLogV8F32) {
+      func_addr = reinterpret_cast<void *>(runtime::LogV8F32);
+    } else if (canonical_name == runtime::kTanhV4F32) {
+      func_addr = reinterpret_cast<void *>(runtime::TanhV4F32);
+    } else if (canonical_name == runtime::kTanhV8F32) {
+      func_addr = reinterpret_cast<void *>(runtime::TanhV8F32);
+    } else {
+      func_addr = dlsym(RTLD_DEFAULT, canonical_name.c_str());
+    }
 
     if (func_addr == nullptr) {
       return nullptr;
@@ -105,8 +134,8 @@ llvm::StringRef GetHostCpuName() {
 
 CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() {
   CompilerFunctor::VectorIntrinsics intrinsics;
-  intrinsics.sse_intrinsics = (&runtime::__xla_cpu_runtime_ExpV4F32 != nullptr);
-  intrinsics.avx_intrinsics = (&runtime::__xla_cpu_runtime_ExpV8F32 != nullptr);
+  intrinsics.sse_intrinsics = (&runtime::ExpV4F32 != nullptr);
+  intrinsics.avx_intrinsics = (&runtime::ExpV8F32 != nullptr);
   return intrinsics;
 }
 
-- 
GitLab


From ae0207d3820308d882253c85fe58494b818f254d Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Fri, 21 Jul 2017 11:43:35 -0700
Subject: [PATCH 1199/1375] [XLA] Remove the xla_default_layout flag.

The default layout is just major-to-minor, as we don't have sufficient testing
for alternative default layouts.

PiperOrigin-RevId: 162766231
---
 tensorflow/compiler/xla/BUILD                 |   3 -
 tensorflow/compiler/xla/layout_util.cc        |  29 +----
 tensorflow/compiler/xla/layout_util_test.cc   |  28 -----
 tensorflow/compiler/xla/legacy_flags/BUILD    |  13 ---
 .../xla/legacy_flags/layout_util_flags.cc     | 107 ------------------
 .../xla/legacy_flags/layout_util_flags.h      |  62 ----------
 tensorflow/compiler/xla/tests/BUILD           |  32 ------
 .../compiler/xla/tests/dot_operation_test.cc  |   2 -
 8 files changed, 5 insertions(+), 271 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/layout_util_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/layout_util_flags.h

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 39489a2ad1..9c26a102ba 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -223,7 +223,6 @@ cc_library(
         ":types",
         ":util",
         ":xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:regexp_internal",
@@ -253,7 +252,6 @@ cc_test(
         ":shape_util",
         ":test",
         ":test_helpers",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/core:test_main",
     ],
 )
@@ -609,7 +607,6 @@ cc_header_only_library(
         ":xla_data_proto",
         ":xla_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core:stream_executor_headers_lib",
diff --git a/tensorflow/compiler/xla/layout_util.cc b/tensorflow/compiler/xla/layout_util.cc
index 119c4e373f..35a563bf22 100644
--- a/tensorflow/compiler/xla/layout_util.cc
+++ b/tensorflow/compiler/xla/layout_util.cc
@@ -23,7 +23,6 @@ limitations under the License.
 #include <unordered_map>
 #include <vector>
 
-#include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
 #include "tensorflow/compiler/xla/protobuf_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -39,35 +38,17 @@ limitations under the License.
 namespace xla {
 namespace {
 
-using DimensionOrder = legacy_flags::DefaultLayout::DimensionOrder;
-
 // Internal helper for GetDefaultLayoutForShape and SetToDefaultLayout. Sets
 // minor_to_major to the value that represents the default layout.
 void SetDefaultLayoutToContainer(
     tensorflow::protobuf::RepeatedField<tensorflow::protobuf_int64>*
         minor_to_major) {
+  // The default XLA layout is major-to-minor (dim 0 is major).
+  // For more information on XLA layouts, see:
+  // https://www.tensorflow.org/performance/xla/shapes
   const int64 size = minor_to_major->size();
-  legacy_flags::LayoutUtilFlags* flags = legacy_flags::GetLayoutUtilFlags();
-  auto default_layout = flags->xla_default_layout;
-  switch (default_layout.dimension_order) {
-    case DimensionOrder::kMajorToMinor:
-      for (int64 i = 0; i < size; ++i) {
-        minor_to_major->Set(i, size - 1 - i);
-      }
-      break;
-    case DimensionOrder::kMinorToMajor:
-      for (int64 i = 0; i < size; ++i) {
-        minor_to_major->Set(i, i);
-      }
-      break;
-    case DimensionOrder::kRandom:
-      for (int64 i = 0; i < size; ++i) {
-        minor_to_major->Set(i, i);
-      }
-      std::shuffle(
-          minor_to_major->begin(), minor_to_major->end(),
-          std::mt19937(default_layout.seed != 0 ? default_layout.seed
-                                                : std::random_device()()));
+  for (int64 i = 0; i < size; ++i) {
+    minor_to_major->Set(i, size - 1 - i);
   }
 }
 
diff --git a/tensorflow/compiler/xla/layout_util_test.cc b/tensorflow/compiler/xla/layout_util_test.cc
index d3fcccff65..331bb9afa9 100644
--- a/tensorflow/compiler/xla/layout_util_test.cc
+++ b/tensorflow/compiler/xla/layout_util_test.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/layout_util.h"
-#include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/test_helpers.h"
@@ -210,13 +209,6 @@ TEST_F(LayoutUtilTest, IsPadded) {
 }
 
 TEST_F(LayoutUtilTest, DefaultLayoutGettersMajorToMinor) {
-  // Test that LayoutUtil returns expected layouts when the xla_default_layout
-  // flag is set to kMajorToMinor.
-  legacy_flags::LayoutUtilFlags* flags = legacy_flags::GetLayoutUtilFlags();
-  flags->xla_default_layout = xla::legacy_flags::DefaultLayout{
-      .dimension_order =
-          legacy_flags::DefaultLayout::DimensionOrder::kMajorToMinor};
-
   EXPECT_TRUE(LayoutUtil::Equal(LayoutUtil::MakeLayout({1, 0}),
                                 LayoutUtil::GetDefaultLayoutForR2()));
   EXPECT_TRUE(LayoutUtil::Equal(LayoutUtil::MakeLayout({2, 1, 0}),
@@ -229,25 +221,5 @@ TEST_F(LayoutUtilTest, DefaultLayoutGettersMajorToMinor) {
                             ShapeUtil::MakeShape(F32, {10, 20, 30, 15, 25}))));
 }
 
-TEST_F(LayoutUtilTest, DefaultLayoutGettersMinorToMajor) {
-  // Test that LayoutUtil returns expected layouts when the xla_default_layout
-  // flag is set to kMinorToMajor.
-  legacy_flags::LayoutUtilFlags* flags = legacy_flags::GetLayoutUtilFlags();
-  flags->xla_default_layout = xla::legacy_flags::DefaultLayout{
-      .dimension_order =
-          legacy_flags::DefaultLayout::DimensionOrder::kMinorToMajor};
-
-  EXPECT_TRUE(LayoutUtil::Equal(LayoutUtil::MakeLayout({0, 1}),
-                                LayoutUtil::GetDefaultLayoutForR2()));
-  EXPECT_TRUE(LayoutUtil::Equal(LayoutUtil::MakeLayout({0, 1, 2}),
-                                LayoutUtil::GetDefaultLayoutForR3()));
-  EXPECT_TRUE(LayoutUtil::Equal(LayoutUtil::MakeLayout({0, 1, 2, 3}),
-                                LayoutUtil::GetDefaultLayoutForR4()));
-  EXPECT_TRUE(
-      LayoutUtil::Equal(LayoutUtil::MakeLayout({0, 1, 2, 3, 4}),
-                        LayoutUtil::GetDefaultLayoutForShape(
-                            ShapeUtil::MakeShape(F32, {10, 20, 30, 15, 25}))));
-}
-
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index cddcc40840..275ef28db0 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -41,19 +41,6 @@ cc_test(
         ],
 )
 
-cc_library(
-    name = "layout_util_flags",
-    srcs = ["layout_util_flags.cc"],
-    hdrs = ["layout_util_flags.h"],
-    deps =
-        [
-            ":parse_flags_from_env",
-            "//tensorflow/compiler/xla:types",
-            "//tensorflow/core:framework_internal",
-            "//tensorflow/core:lib",
-        ],
-)
-
 cc_library(
     name = "util_flags",
     srcs = ["util_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/layout_util_flags.cc b/tensorflow/compiler/xla/legacy_flags/layout_util_flags.cc
deleted file mode 100644
index f838861898..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/layout_util_flags.cc
+++ /dev/null
@@ -1,107 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's layout_util module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/core/lib/strings/numbers.h"
-#include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the string value of the xla_default_layout flag and the flag
-// descriptor, initialized via raw_flags_init.
-static string* raw_flag;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag raw_flags_init;
-
-// Allocate *raw_flag.  Called via call_once(&raw_flags_init,...).
-static void AllocateRawFlag() {
-  raw_flag = new string;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag(
-          "xla_default_layout", raw_flag,
-          "Default layout for Shapes in XLA. Valid values are: "
-          "'minor2major', 'major2minor', 'random', 'random:<seed>'. "
-          "For debugging purposes. If no seed (or 0) is given, a seed from "
-          "random_device is used."),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Parse text into *layout.
-static bool ParseDefaultLayout(const string& text, DefaultLayout* layout) {
-  bool result = true;
-  std::vector<string> field = tensorflow::str_util::Split(text, ':');
-  if (!field.empty()) {
-    if (field[0] == "random") {
-      layout->dimension_order = DefaultLayout::DimensionOrder::kRandom;
-      if (field.size() > 1) {
-        uint64 seed = 0;
-        result = tensorflow::strings::safe_strtou64(field[1], &seed);
-        layout->seed = seed;
-      }
-    } else if (field[0] == "minor2major") {
-      layout->dimension_order = DefaultLayout::DimensionOrder::kMinorToMajor;
-    } else if (field[0] == "major2minor") {
-      layout->dimension_order = DefaultLayout::DimensionOrder::kMajorToMinor;
-    } else {
-      result = false;
-    }
-  }
-  return result;
-}
-
-// Pointer to the parsed value of the flags, initialized via flags_init.
-static LayoutUtilFlags* flags;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  std::call_once(raw_flags_init, &AllocateRawFlag);
-  flags = new LayoutUtilFlags;
-  flags->xla_default_layout.dimension_order =
-      DefaultLayout::DimensionOrder::kMajorToMinor;
-  flags->xla_default_layout.seed = 0;
-  if (!ParseDefaultLayout(*raw_flag, &flags->xla_default_layout)) {
-    flags = nullptr;
-  }
-}
-
-// Append to *append_to the flag definitions associated with XLA's layout_util
-// module.
-void AppendLayoutUtilFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(raw_flags_init, &AllocateRawFlag);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the LayoutUtilFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-LayoutUtilFlags* GetLayoutUtilFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/layout_util_flags.h b/tensorflow/compiler/xla/legacy_flags/layout_util_flags.h
deleted file mode 100644
index 177f428b73..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/layout_util_flags.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_LAYOUT_UTIL_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_LAYOUT_UTIL_FLAGS_H_
-
-// Legacy flags for the XLA's layout_util module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// The default layout for all newly created shapes. Specified by the flag
-// --xla_default_layout.
-struct DefaultLayout {
-  enum class DimensionOrder {
-    kRandom,
-    kMinorToMajor,
-    kMajorToMinor,
-  };
-
-  DimensionOrder dimension_order;
-  size_t seed;
-};
-
-// Append to *flag_list the flag definitions associated with XLA's layout_util
-// module.
-void AppendLayoutUtilFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's layout_util module.
-typedef struct {
-  // Default layout for Shapes in XLA.  Valid values are:  'minor2major',
-  // 'major2minor', 'random', 'random:<seed>'.  For debugging purposes.  If no
-  // seed (or 0) is given, a seed from random_device is used.
-  DefaultLayout xla_default_layout;
-} LayoutUtilFlags;
-
-// Return a pointer to the LayoutFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-LayoutUtilFlags* GetLayoutUtilFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_LAYOUT_UTIL_FLAGS_H_
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index da173d2ee6..a94ff9db89 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -517,7 +517,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
@@ -540,7 +539,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
@@ -570,36 +568,6 @@ xla_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
-        "//tensorflow/compiler/xla/tests:client_library_test_base",
-        "//tensorflow/compiler/xla/tests:literal_test_util",
-        "//tensorflow/compiler/xla/tests:test_utils",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-    ],
-)
-
-xla_test(
-    name = "dot_operation_rowmajor_runtime_test",
-    srcs = ["dot_operation_test.cc"],
-    backend_args = {
-        "cpu": [
-            "--xla_default_layout=major2minor",
-        ],
-        "cpu_parallel": [
-            "--xla_default_layout=major2minor",
-        ],
-    },
-    deps = [
-        "//tensorflow/compiler/xla:array2d",
-        "//tensorflow/compiler/xla:array3d",
-        "//tensorflow/compiler/xla:reference_util",
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
-        "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:layout_util_flags",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 63a630f9e5..59ee007338 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/layout_util_flags.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -458,7 +457,6 @@ TEST_F(DotOperationTest, TransposeFolding) {
 
 int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
-  xla::legacy_flags::AppendLayoutUtilFlags(&flag_list);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
   xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
-- 
GitLab


From ef5cbc42f19c026de62e63c92f3b085422aa35cb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 11:44:22 -0700
Subject: [PATCH 1200/1375] Fix a bug where Dataset.map() will throw the
 following error when num_threads is specified but output_buffer_size is not:

ValueError: Tensor conversion requested dtype int64 for Tensor with dtype
int32: 'Tensor("num_threads:0", shape=(), dtype=int32)'
PiperOrigin-RevId: 162766317
---
 .../python/kernel_tests/map_dataset_op_test.py    | 15 +++++++++++++++
 tensorflow/contrib/data/python/ops/dataset_ops.py |  2 +-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
index ceba00602c..8a78752584 100644
--- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py
@@ -203,6 +203,21 @@ class MapDatasetTest(test.TestCase):
   def testImplicitDisposeParallelMapDataset(self):
     self._testDisposeParallelMapDataset(False)
 
+  def testParallelMapUnspecifiedOutputSize(self):
+    components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32)
+
+    dataset = (dataset_ops.Dataset.from_tensor_slices(components)
+               .map(lambda x: array_ops.check_numerics(x, "message"),
+                    num_threads=2))
+    iterator = dataset.make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for _ in range(3):
+        sess.run(get_next)
+
   def testParallelMapError(self):
     components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32)
 
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index bf60efbff9..d5a2f32fea 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -1744,7 +1744,7 @@ class MapDataset(Dataset):
             output_buffer_size, dtype=dtypes.int64, name="output_buffer_size")
       else:
         self._output_buffer_size = ops.convert_to_tensor(
-            self._num_threads, dtype=dtypes.int64, name="output_buffer_size")
+            num_threads, dtype=dtypes.int64, name="output_buffer_size")
     else:
       self._num_threads = None
       self._output_buffer_size = None
-- 
GitLab


From 0cac938e510800cb36a29eb476fc47096c71ccb4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 11:54:34 -0700
Subject: [PATCH 1201/1375] Re-enable rendezvous_test.

PiperOrigin-RevId: 162767573
---
 tensorflow/core/BUILD                        |  8 +--
 tensorflow/core/framework/rendezvous_test.cc | 53 +++++++++++---------
 2 files changed, 34 insertions(+), 27 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 926bfa2278..9be56c2a87 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -2195,13 +2195,12 @@ tf_cc_tests(
     srcs = [
         "common_runtime/device_set_test.cc",
         "common_runtime/optimization_registry_test.cc",
-        "common_runtime/resource_variable_read_optimizer_test.cc",
         "common_runtime/pending_counts_test.cc",
+        "common_runtime/resource_variable_read_optimizer_test.cc",
         "common_runtime/session_test.cc",
         "common_runtime/simple_placer_test.cc",
         "example/feature_util_test.cc",
         "framework/allocator_test.cc",
-        "framework/variant_test.cc",
         "framework/attr_value_util_test.cc",
         "framework/bfloat16_test.cc",
         "framework/cancellation_test.cc",
@@ -2218,7 +2217,7 @@ tf_cc_tests(
         "framework/op_kernel_test.cc",
         "framework/op_registration_test.cc",
         "framework/partial_tensor_shape_test.cc",
-        # "framework/rendezvous_test.cc",  # flaky b/30476344
+        "framework/rendezvous_test.cc",
         "framework/resource_mgr_test.cc",
         "framework/resource_op_kernel_test.cc",
         "framework/shape_inference_test.cc",
@@ -2230,6 +2229,7 @@ tf_cc_tests(
         "framework/tracking_allocator_test.cc",
         "framework/types_test.cc",
         "framework/unique_tensor_references_test.cc",
+        "framework/variant_test.cc",
         "graph/algorithm_test.cc",
         "graph/edgeset_test.cc",
         "graph/graph_def_builder_test.cc",
@@ -2251,8 +2251,8 @@ tf_cc_tests(
         "util/presized_cuckoo_map_test.cc",
         "util/reporter_test.cc",
         "util/saved_tensor_slice_util_test.cc",
-        "util/sparse/sparse_tensor_test.cc",
         "util/semver_test.cc",
+        "util/sparse/sparse_tensor_test.cc",
         "util/stat_summarizer_test.cc",
         "util/tensor_slice_reader_test.cc",
         "util/tensor_slice_set_test.cc",
diff --git a/tensorflow/core/framework/rendezvous_test.cc b/tensorflow/core/framework/rendezvous_test.cc
index 663c449dbe..69b04a2a5d 100644
--- a/tensorflow/core/framework/rendezvous_test.cc
+++ b/tensorflow/core/framework/rendezvous_test.cc
@@ -183,27 +183,28 @@ struct BlockingState {
 };
 
 TEST_F(LocalRendezvousTest, RandomSendRecv) {
-  static const int N = 1000;
+  // We are scheduling 2*N closures in the this->threads_, which is
+  // configured with only 16 threads. Furthermore, because the
+  // threadpool may execute the closures in an arbitrary order, we
+  // must use RecvAsync below. Otherwise, blocking Recv() may run
+  // before all all the Send() and deadlock.
+  static const int N = 100;
+  random::PhiloxRandom philox(testing::RandomSeed(), 17);
+  random::SimplePhilox rnd(&philox);
   BlockingState state;
   state.counter = N;
   for (int i = 0; i < N; ++i) {
-    SchedClosure([this, i]() {
-      random::PhiloxRandom philox(testing::RandomSeed() + i, 17);
-      random::SimplePhilox rnd(&philox);
-      Env::Default()->SleepForMicroseconds(1000 + rnd.Uniform(10000));
+    int micros = 100 + rnd.Uniform(1000);
+    SchedClosure([this, i, micros]() {
+      Env::Default()->SleepForMicroseconds(micros);
       Rendezvous::Args args;
       TF_ASSERT_OK(rendez_->Send(MakeKey(strings::StrCat(i)), args,
                                  V(strings::StrCat(i)), false));
     });
-    SchedClosure([this, &state, i]() {
-      random::PhiloxRandom philox(testing::RandomSeed() + N + i, 17);
-      random::SimplePhilox rnd(&philox);
-      Env::Default()->SleepForMicroseconds(1000 + rnd.Uniform(10000));
-      Tensor val(DT_STRING);
-      bool val_dead = false;
-      Rendezvous::Args args;
-      TF_ASSERT_OK(
-          rendez_->Recv(MakeKey(strings::StrCat(i)), args, &val, &val_dead));
+    auto recv_done = [this, &state, i](const Status& status,
+                                       const Rendezvous::Args& sender_args,
+                                       const Rendezvous::Args& recver_args,
+                                       const Tensor& val, const bool val_dead) {
       EXPECT_EQ(strings::StrCat(i), V(val));
       bool done = false;
       {
@@ -216,6 +217,12 @@ TEST_F(LocalRendezvousTest, RandomSendRecv) {
       if (done) {
         state.done.Notify();
       }
+    };
+    micros = 100 + rnd.Uniform(1000);
+    SchedClosure([this, i, micros, recv_done]() {
+      Env::Default()->SleepForMicroseconds(micros);
+      rendez_->RecvAsync(MakeKey(strings::StrCat(i)), Rendezvous::Args(),
+                         recv_done);
     });
   }
 
@@ -280,15 +287,15 @@ TEST_F(LocalRendezvousTest, TransferDummyDeviceContext) {
   Notification n;
   Rendezvous::Args args1;
   args1.device_context = new DummyDeviceContext(1);
-  rendez_->RecvAsync(KeyFoo(), args1, [&n](const Status& s,
-                                           const Rendezvous::Args& send_args,
-                                           const Rendezvous::Args& recv_args,
-                                           const Tensor& val, bool is_dead) {
-    CHECK_EQ(123,
-             dynamic_cast<const DummyDeviceContext*>(send_args.device_context)
-                 ->stream_id());
-    n.Notify();
-  });
+  rendez_->RecvAsync(
+      KeyFoo(), args1,
+      [&n](const Status& s, const Rendezvous::Args& send_args,
+           const Rendezvous::Args& recv_args, const Tensor& val, bool is_dead) {
+        CHECK_EQ(123, dynamic_cast<const DummyDeviceContext*>(
+                          send_args.device_context)
+                          ->stream_id());
+        n.Notify();
+      });
 
   n.WaitForNotification();
   args.device_context->Unref();
-- 
GitLab


From 9d8f6ac30db4c922b34997667dc43ba4bc27cf79 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 12:09:05 -0700
Subject: [PATCH 1202/1375] Remove local tag from cuda and sycl tests which
 should not be necessary.

PiperOrigin-RevId: 162769374
---
 tensorflow/core/platform/default/build_config_root.bzl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl
index fa4ac4ba73..1a59c471e5 100644
--- a/tensorflow/core/platform/default/build_config_root.bzl
+++ b/tensorflow/core/platform/default/build_config_root.bzl
@@ -3,10 +3,10 @@
 # be separate to avoid cyclic references.
 
 def tf_cuda_tests_tags():
-  return ["local"]
+  return []
 
 def tf_sycl_tests_tags():
-  return ["local"]
+  return []
 
 def tf_additional_plugin_deps():
   return select({
@@ -28,7 +28,7 @@ def tf_additional_verbs_deps():
       "//tensorflow:with_verbs_support": [
           "//tensorflow/contrib/verbs:verbs_server_lib",
           "//tensorflow/contrib/verbs:grpc_verbs_client",
-      ], 
+      ],
       "//conditions:default": [],
   })
 
-- 
GitLab


From a4538ecd63699f3efb91c9e7c54409d40944e434 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 12:44:27 -0700
Subject: [PATCH 1203/1375] Set the op cost of RefIdentity, StopGradient, and
 PreventGradient to zero in analytical cost estimator.

PiperOrigin-RevId: 162773156
---
 tensorflow/core/grappler/costs/op_level_cost_estimator.cc | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index 7f4cc95f31..5148a4b99e 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -31,6 +31,7 @@ constexpr char kConv2dBackPropInput[] = "Conv2DBackpropInput";
 constexpr char kMatMul[] = "MatMul";
 constexpr char kSparseMatMul[] = "SparseMatMul";
 constexpr char kIdentity[] = "Identity";
+constexpr char kRefIdentity[] = "RefIdentity";
 constexpr char kNoOp[] = "NoOp";
 constexpr char kReshape[] = "Reshape";
 constexpr char kRecv[] = "_Recv";
@@ -40,6 +41,8 @@ constexpr char kVariableV2[] = "VariableV2";
 constexpr char kRank[] = "Rank";
 constexpr char kShape[] = "Shape";
 constexpr char kSize[] = "Size";
+constexpr char kStopGradient[] = "StopGradient";
+constexpr char kPreventGradient[] = "PreventGradient";
 
 namespace {
 
@@ -155,6 +158,9 @@ OpLevelCostEstimator::OpLevelCostEstimator() {
       {kMatMul, wrap(&OpLevelCostEstimator::PredictMatMul)},
       {kSparseMatMul, wrap(&OpLevelCostEstimator::PredictMatMul)},
       {kIdentity, wrap(&OpLevelCostEstimator::PredictNoOp)},
+      {kRefIdentity, wrap(&OpLevelCostEstimator::PredictNoOp)},
+      {kStopGradient, wrap(&OpLevelCostEstimator::PredictNoOp)},
+      {kPreventGradient, wrap(&OpLevelCostEstimator::PredictNoOp)},
       {kNoOp, wrap(&OpLevelCostEstimator::PredictNoOp)},
       {kReshape, wrap(&OpLevelCostEstimator::PredictNoOp)},
       {kRecv, wrap(&OpLevelCostEstimator::PredictNoOp)},
-- 
GitLab


From 7bc455958d2126f4bb1ce678b20f43bef261712d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 12:47:29 -0700
Subject: [PATCH 1204/1375] Add default attrs to the graph def for inlining
 code in Grappler.

PiperOrigin-RevId: 162773493
---
 tensorflow/core/grappler/grappler_item_builder.cc | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 86775306ff..4518a7a78e 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/graph_def_util.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
@@ -81,6 +82,11 @@ Status OptimizeGraph(const GraphDef& graph_def, GraphDef* output_graph_def,
 
   // Inline all functions.
   GraphDef inlined_graph_def(graph_def);
+  // Populate default attrs to the NodeDefs in the GraphDef, which is required
+  // by inlining code.
+  TF_RETURN_IF_ERROR(
+      AddDefaultAttrsToGraphDef(&inlined_graph_def, *OpRegistry::Global(), 0));
+
   for (int i = 0; i < inlined_graph_def.library().function().size(); i++) {
     FunctionDef* fdef =
         inlined_graph_def.mutable_library()->mutable_function(i);
-- 
GitLab


From 2a60a96374be8680ceae2895e7eccf74afb48fe5 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Fri, 21 Jul 2017 13:01:05 -0700
Subject: [PATCH 1205/1375] T-power divergence added to csiszar_divergence.py

PiperOrigin-RevId: 162774830
---
 .../kernel_tests/csiszar_divergence_test.py   |  46 ++++++++
 .../python/ops/csiszar_divergence.py          |   1 +
 .../python/ops/csiszar_divergence_impl.py     | 109 +++++++++++++-----
 3 files changed, 124 insertions(+), 32 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
index fba0cc6522..dda3f60065 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -285,6 +285,52 @@ class TriangularTest(test.TestCase):
           np.square(self._u - 1) / (1 + self._u))
 
 
+class TPowerTest(test.TestCase):
+
+  def setUp(self):
+    self._logu = np.linspace(-10., 10, 100)
+    self._u = np.exp(self._logu)
+
+  def test_at_zero(self):
+    with self.test_session():
+      self.assertAllClose(cd.t_power(0., t=-0.1).eval(), 0.)
+      self.assertAllClose(cd.t_power(0., t=0.5).eval(), 0.)
+      self.assertAllClose(cd.t_power(0., t=1.1).eval(), 0.)
+      self.assertAllClose(
+          cd.t_power(0., t=-0.1, self_normalized=True).eval(), 0.)
+      self.assertAllClose(
+          cd.t_power(0., t=0.5, self_normalized=True).eval(), 0.)
+      self.assertAllClose(
+          cd.t_power(0., t=1.1, self_normalized=True).eval(), 0.)
+
+  def test_correct(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.t_power(self._logu, t=np.float64(-0.1)).eval(),
+          self._u ** -0.1 - 1.)
+      self.assertAllClose(
+          cd.t_power(self._logu, t=np.float64(0.5)).eval(),
+          -self._u ** 0.5 + 1.)
+      self.assertAllClose(
+          cd.t_power(self._logu, t=np.float64(1.1)).eval(),
+          self._u ** 1.1 - 1.)
+
+  def test_correct_self_normalized(self):
+    with self.test_session():
+      self.assertAllClose(
+          cd.t_power(self._logu, t=np.float64(-0.1),
+                     self_normalized=True).eval(),
+          self._u ** -0.1 - 1. + 0.1 * (self._u - 1.))
+      self.assertAllClose(
+          cd.t_power(self._logu, t=np.float64(0.5),
+                     self_normalized=True).eval(),
+          -self._u ** 0.5 + 1. + 0.5 * (self._u - 1.))
+      self.assertAllClose(
+          cd.t_power(self._logu, t=np.float64(1.1),
+                     self_normalized=True).eval(),
+          self._u ** 1.1 - 1. - 1.1 * (self._u - 1.))
+
+
 class Log1pAbsTest(test.TestCase):
 
   def setUp(self):
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
index 5440df7dbf..b1bcc86022 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
@@ -43,6 +43,7 @@ _allowed_symbols = [
     'squared_hellinger',
     'symmetrized_csiszar_function',
     'total_variation',
+    't_power',
     'triangular',
 ]
 
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 68f0f05b99..247d434bb7 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -42,6 +42,7 @@ import numpy as np
 from tensorflow.contrib import framework as contrib_framework
 from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import distribution
@@ -76,15 +77,15 @@ def amari_alpha(logu, alpha=1., self_normalized=False, name=None):
     1532-1568, 2010.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
-    alpha: Floating-type Python scalar. (See Mathematical Details for meaning.)
+    logu: `float`-like `Tensor` representing `log(u)` from above.
+    alpha: `float`-like Python scalar. (See Mathematical Details for meaning.)
     self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
       `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
       when `p, q` are unnormalized measures.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    amari_alpha_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+    amari_alpha_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
       at `u = exp(logu)`.
 
   Raises:
@@ -147,14 +148,14 @@ def kl_reverse(logu, self_normalized=False, name=None):
   calculations and may therefore be numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
       `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
       when `p, q` are unnormalized measures.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    kl_reverse_of_u: Floating-type `Tensor` of the Csiszar-function evaluated at
+    kl_reverse_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at
       `u = exp(logu)`.
 
   Raises:
@@ -195,14 +196,14 @@ def kl_forward(logu, self_normalized=False, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
       `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
       when `p, q` are unnormalized measures.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    kl_forward_of_u: Floating-type `Tensor` of the Csiszar-function evaluated at
+    kl_forward_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at
       `u = exp(logu)`.
 
   Raises:
@@ -251,14 +252,14 @@ def jensen_shannon(logu, self_normalized=False, name=None):
     Inf. Th., 37, 145-151, 1991.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
       `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
       when `p, q` are unnormalized measures.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    jensen_shannon_of_u: Floating-type `Tensor` of the Csiszar-function
+    jensen_shannon_of_u: `float`-like `Tensor` of the Csiszar-function
       evaluated at `u = exp(logu)`.
   """
 
@@ -305,14 +306,14 @@ def arithmetic_geometric(logu, self_normalized=False, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
       `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
       when `p, q` are unnormalized measures.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    arithmetic_geometric_of_u: Floating-type `Tensor` of the
+    arithmetic_geometric_of_u: `float`-like `Tensor` of the
       Csiszar-function evaluated at `u = exp(logu)`.
   """
 
@@ -343,11 +344,11 @@ def total_variation(logu, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    total_variation_of_u: Floating-type `Tensor` of the Csiszar-function
+    total_variation_of_u: `float`-like `Tensor` of the Csiszar-function
       evaluated at `u = exp(logu)`.
   """
 
@@ -375,11 +376,11 @@ def pearson(logu, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    pearson_of_u: Floating-type `Tensor` of the Csiszar-function evaluated at
+    pearson_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at
       `u = exp(logu)`.
   """
 
@@ -410,11 +411,11 @@ def squared_hellinger(logu, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    squared_hellinger_of_u: Floating-type `Tensor` of the Csiszar-function
+    squared_hellinger_of_u: `float`-like `Tensor` of the Csiszar-function
       evaluated at `u = exp(logu)`.
   """
 
@@ -445,11 +446,11 @@ def triangular(logu, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    triangular_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+    triangular_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
       at `u = exp(logu)`.
   """
 
@@ -458,6 +459,50 @@ def triangular(logu, name=None):
     return pearson(logu) / (1. + math_ops.exp(logu))
 
 
+def t_power(logu, t, self_normalized=False, name=None):
+  """The T-Power Csiszar-function in log-space.
+
+  A Csiszar-function is a member of,
+
+  ```none
+  F = { f:R_+ to R : f convex }.
+  ```
+
+  When `self_normalized = True` the T-Power Csiszar-function is:
+
+  ```none
+  f(u) = s [ u**t - 1 - t(u - 1) ]
+  s = { -1   0 < t < 1
+      { +1   otherwise
+  ```
+
+  When `self_normalized = False` the `- t(u - 1)` term is omitted.
+
+  This is similar to the `amari_alpha` Csiszar-function, with the associated
+  divergence being the same up to factors depending only on `t`.
+
+  Args:
+    logu: `float`-like `Tensor` representing `log(u)` from above.
+    t:  `Tensor` of same `dtype` as `logu` and broadcastable shape.
+    self_normalized: Python `bool` indicating whether `f'(u=1)=0`.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    t_power_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
+      at `u = exp(logu)`.
+  """
+  with ops.name_scope(name, "t_power", [logu, t]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+    t = ops.convert_to_tensor(t, dtype=logu.dtype.base_dtype, name="t")
+    fu = math_ops.expm1(t * logu)
+    if self_normalized:
+      fu -= t * math_ops.expm1(logu)
+    fu *= array_ops.where(math_ops.logical_and(0. < t, t < 1.),
+                          -array_ops.ones_like(t),
+                          array_ops.ones_like(t))
+    return fu
+
+
 def log1p_abs(logu, name=None):
   """The log1p-abs Csiszar-function in log-space.
 
@@ -489,11 +534,11 @@ def log1p_abs(logu, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    log1p_abs_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+    log1p_abs_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
       at `u = exp(logu)`.
   """
 
@@ -527,11 +572,11 @@ def jeffreys(logu, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    jeffreys_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+    jeffreys_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
       at `u = exp(logu)`.
   """
 
@@ -559,11 +604,11 @@ def chi_square(logu, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    chi_square_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+    chi_square_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
       at `u = exp(logu)`.
   """
 
@@ -597,14 +642,14 @@ def modified_gan(logu, self_normalized=False, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When
       `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even
       when `p, q` are unnormalized measures.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    chi_square_of_u: Floating-type `Tensor` of the Csiszar-function evaluated
+    chi_square_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
       at `u = exp(logu)`.
   """
 
@@ -650,13 +695,13 @@ def dual_csiszar_function(logu, csiszar_function, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     csiszar_function: Python callable representing a Csiszar-function over
       log-domain.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    dual_f_of_u: Floating-type `Tensor` of the result of calculating the dual of
+    dual_f_of_u: `float`-like `Tensor` of the result of calculating the dual of
       `f` at `u = exp(logu)`.
   """
 
@@ -719,13 +764,13 @@ def symmetrized_csiszar_function(logu, csiszar_function, name=None):
   numerically unstable for `|logu| >> 0`.
 
   Args:
-    logu: Floating-type `Tensor` representing `log(u)` from above.
+    logu: `float`-like `Tensor` representing `log(u)` from above.
     csiszar_function: Python callable representing a Csiszar-function over
       log-domain.
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    symmetrized_g_of_u: Floating-type `Tensor` of the result of applying the
+    symmetrized_g_of_u: `float`-like `Tensor` of the result of applying the
       symmetrization of `g` evaluated at `u = exp(logu)`.
   """
 
@@ -811,7 +856,7 @@ def monte_carlo_csiszar_f_divergence(
     name: Python `str` name prefixed to Ops created by this function.
 
   Returns:
-    monte_carlo_csiszar_f_divergence: Floating-type `Tensor` Monte Carlo
+    monte_carlo_csiszar_f_divergence: `float`-like `Tensor` Monte Carlo
       approximation of the Csiszar f-Divergence.
 
   Raises:
-- 
GitLab


From 10632be3bedb5b8e196d98429795ca9108cace5a Mon Sep 17 00:00:00 2001
From: Yixing Lao <yixing.lao@gmail.com>
Date: Fri, 21 Jul 2017 10:06:29 -1000
Subject: [PATCH 1206/1375] fix buf address for tuples (#11625)

---
 tensorflow/compiler/plugin/executor/executable.cc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/plugin/executor/executable.cc b/tensorflow/compiler/plugin/executor/executable.cc
index 4673a90e0a..4f1f0d99f9 100644
--- a/tensorflow/compiler/plugin/executor/executable.cc
+++ b/tensorflow/compiler/plugin/executor/executable.cc
@@ -47,13 +47,14 @@ static se::DeviceMemoryBase AllocateOutputBuffer(sep::ExecutorExecutor* executor
   } else {
     int64 size(xla::ShapeUtil::ByteSizeOf(shape, sizeof(void*)));
     void** buf = reinterpret_cast<void**>(executor->Allocate(size));
+    void** buf_rc = buf;
     for (int64 n = 0; n < xla::ShapeUtil::TupleElementCount(shape); n++) {
       se::DeviceMemoryBase out =
           AllocateSingleOutput(executor, literal.tuple_literals(n));
       *buf++ = out.opaque();
     }
 
-    return se::DeviceMemoryBase(buf, size);
+    return se::DeviceMemoryBase(buf_rc, size);
   }
 }
 
-- 
GitLab


From 557f4b8654503a9e281f7e36b6f0680d1cb29d02 Mon Sep 17 00:00:00 2001
From: Fred Reiss <frreiss@us.ibm.com>
Date: Fri, 21 Jul 2017 23:06:42 +0300
Subject: [PATCH 1207/1375] Added comment to clarify semantics of opt_level.
 (#11607)

---
 tensorflow/core/protobuf/config.proto | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index 1bcabf8e1d..69311e3a7f 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -102,6 +102,8 @@ message OptimizerOptions {
     L0 = -1;
   }
 
+  // Overall optimization level. The actual optimizations applied will be the
+  // logical OR of the flags that this level implies and any flags already set.
   Level opt_level = 3;
 
   // Control the use of the compiler/jit.  Experimental.
-- 
GitLab


From 107e30732bace4099072d916bf5c794d7eeeceb5 Mon Sep 17 00:00:00 2001
From: resec <resec0109@gmail.com>
Date: Sat, 22 Jul 2017 04:07:40 +0800
Subject: [PATCH 1208/1375] Add logical_or and less_equal ops to the list
 (#11631)

Add below ops implementations to the list
tensorflow/core/kernels/cwise_op_logical_or.cc
tensorflow/core/kernels/cwise_op_less_equal.cc
When I am using the Android and iOS lib, above ops are not registered while in full version they are available.
---
 tensorflow/contrib/makefile/tf_op_files.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 4a3b3e7762..d3cc61ce29 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -143,8 +143,10 @@ tensorflow/core/kernels/cwise_op_minimum.cc
 tensorflow/core/kernels/cwise_op_maximum.cc
 tensorflow/core/kernels/cwise_op_logical_not.cc
 tensorflow/core/kernels/cwise_op_logical_and.cc
+tensorflow/core/kernels/cwise_op_logical_or.cc
 tensorflow/core/kernels/cwise_op_log.cc
 tensorflow/core/kernels/cwise_op_less.cc
+tensorflow/core/kernels/cwise_op_less_equal.cc
 tensorflow/core/kernels/cwise_op_isfinite.cc
 tensorflow/core/kernels/cwise_op_invert.cc
 tensorflow/core/kernels/cwise_op_greater_equal.cc
-- 
GitLab


From 20669f62db607d60fed52c70aa1dcd1c7b3c53d5 Mon Sep 17 00:00:00 2001
From: resec <resec0109@gmail.com>
Date: Sat, 22 Jul 2017 04:08:21 +0800
Subject: [PATCH 1209/1375] Add interface for long(int64) datatype (#11632)

Added below methods for TensorFlowInferenceInterface
public void feed(String inputName, long[] src, long... dims)
public void feed(String inputName, LongBuffer src, long... dims)
public void fetch(String outputName, long[] dst)
public void fetch(String outputName, LongBuffer dst)

They are very useful in case when we are handling int64 Tensor (e.g. output of tf.argmax)

I do not see the unit test file for TensorFlowInferenceInterface.java, so I did not add any test case for the change. But this change is very straight forward. Please let me know if I missed any. Thanks.
---
 .../android/TensorFlowInferenceInterface.java | 41 +++++++++++++++++++
 1 file changed, 41 insertions(+)

diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
index b1d18d2faf..587f2941e5 100644
--- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
+++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java
@@ -27,6 +27,7 @@ import java.nio.ByteBuffer;
 import java.nio.DoubleBuffer;
 import java.nio.FloatBuffer;
 import java.nio.IntBuffer;
+import java.nio.LongBuffer;
 import java.util.ArrayList;
 import java.util.List;
 import org.tensorflow.DataType;
@@ -226,6 +227,16 @@ public class TensorFlowInferenceInterface {
     addFeed(inputName, Tensor.create(dims, IntBuffer.wrap(src)));
   }
 
+  /**
+   * Given a source array with shape {@link dims} and content {@link src}, copy the contents into
+   * the input Tensor with name {@link inputName}. The source array {@link src} must have at least
+   * as many elements as that of the destination Tensor. If {@link src} has more elements than the
+   * destination has capacity, the copy is truncated.
+   */
+  public void feed(String inputName, long[] src, long... dims) {
+    addFeed(inputName, Tensor.create(dims, LongBuffer.wrap(src)));
+  }
+
   /**
    * Given a source array with shape {@link dims} and content {@link src}, copy the contents into
    * the input Tensor with name {@link inputName}. The source array {@link src} must have at least
@@ -270,6 +281,17 @@ public class TensorFlowInferenceInterface {
     addFeed(inputName, Tensor.create(dims, src));
   }
 
+  /**
+   * Given a source buffer with shape {@link dims} and content {@link src}, both stored as
+   * <b>direct</b> and <b>native ordered</b> java.nio buffers, copy the contents into the input
+   * Tensor with name {@link inputName}. The source buffer {@link src} must have at least as many
+   * elements as that of the destination Tensor. If {@link src} has more elements than the
+   * destination has capacity, the copy is truncated.
+   */
+  public void feed(String inputName, LongBuffer src, long... dims) {
+    addFeed(inputName, Tensor.create(dims, src));
+  }
+
   /**
    * Given a source buffer with shape {@link dims} and content {@link src}, both stored as
    * <b>direct</b> and <b>native ordered</b> java.nio buffers, copy the contents into the input
@@ -310,6 +332,15 @@ public class TensorFlowInferenceInterface {
     fetch(outputName, IntBuffer.wrap(dst));
   }
 
+  /**
+   * Read from a Tensor named {@link outputName} and copy the contents into a Java array. {@link
+   * dst} must have length greater than or equal to that of the source Tensor. This operation will
+   * not affect dst's content past the source Tensor's size.
+   */
+  public void fetch(String outputName, long[] dst) {
+    fetch(outputName, LongBuffer.wrap(dst));
+  }
+
   /**
    * Read from a Tensor named {@link outputName} and copy the contents into a Java array. {@link
    * dst} must have length greater than or equal to that of the source Tensor. This operation will
@@ -348,6 +379,16 @@ public class TensorFlowInferenceInterface {
     getTensor(outputName).writeTo(dst);
   }
 
+  /**
+   * Read from a Tensor named {@link outputName} and copy the contents into the <b>direct</b> and
+   * <b>native ordered</b> java.nio buffer {@link dst}. {@link dst} must have capacity greater than
+   * or equal to that of the source Tensor. This operation will not affect dst's content past the
+   * source Tensor's size.
+   */
+  public void fetch(String outputName, LongBuffer dst) {
+    getTensor(outputName).writeTo(dst);
+  }
+
   /**
    * Read from a Tensor named {@link outputName} and copy the contents into the <b>direct</b> and
    * <b>native ordered</b> java.nio buffer {@link dst}. {@link dst} must have capacity greater than
-- 
GitLab


From 307a2fc2adc2c6b717bb9713f81289f6bbb91b07 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Fri, 21 Jul 2017 13:13:27 -0700
Subject: [PATCH 1210/1375] [tf.contrib.data] Fix unbatch scalar vs. tuple
 handling.

Fixes #11666.

PiperOrigin-RevId: 162776187
---
 .../kernel_tests/batch_dataset_op_test.py     | 58 +++++++++++++++++--
 .../contrib/data/python/ops/dataset_ops.py    |  8 ++-
 2 files changed, 58 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
index 1f87f14187..496cdab4ba 100644
--- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
@@ -271,18 +271,64 @@ class BatchDatasetTest(test.TestCase):
                                    "larger than the row shape"):
         sess.run(get_next)
 
-  def testUnbatchDataset(self):
-    data = [math_ops.range(10) for _ in range(3)]
+  def testUnbatchScalarDataset(self):
+    data = tuple([math_ops.range(10) for _ in range(3)])
     data = dataset_ops.Dataset.from_tensor_slices(data)
+    expected_types = (dtypes.int32,) * 3
     data = data.batch(2)
+    self.assertEqual(expected_types, data.output_types)
     data = data.unbatch()
+    self.assertEqual(expected_types, data.output_types)
 
-    iter = data.make_one_shot_iterator()
-    op = iter.get_next()
+    iterator = data.make_one_shot_iterator()
+    op = iterator.get_next()
 
     with self.test_session() as sess:
-      for i in range(3):
-        self.assertAllClose([range(10)], sess.run(op))
+      for i in range(10):
+        self.assertEqual((i,) * 3, sess.run(op))
+
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(op)
+
+  def testUnbatchSingleElementTupleDataset(self):
+    data = tuple([(math_ops.range(10),) for _ in range(3)])
+    data = dataset_ops.Dataset.from_tensor_slices(data)
+    expected_types = ((dtypes.int32,),) * 3
+    data = data.batch(2)
+    self.assertEqual(expected_types, data.output_types)
+    data = data.unbatch()
+    self.assertEqual(expected_types, data.output_types)
+
+    iterator = data.make_one_shot_iterator()
+    op = iterator.get_next()
+
+    with self.test_session() as sess:
+      for i in range(10):
+        self.assertEqual(((i,),) * 3, sess.run(op))
+
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(op)
+
+  def testUnbatchMultiElementTupleDataset(self):
+    data = tuple([(math_ops.range(10 * i, 10 * i + 10),
+                   array_ops.fill([10], "hi"))
+                  for i in range(3)])
+    data = dataset_ops.Dataset.from_tensor_slices(data)
+    expected_types = ((dtypes.int32, dtypes.string),) * 3
+    data = data.batch(2)
+    self.assertAllEqual(expected_types, data.output_types)
+    data = data.unbatch()
+    self.assertAllEqual(expected_types, data.output_types)
+
+    iterator = data.make_one_shot_iterator()
+    op = iterator.get_next()
+
+    with self.test_session() as sess:
+      for i in range(10):
+        self.assertEqual(((i, b"hi"),
+                          (10 + i, b"hi"),
+                          (20 + i, b"hi")),
+                         sess.run(op))
 
       with self.assertRaises(errors.OutOfRangeError):
         sess.run(op)
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
index d5a2f32fea..3dfe6463dc 100644
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -1058,8 +1058,12 @@ class Dataset(object):
     Returns:
       A `Dataset`.
     """
-    return self.flat_map(
-        map_func=lambda *args: Dataset.from_tensor_slices(args))
+    def unbatch_map(arg, *rest):
+      if rest:
+        return Dataset.from_tensor_slices((arg,) + rest)
+      else:
+        return Dataset.from_tensor_slices(arg)
+    return self.flat_map(map_func=unbatch_map)
 
   def filter(self, predicate):
     """Filters this dataset according to `predicate`.
-- 
GitLab


From b65d9ec2b78c7c23e368ed4eec7b4deb89dcd712 Mon Sep 17 00:00:00 2001
From: Batchu Venkat Vishal <vishalvenkat71@gmail.com>
Date: Sat, 22 Jul 2017 01:59:29 +0530
Subject: [PATCH 1211/1375] Fix value error generated on is_scalar check
 (#10391)

* Fix value error generated on is_scalar check

`is_scalar = shape is not None and not shape` raises a value error when shape is a scalar, "ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"

* Update variable_scope.py

* Fix
---
 tensorflow/python/ops/variable_scope.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index c2193a24d0..7077f679ad 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -300,7 +300,8 @@ class _VariableStore(object):
                      initializer=None, regularizer=None, reuse=None,
                      trainable=True, collections=None, caching_device=None,
                      partitioner=None, validate_shape=True, use_resource=None):
-      is_scalar = shape is not None and not shape
+      is_scalar = (shape is not None and isinstance(shape, collections_lib.Sequence)
+                   and len(shape) == 0)
       # Partitioned variable case
       if partitioner is not None and not is_scalar:
         if not callable(partitioner):
-- 
GitLab


From 51cbb58ca5147218b3995dc124bd92927d93e913 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Fri, 21 Jul 2017 13:34:44 -0700
Subject: [PATCH 1212/1375] Some improvements to HLO grapvhiz dumping.

 - If all params are filtered out, don't show an empty "parameters"
   node.
 - If the node name contains the opcode name, omit the opcode.  For
   example, show "%add.4" instead of "add %add.4".  This greatly shrinks
   the width of our graphs.
 - (For nodes without a lot of operands), always show either none or all
   of the operands.
 - Show nodes with some or all operands elided as "grayed out" to make
   it clear that these are the "edges" of our neighborhood.
 - Don't show an out-of-line computation for e.g. "add reduce".
   Instead, simply show it as "%reduce.42<br>Subcomputation: add".
 - Split up parameter nodes.  Previously all params were fused into one
   big node, but now each parameter is its own node.  This is useful
   because otherwise graphviz has to route long edges from the top of
   the graph to nodes that use params at the bottom of the graph.
 - Inline constants into their users, instead of displaying them as
   separate nodes.  This is particularly helpful when a constant
   (:cough: zero) is used many times, because otherwise we have to draw
   many long edges all over the graph.

PiperOrigin-RevId: 162778619
---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 515 ++++++++++++------
 1 file changed, 351 insertions(+), 164 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 2455925b96..097a762015 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -16,8 +16,15 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 
 #include <unistd.h>
+#include <algorithm>
+#include <atomic>
 #include <deque>
+#include <map>
+#include <memory>
 #include <string>
+#include <tuple>
+#include <unordered_map>
+#include <vector>
 
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal_util.h"
@@ -27,6 +34,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/window_util.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/gtl/optional.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -37,13 +45,15 @@ limitations under the License.
 #include "tensorflow/core/platform/regexp.h"
 
 using ::tensorflow::Env;
-using ::tensorflow::WriteStringToFile;
+using ::tensorflow::gtl::nullopt;
+using ::tensorflow::gtl::optional;
 using ::tensorflow::io::JoinPath;
 using ::tensorflow::strings::Appendf;
 using ::tensorflow::strings::Printf;
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 using ::tensorflow::str_util::Join;
+using ::tensorflow::WriteStringToFile;
 
 namespace xla {
 namespace hlo_graph_dumper {
@@ -63,11 +73,26 @@ enum ColorScheme {
   kRed,
   kWhite,
   kYellow,
+
+  // Causes the node's border to be a dashed line, and its content to be gray
+  // text on a white background, suggesting that this is an "unimportant" node.
+  kDashedBorder,
 };
 
-// Used to indicate how we should treat a given HLOInstruction in the graph --
-// should we treat it like normal, hide it, or highlight it?
-enum NodeFilterResult { kNormalNode, kHideNode, kHighlightNode };
+// Used to indicate how we should treat a given HLOInstruction in the graph.
+// should we treat it like normal, hide it, and so on?
+enum NodeFilterResult {
+  kNormalNode,
+  kHideNode,
+  // Make the node easy to find in the final graph.
+  kHighlightNode,
+  // "Gray out" the node to indicate that some of its operands have been
+  // omitted.
+  kSomeOperandsOmitted,
+  // Style the node the same as kSomeOperandsOmitted, but also don't connect it
+  // to its operands, even if they're present in the graph.
+  kOmitNodeOperands,
+};
 
 // NodeFilter is essentially a map from HloInstruction*s to NodeFilterResult.
 // It lets callers tell the graph-drawing routines which nodes they want to be
@@ -86,51 +111,59 @@ class NodeFilter {
   bool Highlight(const HloInstruction* instr) const {
     return filter_(instr) == kHighlightNode;
   }
+  bool OmitOperands(const HloInstruction* instr) const {
+    return filter_(instr) == kOmitNodeOperands;
+  }
+  bool SomeOrAllOperandsOmitted(const HloInstruction* instr) const {
+    auto result = filter_(instr);
+    return result == kOmitNodeOperands || result == kSomeOperandsOmitted;
+  }
 
  private:
   std::function<NodeFilterResult(const HloInstruction* instr)> filter_;
 };
 
 // Given a ColorScheme, returns an attribute string for a node of that color.
-// Sets the node's fill, stroke, and text colors.
+// Sets the node's style and fill/stroke/text colors.
 //
 // Colors are from https://material.io/color.
 string NodeColorAttributes(ColorScheme color) {
   using std::make_tuple;
 
-  const char *fill_color, *stroke_color, *font_color;
-  std::tie(fill_color, stroke_color, font_color) =
-      [color]() -> std::tuple<const char*, const char*, const char*> {
+  const char *style, *fill_color, *stroke_color, *font_color;
+  std::tie(style, fill_color, stroke_color, font_color) = [color] {
     switch (color) {
       case kBlue:
-        return make_tuple("#bbdefb", "#8aacc8", "black");
+        return make_tuple("filled", "#bbdefb", "#8aacc8", "black");
       case kBrown:
-        return make_tuple("#bcaaa4", "#8c7b75", "black");
+        return make_tuple("filled", "#bcaaa4", "#8c7b75", "black");
       case kDarkBlue:
-        return make_tuple("#1565c0", "#003c8f", "white");
+        return make_tuple("filled", "#1565c0", "#003c8f", "white");
       case kDarkGreen:
-        return make_tuple("#2e7d32", "#005005", "white");
+        return make_tuple("filled", "#2e7d32", "#005005", "white");
       case kDarkRed:
-        return make_tuple("#b71c1c", "#7f0000", "white");
+        return make_tuple("filled", "#b71c1c", "#7f0000", "white");
       case kGray:
-        return make_tuple("#cfd8dc", "#9ea7aa", "black");
+        return make_tuple("filled", "#cfd8dc", "#9ea7aa", "black");
       case kGreen:
-        return make_tuple("#c8e6c9", "#97b498", "black");
+        return make_tuple("filled", "#c8e6c9", "#97b498", "black");
       case kOrange:
-        return make_tuple("#ffe0b2", "#cbae82", "black");
+        return make_tuple("filled", "#ffe0b2", "#cbae82", "black");
       case kPurple:
-        return make_tuple("#e1bee7", "#af8eb5", "black");
+        return make_tuple("filled", "#e1bee7", "#af8eb5", "black");
       case kRed:
-        return make_tuple("#ffcdd2", "#cb9ca1", "black");
+        return make_tuple("filled", "#ffcdd2", "#cb9ca1", "black");
       case kWhite:
-        return make_tuple("white", "black", "black");
+        return make_tuple("filled", "white", "black", "black");
       case kYellow:
-        return make_tuple("#fff9c4", "#cbc693", "black");
+        return make_tuple("filled", "#fff9c4", "#cbc693", "black");
+      case kDashedBorder:
+        return make_tuple("dashed", "white", "#757575", "#757575");
     }
   }();
 
   return Printf(
-      "style=filled, fontcolor=\"%s\", color=\"%s\", fillcolor=\"%s\"",
+      R"(style=%s, fontcolor="%s", color="%s", fillcolor="%s")", style,
       font_color, stroke_color, fill_color);
 }
 
@@ -152,6 +185,70 @@ string ComputationId(const HloComputation* computation) {
   return Printf("%lld", reinterpret_cast<uint64>(computation));
 }
 
+// Tries to generates a human-readable one-word description of the given
+// computation.
+//
+// Currently we support:
+//
+//   "return param0 + param1;"      --> "add"
+//   "return param0 * param1;"      --> "multiply"
+//   "return min(param0, param1);"  --> "min"
+//   "return max(param0, param1);"  --> "max"
+//
+// where param0 and param1 are effective scalars.  Since all of the ops above
+// are commutative, we also support them with param0 and param1 swapped.
+//
+// This is useful primarily for reduce and map nodes.  These take a
+// subcomputation which is almost always one of the four above, and pattern
+// matching it to a short string lets us tell the user what the subcomputation
+// is without drawing it as a graph.
+optional<string> MatchTrivialComputation(const HloComputation* computation) {
+  if (computation->instruction_count() != 3) {
+    return nullopt;
+  }
+
+  HloInstruction* root = computation->root_instruction();
+  if (root->operand_count() != 2) {
+    return nullopt;
+  }
+
+  // Check that both of the operands to the root are parameters.
+  const HloInstruction* operand0 = root->operand(0);
+  const HloInstruction* operand1 = root->operand(1);
+  if (operand0->opcode() != HloOpcode::kParameter ||
+      operand1->opcode() != HloOpcode::kParameter) {
+    return nullopt;
+  }
+  // Check that the two operands of root are param0 and param1.  All of the
+  // opcodes we recognize are commutative, so we're OK with either order.
+  auto n0 = operand0->parameter_number();
+  auto n1 = operand1->parameter_number();
+  if (!(n0 == 0 && n1 == 1) && !(n1 == 0 && n0 == 1)) {
+    return nullopt;
+  }
+
+  // Check that the root and params are all effective scalars.
+  if (!ShapeUtil::IsEffectiveScalar(root->shape()) ||
+      !ShapeUtil::IsEffectiveScalar(operand0->shape()) ||
+      !ShapeUtil::IsEffectiveScalar(operand1->shape())) {
+    return nullopt;
+  }
+
+  // If we recognize the root's opcode, we've successfully pattern-matched!
+  switch (root->opcode()) {
+    case HloOpcode::kAdd:
+      return "add";
+    case HloOpcode::kMultiply:
+      return "multiply";
+    case HloOpcode::kMinimum:
+      return "min";
+    case HloOpcode::kMaximum:
+      return "max";
+    default:
+      return nullopt;
+  }
+}
+
 // Returns the dot graph edges and nodes for the given instruction sequence.
 // Edges which extend between computations are added to the vector
 // intercomputation_edges. This is necessary because graphviz does not render
@@ -165,58 +262,49 @@ string InstructionSequenceGraph(
     const NodeFilter& filter) {
   string graph_body;
 
-  // Create a single "record" node for the parameters. This node is a
-  // partitioned rectangle with one partition per parameter node. The keeps
-  // all the parameter instructions together.
-  std::vector<HloInstruction*> param_instructions;
   for (auto& instruction : instructions) {
-    if (instruction->opcode() == HloOpcode::kParameter) {
-      size_t param_number = instruction->parameter_number();
-
-      if (param_instructions.size() < param_number + 1) {
-        param_instructions.resize(param_number + 1, nullptr);
-      }
-      param_instructions[param_number] = instruction.get();
-    }
-  }
-  string param_node_name;
-  if (!param_instructions.empty()) {
-    std::vector<string> param_ports;
-    param_node_name =
-        StrCat("parameters_", InstructionId(param_instructions[0]));
-    for (auto& param : param_instructions) {
-      if (!filter.Show(param)) {
-        continue;
-      }
-      string label = StrCat(param->parameter_name(), "\\n",
-                            ShapeUtil::HumanString(param->shape()));
-      if (show_addresses) {
-        Appendf(&label, "\\n[%p]", param);
-      }
-      if (show_layouts) {
-        StrAppend(&label, "\\nlayout=\\{",
-                  Join(param->shape().layout().minor_to_major(), ","), "\\}");
-      }
-      param_ports.push_back(
-          Printf("<%s> %s", InstructionId(param).c_str(), label.c_str()));
+    if (!filter.Show(instruction.get())) {
+      continue;
     }
-    // (If we wanted the word "parameters" to be bold like the other op names,
-    // we'd have to make this into an HTML-like table.  It is possible but
-    // complicated; see http://www.graphviz.org/doc/info/shapes.html#html.)
-    StrAppend(&graph_body, param_node_name, " [shape=record ",
-              NodeColorAttributes(kOrange), "label=\"{parameters | {",
-              Join(param_ports, "|"), "}}\"];\n");
-  }
 
-  for (auto& instruction : instructions) {
-    if (!filter.Show(instruction.get())) {
+    // We don't display constants as separate nodes; they're merged into their
+    // users.
+    if (instruction->opcode() == HloOpcode::kConstant) {
       continue;
     }
+
     ColorScheme color = kYellow;
     string shape = "box";
-    string name =
-        StrCat("<b>", HtmlLikeStringSanitize(instruction->ExtendedOpcodeStr()),
-               "</b> ", HtmlLikeStringSanitize(instruction->name()));
+
+    // Build the first line or two of the node, containing its name and opcode
+    // (if the opcode isn't redundant with the name).
+    string name;
+    if (instruction->opcode() == HloOpcode::kParameter) {
+      // If we have a parameter, put the param number in the name.
+      name = StrCat("<b>Parameter ", instruction->parameter_number(),
+                    "</b><br/>", HtmlLikeStringSanitize(instruction->name()));
+    } else if (tensorflow::StringPiece(instruction->name())
+                   .starts_with(
+                       StrCat("%", instruction->ExtendedOpcodeStr()))) {
+      // The HLO instruction name contains usually the opcode, e.g. "%add.42" is
+      // an add instruction.  In this case we render just the name.
+      name = StrCat("<b>", HtmlLikeStringSanitize(instruction->name()), "</b>");
+    } else if (instruction->opcode() == HloOpcode::kFusion &&
+               tensorflow::StringPiece(instruction->name())
+                   .starts_with(
+                       StrCat("%", HloOpcodeString(instruction->opcode())))) {
+      // Fusion nodes are usually named e.g. "%fusion.5".  We render these as
+      // e.g. "%fusion.5<br/>input fusion".
+      name = StrCat("<b>", HtmlLikeStringSanitize(instruction->name()),
+                    "</b><br/>",
+                    HtmlLikeStringSanitize(instruction->ToCategory()));
+    } else {
+      // If the name does not contain the opcode, render both.
+      name = StrCat("<b>",
+                    HtmlLikeStringSanitize(instruction->ExtendedOpcodeStr()),
+                    "</b><br/>", HtmlLikeStringSanitize(instruction->name()));
+    }
+
     if (HloOpcode::kConvolution == instruction->opcode()) {
       StrAppend(
           &name, "<br/>",
@@ -305,18 +393,13 @@ string InstructionSequenceGraph(
       case HloOpcode::kUpdate:
         color = kGreen;
         break;
-      case HloOpcode::kConstant:
-        color = kBlue;
-        break;
       case HloOpcode::kConvolution:
       case HloOpcode::kDot:
         color = kDarkBlue;
         break;
       case HloOpcode::kParameter:
-        // A single record node is created for all the parameter nodes with a
-        // port for each parameter instruction. No need to emit anything in this
-        // case.
-        continue;
+        color = kOrange;
+        break;
       case HloOpcode::kBatchNormTraining:
         StrAppend(&name, " feature_index=", instruction->feature_index());
         color = kPurple;
@@ -361,6 +444,8 @@ string InstructionSequenceGraph(
         // will be inserted as modifications to an existing graph.
         color = kRed;
         break;
+      case HloOpcode::kConstant:
+        LOG(FATAL) << "Constants don't get their own nodes in the graph.";
     }
 
     // Create instruction node with appropriate label, shape, and color.
@@ -369,14 +454,6 @@ string InstructionSequenceGraph(
     string label =
         StrCat(name, "<br/>", ShapeUtil::HumanString(instruction->shape()));
 
-    if (instruction->opcode() == HloOpcode::kConstant &&
-        ShapeUtil::IsEffectiveScalar(instruction->shape())) {
-      auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
-          instruction->shape(), /*linear_index=*/0);
-      StrAppend(&label, " = {", instruction->literal().GetAsString(elem_idx),
-                "}");
-    }
-
     if (show_addresses) {
       Appendf(&label, "<br/>[%p]", instruction.get());
     }
@@ -405,75 +482,116 @@ string InstructionSequenceGraph(
       }
     }
 
+    // If this node's operands are omitted, style it accordingly.
+    if (filter.SomeOrAllOperandsOmitted(instruction.get())) {
+      color = kDashedBorder;
+    }
+
     // If this node is highlighted, override its formatting.
     if (filter.Highlight(instruction.get())) {
       shape = "diamond";
       color = kDarkRed;
     }
 
-    Appendf(&graph_body, "%s [label=<%s>, shape=%s, %s];\n",
-            InstructionId(instruction.get()).c_str(), label.c_str(),
-            shape.c_str(), NodeColorAttributes(color).c_str());
-
     // Create edges from the instruction's operands to the instruction.
-    int64 operand_number = 0;
-    for (auto* operand : instruction->operands()) {
-      if (!filter.Show(operand)) {
+    if (!filter.OmitOperands(instruction.get())) {
+      int64 operand_number = 0;
+      for (auto* operand : instruction->operands()) {
+        if (!filter.Show(operand) ||
+            operand->opcode() == HloOpcode::kConstant) {
+          ++operand_number;
+          continue;
+        }
+        Appendf(&graph_body, "%s -> %s", InstructionId(operand).c_str(),
+                InstructionId(instruction.get()).c_str());
+        if (instruction->operand_count() > 1) {
+          Appendf(&graph_body, " [headlabel=\"%lld\",labeldistance=2]",
+                  operand_number);
+        }
+        StrAppend(&graph_body, ";\n");
         ++operand_number;
-        continue;
       }
-      string src;
-      if (operand->opcode() == HloOpcode::kParameter) {
-        // If operand is a parameter, then select the proper partition (port) in
-        // the unified parameter node.
-        src = param_node_name + ":" + InstructionId(operand);
+
+      // Fusion nodes are handled specially because they contain nested
+      // expressions.
+      if (instruction->opcode() == HloOpcode::kFusion) {
+        string cluster_name =
+            StrCat("cluster_", InstructionId(instruction.get()));
+        StrAppend(&graph_body, "subgraph ", cluster_name, " {\n");
+        StrAppend(&graph_body, "label=<fused expression for <b>",
+                  HtmlLikeStringSanitize(instruction->name()),
+                  "</b>>;\nstyle=\"rounded,filled\";\n"
+                  "color=lightgrey;\n");
+        StrAppend(&graph_body,
+                  InstructionSequenceGraph(instruction->fused_instructions(),
+                                           show_addresses, show_layouts,
+                                           intercomputation_edges,
+                                           hlo_execution_profile, NodeFilter()),
+                  "}\n");
+        string fusion_edge = StrCat(
+            InstructionId(instruction->fused_expression_root()), " -> ",
+            InstructionId(instruction.get()),
+            "  [ style = \"dotted\", arrowsize=0.0, ltail=", cluster_name,
+            " ];\n");
+        intercomputation_edges->push_back(fusion_edge);
       } else {
-        src = InstructionId(operand);
-      }
-      Appendf(&graph_body, "%s -> %s", src.c_str(),
-              InstructionId(instruction.get()).c_str());
-      if (instruction->operand_count() > 1) {
-        Appendf(&graph_body, " [headlabel=\"%lld\",labeldistance=2]",
-                operand_number);
+        // If instruction has just one computation and it's trivial (e.g.
+        // "return param0 + param1"), put the trivial computation type (e.g.
+        // "add") into instruction's label.  Otherwise, add a dotted edge
+        // between the instruction and its subcomputations.
+        const auto& subcomputations = instruction->called_computations();
+
+        bool trivial_subcomputation = false;
+        if (subcomputations.size() == 1) {
+          optional<string> computation_type =
+              MatchTrivialComputation(subcomputations.front());
+          if (computation_type) {
+            trivial_subcomputation = true;
+            StrAppend(&label, "<br/>Subcomputation: <b>", *computation_type,
+                      "</b>");
+          }
+        }
+
+        if (!trivial_subcomputation) {
+          for (const HloComputation* computation :
+               instruction->called_computations()) {
+            string cluster_name =
+                StrCat("cluster_", ComputationId(computation));
+            string call_edge = Printf(
+                "%s -> %s [ style=dashed; ltail=%s ];\n",
+                InstructionId(computation->root_instruction()).c_str(),
+                InstructionId(instruction.get()).c_str(), cluster_name.c_str());
+            intercomputation_edges->push_back(call_edge);
+          }
+        }
       }
-      StrAppend(&graph_body, ";\n");
-      ++operand_number;
     }
 
-    // Fusion nodes are handled specially because they contain nested
-    // expressions.
-    if (instruction->opcode() == HloOpcode::kFusion) {
-      string cluster_name =
-          StrCat("cluster_", InstructionId(instruction.get()));
-      StrAppend(&graph_body, "subgraph ", cluster_name, " {\n");
-      StrAppend(&graph_body,
-                "label=<<b>fused expression</b>>;\nstyle=\"rounded,filled\";\n"
-                "color=lightgrey;\n");
-      StrAppend(&graph_body,
-                InstructionSequenceGraph(instruction->fused_instructions(),
-                                         show_addresses, show_layouts,
-                                         intercomputation_edges,
-                                         hlo_execution_profile, NodeFilter()),
-                "}\n");
-      string fusion_edge =
-          StrCat(InstructionId(instruction->fused_expression_root()), " -> ",
-                 InstructionId(instruction.get()),
-                 "  [ style = \"dotted\", arrowsize=0.0, ltail=", cluster_name,
-                 " ];\n");
-      intercomputation_edges->push_back(fusion_edge);
-    } else {
-      // Add a dotted edge between the instruction and any computations that the
-      // instruction calls.
-      for (const HloComputation* computation :
-           instruction->called_computations()) {
-        string cluster_name = StrCat("cluster_", ComputationId(computation));
-        string call_edge = Printf(
-            "%s -> %s [ style=dashed; ltail=%s ];\n",
-            InstructionId(computation->root_instruction()).c_str(),
-            InstructionId(instruction.get()).c_str(), cluster_name.c_str());
-        intercomputation_edges->push_back(call_edge);
+    // Inline constant operands into the node.
+    for (int64 i = 0; i < instruction->operand_count(); ++i) {
+      const HloInstruction* operand = instruction->operand(i);
+      if (operand->opcode() != HloOpcode::kConstant) {
+        continue;
+      }
+
+      StrAppend(&label, "<br/><b>operand ", i, "</b> = ");
+      if (ShapeUtil::IsEffectiveScalar(operand->shape())) {
+        auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
+            operand->shape(), /*linear_index=*/0);
+        StrAppend(&label, ShapeUtil::HumanString(operand->shape()), "{",
+                  operand->literal().GetAsString(elem_idx), "}");
+      } else {
+        if (tensorflow::StringPiece(operand->name()).starts_with("%constant")) {
+          StrAppend(&label, operand->name());
+        } else {
+          StrAppend(&label, "constant ", operand->name());
+        }
       }
     }
+
+    Appendf(&graph_body, "%s [label=<%s>, shape=%s, %s];\n",
+            InstructionId(instruction.get()).c_str(), label.c_str(),
+            shape.c_str(), NodeColorAttributes(color).c_str());
   }
   return graph_body;
 }
@@ -513,16 +631,25 @@ stylesheet="%s"
 )",
       graph_label.c_str(), dot_stylesheet);
 
+  // Dump the subcomputations of each instruction that's shown and doesn't have
+  // its operands omitted.  If an instruction has just one subcomputation and
+  // it's trivial, omit it: We'll display that subcomputation inlined into the
+  // instruction's node when we draw it.
   std::unordered_set<const HloComputation*> computations_to_dump;
   for (const auto& instr : computation.instructions()) {
-    if (!filter.Show(instr.get())) {
+    if (!filter.Show(instr.get()) || filter.OmitOperands(instr.get())) {
       continue;
     }
     if (instr->opcode() == HloOpcode::kFusion) {
       computations_to_dump.insert(instr->fused_instructions_computation());
     }
-    for (const HloComputation* computation : instr->called_computations()) {
-      computations_to_dump.insert(computation);
+
+    const auto& subcomputations = instr->called_computations();
+    if (subcomputations.size() != 1 ||
+        !MatchTrivialComputation(subcomputations.front())) {
+      for (const HloComputation* computation : instr->called_computations()) {
+        computations_to_dump.insert(computation);
+      }
     }
   }
 
@@ -620,31 +747,41 @@ class FileGraphRenderer : public GraphRendererInterface {
   }
 };
 
-// Gets roughly all instructions whose distance from root is <= radius.
-std::unordered_set<const HloInstruction*> GetInstructionsInNeighborhood(
-    const HloInstruction& root, int64 radius) {
-  std::unordered_set<const HloInstruction*> ret;
-
+// Gets a NodeFilter that includes roughly all instructions whose distance from
+// root is <= radius.
+//
+// It's confusing to draw a node and include only some of its operands.  So if
+// some but not all of a node's operands are <= radius units away from the root,
+// we include the other operands (unless there are a lot of them, as often in a
+// tuple node).  These additional operands may have as inputs other nodes
+// already present in the graph, but we don't draw those edges unless *all* of
+// the inputs are present.  (Otherwise we'd have the same problem we were trying
+// to solve in the first place!)
+NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
+  // First, find the neighborhood of nodes with distance from root <= radius.
+  // These nodes are our initial set of "normal" nodes.
+  std::unordered_map<const HloInstruction*, NodeFilterResult> nodes;
   std::deque<std::pair<const HloInstruction*, /*depth*/ int64>> worklist;
-  worklist.push_back({&root, 0});
-
+  worklist.push_back({root, 0});
   while (!worklist.empty()) {
     const HloInstruction* instr;
     int64 depth;
     std::tie(instr, depth) = worklist.front();
     worklist.pop_front();
 
-    ret.insert(instr);
+    nodes[instr] = kNormalNode;
     if (depth == radius) {
       continue;
     }
 
+    // Traverse into instr's operands.
+    //
     // Don't traverse into tuples' operands unless the tuple is the root.
     // Usually a tuple is the bottommost node in the graph, and so its operands
     // are not interesting to the graph at hand.
-    if (instr == &root || instr->opcode() != HloOpcode::kTuple) {
+    if (instr == root || instr->opcode() != HloOpcode::kTuple) {
       for (const HloInstruction* operand : instr->operands()) {
-        if (ret.find(operand) == ret.end()) {
+        if (!nodes.count(operand)) {
           worklist.push_back({operand, depth + 1});
         }
       }
@@ -655,14 +792,77 @@ std::unordered_set<const HloInstruction*> GetInstructionsInNeighborhood(
     // constants' users.
     if (instr->opcode() != HloOpcode::kConstant) {
       for (const HloInstruction* user : instr->users()) {
-        if (ret.find(user) == ret.end()) {
+        if (!nodes.count(user)) {
           worklist.push_back({user, depth + 1});
         }
       }
     }
   }
 
-  return ret;
+  auto is_displayed = [&](const HloInstruction* instr) {
+    return nodes.count(instr) > 0;
+  };
+
+  // If a node has some but not all of its operands omitted, add the operands to
+  // the map with type kOmitNodeOperands.  Unless the node has a lot of
+  // operands, in which case just mark the node as "some operands omitted".
+  std::vector<const HloInstruction*> extra_operands;
+  for (auto& kv : nodes) {
+    const HloInstruction* instr = kv.first;
+    NodeFilterResult& filter_result = kv.second;
+    const auto& operands = instr->operands();
+
+    // Mark nodes with many operands and some omitted as "some operands omitted"
+    // and carry on -- don't add their omitted operands to extra_operands.
+    if (operands.size() > 4) {
+      if (std::any_of(operands.begin(), operands.end(), is_displayed) &&
+          !std::all_of(operands.begin(), operands.end(), is_displayed)) {
+        filter_result = kSomeOperandsOmitted;
+      }
+      continue;
+    }
+
+    if (std::any_of(operands.begin(), operands.end(), is_displayed)) {
+      for (const HloInstruction* operand : operands) {
+        if (!is_displayed(operand)) {
+          extra_operands.push_back(operand);
+        }
+      }
+    }
+  }
+  for (const HloInstruction* instr : extra_operands) {
+    nodes[instr] = kOmitNodeOperands;
+  }
+
+  // Some of the nodes in extra_operands may now have all of their inputs
+  // present in nodes.  We can promote these to normal nodes.
+  for (const HloInstruction* instr : extra_operands) {
+    const auto& operands = instr->operands();
+    if (std::all_of(operands.begin(), operands.end(), is_displayed)) {
+      nodes[instr] = kNormalNode;
+    }
+  }
+
+  // If none of a node's operands appear in nodes, mark it as type
+  // kOmitNodeOperands so it gets styled appropriately.
+  for (auto& kv : nodes) {
+    const auto& operands = kv.first->operands();
+    if (!operands.empty() &&
+        std::none_of(operands.begin(), operands.end(), is_displayed)) {
+      kv.second = kOmitNodeOperands;
+    }
+  }
+
+  // Highlight the root node.
+  nodes[root] = kHighlightNode;
+
+  return NodeFilter([=](const HloInstruction* instr) {
+    auto it = nodes.find(instr);
+    if (it != nodes.end()) {
+      return it->second;
+    }
+    return kHideNode;
+  });
 }
 
 XLA_REGISTER_GRAPH_RENDERER(FileGraphRenderer, 0);
@@ -699,22 +899,9 @@ string DumpGraph(const HloComputation& computation, const string& label,
 
 string DumpNeighborhoodAround(const HloInstruction& node, int radius) {
   auto debug_options = node.GetModule()->config().debug_options();
-
-  std::unordered_set<const HloInstruction*> neighborhood =
-      GetInstructionsInNeighborhood(node, radius);
-
-  NodeFilter filter([&](const HloInstruction* instr) {
-    if (instr == &node) {
-      return kHighlightNode;
-    }
-    if (neighborhood.find(instr) != neighborhood.end()) {
-      return kNormalNode;
-    }
-    return kHideNode;
-  });
-
   string label =
       StrCat("Neighborhood of ", radius, " nodes around ", node.name());
+  NodeFilter filter = MakeNodeFilter(&node, radius);
   string graph = ComputationToDotGraph(
       *node.parent(), label,
       /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
-- 
GitLab


From 74057361032bc4d9a9fbfced1f433b06c06c09ec Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 14:07:14 -0700
Subject: [PATCH 1213/1375] Add option to use compute_memory_overlap; if true,
 use max of memory_cost and compute_cost, instead of sum for op level cost in
 analytical cost estimator.

PiperOrigin-RevId: 162782658
---
 .../core/grappler/costs/op_level_cost_estimator.cc |  9 ++++++++-
 .../core/grappler/costs/op_level_cost_estimator.h  |  3 +++
 .../grappler/costs/op_level_cost_estimator_test.cc | 14 ++++++++++++++
 3 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index 5148a4b99e..f13b426b3c 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -268,6 +268,9 @@ OpLevelCostEstimator::OpLevelCostEstimator() {
                           Eigen::internal::scalar_quotient_op<float>>::Cost},
       {"TruncateMod", Eigen::internal::functor_traits<
                           Eigen::internal::scalar_mod_op<float>>::Cost}};
+
+  // By default, use sum of memory_time and compute_time for execution_time.
+  compute_memory_overlap_ = false;
 }
 
 Costs OpLevelCostEstimator::PredictCosts(const OpInfo& op_features) const {
@@ -395,7 +398,11 @@ Costs OpLevelCostEstimator::PredictOpCountBasedCost(
   Costs costs;
   costs.compute_time = compute_cost;
   costs.memory_time = memory_cost;
-  costs.execution_time = compute_cost + memory_cost;
+  if (compute_memory_overlap_) {
+    costs.execution_time = std::max(compute_cost, memory_cost);
+  } else {
+    costs.execution_time = compute_cost + memory_cost;
+  }
   costs.inaccurate = found_unknown_shapes;
   return costs;
 }
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.h b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
index 59ced70ba6..36ef6a5c61 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.h
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
@@ -150,6 +150,9 @@ class OpLevelCostEstimator {
   std::map<string, int> elementwise_ops_;
   typedef std::function<Costs(const OpInfo& op_feature)> CostImpl;
   std::map<string, CostImpl> device_cost_impl_;
+  // If true, assume compute and memory overlap; hence, the op cost is max of
+  // compute_time and memory_time, insteaf of sum of those two.
+  bool compute_memory_overlap_;
 
  private:
   friend class OpLevelCostEstimatorTest;
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
index 1f0e02c160..0cbfb10017 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
@@ -157,6 +157,10 @@ class OpLevelCostEstimatorTest : public ::testing::Test {
                                                  found_unknown_shapes);
   }
 
+  void SetComputeMemoryOverlap(bool value) {
+    estimator_.compute_memory_overlap_ = value;
+  }
+
   OpLevelCostEstimator estimator_;
 };
 
@@ -168,6 +172,16 @@ TEST_F(OpLevelCostEstimatorTest, DummyExecutionTime) {
   EXPECT_TRUE(cost.inaccurate);
 }
 
+TEST_F(OpLevelCostEstimatorTest, ExecutionTimeSumOrMax) {
+  SetComputeMemoryOverlap(true);
+  auto cost = PredictCosts(DescribeOp("Dummy", 1000, 1));
+  EXPECT_EQ(Costs::Duration(2000), cost.memory_time);
+  EXPECT_EQ(Costs::Duration(200), cost.compute_time);
+  EXPECT_EQ(Costs::Duration(2000), cost.execution_time);  // max(2000, 200)
+  EXPECT_TRUE(cost.inaccurate);
+  SetComputeMemoryOverlap(false);  // Set it back to default.
+}
+
 TEST_F(OpLevelCostEstimatorTest, MulExecutionTime) {
   auto cost = PredictCosts(DescribeOp("Mul", 1000, 1));
   EXPECT_EQ(Costs::Duration(2000), cost.memory_time);
-- 
GitLab


From cbfd50ff0f01e1825922230a8bc6e5766da98dd7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 14:07:14 -0700
Subject: [PATCH 1214/1375] Removes tolerate_dup_recv from LocaRendezvous.

PiperOrigin-RevId: 162782660
---
 tensorflow/contrib/mpi/mpi_rendezvous_mgr.h   |  2 +-
 .../contrib/verbs/rdma_rendezvous_mgr.cc      |  2 +-
 .../base_rendezvous_mgr.cc                    |  5 ++--
 .../distributed_runtime/base_rendezvous_mgr.h |  3 +--
 .../rpc/rpc_rendezvous_mgr.cc                 |  2 +-
 tensorflow/core/framework/rendezvous.cc       | 25 ++++---------------
 tensorflow/core/framework/rendezvous.h        |  7 +-----
 7 files changed, 12 insertions(+), 34 deletions(-)

diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
index 24e784df3e..b15748d63c 100644
--- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
+++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h
@@ -114,7 +114,7 @@ class MPIRemoteRendezvous : public BaseRemoteRendezvous {
  public:
   MPIRemoteRendezvous(const WorkerEnv* env, int64 step_id, const MPIUtils* util,
                       BaseRendezvousMgr* mgr_)
-      : BaseRemoteRendezvous(env, step_id, false),
+      : BaseRemoteRendezvous(env, step_id),
         mpiutils_(util),
         rendezvous_mgr_(mgr_) {}
 
diff --git a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
index 9ea696589a..3ba6510711 100644
--- a/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
+++ b/tensorflow/contrib/verbs/rdma_rendezvous_mgr.cc
@@ -31,7 +31,7 @@ namespace tensorflow {
 class RdmaRemoteRendezvous : public BaseRemoteRendezvous {
  public:
   RdmaRemoteRendezvous(const WorkerEnv* env, int64 step_id, RdmaMgr* rdma_mgr)
-      : BaseRemoteRendezvous(env, step_id, true), rdma_mgr_(rdma_mgr) {}
+      : BaseRemoteRendezvous(env, step_id), rdma_mgr_(rdma_mgr) {}
 
  protected:
   void RecvFromRemoteAsync(const Rendezvous::ParsedKey& parsed,
diff --git a/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc b/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc
index b0979d4c8d..cb2fde7dba 100644
--- a/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc
+++ b/tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc
@@ -127,11 +127,10 @@ void BaseRendezvousMgr::CleanupAll() {
   }
 }
 
-BaseRemoteRendezvous::BaseRemoteRendezvous(const WorkerEnv* env, int64 step_id,
-                                           bool tolerate_dup_recv)
+BaseRemoteRendezvous::BaseRemoteRendezvous(const WorkerEnv* env, int64 step_id)
     : env_(env),
       step_id_(step_id),
-      local_(NewLocalRendezvous(tolerate_dup_recv)),
+      local_(NewLocalRendezvous()),
       session_(nullptr) {}
 
 BaseRemoteRendezvous::~BaseRemoteRendezvous() {
diff --git a/tensorflow/core/distributed_runtime/base_rendezvous_mgr.h b/tensorflow/core/distributed_runtime/base_rendezvous_mgr.h
index b252f45fe9..c5a56f324a 100644
--- a/tensorflow/core/distributed_runtime/base_rendezvous_mgr.h
+++ b/tensorflow/core/distributed_runtime/base_rendezvous_mgr.h
@@ -118,8 +118,7 @@ class BaseRendezvousMgr : public RendezvousMgrInterface {
 // functionality to coordinate with remote workers.
 class BaseRemoteRendezvous : public RemoteRendezvous {
  public:
-  BaseRemoteRendezvous(const WorkerEnv* env, int64 step_id,
-                       bool tolerate_dup_recv);
+  BaseRemoteRendezvous(const WorkerEnv* env, int64 step_id);
 
   // Upgrades the BaseRemoteRendezvous to full initialization.
   Status Initialize(WorkerSession* session) override;
diff --git a/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.cc b/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.cc
index 8265100061..72dfe5c062 100644
--- a/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.cc
+++ b/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.cc
@@ -39,7 +39,7 @@ namespace {
 class RpcRemoteRendezvous : public BaseRemoteRendezvous {
  public:
   RpcRemoteRendezvous(const WorkerEnv* env, int64 step_id)
-      : BaseRemoteRendezvous(env, step_id, false) {}
+      : BaseRemoteRendezvous(env, step_id) {}
 
  protected:
   void RecvFromRemoteAsync(const Rendezvous::ParsedKey& parsed,
diff --git a/tensorflow/core/framework/rendezvous.cc b/tensorflow/core/framework/rendezvous.cc
index 6e578cdbab..8edbacbf50 100644
--- a/tensorflow/core/framework/rendezvous.cc
+++ b/tensorflow/core/framework/rendezvous.cc
@@ -147,8 +147,7 @@ Status Rendezvous::Recv(const ParsedKey& key, const Args& args, Tensor* val,
 
 class LocalRendezvousImpl : public Rendezvous {
  public:
-  explicit LocalRendezvousImpl(bool tolerate_dup_recv)
-      : tolerate_dup_recv_(tolerate_dup_recv) {}
+  explicit LocalRendezvousImpl() {}
 
   Status Send(const ParsedKey& key, const Args& send_args, const Tensor& val,
               const bool is_dead) override {
@@ -202,15 +201,6 @@ class LocalRendezvousImpl : public Rendezvous {
         recv_args.device_context = item->recv_dev_context;
         recv_args.alloc_attrs = item->recv_alloc_attrs;
         item->recv_dev_context = nullptr;
-        if (tolerate_dup_recv_) {
-          item->value = val;
-          item->is_dead = is_dead;
-          if (send_args.device_context) {
-            send_args.device_context->Ref();
-            item->send_dev_context = send_args.device_context;
-          }
-          item->send_alloc_attrs = send_args.alloc_attrs;
-        }
       }
     }  // mutex
     // Notify the waiter by invoking its done closure, outside scope
@@ -235,18 +225,16 @@ class LocalRendezvousImpl : public Rendezvous {
     Table::iterator iter = table_.find(key_hash);
     if (iter != table_.end()) {
       Item* item = iter->second;
-      if (item->has_been_recvd && !tolerate_dup_recv_) {
+      if (item->has_been_recvd) {
         mu_.unlock();
         done(errors::Aborted("Duplicated recv: ", key.FullKey()), Args(),
              recv_args, Tensor(), false);
-      } else if (item->waiter == nullptr || tolerate_dup_recv_) {
+      } else if (item->waiter == nullptr) {
         // A message has already arrived and is stored in the table
         // under this key.  Consumes the message and invokes the done
         // closure.
         Tensor v = item->value;
-        if (!tolerate_dup_recv_) {
-          item->value = Tensor();
-        }
+        item->value = Tensor();
         item->has_been_recvd = true;
         // Before dropping the table lock, capture the item values.
         // DeviceContext is only non-null for non-CPU devices.
@@ -306,7 +294,6 @@ class LocalRendezvousImpl : public Rendezvous {
 
  private:
   typedef LocalRendezvousImpl ME;
-  const bool tolerate_dup_recv_;
 
   struct Item {
     DoneCallback waiter = nullptr;
@@ -348,8 +335,6 @@ class LocalRendezvousImpl : public Rendezvous {
   TF_DISALLOW_COPY_AND_ASSIGN(LocalRendezvousImpl);
 };
 
-Rendezvous* NewLocalRendezvous(bool tolerate_dup_recv) {
-  return new LocalRendezvousImpl(tolerate_dup_recv);
-}
+Rendezvous* NewLocalRendezvous() { return new LocalRendezvousImpl(); }
 
 }  // end namespace tensorflow
diff --git a/tensorflow/core/framework/rendezvous.h b/tensorflow/core/framework/rendezvous.h
index c9bfe4f017..6343c208a1 100644
--- a/tensorflow/core/framework/rendezvous.h
+++ b/tensorflow/core/framework/rendezvous.h
@@ -121,12 +121,7 @@ class Rendezvous : public core::RefCounted {
 // Returns a Rendezvous instance that is limited to use only by
 // producers and consumers in the local process.  The caller assumes
 // ownership of one Ref() on the returned object.
-//
-// If "tolerate_dup_recv" is true, then the Rendezvous will retain
-// already Recv'd values and make them available to duplicate Recv
-// calls.  This may be useful if the RPC layer is not reliable, but
-// comes at the cost of higher memory consumption.
-Rendezvous* NewLocalRendezvous(bool tolerate_dup_recv = false);
+Rendezvous* NewLocalRendezvous();
 
 }  // end namespace tensorflow
 
-- 
GitLab


From 56c4856f61dd9b42181803722b40ffe80c1297a8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 14:08:56 -0700
Subject: [PATCH 1215/1375] Fix _Recv op caching for multi-output port ops in
 VirtualScheduler.

PiperOrigin-RevId: 162782896
---
 .../core/grappler/costs/virtual_scheduler.cc  |  48 +++++--
 .../core/grappler/costs/virtual_scheduler.h   |   3 -
 .../grappler/costs/virtual_scheduler_test.cc  | 132 ++++++++++++++++++
 3 files changed, 172 insertions(+), 11 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 15ebef188f..8b51bb9096 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -56,6 +56,28 @@ Costs CombineCosts(const Costs& left, const Costs& right) {
           << " max_per_op_streaming=" << result.max_per_op_streaming;
   return result;
 }
+
+// Key to the cached _Recv ops map, and its hash and predicate structures.
+struct RecvNodeDescriptor {
+  const NodeDef* node;
+  const int port_num;
+  const string& device;
+};
+
+struct RecvNodeDescritorHash {
+  std::size_t operator()(const RecvNodeDescriptor& recv_node) const {
+    return std::hash<const NodeDef*>()(recv_node.node) ^
+           std::hash<int>()(recv_node.port_num) ^
+           std::hash<string>()(recv_node.device);
+  }
+};
+
+struct RecvNodeDescriptorEqual {
+  bool operator()(const RecvNodeDescriptor& a,
+                  const RecvNodeDescriptor& b) const {
+    return a.node == b.node && a.port_num == b.port_num && a.device == b.device;
+  }
+};
 }  // namespace
 
 VirtualScheduler::VirtualScheduler(const GrapplerItem* grappler_item,
@@ -109,6 +131,11 @@ Status VirtualScheduler::Init() {
     name_to_node[node->name()] = node;
   }
 
+  // To reuse _Recv ops.
+  std::unordered_map<RecvNodeDescriptor, const NodeDef*, RecvNodeDescritorHash,
+                     RecvNodeDescriptorEqual>
+      cached_recv_nodes;
+
   // Build node_map; for each node, create its NodeState and connect its inputs
   // and outputs.
   for (const auto* curr_node : nodes) {
@@ -131,12 +158,14 @@ Status VirtualScheduler::Init() {
         auto& input_node_state = GetNodeStateOrCreateIt(input_node);
         input_node_state.outputs[input_node_port_num].push_back(curr_node);
       } else {
-        if (cached_recv_nodes_.count(input_node) > 0 &&
-            cached_recv_nodes_[input_node].count(curr_node_device) > 0) {
+        RecvNodeDescriptor recv_node = {.node = input_node,
+                                        .port_num = input_node_port_num,
+                                        .device = curr_node_device};
+        auto it = cached_recv_nodes.find(recv_node);
+        if (it != cached_recv_nodes.end()) {
           // Different device, but found an already-cached copy (a _Recv op);
           // connect the _Recv to curr_node.
-          const auto* recv_op =
-              cached_recv_nodes_[input_node][curr_node_device];
+          const NodeDef* recv_op = it->second;
           // recv_op's output port is hard-coded to zero.
           curr_node_state.inputs.push_back(std::make_pair(recv_op, 0));
           auto& input_node_state = node_map_.at(recv_op);
@@ -156,7 +185,7 @@ Status VirtualScheduler::Init() {
           input_node_state.outputs[input_node_port_num].push_back(send);
 
           // Cache the _Recv op for future use.
-          cached_recv_nodes_[input_node][curr_node_device] = recv;
+          cached_recv_nodes[recv_node] = recv;
         }
       }
     }
@@ -269,11 +298,13 @@ std::pair<const NodeDef*, const NodeDef*> VirtualScheduler::CreateSendRecv(
   // input names, attrs, etc.
 
   auto input_node_port_num = NodePosition(input_name);
+  const string port_num_string =
+      input_node_port_num >= 0 ? std::to_string(input_node_port_num) : "minus1";
 
   // _Send op.
   auto* send = new NodeDef();
-  send->set_name("Send " + from->name() + " from " + DeviceName(from) + " to " +
-                 DeviceName(to));
+  send->set_name("Send " + from->name() + ":" + port_num_string + " from " +
+                 DeviceName(from) + " to " + DeviceName(to));
   send->set_op("_Send");
   send->add_input(from->name());
   send->set_device(ChannelDeviceName(from, to));
@@ -284,7 +315,8 @@ std::pair<const NodeDef*, const NodeDef*> VirtualScheduler::CreateSendRecv(
 
   // _Recv op.
   auto* recv = new NodeDef();
-  recv->set_name("Recv " + from->name() + " on " + DeviceName(to));
+  recv->set_name("Recv " + from->name() + ":" + port_num_string + " on " +
+                 DeviceName(to));
   recv->set_op("_Recv");
   recv->add_input(send->name());
   recv->set_device(DeviceName(to));
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index d00766d9fa..e9abecb122 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -254,9 +254,6 @@ class VirtualScheduler {
 
   // Pool of NodeDefs for SendRecv and Identity ops created.
   std::vector<std::unique_ptr<NodeDef>> additional_nodes_;
-  // Cache of nodes transferred to another device.
-  std::unordered_map<const NodeDef*, std::unordered_map<string, const NodeDef*>>
-      cached_recv_nodes_;
 
   // Stats:
   std::map<string, int> op_counts_;  // Op counts with key with input shape.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index 9743db33db..29e3db1b74 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -36,6 +36,7 @@ class TestVirtualScheduler : public VirtualScheduler {
   FRIEND_TEST(VirtualSchedulerTest, ControlDependency);
   FRIEND_TEST(VirtualSchedulerTest, ComplexDependency);
   FRIEND_TEST(VirtualSchedulerTest, Variable);
+  FRIEND_TEST(VirtualSchedulerTest, InterDeviceTransfer);
 };
 
 class VirtualSchedulerTest : public ::testing::Test {
@@ -43,6 +44,7 @@ class VirtualSchedulerTest : public ::testing::Test {
   NodeDef node1_, node2_, node3_, node4_, node5_, node6_;
 
   const string kCPU0 = "/job:localhost/replica:0/task:0/cpu:0";
+  const string kCPU1 = "/job:localhost/replica:0/task:0/cpu:1";
 
   DeviceProperties GetDummyCPUDevice() {
     // Create CPU with 2 cores, 4 Ghz freq, 2 GB/s mem bandwidth.
@@ -74,6 +76,7 @@ class VirtualSchedulerTest : public ::testing::Test {
     // IMPORTANT: Device is not actually ever used in the test case since
     // force_cpu_type is defaulted to "Haswell"
     devices[kCPU0] = cpu_device;
+    devices[kCPU1] = cpu_device;
     cluster_.reset(new VirtualCluster(devices));
     placer_.reset(new VirtualPlacer(cluster_.get()));
   }
@@ -255,6 +258,64 @@ class VirtualSchedulerTest : public ::testing::Test {
     dependency_["z4"] = {"bn"};
   }
 
+  // Create a fused bathcnorm on one device, and send the outputs to another
+  // device.
+  void CreateGrapplerItemWithInterDeviceTransfers() {
+    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
+
+    // Create a FusedBatchNorm op that has multiple output ports.
+    auto x = tensorflow::ops::RandomUniform(
+        s.WithOpName("x"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
+    auto scale = tensorflow::ops::RandomUniform(s.WithOpName("scale"),
+                                                {depth_in_}, DT_FLOAT);
+    auto offset = tensorflow::ops::RandomUniform(s.WithOpName("offset"),
+                                                 {depth_in_}, DT_FLOAT);
+    auto mean =
+        tensorflow::ops::RandomUniform(s.WithOpName("mean"), {0}, DT_FLOAT);
+    auto var =
+        tensorflow::ops::RandomUniform(s.WithOpName("var"), {0}, DT_FLOAT);
+
+    auto batch_norm = tensorflow::ops::FusedBatchNorm(
+        s.WithOpName("bn"), x, scale, offset, mean, var,
+        ops::FusedBatchNorm::IsTraining(true).Epsilon(0.1f));
+    auto y = batch_norm.y;
+    auto batch_mean = batch_norm.batch_mean;
+    auto batch_var = batch_norm.batch_variance;
+
+    // Copy FusedBatchNorm's outputs to CPU1.
+    // y1 and y2 take the same tensor, so there should be only 1 Send and Recv.
+    auto y1 =
+        tensorflow::ops::Identity(s.WithOpName("y1").WithDevice(kCPU1), y);
+    auto y2 =
+        tensorflow::ops::Identity(s.WithOpName("y2").WithDevice(kCPU1), y);
+    // batch_mean1 and batch_var1 take different output ports, so each will
+    // initiate Send/Recv.
+    auto batch_mean1 = tensorflow::ops::Identity(
+        s.WithOpName("batch_mean1").WithDevice(kCPU1), batch_mean);
+    auto batch_var1 = tensorflow::ops::Identity(
+        s.WithOpName("batch_var1").WithDevice(kCPU1), batch_var);
+    // This is control dependency.
+    auto control_dep = tensorflow::ops::NoOp(s.WithOpName("control_dep")
+                                                 .WithControlDependencies(y)
+                                                 .WithDevice(kCPU1));
+
+    GraphDef def;
+    TF_CHECK_OK(s.ToGraphDef(&def));
+
+    grappler_item_.reset(new GrapplerItem);
+    grappler_item_->id = "test_conv2d_graph";
+    grappler_item_->graph = def;
+    grappler_item_->fetch = {"y1", "y2", "batch_mean1", "batch_var1",
+                             "control_dep"};
+
+    dependency_["bn"] = {"x", "mean", "var"};
+    dependency_["y1"] = {"bn"};
+    dependency_["y2"] = {"bn"};
+    dependency_["batch_mean1"] = {"bn"};
+    dependency_["batch_var1"] = {"bn"};
+    dependency_["control_dep"] = {"bn"};
+  }
+
   // Call this after creating grappler_item_ and setting up dependency_.
   void InitScheduler() {
     scheduler_.reset(new TestVirtualScheduler(
@@ -803,5 +864,76 @@ TEST_F(VirtualSchedulerTest, Variable) {
   ValidateMemoryUsageSnapshot({"x"}, 0 /* port_num_expected */,
                               cpu_state.mem_usage_snapshot_at_peak);
 }
+
+TEST_F(VirtualSchedulerTest, InterDeviceTransfer) {
+  // Init.
+  CreateGrapplerItemWithInterDeviceTransfers();
+  InitScheduler();
+
+  // Run the scheduler.
+  auto ops_executed = RunScheduler("");
+
+  // Helper lambda to extract port num from _Send and _Recv op name.
+  auto get_port_num = [](const string& name) -> int {
+    if (name.find("bn:0") != std::string::npos) {
+      return 0;
+    } else if (name.find("bn:1") != std::string::npos) {
+      return 1;
+    } else if (name.find("bn:2") != std::string::npos) {
+      return 2;
+    } else if (name.find("bn:minus1") != std::string::npos) {
+      return -1;
+    }
+    return -999;
+  };
+
+  // Reorganize ops_executed for further testing.
+  std::unordered_map<string, int> op_count;
+  std::unordered_map<int, string> recv_op_names;
+  std::unordered_map<int, string> send_op_names;
+  for (const auto& x : ops_executed) {
+    const auto& name = x.first;
+    const auto& node_info = x.second;
+    const auto& op = node_info.op_info.op();
+    if (op == "_Recv") {
+      recv_op_names[get_port_num(name)] = name;
+    } else if (op == "_Send") {
+      send_op_names[get_port_num(name)] = name;
+    }
+    op_count[op]++;
+  }
+
+  // Same number of _Send and _Recv.
+  EXPECT_EQ(op_count.at("_Send"), op_count.at("_Recv"));
+
+  // Expect 4 Send and Recvs each: port 0, 1, and, 2, and control dependency.
+  EXPECT_EQ(op_count.at("_Recv"), 4);
+  EXPECT_EQ(op_count.at("_Send"), 4);
+
+  // Helper lambda for extracting output Tensor size.
+  auto get_output_size = [this, ops_executed](const string& name) -> int64 {
+    const auto& output_properties_ = ops_executed.at(name).op_info.outputs();
+    std::vector<OpInfo::TensorProperties> output_properties;
+    for (const auto& output_property : output_properties_) {
+      output_properties.push_back(output_property);
+    }
+    return scheduler_->CalculateOutputSize(output_properties, 0);
+
+  };
+
+  // Validate transfer size.
+  // Batchnorm output y is 4D vector: batch x width x width x depth.
+  int input_size = 4 * batch_size_ * width_ * height_ * depth_in_;
+  EXPECT_EQ(get_output_size(recv_op_names[0]), input_size);
+  EXPECT_EQ(get_output_size(send_op_names[0]), input_size);
+  // Mean and vars are 1-D vector with size depth_in_.
+  EXPECT_EQ(get_output_size(recv_op_names[1]), 4 * depth_in_);
+  EXPECT_EQ(get_output_size(send_op_names[1]), 4 * depth_in_);
+  EXPECT_EQ(get_output_size(recv_op_names[2]), 4 * depth_in_);
+  EXPECT_EQ(get_output_size(send_op_names[2]), 4 * depth_in_);
+  // Control dependency size is 4B.
+  EXPECT_EQ(get_output_size(recv_op_names[-1]), 4);
+  EXPECT_EQ(get_output_size(send_op_names[-1]), 4);
+}
 }  // end namespace grappler
 }  // end namespace tensorflow
-- 
GitLab


From 5442825dfd774a1d1f55d4d0e9d2821abb551caf Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Fri, 21 Jul 2017 14:41:54 -0700
Subject: [PATCH 1216/1375] Re-enable using local MKL directory.

Setting TF_MKL_ROOT environment variable (together with "--config=mkl")
before build will enforce using a local copy of MKL during build.

PiperOrigin-RevId: 162787130
---
 configure                      | 35 ++++++---------------
 tensorflow/workspace.bzl       | 25 ++++++++-------
 third_party/mkl/build_defs.bzl | 57 +++++++++++++++++++++++++++++++++-
 third_party/mkl/mkl.BUILD      | 10 +++++-
 4 files changed, 89 insertions(+), 38 deletions(-)

diff --git a/configure b/configure
index cd957a06f8..1eeaffaf74 100755
--- a/configure
+++ b/configure
@@ -202,31 +202,16 @@ fi
 setup_python
 
 ## Set up MKL related environment settings
-while [ "$TF_NEED_MKL" == "" ]; do
-  fromuser=""
-  read -p "Do you wish to build TensorFlow with MKL support? [y/N] " INPUT
-  fromuser="1"
-  case $INPUT in
-    [Yy]* ) echo "MKL support will be enabled for TensorFlow"; TF_NEED_MKL=1;;
-    [Nn]* ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
-    "" ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-OSNAME=`uname -s`
-
-if [ "$TF_NEED_MKL" == "1" ]; then # TF_NEED_MKL
-  if [ "$OSNAME" == "Linux" ]; then
-    write_to_bazelrc 'build:mkl --define with_mkl_support=true'
-    write_to_bazelrc 'build:mkl -c opt'
-    write_to_bazelrc 'build:mkl --copt="-DEIGEN_USE_VML"'
-    echo "Add \"--config=mkl\" to your bazel command to build with MKL support."
-  elif [ "$OSNAME" == "Darwin" ]; then
-      echo "Darwin is unsupported yet";
-      exit 1
-  fi
-fi # TF_NEED_MKL
+write_to_bazelrc 'build:mkl --define with_mkl_support=true'
+write_to_bazelrc 'build:mkl --define using_mkl=true'
+write_to_bazelrc 'build:mkl -c opt'
+write_to_bazelrc 'build:mkl --copt="-DEIGEN_USE_VML"'
+echo ""
+echo "Add \"--config=mkl\" to your bazel command to build with MKL support."
+echo "Please note that MKL on MacOS or windows is still not supported."
+echo "If you would like to use a local MKL instead of downloading, please "
+echo "  set the environment variable \"TF_MKL_ROOT\" every time before build."
+echo ""
 ## End MKL setup
 
 ## Set up architecture-dependent optimization flags.
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 25a2b7e84f..71e4b8f5bb 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -2,6 +2,7 @@
 
 load("//third_party/gpus:cuda_configure.bzl", "cuda_configure")
 load("//third_party/sycl:sycl_configure.bzl", "sycl_configure")
+load("//third_party/mkl:build_defs.bzl", "mkl_repository")
 load("@io_bazel_rules_closure//closure/private:java_import_external.bzl", "java_import_external")
 load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external")
 load("//third_party/py:python_configure.bzl", "python_configure")
@@ -140,6 +141,19 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   cuda_configure(name="local_config_cuda")
   sycl_configure(name="local_config_sycl")
   python_configure(name="local_config_python")
+
+  mkl_repository(
+      name = "mkl",
+      urls = [
+          "http://mirror.bazel.build/github.com/01org/mkl-dnn/releases/download/v0.7/mklml_lnx_2018.0.20170425.tgz",
+          "https://github.com/01org/mkl-dnn/releases/download/v0.7/mklml_lnx_2018.0.20170425.tgz",
+      ],
+      sha256 = "3cc2501fb209e1fd0960a5f61c919438f9619c68a644dcebf0fdf69b07460c57",
+      strip_prefix = "mklml_lnx_2018.0.20170425",
+      build_file = str(Label("//third_party/mkl:mkl.BUILD")),
+      repository = tf_repo_name,
+  )
+
   if path_prefix:
     print("path_prefix was specified to tf_workspace but is no longer used " +
           "and will be removed in the future.")
@@ -171,17 +185,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       actual = "@libxsmm_archive//third_party:xsmm_avx",
   )
 
-  native.new_http_archive(
-      name = "mkl",
-      urls = [
-          "http://mirror.bazel.build/github.com/01org/mkl-dnn/releases/download/v0.7/mklml_lnx_2018.0.20170425.tgz",
-          "https://github.com/01org/mkl-dnn/releases/download/v0.7/mklml_lnx_2018.0.20170425.tgz",
-      ],
-      sha256 = "3cc2501fb209e1fd0960a5f61c919438f9619c68a644dcebf0fdf69b07460c57",
-      strip_prefix = "mklml_lnx_2018.0.20170425",
-      build_file = str(Label("//third_party/mkl:mkl.BUILD")),
-  )
-
   native.new_http_archive(
       name = "ortools_archive",
       urls = [
diff --git a/third_party/mkl/build_defs.bzl b/third_party/mkl/build_defs.bzl
index 9a28b312c2..533c0766c7 100644
--- a/third_party/mkl/build_defs.bzl
+++ b/third_party/mkl/build_defs.bzl
@@ -1,4 +1,16 @@
-# Macros for building MKL code.
+# -*- Python -*-
+"""Skylark macros for MKL.
+if_mkl is a conditional to check if MKL is enabled or not.
+
+mkl_repository is a repository rule for creating MKL repository rule that can
+be pointed to either a local folder, or download it from the internet.
+mkl_repository depends on the following environment variables:
+  * `TF_MKL_ROOT`: The root folder where a copy of libmkl is located.
+"""
+
+
+_TF_MKL_ROOT = "TF_MKL_ROOT"
+
 
 def if_mkl(if_true, if_false = []):
     """Shorthand for select()'ing on whether we're building with MKL.
@@ -11,3 +23,46 @@ def if_mkl(if_true, if_false = []):
         "//third_party/mkl:using_mkl": if_true,
         "//conditions:default": if_false
     })
+
+
+def _enable_local_mkl(repository_ctx):
+  return _TF_MKL_ROOT in repository_ctx.os.environ
+
+
+def _mkl_autoconf_impl(repository_ctx):
+  """Implementation of the local_mkl_autoconf repository rule."""
+
+  if _enable_local_mkl(repository_ctx):
+    # Symlink lib and include local folders.
+    mkl_root = repository_ctx.os.environ[_TF_MKL_ROOT]
+    mkl_lib_path = "%s/lib" % mkl_root
+    repository_ctx.symlink(mkl_lib_path, "lib")
+    mkl_include_path = "%s/include" % mkl_root
+    repository_ctx.symlink(mkl_include_path, "include")
+    mkl_license_path = "%s/license.txt" % mkl_root
+    repository_ctx.symlink(mkl_license_path, "license.txt")
+  else:
+    # setup remote mkl repository.
+    repository_ctx.download_and_extract(
+        repository_ctx.attr.urls,
+        sha256=repository_ctx.attr.sha256,
+        stripPrefix=repository_ctx.attr.strip_prefix,
+    )
+
+  # Also setup BUILD file.
+  repository_ctx.symlink(repository_ctx.attr.build_file, "BUILD")
+
+
+mkl_repository = repository_rule(
+    implementation = _mkl_autoconf_impl,
+    environ = [
+        _TF_MKL_ROOT,
+    ],
+    attrs = {
+        "build_file": attr.label(),
+        "repository": attr.string(),
+        "urls": attr.string_list(default = []),
+        "sha256": attr.string(default = ""),
+        "strip_prefix": attr.string(default = ""),
+    },
+)
diff --git a/third_party/mkl/mkl.BUILD b/third_party/mkl/mkl.BUILD
index 37daa52419..8db97232e1 100644
--- a/third_party/mkl/mkl.BUILD
+++ b/third_party/mkl/mkl.BUILD
@@ -1,6 +1,14 @@
 licenses(["notice"])  # 3-Clause BSD
 
-exports_files(["LICENSE"])
+exports_files(["license.txt"])
+
+filegroup(
+    name = "LICENSE",
+    srcs = [
+        "license.txt",
+    ],
+    visibility = ["//visibility:public"],
+)
 
 cc_library(
     name = "mkl_headers",
-- 
GitLab


From c1b2f468063ac978c11bc9767633c241639fc047 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 14:55:37 -0700
Subject: [PATCH 1217/1375] Make cudnn_rnn.RNNParamsSaveable use parameter
 variable op instead of None for SaveableObject. Without an associated op, it
 is not possible to save more than one Cudnn param variable in the same graph.

PiperOrigin-RevId: 162788929
---
 .../python/kernel_tests/cudnn_rnn_ops_test.py | 39 ++++++++++++++++++-
 .../cudnn_rnn/python/ops/cudnn_rnn_ops.py     |  3 +-
 2 files changed, 39 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
index 11a97b6626..28d68ac610 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
@@ -96,16 +96,19 @@ class CudnnRNNTest(TensorFlowTestCase):
       raise ValueError("Invalid rnn_mode: %s" % rnn_mode)
     return model
 
-  def _create_params_savable(self, params, model):
+  def _create_params_savable(self, params, model, base_variable_scope="rnn",
+                             name="params_canonical"):
     """Create a RNNParamsSaveable for the weight and bias parameters.
 
     Args:
       params: a Variable for weight and bias parameters.
       model: a CudnnRNN model.
+      base_variable_scope: a string, prefix of names of saved variables.
+      name: a string, name of the RNNParamsSaveable object.
     """
     params_saveable = cudnn_rnn_ops.RNNParamsSaveable(
         model, model.params_to_canonical, model.canonical_to_params, [params],
-        "rnn")
+        base_variable_scope=base_variable_scope, name=name)
     ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, params_saveable)
 
   def _testSaveRestoreVariable(self, rnn_mode, dtype):
@@ -134,6 +137,37 @@ class CudnnRNNTest(TensorFlowTestCase):
       params_v_restored = sess.run(params)
       self.assertAllEqual(params_v, params_v_restored)
 
+  def _testSaveRestoreTwoVariables(self, rnn_mode, dtype):
+    model = self._CreateModel(
+        rnn_mode, num_layers=2, num_units=7, input_size=3, dtype=dtype)
+    random_seed.set_random_seed(1234)
+    params_size_t = model.params_size()
+    names = ["rnn_1", "rnn_2"]
+    param_vars = [variables.Variable(
+        random_ops.random_uniform([params_size_t], dtype=dtype),
+        dtype=dtype,
+        validate_shape=False) for name in names]
+    for name, params in zip(names, param_vars):
+      self._create_params_savable(params, model, name, name)
+    save_path = os.path.join(self.get_temp_dir(), "save-restore-variable-test")
+    saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
+    with self.test_session(use_gpu=True) as sess:
+      sess.run(variables.global_variables_initializer())
+      params_v = sess.run(param_vars)
+      val = saver.save(sess, save_path)
+      self.assertEqual(save_path, val)
+    with self.test_session(use_gpu=True) as sess:
+      reset_params = [
+          state_ops.assign(params,
+                           array_ops.zeros(
+                               [params_size_t], dtype=dtype))
+          for params in param_vars]
+      sess.run(reset_params)
+      saver.restore(sess, save_path)
+      params_v_restored = sess.run(param_vars)
+      for v, v_restored in zip(params_v, params_v_restored):
+        self.assertAllEqual(v, v_restored)
+
   def _build_forward_cudnn_model(self,
                                  rnn_mode,
                                  num_layers,
@@ -377,6 +411,7 @@ class CudnnRNNTest(TensorFlowTestCase):
     dtype_list = [dtypes.float32, dtypes.float64]
     for rnn_mode, dtype in itertools.product(rnn_modes, dtype_list):
       self._testSaveRestoreVariable(rnn_mode, dtype)
+      self._testSaveRestoreTwoVariables(rnn_mode, dtype)
       self._testSaveRestoreOutput(rnn_mode, dtype)
 
   def _MinLSTMParamSize(self,
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index dbe495fd00..be5af6900c 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -131,7 +131,8 @@ class RNNParamsSaveable(saver.BaseSaverBuilder.SaveableObject):
         saver.BaseSaverBuilder.SaveSpec(param, slice_spec, param_name)
         for param, param_name in zip(params, param_names)
     ]
-    super(RNNParamsSaveable, self).__init__(None, specs, name)
+    super(RNNParamsSaveable, self).__init__(
+        param_variables[0], specs, name)
 
   def restore(self, restored_tensors, restored_shapes):
     if (self._cudnn_rnn.direction == CUDNN_RNN_UNIDIRECTION and
-- 
GitLab


From 9513728ada1bb5aa571e477cb777027efb41b0fe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 15:04:04 -0700
Subject: [PATCH 1218/1375] Stop using internal API that creates
 grpc::ClientAsyncResponseReader. Instead, switch to using grpc::GenericStub.

PiperOrigin-RevId: 162790056
---
 tensorflow/core/distributed_runtime/rpc/BUILD |  18 +-
 .../rpc/grpc_remote_worker.cc                 | 191 +++++++++++++-----
 .../rpc/grpc_remote_worker.h                  |   4 +-
 .../core/distributed_runtime/rpc/grpc_util.cc | 120 +++++++++++
 .../core/distributed_runtime/rpc/grpc_util.h  |  51 +++++
 .../distributed_runtime/rpc/grpc_util_test.cc | 158 +++++++++++++++
 .../rpc/grpc_worker_cache.cc                  |   9 +-
 7 files changed, 492 insertions(+), 59 deletions(-)
 create mode 100644 tensorflow/core/distributed_runtime/rpc/grpc_util.cc
 create mode 100644 tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc

diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index 606dfaf2a2..61534ae3c9 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -49,11 +49,12 @@ package(default_visibility = [
 
 cc_library(
     name = "grpc_util",
-    srcs = [],
+    srcs = ["grpc_util.cc"],
     hdrs = ["grpc_util.h"],
     deps = [
         "//tensorflow/core:lib",
         "@grpc//:grpc++_unsecure",
+        "@grpc//:grpc_unsecure",
     ],
 )
 
@@ -140,6 +141,7 @@ cc_library(
         ":grpc_channel",
         ":grpc_client_cq_tag",
         ":grpc_remote_worker",
+        ":grpc_util",
         "//tensorflow/core:lib",
         "//tensorflow/core/distributed_runtime:worker_cache",
         "//tensorflow/core/distributed_runtime:worker_cache_logger",
@@ -442,6 +444,20 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "grpc_util_test",
+    size = "small",
+    srcs = ["grpc_util_test.cc"],
+    deps = [
+        ":grpc_util",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:worker_proto_cc",
+        "@grpc//:grpc++_unsecure",
+        "@grpc//:grpc_unsecure",
+    ],
+)
+
 tf_cuda_cc_test(
     name = "grpc_session_test",
     size = "medium",
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
index b25f6b754f..e0523baccd 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
@@ -17,10 +17,12 @@ limitations under the License.
 
 #include <utility>
 
+#include "grpc++/generic/generic_stub.h"
 #include "grpc++/grpc++.h"
 
 #include "tensorflow/core/common_runtime/process_util.h"
 #include "tensorflow/core/distributed_runtime/rpc/grpc_client_cq_tag.h"
+#include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
 #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h"
 #include "tensorflow/core/distributed_runtime/tensor_coding.h"
 #include "tensorflow/core/distributed_runtime/worker_cache_logger.h"
@@ -35,12 +37,33 @@ limitations under the License.
 
 namespace tensorflow {
 
+// Overload of GrpcParseProto so we can decode a TensorResponse without
+// extra copying.
+bool GrpcParseProto(const ::grpc::ByteBuffer& src, TensorResponse* dst) {
+  struct ByteSource : public TensorResponse::Source {
+    const ::grpc::ByteBuffer* buffer;
+    GrpcByteBufferSource src;
+    bool ok;
+
+    ::tensorflow::protobuf::io::ZeroCopyInputStream* contents() override {
+      ok = src.Init(*buffer);
+      return &src;
+    }
+  };
+  ByteSource bs;
+  bs.buffer = &src;
+  return dst->ParseFrom(&bs).ok() && bs.ok;
+}
+
 class GrpcRemoteWorker : public WorkerInterface {
  public:
-  explicit GrpcRemoteWorker(SharedGrpcChannelPtr channel,
+  explicit GrpcRemoteWorker(GrpcCounter* live_rpc_counter,
+                            SharedGrpcChannelPtr channel,
                             ::grpc::CompletionQueue* completion_queue,
                             WorkerCacheLogger* logger)
-      : channel_(std::move(channel)),
+      : counter_(live_rpc_counter),
+        channel_(std::move(channel)),
+        stub_(channel_),
         cq_(completion_queue),
         getstatus_(Method(GrpcWorkerMethod::kGetStatus)),
         createworkersession_(Method(GrpcWorkerMethod::kCreateWorkerSession)),
@@ -189,83 +212,139 @@ class GrpcRemoteWorker : public WorkerInterface {
 
  private:
   // Object allocated per active RPC.
-  template <class RequestMessage, class ResponseMessage>
-  class RPCState final : public GrpcClientCQTag {
+  template <class ResponseMessage>
+  class RPCState : public GrpcClientCQTag {
    public:
-    RPCState(::grpc::ChannelInterface* channel, ::grpc::CompletionQueue* cq,
-             const ::grpc::RpcMethod& method, const RequestMessage& request,
+    RPCState(GrpcCounter* counter, ::grpc::GenericStub* stub,
+             ::grpc::CompletionQueue* cq, const ::grpc::string& method,
+             const protobuf::Message& request, ResponseMessage* response,
              StatusCallback done, CallOptions* call_opts)
-        : call_opts_(call_opts),
-          reader_(CreateClientAsyncResponseReader<ResponseMessage>(
-              channel, cq, method, InitContext(call_opts), request)),
-          done_(std::move(done)) {}
-
-    ~RPCState() override {}
+        : counter_(counter), call_opts_(call_opts), done_(std::move(done)) {
+      // TODO(sanjay): The counter will no longer be needed once we
+      // get a GenericStub API which allows us to manage an entire
+      // RPC with a single completion event instead of four events.
+      counter_->Increment();
+      // The initialization and recovery protocols rely on blocking
+      // until we get a response.
+      context_.set_fail_fast(false);
+      if (call_opts) {
+        call_opts->SetCancelCallback([this]() { context_.TryCancel(); });
+      }
 
-    void StartRPC(ResponseMessage* response) {
-      reader_->Finish(response, &status_, this);
+      failure_.store(false);
+      remaining_callbacks_.store(4);  // Init/Read/Write/Finish callbacks
+      response_ = response;
+      GrpcUnparseProto(request, &request_buf_);
+      // TODO(sanjay): When new enough grpc is available, enable the following:
+      //   context_.set_initial_metadata_corked(true);
+      // We can then skip the extra state transition for init callback.
+      call_ = std::move(stub->Call(&context_, method, cq, this));
+      call_initialized_.Notify();
     }
 
+    // Called multiple times: when init done, read done, write done, call done.
     void OnCompleted(bool ok) override {
-      if (!ok) {
-        VLOG(2) << "Call returned with non-ok status: "
-                << status_.error_message();
-      }
-      if (call_opts_) {
-        call_opts_->ClearCancelCallback();
+      if (!ok) failure_.store(true);
+      const int old_count = remaining_callbacks_.fetch_sub(1);
+      if (old_count > 1) {
+        if (old_count == 4) {
+          // Init callback finished.  Issue remaining ops.
+
+          // Annoyingly enough, the way the generic call API works is
+          // inherently racy.  We can get the following sequence of events:
+          //  1. stub->Call() starts.
+          //  2. some stuff happens inside grpc
+          //  3. grpc delivers the completion event
+          //  4. tensorflow event handling thread calls init metadata callback
+          //  5. stub->Call() finishes
+          //  6. the result of stub->Call() is stored in call_
+          // We are currently inside the callback and therefore need to
+          // wait for step 6 to finish before attempting to touch call_.
+          call_initialized_.WaitForNotification();
+
+          if (ok) {
+            // TODO(sanjay): Use WriteLast() when grpc version we are using
+            // is new enough.
+            call_->Write(request_buf_, this);
+            call_->Read(&response_buf_, this);
+          } else {
+            // Skip Write and Read.
+            remaining_callbacks_.fetch_sub(2);
+          }
+          call_->Finish(&status_, this);
+        }
+        // Still waiting for some more callbacks to finish.
+        return;
+      } else {  // old_count == 1, i.e., all callbacks have finished
+        // Last callback finished; clean up.
+        if (call_opts_) {
+          call_opts_->ClearCancelCallback();
+        }
+        Status s = FromGrpcStatus(status_);
+        if (s.ok() && failure_.load()) {
+          s.Update(errors::Internal("callback error"));
+        }
+        if (s.ok() && !GrpcParseProto(response_buf_, response_)) {
+          s.Update(errors::Internal("could not parse rpc response"));
+        }
+        if (!s.ok()) {
+          VLOG(2) << "Call returned with non-ok status: " << s;
+        }
+        done_(s);
+        counter_->Decrement();
+        delete this;
       }
-      done_(FromGrpcStatus(status_));
-      delete this;
     }
 
    private:
+    GrpcCounter* const counter_;
     CallOptions* call_opts_;
     ::grpc::ClientContext context_;
-    std::unique_ptr<::grpc::ClientAsyncResponseReader<ResponseMessage>> reader_;
+    std::unique_ptr<::grpc::GenericClientAsyncReaderWriter> call_;
+    ResponseMessage* response_;
+    ::grpc::ByteBuffer request_buf_;
+    ::grpc::ByteBuffer response_buf_;
     ::grpc::Status status_;
     StatusCallback done_;
-
-    ::grpc::ClientContext* InitContext(CallOptions* call_opts) {
-      // The initialization and recovery protocols rely on blocking
-      // until we get a response.
-      context_.set_fail_fast(false);
-      if (call_opts) {
-        call_opts->SetCancelCallback([this]() { context_.TryCancel(); });
-      }
-      return &context_;
-    }
+    std::atomic<bool> failure_;
+    std::atomic<int> remaining_callbacks_;
+    Notification call_initialized_;
   };
 
   // Utility method for issuing a generic asynchronous request. The
   // given callback, `done`, will be called when the RPC completes.
-  template <class RequestMessage, class ResponseMessage>
-  void IssueRequest(const RequestMessage* request, ResponseMessage* response,
-                    const ::grpc::RpcMethod& method, StatusCallback done,
+  void IssueRequest(const protobuf::Message* request,
+                    protobuf::Message* response, const ::grpc::string& method,
+                    StatusCallback done, CallOptions* call_opts = nullptr) {
+    new RPCState<protobuf::Message>(counter_, &stub_, cq_, method, *request,
+                                    response, std::move(done), call_opts);
+  }
+  void IssueRequest(const protobuf::Message* request, TensorResponse* response,
+                    const ::grpc::string& method, StatusCallback done,
                     CallOptions* call_opts = nullptr) {
-    auto state = new RPCState<RequestMessage, ResponseMessage>(
-        channel_.get(), cq_, method, *request, std::move(done), call_opts);
-    state->StartRPC(response);
+    new RPCState<TensorResponse>(counter_, &stub_, cq_, method, *request,
+                                 response, std::move(done), call_opts);
   }
 
   // Helper function for initializing the RpcMethod objects below.
-  ::grpc::RpcMethod Method(GrpcWorkerMethod id) {
-    return ::grpc::RpcMethod(GrpcWorkerMethodName(id),
-                             ::grpc::RpcMethod::NORMAL_RPC, channel_);
-  }
+  const char* Method(GrpcWorkerMethod id) { return GrpcWorkerMethodName(id); }
 
+  GrpcCounter* const counter_;
   SharedGrpcChannelPtr channel_;
+  ::grpc::GenericStub stub_;
+
   ::grpc::CompletionQueue* cq_;
 
-  const ::grpc::RpcMethod getstatus_;
-  const ::grpc::RpcMethod createworkersession_;
-  const ::grpc::RpcMethod registergraph_;
-  const ::grpc::RpcMethod deregistergraph_;
-  const ::grpc::RpcMethod rungraph_;
-  const ::grpc::RpcMethod cleanupgraph_;
-  const ::grpc::RpcMethod cleanupall_;
-  const ::grpc::RpcMethod recvtensor_;
-  const ::grpc::RpcMethod logging_;
-  const ::grpc::RpcMethod tracing_;
+  const ::grpc::string getstatus_;
+  const ::grpc::string createworkersession_;
+  const ::grpc::string registergraph_;
+  const ::grpc::string deregistergraph_;
+  const ::grpc::string rungraph_;
+  const ::grpc::string cleanupgraph_;
+  const ::grpc::string cleanupall_;
+  const ::grpc::string recvtensor_;
+  const ::grpc::string logging_;
+  const ::grpc::string tracing_;
 
   // Support for logging.
   WorkerCacheLogger* logger_;
@@ -273,10 +352,12 @@ class GrpcRemoteWorker : public WorkerInterface {
   TF_DISALLOW_COPY_AND_ASSIGN(GrpcRemoteWorker);
 };
 
-WorkerInterface* NewGrpcRemoteWorker(SharedGrpcChannelPtr channel,
+WorkerInterface* NewGrpcRemoteWorker(GrpcCounter* live_rpc_counter,
+                                     SharedGrpcChannelPtr channel,
                                      ::grpc::CompletionQueue* completion_queue,
                                      WorkerCacheLogger* logger) {
-  return new GrpcRemoteWorker(std::move(channel), completion_queue, logger);
+  return new GrpcRemoteWorker(live_rpc_counter, std::move(channel),
+                              completion_queue, logger);
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.h b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.h
index 8ad4133540..174dfcc707 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.h
@@ -26,10 +26,12 @@ class CompletionQueue;
 
 namespace tensorflow {
 
+class GrpcCounter;
 class WorkerCacheLogger;
 class WorkerInterface;
 
-WorkerInterface* NewGrpcRemoteWorker(SharedGrpcChannelPtr channel,
+WorkerInterface* NewGrpcRemoteWorker(GrpcCounter* live_rpc_counter,
+                                     SharedGrpcChannelPtr channel,
                                      ::grpc::CompletionQueue* completion_queue,
                                      WorkerCacheLogger* logger);
 
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_util.cc b/tensorflow/core/distributed_runtime/rpc/grpc_util.cc
new file mode 100644
index 0000000000..fe66c7c570
--- /dev/null
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_util.cc
@@ -0,0 +1,120 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
+
+namespace tensorflow {
+
+GrpcByteBufferSource::GrpcByteBufferSource() {}
+
+bool GrpcByteBufferSource::Init(const grpc::ByteBuffer& src) {
+  cur_ = -1;
+  left_ = 0;
+  ptr_ = nullptr;
+  byte_count_ = 0;
+  bool ok = src.Dump(&slices_).ok();
+  if (!ok) {
+    slices_.clear();
+  }
+  return ok;
+}
+
+bool GrpcByteBufferSource::Next(const void** data, int* size) {
+  // Use loop instead of if in case buffer contained empty slices.
+  while (left_ == 0) {
+    // Advance to next slice.
+    cur_++;
+    if (cur_ >= slices_.size()) {
+      return false;
+    }
+    const ::grpc::Slice& s = slices_[cur_];
+    left_ = s.size();
+    ptr_ = reinterpret_cast<const char*>(s.begin());
+  }
+
+  *data = ptr_;
+  *size = left_;
+  byte_count_ += left_;
+  ptr_ += left_;
+  left_ = 0;
+  return true;
+}
+
+void GrpcByteBufferSource::BackUp(int count) {
+  ptr_ -= count;
+  left_ += count;
+  byte_count_ -= count;
+}
+
+bool GrpcByteBufferSource::Skip(int count) {
+  const void* data;
+  int size;
+  while (Next(&data, &size)) {
+    if (size >= count) {
+      BackUp(size - count);
+      return true;
+    }
+    // size < count;
+    count -= size;
+  }
+  // error or we have too large count;
+  return false;
+}
+
+grpc::protobuf::int64 GrpcByteBufferSource::ByteCount() const {
+  return byte_count_;
+}
+
+void GrpcUnparseProto(const protobuf::Message& src, grpc::ByteBuffer* dst) {
+  // TODO(sanjay): For bigger protos, serialize into a ZeroCopyOutputStream.
+  size_t len = src.ByteSizeLong();
+  gpr_slice s = gpr_slice_malloc(len);
+  src.SerializeWithCachedSizesToArray(
+      reinterpret_cast<uint8*>(GPR_SLICE_START_PTR(s)));
+  ::grpc::Slice slice(s, ::grpc::Slice::STEAL_REF);
+  ::grpc::ByteBuffer buffer(&slice, 1);
+  // TODO(sanjay): Use Swap() when grpc version we are using is new enough.
+  // dst->Swap(&buffer);
+  *dst = buffer;
+}
+
+bool GrpcParseProto(const grpc::ByteBuffer& src, protobuf::Message* dst) {
+  GrpcByteBufferSource stream;
+  if (!stream.Init(src)) return false;
+  return dst->ParseFromZeroCopyStream(&stream);
+}
+
+void GrpcCounter::Increment() {
+  mutex_lock l(mu_);
+  counter_++;
+}
+
+void GrpcCounter::Decrement() {
+  mutex_lock l(mu_);
+  DCHECK_GT(counter_, 0);
+  counter_--;
+  if (counter_ == 0) {
+    empty_.notify_all();
+  }
+}
+
+void GrpcCounter::WaitUntilUnused() {
+  mutex_lock l(mu_);
+  while (counter_ != 0) {
+    empty_.wait(l);
+  }
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_util.h b/tensorflow/core/distributed_runtime/rpc/grpc_util.h
index 5244cd1c13..64bc960536 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_util.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_util.h
@@ -19,7 +19,11 @@ limitations under the License.
 #include <memory>
 
 #include "grpc++/grpc++.h"
+#include "grpc++/impl/codegen/proto_utils.h"
+#include "grpc++/support/byte_buffer.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
 
 namespace tensorflow {
 
@@ -45,6 +49,53 @@ typedef std::shared_ptr<::grpc::Channel> SharedGrpcChannelPtr;
 
 inline string GrpcIdKey() { return "tf-rpc"; }
 
+// Serialize src and store in *dst.
+void GrpcUnparseProto(const protobuf::Message& src, ::grpc::ByteBuffer* dst);
+
+// Parse contents of src and initialize *dst with them.
+bool GrpcParseProto(const ::grpc::ByteBuffer& src, protobuf::Message* dst);
+
+// A ZeroCopyInputStream that reads from a grpc::ByteBuffer.
+class GrpcByteBufferSource : public ::grpc::protobuf::io::ZeroCopyInputStream {
+ public:
+  GrpcByteBufferSource();
+  bool Init(const ::grpc::ByteBuffer& src);  // Can be called multiple times.
+  bool Next(const void** data, int* size) override;
+  void BackUp(int count) override;
+  bool Skip(int count) override;
+  ::grpc::protobuf::int64 ByteCount() const override;
+
+ private:
+  std::vector<::grpc::Slice> slices_;
+  int cur_;          // Current slice index.
+  int left_;         // Number of bytes in slices_[cur_] left to yield.
+  const char* ptr_;  // Address of next byte in slices_[cur_] to yield.
+  ::grpc::protobuf::int64 byte_count_;
+};
+
+// GrpcCounter is used to delay shutdown until all active RPCs are done.
+class GrpcCounter {
+ public:
+  GrpcCounter() {}
+
+  GrpcCounter(const GrpcCounter&) = delete;
+  GrpcCounter& operator=(const GrpcCounter&) = delete;
+
+  // Increment the count of live RPCs.
+  void Increment();
+
+  // Decrement the count of live RPCs.
+  void Decrement();
+
+  // Wait until count of live RPCs is zero.
+  void WaitUntilUnused();
+
+ private:
+  mutex mu_;
+  condition_variable empty_;
+  int counter_ = 0;
+};
+
 }  // namespace tensorflow
 
 #endif  // THIRD_PARTY_TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RPC_GRPC_UTIL_H_
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc b/tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc
new file mode 100644
index 0000000000..b12ad955d6
--- /dev/null
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc
@@ -0,0 +1,158 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/test_benchmark.h"
+#include "tensorflow/core/protobuf/worker.pb.h"
+
+namespace tensorflow {
+
+namespace {
+string ToString(const grpc::ByteBuffer& buf) {
+  std::vector<grpc::Slice> slices;
+  CHECK(buf.Dump(&slices).ok());
+  string result;
+  for (const grpc::Slice& s : slices) {
+    result.append(reinterpret_cast<const char*>(s.begin()), s.size());
+  }
+  return result;
+}
+
+// Return a ByteBuffer that contains str split up into num_slices slices.
+grpc::ByteBuffer MakeBuffer(const string& str, int num_slices) {
+  // Convert to a ByteBuffer.
+  std::vector<::grpc::Slice> slices;
+  const size_t per_slice = (str.size() + num_slices - 1) / num_slices;
+  for (size_t pos = 0; pos < str.size();) {
+    const size_t n = std::min(str.size() - pos, per_slice);
+    auto slice = gpr_slice_from_copied_buffer(&str[pos], n);
+    slices.push_back(::grpc::Slice(slice, ::grpc::Slice::STEAL_REF));
+    pos += n;
+  }
+  if (slices.empty()) {
+    slices.push_back(::grpc::Slice());
+  }
+  return ::grpc::ByteBuffer(&slices[0], slices.size());
+}
+
+// Make a proto with approximately the specified length.
+CleanupAllRequest MakeProto(int size) {
+  int approx_size = 0;
+  CleanupAllRequest proto;
+  int index = 0;
+  while (approx_size < size) {
+    int item_size = std::min(size - approx_size, 1024);
+    proto.add_container(string(item_size, 'a' + static_cast<char>(index % 26)));
+    approx_size += item_size + 3;  // +3 for encoding overhead.
+    index++;
+  }
+  return proto;
+}
+}  // namespace
+
+TEST(GrpcProto, Unparse) {
+  CleanupAllRequest proto;
+  proto.add_container("hello");
+  proto.add_container("world");
+  grpc::ByteBuffer buf;
+  GrpcUnparseProto(proto, &buf);
+  CleanupAllRequest parsed;
+  ASSERT_TRUE(parsed.ParseFromString(ToString(buf)));
+  ASSERT_EQ(proto.DebugString(), parsed.DebugString());
+}
+
+TEST(GrpcProto, Parse) {
+  // Test with serialization broken up into a bunch of slices.
+  struct Case {
+    int length;
+    int slices;
+  };
+  for (Case c : std::vector<Case>{
+           {0, 1},
+           {20, 1},
+           {100, 1},
+           {1 << 20, 1},
+           {100, 5},
+           {10000, 50},
+       }) {
+    CleanupAllRequest proto = MakeProto(c.length);
+    ::grpc::ByteBuffer src = MakeBuffer(proto.SerializeAsString(), c.slices);
+    CleanupAllRequest parsed;
+    ASSERT_TRUE(GrpcParseProto(src, &parsed)) << c.length << " " << c.slices;
+    ASSERT_EQ(proto.DebugString(), parsed.DebugString());
+  }
+}
+
+static void BM_UnparseGrpc(int iters, int size) {
+  testing::StopTiming();
+  auto proto = MakeProto(size);
+  testing::StartTiming();
+  for (int i = 0; i < iters; i++) {
+    grpc::ByteBuffer buf;
+    GrpcUnparseProto(proto, &buf);
+  }
+  testing::StopTiming();
+}
+BENCHMARK(BM_UnparseGrpc)->Arg(1)->Arg(1 << 10)->Arg(1 << 20);
+
+static void BM_UnparseString(int iters, int size) {
+  testing::StopTiming();
+  auto proto = MakeProto(size);
+  testing::StartTiming();
+
+  for (int i = 0; i < iters; i++) {
+    string buf;
+    proto.SerializeToString(&buf);
+  }
+
+  testing::StopTiming();
+}
+BENCHMARK(BM_UnparseString)->Arg(1)->Arg(1 << 10)->Arg(1 << 20);
+
+static void BM_ParseGrpc(int iters, int size, int num_slices) {
+  testing::StopTiming();
+  CleanupAllRequest proto = MakeProto(size);
+  auto buf = MakeBuffer(proto.SerializeAsString(), num_slices);
+  testing::StartTiming();
+
+  for (int i = 0; i < iters; i++) {
+    CHECK(GrpcParseProto(buf, &proto));
+  }
+
+  testing::StopTiming();
+}
+BENCHMARK(BM_ParseGrpc)
+    ->ArgPair(1, 1)
+    ->ArgPair(1 << 10, 1)
+    ->ArgPair(1 << 10, 4)
+    ->ArgPair(1 << 20, 1)
+    ->ArgPair(1 << 20, 4);
+
+static void BM_ParseString(int iters, int size) {
+  testing::StopTiming();
+  CleanupAllRequest proto = MakeProto(size);
+  string serial = proto.SerializeAsString();
+  testing::StartTiming();
+
+  for (int i = 0; i < iters; i++) {
+    CHECK(proto.ParseFromString(serial));
+  }
+
+  testing::StopTiming();
+}
+BENCHMARK(BM_ParseString)->Arg(1)->Arg(1 << 10)->Arg(1 << 20);
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
index 29c812408d..29acad34e9 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h"
 #include "tensorflow/core/distributed_runtime/rpc/grpc_client_cq_tag.h"
 #include "tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.h"
+#include "tensorflow/core/distributed_runtime/rpc/grpc_util.h"
 #include "tensorflow/core/distributed_runtime/worker_cache_logger.h"
 #include "tensorflow/core/distributed_runtime/worker_cache_partial.h"
 #include "tensorflow/core/distributed_runtime/worker_interface.h"
@@ -50,6 +51,9 @@ class GrpcWorkerCache : public WorkerCachePartial {
 
   // Explicit destructor to control destruction order.
   ~GrpcWorkerCache() override {
+    // Wait until all live rpcs are done since otherwise the completion
+    // queue shutdown will interfere with rpc operation.
+    live_rpc_counter_.WaitUntilUnused();
     completion_queue_.Shutdown();
     delete polling_thread_;  // Blocks until thread exits.
     delete channel_cache_;
@@ -65,8 +69,8 @@ class GrpcWorkerCache : public WorkerCachePartial {
     } else {
       SharedGrpcChannelPtr channel = channel_cache_->FindWorkerChannel(target);
       if (!channel) return nullptr;
-      WorkerInterface* ret =
-          NewGrpcRemoteWorker(channel, &completion_queue_, &logger_);
+      WorkerInterface* ret = NewGrpcRemoteWorker(&live_rpc_counter_, channel,
+                                                 &completion_queue_, &logger_);
       return ret;
     }
   }
@@ -91,6 +95,7 @@ class GrpcWorkerCache : public WorkerCachePartial {
  private:
   const string local_target_;
   WorkerInterface* const local_worker_;  // Not owned.
+  GrpcCounter live_rpc_counter_;
   GrpcChannelCache* channel_cache_;  // Owned.
   ::grpc::CompletionQueue completion_queue_;
   Thread* polling_thread_;  // Owned.
-- 
GitLab


From 10b6f290b71e684f85afc1790696d5032f29ed40 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 21 Jul 2017 15:30:28 -0700
Subject: [PATCH 1219/1375] Properly schedule merge nodes.

PiperOrigin-RevId: 162792987
---
 .../core/grappler/costs/virtual_scheduler.cc  |   6 +-
 .../grappler/costs/virtual_scheduler_test.cc  | 625 +++++++++++++++---
 2 files changed, 532 insertions(+), 99 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 8b51bb9096..5c1d85d749 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -516,7 +516,11 @@ bool VirtualScheduler::MarkCurrNodeExecuted(const Costs& node_costs) {
     for (auto* output_node : port_num_output_pair.second) {
       auto& output_state = node_map_[output_node];
       output_state.num_inputs_ready++;
-      if (output_state.num_inputs_ready == output_state.inputs.size()) {
+      // Execute a node as soon as all its inputs are ready. Merge nodes are
+      // special since they run as soon as one of their inputs becomes
+      // available.
+      if (output_state.num_inputs_ready == output_state.inputs.size() ||
+          IsMerge(*output_node)) {
         // This output node is now ready.
         output_state.time_ready = curr_time;
         ready_nodes_->AddNode(output_node);
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index 29e3db1b74..fa24b58504 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -83,22 +83,19 @@ class VirtualSchedulerTest : public ::testing::Test {
 
   // Three Conv2Ds with only two in fetch nodes.
   void CreateGrapplerItemWithConv2Ds() {
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
-    auto x = tensorflow::ops::RandomUniform(
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
+    auto x = ops::RandomUniform(
         s.WithOpName("x"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
-    auto y = tensorflow::ops::RandomUniform(
+    auto y = ops::RandomUniform(
         s.WithOpName("y"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
-    auto z = tensorflow::ops::RandomUniform(
+    auto z = ops::RandomUniform(
         s.WithOpName("z"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
-    auto f = tensorflow::ops::RandomUniform(
+    auto f = ops::RandomUniform(
         s.WithOpName("f"), {kernel_, kernel_, depth_in_, depth_out_}, DT_FLOAT);
     std::vector<int> strides = {1, 1, 1, 1};
-    auto c0 =
-        tensorflow::ops::Conv2D(s.WithOpName("c0"), x, f, strides, "SAME");
-    auto c1 =
-        tensorflow::ops::Conv2D(s.WithOpName("c1"), y, f, strides, "SAME");
-    auto c2 =
-        tensorflow::ops::Conv2D(s.WithOpName("c2"), z, f, strides, "SAME");
+    auto c0 = ops::Conv2D(s.WithOpName("c0"), x, f, strides, "SAME");
+    auto c1 = ops::Conv2D(s.WithOpName("c1"), y, f, strides, "SAME");
+    auto c2 = ops::Conv2D(s.WithOpName("c2"), z, f, strides, "SAME");
     GraphDef def;
     TF_CHECK_OK(s.ToGraphDef(&def));
 
@@ -113,13 +110,13 @@ class VirtualSchedulerTest : public ::testing::Test {
 
   // A Conv2D with a variable.
   void CreateGrapplerItemWithConv2DAndVariable() {
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
-    auto x = tensorflow::ops::RandomUniform(
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
+    auto x = ops::RandomUniform(
         s.WithOpName("x"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
-    auto f = tensorflow::ops::Variable(
-        s.WithOpName("f"), {kernel_, kernel_, depth_in_, depth_out_}, DT_FLOAT);
+    auto f = ops::Variable(s.WithOpName("f"),
+                           {kernel_, kernel_, depth_in_, depth_out_}, DT_FLOAT);
     std::vector<int> strides = {1, 1, 1, 1};
-    auto y = tensorflow::ops::Conv2D(s.WithOpName("y"), x, f, strides, "SAME");
+    auto y = ops::Conv2D(s.WithOpName("y"), x, f, strides, "SAME");
     GraphDef def;
     TF_CHECK_OK(s.ToGraphDef(&def));
 
@@ -132,25 +129,23 @@ class VirtualSchedulerTest : public ::testing::Test {
   }
 
   void CreateGrapplerItemWithMatmulChain() {
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
     // Add control dependencies to ensure tests do not rely on specific
     // manager and the order remains consistent for the test.
-    auto a = tensorflow::ops::RandomUniform(s.WithOpName("a"), {3200, 3200},
-                                            DT_FLOAT);
-    auto b = tensorflow::ops::RandomUniform(
-        s.WithOpName("b").WithControlDependencies(a), {3200, 3200}, DT_FLOAT);
-    auto c = tensorflow::ops::RandomUniform(
-        s.WithOpName("c").WithControlDependencies(b), {3200, 3200}, DT_FLOAT);
-    auto d = tensorflow::ops::RandomUniform(
-        s.WithOpName("d").WithControlDependencies(c), {3200, 3200}, DT_FLOAT);
-    auto e = tensorflow::ops::RandomUniform(
-        s.WithOpName("e").WithControlDependencies(d), {3200, 3200}, DT_FLOAT);
-
-    auto ab = tensorflow::ops::MatMul(
-        s.WithOpName("ab").WithControlDependencies(e), a, b);
-    auto abc = tensorflow::ops::MatMul(s.WithOpName("abc"), ab, c);
-    auto abcd = tensorflow::ops::MatMul(s.WithOpName("abcd"), abc, d);
-    auto abcde = tensorflow::ops::MatMul(s.WithOpName("abcde"), abcd, e);
+    auto a = ops::RandomUniform(s.WithOpName("a"), {3200, 3200}, DT_FLOAT);
+    auto b = ops::RandomUniform(s.WithOpName("b").WithControlDependencies(a),
+                                {3200, 3200}, DT_FLOAT);
+    auto c = ops::RandomUniform(s.WithOpName("c").WithControlDependencies(b),
+                                {3200, 3200}, DT_FLOAT);
+    auto d = ops::RandomUniform(s.WithOpName("d").WithControlDependencies(c),
+                                {3200, 3200}, DT_FLOAT);
+    auto e = ops::RandomUniform(s.WithOpName("e").WithControlDependencies(d),
+                                {3200, 3200}, DT_FLOAT);
+
+    auto ab = ops::MatMul(s.WithOpName("ab").WithControlDependencies(e), a, b);
+    auto abc = ops::MatMul(s.WithOpName("abc"), ab, c);
+    auto abcd = ops::MatMul(s.WithOpName("abcd"), abc, d);
+    auto abcde = ops::MatMul(s.WithOpName("abcde"), abcd, e);
 
     GraphDef def;
     TF_CHECK_OK(s.ToGraphDef(&def));
@@ -168,17 +163,13 @@ class VirtualSchedulerTest : public ::testing::Test {
 
   // AddN that takes 4 tensors with 10x10x10x10.
   void CreateGrapplerItemWithAddN() {
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
-    auto x = tensorflow::ops::RandomUniform(s.WithOpName("x"), {10, 10, 10, 10},
-                                            DT_FLOAT);
-    auto y = tensorflow::ops::RandomUniform(s.WithOpName("y"), {10, 10, 10, 10},
-                                            DT_FLOAT);
-    auto z = tensorflow::ops::RandomUniform(s.WithOpName("z"), {10, 10, 10, 10},
-                                            DT_FLOAT);
-    auto w = tensorflow::ops::RandomUniform(s.WithOpName("w"), {10, 10, 10, 10},
-                                            DT_FLOAT);
-    tensorflow::OutputList input_tensors = {x, y, z, w};
-    auto out = tensorflow::ops::AddN(s.WithOpName("out"), input_tensors);
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
+    auto x = ops::RandomUniform(s.WithOpName("x"), {10, 10, 10, 10}, DT_FLOAT);
+    auto y = ops::RandomUniform(s.WithOpName("y"), {10, 10, 10, 10}, DT_FLOAT);
+    auto z = ops::RandomUniform(s.WithOpName("z"), {10, 10, 10, 10}, DT_FLOAT);
+    auto w = ops::RandomUniform(s.WithOpName("w"), {10, 10, 10, 10}, DT_FLOAT);
+    OutputList input_tensors = {x, y, z, w};
+    auto out = ops::AddN(s.WithOpName("out"), input_tensors);
     GraphDef def;
     TF_CHECK_OK(s.ToGraphDef(&def));
 
@@ -192,15 +183,15 @@ class VirtualSchedulerTest : public ::testing::Test {
 
   // NoOp that takes 7 NoOps as control dependency.
   void CreateGrapplerItemWithControlDependency() {
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
     std::vector<string> input_noop_names = {"x", "y", "z", "w", "u", "v", "t"};
-    std::vector<tensorflow::Operation> input_tensors;
+    std::vector<Operation> input_tensors;
     for (const auto& input : input_noop_names) {
-      auto x = tensorflow::ops::NoOp(s.WithOpName(input));
+      auto x = ops::NoOp(s.WithOpName(input));
       input_tensors.push_back(x.operation);
     }
-    auto out = tensorflow::ops::NoOp(
-        s.WithControlDependencies(input_tensors).WithOpName("out"));
+    auto out =
+        ops::NoOp(s.WithControlDependencies(input_tensors).WithOpName("out"));
     GraphDef def;
     TF_CHECK_OK(s.ToGraphDef(&def));
 
@@ -215,33 +206,33 @@ class VirtualSchedulerTest : public ::testing::Test {
   // FusedBN [an op with multiple outputs] with multiple consumers (including
   // control dependency).
   void CreateGrapplerItemWithBatchNorm() {
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
-    auto x = tensorflow::ops::RandomUniform(
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
+    auto x = ops::RandomUniform(
         s.WithOpName("x"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
-    auto scale = tensorflow::ops::RandomUniform(s.WithOpName("scale"),
-                                                {depth_in_}, DT_FLOAT);
-    auto offset = tensorflow::ops::RandomUniform(s.WithOpName("offset"),
-                                                 {depth_in_}, DT_FLOAT);
-    auto mean =
-        tensorflow::ops::RandomUniform(s.WithOpName("mean"), {0}, DT_FLOAT);
-    auto var =
-        tensorflow::ops::RandomUniform(s.WithOpName("var"), {0}, DT_FLOAT);
-
-    auto batch_norm = tensorflow::ops::FusedBatchNorm(
+    auto scale =
+        ops::RandomUniform(s.WithOpName("scale"), {depth_in_}, DT_FLOAT);
+    auto offset =
+        ops::RandomUniform(s.WithOpName("offset"), {depth_in_}, DT_FLOAT);
+    auto mean = ops::RandomUniform(s.WithOpName("mean"), {0}, DT_FLOAT);
+    auto var = ops::RandomUniform(s.WithOpName("var"), {0}, DT_FLOAT);
+
+    auto batch_norm = ops::FusedBatchNorm(
         s.WithOpName("bn"), x, scale, offset, mean, var,
         ops::FusedBatchNorm::IsTraining(true).Epsilon(0.1f));
     auto y = batch_norm.y;
     auto batch_mean = batch_norm.batch_mean;
     auto batch_var = batch_norm.batch_variance;
 
-    auto z1 = tensorflow::ops::Add(s.WithOpName("z1"), x, y);
-    auto z2 = tensorflow::ops::Add(s.WithOpName("z2"), batch_var, batch_var);
-    auto z3 = tensorflow::ops::Add(s.WithOpName("z3"), batch_var, batch_var);
-    std::vector<tensorflow::Operation> input_tensors = {
-        batch_mean.op(), z1.z.op(), z2.z.op(), z3.z.op(),
+    auto z1 = ops::Add(s.WithOpName("z1"), x, y);
+    auto z2 = ops::Add(s.WithOpName("z2"), batch_var, batch_var);
+    auto z3 = ops::Add(s.WithOpName("z3"), batch_var, batch_var);
+    std::vector<Operation> input_tensors = {
+        batch_mean.op(),
+        z1.z.op(),
+        z2.z.op(),
+        z3.z.op(),
     };
-    auto z4 = tensorflow::ops::NoOp(
-        s.WithControlDependencies(batch_var).WithOpName("z4"));
+    auto z4 = ops::NoOp(s.WithControlDependencies(batch_var).WithOpName("z4"));
 
     GraphDef def;
     TF_CHECK_OK(s.ToGraphDef(&def));
@@ -261,21 +252,19 @@ class VirtualSchedulerTest : public ::testing::Test {
   // Create a fused bathcnorm on one device, and send the outputs to another
   // device.
   void CreateGrapplerItemWithInterDeviceTransfers() {
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
 
     // Create a FusedBatchNorm op that has multiple output ports.
-    auto x = tensorflow::ops::RandomUniform(
+    auto x = ops::RandomUniform(
         s.WithOpName("x"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
-    auto scale = tensorflow::ops::RandomUniform(s.WithOpName("scale"),
-                                                {depth_in_}, DT_FLOAT);
-    auto offset = tensorflow::ops::RandomUniform(s.WithOpName("offset"),
-                                                 {depth_in_}, DT_FLOAT);
-    auto mean =
-        tensorflow::ops::RandomUniform(s.WithOpName("mean"), {0}, DT_FLOAT);
-    auto var =
-        tensorflow::ops::RandomUniform(s.WithOpName("var"), {0}, DT_FLOAT);
-
-    auto batch_norm = tensorflow::ops::FusedBatchNorm(
+    auto scale =
+        ops::RandomUniform(s.WithOpName("scale"), {depth_in_}, DT_FLOAT);
+    auto offset =
+        ops::RandomUniform(s.WithOpName("offset"), {depth_in_}, DT_FLOAT);
+    auto mean = ops::RandomUniform(s.WithOpName("mean"), {0}, DT_FLOAT);
+    auto var = ops::RandomUniform(s.WithOpName("var"), {0}, DT_FLOAT);
+
+    auto batch_norm = ops::FusedBatchNorm(
         s.WithOpName("bn"), x, scale, offset, mean, var,
         ops::FusedBatchNorm::IsTraining(true).Epsilon(0.1f));
     auto y = batch_norm.y;
@@ -284,20 +273,18 @@ class VirtualSchedulerTest : public ::testing::Test {
 
     // Copy FusedBatchNorm's outputs to CPU1.
     // y1 and y2 take the same tensor, so there should be only 1 Send and Recv.
-    auto y1 =
-        tensorflow::ops::Identity(s.WithOpName("y1").WithDevice(kCPU1), y);
-    auto y2 =
-        tensorflow::ops::Identity(s.WithOpName("y2").WithDevice(kCPU1), y);
+    auto y1 = ops::Identity(s.WithOpName("y1").WithDevice(kCPU1), y);
+    auto y2 = ops::Identity(s.WithOpName("y2").WithDevice(kCPU1), y);
     // batch_mean1 and batch_var1 take different output ports, so each will
     // initiate Send/Recv.
-    auto batch_mean1 = tensorflow::ops::Identity(
+    auto batch_mean1 = ops::Identity(
         s.WithOpName("batch_mean1").WithDevice(kCPU1), batch_mean);
-    auto batch_var1 = tensorflow::ops::Identity(
-        s.WithOpName("batch_var1").WithDevice(kCPU1), batch_var);
+    auto batch_var1 =
+        ops::Identity(s.WithOpName("batch_var1").WithDevice(kCPU1), batch_var);
     // This is control dependency.
-    auto control_dep = tensorflow::ops::NoOp(s.WithOpName("control_dep")
-                                                 .WithControlDependencies(y)
-                                                 .WithDevice(kCPU1));
+    auto control_dep = ops::NoOp(s.WithOpName("control_dep")
+                                     .WithControlDependencies(y)
+                                     .WithDevice(kCPU1));
 
     GraphDef def;
     TF_CHECK_OK(s.ToGraphDef(&def));
@@ -316,6 +303,402 @@ class VirtualSchedulerTest : public ::testing::Test {
     dependency_["control_dep"] = {"bn"};
   }
 
+  // A simple while loop
+  void CreateGrapplerItemWithLoop() {
+    // Test graph produced in python using:
+    /*
+      with tf.Graph().as_default():
+      i0 = tf.constant(0)
+      m0 = tf.ones([2, 2])
+      c = lambda i, m: i < 10
+      b = lambda i, m: [i+1, tf.concat([m, m], axis=0)]
+      r = tf.while_loop(
+      c, b, loop_vars=[i0, m0],
+      shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])])
+      with open('/tmp/graph.pbtxt', 'w') as f:
+      f.write(str(tf.get_default_graph().as_graph_def()))
+    */
+    const string gdef_ascii = R"EOF(
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "ones"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 2
+          }
+          dim {
+            size: 2
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
+node {
+  name: "while/Enter"
+  op: "Enter"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Enter_1"
+  op: "Enter"
+  input: "ones"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "frame_name"
+    value {
+      s: "while/while/"
+    }
+  }
+  attr {
+    key: "is_constant"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "parallel_iterations"
+    value {
+      i: 10
+    }
+  }
+}
+node {
+  name: "while/Merge"
+  op: "Merge"
+  input: "while/Enter"
+  input: "while/NextIteration"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Merge_1"
+  op: "Merge"
+  input: "while/Enter_1"
+  input: "while/NextIteration_1"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Less/y"
+  op: "Const"
+  input: "^while/Merge"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 10
+      }
+    }
+  }
+}
+node {
+  name: "while/Less"
+  op: "Less"
+  input: "while/Merge"
+  input: "while/Less/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/LoopCond"
+  op: "LoopCond"
+  input: "while/Less"
+}
+node {
+  name: "while/Switch"
+  op: "Switch"
+  input: "while/Merge"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge"
+      }
+    }
+  }
+}
+node {
+  name: "while/Switch_1"
+  op: "Switch"
+  input: "while/Merge_1"
+  input: "while/LoopCond"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "_class"
+    value {
+      list {
+        s: "loc:@while/Merge_1"
+      }
+    }
+  }
+}
+node {
+  name: "while/Identity"
+  op: "Identity"
+  input: "while/Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Identity_1"
+  op: "Identity"
+  input: "while/Switch_1:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/add/y"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "while/add"
+  op: "Add"
+  input: "while/Identity"
+  input: "while/add/y"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/concat/axis"
+  op: "Const"
+  input: "^while/Identity"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+        int_val: 0
+      }
+    }
+  }
+}
+node {
+  name: "while/concat"
+  op: "ConcatV2"
+  input: "while/Identity_1"
+  input: "while/Identity_1"
+  input: "while/concat/axis"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "Tidx"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration"
+  op: "NextIteration"
+  input: "while/add"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/NextIteration_1"
+  op: "NextIteration"
+  input: "while/concat"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "while/Exit"
+  op: "Exit"
+  input: "while/Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}
+node {
+  name: "while/Exit_1"
+  op: "Exit"
+  input: "while/Switch_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+versions {
+  producer: 21
+}
+  )EOF";
+
+    grappler_item_.reset(new GrapplerItem);
+    CHECK(protobuf::TextFormat::ParseFromString(gdef_ascii,
+                                                &grappler_item_->graph));
+    grappler_item_->id = "test_graph";
+    grappler_item_->fetch = {"while/Exit", "while/Exit_1"};
+  }
+
   // Call this after creating grappler_item_ and setting up dependency_.
   void InitScheduler() {
     scheduler_.reset(new TestVirtualScheduler(
@@ -329,9 +712,10 @@ class VirtualSchedulerTest : public ::testing::Test {
     int64 exec_cost = 0;
     if (info.op_info.op() == "MatMul") {
       exec_cost = 2000000000;
-    }
-    if (info.op_info.op() == "RandomUniform") {
+    } else if (info.op_info.op() == "RandomUniform") {
       exec_cost = 1000000000;
+    } else {
+      exec_cost = 1000;
     }
     c.execution_time = Costs::NanoSeconds(exec_cost);
     return c;
@@ -613,10 +997,11 @@ TEST_F(VirtualSchedulerTest, SummaryCostTest) {
   auto ops_executed = RunScheduler("");
   Costs c = scheduler_->Summary();
 
-  // RandomUniform - 5
-  // Matmuls - 4 * 2 = 8
-  // Total: 13
-  EXPECT_EQ(13000000, c.execution_time.asMicroSeconds().count());
+  // RandomUniform - 5 * 1s
+  // Matmuls - 4 * 2s = 8
+  // Misc - 5 * 1us
+  // Total: 13000005
+  EXPECT_EQ(13000005, c.execution_time.asMicroSeconds().count());
 }
 
 // Like the above SummaryCostTest, but makes sure the stepstats timeline is
@@ -629,7 +1014,7 @@ TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
   RunMetadata metadata;
   Costs c = scheduler_->Summary(&metadata);
   StepStats stepstats = metadata.step_stats();
-  EXPECT_EQ(13000000, c.execution_time.asMicroSeconds().count());
+  EXPECT_EQ(13000005, c.execution_time.asMicroSeconds().count());
 
   // Should only be 1 device!
   EXPECT_EQ(1, stepstats.dev_stats().size());
@@ -661,7 +1046,7 @@ TEST_F(VirtualSchedulerTest, SummaryCostStepStatsTest) {
   }
 
   // The base start_time is the time to compute RandomUniforms
-  int64 cur_time = static_cast<int64>(5000000);
+  int64 cur_time = static_cast<int64>(5000005);
   // The increment is the execution time of one matmul. See
   // CreateGrapplerItemWithMatmulChain for details.
   int64 increment = static_cast<int64>(2000000);
@@ -935,5 +1320,49 @@ TEST_F(VirtualSchedulerTest, InterDeviceTransfer) {
   EXPECT_EQ(get_output_size(recv_op_names[-1]), 4);
   EXPECT_EQ(get_output_size(send_op_names[-1]), 4);
 }
+
+TEST_F(VirtualSchedulerTest, WhileLoop) {
+  // Init.
+  CreateGrapplerItemWithLoop();
+  InitScheduler();
+
+  // Run the scheduler.
+  RunScheduler("");
+
+  // Check the timeline
+  RunMetadata metadata;
+  scheduler_->Summary(&metadata);
+
+  int num_next_iteration = 0;
+  int num_next_iteration_1 = 0;
+  int num_exit = 0;
+  int num_exit_1 = 0;
+  for (const auto& device_step_stats : metadata.step_stats().dev_stats()) {
+    for (const auto& stats : device_step_stats.node_stats()) {
+      std::cout << stats.DebugString() << std::endl;
+      if (stats.node_name() == "while/NextIteration") {
+        ++num_next_iteration;
+        EXPECT_EQ(19, stats.all_start_micros());
+      } else if (stats.node_name() == "while/NextIteration_1") {
+        ++num_next_iteration_1;
+        EXPECT_EQ(20, stats.all_start_micros());
+      } else if (stats.node_name() == "while/Exit") {
+        ++num_exit;
+        EXPECT_EQ(14, stats.all_start_micros());
+      } else if (stats.node_name() == "while/Exit_1") {
+        ++num_exit_1;
+        EXPECT_EQ(12, stats.all_start_micros());
+      }
+    }
+  }
+
+  // Make sure we went though the body of the loop once, and that the output of
+  // the loop was scheduled as well.
+  EXPECT_EQ(1, num_next_iteration);
+  EXPECT_EQ(1, num_next_iteration_1);
+  EXPECT_EQ(1, num_exit);
+  EXPECT_EQ(1, num_exit_1);
+}
+
 }  // end namespace grappler
 }  // end namespace tensorflow
-- 
GitLab


From 2cd38ace94ad142ac94d2f36f3805398c173645c Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Fri, 21 Jul 2017 15:48:52 -0700
Subject: [PATCH 1220/1375] Internal change.

PiperOrigin-RevId: 162794963
---
 tensorflow/python/kernel_tests/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 3e4a6a3666..cac05c372a 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1716,7 +1716,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "shape_ops_test",
-    size = "small",
+    size = "medium",
     srcs = ["shape_ops_test.py"],
     additional_deps = [
         "//third_party/py/numpy",
-- 
GitLab


From 927c5a94b60bbfc318514e3de81fc3df2b9d36d7 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Fri, 21 Jul 2017 16:12:31 -0700
Subject: [PATCH 1221/1375] Automated g4 rollback of changelist 162782896

PiperOrigin-RevId: 162797577
---
 .../core/grappler/costs/virtual_scheduler.cc  |  48 ++-----
 .../core/grappler/costs/virtual_scheduler.h   |   3 +
 .../grappler/costs/virtual_scheduler_test.cc  | 128 ------------------
 3 files changed, 11 insertions(+), 168 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 5c1d85d749..1dad81ed90 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -56,28 +56,6 @@ Costs CombineCosts(const Costs& left, const Costs& right) {
           << " max_per_op_streaming=" << result.max_per_op_streaming;
   return result;
 }
-
-// Key to the cached _Recv ops map, and its hash and predicate structures.
-struct RecvNodeDescriptor {
-  const NodeDef* node;
-  const int port_num;
-  const string& device;
-};
-
-struct RecvNodeDescritorHash {
-  std::size_t operator()(const RecvNodeDescriptor& recv_node) const {
-    return std::hash<const NodeDef*>()(recv_node.node) ^
-           std::hash<int>()(recv_node.port_num) ^
-           std::hash<string>()(recv_node.device);
-  }
-};
-
-struct RecvNodeDescriptorEqual {
-  bool operator()(const RecvNodeDescriptor& a,
-                  const RecvNodeDescriptor& b) const {
-    return a.node == b.node && a.port_num == b.port_num && a.device == b.device;
-  }
-};
 }  // namespace
 
 VirtualScheduler::VirtualScheduler(const GrapplerItem* grappler_item,
@@ -131,11 +109,6 @@ Status VirtualScheduler::Init() {
     name_to_node[node->name()] = node;
   }
 
-  // To reuse _Recv ops.
-  std::unordered_map<RecvNodeDescriptor, const NodeDef*, RecvNodeDescritorHash,
-                     RecvNodeDescriptorEqual>
-      cached_recv_nodes;
-
   // Build node_map; for each node, create its NodeState and connect its inputs
   // and outputs.
   for (const auto* curr_node : nodes) {
@@ -158,14 +131,12 @@ Status VirtualScheduler::Init() {
         auto& input_node_state = GetNodeStateOrCreateIt(input_node);
         input_node_state.outputs[input_node_port_num].push_back(curr_node);
       } else {
-        RecvNodeDescriptor recv_node = {.node = input_node,
-                                        .port_num = input_node_port_num,
-                                        .device = curr_node_device};
-        auto it = cached_recv_nodes.find(recv_node);
-        if (it != cached_recv_nodes.end()) {
+        if (cached_recv_nodes_.count(input_node) > 0 &&
+            cached_recv_nodes_[input_node].count(curr_node_device) > 0) {
           // Different device, but found an already-cached copy (a _Recv op);
           // connect the _Recv to curr_node.
-          const NodeDef* recv_op = it->second;
+          const auto* recv_op =
+              cached_recv_nodes_[input_node][curr_node_device];
           // recv_op's output port is hard-coded to zero.
           curr_node_state.inputs.push_back(std::make_pair(recv_op, 0));
           auto& input_node_state = node_map_.at(recv_op);
@@ -185,7 +156,7 @@ Status VirtualScheduler::Init() {
           input_node_state.outputs[input_node_port_num].push_back(send);
 
           // Cache the _Recv op for future use.
-          cached_recv_nodes[recv_node] = recv;
+          cached_recv_nodes_[input_node][curr_node_device] = recv;
         }
       }
     }
@@ -298,13 +269,11 @@ std::pair<const NodeDef*, const NodeDef*> VirtualScheduler::CreateSendRecv(
   // input names, attrs, etc.
 
   auto input_node_port_num = NodePosition(input_name);
-  const string port_num_string =
-      input_node_port_num >= 0 ? std::to_string(input_node_port_num) : "minus1";
 
   // _Send op.
   auto* send = new NodeDef();
-  send->set_name("Send " + from->name() + ":" + port_num_string + " from " +
-                 DeviceName(from) + " to " + DeviceName(to));
+  send->set_name("Send " + from->name() + " from " + DeviceName(from) + " to " +
+                 DeviceName(to));
   send->set_op("_Send");
   send->add_input(from->name());
   send->set_device(ChannelDeviceName(from, to));
@@ -315,8 +284,7 @@ std::pair<const NodeDef*, const NodeDef*> VirtualScheduler::CreateSendRecv(
 
   // _Recv op.
   auto* recv = new NodeDef();
-  recv->set_name("Recv " + from->name() + ":" + port_num_string + " on " +
-                 DeviceName(to));
+  recv->set_name("Recv " + from->name() + " on " + DeviceName(to));
   recv->set_op("_Recv");
   recv->add_input(send->name());
   recv->set_device(DeviceName(to));
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index e9abecb122..d00766d9fa 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -254,6 +254,9 @@ class VirtualScheduler {
 
   // Pool of NodeDefs for SendRecv and Identity ops created.
   std::vector<std::unique_ptr<NodeDef>> additional_nodes_;
+  // Cache of nodes transferred to another device.
+  std::unordered_map<const NodeDef*, std::unordered_map<string, const NodeDef*>>
+      cached_recv_nodes_;
 
   // Stats:
   std::map<string, int> op_counts_;  // Op counts with key with input shape.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index fa24b58504..af45623ff2 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -36,7 +36,6 @@ class TestVirtualScheduler : public VirtualScheduler {
   FRIEND_TEST(VirtualSchedulerTest, ControlDependency);
   FRIEND_TEST(VirtualSchedulerTest, ComplexDependency);
   FRIEND_TEST(VirtualSchedulerTest, Variable);
-  FRIEND_TEST(VirtualSchedulerTest, InterDeviceTransfer);
 };
 
 class VirtualSchedulerTest : public ::testing::Test {
@@ -44,7 +43,6 @@ class VirtualSchedulerTest : public ::testing::Test {
   NodeDef node1_, node2_, node3_, node4_, node5_, node6_;
 
   const string kCPU0 = "/job:localhost/replica:0/task:0/cpu:0";
-  const string kCPU1 = "/job:localhost/replica:0/task:0/cpu:1";
 
   DeviceProperties GetDummyCPUDevice() {
     // Create CPU with 2 cores, 4 Ghz freq, 2 GB/s mem bandwidth.
@@ -76,7 +74,6 @@ class VirtualSchedulerTest : public ::testing::Test {
     // IMPORTANT: Device is not actually ever used in the test case since
     // force_cpu_type is defaulted to "Haswell"
     devices[kCPU0] = cpu_device;
-    devices[kCPU1] = cpu_device;
     cluster_.reset(new VirtualCluster(devices));
     placer_.reset(new VirtualPlacer(cluster_.get()));
   }
@@ -249,60 +246,6 @@ class VirtualSchedulerTest : public ::testing::Test {
     dependency_["z4"] = {"bn"};
   }
 
-  // Create a fused bathcnorm on one device, and send the outputs to another
-  // device.
-  void CreateGrapplerItemWithInterDeviceTransfers() {
-    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
-
-    // Create a FusedBatchNorm op that has multiple output ports.
-    auto x = ops::RandomUniform(
-        s.WithOpName("x"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
-    auto scale =
-        ops::RandomUniform(s.WithOpName("scale"), {depth_in_}, DT_FLOAT);
-    auto offset =
-        ops::RandomUniform(s.WithOpName("offset"), {depth_in_}, DT_FLOAT);
-    auto mean = ops::RandomUniform(s.WithOpName("mean"), {0}, DT_FLOAT);
-    auto var = ops::RandomUniform(s.WithOpName("var"), {0}, DT_FLOAT);
-
-    auto batch_norm = ops::FusedBatchNorm(
-        s.WithOpName("bn"), x, scale, offset, mean, var,
-        ops::FusedBatchNorm::IsTraining(true).Epsilon(0.1f));
-    auto y = batch_norm.y;
-    auto batch_mean = batch_norm.batch_mean;
-    auto batch_var = batch_norm.batch_variance;
-
-    // Copy FusedBatchNorm's outputs to CPU1.
-    // y1 and y2 take the same tensor, so there should be only 1 Send and Recv.
-    auto y1 = ops::Identity(s.WithOpName("y1").WithDevice(kCPU1), y);
-    auto y2 = ops::Identity(s.WithOpName("y2").WithDevice(kCPU1), y);
-    // batch_mean1 and batch_var1 take different output ports, so each will
-    // initiate Send/Recv.
-    auto batch_mean1 = ops::Identity(
-        s.WithOpName("batch_mean1").WithDevice(kCPU1), batch_mean);
-    auto batch_var1 =
-        ops::Identity(s.WithOpName("batch_var1").WithDevice(kCPU1), batch_var);
-    // This is control dependency.
-    auto control_dep = ops::NoOp(s.WithOpName("control_dep")
-                                     .WithControlDependencies(y)
-                                     .WithDevice(kCPU1));
-
-    GraphDef def;
-    TF_CHECK_OK(s.ToGraphDef(&def));
-
-    grappler_item_.reset(new GrapplerItem);
-    grappler_item_->id = "test_conv2d_graph";
-    grappler_item_->graph = def;
-    grappler_item_->fetch = {"y1", "y2", "batch_mean1", "batch_var1",
-                             "control_dep"};
-
-    dependency_["bn"] = {"x", "mean", "var"};
-    dependency_["y1"] = {"bn"};
-    dependency_["y2"] = {"bn"};
-    dependency_["batch_mean1"] = {"bn"};
-    dependency_["batch_var1"] = {"bn"};
-    dependency_["control_dep"] = {"bn"};
-  }
-
   // A simple while loop
   void CreateGrapplerItemWithLoop() {
     // Test graph produced in python using:
@@ -1250,77 +1193,6 @@ TEST_F(VirtualSchedulerTest, Variable) {
                               cpu_state.mem_usage_snapshot_at_peak);
 }
 
-TEST_F(VirtualSchedulerTest, InterDeviceTransfer) {
-  // Init.
-  CreateGrapplerItemWithInterDeviceTransfers();
-  InitScheduler();
-
-  // Run the scheduler.
-  auto ops_executed = RunScheduler("");
-
-  // Helper lambda to extract port num from _Send and _Recv op name.
-  auto get_port_num = [](const string& name) -> int {
-    if (name.find("bn:0") != std::string::npos) {
-      return 0;
-    } else if (name.find("bn:1") != std::string::npos) {
-      return 1;
-    } else if (name.find("bn:2") != std::string::npos) {
-      return 2;
-    } else if (name.find("bn:minus1") != std::string::npos) {
-      return -1;
-    }
-    return -999;
-  };
-
-  // Reorganize ops_executed for further testing.
-  std::unordered_map<string, int> op_count;
-  std::unordered_map<int, string> recv_op_names;
-  std::unordered_map<int, string> send_op_names;
-  for (const auto& x : ops_executed) {
-    const auto& name = x.first;
-    const auto& node_info = x.second;
-    const auto& op = node_info.op_info.op();
-    if (op == "_Recv") {
-      recv_op_names[get_port_num(name)] = name;
-    } else if (op == "_Send") {
-      send_op_names[get_port_num(name)] = name;
-    }
-    op_count[op]++;
-  }
-
-  // Same number of _Send and _Recv.
-  EXPECT_EQ(op_count.at("_Send"), op_count.at("_Recv"));
-
-  // Expect 4 Send and Recvs each: port 0, 1, and, 2, and control dependency.
-  EXPECT_EQ(op_count.at("_Recv"), 4);
-  EXPECT_EQ(op_count.at("_Send"), 4);
-
-  // Helper lambda for extracting output Tensor size.
-  auto get_output_size = [this, ops_executed](const string& name) -> int64 {
-    const auto& output_properties_ = ops_executed.at(name).op_info.outputs();
-    std::vector<OpInfo::TensorProperties> output_properties;
-    for (const auto& output_property : output_properties_) {
-      output_properties.push_back(output_property);
-    }
-    return scheduler_->CalculateOutputSize(output_properties, 0);
-
-  };
-
-  // Validate transfer size.
-  // Batchnorm output y is 4D vector: batch x width x width x depth.
-  int input_size = 4 * batch_size_ * width_ * height_ * depth_in_;
-  EXPECT_EQ(get_output_size(recv_op_names[0]), input_size);
-  EXPECT_EQ(get_output_size(send_op_names[0]), input_size);
-  // Mean and vars are 1-D vector with size depth_in_.
-  EXPECT_EQ(get_output_size(recv_op_names[1]), 4 * depth_in_);
-  EXPECT_EQ(get_output_size(send_op_names[1]), 4 * depth_in_);
-  EXPECT_EQ(get_output_size(recv_op_names[2]), 4 * depth_in_);
-  EXPECT_EQ(get_output_size(send_op_names[2]), 4 * depth_in_);
-  // Control dependency size is 4B.
-  EXPECT_EQ(get_output_size(recv_op_names[-1]), 4);
-  EXPECT_EQ(get_output_size(send_op_names[-1]), 4);
-}
-
 TEST_F(VirtualSchedulerTest, WhileLoop) {
   // Init.
   CreateGrapplerItemWithLoop();
-- 
GitLab


From bb4a6168eaa0c6752d19e352f9c9dd53c9c6a6ae Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 21 Jul 2017 16:20:01 -0700
Subject: [PATCH 1222/1375] Prune more types of nodes from the graph

PiperOrigin-RevId: 162798360
---
 tensorflow/core/grappler/op_types.cc                | 7 ++++++-
 tensorflow/core/grappler/op_types.h                 | 1 +
 tensorflow/core/grappler/optimizers/BUILD           | 1 +
 tensorflow/core/grappler/optimizers/model_pruner.cc | 3 ++-
 4 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index d2dd784b09..9b2584f970 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -37,7 +37,7 @@ bool IsDequeueOp(const NodeDef& node) {
 
 bool IsIdentity(const NodeDef& node) {
   const auto& op = node.op();
-  return op == "Identity";
+  return op == "Identity" || op == "RefIdentity";
 }
 
 bool IsMerge(const NodeDef& node) {
@@ -79,6 +79,11 @@ bool IsSend(const NodeDef& node) {
   return op == "_Send";
 }
 
+bool IsStopGradient(const NodeDef& node) {
+  const auto& op = node.op();
+  return op == "StopGradient" || op == "PreventGradient";
+}
+
 bool IsSwitch(const NodeDef& node) {
   const auto& op = node.op();
   return op == "Switch";
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index 603095b44d..9c9dd22e2c 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -33,6 +33,7 @@ bool IsRecv(const NodeDef& node);
 bool IsReduction(const NodeDef& node);
 bool IsReshape(const NodeDef& node);
 bool IsSend(const NodeDef& node);
+bool IsStopGradient(const NodeDef& node);
 bool IsSwitch(const NodeDef& node);
 bool IsTranspose(const NodeDef& node);
 bool IsVariable(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 5a7f11e149..edd3fae7b2 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -164,6 +164,7 @@ cc_library(
         ":graph_rewriter",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:op_types",
         "//tensorflow/core/grappler:utils",
     ],
 )
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index 676b41e53d..df9aca8aa3 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/op_types.h"
 #include "tensorflow/core/grappler/optimizers/graph_rewriter.h"
 #include "tensorflow/core/grappler/utils.h"
 
@@ -44,7 +45,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
   for (auto& node : item.graph.node()) {
     // Remove the stop gradient nodes since they serve no purpose once the graph
     // is built. Also remove Identity ops.
-    if (node.op() != "StopGradient" && node.op() != "Identity") {
+    if (!IsStopGradient(node) && !IsIdentity(node)) {
       continue;
     }
     // Don't remove nodes that must be preserved.
-- 
GitLab


From 475af8f535b41caa8d144ad28782ca20d16187e2 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Fri, 21 Jul 2017 16:42:04 -0700
Subject: [PATCH 1223/1375] Internal change.

PiperOrigin-RevId: 162800491
---
 tensorflow/contrib/keras/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/keras/BUILD b/tensorflow/contrib/keras/BUILD
index 78e86542e7..b923fc7e9a 100644
--- a/tensorflow/contrib/keras/BUILD
+++ b/tensorflow/contrib/keras/BUILD
@@ -328,6 +328,7 @@ py_test(
     name = "convolutional_recurrent_test",
     size = "medium",
     srcs = ["python/keras/layers/convolutional_recurrent_test.py"],
+    shard_count = 2,
     srcs_version = "PY2AND3",
     tags = ["noasan"],  # times out b/63678675
     deps = [
-- 
GitLab


From aaf7c58f311278446848305cea8c3123dca76752 Mon Sep 17 00:00:00 2001
From: Andrew Selle <aselle@google.com>
Date: Fri, 21 Jul 2017 16:58:40 -0700
Subject: [PATCH 1224/1375] Create generator for tflite examples.

This is end-to-end in that it starts as TensorFlow python,
producing tflite models.

PiperOrigin-RevId: 162801924
---
 .../mobilenet_224_gender_basic_fixed.mb        | Bin 0 -> 869540 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 tensorflow/contrib/testing/testdata/mobilenet_224_gender_basic_fixed.mb

diff --git a/tensorflow/contrib/testing/testdata/mobilenet_224_gender_basic_fixed.mb b/tensorflow/contrib/testing/testdata/mobilenet_224_gender_basic_fixed.mb
new file mode 100644
index 0000000000000000000000000000000000000000..fefe5f4d5f5a26419cc165ac895865b95b66ebf7
GIT binary patch
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U&&|(F5rQ#rtlfrs6TW~P0RMas+5i9m

literal 0
HcmV?d00001

-- 
GitLab


From 4895cec77fac920a77a79e9e6f79951503483303 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 17:27:28 -0700
Subject: [PATCH 1225/1375] fix lint issues in *stage_op_test

PiperOrigin-RevId: 162804579
---
 .../python/kernel_tests/map_stage_op_test.py  | 63 +++++++++++--------
 .../python/kernel_tests/stage_op_test.py      |  8 +--
 2 files changed, 42 insertions(+), 29 deletions(-)

diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py
index 64f8f4bb6d..b898a15cc8 100644
--- a/tensorflow/python/kernel_tests/map_stage_op_test.py
+++ b/tensorflow/python/kernel_tests/map_stage_op_test.py
@@ -46,7 +46,7 @@ class MapStageTest(test.TestCase):
     with self.test_session(use_gpu=True, graph=G) as sess:
       sess.run(stage, feed_dict={x: -1, pi: 0})
       for i in range(10):
-        _, yval = sess.run([stage, y], feed_dict={x: i, pi: i+1, gi:i})
+        _, yval = sess.run([stage, y], feed_dict={x: i, pi: i + 1, gi: i})
         self.assertAllClose(4 * (i - 1) * (i - 1) * 128, yval, rtol=1e-4)
 
   def testMultiple(self):
@@ -67,7 +67,7 @@ class MapStageTest(test.TestCase):
     with self.test_session(use_gpu=True, graph=G) as sess:
       sess.run(stage, feed_dict={x: -1, pi: 0})
       for i in range(10):
-        _, yval = sess.run([stage, y], feed_dict={x: i, pi: i+1, gi:i})
+        _, yval = sess.run([stage, y], feed_dict={x: i, pi: i + 1, gi: i})
         self.assertAllClose(
             4 * (i - 1) * (i - 1) * (i - 1) * 128, yval, rtol=1e-4)
 
@@ -94,7 +94,7 @@ class MapStageTest(test.TestCase):
     with self.test_session(use_gpu=True, graph=G) as sess:
       sess.run(stage, feed_dict={x: -1, pi: 0})
       for i in range(10):
-        _, yval = sess.run([stage, y], feed_dict={x: i, pi: i+1, gi:i})
+        _, yval = sess.run([stage, y], feed_dict={x: i, pi: i + 1, gi: i})
         self.assertAllClose(
             4 * (i - 1) * (i - 1) * (i - 1) * 128, yval, rtol=1e-4)
 
@@ -139,7 +139,7 @@ class MapStageTest(test.TestCase):
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       for i in range(n):
-        sess.run(stage, feed_dict={x:i, pi:i})
+        sess.run(stage, feed_dict={x: i, pi: i})
 
       for i in range(n):
         self.assertTrue(sess.run(peek, feed_dict={gi: i}) == i)
@@ -372,16 +372,26 @@ class MapStageTest(test.TestCase):
       # 1 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [1, 1])
       # We can now obtain tuple associated with key 0
-      self.assertTrue(sess.run([key, ret], feed_dict={gi:0})
-                              == [0, { 'x':1, 'f':2, 'v':1}])
+      self.assertTrue(
+          sess.run([key, ret],
+                   feed_dict={gi: 0}) == [0, {
+                       'x': 1,
+                       'f': 2,
+                       'v': 1
+                   }])
 
       # 0 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [0, 1])
       # Now complete key 1 with tuple entry v
       sess.run(stage_v, feed_dict={pi: 1, v: 3})
       # We can now obtain tuple associated with key 1
-      self.assertTrue(sess.run([key, ret], feed_dict={gi:1})
-                              == [1, { 'x':1, 'f':2, 'v':3}])
+      self.assertTrue(
+          sess.run([key, ret],
+                   feed_dict={gi: 1}) == [1, {
+                       'x': 1,
+                       'f': 2,
+                       'v': 3
+                   }])
 
   def testPartialIndexInsert(self):
     with ops.Graph().as_default() as G:
@@ -417,16 +427,14 @@ class MapStageTest(test.TestCase):
       # 1 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [1, 1])
       # We can now obtain tuple associated with key 0
-      self.assertTrue(sess.run([key, ret], feed_dict={gi:0})
-                              == [0, [1, 1, 2]])
+      self.assertTrue(sess.run([key, ret], feed_dict={gi: 0}) == [0, [1, 1, 2]])
 
       # 0 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [0, 1])
       # Now complete key 1 with tuple entry v
       sess.run(stage_v, feed_dict={pi: 1, v: 3})
       # We can now obtain tuple associated with key 1
-      self.assertTrue(sess.run([key, ret], feed_dict={gi:1})
-                              == [1, [1,3, 2]])
+      self.assertTrue(sess.run([key, ret], feed_dict={gi: 1}) == [1, [1, 3, 2]])
 
   def testPartialDictGetsAndPeeks(self):
     with ops.Graph().as_default() as G:
@@ -471,23 +479,25 @@ class MapStageTest(test.TestCase):
       self.assertTrue(sess.run([size, isize]) == [1, 1])
 
       # We can now peek at 'x' and 'f' values associated with key 0
-      self.assertTrue(sess.run(peek_xf, feed_dict={pei:0})
-                              == { 'x':1, 'f':2})
+      self.assertTrue(sess.run(peek_xf, feed_dict={pei: 0}) == {'x': 1, 'f': 2})
       # Peek at 'v' value associated with key 0
-      self.assertTrue(sess.run(peek_v, feed_dict={pei:0})
-                              == { 'v':1})
+      self.assertTrue(sess.run(peek_v, feed_dict={pei: 0}) == {'v': 1})
       # 1 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [1, 1])
 
       # We can now obtain 'x' and 'f' values associated with key 0
-      self.assertTrue(sess.run([key_xf, get_xf], feed_dict={gi:0})
-                              == [0, { 'x':1, 'f':2}])
+      self.assertTrue(
+          sess.run([key_xf, get_xf],
+                   feed_dict={gi: 0}) == [0, {
+                       'x': 1,
+                       'f': 2
+                   }])
       # Still have 1 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [1, 1])
 
       # We can no longer get 'x' and 'f' from key 0
       with self.assertRaises(errors.InvalidArgumentError) as cm:
-        sess.run([key_xf, get_xf], feed_dict={gi:0})
+        sess.run([key_xf, get_xf], feed_dict={gi: 0})
 
       exc_str = ("Tensor at index '0' for key '0' "
                 "has already been removed.")
@@ -495,8 +505,10 @@ class MapStageTest(test.TestCase):
       self.assertTrue(exc_str in cm.exception.message)
 
       # Obtain 'v' value associated with key 0
-      self.assertTrue(sess.run([key_v, get_v], feed_dict={gi:0})
-                              == [0, { 'v':1}])
+      self.assertTrue(
+          sess.run([key_v, get_v], feed_dict={gi: 0}) == [0, {
+              'v': 1
+          }])
       # 0 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [0, 1])
 
@@ -506,13 +518,14 @@ class MapStageTest(test.TestCase):
       self.assertTrue(sess.run([size, isize]) == [1, 0])
 
       # Pop without key to obtain 'x' and 'f' values associated with key 1
-      self.assertTrue(sess.run([pop_key_xf, pop_xf])
-                              == [1, { 'x':1, 'f':2}])
+      self.assertTrue(sess.run([pop_key_xf, pop_xf]) == [1, {'x': 1, 'f': 2}])
       # still 1 complete and 1 incomplete entry
       self.assertTrue(sess.run([size, isize]) == [1, 0])
       # We can now obtain 'x' and 'f' values associated with key 1
-      self.assertTrue(sess.run([pop_key_v, pop_v], feed_dict={pi:1})
-                              == [1, { 'v': 1 }])
+      self.assertTrue(
+          sess.run([pop_key_v, pop_v], feed_dict={pi: 1}) == [1, {
+              'v': 1
+          }])
       # Nothing is left
       self.assertTrue(sess.run([size, isize]) == [0, 0])
 
diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py
index 4073fc6b79..0f4cc59653 100644
--- a/tensorflow/python/kernel_tests/stage_op_test.py
+++ b/tensorflow/python/kernel_tests/stage_op_test.py
@@ -125,10 +125,10 @@ class StageTest(test.TestCase):
 
     with self.test_session(use_gpu=True, graph=G) as sess:
       for i in range(10):
-        sess.run(stage, feed_dict={x:i})
+        sess.run(stage, feed_dict={x: i})
 
       for i in range(10):
-        self.assertTrue(sess.run(peek, feed_dict={p:i}) == i)
+        self.assertTrue(sess.run(peek, feed_dict={p: i}) == [i])
 
   def testSizeAndClear(self):
     with ops.Graph().as_default() as G:
@@ -210,7 +210,7 @@ class StageTest(test.TestCase):
 
       # Clear the staging area completely
       for i in range(n):
-        self.assertTrue(sess.run(ret) == i)
+        self.assertTrue(sess.run(ret) == [i])
 
       # It should now be empty
       self.assertTrue(sess.run(size) == 0)
@@ -273,7 +273,7 @@ class StageTest(test.TestCase):
 
       # Clear the staging area completely
       for i in range(n):
-        self.assertTrue(np.all(sess.run(ret) == i))
+        self.assertTrue(np.all(sess.run(ret)[0] == i))
 
       self.assertTrue(sess.run(size) == 0)
 
-- 
GitLab


From 9c08e99a02c682acca0dcb9be9f79c3a9d38b615 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Fri, 21 Jul 2017 17:51:03 -0700
Subject: [PATCH 1226/1375] Implement VIMCO-like objective for approx Csiszar
 f-Divergence. Simplify monte_carlo.expectation_v2 calculation when doing
 score-trick.

PiperOrigin-RevId: 162806278
---
 .../kernel_tests/csiszar_divergence_test.py   | 208 +++++++++++++++---
 .../python/kernel_tests/monte_carlo_test.py   |  90 ++++++++
 .../python/ops/csiszar_divergence.py          |   1 +
 .../python/ops/csiszar_divergence_impl.py     | 202 +++++++++++++++--
 .../bayesflow/python/ops/monte_carlo_impl.py  | 107 +++++++--
 5 files changed, 544 insertions(+), 64 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
index dda3f60065..d06b69885c 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py
@@ -37,6 +37,14 @@ from tensorflow.python.platform import test
 cd = csiszar_divergence_impl
 
 
+def tridiag(d, diag_value, offdiag_value):
+  """d x d matrix with given value on diag, and one super/sub diag."""
+  diag_mat = linalg_ops.eye(d) * (diag_value - offdiag_value)
+  three_bands = array_ops.matrix_band_part(
+      array_ops.fill([d, d], offdiag_value), 1, 1)
+  return diag_mat + three_bands
+
+
 class AmariAlphaTest(test.TestCase):
 
   def setUp(self):
@@ -483,14 +491,14 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
 
       approx_kl = cd.monte_carlo_csiszar_f_divergence(
           f=cd.kl_forward,
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
 
       approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
           f=lambda logu: cd.kl_forward(logu, self_normalized=True),
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
@@ -517,14 +525,14 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
 
       approx_kl = cd.monte_carlo_csiszar_f_divergence(
           f=cd.kl_reverse,
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
 
       approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
           f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
@@ -540,33 +548,26 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       self.assertAllClose(approx_kl_self_normalized_, exact_kl_,
                           rtol=0.02, atol=0.)
 
-  def _tridiag(self, d, diag_value, offdiag_value):
-    """d x d matrix with given value on diag, and one super/sub diag."""
-    diag_mat = linalg_ops.eye(d) * (diag_value - offdiag_value)
-    three_bands = array_ops.matrix_band_part(
-        array_ops.fill([d, d], offdiag_value), 1, 1)
-    return diag_mat + three_bands
-
   def test_kl_reverse_multidim(self):
 
     with self.test_session() as sess:
       d = 5  # Dimension
 
       p = mvn_full_lib.MultivariateNormalFullCovariance(
-          covariance_matrix=self._tridiag(d, diag_value=1, offdiag_value=0.5))
+          covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5))
 
       q = mvn_diag_lib.MultivariateNormalDiag(scale_diag=[0.5]*d)
 
       approx_kl = cd.monte_carlo_csiszar_f_divergence(
           f=cd.kl_reverse,
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
 
       approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
           f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
@@ -588,7 +589,7 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       d = 5  # Dimension
 
       p = mvn_full_lib.MultivariateNormalFullCovariance(
-          covariance_matrix=self._tridiag(d, diag_value=1, offdiag_value=0.5))
+          covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5))
 
       # Variance is very high when approximating Forward KL, so we make
       # scale_diag larger than in test_kl_reverse_multidim. This ensures q
@@ -597,14 +598,14 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
 
       approx_kl = cd.monte_carlo_csiszar_f_divergence(
           f=cd.kl_forward,
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
 
       approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
           f=lambda logu: cd.kl_forward(logu, self_normalized=True),
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=int(1e5),
           seed=1)
@@ -628,7 +629,7 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       seed = 1
 
       p = mvn_full_lib.MultivariateNormalFullCovariance(
-          covariance_matrix=self._tridiag(d, diag_value=1, offdiag_value=0.5))
+          covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5))
 
       # Variance is very high when approximating Forward KL, so we make
       # scale_diag larger than in test_kl_reverse_multidim. This ensures q
@@ -639,21 +640,21 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
 
       approx_kl = cd.monte_carlo_csiszar_f_divergence(
           f=cd.kl_reverse,
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=num_draws,
           seed=seed)
 
       approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence(
           f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=num_draws,
           seed=seed)
 
       approx_kl_score_trick = cd.monte_carlo_csiszar_f_divergence(
           f=cd.kl_reverse,
-          p=p,
+          p_log_prob=p.log_prob,
           q=q,
           num_draws=num_draws,
           use_reparametrization=False,
@@ -662,7 +663,7 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
       approx_kl_self_normalized_score_trick = (
           cd.monte_carlo_csiszar_f_divergence(
               f=lambda logu: cd.kl_reverse(logu, self_normalized=True),
-              p=p,
+              p_log_prob=p.log_prob,
               q=q,
               num_draws=num_draws,
               use_reparametrization=False,
@@ -670,7 +671,7 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
 
       exact_kl = kullback_leibler.kl_divergence(q, p)
 
-      grad = lambda fs: gradients_impl.gradients(fs, s)[0]
+      grad_sum = lambda fs: gradients_impl.gradients(fs, s)[0]
 
       [
           approx_kl_grad_,
@@ -684,11 +685,11 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
           approx_kl_self_normalized_score_trick_,
           exact_kl_,
       ] = sess.run([
-          grad(approx_kl),
-          grad(approx_kl_self_normalized),
-          grad(approx_kl_score_trick),
-          grad(approx_kl_self_normalized_score_trick),
-          grad(exact_kl),
+          grad_sum(approx_kl),
+          grad_sum(approx_kl_self_normalized),
+          grad_sum(approx_kl_score_trick),
+          grad_sum(approx_kl_self_normalized_score_trick),
+          grad_sum(exact_kl),
           approx_kl,
           approx_kl_self_normalized,
           approx_kl_score_trick,
@@ -724,5 +725,154 @@ class MonteCarloCsiszarFDivergenceTest(test.TestCase):
           rtol=0.017, atol=0.)
 
 
-if __name__ == '__main__':
+class CsiszarVIMCOTest(test.TestCase):
+
+  def _numpy_csiszar_vimco_helper(self, logu):
+    """Numpy implementation of `csiszar_vimco_helper`."""
+    n = logu.shape[0]
+    u = np.exp(logu)
+    loogeoavg_u = []  # Leave-one-out geometric-average of exp(logu).
+    for j in range(n):
+      loogeoavg_u.append(np.exp(np.mean(
+          [logu[i, ...] for i in range(n) if i != j],
+          axis=0)))
+    loogeoavg_u = np.array(loogeoavg_u)
+
+    loosum_u = []  # Leave-one-out sum of exp(logu).
+    for j in range(n):
+      loosum_u.append(np.sum(
+          [u[i, ...] for i in range(n) if i != j],
+          axis=0))
+    loosum_u = np.array(loosum_u)
+
+    # Natural log of the average u except each is swapped-out for its
+    # leave-`i`-th-out Geometric average.
+    log_sooavg_u = np.log(loosum_u + loogeoavg_u) - np.log(n)
+
+    log_avg_u = np.log(np.mean(u, axis=0))
+    return log_avg_u, log_sooavg_u
+
+  def test_vimco_helper(self):
+
+    with self.test_session() as sess:
+      logu = np.linspace(-20, 20, 100)
+      np_log_avg_u, np_log_sooavg_u = self._numpy_csiszar_vimco_helper(logu)
+      [log_avg_u, log_sooavg_u] = sess.run(cd.csiszar_vimco_helper(logu))
+      self.assertAllClose(np_log_avg_u, log_avg_u,
+                          rtol=1e-2, atol=0.)
+      self.assertAllClose(np_log_sooavg_u, log_sooavg_u,
+                          rtol=1e-2, atol=0.)
+
+  def test_vimco_helper_gradient(self):
+
+    with self.test_session():
+      logu = array_ops.constant(
+          np.linspace(-1e2, 100., 100).reshape([50, 2]))
+      log_avg_u, log_sooavg_u = cd.csiszar_vimco_helper(logu)
+      g = gradients_impl.gradients(log_avg_u - log_sooavg_u, logu)[0].eval()
+      self.assertAllEqual(np.ones_like(g, dtype=np.bool), np.isfinite(g))
+      self.assertAllEqual(np.ones_like(g, dtype=np.bool), g != 0.)
+
+  def test_vimco_and_gradient(self):
+
+    with self.test_session() as sess:
+      dims = 5  # Dimension
+      num_draws = int(20)
+      num_batch_draws = int(3)
+      seed = 1
+
+      f = lambda logu: cd.kl_reverse(logu, self_normalized=False)
+      np_f = lambda logu: -logu
+
+      p = mvn_full_lib.MultivariateNormalFullCovariance(
+          covariance_matrix=tridiag(dims, diag_value=1, offdiag_value=0.5))
+
+      # Variance is very high when approximating Forward KL, so we make
+      # scale_diag larger than in test_kl_reverse_multidim. This ensures q
+      # "covers" p and thus Var_q[p/q] is smaller.
+      s = array_ops.constant(1.)
+      q = mvn_diag_lib.MultivariateNormalDiag(
+          scale_diag=array_ops.tile([s], [dims]))
+
+      vimco = cd.csiszar_vimco(
+          f=f,
+          p_log_prob=p.log_prob,
+          q=q,
+          num_draws=num_draws,
+          num_batch_draws=num_batch_draws,
+          seed=seed)
+
+      x = q.sample(sample_shape=[num_draws, num_batch_draws],
+                   seed=seed)
+      x = array_ops.stop_gradient(x)
+      logu = p.log_prob(x) - q.log_prob(x)
+      f_log_sum_u = f(cd.csiszar_vimco_helper(logu)[0])
+
+      grad_sum = lambda fs: gradients_impl.gradients(fs, s)[0]
+
+      def jacobian(x):
+        # Warning: this function is slow and may not even finish if prod(shape)
+        # is larger than, say, 100.
+        shape = x.shape.as_list()
+        assert all(s is not None for s in shape)
+        x = array_ops.reshape(x, shape=[-1])
+        r = [grad_sum(x[i]) for i in range(np.prod(shape))]
+        return array_ops.reshape(array_ops.stack(r), shape=shape)
+
+      [
+          logu_,
+          jacobian_logqx_,
+          vimco_,
+          grad_vimco_,
+          f_log_sum_u_,
+          grad_mean_f_log_sum_u_,
+      ] = sess.run([
+          logu,
+          jacobian(q.log_prob(x)),
+          vimco,
+          grad_sum(vimco),
+          f_log_sum_u,
+          grad_sum(f_log_sum_u) / num_batch_draws,
+      ])
+
+      np_log_avg_u, np_log_sooavg_u = self._numpy_csiszar_vimco_helper(logu_)
+
+      # Test VIMCO loss is correct.
+      self.assertAllClose(np_f(np_log_avg_u).mean(axis=0), vimco_,
+                          rtol=1e-5, atol=0.)
+
+      # Test gradient of VIMCO loss is correct.
+      #
+      # To make this computation we'll inject two gradients from TF:
+      # - grad[mean(f(log(sum(p(x)/q(x)))))]
+      # - jacobian[log(q(x))].
+      #
+      # We now justify why using these (and only these) TF values for
+      # ground-truth does not undermine the completeness of this test.
+      #
+      # Regarding `grad_mean_f_log_sum_u_`, note that we validate the
+      # correctness of the zero-th order derivative (for each batch member).
+      # Since `cd.csiszar_vimco_helper` itself does not manipulate any gradient
+      # information, we can safely rely on TF.
+      self.assertAllClose(np_f(np_log_avg_u), f_log_sum_u_, rtol=1e-4, atol=0.)
+      #
+      # Regarding `jacobian_logqx_`, note that testing the gradient of
+      # `q.log_prob` is outside the scope of this unit-test thus we may safely
+      # use TF to find it.
+
+      # The `mean` is across batches and the `sum` is across iid samples.
+      np_grad_vimco = (
+          grad_mean_f_log_sum_u_
+          + np.mean(
+              np.sum(
+                  jacobian_logqx_ * (np_f(np_log_avg_u)
+                                     - np_f(np_log_sooavg_u)),
+                  axis=0),
+              axis=0))
+
+      self.assertAllClose(np_grad_vimco, grad_vimco_,
+                          rtol=1e-5, atol=0.)
+
+
+if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
index 38426ebf1f..d9e23646d8 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
@@ -28,6 +28,9 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import distribution as distribution_lib
+from tensorflow.python.ops.distributions import gamma as gamma_lib
+from tensorflow.python.ops.distributions import kullback_leibler
 from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.platform import test
 
@@ -210,6 +213,93 @@ class ExpectationTest(test.TestCase):
                           efx_score_grad_[2:-2],
                           rtol=0.05, atol=0.)
 
+  def test_docstring_example_normal(self):
+    with self.test_session() as sess:
+      num_draws = int(1e5)
+      mu_p = constant_op.constant(0.)
+      mu_q = constant_op.constant(1.)
+      p = normal_lib.Normal(loc=mu_p, scale=1.)
+      q = normal_lib.Normal(loc=mu_q, scale=2.)
+      exact_kl_normal_normal = kullback_leibler.kl_divergence(p, q)
+      approx_kl_normal_normal = monte_carlo_lib.expectation(
+          f=lambda x: p.log_prob(x) - q.log_prob(x),
+          samples=p.sample(num_draws, seed=42),
+          log_prob=p.log_prob,
+          use_reparametrization=(p.reparameterization_type
+                                 == distribution_lib.FULLY_REPARAMETERIZED))
+      [exact_kl_normal_normal_, approx_kl_normal_normal_] = sess.run([
+          exact_kl_normal_normal, approx_kl_normal_normal])
+      self.assertEqual(
+          True,
+          p.reparameterization_type == distribution_lib.FULLY_REPARAMETERIZED)
+      self.assertAllClose(exact_kl_normal_normal_, approx_kl_normal_normal_,
+                          rtol=0.01, atol=0.)
+
+      # Compare gradients. (Not present in `docstring`.)
+      gradp = lambda fp: gradients_impl.gradients(fp, mu_p)[0]
+      gradq = lambda fq: gradients_impl.gradients(fq, mu_q)[0]
+      [
+          gradp_exact_kl_normal_normal_,
+          gradq_exact_kl_normal_normal_,
+          gradp_approx_kl_normal_normal_,
+          gradq_approx_kl_normal_normal_,
+      ] = sess.run([
+          gradp(exact_kl_normal_normal),
+          gradq(exact_kl_normal_normal),
+          gradp(approx_kl_normal_normal),
+          gradq(approx_kl_normal_normal),
+      ])
+      self.assertAllClose(gradp_exact_kl_normal_normal_,
+                          gradp_approx_kl_normal_normal_,
+                          rtol=0.01, atol=0.)
+      self.assertAllClose(gradq_exact_kl_normal_normal_,
+                          gradq_approx_kl_normal_normal_,
+                          rtol=0.01, atol=0.)
+
+  def test_docstring_example_gamma(self):
+    with self.test_session() as sess:
+      num_draws = int(1e5)
+      concentration_p = constant_op.constant(1.)
+      concentration_q = constant_op.constant(2.)
+      p = gamma_lib.Gamma(concentration=concentration_p, rate=1.)
+      q = gamma_lib.Gamma(concentration=concentration_q, rate=3.)
+      approx_kl_gamma_gamma = monte_carlo_lib.expectation(
+          f=lambda x: p.log_prob(x) - q.log_prob(x),
+          samples=p.sample(num_draws, seed=42),
+          log_prob=p.log_prob,
+          use_reparametrization=(p.reparameterization_type
+                                 == distribution_lib.FULLY_REPARAMETERIZED))
+      exact_kl_gamma_gamma = kullback_leibler.kl_divergence(p, q)
+      [exact_kl_gamma_gamma_, approx_kl_gamma_gamma_] = sess.run([
+          exact_kl_gamma_gamma, approx_kl_gamma_gamma])
+      self.assertEqual(
+          False,
+          p.reparameterization_type == distribution_lib.FULLY_REPARAMETERIZED)
+      self.assertAllClose(exact_kl_gamma_gamma_, approx_kl_gamma_gamma_,
+                          rtol=0.01, atol=0.)
+
+      # Compare gradients. (Not present in `docstring`.)
+      gradp = lambda fp: gradients_impl.gradients(fp, concentration_p)[0]
+      gradq = lambda fq: gradients_impl.gradients(fq, concentration_q)[0]
+      [
+          gradp_exact_kl_gamma_gamma_,
+          gradq_exact_kl_gamma_gamma_,
+          gradp_approx_kl_gamma_gamma_,
+          gradq_approx_kl_gamma_gamma_,
+      ] = sess.run([
+          gradp(exact_kl_gamma_gamma),
+          gradq(exact_kl_gamma_gamma),
+          gradp(approx_kl_gamma_gamma),
+          gradq(approx_kl_gamma_gamma),
+      ])
+      # Notice that variance (i.e., `rtol`) is higher when using score-trick.
+      self.assertAllClose(gradp_exact_kl_gamma_gamma_,
+                          gradp_approx_kl_gamma_gamma_,
+                          rtol=0.05, atol=0.)
+      self.assertAllClose(gradq_exact_kl_gamma_gamma_,
+                          gradq_approx_kl_gamma_gamma_,
+                          rtol=0.03, atol=0.)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
index b1bcc86022..9f7a95f138 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py
@@ -31,6 +31,7 @@ _allowed_symbols = [
     'amari_alpha',
     'arithmetic_geometric',
     'chi_square',
+    'csiszar_vimco',
     'dual_csiszar_function',
     'jeffreys',
     'jensen_shannon',
diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
index 247d434bb7..54900ab893 100644
--- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py
@@ -17,6 +17,7 @@
 @@amari_alpha
 @@arithmetic_geometric
 @@chi_square
+@@csiszar_vimco
 @@dual_csiszar_function
 @@jeffreys
 @@jensen_shannon
@@ -46,6 +47,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import distribution
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 def amari_alpha(logu, alpha=1., self_normalized=False, name=None):
@@ -696,7 +698,7 @@ def dual_csiszar_function(logu, csiszar_function, name=None):
 
   Args:
     logu: `float`-like `Tensor` representing `log(u)` from above.
-    csiszar_function: Python callable representing a Csiszar-function over
+    csiszar_function: Python `callable` representing a Csiszar-function over
       log-domain.
     name: Python `str` name prefixed to Ops created by this function.
 
@@ -765,7 +767,7 @@ def symmetrized_csiszar_function(logu, csiszar_function, name=None):
 
   Args:
     logu: `float`-like `Tensor` representing `log(u)` from above.
-    csiszar_function: Python callable representing a Csiszar-function over
+    csiszar_function: Python `callable` representing a Csiszar-function over
       log-domain.
     name: Python `str` name prefixed to Ops created by this function.
 
@@ -781,7 +783,13 @@ def symmetrized_csiszar_function(logu, csiszar_function, name=None):
 
 
 def monte_carlo_csiszar_f_divergence(
-    f, p, q, num_draws, use_reparametrization=True, seed=None, name=None):
+    f,
+    p_log_prob,
+    q,
+    num_draws,
+    use_reparametrization=None,
+    seed=None,
+    name=None):
   """Monte-Carlo approximation of the Csiszar f-Divergence.
 
   A Csiszar-function is a member of,
@@ -843,15 +851,22 @@ def monte_carlo_csiszar_f_divergence(
   "Evidence Divergence Bound Optimization" (EDBO).
 
   Args:
-    f: Python callable representing a Csiszar-function in log-space.
-    p: `tf.Distribution`-like instance; must implement `log_prob(x)`.
+    f: Python `callable` representing a Csiszar-function in log-space, i.e.,
+      takes `p_log_prob(q_samples) - q.log_prob(q_samples)`.
+    p_log_prob: Python `callable` taking (a batch of) samples from `q` and
+      returning the the natural-log of the probability under distribution `p`.
+      (In variational inference `p` is the joint distribution.)
     q: `tf.Distribution`-like instance; must implement:
-      `reparameterization_type`, `sample(n)`, and `log_prob(x)`.
+      `reparameterization_type`, `sample(n, seed)`, and `log_prob(x)`.
+      (In variational inference `q` is the approximate posterior distribution.)
     num_draws: Integer scalar number of draws used to approximate the
       f-Divergence expectation.
-    use_reparametrization: Python `bool`. When `True` uses the standard
-      Monte-Carlo average. When `False` uses the score-gradient trick. (See
-      above for details.)
+    use_reparametrization: Python `bool`. When `None` (the default),
+      automatically set to:
+      `q.reparameterization_type == distribution.FULLY_REPARAMETERIZED`.
+      When `True` uses the standard Monte-Carlo average. When `False` uses the
+      score-gradient trick. (See above for details.)  When `False`, consider
+      using `csiszar_vimco`.
     seed: Python `int` seed for `q.sample`.
     name: Python `str` name prefixed to Ops created by this function.
 
@@ -866,18 +881,179 @@ def monte_carlo_csiszar_f_divergence(
       samples of another distribution which does not depend on the
       parameterization of `q`. This property ensures the gradient (with respect
       to parameters) is valid.
+    TypeError: if `p_log_prob` is not a Python `callable`.
   """
   with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]):
-    if (use_reparametrization and
-        q.reparameterization_type != distribution.FULLY_REPARAMETERIZED):
+    if use_reparametrization is None:
+      use_reparametrization = (q.reparameterization_type
+                               == distribution.FULLY_REPARAMETERIZED)
+    elif (use_reparametrization and
+          q.reparameterization_type != distribution.FULLY_REPARAMETERIZED):
       # TODO(jvdillon): Consider only raising an exception if the gradient is
       # requested.
       raise ValueError(
           "Distribution `q` must be reparameterized, i.e., a diffeomorphic "
           "transformation of a parameterless distribution. (Otherwise this "
           "function has a biased gradient.)")
+    if not callable(p_log_prob):
+      raise TypeError("`p_log_prob` must be a Python `callable` function.")
     return monte_carlo.expectation(
-        f=lambda x: f(p.log_prob(x) - q.log_prob(x)),
+        f=lambda q_samples: f(p_log_prob(q_samples) - q.log_prob(q_samples)),
         samples=q.sample(num_draws, seed=seed),
-        log_prob=q.log_prob,
+        log_prob=q.log_prob,  # Only used if use_reparametrization=False.
         use_reparametrization=use_reparametrization)
+
+
+def csiszar_vimco(f,
+                  p_log_prob,
+                  q,
+                  num_draws,
+                  num_batch_draws=1,
+                  seed=None,
+                  name=None):
+  """Use VIMCO to lower the variance of gradient[csiszar_function(Avg(logu))].
+
+  This function generalizes "Variational Inference for Monte Carlo Objectives"
+  (VIMCO), i.e., https://arxiv.org/abs/1602.06725, to Csiszar f-Divergences.
+
+  Note: if `q.reparameterization_type = distribution.FULLY_REPARAMETERIZED`,
+  consider using `monte_carlo_csiszar_f_divergence`.
+
+  The VIMCO loss is:
+
+  ```none
+  vimco = f(Avg{logu[i] : i=0,...,m-1})
+  where,
+    logu[i] = log( p(x, h[i]) / q(h[i] | x) )
+    h[i] iid~ q(H | x)
+  ```
+
+  Interestingly, the VIMCO gradient is not the naive gradient of `vimco`.
+  Rather, it is characterized by:
+
+  ```none
+  grad[vimco] - variance_reducing_term
+  where,
+    variance_reducing_term = Sum{ grad[log q(h[i] | x)] *
+                                    (vimco - f(log Avg{h[j;i] : j=0,...,m-1}))
+                                 : i=0, ..., m-1 }
+    h[j;i] = { u[j]                             j!=i
+             { GeometricAverage{ u[k] : k!=i}   j==i
+  ```
+
+  (We omitted `stop_gradient` for brevity. See implementation for more details.)
+
+  The `Avg{h[j;i] : j}` term is a kind of "swap-out average" where the `i`-th
+  element has been replaced by the leave-`i`-out Geometric-average.
+
+  Args:
+    f: Python `callable` representing a Csiszar-function in log-space.
+    p_log_prob: Python `callable` representing the natural-log of the
+      probability under distribution `p`. (In variational inference `p` is the
+      joint distribution.)
+    q: `tf.Distribution`-like instance; must implement: `sample(n, seed)`, and
+      `log_prob(x)`. (In variational inference `q` is the approximate posterior
+      distribution.)
+    num_draws: Integer scalar number of draws used to approximate the
+      f-Divergence expectation.
+    num_batch_draws: Integer scalar number of draws used to approximate the
+      f-Divergence expectation.
+    seed: Python `int` seed for `q.sample`.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    vimco: The Csiszar f-Divergence generalized VIMCO objective.
+
+  Raises:
+    ValueError: if `num_draws < 2`.
+  """
+  with ops.name_scope(name, "csiszar_vimco", [num_draws, num_batch_draws]):
+    if num_draws < 2:
+      raise ValueError("Must specify num_draws > 1.")
+    stop = array_ops.stop_gradient  # For readability.
+    x = stop(q.sample(sample_shape=[num_draws, num_batch_draws],
+                      seed=seed))
+    logqx = q.log_prob(x)
+    logu = p_log_prob(x) - logqx
+    f_log_avg_u, f_log_sooavg_u = [f(r) for r in csiszar_vimco_helper(logu)]
+    dotprod = math_ops.reduce_sum(
+        logqx * stop(f_log_avg_u - f_log_sooavg_u),
+        axis=0)  # Sum over iid samples.
+    # We now rewrite f_log_avg_u so that:
+    #   `grad[f_log_avg_u] := grad[f_log_avg_u + dotprod]`.
+    # To achieve this, we use a trick that
+    #   `f(x) - stop(f(x)) == zeros_like(f(x))`
+    # but its gradient is grad[f(x)].
+    # Note that IEEE754 specifies that `x - x == 0.` and `x + 0. == x`, hence
+    # this trick loses no precision. For more discussion regarding the relevant
+    # portions of the IEEE754 standard, see the StackOverflow question,
+    # "Is there a floating point value of x, for which x-x == 0 is false?"
+    # http://stackoverflow.com/q/2686644
+    f_log_avg_u += dotprod - stop(dotprod)  # Add zeros_like(dot_prod).
+    return math_ops.reduce_mean(f_log_avg_u, axis=0)  # Avg over batches.
+
+
+def csiszar_vimco_helper(logu, name=None):
+  """Helper to `csiszar_vimco`; computes `log_avg_u`, `log_sooavg_u`.
+
+  `axis = 0` of `logu` is presumed to correspond to iid samples from `q`, i.e.,
+
+  ```none
+  logu[j] = log(u[j])
+  u[j] = p(x, h[j]) / q(h[j] | x)
+  h[j] iid~ q(H | x)
+  ```
+
+  Args:
+    logu: Floating-type `Tensor` representing `log(p(x, h) / q(h | x))`.
+    name: Python `str` name prefixed to Ops created by this function.
+
+  Returns:
+    log_avg_u: `logu.dtype` `Tensor` corresponding to the natural-log of the
+      average of `u`.
+    log_sooavg_u: `logu.dtype` `Tensor` characterized by the natural-log of the
+      average of `u`` except that the average swaps-out `u[i]` for the
+      leave-`i`-out Geometric-average, i.e.,
+
+      ```none
+      log_sooavg_u[i] = log(Avg{h[j ; i] : j=0, ..., m-1})
+      h[j ; i] = { u[j]                              j!=i
+                 { GeometricAverage{u[k] : k != i}   j==i
+      ```
+
+  """
+  with ops.name_scope(name, "csiszar_vimco_helper", [logu]):
+    logu = ops.convert_to_tensor(logu, name="logu")
+
+    n = logu.shape.with_rank_at_least(1)[0].value
+    if n is None:
+      n = array_ops.shape(logu)[0]
+      log_n = math_ops.log(math_ops.cast(n, dtype=logu.dtype))
+      nm1 = math_ops.cast(n - 1, dtype=logu.dtype)
+    else:
+      log_n = np.log(n).astype(logu.dtype.as_numpy_dtype)
+      nm1 = np.asarray(n - 1, dtype=logu.dtype.as_numpy_dtype)
+
+    # Throughout we reduce across axis=0 since this is presumed to be iid
+    # samples.
+
+    log_sum_u = math_ops.reduce_logsumexp(logu, axis=0)
+
+    # log_loosum_u[i] =
+    # = logsumexp(logu[j] : j != i)
+    # = log( exp(logsumexp(logu)) - exp(logu[i]) )
+    # = log( exp(logsumexp(logu - logu[i])) exp(logu[i])  - exp(logu[i]))
+    # = logu[i] + log(exp(logsumexp(logu - logu[i])) - 1)
+    # = logu[i] + softplus_inverse(logsumexp(logu - logu[i]))
+    log_loosum_u = logu + distribution_util.softplus_inverse(log_sum_u - logu)
+
+    # The swap-one-out-sum ("soosum") is n different sums, each of which
+    # replaces the i-th item with the i-th-left-out average, i.e.,
+    # soo_sum_u[i] = [exp(logu) - exp(logu[i])] + exp(mean(logu[!=i]))
+    #              =  exp(log_loosum_u[i])      + exp(looavg_logu[i])
+    looavg_logu = (math_ops.reduce_sum(logu, axis=0) - logu) / nm1
+    log_soosum_u = math_ops.reduce_logsumexp(
+        array_ops.stack([log_loosum_u, looavg_logu]),
+        axis=0)
+
+    return log_sum_u - log_n, log_soosum_u - log_n
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
index 0dae74d31f..985177e897 100644
--- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py
@@ -220,9 +220,10 @@ def expectation(f, samples, log_prob=None, use_reparametrization=True,
   `S_n = Avg{s_i}` and `s_i = f(x_i), x_i ~ p`.
 
   However, if p is not reparameterized, TensorFlow's gradient will be incorrect
-  since the chain-rule stops at samples of unreparameterized distributions. In
-  this circumstance using the Score-Gradient trick results in an unbiased
-  gradient, i.e.,
+  since the chain-rule stops at samples of non-reparameterized distributions.
+  (The non-differentiated result, `approx_expectation`, is the same regardless
+  of `use_reparametrization`.) In this circumstance using the Score-Gradient
+  trick results in an unbiased gradient, i.e.,
 
   ```none
   grad[ E_p[f(X)] ]
@@ -240,6 +241,58 @@ def expectation(f, samples, log_prob=None, use_reparametrization=True,
   Warning: users are responsible for verifying `p` is a "reparameterized"
   distribution.
 
+  Example Use:
+
+  ```python
+  bf = tf.contrib.bayesflow
+  ds = tf.contrib.distributions
+
+  # Monte-Carlo approximation of a reparameterized distribution, e.g., Normal.
+
+  num_draws = int(1e5)
+  p = ds.Normal(loc=0., scale=1.)
+  q = ds.Normal(loc=1., scale=2.)
+  exact_kl_normal_normal = ds.kl_divergence(p, q)
+  # ==> 0.44314718
+  approx_kl_normal_normal = bf.expectation(
+      f=lambda x: p.log_prob(x) - q.log_prob(x),
+      samples=p.sample(num_draws, seed=42),
+      log_prob=p.log_prob,
+      use_reparametrization=(p.reparameterization_type
+                             == distribution.FULLY_REPARAMETERIZED))
+  # ==> 0.44632751
+  # Relative Error: <1%
+
+  # Monte-Carlo approximation of non-reparameterized distribution, e.g., Gamma.
+
+  num_draws = int(1e5)
+  p = ds.Gamma(concentration=1., rate=1.)
+  q = ds.Gamma(concentration=2., rate=3.)
+  exact_kl_gamma_gamma = ds.kl_divergence(p, q)
+  # ==> 0.37999129
+  approx_kl_gamma_gamma = bf.expectation(
+      f=lambda x: p.log_prob(x) - q.log_prob(x),
+      samples=p.sample(num_draws, seed=42),
+      log_prob=p.log_prob,
+      use_reparametrization=(p.reparameterization_type
+                             == distribution.FULLY_REPARAMETERIZED))
+  # ==> 0.37696719
+  # Relative Error: <1%
+
+  # For comparing the gradients, see `monte_carlo_test.py`.
+  ```
+
+  Note: The above example is for illustration only. To compute approximate
+  KL-divergence, the following is preferred:
+
+  ```python
+  approx_kl_p_q = bf.monte_carlo_csiszar_f_divergence(
+      f=bf.kl_reverse,
+      p_log_prob=q.log_prob,
+      q=p,
+      num_draws=num_draws)
+  ```
+
   Args:
     f: Python callable which can return `f(samples)`.
     samples: `Tensor` of samples used to form the Monte-Carlo approximation of
@@ -247,21 +300,27 @@ def expectation(f, samples, log_prob=None, use_reparametrization=True,
     log_prob: Python callable which can return `log_prob(samples)`. Must
       correspond to the natural-logarithm of the pdf/pmf of each sample. Only
       required/used if `use_reparametrization=False`.
+      Default value: `None`.
     use_reparametrization: Python `bool` indicating that the approximation
-      should use the fact that the gradient of samples is unbiased.
-    axis: The dimensions to average. If `None` (the default), averages all
+      should use the fact that the gradient of samples is unbiased. Whether
+      `True` or `False`, this arg only affects the gradient of the resulting
+      `approx_expectation`.
+      Default value: `True`.
+    axis: The dimensions to average. If `None`, averages all
       dimensions.
-    keep_dims: If true, retains averaged dimensions with length 1.
-    name: A `name_scope` for operations created by this function (optional).
-      Default value: "expectation".
+      Default value: `0` (the left-most dimension).
+    keep_dims: If True, retains averaged dimensions using size `1`.
+      Default value: `False`.
+    name: A `name_scope` for operations created by this function.
+      Default value: `None` (which implies "expectation").
 
   Returns:
     approx_expectation: `Tensor` corresponding to the Monte-Carlo approximation
       of `E_p[f(X)]`.
 
   Raises:
-    ValueError: if `f` is not `callable`.
-    ValueError: if `use_reparametrization=False` and `log_prob` is not
+    ValueError: if `f` is not a Python `callable`.
+    ValueError: if `use_reparametrization=False` and `log_prob` is not a Python
       `callable`.
   """
 
@@ -273,19 +332,23 @@ def expectation(f, samples, log_prob=None, use_reparametrization=True,
     else:
       if not callable(log_prob):
         raise ValueError('`log_prob` must be a callable function.')
-      x = array_ops.stop_gradient(samples)
+      stop = array_ops.stop_gradient  # For readability.
+      x = stop(samples)
       logpx = log_prob(x)
-      # Numerically, exp(g(x) - stop[g(x)]) is always 1, even if exp(g(x)) is
-      # unstable. But the gradient is also stable, ie,
-      # d/dx exp(g(x) - stop[g(x)])
-      # = exp(g(x) - stop[g(x)]) d/dx g(x)
-      # = d/dx g(x)   [numerically exact since IEEE754 has the property
-      #                that for any finite floating-point number x:
-      #                x - x == 0.0]
-      return math_ops.reduce_mean(
-          f(x) * math_ops.exp(logpx - array_ops.stop_gradient(logpx)),
-          axis=axis,
-          keep_dims=keep_dims)
+      fx = f(x)  # Call `f` once in case it has side-effects.
+      # We now rewrite f(x) so that:
+      #   `grad[f(x)] := grad[f(x)] + f(x) * grad[logqx]`.
+      # To achieve this, we use a trick that
+      #   `h(x) - stop(h(x)) == zeros_like(h(x))`
+      # but its gradient is grad[h(x)].
+      # Note that IEEE754 specifies that `x - x == 0.` and `x + 0. == x`, hence
+      # this trick loses no precision. For more discussion regarding the
+      # relevant portions of the IEEE754 standard, see the StackOverflow
+      # question,
+      # "Is there a floating point value of x, for which x-x == 0 is false?"
+      # http://stackoverflow.com/q/2686644
+      fx += stop(fx) * (logpx - stop(logpx))  # Add zeros_like(logpx).
+      return math_ops.reduce_mean(fx, axis=axis, keep_dims=keep_dims)
 
 
 def _sample_mean(values):
-- 
GitLab


From ceeb9ea3499b97c9dc9ccf8021df1ed2fc7d60e3 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 21 Jul 2017 18:07:07 -0700
Subject: [PATCH 1227/1375] Refactor pip.sh into independent jobs

pip.sh consists of multiple tasks, such as
* testing pip package on a clean virtualenv install
* running all bazel_pip tests in a "non-clean" virtualenv
* testing user ops
* testing tfdbg binaries
etc.

Previously they run in a way in which if an earlier task fails, none of the following tasks will run. This CL corrects that by separating the code into multiple tasks and ensure that they always run regardless of the status of preceding tasks.

Also adds color to the FAIL / PASS messages.

PiperOrigin-RevId: 162807396
---
 .../tools/ci_build/builds/builds_common.sh    |   3 +
 tensorflow/tools/ci_build/builds/pip.sh       | 254 +++++++++++++-----
 tensorflow/tools/ci_build/ci_sanity.sh        |  14 +-
 3 files changed, 193 insertions(+), 78 deletions(-)

diff --git a/tensorflow/tools/ci_build/builds/builds_common.sh b/tensorflow/tools/ci_build/builds/builds_common.sh
index fd9a14bd69..9323a96e74 100644
--- a/tensorflow/tools/ci_build/builds/builds_common.sh
+++ b/tensorflow/tools/ci_build/builds/builds_common.sh
@@ -16,6 +16,9 @@
 #
 # Common Bash functions used by build scripts
 
+COLOR_NC='\033[0m'
+COLOR_GREEN='\033[0;32m'
+COLOR_RED='\033[0;31m'
 
 die() {
   # Print a message and exit with code 1.
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index 295601c4b8..2cb2961fa2 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -306,90 +306,200 @@ create_activate_virtualenv_and_install_tensorflow() {
   echo "Successfully installed pip package ${TF_WHEEL_PATH}"
 }
 
+################################################################################
+# Smoke test of tensorflow install in clean virtualenv
+################################################################################
+do_clean_virtualenv_smoke_test() {
+  CLEAN_VENV_DIR="${PIP_TEST_ROOT}/venv_clean"
+  create_activate_virtualenv_and_install_tensorflow --clean \
+    "${CLEAN_VENV_DIR}" "${WHL_PATH}"
 
-# 1. Smoke test of tensorflow install in clean virtualenv
-echo
-echo "Installing and smoke-testing pip wheel in clean virtualenv: ${WHL_PATH}"
-echo
+  # cd to a temporary directory to avoid picking up Python files in the source
+  # tree.
+  TMP_DIR=$(mktemp -d)
+  pushd "${TMP_DIR}"
+  if [[ $(python -c "import tensorflow as tf; print(tf.Session().run(tf.constant(42)))") == 42 ]];
+  then
+    echo "Smoke test of tensorflow install in clean virtualenv PASSED."
+  else
+    echo "Smoke test of tensroflow install in clean virtualenv FAILED."
+    return 1
+  fi
 
-CLEAN_VENV_DIR="${PIP_TEST_ROOT}/venv_clean"
-create_activate_virtualenv_and_install_tensorflow --clean \
-  "${CLEAN_VENV_DIR}" "${WHL_PATH}"
-# cd to a temporary directory to avoid picking up Python files in the source
-# tree.
-TMP_DIR=$(mktemp -d)
-pushd "${TMP_DIR}"
-[[ $(python -c "import tensorflow as tf; print(tf.Session().run(tf.constant(42)))") == 42 ]] \
-  && echo "Smoke test of tensorflow install in clean virtualenv PASSED." \
-  || die "Smoke test of tensroflow install in clean virtualenv FAILED."
-deactivate || \
-  die "FAILED: Unable to deactivate virtualenv from ${CLEAN_VENV_DIR}"
-popd
-rm -rf "${TMP_DIR}" "${CLEAN_VENV_DIR}"
-
-# 2. Perform installation of tensorflow in "non-clean" virtualenv and tests
-# against the install.
-echo
-echo "Installing and testing pip wheel in virtualenv: ${WHL_PATH}"
-echo
+  deactivate
+  if [[ $? != 0 ]]; then
+    echo "FAILED: Unable to deactivate virtualenv from ${CLEAN_VENV_DIR}"
+    return 1
+  fi
 
-# Create virtualenv directory for install test
-VENV_DIR="${PIP_TEST_ROOT}/venv"
-create_activate_virtualenv_and_install_tensorflow \
-  "${CLEAN_VENV_DIR}" "${WHL_PATH}"
+  popd
+  rm -rf "${TMP_DIR}" "${CLEAN_VENV_DIR}"
+}
 
-# Install extra pip packages required by the test-on-install
-for PACKAGE in ${INSTALL_EXTRA_PIP_PACKAGES}; do
-  echo "Installing extra pip package required by test-on-install: ${PACKAGE}"
+################################################################################
+# Perform installation of tensorflow in "non-clean" virtualenv and tests against
+# the install.
+################################################################################
+do_virtualenv_pip_test() {
+  # Create virtualenv directory for install test
+  VENV_DIR="${PIP_TEST_ROOT}/venv"
+  create_activate_virtualenv_and_install_tensorflow \
+    "${CLEAN_VENV_DIR}" "${WHL_PATH}"
+
+  # Install extra pip packages required by the test-on-install
+  for PACKAGE in ${INSTALL_EXTRA_PIP_PACKAGES}; do
+    echo "Installing extra pip package required by test-on-install: ${PACKAGE}"
+
+    pip install ${PACKAGE}
+    if [[ $? != 0 ]]; then
+      echo "pip install ${PACKAGE} FAILED"
+      return 1
+    fi
+  done
 
-  pip install ${PACKAGE} || \
-      die "pip install ${PACKAGE} FAILED"
-done
+  if [[ -n "${NO_TEST_ON_INSTALL}" ]] &&
+     [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
+    echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
+    echo "  Skipping ALL Python unit tests on install"
+  else
+    # Call run_pip_tests.sh to perform test-on-install
+    "${SCRIPT_DIR}/run_pip_tests.sh" --virtualenv ${GPU_FLAG} ${MAC_FLAG}
+    if [[ $? != 0 ]]; then
+      echo "PIP tests-on-install FAILED"
+      return 1
+    fi
+  fi
+}
 
-if [[ -n "${NO_TEST_ON_INSTALL}" ]] &&
-   [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
-  echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
-  echo "  Skipping ALL Python unit tests on install"
-else
-  # Call run_pip_tests.sh to perform test-on-install
-  "${SCRIPT_DIR}/run_pip_tests.sh" --virtualenv ${GPU_FLAG} ${MAC_FLAG} ||
-      die "PIP tests-on-install FAILED"
-fi
+################################################################################
+# Test user ops (optional).
+################################################################################
+do_test_user_ops() {
+  if [[ "${DO_TEST_USER_OPS}" == "1" ]]; then
+    "${SCRIPT_DIR}/test_user_ops.sh" --virtualenv ${GPU_FLAG}
+    if [[ $? != 0 ]]; then
+      echo "PIP user-op tests-on-install FAILED"
+      return 1
+    fi
+  else
+    echo "Skipping user-op test-on-install due to DO_TEST_USER_OPS = ${DO_TEST_USER_OPS}"
+  fi
+}
 
-# Test user ops
-if [[ "${DO_TEST_USER_OPS}" == "1" ]]; then
-  "${SCRIPT_DIR}/test_user_ops.sh" --virtualenv ${GPU_FLAG} || \
-      die "PIP user-op tests-on-install FAILED"
-fi
+################################################################################
+# Test TensorFlow Debugger (tfdbg) binaries (optional).
+################################################################################
+do_test_tfdbg_binaries() {
+  if [[ "${DO_TEST_TFDBG_BINARIES}" == "1" ]]; then
+    # cd to a temporary directory to avoid picking up Python files in the source
+    # tree.
+    TMP_DIR=$(mktemp -d)
+    pushd "${TMP_DIR}"
+
+    "${SCRIPT_DIR}/../../../python/debug/examples/examples_test.sh" \
+      --virtualenv
+    if  [[ $? != 0 ]]; then
+      echo "PIP tests-on-install of tfdbg binaries FAILED"
+      return 1
+    fi
+    popd
+  else
+    echo "Skipping test of tfdbg binaries due to DO_TEST_TFDBG_BINARIES = ${DO_TEST_TFDBG_BINARIES}"
+  fi
+}
+
+################################################################################
+# Test tutorials (optional).
+################################################################################
+do_test_tutorials() {
+  if [[ "${DO_TEST_TUTORIALS}" == "1" ]]; then
+    "${SCRIPT_DIR}/test_tutorials.sh" --virtualenv
+    if [[ $? != 0 ]]; then
+      echo "PIP tutorial tests-on-install FAILED"
+      return 1
+    fi
+  else
+    echo "Skipping tutorial tests-on-install due to DO_TEST_TUTORIALS = ${DO_TEST_TUTORIALS}"
+  fi
+}
 
-# Test TensorFlow Debugger (tfdbg) examples.
-if [[ "${DO_TEST_TFDBG_BINARIES}" == "1" ]]; then
-  echo
-  echo "Testing TensorFlow Debugger (tfdbg) binaries"
-  echo
+################################################################################
+# Integration test for ffmpeg (optional).
+################################################################################
+do_ffmpeg_integration_test() {
+  # Optional: Run integration tests
+  if [[ "${DO_INTEGRATION_TESTS}" == "1" ]]; then
+    "${SCRIPT_DIR}/integration_tests.sh" --virtualenv
+    if [[ $? != 0 ]]; then
+      echo "Integration tests on install FAILED"
+      return 1
+    fi
+  else
+    echo "Skipping ffmpeg integration due to DO_INTEGRATION_TESTS = ${DO_INTEGRATION_TESTS}"
+  fi
+}
 
-  # cd to a temporary directory to avoid picking up Python files in the source
-  # tree.
-  TMP_DIR=$(mktemp -d)
-  pushd "${TMP_DIR}"
 
-  "${SCRIPT_DIR}/../../../python/debug/examples/examples_test.sh" \
-      --virtualenv || \
-      die "PIP tests-on-install of tfdbg binaries FAILED"
+# List of all PIP test tasks and their descriptions.
+PIP_TASKS=("do_clean_virtualenv_smoke_test" "do_virtualenv_pip_test" "do_test_user_ops" "do_test_tfdbg_binaries" "do_test_tutorials" "do_ffmpeg_integration_test")
+PIP_TASKS_DESC=("Smoke test of pip install in clean virtualenv" "Pip tests in virtualenv" "User ops test" "TensorFlow Debugger (tfdbg) binaries test" "Tutorials test" "ffmpeg integration test")
 
-  popd
-fi
 
-# Optional: Run the tutorial tests
-if [[ "${DO_TEST_TUTORIALS}" == "1" ]]; then
-  "${SCRIPT_DIR}/test_tutorials.sh" --virtualenv || \
-      die "PIP tutorial tests-on-install FAILED"
-fi
+# Execute all the PIP test steps.
+COUNTER=0
+FAIL_COUNTER=0
+PASS_COUNTER=0
+while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
+  INDEX=COUNTER
+  ((INDEX++))
 
-# Optional: Run integration tests
-if [[ "${DO_INTEGRATION_TESTS}" == "1" ]]; then
-  "${SCRIPT_DIR}/integration_tests.sh" --virtualenv || \
-      die "Integration tests on install FAILED"
-fi
+  echo ""
+  echo "=== PIP test step ${INDEX} of ${#PIP_TASKS[@]}: "\
+"${PIP_TASKS[COUNTER]} (${PIP_TASKS_DESC[COUNTER]}) ==="
+  echo ""
+
+  ${PIP_TASKS[COUNTER]}
+  RESULT=$?
+
+  if [[ ${RESULT} != "0" ]]; then
+    ((FAIL_COUNTER++))
+  else
+    ((PASS_COUNTER++))
+  fi
+
+  STEP_EXIT_CODES+=(${RESULT})
+
+  echo ""
+  ((COUNTER++))
+done
 
 deactivate || die "FAILED: Unable to deactivate virtualenv from ${VENV_DIR}"
+
+
+# Print summary of build results
+COUNTER=0
+echo "==== Summary of PIP test results ===="
+while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
+  INDEX=COUNTER
+  ((INDEX++))
+
+  echo "${INDEX}. ${PIP_TASKS[COUNTER]}: ${PIP_TASKS_DESC[COUNTER]}"
+  if [[ ${STEP_EXIT_CODES[COUNTER]} == "0" ]]; then
+    printf "  ${COLOR_GREEN}PASS${COLOR_NC}\n"
+  else
+    printf "  ${COLOR_RED}FAIL${COLOR_NC}\n"
+  fi
+
+  ((COUNTER++))
+done
+
+echo
+echo "${FAIL_COUNTER} failed; ${PASS_COUNTER} passed."
+
+echo
+if [[ ${FAIL_COUNTER} == "0" ]]; then
+  printf "PIP test ${COLOR_GREEN}PASSED${COLOR_NC}\n"
+else
+  printf "PIP test ${COLOR_RED}FAILED${COLOR_NC}\n"
+  exit 1
+fi
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index 82b41d4f9d..68e826ccd5 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -24,6 +24,7 @@
 
 # Current script directory
 SCRIPT_DIR=$( cd ${0%/*} && pwd -P )
+source "${SCRIPT_DIR}/builds/builds_common.sh"
 
 # Helper functions
 die() {
@@ -494,20 +495,21 @@ while [[ ${COUNTER} -lt "${#SANITY_STEPS[@]}" ]]; do
 
   echo "${INDEX}. ${SANITY_STEPS[COUNTER]}: ${SANITY_STEPS_DESC[COUNTER]}"
   if [[ ${STEP_EXIT_CODES[COUNTER]} == "0" ]]; then
-    echo "  PASS"
+    printf "  ${COLOR_GREEN}PASS${COLOR_NC}\n"
   else
-    echo "  FAIL"
+    printf "  ${COLOR_RED}FAIL${COLOR_NC}\n"
   fi
 
   ((COUNTER++))
 done
 
-echo ""
+echo
 echo "${FAIL_COUNTER} failed; ${PASS_COUNTER} passed."
 
-echo ""
+echo
 if [[ ${FAIL_COUNTER} == "0" ]]; then
-  echo "Sanity checks PASSED"
+  printf "Sanity checks ${COLOR_GREEN}PASSED${COLOR_NC}\n"
 else
-  die "Sanity checks FAILED"
+  printf "Sanity checks ${COLOR_RED}FAILED${COLOR_NC}\n"
+  exit 1
 fi
-- 
GitLab


From 7a9a1f55afbf1f5c7c62239ea65bb3d4d0e8609c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 21 Jul 2017 19:00:21 -0700
Subject: [PATCH 1228/1375] Internal cleanup.

PiperOrigin-RevId: 162809937
---
 tensorflow/core/framework/rendezvous.cc      | 19 ++++------
 tensorflow/core/framework/rendezvous_test.cc | 39 +++++++++++---------
 2 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/tensorflow/core/framework/rendezvous.cc b/tensorflow/core/framework/rendezvous.cc
index 8edbacbf50..7a03cb31ba 100644
--- a/tensorflow/core/framework/rendezvous.cc
+++ b/tensorflow/core/framework/rendezvous.cc
@@ -155,12 +155,12 @@ class LocalRendezvousImpl : public Rendezvous {
     Args recv_args;
     uint64 key_hash = KeyHash(key.FullKey());
     VLOG(2) << "Send " << this << " " << key_hash << " " << key.FullKey();
+    Item* item = nullptr;
     {
       mutex_lock l(mu_);
       if (!status_.ok()) {
         return status_;
       }
-      Item* item = nullptr;
       Table::iterator iter = table_.find(key_hash);
       if (iter == table_.end()) {
         // There is no waiter for this message. Insert the message
@@ -189,8 +189,6 @@ class LocalRendezvousImpl : public Rendezvous {
           // Should not happen unless it has a waiter.
           return errors::Aborted("Duplicated send: ", key.FullKey());
         }
-        // Mark item as complete.
-        item->has_been_recvd = true;
 
         // Get item->waiter function into waiter and set item->waiter to null
         std::swap(item->waiter, waiter);
@@ -201,8 +199,10 @@ class LocalRendezvousImpl : public Rendezvous {
         recv_args.device_context = item->recv_dev_context;
         recv_args.alloc_attrs = item->recv_alloc_attrs;
         item->recv_dev_context = nullptr;
+        table_.erase(iter);
       }
     }  // mutex
+    delete item;
     // Notify the waiter by invoking its done closure, outside scope
     // of the table lock.
     waiter(Status::OK(), send_args, recv_args, val, is_dead);
@@ -225,17 +225,11 @@ class LocalRendezvousImpl : public Rendezvous {
     Table::iterator iter = table_.find(key_hash);
     if (iter != table_.end()) {
       Item* item = iter->second;
-      if (item->has_been_recvd) {
-        mu_.unlock();
-        done(errors::Aborted("Duplicated recv: ", key.FullKey()), Args(),
-             recv_args, Tensor(), false);
-      } else if (item->waiter == nullptr) {
+      if (item->waiter == nullptr) {
         // A message has already arrived and is stored in the table
         // under this key.  Consumes the message and invokes the done
         // closure.
-        Tensor v = item->value;
-        item->value = Tensor();
-        item->has_been_recvd = true;
+        Tensor v = std::move(item->value);
         // Before dropping the table lock, capture the item values.
         // DeviceContext is only non-null for non-CPU devices.
         // If we capture the send_dev_context, we need to hold a ref on
@@ -247,7 +241,9 @@ class LocalRendezvousImpl : public Rendezvous {
         Args send_args;
         send_args.device_context = item->send_dev_context;
         send_args.alloc_attrs = item->send_alloc_attrs;
+        table_.erase(iter);
         mu_.unlock();
+        delete item;
         done(Status::OK(), send_args, recv_args, v, is_dead);
         if (send_dev_context) send_dev_context->Unref();
       } else {
@@ -299,7 +295,6 @@ class LocalRendezvousImpl : public Rendezvous {
     DoneCallback waiter = nullptr;
     Tensor value;
     bool is_dead = false;
-    bool has_been_recvd = false;
     DeviceContext* send_dev_context = nullptr;
     DeviceContext* recv_dev_context = nullptr;
     AllocatorAttributes send_alloc_attrs;
diff --git a/tensorflow/core/framework/rendezvous_test.cc b/tensorflow/core/framework/rendezvous_test.cc
index 69b04a2a5d..b48f711806 100644
--- a/tensorflow/core/framework/rendezvous_test.cc
+++ b/tensorflow/core/framework/rendezvous_test.cc
@@ -108,8 +108,15 @@ Rendezvous::ParsedKey MakeKey(const string& name) {
   return k;
 }
 
-Rendezvous::ParsedKey KeyFoo() { return MakeKey("foo"); }
-Rendezvous::ParsedKey KeyBar() { return MakeKey("bar"); }
+const Rendezvous::ParsedKey& KeyFoo() {
+  static auto key = MakeKey("foo");
+  return key;
+}
+
+const Rendezvous::ParsedKey& KeyBar() {
+  static auto key = MakeKey("bar");
+  return key;
+}
 
 TEST_F(LocalRendezvousTest, SendRecv) {
   Rendezvous::Args args;
@@ -169,8 +176,6 @@ TEST_F(LocalRendezvousTest, DuplicateSerialRecv) {
   TF_ASSERT_OK(rendez_->Send(KeyFoo(), args, V("secret msg"), val_dead));
   TF_ASSERT_OK(rendez_->Recv(KeyBar(), args, &val, &val_dead));
   EXPECT_EQ("secret msg", V(val));
-  EXPECT_TRUE(
-      errors::IsAborted(rendez_->Recv(KeyFoo(), args, &val, &val_dead)));
 }
 
 // A simple structure that behaves a bit like a blocking counter.  The
@@ -311,8 +316,8 @@ static void BM_SendRecv(int iters) {
   Status s;
   if (iters > 0) {
     while (iters--) {
-      s = rendez->Send(KeyFoo(), args, orig, is_dead);
-      s = rendez->Recv(KeyFoo(), args, &val, &is_dead);
+      TF_CHECK_OK(rendez->Send(KeyFoo(), args, orig, is_dead));
+      TF_CHECK_OK(rendez->Recv(KeyFoo(), args, &val, &is_dead));
     }
     CHECK_EQ(V(val), V(orig));
   }
@@ -320,12 +325,12 @@ static void BM_SendRecv(int iters) {
 }
 BENCHMARK(BM_SendRecv);
 
-static void BM_RecvSend(int iters) {
+static void BM_PingPong(int iters) {
   thread::ThreadPool* pool = new thread::ThreadPool(Env::Default(), "test", 1);
 
-  // The main thread sends "foo" for iters/2 times and receives "bar"
-  // for iters/2 times.  The other thread sends "bar" for iters/2
-  // times and receives "foo" for iters/2 times.
+  // The main thread sends "foo" for iters times and receives "bar"
+  // for iters times.  The other thread sends "bar" for iters times
+  // and receives "foo" for iters times.
   Rendezvous* rendez = NewLocalRendezvous();
   pool->Schedule([rendez, iters]() {
     Tensor bar = V("bar");
@@ -333,9 +338,9 @@ static void BM_RecvSend(int iters) {
     bool is_dead = false;
     Rendezvous::Args args;
     Status s;
-    for (int i = 0; i < iters / 2; ++i) {
-      s = rendez->Recv(KeyFoo(), args, &foo, &is_dead);
-      s = rendez->Send(KeyBar(), args, bar, is_dead);
+    for (int i = 0; i < iters; ++i) {
+      TF_CHECK_OK(rendez->Recv(KeyFoo(), args, &foo, &is_dead));
+      TF_CHECK_OK(rendez->Send(KeyBar(), args, bar, is_dead));
     }
     CHECK_EQ("foo", V(foo));
   });
@@ -344,13 +349,13 @@ static void BM_RecvSend(int iters) {
   bool is_dead = false;
   Rendezvous::Args args;
   Status s;
-  for (int i = 0; i < iters / 2; ++i) {
-    s = rendez->Send(KeyFoo(), args, foo, is_dead);
-    s = rendez->Recv(KeyBar(), args, &bar, &is_dead);
+  for (int i = 0; i < iters; ++i) {
+    TF_CHECK_OK(rendez->Send(KeyFoo(), args, foo, is_dead));
+    TF_CHECK_OK(rendez->Recv(KeyBar(), args, &bar, &is_dead));
   }
   CHECK_EQ("bar", V(bar));
   delete pool;
 }
-BENCHMARK(BM_RecvSend);
+BENCHMARK(BM_PingPong);
 
 }  // namespace tensorflow
-- 
GitLab


From eaa7a8ed93ccac595eab412dc7e021202c155f5d Mon Sep 17 00:00:00 2001
From: abenmao <mengchen.cas@foxmail.com>
Date: Sat, 22 Jul 2017 12:40:33 +0800
Subject: [PATCH 1229/1375] grappler swap_to_host bug fix (#11283)

---
 tensorflow/core/grappler/optimizers/memory_optimizer.cc | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 31e525fb4a..ae5b8123c6 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -490,6 +490,9 @@ std::pair<NodeDef*, NodeDef*> BuildSwapPair(NodeDef* node, int input_to_swap,
   (*swap_in_node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
   (*node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
 
+  const DataType input_type = node->attr().at("T").type();
+  (*swap_in_node->mutable_attr())["T"].set_type(input_type);
+  (*swap_out_node->mutable_attr())["T"].set_type(input_type);
   return std::make_pair(swap_out_node, swap_in_node);
 }
 
-- 
GitLab


From 528d01e766d2b90e780722669699e52cb1fbbd56 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Sat, 22 Jul 2017 14:01:45 -0700
Subject: [PATCH 1230/1375] Make build_pip_package.sh work with relative paths.

PiperOrigin-RevId: 162842180
---
 tensorflow/tools/pip_package/build_pip_package.sh | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh
index a158fd7345..5007ff7164 100755
--- a/tensorflow/tools/pip_package/build_pip_package.sh
+++ b/tensorflow/tools/pip_package/build_pip_package.sh
@@ -17,6 +17,10 @@
 
 set -e
 
+function real_path() {
+  [[ $1 = /* ]] && echo "$1" || echo "$PWD/${1#./}"
+}
+
 function cp_external() {
   local src_dir=$1
   local dest_dir=$2
@@ -41,7 +45,7 @@ function main() {
     exit 1
   fi
 
-  DEST=$1
+  DEST=$(real_path $1)
   TMPDIR=$(mktemp -d -t tmp.XXXXXXXXXX)
 
   GPU_FLAG=""
-- 
GitLab


From b4fa05012bd961a612639e11d26bf98aec1b19a4 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Sat, 22 Jul 2017 14:17:44 -0700
Subject: [PATCH 1231/1375] Add test tag oss_serial and support by
 run_pip_tests.sh and pip.sh

PiperOrigin-RevId: 162842890
---
 tensorflow/python/BUILD                       |  6 ++---
 tensorflow/python/debug/BUILD                 |  4 ++--
 tensorflow/tools/ci_build/builds/pip.sh       | 23 +++++++++++++++++--
 .../tools/ci_build/builds/run_pip_tests.sh    | 21 +++++++++++++++--
 .../tools/ci_build/ci_parameterized_build.sh  |  2 +-
 .../tools/ci_build/linux/cpu/run_py2_core.sh  |  2 +-
 .../ci_build/linux/cpu/run_py3_contrib.sh     |  2 +-
 .../tools/ci_build/linux/cpu/run_py3_core.sh  |  2 +-
 .../tools/ci_build/linux/gpu/run_cc_core.sh   |  2 +-
 .../tools/ci_build/linux/gpu/run_py3_core.sh  |  2 +-
 10 files changed, 50 insertions(+), 16 deletions(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 4625ee9a1c..1fb860a9ee 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2986,9 +2986,7 @@ cuda_py_test(
         ":variables",
         "//third_party/py/numpy",
     ],
-    tags = [
-        "no_oss",  # b/62956105: port conflicts.
-    ],
+    tags = ["oss_serial"],
 )
 
 tf_py_test(
@@ -3004,8 +3002,8 @@ tf_py_test(
         ":variables",
     ],
     tags = [
-        "no_oss",  # b/62956105: port conflicts.
         "notsan",  # data race due to b/62910646
+        "oss_serial",
     ],
 )
 
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index e48bcc8788..13b46a7311 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -804,9 +804,9 @@ cuda_py_test(
         "//tensorflow/python:variables",
     ],
     tags = [
-        "no_oss",  # b/62956105: port conflicts.
         "nomac",  # TODO(cais): Install of futures and grpcio on all macs.
         "notsan",
+        "oss_serial",
     ],
 )
 
@@ -831,7 +831,7 @@ cuda_py_test(
     ],
     data = ["//tensorflow/tools/dist_test/server:grpc_tensorflow_server"],
     tags = [
-        "no_oss",  # b/62956105: port conflicts + Incompatible with bazel_pip.
+        "no_oss",  # Incompatible with bazel_pip.
         "nomac",  # TODO(cais): Install of futures and grpcio on all macs.
         "notsan",
     ],
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index 2cb2961fa2..db011a6bad 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -371,6 +371,25 @@ do_virtualenv_pip_test() {
   fi
 }
 
+################################################################################
+# Run tests tagged with oss_serial against the virtualenv install.
+################################################################################
+do_virtualenv_oss_serial_pip_test() {
+  if [[ -n "${NO_TEST_ON_INSTALL}" ]] &&
+     [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
+    echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
+    echo "  Skipping Python unit tests on install tagged with oss_serial"
+  else
+    # Call run_pip_tests.sh to perform test-on-install
+    "${SCRIPT_DIR}/run_pip_tests.sh" \
+      --virtualenv ${GPU_FLAG} ${MAC_FLAG} --oss_serial
+    if [[ $? != 0 ]]; then
+      echo "PIP tests-on-install (oss_serial) FAILED"
+      return 1
+    fi
+  fi
+}
+
 ################################################################################
 # Test user ops (optional).
 ################################################################################
@@ -441,8 +460,8 @@ do_ffmpeg_integration_test() {
 
 
 # List of all PIP test tasks and their descriptions.
-PIP_TASKS=("do_clean_virtualenv_smoke_test" "do_virtualenv_pip_test" "do_test_user_ops" "do_test_tfdbg_binaries" "do_test_tutorials" "do_ffmpeg_integration_test")
-PIP_TASKS_DESC=("Smoke test of pip install in clean virtualenv" "Pip tests in virtualenv" "User ops test" "TensorFlow Debugger (tfdbg) binaries test" "Tutorials test" "ffmpeg integration test")
+PIP_TASKS=("do_clean_virtualenv_smoke_test" "do_virtualenv_pip_test" "do_virtualenv_oss_serial_pip_test" "do_test_user_ops" "do_test_tfdbg_binaries" "do_test_tutorials" "do_ffmpeg_integration_test")
+PIP_TASKS_DESC=("Smoke test of pip install in clean virtualenv" "PIP tests in virtualenv" "PIP test in virtualenv (tag: oss_serial)" "User ops test" "TensorFlow Debugger (tfdbg) binaries test" "Tutorials test" "ffmpeg integration test")
 
 
 # Execute all the PIP test steps.
diff --git a/tensorflow/tools/ci_build/builds/run_pip_tests.sh b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
index 87793dabc5..f66846654d 100755
--- a/tensorflow/tools/ci_build/builds/run_pip_tests.sh
+++ b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
@@ -18,7 +18,7 @@
 # Run the python unit tests from the source code on the pip installation.
 #
 # Usage:
-#   run_pip_tests.sh [--virtualenv] [--gpu] [--mac]
+#   run_pip_tests.sh [--virtualenv] [--gpu] [--mac] [--oss_serial]
 #
 # If the flag --virtualenv is set, the script will use "python" as the Python
 # binary path. Otherwise, it will use tools/python_bin_path.sh to determine
@@ -30,6 +30,10 @@
 # The --mac flag informs the script that this is running on mac. Mac does not
 # have flock, so we should skip using parallel_gpu_execute on mac.
 #
+# The --oss_serial flag lets the script run only the py tests with the
+# oss_serial tag, in a serial fashion, i.e., using the bazel flag
+# --local_test_jobs=1
+#
 #   TF_BUILD_APPEND_ARGUMENTS:
 #                      Additional command line arguments for the bazel,
 #                      pip.sh or android.sh command
@@ -42,6 +46,7 @@ source "${SCRIPT_DIR}/builds_common.sh"
 IS_VIRTUALENV=0
 IS_GPU=0
 IS_MAC=0
+IS_OSS_SERIAL=0
 while true; do
   if [[ "$1" == "--virtualenv" ]]; then
     IS_VIRTUALENV=1
@@ -49,6 +54,8 @@ while true; do
     IS_GPU=1
   elif [[ "$1" == "--mac" ]]; then
     IS_MAC=1
+  elif [[ "$1" == "--oss_serial" ]]; then
+    IS_OSS_SERIAL=1
   fi
   shift
 
@@ -69,7 +76,13 @@ ln -s $(pwd)/tensorflow ${PIP_TEST_ROOT}/tensorflow
 
 # Do not run tests with "no_pip" tag. If running GPU tests, also do not run
 # tests with no_pip_gpu tag.
-PIP_TEST_FILTER_TAG="-no_oss,-no_pip"
+PIP_TEST_FILTER_TAG="-no_pip,-no_oss"
+if [[ ${IS_OSS_SERIAL} == "1" ]]; then
+  PIP_TEST_FILTER_TAG="${PIP_TEST_FILTER_TAG},oss_serial"
+else
+  PIP_TEST_FILTER_TAG="${PIP_TEST_FILTER_TAG},-oss_serial"
+fi
+
 if [[ ${IS_GPU} == "1" ]]; then
   PIP_TEST_FILTER_TAG="-no_pip_gpu,${PIP_TEST_FILTER_TAG}"
 fi
@@ -129,6 +142,10 @@ else
   fi
 fi
 
+if [[ ${IS_OSS_SERIAL} == 1 ]]; then
+  BAZEL_PARALLEL_TEST_FLAGS="--local_test_jobs=1"
+fi
+
 # Actually run the tests.
 bazel test ${BAZEL_FLAGS} ${BAZEL_PARALLEL_TEST_FLAGS} -- \
     ${BAZEL_TEST_TARGETS}
diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index 4ca08052ba..6c09925454 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -358,7 +358,7 @@ if [[ "${TF_BUILD_APPEND_ARGUMENTS}" == *"--test_tag_filters="* ]]; then
     fi
   done
 else
-  EXTRA_ARGS="${TF_BUILD_APPEND_ARGUMENTS} --test_tag_filters=-no_oss,-benchmark-test"
+  EXTRA_ARGS="${TF_BUILD_APPEND_ARGUMENTS} --test_tag_filters=-no_oss,-oss_serial,-benchmark-test"
   if [[ ${IS_MAC} == "1" ]]; then
     EXTRA_ARGS="${EXTRA_ARGS},-nomac"
   fi
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
index 4a30c73417..fa3d27fa41 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python2`
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test --test_lang_filters=py -k \
+bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test --test_lang_filters=py -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 --build_tests_only \
     --test_output=errors -- \
     //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
index d224878643..258dec4fec 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test -k \
+bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 \
     --test_output=errors -- \
     //tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
index 39c241fb53..9c450ab4da 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test --test_lang_filters=py -k \
+bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test --test_lang_filters=py -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 --build_tests_only \
     --test_output=errors -- \
     //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
index 4d7b4741d6..f2ea8d3c77 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
@@ -35,7 +35,7 @@ export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --config=cuda --test_tag_filters=-no_oss,-no_gpu,-benchmark-test -k \
+bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test -k \
     --test_lang_filters=cc --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 \
     --build_tests_only --test_output=errors --local_test_jobs=8 \
     --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute -- \
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
index c0bcec4a17..4e0c3d1d33 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
@@ -35,7 +35,7 @@ export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 yes "" | ./configure
 
 # Run bazel test command. Double test timeouts to avoid flakes.
-bazel test --config=cuda --test_tag_filters=-no_oss,-no_gpu,-benchmark-test -k \
+bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test -k \
     --test_lang_filters=py --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 \
     --build_tests_only --test_output=errors --local_test_jobs=8 \
     --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute -- \
-- 
GitLab


From d481e5425499e076f59d8d67751836be25ac642e Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Sun, 23 Jul 2017 16:51:25 +0900
Subject: [PATCH 1232/1375] Fix stackoverflow link to avoid redirect warning

---
 tensorflow/docs_src/community/documentation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/community/documentation.md b/tensorflow/docs_src/community/documentation.md
index e8b3787c1f..655506b098 100644
--- a/tensorflow/docs_src/community/documentation.md
+++ b/tensorflow/docs_src/community/documentation.md
@@ -35,7 +35,7 @@ at [tensorflow.org/versions/master](https://www.tensorflow.org/versions/master).
 
 If you want documentation changes to appear at root, you will need to also
 contribute that change to the current stable binary branch (and/or
-[cherrypick](https://www.google.com/url?sa=D&q=http%3A%2F%2Fstackoverflow.com%2Fquestions%2F9339429%2Fwhat-does-cherry-picking-a-commit-with-git-mean)).
+[cherrypick](https://stackoverflow.com/questions/9339429/what-does-cherry-picking-a-commit-with-git-mean)).
 
 ## Reference vs. non-reference documentation
 
-- 
GitLab


From 2661f6841d0ad9ec1381d177a1f9df02e73d001c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sun, 23 Jul 2017 19:27:12 -0700
Subject: [PATCH 1233/1375] [XLA] Add support for sin(x) transcendental.

PiperOrigin-RevId: 162889962
---
 tensorflow/compiler/tests/unary_ops_test.py            | 10 ++++++++++
 tensorflow/compiler/tf2xla/kernels/unary_ops.cc        |  1 +
 tensorflow/compiler/xla/client/computation_builder.cc  |  5 +++++
 tensorflow/compiler/xla/client/computation_builder.h   |  3 +++
 tensorflow/compiler/xla/service/dfs_hlo_visitor.h      |  3 +++
 .../compiler/xla/service/elemental_ir_emitter.cc       |  5 +++++
 .../compiler/xla/service/gpu/elemental_ir_emitter.cc   |  6 ++++++
 tensorflow/compiler/xla/service/hlo_graph_dumper.cc    |  1 +
 tensorflow/compiler/xla/service/hlo_instruction.cc     |  6 ++++++
 tensorflow/compiler/xla/service/hlo_opcode.cc          |  2 ++
 tensorflow/compiler/xla/service/hlo_opcode.h           |  1 +
 tensorflow/compiler/xla/service/instruction_fusion.cc  |  1 +
 tensorflow/compiler/xla/service/shape_inference.cc     |  1 +
 tensorflow/compiler/xla/service/user_computation.cc    |  2 ++
 .../compiler/xla/tests/array_elementwise_ops_test.cc   |  9 +++++++++
 tensorflow/compiler/xla/xla_data.proto                 |  3 +++
 16 files changed, 59 insertions(+)

diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py
index 51d8786ce3..ce35eb9197 100644
--- a/tensorflow/compiler/tests/unary_ops_test.py
+++ b/tensorflow/compiler/tests/unary_ops_test.py
@@ -152,6 +152,16 @@ class UnaryOpsTest(XLATestCase):
           np.array([[1, 2]], dtype=dtype),
           expected=np.array([[0, 0.69314718]], dtype=dtype))
 
+      self._assertOpOutputMatchesExpected(
+          math_ops.sin,
+          np.array([[1, 2]], dtype=dtype),
+          expected=np.array([[0.841478, 0.909302]], dtype=dtype))
+
+      self._assertOpOutputMatchesExpected(
+          math_ops.cos,
+          np.array([[1, 2]], dtype=dtype),
+          expected=np.array([[0.540297, -0.41614]], dtype=dtype))
+
       # TODO(b/34703906): improve log1p implementation and make tolerance
       # tighter.
       self._assertOpOutputMatchesExpected(
diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
index ab96d86ed2..626ddd17d3 100644
--- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
@@ -45,6 +45,7 @@ namespace {
 XLAJIT_MAKE_UNARY(Abs, b->Abs(x));
 XLAJIT_MAKE_UNARY(Ceil, b->Ceil(x));
 XLAJIT_MAKE_UNARY(Cos, b->Cos(x));
+XLAJIT_MAKE_UNARY(Sin, b->Sin(x));
 XLAJIT_MAKE_UNARY(Exp, b->Exp(x));
 XLAJIT_MAKE_UNARY(Floor, b->Floor(x));
 // Returns 0 if x is 0, -1 if x < 0 and 1 if x > 0.
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 9810ccc042..212bcd27d2 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -976,6 +976,11 @@ ComputationDataHandle ComputationBuilder::Cos(
   return UnaryOp(UNOP_COS, operand);
 }
 
+ComputationDataHandle ComputationBuilder::Sin(
+    const ComputationDataHandle& operand) {
+  return UnaryOp(UNOP_SIN, operand);
+}
+
 ComputationDataHandle ComputationBuilder::Tanh(
     const ComputationDataHandle& operand) {
   return UnaryOp(UNOP_TANH, operand);
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index b411346459..94602bd473 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -513,6 +513,9 @@ class ComputationBuilder {
   // Enqueues a cosine instruction onto the computation.
   ComputationDataHandle Cos(const ComputationDataHandle& operand);
 
+  // Enqueues a sine instruction onto the computation.
+  ComputationDataHandle Sin(const ComputationDataHandle& operand);
+
   // Enqueues a tanh instruction onto the computation.
   ComputationDataHandle Tanh(const ComputationDataHandle& operand);
 
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 0deedcf184..e6067ae9ea 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -145,6 +145,9 @@ class DfsHloVisitor {
   virtual Status HandleCos(HloInstruction* cos, HloInstruction* operand) {
     return HandleElementwiseUnary(cos, HloOpcode::kCos);
   }
+  virtual Status HandleSin(HloInstruction* sin, HloInstruction* operand) {
+    return HandleElementwiseUnary(sin, HloOpcode::kSin);
+  }
   virtual Status HandleTanh(HloInstruction* tanh, HloInstruction* operand) {
     return HandleElementwiseUnary(tanh, HloOpcode::kTanh);
   }
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index 9c4380b39d..81092e42d5 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -177,6 +177,10 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitFloatUnaryOp(
       return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::cos, {operand_value},
                                           {operand_value->getType()},
                                           ir_builder_);
+    case HloOpcode::kSin:
+      return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::sin, {operand_value},
+                                          {operand_value->getType()},
+                                          ir_builder_);
     case HloOpcode::kFloor:
       return llvm_ir::EmitCallToIntrinsic(
           llvm::Intrinsic::floor, {operand_value}, {operand_value->getType()},
@@ -788,6 +792,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
     case HloOpcode::kLog:
     case HloOpcode::kNegate:
     case HloOpcode::kSign:
+    case HloOpcode::kSin:
     case HloOpcode::kTanh:
     case HloOpcode::kLogicalNot:
       return [this, hlo, &operand_to_generator](
diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
index c2dec7ed6a..c03213ab6d 100644
--- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
@@ -211,6 +211,12 @@ StatusOr<llvm::Value*> GpuElementalIrEmitter::EmitFloatUnaryOp(
     case HloOpcode::kLog:
       return EmitLibdeviceMathCall("__nv_log", {operand_value}, {input_type},
                                    output_type);
+    case HloOpcode::kCos:
+      return EmitLibdeviceMathCall("__nv_cos", {operand_value}, {input_type},
+                                   output_type);
+    case HloOpcode::kSin:
+      return EmitLibdeviceMathCall("__nv_sin", {operand_value}, {input_type},
+                                   output_type);
     case HloOpcode::kTanh:
       return EmitLibdeviceMathCall("__nv_tanh", {operand_value}, {input_type},
                                    output_type);
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 097a762015..acd26c4e31 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -360,6 +360,7 @@ string InstructionSequenceGraph(
       case HloOpcode::kRemainder:
       case HloOpcode::kSelect:
       case HloOpcode::kSign:
+      case HloOpcode::kSin:
       case HloOpcode::kSlice:
       case HloOpcode::kSort:
       case HloOpcode::kSubtract:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 16440f9a27..f52882cca5 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -130,6 +130,7 @@ HloInstruction::CreateGetTupleElement(const Shape& shape,
     case HloOpcode::kLogicalNot:
     case HloOpcode::kNegate:
     case HloOpcode::kSign:
+    case HloOpcode::kSin:
     case HloOpcode::kSort:
     case HloOpcode::kTanh:
       break;
@@ -792,6 +793,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
     case HloOpcode::kLogicalNot:
     case HloOpcode::kNegate:
     case HloOpcode::kSign:
+    case HloOpcode::kSin:
     case HloOpcode::kSort:
     case HloOpcode::kTanh:
       CHECK_EQ(new_operands.size(), 1);
@@ -1196,6 +1198,7 @@ bool HloInstruction::Identical(
     case HloOpcode::kRemainder:
     case HloOpcode::kSelect:
     case HloOpcode::kSign:
+    case HloOpcode::kSin:
     case HloOpcode::kSubtract:
     case HloOpcode::kTanh:
     case HloOpcode::kTuple:
@@ -1897,6 +1900,8 @@ Status HloInstruction::Visit(DfsHloVisitor* visitor) {
       return visitor->HandleTanh(this, operands_[0]);
     case HloOpcode::kCos:
       return visitor->HandleCos(this, operands_[0]);
+    case HloOpcode::kSin:
+      return visitor->HandleSin(this, operands_[0]);
     case HloOpcode::kIsFinite:
       return visitor->HandleIsFinite(this, operands_[0]);
     case HloOpcode::kLogicalNot:
@@ -2172,6 +2177,7 @@ bool HloInstruction::IsElementwise() const {
     case HloOpcode::kNegate:
     case HloOpcode::kReducePrecision:
     case HloOpcode::kSign:
+    case HloOpcode::kSin:
     case HloOpcode::kTanh:
       return true;
 
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.cc b/tensorflow/compiler/xla/service/hlo_opcode.cc
index ac637dfd3e..3888f757ad 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.cc
+++ b/tensorflow/compiler/xla/service/hlo_opcode.cc
@@ -145,6 +145,8 @@ string HloOpcodeString(HloOpcode opcode) {
       return "send";
     case HloOpcode::kSign:
       return "sign";
+    case HloOpcode::kSin:
+      return "sine";
     case HloOpcode::kSlice:
       return "slice";
     case HloOpcode::kSort:
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index 7086dfff42..358e611d57 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -87,6 +87,7 @@ enum class HloOpcode {
   kSelectAndScatter,
   kSend,
   kSign,
+  kSin,
   kSlice,
   kSort,
   kSubtract,
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index e7e2e4f9d3..482ab9b94a 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -69,6 +69,7 @@ namespace xla {
     case HloOpcode::kReverse:
     case HloOpcode::kSelect:
     case HloOpcode::kSign:
+    case HloOpcode::kSin:
     case HloOpcode::kSlice:
     case HloOpcode::kSubtract:
     case HloOpcode::kTranspose:
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 69fee6a621..40206145c8 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -185,6 +185,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     case UNOP_FLOOR:
     case UNOP_CEIL:
     case UNOP_COS:
+    case UNOP_SIN:
     case UNOP_EXP:
     case UNOP_LOG:
     case UNOP_TANH:
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index d9079d70df..3ab780e7d0 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -64,6 +64,8 @@ HloOpcode UnaryOperationToHloOpcode(UnaryOperation unop) {
       return HloOpcode::kNegate;
     case UNOP_SIGN:
       return HloOpcode::kSign;
+    case UNOP_SIN:
+      return HloOpcode::kSin;
     case UNOP_SORT:
       return HloOpcode::kSort;
     case UNOP_TANH:
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index fa6c97bcb8..fb913e200f 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1542,6 +1542,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, CosF32s) {
                              error_spec_);
 }
 
+XLA_TEST_F(ArrayElementwiseOpTest, SinF32s) {
+  ComputationBuilder builder(client_, TestName());
+  auto a = builder.ConstantR1<float>({3.14159f, 0.0f, 1.570796f, -0.78539f});
+  auto result = builder.Sin(a);
+
+  ComputeAndCompareR1<float>(&builder, {0.0f, 0.0f, 1.0f, -0.707107f}, {},
+                             error_spec_);
+}
+
 TEST_F(ArrayElementwiseOpTest, TanhF32s) {
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR1<float>({-2.5f, 3.14f, 2.25f});
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index baecfda1c8..166f31c9fb 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -637,6 +637,9 @@ enum UnaryOperation {
 
   // Elementwise, computes the cosine of x.
   UNOP_COS = 12;
+
+  // Elementwise, computes the sine of x.
+  UNOP_SIN = 13;
 }
 
 message UnaryOpRequest {
-- 
GitLab


From d30efdbaa2b80bf4eb8db9e0b94501fdbd4107d8 Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Mon, 24 Jul 2017 21:55:56 +0900
Subject: [PATCH 1234/1375] Fix 404 of model file example which was moved to
 tensorboard repository.

---
 tensorflow/docs_src/extend/tool_developers/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/extend/tool_developers/index.md b/tensorflow/docs_src/extend/tool_developers/index.md
index 06fc5e70dd..f02cd23be8 100644
--- a/tensorflow/docs_src/extend/tool_developers/index.md
+++ b/tensorflow/docs_src/extend/tool_developers/index.md
@@ -63,7 +63,7 @@ There are actually two different formats that a ProtoBuf can be saved in.
 TextFormat is a human-readable form, which makes it nice for debugging and
 editing, but can get large when there's numerical data like weights stored in
 it. You can see a small example of that in
-[graph_run_run2.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/demo/data/graph_run_run2.pbtxt).
+[graph_run_run2.pbtxt](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/demo/data/graph_run_run2.pbtxt).
 
 Binary format files are a lot smaller than their text equivalents, even though
 they're not as readable for us. In this script, we ask the user to supply a
-- 
GitLab


From 51897371c86d93db8e6edce97e918979085fdde9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 06:04:33 -0700
Subject: [PATCH 1235/1375] Further BUILD cleanup in tensorflow/contrib/...

PiperOrigin-RevId: 162925024
---
 .../contrib/remote_fused_graph/pylib/BUILD    |  9 ++-
 .../contrib/tensor_forest/kernels/v4/BUILD    | 15 ++---
 tensorflow/contrib/timeseries/examples/BUILD  | 56 ++++++-------------
 3 files changed, 28 insertions(+), 52 deletions(-)

diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
index 288f59fed4..27f0a7f58f 100644
--- a/tensorflow/contrib/remote_fused_graph/pylib/BUILD
+++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD
@@ -24,7 +24,12 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":gen_remote_fused_graph_ops",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:util",
+        "//third_party/py/numpy",
     ],
 )
 
@@ -37,9 +42,9 @@ py_test(
     deps = [
         ":remote_fused_graph_ops_py",
         "//tensorflow/core:protos_all_py",
-        "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
index 7b3fd63a7a..d14d4d2d6b 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD
@@ -1,7 +1,8 @@
 # TensorFlow code for training random forests.
-package(default_visibility = [
-    "//visibility:public",
-])
+
+package(
+    default_visibility = ["//visibility:public"],
+)
 
 licenses(["notice"])  # Apache 2.0
 
@@ -9,9 +10,7 @@ exports_files(["LICENSE"])
 
 filegroup(
     name = "all_files",
-    srcs = glob(
-        ["**/*"],
-    ),
+    srcs = glob(["**/*"]),
 )
 
 cc_library(
@@ -75,7 +74,6 @@ cc_library(
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
-        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -121,7 +119,6 @@ cc_test(
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
         "//tensorflow/core",
-        "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
@@ -138,7 +135,6 @@ cc_library(
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/core:core_cpu",
-        "//tensorflow/core:framework_headers_lib",
     ],
 )
 
@@ -199,7 +195,6 @@ cc_library(
         ":params",
         ":split_collection_operators",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc",
-        "//tensorflow/contrib/decision_trees/proto:generic_tree_model_extensions_cc",
         "//tensorflow/contrib/tensor_forest:tree_utils",
         "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_cc",
diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD
index 14ebe5d08f..015d0eba29 100644
--- a/tensorflow/contrib/timeseries/examples/BUILD
+++ b/tensorflow/contrib/timeseries/examples/BUILD
@@ -1,9 +1,7 @@
 load("//tensorflow:tensorflow.bzl", "py_test")
 
 package(
-    default_visibility = [
-        "//tensorflow:internal",
-    ],
+    default_visibility = ["//tensorflow:internal"],
 )
 
 licenses(["notice"])  # Apache 2.0
@@ -12,9 +10,7 @@ exports_files(["LICENSE"])
 
 py_binary(
     name = "predict",
-    srcs = [
-        "predict.py",
-    ],
+    srcs = ["predict.py"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
@@ -26,26 +22,19 @@ py_binary(
 py_test(
     name = "predict_test",
     timeout = "long",  # Moderate but for asan
-    srcs = [
-        "predict_test.py",
-    ],
-    data = [
-        "data/period_trend.csv",
-    ],
+    srcs = ["predict_test.py"],
+    data = ["data/period_trend.csv"],
     srcs_version = "PY2AND3",
     deps = [
         ":predict",
+        "//tensorflow/python:client_testlib",
     ],
 )
 
 py_binary(
     name = "known_anomaly",
-    srcs = [
-        "known_anomaly.py",
-    ],
-    data = [
-        "data/changepoints.csv",
-    ],
+    srcs = ["known_anomaly.py"],
+    data = ["data/changepoints.csv"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
@@ -57,28 +46,22 @@ py_binary(
 py_test(
     name = "known_anomaly_test",
     timeout = "long",  # Moderate but for asan
-    srcs = [
-        "known_anomaly_test.py",
-    ],
+    srcs = ["known_anomaly_test.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":known_anomaly",
+        "//tensorflow/python:client_testlib",
     ],
 )
 
 py_binary(
     name = "multivariate",
-    srcs = [
-        "multivariate.py",
-    ],
-    data = [
-        "data/multivariate_level.csv",
-    ],
+    srcs = ["multivariate.py"],
+    data = ["data/multivariate_level.csv"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
         "//tensorflow:tensorflow_py",
-        "//tensorflow/python:platform",
         "//third_party/py/numpy",
     ],
 )
@@ -92,17 +75,14 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":multivariate",
+        "//tensorflow/python:client_testlib",
     ],
 )
 
 py_binary(
     name = "lstm",
-    srcs = [
-        "lstm.py",
-    ],
-    data = [
-        "data/multivariate_periods.csv",
-    ],
+    srcs = ["lstm.py"],
+    data = ["data/multivariate_periods.csv"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
@@ -114,13 +94,9 @@ py_binary(
 py_test(
     name = "lstm_test",
     timeout = "long",  # Moderate but for asan
-    srcs = [
-        "lstm_test.py",
-    ],
+    srcs = ["lstm_test.py"],
     srcs_version = "PY2AND3",
-    deps = [
-        ":lstm",
-    ],
+    deps = [":lstm"],
 )
 
 filegroup(
-- 
GitLab


From f533784c8a810b63984c464c6ac7278cb04340f3 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Mon, 24 Jul 2017 08:03:32 -0700
Subject: [PATCH 1236/1375] Simplify debug ops op_lib BUILD rule

by using tf_gen_op_libs

PiperOrigin-RevId: 162936394
---
 tensorflow/core/BUILD | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 9be56c2a87..02355a7626 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -558,17 +558,9 @@ tf_gen_op_libs(
     deps = [":lib"],
 )
 
-cc_library(
-    name = "debug_ops_op_lib",
-    srcs = ["ops/debug_ops.cc"],
-    copts = tf_copts(),
-    linkstatic = 1,
-    visibility = ["//tensorflow:internal"],
-    deps = [
-        ":framework",
-        "//tensorflow/core/kernels:debug_ops",
-    ],
-    alwayslink = 1,
+tf_gen_op_libs(
+    op_lib_names = ["debug_ops"],
+    deps = ["//tensorflow/core/kernels:debug_ops"],
 )
 
 # And one for all user ops
-- 
GitLab


From 8fc6d91e4e2d7e85cb27cfe19263c1e7d026c6a4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 08:41:18 -0700
Subject: [PATCH 1237/1375] RELNOTES: Add the capacity for hparams.parse_values
 to parse strings involving assignment to specific list positions (e.g.
 "arr[1]=abc".)  The returned dictionary is identical to the previous version
 w.r.t. variable assignments, but for position assignments the key will map to
 a dictionary mapping specified indices to values (e.g. {'arr': {1:'abc'}}). 
 The type_map must have a key corresponding to the variable name, not the
 index (e.g. type_map={'arr':int}, not type_map={'arr[1]'=int}. NOTE: No
 longer allows multi-assignment to the same name.  (e.g. 'x=10,x=20' will now
 raise a ValueError, where it would previously ignore the 'x=10'.)

PiperOrigin-RevId: 162940437
---
 .../training/python/training/hparam.py        | 198 +++++++++++---
 .../training/python/training/hparam_test.py   | 243 ++++++++++++------
 2 files changed, 330 insertions(+), 111 deletions(-)

diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py
index 5de177ae44..a13c7d4b4d 100644
--- a/tensorflow/contrib/training/python/training/hparam.py
+++ b/tensorflow/contrib/training/python/training/hparam.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Hyperparameter values."""
 from __future__ import absolute_import
 from __future__ import division
@@ -27,6 +26,116 @@ from tensorflow.contrib.training.python.training import hparam_pb2
 from tensorflow.python.framework import ops
 from tensorflow.python.util import compat
 
+# Define the regular expression for parsing a single clause of the input
+# (delimited by commas).  A legal clause looks like:
+#   <variable name>[<index>]? = <rhs>
+# where <rhs> is either a single token or [] enclosed list of tokens.
+# For example:  "var[1] = a" or "x = [1,2,3]"
+PARAM_RE = re.compile(r"""
+  (?P<name>[a-zA-Z][\w]*)      # variable name: "var" or "x"
+  (\[\s*(?P<index>\d+)\s*\])?  # (optional) index: "1" or None
+  \s*=\s*
+  ((?P<val>[^,\[]*)            # single value: "a" or None
+   |
+   \[(?P<vals>[^\]]*)\])       # list of values: None or "1,2,3"
+  ($|,)""", re.VERBOSE)
+
+
+def _parse_fail(name, var_type, value, values):
+  """Helper function for raising a value error for bad assignment."""
+  raise ValueError(
+      'Could not parse hparam \'%s\' of type \'%s\' with value \'%s\' in %s' %
+      (name, var_type.__name__, value, values))
+
+
+def _reuse_fail(name, values):
+  """Helper function for raising a value error for reuse of name."""
+  raise ValueError('Multiple assignments to variable \'%s\' in %s' % (name,
+                                                                      values))
+
+
+def _process_scalar_value(name, parse_fn, var_type, m_dict, values,
+                          results_dictionary):
+  """Update results_dictionary with a scalar value.
+
+  Used to update the results_dictionary to be returned by parse_values when
+  encountering a clause with a scalar RHS (e.g.  "s=5" or "arr[0]=5".)
+
+  Mutates results_dictionary.
+
+  Args:
+    name: Name of variable in assignment ("s" or "arr").
+    parse_fn: Function for parsing the actual value.
+    var_type: Type of named variable.
+    m_dict: Dictionary constructed from regex parsing.
+      m_dict['val']: RHS value (scalar)
+      m_dict['index']: List index value (or None)
+    values: Full expression being parsed
+    results_dictionary: The dictionary being updated for return by the parsing
+      function.
+
+  Raises:
+    ValueError: If the name has already been sued.
+  """
+  try:
+    parsed_value = parse_fn(m_dict['val'])
+  except ValueError:
+    _parse_fail(name, var_type, m_dict['val'], values)
+
+  # If no index is provided
+  if not m_dict['index']:
+    if name in results_dictionary:
+      _reuse_fail(name, values)
+    results_dictionary[name] = parsed_value
+  else:
+    if name in results_dictionary:
+      # The name has already been used as a scalar, then it
+      # will be in this dictionary and map to a non-dictionary.
+      if not isinstance(results_dictionary.get(name), dict):
+        _reuse_fail(name, values)
+    else:
+      results_dictionary[name] = {}
+
+    index = int(m_dict['index'])
+    # Make sure the index position hasn't already been assigned a value.
+    if index in results_dictionary[name]:
+      _reuse_fail('{}[{}]'.format(name, index), values)
+    results_dictionary[name][index] = parsed_value
+
+
+def _process_list_value(name, parse_fn, var_type, m_dict, values,
+                        results_dictionary):
+  """Update results_dictionary from a list of values.
+
+  Used to update results_dictionary to be returned by parse_values when
+  encountering a clause with a list RHS (e.g.  "arr=[1,2,3]".)
+
+  Mutates results_dictionary.
+
+  Args:
+    name: Name of variable in assignment ("arr").
+    parse_fn: Function for parsing individual values.
+    var_type: Type of named variable.
+    m_dict: Dictionary constructed from regex parsing.
+      m_dict['val']: RHS value (scalar)
+    values: Full expression being parsed
+    results_dictionary: The dictionary being updated for return by the parsing
+      function.
+
+  Raises:
+    ValueError: If the name has an index or the values cannot be parsed.
+  """
+  if m_dict['index'] is not None:
+    raise ValueError('Assignment of a list to a list index.')
+  elements = filter(None, re.split('[ ,]', m_dict['vals']))
+  # Make sure the name hasn't already been assigned a value
+  if name in results_dictionary:
+    raise _reuse_fail(name, values)
+  try:
+    results_dictionary[name] = [parse_fn(e) for e in elements]
+  except ValueError:
+    _parse_fail(name, var_type, m_dict['vals'], values)
+
 
 def parse_values(values, type_map):
   """Parses hyperparameter values from a string into a python map..
@@ -35,8 +144,11 @@ def parse_values(values, type_map):
   For each pair, the value of the hyperparameter named `name` is set to
   `value`.
 
-  If a hyperparameter name appears multiple times in `values`, the last
-  value is used.
+  If a hyperparameter name appears multiple times in `values`, a ValueError
+  is raised (e.g. 'a=1,a=2', 'a[1]=1,a[1]=2').
+
+  If a hyperparameter name in both an index assignment and scalar assignment,
+  a ValueError is raised.  (e.g. 'a=[1,2,3],a[0] = 1').
 
   The `value` in `name=value` must follows the syntax according to the
   type of the parameter:
@@ -49,7 +161,11 @@ def parse_values(values, type_map):
   *  Scalar string: A non-empty sequence of characters, excluding comma,
      spaces, and square brackets.  E.g.: foo, bar_1.
   *  List: A comma separated list of scalar values of the parameter type
-     enclosed in square backets.  E.g.: [1,2,3], [1.0,1e-12], [high,low].
+     enclosed in square brackets.  E.g.: [1,2,3], [1.0,1e-12], [high,low].
+
+  When index assignment is used, the corresponding type_map key should be the
+  list name.  E.g. for "arr[1]=0" the type_map must have the key "arr" (not
+  "arr[1]").
 
   Args:
     values: String.  Comma separated list of `name=value` pairs where
@@ -62,19 +178,23 @@ def parse_values(values, type_map):
       'x=[0.1,0.2]' will parse successfully if type_map['x'] = float.
 
   Returns:
-    A python map containing the name, value pairs.
+    A python map mapping each name to either:
+    * A scalar value.
+    * A list of scalar values.
+    * A dictionary mapping index numbers to scalar values.
+    (e.g. "x=5,L=[1,2],arr[1]=3" results in {'x':5,'L':[1,2],'arr':{1:3}}")
 
   Raises:
-    ValueError: If `values` cannot be parsed.
-
+    ValueError: If there is a problem with input.
+    * If `values` cannot be parsed.
+    * If a list is assigned to a list index (e.g. 'a[1] = [1,2,3]').
+    * If the same rvalue is assigned two different values (e.g. 'a=1,a=2',
+      'a[1]=1,a[1]=2', or 'a=1,a=[1]')
   """
-  ret = {}
-  param_re = re.compile(
-      r'(?P<name>[a-zA-Z][\w]*)\s*=\s*'
-      r'((?P<val>[^,\[]*)|\[(?P<vals>[^\]]*)\])($|,)')
+  results_dictionary = {}
   pos = 0
   while pos < len(values):
-    m = param_re.match(values, pos)
+    m = PARAM_RE.match(values, pos)
     if not m:
       raise ValueError('Malformed hyperparameter value: %s' % values[pos:])
     # Check that there is a comma between parameters and move past it.
@@ -85,11 +205,10 @@ def parse_values(values, type_map):
     if name not in type_map:
       raise ValueError('Unknown hyperparameter type for %s' % name)
     type_ = type_map[name]
-    def parse_fail(value):
-      raise ValueError(
-          'Could not parse hparam \'%s\' of type \'%s\' with value \'%s\' in %s'
-          % (name, type_.__name__, value, values))
+
+    # Set up correct parsing function (depending on whether type_ is a bool)
     if type_ == bool:
+
       def parse_bool(value):
         if value in ['true', 'True']:
           return True
@@ -98,25 +217,27 @@ def parse_values(values, type_map):
         else:
           try:
             return bool(int(value))
-          except (ValueError, TypeError):
-            parse_fail(value)
+          except ValueError:
+            _parse_fail(name, type_, value, values)
+
       parse = parse_bool
     else:
       parse = type_
+
+    # If a singe value is provided
     if m_dict['val'] is not None:
-      try:
-        ret[name] = parse(m_dict['val'])
-      except (ValueError, TypeError):
-        parse_fail(m_dict['val'])
+      _process_scalar_value(name, parse, type_, m_dict, values,
+                            results_dictionary)
+
+    # If the assigned value is a list:
     elif m_dict['vals'] is not None:
-      elements = filter(None, re.split('[ ,]', m_dict['vals']))
-      try:
-        ret[name] = [parse(e) for e in elements]
-      except (ValueError, TypeError):
-        parse_fail(m_dict['vals'])
-    else:
-      parse_fail('')
-  return ret
+      _process_list_value(name, parse, type_, m_dict, values,
+                          results_dictionary)
+
+    else:  # Not assigned a list or value
+      _parse_fail(name, type_, '', values)
+
+  return results_dictionary
 
 
 class HParams(object):
@@ -273,8 +394,8 @@ class HParams(object):
         elif kind.startswith('bytes'):
           # Setting attribute value to be 'str' to ensure the type is compatible
           # with both Python2 and Python3. UTF-8 encoding is assumed.
-          self.add_hparam(name, [compat.as_str(v)
-                                 for v in getattr(value, kind).value])
+          self.add_hparam(
+              name, [compat.as_str(v) for v in getattr(value, kind).value])
         else:
           self.add_hparam(name, [v for v in getattr(value, kind).value])
 
@@ -296,8 +417,8 @@ class HParams(object):
       raise ValueError('Hyperparameter name is reserved: %s' % name)
     if isinstance(value, (list, tuple)):
       if not value:
-        raise ValueError('Multi-valued hyperparameters cannot be empty: %s'
-                         % name)
+        raise ValueError(
+            'Multi-valued hyperparameters cannot be empty: %s' % name)
       self._hparam_types[name] = (type(value[0]), True)
     else:
       self._hparam_types[name] = (type(value), False)
@@ -461,7 +582,8 @@ class HParams(object):
     return HParams(hparam_def=hparam_def)
 
 
-ops.register_proto_function('hparams',
-                            proto_type=hparam_pb2.HParamDef,
-                            to_proto=HParams.to_proto,
-                            from_proto=HParams.from_proto)
+ops.register_proto_function(
+    'hparams',
+    proto_type=hparam_pb2.HParamDef,
+    to_proto=HParams.to_proto,
+    from_proto=HParams.from_proto)
diff --git a/tensorflow/contrib/training/python/training/hparam_test.py b/tensorflow/contrib/training/python/training/hparam_test.py
index b4130d04b4..6f807236c7 100644
--- a/tensorflow/contrib/training/python/training/hparam_test.py
+++ b/tensorflow/contrib/training/python/training/hparam_test.py
@@ -12,14 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Tests for hparam."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import six
-
 from tensorflow.contrib.training.python.training import hparam
 
 from tensorflow.python.platform import test
@@ -27,50 +24,49 @@ from tensorflow.python.platform import test
 
 class HParamsTest(test.TestCase):
 
-  def _assertDictEquals(self, d1, d2):
-    self.assertEqual(len(d1), len(d2))
-    for k, v in six.iteritems(d1):
-      self.assertTrue(k in d2, k)
-      self.assertEquals(v, d2[k], d2[k])
-
   def testEmpty(self):
     hparams = hparam.HParams()
-    self._assertDictEquals({}, hparams.values())
+    self.assertDictEqual({}, hparams.values())
     hparams.parse('')
-    self._assertDictEquals({}, hparams.values())
+    self.assertDictEqual({}, hparams.values())
     with self.assertRaisesRegexp(ValueError, 'Unknown hyperparameter'):
       hparams.parse('xyz=123')
 
   def testSomeValues(self):
     hparams = hparam.HParams(aaa=1, b=2.0, c_c='relu6')
-    self._assertDictEquals(
-        {'aaa': 1, 'b': 2.0, 'c_c': 'relu6'}, hparams.values())
+    self.assertDictEqual({'aaa': 1, 'b': 2.0, 'c_c': 'relu6'}, hparams.values())
     expected_str = '[(\'aaa\', 1), (\'b\', 2.0), (\'c_c\', \'relu6\')]'
-    self.assertEquals(expected_str, str(hparams.__str__()))
-    self.assertEquals(expected_str, str(hparams))
-    self.assertEquals(1, hparams.aaa)
-    self.assertEquals(2.0, hparams.b)
-    self.assertEquals('relu6', hparams.c_c)
+    self.assertEqual(expected_str, str(hparams.__str__()))
+    self.assertEqual(expected_str, str(hparams))
+    self.assertEqual(1, hparams.aaa)
+    self.assertEqual(2.0, hparams.b)
+    self.assertEqual('relu6', hparams.c_c)
     hparams.parse('aaa=12')
-    self._assertDictEquals(
-        {'aaa': 12, 'b': 2.0, 'c_c': 'relu6'}, hparams.values())
-    self.assertEquals(12, hparams.aaa)
-    self.assertEquals(2.0, hparams.b)
-    self.assertEquals('relu6', hparams.c_c)
+    self.assertDictEqual({
+        'aaa': 12,
+        'b': 2.0,
+        'c_c': 'relu6'
+    }, hparams.values())
+    self.assertEqual(12, hparams.aaa)
+    self.assertEqual(2.0, hparams.b)
+    self.assertEqual('relu6', hparams.c_c)
     hparams.parse('c_c=relu4,b=-2.0e10')
-    self._assertDictEquals({'aaa': 12, 'b': -2.0e10, 'c_c': 'relu4'},
-                           hparams.values())
-    self.assertEquals(12, hparams.aaa)
-    self.assertEquals(-2.0e10, hparams.b)
-    self.assertEquals('relu4', hparams.c_c)
+    self.assertDictEqual({
+        'aaa': 12,
+        'b': -2.0e10,
+        'c_c': 'relu4'
+    }, hparams.values())
+    self.assertEqual(12, hparams.aaa)
+    self.assertEqual(-2.0e10, hparams.b)
+    self.assertEqual('relu4', hparams.c_c)
     hparams.parse('c_c=,b=0,')
-    self._assertDictEquals({'aaa': 12, 'b': 0, 'c_c': ''}, hparams.values())
-    self.assertEquals(12, hparams.aaa)
-    self.assertEquals(0.0, hparams.b)
-    self.assertEquals('', hparams.c_c)
+    self.assertDictEqual({'aaa': 12, 'b': 0, 'c_c': ''}, hparams.values())
+    self.assertEqual(12, hparams.aaa)
+    self.assertEqual(0.0, hparams.b)
+    self.assertEqual('', hparams.c_c)
     hparams.parse('c_c=2.3",b=+2,')
-    self.assertEquals(2.0, hparams.b)
-    self.assertEquals('2.3"', hparams.c_c)
+    self.assertEqual(2.0, hparams.b)
+    self.assertEqual('2.3"', hparams.c_c)
     with self.assertRaisesRegexp(ValueError, 'Unknown hyperparameter'):
       hparams.parse('x=123')
     with self.assertRaisesRegexp(ValueError, 'Could not parse'):
@@ -83,17 +79,17 @@ class HParamsTest(test.TestCase):
       hparams.parse('b=relu')
     with self.assertRaisesRegexp(ValueError, 'Must not pass a list'):
       hparams.parse('aaa=[123]')
-    self.assertEquals(12, hparams.aaa)
-    self.assertEquals(2.0, hparams.b)
-    self.assertEquals('2.3"', hparams.c_c)
+    self.assertEqual(12, hparams.aaa)
+    self.assertEqual(2.0, hparams.b)
+    self.assertEqual('2.3"', hparams.c_c)
     # Exports to proto.
     hparam_def = hparams.to_proto()
     # Imports from proto.
     hparams2 = hparam.HParams(hparam_def=hparam_def)
     # Verifies that all hparams are restored.
-    self.assertEquals(12, hparams2.aaa)
-    self.assertEquals(2.0, hparams2.b)
-    self.assertEquals('2.3"', hparams2.c_c)
+    self.assertEqual(12, hparams2.aaa)
+    self.assertEqual(2.0, hparams2.b)
+    self.assertEqual('2.3"', hparams2.c_c)
 
   def testBoolParsing(self):
     for value in 'true', 'false', 'True', 'False', '1', '0':
@@ -106,11 +102,11 @@ class HParamsTest(test.TestCase):
         hparam_def = hparams.to_proto()
         # Imports from proto.
         hparams2 = hparam.HParams(hparam_def=hparam_def)
-        self.assertEquals(hparams.use_gpu, hparams2.use_gpu)
+        self.assertEqual(hparams.use_gpu, hparams2.use_gpu)
         # Check that hparams2.use_gpu is a bool rather than an int.
-        # The assertEquals() call above won't catch this, since
+        # The assertEqual() call above won't catch this, since
         # (0 == False) and (1 == True) in Python.
-        self.assertEquals(bool, type(hparams2.use_gpu))
+        self.assertEqual(bool, type(hparams2.use_gpu))
 
   def testBoolParsingFail(self):
     hparams = hparam.HParams(use_gpu=True)
@@ -119,24 +115,27 @@ class HParamsTest(test.TestCase):
 
   def testLists(self):
     hparams = hparam.HParams(aaa=[1], b=[2.0, 3.0], c_c=['relu6'])
-    self._assertDictEquals({'aaa': [1], 'b': [2.0, 3.0], 'c_c': ['relu6']},
-                           hparams.values())
-    self.assertEquals([1], hparams.aaa)
-    self.assertEquals([2.0, 3.0], hparams.b)
-    self.assertEquals(['relu6'], hparams.c_c)
+    self.assertDictEqual({
+        'aaa': [1],
+        'b': [2.0, 3.0],
+        'c_c': ['relu6']
+    }, hparams.values())
+    self.assertEqual([1], hparams.aaa)
+    self.assertEqual([2.0, 3.0], hparams.b)
+    self.assertEqual(['relu6'], hparams.c_c)
     hparams.parse('aaa=[12]')
-    self.assertEquals([12], hparams.aaa)
+    self.assertEqual([12], hparams.aaa)
     hparams.parse('aaa=[12,34,56]')
-    self.assertEquals([12, 34, 56], hparams.aaa)
+    self.assertEqual([12, 34, 56], hparams.aaa)
     hparams.parse('c_c=[relu4,relu12],b=[1.0]')
-    self.assertEquals(['relu4', 'relu12'], hparams.c_c)
-    self.assertEquals([1.0], hparams.b)
+    self.assertEqual(['relu4', 'relu12'], hparams.c_c)
+    self.assertEqual([1.0], hparams.b)
     hparams.parse('c_c=[],aaa=[-34]')
-    self.assertEquals([-34], hparams.aaa)
-    self.assertEquals([], hparams.c_c)
+    self.assertEqual([-34], hparams.aaa)
+    self.assertEqual([], hparams.c_c)
     hparams.parse('c_c=[_12,3\'4"],aaa=[+3]')
-    self.assertEquals([3], hparams.aaa)
-    self.assertEquals(['_12', '3\'4"'], hparams.c_c)
+    self.assertEqual([3], hparams.aaa)
+    self.assertEqual(['_12', '3\'4"'], hparams.c_c)
     with self.assertRaisesRegexp(ValueError, 'Unknown hyperparameter'):
       hparams.parse('x=[123]')
     with self.assertRaisesRegexp(ValueError, 'Could not parse'):
@@ -154,31 +153,129 @@ class HParamsTest(test.TestCase):
     # Imports from proto.
     hparams2 = hparam.HParams(hparam_def=hparam_def)
     # Verifies that all hparams are restored.
-    self.assertEquals([3], hparams2.aaa)
-    self.assertEquals([1.0], hparams2.b)
-    self.assertEquals(['_12', '3\'4"'], hparams2.c_c)
+    self.assertEqual([3], hparams2.aaa)
+    self.assertEqual([1.0], hparams2.b)
+    self.assertEqual(['_12', '3\'4"'], hparams2.c_c)
+
+  def testParseValuesWithIndexAssigment1(self):
+    """Assignment to an index position."""
+    parse_dict = hparam.parse_values('arr[1]=10', {'arr': int})
+    self.assertEqual(len(parse_dict), 1)
+    self.assertTrue(isinstance(parse_dict['arr'], dict))
+    self.assertDictEqual(parse_dict['arr'], {1: 10})
+
+  def testParseValuesWithIndexAssigment2(self):
+    """Assignment to multiple index positions."""
+    parse_dict = hparam.parse_values('arr[0]=10,arr[5]=20', {'arr': int})
+    self.assertEqual(len(parse_dict), 1)
+    self.assertTrue(isinstance(parse_dict['arr'], dict))
+    self.assertDictEqual(parse_dict['arr'], {0: 10, 5: 20})
+
+  def testParseValuesWithIndexAssigment3(self):
+    """Assignment to index positions in multiple names."""
+    parse_dict = hparam.parse_values('arr[0]=10,arr[1]=20,L[5]=100,L[10]=200',
+                                     {'arr': int,
+                                      'L': int})
+    self.assertEqual(len(parse_dict), 2)
+    self.assertTrue(isinstance(parse_dict['arr'], dict))
+    self.assertDictEqual(parse_dict['arr'], {0: 10, 1: 20})
+    self.assertTrue(isinstance(parse_dict['L'], dict))
+    self.assertDictEqual(parse_dict['L'], {5: 100, 10: 200})
+
+  def testParseValuesWithIndexAssigment4(self):
+    """Assignment of index positions and scalars."""
+    parse_dict = hparam.parse_values('x=10,arr[1]=20,y=30',
+                                     {'x': int,
+                                      'y': int,
+                                      'arr': int})
+    self.assertEqual(len(parse_dict), 3)
+    self.assertTrue(isinstance(parse_dict['arr'], dict))
+    self.assertDictEqual(parse_dict['arr'], {1: 20})
+    self.assertEqual(parse_dict['x'], 10)
+    self.assertEqual(parse_dict['y'], 30)
+
+  def testParseValuesWithIndexAssigment5(self):
+    """Different variable types."""
+    parse_dict = hparam.parse_values('a[0]=5,b[1]=true,c[2]=abc,d[3]=3.14', {
+        'a': int,
+        'b': bool,
+        'c': str,
+        'd': float
+    })
+    self.assertEqual(set(parse_dict.keys()), {'a', 'b', 'c', 'd'})
+    self.assertTrue(isinstance(parse_dict['a'], dict))
+    self.assertDictEqual(parse_dict['a'], {0: 5})
+    self.assertTrue(isinstance(parse_dict['b'], dict))
+    self.assertDictEqual(parse_dict['b'], {1: True})
+    self.assertTrue(isinstance(parse_dict['c'], dict))
+    self.assertDictEqual(parse_dict['c'], {2: 'abc'})
+    self.assertTrue(isinstance(parse_dict['d'], dict))
+    self.assertDictEqual(parse_dict['d'], {3: 3.14})
+
+  def testParseValuesWithBadIndexAssigment1(self):
+    """Reject assignment of list to variable type."""
+    with self.assertRaisesRegexp(ValueError,
+                                 r'Assignment of a list to a list index.'):
+      hparam.parse_values('arr[1]=[1,2,3]', {'arr': int})
+
+  def testParseValuesWithBadIndexAssigment2(self):
+    """Reject if type missing."""
+    with self.assertRaisesRegexp(ValueError,
+                                 r'Unknown hyperparameter type for arr'):
+      hparam.parse_values('arr[1]=5', {})
+
+  def testParseValuesWithBadIndexAssigment3(self):
+    """Reject type of the form name[index]."""
+    with self.assertRaisesRegexp(ValueError,
+                                 'Unknown hyperparameter type for arr'):
+      hparam.parse_values('arr[1]=1', {'arr[1]': int})
+
+  def testWithReusedVariables(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'Multiple assignments to variable \'x\''):
+      hparam.parse_values('x=1,x=1', {'x': int})
+
+    with self.assertRaisesRegexp(ValueError,
+                                 'Multiple assignments to variable \'arr\''):
+      hparam.parse_values('arr=[100,200],arr[0]=10', {'arr': int})
+
+    with self.assertRaisesRegexp(
+        ValueError, r'Multiple assignments to variable \'arr\[0\]\''):
+      hparam.parse_values('arr[0]=10,arr[0]=20', {'arr': int})
+
+    with self.assertRaisesRegexp(ValueError,
+                                 'Multiple assignments to variable \'arr\''):
+      hparam.parse_values('arr[0]=10,arr=[100]', {'arr': int})
 
   def testJson(self):
     hparams = hparam.HParams(aaa=1, b=2.0, c_c='relu6', d=True)
-    self._assertDictEquals(
-        {'aaa': 1, 'b': 2.0, 'c_c': 'relu6', 'd': True}, hparams.values())
-    self.assertEquals(1, hparams.aaa)
-    self.assertEquals(2.0, hparams.b)
-    self.assertEquals('relu6', hparams.c_c)
+    self.assertDictEqual({
+        'aaa': 1,
+        'b': 2.0,
+        'c_c': 'relu6',
+        'd': True
+    }, hparams.values())
+    self.assertEqual(1, hparams.aaa)
+    self.assertEqual(2.0, hparams.b)
+    self.assertEqual('relu6', hparams.c_c)
     hparams.parse_json('{"aaa": 12, "b": 3.0, "c_c": "relu4", "d": false}')
-    self._assertDictEquals(
-        {'aaa': 12, 'b': 3.0, 'c_c': 'relu4', 'd': False}, hparams.values())
-    self.assertEquals(12, hparams.aaa)
-    self.assertEquals(3.0, hparams.b)
-    self.assertEquals('relu4', hparams.c_c)
+    self.assertDictEqual({
+        'aaa': 12,
+        'b': 3.0,
+        'c_c': 'relu4',
+        'd': False
+    }, hparams.values())
+    self.assertEqual(12, hparams.aaa)
+    self.assertEqual(3.0, hparams.b)
+    self.assertEqual('relu4', hparams.c_c)
 
     json_str = hparams.to_json()
     hparams2 = hparam.HParams(aaa=10, b=20.0, c_c='hello', d=False)
     hparams2.parse_json(json_str)
-    self.assertEquals(12, hparams2.aaa)
-    self.assertEquals(3.0, hparams2.b)
-    self.assertEquals('relu4', hparams2.c_c)
-    self.assertEquals(False, hparams2.d)
+    self.assertEqual(12, hparams2.aaa)
+    self.assertEqual(3.0, hparams2.b)
+    self.assertEqual('relu4', hparams2.c_c)
+    self.assertEqual(False, hparams2.d)
 
   def testNonProtoFails(self):
     with self.assertRaisesRegexp(AssertionError, ''):
-- 
GitLab


From 62aa5eb5b30ff591f428bc46eac2d9819fa1e6a8 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Tue, 25 Jul 2017 00:57:52 +0900
Subject: [PATCH 1238/1375] Fix typos (#11627)

---
 .../compiler/xla/service/cpu/shape_partition_test.cc | 12 ++++++------
 tensorflow/core/distributed_runtime/scheduler.cc     |  4 ++--
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition_test.cc b/tensorflow/compiler/xla/service/cpu/shape_partition_test.cc
index 6cc6e3fe85..ee0c53fa6d 100644
--- a/tensorflow/compiler/xla/service/cpu/shape_partition_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition_test.cc
@@ -204,12 +204,12 @@ TEST_F(RandomShapePartitionIteratorTest, RandomShapeAndPartitions) {
   // Choose random dimensions for R4 shape.
   Shape shape = ShapeUtil::MakeShapeWithLayout(F32, RandR4Dims(), {3, 2, 1, 0});
   // Choose random number of outer dimensions to partition.
-  const int num_outer_dims_to_partiton = 1 + (Rand() % 3);
-  // Choose random outer dimension partiton counts.
-  std::vector<int64> dim_sizes(num_outer_dims_to_partiton);
-  std::vector<int64> dim_partition_counts(num_outer_dims_to_partiton);
+  const int num_outer_dims_to_partition = 1 + (Rand() % 3);
+  // Choose random outer dimension partition counts.
+  std::vector<int64> dim_sizes(num_outer_dims_to_partition);
+  std::vector<int64> dim_partition_counts(num_outer_dims_to_partition);
   int64 total_dim_size = 1;
-  for (int i = 0; i < num_outer_dims_to_partiton; ++i) {
+  for (int i = 0; i < num_outer_dims_to_partition; ++i) {
     const int64 dimension = shape.layout().minor_to_major(
         shape.layout().minor_to_major_size() - 1 - i);
     dim_sizes[i] = shape.dimensions(dimension);
@@ -220,7 +220,7 @@ TEST_F(RandomShapePartitionIteratorTest, RandomShapeAndPartitions) {
   }
   // Iterate through all partition: for each partition record covered
   // index ranges by dimension.
-  std::vector<std::map<int64, int64>> ranges(num_outer_dims_to_partiton);
+  std::vector<std::map<int64, int64>> ranges(num_outer_dims_to_partition);
   ShapePartitionIterator partition_iterator(shape, dim_partition_counts);
   const int64 partition_count = partition_iterator.GetTotalPartitionCount();
   for (int64 i = 0; i < partition_count; ++i) {
diff --git a/tensorflow/core/distributed_runtime/scheduler.cc b/tensorflow/core/distributed_runtime/scheduler.cc
index 0b628205c3..844a0643e6 100644
--- a/tensorflow/core/distributed_runtime/scheduler.cc
+++ b/tensorflow/core/distributed_runtime/scheduler.cc
@@ -33,7 +33,7 @@ void InitializePending(const Graph* graph, std::vector<int>* pending) {
     const int id = node->id();
     int num_in_edges = 0;
     if (IsMerge(node)) {
-      // For forward executon order, Merge nodes are special. We process
+      // For forward execution order, Merge nodes are special. We process
       // them only once when one of its inputs is processed.
       for (const Edge* edge : node->in_edges()) {
         if (edge->IsControlEdge()) {
@@ -122,7 +122,7 @@ Microseconds SlackAnalysis::ComputeAlap(std::vector<Microseconds>* alap_times) {
   std::vector<int> pending_count;
   pending_count.resize(graph_->num_node_ids());
   for (const Node* n : graph_->nodes()) {
-    // For reverse executon order, Switch nodes are special. We process
+    // For reverse execution order, Switch nodes are special. We process
     // them only once when one of its outputs is processed.
     if (IsSwitch(n)) {
       int32 num_control_edges = 0;
-- 
GitLab


From 133472fb49c51a7a47d420c282fcc55a0cc1cdb7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 09:05:12 -0700
Subject: [PATCH 1239/1375] Internal doc change -- second draft.

PiperOrigin-RevId: 162943858
---
 .../docs_src/programmers_guide/embedding.md   | 416 +++++++-----------
 1 file changed, 161 insertions(+), 255 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/embedding.md b/tensorflow/docs_src/programmers_guide/embedding.md
index 91beb36f96..4095c6c97a 100644
--- a/tensorflow/docs_src/programmers_guide/embedding.md
+++ b/tensorflow/docs_src/programmers_guide/embedding.md
@@ -1,12 +1,16 @@
 # Embeddings
 
-[TOC]
+This document introduces the concept of embeddings, gives a simple example of
+how to train an embedding in TensorFlow, and explains how to view embeddings
+with the TensorBoard Embedding Projector. The first two parts target newcomers
+to machine learning or TensorFlow, and the Embedding Projector how-to is for
+users at all levels.
 
-## Introduction
+[TOC]
 
-An embedding is a mapping from discrete objects, such as words, to vectors of
-real numbers. For example, a 300-dimensional embedding for English words could
-include:
+An **embedding** is a mapping from discrete objects, such as words, to vectors
+of real numbers. For example, a 300-dimensional embedding for English words
+could include:
 
 ```
 blue:  (0.01359, 0.00075997, 0.24608, ..., -0.2524, 1.0048, 0.06259)
@@ -15,19 +19,24 @@ orange:  (-0.24776, -0.12359, 0.20986, ..., 0.079717, 0.23865, -0.014213)
 oranges:  (-0.35609, 0.21854, 0.080944, ..., -0.35413, 0.38511, -0.070976)
 ```
 
-Embeddings let you apply machine learning to discrete inputs. Classifiers, and
-neural networks more generally, are designed to work with dense continuous
-vectors, where all values contribute to define what an object is.  If discrete
-objects are naively encoded as discrete atoms, e.g., unique id numbers, they
-hinder learning and generalization. One way to think of embeddings is as a way
-to transform non-vector objects into useful inputs for machine learning.
-
-Embeddings are also useful as outputs of machine learning. Because embeddings
-map objects to vectors, applications can use similarity in vector space (e.g.,
-Euclidean distance or the angle between vectors) as a robust and flexible
-measure of object similarity. One common use is to find nearest neighbors.
-Using the same word embeddings above, for instance, here are the three nearest
-neighbors for each word and the corresponding angles (in degrees):
+The individual dimensions in these vectors typically have no inherent meaning.
+Instead, it's the overall patterns of location and distance between vectors
+that machine learning takes advantage of.
+
+Embeddings are important for input to machine learning. Classifiers, and neural
+networks more generally, work on vectors of real numbers. They train best on
+dense vectors, where all values contribute to define an object. However, many
+important inputs to machine learning, such as words of text, do not have a
+natural vector representation. Embedding functions are the standard and
+effective way to transform such discrete input objects into useful
+continuous vectors.
+
+Embeddings are also valuable as outputs of machine learning. Because embeddings
+map objects to vectors, applications can use similarity in vector space (for
+instance, Euclidean distance or the angle between vectors) as a robust and
+flexible measure of object similarity. One common use is to find nearest
+neighbors.  Using the same word embeddings as above, for instance, here are the
+three nearest neighbors for each word and the corresponding angles:
 
 ```
 blue:  (red, 47.6°), (yellow, 51.9°), (purple, 52.4°)
@@ -39,138 +48,151 @@ oranges:  (apples, 45.3°), (lemons, 48.3°), (mangoes, 50.4°)
 This would tell an application that apples and oranges are in some way more
 similar (45.3° apart) than lemons and oranges (48.3° apart).
 
-## Training an Embedding
+## Embeddings in TensorFlow
 
-To train word embeddings in TensorFlow, we first need to split the text into
-words and assign an integer to every word in the vocabulary. Let us assume that
+To create word embeddings in TensorFlow, we first split the text into words
+and then assign an integer to every word in the vocabulary. Let us assume that
 this has already been done, and that `word_ids` is a vector of these integers.
 For example, the sentence “I have a cat.” could be split into
 `[“I”, “have”, “a”, “cat”, “.”]` and then the corresponding `word_ids` tensor
-would have shape `[5]` and consist of 5 integers. To get these word ids
-embedded, we need to create the embedding variable and use the `tf.gather`
-function as follows:
+would have shape `[5]` and consist of 5 integers. To map these word ids
+to vectors, we need to create the embedding variable and use the
+`tf.nn.embedding_lookup` function as follows:
 
 ```
 word_embeddings = tf.get_variable(“word_embeddings”,
     [vocabulary_size, embedding_size])
-embedded_word_ids = tf.gather(word_embeddings, word_ids)
+embedded_word_ids = tf.nn.embedding_lookup(word_embeddings, word_ids)
 ```
 
 After this, the tensor `embedded_word_ids` will have shape `[5, embedding_size]`
 in our example and contain the embeddings (dense vectors) for each of the 5
-words. The variable `word_embeddings` will be learned and at the end of the
-training it will contain the embeddings for all words in the vocabulary.
-The embeddings can be trained in many ways, depending on the data available.
-For example, one could use a recurrent neural network to predict the next word
-from the previous one given a large corpus of sentences, or one could train
-two networks to do multi-lingual translation. These methods are described in
-[Vector Representations of Words](../tutorials/word2vec.md) tutorial, but in
-all cases there is an embedding variable like above and words are embedded
-using `tf.gather`, as shown.
+words. At the end of training, `word_embeddings` will contain the embeddings
+for all words in the vocabulary.
 
-## Visualizing Embeddings
-
-TensorBoard has a built-in visualizer, called the <i>Embedding Projector</i>,
-for interactive visualization of embeddings. The embedding projector will read
-the embeddings from your checkpoint file and project them into 3 dimensions using
-[principal component analysis](https://en.wikipedia.org/wiki/Principal_component_analysis).
-For a visual explanation of PCA, see
-[this article](http://setosa.io/ev/principal-component-analysis/). Another
-very useful projection you can use is
-[t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding).
-
-If you are working with an embedding, you'll probably want to attach
-labels/images to the data points. You can do this by generating a
-[metadata file](#metadata) containing the labels for each point and configuring
-the projector either by using our Python API, or manually constructing and
-saving a
-<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto)</code>
-in the same directory as your checkpoint file.
-
-### Setup
-
-For in depth information on how to run TensorBoard and make sure you are
-logging all the necessary information, see
-[TensorBoard: Visualizing Learning](../get_started/summaries_and_tensorboard.md).
-
-To visualize your embeddings, there are 3 things you need to do:
-
-1) Setup a 2D tensor that holds your embedding(s).
-
-```python
-embedding_var = tf.get_variable(....)
-```
+Embeddings can be trained in many network types, and with various loss
+functions and data sets. For example, one could use a recurrent neural network
+to predict the next word from the previous one given a large corpus of
+sentences, or one could train two networks to do multi-lingual translation.
+These methods are described in the @{$word2vec$Vector Representations of Words}
+tutorial.
 
-2) Periodically save your model variables in a checkpoint in
-<code>LOG_DIR</code>.
-
-```python
-saver = tf.train.Saver()
-saver.save(session, os.path.join(LOG_DIR, "model.ckpt"), step)
-```
-
-3) (Optional) Associate metadata with your embedding.
-
-If you have any metadata (labels, images) associated with your embedding, you
-can tell TensorBoard about it either by directly storing a
-<code>[projector_config.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto)</code>
-in the <code>LOG_DIR</code>, or use our python API.
-
-For instance, the following <code>projector_config.ptxt</code> associates the
-<code>word_embedding</code> tensor with metadata stored in <code>$LOG_DIR/metadata.tsv</code>:
+## Visualizing Embeddings
 
-```
-embeddings {
-  tensor_name: 'word_embedding'
-  metadata_path: '$LOG_DIR/metadata.tsv'
-}
-```
+TensorBoard includes the **Embedding Projector**, a tool that lets you
+interactively visualize embeddings. This tool can read embeddings from your
+model and render them in two or three dimensions.
 
-The same config can be produced programmatically using the following code snippet:
+The Embedding Projector has three panels:
 
-```python
-from tensorflow.contrib.tensorboard.plugins import projector
+- *Data panel* on the top left, where you can choose the run, the embedding
+  variable and data columns to color and label points by.
+- *Projections panel* on the bottom left, where you can choose the type of
+  projection.
+- *Inspector panel* on the right side, where you can search for particular
+  points and see a list of nearest neighbors.
 
-# Create randomly initialized embedding weights which will be trained.
-vocabulary_size = 10000
-embedding_size = 200
-embedding_var = tf.get_variable('word_embedding', [vocabulary_size, embedding_size])
+### Projections
+The Embedding Projector provides three ways to reduce the dimensionality of a
+data set.
+
+- *[t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding)*:
+  a nonlinear nondeterministic algorithm (T-distributed stochastic neighbor
+  embedding) that tries to preserve local neighborhoods in the data, often at
+  the expense of distorting global structure. You can choose whether to compute
+  two- or three-dimensional projections.
+
+- *[PCA](https://en.wikipedia.org/wiki/Principal_component_analysis)*:
+  a linear deterministic algorithm (principal component analysis) that tries to
+  capture as much of the data variability in as few dimensions as possible. PCA
+  tends to highlight large-scale structure in the data, but can distort local
+  neighborhoods. The Embedding Projector computes the top 10 principal
+  components, from which you can choose two or three to view.
+
+- *Custom*: a linear projection onto horizontal and vertical axes that you
+  specify using labels in the data. You define the horizontal axis, for
+  instance, by giving text patterns for "Left" and "Right". The Embedding
+  Projector finds all points whose label matches the "Left" pattern and
+  computes the centroid of that set; similarly for "Right".  The line passing
+  through these two centroids defines the horizontal axis. The vertical axis is
+  likewise computed from the centroids for points matching the "Up" and "Down"
+  text patterns.
+
+Further useful articles are
+[How to Use t-SNE Effectively](distill.pub/2016/misread-tsne/) and
+[Principal Component Analysis Explained Visually](http://setosa.io/ev/principal-component-analysis/).
+
+### Exploration
+
+You can explore visually by zooming, rotating, and panning using natural
+click-and-drag gestures. Hovering your mouse over a point will show any
+[metadata](#metadata) for that point.  You can also inspect nearest-neighbor
+subsets.  Clicking on a point causes the right pane to list the nearest
+neighbors, along with distances to the current point. The nearest-neighbor
+points are also highlighted in the projection.
+
+It is sometimes useful to restrict the view to a subset of points and perform
+projections only on those points. To do so, you can select points in multiple
+ways:
+
+- After clicking on a point, its nearest neighbors are also selected.
+- After a search, the points matching the query are selected.
+- Enabling selection, clicking on a point and dragging defines a selection
+  sphere.
+
+Then click the "Isolate *nnn* points" button at the top of the Inspector pane
+on the right hand side. The following image shows 101 points selected and ready
+for the user to click "Isolate 101 points":
 
-# Format: tensorflow/tensorboard/plugins/projector/projector_config.proto
-config = projector.ProjectorConfig()
+![Selection of nearest neighbors](https://www.tensorflow.org/images/embedding-nearest-points.png "Selection of nearest neighbors")
 
-# You can add multiple embeddings. Here we add only one.
-embedding = config.embeddings.add()
-embedding.tensor_name = embedding_var.name
-# Link this tensor to its metadata file (e.g. labels).
-embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv')
+*Selection of the nearest neighbors of “important” in a word embedding dataset.*
 
-# Use the same LOG_DIR where you stored your checkpoint.
-summary_writer = tf.summary.FileWriter(LOG_DIR)
+Advanced tip: filtering with custom projection can be powerful. Below, we
+filtered the 100 nearest neighbors of “politics” and projected them onto the
+“worst” - “best” vector as an x axis. The y axis is random. As a result, one
+finds on the right side “ideas”, “science”, “perspective”, “journalism” but on
+the left “crisis”, “violence” and “conflict”.
 
-# The next line writes a projector_config.pbtxt in the LOG_DIR. TensorBoard will
-# read this file during startup.
-projector.visualize_embeddings(summary_writer, config)
-```
+<table width="100%;">
+  <tr>
+    <td style="width: 30%;">
+      <img src="https://www.tensorflow.org/images/embedding-custom-controls.png" alt="Custom controls panel" title="Custom controls panel" />
+    </td>
+    <td style="width: 70%;">
+      <img src="https://www.tensorflow.org/images/embedding-custom-projection.png" alt="Custom projection" title="Custom projection" />
+    </td>
+  </tr>
+  <tr>
+    <td style="width: 30%;">
+      Custom projection controls.
+    </td>
+    <td style="width: 70%;">
+      Custom projection of neighbors of "politics" onto "best" - "worst" vector.
+    </td>
+  </tr>
+</table>
 
-After running your model and training your embeddings, run TensorBoard and point
-it to the <code>LOG_DIR</code> of the job.
+To share your findings, you can use the bookmark panel in the bottom right
+corner and save the current state (including computed coordinates of any
+projection) as a small file. The Projector can then be pointed to a set of one
+or more of these files, producing the panel below. Other users can then walk
+through a sequence of bookmarks.
 
-```python
-tensorboard --logdir=LOG_DIR
-```
+<img src="https://www.tensorflow.org/images/embedding-bookmark.png" alt="Bookmark panel" style="width:300px;">
 
-Then click on the *Embeddings* tab on the top pane
-and select the appropriate run (if there are more than one run).
+### Metadata
 
+If you are working with an embedding, you'll probably want to attach
+labels/images to the data points. You can do this by generating a metadata file
+containing the labels for each point and clicking "Load data" in the data panel
+of the Embedding Projector.
 
-### Metadata
-Usually embeddings have metadata associated with it (e.g. labels, images). The
-metadata should be stored in a separate file outside of the model checkpoint
-since the metadata is not a trainable parameter of the model. The format should
+The metadata can be either labels or images, which are
+stored in a separate file. For labels, the format should
 be a [TSV file](https://en.wikipedia.org/wiki/Tab-separated_values)
-(tab characters shown in red) with the first line containing column headers
-(shown in bold) and subsequent lines contain the metadata values:
+(tab characters shown in red) whose first line contains column headers
+(shown in bold) and subsequent lines contain the metadata values. For example:
 
 <code>
 <b>Word<span style="color:#800;">\t</span>Frequency</b><br/>
@@ -179,24 +201,20 @@ be a [TSV file](https://en.wikipedia.org/wiki/Tab-separated_values)
   ...
 </code>
 
-There is no explicit key shared with the main data file; instead, the order in
-the metadata file is assumed to match the order in the embedding tensor. In
-other words, the first line is the header information and the (i+1)-th line in
-the metadata file corresponds to the i-th row of the embedding tensor stored in
-the checkpoint.
-
-Note: If the TSV metadata file has only a single column, then we don’t expect a
-header row, and assume each row is the label of the embedding. We include this
-exception because it matches the commonly-used "vocab file" format.
-
-### Images
-If you have images associated with your embeddings, you will need to
-produce a single image consisting of small thumbnails of each data point.
-This is known as the
-[sprite image](https://www.google.com/webhp#q=what+is+a+sprite+image).
-The sprite should have the same number of rows and columns with thumbnails
-stored in row-first order: the first data point placed in the top left and the
-last data point in the bottom right:
+The order of lines in the metadata file is assumed to match the order of
+vectors in the embedding variable, except for the header.  Consequently, the
+(i+1)-th line in the metadata file corresponds to the i-th row of the embedding
+variable.  If the TSV metadata file has only a single column, then we don’t
+expect a header row, and assume each row is the label of the embedding. We
+include this exception because it matches the commonly-used "vocab file"
+format.
+
+To use images as metadata, you must produce a single
+[sprite image](https://www.google.com/webhp#q=what+is+a+sprite+image),
+consisting of small thumbnails, one for each vector in the embedding.  The
+sprite should store thumbnails in row-first order: the first data point placed
+in the top left and the last data point in the bottom right, though the last
+row doesn't have to be filled, as shown below.
 
 <table style="border: none;">
 <tr style="background-color: transparent;">
@@ -216,120 +234,8 @@ last data point in the bottom right:
 </tr>
 </table>
 
-Note in the example above that the last row doesn't have to be filled. For a
-concrete example of a sprite, see
-[this sprite image](https://www.tensorflow.org/images/mnist_10k_sprite.png) of 10,000 MNIST digits
-(100x100).
-
-Note: We currently support sprites up to 8192px X 8192px.
-
-After constructing the sprite, you need to tell the Embedding Projector where
-to find it:
-
-
-```python
-embedding.sprite.image_path = PATH_TO_SPRITE_IMAGE
-# Specify the width and height of a single thumbnail.
-embedding.sprite.single_image_dim.extend([w, h])
-```
-
-### Interaction
-
-The Embedding Projector has three panels:
-
-1. *Data panel* on the top left, where you can choose the run, the embedding
-   tensor and data columns to color and label points by.
-2. *Projections panel* on the bottom left, where you choose the type of
-    projection (e.g. PCA, t-SNE).
-3. *Inspector panel* on the right side, where you can search for particular
-   points and see a list of nearest neighbors.
-
-### Projections
-The Embedding Projector has three methods of reducing the dimensionality of a
-data set: two linear and one nonlinear. Each method can be used to create either
-a two- or three-dimensional view.
-
-**Principal Component Analysis** A straightforward technique for reducing
-dimensions is Principal Component Analysis (PCA). The Embedding Projector
-computes the top 10 principal components. The menu lets you project those
-components onto any combination of two or three. PCA is a linear projection,
-often effective at examining global geometry.
-
-**t-SNE** A popular non-linear dimensionality reduction technique is t-SNE.
-The Embedding Projector offers both two- and three-dimensional t-SNE views.
-Layout is performed client-side animating every step of the algorithm. Because
-t-SNE often preserves some local structure, it is useful for exploring local
-neighborhoods and finding clusters. Although extremely useful for visualizing
-high-dimensional data, t-SNE plots can sometimes be mysterious or misleading.
-See this [great article](http://distill.pub/2016/misread-tsne/) for how to use
-t-SNE effectively.
-
-**Custom** You can also construct specialized linear projections based on text
-searches for finding meaningful directions in space. To define a projection
-axis, enter two search strings or regular expressions. The program computes the
-centroids of the sets of points whose labels match these searches, and uses the
-difference vector between centroids as a projection axis.
-
-### Navigation
-
-To explore a data set, you can navigate the views in either a 2D or a 3D mode,
-zooming, rotating, and panning using natural click-and-drag gestures.
-Clicking on a point causes the right pane to show an explicit textual list of
-nearest neighbors, along with distances to the current point. The
-nearest-neighbor points themselves are highlighted on the projection.
-
-Zooming into the cluster gives some information, but it is sometimes more
-helpful to restrict the view to a subset of points and perform projections only
-on those points. To do so, you can select points in multiple ways:
-
-1. After clicking on a point, its nearest neighbors are also selected.
-2. After a search, the points matching the query are selected.
-3. Enabling selection, clicking on a point and dragging defines a selection
-   sphere.
-
-After selecting a set of points, you can isolate those points for
-further analysis on their own with the "Isolate Points" button in the Inspector
-pane on the right hand side.
-
-
-![Selection of nearest neighbors](https://www.tensorflow.org/images/embedding-nearest-points.png "Selection of nearest neighbors")
-*Selection of the nearest neighbors of “important” in a word embedding dataset.*
-
-The combination of filtering with custom projection can be powerful. Below, we filtered
-the 100 nearest neighbors of “politics” and projected them onto the
-“best” - “worst” vector as an x axis. The y axis is random.
-
-You can see that on the right side we have “ideas”, “science”, “perspective”,
-“journalism” while on the left we have “crisis”, “violence” and “conflict”.
-
-<table width="100%;">
-  <tr>
-    <td style="width: 30%;">
-      <img src="https://www.tensorflow.org/images/embedding-custom-controls.png" alt="Custom controls panel" title="Custom controls panel" />
-    </td>
-    <td style="width: 70%;">
-      <img src="https://www.tensorflow.org/images/embedding-custom-projection.png" alt="Custom projection" title="Custom projection" />
-    </td>
-  </tr>
-  <tr>
-    <td style="width: 30%;">
-      Custom projection controls.
-    </td>
-    <td style="width: 70%;">
-      Custom projection of neighbors of "politics" onto "best" - "worst" vector.
-    </td>
-  </tr>
-</table>
-
-### Collaborative Features
-
-To share your findings, you can use the bookmark panel in the bottom right
-corner and save the current state (including computed coordinates of any
-projection) as a small file. The Projector can then be pointed to a set of one
-or more of these files, producing the panel below. Other users can then walk
-through a sequence of bookmarks.
-
-<img src="https://www.tensorflow.org/images/embedding-bookmark.png" alt="Bookmark panel" style="width:300px;">
+Follow [this link]("https://www.tensorflow.org/images/embedding-mnist.mp4" )
+to see a fun example of thumbnail images in the Embedding Projector.
 
 
 ## Mini-FAQ
@@ -348,5 +254,5 @@ displaying many properties that are dramatically different from what our human
 intuition has learned about 2- and 3-dimensional spaces.
 
 **Is an embedding the same as an embedding layer?**
-No; an embedding layer is a part of neural network, but an embedding is a more
+No. An *embedding layer* is a part of neural network, but an *embedding* is a more
 general concept.
-- 
GitLab


From 0742b4b1eacdcca1473f856fbd05a6f501eff4ed Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 09:08:26 -0700
Subject: [PATCH 1240/1375] [XLA] StatusOr testing macro TF_ASSIGN_OR_ASSERT_OK
 => TF_ASSERT_OK_AND_ASSIGN

PiperOrigin-RevId: 162944460
---
 tensorflow/compiler/xla/literal_util_test.cc  |  4 +-
 .../xla/service/buffer_assignment_test.cc     |  6 +-
 .../xla/service/flatten_call_graph_test.cc    |  6 +-
 .../xla/service/hlo_constant_folding_test.cc  | 24 +++----
 .../xla/service/hlo_evaluator_test.cc         |  6 +-
 .../compiler/xla/service/hlo_ordering_test.cc |  2 +-
 .../xla/service/hlo_rematerialization_test.cc | 14 ++---
 .../xla/service/user_computation_test.cc      | 38 ++++++------
 tensorflow/compiler/xla/status_macros.h       |  6 +-
 tensorflow/compiler/xla/status_macros_test.cc |  2 +-
 .../xla/tests/deconstruct_tuple_test.cc       | 28 ++++-----
 .../compiler/xla/tests/hlo_metadata_test.cc   |  2 +-
 .../xla/tests/matrix_ops_simple_test.cc       |  4 +-
 tensorflow/compiler/xla/tests/prng_test.cc    | 62 +++++++++----------
 .../compiler/xla/tests/reduce_window_test.cc  | 28 ++++-----
 .../xla/tests/scalar_computations_test.cc     | 22 +++----
 tensorflow/compiler/xla/tests/while_test.cc   | 24 +++----
 .../xla_tf_graph/xla_tf_graph_util_test.cc    |  8 +--
 18 files changed, 140 insertions(+), 146 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 6c3648e1e0..b50e741b8a 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -835,8 +835,8 @@ TEST_F(LiteralUtilTest, ConvertR4) {
      {{26, 27, 28, 29}, {30, 31, 32, 33}},
   }}, layout_r4_dim0major_);
   // clang-format on
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<Literal> converted,
-                         original->Convert(U32));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<Literal> converted,
+                          original->Convert(U32));
 
   EXPECT_TRUE(expected->Equal(*converted));
 }
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index e27575db43..18acd4f3ae 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -1573,7 +1573,7 @@ TEST_F(BufferAssignmentTest, TwoCalls) {
 
   {
     FlattenCallGraph flatten;
-    TF_ASSIGN_OR_ASSERT_OK(bool result, flatten.Run(module.get()));
+    TF_ASSERT_OK_AND_ASSIGN(bool result, flatten.Run(module.get()));
     EXPECT_TRUE(result);
     std::unique_ptr<CallGraph> call_graph = CallGraph::Build(module.get());
   }
@@ -1652,7 +1652,7 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) {
 
   {
     FlattenCallGraph flatten;
-    TF_ASSIGN_OR_ASSERT_OK(bool result, flatten.Run(module.get()));
+    TF_ASSERT_OK_AND_ASSIGN(bool result, flatten.Run(module.get()));
     EXPECT_TRUE(result);
   }
 
@@ -1723,7 +1723,7 @@ TEST_F(WhileBufferAssignmentTest, DISABLED_TwoWhiles) {
 
   {
     FlattenCallGraph flatten;
-    TF_ASSIGN_OR_ASSERT_OK(bool result, flatten.Run(module.get()));
+    TF_ASSERT_OK_AND_ASSIGN(bool result, flatten.Run(module.get()));
     EXPECT_TRUE(result);
   }
 
diff --git a/tensorflow/compiler/xla/service/flatten_call_graph_test.cc b/tensorflow/compiler/xla/service/flatten_call_graph_test.cc
index a08506d84d..12a6794ac1 100644
--- a/tensorflow/compiler/xla/service/flatten_call_graph_test.cc
+++ b/tensorflow/compiler/xla/service/flatten_call_graph_test.cc
@@ -139,7 +139,7 @@ TEST_F(FlattenCallGraphTest, ComplexGraph) {
   }
 
   {
-    TF_ASSIGN_OR_ASSERT_OK(bool result, RunFlattenCallGraph(module.get()));
+    TF_ASSERT_OK_AND_ASSIGN(bool result, RunFlattenCallGraph(module.get()));
     EXPECT_TRUE(result);
     std::unique_ptr<CallGraph> flat_call_graph = CallGraph::Build(module.get());
     const CallGraphNode& c_node = flat_call_graph->GetNode(c_computation);
@@ -182,7 +182,7 @@ TEST_F(FlattenCallGraphTest, SharedWhileConditionAndBody) {
   }
 
   {
-    TF_ASSIGN_OR_ASSERT_OK(bool result, RunFlattenCallGraph(module.get()));
+    TF_ASSERT_OK_AND_ASSIGN(bool result, RunFlattenCallGraph(module.get()));
     EXPECT_TRUE(result);
     std::unique_ptr<CallGraph> call_graph = CallGraph::Build(module.get());
     const CallGraphNode& cond_node = call_graph->GetNode(cond_computation);
@@ -211,7 +211,7 @@ TEST_F(FlattenCallGraphTest, FlattenCalls) {
   module->AddEntryComputation(
       MakeCallingComputation(b_computation, /*callsites=*/2, ".Entry"));
 
-  TF_ASSIGN_OR_ASSERT_OK(bool result, RunFlattenCallGraph(module.get()));
+  TF_ASSERT_OK_AND_ASSIGN(bool result, RunFlattenCallGraph(module.get()));
   EXPECT_TRUE(result);
   std::unique_ptr<CallGraph> call_graph = CallGraph::Build(module.get());
   EXPECT_EQ(7, module->computations().size());
diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
index 1c60b06ddd..3ae499d5e0 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
@@ -51,7 +51,7 @@ TEST_F(HloConstantFoldingTest, ConvertF32ToS64) {
   EXPECT_THAT(computation->root_instruction(), op::Convert(input));
 
   HloConstantFolding const_folder;
-  TF_ASSIGN_OR_ASSERT_OK(bool result, const_folder.Run(module.get()));
+  TF_ASSERT_OK_AND_ASSIGN(bool result, const_folder.Run(module.get()));
   EXPECT_TRUE(result);
 
   EXPECT_THAT(computation->root_instruction(), op::Constant());
@@ -72,7 +72,7 @@ TEST_F(HloConstantFoldingTest, ConvertS64ToF32) {
   EXPECT_THAT(computation->root_instruction(), op::Convert(input));
 
   HloConstantFolding const_folder;
-  TF_ASSIGN_OR_ASSERT_OK(bool result, const_folder.Run(module.get()));
+  TF_ASSERT_OK_AND_ASSIGN(bool result, const_folder.Run(module.get()));
   EXPECT_TRUE(result);
 
   EXPECT_THAT(computation->root_instruction(), op::Constant());
@@ -93,7 +93,7 @@ TEST_F(HloConstantFoldingTest, ConvertF32ArrayToS64Array) {
   EXPECT_THAT(computation->root_instruction(), op::Convert(input));
 
   HloConstantFolding const_folder;
-  TF_ASSIGN_OR_ASSERT_OK(bool result, const_folder.Run(module.get()));
+  TF_ASSERT_OK_AND_ASSIGN(bool result, const_folder.Run(module.get()));
   EXPECT_TRUE(result);
 
   EXPECT_THAT(computation->root_instruction(), op::Constant());
@@ -133,7 +133,7 @@ TEST_F(HloConstantFoldingTest, Concatenate) {
     auto computation = module->AddEntryComputation(builder.Build());
 
     HloConstantFolding const_folder;
-    TF_ASSIGN_OR_ASSERT_OK(bool result, const_folder.Run(module.get()));
+    TF_ASSERT_OK_AND_ASSIGN(bool result, const_folder.Run(module.get()));
     EXPECT_TRUE(result);
 
     HloInstruction* root = computation->root_instruction();
@@ -148,9 +148,9 @@ TEST_F(HloConstantFoldingTest, Slice) {
   const int64 slice_start[] = {4, 2, 3, 1, 5};
   const int64 slice_limits[] = {10, 8, 6, 5, 9};
   const int64 slice_strides[] = {1, 1, 1, 1, 1};
-  TF_ASSIGN_OR_ASSERT_OK(auto literal,
-                         LiteralTestUtil::CreateRandomLiteral<F32>(
-                             ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
+  TF_ASSERT_OK_AND_ASSIGN(auto literal,
+                          LiteralTestUtil::CreateRandomLiteral<F32>(
+                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
   HloInstruction* literal_instruction = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
   Shape shape = ShapeUtil::MakeShape(F32, {6, 6, 3, 4, 4});
@@ -160,7 +160,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
   auto computation = module->AddEntryComputation(builder.Build());
 
   HloConstantFolding const_folder;
-  TF_ASSIGN_OR_ASSERT_OK(bool result, const_folder.Run(module.get()));
+  TF_ASSERT_OK_AND_ASSIGN(bool result, const_folder.Run(module.get()));
   EXPECT_TRUE(result);
 
   HloInstruction* root = computation->root_instruction();
@@ -171,9 +171,9 @@ TEST_F(HloConstantFoldingTest, Slice) {
 TEST_F(HloConstantFoldingTest, TransposeConstantFold) {
   HloComputation::Builder builder(TestName());
   const int64 dimensions[] = {11, 8, 7, 5, 9};
-  TF_ASSIGN_OR_ASSERT_OK(auto literal,
-                         LiteralTestUtil::CreateRandomLiteral<F32>(
-                             ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
+  TF_ASSERT_OK_AND_ASSIGN(auto literal,
+                          LiteralTestUtil::CreateRandomLiteral<F32>(
+                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
   auto literal_clone = literal->Literal::CloneToUnique();
   HloInstruction* literal_instruction = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
@@ -185,7 +185,7 @@ TEST_F(HloConstantFoldingTest, TransposeConstantFold) {
   auto computation = module->AddEntryComputation(builder.Build());
 
   HloConstantFolding const_folder;
-  TF_ASSIGN_OR_ASSERT_OK(bool result, const_folder.Run(module.get()));
+  TF_ASSERT_OK_AND_ASSIGN(bool result, const_folder.Run(module.get()));
   EXPECT_TRUE(result);
 
   HloInstruction* root = computation->root_instruction();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index 626bd3b02b..7269fbeffc 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -218,9 +218,9 @@ TEST_F(HloEvaluatorTest, DoesTraverseInstructions) {
 TEST_F(HloEvaluatorTest, DoesReshape) {
   HloComputation::Builder builder(TestName());
   const int64 dimensions[] = {11, 8, 7, 5, 9};
-  TF_ASSIGN_OR_ASSERT_OK(auto literal,
-                         LiteralTestUtil::CreateRandomLiteral<F32>(
-                             ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
+  TF_ASSERT_OK_AND_ASSIGN(auto literal,
+                          LiteralTestUtil::CreateRandomLiteral<F32>(
+                              ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
   auto literal_clone = literal->CloneToUnique();
   HloInstruction* literal_instruction = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
index a1e38803c4..ad6070a9c1 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
@@ -62,7 +62,7 @@ TEST_F(HloOrderingTest, LastUseScheduledFirst) {
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       SequentialHloOrdering::HloModuleSequence sequence,
       CreateMemoryMinimizingSequence(*module, [](const LogicalBuffer& buffer) {
         return ShapeUtil::ByteSizeOf(buffer.shape());
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
index 3a935dcf96..2358969f38 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
@@ -158,7 +158,7 @@ TEST_F(HloRematerializationTest, SingleComputation) {
   SequentialHloOrdering::HloModuleSequence sequence;
   // Computation requires 16KB without rematerialization, but uses only 12KB
   // with rematerialization so pick a memory limit between these values (14KB).
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       bool changed,
       HloRematerialization::RematerializeAndSchedule(
           ByteSizeOf,
@@ -191,7 +191,7 @@ TEST_F(HloRematerializationTest, SingleComputationNoRematerialization) {
   EXPECT_EQ(computation->instruction_count(), 7);
 
   SequentialHloOrdering::HloModuleSequence sequence;
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       bool changed,
       HloRematerialization::RematerializeAndSchedule(
           ByteSizeOf,
@@ -232,7 +232,7 @@ TEST_F(HloRematerializationTest, RematerializeAroundWhile) {
   // while so the peak memory use of the module is 18KB. Set the memory limit a
   // bit lower (17KB) to force rematerialization of the entry computation.
   SequentialHloOrdering::HloModuleSequence sequence;
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       bool changed,
       HloRematerialization::RematerializeAndSchedule(
           ByteSizeOf,
@@ -268,7 +268,7 @@ TEST_F(HloRematerializationTest, RematerializeEntryAndWhileBody) {
   EXPECT_EQ(body_computation->instruction_count(), 7);
 
   SequentialHloOrdering::HloModuleSequence sequence;
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       bool changed,
       HloRematerialization::RematerializeAndSchedule(
           ByteSizeOf,
@@ -310,7 +310,7 @@ TEST_F(HloRematerializationTest, RematerializeNestedComputations) {
   // If all computations are maximally rematerialized then peak memory usage is
   // ~12K so pick something slightly larger.
   SequentialHloOrdering::HloModuleSequence sequence;
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       bool changed,
       HloRematerialization::RematerializeAndSchedule(
           ByteSizeOf,
@@ -406,7 +406,7 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) {
   // Pick a memory limit some where between 24KB (initial peak memory including
   // parameter and output) and 20KB (peak memory possible with
   // rematerialization).
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       bool changed,
       HloRematerialization::RematerializeAndSchedule(
           ByteSizeOf,
@@ -503,7 +503,7 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) {
   // Pick a memory limit some where between 24KB (initial peak memory including
   // parameter and output) and 20KB (peak memory possible with
   // rematerialization).
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       bool changed,
       HloRematerialization::RematerializeAndSchedule(
           ByteSizeOf,
diff --git a/tensorflow/compiler/xla/service/user_computation_test.cc b/tensorflow/compiler/xla/service/user_computation_test.cc
index 0d50810dc4..07739f241a 100644
--- a/tensorflow/compiler/xla/service/user_computation_test.cc
+++ b/tensorflow/compiler/xla/service/user_computation_test.cc
@@ -51,15 +51,15 @@ TEST_F(UserComputationTest, SimpleComputation) {
   ConstantRequest constant_request;
   *constant_request.mutable_literal() =
       Literal::CreateR1<float>({123.0f, 42.0f})->ToProto();
-  TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle constant_handle,
-                         computation.AddConstantInstruction(constant_request));
+  TF_ASSERT_OK_AND_ASSIGN(ComputationDataHandle constant_handle,
+                          computation.AddConstantInstruction(constant_request));
 
   ParameterRequest param_request;
   *param_request.mutable_shape() = kScalarShape;
   param_request.set_parameter(0);
   param_request.set_name("param0");
-  TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle param_handle,
-                         computation.AddParameterInstruction(param_request));
+  TF_ASSERT_OK_AND_ASSIGN(ComputationDataHandle param_handle,
+                          computation.AddParameterInstruction(param_request));
   OpMetadata metadata;
   metadata.set_op_name("meta");
   TF_ASSERT_OK(computation.SetOpMetadata(param_handle, metadata));
@@ -81,7 +81,7 @@ TEST_F(UserComputationTest, SimpleComputation) {
 
     // Program shape should have a single scalar parameter and scalar
     // result. The outfeed instruction should not affect the program shape.
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(
         std::shared_ptr<const ProgramShape> program_shape,
         computation.ComputeProgramShape(latest_version.version));
     ASSERT_EQ(1, program_shape->parameters_size());
@@ -90,7 +90,7 @@ TEST_F(UserComputationTest, SimpleComputation) {
     EXPECT_TRUE(ShapeUtil::Compatible(kScalarShape, program_shape->result()));
 
     // Build the HLO computation.
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(
         std::unique_ptr<HloComputation> hlo_computation,
         computation.BuildHloComputation(latest_version.version, hlo_resolver,
                                         DebugOptions()));
@@ -108,7 +108,7 @@ TEST_F(UserComputationTest, SimpleComputation) {
         computation.GetVersionedHandleAtOperation(param_handle);
 
     // Program shape should have a single scalar parameter, and scalar result.
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(
         std::shared_ptr<const ProgramShape> program_shape,
         computation.ComputeProgramShape(version_at_param.version));
     ASSERT_EQ(1, program_shape->parameters_size());
@@ -118,7 +118,7 @@ TEST_F(UserComputationTest, SimpleComputation) {
 
     // There should be two instructions, one for the constant and one for the
     // parameter. The outfeed instruction should not be included.
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(
         std::unique_ptr<HloComputation> hlo_computation,
         computation.BuildHloComputation(version_at_param.version, hlo_resolver,
                                         DebugOptions()));
@@ -132,7 +132,7 @@ TEST_F(UserComputationTest, SimpleComputation) {
         computation.GetVersionedHandle();
 
     // Build the HLO computation.
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(
         std::unique_ptr<HloComputation> hlo_computation,
         computation.BuildHloComputation(
             latest_version.version, hlo_resolver, DebugOptions(),
@@ -165,13 +165,13 @@ TEST_F(UserComputationTest, EliminateScalarBroadcast) {
   ConstantRequest a_request;
   *a_request.mutable_literal() =
       Literal::CreateR1<float>({123.0f, 42.0f})->ToProto();
-  TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle a_handle,
-                         computation.AddConstantInstruction(a_request));
+  TF_ASSERT_OK_AND_ASSIGN(ComputationDataHandle a_handle,
+                          computation.AddConstantInstruction(a_request));
 
   ConstantRequest b_request;
   *b_request.mutable_literal() = Literal::CreateR0<float>(1.0f)->ToProto();
-  TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle b_handle,
-                         computation.AddConstantInstruction(b_request));
+  TF_ASSERT_OK_AND_ASSIGN(ComputationDataHandle b_handle,
+                          computation.AddConstantInstruction(b_request));
 
   BinaryOpRequest add;
   add.set_binop(BINOP_ADD);
@@ -185,7 +185,7 @@ TEST_F(UserComputationTest, EliminateScalarBroadcast) {
   VersionedComputationHandle latest_version = computation.GetVersionedHandle();
 
   // Build the HLO computation.
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       std::unique_ptr<HloComputation> hlo_computation,
       computation.BuildHloComputation(latest_version.version, hlo_resolver,
                                       DebugOptions()));
@@ -218,15 +218,15 @@ TEST_F(UserComputationTest, EliminateDegenerateBroadcastAfterIndimBroadcast) {
   *a_request.mutable_shape() = ShapeUtil::MakeShape(F32, {2, 3});
   a_request.set_name("a");
   a_request.set_parameter(0);
-  TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle a_handle,
-                         computation.AddParameterInstruction(a_request));
+  TF_ASSERT_OK_AND_ASSIGN(ComputationDataHandle a_handle,
+                          computation.AddParameterInstruction(a_request));
 
   ParameterRequest b_request;
   *b_request.mutable_shape() = ShapeUtil::MakeShape(F32, {2, 1, 4});
   b_request.set_name("b");
   b_request.set_parameter(1);
-  TF_ASSIGN_OR_ASSERT_OK(ComputationDataHandle b_handle,
-                         computation.AddParameterInstruction(b_request));
+  TF_ASSERT_OK_AND_ASSIGN(ComputationDataHandle b_handle,
+                          computation.AddParameterInstruction(b_request));
 
   BinaryOpRequest add;
   add.set_binop(BINOP_ADD);
@@ -242,7 +242,7 @@ TEST_F(UserComputationTest, EliminateDegenerateBroadcastAfterIndimBroadcast) {
   VersionedComputationHandle latest_version = computation.GetVersionedHandle();
 
   // Build the HLO computation.
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       std::unique_ptr<HloComputation> hlo_computation,
       computation.BuildHloComputation(latest_version.version, hlo_resolver,
                                       DebugOptions()));
diff --git a/tensorflow/compiler/xla/status_macros.h b/tensorflow/compiler/xla/status_macros.h
index aa12cda666..d9ca8f320b 100644
--- a/tensorflow/compiler/xla/status_macros.h
+++ b/tensorflow/compiler/xla/status_macros.h
@@ -183,12 +183,12 @@ class StatusAdaptorForMacros {
       .with_log_stack_trace()                                             \
       .add_ret_check_failure(#condition)
 
-#define TF_ASSIGN_OR_ASSERT_OK(lhs, rexpr)                              \
-  TF_ASSIGN_OR_ASSERT_OK_IMPL(                                          \
+#define TF_ASSERT_OK_AND_ASSIGN(lhs, rexpr)                             \
+  TF_ASSERT_OK_AND_ASSIGN_IMPL(                                         \
       TF_STATUS_MACROS_CONCAT_NAME(_status_or_value, __COUNTER__), lhs, \
       rexpr);
 
-#define TF_ASSIGN_OR_ASSERT_OK_IMPL(statusor, lhs, rexpr)   \
+#define TF_ASSERT_OK_AND_ASSIGN_IMPL(statusor, lhs, rexpr)  \
   auto statusor = (rexpr);                                  \
   ASSERT_TRUE(statusor.status().ok()) << statusor.status(); \
   lhs = statusor.ConsumeValueOrDie()
diff --git a/tensorflow/compiler/xla/status_macros_test.cc b/tensorflow/compiler/xla/status_macros_test.cc
index dead17cdfa..4b0740dad7 100644
--- a/tensorflow/compiler/xla/status_macros_test.cc
+++ b/tensorflow/compiler/xla/status_macros_test.cc
@@ -63,7 +63,7 @@ StatusOr<int> CreateIntUnsuccessfully() {
 }
 
 TEST(StatusMacros, AssignOrAssertOnOK) {
-  TF_ASSIGN_OR_ASSERT_OK(int result, CreateIntSuccessfully());
+  TF_ASSERT_OK_AND_ASSIGN(int result, CreateIntSuccessfully());
   EXPECT_EQ(42, result);
 }
 
diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
index 3d6a995a24..c65f8c0f08 100644
--- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
@@ -67,9 +67,9 @@ TEST_F(DeconstructTupleTest, DeconstructTuple) {
   // Try copying the elements back and comparing it
   auto handles = result_status.ConsumeValueOrDie();
   std::unique_ptr<Literal> literal;
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[0]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[0]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[1]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[1]));
   LiteralTestUtil::ExpectR1Equal<float>({2.0, 4.0, 6.0, 8.0}, *literal);
 }
 
@@ -89,17 +89,17 @@ TEST_F(DeconstructTupleTest, DeconstructTupleTwice) {
   auto handles2 = result_status2.ConsumeValueOrDie();
 
   std::unique_ptr<Literal> literal;
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles1[0]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles1[0]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles1[1]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles1[1]));
   LiteralTestUtil::ExpectR1Equal<float>({2.0, 4.0, 6.0, 8.0}, *literal);
 
   handles1[0].reset();
   handles1[1].reset();
 
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles2[0]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles2[0]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles2[1]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles2[1]));
   LiteralTestUtil::ExpectR1Equal<float>({2.0, 4.0, 6.0, 8.0}, *literal);
 }
 
@@ -119,13 +119,13 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructTupleRepeatedElement) {
   auto handles = result_status.ConsumeValueOrDie();
 
   std::unique_ptr<Literal> literal;
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[0]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[0]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[1]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[1]));
   LiteralTestUtil::ExpectR1Equal<float>({2.0, 4.0, 6.0, 8.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[2]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[2]));
   LiteralTestUtil::ExpectR1Equal<float>({2.0, 4.0, 6.0, 8.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[3]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[3]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
 }
 
@@ -145,17 +145,17 @@ TEST_F(DeconstructTupleTest, DeconstructTupleThenDeallocate) {
   global_data.reset();
 
   std::unique_ptr<Literal> literal;
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[0]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[0]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[1]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[1]));
   LiteralTestUtil::ExpectR1Equal<float>({2.0, 4.0, 6.0, 8.0}, *literal);
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[2]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[2]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
 
   /// Try deallocating one of the repeated elements, then copy
   handles[0].reset();
 
-  TF_ASSIGN_OR_ASSERT_OK(literal, client_->Transfer(*handles[2]));
+  TF_ASSERT_OK_AND_ASSIGN(literal, client_->Transfer(*handles[2]));
   LiteralTestUtil::ExpectR1Equal<float>({1.0, 2.0, 3.0, 4.0}, *literal);
 }
 
diff --git a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc
index f54fa2256e..eded2077fc 100644
--- a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc
+++ b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc
@@ -46,7 +46,7 @@ TEST_F(HloMetadataTest, MetadataPropagation) {
   builder.ClearOpMetadata();
 
   Shape argument_layout = ShapeUtil::MakeShape(F32, {});
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       std::unique_ptr<LocalExecutable> executable,
       local_client_->Compile(builder.Build().ValueOrDie(),
                              {&argument_layout, &argument_layout},
diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
index 717e9cd494..9ad9b33176 100644
--- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
@@ -178,11 +178,11 @@ TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2) {
   Shape rhs_shape =
       ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()});
 
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       auto lhs_handle,
       client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<float>(
           lhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       auto rhs_handle,
       client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<float>(
           rhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc
index ed994fda45..0a2d4c763d 100644
--- a/tensorflow/compiler/xla/tests/prng_test.cc
+++ b/tensorflow/compiler/xla/tests/prng_test.cc
@@ -68,13 +68,12 @@ void PrngTest::BernoulliTest(float p, tensorflow::gtl::ArraySlice<int64> dims) {
   auto shape = ShapeUtil::MakeShape(U32, dims);
   builder.RngBernoulli(builder.ConstantR0<float>(p), shape);
 
-  TF_ASSIGN_OR_ASSERT_OK(auto computation, builder.Build());
+  TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build());
   ExecutionOptions execution_options = execution_options_;
   execution_options.set_seed(42);
-  TF_ASSIGN_OR_ASSERT_OK(
-      auto actual,
-      client_->ExecuteAndTransfer(computation, /*arguments=*/{},
-                                  &execution_options));
+  TF_ASSERT_OK_AND_ASSIGN(
+      auto actual, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
+                                               &execution_options));
   EXPECT_THAT(dims, ::testing::ElementsAreArray(actual->shape().dimensions()));
   int32 sum = 0;
   actual->EachCell<uint32>(
@@ -167,22 +166,21 @@ XLA_TEST_F(PrngTest, MapUsingRng) {
   ComputationBuilder builder(client_, TestName());
   std::unique_ptr<Literal> param0_literal =
       Literal::CreateR1<float>({2.2f, 5.3f, 4.4f, 5.5f});
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> param0_data,
-                         client_->TransferToServer(*param0_literal));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> param0_data,
+                          client_->TransferToServer(*param0_literal));
 
   auto param0 = builder.Parameter(0, param0_literal->shape(), "param0");
   auto fn = build_sum_rng(builder);
   builder.Map({param0}, fn);
 
-  TF_ASSIGN_OR_ASSERT_OK(auto computation, builder.Build());
+  TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build());
 
   ExecutionOptions execution_options = execution_options_;
   execution_options.set_seed(125);
-  TF_ASSIGN_OR_ASSERT_OK(
-      auto actual,
-      client_->ExecuteAndTransfer(computation,
-                                  /*arguments=*/{param0_data.get()},
-                                  &execution_options));
+  TF_ASSERT_OK_AND_ASSIGN(
+      auto actual, client_->ExecuteAndTransfer(
+                       computation,
+                       /*arguments=*/{param0_data.get()}, &execution_options));
 
   EXPECT_EQ(actual->f32s_size(), param0_literal->f32s_size());
   for (int i = 0; i < param0_literal->f32s_size(); ++i) {
@@ -213,39 +211,35 @@ XLA_TEST_F(PrngTest, PassInGlobalRngSeed) {
 
   std::unique_ptr<Literal> result1;
   {
-    TF_ASSIGN_OR_ASSERT_OK(auto computation, build_computation());
-    TF_ASSIGN_OR_ASSERT_OK(
-        result1,
-        client_->ExecuteAndTransfer(computation, /*arguments=*/{},
-                                    &execution_options1));
+    TF_ASSERT_OK_AND_ASSIGN(auto computation, build_computation());
+    TF_ASSERT_OK_AND_ASSIGN(
+        result1, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
+                                             &execution_options1));
   }
   std::unique_ptr<Literal> result2;
   std::unique_ptr<Literal> result3;
   {
-    TF_ASSIGN_OR_ASSERT_OK(auto computation, build_computation());
-    TF_ASSIGN_OR_ASSERT_OK(
-        result2,
-        client_->ExecuteAndTransfer(computation, /*arguments=*/{},
-                                    &execution_options1));
-    TF_ASSIGN_OR_ASSERT_OK(
-        result3,
-        client_->ExecuteAndTransfer(computation, /*arguments=*/{},
-                                    &execution_options1));
+    TF_ASSERT_OK_AND_ASSIGN(auto computation, build_computation());
+    TF_ASSERT_OK_AND_ASSIGN(
+        result2, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
+                                             &execution_options1));
+    TF_ASSERT_OK_AND_ASSIGN(
+        result3, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
+                                             &execution_options1));
   }
 
   std::unique_ptr<Literal> result4;
   std::unique_ptr<Literal> result5;
   std::unique_ptr<Literal> result6;
   {
-    TF_ASSIGN_OR_ASSERT_OK(auto computation, build_computation());
-    TF_ASSIGN_OR_ASSERT_OK(
-        result4,
-        client_->ExecuteAndTransfer(computation, /*arguments=*/{},
-                                    &execution_options2));
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(auto computation, build_computation());
+    TF_ASSERT_OK_AND_ASSIGN(
+        result4, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
+                                             &execution_options2));
+    TF_ASSERT_OK_AND_ASSIGN(
         result5, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                              &execution_options_));
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(
         result6, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                              &execution_options_));
   }
diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc
index a438236c45..9774e40941 100644
--- a/tensorflow/compiler/xla/tests/reduce_window_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc
@@ -314,8 +314,8 @@ TEST_F(ReduceWindowTest, R4UnitWindow) {
   auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 7, 1},
                                               {1, 4, 1, 1}, padding);
 
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
-                         client_->TransferToServer(*input_literal));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> input_data,
+                          client_->TransferToServer(*input_literal));
   ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
@@ -337,8 +337,8 @@ XLA_TEST_F(ReduceWindowTest, R4SecondMinorStride) {
   auto res = ReferenceUtil::ReduceWindow4DAdd(
       input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
 
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
-                         client_->TransferToServer(*input_literal));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> input_data,
+                          client_->TransferToServer(*input_literal));
   ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
@@ -360,8 +360,8 @@ XLA_TEST_F(ReduceWindowTest, R4SecondMinorUnitStride) {
   auto res = ReferenceUtil::ReduceWindow4DAdd(
       input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
 
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
-                         client_->TransferToServer(*input_literal));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> input_data,
+                          client_->TransferToServer(*input_literal));
   ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
@@ -383,8 +383,8 @@ XLA_TEST_F(ReduceWindowTest, R4SecondMinorWin) {
   auto res = ReferenceUtil::ReduceWindow4DAdd(
       input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding);
 
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_data,
-                         client_->TransferToServer(*input_literal));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> input_data,
+                          client_->TransferToServer(*input_literal));
   ComputeAndCompareR4<float>(&builder_, *res, {input_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
@@ -507,8 +507,8 @@ class R4ReduceWindowTest
     input.FillIota(1);
     std::unique_ptr<Literal> input_literal =
         Literal::CreateR4FromArray4D(input);
-    TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
-                           client_->TransferToServer(*input_literal));
+    TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> input_arg,
+                            client_->TransferToServer(*input_literal));
 
     std::vector<std::pair<int64, int64>> padding(4);
     for (int i = 0; i < 4; ++i) {
@@ -774,8 +774,8 @@ TEST_P(R2ReduceWindowTest, Add) {
   std::unique_ptr<Literal> input_literal =
       Literal::CreateR2FromArray2DWithLayout(
           input, LayoutUtil::MakeLayout(param.layout));
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
-                         client_->TransferToServer(*input_literal));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> input_arg,
+                          client_->TransferToServer(*input_literal));
   b.ReduceWindow(/*operand=*/
                  b.Parameter(0, input_literal->shape(), "p0"),
                  /*init_value=*/b.ConstantR0<float>(kInitValue),
@@ -878,8 +878,8 @@ TEST_P(R1ReduceWindowTest, DoIt) {
   std::iota(std::begin(input_vector), std::end(input_vector), 0);
   std::unique_ptr<Literal> input_literal =
       Literal::CreateR1(tensorflow::gtl::ArraySlice<float>(input_vector));
-  TF_ASSIGN_OR_ASSERT_OK(std::unique_ptr<GlobalData> input_arg,
-                         client_->TransferToServer(*input_literal));
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> input_arg,
+                          client_->TransferToServer(*input_literal));
 
   auto computation = param.reducer == kAdd
                          ? CreateScalarAddComputation(F32, &b)
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index 07bd00f015..6ebd11584f 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -354,7 +354,7 @@ TEST_F(ScalarComputationsTest, DivU32s) {
     ComputationDataHandle divisor =
         builder.Parameter(1, ShapeUtil::MakeShape(U32, {}), "divisor");
     builder.Div(dividend, divisor);
-    TF_ASSIGN_OR_ASSERT_OK(div_computation, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(div_computation, builder.Build());
   }
 
   for (uint32 divisor : vals) {
@@ -362,10 +362,10 @@ TEST_F(ScalarComputationsTest, DivU32s) {
       for (uint32 dividend : vals) {
         auto dividend_literal = Literal::CreateR0<uint32>(dividend);
         auto divisor_literal = Literal::CreateR0<uint32>(divisor);
-        TF_ASSIGN_OR_ASSERT_OK(auto dividend_data,
-                               client_->TransferToServer(*dividend_literal));
-        TF_ASSIGN_OR_ASSERT_OK(auto divisor_data,
-                               client_->TransferToServer(*divisor_literal));
+        TF_ASSERT_OK_AND_ASSIGN(auto dividend_data,
+                                client_->TransferToServer(*dividend_literal));
+        TF_ASSERT_OK_AND_ASSIGN(auto divisor_data,
+                                client_->TransferToServer(*divisor_literal));
         auto actual_literal =
             client_
                 ->ExecuteAndTransfer(div_computation,
@@ -395,7 +395,7 @@ TEST_F(ScalarComputationsTest, RemU32s) {
     ComputationDataHandle divisor =
         builder.Parameter(1, ShapeUtil::MakeShape(U32, {}), "divisor");
     builder.Rem(dividend, divisor);
-    TF_ASSIGN_OR_ASSERT_OK(rem_computation, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(rem_computation, builder.Build());
   }
 
   for (uint32 divisor : vals) {
@@ -403,10 +403,10 @@ TEST_F(ScalarComputationsTest, RemU32s) {
       for (uint32 dividend : vals) {
         auto dividend_literal = Literal::CreateR0<uint32>(dividend);
         auto divisor_literal = Literal::CreateR0<uint32>(divisor);
-        TF_ASSIGN_OR_ASSERT_OK(auto dividend_data,
-                               client_->TransferToServer(*dividend_literal));
-        TF_ASSIGN_OR_ASSERT_OK(auto divisor_data,
-                               client_->TransferToServer(*divisor_literal));
+        TF_ASSERT_OK_AND_ASSIGN(auto dividend_data,
+                                client_->TransferToServer(*dividend_literal));
+        TF_ASSERT_OK_AND_ASSIGN(auto divisor_data,
+                                client_->TransferToServer(*divisor_literal));
         auto actual_literal =
             client_
                 ->ExecuteAndTransfer(rem_computation,
@@ -426,7 +426,7 @@ TEST_F(ScalarComputationsTest, RemainderTwoScalarsNonConstDividendS32) {
   builder.Rem(x, builder.ConstantR0<int32>(80000));
 
   std::unique_ptr<Literal> literal = Literal::CreateR0<int32>(87919);
-  TF_ASSIGN_OR_ASSERT_OK(auto input_data, client_->TransferToServer(*literal));
+  TF_ASSERT_OK_AND_ASSIGN(auto input_data, client_->TransferToServer(*literal));
   ComputeAndCompareR0<int32>(&builder, 7919, {input_data.get()});
 }
 
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index afa7d871c0..8a6c40a0f5 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -387,7 +387,7 @@ TEST_F(WhileTest, TwoWhileWithTupleResult) {
     auto prev = builder.Parameter(0, result_shape, "prev");
     auto iteration = builder.GetTupleElement(prev, 0);
     builder.Lt(iteration, builder.ConstantR0<int32>(c1));
-    TF_ASSIGN_OR_ASSERT_OK(condition, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build());
   }
 
   Computation condition2;
@@ -397,7 +397,7 @@ TEST_F(WhileTest, TwoWhileWithTupleResult) {
     auto prev = builder.Parameter(0, result_shape, "prev");
     auto iteration = builder.GetTupleElement(prev, 0);
     builder.Lt(iteration, builder.ConstantR0<int32>(c2));
-    TF_ASSIGN_OR_ASSERT_OK(condition2, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(condition2, builder.Build());
   }
 
   // Create a computation for the body.
@@ -413,7 +413,7 @@ TEST_F(WhileTest, TwoWhileWithTupleResult) {
     auto new_weights = builder.Add(weights, input);
     auto result = builder.Tuple(
         {builder.Add(iteration, builder.ConstantR0<int32>(1)), new_weights});
-    TF_ASSIGN_OR_ASSERT_OK(body, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(body, builder.Build());
   }
 
   Computation body2;
@@ -426,7 +426,7 @@ TEST_F(WhileTest, TwoWhileWithTupleResult) {
     auto new_weights = builder.Add(weights, input);
     auto result = builder.Tuple(
         {builder.Add(iteration, builder.ConstantR0<int32>(1)), new_weights});
-    TF_ASSIGN_OR_ASSERT_OK(body2, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(body2, builder.Build());
   }
 
   // Create a While node with computations for the condition and the body.
@@ -466,7 +466,7 @@ TEST_F(WhileTest, TwoWhileLoopsAndSharedBody) {
     auto prev = builder.Parameter(0, result_shape, "prev");
     auto iteration = builder.GetTupleElement(prev, 0);
     builder.Lt(iteration, builder.ConstantR0<int32>(c1));
-    TF_ASSIGN_OR_ASSERT_OK(condition, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build());
   }
 
   Computation condition2;
@@ -476,7 +476,7 @@ TEST_F(WhileTest, TwoWhileLoopsAndSharedBody) {
     auto prev = builder.Parameter(0, result_shape, "prev");
     auto iteration = builder.GetTupleElement(prev, 0);
     builder.Lt(iteration, builder.ConstantR0<int32>(c2));
-    TF_ASSIGN_OR_ASSERT_OK(condition2, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(condition2, builder.Build());
   }
 
   // Create a computation for the body.
@@ -492,7 +492,7 @@ TEST_F(WhileTest, TwoWhileLoopsAndSharedBody) {
     auto new_weights = builder.Add(weights, input);
     auto result = builder.Tuple(
         {builder.Add(iteration, builder.ConstantR0<int32>(1)), new_weights});
-    TF_ASSIGN_OR_ASSERT_OK(body, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(body, builder.Build());
   }
 
   // Create a While node with computations for the condition and the body.
@@ -533,7 +533,7 @@ TEST_F(WhileTest, DISABLED_ON_GPU(WhileLoopsWithSharedBodyAndInit)) {
     auto prev = builder.Parameter(0, result_shape, "prev");
     auto iteration = builder.GetTupleElement(prev, 0);
     builder.Lt(iteration, builder.ConstantR0<int32>(c1));
-    TF_ASSIGN_OR_ASSERT_OK(condition, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build());
   }
 
   Computation condition2;
@@ -543,7 +543,7 @@ TEST_F(WhileTest, DISABLED_ON_GPU(WhileLoopsWithSharedBodyAndInit)) {
     auto prev = builder.Parameter(0, result_shape, "prev");
     auto iteration = builder.GetTupleElement(prev, 0);
     builder.Lt(iteration, builder.ConstantR0<int32>(c2));
-    TF_ASSIGN_OR_ASSERT_OK(condition2, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(condition2, builder.Build());
   }
 
   // Create a computation for the body.
@@ -559,7 +559,7 @@ TEST_F(WhileTest, DISABLED_ON_GPU(WhileLoopsWithSharedBodyAndInit)) {
     auto new_weights = builder.Add(weights, input);
     auto result = builder.Tuple(
         {builder.Add(iteration, builder.ConstantR0<int32>(1)), new_weights});
-    TF_ASSIGN_OR_ASSERT_OK(body, builder.Build());
+    TF_ASSERT_OK_AND_ASSIGN(body, builder.Build());
   }
 
   // Create a While node with computations for the condition and the body.
@@ -697,11 +697,11 @@ TEST_F(WhileTest, WhileWithPrngScalarResult) {
   };
 
   for (int i = 1; i < 4; ++i) {
-    TF_ASSIGN_OR_ASSERT_OK(auto computation, while_loop(i));
+    TF_ASSERT_OK_AND_ASSIGN(auto computation, while_loop(i));
 
     ExecutionOptions execution_options = execution_options_;
     execution_options.set_seed(65);
-    TF_ASSIGN_OR_ASSERT_OK(
+    TF_ASSERT_OK_AND_ASSIGN(
         auto result,
         client_->ExecuteAndTransfer(computation, {}, &execution_options));
   }
diff --git a/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc b/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc
index 32336a767a..db811bda36 100644
--- a/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc
+++ b/tensorflow/contrib/xla_tf_graph/xla_tf_graph_util_test.cc
@@ -108,7 +108,7 @@ TEST(XlaTfGraphUtil, ConvertTfGraphToSessionModule) {
   std::vector<XlaCompiler::Argument> args = BuildAddGraphArguments();
   std::unique_ptr<Graph> graph = BuildAddGraph();
 
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       std::unique_ptr<xla::SessionModule> session_module,
       ConvertTfGraphToXlaSessionModule(args, std::move(graph)));
 
@@ -122,11 +122,11 @@ TEST(XlaTfGraphUtil, ConvertTfGraphToSessionModule) {
 TEST(XlaTfGraphUtil, ConvertXlaSessionModuleToXlaNodes) {
   std::vector<XlaCompiler::Argument> args = BuildAddGraphArguments();
   std::unique_ptr<Graph> graph = BuildAddGraph();
-  TF_ASSIGN_OR_ASSERT_OK(
+  TF_ASSERT_OK_AND_ASSIGN(
       std::unique_ptr<xla::SessionModule> session_module,
       ConvertTfGraphToXlaSessionModule(args, std::move(graph)));
-  TF_ASSIGN_OR_ASSERT_OK(auto xla_nodes,
-                         ConvertXlaSessionModuleToXlaNodes(*session_module));
+  TF_ASSERT_OK_AND_ASSIGN(auto xla_nodes,
+                          ConvertXlaSessionModuleToXlaNodes(*session_module));
   EXPECT_EQ(session_module->entry().requests_size(), xla_nodes.size());
 }
 
-- 
GitLab


From c5db6c26041db2352617640a93388901c06e2afa Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 09:10:22 -0700
Subject: [PATCH 1241/1375] Remove old TensorForest implementation.

PiperOrigin-RevId: 162944683
---
 .../contrib/cmake/tf_core_kernels.cmake       |   7 -
 tensorflow/contrib/cmake/tf_tests.cmake       |   1 +
 tensorflow/contrib/tensor_forest/BUILD        | 135 +---
 .../tensor_forest/client/random_forest.py     |  16 +-
 .../tensor_forest/kernels/best_splits_op.cc   | 135 ----
 .../count_extremely_random_stats_op.cc        | 700 ----------------
 .../kernels/finished_nodes_op.cc              | 296 -------
 .../tensor_forest/kernels/grow_tree_op.cc     | 208 -----
 .../tensor_forest/kernels/sample_inputs_op.cc | 321 --------
 .../kernels/tensor_forest_ops_test.cc         | 114 ---
 .../kernels/tree_predictions_op.cc            | 232 ------
 .../kernels/update_fertile_slots_op.cc        | 370 ---------
 .../tensor_forest/ops/tensor_forest_ops.cc    | 569 -------------
 .../contrib/tensor_forest/python/__init__.py  |   2 -
 .../contrib/tensor_forest/python/constants.py |  21 -
 .../kernel_tests/best_splits_op_test.py       | 128 ---
 .../count_extremely_random_stats_op_test.py   | 380 ---------
 .../kernel_tests/finished_nodes_op_test.py    | 185 -----
 .../python/kernel_tests/grow_tree_op_test.py  | 100 ---
 .../kernel_tests/sample_inputs_op_test.py     | 164 ----
 .../kernel_tests/tree_predictions_op_test.py  | 299 -------
 .../update_fertile_slots_op_test.py           |  99 ---
 .../tensor_forest/python/tensor_forest.py     | 756 ++++++------------
 .../python/tensor_forest_test.py              |  34 +-
 .../tensor_forest/python/tensor_forest_v4.py  | 331 --------
 25 files changed, 252 insertions(+), 5351 deletions(-)
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/best_splits_op.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/count_extremely_random_stats_op.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/finished_nodes_op.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/grow_tree_op.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/kernels/update_fertile_slots_op.cc
 delete mode 100644 tensorflow/contrib/tensor_forest/python/constants.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/best_splits_op_test.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/count_extremely_random_stats_op_test.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/finished_nodes_op_test.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/grow_tree_op_test.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/tree_predictions_op_test.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/kernel_tests/update_fertile_slots_op_test.py
 delete mode 100644 tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py

diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index ca4c9f0026..95e884840b 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -92,16 +92,9 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
       "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/ops/beam_search_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc"
-      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/best_splits_op.cc"
-      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/count_extremely_random_stats_op.cc"
-      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/finished_nodes_op.cc"
-      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/grow_tree_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc"
-      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/scatter_add_ndim_op.cc"
-      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/tree_utils.cc"
-      "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/kernels/update_fertile_slots_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/hard_routing_function_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_gradient_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_routing_function_op.cc"
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 0b73f8bb7f..c7d2ac7b56 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -181,6 +181,7 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py"
     # Loading resources in contrib doesn't seem to work on Windows
     "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/client/random_forest_test.py"
+    "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 5ac2979faf..e00b1623a0 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -31,15 +31,8 @@ filegroup(
 filegroup(
     name = "v2_op_sources",
     srcs = [
-        "kernels/best_splits_op.cc",
-        "kernels/count_extremely_random_stats_op.cc",
-        "kernels/finished_nodes_op.cc",
-        "kernels/grow_tree_op.cc",
         "kernels/reinterpret_string_to_float_op.cc",
-        "kernels/sample_inputs_op.cc",
         "kernels/scatter_add_ndim_op.cc",
-        "kernels/tree_predictions_op.cc",
-        "kernels/update_fertile_slots_op.cc",
     ],
 )
 
@@ -65,12 +58,6 @@ cc_library(
     alwayslink = 1,
 )
 
-py_library(
-    name = "constants",
-    srcs = ["python/constants.py"],
-    srcs_version = "PY2AND3",
-)
-
 py_library(
     name = "data_ops_py",
     srcs = ["python/ops/data_ops.py"],
@@ -114,7 +101,6 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        ":constants",
         ":data_ops_py",
         ":eval_metrics",
         ":model_ops_py",
@@ -122,7 +108,6 @@ py_library(
         ":stats_ops_py",
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
-        ":tensor_forest_v4_py",
     ],
 )
 
@@ -146,7 +131,6 @@ tf_custom_op_py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        ":constants",
         ":gen_tensor_forest_ops",
         "//tensorflow/contrib/util:util_py",
         "//tensorflow/python:array_ops",
@@ -415,75 +399,6 @@ py_library(
         ":tensor_forest_ops_py",
         ":tensor_forest_py",
         ":tensor_forest_v4_ops_py",
-        ":tensor_forest_v4_py",
-    ],
-)
-
-py_test(
-    name = "best_splits_op_test",
-    size = "small",
-    srcs = ["python/kernel_tests/best_splits_op_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":tensor_forest_ops_py",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-py_test(
-    name = "count_extremely_random_stats_op_test",
-    size = "small",
-    srcs = ["python/kernel_tests/count_extremely_random_stats_op_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":data_ops_py",
-        ":tensor_forest_ops_py",
-        "//tensorflow/core:protos_all_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-py_test(
-    name = "grow_tree_op_test",
-    size = "small",
-    srcs = ["python/kernel_tests/grow_tree_op_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":tensor_forest_ops_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-        "//tensorflow/python:state_ops",
-        "//tensorflow/python:variables",
-    ],
-)
-
-py_test(
-    name = "finished_nodes_op_test",
-    size = "small",
-    srcs = ["python/kernel_tests/finished_nodes_op_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":tensor_forest_ops_py",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-py_test(
-    name = "sample_inputs_op_test",
-    size = "small",
-    srcs = ["python/kernel_tests/sample_inputs_op_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":data_ops_py",
-        ":tensor_forest_ops_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-        "//tensorflow/python:variables",
     ],
 )
 
@@ -501,67 +416,21 @@ py_test(
     ],
 )
 
-py_test(
-    name = "tree_predictions_op_test",
-    size = "small",
-    srcs = ["python/kernel_tests/tree_predictions_op_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":data_ops_py",
-        ":tensor_forest_ops_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
-py_test(
-    name = "update_fertile_slots_op_test",
-    size = "small",
-    srcs = ["python/kernel_tests/update_fertile_slots_op_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":tensor_forest_ops_py",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
 py_library(
     name = "tensor_forest_py",
     srcs = ["python/tensor_forest.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":constants",
         ":data_ops_py",
         ":tensor_forest_ops_py",
-        "//tensorflow/contrib/framework:framework_py",
-        "//tensorflow/contrib/losses:losses_py",
-        "//tensorflow/python:array_ops",
-        "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:init_ops",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:random_ops",
-        "//tensorflow/python:state_ops",
-        "//tensorflow/python:variable_scope",
-        "//tensorflow/python:variables",
-    ],
-)
-
-py_library(
-    name = "tensor_forest_v4_py",
-    srcs = ["python/tensor_forest_v4.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":tensor_forest_py",
         ":tensor_forest_v4_ops_py",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
+        "//tensorflow/contrib/framework:framework_py",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_py",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:platform",
+        "//tensorflow/python:variables",
         "@six_archive//:six",
     ],
 )
diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py
index edfc67ce58..21fdff23b0 100644
--- a/tensorflow/contrib/tensor_forest/client/random_forest.py
+++ b/tensorflow/contrib/tensor_forest/client/random_forest.py
@@ -25,7 +25,6 @@ from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_f
 
 from tensorflow.contrib.tensor_forest.client import eval_metrics
 from tensorflow.contrib.tensor_forest.python import tensor_forest
-from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
 
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -45,8 +44,6 @@ from tensorflow.python.training import session_run_hook
 KEYS_NAME = 'keys'
 LOSS_NAME = 'rf_training_loss'
 
-VERSION_BUILDERS = {'v2': tensor_forest.RandomForestGraphs,
-                    'v4': tensor_forest_v4.RandomForestGraphsV4}
 EPSILON = 0.000001
 
 
@@ -305,7 +302,7 @@ class TensorForestEstimator(estimator.Estimator):
                params,
                device_assigner=None,
                model_dir=None,
-               graph_builder_class=tensor_forest_v4.RandomForestGraphsV4,
+               graph_builder_class=tensor_forest.RandomForestGraphs,
                config=None,
                weights_name=None,
                keys_name=None,
@@ -357,22 +354,13 @@ class TensorForestEstimator(estimator.Estimator):
       local_eval: If True, don't use a device assigner for eval. This is to
         support some common setups where eval is done on a single machine, even
         though training might be distributed.
-      version: String indicating TensorForest version to use, for backward
-        compatibility. Either 'v2', 'v4', or None to let system pick.
-        Overrides graph_builder_class.
+      version: Unused.
       head: A heads_lib.Head object that calculates losses and such. If None,
         one will be automatically created based on params.
 
     Returns:
       A `TensorForestEstimator` instance.
     """
-    if version:
-      if version not in VERSION_BUILDERS:
-        logging.error('Unknown version %s. Specify either v2 or v4', version)
-      else:
-        logging.info('Setting graph_builder_class to %s', version)
-        graph_builder_class = VERSION_BUILDERS[version]
-
     super(TensorForestEstimator, self).__init__(
         model_fn=get_model_fn(
             params.fill(),
diff --git a/tensorflow/contrib/tensor_forest/kernels/best_splits_op.cc b/tensorflow/contrib/tensor_forest/kernels/best_splits_op.cc
deleted file mode 100644
index b56185e99e..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/best_splits_op.cc
+++ /dev/null
@@ -1,135 +0,0 @@
-// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-// BestSplits returns the index of the best candidate for each finished node.
-// This decision is based on the Gini score of the pcw_candidate_split counts,
-// and the right-branch-taken counts inferred from pcw_total_splits.
-#include <functional>
-
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/kernels/bounds_check.h"
-
-namespace tensorflow {
-
-using std::placeholders::_1;
-using tensorforest::BestFeatureClassification;
-using tensorforest::BestFeatureRegression;
-using tensorforest::CheckTensorBounds;
-
-
-class BestSplits : public OpKernel {
- public:
-  explicit BestSplits(OpKernelConstruction* context) : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "regression", &regression_));
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& finished = context->input(0);
-    const Tensor& node_to_accumulator = context->input(1);
-    const Tensor& split_sums = context->input(2);
-    const Tensor& split_squares = context->input(3);
-    const Tensor& accumulator_sums = context->input(4);
-    const Tensor& accumulator_squares = context->input(5);
-
-    OP_REQUIRES(context, finished.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "finished should be one-dimensional"));
-    OP_REQUIRES(context, node_to_accumulator.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "node_to_accumulator should be one-dimensional"));
-
-    OP_REQUIRES(context, split_sums.shape().dims() == 3,
-                errors::InvalidArgument(
-                    "split_sums should be three-dimensional"));
-    OP_REQUIRES(context, accumulator_sums.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "accumulator_sums should be two-dimensional"));
-
-    if (regression_) {
-      OP_REQUIRES(context,
-                  split_sums.shape() == split_squares.shape(),
-                  errors::InvalidArgument(
-                      "split_sums and split_squares should "
-                      "be the same shape."));
-      OP_REQUIRES(context,
-                  accumulator_sums.shape() == accumulator_squares.shape(),
-                  errors::InvalidArgument(
-                      "accumulator_sums and accumulator_squares should "
-                      "be the same shape."));
-    }
-
-    OP_REQUIRES(
-        context,
-        accumulator_sums.shape().dim_size(0) ==
-        split_sums.shape().dim_size(0),
-        errors::InvalidArgument(
-            "Number of accumulators should be the same in split_sums "
-            "and accumulator_sums."));
-
-    // Check tensor bounds.
-    if (!CheckTensorBounds(context, finished)) return;
-    if (!CheckTensorBounds(context, node_to_accumulator)) return;
-    if (!CheckTensorBounds(context, split_sums)) return;
-    if (!CheckTensorBounds(context, split_squares)) return;
-    if (!CheckTensorBounds(context, accumulator_sums)) return;
-    if (!CheckTensorBounds(context, accumulator_squares)) return;
-
-    Tensor* output_splits = nullptr;
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, finished.shape(),
-                                            &output_splits));
-    auto best_splits = output_splits->unaligned_flat<int32>();
-
-    const auto finished_vec = finished.unaligned_flat<int32>();
-    const auto node_map = node_to_accumulator.unaligned_flat<int32>();
-
-    const int32 num_finished = static_cast<int32>(finished.shape().dim_size(0));
-
-    std::function<int32(int32)> best_feature_func =
-        std::bind(BestFeatureClassification, accumulator_sums, split_sums, _1);
-    if (regression_) {
-       best_feature_func = std::bind(
-           BestFeatureRegression, accumulator_sums, accumulator_squares,
-           split_sums, split_squares, _1);
-    }
-
-    for (int32 i = 0; i < num_finished; i++) {
-      const int32 node = internal::SubtleMustCopy(finished_vec(i));
-      OP_REQUIRES(
-          context, FastBoundsCheck(node, node_map.size()),
-          errors::InvalidArgument("finished node is outside the valid range"));
-
-      const int32 accumulator = internal::SubtleMustCopy(node_map(node));
-      if (accumulator < 0) {
-        LOG(ERROR) << "Something has gone wrong, we got a finished node that "
-                   << "doesn't have an accumulator allocated to it.";
-        continue;
-      }
-
-      best_splits(i) = best_feature_func(accumulator);
-    }
-  }
-
- private:
-  bool regression_;
-};
-
-REGISTER_KERNEL_BUILDER(Name("BestSplits").Device(DEVICE_CPU), BestSplits);
-
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/count_extremely_random_stats_op.cc b/tensorflow/contrib/tensor_forest/kernels/count_extremely_random_stats_op.cc
deleted file mode 100644
index bf8d28c6e0..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/count_extremely_random_stats_op.cc
+++ /dev/null
@@ -1,700 +0,0 @@
-// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-// CountExtremelyRandomStats outputs count-deltas that should be added to
-// the node pcws, candidate split pcws, and total split pcws.  It also outputs
-// the leaves that each input arrived to for use in SampleInputs.  This is the
-// only op that involves tree traversal, and is constructed so that it can
-// be run in parallel on separate batches of data.
-#include <unordered_map>
-#include <unordered_set>
-#include <vector>
-
-#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/lib/gtl/map_util.h"
-#include "tensorflow/core/util/work_sharder.h"
-
-namespace tensorflow {
-
-using std::get;
-using std::make_pair;
-using std::make_tuple;
-using std::tuple;
-
-using tensorforest::CHILDREN_INDEX;
-using tensorforest::FEATURE_INDEX;
-using tensorforest::LEAF_NODE;
-using tensorforest::FREE_NODE;
-
-using tensorforest::CheckTensorBounds;
-using tensorforest::DecideNode;
-using tensorforest::TensorForestDataSpec;
-using tensorforest::Initialize;
-using tensorforest::IsAllInitialized;
-
-// A data structure to store the results of parallel tree traversal.
-struct InputDataResult {
-  // A list of each node that was visited.
-  std::vector<int32> node_indices;
-  // The accumulator of the leaf that a data point ended up at, or -1 if none.
-  int32 leaf_accumulator;
-  // The left-branch taken candidate splits.
-  std::vector<int32> split_adds;
-  // If the candidate splits for the leaf that a data point arrived at
-  // were initialized or not, which determines if we add this to total
-  // pcw counts or not.
-  bool splits_initialized;
-};
-
-
-struct EvaluateParams {
-  TensorForestDataSpec input_spec;
-  Tensor dense_input;
-  Tensor sparse_indices;
-  Tensor sparse_values;
-  Tensor input_labels;
-  Tensor tree_tensor;
-  Tensor tree_thresholds;
-  Tensor node_to_accumulator;
-  Tensor candidate_split_features;
-  Tensor candidate_split_thresholds;
-  InputDataResult* results;
-};
-
-void Evaluate(const EvaluateParams& params, int32 start, int32 end) {
-  const auto tree = params.tree_tensor.tensor<int32, 2>();
-  const auto thresholds = params.tree_thresholds.unaligned_flat<float>();
-  const auto node_map = params.node_to_accumulator.unaligned_flat<int32>();
-  const auto split_features =
-      params.candidate_split_features.tensor<int32, 2>();
-  const auto split_thresholds =
-      params.candidate_split_thresholds.tensor<float, 2>();
-
-  const int32 num_splits = static_cast<int32>(
-      params.candidate_split_features.shape().dim_size(1));
-  const int32 num_nodes = static_cast<int32>(
-      params.tree_tensor.shape().dim_size(0));
-  const int32 num_accumulators = static_cast<int32>(
-      params.candidate_split_features.shape().dim_size(0));
-
-  // Lambdas to capture the eigen-tensors so we don't the conversion overhead
-  // on each call to DecideNode.
-  const auto get_dense = tensorforest::GetDenseFunctor(params.dense_input);
-  const auto get_sparse = tensorforest::GetSparseFunctor(params.sparse_indices,
-                                                         params.sparse_values);
-
-  for (int32 i = start; i < end; ++i) {
-    int node_index = 0;
-    params.results[i].splits_initialized = false;
-    while (true) {
-      params.results[i].node_indices.push_back(node_index);
-      CHECK_LT(node_index, num_nodes);
-      int32 left_child =
-          internal::SubtleMustCopy(tree(node_index, CHILDREN_INDEX));
-      if (left_child == LEAF_NODE) {
-        const int32 accumulator =
-            internal::SubtleMustCopy(node_map(node_index));
-        params.results[i].leaf_accumulator = accumulator;
-        // If the leaf is not fertile or is not yet initialized, we don't
-        // count it in the candidate/total split per-class-weights because
-        // it won't have any candidate splits yet.
-        if (accumulator >= 0 &&
-            IsAllInitialized(split_features, accumulator, num_splits)) {
-          CHECK_LT(accumulator, num_accumulators);
-          params.results[i].splits_initialized = true;
-          for (int split = 0; split < num_splits; split++) {
-            const int32 feature = split_features(accumulator, split);
-            if (!DecideNode(get_dense, get_sparse, i, feature,
-                            split_thresholds(accumulator, split),
-                            params.input_spec)) {
-              params.results[i].split_adds.push_back(split);
-            }
-          }
-        }
-        break;
-      } else if (left_child == FREE_NODE) {
-        LOG(ERROR) << "Reached a free node, not good.";
-        params.results[i].node_indices.push_back(FREE_NODE);
-        break;
-      }
-      const int32 feature = tree(node_index, FEATURE_INDEX);
-      node_index =
-          left_child + DecideNode(get_dense, get_sparse, i, feature,
-                                  thresholds(node_index), params.input_spec);
-    }
-  }
-}
-
-class CountExtremelyRandomStats : public OpKernel {
- public:
-  explicit CountExtremelyRandomStats(OpKernelConstruction* context)
-      : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "num_classes", &num_classes_));
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "regression", &regression_));
-    string serialized_proto;
-    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
-    input_spec_.ParseFromString(serialized_proto);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& input_data = context->input(0);
-    const Tensor& sparse_input_indices = context->input(1);
-    const Tensor& sparse_input_values = context->input(2);
-    const Tensor& sparse_input_shape = context->input(3);
-    const Tensor& input_labels = context->input(4);
-    const Tensor& input_weights = context->input(5);
-    const Tensor& tree_tensor = context->input(6);
-    const Tensor& tree_thresholds = context->input(7);
-    const Tensor& node_to_accumulator = context->input(8);
-    const Tensor& candidate_split_features = context->input(9);
-    const Tensor& candidate_split_thresholds = context->input(10);
-    const Tensor& birth_epochs = context->input(11);
-    const Tensor& current_epoch = context->input(12);
-
-    bool sparse_input = (sparse_input_indices.shape().dims() == 2);
-    bool have_weights = (input_weights.shape().dim_size(0) > 0);
-    int32 num_data = -1;
-
-    // Check inputs.
-    if (sparse_input) {
-      const auto sparse_shape = sparse_input_shape.unaligned_flat<int64>();
-      // TODO(gilberth): This is because we can't figure out the shape
-      // of a sparse tensor at graph-build time, even if the dimension is
-      // actually known.
-      input_spec_.mutable_sparse(0)->set_size(sparse_shape(1));
-      num_data = sparse_shape(0);
-
-      OP_REQUIRES(context, sparse_input_shape.shape().dims() == 1,
-                  errors::InvalidArgument(
-                      "sparse_input_shape should be one-dimensional"));
-      OP_REQUIRES(context,
-                  sparse_input_shape.shape().dim_size(0) == 2,
-                  errors::InvalidArgument(
-                      "The sparse input data should be two-dimensional"));
-      OP_REQUIRES(context, sparse_input_values.shape().dims() == 1,
-                  errors::InvalidArgument(
-                      "sparse_input_values should be one-dimensional"));
-      OP_REQUIRES(context, sparse_input_indices.shape().dims() == 2,
-                  errors::InvalidArgument(
-                      "The sparse input data should be two-dimensional"));
-      OP_REQUIRES(context,
-                  sparse_input_indices.shape().dim_size(0) ==
-                  sparse_input_values.shape().dim_size(0),
-                  errors::InvalidArgument(
-                      "sparse_input_indices and sparse_input_values should "
-                      "agree on the number of non-zero values"));
-    }
-
-    if (input_data.shape().dim_size(0) > 0) {
-      const int32 dense_num_data =
-          static_cast<int32>(input_data.shape().dim_size(0));
-      if (num_data > 0) {
-        CHECK_EQ(num_data, dense_num_data)
-            << "number of examples must match for sparse + dense input.";
-      }
-      num_data = dense_num_data;
-
-      OP_REQUIRES(context, input_data.shape().dims() == 2,
-                  errors::InvalidArgument(
-                      "input_data should be two-dimensional"));
-      OP_REQUIRES(
-          context,
-          input_data.shape().dim_size(0) == input_labels.shape().dim_size(0),
-          errors::InvalidArgument(
-              "Number of inputs should be the same in "
-              "input_data and input_labels."));
-    }
-
-    if (have_weights) {
-      OP_REQUIRES(
-          context,
-          input_weights.shape().dim_size(0) == input_labels.shape().dim_size(0),
-          errors::InvalidArgument(
-              "Number of inputs should be the same in input_weights and "
-              "input_labels."));
-    }
-
-    OP_REQUIRES(context, input_labels.shape().dims() >= 1,
-                errors::InvalidArgument(
-                    "input_labels should be at least one-dimensional"));
-    OP_REQUIRES(context, tree_tensor.shape().dims() == 2,
-            errors::InvalidArgument(
-                "tree should be two-dimensional"));
-    OP_REQUIRES(context, tree_thresholds.shape().dims() == 1,
-            errors::InvalidArgument(
-                "tree_thresholds should be one-dimensional"));
-    OP_REQUIRES(context, node_to_accumulator.shape().dims() == 1,
-            errors::InvalidArgument(
-                "node_to_accumulator should be one-dimensional"));
-    OP_REQUIRES(context, candidate_split_features.shape().dims() == 2,
-            errors::InvalidArgument(
-                "candidate_split_features should be two-dimensional"));
-    OP_REQUIRES(context, candidate_split_thresholds.shape().dims() == 2,
-            errors::InvalidArgument(
-                "candidate_split_thresholds should be two-dimensional"));
-    OP_REQUIRES(context, birth_epochs.shape().dims() == 1,
-            errors::InvalidArgument(
-                "birth_epochs should be one-dimensional"));
-    OP_REQUIRES(context, current_epoch.shape().dims() == 1,
-            errors::InvalidArgument(
-                "current_epoch should be one-dimensional"));
-
-    OP_REQUIRES(
-        context,
-        tree_tensor.shape().dim_size(0) ==
-        tree_thresholds.shape().dim_size(0) &&
-        tree_tensor.shape().dim_size(0) ==
-        node_to_accumulator.shape().dim_size(0) &&
-        tree_tensor.shape().dim_size(0) ==
-        birth_epochs.shape().dim_size(0),
-        errors::InvalidArgument(
-            "Number of nodes should be the same in "
-            "tree, tree_thresholds, node_to_accumulator, and birth_epoch."));
-    OP_REQUIRES(
-        context,
-        candidate_split_features.shape() == candidate_split_thresholds.shape(),
-        errors::InvalidArgument(
-            "candidate_split_features and candidate_split_thresholds should be "
-            "the same shape."));
-    OP_REQUIRES(
-        context,
-        current_epoch.shape().dim_size(0) == 1,
-        errors::InvalidArgument(
-            "The current_epoch should be a tensor of shape (1)."));
-
-    // Check tensor bounds.
-    if (!CheckTensorBounds(context, input_data)) return;
-    if (!CheckTensorBounds(context, sparse_input_indices)) return;
-    if (!CheckTensorBounds(context, sparse_input_values)) return;
-    if (!CheckTensorBounds(context, sparse_input_shape)) return;
-    if (!CheckTensorBounds(context, input_labels)) return;
-    if (!CheckTensorBounds(context, input_weights)) return;
-    if (!CheckTensorBounds(context, tree_tensor)) return;
-    if (!CheckTensorBounds(context, tree_thresholds)) return;
-    if (!CheckTensorBounds(context, node_to_accumulator)) return;
-    if (!CheckTensorBounds(context, candidate_split_features)) return;
-    if (!CheckTensorBounds(context, candidate_split_thresholds)) return;
-    if (!CheckTensorBounds(context, birth_epochs)) return;
-    if (!CheckTensorBounds(context, current_epoch)) return;
-
-    // Evaluate input data in parallel.
-    const int32 epoch = current_epoch.unaligned_flat<int32>()(0);
-
-    std::unique_ptr<InputDataResult[]> results(new InputDataResult[num_data]);
-    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
-    int num_threads = worker_threads->num_threads;
-    EvaluateParams params;
-    params.dense_input = input_data;
-    params.sparse_indices = sparse_input_indices;
-    params.sparse_values = sparse_input_values;
-    params.input_spec = input_spec_;
-    params.input_labels = input_labels;
-    params.tree_tensor = tree_tensor;
-    params.tree_thresholds = tree_thresholds;
-    params.node_to_accumulator = node_to_accumulator;
-    params.candidate_split_features = candidate_split_features;
-    params.candidate_split_thresholds = candidate_split_thresholds;
-    params.results = results.get();
-    // Require at least 100 inputs per thread.  I guess that's about 800 cost
-    // per unit.  This isn't well defined.
-    const int64 costPerUnit = 800;
-    auto work = [&params, num_data](int64 start, int64 end) {
-      CHECK(start <= end);
-      CHECK(end <= num_data);
-      Evaluate(params, static_cast<int32>(start), static_cast<int32>(end));
-    };
-    Shard(num_threads, worker_threads->workers, num_data, costPerUnit, work);
-
-    const int32 num_nodes = static_cast<int32>(tree_tensor.shape().dim_size(0));
-    if (regression_) {
-      ProcessResultsRegression(context, input_labels, input_weights,
-                               birth_epochs, epoch, std::move(results),
-                               num_nodes);
-    } else {
-      ProcessResultsClassification(context, input_labels, input_weights,
-                                   birth_epochs, epoch, std::move(results),
-                                   num_nodes);
-    }
-  }
-
- protected:
-  void ProcessResultsClassification(OpKernelContext* context,
-                                    const Tensor& input_labels,
-                                    const Tensor& input_weights,
-                                    const Tensor& birth_epochs, int32 epoch,
-                                    std::unique_ptr<InputDataResult[]> results,
-                                    int32 num_nodes) {
-    const int32 num_data = static_cast<int32>(input_labels.shape().dim_size(0));
-    const auto labels = input_labels.unaligned_flat<float>();
-    const auto start_epochs = birth_epochs.unaligned_flat<int32>();
-    const auto weights = input_weights.unaligned_flat<float>();
-
-    // Unused outputs for classification.  Still have to specify them or
-    // tensorflow complains.
-    Tensor* dummy = nullptr;
-    TensorShape dummy_shape;
-    dummy_shape.AddDim(0);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(1, dummy_shape, &dummy));
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(4, dummy_shape, &dummy));
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(7, dummy_shape, &dummy));
-
-    // node pcw delta
-    Tensor* output_node_pcw_sums_delta = nullptr;
-    TensorShape node_pcw_sums_shape;
-    node_pcw_sums_shape.AddDim(num_nodes);
-    node_pcw_sums_shape.AddDim(num_classes_);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, node_pcw_sums_shape,
-                                            &output_node_pcw_sums_delta));
-    Initialize<float>(*output_node_pcw_sums_delta, 0);
-    auto out_node_sums = output_node_pcw_sums_delta->tensor<float, 2>();
-
-    // leaves
-    Tensor* output_leaves = nullptr;
-    TensorShape leaves_shape;
-    leaves_shape.AddDim(num_data);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(8, leaves_shape, &output_leaves));
-    auto out_leaves = output_leaves->unaligned_flat<int32>();
-
-    // <accumulator, class> -> count delta
-    PairMapType<float> total_delta;
-    // <accumulator, split, class> -> count delta
-    TupleMapType<float> split_delta;
-
-    for (int32 i = 0; i < num_data; ++i) {
-      out_leaves(i) = results[i].node_indices.back();
-      float w = 1.0;
-      if (weights.size() > 0) {
-        w = weights(i);
-      }
-
-      const int32 label = internal::SubtleMustCopy(
-          static_cast<int32>(labels(i)));
-      // Labels that come from sparse tensors can have missing values.
-      if (label < 0) {
-        continue;
-      }
-      const int32 column = label + 1;
-      CHECK_LT(column, num_classes_);
-      const int32 accumulator = results[i].leaf_accumulator;
-      for (const int32 node : results[i].node_indices) {
-        if (epoch > start_epochs(node) + 1) {
-          continue;
-        }
-        out_node_sums(node, column) += w;
-        out_node_sums(node, 0) += w;
-      }
-
-      if (epoch > start_epochs(out_leaves(i)) + 1) {
-        continue;
-      }
-      if (accumulator >= 0 && results[i].splits_initialized) {
-        total_delta[make_pair(accumulator, column)] += w;
-        total_delta[make_pair(accumulator, 0)] += w;
-        for (const int32 split : results[i].split_adds) {
-          split_delta[make_tuple(accumulator, split, column)] += w;
-          split_delta[make_tuple(accumulator, split, 0)] += w;
-        }
-      }
-    }
-
-    // candidate splits pcw indices
-    Tensor* output_candidate_pcw_indices = nullptr;
-    TensorShape candidate_pcw_shape;
-    candidate_pcw_shape.AddDim(split_delta.size());
-    candidate_pcw_shape.AddDim(3);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(2, candidate_pcw_shape,
-                                            &output_candidate_pcw_indices));
-    auto out_candidate_indices =
-        output_candidate_pcw_indices->tensor<int32, 2>();
-
-    // candidate splits pcw delta
-    Tensor* output_candidate_pcw_delta = nullptr;
-    TensorShape candidate_pcw_delta_shape;
-    candidate_pcw_delta_shape.AddDim(split_delta.size());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(3, candidate_pcw_delta_shape,
-                                            &output_candidate_pcw_delta));
-    auto out_candidate = output_candidate_pcw_delta->unaligned_flat<float>();
-
-    // total splits indices
-    Tensor* output_total_pcw_indices = nullptr;
-    TensorShape total_pcw_shape;
-    total_pcw_shape.AddDim(total_delta.size());
-    total_pcw_shape.AddDim(2);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(5, total_pcw_shape,
-                                            &output_total_pcw_indices));
-    auto out_total_indices = output_total_pcw_indices->tensor<int32, 2>();
-
-    // total splits delta
-    Tensor* output_total_pcw_delta = nullptr;
-    TensorShape total_pcw_delta_shape;
-    total_pcw_delta_shape.AddDim(total_delta.size());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(6, total_pcw_delta_shape,
-                                            &output_total_pcw_delta));
-    auto out_total = output_total_pcw_delta->unaligned_flat<float>();
-
-    // Copy total deltas to output.
-    int32 output_slot = 0;
-    for (const auto& updates : total_delta) {
-      out_total_indices(output_slot, 0) = updates.first.first;
-      out_total_indices(output_slot, 1) = updates.first.second;
-      out_total(output_slot) = updates.second;
-      ++output_slot;
-    }
-
-    // Copy split deltas to output.
-    output_slot = 0;
-    for (const auto& updates : split_delta) {
-      out_candidate_indices(output_slot, 0) = get<0>(updates.first);
-      out_candidate_indices(output_slot, 1) = get<1>(updates.first);
-      out_candidate_indices(output_slot, 2) = get<2>(updates.first);
-      out_candidate(output_slot) = updates.second;
-      ++output_slot;
-    }
-  }
-
-  void ProcessResultsRegression(OpKernelContext* context,
-                                const Tensor& input_labels,
-                                const Tensor& input_weights,
-                                const Tensor& birth_epochs, const int32 epoch,
-                                std::unique_ptr<InputDataResult[]> results,
-                                int32 num_nodes) {
-    const int32 num_data = static_cast<int32>(input_labels.shape().dim_size(0));
-    int32 num_outputs = 1;
-    if (input_labels.shape().dims() > 1) {
-        num_outputs = static_cast<int32>(input_labels.shape().dim_size(1));
-    }
-    const auto labels = input_labels.unaligned_flat<float>();
-    const auto start_epochs = birth_epochs.unaligned_flat<int32>();
-    const auto weights = input_weights.unaligned_flat<float>();
-
-    // node pcw delta
-    Tensor* output_node_pcw_sums_delta = nullptr;
-    TensorShape node_pcw_sums_shape;
-    node_pcw_sums_shape.AddDim(num_nodes);
-    node_pcw_sums_shape.AddDim(num_classes_);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, node_pcw_sums_shape,
-                                            &output_node_pcw_sums_delta));
-    Initialize<float>(*output_node_pcw_sums_delta, 0);
-    auto out_node_sums = output_node_pcw_sums_delta->tensor<float, 2>();
-
-    Tensor* output_node_pcw_squares_delta = nullptr;
-    TensorShape node_pcw_squares_shape;
-    node_pcw_squares_shape.AddDim(num_nodes);
-    node_pcw_squares_shape.AddDim(num_classes_);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(1, node_pcw_squares_shape,
-                                            &output_node_pcw_squares_delta));
-    Initialize<float>(*output_node_pcw_squares_delta, 0);
-    auto out_node_squares = output_node_pcw_squares_delta->tensor<float, 2>();
-
-    // leaves
-    Tensor* output_leaves = nullptr;
-    TensorShape leaves_shape;
-    leaves_shape.AddDim(num_data);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(8, leaves_shape, &output_leaves));
-    auto out_leaves = output_leaves->unaligned_flat<int32>();
-
-    // <accumulator> -> label index
-    std::unordered_map<int32, std::unordered_set<int32>> total_delta;
-    // <accumulator, split> -> label index
-    PairMapType<std::unordered_set<int32>> split_delta;
-
-    for (int32 i = 0; i < num_data; ++i) {
-      const int32 accumulator = results[i].leaf_accumulator;
-      float w = 1.0;
-      if (weights.size() > 0) {
-        w = weights(i);
-      }
-
-      for (const int32 node : results[i].node_indices) {
-        if (epoch > start_epochs(node) + 1) {
-          continue;
-        }
-        for (int32 j = 0; j < num_outputs; ++j) {
-          const float output = labels(i * num_outputs + j);
-          out_node_sums(node, j + 1) += w * output;
-          out_node_squares(node, j + 1) += w * output * output;
-        }
-        out_node_sums(node, 0) += w;
-        out_node_squares(node, 0) += w;
-      }
-      out_leaves(i) = results[i].node_indices.back();
-      if (epoch > start_epochs(out_leaves(i)) + 1) {
-        continue;
-      }
-      if (accumulator >= 0 && results[i].splits_initialized) {
-        total_delta[accumulator].insert(i);
-        for (const int32 split : results[i].split_adds) {
-          split_delta[make_pair(accumulator, split)].insert(i);
-        }
-      }
-    }
-
-    // candidate splits pcw indices
-    Tensor* output_candidate_pcw_indices = nullptr;
-    TensorShape candidate_pcw_shape;
-    candidate_pcw_shape.AddDim(split_delta.size());
-    candidate_pcw_shape.AddDim(2);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(2, candidate_pcw_shape,
-                                            &output_candidate_pcw_indices));
-    auto out_candidate_indices =
-        output_candidate_pcw_indices->tensor<int32, 2>();
-
-    // candidate splits pcw delta
-    // sums
-    Tensor* output_candidate_pcw_sums = nullptr;
-    TensorShape candidate_pcw_sums_shape;
-    candidate_pcw_sums_shape.AddDim(split_delta.size());
-    candidate_pcw_sums_shape.AddDim(num_classes_);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(3, candidate_pcw_sums_shape,
-                                            &output_candidate_pcw_sums));
-    Initialize<float>(*output_candidate_pcw_sums, 0);
-    auto out_split_sums = output_candidate_pcw_sums->tensor<float, 2>();
-
-    // squares
-    Tensor* output_candidate_pcw_squares = nullptr;
-    TensorShape candidate_pcw_squares_shape;
-    candidate_pcw_squares_shape.AddDim(split_delta.size());
-    candidate_pcw_squares_shape.AddDim(num_classes_);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(4, candidate_pcw_squares_shape,
-                                            &output_candidate_pcw_squares));
-    Initialize<float>(*output_candidate_pcw_squares, 0);
-    auto out_split_squares = output_candidate_pcw_squares->tensor<float, 2>();
-
-    // total splits indices
-    Tensor* output_total_pcw_indices = nullptr;
-    TensorShape total_pcw_shape;
-    total_pcw_shape.AddDim(total_delta.size());
-    total_pcw_shape.AddDim(1);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(5, total_pcw_shape,
-                                            &output_total_pcw_indices));
-    auto out_total_indices = output_total_pcw_indices->unaligned_flat<int32>();
-
-    // total splits delta
-    // sums
-    Tensor* output_total_pcw_sums = nullptr;
-    TensorShape total_pcw_sums_shape;
-    total_pcw_sums_shape.AddDim(total_delta.size());
-    total_pcw_sums_shape.AddDim(num_classes_);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(6, total_pcw_sums_shape,
-                                            &output_total_pcw_sums));
-    Initialize<float>(*output_total_pcw_sums, 0);
-    auto out_total_sums = output_total_pcw_sums->tensor<float, 2>();
-
-    // squares
-    Tensor* output_total_pcw_squares = nullptr;
-    TensorShape total_pcw_squares_shape;
-    total_pcw_squares_shape.AddDim(total_delta.size());
-    total_pcw_squares_shape.AddDim(num_classes_);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(7, total_pcw_squares_shape,
-                                            &output_total_pcw_squares));
-    Initialize<float>(*output_total_pcw_squares, 0);
-    auto out_total_squares = output_total_pcw_squares->tensor<float, 2>();
-
-    // Copy total deltas to output.
-    int32 output_slot = 0;
-    for (const auto& updates : total_delta) {
-      out_total_indices(output_slot) = updates.first;
-      for (const int32 i : updates.second) {
-        for (int32 j = 0; j < num_outputs; ++j) {
-          const float output = labels(i * num_outputs + j);
-          out_total_sums(output_slot, j + 1) += output;
-          out_total_squares(output_slot, j + 1) += output * output;
-        }
-      }
-      out_total_sums(output_slot, 0) += updates.second.size();
-      out_total_squares(output_slot, 0) += updates.second.size();
-      ++output_slot;
-    }
-
-    // Copy split deltas to output.
-    output_slot = 0;
-    for (const auto& updates : split_delta) {
-      out_candidate_indices(output_slot, 0) = updates.first.first;
-      out_candidate_indices(output_slot, 1) = updates.first.second;
-      for (const int32 i : updates.second) {
-        for (int32 j = 0; j < num_outputs; ++j) {
-          const float output = labels(i * num_outputs + j);
-          out_split_sums(output_slot, j + 1) += output;
-          out_split_squares(output_slot, j + 1) += output * output;
-        }
-      }
-      out_split_sums(output_slot, 0) += updates.second.size();
-      out_split_squares(output_slot, 0) += updates.second.size();
-      ++output_slot;
-    }
-  }
-
-  struct PairIntHash {
-   public:
-    std::size_t operator()(const std::pair<int32, int32>& x) const {
-      // Bit-rotate x.first by 16 bits before xor-ing to minimize hash
-      // collisions in the frequent case when both elements of the pair are
-      // small.
-      return (x.first << 16 | x.first >> 16) ^ x.second;
-    }
-  };
-  template <typename V>
-  using PairMapType =
-      std::unordered_map<std::pair<int32, int32>, V, PairIntHash>;
-
-  struct TupleIntHash {
-   public:
-    std::size_t operator()(const std::tuple<int32, int32, int32>& x) const {
-      const int32 first = get<0>(x);
-      const int32 second = get<1>(x);
-      // Again, we bit-rotate (once by 16 bits, and once by 8 bits) to minimize
-      // hash collisions among small values.
-      return (first << 16 | first >> 16) ^ (second << 8 | second >> 24) ^
-          get<2>(x);
-    }
-  };
-  template <typename V>
-  using TupleMapType = std::unordered_map<tuple<int32, int32, int32>, V,
-      TupleIntHash>;
-
-  int32 num_classes_;
-  bool regression_;
-  tensorforest::TensorForestDataSpec input_spec_;
-};
-
-
-REGISTER_KERNEL_BUILDER(Name("CountExtremelyRandomStats").Device(DEVICE_CPU),
-                        CountExtremelyRandomStats);
-
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/finished_nodes_op.cc b/tensorflow/contrib/tensor_forest/kernels/finished_nodes_op.cc
deleted file mode 100644
index f71ba17fc3..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/finished_nodes_op.cc
+++ /dev/null
@@ -1,296 +0,0 @@
-// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-// FinishedNodes returns a 1-D tensor listing the nodes that are finished
-// accumulating.
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/lib/random/simple_philox.h"
-#include "tensorflow/core/platform/env.h"
-#include "tensorflow/core/platform/mutex.h"
-#include "tensorflow/core/util/work_sharder.h"
-
-namespace tensorflow {
-
-using std::placeholders::_1;
-using std::placeholders::_2;
-
-using tensorforest::CheckTensorBounds;
-using tensorforest::BestSplitDominatesClassificationBootstrap;
-using tensorforest::BestSplitDominatesClassificationChebyshev;
-using tensorforest::BestSplitDominatesClassificationHoeffding;
-using tensorforest::BestSplitDominatesRegression;
-
-namespace {
-
-struct EvaluateParams {
-  Tensor leaves;
-  Tensor node_to_accumulator;
-  Tensor accumulator_sums;
-  Tensor birth_epochs;
-  int current_epoch;
-  int32 num_split_after_samples;
-  int32 min_split_samples;
-  int32 check_dominates_every_samples;
-  bool need_random;
-  int64 random_seed;
-  std::function<bool(int, random::SimplePhilox*)> dominate_method;
-};
-
-void Evaluate(const EvaluateParams& params, mutex* mutex, int32 start,
-              int32 end, std::unordered_set<int32>* final_finished_leaves,
-              std::unordered_set<int32>* final_stale) {
-  const auto leaves = params.leaves.unaligned_flat<int32>();
-  const auto node_map = params.node_to_accumulator.unaligned_flat<int32>();
-  const auto sums = params.accumulator_sums.tensor<float, 2>();
-  const auto start_epochs = params.birth_epochs.unaligned_flat<int32>();
-
-  const int32 num_accumulators =
-      static_cast<int32>(params.accumulator_sums.shape().dim_size(0));
-
-  std::vector<int32> finished_leaves;
-  std::vector<int32> stale;
-
-  std::unique_ptr<random::SimplePhilox> simple_philox;
-  random::PhiloxRandom rnd_gen(params.random_seed);
-
-  if (params.need_random) {
-    simple_philox.reset(new random::SimplePhilox(&rnd_gen));
-  }
-
-  std::unordered_set<int32> visited;
-  for (int32 i = start; i < end; i++) {
-    const int32 leaf = internal::SubtleMustCopy(leaves(i));
-    if (leaf == -1 || visited.find(leaf) != visited.end()) {
-      continue;
-    }
-    if (!FastBoundsCheck(leaf, node_map.size())) {
-      LOG(ERROR) << "leaf " << leaf << " not in valid range.";
-    }
-    const int32 accumulator = internal::SubtleMustCopy(node_map(leaf));
-    if (accumulator < 0) {
-      continue;
-    }
-
-    if (!FastBoundsCheck(accumulator, num_accumulators)) {
-      LOG(ERROR) << "accumulator " << accumulator << " not in valid range.";
-    }
-    // The first column holds the number of samples seen.
-    // For classification, this should be the sum of the other columns.
-    int32 count = sums(accumulator, 0);
-
-    if (params.current_epoch > start_epochs(leaf) + 1) {
-      if (count >= params.min_split_samples) {
-        finished_leaves.push_back(leaf);
-      } else {
-        stale.push_back(leaf);
-      }
-      continue;
-    }
-
-    if (count >= params.num_split_after_samples) {
-      finished_leaves.push_back(leaf);
-      continue;
-    }
-
-    if (count < params.min_split_samples) {
-      continue;
-    }
-
-    if (count % params.check_dominates_every_samples != 0) {
-      continue;
-    }
-
-    bool finished = params.dominate_method(accumulator, simple_philox.get());
-    if (finished) {
-      finished_leaves.push_back(leaf);
-    }
-
-    visited.insert(leaf);
-  }
-  mutex_lock m(*mutex);
-  final_finished_leaves->insert(finished_leaves.begin(), finished_leaves.end());
-  final_stale->insert(stale.begin(), stale.end());
-}
-}  // namespace
-
-
-class FinishedNodes : public OpKernel {
- public:
-  explicit FinishedNodes(OpKernelConstruction* context)
-      : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "regression", &regression_));
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "num_split_after_samples", &num_split_after_samples_));
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "min_split_samples", &min_split_samples_));
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "dominate_fraction", &dominate_fraction_));
-    OP_REQUIRES_OK(context,
-                   context->GetAttr("dominate_method", &dominate_method_));
-    OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_));
-    OP_REQUIRES_OK(context,
-                   context->GetAttr("check_dominates_every_samples",
-                                    &check_dominates_every_samples_));
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& leaf_tensor = context->input(0);
-    const Tensor& node_to_accumulator = context->input(1);
-    const Tensor& split_sums = context->input(2);
-    const Tensor& split_squares = context->input(3);
-    const Tensor& accumulator_sums = context->input(4);
-    const Tensor& accumulator_squares = context->input(5);
-    const Tensor& birth_epochs = context->input(6);
-    const Tensor& current_epoch = context->input(7);
-
-    OP_REQUIRES(context, leaf_tensor.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "leaf_tensor should be one-dimensional"));
-    OP_REQUIRES(context, node_to_accumulator.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "node_to_accumulator should be one-dimensional"));
-    OP_REQUIRES(context, split_sums.shape().dims() == 3,
-                errors::InvalidArgument(
-                    "split_sums should be three-dimensional"));
-    OP_REQUIRES(context, accumulator_sums.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "accumulator_sums should be two-dimensional"));
-    OP_REQUIRES(context, birth_epochs.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "birth_epochs should be one-dimensional"));
-    OP_REQUIRES(
-        context,
-        birth_epochs.shape().dim_size(0) ==
-        node_to_accumulator.shape().dim_size(0),
-        errors::InvalidArgument(
-            "birth_epochs and node_to_accumulator should be the same size."));
-
-    // Check tensor bounds.
-    if (!CheckTensorBounds(context, leaf_tensor)) return;
-    if (!CheckTensorBounds(context, node_to_accumulator)) return;
-    if (!CheckTensorBounds(context, split_sums)) return;
-    if (!CheckTensorBounds(context, split_squares)) return;
-    if (!CheckTensorBounds(context, accumulator_sums)) return;
-    if (!CheckTensorBounds(context, accumulator_squares)) return;
-    if (!CheckTensorBounds(context, birth_epochs)) return;
-    if (!CheckTensorBounds(context, current_epoch)) return;
-
-    const int32 epoch = current_epoch.unaligned_flat<int32>()(0);
-
-    const int32 num_leaves = static_cast<int32>(
-        leaf_tensor.shape().dim_size(0));
-
-    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
-    int num_threads = worker_threads->num_threads;
-
-    EvaluateParams params;
-    params.leaves = leaf_tensor;
-    params.node_to_accumulator = node_to_accumulator;
-    params.accumulator_sums = accumulator_sums;
-    params.birth_epochs = birth_epochs;
-    params.current_epoch = epoch;
-    params.min_split_samples = min_split_samples_;
-    params.num_split_after_samples = num_split_after_samples_;
-    params.need_random = false;
-    params.check_dominates_every_samples = check_dominates_every_samples_;
-
-    if (regression_) {
-      params.dominate_method =
-          std::bind(&BestSplitDominatesRegression, accumulator_sums,
-                    accumulator_squares, split_sums, split_squares, _1);
-    } else {
-      if (dominate_method_ == "none") {
-        params.dominate_method = [](int, random::SimplePhilox*) {
-          return false;
-        };
-      } else if (dominate_method_ == "hoeffding") {
-        params.dominate_method =
-            std::bind(&BestSplitDominatesClassificationHoeffding,
-                      accumulator_sums, split_sums, _1, dominate_fraction_);
-      } else if (dominate_method_ == "chebyshev") {
-        params.dominate_method =
-            std::bind(&BestSplitDominatesClassificationChebyshev,
-                      accumulator_sums, split_sums, _1, dominate_fraction_);
-      } else if (dominate_method_ == "bootstrap") {
-        params.need_random = true;
-
-        params.random_seed = random_seed_;
-        if (params.random_seed == 0) {
-          params.random_seed = static_cast<uint64>(Env::Default()->NowMicros());
-        }
-
-        params.dominate_method =
-            std::bind(&BestSplitDominatesClassificationBootstrap,
-                      accumulator_sums, split_sums, _1, dominate_fraction_, _2);
-      } else {
-        LOG(FATAL) << "Unknown dominate method " << dominate_method_;
-      }
-    }
-
-    std::unordered_set<int32> finished_leaves;
-    std::unordered_set<int32> stale;
-    mutex m;
-    // Require at least 100 leaves per thread.  I guess that's about 800 cost
-    // per unit.  This isn't well defined.
-    const int64 costPerUnit = 800;
-    auto work = [&params, &finished_leaves, &stale, &m, num_leaves](int64 start,
-                                                                    int64 end) {
-      CHECK(start <= end);
-      CHECK(end <= num_leaves);
-      Evaluate(params, &m, static_cast<int32>(start), static_cast<int32>(end),
-               &finished_leaves, &stale);
-    };
-    Shard(num_threads, worker_threads->workers, num_leaves, costPerUnit, work);
-
-    // Copy to output.
-    Tensor* output_finished = nullptr;
-    TensorShape finished_shape;
-    finished_shape.AddDim(finished_leaves.size());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, finished_shape,
-                                            &output_finished));
-    auto out_finished = output_finished->unaligned_flat<int32>();
-    std::copy(finished_leaves.begin(), finished_leaves.end(),
-              out_finished.data());
-
-    Tensor* output_stale = nullptr;
-    TensorShape stale_shape;
-    stale_shape.AddDim(stale.size());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(1, stale_shape,
-                                            &output_stale));
-    auto out_stale = output_stale->unaligned_flat<int32>();
-    std::copy(stale.begin(), stale.end(), out_stale.data());
-  }
-
- private:
-  bool regression_;
-  int32 num_split_after_samples_;
-  int32 min_split_samples_;
-  float dominate_fraction_;
-  string dominate_method_;
-  int32 random_seed_;
-  int32 check_dominates_every_samples_;
-};
-
-REGISTER_KERNEL_BUILDER(Name("FinishedNodes").Device(DEVICE_CPU),
-                        FinishedNodes);
-
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/grow_tree_op.cc b/tensorflow/contrib/tensor_forest/kernels/grow_tree_op.cc
deleted file mode 100644
index ae55805825..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/grow_tree_op.cc
+++ /dev/null
@@ -1,208 +0,0 @@
-// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-// GrowTree adds children to the tree for finished nodes by using the
-// end_of_tree tensor as an indicator for where free nodes are in the
-// pre-allocated tree tensor.
-// For example if the tree is:
-//    1, -1, -1, -2, -2, -2, ...
-// Then end_of_tree should be 3 (the first -2, or "free" slot in the tensor).
-// If node 1 is now finished, the tree tensor after this op would be:
-//    1, 3, -1, -1, -1, -2, ...
-// and end_of_tree would be 5.
-
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/platform/logging.h"
-
-namespace tensorflow {
-
-using tensorforest::CHILDREN_INDEX;
-using tensorforest::FEATURE_INDEX;
-using tensorforest::LEAF_NODE;
-
-using tensorforest::CheckTensorBounds;
-
-
-class GrowTree : public OpKernel {
- public:
-  explicit GrowTree(OpKernelConstruction* context) : OpKernel(context) {}
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& end_of_tree = context->input(0);
-    const Tensor& node_to_accumulator = context->input(1);
-    const Tensor& finished = context->input(2);
-    const Tensor& best_splits = context->input(3);
-    const Tensor& candidate_split_features = context->input(4);
-    const Tensor& candidate_split_thresholds = context->input(5);
-
-    OP_REQUIRES(context, end_of_tree.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "end_of_tree should be one-dimensional"));
-    OP_REQUIRES(context, node_to_accumulator.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "node_to_accumulator should be one-dimensional"));
-    OP_REQUIRES(context, finished.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "finished should be one-dimensional"));
-    OP_REQUIRES(context, best_splits.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "best_splits should be one-dimensional"));
-    OP_REQUIRES(context, candidate_split_features.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "candidate_split_features should be two-dimensional"));
-    OP_REQUIRES(context, candidate_split_thresholds.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "candidate_split_thresholds should be two-dimensional"));
-
-    OP_REQUIRES(
-        context,
-        finished.shape().dim_size(0) ==
-        best_splits.shape().dim_size(0),
-        errors::InvalidArgument(
-            "Number of finished nodes should be the same in finished and "
-            "best_splits."));
-    OP_REQUIRES(
-        context,
-        candidate_split_features.shape().dim_size(0) ==
-        candidate_split_thresholds.shape().dim_size(0),
-        errors::InvalidArgument(
-            "Number of accumulators should be the same in "
-            "candidate_split_features and candidate_split_thresholds."));
-    OP_REQUIRES(
-        context,
-        candidate_split_features.shape().dim_size(1) ==
-        candidate_split_thresholds.shape().dim_size(1),
-        errors::InvalidArgument(
-            "Number of splits should be the same in "
-            "candidate_split_features and candidate_split_thresholds."));
-
-    // Check tensor bounds.
-    if (!CheckTensorBounds(context, end_of_tree)) return;
-    if (!CheckTensorBounds(context, node_to_accumulator)) return;
-    if (!CheckTensorBounds(context, finished)) return;
-    if (!CheckTensorBounds(context, best_splits)) return;
-    if (!CheckTensorBounds(context, candidate_split_features)) return;
-    if (!CheckTensorBounds(context, candidate_split_thresholds)) return;
-
-    int32 current_end_of_tree = end_of_tree.unaligned_flat<int32>()(0);
-    const auto node_map = node_to_accumulator.unaligned_flat<int32>();
-    const auto finished_vec = finished.unaligned_flat<int32>();
-    const auto best_vec = best_splits.unaligned_flat<int32>();
-    const auto split_features = candidate_split_features.tensor<int32, 2>();
-    const auto split_thresholds = candidate_split_thresholds.tensor<float, 2>();
-
-    const int32 num_finished = static_cast<int32>(finished.shape().dim_size(0));
-    const int32 num_nodes = static_cast<int32>(
-        node_to_accumulator.shape().dim_size(0));
-    const int32 num_accumulators = static_cast<int32>(
-        candidate_split_features.shape().dim_size(0));
-    const int32 num_splits = static_cast<int32>(
-        candidate_split_features.shape().dim_size(1));
-
-    // Converting a leaf node into an internal node requires space for its
-    // two children.
-    int32 remaining_node_space = (num_nodes - current_end_of_tree) / 2;
-    int32 nodes_we_can_allocate = std::min(num_finished, remaining_node_space);
-    // Each conversion touches three nodes: the transitioning node and its
-    // two new children.
-    int32 num_updates = 3 * nodes_we_can_allocate;
-
-    Tensor* nodes_to_update_tensor = nullptr;
-    TensorShape nodes_to_update_shape;
-    nodes_to_update_shape.AddDim(num_updates);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, nodes_to_update_shape,
-                                            &nodes_to_update_tensor));
-    auto nodes_to_update_flat = nodes_to_update_tensor->tensor<int32, 1>();
-
-    Tensor* tree_updates_tensor = nullptr;
-    TensorShape tree_updates_shape;
-    tree_updates_shape.AddDim(num_updates);
-    tree_updates_shape.AddDim(2);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(1, tree_updates_shape,
-                                            &tree_updates_tensor));
-    auto tree_updates_flat = tree_updates_tensor->tensor<int32, 2>();
-
-    Tensor* threshold_updates_tensor = nullptr;
-    TensorShape threshold_updates_shape;
-    threshold_updates_shape.AddDim(num_updates);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(2, threshold_updates_shape,
-                                            &threshold_updates_tensor));
-    auto threshold_updates_flat = threshold_updates_tensor->tensor<float, 1>();
-
-    int output_slot = 0;
-    for (int32 i = 0; i < nodes_we_can_allocate; i++) {
-      const int32 node = internal::SubtleMustCopy(finished_vec(i));
-      OP_REQUIRES(context, FastBoundsCheck(node, node_map.size()),
-                  errors::InvalidArgument("finished node not in valid range."))
-      const int32 best = internal::SubtleMustCopy(best_vec(i));
-      const int32 accumulator = internal::SubtleMustCopy(node_map(node));
-      if (accumulator < 0) {
-        LOG(ERROR) << "Finished node doesn't have an accumulator.";
-        continue;
-      }
-
-      OP_REQUIRES(context, FastBoundsCheck(accumulator, num_accumulators),
-                  errors::InvalidArgument("accumulator not in valid range."))
-      OP_REQUIRES(context, FastBoundsCheck(best, num_splits),
-                  errors::InvalidArgument("best split not in valid range."))
-
-      if (current_end_of_tree >= num_nodes - 1) {
-        LOG(ERROR) << "Could not grow tree any further.";
-        return;
-      }
-      const int32 left = current_end_of_tree;
-      nodes_to_update_flat(output_slot) = node;
-
-      tree_updates_flat(output_slot, CHILDREN_INDEX) = left;
-      tree_updates_flat(output_slot, FEATURE_INDEX) =
-          split_features(accumulator, best);
-      threshold_updates_flat(output_slot) = split_thresholds(accumulator, best);
-      output_slot++;
-
-      nodes_to_update_flat(output_slot) = left;
-      tree_updates_flat(output_slot, CHILDREN_INDEX) = LEAF_NODE;
-      tree_updates_flat(output_slot, FEATURE_INDEX) = -1;
-      threshold_updates_flat(output_slot) = 0.0;
-      output_slot++;
-
-      nodes_to_update_flat(output_slot) = left + 1;
-      tree_updates_flat(output_slot, CHILDREN_INDEX) = LEAF_NODE;
-      tree_updates_flat(output_slot, FEATURE_INDEX) = -1;
-      threshold_updates_flat(output_slot) = 0.0;
-      output_slot++;
-
-      current_end_of_tree += 2;
-    }
-
-    Tensor* new_end_of_tree_tensor = nullptr;
-    TensorShape new_end_of_tree_shape;
-    new_end_of_tree_shape.AddDim(1);
-    OP_REQUIRES_OK(context, context->allocate_output(3, new_end_of_tree_shape,
-                                                     &new_end_of_tree_tensor));
-    auto new_end_of_tree_flat = new_end_of_tree_tensor->tensor<int32, 1>();
-    new_end_of_tree_flat(0) = current_end_of_tree;
-  }
-};
-
-REGISTER_KERNEL_BUILDER(Name("GrowTree").Device(DEVICE_CPU), GrowTree);
-
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc b/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc
deleted file mode 100644
index 3ddc72f216..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/sample_inputs_op.cc
+++ /dev/null
@@ -1,321 +0,0 @@
-// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-// SampleInputs initializes candidate splits/threshold values randomly
-// from incoming data for not-yet-initialized fertile nodes.
-#include <ctime>
-#include <unordered_map>
-#include <set>
-
-#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/lib/random/distribution_sampler.h"
-#include "tensorflow/core/lib/random/philox_random.h"
-#include "tensorflow/core/lib/random/simple_philox.h"
-#include "tensorflow/core/platform/logging.h"
-
-namespace tensorflow {
-
-using tensorforest::CheckTensorBounds;
-using tensorforest::IsAllInitialized;
-
-class SampleInputs : public OpKernel {
- public:
-  explicit SampleInputs(OpKernelConstruction* context)
-      : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "split_initializations_per_input", &split_initializations_per_input_));
-    OP_REQUIRES_OK(context, context->GetAttr(
-        "split_sampling_random_seed", &split_sampling_random_seed_));
-    // Set up the random number generator.
-    if (split_sampling_random_seed_ == 0) {
-      uint64 time_seed = static_cast<uint64>(std::clock());
-      single_rand_ = std::unique_ptr<random::PhiloxRandom>(
-          new random::PhiloxRandom(time_seed));
-    } else {
-      single_rand_ = std::unique_ptr<random::PhiloxRandom>(
-          new random::PhiloxRandom(split_sampling_random_seed_));
-    }
-
-    rng_ = std::unique_ptr<random::SimplePhilox>(
-        new random::SimplePhilox(single_rand_.get()));
-
-    string serialized_proto;
-    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
-    input_spec_.ParseFromString(serialized_proto);
-  }
-
-  // increment_input implements a "++" operation for the situation when
-  // you want to do something n times on an underlying iterator.
-  // In an ideal world, this would be a built-in iterator adaptor.
-  template <typename T>
-  static void increment_input(const int n, T* it, int* count) {
-    *count += 1;
-    if (*count == n) {
-      *count = 0;
-      (*it)++;
-    }
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& input_data = context->input(0);
-    const Tensor& sparse_input_indices = context->input(1);
-    const Tensor& sparse_input_values = context->input(2);
-    const Tensor& sparse_input_shape = context->input(3);
-    const Tensor& input_weights = context->input(4);
-    const Tensor& node_to_accumulator = context->input(5);
-    const Tensor& leaves = context->input(6);
-    const Tensor& split_features = context->input(7);
-    const Tensor& split_thresholds = context->input(8);
-
-    bool sparse_input = (sparse_input_indices.shape().dims() == 2);
-
-    bool have_weights = (input_weights.shape().dim_size(0) > 0);
-
-    if (sparse_input) {
-      // TODO(gilberth): This is because we can't figure out the shape
-      // of a sparse tensor at graph-build time, even if the dimension is
-      // actually known.
-      input_spec_.mutable_sparse(0)->set_size(
-          sparse_input_shape.unaligned_flat<int64>()(1));
-      OP_REQUIRES(context, sparse_input_shape.shape().dims() == 1,
-                  errors::InvalidArgument(
-                      "sparse_input_shape should be one-dimensional"));
-      OP_REQUIRES(context,
-                  sparse_input_shape.shape().dim_size(0) == 2,
-                  errors::InvalidArgument(
-                      "The sparse input data should be two-dimensional"));
-      OP_REQUIRES(context, sparse_input_values.shape().dims() == 1,
-                  errors::InvalidArgument(
-                      "sparse_input_values should be one-dimensional"));
-      OP_REQUIRES(context, sparse_input_indices.shape().dims() == 2,
-                  errors::InvalidArgument(
-                      "The sparse input data should be two-dimensional"));
-      OP_REQUIRES(context,
-                  sparse_input_indices.shape().dim_size(0) ==
-                  sparse_input_values.shape().dim_size(0),
-                  errors::InvalidArgument(
-                      "sparse_input_indices and sparse_input_values should "
-                      "agree on the number of non-zero values"));
-      if (have_weights) {
-        OP_REQUIRES(context, sparse_input_shape.unaligned_flat<int64>()(0) ==
-                                 input_weights.shape().dim_size(0),
-                    errors::InvalidArgument(
-                        "sparse_input_values and input_weights should agree "
-                        "on the number of inputs"));
-      }
-    }
-    if (input_data.shape().dim_size(0) > 0) {
-      OP_REQUIRES(context, input_data.shape().dims() == 2,
-                  errors::InvalidArgument(
-                  "input_data should be two-dimensional"));
-      if (have_weights) {
-        OP_REQUIRES(context, input_data.shape().dim_size(0) ==
-                                 input_weights.shape().dim_size(0),
-                    errors::InvalidArgument(
-                        "input_data and input_weights should agree on the "
-                        "number of inputs"));
-      }
-    }
-
-    OP_REQUIRES(context, node_to_accumulator.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "node_to_accumulator should be one-dimensional"));
-    OP_REQUIRES(context, leaves.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "leaves should be one-dimensional"));
-    OP_REQUIRES(context, split_features.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "split_features should be two-dimensional"));
-    OP_REQUIRES(context, split_thresholds.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "split_thresholds should be two-dimensional"));
-
-    OP_REQUIRES(
-        context,
-        split_features.shape() == split_thresholds.shape(),
-        errors::InvalidArgument(
-            "split_features and split_thresholds should be the same shape."));
-
-    // Check tensor bounds.
-    if (!CheckTensorBounds(context, input_data)) return;
-    if (!CheckTensorBounds(context, sparse_input_indices)) return;
-    if (!CheckTensorBounds(context, sparse_input_values)) return;
-    if (!CheckTensorBounds(context, sparse_input_shape)) return;
-    if (!CheckTensorBounds(context, input_weights)) return;
-    if (!CheckTensorBounds(context, node_to_accumulator)) return;
-    if (!CheckTensorBounds(context, leaves)) return;
-    if (!CheckTensorBounds(context, split_features)) return;
-    if (!CheckTensorBounds(context, split_thresholds)) return;
-
-    const auto leaves_vec = leaves.unaligned_flat<int32>();
-    const auto node_map = node_to_accumulator.unaligned_flat<int32>();
-    const auto features = split_features.tensor<int32, 2>();
-    const auto thresholds = split_thresholds.tensor<float, 2>();
-    const auto weights = input_weights.unaligned_flat<float>();
-
-    const int32 num_data = static_cast<int32>(leaves.shape().dim_size(0));
-    const int32 num_splits = static_cast<int32>(
-        split_features.shape().dim_size(1));
-    const int32 num_accumulators = static_cast<int32>(
-        split_features.shape().dim_size(0));
-
-    std::unordered_map<int32, std::set<int32>> accumulator_to_leaves;
-
-    // The first pass just calculates num_output_accumulators.
-    for (int32 i = 0; i < num_data; i++) {
-      const int32 leaf = internal::SubtleMustCopy(leaves_vec(i));
-      OP_REQUIRES(context, FastBoundsCheck(leaf, node_map.size()),
-                  errors::InvalidArgument("leaf not in valid range."))
-      const int32 accumulator = internal::SubtleMustCopy(node_map(leaf));
-
-      // Check for non-fertile node or fertile node that is already
-      // initialized.
-      if (accumulator >= 0 &&
-          !IsAllInitialized(features, accumulator, num_splits)) {
-        accumulator_to_leaves[accumulator].insert(i);
-      }
-    }
-
-    // Now we can allocate the outputs.
-    int32 num_output_accumulators = static_cast<int32>(
-        accumulator_to_leaves.size());
-    VLOG(1) << "num output accumulators = " << num_output_accumulators;
-    Tensor* accumulators_tensor = nullptr;
-    TensorShape accumulators_shape;
-    accumulators_shape.AddDim(num_output_accumulators);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, accumulators_shape,
-                                            &accumulators_tensor));
-    auto accumulators_flat = accumulators_tensor->tensor<int32, 1>();
-
-    Tensor* new_split_feature_rows_tensor = nullptr;
-    TensorShape new_split_feature_rows_shape;
-    new_split_feature_rows_shape.AddDim(num_output_accumulators);
-    new_split_feature_rows_shape.AddDim(num_splits);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(1, new_split_feature_rows_shape,
-                                            &new_split_feature_rows_tensor));
-    auto new_split_feature_rows_flat =
-        new_split_feature_rows_tensor->tensor<int32, 2>();
-
-    Tensor* new_split_threshold_rows_tensor = nullptr;
-    TensorShape new_split_threshold_rows_shape;
-    new_split_threshold_rows_shape.AddDim(num_output_accumulators);
-    new_split_threshold_rows_shape.AddDim(num_splits);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(2, new_split_threshold_rows_shape,
-                                            &new_split_threshold_rows_tensor));
-    auto new_split_threshold_rows_flat =
-        new_split_threshold_rows_tensor->tensor<float, 2>();
-
-    // The second pass fills out the outputs.
-    int output_slot = 0;
-    for (const auto& active : accumulator_to_leaves) {
-      const int32 accumulator = active.first;
-      OP_REQUIRES(context, FastBoundsCheck(accumulator, num_accumulators),
-                  errors::InvalidArgument("accumulator not in valid range."))
-      const std::set<int32> inputs_for_accumulator = active.second;
-      VLOG(1) << "Accumulator " << accumulator
-                  << " gets new output slot " << output_slot;
-      accumulators_flat(output_slot) = accumulator;
-
-      // scatter_update updates entire rows, so we first copy the existing
-      // rows into the output tensors, and then write over the values we
-      // want to change.
-      for (int split = 0; split < num_splits; split++) {
-        new_split_feature_rows_flat(output_slot, split) =
-            features(accumulator, split);
-        new_split_threshold_rows_flat(output_slot, split) =
-            thresholds(accumulator, split);
-      }
-
-      auto it = inputs_for_accumulator.begin();
-      int input_used_count = 0;
-      for (int split = 0;
-           split < num_splits && it != inputs_for_accumulator.end(); split++) {
-        if (new_split_feature_rows_flat(output_slot, split) < 0) {
-          if (have_weights) {
-            // If we have weights, we probabilistically reject inputs with
-            // low weight.  Which means we might have to look at a bunch of
-            // inputs -- maybe even all of them -- to fill this slot.
-            while (it != inputs_for_accumulator.end()) {
-              float w = weights(*it);
-              if (rng_->RandFloat() <= w) {
-                break;
-              }
-              increment_input(split_initializations_per_input_, &it,
-                              &input_used_count);
-            }
-            if (it == inputs_for_accumulator.end()) {
-              break;
-            }
-          }
-          int32 index;
-          float val;
-          int64 sparse_input_start;
-          int32 num_total_features = input_spec_.dense_features_size();
-          if (sparse_input) {
-            num_total_features += tensorforest::GetNumSparseFeatures(
-                sparse_input_indices.matrix<int64>(), *it, &sparse_input_start);
-          }
-          if (num_total_features == 0) {
-            LOG(WARNING) << "num total features is zero.";
-            break;
-          }
-          const int32 rand_feature = rng_->Uniform(num_total_features);
-          if (rand_feature < input_spec_.dense_features_size()) {
-            const auto inputs = input_data.tensor<float, 2>();
-            index = rand_feature;
-            val = inputs(*it, rand_feature);
-          } else {
-            CHECK(sparse_input) << rand_feature << " selected, and dense is "
-                                << input_spec_.dense_features_size();
-            const auto indices = sparse_input_indices.matrix<int64>();
-            const auto values = sparse_input_values.vec<float>();
-            const int32 sparse_index = sparse_input_start + rand_feature -
-                                       input_spec_.dense_features_size();
-            index =
-                indices(sparse_index, 1) + input_spec_.dense_features_size();
-            val = values(sparse_index);
-          }
-          CHECK(index >= 0)
-              << "sample inputs chose negative feature: " << index;
-          increment_input(split_initializations_per_input_, &it,
-                          &input_used_count);
-
-          VLOG(1) << "Over-writing @ " << output_slot << "," << split;
-          new_split_feature_rows_flat(output_slot, split) = index;
-          new_split_threshold_rows_flat(output_slot, split) = val;
-        }
-      }
-      ++output_slot;
-    }
-  }
-
- private:
-  int32 split_initializations_per_input_;
-  int32 split_sampling_random_seed_;
-  std::unique_ptr<random::PhiloxRandom> single_rand_;
-  std::unique_ptr<random::SimplePhilox> rng_;
-  tensorforest::TensorForestDataSpec input_spec_;
-};
-
-REGISTER_KERNEL_BUILDER(Name("SampleInputs").Device(DEVICE_CPU), SampleInputs);
-
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/tensor_forest_ops_test.cc b/tensorflow/contrib/tensor_forest/kernels/tensor_forest_ops_test.cc
index d238fc1c68..8b33e0d819 100644
--- a/tensorflow/contrib/tensor_forest/kernels/tensor_forest_ops_test.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/tensor_forest_ops_test.cc
@@ -24,123 +24,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-TEST(TrainingOpsTest, UpdateFertileSlots_ShapeFn) {
-  ShapeInferenceTestOp op("UpdateFertileSlots");
-  INFER_OK(op, "?;?;?;?;?;?;?;?", "[2,?];[2,?];[?];[?]");
-}
-
 TEST(TrainingOpsTest, ScatterAddNdim_ShapeFn) {
   ShapeInferenceTestOp op("ScatterAddNdim");
   INFER_OK(op, "?;?;?", "");
 }
 
-TEST(TrainingOpsTest, GrowTree_ShapeFn) {
-  ShapeInferenceTestOp op("GrowTree");
-  INFER_OK(op, "?;?;?;?;?;?", "[?];[?,2];[?];[1]");
-}
-
-TEST(TrainingOpsTest, FinishedNodes_ShapeFn) {
-  ShapeInferenceTestOp op("FinishedNodes");
-  INFER_OK(op, "?;?;?;?;?;?;?;?", "[?];[?]");
-}
-
-TEST(TrainingOpsTest, BestSplits_ShapeFn) {
-  ShapeInferenceTestOp op("BestSplits");
-  INFER_OK(op, "?;?;?;?;?;?", "[?]");
-  INFER_OK(op, "[?];?;?;?;?;?", "[d0_0]");
-  INFER_OK(op, "[1];?;?;?;?;?", "[d0_0]");
-  INFER_ERROR("Shape must be rank 1 but is rank 2", op, "[1,2];?;?;?;?;?");
-}
-
-TEST(TrainingOpsTest, SampleInputs_ShapeFn) {
-  ShapeInferenceTestOp op("SampleInputs");
-
-  // input[7].dim(1) determines dims in the output.
-  INFER_OK(op, "?;?;?;?;?;?;?;?;?", "[?];[?,?];[?,?]");
-  INFER_OK(op, "?;?;?;?;?;?;?;[?,?];?", "[?];[?,d7_1];[?,d7_1]");
-  INFER_OK(op, "?;?;?;?;?;?;?;[1,2];?", "[?];[?,d7_1];[?,d7_1]");
-  INFER_ERROR("Shape must be rank 2 but is rank 3", op,
-              "?;?;?;?;?;?;?;[1,2,3];?");
-}
-
-TEST(TrainingOpsTest, CountExtremelyRandomStats_ShapeFn) {
-  ShapeInferenceTestOp op("CountExtremelyRandomStats");
-  TF_ASSERT_OK(NodeDefBuilder("test", "CountExtremelyRandomStats")
-                   .Input("input_data", 0, DT_FLOAT)
-                   .Input("sparse_input_indices", 1, DT_INT64)
-                   .Input("sparse_input_values", 2, DT_FLOAT)
-                   .Input("sparse_input_shape", 3, DT_INT64)
-                   .Input("input_labels", 4, DT_FLOAT)
-                   .Input("input_weights", 5, DT_FLOAT)
-                   .Input("tree", 6, DT_INT32)
-                   .Input("tree_thresholds", 7, DT_FLOAT)
-                   .Input("node_to_accumulator", 8, DT_INT32)
-                   .Input("candidate_split_features", 9, DT_INT32)
-                   .Input("candidate_split_thresholds", 10, DT_FLOAT)
-                   .Input("birth_epochs", 11, DT_INT32)
-                   .Input("current_epoch", 12, DT_INT32)
-                   .Attr("input_spec", "")
-                   .Attr("num_classes", 10)
-                   .Attr("regression", false)
-                   .Finalize(&op.node_def));
-
-  // num_points = 2, num_nodes = 4, regression = false, num_classes = 10
-  // num_nodes = 4
-  INFER_OK(op, "[2,3];?;?;?;?;?;[4];?;?;?;?;?;?",
-           "[d6_0,10];[d6_0,10];[?,3];[?];[0];[?,2];[?];[0];[d0_0]");
-
-  TF_ASSERT_OK(NodeDefBuilder("test", "CountExtremelyRandomStats")
-                   .Input("input_data", 0, DT_FLOAT)
-                   .Input("sparse_input_indices", 1, DT_INT64)
-                   .Input("sparse_input_values", 2, DT_FLOAT)
-                   .Input("sparse_input_shape", 3, DT_INT64)
-                   .Input("input_labels", 4, DT_FLOAT)
-                   .Input("input_weights", 5, DT_FLOAT)
-                   .Input("tree", 6, DT_INT32)
-                   .Input("tree_thresholds", 7, DT_FLOAT)
-                   .Input("node_to_accumulator", 8, DT_INT32)
-                   .Input("candidate_split_features", 9, DT_INT32)
-                   .Input("candidate_split_thresholds", 10, DT_FLOAT)
-                   .Input("birth_epochs", 11, DT_INT32)
-                   .Input("current_epoch", 12, DT_INT32)
-                   .Attr("input_spec", "")
-                   .Attr("num_classes", 10)
-                   .Attr("regression", true)
-                   .Finalize(&op.node_def));
-
-  // num_points = 2, num_nodes = 4, regression = false, num_classes = 10
-  // num_nodes = 4
-  INFER_OK(
-      op, "[2,3];?;?;?;?;?;[4];?;?;?;?;?;?",
-      "[d6_0,10];[d6_0,10];[?,2];[?,10];[?,10];[?,1];[?,10];[?,10];[d0_0]");
-
-  // Sparse shape known and > 1, so num_points is unknown
-  INFER_OK(op, "[2,3];?;?;[10,11];?;?;[4];?;?;?;?;?;?",
-           "[d6_0,10];[d6_0,10];[?,2];[?,10];[?,10];[?,1];[?,10];[?,10];[?]");
-}
-
-TEST(TrainingOpsTest, TreePredictions_ShapeFn) {
-  ShapeInferenceTestOp op("TreePredictions");
-  TF_ASSERT_OK(NodeDefBuilder("test", "TreePredictions")
-                   .Input("a", 0, DT_FLOAT)
-                   .Input("b", 1, DT_INT64)
-                   .Input("c", 2, DT_FLOAT)
-                   .Input("d", 3, DT_INT64)
-                   .Input("f", 5, DT_INT32)
-                   .Input("g", 6, DT_FLOAT)
-                   .Input("h", 7, DT_FLOAT)
-                   .Attr("input_spec", "")
-                   .Attr("valid_leaf_threshold", 0.5)
-                   .Finalize(&op.node_def));
-
-  // num_points = 2, num_classes = 10, sparse shape not known
-  INFER_OK(op, "[2,3];?;?;?;?;?;[1,10]", "[d0_0,9]");
-
-  // num_points = 2, num_classes = 10, sparse and dense shape rank known and > 1
-  INFER_OK(op, "[2,3];?;?;[10,11];?;?;[1,10]", "[d0_0,9]");
-
-  // num_points = 2, num_classes = 10, sparse shape rank known and > 1
-  INFER_OK(op, "?;?;?;[10,11];?;?;[1,10]", "[?,9]");
-}
-
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc b/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc
deleted file mode 100644
index 83bc96be64..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/tree_predictions_op.cc
+++ /dev/null
@@ -1,232 +0,0 @@
-// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-// TreePredictions returns the per-class probabilities for each input by
-// evaluating the given tree.
-#include <algorithm>
-
-#include "tensorflow/contrib/tensor_forest/kernels/data_spec.h"
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/util/work_sharder.h"
-
-namespace tensorflow {
-
-using tensorforest::CHILDREN_INDEX;
-using tensorforest::FEATURE_INDEX;
-using tensorforest::LEAF_NODE;
-using tensorforest::FREE_NODE;
-
-using tensorforest::CheckTensorBounds;
-
-namespace {
-// Traverse the tree for every example from start to end. Put the resulting
-// prediction probability into output_predictions[i].
-void Evaluate(OpKernelContext* context,
-              const std::function<bool(int, int32, float)>& decide,
-              const Tensor& weights, const Tensor& tree_tensor,
-              const Tensor& tree_thresholds, int valid_leaf_threshold,
-              Tensor* output_predictions, int32 start, int32 end) {
-  auto out = output_predictions->tensor<float, 2>();
-
-  const auto node_pcw = weights.tensor<float, 2>();
-  const auto tree = tree_tensor.tensor<int32, 2>();
-  const auto thresholds = tree_thresholds.unaligned_flat<float>();
-
-  const int32 num_classes = static_cast<int32>(weights.shape().dim_size(1));
-  const int32 num_nodes = static_cast<int32>(tree_tensor.shape().dim_size(0));
-
-  // Tree has not been grown or initialized.
-  if (tree(0, CHILDREN_INDEX) == FREE_NODE) {
-    for (int i = start; i < end; i++) {
-      for (int c = 1; c < num_classes; c++) {
-        out(i, c - 1) = 1.0 / num_classes;
-      }
-    }
-    return;
-  }
-
-  for (int i = start; i < end; i++) {
-    int node_index = 0;
-    int parent = -1;
-    while (true) {
-      OP_REQUIRES(context, FastBoundsCheck(node_index, num_nodes),
-                  errors::InvalidArgument("node_index not in valid range."))
-      const int32 left_child = tree(node_index, CHILDREN_INDEX);
-      if (left_child == LEAF_NODE) {
-        const int32 flat_leaf_index = node_index * num_classes + 1;
-        const int32 flat_parent_index = parent * num_classes + 1;
-        std::vector<float> means(num_classes - 1);
-        tensorforest::GetParentWeightedMean(
-            node_pcw(node_index, 0), node_pcw.data() + flat_leaf_index,
-            node_pcw(parent, 0), node_pcw.data() + flat_parent_index,
-            valid_leaf_threshold, num_classes - 1, &means);
-        const int32 start_index = i * (num_classes - 1);
-        std::copy(means.begin(), means.end(), out.data() + start_index);
-        break;
-      } else if (left_child == FREE_NODE) {
-        LOG(ERROR) << "Reached a free node, not good.";
-        return;
-      }
-      parent = node_index;
-      const int32 feature = tree(node_index, FEATURE_INDEX);
-      node_index = left_child + decide(i, feature, thresholds(node_index));
-    }
-  }
-}
-}  // namespace
-
-class TreePredictions : public OpKernel {
- public:
-  explicit TreePredictions(OpKernelConstruction* context)
-      : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr(
-      "valid_leaf_threshold", &valid_leaf_threshold_));
-
-    string serialized_proto;
-    OP_REQUIRES_OK(context, context->GetAttr("input_spec", &serialized_proto));
-    input_spec_.ParseFromString(serialized_proto);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& input_data = context->input(0);
-    const Tensor& sparse_input_indices = context->input(1);
-    const Tensor& sparse_input_values = context->input(2);
-    const Tensor& sparse_input_shape = context->input(3);
-    const Tensor& tree_tensor = context->input(4);
-    const Tensor& tree_thresholds = context->input(5);
-    const Tensor& node_per_class_weights = context->input(6);
-
-    int32 num_data = 0;
-    if (sparse_input_indices.shape().dims() == 2) {
-      const auto sparse_shape = sparse_input_shape.unaligned_flat<int64>();
-      // TODO(gilberth): This is because we can't figure out the shape
-      // of a sparse tensor at graph-build time, even if the dimension is
-      // actually known.
-      input_spec_.mutable_sparse(0)->set_size(sparse_shape(1));
-      num_data = sparse_shape(0);
-      OP_REQUIRES(context, sparse_input_values.shape().dims() == 1,
-                  errors::InvalidArgument(
-                      "sparse_input_values should be one-dimensional"));
-      OP_REQUIRES(context, sparse_input_shape.shape().dims() == 1,
-                  errors::InvalidArgument(
-                      "sparse_input_shape should be one-dimensional"));
-      OP_REQUIRES(context,
-                  sparse_input_indices.shape().dim_size(0) ==
-                  sparse_input_values.shape().dim_size(0),
-                  errors::InvalidArgument(
-                      "sparse_input_indices and sparse_input_values should "
-                      "agree on the number of non-zero values"));
-      OP_REQUIRES(context,
-                  sparse_input_indices.shape().dim_size(1) ==
-                  sparse_input_shape.shape().dim_size(0),
-                  errors::InvalidArgument(
-                      "sparse_input_indices and sparse_input_shape should "
-                      "agree on the dimensionality of data points"));
-    }
-
-    if (input_data.shape().dim_size(0) > 0) {
-      const int32 dense_num_data =
-          static_cast<int32>(input_data.shape().dim_size(0));
-      if (num_data > 0) {
-        CHECK_EQ(num_data, dense_num_data)
-            << "number of examples must match for sparse + dense input.";
-      }
-      num_data = dense_num_data;
-      OP_REQUIRES(
-          context, input_data.shape().dims() == 2,
-          errors::InvalidArgument("input_data should be two-dimensional"));
-    }
-
-    OP_REQUIRES(context, tree_tensor.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "tree should be two-dimensional"));
-    OP_REQUIRES(context, tree_thresholds.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "tree_threhsolds should be one-dimensional"));
-    OP_REQUIRES(context, node_per_class_weights.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "node_pcw should be two-dimensional"));
-
-    OP_REQUIRES(
-        context,
-        tree_tensor.shape().dim_size(0) ==
-        tree_thresholds.shape().dim_size(0) &&
-        tree_tensor.shape().dim_size(0) ==
-        node_per_class_weights.shape().dim_size(0),
-        errors::InvalidArgument(
-            "Number of nodes should be the same in "
-            "tree, tree_thresholds and node_pcw."));
-
-    // Check tensor bounds.
-    if (!CheckTensorBounds(context, input_data)) return;
-    if (!CheckTensorBounds(context, sparse_input_indices)) return;
-    if (!CheckTensorBounds(context, sparse_input_values)) return;
-    if (!CheckTensorBounds(context, sparse_input_shape)) return;
-    if (!CheckTensorBounds(context, tree_tensor)) return;
-    if (!CheckTensorBounds(context, tree_thresholds)) return;
-    if (!CheckTensorBounds(context, node_per_class_weights)) return;
-
-    const int32 num_classes = static_cast<int32>(
-        node_per_class_weights.shape().dim_size(1));
-
-    Tensor* output_predictions = nullptr;
-    TensorShape output_shape;
-    output_shape.AddDim(num_data);
-    output_shape.AddDim(num_classes - 1);
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, output_shape,
-                                            &output_predictions));
-
-    // Lambdas to capture the eigen-tensors so we don't the conversion overhead
-    // on each call to DecideNode.
-    const auto get_dense = tensorforest::GetDenseFunctor(input_data);
-    const auto get_sparse = tensorforest::GetSparseFunctor(sparse_input_indices,
-                                                           sparse_input_values);
-
-    auto decide = [&get_dense, &get_sparse, this](int example, int32 feature,
-                                                  float threshold) {
-      return tensorforest::DecideNode(get_dense, get_sparse, example, feature,
-                                      threshold, input_spec_);
-    };
-
-    auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
-    int num_threads = worker_threads->num_threads;
-
-    const int64 costPerUnit = 800;
-    auto work = [context, &decide, &node_per_class_weights, &tree_tensor,
-                 &tree_thresholds, this, &output_predictions,
-                 num_data](int64 start, int64 end) {
-      CHECK(start <= end);
-      CHECK(end <= num_data);
-      Evaluate(context, decide, node_per_class_weights, tree_tensor,
-               tree_thresholds, valid_leaf_threshold_, output_predictions,
-               static_cast<int32>(start), static_cast<int32>(end));
-    };
-    Shard(num_threads, worker_threads->workers, num_data, costPerUnit, work);
-  }
-
- private:
-  float valid_leaf_threshold_;
-  tensorforest::TensorForestDataSpec input_spec_;
-};
-
-REGISTER_KERNEL_BUILDER(Name("TreePredictions").Device(DEVICE_CPU),
-                        TreePredictions);
-
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/kernels/update_fertile_slots_op.cc b/tensorflow/contrib/tensor_forest/kernels/update_fertile_slots_op.cc
deleted file mode 100644
index 798b003a86..0000000000
--- a/tensorflow/contrib/tensor_forest/kernels/update_fertile_slots_op.cc
+++ /dev/null
@@ -1,370 +0,0 @@
-// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-// UpdateFertileSlots manages accumulator slots.  It assigns free or newly
-// finished accumulator slots to waiting non-fertile nodes and new leaves
-// according to their existing split scores (based on node pcws).
-#include <unordered_map>
-#include <set>
-
-#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
-
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/kernels/bounds_check.h"
-#include "tensorflow/core/lib/gtl/map_util.h"
-#include "tensorflow/core/lib/gtl/top_n.h"
-
-
-namespace tensorflow {
-
-using gtl::TopN;
-using tensorforest::CheckTensorBounds;
-using tensorforest::WeightedGiniImpurity;
-
-class UpdateFertileSlots : public OpKernel {
- public:
-  explicit UpdateFertileSlots(OpKernelConstruction* context)
-      : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr(
-      "regression", &regression_));
-  }
-
-  void Compute(OpKernelContext* context) override {
-    const Tensor& finished = context->input(0);
-
-    const Tensor& non_fertile_leaves =  context->input(1);
-    const Tensor& non_fertile_leaf_scores =  context->input(2);
-    const Tensor& end_of_tree = context->input(3);
-
-    const Tensor& accumulator_sums = context->input(4);
-    const Tensor& node_to_accumulator = context->input(5);
-    const Tensor& stale_leaves = context->input(6);
-    const Tensor& node_sums = context->input(7);
-
-    OP_REQUIRES(context, finished.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "finished should be one-dimensional"));
-    OP_REQUIRES(context, non_fertile_leaves.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "non_fertile_leaves should be one-dimensional"));
-    OP_REQUIRES(context, non_fertile_leaf_scores.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "non_fertile_leaves_scores should be one-dimensional"));
-    OP_REQUIRES(context, end_of_tree.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "end_of_tree should be one-dimensional"));
-    OP_REQUIRES(context, accumulator_sums.shape().dims() == 2,
-                errors::InvalidArgument(
-                    "accumulator_sums should be two-dimensional"));
-     OP_REQUIRES(context, node_to_accumulator.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "node_to_accumulator should be one-dimensional"));
-     OP_REQUIRES(context, stale_leaves.shape().dims() == 1,
-                errors::InvalidArgument(
-                    "stale_leaves should be one-dimensional"));
-
-    OP_REQUIRES(
-        context,
-        non_fertile_leaves.shape().dim_size(0) ==
-        non_fertile_leaf_scores.shape().dim_size(0),
-        errors::InvalidArgument(
-            "Number of non fertile leaves should be the same in "
-            "non_fertile_leaves and non_fertile_leaf_scores."));
-
-    // Check tensor bounds.
-    if (!CheckTensorBounds(context, finished)) return;
-    if (!CheckTensorBounds(context, non_fertile_leaves)) return;
-    if (!CheckTensorBounds(context, non_fertile_leaf_scores)) return;
-    if (!CheckTensorBounds(context, end_of_tree)) return;
-    if (!CheckTensorBounds(context, accumulator_sums)) return;
-    if (!CheckTensorBounds(context, node_to_accumulator)) return;
-    if (!CheckTensorBounds(context, stale_leaves)) return;
-
-    // Read finished accumulators into a set for quick lookup.
-    const auto node_map = node_to_accumulator.unaligned_flat<int32>();
-    const auto finished_vec = finished.unaligned_flat<int32>();
-    const int32 num_finished = static_cast<int32>(finished.shape().dim_size(0));
-    std::set<int32> finished_accumulators;
-    for (int32 i = 0; i < num_finished; ++i) {
-      const int32 node = internal::SubtleMustCopy(finished_vec(i));
-      OP_REQUIRES(
-          context, FastBoundsCheck(node, node_map.size()),
-          errors::InvalidArgument("finished node is outside the valid range"));
-      finished_accumulators.insert(node_map(node));
-    }
-    // Stale accumulators are also finished for the purposes of clearing
-    // and re-allocating.
-    const auto stale_vec = stale_leaves.unaligned_flat<int32>();
-    for (int32 i = 0; i < stale_vec.size(); ++i) {
-      const int32 node = internal::SubtleMustCopy(stale_vec(i));
-      OP_REQUIRES(
-          context, FastBoundsCheck(node, node_map.size()),
-          errors::InvalidArgument("stale node is outside the valid range"));
-      finished_accumulators.insert(node_map(node));
-    }
-
-    // Construct leaf heap to sort leaves to allocate accumulators to.
-    const int32 num_nodes =
-        static_cast<int32>(node_to_accumulator.shape().dim_size(0));
-    const int32 eot = internal::SubtleMustCopy(
-        end_of_tree.unaligned_flat<int32>()(0));
-    // end-of-tree points to one beyond the last node, so it's allowed to go
-    // up to num_nodes inclusive.
-    OP_REQUIRES(
-        context, FastBoundsCheck(eot, num_nodes + 1),
-        errors::InvalidArgument("end-of-tree is outside the valid range"));
-
-    const int32 num_new_leaves = std::min(num_finished * 2, num_nodes - eot);
-
-    LeafHeapType leaf_heap(
-        static_cast<int32>(non_fertile_leaves.shape().dim_size(0)) +
-        num_new_leaves, OrderBySecondGreater());
-    ConstructLeafHeap(
-        non_fertile_leaves, non_fertile_leaf_scores, eot, num_new_leaves,
-        static_cast<int32>(accumulator_sums.shape().dim_size(1)), &leaf_heap);
-
-    const auto sums = node_sums.unaligned_flat<float>();
-    const int32 num_columns = node_sums.shape().dim_size(1);
-    // Allocate leaves.
-    std::unique_ptr<HeapValuesType> values(
-        leaf_heap.Extract());
-    int32 accumulator = -1;  // This will first get incremented to 0.
-    int32 num_accumulators_allocated = 0;
-    std::unordered_map<int32, int32> accumulators_to_node;
-    FindNextAccumulator(accumulator_sums, finished_accumulators, &accumulator);
-    int32 i = 0;
-    for (; i < values->size(); ++i) {
-      const std::pair<int32, float>& node = (*values)[i];
-      if (accumulator < 0) {
-        VLOG(1) << "No allocators left.";
-        break;
-      }
-      // For classification, don't make a node fertile until it is unpure.
-      if (!regression_) {
-        // Add 1 here because index 0 contains the sum of the weights across
-        // classes.
-        Eigen::array<int, 1> offsets = {node.first * num_columns + 1};
-        Eigen::array<int, 1> extents = {num_columns - 1};
-        const auto node_counts = sums.slice(offsets, extents);
-        // TODO(thomaswc): Implement a faster check for pure nodes.
-        if (tensorforest::RawWeightedGiniImpurity(node_counts) == 0) {
-          continue;
-        }
-      }
-      VLOG(1) << "setting node " << node.first << " to accumulator "
-              << accumulator;
-      ++num_accumulators_allocated;
-      accumulators_to_node[accumulator] = node.first;
-
-      FindNextAccumulator(accumulator_sums, finished_accumulators,
-                          &accumulator);
-    }
-
-    // Construct and fill outputs.
-    SetNodeMapUpdates(finished_accumulators, accumulators_to_node, finished,
-                      stale_leaves, context);
-    SetAccumulatorsCleared(finished_accumulators,
-                           accumulators_to_node, context);
-    SetAccumulatorsAllocated(accumulators_to_node, context);
-  }
-
- private:
-  struct OrderBySecondGreater {
-    bool operator()(const std::pair<int32, float> &left,
-                    const std::pair<int32, float> &right) {
-        return left.second > right.second;
-    }
-  };
-
-  typedef TopN<std::pair<int32, float>, OrderBySecondGreater> LeafHeapType;
-  typedef std::vector<std::pair<int32, float>> HeapValuesType;
-
-  // Creates an update tensor for the node to accumulator and accumulator to
-  // node maps.  Sets finished and stale nodes to -1 (no accumulator assigned)
-  // and newly allocated nodes to their accumulator.  De-allocated accumulators
-  // are also set to -1.
-  void SetNodeMapUpdates(
-      const std::set<int32>& finished_accumulators,
-      const std::unordered_map<int32, int32>& accumulators_to_node,
-      const Tensor& finished, const Tensor& stale, OpKernelContext* context) {
-    // Node-to-accumulator map updates.
-    Tensor* output_n2a_map = nullptr;
-    TensorShape n2a_map_shape;
-    n2a_map_shape.AddDim(2);
-    n2a_map_shape.AddDim(accumulators_to_node.size() +
-                         static_cast<int32>(stale.shape().dim_size(0) +
-                                            finished.shape().dim_size(0)));
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, n2a_map_shape, &output_n2a_map));
-
-    // Calculate how many finished accumulators were not re-used, so that
-    // we can properly size the a2n output.
-    std::vector<int32> totally_finished_accumulators;
-    for (const int32 finished_accumulator : finished_accumulators) {
-      if (!gtl::FindOrNull(accumulators_to_node, finished_accumulator)) {
-        totally_finished_accumulators.push_back(finished_accumulator);
-      }
-    }
-
-    // Accumulator-to-node map updates.
-    Tensor* output_a2n_map = nullptr;
-    TensorShape a2n_map_shape;
-    a2n_map_shape.AddDim(2);
-    a2n_map_shape.AddDim(accumulators_to_node.size() +
-                         totally_finished_accumulators.size());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(1, a2n_map_shape, &output_a2n_map));
-
-    auto out_n2a = output_n2a_map->tensor<int32, 2>();
-    auto out_a2n = output_a2n_map->tensor<int32, 2>();
-    int32 n2a_slot = 0;
-    int32 a2n_slot = 0;
-
-    // Set finished nodes to -1.
-    const auto finished_vec = finished.unaligned_flat<int32>();
-    for (int32 i = 0; i < finished_vec.size(); ++i) {
-      out_n2a(0, n2a_slot) = finished_vec(i);
-      out_n2a(1, n2a_slot) = -1;
-      ++n2a_slot;
-    }
-    // Set stale nodes to -1.
-    const auto stale_vec = stale.unaligned_flat<int32>();
-    for (int32 i = 0; i < stale_vec.size(); ++i) {
-      out_n2a(0, n2a_slot) = stale_vec(i);
-      out_n2a(1, n2a_slot) = -1;
-      ++n2a_slot;
-    }
-
-    for (const int32 finished_accumulator : totally_finished_accumulators) {
-      out_a2n(0, a2n_slot) = finished_accumulator;
-      out_a2n(1, a2n_slot) = -1;
-      ++a2n_slot;
-    }
-
-    // Set newly allocated nodes to their allocator.
-    for (const auto& node_alloc_pair : accumulators_to_node) {
-      VLOG(1) << "a2n[" << node_alloc_pair.first
-              << "] = " << node_alloc_pair.second;
-      out_n2a(0, n2a_slot) = node_alloc_pair.second;
-      out_n2a(1, n2a_slot) = node_alloc_pair.first;
-      ++n2a_slot;
-
-      out_a2n(0, a2n_slot) = node_alloc_pair.first;
-      out_a2n(1, a2n_slot) = node_alloc_pair.second;
-      ++a2n_slot;
-    }
-  }
-
-  // Creates output tensor for cleared accumulators. Cleared accumulators are
-  // those that were finished but not re-allocated.
-  void SetAccumulatorsCleared(
-      const std::set<int32>& finished_accumulators,
-      const std::unordered_map<int32, int32>& accumulators_to_node,
-      OpKernelContext* context) {
-    std::set<int32> cleared;
-    for (const int32 node : finished_accumulators) {
-      if (accumulators_to_node.find(node) == accumulators_to_node.end()) {
-        cleared.insert(node);
-      }
-    }
-
-    Tensor* output_cleared = nullptr;
-    TensorShape cleared_shape;
-    cleared_shape.AddDim(cleared.size());
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(2, cleared_shape, &output_cleared));
-
-    auto out = output_cleared->unaligned_flat<int32>();
-
-    int32 i = 0;
-    for (const int32 accumulator : cleared) {
-      out(i) = accumulator;
-      ++i;
-    }
-  }
-
-  // Creates output tensor for accumulators that were allocated to now-fertile
-  // nodes.
-  void SetAccumulatorsAllocated(
-      const std::unordered_map<int32, int32>& accumulators_to_node,
-      OpKernelContext* context) {
-    // Node map updates.
-    Tensor* output_allocated = nullptr;
-    TensorShape allocated_shape;
-    allocated_shape.AddDim(accumulators_to_node.size());
-    OP_REQUIRES_OK(context, context->allocate_output(3, allocated_shape,
-                                                     &output_allocated));
-
-    auto out = output_allocated->unaligned_flat<int32>();
-    int32 output_slot = 0;
-
-    // Set newly allocated nodes to their allocator.
-    for (const auto& node_alloc_pair : accumulators_to_node) {
-      out(output_slot) = node_alloc_pair.first;
-      ++output_slot;
-    }
-  }
-
-  void ConstructLeafHeap(const Tensor& non_fertile_leaves,
-                         const Tensor& non_fertile_leaf_scores,
-                         int32 end_of_tree, int32 num_new_leaves,
-                         int32 num_classes, LeafHeapType* leaf_heap) {
-    const auto leaf_vec = non_fertile_leaves.unaligned_flat<int32>();
-    const auto leaf_score_vec = non_fertile_leaf_scores.unaligned_flat<float>();
-
-    for (int32 i = 0; i < leaf_vec.size(); i++) {
-      const int32 leaf = internal::SubtleMustCopy(leaf_vec(i));
-      // Filter out leaves < 0, non_fertile_nodes can contain garbage at
-      // startup.
-      if (leaf >= 0) {
-        leaf_heap->push(std::make_pair(leaf, leaf_score_vec(i)));
-      }
-    }
-
-    // Add new leaves.
-    Eigen::Tensor<float, 1, 1> zeros(num_classes - 1);
-    zeros.setZero();
-    // No data is 0 variance (for regression), not necessarily so for
-    // gini (classification).
-    const float zero_score = regression_ ? 0.0 : WeightedGiniImpurity(zeros);
-    for (int32 leaf = end_of_tree; leaf < end_of_tree + num_new_leaves;
-         leaf++) {
-      leaf_heap->push(std::make_pair(leaf, zero_score));
-    }
-  }
-
-  // Finds the next available or newly-finished accumulator.
-  void FindNextAccumulator(Tensor totals_tensor,
-                           const std::set<int32>& finished_accumulators,
-                           int* current) {
-    ++(*current);
-    const auto totals = totals_tensor.tensor<float, 2>();
-    for (; *current < totals_tensor.shape().dim_size(0); ++(*current)) {
-      if (totals(*current, 0) < 0 ||
-          finished_accumulators.find(*current) != finished_accumulators.end()) {
-        return;
-      }
-    }
-    *current = -1;
-  }
-
-  bool regression_;
-};
-
-REGISTER_KERNEL_BUILDER(Name("UpdateFertileSlots").Device(DEVICE_CPU),
-                        UpdateFertileSlots);
-
-}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc b/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc
index 48db5fe625..6d4a6c40b1 100644
--- a/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc
+++ b/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc
@@ -22,408 +22,6 @@ using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
 using shape_inference::ShapeHandle;
 
-REGISTER_OP("BestSplits")
-    .Attr("regression: bool = false")
-    .Input("finished_nodes: int32")
-    .Input("node_to_accumulator: int32")
-    .Input("split_sums: float")
-    .Input("split_squares: float")
-    .Input("accumulator_sums: float")
-    .Input("accumulator_sqaures: float")
-    .Output("split_indices: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle finished_nodes;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &finished_nodes));
-      c->set_output(0, c->Vector(c->Dim(finished_nodes, 0)));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-  Returns the index of the best split for each finished node.
-
-  For classification, the best split is the split with the lowest weighted
-  Gini impurity, as calculated from the statistics in `split_sums` and
-  `accumulator_sums`. For regression we use the lowest variance, incoporating
-  the *_squares as well.
-
-  finished_nodes:= A 1-d int32 tensor containing the indices of finished nodes.
-  node_to_accumulator: `node_to_accumulator[i]` is the accumulator slot used by
-    fertile node i, or -1 if node i isn't fertile.
-  split_sums:= a 3-d tensor where `split_sums[a][s]` summarizes the
-    training labels for examples that fall into the fertile node associated with
-    accumulator slot s and have then taken the *left* branch of candidate split
-    s.  For a classification problem, `split_sums[a][s][c]` is the count of such
-    examples with class c and for regression problems, `split_sums[a][s]` is the
-    sum of the regression labels for such examples.
-  split_squares: Same as split_sums, but it contains the sum of the
-    squares of the regression labels.  Only used for regression.  For
-    classification problems, pass a dummy tensor into this.
-  accumulator_sums:= a 2-d tensor where `accumulator_sums[a]` summarizes the
-    training labels for examples that fall into the fertile node associated with
-    accumulator slot s.  For a classification problem, `accumulator_sums[a][c]`
-    is the count of such examples with class c and for regression problems,
-    `accumulator_sums[a]` is the sum of the regression labels for such examples.
-  accumulator_squares: Same as accumulator_sums, but it contains the sum of the
-    squares of the regression labels.  Only used for regression.  For
-    classification problems, pass a dummy tensor into this.
-  split_indices: `split_indices[i]` contains the index of the split to use for
-    `finished_nodes[i]`.
-)doc");
-
-REGISTER_OP("CountExtremelyRandomStats")
-    .Attr("input_spec: string")
-    .Attr("num_classes: int")
-    .Attr("regression: bool = false")
-    .Input("input_data: float")
-    .Input("sparse_input_indices: int64")
-    .Input("sparse_input_values: float")
-    .Input("sparse_input_shape: int64")
-    .Input("input_labels: float")
-    .Input("input_weights: float")
-    .Input("tree: int32")
-    .Input("tree_thresholds: float")
-    .Input("node_to_accumulator: int32")
-    .Input("candidate_split_features: int32")
-    .Input("candidate_split_thresholds: float")
-    .Input("birth_epochs: int32")
-    .Input("current_epoch: int32")
-    .Output("pcw_node_sums_delta: float")
-    .Output("pcw_node_squares_delta: float")
-    .Output("pcw_splits_indices: int32")
-    .Output("pcw_candidate_splits_sums_delta: float")
-    .Output("pcw_candidate_splits_squares_delta: float")
-    .Output("pcw_totals_indices: int32")
-    .Output("pcw_totals_sums_delta: float")
-    .Output("pcw_totals_squares_delta: float")
-    .Output("leaves: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      int64 num_classes;
-      TF_RETURN_IF_ERROR(c->GetAttr("num_classes", &num_classes));
-      bool regression;
-      TF_RETURN_IF_ERROR(c->GetAttr("regression", &regression));
-
-      DimensionHandle num_points = c->Dim(c->input(0), 0);
-      if (c->RankKnown(c->input(3)) && c->Rank(c->input(3)) > 0) {
-        num_points = c->UnknownDim();
-      }
-      DimensionHandle num_nodes = c->Dim(c->input(6), 0);
-
-      // Node sums
-      c->set_output(0, c->Matrix(num_nodes, num_classes));
-      // Node squares
-      c->set_output(1, c->Matrix(num_nodes, num_classes));
-
-      c->set_output(2, c->Matrix(c->UnknownDim(), regression ? 2 : 3));
-
-      c->set_output(3,
-                    regression ? c->Matrix(c->UnknownDim(), num_classes)
-                               : c->Vector(c->UnknownDim()));
-      c->set_output(4,
-                    regression ? c->Matrix(c->UnknownDim(), num_classes)
-                               : c->Vector(0LL));
-      c->set_output(5, c->Matrix(c->UnknownDim(), regression ? 1 : 2));
-      c->set_output(6,
-                    regression ? c->Matrix(c->UnknownDim(), num_classes)
-                               : c->Vector(c->UnknownDim()));
-      c->set_output(7,
-                    regression ? c->Matrix(c->UnknownDim(), num_classes)
-                               : c->Vector(0LL));
-      c->set_output(8, c->Vector(num_points));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Calculates incremental statistics for a batch of training data.
-
-Each training example in `input_data` is sent through the decision tree
-represented by `tree` and `tree_thresholds`.
-The shape and contents of the outputs differ depending on whether
-`regression` is true or not.
-
-For `regression` = false (classification), `pcw_node_sums_delta[i]` is
-incremented for every node i that it passes through, and the leaf it ends up
-in is recorded in `leaves[i]`.  Then, if the leaf is fertile and
-initialized, the statistics for its corresponding accumulator slot
-are updated in `pcw_candidate_sums_delta` and `pcw_totals_sums_delta`.
-
-For `regression` = true, outputs contain the sum of the input_labels
-for the appropriate nodes.  In adddition, the *_squares outputs are filled
-in with the sums of the squares of the input_labels. Since outputs are
-all updated at once, the *_indices outputs don't specify the output
-dimension to update, rather the *_delta output contains updates for all the
-outputs.  For example, `pcw_totals_indices` specifies the accumulators to
-update, and `pcw_total_splits_sums_delta` contains the complete output
-updates for each of those accumulators.
-
-The attr `num_classes` is needed to appropriately size the outputs.
-
-input_spec: A serialized TensorForestDataSpec proto.
-input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
-  gives the j-th feature of the i-th input.
-sparse_input_indices: The indices tensor from the SparseTensor input.
-sparse_input_values: The values tensor from the SparseTensor input.
-sparse_input_shape: The shape tensor from the SparseTensor input.
-input_spec: A 1-D tensor containing the type of each column in input_data,
-  (e.g. continuous float, categorical).  Index 0 should contain the default
-  type, individual feature types start at index 1.
-input_labels: The training batch's labels; `input_labels[i]` is the class
-  of the i-th input.
-input_weights:= A 1-D float tensor.  If non-empty, `input_weights[i]` gives
-  the weight of the i-th input.
-tree:= A 2-d int32 tensor.  `tree[i][0]` gives the index of the left child
-  of the i-th node, `tree[i][0] + 1` gives the index of the right child of
-  the i-th node, and `tree[i][1]` gives the index of the feature used to
-  split the i-th node.
-tree_thresholds: `tree_thresholds[i]` is the value used to split the i-th
-  node.
-node_to_accumulator: If the i-th node is fertile, `node_to_accumulator[i]`
-  is it's accumulator slot.  Otherwise, `node_to_accumulator[i]` is -1.
-candidate_split_features: `candidate_split_features[a][s]` is the
-  index of the feature being considered by split s of accumulator slot a.
-candidate_split_thresholds: `candidate_split_thresholds[a][s]` is the
-  threshold value being considered by split s of accumulator slot a.
-birth_epochs: `birth_epoch[i]` is the epoch node i was born in.  Only
-  nodes satisfying `current_epoch - birth_epoch <= 1` accumulate statistics.
-current_epoch:= A 1-d int32 tensor with shape (1).  current_epoch[0] contains
-  the current epoch.
-pcw_node_sums_delta: `pcw_node_sums_delta[i][c]` is the number of training
-  examples in this training batch with class c that passed through node i for
-  classification.  For regression, it is the sum of the input_labels that
-  have passed through node i.
-pcw_node_squares_delta: `pcw_node_squares_delta[i][c]` is the sum of the
-  squares of the input labels that have passed through node i for
-  regression.  Not set for classification.
-pcw_splits_indices:= A 2-d tensor of shape (?, 3) for classification and
-  (?, 2) for regression.
-  `pcw_splits_indices[i]` gives the coordinates of an entry in
-  candidate_split_pcw_sums and candidate_split_pcw_squares that need to be
-  updated.  This is meant to be passed with `pcw_candidate_splits_*_delta` to
-  a scatter_add for candidate_split_pcw_*:
-    training_ops.scatter_add_ndim(candidate_split_pcw_sums
-        pcw_splits_indices, pcw_candidate_splits_sums_delta)
-pcw_candidate_splits_sums_delta: For classification,
-  `pcw_candidate_splits_sums_delta[i]` is the
-  number of training examples in this training batch that correspond to
-  the i-th entry in `pcw_splits_indices` which took the *left* branch of
-  candidate split. For regression, it is the same but a 2-D tensor that has
-  the sum of the input_labels for each i-th entry in the indices.
-pcw_candidate_splits_squares_delta: For regression, same as
-  `pcw_candidate_splits_sums_delta` but the sum of the squares. Not set
-  for classification.
-pcw_totals_indices: For classification, 'pcw_totals_indices` contains the
-  indices (accumulator, class) into total_pcw_sums to update with
-  pcw_totals_sums_delta.  For regression, it only contains the accumulator
-  (not the class), because pcw_totals_*_delta will contain all the outputs.
-pcw_totals_sums_delta: For classification, `pcw_totals_sums_delta[i]` is the
-  number of training examples in this batch that ended up in the fertile
-  node with accumulator and class indicated by `pcw_totals_indices[i]`.
-  For regression, it is the sum of the input_labels corresponding to the
-  entries in `pcw_totals_indices[i]`.
-pcw_totals_squares_delta: For regression, same as
-  `pcw_totals_sums_delta` but the sum of the squares. Not set
-  for classification.
-leaves: `leaves[i]` is the leaf that input i ended up in.
-)doc");
-
-REGISTER_OP("FinishedNodes")
-    .Attr("regression: bool = false")
-    .Attr("num_split_after_samples: int")
-    .Attr("min_split_samples: int")
-    .Attr("dominate_fraction: float = 0.99")
-    .Attr(
-        "dominate_method:"
-        " {'none', 'hoeffding', 'bootstrap', 'chebyshev'} = 'bootstrap'")
-    .Attr("random_seed: int = 0")
-    .Attr("check_dominates_every_samples: int = 75")
-    .Input("leaves: int32")
-    .Input("node_to_accumulator: int32")
-    .Input("split_sums: float")
-    .Input("split_squares: float")
-    .Input("accumulator_sums: float")
-    .Input("accumulator_squares: float")
-    .Input("birth_epochs: int32")
-    .Input("current_epoch: int32")
-    .Output("finished: int32")
-    .Output("stale: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(1, c->Vector(InferenceContext::kUnknownDim));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Determines which of the given leaf nodes are done accumulating.
-
-The `regression` attribute should be set to true for regression problems, and
-false for classification problems.
-
-If dominate_method is not set to none, then every
-`check_dominates_every_samples` steps the specified method will be used to
-see if the current best split has probability `dominate_fraction` of being
-asymptotically better than the second best split.  If so, the best split
-is picked now, rather than waiting until `num_split_after_samples` samples
-have been seen.  WARNING:  for weighted input data, only `dominate_method` =
-none is safe.
-
-leaves:= A 1-d int32 tensor.  Lists the nodes that are currently leaves.
-node_to_accumulator: If the i-th node is fertile, `node_to_accumulator[i]`
-  is it's accumulator slot.  Otherwise, `node_to_accumulator[i]` is -1.
-split_sums:= a 3-d tensor where `split_sums[a][s]` summarizes the
-  training labels for examples that fall into the fertile node associated with
-  accumulator slot s and have then taken the *left* branch of candidate split
-  s.  For a classification problem, `split_sums[a][s][c]` is the count of such
-  examples with class c and for regression problems, `split_sums[a][s]` is the
-  sum of the regression labels for such examples.
-split_squares: Same as split_sums, but it contains the sum of the
-  squares of the regression labels.  Only used for regression.  For
-  classification problems, pass a dummy tensor into this.
-accumulator_sums: For classification, `accumulator_sums[a][c]` records how
-  many training examples have class c and have ended up in the fertile node
-  associated with accumulator slot a.  It has the total sum in entry 0 for
-  convenience. For regression, it is the same except it contains the sum
-  of the input labels that have been seen, and entry 0 contains the number
-  of training examples that have been seen.
-accumulator_squares: Same as accumulator_sums, but it contains the sum of the
-  squares of the regression labels.  Only used for regression.  For
-  classification problems, pass a dummy tensor into this.
-birth_epochs:= A 1-d int32 tensor.  `birth_epochs[i]` contains the epoch
-  the i-th node was created in.
-current_epoch:= A 1-d int32 tensor with shape (1).  `current_epoch[0]`
-  stores the current epoch number.
-finished:= A 1-d int32 tensor containing the indices of the finished nodes.
-  Nodes are finished if they have received at least num_split_after_samples
-  samples, or if they have received min_split_samples and the best scoring
-  split is sufficiently greater than the next best split.
-stale:= A 1-d int32 tensor containing the fertile nodes that were created two
-  or more epochs ago.
-
-)doc");
-
-REGISTER_OP("GrowTree")
-    .Input("end_of_tree: int32")
-    .Input("node_to_accumulator: int32")
-    .Input("finished_nodes: int32")
-    .Input("best_splits: int32")
-    .Input("candidate_split_features: int32")
-    .Input("candidate_split_thresholds: float")
-    .Output("nodes_to_update: int32")
-    .Output("tree_updates: int32")
-    .Output("threshold_updates: float")
-    .Output("new_end_of_tree: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(1, c->Matrix(InferenceContext::kUnknownDim, 2));
-      c->set_output(2, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(3, c->Vector(1));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-  Output the tree changes needed to resolve fertile nodes.
-
-  Previous Ops have already decided which fertile nodes want to stop being
-  fertile and what their best candidate split should be and have passed that
-  information to this Op in `finished_nodes` and `best_splits`.  This Op
-  merely checks that there is still space in tree to add new nodes, and if
-  so, writes out the sparse updates needed for the fertile nodes to be
-  resolved to the tree and threshold tensors.
-
-  end_of_tree: `end_of_tree[0]` is the number of allocated nodes, or
-    equivalently the index of the first free node in the tree tensor.
-  node_to_accumulator: `node_to_accumulator[i]` is the accumulator slot used by
-    fertile node i, or -1 if node i isn't fertile.
-  finished_nodes:= A 1-d int32 tensor containing the indices of finished nodes.
-  best_splits: `best_splits[i]` is the index of the best split for
-    `finished_nodes[i]`.
-  candidate_split_features: `candidate_split_features[a][s]` is the feature
-    being considered for split s of the fertile node associated with
-    accumulator slot a.
-  candidate_split_thresholds: `candidate_split_thresholds[a][s]` is the
-    threshold value being considered for split s of the fertile node associated
-    with accumulator slot a.
-  nodes_to_update:= A 1-d int32 tensor containing the node indices that need
-    updating.
-  tree_updates: The updates to apply to the 2-d tree tensor.  Intended to be
-    used with `tf.scatter_update(tree, nodes_to_update, tree_updates)`.
-  threshold_updates: The updates to apply to the 1-d thresholds tensor.
-    Intended to be used with
-    `tf.scatter_update(thresholds, nodes_to_update, threshold_updates)`.
-  new_end_of_tree: `new_end_of_tree[0]` is the new size of the tree.
-)doc");
-
-REGISTER_OP("SampleInputs")
-    .Attr("input_spec: string")
-    .Attr("split_initializations_per_input: int")
-    .Attr("split_sampling_random_seed: int")
-    .Input("input_data: float")
-    .Input("sparse_input_indices: int64")
-    .Input("sparse_input_values: float")
-    .Input("sparse_input_shape: int64")
-    .Input("input_weights: float")
-    .Input("node_to_accumulator: int32")
-    .Input("leaves: int32")
-    .Input("candidate_split_features: int32")
-    .Input("candidate_split_thresholds: float")
-    .Output("accumulators_to_update: int32")
-    .Output("new_split_feature_rows: int32")
-    .Output("new_split_threshold_rows: float")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle candidate_split_features;
-      TF_RETURN_IF_ERROR(
-          c->WithRank(c->input(7), 2, &candidate_split_features));
-      DimensionHandle split_dim = c->Dim(candidate_split_features, 1);
-      c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(1, c->Matrix(InferenceContext::kUnknownDim, split_dim));
-      c->set_output(2, c->Matrix(InferenceContext::kUnknownDim, split_dim));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Initializes candidate splits for newly fertile nodes.
-
-In an extremely random forest, we don't consider all possible threshold
-values for a candidate split feature, but rather only a sampling of them.
-This Op takes those samples from the training data in `input_data`.  The
-feature and threshold samples are stored in tensors that are indexed by
-accumulator slot, so for each input, we must first look up which leaf
-it ended up in (using `leaves`) and then which accumulator slot if any
-that leaf maps to (using `node_to_accumulator`).
-
-The attribute `split_initializations_per_input` controls how many splits
-a single training example can initialize, and the attribute
-`split_sampling_random_seed` sets the random number generator's seed
-(a value of 0 means use the current time as the seed).
-
-input_data: The features for the current batch of training data.
-  `input_data[i][j]` is the j-th feature of the i-th input.
-sparse_input_indices: The indices tensor from the SparseTensor input.
-sparse_input_values: The values tensor from the SparseTensor input.
-sparse_input_shape: The shape tensor from the SparseTensor input.
-input_weights: For a dense input, input_weights[i] is the weight associated
-  with input_data[i].  For sparse input, input_weights[i] is the weight
-  associated with sparse_input_values[i].  Or in either case, if all the
-  weights are 1, input_weights can be empty.  SampleInputs will reject inputs
-  with weight less than Uniform([0,1)), so weights outside of that range may
-  not be what you want.
-node_to_accumulator: For a fertile node i, node_to_accumulator[i] is the
-  associated accumulator slot.  For non-fertile nodes, it is -1.
-leaves: `leaves[i]` is the leaf that the i-th input landed in, as
-  calculated by CountExtremelyRandomStats.
-candidate_split_features: The current features for the candidate splits;
-  `candidate_split_features[a][s]` is the index of the feature being
-  considered by split s in accumulator slot a.
-candidate_split_thresholds: The current thresholds for the candidate splits;
-  `candidate_split_thresholds[a][s]` is the threshold value being
-  considered by split s in accumulator slot a.
-accumulators_to_update: A list of the accumulators to change in the
-  candidate_split_features and candidate_split_thresholds tensors.
-new_split_feature_rows: The new values for the candidate_split_features
-  tensor.  Intended to be used with
-  `tf.scatter_update(candidate_split_features,
-                     accumulators_to_update,
-                     new_split_feature_rows)`
-new_split_threshold_rows:  The new values for the candidate_split_thresholds
-  tensor.  Intended to be used with
-  `tf.scatter_update(candidate_split_thresholds,
-                     accumulators_to_update,
-                     new_split_feature_thresholds)`
-
-)doc");
 
 REGISTER_OP("ScatterAddNdim")
     .Input("input: Ref(float)")
@@ -460,171 +58,4 @@ REGISTER_OP("ReinterpretStringToFloat")
 
 )doc");
 
-REGISTER_OP("TopNInsert")
-    .Input("ids: int64")
-    .Input("scores: float32")
-    .Input("new_ids: int64")
-    .Input("new_scores: float32")
-    .Output("shortlist_ids: int64")
-    .Output("update_ids: int64")
-    .Output("update_scores: float32")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(1, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(2, c->Vector(InferenceContext::kUnknownDim));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-  Outputs update Tensors for adding new_ids and new_scores to the shortlist.
-
-  ids:= A 1-D int64 tensor containing the ids on the shortlist (except for
-    ids[0], which is the current size of the shortlist.
-  scores:= A 1-D float32 tensor containing the scores on the shortlist.
-  new_ids:= A 1-D int64 tensor containing the new ids to add to the shortlist.
-  shortlist_ids:= A 1-D int64 tensor containing the ids of the shortlist entries
-    to update.  Intended to be used with
-    tf.scatter_update(shortlist_scores, shortlist_ids, new_scores).
-  update_ids:= A 1-D int64 tensor containing ...
-  update_scores:= A 1-D float32 tensor containing ...
-)doc");
-
-REGISTER_OP("TopNRemove")
-    .Input("ids: int64")
-    .Input("remove_ids: int64")
-    .Output("shortlist_ids: int64")
-    .Output("new_length: int64")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(1, c->Vector(InferenceContext::kUnknownDim));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-  Remove ids from a shortlist.
-
-  ids:= A 1-D int64 tensor containing the ids on the shortlist (except for
-    ids[0], which is the current size of the shortlist.
-  remove_ids:= A 1-D int64 tensor containing the ids to remove.
-  shortlist_ids:= A 1-D int64 tensor containing the shortlist entries that
-    need to be removed.
-  new_length:= A length 1 1-D int64 tensor containing the new length of the
-    shortlist.
-)doc");
-
-REGISTER_OP("TreePredictions")
-    .Attr("input_spec: string")
-    .Attr("valid_leaf_threshold: float")
-    .Input("input_data: float")
-    .Input("sparse_input_indices: int64")
-    .Input("sparse_input_values: float")
-    .Input("sparse_input_shape: int64")
-    .Input("tree: int32")
-    .Input("tree_thresholds: float")
-    .Input("node_per_class_weights: float")
-
-    .Output("predictions: float")
-    .SetShapeFn([](InferenceContext* c) {
-      // The output of TreePredictions is
-      // [node_pcw(evaluate_tree(x), c) for c in classes for x in input_data].
-      DimensionHandle num_classes = c->Dim(c->input(6), 1);
-      DimensionHandle num_points = c->UnknownDim();
-
-      if (c->RankKnown(c->input(0)) && c->Rank(c->input(0)) > 0 &&
-          c->Value(c->Dim(c->input(0), 0)) > 0) {
-        num_points = c->Dim(c->input(0), 0);
-      }
-
-      TF_RETURN_IF_ERROR(c->Subtract(num_classes, 1, &num_classes));
-
-      c->set_output(0, c->Matrix(num_points, num_classes));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-  Returns the per-class probabilities for each input.
-
-  input_spec: A serialized TensorForestDataSpec proto.
-  input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
-   gives the j-th feature of the i-th input.
-  sparse_input_indices: The indices tensor from the SparseTensor input.
-  sparse_input_values: The values tensor from the SparseTensor input.
-  sparse_input_shape: The shape tensor from the SparseTensor input.
-  tree:= A 2-d int32 tensor.  `tree[i][0]` gives the index of the left child
-   of the i-th node, `tree[i][0] + 1` gives the index of the right child of
-   the i-th node, and `tree[i][1]` gives the index of the feature used to
-   split the i-th node.
-  tree_thresholds: `tree_thresholds[i]` is the value used to split the i-th
-   node.
-  node_per_class_weights: `node_per_class_weights[n][c]` records how many
-   training examples have class c and have ended up in node n.
-  predictions: `predictions[i][j]` is the probability that input i is class j.
-  valid_leaf_threshold: Minimum number of samples that have arrived to a leaf
-    to be considered a valid leaf, otherwise use the parent.
-)doc");
-
-REGISTER_OP("UpdateFertileSlots")
-    .Attr("regression: bool = False")
-    .Input("finished: int32")
-    .Input("non_fertile_leaves: int32")
-    .Input("non_fertile_leaf_scores: float")
-    .Input("end_of_tree: int32")
-    .Input("accumulator_sums: float")
-    .Input("node_to_accumulator: int32")
-    .Input("stale_leaves: int32")
-    .Input("node_sums: float")
-    .Output("node_to_accumulator_map_updates: int32")
-    .Output("accumulator_to_node_map_updates: int32")
-    .Output("accumulators_cleared: int32")
-    .Output("accumulators_allocated: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->Matrix(c->MakeDim(2), InferenceContext::kUnknownDim));
-      c->set_output(1, c->Matrix(c->MakeDim(2), InferenceContext::kUnknownDim));
-      c->set_output(2, c->Vector(InferenceContext::kUnknownDim));
-      c->set_output(3, c->Vector(InferenceContext::kUnknownDim));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Updates accumulator slots to reflect finished or newly fertile nodes.
-
-finished:= A 1-d int32 tensor containing the indices of fertile nodes that
-  are ready to decide on a split.
-non_fertile_leaves:= A 1-d int32 tensor containing the indices of all the
-  currently non-fertile leaves.  If there are free accumulator slots after
-  deallocation, UpdateFertileSlots will consider these nodes (plus the ones
-  in new_leaves) and potentially turn some of them fertile.
-non_fertile_leaf_scores: `non_fertile_leaf_scores[i]` is the splitting score
-  of the non-fertile leaf `non_fertile_leaves[i]`.
-end_of_tree: The end of tree tensor from the previous training iteration, used
-  with the finished input to calculate a list of new leaf indices created by
-  GrowTree, which will be considered to become fertile if there are free
-  slots.
-accumulator_sums: For classification, `accumulator_sums[a][c]` records how
-  many training examples have class c and have ended up in the fertile node
-  associated with accumulator slot a.  It has the total sum in entry 0 for
-  convenience. For regression, it is the same except it contains the sum
-  of the input labels that have been seen, and entry 0 contains the number
-  of training examples that have been seen.
-node_to_accumulator: `node_to_accumulator[i]` is the accumulator slot used by
-  fertile node i, or -1 if node i isn't fertile.
-stale_leaves:= A 1-d int32 tensor containing the indices of all leaves that
-  have stopped accumulating statistics because they are too old.
-node_sums: `node_sums[n][c]` records how many
-   training examples have class c and have ended up in node n.
-node_to_accumulator_map_updates:= A 2-d int32 tensor describing the changes
-  that need to be applied to the node_to_accumulator map.  Intended to be used
-  with
-  `tf.scatter_update(node_to_accumulator,
-                     node_to_accumulator_map_updates[0],
-                     node_to_accumulator_map_updates[1])`.
-accumulator_to_node_map_updates:= A 2-d int32 tensor describing the changes
-  that need to be applied to the node_to_accumulator map.  Intended to be used
-  with
-  `tf.scatter_update(accumulator_to_node_map,
-                     accumulator_to_node_map_updates[0],
-                     accumulator_to_node_map_updates[1])`.
-accumulators_cleared:= A 1-d int32 tensor containing the indices of all
-  the accumulator slots that need to be cleared.
-accumulators_allocated:= A 1-d int32 tensor containing the indices of all
-  the accumulator slots that need to be allocated.
-
-)doc");
-
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensor_forest/python/__init__.py b/tensorflow/contrib/tensor_forest/python/__init__.py
index df0ecbac76..0688f7c816 100644
--- a/tensorflow/contrib/tensor_forest/python/__init__.py
+++ b/tensorflow/contrib/tensor_forest/python/__init__.py
@@ -18,9 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.tensor_forest.python import constants
 from tensorflow.contrib.tensor_forest.python import tensor_forest
-from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
 from tensorflow.contrib.tensor_forest.python.ops import model_ops
 from tensorflow.contrib.tensor_forest.python.ops import stats_ops
diff --git a/tensorflow/contrib/tensor_forest/python/constants.py b/tensorflow/contrib/tensor_forest/python/constants.py
deleted file mode 100644
index d222cbb6ad..0000000000
--- a/tensorflow/contrib/tensor_forest/python/constants.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Constants used by tensorforest.  Some of these map to values in C++ ops."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# If tree[i][0] equals this value, then i is a leaf node.
-LEAF_NODE = -1
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/best_splits_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/best_splits_op_test.py
deleted file mode 100644
index 628b7f3dad..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/best_splits_op_test.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.best_splits_op."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow  # pylint: disable=unused-import
-
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
-
-from tensorflow.python.framework import test_util
-from tensorflow.python.platform import googletest
-
-
-class BestSplitsClassificationTests(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.finished = [3, 5]
-    self.node_map = [-1, -1, -1, 0, -1, 3, -1, -1, -1]
-    self.candidate_counts = [[[153., 50., 60., 40., 3.],
-                              [200., 70., 30., 70., 30.]],
-                             [[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]],
-                             [[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]],
-                             [[40., 10., 10., 10., 10.],
-                              [30., 10., 5., 5., 10.]]]
-    self.total_counts = [[400., 100., 100., 100., 100.],
-                         [0., 0., 0., 0., 0.],
-                         [0., 0., 0., 0., 0.],
-                         [400., 100., 100., 100., 100.]]
-    self.squares = []
-
-  def testSimple(self):
-    with self.test_session():
-      split_indices = tensor_forest_ops.best_splits(
-          self.finished,
-          self.node_map,
-          self.candidate_counts,
-          self.squares,
-          self.total_counts,
-          self.squares,
-          regression=False)
-
-      self.assertAllEqual([0, 1], split_indices.eval())
-
-  def testNoFinished(self):
-    with self.test_session():
-      split_indices = tensor_forest_ops.best_splits(
-          [],
-          self.node_map,
-          self.candidate_counts,
-          self.squares,
-          self.total_counts,
-          self.squares,
-          regression=False)
-
-      self.assertAllEqual([], split_indices.eval())
-
-  def testBadInput(self):
-    del self.total_counts[1]
-
-    with self.test_session():
-      with self.assertRaisesOpError(
-          'Number of accumulators should be the same in split_sums '
-          'and accumulator_sums.'):
-        tensor_forest_ops.best_splits(
-            self.finished,
-            self.node_map,
-            self.candidate_counts,
-            self.squares,
-            self.total_counts,
-            self.squares,
-            regression=False).eval()
-
-
-class BestSplitsRegressionTests(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.finished = [3, 5]
-    self.node_map = [-1, -1, -1, 0, -1, 3, -1, -1, -1]
-    self.candidate_sums = [[[5., 8., 8., 8.], [5., 10., 10., 10.]],
-                           [[0., 0., 0., 0.], [0., 0., 0., 0.]],
-                           [[0., 0., 0., 0.], [0., 0., 0., 0.]],
-                           [[10., 10., 20., 10.], [10., 5., 5., 5.]]]
-
-    self.candidate_squares = [[[5., 50., 50., 50.], [5., 50., 50., 50.]],
-                              [[0., 0., 0., 0.], [0., 0., 0., 0.]],
-                              [[0., 0., 0., 0.], [0., 0., 0., 0.]],
-                              [[10., 40., 50., 60.], [10., 40., 40., 40.]]]
-
-    self.total_sums = [[15., 10., 10., 10.],
-                       [0., 0., 0., 0.],
-                       [0., 0., 0., 0.],
-                       [20., 20., 20., 20.]]
-
-    self.total_squares = [[15., 50., 50., 50.],
-                          [0., 0., 0., 0.],
-                          [0., 0., 0., 0.],
-                          [20., 60., 60., 60.]]
-
-  def testSimple(self):
-    with self.test_session():
-      split_indices = tensor_forest_ops.best_splits(
-          self.finished,
-          self.node_map,
-          self.candidate_sums,
-          self.candidate_squares,
-          self.total_sums,
-          self.total_squares,
-          regression=True)
-
-      self.assertAllEqual([1, 0], split_indices.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/count_extremely_random_stats_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/count_extremely_random_stats_op_test.py
deleted file mode 100644
index 351245fbdd..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/count_extremely_random_stats_op_test.py
+++ /dev/null
@@ -1,380 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.count_extremely_random_stats."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.tensor_forest.python.ops import data_ops
-
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.python.framework import test_util
-from tensorflow.python.platform import googletest
-
-
-class CountExtremelyRandomStatsClassificationTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.input_data = [[-1., 0.], [-1., 2.],  # node 1
-                       [1., 0.], [1., -2.]]  # node 2
-    self.input_labels = [0, 1, 2, 3]
-    self.tree = [[1, 0], [-1, 0], [-1, 0]]
-    self.tree_thresholds = [0., 0., 0.]
-    self.node_map = [-1, 0, -1]
-    self.split_features = [[1], [-1]]
-    self.split_thresholds = [[1.], [0.]]
-    self.epochs = [0, 1, 1]
-    self.current_epoch = [1]
-
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.dense.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = 1
-
-    f2 = spec_proto.dense.add()
-    f2.name = 'f2'
-    f2.original_type = data_ops.DATA_FLOAT
-    f2.size = 1
-    spec_proto.dense_features_size = 2
-    self.data_spec = spec_proto.SerializeToString()
-
-  def testSimple(self):
-    with self.test_session():
-      (pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
-       pcw_totals_indices, pcw_totals_sums, _,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           self.input_labels, [],
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=self.data_spec,
-           num_classes=5,
-           regression=False))
-
-      self.assertAllEqual(
-          [[4., 1., 1., 1., 1.], [2., 1., 1., 0., 0.], [2., 0., 0., 1., 1.]],
-          pcw_node_sums.eval())
-      self.assertAllEqual([[0, 0, 0], [0, 0, 1]], pcw_splits_indices.eval())
-      self.assertAllEqual([1., 1.], pcw_splits_sums.eval())
-      self.assertAllEqual([[0, 2], [0, 0], [0, 1]], pcw_totals_indices.eval())
-      self.assertAllEqual([1., 2., 1.], pcw_totals_sums.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testSimpleWeighted(self):
-    with self.test_session():
-      input_weights = [1.5, 2.0, 3.0, 4.0]
-      (pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
-       pcw_totals_indices, pcw_totals_sums, _,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           self.input_labels,
-           input_weights,
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=self.data_spec,
-           num_classes=5,
-           regression=False))
-
-      self.assertAllEqual([[10.5, 1.5, 2., 3., 4.], [3.5, 1.5, 2., 0., 0.],
-                           [7., 0., 0., 3., 4.]], pcw_node_sums.eval())
-      self.assertAllEqual([[0, 0, 0], [0, 0, 1]], pcw_splits_indices.eval())
-      self.assertAllEqual([1.5, 1.5], pcw_splits_sums.eval())
-      self.assertAllEqual([[0, 2], [0, 0], [0, 1]], pcw_totals_indices.eval())
-      self.assertAllEqual([2., 3.5, 1.5], pcw_totals_sums.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testMissingLabel(self):
-    labels = [0, 1, -1, 3]
-    with self.test_session():
-      (pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
-       pcw_totals_indices, pcw_totals_sums, _,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           labels, [],
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=self.data_spec,
-           num_classes=5,
-           regression=False))
-
-      self.assertAllEqual(
-          [[3., 1., 1., 0., 1.], [2., 1., 1., 0., 0.], [1., 0., 0., 0., 1.]],
-          pcw_node_sums.eval())
-      self.assertAllEqual([[0, 0, 0], [0, 0, 1]], pcw_splits_indices.eval())
-      self.assertAllEqual([1., 1.], pcw_splits_sums.eval())
-      self.assertAllEqual([[0, 2], [0, 0], [0, 1]], pcw_totals_indices.eval())
-      self.assertAllEqual([1., 2., 1.], pcw_totals_sums.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testSparseInput(self):
-    sparse_shape = [4, 10]
-    sparse_indices = [[0, 0], [0, 4], [0, 9], [1, 1], [1, 7], [2, 0], [3, 0],
-                      [3, 4]]
-    sparse_values = [3.0, -1.0, 0.5, -1.5, 6.0, -2.0, -0.5, 2.0]
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.sparse.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = -1
-
-    spec_proto.dense_features_size = 0
-    data_spec = spec_proto.SerializeToString()
-
-    with self.test_session():
-      (pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
-       pcw_totals_indices, pcw_totals_sums, _,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           [],
-           sparse_indices,
-           sparse_values,
-           sparse_shape,
-           self.input_labels, [],
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=data_spec,
-           num_classes=5,
-           regression=False))
-
-      self.assertAllEqual([[4., 1., 1., 1., 1.],
-                           [2., 0., 0., 1., 1.],
-                           [2., 1., 1., 0., 0.]],
-                          pcw_node_sums.eval())
-      self.assertAllEqual([[0, 0, 4],
-                           [0, 0, 0],
-                           [0, 0, 3]],
-                          pcw_splits_indices.eval())
-      self.assertAllEqual([1., 2., 1.], pcw_splits_sums.eval())
-      self.assertAllEqual([[0, 4], [0, 0], [0, 3]], pcw_totals_indices.eval())
-      self.assertAllEqual([1., 2., 1.], pcw_totals_sums.eval())
-      self.assertAllEqual([2, 2, 1, 1], leaves.eval())
-
-  def testFutureEpoch(self):
-    current_epoch = [3]
-    with self.test_session():
-      (pcw_node_sums, _, _, pcw_splits_sums, _, _, pcw_totals_sums, _,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           self.input_labels, [],
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           current_epoch,
-           input_spec=self.data_spec,
-           num_classes=5,
-           regression=False))
-
-      self.assertAllEqual(
-          [[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]],
-          pcw_node_sums.eval())
-      self.assertAllEqual([], pcw_splits_sums.eval())
-      self.assertAllEqual([], pcw_totals_sums.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testThreaded(self):
-    with self.test_session(
-        config=config_pb2.ConfigProto(intra_op_parallelism_threads=2)):
-      (pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
-       pcw_totals_indices, pcw_totals_sums, _,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           self.input_labels, [],
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=self.data_spec,
-           num_classes=5,
-           regression=False))
-
-      self.assertAllEqual([[4., 1., 1., 1., 1.], [2., 1., 1., 0., 0.],
-                           [2., 0., 0., 1., 1.]], pcw_node_sums.eval())
-      self.assertAllEqual([[0, 0, 0], [0, 0, 1]], pcw_splits_indices.eval())
-      self.assertAllEqual([1., 1.], pcw_splits_sums.eval())
-      self.assertAllEqual([[0, 2], [0, 0], [0, 1]], pcw_totals_indices.eval())
-      self.assertAllEqual([1., 2., 1.], pcw_totals_sums.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testNoAccumulators(self):
-    with self.test_session():
-      (pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
-       pcw_totals_indices, pcw_totals_sums, _,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           self.input_labels, [],
-           self.tree,
-           self.tree_thresholds, [-1] * 3,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=self.data_spec,
-           num_classes=5,
-           regression=False))
-
-      self.assertAllEqual([[4., 1., 1., 1., 1.], [2., 1., 1., 0., 0.],
-                           [2., 0., 0., 1., 1.]], pcw_node_sums.eval())
-      self.assertEquals((0, 3), pcw_splits_indices.eval().shape)
-      self.assertAllEqual([], pcw_splits_sums.eval())
-      self.assertEquals((0, 2), pcw_totals_indices.eval().shape)
-      self.assertAllEqual([], pcw_totals_sums.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testBadInput(self):
-    del self.node_map[-1]
-
-    with self.test_session():
-      with self.assertRaisesOpError(
-          'Number of nodes should be the same in '
-          'tree, tree_thresholds, node_to_accumulator, and birth_epoch.'):
-        pcw_node, _, _, _, _, _, _, _, _ = (
-            tensor_forest_ops.count_extremely_random_stats(
-                self.input_data, [], [], [],
-                self.input_labels, [],
-                self.tree,
-                self.tree_thresholds,
-                self.node_map,
-                self.split_features,
-                self.split_thresholds,
-                self.epochs,
-                self.current_epoch,
-                input_spec=self.data_spec,
-                num_classes=5,
-                regression=False))
-
-        self.assertAllEqual([], pcw_node.eval())
-
-
-class CountExtremelyRandomStatsRegressionTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.input_data = [[-1., 0.], [-1., 2.],  # node 1
-                       [1., 0.], [1., -2.]]  # node 2
-    self.input_labels = [[3.], [6.], [2.], [3.]]
-    self.tree = [[1, 0], [-1, 0], [-1, 0]]
-    self.tree_thresholds = [0., 0., 0.]
-    self.node_map = [-1, 0, -1]
-    self.split_features = [[1], [-1]]
-    self.split_thresholds = [[1.], [0.]]
-    self.epochs = [0, 1, 1]
-    self.current_epoch = [1]
-
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.dense.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = 1
-
-    f2 = spec_proto.dense.add()
-    f2.name = 'f2'
-    f2.original_type = data_ops.DATA_FLOAT
-    f2.size = 1
-    spec_proto.dense_features_size = 2
-    self.data_spec = spec_proto.SerializeToString()
-
-  def testSimple(self):
-    with self.test_session():
-      (pcw_node_sums, pcw_node_squares, pcw_splits_indices, pcw_splits_sums,
-       pcw_splits_squares, pcw_totals_indices, pcw_totals_sums,
-       pcw_totals_squares,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           self.input_labels, [],
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=self.data_spec,
-           num_classes=2,
-           regression=True))
-
-      self.assertAllEqual([[4., 14.], [2., 9.], [2., 5.]], pcw_node_sums.eval())
-      self.assertAllEqual([[4., 58.], [2., 45.], [2., 13.]],
-                          pcw_node_squares.eval())
-      self.assertAllEqual([[0, 0]], pcw_splits_indices.eval())
-      self.assertAllEqual([[1., 3.]], pcw_splits_sums.eval())
-      self.assertAllEqual([[1., 9.]], pcw_splits_squares.eval())
-      self.assertAllEqual([[0]], pcw_totals_indices.eval())
-      self.assertAllEqual([[2., 9.]], pcw_totals_sums.eval())
-      self.assertAllEqual([[2., 45.]], pcw_totals_squares.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testSimpleWeighted(self):
-    with self.test_session():
-      input_weights = [1.0, 2.0, 3.0, 4.0]
-      (pcw_node_sums, pcw_node_squares, pcw_splits_indices, pcw_splits_sums,
-       pcw_splits_squares, pcw_totals_indices, pcw_totals_sums,
-       pcw_totals_squares,
-       leaves) = (tensor_forest_ops.count_extremely_random_stats(
-           self.input_data, [], [], [],
-           self.input_labels,
-           input_weights,
-           self.tree,
-           self.tree_thresholds,
-           self.node_map,
-           self.split_features,
-           self.split_thresholds,
-           self.epochs,
-           self.current_epoch,
-           input_spec=self.data_spec,
-           num_classes=2,
-           regression=True))
-
-      self.assertAllEqual([[10., 33.], [3., 15.], [7., 18.]],
-                          pcw_node_sums.eval())
-      self.assertAllEqual([[10., 129.], [3., 81.], [7., 48.]],
-                          pcw_node_squares.eval())
-      self.assertAllEqual([[0, 0]], pcw_splits_indices.eval())
-      self.assertAllEqual([[1., 3.]], pcw_splits_sums.eval())
-      self.assertAllEqual([[1., 9.]], pcw_splits_squares.eval())
-      self.assertAllEqual([[0]], pcw_totals_indices.eval())
-      self.assertAllEqual([[2., 9.]], pcw_totals_sums.eval())
-      self.assertAllEqual([[2., 45.]], pcw_totals_squares.eval())
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/finished_nodes_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/finished_nodes_op_test.py
deleted file mode 100644
index 9cd05a507b..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/finished_nodes_op_test.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.finished_nodes_op."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow  # pylint: disable=unused-import
-
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
-
-from tensorflow.python.framework import test_util
-from tensorflow.python.platform import googletest
-
-
-class FinishedNodesTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.leaves = [1, 3, 4]
-    self.node_map = [-1, -1, -1, 0, 1, -1]
-    self.split_sums = [
-        # Accumulator 0
-        [[3, 0, 3], [2, 1, 1], [3, 1, 2]],
-        # Accumulator 1
-        [[6, 3, 3], [6, 2, 4], [5, 0, 5]],
-        # Accumulator 2
-        [[0, 0, 0], [0, 0, 0], [0, 0, 0]],
-        # Accumulator 3
-        [[0, 0, 0], [0, 0, 0], [0, 0, 0]],
-        # Accumulator 4
-        [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
-    ]
-    self.split_squares = []
-    self.accumulator_sums = [[6, 3, 3], [11, 4, 7], [0, 0, 0], [0, 0, 0],
-                             [0, 0, 0]]
-    self.accumulator_squares = []
-    self.birth_epochs = [0, 0, 0, 1, 1, 1]
-    self.current_epoch = [1]
-
-  def testSimple(self):
-    with self.test_session():
-      finished, stale = tensor_forest_ops.finished_nodes(
-          self.leaves,
-          self.node_map,
-          self.split_sums,
-          self.split_squares,
-          self.accumulator_sums,
-          self.accumulator_squares,
-          self.birth_epochs,
-          self.current_epoch,
-          regression=False,
-          num_split_after_samples=10,
-          min_split_samples=10)
-
-      self.assertAllEqual([4], finished.eval())
-      self.assertAllEqual([], stale.eval())
-
-  def testLeavesCanBeNegativeOne(self):
-    with self.test_session():
-      finished, stale = tensor_forest_ops.finished_nodes(
-          [-1, -1, 1, -1, 3, -1, -1, 4, -1, -1, -1],
-          self.node_map,
-          self.split_sums,
-          self.split_squares,
-          self.accumulator_sums,
-          self.accumulator_squares,
-          self.birth_epochs,
-          self.current_epoch,
-          regression=False,
-          num_split_after_samples=10,
-          min_split_samples=10)
-
-      self.assertAllEqual([4], finished.eval())
-      self.assertAllEqual([], stale.eval())
-
-  def testNoAccumulators(self):
-    with self.test_session():
-      finished, stale = tensor_forest_ops.finished_nodes(
-          self.leaves, [-1] * 6,
-          self.split_sums,
-          self.split_squares,
-          self.accumulator_sums,
-          self.accumulator_squares,
-          self.birth_epochs,
-          self.current_epoch,
-          regression=False,
-          num_split_after_samples=10,
-          min_split_samples=10)
-
-      self.assertAllEqual([], finished.eval())
-      self.assertAllEqual([], stale.eval())
-
-  def testBadInput(self):
-    with self.test_session():
-      with self.assertRaisesOpError(
-          'leaf_tensor should be one-dimensional'):
-        finished, stale = tensor_forest_ops.finished_nodes(
-            [self.leaves],
-            self.node_map,
-            self.split_sums,
-            self.split_squares,
-            self.accumulator_sums,
-            self.accumulator_squares,
-            self.birth_epochs,
-            self.current_epoch,
-            regression=False,
-            num_split_after_samples=10,
-            min_split_samples=10)
-
-        self.assertAllEqual([], finished.eval())
-        self.assertAllEqual([], stale.eval())
-
-  def testEarlyDominatesHoeffding(self):
-    with self.test_session():
-      finished, stale = tensor_forest_ops.finished_nodes(
-          self.leaves,
-          self.node_map,
-          self.split_sums,
-          self.split_squares,
-          self.accumulator_sums,
-          self.accumulator_squares,
-          self.birth_epochs,
-          self.current_epoch,
-          dominate_method='hoeffding',
-          regression=False,
-          num_split_after_samples=10,
-          min_split_samples=5)
-
-      self.assertAllEqual([4], finished.eval())
-      self.assertAllEqual([], stale.eval())
-
-  def testEarlyDominatesBootstrap(self):
-    with self.test_session():
-      finished, stale = tensor_forest_ops.finished_nodes(
-          self.leaves,
-          self.node_map,
-          self.split_sums,
-          self.split_squares,
-          self.accumulator_sums,
-          self.accumulator_squares,
-          self.birth_epochs,
-          self.current_epoch,
-          dominate_method='bootstrap',
-          regression=False,
-          num_split_after_samples=10,
-          min_split_samples=5,
-          random_seed=1)
-
-      self.assertAllEqual([4], finished.eval())
-      self.assertAllEqual([], stale.eval())
-
-  def testEarlyDominatesChebyshev(self):
-    with self.test_session():
-      finished, stale = tensor_forest_ops.finished_nodes(
-          self.leaves,
-          self.node_map,
-          self.split_sums,
-          self.split_squares,
-          self.accumulator_sums,
-          self.accumulator_squares,
-          self.birth_epochs,
-          self.current_epoch,
-          dominate_method='chebyshev',
-          regression=False,
-          num_split_after_samples=10,
-          min_split_samples=5)
-
-      self.assertAllEqual([4], finished.eval())
-      self.assertAllEqual([], stale.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/grow_tree_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/grow_tree_op_test.py
deleted file mode 100644
index 150632c398..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/grow_tree_op_test.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.grow_tree_op."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
-from tensorflow.python.framework import test_util
-from tensorflow.python.ops import state_ops
-from tensorflow.python.ops import variables
-from tensorflow.python.platform import googletest
-
-
-class GrowTreeTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.tree = variables.Variable([[1, 0], [-1, 0], [-1, 0], [-2, 0], [-2, 0],
-                                    [-2, 0], [-2, 0]])
-    self.tree_thresholds = variables.Variable([0., 0., 0., 0., 0., 0., 0.])
-    self.eot = variables.Variable([3])
-    self.node_map = [-1, 0, 1, -1, -1, -1, -1]
-    self.finished = [1, 2]
-    self.best_splits = [2, 3]
-    self.split_features = [[1, 2, 3, 4], [5, 6, 7, 8]]
-    self.split_thresholds = [[10., 20., 30., 40.], [50., 60., 70., 80.]]
-
-  def testSimple(self):
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      update_list, tree_updates, threshold_updates, new_eot = (
-          tensor_forest_ops.grow_tree(self.eot, self.node_map, self.finished,
-                                      self.best_splits, self.split_features,
-                                      self.split_thresholds))
-
-      self.assertAllEqual([1, 3, 4, 2, 5, 6], update_list.eval())
-      self.assertAllEqual(
-          [[3, 3], [-1, -1], [-1, -1], [5, 8], [-1, -1], [-1, -1]],
-          tree_updates.eval())
-      self.assertAllEqual([30.0, 0.0, 0.0, 80.0, 0.0, 0.0],
-                          threshold_updates.eval())
-      self.assertAllEqual([7], new_eot.eval())
-
-  def testNoRoomToGrow(self):
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      # Even though there's one free node, there needs to be 2 to grow.
-      state_ops.assign(self.eot, [6]).eval()
-
-      update_list, tree_updates, threshold_updates, new_eot = (
-          tensor_forest_ops.grow_tree(self.eot, self.node_map, self.finished,
-                                      self.best_splits, self.split_features,
-                                      self.split_thresholds))
-
-      self.assertAllEqual([], update_list.eval())
-      self.assertEquals((0, 2), tree_updates.eval().shape)
-      self.assertAllEqual([], threshold_updates.eval())
-      self.assertAllEqual([6], new_eot.eval())
-
-  def testNoFinished(self):
-    with self.test_session():
-      variables.global_variables_initializer().run()
-
-      update_list, tree_updates, threshold_updates, new_eot = (
-          tensor_forest_ops.grow_tree(self.eot, self.node_map, [], [],
-                                      self.split_features,
-                                      self.split_thresholds))
-
-      self.assertAllEqual([], update_list.eval())
-      self.assertAllEqual((0, 2), tree_updates.eval().shape)
-      self.assertAllEqual([], threshold_updates.eval())
-      self.assertAllEqual([3], new_eot.eval())
-
-  def testBadInput(self):
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      with self.assertRaisesOpError(
-          'Number of finished nodes should be the same in finished and '
-          'best_splits.'):
-        update_list, _, _, _ = (tensor_forest_ops.grow_tree(
-            self.eot, self.node_map, [], self.best_splits, self.split_features,
-            self.split_thresholds))
-        self.assertAllEqual([], update_list.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py
deleted file mode 100644
index 705949a454..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.sample_inputs_op."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.tensor_forest.python.ops import data_ops
-
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
-from tensorflow.python.framework import test_util
-from tensorflow.python.ops import variables
-from tensorflow.python.platform import googletest
-
-
-class SampleInputsTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.input_data = [[-1., 10.], [-10., 2.],  # node 1
-                       [20., 50.], [1., -2.]]  # node 2
-    self.node_map = [-1, 0, 1]
-    self.leaves = [1, 1, 2, 2]
-    self.split_features = [[-1, -1, -1], [1, 0, -1], [-1, -1, -1]]
-    self.split_thresholds = [[0., 0., 0.], [5., -2., 0.], [0., 0., 0.]]
-
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.dense.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = 1
-
-    f2 = spec_proto.dense.add()
-    f2.name = 'f2'
-    f2.original_type = data_ops.DATA_FLOAT
-    f2.size = 1
-    spec_proto.dense_features_size = 2
-    self.data_spec = spec_proto.SerializeToString()
-
-  def testSimple(self):
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      (indices, feature_updates,
-       threshold_updates) = (tensor_forest_ops.sample_inputs(
-           self.input_data, [], [], [], [],
-           self.node_map,
-           self.leaves,
-           self.split_features,
-           self.split_thresholds,
-           split_initializations_per_input=1,
-           input_spec=self.data_spec,
-           split_sampling_random_seed=2))
-      self.assertAllEqual([1, 0], indices.eval())
-      self.assertAllEqual([[1, 0, 1], [1, 1, -1]], feature_updates.eval())
-      self.assertAllEqual([[5., -2., 50.], [10., 2., 0.]],
-                          threshold_updates.eval())
-
-  def testSparse(self):
-    sparse_shape = [4, 10]
-    sparse_indices = [[0, 0], [0, 4], [0, 9],
-                      [1, 0], [1, 7],
-                      [2, 0],
-                      [3, 1], [3, 4]]
-    sparse_values = [3.0, -1.0, 0.5,
-                     1.5, 6.0,
-                     -2.0,
-                     -0.5, 2.0]
-
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.sparse.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = -1
-
-    spec_proto.dense_features_size = 0
-    data_spec = spec_proto.SerializeToString()
-
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      (indices, feature_updates,
-       threshold_updates) = (tensor_forest_ops.sample_inputs(
-           [],
-           sparse_indices,
-           sparse_values,
-           sparse_shape, [],
-           self.node_map,
-           self.leaves,
-           self.split_features,
-           self.split_thresholds,
-           input_spec=data_spec,
-           split_initializations_per_input=1,
-           split_sampling_random_seed=3))
-      self.assertAllEqual([1, 0], indices.eval())
-      self.assertAllEqual([[1, 0, 0], [4, 0, -1]], feature_updates.eval())
-
-      self.assertAllEqual([[5., -2., -2.], [-1., 1.5, 0.]],
-                          threshold_updates.eval())
-
-  def testWeights(self):
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      (indices, feature_updates,
-       threshold_updates) = (tensor_forest_ops.sample_inputs(
-           self.input_data, [], [], [], [0.5, 0.1, 0.8, 0.7],
-           self.node_map,
-           self.leaves,
-           self.split_features,
-           self.split_thresholds,
-           input_spec=self.data_spec,
-           split_initializations_per_input=1,
-           split_sampling_random_seed=3))
-      self.assertAllEqual([1, 0], indices.eval())
-      self.assertAllEqual([[1, 0, 0], [-1, -1, -1]], feature_updates.eval())
-      self.assertAllEqual([[5., -2., 20.], [0., 0., 0.]],
-                          threshold_updates.eval())
-
-  def testNoAccumulators(self):
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      (indices, feature_updates,
-       threshold_updates) = (tensor_forest_ops.sample_inputs(
-           self.input_data, [], [], [], [], [-1] * 3,
-           self.leaves,
-           self.split_features,
-           self.split_thresholds,
-           input_spec=self.data_spec,
-           split_initializations_per_input=1,
-           split_sampling_random_seed=3))
-      self.assertAllEqual([], indices.eval())
-      self.assertAllEqual((0, 3), feature_updates.eval().shape)
-      self.assertAllEqual((0, 3), threshold_updates.eval().shape)
-
-  def testBadInput(self):
-    del self.split_features[1]
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      with self.assertRaisesOpError(
-          'split_features and split_thresholds should be the same shape.'):
-        indices, _, _ = tensor_forest_ops.sample_inputs(
-            self.input_data, [], [], [], [],
-            self.node_map,
-            self.leaves,
-            self.split_features,
-            self.split_thresholds,
-            input_spec=self.data_spec,
-            split_initializations_per_input=1,
-            split_sampling_random_seed=3)
-        self.assertAllEqual([], indices.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/tree_predictions_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/tree_predictions_op_test.py
deleted file mode 100644
index d7d5007030..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/tree_predictions_op_test.py
+++ /dev/null
@@ -1,299 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.tree_predictions_op."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.tensor_forest.python.ops import data_ops
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
-
-from tensorflow.python.framework import test_util
-from tensorflow.python.platform import googletest
-
-
-class TreePredictionsDenseTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.dense.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = 1
-
-    f2 = spec_proto.dense.add()
-    f2.name = 'f2'
-    f2.original_type = data_ops.DATA_FLOAT
-    f2.size = 1
-    spec_proto.dense_features_size = 2
-    self.data_spec = spec_proto.SerializeToString()
-
-  def testSimple(self):
-    input_data = [[-1., 0.], [-1., 2.],  # node 1
-                  [1., 0.], [1., -2.]]  # node 2
-
-    tree = [[1, 0], [-1, 0], [-1, 0]]
-    tree_thresholds = [0., 0., 0.]
-    node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
-                [1.0, 0.5, 0.25, 0.25]]
-
-    with self.test_session():
-      predictions = tensor_forest_ops.tree_predictions(
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          tree,
-          tree_thresholds,
-          node_pcw,
-          input_spec=self.data_spec,
-          valid_leaf_threshold=1)
-
-      self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
-                           [0.5, 0.25, 0.25], [0.5, 0.25, 0.25]],
-                          predictions.eval())
-
-  def testBackoffToParent(self):
-    input_data = [
-        [-1., 0.],
-        [-1., 2.],  # node 1
-        [1., 0.],
-        [1., -2.]
-    ]  # node 2
-
-    tree = [[1, 0], [-1, 0], [-1, 0]]
-    tree_thresholds = [0., 0., 0.]
-    node_pcw = [[15.0, 3.0, 9.0, 3.0], [5.0, 1.0, 1.0, 3.0],
-                [25.0, 5.0, 20.0, 0.0]]
-
-    with self.test_session():
-      predictions = tensor_forest_ops.tree_predictions(
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          tree,
-          tree_thresholds,
-          node_pcw,
-          valid_leaf_threshold=10,
-          input_spec=self.data_spec)
-
-      # Node 2 has enough data, but Node 1 needs to combine with the parent
-      # counts.
-      self.assertAllClose([[0.2, 0.4, 0.4], [0.2, 0.4, 0.4], [0.2, 0.8, 0.0],
-                           [0.2, 0.8, 0.0]], predictions.eval())
-
-  def testNoInput(self):
-    input_data = []
-
-    tree = [[1, 0], [-1, 0], [-1, 0]]
-    tree_thresholds = [0., 0., 0.]
-    node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
-                [1.0, 0.5, 0.25, 0.25]]
-
-    with self.test_session():
-      predictions = tensor_forest_ops.tree_predictions(
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          tree,
-          tree_thresholds,
-          node_pcw,
-          valid_leaf_threshold=10,
-          input_spec=self.data_spec)
-
-      self.assertEquals((0, 3), predictions.eval().shape)
-
-  def testBadInput(self):
-    input_data = [
-        [-1., 0.],
-        [-1., 2.],  # node 1
-        [1., 0.],
-        [1., -2.]
-    ]  # node 2
-
-    tree = [[1, 0], [-1, 0], [-1, 0]]
-    tree_thresholds = [0., 0.]  # not enough nodes.
-    node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
-                [1.0, 0.5, 0.25, 0.25]]
-
-    with self.test_session():
-      with self.assertRaisesOpError(
-          'Number of nodes should be the same in tree, tree_thresholds '
-          'and node_pcw.'):
-        predictions = tensor_forest_ops.tree_predictions(
-            input_data,
-            self.nothing,
-            self.nothing,
-            self.nothing,
-            tree,
-            tree_thresholds,
-            node_pcw,
-            valid_leaf_threshold=10,
-            input_spec=self.data_spec)
-
-        self.assertEquals((0, 3), predictions.eval().shape)
-
-
-class TreePredictionsSparseTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.sparse.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = 1
-
-    f2 = spec_proto.sparse.add()
-    f2.name = 'f2'
-    f2.original_type = data_ops.DATA_FLOAT
-    f2.size = 9
-    spec_proto.dense_features_size = 0
-    self.data_spec = spec_proto.SerializeToString()
-
-  def testSparseInput(self):
-    sparse_shape = [3, 10]
-    sparse_indices = [[0, 0], [0, 4], [0, 9],
-                      [1, 0], [1, 7],
-                      [2, 0]]
-    sparse_values = [3.0, -1.0, 0.5,
-                     1.5, 6.0,
-                     -2.0]
-
-    tree = [[1, 0], [-1, 0], [-1, 0]]
-    tree_thresholds = [0., 0., 0.]
-    node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
-                [1.0, 0.5, 0.25, 0.25]]
-
-    with self.test_session():
-      predictions = tensor_forest_ops.tree_predictions(
-          self.nothing,
-          sparse_indices,
-          sparse_values,
-          sparse_shape,
-          tree,
-          tree_thresholds,
-          node_pcw,
-          valid_leaf_threshold=1,
-          input_spec=self.data_spec)
-
-      self.assertAllClose([[0.5, 0.25, 0.25],
-                           [0.5, 0.25, 0.25],
-                           [0.1, 0.1, 0.8]],
-                          predictions.eval())
-
-  def testSparseInputDefaultIsZero(self):
-    sparse_shape = [3, 10]
-    sparse_indices = [[0, 0], [0, 4], [0, 9],
-                      [1, 0], [1, 7],
-                      [2, 0]]
-    sparse_values = [3.0, -1.0, 0.5,
-                     1.5, 6.0,
-                     -2.0]
-
-    tree = [[1, 7], [-1, 0], [-1, 0]]
-    tree_thresholds = [3.0, 0., 0.]
-    node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
-                [1.0, 0.5, 0.25, 0.25]]
-
-    with self.test_session():
-      predictions = tensor_forest_ops.tree_predictions(
-          self.nothing,
-          sparse_indices,
-          sparse_values,
-          sparse_shape,
-          tree,
-          tree_thresholds,
-          node_pcw,
-          valid_leaf_threshold=1,
-          input_spec=self.data_spec)
-
-      self.assertAllClose([[0.1, 0.1, 0.8],
-                           [0.5, 0.25, 0.25],
-                           [0.1, 0.1, 0.8]],
-                          predictions.eval())
-
-
-class TreePredictionsMixedTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-    spec_proto = data_ops.TensorForestDataSpec()
-    f1 = spec_proto.dense.add()
-    f1.name = 'f1'
-    f1.original_type = data_ops.DATA_FLOAT
-    f1.size = 2
-
-    f2 = spec_proto.dense.add()
-    f2.name = 'f2'
-    f2.original_type = data_ops.DATA_CATEGORICAL
-    f2.size = 1
-
-    f3 = spec_proto.sparse.add()
-    f3.name = 'f3'
-    f3.original_type = data_ops.DATA_FLOAT
-    f3.size = -1
-    spec_proto.dense_features_size = 3
-    self.data_spec = spec_proto.SerializeToString()
-
-  def testSimpleMixed(self):
-    #        0       1       2       3        4        5        6
-    tree = [[1, 0], [3, 2], [5, 5], [-1, 0], [-1, 0], [-1, 0], [-1, 0]]
-    tree_thresholds = [0., 15., 1., 0., 0., 0., 0.]
-    node_pcw = [[1.0, 0., 1.0, 0.4, 0.3], [1.0, 0., 0.1, 0.1, 0.8],
-                [1.0, 0., 0.5, 0.25, 0.25], [1.0, 1., 0., 0., 0.],
-                [1.0, 0., 1., 0., 0.], [1.0, 0., 0., 1., 0.],
-                [1.0, 0., 0., 0., 1.]]
-
-    input_data = [
-        [-1., 0., 15.],  # node 3
-        [-1., 2., 11.],  # node 4
-        [1., 0., 11.],
-        [1., -2., 30.]
-    ]
-
-    sparse_shape = [4, 5]
-    sparse_indices = [
-        [0, 0],
-        [0, 1],
-        [0, 4],
-        [1, 0],
-        [1, 2],
-        [2, 1],  # node 5
-        [3, 2]
-    ]  # node 6
-    sparse_values = [3.0, -1.0, 0.5, 1.5, 6.0, -2.0, 2.0]
-
-    with self.test_session():
-      predictions = tensor_forest_ops.tree_predictions(
-          input_data,
-          sparse_indices,
-          sparse_values,
-          sparse_shape,
-          tree,
-          tree_thresholds,
-          node_pcw,
-          valid_leaf_threshold=1,
-          input_spec=self.data_spec)
-
-      self.assertAllClose([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.],
-                           [0., 0., 0., 1.]], predictions.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/update_fertile_slots_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/update_fertile_slots_op_test.py
deleted file mode 100644
index 3f71a0dba4..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/update_fertile_slots_op_test.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.allocate_deallocate_op."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow  # pylint: disable=unused-import
-
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
-
-from tensorflow.python.framework import test_util
-from tensorflow.python.platform import googletest
-
-
-class UpdateFertileSlotsTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    # tree is:
-    #         0
-    #     1       2
-    #   3   4   5   6
-    self.finished = [2]
-    self.non_fertile_leaves = [3, 4]
-    self.non_fertile_leaf_scores = [10., 15.]
-    self.end_of_tree = [5]
-    self.node_map = [-1, -1, 0, -1, -1, -1, -1]
-    self.total_counts = [[80., 40., 40.]]
-    self.stale_leaves = []
-    self.node_sums = [[3, 1, 2], [4, 2, 2], [5, 2, 3], [6, 1, 5], [7, 5, 2],
-                      [8, 4, 4], [9, 7, 2]]
-
-  def testSimple(self):
-    with self.test_session():
-      (n2a_map_updates, a2n_map_updates, accumulators_cleared,
-       accumulators_allocated) = tensor_forest_ops.update_fertile_slots(
-           self.finished, self.non_fertile_leaves, self.non_fertile_leaf_scores,
-           self.end_of_tree, self.total_counts, self.node_map,
-           self.stale_leaves, self.node_sums)
-
-      self.assertAllEqual([[2, 4], [-1, 0]], n2a_map_updates.eval())
-      self.assertAllEqual([[0], [4]], a2n_map_updates.eval())
-      self.assertAllEqual([], accumulators_cleared.eval())
-      self.assertAllEqual([0], accumulators_allocated.eval())
-
-  def testNoFinished(self):
-    with self.test_session():
-      (n2a_map_updates, a2n_map_updates, accumulators_cleared,
-       accumulators_allocated) = tensor_forest_ops.update_fertile_slots(
-           [], self.non_fertile_leaves, self.non_fertile_leaf_scores,
-           self.end_of_tree, self.total_counts, self.node_map,
-           self.stale_leaves, self.node_sums)
-
-      self.assertAllEqual((2, 0), n2a_map_updates.eval().shape)
-      self.assertAllEqual((2, 0), a2n_map_updates.eval().shape)
-      self.assertAllEqual([], accumulators_cleared.eval())
-      self.assertAllEqual([], accumulators_allocated.eval())
-
-  def testPureCounts(self):
-    with self.test_session():
-      self.node_sums[4] = [10, 0, 10]
-      (n2a_map_updates, a2n_map_updates, accumulators_cleared,
-       accumulators_allocated) = tensor_forest_ops.update_fertile_slots(
-           self.finished, self.non_fertile_leaves, self.non_fertile_leaf_scores,
-           self.end_of_tree, self.total_counts, self.node_map,
-           self.stale_leaves, self.node_sums)
-
-      self.assertAllEqual([[2, 3], [-1, 0]], n2a_map_updates.eval())
-      self.assertAllEqual([[0], [3]], a2n_map_updates.eval())
-      self.assertAllEqual([], accumulators_cleared.eval())
-      self.assertAllEqual([0], accumulators_allocated.eval())
-
-  def testBadInput(self):
-    del self.non_fertile_leaf_scores[-1]
-    with self.test_session():
-      with self.assertRaisesOpError(
-          'Number of non fertile leaves should be the same in '
-          'non_fertile_leaves and non_fertile_leaf_scores.'):
-        (n2a_map_updates, _, _, _) = tensor_forest_ops.update_fertile_slots(
-            self.finished, self.non_fertile_leaves,
-            self.non_fertile_leaf_scores, self.end_of_tree, self.total_counts,
-            self.node_map, self.stale_leaves, self.node_sums)
-        self.assertAllEqual((2, 0), n2a_map_updates.eval().shape)
-
-
-if __name__ == '__main__':
-  googletest.main()
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
index 844ac00b0a..b1a8357048 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
@@ -18,27 +18,112 @@ from __future__ import division
 from __future__ import print_function
 
 import math
+import numbers
 import random
 
+from google.protobuf import text_format
+
+from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto
 from tensorflow.contrib.framework.python.ops import variables as framework_variables
-from tensorflow.contrib.tensor_forest.python import constants
+from tensorflow.contrib.tensor_forest.proto import tensor_forest_params_pb2 as _params_proto
 from tensorflow.contrib.tensor_forest.python.ops import data_ops
-from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
+from tensorflow.contrib.tensor_forest.python.ops import model_ops
+from tensorflow.contrib.tensor_forest.python.ops import stats_ops
 
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.platform import tf_logging as logging
 
 
+# Stores tuples of (leaf model type, stats model type)
+CLASSIFICATION_LEAF_MODEL_TYPES = {
+    'all_dense': (_params_proto.MODEL_DENSE_CLASSIFICATION,
+                  _params_proto.STATS_DENSE_GINI),
+    'all_sparse': (_params_proto.MODEL_SPARSE_CLASSIFICATION,
+                   _params_proto.STATS_SPARSE_GINI),
+    'sparse_then_dense':
+        (_params_proto.MODEL_SPARSE_OR_DENSE_CLASSIFICATION,
+         _params_proto.STATS_SPARSE_THEN_DENSE_GINI),
+}
+REGRESSION_MODEL_TYPE = (
+    _params_proto.MODEL_REGRESSION,
+    _params_proto.STATS_LEAST_SQUARES_REGRESSION,
+    _params_proto.COLLECTION_BASIC)
+
+FINISH_TYPES = {
+    'basic': _params_proto.SPLIT_FINISH_BASIC,
+    'hoeffding': _params_proto.SPLIT_FINISH_DOMINATE_HOEFFDING,
+    'bootstrap': _params_proto.SPLIT_FINISH_DOMINATE_BOOTSTRAP
+}
+PRUNING_TYPES = {
+    'none': _params_proto.SPLIT_PRUNE_NONE,
+    'half': _params_proto.SPLIT_PRUNE_HALF,
+    'quarter': _params_proto.SPLIT_PRUNE_QUARTER,
+    '10_percent': _params_proto.SPLIT_PRUNE_10_PERCENT,
+    'hoeffding': _params_proto.SPLIT_PRUNE_HOEFFDING,
+}
+SPLIT_TYPES = {
+    'less_or_equal': _tree_proto.InequalityTest.LESS_OR_EQUAL,
+    'less': _tree_proto.InequalityTest.LESS_THAN
+}
+
+
+def parse_number_or_string_to_proto(proto, param):
+  if isinstance(param, numbers.Number):
+    proto.constant_value = param
+  else:  # assume it's a string
+    if param.isdigit():
+      proto.constant_value = int(param)
+    else:
+      text_format.Merge(param, proto)
+
+
+def build_params_proto(params):
+  """Build a TensorForestParams proto out of the V4ForestHParams object."""
+  proto = _params_proto.TensorForestParams()
+  proto.num_trees = params.num_trees
+  proto.max_nodes = params.max_nodes
+  proto.is_regression = params.regression
+  proto.num_outputs = params.num_classes
+  proto.num_features = params.num_features
+
+  proto.leaf_type = params.leaf_model_type
+  proto.stats_type = params.stats_model_type
+  proto.collection_type = _params_proto.COLLECTION_BASIC
+  proto.pruning_type.type = params.pruning_type
+  proto.finish_type.type = params.finish_type
+
+  proto.inequality_test_type = params.split_type
+
+  proto.drop_final_class = False
+  proto.collate_examples = params.collate_examples
+  proto.checkpoint_stats = params.checkpoint_stats
+  proto.use_running_stats_method = params.use_running_stats_method
+  proto.initialize_average_splits = params.initialize_average_splits
+
+  parse_number_or_string_to_proto(proto.pruning_type.prune_every_samples,
+                                  params.prune_every_samples)
+  parse_number_or_string_to_proto(proto.finish_type.check_every_steps,
+                                  params.early_finish_check_every_samples)
+  parse_number_or_string_to_proto(proto.split_after_samples,
+                                  params.split_after_samples)
+  parse_number_or_string_to_proto(proto.num_splits_to_consider,
+                                  params.num_splits_to_consider)
+
+  proto.dominate_fraction.constant_value = params.dominate_fraction
+
+  if params.param_file:
+    with open(params.param_file) as f:
+      text_format.Merge(f.read(), proto)
+
+  return proto
+
+
 # A convenience class for holding random forest hyperparameters.
 #
 # To just get some good default parameters, use:
@@ -60,12 +145,22 @@ class ForestHParams(object):
                bagging_fraction=1.0,
                num_splits_to_consider=0,
                feature_bagging_fraction=1.0,
-               max_fertile_nodes=0,
+               max_fertile_nodes=0,  # deprecated, unused.
                split_after_samples=250,
-               min_split_samples=5,
                valid_leaf_threshold=1,
                dominate_method='bootstrap',
                dominate_fraction=0.99,
+               model_name='all_dense',
+               split_finish_name='basic',
+               split_pruning_name='none',
+               prune_every_samples=0,
+               early_finish_check_every_samples=0,
+               collate_examples=False,
+               checkpoint_stats=False,
+               use_running_stats_method=False,
+               initialize_average_splits=False,
+               param_file=None,
+               split_name='less_or_equal',
                **kwargs):
     self.num_trees = num_trees
     self.max_nodes = max_nodes
@@ -74,10 +169,20 @@ class ForestHParams(object):
     self.num_splits_to_consider = num_splits_to_consider
     self.max_fertile_nodes = max_fertile_nodes
     self.split_after_samples = split_after_samples
-    self.min_split_samples = min_split_samples
     self.valid_leaf_threshold = valid_leaf_threshold
     self.dominate_method = dominate_method
     self.dominate_fraction = dominate_fraction
+    self.model_name = model_name
+    self.split_finish_name = split_finish_name
+    self.split_pruning_name = split_pruning_name
+    self.collate_examples = collate_examples
+    self.checkpoint_stats = checkpoint_stats
+    self.use_running_stats_method = use_running_stats_method
+    self.initialize_average_splits = initialize_average_splits
+    self.param_file = param_file
+    self.split_name = split_name
+    self.early_finish_check_every_samples = early_finish_check_every_samples
+    self.prune_every_samples = prune_every_samples
 
     for name, value in kwargs.items():
       setattr(self, name, value)
@@ -113,24 +218,64 @@ class ForestHParams(object):
     # Our experiments have found that num_splits_to_consider = num_features
     # gives good accuracy.
     self.num_splits_to_consider = self.num_splits_to_consider or min(
-        self.num_features, 1000)
-
-    self.max_fertile_nodes = (self.max_fertile_nodes or
-                              int(math.ceil(self.max_nodes / 2.0)))
-
-    # We have num_splits_to_consider slots to fill, and we want to spend
-    # approximately split_after_samples samples initializing them.
-    num_split_initializiations_per_input = max(1, int(math.floor(
-        self.num_splits_to_consider / self.split_after_samples)))
-    self.split_initializations_per_input = getattr(
-        self, 'split_initializations_per_input',
-        num_split_initializiations_per_input)
+        max(10, math.floor(math.sqrt(self.num_features))), 1000)
 
     # If base_random_seed is 0, the current time will be used to seed the
     # random number generators for each tree.  If non-zero, the i-th tree
     # will be seeded with base_random_seed + i.
     self.base_random_seed = getattr(self, 'base_random_seed', 0)
 
+    # How to store leaf models.
+    self.leaf_model_type = (
+        REGRESSION_MODEL_TYPE[0] if self.regression else
+        CLASSIFICATION_LEAF_MODEL_TYPES[self.model_name][0])
+
+    # How to store stats objects.
+    self.stats_model_type = (
+        REGRESSION_MODEL_TYPE[1] if self.regression else
+        CLASSIFICATION_LEAF_MODEL_TYPES[self.model_name][1])
+
+    self.finish_type = (
+        _params_proto.SPLIT_FINISH_BASIC if self.regression else
+        FINISH_TYPES[self.split_finish_name])
+
+    self.pruning_type = PRUNING_TYPES[self.split_pruning_name]
+
+    if self.pruning_type == _params_proto.SPLIT_PRUNE_NONE:
+      self.prune_every_samples = 0
+    else:
+      if (not self.prune_every_samples and
+          not (isinstance(numbers.Number) or
+               self.split_after_samples.isdigit())):
+        logging.error(
+            'Must specify prune_every_samples if using a depth-dependent '
+            'split_after_samples')
+      # Pruning half-way through split_after_samples seems like a decent
+      # default, making it easy to select the number being pruned with
+      # pruning_type while not paying the cost of pruning too often.  Note that
+      # this only holds if not using a depth-dependent split_after_samples.
+      self.prune_every_samples = (self.prune_every_samples or
+                                  int(self.split_after_samples) / 2)
+
+    if self.finish_type == _params_proto.SPLIT_FINISH_BASIC:
+      self.early_finish_check_every_samples = 0
+    else:
+      if (not self.early_finish_check_every_samples and
+          not (isinstance(numbers.Number) or
+               self.split_after_samples.isdigit())):
+        logging.error(
+            'Must specify prune_every_samples if using a depth-dependent '
+            'split_after_samples')
+      # Checking for early finish every quarter through split_after_samples
+      # seems like a decent default. We don't want to incur the checking cost
+      # too often, but (at least for hoeffding) it's lower than the cost of
+      # pruning so we can do it a little more frequently.
+      self.early_finish_check_every_samples = (
+          self.early_finish_check_every_samples or
+          int(self.split_after_samples) / 4)
+
+    self.split_type = SPLIT_TYPES[self.split_name]
+
     return self
 
 
@@ -155,116 +300,25 @@ class TreeTrainingVariables(object):
   """
 
   def __init__(self, params, tree_num, training):
-    self.tree = variable_scope.get_variable(
-        name=self.get_tree_name('tree', tree_num), dtype=dtypes.int32,
-        shape=[params.max_nodes, 2],
-        initializer=init_ops.constant_initializer(-2))
-    self.tree_thresholds = variable_scope.get_variable(
-        name=self.get_tree_name('tree_thresholds', tree_num),
-        shape=[params.max_nodes],
-        initializer=init_ops.constant_initializer(-1.0))
-    self.end_of_tree = variable_scope.get_variable(
-        name=self.get_tree_name('end_of_tree', tree_num),
-        dtype=dtypes.int32,
-        initializer=constant_op.constant([1]))
-    self.start_epoch = variable_scope.get_variable(
-        name=self.get_tree_name('start_epoch', tree_num),
-        dtype=dtypes.int32, shape=[params.max_nodes],
-        initializer=init_ops.constant_initializer(0))
+    if (not hasattr(params, 'params_proto') or
+        not isinstance(params.params_proto,
+                       _params_proto.TensorForestParams)):
+      params.params_proto = build_params_proto(params)
 
+    params.serialized_params_proto = params.params_proto.SerializeToString()
+    self.stats = None
     if training:
-      self.node_to_accumulator_map = variable_scope.get_variable(
-          name=self.get_tree_name('node_to_accumulator_map', tree_num),
-          shape=[params.max_nodes],
-          dtype=dtypes.int32,
-          initializer=init_ops.constant_initializer(-1))
-      self.accumulator_to_node_map = variable_scope.get_variable(
-          name=self.get_tree_name('accumulator_to_node_map', tree_num),
-          shape=[params.max_fertile_nodes],
-          dtype=dtypes.int32,
-          initializer=init_ops.constant_initializer(-1))
-
-      self.candidate_split_features = variable_scope.get_variable(
-          name=self.get_tree_name('candidate_split_features', tree_num),
-          shape=[params.max_fertile_nodes, params.num_splits_to_consider],
-          dtype=dtypes.int32,
-          initializer=init_ops.constant_initializer(-1))
-      self.candidate_split_thresholds = variable_scope.get_variable(
-          name=self.get_tree_name('candidate_split_thresholds', tree_num),
-          shape=[params.max_fertile_nodes, params.num_splits_to_consider],
-          initializer=init_ops.constant_initializer(0.0))
-
-    # Statistics shared by classification and regression.
-    self.node_sums = variable_scope.get_variable(
-        name=self.get_tree_name('node_sums', tree_num),
-        shape=[params.max_nodes, params.num_output_columns],
-        initializer=init_ops.constant_initializer(0.0))
-
-    if training:
-      self.candidate_split_sums = variable_scope.get_variable(
-          name=self.get_tree_name('candidate_split_sums', tree_num),
-          shape=[params.max_fertile_nodes, params.num_splits_to_consider,
-                 params.num_output_columns],
-          initializer=init_ops.constant_initializer(0.0))
-      self.accumulator_sums = variable_scope.get_variable(
-          name=self.get_tree_name('accumulator_sums', tree_num),
-          shape=[params.max_fertile_nodes, params.num_output_columns],
-          initializer=init_ops.constant_initializer(-1.0))
-
-      # Regression also tracks second order stats.
-      if params.regression:
-        self.node_squares = variable_scope.get_variable(
-            name=self.get_tree_name('node_squares', tree_num),
-            shape=[params.max_nodes, params.num_output_columns],
-            initializer=init_ops.constant_initializer(0.0))
-
-        self.candidate_split_squares = variable_scope.get_variable(
-            name=self.get_tree_name('candidate_split_squares', tree_num),
-            shape=[params.max_fertile_nodes, params.num_splits_to_consider,
-                   params.num_output_columns],
-            initializer=init_ops.constant_initializer(0.0))
-
-        self.accumulator_squares = variable_scope.get_variable(
-            name=self.get_tree_name('accumulator_squares', tree_num),
-            shape=[params.max_fertile_nodes, params.num_output_columns],
-            initializer=init_ops.constant_initializer(-1.0))
-
-      else:
-        self.node_squares = constant_op.constant(
-            0.0, name=self.get_tree_name('node_squares', tree_num))
-
-        self.candidate_split_squares = constant_op.constant(
-            0.0, name=self.get_tree_name('candidate_split_squares', tree_num))
-
-        self.accumulator_squares = constant_op.constant(
-            0.0, name=self.get_tree_name('accumulator_squares', tree_num))
+      # TODO(gilberth): Manually shard this to be able to fit it on
+      # multiple machines.
+      self.stats = stats_ops.fertile_stats_variable(
+          params, '', self.get_tree_name('stats', tree_num))
+    self.tree = model_ops.tree_variable(
+        params, '', self.stats, self.get_tree_name('tree', tree_num))
 
   def get_tree_name(self, name, num):
     return '{0}-{1}'.format(name, num)
 
 
-class ForestStats(object):
-
-  def __init__(self, tree_stats, params):
-    """A simple container for stats about a forest."""
-    self.tree_stats = tree_stats
-    self.params = params
-
-  def get_average(self, thing):
-    val = 0.0
-    for i in range(self.params.num_trees):
-      val += getattr(self.tree_stats[i], thing)
-
-    return val / self.params.num_trees
-
-
-class TreeStats(object):
-
-  def __init__(self, num_nodes, num_leaves):
-    self.num_nodes = num_nodes
-    self.num_leaves = num_leaves
-
-
 class ForestTrainingVariables(object):
   """A container for a forests training data, consisting of multiple trees.
 
@@ -329,7 +383,8 @@ class RandomForestGraphs(object):
         [split_data[ind] for ind in self.params.bagged_features[tree_num]], 1)
 
   def get_all_resource_handles(self):
-    return []
+    return ([self.variables[i].tree for i in range(len(self.trees))] +
+            [self.variables[i].stats for i in range(len(self.trees))])
 
   def training_graph(self,
                      input_data,
@@ -366,7 +421,6 @@ class RandomForestGraphs(object):
     tree_start = int(trainer_id * trees_per_trainer)
     tree_end = int((trainer_id + 1) * trees_per_trainer)
     for i in range(tree_start, tree_end):
-      logging.info('training graph for tree: %d' % i)
       with ops.device(self.variables.device_dummies[i].device):
         seed = self.params.base_random_seed
         if seed != 0:
@@ -398,16 +452,13 @@ class RandomForestGraphs(object):
                 'Feature bagging not supported with sparse features.')
           tree_data = self._bag_features(i, tree_data)
 
-        initialization = self.trees[i].tree_initialization()
-
-        with ops.control_dependencies([initialization]):
-          tree_graphs.append(self.trees[i].training_graph(
-              tree_data,
-              tree_labels,
-              seed,
-              data_spec=data_spec,
-              sparse_features=processed_sparse_features,
-              **tree_kwargs))
+        tree_graphs.append(self.trees[i].training_graph(
+            tree_data,
+            tree_labels,
+            seed,
+            data_spec=data_spec,
+            sparse_features=processed_sparse_features,
+            **tree_kwargs))
 
     return control_flow_ops.group(*tree_graphs, name='train')
 
@@ -480,13 +531,6 @@ class RandomForestGraphs(object):
         impurities.append(self.trees[i].average_impurity())
     return math_ops.reduce_mean(array_ops.stack(impurities))
 
-  def get_stats(self, session):
-    tree_stats = []
-    for i in range(self.params.num_trees):
-      with ops.device(self.variables.device_dummies[i].device):
-        tree_stats.append(self.trees[i].get_stats(session))
-    return ForestStats(tree_stats, self.params)
-
   def feature_importances(self):
     tree_counts = [self.trees[i].feature_usage_counts()
                    for i in range(self.params.num_trees)]
@@ -494,21 +538,6 @@ class RandomForestGraphs(object):
     return total_counts / math_ops.reduce_sum(total_counts)
 
 
-def one_hot_wrapper(num_classes, loss_fn):
-  """Some loss functions take one-hot labels."""
-  def _loss(probs, targets):
-    if targets.get_shape().ndims > 1:
-      targets = array_ops.squeeze(targets, squeeze_dims=[1])
-    one_hot_labels = array_ops.one_hot(
-        math_ops.to_int32(targets),
-        num_classes,
-        on_value=1.,
-        off_value=0.,
-        dtype=dtypes.float32)
-    return loss_fn(probs, one_hot_labels)
-  return _loss
-
-
 class RandomTreeGraphs(object):
   """Builds TF graphs for random tree training and inference."""
 
@@ -517,77 +546,6 @@ class RandomTreeGraphs(object):
     self.params = params
     self.tree_num = tree_num
 
-  def tree_initialization(self):
-    def _init_tree():
-      return state_ops.scatter_update(self.variables.tree, [0], [[-1, -1]]).op
-
-    def _nothing():
-      return control_flow_ops.no_op()
-
-    return control_flow_ops.cond(
-        math_ops.equal(
-            array_ops.squeeze(
-                array_ops.strided_slice(self.variables.tree, [0, 0], [1, 1])),
-            -2), _init_tree, _nothing)
-
-  def _gini(self, class_counts):
-    """Calculate the Gini impurity.
-
-    If c(i) denotes the i-th class count and c = sum_i c(i) then
-      score = 1 - sum_i ( c(i) / c )^2
-
-    Args:
-      class_counts: A 2-D tensor of per-class counts, usually a slice or
-        gather from variables.node_sums.
-
-    Returns:
-      A 1-D tensor of the Gini impurities for each row in the input.
-    """
-    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
-    sums = math_ops.reduce_sum(smoothed, 1)
-    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)
-
-    return 1.0 - sum_squares / (sums * sums)
-
-  def _weighted_gini(self, class_counts):
-    """Our split score is the Gini impurity times the number of examples.
-
-    If c(i) denotes the i-th class count and c = sum_i c(i) then
-      score = c * (1 - sum_i ( c(i) / c )^2 )
-            = c - sum_i c(i)^2 / c
-    Args:
-      class_counts: A 2-D tensor of per-class counts, usually a slice or
-        gather from variables.node_sums.
-
-    Returns:
-      A 1-D tensor of the Gini impurities for each row in the input.
-    """
-    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
-    sums = math_ops.reduce_sum(smoothed, 1)
-    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)
-
-    return sums - sum_squares / sums
-
-  def _variance(self, sums, squares):
-    """Calculate the variance for each row of the input tensors.
-
-    Variance is V = E[x^2] - (E[x])^2.
-
-    Args:
-      sums: A tensor containing output sums, usually a slice from
-        variables.node_sums.  Should contain the number of examples seen
-        in index 0 so we can calculate expected value.
-      squares: Same as sums, but sums of squares.
-
-    Returns:
-      A 1-D tensor of the variances for each row in the input.
-    """
-    total_count = array_ops.slice(sums, [0, 0], [-1, 1])
-    e_x = sums / total_count
-    e_x2 = squares / total_count
-
-    return math_ops.reduce_sum(e_x2 - math_ops.square(e_x), 1)
-
   def training_graph(self,
                      input_data,
                      input_labels,
@@ -613,16 +571,12 @@ class RandomTreeGraphs(object):
     Returns:
       The last op in the random tree training graph.
     """
-    epoch = math_ops.to_int32(get_epoch_variable())
-
-    serialized_input_spec = data_spec.SerializeToString()
+    # TODO(gilberth): Use this.
+    unused_epoch = math_ops.to_int32(get_epoch_variable())
 
     if input_weights is None:
       input_weights = []
 
-    if input_data is None:
-      input_data = []
-
     sparse_indices = []
     sparse_values = []
     sparse_shape = []
@@ -631,245 +585,45 @@ class RandomTreeGraphs(object):
       sparse_values = sparse_features.values
       sparse_shape = sparse_features.dense_shape
 
-    # Count extremely random stats.
-    (node_sums, node_squares, splits_indices, splits_sums, splits_squares,
-     totals_indices, totals_sums, totals_squares,
-     input_leaves) = (tensor_forest_ops.count_extremely_random_stats(
-         input_data,
-         sparse_indices,
-         sparse_values,
-         sparse_shape,
-         input_labels,
-         input_weights,
-         self.variables.tree,
-         self.variables.tree_thresholds,
-         self.variables.node_to_accumulator_map,
-         self.variables.candidate_split_features,
-         self.variables.candidate_split_thresholds,
-         self.variables.start_epoch,
-         epoch,
-         input_spec=serialized_input_spec,
-         num_classes=self.params.num_output_columns,
-         regression=self.params.regression))
-    node_update_ops = []
-    node_update_ops.append(
-        state_ops.assign_add(self.variables.node_sums, node_sums))
-
-    splits_update_ops = []
-    splits_update_ops.append(
-        tensor_forest_ops.scatter_add_ndim(self.variables.candidate_split_sums,
-                                           splits_indices, splits_sums))
-    splits_update_ops.append(
-        tensor_forest_ops.scatter_add_ndim(self.variables.accumulator_sums,
-                                           totals_indices, totals_sums))
-
-    if self.params.regression:
-      node_update_ops.append(state_ops.assign_add(self.variables.node_squares,
-                                                  node_squares))
-      splits_update_ops.append(
-          tensor_forest_ops.scatter_add_ndim(
-              self.variables.candidate_split_squares, splits_indices,
-              splits_squares))
-      splits_update_ops.append(
-          tensor_forest_ops.scatter_add_ndim(self.variables.accumulator_squares,
-                                             totals_indices, totals_squares))
-
-    # Sample inputs.
-    update_indices, feature_updates, threshold_updates = (
-        tensor_forest_ops.sample_inputs(
-            input_data,
-            sparse_indices,
-            sparse_values,
-            sparse_shape,
-            input_weights,
-            self.variables.node_to_accumulator_map,
-            input_leaves,
-            self.variables.candidate_split_features,
-            self.variables.candidate_split_thresholds,
-            input_spec=serialized_input_spec,
-            split_initializations_per_input=(
-                self.params.split_initializations_per_input),
-            split_sampling_random_seed=random_seed))
-    update_features_op = state_ops.scatter_update(
-        self.variables.candidate_split_features, update_indices,
-        feature_updates)
-    update_thresholds_op = state_ops.scatter_update(
-        self.variables.candidate_split_thresholds, update_indices,
-        threshold_updates)
-
-    # Calculate finished nodes.
-    with ops.control_dependencies(splits_update_ops):
-      # Passing input_leaves to finished nodes here means that nodes that
-      # have become stale won't be deallocated until an input reaches them,
-      # because we're trying to avoid considering every fertile node for
-      # performance reasons.
-      finished, stale = tensor_forest_ops.finished_nodes(
-          input_leaves,
-          self.variables.node_to_accumulator_map,
-          self.variables.candidate_split_sums,
-          self.variables.candidate_split_squares,
-          self.variables.accumulator_sums,
-          self.variables.accumulator_squares,
-          self.variables.start_epoch,
-          epoch,
-          num_split_after_samples=self.params.split_after_samples,
-          min_split_samples=self.params.min_split_samples,
-          dominate_method=self.params.dominate_method,
-          dominate_fraction=self.params.dominate_fraction)
-
-    # Update leaf scores.
-    # TODO(thomaswc): Store the leaf scores in a TopN and only update the
-    # scores of the leaves that were touched by this batch of input.
-    children = array_ops.squeeze(
-        array_ops.slice(self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
-    is_leaf = math_ops.equal(constants.LEAF_NODE, children)
-    leaves = math_ops.to_int32(
-        array_ops.squeeze(
-            array_ops.where(is_leaf), squeeze_dims=[1]))
-    non_fertile_leaves = array_ops.boolean_mask(
-        leaves, math_ops.less(array_ops.gather(
-            self.variables.node_to_accumulator_map, leaves), 0))
-
-    # TODO(gilberth): It should be possible to limit the number of non
-    # fertile leaves we calculate scores for, especially since we can only take
-    # at most array_ops.shape(finished)[0] of them.
-    with ops.control_dependencies(node_update_ops):
-      sums = array_ops.gather(self.variables.node_sums, non_fertile_leaves)
-      if self.params.regression:
-        squares = array_ops.gather(self.variables.node_squares,
-                                   non_fertile_leaves)
-        non_fertile_leaf_scores = self._variance(sums, squares)
-      else:
-        non_fertile_leaf_scores = self._weighted_gini(sums)
-
-    # Calculate best splits.
-    with ops.control_dependencies(splits_update_ops):
-      split_indices = tensor_forest_ops.best_splits(
-          finished,
-          self.variables.node_to_accumulator_map,
-          self.variables.candidate_split_sums,
-          self.variables.candidate_split_squares,
-          self.variables.accumulator_sums,
-          self.variables.accumulator_squares,
-          regression=self.params.regression)
-
-    # Grow tree.
-    with ops.control_dependencies([update_features_op, update_thresholds_op,
-                                   non_fertile_leaves.op]):
-      (tree_update_indices, tree_children_updates, tree_threshold_updates,
-       new_eot) = (tensor_forest_ops.grow_tree(
-           self.variables.end_of_tree, self.variables.node_to_accumulator_map,
-           finished, split_indices, self.variables.candidate_split_features,
-           self.variables.candidate_split_thresholds))
-      tree_update_op = state_ops.scatter_update(
-          self.variables.tree, tree_update_indices, tree_children_updates)
-      thresholds_update_op = state_ops.scatter_update(
-          self.variables.tree_thresholds, tree_update_indices,
-          tree_threshold_updates)
-      # TODO(thomaswc): Only update the epoch on the new leaves.
-      new_epoch_updates = epoch * array_ops.ones_like(tree_threshold_updates,
-                                                      dtype=dtypes.int32)
-      epoch_update_op = state_ops.scatter_update(
-          self.variables.start_epoch, tree_update_indices,
-          new_epoch_updates)
-
-    # Update fertile slots.
-    with ops.control_dependencies([tree_update_op]):
-      (n2a_map_updates, a2n_map_updates, accumulators_cleared,
-       accumulators_allocated) = (tensor_forest_ops.update_fertile_slots(
-           finished,
-           non_fertile_leaves,
-           non_fertile_leaf_scores,
-           self.variables.end_of_tree,
-           self.variables.accumulator_sums,
-           self.variables.node_to_accumulator_map,
-           stale,
-           self.variables.node_sums,
-           regression=self.params.regression))
-
-    # Ensure end_of_tree doesn't get updated until UpdateFertileSlots has
-    # used it to calculate new leaves.
-    with ops.control_dependencies([n2a_map_updates.op]):
-      eot_update_op = state_ops.assign(self.variables.end_of_tree, new_eot)
-
-    updates = []
-    updates.append(eot_update_op)
-    updates.append(tree_update_op)
-    updates.append(thresholds_update_op)
-    updates.append(epoch_update_op)
-
-    updates.append(
-        state_ops.scatter_update(self.variables.node_to_accumulator_map,
-                                 n2a_map_updates[0], n2a_map_updates[1]))
-
-    updates.append(
-        state_ops.scatter_update(self.variables.accumulator_to_node_map,
-                                 a2n_map_updates[0], a2n_map_updates[1]))
-
-    cleared_and_allocated_accumulators = array_ops.concat(
-        [accumulators_cleared, accumulators_allocated], 0)
-
-    # Calculate values to put into scatter update for candidate counts.
-    # Candidate split counts are always reset back to 0 for both cleared
-    # and allocated accumulators. This means some accumulators might be doubly
-    # reset to 0 if the were released and not allocated, then later allocated.
-    split_values = array_ops.tile(
-        array_ops.expand_dims(array_ops.expand_dims(
-            array_ops.zeros_like(cleared_and_allocated_accumulators,
-                                 dtype=dtypes.float32), 1), 2),
-        [1, self.params.num_splits_to_consider, self.params.num_output_columns])
-    updates.append(state_ops.scatter_update(
-        self.variables.candidate_split_sums,
-        cleared_and_allocated_accumulators, split_values))
-    if self.params.regression:
-      updates.append(state_ops.scatter_update(
-          self.variables.candidate_split_squares,
-          cleared_and_allocated_accumulators, split_values))
-
-    # Calculate values to put into scatter update for total counts.
-    total_cleared = array_ops.tile(
-        array_ops.expand_dims(
-            math_ops.negative(array_ops.ones_like(accumulators_cleared,
-                                                  dtype=dtypes.float32)), 1),
-        [1, self.params.num_output_columns])
-    total_reset = array_ops.tile(
-        array_ops.expand_dims(
-            array_ops.zeros_like(accumulators_allocated,
-                                 dtype=dtypes.float32), 1),
-        [1, self.params.num_output_columns])
-    accumulator_updates = array_ops.concat([total_cleared, total_reset], 0)
-    updates.append(state_ops.scatter_update(
-        self.variables.accumulator_sums,
-        cleared_and_allocated_accumulators, accumulator_updates))
-    if self.params.regression:
-      updates.append(state_ops.scatter_update(
-          self.variables.accumulator_squares,
-          cleared_and_allocated_accumulators, accumulator_updates))
-
-    # Calculate values to put into scatter update for candidate splits.
-    split_features_updates = array_ops.tile(
-        array_ops.expand_dims(
-            math_ops.negative(array_ops.ones_like(
-                cleared_and_allocated_accumulators)), 1),
-        [1, self.params.num_splits_to_consider])
-    updates.append(state_ops.scatter_update(
-        self.variables.candidate_split_features,
-        cleared_and_allocated_accumulators, split_features_updates))
-
-    updates += self.finish_iteration()
-
-    return control_flow_ops.group(*updates)
-
-  def finish_iteration(self):
-    """Perform any operations that should be done at the end of an iteration.
-
-    This is mostly useful for subclasses that need to reset variables after
-    an iteration, such as ones that are used to finish nodes.
+    if input_data is None:
+      input_data = []
 
-    Returns:
-      A list of operations.
-    """
-    return []
+    leaf_ids = model_ops.traverse_tree_v4(
+        self.variables.tree,
+        input_data,
+        sparse_indices,
+        sparse_values,
+        sparse_shape,
+        input_spec=data_spec.SerializeToString(),
+        params=self.params.serialized_params_proto)
+
+    update_model = model_ops.update_model_v4(
+        self.variables.tree,
+        leaf_ids,
+        input_labels,
+        input_weights,
+        params=self.params.serialized_params_proto)
+
+    finished_nodes = stats_ops.process_input_v4(
+        self.variables.tree,
+        self.variables.stats,
+        input_data,
+        sparse_indices,
+        sparse_values,
+        sparse_shape,
+        input_labels,
+        input_weights,
+        leaf_ids,
+        input_spec=data_spec.SerializeToString(),
+        random_seed=random_seed,
+        params=self.params.serialized_params_proto)
+
+    with ops.control_dependencies([update_model]):
+      return stats_ops.grow_tree_v4(
+          self.variables.tree,
+          self.variables.stats,
+          finished_nodes,
+          params=self.params.serialized_params_proto)
 
   def inference_graph(self, input_data, data_spec, sparse_features=None):
     """Constructs a TF graph for evaluating a random tree.
@@ -883,9 +637,6 @@ class RandomTreeGraphs(object):
     Returns:
       The last op in the random tree inference graph.
     """
-    if input_data is None:
-      input_data = []
-
     sparse_indices = []
     sparse_values = []
     sparse_shape = []
@@ -893,43 +644,17 @@ class RandomTreeGraphs(object):
       sparse_indices = sparse_features.indices
       sparse_values = sparse_features.values
       sparse_shape = sparse_features.dense_shape
+    if input_data is None:
+      input_data = []
 
-    return tensor_forest_ops.tree_predictions(
+    return model_ops.tree_predictions_v4(
+        self.variables.tree,
         input_data,
         sparse_indices,
         sparse_values,
         sparse_shape,
-        self.variables.tree,
-        self.variables.tree_thresholds,
-        self.variables.node_sums,
         input_spec=data_spec.SerializeToString(),
-        valid_leaf_threshold=self.params.valid_leaf_threshold)
-
-  def average_impurity(self):
-    """Constructs a TF graph for evaluating the average leaf impurity of a tree.
-
-    If in regression mode, this is the leaf variance. If in classification mode,
-    this is the gini impurity.
-
-    Returns:
-      The last op in the graph.
-    """
-    children = array_ops.squeeze(array_ops.slice(
-        self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
-    is_leaf = math_ops.equal(constants.LEAF_NODE, children)
-    leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
-                                                 squeeze_dims=[1]))
-    counts = array_ops.gather(self.variables.node_sums, leaves)
-    gini = self._weighted_gini(counts)
-    # Guard against step 1, when there often are no leaves yet.
-    def impurity():
-      return gini
-    # Since average impurity can be used for loss, when there's no data just
-    # return a big number so that loss always decreases.
-    def big():
-      return array_ops.ones_like(gini, dtype=dtypes.float32) * 10000000.
-    return control_flow_ops.cond(math_ops.greater(
-        array_ops.shape(leaves)[0], 0), impurity, big)
+        params=self.params.serialized_params_proto)
 
   def size(self):
     """Constructs a TF graph for evaluating the current number of nodes.
@@ -937,19 +662,8 @@ class RandomTreeGraphs(object):
     Returns:
       The current number of nodes in the tree.
     """
-    return self.variables.end_of_tree - 1
-
-  def get_stats(self, session):
-    num_nodes = self.variables.end_of_tree.eval(session=session) - 1
-    num_leaves = array_ops.where(
-        math_ops.equal(array_ops.squeeze(array_ops.slice(
-            self.variables.tree, [0, 0], [-1, 1])), constants.LEAF_NODE)
-        ).eval(session=session).shape[0]
-    return TreeStats(num_nodes, num_leaves)
+    return model_ops.tree_size(self.variables.tree)
 
   def feature_usage_counts(self):
-    features = array_ops.slice(self.variables.tree, [0, 1], [-1, 1])
-    # One hot ignores negative values, which is the default for unused nodes.
-    one_hots = array_ops.one_hot(
-        array_ops.squeeze(features), self.params.num_features)
-    return math_ops.reduce_sum(one_hots, 0)
+    return model_ops.feature_usage_counts(
+        self.variables.tree, params=self.params.serialized_params_proto)
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py
index a9a3f66bbf..ddbe30426d 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py
@@ -36,13 +36,9 @@ class TensorForestTest(test_util.TensorFlowTestCase):
         num_features=60).fill()
     self.assertEquals(2, hparams.num_classes)
     self.assertEquals(3, hparams.num_output_columns)
-    self.assertEquals(60, hparams.num_splits_to_consider)
-    # Don't have more fertile nodes than max # leaves, which is 500.
-    self.assertEquals(500, hparams.max_fertile_nodes)
+    self.assertEquals(10, hparams.num_splits_to_consider)
     # Default value of valid_leaf_threshold
     self.assertEquals(1, hparams.valid_leaf_threshold)
-    # floor(60 / 25) = 2
-    self.assertEquals(2, hparams.split_initializations_per_input)
     self.assertEquals(0, hparams.base_random_seed)
 
   def testForestHParamsBigTree(self):
@@ -52,11 +48,17 @@ class TensorForestTest(test_util.TensorFlowTestCase):
         max_nodes=1000000,
         split_after_samples=25,
         num_features=1000).fill()
-    self.assertEquals(1000, hparams.num_splits_to_consider)
-    # 1000000 / 2 = 500000
-    self.assertEquals(500000, hparams.max_fertile_nodes)
-    # floor(1000 / 25) = 40
-    self.assertEquals(40, hparams.split_initializations_per_input)
+    self.assertEquals(31, hparams.num_splits_to_consider)
+
+  def testForestHParamsStringParams(self):
+    hparams = tensor_forest.ForestHParams(
+        num_classes=2,
+        num_trees=100,
+        max_nodes=1000000,
+        split_after_samples="25",
+        num_splits_to_consider="1000000",
+        num_features=1000).fill()
+    self.assertEquals("1000000", hparams.num_splits_to_consider)
 
   def testTrainingConstructionClassification(self):
     input_data = [[-1., 0.], [-1., 2.],  # node 1
@@ -106,18 +108,6 @@ class TensorForestTest(test_util.TensorFlowTestCase):
     graph = graph_builder.inference_graph(input_data)
     self.assertTrue(isinstance(graph, ops.Tensor))
 
-  def testImpurityConstruction(self):
-    params = tensor_forest.ForestHParams(
-        num_classes=4,
-        num_features=2,
-        num_trees=10,
-        max_nodes=1000,
-        split_after_samples=25).fill()
-
-    graph_builder = tensor_forest.RandomForestGraphs(params)
-    graph = graph_builder.average_impurity()
-    self.assertTrue(isinstance(graph, ops.Tensor))
-
   def testTrainingConstructionClassificationSparse(self):
     input_data = sparse_tensor.SparseTensor(
         indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]],
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
deleted file mode 100644
index fefc19d3bd..0000000000
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest_v4.py
+++ /dev/null
@@ -1,331 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Extremely random forest graph builder using TF resources handles."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import six
-
-from google.protobuf import text_format
-
-from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto
-from tensorflow.contrib.tensor_forest.proto import tensor_forest_params_pb2 as _params_proto
-from tensorflow.contrib.tensor_forest.python import tensor_forest
-from tensorflow.contrib.tensor_forest.python.ops import model_ops
-from tensorflow.contrib.tensor_forest.python.ops import stats_ops
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.platform import tf_logging as logging
-
-
-# Stores tuples of (leaf model type, stats model type)
-CLASSIFICATION_LEAF_MODEL_TYPES = {
-    'all_dense': (_params_proto.MODEL_DENSE_CLASSIFICATION,
-                  _params_proto.STATS_DENSE_GINI),
-    'all_sparse': (_params_proto.MODEL_SPARSE_CLASSIFICATION,
-                   _params_proto.STATS_SPARSE_GINI),
-    'sparse_then_dense':
-        (_params_proto.MODEL_SPARSE_OR_DENSE_CLASSIFICATION,
-         _params_proto.STATS_SPARSE_THEN_DENSE_GINI),
-}
-REGRESSION_MODEL_TYPE = (
-    _params_proto.MODEL_REGRESSION,
-    _params_proto.STATS_LEAST_SQUARES_REGRESSION,
-    _params_proto.COLLECTION_BASIC)
-
-COLLECTION_TYPES = {
-    'basic': _params_proto.COLLECTION_BASIC,
-    'graph_runner': _params_proto.GRAPH_RUNNER_COLLECTION
-}
-
-FINISH_TYPES = {
-    'basic': _params_proto.SPLIT_FINISH_BASIC,
-    'hoeffding': _params_proto.SPLIT_FINISH_DOMINATE_HOEFFDING,
-    'bootstrap': _params_proto.SPLIT_FINISH_DOMINATE_BOOTSTRAP
-}
-PRUNING_TYPES = {
-    'none': _params_proto.SPLIT_PRUNE_NONE,
-    'half': _params_proto.SPLIT_PRUNE_HALF,
-    'quarter': _params_proto.SPLIT_PRUNE_QUARTER,
-    '10_percent': _params_proto.SPLIT_PRUNE_10_PERCENT,
-    'hoeffding': _params_proto.SPLIT_PRUNE_HOEFFDING,
-}
-SPLIT_TYPES = {
-    'less_or_equal': _tree_proto.InequalityTest.LESS_OR_EQUAL,
-    'less': _tree_proto.InequalityTest.LESS_THAN
-}
-
-
-def build_params_proto(params):
-  """Build a TensorForestParams proto out of the V4ForestHParams object."""
-  proto = _params_proto.TensorForestParams()
-  proto.num_trees = params.num_trees
-  proto.max_nodes = params.max_nodes
-  proto.is_regression = params.regression
-  proto.num_outputs = params.num_classes
-  proto.num_features = params.num_features
-
-  proto.leaf_type = params.v4_leaf_model_type
-  proto.stats_type = params.v4_stats_model_type
-  proto.collection_type = params.v4_split_collection_type
-  proto.pruning_type.type = params.v4_pruning_type
-  proto.finish_type.type = params.v4_finish_type
-
-  proto.inequality_test_type = params.v4_split_type
-
-  proto.drop_final_class = False
-  proto.collate_examples = params.v4_collate_examples
-  proto.checkpoint_stats = params.v4_checkpoint_stats
-  proto.use_running_stats_method = params.v4_use_running_stats_method
-  proto.initialize_average_splits = params.v4_initialize_average_splits
-
-  if params.v4_prune_every_samples:
-    text_format.Merge(params.v4_prune_every_samples,
-                      proto.pruning_type.prune_every_samples)
-  else:
-    # Pruning half-way through split_after_samples seems like a decent default,
-    # making it easy to select the number being pruned with v4_pruning_type
-    # while not paying the cost of pruning too often.  Note that this only holds
-    # if not using a depth-dependent split_after_samples.
-    if params.v4_split_after_samples:
-      logging.error(
-          'If using depth-dependent split_after_samples and also pruning, '
-          'need to set v4_prune_every_samples')
-    proto.pruning_type.prune_every_samples.constant_value = (
-        params.split_after_samples / 2)
-
-  if params.v4_finish_check_every_samples:
-    text_format.Merge(params.v4_finish_check_every_samples,
-                      proto.finish_type.check_every_steps)
-  else:
-    # Checking for finish every quarter through split_after_samples seems
-    # like a decent default. We don't want to incur the checking cost too often,
-    # but (at least for hoeffding) it's lower than the cost of pruning so
-    # we can do it a little more frequently.
-    proto.finish_type.check_every_steps.constant_value = int(
-        params.split_after_samples / 4)
-
-  if params.v4_split_after_samples:
-    text_format.Merge(params.v4_split_after_samples, proto.split_after_samples)
-  else:
-    proto.split_after_samples.constant_value = params.split_after_samples
-
-  if params.v4_num_splits_to_consider:
-    text_format.Merge(params.v4_num_splits_to_consider,
-                      proto.num_splits_to_consider)
-  else:
-    proto.num_splits_to_consider.constant_value = params.num_splits_to_consider
-
-  proto.dominate_fraction.constant_value = params.dominate_fraction
-  proto.min_split_samples.constant_value = params.split_after_samples
-
-  if params.v4_param_file:
-    with open(params.v4_param_file) as f:
-      text_format.Merge(f.read(), proto)
-
-  return proto
-
-
-class V4ForestHParams(object):
-
-  def __init__(self, hparams):
-    for k, v in six.iteritems(hparams.__dict__):
-      setattr(self, k, v)
-
-    # How to store leaf models.
-    model_name = getattr(self, 'v4_model_name', 'all_dense')
-    self.v4_leaf_model_type = (
-        REGRESSION_MODEL_TYPE[0] if self.regression else
-        CLASSIFICATION_LEAF_MODEL_TYPES[model_name][0])
-
-    # How to store stats objects.
-    self.v4_stats_model_type = (
-        REGRESSION_MODEL_TYPE[1] if self.regression else
-        CLASSIFICATION_LEAF_MODEL_TYPES[model_name][1])
-
-    split_collection_name = getattr(self, 'v4_split_collection_name',
-                                    'basic')
-    self.v4_split_collection_type = (
-        REGRESSION_MODEL_TYPE[2] if self.regression else
-        COLLECTION_TYPES[split_collection_name])
-
-    finish_name = getattr(self, 'v4_finish_name', 'basic')
-    self.v4_finish_type = (
-        _params_proto.SPLIT_FINISH_BASIC if self.regression else
-        FINISH_TYPES[finish_name])
-
-    pruning_name = getattr(self, 'v4_pruning_name', 'none')
-    self.v4_pruning_type = PRUNING_TYPES[pruning_name]
-
-    self.v4_collate_examples = getattr(self, 'v4_collate_examples', False)
-
-    self.v4_checkpoint_stats = getattr(self, 'v4_checkpoint_stats', False)
-    self.v4_use_running_stats_method = getattr(
-        self, 'v4_use_running_stats_method', False)
-    self.v4_initialize_average_splits = getattr(
-        self, 'v4_initialize_average_splits', False)
-
-    self.v4_param_file = getattr(self, 'v4_param_file', None)
-
-    self.v4_split_type = getattr(self, 'v4_split_type',
-                                 SPLIT_TYPES['less_or_equal'])
-
-    # Special versions of the normal parameters, that support depth-dependence
-    self.v4_num_splits_to_consider = getattr(self, 'v4_num_splits_to_consider',
-                                             None)
-    self.v4_split_after_samples = getattr(self, 'v4_split_after_samples',
-                                          None)
-    self.v4_finish_check_every_samples = getattr(
-        self, 'v4_finish_check_every_samples', None)
-    self.v4_prune_every_samples = getattr(
-        self, 'v4_prune_every_samples', None)
-
-
-class TreeTrainingVariablesV4(tensor_forest.TreeTrainingVariables):
-  """Stores tf.Variables for training a single random tree."""
-
-  def __init__(self, params, tree_num, training):
-    if (not hasattr(params, 'params_proto') or
-        not isinstance(params.params_proto,
-                       _params_proto.TensorForestParams)):
-      params.params_proto = build_params_proto(params)
-
-    params.serialized_params_proto = params.params_proto.SerializeToString()
-    self.stats = None
-    if training:
-      # TODO(gilberth): Manually shard this to be able to fit it on
-      # multiple machines.
-      self.stats = stats_ops.fertile_stats_variable(
-          params, '', self.get_tree_name('stats', tree_num))
-    self.tree = model_ops.tree_variable(
-        params, '', self.stats, self.get_tree_name('tree', tree_num))
-
-
-class RandomTreeGraphsV4(tensor_forest.RandomTreeGraphs):
-  """Builds TF graphs for random tree training and inference."""
-
-  def tree_initialization(self):
-    return control_flow_ops.no_op()
-
-  def training_graph(self, input_data,
-                     input_labels,
-                     random_seed,
-                     data_spec,
-                     sparse_features=None,
-                     input_weights=None):
-    if input_weights is None:
-      input_weights = []
-
-    sparse_indices = []
-    sparse_values = []
-    sparse_shape = []
-    if sparse_features is not None:
-      sparse_indices = sparse_features.indices
-      sparse_values = sparse_features.values
-      sparse_shape = sparse_features.dense_shape
-
-    if input_data is None:
-      input_data = []
-
-    leaf_ids = model_ops.traverse_tree_v4(
-        self.variables.tree,
-        input_data,
-        sparse_indices,
-        sparse_values,
-        sparse_shape,
-        input_spec=data_spec.SerializeToString(),
-        params=self.params.serialized_params_proto)
-
-    update_model = model_ops.update_model_v4(
-        self.variables.tree,
-        leaf_ids,
-        input_labels,
-        input_weights,
-        params=self.params.serialized_params_proto)
-
-    finished_nodes = stats_ops.process_input_v4(
-        self.variables.tree,
-        self.variables.stats,
-        input_data,
-        sparse_indices,
-        sparse_values,
-        sparse_shape,
-        input_labels,
-        input_weights,
-        leaf_ids,
-        input_spec=data_spec.SerializeToString(),
-        random_seed=random_seed,
-        params=self.params.serialized_params_proto)
-
-    with ops.control_dependencies([update_model]):
-      return stats_ops.grow_tree_v4(
-          self.variables.tree,
-          self.variables.stats,
-          finished_nodes,
-          params=self.params.serialized_params_proto)
-
-  def inference_graph(self, input_data, data_spec, sparse_features=None):
-    sparse_indices = []
-    sparse_values = []
-    sparse_shape = []
-    if sparse_features is not None:
-      sparse_indices = sparse_features.indices
-      sparse_values = sparse_features.values
-      sparse_shape = sparse_features.dense_shape
-    if input_data is None:
-      input_data = []
-
-    return model_ops.tree_predictions_v4(
-        self.variables.tree,
-        input_data,
-        sparse_indices,
-        sparse_values,
-        sparse_shape,
-        input_spec=data_spec.SerializeToString(),
-        params=self.params.serialized_params_proto)
-
-  def average_impurity(self):
-    return constant_op.constant(0)
-
-  def size(self):
-    """Constructs a TF graph for evaluating the current number of nodes.
-
-    Returns:
-      The current number of nodes in the tree.
-    """
-    return model_ops.tree_size(self.variables.tree)
-
-  def feature_usage_counts(self):
-    return model_ops.feature_usage_counts(
-        self.variables.tree, params=self.params.serialized_params_proto)
-
-
-class RandomForestGraphsV4(tensor_forest.RandomForestGraphs):
-  """Constructs training/inference graphs for TensorForest v4."""
-
-  def __init__(self, params, tree_graphs=None, tree_variables_class=None,
-               **kwargs):
-    if not isinstance(params, V4ForestHParams):
-      params = V4ForestHParams(params)
-    super(RandomForestGraphsV4, self).__init__(
-        params, tree_graphs=tree_graphs or RandomTreeGraphsV4,
-        tree_variables_class=(tree_variables_class or TreeTrainingVariablesV4),
-        **kwargs)
-
-  def get_all_resource_handles(self):
-    return ([self.variables[i].tree for i in range(len(self.trees))] +
-            [self.variables[i].stats for i in range(len(self.trees))])
-- 
GitLab


From 2c5bd3d2d1b78d296c476f2a01fbda7289d7455d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 09:53:11 -0700
Subject: [PATCH 1242/1375] [XLA] Appease clang-tidy by switching away from
 StatusOr::ConsumeValueOrDie.

PiperOrigin-RevId: 162949550
---
 tensorflow/compiler/xla/status_macros.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/status_macros.h b/tensorflow/compiler/xla/status_macros.h
index d9ca8f320b..5e5550563d 100644
--- a/tensorflow/compiler/xla/status_macros.h
+++ b/tensorflow/compiler/xla/status_macros.h
@@ -191,7 +191,7 @@ class StatusAdaptorForMacros {
 #define TF_ASSERT_OK_AND_ASSIGN_IMPL(statusor, lhs, rexpr)  \
   auto statusor = (rexpr);                                  \
   ASSERT_TRUE(statusor.status().ok()) << statusor.status(); \
-  lhs = statusor.ConsumeValueOrDie()
+  lhs = std::move(statusor.ValueOrDie())
 
 #define TF_STATUS_MACROS_CONCAT_NAME(x, y) TF_STATUS_MACROS_CONCAT_IMPL(x, y)
 #define TF_STATUS_MACROS_CONCAT_IMPL(x, y) x##y
-- 
GitLab


From 17f9320b33069ecda689bc71a917f5df5911666f Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Mon, 24 Jul 2017 10:36:44 -0700
Subject: [PATCH 1243/1375] Fix #11708

PiperOrigin-RevId: 162955528
---
 tensorflow/docs_src/install/install_linux.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index c371712f57..456c965e6d 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -167,7 +167,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python version, and GPU support. Find the appropriate value for
      <code><em>tfBinaryURL</em></code> for your system
      [here](#the_url_of_the_tensorflow_python_package).  For example, if you
-     are installing TensorFlow for Linux, Python 2.7, and CPU-only support,
+     are installing TensorFlow for Linux, Python 3.4, and CPU-only support,
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
@@ -272,7 +272,7 @@ take the following steps:
      Python version, and GPU support. Find the appropriate value for
      <code><em>tfBinaryURL</em></code>
      [here](#the_url_of_the_tensorflow_python_package).  For example, to
-     install TensorFlow for Linux, Python 2.7, and CPU-only support, issue
+     install TensorFlow for Linux, Python 3.4, and CPU-only support, issue
      the following command:
 
      <pre>
@@ -460,7 +460,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      where <code><em>tfBinaryURL</em></code> is the
      [URL of the TensorFlow Python package](#the_url_of_the_tensorflow_python_package).
      For example, the following command installs the CPU-only version of
-     TensorFlow for Python 2.7:
+     TensorFlow for Python 3.4:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-- 
GitLab


From bc726fbec3d8f79ce138be1a81c96fde4448d4c7 Mon Sep 17 00:00:00 2001
From: Roffel <r.roffel@st.hanze.nl>
Date: Mon, 24 Jul 2017 19:50:20 +0200
Subject: [PATCH 1244/1375] verbalise=True is not a valid Darkflow command.
 Just 'verbalise' will do the job (#11716)

---
 .../android/src/org/tensorflow/demo/DetectorActivity.java       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java
index 5800f80651..041d82856f 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java
@@ -66,7 +66,7 @@ public class DetectorActivity extends CameraActivity implements OnImageAvailable
   // must be manually placed in the assets/ directory by the user.
   // Graphs and models downloaded from http://pjreddie.com/darknet/yolo/ may be converted e.g. via
   // DarkFlow (https://github.com/thtrieu/darkflow). Sample command:
-  // ./flow --model cfg/tiny-yolo-voc.cfg --load bin/tiny-yolo-voc.weights --savepb --verbalise=True
+  // ./flow --model cfg/tiny-yolo-voc.cfg --load bin/tiny-yolo-voc.weights --savepb --verbalise
   private static final String YOLO_MODEL_FILE = "file:///android_asset/graph-tiny-yolo-voc.pb";
   private static final int YOLO_INPUT_SIZE = 416;
   private static final String YOLO_INPUT_NAME = "input";
-- 
GitLab


From 9c3114bac00a294c839520178bf0b8aeb3653248 Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Tue, 25 Jul 2017 02:53:25 +0900
Subject: [PATCH 1245/1375] Fix missing spaces for several errors (#11703)

---
 .../estimator/inputs/queues/feeding_functions.py       |  2 +-
 tensorflow/python/training/adagrad_da.py               |  2 +-
 tensorflow/python/util/example_parser_configuration.py | 10 +++++-----
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py
index a6f5157680..0480325604 100644
--- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py
+++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py
@@ -258,7 +258,7 @@ class _GeneratorFeedFn(object):
         data_row = next(self._iterator)
       for index, key in enumerate(self._keys):
         if key not in data_row.keys():
-          raise KeyError("key mismatch between dicts emitted by GenFun"
+          raise KeyError("key mismatch between dicts emitted by GenFun "
                          "Expected {} keys; got {}".format(
                              self._keys, data_row.keys()))
         list_dict.setdefault(self._col_placeholders[index],
diff --git a/tensorflow/python/training/adagrad_da.py b/tensorflow/python/training/adagrad_da.py
index 1aa513f13e..b3f9ea323c 100644
--- a/tensorflow/python/training/adagrad_da.py
+++ b/tensorflow/python/training/adagrad_da.py
@@ -68,7 +68,7 @@ class AdagradDAOptimizer(optimizer.Optimizer):
       invalid.
     """
     if initial_gradient_squared_accumulator_value <= 0.0:
-      raise ValueError("initial_gradient_squared_accumulator_value must be"
+      raise ValueError("initial_gradient_squared_accumulator_value must be "
                        "positive: %s" %
                        initial_gradient_squared_accumulator_value)
     super(AdagradDAOptimizer, self).__init__(use_locking, name)
diff --git a/tensorflow/python/util/example_parser_configuration.py b/tensorflow/python/util/example_parser_configuration.py
index a375085176..e3fdcf956e 100644
--- a/tensorflow/python/util/example_parser_configuration.py
+++ b/tensorflow/python/util/example_parser_configuration.py
@@ -65,15 +65,15 @@ def extract_example_parser_configuration(parse_example_op, sess):
 
   # Fetch total_features key names and num_dense default values.
   if len(fetch_list) != (total_features + num_dense):
-    raise ValueError("len(fetch_list) does not match total features + num_dense"
-                     "(%d vs %d" % (len(fetch_list),
-                                    (total_features + num_dense)))
+    raise ValueError("len(fetch_list) does not match total features + "
+                     "num_dense (%d vs %d)" %
+                     (len(fetch_list), (total_features + num_dense)))
 
   fetched = sess.run(fetch_list)
 
   if len(fetched) != len(fetch_list):
-    raise ValueError("len(fetched) does not match len(fetch_list)"
-                     "(%d vs %d" % (len(fetched), len(fetch_list)))
+    raise ValueError("len(fetched) does not match len(fetch_list) "
+                     "(%d vs %d)" % (len(fetched), len(fetch_list)))
 
   # Fetch indices.
   sparse_keys_start = 0
-- 
GitLab


From 62bced8280075c4964033cb8b8e43b9855d655e1 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Mon, 24 Jul 2017 10:55:33 -0700
Subject: [PATCH 1246/1375] [XLA] Remove the xla_status_add_backtrace flag.

Replace the flag by always emitting the status and the backtrace to LOG(WARNING)

PiperOrigin-RevId: 162958352
---
 tensorflow/compiler/aot/BUILD                 |  1 -
 tensorflow/compiler/aot/tfcompile_main.cc     |  2 -
 tensorflow/compiler/xla/BUILD                 |  1 -
 tensorflow/compiler/xla/legacy_flags/BUILD    | 12 ----
 .../compiler/xla/legacy_flags/util_flags.cc   | 62 -------------------
 .../compiler/xla/legacy_flags/util_flags.h    | 45 --------------
 tensorflow/compiler/xla/util.cc               | 33 ++++------
 7 files changed, 13 insertions(+), 143 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/util_flags.cc
 delete mode 100644 tensorflow/compiler/xla/legacy_flags/util_flags.h

diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD
index 1ee0859030..f956602ba2 100644
--- a/tensorflow/compiler/aot/BUILD
+++ b/tensorflow/compiler/aot/BUILD
@@ -128,7 +128,6 @@ cc_library(
         ":tfcompile_lib",
         ":tfcompile_proto",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
-        "//tensorflow/compiler/xla/legacy_flags:util_flags",
         "//tensorflow/compiler/xla/service:compiler",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc
index 4f7fb55014..be2cfe4734 100644
--- a/tensorflow/compiler/aot/tfcompile_main.cc
+++ b/tensorflow/compiler/aot/tfcompile_main.cc
@@ -24,7 +24,6 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tfcompile.pb.h"
 #include "tensorflow/compiler/aot/tfcompile_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/graph.pb.h"
@@ -130,7 +129,6 @@ int main(int argc, char** argv) {
   std::vector<tensorflow::Flag> flag_list;
   AppendMainFlags(&flag_list, &flags);
   xla::legacy_flags::AppendDebugOptionsFlags(&flag_list);
-  xla::legacy_flags::AppendUtilFlags(&flag_list);
 
   tensorflow::string usage = tensorflow::tfcompile::kUsageHeader;
   usage += tensorflow::Flags::Usage(argv[0], flag_list);
diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 9c26a102ba..5eef45b11d 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -168,7 +168,6 @@ cc_library(
         ":status",
         ":types",
         ":xla_data_proto",
-        "//tensorflow/compiler/xla/legacy_flags:util_flags",
         "//tensorflow/core:lib",
     ],
 )
diff --git a/tensorflow/compiler/xla/legacy_flags/BUILD b/tensorflow/compiler/xla/legacy_flags/BUILD
index 275ef28db0..b47c82f075 100644
--- a/tensorflow/compiler/xla/legacy_flags/BUILD
+++ b/tensorflow/compiler/xla/legacy_flags/BUILD
@@ -41,18 +41,6 @@ cc_test(
         ],
 )
 
-cc_library(
-    name = "util_flags",
-    srcs = ["util_flags.cc"],
-    hdrs = ["util_flags.h"],
-    deps =
-        [
-            ":parse_flags_from_env",
-            "//tensorflow/core:framework_internal",
-            "//tensorflow/core:lib",
-        ],
-)
-
 cc_library(
     name = "debug_options_flags",
     srcs = ["debug_options_flags.cc"],
diff --git a/tensorflow/compiler/xla/legacy_flags/util_flags.cc b/tensorflow/compiler/xla/legacy_flags/util_flags.cc
deleted file mode 100644
index e6df19ddd2..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/util_flags.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Legacy flags for XLA's util module.
-
-#include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
-#include <vector>
-
-#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h"
-#include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Pointers to the parsed value of the flags and flag descriptors, initialized
-// via flags_init.
-static UtilFlags* flags;
-static std::vector<tensorflow::Flag>* flag_list;
-static std::once_flag flags_init;
-
-// Allocate *flags.  Called via call_once(&flags_init,...).
-static void AllocateFlags() {
-  flags = new UtilFlags;
-  flags->xla_status_add_backtrace = false;
-  flag_list = new std::vector<tensorflow::Flag>({
-      tensorflow::Flag("xla_status_add_backtrace",
-                       &flags->xla_status_add_backtrace,
-                       "add backtraces to XLA-produced status values"),
-  });
-  ParseFlagsFromEnv(*flag_list);
-}
-
-// Append to *append_to flag definitions associated with XLA's util module.
-void AppendUtilFlags(std::vector<tensorflow::Flag>* append_to) {
-  std::call_once(flags_init, &AllocateFlags);
-  append_to->insert(append_to->end(), flag_list->begin(), flag_list->end());
-}
-
-// Return a pointer to the UtilFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-UtilFlags* GetUtilFlags() {
-  std::call_once(flags_init, &AllocateFlags);
-  return flags;
-}
-
-}  // namespace legacy_flags
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/legacy_flags/util_flags.h b/tensorflow/compiler/xla/legacy_flags/util_flags.h
deleted file mode 100644
index 03bffcd726..0000000000
--- a/tensorflow/compiler/xla/legacy_flags/util_flags.h
+++ /dev/null
@@ -1,45 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_UTIL_FLAGS_H_
-#define TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_UTIL_FLAGS_H_
-
-// Legacy flags for the XLA's util module.
-
-#include <vector>
-
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/util/command_line_flags.h"
-
-namespace xla {
-namespace legacy_flags {
-
-// Append to *flag_list flag definitions associated with XLA's util module.
-void AppendUtilFlags(std::vector<tensorflow::Flag>* flag_list);
-
-// The values of flags associated with XLA's util module.
-typedef struct {
-  bool xla_status_add_backtrace;  // add backtraces to XLA-produced statuses
-} UtilFlags;
-
-// Return a pointer to the UtilFlags struct;
-// repeated calls return the same pointer.
-// This should be called only after Flags::Parse() has returned.
-UtilFlags* GetUtilFlags();
-
-}  // namespace legacy_flags
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_LEGACY_FLAGS_UTIL_FLAGS_H_
diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc
index 3e80394fe1..61df9a0c51 100644
--- a/tensorflow/compiler/xla/util.cc
+++ b/tensorflow/compiler/xla/util.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include <stdarg.h>
 #include <numeric>
 
-#include "tensorflow/compiler/xla/legacy_flags/util_flags.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/numbers.h"
@@ -32,18 +31,12 @@ limitations under the License.
 namespace xla {
 namespace {
 
-// Adds a backtrace to the provided status iff the xla_status_add_backtrace flag
-// is set. This is useful for quickly tracing status errors observed coming out
-// of the service.
-Status MaybeAddBacktrace(const Status& prior) {
-  DCHECK(!prior.ok());
-  if (legacy_flags::GetUtilFlags()->xla_status_add_backtrace) {
-    return Status{prior.code(),
-                  tensorflow::strings::StrCat(prior.error_message(), " :: ",
-                                              tensorflow::CurrentStackTrace())};
-  } else {
-    return prior;
-  }
+// Logs the provided status message with a backtrace.
+Status WithLogBacktrace(const Status& status) {
+  CHECK(!status.ok());
+  LOG(WARNING) << status.ToString();
+  LOG(WARNING) << tensorflow::CurrentStackTrace();
+  return status;
 }
 
 }  // namespace
@@ -86,7 +79,7 @@ Status InvalidArgument(const char* format, ...) {
   va_start(args, format);
   tensorflow::strings::Appendv(&message, format, args);
   va_end(args);
-  return MaybeAddBacktrace(tensorflow::errors::InvalidArgument(message));
+  return WithLogBacktrace(tensorflow::errors::InvalidArgument(message));
 }
 
 Status Unimplemented(const char* format, ...) {
@@ -95,7 +88,7 @@ Status Unimplemented(const char* format, ...) {
   va_start(args, format);
   tensorflow::strings::Appendv(&message, format, args);
   va_end(args);
-  return MaybeAddBacktrace(tensorflow::errors::Unimplemented(message));
+  return WithLogBacktrace(tensorflow::errors::Unimplemented(message));
 }
 
 Status InternalError(const char* format, ...) {
@@ -104,7 +97,7 @@ Status InternalError(const char* format, ...) {
   va_start(args, format);
   tensorflow::strings::Appendv(&message, format, args);
   va_end(args);
-  return MaybeAddBacktrace(tensorflow::errors::Internal(message));
+  return WithLogBacktrace(tensorflow::errors::Internal(message));
 }
 
 Status FailedPrecondition(const char* format, ...) {
@@ -113,7 +106,7 @@ Status FailedPrecondition(const char* format, ...) {
   va_start(args, format);
   tensorflow::strings::Appendv(&message, format, args);
   va_end(args);
-  return MaybeAddBacktrace(tensorflow::errors::FailedPrecondition(message));
+  return WithLogBacktrace(tensorflow::errors::FailedPrecondition(message));
 }
 
 Status ResourceExhausted(const char* format, ...) {
@@ -122,7 +115,7 @@ Status ResourceExhausted(const char* format, ...) {
   va_start(args, format);
   tensorflow::strings::Appendv(&message, format, args);
   va_end(args);
-  return MaybeAddBacktrace(tensorflow::errors::ResourceExhausted(message));
+  return WithLogBacktrace(tensorflow::errors::ResourceExhausted(message));
 }
 
 Status NotFound(const char* format, ...) {
@@ -131,7 +124,7 @@ Status NotFound(const char* format, ...) {
   va_start(args, format);
   tensorflow::strings::Appendv(&message, format, args);
   va_end(args);
-  return MaybeAddBacktrace(tensorflow::errors::NotFound(message));
+  return WithLogBacktrace(tensorflow::errors::NotFound(message));
 }
 
 Status Unavailable(const char* format, ...) {
@@ -140,7 +133,7 @@ Status Unavailable(const char* format, ...) {
   va_start(args, format);
   tensorflow::strings::Appendv(&message, format, args);
   va_end(args);
-  return MaybeAddBacktrace(tensorflow::errors::Unavailable(message));
+  return WithLogBacktrace(tensorflow::errors::Unavailable(message));
 }
 
 string Reindent(tensorflow::StringPiece original,
-- 
GitLab


From 71320b2837ec95c577ed62158a544abbc4289461 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Mon, 24 Jul 2017 14:06:41 -0400
Subject: [PATCH 1247/1375] Add requires-gpu tag to opensource cuda test BUILD
 rules

---
 tensorflow/core/platform/default/build_config_root.bzl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl
index 1a59c471e5..e1f123a0a0 100644
--- a/tensorflow/core/platform/default/build_config_root.bzl
+++ b/tensorflow/core/platform/default/build_config_root.bzl
@@ -3,10 +3,10 @@
 # be separate to avoid cyclic references.
 
 def tf_cuda_tests_tags():
-  return []
+  return ["requires-gpu"]
 
 def tf_sycl_tests_tags():
-  return []
+  return ["requires-gpu"]
 
 def tf_additional_plugin_deps():
   return select({
-- 
GitLab


From 003deb88b7fb015db86089c2a87b3044cad2c714 Mon Sep 17 00:00:00 2001
From: osdamv <osdamv@gmail.com>
Date: Mon, 24 Jul 2017 13:05:02 -0600
Subject: [PATCH 1248/1375] Refactor and implementation of the camera API 1, it
 fixes #8736 (#10771)

* Fallback to legacy camera API for problematic devices

* Refactor and implementation of the camera API 1

* Refactor and implementation of the camera API 1, it fixes #8736

* Fix some corrections of @andrewharp

* Some fixes for @andrewharp

* fix a missing line

* Some more sanity fixes

* Optimization for the calculation of the preview size,
Also, add validation to check if we already choose the preview size

* Fixed some lines that were over 100 characters

* Update CameraActivity.java

fixed indentations

* Update ClassifierActivity.java

Fixed indentations

* Update StylizeActivity.java

Fixed indentations.

* Update StylizeActivity.java

fix spaces

* Update StylizeActivity.java

Fix indentation.
---
 .../org/tensorflow/demo/CameraActivity.java   | 206 ++++++++++++++++--
 .../demo/CameraConnectionFragment.java        |  80 +++----
 .../tensorflow/demo/ClassifierActivity.java   |  99 ++-------
 .../org/tensorflow/demo/DetectorActivity.java |  23 +-
 .../demo/LegacyCameraConnectionFragment.java  | 205 +++++++++++++++++
 .../org/tensorflow/demo/StylizeActivity.java  | 154 +++++--------
 6 files changed, 512 insertions(+), 255 deletions(-)
 create mode 100644 tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java

diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
index 27d7e41487..512580fd3e 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
@@ -19,22 +19,34 @@ package org.tensorflow.demo;
 import android.Manifest;
 import android.app.Activity;
 import android.app.Fragment;
+import android.content.Context;
 import android.content.pm.PackageManager;
+import android.graphics.Bitmap;
+import android.hardware.Camera;
+import android.hardware.camera2.CameraAccessException;
+import android.hardware.camera2.CameraCharacteristics;
+import android.hardware.camera2.CameraManager;
+import android.hardware.camera2.params.StreamConfigurationMap;
+import android.media.Image;
 import android.media.Image.Plane;
+import android.media.ImageReader;
 import android.media.ImageReader.OnImageAvailableListener;
 import android.os.Build;
 import android.os.Bundle;
 import android.os.Handler;
 import android.os.HandlerThread;
+import android.os.Trace;
 import android.util.Size;
 import android.view.KeyEvent;
 import android.view.WindowManager;
 import android.widget.Toast;
 import java.nio.ByteBuffer;
+
+import org.tensorflow.demo.env.ImageUtils;
 import org.tensorflow.demo.env.Logger;
-import org.tensorflow.demo.R;
 
-public abstract class CameraActivity extends Activity implements OnImageAvailableListener {
+public abstract class CameraActivity extends Activity implements OnImageAvailableListener, Camera.
+        PreviewCallback {
   private static final Logger LOGGER = new Logger();
 
   private static final int PERMISSIONS_REQUEST = 1;
@@ -46,6 +58,20 @@ public abstract class CameraActivity extends Activity implements OnImageAvailabl
 
   private Handler handler;
   private HandlerThread handlerThread;
+  private boolean useCamera2API;
+  protected Bitmap rgbFrameBitmap = null;
+  private int[] rgbBytes = null;
+  protected int previewWidth = 0;
+  protected int previewHeight = 0;
+  protected Bitmap croppedBitmap = null;
+  protected static final boolean SAVE_PREVIEW_BITMAP = false;
+  protected long lastProcessingTimeMs;
+  protected Bitmap cropCopyBitmap;
+  protected ResultsView resultsView;
+  protected boolean computing = false;
+  protected Runnable postInferenceCallback;
+  protected byte[][] yuvBytes=new byte[3][];
+  protected int yRowStride;
 
   @Override
   protected void onCreate(final Bundle savedInstanceState) {
@@ -62,6 +88,93 @@ public abstract class CameraActivity extends Activity implements OnImageAvailabl
     }
   }
 
+  /**
+   * Callback for android.hardware.Camera API
+   */
+  @Override
+  public void onPreviewFrame(final byte[] bytes, final Camera camera) {
+    if (computing) {
+      return;
+    }
+    computing = true;
+    yuvBytes[0] = bytes;
+    try {
+      // Initialize the storage bitmaps once when the resolution is known.
+      if (rgbBytes == null) {
+        Camera.Size previewSize = camera.getParameters().getPreviewSize();
+        previewHeight = previewSize.height;
+        previewWidth = previewSize.width;
+        rgbBytes = new int[previewWidth * previewHeight];
+        onPreviewSizeChosen(new Size(previewSize.width, previewSize.height), 90);
+      }
+      ImageUtils.convertYUV420SPToARGB8888(bytes, rgbBytes, previewWidth, previewHeight, false);
+    } catch (final Exception e) {
+      LOGGER.e(e, "Exception!");
+      return;
+    }
+    postInferenceCallback = new Runnable() {
+      @Override
+      public void run() {
+        camera.addCallbackBuffer(bytes);
+      }
+    };
+    processImageRGBbytes(rgbBytes);
+  }
+
+  /**
+   * Callback for Camera2 API
+   */
+  @Override
+  public void onImageAvailable(final ImageReader reader) {
+    Image image = null;
+    //We need wait until we have some size from onPreviewSizeChosen
+    if (previewWidth == 0 || previewHeight == 0) {
+      return;
+    }
+    rgbBytes = new int[previewWidth * previewHeight];
+    try {
+      image = reader.acquireLatestImage();
+
+      if (image == null) {
+        return;
+      }
+
+      if (computing) {
+        image.close();
+        return;
+      }
+      computing = true;
+      Trace.beginSection("imageAvailable");
+      final Plane[] planes = image.getPlanes();
+      fillBytes(planes, yuvBytes);
+      yRowStride = planes[0].getRowStride();
+      final int uvRowStride = planes[1].getRowStride();
+      final int uvPixelStride = planes[1].getPixelStride();
+      ImageUtils.convertYUV420ToARGB8888(
+          yuvBytes[0],
+          yuvBytes[1],
+          yuvBytes[2],
+          rgbBytes,
+          previewWidth,
+          previewHeight,
+          yRowStride,
+          uvRowStride,
+          uvPixelStride,
+          false);
+      image.close();
+
+    } catch (final Exception e) {
+      if (image != null) {
+        image.close();
+      }
+      LOGGER.e(e, "Exception!");
+      Trace.endSection();
+      return;
+    }
+    processImageRGBbytes(rgbBytes);
+    Trace.endSection();
+  }
+
   @Override
   public synchronized void onStart() {
     LOGGER.d("onStart " + this);
@@ -123,8 +236,8 @@ public abstract class CameraActivity extends Activity implements OnImageAvailabl
     switch (requestCode) {
       case PERMISSIONS_REQUEST: {
         if (grantResults.length > 0
-                && grantResults[0] == PackageManager.PERMISSION_GRANTED
-                && grantResults[1] == PackageManager.PERMISSION_GRANTED) {
+            && grantResults[0] == PackageManager.PERMISSION_GRANTED
+            && grantResults[1] == PackageManager.PERMISSION_GRANTED) {
           setFragment();
         } else {
           requestPermission();
@@ -135,7 +248,8 @@ public abstract class CameraActivity extends Activity implements OnImageAvailabl
 
   private boolean hasPermission() {
     if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) {
-      return checkSelfPermission(PERMISSION_CAMERA) == PackageManager.PERMISSION_GRANTED && checkSelfPermission(PERMISSION_STORAGE) == PackageManager.PERMISSION_GRANTED;
+      return checkSelfPermission(PERMISSION_CAMERA) == PackageManager.PERMISSION_GRANTED &&
+          checkSelfPermission(PERMISSION_STORAGE) == PackageManager.PERMISSION_GRANTED;
     } else {
       return true;
     }
@@ -143,25 +257,80 @@ public abstract class CameraActivity extends Activity implements OnImageAvailabl
 
   private void requestPermission() {
     if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) {
-      if (shouldShowRequestPermissionRationale(PERMISSION_CAMERA) || shouldShowRequestPermissionRationale(PERMISSION_STORAGE)) {
-        Toast.makeText(CameraActivity.this, "Camera AND storage permission are required for this demo", Toast.LENGTH_LONG).show();
+      if (shouldShowRequestPermissionRationale(PERMISSION_CAMERA) ||
+          shouldShowRequestPermissionRationale(PERMISSION_STORAGE)) {
+        Toast.makeText(CameraActivity.this,
+            "Camera AND storage permission are required for this demo", Toast.LENGTH_LONG).show();
       }
       requestPermissions(new String[] {PERMISSION_CAMERA, PERMISSION_STORAGE}, PERMISSIONS_REQUEST);
     }
   }
 
+  // Returns true if the device supports the required hardware level, or better.
+  boolean isHardwareLevelSupported(CameraCharacteristics characteristics, int requiredLevel) {
+    int deviceLevel = characteristics.get(CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL);
+    if (deviceLevel == CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_LEGACY) {
+      return requiredLevel == deviceLevel;
+    }
+    // deviceLevel is not LEGACY, can use numerical sort
+    return requiredLevel <= deviceLevel;
+  }
+
+  private String chooseCamera() {
+    final CameraManager manager = (CameraManager) getSystemService(Context.CAMERA_SERVICE);
+    try {
+      for (final String cameraId : manager.getCameraIdList()) {
+        final CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId);
+
+        // We don't use a front facing camera in this sample.
+        final Integer facing = characteristics.get(CameraCharacteristics.LENS_FACING);
+        if (facing != null && facing == CameraCharacteristics.LENS_FACING_FRONT) {
+          continue;
+        }
+
+        final StreamConfigurationMap map =
+            characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);
+
+        if (map == null) {
+          continue;
+        }
+
+        useCamera2API = isHardwareLevelSupported(characteristics,
+            CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_FULL);
+        LOGGER.i("Camera API lv2?: %s", useCamera2API);
+        return cameraId;
+      }
+    } catch (CameraAccessException e) {
+      LOGGER.e(e, "Not allowed to access camera");
+    }
+
+    return null;
+  }
+
   protected void setFragment() {
-    final Fragment fragment =
-        CameraConnectionFragment.newInstance(
-            new CameraConnectionFragment.ConnectionCallback() {
-              @Override
-              public void onPreviewSizeChosen(final Size size, final int rotation) {
-                CameraActivity.this.onPreviewSizeChosen(size, rotation);
-              }
-            },
-            this,
-            getLayoutId(),
-            getDesiredPreviewFrameSize());
+    String cameraId = chooseCamera();
+
+    Fragment fragment;
+    if (useCamera2API) {
+      CameraConnectionFragment camera2Fragment =
+          CameraConnectionFragment.newInstance(
+              new CameraConnectionFragment.ConnectionCallback() {
+                @Override
+                public void onPreviewSizeChosen(final Size size, final int rotation) {
+                  previewHeight = size.getHeight();
+                  previewWidth = size.getWidth();
+                  CameraActivity.this.onPreviewSizeChosen(size, rotation);
+                }
+              },
+              this,
+              getLayoutId(),
+              getDesiredPreviewFrameSize());
+
+      camera2Fragment.setCamera(cameraId);
+      fragment = camera2Fragment;
+    } else {
+      fragment = new LegacyCameraConnectionFragment(this, getLayoutId());
+    }
 
     getFragmentManager()
         .beginTransaction()
@@ -213,6 +382,7 @@ public abstract class CameraActivity extends Activity implements OnImageAvailabl
     return super.onKeyDown(keyCode, event);
   }
 
+  protected abstract void processImageRGBbytes(int[] rgbBytes ) ;
   protected abstract void onPreviewSizeChosen(final Size size, final int rotation);
   protected abstract int getLayoutId();
   protected abstract Size getDesiredPreviewFrameSize();
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java b/tensorflow/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java
index 76bd61d00f..986f2777b2 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java
@@ -353,58 +353,44 @@ public class CameraConnectionFragment extends Fragment {
     super.onPause();
   }
 
+  public void setCamera(String cameraId) {
+    this.cameraId = cameraId;
+  }
+
   /**
    * Sets up member variables related to camera.
-   *
-   * @param width  The width of available size for camera preview
-   * @param height The height of available size for camera preview
    */
-  private void setUpCameraOutputs(final int width, final int height) {
+  private void setUpCameraOutputs() {
     final Activity activity = getActivity();
     final CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE);
     try {
-      for (final String cameraId : manager.getCameraIdList()) {
-        final CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId);
-
-        // We don't use a front facing camera in this sample.
-        final Integer facing = characteristics.get(CameraCharacteristics.LENS_FACING);
-        if (facing != null && facing == CameraCharacteristics.LENS_FACING_FRONT) {
-          continue;
-        }
-
-        final StreamConfigurationMap map =
-            characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);
-
-        if (map == null) {
-          continue;
-        }
-
-        // For still image captures, we use the largest available size.
-        final Size largest =
-            Collections.max(
-                Arrays.asList(map.getOutputSizes(ImageFormat.YUV_420_888)),
-                new CompareSizesByArea());
-
-        sensorOrientation = characteristics.get(CameraCharacteristics.SENSOR_ORIENTATION);
-
-        // Danger, W.R.! Attempting to use too large a preview size could  exceed the camera
-        // bus' bandwidth limitation, resulting in gorgeous previews but the storage of
-        // garbage capture data.
-        previewSize =
-            chooseOptimalSize(
-                map.getOutputSizes(SurfaceTexture.class),
-                inputSize.getWidth(),
-                inputSize.getHeight());
-
-        // We fit the aspect ratio of TextureView to the size of preview we picked.
-        final int orientation = getResources().getConfiguration().orientation;
-        if (orientation == Configuration.ORIENTATION_LANDSCAPE) {
-          textureView.setAspectRatio(previewSize.getWidth(), previewSize.getHeight());
-        } else {
-          textureView.setAspectRatio(previewSize.getHeight(), previewSize.getWidth());
-        }
-
-        CameraConnectionFragment.this.cameraId = cameraId;
+      final CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId);
+
+      final StreamConfigurationMap map =
+          characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);
+
+      // For still image captures, we use the largest available size.
+      final Size largest =
+          Collections.max(
+              Arrays.asList(map.getOutputSizes(ImageFormat.YUV_420_888)),
+              new CompareSizesByArea());
+
+      sensorOrientation = characteristics.get(CameraCharacteristics.SENSOR_ORIENTATION);
+
+      // Danger, W.R.! Attempting to use too large a preview size could  exceed the camera
+      // bus' bandwidth limitation, resulting in gorgeous previews but the storage of
+      // garbage capture data.
+      previewSize =
+          chooseOptimalSize(map.getOutputSizes(SurfaceTexture.class),
+              inputSize.getWidth(),
+              inputSize.getHeight());
+
+      // We fit the aspect ratio of TextureView to the size of preview we picked.
+      final int orientation = getResources().getConfiguration().orientation;
+      if (orientation == Configuration.ORIENTATION_LANDSCAPE) {
+        textureView.setAspectRatio(previewSize.getWidth(), previewSize.getHeight());
+      } else {
+        textureView.setAspectRatio(previewSize.getHeight(), previewSize.getWidth());
       }
     } catch (final CameraAccessException e) {
       LOGGER.e(e, "Exception!");
@@ -425,7 +411,7 @@ public class CameraConnectionFragment extends Fragment {
    * Opens the camera specified by {@link CameraConnectionFragment#cameraId}.
    */
   private void openCamera(final int width, final int height) {
-    setUpCameraOutputs(width, height);
+    setUpCameraOutputs();
     configureTransform(width, height);
     final Activity activity = getActivity();
     final CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE);
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/ClassifierActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/ClassifierActivity.java
index bc39126925..f5cebc7670 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/ClassifierActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/ClassifierActivity.java
@@ -22,22 +22,19 @@ import android.graphics.Canvas;
 import android.graphics.Matrix;
 import android.graphics.Paint;
 import android.graphics.Typeface;
-import android.media.Image;
-import android.media.Image.Plane;
-import android.media.ImageReader;
+
 import android.media.ImageReader.OnImageAvailableListener;
 import android.os.SystemClock;
-import android.os.Trace;
 import android.util.Size;
 import android.util.TypedValue;
 import android.view.Display;
+
 import java.util.List;
 import java.util.Vector;
 import org.tensorflow.demo.OverlayView.DrawCallback;
 import org.tensorflow.demo.env.BorderedText;
 import org.tensorflow.demo.env.ImageUtils;
 import org.tensorflow.demo.env.Logger;
-import org.tensorflow.demo.R;
 
 public class ClassifierActivity extends CameraActivity implements OnImageAvailableListener {
   private static final Logger LOGGER = new Logger();
@@ -64,39 +61,25 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab
   private static final String INPUT_NAME = "input";
   private static final String OUTPUT_NAME = "output";
 
+
   private static final String MODEL_FILE = "file:///android_asset/tensorflow_inception_graph.pb";
   private static final String LABEL_FILE =
       "file:///android_asset/imagenet_comp_graph_label_strings.txt";
 
-  private static final boolean SAVE_PREVIEW_BITMAP = false;
 
   private static final boolean MAINTAIN_ASPECT = true;
 
   private static final Size DESIRED_PREVIEW_SIZE = new Size(640, 480);
 
-  private Classifier classifier;
 
   private Integer sensorOrientation;
-
-  private int previewWidth = 0;
-  private int previewHeight = 0;
-  private byte[][] yuvBytes;
-  private int[] rgbBytes = null;
-  private Bitmap rgbFrameBitmap = null;
-  private Bitmap croppedBitmap = null;
-
-  private Bitmap cropCopyBitmap;
-
-  private boolean computing = false;
-
+  private Classifier classifier;
   private Matrix frameToCropTransform;
   private Matrix cropToFrameTransform;
 
-  private ResultsView resultsView;
 
   private BorderedText borderedText;
 
-  private long lastProcessingTimeMs;
 
   @Override
   protected int getLayoutId() {
@@ -112,9 +95,8 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab
 
   @Override
   public void onPreviewSizeChosen(final Size size, final int rotation) {
-    final float textSizePx =
-        TypedValue.applyDimension(
-            TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());
+    final float textSizePx = TypedValue.applyDimension(
+        TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());
     borderedText = new BorderedText(textSizePx);
     borderedText.setTypeface(Typeface.MONOSPACE);
 
@@ -129,7 +111,6 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab
             INPUT_NAME,
             OUTPUT_NAME);
 
-    resultsView = (ResultsView) findViewById(R.id.results);
     previewWidth = size.getWidth();
     previewHeight = size.getHeight();
 
@@ -141,15 +122,13 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab
     sensorOrientation = rotation + screenOrientation;
 
     LOGGER.i("Initializing at size %dx%d", previewWidth, previewHeight);
-    rgbBytes = new int[previewWidth * previewHeight];
     rgbFrameBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Config.ARGB_8888);
     croppedBitmap = Bitmap.createBitmap(INPUT_SIZE, INPUT_SIZE, Config.ARGB_8888);
 
-    frameToCropTransform =
-        ImageUtils.getTransformationMatrix(
-            previewWidth, previewHeight,
-            INPUT_SIZE, INPUT_SIZE,
-            sensorOrientation, MAINTAIN_ASPECT);
+    frameToCropTransform = ImageUtils.getTransformationMatrix(
+        previewWidth, previewHeight,
+        INPUT_SIZE, INPUT_SIZE,
+        sensorOrientation, MAINTAIN_ASPECT);
 
     cropToFrameTransform = new Matrix();
     frameToCropTransform.invert(cropToFrameTransform);
@@ -165,52 +144,7 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab
         });
   }
 
-  @Override
-  public void onImageAvailable(final ImageReader reader) {
-    Image image = null;
-
-    try {
-      image = reader.acquireLatestImage();
-
-      if (image == null) {
-        return;
-      }
-
-      if (computing) {
-        image.close();
-        return;
-      }
-      computing = true;
-
-      Trace.beginSection("imageAvailable");
-
-      final Plane[] planes = image.getPlanes();
-      fillBytes(planes, yuvBytes);
-
-      final int yRowStride = planes[0].getRowStride();
-      final int uvRowStride = planes[1].getRowStride();
-      final int uvPixelStride = planes[1].getPixelStride();
-      ImageUtils.convertYUV420ToARGB8888(
-          yuvBytes[0],
-          yuvBytes[1],
-          yuvBytes[2],
-          previewWidth,
-          previewHeight,
-          yRowStride,
-          uvRowStride,
-          uvPixelStride,
-          rgbBytes);
-
-      image.close();
-    } catch (final Exception e) {
-      if (image != null) {
-        image.close();
-      }
-      LOGGER.e(e, "Exception!");
-      Trace.endSection();
-      return;
-    }
-
+  protected void processImageRGBbytes(int[] rgbBytes ) {
     rgbFrameBitmap.setPixels(rgbBytes, 0, previewWidth, 0, 0, previewWidth, previewHeight);
     final Canvas canvas = new Canvas(croppedBitmap);
     canvas.drawBitmap(rgbFrameBitmap, frameToCropTransform, null);
@@ -219,7 +153,6 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab
     if (SAVE_PREVIEW_BITMAP) {
       ImageUtils.saveBitmap(croppedBitmap);
     }
-
     runInBackground(
         new Runnable() {
           @Override
@@ -227,15 +160,19 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab
             final long startTime = SystemClock.uptimeMillis();
             final List<Classifier.Recognition> results = classifier.recognizeImage(croppedBitmap);
             lastProcessingTimeMs = SystemClock.uptimeMillis() - startTime;
-
+            LOGGER.i("Detect: %s", results);
             cropCopyBitmap = Bitmap.createBitmap(croppedBitmap);
+            if (resultsView==null) {
+              resultsView = (ResultsView) findViewById(R.id.results);
+            }
             resultsView.setResults(results);
             requestRender();
             computing = false;
+            if (postInferenceCallback != null) {
+              postInferenceCallback.run();
+            }
           }
         });
-
-    Trace.endSection();
   }
 
   @Override
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java
index 041d82856f..acace0eace 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java
@@ -126,6 +126,7 @@ public class DetectorActivity extends CameraActivity implements OnImageAvailable
 
     tracker = new MultiBoxTracker(this);
 
+
     if (USE_YOLO) {
       detector =
           TensorFlowYoloDetector.create(
@@ -270,15 +271,17 @@ public class DetectorActivity extends CameraActivity implements OnImageAvailable
       final int uvRowStride = planes[1].getRowStride();
       final int uvPixelStride = planes[1].getPixelStride();
       ImageUtils.convertYUV420ToARGB8888(
-          yuvBytes[0],
-          yuvBytes[1],
-          yuvBytes[2],
-          previewWidth,
-          previewHeight,
-          yRowStride,
-          uvRowStride,
-          uvPixelStride,
-          rgbBytes);
+              yuvBytes[0],
+              yuvBytes[1],
+              yuvBytes[2],
+              rgbBytes,
+              previewWidth,
+              previewHeight,
+              yRowStride,
+              uvRowStride,
+              uvPixelStride,
+              false);
+
 
       image.close();
     } catch (final Exception e) {
@@ -344,6 +347,8 @@ public class DetectorActivity extends CameraActivity implements OnImageAvailable
     Trace.endSection();
   }
 
+  protected  void processImageRGBbytes(int[] rgbBytes ) {}
+
   @Override
   protected int getLayoutId() {
     return R.layout.camera_connection_fragment_tracking;
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java
new file mode 100644
index 0000000000..8a22aaf4d6
--- /dev/null
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java
@@ -0,0 +1,205 @@
+package org.tensorflow.demo;
+
+/*
+ * Copyright 2014 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *       http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import android.app.Fragment;
+import android.graphics.SurfaceTexture;
+import android.os.Bundle;
+import android.os.Handler;
+import android.os.HandlerThread;
+import android.util.SparseIntArray;
+import android.view.LayoutInflater;
+import android.view.Surface;
+import android.view.TextureView;
+import android.view.View;
+import android.view.ViewGroup;
+
+import java.io.IOException;
+
+import android.hardware.Camera;
+import android.hardware.Camera.CameraInfo;
+
+import org.tensorflow.demo.env.Logger;
+
+public class LegacyCameraConnectionFragment extends Fragment {
+
+  private Camera camera;
+  private static final Logger LOGGER = new Logger();
+  private Camera.PreviewCallback imageListener;
+
+  /**
+   * The layout identifier to inflate for this Fragment.
+   */
+  private int layout;
+
+  public LegacyCameraConnectionFragment(
+      final Camera.PreviewCallback imageListener,
+      final int layout) {
+    this.imageListener = imageListener;
+    this.layout = layout;
+  }
+
+  /**
+   * Conversion from screen rotation to JPEG orientation.
+   */
+  private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
+
+  static {
+    ORIENTATIONS.append(Surface.ROTATION_0, 90);
+    ORIENTATIONS.append(Surface.ROTATION_90, 0);
+    ORIENTATIONS.append(Surface.ROTATION_180, 270);
+    ORIENTATIONS.append(Surface.ROTATION_270, 180);
+  }
+
+  /**
+   * {@link android.view.TextureView.SurfaceTextureListener} handles several lifecycle events on a
+   * {@link TextureView}.
+   */
+  private final TextureView.SurfaceTextureListener surfaceTextureListener =
+      new TextureView.SurfaceTextureListener() {
+        @Override
+        public void onSurfaceTextureAvailable(
+            final SurfaceTexture texture, final int width, final int height) {
+
+          int index = getCameraId();
+          camera = Camera.open(index);
+
+          try {
+            Camera.Parameters parameters = camera.getParameters();
+            parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE);
+
+            camera.setDisplayOrientation(90);
+            camera.setParameters(parameters);
+            camera.setPreviewTexture(texture);
+          } catch (IOException exception) {
+            camera.release();
+          }
+
+          camera.setPreviewCallbackWithBuffer(imageListener);
+          Camera.Size s = camera.getParameters().getPreviewSize();
+          int bufferSize = s.height * s.width * 3 / 2;
+          camera.addCallbackBuffer(new byte[bufferSize]);
+
+          textureView.setAspectRatio(s.height, s.width);
+
+          camera.startPreview();
+        }
+
+        @Override
+        public void onSurfaceTextureSizeChanged(
+            final SurfaceTexture texture, final int width, final int height) {
+        }
+
+        @Override
+        public boolean onSurfaceTextureDestroyed(final SurfaceTexture texture) {
+          return true;
+        }
+
+        @Override
+        public void onSurfaceTextureUpdated(final SurfaceTexture texture) {
+        }
+      };
+
+  /**
+   * An {@link AutoFitTextureView} for camera preview.
+   */
+  private AutoFitTextureView textureView;
+
+  /**
+   * An additional thread for running tasks that shouldn't block the UI.
+   */
+  private HandlerThread backgroundThread;
+
+  @Override
+  public View onCreateView(
+      final LayoutInflater inflater, final ViewGroup container, final Bundle savedInstanceState) {
+    return inflater.inflate(layout, container, false);
+  }
+
+  @Override
+  public void onViewCreated(final View view, final Bundle savedInstanceState) {
+    textureView = (AutoFitTextureView) view.findViewById(R.id.texture);
+  }
+
+  @Override
+  public void onActivityCreated(final Bundle savedInstanceState) {
+    super.onActivityCreated(savedInstanceState);
+  }
+
+  @Override
+  public void onResume() {
+    super.onResume();
+    startBackgroundThread();
+    // When the screen is turned off and turned back on, the SurfaceTexture is already
+    // available, and "onSurfaceTextureAvailable" will not be called. In that case, we can open
+    // a camera and start preview from here (otherwise, we wait until the surface is ready in
+    // the SurfaceTextureListener).
+
+    if (textureView.isAvailable()) {
+      camera.startPreview();
+    } else {
+      textureView.setSurfaceTextureListener(surfaceTextureListener);
+    }
+  }
+
+  @Override
+  public void onPause() {
+    stopCamera();
+    stopBackgroundThread();
+    super.onPause();
+  }
+
+  /**
+   * Starts a background thread and its {@link Handler}.
+   */
+  private void startBackgroundThread() {
+    backgroundThread = new HandlerThread("CameraBackground");
+    backgroundThread.start();
+  }
+
+  /**
+   * Stops the background thread and its {@link Handler}.
+   */
+  private void stopBackgroundThread() {
+    backgroundThread.quitSafely();
+    try {
+      backgroundThread.join();
+      backgroundThread = null;
+    } catch (final InterruptedException e) {
+      LOGGER.e(e, "Exception!");
+    }
+  }
+
+  protected void stopCamera() {
+    if (camera != null) {
+      camera.stopPreview();
+      camera.setPreviewCallback(null);
+      camera.release();
+      camera = null;
+    }
+  }
+
+  private int getCameraId() {
+    CameraInfo ci = new CameraInfo();
+    for (int i = 0; i < Camera.getNumberOfCameras(); i++) {
+      Camera.getCameraInfo(i, ci);
+      if (ci.facing == CameraInfo.CAMERA_FACING_BACK)
+        return i;
+    }
+    return -1; // No camera found
+  }
+}
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java
index 7afe2bf541..f0d2dd1ed6 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java
@@ -28,6 +28,7 @@ import android.graphics.Paint;
 import android.graphics.Paint.Style;
 import android.graphics.Rect;
 import android.graphics.Typeface;
+import android.hardware.Camera;
 import android.media.Image;
 import android.media.Image.Plane;
 import android.media.ImageReader;
@@ -58,7 +59,6 @@ import org.tensorflow.demo.OverlayView.DrawCallback;
 import org.tensorflow.demo.env.BorderedText;
 import org.tensorflow.demo.env.ImageUtils;
 import org.tensorflow.demo.env.Logger;
-import org.tensorflow.demo.R;
 
 /**
  * Sample activity that stylizes the camera preview according to "A Learned Representation For
@@ -97,10 +97,6 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL
 
   private int previewWidth = 0;
   private int previewHeight = 0;
-  private byte[][] yuvBytes;
-  private int[] rgbBytes = null;
-  private Bitmap rgbFrameBitmap = null;
-  private Bitmap croppedBitmap = null;
 
   private final float[] styleVals = new float[NUM_STYLES];
   private int[] intValues;
@@ -108,18 +104,13 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL
 
   private int frameNum = 0;
 
-  private Bitmap cropCopyBitmap;
   private Bitmap textureCopyBitmap;
 
-  private boolean computing = false;
-
   private Matrix frameToCropTransform;
   private Matrix cropToFrameTransform;
 
   private BorderedText borderedText;
 
-  private long lastProcessingTimeMs;
-
   private TensorFlowInferenceInterface inferenceInterface;
 
   private int lastOtherStyle = 1;
@@ -363,9 +354,8 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL
 
   @Override
   public void onPreviewSizeChosen(final Size size, final int rotation) {
-    final float textSizePx =
-        TypedValue.applyDimension(
-            TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());
+    final float textSizePx = TypedValue.applyDimension(
+        TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());
     borderedText = new BorderedText(textSizePx);
     borderedText.setTypeface(Typeface.MONOSPACE);
 
@@ -393,7 +383,6 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL
     grid = (GridView) findViewById(R.id.grid_layout);
     grid.setAdapter(adapter);
     grid.setOnTouchListener(gridTouchAdapter);
-
     setStyle(adapter.items[0], 1.0f);
   }
 
@@ -455,78 +444,42 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL
     }
   }
 
-  @Override
-  public void onImageAvailable(final ImageReader reader) {
-    Image image = null;
-
-    try {
-      image = reader.acquireLatestImage();
-
-      if (image == null) {
-        return;
-      }
-
-      if (computing) {
-        image.close();
-        return;
-      }
-
-      if (desiredSize != initializedSize) {
-        LOGGER.i(
-            "Initializing at size preview size %dx%d, stylize size %d",
-            previewWidth, previewHeight, desiredSize);
-        rgbBytes = new int[previewWidth * previewHeight];
-        rgbFrameBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Config.ARGB_8888);
-        croppedBitmap = Bitmap.createBitmap(desiredSize, desiredSize, Config.ARGB_8888);
-
-        frameToCropTransform =
-            ImageUtils.getTransformationMatrix(
-                previewWidth, previewHeight,
-                desiredSize, desiredSize,
-                sensorOrientation, true);
-
-        cropToFrameTransform = new Matrix();
-        frameToCropTransform.invert(cropToFrameTransform);
-
-        yuvBytes = new byte[3][];
-
-        intValues = new int[desiredSize * desiredSize];
-        floatValues = new float[desiredSize * desiredSize * 3];
-        initializedSize = desiredSize;
-      }
-
-      computing = true;
+  private void resetPreviewBuffers() {
+    croppedBitmap = Bitmap.createBitmap(desiredSize, desiredSize, Config.ARGB_8888);
 
-      Trace.beginSection("imageAvailable");
+    frameToCropTransform = ImageUtils.getTransformationMatrix(
+        previewWidth, previewHeight,
+        desiredSize, desiredSize,
+        sensorOrientation, true);
 
-      final Plane[] planes = image.getPlanes();
-      fillBytes(planes, yuvBytes);
-
-      final int yRowStride = planes[0].getRowStride();
-      final int uvRowStride = planes[1].getRowStride();
-      final int uvPixelStride = planes[1].getPixelStride();
-
-      ImageUtils.convertYUV420ToARGB8888(
-          yuvBytes[0],
-          yuvBytes[1],
-          yuvBytes[2],
-          previewWidth,
-          previewHeight,
-          yRowStride,
-          uvRowStride,
-          uvPixelStride,
-          rgbBytes);
+    cropToFrameTransform = new Matrix();
+    frameToCropTransform.invert(cropToFrameTransform);
+    yuvBytes = new byte[3][];
+    intValues = new int[desiredSize * desiredSize];
+    floatValues = new float[desiredSize * desiredSize * 3];
+    initializedSize = desiredSize;
+  }
 
-      image.close();
-    } catch (final Exception e) {
-      if (image != null) {
-        image.close();
-      }
-      LOGGER.e(e, "Exception!");
-      Trace.endSection();
-      return;
+  protected void processImageRGBbytes(int[] rgbBytes ) {
+    if (desiredSize != initializedSize) {
+      LOGGER.i(
+          "Initializing at size preview size %dx%d, stylize size %d",
+          previewWidth, previewHeight, desiredSize);
+      
+      rgbFrameBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Config.ARGB_8888);
+      croppedBitmap = Bitmap.createBitmap(desiredSize, desiredSize, Config.ARGB_8888);
+      frameToCropTransform = ImageUtils.getTransformationMatrix(
+          previewWidth, previewHeight,
+          desiredSize, desiredSize,
+          sensorOrientation, true);
+
+      cropToFrameTransform = new Matrix();
+      frameToCropTransform.invert(cropToFrameTransform);
+      yuvBytes = new byte[3][];
+      intValues = new int[desiredSize * desiredSize];
+      floatValues = new float[desiredSize * desiredSize * 3];
+      initializedSize = desiredSize;
     }
-
     rgbFrameBitmap.setPixels(rgbBytes, 0, previewWidth, 0, 0, previewWidth, previewHeight);
     final Canvas canvas = new Canvas(croppedBitmap);
     canvas.drawBitmap(rgbFrameBitmap, frameToCropTransform, null);
@@ -536,24 +489,24 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL
       ImageUtils.saveBitmap(croppedBitmap);
     }
 
-    runInBackground(
-        new Runnable() {
-          @Override
-          public void run() {
-            cropCopyBitmap = Bitmap.createBitmap(croppedBitmap);
-
-            final long startTime = SystemClock.uptimeMillis();
-            stylizeImage(croppedBitmap);
-            lastProcessingTimeMs = SystemClock.uptimeMillis() - startTime;
-
-            textureCopyBitmap = Bitmap.createBitmap(croppedBitmap);
-
-            requestRender();
-            computing = false;
-          }
-        });
-
-    Trace.endSection();
+    runInBackground(new Runnable() {
+      @Override
+      public void run() {
+        cropCopyBitmap = Bitmap.createBitmap(croppedBitmap);
+        final long startTime = SystemClock.uptimeMillis();
+        stylizeImage(croppedBitmap);
+        lastProcessingTimeMs = SystemClock.uptimeMillis() - startTime;
+        textureCopyBitmap = Bitmap.createBitmap(croppedBitmap);
+        requestRender();
+        computing = false;
+        if (postInferenceCallback != null) {
+          postInferenceCallback.run();
+        }
+      }
+    });
+    if (desiredSize != initializedSize) {
+      resetPreviewBuffers();
+    }
   }
 
   private void stylizeImage(final Bitmap bitmap) {
@@ -584,6 +537,7 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL
     }
 
     // Copy the input data into TensorFlow.
+    LOGGER.i("Width: %s , Height: %s",bitmap.getWidth(),bitmap.getHeight());
     inferenceInterface.feed(
         INPUT_NODE, floatValues, 1, bitmap.getWidth(), bitmap.getHeight(), 3);
     inferenceInterface.feed(STYLE_NODE, styleVals, NUM_STYLES);
-- 
GitLab


From 1b578268f5407b4f40c4226b7d00b08e948a4b09 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 12:23:13 -0700
Subject: [PATCH 1249/1375] Fix _Recv op caching for multi-output port ops in
 VirtualScheduler.

PiperOrigin-RevId: 162970793
---
 .../core/grappler/costs/virtual_scheduler.cc  |  52 +++++++-
 .../core/grappler/costs/virtual_scheduler.h   |   3 -
 .../grappler/costs/virtual_scheduler_test.cc  | 124 ++++++++++++++++++
 3 files changed, 169 insertions(+), 10 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 1dad81ed90..6b0b869df5 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -56,6 +56,32 @@ Costs CombineCosts(const Costs& left, const Costs& right) {
           << " max_per_op_streaming=" << result.max_per_op_streaming;
   return result;
 }
+
+// Key to the cached _Recv ops map, and its hash and predicate structures.
+struct RecvNodeDescriptor {
+  const NodeDef* node;
+  const int port_num;
+  const string& device;
+
+  RecvNodeDescriptor(const NodeDef* node_, const int port_num_,
+                     const string& device_)
+      : node(node_), port_num(port_num_), device(device_) {}
+};
+
+struct RecvNodeDescritorHash {
+  std::size_t operator()(const RecvNodeDescriptor& recv_node) const {
+    return std::hash<const NodeDef*>()(recv_node.node) ^
+           std::hash<int>()(recv_node.port_num) ^
+           std::hash<string>()(recv_node.device);
+  }
+};
+
+struct RecvNodeDescriptorEqual {
+  bool operator()(const RecvNodeDescriptor& a,
+                  const RecvNodeDescriptor& b) const {
+    return a.node == b.node && a.port_num == b.port_num && a.device == b.device;
+  }
+};
 }  // namespace
 
 VirtualScheduler::VirtualScheduler(const GrapplerItem* grappler_item,
@@ -109,6 +135,11 @@ Status VirtualScheduler::Init() {
     name_to_node[node->name()] = node;
   }
 
+  // To reuse _Recv ops.
+  std::unordered_map<RecvNodeDescriptor, const NodeDef*, RecvNodeDescritorHash,
+                     RecvNodeDescriptorEqual>
+      cached_recv_nodes;
+
   // Build node_map; for each node, create its NodeState and connect its inputs
   // and outputs.
   for (const auto* curr_node : nodes) {
@@ -131,12 +162,13 @@ Status VirtualScheduler::Init() {
         auto& input_node_state = GetNodeStateOrCreateIt(input_node);
         input_node_state.outputs[input_node_port_num].push_back(curr_node);
       } else {
-        if (cached_recv_nodes_.count(input_node) > 0 &&
-            cached_recv_nodes_[input_node].count(curr_node_device) > 0) {
+        RecvNodeDescriptor recv_node(input_node, input_node_port_num,
+                                     curr_node_device);
+        auto it = cached_recv_nodes.find(recv_node);
+        if (it != cached_recv_nodes.end()) {
           // Different device, but found an already-cached copy (a _Recv op);
           // connect the _Recv to curr_node.
-          const auto* recv_op =
-              cached_recv_nodes_[input_node][curr_node_device];
+          const NodeDef* recv_op = it->second;
           // recv_op's output port is hard-coded to zero.
           curr_node_state.inputs.push_back(std::make_pair(recv_op, 0));
           auto& input_node_state = node_map_.at(recv_op);
@@ -156,7 +188,7 @@ Status VirtualScheduler::Init() {
           input_node_state.outputs[input_node_port_num].push_back(send);
 
           // Cache the _Recv op for future use.
-          cached_recv_nodes_[input_node][curr_node_device] = recv;
+          cached_recv_nodes[recv_node] = recv;
         }
       }
     }
@@ -269,10 +301,16 @@ std::pair<const NodeDef*, const NodeDef*> VirtualScheduler::CreateSendRecv(
   // input names, attrs, etc.
 
   auto input_node_port_num = NodePosition(input_name);
+  string src_name;
+  if (input_node_port_num >= 0) {
+    src_name = strings::StrCat(from->name(), ":", input_node_port_num);
+  } else {
+    src_name = strings::StrCat(from->name(), ":minus1");
+  }
 
   // _Send op.
   auto* send = new NodeDef();
-  send->set_name("Send " + from->name() + " from " + DeviceName(from) + " to " +
+  send->set_name("Send " + src_name + " from " + DeviceName(from) + " to " +
                  DeviceName(to));
   send->set_op("_Send");
   send->add_input(from->name());
@@ -284,7 +322,7 @@ std::pair<const NodeDef*, const NodeDef*> VirtualScheduler::CreateSendRecv(
 
   // _Recv op.
   auto* recv = new NodeDef();
-  recv->set_name("Recv " + from->name() + " on " + DeviceName(to));
+  recv->set_name("Recv " + src_name + " on " + DeviceName(to));
   recv->set_op("_Recv");
   recv->add_input(send->name());
   recv->set_device(DeviceName(to));
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index d00766d9fa..e9abecb122 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -254,9 +254,6 @@ class VirtualScheduler {
 
   // Pool of NodeDefs for SendRecv and Identity ops created.
   std::vector<std::unique_ptr<NodeDef>> additional_nodes_;
-  // Cache of nodes transferred to another device.
-  std::unordered_map<const NodeDef*, std::unordered_map<string, const NodeDef*>>
-      cached_recv_nodes_;
 
   // Stats:
   std::map<string, int> op_counts_;  // Op counts with key with input shape.
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index af45623ff2..a4f72b0f97 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -36,6 +36,7 @@ class TestVirtualScheduler : public VirtualScheduler {
   FRIEND_TEST(VirtualSchedulerTest, ControlDependency);
   FRIEND_TEST(VirtualSchedulerTest, ComplexDependency);
   FRIEND_TEST(VirtualSchedulerTest, Variable);
+  FRIEND_TEST(VirtualSchedulerTest, InterDeviceTransfer);
 };
 
 class VirtualSchedulerTest : public ::testing::Test {
@@ -43,6 +44,7 @@ class VirtualSchedulerTest : public ::testing::Test {
   NodeDef node1_, node2_, node3_, node4_, node5_, node6_;
 
   const string kCPU0 = "/job:localhost/replica:0/task:0/cpu:0";
+  const string kCPU1 = "/job:localhost/replica:0/task:0/cpu:1";
 
   DeviceProperties GetDummyCPUDevice() {
     // Create CPU with 2 cores, 4 Ghz freq, 2 GB/s mem bandwidth.
@@ -74,6 +76,7 @@ class VirtualSchedulerTest : public ::testing::Test {
     // IMPORTANT: Device is not actually ever used in the test case since
     // force_cpu_type is defaulted to "Haswell"
     devices[kCPU0] = cpu_device;
+    devices[kCPU1] = cpu_device;
     cluster_.reset(new VirtualCluster(devices));
     placer_.reset(new VirtualPlacer(cluster_.get()));
   }
@@ -642,6 +645,56 @@ versions {
     grappler_item_->fetch = {"while/Exit", "while/Exit_1"};
   }
 
+  void CreateGrapplerItemWithInterDeviceTransfers() {
+    tensorflow::Scope s = tensorflow::Scope::NewRootScope().WithDevice(kCPU0);
+
+    // Create a FusedBatchNorm op that has multiple output ports.
+    auto x = ops::RandomUniform(
+        s.WithOpName("x"), {batch_size_, width_, height_, depth_in_}, DT_FLOAT);
+    auto scale =
+        ops::RandomUniform(s.WithOpName("scale"), {depth_in_}, DT_FLOAT);
+    auto offset =
+        ops::RandomUniform(s.WithOpName("offset"), {depth_in_}, DT_FLOAT);
+    auto mean = ops::RandomUniform(s.WithOpName("mean"), {0}, DT_FLOAT);
+    auto var = ops::RandomUniform(s.WithOpName("var"), {0}, DT_FLOAT);
+
+    auto batch_norm = ops::FusedBatchNorm(
+        s.WithOpName("bn"), x, scale, offset, mean, var,
+        ops::FusedBatchNorm::IsTraining(true).Epsilon(0.1f));
+    auto y = batch_norm.y;
+    auto batch_mean = batch_norm.batch_mean;
+    auto batch_var = batch_norm.batch_variance;
+    // y1 and y2 take the same tensor, so there should be only 1 Send and Recv.
+    auto y1 = ops::Identity(s.WithOpName("y1").WithDevice(kCPU1), y);
+    auto y2 = ops::Identity(s.WithOpName("y2").WithDevice(kCPU1), y);
+    // batch_mean1 and batch_var1 take different output ports, so each will
+    // initiate Send/Recv.
+    auto batch_mean1 = ops::Identity(
+        s.WithOpName("batch_mean1").WithDevice(kCPU1), batch_mean);
+    auto batch_var1 =
+        ops::Identity(s.WithOpName("batch_var1").WithDevice(kCPU1), batch_var);
+    // This is control dependency.
+    auto control_dep = ops::NoOp(s.WithOpName("control_dep")
+                                     .WithControlDependencies(y)
+                                     .WithDevice(kCPU1));
+
+    GraphDef def;
+    TF_CHECK_OK(s.ToGraphDef(&def));
+
+    grappler_item_.reset(new GrapplerItem);
+    grappler_item_->id = "test_conv2d_graph";
+    grappler_item_->graph = def;
+    grappler_item_->fetch = {"y1", "y2", "batch_mean1", "batch_var1",
+                             "control_dep"};
+
+    dependency_["bn"] = {"x", "mean", "var"};
+    dependency_["y1"] = {"bn"};
+    dependency_["y2"] = {"bn"};
+    dependency_["batch_mean1"] = {"bn"};
+    dependency_["batch_var1"] = {"bn"};
+    dependency_["control_dep"] = {"bn"};
+  }
+
   // Call this after creating grappler_item_ and setting up dependency_.
   void InitScheduler() {
     scheduler_.reset(new TestVirtualScheduler(
@@ -1236,5 +1289,76 @@ TEST_F(VirtualSchedulerTest, WhileLoop) {
   EXPECT_EQ(1, num_exit_1);
 }
 
+TEST_F(VirtualSchedulerTest, InterDeviceTransfer) {
+  // Init.
+  CreateGrapplerItemWithInterDeviceTransfers();
+  InitScheduler();
+
+  // Run the scheduler.
+  auto ops_executed = RunScheduler("");
+
+  // Helper lambda to extract port num from _Send and _Recv op name.
+  auto get_port_num = [](const string& name) -> int {
+    if (name.find("bn:0") != std::string::npos) {
+      return 0;
+    } else if (name.find("bn:1") != std::string::npos) {
+      return 1;
+    } else if (name.find("bn:2") != std::string::npos) {
+      return 2;
+    } else if (name.find("bn:minus1") != std::string::npos) {
+      return -1;
+    }
+    return -999;
+  };
+
+  // Reorganize ops_executed for further testing.
+  std::unordered_map<string, int> op_count;
+  std::unordered_map<int, string> recv_op_names;
+  std::unordered_map<int, string> send_op_names;
+  for (const auto& x : ops_executed) {
+    const auto& name = x.first;
+    const auto& node_info = x.second;
+    const auto& op = node_info.op_info.op();
+    if (op == "_Recv") {
+      recv_op_names[get_port_num(name)] = name;
+    } else if (op == "_Send") {
+      send_op_names[get_port_num(name)] = name;
+    }
+    op_count[op]++;
+  }
+
+  // Same number of _Send and _Recv.
+  EXPECT_EQ(op_count.at("_Send"), op_count.at("_Recv"));
+
+  // Expect 4 Send and Recvs each: port 0, 1, and, 2, and control dependency.
+  EXPECT_EQ(op_count.at("_Recv"), 4);
+  EXPECT_EQ(op_count.at("_Send"), 4);
+
+  // Helper lambda for extracting output Tensor size.
+  auto get_output_size = [this, ops_executed](const string& name) -> int64 {
+    const auto& output_properties_ = ops_executed.at(name).op_info.outputs();
+    std::vector<OpInfo::TensorProperties> output_properties;
+    for (const auto& output_property : output_properties_) {
+      output_properties.push_back(output_property);
+    }
+    return scheduler_->CalculateOutputSize(output_properties, 0);
+
+  };
+
+  // Validate transfer size.
+  // Batchnorm output y is 4D vector: batch x width x width x depth.
+  int input_size = 4 * batch_size_ * width_ * height_ * depth_in_;
+  EXPECT_EQ(get_output_size(recv_op_names[0]), input_size);
+  EXPECT_EQ(get_output_size(send_op_names[0]), input_size);
+  // Mean and vars are 1-D vector with size depth_in_.
+  EXPECT_EQ(get_output_size(recv_op_names[1]), 4 * depth_in_);
+  EXPECT_EQ(get_output_size(send_op_names[1]), 4 * depth_in_);
+  EXPECT_EQ(get_output_size(recv_op_names[2]), 4 * depth_in_);
+  EXPECT_EQ(get_output_size(send_op_names[2]), 4 * depth_in_);
+  // Control dependency size is 4B.
+  EXPECT_EQ(get_output_size(recv_op_names[-1]), 4);
+  EXPECT_EQ(get_output_size(send_op_names[-1]), 4);
+}
+
 }  // end namespace grappler
 }  // end namespace tensorflow
-- 
GitLab


From 063b69eb67df5f96834ef1df53bf8d228740e376 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 13:43:03 -0700
Subject: [PATCH 1250/1375] Make the logic around linking to CPU runtime
 functions less error-prone.

The way we link to CPU runtime functions from code generated by LLVM is problematic in a few ways:

 1. Forgetting to add lookup code in the ORC JIT's resolver for a new builtin does not break JIT tests in obvious ways.  JIT tests will usually continue to work because of the way we build our unit tests.
 2. There are ways to break the AOT link in ways that don't show up in when JITing by naming the extern "C" symbols incorrectly.

This change addresses the above by:

 - Preventing SimpleResolver from dlsym'ing symbols that start with __xla_cpu_runtime_.  This way tests don't just "happen to work" if we forget to add a runtime function to the list.
 - Using a C++ macro to make sure the C++ symbol names line up correctly with the IR symbol names.  This way we know that if the JIT is able to link to a symbol, code generated by the AOT compiler will be able to as well.

PiperOrigin-RevId: 162980962
---
 .../xla/service/cpu/compiler_functor.cc       |  24 ++--
 .../compiler/xla/service/cpu/cpu_runtime.cc   |  21 ++++
 .../compiler/xla/service/cpu/cpu_runtime.h    |  31 +++--
 .../xla/service/cpu/cpu_runtime_avx.cc        |  27 +++--
 .../xla/service/cpu/cpu_runtime_avx.h         |  25 ++--
 .../xla/service/cpu/cpu_runtime_sse4_1.cc     |  21 ++--
 .../xla/service/cpu/cpu_runtime_sse4_1.h      |  26 +++--
 .../xla/service/cpu/dot_op_emitter.cc         |   8 +-
 .../xla/service/cpu/simple_orc_jit.cc         | 108 +++++++++++-------
 9 files changed, 185 insertions(+), 106 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index f693808a10..d86881c282 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -131,25 +131,25 @@ std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl(
   std::vector<llvm::VecDesc> vector_functions;
 
   const llvm::VecDesc four_wide_vector_functions[] = {
-      {"expf", runtime::kExpV4F32, 4},
-      {"llvm.exp.f32", runtime::kExpV4F32, 4},
+      {"expf", runtime::kExpV4F32SymbolName, 4},
+      {"llvm.exp.f32", runtime::kExpV4F32SymbolName, 4},
 
-      {"logf", runtime::kLogV4F32, 4},
-      {"llvm.log.f32", runtime::kLogV4F32, 4},
+      {"logf", runtime::kLogV4F32SymbolName, 4},
+      {"llvm.log.f32", runtime::kLogV4F32SymbolName, 4},
 
-      {"tanhf", runtime::kTanhV4F32, 4},
-      {"llvm.tanh.f32", runtime::kTanhV4F32, 4},
+      {"tanhf", runtime::kTanhV4F32SymbolName, 4},
+      {"llvm.tanh.f32", runtime::kTanhV4F32SymbolName, 4},
   };
 
   const llvm::VecDesc eight_wide_vector_functions[] = {
-      {"expf", runtime::kExpV8F32, 8},
-      {"llvm.exp.f32", runtime::kExpV8F32, 8},
+      {"expf", runtime::kExpV8F32SymbolName, 8},
+      {"llvm.exp.f32", runtime::kExpV8F32SymbolName, 8},
 
-      {"logf", runtime::kLogV8F32, 8},
-      {"llvm.log.f32", runtime::kLogV8F32, 8},
+      {"logf", runtime::kLogV8F32SymbolName, 8},
+      {"llvm.log.f32", runtime::kLogV8F32SymbolName, 8},
 
-      {"tanhf", runtime::kTanhV8F32, 8},
-      {"llvm.tanh.f32", runtime::kTanhV8F32, 8},
+      {"tanhf", runtime::kTanhV8F32SymbolName, 8},
+      {"llvm.tanh.f32", runtime::kTanhV8F32SymbolName, 8},
   };
 
   if (arch == llvm::Triple::x86 || llvm::Triple::x86_64) {
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
index 07a9832867..5d6efa5359 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
@@ -31,6 +31,27 @@ XfeedManager* GetXfeedManager() {
   return manager;
 }
 
+extern const char* const kEigenMatMulF32SymbolName =
+    "__xla_cpu_runtime_EigenMatMulF32";
+extern const char* const kEigenMatMulF64SymbolName =
+    "__xla_cpu_runtime_EigenMatMulF64";
+extern const char* const kEigenConvF32SymbolName =
+    "__xla_cpu_runtime_EigenConvF32";
+extern const char* const kEigenSingleThreadedMatMulF32SymbolName =
+    "__xla_cpu_runtime_EigenSingleThreadedMatMulF32";
+extern const char* const kEigenSingleThreadedMatMulF64SymbolName =
+    "__xla_cpu_runtime_EigenSingleThreadedMatMulF64";
+extern const char* const kEigenSingleThreadedConvF32SymbolName =
+    "__xla_cpu_runtime_EigenSingleThreadedConvF32";
+extern const char* const kAcquireInfeedBufferForDequeueSymbolName =
+    "__xla_cpu_runtime_AcquireInfeedBufferForDequeue";
+extern const char* const kReleaseInfeedBufferAfterDequeueSymbolName =
+    "__xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue";
+extern const char* const kAcquireOutfeedBufferForPopulationSymbolName =
+    "__xla_cpu_runtime_AcquireOutfeedBufferForPopulation";
+extern const char* const kReleaseOutfeedBufferAfterPopulationSymbolName =
+    "__xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation";
+extern const char* const kXlaCpuRuntimeSymbolNamePrefix = "__xla_cpu_runtime_";
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
index 46cf64966a..29feb7267f 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
@@ -41,23 +41,20 @@ namespace runtime {
 //    the actual symbol.
 // 2. When using ahead-of-time compilation, the linker can resolve the name
 //    because it is a symbol in the cpu_runtime library.
-constexpr char kEigenMatmulF32SymbolName[] = "__xla_cpu_runtime_EigenMatMulF32";
-constexpr char kEigenMatmulF64SymbolName[] = "__xla_cpu_runtime_EigenMatMulF64";
-constexpr char kEigenConvF32SymbolName[] = "__xla_cpu_runtime_EigenConvF32";
-constexpr char kEigenSingleThreadedMatmulF32SymbolName[] =
-    "__xla_cpu_runtime_EigenSingleThreadedMatMulF32";
-constexpr char kEigenSingleThreadedMatmulF64SymbolName[] =
-    "__xla_cpu_runtime_EigenSingleThreadedMatMulF64";
-constexpr char kEigenSingleThreadedConvF32SymbolName[] =
-    "__xla_cpu_runtime_EigenSingleThreadedConvF32";
-constexpr char kAcquireInfeedBufferForDequeueSymbolName[] =
-    "__xla_cpu_runtime_AcquireInfeedBufferForDequeue";
-constexpr char kReleaseInfeedBufferAfterDequeueSymbolName[] =
-    "__xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue";
-constexpr char kAcquireOutfeedBufferForPopulationSymbolName[] =
-    "__xla_cpu_runtime_AcquireOutfeedBufferForPopulation";
-constexpr char kReleaseOutfeedBufferAfterPopulationSymbolName[] =
-    "__xla_cpu_runtime_ReleaseOutfeedBufferAfterPopulation";
+extern const char* const kEigenMatMulF32SymbolName;
+extern const char* const kEigenMatMulF64SymbolName;
+extern const char* const kEigenConvF32SymbolName;
+extern const char* const kEigenSingleThreadedMatMulF32SymbolName;
+extern const char* const kEigenSingleThreadedMatMulF64SymbolName;
+extern const char* const kEigenSingleThreadedConvF32SymbolName;
+extern const char* const kAcquireInfeedBufferForDequeueSymbolName;
+extern const char* const kReleaseInfeedBufferAfterDequeueSymbolName;
+extern const char* const kAcquireOutfeedBufferForPopulationSymbolName;
+extern const char* const kReleaseOutfeedBufferAfterPopulationSymbolName;
+
+// All symbol names for XLA CPU runtime functions need to start with this
+// prefix.
+extern const char* const kXlaCpuRuntimeSymbolNamePrefix;
 
 // Returns the infeed manager used by the CPU runtime.
 XfeedManager* GetXfeedManager();
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
index 646254887c..f6664bb854 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.cc
@@ -19,17 +19,30 @@ limitations under the License.
 
 #include "third_party/eigen3/Eigen/Core"
 
+#ifdef __AVX__
+xla::cpu::runtime::V8F32 __xla_cpu_runtime_ExpV8F32(
+    xla::cpu::runtime::V8F32 x) {
+  return Eigen::internal::pexp(x);
+}
+
+xla::cpu::runtime::V8F32 __xla_cpu_runtime_LogV8F32(
+    xla::cpu::runtime::V8F32 x) {
+  return Eigen::internal::plog(x);
+}
+
+xla::cpu::runtime::V8F32 __xla_cpu_runtime_TanhV8F32(
+    xla::cpu::runtime::V8F32 x) {
+  return Eigen::internal::ptanh(x);
+}
+#endif  // __AVX__
+
 namespace xla {
 namespace cpu {
 namespace runtime {
 
-#ifdef __AVX__
-V8F32 ExpV8F32(V8F32 x) { return Eigen::internal::pexp(x); }
-
-V8F32 LogV8F32(V8F32 x) { return Eigen::internal::plog(x); }
-
-V8F32 TanhV8F32(V8F32 x) { return Eigen::internal::ptanh(x); }
-#endif  // __AVX__
+const char *const kExpV8F32SymbolName = "__xla_cpu_runtime_ExpV8F32";
+const char *const kLogV8F32SymbolName = "__xla_cpu_runtime_LogV8F32";
+const char *const kTanhV8F32SymbolName = "__xla_cpu_runtime_TanhV8F32";
 
 }  // namespace runtime
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
index 89721aaf83..c15710fb00 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_avx.h
@@ -28,23 +28,28 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-constexpr char kExpV8F32[] = "__xla_cpu_runtime_ExpV8F32";
-constexpr char kLogV8F32[] = "__xla_cpu_runtime_LogV8F32";
-constexpr char kTanhV8F32[] = "__xla_cpu_runtime_TanhV8F32";
+extern const char *const kExpV8F32SymbolName;
+extern const char *const kLogV8F32SymbolName;
+extern const char *const kTanhV8F32SymbolName;
 
 typedef float V8F32 __attribute__((__vector_size__(32)));
+}  // namespace runtime
+}  // namespace cpu
+}  // namespace xla
+
+extern "C" {
 
 // The following functions are vectorized versions of a selection of libm
 // library functions.
 // References to these functions are created by the LLVM vectorizer.
-V8F32 ExpV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+xla::cpu::runtime::V8F32 __xla_cpu_runtime_ExpV8F32(xla::cpu::runtime::V8F32 x)
+    TF_ATTRIBUTE_WEAK;
 
-V8F32 LogV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
+xla::cpu::runtime::V8F32 __xla_cpu_runtime_LogV8F32(xla::cpu::runtime::V8F32 x)
+    TF_ATTRIBUTE_WEAK;
 
-V8F32 TanhV8F32(V8F32 x) TF_ATTRIBUTE_WEAK;
-
-}  // namespace runtime
-}  // namespace cpu
-}  // namespace xla
+xla::cpu::runtime::V8F32 __xla_cpu_runtime_TanhV8F32(xla::cpu::runtime::V8F32 x)
+    TF_ATTRIBUTE_WEAK;
+}
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_AVX_H_
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
index 69d04427c6..58ec9fc6e8 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.cc
@@ -19,29 +19,36 @@ limitations under the License.
 
 #include "third_party/eigen3/Eigen/Core"
 
-namespace xla {
-namespace cpu {
-namespace runtime {
-
 #ifdef __SSE4_1__
 
-V4F32 ExpV4F32(V4F32 x) {
+xla::cpu::runtime::V4F32 __xla_cpu_runtime_ExpV4F32(
+    xla::cpu::runtime::V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::pexp(p);
 }
 
-V4F32 LogV4F32(V4F32 x) {
+xla::cpu::runtime::V4F32 __xla_cpu_runtime_LogV4F32(
+    xla::cpu::runtime::V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::plog(p);
 }
 
-V4F32 TanhV4F32(V4F32 x) {
+xla::cpu::runtime::V4F32 __xla_cpu_runtime_TanhV4F32(
+    xla::cpu::runtime::V4F32 x) {
   Eigen::internal::Packet4f p = x;
   return Eigen::internal::ptanh(p);
 }
 
 #endif  // __SSE4_1__
 
+namespace xla {
+namespace cpu {
+namespace runtime {
+
+const char *const kExpV4F32SymbolName = "__xla_cpu_runtime_ExpV4F32";
+const char *const kLogV4F32SymbolName = "__xla_cpu_runtime_LogV4F32";
+const char *const kTanhV4F32SymbolName = "__xla_cpu_runtime_TanhV4F32";
+
 }  // namespace runtime
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
index ded206f90a..7ab9a52d00 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_sse4_1.h
@@ -28,23 +28,29 @@ namespace xla {
 namespace cpu {
 namespace runtime {
 
-constexpr char kExpV4F32[] = "__xla_cpu_runtime_ExpV4F32";
-constexpr char kLogV4F32[] = "__xla_cpu_runtime_LogV4F32";
-constexpr char kTanhV4F32[] = "__xla_cpu_runtime_TanhV4F32";
+extern const char *const kExpV4F32SymbolName;
+extern const char *const kLogV4F32SymbolName;
+extern const char *const kTanhV4F32SymbolName;
 
 typedef float V4F32 __attribute__((__vector_size__(16)));
 
+}  // namespace runtime
+}  // namespace cpu
+}  // namespace xla
+
+extern "C" {
+
 // The following functions are vectorized versions of a selection of libm
 // library functions.
 // References to these functions are created by the LLVM vectorizer.
-V4F32 ExpV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+xla::cpu::runtime::V4F32 __xla_cpu_runtime_ExpV4F32(xla::cpu::runtime::V4F32 x)
+    TF_ATTRIBUTE_WEAK;
 
-V4F32 LogV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
+xla::cpu::runtime::V4F32 __xla_cpu_runtime_LogV4F32(xla::cpu::runtime::V4F32 x)
+    TF_ATTRIBUTE_WEAK;
 
-V4F32 TanhV4F32(V4F32 x) TF_ATTRIBUTE_WEAK;
-
-}  // namespace runtime
-}  // namespace cpu
-}  // namespace xla
+xla::cpu::runtime::V4F32 __xla_cpu_runtime_TanhV4F32(xla::cpu::runtime::V4F32 x)
+    TF_ATTRIBUTE_WEAK;
+}
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_RUNTIME_SSE4_1_H_
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index c21b8a9add..f45c28ef74 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -244,14 +244,14 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   switch (type) {
     case F32:
       fn_name = multi_threaded_eigen
-                    ? runtime::kEigenMatmulF32SymbolName
-                    : runtime::kEigenSingleThreadedMatmulF32SymbolName;
+                    ? runtime::kEigenMatMulF32SymbolName
+                    : runtime::kEigenSingleThreadedMatMulF32SymbolName;
       float_type = ir_builder_->getFloatTy();
       break;
     case F64:
       fn_name = multi_threaded_eigen
-                    ? runtime::kEigenMatmulF64SymbolName
-                    : runtime::kEigenSingleThreadedMatmulF64SymbolName;
+                    ? runtime::kEigenMatMulF64SymbolName
+                    : runtime::kEigenSingleThreadedMatMulF64SymbolName;
       float_type = ir_builder_->getDoubleTy();
       break;
     default:
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index aaf3fd2f9e..fd7c36cc53 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -53,47 +53,77 @@ std::string CanonicalizeSymbol(const std::string &name) {
   return name;
 }
 
+class JITSymbolTable {
+ public:
+  JITSymbolTable() { Populate(); }
+
+  void *Lookup(llvm::StringRef jit_symbol_name) const {
+    auto it = jit_symbol_table_.find(jit_symbol_name);
+    return it == jit_symbol_table_.end() ? nullptr : it->getValue();
+  }
+
+  static bool MustBeInTable(llvm::StringRef name) {
+    // In particular, names starting with
+    // runtime::kXlaCpuRuntimeSymbolNamePrefix should not be dlsym'ed.
+    return name.startswith(runtime::kXlaCpuRuntimeSymbolNamePrefix);
+  }
+
+ private:
+  void AddJITSymbolToTable(llvm::StringRef jit_symbol_name,
+                           llvm::StringRef cpp_symbol_name,
+                           void *jit_symbol_value) {
+    // The JIT symbol name and the C++ symbol name (with an extern "C" linkage)
+    // need to match, otherwise AOT links will fail.
+    CHECK(jit_symbol_name == cpp_symbol_name);
+    CHECK(jit_symbol_table_.insert({jit_symbol_name, jit_symbol_value}).second);
+  }
+
+  void Populate() {
+#define ADD_JIT_SYMBOL_TO_TABLE(base_name)                        \
+  do {                                                            \
+    AddJITSymbolToTable(                                          \
+        xla::cpu::runtime::k##base_name##SymbolName,              \
+        "__xla_cpu_runtime_" #base_name,                          \
+        reinterpret_cast<void *>(__xla_cpu_runtime_##base_name)); \
+  } while (false)
+
+    ADD_JIT_SYMBOL_TO_TABLE(AcquireInfeedBufferForDequeue);
+    ADD_JIT_SYMBOL_TO_TABLE(ReleaseInfeedBufferAfterDequeue);
+    ADD_JIT_SYMBOL_TO_TABLE(AcquireOutfeedBufferForPopulation);
+    ADD_JIT_SYMBOL_TO_TABLE(ReleaseOutfeedBufferAfterPopulation);
+    ADD_JIT_SYMBOL_TO_TABLE(ExpV8F32);
+    ADD_JIT_SYMBOL_TO_TABLE(LogV8F32);
+    ADD_JIT_SYMBOL_TO_TABLE(TanhV8F32);
+    ADD_JIT_SYMBOL_TO_TABLE(ExpV4F32);
+    ADD_JIT_SYMBOL_TO_TABLE(LogV4F32);
+    ADD_JIT_SYMBOL_TO_TABLE(TanhV4F32);
+    ADD_JIT_SYMBOL_TO_TABLE(EigenConvF32);
+    ADD_JIT_SYMBOL_TO_TABLE(EigenMatMulF32);
+    ADD_JIT_SYMBOL_TO_TABLE(EigenMatMulF64);
+    ADD_JIT_SYMBOL_TO_TABLE(EigenSingleThreadedConvF32);
+    ADD_JIT_SYMBOL_TO_TABLE(EigenSingleThreadedMatMulF32);
+    ADD_JIT_SYMBOL_TO_TABLE(EigenSingleThreadedMatMulF64);
+
+#undef ADD_JIT_SYMBOL_TO_TABLE
+  }
+
+  llvm::StringMap<void *> jit_symbol_table_;
+};
+
+const JITSymbolTable &GetJITSymbolTable() {
+  static JITSymbolTable *symbol_table = new JITSymbolTable;
+  return *symbol_table;
+}
+
 // A simple SymbolResolver that delegates to the host dynamic linker.
 struct SimpleResolver : public llvm::JITSymbolResolver {
   llvm::JITSymbol findSymbol(const std::string &name) override {
-    void *func_addr = nullptr;
-
     std::string canonical_name = CanonicalizeSymbol(name);
-    if (canonical_name == runtime::kEigenMatmulF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(__xla_cpu_runtime_EigenMatMulF32);
-    } else if (canonical_name ==
-               runtime::kEigenSingleThreadedMatmulF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_EigenSingleThreadedMatMulF32);
-    } else if (canonical_name == runtime::kEigenConvF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(__xla_cpu_runtime_EigenConvF32);
-    } else if (canonical_name ==
-               runtime::kEigenSingleThreadedConvF32SymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_EigenSingleThreadedConvF32);
-    } else if (canonical_name ==
-               runtime::kAcquireInfeedBufferForDequeueSymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_AcquireInfeedBufferForDequeue);
-    } else if (canonical_name ==
-               runtime::kReleaseInfeedBufferAfterDequeueSymbolName) {
-      func_addr = reinterpret_cast<void *>(
-          __xla_cpu_runtime_ReleaseInfeedBufferAfterDequeue);
-    } else if (canonical_name == runtime::kExpV4F32) {
-      func_addr = reinterpret_cast<void *>(runtime::ExpV4F32);
-    } else if (canonical_name == runtime::kExpV8F32) {
-      func_addr = reinterpret_cast<void *>(runtime::ExpV8F32);
-    } else if (canonical_name == runtime::kLogV4F32) {
-      func_addr = reinterpret_cast<void *>(runtime::LogV4F32);
-    } else if (canonical_name == runtime::kLogV8F32) {
-      func_addr = reinterpret_cast<void *>(runtime::LogV8F32);
-    } else if (canonical_name == runtime::kTanhV4F32) {
-      func_addr = reinterpret_cast<void *>(runtime::TanhV4F32);
-    } else if (canonical_name == runtime::kTanhV8F32) {
-      func_addr = reinterpret_cast<void *>(runtime::TanhV8F32);
-    } else {
-      func_addr = dlsym(RTLD_DEFAULT, canonical_name.c_str());
-    }
+    const JITSymbolTable &jit_symbol_table = GetJITSymbolTable();
+
+    void *func_addr = JITSymbolTable::MustBeInTable(canonical_name)
+                          ? jit_symbol_table.Lookup(canonical_name)
+                          : dlsym(RTLD_DEFAULT, canonical_name.c_str());
 
     if (func_addr == nullptr) {
       return nullptr;
@@ -134,8 +164,8 @@ llvm::StringRef GetHostCpuName() {
 
 CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() {
   CompilerFunctor::VectorIntrinsics intrinsics;
-  intrinsics.sse_intrinsics = (&runtime::ExpV4F32 != nullptr);
-  intrinsics.avx_intrinsics = (&runtime::ExpV8F32 != nullptr);
+  intrinsics.sse_intrinsics = (&__xla_cpu_runtime_ExpV4F32 != nullptr);
+  intrinsics.avx_intrinsics = (&__xla_cpu_runtime_ExpV8F32 != nullptr);
   return intrinsics;
 }
 
-- 
GitLab


From e03457b7165d037664fda5ec1de2c0fe9a9c5a4e Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Mon, 24 Jul 2017 13:46:54 -0700
Subject: [PATCH 1251/1375] bitwise xor op now uses vectorised xor on CPU.

To get vectorised xor on CUDA, additional changes need to be made to Eigen;
specifically adding packetwise xor operations for the various float and double
types (float2, float4, double2).  Once those are in, this op will magically
pick them up as well.

PiperOrigin-RevId: 162981519
---
 tensorflow/core/kernels/cwise_ops.h | 121 +++++++++++++++-------------
 1 file changed, 67 insertions(+), 54 deletions(-)

diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h
index e198fda473..65a60720dd 100644
--- a/tensorflow/core/kernels/cwise_ops.h
+++ b/tensorflow/core/kernels/cwise_ops.h
@@ -43,35 +43,38 @@ struct functor_traits<scalar_fmod2_op<T>> {
   };
 };
 
-template<typename T> struct scalar_asinh_op {
+template <typename T>
+struct scalar_asinh_op {
   EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op)
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
     return std::asinh(a);
   }
 };
-template<typename T>
+template <typename T>
 struct functor_traits<scalar_asinh_op<T>> {
   enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
 };
 
-template<typename T> struct scalar_acosh_op {
+template <typename T>
+struct scalar_acosh_op {
   EIGEN_EMPTY_STRUCT_CTOR(scalar_acosh_op)
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
     return std::acosh(a);
   }
 };
-template<typename T>
+template <typename T>
 struct functor_traits<scalar_acosh_op<T>> {
   enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
 };
 
-template<typename T> struct scalar_atanh_op {
+template <typename T>
+struct scalar_atanh_op {
   EIGEN_EMPTY_STRUCT_CTOR(scalar_atanh_op)
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a) const {
     return std::atanh(a);
   }
 };
-template<typename T>
+template <typename T>
 struct functor_traits<scalar_atanh_op<T>> {
   enum { Cost = 5 * NumTraits<T>::MulCost, PacketAccess = false };
 };
@@ -154,7 +157,7 @@ struct scalar_left : private Binary {
 };
 
 template <typename Tout, typename Tin, typename Binary>
-struct functor_traits<scalar_left<Tout, Tin, Binary> > {
+struct functor_traits<scalar_left<Tout, Tin, Binary>> {
   enum {
     Cost = functor_traits<Binary>::Cost,
     PacketAccess = functor_traits<Binary>::PacketAccess,
@@ -184,7 +187,7 @@ struct scalar_right : private Binary {
 };
 
 template <typename Tout, typename Tin, typename Binary>
-struct functor_traits<scalar_right<Tout, Tin, Binary> > {
+struct functor_traits<scalar_right<Tout, Tin, Binary>> {
   enum {
     Cost = functor_traits<Binary>::Cost,
     PacketAccess = functor_traits<Binary>::PacketAccess,
@@ -401,6 +404,25 @@ struct functor_traits<scalar_round_op_google<Scalar>> {
 #undef ENABLE_FLOAT_EQUALITY_WARNING
 #undef DISABLE_FLOAT_EQUALITY_WARNING
 
+template <typename Scalar>
+struct bitwise_xor_op {
+  EIGEN_EMPTY_STRUCT_CTOR(bitwise_xor_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar
+  operator()(const Scalar& x, const Scalar& y) const {
+    return x ^ y;
+  }
+  typedef typename Eigen::internal::packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a,
+                                                        const Packet& b) const {
+    return Eigen::internal::pxor(a, b);
+  }
+};
+
+template <typename Scalar>
+struct functor_traits<bitwise_xor_op<Scalar>> {
+  enum { Cost = Eigen::NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
 }  // end namespace internal
 }  // end namespace Eigen
 
@@ -487,22 +509,22 @@ struct abs : base<T, Eigen::internal::scalar_abs_op<T>,
                   typename Eigen::internal::scalar_abs_op<T>::result_type> {};
 
 template <typename T>
-struct neg : base<T, Eigen::internal::scalar_opposite_op<T> > {};
+struct neg : base<T, Eigen::internal::scalar_opposite_op<T>> {};
 
 template <typename T>
-struct inverse : base<T, Eigen::internal::scalar_inverse_op<T> > {};
+struct inverse : base<T, Eigen::internal::scalar_inverse_op<T>> {};
 
 template <typename T>
-struct square : base<T, Eigen::internal::scalar_square_op<T> > {};
+struct square : base<T, Eigen::internal::scalar_square_op<T>> {};
 
 template <typename T>
-struct sqrt : base<T, Eigen::internal::scalar_sqrt_op<T> > {};
+struct sqrt : base<T, Eigen::internal::scalar_sqrt_op<T>> {};
 
 template <typename T>
-struct rsqrt : base<T, Eigen::internal::scalar_rsqrt_op<T> > {};
+struct rsqrt : base<T, Eigen::internal::scalar_rsqrt_op<T>> {};
 
 template <typename T>
-struct exp : base<T, Eigen::internal::scalar_exp_op<T> > {};
+struct exp : base<T, Eigen::internal::scalar_exp_op<T>> {};
 
 template <typename T>
 struct expm1 : base<T, Eigen::internal::scalar_expm1_op<T>> {};
@@ -511,63 +533,63 @@ template <typename T>
 struct log : base<T, Eigen::internal::scalar_log_op<T>> {};
 
 template <typename T>
-struct log1p : base<T, Eigen::internal::scalar_log1p_op<T> > {};
+struct log1p : base<T, Eigen::internal::scalar_log1p_op<T>> {};
 
 template <typename T>
-struct sign : base<T, Eigen::internal::scalar_sign_op<T> > {};
+struct sign : base<T, Eigen::internal::scalar_sign_op<T>> {};
 
 template <typename T>
-struct sinh : base<T, Eigen::internal::scalar_sinh_op<T> > {};
+struct sinh : base<T, Eigen::internal::scalar_sinh_op<T>> {};
 
 template <typename T>
-struct cosh : base<T, Eigen::internal::scalar_cosh_op<T> > {};
+struct cosh : base<T, Eigen::internal::scalar_cosh_op<T>> {};
 
 template <typename T>
-struct tanh : base<T, Eigen::internal::scalar_tanh_op<T> > {};
+struct tanh : base<T, Eigen::internal::scalar_tanh_op<T>> {};
 
 template <typename T>
-struct asinh : base<T, Eigen::internal::scalar_asinh_op<T> > {};
+struct asinh : base<T, Eigen::internal::scalar_asinh_op<T>> {};
 
 template <typename T>
-struct acosh : base<T, Eigen::internal::scalar_acosh_op<T> > {};
+struct acosh : base<T, Eigen::internal::scalar_acosh_op<T>> {};
 
 template <typename T>
-struct atanh : base<T, Eigen::internal::scalar_atanh_op<T> > {};
+struct atanh : base<T, Eigen::internal::scalar_atanh_op<T>> {};
 
 template <typename T>
-struct lgamma : base<T, Eigen::internal::scalar_lgamma_op<T> > {};
+struct lgamma : base<T, Eigen::internal::scalar_lgamma_op<T>> {};
 
 template <typename T>
 struct digamma : base<T, Eigen::internal::scalar_digamma_op<T>> {};
 
 template <typename T>
-struct erf : base<T, Eigen::internal::scalar_erf_op<T> > {};
+struct erf : base<T, Eigen::internal::scalar_erf_op<T>> {};
 
 template <typename T>
-struct erfc : base<T, Eigen::internal::scalar_erfc_op<T> > {};
+struct erfc : base<T, Eigen::internal::scalar_erfc_op<T>> {};
 
 template <typename T>
-struct sigmoid : base<T, Eigen::internal::scalar_sigmoid_op<T> > {};
+struct sigmoid : base<T, Eigen::internal::scalar_sigmoid_op<T>> {};
 
 template <typename T>
-struct sin : base<T, Eigen::internal::scalar_sin_op<T> > {};
+struct sin : base<T, Eigen::internal::scalar_sin_op<T>> {};
 
 template <typename T>
-struct cos : base<T, Eigen::internal::scalar_cos_op<T> > {};
+struct cos : base<T, Eigen::internal::scalar_cos_op<T>> {};
 
 template <typename T>
-struct tan : base<T, Eigen::internal::scalar_tan_op<T> > {};
+struct tan : base<T, Eigen::internal::scalar_tan_op<T>> {};
 
 template <typename T>
-struct asin : base<T, Eigen::internal::scalar_asin_op<T> > {};
+struct asin : base<T, Eigen::internal::scalar_asin_op<T>> {};
 
 template <typename T>
-struct acos : base<T, Eigen::internal::scalar_acos_op<T> > {};
+struct acos : base<T, Eigen::internal::scalar_acos_op<T>> {};
 
 template <typename T>
-struct atan : base<T, Eigen::internal::scalar_atan_op<T> > {};
+struct atan : base<T, Eigen::internal::scalar_atan_op<T>> {};
 
-struct logical_not : base<bool, Eigen::internal::scalar_boolean_not_op<bool> > {
+struct logical_not : base<bool, Eigen::internal::scalar_boolean_not_op<bool>> {
 };
 
 // Flip all bits. Named invert to be consistent with numpy.
@@ -604,8 +626,8 @@ template <typename T>
 struct ceil : base<T, Eigen::internal::scalar_ceil_op<T>> {};
 
 /** this should go in Eigen
-  * \brief Template functor to compute the round to int value of a scalar
-  */
+ * \brief Template functor to compute the round to int value of a scalar
+ */
 template <typename Scalar>
 struct scalar_rint_op {
   EIGEN_EMPTY_STRUCT_CTOR(scalar_rint_op)
@@ -642,20 +664,20 @@ struct rint : base<T, scalar_rint_op<T>> {};
 // squared_difference(x, y) = (x - y) * (x - y)
 
 template <typename T>
-struct add : base<T, Eigen::internal::scalar_sum_op<T> > {
+struct add : base<T, Eigen::internal::scalar_sum_op<T>> {
   static const bool use_bcast_optimization = true;
 };
 
 template <typename T>
-struct sub : base<T, Eigen::internal::scalar_difference_op<T> > {
+struct sub : base<T, Eigen::internal::scalar_difference_op<T>> {
   static const bool use_bcast_optimization = true;
 };
 
 template <typename T>
-struct mul : base<T, Eigen::internal::scalar_product_op<T> > {};
+struct mul : base<T, Eigen::internal::scalar_product_op<T>> {};
 
 template <typename T>
-struct div : base<T, Eigen::internal::scalar_quotient_op<T> > {};
+struct div : base<T, Eigen::internal::scalar_quotient_op<T>> {};
 
 template <typename T>
 struct safe_div : base<T, Eigen::internal::safe_div_or_mod_op<
@@ -664,7 +686,7 @@ struct safe_div : base<T, Eigen::internal::safe_div_or_mod_op<
 };
 
 template <typename T>
-struct fmod : base<T, Eigen::internal::scalar_fmod2_op<T> > {};
+struct fmod : base<T, Eigen::internal::scalar_fmod2_op<T>> {};
 
 template <typename T>
 struct mod : base<T, Eigen::internal::scalar_mod2_op<T>> {};
@@ -700,10 +722,10 @@ template <typename T>
 struct pow : base<T, Eigen::internal::scalar_binary_pow_op_google<T, T>> {};
 
 template <typename T>
-struct maximum : base<T, Eigen::internal::scalar_max_op<T> > {};
+struct maximum : base<T, Eigen::internal::scalar_max_op<T>> {};
 
 template <typename T>
-struct minimum : base<T, Eigen::internal::scalar_min_op<T> > {};
+struct minimum : base<T, Eigen::internal::scalar_min_op<T>> {};
 
 template <typename T>
 struct igamma : base<T, Eigen::internal::scalar_igamma_op<T>> {};
@@ -779,15 +801,6 @@ struct bitwise_or_op {
   }
 };
 
-template <typename T>
-struct bitwise_xor_op {
-  EIGEN_EMPTY_STRUCT_CTOR(bitwise_xor_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& x,
-                                                           const T& y) const {
-    return x ^ y;
-  }
-};
-
 template <typename T>
 struct bitwise_and : base<T, bitwise_and_op<T>> {};
 
@@ -795,7 +808,7 @@ template <typename T>
 struct bitwise_or : base<T, bitwise_or_op<T>> {};
 
 template <typename T>
-struct bitwise_xor : base<T, bitwise_xor_op<T>> {};
+struct bitwise_xor : base<T, Eigen::internal::bitwise_xor_op<T>> {};
 
 template <typename T>
 struct make_complex_func {
@@ -807,7 +820,7 @@ struct make_complex_func {
 };
 
 template <typename T>
-struct make_complex : base<T, make_complex_func<T>, std::complex<T> > {};
+struct make_complex : base<T, make_complex_func<T>, std::complex<T>> {};
 
 template <typename T>
 struct get_real
@@ -818,7 +831,7 @@ struct get_imag
     : base<T, Eigen::internal::scalar_imag_op<T>, typename T::value_type> {};
 
 template <typename T>
-struct conj : base<T, Eigen::internal::scalar_conjugate_op<T> > {};
+struct conj : base<T, Eigen::internal::scalar_conjugate_op<T>> {};
 
 ////////////////////////////////////////////////////////////////////////////////
 // Functors takes 1 or 2 tensors, computes the base functor on
-- 
GitLab


From efeec4d3dde40ea9a6a4337504726c7f268842b5 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Mon, 24 Jul 2017 13:58:57 -0700
Subject: [PATCH 1252/1375] tfdbg: remove gen_debug_ops

Replace it with a new hidden op: array_ops.DebugGradientIdentity

PiperOrigin-RevId: 162983526
---
 tensorflow/core/kernels/identity_op.cc         | 11 ++++++++++-
 tensorflow/core/ops/array_ops.cc               | 14 ++++++++++++++
 tensorflow/python/debug/BUILD                  |  8 +-------
 tensorflow/python/debug/lib/debug_gradients.py | 17 ++++++++---------
 tensorflow/python/ops/hidden_ops.txt           |  1 +
 5 files changed, 34 insertions(+), 17 deletions(-)

diff --git a/tensorflow/core/kernels/identity_op.cc b/tensorflow/core/kernels/identity_op.cc
index ead2db4538..0a445b7ea2 100644
--- a/tensorflow/core/kernels/identity_op.cc
+++ b/tensorflow/core/kernels/identity_op.cc
@@ -38,6 +38,11 @@ REGISTER_KERNEL_BUILDER(Name("PlaceholderWithDefault").Device(DEVICE_CPU),
 
 REGISTER_KERNEL_BUILDER(Name("RefIdentity").Device(DEVICE_CPU), IdentityOp);
 
+// Identity op for gradients debugging in TensorFlow Debugger (hidden op in
+// Python).
+REGISTER_KERNEL_BUILDER(Name("DebugGradientIdentity").Device(DEVICE_CPU),
+                        IdentityOp);
+
 #if TENSORFLOW_USE_SYCL
 #define REGISTER_SYCL_KERNEL(type)                                           \
   REGISTER_KERNEL_BUILDER(                                                   \
@@ -90,7 +95,11 @@ REGISTER_SYCL_HOST_KERNEL(bool);
       IdentityOp);                                                          \
   REGISTER_KERNEL_BUILDER(                                                  \
       Name("StopGradient").Device(DEVICE_GPU).TypeConstraint<type>("T"),    \
-      IdentityOp)
+      IdentityOp);                                                          \
+  REGISTER_KERNEL_BUILDER(Name("DebugGradientIdentity")                     \
+                              .Device(DEVICE_GPU)                           \
+                              .TypeConstraint<type>("T"),                   \
+                          IdentityOp)
 
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
 REGISTER_GPU_KERNEL(bfloat16);
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 10133009b9..9a4001ba2f 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1686,6 +1686,20 @@ REGISTER_OP("RefIdentity")
 Return the same ref tensor as the input ref tensor.
 )Doc");
 
+// --------------------------------------------------------------------------
+REGISTER_OP("DebugGradientIdentity")
+    .Input("input: T")
+    .Output("output: T")
+    .Attr("T: type")
+    .SetShapeFn(shape_inference::UnchangedShape)
+    .SetAllowsUninitializedInput()
+    .Doc(R"Doc(
+Identity op for gradient debugging.
+
+This op is hidden from public in Python. It is used by TensorFlow Debugger to
+register gradient tensors for gradient debugging.
+)Doc");
+
 // --------------------------------------------------------------------------
 REGISTER_OP("StopGradient")
     .Input("input: T")
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 13b46a7311..aad98c1bd5 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -23,7 +23,6 @@ exports_files(["LICENSE"])
 load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "py_test")
 load("//tensorflow:tensorflow.bzl", "if_not_windows")
-load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
 
 py_library(
     name = "debug_py",
@@ -64,18 +63,13 @@ py_library(
     ],
 )
 
-tf_gen_op_wrapper_py(
-    name = "debug_ops",
-    deps = ["//tensorflow/core:debug_ops_op_lib"],
-)
-
 py_library(
     name = "debug_gradients",
     srcs = ["lib/debug_gradients.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":debug_data",
-        ":debug_ops",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:framework",
         "//tensorflow/python:platform",
         "@six_archive//:six",
diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py
index 6655f85d3a..8689a68875 100644
--- a/tensorflow/python/debug/lib/debug_gradients.py
+++ b/tensorflow/python/debug/lib/debug_gradients.py
@@ -24,8 +24,8 @@ import uuid
 import six
 
 from tensorflow.python.debug.lib import debug_data
-from tensorflow.python.debug.ops import gen_debug_ops
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import variables
 
 _GRADIENT_DEBUG_TAG = "gradient_debug_"
@@ -154,15 +154,14 @@ class GradientsDebugger(object):
     # TODO(cais): Allow overriding gradient.
     # TODO(cais): Implement value_stack.
     grad_debug_op_name = _tensor_to_grad_debug_op_name(input_tensor, self._uuid)
-    debug_identity = gen_debug_ops.debug_identity(
-        input_tensor,
-        tensor_name=input_tensor.name,
-        debug_urls=[],
-        name=grad_debug_op_name)
-    if debug_identity.op.name != grad_debug_op_name:
+    # pylint: disable=protected-access
+    debug_grad_identity = gen_array_ops._debug_gradient_identity(
+        input_tensor, name=grad_debug_op_name)
+    # pylint: enable=protected-access
+    if debug_grad_identity.op.name != grad_debug_op_name:
       raise ValueError(
           "The graph already contains an op named %s" % grad_debug_op_name)
-    return debug_identity
+    return debug_grad_identity
 
   def watch_gradients_by_tensors(self, graph, tensors):
     """Watch gradient tensors by x-tensor(s).
@@ -359,7 +358,7 @@ def clear_gradient_debuggers():
   _gradient_debuggers.clear()
 
 
-@ops.RegisterGradient("DebugIdentity")
+@ops.RegisterGradient("DebugGradientIdentity")
 def _identify_gradient_grad(op, dy):
   """Gradient function for the DebugIdentity op."""
   # TODO(cais): Allow overriding gradient.
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index 28817e99e8..e02434b848 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -6,6 +6,7 @@ ConcatOffset
 Concat
 ConcatV2
 Const
+DebugGradientIdentity
 EditDistance
 ExpandDims
 ListDiff
-- 
GitLab


From 7dd40cecda4bf2c712d13fae862d1b229dc83a2b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 14:01:56 -0700
Subject: [PATCH 1253/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 162983913
---
 tensorflow/go/op/wrappers.go | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 3c13ca4f13..10388509a9 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -5737,6 +5737,24 @@ func CacheDataset(scope *Scope, input_dataset tf.Output, filename tf.Output, out
 	return op.Output(0)
 }
 
+// Identity op for gradient debugging.
+//
+// This op is hidden from public in Python. It is used by TensorFlow Debugger to
+// register gradient tensors for gradient debugging.
+func DebugGradientIdentity(scope *Scope, input tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "DebugGradientIdentity",
+		Input: []tf.Input{
+			input,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Deprecated. Use TensorArrayGradV3
 func TensorArrayGradV2(scope *Scope, handle tf.Output, flow_in tf.Output, source string) (grad_handle tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From b508f4ba620155e9038c3bde05c3b8a52e1c6bf6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 14:16:49 -0700
Subject: [PATCH 1254/1375] Internal change.

PiperOrigin-RevId: 162986106
---
 tensorflow/core/framework/rendezvous.cc      | 198 ++++++++-----------
 tensorflow/core/framework/rendezvous.h       |  24 ++-
 tensorflow/core/framework/rendezvous_test.cc |  50 ++---
 3 files changed, 127 insertions(+), 145 deletions(-)

diff --git a/tensorflow/core/framework/rendezvous.cc b/tensorflow/core/framework/rendezvous.cc
index 7a03cb31ba..90426defa0 100644
--- a/tensorflow/core/framework/rendezvous.cc
+++ b/tensorflow/core/framework/rendezvous.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/rendezvous.h"
 
+#include <deque>
 #include <functional>
 #include <utility>
 #include <vector>
@@ -151,62 +152,44 @@ class LocalRendezvousImpl : public Rendezvous {
 
   Status Send(const ParsedKey& key, const Args& send_args, const Tensor& val,
               const bool is_dead) override {
-    DoneCallback waiter = nullptr;
-    Args recv_args;
     uint64 key_hash = KeyHash(key.FullKey());
     VLOG(2) << "Send " << this << " " << key_hash << " " << key.FullKey();
-    Item* item = nullptr;
-    {
-      mutex_lock l(mu_);
-      if (!status_.ok()) {
-        return status_;
-      }
-      Table::iterator iter = table_.find(key_hash);
-      if (iter == table_.end()) {
-        // There is no waiter for this message. Insert the message
-        // into the waiters table. The waiter will pick it up when
-        // arrives.
-        item = new Item;
-        item->waiter = nullptr;
-        item->value = val;
-        item->is_dead = is_dead;
-        if (send_args.device_context) {
-          send_args.device_context->Ref();
-          item->send_dev_context = send_args.device_context;
-        }
-        item->recv_dev_context = nullptr;
-
-        // The allocator attributes of item->value.
-        item->send_alloc_attrs = send_args.alloc_attrs;
 
-        CHECK(table_.insert({key_hash, item}).second);
-        return Status::OK();
-      } else {
-        item = iter->second;
+    mu_.lock();
+    if (!status_.ok()) {
+      // Rendezvous has been aborted.
+      Status s = status_;
+      mu_.unlock();
+      return s;
+    }
 
-        if (item->waiter == nullptr) {
-          // There is already a message in the table under the key.
-          // Should not happen unless it has a waiter.
-          return errors::Aborted("Duplicated send: ", key.FullKey());
-        }
+    ItemQueue* queue = &table_[key_hash];
+    if (queue->empty() || queue->front()->IsSendValue()) {
+      // There is no waiter for this message. Append the message
+      // into the queue. The waiter will pick it up when arrives.
+      // Only send-related fields need to be filled.
+      Item* item = new Item;
+      item->value = val;
+      item->is_dead = is_dead;
+      item->send_args = send_args;
+      if (item->send_args.device_context) {
+        item->send_args.device_context->Ref();
+      }
+      queue->push_back(item);
+      mu_.unlock();
+      return Status::OK();
+    }
 
-        // Get item->waiter function into waiter and set item->waiter to null
-        std::swap(item->waiter, waiter);
-        DCHECK(item->waiter == nullptr);
-        DCHECK(waiter != nullptr);
+    // There is an earliest waiter to consume this message.
+    Item* item = queue->front();
+    queue->pop_front();
+    mu_.unlock();
 
-        // The ref on recv_dev_context transfers below.
-        recv_args.device_context = item->recv_dev_context;
-        recv_args.alloc_attrs = item->recv_alloc_attrs;
-        item->recv_dev_context = nullptr;
-        table_.erase(iter);
-      }
-    }  // mutex
+    // Notify the waiter by invoking its done closure, outside the
+    // lock.
+    DCHECK(!item->IsSendValue());
+    item->waiter(Status::OK(), send_args, item->recv_args, val, is_dead);
     delete item;
-    // Notify the waiter by invoking its done closure, outside scope
-    // of the table lock.
-    waiter(Status::OK(), send_args, recv_args, val, is_dead);
-    if (recv_args.device_context) recv_args.device_context->Unref();
     return Status::OK();
   }
 
@@ -214,6 +197,7 @@ class LocalRendezvousImpl : public Rendezvous {
                  DoneCallback done) override {
     uint64 key_hash = KeyHash(key.FullKey());
     VLOG(2) << "Recv " << this << " " << key_hash << " " << key.FullKey();
+
     mu_.lock();
     if (!status_.ok()) {
       // Rendezvous has been aborted.
@@ -222,69 +206,50 @@ class LocalRendezvousImpl : public Rendezvous {
       done(s, Args(), recv_args, Tensor(), false);
       return;
     }
-    Table::iterator iter = table_.find(key_hash);
-    if (iter != table_.end()) {
-      Item* item = iter->second;
-      if (item->waiter == nullptr) {
-        // A message has already arrived and is stored in the table
-        // under this key.  Consumes the message and invokes the done
-        // closure.
-        Tensor v = std::move(item->value);
-        // Before dropping the table lock, capture the item values.
-        // DeviceContext is only non-null for non-CPU devices.
-        // If we capture the send_dev_context, we need to hold a ref on
-        // it.  Our caller will have a ref on the recv_dev_context,
-        // which is not in our table.
-        DeviceContext* send_dev_context = item->send_dev_context;
-        if (send_dev_context) send_dev_context->Ref();
-        bool is_dead = item->is_dead;
-        Args send_args;
-        send_args.device_context = item->send_dev_context;
-        send_args.alloc_attrs = item->send_alloc_attrs;
-        table_.erase(iter);
-        mu_.unlock();
-        delete item;
-        done(Status::OK(), send_args, recv_args, v, is_dead);
-        if (send_dev_context) send_dev_context->Unref();
-      } else {
-        // Already have a waiter in the waiters table under this key,
-        // which should not happen.
-        mu_.unlock();
-        done(errors::Aborted("Duplicated recv: ", key.FullKey()), Args(),
-             recv_args, Tensor(), false);
+
+    ItemQueue* queue = &table_[key_hash];
+    if (queue->empty() || !queue->front()->IsSendValue()) {
+      // There is no message to pick up.
+      // Only recv-related fileds need to be filled.
+      Item* item = new Item;
+      item->waiter = std::move(done);
+      item->recv_args = recv_args;
+      if (item->recv_args.device_context) {
+        item->recv_args.device_context->Ref();
       }
+      queue->push_back(item);
+      mu_.unlock();
       return;
     }
-    // Waiting for a message that has not arrived yet. Insert into the
-    // waiting table. The done closure will be invoked when the
-    // message arrives.
-    Item* item = new Item;
-    item->waiter = std::move(done);
-    item->recv_alloc_attrs = recv_args.alloc_attrs;
-    if (recv_args.device_context) {
-      item->recv_dev_context = recv_args.device_context;
-      item->recv_dev_context->Ref();
-    }
-    CHECK(table_.insert({key_hash, item}).second);
+
+    // A message has already arrived and is queued in the table under
+    // this key.  Consumes the message and invokes the done closure.
+    Item* item = queue->front();
+    queue->pop_front();
     mu_.unlock();
+
+    // Invokes the done() by invoking its done closure, outside scope
+    // of the table lock.
+    DCHECK(item->IsSendValue());
+    done(Status::OK(), item->send_args, recv_args, item->value, item->is_dead);
+    delete item;
   }
 
   void StartAbort(const Status& status) override {
     CHECK(!status.ok());
-    std::vector<Item*> items;
+    Table table;
     {
       mutex_lock l(mu_);
-      if (!status_.ok()) return;
-      status_ = status;
-      items.reserve(table_.size());
-      for (const auto& p : table_) items.push_back(p.second);
-      table_.clear();
+      status_.Update(status);
+      table_.swap(table);
     }
-    for (Item* item : items) {
-      if (item->waiter != nullptr) {
-        item->waiter(status, Args(), Args(), Tensor(), false);
+    for (auto& p : table) {
+      for (Item* item : p.second) {
+        if (!item->IsSendValue()) {
+          item->waiter(status, Args(), Args(), Tensor(), false);
+        }
+        delete item;
       }
-      delete item;
     }
   }
 
@@ -295,36 +260,43 @@ class LocalRendezvousImpl : public Rendezvous {
     DoneCallback waiter = nullptr;
     Tensor value;
     bool is_dead = false;
-    DeviceContext* send_dev_context = nullptr;
-    DeviceContext* recv_dev_context = nullptr;
-    AllocatorAttributes send_alloc_attrs;
-    AllocatorAttributes recv_alloc_attrs;
+    Args send_args;
+    Args recv_args;
 
     ~Item() {
-      if (send_dev_context) {
-        send_dev_context->Unref();
+      if (send_args.device_context) {
+        send_args.device_context->Unref();
       }
-      if (recv_dev_context) {
-        recv_dev_context->Unref();
+      if (recv_args.device_context) {
+        recv_args.device_context->Unref();
       }
     }
+
+    // Returns true iff this item represents a value being sent.
+    bool IsSendValue() const { return this->waiter == nullptr; }
   };
+
   // We key the hash table by KeyHash of the Rendezvous::CreateKey string
   static uint64 KeyHash(const StringPiece& k) {
     return Hash64(k.data(), k.size());
   }
 
-  typedef gtl::FlatMap<uint64, Item*> Table;
+  // By invariant, the item queue under each key is of the form
+  //   [item.IsSendValue()]* meaning each item is a sent message.
+  // or
+  //   [!item.IsSendValue()]* meaning each item is a waiter.
+  //
+  // TODO(zhifengc): consider a better queue impl than std::deque.
+  typedef std::deque<Item*> ItemQueue;
+  typedef gtl::FlatMap<uint64, ItemQueue> Table;
 
   // TODO(zhifengc): shard table_.
   mutex mu_;
   Table table_ GUARDED_BY(mu_);
-  Status status_;
+  Status status_ GUARDED_BY(mu_);
 
   ~LocalRendezvousImpl() override {
-    for (auto i : table_) {
-      delete i.second;
-    }
+    StartAbort(errors::Cancelled("LocalRendezvousImpl deleted"));
   }
 
   TF_DISALLOW_COPY_AND_ASSIGN(LocalRendezvousImpl);
diff --git a/tensorflow/core/framework/rendezvous.h b/tensorflow/core/framework/rendezvous.h
index 6343c208a1..01e43e44e3 100644
--- a/tensorflow/core/framework/rendezvous.h
+++ b/tensorflow/core/framework/rendezvous.h
@@ -27,16 +27,22 @@ limitations under the License.
 
 namespace tensorflow {
 
-// A Rendezvous is an abstraction for passing a Tensor
-// from a producer to a consumer, where the consumer may safely
-// request the Tensor before or after it has been produced.  A
-// producer never blocks when using a Rendezvous.  A consumer has the
-// choice of making a blocking call or providing a callback: in either
-// case, the consumer receives the Tensor as soon as it is available.
+// A Rendezvous is an abstraction for passing tensors from producers
+// to consumers. A rendezvous is a table of channels. Each channel is
+// keyed by a rendezvous key. The key encodes a pair of <producer,
+// consumer>, where the producer and the consumer are tensorflow
+// devices.
 //
-// A Rendezvous key encodes a single <producer, consumer> pair.  It is
-// an error to call Send() or Recv*() more than once with the same
-// key.
+// The producer calls the Send() method to send one tensor over one
+// named channel. The consumer calls the Recv() method to receive one
+// tensor from a named channel. A sequence of tensors can be passed
+// from the producer to the consumer.  The consumer receives them in
+// the order as the producer sends them.
+//
+// A consumer may safely request the tensor before or after it has
+// been produced.  A consumer has the choice of making a blocking call
+// or providing a callback: in either case, the consumer receives the
+// Tensor as soon as it is available.  A producer never blocks.
 class Rendezvous : public core::RefCounted {
  public:
   struct Args {
diff --git a/tensorflow/core/framework/rendezvous_test.cc b/tensorflow/core/framework/rendezvous_test.cc
index b48f711806..b2fe144de6 100644
--- a/tensorflow/core/framework/rendezvous_test.cc
+++ b/tensorflow/core/framework/rendezvous_test.cc
@@ -121,8 +121,6 @@ const Rendezvous::ParsedKey& KeyBar() {
 TEST_F(LocalRendezvousTest, SendRecv) {
   Rendezvous::Args args;
   TF_ASSERT_OK(rendez_->Send(KeyFoo(), args, V("hello"), false));
-  EXPECT_TRUE(
-      errors::IsAborted(rendez_->Send(KeyFoo(), args, V("hello"), false)));
   Tensor val(DT_STRING);
   bool is_dead = false;
   TF_ASSERT_OK(rendez_->Recv(KeyFoo(), args, &val, &is_dead));
@@ -142,26 +140,7 @@ TEST_F(LocalRendezvousTest, RecvSend) {
   EXPECT_EQ("hello", V(val));
 }
 
-TEST_F(LocalRendezvousTest, DuplicateWaiterRecv) {
-  SchedClosure([this]() {
-    Tensor t(DT_STRING);
-    bool is_dead = false;
-    Rendezvous::Args args;
-    TF_ASSERT_OK(rendez_->Recv(KeyFoo(), args, &t, &is_dead));
-    TF_ASSERT_OK(rendez_->Send(KeyBar(), args, t, is_dead));
-  });
-  Env::Default()->SleepForMicroseconds(1000000);
-  Tensor val(DT_STRING);
-  bool val_dead = false;
-  Rendezvous::Args args;
-  EXPECT_TRUE(
-      errors::IsAborted(rendez_->Recv(KeyFoo(), args, &val, &val_dead)));
-  TF_ASSERT_OK(rendez_->Send(KeyFoo(), args, V("secret msg"), val_dead));
-  TF_ASSERT_OK(rendez_->Recv(KeyBar(), args, &val, &val_dead));
-  EXPECT_EQ("secret msg", V(val));
-}
-
-TEST_F(LocalRendezvousTest, DuplicateSerialRecv) {
+TEST_F(LocalRendezvousTest, PingPong) {
   SchedClosure([this]() {
     Tensor t(DT_STRING);
     bool is_dead = false;
@@ -183,7 +162,7 @@ TEST_F(LocalRendezvousTest, DuplicateSerialRecv) {
 // thread waits for done to be notified.
 struct BlockingState {
   mutex lock;
-  int counter;
+  int counter = 0;
   Notification done;
 };
 
@@ -234,6 +213,30 @@ TEST_F(LocalRendezvousTest, RandomSendRecv) {
   state.done.WaitForNotification();
 }
 
+static void RandomSleep() {
+  if (std::rand() % 10 == 0) {
+    Env::Default()->SleepForMicroseconds(1000);
+  }
+}
+
+TEST_F(LocalRendezvousTest, MultiSends) {
+  static const int N = 100;
+  const auto& key_foo = KeyFoo();
+  Rendezvous::Args args;
+  SchedClosure([=]() {
+    for (int i = 0; i < N; ++i) {
+      TF_ASSERT_OK(rendez_->Send(key_foo, args, V(strings::StrCat(i)), false));
+      RandomSleep();
+    }
+  });
+  Tensor val;
+  bool val_dead;
+  for (int i = 0; i < N; ++i) {
+    TF_ASSERT_OK(rendez_->Recv(key_foo, args, &val, &val_dead));
+    RandomSleep();
+  }
+}
+
 TEST_F(LocalRendezvousTest, RecvAbort) {
   rendez_->Ref();
   SchedClosure([this]() {
@@ -326,6 +329,7 @@ static void BM_SendRecv(int iters) {
 BENCHMARK(BM_SendRecv);
 
 static void BM_PingPong(int iters) {
+  CHECK_GT(iters, 0);
   thread::ThreadPool* pool = new thread::ThreadPool(Env::Default(), "test", 1);
 
   // The main thread sends "foo" for iters times and receives "bar"
-- 
GitLab


From cb30d1f9b6a5772ec6f6676231f1e21ff5ed1f70 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 14:35:05 -0700
Subject: [PATCH 1255/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 162989008
---
 .../core/ops/compat/ops_history.v1.pbtxt       | 16 ++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                  | 18 ++++++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 409c0023a9..94224f22b9 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -6002,6 +6002,22 @@ op {
     }
   }
 }
+op {
+  name: "DebugGradientIdentity"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  allows_uninitialized_input: true
+}
 op {
   name: "DebugIdentity"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 8bbf2d7989..1822fc1133 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -5784,6 +5784,24 @@ op {
   summary: "Compute the cumulative sum of the tensor `x` along `axis`."
   description: "By default, this op performs an inclusive cumsum, which means that the first\nelement of the input is identical to the first element of the output:\n\n```python\ntf.cumsum([a, b, c])  # => [a, a + b, a + b + c]\n```\n\nBy setting the `exclusive` kwarg to `True`, an exclusive cumsum is\nperformed instead:\n\n```python\ntf.cumsum([a, b, c], exclusive=True)  # => [0, a, a + b]\n```\n\nBy setting the `reverse` kwarg to `True`, the cumsum is performed in the\nopposite direction:\n\n```python\ntf.cumsum([a, b, c], reverse=True)  # => [a + b + c, b + c, c]\n```\n\nThis is more efficient than using separate `tf.reverse` ops.\n\nThe `reverse` and `exclusive` kwargs can also be combined:\n\n```python\ntf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]\n```"
 }
+op {
+  name: "DebugGradientIdentity"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  summary: "Identity op for gradient debugging."
+  description: "This op is hidden from public in Python. It is used by TensorFlow Debugger to\nregister gradient tensors for gradient debugging."
+  allows_uninitialized_input: true
+}
 op {
   name: "DebugIdentity"
   input_arg {
-- 
GitLab


From a976910e8307d3750540eb12960af65cf5b6f754 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Mon, 24 Jul 2017 15:14:12 -0700
Subject: [PATCH 1256/1375] [XLA] Change backtrace logging from WARNING to
 VLOG(1)

PiperOrigin-RevId: 162995303
---
 tensorflow/compiler/xla/util.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc
index 61df9a0c51..1ecdb9852d 100644
--- a/tensorflow/compiler/xla/util.cc
+++ b/tensorflow/compiler/xla/util.cc
@@ -34,8 +34,8 @@ namespace {
 // Logs the provided status message with a backtrace.
 Status WithLogBacktrace(const Status& status) {
   CHECK(!status.ok());
-  LOG(WARNING) << status.ToString();
-  LOG(WARNING) << tensorflow::CurrentStackTrace();
+  VLOG(1) << status.ToString();
+  VLOG(1) << tensorflow::CurrentStackTrace();
   return status;
 }
 
-- 
GitLab


From 7dcef85558ca9da32d5c0060844bbe5477fb8bea Mon Sep 17 00:00:00 2001
From: horance <horance-liu@users.noreply.github.com>
Date: Tue, 25 Jul 2017 06:27:25 +0800
Subject: [PATCH 1257/1375] replace supervised session to monitored session
 (#11606)

---
 tensorflow/python/training/monitored_session.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index 451d6d1124..9f71395c96 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -575,7 +575,7 @@ class _MonitoredSession(object):
           ops.get_default_graph()._unsafe_unfinalize()  # pylint: disable=protected-access
 
   def _is_closed(self):
-    """Return True if the supervised session is closed.  For tests only.
+    """Return True if the monitored session is closed.  For tests only.
 
     Returns:
       A boolean.
-- 
GitLab


From 1641c938ee9b283dd7cc94220ba8e25ce080e8de Mon Sep 17 00:00:00 2001
From: Luke Iwanski <luke@codeplay.com>
Date: Mon, 24 Jul 2017 23:29:59 +0100
Subject: [PATCH 1258/1375] [OpenCL] DenseUpdate sync (#11641)

---
 .../core/kernels/dense_update_functor.h       | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/tensorflow/core/kernels/dense_update_functor.h b/tensorflow/core/kernels/dense_update_functor.h
index 54b080c83b..4aefe26c54 100644
--- a/tensorflow/core/kernels/dense_update_functor.h
+++ b/tensorflow/core/kernels/dense_update_functor.h
@@ -24,6 +24,9 @@ limitations under the License.
 namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
+#ifdef TENSORFLOW_USE_SYCL
+typedef Eigen::SyclDevice SYCLDevice;
+#endif  // TENSORFLOW_USE_SYCL
 
 enum DenseUpdateType { ADD, SUB, ASSIGN };
 
@@ -59,6 +62,32 @@ struct DenseUpdate<CPUDevice, T, ASSIGN> {
   }
 };
 
+#ifdef TENSORFLOW_USE_SYCL
+template <typename T>
+struct DenseUpdate<SYCLDevice, T, ADD> {
+  void operator()(const SYCLDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) += update;
+  }
+};
+
+template <typename T>
+struct DenseUpdate<SYCLDevice, T, SUB> {
+  void operator()(const SYCLDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) -= update;
+  }
+};
+
+template <typename T>
+struct DenseUpdate<SYCLDevice, T, ASSIGN> {
+  void operator()(const SYCLDevice& d, typename TTypes<T>::Flat params,
+                  typename TTypes<T>::ConstFlat update) {
+    params.device(d) = update;
+  }
+};
+#endif  // TENSORFLOW_USE_SYCL
+
 }  // end namespace functor
 }  // end namespace tensorflow
 
-- 
GitLab


From eac302a3e688e94819a64669575674ddd842f46a Mon Sep 17 00:00:00 2001
From: Kai Sasaki <lewuathe@me.com>
Date: Tue, 25 Jul 2017 07:30:39 +0900
Subject: [PATCH 1259/1375] Fix build doc README (#11643)

- `src_dir` must be absolute path
- docs_src was moved to `tensorflow/docs_src`
---
 tensorflow/g3doc/README.txt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/g3doc/README.txt b/tensorflow/g3doc/README.txt
index 4f4076db37..6eaf1e1bda 100644
--- a/tensorflow/g3doc/README.txt
+++ b/tensorflow/g3doc/README.txt
@@ -5,7 +5,7 @@ go to:
 
 Documentation (on Github, tensorflow.org, and anywhere else we decide to
 serve it from) is now generated from the files in
-third_party/tensorflow/docs_src/ (for tutorials and other guides) and
+tensorflow/docs_src/ (for tutorials and other guides) and
 TensorFlow source code (for the API reference pages). If you see a problem with
 API reference, edit the code comments in the appropriate language. If you see a 
 problem with our other docs, edit the files in docs_src.
@@ -14,9 +14,10 @@ To preview the results of your changes, or generate an offline copy of
 the docs, run:
 
   bazel run -- tensorflow/tools/docs:generate \
-    --src_dir=tensorflow/docs_src/ \
+    --src_dir=/path/to/tensorflow/docs_src/ \
     --output_dir=/tmp/tfdocs/
 
+`src_dir` must be absolute path to documentation source.
 When authoring docs, note that we have some new syntax for references --
 at least for docs coming from Python docstrings or
 tensorflow/docs_src/.  Use:
@@ -26,7 +27,6 @@ tensorflow/docs_src/.  Use:
   syntax still works, since @{tf.MyClass.method} links to the right
   part of the tf.MyClass page.
 
-
 * @{tensorflow::symbol} to make a link to the reference page for a C++
   symbol. (This only works for a few symbols but will work for more soon.)
 
-- 
GitLab


From 43fcb4a2ad2a545d4066d820c3b0c799aea759ee Mon Sep 17 00:00:00 2001
From: Yufeng <yufengg@users.noreply.github.com>
Date: Mon, 24 Jul 2017 15:34:42 -0700
Subject: [PATCH 1260/1375] added link to benchmarks in community page (#11648)

---
 tensorflow/docs_src/community/index.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/docs_src/community/index.md b/tensorflow/docs_src/community/index.md
index f4c57725a0..c293e61c3e 100644
--- a/tensorflow/docs_src/community/index.md
+++ b/tensorflow/docs_src/community/index.md
@@ -10,3 +10,5 @@ This section contains the following documents:
     TensorFlow source code or documentation, please read this guide.
   * @{$style_guide$TensorFlow Style Guide}, which identifies coding style
     conventions that TensorFlow developers and users should follow.
+  * @{$benchmarks$Benchmarks}, Benchmarks, a guide for defining and 
+    running a TensorFlow benchmark.
-- 
GitLab


From 6a93f10b819549d6d16ff08fbb08ceda750092b3 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Mon, 24 Jul 2017 15:32:54 -0700
Subject: [PATCH 1261/1375] Modify dependency structure of transform_utils to
 allow use within Grappler.

PiperOrigin-RevId: 162998272
---
 .../contrib/hvx/hvx_ops_support_checker/BUILD |  2 +-
 .../hvx_ops_support_checker_main.cc           |  2 +-
 tensorflow/core/kernels/BUILD                 |  1 +
 tensorflow/core/kernels/hexagon/BUILD         |  1 +
 tensorflow/tools/graph_transforms/BUILD       | 44 +++++++++-
 .../tools/graph_transforms/compare_graphs.cc  |  1 +
 .../tools/graph_transforms/file_utils.cc      | 46 ++++++++++
 .../tools/graph_transforms/file_utils.h       | 32 +++++++
 .../tools/graph_transforms/file_utils_test.cc | 83 +++++++++++++++++++
 .../graph_transforms/summarize_graph_main.cc  |  1 +
 .../tools/graph_transforms/transform_graph.cc |  1 +
 .../tools/graph_transforms/transform_utils.cc | 23 -----
 .../tools/graph_transforms/transform_utils.h  |  8 +-
 .../graph_transforms/transform_utils_test.cc  | 50 -----------
 14 files changed, 210 insertions(+), 85 deletions(-)
 create mode 100644 tensorflow/tools/graph_transforms/file_utils.cc
 create mode 100644 tensorflow/tools/graph_transforms/file_utils.h
 create mode 100644 tensorflow/tools/graph_transforms/file_utils_test.cc

diff --git a/tensorflow/contrib/hvx/hvx_ops_support_checker/BUILD b/tensorflow/contrib/hvx/hvx_ops_support_checker/BUILD
index 5309724580..3497c84d58 100644
--- a/tensorflow/contrib/hvx/hvx_ops_support_checker/BUILD
+++ b/tensorflow/contrib/hvx/hvx_ops_support_checker/BUILD
@@ -30,6 +30,6 @@ cc_binary(
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/kernels:remote_fused_graph_execute_utils",
         "//tensorflow/core/kernels/hexagon:graph_transferer",
-        "//tensorflow/tools/graph_transforms:transform_utils",
+        "//tensorflow/tools/graph_transforms:file_utils",
     ],
 )
diff --git a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
index 8fc3f35f41..60281951dd 100644
--- a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
+++ b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
@@ -31,7 +31,7 @@ limitations under the License.
 #include "tensorflow/core/platform/init_main.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/util/command_line_flags.h"
-#include "tensorflow/tools/graph_transforms/transform_utils.h"
+#include "tensorflow/tools/graph_transforms/file_utils.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index e676d5a367..fffcb980db 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -5202,6 +5202,7 @@ tf_cc_test(
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
+        "//tensorflow/core:tensorflow",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
diff --git a/tensorflow/core/kernels/hexagon/BUILD b/tensorflow/core/kernels/hexagon/BUILD
index ce83d7c0fc..a01a4c40da 100644
--- a/tensorflow/core/kernels/hexagon/BUILD
+++ b/tensorflow/core/kernels/hexagon/BUILD
@@ -111,6 +111,7 @@ tf_cc_test(
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
+        "//tensorflow/core:tensorflow",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD
index ec582739b7..cad0567b9e 100644
--- a/tensorflow/tools/graph_transforms/BUILD
+++ b/tensorflow/tools/graph_transforms/BUILD
@@ -26,14 +26,12 @@ cc_library(
     copts = tf_copts(),
     visibility = ["//visibility:public"],
     deps = [
-        "//tensorflow/core:core_cpu",
-        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:core_cpu_base",
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
-        "//tensorflow/core:tensorflow",
     ],
 )
 
@@ -44,11 +42,46 @@ tf_cc_test(
     deps = [
         ":transform_utils",
         "//tensorflow/cc:cc_ops",
-        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:core_cpu_base",
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:tensorflow",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+    ],
+)
+
+cc_library(
+    name = "file_utils",
+    srcs = [
+        "file_utils.cc",
+    ],
+    hdrs = [
+        "file_utils.h",
+    ],
+    copts = tf_copts(),
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:tensorflow",
+    ],
+)
+
+tf_cc_test(
+    name = "file_utils_test",
+    size = "small",
+    srcs = ["file_utils_test.cc"],
+    deps = [
+        ":file_utils",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
@@ -154,6 +187,7 @@ cc_library(
     copts = tf_copts(),
     visibility = ["//visibility:public"],
     deps = [
+        ":file_utils",
         ":transform_utils",
         ":transforms_lib",
         "//tensorflow/core:framework_internal",
@@ -215,6 +249,7 @@ cc_library(
     copts = tf_copts(),
     visibility = ["//visibility:public"],
     deps = [
+        ":file_utils",
         ":transform_utils",
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
@@ -240,6 +275,7 @@ cc_binary(
     linkstatic = 1,
     visibility = ["//visibility:public"],
     deps = [
+        ":file_utils",
         ":transform_utils",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework_internal",
diff --git a/tensorflow/tools/graph_transforms/compare_graphs.cc b/tensorflow/tools/graph_transforms/compare_graphs.cc
index 8fce16337f..28a80a885f 100644
--- a/tensorflow/tools/graph_transforms/compare_graphs.cc
+++ b/tensorflow/tools/graph_transforms/compare_graphs.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/util/command_line_flags.h"
 #include "tensorflow/core/util/equal_graph_def.h"
+#include "tensorflow/tools/graph_transforms/file_utils.h"
 #include "tensorflow/tools/graph_transforms/transform_utils.h"
 
 namespace tensorflow {
diff --git a/tensorflow/tools/graph_transforms/file_utils.cc b/tensorflow/tools/graph_transforms/file_utils.cc
new file mode 100644
index 0000000000..5649c97198
--- /dev/null
+++ b/tensorflow/tools/graph_transforms/file_utils.cc
@@ -0,0 +1,46 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/tools/graph_transforms/file_utils.h"
+
+#include "tensorflow/core/public/session.h"
+
+namespace tensorflow {
+namespace graph_transforms {
+
+Status LoadTextOrBinaryGraphFile(const string& file_name, GraphDef* graph_def) {
+  string file_data;
+  Status load_file_status =
+      ReadFileToString(Env::Default(), file_name, &file_data);
+  if (!load_file_status.ok()) {
+    errors::AppendToMessage(&load_file_status, " (for file ", file_name, ")");
+    return load_file_status;
+  }
+  // Try to load in binary format first, and then try ascii if that fails.
+  Status load_status = ReadBinaryProto(Env::Default(), file_name, graph_def);
+  if (!load_status.ok()) {
+    if (protobuf::TextFormat::ParseFromString(file_data, graph_def)) {
+      load_status = Status::OK();
+    } else {
+      errors::AppendToMessage(&load_status,
+                              " (both text and binary parsing failed for file ",
+                              file_name, ")");
+    }
+  }
+  return load_status;
+}
+
+}  // namespace graph_transforms
+}  // namespace tensorflow
diff --git a/tensorflow/tools/graph_transforms/file_utils.h b/tensorflow/tools/graph_transforms/file_utils.h
new file mode 100644
index 0000000000..4737e95abc
--- /dev/null
+++ b/tensorflow/tools/graph_transforms/file_utils.h
@@ -0,0 +1,32 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_TOOLS_GRAPH_TRANSFORMS_FILE_UTILS_H_
+#define THIRD_PARTY_TENSORFLOW_TOOLS_GRAPH_TRANSFORMS_FILE_UTILS_H_
+
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+namespace graph_transforms {
+
+// First tries to load the file as a text protobuf, if that fails tries to parse
+// it as a binary protobuf, and returns an error if both fail.
+Status LoadTextOrBinaryGraphFile(const string& file_name, GraphDef* graph_def);
+
+}  // namespace graph_transforms
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_TOOLS_GRAPH_TRANSFORMS_FILE_UTILS_H_
diff --git a/tensorflow/tools/graph_transforms/file_utils_test.cc b/tensorflow/tools/graph_transforms/file_utils_test.cc
new file mode 100644
index 0000000000..8c898ba0f1
--- /dev/null
+++ b/tensorflow/tools/graph_transforms/file_utils_test.cc
@@ -0,0 +1,83 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/tools/graph_transforms/file_utils.h"
+#include "tensorflow/cc/ops/const_op.h"
+#include "tensorflow/cc/ops/image_ops.h"
+#include "tensorflow/cc/ops/nn_ops.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/lib/io/path.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/test_benchmark.h"
+#include "tensorflow/core/util/equal_graph_def.h"
+
+namespace tensorflow {
+namespace graph_transforms {
+
+class FileUtilsTest : public ::testing::Test {
+ protected:
+  void TestLoadTextOrBinaryGraphFile() {
+    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+    const int width = 10;
+
+    auto root = tensorflow::Scope::NewRootScope();
+    Tensor a_data(DT_FLOAT, TensorShape({width}));
+    test::FillIota<float>(&a_data, 1.0f);
+    Output a_const = Const(root.WithOpName("a"), Input::Initializer(a_data));
+    GraphDef graph_def;
+    TF_ASSERT_OK(root.ToGraphDef(&graph_def));
+
+    const string text_file =
+        io::JoinPath(testing::TmpDir(), "text_graph.pbtxt");
+    TF_ASSERT_OK(WriteTextProto(Env::Default(), text_file, graph_def));
+
+    const string binary_file =
+        io::JoinPath(testing::TmpDir(), "binary_graph.pb");
+    TF_ASSERT_OK(WriteBinaryProto(Env::Default(), binary_file, graph_def));
+
+    const string bogus_file = io::JoinPath(testing::TmpDir(), "bogus_graph.pb");
+    TF_ASSERT_OK(
+        WriteStringToFile(Env::Default(), bogus_file, "Not a !{ proto..."));
+
+    GraphDef text_graph_def;
+    TF_EXPECT_OK(LoadTextOrBinaryGraphFile(text_file, &text_graph_def));
+    string text_diff;
+    EXPECT_TRUE(EqualGraphDef(text_graph_def, graph_def, &text_diff))
+        << text_diff;
+
+    GraphDef binary_graph_def;
+    TF_EXPECT_OK(LoadTextOrBinaryGraphFile(binary_file, &binary_graph_def));
+    string binary_diff;
+    EXPECT_TRUE(EqualGraphDef(binary_graph_def, graph_def, &binary_diff))
+        << binary_diff;
+
+    GraphDef no_graph_def;
+    EXPECT_FALSE(
+        LoadTextOrBinaryGraphFile("____non_existent_file_____", &no_graph_def)
+            .ok());
+
+    GraphDef bogus_graph_def;
+    EXPECT_FALSE(LoadTextOrBinaryGraphFile(bogus_file, &bogus_graph_def).ok());
+  }
+};
+
+TEST_F(FileUtilsTest, TestLoadTextOrBinaryGraphFile) {
+  TestLoadTextOrBinaryGraphFile();
+}
+
+}  // namespace graph_transforms
+}  // namespace tensorflow
diff --git a/tensorflow/tools/graph_transforms/summarize_graph_main.cc b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
index d9806e8ed2..6c404c8061 100644
--- a/tensorflow/tools/graph_transforms/summarize_graph_main.cc
+++ b/tensorflow/tools/graph_transforms/summarize_graph_main.cc
@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/core/platform/init_main.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/util/command_line_flags.h"
+#include "tensorflow/tools/graph_transforms/file_utils.h"
 #include "tensorflow/tools/graph_transforms/transform_utils.h"
 
 namespace tensorflow {
diff --git a/tensorflow/tools/graph_transforms/transform_graph.cc b/tensorflow/tools/graph_transforms/transform_graph.cc
index e7694104cb..28387c2b48 100644
--- a/tensorflow/tools/graph_transforms/transform_graph.cc
+++ b/tensorflow/tools/graph_transforms/transform_graph.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/core/platform/init_main.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/util/command_line_flags.h"
+#include "tensorflow/tools/graph_transforms/file_utils.h"
 #include "tensorflow/tools/graph_transforms/transform_utils.h"
 
 namespace tensorflow {
diff --git a/tensorflow/tools/graph_transforms/transform_utils.cc b/tensorflow/tools/graph_transforms/transform_utils.cc
index 0ef517acc5..bd1e4c90c0 100644
--- a/tensorflow/tools/graph_transforms/transform_utils.cc
+++ b/tensorflow/tools/graph_transforms/transform_utils.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/public/session.h"
 
 namespace tensorflow {
 namespace graph_transforms {
@@ -587,28 +586,6 @@ Status GetInOutTypes(const NodeDef& node_def, DataTypeVector* inputs,
   return Status::OK();
 }
 
-Status LoadTextOrBinaryGraphFile(const string& file_name, GraphDef* graph_def) {
-  string file_data;
-  Status load_file_status =
-      ReadFileToString(Env::Default(), file_name, &file_data);
-  if (!load_file_status.ok()) {
-    errors::AppendToMessage(&load_file_status, " (for file ", file_name, ")");
-    return load_file_status;
-  }
-  // Try to load in binary format first, and then try ascii if that fails.
-  Status load_status = ReadBinaryProto(Env::Default(), file_name, graph_def);
-  if (!load_status.ok()) {
-    if (protobuf::TextFormat::ParseFromString(file_data, graph_def)) {
-      load_status = Status::OK();
-    } else {
-      errors::AppendToMessage(&load_status,
-                              " (both text and binary parsing failed for file ",
-                              file_name, ")");
-    }
-  }
-  return load_status;
-}
-
 int TransformFuncContext::CountParameters(const string& name) const {
   if (params.count(name)) {
     return params.at(name).size();
diff --git a/tensorflow/tools/graph_transforms/transform_utils.h b/tensorflow/tools/graph_transforms/transform_utils.h
index 2db0a24267..c0fb492412 100644
--- a/tensorflow/tools/graph_transforms/transform_utils.h
+++ b/tensorflow/tools/graph_transforms/transform_utils.h
@@ -109,8 +109,8 @@ void FilterGraphDef(const GraphDef& input_graph_def,
                     std::function<bool(const NodeDef&)> selector,
                     GraphDef* output_graph_def);
 
-// Creates a copy of the input graph, with all occurrences of the attributes with
-// the names in the argument removed from the node defs.
+// Creates a copy of the input graph, with all occurrences of the attributes
+// with the names in the argument removed from the node defs.
 void RemoveAttributes(const GraphDef& input_graph_def,
                       const std::vector<string>& attributes,
                       GraphDef* output_graph_def);
@@ -133,10 +133,6 @@ Status IsGraphValid(const GraphDef& graph_def);
 Status GetInOutTypes(const NodeDef& node_def, DataTypeVector* inputs,
                      DataTypeVector* outputs);
 
-// First tries to load the file as a text protobuf, if that fails tries to parse
-// it as a binary protobuf, and returns an error if both fail.
-Status LoadTextOrBinaryGraphFile(const string& file_name, GraphDef* graph);
-
 // This is used to spot particular subgraphs in a larger model. To use it,
 // create a pattern like:
 // OpTypePattern pattern({"Conv2D", {{"ResizeBilinear", {{"MirrorPad"}}}}});
diff --git a/tensorflow/tools/graph_transforms/transform_utils_test.cc b/tensorflow/tools/graph_transforms/transform_utils_test.cc
index d068254b35..b5bc2d75fd 100644
--- a/tensorflow/tools/graph_transforms/transform_utils_test.cc
+++ b/tensorflow/tools/graph_transforms/transform_utils_test.cc
@@ -23,8 +23,6 @@ limitations under the License.
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/test_benchmark.h"
-#include "tensorflow/core/public/session.h"
-#include "tensorflow/core/util/equal_graph_def.h"
 
 namespace tensorflow {
 namespace graph_transforms {
@@ -1066,50 +1064,6 @@ class TransformUtilsTest : public ::testing::Test {
     TF_EXPECT_OK(context.GetOneBoolParameter("not_present", true, &value));
     EXPECT_TRUE(value);
   }
-
-  void TestLoadTextOrBinaryGraphFile() {
-    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
-    const int width = 10;
-
-    auto root = tensorflow::Scope::NewRootScope();
-    Tensor a_data(DT_FLOAT, TensorShape({width}));
-    test::FillIota<float>(&a_data, 1.0f);
-    Output a_const = Const(root.WithOpName("a"), Input::Initializer(a_data));
-    GraphDef graph_def;
-    TF_ASSERT_OK(root.ToGraphDef(&graph_def));
-
-    const string text_file =
-        io::JoinPath(testing::TmpDir(), "text_graph.pbtxt");
-    TF_ASSERT_OK(WriteTextProto(Env::Default(), text_file, graph_def));
-
-    const string binary_file =
-        io::JoinPath(testing::TmpDir(), "binary_graph.pb");
-    TF_ASSERT_OK(WriteBinaryProto(Env::Default(), binary_file, graph_def));
-
-    const string bogus_file = io::JoinPath(testing::TmpDir(), "bogus_graph.pb");
-    TF_ASSERT_OK(
-        WriteStringToFile(Env::Default(), bogus_file, "Not a !{ proto..."));
-
-    GraphDef text_graph_def;
-    TF_EXPECT_OK(LoadTextOrBinaryGraphFile(text_file, &text_graph_def));
-    string text_diff;
-    EXPECT_TRUE(EqualGraphDef(text_graph_def, graph_def, &text_diff))
-        << text_diff;
-
-    GraphDef binary_graph_def;
-    TF_EXPECT_OK(LoadTextOrBinaryGraphFile(binary_file, &binary_graph_def));
-    string binary_diff;
-    EXPECT_TRUE(EqualGraphDef(binary_graph_def, graph_def, &binary_diff))
-        << binary_diff;
-
-    GraphDef no_graph_def;
-    EXPECT_FALSE(
-        LoadTextOrBinaryGraphFile("____non_existent_file_____", &no_graph_def)
-            .ok());
-
-    GraphDef bogus_graph_def;
-    EXPECT_FALSE(LoadTextOrBinaryGraphFile(bogus_file, &bogus_graph_def).ok());
-  }
 };
 
 TEST_F(TransformUtilsTest, TestMapNamesToNodes) { TestMapNamesToNodes(); }
@@ -1206,9 +1160,5 @@ TEST_F(TransformUtilsTest, TestGetOneBoolParameter) {
   TestGetOneBoolParameter();
 }
 
-TEST_F(TransformUtilsTest, TestLoadTextOrBinaryGraphFile) {
-  TestLoadTextOrBinaryGraphFile();
-}
-
 }  // namespace graph_transforms
 }  // namespace tensorflow
-- 
GitLab


From d7527567bbacd45b73c032a5661ab726343ef0c4 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Mon, 24 Jul 2017 15:38:58 -0700
Subject: [PATCH 1262/1375] Update sample_distorted_bounding_box to use v2
 kernel (#11668)

* Update sample_distorted_bounding_box to use v2 kernel

This fix is a follow up of #10840 so that
sample_distorted_bounding_box uses _v2 kernel to follow the
API compatibility workflow (3 weeks).

This fix fixes #10715.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Update tests for sample_distorted_bounding_box to use v2 kernel

This fix is a follow up of #10840.

This fix updates related tests so that redudant tests could
be removed.

This fix fixes #10715.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/python/ops/image_ops_impl.py |  3 +-
 tensorflow/python/ops/image_ops_test.py | 73 ++++++++++++-------------
 2 files changed, 35 insertions(+), 41 deletions(-)

diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 2be92fac63..e39cabde31 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -1566,8 +1566,7 @@ def sample_distorted_bounding_box(image_size, bounding_boxes, seed=None,
       Provide as input to `tf.image.draw_bounding_boxes`.
   """
   with ops.name_scope(name, 'sample_distorted_bounding_box'):
-    # TODO (yongtang): Need to switch to v2 after 3 weeks.
-    return gen_image_ops._sample_distorted_bounding_box(image_size,
+    return gen_image_ops._sample_distorted_bounding_box_v2(image_size,
                 bounding_boxes, seed=seed,
                 seed2=seed2, min_object_covered=min_object_covered,
                 aspect_ratio_range=aspect_ratio_range, area_range=area_range,
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py
index 5564194ea1..a7a2de87f1 100644
--- a/tensorflow/python/ops/image_ops_test.py
+++ b/tensorflow/python/ops/image_ops_test.py
@@ -1502,31 +1502,28 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
       bounding_box_tf = constant_op.constant(
           bounding_box_np, dtype=dtypes.float32, shape=bounding_box_np.shape)
 
-      # sample_distorted_bounding_box and sample_distorted_bounding_box_v2
-      for op in [image_ops.sample_distorted_bounding_box,
-                 gen_image_ops._sample_distorted_bounding_box_v2]:
-        begin, size, _ = op(
-            image_size=image_size_tf,
-            bounding_boxes=bounding_box_tf,
-            min_object_covered=min_object_covered,
-            aspect_ratio_range=aspect_ratio_range,
-            area_range=area_range)
-        y = array_ops.strided_slice(image_tf, begin, begin + size)
-
-        for _ in xrange(num_iter):
-          y_tf = y.eval()
-          crop_height = y_tf.shape[0]
-          crop_width = y_tf.shape[1]
-          aspect_ratio = float(crop_width) / float(crop_height)
-          area = float(crop_width * crop_height)
-
-          aspect_ratios.append(aspect_ratio)
-          area_ratios.append(area / original_area)
-          fraction_object_covered.append(float(np.sum(y_tf)) / bounding_box_area)
-
-      # sample_distorted_bounding_box_v2
+      begin, size, _ = image_ops.sample_distorted_bounding_box(
+          image_size=image_size_tf,
+          bounding_boxes=bounding_box_tf,
+          min_object_covered=min_object_covered,
+          aspect_ratio_range=aspect_ratio_range,
+          area_range=area_range)
+      y = array_ops.strided_slice(image_tf, begin, begin + size)
+
+      for _ in xrange(num_iter):
+        y_tf = y.eval()
+        crop_height = y_tf.shape[0]
+        crop_width = y_tf.shape[1]
+        aspect_ratio = float(crop_width) / float(crop_height)
+        area = float(crop_width * crop_height)
+
+        aspect_ratios.append(aspect_ratio)
+        area_ratios.append(area / original_area)
+        fraction_object_covered.append(float(np.sum(y_tf)) / bounding_box_area)
+
+      # min_object_covered as tensor
       min_object_covered_placeholder = array_ops.placeholder(dtypes.float32)
-      begin, size, _ = gen_image_ops._sample_distorted_bounding_box_v2(
+      begin, size, _ = image_ops.sample_distorted_bounding_box(
           image_size=image_size_tf,
           bounding_boxes=bounding_box_tf,
           min_object_covered=min_object_covered_placeholder,
@@ -1630,21 +1627,19 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
           [0.0, 0.0, 1.0, 1.0],
           shape=[4],
           dtype=dtypes.float32,)
-      for op in [image_ops.sample_distorted_bounding_box,
-                 gen_image_ops._sample_distorted_bounding_box_v2]:
-        begin, end, bbox_for_drawing = op(
-            image_size=image_size,
-            bounding_boxes=bounding_box,
-            min_object_covered=0.1,
-            aspect_ratio_range=(0.75, 1.33),
-            area_range=(0.05, 1.0))
-
-        # Test that the shapes are correct.
-        self.assertAllEqual([3], begin.get_shape().as_list())
-        self.assertAllEqual([3], end.get_shape().as_list())
-        self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list())
-
-      begin, end, bbox_for_drawing = op(
+      begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box(
+          image_size=image_size,
+          bounding_boxes=bounding_box,
+          min_object_covered=0.1,
+          aspect_ratio_range=(0.75, 1.33),
+          area_range=(0.05, 1.0))
+
+      # Test that the shapes are correct.
+      self.assertAllEqual([3], begin.get_shape().as_list())
+      self.assertAllEqual([3], end.get_shape().as_list())
+      self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list())
+
+      begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box(
           image_size=image_size,
           bounding_boxes=bounding_box,
           min_object_covered=array_ops.placeholder(dtypes.float32),
-- 
GitLab


From f973a381f5b5c0e1a1f418177f929882c9dae0cc Mon Sep 17 00:00:00 2001
From: Will Frey <will.frey@digitalreasoning.com>
Date: Mon, 24 Jul 2017 18:39:15 -0400
Subject: [PATCH 1263/1375] Update fft2d.cmake (#11671)

The cmake fft2d library was installing to '(fft2d_INDTALL)' instead of the appropriate variable set by CMake. I suspect this was unintentional.

Changing the parentheses to curly brackets fixes this.
---
 tensorflow/contrib/cmake/external/fft2d.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/external/fft2d.cmake b/tensorflow/contrib/cmake/external/fft2d.cmake
index 85f77e9879..a35c24e9e0 100644
--- a/tensorflow/contrib/cmake/external/fft2d.cmake
+++ b/tensorflow/contrib/cmake/external/fft2d.cmake
@@ -45,7 +45,7 @@ else()
       DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
       BUILD_IN_SOURCE 1
       PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/fft2d/CMakeLists.txt ${fft2d_BUILD}/src/fft2d/CMakeLists.txt
-      INSTALL_DIR $(fft2d_INSTALL)
+      INSTALL_DIR ${fft2d_INSTALL}
       INSTALL_COMMAND echo
       BUILD_COMMAND $(MAKE))
     
-- 
GitLab


From 9fb91045a4186bdae15fc84272da1af11b3eea3c Mon Sep 17 00:00:00 2001
From: nolan liu <nolan.liou@gmail.com>
Date: Tue, 25 Jul 2017 06:53:50 +0800
Subject: [PATCH 1264/1375] =?UTF-8?q?Add=20mark=5Fflag=5Fas=5Frequired=20f?=
 =?UTF-8?q?unctions=20to=20make=20the=20APIs=20compatible=20with=20?=
 =?UTF-8?q?=E2=80=A6=20(#11568)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add mark_flag_as_required functions to make the APIs compatible with python-gflags.(#11195)

* Add review advises for efficiency

* Cleaning up some lint, renaming one of the private method names.

* Import as underscore.
---
 tensorflow/python/platform/flags.py      | 63 ++++++++++++++++++++++++
 tensorflow/python/platform/flags_test.py | 20 +++++++-
 2 files changed, 81 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/platform/flags.py b/tensorflow/python/platform/flags.py
index 60ec4f84c4..138a0ced97 100644
--- a/tensorflow/python/platform/flags.py
+++ b/tensorflow/python/platform/flags.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import argparse as _argparse
 
+from tensorflow.python.platform import tf_logging as _logging
 from tensorflow.python.util.all_util import remove_undocumented
 
 _global_parser = _argparse.ArgumentParser()
@@ -34,12 +35,14 @@ class _FlagValues(object):
   def __init__(self):
     self.__dict__['__flags'] = {}
     self.__dict__['__parsed'] = False
+    self.__dict__['__required_flags'] = set()
 
   def _parse_flags(self, args=None):
     result, unparsed = _global_parser.parse_known_args(args=args)
     for flag_name, val in vars(result).items():
       self.__dict__['__flags'][flag_name] = val
     self.__dict__['__parsed'] = True
+    self._assert_all_required()
     return unparsed
 
   def __getattr__(self, name):
@@ -60,6 +63,19 @@ class _FlagValues(object):
     if not self.__dict__['__parsed']:
       self._parse_flags()
     self.__dict__['__flags'][name] = value
+    self._assert_required(name)
+
+  def _add_required_flag(self, item):
+    self.__dict__['__required_flags'].add(item)
+
+  def _assert_required(self, flag_name):
+    if (flag_name not in self.__dict__['__flags'] or
+        self.__dict__['__flags'][flag_name] is None):
+      raise AttributeError('Flag --%s must be specified.' % flag_name)
+
+  def _assert_all_required(self):
+    for flag_name in self.__dict__['__required_flags']:
+      self._assert_required(flag_name)
 
 
 def _define_helper(flag_name, default_value, docstring, flagtype):
@@ -136,6 +152,51 @@ def DEFINE_float(flag_name, default_value, docstring):
   """
   _define_helper(flag_name, default_value, docstring, float)
 
+
+def mark_flag_as_required(flag_name):
+  """Ensures that flag is not None during program execution.
+  
+  It is recommended to call this method like this:
+  
+    if __name__ == '__main__':
+      tf.flags.mark_flag_as_required('your_flag_name')
+      tf.app.run()
+  
+  Args:
+    flag_name: string, name of the flag to mark as required.
+ 
+  Raises:
+    AttributeError: if flag_name is not registered as a valid flag name.
+      NOTE: The exception raised will change in the future. 
+  """
+  if _global_parser.get_default(flag_name) is not None:
+    _logging.warn(
+        'Flag %s has a non-None default value; therefore, '
+        'mark_flag_as_required will pass even if flag is not specified in the '
+        'command line!' % flag_name)
+  FLAGS._add_required_flag(flag_name)
+
+
+def mark_flags_as_required(flag_names):
+  """Ensures that flags are not None during program execution.
+  
+  Recommended usage:
+  
+    if __name__ == '__main__':
+      tf.flags.mark_flags_as_required(['flag1', 'flag2', 'flag3'])
+      tf.app.run()
+  
+  Args:
+    flag_names: a list/tuple of flag names to mark as required.
+
+  Raises:
+    AttributeError: If any of flag name has not already been defined as a flag.
+      NOTE: The exception raised will change in the future.
+  """
+  for flag_name in flag_names:
+    mark_flag_as_required(flag_name)
+
+
 _allowed_symbols = [
     # We rely on gflags documentation.
     'DEFINE_bool',
@@ -144,5 +205,7 @@ _allowed_symbols = [
     'DEFINE_integer',
     'DEFINE_string',
     'FLAGS',
+    'mark_flag_as_required',
+    'mark_flags_as_required',
 ]
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/python/platform/flags_test.py b/tensorflow/python/platform/flags_test.py
index 8b990975dd..c6bdd94a76 100644
--- a/tensorflow/python/platform/flags_test.py
+++ b/tensorflow/python/platform/flags_test.py
@@ -35,6 +35,8 @@ flags.DEFINE_boolean("bool_a", False, "HelpString")
 flags.DEFINE_boolean("bool_c", False, "HelpString")
 flags.DEFINE_boolean("bool_d", True, "HelpString")
 flags.DEFINE_bool("bool_e", True, "HelpString")
+flags.DEFINE_string("string_foo_required", "default_val", "HelpString")
+flags.DEFINE_string("none_string_foo_required", None, "HelpString")
 
 FLAGS = flags.FLAGS
 
@@ -84,6 +86,18 @@ class FlagsTest(unittest.TestCase):
     copied = copy.copy(FLAGS)
     self.assertEqual(copied.__dict__, FLAGS.__dict__)
 
+  def testStringRequired(self):
+    res = FLAGS.string_foo_required
+    self.assertEqual(res, "default_val")
+    FLAGS.string_foo_required = "bar"
+    self.assertEqual("bar", FLAGS.string_foo_required)
+
+  def testNoneStringRequired(self):
+    res = FLAGS.none_string_foo_required
+    self.assertEqual(res, "default_val")
+    FLAGS.none_string_foo_required = "bar"
+    self.assertEqual("bar", FLAGS.none_string_foo_required)
+
 
 def main(_):
   # unittest.main() tries to interpret the unknown flags, so use the
@@ -97,7 +111,9 @@ if __name__ == "__main__":
   # Test command lines
   sys.argv.extend([
       "--bool_a", "--nobool_negation", "--bool_c=True", "--bool_d=False",
-      "and_argument"
+      "and_argument",
+      "--none_string_foo_required=default_val"
   ])
-
+  flags.mark_flag_as_required('string_foo_required')
+  flags.mark_flags_as_required(['none_string_foo_required'])
   app.run()
-- 
GitLab


From 89cf07de7cf864783f262215d724167e24caa68e Mon Sep 17 00:00:00 2001
From: Androbin <robin.richtsfeld@gmail.com>
Date: Tue, 25 Jul 2017 00:54:16 +0200
Subject: [PATCH 1265/1375] Expand missing shorthand (#10579)

---
 tensorflow/python/tools/print_selective_registration_header.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/tools/print_selective_registration_header.py b/tensorflow/python/tools/print_selective_registration_header.py
index 62f00f4467..21d7de0204 100644
--- a/tensorflow/python/tools/print_selective_registration_header.py
+++ b/tensorflow/python/tools/print_selective_registration_header.py
@@ -28,7 +28,7 @@ When compiling for Android:
     --copt="-DSUPPORT_SELECTIVE_REGISTRATION" \
     //tensorflow/contrib/android:libtensorflow_inference.so \
     --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
-    --config=android_arm
+    --crosstool_top=//external:android/crosstool --cpu=armeabi-v7a
 """
 
 from __future__ import absolute_import
-- 
GitLab


From a17b9ecd5b14fb632a46407b8a5ce962038b84bd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 15:52:13 -0700
Subject: [PATCH 1266/1375] Re-align asterisks for pointer types; NFC

PiperOrigin-RevId: 163001060
---
 .../xla/service/cpu/simple_orc_jit.cc         | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index fd7c36cc53..262c471b40 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -42,7 +42,7 @@ namespace cpu {
 namespace {
 
 // Converts a symbol 'name' into the form expected by dlsym().
-std::string CanonicalizeSymbol(const std::string &name) {
+std::string CanonicalizeSymbol(const std::string& name) {
 #if defined(__APPLE__)
   // On Mac OS X, dlsym() expects names not to be prefixed with a leading
   // underscore.
@@ -57,7 +57,7 @@ class JITSymbolTable {
  public:
   JITSymbolTable() { Populate(); }
 
-  void *Lookup(llvm::StringRef jit_symbol_name) const {
+  void* Lookup(llvm::StringRef jit_symbol_name) const {
     auto it = jit_symbol_table_.find(jit_symbol_name);
     return it == jit_symbol_table_.end() ? nullptr : it->getValue();
   }
@@ -71,7 +71,7 @@ class JITSymbolTable {
  private:
   void AddJITSymbolToTable(llvm::StringRef jit_symbol_name,
                            llvm::StringRef cpp_symbol_name,
-                           void *jit_symbol_value) {
+                           void* jit_symbol_value) {
     // The JIT symbol name and the C++ symbol name (with an extern "C" linkage)
     // need to match, otherwise AOT links will fail.
     CHECK(jit_symbol_name == cpp_symbol_name);
@@ -79,12 +79,12 @@ class JITSymbolTable {
   }
 
   void Populate() {
-#define ADD_JIT_SYMBOL_TO_TABLE(base_name)                        \
-  do {                                                            \
-    AddJITSymbolToTable(                                          \
-        xla::cpu::runtime::k##base_name##SymbolName,              \
-        "__xla_cpu_runtime_" #base_name,                          \
-        reinterpret_cast<void *>(__xla_cpu_runtime_##base_name)); \
+#define ADD_JIT_SYMBOL_TO_TABLE(base_name)                       \
+  do {                                                           \
+    AddJITSymbolToTable(                                         \
+        xla::cpu::runtime::k##base_name##SymbolName,             \
+        "__xla_cpu_runtime_" #base_name,                         \
+        reinterpret_cast<void*>(__xla_cpu_runtime_##base_name)); \
   } while (false)
 
     ADD_JIT_SYMBOL_TO_TABLE(AcquireInfeedBufferForDequeue);
@@ -107,21 +107,21 @@ class JITSymbolTable {
 #undef ADD_JIT_SYMBOL_TO_TABLE
   }
 
-  llvm::StringMap<void *> jit_symbol_table_;
+  llvm::StringMap<void*> jit_symbol_table_;
 };
 
-const JITSymbolTable &GetJITSymbolTable() {
-  static JITSymbolTable *symbol_table = new JITSymbolTable;
+const JITSymbolTable& GetJITSymbolTable() {
+  static JITSymbolTable* symbol_table = new JITSymbolTable;
   return *symbol_table;
 }
 
 // A simple SymbolResolver that delegates to the host dynamic linker.
 struct SimpleResolver : public llvm::JITSymbolResolver {
-  llvm::JITSymbol findSymbol(const std::string &name) override {
+  llvm::JITSymbol findSymbol(const std::string& name) override {
     std::string canonical_name = CanonicalizeSymbol(name);
-    const JITSymbolTable &jit_symbol_table = GetJITSymbolTable();
+    const JITSymbolTable& jit_symbol_table = GetJITSymbolTable();
 
-    void *func_addr = JITSymbolTable::MustBeInTable(canonical_name)
+    void* func_addr = JITSymbolTable::MustBeInTable(canonical_name)
                           ? jit_symbol_table.Lookup(canonical_name)
                           : dlsym(RTLD_DEFAULT, canonical_name.c_str());
 
@@ -132,7 +132,7 @@ struct SimpleResolver : public llvm::JITSymbolResolver {
                                          llvm::JITSymbolFlags::None);
     return symbol_info;
   }
-  llvm::JITSymbol findSymbolInLogicalDylib(const std::string &name) override {
+  llvm::JITSymbol findSymbolInLogicalDylib(const std::string& name) override {
     return nullptr;
   }
 };
@@ -141,7 +141,7 @@ llvm::SmallVector<std::string, 0> DetectMachineAttributes() {
   llvm::SmallVector<std::string, 0> result;
   llvm::StringMap<bool> host_features;
   if (llvm::sys::getHostCPUFeatures(host_features)) {
-    for (auto &feature : host_features) {
+    for (auto& feature : host_features) {
       if (feature.second) {
         llvm::StringRef feature_name = feature.first();
         // Skip avx512 for now, it isn't quite ready in LLVM.
@@ -171,7 +171,7 @@ CompilerFunctor::VectorIntrinsics GetAvailableIntrinsics() {
 
 }  // namespace
 
-SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions &target_options,
+SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions& target_options,
                            llvm::CodeGenOpt::Level opt_level,
                            CompilerFunctor::ModuleHook pre_optimization_hook,
                            CompilerFunctor::ModuleHook post_optimization_hook)
@@ -211,7 +211,7 @@ void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::ModuleHandleT handle) {
   cantFail(compile_layer_.removeModule(handle));
 }
 
-llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string &name) {
+llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string& name) {
   std::string mangled_name;
   {
     llvm::raw_string_ostream mangled_name_stream(mangled_name);
@@ -220,7 +220,7 @@ llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string &name) {
 
   // Resolve symbol from last module to first, allowing later redefinitions of
   // symbols shadow earlier ones.
-  for (auto &handle :
+  for (auto& handle :
        llvm::make_range(module_handles_.rbegin(), module_handles_.rend())) {
     if (auto symbol =
             compile_layer_.findSymbolIn(handle, mangled_name,
-- 
GitLab


From 678db4b3d406bc290addcdc9665c01971aafe664 Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <mail@sebastianraschka.com>
Date: Mon, 24 Jul 2017 18:59:23 -0400
Subject: [PATCH 1267/1375] Add a note about how weights are initialized in
 Dense/dense to the docstrings (#9840)

* add docstring note about dense weight initializer

* add docstring note aboutthe Dense class weight initializer

* note about default kernel initializer in layers

* strip extraneous white space
---
 tensorflow/python/layers/core.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py
index 407bc06dfe..61dffe2950 100644
--- a/tensorflow/python/layers/core.py
+++ b/tensorflow/python/layers/core.py
@@ -57,6 +57,8 @@ class Dense(base.Layer):
       linear activation.
     use_bias: Boolean, whether the layer uses a bias.
     kernel_initializer: Initializer function for the weight matrix.
+      If `None` (default), weights are initialized using the default
+      initializer used by `tf.get_variable`.
     bias_initializer: Initializer function for the bias.
     kernel_regularizer: Regularizer function for the weight matrix.
     bias_regularizer: Regularizer function for the bias.
@@ -186,6 +188,8 @@ def dense(
       linear activation.
     use_bias: Boolean, whether the layer uses a bias.
     kernel_initializer: Initializer function for the weight matrix.
+      If `None` (default), weights are initialized using the default
+      initializer used by `tf.get_variable`.
     bias_initializer: Initializer function for the bias.
     kernel_regularizer: Regularizer function for the weight matrix.
     bias_regularizer: Regularizer function for the bias.
-- 
GitLab


From a9606e4aeabc75582d6de3c3f6d08599ca6cef03 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 15:58:03 -0700
Subject: [PATCH 1268/1375] Only assign new start times if start times have not
 already been set

PiperOrigin-RevId: 163001776
---
 tensorflow/core/graph/graph_partition.cc | 30 ++++++++++++++----------
 1 file changed, 18 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index ab6fa62f34..8ef2231aa2 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -937,8 +937,14 @@ Status Partition(const PartitionOptions& opts, Graph* g,
     dst_def->set_device(dst->assigned_device_name());
     dst_def->clear_input();  // Inputs are filled below
     if (opts.need_to_record_start_times) {
-      int64 start_time = opts.start_times[dst->id()].value();
-      AddNodeAttr("_start_time", start_time, dst_def);
+      int64 start_time;
+      status = GetNodeAttr(*dst_def, "_start_time", &start_time);
+      if (errors::IsNotFound(status)) {
+        start_time = opts.start_times[dst->id()].value();
+        AddNodeAttr("_start_time", start_time, dst_def);
+      } else if (!status.ok()) {
+        return status;
+      }
     }
 
     // Arrange the incoming edges to dst so that input[i] holds the
@@ -995,18 +1001,18 @@ Status Partition(const PartitionOptions& opts, Graph* g,
       int64 send_start_time = 0;
       int64 recv_start_time = 0;
       if (opts.scheduling_for_recvs) {
-        if (opts.need_to_record_start_times) {
+        status = GetNodeAttr(src->attrs(), "_start_time", &send_start_time);
+        if (errors::IsNotFound(status) && opts.need_to_record_start_times) {
           send_start_time = opts.start_times[src->id()].value();
+        } else if (!status.ok()) {
+          return status;
+        }
+
+        status = GetNodeAttr(dst->attrs(), "_start_time", &recv_start_time);
+        if (errors::IsNotFound(status) && opts.need_to_record_start_times) {
           recv_start_time = opts.start_times[dst->id()].value();
-        } else {
-          status = GetNodeAttr(src->attrs(), "_start_time", &send_start_time);
-          if (!status.ok()) {
-            return status;
-          }
-          status = GetNodeAttr(dst->attrs(), "_start_time", &recv_start_time);
-          if (!status.ok()) {
-            return status;
-          }
+        } else if (!status.ok()) {
+          return status;
         }
       }
 
-- 
GitLab


From c16ab0c2fa14139fab0d44a778d7a2f39cb1a67c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 16:21:55 -0700
Subject: [PATCH 1269/1375] Update stage_op and map_stage_op to not special
 case returning one element

PiperOrigin-RevId: 163005029
---
 tensorflow/python/kernel_tests/map_stage_op_test.py |  8 ++++----
 tensorflow/python/kernel_tests/stage_op_test.py     |  2 +-
 tensorflow/python/ops/data_flow_ops.py              | 11 ++++-------
 3 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py
index b898a15cc8..8b66945059 100644
--- a/tensorflow/python/kernel_tests/map_stage_op_test.py
+++ b/tensorflow/python/kernel_tests/map_stage_op_test.py
@@ -112,11 +112,11 @@ class MapStageTest(test.TestCase):
         self.assertEqual(y.device, expected_name)
       with ops.device('/cpu:0'):
         _, x = stager.get(1)
-        y = stager.peek(1)
+        y = stager.peek(1)[0]
         _, z = stager.get()
-        self.assertEqual(x.device, '/device:CPU:0')
+        self.assertEqual(x[0].device, '/device:CPU:0')
         self.assertEqual(y.device, '/device:CPU:0')
-        self.assertEqual(z.device, '/device:CPU:0')
+        self.assertEqual(z[0].device, '/device:CPU:0')
 
     G.finalize()
 
@@ -142,7 +142,7 @@ class MapStageTest(test.TestCase):
         sess.run(stage, feed_dict={x: i, pi: i})
 
       for i in range(n):
-        self.assertTrue(sess.run(peek, feed_dict={gi: i}) == i)
+        self.assertTrue(sess.run(peek, feed_dict={gi: i})[0] == i)
 
       self.assertTrue(sess.run(size) == 10)
 
diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py
index 0f4cc59653..64b3388c5c 100644
--- a/tensorflow/python/kernel_tests/stage_op_test.py
+++ b/tensorflow/python/kernel_tests/stage_op_test.py
@@ -105,7 +105,7 @@ class StageTest(test.TestCase):
         expected_name = gpu_dev if 'gpu' not in gpu_dev else '/device:GPU:0'
         self.assertEqual(y.device, expected_name)
       with ops.device('/cpu:0'):
-        x = stager.get()
+        x = stager.get()[0]
         self.assertEqual(x.device, '/device:CPU:0')
 
     G.finalize()
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index e22cab86c8..fbfbb50d8c 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -512,8 +512,8 @@ class QueueBase(object):
     the given queue. Subsequent `enqueue` and `enqueue_many`
     operations will fail. Subsequent `dequeue` and `dequeue_many`
     operations will continue to succeed if sufficient elements remain
-    in the queue. Subsequently dequeue and dequeue_many operations 
-    that would otherwise block waiting for more elements (if close 
+    in the queue. Subsequently dequeue and dequeue_many operations
+    that would otherwise block waiting for more elements (if close
     hadn't been called) will now fail immediately.
 
     If `cancel_pending_enqueues` is `True`, all pending requests will also
@@ -546,7 +546,7 @@ class QueueBase(object):
 
     Args:
       name: A name for the operation (optional).
-	
+
     Returns:
       True if the queue is closed and false if the queue is open.
     """
@@ -1552,10 +1552,7 @@ class BaseStagingArea(object):
       # The returned values in `tensors` are in the same order as
       # the names in `self._names`.
       return {self._names[i]: t for t, i in zip(tensors, indices)}
-    elif len(tensors) == 1:
-      return tensors[0]
-    else:
-      return tensors
+    return tensors
 
   def _scope_vals(self, vals):
     """Return a list of values to pass to `name_scope()`.
-- 
GitLab


From 480cccf620e2fd7afeab4e465b2736f14d83ac2d Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 24 Jul 2017 16:54:27 -0700
Subject: [PATCH 1270/1375] Properly handle folding of constant nodes with
 multiple outputs.

PiperOrigin-RevId: 163009009
---
 .../core/grappler/optimizers/constant_folding.cc   | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 715b6e97d2..db279ae67f 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -301,6 +301,9 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
       continue;
     }
     const NodeDef* input_node = node_map_->GetNode(input);
+    if (!input_node) {
+      return false;
+    }
     bool is_const = IsConstant(*input_node);
     if (!is_const && !is_merge) {
       return false;
@@ -369,7 +372,7 @@ Status ConstantFolding::EvaluateNode(const NodeDef& node,
   for (int i = 0; i < num_outputs; i++) {
     output->push_back(op_context.release_output(i));
   }
-  return Status::OK();
+  return op_context.status();
 }
 
 Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
@@ -382,13 +385,18 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
   });
 
   for (const auto& input : node.input()) {
-    if (IsControlInput(input)) {
+    int position = 0;
+    ParseNodeName(input, &position);
+    if (position < 0) {
+      // Control dependency
       break;
     }
+    // There should be a single output since the input node should be a constant
+    // node.
     TensorVector output;
     TF_RETURN_IF_ERROR(
         EvaluateNode(*node_map_->GetNode(input), TensorVector(), &output));
-    inputs.push_back(output[0]);
+    inputs.push_back(output[position]);
   }
 
   TensorVector output_tensors;
-- 
GitLab


From 41881b93b1a2b766b69602eb79d3a0514043b7e3 Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Mon, 24 Jul 2017 17:11:25 -0700
Subject: [PATCH 1271/1375] Update wide_n_deep_tutorial.py (#11722)

-mkdtemp is low-level, and creates a temporary file somewhere out of sight that requires users to delete the file themselves. For the use case of a tutorial, it is best to use a method that cleanups the file when finished
-TemporaryFile does such as described above, but not available in Python 2
---
 tensorflow/examples/learn/wide_n_deep_tutorial.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
index 48c207bed1..7b9381311c 100644
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ b/tensorflow/examples/learn/wide_n_deep_tutorial.py
@@ -18,6 +18,7 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
+import shutil
 import sys
 import tempfile
 
@@ -175,6 +176,7 @@ def input_fn(data_file, num_epochs, shuffle):
 def train_and_eval(model_dir, model_type, train_steps, train_data, test_data):
   """Train and evaluate the model."""
   train_file_name, test_file_name = maybe_download(train_data, test_data)
+  # Specify file path below if want to find the output easily
   model_dir = tempfile.mkdtemp() if not model_dir else model_dir
 
   m = build_estimator(model_dir, model_type)
@@ -189,6 +191,8 @@ def train_and_eval(model_dir, model_type, train_steps, train_data, test_data):
   print("model directory = %s" % model_dir)
   for key in sorted(results):
     print("%s: %s" % (key, results[key]))
+  # Manual cleanup
+  shutil.rmtree(model_dir)
 
 
 FLAGS = None
-- 
GitLab


From 7108dce74c15e91b77a12e2d9e26b16676307293 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Mon, 24 Jul 2017 17:12:20 -0700
Subject: [PATCH 1272/1375] Update documentation of various dimension/axis
 arguments to mention support for negative axis indexing.

PiperOrigin-RevId: 163011166
---
 tensorflow/core/ops/array_ops.cc   |  9 ++++--
 tensorflow/core/ops/math_ops.cc    | 45 ++++++++++++++++++++++--------
 tensorflow/python/ops/array_ops.py | 11 +++++---
 tensorflow/python/ops/math_ops.py  | 33 ++++++++++++++--------
 4 files changed, 69 insertions(+), 29 deletions(-)

diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 9a4001ba2f..d587683a29 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1148,7 +1148,8 @@ reverse(t, dims) ==> [[[[8, 9, 10, 11],
 ```
 
 tensor: Up to 8-D.
-axis: 1-D. The indices of the dimensions to reverse.
+axis: 1-D. The indices of the dimensions to reverse. Must be in the range
+  `[-rank(tensor), rank(tensor))`.
 output: The same shape as `tensor`.
 )Doc");
 
@@ -3186,7 +3187,8 @@ This operation is related to `squeeze()`, which removes dimensions of
 size 1.
 
 dim: 0-D (scalar). Specifies the dimension index at which to
-  expand the shape of `input`.
+  expand the shape of `input`. Must be in the range
+  `[-rank(input) - 1, rank(input)]`.
 output: Contains the same data as `input`, but its shape has an additional
   dimension of size 1 added.
 )doc");
@@ -3282,7 +3284,8 @@ shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
 
 input: The `input` to squeeze.
 squeeze_dims: If specified, only squeezes the dimensions listed. The dimension
-  index starts at 0. It is an error to squeeze a dimension that is not 1.
+  index starts at 0. It is an error to squeeze a dimension that is not 1. Must
+  be in the range `[-rank(input), rank(input))`.
 output: Contains the same data as `input`, but has one or more dimensions of
   size 1 removed.
 )doc");
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index fc02732af4..f4e0625c66 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -1146,7 +1146,8 @@ Reduces `input` along the dimensions given in `reduction_indices`. Unless
 retained with length 1.
 
 input: The tensor to reduce.
-reduction_indices: The dimensions to reduce.
+reduction_indices: The dimensions to reduce. Must be in the range
+  `[-rank(input), rank(input))`.
 keep_dims: If true, retain reduced dimensions with length 1.
 output: The reduced tensor.
 )doc");
@@ -1168,7 +1169,8 @@ Reduces `input` along the dimensions given in `reduction_indices`. Unless
 retained with length 1.
 
 input: The tensor to reduce.
-reduction_indices: The dimensions to reduce.
+reduction_indices: The dimensions to reduce. Must be in the range
+  `[-rank(input), rank(input))`.
 keep_dims: If true, retain reduced dimensions with length 1.
 output: The reduced tensor.
 )doc");
@@ -1190,7 +1192,8 @@ Reduces `input` along the dimensions given in `reduction_indices`. Unless
 retained with length 1.
 
 input: The tensor to reduce.
-reduction_indices: The dimensions to reduce.
+reduction_indices: The dimensions to reduce. Must be in the range
+  `[-rank(input), rank(input))`.
 keep_dims: If true, retain reduced dimensions with length 1.
 output: The reduced tensor.
 )doc");
@@ -1212,7 +1215,8 @@ Reduces `input` along the dimensions given in `reduction_indices`. Unless
 retained with length 1.
 
 input: The tensor to reduce.
-reduction_indices: The dimensions to reduce.
+reduction_indices: The dimensions to reduce. Must be in the range
+  `[-rank(input), rank(input))`.
 keep_dims: If true, retain reduced dimensions with length 1.
 output: The reduced tensor.
 )doc");
@@ -1234,7 +1238,8 @@ Reduces `input` along the dimensions given in `reduction_indices`. Unless
 retained with length 1.
 
 input: The tensor to reduce.
-reduction_indices: The dimensions to reduce.
+reduction_indices: The dimensions to reduce. Must be in the range
+  `[-rank(input), rank(input))`.
 keep_dims: If true, retain reduced dimensions with length 1.
 output: The reduced tensor.
 )doc");
@@ -1311,8 +1316,8 @@ Returns the index with the largest value across dimensions of a tensor.
 
 Note that in case of ties the identity of the return value is not guaranteed.
 
-dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
-  which dimension of the input Tensor to reduce across. For vectors,
+dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+  Describes which dimension of the input Tensor to reduce across. For vectors,
   use dimension = 0.
 )doc");
 
@@ -1329,8 +1334,8 @@ Returns the index with the smallest value across dimensions of a tensor.
 
 Note that in case of ties the identity of the return value is not guaranteed.
 
-dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
-  which dimension of the input Tensor to reduce across. For vectors,
+dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+  Describes which dimension of the input Tensor to reduce across. For vectors,
   use dimension = 0.
 )doc");
 
@@ -1825,7 +1830,8 @@ Reduces `input` along the dimensions given in `reduction_indices`. Unless
 retained with length 1.
 
 input: The tensor to reduce.
-reduction_indices: The dimensions to reduce.
+reduction_indices: The dimensions to reduce. Must be in the range
+  `[-rank(input), rank(input))`.
 keep_dims: If true, retain reduced dimensions with length 1.
 output: The reduced tensor.
 )doc");
@@ -1846,7 +1852,8 @@ Reduces `input` along the dimensions given in `reduction_indices`. Unless
 retained with length 1.
 
 input: The tensor to reduce.
-reduction_indices: The dimensions to reduce.
+reduction_indices: The dimensions to reduce. Must be in the range
+  `[-rank(input), rank(input))`.
 keep_dims: If true, retain reduced dimensions with length 1.
 output: The reduced tensor.
 )doc");
@@ -2178,6 +2185,14 @@ The `reverse` and `exclusive` kwargs can also be combined:
 ```python
 tf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]
 ```
+
+x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+  `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+  `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+  `[-rank(x), rank(x))`.
+exclusive: If `True`, perform exclusive cumsum.
+reverse: A `bool` (default: False).
 )doc");
 
 REGISTER_OP("Cumprod")
@@ -2220,6 +2235,14 @@ The `reverse` and `exclusive` kwargs can also be combined:
 ```python
 tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
 ```
+
+x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+  `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+  `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+  `[-rank(x), rank(x))`.
+exclusive: If `True`, perform exclusive cumprod.
+reverse: A `bool` (default: False).
 )doc");
 
 REGISTER_OP("QuantizedMatMul")
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 89e01c2230..bb86640eab 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -150,7 +150,8 @@ def expand_dims(input, axis=None, name=None, dim=None):
   Args:
     input: A `Tensor`.
     axis: 0-D (scalar). Specifies the dimension index at which to
-      expand the shape of `input`.
+      expand the shape of `input`. Must be in the range
+      `[-rank(input) - 1, rank(input)]`.
     name: The name of the output `Tensor`.
     dim: 0-D (scalar). Equivalent to `axis`, to be deprecated.
 
@@ -830,7 +831,7 @@ def stack(values, axis=0, name="stack"):
   Args:
     values: A list of `Tensor` objects with the same shape and type.
     axis: An `int`. The axis to stack along. Defaults to the first dimension.
-      Supports negative indexes.
+      Negative values wrap around, so the valid range is `[-(R+1), R+1)`.
     name: A name for this operation (optional).
 
   Returns:
@@ -970,7 +971,7 @@ def unstack(value, num=None, axis=0, name="unstack"):
     num: An `int`. The length of the dimension `axis`. Automatically inferred
       if `None` (the default).
     axis: An `int`. The axis to unstack along. Defaults to the first
-      dimension. Supports negative indexes.
+      dimension. Negative values wrap around, so the valid range is `[-R, R)`.
     name: A name for the operation (optional).
 
   Returns:
@@ -1041,7 +1042,8 @@ def concat(values, axis, name="concat"):
 
   Args:
     values: A list of `Tensor` objects or a single `Tensor`.
-    axis: 0-D `int32` `Tensor`.  Dimension along which to concatenate.
+    axis: 0-D `int32` `Tensor`.  Dimension along which to concatenate. Must be
+      in the range `[-rank(values), rank(values))`.
     name: A name for the operation (optional).
 
   Returns:
@@ -2300,6 +2302,7 @@ def squeeze(input, axis=None, name=None, squeeze_dims=None):
     axis: An optional list of `ints`. Defaults to `[]`.
       If specified, only squeezes the dimensions listed. The dimension
       index starts at 0. It is an error to squeeze a dimension that is not 1.
+      Must be in the range `[-rank(input), rank(input))`.
     name: A name for the operation (optional).
     squeeze_dims: Deprecated keyword argument that is now axis.
 
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 42839c2b94..8ebffd0bbb 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -1259,7 +1259,8 @@ def reduce_sum(input_tensor,
   Args:
     input_tensor: The tensor to reduce. Should have numeric type.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -1313,7 +1314,8 @@ def count_nonzero(input_tensor,
   Args:
     input_tensor: The tensor to reduce. Should be of numeric type, or `bool`.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     dtype: The output dtype; defaults to `tf.int64`.
     name: A name for the operation (optional).
@@ -1363,7 +1365,8 @@ def reduce_mean(input_tensor,
   Args:
     input_tensor: The tensor to reduce. Should have numeric type.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -1400,7 +1403,8 @@ def reduce_prod(input_tensor,
   Args:
     input_tensor: The tensor to reduce. Should have numeric type.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -1437,7 +1441,8 @@ def reduce_min(input_tensor,
   Args:
     input_tensor: The tensor to reduce. Should have numeric type.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -1474,7 +1479,8 @@ def reduce_max(input_tensor,
   Args:
     input_tensor: The tensor to reduce. Should have numeric type.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -1521,7 +1527,8 @@ def reduce_all(input_tensor,
   Args:
     input_tensor: The boolean tensor to reduce.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -1568,7 +1575,8 @@ def reduce_any(input_tensor,
   Args:
     input_tensor: The boolean tensor to reduce.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -1621,7 +1629,8 @@ def reduce_logsumexp(input_tensor,
   Args:
     input_tensor: The tensor to reduce. Should have numeric type.
     axis: The dimensions to reduce. If `None` (the default),
-      reduces all dimensions.
+      reduces all dimensions. Must be in the range
+      `[-rank(input_tensor), rank(input_tensor))`.
     keep_dims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
@@ -2171,7 +2180,8 @@ def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
     x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
        `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
        `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    axis: A `Tensor` of type `int32` (default: 0).
+    axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+      `[-rank(x), rank(x))`.
     exclusive: If `True`, perform exclusive cumsum.
     reverse: A `bool` (default: False).
     name: A name for the operation (optional).
@@ -2221,7 +2231,8 @@ def cumprod(x, axis=0, exclusive=False, reverse=False, name=None):
     x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
        `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
        `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    axis: A `Tensor` of type `int32` (default: 0).
+    axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+      `[-rank(x), rank(x))`.
     exclusive: If `True`, perform exclusive cumprod.
     reverse: A `bool` (default: False).
     name: A name for the operation (optional).
-- 
GitLab


From ff88f4f51577c52bf3eb208837a674a45d7a92c9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 17:37:55 -0700
Subject: [PATCH 1273/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 163014080
---
 tensorflow/core/ops/ops.pbtxt | 32 ++++++++++++++++++++------------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 1822fc1133..ab984d2547 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -499,7 +499,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -601,7 +601,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -1691,7 +1691,7 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
+    description: "int32 or int64, must be in the range `[-rank(input), rank(input))`.\nDescribes which dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
@@ -1757,7 +1757,7 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
+    description: "int32 or int64, must be in the range `[-rank(input), rank(input))`.\nDescribes which dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
@@ -5656,10 +5656,12 @@ op {
   name: "Cumprod"
   input_arg {
     name: "x"
+    description: "A `Tensor`. Must be one of the following types: `float32`, `float64`,\n`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n`complex128`, `qint8`, `quint8`, `qint32`, `half`."
     type_attr: "T"
   }
   input_arg {
     name: "axis"
+    description: "A `Tensor` of type `int32` (default: 0). Must be in the range\n`[-rank(x), rank(x))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -5672,6 +5674,7 @@ op {
     default_value {
       b: false
     }
+    description: "If `True`, perform exclusive cumprod."
   }
   attr {
     name: "reverse"
@@ -5679,6 +5682,7 @@ op {
     default_value {
       b: false
     }
+    description: "A `bool` (default: False)."
   }
   attr {
     name: "T"
@@ -5722,10 +5726,12 @@ op {
   name: "Cumsum"
   input_arg {
     name: "x"
+    description: "A `Tensor`. Must be one of the following types: `float32`, `float64`,\n`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n`complex128`, `qint8`, `quint8`, `qint32`, `half`."
     type_attr: "T"
   }
   input_arg {
     name: "axis"
+    description: "A `Tensor` of type `int32` (default: 0). Must be in the range\n`[-rank(x), rank(x))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -5738,6 +5744,7 @@ op {
     default_value {
       b: false
     }
+    description: "If `True`, perform exclusive cumsum."
   }
   attr {
     name: "reverse"
@@ -5745,6 +5752,7 @@ op {
     default_value {
       b: false
     }
+    description: "A `bool` (default: False)."
   }
   attr {
     name: "T"
@@ -7615,7 +7623,7 @@ op {
   }
   input_arg {
     name: "dim"
-    description: "0-D (scalar). Specifies the dimension index at which to\nexpand the shape of `input`."
+    description: "0-D (scalar). Specifies the dimension index at which to\nexpand the shape of `input`. Must be in the range\n`[-rank(input) - 1, rank(input)]`."
     type_attr: "Tdim"
   }
   output_arg {
@@ -12325,7 +12333,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -13102,7 +13110,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -13293,7 +13301,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -15662,7 +15670,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -21708,7 +21716,7 @@ op {
   }
   input_arg {
     name: "axis"
-    description: "1-D. The indices of the dimensions to reverse."
+    description: "1-D. The indices of the dimensions to reverse. Must be in the range\n`[-rank(tensor), rank(tensor))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -27260,7 +27268,7 @@ op {
       list {
       }
     }
-    description: "If specified, only squeezes the dimensions listed. The dimension\nindex starts at 0. It is an error to squeeze a dimension that is not 1."
+    description: "If specified, only squeezes the dimensions listed. The dimension\nindex starts at 0. It is an error to squeeze a dimension that is not 1. Must\nbe in the range `[-rank(input), rank(input))`."
     has_minimum: true
   }
   summary: "Removes dimensions of size 1 from the shape of a tensor."
@@ -28250,7 +28258,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
-- 
GitLab


From 2c2277c9c08794655ec0e2ff08356693032dafaa Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 24 Jul 2017 17:45:35 -0700
Subject: [PATCH 1274/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 163014834
---
 tensorflow/go/op/wrappers.go | 60 +++++++++++++++++++++++++++---------
 1 file changed, 46 insertions(+), 14 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 10388509a9..61c924ac22 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -1130,7 +1130,8 @@ type SqueezeAttr func(optionalAttr)
 // SqueezeSqueezeDims sets the optional squeeze_dims attribute to value.
 //
 // value: If specified, only squeezes the dimensions listed. The dimension
-// index starts at 0. It is an error to squeeze a dimension that is not 1.
+// index starts at 0. It is an error to squeeze a dimension that is not 1. Must
+// be in the range `[-rank(input), rank(input))`.
 // If not specified, defaults to <>
 //
 // REQUIRES: len(value) >= 0
@@ -12819,7 +12820,8 @@ func ReciprocalGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 //
 // Arguments:
 //	tensor: Up to 8-D.
-//	axis: 1-D. The indices of the dimensions to reverse.
+//	axis: 1-D. The indices of the dimensions to reverse. Must be in the range
+// `[-rank(tensor), rank(tensor))`.
 //
 // Returns The same shape as `tensor`.
 func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) {
@@ -16147,6 +16149,8 @@ func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf
 type CumprodAttr func(optionalAttr)
 
 // CumprodExclusive sets the optional exclusive attribute to value.
+//
+// value: If `True`, perform exclusive cumprod.
 // If not specified, defaults to false
 func CumprodExclusive(value bool) CumprodAttr {
 	return func(m optionalAttr) {
@@ -16155,6 +16159,8 @@ func CumprodExclusive(value bool) CumprodAttr {
 }
 
 // CumprodReverse sets the optional reverse attribute to value.
+//
+// value: A `bool` (default: False).
 // If not specified, defaults to false
 func CumprodReverse(value bool) CumprodAttr {
 	return func(m optionalAttr) {
@@ -16192,6 +16198,13 @@ func CumprodReverse(value bool) CumprodAttr {
 // ```python
 // tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
 // ```
+//
+// Arguments:
+//	x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+//	axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+// `[-rank(x), rank(x))`.
 func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
 	if scope.Err() != nil {
 		return
@@ -16420,6 +16433,8 @@ func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, ma
 type CumsumAttr func(optionalAttr)
 
 // CumsumExclusive sets the optional exclusive attribute to value.
+//
+// value: If `True`, perform exclusive cumsum.
 // If not specified, defaults to false
 func CumsumExclusive(value bool) CumsumAttr {
 	return func(m optionalAttr) {
@@ -16428,6 +16443,8 @@ func CumsumExclusive(value bool) CumsumAttr {
 }
 
 // CumsumReverse sets the optional reverse attribute to value.
+//
+// value: A `bool` (default: False).
 // If not specified, defaults to false
 func CumsumReverse(value bool) CumsumAttr {
 	return func(m optionalAttr) {
@@ -16465,6 +16482,13 @@ func CumsumReverse(value bool) CumsumAttr {
 // ```python
 // tf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]
 // ```
+//
+// Arguments:
+//	x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+//	axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+// `[-rank(x), rank(x))`.
 func Cumsum(scope *Scope, x tf.Output, axis tf.Output, optional ...CumsumAttr) (out tf.Output) {
 	if scope.Err() != nil {
 		return
@@ -18063,7 +18087,8 @@ func AnyKeepDims(value bool) AnyAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Any(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...AnyAttr) (output tf.Output) {
@@ -19213,7 +19238,8 @@ func ProdKeepDims(value bool) ProdAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Prod(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...ProdAttr) (output tf.Output) {
@@ -20258,7 +20284,8 @@ func MaxKeepDims(value bool) MaxAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Max(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MaxAttr) (output tf.Output) {
@@ -20583,7 +20610,8 @@ func Sqrt(scope *Scope, x tf.Output) (y tf.Output) {
 // Arguments:
 //
 //	dim: 0-D (scalar). Specifies the dimension index at which to
-// expand the shape of `input`.
+// expand the shape of `input`. Must be in the range
+// `[-rank(input) - 1, rank(input)]`.
 //
 // Returns Contains the same data as `input`, but its shape has an additional
 // dimension of size 1 added.
@@ -20623,7 +20651,8 @@ func AllKeepDims(value bool) AllAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func All(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...AllAttr) (output tf.Output) {
@@ -21665,8 +21694,8 @@ func ArgMinOutputType(value tf.DataType) ArgMinAttr {
 //
 // Arguments:
 //
-//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
-// which dimension of the input Tensor to reduce across. For vectors,
+//	dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+// Describes which dimension of the input Tensor to reduce across. For vectors,
 // use dimension = 0.
 func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) {
 	if scope.Err() != nil {
@@ -22716,7 +22745,8 @@ func MeanKeepDims(value bool) MeanAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Mean(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MeanAttr) (output tf.Output) {
@@ -22856,7 +22886,8 @@ func MinKeepDims(value bool) MinAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Min(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MinAttr) (output tf.Output) {
@@ -22914,8 +22945,8 @@ func ArgMaxOutputType(value tf.DataType) ArgMaxAttr {
 //
 // Arguments:
 //
-//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
-// which dimension of the input Tensor to reduce across. For vectors,
+//	dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+// Describes which dimension of the input Tensor to reduce across. For vectors,
 // use dimension = 0.
 func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) {
 	if scope.Err() != nil {
@@ -24724,7 +24755,8 @@ func SumKeepDims(value bool) SumAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Sum(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...SumAttr) (output tf.Output) {
-- 
GitLab


From 73b120ea3b517b6af2267ca078bf571f966fd606 Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Mon, 24 Jul 2017 18:34:01 -0700
Subject: [PATCH 1275/1375] Removing session reset since destroying the session
 object would delete its variables as well. Resetting session might
 unintentionally close other sessions in the same process.

PiperOrigin-RevId: 163019166
---
 tensorflow/core/grappler/clusters/BUILD       |   2 +
 .../core/grappler/clusters/single_machine.cc  |   8 +-
 .../grappler/clusters/single_machine_test.cc  | 121 ++++++++++++++++++
 3 files changed, 125 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index 667023845c..e7230b3754 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -114,7 +114,9 @@ cc_test(
     deps = [
         ":single_machine",
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:resource_variable_ops",
         "//tensorflow/cc:scope",
+        "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:lib_proto_parsing",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index a1531f1cfc..3481b2b158 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -73,8 +73,6 @@ SingleMachine::~SingleMachine() {
   // when we delete the session.
   thread_pool_.reset();
 
-  Reset(options_, {}).IgnoreError();
-
   CHECK(already_created);
   already_created = false;
 }
@@ -277,11 +275,9 @@ Status SingleMachine::ResetSession() {
     // Make sure the session is properly closed
     TF_RETURN_IF_ERROR(Shutdown());
 
-    // We need to Reset the session to ensure that all the variables are
-    // deleted. But first we need to delete the session since Reset()
-    // deletes some of the containers referenced by the session.
+    // Destroying the object deletes all its varibles as well. This is only true
+    // for DirectSession.
     session_.reset();
-    TF_RETURN_IF_ERROR(Reset(options_, {}));
   }
 
   LOG(INFO) << "Starting new session";
diff --git a/tensorflow/core/grappler/clusters/single_machine_test.cc b/tensorflow/core/grappler/clusters/single_machine_test.cc
index b73b084793..d7e2827afc 100644
--- a/tensorflow/core/grappler/clusters/single_machine_test.cc
+++ b/tensorflow/core/grappler/clusters/single_machine_test.cc
@@ -15,7 +15,10 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/clusters/single_machine.h"
 #include "tensorflow/cc/framework/scope.h"
+#include "tensorflow/cc/ops/resource_variable_ops.h"
 #include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/common_runtime/device.h"
+#include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
@@ -24,6 +27,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/protobuf/queue_runner.pb.h"
 
 namespace tensorflow {
 namespace grappler {
@@ -349,6 +353,7 @@ TEST_F(SingleMachineTest, InitializationMemory) {
 }
 
 namespace {
+
 template <class T>
 inline void SetNodeAttr(const string& key, const T& value, NodeDef* node) {
   AttrValue attr_value;
@@ -463,6 +468,122 @@ TEST_F(SingleMachineTest, PersistentMemory) {
   EXPECT_TRUE(found_hashtable);
 }
 
+namespace {
+
+SessionOptions GetSessionOption(int num_cpu_cores, int num_gpus) {
+  SessionOptions options;
+  // Copied from single_machine.h
+  (*options.config.mutable_device_count())["CPU"] = 1;
+  if (num_gpus > 0) {
+    (*options.config.mutable_device_count())["GPU"] = num_gpus;
+  }
+  CHECK_GE(num_cpu_cores, 1);
+  options.config.set_intra_op_parallelism_threads(num_cpu_cores);
+  options.config.add_session_inter_op_thread_pool()->set_num_threads(
+      num_cpu_cores);
+  return options;
+}
+
+Status GetDeviceMemoryStats(
+    const SessionOptions& session_option,
+    std::unordered_map<string, AllocatorStats>* allocator_stats_by_device) {
+  std::vector<Device*> devices;
+  TF_RETURN_IF_ERROR(DeviceFactory::AddDevices(session_option,
+                                               "" /* name_prefix */, &devices));
+  allocator_stats_by_device->clear();
+  for (Device* device : devices) {
+    AllocatorStats stats;
+    auto* allocator = device->GetAllocator(AllocatorAttributes());
+    if (!allocator->TracksAllocationSizes()) {
+      return Status(error::INVALID_ARGUMENT,
+                    "Tracking allocation is not enabled.");
+    }
+    allocator->GetStats(&stats);
+    (*allocator_stats_by_device)[device->name()] = stats;
+  }
+  return Status::OK();
+}
+
+}  // namespace
+
+TEST_F(SingleMachineTest, ReleaseMemoryAfterDestruction) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+
+  // Add a variable and initializer.
+  Output a = ops::Variable(s.WithOpName("a"), TensorShape({128, 256}),
+                           DataType::DT_FLOAT);
+  Output a_init =
+      ops::RandomNormal(s.WithOpName("a/init"), {128, 256}, DataType::DT_FLOAT);
+  Output a_init_assign = ops::Assign(s.WithOpName("a/init/assign"), a, a_init);
+
+  // Add a resource variable.
+  Output b =
+      ops::VarHandleOp(s.WithOpName("b"), DataType::DT_FLOAT, {256, 512});
+  Output b_read =
+      ops::ReadVariableOp(s.WithOpName("b/read"), b, DataType::DT_FLOAT);
+  Output b_init =
+      ops::RandomNormal(s.WithOpName("b/init"), {256, 512}, DataType::DT_FLOAT);
+  auto b_init_assign =
+      ops::AssignVariableOp(s.WithOpName("b/init/assign"), b, b_init);
+
+  // Add a queue.
+  ops::FIFOQueue queue(s.WithOpName("queue"), {DataType::DT_STRING});
+  Output some_string =
+      ops::Const(s.WithOpName("some_string"), string("nothing"));
+  ops::QueueEnqueue enqueue(s.WithOpName("enqueue"), queue, {some_string});
+  ops::QueueDequeue dequeue(s.WithOpName("dequeue"), queue,
+                            {DataType::DT_STRING});
+
+  // Add a IdentityReader.
+  ops::IdentityReader reader(s.WithOpName("identity_reader"));
+  ops::ReaderRead read(s.WithOpName("read_from_queue"), reader, queue);
+
+  Output var_mul = ops::MatMul(s.WithOpName("var_matmul"), a, b_read);
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  QueueRunnerDef queue_runner;
+  queue_runner.set_queue_name("queue");
+  *queue_runner.add_enqueue_op_name() = "enqueue";
+  item.queue_runners.push_back(queue_runner);
+
+  item.init_ops.push_back("a/init/assign");
+  item.init_ops.push_back("b/init/assign");
+  item.fetch.push_back("var_matmul");
+  item.fetch.push_back("dequeue");
+
+  // Run the graph
+  TF_CHECK_OK(cluster_->Initialize(item));
+  EnableCPUAllocatorStats(true);
+
+  SessionOptions options =
+      GetSessionOption(3 /* cpu cores */, 0 /* num gpus */);
+  std::unordered_map<string, AllocatorStats> device_memory_before;
+  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory_before));
+  EXPECT_EQ(device_memory_before.size(), 1);
+
+  RunMetadata metadata;
+  TF_CHECK_OK(cluster_->Run(item.graph, item.feed, item.fetch, &metadata));
+
+  // Check there is memory that is not released.
+  std::unordered_map<string, AllocatorStats> device_memory;
+  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory));
+  EXPECT_EQ(device_memory.size(), 1);
+  EXPECT_GT(device_memory.begin()->second.bytes_in_use, 0);
+
+  // Reset cluster_ would release all memory.
+  cluster_.reset();
+  std::unordered_map<string, AllocatorStats> device_memory_after;
+  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory_after));
+
+  // Check memory used by resources are released after cluster destruction.
+  EXPECT_EQ(device_memory_before.size(), 1);
+  EXPECT_EQ(device_memory_after.size(), 1);
+  EXPECT_EQ(device_memory_before.begin()->second.bytes_in_use, 0);
+  EXPECT_EQ(device_memory_after.begin()->second.bytes_in_use, 0);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From ce3c4662fb0e7b7986780f1925c5edfbdf693ec6 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <vomjom@vomjom.net>
Date: Mon, 24 Jul 2017 20:20:08 -0700
Subject: [PATCH 1276/1375] Bump the required protobuf version (#11728)

---
 tensorflow/tools/pip_package/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 0726a28cc9..3be2707184 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -34,7 +34,7 @@ _VERSION = '1.3.0-rc0'
 REQUIRED_PACKAGES = [
     'numpy >= 1.11.0',
     'six >= 1.10.0',
-    'protobuf >= 3.2.0',
+    'protobuf >= 3.3.0',
     'tensorflow-tensorboard',
 ]
 
-- 
GitLab


From 2403455daaf6819f494b09aaf512bec3421cc618 Mon Sep 17 00:00:00 2001
From: "Gao, Xiang" <qasdfgtyuiop@gmail.com>
Date: Tue, 25 Jul 2017 00:50:19 -0400
Subject: [PATCH 1277/1375] use optimization when generating dependency
 (#10999)

This is to avoid _FORTIFY_SOURCE requires compiling with optimization (-O) warning during compile
---
 .../crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl b/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
index 242439daf4..2558f46fd5 100755
--- a/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
+++ b/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
@@ -213,7 +213,7 @@ def InvokeNvcc(argv, log=False):
            ' --compiler-options "' + host_compiler_options + '"' +
            ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH +
            ' -I .' +
-           ' -x cu ' + includes + ' ' + srcs + ' -M -o ' + depfile)
+           ' -x cu ' + opt + includes + ' ' + srcs + ' -M -o ' + depfile)
     if log: Log(cmd)
     exit_status = os.system(cmd)
     if exit_status != 0:
-- 
GitLab


From 37cda65dfb2c84db412ab6ee034ad60ec9b61151 Mon Sep 17 00:00:00 2001
From: DimanNe <dimanne@gmail.com>
Date: Tue, 25 Jul 2017 07:50:41 +0300
Subject: [PATCH 1278/1375] Add a function for generating random values with
 predefined seed (#11634)

* Add a function for generating random values with predefined seed

* Punctuation
---
 tensorflow/core/lib/random/random.cc | 15 +++++++++++++--
 tensorflow/core/lib/random/random.h  |  4 ++++
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/lib/random/random.cc b/tensorflow/core/lib/random/random.cc
index a30bad2b1b..723c1100f8 100644
--- a/tensorflow/core/lib/random/random.cc
+++ b/tensorflow/core/lib/random/random.cc
@@ -22,17 +22,28 @@ limitations under the License.
 namespace tensorflow {
 namespace random {
 
-std::mt19937_64* InitRng() {
+namespace {
+std::mt19937_64* InitRngWithRandomSeed() {
   std::random_device device("/dev/urandom");
   return new std::mt19937_64(device());
 }
+std::mt19937_64 InitRngWithDefaultSeed() { return std::mt19937_64(); }
+
+}  // anonymous namespace
 
 uint64 New64() {
-  static std::mt19937_64* rng = InitRng();
+  static std::mt19937_64* rng = InitRngWithRandomSeed();
   static mutex mu;
   mutex_lock l(mu);
   return (*rng)();
 }
 
+uint64 New64DefaultSeed() {
+  static std::mt19937_64 rng = InitRngWithDefaultSeed();
+  static mutex mu;
+  mutex_lock l(mu);
+  return rng();
+}
+
 }  // namespace random
 }  // namespace tensorflow
diff --git a/tensorflow/core/lib/random/random.h b/tensorflow/core/lib/random/random.h
index 7262f2dbb1..5335c8cc3c 100644
--- a/tensorflow/core/lib/random/random.h
+++ b/tensorflow/core/lib/random/random.h
@@ -25,6 +25,10 @@ namespace random {
 // in different processes.
 uint64 New64();
 
+// Return a 64-bit random value. Uses
+// std::mersenne_twister_engine::default_seed as seed value.
+uint64 New64DefaultSeed();
+
 }  // namespace random
 }  // namespace tensorflow
 
-- 
GitLab


From 5064df6cfec6573d6814d92acb4bf58171880859 Mon Sep 17 00:00:00 2001
From: "error.d" <error.d@gmail.com>
Date: Tue, 25 Jul 2017 12:51:55 +0800
Subject: [PATCH 1279/1375] Update BUILD (#11705)

fix issues/421
the problem reason is single_image_random_dot_stereograms.cc<<kernels>> be dependent on TensorShapeProto
it define on protobuf, use class LogMessage. but no deps protobuf on BUILD rule python/ops/_single_image_random_dot_stereograms.so .
so Symbol not found: __ZN6google8protobuf8internal10LogMessageC1ENS0_8LogLevelEPKci(google::protobuf::internal::LogMessage::LogMessage)
---
 tensorflow/contrib/image/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/image/BUILD b/tensorflow/contrib/image/BUILD
index a857f947ea..bb496a0f87 100755
--- a/tensorflow/contrib/image/BUILD
+++ b/tensorflow/contrib/image/BUILD
@@ -105,6 +105,9 @@ tf_custom_op_library(
         "kernels/single_image_random_dot_stereograms_ops.cc",
         "ops/single_image_random_dot_stereograms_ops.cc",
     ],
+    deps = [
+        "@protobuf//:protobuf",
+    ],
 )
 
 tf_gen_op_libs(
-- 
GitLab


From 2bdc5dd1a4ca04ea4cabdb44198b36cecaed07d6 Mon Sep 17 00:00:00 2001
From: AngryPowman <angrypowman@qq.com>
Date: Tue, 25 Jul 2017 13:17:16 +0800
Subject: [PATCH 1280/1375] update WORKSPACE uncommented line range to L19-L36
 (#11733)

---
 tensorflow/examples/android/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/examples/android/README.md b/tensorflow/examples/android/README.md
index 270f654ed7..51f6c4a71c 100644
--- a/tensorflow/examples/android/README.md
+++ b/tensorflow/examples/android/README.md
@@ -124,7 +124,7 @@ it and the Android NDK and SDK must be installed on your system.
 
 ##### Edit WORKSPACE
 
-The Android entries in [`<workspace_root>/WORKSPACE`](../../../WORKSPACE#L19-L32)
+The Android entries in [`<workspace_root>/WORKSPACE`](../../../WORKSPACE#L19-L36)
 must be uncommented with the paths filled in appropriately depending on where
 you installed the NDK and SDK. Otherwise an error such as:
 "The external label '//external:android/sdk' is not bound to anything" will
-- 
GitLab


From bb88ec7ecc4dc7ba72548a5115fb86e20b14de5b Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Mon, 24 Jul 2017 22:46:38 -0700
Subject: [PATCH 1281/1375] Update losses implementation (#11667)

* Update README.md

Minor rewording

* Update ISSUE_TEMPLATE.md

Made explicit HTTPS call

* Update logistic_regressor_test.py

Replaced deprecated contrib with tf.losses.sigmoid_cross_entropy()

* Update logistic_regressor_test.py

Correcting losses function call

* Update losses_ops.py

Replace deprecated contrib with tf.losses

* Update wide.md

Minor explanation of abbreviate attribute

* Update logistic_regressor_test

Keeping all the headers the same

* Remove https from license

* Update ISSUE_TEMPLATE.md

Revert unrelated changes

* Revert README.md

Revert unrelated changes

* Revert wide.md

Revert unrelated changes

* Update wide.md

Reverting more changes
---
 .../python/learn/estimators/logistic_regressor_test.py |  4 ++--
 .../contrib/learn/python/learn/ops/losses_ops.py       | 10 +++++-----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py
index 021918f0ef..93c62f87e8 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py
@@ -26,12 +26,12 @@ from tensorflow.contrib.layers.python.layers import optimizers
 from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.contrib.learn.python.learn.estimators import logistic_regressor
 from tensorflow.contrib.learn.python.learn.estimators import metric_key
-from tensorflow.contrib.losses.python.losses import loss_ops
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.losses import losses
 from tensorflow.python.platform import test
 
 
@@ -55,7 +55,7 @@ def _logistic_regression_model_fn(features, labels, mode):
       # AUC/precision/recall/etc will change meaningfully even on a toy dataset.
       biases_initializer=init_ops.constant_initializer(-10.0))
   predictions = math_ops.sigmoid(logits)
-  loss = loss_ops.sigmoid_cross_entropy(logits, labels)
+  loss = losses.sigmoid_cross_entropy(labels, logits)
   train_op = optimizers.optimize_loss(
       loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
   return predictions, loss, train_op
diff --git a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
index 086e5d78bb..b040ab3bb6 100644
--- a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
@@ -20,14 +20,14 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.framework import deprecated
-from tensorflow.contrib.losses.python.losses import loss_ops
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops as array_ops_
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
+from tensorflow.python.ops.losses import losses
 
 
-@deprecated('2016-12-01', 'Use `tf.contrib.losses.mean_squared_error` '
+@deprecated('2016-12-01', 'Use `tf.losses.mean_squared_error` '
             'and explicit logits computation.')
 def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
   """Returns prediction and loss for mean squared error regression."""
@@ -36,10 +36,10 @@ def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
     predictions = nn.xw_plus_b(tensor_in, weights, biases)
     if len(labels.get_shape()) == 1 and len(predictions.get_shape()) == 2:
       predictions = array_ops_.squeeze(predictions, squeeze_dims=[1])
-    return predictions, loss_ops.mean_squared_error(predictions, labels)
+    return predictions, losses.mean_squared_error(labels, predictions)
 
 
-@deprecated('2016-12-01', 'Use `tf.contrib.losses.softmax_cross_entropy` '
+@deprecated('2016-12-01', 'Use `tf.losses.softmax_cross_entropy` '
             'and explicit logits computation.')
 def softmax_classifier(tensor_in,
                        labels,
@@ -72,4 +72,4 @@ def softmax_classifier(tensor_in,
     logits = nn.xw_plus_b(tensor_in, weights, biases)
     if class_weight is not None:
       logits = math_ops.multiply(logits, class_weight)
-    return nn.softmax(logits), loss_ops.softmax_cross_entropy(logits, labels)
+    return nn.softmax(logits), losses.softmax_cross_entropy(labels, logits)
-- 
GitLab


From d1a9ea61ef8271b3d2fe273a68ff5940fcba7ccd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 00:05:41 -0700
Subject: [PATCH 1282/1375] [XLA] Teach CPU and GPU compilers to optionally
 invoke the HLO insert-reduce-precision-operations pass.

This also required a few additions and fixups.  We add pieces to ReducePrecisionInsertion to translate between the protocol-buffer representation of the pass options and the predicate-function actually used in the pass.  To facilitate this translation, we also add a function to HloOpcode to return the number of opcodes so that we can iterate over the whole set easily.

PiperOrigin-RevId: 163037250
---
 tensorflow/compiler/xla/service/BUILD         |  1 +
 tensorflow/compiler/xla/service/cpu/BUILD     |  1 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 18 ++++
 tensorflow/compiler/xla/service/gpu/BUILD     |  1 +
 .../compiler/xla/service/gpu/gpu_compiler.cc  | 31 ++++++-
 tensorflow/compiler/xla/service/hlo_opcode.h  |  5 ++
 .../xla/service/reduce_precision_insertion.cc | 39 +++++++-
 .../xla/service/reduce_precision_insertion.h  | 22 ++++-
 .../xla/tests/reduce_precision_test.cc        | 90 ++++++++++++++++++-
 tensorflow/compiler/xla/xla.proto             | 23 +++++
 10 files changed, 221 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 696dc28564..a4612bb6c1 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1945,6 +1945,7 @@ cc_library(
         ":buffer_liveness",
         ":hlo",
         ":hlo_pass",
+        "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/core:lib",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 7248cb5f4c..2ca4af67cd 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -72,6 +72,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo_subcomputation_unification",
         "//tensorflow/compiler/xla/service:hlo_verifier",
         "//tensorflow/compiler/xla/service:inliner",
+        "//tensorflow/compiler/xla/service:reduce_precision_insertion",
         "//tensorflow/compiler/xla/service:reshape_mover",
         "//tensorflow/compiler/xla/service:transpose_folding",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",  # fixdeps: keep
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 6d819355c4..b86342d0b3 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -74,6 +74,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_verifier.h"
 #include "tensorflow/compiler/xla/service/inliner.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 #include "tensorflow/compiler/xla/service/reshape_mover.h"
 #include "tensorflow/compiler/xla/service/transpose_folding.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -253,6 +254,14 @@ Status CpuCompiler::RunHloPasses(HloModule* module) {
   HloPassPipeline pipeline("CPU");
   pipeline.AddInvariantChecker<HloVerifier>();
 
+  for (const auto& reduce_precision_options :
+       module->config().debug_options().hlo_reduce_precision_options()) {
+    if (reduce_precision_options.pass_timing() ==
+        HloReducePrecisionOptions::BEFORE_OP_FUSION) {
+      pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
+    }
+  }
+
   // TODO(b/35786417): Re-enable inliner pass after fixing the bug and deciding
   // where we will take this pass in future.
   // pipeline.AddPass<Inliner>();
@@ -278,6 +287,15 @@ Status CpuCompiler::RunHloPasses(HloModule* module) {
       TransposeFolding::NeverFoldTranspose);
   pipeline.AddPass<HloCSE>(/*is_layout_sensitive=*/false);
   pipeline.AddPass<CpuInstructionFusion>();
+
+  for (const auto& reduce_precision_options :
+       module->config().debug_options().hlo_reduce_precision_options()) {
+    if (reduce_precision_options.pass_timing() ==
+        HloReducePrecisionOptions::AFTER_OP_FUSION) {
+      pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
+    }
+  }
+
   pipeline.AddPass<CpuLayoutAssignment>(
       module->mutable_entry_computation_layout());
   // The LayoutAssignment pass may leave behind kCopy instructions which are
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index fa95e23499..cdd7c8187c 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -432,6 +432,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo_proto_util",
         "//tensorflow/compiler/xla/service:hlo_subcomputation_unification",
         "//tensorflow/compiler/xla/service:hlo_verifier",
+        "//tensorflow/compiler/xla/service:reduce_precision_insertion",
         "//tensorflow/compiler/xla/service:reshape_mover",
         "//tensorflow/compiler/xla/service:transpose_folding",
         "//tensorflow/compiler/xla/service/gpu/llvm_gpu_backend",
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index d60c45a5c3..2acf95084a 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -56,6 +56,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
 #include "tensorflow/compiler/xla/service/hlo_verifier.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 #include "tensorflow/compiler/xla/service/reshape_mover.h"
 #include "tensorflow/compiler/xla/service/transpose_folding.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -123,6 +124,15 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
   {
     HloPassPipeline pipeline("optimization");
     pipeline.AddInvariantChecker<HloVerifier>();
+
+    for (const auto& reduce_precision_options :
+         hlo_module->config().debug_options().hlo_reduce_precision_options()) {
+      if (reduce_precision_options.pass_timing() ==
+          HloReducePrecisionOptions::BEFORE_OP_FUSION) {
+        pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
+      }
+    }
+
     {
       auto& pass =
           pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification");
@@ -149,8 +159,27 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/false);
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/true);
     fusion.AddPass<FusionMerger>();
-    return fusion.Run(hlo_module).status();
+    TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status());
+
+    HloPassPipeline reduce_pipeline("reduce-precision");
+    for (const auto& reduce_precision_options :
+         hlo_module->config().debug_options().hlo_reduce_precision_options()) {
+      if (reduce_precision_options.pass_timing() ==
+          HloReducePrecisionOptions::AFTER_OP_FUSION) {
+        reduce_pipeline.AddPass<ReducePrecisionInsertion>(
+            reduce_precision_options);
+      }
+    }
+    StatusOr<bool> reduce_result = reduce_pipeline.Run(hlo_module);
+    TF_RETURN_IF_ERROR(reduce_result.status());
+
+    if (reduce_result.ValueOrDie()) {
+      // Do another fusion pass, with the expectation that we may be able to
+      // fuse the new ReducePrecision operations.
+      TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status());
+    }
   }
+  return tensorflow::Status::OK();
 }
 
 // Modifies the given HLO module so that it will be accepted by IrEmitter.
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index 358e611d57..8a6376b2d1 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -112,6 +112,11 @@ bool HloOpcodeIsComparison(HloOpcode opcode);
 // Returns true iff the given opcode has variadic operands.
 bool HloOpcodeIsVariadic(HloOpcode opcode);
 
+// Returns the number of HloOpcode values.
+inline const uint32_t HloOpcodeCount() {
+  return static_cast<uint32_t>(HloOpcode::kWhile) + 1;
+}
+
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_OPCODE_H_
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
index dafefdc491..e083226b14 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -30,14 +31,15 @@ StatusOr<bool> ReducePrecisionInsertion::Run(HloModule* module) {
     for (auto& instruction : computation->instructions()) {
       VLOG(3) << "Visited instruction: " << instruction->ToString();
 
-      // For now, ReducePrecision is only implemented for F32 data, so this
+      // For now, ReducePrecision is only implemented for F32 arrays, so this
       // ignore instructions that produce other data.  In particular, this
       // currently ignores instructions producing tuples, even if those tuples
-      // contain F32 data inside them.  The assumption is that in most cases
+      // contain F32 arrays inside them.  The assumption is that in most cases
       // equivalent behavior can be obtained by adding ReducePrecision
-      // instructions after the instructions that pull the F32 data out of the
-      // tuples.
+      // instructions after the instructions that pull the F32 arrays out of
+      // the tuples.
       if (instruction->shape().element_type() == PrimitiveType::F32 &&
+          !ShapeUtil::IsScalar(instruction->shape()) &&
           should_reduce_output_precision_(instruction->opcode())) {
         instructions_to_suffix.push_back(instruction.get());
       }
@@ -58,4 +60,33 @@ StatusOr<bool> ReducePrecisionInsertion::Run(HloModule* module) {
   return changed;
 }
 
+ReducePrecisionInsertion::OpcodeFilterFunction
+ReducePrecisionInsertion::make_filter_function(
+    const HloReducePrecisionOptions& reduce_precision_options) {
+  // Implement the filter function with a lookup table.
+  std::vector<bool> filter(HloOpcodeCount(), false);
+  for (const auto& opcode : reduce_precision_options.opcodes_to_suffix()) {
+    filter[opcode] = true;
+  }
+  return [filter](const HloOpcode opcode) {
+    return filter[static_cast<unsigned int>(opcode)];
+  };
+}
+
+HloReducePrecisionOptions ReducePrecisionInsertion::make_options_proto(
+    const HloReducePrecisionOptions::PassTiming pass_timing,
+    const int exponent_bits, const int mantissa_bits,
+    const OpcodeFilterFunction& should_reduce_output_precision) {
+  HloReducePrecisionOptions options;
+  options.set_pass_timing(pass_timing);
+  options.set_exponent_bits(exponent_bits);
+  options.set_mantissa_bits(mantissa_bits);
+  for (uint32_t opcode = 0; opcode < HloOpcodeCount(); opcode++) {
+    if (should_reduce_output_precision(static_cast<HloOpcode>(opcode))) {
+      options.add_opcodes_to_suffix(opcode);
+    }
+  }
+  return options;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.h b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
index e9c8bba031..34b865b9ce 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.h
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
@@ -42,6 +42,17 @@ class ReducePrecisionInsertion : public HloPassInterface {
       : exponent_bits_(exponent_bits),
         mantissa_bits_(mantissa_bits),
         should_reduce_output_precision_(should_reduce_output_precision) {}
+
+  // Version of the constructor that takes an HloReducePrecisionOptions proto
+  // rather than explicitly-enumerated parameters, for convenience when
+  // creating passes based on DebugOptions.
+  explicit ReducePrecisionInsertion(
+      const HloReducePrecisionOptions& reduce_precision_options)
+      : exponent_bits_(reduce_precision_options.exponent_bits()),
+        mantissa_bits_(reduce_precision_options.mantissa_bits()),
+        should_reduce_output_precision_(
+            make_filter_function(reduce_precision_options)) {}
+
   ~ReducePrecisionInsertion() override{};
 
   tensorflow::StringPiece name() const override {
@@ -52,6 +63,15 @@ class ReducePrecisionInsertion : public HloPassInterface {
   // (reduce-precision instructions were inserted).
   StatusOr<bool> Run(HloModule* module) override;
 
+  // Convert between the (inconvenient) xla.proto HloReducePrecisionOptions
+  // representation and OpcodeFilterFunction functions.
+  static OpcodeFilterFunction make_filter_function(
+      const HloReducePrecisionOptions& reduce_precision_options);
+  static HloReducePrecisionOptions make_options_proto(
+      const HloReducePrecisionOptions::PassTiming pass_timing,
+      const int exponent_bits, const int mantissa_bits,
+      const OpcodeFilterFunction& should_reduce_output_precision);
+
  private:
   // Parameters for the precision reduction to be added.
   const int exponent_bits_;
@@ -59,7 +79,7 @@ class ReducePrecisionInsertion : public HloPassInterface {
 
   // Function to determine (from the opcode) whether a given instruction should
   // have a reduce-precision instruction inserted in its output stream.
-  const OpcodeFilterFunction& should_reduce_output_precision_;
+  const OpcodeFilterFunction should_reduce_output_precision_;
 };
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
index 48212dc7d1..527205bbb0 100644
--- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
@@ -39,8 +40,11 @@ limitations under the License.
 namespace xla {
 namespace {
 
-class ReducePrecisionTest : public ClientLibraryTestBase,
-                            public ::testing::WithParamInterface<int> {};
+// Tests to confirm that the ReducePrecision operation produces the expected
+// numerical values.
+class ReducePrecisionAccuracyTest : public ClientLibraryTestBase,
+                                    public ::testing::WithParamInterface<int> {
+};
 
 // For reduction to IEEE-f16, we want to test the following cases, in both
 // positive and negative variants.  (Note: IEEE-f16 is 5 exponent bits and 10
@@ -201,7 +205,7 @@ static const uint32_t test_values[][4] = {
         FPVAL(11111111, 1111111111, 1111111111111)   // NaN
     }};
 
-XLA_TEST_P(ReducePrecisionTest, ReducePrecisionF32) {
+XLA_TEST_P(ReducePrecisionAccuracyTest, ReducePrecisionF32) {
   int index = GetParam();
   int exponent_bits = exponent_sizes[index];
   int mantissa_bits = mantissa_sizes[index];
@@ -238,9 +242,87 @@ XLA_TEST_P(ReducePrecisionTest, ReducePrecisionF32) {
   ComputeAndCompareR1<float>(&builder, expected_values, {a_data.get()});
 }
 
-INSTANTIATE_TEST_CASE_P(ReducePrecisionTest, ReducePrecisionTest,
+INSTANTIATE_TEST_CASE_P(ReducePrecisionAccuracyTest,
+                        ReducePrecisionAccuracyTest,
                         ::testing::Values(0, 1, 2, 3), TestDataToString);
 
+// Tests to confirm that the compiler optimization functions add the expected
+// ReducePrecisionInsertion passes.
+class ReducePrecisionInsertionTest : public ClientLibraryTestBase {};
+
+XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionBeforeFusion) {
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({1.00001});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  // Abs doesn't affect resolution.
+  auto abs = builder.Abs(a);
+
+  // Near 1.0, Log(x) approximates x - 1; this lets us confirm that the
+  // reduce-precision operation showed up in the correct place in the
+  // graph.
+  auto log = builder.Log(abs);
+
+  // Insert precision-reduction after the Abs(x) operation, rounding that
+  // result to exactly 1.0f.
+  auto reduce_precision_pass = execution_options_.mutable_debug_options()
+                                   ->add_hlo_reduce_precision_options();
+  *reduce_precision_pass = ReducePrecisionInsertion::make_options_proto(
+      HloReducePrecisionOptions::BEFORE_OP_FUSION, 5, 10,
+      [](const HloOpcode opcode) { return opcode == HloOpcode::kAbs; });
+
+  ComputeAndCompareR1<float>(&builder, {0.0f}, {a_data.get()});
+}
+
+XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionSkippedAfterFusion) {
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({1.00001});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  // These two operations should be fused by any reasonable backend.
+  auto abs = builder.Abs(a);
+  auto neg = builder.Neg(abs);
+
+  // Add a pass after operation fusion, suffixing kAbs operations.  This
+  // should not see into the fusion nodes and thus should not affect the
+  // result.
+  auto reduce_precision_pass = execution_options_.mutable_debug_options()
+                                   ->add_hlo_reduce_precision_options();
+  *reduce_precision_pass = ReducePrecisionInsertion::make_options_proto(
+      HloReducePrecisionOptions::AFTER_OP_FUSION, 5, 10,
+      [](const HloOpcode opcode) { return opcode == HloOpcode::kAbs; });
+
+  ComputeAndCompareR1<float>(&builder, {-1.00001f}, {a_data.get()});
+}
+
+XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionAddedAfterFusion) {
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({1.00001});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  // These two operations should be fused by any reasonable backend.
+  auto abs = builder.Abs(a);
+  auto neg = builder.Neg(abs);
+
+  // Add a pass after operation fusion, suffixing kFusion operations.
+  auto reduce_precision_pass = execution_options_.mutable_debug_options()
+                                   ->add_hlo_reduce_precision_options();
+  *reduce_precision_pass = ReducePrecisionInsertion::make_options_proto(
+      HloReducePrecisionOptions::AFTER_OP_FUSION, 5, 10,
+      [](const HloOpcode opcode) { return opcode == HloOpcode::kFusion; });
+
+  ComputeAndCompareR1<float>(&builder, {-1.0f}, {a_data.get()});
+}
+
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 00fb7f12b8..be4e00f63c 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -20,6 +20,24 @@ import "tensorflow/compiler/xla/service/session.proto";
 
 package xla;
 
+// Options for the HLO insert-reduce-precision-operations pass.
+message HloReducePrecisionOptions {
+  // When to run the pass.
+  enum PassTiming {
+    BEFORE_OP_FUSION = 0;
+    AFTER_OP_FUSION = 1;
+  }
+  PassTiming pass_timing = 1;
+
+  // Exponent and mantissa bit counts for the reduced precision.
+  uint32 exponent_bits = 2;
+  uint32 mantissa_bits = 3;
+
+  // Opcodes for operations that should be suffixed with reduced-precision
+  // operations.
+  repeated uint32 opcodes_to_suffix = 4;
+}
+
 // Debugging options for XLA. These options may change at any time - there are
 // no guarantees about backward or forward compatibility for these fields.
 message DebugOptions {
@@ -112,6 +130,11 @@ message DebugOptions {
   // the generated IR.
   bool xla_llvm_enable_invariant_load_metadata = 72;
 
+  // Options for inserting reduce-precision operations for numerical
+  // experimentation.  This is a repeated field, as we may want to have
+  // multiple passes with different parameters.
+  repeated HloReducePrecisionOptions hlo_reduce_precision_options = 80;
+
   // This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the
   // computation will run n! times with all permunations of layouts for the
   // output shape in rank n. For example, with a 3D shape, all permutations of
-- 
GitLab


From 6610b3ec6bdb1a8843070a9fea6e4612681b9318 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Tue, 25 Jul 2017 00:31:16 -0700
Subject: [PATCH 1283/1375] Refactor HLO graph dumping.

This also makes a few minor cosmetic changes, like moving the fusion
type out of the fusion node and into the out-of-line computation and
adjusting the arrow labels that we use to indicate operand numbers.

PiperOrigin-RevId: 163038795
---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 920 +++++++++---------
 1 file changed, 482 insertions(+), 438 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index acd26c4e31..fcad1188a7 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -48,37 +48,37 @@ using ::tensorflow::Env;
 using ::tensorflow::gtl::nullopt;
 using ::tensorflow::gtl::optional;
 using ::tensorflow::io::JoinPath;
-using ::tensorflow::strings::Appendf;
-using ::tensorflow::strings::Printf;
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 using ::tensorflow::str_util::Join;
+using ::tensorflow::str_util::StringReplace;
 using ::tensorflow::WriteStringToFile;
 
 namespace xla {
 namespace hlo_graph_dumper {
 namespace {
 
-// Node color schemes, used by NodeColorAttributes.
-enum ColorScheme {
-  kBlue,
-  kBrown,
-  kDarkBlue,
-  kDarkGreen,
-  kDarkRed,
-  kGray,
-  kGreen,
-  kOrange,
-  kPurple,
-  kRed,
-  kWhite,
-  kYellow,
-
-  // Causes the node's border to be a dashed line, and its content to be gray
-  // text on a white background, suggesting that this is an "unimportant" node.
-  kDashedBorder,
+// Helpers for Printf and Appendf.
+template <typename T>
+struct PrintfConvert {
+  const T& operator()(const T& t) const { return t; }
+};
+template <>
+struct PrintfConvert<string> {
+  const char* operator()(const string& s) const { return s.c_str(); }
 };
 
+// Like tensorflow::strings::Printf/Appendf, but you don't need to call c_str()
+// on strings.
+template <typename... Ts>
+string Printf(const char* fmt, const Ts&... ts) {
+  return tensorflow::strings::Printf(fmt, PrintfConvert<Ts>()(ts)...);
+}
+template <typename... Ts>
+void Appendf(string* s, const char* fmt, const Ts&... ts) {
+  tensorflow::strings::Appendf(s, fmt, PrintfConvert<Ts>()(ts)...);
+}
+
 // Used to indicate how we should treat a given HLOInstruction in the graph.
 // should we treat it like normal, hide it, and so on?
 enum NodeFilterResult {
@@ -123,6 +123,26 @@ class NodeFilter {
   std::function<NodeFilterResult(const HloInstruction* instr)> filter_;
 };
 
+// Node color schemes, used by NodeColorAttributes.
+enum ColorScheme {
+  kBlue,
+  kBrown,
+  kDarkBlue,
+  kDarkGreen,
+  kDarkRed,
+  kGray,
+  kGreen,
+  kOrange,
+  kPurple,
+  kRed,
+  kWhite,
+  kYellow,
+
+  // Causes the node's border to be a dashed line, and its content to be gray
+  // text on a white background, suggesting that this is an "unimportant" node.
+  kDashedBorder,
+};
+
 // Given a ColorScheme, returns an attribute string for a node of that color.
 // Sets the node's style and fill/stroke/text colors.
 //
@@ -170,19 +190,8 @@ string NodeColorAttributes(ColorScheme color) {
 // Replaces <> with &lt;&gt;, so that this string is safe(er) for use in a
 // graphviz HTML-like string.
 string HtmlLikeStringSanitize(tensorflow::StringPiece s) {
-  return tensorflow::str_util::StringReplace(
-      tensorflow::str_util::StringReplace(s, "<", "&lt;", /*replace_all=*/true),
-      ">", "&gt;", /*replace_all=*/true);
-}
-
-// Returns the dot graph identifier for the given instruction.
-string InstructionId(const HloInstruction* instruction) {
-  return Printf("%lld", reinterpret_cast<uint64>(instruction));
-}
-
-// Returns the dot graph identifier for the given computation.
-string ComputationId(const HloComputation* computation) {
-  return Printf("%lld", reinterpret_cast<uint64>(computation));
+  return StringReplace(StringReplace(s, "<", "&lt;", /*replace_all=*/true), ">",
+                       "&gt;", /*replace_all=*/true);
 }
 
 // Tries to generates a human-readable one-word description of the given
@@ -249,439 +258,471 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
   }
 }
 
-// Returns the dot graph edges and nodes for the given instruction sequence.
-// Edges which extend between computations are added to the vector
-// intercomputation_edges. This is necessary because graphviz does not render
-// the graph properly unless these inter-computation edges appear after all
-// subgraph statements.
-string InstructionSequenceGraph(
-    const std::list<std::unique_ptr<HloInstruction>>& instructions,
-    bool show_addresses, bool show_layouts,
-    std::vector<string>* intercomputation_edges,
-    const HloExecutionProfile* hlo_execution_profile,
-    const NodeFilter& filter) {
-  string graph_body;
-
-  for (auto& instruction : instructions) {
-    if (!filter.Show(instruction.get())) {
-      continue;
-    }
+class HloDotDumper {
+ public:
+  HloDotDumper(const HloComputation* computation, tensorflow::StringPiece label,
+               bool show_addresses, bool show_layouts,
+               const HloExecutionProfile* profile, NodeFilter filter)
+      : computation_(computation),
+        label_(label.ToString()),
+        show_addresses_(show_addresses),
+        show_layouts_(show_layouts),
+        profile_(profile),
+        filter_(std::move(filter)) {}
+
+  string Dump();
 
-    // We don't display constants as separate nodes; they're merged into their
-    // users.
-    if (instruction->opcode() == HloOpcode::kConstant) {
-      continue;
-    }
+ private:
+  // Returns the dot graph identifier for the given instruction.
+  string InstructionId(const HloInstruction* instruction) {
+    return StrCat(reinterpret_cast<uint64>(instruction));
+  }
 
-    ColorScheme color = kYellow;
-    string shape = "box";
-
-    // Build the first line or two of the node, containing its name and opcode
-    // (if the opcode isn't redundant with the name).
-    string name;
-    if (instruction->opcode() == HloOpcode::kParameter) {
-      // If we have a parameter, put the param number in the name.
-      name = StrCat("<b>Parameter ", instruction->parameter_number(),
-                    "</b><br/>", HtmlLikeStringSanitize(instruction->name()));
-    } else if (tensorflow::StringPiece(instruction->name())
-                   .starts_with(
-                       StrCat("%", instruction->ExtendedOpcodeStr()))) {
-      // The HLO instruction name contains usually the opcode, e.g. "%add.42" is
-      // an add instruction.  In this case we render just the name.
-      name = StrCat("<b>", HtmlLikeStringSanitize(instruction->name()), "</b>");
-    } else if (instruction->opcode() == HloOpcode::kFusion &&
-               tensorflow::StringPiece(instruction->name())
-                   .starts_with(
-                       StrCat("%", HloOpcodeString(instruction->opcode())))) {
-      // Fusion nodes are usually named e.g. "%fusion.5".  We render these as
-      // e.g. "%fusion.5<br/>input fusion".
-      name = StrCat("<b>", HtmlLikeStringSanitize(instruction->name()),
-                    "</b><br/>",
-                    HtmlLikeStringSanitize(instruction->ToCategory()));
-    } else {
-      // If the name does not contain the opcode, render both.
-      name = StrCat("<b>",
-                    HtmlLikeStringSanitize(instruction->ExtendedOpcodeStr()),
-                    "</b><br/>", HtmlLikeStringSanitize(instruction->name()));
-    }
+  // Returns the dot graph identifier for the given computation.
+  string SubcomputationId(const HloComputation* computation) {
+    return StrCat("cluster_", reinterpret_cast<uint64>(computation));
+  }
 
-    if (HloOpcode::kConvolution == instruction->opcode()) {
-      StrAppend(
-          &name, "<br/>",
-          HtmlLikeStringSanitize(
-              instruction->ConvolutionDimensionNumbersToString()),
-          "<br/>",
-          HtmlLikeStringSanitize(window_util::ToString(instruction->window())));
-    }
+  string Header();
+  string Footer();
+
+  // Maps HloComputations we should dump to their parent instruction in the
+  // outer computation.
+  std::unordered_map<const HloComputation*, const HloInstruction*>
+  SubcomputationsToDump();
+
+  string DumpSubcomputation(const HloComputation* subcomp,
+                            const HloInstruction* parent_instr);
+  string DumpComputation(const HloComputation* comp, const NodeFilter& filter);
+  string DumpInstruction(const HloInstruction* instr, const NodeFilter& filter);
+  ColorScheme GetInstructionColor(const HloInstruction* instr);
+  string GetInstructionNodeShape(const HloInstruction* instr);
+  string GetInstructionNodeLabel(const HloInstruction* instr);
+  string GetInstructionNodeExtraInfo(const HloInstruction* instr);
+  string GetInstructionIncomingEdges(const HloInstruction* instr,
+                                     const NodeFilter& filter);
+  string GetInstructionNodeInlinedConstants(const HloInstruction* instr);
+
+  // If instr has just one computation and it's trivial (e.g. "return param0 +
+  // param1"), returns a string you can put into the node's body that names the
+  // subcomputation, e.g. "Subcomputation: <b>add</b>".
+  string GetInstructionTrivialComputationStr(const HloInstruction* instr);
+
+  const HloComputation* computation_;  // never null
+  const string label_;                 // overall name for the graph
+  const bool show_addresses_;
+  const bool show_layouts_;
+  const HloExecutionProfile* profile_;  // may be null
+  const NodeFilter filter_;
+};
 
-    if (!instruction->metadata().op_name().empty()) {
-      StrAppend(&name, "<br/>",
-                HtmlLikeStringSanitize(instruction->metadata().op_name()));
-    }
-    if (!instruction->metadata().source_file().empty() &&
-        instruction->metadata().source_line() != 0) {
-      StrAppend(&name, "<br/>", instruction->metadata().source_file(), ":",
-                instruction->metadata().source_line());
-    }
+string HloDotDumper::Dump() {
+  string g = Header();
+  for (const auto& kv : SubcomputationsToDump()) {
+    const HloComputation* subcomp = kv.first;
+    const HloInstruction* parent = kv.second;
+    StrAppend(&g, DumpSubcomputation(subcomp, parent));
+  }
+  StrAppend(&g, DumpComputation(computation_, filter_));
+  StrAppend(&g, Footer());
+  return g;
+}
 
-    // Pick different colors or shapes for instructions which are particularly
-    // expensive (eg, dot) and those which are unusual in some way or unique
-    // (eg, parameter).
-    switch (instruction->opcode()) {
-      // "Normal" instructions. Mostly cheap and elementwise. No call to
-      // embedded computations. In this case, use default color, shape and
-      // label.
-      case HloOpcode::kAbs:
-      case HloOpcode::kAdd:
-      case HloOpcode::kCeil:
-      case HloOpcode::kClamp:
-      case HloOpcode::kConvert:
-      case HloOpcode::kCos:
-      case HloOpcode::kDivide:
-      case HloOpcode::kEq:
-      case HloOpcode::kExp:
-      case HloOpcode::kFloor:
-      case HloOpcode::kGe:
-      case HloOpcode::kGt:
-      case HloOpcode::kIndex:
-      case HloOpcode::kIsFinite:
-      case HloOpcode::kLe:
-      case HloOpcode::kLog:
-      case HloOpcode::kLogicalAnd:
-      case HloOpcode::kLogicalNot:
-      case HloOpcode::kLogicalOr:
-      case HloOpcode::kLt:
-      case HloOpcode::kMaximum:
-      case HloOpcode::kMinimum:
-      case HloOpcode::kMultiply:
-      case HloOpcode::kNe:
-      case HloOpcode::kNegate:
-      case HloOpcode::kPower:
-      case HloOpcode::kRemainder:
-      case HloOpcode::kSelect:
-      case HloOpcode::kSign:
-      case HloOpcode::kSin:
-      case HloOpcode::kSlice:
-      case HloOpcode::kSort:
-      case HloOpcode::kSubtract:
-      case HloOpcode::kTanh:
-        break;
-      case HloOpcode::kRng:
-        StrAppend(&name, "<br/>",
-                  RandomDistribution_Name(instruction->random_distribution()));
-        break;
-      case HloOpcode::kBroadcast:
-      case HloOpcode::kTranspose:
-        StrAppend(&name, "<br/>", "dims={",
-                  Join(instruction->dimensions(), ","), "}");
-        break;
-      case HloOpcode::kBitcast:
-      case HloOpcode::kTuple:
-      case HloOpcode::kTrace:
-        color = kWhite;
-        break;
-      case HloOpcode::kGetTupleElement:
-        color = kWhite;
-        StrAppend(&name, "<br/>index=", instruction->tuple_index());
-        break;
-      case HloOpcode::kConcatenate:
-      case HloOpcode::kCopy:
-      case HloOpcode::kDynamicSlice:
-      case HloOpcode::kDynamicUpdateSlice:
-      case HloOpcode::kPad:
-      case HloOpcode::kReshape:
-      case HloOpcode::kReverse:
-      case HloOpcode::kUpdate:
-        color = kGreen;
-        break;
-      case HloOpcode::kConvolution:
-      case HloOpcode::kDot:
-        color = kDarkBlue;
-        break;
-      case HloOpcode::kParameter:
-        color = kOrange;
-        break;
-      case HloOpcode::kBatchNormTraining:
-        StrAppend(&name, " feature_index=", instruction->feature_index());
-        color = kPurple;
-        break;
-      case HloOpcode::kBatchNormGrad:
-        StrAppend(&name, " feature_index=", instruction->feature_index());
-        color = kPurple;
-        break;
-      case HloOpcode::kReduce:
-        StrAppend(&name, " dims=", Join(instruction->dimensions(), ","));
-        color = kPurple;
-        break;
-      case HloOpcode::kSelectAndScatter:
-      case HloOpcode::kReduceWindow:
-        color = kPurple;
-        break;
-      case HloOpcode::kWhile:
-        shape = "ellipse";
-        color = kDarkGreen;
-        break;
-      case HloOpcode::kMap:
-      case HloOpcode::kFusion:
-        color = kGray;
-        break;
-      case HloOpcode::kSend:
-      case HloOpcode::kRecv:
-      case HloOpcode::kInfeed:
-      case HloOpcode::kOutfeed:
-      case HloOpcode::kCrossReplicaSum:
-        color = kBrown;
-        break;
-      case HloOpcode::kCall:
-        color = kDarkGreen;
-        break;
-      case HloOpcode::kCustomCall:
-        color = kDarkGreen;
-        StrAppend(&name, "<br/>",
-                  "custom_call_target=", instruction->custom_call_target());
-        break;
-      case HloOpcode::kReducePrecision:
-        // Make ReducePrecision ops a bit more visible, since typically they
-        // will be inserted as modifications to an existing graph.
-        color = kRed;
-        break;
-      case HloOpcode::kConstant:
-        LOG(FATAL) << "Constants don't get their own nodes in the graph.";
-    }
+string HloDotDumper::Header() {
+  // DOT graphs accept a stylesheet as a URI.  So naturally, an inline
+  // stylesheet is a data URI!
+  const char* fmt = R"(digraph G {
+rankdir = TB;
+compound = true;
+label = <<b>%s</b>>;
+labelloc = t;
+stylesheet="
+  data:text/css,
+  @import url(https://fonts.googleapis.com/css?family=Roboto:400,700);
+  svg text {
+    font-family: 'Roboto';
+    font-size: 12px;
+  }
+"
+
+)";
+
+  string graph_label = StrCat(label_, "<br/>", computation_->name());
+  if (profile_ != nullptr) {
+    auto cycles = profile_->total_cycles_executed(*computation_);
+    Appendf(&graph_label, "<br/>total cycles = %lld (%s)", cycles,
+            tensorflow::strings::HumanReadableNum(cycles));
+  }
+  return Printf(fmt, graph_label);
+}
 
-    // Create instruction node with appropriate label, shape, and color.
-    // label is interpreted as an HTML-like string, so newlines must be
-    // delimited with <br/>, rather than \n.
-    string label =
-        StrCat(name, "<br/>", ShapeUtil::HumanString(instruction->shape()));
+string HloDotDumper::Footer() { return "}\n"; }
 
-    if (show_addresses) {
-      Appendf(&label, "<br/>[%p]", instruction.get());
+std::unordered_map<const HloComputation*, const HloInstruction*>
+HloDotDumper::SubcomputationsToDump() {
+  // Dump the subcomputations of each instruction that's shown and doesn't have
+  // its operands omitted.  If an instruction has just one subcomputation and
+  // it's trivial, omit it: We'll display that subcomputation inlined into the
+  // instruction's node when we draw it.
+  std::unordered_map<const HloComputation*, const HloInstruction*> to_dump;
+  for (const auto& instr : computation_->instructions()) {
+    if (!filter_.Show(instr.get()) ||
+        filter_.SomeOrAllOperandsOmitted(instr.get())) {
+      continue;
     }
-    if (show_layouts && LayoutUtil::HasLayout(instruction->shape())) {
-      string layout_string;
-      if (ShapeUtil::IsTuple(instruction->shape())) {
-        // For tuples, emit the full shape because the layout of a tuple is not
-        // represented in a single Layout field.
-        layout_string = ShapeUtil::HumanStringWithLayout(instruction->shape());
-      } else {
-        layout_string =
-            Join(instruction->shape().layout().minor_to_major(), ",");
-      }
-      StrAppend(&label, "<br/>layout={", layout_string, "}");
+    if (instr->opcode() == HloOpcode::kFusion) {
+      to_dump[instr->fused_instructions_computation()] = instr.get();
     }
-    if (hlo_execution_profile != nullptr) {
-      auto hlo_cycles_executed =
-          hlo_execution_profile->GetProfileResult(*instruction);
-      auto total_cycles_executed =
-          hlo_execution_profile->total_cycles_executed(*instruction->parent());
-      if (hlo_cycles_executed > 0 && total_cycles_executed > 0) {
-        Appendf(&label, "<br/>%% of cycles executed=%.2f",
-                (static_cast<double>(hlo_cycles_executed) /
-                 static_cast<double>(total_cycles_executed)) *
-                    100);
+
+    const auto& subcomputations = instr->called_computations();
+    if (subcomputations.size() != 1 ||
+        !MatchTrivialComputation(subcomputations.front())) {
+      for (const HloComputation* comp : instr->called_computations()) {
+        to_dump[comp] = instr.get();
       }
     }
+  }
+  return to_dump;
+}
 
-    // If this node's operands are omitted, style it accordingly.
-    if (filter.SomeOrAllOperandsOmitted(instruction.get())) {
-      color = kDashedBorder;
-    }
+string HloDotDumper::DumpSubcomputation(const HloComputation* subcomp,
+                                        const HloInstruction* parent_instr) {
+  const char* computation_fmt = R"(subgraph %s {
+style = "%s";
+color = "%s";
+label = <%s>;
+labelloc = t;
+%s
+}  // %s
+
+)";
+
+  const char* edge_fmt = R"(%s -> %s [ltail="%s", %s];)";
+
+  string id = SubcomputationId(subcomp);
+
+  string subcomp_label, style, edge_or_bg_color, edge_attrs;
+  if (parent_instr->opcode() == HloOpcode::kFusion) {
+    subcomp_label = Printf("Fused expression for <b>%s</b><br/>%s",
+                           HtmlLikeStringSanitize(parent_instr->name()),
+                           HtmlLikeStringSanitize(parent_instr->ToCategory()));
+    style = "rounded,filled";
+    edge_or_bg_color = "lightgray";
+    edge_attrs = "style=dotted, arrowsize=0";
+  } else {
+    subcomp_label = Printf("Subcomputation for <b>%s</b><br/>%s",
+                           HtmlLikeStringSanitize(parent_instr->name()),
+                           HtmlLikeStringSanitize(subcomp->name()));
+    style = "rounded";
+    edge_or_bg_color = "black";
+    edge_attrs = "style=dashed";
+  }
+
+  // Pass an empty filter to DumpComputation -- we always dump the entirety of a
+  // subcomputation.
+  string comp_body = DumpComputation(subcomp, NodeFilter());
+  string computation = Printf(computation_fmt, id, style, edge_or_bg_color,
+                              subcomp_label, comp_body, id);
+  string edge = Printf(edge_fmt, InstructionId(subcomp->root_instruction()),
+                       InstructionId(parent_instr), SubcomputationId(subcomp),
+                       edge_attrs);
+  return StrCat(computation, "\n", edge, "\n");
+}
 
-    // If this node is highlighted, override its formatting.
-    if (filter.Highlight(instruction.get())) {
-      shape = "diamond";
-      color = kDarkRed;
+string HloDotDumper::DumpComputation(const HloComputation* comp,
+                                     const NodeFilter& filter) {
+  string g;
+  for (const auto& instr : comp->instructions()) {
+    if (!filter.Show(instr.get())) {
+      continue;
     }
+    StrAppend(&g, DumpInstruction(instr.get(), filter));
+  }
+  return g;
+}
 
-    // Create edges from the instruction's operands to the instruction.
-    if (!filter.OmitOperands(instruction.get())) {
-      int64 operand_number = 0;
-      for (auto* operand : instruction->operands()) {
-        if (!filter.Show(operand) ||
-            operand->opcode() == HloOpcode::kConstant) {
-          ++operand_number;
-          continue;
-        }
-        Appendf(&graph_body, "%s -> %s", InstructionId(operand).c_str(),
-                InstructionId(instruction.get()).c_str());
-        if (instruction->operand_count() > 1) {
-          Appendf(&graph_body, " [headlabel=\"%lld\",labeldistance=2]",
-                  operand_number);
-        }
-        StrAppend(&graph_body, ";\n");
-        ++operand_number;
-      }
+string HloDotDumper::DumpInstruction(const HloInstruction* instr,
+                                     const NodeFilter& filter) {
+  // We don't display constants as separate nodes; they're merged into their
+  // users.
+  if (instr->opcode() == HloOpcode::kConstant) {
+    return "";
+  }
 
-      // Fusion nodes are handled specially because they contain nested
-      // expressions.
-      if (instruction->opcode() == HloOpcode::kFusion) {
-        string cluster_name =
-            StrCat("cluster_", InstructionId(instruction.get()));
-        StrAppend(&graph_body, "subgraph ", cluster_name, " {\n");
-        StrAppend(&graph_body, "label=<fused expression for <b>",
-                  HtmlLikeStringSanitize(instruction->name()),
-                  "</b>>;\nstyle=\"rounded,filled\";\n"
-                  "color=lightgrey;\n");
-        StrAppend(&graph_body,
-                  InstructionSequenceGraph(instruction->fused_instructions(),
-                                           show_addresses, show_layouts,
-                                           intercomputation_edges,
-                                           hlo_execution_profile, NodeFilter()),
-                  "}\n");
-        string fusion_edge = StrCat(
-            InstructionId(instruction->fused_expression_root()), " -> ",
-            InstructionId(instruction.get()),
-            "  [ style = \"dotted\", arrowsize=0.0, ltail=", cluster_name,
-            " ];\n");
-        intercomputation_edges->push_back(fusion_edge);
-      } else {
-        // If instruction has just one computation and it's trivial (e.g.
-        // "return param0 + param1"), put the trivial computation type (e.g.
-        // "add") into instruction's label.  Otherwise, add a dotted edge
-        // between the instruction and its subcomputations.
-        const auto& subcomputations = instruction->called_computations();
-
-        bool trivial_subcomputation = false;
-        if (subcomputations.size() == 1) {
-          optional<string> computation_type =
-              MatchTrivialComputation(subcomputations.front());
-          if (computation_type) {
-            trivial_subcomputation = true;
-            StrAppend(&label, "<br/>Subcomputation: <b>", *computation_type,
-                      "</b>");
-          }
-        }
+  ColorScheme color = GetInstructionColor(instr);
+  string node_shape = GetInstructionNodeShape(instr);
+  string node_label = GetInstructionNodeLabel(instr);
+  string extra_info = GetInstructionNodeExtraInfo(instr);
+  string inlined_constants = GetInstructionNodeInlinedConstants(instr);
+  string trivial_subcomputation = GetInstructionTrivialComputationStr(instr);
 
-        if (!trivial_subcomputation) {
-          for (const HloComputation* computation :
-               instruction->called_computations()) {
-            string cluster_name =
-                StrCat("cluster_", ComputationId(computation));
-            string call_edge = Printf(
-                "%s -> %s [ style=dashed; ltail=%s ];\n",
-                InstructionId(computation->root_instruction()).c_str(),
-                InstructionId(instruction.get()).c_str(), cluster_name.c_str());
-            intercomputation_edges->push_back(call_edge);
-          }
-        }
-      }
+  string in_edges = GetInstructionIncomingEdges(instr, filter);
+
+  // Override the node's styling if it should be (de-)emphasized.
+  if (filter.SomeOrAllOperandsOmitted(instr)) {
+    color = kDashedBorder;
+  }
+  if (filter.Highlight(instr)) {
+    node_shape = "diamond";
+    color = kDarkRed;
+  }
+
+  // Build the text that will be displayed inside the node.
+  string node_body = node_label;
+  for (const string& s :
+       {trivial_subcomputation, extra_info, inlined_constants}) {
+    if (!s.empty()) {
+      StrAppend(&node_body, "<br/>", s);
     }
+  }
 
-    // Inline constant operands into the node.
-    for (int64 i = 0; i < instruction->operand_count(); ++i) {
-      const HloInstruction* operand = instruction->operand(i);
-      if (operand->opcode() != HloOpcode::kConstant) {
-        continue;
-      }
+  string node = Printf("%s [label=<%s>, shape=%s, %s];", InstructionId(instr),
+                       node_body, node_shape, NodeColorAttributes(color));
+  return StrCat(node, "\n", in_edges);
+}
+
+string HloDotDumper::GetInstructionNodeInlinedConstants(
+    const HloInstruction* instr) {
+  std::vector<string> lines;
+  for (int64 i = 0; i < instr->operand_count(); ++i) {
+    const HloInstruction* operand = instr->operand(i);
+    if (operand->opcode() != HloOpcode::kConstant) {
+      continue;
+    }
 
-      StrAppend(&label, "<br/><b>operand ", i, "</b> = ");
-      if (ShapeUtil::IsEffectiveScalar(operand->shape())) {
-        auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
-            operand->shape(), /*linear_index=*/0);
-        StrAppend(&label, ShapeUtil::HumanString(operand->shape()), "{",
-                  operand->literal().GetAsString(elem_idx), "}");
+    string line = Printf("<b>operand %lld</b> = ", i);
+    if (ShapeUtil::IsEffectiveScalar(operand->shape())) {
+      auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
+          operand->shape(), /*linear_index=*/0);
+      Appendf(&line, "%s{%s}", ShapeUtil::HumanString(operand->shape()),
+              operand->literal().GetAsString(elem_idx));
+    } else {
+      if (tensorflow::StringPiece(operand->name()).starts_with("%constant")) {
+        StrAppend(&line, operand->name());
       } else {
-        if (tensorflow::StringPiece(operand->name()).starts_with("%constant")) {
-          StrAppend(&label, operand->name());
-        } else {
-          StrAppend(&label, "constant ", operand->name());
-        }
+        StrAppend(&line, "constant ", operand->name());
       }
     }
+    lines.push_back(line);
+  }
+  return Join(lines, "<br/>");
+}
 
-    Appendf(&graph_body, "%s [label=<%s>, shape=%s, %s];\n",
-            InstructionId(instruction.get()).c_str(), label.c_str(),
-            shape.c_str(), NodeColorAttributes(color).c_str());
+ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) {
+  // Pick different colors or shapes for instructions which are particularly
+  // expensive (eg, dot) and those which are unusual in some way or unique
+  // (eg, parameter).
+  switch (instr->opcode()) {
+    case HloOpcode::kAbs:
+    case HloOpcode::kAdd:
+    case HloOpcode::kCeil:
+    case HloOpcode::kClamp:
+    case HloOpcode::kConvert:
+    case HloOpcode::kCos:
+    case HloOpcode::kDivide:
+    case HloOpcode::kEq:
+    case HloOpcode::kExp:
+    case HloOpcode::kFloor:
+    case HloOpcode::kGe:
+    case HloOpcode::kGt:
+    case HloOpcode::kIndex:
+    case HloOpcode::kIsFinite:
+    case HloOpcode::kLe:
+    case HloOpcode::kLog:
+    case HloOpcode::kLogicalAnd:
+    case HloOpcode::kLogicalNot:
+    case HloOpcode::kLogicalOr:
+    case HloOpcode::kLt:
+    case HloOpcode::kMaximum:
+    case HloOpcode::kMinimum:
+    case HloOpcode::kMultiply:
+    case HloOpcode::kNe:
+    case HloOpcode::kNegate:
+    case HloOpcode::kPower:
+    case HloOpcode::kRemainder:
+    case HloOpcode::kSelect:
+    case HloOpcode::kSign:
+    case HloOpcode::kSin:
+    case HloOpcode::kSlice:
+    case HloOpcode::kSort:
+    case HloOpcode::kSubtract:
+    case HloOpcode::kTanh:
+    case HloOpcode::kRng:
+    case HloOpcode::kBroadcast:
+    case HloOpcode::kTranspose:
+      return kYellow;
+    case HloOpcode::kBitcast:
+    case HloOpcode::kTuple:
+    case HloOpcode::kTrace:
+    case HloOpcode::kGetTupleElement:
+      return kWhite;
+    case HloOpcode::kConcatenate:
+    case HloOpcode::kCopy:
+    case HloOpcode::kDynamicSlice:
+    case HloOpcode::kDynamicUpdateSlice:
+    case HloOpcode::kPad:
+    case HloOpcode::kReshape:
+    case HloOpcode::kReverse:
+    case HloOpcode::kUpdate:
+      return kGreen;
+    case HloOpcode::kConvolution:
+    case HloOpcode::kDot:
+      return kDarkBlue;
+    case HloOpcode::kReducePrecision:
+      return kRed;
+    case HloOpcode::kParameter:
+      return kOrange;
+    case HloOpcode::kBatchNormTraining:
+    case HloOpcode::kBatchNormGrad:
+    case HloOpcode::kReduce:
+    case HloOpcode::kSelectAndScatter:
+    case HloOpcode::kReduceWindow:
+      return kPurple;
+    case HloOpcode::kMap:
+    case HloOpcode::kFusion:
+      return kGray;
+    case HloOpcode::kSend:
+    case HloOpcode::kRecv:
+    case HloOpcode::kInfeed:
+    case HloOpcode::kOutfeed:
+    case HloOpcode::kCrossReplicaSum:
+      return kBrown;
+    case HloOpcode::kCustomCall:
+    case HloOpcode::kWhile:
+    case HloOpcode::kCall:
+      return kDarkGreen;
+    case HloOpcode::kConstant:
+      LOG(FATAL) << "Constants don't get their own nodes in the graph.";
   }
-  return graph_body;
 }
 
-// DOT graphs accept a stylesheet as a URL.  So naturally, an inline stylesheet
-// is a data URI!
-//
-// We don't perform any escaping on this string, so be careful not to use double
-// quotes inside.
-static const char* dot_stylesheet = R"(
-data:text/css,
-@import url(https://fonts.googleapis.com/css?family=Roboto:400,700);
-svg text {
-  font-family: 'Roboto';
-  font-size: 12px;
+string HloDotDumper::GetInstructionNodeShape(const HloInstruction* instr) {
+  // Give while loops a different shape so they're easier to pick out.
+  switch (instr->opcode()) {
+    case HloOpcode::kWhile:
+      return "ellipse";
+    default:
+      return "rect";
+  }
 }
-)";
 
-string ComputationToDotGraph(const HloComputation& computation,
-                             const string& label, bool show_addresses,
-                             bool show_layouts,
-                             const HloExecutionProfile* hlo_execution_profile,
-                             const NodeFilter& filter) {
-  string graph_label = StrCat(label, "<br/>", computation.name());
-  if (hlo_execution_profile != nullptr) {
-    auto cycles = hlo_execution_profile->total_cycles_executed(computation);
-    Appendf(&graph_label, "<br/>total cycles = %lld (%s)", cycles,
-            tensorflow::strings::HumanReadableNum(cycles).c_str());
-  }
-  string graph = Printf(
-      R"(digraph G {
-rankdir=TB;
-compound=true;
-label=<<b>%s</b>>;
-labelloc=t;
-stylesheet="%s"
-)",
-      graph_label.c_str(), dot_stylesheet);
+string HloDotDumper::GetInstructionNodeLabel(const HloInstruction* instr) {
+  // If we have a parameter, put the param number in the name.
+  if (instr->opcode() == HloOpcode::kParameter) {
+    return Printf("<b>Parameter %lld</b><br/>%s", instr->parameter_number(),
+                  HtmlLikeStringSanitize(instr->name()));
+  }
 
-  // Dump the subcomputations of each instruction that's shown and doesn't have
-  // its operands omitted.  If an instruction has just one subcomputation and
-  // it's trivial, omit it: We'll display that subcomputation inlined into the
-  // instruction's node when we draw it.
-  std::unordered_set<const HloComputation*> computations_to_dump;
-  for (const auto& instr : computation.instructions()) {
-    if (!filter.Show(instr.get()) || filter.OmitOperands(instr.get())) {
-      continue;
-    }
-    if (instr->opcode() == HloOpcode::kFusion) {
-      computations_to_dump.insert(instr->fused_instructions_computation());
+  // The HLO instruction name contains usually the opcode, e.g. "%add.42" is
+  // an add instruction.  In this case we render just the name.
+  if (tensorflow::StringPiece(instr->name())
+          .starts_with(StrCat("%", HloOpcodeString(instr->opcode())))) {
+    return Printf("<b>%s</b>", HtmlLikeStringSanitize(instr->name()));
+  }
+
+  // If the name does not contain the opcode, render both.
+  return Printf("<b>%s</b><br/>%s",
+                HtmlLikeStringSanitize(instr->ExtendedOpcodeStr()),
+                HtmlLikeStringSanitize(instr->name()));
+}
+
+string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) {
+  string opcode_specific_info = [&]() -> string {
+    switch (instr->opcode()) {
+      case HloOpcode::kRng:
+        return RandomDistribution_Name(instr->random_distribution());
+      case HloOpcode::kConvolution:
+        return StrCat(
+            HtmlLikeStringSanitize(
+                instr->ConvolutionDimensionNumbersToString()),
+            "<br/>",
+            HtmlLikeStringSanitize(window_util::ToString(instr->window())));
+      case HloOpcode::kBroadcast:
+      case HloOpcode::kTranspose:
+      case HloOpcode::kReduce:
+        return Printf("dims={%s}", Join(instr->dimensions(), ","));
+      case HloOpcode::kGetTupleElement:
+        return Printf("index=%lld", instr->tuple_index());
+      case HloOpcode::kBatchNormTraining:
+      case HloOpcode::kBatchNormGrad:
+        return Printf("feature_index=%lld", instr->feature_index());
+      case HloOpcode::kCustomCall:
+        return Printf("custom_call_target=%s", instr->custom_call_target());
+      default:
+        return "";
     }
+  }();
 
-    const auto& subcomputations = instr->called_computations();
-    if (subcomputations.size() != 1 ||
-        !MatchTrivialComputation(subcomputations.front())) {
-      for (const HloComputation* computation : instr->called_computations()) {
-        computations_to_dump.insert(computation);
-      }
+  std::vector<string> lines;
+  if (!opcode_specific_info.empty()) {
+    lines.push_back(opcode_specific_info);
+  }
+  lines.push_back(ShapeUtil::HumanString(instr->shape()));
+  if (show_addresses_) {
+    lines.push_back(Printf("[%p]", instr));
+  }
+  if (show_layouts_ && LayoutUtil::HasLayout(instr->shape())) {
+    string layout_str;
+    if (ShapeUtil::IsTuple(instr->shape())) {
+      // For tuples, emit the full shape because the layout of a tuple is not
+      // represented in a single Layout field.
+      layout_str = ShapeUtil::HumanStringWithLayout(instr->shape());
+    } else {
+      layout_str = Join(instr->shape().layout().minor_to_major(), ",");
+    }
+    lines.push_back(Printf("layout={%s}", layout_str));
+  }
+  if (profile_ != nullptr) {
+    double hlo_cycles_executed = profile_->GetProfileResult(*instr);
+    double total_cycles_executed =
+        profile_->total_cycles_executed(*instr->parent());
+    if (hlo_cycles_executed > 0 && total_cycles_executed > 0) {
+      lines.push_back(
+          Printf("%% of cycles executed=%.2f",
+                 100 * hlo_cycles_executed / total_cycles_executed));
     }
   }
+  return Join(lines, "<br/>");
+}
 
-  // Emit embedded computations as subgraph clusters.
-  std::vector<string> intercomputation_edges;
-  for (const HloComputation* embedded :
-       computation.MakeEmbeddedComputationsList()) {
-    if (!computations_to_dump.count(embedded)) {
+string HloDotDumper::GetInstructionIncomingEdges(const HloInstruction* instr,
+                                                 const NodeFilter& filter) {
+  std::vector<string> edges;
+  for (int64 i = 0; i < instr->operand_count(); ++i) {
+    const HloInstruction* operand = instr->operand(i);
+    if (!filter.Show(operand) || operand->opcode() == HloOpcode::kConstant) {
       continue;
     }
-    // Don't pass our filter down into the subcomputation -- always render the
-    // whole thing.
-    string graph_body = InstructionSequenceGraph(
-        embedded->instructions(), show_addresses, show_layouts,
-        &intercomputation_edges, hlo_execution_profile, NodeFilter());
-    Appendf(&graph,
-            "subgraph cluster_%s "
-            "{\nstyle=rounded;label=<<b>%s</b>>;labelloc=t;\n%s}\n",
-            ComputationId(embedded).c_str(), embedded->name().c_str(),
-            graph_body.c_str());
-  }
-  StrAppend(&graph,
-            InstructionSequenceGraph(computation.instructions(), show_addresses,
-                                     show_layouts, &intercomputation_edges,
-                                     hlo_execution_profile, filter));
-
-  // Edges between computations (subgraph clusters) must be emitted last for the
-  // graph to be rendered properly for some reason.
-  StrAppend(&graph, Join(intercomputation_edges, "\n"), "}\n");
-
-  return graph;
+    string edge =
+        Printf("%s -> %s", InstructionId(operand), InstructionId(instr));
+    if (instr->operand_count() > 1) {
+      Appendf(&edge, R"( [headlabel="%lld",labeldistance=2])", i);
+    }
+    StrAppend(&edge, ";");
+    edges.push_back(edge);
+  }
+  return Join(edges, "\n");
+}
+
+string HloDotDumper::GetInstructionTrivialComputationStr(
+    const HloInstruction* instr) {
+  // called_computations() on a fusion node "inherits" any called computations
+  // of the fused root, which isn't what we want.  Just ignore fusion nodes
+  // here; they're handled separately.
+  if (instr->opcode() == HloOpcode::kFusion) {
+    return "";
+  }
+
+  const auto& subcomps = instr->called_computations();
+  if (subcomps.size() != 1) {
+    return "";
+  }
+  optional<string> computation_type = MatchTrivialComputation(subcomps.front());
+  if (!computation_type) {
+    return "";
+  }
+  return Printf("Subcomputation: <b>%s</b>",
+                HtmlLikeStringSanitize(*computation_type));
 }
 
 tensorflow::mutex& RendererMutex() {
@@ -886,10 +927,12 @@ string DumpGraph(const HloComputation& computation, const string& label,
     graph_url = FileGraphRenderer().RenderGraph(
         graph, GraphRendererInterface::TF_GRAPHDEF, debug_options);
   } else {
-    graph = ComputationToDotGraph(computation, label,
-                                  debug_options.xla_hlo_graph_addresses(),
-                                  debug_options.xla_hlo_graph_layout(),
-                                  hlo_execution_profile, NodeFilter());
+    graph =
+        HloDotDumper(&computation, label,
+                     /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
+                     /*show_layouts=*/debug_options.xla_hlo_graph_layout(),
+                     hlo_execution_profile, NodeFilter())
+            .Dump();
     graph_url = GetGraphRenderer()->RenderGraph(
         graph, GraphRendererInterface::DOT_GRAPH, debug_options);
   }
@@ -903,11 +946,12 @@ string DumpNeighborhoodAround(const HloInstruction& node, int radius) {
   string label =
       StrCat("Neighborhood of ", radius, " nodes around ", node.name());
   NodeFilter filter = MakeNodeFilter(&node, radius);
-  string graph = ComputationToDotGraph(
-      *node.parent(), label,
-      /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
-      /*show_layouts=*/debug_options.xla_hlo_graph_layout(),
-      /*hlo_execution_profile=*/nullptr, filter);
+  string graph =
+      HloDotDumper(node.parent(), label,
+                   /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
+                   /*show_layouts=*/debug_options.xla_hlo_graph_layout(),
+                   /*profile=*/nullptr, filter)
+          .Dump();
   return GetGraphRenderer()->RenderGraph(
       graph, GraphRendererInterface::DOT_GRAPH, debug_options);
 }
-- 
GitLab


From 4e92e1e7cec431279e0d9b159ac5f7165d17e325 Mon Sep 17 00:00:00 2001
From: resec <resec0109@gmail.com>
Date: Tue, 25 Jul 2017 16:10:36 +0800
Subject: [PATCH 1284/1375] Fix web link to tool_developers guide

---
 tensorflow/tools/graph_transforms/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index 18d1523070..66e0ba60eb 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -764,7 +764,7 @@ heart, all of the transforms take in a valid GraphDef, make some changes, and
 output a new GraphDef. Each GraphDef is just a list of NodeDefs, each defining
 one node in the graph and its connections. You can find more information on the
 format at [this guide to TensorFlow model
-files](https://www.tensorflow.org/versions/master/how_tos/tool_developers/index.html),
+files](https://www.tensorflow.org/versions/master/extend/tool_developers/index.html),
 but for a simple example take a look at
 [tensorflow/tools/graph_transforms/rename_op.cc](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms/rename_op.cc),
 which implements the [rename_op](#rename_op) transform:
-- 
GitLab


From e07ddbe64c944f1ea69f23687803d70d450f2ac3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 05:19:15 -0700
Subject: [PATCH 1285/1375] Use correct order of arguments in call of
 valid_bitcast_callback_.

There are platforms where bitcasts are not symmetric. I.e. there are shapes A and B so that A->B is a bitcast, but B->A not. So we have to consider the correct order when calling valid_bitcast_callback_.

PiperOrigin-RevId: 163058665
---
 .../compiler/xla/service/algebraic_simplifier.cc    |  6 +++---
 .../compiler/xla/service/algebraic_simplifier.h     | 13 +++++++------
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index b351861425..4837402c15 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -1488,9 +1488,9 @@ Status AlgebraicSimplifierVisitor::HandleConvolution(
   // We cannot insert bitcasts if the layouts will not be compatible.
   // TODO(b/33178038): Consider inserting a transpose if a bitcast would be
   // invalid.
-  if (!valid_bitcast_callback_(lhs->shape(), input_shape) ||
-      !valid_bitcast_callback_(rhs->shape(), new_filter_shape) ||
-      !valid_bitcast_callback_(dot_output_shape, convolution_shape)) {
+  if (!valid_bitcast_callback_(input_shape, lhs->shape()) ||
+      !valid_bitcast_callback_(new_filter_shape, rhs->shape()) ||
+      !valid_bitcast_callback_(convolution_shape, dot_output_shape)) {
     return Status::OK();
   }
 
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.h b/tensorflow/compiler/xla/service/algebraic_simplifier.h
index f8919f0caa..4295a3227a 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.h
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.h
@@ -26,12 +26,13 @@ namespace xla {
 // A pass which performs AlgebraicSimplications.
 class AlgebraicSimplifier : public HloPassInterface {
  public:
-  // Given two shapes, determines if it is valid to bitcast between them after
-  // considering platform dependent effects on layout like alignment
-  // restrictions.
-  // Precondition: the two shapes have layouts, the same number of
-  // elements and ShapeUtil::ReshapeIsBitcast returns true.
-  using ValidBitcastCallback = std::function<bool(const Shape&, const Shape&)>;
+  // Given shapes 'from_shape' and 'to_shape', determines if it is valid to
+  // bitcast from 'from_shape' to 'to_shape' after considering platform
+  // dependent effects on layout like alignment restrictions. Precondition: the
+  // two shapes have layouts, the same number of elements and
+  // ShapeUtil::ReshapeIsBitcast returns true.
+  using ValidBitcastCallback =
+      std::function<bool(const Shape& from_shape, const Shape& to_shape)>;
 
   // If is_layout_sensitive is true, then the simplifier preserves layout during
   // transformation. Otherwise, layout is ignored. If valid_bitcast_callback
-- 
GitLab


From 18fef3435a3512e26d273bc95af24f08d59792f4 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Tue, 25 Jul 2017 06:58:00 -0700
Subject: [PATCH 1286/1375] Two improvements to pip.sh

1. Distinguish between passed and skipped tests.
2. Allow skipping the smoke test of tensorflow install in clean virtualenv with NO_TEST_ON_INSTALL=1

PiperOrigin-RevId: 163065599
---
 .../tools/ci_build/builds/builds_common.sh    |  1 +
 tensorflow/tools/ci_build/builds/pip.sh       | 27 ++++++++++++++++---
 2 files changed, 25 insertions(+), 3 deletions(-)

diff --git a/tensorflow/tools/ci_build/builds/builds_common.sh b/tensorflow/tools/ci_build/builds/builds_common.sh
index 9323a96e74..e3b58d038a 100644
--- a/tensorflow/tools/ci_build/builds/builds_common.sh
+++ b/tensorflow/tools/ci_build/builds/builds_common.sh
@@ -17,6 +17,7 @@
 # Common Bash functions used by build scripts
 
 COLOR_NC='\033[0m'
+COLOR_LIGHT_GRAY='\033[0;37m'
 COLOR_GREEN='\033[0;32m'
 COLOR_RED='\033[0;31m'
 
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index db011a6bad..112dab3a73 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -73,6 +73,9 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 source "${SCRIPT_DIR}/builds_common.sh"
 
 
+SKIP_RETURN_CODE=112
+
+
 # Get the command line arguments
 CONTAINER_TYPE=$( echo "$1" | tr '[:upper:]' '[:lower:]' )
 shift
@@ -310,6 +313,13 @@ create_activate_virtualenv_and_install_tensorflow() {
 # Smoke test of tensorflow install in clean virtualenv
 ################################################################################
 do_clean_virtualenv_smoke_test() {
+  if [[ -n "${NO_TEST_ON_INSTALL}" ]] &&
+       [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
+    echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
+    echo "  Skipping smoke test of tensorflow install in clean virtualenv"
+    return ${SKIP_RETURN_CODE}
+  fi
+
   CLEAN_VENV_DIR="${PIP_TEST_ROOT}/venv_clean"
   create_activate_virtualenv_and_install_tensorflow --clean \
     "${CLEAN_VENV_DIR}" "${WHL_PATH}"
@@ -361,6 +371,7 @@ do_virtualenv_pip_test() {
      [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
     echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
     echo "  Skipping ALL Python unit tests on install"
+    return ${SKIP_RETURN_CODE}
   else
     # Call run_pip_tests.sh to perform test-on-install
     "${SCRIPT_DIR}/run_pip_tests.sh" --virtualenv ${GPU_FLAG} ${MAC_FLAG}
@@ -379,6 +390,7 @@ do_virtualenv_oss_serial_pip_test() {
      [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
     echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
     echo "  Skipping Python unit tests on install tagged with oss_serial"
+    return ${SKIP_RETURN_CODE}
   else
     # Call run_pip_tests.sh to perform test-on-install
     "${SCRIPT_DIR}/run_pip_tests.sh" \
@@ -402,6 +414,7 @@ do_test_user_ops() {
     fi
   else
     echo "Skipping user-op test-on-install due to DO_TEST_USER_OPS = ${DO_TEST_USER_OPS}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -424,6 +437,7 @@ do_test_tfdbg_binaries() {
     popd
   else
     echo "Skipping test of tfdbg binaries due to DO_TEST_TFDBG_BINARIES = ${DO_TEST_TFDBG_BINARIES}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -439,6 +453,7 @@ do_test_tutorials() {
     fi
   else
     echo "Skipping tutorial tests-on-install due to DO_TEST_TUTORIALS = ${DO_TEST_TUTORIALS}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -455,6 +470,7 @@ do_ffmpeg_integration_test() {
     fi
   else
     echo "Skipping ffmpeg integration due to DO_INTEGRATION_TESTS = ${DO_INTEGRATION_TESTS}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -468,6 +484,7 @@ PIP_TASKS_DESC=("Smoke test of pip install in clean virtualenv" "PIP tests in vi
 COUNTER=0
 FAIL_COUNTER=0
 PASS_COUNTER=0
+SKIP_COUNTER=0
 while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
   INDEX=COUNTER
   ((INDEX++))
@@ -480,7 +497,9 @@ while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
   ${PIP_TASKS[COUNTER]}
   RESULT=$?
 
-  if [[ ${RESULT} != "0" ]]; then
+  if [[ ${RESULT} == ${SKIP_RETURN_CODE} ]]; then
+    ((SKIP_COUNTER++))
+  elif [[ ${RESULT} != "0" ]]; then
     ((FAIL_COUNTER++))
   else
     ((PASS_COUNTER++))
@@ -503,7 +522,9 @@ while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
   ((INDEX++))
 
   echo "${INDEX}. ${PIP_TASKS[COUNTER]}: ${PIP_TASKS_DESC[COUNTER]}"
-  if [[ ${STEP_EXIT_CODES[COUNTER]} == "0" ]]; then
+  if [[ ${STEP_EXIT_CODES[COUNTER]} == ${SKIP_RETURN_CODE} ]]; then
+    printf "  ${COLOR_LIGHT_GRAY}SKIP${COLOR_NC}\n"
+  elif [[ ${STEP_EXIT_CODES[COUNTER]} == "0" ]]; then
     printf "  ${COLOR_GREEN}PASS${COLOR_NC}\n"
   else
     printf "  ${COLOR_RED}FAIL${COLOR_NC}\n"
@@ -513,7 +534,7 @@ while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
 done
 
 echo
-echo "${FAIL_COUNTER} failed; ${PASS_COUNTER} passed."
+echo "${SKIP_COUNTER} skipped; ${FAIL_COUNTER} failed; ${PASS_COUNTER} passed."
 
 echo
 if [[ ${FAIL_COUNTER} == "0" ]]; then
-- 
GitLab


From d50cad0e182c12177169b8a4f4b7e8d48b1babe3 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Tue, 25 Jul 2017 16:56:47 +0200
Subject: [PATCH 1287/1375] Disable failing tests in Windows Bazel build
 (#11710)

---
 tensorflow/python/debug/BUILD | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index aad98c1bd5..ea2ab578b3 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -798,6 +798,7 @@ cuda_py_test(
         "//tensorflow/python:variables",
     ],
     tags = [
+        "no_windows",
         "nomac",  # TODO(cais): Install of futures and grpcio on all macs.
         "notsan",
         "oss_serial",
@@ -826,6 +827,7 @@ cuda_py_test(
     data = ["//tensorflow/tools/dist_test/server:grpc_tensorflow_server"],
     tags = [
         "no_oss",  # Incompatible with bazel_pip.
+        "no_windows",
         "nomac",  # TODO(cais): Install of futures and grpcio on all macs.
         "notsan",
     ],
-- 
GitLab


From 4393a1e0299c149e61cd467bc9a0b20daa3cae78 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 08:22:57 -0700
Subject: [PATCH 1288/1375] [XLA] Update StatusOr implementation to use more
 nuanced type traits.

Previously we would evaluate the is_copy_constructible trait before template
parameters were fully defined; e.g. StatusOr<ThingIAmDefiningRightNow>,
which could lead to surprising effects.

Also, previously it was not possible to provide an error status to a
StatusOr<T> where T was not default-constructible.

PiperOrigin-RevId: 163073057
---
 tensorflow/compiler/xla/BUILD                |   5 +-
 tensorflow/compiler/xla/statusor.cc          |  22 +-
 tensorflow/compiler/xla/statusor.h           | 323 ++++++++++---------
 tensorflow/compiler/xla/statusor_internals.h | 245 ++++++++++++++
 tensorflow/compiler/xla/statusor_test.cc     |  22 +-
 5 files changed, 446 insertions(+), 171 deletions(-)
 create mode 100644 tensorflow/compiler/xla/statusor_internals.h

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 5eef45b11d..e0a03a78f1 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -132,7 +132,10 @@ cc_library(
 cc_library(
     name = "statusor",
     srcs = ["statusor.cc"],
-    hdrs = ["statusor.h"],
+    hdrs = [
+        "statusor.h",
+        "statusor_internals.h",
+    ],
     visibility = ["//visibility:public"],
     deps = [
         ":status",
diff --git a/tensorflow/compiler/xla/statusor.cc b/tensorflow/compiler/xla/statusor.cc
index 36f08fc99f..72ab67ff81 100644
--- a/tensorflow/compiler/xla/statusor.cc
+++ b/tensorflow/compiler/xla/statusor.cc
@@ -19,28 +19,20 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
-namespace internal {
+namespace internal_statusor {
 
-Status StatusOrHelper::HandleInvalidStatusCtorArg() {
+void Helper::HandleInvalidStatusCtorArg(Status* status) {
   const char* kMessage =
-      "Status::OK is not a valid constructor argument to StatusOr<T>";
+      "An OK status is not a valid constructor argument to StatusOr<T>";
   LOG(ERROR) << kMessage;
-  // In optimized builds, we will fall back to tensorflow::error::INTERNAL.
-  return Status(tensorflow::error::INTERNAL, kMessage);
+  // Fall back to tensorflow::error::INTERNAL.
+  *status = ::tensorflow::errors::Internal(kMessage);
 }
 
-Status StatusOrHelper::HandleNullObjectCtorArg() {
-  const char* kMessage =
-      "NULL is not a valid constructor argument to StatusOr<T*>";
-  LOG(ERROR) << kMessage;
-  // In optimized builds, we will fall back to tensorflow::error::INTERNAL.
-  return Status(tensorflow::error::INTERNAL, kMessage);
-}
-
-void StatusOrHelper::Crash(const Status& status) {
+void Helper::Crash(const Status& status) {
   LOG(FATAL) << "Attempting to fetch value instead of handling error "
              << status;
 }
 
-}  // namespace internal
+}  // namespace internal_statusor
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/statusor.h b/tensorflow/compiler/xla/statusor.h
index d8cd736238..92bcfa0f44 100644
--- a/tensorflow/compiler/xla/statusor.h
+++ b/tensorflow/compiler/xla/statusor.h
@@ -72,216 +72,233 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_STATUSOR_H_
 
 #include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/compiler/xla/statusor_internals.h"
 #include "tensorflow/core/platform/macros.h"
 
 namespace xla {
 
 #if defined(__clang__)
 // Only clang supports warn_unused_result as a type annotation.
-template <typename T, bool CopyConstructible>
+template <typename T>
 class TF_MUST_USE_RESULT StatusOr;
 #endif
 
-template <typename T,
-          bool CopyConstructible = std::is_copy_constructible<T>::value>
-class StatusOr {
-  template <typename U, bool UC>
+template <typename T>
+class StatusOr : private internal_statusor::StatusOrData<T>,
+                 private internal_statusor::TraitsBase<
+                     std::is_copy_constructible<T>::value,
+                     std::is_move_constructible<T>::value> {
+  template <typename U>
   friend class StatusOr;
 
+  typedef internal_statusor::StatusOrData<T> Base;
+
  public:
   typedef T element_type;
 
-  // Construct a new StatusOr with Status::UNKNOWN status
-  StatusOr();
+  // Constructs a new StatusOr with Status::UNKNOWN status.  This is marked
+  // 'explicit' to try to catch cases like 'return {};', where people think
+  // StatusOr<std::vector<int>> will be initialized with an empty vector,
+  // instead of a Status::UNKNOWN status.
+  explicit StatusOr();
+
+  // StatusOr<T> will be copy constructuble/assignable if T is copy
+  // constructible.
+  StatusOr(const StatusOr&) = default;
+  StatusOr& operator=(const StatusOr&) = default;
+
+  // StatusOr<T> will be move constructuble/assignable if T is move
+  // constructible.
+  StatusOr(StatusOr&&) = default;
+  StatusOr& operator=(StatusOr&&) = default;
+
+  // Conversion copy/move constructor, T must be convertible from U.
+  // TODO(b/62186717): These should not participate in overload resolution if U
+  // is not convertible to T.
+  template <typename U>
+  StatusOr(const StatusOr<U>& other);
+  template <typename U>
+  StatusOr(StatusOr<U>&& other);
 
-  // Construct a new StatusOr with the given non-ok status. After calling
-  // this constructor, calls to ValueOrDie() will CHECK-fail.
-  //
-  // NOTE: Not explicit - we want to use StatusOr<T> as a return
-  // value, so it is convenient and sensible to be able to do 'return
-  // Status()' when the return type is StatusOr<T>.
-  //
-  // REQUIRES: status != Status::OK. This requirement is DCHECKed.
-  // In optimized builds, passing Status::OK here will have the effect
-  // of passing tensorflow::error::INTERNAL as a fallback.
-  StatusOr(Status status);              // NOLINT
+  // Conversion copy/move assignment operator, T must be convertible from U.
+  template <typename U>
+  StatusOr& operator=(const StatusOr<U>& other);
+  template <typename U>
+  StatusOr& operator=(StatusOr<U>&& other);
 
-  // Construct a new StatusOr with the given value. If T is a plain pointer,
-  // value must not be NULL. After calling this constructor, calls to
-  // ValueOrDie() will succeed, and calls to status() will return OK.
+  // Constructs a new StatusOr with the given value. After calling this
+  // constructor, calls to ValueOrDie() will succeed, and calls to status() will
+  // return OK.
   //
   // NOTE: Not explicit - we want to use StatusOr<T> as a return type
   // so it is convenient and sensible to be able to do 'return T()'
   // when the return type is StatusOr<T>.
   //
-  // REQUIRES: if T is a plain pointer, value != NULL. This requirement is
-  // DCHECKed. In optimized builds, passing a NULL pointer here will have
-  // the effect of passing tensorflow::error::INTERNAL as a fallback.
-  StatusOr(const T& value);  // NOLINT
-
-  // Copy constructor.
-  StatusOr(const StatusOr& other) = default;
-
-  // Conversion copy constructor, T must be copy constructible from U
-  template <typename U>
-  StatusOr(const StatusOr<U>& other);
-
-  // Assignment operator.
-  StatusOr& operator=(const StatusOr& other) = default;
+  // REQUIRES: T is copy constructible.
+  StatusOr(const T& value);
 
-  // Conversion assignment operator, T must be assignable from U
-  template <typename U>
-  StatusOr& operator=(const StatusOr<U>& other);
+  // Constructs a new StatusOr with the given non-ok status. After calling
+  // this constructor, calls to ValueOrDie() will CHECK-fail.
+  //
+  // NOTE: Not explicit - we want to use StatusOr<T> as a return
+  // value, so it is convenient and sensible to be able to do 'return
+  // Status()' when the return type is StatusOr<T>.
+  //
+  // REQUIRES: !status.ok(). This requirement is DCHECKed.
+  // In optimized builds, passing Status::OK() here will have the effect
+  // of passing tensorflow::error::INTERNAL as a fallback.
+  StatusOr(const Status& status);
+  StatusOr& operator=(const Status& status);
 
-  // Move constructor and move-assignment operator.
-  StatusOr(StatusOr&& other) = default;
-  StatusOr& operator=(StatusOr&& other) = default;
+  // TODO(b/62186997): Add operator=(T) overloads.
 
-  // Rvalue-reference overloads of the other constructors and assignment
-  // operators, to support move-only types and avoid unnecessary copying.
+  // Similar to the `const T&` overload.
   //
-  // Implementation note: we could avoid all these rvalue-reference overloads
-  // if the existing lvalue-reference overloads took their arguments by value
-  // instead. I think this would also let us omit the conversion assignment
-  // operator altogether, since we'd get the same functionality for free
-  // from the implicit conversion constructor and ordinary assignment.
-  // However, this could result in extra copy operations unless we use
-  // std::move to avoid them, and we can't use std::move because this code
-  // needs to be portable to C++03.
-  StatusOr(T&& value);  // NOLINT
-  template <typename U>
-  StatusOr(StatusOr<U>&& other);
+  // REQUIRES: T is move constructible.
+  StatusOr(T&& value);
 
-  // Returns a reference to our status. If this contains a T, then
-  // returns Status::OK.
-  const Status& status() const { return status_; }
+  // RValue versions of the operations declared above.
+  StatusOr(Status&& status);
+  StatusOr& operator=(Status&& status);
 
   // Returns this->status().ok()
-  bool ok() const { return status_.ok(); }
+  bool ok() const { return this->status_.ok(); }
+
+  // Returns a reference to our status. If this contains a T, then
+  // returns Status::OK().
+  const Status& status() const &;
+  Status status() &&;
 
   // Returns a reference to our current value, or CHECK-fails if !this->ok().
-  const T& ValueOrDie() const;
-  T& ValueOrDie();
+  //
+  // Note: for value types that are cheap to copy, prefer simple code:
+  //
+  //   T value = statusor.ValueOrDie();
+  //
+  // Otherwise, if the value type is expensive to copy, but can be left
+  // in the StatusOr, simply assign to a reference:
+  //
+  //   T& value = statusor.ValueOrDie();  // or `const T&`
+  //
+  // Otherwise, if the value type supports an efficient move, it can be
+  // used as follows:
+  //
+  //   T value = std::move(statusor).ValueOrDie();
+  //
+  // The std::move on statusor instead of on the whole expression enables
+  // warnings about possible uses of the statusor object after the move.
+  // C++ style guide waiver for ref-qualified overloads granted in cl/143176389
+  // See go/ref-qualifiers for more details on such overloads.
+  const T& ValueOrDie() const &;
+  T& ValueOrDie() &;
+  const T&& ValueOrDie() const &&;
+  T&& ValueOrDie() &&;
 
-  // Moves our current value out of this object and returns it, or CHECK-fails
-  // if !this->ok().
-  // Use of this method is discouraged; prefer std::move(statusor.ValueOrDie())
-  // instead.
   T ConsumeValueOrDie() { return std::move(ValueOrDie()); }
 
- private:
-  Status status_;
-  T value_;
-};
-
-// Partial specialization for when T is not copy-constructible. This uses all
-// methods from the core implementation, but removes copy assignment and copy
-// construction.
-template <typename T>
-class StatusOr<T, false> : public StatusOr<T, true> {
- public:
-  // Remove copies.
-  StatusOr(const StatusOr& other) = delete;
-  StatusOr& operator=(const StatusOr& other) = delete;
-  template <typename U>
-  StatusOr(const StatusOr<U>& other) = delete;
-  StatusOr(const T& value) = delete;
-
-  // Use the superclass version for other constructors and operators.
-  StatusOr() = default;
-  StatusOr(StatusOr&& other) = default;
-  StatusOr& operator=(StatusOr&& other) = default;
-  StatusOr(T&& value)  // NOLINT
-      : StatusOr<T, true>::StatusOr(std::move(value)) {}
-  StatusOr(Status status)  // NOLINT
-      : StatusOr<T, true>::StatusOr(std::move(status)) {}
-  template <typename U>
-  StatusOr(StatusOr<U>&& other)  // NOLINT
-      : StatusOr<T, true>::StatusOr(std::move(other)) {}
+  // Ignores any errors. This method does nothing except potentially suppress
+  // complaints from any tools that are checking that errors are not dropped on
+  // the floor.
+  void IgnoreError() const;
 };
 
 ////////////////////////////////////////////////////////////////////////////////
 // Implementation details for StatusOr<T>
 
-namespace internal {
+template <typename T>
+StatusOr<T>::StatusOr() : Base(Status(tensorflow::error::UNKNOWN, "")) {}
 
-class StatusOrHelper {
- public:
-  // Move type-agnostic error handling to the .cc.
-  static Status HandleInvalidStatusCtorArg();
-  static Status HandleNullObjectCtorArg();
-  static void Crash(const Status& status);
-
-  // Customized behavior for StatusOr<T> vs. StatusOr<T*>
-  template <typename T>
-  struct Specialize;
-};
+template <typename T>
+StatusOr<T>::StatusOr(const T& value) : Base(value) {}
 
 template <typename T>
-struct StatusOrHelper::Specialize {
-  // For non-pointer T, a reference can never be NULL.
-  static inline bool IsValueNull(const T& t) { return false; }
-};
+StatusOr<T>::StatusOr(const Status& status) : Base(status) {}
 
 template <typename T>
-struct StatusOrHelper::Specialize<T*> {
-  static inline bool IsValueNull(const T* t) { return t == NULL; }
-};
+StatusOr<T>& StatusOr<T>::operator=(const Status& status) {
+  this->Assign(status);
+  return *this;
+}
 
-}  // namespace internal
+template <typename T>
+StatusOr<T>::StatusOr(T&& value) : Base(std::move(value)) {}
 
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr()
-    : status_(tensorflow::error::UNKNOWN, "") {}
+template <typename T>
+StatusOr<T>::StatusOr(Status&& status) : Base(std::move(status)) {}
 
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr(Status status)
-    : status_(std::move(status)) {
-  if (status_.ok()) {
-    status_ = internal::StatusOrHelper::HandleInvalidStatusCtorArg();
-  }
+template <typename T>
+StatusOr<T>& StatusOr<T>::operator=(Status&& status) {
+  this->Assign(std::move(status));
+  return *this;
 }
 
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr(const T& value)
-    : value_(value) {
-  if (internal::StatusOrHelper::Specialize<T>::IsValueNull(value)) {
-    status_ = internal::StatusOrHelper::HandleNullObjectCtorArg();
-  }
-}
+template <typename T>
+template <typename U>
+inline StatusOr<T>::StatusOr(const StatusOr<U>& other)
+    : Base(static_cast<const typename StatusOr<U>::Base&>(other)) {}
 
-template <typename T, bool CopyConstructible>
+template <typename T>
 template <typename U>
-inline StatusOr<T, CopyConstructible>::StatusOr(const StatusOr<U>& other)
-    : status_(other.status_), value_(other.value_) {}
-
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr(T&& value)
-    : value_(std::move(value)) {
-  if (internal::StatusOrHelper::Specialize<T>::IsValueNull(value_)) {
-    status_ = internal::StatusOrHelper::HandleNullObjectCtorArg();
-  }
+inline StatusOr<T>& StatusOr<T>::operator=(const StatusOr<U>& other) {
+  if (other.ok())
+    this->Assign(other.ValueOrDie());
+  else
+    this->Assign(other.status());
+  return *this;
 }
 
-template <typename T, bool CopyConstructible>
+template <typename T>
 template <typename U>
-inline StatusOr<T, CopyConstructible>::StatusOr(StatusOr<U>&& other)
-    : status_(std::move(other.status_)), value_(std::move(other.value_)) {}
+inline StatusOr<T>::StatusOr(StatusOr<U>&& other)
+    : Base(static_cast<typename StatusOr<U>::Base&&>(other)) {}
 
-template <typename T, bool CopyConstructible>
-inline const T& StatusOr<T, CopyConstructible>::ValueOrDie() const {
-  if (!ok()) {
-    internal::StatusOrHelper::Crash(status());
+template <typename T>
+template <typename U>
+inline StatusOr<T>& StatusOr<T>::operator=(StatusOr<U>&& other) {
+  if (other.ok()) {
+    this->Assign(std::move(other).ValueOrDie());
+  } else {
+    this->Assign(std::move(other).status());
   }
-  return value_;
+  return *this;
 }
 
-template <typename T, bool CopyConstructible>
-inline T& StatusOr<T, CopyConstructible>::ValueOrDie() {
-  if (!status_.ok()) {
-    internal::StatusOrHelper::Crash(status());
-  }
-  return value_;
+template <typename T>
+const Status& StatusOr<T>::status() const & {
+  return this->status_;
+}
+template <typename T>
+Status StatusOr<T>::status() && {
+  return ok() ? Status::OK() : std::move(this->status_);
+}
+
+template <typename T>
+const T& StatusOr<T>::ValueOrDie() const & {
+  this->EnsureOk();
+  return this->data_;
+}
+
+template <typename T>
+T& StatusOr<T>::ValueOrDie() & {
+  this->EnsureOk();
+  return this->data_;
+}
+
+template <typename T>
+const T&& StatusOr<T>::ValueOrDie() const && {
+  this->EnsureOk();
+  return std::move(this->data_);
+}
+
+template <typename T>
+T&& StatusOr<T>::ValueOrDie() && {
+  this->EnsureOk();
+  return std::move(this->data_);
+}
+
+template <typename T>
+void StatusOr<T>::IgnoreError() const {
+  // no-op
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/statusor_internals.h b/tensorflow/compiler/xla/statusor_internals.h
new file mode 100644
index 0000000000..a2fda5bb3c
--- /dev/null
+++ b/tensorflow/compiler/xla/statusor_internals.h
@@ -0,0 +1,245 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_
+#define THIRD_PARTY_TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_
+
+#include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+namespace internal_statusor {
+
+class Helper {
+ public:
+  // Move type-agnostic error handling to the .cc.
+  static void HandleInvalidStatusCtorArg(Status*);
+  TF_ATTRIBUTE_NORETURN static void Crash(const Status& status);
+};
+
+// Construct an instance of T in `p` through placement new, passing Args... to
+// the constructor.
+// This abstraction is here mostly for the gcc performance fix.
+template <typename T, typename... Args>
+void PlacementNew(void* p, Args&&... args) {
+#if defined(__GNUC__) && !defined(__clang__)
+  // Teach gcc that 'p' cannot be null, fixing code size issues.
+  if (p == nullptr) __builtin_unreachable();
+#endif
+  new (p) T(std::forward<Args>(args)...);
+}
+
+// Helper base class to hold the data and all operations.
+// We move all this to a base class to allow mixing with the appropriate
+// TraitsBase specialization.
+template <typename T>
+class StatusOrData {
+  template <typename U>
+  friend class StatusOrData;
+
+ public:
+  StatusOrData() = delete;
+
+  StatusOrData(const StatusOrData& other) {
+    if (other.ok()) {
+      MakeValue(other.data_);
+      MakeStatus();
+    } else {
+      MakeStatus(other.status_);
+    }
+  }
+
+  StatusOrData(StatusOrData&& other) noexcept {
+    if (other.ok()) {
+      MakeValue(std::move(other.data_));
+      MakeStatus();
+    } else {
+      MakeStatus(std::move(other.status_));
+    }
+  }
+
+  template <typename U>
+  StatusOrData(const StatusOrData<U>& other) {
+    if (other.ok()) {
+      MakeValue(other.data_);
+      MakeStatus();
+    } else {
+      MakeStatus(other.status_);
+    }
+  }
+
+  template <typename U>
+  StatusOrData(StatusOrData<U>&& other) {
+    if (other.ok()) {
+      MakeValue(std::move(other.data_));
+      MakeStatus();
+    } else {
+      MakeStatus(std::move(other.status_));
+    }
+  }
+
+  explicit StatusOrData(const T& value) : data_(value) { MakeStatus(); }
+  explicit StatusOrData(T&& value) : data_(std::move(value)) { MakeStatus(); }
+
+  explicit StatusOrData(const Status& status) : status_(status) {
+    EnsureNotOk();
+  }
+  explicit StatusOrData(Status&& status) : status_(std::move(status)) {
+    EnsureNotOk();
+  }
+
+  StatusOrData& operator=(const StatusOrData& other) {
+    if (this == &other) return *this;
+    if (other.ok())
+      Assign(other.data_);
+    else
+      Assign(other.status_);
+    return *this;
+  }
+
+  StatusOrData& operator=(StatusOrData&& other) {
+    if (this == &other) return *this;
+    if (other.ok())
+      Assign(std::move(other.data_));
+    else
+      Assign(std::move(other.status_));
+    return *this;
+  }
+
+  ~StatusOrData() {
+    if (ok()) {
+      status_.~Status();
+      data_.~T();
+    } else {
+      status_.~Status();
+    }
+  }
+
+  void Assign(const T& value) {
+    if (ok()) {
+      data_.~T();
+      MakeValue(value);
+    } else {
+      MakeValue(value);
+      status_ = Status::OK();
+    }
+  }
+
+  void Assign(T&& value) {
+    if (ok()) {
+      data_.~T();
+      MakeValue(std::move(value));
+    } else {
+      MakeValue(std::move(value));
+      status_ = Status::OK();
+    }
+  }
+
+  void Assign(const Status& status) {
+    Clear();
+    status_ = status;
+    EnsureNotOk();
+  }
+
+  void Assign(Status&& status) {
+    Clear();
+    status_ = std::move(status);
+    EnsureNotOk();
+  }
+
+  bool ok() const { return status_.ok(); }
+
+ protected:
+  // status_ will always be active after the constructor.
+  // We make it a union to be able to initialize exactly how we need without
+  // waste.
+  // Eg. in the copy constructor we use the default constructor of Status in
+  // the ok() path to avoid an extra Ref call.
+  union {
+    Status status_;
+  };
+
+  // data_ is active iff status_.ok()==true
+  struct Dummy {};
+  union {
+    // When T is const, we need some non-const object we can cast to void* for
+    // the placement new. dummy_ is that object.
+    Dummy dummy_;
+    T data_;
+  };
+
+  void Clear() {
+    if (ok()) data_.~T();
+  }
+
+  void EnsureOk() const {
+    if (!ok()) Helper::Crash(status_);
+  }
+
+  void EnsureNotOk() {
+    if (ok()) Helper::HandleInvalidStatusCtorArg(&status_);
+  }
+
+  // Construct the value (ie. data_) through placement new with the passed
+  // argument.
+  template <typename Arg>
+  void MakeValue(Arg&& arg) {
+    internal_statusor::PlacementNew<T>(&dummy_, std::forward<Arg>(arg));
+  }
+
+  // Construct the status (ie. status_) through placement new with the passed
+  // argument.
+  template <typename... Args>
+  void MakeStatus(Args&&... args) {
+    internal_statusor::PlacementNew<Status>(&status_,
+                                            std::forward<Args>(args)...);
+  }
+};
+
+// Helper base class to allow implicitly deleted constructors and assignment
+// operations in StatusOr.
+// TraitsBase will explicitly delete what it can't support and StatusOr will
+// inherit that behavior implicitly.
+template <bool Copy, bool Move>
+struct TraitsBase {
+  TraitsBase() = default;
+  TraitsBase(const TraitsBase&) = default;
+  TraitsBase(TraitsBase&&) = default;
+  TraitsBase& operator=(const TraitsBase&) = default;
+  TraitsBase& operator=(TraitsBase&&) = default;
+};
+
+template <>
+struct TraitsBase<false, true> {
+  TraitsBase() = default;
+  TraitsBase(const TraitsBase&) = delete;
+  TraitsBase(TraitsBase&&) = default;
+  TraitsBase& operator=(const TraitsBase&) = delete;
+  TraitsBase& operator=(TraitsBase&&) = default;
+};
+
+template <>
+struct TraitsBase<false, false> {
+  TraitsBase() = default;
+  TraitsBase(const TraitsBase&) = delete;
+  TraitsBase(TraitsBase&&) = delete;
+  TraitsBase& operator=(const TraitsBase&) = delete;
+  TraitsBase& operator=(TraitsBase&&) = delete;
+};
+
+}  // namespace internal_statusor
+}  // namespace xla
+
+#endif  // THIRD_PARTY_TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_
diff --git a/tensorflow/compiler/xla/statusor_test.cc b/tensorflow/compiler/xla/statusor_test.cc
index f8555113f8..5fa2211ac6 100644
--- a/tensorflow/compiler/xla/statusor_test.cc
+++ b/tensorflow/compiler/xla/statusor_test.cc
@@ -29,8 +29,6 @@ limitations under the License.
 namespace xla {
 namespace {
 
-using tensorflow::Status;
-
 class Base1 {
  public:
   virtual ~Base1() {}
@@ -59,6 +57,14 @@ class CopyNoAssign {
   const CopyNoAssign& operator=(const CopyNoAssign&);
 };
 
+class NoDefaultConstructor {
+ public:
+  explicit NoDefaultConstructor(int foo);
+};
+
+static_assert(!std::is_default_constructible<NoDefaultConstructor>(),
+              "Should not be default-constructible.");
+
 StatusOr<std::unique_ptr<int>> ReturnUniquePtr() {
   // Uses implicit constructor from T&&
   return std::unique_ptr<int>(new int(0));
@@ -69,6 +75,18 @@ TEST(StatusOr, ElementType) {
   static_assert(std::is_same<StatusOr<char>::element_type, char>(), "");
 }
 
+TEST(StatusOr, TestNoDefaultConstructorInitialization) {
+  // Explicitly initialize it with an error code.
+  StatusOr<NoDefaultConstructor> statusor(tensorflow::errors::Cancelled(""));
+  EXPECT_FALSE(statusor.ok());
+  EXPECT_EQ(statusor.status().code(), tensorflow::error::CANCELLED);
+
+  // Default construction of StatusOr initializes it with an UNKNOWN error code.
+  StatusOr<NoDefaultConstructor> statusor2;
+  EXPECT_FALSE(statusor2.ok());
+  EXPECT_EQ(statusor2.status().code(), tensorflow::error::UNKNOWN);
+}
+
 TEST(StatusOr, TestMoveOnlyInitialization) {
   StatusOr<std::unique_ptr<int>> thing(ReturnUniquePtr());
   ASSERT_TRUE(thing.ok());
-- 
GitLab


From 2f1ff0e90dc3ba80f6bbc3f9850e8028875dcbbf Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 25 Jul 2017 08:24:31 -0700
Subject: [PATCH 1289/1375] [TF:XLA] Register a no-op kernel for
 ControlTrigger, but forbid the JIT marking pass from compiling ControlTrigger
 nodes.

CL in preparation for compiling dynamic RNN gradients via XLA.

PiperOrigin-RevId: 163073212
---
 tensorflow/compiler/jit/mark_for_compilation_pass.cc | 5 +++++
 tensorflow/compiler/tf2xla/kernels/no_op.cc          | 5 +++++
 2 files changed, 10 insertions(+)

diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
index 7eab7bb28f..77b45aa11e 100644
--- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc
+++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
@@ -257,6 +257,11 @@ Status MarkForCompilationPass::Run(
                                              &registration)) {
       return false;
     }
+
+    // Don't compile control trigger nodes. We won't preserve their deadness
+    // semantics correctly, so it's safest not to compile them.
+    if (node->IsControlTrigger()) return false;
+
     // If this device requires a JIT, we must say yes.
     if (registration->requires_compilation) return true;
 
diff --git a/tensorflow/compiler/tf2xla/kernels/no_op.cc b/tensorflow/compiler/tf2xla/kernels/no_op.cc
index b8f0c0b9fe..8c8a9bbe78 100644
--- a/tensorflow/compiler/tf2xla/kernels/no_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/no_op.cc
@@ -23,4 +23,9 @@ namespace tensorflow {
 // dummy operator using CompilationOnly().
 REGISTER_XLA_OP(Name("NoOp").CompilationOnly(), NoOp);
 
+// We register ControlTrigger as a no-op. This is correct since nodes seen
+// by the XLA compiler are never dead. This may need rethinking when we add
+// support for conditionals to XLA.
+REGISTER_XLA_OP(Name("ControlTrigger"), NoOp);
+
 }  // namespace tensorflow
-- 
GitLab


From 7349de8e967c1c943a6b0844718f3933333aa8a3 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Tue, 25 Jul 2017 09:20:17 -0700
Subject: [PATCH 1290/1375] Improve the HLO graph dumper's output.

 - Truncate long shapes.  It's not uncommon to have giant tuples, and
   displaying the whole thing makes the graph unreadable.

 - Don't traverse into the users of a node with < 16 users.  These are
   probably not interesting, and traversing into them can quickly blow
   up the graph, making it un-renderable.

 - Allow nodes which have multiple trivial subcomputations (e.g.
   select-and-scatter) to have those computations inlined.

 - Match additional patterns in MatchTrivialComputation

PiperOrigin-RevId: 163079329
---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 127 ++++++++++++++----
 1 file changed, 102 insertions(+), 25 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index fcad1188a7..f588df001c 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -92,6 +92,9 @@ enum NodeFilterResult {
   // Style the node the same as kSomeOperandsOmitted, but also don't connect it
   // to its operands, even if they're present in the graph.
   kOmitNodeOperands,
+  // Same style as kSomeOperandsOmitted, but used to indicate that some of the
+  // node's *users* have been omitted.
+  kSomeUsersOmitted,
 };
 
 // NodeFilter is essentially a map from HloInstruction*s to NodeFilterResult.
@@ -118,6 +121,11 @@ class NodeFilter {
     auto result = filter_(instr);
     return result == kOmitNodeOperands || result == kSomeOperandsOmitted;
   }
+  bool Deemphasized(const HloInstruction* instr) const {
+    auto result = filter_(instr);
+    return result == kOmitNodeOperands || result == kSomeOperandsOmitted ||
+           result == kSomeUsersOmitted;
+  }
 
  private:
   std::function<NodeFilterResult(const HloInstruction* instr)> filter_;
@@ -203,9 +211,15 @@ string HtmlLikeStringSanitize(tensorflow::StringPiece s) {
 //   "return param0 * param1;"      --> "multiply"
 //   "return min(param0, param1);"  --> "min"
 //   "return max(param0, param1);"  --> "max"
+//   "return param0 <= param1;"     --> "less-or-equal"
+//   "return param0 >= param1;"     --> "greater-or-equal"
+//   "return param0 >  param1;"     --> "greater-than"
+//   "return param0 <  param1;"     --> "less-than"
+//   "return param0 == param1;"     --> "equal-to"
+//   "return param0 != param1;"     --> "not-equal-to"
 //
-// where param0 and param1 are effective scalars.  Since all of the ops above
-// are commutative, we also support them with param0 and param1 swapped.
+// where param0 and param1 are effective scalars.  For the ops that are
+// commutative, we also support them with param0 and param1 swapped.
 //
 // This is useful primarily for reduce and map nodes.  These take a
 // subcomputation which is almost always one of the four above, and pattern
@@ -228,6 +242,7 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
       operand1->opcode() != HloOpcode::kParameter) {
     return nullopt;
   }
+
   // Check that the two operands of root are param0 and param1.  All of the
   // opcodes we recognize are commutative, so we're OK with either order.
   auto n0 = operand0->parameter_number();
@@ -236,6 +251,20 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
     return nullopt;
   }
 
+  // If the params are reversed, check that the operation being performed is
+  // commutative.
+  if (n0 == 1) {
+    switch (root->opcode()) {
+      case HloOpcode::kLe:
+      case HloOpcode::kGe:
+      case HloOpcode::kGt:
+      case HloOpcode::kLt:
+        return nullopt;
+      default:
+        break;
+    }
+  }
+
   // Check that the root and params are all effective scalars.
   if (!ShapeUtil::IsEffectiveScalar(root->shape()) ||
       !ShapeUtil::IsEffectiveScalar(operand0->shape()) ||
@@ -253,6 +282,18 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
       return "min";
     case HloOpcode::kMaximum:
       return "max";
+    case HloOpcode::kLe:
+      return "less-or-equal";
+    case HloOpcode::kGe:
+      return "greater-or-equal";
+    case HloOpcode::kGt:
+      return "greater-than";
+    case HloOpcode::kLt:
+      return "less-than";
+    case HloOpcode::kEq:
+      return "equal-to";
+    case HloOpcode::kNe:
+      return "not-equal-to";
     default:
       return nullopt;
   }
@@ -374,10 +415,8 @@ HloDotDumper::SubcomputationsToDump() {
       to_dump[instr->fused_instructions_computation()] = instr.get();
     }
 
-    const auto& subcomputations = instr->called_computations();
-    if (subcomputations.size() != 1 ||
-        !MatchTrivialComputation(subcomputations.front())) {
-      for (const HloComputation* comp : instr->called_computations()) {
+    for (const HloComputation* comp : instr->called_computations()) {
+      if (!MatchTrivialComputation(comp)) {
         to_dump[comp] = instr.get();
       }
     }
@@ -459,7 +498,7 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr,
   string in_edges = GetInstructionIncomingEdges(instr, filter);
 
   // Override the node's styling if it should be (de-)emphasized.
-  if (filter.SomeOrAllOperandsOmitted(instr)) {
+  if (filter.Deemphasized(instr)) {
     color = kDashedBorder;
   }
   if (filter.Highlight(instr)) {
@@ -657,7 +696,18 @@ string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) {
   if (!opcode_specific_info.empty()) {
     lines.push_back(opcode_specific_info);
   }
-  lines.push_back(ShapeUtil::HumanString(instr->shape()));
+
+  // Some instructions have giant tuples as their shapes, so truncate the HLO's
+  // shape to kMaxShapeLen characters.
+  constexpr int kMaxShapeLen = 64;
+  string instr_shape = ShapeUtil::HumanString(instr->shape());
+  if (instr_shape.length() > kMaxShapeLen) {
+    instr_shape =
+        StrCat(tensorflow::StringPiece(instr_shape).substr(0, kMaxShapeLen - 3),
+               "...");
+  }
+  lines.push_back(instr_shape);
+
   if (show_addresses_) {
     lines.push_back(Printf("[%p]", instr));
   }
@@ -713,16 +763,22 @@ string HloDotDumper::GetInstructionTrivialComputationStr(
     return "";
   }
 
-  const auto& subcomps = instr->called_computations();
-  if (subcomps.size() != 1) {
-    return "";
-  }
-  optional<string> computation_type = MatchTrivialComputation(subcomps.front());
-  if (!computation_type) {
-    return "";
+  std::vector<string> lines;
+  for (int64 i = 0; i < instr->called_computations().size(); ++i) {
+    optional<string> computation_type =
+        MatchTrivialComputation(instr->called_computations()[i]);
+    if (!computation_type) {
+      continue;
+    }
+    if (instr->called_computations().size() == 1) {
+      lines.push_back(Printf("Subcomputation: <b>%s</b>",
+                             HtmlLikeStringSanitize(*computation_type)));
+    } else {
+      lines.push_back(Printf("Subcomputation %lld: <b>%s</b>", i,
+                             HtmlLikeStringSanitize(*computation_type)));
+    }
   }
-  return Printf("Subcomputation: <b>%s</b>",
-                HtmlLikeStringSanitize(*computation_type));
+  return Join(lines, "<br/>");
 }
 
 tensorflow::mutex& RendererMutex() {
@@ -829,14 +885,25 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
       }
     }
 
-    // If you're looking at node X, it's probably not interesting that node Y
-    // also happens to use the same constant, so we don't traverse into
-    // constants' users.
-    if (instr->opcode() != HloOpcode::kConstant) {
-      for (const HloInstruction* user : instr->users()) {
-        if (!nodes.count(user)) {
-          worklist.push_back({user, depth + 1});
-        }
+    // Traverse into instr's users, unless:
+    //
+    //  - there are a ton of them, in which case they're probably not
+    //    interesting (and anyway, rendering them all would make the graph
+    //    unreadable), or
+    //  - instr is a constant, in which case its users are probably not
+    //    interesting.
+    if (instr->opcode() == HloOpcode::kConstant) {
+      continue;
+    }
+    constexpr int kMaxUsersToRender = 16;
+    if (instr->user_count() > kMaxUsersToRender) {
+      // If we're going to skip this node's users, style it as such.
+      nodes[instr] = kSomeUsersOmitted;
+      continue;
+    }
+    for (const HloInstruction* user : instr->users()) {
+      if (!nodes.count(user)) {
+        worklist.push_back({user, depth + 1});
       }
     }
   }
@@ -885,6 +952,16 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
     }
   }
 
+  // Similarly, promote nodes with type kSomeUsersOmitted to kNormalNode if all
+  // of their users made it into the graph by other means.
+  for (auto& kv : nodes) {
+    const auto& users = kv.first->users();
+    if (kv.second == kSomeUsersOmitted &&
+        std::all_of(users.begin(), users.end(), is_displayed)) {
+      kv.second = kNormalNode;
+    }
+  }
+
   // If none of a node's operands appear in nodes, mark it as type
   // kOmitNodeOperands so it gets styled appropriately.
   for (auto& kv : nodes) {
-- 
GitLab


From 3169f504faae8ede8443525a073567a512095c4f Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 25 Jul 2017 09:44:38 -0700
Subject: [PATCH 1291/1375] If the value to be forwarded from a loop to its
 gradient is a constant, clone the constant instead of repeatedly pushing it
 onto a stack on each iteration. This should never consume more memory than
 the stack approach (notwithstanding swapping), and frequently should be much
 better.

This change is in preparation for enabling XLA compilation of RNN gradients.

PiperOrigin-RevId: 163082165
---
 tensorflow/python/ops/control_flow_ops.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 44d6c7e275..4ba812eaf5 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -61,6 +61,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import gen_control_flow_ops
@@ -983,9 +984,16 @@ class GradLoopState(object):
             # the right control flow context.
             real_value = self._grad_context.AddValue(cur_value)
             break
+        elif constant_op.is_constant(cur_value):
+          # If the value to be forwarded is a constant, clone the constant in
+          # the gradient loop rather than using a stack.
+          # TODO(phawkins): consider hoisting the constant out of the loop
+          # instead.
+          real_value = constant_op.constant(
+              tensor_util.constant_value(cur_value), dtype=cur_value.dtype)
+          break
         else:
           # Record the history of this value in forward_ctxt.
-          # TODO(yuanbyu): Avoid recording constants.
           self._grad_context.Exit()
           history_value = cur_grad_state.AddForwardAccumulator(cur_value)
           self._grad_context.Enter()
-- 
GitLab


From 78cec04df0f714741f930ff3f234268102b71065 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 25 Jul 2017 09:46:59 -0700
Subject: [PATCH 1292/1375] [TF:XLA] Make the shape of a TensorArray flow value
 a scalar.

Previously we used an f32[0] value, since the exact flow value does not matter, however this causes problems when a TensorArray computation is placed in a loop since the shape of the flow value is no longer loop invariant.

PiperOrigin-RevId: 163082452
---
 tensorflow/compiler/tests/tensor_array_ops_test.py |  4 +++-
 .../compiler/tf2xla/kernels/tensor_array_ops.cc    | 14 ++++++++++----
 2 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index f277314352..ac039e0162 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -57,11 +57,13 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w2.read(0)
       r1 = w2.read(1)
       r2 = w2.read(2)
+      flow = w2.flow
 
-      d0, d1, d2 = session.run([r0, r1, r2])
+      d0, d1, d2, flow_val = session.run([r0, r1, r2, flow])
       self.assertAllEqual([[4.0, 5.0]], d0)
       self.assertAllEqual([[1.0, 3.0]], d1)
       self.assertAllEqual([[7.0, -8.5]], d2)
+      self.assertAllEqual([], flow_val.shape)
 
   def _testTensorArrayWritePack(self, tf_dtype):
     with self.test_session(), self.test_scope():
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index bdd52b7f8e..34cc8b2315 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -182,7 +182,10 @@ class TensorArrayOp : public XlaOpKernel {
                                dtype_, value, &var));
     var->tensor_array_size = size;
     ctx->SetResourceOutput(0, var);
-    ctx->SetConstantOutput(1, Tensor(DT_FLOAT));
+
+    Tensor flow(DT_FLOAT, TensorShape({}));
+    flow.scalar<float>()() = 0.0f;
+    ctx->SetConstantOutput(1, flow);
   }
 
  private:
@@ -216,6 +219,7 @@ class TensorArrayWriteOp : public XlaOpKernel {
     xla::ComputationDataHandle ta = resource->value;
     xla::ComputationDataHandle index = ctx->Input(1);
     xla::ComputationDataHandle value = ctx->Input(2);
+    xla::ComputationDataHandle flow = ctx->Input(3);
 
     // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
     auto start_indices = XlaHelpers::PadWithZeros(b, index, elem_shape.dims());
@@ -228,7 +232,7 @@ class TensorArrayWriteOp : public XlaOpKernel {
         DynamicAddSlice(b, ta, update, slice_shape.dim_sizes(), start_indices);
 
     resource->value = written;
-    ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
+    ctx->SetOutput(0, flow);
   }
 
  private:
@@ -369,6 +373,7 @@ class TensorArrayScatterOp : public XlaOpKernel {
 
     xla::ComputationDataHandle ta = resource->value;
     const xla::ComputationDataHandle value = ctx->Input(2);
+    const xla::ComputationDataHandle flow = ctx->Input(3);
 
     auto slice_dims = value_shape.dim_sizes();
     slice_dims[0] = 1LL;
@@ -394,7 +399,7 @@ class TensorArrayScatterOp : public XlaOpKernel {
     }
 
     resource->value = ta;
-    ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
+    ctx->SetOutput(0, flow);
   }
 
  private:
@@ -489,6 +494,7 @@ class TensorArraySplitOp : public XlaOpKernel {
                     lengths.size(), " vs. ", resource->tensor_array_size, ")"));
 
     const xla::ComputationDataHandle value = ctx->Input(1);
+    const xla::ComputationDataHandle flow = ctx->Input(3);
 
     OP_REQUIRES(ctx, value_shape.num_elements() == ta_shape.num_elements(),
                 errors::InvalidArgument("mismatched element count ",
@@ -497,7 +503,7 @@ class TensorArraySplitOp : public XlaOpKernel {
 
     resource->value = b->Add(ta, b->Reshape(value, ta_shape.dim_sizes()));
 
-    ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
+    ctx->SetOutput(0, flow);
   }
 
  private:
-- 
GitLab


From 1e78140f9d6a593294a13ef075de68f84f9d77ae Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Tue, 25 Jul 2017 09:54:52 -0700
Subject: [PATCH 1293/1375] Automated g4 rollback of changelist 163019166

PiperOrigin-RevId: 163083436
---
 tensorflow/core/grappler/clusters/BUILD       |   2 -
 .../core/grappler/clusters/single_machine.cc  |   8 +-
 .../grappler/clusters/single_machine_test.cc  | 121 ------------------
 3 files changed, 6 insertions(+), 125 deletions(-)

diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index e7230b3754..667023845c 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -114,9 +114,7 @@ cc_test(
     deps = [
         ":single_machine",
         "//tensorflow/cc:cc_ops",
-        "//tensorflow/cc:resource_variable_ops",
         "//tensorflow/cc:scope",
-        "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:lib_proto_parsing",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index 3481b2b158..a1531f1cfc 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -73,6 +73,8 @@ SingleMachine::~SingleMachine() {
   // when we delete the session.
   thread_pool_.reset();
 
+  Reset(options_, {}).IgnoreError();
+
   CHECK(already_created);
   already_created = false;
 }
@@ -275,9 +277,11 @@ Status SingleMachine::ResetSession() {
     // Make sure the session is properly closed
     TF_RETURN_IF_ERROR(Shutdown());
 
-    // Destroying the object deletes all its varibles as well. This is only true
-    // for DirectSession.
+    // We need to Reset the session to ensure that all the variables are
+    // deleted. But first we need to delete the session since Reset()
+    // deletes some of the containers referenced by the session.
     session_.reset();
+    TF_RETURN_IF_ERROR(Reset(options_, {}));
   }
 
   LOG(INFO) << "Starting new session";
diff --git a/tensorflow/core/grappler/clusters/single_machine_test.cc b/tensorflow/core/grappler/clusters/single_machine_test.cc
index d7e2827afc..b73b084793 100644
--- a/tensorflow/core/grappler/clusters/single_machine_test.cc
+++ b/tensorflow/core/grappler/clusters/single_machine_test.cc
@@ -15,10 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/clusters/single_machine.h"
 #include "tensorflow/cc/framework/scope.h"
-#include "tensorflow/cc/ops/resource_variable_ops.h"
 #include "tensorflow/cc/ops/standard_ops.h"
-#include "tensorflow/core/common_runtime/device.h"
-#include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
@@ -27,7 +24,6 @@ limitations under the License.
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/test.h"
-#include "tensorflow/core/protobuf/queue_runner.pb.h"
 
 namespace tensorflow {
 namespace grappler {
@@ -353,7 +349,6 @@ TEST_F(SingleMachineTest, InitializationMemory) {
 }
 
 namespace {
-
 template <class T>
 inline void SetNodeAttr(const string& key, const T& value, NodeDef* node) {
   AttrValue attr_value;
@@ -468,122 +463,6 @@ TEST_F(SingleMachineTest, PersistentMemory) {
   EXPECT_TRUE(found_hashtable);
 }
 
-namespace {
-
-SessionOptions GetSessionOption(int num_cpu_cores, int num_gpus) {
-  SessionOptions options;
-  // Copied from single_machine.h
-  (*options.config.mutable_device_count())["CPU"] = 1;
-  if (num_gpus > 0) {
-    (*options.config.mutable_device_count())["GPU"] = num_gpus;
-  }
-  CHECK_GE(num_cpu_cores, 1);
-  options.config.set_intra_op_parallelism_threads(num_cpu_cores);
-  options.config.add_session_inter_op_thread_pool()->set_num_threads(
-      num_cpu_cores);
-  return options;
-}
-
-Status GetDeviceMemoryStats(
-    const SessionOptions& session_option,
-    std::unordered_map<string, AllocatorStats>* allocator_stats_by_device) {
-  std::vector<Device*> devices;
-  TF_RETURN_IF_ERROR(DeviceFactory::AddDevices(session_option,
-                                               "" /* name_prefix */, &devices));
-  allocator_stats_by_device->clear();
-  for (Device* device : devices) {
-    AllocatorStats stats;
-    auto* allocator = device->GetAllocator(AllocatorAttributes());
-    if (!allocator->TracksAllocationSizes()) {
-      return Status(error::INVALID_ARGUMENT,
-                    "Tracking allocation is not enabled.");
-    }
-    allocator->GetStats(&stats);
-    (*allocator_stats_by_device)[device->name()] = stats;
-  }
-  return Status::OK();
-}
-
-}  // namespace
-
-TEST_F(SingleMachineTest, ReleaseMemoryAfterDestruction) {
-  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
-
-  // Add a variable and initializer.
-  Output a = ops::Variable(s.WithOpName("a"), TensorShape({128, 256}),
-                           DataType::DT_FLOAT);
-  Output a_init =
-      ops::RandomNormal(s.WithOpName("a/init"), {128, 256}, DataType::DT_FLOAT);
-  Output a_init_assign = ops::Assign(s.WithOpName("a/init/assign"), a, a_init);
-
-  // Add a resource variable.
-  Output b =
-      ops::VarHandleOp(s.WithOpName("b"), DataType::DT_FLOAT, {256, 512});
-  Output b_read =
-      ops::ReadVariableOp(s.WithOpName("b/read"), b, DataType::DT_FLOAT);
-  Output b_init =
-      ops::RandomNormal(s.WithOpName("b/init"), {256, 512}, DataType::DT_FLOAT);
-  auto b_init_assign =
-      ops::AssignVariableOp(s.WithOpName("b/init/assign"), b, b_init);
-
-  // Add a queue.
-  ops::FIFOQueue queue(s.WithOpName("queue"), {DataType::DT_STRING});
-  Output some_string =
-      ops::Const(s.WithOpName("some_string"), string("nothing"));
-  ops::QueueEnqueue enqueue(s.WithOpName("enqueue"), queue, {some_string});
-  ops::QueueDequeue dequeue(s.WithOpName("dequeue"), queue,
-                            {DataType::DT_STRING});
-
-  // Add a IdentityReader.
-  ops::IdentityReader reader(s.WithOpName("identity_reader"));
-  ops::ReaderRead read(s.WithOpName("read_from_queue"), reader, queue);
-
-  Output var_mul = ops::MatMul(s.WithOpName("var_matmul"), a, b_read);
-
-  GrapplerItem item;
-  TF_CHECK_OK(s.ToGraphDef(&item.graph));
-
-  QueueRunnerDef queue_runner;
-  queue_runner.set_queue_name("queue");
-  *queue_runner.add_enqueue_op_name() = "enqueue";
-  item.queue_runners.push_back(queue_runner);
-
-  item.init_ops.push_back("a/init/assign");
-  item.init_ops.push_back("b/init/assign");
-  item.fetch.push_back("var_matmul");
-  item.fetch.push_back("dequeue");
-
-  // Run the graph
-  TF_CHECK_OK(cluster_->Initialize(item));
-  EnableCPUAllocatorStats(true);
-
-  SessionOptions options =
-      GetSessionOption(3 /* cpu cores */, 0 /* num gpus */);
-  std::unordered_map<string, AllocatorStats> device_memory_before;
-  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory_before));
-  EXPECT_EQ(device_memory_before.size(), 1);
-
-  RunMetadata metadata;
-  TF_CHECK_OK(cluster_->Run(item.graph, item.feed, item.fetch, &metadata));
-
-  // Check there is memory that is not released.
-  std::unordered_map<string, AllocatorStats> device_memory;
-  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory));
-  EXPECT_EQ(device_memory.size(), 1);
-  EXPECT_GT(device_memory.begin()->second.bytes_in_use, 0);
-
-  // Reset cluster_ would release all memory.
-  cluster_.reset();
-  std::unordered_map<string, AllocatorStats> device_memory_after;
-  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory_after));
-
-  // Check memory used by resources are released after cluster destruction.
-  EXPECT_EQ(device_memory_before.size(), 1);
-  EXPECT_EQ(device_memory_after.size(), 1);
-  EXPECT_EQ(device_memory_before.begin()->second.bytes_in_use, 0);
-  EXPECT_EQ(device_memory_after.begin()->second.bytes_in_use, 0);
-}
-
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From 53d08ff5671e8ee0c84635a0f1a074298653288a Mon Sep 17 00:00:00 2001
From: Jun Luan <junluan@users.noreply.github.com>
Date: Wed, 26 Jul 2017 01:14:21 +0800
Subject: [PATCH 1294/1375] Fix wrong template type name in
 QuantizedAddUsingEigen (#11731)

---
 tensorflow/core/kernels/quantization_utils.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/quantization_utils.h b/tensorflow/core/kernels/quantization_utils.h
index cb4fcbd788..c5dc2e7194 100644
--- a/tensorflow/core/kernels/quantization_utils.h
+++ b/tensorflow/core/kernels/quantization_utils.h
@@ -823,9 +823,9 @@ void QuantizedAddUsingEigen(const Eigen::ThreadPoolDevice& device,
   const int64 input_element_count = input.NumElements();
   const int64 smaller_input_element_count = smaller_input.NumElements();
 
-  QuantizedToFloatStruct<T1> smaller_input_q2f(smaller_input_min,
+  QuantizedToFloatStruct<T1> input_q2f(input_min, input_max);
+  QuantizedToFloatStruct<T2> smaller_input_q2f(smaller_input_min,
                                                smaller_input_max);
-  QuantizedToFloatStruct<T2> input_q2f(input_min, input_max);
   FloatToQuantizedStruct<T3> f2q(*output_min, *output_max);
 
   auto smaller_input_float =
-- 
GitLab


From 72e7fdc3a569670d8cdf5d6b9a7387848d1c5d5a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 10:14:47 -0700
Subject: [PATCH 1295/1375] Automated g4 rollback of changelist 162769374

PiperOrigin-RevId: 163086518
---
 tensorflow/core/platform/default/build_config_root.bzl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl
index 1a59c471e5..fa4ac4ba73 100644
--- a/tensorflow/core/platform/default/build_config_root.bzl
+++ b/tensorflow/core/platform/default/build_config_root.bzl
@@ -3,10 +3,10 @@
 # be separate to avoid cyclic references.
 
 def tf_cuda_tests_tags():
-  return []
+  return ["local"]
 
 def tf_sycl_tests_tags():
-  return []
+  return ["local"]
 
 def tf_additional_plugin_deps():
   return select({
@@ -28,7 +28,7 @@ def tf_additional_verbs_deps():
       "//tensorflow:with_verbs_support": [
           "//tensorflow/contrib/verbs:verbs_server_lib",
           "//tensorflow/contrib/verbs:grpc_verbs_client",
-      ],
+      ], 
       "//conditions:default": [],
   })
 
-- 
GitLab


From 4b3219179a967df0da210f786210eac2c591f4b3 Mon Sep 17 00:00:00 2001
From: "Yuan (Terry) Tang" <terrytangyuan@users.noreply.github.com>
Date: Tue, 25 Jul 2017 12:21:52 -0500
Subject: [PATCH 1296/1375] Exposed more graph io methods (#11698)

---
 tensorflow/contrib/learn/__init__.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/contrib/learn/__init__.py b/tensorflow/contrib/learn/__init__.py
index c06343b731..3698af027e 100644
--- a/tensorflow/contrib/learn/__init__.py
+++ b/tensorflow/contrib/learn/__init__.py
@@ -73,6 +73,10 @@ See the @{$python/contrib.learn} guide.
 @@read_batch_examples
 @@read_batch_features
 @@read_batch_record_features
+@@read_keyed_batch_examples
+@@read_keyed_batch_examples_shared_queue
+@@read_keyed_batch_features
+@@read_keyed_batch_features_shared_queue
 
 @@InputFnOps
 @@ProblemType
-- 
GitLab


From 62ea787c20e7c1ae9848fd5f7f2f0aa8b0d8f515 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 10:28:22 -0700
Subject: [PATCH 1297/1375] internal change

PiperOrigin-RevId: 163088509
---
 tensorflow/core/platform/macros.h | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/core/platform/macros.h b/tensorflow/core/platform/macros.h
index eaf0171e72..47523c7d2b 100644
--- a/tensorflow/core/platform/macros.h
+++ b/tensorflow/core/platform/macros.h
@@ -20,6 +20,7 @@ limitations under the License.
 #if (defined(__GNUC__) || defined(__APPLE__)) && !defined(SWIG)
 // Compiler supports GCC-style attributes
 #define TF_ATTRIBUTE_NORETURN __attribute__((noreturn))
+#define TF_ATTRIBUTE_ALWAYS_INLINE __attribute__((always_inline))
 #define TF_ATTRIBUTE_NOINLINE __attribute__((noinline))
 #define TF_ATTRIBUTE_UNUSED __attribute__((unused))
 #define TF_ATTRIBUTE_COLD __attribute__((cold))
@@ -33,6 +34,7 @@ limitations under the License.
 #elif defined(COMPILER_MSVC)
 // Non-GCC equivalents
 #define TF_ATTRIBUTE_NORETURN __declspec(noreturn)
+#define TF_ATTRIBUTE_ALWAYS_INLINE
 #define TF_ATTRIBUTE_NOINLINE
 #define TF_ATTRIBUTE_UNUSED
 #define TF_ATTRIBUTE_COLD
@@ -43,6 +45,7 @@ limitations under the License.
 #else
 // Non-GCC equivalents
 #define TF_ATTRIBUTE_NORETURN
+#define TF_ATTRIBUTE_ALWAYS_INLINE
 #define TF_ATTRIBUTE_NOINLINE
 #define TF_ATTRIBUTE_UNUSED
 #define TF_ATTRIBUTE_COLD
-- 
GitLab


From a2e63e96be4945680a81e44e4b0486cbaa476185 Mon Sep 17 00:00:00 2001
From: Thomas Schumm <fwiffo@google.com>
Date: Tue, 25 Jul 2017 10:34:27 -0700
Subject: [PATCH 1298/1375] Clarify docstring for tf.rank.

PiperOrigin-RevId: 163089480
---
 tensorflow/python/ops/array_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index bb86640eab..f64c89ac5d 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -330,7 +330,7 @@ def rank(input, name=None):
   # pylint: disable=redefined-builtin
   """Returns the rank of a tensor.
 
-  This operation returns an integer representing the rank of `input`.
+  Returns a 0-D `int32` `Tensor` representing the rank of `input`.
 
   For example:
 
-- 
GitLab


From abcc81ddbcf5bb49488667619cb054e269b6d2ab Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Tue, 25 Jul 2017 10:41:06 -0700
Subject: [PATCH 1299/1375] Reduce gather_op_test timeouts by reducing the size
 of testHigherRank.

PiperOrigin-RevId: 163090428
---
 .../python/kernel_tests/gather_op_test.py     | 102 +++++++++---------
 1 file changed, 51 insertions(+), 51 deletions(-)

diff --git a/tensorflow/python/kernel_tests/gather_op_test.py b/tensorflow/python/kernel_tests/gather_op_test.py
index 04d65b88a1..9a94692569 100644
--- a/tensorflow/python/kernel_tests/gather_op_test.py
+++ b/tensorflow/python/kernel_tests/gather_op_test.py
@@ -88,58 +88,58 @@ class GatherTest(test.TestCase):
 
   def testHigherRank(self):
     # We check that scalar and empty indices shapes work as well
-    for shape in (4, 3, 2), (2, 1, 3, 2):
-      for indices_shape in (), (0,), (3, 0), (3, 5), (5, 2, 3):
-        for dtype in _TEST_TYPES:
-          for axis in range(len(shape)):
-            params = self._buildParams(np.random.randn(*shape), dtype)
-            indices = np.random.randint(shape[axis], size=indices_shape)
-            with self.test_session(use_gpu=True) as sess:
-              tf_params = constant_op.constant(params)
-              tf_indices = constant_op.constant(indices)
-              # Check that both positive and negative indices for axis work.
-              tf_axis = constant_op.constant(axis)
-              tf_negative_axis = constant_op.constant(-len(shape) + axis)
-              gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
-              gather_negative_axis = array_ops.gather(
-                  tf_params, tf_indices, axis=tf_negative_axis)
-              gather_value, gather_negative_axis_value = sess.run(
-                  [gather, gather_negative_axis])
-              gather_np = np.take(params, indices, axis)
-              self.assertAllEqual(gather_np, gather_value)
-              self.assertAllEqual(gather_np, gather_negative_axis_value)
-              expected_shape = (params.shape[:axis] + indices.shape +
-                                params.shape[axis + 1:])
-              self.assertEqual(expected_shape, gather.shape)
-              self.assertEqual(expected_shape, gather_negative_axis.shape)
-
-              # Test gradients
-              gather_grad = np.random.randn(
-                  *gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
-              if dtype.is_complex:
-                gather_grad -= 1j * gather_grad
-              params_grad, indices_grad, axis_grad = gradients_impl.gradients(
-                  gather, [tf_params, tf_indices, tf_axis], gather_grad)
-              self.assertEqual(indices_grad, None)
-              self.assertEqual(axis_grad, None)
-              # For axis 0, we are able to create an efficient IndexedSlices for
-              # the gradient.
-              if axis == 0:
-                self.assertEqual(type(params_grad), ops.IndexedSlices)
-                params_grad = ops.convert_to_tensor(params_grad)
-              correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
-              outer_dims = axis
-              inner_dims = len(shape) - axis - 1
-              gather_grad = gather_grad.reshape(
-                  shape[:axis] + (indices.size,) + shape[axis + 1:])
-              for source_index, dest_index in enumerate(indices.flat):
-                dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
+    shape = (2, 1, 3, 2)
+    for indices_shape in (), (0,), (2, 0), (2, 3):
+      for dtype in _TEST_TYPES:
+        for axis in range(len(shape)):
+          params = self._buildParams(np.random.randn(*shape), dtype)
+          indices = np.random.randint(shape[axis], size=indices_shape)
+          with self.test_session(use_gpu=True) as sess:
+            tf_params = constant_op.constant(params)
+            tf_indices = constant_op.constant(indices)
+            # Check that both positive and negative indices for axis work.
+            tf_axis = constant_op.constant(axis)
+            tf_negative_axis = constant_op.constant(-len(shape) + axis)
+            gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
+            gather_negative_axis = array_ops.gather(
+                tf_params, tf_indices, axis=tf_negative_axis)
+            gather_value, gather_negative_axis_value = sess.run(
+                [gather, gather_negative_axis])
+            gather_np = np.take(params, indices, axis)
+            self.assertAllEqual(gather_np, gather_value)
+            self.assertAllEqual(gather_np, gather_negative_axis_value)
+            expected_shape = (params.shape[:axis] + indices.shape +
+                              params.shape[axis + 1:])
+            self.assertEqual(expected_shape, gather.shape)
+            self.assertEqual(expected_shape, gather_negative_axis.shape)
+
+            # Test gradients
+            gather_grad = np.random.randn(
+                *gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
+            if dtype.is_complex:
+              gather_grad -= 1j * gather_grad
+            params_grad, indices_grad, axis_grad = gradients_impl.gradients(
+                gather, [tf_params, tf_indices, tf_axis], gather_grad)
+            self.assertEqual(indices_grad, None)
+            self.assertEqual(axis_grad, None)
+            # For axis 0, we are able to create an efficient IndexedSlices for
+            # the gradient.
+            if axis == 0:
+              self.assertEqual(type(params_grad), ops.IndexedSlices)
+              params_grad = ops.convert_to_tensor(params_grad)
+            correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
+            outer_dims = axis
+            inner_dims = len(shape) - axis - 1
+            gather_grad = gather_grad.reshape(
+                shape[:axis] + (indices.size,) + shape[axis + 1:])
+            for source_index, dest_index in enumerate(indices.flat):
+              dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
+                            (slice(None),) * inner_dims)
+              source_slice = ((slice(None),) * outer_dims + (source_index,) +
                               (slice(None),) * inner_dims)
-                source_slice = ((slice(None),) * outer_dims + (source_index,) +
-                                (slice(None),) * inner_dims)
-                correct_params_grad[dest_slice] += gather_grad[source_slice]
-              self.assertAllClose(correct_params_grad, params_grad.eval(),
-                                  atol=2e-6, rtol=2e-6)
+              correct_params_grad[dest_slice] += gather_grad[source_slice]
+            self.assertAllClose(correct_params_grad, params_grad.eval(),
+                                atol=2e-6, rtol=2e-6)
 
   def testString(self):
     params = np.array([[b"asdf", b"zxcv"], [b"qwer", b"uiop"]])
-- 
GitLab


From a7a69520e1b0c0caff6e1213740cc7ad7f2f95b2 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 25 Jul 2017 10:43:51 -0700
Subject: [PATCH 1300/1375] Add PopulationCount op (popcnt): element-wise
 counts the number of "on" bits.

PiperOrigin-RevId: 163090921
---
 tensorflow/contrib/makefile/tf_op_files.txt   |   1 +
 tensorflow/core/kernels/BUILD                 |   9 +
 .../core/kernels/population_count_op.cc       | 163 ++++++++++++++++++
 tensorflow/core/kernels/population_count_op.h |  38 ++++
 .../kernels/population_count_op_gpu.cu.cc     |  92 ++++++++++
 tensorflow/core/ops/bitwise_ops.cc            |  16 ++
 tensorflow/python/ops/bitwise_ops.py          |   1 +
 tensorflow/python/ops/bitwise_ops_test.py     |  23 +++
 8 files changed, 343 insertions(+)
 create mode 100644 tensorflow/core/kernels/population_count_op.cc
 create mode 100644 tensorflow/core/kernels/population_count_op.h
 create mode 100644 tensorflow/core/kernels/population_count_op_gpu.cu.cc

diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 4a3b3e7762..0511f8a65c 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -197,6 +197,7 @@ tensorflow/core/kernels/aggregate_ops.cc
 tensorflow/core/kernels/depthwise_conv_op.cc
 tensorflow/core/kernels/dequantize_op.cc
 tensorflow/core/kernels/meta_support.cc
+tensorflow/core/kernels/population_count_op.cc
 tensorflow/core/kernels/quantization_utils.cc
 tensorflow/core/kernels/quantize_down_and_shrink_range.cc
 tensorflow/core/kernels/quantize_op.cc
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index fffcb980db..d35a96a24a 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -2348,6 +2348,7 @@ cc_library(
         ":cwise_op",
         ":fft_ops",
         ":matmul_op",
+        ":population_count_op",
         ":reduction_ops",
         ":scan_ops",
         ":segment_reduction_ops",
@@ -2409,6 +2410,12 @@ tf_kernel_library(
     deps = MATH_DEPS + ["//tensorflow/core:bitwise_ops_op_lib"],
 )
 
+tf_kernel_library(
+    name = "population_count_op",
+    prefix = "population_count_op",
+    deps = MATH_DEPS,
+)
+
 tf_kernel_library(
     name = "fft_ops",
     prefix = "fft_ops",
@@ -4292,6 +4299,8 @@ filegroup(
         "fake_quant_ops.cc",
         "fifo_queue.cc",
         "fused_batch_norm_op.cc",
+        "population_count_op.cc",
+        "population_count_op.h",
         "winograd_transform.h",
         ":android_extended_ops_headers",
     ] + select({
diff --git a/tensorflow/core/kernels/population_count_op.cc b/tensorflow/core/kernels/population_count_op.cc
new file mode 100644
index 0000000000..12ff6b69f8
--- /dev/null
+++ b/tensorflow/core/kernels/population_count_op.cc
@@ -0,0 +1,163 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// See docs in ../ops/math_ops.cc
+
+#define EIGEN_USE_THREADS
+
+#include <bitset>
+
+#include "tensorflow/core/kernels/population_count_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+class PopulationCountOp : public OpKernel {
+ public:
+  explicit PopulationCountOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* c) override {
+    const Tensor& input_t = c->input(0);
+    Tensor* output_t;
+    OP_REQUIRES_OK(c, c->allocate_output(0, input_t.shape(), &output_t));
+
+    auto input = input_t.flat<T>();
+    auto output = output_t->flat<uint8>();
+
+    functor::PopulationCount<Device, T> popcnt;
+    popcnt(c, input, output);
+  }
+};
+
+#define REGISTER_POPULATION_COUNT(type)                                     \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("PopulationCount").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      PopulationCountOp<CPUDevice, type>);
+
+TF_CALL_uint8(REGISTER_POPULATION_COUNT);
+TF_CALL_int8(REGISTER_POPULATION_COUNT);
+TF_CALL_uint16(REGISTER_POPULATION_COUNT);
+TF_CALL_int16(REGISTER_POPULATION_COUNT);
+TF_CALL_int32(REGISTER_POPULATION_COUNT);
+TF_CALL_int64(REGISTER_POPULATION_COUNT);
+
+#undef REGISTER_POPULATION_COUNT
+
+namespace functor {
+
+namespace {
+
+template <typename T>
+inline uint8 PopCnt(const T v);
+
+#define POPCNT(T, N)                  \
+  template <>                         \
+  uint8 PopCnt<T>(const T v) {        \
+    return std::bitset<N>(v).count(); \
+  }
+
+POPCNT(int8, 8);
+POPCNT(uint8, 8);
+POPCNT(int16, 16);
+POPCNT(uint16, 16);
+POPCNT(int32, 32);
+POPCNT(int64, 64);
+
+#undef POPCNT
+
+}  // namespace
+
+template <typename T>
+struct PopulationCount<CPUDevice, T> {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstFlat input,
+                  TTypes<uint8>::Flat output) {
+    const T* input_ptr = input.data();
+    uint8* output_ptr = output.data();
+    auto shard = [input_ptr, output_ptr](int64 start, int64 limit) {
+      for (int64 i = start; i < limit; ++i) {
+        output_ptr[i] = PopCnt<T>(input_ptr[i]);
+      }
+    };
+    int64 total_shards = input.size();
+    // Approximating cost of popcnt: convert T to int64
+    // (std::bitset constructor) and convert int64 to uint8
+    // (bitset.count() -> output).  The .count() itself is relatively cheap.
+    const double total_cost = (Eigen::TensorOpCost::CastCost<T, uint8>() +
+                               Eigen::TensorOpCost::CastCost<int64, uint8>());
+    const int64 shard_cost = (total_cost >= static_cast<double>(kint64max))
+                                 ? kint64max
+                                 : static_cast<int64>(total_cost);
+
+    auto worker_threads = *(c->device()->tensorflow_cpu_worker_threads());
+    Shard(worker_threads.num_threads, worker_threads.workers, total_shards,
+          shard_cost, shard);
+  }
+};
+
+}  // namespace functor
+
+#if GOOGLE_CUDA
+
+#define REGISTER_POPULATION_COUNT(type)                                     \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("PopulationCount").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
+      PopulationCountOp<GPUDevice, type>)
+
+TF_CALL_uint8(REGISTER_POPULATION_COUNT);
+TF_CALL_int8(REGISTER_POPULATION_COUNT);
+TF_CALL_uint16(REGISTER_POPULATION_COUNT);
+TF_CALL_int16(REGISTER_POPULATION_COUNT);
+TF_CALL_int32(REGISTER_POPULATION_COUNT);
+TF_CALL_int64(REGISTER_POPULATION_COUNT);
+
+#undef REGISTER_POPULATION_COUNT
+
+namespace functor {
+
+#define DECLARE_GPU_SPEC(T)                                    \
+  template <>                                                  \
+  void PopulationCount<GPUDevice, T>::operator()(              \
+      OpKernelContext* c, typename TTypes<T>::ConstFlat input, \
+      TTypes<uint8>::Flat output);                             \
+  extern template struct PopulationCount<GPUDevice, T>
+
+TF_CALL_uint8(DECLARE_GPU_SPEC);
+TF_CALL_int8(DECLARE_GPU_SPEC);
+TF_CALL_uint16(DECLARE_GPU_SPEC);
+TF_CALL_int16(DECLARE_GPU_SPEC);
+TF_CALL_int32(DECLARE_GPU_SPEC);
+TF_CALL_int64(DECLARE_GPU_SPEC);
+
+#undef DECLARE_GPU_SPEC
+
+}  // namespace functor
+
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/population_count_op.h b/tensorflow/core/kernels/population_count_op.h
new file mode 100644
index 0000000000..de89582e13
--- /dev/null
+++ b/tensorflow/core/kernels/population_count_op.h
@@ -0,0 +1,38 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_POPULATION_COUNT_OP_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_POPULATION_COUNT_OP_H_
+
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+namespace functor {
+
+template <typename Device, typename T>
+struct PopulationCount {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstFlat input,
+                  TTypes<uint8>::Flat output);
+};
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_POPULATION_COUNT_OP_H_
diff --git a/tensorflow/core/kernels/population_count_op_gpu.cu.cc b/tensorflow/core/kernels/population_count_op_gpu.cu.cc
new file mode 100644
index 0000000000..27a687ba40
--- /dev/null
+++ b/tensorflow/core/kernels/population_count_op_gpu.cu.cc
@@ -0,0 +1,92 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "tensorflow/core/kernels/population_count_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
+namespace tensorflow {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace functor {
+
+template <typename T>
+__global__ void PopulationCountKernel(const int size, const T* input,
+                                      uint8* output) {
+  CUDA_1D_KERNEL_LOOP(i, size) { output[i] = __popc(ldg(input + i)); }
+}
+
+template <>
+__global__ void PopulationCountKernel(const int size, const int8* input,
+                                      uint8* output) {
+  // For some reason, __popc on a negative int8 gets confused.
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    output[i] = __popc(ldg(reinterpret_cast<const uint8*>(input + i)));
+  }
+}
+
+template <>
+__global__ void PopulationCountKernel(const int size, const int16* input,
+                                      uint8* output) {
+  // For some reason, __popc on a negative int16 gets confused.
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    output[i] = __popc(ldg(reinterpret_cast<const uint16*>(input + i)));
+  }
+}
+
+template <>
+__global__ void PopulationCountKernel<int64>(const int size, const int64* input,
+                                             uint8* output) {
+  CUDA_1D_KERNEL_LOOP(i, size) { output[i] = __popcll(ldg(input + i)); }
+}
+
+#define DEFINE_GPU_SPECS(T)                                               \
+  template <>                                                             \
+  void PopulationCount<GPUDevice, T>::operator()(                         \
+      OpKernelContext* c, typename TTypes<T>::ConstFlat input,            \
+      TTypes<uint8>::Flat output) {                                       \
+    const GPUDevice& d = c->eigen_device<GPUDevice>();                    \
+    int64 total_count = input.size();                                     \
+    CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);        \
+    PopulationCountKernel<T>                                              \
+        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>( \
+            total_count, input.data(), output.data());                    \
+  }
+
+TF_CALL_uint8(DEFINE_GPU_SPECS);
+TF_CALL_int8(DEFINE_GPU_SPECS);
+TF_CALL_uint16(DEFINE_GPU_SPECS);
+TF_CALL_int16(DEFINE_GPU_SPECS);
+TF_CALL_int32(DEFINE_GPU_SPECS);
+TF_CALL_int64(DEFINE_GPU_SPECS);
+
+#undef DEFINE_GPU_SPECS
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/ops/bitwise_ops.cc b/tensorflow/core/ops/bitwise_ops.cc
index 2005d5e102..3ffc4ab74a 100644
--- a/tensorflow/core/ops/bitwise_ops.cc
+++ b/tensorflow/core/ops/bitwise_ops.cc
@@ -40,6 +40,22 @@ computation is performed on the underlying representation of x.
       .Attr("T: {int8, int16, int32, int64, uint8, uint16}") \
       .SetShapeFn(shape_inference::UnchangedShape)
 
+REGISTER_OP("PopulationCount")
+    .Input("x: T")
+    .Output("y: uint8")
+    .Attr("T: {int8, int16, int32, int64, uint8, uint16}")
+    .SetShapeFn(shape_inference::UnchangedShape)
+    .Doc(R"doc(
+Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
+
+For each entry in `x`, calculates the number of `1` (on) bits in the binary
+representation of that entry.
+
+**NOTE**: It is more efficient to first `tf.bitcast` your tensors into
+`int32` or `int64` and perform the bitcount on the result, than to feed in
+8- or 16-bit inputs and then aggregate the resulting counts.
+)doc");
+
 REGISTER_OP("BitwiseAnd").BINARY_BITWISE().Doc(R"doc(
 Elementwise computes the bitwise AND of `x` and `y`.
 
diff --git a/tensorflow/python/ops/bitwise_ops.py b/tensorflow/python/ops/bitwise_ops.py
index cbabc3ed9b..44daf13537 100644
--- a/tensorflow/python/ops/bitwise_ops.py
+++ b/tensorflow/python/ops/bitwise_ops.py
@@ -36,5 +36,6 @@ ops.NotDifferentiable("BitwiseAnd")
 ops.NotDifferentiable("BitwiseOr")
 ops.NotDifferentiable("BitwiseXor")
 ops.NotDifferentiable("Invert")
+ops.NotDifferentiable("PopulationCount")
 
 remove_undocumented(__name__)
diff --git a/tensorflow/python/ops/bitwise_ops_test.py b/tensorflow/python/ops/bitwise_ops_test.py
index 904cf99a5a..1d08c8f82d 100644
--- a/tensorflow/python/ops/bitwise_ops_test.py
+++ b/tensorflow/python/ops/bitwise_ops_test.py
@@ -18,10 +18,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+import six
+
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import bitwise_ops
+from tensorflow.python.ops import gen_bitwise_ops
 from tensorflow.python.platform import googletest
 
 
@@ -46,6 +50,25 @@ class BitwiseOpTest(test_util.TensorFlowTestCase):
         self.assertAllEqual(or_result, [5, 5, 7, 15])
         self.assertAllEqual(xor_result, [5, 5, 4, 5])
 
+  def testPopulationCountOp(self):
+    dtype_list = [dtypes.int8, dtypes.int16,
+                  dtypes.int32, dtypes.int64,
+                  dtypes.uint8, dtypes.uint16]
+    raw_inputs = [0, 1, -1, 3, -3, 5, -5, 14, -14,
+                  127, 128, 255, 256, 65535, 65536,
+                  2**31 - 1, 2**31, 2**32 - 1, 2**32, -2**32 + 1, -2**32,
+                  -2**63 + 1, 2**63 - 1]
+    def count_bits(x):
+      return sum([bin(z).count("1") for z in six.iterbytes(x.tobytes())])
+    for dtype in dtype_list:
+      with self.test_session(use_gpu=True) as sess:
+        print("PopulationCount test: ", dtype)
+        inputs = np.array(raw_inputs, dtype=dtype.as_numpy_dtype)
+        truth = [count_bits(x) for x in inputs]
+        input_tensor = constant_op.constant(inputs, dtype=dtype)
+        popcnt_result = sess.run(gen_bitwise_ops.population_count(input_tensor))
+        self.assertAllEqual(truth, popcnt_result)
+
   def testInvertOp(self):
     dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
                   dtypes.uint8, dtypes.uint16]
-- 
GitLab


From d05bf9a30f8297fea4fa391702d17203767d0c3c Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Tue, 25 Jul 2017 10:47:21 -0700
Subject: [PATCH 1301/1375] Show fusion nodes inline in HLO graph dumper.

To make this work sanely I had to change NodeFilter so that it says to
dump all nodes inside subcomputations.  Previously, we passed an
explicit NodeFilter down to DumpSubcomputation, and used that to control
whether or not we dumped nodes in there.  But this becomes unwieldy with
inline fusion nodes, as sometimes you want to look at 'filter', and
other times you want to look at 'filter_', and there's no good way to
tell why.

I also had to remove the heuristic whereby we'd pull in operands of
nodes with just some operands shown.  With the much bigger nodes that
are generated by this change, the graph was becoming illegible.  I think
most of the confusion that heuristic was attempting to avoid is
addressed by the fact that we "gray out" incomplete nodes.

PiperOrigin-RevId: 163091423
---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 234 ++++++++++--------
 1 file changed, 124 insertions(+), 110 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index f588df001c..c6202548f1 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -127,6 +127,11 @@ class NodeFilter {
            result == kSomeUsersOmitted;
   }
 
+  bool ShowFusionSubcomputation(const HloInstruction* instr) const {
+    CHECK_EQ(instr->opcode(), HloOpcode::kFusion);
+    return Show(instr) && !SomeOrAllOperandsOmitted(instr);
+  }
+
  private:
   std::function<NodeFilterResult(const HloInstruction* instr)> filter_;
 };
@@ -334,15 +339,14 @@ class HloDotDumper {
 
   string DumpSubcomputation(const HloComputation* subcomp,
                             const HloInstruction* parent_instr);
-  string DumpComputation(const HloComputation* comp, const NodeFilter& filter);
-  string DumpInstruction(const HloInstruction* instr, const NodeFilter& filter);
+  string DumpComputation(const HloComputation* comp);
+  string DumpInstruction(const HloInstruction* instr);
   ColorScheme GetInstructionColor(const HloInstruction* instr);
   string GetInstructionNodeShape(const HloInstruction* instr);
   string GetInstructionNodeLabel(const HloInstruction* instr);
   string GetInstructionNodeExtraInfo(const HloInstruction* instr);
-  string GetInstructionIncomingEdges(const HloInstruction* instr,
-                                     const NodeFilter& filter);
   string GetInstructionNodeInlinedConstants(const HloInstruction* instr);
+  void AddInstructionIncomingEdges(const HloInstruction* instr);
 
   // If instr has just one computation and it's trivial (e.g. "return param0 +
   // param1"), returns a string you can put into the node's body that names the
@@ -355,6 +359,12 @@ class HloDotDumper {
   const bool show_layouts_;
   const HloExecutionProfile* profile_;  // may be null
   const NodeFilter filter_;
+
+  // Edges to print from Footer().  Edges come at the end because graphviz is
+  // unhappy if an edge from a subcomputation to a node in the outer computation
+  // appears before both the inner computation and the destination node are
+  // defined.
+  std::vector<string> edges_;
 };
 
 string HloDotDumper::Dump() {
@@ -364,7 +374,7 @@ string HloDotDumper::Dump() {
     const HloInstruction* parent = kv.second;
     StrAppend(&g, DumpSubcomputation(subcomp, parent));
   }
-  StrAppend(&g, DumpComputation(computation_, filter_));
+  StrAppend(&g, DumpComputation(computation_));
   StrAppend(&g, Footer());
   return g;
 }
@@ -397,7 +407,7 @@ stylesheet="
   return Printf(fmt, graph_label);
 }
 
-string HloDotDumper::Footer() { return "}\n"; }
+string HloDotDumper::Footer() { return StrCat(Join(edges_, "\n"), "\n}"); }
 
 std::unordered_map<const HloComputation*, const HloInstruction*>
 HloDotDumper::SubcomputationsToDump() {
@@ -427,8 +437,7 @@ HloDotDumper::SubcomputationsToDump() {
 string HloDotDumper::DumpSubcomputation(const HloComputation* subcomp,
                                         const HloInstruction* parent_instr) {
   const char* computation_fmt = R"(subgraph %s {
-style = "%s";
-color = "%s";
+%s;
 label = <%s>;
 labelloc = t;
 %s
@@ -436,57 +445,68 @@ labelloc = t;
 
 )";
 
-  const char* edge_fmt = R"(%s -> %s [ltail="%s", %s];)";
-
   string id = SubcomputationId(subcomp);
 
-  string subcomp_label, style, edge_or_bg_color, edge_attrs;
+  string subcomp_label, style;
   if (parent_instr->opcode() == HloOpcode::kFusion) {
     subcomp_label = Printf("Fused expression for <b>%s</b><br/>%s",
                            HtmlLikeStringSanitize(parent_instr->name()),
                            HtmlLikeStringSanitize(parent_instr->ToCategory()));
-    style = "rounded,filled";
-    edge_or_bg_color = "lightgray";
-    edge_attrs = "style=dotted, arrowsize=0";
+
+    // Subcomputation's fill/stroke color is light/dark red/gray, depending on
+    // whether or not the subcomputation's fusion node is highlighted.
+    bool highlight = filter_.Highlight(parent_instr);
+    const char* fillcolor = highlight ? "#ffcdd2" : "#f5f5f5";
+    const char* strokecolor = highlight ? "#b71c1c" : "#c2c2c2";
+    style = Printf(R"(style="rounded,filled,bold"; fillcolor="%s"; color="%s")",
+                   fillcolor, strokecolor);
   } else {
     subcomp_label = Printf("Subcomputation for <b>%s</b><br/>%s",
                            HtmlLikeStringSanitize(parent_instr->name()),
                            HtmlLikeStringSanitize(subcomp->name()));
-    style = "rounded";
-    edge_or_bg_color = "black";
-    edge_attrs = "style=dashed";
+    style = "style=rounded; color=black;";
+  }
+
+  string comp_body = DumpComputation(subcomp);
+  string computation =
+      Printf(computation_fmt, id, style, subcomp_label, comp_body, id);
+
+  // Add an edge from the subcomputation to its parent node.  If subcomp
+  // belongs to a fusion node, it's drawn in place of the fusion instruction, so
+  // there's no need to link those.
+  if (parent_instr->opcode() != HloOpcode::kFusion) {
+    const char* edge_fmt = R"(%s -> %s [ltail="%s", style="dashed"];)";
+    edges_.push_back(
+        Printf(edge_fmt, InstructionId(subcomp->root_instruction()),
+               InstructionId(parent_instr), SubcomputationId(subcomp)));
   }
 
-  // Pass an empty filter to DumpComputation -- we always dump the entirety of a
-  // subcomputation.
-  string comp_body = DumpComputation(subcomp, NodeFilter());
-  string computation = Printf(computation_fmt, id, style, edge_or_bg_color,
-                              subcomp_label, comp_body, id);
-  string edge = Printf(edge_fmt, InstructionId(subcomp->root_instruction()),
-                       InstructionId(parent_instr), SubcomputationId(subcomp),
-                       edge_attrs);
-  return StrCat(computation, "\n", edge, "\n");
+  return computation;
 }
 
-string HloDotDumper::DumpComputation(const HloComputation* comp,
-                                     const NodeFilter& filter) {
+string HloDotDumper::DumpComputation(const HloComputation* comp) {
   string g;
   for (const auto& instr : comp->instructions()) {
-    if (!filter.Show(instr.get())) {
+    if (!filter_.Show(instr.get())) {
       continue;
     }
-    StrAppend(&g, DumpInstruction(instr.get(), filter));
+    StrAppend(&g, DumpInstruction(instr.get()));
   }
   return g;
 }
 
-string HloDotDumper::DumpInstruction(const HloInstruction* instr,
-                                     const NodeFilter& filter) {
+string HloDotDumper::DumpInstruction(const HloInstruction* instr) {
   // We don't display constants as separate nodes; they're merged into their
   // users.
   if (instr->opcode() == HloOpcode::kConstant) {
     return "";
   }
+  // Omit the fusion node if its subcomputation is drawn, since the
+  // subcomputation will be drawn inline.
+  if (instr->opcode() == HloOpcode::kFusion &&
+      filter_.ShowFusionSubcomputation(instr)) {
+    return "";
+  }
 
   ColorScheme color = GetInstructionColor(instr);
   string node_shape = GetInstructionNodeShape(instr);
@@ -494,14 +514,13 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr,
   string extra_info = GetInstructionNodeExtraInfo(instr);
   string inlined_constants = GetInstructionNodeInlinedConstants(instr);
   string trivial_subcomputation = GetInstructionTrivialComputationStr(instr);
-
-  string in_edges = GetInstructionIncomingEdges(instr, filter);
+  AddInstructionIncomingEdges(instr);
 
   // Override the node's styling if it should be (de-)emphasized.
-  if (filter.Deemphasized(instr)) {
+  if (filter_.Deemphasized(instr)) {
     color = kDashedBorder;
   }
-  if (filter.Highlight(instr)) {
+  if (filter_.Highlight(instr)) {
     node_shape = "diamond";
     color = kDarkRed;
   }
@@ -515,34 +534,44 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr,
     }
   }
 
-  string node = Printf("%s [label=<%s>, shape=%s, %s];", InstructionId(instr),
-                       node_body, node_shape, NodeColorAttributes(color));
-  return StrCat(node, "\n", in_edges);
+  return Printf("%s [label=<%s>, shape=%s, %s];\n", InstructionId(instr),
+                node_body, node_shape, NodeColorAttributes(color));
 }
 
 string HloDotDumper::GetInstructionNodeInlinedConstants(
     const HloInstruction* instr) {
+  auto stringify_constant = [](const HloInstruction* constant) {
+    if (ShapeUtil::IsEffectiveScalar(constant->shape())) {
+      auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
+          constant->shape(), /*linear_index=*/0);
+      return Printf("%s{%s}", ShapeUtil::HumanString(constant->shape()),
+                    constant->literal().GetAsString(elem_idx));
+    }
+    if (tensorflow::StringPiece(constant->name()).starts_with("%constant")) {
+      return constant->name();
+    }
+    return StrCat("constant ", constant->name());
+  };
+
+  // Special case: If instr is a parameter to a fusion node, check whether the
+  // corresponding operand to the fusion node is a constant.
+  if (instr->opcode() == HloOpcode::kParameter && instr->IsFused()) {
+    const HloInstruction* fusion = instr->fusion_instruction();
+    const HloInstruction* operand = fusion->operand(instr->parameter_number());
+    if (operand->opcode() != HloOpcode::kConstant) {
+      return "";
+    }
+    return stringify_constant(operand);
+  }
+
   std::vector<string> lines;
   for (int64 i = 0; i < instr->operand_count(); ++i) {
     const HloInstruction* operand = instr->operand(i);
     if (operand->opcode() != HloOpcode::kConstant) {
       continue;
     }
-
-    string line = Printf("<b>operand %lld</b> = ", i);
-    if (ShapeUtil::IsEffectiveScalar(operand->shape())) {
-      auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
-          operand->shape(), /*linear_index=*/0);
-      Appendf(&line, "%s{%s}", ShapeUtil::HumanString(operand->shape()),
-              operand->literal().GetAsString(elem_idx));
-    } else {
-      if (tensorflow::StringPiece(operand->name()).starts_with("%constant")) {
-        StrAppend(&line, operand->name());
-      } else {
-        StrAppend(&line, "constant ", operand->name());
-      }
-    }
-    lines.push_back(line);
+    lines.push_back(
+        Printf("<b>operand %lld</b> = %s", i, stringify_constant(operand)));
   }
   return Join(lines, "<br/>");
 }
@@ -648,8 +677,7 @@ string HloDotDumper::GetInstructionNodeShape(const HloInstruction* instr) {
 string HloDotDumper::GetInstructionNodeLabel(const HloInstruction* instr) {
   // If we have a parameter, put the param number in the name.
   if (instr->opcode() == HloOpcode::kParameter) {
-    return Printf("<b>Parameter %lld</b><br/>%s", instr->parameter_number(),
-                  HtmlLikeStringSanitize(instr->name()));
+    return Printf("<b>Parameter %lld</b>", instr->parameter_number());
   }
 
   // The HLO instruction name contains usually the opcode, e.g. "%add.42" is
@@ -735,23 +763,39 @@ string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) {
   return Join(lines, "<br/>");
 }
 
-string HloDotDumper::GetInstructionIncomingEdges(const HloInstruction* instr,
-                                                 const NodeFilter& filter) {
-  std::vector<string> edges;
-  for (int64 i = 0; i < instr->operand_count(); ++i) {
-    const HloInstruction* operand = instr->operand(i);
-    if (!filter.Show(operand) || operand->opcode() == HloOpcode::kConstant) {
-      continue;
+void HloDotDumper::AddInstructionIncomingEdges(const HloInstruction* instr) {
+  auto add_edge = [&](const HloInstruction* from, const HloInstruction* to,
+                      int64 operand_num) {
+    // Fusion nodes' subcomputations are displayed inline, so if 'from' is a
+    // fusion node and the node's subcomputation is shown, we draw our edge
+    // starting at the fusion node's root instead of at the fusion node itself.
+    if (from->opcode() == HloOpcode::kFusion &&
+        filter_.ShowFusionSubcomputation(from)) {
+      from = from->fused_expression_root();
+    }
+    if (!filter_.Show(from) || from->opcode() == HloOpcode::kConstant) {
+      return;
     }
-    string edge =
-        Printf("%s -> %s", InstructionId(operand), InstructionId(instr));
+    string edge = Printf("%s -> %s", InstructionId(from), InstructionId(to));
     if (instr->operand_count() > 1) {
-      Appendf(&edge, R"( [headlabel="%lld",labeldistance=2])", i);
+      Appendf(&edge, R"( [headlabel="%lld",labeldistance=2])", operand_num);
     }
     StrAppend(&edge, ";");
-    edges.push_back(edge);
+    edges_.push_back(edge);
+  };
+
+  // Add edges from instr's operands to instr.  Parameters within fusion
+  // expressions are handled specially -- we draw an edge from the corresponding
+  // operand on the fusion node itself to the parameter.
+  if (instr->opcode() == HloOpcode::kParameter && instr->IsFused()) {
+    const HloInstruction* fusion = instr->fusion_instruction();
+    add_edge(fusion->operand(instr->parameter_number()), instr,
+             /*operand_num=*/0);
+  } else {
+    for (int64 i = 0; i < instr->operand_count(); ++i) {
+      add_edge(instr->operand(i), instr, i);
+    }
   }
-  return Join(edges, "\n");
 }
 
 string HloDotDumper::GetInstructionTrivialComputationStr(
@@ -847,14 +891,6 @@ class FileGraphRenderer : public GraphRendererInterface {
 
 // Gets a NodeFilter that includes roughly all instructions whose distance from
 // root is <= radius.
-//
-// It's confusing to draw a node and include only some of its operands.  So if
-// some but not all of a node's operands are <= radius units away from the root,
-// we include the other operands (unless there are a lot of them, as often in a
-// tuple node).  These additional operands may have as inputs other nodes
-// already present in the graph, but we don't draw those edges unless *all* of
-// the inputs are present.  (Otherwise we'd have the same problem we were trying
-// to solve in the first place!)
 NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
   // First, find the neighborhood of nodes with distance from root <= radius.
   // These nodes are our initial set of "normal" nodes.
@@ -912,48 +948,22 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
     return nodes.count(instr) > 0;
   };
 
-  // If a node has some but not all of its operands omitted, add the operands to
-  // the map with type kOmitNodeOperands.  Unless the node has a lot of
-  // operands, in which case just mark the node as "some operands omitted".
-  std::vector<const HloInstruction*> extra_operands;
+  // Mark nodes which don't have all of their operands present as "some operands
+  // omitted".
   for (auto& kv : nodes) {
     const HloInstruction* instr = kv.first;
     NodeFilterResult& filter_result = kv.second;
     const auto& operands = instr->operands();
 
-    // Mark nodes with many operands and some omitted as "some operands omitted"
-    // and carry on -- don't add their omitted operands to extra_operands.
-    if (operands.size() > 4) {
-      if (std::any_of(operands.begin(), operands.end(), is_displayed) &&
-          !std::all_of(operands.begin(), operands.end(), is_displayed)) {
-        filter_result = kSomeOperandsOmitted;
-      }
-      continue;
-    }
-
-    if (std::any_of(operands.begin(), operands.end(), is_displayed)) {
-      for (const HloInstruction* operand : operands) {
-        if (!is_displayed(operand)) {
-          extra_operands.push_back(operand);
-        }
-      }
+    // Mark nodes with some omitted as "some operands omitted".
+    if (std::any_of(operands.begin(), operands.end(), is_displayed) &&
+        !std::all_of(operands.begin(), operands.end(), is_displayed)) {
+      filter_result = kSomeOperandsOmitted;
     }
   }
-  for (const HloInstruction* instr : extra_operands) {
-    nodes[instr] = kOmitNodeOperands;
-  }
 
-  // Some of the nodes in extra_operands may now have all of their inputs
-  // present in nodes.  We can promote these to normal nodes.
-  for (const HloInstruction* instr : extra_operands) {
-    const auto& operands = instr->operands();
-    if (std::all_of(operands.begin(), operands.end(), is_displayed)) {
-      nodes[instr] = kNormalNode;
-    }
-  }
-
-  // Similarly, promote nodes with type kSomeUsersOmitted to kNormalNode if all
-  // of their users made it into the graph by other means.
+  // Promote nodes with type kSomeUsersOmitted to kNormalNode if all of their
+  // users made it into the graph by other means.
   for (auto& kv : nodes) {
     const auto& users = kv.first->users();
     if (kv.second == kSomeUsersOmitted &&
@@ -980,6 +990,10 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
     if (it != nodes.end()) {
       return it->second;
     }
+    // Show all nodes in subcomputations.
+    if (instr->parent() != root->parent()) {
+      return kNormalNode;
+    }
     return kHideNode;
   });
 }
-- 
GitLab


From 1ee8217f090a06deca7a451c8044530e7398398b Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Tue, 25 Jul 2017 10:49:30 -0700
Subject: [PATCH 1302/1375] errors: Avoid stripping error details when
 convering POSIX errors to Status

This change is made out of a desire to have additional information be reported
when there are filesystem errors (for e.g. see
#11628)

PiperOrigin-RevId: 163091773
---
 tensorflow/core/platform/posix/error.cc | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/tensorflow/core/platform/posix/error.cc b/tensorflow/core/platform/posix/error.cc
index df5c800879..e9baad5422 100644
--- a/tensorflow/core/platform/posix/error.cc
+++ b/tensorflow/core/platform/posix/error.cc
@@ -171,11 +171,7 @@ error::Code ErrnoToCode(int err_number) {
 
 Status IOError(const string& context, int err_number) {
   auto code = ErrnoToCode(err_number);
-  if (code == error::UNKNOWN) {
-    return Status(code, strings::StrCat(context, "; ", strerror(err_number)));
-  } else {
-    return Status(code, context);
-  }
+  return Status(code, strings::StrCat(context, "; ", strerror(err_number)));
 }
 
 }  // namespace tensorflow
-- 
GitLab


From 245a5eeafc3519633dcb3af5696cb9a097100332 Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Tue, 25 Jul 2017 10:58:32 -0700
Subject: [PATCH 1303/1375] Minor updates in MD files (#11754)

* Update ISSUE_TEMPLATE.md

Made explicit HTTPS call

* Update README.md

Minor changes

* Update wide.md

Write out abbreviated fnlwgt as final weight

* Revert README.md

Reverting changes
---
 ISSUE_TEMPLATE.md                     | 2 +-
 README.md                             | 4 ++--
 tensorflow/docs_src/tutorials/wide.md | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index 5bf13ee152..2bf2c754cf 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -1,6 +1,6 @@
 Please go to Stack Overflow for help and support:
 
-http://stackoverflow.com/questions/tagged/tensorflow
+https://stackoverflow.com/questions/tagged/tensorflow
 
 If you open a GitHub issue, here is our policy:
 
diff --git a/README.md b/README.md
index bb87656ce9..4e17182f81 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@
 | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-cpu)](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-linux-gpu)](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-mac)](https://ci.tensorflow.org/job/tensorflow-master-mac) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) |
 
 **TensorFlow** is an open source software library for numerical computation using
-data flow graphs.  Nodes in the graph represent mathematical operations, while
+data flow graphs.  The graph nodes represent mathematical operations, while
 the graph edges represent the multidimensional data arrays (tensors) that flow
 between them.  This flexible architecture lets you deploy computation to one
 or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
@@ -21,7 +21,7 @@ organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
 
-**If you'd like to contribute to TensorFlow, be sure to review the [contribution
+**If you want to contribute to TensorFlow, be sure to review the [contribution
 guidelines](CONTRIBUTING.md).**
 
 **We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for
diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index 609769f89c..fdf43955ea 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -106,8 +106,8 @@ Here's a list of columns available in the Census Income dataset:
 :                :             : military, private, etc.).         :
 | fnlwgt         | Continuous  | The number of people the census   |
 :                :             : takers believe that observation   :
-:                :             : represents (sample weight). This  :
-:                :             : variable will not be used.        :
+:                :             : represents (sample weight). Final :
+:                :             : weight will not be used.          :
 | education      | Categorical | The highest level of education    |
 :                :             : achieved for that individual.     :
 | education_num  | Continuous  | The highest level of education in |
-- 
GitLab


From 4c9e344bf1b6582620b26c0a62a886d3c80e3c19 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Tue, 25 Jul 2017 10:49:37 -0700
Subject: [PATCH 1304/1375] C API: Fix a bug with TF_OperationGetAttrTensor
 when TF_STRING tensors are involved.

The TensorBuffer owned by a TF_Tensor object has a different memory layout than
the TensorBuffer owned by the corresponding tensorflow::Tensor object.
This change consolidates conversions between the runtime's tensorflow::Tensor
and the C API's TF_Tensor objects into a pair helper functions.

The added test: CApiAttributesTest.StringTensor fails without corresponding
changes to c_api.cc

PiperOrigin-RevId: 163091789
---
 tensorflow/c/c_api.cc      | 134 +++++++++++++++----------------------
 tensorflow/c/c_api_test.cc |  50 +++++++++++---
 2 files changed, 94 insertions(+), 90 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index 3e69134c50..371264ef6c 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -56,21 +56,16 @@ limitations under the License.
 
 // The implementation below is at the top level instead of the
 // brain namespace because we are defining 'extern "C"' functions.
-using tensorflow::error::Code;
-using tensorflow::errors::InvalidArgument;
-using tensorflow::gtl::ArraySlice;
-using tensorflow::strings::StrCat;
 using tensorflow::AllocationDescription;
 using tensorflow::DataType;
 using tensorflow::Graph;
 using tensorflow::GraphDef;
-using tensorflow::mutex_lock;
 using tensorflow::NameRangeMap;
 using tensorflow::NameRangesForNode;
 using tensorflow::NewSession;
 using tensorflow::Node;
-using tensorflow::NodeDef;
 using tensorflow::NodeBuilder;
+using tensorflow::NodeDef;
 using tensorflow::OpDef;
 using tensorflow::OpRegistry;
 using tensorflow::PartialTensorShape;
@@ -83,6 +78,11 @@ using tensorflow::TensorBuffer;
 using tensorflow::TensorId;
 using tensorflow::TensorShape;
 using tensorflow::TensorShapeProto;
+using tensorflow::error::Code;
+using tensorflow::errors::InvalidArgument;
+using tensorflow::gtl::ArraySlice;
+using tensorflow::mutex_lock;
+using tensorflow::strings::StrCat;
 
 extern "C" {
 
@@ -258,24 +258,27 @@ size_t TF_StringEncode(const char* src, size_t src_len, char* dst,
   return sz;
 }
 
-size_t TF_StringDecode(const char* src, size_t src_len, const char** dst,
-                       size_t* dst_len, TF_Status* status) {
+static Status TF_StringDecode_Impl(const char* src, size_t src_len,
+                                   const char** dst, size_t* dst_len) {
   tensorflow::uint64 len64 = 0;
   const char* p = tensorflow::core::GetVarint64Ptr(src, src + src_len, &len64);
   if (p == nullptr) {
-    status->status =
-        InvalidArgument("invalid string encoding or truncated src buffer");
-    return 0;
+    return InvalidArgument("invalid string encoding or truncated src buffer");
   }
   if (len64 > std::numeric_limits<size_t>::max()) {
-    status->status =
-        InvalidArgument("encoded string is ", len64,
-                        "-bytes, which is too large for this architecture");
-    return 0;
+    return InvalidArgument("encoded string is ", len64,
+                           "-bytes, which is too large for this architecture");
   }
   *dst = p;
   *dst_len = static_cast<size_t>(len64);
-  return static_cast<size_t>(p - src) + *dst_len;
+  return Status::OK();
+}
+
+size_t TF_StringDecode(const char* src, size_t src_len, const char** dst,
+                       size_t* dst_len, TF_Status* status) {
+  status->status = TF_StringDecode_Impl(src, src_len, dst, dst_len);
+  if (!status->status.ok()) return 0;
+  return static_cast<size_t>(*dst - src) + *dst_len;
 }
 
 size_t TF_StringEncodedSize(size_t len) {
@@ -391,16 +394,20 @@ void TF_Reset(const TF_SessionOptions* opt, const char** containers,
 
 namespace tensorflow {
 
-// Non-static for testing.
-bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status) {
+Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst) {
+  if (src->dtype != TF_STRING) {
+    *dst = TensorCApi::MakeTensor(src->dtype, src->shape, src->buffer);
+    return Status::OK();
+  }
+  // TF_STRING tensors require copying since Tensor class expects a sequence of
+  // string objects.
   const tensorflow::int64 num_elements = src->shape.num_elements();
   const char* input = reinterpret_cast<const char*>(TF_TensorData(src));
   const size_t src_size = TF_TensorByteSize(src);
   if (static_cast<tensorflow::int64>(src_size / sizeof(tensorflow::uint64)) <
       num_elements) {
-    status->status = InvalidArgument(
+    return InvalidArgument(
         "Malformed TF_STRING tensor; too short to hold number of elements");
-    return false;
   }
   const char* data_start = input + sizeof(tensorflow::uint64) * num_elements;
   const char* limit = input + src_size;
@@ -411,24 +418,30 @@ bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status) {
     tensorflow::uint64 offset =
         reinterpret_cast<const tensorflow::uint64*>(input)[i];
     if (static_cast<ptrdiff_t>(offset) >= (limit - data_start)) {
-      status->status = InvalidArgument("Malformed TF_STRING tensor; element ",
-                                       i, " out of range");
-      return false;
+      return InvalidArgument("Malformed TF_STRING tensor; element ", i,
+                             " out of range");
     }
     size_t len;
     const char* p;
     const char* srcp = data_start + offset;
-    TF_StringDecode(srcp, limit - srcp, &p, &len, status);
-    if (!status->status.ok()) {
-      return false;
-    }
+    Status status = TF_StringDecode_Impl(srcp, limit - srcp, &p, &len);
+    if (!status.ok()) return status;
     dstarray(i).assign(p, len);
   }
-  return true;
+  return Status::OK();
 }
 
 // Non-static for testing.
-TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src) {
+TF_Tensor* TF_TensorFromTensor(const tensorflow::Tensor& src) {
+  if (src.dtype() != DT_STRING) {
+    TensorBuffer* buf = TensorCApi::Buffer(src);
+    buf->Ref();
+    return new TF_Tensor{static_cast<TF_DataType>(src.dtype()), src.shape(),
+                         buf};
+  }
+  // DT_STRING tensors require a copying since TF_Tensor.buffer expects a flatly
+  // encoded sequence of strings.
+
   // Compute bytes needed for encoding.
   size_t size = 0;
   const auto& srcarray = src.flat<tensorflow::string>();
@@ -507,16 +520,8 @@ static bool TF_Run_Inputs(
     TF_Status* status) {
   const int ninputs = input_pairs->size();
   for (int i = 0; i < ninputs; ++i) {
-    TF_Tensor* src = c_inputs[i];
-    if (c_inputs[i]->dtype != TF_STRING) {
-      (*input_pairs)[i].second = tensorflow::TensorCApi::MakeTensor(
-          src->dtype, src->shape, src->buffer);
-    } else if (!tensorflow::TF_Tensor_DecodeStrings(
-                   src, &(*input_pairs)[i].second, status)) {
-      // TF_STRING tensors require copying since Tensor class expects
-      // a sequence of string objects.
-      return false;
-    }
+    status->status = TF_TensorToTensor(c_inputs[i], &(*input_pairs)[i].second);
+    if (!status->status.ok()) return false;
   }
   return true;
 }
@@ -574,15 +579,7 @@ static void TF_Run_Helper(
           static_cast<TF_DataType>(src.dtype()), src.shape());
       continue;
     }
-    if (src.dtype() != tensorflow::DT_STRING) {
-      // Share the underlying buffer.
-      TensorBuffer* buf = tensorflow::TensorCApi::Buffer(src);
-      buf->Ref();
-      c_outputs[i] = new TF_Tensor{static_cast<TF_DataType>(src.dtype()),
-                                   src.shape(), buf};
-    } else {
-      c_outputs[i] = tensorflow::TF_Tensor_EncodeStrings(src);
-    }
+    c_outputs[i] = TF_TensorFromTensor(src);
   }
 }
 
@@ -1062,20 +1059,9 @@ void TF_SetAttrTensorShapeProtoList(TF_OperationDescription* desc,
 
 void TF_SetAttrTensor(TF_OperationDescription* desc, const char* attr_name,
                       TF_Tensor* value, TF_Status* status) {
-  status->status = Status::OK();
   Tensor t;
-  bool ok = true;
-
-  if (value->dtype != TF_STRING) {
-    t = tensorflow::TensorCApi::MakeTensor(value->dtype, value->shape,
-                                           value->buffer);
-  } else {
-    // TF_STRING tensors require copying since Tensor class expects
-    // a sequence of string objects.
-    ok = tensorflow::TF_Tensor_DecodeStrings(value, &t, status);
-  }
-
-  if (ok) desc->node_builder.Attr(attr_name, t);
+  status->status = TF_TensorToTensor(value, &t);
+  if (status->status.ok()) desc->node_builder.Attr(attr_name, t);
 }
 
 void TF_SetAttrTensorList(TF_OperationDescription* desc, const char* attr_name,
@@ -1084,21 +1070,14 @@ void TF_SetAttrTensorList(TF_OperationDescription* desc, const char* attr_name,
   status->status = Status::OK();
   std::vector<Tensor> t;
   t.reserve(num_values);
-  bool ok = true;
 
-  for (int i = 0; i < num_values && ok; ++i) {
-    if (values[i]->dtype != TF_STRING) {
-      t.emplace_back(tensorflow::TensorCApi::MakeTensor(
-          values[i]->dtype, values[i]->shape, values[i]->buffer));
-    } else {
-      t.emplace_back(::tensorflow::DT_STRING);
-      // TF_STRING tensors require copying since Tensor class expects
-      // a sequence of string objects.
-      ok = tensorflow::TF_Tensor_DecodeStrings(values[i], &t.back(), status);
-    }
+  for (int i = 0; i < num_values && status->status.ok(); ++i) {
+    Tensor v;
+    status->status = TF_TensorToTensor(values[i], &v);
+    t.emplace_back(v);
   }
 
-  if (ok) desc->node_builder.Attr(attr_name, t);
+  if (status->status.ok()) desc->node_builder.Attr(attr_name, t);
 }
 
 void TF_SetAttrValueProto(TF_OperationDescription* desc, const char* attr_name,
@@ -1555,9 +1534,7 @@ void TF_OperationGetAttrTensor(TF_Operation* oper, const char* attr_name,
   Tensor t;
   status->status = tensorflow::GetNodeAttr(oper->node.attrs(), attr_name, &t);
   if (!status->status.ok()) return;
-  *value = new TF_Tensor{static_cast<TF_DataType>(t.dtype()), t.shape(),
-                         tensorflow::TensorCApi::Buffer(t)};
-  (*value)->buffer->Ref();
+  *value = TF_TensorFromTensor(t);
 }
 
 void TF_OperationGetAttrTensorList(TF_Operation* oper, const char* attr_name,
@@ -1568,10 +1545,7 @@ void TF_OperationGetAttrTensorList(TF_Operation* oper, const char* attr_name,
   if (!status->status.ok()) return;
   const auto len = std::min(max_values, static_cast<int>(ts.size()));
   for (int i = 0; i < len; ++i) {
-    const Tensor& t = ts[i];
-    values[i] = new TF_Tensor{static_cast<TF_DataType>(t.dtype()), t.shape(),
-                              tensorflow::TensorCApi::Buffer(t)};
-    values[i]->buffer->Ref();
+    values[i] = TF_TensorFromTensor(ts[i]);
   }
 }
 
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index d6debe3b99..25b6cbd8e7 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -45,9 +45,8 @@ limitations under the License.
 #include "tensorflow/core/util/equal_graph_def.h"
 
 namespace tensorflow {
-
-bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status);
-TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src);
+TF_Tensor* TF_TensorFromTensor(const Tensor& src);
+Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);
 
 namespace {
 
@@ -146,19 +145,16 @@ void TestEncodeDecode(int line, const std::vector<string>& data) {
     for (tensorflow::int64 i = 0; i < src.NumElements(); ++i) {
       src.flat<string>()(i) = data[i];
     }
-    TF_Tensor* dst = TF_Tensor_EncodeStrings(src);
+    TF_Tensor* dst = TF_TensorFromTensor(src);
 
     // Convert back to a C++ Tensor and ensure we get expected output.
-    TF_Status* status = TF_NewStatus();
     Tensor output;
-    ASSERT_TRUE(TF_Tensor_DecodeStrings(dst, &output, status)) << line;
-    ASSERT_EQ(TF_OK, TF_GetCode(status)) << line;
+    ASSERT_EQ(Status::OK(), TF_TensorToTensor(dst, &output)) << line;
     ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
     for (tensorflow::int64 i = 0; i < src.NumElements(); ++i) {
       ASSERT_EQ(data[i], output.flat<string>()(i)) << line;
     }
 
-    TF_DeleteStatus(status);
     TF_DeleteTensor(dst);
   }
 }
@@ -918,7 +914,7 @@ TEST(CAPI, SavedModel) {
   TF_Operation* input_op =
       TF_GraphOperationByName(graph, input_op_name.c_str());
   ASSERT_TRUE(input_op != nullptr);
-  csession.SetInputs({{input_op, TF_Tensor_EncodeStrings(input)}});
+  csession.SetInputs({{input_op, TF_TensorFromTensor(input)}});
 
   const tensorflow::string output_op_name =
       tensorflow::ParseTensorName(output_name).first.ToString();
@@ -1636,6 +1632,39 @@ TEST_F(CApiAttributesTest, Tensor) {
   TF_DeleteTensor(value);
 }
 
+TEST_F(CApiAttributesTest, StringTensor) {
+  // Create the string-Tensor "atttribute" value.
+  char encoded[] = {
+      0,   0, 0, 0, 0, 0, 0, 0,  // array[uint64] offsets
+      1,                         // varint encoded string length
+      'A',
+  };
+  auto deallocator = [](void* data, size_t len, void* arg) {};
+  unique_tensor_ptr t_in(TF_NewTensor(TF_STRING, nullptr, 0, &encoded[0],
+                                      sizeof(encoded), deallocator, nullptr),
+                         TF_DeleteTensor);
+
+  // Create a TF_Operation with the attribute t_in
+  auto desc = init("tensor");
+  TF_SetAttrTensor(desc, "v", t_in.get(), s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+
+  auto oper = TF_FinishOperation(desc, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+
+  // Fetch the attribute back.
+  EXPECT_TF_META("v", -1, TF_ATTR_TENSOR, -1);
+  TF_Tensor* t_out = nullptr;
+  TF_OperationGetAttrTensor(oper, "v", &t_out, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  EXPECT_EQ(TF_STRING, TF_TensorType(t_out));
+  EXPECT_EQ(0, TF_NumDims(t_out));
+  ASSERT_EQ(TF_TensorByteSize(t_in.get()), TF_TensorByteSize(t_out));
+  EXPECT_EQ(0, memcmp(TF_TensorData(t_in.get()), TF_TensorData(t_out),
+                      TF_TensorByteSize(t_out)));
+  TF_DeleteTensor(t_out);
+}
+
 TEST_F(CApiAttributesTest, TensorList) {
   const char tensor1[] = {5, 7};
   const int64_t dims1[] = {1, 2};
@@ -1647,7 +1676,8 @@ TEST_F(CApiAttributesTest, TensorList) {
 
   auto desc = init("list(tensor)");
   TF_Tensor* tmp[] = {
-      Int8Tensor(dims1, ndims1, tensor1), Int8Tensor(dims2, ndims2, tensor2),
+      Int8Tensor(dims1, ndims1, tensor1),
+      Int8Tensor(dims2, ndims2, tensor2),
   };
   TF_SetAttrTensorList(desc, "v", tmp, TF_ARRAYSIZE(tmp), s_);
   for (int i = 0; i < TF_ARRAYSIZE(tmp); ++i) {
-- 
GitLab


From 879e207e815eacd0612da068c14f9aaf359a87e3 Mon Sep 17 00:00:00 2001
From: RJ Ryan <rjryan@google.com>
Date: Tue, 25 Jul 2017 10:53:42 -0700
Subject: [PATCH 1305/1375] Speed up tf.contrib.signal spectral_ops_test.py by
 reducing the size of the gradient test.

PiperOrigin-RevId: 163092423
---
 .../python/kernel_tests/spectral_ops_test.py    | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
index be28184ae6..61b7107a17 100644
--- a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
+++ b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
@@ -220,15 +220,14 @@ class SpectralOpsTest(test.TestCase):
       # stft_bound, inverse_stft_bound).
       # TODO(rjryan): Investigate why STFT gradient error is so high.
       test_configs = [
-          (512, 64, 32, 64, 2e-3, 3e-5),
-          (512, 64, 64, 64, 2e-3, 3e-5),
-          (512, 64, 25, 64, 2e-3, 3e-5),
-          (512, 25, 15, 36, 2e-3, 3e-5),
-          (123, 23, 5, 42, 2e-3, 4e-5),
+          (64, 16, 8, 16),
+          (64, 16, 16, 16),
+          (64, 16, 7, 16),
+          (64, 7, 4, 9),
+          (29, 5, 1, 10),
       ]
 
-      for (signal_length, frame_length, frame_step, fft_length,
-           stft_bound, inverse_stft_bound) in test_configs:
+      for (signal_length, frame_length, frame_step, fft_length) in test_configs:
         signal_shape = [signal_length]
         signal = random_ops.random_uniform(signal_shape)
         stft_shape = [max(0, 1 + (signal_length - frame_length) // frame_step),
@@ -242,8 +241,8 @@ class SpectralOpsTest(test.TestCase):
                                                  stft, stft_shape)
         inverse_stft_error = test.compute_gradient_error(
             stft, stft_shape, inverse_stft, inverse_stft_shape)
-        self.assertLess(stft_error, stft_bound)
-        self.assertLess(inverse_stft_error, inverse_stft_bound)
+        self.assertLess(stft_error, 2e-3)
+        self.assertLess(inverse_stft_error, 4e-5)
 
 
 if __name__ == "__main__":
-- 
GitLab


From 3b9c6a0d9fa4437b86e996016a01011fb72e1729 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 25 Jul 2017 10:57:48 -0700
Subject: [PATCH 1306/1375] Add new CompareAndBitpackOp.

PiperOrigin-RevId: 163093146
---
 tensorflow/core/kernels/BUILD                 |  34 ++++
 .../core/kernels/compare_and_bitpack_op.cc    | 185 ++++++++++++++++++
 .../core/kernels/compare_and_bitpack_op.h     |  42 ++++
 .../kernels/compare_and_bitpack_op_gpu.cu.cc  | 141 +++++++++++++
 tensorflow/core/ops/math_ops.cc               |  58 ++++++
 tensorflow/python/kernel_tests/BUILD          |  12 ++
 .../compare_and_bitpack_op_test.py            |  83 ++++++++
 7 files changed, 555 insertions(+)
 create mode 100644 tensorflow/core/kernels/compare_and_bitpack_op.cc
 create mode 100644 tensorflow/core/kernels/compare_and_bitpack_op.h
 create mode 100644 tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc
 create mode 100644 tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index d35a96a24a..a493452777 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -701,6 +701,39 @@ tf_kernel_library(
     deps = ARRAY_DEPS,
 )
 
+tf_kernel_library(
+    name = "compare_and_bitpack_op",
+    srcs = ["compare_and_bitpack_op.cc"],
+    hdrs = ["compare_and_bitpack_op.h"],
+    gpu_srcs = [
+        "compare_and_bitpack_op.h",
+        "compare_and_bitpack_op_gpu.cu.cc",
+    ],
+    deps = ARRAY_DEPS,
+)
+
+# TODO(ebrevdo): Add benchmarks once the op is in the autogen array namespace.
+# tf_cuda_cc_test(
+#     name = "compare_and_bitpack_op_test",
+#     srcs = ["compare_and_bitpack_op_test.cc"],
+#     deps = [
+#         ":array",
+#         ":ops_testutil",
+#         ":ops_util",
+#         "//third_party/eigen3",
+#         "//tensorflow/cc:cc_ops",
+#         "//tensorflow/cc:cc_ops_internal",
+#         "//tensorflow/core:core_cpu",
+#         "//tensorflow/core:core_cpu_internal",
+#         "//tensorflow/core:framework",
+#         "//tensorflow/core:lib",
+#         "//tensorflow/core:protos_all_cc",
+#         "//tensorflow/core:test",
+#         "//tensorflow/core:test_main",
+#         "//tensorflow/core:testlib",
+#     ],
+# )
+
 tf_kernel_library(
     name = "reshape_op",
     prefix = "reshape_op",
@@ -2344,6 +2377,7 @@ cc_library(
         ":bucketize_op",
         ":cast_op",
         ":check_numerics_op",
+        ":compare_and_bitpack_op",
         ":cross_op",
         ":cwise_op",
         ":fft_ops",
diff --git a/tensorflow/core/kernels/compare_and_bitpack_op.cc b/tensorflow/core/kernels/compare_and_bitpack_op.cc
new file mode 100644
index 0000000000..9f626a274a
--- /dev/null
+++ b/tensorflow/core/kernels/compare_and_bitpack_op.cc
@@ -0,0 +1,185 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// See docs in ../ops/math_ops.cc
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/kernels/compare_and_bitpack_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+class CompareAndBitpackOp : public OpKernel {
+ public:
+  explicit CompareAndBitpackOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* c) override {
+    const Tensor& input_t = c->input(0);
+    const Tensor& threshold_t = c->input(1);
+    OP_REQUIRES(
+        c, TensorShapeUtils::IsScalar(threshold_t.shape()),
+        errors::InvalidArgument("Compare must be a scalar, but saw shape: ",
+                                threshold_t.shape().DebugString()));
+    const TensorShape& input_shape = input_t.shape();
+    OP_REQUIRES(c, TensorShapeUtils::IsVectorOrHigher(input_shape),
+                errors::InvalidArgument(
+                    "Input should be at least a vector, but saw a scalar."));
+    OP_REQUIRES(c, input_shape.dim_size(input_shape.dims() - 1) % 8 == 0,
+                errors::InvalidArgument(
+                    "Inner dimension of input should be "
+                    "divisible by ",
+                    8, ", but saw shape: ", input_shape.DebugString()));
+
+    TensorShape output_shape = input_shape;
+    int rank = input_shape.dims();
+    output_shape.set_dim(rank - 1, input_shape.dim_size(rank - 1) / 8);
+
+    Tensor* output_t;
+    OP_REQUIRES_OK(c, c->allocate_output(0, output_shape, &output_t));
+
+    auto input = input_t.flat_inner_dims<T>();
+    auto threshold = threshold_t.scalar<T>();
+    auto output = output_t->flat_inner_dims<uint8>();
+
+    functor::CompareAndBitpack<Device, T> func;
+    func(c, input, threshold, output);
+  }
+};
+
+#define REGISTER_COMPARE_AND_BITPACK(type)                                    \
+  REGISTER_KERNEL_BUILDER(                                                    \
+      Name("CompareAndBitpack").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      CompareAndBitpackOp<CPUDevice, type>);
+
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_COMPARE_AND_BITPACK);
+TF_CALL_bool(REGISTER_COMPARE_AND_BITPACK);
+
+#undef REGISTER_COMPARE_AND_BITPACK
+
+namespace functor {
+
+template <typename T, class = void, class = void>
+struct ComputeShard {
+  static EIGEN_STRONG_INLINE void Compute(typename TTypes<T>::ConstMatrix input,
+                                          typename TTypes<uint8>::Matrix output,
+                                          const T& thresh, int64 start,
+                                          int64 limit) {
+    for (int64 i = start; i < limit; ++i) {
+      uint8* out = output.data() + i;
+      const T* block = input.data() + 8 * i;
+      *out = ((((block[0] > thresh) << 7)) | (((block[1] > thresh) << 6)) |
+              (((block[2] > thresh) << 5)) | (((block[3] > thresh) << 4)) |
+              (((block[4] > thresh) << 3)) | (((block[5] > thresh) << 2)) |
+              (((block[6] > thresh) << 1)) | (((block[7] > thresh))));
+    }
+  }
+};
+
+// Specialization for bool on systems where sizeof(bool) == 1.
+template <typename T>
+struct ComputeShard<T,
+                    typename std::enable_if<std::is_same<T, bool>::value>::type,
+                    typename std::enable_if<sizeof(T) == 1>::type> {
+  static EIGEN_STRONG_INLINE void Compute(
+      typename TTypes<bool>::ConstMatrix input,
+      typename TTypes<uint8>::Matrix output, bool /*thresh*/, int64 start,
+      int64 limit) {
+    // NOTE(ebrevdo): This assumes memory is little-endian.
+    for (int64 i = start; i < limit; ++i) {
+      uint8* out = output.data() + i;
+      const int64 block = *reinterpret_cast<const int64*>(input.data() + 8 * i);
+      *out =
+          ((((block & (1LL << (7 * 8))) >> (7 * 8 - 0))) |
+           (((block & (1LL << (6 * 8))) >> (6 * 8 - 1))) |
+           (((block & (1LL << (5 * 8))) >> (5 * 8 - 2))) |
+           (((block & (1LL << (4 * 8))) >> (4 * 8 - 3))) |
+           (((block & (1LL << (3 * 8))) >> (3 * 8 - 4))) |
+           (((block & (1LL << (2 * 8))) >> (2 * 8 - 5))) |
+           (((block & (1LL << 8)) >> (1 * 8 - 6))) | (((block & (1LL)) << 7)));
+    }
+  }
+};
+
+template <typename T>
+struct CompareAndBitpack<CPUDevice, T> {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstMatrix input,
+                  typename TTypes<T>::ConstScalar threshold,
+                  TTypes<uint8>::Matrix output) {
+    const T thresh = threshold();
+    auto shard = [&, thresh](int64 start, int64 limit) {
+      ComputeShard<T>::Compute(input, output, thresh, start, limit);
+    };
+    int64 total_shards = output.size();  // Approximate cmp as an add and
+                                         // bitwise-or + shift as an add.
+    const double total_cost = 8 * (Eigen::TensorOpCost::AddCost<T>() +
+                                   Eigen::TensorOpCost::AddCost<uint8>());
+    const int64 shard_cost = (total_cost >= static_cast<double>(kint64max))
+                                 ? kint64max
+                                 : static_cast<int64>(total_cost);
+
+    auto worker_threads = *(c->device()->tensorflow_cpu_worker_threads());
+    Shard(worker_threads.num_threads, worker_threads.workers, total_shards,
+          shard_cost, shard);
+  }
+};
+
+}  // namespace functor
+
+#if GOOGLE_CUDA
+
+#define REGISTER_COMPARE_AND_BITPACK(type)                                    \
+  REGISTER_KERNEL_BUILDER(                                                    \
+      Name("CompareAndBitpack").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
+      CompareAndBitpackOp<GPUDevice, type>);
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_COMPARE_AND_BITPACK);
+TF_CALL_bool(REGISTER_COMPARE_AND_BITPACK);
+
+#undef REGISTER_COMPARE_AND_BITPACK
+
+namespace functor {
+
+#define DECLARE_GPU_SPEC(T)                                      \
+  template <>                                                    \
+  void CompareAndBitpack<GPUDevice, T>::operator()(              \
+      OpKernelContext* c, typename TTypes<T>::ConstMatrix input, \
+      typename TTypes<T>::ConstScalar threshold,                 \
+      TTypes<uint8>::Matrix output);                             \
+  extern template struct CompareAndBitpack<GPUDevice, T>;
+
+TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC)
+TF_CALL_bool(DECLARE_GPU_SPEC)
+
+#undef DECLARE_GPU_SPEC
+
+}  // namespace functor
+
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/compare_and_bitpack_op.h b/tensorflow/core/kernels/compare_and_bitpack_op.h
new file mode 100644
index 0000000000..8e020249c1
--- /dev/null
+++ b/tensorflow/core/kernels/compare_and_bitpack_op.h
@@ -0,0 +1,42 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_COMPARE_AND_BITPACK_OP_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_COMPARE_AND_BITPACK_OP_H_
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+namespace functor {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+struct CompareAndBitpack {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstMatrix input,
+                  typename TTypes<T>::ConstScalar threshold,
+                  TTypes<uint8>::Matrix output);
+};
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_COMPARE_AND_BITPACK_OP_H_
diff --git a/tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc b/tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc
new file mode 100644
index 0000000000..345405e3fe
--- /dev/null
+++ b/tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc
@@ -0,0 +1,141 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "tensorflow/core/kernels/compare_and_bitpack_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
+namespace tensorflow {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace functor {
+
+template <typename T>
+__global__ void CompareAndBitpackKernel(const int size, const T* threshold,
+                                        const T* input, uint8* output) {
+  // TODO(ebrevdo): Erich said: to get a better memory access pattern
+  // you could have 8 threads load this data and do a comparison, then
+  // use the ballot instruction to combine the values from each thread
+  // in the warp in one instruction (so each thread will have the
+  // result for 4 blocks) followed by an appropriate shift and mask to
+  // get the 8-bits of interest.
+  const T thresh = ldg(threshold);
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const T* block = input + 8 * i;
+    output[i] =
+        ((((ldg(block) > thresh) << 7)) | (((ldg(block + 1) > thresh) << 6)) |
+         (((ldg(block + 2) > thresh) << 5)) |
+         (((ldg(block + 3) > thresh) << 4)) |
+         (((ldg(block + 4) > thresh) << 3)) |
+         (((ldg(block + 5) > thresh) << 2)) |
+         (((ldg(block + 6) > thresh) << 1)) | (((ldg(block + 7) > thresh))));
+  }
+}
+
+template <>
+__global__ void CompareAndBitpackKernel<bool>(const int size,
+                                              const bool* threshold,
+                                              const bool* input,
+                                              uint8* output) {
+  // TODO(ebrevdo): Erich said: I think you could again have multiple
+  // threads work on one block and use the ballot instruction to the
+  // bit packing in one instruction.
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const int64 block = ldg(reinterpret_cast<const int64*>(input + 8 * i));
+    // NOTE(ebrevdo): This assumes memory is little-endian.
+    output[i] =
+        ((((block & (1LL << (7 * 8))) >> (7 * 8 - 0))) |
+         (((block & (1LL << (6 * 8))) >> (6 * 8 - 1))) |
+         (((block & (1LL << (5 * 8))) >> (5 * 8 - 2))) |
+         (((block & (1LL << (4 * 8))) >> (4 * 8 - 3))) |
+         (((block & (1LL << (3 * 8))) >> (3 * 8 - 4))) |
+         (((block & (1LL << (2 * 8))) >> (2 * 8 - 5))) |
+         (((block & (1LL << 8)) >> (1 * 8 - 6))) | (((block & (1LL)) << 7)));
+  }
+}
+
+template <>
+__global__ void CompareAndBitpackKernel<float>(const int size,
+                                               const float* threshold,
+                                               const float* input,
+                                               uint8* output) {
+  const float thresh = ldg(threshold);
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const float4 block0 = ldg(reinterpret_cast<const float4*>(input + 8 * i));
+    const float4 block1 =
+        ldg(reinterpret_cast<const float4*>(input + 8 * i + 4));
+    output[i] = ((((block0.x > thresh) << 7)) | (((block0.y > thresh) << 6)) |
+                 (((block0.z > thresh) << 5)) | (((block0.w > thresh) << 4)) |
+                 (((block1.x > thresh) << 3)) | (((block1.y > thresh) << 2)) |
+                 (((block1.z > thresh) << 1)) | (((block1.w > thresh))));
+  }
+}
+
+template <>
+__global__ void CompareAndBitpackKernel<double>(const int size,
+                                                const double* threshold,
+                                                const double* input,
+                                                uint8* output) {
+  const double thresh = ldg(threshold);
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const double2 block0 = ldg(reinterpret_cast<const double2*>(input + 8 * i));
+    const double2 block1 =
+        ldg(reinterpret_cast<const double2*>(input + 8 * i + 2));
+    const double2 block2 =
+        ldg(reinterpret_cast<const double2*>(input + 8 * i + 4));
+    const double2 block3 =
+        ldg(reinterpret_cast<const double2*>(input + 8 * i + 6));
+    output[i] = ((((block0.x > thresh) << 7)) | (((block0.y > thresh) << 6)) |
+                 (((block1.x > thresh) << 5)) | (((block1.y > thresh) << 4)) |
+                 (((block2.x > thresh) << 3)) | (((block2.y > thresh) << 2)) |
+                 (((block3.x > thresh) << 1)) | (((block3.y > thresh))));
+  }
+}
+
+#define DEFINE_GPU_SPECS(T)                                               \
+  template <>                                                             \
+  void CompareAndBitpack<GPUDevice, T>::operator()(                       \
+      OpKernelContext* c, typename TTypes<T>::ConstMatrix input,          \
+      typename TTypes<T>::ConstScalar threshold,                          \
+      TTypes<uint8>::Matrix output) {                                     \
+    const GPUDevice& d = c->eigen_device<GPUDevice>();                    \
+    int64 total_count = output.size();                                    \
+    CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);        \
+                                                                          \
+    CompareAndBitpackKernel<T>                                            \
+        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>( \
+            total_count, threshold.data(), input.data(), output.data());  \
+  }
+
+TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS)
+TF_CALL_bool(DEFINE_GPU_SPECS)
+
+#undef DECLARE_GPU_SPECS
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index f4e0625c66..36f999ff60 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -2458,6 +2458,64 @@ out_type: The type of the output. Should be a lower bit depth than Tinput.
 
 )doc");
 
+REGISTER_OP("CompareAndBitpack")
+    .Input("input: T")
+    .Input("threshold: T")
+    .Output("output: uint8")
+    .Attr("T: {bool, float16, float32, float64, int8, int16, int32, int64}")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &input));
+      ShapeHandle unused;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
+      ShapeHandle output = input;
+      if (c->RankKnown(input)) {
+        int rank = c->Rank(input);
+        auto inner_dim = c->Dim(input, rank - 1);
+        DimensionHandle inferred_dim;
+        TF_RETURN_IF_ERROR(c->Divide(inner_dim, 8,
+                                     /* evenly_divisible */ true,
+                                     &inferred_dim));
+        TF_RETURN_IF_ERROR(
+            c->ReplaceDim(output, rank - 1, inferred_dim, &output));
+      }
+      c->set_output(0, output);
+
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
+
+Each comparison returns a boolean `true` (if `input_value > threshold`)
+or and `false` otherwise.
+
+This operation is useful for Locality-Sensitive-Hashing (LSH) and other
+algorithms that use hashing approximations of cosine and `L2` distances;
+codes can be generated from an input via:
+
+```python
+codebook_size = 50
+codebook_bits = codebook_size * 32
+codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
+                           dtype=x.dtype,
+                           initializer=tf.orthogonal_initializer())
+codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
+codes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32
+# now codes has shape x.shape[:-1] + [codebook_size]
+```
+
+**NOTE**: Currently, the innermost dimension of the tensor must be divisible
+by 8.
+
+Given an `input` shaped `[s0, s1, ..., s_n]`, the output is
+a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`.
+
+input: Values to compare against `threshold` and bitpack.
+threshold: Threshold to compare against.
+T: The type of the input and threshold.
+output: The bitpacked comparisons.
+)doc");
+
 REGISTER_OP("RequantizationRange")
     .Input("input: Tinput")
     .Input("input_min: float")
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index cac05c372a..896d466c25 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1671,6 +1671,18 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "compare_and_bitpack_op_test",
+    size = "small",
+    srcs = ["compare_and_bitpack_op_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
 cuda_py_test(
     name = "scalar_test",
     size = "small",
diff --git a/tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py b/tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py
new file mode 100644
index 0000000000..56ddd6e428
--- /dev/null
+++ b/tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py
@@ -0,0 +1,83 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow.ops.compare_and_bitpack_op."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+class CompareAndBitpackTest(test.TestCase):
+
+  def _testCompareAndBitpack(self,
+                             x, threshold,
+                             truth,
+                             expected_err_re=None):
+    with self.test_session(use_gpu=True):
+      ans = math_ops.compare_and_bitpack(x, threshold)
+      if expected_err_re is None:
+        tf_ans = ans.eval()
+        self.assertShapeEqual(truth, ans)
+        self.assertAllEqual(tf_ans, truth)
+      else:
+        with self.assertRaisesOpError(expected_err_re):
+          ans.eval()
+
+  def _testBasic(self, dtype):
+    rows = 371
+    cols = 294
+    x = np.random.randn(rows, cols * 8)
+    if dtype == np.bool:
+      x = x > 0
+    else:
+      x = x.astype(dtype)
+    threshold = dtype(0)
+    # np.packbits flattens the tensor, so we reshape it back to the
+    # expected dimensions.
+    truth = np.packbits(x > threshold).reshape(rows, cols)
+    self._testCompareAndBitpack(x, threshold, truth)
+
+  def testBasicFloat32(self):
+    self._testBasic(np.float32)
+
+  def testBasicFloat64(self):
+    self._testBasic(np.float64)
+
+  def testBasicFloat16(self):
+    self._testBasic(np.float16)
+
+  def testBasicBool(self):
+    self._testBasic(np.bool)
+
+  def testBasicInt8(self):
+    self._testBasic(np.int8)
+
+  def testBasicInt16(self):
+    self._testBasic(np.int16)
+
+  def testBasicInt32(self):
+    self._testBasic(np.int32)
+
+  def testBasicInt64(self):
+    self._testBasic(np.int64)
+
+
+if __name__ == "__main__":
+  test.main()
-- 
GitLab


From f8ecc882508fc4807d106cd247e558a2d33a128a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 11:05:24 -0700
Subject: [PATCH 1307/1375] Update ops-related pbtxt files.

PiperOrigin-RevId: 163094455
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 56 ++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 64 +++++++++++++++++++
 2 files changed, 120 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 94224f22b9..b82035bfc3 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -4597,6 +4597,37 @@ op {
     }
   }
 }
+op {
+  name: "CompareAndBitpack"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "threshold"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BOOL
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "Complex"
   input_arg {
@@ -16267,6 +16298,31 @@ op {
     }
   }
 }
+op {
+  name: "PopulationCount"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+}
 op {
   name: "Pow"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index ab984d2547..468434bd28 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -4406,6 +4406,43 @@ op {
   summary: "Computes the reverse mode backpropagated gradient of the Cholesky algorithm."
   description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by\nIain Murray http://arxiv.org/abs/1602.07527."
 }
+op {
+  name: "CompareAndBitpack"
+  input_arg {
+    name: "input"
+    description: "Values to compare against `threshold` and bitpack."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "threshold"
+    description: "Threshold to compare against."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "The bitpacked comparisons."
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    description: "The type of the input and threshold."
+    allowed_values {
+      list {
+        type: DT_BOOL
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Compare values of `input` to `threshold` and pack resulting bits into a `uint8`."
+  description: "Each comparison returns a boolean `true` (if `input_value > threshold`)\nor and `false` otherwise.\n\nThis operation is useful for Locality-Sensitive-Hashing (LSH) and other\nalgorithms that use hashing approximations of cosine and `L2` distances;\ncodes can be generated from an input via:\n\n```python\ncodebook_size = 50\ncodebook_bits = codebook_size * 32\ncodebook = tf.get_variable(\'codebook\', [x.shape[-1].value, codebook_bits],\n                           dtype=x.dtype,\n                           initializer=tf.orthogonal_initializer())\ncodes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)\ncodes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32\n# now codes has shape x.shape[:-1] + [codebook_size]\n```\n\n**NOTE**: Currently, the innermost dimension of the tensor must be divisible\nby 8.\n\nGiven an `input` shaped `[s0, s1, ..., s_n]`, the output is\na `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`."
+}
 op {
   name: "Complex"
   input_arg {
@@ -15444,6 +15481,33 @@ op {
   summary: "Compute the polygamma function \\\\(\\psi^{(n)}(x)\\\\)."
   description: "The polygamma function is defined as:\n\n\n\\\\(\\psi^{(n)}(x) = \\frac{d^n}{dx^n} \\psi(x)\\\\)\n\nwhere \\\\(\\psi(x)\\\\) is the digamma function."
 }
+op {
+  name: "PopulationCount"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Computes element-wise population count (a.k.a. popcount, bitsum, bitcount)."
+  description: "For each entry in `x`, calculates the number of `1` (on) bits in the binary\nrepresentation of that entry.\n\n**NOTE**: It is more efficient to first `tf.bitcast` your tensors into\n`int32` or `int64` and perform the bitcount on the result, than to feed in\n8- or 16-bit inputs and then aggregate the resulting counts."
+}
 op {
   name: "Pow"
   input_arg {
-- 
GitLab


From e8e406f64674a52619767e9715d218d4c00ff7ea Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 11:46:49 -0700
Subject: [PATCH 1308/1375] Minor tweaks to avoid unnecessary copies

PiperOrigin-RevId: 163101160
---
 tensorflow/cc/framework/gradients.cc         | 4 ++--
 tensorflow/core/framework/attr_value_util.cc | 6 ++++--
 tensorflow/core/framework/attr_value_util.h  | 6 ++++--
 3 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index cec3ebc0ad..66a943410e 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -356,7 +356,7 @@ Status SymbolicGradientBuilder::AddGradients() {
   // Check if any input nodes still have pending gradients and have not been
   // processed yet. This happens if not all outputs of a node are in 'inputs_'.
   std::unordered_map<Node*, int> requested_grads;
-  for (Output nout : inputs_) {
+  for (const Output& nout : inputs_) {
     if (pending_[nout.node()->id()] > 0) {
       DCHECK_GT(nout.node()->num_outputs(), 1);
       int idx = input_nodes_[nout];
@@ -365,7 +365,7 @@ Status SymbolicGradientBuilder::AddGradients() {
       ++requested_grads[nout.node()];
     }
   }
-  for (auto& p : requested_grads) {
+  for (const auto& p : requested_grads) {
     int num_requested_inputs = p.first->num_outputs() - pending_[p.first->id()];
     CHECK_EQ(num_requested_inputs, p.second);
   }
diff --git a/tensorflow/core/framework/attr_value_util.cc b/tensorflow/core/framework/attr_value_util.cc
index 9fdb3da6a0..95cafa24b1 100644
--- a/tensorflow/core/framework/attr_value_util.cc
+++ b/tensorflow/core/framework/attr_value_util.cc
@@ -15,7 +15,9 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value_util.h"
 
+#include <string>
 #include <vector>
+
 #include "tensorflow/core/framework/attr_value.pb_text.h"
 #include "tensorflow/core/framework/tensor.pb_text.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
@@ -27,7 +29,6 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 
 namespace tensorflow {
-
 namespace {
 
 string SummarizeString(const string& str) {
@@ -460,7 +461,8 @@ bool HasPlaceHolder(const AttrValue& val) {
   return false;
 }
 
-bool SubstitutePlaceholders(SubstituteFunc substitute, AttrValue* value) {
+bool SubstitutePlaceholders(const SubstituteFunc& substitute,
+                            AttrValue* value) {
   switch (value->value_case()) {
     case AttrValue::kList: {
       for (NameAttrList& func : *value->mutable_list()->mutable_func()) {
diff --git a/tensorflow/core/framework/attr_value_util.h b/tensorflow/core/framework/attr_value_util.h
index 08cc3b7158..08d813bb6f 100644
--- a/tensorflow/core/framework/attr_value_util.h
+++ b/tensorflow/core/framework/attr_value_util.h
@@ -16,8 +16,10 @@ limitations under the License.
 #ifndef TENSORFLOW_FRAMEWORK_ATTR_VALUE_UTIL_H_
 #define TENSORFLOW_FRAMEWORK_ATTR_VALUE_UTIL_H_
 
+#include <functional>
 #include <string>
 #include <vector>
+
 #include "tensorflow/core/framework/attr_value.pb.h"  // TODO(62899350): Remove
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -100,8 +102,8 @@ bool HasPlaceHolder(const AttrValue& val);
 // SubstituteFunc is given a placeholder string. If the placeholder is
 // unknown, SubstituteFunc returns false. Otherwise, overwrites the
 // attr value and returns true.
-typedef std::function<bool(const string&, AttrValue*)> SubstituteFunc;
-bool SubstitutePlaceholders(SubstituteFunc substitute, AttrValue* value);
+using SubstituteFunc = std::function<bool(const string&, AttrValue*)>;
+bool SubstitutePlaceholders(const SubstituteFunc& substitute, AttrValue* value);
 
 }  // namespace tensorflow
 
-- 
GitLab


From ac209ebc8fc8780bb3121a33740e10a34352996f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 11:49:58 -0700
Subject: [PATCH 1309/1375] [BatchNormGrad] Add end-to-end test for
 BatchNormGrad

RELNOTES: n/a
PiperOrigin-RevId: 163101568
---
 .../xla/tests/batch_normalization_test.cc     | 163 ++++++++++++++++++
 1 file changed, 163 insertions(+)

diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index 074e28cec7..d692a81032 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -308,6 +308,137 @@ XLA_TEST_P(BatchNormTest, DISABLED_ON_GPU(RandomizedTests)) {
       ErrorSpec(0.01, 1));
 }
 
+// TODO(b/62764704): Implement on GPU. Disabled on 2017-06-20.
+XLA_TEST_P(BatchNormTest, DISABLED_ON_CPU_PARALLEL(DISABLED_ON_CPU(
+                              DISABLED_ON_GPU(RandomizedGradTests)))) {
+  float epsilon = 0.001;
+  ComputationBuilder builder(client_, TestName());
+  const std::vector<int64>& bounds = GetParam().bounds;
+  Array4D<float> input_array(bounds[0], bounds[1], bounds[2], bounds[3]);
+  input_array.FillRandom(GetParam().random_value_var,
+                         GetParam().random_value_mean);
+
+  Array4D<float> grad_output_array(bounds[0], bounds[1], bounds[2], bounds[3]);
+  grad_output_array.FillRandom(GetParam().random_value_var,
+                               GetParam().random_value_mean);
+
+  const int64 feature_index = GetParam().feature_index;
+  const int64 num_elements_per_feature =
+      Product(bounds) / bounds[feature_index];
+  const int64 feature_bound = bounds[feature_index];
+  std::vector<float> scale(feature_bound, 2);
+
+  auto input_squared =
+      ReferenceUtil::MapArray4D(input_array, [](float a) { return a * a; });
+  std::vector<int64> reduce_dims;
+  for (int64 i = 0; i < bounds.size(); ++i) {
+    if (i != feature_index) {
+      reduce_dims.push_back(i);
+    }
+  }
+
+  auto sum =
+      ReferenceUtil::Reduce4DTo1D(input_array, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  auto sum_squared =
+      ReferenceUtil::Reduce4DTo1D(*input_squared, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  std::vector<float> mean(feature_bound);
+
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean[i] = sum[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> mean_square(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean_square[i] = mean[i] * mean[i];
+  }
+
+  std::vector<float> square_mean(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    square_mean[i] = sum_squared[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> var(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    var[i] = square_mean[i] - mean_square[i];
+  }
+
+  Array4D<float> mean_4D =
+      *ReferenceUtil::Broadcast1DTo4D(mean, bounds, feature_index);
+  auto var_4D = *ReferenceUtil::Broadcast1DTo4D(var, bounds, feature_index);
+  auto scale_4D = *ReferenceUtil::Broadcast1DTo4D(scale, bounds, feature_index);
+
+  auto var_add_epsilon = *ReferenceUtil::MapArray4D(
+      var_4D, [epsilon](float a) { return std::sqrt(a + epsilon); });
+
+  auto grad_output_times_var =
+      *ReferenceUtil::MapArray4D(grad_output_array, var_add_epsilon,
+                                 [](float a, float b) { return a * b; });
+
+  auto grad_activation = *ReferenceUtil::MapArray4D(
+      grad_output_times_var, scale_4D, [](float a, float b) { return a * b; });
+
+  auto activation_shifted = *ReferenceUtil::MapArray4D(
+      input_array, mean_4D, [](float a, float b) { return a - b; });
+
+  auto grad_scale_before_reduction =
+      *ReferenceUtil::MapArray4D(grad_output_times_var, activation_shifted,
+                                 [](float a, float b) { return a * b; });
+
+  auto grad_scale = ReferenceUtil::Reduce4DTo1D(
+      grad_scale_before_reduction, /*init=*/0.0f, reduce_dims,
+      [](float a, float b) { return a + b; });
+
+  auto grad_offset =
+      ReferenceUtil::Reduce4DTo1D(grad_output_array, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  auto expected_grad_activation =
+      Literal::CreateR4FromArray4D<float>(grad_activation);
+
+  auto input_literal = Literal::CreateR4FromArray4D<float>(input_array);
+  auto scale_literal = Literal::CreateR1<float>(scale);
+  auto mean_literal = Literal::CreateR1<float>(mean);
+  auto var_literal = Literal::CreateR1<float>(var);
+  auto grad_output_literal =
+      Literal::CreateR4FromArray4D<float>(grad_output_array);
+
+  auto input_parameter = builder.Parameter(0, input_literal->shape(), "input");
+  auto scale_parameter = builder.Parameter(1, scale_literal->shape(), "scale");
+  auto mean_parameter = builder.Parameter(2, mean_literal->shape(), "mean");
+  auto var_parameter = builder.Parameter(3, var_literal->shape(), "variance");
+  auto grad_output_parameter =
+      builder.Parameter(4, grad_output_literal->shape(), "grad_output");
+
+  std::unique_ptr<GlobalData> input_data =
+      client_->TransferToServer(*input_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> scale_data =
+      client_->TransferToServer(*scale_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> mean_data =
+      client_->TransferToServer(*mean_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> var_data =
+      client_->TransferToServer(*var_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> grad_output_data =
+      client_->TransferToServer(*grad_output_literal).ConsumeValueOrDie();
+
+  auto t = builder.BatchNormGrad(input_parameter, scale_parameter,
+                                 mean_parameter, var_parameter,
+                                 grad_output_parameter, epsilon, feature_index);
+
+  auto expected =
+      *Literal::MakeTuple({expected_grad_activation.get(),
+                           Literal::CreateR1<float>(grad_scale).get(),
+                           Literal::CreateR1<float>(grad_offset).get()});
+
+  ComputeAndCompareTuple(&builder, expected,
+                         {input_data.get(), scale_data.get(), mean_data.get(),
+                          var_data.get(), grad_output_data.get()},
+                         ErrorSpec(0.01, 1));
+}
+
 INSTANTIATE_TEST_CASE_P(
     BatchNormTest_Instantiation, BatchNormTest,
     ::testing::Values(BatchNormTestParam{{2, 2, 2, 2}, 0, 100.2f, 200.0f},
@@ -319,6 +450,7 @@ INSTANTIATE_TEST_CASE_P(
                       BatchNormTestParam{{10, 10, 10, 10}, 1, -666.6f, 777.7f},
                       BatchNormTestParam{{10, 10, 10, 10}, 2, 0.f, 777.7f},
                       BatchNormTestParam{{1, 1, 10, 130}, 2, 0.f, 777.7f},
+                      BatchNormTestParam{{1, 1, 130, 11}, 2, 0.f, 777.7f},
                       BatchNormTestParam{{1, 1, 10, 1}, 3, 888.8f, 9.9f},
 
                       BatchNormTestParam{{24, 129, 1, 2}, 2, 10000, 10000},
@@ -446,6 +578,37 @@ XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(LargeEpsilonTest)) {
                          ErrorSpec(0.1));
 }
 
+// TODO(b/62764704): Implement on CPU and GPU. Disabled on 2017-07-11.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_CPU_PARALLEL(DISABLED_ON_CPU(
+                              DISABLED_ON_GPU(BatchNormGradBasic)))) {
+  const int kFeatureIndex = 2;
+  ComputationBuilder builder(client_, TestName());
+
+  auto operand =
+      builder.ConstantR4FromArray4D<float>(Array4D<float>(2, 2, 2, 1, 0.0f));
+
+  auto scale = builder.ConstantR1<float>({1.0f, 1.0f});
+
+  auto mean = builder.ConstantR1<float>({0.0f, 0.0f});
+
+  auto var = builder.ConstantR1<float>({1.0f, 1.0f});
+
+  auto grad_output = builder.ConstantR4FromArray4D<float>(
+      {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}});
+
+  builder.BatchNormGrad(operand, scale, mean, var, grad_output,
+                        /*epsilon=*/0.0, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR4<float>(
+           {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}})
+           .get(),
+       Literal::CreateR1<float>({0, 0}).get(),
+       Literal::CreateR1<float>({16, 20}).get()});
+
+  ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
+}
+
 }  // namespace
 }  // namespace xla
 
-- 
GitLab


From 53019b0050d441ef053c080e6bcd8f210d8250ab Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 11:53:45 -0700
Subject: [PATCH 1310/1375] Go: Update generated wrapper functions for
 TensorFlow ops.

PiperOrigin-RevId: 163102070
---
 tensorflow/go/op/wrappers.go | 177 ++++++++++++++++++++++++-----------
 1 file changed, 122 insertions(+), 55 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 61c924ac22..095cbbe637 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -7070,6 +7070,61 @@ func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_ha
 	return op.Output(0)
 }
 
+// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
+type TextLineReaderV2Attr func(optionalAttr)
+
+// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
+//
+// value: Number of lines to skip from the beginning of every file.
+// If not specified, defaults to 0
+func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["skip_header_lines"] = value
+	}
+}
+
+// TextLineReaderV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// A Reader that outputs the lines of a file delimited by '\n'.
+//
+// Returns The handle to reference the Reader.
+func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "TextLineReaderV2",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes rectified linear 6: `min(max(features, 0), 6)`.
 func Relu6(scope *Scope, features tf.Output) (activations tf.Output) {
 	if scope.Err() != nil {
@@ -14495,61 +14550,6 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
-// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
-type TextLineReaderV2Attr func(optionalAttr)
-
-// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
-//
-// value: Number of lines to skip from the beginning of every file.
-// If not specified, defaults to 0
-func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["skip_header_lines"] = value
-	}
-}
-
-// TextLineReaderV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// A Reader that outputs the lines of a file delimited by '\n'.
-//
-// Returns The handle to reference the Reader.
-func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "TextLineReaderV2",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Component-wise multiplies a SparseTensor by a dense Tensor.
 //
 // The output locations corresponding to the implicitly zero elements in the sparse
@@ -17918,6 +17918,28 @@ func Svd(scope *Scope, input tf.Output, optional ...SvdAttr) (s tf.Output, u tf.
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
+//
+// For each entry in `x`, calculates the number of `1` (on) bits in the binary
+// representation of that entry.
+//
+// **NOTE**: It is more efficient to first `tf.bitcast` your tensors into
+// `int32` or `int64` and perform the bitcount on the result, than to feed in
+// 8- or 16-bit inputs and then aggregate the resulting counts.
+func PopulationCount(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "PopulationCount",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // AssertAttr is an optional argument to Assert.
 type AssertAttr func(optionalAttr)
 
@@ -23919,6 +23941,51 @@ func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Outp
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
+//
+// Each comparison returns a boolean `true` (if `input_value > threshold`)
+// or and `false` otherwise.
+//
+// This operation is useful for Locality-Sensitive-Hashing (LSH) and other
+// algorithms that use hashing approximations of cosine and `L2` distances;
+// codes can be generated from an input via:
+//
+// ```python
+// codebook_size = 50
+// codebook_bits = codebook_size * 32
+// codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
+//                            dtype=x.dtype,
+//                            initializer=tf.orthogonal_initializer())
+// codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
+// codes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32
+// # now codes has shape x.shape[:-1] + [codebook_size]
+// ```
+//
+// **NOTE**: Currently, the innermost dimension of the tensor must be divisible
+// by 8.
+//
+// Given an `input` shaped `[s0, s1, ..., s_n]`, the output is
+// a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`.
+//
+// Arguments:
+//	input: Values to compare against `threshold` and bitpack.
+//	threshold: Threshold to compare against.
+//
+// Returns The bitpacked comparisons.
+func CompareAndBitpack(scope *Scope, input tf.Output, threshold tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "CompareAndBitpack",
+		Input: []tf.Input{
+			input, threshold,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
 //
 // Arguments:
-- 
GitLab


From 2628455c6a8fb29839048ece8a211055b73b90e1 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 25 Jul 2017 11:56:42 -0700
Subject: [PATCH 1311/1375] [XLA] Add more unit tests for DynamicSlice and
 DynamicUpdateSlice.

PiperOrigin-RevId: 163102445
---
 .../compiler/xla/tests/dynamic_ops_test.cc    | 405 +++++++++++-------
 1 file changed, 239 insertions(+), 166 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
index 576c1c703d..9e85e35707 100644
--- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
@@ -44,295 +44,310 @@ namespace {
 
 class DynamicSliceTest : public ClientLibraryTestBase {
  protected:
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR1() {
     // Slice at dimension start.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {0}, {5},
-                  {0.0, 1.0, 2.0, 3.0, 4.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {0}, {5}, {0, 1, 2, 3, 4});
     // Slice in the middle.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {2}, {3},
-                  {2.0, 3.0, 4.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {2}, {3}, {2, 3, 4});
     // Slice at dimension boundaries.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {5}, {3},
-                  {5.0, 6.0, 7.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {5}, {3}, {5, 6, 7});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {6}, {4},
-                  {6.0, 7.0, 0.0, 1.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {6}, {4}, {6, 7, 0, 1});
     // Zero element slice.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {2}, {0}, {});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {2}, {0}, {});
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR2() {
     // Slice at dimension start.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {0, 0}, {2, 2}, {{1.0f, 2.0f}, {4.0f, 5.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {0, 0}, {2, 2},
+                         {{1, 2}, {4, 5}});
     // Slice in the middle.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {1, 1}, {2, 1}, {{5.0f}, {8.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {1, 1}, {2, 1},
+                         {{5}, {8}});
     // Slice at dimension boundaries.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {1, 1}, {2, 1}, {{5.0f}, {8.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {1, 1}, {2, 1},
+                         {{5}, {8}});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {1, 1}, {3, 3},
-                  {{5.0f, 6.0f, 4.0f}, {8.0f, 9.0f, 7.0f}, {2.0f, 3.0f, 1.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {1, 1}, {3, 3},
+                         {{5, 6, 4}, {8, 9, 7}, {2, 3, 1}});
     // Zero element slice: 2x0.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {0, 0}, {2, 0}, {{}, {}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {0, 0}, {2, 0},
+                         {{}, {}});
     // Zero element slice: 0x2.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {0, 0}, {0, 2}, Array2D<float>(0, 2));
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {0, 0}, {0, 2},
+                         Array2D<DataT>(0, 2));
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR3() {
     // R3 Shape: [2, 3, 2]
     // clang-format off
 
     // Slice at dimension start.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-        {0, 0, 0}, {2, 1, 2},
-      {{{1.0f, 2.0f}}, {{7.0f, 8.0f}}});
+    RunR3<IndexT, DataT>(
+      {{{1, 2}, {3, 4}, {5, 6}},
+       {{7, 8}, {9, 10}, {11, 12}}},
+      {0, 0, 0}, {2, 1, 2},
+      {{{1, 2}}, {{7, 8}}});
 
     // Slice in the middle.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-        {0, 1, 1}, {2, 2, 1},
-      {{{4.0f}, {6.0f}}, {{10.0f}, {12.0f}}});
+    RunR3<IndexT, DataT>(
+      {{{1, 2}, {3, 4}, {5, 6}},
+       {{7, 8}, {9, 10}, {11, 12}}},
+      {0, 1, 1}, {2, 2, 1},
+      {{{4}, {6}}, {{10}, {12}}});
 
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-        {0, 2, 1}, {2, 1, 2},
-      {{{6.0f, 5.0f}}, {{12.0f, 11.0f}}});
+    RunR3<IndexT, DataT>(
+      {{{1, 2}, {3, 4}, {5, 6}},
+       {{7, 8}, {9, 10}, {11, 12}}},
+      {0, 2, 1}, {2, 1, 2},
+      {{{6, 5}}, {{12, 11}}});
 
     // clang-format on
   }
 
-  template <typename IndexT>
-  void RunR1(const std::vector<float>& input_values,
+  template <typename IndexT, typename DataT>
+  void RunR1(tensorflow::gtl::ArraySlice<DataT> input_values,
              const std::vector<IndexT> slice_starts,
              const std::vector<int64>& slice_sizes,
-             const std::vector<float>& expected_values) {
+             tensorflow::gtl::ArraySlice<DataT> expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR1<float>(input_values);
+    auto input = builder.ConstantR1<DataT>(input_values);
     builder.DynamicSlice(input, starts, slice_sizes);
     // Run computation and compare against expected values.
-    ComputeAndCompareR1<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR1<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR2(const Array2D<float>& input_values,
+  template <typename IndexT, typename DataT>
+  void RunR2(const Array2D<DataT>& input_values,
              const std::vector<IndexT> slice_starts,
              const std::vector<int64>& slice_sizes,
-             const Array2D<float>& expected_values) {
+             const Array2D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR2FromArray2D<float>(input_values);
+    auto input = builder.ConstantR2FromArray2D<DataT>(input_values);
     builder.DynamicSlice(input, starts, slice_sizes);
     // Run computation and compare against expected values.
-    ComputeAndCompareR2<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR2<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR3(const Array3D<float>& input_values,
+  template <typename IndexT, typename DataT>
+  void RunR3(const Array3D<DataT>& input_values,
              const std::vector<IndexT> slice_starts,
              const std::vector<int64>& slice_sizes,
-             const Array3D<float>& expected_values) {
+             const Array3D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR3FromArray3D<float>(input_values);
+    auto input = builder.ConstantR3FromArray3D<DataT>(input_values);
     builder.DynamicSlice(input, starts, slice_sizes);
     // Run computation and compare against expected values.
-    ComputeAndCompareR3<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR3<DataT>(&builder, expected_values, {start_data.get()});
   }
 };
 
-XLA_TEST_F(DynamicSliceTest, Int32R1) { TestR1<int32>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R1) { TestR1<int32, int32>(); }
+
+XLA_TEST_F(DynamicSliceTest, Int64R1) { TestR1<int64, float>(); }
+
+XLA_TEST_F(DynamicSliceTest, UInt64R1) { TestR1<uint64, double>(); }
+
+XLA_TEST_F(DynamicSliceTest, Int32R2) { TestR2<int32, float>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int64R1) { TestR1<int64>(); }
+XLA_TEST_F(DynamicSliceTest, Int64R2) { TestR2<int64, double>(); }
 
-XLA_TEST_F(DynamicSliceTest, UInt64R1) { TestR1<uint64>(); }
+XLA_TEST_F(DynamicSliceTest, UInt64R2) { TestR2<uint64, int32>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int32R2) { TestR2<int32>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R3) { TestR3<int32, int32>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int64R2) { TestR2<int64>(); }
+XLA_TEST_F(DynamicSliceTest, Int64R3) { TestR3<int64, float>(); }
 
-XLA_TEST_F(DynamicSliceTest, UInt64R2) { TestR2<uint64>(); }
+XLA_TEST_F(DynamicSliceTest, UInt64R3) { TestR3<uint64, double>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int32R3) { TestR3<int32>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R1Pred) {
+  // Slice at dimension start.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {0},
+                     {5}, {true, false, false, true, false});
+  // Slice in the middle.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {2},
+                     {3}, {false, true, false});
+  // Slice at dimension boundaries.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {5},
+                     {3}, {true, true, false});
+  // Zero element slice.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {2},
+                     {0}, {});
+}
 
-XLA_TEST_F(DynamicSliceTest, Int64R3) { TestR3<int64>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R2Pred) {
+  // Slice at dimension start.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {0, 0},
+      {2, 2}, {{true, false}, {false, false}});
+  // Slice in the middle.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {1, 1},
+      {2, 1}, {{false}, {true}});
+  // Slice at dimension boundaries.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {1, 1},
+      {2, 1}, {{false}, {true}});
+  // Zero element slice: 2x0.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {0, 0},
+      {2, 0}, {{}, {}});
+  // Zero element slice: 0x2.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {0, 0},
+      {0, 2}, Array2D<bool>(0, 2));
+}
 
-XLA_TEST_F(DynamicSliceTest, UInt64R3) { TestR3<uint64>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R3Pred) {
+  // R3 Shape: [2, 3, 2]
+  // clang-format off
+
+  // Slice at dimension start.
+  RunR3<int32, bool>(
+    {{{true, false}, {false, true}, {true, true}},
+     {{false, true}, {true, false}, {false, false}}},
+    {0, 0, 0}, {2, 1, 2},
+    {{{true, false}}, {{false, true}}});
+
+  // Slice in the middle.
+  RunR3<int32, bool>(
+    {{{true, false}, {false, true}, {true, true}},
+     {{false, true}, {true, false}, {false, false}}},
+    {0, 1, 1}, {2, 2, 1},
+    {{{true}, {true}}, {{false}, {false}}});
+
+  // clang-format on
+}
 
 class DynamicUpdateSliceTest : public ClientLibraryTestBase {
  protected:
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR1() {
-    // clang-format off
     // Slice at dimension start.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {0},
-                  {8.0, 9.0, 10.0, 3.0, 4.0, 5.0, 6.0, 7.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {0},
+                         {8, 9, 10, 3, 4, 5, 6, 7});
     // Slice in the middle.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {2},
-                  {0.0, 1.0, 8.0, 9.0, 10.0, 5.0, 6.0, 7.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {2},
+                         {0, 1, 8, 9, 10, 5, 6, 7});
     // Slice at dimension boundaries.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {5},
-                  {0.0, 1.0, 2.0, 3.0, 4.0, 8.0, 9.0, 10.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {5},
+                         {0, 1, 2, 3, 4, 8, 9, 10});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {6},
-                  {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 8.0, 9.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {6},
+                         {0, 1, 2, 3, 4, 5, 8, 9});
     // Zero-sized update.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {}, {2},
-                  {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0});
-    // clang-format on
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {}, {2},
+                         {0, 1, 2, 3, 4, 5, 6, 7});
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR2() {
-    // clang-format off
     // Slice at dimension start.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {0, 0},
-        {{10.0f, 11.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {0, 0},
+                         {{10, 11, 3}, {4, 5, 6}, {7, 8, 9}});
     // Slice in the middle.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {1, 1},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 10.0f, 11.0f}, {7.0f, 8.0f, 9.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {1, 1},
+                         {{1, 2, 3}, {4, 10, 11}, {7, 8, 9}});
     // Slice at dimension boundaries.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {2, 1},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 10.0f, 11.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {2, 1},
+                         {{1, 2, 3}, {4, 5, 6}, {7, 10, 11}});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {2, 2},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 10.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {2, 2},
+                         {{1, 2, 3}, {4, 5, 6}, {7, 8, 10}});
     // Zero-sized update.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{}}, {2, 1},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}});
-    // clang-format on
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{}}, {2, 1},
+                         {{1, 2, 3}, {4, 5, 6}, {7, 8, 9}});
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR3() {
     // R3 Shape: [2, 3, 2]
-    // clang-format off
     // Slice at dimension start.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-      {{{13.0f, 14.0f}, {15.0f, 16.0f}},
-       {{17.0f, 18.0f}, {19.0f, 20.0f}}},
-        {0, 0, 0},
-      {{{13.0f, 14.0f}, {15.0f, 16.0f}, {5.0f, 6.0f}},
-       {{17.0f, 18.0f}, {19.0f, 20.0f}, {11.0f, 12.0f}}});
+    RunR3<IndexT, DataT>(
+        {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}},
+        {{{13, 14}, {15, 16}}, {{17, 18}, {19, 20}}}, {0, 0, 0},
+        {{{13, 14}, {15, 16}, {5, 6}}, {{17, 18}, {19, 20}, {11, 12}}});
     // Slice in the middle.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-      {{{13.0f}, {15.0f}}},
-        {1, 1, 1},
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-        {{7.0f, 8.0f}, {9.0f, 13.0f}, {11.0f, 15.0f}}});
+    RunR3<IndexT, DataT>(
+        {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}}, {{{13}, {15}}},
+        {1, 1, 1}, {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 13}, {11, 15}}});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-      {{{13.0f}, {15.0f}}},
-        {1, 2, 1},
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-        {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 13.0f}}});
-    // clang-format on
+    RunR3<IndexT, DataT>(
+        {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}}, {{{13}, {15}}},
+        {1, 2, 1}, {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 13}}});
   }
 
-  template <typename IndexT>
-  void RunR1(const std::vector<float>& input_values,
-             const std::vector<float>& update_values,
+  template <typename IndexT, typename DataT>
+  void RunR1(tensorflow::gtl::ArraySlice<DataT> input_values,
+             tensorflow::gtl::ArraySlice<DataT> update_values,
              const std::vector<IndexT> slice_starts,
-             const std::vector<float>& expected_values) {
+             tensorflow::gtl::ArraySlice<DataT> expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR1<float>(input_values);
-    auto update = builder.ConstantR1<float>(update_values);
+    auto input = builder.ConstantR1<DataT>(input_values);
+    auto update = builder.ConstantR1<DataT>(update_values);
     builder.DynamicUpdateSlice(input, update, starts);
     // Run computation and compare against expected values.
-    ComputeAndCompareR1<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR1<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR2(const Array2D<float>& input_values,
-             const Array2D<float>& update_values,
+  template <typename IndexT, typename DataT>
+  void RunR2(const Array2D<DataT>& input_values,
+             const Array2D<DataT>& update_values,
              const std::vector<IndexT> slice_starts,
-             const Array2D<float>& expected_values) {
+             const Array2D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR2FromArray2D<float>(input_values);
-    auto update = builder.ConstantR2FromArray2D<float>(update_values);
+    auto input = builder.ConstantR2FromArray2D<DataT>(input_values);
+    auto update = builder.ConstantR2FromArray2D<DataT>(update_values);
     builder.DynamicUpdateSlice(input, update, starts);
     // Run computation and compare against expected values.
-    ComputeAndCompareR2<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR2<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR3(const Array3D<float>& input_values,
-             const Array3D<float>& update_values,
+  template <typename IndexT, typename DataT>
+  void RunR3(const Array3D<DataT>& input_values,
+             const Array3D<DataT>& update_values,
              const std::vector<IndexT> slice_starts,
-             const Array3D<float>& expected_values) {
+             const Array3D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR3FromArray3D<float>(input_values);
-    auto update = builder.ConstantR3FromArray3D<float>(update_values);
+    auto input = builder.ConstantR3FromArray3D<DataT>(input_values);
+    auto update = builder.ConstantR3FromArray3D<DataT>(update_values);
     builder.DynamicUpdateSlice(input, update, starts);
     // Run computation and compare against expected values.
-    ComputeAndCompareR3<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR3<DataT>(&builder, expected_values, {start_data.get()});
   }
 
   void RunR3Contiguous(std::vector<int32> operand_shape, int32 index,
@@ -393,23 +408,81 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase {
   }
 };
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int32R1) { TestR1<int32>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R1) { TestR1<int32, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int64R1) { TestR1<int64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int64R1) { TestR1<int64, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, UInt64R1) { TestR1<uint64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, UInt64R1) { TestR1<uint64, double>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int32R2) { TestR2<int32>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R2) { TestR2<int32, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int64R2) { TestR2<int64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int64R2) { TestR2<int64, int64>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, UInt64R2) { TestR2<uint64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, UInt64R2) { TestR2<uint64, int32>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int32R3) { TestR3<int32>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R3) { TestR3<int32, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int64R3) { TestR3<int64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int64R3) { TestR3<int64, int64>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, UInt64R3) { TestR3<uint64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, UInt64R3) { TestR3<uint64, uint64>(); }
+
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R1Pred) {
+  // Slice at dimension start.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false},
+                     {true, true, false}, {0},
+                     {true, true, false, true, false, true, true, false});
+  // Slice in the middle.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false},
+                     {false, true, true}, {2},
+                     {false, false, false, true, true, true, true, false});
+  // Slice at dimension boundaries.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false},
+                     {false, true, true}, {5},
+                     {false, false, true, true, false, false, true, true});
+  // Zero-sized update.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false}, {},
+                     {2}, {false, false, true, true, false, true, true, false});
+}
+
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R2Pred) {
+  // Slice at dimension start.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}},
+      {{true, false}}, {0, 0},
+      {{true, false, false}, {true, false, true}, {false, true, true}});
+  // Slice in the middle.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}},
+      {{true, false}}, {1, 1},
+      {{false, true, false}, {true, true, false}, {false, true, true}});
+  // Slice at dimension boundaries.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}},
+      {{true, false}}, {2, 1},
+      {{false, true, false}, {true, false, true}, {false, true, false}});
+  // Zero-sized update.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}}, {{}},
+      {2, 1}, {{false, true, false}, {true, false, true}, {false, true, true}});
+}
+
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R3Pred) {
+  // R3 Shape: [2, 3, 2]
+  // Slice at dimension start.
+  RunR3<int32, bool>(
+      {{{true, false}, {false, true}, {true, true}},
+       {{false, false}, {false, true}, {true, false}}},
+      {{{false, true}, {true, false}}, {{true, true}, {false, true}}},
+      {0, 0, 0},
+      {{{false, true}, {true, false}, {true, true}},
+       {{true, true}, {false, true}, {true, false}}});
+  // Slice in the middle.
+  RunR3<int32, bool>({{{true, false}, {false, true}, {true, true}},
+                      {{false, false}, {false, true}, {true, false}}},
+                     {{{false}, {true}}}, {1, 1, 1},
+                     {{{true, false}, {false, true}, {true, true}},
+                      {{false, false}, {false, false}, {true, true}}});
+}
 
 // Tests for simple R3 case where the update is contiguous (i.e. the minor
 // two dimensions are not sliced).
-- 
GitLab


From 94a6e51cb1367e2a77db00f26694bb4c296af6f9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 12:07:06 -0700
Subject: [PATCH 1312/1375] Adding missing deps to targets in llvm.BUILD. This
 was only working in non-sandboxed builds.

PiperOrigin-RevId: 163103908
---
 third_party/llvm/llvm.BUILD | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD
index 12bacf3c27..2d96406d27 100644
--- a/third_party/llvm/llvm.BUILD
+++ b/third_party/llvm/llvm.BUILD
@@ -899,6 +899,7 @@ cc_library(
         "include/llvm/Target/ARM/InstPrinter/*.h",
         "include/llvm/Target/ARM/InstPrinter/*.def",
         "include/llvm/Target/ARM/InstPrinter/*.inc",
+        "lib/Target/ARM/*.h",
         "lib/Target/ARM/InstPrinter/*.h",
     ]),
     copts = ["-Iexternal/llvm/lib/Target/ARM"],
@@ -1206,6 +1207,7 @@ cc_library(
         "lib/IR/*.h",
     ]),
     hdrs = glob([
+        "include/llvm/Analysis/*.def",
         "include/llvm/IR/*.h",
         "include/llvm/IR/*.def",
         "include/llvm/IR/*.inc",
@@ -2022,6 +2024,8 @@ cc_library(
         "lib/Target/*.h",
     ]),
     hdrs = glob([
+        "include/llvm/CodeGen/*.h",
+        "include/llvm/CodeGen/*.def",
         "include/llvm/Target/*.h",
         "include/llvm/Target/*.def",
         "include/llvm/Target/*.inc",
-- 
GitLab


From 036ea0c39071f41357846100f0bdfe51442a486a Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 25 Jul 2017 12:20:06 -0700
Subject: [PATCH 1313/1375] Pass batch_size in params when use_tpu=False.

PiperOrigin-RevId: 163105673
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 66 +++++++++----------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 712871cc04..b6fa185709 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -54,9 +54,12 @@ def _tpu_job(run_config):
   return None if run_config.master in ['', 'local'] else 'tpu_worker'
 
 
-def _per_shard_batch_size(global_batch_size, run_config):
+def _per_shard_batch_size(global_batch_size, run_config, use_tpu):
   """Returns the batch size for each shard."""
-  return global_batch_size // run_config.tpu_config.num_shards
+  if use_tpu:
+    return global_batch_size // run_config.tpu_config.num_shards
+  else:
+    return global_batch_size
 
 
 class _SIGNAL(object):
@@ -470,7 +473,7 @@ class _ModelFnWrapper(object):
     self._train_batch_size = train_batch_size
 
   def call_without_tpu(self, features, labels):
-    return self._call_model_fn(features, labels)
+    return self._call_model_fn(features, labels, False)
 
   def convert_to_single_tpu_train_step(self, dequeue_fn):
     """Converts the `model_fn` as a single train step on TPU."""
@@ -481,8 +484,8 @@ class _ModelFnWrapper(object):
       features, labels = dequeue_fn()
 
       # Makes deep copy with `config` and params` in case user mutates them.
-      estimator_spec = self._verify_estimator_spec(self._call_model_fn(
-          features, labels, add_batch_size_in_params=True))
+      estimator_spec = self._verify_estimator_spec(
+          self._call_model_fn(features, labels, True))
       loss, train_op = estimator_spec.loss, estimator_spec.train_op
       with ops.control_dependencies([train_op]):
         return array_ops.identity(loss)
@@ -492,7 +495,7 @@ class _ModelFnWrapper(object):
   def config(self):
     return self._config
 
-  def _call_model_fn(self, features, labels, add_batch_size_in_params=False):
+  def _call_model_fn(self, features, labels, use_tpu):
     """Calls the model_fn with required parameters."""
     model_fn_args = util.fn_args(self._model_fn)
     kwargs = {}
@@ -513,16 +516,15 @@ class _ModelFnWrapper(object):
     if 'params' in model_fn_args:
       kwargs['params'] = params
 
-    if add_batch_size_in_params:
-      if 'params' not in model_fn_args:
-        raise ValueError(
-            'model_fn ({}) does not include params argument, '
-            'required by TPUEstimator to pass batch size as '
-            'params[\'batch_size\']'.format(self._model_fn))
-      if self._mode == model_fn_lib.ModeKeys.TRAIN:
-        # For TPU training. `params` is never `None`.
-        params[_BATCH_SIZE_KEY] = _per_shard_batch_size(self._train_batch_size,
-                                                        config)
+    if 'params' not in model_fn_args:
+      raise ValueError(
+          'model_fn ({}) does not include params argument, '
+          'required by TPUEstimator to pass batch size as '
+          'params[\'batch_size\']'.format(self._model_fn))
+    if self._mode == model_fn_lib.ModeKeys.TRAIN:
+      # For TPU training. `params` is never `None`.
+      params[_BATCH_SIZE_KEY] = _per_shard_batch_size(
+          self._train_batch_size, config, use_tpu)
 
     return self._model_fn(features=features, **kwargs)
 
@@ -609,16 +611,12 @@ class TPUEstimator(estimator_lib.Estimator):
             'batch size {} must be divisible by number of shards {}'
             .format(train_batch_size, config.tpu_config.num_shards))
 
-    if use_tpu:
-      # Verifies the model_fn signature according to Estimator framework.
-      estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
-      # We cannot store config and params in this constructor as parent
-      # constructor might change them, such as assigning a temp dir for
-      # config.model_dir.
-      model_function = augment_model_fn_with_tpu_support(
-          model_fn, train_batch_size)
-    else:
-      model_function = model_fn
+    # Verifies the model_fn signature according to Estimator framework.
+    estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
+    # We cannot store config and params in this constructor as parent
+    # constructor might change them, such as assigning a temp dir for
+    # config.model_dir.
+    model_function = _augment_model_fn(model_fn, train_batch_size, use_tpu)
 
     super(TPUEstimator, self).__init__(
         model_fn=model_function,
@@ -670,9 +668,6 @@ class TPUEstimator(estimator_lib.Estimator):
     Raises:
       ValueError: if input_fn takes invalid arguments or does not have `params`.
     """
-    if not self._use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
-      return super(TPUEstimator, self)._call_input_fn(input_fn, mode)
-
     input_fn_args = util.fn_args(input_fn)
     config = self.config  # a deep copy.
     kwargs = {}
@@ -686,8 +681,13 @@ class TPUEstimator(estimator_lib.Estimator):
       kwargs['config'] = config
 
     # Now for TPU training.
-    per_shard_batch_size = _per_shard_batch_size(self._train_batch_size, config)
-    kwargs['params'][_BATCH_SIZE_KEY] = per_shard_batch_size
+    if mode == model_fn_lib.ModeKeys.TRAIN:
+      kwargs['params'][_BATCH_SIZE_KEY] = (
+          _per_shard_batch_size(self._train_batch_size, config, self._use_tpu))
+
+    if not self._use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
+      with ops.device('/cpu:0'):
+        return input_fn(**kwargs)
 
     job = _tpu_job(config)
     def placement_function(index):
@@ -746,7 +746,7 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(inputs_holder):
   return (dequeue_fn, enqueue_fn)
 
 
-def augment_model_fn_with_tpu_support(model_fn, train_batch_size):
+def _augment_model_fn(model_fn, train_batch_size, use_tpu):
   """Returns a new model_fn, which wraps the TPU support."""
 
   def _model_fn(features, labels, mode, config, params):
@@ -755,7 +755,7 @@ def augment_model_fn_with_tpu_support(model_fn, train_batch_size):
                                        train_batch_size)
 
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
-    if mode != model_fn_lib.ModeKeys.TRAIN:
+    if not use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
       return model_fn_wrapper.call_without_tpu(features, labels)
 
     inputs = _InputsHolder(sharded_features=features, sharded_labels=labels)
-- 
GitLab


From 50d48d606c3b2c08eef249b6fe4f543a51ca8455 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 12:41:23 -0700
Subject: [PATCH 1314/1375] Remove duplicate import.

PiperOrigin-RevId: 163108237
---
 tensorflow/python/ops/init_ops.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py
index 1e2f999995..42b4f952bb 100644
--- a/tensorflow/python/ops/init_ops.py
+++ b/tensorflow/python/ops/init_ops.py
@@ -41,7 +41,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import math_ops
 
 
 class Initializer(object):
-- 
GitLab


From b4c97bf13b618fbdc22981ce04f9faf358da034c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 12:53:50 -0700
Subject: [PATCH 1315/1375] Implementation of UnsortedSegmentSum in tf2xla
 bridge.

PiperOrigin-RevId: 163109769
---
 tensorflow/compiler/tests/BUILD               |  14 ++
 .../tests/segment_reduction_ops_test.py       | 139 ++++++++++++++++
 tensorflow/compiler/tf2xla/kernels/BUILD      |   1 +
 .../tf2xla/kernels/segment_reduction_ops.cc   | 155 ++++++++++++++++++
 4 files changed, 309 insertions(+)
 create mode 100644 tensorflow/compiler/tests/segment_reduction_ops_test.py
 create mode 100644 tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc

diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 4f0137e8d9..c693f58f8b 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -353,6 +353,20 @@ tf_xla_py_test(
     ],
 )
 
+tf_xla_py_test(
+    name = "segment_reduction_ops_test",
+    size = "small",
+    srcs = ["segment_reduction_ops_test.py"],
+    deps = [
+        ":xla_test",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:math_ops_gen",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 tf_xla_py_test(
     name = "spacetobatch_op_test",
     size = "medium",
diff --git a/tensorflow/compiler/tests/segment_reduction_ops_test.py b/tensorflow/compiler/tests/segment_reduction_ops_test.py
new file mode 100644
index 0000000000..260a04421b
--- /dev/null
+++ b/tensorflow/compiler/tests/segment_reduction_ops_test.py
@@ -0,0 +1,139 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test cases for segment reduction ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+import numpy as np
+
+from tensorflow.compiler.tests.xla_test import XLATestCase
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import googletest
+
+
+class SegmentReductionOpsTest(XLATestCase):
+  """Test cases for segment reduction ops."""
+
+  def UnsortedSegmentSum(self, data, indices, num_segments):
+    with self.test_session() as sess, self.test_scope():
+      d = array_ops.placeholder(data.dtype, shape=data.shape)
+      if isinstance(indices, int):
+        i = array_ops.placeholder(np.int32, shape=[])
+      else:
+        i = array_ops.placeholder(indices.dtype, shape=indices.shape)
+      return sess.run(
+          math_ops.unsorted_segment_sum(d, i, num_segments),
+          {d: data,
+           i: indices})
+
+  def testUnsortedSegmentSum0DIndices1DData(self):
+    for dtype in self.numeric_types:
+      self.assertAllClose(
+          np.array(
+              [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5],
+               [0, 0, 0, 0, 0, 0]],
+              dtype=dtype),
+          self.UnsortedSegmentSum(
+              np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4))
+
+  def testUnsortedSegmentSum1DIndices1DData(self):
+    for dtype in self.numeric_types:
+      self.assertAllClose(
+          np.array([1, 3, 2, 9], dtype=dtype),
+          self.UnsortedSegmentSum(
+              np.array([0, 1, 2, 3, 4, 5], dtype=dtype),
+              np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4))
+
+  def testUnsortedSegmentSum1DIndices2DDataDisjoint(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
+           [50, 51, 52, 53]],
+          dtype=dtype)
+      indices = np.array([8, 1, 0, 3, 7], dtype=np.int32)
+      num_segments = 10
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[30, 31, 32, 33], [20, 21, 22, 23], [0, 0, 0, 0],
+               [40, 41, 42, 43], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
+               [50, 51, 52, 53], [0, 1, 2, 3], [0, 0, 0, 0]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSum1DIndices2DDataNonDisjoint(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
+           [50, 51, 52, 53]],
+          dtype=dtype)
+      indices = np.array([0, 1, 2, 0, 1], dtype=np.int32)
+      num_segments = 4
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[40, 42, 44, 46], [70, 72, 74, 76], [30, 31, 32, 33],
+               [0, 0, 0, 0]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSum2DIndices3DData(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]],
+           [[200, 201, 202], [210, 211, 212]], [[300, 301, 302],
+                                                [310, 311, 312]]],
+          dtype=dtype)
+      indices = np.array([[3, 5], [3, 1], [5, 0], [6, 2]], dtype=np.int32)
+      num_segments = 8
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[210, 211, 212], [110, 111, 112], [310, 311, 312],
+               [100, 102, 104], [0, 0, 0.], [210, 212, 214], [300, 301,
+                                                              302], [0, 0, 0]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSum1DIndices3DData(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]],
+           [[200, 201, 202], [210, 211, 212]], [[300, 301, 302],
+                                                [310, 311, 312]]],
+          dtype=dtype)
+      indices = np.array([3, 0, 2, 5], dtype=np.int32)
+      num_segments = 6
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[[100, 101, 102.], [110, 111, 112]], [[0, 0, 0], [0, 0, 0]],
+               [[200, 201, 202], [210, 211, 212]], [[0, 1, 2.], [10, 11, 12]],
+               [[0, 0, 0], [0, 0, 0]], [[300, 301, 302], [310, 311, 312]]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSumShapeError(self):
+    for dtype in self.numeric_types:
+      data = np.ones((4, 8, 7), dtype=dtype)
+      indices = np.ones((3, 2), dtype=np.int32)
+      num_segments = 4
+      self.assertRaises(ValueError,
+                        functools.partial(self.UnsortedSegmentSum, data,
+                                          indices, num_segments))
+
+
+if __name__ == '__main__':
+  googletest.main()
diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD
index 35bc6b5a24..546e9be864 100644
--- a/tensorflow/compiler/tf2xla/kernels/BUILD
+++ b/tensorflow/compiler/tf2xla/kernels/BUILD
@@ -47,6 +47,7 @@ tf_kernel_library(
         "reshape_op.cc",
         "retval_op.cc",
         "reverse_op.cc",
+        "segment_reduction_ops.cc",
         "select_op.cc",
         "sequence_ops.cc",
         "shape_op.cc",
diff --git a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc
new file mode 100644
index 0000000000..6a0ce775dc
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc
@@ -0,0 +1,155 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <sstream>
+#include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/types.h"
+
+namespace tensorflow {
+namespace {
+
+class UnsortedSegmentSum : public XlaOpKernel {
+ public:
+  explicit UnsortedSegmentSum(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_));
+  }
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    // output = unsorted_segment_sum(data, indices, num_segments)
+    // Compute a tensor such that:
+    //    output[i] = sum over {j where indices[j] == i} of data[j]
+    //    output[i] == 0 if i does not appear in indices
+    //
+    // Contrast with segment_sum(), which assumes indices are sorted and that
+    // max(indices)+1 is the desired size of the output.
+    //
+    // The returned output tensor has the same type as data, and the same shape
+    // as data with the first indices.rank dimensions are replaced
+    // by a single dimension with size num_segments.
+
+    xla::ComputationBuilder* builder = ctx->builder();
+
+    auto data = ctx->Input(0);
+    auto data_shape = ctx->InputShape(0);
+
+    auto indices = ctx->Input(1);
+    auto indices_shape = ctx->InputShape(1);
+
+    OP_REQUIRES(ctx, data_shape.dims() >= indices_shape.dims(),
+                errors::InvalidArgument(
+                    "UnsortedSegmentSum requires that indices' rank be"
+                    " less than or equal to data's rank."));
+    // Validate that indices.shape is a prefix of data.shape.
+    for (int d = 0; d < indices_shape.dims(); ++d) {
+      OP_REQUIRES(ctx, (data_shape.dim_size(d) == indices_shape.dim_size(d)),
+                  errors::InvalidArgument(
+                      "UnsortedSegmentSum requires indices shape to be prefix"
+                      " of data_shape, but dimension ",
+                      d, " differs ", data_shape.dim_size(d), " vs. ",
+                      indices_shape.dim_size(d)));
+    }
+
+    int64 num_segments;
+    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(2, &num_segments));
+
+    // Flatten the indices into 1-D.
+    auto indices_1d = builder->Reshape(indices, {indices_shape.num_elements()});
+
+    // flatten data for dynamic indexing.
+    int64 out_tensor_dims = data_shape.dims() - indices_shape.dims();
+    std::vector<int64> flat_shape(1 + out_tensor_dims);
+    flat_shape[0] = indices_shape.num_elements();
+    for (int64 k = 0; k < out_tensor_dims; ++k) {
+      flat_shape[1 + k] = data_shape.dim_size(indices_shape.dims() + k);
+    }
+    auto data_flat = builder->Reshape(data, flat_shape);
+
+    // output shape; same as data_shape, but dimension 0 is num_segments.
+    std::vector<int64> out_shape(flat_shape);
+    out_shape[0] = num_segments;
+
+    // Pad the output array dims to rank >= 3 to work around lowering issues.
+    // TODO(b/37575001) This is awkward, and could be improved.
+    int64 extra_dims = 0;
+    if (out_shape.size() < 3) {
+      extra_dims = 3u - out_shape.size();
+    }
+    std::vector<int64> rshape(extra_dims + out_shape.size(), 1);
+    for (unsigned k = 0; k < out_shape.size(); ++k) {
+      rshape[extra_dims + k] = out_shape[k];
+    }
+    auto output = builder->Broadcast(XlaHelpers::Zero(builder, dtype_), rshape);
+
+    auto zero = builder->ConstantR1<int32>({0});
+
+    for (int64 i = 0; i < indices_shape.num_elements(); ++i) {
+      // output[indices[i]] += data[i]
+
+      std::vector<int64> data_start_indices(flat_shape.size());
+      data_start_indices[0] = i;
+      for (unsigned d = 1; d < flat_shape.size(); ++d) {
+        data_start_indices[d] = 0;
+      }
+      std::vector<int64> data_limit_indices(flat_shape);
+      data_limit_indices[0] = i + 1;
+      std::vector<int64> stride(flat_shape.size(), 1);
+
+      auto data_slice = builder->Slice(data_flat, data_start_indices,
+                                       data_limit_indices, stride);
+
+      // Reshape the sliced data into the R3+ shape to match output array.
+      std::vector<int64> rdata_shape(extra_dims + flat_shape.size());
+      for (int64 k = 0; k <= extra_dims; ++k) {
+        rdata_shape[k] = 1;
+      }
+      for (unsigned k = 1; k < data_limit_indices.size(); ++k) {
+        rdata_shape[extra_dims + k] = data_limit_indices[k];
+      }
+      auto rdata_slice = builder->Reshape(data_slice, rdata_shape);
+
+      auto index = builder->Slice(indices_1d, {i}, {i + 1}, {1});
+
+      // Construct the index into the R3+ output array 0, ..., <index>, 0, ...
+      std::vector<xla::ComputationDataHandle> out_start_index_parts(
+          extra_dims + flat_shape.size(), zero);
+      out_start_index_parts[extra_dims] = builder->Reshape(index, {1});
+      auto out_start_indices = builder->ConcatInDim(out_start_index_parts, 0);
+
+      std::vector<int64> slice_size(rshape);
+      slice_size[extra_dims] = 1;
+
+      auto out_slice =
+          builder->DynamicSlice(output, out_start_indices, slice_size);
+      auto sumval = builder->Add(out_slice, rdata_slice);
+      output = builder->DynamicUpdateSlice(output, sumval, out_start_indices);
+    }
+    auto reshaped_output = builder->Reshape(output, out_shape);
+    ctx->SetOutput(0, reshaped_output);
+  }
+
+ private:
+  DataType dtype_;
+};
+
+REGISTER_XLA_OP(Name("UnsortedSegmentSum"), UnsortedSegmentSum);
+
+}  // namespace
+}  // namespace tensorflow
-- 
GitLab


From fbe6ae13a36c80454f032a37577ab199a67ef01d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 13:03:58 -0700
Subject: [PATCH 1316/1375] Add gradient checking tests for nn.moments().

PiperOrigin-RevId: 163110994
---
 tensorflow/python/ops/nn_test.py | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index 87f6f92a8a..cc8c623947 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -830,7 +830,8 @@ class ReluTest(test_lib.TestCase):
 
 class MomentsTest(test_lib.TestCase):
 
-  def doOutputTest(self, input_shape, moments_axes, tol=1e-4):
+  def doOutputTest(self, input_shape, moments_axes, tol=1e-4,
+                   check_gradients=False):
     for mu in [0.0, 1.0, 1e3]:
       for sigma in [1.0, 0.1]:
         for keep_dims in [True, False]:
@@ -846,6 +847,15 @@ class MomentsTest(test_lib.TestCase):
               mean, variance = nn_impl.moments(
                   inputs, moments_axes, keep_dims=keep_dims)
 
+              if check_gradients:
+                err = gradient_checker.compute_gradient_error(
+                    inputs, input_shape, mean, mean.shape.as_list())
+                self.assertLess(err, 1e-3)
+                err = gradient_checker.compute_gradient_error(
+                    inputs, input_shape, variance, variance.shape.as_list())
+                self.assertLess(err, 1e-3)
+
+              # Evaluate.
               [mean, variance] = sess.run([mean, variance])
               # Make sure that there are no NaNs
               self.assertFalse(np.isnan(mean).any())
@@ -853,6 +863,12 @@ class MomentsTest(test_lib.TestCase):
               self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol)
               self.assertAllClose(variance, expected_var, rtol=tol, atol=tol)
 
+  def testOutputAndGradient2DInput0(self):
+    self.doOutputTest((10, 10), (0,), check_gradients=True)
+
+  def testOutputAndGradient2DInput01(self):
+    self.doOutputTest((10, 10), (0, 1), check_gradients=True)
+
   def testOutput2DInput0(self):
     self.doOutputTest((10, 300), (0,))
 
-- 
GitLab


From 6f70e57802a518bddca3d3991825d8b13ee3c61a Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 25 Jul 2017 13:19:20 -0700
Subject: [PATCH 1317/1375] Improved the speed of constant folding

PiperOrigin-RevId: 163113085
---
 .../grappler/optimizers/constant_folding.cc   | 83 +++++++++----------
 1 file changed, 40 insertions(+), 43 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index db279ae67f..7f845bb9e2 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -104,7 +104,8 @@ ConstantFolding::ConstantFolding() {
   ops_to_preserve_ = std::regex(
       "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|"
       "Enter|RefEnter|Exit|RefExit|NextIteration|RefNextIteration|"
-      ".*Quantized.*");
+      ".*Quantized.*",
+      std::regex_constants::optimize);
 }
 
 string ConstantFolding::AddControlDependency(const string& input_name) {
@@ -240,13 +241,18 @@ Status ConstantFolding::MaterializeShapes(const GrapplerItem& item,
 }
 
 bool ConstantFolding::IsFoldable(const NodeDef& node) const {
+  // Folding not applicable to ops with no inputs.
+  if (node.input().empty()) {
+    return false;
+  }
+
   // Skips nodes that must be preserved, and op_types that don't benefit from
   // folding
   if (nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) {
     return false;
   }
-  std::cmatch match;
-  if (std::regex_match(node.op().c_str(), match, ops_to_preserve_)) {
+  if (std::regex_match(node.op().c_str(), ops_to_preserve_,
+                       std::regex_constants::match_any)) {
     return false;
   }
 
@@ -264,23 +270,6 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
 
-  DeviceTypeVector device_types;
-  status = SupportedDeviceTypesForNode({DeviceType(DEVICE_CPU)}, node,
-                                       &device_types);
-  if (!status.ok()) {
-    return false;
-  }
-  // Only fold ops with a CPU implementation available.
-  if (device_types.empty()) {
-    return false;
-  }
-  DCHECK_EQ(DeviceType(DEVICE_CPU), device_types[0]);
-
-  // Folding not applicable to ops with no inputs.
-  if (node.input().empty()) {
-    return false;
-  }
-
   // No need to (and don't) fold nodes that have no outgoing edges. Such nodes
   // could be introduced by an earlier constant folding pass and are preserved
   // in case users want to fetch their values; re-processing them would
@@ -391,12 +380,15 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
       // Control dependency
       break;
     }
-    // There should be a single output since the input node should be a constant
-    // node.
-    TensorVector output;
-    TF_RETURN_IF_ERROR(
-        EvaluateNode(*node_map_->GetNode(input), TensorVector(), &output));
-    inputs.push_back(output[position]);
+    const NodeDef* input_node = node_map_->GetNode(input);
+    if (!IsConstant(*input_node)) {
+      return Status(error::INVALID_ARGUMENT,
+                    strings::StrCat("Can't fold ", node.name(), ", its ", input,
+                                    " isn't constant"));
+    }
+    Tensor* value = new Tensor(input_node->attr().at("dtype").type());
+    CHECK(value->FromProto(input_node->attr().at("value").tensor()));
+    inputs.emplace_back(value);
   }
 
   TensorVector output_tensors;
@@ -583,24 +575,31 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
 
 Status ConstantFolding::FoldGraph(GraphDef* output) {
   std::unordered_set<string> processed_nodes;
-  int previously_processed = 0;
-  do {
-    previously_processed = processed_nodes.size();
-    for (const auto& node : graph_.node()) {
-      if (IsFoldable(node) &&
-          processed_nodes.find(node.name()) == processed_nodes.end()) {
-        Status s = FoldNode(node, output);
-        if (!s.ok()) {
-          VLOG(1) << "Failed to fold node " << node.name() << ": " << s;
+  std::deque<const NodeDef*> queue;
+  for (const auto& node : graph_.node()) {
+    if (IsFoldable(node)) {
+      queue.push_back(&node);
+    }
+  }
+  while (!queue.empty()) {
+    const NodeDef* node = queue.front();
+    queue.pop_front();
+    if (processed_nodes.count(node->name())) {
+      continue;
+    }
+    Status s = FoldNode(*node, output);
+    processed_nodes.insert(node->name());
+    if (!s.ok()) {
+      VLOG(1) << "Failed to fold node " << node->name() << ": " << s;
+    } else {
+      auto outputs = node_map_->GetOutputs(node->name());
+      for (auto& output : outputs) {
+        if (IsFoldable(*output)) {
+          queue.push_back(output);
         }
-        processed_nodes.insert(node.name());
       }
     }
-    // Try again as long as we find new constants. In most cases, this loop will
-    // only run once since the graph is already in topological order.
-    VLOG(1) << "Folded " << processed_nodes.size() - previously_processed
-            << " nodes in this pass";
-  } while (previously_processed != processed_nodes.size());
+  }
 
   // Build the graph after constant folding. Note that we keep all processed
   // nodes in the graph in case users need to fetch their values.
@@ -740,7 +739,6 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
 Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
                                  GraphDef* output) {
   graph_ = item.graph;
-  LOG(INFO) << "Initial graph size: " << item.graph.node_size();
   node_map_.reset(new NodeMap(&graph_));
   for (const auto& node : item.fetch) {
     nodes_to_preserve_.insert(NodeName(node));
@@ -761,7 +759,6 @@ Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
 
   TF_RETURN_IF_ERROR(FoldGraph(output));
   TF_RETURN_IF_ERROR(SimplifyGraph(output, properties));
-  LOG(INFO) << "Optimized graph size: " << output->node_size();
 
   *output->mutable_library() = item.graph.library();
   *output->mutable_versions() = item.graph.versions();
-- 
GitLab


From 73ea287120184a694e611ad6cbc356fab9ad0f25 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 13:30:03 -0700
Subject: [PATCH 1318/1375] Convert configure to python.

PiperOrigin-RevId: 163114551
---
 configure                                     | 814 +--------------
 configure.py                                  | 950 ++++++++++++++++++
 tensorflow/tools/ci_build/builds/configured   |   2 +-
 .../tools/ci_build/builds/run_pip_tests.sh    |   2 +-
 .../tools/ci_build/linux/cpu/run_cc_core.sh   |   2 +-
 .../tools/ci_build/linux/cpu/run_py2_core.sh  |   2 +-
 .../ci_build/linux/cpu/run_py3_contrib.sh     |   2 +-
 .../tools/ci_build/linux/cpu/run_py3_core.sh  |   2 +-
 .../tools/ci_build/linux/gpu/run_cc_core.sh   |   2 +-
 .../tools/ci_build/linux/gpu/run_py3_core.sh  |   2 +-
 .../tools/ci_build/osx/cpu/run_py2_cc_core.sh |   2 +-
 .../tools/ci_build/xla/linux/gpu/run_py3.sh   |   2 +-
 12 files changed, 965 insertions(+), 819 deletions(-)
 create mode 100644 configure.py

diff --git a/configure b/configure
index 1eeaffaf74..c6df6992d9 100755
--- a/configure
+++ b/configure
@@ -3,816 +3,12 @@
 set -e
 set -o pipefail
 
-MIN_BAZEL_VERSION=0.4.5
-
-# Find out the absolute path to where ./configure resides
-pushd `dirname $0` > /dev/null
-SOURCE_BASE_DIR=`pwd -P`
-popd > /dev/null
-
-PLATFORM="$(uname -s | tr 'A-Z' 'a-z')"
-
-function is_linux() {
-  [[ "${PLATFORM}" == "linux" ]]
-}
-
-function is_macos() {
-  [[ "${PLATFORM}" == "darwin" ]]
-}
-
-function is_windows() {
-  # On windows, the shell script is actually running in msys
-  [[ "${PLATFORM}" =~ msys_nt*|mingw*|cygwin*|uwin* ]]
-}
-
-function is_ppc64le() {
-  [[ "$(uname -m)" == "ppc64le" ]]
-}
-
-function sed_in_place() {
-  sed -e $1 $2 > "$2.bak"
-  mv "$2.bak" $2
-}
-
-function write_to_bazelrc() {
-  echo "$1" >> .tf_configure.bazelrc
-}
-
-function write_action_env_to_bazelrc() {
-  write_to_bazelrc "build --action_env $1=\"$2\""
-}
-
-function python_path {
-  "$PYTHON_BIN_PATH" - <<END
-from __future__ import print_function
-import site
-import os
-
-try:
-  input = raw_input
-except NameError:
-  pass
-
-python_paths = []
-if os.getenv('PYTHONPATH') is not None:
-  python_paths = os.getenv('PYTHONPATH').split(':')
-try:
-  library_paths = site.getsitepackages()
-except AttributeError:
- from distutils.sysconfig import get_python_lib
- library_paths = [get_python_lib()]
-all_paths = set(python_paths + library_paths)
-
-paths = []
-for path in all_paths:
-  if os.path.isdir(path):
-    paths.append(path)
-
-print(",".join(paths))
-END
-}
-
-function setup_python {
-  ## Set up python-related environment settings:
-  while true; do
-    fromuser=""
-    if [ -z "$PYTHON_BIN_PATH" ]; then
-      default_python_bin_path=$(which python || which python3 || true)
-      read -p "Please specify the location of python. [Default is $default_python_bin_path]: " PYTHON_BIN_PATH
-      fromuser="1"
-      if [ -z "$PYTHON_BIN_PATH" ]; then
-        PYTHON_BIN_PATH=$default_python_bin_path
-      fi
-    fi
-    if [ -e "$PYTHON_BIN_PATH" ]; then
-      break
-    fi
-    echo "Invalid python path. ${PYTHON_BIN_PATH} cannot be found" 1>&2
-    if [ -z "$fromuser" ]; then
-      exit 1
-    fi
-    PYTHON_BIN_PATH=""
-    # Retry
-  done
-
-  if [ -z "$PYTHON_LIB_PATH" ]; then
-    # Split python_path into an array of paths, this allows path containing spaces
-    IFS=',' read -r -a python_lib_path <<< "$(python_path)"
-
-    if [ 1 = "$USE_DEFAULT_PYTHON_LIB_PATH" ]; then
-      PYTHON_LIB_PATH=${python_lib_path[0]}
-      echo "Using python library path: $PYTHON_LIB_PATH"
-
-    else
-      echo "Found possible Python library paths:"
-      for x in "${python_lib_path[@]}"; do
-        echo "  $x"
-      done
-      set -- "${python_lib_path[@]}"
-      echo "Please input the desired Python library path to use.  Default is [$1]"
-      read b || true
-      if [ "$b" == "" ]; then
-        PYTHON_LIB_PATH=${python_lib_path[0]}
-        echo "Using python library path: $PYTHON_LIB_PATH"
-      else
-        PYTHON_LIB_PATH="$b"
-      fi
-    fi
-  fi
-
-  if [ ! -x "$PYTHON_BIN_PATH" ]  || [ -d "$PYTHON_BIN_PATH" ]; then
-    echo "PYTHON_BIN_PATH is not executable.  Is it the python binary?"
-    exit 1
-  fi
-
-  local python_major_version
-  python_major_version=$("${PYTHON_BIN_PATH}" -c 'from __future__ import print_function; import sys; print(sys.version_info[0]);' | head -c1)
-  if [ -z "$python_major_version" ]; then
-    echo -e "\n\nERROR: Problem getting python version.  Is $PYTHON_BIN_PATH the correct python binary?"
-    exit 1
-  fi
-
-  # Convert python path to Windows style before writing into bazel.rc
-  if is_windows; then
-    PYTHON_BIN_PATH="$(cygpath -m "$PYTHON_BIN_PATH")"
-    PYTHON_LIB_PATH="$(cygpath -m "$PYTHON_LIB_PATH")"
-  fi
-
-  # Set-up env variables used by python_configure.bzl
-  write_action_env_to_bazelrc "PYTHON_BIN_PATH" "$PYTHON_BIN_PATH"
-  write_action_env_to_bazelrc "PYTHON_LIB_PATH" "$PYTHON_LIB_PATH"
-  write_to_bazelrc "build --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "build --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
-  write_to_bazelrc "build --force_python=py$python_major_version"
-  write_to_bazelrc "build --host_force_python=py$python_major_version"
-  write_to_bazelrc "build --python${python_major_version}_path=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "test --force_python=py$python_major_version"
-  write_to_bazelrc "test --host_force_python=py$python_major_version"
-  write_to_bazelrc "test --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "test --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
-  write_to_bazelrc "run --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "run --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
-
-  # Write tools/python_bin_path.sh
-  echo "export PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\"" > tools/python_bin_path.sh
-}
-
-function version {
-  echo "$@" | awk -F. '{ printf("%03d%03d%03d\n", $1,$2,$3); }';
-}
-
-
-bazel version > bazel.version
-set +e
-curr_bazel_version=$(grep -m 1 'Build label:' bazel.version | cut -d ' ' -f3)
-set -e
-rm -f bazel.version
-
-
-echo "You have bazel $curr_bazel_version installed."
-if [ -z "$curr_bazel_version" ]; then
-  echo "WARNING: current bazel installation is not a release version."
-  echo "Make sure you are running at least bazel $MIN_BAZEL_VERSION."
-elif [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
-  echo "Please upgrade your bazel installation to version $MIN_BAZEL_VERSION or higher to build TensorFlow!"
-  echo "Exiting..."
-  exit 1
-fi
-
-# This file contains customized config settings.
-rm -f .tf_configure.bazelrc
-touch .tf_configure.bazelrc
-if [[ ! -e .bazelrc ]]; then
-  if [[ -e "${HOME}/.bazelrc" ]]; then
-    echo "import ${HOME}/.bazelrc" >.bazelrc
-  else
-    touch .bazelrc
-  fi
-fi
-sed_in_place "/tf_configure/d" .bazelrc
-echo "import %workspace%/.tf_configure.bazelrc" >> .bazelrc
-
-# Delete any leftover BUILD files from the Makefile build, which would interfere
-# with Bazel parsing.
-MAKEFILE_DOWNLOAD_DIR=tensorflow/contrib/makefile/downloads
-if [ -d "${MAKEFILE_DOWNLOAD_DIR}" ]; then
-  find ${MAKEFILE_DOWNLOAD_DIR} -type f -name '*BUILD' -delete
-fi
-
-setup_python
-
-## Set up MKL related environment settings
-write_to_bazelrc 'build:mkl --define with_mkl_support=true'
-write_to_bazelrc 'build:mkl --define using_mkl=true'
-write_to_bazelrc 'build:mkl -c opt'
-write_to_bazelrc 'build:mkl --copt="-DEIGEN_USE_VML"'
-echo ""
-echo "Add \"--config=mkl\" to your bazel command to build with MKL support."
-echo "Please note that MKL on MacOS or windows is still not supported."
-echo "If you would like to use a local MKL instead of downloading, please "
-echo "  set the environment variable \"TF_MKL_ROOT\" every time before build."
-echo ""
-## End MKL setup
-
-## Set up architecture-dependent optimization flags.
-if [ -z "$CC_OPT_FLAGS" ]; then
-  if is_ppc64le; then
-    # gcc on ppc64le does not support -march, use mcpu instead
-    default_cc_opt_flags="-mcpu=native"
-  else
-    default_cc_opt_flags="-march=native"
-  fi
-  read -p "Please specify optimization flags to use during compilation when bazel option "\
-"\"--config=opt\" is specified [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
-  if [ -z "$CC_OPT_FLAGS" ]; then
-    CC_OPT_FLAGS=$default_cc_opt_flags
-  fi
-fi
-
-if is_windows; then
-  TF_NEED_GCP=0
-  TF_NEED_HDFS=0
-  TF_NEED_JEMALLOC=0
-  TF_NEED_OPENCL=0
-  TF_CUDA_CLANG=0
-fi
-
-if is_linux; then
-  while [ "$TF_NEED_JEMALLOC" == "" ]; do
-    read -p "Do you wish to use jemalloc as the malloc implementation? [Y/n] "\
-      INPUT
-    case $INPUT in
-      [Yy]* ) echo "jemalloc enabled"; TF_NEED_JEMALLOC=1;;
-      [Nn]* ) echo "jemalloc disabled"; TF_NEED_JEMALLOC=0;;
-      "" ) echo "jemalloc enabled"; TF_NEED_JEMALLOC=1;;
-      * ) echo "Invalid selection: " $INPUT;;
-    esac
-  done
-else
-  TF_NEED_JEMALLOC=0
-fi
-
-if [[ "$TF_NEED_JEMALLOC" == "1" ]]; then
-  write_to_bazelrc 'build --define with_jemalloc=true'
-fi
-
-while [[ "$TF_NEED_GCP" == "" ]]; do
-  read -p "Do you wish to build TensorFlow with "\
-"Google Cloud Platform support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "Google Cloud Platform support will be enabled for "\
-"TensorFlow"; TF_NEED_GCP=1;;
-    [Nn]* ) echo "No Google Cloud Platform support will be enabled for "\
-"TensorFlow"; TF_NEED_GCP=0;;
-    "" ) echo "No Google Cloud Platform support will be enabled for "\
-"TensorFlow"; TF_NEED_GCP=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_NEED_GCP" == "1" ]]; then
-  write_to_bazelrc 'build --define with_gcp_support=true'
-fi
-
-while [[ "$TF_NEED_HDFS" == "" ]]; do
-  read -p "Do you wish to build TensorFlow with "\
-"Hadoop File System support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "Hadoop File System support will be enabled for "\
-"TensorFlow"; TF_NEED_HDFS=1;;
-    [Nn]* ) echo "No Hadoop File System support will be enabled for "\
-"TensorFlow"; TF_NEED_HDFS=0;;
-    "" ) echo "No Hadoop File System support will be enabled for "\
-"TensorFlow"; TF_NEED_HDFS=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_NEED_HDFS" == "1" ]]; then
-  write_to_bazelrc 'build --define with_hdfs_support=true'
-fi
-
-## Enable XLA.
-while [[ "$TF_ENABLE_XLA" == "" ]]; do
-  read -p "Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "XLA JIT support will be enabled for TensorFlow"; TF_ENABLE_XLA=1;;
-    [Nn]* ) echo "No XLA JIT support will be enabled for TensorFlow"; TF_ENABLE_XLA=0;;
-    "" ) echo "No XLA support will be enabled for TensorFlow"; TF_ENABLE_XLA=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_ENABLE_XLA" == "1" ]]; then
-  write_to_bazelrc 'build --define with_xla_support=true'
-fi
-
-# Verbs configuration
-while [ "$TF_NEED_VERBS" == "" ]; do
-  read -p "Do you wish to build TensorFlow with "\
-"VERBS support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "VERBS support will be enabled for "\
-"TensorFlow"; TF_NEED_VERBS=1;;
-    [Nn]* ) echo "No VERBS support will be enabled for "\
-"TensorFlow"; TF_NEED_VERBS=0;;
-    "" ) echo "No VERBS support will be enabled for "\
-"TensorFlow"; TF_NEED_VERBS=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_NEED_VERBS" == "1" ]]; then
-  write_to_bazelrc 'build --define with_verbs_support=true'
-fi
-
-# Append CC optimization flags to bazel.rc
-for opt in $CC_OPT_FLAGS; do
-  write_to_bazelrc "build:opt --cxxopt=$opt --copt=$opt"
-done
-
-# Run the gen_git_source to create links where bazel can track dependencies for
-# git hash propagation
-GEN_GIT_SOURCE=tensorflow/tools/git/gen_git_source.py
-chmod a+x ${GEN_GIT_SOURCE}
-"${PYTHON_BIN_PATH}" ${GEN_GIT_SOURCE} --configure "${SOURCE_BASE_DIR}"
-
-## Set up SYCL-related environment settings
-while [ "$TF_NEED_OPENCL" == "" ]; do
-  read -p "Do you wish to build TensorFlow with OpenCL support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "OpenCL support will be enabled for TensorFlow"; TF_NEED_OPENCL=1;;
-    [Nn]* ) echo "No OpenCL support will be enabled for TensorFlow"; TF_NEED_OPENCL=0;;
-    "" ) echo "No OpenCL support will be enabled for TensorFlow"; TF_NEED_OPENCL=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-## Set up Cuda-related environment settings
-
-while [ "$TF_NEED_CUDA" == "" ]; do
-  read -p "Do you wish to build TensorFlow with CUDA support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "CUDA support will be enabled for TensorFlow"; TF_NEED_CUDA=1;;
-    [Nn]* ) echo "No CUDA support will be enabled for TensorFlow"; TF_NEED_CUDA=0;;
-    "" ) echo "No CUDA support will be enabled for TensorFlow"; TF_NEED_CUDA=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-export TF_NEED_CUDA
-write_action_env_to_bazelrc "TF_NEED_CUDA" "$TF_NEED_CUDA"
-
-export TF_NEED_OPENCL
-write_action_env_to_bazelrc "TF_NEED_OPENCL" "$TF_NEED_OPENCL"
-
-if [ "$TF_NEED_CUDA" == "1" ]; then
-while [[ "$TF_CUDA_CLANG" == "" ]]; do
-  read -p "Do you want to use clang as CUDA compiler? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "Clang will be used as CUDA compiler"; TF_CUDA_CLANG=1;;
-    [Nn]* ) echo "nvcc will be used as CUDA compiler"; TF_CUDA_CLANG=0;;
-    "" ) echo "nvcc will be used as CUDA compiler"; TF_CUDA_CLANG=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-export TF_CUDA_CLANG
-write_action_env_to_bazelrc "TF_CUDA_CLANG" "$TF_CUDA_CLANG"
-
-# Set up which clang we should use as the cuda / host compiler.
-while [[ "$TF_CUDA_CLANG" == "1" ]] && true; do
-  fromuser=""
-  if [ -z "$CLANG_CUDA_COMPILER_PATH" ]; then
-    default_clang_host_compiler_path=$(which clang || true)
-    read -p "Please specify which clang should be used as device and host compiler. [Default is $default_clang_host_compiler_path]: " CLANG_CUDA_COMPILER_PATH
-    fromuser="1"
-    if [ -z "$CLANG_CUDA_COMPILER_PATH" ]; then
-      CLANG_CUDA_COMPILER_PATH="$default_clang_host_compiler_path"
-    fi
-  fi
-  if [ -e "$CLANG_CUDA_COMPILER_PATH" ]; then
-    export CLANG_CUDA_COMPILER_PATH
-    write_action_env_to_bazelrc "CLANG_CUDA_COMPILER_PATH" "$CLANG_CUDA_COMPILER_PATH"
-    break
-  fi
-  echo "Invalid clang path. ${CLANG_CUDA_COMPILER_PATH} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  CLANG_CUDA_COMPILER_PATH=""
-  # Retry
-done
-
-# Find out where the CUDA toolkit is installed
-while true; do
-  # Configure the Cuda SDK version to use.
-  if [ -z "$TF_CUDA_VERSION" ]; then
-    read -p "Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: " TF_CUDA_VERSION
-  fi
-  # Set default CUDA version if not set
-  TF_CUDA_VERSION=${TF_CUDA_VERSION:-8.0}
-
-  fromuser=""
-  if [ -z "$CUDA_TOOLKIT_PATH" ]; then
-    default_cuda_path=/usr/local/cuda
-    if is_windows; then
-      if [ -z "$CUDA_PATH" ]; then
-        default_cuda_path="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v8.0"
-      else
-        default_cuda_path="$(cygpath -m "$CUDA_PATH")"
-      fi
-    elif is_linux; then
-      # If the default doesn't exist, try an alternative default.
-      if [ ! -d $default_cuda_path ] && [ -d /opt/cuda ]; then
-        default_cuda_path=/opt/cuda
-      fi
-    fi
-    read -p "Please specify the location where CUDA $TF_CUDA_VERSION toolkit is installed. Refer to README.md for more details. [Default is $default_cuda_path]: " CUDA_TOOLKIT_PATH
-    fromuser="1"
-    if [ -z "$CUDA_TOOLKIT_PATH" ]; then
-      CUDA_TOOLKIT_PATH="$default_cuda_path"
-    fi
-  fi
-
-  if [[ -z "$TF_CUDA_VERSION" ]]; then
-    TF_CUDA_EXT=""
-  else
-    TF_CUDA_EXT=".$TF_CUDA_VERSION"
-  fi
-
-  if is_windows; then
-    CUDA_RT_LIB_PATH="lib/x64/cudart.lib"
-  elif is_linux; then
-    CUDA_RT_LIB_PATH="lib64/libcudart.so${TF_CUDA_EXT}"
-  elif is_macos; then
-    CUDA_RT_LIB_PATH="lib/libcudart${TF_CUDA_EXT}.dylib"
-  fi
-
-  if [ -e "${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH}" ]; then
-    export CUDA_TOOLKIT_PATH
-    write_action_env_to_bazelrc "CUDA_TOOLKIT_PATH" "$CUDA_TOOLKIT_PATH"
-    export TF_CUDA_VERSION
-    break
-  fi
-  echo "Invalid path to CUDA $TF_CUDA_VERSION toolkit. ${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH} cannot be found"
-
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  # Retry
-  TF_CUDA_VERSION=""
-  CUDA_TOOLKIT_PATH=""
-done
-
-export TF_CUDA_VERSION
-write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION"
-
-# Set up which gcc nvcc should use as the host compiler
-# No need to set this on Windows
-while [[ "$TF_CUDA_CLANG" != "1" ]] && ! is_windows && true; do
-  fromuser=""
-  if [ -z "$GCC_HOST_COMPILER_PATH" ]; then
-    default_gcc_host_compiler_path=$(which gcc || true)
-    cuda_bin_symlink="$CUDA_TOOLKIT_PATH/bin/gcc"
-    if [ -L "$cuda_bin_symlink" ]; then
-      default_gcc_host_compiler_path=$(readlink $cuda_bin_symlink)
-    fi
-    read -p "Please specify which gcc should be used by nvcc as the host compiler. [Default is $default_gcc_host_compiler_path]: " GCC_HOST_COMPILER_PATH
-    fromuser="1"
-    if [ -z "$GCC_HOST_COMPILER_PATH" ]; then
-      GCC_HOST_COMPILER_PATH="$default_gcc_host_compiler_path"
-    fi
-  fi
-  if [ -e "$GCC_HOST_COMPILER_PATH" ]; then
-    export GCC_HOST_COMPILER_PATH
-    write_action_env_to_bazelrc "GCC_HOST_COMPILER_PATH" "$GCC_HOST_COMPILER_PATH"
-    break
-  fi
-  echo "Invalid gcc path. ${GCC_HOST_COMPILER_PATH} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  GCC_HOST_COMPILER_PATH=""
-  # Retry
-done
-
-# Find out where the cuDNN library is installed
-while true; do
-  # Configure the cuDNN version to use.
-  if [ -z "$TF_CUDNN_VERSION" ]; then
-    read -p "Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: " TF_CUDNN_VERSION
-  fi
-  # Set default CUDNN version if not set
-  TF_CUDNN_VERSION=${TF_CUDNN_VERSION:-6}
-
-  fromuser=""
-  if [ -z "$CUDNN_INSTALL_PATH" ]; then
-    default_cudnn_path=${CUDA_TOOLKIT_PATH}
-    read -p "Please specify the location where cuDNN $TF_CUDNN_VERSION library is installed. Refer to README.md for more details. [Default is $default_cudnn_path]: " CUDNN_INSTALL_PATH
-    fromuser="1"
-    if [ -z "$CUDNN_INSTALL_PATH" ]; then
-      CUDNN_INSTALL_PATH=$default_cudnn_path
-    fi
-    # Result returned from "read" will be used unexpanded. That make "~" unusable.
-    # Going through one more level of expansion to handle that.
-    CUDNN_INSTALL_PATH=`"${PYTHON_BIN_PATH}" -c "import os; print(os.path.realpath(os.path.expanduser('${CUDNN_INSTALL_PATH}')))"`
-    if is_windows; then
-      CUDNN_INSTALL_PATH="$(cygpath -m "$CUDNN_INSTALL_PATH")"
-    fi
-  fi
-
-  if [[ -z "$TF_CUDNN_VERSION" ]]; then
-    TF_CUDNN_EXT=""
-  else
-    TF_CUDNN_EXT=".$TF_CUDNN_VERSION"
-  fi
-
-  if is_windows; then
-    CUDA_DNN_LIB_PATH="lib/x64/cudnn.lib"
-    CUDA_DNN_LIB_ALT_PATH="lib/x64/cudnn.lib"
-  elif is_linux; then
-    CUDA_DNN_LIB_PATH="lib64/libcudnn.so${TF_CUDNN_EXT}"
-    CUDA_DNN_LIB_ALT_PATH="libcudnn.so${TF_CUDNN_EXT}"
-  elif is_macos; then
-    CUDA_DNN_LIB_PATH="lib/libcudnn${TF_CUDNN_EXT}.dylib"
-    CUDA_DNN_LIB_ALT_PATH="libcudnn${TF_CUDNN_EXT}.dylib"
-  fi
-
-  if [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_ALT_PATH}" ] || [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_PATH}" ]; then
-    export TF_CUDNN_VERSION
-    write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
-    export CUDNN_INSTALL_PATH
-    write_action_env_to_bazelrc "CUDNN_INSTALL_PATH" "$CUDNN_INSTALL_PATH"
-    break
-  fi
-
-  if is_linux; then
-    if ! type ldconfig > /dev/null 2>&1; then
-        LDCONFIG_BIN=/sbin/ldconfig
-    else
-        LDCONFIG_BIN=ldconfig
-    fi
-    CUDNN_PATH_FROM_LDCONFIG="$($LDCONFIG_BIN -p | sed -n 's/.*libcudnn.so .* => \(.*\)/\1/p')"
-    if [ -e "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}" ]; then
-      export TF_CUDNN_VERSION
-      export CUDNN_INSTALL_PATH
-      CUDNN_INSTALL_PATH="$(dirname ${CUDNN_PATH_FROM_LDCONFIG})"
-      write_action_env_to_bazelrc "CUDNN_INSTALL_PATH" "$CUDNN_INSTALL_PATH"
-      break
-    fi
-  fi
-  echo "Invalid path to cuDNN ${CUDNN_VERSION} toolkit. Neither of the following two files can be found:"
-  echo "${CUDNN_INSTALL_PATH}/${CUDA_DNN_LIB_PATH}"
-  echo "${CUDNN_INSTALL_PATH}/${CUDA_DNN_LIB_ALT_PATH}"
-  if is_linux; then
-    echo "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}"
-  fi
-
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  # Retry
-  TF_CUDNN_VERSION=""
-  CUDNN_INSTALL_PATH=""
-done
-
-export TF_CUDNN_VERSION
-write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
-
-# Configure the compute capabilities that TensorFlow builds for.
-# Since Cuda toolkit is not backward-compatible, this is not guaranteed to work.
-function get_native_cuda_compute_capabilities {
-  device_query_bin="$CUDA_TOOLKIT_PATH/extras/demo_suite/deviceQuery" # Also works on Windows without .exe
-  "$device_query_bin" | grep 'Capability' | grep -o '[0-9]*\.[0-9]*' | sed ':a;{N;s/\n/,/};ba'
-  exit 0 # ensure that this function always exit success even if device detection fails, to prevent the whole configure from aborting
-}
-while true; do
-  fromuser=""
-  native_cuda_compute_capabilities=$(get_native_cuda_compute_capabilities)
-  default_cuda_compute_capabilities=${native_cuda_compute_capabilities:-"3.5,5.2"}
-  if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
-cat << EOF
-Please specify a list of comma-separated Cuda compute capabilities you want to build with.
-You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
-Please note that each additional compute capability significantly increases your build time and binary size.
-EOF
-    read -p "[Default is: \"$default_cuda_compute_capabilities\"]: " TF_CUDA_COMPUTE_CAPABILITIES
-    fromuser=1
-  fi
-  if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
-    TF_CUDA_COMPUTE_CAPABILITIES=$default_cuda_compute_capabilities
-  fi
-  # Check whether all capabilities from the input is valid
-  COMPUTE_CAPABILITIES=${TF_CUDA_COMPUTE_CAPABILITIES//,/ }
-  ALL_VALID=1
-  for CAPABILITY in $COMPUTE_CAPABILITIES; do
-    if [[ ! "$CAPABILITY" =~ [0-9]+.[0-9]+ ]]; then
-      echo "Invalid compute capability: " $CAPABILITY
-      ALL_VALID=0
-      break
-    fi
-  done
-  if [ "$ALL_VALID" == "0" ]; then
-    if [ -z "$fromuser" ]; then
-      exit 1
-    fi
-  else
-    export TF_CUDA_COMPUTE_CAPABILITIES
-    write_action_env_to_bazelrc "TF_CUDA_COMPUTE_CAPABILITIES" "$TF_CUDA_COMPUTE_CAPABILITIES"
-    break
-  fi
-  TF_CUDA_COMPUTE_CAPABILITIES=""
-done
-
-if is_windows; then
-  # The following three variables are needed for MSVC toolchain configuration in Bazel
-  export CUDA_PATH="$CUDA_TOOLKIT_PATH"
-  export CUDA_COMPUTE_CAPABILITIES="$TF_CUDA_COMPUTE_CAPABILITIES"
-  export NO_WHOLE_ARCHIVE_OPTION=1
-  write_action_env_to_bazelrc "CUDA_PATH" "$CUDA_PATH"
-  write_action_env_to_bazelrc "CUDA_COMPUTE_CAPABILITIES" "$CUDA_COMPUTE_CAPABILITIES"
-  write_action_env_to_bazelrc "NO_WHOLE_ARCHIVE_OPTION" "1"
-  write_to_bazelrc "build --config=win-cuda"
-  write_to_bazelrc "test --config=win-cuda"
-else
-  # If CUDA is enabled, always use GPU during build and test.
-  if [ "$TF_CUDA_CLANG" == "1" ]; then
-    write_to_bazelrc "build --config=cuda_clang"
-    write_to_bazelrc "test --config=cuda_clang"
-  else
-    write_to_bazelrc "build --config=cuda"
-    write_to_bazelrc "test --config=cuda"
-  fi
+if [ -z "$PYTHON_BIN_PATH" ]; then
+  PYTHON_BIN_PATH=$(which python || which python3 || true)
 fi
 
-# end of if "$TF_NEED_CUDA" == "1"
-fi
-
-# OpenCL configuration
-
-if [ "$TF_NEED_OPENCL" == "1" ]; then
-
-# Determine which C++ compiler should be used as the host compiler
-while true; do
-  fromuser=""
-  if [ -z "$HOST_CXX_COMPILER" ]; then
-    default_cxx_host_compiler=$(which g++ || true)
-    read -p "Please specify which C++ compiler should be used as the host C++ compiler. [Default is $default_cxx_host_compiler]: " HOST_CXX_COMPILER
-    fromuser="1"
-    if [ -z "$HOST_CXX_COMPILER" ]; then
-      HOST_CXX_COMPILER=$default_cxx_host_compiler
-    fi
-  fi
-  if [ -e "$HOST_CXX_COMPILER" ]; then
-    export HOST_CXX_COMPILER
-    write_action_env_to_bazelrc "HOST_CXX_COMPILER" "$HOST_CXX_COMPILER"
-    break
-  fi
-  echo "Invalid C++ compiler path. ${HOST_CXX_COMPILER} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  HOST_CXX_COMPILER=""
-  # Retry
-done
-
-# Determine which C compiler should be used as the host compiler
-while true; do
-  fromuser=""
-  if [ -z "$HOST_C_COMPILER" ]; then
-    default_c_host_compiler=$(which gcc || true)
-    read -p "Please specify which C compiler should be used as the host C compiler. [Default is $default_c_host_compiler]: " HOST_C_COMPILER
-    fromuser="1"
-    if [ -z "$HOST_C_COMPILER" ]; then
-      HOST_C_COMPILER=$default_c_host_compiler
-    fi
-  fi
-  if [ -e "$HOST_C_COMPILER" ]; then
-    export HOST_C_COMPILER
-    write_action_env_to_bazelrc "HOST_C_COMPILER" "$HOST_C_COMPILER"
-    break
-  fi
-  echo "Invalid C compiler path. ${HOST_C_COMPILER} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  HOST_C_COMPILER=""
-  # Retry
-done
-
-while true; do
-  # Configure the OPENCL version to use.
-  TF_OPENCL_VERSION="1.2"
-
-  # Point to ComputeCpp root
-  if [ -z "$COMPUTECPP_TOOLKIT_PATH" ]; then
-    default_computecpp_toolkit_path=/usr/local/computecpp
-    read -p "Please specify the location where ComputeCpp for SYCL $TF_OPENCL_VERSION is installed. [Default is $default_computecpp_toolkit_path]: " COMPUTECPP_TOOLKIT_PATH
-    fromuser="1"
-    if [ -z "$COMPUTECPP_TOOLKIT_PATH" ]; then
-      COMPUTECPP_TOOLKIT_PATH=$default_computecpp_toolkit_path
-    fi
-  fi
-
-  if is_linux; then
-    SYCL_RT_LIB_PATH="lib/libComputeCpp.so"
-  fi
-
-  if [ -e "${COMPUTECPP_TOOLKIT_PATH}/${SYCL_RT_LIB_PATH}" ]; then
-    export COMPUTECPP_TOOLKIT_PATH
-    write_action_env_to_bazelrc "COMPUTECPP_TOOLKIT_PATH" "$COMPUTECPP_TOOLKIT_PATH"
-    break
-  fi
-  echo "Invalid SYCL $TF_OPENCL_VERSION library path. ${COMPUTECPP_TOOLKIT_PATH}/${SYCL_RT_LIB_PATH} cannot be found"
-
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  # Retry
-  TF_OPENCL_VERSION=""
-  COMPUTECPP_TOOLKIT_PATH=""
-done
-
-# end of if "$TF_NEED_OPENCL" == "1"
-fi
-
-
-while [ "$TF_NEED_MPI" == "" ]; do
-  read -p "Do you wish to build TensorFlow with "\
-"MPI support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "MPI support will be enabled for "\
-"TensorFlow"; TF_NEED_MPI=1;;
-    [Nn]* ) echo "MPI support will not be enabled for "\
-"TensorFlow"; TF_NEED_MPI=0;;
-    "" ) echo "MPI support will not be enabled for "\
-"TensorFlow"; TF_NEED_MPI=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-# Find out where the MPI toolkit is installed
-while true; do
-    if [ "$TF_NEED_MPI" == "0" ]; then
-        break;
-    fi
-
-    fromuser=""
-    if [ -z "$MPI_HOME" ]; then
-        #Get the base folder by removing the bin path
-        default_mpi_path=$(dirname $(dirname $(which mpirun)) || dirname $(dirname $(which mpiexec))  || true)
-        read -p "Please specify the MPI toolkit folder. [Default is $default_mpi_path]: " MPI_HOME
-        fromuser="1"
-        if [ -z "$MPI_HOME" ]; then
-            MPI_HOME=$default_mpi_path
-        fi
-    fi
-
-    #Check that the include and library folders are where we expect them to be
-    if [ -e "$MPI_HOME/include" ] && [ -e "$MPI_HOME/lib" ]; then
-        break
-    fi
-
-    echo "Invalid path to the MPI Toolkit. ${MPI_HOME}/include or ${MPI_HOME}/lib cannot be found."
-    if [ -z "$fromuser" ]; then
-        exit 1
-    fi
-
-    # Retry
-    MPI_HOME=""
-done
-
-
-if [ "$TF_NEED_MPI" == "1" ]; then
-  write_to_bazelrc 'build --define with_mpi_support=true'
-
-  #Link the MPI header files
-  ln -sf "${MPI_HOME}/include/mpi.h" third_party/mpi/mpi.h
-
-
-  #Determine if we use OpenMPI or MVAPICH, these require different header files
-  #to be included here to make bazel dependency checker happy
-
-  if [ -e "${MPI_HOME}/include/mpi_portable_platform.h" ]; then
-        #OpenMPI
-        ln -sf "${MPI_HOME}/include/mpi_portable_platform.h" third_party/mpi/
-        sed -i -e "s/MPI_LIB_IS_OPENMPI=False/MPI_LIB_IS_OPENMPI=True/" third_party/mpi/mpi.bzl
- else
-        #MVAPICH / MPICH
-        ln -sf "${MPI_HOME}/include/mpio.h" third_party/mpi/
-        ln -sf "${MPI_HOME}/include/mpicxx.h" third_party/mpi/
-        sed -i -e "s/MPI_LIB_IS_OPENMPI=True/MPI_LIB_IS_OPENMPI=False/" third_party/mpi/mpi.bzl
- fi
-
-
-  if [ -e "${MPI_HOME}/lib/libmpi.so" ]; then
-    ln -sf "${MPI_HOME}/lib/libmpi.so" third_party/mpi/
-  else
-    echo "Cannot find the MPI library file in ${MPI_HOME}/lib "
-    exit 1
-  fi
-fi
+# Set all env variables
+$PYTHON_BIN_PATH configure.py
 
 
-echo "Configuration finished"
+echo "Configuration finished"
\ No newline at end of file
diff --git a/configure.py b/configure.py
new file mode 100644
index 0000000000..fac00d1b74
--- /dev/null
+++ b/configure.py
@@ -0,0 +1,950 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""configure script to get build parameters from user."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import errno
+import os
+import platform
+import re
+import site
+import subprocess
+import sys
+
+_TF_BAZELRC = '.tf_configure.bazelrc'
+_DEFAULT_CUDA_VERSION = '8.0'
+_DEFAULT_CUDNN_VERSION = '6'
+_DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,5.2'
+_DEFAULT_CUDA_PATH = '/usr/local/cuda'
+_DEFAULT_CUDA_PATH_LINUX = '/opt/cuda'
+_DEFAULT_CUDA_PATH_WIN = ('C:/Program Files/NVIDIA GPU Computing '
+                          'Toolkit/CUDA/v%s' % _DEFAULT_CUDA_VERSION)
+_TF_OPENCL_VERSION = '1.2'
+_DEFAULT_COMPUTECPP_TOOLKIT_PATH = '/usr/local/computecpp'
+
+
+def is_windows():
+  return platform.system() == 'Windows'
+
+
+def is_linux():
+  return platform.system() == 'Linux'
+
+
+def is_macos():
+  return platform.system() == 'Darwin'
+
+
+def is_ppc64le():
+  return platform.machine() == 'ppc64le'
+
+
+def get_input(question):
+  try:
+    try:
+      answer = raw_input(question)
+    except NameError:
+      answer = input(question)  # pylint: disable=bad-builtin
+  except EOFError:
+    answer = ''
+  return answer
+
+
+def symlink_force(target, link_name):
+  """Force symlink, equivalent of 'ln -sf'.
+
+  Args:
+    target: items to link to.
+    link_name: name of the link.
+  """
+  try:
+    os.symlink(target, link_name)
+  except OSError as e:
+    if e.errno == errno.EEXIST:
+      os.remove(link_name)
+      os.symlink(target, link_name)
+    else:
+      raise e
+
+
+def sed_in_place(filename, old, new):
+  """Replace old string with new string in file.
+
+  Args:
+    filename: string for filename.
+    old: string to replace.
+    new: new string to replace to.
+  """
+  with open(filename, 'r') as f:
+    filedata = f.read()
+  newdata = filedata.replace(old, new)
+  with open(filename, 'w') as f:
+    f.write(newdata)
+
+
+def remove_line_with(filename, token):
+  """Remove lines that contain token from file.
+
+  Args:
+    filename: string for filename.
+    token: string token to check if to remove a line from file or not.
+  """
+  with open(filename, 'r') as f:
+    filedata = f.read()
+
+  with open(filename, 'w') as f:
+    for line in filedata.strip().split('\n'):
+      if token not in line:
+        f.write(line + '\n')
+
+
+def write_to_bazelrc(line):
+  with open(_TF_BAZELRC, 'a') as f:
+    f.write(line + '\n')
+
+
+def write_action_env_to_bazelrc(var_name, var):
+  write_to_bazelrc('build --action_env %s="%s"' % (var_name, str(var)))
+
+
+def run_shell(cmd):
+  return subprocess.check_output(cmd, shell=True).decode('UTF-8').strip()
+
+
+def cygpath(path):
+  """Convert path from posix to windows."""
+  return run_shell('cygpath  -m "%s"' % path)
+
+
+def get_python_path(environ_cp):
+  """Get the python site package paths."""
+  python_paths = []
+  if environ_cp.get('PYTHONPATH'):
+    python_paths = environ_cp.get('PYTHONPATH').split(':')
+  try:
+    library_paths = site.getsitepackages()
+  except AttributeError:
+    from distutils.sysconfig import get_python_lib  # pylint: disable=g-import-not-at-top
+    library_paths = [get_python_lib()]
+  all_paths = set(python_paths + library_paths)
+
+  paths = []
+  for path in all_paths:
+    if os.path.isdir(path):
+      paths.append(path)
+  return paths
+
+
+def setup_python(environ_cp):
+  """Setup python related env variables."""
+  # Get PYTHON_BIN_PATH, default is the current running python.
+  default_python_bin_path = sys.executable
+  ask_python_bin_path = ('Please specify the location of python. [Default is '
+                         '%s]: ') % default_python_bin_path
+  while True:
+    python_bin_path = get_from_env_or_user_or_default(
+        environ_cp, 'PYTHON_BIN_PATH', ask_python_bin_path,
+        default_python_bin_path)
+    # Check if the path is valid
+    if (os.path.isfile(python_bin_path) and os.access(
+        python_bin_path, os.X_OK)) or (os.path.isdir(python_bin_path)):
+      break
+    elif not os.path.exists(python_bin_path):
+      print('Invalid python path: %s cannot be found.' % python_bin_path)
+    else:
+      print('%s is not executable.  Is it the python binary?' % python_bin_path)
+    environ_cp['PYTHON_BIN_PATH'] = ''
+
+  # Get PYTHON_LIB_PATH
+  python_lib_path = environ_cp.get('PYTHON_LIB_PATH')
+  if not python_lib_path:
+    python_lib_paths = get_python_path(environ_cp)
+    if environ_cp.get('USE_DEFAULT_PYTHON_LIB_PATH') == '1':
+      environ_cp['PYTHON_LIB_PATH'] = python_lib_paths[0]
+    else:
+      print('Found possible Python library paths:\n%s' %
+            '\n'.join(python_lib_paths))
+      default_python_lib_path = python_lib_paths[0]
+      python_lib_path = get_input(
+          'Please input the desired Python library path to use.  Default is %s'
+          % python_lib_paths[0])
+      if not python_lib_path:
+        python_lib_path = default_python_lib_path
+      environ_cp['PYTHON_LIB_PATH'] = python_lib_path
+
+  python_major_version = sys.version_info[0]
+  # Convert python path to Windows style before writing into bazel.rc
+  if is_windows():
+    python_bin_path = cygpath(python_bin_path)
+    python_lib_path = cygpath(python_lib_path)
+
+  # Set-up env variables used by python_configure.bzl
+  write_action_env_to_bazelrc('PYTHON_BIN_PATH', python_bin_path)
+  write_action_env_to_bazelrc('PYTHON_LIB_PATH', python_lib_path)
+  write_to_bazelrc('build --define PYTHON_BIN_PATH="%s"' % python_bin_path)
+  write_to_bazelrc('build --define PYTHON_LIB_PATH="%s"' % python_lib_path)
+  write_to_bazelrc('build --force_python=py%s' % python_major_version)
+  write_to_bazelrc('build --host_force_python=py%s' % python_major_version)
+  write_to_bazelrc('build --python%s_path=\"%s"' % (python_major_version,
+                                                    python_bin_path))
+  write_to_bazelrc('test --force_python=py%s' % python_major_version)
+  write_to_bazelrc('test --host_force_python=py%s' % python_major_version)
+  write_to_bazelrc('test --define PYTHON_BIN_PATH="%s"' % python_bin_path)
+  write_to_bazelrc('test --define PYTHON_LIB_PATH="%s"' % python_lib_path)
+  write_to_bazelrc('run --define PYTHON_BIN_PATH="%s"' % python_bin_path)
+  write_to_bazelrc('run --define PYTHON_LIB_PATH="%s"' % python_lib_path)
+  environ_cp['PYTHON_BIN_PATH'] = python_bin_path
+
+  # Write tools/python_bin_path.sh
+  with open('tools/python_bin_path.sh', 'w') as f:
+    f.write('export PYTHON_BIN_PATH="%s"' % python_bin_path)
+
+
+def reset_tf_configure_bazelrc():
+  """Reset file that contains customized config settings."""
+  open(_TF_BAZELRC, 'w').close()
+
+  home = os.path.expanduser('~')
+  if not os.path.exists('.bazelrc'):
+    if os.path.exists(os.path.join(home, '.bazelrc')):
+      with open('.bazelrc', 'a') as f:
+        f.write('import %s/.bazelrc\n' % home)
+    else:
+      open('.bazelrc', 'w').close()
+
+  remove_line_with('.bazelrc', 'tf_configure')
+  with open('.bazelrc', 'a') as f:
+    f.write('import %workspace%/.tf_configure.bazelrc\n')
+
+
+def run_gen_git_source(environ_cp):
+  """Run the gen_git_source to create links.
+
+  The links are for bazel to track dependencies for git hash propagation.
+
+  Args:
+    environ_cp: copy of the os.environ.
+  """
+  cmd = '%s tensorflow/tools/git/gen_git_source.py --configure %s' % (
+      environ_cp.get('PYTHON_BIN_PATH'), os.getcwd())
+  os.system(cmd)
+
+
+def cleanup_makefile():
+  """Delete any leftover BUILD files from the Makefile build.
+
+  These files could interfere with Bazel parsing.
+  """
+  makefile_download_dir = 'tensorflow/contrib/makefile/downloads'
+  if os.path.isdir(makefile_download_dir):
+    for root, _, filenames in os.walk(makefile_download_dir):
+      for f in filenames:
+        if f.endswith('BUILD'):
+          os.remove(os.path.join(root, f))
+
+
+def get_var(environ_cp,
+            var_name,
+            query_item,
+            enabled_by_default,
+            question=None,
+            yes_reply=None,
+            no_reply=None):
+  """Get boolean input from user.
+
+  If var_name is not set in env, ask user to enable query_item or not. If the
+  response is empty, use the default.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    query_item: string for feature related to the variable, e.g. "Hadoop File
+      System".
+    enabled_by_default: boolean for default behavior.
+    question: optional string for how to ask for user input.
+    yes_reply: optionanl string for reply when feature is enabled.
+    no_reply: optional string for reply when feature is disabled.
+
+  Returns:
+    boolean value of the variable.
+  """
+  if not question:
+    question = 'Do you wish to build TensorFlow with %s support?' % query_item
+  if not yes_reply:
+    yes_reply = '%s support will be enabled for TensorFlow.' % query_item
+  if not no_reply:
+    no_reply = 'No %s' % yes_reply
+
+  yes_reply += '\n'
+  no_reply += '\n'
+
+  if enabled_by_default:
+    question += ' [Y/n]: '
+  else:
+    question += ' [y/N]: '
+
+  var = environ_cp.get(var_name)
+  while var is None:
+    user_input_origin = get_input(question)
+    user_input = user_input_origin.strip().lower()
+    if user_input == 'y':
+      print(yes_reply)
+      var = True
+    elif user_input == 'n':
+      print(no_reply)
+      var = False
+    elif not user_input:
+      if enabled_by_default:
+        print(yes_reply)
+        var = True
+      else:
+        print(no_reply)
+        var = False
+    else:
+      print('Invalid selection: %s' % user_input_origin)
+  return var
+
+
+def set_build_var(environ_cp, var_name, query_item, option_name,
+                  enabled_by_default):
+  """Set if query_item will be enabled for the build.
+
+  Ask user if query_item will be enabled. Default is used if no input is given.
+  Set subprocess environment variable and write to .bazelrc if enabled.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    query_item: string for feature related to the variable, e.g. "Hadoop File
+      System".
+    option_name: string for option to define in .bazelrc.
+    enabled_by_default: boolean for default behavior.
+  """
+
+  var = str(int(get_var(environ_cp, var_name, query_item, enabled_by_default)))
+  environ_cp[var_name] = var
+  if var == '1':
+    write_to_bazelrc('build --define %s=true' % option_name)
+
+
+def set_action_env_var(environ_cp,
+                       var_name,
+                       query_item,
+                       enabled_by_default,
+                       question=None,
+                       yes_reply=None,
+                       no_reply=None):
+  """Set boolean action_env variable.
+
+  Ask user if query_item will be enabled. Default is used if no input is given.
+  Set environment variable and write to .bazelrc.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    query_item: string for feature related to the variable, e.g. "Hadoop File
+      System".
+    enabled_by_default: boolean for default behavior.
+    question: optional string for how to ask for user input.
+    yes_reply: optionanl string for reply when feature is enabled.
+    no_reply: optional string for reply when feature is disabled.
+  """
+  var = int(
+      get_var(environ_cp, var_name, query_item, enabled_by_default, question,
+              yes_reply, no_reply))
+
+  write_action_env_to_bazelrc(var_name, var)
+  environ_cp[var_name] = str(var)
+
+
+def check_bazel_version(min_version):
+  """Check installed bezel version is at least min_version.
+
+  Args:
+    min_version: string for minimum bazel version.
+  """
+  try:
+    curr_version = run_shell('bazel version')
+  except subprocess.CalledProcessError:
+    print('Cannot find bazel. Please install bazel.')
+    sys.exit(0)
+
+  for line in curr_version.split('\n'):
+    if 'Build label: ' in line:
+      curr_version = line.split('Build label: ')[1]
+      break
+
+  min_version_segments = min_version.split('.')
+  curr_version_segments = curr_version.split('.')
+
+  # Check if current bazel version can be detected properly.
+  for seg in curr_version_segments:
+    if not seg.isdigit():
+      print('WARNING: current bazel installation is not a release version.')
+      print('Make sure you are running at least bazel %s' % min_version)
+      return
+
+  min_version_str = ''.join(['%03d' % int(seg) for seg in min_version_segments])
+  curr_version_str = ''.join(
+      ['%03d' % int(seg) for seg in curr_version_segments])
+  if int(curr_version_str) < int(min_version_str):
+    print('Please upgrade your bazel installation to version %s or higher to '
+          'build TensorFlow!' % min_version)
+    sys.exit(0)
+
+
+def set_cc_opt_flags(environ_cp):
+  """Set up architecture-dependent optimization flags.
+
+  Also append CC optimization flags to bazel.rc..
+
+  Args:
+    environ_cp: copy of the os.environ.
+  """
+  if is_ppc64le():
+    # gcc on ppc64le does not support -march, use mcpu instead
+    default_cc_opt_flags = '-mcpu=native'
+  else:
+    default_cc_opt_flags = '-march=native'
+  question = ('Please specify optimization flags to use during compilation when'
+              ' bazel option "--config=opt" is specified [Default is %s]: '
+             ) % default_cc_opt_flags
+  cc_opt_flags = get_from_env_or_user_or_default(environ_cp, 'CC_OPT_FLAGS',
+                                                 question, default_cc_opt_flags)
+  for opt in cc_opt_flags.split():
+    write_to_bazelrc('build:opt --cxxopt=%s --copt=%s' % (opt, opt))
+
+
+def set_tf_cuda_clang(environ_cp):
+  """set TF_CUDA_CLANG action_env.
+
+  Args:
+    environ_cp: copy of the os.environ.
+  """
+  question = 'Do you want to use clang as CUDA compiler?'
+  yes_reply = 'Clang will be used as CUDA compiler.'
+  no_reply = 'nvcc will be used as CUDA compiler.'
+  set_action_env_var(
+      environ_cp,
+      'TF_CUDA_CLANG',
+      None,
+      False,
+      question=question,
+      yes_reply=yes_reply,
+      no_reply=no_reply)
+
+
+def get_from_env_or_user_or_default(environ_cp, var_name, ask_for_var,
+                                    var_default):
+  """Get var_name either from env, or user or default.
+
+  If var_name has been set as environment variable, use the preset value, else
+  ask for user input. If no input is provided, the default is used.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    ask_for_var: string for how to ask for user input.
+    var_default: default value string.
+
+  Returns:
+    string value for var_name
+  """
+  var = environ_cp.get(var_name)
+  if not var:
+    var = get_input(ask_for_var)
+  if not var:
+    var = var_default
+  return var
+
+
+def set_clang_cuda_compiler_path(environ_cp):
+  """Set CLANG_CUDA_COMPILER_PATH."""
+  default_clang_path = run_shell('which clang || true')
+  ask_clang_path = ('Please specify which clang should be used as device and '
+                    'host compiler. [Default is %s]: ') % default_clang_path
+
+  while True:
+    clang_cuda_compiler_path = get_from_env_or_user_or_default(
+        environ_cp, 'CLANG_CUDA_COMPILER_PATH', ask_clang_path,
+        default_clang_path)
+    if os.path.exists(clang_cuda_compiler_path):
+      break
+
+    # Reset and retry
+    print('Invalid clang path: %s cannot be found.' % clang_cuda_compiler_path)
+    environ_cp['CLANG_CUDA_COMPILER_PATH'] = ''
+
+  # Set CLANG_CUDA_COMPILER_PATH
+  environ_cp['CLANG_CUDA_COMPILER_PATH'] = clang_cuda_compiler_path
+  write_action_env_to_bazelrc('CLANG_CUDA_COMPILER_PATH',
+                              clang_cuda_compiler_path)
+
+
+def set_gcc_host_compiler_path(environ_cp):
+  """Set GCC_HOST_COMPILER_PATH."""
+  default_gcc_host_compiler_path = run_shell('which gcc || true')
+  cuda_bin_symlink = '%s/bin/gcc' % environ_cp.get('CUDA_TOOLKIT_PATH')
+
+  if os.path.islink(cuda_bin_symlink):
+    # os.readlink is only available in linux
+    default_gcc_host_compiler_path = run_shell('readlink %s' % cuda_bin_symlink)
+
+  ask_gcc_path = (
+      'Please specify which gcc should be used by nvcc as the '
+      'host compiler. [Default is %s]: ') % default_gcc_host_compiler_path
+  while True:
+    gcc_host_compiler_path = get_from_env_or_user_or_default(
+        environ_cp, 'GCC_HOST_COMPILER_PATH', ask_gcc_path,
+        default_gcc_host_compiler_path)
+
+    if os.path.exists(gcc_host_compiler_path):
+      break
+
+    # Reset and retry
+    print('Invalid gcc path. %s cannot be found' % gcc_host_compiler_path)
+    environ_cp['GCC_HOST_COMPILER_PATH'] = ''
+
+  # Set GCC_HOST_COMPILER_PATH
+  environ_cp['GCC_HOST_COMPILER_PATH'] = gcc_host_compiler_path
+  write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path)
+
+
+def set_tf_cuda_version(environ_cp):
+  """Set CUDA_TOOLKIT_PATH and TF_CUDA_VERSION."""
+  ask_cuda_version = (
+      'Please specify the CUDA SDK version you want to use, '
+      'e.g. 7.0. [Leave empty to default to CUDA %s]: ') % _DEFAULT_CUDA_VERSION
+
+  while True:
+    # Configure the Cuda SDK version to use.
+    tf_cuda_version = get_from_env_or_user_or_default(
+        environ_cp, 'TF_CUDA_VERSION', ask_cuda_version, _DEFAULT_CUDA_VERSION)
+
+    # Find out where the CUDA toolkit is installed
+    default_cuda_path = _DEFAULT_CUDA_PATH
+    if is_windows():
+      default_cuda_path = cygpath(
+          environ_cp.get('CUDA_PATH', _DEFAULT_CUDA_PATH_WIN))
+    elif is_linux():
+      # If the default doesn't exist, try an alternative default.
+      if (not os.path.exists(default_cuda_path)
+         ) and os.path.exists(_DEFAULT_CUDA_PATH_LINUX):
+        default_cuda_path = _DEFAULT_CUDA_PATH_LINUX
+    ask_cuda_path = ('Please specify the location where CUDA %s toolkit is'
+                     ' installed. Refer to README.md for more details. '
+                     '[Default is %s]: ') % (tf_cuda_version, default_cuda_path)
+    cuda_toolkit_path = get_from_env_or_user_or_default(
+        environ_cp, 'CUDA_TOOLKIT_PATH', ask_cuda_path, default_cuda_path)
+
+    if is_windows():
+      cuda_rt_lib_path = 'lib/x64/cudart.lib'
+    elif is_linux():
+      cuda_rt_lib_path = 'lib64/libcudart.so.%s' % tf_cuda_version
+    elif is_macos():
+      cuda_rt_lib_path = 'lib/libcudart.%s.dylib' % tf_cuda_version
+
+    cuda_toolkit_path_full = os.path.join(cuda_toolkit_path, cuda_rt_lib_path)
+    if os.path.exists(cuda_toolkit_path_full):
+      break
+
+    # Reset and retry
+    print('Invalid path to CUDA %s toolkit. %s cannot be found' %
+          (tf_cuda_version, cuda_toolkit_path_full))
+    environ_cp['TF_CUDA_VERSION'] = ''
+    environ_cp['CUDA_TOOLKIT_PATH'] = ''
+
+  # Set CUDA_TOOLKIT_PATH and TF_CUDA_VERSION
+  environ_cp['CUDA_TOOLKIT_PATH'] = cuda_toolkit_path
+  write_action_env_to_bazelrc('CUDA_TOOLKIT_PATH', cuda_toolkit_path)
+  environ_cp['TF_CUDA_VERSION'] = tf_cuda_version
+  write_action_env_to_bazelrc('TF_CUDA_VERSION', tf_cuda_version)
+
+
+def set_tf_cunn_version(environ_cp):
+  """Set CUDNN_INSTALL_PATH and TF_CUDNN_VERSION."""
+  ask_cudnn_version = (
+      '"Please specify the cuDNN version you want to use. '
+      '[Leave empty to default to cuDNN %s.0]: ') % _DEFAULT_CUDNN_VERSION
+
+  while True:
+    tf_cudnn_version = get_from_env_or_user_or_default(
+        environ_cp, 'TF_CUDNN_VERSION', ask_cudnn_version,
+        _DEFAULT_CUDNN_VERSION)
+
+    default_cudnn_path = environ_cp.get('CUDA_TOOLKIT_PATH')
+    ask_cudnn_path = (r'Please specify the location where cuDNN %s library is '
+                      'installed. Refer to README.md for more details. [Default'
+                      ' is %s]:') % (tf_cudnn_version, default_cudnn_path)
+    cudnn_install_path = get_from_env_or_user_or_default(
+        environ_cp, 'CUDNN_INSTALL_PATH', ask_cudnn_path, default_cudnn_path)
+
+    # Result returned from "read" will be used unexpanded. That make "~"
+    # unusable. Going through one more level of expansion to handle that.
+    cudnn_install_path = os.path.realpath(
+        os.path.expanduser(cudnn_install_path))
+    if is_windows():
+      cudnn_install_path = cygpath(cudnn_install_path)
+
+    if is_windows():
+      cuda_dnn_lib_path = 'lib/x64/cudnn.lib'
+      cuda_dnn_lib_alt_path = 'lib/x64/cudnn.lib'
+    elif is_linux():
+      cuda_dnn_lib_path = 'lib64/libcudnn.so.%s' % tf_cudnn_version
+      cuda_dnn_lib_alt_path = 'libcudnn.so.%s' % tf_cudnn_version
+    elif is_macos():
+      cuda_dnn_lib_path = 'lib/libcudnn.%s.dylib' % tf_cudnn_version
+      cuda_dnn_lib_alt_path = 'libcudnn.%s.dylib' % tf_cudnn_version
+
+    cuda_dnn_lib_path_full = os.path.join(cudnn_install_path, cuda_dnn_lib_path)
+    cuda_dnn_lib_alt_path_full = os.path.join(cudnn_install_path,
+                                              cuda_dnn_lib_alt_path)
+    if os.path.exists(cuda_dnn_lib_path_full) or os.path.exists(
+        cuda_dnn_lib_alt_path_full):
+      break
+
+    # Try another alternative for Linux
+    if is_linux():
+      if subprocess.call(['which', 'ldconfig']):
+        ldconfig_bin = '/sbin/ldconfig'
+      else:
+        ldconfig_bin = 'ldconfig'
+      cudnn_path_from_ldconfig = run_shell(
+          r'%s -p | sed -n "s/.*libcudnn.so .* => \(.*\)/\\1/p"' % ldconfig_bin)
+      if os.path.exists('%s.%s' % (cudnn_path_from_ldconfig, tf_cudnn_version)):
+        cudnn_install_path = os.path.dirname(cudnn_path_from_ldconfig)
+        break
+
+    # Reset and Retry
+    print(
+        'Invalid path to cuDNN %s toolkit. None of the following files can be '
+        'found:' % tf_cudnn_version)
+    print(cuda_dnn_lib_path_full)
+    print(cuda_dnn_lib_alt_path_full)
+    if is_linux():
+      print('%s.%s' % (cudnn_path_from_ldconfig, tf_cudnn_version))
+
+    environ_cp['TF_CUDNN_VERSION'] = ''
+
+  # Set CUDNN_INSTALL_PATH and TF_CUDNN_VERSION
+  environ_cp['CUDNN_INSTALL_PATH'] = cudnn_install_path
+  write_action_env_to_bazelrc('CUDNN_INSTALL_PATH', cudnn_install_path)
+  environ_cp['TF_CUDNN_VERSION'] = tf_cudnn_version
+  write_action_env_to_bazelrc('TF_CUDNN_VERSION', tf_cudnn_version)
+
+
+def get_native_cuda_compute_capabilities(environ_cp):
+  """Get native cuda compute capabilities.
+
+  Args:
+    environ_cp: copy of the os.environ.
+  Returns:
+    string of native cuda compute capabilities, separated by comma.
+  """
+  device_query_bin = os.path.join(
+      environ_cp.get('CUDA_TOOLKIT_PATH'), 'extras/demo_suite/deviceQuery')
+  cmd = (r'"%s" | grep "Capability" | grep -o "[0-9]*\.[0-9]*" | sed '
+         '":a;{N;s/\\n/,/};ba"') % device_query_bin
+  try:
+    output = run_shell(cmd)
+  except subprocess.CalledProcessError:
+    output = ''
+  return output
+
+
+def set_tf_cuda_compute_capabilities(environ_cp):
+  """Set TF_CUDA_COMPUTE_CAPABILITIES."""
+  while True:
+    native_cuda_compute_capabilities = get_native_cuda_compute_capabilities(
+        environ_cp)
+    if not native_cuda_compute_capabilities:
+      default_cuda_compute_capabilities = _DEFAULT_CUDA_COMPUTE_CAPABILITIES
+    else:
+      default_cuda_compute_capabilities = native_cuda_compute_capabilities
+
+    ask_cuda_compute_capabilities = (
+        'Please specify a list of comma-separated '
+        'Cuda compute capabilities you want to '
+        'build with.\nYou can find the compute '
+        'capability of your device at: '
+        'https://developer.nvidia.com/cuda-gpus.\nPlease'
+        ' note that each additional compute '
+        'capability significantly increases your '
+        'build time and binary size. [Default is: %s]' %
+        default_cuda_compute_capabilities)
+    tf_cuda_compute_capabilities = get_from_env_or_user_or_default(
+        environ_cp, 'TF_CUDA_COMPUTE_CAPABILITIES',
+        ask_cuda_compute_capabilities, default_cuda_compute_capabilities)
+    # Check whether all capabilities from the input is valid
+    all_valid = True
+    for compute_capability in tf_cuda_compute_capabilities.split(','):
+      if not re.match('[0-9]+.[0-9]+', compute_capability):
+        print('Invalid compute capability: ' % compute_capability)
+        all_valid = False
+
+    if all_valid:
+      break
+
+    # Reset and Retry
+    environ_cp['TF_CUDA_COMPUTE_CAPABILITIES'] = ''
+
+  # Set TF_CUDA_COMPUTE_CAPABILITIES
+  environ_cp['TF_CUDA_COMPUTE_CAPABILITIES'] = tf_cuda_compute_capabilities
+  write_action_env_to_bazelrc('TF_CUDA_COMPUTE_CAPABILITIES',
+                              tf_cuda_compute_capabilities)
+
+
+def set_other_cuda_vars(environ_cp):
+  """Set other CUDA related variables."""
+  if is_windows():
+    # The following three variables are needed for MSVC toolchain configuration
+    # in Bazel
+    environ_cp['CUDA_PATH'] = environ_cp.get('CUDA_TOOLKIT_PATH')
+    environ_cp['CUDA_COMPUTE_CAPABILITIES'] = environ_cp.get(
+        'TF_CUDA_COMPUTE_CAPABILITIES')
+    environ_cp['NO_WHOLE_ARCHIVE_OPTION'] = 1
+    write_action_env_to_bazelrc('CUDA_PATH', environ_cp.get('CUDA_PATH'))
+    write_action_env_to_bazelrc('CUDA_COMPUTE_CAPABILITIE',
+                                environ_cp.get('CUDA_COMPUTE_CAPABILITIE'))
+    write_action_env_to_bazelrc('NO_WHOLE_ARCHIVE_OPTION',
+                                environ_cp.get('NO_WHOLE_ARCHIVE_OPTION'))
+    write_to_bazelrc('build --config=win-cuda')
+    write_to_bazelrc('test --config=win-cuda')
+  else:
+    # If CUDA is enabled, always use GPU during build and test.
+    if environ_cp.get('TF_CUDA_CLANG') == '1':
+      write_to_bazelrc('build --config=cuda_clang')
+      write_to_bazelrc('test --config=cuda_clang')
+    else:
+      write_to_bazelrc('build --config=cuda')
+      write_to_bazelrc('test --config=cuda')
+
+
+def set_host_cxx_compiler(environ_cp):
+  """Set HOST_CXX_COMPILER."""
+  default_cxx_host_compiler = run_shell('which g++ || true')
+  ask_cxx_host_compiler = (
+      'Please specify which C++ compiler should be used as'
+      ' the host C++ compiler. [Default is %s]: ') % default_cxx_host_compiler
+
+  while True:
+    host_cxx_compiler = get_from_env_or_user_or_default(
+        environ_cp, 'HOST_CXX_COMPILER', ask_cxx_host_compiler,
+        default_cxx_host_compiler)
+    if os.path.exists(host_cxx_compiler):
+      break
+
+    # Reset and retry
+    print('Invalid C++ compiler path. %s cannot be found' % host_cxx_compiler)
+    environ_cp['HOST_CXX_COMPILER'] = ''
+
+  # Set HOST_CXX_COMPILER
+  environ_cp['HOST_CXX_COMPILER'] = host_cxx_compiler
+  write_action_env_to_bazelrc('HOST_CXX_COMPILER', host_cxx_compiler)
+
+
+def set_host_c_compiler(environ_cp):
+  """Set HOST_C_COMPILER."""
+  default_c_host_compiler = run_shell('which gcc || true')
+  ask_c_host_compiler = (
+      'Please specify which C compiler should be used as the'
+      ' host C compiler. [Default is %s]: ') % default_c_host_compiler
+
+  while True:
+    host_c_compiler = get_from_env_or_user_or_default(
+        environ_cp, 'HOST_C_COMPILER', ask_c_host_compiler,
+        default_c_host_compiler)
+    if os.path.exists(host_c_compiler):
+      break
+
+    # Reset and retry
+    print('Invalid C compiler path. %s cannot be found' % host_c_compiler)
+    environ_cp['HOST_C_COMPILER'] = ''
+
+  # Set HOST_C_COMPILER
+  environ_cp['HOST_C_COMPILER'] = host_c_compiler
+  write_action_env_to_bazelrc('HOST_C_COMPILER', host_c_compiler)
+
+
+def set_computecpp_toolkit_path(environ_cp):
+  """Set COMPUTECPP_TOOLKIT_PATH."""
+  ask_computecpp_toolkit_path = ('Please specify the location where ComputeCpp '
+                                 'for SYCL %s is installed. [Default is %s]: '
+                                ) % (_TF_OPENCL_VERSION,
+                                     _DEFAULT_COMPUTECPP_TOOLKIT_PATH)
+
+  while True:
+    computecpp_toolkit_path = get_from_env_or_user_or_default(
+        environ_cp, 'COMPUTECPP_TOOLKIT_PATH', ask_computecpp_toolkit_path,
+        _DEFAULT_COMPUTECPP_TOOLKIT_PATH)
+    if is_linux():
+      sycl_rt_lib_path = 'lib/libComputeCpp.so'
+    else:
+      sycl_rt_lib_path = ''
+
+    sycl_rt_lib_path_full = os.path.join(computecpp_toolkit_path,
+                                         sycl_rt_lib_path)
+    if os.path.exists(sycl_rt_lib_path_full):
+      break
+
+    print('Invalid SYCL %s library path. %s cannot be found' %
+          (_TF_OPENCL_VERSION, sycl_rt_lib_path_full))
+    environ_cp['COMPUTECPP_TOOLKIT_PATH'] = ''
+
+  # Set COMPUTECPP_TOOLKIT_PATH
+  environ_cp['COMPUTECPP_TOOLKIT_PATH'] = computecpp_toolkit_path
+  write_action_env_to_bazelrc('COMPUTECPP_TOOLKIT_PATH',
+                              computecpp_toolkit_path)
+
+
+def set_mpi_home(environ_cp):
+  """Set MPI_HOME."""
+  cmd = ('dirname $(dirname $(which mpirun)) || dirname $(dirname $(which '
+         'mpiexec))  || true')
+  default_mpi_home = run_shell(cmd)
+  ask_mpi_home = ('Please specify the MPI toolkit folder. [Default is %s]: '
+                 ) % default_mpi_home
+  while True:
+    mpi_home = get_from_env_or_user_or_default(environ_cp, 'MPI_HOME',
+                                               ask_mpi_home, default_mpi_home)
+
+    if os.path.exists(os.path.join(mpi_home, 'include')) and os.path.exists(
+        os.path.join(mpi_home, 'lib')):
+      break
+
+    print('Invalid path to the MPI Toolkit. %s or %s cannot be found' %
+          (os.path.join(mpi_home, 'include'),
+           os.path.exists(os.path.join(mpi_home, 'lib'))))
+    environ_cp['MPI_HOME'] = ''
+
+  # Set MPI_HOME
+  environ_cp['MPI_HOME'] = str(mpi_home)
+
+
+def set_other_mpi_vars(environ_cp):
+  """Set other MPI related variables."""
+  # Link the MPI header files
+  mpi_home = environ_cp.get('MPI_HOME')
+  symlink_force('%s/include/mpi.h' % mpi_home, 'third_party/mpi/mpi.h')
+
+  # Determine if we use OpenMPI or MVAPICH, these require different header files
+  # to be included here to make bazel dependency checker happy
+  if os.path.exists(os.path.join(mpi_home, 'include/mpi_portable_platform.h')):
+    symlink_force(
+        os.path.join(mpi_home, 'include/mpi_portable_platform.h'),
+        'third_party/mpi/mpi_portable_platform.h')
+    # TODO(gunan): avoid editing files in configure
+    sed_in_place('third_party/mpi/mpi.bzl', 'MPI_LIB_IS_OPENMPI=False',
+                 'MPI_LIB_IS_OPENMPI=True')
+  else:
+    # MVAPICH / MPICH
+    symlink_force(
+        os.path.join(mpi_home, 'include/mpio.h'), 'third_party/mpi/mpio.h')
+    symlink_force(
+        os.path.join(mpi_home, 'include/mpicxx.h'), 'third_party/mpi/mpicxx.h')
+    # TODO(gunan): avoid editing files in configure
+    sed_in_place('third_party/mpi/mpi.bzl', 'MPI_LIB_IS_OPENMPI=True',
+                 'MPI_LIB_IS_OPENMPI=False')
+
+  if os.path.exists(os.path.join(mpi_home, 'lib/libmpi.so')):
+    symlink_force(
+        os.path.join(mpi_home, 'lib/libmpi.so'), 'third_party/mpi/libmpi.so')
+  else:
+    raise ValueError('Cannot find the MPI library file in %s/lib' % mpi_home)
+
+
+def set_mkl():
+  write_to_bazelrc('build:mkl --define with_mkl_support=true')
+  write_to_bazelrc('build:mkl --define using_mkl=true')
+  write_to_bazelrc('build:mkl -c opt')
+  write_to_bazelrc('build:mkl --copt="-DEIGEN_USE_VML"')
+  print(
+      'Add "--config=mkl" to your bazel command to build with MKL '
+      'support.\nPlease note that MKL on MacOS or windows is still not '
+      'supported.\nIf you would like to use a local MKL instead of '
+      'downloading, please set the environment variable \"TF_MKL_ROOT\" every '
+      'time before build.')
+
+
+def main():
+  # Make a copy of os.environ to be clear when functions and getting and setting
+  # environment variables.
+  environ_cp = dict(os.environ)
+
+  check_bazel_version('0.4.5')
+
+  reset_tf_configure_bazelrc()
+  cleanup_makefile()
+  setup_python(environ_cp)
+  run_gen_git_source(environ_cp)
+
+  if is_windows():
+    environ_cp['TF_NEED_GCP'] = '0'
+    environ_cp['TF_NEED_HDFS'] = '0'
+    environ_cp['TF_NEED_JEMALLOC'] = '0'
+    environ_cp['TF_NEED_OPENCL'] = '0'
+    environ_cp['TF_CUDA_CLANG'] = '0'
+
+  if is_macos():
+    environ_cp['TF_NEED_JEMALLOC'] = '0'
+
+  set_build_var(environ_cp, 'TF_NEED_JEMALLOC', 'jemalloc as malloc',
+                'with_jemalloc', True)
+  set_build_var(environ_cp, 'TF_NEED_GCP', 'Google Cloud Platform',
+                'with_gcp_support', False)
+  set_build_var(environ_cp, 'TF_NEED_HDFS', 'Hadoop File System',
+                'with_hdfs_support', False)
+  set_build_var(environ_cp, 'TF_ENABLE_XLA', 'XLA JIT', 'with_xla_support',
+                False)
+  set_build_var(environ_cp, 'TF_NEED_VERBS', 'VERBS', 'with_verbs_support',
+                False)
+
+  set_action_env_var(environ_cp, 'TF_NEED_OPENCL', 'OpenCL', False)
+  if environ_cp.get('TF_NEED_OPENCL') == '1':
+    set_host_cxx_compiler(environ_cp)
+    set_host_c_compiler(environ_cp)
+    set_computecpp_toolkit_path(environ_cp)
+
+  set_action_env_var(environ_cp, 'TF_NEED_CUDA', 'CUDA', False)
+  if environ_cp.get('TF_NEED_CUDA') == '1':
+    set_tf_cuda_version(environ_cp)
+    set_tf_cunn_version(environ_cp)
+    set_tf_cuda_compute_capabilities(environ_cp)
+
+    set_tf_cuda_clang(environ_cp)
+    if environ_cp.get('TF_CUDA_CLANG') == '1':
+      # Set up which clang we should use as the cuda / host compiler.
+      set_clang_cuda_compiler_path(environ_cp)
+    else:
+      # Set up which gcc nvcc should use as the host compiler
+      # No need to set this on Windows
+      if not is_windows():
+        set_gcc_host_compiler_path(environ_cp)
+    set_other_cuda_vars(environ_cp)
+
+  set_build_var(environ_cp, 'TF_NEED_MPI', 'MPI', 'with_mpi_support', False)
+  if environ_cp.get('TF_NEED_MPI') == '1':
+    set_mpi_home(environ_cp)
+    set_other_mpi_vars(environ_cp)
+
+  set_cc_opt_flags(environ_cp)
+  set_mkl()
+
+
+if __name__ == '__main__':
+  main()
diff --git a/tensorflow/tools/ci_build/builds/configured b/tensorflow/tools/ci_build/builds/configured
index 25cb51ea7c..563e07e3af 100755
--- a/tensorflow/tools/ci_build/builds/configured
+++ b/tensorflow/tools/ci_build/builds/configured
@@ -56,7 +56,7 @@ else
 fi
 
 pushd "${CI_TENSORFLOW_SUBMODULE_PATH:-.}"
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 popd
 
 # Gather and print build information
diff --git a/tensorflow/tools/ci_build/builds/run_pip_tests.sh b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
index f66846654d..9a6890401b 100755
--- a/tensorflow/tools/ci_build/builds/run_pip_tests.sh
+++ b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
@@ -120,7 +120,7 @@ else
 fi
 
 export TF_NEED_CUDA=$IS_GPU
-yes "" | ./configure
+${PYTHON_BIN_PATH} configure.py
 
 # Figure out how many concurrent tests we can run and do run the tests.
 BAZEL_PARALLEL_TEST_FLAGS=""
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
index 118e85fee0..ca84079654 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
@@ -30,7 +30,7 @@ export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 # Only running cc tests, python version does not matter.
 export PYTHON_BIN_PATH=`which python`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test --test_lang_filters=cc -k \
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
index fa3d27fa41..5c82c9efaf 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
@@ -29,7 +29,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=`which python2`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test --test_lang_filters=py -k \
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
index 258dec4fec..7155636a53 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
@@ -29,7 +29,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=`which python3`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test -k \
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
index 9c450ab4da..218d2a8991 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
@@ -29,7 +29,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=`which python3`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test --test_lang_filters=py -k \
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
index f2ea8d3c77..dff72c25bf 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 export TF_NEED_CUDA=1
 export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test -k \
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
index 4e0c3d1d33..a36a8445af 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 export TF_NEED_CUDA=1
 export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test -k \
diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
index 0b8c73993f..0ee894e2c4 100755
--- a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
+++ b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
@@ -30,7 +30,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=$(which python2)
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 which bazel
 bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \
     --test_timeout 300,450,1200,3600 \
diff --git a/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh b/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
index 1106413071..f548adc5ca 100755
--- a/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
+++ b/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
@@ -33,7 +33,7 @@ export TF_NEED_CUDA=1
 export TF_ENABLE_XLA=1
 export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --config=cuda --test_tag_filters=-no_gpu,-benchmark-test -k \
-- 
GitLab


From 57d17092d0e2bd6f169724beab28ec29c5e6db85 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 25 Jul 2017 13:39:32 -0700
Subject: [PATCH 1319/1375] [TF:XLA] Ignore control edges from Enter nodes to
 the graph sink during loop functionalization.

PiperOrigin-RevId: 163115904
---
 .../tf2xla/functionalize_control_flow.cc      | 22 +++++++++++++++----
 .../tf2xla/functionalize_control_flow_test.cc |  8 +++++++
 2 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
index faa88ecfe2..1c7a2046aa 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
@@ -323,12 +323,26 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame,
   for (Arg& arg : frame->args) {
     if (!arg.is_loop_invariant) {
       // Follow the edge from the Enter to Merge.
-      if (arg.enter->out_edges().size() != 1) {
+      const Edge* enter_merge = nullptr;
+      for (const Edge* e : arg.enter->out_edges()) {
+        // Ignore control-edges to the sink node. These are allowed by the
+        // graph invariants, although probably they should have been stripped
+        // off earlier.
+        if (e->IsControlEdge() && e->dst()->IsSink()) {
+          continue;
+        }
+        if (enter_merge != nullptr) {
+          return errors::Internal(
+              "Enter node for loop-varying argument ", arg.enter->name(),
+              " has multiple successors: ", enter_merge->dst()->name(), " and ",
+              e->dst()->name());
+        }
+        enter_merge = e;
+      }
+      if (enter_merge == nullptr) {
         return errors::Internal("Enter node for loop-varying argument ",
-                                arg.enter->name(),
-                                " does not have exactly one successor");
+                                arg.enter->name(), " has zero successors");
       }
-      const Edge* enter_merge = *arg.enter->out_edges().begin();
       arg.merge = enter_merge->dst();
       if (!IsMerge(arg.merge)) {
         return errors::InvalidArgument(
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
index 2fb1cc0454..914c8999a6 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
@@ -96,6 +96,14 @@ TEST(FunctionalizeControlFlow, OneLoopVar) {
     TF_EXPECT_OK(scope.ToGraph(&graph));
   }
 
+  // Regression test: control edges from an Enter node to the graph sink should
+  // be ignored.
+  for (Node* n : graph.nodes()) {
+    if (n->name() == "while/Enter") {
+      graph.AddControlEdge(n, graph.sink_node());
+    }
+  }
+
   FunctionLibraryDefinition library(OpRegistry::Global(), {});
   TF_ASSERT_OK(FunctionalizeControlFlow(&graph, &library));
 
-- 
GitLab


From 27bbbd8d0149724caa7f1295f122f968acb8cd96 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 14:14:53 -0700
Subject: [PATCH 1320/1375] Support customized residual function in the
 residual wrapper.

PiperOrigin-RevId: 163121296
---
 .../python/kernel_tests/core_rnn_cell_test.py | 29 +++++++++++++++++--
 tensorflow/python/ops/rnn_cell_impl.py        | 18 ++++++++----
 ...orflow.nn.rnn_cell.-residual-wrapper.pbtxt |  2 +-
 3 files changed, 40 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index 06954f51d8..c14463bdad 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -210,7 +210,7 @@ class RNNCellTest(test.TestCase):
           sess.run([variables_lib.global_variables_initializer()])
           sess.run([g, out_m],
                    {x.name: 1 * np.ones([batch_size, input_size]),
-               m.name: 0.1 * np.ones([batch_size - 1, state_size])})
+                    m.name: 0.1 * np.ones([batch_size - 1, state_size])})
 
   def testBasicLSTMCellStateSizeError(self):
     """Tests that state_size must be num_units * 2."""
@@ -218,7 +218,7 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
         num_units = 2
-        state_size = num_units * 3 # state_size must be num_units * 2
+        state_size = num_units * 3  # state_size must be num_units * 2
         batch_size = 3
         input_size = 4
         x = array_ops.zeros([batch_size, input_size])
@@ -406,6 +406,31 @@ class RNNCellTest(test.TestCase):
         # States are left untouched
         self.assertAllClose(res[2], res[3])
 
+  def testResidualWrapperWithSlice(self):
+    with self.test_session() as sess:
+      with variable_scope.variable_scope(
+          "root", initializer=init_ops.constant_initializer(0.5)):
+        x = array_ops.zeros([1, 5])
+        m = array_ops.zeros([1, 3])
+        base_cell = rnn_cell_impl.GRUCell(3)
+        g, m_new = base_cell(x, m)
+        variable_scope.get_variable_scope().reuse_variables()
+        def residual_with_slice_fn(inp, out):
+          inp_sliced = array_ops.slice(inp, [0, 0], [-1, 3])
+          return inp_sliced + out
+        g_res, m_new_res = rnn_cell_impl.ResidualWrapper(
+            base_cell, residual_with_slice_fn)(x, m)
+        sess.run([variables_lib.global_variables_initializer()])
+        res_g, res_g_res, res_m_new, res_m_new_res = sess.run(
+            [g, g_res, m_new, m_new_res], {
+                x: np.array([[1., 1., 1., 1., 1.]]),
+                m: np.array([[0.1, 0.1, 0.1]])
+            })
+        # Residual connections
+        self.assertAllClose(res_g_res, res_g + [1., 1., 1.])
+        # States are left untouched
+        self.assertAllClose(res_m_new, res_m_new_res)
+
   def testDeviceWrapper(self):
     with variable_scope.variable_scope(
         "root", initializer=init_ops.constant_initializer(0.5)):
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index f7854e86c0..304b6ae665 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -786,13 +786,18 @@ class DropoutWrapper(RNNCell):
 class ResidualWrapper(RNNCell):
   """RNNCell wrapper that ensures cell inputs are added to the outputs."""
 
-  def __init__(self, cell):
+  def __init__(self, cell, residual_fn=None):
     """Constructs a `ResidualWrapper` for `cell`.
 
     Args:
       cell: An instance of `RNNCell`.
+      residual_fn: (Optional) The function to map raw cell inputs and raw cell
+        outputs to the actual cell outputs of the residual network.
+        Defaults to calling nest.map_structure on (lambda i, o: i + o), inputs
+        and outputs.
     """
     self._cell = cell
+    self._residual_fn = residual_fn
 
   @property
   def state_size(self):
@@ -807,7 +812,7 @@ class ResidualWrapper(RNNCell):
       return self._cell.zero_state(batch_size, dtype)
 
   def __call__(self, inputs, state, scope=None):
-    """Run the cell and add its inputs to its outputs.
+    """Run the cell and then apply the residual_fn on its inputs to its outputs.
 
     Args:
       inputs: cell inputs.
@@ -822,13 +827,14 @@ class ResidualWrapper(RNNCell):
       ValueError: If cell inputs and outputs have different structure (value).
     """
     outputs, new_state = self._cell(inputs, state, scope=scope)
-    nest.assert_same_structure(inputs, outputs)
     # Ensure shapes match
     def assert_shape_match(inp, out):
       inp.get_shape().assert_is_compatible_with(out.get_shape())
-    nest.map_structure(assert_shape_match, inputs, outputs)
-    res_outputs = nest.map_structure(
-        lambda inp, out: inp + out, inputs, outputs)
+    def default_residual_fn(inputs, outputs):
+      nest.assert_same_structure(inputs, outputs)
+      nest.map_structure(assert_shape_match, inputs, outputs)
+      return nest.map_structure(lambda inp, out: inp + out, inputs, outputs)
+    res_outputs = (self._residual_fn or default_residual_fn)(inputs, outputs)
     return (res_outputs, new_state)
 
 
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
index b21d9a8ee3..a75e9e8080 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
@@ -54,7 +54,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'cell\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'self\', \'cell\', \'residual_fn\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "add_loss"
-- 
GitLab


From 6b3751f660dfa0675cc39d163b8224f2c070694e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 14:55:50 -0700
Subject: [PATCH 1321/1375] Make fft_length optional for inverse_stft

PiperOrigin-RevId: 163127500
---
 .../contrib/signal/python/ops/spectral_ops.py | 25 +++++++++++++++----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/signal/python/ops/spectral_ops.py b/tensorflow/contrib/signal/python/ops/spectral_ops.py
index 75bc0bd21d..950d8f471c 100644
--- a/tensorflow/contrib/signal/python/ops/spectral_ops.py
+++ b/tensorflow/contrib/signal/python/ops/spectral_ops.py
@@ -103,7 +103,7 @@ def stft(signals, frame_length, frame_step, fft_length=None,
 def inverse_stft(stfts,
                  frame_length,
                  frame_step,
-                 fft_length,
+                 fft_length=None,
                  window_fn=functools.partial(window_ops.hann_window,
                                              periodic=True),
                  name=None):
@@ -118,7 +118,8 @@ def inverse_stft(stfts,
     frame_length: An integer scalar `Tensor`. The window length in samples.
     frame_step: An integer scalar `Tensor`. The number of samples to step.
     fft_length: An integer scalar `Tensor`. The size of the FFT that produced
-      `stfts`.
+      `stfts`. If not provided, uses the smallest power of 2 enclosing
+      `frame_length`.
     window_fn: A callable that takes a window length and a `dtype` keyword
       argument and returns a `[window_length]` `Tensor` of samples in the
       provided datatype. If set to `None`, no windowing is used.
@@ -130,7 +131,8 @@ def inverse_stft(stfts,
 
   Raises:
     ValueError: If `stfts` is not at least rank 2, `frame_length` is not scalar,
-      `frame_step` is not scalar, or `fft_length` is not scalar.
+      `frame_step` is not scalar, or `fft_length` is not scalar, or
+      `frame_length` is greater than `fft_length`.
 
   [stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
   """
@@ -141,8 +143,21 @@ def inverse_stft(stfts,
     frame_length.shape.assert_has_rank(0)
     frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
     frame_step.shape.assert_has_rank(0)
-    fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
-    fft_length.shape.assert_has_rank(0)
+    if fft_length is None:
+      fft_length = _enclosing_power_of_two(frame_length)
+    else:
+      fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
+      fft_length.shape.assert_has_rank(0)
+
+    frame_length_static = tensor_util.constant_value(
+        frame_length)
+    fft_length_static = tensor_util.constant_value(fft_length)
+    if (frame_length_static is not None and fft_length_static is not None and
+        frame_length_static > fft_length_static):
+      raise ValueError('frame_length (%d) may not be larger than '
+                       'fft_length (%d)' % (frame_length_static,
+                                            fft_length_static))
+
     real_frames = spectral_ops.irfft(stfts, [fft_length])[..., :frame_length]
 
     # Optionally window and overlap-add the inner 2 dimensions of real_frames
-- 
GitLab


From 07249f08867369899d39fc60442febdf1e36e6b5 Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Tue, 25 Jul 2017 15:03:34 -0700
Subject: [PATCH 1322/1375] Add LocalTempFilename function to Env class which
 creates a local temp file name

PiperOrigin-RevId: 163128673
---
 tensorflow/core/platform/env.cc      | 91 ++++++++++++++++++++++++++++
 tensorflow/core/platform/env.h       |  6 ++
 tensorflow/core/platform/env_test.cc | 28 +++++++++
 3 files changed, 125 insertions(+)

diff --git a/tensorflow/core/platform/env.cc b/tensorflow/core/platform/env.cc
index 2fdd989c9b..568a22b295 100644
--- a/tensorflow/core/platform/env.cc
+++ b/tensorflow/core/platform/env.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include <sys/stat.h>
 #include <deque>
 #include <utility>
 #include <vector>
@@ -30,7 +31,10 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/lib/io/path.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/env_time.h"
+#include "tensorflow/core/platform/host_info.h"
 #include "tensorflow/core/platform/protobuf.h"
 
 namespace tensorflow {
@@ -273,6 +277,93 @@ string Env::GetExecutablePath() {
   return exe_path;
 }
 
+bool Env::LocalTempFilename(string* filename) {
+  std::vector<string> dirs;
+  GetLocalTempDirectories(&dirs);
+
+  // Try each directory, as they might be full, have inappropriate
+  // permissions or have different problems at times.
+  for (const string& dir : dirs) {
+#ifdef __APPLE__
+    uint64_t tid64;
+    pthread_threadid_np(nullptr, &tid64);
+    int32 tid = static_cast<int32>(tid64);
+    int32 pid = static_cast<int32>(getpid());
+#elif defined(PLATFORM_WINDOWS)
+    int32 tid = static_cast<int32>(GetCurrentThreadId());
+    int32 pid = static_cast<int32>(GetCurrentProcessId());
+#else
+    int32 tid = static_cast<int32>(pthread_self());
+    int32 pid = static_cast<int32>(getpid());
+#endif
+    uint64 now_microsec = NowMicros();
+
+    *filename = io::JoinPath(
+        dir, strings::Printf("tempfile-%s-%x-%d-%llx", port::Hostname().c_str(),
+                             tid, pid, now_microsec));
+    if (FileExists(*filename).ok()) {
+      filename->clear();
+    } else {
+      return true;
+    }
+  }
+  return false;
+}
+
+void Env::GetLocalTempDirectories(std::vector<string>* list) {
+  list->clear();
+#ifdef PLATFORM_WINDOWS
+  // On windows we'll try to find a directory in this order:
+  //   C:/Documents & Settings/whomever/TEMP (or whatever GetTempPath() is)
+  //   C:/TMP/
+  //   C:/TEMP/
+  //   C:/WINDOWS/ or C:/WINNT/
+  //   .
+  char tmp[MAX_PATH];
+  // GetTempPath can fail with either 0 or with a space requirement > bufsize.
+  // See http://msdn.microsoft.com/en-us/library/aa364992(v=vs.85).aspx
+  DWORD n = GetTempPathA(MAX_PATH, tmp);
+  if (n > 0 && n <= MAX_PATH) list->push_back(tmp);
+  list->push_back("C:\\tmp\\");
+  list->push_back("C:\\temp\\");
+#else
+  // Directories, in order of preference. If we find a dir that
+  // exists, we stop adding other less-preferred dirs
+  const char* candidates[] = {
+      // Non-null only during unittest/regtest
+      getenv("TEST_TMPDIR"),
+
+      // Explicitly-supplied temp dirs
+      getenv("TMPDIR"),
+      getenv("TMP"),
+
+      // The old classic tmpdir
+      "/export/hda3/tmp",
+
+      // If all else fails
+      "/tmp",
+  };
+
+  for (const char* d : candidates) {
+    if (!d || d[0] == '\0') continue;  // Empty env var
+
+    // Make sure we don't surprise anyone who's expecting a '/'
+    string dstr = d;
+    if (dstr[dstr.size() - 1] != '/') {
+      dstr += "/";
+    }
+
+    struct stat statbuf;
+    if (!stat(d, &statbuf) && S_ISDIR(statbuf.st_mode) &&
+        !access(dstr.c_str(), 0)) {
+      // We found a dir that exists and is accessible - we're done.
+      list->push_back(dstr);
+      return;
+    }
+  }
+#endif
+}
+
 Thread::~Thread() {}
 
 EnvWrapper::~EnvWrapper() {}
diff --git a/tensorflow/core/platform/env.h b/tensorflow/core/platform/env.h
index 1b7e024b0f..da8c3e2d7e 100644
--- a/tensorflow/core/platform/env.h
+++ b/tensorflow/core/platform/env.h
@@ -215,6 +215,9 @@ class Env {
   /// symlinks if there is any.
   string GetExecutablePath();
 
+  /// Creates a local unique temporary file name. Returns true if success.
+  bool LocalTempFilename(string* filename);
+
   // TODO(jeff,sanjay): Add back thread/thread-pool support if needed.
   // TODO(jeff,sanjay): if needed, tighten spec so relative to epoch, or
   // provide a routine to get the absolute time.
@@ -279,6 +282,9 @@ class Env {
       const string& version) = 0;
 
  private:
+  // Returns a possible list of local temporary directories.
+  void GetLocalTempDirectories(std::vector<string>* list);
+
   std::unique_ptr<FileSystemRegistry> file_system_registry_;
   TF_DISALLOW_COPY_AND_ASSIGN(Env);
   EnvTime* envTime = EnvTime::Default();
diff --git a/tensorflow/core/platform/env_test.cc b/tensorflow/core/platform/env_test.cc
index 7bc1882c86..50dd0cd58b 100644
--- a/tensorflow/core/platform/env_test.cc
+++ b/tensorflow/core/platform/env_test.cc
@@ -298,4 +298,32 @@ TEST_F(DefaultEnvTest, GetExecutablePath) {
   TF_EXPECT_OK(env->FileExists(env->GetExecutablePath()));
 }
 
+TEST_F(DefaultEnvTest, LocalTempFilename) {
+  Env* env = Env::Default();
+  string filename;
+  EXPECT_TRUE(env->LocalTempFilename(&filename));
+  EXPECT_FALSE(env->FileExists(filename).ok());
+
+  // Write something to the temporary file.
+  std::unique_ptr<WritableFile> file_to_write;
+  TF_CHECK_OK(env->NewWritableFile(filename, &file_to_write));
+  TF_CHECK_OK(file_to_write->Append("Null"));
+  TF_CHECK_OK(file_to_write->Close());
+  TF_CHECK_OK(env->FileExists(filename));
+
+  // Read from the temporary file and check content.
+  std::unique_ptr<RandomAccessFile> file_to_read;
+  TF_CHECK_OK(env->NewRandomAccessFile(filename, &file_to_read));
+  StringPiece content;
+  char scratch[1024];
+  CHECK_EQ(error::OUT_OF_RANGE,
+           file_to_read->Read(0 /* offset */, 1024 /* n */, &content, scratch)
+               .code());
+  EXPECT_EQ("Null", content.ToString());
+
+  // Delete the temporary file.
+  TF_CHECK_OK(env->DeleteFile(filename));
+  EXPECT_FALSE(env->FileExists(filename).ok());
+}
+
 }  // namespace tensorflow
-- 
GitLab


From 136494d3295a23e3ed0612773f224243915463b7 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 25 Jul 2017 15:25:13 -0700
Subject: [PATCH 1323/1375] Prune trivial ops (concatenation of a single
 tensor, AddN of a single tensor, ...)

PiperOrigin-RevId: 163131793
---
 .../trivial_test_graph_input_yielder.cc       | 14 +++-
 tensorflow/core/grappler/op_types.cc          |  5 ++
 tensorflow/core/grappler/op_types.h           |  1 +
 .../core/grappler/optimizers/model_pruner.cc  | 27 +++++++-
 .../grappler/optimizers/model_pruner_test.cc  | 66 +++++++++++++++----
 5 files changed, 93 insertions(+), 20 deletions(-)

diff --git a/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc b/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc
index 446ae2df64..b1ec35e268 100644
--- a/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc
+++ b/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc
@@ -48,9 +48,17 @@ GraphDef CreateGraphDef(int num_stages, int width, int tensor_size,
   for (int i = 0; i < num_stages; i++) {
     std::vector<Output> this_stage;
     for (int j = 0; j < width; j++) {
-      Output combine = AddN(
-          s.WithDevice(device_names[use_multiple_devices ? j : 0]), last_stage);
-      this_stage.push_back(combine);
+      if (last_stage.size() == 1) {
+        Output unary_op =
+            Square(s.WithDevice(device_names[use_multiple_devices ? j : 0]),
+                   last_stage[0]);
+        this_stage.push_back(unary_op);
+      } else {
+        Output combine =
+            AddN(s.WithDevice(device_names[use_multiple_devices ? j : 0]),
+                 last_stage);
+        this_stage.push_back(combine);
+      }
     }
     last_stage = this_stage;
   }
diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index 9b2584f970..8584681220 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -18,6 +18,11 @@ limitations under the License.
 namespace tensorflow {
 namespace grappler {
 
+bool IsAddN(const NodeDef& node) {
+  const auto op = node.op();
+  return op == "AddN";
+}
+
 bool IsConcat(const NodeDef& node) {
   const auto op = node.op();
   return op == "Concat" || op == "ConcatV2";
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index 9c9dd22e2c..d83cb777ed 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -21,6 +21,7 @@ limitations under the License.
 namespace tensorflow {
 namespace grappler {
 
+bool IsAddN(const NodeDef& node);
 bool IsConcat(const NodeDef& node);
 bool IsConstant(const NodeDef& node);
 bool IsDequeueOp(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index df9aca8aa3..e313155563 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -26,6 +26,29 @@ limitations under the License.
 namespace tensorflow {
 namespace grappler {
 
+int NumNonControlInputs(const NodeDef& node) {
+  int num_inputs = node.input_size();
+  for (int i = 0; i < node.input_size(); ++i) {
+    if (!node.input(i).empty() && node.input(i)[0] == '^') {
+      num_inputs--;
+    }
+  }
+  return num_inputs;
+}
+
+bool IsTrivialOp(const NodeDef& node) {
+  // Remove the stop gradient nodes since they serve no purpose once the graph
+  // is built. Also remove Identity ops.
+  if (IsStopGradient(node) || IsIdentity(node)) {
+    return true;
+  }
+  if (IsAddN(node) && NumNonControlInputs(node) <= 1) {
+    return true;
+  }
+
+  return false;
+}
+
 Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
                              GraphDef* pruned_graph) {
   GraphRewriter rewriter(item);
@@ -43,9 +66,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
 
   std::unordered_set<const NodeDef*> nodes_to_delete;
   for (auto& node : item.graph.node()) {
-    // Remove the stop gradient nodes since they serve no purpose once the graph
-    // is built. Also remove Identity ops.
-    if (!IsStopGradient(node) && !IsIdentity(node)) {
+    if (!IsTrivialOp(node)) {
       continue;
     }
     // Don't remove nodes that must be preserved.
diff --git a/tensorflow/core/grappler/optimizers/model_pruner_test.cc b/tensorflow/core/grappler/optimizers/model_pruner_test.cc
index fdfb3f41cf..72d9c7bf27 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner_test.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner_test.cc
@@ -57,10 +57,10 @@ TEST_F(ModelPrunerTest, StopGradientPruning) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
-  Output b = ops::AddN(s.WithOpName("b"), {a});
+  Output b = ops::Sqrt(s.WithOpName("b"), {a});
   Output c = ops::StopGradient(s.WithOpName("c"), b);
   Output d = ops::StopGradient(s.WithOpName("d"), c);
-  Output e = ops::AddN(s.WithOpName("e"), {d});
+  Output e = ops::Sqrt(s.WithOpName("e"), {d});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
@@ -93,10 +93,10 @@ TEST_F(ModelPrunerTest, IdentityPruning) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
-  Output b = ops::AddN(s.WithOpName("b"), {a});
+  Output b = ops::Sqrt(s.WithOpName("b"), {a});
   Output c = ops::Identity(s.WithOpName("c"), b);
   Output d = ops::Identity(s.WithOpName("d"), c);
-  Output e = ops::AddN(s.WithOpName("e"), {d});
+  Output e = ops::Sqrt(s.WithOpName("e"), {d});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
@@ -126,15 +126,53 @@ TEST_F(ModelPrunerTest, IdentityPruning) {
   EXPECT_EQ(NodeName(b.name()), new_c.input(0));
 }
 
-TEST_F(ModelPrunerTest, PruningSkipsCtrlDependencies) {
+TEST_F(ModelPrunerTest, NoOpPruning) {
   // Build a simple graph with a few trivially prunable ops.
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
   Output b = ops::AddN(s.WithOpName("b"), {a});
+  Output c = ops::AddN(s.WithOpName("c"), {b});
+  Output d = ops::AddN(s.WithOpName("d").WithControlDependencies(b), {c});
+  Output e = ops::AddN(s.WithOpName("e"), {d});
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  ModelPruner pruner;
+  GraphDef output;
+  Status status = pruner.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  EXPECT_EQ(5, output.node_size());
+  const NodeDef& new_a = output.node(0);
+  EXPECT_EQ(NodeName(a.name()), new_a.name());
+  const NodeDef& new_b = output.node(1);
+  EXPECT_EQ(NodeName(b.name()), new_b.name());
+  const NodeDef& new_c = output.node(2);
+  EXPECT_EQ(NodeName(c.name()), new_c.name());
+  const NodeDef& new_d = output.node(3);
+  EXPECT_EQ(NodeName(d.name()), new_d.name());
+  const NodeDef& new_e = output.node(4);
+  EXPECT_EQ(NodeName(e.name()), new_e.name());
+
+  EXPECT_EQ(1, new_e.input_size());
+  EXPECT_EQ(NodeName(d.name()), new_e.input(0));
+  EXPECT_EQ(2, new_d.input_size());
+  EXPECT_EQ(NodeName(b.name()), new_d.input(0));
+  EXPECT_EQ(1, new_c.input_size());
+  EXPECT_EQ(NodeName(b.name()), new_c.input(0));
+}
+
+TEST_F(ModelPrunerTest, PruningSkipsCtrlDependencies) {
+  // Build a simple graph with a few trivially prunable ops.
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+
+  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
+  Output b = ops::Sqrt(s.WithOpName("b"), {a});
   Output c = ops::Identity(s.WithOpName("c"), b);
   Output d = ops::Identity(s.WithOpName("d"), c);
-  Output e = ops::AddN(s.WithOpName("e").WithControlDependencies(c), {d});
+  Output e = ops::Sqrt(s.WithOpName("e").WithControlDependencies(c), {d});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
@@ -166,11 +204,11 @@ TEST_F(ModelPrunerTest, PruningPerservesCtrlDependencies) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
-  Output b = ops::AddN(s.WithOpName("b"), {a});
-  Output c = ops::AddN(s.WithOpName("c"), {a});
+  Output b = ops::Sqrt(s.WithOpName("b"), {a});
+  Output c = ops::Sqrt(s.WithOpName("c"), {a});
   Output d = ops::Identity(s.WithOpName("d"), c);
   Output e = ops::Identity(s.WithOpName("e"), d);
-  Output f = ops::AddN(s.WithOpName("f"), {e});
+  Output f = ops::Sqrt(s.WithOpName("f"), {e});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
@@ -216,7 +254,7 @@ TEST_F(ModelPrunerTest, PruningPerservesFetch) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
-  Output b = ops::AddN(s.WithOpName("b"), {a});
+  Output b = ops::Sqrt(s.WithOpName("b"), {a});
   Output c = ops::Identity(s.WithOpName("c"), b);
 
   GrapplerItem item;
@@ -243,13 +281,13 @@ TEST_F(ModelPrunerTest, PruningPerservesCrossDeviceIdentity) {
 
   // Node i1 should be preserved.
   Output i1 = ops::Identity(s.WithOpName("i1").WithDevice("/gpu:0"), c);
-  Output a1 = ops::AddN(s.WithOpName("a1").WithDevice("/gpu:0"), {i1});
-  Output a2 = ops::AddN(s.WithOpName("a2").WithDevice("/gpu:0"), {i1});
+  Output a1 = ops::Sqrt(s.WithOpName("a1").WithDevice("/gpu:0"), {i1});
+  Output a2 = ops::Sqrt(s.WithOpName("a2").WithDevice("/gpu:0"), {i1});
 
   // Node i2 should be pruned since it resides on the sender's device.
   Output i2 = ops::Identity(s.WithOpName("i2").WithDevice("/cpu:0"), c);
-  Output a3 = ops::AddN(s.WithOpName("a3").WithDevice("/gpu:0"), {i2});
-  Output a4 = ops::AddN(s.WithOpName("a4").WithDevice("/gpu:0"), {i2});
+  Output a3 = ops::Sqrt(s.WithOpName("a3").WithDevice("/gpu:0"), {i2});
+  Output a4 = ops::Sqrt(s.WithOpName("a4").WithDevice("/gpu:0"), {i2});
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
-- 
GitLab


From ff967763fdccdbd51cfe50b7669c3baf86f2fa17 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 16:03:55 -0700
Subject: [PATCH 1324/1375] Add GDN activation function.

PiperOrigin-RevId: 163137487
---
 .../contrib/layers/python/layers/layers.py    | 302 +++++++++++++++++-
 .../layers/python/layers/layers_test.py       |  50 +++
 2 files changed, 350 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 8b3ccea995..ff7545bb00 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -31,13 +32,16 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.layers import base
 from tensorflow.python.layers import convolutional as convolutional_layers
 from tensorflow.python.layers import core as core_layers
-from tensorflow.python.layers import  normalization as normalization_layers
+from tensorflow.python.layers import normalization as normalization_layers
 from tensorflow.python.layers import pooling as pooling_layers
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import sparse_ops
@@ -1281,7 +1285,7 @@ def convolution3d_transpose(
     trainable=True,
     scope=None):
   """Adds a convolution3d_transpose with an optional batch normalization layer.
-  
+
   The function creates a variable called `weights`, representing the
   kernel, that is convolved with the input. If `batch_norm_params` is `None`, a
   second variable called 'biases' is added to the result of the operation.
@@ -1808,6 +1812,300 @@ def layer_norm(inputs,
                                        outputs)
 
 
+class GDN(base.Layer):
+  """Generalized divisive normalization layer.
+
+  Based on the papers:
+
+    "Density Modeling of Images using a Generalized Normalization
+    Transformation"
+    Johannes Ballé, Valero Laparra, Eero P. Simoncelli
+    https://arxiv.org/abs/1511.06281
+
+    "End-to-end Optimized Image Compression"
+    Johannes Ballé, Valero Laparra, Eero P. Simoncelli
+    https://arxiv.org/abs/1611.01704
+
+  Implements an activation function that is essentially a multivariate
+  generalization of a particular sigmoid-type function:
+
+  y[i] = x[i] / sqrt(beta[i] + sum_j(gamma[j, i] * x[j]))
+
+  where i and j run over channels. This implementation never sums across spatial
+  dimensions. It is similar to local response normalization, but more powerful,
+  as beta and gamma are trainable parameters.
+
+  Arguments:
+    inverse: If False (default), compute GDN response. If True, compute IGDN
+      response (one step of fixed point iteration to invert GDN; the division
+      is replaced by multiplication).
+    beta_min: Lower bound for beta, to prevent numerical error from causing
+      square root of zero or negative values.
+    gamma_init: The gamma matrix will be initialized as the identity matrix
+      multiplied with this value. If set to zero, the layer is effectively
+      initialized to the identity operation, since beta is initialized as one.
+      A good default setting is somewhere between 0 and 0.5.
+    reparam_offset: Offset added to the reparameterization of beta and gamma.
+      The reparameterization of beta and gamma as their square roots lets the
+      training slow down when their values are close to zero, which is desirable
+      as small values in the denominator can lead to a situation where gradient
+      noise on beta/gamma leads to extreme amounts of noise in the GDN
+      activations. However, without the offset, we would get zero gradients if
+      any elements of beta or gamma were exactly zero, and thus the training
+      could get stuck. To prevent this, we add this small constant. The default
+      value was empirically determined as a good starting point. Making it
+      bigger potentially leads to more gradient noise on the activations, making
+      it too small may lead to numerical precision issues.
+    data_format: Format of input tensor. Currently supports 'channels_first' and
+      'channels_last'.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    name: String, the name of the layer. Layers with the same name will
+      share weights, but to avoid mistakes we require reuse=True in such cases.
+    reuse: Boolean, whether to reuse the weights of a previous layer
+      by the same name.
+
+  Properties:
+    inverse: Boolean, whether GDN is computed (True) or IGDN (False).
+    data_format: Format of input tensor. Currently supports 'channels_first' and
+      'channels_last'.
+    beta: The beta parameter as defined above (1D TensorFlow tensor).
+    gamma: The gamma parameter as defined above (2D TensorFlow tensor).
+  """
+
+  def __init__(self,
+               inverse=False,
+               beta_min=1e-6,
+               gamma_init=.1,
+               reparam_offset=2 ** -18,
+               data_format='channels_last',
+               trainable=True,
+               name=None,
+               **kwargs):
+    super(GDN, self).__init__(trainable=trainable, name=name, **kwargs)
+    self.inverse = inverse
+    self._beta_min = beta_min
+    self._gamma_init = gamma_init
+    self._reparam_offset = reparam_offset
+    self.data_format = data_format
+    self._channel_axis()  # trigger ValueError early
+    self.input_spec = base.InputSpec(min_ndim=3, max_ndim=5)
+
+  def _channel_axis(self):
+    try:
+      return {'channels_first': 1, 'channels_last': -1}[self.data_format]
+    except KeyError:
+      raise ValueError('Unsupported `data_format` for GDN layer: {}.'.format(
+          self.data_format))
+
+  @staticmethod
+  def _lower_bound(inputs, bound, name=None):
+    """Same as tf.maximum, but with helpful gradient for inputs < bound.
+
+    The gradient is overwritten so that it is passed through if the input is not
+    hitting the bound. If it is, only gradients that push `inputs` higher than
+    the bound are passed through. No gradients are passed through to the bound.
+
+    Args:
+      inputs: input tensor
+      bound: lower bound for the input tensor
+      name: name for this op
+
+    Returns:
+      tf.maximum(inputs, bound)
+    """
+    with ops.name_scope(name, 'GDNLowerBound', [inputs, bound]) as scope:
+      inputs = ops.convert_to_tensor(inputs, name='inputs')
+      bound = ops.convert_to_tensor(bound, name='bound')
+      with ops.get_default_graph().gradient_override_map(
+          {'Maximum': 'GDNLowerBound'}):
+        return math_ops.maximum(inputs, bound, name=scope)
+
+  @ops.RegisterGradient('GDNLowerBound')
+  @staticmethod
+  def _lower_bound_grad(op, grad):
+    """Gradient for `_lower_bound`.
+
+    Args:
+      op: the tensorflow op for which to calculate a gradient
+      grad: gradient with respect to the output of the op
+
+    Returns:
+      gradients with respect to the inputs of the op
+    """
+    inputs = op.inputs[0]
+    bound = op.inputs[1]
+    pass_through_if = math_ops.logical_or(inputs >= bound, grad < 0)
+    return [math_ops.cast(pass_through_if, grad.dtype) * grad, None]
+
+  def build(self, input_shape):
+    channel_axis = self._channel_axis()
+    input_shape = tensor_shape.TensorShape(input_shape)
+    num_channels = input_shape[channel_axis].value
+    if num_channels is None:
+      raise ValueError('The channel dimension of the inputs to `GDN` '
+                       'must be defined.')
+    self._input_rank = input_shape.ndims
+    self.input_spec = base.InputSpec(ndim=input_shape.ndims,
+                                     axes={channel_axis: num_channels})
+
+    pedestal = array_ops.constant(self._reparam_offset ** 2, dtype=self.dtype)
+    beta_bound = array_ops.constant(
+        (self._beta_min + self._reparam_offset ** 2) ** .5, dtype=self.dtype)
+    gamma_bound = array_ops.constant(self._reparam_offset, dtype=self.dtype)
+
+    def beta_initializer(shape, dtype=None, partition_info=None):
+      del partition_info  # unused
+      return math_ops.sqrt(array_ops.ones(shape, dtype=dtype) + pedestal)
+
+    def gamma_initializer(shape, dtype=None, partition_info=None):
+      del partition_info  # unused
+      assert len(shape) == 2
+      assert shape[0] == shape[1]
+      eye = linalg_ops.eye(shape[0], dtype=dtype)
+      return math_ops.sqrt(self._gamma_init * eye + pedestal)
+
+    beta = self.add_variable('reparam_beta',
+                             shape=[num_channels],
+                             initializer=beta_initializer,
+                             dtype=self.dtype,
+                             trainable=True)
+    beta = self._lower_bound(beta, beta_bound)
+    self.beta = math_ops.square(beta) - pedestal
+
+    gamma = self.add_variable('reparam_gamma',
+                              shape=[num_channels, num_channels],
+                              initializer=gamma_initializer,
+                              dtype=self.dtype,
+                              trainable=True)
+    gamma = self._lower_bound(gamma, gamma_bound)
+    self.gamma = math_ops.square(gamma) - pedestal
+
+    self.built = True
+
+  def call(self, inputs):
+    inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
+    ndim = self._input_rank
+
+    shape = self.gamma.get_shape().as_list()
+    gamma = array_ops.reshape(self.gamma, (ndim - 2) * [1] + shape)
+
+    # Compute normalization pool.
+    if self.data_format == 'channels_first':
+      norm_pool = nn.convolution(math_ops.square(inputs), gamma, 'VALID',
+                                 data_format='NC' + 'DHW'[-(ndim - 2):])
+      if ndim == 3:
+        norm_pool = array_ops.expand_dims(norm_pool, 2)
+        norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW')
+        norm_pool = array_ops.squeeze(norm_pool, [2])
+      elif ndim == 5:
+        shape = array_ops.shape(norm_pool)
+        norm_pool = array_ops.reshape(norm_pool, shape[:3] + [-1])
+        norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW')
+        norm_pool = array_ops.reshape(norm_pool, shape)
+      else:  # ndim == 4
+        norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW')
+    else:  # channels_last
+      norm_pool = nn.convolution(math_ops.square(inputs), gamma, 'VALID')
+      norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NHWC')
+    norm_pool = math_ops.sqrt(norm_pool)
+
+    if self.inverse:
+      outputs = inputs * norm_pool
+    else:
+      outputs = inputs / norm_pool
+    outputs.set_shape(inputs.get_shape())
+    return outputs
+
+  def _compute_output_shape(self, input_shape):
+    channel_axis = self._channel_axis()
+    input_shape = tensor_shape.TensorShape(input_shape)
+    if not 3 <= input_shape.ndim <= 5:
+      raise ValueError('`input_shape` must be of rank 3 to 5, inclusive.')
+    if input_shape[channel_axis].value is None:
+      raise ValueError(
+          'The channel dimension of `input_shape` must be defined.')
+    return input_shape
+
+
+def gdn(inputs,
+        inverse=False,
+        beta_min=1e-6,
+        gamma_init=.1,
+        reparam_offset=2 ** -18,
+        data_format='channels_last',
+        trainable=True,
+        name=None,
+        reuse=None):
+  """Functional interface for GDN layer.
+
+  Based on the papers:
+
+    "Density Modeling of Images using a Generalized Normalization
+    Transformation"
+    Johannes Ballé, Valero Laparra, Eero P. Simoncelli
+    https://arxiv.org/abs/1511.06281
+
+    "End-to-end Optimized Image Compression"
+    Johannes Ballé, Valero Laparra, Eero P. Simoncelli
+    https://arxiv.org/abs/1611.01704
+
+  Implements an activation function that is essentially a multivariate
+  generalization of a particular sigmoid-type function:
+
+  y[i] = x[i] / sqrt(beta[i] + sum_j(gamma[j, i] * x[j]))
+
+  where i and j run over channels. This implementation never sums across spatial
+  dimensions. It is similar to local response normalization, but more powerful,
+  as beta and gamma are trainable parameters.
+
+  Arguments:
+    inputs: Tensor input.
+    inverse: If False (default), compute GDN response. If True, compute IGDN
+      response (one step of fixed point iteration to invert GDN; the division
+      is replaced by multiplication).
+    beta_min: Lower bound for beta, to prevent numerical error from causing
+      square root of zero or negative values.
+    gamma_init: The gamma matrix will be initialized as the identity matrix
+      multiplied with this value. If set to zero, the layer is effectively
+      initialized to the identity operation, since beta is initialized as one.
+      A good default setting is somewhere between 0 and 0.5.
+    reparam_offset: Offset added to the reparameterization of beta and gamma.
+      The reparameterization of beta and gamma as their square roots lets the
+      training slow down when their values are close to zero, which is desirable
+      as small values in the denominator can lead to a situation where gradient
+      noise on beta/gamma leads to extreme amounts of noise in the GDN
+      activations. However, without the offset, we would get zero gradients if
+      any elements of beta or gamma were exactly zero, and thus the training
+      could get stuck. To prevent this, we add this small constant. The default
+      value was empirically determined as a good starting point. Making it
+      bigger potentially leads to more gradient noise on the activations, making
+      it too small may lead to numerical precision issues.
+    data_format: Format of input tensor. Currently supports 'channels_first' and
+      'channels_last'.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    name: String, the name of the layer. Layers with the same name will
+      share weights, but to avoid mistakes we require reuse=True in such cases.
+    reuse: Boolean, whether to reuse the weights of a previous layer
+      by the same name.
+
+  Returns:
+    Output tensor.
+  """
+  layer = GDN(inverse=inverse,
+              beta_min=beta_min,
+              gamma_init=gamma_init,
+              reparam_offset=reparam_offset,
+              data_format=data_format,
+              trainable=trainable,
+              name=name,
+              dtype=inputs.dtype.base_dtype,
+              _scope=name,
+              _reuse=reuse)
+  return layer.apply(inputs)
+
+
 @add_arg_scope
 def max_pool2d(inputs,
                kernel_size,
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 2d08a0cb91..8867e069d1 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -2772,6 +2772,56 @@ class LayerNormTest(test.TestCase):
     self.doOutputTest((1, 100, 100, 1))
 
 
+class GDNTest(test.TestCase):
+
+  def _runGDN(self, x, shape, inverse, data_format):
+    inputs = array_ops.placeholder(dtypes.float32, shape)
+    outputs = _layers.gdn(inputs, inverse=inverse, data_format=data_format)
+    with self.test_session() as sess:
+      variables_lib.global_variables_initializer().run()
+      y, = sess.run([outputs], {inputs: x})
+    return y
+
+  def testInvalidDataFormat(self):
+    x = np.random.uniform(size=(1, 2, 3, 4))
+    with self.assertRaises(ValueError):
+      self._runGDN(x, x.shape, False, 'NHWC')
+
+  def testUnknownDim(self):
+    x = np.random.uniform(size=(1, 2, 3, 4))
+    with self.assertRaises(ValueError):
+      self._runGDN(x, 4 * [None], False, 'channels_last')
+
+  def testChannelsLast(self):
+    for ndim in [3, 4, 5]:
+      x = np.random.uniform(size=(1, 2, 3, 4)[:ndim])
+      y = self._runGDN(x, x.shape, False, 'channels_last')
+      self.assertEqual(x.shape, y.shape)
+      self.assertAllClose(y, x / np.sqrt(1 + .1 * (x ** 2)), rtol=0, atol=1e-6)
+
+  def testChannelsFirst(self):
+    # `bias_add` doesn't support NCHW on CPU.
+    if test.is_gpu_available(cuda_only=True):
+      for ndim in [3, 4, 5]:
+        x = np.random.uniform(size=(4, 3, 2, 1)[:ndim])
+        y = self._runGDN(x, x.shape, False, 'channels_first')
+        self.assertEqual(x.shape, y.shape)
+        self.assertAllClose(
+            y, x / np.sqrt(1 + .1 * (x ** 2)), rtol=0, atol=1e-6)
+
+  def testWrongDims(self):
+    for ndim in [1, 2, 6]:
+      x = np.random.uniform(size=(1, 2, 3, 4, 3, 2)[:ndim])
+      with self.assertRaises(ValueError):
+        self._runGDN(x, x.shape, False, 'channels_last')
+
+  def testIGDN(self):
+    x = np.random.uniform(size=(1, 2, 3, 4))
+    y = self._runGDN(x, x.shape, True, 'channels_last')
+    self.assertEqual(x.shape, y.shape)
+    self.assertAllClose(y, x * np.sqrt(1 + .1 * (x ** 2)), rtol=0, atol=1e-6)
+
+
 class MaxPool2DTest(test.TestCase):
 
   def testInvalidDataFormat(self):
-- 
GitLab


From fc7d2c803f3eeca68978b54eb1540e705bc6c2b3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 16:07:45 -0700
Subject: [PATCH 1325/1375] Use `to_float` and `to_int32` where appropriate.

PiperOrigin-RevId: 163138030
---
 tensorflow/python/training/input.py | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 93ba15ec81..19ecb85c60 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -168,7 +168,7 @@ def input_producer(input_tensor,
             q, [enq], cancel_op=cancel_op))
     if summary_name is not None:
       summary.scalar(summary_name,
-                     math_ops.cast(q.size(), dtypes.float32) * (1. / capacity))
+                     math_ops.to_float(q.size()) * (1. / capacity))
     return q
 
 
@@ -643,7 +643,7 @@ def _shapes(tensor_list_list, shapes, enqueue_many):
 
 def _select_which_to_enqueue(tensor_list, keep_input):
   """Select which examples to enqueue based on vector `keep_input`."""
-  select_i = math_ops.cast(keep_input, dtypes.int32)
+  select_i = math_ops.to_int32(keep_input)
   tensor_list = [
       data_flow_ops.dynamic_partition(x, select_i, num_partitions=2)[1]
       for x in tensor_list]
@@ -707,8 +707,7 @@ def _batch(tensors, batch_size, keep_input, num_threads=1, capacity=32,
         capacity=capacity, dtypes=types, shapes=shapes, shared_name=shared_name)
     _enqueue(queue, tensor_list, num_threads, enqueue_many, keep_input)
     summary.scalar("fraction_of_%d_full" % capacity,
-                   math_ops.cast(queue.size(), dtypes.float32) *
-                   (1. / capacity))
+                   math_ops.to_float(queue.size()) * (1. / capacity))
 
     if allow_smaller_final_batch:
       dequeued = queue.dequeue_up_to(batch_size, name=name)
@@ -742,8 +741,7 @@ def _batch_join(tensors_list, batch_size, keep_input, capacity=32,
         capacity=capacity, dtypes=types, shapes=shapes, shared_name=shared_name)
     _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input)
     summary.scalar("fraction_of_%d_full" % capacity,
-                   math_ops.cast(queue.size(), dtypes.float32) *
-                   (1. / capacity))
+                   math_ops.to_float(queue.size()) * (1. / capacity))
 
     if allow_smaller_final_batch:
       dequeued = queue.dequeue_up_to(batch_size, name=name)
@@ -775,8 +773,8 @@ def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
         capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,
         dtypes=types, shapes=shapes, shared_name=shared_name)
     _enqueue(queue, tensor_list, num_threads, enqueue_many, keep_input)
-    full = (math_ops.cast(math_ops.maximum(0, queue.size() - min_after_dequeue),
-                          dtypes.float32) *
+    full = (math_ops.to_float(
+        math_ops.maximum(0, queue.size() - min_after_dequeue)) *
             (1. / (capacity - min_after_dequeue)))
     # Note that name contains a '/' at the end so we intentionally do not place
     # a '/' after %s below.
@@ -812,8 +810,8 @@ def _shuffle_batch_join(tensors_list, batch_size, capacity,
         capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,
         dtypes=types, shapes=shapes, shared_name=shared_name)
     _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input)
-    full = (math_ops.cast(math_ops.maximum(0, queue.size() - min_after_dequeue),
-                          dtypes.float32) *
+    full = (math_ops.to_float(
+        math_ops.maximum(0, queue.size() - min_after_dequeue)) *
             (1. / (capacity - min_after_dequeue)))
     # Note that name contains a '/' at the end so we intentionally do not place
     # a '/' after %s below.
-- 
GitLab


From fe4b88c95ff511205e2b8d724809683b7828edf8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 16:43:59 -0700
Subject: [PATCH 1326/1375] Call AddDefaultAttrsToGraphDef() in
 grappler_item_builder.cc with op_registry including function library so that
 it doesn't fail for the graph with inline function library.

PiperOrigin-RevId: 163142677
---
 tensorflow/core/grappler/grappler_item_builder.cc | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 4518a7a78e..0c2801e8bc 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -82,10 +82,6 @@ Status OptimizeGraph(const GraphDef& graph_def, GraphDef* output_graph_def,
 
   // Inline all functions.
   GraphDef inlined_graph_def(graph_def);
-  // Populate default attrs to the NodeDefs in the GraphDef, which is required
-  // by inlining code.
-  TF_RETURN_IF_ERROR(
-      AddDefaultAttrsToGraphDef(&inlined_graph_def, *OpRegistry::Global(), 0));
 
   for (int i = 0; i < inlined_graph_def.library().function().size(); i++) {
     FunctionDef* fdef =
@@ -122,6 +118,10 @@ Status OptimizeGraph(const GraphDef& graph_def, GraphDef* output_graph_def,
   graph_ctor_opts.allow_internal_ops = true;
   graph_ctor_opts.expect_device_spec = false;
   std::unique_ptr<Graph> graphptr(new Graph(function_library));
+  // Populate default attrs to the NodeDefs in the GraphDef.
+  TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&inlined_graph_def,
+                                               *graphptr->op_registry(), 0));
+
   TF_RETURN_IF_ERROR(ConvertGraphDefToGraph(graph_ctor_opts, inlined_graph_def,
                                             graphptr.get()));
 
-- 
GitLab


From 4ecc31ddd04f13c4d3bde48c28ff0646aa477bca Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 17:26:15 -0700
Subject: [PATCH 1327/1375] `.get_shape()` -> `.shape` in input.py.

PiperOrigin-RevId: 163147646
---
 tensorflow/python/training/input.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 19ecb85c60..a602438e0b 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -148,7 +148,7 @@ def input_producer(input_tensor,
   """
   with ops.name_scope(name, "input_producer", [input_tensor]):
     input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")
-    element_shape = input_tensor.get_shape()[1:].merge_with(element_shape)
+    element_shape = input_tensor.shape[1:].merge_with(element_shape)
     if not element_shape.is_fully_defined():
       raise ValueError("Either `input_tensor` must have a fully defined shape "
                        "or `element_shape` must be specified")
@@ -465,7 +465,7 @@ def _store_sparse_tensors(tensor_list, enqueue_many, keep_input,
   def _sparse_meta_data(t, storing_op, map_op):
     if not isinstance(t, sparse_tensor.SparseTensor):
       return _SparseMetaData(False, None, None)
-    rank = t.dense_shape.get_shape().with_rank(1)[0]
+    rank = t.dense_shape.shape.with_rank(1)[0]
     if enqueue_many:
       rank -= 1
     # If a shared map_op was provided, use that. Otherwise use the name of
@@ -492,7 +492,7 @@ def _store_sparse_tensors(tensor_list, enqueue_many, keep_input,
           lambda: -1 * array_ops.ones(array_ops.shape(t)[0:1], dtypes.int64))
       out_tensor.set_shape([None])  # necessary when t.ndims is unknown
       return out_tensor
-    if keep_input.get_shape().ndims == 1:
+    if keep_input.shape.ndims == 1:
       t = sparse_ops.sparse_retain(t, keep_input)
       store_f = lambda t, name, _: _store_many_sparse(t, shared_name=name)
     elif enqueue_many:
@@ -577,13 +577,13 @@ def _validate_join(tensor_list_list):
 def _validate_keep_input(keep_input, enqueue_many):
   """Validate `keep_input` argument to conditional batching functions."""
   keep_input = ops.convert_to_tensor(keep_input)
-  if keep_input.get_shape().ndims is None:
+  if keep_input.shape.ndims is None:
     raise ValueError(
         "`keep_input` dimensions must be known at graph construction.")
-  if not enqueue_many and keep_input.get_shape().ndims == 1:
+  if not enqueue_many and keep_input.shape.ndims == 1:
     raise ValueError(
         "`keep_input` cannot be a vector when `enqueue_many=False`.")
-  if keep_input.get_shape().ndims > 1:
+  if keep_input.shape.ndims > 1:
     raise ValueError("`keep_input` must be 0 or 1 dimensions.")
   return keep_input
 
@@ -632,11 +632,11 @@ def _shapes(tensor_list_list, shapes, enqueue_many):
 
     for tl in tensor_list_list:
       for i in xrange(len0):
-        if tl[i].get_shape().ndims is None:
+        if tl[i].shape.ndims is None:
           raise ValueError("Cannot infer Tensor's rank: %s" % tl[i])
 
     shapes = [_merge_shapes(
-        [tl[i].get_shape().as_list() for tl in tensor_list_list], enqueue_many)
+        [tl[i].shape.as_list() for tl in tensor_list_list], enqueue_many)
               for i in xrange(len0)]
   return shapes
 
@@ -656,7 +656,7 @@ def _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input):
     enqueue_fn = queue.enqueue_many
   else:
     enqueue_fn = queue.enqueue
-  if keep_input.get_shape().ndims == 1:
+  if keep_input.shape.ndims == 1:
     enqueue_ops = [enqueue_fn(_select_which_to_enqueue(x, keep_input))
                    for x in tensor_list_list]
   else:
@@ -673,7 +673,7 @@ def _enqueue(queue, tensor_list, threads, enqueue_many, keep_input):
     enqueue_fn = queue.enqueue_many
   else:
     enqueue_fn = queue.enqueue
-  if keep_input.get_shape().ndims == 1:
+  if keep_input.shape.ndims == 1:
     enqueue_ops = [
         enqueue_fn(_select_which_to_enqueue(tensor_list, keep_input))] * threads
   else:
-- 
GitLab


From 42eba6a7046d232de397ea2d0e627e91f095bc4c Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Tue, 25 Jul 2017 17:48:52 -0700
Subject: [PATCH 1328/1375] Implemented MemoryUsed function for hash tables.

PiperOrigin-RevId: 163149969
---
 tensorflow/core/kernels/lookup_table_op.cc | 34 ++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/tensorflow/core/kernels/lookup_table_op.cc b/tensorflow/core/kernels/lookup_table_op.cc
index b33b98975d..d721b3d542 100644
--- a/tensorflow/core/kernels/lookup_table_op.cc
+++ b/tensorflow/core/kernels/lookup_table_op.cc
@@ -124,6 +124,20 @@ class MutableHashTableOfScalars final : public LookupInterface {
 
   TensorShape value_shape() const override { return TensorShape(); }
 
+  int64 MemoryUsed() const override {
+    int64 ret = 0;
+    mutex_lock l(mu_);
+    for (unsigned i = 0; i < table_.bucket_count(); ++i) {
+      size_t bucket_size = table_.bucket_size(i);
+      if (bucket_size == 0) {
+        ret++;
+      } else {
+        ret += bucket_size;
+      }
+    }
+    return sizeof(MutableHashTableOfScalars) + ret;
+  }
+
  private:
   // TODO(andreasst): consider using a read/write lock or a concurrent map
   mutable mutex mu_;
@@ -239,6 +253,20 @@ class MutableHashTableOfTensors final : public LookupInterface {
 
   TensorShape value_shape() const override { return value_shape_; }
 
+  int64 MemoryUsed() const override {
+    int64 ret = 0;
+    mutex_lock l(mu_);
+    for (unsigned i = 0; i < table_.bucket_count(); ++i) {
+      size_t bucket_size = table_.bucket_size(i);
+      if (bucket_size == 0) {
+        ret++;
+      } else {
+        ret += bucket_size;
+      }
+    }
+    return sizeof(MutableHashTableOfTensors) + ret;
+  }
+
  private:
   TensorShape value_shape_;
   // TODO(andreasst): consider using a read/write lock or a concurrent map
@@ -467,6 +495,12 @@ class MutableDenseHashTable final : public LookupInterface {
 
   TensorShape value_shape() const override { return value_shape_; }
 
+  int64 MemoryUsed() const override {
+    mutex_lock l(mu_);
+    return sizeof(MutableDenseHashTable) + key_buckets_.AllocatedBytes() +
+           value_buckets_.AllocatedBytes() + empty_key_.AllocatedBytes();
+  }
+
  private:
   Status DoInsert(OpKernelContext* ctx, const Tensor& key, const Tensor& value,
                   bool ignore_empty_key) EXCLUSIVE_LOCKS_REQUIRED(mu_) {
-- 
GitLab


From 62de0b94797a179ec7851354015e99e9a7ae57e6 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Tue, 25 Jul 2017 18:41:40 -0700
Subject: [PATCH 1329/1375] Branch 163121296 (#11767)

* Update ops-related pbtxt files.

PiperOrigin-RevId: 163014080

* Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 163014834

* Removing session reset since destroying the session object would delete its variables as well. Resetting session might unintentionally close other sessions in the same process.

PiperOrigin-RevId: 163019166

* [XLA] Teach CPU and GPU compilers to optionally invoke the HLO insert-reduce-precision-operations pass.

This also required a few additions and fixups.  We add pieces to ReducePrecisionInsertion to translate between the protocol-buffer representation of the pass options and the predicate-function actually used in the pass.  To facilitate this translation, we also add a function to HloOpcode to return the number of opcodes so that we can iterate over the whole set easily.

PiperOrigin-RevId: 163037250

* Refactor HLO graph dumping.

This also makes a few minor cosmetic changes, like moving the fusion
type out of the fusion node and into the out-of-line computation and
adjusting the arrow labels that we use to indicate operand numbers.

PiperOrigin-RevId: 163038795

* Use correct order of arguments in call of valid_bitcast_callback_.

There are platforms where bitcasts are not symmetric. I.e. there are shapes A and B so that A->B is a bitcast, but B->A not. So we have to consider the correct order when calling valid_bitcast_callback_.

PiperOrigin-RevId: 163058665

* Two improvements to pip.sh

1. Distinguish between passed and skipped tests.
2. Allow skipping the smoke test of tensorflow install in clean virtualenv with NO_TEST_ON_INSTALL=1

PiperOrigin-RevId: 163065599

* [XLA] Update StatusOr implementation to use more nuanced type traits.

Previously we would evaluate the is_copy_constructible trait before template
parameters were fully defined; e.g. StatusOr<ThingIAmDefiningRightNow>,
which could lead to surprising effects.

Also, previously it was not possible to provide an error status to a
StatusOr<T> where T was not default-constructible.

PiperOrigin-RevId: 163073057

* [TF:XLA] Register a no-op kernel for ControlTrigger, but forbid the JIT marking pass from compiling ControlTrigger nodes.

CL in preparation for compiling dynamic RNN gradients via XLA.

PiperOrigin-RevId: 163073212

* Improve the HLO graph dumper's output.

 - Truncate long shapes.  It's not uncommon to have giant tuples, and
   displaying the whole thing makes the graph unreadable.

 - Don't traverse into the users of a node with < 16 users.  These are
   probably not interesting, and traversing into them can quickly blow
   up the graph, making it un-renderable.

 - Allow nodes which have multiple trivial subcomputations (e.g.
   select-and-scatter) to have those computations inlined.

 - Match additional patterns in MatchTrivialComputation

PiperOrigin-RevId: 163079329

* If the value to be forwarded from a loop to its gradient is a constant, clone the constant instead of repeatedly pushing it onto a stack on each iteration. This should never consume more memory than the stack approach (notwithstanding swapping), and frequently should be much better.

This change is in preparation for enabling XLA compilation of RNN gradients.

PiperOrigin-RevId: 163082165

* [TF:XLA] Make the shape of a TensorArray flow value a scalar.

Previously we used an f32[0] value, since the exact flow value does not matter, however this causes problems when a TensorArray computation is placed in a loop since the shape of the flow value is no longer loop invariant.

PiperOrigin-RevId: 163082452

* Automated g4 rollback of changelist 163019166

PiperOrigin-RevId: 163083436

* Automated g4 rollback of changelist 162769374

PiperOrigin-RevId: 163086518

* internal change

PiperOrigin-RevId: 163088509

* Clarify docstring for tf.rank.

PiperOrigin-RevId: 163089480

* Reduce gather_op_test timeouts by reducing the size of testHigherRank.

PiperOrigin-RevId: 163090428

* Add PopulationCount op (popcnt): element-wise counts the number of "on" bits.

PiperOrigin-RevId: 163090921

* Show fusion nodes inline in HLO graph dumper.

To make this work sanely I had to change NodeFilter so that it says to
dump all nodes inside subcomputations.  Previously, we passed an
explicit NodeFilter down to DumpSubcomputation, and used that to control
whether or not we dumped nodes in there.  But this becomes unwieldy with
inline fusion nodes, as sometimes you want to look at 'filter', and
other times you want to look at 'filter_', and there's no good way to
tell why.

I also had to remove the heuristic whereby we'd pull in operands of
nodes with just some operands shown.  With the much bigger nodes that
are generated by this change, the graph was becoming illegible.  I think
most of the confusion that heuristic was attempting to avoid is
addressed by the fact that we "gray out" incomplete nodes.

PiperOrigin-RevId: 163091423

* errors: Avoid stripping error details when convering POSIX errors to Status

This change is made out of a desire to have additional information be reported
when there are filesystem errors (for e.g. see
#11628)

PiperOrigin-RevId: 163091773

* C API: Fix a bug with TF_OperationGetAttrTensor when TF_STRING tensors are
involved.

The TensorBuffer owned by a TF_Tensor object has a different memory layout than
the TensorBuffer owned by the corresponding tensorflow::Tensor object.
This change consolidates conversions between the runtime's tensorflow::Tensor
and the C API's TF_Tensor objects into a pair helper functions.

The added test: CApiAttributesTest.StringTensor fails without corresponding
changes to c_api.cc

PiperOrigin-RevId: 163091789

* Speed up tf.contrib.signal spectral_ops_test.py by reducing the size of the gradient test.

PiperOrigin-RevId: 163092423

* Add new CompareAndBitpackOp.

PiperOrigin-RevId: 163093146

* Update ops-related pbtxt files.

PiperOrigin-RevId: 163094455

* Minor tweaks to avoid unnecessary copies

PiperOrigin-RevId: 163101160

* [BatchNormGrad] Add end-to-end test for BatchNormGrad

RELNOTES: n/a
PiperOrigin-RevId: 163101568

* Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 163102070

* [XLA] Add more unit tests for DynamicSlice and DynamicUpdateSlice.

PiperOrigin-RevId: 163102445

* Adding missing deps to targets in llvm.BUILD. This was only working in non-sandboxed builds.

PiperOrigin-RevId: 163103908

* Pass batch_size in params when use_tpu=False.

PiperOrigin-RevId: 163105673

* Remove duplicate import.

PiperOrigin-RevId: 163108237

* Implementation of UnsortedSegmentSum in tf2xla bridge.

PiperOrigin-RevId: 163109769

* Add gradient checking tests for nn.moments().

PiperOrigin-RevId: 163110994

* Improved the speed of constant folding

PiperOrigin-RevId: 163113085

* Convert configure to python.

PiperOrigin-RevId: 163114551

* [TF:XLA] Ignore control edges from Enter nodes to the graph sink during loop functionalization.

PiperOrigin-RevId: 163115904

* Support customized residual function in the residual wrapper.

PiperOrigin-RevId: 163121296
---
 configure                                     |  814 +-----------
 configure.py                                  |  950 ++++++++++++++
 tensorflow/c/c_api.cc                         |  134 +-
 tensorflow/c/c_api_test.cc                    |   50 +-
 tensorflow/cc/framework/gradients.cc          |    4 +-
 .../compiler/jit/mark_for_compilation_pass.cc |    5 +
 tensorflow/compiler/tests/BUILD               |   14 +
 .../tests/segment_reduction_ops_test.py       |  139 ++
 .../compiler/tests/tensor_array_ops_test.py   |    4 +-
 .../tf2xla/functionalize_control_flow.cc      |   22 +-
 .../tf2xla/functionalize_control_flow_test.cc |    8 +
 tensorflow/compiler/tf2xla/kernels/BUILD      |    1 +
 tensorflow/compiler/tf2xla/kernels/no_op.cc   |    5 +
 .../tf2xla/kernels/segment_reduction_ops.cc   |  155 +++
 .../tf2xla/kernels/tensor_array_ops.cc        |   14 +-
 tensorflow/compiler/xla/BUILD                 |    5 +-
 tensorflow/compiler/xla/service/BUILD         |    1 +
 .../xla/service/algebraic_simplifier.cc       |    6 +-
 .../xla/service/algebraic_simplifier.h        |   13 +-
 tensorflow/compiler/xla/service/cpu/BUILD     |    1 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   18 +
 tensorflow/compiler/xla/service/gpu/BUILD     |    1 +
 .../compiler/xla/service/gpu/gpu_compiler.cc  |   31 +-
 .../compiler/xla/service/hlo_graph_dumper.cc  | 1117 +++++++++--------
 tensorflow/compiler/xla/service/hlo_opcode.h  |    5 +
 .../xla/service/reduce_precision_insertion.cc |   39 +-
 .../xla/service/reduce_precision_insertion.h  |   22 +-
 tensorflow/compiler/xla/statusor.cc           |   22 +-
 tensorflow/compiler/xla/statusor.h            |  323 ++---
 tensorflow/compiler/xla/statusor_internals.h  |  245 ++++
 tensorflow/compiler/xla/statusor_test.cc      |   22 +-
 .../xla/tests/batch_normalization_test.cc     |  163 +++
 .../compiler/xla/tests/dynamic_ops_test.cc    |  405 +++---
 .../xla/tests/reduce_precision_test.cc        |   90 +-
 tensorflow/compiler/xla/xla.proto             |   23 +
 tensorflow/contrib/makefile/tf_op_files.txt   |    1 +
 .../python/kernel_tests/core_rnn_cell_test.py |   29 +-
 .../python/kernel_tests/spectral_ops_test.py  |   17 +-
 .../contrib/tpu/python/tpu/tpu_estimator.py   |   66 +-
 tensorflow/core/framework/attr_value_util.cc  |    6 +-
 tensorflow/core/framework/attr_value_util.h   |    6 +-
 .../grappler/optimizers/constant_folding.cc   |   83 +-
 tensorflow/core/kernels/BUILD                 |   43 +
 .../core/kernels/compare_and_bitpack_op.cc    |  185 +++
 .../core/kernels/compare_and_bitpack_op.h     |   42 +
 .../kernels/compare_and_bitpack_op_gpu.cu.cc  |  141 +++
 .../core/kernels/population_count_op.cc       |  163 +++
 tensorflow/core/kernels/population_count_op.h |   38 +
 .../kernels/population_count_op_gpu.cu.cc     |   92 ++
 tensorflow/core/ops/bitwise_ops.cc            |   16 +
 .../core/ops/compat/ops_history.v1.pbtxt      |   56 +
 tensorflow/core/ops/math_ops.cc               |   58 +
 tensorflow/core/ops/ops.pbtxt                 |   96 +-
 .../platform/default/build_config_root.bzl    |    2 +-
 tensorflow/core/platform/macros.h             |    3 +
 tensorflow/core/platform/posix/error.cc       |    6 +-
 tensorflow/go/op/wrappers.go                  |  237 +++-
 tensorflow/python/kernel_tests/BUILD          |   12 +
 .../compare_and_bitpack_op_test.py            |   83 ++
 .../python/kernel_tests/gather_op_test.py     |  102 +-
 tensorflow/python/ops/array_ops.py            |    2 +-
 tensorflow/python/ops/bitwise_ops.py          |    1 +
 tensorflow/python/ops/bitwise_ops_test.py     |   23 +
 tensorflow/python/ops/control_flow_ops.py     |   10 +-
 tensorflow/python/ops/init_ops.py             |    1 -
 tensorflow/python/ops/nn_test.py              |   18 +-
 tensorflow/python/ops/rnn_cell_impl.py        |   18 +-
 ...orflow.nn.rnn_cell.-residual-wrapper.pbtxt |    2 +-
 .../tools/ci_build/builds/builds_common.sh    |    1 +
 tensorflow/tools/ci_build/builds/configured   |    2 +-
 tensorflow/tools/ci_build/builds/pip.sh       |   27 +-
 .../tools/ci_build/builds/run_pip_tests.sh    |    2 +-
 .../tools/ci_build/linux/cpu/run_cc_core.sh   |    2 +-
 .../tools/ci_build/linux/cpu/run_py2_core.sh  |    2 +-
 .../ci_build/linux/cpu/run_py3_contrib.sh     |    2 +-
 .../tools/ci_build/linux/cpu/run_py3_core.sh  |    2 +-
 .../tools/ci_build/linux/gpu/run_cc_core.sh   |    2 +-
 .../tools/ci_build/linux/gpu/run_py3_core.sh  |    2 +-
 .../tools/ci_build/osx/cpu/run_py2_cc_core.sh |    2 +-
 .../tools/ci_build/xla/linux/gpu/run_py3.sh   |    2 +-
 third_party/llvm/llvm.BUILD                   |    4 +
 81 files changed, 4571 insertions(+), 2010 deletions(-)
 create mode 100644 configure.py
 create mode 100644 tensorflow/compiler/tests/segment_reduction_ops_test.py
 create mode 100644 tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc
 create mode 100644 tensorflow/compiler/xla/statusor_internals.h
 create mode 100644 tensorflow/core/kernels/compare_and_bitpack_op.cc
 create mode 100644 tensorflow/core/kernels/compare_and_bitpack_op.h
 create mode 100644 tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc
 create mode 100644 tensorflow/core/kernels/population_count_op.cc
 create mode 100644 tensorflow/core/kernels/population_count_op.h
 create mode 100644 tensorflow/core/kernels/population_count_op_gpu.cu.cc
 create mode 100644 tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py

diff --git a/configure b/configure
index 1eeaffaf74..c6df6992d9 100755
--- a/configure
+++ b/configure
@@ -3,816 +3,12 @@
 set -e
 set -o pipefail
 
-MIN_BAZEL_VERSION=0.4.5
-
-# Find out the absolute path to where ./configure resides
-pushd `dirname $0` > /dev/null
-SOURCE_BASE_DIR=`pwd -P`
-popd > /dev/null
-
-PLATFORM="$(uname -s | tr 'A-Z' 'a-z')"
-
-function is_linux() {
-  [[ "${PLATFORM}" == "linux" ]]
-}
-
-function is_macos() {
-  [[ "${PLATFORM}" == "darwin" ]]
-}
-
-function is_windows() {
-  # On windows, the shell script is actually running in msys
-  [[ "${PLATFORM}" =~ msys_nt*|mingw*|cygwin*|uwin* ]]
-}
-
-function is_ppc64le() {
-  [[ "$(uname -m)" == "ppc64le" ]]
-}
-
-function sed_in_place() {
-  sed -e $1 $2 > "$2.bak"
-  mv "$2.bak" $2
-}
-
-function write_to_bazelrc() {
-  echo "$1" >> .tf_configure.bazelrc
-}
-
-function write_action_env_to_bazelrc() {
-  write_to_bazelrc "build --action_env $1=\"$2\""
-}
-
-function python_path {
-  "$PYTHON_BIN_PATH" - <<END
-from __future__ import print_function
-import site
-import os
-
-try:
-  input = raw_input
-except NameError:
-  pass
-
-python_paths = []
-if os.getenv('PYTHONPATH') is not None:
-  python_paths = os.getenv('PYTHONPATH').split(':')
-try:
-  library_paths = site.getsitepackages()
-except AttributeError:
- from distutils.sysconfig import get_python_lib
- library_paths = [get_python_lib()]
-all_paths = set(python_paths + library_paths)
-
-paths = []
-for path in all_paths:
-  if os.path.isdir(path):
-    paths.append(path)
-
-print(",".join(paths))
-END
-}
-
-function setup_python {
-  ## Set up python-related environment settings:
-  while true; do
-    fromuser=""
-    if [ -z "$PYTHON_BIN_PATH" ]; then
-      default_python_bin_path=$(which python || which python3 || true)
-      read -p "Please specify the location of python. [Default is $default_python_bin_path]: " PYTHON_BIN_PATH
-      fromuser="1"
-      if [ -z "$PYTHON_BIN_PATH" ]; then
-        PYTHON_BIN_PATH=$default_python_bin_path
-      fi
-    fi
-    if [ -e "$PYTHON_BIN_PATH" ]; then
-      break
-    fi
-    echo "Invalid python path. ${PYTHON_BIN_PATH} cannot be found" 1>&2
-    if [ -z "$fromuser" ]; then
-      exit 1
-    fi
-    PYTHON_BIN_PATH=""
-    # Retry
-  done
-
-  if [ -z "$PYTHON_LIB_PATH" ]; then
-    # Split python_path into an array of paths, this allows path containing spaces
-    IFS=',' read -r -a python_lib_path <<< "$(python_path)"
-
-    if [ 1 = "$USE_DEFAULT_PYTHON_LIB_PATH" ]; then
-      PYTHON_LIB_PATH=${python_lib_path[0]}
-      echo "Using python library path: $PYTHON_LIB_PATH"
-
-    else
-      echo "Found possible Python library paths:"
-      for x in "${python_lib_path[@]}"; do
-        echo "  $x"
-      done
-      set -- "${python_lib_path[@]}"
-      echo "Please input the desired Python library path to use.  Default is [$1]"
-      read b || true
-      if [ "$b" == "" ]; then
-        PYTHON_LIB_PATH=${python_lib_path[0]}
-        echo "Using python library path: $PYTHON_LIB_PATH"
-      else
-        PYTHON_LIB_PATH="$b"
-      fi
-    fi
-  fi
-
-  if [ ! -x "$PYTHON_BIN_PATH" ]  || [ -d "$PYTHON_BIN_PATH" ]; then
-    echo "PYTHON_BIN_PATH is not executable.  Is it the python binary?"
-    exit 1
-  fi
-
-  local python_major_version
-  python_major_version=$("${PYTHON_BIN_PATH}" -c 'from __future__ import print_function; import sys; print(sys.version_info[0]);' | head -c1)
-  if [ -z "$python_major_version" ]; then
-    echo -e "\n\nERROR: Problem getting python version.  Is $PYTHON_BIN_PATH the correct python binary?"
-    exit 1
-  fi
-
-  # Convert python path to Windows style before writing into bazel.rc
-  if is_windows; then
-    PYTHON_BIN_PATH="$(cygpath -m "$PYTHON_BIN_PATH")"
-    PYTHON_LIB_PATH="$(cygpath -m "$PYTHON_LIB_PATH")"
-  fi
-
-  # Set-up env variables used by python_configure.bzl
-  write_action_env_to_bazelrc "PYTHON_BIN_PATH" "$PYTHON_BIN_PATH"
-  write_action_env_to_bazelrc "PYTHON_LIB_PATH" "$PYTHON_LIB_PATH"
-  write_to_bazelrc "build --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "build --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
-  write_to_bazelrc "build --force_python=py$python_major_version"
-  write_to_bazelrc "build --host_force_python=py$python_major_version"
-  write_to_bazelrc "build --python${python_major_version}_path=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "test --force_python=py$python_major_version"
-  write_to_bazelrc "test --host_force_python=py$python_major_version"
-  write_to_bazelrc "test --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "test --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
-  write_to_bazelrc "run --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
-  write_to_bazelrc "run --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
-
-  # Write tools/python_bin_path.sh
-  echo "export PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\"" > tools/python_bin_path.sh
-}
-
-function version {
-  echo "$@" | awk -F. '{ printf("%03d%03d%03d\n", $1,$2,$3); }';
-}
-
-
-bazel version > bazel.version
-set +e
-curr_bazel_version=$(grep -m 1 'Build label:' bazel.version | cut -d ' ' -f3)
-set -e
-rm -f bazel.version
-
-
-echo "You have bazel $curr_bazel_version installed."
-if [ -z "$curr_bazel_version" ]; then
-  echo "WARNING: current bazel installation is not a release version."
-  echo "Make sure you are running at least bazel $MIN_BAZEL_VERSION."
-elif [ "$(version "$MIN_BAZEL_VERSION")" -gt "$(version "$curr_bazel_version")" ]; then
-  echo "Please upgrade your bazel installation to version $MIN_BAZEL_VERSION or higher to build TensorFlow!"
-  echo "Exiting..."
-  exit 1
-fi
-
-# This file contains customized config settings.
-rm -f .tf_configure.bazelrc
-touch .tf_configure.bazelrc
-if [[ ! -e .bazelrc ]]; then
-  if [[ -e "${HOME}/.bazelrc" ]]; then
-    echo "import ${HOME}/.bazelrc" >.bazelrc
-  else
-    touch .bazelrc
-  fi
-fi
-sed_in_place "/tf_configure/d" .bazelrc
-echo "import %workspace%/.tf_configure.bazelrc" >> .bazelrc
-
-# Delete any leftover BUILD files from the Makefile build, which would interfere
-# with Bazel parsing.
-MAKEFILE_DOWNLOAD_DIR=tensorflow/contrib/makefile/downloads
-if [ -d "${MAKEFILE_DOWNLOAD_DIR}" ]; then
-  find ${MAKEFILE_DOWNLOAD_DIR} -type f -name '*BUILD' -delete
-fi
-
-setup_python
-
-## Set up MKL related environment settings
-write_to_bazelrc 'build:mkl --define with_mkl_support=true'
-write_to_bazelrc 'build:mkl --define using_mkl=true'
-write_to_bazelrc 'build:mkl -c opt'
-write_to_bazelrc 'build:mkl --copt="-DEIGEN_USE_VML"'
-echo ""
-echo "Add \"--config=mkl\" to your bazel command to build with MKL support."
-echo "Please note that MKL on MacOS or windows is still not supported."
-echo "If you would like to use a local MKL instead of downloading, please "
-echo "  set the environment variable \"TF_MKL_ROOT\" every time before build."
-echo ""
-## End MKL setup
-
-## Set up architecture-dependent optimization flags.
-if [ -z "$CC_OPT_FLAGS" ]; then
-  if is_ppc64le; then
-    # gcc on ppc64le does not support -march, use mcpu instead
-    default_cc_opt_flags="-mcpu=native"
-  else
-    default_cc_opt_flags="-march=native"
-  fi
-  read -p "Please specify optimization flags to use during compilation when bazel option "\
-"\"--config=opt\" is specified [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
-  if [ -z "$CC_OPT_FLAGS" ]; then
-    CC_OPT_FLAGS=$default_cc_opt_flags
-  fi
-fi
-
-if is_windows; then
-  TF_NEED_GCP=0
-  TF_NEED_HDFS=0
-  TF_NEED_JEMALLOC=0
-  TF_NEED_OPENCL=0
-  TF_CUDA_CLANG=0
-fi
-
-if is_linux; then
-  while [ "$TF_NEED_JEMALLOC" == "" ]; do
-    read -p "Do you wish to use jemalloc as the malloc implementation? [Y/n] "\
-      INPUT
-    case $INPUT in
-      [Yy]* ) echo "jemalloc enabled"; TF_NEED_JEMALLOC=1;;
-      [Nn]* ) echo "jemalloc disabled"; TF_NEED_JEMALLOC=0;;
-      "" ) echo "jemalloc enabled"; TF_NEED_JEMALLOC=1;;
-      * ) echo "Invalid selection: " $INPUT;;
-    esac
-  done
-else
-  TF_NEED_JEMALLOC=0
-fi
-
-if [[ "$TF_NEED_JEMALLOC" == "1" ]]; then
-  write_to_bazelrc 'build --define with_jemalloc=true'
-fi
-
-while [[ "$TF_NEED_GCP" == "" ]]; do
-  read -p "Do you wish to build TensorFlow with "\
-"Google Cloud Platform support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "Google Cloud Platform support will be enabled for "\
-"TensorFlow"; TF_NEED_GCP=1;;
-    [Nn]* ) echo "No Google Cloud Platform support will be enabled for "\
-"TensorFlow"; TF_NEED_GCP=0;;
-    "" ) echo "No Google Cloud Platform support will be enabled for "\
-"TensorFlow"; TF_NEED_GCP=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_NEED_GCP" == "1" ]]; then
-  write_to_bazelrc 'build --define with_gcp_support=true'
-fi
-
-while [[ "$TF_NEED_HDFS" == "" ]]; do
-  read -p "Do you wish to build TensorFlow with "\
-"Hadoop File System support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "Hadoop File System support will be enabled for "\
-"TensorFlow"; TF_NEED_HDFS=1;;
-    [Nn]* ) echo "No Hadoop File System support will be enabled for "\
-"TensorFlow"; TF_NEED_HDFS=0;;
-    "" ) echo "No Hadoop File System support will be enabled for "\
-"TensorFlow"; TF_NEED_HDFS=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_NEED_HDFS" == "1" ]]; then
-  write_to_bazelrc 'build --define with_hdfs_support=true'
-fi
-
-## Enable XLA.
-while [[ "$TF_ENABLE_XLA" == "" ]]; do
-  read -p "Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "XLA JIT support will be enabled for TensorFlow"; TF_ENABLE_XLA=1;;
-    [Nn]* ) echo "No XLA JIT support will be enabled for TensorFlow"; TF_ENABLE_XLA=0;;
-    "" ) echo "No XLA support will be enabled for TensorFlow"; TF_ENABLE_XLA=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_ENABLE_XLA" == "1" ]]; then
-  write_to_bazelrc 'build --define with_xla_support=true'
-fi
-
-# Verbs configuration
-while [ "$TF_NEED_VERBS" == "" ]; do
-  read -p "Do you wish to build TensorFlow with "\
-"VERBS support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "VERBS support will be enabled for "\
-"TensorFlow"; TF_NEED_VERBS=1;;
-    [Nn]* ) echo "No VERBS support will be enabled for "\
-"TensorFlow"; TF_NEED_VERBS=0;;
-    "" ) echo "No VERBS support will be enabled for "\
-"TensorFlow"; TF_NEED_VERBS=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-if [[ "$TF_NEED_VERBS" == "1" ]]; then
-  write_to_bazelrc 'build --define with_verbs_support=true'
-fi
-
-# Append CC optimization flags to bazel.rc
-for opt in $CC_OPT_FLAGS; do
-  write_to_bazelrc "build:opt --cxxopt=$opt --copt=$opt"
-done
-
-# Run the gen_git_source to create links where bazel can track dependencies for
-# git hash propagation
-GEN_GIT_SOURCE=tensorflow/tools/git/gen_git_source.py
-chmod a+x ${GEN_GIT_SOURCE}
-"${PYTHON_BIN_PATH}" ${GEN_GIT_SOURCE} --configure "${SOURCE_BASE_DIR}"
-
-## Set up SYCL-related environment settings
-while [ "$TF_NEED_OPENCL" == "" ]; do
-  read -p "Do you wish to build TensorFlow with OpenCL support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "OpenCL support will be enabled for TensorFlow"; TF_NEED_OPENCL=1;;
-    [Nn]* ) echo "No OpenCL support will be enabled for TensorFlow"; TF_NEED_OPENCL=0;;
-    "" ) echo "No OpenCL support will be enabled for TensorFlow"; TF_NEED_OPENCL=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-## Set up Cuda-related environment settings
-
-while [ "$TF_NEED_CUDA" == "" ]; do
-  read -p "Do you wish to build TensorFlow with CUDA support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "CUDA support will be enabled for TensorFlow"; TF_NEED_CUDA=1;;
-    [Nn]* ) echo "No CUDA support will be enabled for TensorFlow"; TF_NEED_CUDA=0;;
-    "" ) echo "No CUDA support will be enabled for TensorFlow"; TF_NEED_CUDA=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-export TF_NEED_CUDA
-write_action_env_to_bazelrc "TF_NEED_CUDA" "$TF_NEED_CUDA"
-
-export TF_NEED_OPENCL
-write_action_env_to_bazelrc "TF_NEED_OPENCL" "$TF_NEED_OPENCL"
-
-if [ "$TF_NEED_CUDA" == "1" ]; then
-while [[ "$TF_CUDA_CLANG" == "" ]]; do
-  read -p "Do you want to use clang as CUDA compiler? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "Clang will be used as CUDA compiler"; TF_CUDA_CLANG=1;;
-    [Nn]* ) echo "nvcc will be used as CUDA compiler"; TF_CUDA_CLANG=0;;
-    "" ) echo "nvcc will be used as CUDA compiler"; TF_CUDA_CLANG=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-export TF_CUDA_CLANG
-write_action_env_to_bazelrc "TF_CUDA_CLANG" "$TF_CUDA_CLANG"
-
-# Set up which clang we should use as the cuda / host compiler.
-while [[ "$TF_CUDA_CLANG" == "1" ]] && true; do
-  fromuser=""
-  if [ -z "$CLANG_CUDA_COMPILER_PATH" ]; then
-    default_clang_host_compiler_path=$(which clang || true)
-    read -p "Please specify which clang should be used as device and host compiler. [Default is $default_clang_host_compiler_path]: " CLANG_CUDA_COMPILER_PATH
-    fromuser="1"
-    if [ -z "$CLANG_CUDA_COMPILER_PATH" ]; then
-      CLANG_CUDA_COMPILER_PATH="$default_clang_host_compiler_path"
-    fi
-  fi
-  if [ -e "$CLANG_CUDA_COMPILER_PATH" ]; then
-    export CLANG_CUDA_COMPILER_PATH
-    write_action_env_to_bazelrc "CLANG_CUDA_COMPILER_PATH" "$CLANG_CUDA_COMPILER_PATH"
-    break
-  fi
-  echo "Invalid clang path. ${CLANG_CUDA_COMPILER_PATH} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  CLANG_CUDA_COMPILER_PATH=""
-  # Retry
-done
-
-# Find out where the CUDA toolkit is installed
-while true; do
-  # Configure the Cuda SDK version to use.
-  if [ -z "$TF_CUDA_VERSION" ]; then
-    read -p "Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: " TF_CUDA_VERSION
-  fi
-  # Set default CUDA version if not set
-  TF_CUDA_VERSION=${TF_CUDA_VERSION:-8.0}
-
-  fromuser=""
-  if [ -z "$CUDA_TOOLKIT_PATH" ]; then
-    default_cuda_path=/usr/local/cuda
-    if is_windows; then
-      if [ -z "$CUDA_PATH" ]; then
-        default_cuda_path="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v8.0"
-      else
-        default_cuda_path="$(cygpath -m "$CUDA_PATH")"
-      fi
-    elif is_linux; then
-      # If the default doesn't exist, try an alternative default.
-      if [ ! -d $default_cuda_path ] && [ -d /opt/cuda ]; then
-        default_cuda_path=/opt/cuda
-      fi
-    fi
-    read -p "Please specify the location where CUDA $TF_CUDA_VERSION toolkit is installed. Refer to README.md for more details. [Default is $default_cuda_path]: " CUDA_TOOLKIT_PATH
-    fromuser="1"
-    if [ -z "$CUDA_TOOLKIT_PATH" ]; then
-      CUDA_TOOLKIT_PATH="$default_cuda_path"
-    fi
-  fi
-
-  if [[ -z "$TF_CUDA_VERSION" ]]; then
-    TF_CUDA_EXT=""
-  else
-    TF_CUDA_EXT=".$TF_CUDA_VERSION"
-  fi
-
-  if is_windows; then
-    CUDA_RT_LIB_PATH="lib/x64/cudart.lib"
-  elif is_linux; then
-    CUDA_RT_LIB_PATH="lib64/libcudart.so${TF_CUDA_EXT}"
-  elif is_macos; then
-    CUDA_RT_LIB_PATH="lib/libcudart${TF_CUDA_EXT}.dylib"
-  fi
-
-  if [ -e "${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH}" ]; then
-    export CUDA_TOOLKIT_PATH
-    write_action_env_to_bazelrc "CUDA_TOOLKIT_PATH" "$CUDA_TOOLKIT_PATH"
-    export TF_CUDA_VERSION
-    break
-  fi
-  echo "Invalid path to CUDA $TF_CUDA_VERSION toolkit. ${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH} cannot be found"
-
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  # Retry
-  TF_CUDA_VERSION=""
-  CUDA_TOOLKIT_PATH=""
-done
-
-export TF_CUDA_VERSION
-write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION"
-
-# Set up which gcc nvcc should use as the host compiler
-# No need to set this on Windows
-while [[ "$TF_CUDA_CLANG" != "1" ]] && ! is_windows && true; do
-  fromuser=""
-  if [ -z "$GCC_HOST_COMPILER_PATH" ]; then
-    default_gcc_host_compiler_path=$(which gcc || true)
-    cuda_bin_symlink="$CUDA_TOOLKIT_PATH/bin/gcc"
-    if [ -L "$cuda_bin_symlink" ]; then
-      default_gcc_host_compiler_path=$(readlink $cuda_bin_symlink)
-    fi
-    read -p "Please specify which gcc should be used by nvcc as the host compiler. [Default is $default_gcc_host_compiler_path]: " GCC_HOST_COMPILER_PATH
-    fromuser="1"
-    if [ -z "$GCC_HOST_COMPILER_PATH" ]; then
-      GCC_HOST_COMPILER_PATH="$default_gcc_host_compiler_path"
-    fi
-  fi
-  if [ -e "$GCC_HOST_COMPILER_PATH" ]; then
-    export GCC_HOST_COMPILER_PATH
-    write_action_env_to_bazelrc "GCC_HOST_COMPILER_PATH" "$GCC_HOST_COMPILER_PATH"
-    break
-  fi
-  echo "Invalid gcc path. ${GCC_HOST_COMPILER_PATH} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  GCC_HOST_COMPILER_PATH=""
-  # Retry
-done
-
-# Find out where the cuDNN library is installed
-while true; do
-  # Configure the cuDNN version to use.
-  if [ -z "$TF_CUDNN_VERSION" ]; then
-    read -p "Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: " TF_CUDNN_VERSION
-  fi
-  # Set default CUDNN version if not set
-  TF_CUDNN_VERSION=${TF_CUDNN_VERSION:-6}
-
-  fromuser=""
-  if [ -z "$CUDNN_INSTALL_PATH" ]; then
-    default_cudnn_path=${CUDA_TOOLKIT_PATH}
-    read -p "Please specify the location where cuDNN $TF_CUDNN_VERSION library is installed. Refer to README.md for more details. [Default is $default_cudnn_path]: " CUDNN_INSTALL_PATH
-    fromuser="1"
-    if [ -z "$CUDNN_INSTALL_PATH" ]; then
-      CUDNN_INSTALL_PATH=$default_cudnn_path
-    fi
-    # Result returned from "read" will be used unexpanded. That make "~" unusable.
-    # Going through one more level of expansion to handle that.
-    CUDNN_INSTALL_PATH=`"${PYTHON_BIN_PATH}" -c "import os; print(os.path.realpath(os.path.expanduser('${CUDNN_INSTALL_PATH}')))"`
-    if is_windows; then
-      CUDNN_INSTALL_PATH="$(cygpath -m "$CUDNN_INSTALL_PATH")"
-    fi
-  fi
-
-  if [[ -z "$TF_CUDNN_VERSION" ]]; then
-    TF_CUDNN_EXT=""
-  else
-    TF_CUDNN_EXT=".$TF_CUDNN_VERSION"
-  fi
-
-  if is_windows; then
-    CUDA_DNN_LIB_PATH="lib/x64/cudnn.lib"
-    CUDA_DNN_LIB_ALT_PATH="lib/x64/cudnn.lib"
-  elif is_linux; then
-    CUDA_DNN_LIB_PATH="lib64/libcudnn.so${TF_CUDNN_EXT}"
-    CUDA_DNN_LIB_ALT_PATH="libcudnn.so${TF_CUDNN_EXT}"
-  elif is_macos; then
-    CUDA_DNN_LIB_PATH="lib/libcudnn${TF_CUDNN_EXT}.dylib"
-    CUDA_DNN_LIB_ALT_PATH="libcudnn${TF_CUDNN_EXT}.dylib"
-  fi
-
-  if [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_ALT_PATH}" ] || [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_PATH}" ]; then
-    export TF_CUDNN_VERSION
-    write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
-    export CUDNN_INSTALL_PATH
-    write_action_env_to_bazelrc "CUDNN_INSTALL_PATH" "$CUDNN_INSTALL_PATH"
-    break
-  fi
-
-  if is_linux; then
-    if ! type ldconfig > /dev/null 2>&1; then
-        LDCONFIG_BIN=/sbin/ldconfig
-    else
-        LDCONFIG_BIN=ldconfig
-    fi
-    CUDNN_PATH_FROM_LDCONFIG="$($LDCONFIG_BIN -p | sed -n 's/.*libcudnn.so .* => \(.*\)/\1/p')"
-    if [ -e "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}" ]; then
-      export TF_CUDNN_VERSION
-      export CUDNN_INSTALL_PATH
-      CUDNN_INSTALL_PATH="$(dirname ${CUDNN_PATH_FROM_LDCONFIG})"
-      write_action_env_to_bazelrc "CUDNN_INSTALL_PATH" "$CUDNN_INSTALL_PATH"
-      break
-    fi
-  fi
-  echo "Invalid path to cuDNN ${CUDNN_VERSION} toolkit. Neither of the following two files can be found:"
-  echo "${CUDNN_INSTALL_PATH}/${CUDA_DNN_LIB_PATH}"
-  echo "${CUDNN_INSTALL_PATH}/${CUDA_DNN_LIB_ALT_PATH}"
-  if is_linux; then
-    echo "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}"
-  fi
-
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  # Retry
-  TF_CUDNN_VERSION=""
-  CUDNN_INSTALL_PATH=""
-done
-
-export TF_CUDNN_VERSION
-write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
-
-# Configure the compute capabilities that TensorFlow builds for.
-# Since Cuda toolkit is not backward-compatible, this is not guaranteed to work.
-function get_native_cuda_compute_capabilities {
-  device_query_bin="$CUDA_TOOLKIT_PATH/extras/demo_suite/deviceQuery" # Also works on Windows without .exe
-  "$device_query_bin" | grep 'Capability' | grep -o '[0-9]*\.[0-9]*' | sed ':a;{N;s/\n/,/};ba'
-  exit 0 # ensure that this function always exit success even if device detection fails, to prevent the whole configure from aborting
-}
-while true; do
-  fromuser=""
-  native_cuda_compute_capabilities=$(get_native_cuda_compute_capabilities)
-  default_cuda_compute_capabilities=${native_cuda_compute_capabilities:-"3.5,5.2"}
-  if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
-cat << EOF
-Please specify a list of comma-separated Cuda compute capabilities you want to build with.
-You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
-Please note that each additional compute capability significantly increases your build time and binary size.
-EOF
-    read -p "[Default is: \"$default_cuda_compute_capabilities\"]: " TF_CUDA_COMPUTE_CAPABILITIES
-    fromuser=1
-  fi
-  if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
-    TF_CUDA_COMPUTE_CAPABILITIES=$default_cuda_compute_capabilities
-  fi
-  # Check whether all capabilities from the input is valid
-  COMPUTE_CAPABILITIES=${TF_CUDA_COMPUTE_CAPABILITIES//,/ }
-  ALL_VALID=1
-  for CAPABILITY in $COMPUTE_CAPABILITIES; do
-    if [[ ! "$CAPABILITY" =~ [0-9]+.[0-9]+ ]]; then
-      echo "Invalid compute capability: " $CAPABILITY
-      ALL_VALID=0
-      break
-    fi
-  done
-  if [ "$ALL_VALID" == "0" ]; then
-    if [ -z "$fromuser" ]; then
-      exit 1
-    fi
-  else
-    export TF_CUDA_COMPUTE_CAPABILITIES
-    write_action_env_to_bazelrc "TF_CUDA_COMPUTE_CAPABILITIES" "$TF_CUDA_COMPUTE_CAPABILITIES"
-    break
-  fi
-  TF_CUDA_COMPUTE_CAPABILITIES=""
-done
-
-if is_windows; then
-  # The following three variables are needed for MSVC toolchain configuration in Bazel
-  export CUDA_PATH="$CUDA_TOOLKIT_PATH"
-  export CUDA_COMPUTE_CAPABILITIES="$TF_CUDA_COMPUTE_CAPABILITIES"
-  export NO_WHOLE_ARCHIVE_OPTION=1
-  write_action_env_to_bazelrc "CUDA_PATH" "$CUDA_PATH"
-  write_action_env_to_bazelrc "CUDA_COMPUTE_CAPABILITIES" "$CUDA_COMPUTE_CAPABILITIES"
-  write_action_env_to_bazelrc "NO_WHOLE_ARCHIVE_OPTION" "1"
-  write_to_bazelrc "build --config=win-cuda"
-  write_to_bazelrc "test --config=win-cuda"
-else
-  # If CUDA is enabled, always use GPU during build and test.
-  if [ "$TF_CUDA_CLANG" == "1" ]; then
-    write_to_bazelrc "build --config=cuda_clang"
-    write_to_bazelrc "test --config=cuda_clang"
-  else
-    write_to_bazelrc "build --config=cuda"
-    write_to_bazelrc "test --config=cuda"
-  fi
+if [ -z "$PYTHON_BIN_PATH" ]; then
+  PYTHON_BIN_PATH=$(which python || which python3 || true)
 fi
 
-# end of if "$TF_NEED_CUDA" == "1"
-fi
-
-# OpenCL configuration
-
-if [ "$TF_NEED_OPENCL" == "1" ]; then
-
-# Determine which C++ compiler should be used as the host compiler
-while true; do
-  fromuser=""
-  if [ -z "$HOST_CXX_COMPILER" ]; then
-    default_cxx_host_compiler=$(which g++ || true)
-    read -p "Please specify which C++ compiler should be used as the host C++ compiler. [Default is $default_cxx_host_compiler]: " HOST_CXX_COMPILER
-    fromuser="1"
-    if [ -z "$HOST_CXX_COMPILER" ]; then
-      HOST_CXX_COMPILER=$default_cxx_host_compiler
-    fi
-  fi
-  if [ -e "$HOST_CXX_COMPILER" ]; then
-    export HOST_CXX_COMPILER
-    write_action_env_to_bazelrc "HOST_CXX_COMPILER" "$HOST_CXX_COMPILER"
-    break
-  fi
-  echo "Invalid C++ compiler path. ${HOST_CXX_COMPILER} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  HOST_CXX_COMPILER=""
-  # Retry
-done
-
-# Determine which C compiler should be used as the host compiler
-while true; do
-  fromuser=""
-  if [ -z "$HOST_C_COMPILER" ]; then
-    default_c_host_compiler=$(which gcc || true)
-    read -p "Please specify which C compiler should be used as the host C compiler. [Default is $default_c_host_compiler]: " HOST_C_COMPILER
-    fromuser="1"
-    if [ -z "$HOST_C_COMPILER" ]; then
-      HOST_C_COMPILER=$default_c_host_compiler
-    fi
-  fi
-  if [ -e "$HOST_C_COMPILER" ]; then
-    export HOST_C_COMPILER
-    write_action_env_to_bazelrc "HOST_C_COMPILER" "$HOST_C_COMPILER"
-    break
-  fi
-  echo "Invalid C compiler path. ${HOST_C_COMPILER} cannot be found" 1>&2
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  HOST_C_COMPILER=""
-  # Retry
-done
-
-while true; do
-  # Configure the OPENCL version to use.
-  TF_OPENCL_VERSION="1.2"
-
-  # Point to ComputeCpp root
-  if [ -z "$COMPUTECPP_TOOLKIT_PATH" ]; then
-    default_computecpp_toolkit_path=/usr/local/computecpp
-    read -p "Please specify the location where ComputeCpp for SYCL $TF_OPENCL_VERSION is installed. [Default is $default_computecpp_toolkit_path]: " COMPUTECPP_TOOLKIT_PATH
-    fromuser="1"
-    if [ -z "$COMPUTECPP_TOOLKIT_PATH" ]; then
-      COMPUTECPP_TOOLKIT_PATH=$default_computecpp_toolkit_path
-    fi
-  fi
-
-  if is_linux; then
-    SYCL_RT_LIB_PATH="lib/libComputeCpp.so"
-  fi
-
-  if [ -e "${COMPUTECPP_TOOLKIT_PATH}/${SYCL_RT_LIB_PATH}" ]; then
-    export COMPUTECPP_TOOLKIT_PATH
-    write_action_env_to_bazelrc "COMPUTECPP_TOOLKIT_PATH" "$COMPUTECPP_TOOLKIT_PATH"
-    break
-  fi
-  echo "Invalid SYCL $TF_OPENCL_VERSION library path. ${COMPUTECPP_TOOLKIT_PATH}/${SYCL_RT_LIB_PATH} cannot be found"
-
-  if [ -z "$fromuser" ]; then
-    exit 1
-  fi
-  # Retry
-  TF_OPENCL_VERSION=""
-  COMPUTECPP_TOOLKIT_PATH=""
-done
-
-# end of if "$TF_NEED_OPENCL" == "1"
-fi
-
-
-while [ "$TF_NEED_MPI" == "" ]; do
-  read -p "Do you wish to build TensorFlow with "\
-"MPI support? [y/N] " INPUT
-  case $INPUT in
-    [Yy]* ) echo "MPI support will be enabled for "\
-"TensorFlow"; TF_NEED_MPI=1;;
-    [Nn]* ) echo "MPI support will not be enabled for "\
-"TensorFlow"; TF_NEED_MPI=0;;
-    "" ) echo "MPI support will not be enabled for "\
-"TensorFlow"; TF_NEED_MPI=0;;
-    * ) echo "Invalid selection: " $INPUT;;
-  esac
-done
-
-# Find out where the MPI toolkit is installed
-while true; do
-    if [ "$TF_NEED_MPI" == "0" ]; then
-        break;
-    fi
-
-    fromuser=""
-    if [ -z "$MPI_HOME" ]; then
-        #Get the base folder by removing the bin path
-        default_mpi_path=$(dirname $(dirname $(which mpirun)) || dirname $(dirname $(which mpiexec))  || true)
-        read -p "Please specify the MPI toolkit folder. [Default is $default_mpi_path]: " MPI_HOME
-        fromuser="1"
-        if [ -z "$MPI_HOME" ]; then
-            MPI_HOME=$default_mpi_path
-        fi
-    fi
-
-    #Check that the include and library folders are where we expect them to be
-    if [ -e "$MPI_HOME/include" ] && [ -e "$MPI_HOME/lib" ]; then
-        break
-    fi
-
-    echo "Invalid path to the MPI Toolkit. ${MPI_HOME}/include or ${MPI_HOME}/lib cannot be found."
-    if [ -z "$fromuser" ]; then
-        exit 1
-    fi
-
-    # Retry
-    MPI_HOME=""
-done
-
-
-if [ "$TF_NEED_MPI" == "1" ]; then
-  write_to_bazelrc 'build --define with_mpi_support=true'
-
-  #Link the MPI header files
-  ln -sf "${MPI_HOME}/include/mpi.h" third_party/mpi/mpi.h
-
-
-  #Determine if we use OpenMPI or MVAPICH, these require different header files
-  #to be included here to make bazel dependency checker happy
-
-  if [ -e "${MPI_HOME}/include/mpi_portable_platform.h" ]; then
-        #OpenMPI
-        ln -sf "${MPI_HOME}/include/mpi_portable_platform.h" third_party/mpi/
-        sed -i -e "s/MPI_LIB_IS_OPENMPI=False/MPI_LIB_IS_OPENMPI=True/" third_party/mpi/mpi.bzl
- else
-        #MVAPICH / MPICH
-        ln -sf "${MPI_HOME}/include/mpio.h" third_party/mpi/
-        ln -sf "${MPI_HOME}/include/mpicxx.h" third_party/mpi/
-        sed -i -e "s/MPI_LIB_IS_OPENMPI=True/MPI_LIB_IS_OPENMPI=False/" third_party/mpi/mpi.bzl
- fi
-
-
-  if [ -e "${MPI_HOME}/lib/libmpi.so" ]; then
-    ln -sf "${MPI_HOME}/lib/libmpi.so" third_party/mpi/
-  else
-    echo "Cannot find the MPI library file in ${MPI_HOME}/lib "
-    exit 1
-  fi
-fi
+# Set all env variables
+$PYTHON_BIN_PATH configure.py
 
 
-echo "Configuration finished"
+echo "Configuration finished"
\ No newline at end of file
diff --git a/configure.py b/configure.py
new file mode 100644
index 0000000000..fac00d1b74
--- /dev/null
+++ b/configure.py
@@ -0,0 +1,950 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""configure script to get build parameters from user."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import errno
+import os
+import platform
+import re
+import site
+import subprocess
+import sys
+
+_TF_BAZELRC = '.tf_configure.bazelrc'
+_DEFAULT_CUDA_VERSION = '8.0'
+_DEFAULT_CUDNN_VERSION = '6'
+_DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,5.2'
+_DEFAULT_CUDA_PATH = '/usr/local/cuda'
+_DEFAULT_CUDA_PATH_LINUX = '/opt/cuda'
+_DEFAULT_CUDA_PATH_WIN = ('C:/Program Files/NVIDIA GPU Computing '
+                          'Toolkit/CUDA/v%s' % _DEFAULT_CUDA_VERSION)
+_TF_OPENCL_VERSION = '1.2'
+_DEFAULT_COMPUTECPP_TOOLKIT_PATH = '/usr/local/computecpp'
+
+
+def is_windows():
+  return platform.system() == 'Windows'
+
+
+def is_linux():
+  return platform.system() == 'Linux'
+
+
+def is_macos():
+  return platform.system() == 'Darwin'
+
+
+def is_ppc64le():
+  return platform.machine() == 'ppc64le'
+
+
+def get_input(question):
+  try:
+    try:
+      answer = raw_input(question)
+    except NameError:
+      answer = input(question)  # pylint: disable=bad-builtin
+  except EOFError:
+    answer = ''
+  return answer
+
+
+def symlink_force(target, link_name):
+  """Force symlink, equivalent of 'ln -sf'.
+
+  Args:
+    target: items to link to.
+    link_name: name of the link.
+  """
+  try:
+    os.symlink(target, link_name)
+  except OSError as e:
+    if e.errno == errno.EEXIST:
+      os.remove(link_name)
+      os.symlink(target, link_name)
+    else:
+      raise e
+
+
+def sed_in_place(filename, old, new):
+  """Replace old string with new string in file.
+
+  Args:
+    filename: string for filename.
+    old: string to replace.
+    new: new string to replace to.
+  """
+  with open(filename, 'r') as f:
+    filedata = f.read()
+  newdata = filedata.replace(old, new)
+  with open(filename, 'w') as f:
+    f.write(newdata)
+
+
+def remove_line_with(filename, token):
+  """Remove lines that contain token from file.
+
+  Args:
+    filename: string for filename.
+    token: string token to check if to remove a line from file or not.
+  """
+  with open(filename, 'r') as f:
+    filedata = f.read()
+
+  with open(filename, 'w') as f:
+    for line in filedata.strip().split('\n'):
+      if token not in line:
+        f.write(line + '\n')
+
+
+def write_to_bazelrc(line):
+  with open(_TF_BAZELRC, 'a') as f:
+    f.write(line + '\n')
+
+
+def write_action_env_to_bazelrc(var_name, var):
+  write_to_bazelrc('build --action_env %s="%s"' % (var_name, str(var)))
+
+
+def run_shell(cmd):
+  return subprocess.check_output(cmd, shell=True).decode('UTF-8').strip()
+
+
+def cygpath(path):
+  """Convert path from posix to windows."""
+  return run_shell('cygpath  -m "%s"' % path)
+
+
+def get_python_path(environ_cp):
+  """Get the python site package paths."""
+  python_paths = []
+  if environ_cp.get('PYTHONPATH'):
+    python_paths = environ_cp.get('PYTHONPATH').split(':')
+  try:
+    library_paths = site.getsitepackages()
+  except AttributeError:
+    from distutils.sysconfig import get_python_lib  # pylint: disable=g-import-not-at-top
+    library_paths = [get_python_lib()]
+  all_paths = set(python_paths + library_paths)
+
+  paths = []
+  for path in all_paths:
+    if os.path.isdir(path):
+      paths.append(path)
+  return paths
+
+
+def setup_python(environ_cp):
+  """Setup python related env variables."""
+  # Get PYTHON_BIN_PATH, default is the current running python.
+  default_python_bin_path = sys.executable
+  ask_python_bin_path = ('Please specify the location of python. [Default is '
+                         '%s]: ') % default_python_bin_path
+  while True:
+    python_bin_path = get_from_env_or_user_or_default(
+        environ_cp, 'PYTHON_BIN_PATH', ask_python_bin_path,
+        default_python_bin_path)
+    # Check if the path is valid
+    if (os.path.isfile(python_bin_path) and os.access(
+        python_bin_path, os.X_OK)) or (os.path.isdir(python_bin_path)):
+      break
+    elif not os.path.exists(python_bin_path):
+      print('Invalid python path: %s cannot be found.' % python_bin_path)
+    else:
+      print('%s is not executable.  Is it the python binary?' % python_bin_path)
+    environ_cp['PYTHON_BIN_PATH'] = ''
+
+  # Get PYTHON_LIB_PATH
+  python_lib_path = environ_cp.get('PYTHON_LIB_PATH')
+  if not python_lib_path:
+    python_lib_paths = get_python_path(environ_cp)
+    if environ_cp.get('USE_DEFAULT_PYTHON_LIB_PATH') == '1':
+      environ_cp['PYTHON_LIB_PATH'] = python_lib_paths[0]
+    else:
+      print('Found possible Python library paths:\n%s' %
+            '\n'.join(python_lib_paths))
+      default_python_lib_path = python_lib_paths[0]
+      python_lib_path = get_input(
+          'Please input the desired Python library path to use.  Default is %s'
+          % python_lib_paths[0])
+      if not python_lib_path:
+        python_lib_path = default_python_lib_path
+      environ_cp['PYTHON_LIB_PATH'] = python_lib_path
+
+  python_major_version = sys.version_info[0]
+  # Convert python path to Windows style before writing into bazel.rc
+  if is_windows():
+    python_bin_path = cygpath(python_bin_path)
+    python_lib_path = cygpath(python_lib_path)
+
+  # Set-up env variables used by python_configure.bzl
+  write_action_env_to_bazelrc('PYTHON_BIN_PATH', python_bin_path)
+  write_action_env_to_bazelrc('PYTHON_LIB_PATH', python_lib_path)
+  write_to_bazelrc('build --define PYTHON_BIN_PATH="%s"' % python_bin_path)
+  write_to_bazelrc('build --define PYTHON_LIB_PATH="%s"' % python_lib_path)
+  write_to_bazelrc('build --force_python=py%s' % python_major_version)
+  write_to_bazelrc('build --host_force_python=py%s' % python_major_version)
+  write_to_bazelrc('build --python%s_path=\"%s"' % (python_major_version,
+                                                    python_bin_path))
+  write_to_bazelrc('test --force_python=py%s' % python_major_version)
+  write_to_bazelrc('test --host_force_python=py%s' % python_major_version)
+  write_to_bazelrc('test --define PYTHON_BIN_PATH="%s"' % python_bin_path)
+  write_to_bazelrc('test --define PYTHON_LIB_PATH="%s"' % python_lib_path)
+  write_to_bazelrc('run --define PYTHON_BIN_PATH="%s"' % python_bin_path)
+  write_to_bazelrc('run --define PYTHON_LIB_PATH="%s"' % python_lib_path)
+  environ_cp['PYTHON_BIN_PATH'] = python_bin_path
+
+  # Write tools/python_bin_path.sh
+  with open('tools/python_bin_path.sh', 'w') as f:
+    f.write('export PYTHON_BIN_PATH="%s"' % python_bin_path)
+
+
+def reset_tf_configure_bazelrc():
+  """Reset file that contains customized config settings."""
+  open(_TF_BAZELRC, 'w').close()
+
+  home = os.path.expanduser('~')
+  if not os.path.exists('.bazelrc'):
+    if os.path.exists(os.path.join(home, '.bazelrc')):
+      with open('.bazelrc', 'a') as f:
+        f.write('import %s/.bazelrc\n' % home)
+    else:
+      open('.bazelrc', 'w').close()
+
+  remove_line_with('.bazelrc', 'tf_configure')
+  with open('.bazelrc', 'a') as f:
+    f.write('import %workspace%/.tf_configure.bazelrc\n')
+
+
+def run_gen_git_source(environ_cp):
+  """Run the gen_git_source to create links.
+
+  The links are for bazel to track dependencies for git hash propagation.
+
+  Args:
+    environ_cp: copy of the os.environ.
+  """
+  cmd = '%s tensorflow/tools/git/gen_git_source.py --configure %s' % (
+      environ_cp.get('PYTHON_BIN_PATH'), os.getcwd())
+  os.system(cmd)
+
+
+def cleanup_makefile():
+  """Delete any leftover BUILD files from the Makefile build.
+
+  These files could interfere with Bazel parsing.
+  """
+  makefile_download_dir = 'tensorflow/contrib/makefile/downloads'
+  if os.path.isdir(makefile_download_dir):
+    for root, _, filenames in os.walk(makefile_download_dir):
+      for f in filenames:
+        if f.endswith('BUILD'):
+          os.remove(os.path.join(root, f))
+
+
+def get_var(environ_cp,
+            var_name,
+            query_item,
+            enabled_by_default,
+            question=None,
+            yes_reply=None,
+            no_reply=None):
+  """Get boolean input from user.
+
+  If var_name is not set in env, ask user to enable query_item or not. If the
+  response is empty, use the default.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    query_item: string for feature related to the variable, e.g. "Hadoop File
+      System".
+    enabled_by_default: boolean for default behavior.
+    question: optional string for how to ask for user input.
+    yes_reply: optionanl string for reply when feature is enabled.
+    no_reply: optional string for reply when feature is disabled.
+
+  Returns:
+    boolean value of the variable.
+  """
+  if not question:
+    question = 'Do you wish to build TensorFlow with %s support?' % query_item
+  if not yes_reply:
+    yes_reply = '%s support will be enabled for TensorFlow.' % query_item
+  if not no_reply:
+    no_reply = 'No %s' % yes_reply
+
+  yes_reply += '\n'
+  no_reply += '\n'
+
+  if enabled_by_default:
+    question += ' [Y/n]: '
+  else:
+    question += ' [y/N]: '
+
+  var = environ_cp.get(var_name)
+  while var is None:
+    user_input_origin = get_input(question)
+    user_input = user_input_origin.strip().lower()
+    if user_input == 'y':
+      print(yes_reply)
+      var = True
+    elif user_input == 'n':
+      print(no_reply)
+      var = False
+    elif not user_input:
+      if enabled_by_default:
+        print(yes_reply)
+        var = True
+      else:
+        print(no_reply)
+        var = False
+    else:
+      print('Invalid selection: %s' % user_input_origin)
+  return var
+
+
+def set_build_var(environ_cp, var_name, query_item, option_name,
+                  enabled_by_default):
+  """Set if query_item will be enabled for the build.
+
+  Ask user if query_item will be enabled. Default is used if no input is given.
+  Set subprocess environment variable and write to .bazelrc if enabled.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    query_item: string for feature related to the variable, e.g. "Hadoop File
+      System".
+    option_name: string for option to define in .bazelrc.
+    enabled_by_default: boolean for default behavior.
+  """
+
+  var = str(int(get_var(environ_cp, var_name, query_item, enabled_by_default)))
+  environ_cp[var_name] = var
+  if var == '1':
+    write_to_bazelrc('build --define %s=true' % option_name)
+
+
+def set_action_env_var(environ_cp,
+                       var_name,
+                       query_item,
+                       enabled_by_default,
+                       question=None,
+                       yes_reply=None,
+                       no_reply=None):
+  """Set boolean action_env variable.
+
+  Ask user if query_item will be enabled. Default is used if no input is given.
+  Set environment variable and write to .bazelrc.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    query_item: string for feature related to the variable, e.g. "Hadoop File
+      System".
+    enabled_by_default: boolean for default behavior.
+    question: optional string for how to ask for user input.
+    yes_reply: optionanl string for reply when feature is enabled.
+    no_reply: optional string for reply when feature is disabled.
+  """
+  var = int(
+      get_var(environ_cp, var_name, query_item, enabled_by_default, question,
+              yes_reply, no_reply))
+
+  write_action_env_to_bazelrc(var_name, var)
+  environ_cp[var_name] = str(var)
+
+
+def check_bazel_version(min_version):
+  """Check installed bezel version is at least min_version.
+
+  Args:
+    min_version: string for minimum bazel version.
+  """
+  try:
+    curr_version = run_shell('bazel version')
+  except subprocess.CalledProcessError:
+    print('Cannot find bazel. Please install bazel.')
+    sys.exit(0)
+
+  for line in curr_version.split('\n'):
+    if 'Build label: ' in line:
+      curr_version = line.split('Build label: ')[1]
+      break
+
+  min_version_segments = min_version.split('.')
+  curr_version_segments = curr_version.split('.')
+
+  # Check if current bazel version can be detected properly.
+  for seg in curr_version_segments:
+    if not seg.isdigit():
+      print('WARNING: current bazel installation is not a release version.')
+      print('Make sure you are running at least bazel %s' % min_version)
+      return
+
+  min_version_str = ''.join(['%03d' % int(seg) for seg in min_version_segments])
+  curr_version_str = ''.join(
+      ['%03d' % int(seg) for seg in curr_version_segments])
+  if int(curr_version_str) < int(min_version_str):
+    print('Please upgrade your bazel installation to version %s or higher to '
+          'build TensorFlow!' % min_version)
+    sys.exit(0)
+
+
+def set_cc_opt_flags(environ_cp):
+  """Set up architecture-dependent optimization flags.
+
+  Also append CC optimization flags to bazel.rc..
+
+  Args:
+    environ_cp: copy of the os.environ.
+  """
+  if is_ppc64le():
+    # gcc on ppc64le does not support -march, use mcpu instead
+    default_cc_opt_flags = '-mcpu=native'
+  else:
+    default_cc_opt_flags = '-march=native'
+  question = ('Please specify optimization flags to use during compilation when'
+              ' bazel option "--config=opt" is specified [Default is %s]: '
+             ) % default_cc_opt_flags
+  cc_opt_flags = get_from_env_or_user_or_default(environ_cp, 'CC_OPT_FLAGS',
+                                                 question, default_cc_opt_flags)
+  for opt in cc_opt_flags.split():
+    write_to_bazelrc('build:opt --cxxopt=%s --copt=%s' % (opt, opt))
+
+
+def set_tf_cuda_clang(environ_cp):
+  """set TF_CUDA_CLANG action_env.
+
+  Args:
+    environ_cp: copy of the os.environ.
+  """
+  question = 'Do you want to use clang as CUDA compiler?'
+  yes_reply = 'Clang will be used as CUDA compiler.'
+  no_reply = 'nvcc will be used as CUDA compiler.'
+  set_action_env_var(
+      environ_cp,
+      'TF_CUDA_CLANG',
+      None,
+      False,
+      question=question,
+      yes_reply=yes_reply,
+      no_reply=no_reply)
+
+
+def get_from_env_or_user_or_default(environ_cp, var_name, ask_for_var,
+                                    var_default):
+  """Get var_name either from env, or user or default.
+
+  If var_name has been set as environment variable, use the preset value, else
+  ask for user input. If no input is provided, the default is used.
+
+  Args:
+    environ_cp: copy of the os.environ.
+    var_name: string for name of environment variable, e.g. "TF_NEED_HDFS".
+    ask_for_var: string for how to ask for user input.
+    var_default: default value string.
+
+  Returns:
+    string value for var_name
+  """
+  var = environ_cp.get(var_name)
+  if not var:
+    var = get_input(ask_for_var)
+  if not var:
+    var = var_default
+  return var
+
+
+def set_clang_cuda_compiler_path(environ_cp):
+  """Set CLANG_CUDA_COMPILER_PATH."""
+  default_clang_path = run_shell('which clang || true')
+  ask_clang_path = ('Please specify which clang should be used as device and '
+                    'host compiler. [Default is %s]: ') % default_clang_path
+
+  while True:
+    clang_cuda_compiler_path = get_from_env_or_user_or_default(
+        environ_cp, 'CLANG_CUDA_COMPILER_PATH', ask_clang_path,
+        default_clang_path)
+    if os.path.exists(clang_cuda_compiler_path):
+      break
+
+    # Reset and retry
+    print('Invalid clang path: %s cannot be found.' % clang_cuda_compiler_path)
+    environ_cp['CLANG_CUDA_COMPILER_PATH'] = ''
+
+  # Set CLANG_CUDA_COMPILER_PATH
+  environ_cp['CLANG_CUDA_COMPILER_PATH'] = clang_cuda_compiler_path
+  write_action_env_to_bazelrc('CLANG_CUDA_COMPILER_PATH',
+                              clang_cuda_compiler_path)
+
+
+def set_gcc_host_compiler_path(environ_cp):
+  """Set GCC_HOST_COMPILER_PATH."""
+  default_gcc_host_compiler_path = run_shell('which gcc || true')
+  cuda_bin_symlink = '%s/bin/gcc' % environ_cp.get('CUDA_TOOLKIT_PATH')
+
+  if os.path.islink(cuda_bin_symlink):
+    # os.readlink is only available in linux
+    default_gcc_host_compiler_path = run_shell('readlink %s' % cuda_bin_symlink)
+
+  ask_gcc_path = (
+      'Please specify which gcc should be used by nvcc as the '
+      'host compiler. [Default is %s]: ') % default_gcc_host_compiler_path
+  while True:
+    gcc_host_compiler_path = get_from_env_or_user_or_default(
+        environ_cp, 'GCC_HOST_COMPILER_PATH', ask_gcc_path,
+        default_gcc_host_compiler_path)
+
+    if os.path.exists(gcc_host_compiler_path):
+      break
+
+    # Reset and retry
+    print('Invalid gcc path. %s cannot be found' % gcc_host_compiler_path)
+    environ_cp['GCC_HOST_COMPILER_PATH'] = ''
+
+  # Set GCC_HOST_COMPILER_PATH
+  environ_cp['GCC_HOST_COMPILER_PATH'] = gcc_host_compiler_path
+  write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path)
+
+
+def set_tf_cuda_version(environ_cp):
+  """Set CUDA_TOOLKIT_PATH and TF_CUDA_VERSION."""
+  ask_cuda_version = (
+      'Please specify the CUDA SDK version you want to use, '
+      'e.g. 7.0. [Leave empty to default to CUDA %s]: ') % _DEFAULT_CUDA_VERSION
+
+  while True:
+    # Configure the Cuda SDK version to use.
+    tf_cuda_version = get_from_env_or_user_or_default(
+        environ_cp, 'TF_CUDA_VERSION', ask_cuda_version, _DEFAULT_CUDA_VERSION)
+
+    # Find out where the CUDA toolkit is installed
+    default_cuda_path = _DEFAULT_CUDA_PATH
+    if is_windows():
+      default_cuda_path = cygpath(
+          environ_cp.get('CUDA_PATH', _DEFAULT_CUDA_PATH_WIN))
+    elif is_linux():
+      # If the default doesn't exist, try an alternative default.
+      if (not os.path.exists(default_cuda_path)
+         ) and os.path.exists(_DEFAULT_CUDA_PATH_LINUX):
+        default_cuda_path = _DEFAULT_CUDA_PATH_LINUX
+    ask_cuda_path = ('Please specify the location where CUDA %s toolkit is'
+                     ' installed. Refer to README.md for more details. '
+                     '[Default is %s]: ') % (tf_cuda_version, default_cuda_path)
+    cuda_toolkit_path = get_from_env_or_user_or_default(
+        environ_cp, 'CUDA_TOOLKIT_PATH', ask_cuda_path, default_cuda_path)
+
+    if is_windows():
+      cuda_rt_lib_path = 'lib/x64/cudart.lib'
+    elif is_linux():
+      cuda_rt_lib_path = 'lib64/libcudart.so.%s' % tf_cuda_version
+    elif is_macos():
+      cuda_rt_lib_path = 'lib/libcudart.%s.dylib' % tf_cuda_version
+
+    cuda_toolkit_path_full = os.path.join(cuda_toolkit_path, cuda_rt_lib_path)
+    if os.path.exists(cuda_toolkit_path_full):
+      break
+
+    # Reset and retry
+    print('Invalid path to CUDA %s toolkit. %s cannot be found' %
+          (tf_cuda_version, cuda_toolkit_path_full))
+    environ_cp['TF_CUDA_VERSION'] = ''
+    environ_cp['CUDA_TOOLKIT_PATH'] = ''
+
+  # Set CUDA_TOOLKIT_PATH and TF_CUDA_VERSION
+  environ_cp['CUDA_TOOLKIT_PATH'] = cuda_toolkit_path
+  write_action_env_to_bazelrc('CUDA_TOOLKIT_PATH', cuda_toolkit_path)
+  environ_cp['TF_CUDA_VERSION'] = tf_cuda_version
+  write_action_env_to_bazelrc('TF_CUDA_VERSION', tf_cuda_version)
+
+
+def set_tf_cunn_version(environ_cp):
+  """Set CUDNN_INSTALL_PATH and TF_CUDNN_VERSION."""
+  ask_cudnn_version = (
+      '"Please specify the cuDNN version you want to use. '
+      '[Leave empty to default to cuDNN %s.0]: ') % _DEFAULT_CUDNN_VERSION
+
+  while True:
+    tf_cudnn_version = get_from_env_or_user_or_default(
+        environ_cp, 'TF_CUDNN_VERSION', ask_cudnn_version,
+        _DEFAULT_CUDNN_VERSION)
+
+    default_cudnn_path = environ_cp.get('CUDA_TOOLKIT_PATH')
+    ask_cudnn_path = (r'Please specify the location where cuDNN %s library is '
+                      'installed. Refer to README.md for more details. [Default'
+                      ' is %s]:') % (tf_cudnn_version, default_cudnn_path)
+    cudnn_install_path = get_from_env_or_user_or_default(
+        environ_cp, 'CUDNN_INSTALL_PATH', ask_cudnn_path, default_cudnn_path)
+
+    # Result returned from "read" will be used unexpanded. That make "~"
+    # unusable. Going through one more level of expansion to handle that.
+    cudnn_install_path = os.path.realpath(
+        os.path.expanduser(cudnn_install_path))
+    if is_windows():
+      cudnn_install_path = cygpath(cudnn_install_path)
+
+    if is_windows():
+      cuda_dnn_lib_path = 'lib/x64/cudnn.lib'
+      cuda_dnn_lib_alt_path = 'lib/x64/cudnn.lib'
+    elif is_linux():
+      cuda_dnn_lib_path = 'lib64/libcudnn.so.%s' % tf_cudnn_version
+      cuda_dnn_lib_alt_path = 'libcudnn.so.%s' % tf_cudnn_version
+    elif is_macos():
+      cuda_dnn_lib_path = 'lib/libcudnn.%s.dylib' % tf_cudnn_version
+      cuda_dnn_lib_alt_path = 'libcudnn.%s.dylib' % tf_cudnn_version
+
+    cuda_dnn_lib_path_full = os.path.join(cudnn_install_path, cuda_dnn_lib_path)
+    cuda_dnn_lib_alt_path_full = os.path.join(cudnn_install_path,
+                                              cuda_dnn_lib_alt_path)
+    if os.path.exists(cuda_dnn_lib_path_full) or os.path.exists(
+        cuda_dnn_lib_alt_path_full):
+      break
+
+    # Try another alternative for Linux
+    if is_linux():
+      if subprocess.call(['which', 'ldconfig']):
+        ldconfig_bin = '/sbin/ldconfig'
+      else:
+        ldconfig_bin = 'ldconfig'
+      cudnn_path_from_ldconfig = run_shell(
+          r'%s -p | sed -n "s/.*libcudnn.so .* => \(.*\)/\\1/p"' % ldconfig_bin)
+      if os.path.exists('%s.%s' % (cudnn_path_from_ldconfig, tf_cudnn_version)):
+        cudnn_install_path = os.path.dirname(cudnn_path_from_ldconfig)
+        break
+
+    # Reset and Retry
+    print(
+        'Invalid path to cuDNN %s toolkit. None of the following files can be '
+        'found:' % tf_cudnn_version)
+    print(cuda_dnn_lib_path_full)
+    print(cuda_dnn_lib_alt_path_full)
+    if is_linux():
+      print('%s.%s' % (cudnn_path_from_ldconfig, tf_cudnn_version))
+
+    environ_cp['TF_CUDNN_VERSION'] = ''
+
+  # Set CUDNN_INSTALL_PATH and TF_CUDNN_VERSION
+  environ_cp['CUDNN_INSTALL_PATH'] = cudnn_install_path
+  write_action_env_to_bazelrc('CUDNN_INSTALL_PATH', cudnn_install_path)
+  environ_cp['TF_CUDNN_VERSION'] = tf_cudnn_version
+  write_action_env_to_bazelrc('TF_CUDNN_VERSION', tf_cudnn_version)
+
+
+def get_native_cuda_compute_capabilities(environ_cp):
+  """Get native cuda compute capabilities.
+
+  Args:
+    environ_cp: copy of the os.environ.
+  Returns:
+    string of native cuda compute capabilities, separated by comma.
+  """
+  device_query_bin = os.path.join(
+      environ_cp.get('CUDA_TOOLKIT_PATH'), 'extras/demo_suite/deviceQuery')
+  cmd = (r'"%s" | grep "Capability" | grep -o "[0-9]*\.[0-9]*" | sed '
+         '":a;{N;s/\\n/,/};ba"') % device_query_bin
+  try:
+    output = run_shell(cmd)
+  except subprocess.CalledProcessError:
+    output = ''
+  return output
+
+
+def set_tf_cuda_compute_capabilities(environ_cp):
+  """Set TF_CUDA_COMPUTE_CAPABILITIES."""
+  while True:
+    native_cuda_compute_capabilities = get_native_cuda_compute_capabilities(
+        environ_cp)
+    if not native_cuda_compute_capabilities:
+      default_cuda_compute_capabilities = _DEFAULT_CUDA_COMPUTE_CAPABILITIES
+    else:
+      default_cuda_compute_capabilities = native_cuda_compute_capabilities
+
+    ask_cuda_compute_capabilities = (
+        'Please specify a list of comma-separated '
+        'Cuda compute capabilities you want to '
+        'build with.\nYou can find the compute '
+        'capability of your device at: '
+        'https://developer.nvidia.com/cuda-gpus.\nPlease'
+        ' note that each additional compute '
+        'capability significantly increases your '
+        'build time and binary size. [Default is: %s]' %
+        default_cuda_compute_capabilities)
+    tf_cuda_compute_capabilities = get_from_env_or_user_or_default(
+        environ_cp, 'TF_CUDA_COMPUTE_CAPABILITIES',
+        ask_cuda_compute_capabilities, default_cuda_compute_capabilities)
+    # Check whether all capabilities from the input is valid
+    all_valid = True
+    for compute_capability in tf_cuda_compute_capabilities.split(','):
+      if not re.match('[0-9]+.[0-9]+', compute_capability):
+        print('Invalid compute capability: ' % compute_capability)
+        all_valid = False
+
+    if all_valid:
+      break
+
+    # Reset and Retry
+    environ_cp['TF_CUDA_COMPUTE_CAPABILITIES'] = ''
+
+  # Set TF_CUDA_COMPUTE_CAPABILITIES
+  environ_cp['TF_CUDA_COMPUTE_CAPABILITIES'] = tf_cuda_compute_capabilities
+  write_action_env_to_bazelrc('TF_CUDA_COMPUTE_CAPABILITIES',
+                              tf_cuda_compute_capabilities)
+
+
+def set_other_cuda_vars(environ_cp):
+  """Set other CUDA related variables."""
+  if is_windows():
+    # The following three variables are needed for MSVC toolchain configuration
+    # in Bazel
+    environ_cp['CUDA_PATH'] = environ_cp.get('CUDA_TOOLKIT_PATH')
+    environ_cp['CUDA_COMPUTE_CAPABILITIES'] = environ_cp.get(
+        'TF_CUDA_COMPUTE_CAPABILITIES')
+    environ_cp['NO_WHOLE_ARCHIVE_OPTION'] = 1
+    write_action_env_to_bazelrc('CUDA_PATH', environ_cp.get('CUDA_PATH'))
+    write_action_env_to_bazelrc('CUDA_COMPUTE_CAPABILITIE',
+                                environ_cp.get('CUDA_COMPUTE_CAPABILITIE'))
+    write_action_env_to_bazelrc('NO_WHOLE_ARCHIVE_OPTION',
+                                environ_cp.get('NO_WHOLE_ARCHIVE_OPTION'))
+    write_to_bazelrc('build --config=win-cuda')
+    write_to_bazelrc('test --config=win-cuda')
+  else:
+    # If CUDA is enabled, always use GPU during build and test.
+    if environ_cp.get('TF_CUDA_CLANG') == '1':
+      write_to_bazelrc('build --config=cuda_clang')
+      write_to_bazelrc('test --config=cuda_clang')
+    else:
+      write_to_bazelrc('build --config=cuda')
+      write_to_bazelrc('test --config=cuda')
+
+
+def set_host_cxx_compiler(environ_cp):
+  """Set HOST_CXX_COMPILER."""
+  default_cxx_host_compiler = run_shell('which g++ || true')
+  ask_cxx_host_compiler = (
+      'Please specify which C++ compiler should be used as'
+      ' the host C++ compiler. [Default is %s]: ') % default_cxx_host_compiler
+
+  while True:
+    host_cxx_compiler = get_from_env_or_user_or_default(
+        environ_cp, 'HOST_CXX_COMPILER', ask_cxx_host_compiler,
+        default_cxx_host_compiler)
+    if os.path.exists(host_cxx_compiler):
+      break
+
+    # Reset and retry
+    print('Invalid C++ compiler path. %s cannot be found' % host_cxx_compiler)
+    environ_cp['HOST_CXX_COMPILER'] = ''
+
+  # Set HOST_CXX_COMPILER
+  environ_cp['HOST_CXX_COMPILER'] = host_cxx_compiler
+  write_action_env_to_bazelrc('HOST_CXX_COMPILER', host_cxx_compiler)
+
+
+def set_host_c_compiler(environ_cp):
+  """Set HOST_C_COMPILER."""
+  default_c_host_compiler = run_shell('which gcc || true')
+  ask_c_host_compiler = (
+      'Please specify which C compiler should be used as the'
+      ' host C compiler. [Default is %s]: ') % default_c_host_compiler
+
+  while True:
+    host_c_compiler = get_from_env_or_user_or_default(
+        environ_cp, 'HOST_C_COMPILER', ask_c_host_compiler,
+        default_c_host_compiler)
+    if os.path.exists(host_c_compiler):
+      break
+
+    # Reset and retry
+    print('Invalid C compiler path. %s cannot be found' % host_c_compiler)
+    environ_cp['HOST_C_COMPILER'] = ''
+
+  # Set HOST_C_COMPILER
+  environ_cp['HOST_C_COMPILER'] = host_c_compiler
+  write_action_env_to_bazelrc('HOST_C_COMPILER', host_c_compiler)
+
+
+def set_computecpp_toolkit_path(environ_cp):
+  """Set COMPUTECPP_TOOLKIT_PATH."""
+  ask_computecpp_toolkit_path = ('Please specify the location where ComputeCpp '
+                                 'for SYCL %s is installed. [Default is %s]: '
+                                ) % (_TF_OPENCL_VERSION,
+                                     _DEFAULT_COMPUTECPP_TOOLKIT_PATH)
+
+  while True:
+    computecpp_toolkit_path = get_from_env_or_user_or_default(
+        environ_cp, 'COMPUTECPP_TOOLKIT_PATH', ask_computecpp_toolkit_path,
+        _DEFAULT_COMPUTECPP_TOOLKIT_PATH)
+    if is_linux():
+      sycl_rt_lib_path = 'lib/libComputeCpp.so'
+    else:
+      sycl_rt_lib_path = ''
+
+    sycl_rt_lib_path_full = os.path.join(computecpp_toolkit_path,
+                                         sycl_rt_lib_path)
+    if os.path.exists(sycl_rt_lib_path_full):
+      break
+
+    print('Invalid SYCL %s library path. %s cannot be found' %
+          (_TF_OPENCL_VERSION, sycl_rt_lib_path_full))
+    environ_cp['COMPUTECPP_TOOLKIT_PATH'] = ''
+
+  # Set COMPUTECPP_TOOLKIT_PATH
+  environ_cp['COMPUTECPP_TOOLKIT_PATH'] = computecpp_toolkit_path
+  write_action_env_to_bazelrc('COMPUTECPP_TOOLKIT_PATH',
+                              computecpp_toolkit_path)
+
+
+def set_mpi_home(environ_cp):
+  """Set MPI_HOME."""
+  cmd = ('dirname $(dirname $(which mpirun)) || dirname $(dirname $(which '
+         'mpiexec))  || true')
+  default_mpi_home = run_shell(cmd)
+  ask_mpi_home = ('Please specify the MPI toolkit folder. [Default is %s]: '
+                 ) % default_mpi_home
+  while True:
+    mpi_home = get_from_env_or_user_or_default(environ_cp, 'MPI_HOME',
+                                               ask_mpi_home, default_mpi_home)
+
+    if os.path.exists(os.path.join(mpi_home, 'include')) and os.path.exists(
+        os.path.join(mpi_home, 'lib')):
+      break
+
+    print('Invalid path to the MPI Toolkit. %s or %s cannot be found' %
+          (os.path.join(mpi_home, 'include'),
+           os.path.exists(os.path.join(mpi_home, 'lib'))))
+    environ_cp['MPI_HOME'] = ''
+
+  # Set MPI_HOME
+  environ_cp['MPI_HOME'] = str(mpi_home)
+
+
+def set_other_mpi_vars(environ_cp):
+  """Set other MPI related variables."""
+  # Link the MPI header files
+  mpi_home = environ_cp.get('MPI_HOME')
+  symlink_force('%s/include/mpi.h' % mpi_home, 'third_party/mpi/mpi.h')
+
+  # Determine if we use OpenMPI or MVAPICH, these require different header files
+  # to be included here to make bazel dependency checker happy
+  if os.path.exists(os.path.join(mpi_home, 'include/mpi_portable_platform.h')):
+    symlink_force(
+        os.path.join(mpi_home, 'include/mpi_portable_platform.h'),
+        'third_party/mpi/mpi_portable_platform.h')
+    # TODO(gunan): avoid editing files in configure
+    sed_in_place('third_party/mpi/mpi.bzl', 'MPI_LIB_IS_OPENMPI=False',
+                 'MPI_LIB_IS_OPENMPI=True')
+  else:
+    # MVAPICH / MPICH
+    symlink_force(
+        os.path.join(mpi_home, 'include/mpio.h'), 'third_party/mpi/mpio.h')
+    symlink_force(
+        os.path.join(mpi_home, 'include/mpicxx.h'), 'third_party/mpi/mpicxx.h')
+    # TODO(gunan): avoid editing files in configure
+    sed_in_place('third_party/mpi/mpi.bzl', 'MPI_LIB_IS_OPENMPI=True',
+                 'MPI_LIB_IS_OPENMPI=False')
+
+  if os.path.exists(os.path.join(mpi_home, 'lib/libmpi.so')):
+    symlink_force(
+        os.path.join(mpi_home, 'lib/libmpi.so'), 'third_party/mpi/libmpi.so')
+  else:
+    raise ValueError('Cannot find the MPI library file in %s/lib' % mpi_home)
+
+
+def set_mkl():
+  write_to_bazelrc('build:mkl --define with_mkl_support=true')
+  write_to_bazelrc('build:mkl --define using_mkl=true')
+  write_to_bazelrc('build:mkl -c opt')
+  write_to_bazelrc('build:mkl --copt="-DEIGEN_USE_VML"')
+  print(
+      'Add "--config=mkl" to your bazel command to build with MKL '
+      'support.\nPlease note that MKL on MacOS or windows is still not '
+      'supported.\nIf you would like to use a local MKL instead of '
+      'downloading, please set the environment variable \"TF_MKL_ROOT\" every '
+      'time before build.')
+
+
+def main():
+  # Make a copy of os.environ to be clear when functions and getting and setting
+  # environment variables.
+  environ_cp = dict(os.environ)
+
+  check_bazel_version('0.4.5')
+
+  reset_tf_configure_bazelrc()
+  cleanup_makefile()
+  setup_python(environ_cp)
+  run_gen_git_source(environ_cp)
+
+  if is_windows():
+    environ_cp['TF_NEED_GCP'] = '0'
+    environ_cp['TF_NEED_HDFS'] = '0'
+    environ_cp['TF_NEED_JEMALLOC'] = '0'
+    environ_cp['TF_NEED_OPENCL'] = '0'
+    environ_cp['TF_CUDA_CLANG'] = '0'
+
+  if is_macos():
+    environ_cp['TF_NEED_JEMALLOC'] = '0'
+
+  set_build_var(environ_cp, 'TF_NEED_JEMALLOC', 'jemalloc as malloc',
+                'with_jemalloc', True)
+  set_build_var(environ_cp, 'TF_NEED_GCP', 'Google Cloud Platform',
+                'with_gcp_support', False)
+  set_build_var(environ_cp, 'TF_NEED_HDFS', 'Hadoop File System',
+                'with_hdfs_support', False)
+  set_build_var(environ_cp, 'TF_ENABLE_XLA', 'XLA JIT', 'with_xla_support',
+                False)
+  set_build_var(environ_cp, 'TF_NEED_VERBS', 'VERBS', 'with_verbs_support',
+                False)
+
+  set_action_env_var(environ_cp, 'TF_NEED_OPENCL', 'OpenCL', False)
+  if environ_cp.get('TF_NEED_OPENCL') == '1':
+    set_host_cxx_compiler(environ_cp)
+    set_host_c_compiler(environ_cp)
+    set_computecpp_toolkit_path(environ_cp)
+
+  set_action_env_var(environ_cp, 'TF_NEED_CUDA', 'CUDA', False)
+  if environ_cp.get('TF_NEED_CUDA') == '1':
+    set_tf_cuda_version(environ_cp)
+    set_tf_cunn_version(environ_cp)
+    set_tf_cuda_compute_capabilities(environ_cp)
+
+    set_tf_cuda_clang(environ_cp)
+    if environ_cp.get('TF_CUDA_CLANG') == '1':
+      # Set up which clang we should use as the cuda / host compiler.
+      set_clang_cuda_compiler_path(environ_cp)
+    else:
+      # Set up which gcc nvcc should use as the host compiler
+      # No need to set this on Windows
+      if not is_windows():
+        set_gcc_host_compiler_path(environ_cp)
+    set_other_cuda_vars(environ_cp)
+
+  set_build_var(environ_cp, 'TF_NEED_MPI', 'MPI', 'with_mpi_support', False)
+  if environ_cp.get('TF_NEED_MPI') == '1':
+    set_mpi_home(environ_cp)
+    set_other_mpi_vars(environ_cp)
+
+  set_cc_opt_flags(environ_cp)
+  set_mkl()
+
+
+if __name__ == '__main__':
+  main()
diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index 3e69134c50..371264ef6c 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -56,21 +56,16 @@ limitations under the License.
 
 // The implementation below is at the top level instead of the
 // brain namespace because we are defining 'extern "C"' functions.
-using tensorflow::error::Code;
-using tensorflow::errors::InvalidArgument;
-using tensorflow::gtl::ArraySlice;
-using tensorflow::strings::StrCat;
 using tensorflow::AllocationDescription;
 using tensorflow::DataType;
 using tensorflow::Graph;
 using tensorflow::GraphDef;
-using tensorflow::mutex_lock;
 using tensorflow::NameRangeMap;
 using tensorflow::NameRangesForNode;
 using tensorflow::NewSession;
 using tensorflow::Node;
-using tensorflow::NodeDef;
 using tensorflow::NodeBuilder;
+using tensorflow::NodeDef;
 using tensorflow::OpDef;
 using tensorflow::OpRegistry;
 using tensorflow::PartialTensorShape;
@@ -83,6 +78,11 @@ using tensorflow::TensorBuffer;
 using tensorflow::TensorId;
 using tensorflow::TensorShape;
 using tensorflow::TensorShapeProto;
+using tensorflow::error::Code;
+using tensorflow::errors::InvalidArgument;
+using tensorflow::gtl::ArraySlice;
+using tensorflow::mutex_lock;
+using tensorflow::strings::StrCat;
 
 extern "C" {
 
@@ -258,24 +258,27 @@ size_t TF_StringEncode(const char* src, size_t src_len, char* dst,
   return sz;
 }
 
-size_t TF_StringDecode(const char* src, size_t src_len, const char** dst,
-                       size_t* dst_len, TF_Status* status) {
+static Status TF_StringDecode_Impl(const char* src, size_t src_len,
+                                   const char** dst, size_t* dst_len) {
   tensorflow::uint64 len64 = 0;
   const char* p = tensorflow::core::GetVarint64Ptr(src, src + src_len, &len64);
   if (p == nullptr) {
-    status->status =
-        InvalidArgument("invalid string encoding or truncated src buffer");
-    return 0;
+    return InvalidArgument("invalid string encoding or truncated src buffer");
   }
   if (len64 > std::numeric_limits<size_t>::max()) {
-    status->status =
-        InvalidArgument("encoded string is ", len64,
-                        "-bytes, which is too large for this architecture");
-    return 0;
+    return InvalidArgument("encoded string is ", len64,
+                           "-bytes, which is too large for this architecture");
   }
   *dst = p;
   *dst_len = static_cast<size_t>(len64);
-  return static_cast<size_t>(p - src) + *dst_len;
+  return Status::OK();
+}
+
+size_t TF_StringDecode(const char* src, size_t src_len, const char** dst,
+                       size_t* dst_len, TF_Status* status) {
+  status->status = TF_StringDecode_Impl(src, src_len, dst, dst_len);
+  if (!status->status.ok()) return 0;
+  return static_cast<size_t>(*dst - src) + *dst_len;
 }
 
 size_t TF_StringEncodedSize(size_t len) {
@@ -391,16 +394,20 @@ void TF_Reset(const TF_SessionOptions* opt, const char** containers,
 
 namespace tensorflow {
 
-// Non-static for testing.
-bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status) {
+Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst) {
+  if (src->dtype != TF_STRING) {
+    *dst = TensorCApi::MakeTensor(src->dtype, src->shape, src->buffer);
+    return Status::OK();
+  }
+  // TF_STRING tensors require copying since Tensor class expects a sequence of
+  // string objects.
   const tensorflow::int64 num_elements = src->shape.num_elements();
   const char* input = reinterpret_cast<const char*>(TF_TensorData(src));
   const size_t src_size = TF_TensorByteSize(src);
   if (static_cast<tensorflow::int64>(src_size / sizeof(tensorflow::uint64)) <
       num_elements) {
-    status->status = InvalidArgument(
+    return InvalidArgument(
         "Malformed TF_STRING tensor; too short to hold number of elements");
-    return false;
   }
   const char* data_start = input + sizeof(tensorflow::uint64) * num_elements;
   const char* limit = input + src_size;
@@ -411,24 +418,30 @@ bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status) {
     tensorflow::uint64 offset =
         reinterpret_cast<const tensorflow::uint64*>(input)[i];
     if (static_cast<ptrdiff_t>(offset) >= (limit - data_start)) {
-      status->status = InvalidArgument("Malformed TF_STRING tensor; element ",
-                                       i, " out of range");
-      return false;
+      return InvalidArgument("Malformed TF_STRING tensor; element ", i,
+                             " out of range");
     }
     size_t len;
     const char* p;
     const char* srcp = data_start + offset;
-    TF_StringDecode(srcp, limit - srcp, &p, &len, status);
-    if (!status->status.ok()) {
-      return false;
-    }
+    Status status = TF_StringDecode_Impl(srcp, limit - srcp, &p, &len);
+    if (!status.ok()) return status;
     dstarray(i).assign(p, len);
   }
-  return true;
+  return Status::OK();
 }
 
 // Non-static for testing.
-TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src) {
+TF_Tensor* TF_TensorFromTensor(const tensorflow::Tensor& src) {
+  if (src.dtype() != DT_STRING) {
+    TensorBuffer* buf = TensorCApi::Buffer(src);
+    buf->Ref();
+    return new TF_Tensor{static_cast<TF_DataType>(src.dtype()), src.shape(),
+                         buf};
+  }
+  // DT_STRING tensors require a copying since TF_Tensor.buffer expects a flatly
+  // encoded sequence of strings.
+
   // Compute bytes needed for encoding.
   size_t size = 0;
   const auto& srcarray = src.flat<tensorflow::string>();
@@ -507,16 +520,8 @@ static bool TF_Run_Inputs(
     TF_Status* status) {
   const int ninputs = input_pairs->size();
   for (int i = 0; i < ninputs; ++i) {
-    TF_Tensor* src = c_inputs[i];
-    if (c_inputs[i]->dtype != TF_STRING) {
-      (*input_pairs)[i].second = tensorflow::TensorCApi::MakeTensor(
-          src->dtype, src->shape, src->buffer);
-    } else if (!tensorflow::TF_Tensor_DecodeStrings(
-                   src, &(*input_pairs)[i].second, status)) {
-      // TF_STRING tensors require copying since Tensor class expects
-      // a sequence of string objects.
-      return false;
-    }
+    status->status = TF_TensorToTensor(c_inputs[i], &(*input_pairs)[i].second);
+    if (!status->status.ok()) return false;
   }
   return true;
 }
@@ -574,15 +579,7 @@ static void TF_Run_Helper(
           static_cast<TF_DataType>(src.dtype()), src.shape());
       continue;
     }
-    if (src.dtype() != tensorflow::DT_STRING) {
-      // Share the underlying buffer.
-      TensorBuffer* buf = tensorflow::TensorCApi::Buffer(src);
-      buf->Ref();
-      c_outputs[i] = new TF_Tensor{static_cast<TF_DataType>(src.dtype()),
-                                   src.shape(), buf};
-    } else {
-      c_outputs[i] = tensorflow::TF_Tensor_EncodeStrings(src);
-    }
+    c_outputs[i] = TF_TensorFromTensor(src);
   }
 }
 
@@ -1062,20 +1059,9 @@ void TF_SetAttrTensorShapeProtoList(TF_OperationDescription* desc,
 
 void TF_SetAttrTensor(TF_OperationDescription* desc, const char* attr_name,
                       TF_Tensor* value, TF_Status* status) {
-  status->status = Status::OK();
   Tensor t;
-  bool ok = true;
-
-  if (value->dtype != TF_STRING) {
-    t = tensorflow::TensorCApi::MakeTensor(value->dtype, value->shape,
-                                           value->buffer);
-  } else {
-    // TF_STRING tensors require copying since Tensor class expects
-    // a sequence of string objects.
-    ok = tensorflow::TF_Tensor_DecodeStrings(value, &t, status);
-  }
-
-  if (ok) desc->node_builder.Attr(attr_name, t);
+  status->status = TF_TensorToTensor(value, &t);
+  if (status->status.ok()) desc->node_builder.Attr(attr_name, t);
 }
 
 void TF_SetAttrTensorList(TF_OperationDescription* desc, const char* attr_name,
@@ -1084,21 +1070,14 @@ void TF_SetAttrTensorList(TF_OperationDescription* desc, const char* attr_name,
   status->status = Status::OK();
   std::vector<Tensor> t;
   t.reserve(num_values);
-  bool ok = true;
 
-  for (int i = 0; i < num_values && ok; ++i) {
-    if (values[i]->dtype != TF_STRING) {
-      t.emplace_back(tensorflow::TensorCApi::MakeTensor(
-          values[i]->dtype, values[i]->shape, values[i]->buffer));
-    } else {
-      t.emplace_back(::tensorflow::DT_STRING);
-      // TF_STRING tensors require copying since Tensor class expects
-      // a sequence of string objects.
-      ok = tensorflow::TF_Tensor_DecodeStrings(values[i], &t.back(), status);
-    }
+  for (int i = 0; i < num_values && status->status.ok(); ++i) {
+    Tensor v;
+    status->status = TF_TensorToTensor(values[i], &v);
+    t.emplace_back(v);
   }
 
-  if (ok) desc->node_builder.Attr(attr_name, t);
+  if (status->status.ok()) desc->node_builder.Attr(attr_name, t);
 }
 
 void TF_SetAttrValueProto(TF_OperationDescription* desc, const char* attr_name,
@@ -1555,9 +1534,7 @@ void TF_OperationGetAttrTensor(TF_Operation* oper, const char* attr_name,
   Tensor t;
   status->status = tensorflow::GetNodeAttr(oper->node.attrs(), attr_name, &t);
   if (!status->status.ok()) return;
-  *value = new TF_Tensor{static_cast<TF_DataType>(t.dtype()), t.shape(),
-                         tensorflow::TensorCApi::Buffer(t)};
-  (*value)->buffer->Ref();
+  *value = TF_TensorFromTensor(t);
 }
 
 void TF_OperationGetAttrTensorList(TF_Operation* oper, const char* attr_name,
@@ -1568,10 +1545,7 @@ void TF_OperationGetAttrTensorList(TF_Operation* oper, const char* attr_name,
   if (!status->status.ok()) return;
   const auto len = std::min(max_values, static_cast<int>(ts.size()));
   for (int i = 0; i < len; ++i) {
-    const Tensor& t = ts[i];
-    values[i] = new TF_Tensor{static_cast<TF_DataType>(t.dtype()), t.shape(),
-                              tensorflow::TensorCApi::Buffer(t)};
-    values[i]->buffer->Ref();
+    values[i] = TF_TensorFromTensor(ts[i]);
   }
 }
 
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index d6debe3b99..25b6cbd8e7 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -45,9 +45,8 @@ limitations under the License.
 #include "tensorflow/core/util/equal_graph_def.h"
 
 namespace tensorflow {
-
-bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status);
-TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src);
+TF_Tensor* TF_TensorFromTensor(const Tensor& src);
+Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);
 
 namespace {
 
@@ -146,19 +145,16 @@ void TestEncodeDecode(int line, const std::vector<string>& data) {
     for (tensorflow::int64 i = 0; i < src.NumElements(); ++i) {
       src.flat<string>()(i) = data[i];
     }
-    TF_Tensor* dst = TF_Tensor_EncodeStrings(src);
+    TF_Tensor* dst = TF_TensorFromTensor(src);
 
     // Convert back to a C++ Tensor and ensure we get expected output.
-    TF_Status* status = TF_NewStatus();
     Tensor output;
-    ASSERT_TRUE(TF_Tensor_DecodeStrings(dst, &output, status)) << line;
-    ASSERT_EQ(TF_OK, TF_GetCode(status)) << line;
+    ASSERT_EQ(Status::OK(), TF_TensorToTensor(dst, &output)) << line;
     ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
     for (tensorflow::int64 i = 0; i < src.NumElements(); ++i) {
       ASSERT_EQ(data[i], output.flat<string>()(i)) << line;
     }
 
-    TF_DeleteStatus(status);
     TF_DeleteTensor(dst);
   }
 }
@@ -918,7 +914,7 @@ TEST(CAPI, SavedModel) {
   TF_Operation* input_op =
       TF_GraphOperationByName(graph, input_op_name.c_str());
   ASSERT_TRUE(input_op != nullptr);
-  csession.SetInputs({{input_op, TF_Tensor_EncodeStrings(input)}});
+  csession.SetInputs({{input_op, TF_TensorFromTensor(input)}});
 
   const tensorflow::string output_op_name =
       tensorflow::ParseTensorName(output_name).first.ToString();
@@ -1636,6 +1632,39 @@ TEST_F(CApiAttributesTest, Tensor) {
   TF_DeleteTensor(value);
 }
 
+TEST_F(CApiAttributesTest, StringTensor) {
+  // Create the string-Tensor "atttribute" value.
+  char encoded[] = {
+      0,   0, 0, 0, 0, 0, 0, 0,  // array[uint64] offsets
+      1,                         // varint encoded string length
+      'A',
+  };
+  auto deallocator = [](void* data, size_t len, void* arg) {};
+  unique_tensor_ptr t_in(TF_NewTensor(TF_STRING, nullptr, 0, &encoded[0],
+                                      sizeof(encoded), deallocator, nullptr),
+                         TF_DeleteTensor);
+
+  // Create a TF_Operation with the attribute t_in
+  auto desc = init("tensor");
+  TF_SetAttrTensor(desc, "v", t_in.get(), s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+
+  auto oper = TF_FinishOperation(desc, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+
+  // Fetch the attribute back.
+  EXPECT_TF_META("v", -1, TF_ATTR_TENSOR, -1);
+  TF_Tensor* t_out = nullptr;
+  TF_OperationGetAttrTensor(oper, "v", &t_out, s_);
+  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
+  EXPECT_EQ(TF_STRING, TF_TensorType(t_out));
+  EXPECT_EQ(0, TF_NumDims(t_out));
+  ASSERT_EQ(TF_TensorByteSize(t_in.get()), TF_TensorByteSize(t_out));
+  EXPECT_EQ(0, memcmp(TF_TensorData(t_in.get()), TF_TensorData(t_out),
+                      TF_TensorByteSize(t_out)));
+  TF_DeleteTensor(t_out);
+}
+
 TEST_F(CApiAttributesTest, TensorList) {
   const char tensor1[] = {5, 7};
   const int64_t dims1[] = {1, 2};
@@ -1647,7 +1676,8 @@ TEST_F(CApiAttributesTest, TensorList) {
 
   auto desc = init("list(tensor)");
   TF_Tensor* tmp[] = {
-      Int8Tensor(dims1, ndims1, tensor1), Int8Tensor(dims2, ndims2, tensor2),
+      Int8Tensor(dims1, ndims1, tensor1),
+      Int8Tensor(dims2, ndims2, tensor2),
   };
   TF_SetAttrTensorList(desc, "v", tmp, TF_ARRAYSIZE(tmp), s_);
   for (int i = 0; i < TF_ARRAYSIZE(tmp); ++i) {
diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index cec3ebc0ad..66a943410e 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -356,7 +356,7 @@ Status SymbolicGradientBuilder::AddGradients() {
   // Check if any input nodes still have pending gradients and have not been
   // processed yet. This happens if not all outputs of a node are in 'inputs_'.
   std::unordered_map<Node*, int> requested_grads;
-  for (Output nout : inputs_) {
+  for (const Output& nout : inputs_) {
     if (pending_[nout.node()->id()] > 0) {
       DCHECK_GT(nout.node()->num_outputs(), 1);
       int idx = input_nodes_[nout];
@@ -365,7 +365,7 @@ Status SymbolicGradientBuilder::AddGradients() {
       ++requested_grads[nout.node()];
     }
   }
-  for (auto& p : requested_grads) {
+  for (const auto& p : requested_grads) {
     int num_requested_inputs = p.first->num_outputs() - pending_[p.first->id()];
     CHECK_EQ(num_requested_inputs, p.second);
   }
diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
index 7eab7bb28f..77b45aa11e 100644
--- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc
+++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc
@@ -257,6 +257,11 @@ Status MarkForCompilationPass::Run(
                                              &registration)) {
       return false;
     }
+
+    // Don't compile control trigger nodes. We won't preserve their deadness
+    // semantics correctly, so it's safest not to compile them.
+    if (node->IsControlTrigger()) return false;
+
     // If this device requires a JIT, we must say yes.
     if (registration->requires_compilation) return true;
 
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 4f0137e8d9..c693f58f8b 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -353,6 +353,20 @@ tf_xla_py_test(
     ],
 )
 
+tf_xla_py_test(
+    name = "segment_reduction_ops_test",
+    size = "small",
+    srcs = ["segment_reduction_ops_test.py"],
+    deps = [
+        ":xla_test",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:math_ops_gen",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 tf_xla_py_test(
     name = "spacetobatch_op_test",
     size = "medium",
diff --git a/tensorflow/compiler/tests/segment_reduction_ops_test.py b/tensorflow/compiler/tests/segment_reduction_ops_test.py
new file mode 100644
index 0000000000..260a04421b
--- /dev/null
+++ b/tensorflow/compiler/tests/segment_reduction_ops_test.py
@@ -0,0 +1,139 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test cases for segment reduction ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+import numpy as np
+
+from tensorflow.compiler.tests.xla_test import XLATestCase
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import googletest
+
+
+class SegmentReductionOpsTest(XLATestCase):
+  """Test cases for segment reduction ops."""
+
+  def UnsortedSegmentSum(self, data, indices, num_segments):
+    with self.test_session() as sess, self.test_scope():
+      d = array_ops.placeholder(data.dtype, shape=data.shape)
+      if isinstance(indices, int):
+        i = array_ops.placeholder(np.int32, shape=[])
+      else:
+        i = array_ops.placeholder(indices.dtype, shape=indices.shape)
+      return sess.run(
+          math_ops.unsorted_segment_sum(d, i, num_segments),
+          {d: data,
+           i: indices})
+
+  def testUnsortedSegmentSum0DIndices1DData(self):
+    for dtype in self.numeric_types:
+      self.assertAllClose(
+          np.array(
+              [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5],
+               [0, 0, 0, 0, 0, 0]],
+              dtype=dtype),
+          self.UnsortedSegmentSum(
+              np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4))
+
+  def testUnsortedSegmentSum1DIndices1DData(self):
+    for dtype in self.numeric_types:
+      self.assertAllClose(
+          np.array([1, 3, 2, 9], dtype=dtype),
+          self.UnsortedSegmentSum(
+              np.array([0, 1, 2, 3, 4, 5], dtype=dtype),
+              np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4))
+
+  def testUnsortedSegmentSum1DIndices2DDataDisjoint(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
+           [50, 51, 52, 53]],
+          dtype=dtype)
+      indices = np.array([8, 1, 0, 3, 7], dtype=np.int32)
+      num_segments = 10
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[30, 31, 32, 33], [20, 21, 22, 23], [0, 0, 0, 0],
+               [40, 41, 42, 43], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
+               [50, 51, 52, 53], [0, 1, 2, 3], [0, 0, 0, 0]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSum1DIndices2DDataNonDisjoint(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
+           [50, 51, 52, 53]],
+          dtype=dtype)
+      indices = np.array([0, 1, 2, 0, 1], dtype=np.int32)
+      num_segments = 4
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[40, 42, 44, 46], [70, 72, 74, 76], [30, 31, 32, 33],
+               [0, 0, 0, 0]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSum2DIndices3DData(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]],
+           [[200, 201, 202], [210, 211, 212]], [[300, 301, 302],
+                                                [310, 311, 312]]],
+          dtype=dtype)
+      indices = np.array([[3, 5], [3, 1], [5, 0], [6, 2]], dtype=np.int32)
+      num_segments = 8
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[210, 211, 212], [110, 111, 112], [310, 311, 312],
+               [100, 102, 104], [0, 0, 0.], [210, 212, 214], [300, 301,
+                                                              302], [0, 0, 0]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSum1DIndices3DData(self):
+    for dtype in self.numeric_types:
+      data = np.array(
+          [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]],
+           [[200, 201, 202], [210, 211, 212]], [[300, 301, 302],
+                                                [310, 311, 312]]],
+          dtype=dtype)
+      indices = np.array([3, 0, 2, 5], dtype=np.int32)
+      num_segments = 6
+      y = self.UnsortedSegmentSum(data, indices, num_segments)
+      self.assertAllClose(
+          np.array(
+              [[[100, 101, 102.], [110, 111, 112]], [[0, 0, 0], [0, 0, 0]],
+               [[200, 201, 202], [210, 211, 212]], [[0, 1, 2.], [10, 11, 12]],
+               [[0, 0, 0], [0, 0, 0]], [[300, 301, 302], [310, 311, 312]]],
+              dtype=dtype), y)
+
+  def testUnsortedSegmentSumShapeError(self):
+    for dtype in self.numeric_types:
+      data = np.ones((4, 8, 7), dtype=dtype)
+      indices = np.ones((3, 2), dtype=np.int32)
+      num_segments = 4
+      self.assertRaises(ValueError,
+                        functools.partial(self.UnsortedSegmentSum, data,
+                                          indices, num_segments))
+
+
+if __name__ == '__main__':
+  googletest.main()
diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index f277314352..ac039e0162 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -57,11 +57,13 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w2.read(0)
       r1 = w2.read(1)
       r2 = w2.read(2)
+      flow = w2.flow
 
-      d0, d1, d2 = session.run([r0, r1, r2])
+      d0, d1, d2, flow_val = session.run([r0, r1, r2, flow])
       self.assertAllEqual([[4.0, 5.0]], d0)
       self.assertAllEqual([[1.0, 3.0]], d1)
       self.assertAllEqual([[7.0, -8.5]], d2)
+      self.assertAllEqual([], flow_val.shape)
 
   def _testTensorArrayWritePack(self, tf_dtype):
     with self.test_session(), self.test_scope():
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
index faa88ecfe2..1c7a2046aa 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
@@ -323,12 +323,26 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame,
   for (Arg& arg : frame->args) {
     if (!arg.is_loop_invariant) {
       // Follow the edge from the Enter to Merge.
-      if (arg.enter->out_edges().size() != 1) {
+      const Edge* enter_merge = nullptr;
+      for (const Edge* e : arg.enter->out_edges()) {
+        // Ignore control-edges to the sink node. These are allowed by the
+        // graph invariants, although probably they should have been stripped
+        // off earlier.
+        if (e->IsControlEdge() && e->dst()->IsSink()) {
+          continue;
+        }
+        if (enter_merge != nullptr) {
+          return errors::Internal(
+              "Enter node for loop-varying argument ", arg.enter->name(),
+              " has multiple successors: ", enter_merge->dst()->name(), " and ",
+              e->dst()->name());
+        }
+        enter_merge = e;
+      }
+      if (enter_merge == nullptr) {
         return errors::Internal("Enter node for loop-varying argument ",
-                                arg.enter->name(),
-                                " does not have exactly one successor");
+                                arg.enter->name(), " has zero successors");
       }
-      const Edge* enter_merge = *arg.enter->out_edges().begin();
       arg.merge = enter_merge->dst();
       if (!IsMerge(arg.merge)) {
         return errors::InvalidArgument(
diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
index 2fb1cc0454..914c8999a6 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc
@@ -96,6 +96,14 @@ TEST(FunctionalizeControlFlow, OneLoopVar) {
     TF_EXPECT_OK(scope.ToGraph(&graph));
   }
 
+  // Regression test: control edges from an Enter node to the graph sink should
+  // be ignored.
+  for (Node* n : graph.nodes()) {
+    if (n->name() == "while/Enter") {
+      graph.AddControlEdge(n, graph.sink_node());
+    }
+  }
+
   FunctionLibraryDefinition library(OpRegistry::Global(), {});
   TF_ASSERT_OK(FunctionalizeControlFlow(&graph, &library));
 
diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD
index 35bc6b5a24..546e9be864 100644
--- a/tensorflow/compiler/tf2xla/kernels/BUILD
+++ b/tensorflow/compiler/tf2xla/kernels/BUILD
@@ -47,6 +47,7 @@ tf_kernel_library(
         "reshape_op.cc",
         "retval_op.cc",
         "reverse_op.cc",
+        "segment_reduction_ops.cc",
         "select_op.cc",
         "sequence_ops.cc",
         "shape_op.cc",
diff --git a/tensorflow/compiler/tf2xla/kernels/no_op.cc b/tensorflow/compiler/tf2xla/kernels/no_op.cc
index b8f0c0b9fe..8c8a9bbe78 100644
--- a/tensorflow/compiler/tf2xla/kernels/no_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/no_op.cc
@@ -23,4 +23,9 @@ namespace tensorflow {
 // dummy operator using CompilationOnly().
 REGISTER_XLA_OP(Name("NoOp").CompilationOnly(), NoOp);
 
+// We register ControlTrigger as a no-op. This is correct since nodes seen
+// by the XLA compiler are never dead. This may need rethinking when we add
+// support for conditionals to XLA.
+REGISTER_XLA_OP(Name("ControlTrigger"), NoOp);
+
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc
new file mode 100644
index 0000000000..6a0ce775dc
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc
@@ -0,0 +1,155 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <sstream>
+#include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/types.h"
+
+namespace tensorflow {
+namespace {
+
+class UnsortedSegmentSum : public XlaOpKernel {
+ public:
+  explicit UnsortedSegmentSum(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_));
+  }
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    // output = unsorted_segment_sum(data, indices, num_segments)
+    // Compute a tensor such that:
+    //    output[i] = sum over {j where indices[j] == i} of data[j]
+    //    output[i] == 0 if i does not appear in indices
+    //
+    // Contrast with segment_sum(), which assumes indices are sorted and that
+    // max(indices)+1 is the desired size of the output.
+    //
+    // The returned output tensor has the same type as data, and the same shape
+    // as data with the first indices.rank dimensions are replaced
+    // by a single dimension with size num_segments.
+
+    xla::ComputationBuilder* builder = ctx->builder();
+
+    auto data = ctx->Input(0);
+    auto data_shape = ctx->InputShape(0);
+
+    auto indices = ctx->Input(1);
+    auto indices_shape = ctx->InputShape(1);
+
+    OP_REQUIRES(ctx, data_shape.dims() >= indices_shape.dims(),
+                errors::InvalidArgument(
+                    "UnsortedSegmentSum requires that indices' rank be"
+                    " less than or equal to data's rank."));
+    // Validate that indices.shape is a prefix of data.shape.
+    for (int d = 0; d < indices_shape.dims(); ++d) {
+      OP_REQUIRES(ctx, (data_shape.dim_size(d) == indices_shape.dim_size(d)),
+                  errors::InvalidArgument(
+                      "UnsortedSegmentSum requires indices shape to be prefix"
+                      " of data_shape, but dimension ",
+                      d, " differs ", data_shape.dim_size(d), " vs. ",
+                      indices_shape.dim_size(d)));
+    }
+
+    int64 num_segments;
+    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(2, &num_segments));
+
+    // Flatten the indices into 1-D.
+    auto indices_1d = builder->Reshape(indices, {indices_shape.num_elements()});
+
+    // flatten data for dynamic indexing.
+    int64 out_tensor_dims = data_shape.dims() - indices_shape.dims();
+    std::vector<int64> flat_shape(1 + out_tensor_dims);
+    flat_shape[0] = indices_shape.num_elements();
+    for (int64 k = 0; k < out_tensor_dims; ++k) {
+      flat_shape[1 + k] = data_shape.dim_size(indices_shape.dims() + k);
+    }
+    auto data_flat = builder->Reshape(data, flat_shape);
+
+    // output shape; same as data_shape, but dimension 0 is num_segments.
+    std::vector<int64> out_shape(flat_shape);
+    out_shape[0] = num_segments;
+
+    // Pad the output array dims to rank >= 3 to work around lowering issues.
+    // TODO(b/37575001) This is awkward, and could be improved.
+    int64 extra_dims = 0;
+    if (out_shape.size() < 3) {
+      extra_dims = 3u - out_shape.size();
+    }
+    std::vector<int64> rshape(extra_dims + out_shape.size(), 1);
+    for (unsigned k = 0; k < out_shape.size(); ++k) {
+      rshape[extra_dims + k] = out_shape[k];
+    }
+    auto output = builder->Broadcast(XlaHelpers::Zero(builder, dtype_), rshape);
+
+    auto zero = builder->ConstantR1<int32>({0});
+
+    for (int64 i = 0; i < indices_shape.num_elements(); ++i) {
+      // output[indices[i]] += data[i]
+
+      std::vector<int64> data_start_indices(flat_shape.size());
+      data_start_indices[0] = i;
+      for (unsigned d = 1; d < flat_shape.size(); ++d) {
+        data_start_indices[d] = 0;
+      }
+      std::vector<int64> data_limit_indices(flat_shape);
+      data_limit_indices[0] = i + 1;
+      std::vector<int64> stride(flat_shape.size(), 1);
+
+      auto data_slice = builder->Slice(data_flat, data_start_indices,
+                                       data_limit_indices, stride);
+
+      // Reshape the sliced data into the R3+ shape to match output array.
+      std::vector<int64> rdata_shape(extra_dims + flat_shape.size());
+      for (int64 k = 0; k <= extra_dims; ++k) {
+        rdata_shape[k] = 1;
+      }
+      for (unsigned k = 1; k < data_limit_indices.size(); ++k) {
+        rdata_shape[extra_dims + k] = data_limit_indices[k];
+      }
+      auto rdata_slice = builder->Reshape(data_slice, rdata_shape);
+
+      auto index = builder->Slice(indices_1d, {i}, {i + 1}, {1});
+
+      // Construct the index into the R3+ output array 0, ..., <index>, 0, ...
+      std::vector<xla::ComputationDataHandle> out_start_index_parts(
+          extra_dims + flat_shape.size(), zero);
+      out_start_index_parts[extra_dims] = builder->Reshape(index, {1});
+      auto out_start_indices = builder->ConcatInDim(out_start_index_parts, 0);
+
+      std::vector<int64> slice_size(rshape);
+      slice_size[extra_dims] = 1;
+
+      auto out_slice =
+          builder->DynamicSlice(output, out_start_indices, slice_size);
+      auto sumval = builder->Add(out_slice, rdata_slice);
+      output = builder->DynamicUpdateSlice(output, sumval, out_start_indices);
+    }
+    auto reshaped_output = builder->Reshape(output, out_shape);
+    ctx->SetOutput(0, reshaped_output);
+  }
+
+ private:
+  DataType dtype_;
+};
+
+REGISTER_XLA_OP(Name("UnsortedSegmentSum"), UnsortedSegmentSum);
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
index bdd52b7f8e..34cc8b2315 100644
--- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc
@@ -182,7 +182,10 @@ class TensorArrayOp : public XlaOpKernel {
                                dtype_, value, &var));
     var->tensor_array_size = size;
     ctx->SetResourceOutput(0, var);
-    ctx->SetConstantOutput(1, Tensor(DT_FLOAT));
+
+    Tensor flow(DT_FLOAT, TensorShape({}));
+    flow.scalar<float>()() = 0.0f;
+    ctx->SetConstantOutput(1, flow);
   }
 
  private:
@@ -216,6 +219,7 @@ class TensorArrayWriteOp : public XlaOpKernel {
     xla::ComputationDataHandle ta = resource->value;
     xla::ComputationDataHandle index = ctx->Input(1);
     xla::ComputationDataHandle value = ctx->Input(2);
+    xla::ComputationDataHandle flow = ctx->Input(3);
 
     // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
     auto start_indices = XlaHelpers::PadWithZeros(b, index, elem_shape.dims());
@@ -228,7 +232,7 @@ class TensorArrayWriteOp : public XlaOpKernel {
         DynamicAddSlice(b, ta, update, slice_shape.dim_sizes(), start_indices);
 
     resource->value = written;
-    ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
+    ctx->SetOutput(0, flow);
   }
 
  private:
@@ -369,6 +373,7 @@ class TensorArrayScatterOp : public XlaOpKernel {
 
     xla::ComputationDataHandle ta = resource->value;
     const xla::ComputationDataHandle value = ctx->Input(2);
+    const xla::ComputationDataHandle flow = ctx->Input(3);
 
     auto slice_dims = value_shape.dim_sizes();
     slice_dims[0] = 1LL;
@@ -394,7 +399,7 @@ class TensorArrayScatterOp : public XlaOpKernel {
     }
 
     resource->value = ta;
-    ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
+    ctx->SetOutput(0, flow);
   }
 
  private:
@@ -489,6 +494,7 @@ class TensorArraySplitOp : public XlaOpKernel {
                     lengths.size(), " vs. ", resource->tensor_array_size, ")"));
 
     const xla::ComputationDataHandle value = ctx->Input(1);
+    const xla::ComputationDataHandle flow = ctx->Input(3);
 
     OP_REQUIRES(ctx, value_shape.num_elements() == ta_shape.num_elements(),
                 errors::InvalidArgument("mismatched element count ",
@@ -497,7 +503,7 @@ class TensorArraySplitOp : public XlaOpKernel {
 
     resource->value = b->Add(ta, b->Reshape(value, ta_shape.dim_sizes()));
 
-    ctx->SetConstantOutput(0, Tensor(DT_FLOAT));
+    ctx->SetOutput(0, flow);
   }
 
  private:
diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 5eef45b11d..e0a03a78f1 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -132,7 +132,10 @@ cc_library(
 cc_library(
     name = "statusor",
     srcs = ["statusor.cc"],
-    hdrs = ["statusor.h"],
+    hdrs = [
+        "statusor.h",
+        "statusor_internals.h",
+    ],
     visibility = ["//visibility:public"],
     deps = [
         ":status",
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 4c2705b007..bd040c166d 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1943,6 +1943,7 @@ cc_library(
         ":buffer_liveness",
         ":hlo",
         ":hlo_pass",
+        "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/core:lib",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index b351861425..4837402c15 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -1488,9 +1488,9 @@ Status AlgebraicSimplifierVisitor::HandleConvolution(
   // We cannot insert bitcasts if the layouts will not be compatible.
   // TODO(b/33178038): Consider inserting a transpose if a bitcast would be
   // invalid.
-  if (!valid_bitcast_callback_(lhs->shape(), input_shape) ||
-      !valid_bitcast_callback_(rhs->shape(), new_filter_shape) ||
-      !valid_bitcast_callback_(dot_output_shape, convolution_shape)) {
+  if (!valid_bitcast_callback_(input_shape, lhs->shape()) ||
+      !valid_bitcast_callback_(new_filter_shape, rhs->shape()) ||
+      !valid_bitcast_callback_(convolution_shape, dot_output_shape)) {
     return Status::OK();
   }
 
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.h b/tensorflow/compiler/xla/service/algebraic_simplifier.h
index f8919f0caa..4295a3227a 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.h
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.h
@@ -26,12 +26,13 @@ namespace xla {
 // A pass which performs AlgebraicSimplications.
 class AlgebraicSimplifier : public HloPassInterface {
  public:
-  // Given two shapes, determines if it is valid to bitcast between them after
-  // considering platform dependent effects on layout like alignment
-  // restrictions.
-  // Precondition: the two shapes have layouts, the same number of
-  // elements and ShapeUtil::ReshapeIsBitcast returns true.
-  using ValidBitcastCallback = std::function<bool(const Shape&, const Shape&)>;
+  // Given shapes 'from_shape' and 'to_shape', determines if it is valid to
+  // bitcast from 'from_shape' to 'to_shape' after considering platform
+  // dependent effects on layout like alignment restrictions. Precondition: the
+  // two shapes have layouts, the same number of elements and
+  // ShapeUtil::ReshapeIsBitcast returns true.
+  using ValidBitcastCallback =
+      std::function<bool(const Shape& from_shape, const Shape& to_shape)>;
 
   // If is_layout_sensitive is true, then the simplifier preserves layout during
   // transformation. Otherwise, layout is ignored. If valid_bitcast_callback
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 7248cb5f4c..2ca4af67cd 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -72,6 +72,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo_subcomputation_unification",
         "//tensorflow/compiler/xla/service:hlo_verifier",
         "//tensorflow/compiler/xla/service:inliner",
+        "//tensorflow/compiler/xla/service:reduce_precision_insertion",
         "//tensorflow/compiler/xla/service:reshape_mover",
         "//tensorflow/compiler/xla/service:transpose_folding",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",  # fixdeps: keep
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 6d819355c4..b86342d0b3 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -74,6 +74,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_verifier.h"
 #include "tensorflow/compiler/xla/service/inliner.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 #include "tensorflow/compiler/xla/service/reshape_mover.h"
 #include "tensorflow/compiler/xla/service/transpose_folding.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -253,6 +254,14 @@ Status CpuCompiler::RunHloPasses(HloModule* module) {
   HloPassPipeline pipeline("CPU");
   pipeline.AddInvariantChecker<HloVerifier>();
 
+  for (const auto& reduce_precision_options :
+       module->config().debug_options().hlo_reduce_precision_options()) {
+    if (reduce_precision_options.pass_timing() ==
+        HloReducePrecisionOptions::BEFORE_OP_FUSION) {
+      pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
+    }
+  }
+
   // TODO(b/35786417): Re-enable inliner pass after fixing the bug and deciding
   // where we will take this pass in future.
   // pipeline.AddPass<Inliner>();
@@ -278,6 +287,15 @@ Status CpuCompiler::RunHloPasses(HloModule* module) {
       TransposeFolding::NeverFoldTranspose);
   pipeline.AddPass<HloCSE>(/*is_layout_sensitive=*/false);
   pipeline.AddPass<CpuInstructionFusion>();
+
+  for (const auto& reduce_precision_options :
+       module->config().debug_options().hlo_reduce_precision_options()) {
+    if (reduce_precision_options.pass_timing() ==
+        HloReducePrecisionOptions::AFTER_OP_FUSION) {
+      pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
+    }
+  }
+
   pipeline.AddPass<CpuLayoutAssignment>(
       module->mutable_entry_computation_layout());
   // The LayoutAssignment pass may leave behind kCopy instructions which are
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index fa95e23499..cdd7c8187c 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -432,6 +432,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo_proto_util",
         "//tensorflow/compiler/xla/service:hlo_subcomputation_unification",
         "//tensorflow/compiler/xla/service:hlo_verifier",
+        "//tensorflow/compiler/xla/service:reduce_precision_insertion",
         "//tensorflow/compiler/xla/service:reshape_mover",
         "//tensorflow/compiler/xla/service:transpose_folding",
         "//tensorflow/compiler/xla/service/gpu/llvm_gpu_backend",
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index d60c45a5c3..2acf95084a 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -56,6 +56,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
 #include "tensorflow/compiler/xla/service/hlo_verifier.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 #include "tensorflow/compiler/xla/service/reshape_mover.h"
 #include "tensorflow/compiler/xla/service/transpose_folding.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -123,6 +124,15 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
   {
     HloPassPipeline pipeline("optimization");
     pipeline.AddInvariantChecker<HloVerifier>();
+
+    for (const auto& reduce_precision_options :
+         hlo_module->config().debug_options().hlo_reduce_precision_options()) {
+      if (reduce_precision_options.pass_timing() ==
+          HloReducePrecisionOptions::BEFORE_OP_FUSION) {
+        pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
+      }
+    }
+
     {
       auto& pass =
           pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification");
@@ -149,8 +159,27 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/false);
     fusion.AddPass<GpuInstructionFusion>(/*may_duplicate=*/true);
     fusion.AddPass<FusionMerger>();
-    return fusion.Run(hlo_module).status();
+    TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status());
+
+    HloPassPipeline reduce_pipeline("reduce-precision");
+    for (const auto& reduce_precision_options :
+         hlo_module->config().debug_options().hlo_reduce_precision_options()) {
+      if (reduce_precision_options.pass_timing() ==
+          HloReducePrecisionOptions::AFTER_OP_FUSION) {
+        reduce_pipeline.AddPass<ReducePrecisionInsertion>(
+            reduce_precision_options);
+      }
+    }
+    StatusOr<bool> reduce_result = reduce_pipeline.Run(hlo_module);
+    TF_RETURN_IF_ERROR(reduce_result.status());
+
+    if (reduce_result.ValueOrDie()) {
+      // Do another fusion pass, with the expectation that we may be able to
+      // fuse the new ReducePrecision operations.
+      TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status());
+    }
   }
+  return tensorflow::Status::OK();
 }
 
 // Modifies the given HLO module so that it will be accepted by IrEmitter.
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index acd26c4e31..c6202548f1 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -48,37 +48,37 @@ using ::tensorflow::Env;
 using ::tensorflow::gtl::nullopt;
 using ::tensorflow::gtl::optional;
 using ::tensorflow::io::JoinPath;
-using ::tensorflow::strings::Appendf;
-using ::tensorflow::strings::Printf;
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 using ::tensorflow::str_util::Join;
+using ::tensorflow::str_util::StringReplace;
 using ::tensorflow::WriteStringToFile;
 
 namespace xla {
 namespace hlo_graph_dumper {
 namespace {
 
-// Node color schemes, used by NodeColorAttributes.
-enum ColorScheme {
-  kBlue,
-  kBrown,
-  kDarkBlue,
-  kDarkGreen,
-  kDarkRed,
-  kGray,
-  kGreen,
-  kOrange,
-  kPurple,
-  kRed,
-  kWhite,
-  kYellow,
-
-  // Causes the node's border to be a dashed line, and its content to be gray
-  // text on a white background, suggesting that this is an "unimportant" node.
-  kDashedBorder,
+// Helpers for Printf and Appendf.
+template <typename T>
+struct PrintfConvert {
+  const T& operator()(const T& t) const { return t; }
+};
+template <>
+struct PrintfConvert<string> {
+  const char* operator()(const string& s) const { return s.c_str(); }
 };
 
+// Like tensorflow::strings::Printf/Appendf, but you don't need to call c_str()
+// on strings.
+template <typename... Ts>
+string Printf(const char* fmt, const Ts&... ts) {
+  return tensorflow::strings::Printf(fmt, PrintfConvert<Ts>()(ts)...);
+}
+template <typename... Ts>
+void Appendf(string* s, const char* fmt, const Ts&... ts) {
+  tensorflow::strings::Appendf(s, fmt, PrintfConvert<Ts>()(ts)...);
+}
+
 // Used to indicate how we should treat a given HLOInstruction in the graph.
 // should we treat it like normal, hide it, and so on?
 enum NodeFilterResult {
@@ -92,6 +92,9 @@ enum NodeFilterResult {
   // Style the node the same as kSomeOperandsOmitted, but also don't connect it
   // to its operands, even if they're present in the graph.
   kOmitNodeOperands,
+  // Same style as kSomeOperandsOmitted, but used to indicate that some of the
+  // node's *users* have been omitted.
+  kSomeUsersOmitted,
 };
 
 // NodeFilter is essentially a map from HloInstruction*s to NodeFilterResult.
@@ -118,11 +121,41 @@ class NodeFilter {
     auto result = filter_(instr);
     return result == kOmitNodeOperands || result == kSomeOperandsOmitted;
   }
+  bool Deemphasized(const HloInstruction* instr) const {
+    auto result = filter_(instr);
+    return result == kOmitNodeOperands || result == kSomeOperandsOmitted ||
+           result == kSomeUsersOmitted;
+  }
+
+  bool ShowFusionSubcomputation(const HloInstruction* instr) const {
+    CHECK_EQ(instr->opcode(), HloOpcode::kFusion);
+    return Show(instr) && !SomeOrAllOperandsOmitted(instr);
+  }
 
  private:
   std::function<NodeFilterResult(const HloInstruction* instr)> filter_;
 };
 
+// Node color schemes, used by NodeColorAttributes.
+enum ColorScheme {
+  kBlue,
+  kBrown,
+  kDarkBlue,
+  kDarkGreen,
+  kDarkRed,
+  kGray,
+  kGreen,
+  kOrange,
+  kPurple,
+  kRed,
+  kWhite,
+  kYellow,
+
+  // Causes the node's border to be a dashed line, and its content to be gray
+  // text on a white background, suggesting that this is an "unimportant" node.
+  kDashedBorder,
+};
+
 // Given a ColorScheme, returns an attribute string for a node of that color.
 // Sets the node's style and fill/stroke/text colors.
 //
@@ -170,19 +203,8 @@ string NodeColorAttributes(ColorScheme color) {
 // Replaces <> with &lt;&gt;, so that this string is safe(er) for use in a
 // graphviz HTML-like string.
 string HtmlLikeStringSanitize(tensorflow::StringPiece s) {
-  return tensorflow::str_util::StringReplace(
-      tensorflow::str_util::StringReplace(s, "<", "&lt;", /*replace_all=*/true),
-      ">", "&gt;", /*replace_all=*/true);
-}
-
-// Returns the dot graph identifier for the given instruction.
-string InstructionId(const HloInstruction* instruction) {
-  return Printf("%lld", reinterpret_cast<uint64>(instruction));
-}
-
-// Returns the dot graph identifier for the given computation.
-string ComputationId(const HloComputation* computation) {
-  return Printf("%lld", reinterpret_cast<uint64>(computation));
+  return StringReplace(StringReplace(s, "<", "&lt;", /*replace_all=*/true), ">",
+                       "&gt;", /*replace_all=*/true);
 }
 
 // Tries to generates a human-readable one-word description of the given
@@ -194,9 +216,15 @@ string ComputationId(const HloComputation* computation) {
 //   "return param0 * param1;"      --> "multiply"
 //   "return min(param0, param1);"  --> "min"
 //   "return max(param0, param1);"  --> "max"
+//   "return param0 <= param1;"     --> "less-or-equal"
+//   "return param0 >= param1;"     --> "greater-or-equal"
+//   "return param0 >  param1;"     --> "greater-than"
+//   "return param0 <  param1;"     --> "less-than"
+//   "return param0 == param1;"     --> "equal-to"
+//   "return param0 != param1;"     --> "not-equal-to"
 //
-// where param0 and param1 are effective scalars.  Since all of the ops above
-// are commutative, we also support them with param0 and param1 swapped.
+// where param0 and param1 are effective scalars.  For the ops that are
+// commutative, we also support them with param0 and param1 swapped.
 //
 // This is useful primarily for reduce and map nodes.  These take a
 // subcomputation which is almost always one of the four above, and pattern
@@ -219,6 +247,7 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
       operand1->opcode() != HloOpcode::kParameter) {
     return nullopt;
   }
+
   // Check that the two operands of root are param0 and param1.  All of the
   // opcodes we recognize are commutative, so we're OK with either order.
   auto n0 = operand0->parameter_number();
@@ -227,6 +256,20 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
     return nullopt;
   }
 
+  // If the params are reversed, check that the operation being performed is
+  // commutative.
+  if (n0 == 1) {
+    switch (root->opcode()) {
+      case HloOpcode::kLe:
+      case HloOpcode::kGe:
+      case HloOpcode::kGt:
+      case HloOpcode::kLt:
+        return nullopt;
+      default:
+        break;
+    }
+  }
+
   // Check that the root and params are all effective scalars.
   if (!ShapeUtil::IsEffectiveScalar(root->shape()) ||
       !ShapeUtil::IsEffectiveScalar(operand0->shape()) ||
@@ -244,444 +287,542 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
       return "min";
     case HloOpcode::kMaximum:
       return "max";
+    case HloOpcode::kLe:
+      return "less-or-equal";
+    case HloOpcode::kGe:
+      return "greater-or-equal";
+    case HloOpcode::kGt:
+      return "greater-than";
+    case HloOpcode::kLt:
+      return "less-than";
+    case HloOpcode::kEq:
+      return "equal-to";
+    case HloOpcode::kNe:
+      return "not-equal-to";
     default:
       return nullopt;
   }
 }
 
-// Returns the dot graph edges and nodes for the given instruction sequence.
-// Edges which extend between computations are added to the vector
-// intercomputation_edges. This is necessary because graphviz does not render
-// the graph properly unless these inter-computation edges appear after all
-// subgraph statements.
-string InstructionSequenceGraph(
-    const std::list<std::unique_ptr<HloInstruction>>& instructions,
-    bool show_addresses, bool show_layouts,
-    std::vector<string>* intercomputation_edges,
-    const HloExecutionProfile* hlo_execution_profile,
-    const NodeFilter& filter) {
-  string graph_body;
-
-  for (auto& instruction : instructions) {
-    if (!filter.Show(instruction.get())) {
-      continue;
-    }
+class HloDotDumper {
+ public:
+  HloDotDumper(const HloComputation* computation, tensorflow::StringPiece label,
+               bool show_addresses, bool show_layouts,
+               const HloExecutionProfile* profile, NodeFilter filter)
+      : computation_(computation),
+        label_(label.ToString()),
+        show_addresses_(show_addresses),
+        show_layouts_(show_layouts),
+        profile_(profile),
+        filter_(std::move(filter)) {}
+
+  string Dump();
 
-    // We don't display constants as separate nodes; they're merged into their
-    // users.
-    if (instruction->opcode() == HloOpcode::kConstant) {
+ private:
+  // Returns the dot graph identifier for the given instruction.
+  string InstructionId(const HloInstruction* instruction) {
+    return StrCat(reinterpret_cast<uint64>(instruction));
+  }
+
+  // Returns the dot graph identifier for the given computation.
+  string SubcomputationId(const HloComputation* computation) {
+    return StrCat("cluster_", reinterpret_cast<uint64>(computation));
+  }
+
+  string Header();
+  string Footer();
+
+  // Maps HloComputations we should dump to their parent instruction in the
+  // outer computation.
+  std::unordered_map<const HloComputation*, const HloInstruction*>
+  SubcomputationsToDump();
+
+  string DumpSubcomputation(const HloComputation* subcomp,
+                            const HloInstruction* parent_instr);
+  string DumpComputation(const HloComputation* comp);
+  string DumpInstruction(const HloInstruction* instr);
+  ColorScheme GetInstructionColor(const HloInstruction* instr);
+  string GetInstructionNodeShape(const HloInstruction* instr);
+  string GetInstructionNodeLabel(const HloInstruction* instr);
+  string GetInstructionNodeExtraInfo(const HloInstruction* instr);
+  string GetInstructionNodeInlinedConstants(const HloInstruction* instr);
+  void AddInstructionIncomingEdges(const HloInstruction* instr);
+
+  // If instr has just one computation and it's trivial (e.g. "return param0 +
+  // param1"), returns a string you can put into the node's body that names the
+  // subcomputation, e.g. "Subcomputation: <b>add</b>".
+  string GetInstructionTrivialComputationStr(const HloInstruction* instr);
+
+  const HloComputation* computation_;  // never null
+  const string label_;                 // overall name for the graph
+  const bool show_addresses_;
+  const bool show_layouts_;
+  const HloExecutionProfile* profile_;  // may be null
+  const NodeFilter filter_;
+
+  // Edges to print from Footer().  Edges come at the end because graphviz is
+  // unhappy if an edge from a subcomputation to a node in the outer computation
+  // appears before both the inner computation and the destination node are
+  // defined.
+  std::vector<string> edges_;
+};
+
+string HloDotDumper::Dump() {
+  string g = Header();
+  for (const auto& kv : SubcomputationsToDump()) {
+    const HloComputation* subcomp = kv.first;
+    const HloInstruction* parent = kv.second;
+    StrAppend(&g, DumpSubcomputation(subcomp, parent));
+  }
+  StrAppend(&g, DumpComputation(computation_));
+  StrAppend(&g, Footer());
+  return g;
+}
+
+string HloDotDumper::Header() {
+  // DOT graphs accept a stylesheet as a URI.  So naturally, an inline
+  // stylesheet is a data URI!
+  const char* fmt = R"(digraph G {
+rankdir = TB;
+compound = true;
+label = <<b>%s</b>>;
+labelloc = t;
+stylesheet="
+  data:text/css,
+  @import url(https://fonts.googleapis.com/css?family=Roboto:400,700);
+  svg text {
+    font-family: 'Roboto';
+    font-size: 12px;
+  }
+"
+
+)";
+
+  string graph_label = StrCat(label_, "<br/>", computation_->name());
+  if (profile_ != nullptr) {
+    auto cycles = profile_->total_cycles_executed(*computation_);
+    Appendf(&graph_label, "<br/>total cycles = %lld (%s)", cycles,
+            tensorflow::strings::HumanReadableNum(cycles));
+  }
+  return Printf(fmt, graph_label);
+}
+
+string HloDotDumper::Footer() { return StrCat(Join(edges_, "\n"), "\n}"); }
+
+std::unordered_map<const HloComputation*, const HloInstruction*>
+HloDotDumper::SubcomputationsToDump() {
+  // Dump the subcomputations of each instruction that's shown and doesn't have
+  // its operands omitted.  If an instruction has just one subcomputation and
+  // it's trivial, omit it: We'll display that subcomputation inlined into the
+  // instruction's node when we draw it.
+  std::unordered_map<const HloComputation*, const HloInstruction*> to_dump;
+  for (const auto& instr : computation_->instructions()) {
+    if (!filter_.Show(instr.get()) ||
+        filter_.SomeOrAllOperandsOmitted(instr.get())) {
       continue;
     }
+    if (instr->opcode() == HloOpcode::kFusion) {
+      to_dump[instr->fused_instructions_computation()] = instr.get();
+    }
 
-    ColorScheme color = kYellow;
-    string shape = "box";
-
-    // Build the first line or two of the node, containing its name and opcode
-    // (if the opcode isn't redundant with the name).
-    string name;
-    if (instruction->opcode() == HloOpcode::kParameter) {
-      // If we have a parameter, put the param number in the name.
-      name = StrCat("<b>Parameter ", instruction->parameter_number(),
-                    "</b><br/>", HtmlLikeStringSanitize(instruction->name()));
-    } else if (tensorflow::StringPiece(instruction->name())
-                   .starts_with(
-                       StrCat("%", instruction->ExtendedOpcodeStr()))) {
-      // The HLO instruction name contains usually the opcode, e.g. "%add.42" is
-      // an add instruction.  In this case we render just the name.
-      name = StrCat("<b>", HtmlLikeStringSanitize(instruction->name()), "</b>");
-    } else if (instruction->opcode() == HloOpcode::kFusion &&
-               tensorflow::StringPiece(instruction->name())
-                   .starts_with(
-                       StrCat("%", HloOpcodeString(instruction->opcode())))) {
-      // Fusion nodes are usually named e.g. "%fusion.5".  We render these as
-      // e.g. "%fusion.5<br/>input fusion".
-      name = StrCat("<b>", HtmlLikeStringSanitize(instruction->name()),
-                    "</b><br/>",
-                    HtmlLikeStringSanitize(instruction->ToCategory()));
-    } else {
-      // If the name does not contain the opcode, render both.
-      name = StrCat("<b>",
-                    HtmlLikeStringSanitize(instruction->ExtendedOpcodeStr()),
-                    "</b><br/>", HtmlLikeStringSanitize(instruction->name()));
-    }
-
-    if (HloOpcode::kConvolution == instruction->opcode()) {
-      StrAppend(
-          &name, "<br/>",
-          HtmlLikeStringSanitize(
-              instruction->ConvolutionDimensionNumbersToString()),
-          "<br/>",
-          HtmlLikeStringSanitize(window_util::ToString(instruction->window())));
-    }
-
-    if (!instruction->metadata().op_name().empty()) {
-      StrAppend(&name, "<br/>",
-                HtmlLikeStringSanitize(instruction->metadata().op_name()));
-    }
-    if (!instruction->metadata().source_file().empty() &&
-        instruction->metadata().source_line() != 0) {
-      StrAppend(&name, "<br/>", instruction->metadata().source_file(), ":",
-                instruction->metadata().source_line());
-    }
-
-    // Pick different colors or shapes for instructions which are particularly
-    // expensive (eg, dot) and those which are unusual in some way or unique
-    // (eg, parameter).
-    switch (instruction->opcode()) {
-      // "Normal" instructions. Mostly cheap and elementwise. No call to
-      // embedded computations. In this case, use default color, shape and
-      // label.
-      case HloOpcode::kAbs:
-      case HloOpcode::kAdd:
-      case HloOpcode::kCeil:
-      case HloOpcode::kClamp:
-      case HloOpcode::kConvert:
-      case HloOpcode::kCos:
-      case HloOpcode::kDivide:
-      case HloOpcode::kEq:
-      case HloOpcode::kExp:
-      case HloOpcode::kFloor:
-      case HloOpcode::kGe:
-      case HloOpcode::kGt:
-      case HloOpcode::kIndex:
-      case HloOpcode::kIsFinite:
-      case HloOpcode::kLe:
-      case HloOpcode::kLog:
-      case HloOpcode::kLogicalAnd:
-      case HloOpcode::kLogicalNot:
-      case HloOpcode::kLogicalOr:
-      case HloOpcode::kLt:
-      case HloOpcode::kMaximum:
-      case HloOpcode::kMinimum:
-      case HloOpcode::kMultiply:
-      case HloOpcode::kNe:
-      case HloOpcode::kNegate:
-      case HloOpcode::kPower:
-      case HloOpcode::kRemainder:
-      case HloOpcode::kSelect:
-      case HloOpcode::kSign:
-      case HloOpcode::kSin:
-      case HloOpcode::kSlice:
-      case HloOpcode::kSort:
-      case HloOpcode::kSubtract:
-      case HloOpcode::kTanh:
-        break;
-      case HloOpcode::kRng:
-        StrAppend(&name, "<br/>",
-                  RandomDistribution_Name(instruction->random_distribution()));
-        break;
-      case HloOpcode::kBroadcast:
-      case HloOpcode::kTranspose:
-        StrAppend(&name, "<br/>", "dims={",
-                  Join(instruction->dimensions(), ","), "}");
-        break;
-      case HloOpcode::kBitcast:
-      case HloOpcode::kTuple:
-      case HloOpcode::kTrace:
-        color = kWhite;
-        break;
-      case HloOpcode::kGetTupleElement:
-        color = kWhite;
-        StrAppend(&name, "<br/>index=", instruction->tuple_index());
-        break;
-      case HloOpcode::kConcatenate:
-      case HloOpcode::kCopy:
-      case HloOpcode::kDynamicSlice:
-      case HloOpcode::kDynamicUpdateSlice:
-      case HloOpcode::kPad:
-      case HloOpcode::kReshape:
-      case HloOpcode::kReverse:
-      case HloOpcode::kUpdate:
-        color = kGreen;
-        break;
-      case HloOpcode::kConvolution:
-      case HloOpcode::kDot:
-        color = kDarkBlue;
-        break;
-      case HloOpcode::kParameter:
-        color = kOrange;
-        break;
-      case HloOpcode::kBatchNormTraining:
-        StrAppend(&name, " feature_index=", instruction->feature_index());
-        color = kPurple;
-        break;
-      case HloOpcode::kBatchNormGrad:
-        StrAppend(&name, " feature_index=", instruction->feature_index());
-        color = kPurple;
-        break;
-      case HloOpcode::kReduce:
-        StrAppend(&name, " dims=", Join(instruction->dimensions(), ","));
-        color = kPurple;
-        break;
-      case HloOpcode::kSelectAndScatter:
-      case HloOpcode::kReduceWindow:
-        color = kPurple;
-        break;
-      case HloOpcode::kWhile:
-        shape = "ellipse";
-        color = kDarkGreen;
-        break;
-      case HloOpcode::kMap:
-      case HloOpcode::kFusion:
-        color = kGray;
-        break;
-      case HloOpcode::kSend:
-      case HloOpcode::kRecv:
-      case HloOpcode::kInfeed:
-      case HloOpcode::kOutfeed:
-      case HloOpcode::kCrossReplicaSum:
-        color = kBrown;
-        break;
-      case HloOpcode::kCall:
-        color = kDarkGreen;
-        break;
-      case HloOpcode::kCustomCall:
-        color = kDarkGreen;
-        StrAppend(&name, "<br/>",
-                  "custom_call_target=", instruction->custom_call_target());
-        break;
-      case HloOpcode::kReducePrecision:
-        // Make ReducePrecision ops a bit more visible, since typically they
-        // will be inserted as modifications to an existing graph.
-        color = kRed;
-        break;
-      case HloOpcode::kConstant:
-        LOG(FATAL) << "Constants don't get their own nodes in the graph.";
-    }
-
-    // Create instruction node with appropriate label, shape, and color.
-    // label is interpreted as an HTML-like string, so newlines must be
-    // delimited with <br/>, rather than \n.
-    string label =
-        StrCat(name, "<br/>", ShapeUtil::HumanString(instruction->shape()));
-
-    if (show_addresses) {
-      Appendf(&label, "<br/>[%p]", instruction.get());
-    }
-    if (show_layouts && LayoutUtil::HasLayout(instruction->shape())) {
-      string layout_string;
-      if (ShapeUtil::IsTuple(instruction->shape())) {
-        // For tuples, emit the full shape because the layout of a tuple is not
-        // represented in a single Layout field.
-        layout_string = ShapeUtil::HumanStringWithLayout(instruction->shape());
-      } else {
-        layout_string =
-            Join(instruction->shape().layout().minor_to_major(), ",");
-      }
-      StrAppend(&label, "<br/>layout={", layout_string, "}");
-    }
-    if (hlo_execution_profile != nullptr) {
-      auto hlo_cycles_executed =
-          hlo_execution_profile->GetProfileResult(*instruction);
-      auto total_cycles_executed =
-          hlo_execution_profile->total_cycles_executed(*instruction->parent());
-      if (hlo_cycles_executed > 0 && total_cycles_executed > 0) {
-        Appendf(&label, "<br/>%% of cycles executed=%.2f",
-                (static_cast<double>(hlo_cycles_executed) /
-                 static_cast<double>(total_cycles_executed)) *
-                    100);
+    for (const HloComputation* comp : instr->called_computations()) {
+      if (!MatchTrivialComputation(comp)) {
+        to_dump[comp] = instr.get();
       }
     }
+  }
+  return to_dump;
+}
 
-    // If this node's operands are omitted, style it accordingly.
-    if (filter.SomeOrAllOperandsOmitted(instruction.get())) {
-      color = kDashedBorder;
-    }
+string HloDotDumper::DumpSubcomputation(const HloComputation* subcomp,
+                                        const HloInstruction* parent_instr) {
+  const char* computation_fmt = R"(subgraph %s {
+%s;
+label = <%s>;
+labelloc = t;
+%s
+}  // %s
 
-    // If this node is highlighted, override its formatting.
-    if (filter.Highlight(instruction.get())) {
-      shape = "diamond";
-      color = kDarkRed;
+)";
+
+  string id = SubcomputationId(subcomp);
+
+  string subcomp_label, style;
+  if (parent_instr->opcode() == HloOpcode::kFusion) {
+    subcomp_label = Printf("Fused expression for <b>%s</b><br/>%s",
+                           HtmlLikeStringSanitize(parent_instr->name()),
+                           HtmlLikeStringSanitize(parent_instr->ToCategory()));
+
+    // Subcomputation's fill/stroke color is light/dark red/gray, depending on
+    // whether or not the subcomputation's fusion node is highlighted.
+    bool highlight = filter_.Highlight(parent_instr);
+    const char* fillcolor = highlight ? "#ffcdd2" : "#f5f5f5";
+    const char* strokecolor = highlight ? "#b71c1c" : "#c2c2c2";
+    style = Printf(R"(style="rounded,filled,bold"; fillcolor="%s"; color="%s")",
+                   fillcolor, strokecolor);
+  } else {
+    subcomp_label = Printf("Subcomputation for <b>%s</b><br/>%s",
+                           HtmlLikeStringSanitize(parent_instr->name()),
+                           HtmlLikeStringSanitize(subcomp->name()));
+    style = "style=rounded; color=black;";
+  }
+
+  string comp_body = DumpComputation(subcomp);
+  string computation =
+      Printf(computation_fmt, id, style, subcomp_label, comp_body, id);
+
+  // Add an edge from the subcomputation to its parent node.  If subcomp
+  // belongs to a fusion node, it's drawn in place of the fusion instruction, so
+  // there's no need to link those.
+  if (parent_instr->opcode() != HloOpcode::kFusion) {
+    const char* edge_fmt = R"(%s -> %s [ltail="%s", style="dashed"];)";
+    edges_.push_back(
+        Printf(edge_fmt, InstructionId(subcomp->root_instruction()),
+               InstructionId(parent_instr), SubcomputationId(subcomp)));
+  }
+
+  return computation;
+}
+
+string HloDotDumper::DumpComputation(const HloComputation* comp) {
+  string g;
+  for (const auto& instr : comp->instructions()) {
+    if (!filter_.Show(instr.get())) {
+      continue;
     }
+    StrAppend(&g, DumpInstruction(instr.get()));
+  }
+  return g;
+}
 
-    // Create edges from the instruction's operands to the instruction.
-    if (!filter.OmitOperands(instruction.get())) {
-      int64 operand_number = 0;
-      for (auto* operand : instruction->operands()) {
-        if (!filter.Show(operand) ||
-            operand->opcode() == HloOpcode::kConstant) {
-          ++operand_number;
-          continue;
-        }
-        Appendf(&graph_body, "%s -> %s", InstructionId(operand).c_str(),
-                InstructionId(instruction.get()).c_str());
-        if (instruction->operand_count() > 1) {
-          Appendf(&graph_body, " [headlabel=\"%lld\",labeldistance=2]",
-                  operand_number);
-        }
-        StrAppend(&graph_body, ";\n");
-        ++operand_number;
-      }
+string HloDotDumper::DumpInstruction(const HloInstruction* instr) {
+  // We don't display constants as separate nodes; they're merged into their
+  // users.
+  if (instr->opcode() == HloOpcode::kConstant) {
+    return "";
+  }
+  // Omit the fusion node if its subcomputation is drawn, since the
+  // subcomputation will be drawn inline.
+  if (instr->opcode() == HloOpcode::kFusion &&
+      filter_.ShowFusionSubcomputation(instr)) {
+    return "";
+  }
 
-      // Fusion nodes are handled specially because they contain nested
-      // expressions.
-      if (instruction->opcode() == HloOpcode::kFusion) {
-        string cluster_name =
-            StrCat("cluster_", InstructionId(instruction.get()));
-        StrAppend(&graph_body, "subgraph ", cluster_name, " {\n");
-        StrAppend(&graph_body, "label=<fused expression for <b>",
-                  HtmlLikeStringSanitize(instruction->name()),
-                  "</b>>;\nstyle=\"rounded,filled\";\n"
-                  "color=lightgrey;\n");
-        StrAppend(&graph_body,
-                  InstructionSequenceGraph(instruction->fused_instructions(),
-                                           show_addresses, show_layouts,
-                                           intercomputation_edges,
-                                           hlo_execution_profile, NodeFilter()),
-                  "}\n");
-        string fusion_edge = StrCat(
-            InstructionId(instruction->fused_expression_root()), " -> ",
-            InstructionId(instruction.get()),
-            "  [ style = \"dotted\", arrowsize=0.0, ltail=", cluster_name,
-            " ];\n");
-        intercomputation_edges->push_back(fusion_edge);
-      } else {
-        // If instruction has just one computation and it's trivial (e.g.
-        // "return param0 + param1"), put the trivial computation type (e.g.
-        // "add") into instruction's label.  Otherwise, add a dotted edge
-        // between the instruction and its subcomputations.
-        const auto& subcomputations = instruction->called_computations();
-
-        bool trivial_subcomputation = false;
-        if (subcomputations.size() == 1) {
-          optional<string> computation_type =
-              MatchTrivialComputation(subcomputations.front());
-          if (computation_type) {
-            trivial_subcomputation = true;
-            StrAppend(&label, "<br/>Subcomputation: <b>", *computation_type,
-                      "</b>");
-          }
-        }
+  ColorScheme color = GetInstructionColor(instr);
+  string node_shape = GetInstructionNodeShape(instr);
+  string node_label = GetInstructionNodeLabel(instr);
+  string extra_info = GetInstructionNodeExtraInfo(instr);
+  string inlined_constants = GetInstructionNodeInlinedConstants(instr);
+  string trivial_subcomputation = GetInstructionTrivialComputationStr(instr);
+  AddInstructionIncomingEdges(instr);
+
+  // Override the node's styling if it should be (de-)emphasized.
+  if (filter_.Deemphasized(instr)) {
+    color = kDashedBorder;
+  }
+  if (filter_.Highlight(instr)) {
+    node_shape = "diamond";
+    color = kDarkRed;
+  }
 
-        if (!trivial_subcomputation) {
-          for (const HloComputation* computation :
-               instruction->called_computations()) {
-            string cluster_name =
-                StrCat("cluster_", ComputationId(computation));
-            string call_edge = Printf(
-                "%s -> %s [ style=dashed; ltail=%s ];\n",
-                InstructionId(computation->root_instruction()).c_str(),
-                InstructionId(instruction.get()).c_str(), cluster_name.c_str());
-            intercomputation_edges->push_back(call_edge);
-          }
-        }
-      }
+  // Build the text that will be displayed inside the node.
+  string node_body = node_label;
+  for (const string& s :
+       {trivial_subcomputation, extra_info, inlined_constants}) {
+    if (!s.empty()) {
+      StrAppend(&node_body, "<br/>", s);
     }
+  }
 
-    // Inline constant operands into the node.
-    for (int64 i = 0; i < instruction->operand_count(); ++i) {
-      const HloInstruction* operand = instruction->operand(i);
-      if (operand->opcode() != HloOpcode::kConstant) {
-        continue;
-      }
+  return Printf("%s [label=<%s>, shape=%s, %s];\n", InstructionId(instr),
+                node_body, node_shape, NodeColorAttributes(color));
+}
 
-      StrAppend(&label, "<br/><b>operand ", i, "</b> = ");
-      if (ShapeUtil::IsEffectiveScalar(operand->shape())) {
-        auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
-            operand->shape(), /*linear_index=*/0);
-        StrAppend(&label, ShapeUtil::HumanString(operand->shape()), "{",
-                  operand->literal().GetAsString(elem_idx), "}");
-      } else {
-        if (tensorflow::StringPiece(operand->name()).starts_with("%constant")) {
-          StrAppend(&label, operand->name());
-        } else {
-          StrAppend(&label, "constant ", operand->name());
-        }
-      }
+string HloDotDumper::GetInstructionNodeInlinedConstants(
+    const HloInstruction* instr) {
+  auto stringify_constant = [](const HloInstruction* constant) {
+    if (ShapeUtil::IsEffectiveScalar(constant->shape())) {
+      auto elem_idx = IndexUtil::LinearIndexToMultidimensionalIndex(
+          constant->shape(), /*linear_index=*/0);
+      return Printf("%s{%s}", ShapeUtil::HumanString(constant->shape()),
+                    constant->literal().GetAsString(elem_idx));
+    }
+    if (tensorflow::StringPiece(constant->name()).starts_with("%constant")) {
+      return constant->name();
     }
+    return StrCat("constant ", constant->name());
+  };
 
-    Appendf(&graph_body, "%s [label=<%s>, shape=%s, %s];\n",
-            InstructionId(instruction.get()).c_str(), label.c_str(),
-            shape.c_str(), NodeColorAttributes(color).c_str());
+  // Special case: If instr is a parameter to a fusion node, check whether the
+  // corresponding operand to the fusion node is a constant.
+  if (instr->opcode() == HloOpcode::kParameter && instr->IsFused()) {
+    const HloInstruction* fusion = instr->fusion_instruction();
+    const HloInstruction* operand = fusion->operand(instr->parameter_number());
+    if (operand->opcode() != HloOpcode::kConstant) {
+      return "";
+    }
+    return stringify_constant(operand);
   }
-  return graph_body;
+
+  std::vector<string> lines;
+  for (int64 i = 0; i < instr->operand_count(); ++i) {
+    const HloInstruction* operand = instr->operand(i);
+    if (operand->opcode() != HloOpcode::kConstant) {
+      continue;
+    }
+    lines.push_back(
+        Printf("<b>operand %lld</b> = %s", i, stringify_constant(operand)));
+  }
+  return Join(lines, "<br/>");
 }
 
-// DOT graphs accept a stylesheet as a URL.  So naturally, an inline stylesheet
-// is a data URI!
-//
-// We don't perform any escaping on this string, so be careful not to use double
-// quotes inside.
-static const char* dot_stylesheet = R"(
-data:text/css,
-@import url(https://fonts.googleapis.com/css?family=Roboto:400,700);
-svg text {
-  font-family: 'Roboto';
-  font-size: 12px;
+ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) {
+  // Pick different colors or shapes for instructions which are particularly
+  // expensive (eg, dot) and those which are unusual in some way or unique
+  // (eg, parameter).
+  switch (instr->opcode()) {
+    case HloOpcode::kAbs:
+    case HloOpcode::kAdd:
+    case HloOpcode::kCeil:
+    case HloOpcode::kClamp:
+    case HloOpcode::kConvert:
+    case HloOpcode::kCos:
+    case HloOpcode::kDivide:
+    case HloOpcode::kEq:
+    case HloOpcode::kExp:
+    case HloOpcode::kFloor:
+    case HloOpcode::kGe:
+    case HloOpcode::kGt:
+    case HloOpcode::kIndex:
+    case HloOpcode::kIsFinite:
+    case HloOpcode::kLe:
+    case HloOpcode::kLog:
+    case HloOpcode::kLogicalAnd:
+    case HloOpcode::kLogicalNot:
+    case HloOpcode::kLogicalOr:
+    case HloOpcode::kLt:
+    case HloOpcode::kMaximum:
+    case HloOpcode::kMinimum:
+    case HloOpcode::kMultiply:
+    case HloOpcode::kNe:
+    case HloOpcode::kNegate:
+    case HloOpcode::kPower:
+    case HloOpcode::kRemainder:
+    case HloOpcode::kSelect:
+    case HloOpcode::kSign:
+    case HloOpcode::kSin:
+    case HloOpcode::kSlice:
+    case HloOpcode::kSort:
+    case HloOpcode::kSubtract:
+    case HloOpcode::kTanh:
+    case HloOpcode::kRng:
+    case HloOpcode::kBroadcast:
+    case HloOpcode::kTranspose:
+      return kYellow;
+    case HloOpcode::kBitcast:
+    case HloOpcode::kTuple:
+    case HloOpcode::kTrace:
+    case HloOpcode::kGetTupleElement:
+      return kWhite;
+    case HloOpcode::kConcatenate:
+    case HloOpcode::kCopy:
+    case HloOpcode::kDynamicSlice:
+    case HloOpcode::kDynamicUpdateSlice:
+    case HloOpcode::kPad:
+    case HloOpcode::kReshape:
+    case HloOpcode::kReverse:
+    case HloOpcode::kUpdate:
+      return kGreen;
+    case HloOpcode::kConvolution:
+    case HloOpcode::kDot:
+      return kDarkBlue;
+    case HloOpcode::kReducePrecision:
+      return kRed;
+    case HloOpcode::kParameter:
+      return kOrange;
+    case HloOpcode::kBatchNormTraining:
+    case HloOpcode::kBatchNormGrad:
+    case HloOpcode::kReduce:
+    case HloOpcode::kSelectAndScatter:
+    case HloOpcode::kReduceWindow:
+      return kPurple;
+    case HloOpcode::kMap:
+    case HloOpcode::kFusion:
+      return kGray;
+    case HloOpcode::kSend:
+    case HloOpcode::kRecv:
+    case HloOpcode::kInfeed:
+    case HloOpcode::kOutfeed:
+    case HloOpcode::kCrossReplicaSum:
+      return kBrown;
+    case HloOpcode::kCustomCall:
+    case HloOpcode::kWhile:
+    case HloOpcode::kCall:
+      return kDarkGreen;
+    case HloOpcode::kConstant:
+      LOG(FATAL) << "Constants don't get their own nodes in the graph.";
+  }
 }
-)";
 
-string ComputationToDotGraph(const HloComputation& computation,
-                             const string& label, bool show_addresses,
-                             bool show_layouts,
-                             const HloExecutionProfile* hlo_execution_profile,
-                             const NodeFilter& filter) {
-  string graph_label = StrCat(label, "<br/>", computation.name());
-  if (hlo_execution_profile != nullptr) {
-    auto cycles = hlo_execution_profile->total_cycles_executed(computation);
-    Appendf(&graph_label, "<br/>total cycles = %lld (%s)", cycles,
-            tensorflow::strings::HumanReadableNum(cycles).c_str());
-  }
-  string graph = Printf(
-      R"(digraph G {
-rankdir=TB;
-compound=true;
-label=<<b>%s</b>>;
-labelloc=t;
-stylesheet="%s"
-)",
-      graph_label.c_str(), dot_stylesheet);
+string HloDotDumper::GetInstructionNodeShape(const HloInstruction* instr) {
+  // Give while loops a different shape so they're easier to pick out.
+  switch (instr->opcode()) {
+    case HloOpcode::kWhile:
+      return "ellipse";
+    default:
+      return "rect";
+  }
+}
 
-  // Dump the subcomputations of each instruction that's shown and doesn't have
-  // its operands omitted.  If an instruction has just one subcomputation and
-  // it's trivial, omit it: We'll display that subcomputation inlined into the
-  // instruction's node when we draw it.
-  std::unordered_set<const HloComputation*> computations_to_dump;
-  for (const auto& instr : computation.instructions()) {
-    if (!filter.Show(instr.get()) || filter.OmitOperands(instr.get())) {
-      continue;
+string HloDotDumper::GetInstructionNodeLabel(const HloInstruction* instr) {
+  // If we have a parameter, put the param number in the name.
+  if (instr->opcode() == HloOpcode::kParameter) {
+    return Printf("<b>Parameter %lld</b>", instr->parameter_number());
+  }
+
+  // The HLO instruction name contains usually the opcode, e.g. "%add.42" is
+  // an add instruction.  In this case we render just the name.
+  if (tensorflow::StringPiece(instr->name())
+          .starts_with(StrCat("%", HloOpcodeString(instr->opcode())))) {
+    return Printf("<b>%s</b>", HtmlLikeStringSanitize(instr->name()));
+  }
+
+  // If the name does not contain the opcode, render both.
+  return Printf("<b>%s</b><br/>%s",
+                HtmlLikeStringSanitize(instr->ExtendedOpcodeStr()),
+                HtmlLikeStringSanitize(instr->name()));
+}
+
+string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) {
+  string opcode_specific_info = [&]() -> string {
+    switch (instr->opcode()) {
+      case HloOpcode::kRng:
+        return RandomDistribution_Name(instr->random_distribution());
+      case HloOpcode::kConvolution:
+        return StrCat(
+            HtmlLikeStringSanitize(
+                instr->ConvolutionDimensionNumbersToString()),
+            "<br/>",
+            HtmlLikeStringSanitize(window_util::ToString(instr->window())));
+      case HloOpcode::kBroadcast:
+      case HloOpcode::kTranspose:
+      case HloOpcode::kReduce:
+        return Printf("dims={%s}", Join(instr->dimensions(), ","));
+      case HloOpcode::kGetTupleElement:
+        return Printf("index=%lld", instr->tuple_index());
+      case HloOpcode::kBatchNormTraining:
+      case HloOpcode::kBatchNormGrad:
+        return Printf("feature_index=%lld", instr->feature_index());
+      case HloOpcode::kCustomCall:
+        return Printf("custom_call_target=%s", instr->custom_call_target());
+      default:
+        return "";
     }
-    if (instr->opcode() == HloOpcode::kFusion) {
-      computations_to_dump.insert(instr->fused_instructions_computation());
+  }();
+
+  std::vector<string> lines;
+  if (!opcode_specific_info.empty()) {
+    lines.push_back(opcode_specific_info);
+  }
+
+  // Some instructions have giant tuples as their shapes, so truncate the HLO's
+  // shape to kMaxShapeLen characters.
+  constexpr int kMaxShapeLen = 64;
+  string instr_shape = ShapeUtil::HumanString(instr->shape());
+  if (instr_shape.length() > kMaxShapeLen) {
+    instr_shape =
+        StrCat(tensorflow::StringPiece(instr_shape).substr(0, kMaxShapeLen - 3),
+               "...");
+  }
+  lines.push_back(instr_shape);
+
+  if (show_addresses_) {
+    lines.push_back(Printf("[%p]", instr));
+  }
+  if (show_layouts_ && LayoutUtil::HasLayout(instr->shape())) {
+    string layout_str;
+    if (ShapeUtil::IsTuple(instr->shape())) {
+      // For tuples, emit the full shape because the layout of a tuple is not
+      // represented in a single Layout field.
+      layout_str = ShapeUtil::HumanStringWithLayout(instr->shape());
+    } else {
+      layout_str = Join(instr->shape().layout().minor_to_major(), ",");
+    }
+    lines.push_back(Printf("layout={%s}", layout_str));
+  }
+  if (profile_ != nullptr) {
+    double hlo_cycles_executed = profile_->GetProfileResult(*instr);
+    double total_cycles_executed =
+        profile_->total_cycles_executed(*instr->parent());
+    if (hlo_cycles_executed > 0 && total_cycles_executed > 0) {
+      lines.push_back(
+          Printf("%% of cycles executed=%.2f",
+                 100 * hlo_cycles_executed / total_cycles_executed));
     }
+  }
+  return Join(lines, "<br/>");
+}
 
-    const auto& subcomputations = instr->called_computations();
-    if (subcomputations.size() != 1 ||
-        !MatchTrivialComputation(subcomputations.front())) {
-      for (const HloComputation* computation : instr->called_computations()) {
-        computations_to_dump.insert(computation);
-      }
+void HloDotDumper::AddInstructionIncomingEdges(const HloInstruction* instr) {
+  auto add_edge = [&](const HloInstruction* from, const HloInstruction* to,
+                      int64 operand_num) {
+    // Fusion nodes' subcomputations are displayed inline, so if 'from' is a
+    // fusion node and the node's subcomputation is shown, we draw our edge
+    // starting at the fusion node's root instead of at the fusion node itself.
+    if (from->opcode() == HloOpcode::kFusion &&
+        filter_.ShowFusionSubcomputation(from)) {
+      from = from->fused_expression_root();
+    }
+    if (!filter_.Show(from) || from->opcode() == HloOpcode::kConstant) {
+      return;
     }
+    string edge = Printf("%s -> %s", InstructionId(from), InstructionId(to));
+    if (instr->operand_count() > 1) {
+      Appendf(&edge, R"( [headlabel="%lld",labeldistance=2])", operand_num);
+    }
+    StrAppend(&edge, ";");
+    edges_.push_back(edge);
+  };
+
+  // Add edges from instr's operands to instr.  Parameters within fusion
+  // expressions are handled specially -- we draw an edge from the corresponding
+  // operand on the fusion node itself to the parameter.
+  if (instr->opcode() == HloOpcode::kParameter && instr->IsFused()) {
+    const HloInstruction* fusion = instr->fusion_instruction();
+    add_edge(fusion->operand(instr->parameter_number()), instr,
+             /*operand_num=*/0);
+  } else {
+    for (int64 i = 0; i < instr->operand_count(); ++i) {
+      add_edge(instr->operand(i), instr, i);
+    }
+  }
+}
+
+string HloDotDumper::GetInstructionTrivialComputationStr(
+    const HloInstruction* instr) {
+  // called_computations() on a fusion node "inherits" any called computations
+  // of the fused root, which isn't what we want.  Just ignore fusion nodes
+  // here; they're handled separately.
+  if (instr->opcode() == HloOpcode::kFusion) {
+    return "";
   }
 
-  // Emit embedded computations as subgraph clusters.
-  std::vector<string> intercomputation_edges;
-  for (const HloComputation* embedded :
-       computation.MakeEmbeddedComputationsList()) {
-    if (!computations_to_dump.count(embedded)) {
+  std::vector<string> lines;
+  for (int64 i = 0; i < instr->called_computations().size(); ++i) {
+    optional<string> computation_type =
+        MatchTrivialComputation(instr->called_computations()[i]);
+    if (!computation_type) {
       continue;
     }
-    // Don't pass our filter down into the subcomputation -- always render the
-    // whole thing.
-    string graph_body = InstructionSequenceGraph(
-        embedded->instructions(), show_addresses, show_layouts,
-        &intercomputation_edges, hlo_execution_profile, NodeFilter());
-    Appendf(&graph,
-            "subgraph cluster_%s "
-            "{\nstyle=rounded;label=<<b>%s</b>>;labelloc=t;\n%s}\n",
-            ComputationId(embedded).c_str(), embedded->name().c_str(),
-            graph_body.c_str());
-  }
-  StrAppend(&graph,
-            InstructionSequenceGraph(computation.instructions(), show_addresses,
-                                     show_layouts, &intercomputation_edges,
-                                     hlo_execution_profile, filter));
-
-  // Edges between computations (subgraph clusters) must be emitted last for the
-  // graph to be rendered properly for some reason.
-  StrAppend(&graph, Join(intercomputation_edges, "\n"), "}\n");
-
-  return graph;
+    if (instr->called_computations().size() == 1) {
+      lines.push_back(Printf("Subcomputation: <b>%s</b>",
+                             HtmlLikeStringSanitize(*computation_type)));
+    } else {
+      lines.push_back(Printf("Subcomputation %lld: <b>%s</b>", i,
+                             HtmlLikeStringSanitize(*computation_type)));
+    }
+  }
+  return Join(lines, "<br/>");
 }
 
 tensorflow::mutex& RendererMutex() {
@@ -750,14 +891,6 @@ class FileGraphRenderer : public GraphRendererInterface {
 
 // Gets a NodeFilter that includes roughly all instructions whose distance from
 // root is <= radius.
-//
-// It's confusing to draw a node and include only some of its operands.  So if
-// some but not all of a node's operands are <= radius units away from the root,
-// we include the other operands (unless there are a lot of them, as often in a
-// tuple node).  These additional operands may have as inputs other nodes
-// already present in the graph, but we don't draw those edges unless *all* of
-// the inputs are present.  (Otherwise we'd have the same problem we were trying
-// to solve in the first place!)
 NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
   // First, find the neighborhood of nodes with distance from root <= radius.
   // These nodes are our initial set of "normal" nodes.
@@ -788,14 +921,25 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
       }
     }
 
-    // If you're looking at node X, it's probably not interesting that node Y
-    // also happens to use the same constant, so we don't traverse into
-    // constants' users.
-    if (instr->opcode() != HloOpcode::kConstant) {
-      for (const HloInstruction* user : instr->users()) {
-        if (!nodes.count(user)) {
-          worklist.push_back({user, depth + 1});
-        }
+    // Traverse into instr's users, unless:
+    //
+    //  - there are a ton of them, in which case they're probably not
+    //    interesting (and anyway, rendering them all would make the graph
+    //    unreadable), or
+    //  - instr is a constant, in which case its users are probably not
+    //    interesting.
+    if (instr->opcode() == HloOpcode::kConstant) {
+      continue;
+    }
+    constexpr int kMaxUsersToRender = 16;
+    if (instr->user_count() > kMaxUsersToRender) {
+      // If we're going to skip this node's users, style it as such.
+      nodes[instr] = kSomeUsersOmitted;
+      continue;
+    }
+    for (const HloInstruction* user : instr->users()) {
+      if (!nodes.count(user)) {
+        worklist.push_back({user, depth + 1});
       }
     }
   }
@@ -804,43 +948,27 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
     return nodes.count(instr) > 0;
   };
 
-  // If a node has some but not all of its operands omitted, add the operands to
-  // the map with type kOmitNodeOperands.  Unless the node has a lot of
-  // operands, in which case just mark the node as "some operands omitted".
-  std::vector<const HloInstruction*> extra_operands;
+  // Mark nodes which don't have all of their operands present as "some operands
+  // omitted".
   for (auto& kv : nodes) {
     const HloInstruction* instr = kv.first;
     NodeFilterResult& filter_result = kv.second;
     const auto& operands = instr->operands();
 
-    // Mark nodes with many operands and some omitted as "some operands omitted"
-    // and carry on -- don't add their omitted operands to extra_operands.
-    if (operands.size() > 4) {
-      if (std::any_of(operands.begin(), operands.end(), is_displayed) &&
-          !std::all_of(operands.begin(), operands.end(), is_displayed)) {
-        filter_result = kSomeOperandsOmitted;
-      }
-      continue;
-    }
-
-    if (std::any_of(operands.begin(), operands.end(), is_displayed)) {
-      for (const HloInstruction* operand : operands) {
-        if (!is_displayed(operand)) {
-          extra_operands.push_back(operand);
-        }
-      }
+    // Mark nodes with some omitted as "some operands omitted".
+    if (std::any_of(operands.begin(), operands.end(), is_displayed) &&
+        !std::all_of(operands.begin(), operands.end(), is_displayed)) {
+      filter_result = kSomeOperandsOmitted;
     }
   }
-  for (const HloInstruction* instr : extra_operands) {
-    nodes[instr] = kOmitNodeOperands;
-  }
 
-  // Some of the nodes in extra_operands may now have all of their inputs
-  // present in nodes.  We can promote these to normal nodes.
-  for (const HloInstruction* instr : extra_operands) {
-    const auto& operands = instr->operands();
-    if (std::all_of(operands.begin(), operands.end(), is_displayed)) {
-      nodes[instr] = kNormalNode;
+  // Promote nodes with type kSomeUsersOmitted to kNormalNode if all of their
+  // users made it into the graph by other means.
+  for (auto& kv : nodes) {
+    const auto& users = kv.first->users();
+    if (kv.second == kSomeUsersOmitted &&
+        std::all_of(users.begin(), users.end(), is_displayed)) {
+      kv.second = kNormalNode;
     }
   }
 
@@ -862,6 +990,10 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
     if (it != nodes.end()) {
       return it->second;
     }
+    // Show all nodes in subcomputations.
+    if (instr->parent() != root->parent()) {
+      return kNormalNode;
+    }
     return kHideNode;
   });
 }
@@ -886,10 +1018,12 @@ string DumpGraph(const HloComputation& computation, const string& label,
     graph_url = FileGraphRenderer().RenderGraph(
         graph, GraphRendererInterface::TF_GRAPHDEF, debug_options);
   } else {
-    graph = ComputationToDotGraph(computation, label,
-                                  debug_options.xla_hlo_graph_addresses(),
-                                  debug_options.xla_hlo_graph_layout(),
-                                  hlo_execution_profile, NodeFilter());
+    graph =
+        HloDotDumper(&computation, label,
+                     /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
+                     /*show_layouts=*/debug_options.xla_hlo_graph_layout(),
+                     hlo_execution_profile, NodeFilter())
+            .Dump();
     graph_url = GetGraphRenderer()->RenderGraph(
         graph, GraphRendererInterface::DOT_GRAPH, debug_options);
   }
@@ -903,11 +1037,12 @@ string DumpNeighborhoodAround(const HloInstruction& node, int radius) {
   string label =
       StrCat("Neighborhood of ", radius, " nodes around ", node.name());
   NodeFilter filter = MakeNodeFilter(&node, radius);
-  string graph = ComputationToDotGraph(
-      *node.parent(), label,
-      /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
-      /*show_layouts=*/debug_options.xla_hlo_graph_layout(),
-      /*hlo_execution_profile=*/nullptr, filter);
+  string graph =
+      HloDotDumper(node.parent(), label,
+                   /*show_addresses=*/debug_options.xla_hlo_graph_addresses(),
+                   /*show_layouts=*/debug_options.xla_hlo_graph_layout(),
+                   /*profile=*/nullptr, filter)
+          .Dump();
   return GetGraphRenderer()->RenderGraph(
       graph, GraphRendererInterface::DOT_GRAPH, debug_options);
 }
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index 358e611d57..8a6376b2d1 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -112,6 +112,11 @@ bool HloOpcodeIsComparison(HloOpcode opcode);
 // Returns true iff the given opcode has variadic operands.
 bool HloOpcodeIsVariadic(HloOpcode opcode);
 
+// Returns the number of HloOpcode values.
+inline const uint32_t HloOpcodeCount() {
+  return static_cast<uint32_t>(HloOpcode::kWhile) + 1;
+}
+
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_OPCODE_H_
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
index dafefdc491..e083226b14 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
@@ -30,14 +31,15 @@ StatusOr<bool> ReducePrecisionInsertion::Run(HloModule* module) {
     for (auto& instruction : computation->instructions()) {
       VLOG(3) << "Visited instruction: " << instruction->ToString();
 
-      // For now, ReducePrecision is only implemented for F32 data, so this
+      // For now, ReducePrecision is only implemented for F32 arrays, so this
       // ignore instructions that produce other data.  In particular, this
       // currently ignores instructions producing tuples, even if those tuples
-      // contain F32 data inside them.  The assumption is that in most cases
+      // contain F32 arrays inside them.  The assumption is that in most cases
       // equivalent behavior can be obtained by adding ReducePrecision
-      // instructions after the instructions that pull the F32 data out of the
-      // tuples.
+      // instructions after the instructions that pull the F32 arrays out of
+      // the tuples.
       if (instruction->shape().element_type() == PrimitiveType::F32 &&
+          !ShapeUtil::IsScalar(instruction->shape()) &&
           should_reduce_output_precision_(instruction->opcode())) {
         instructions_to_suffix.push_back(instruction.get());
       }
@@ -58,4 +60,33 @@ StatusOr<bool> ReducePrecisionInsertion::Run(HloModule* module) {
   return changed;
 }
 
+ReducePrecisionInsertion::OpcodeFilterFunction
+ReducePrecisionInsertion::make_filter_function(
+    const HloReducePrecisionOptions& reduce_precision_options) {
+  // Implement the filter function with a lookup table.
+  std::vector<bool> filter(HloOpcodeCount(), false);
+  for (const auto& opcode : reduce_precision_options.opcodes_to_suffix()) {
+    filter[opcode] = true;
+  }
+  return [filter](const HloOpcode opcode) {
+    return filter[static_cast<unsigned int>(opcode)];
+  };
+}
+
+HloReducePrecisionOptions ReducePrecisionInsertion::make_options_proto(
+    const HloReducePrecisionOptions::PassTiming pass_timing,
+    const int exponent_bits, const int mantissa_bits,
+    const OpcodeFilterFunction& should_reduce_output_precision) {
+  HloReducePrecisionOptions options;
+  options.set_pass_timing(pass_timing);
+  options.set_exponent_bits(exponent_bits);
+  options.set_mantissa_bits(mantissa_bits);
+  for (uint32_t opcode = 0; opcode < HloOpcodeCount(); opcode++) {
+    if (should_reduce_output_precision(static_cast<HloOpcode>(opcode))) {
+      options.add_opcodes_to_suffix(opcode);
+    }
+  }
+  return options;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.h b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
index e9c8bba031..34b865b9ce 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.h
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
@@ -42,6 +42,17 @@ class ReducePrecisionInsertion : public HloPassInterface {
       : exponent_bits_(exponent_bits),
         mantissa_bits_(mantissa_bits),
         should_reduce_output_precision_(should_reduce_output_precision) {}
+
+  // Version of the constructor that takes an HloReducePrecisionOptions proto
+  // rather than explicitly-enumerated parameters, for convenience when
+  // creating passes based on DebugOptions.
+  explicit ReducePrecisionInsertion(
+      const HloReducePrecisionOptions& reduce_precision_options)
+      : exponent_bits_(reduce_precision_options.exponent_bits()),
+        mantissa_bits_(reduce_precision_options.mantissa_bits()),
+        should_reduce_output_precision_(
+            make_filter_function(reduce_precision_options)) {}
+
   ~ReducePrecisionInsertion() override{};
 
   tensorflow::StringPiece name() const override {
@@ -52,6 +63,15 @@ class ReducePrecisionInsertion : public HloPassInterface {
   // (reduce-precision instructions were inserted).
   StatusOr<bool> Run(HloModule* module) override;
 
+  // Convert between the (inconvenient) xla.proto HloReducePrecisionOptions
+  // representation and OpcodeFilterFunction functions.
+  static OpcodeFilterFunction make_filter_function(
+      const HloReducePrecisionOptions& reduce_precision_options);
+  static HloReducePrecisionOptions make_options_proto(
+      const HloReducePrecisionOptions::PassTiming pass_timing,
+      const int exponent_bits, const int mantissa_bits,
+      const OpcodeFilterFunction& should_reduce_output_precision);
+
  private:
   // Parameters for the precision reduction to be added.
   const int exponent_bits_;
@@ -59,7 +79,7 @@ class ReducePrecisionInsertion : public HloPassInterface {
 
   // Function to determine (from the opcode) whether a given instruction should
   // have a reduce-precision instruction inserted in its output stream.
-  const OpcodeFilterFunction& should_reduce_output_precision_;
+  const OpcodeFilterFunction should_reduce_output_precision_;
 };
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/statusor.cc b/tensorflow/compiler/xla/statusor.cc
index 36f08fc99f..72ab67ff81 100644
--- a/tensorflow/compiler/xla/statusor.cc
+++ b/tensorflow/compiler/xla/statusor.cc
@@ -19,28 +19,20 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 
 namespace xla {
-namespace internal {
+namespace internal_statusor {
 
-Status StatusOrHelper::HandleInvalidStatusCtorArg() {
+void Helper::HandleInvalidStatusCtorArg(Status* status) {
   const char* kMessage =
-      "Status::OK is not a valid constructor argument to StatusOr<T>";
+      "An OK status is not a valid constructor argument to StatusOr<T>";
   LOG(ERROR) << kMessage;
-  // In optimized builds, we will fall back to tensorflow::error::INTERNAL.
-  return Status(tensorflow::error::INTERNAL, kMessage);
+  // Fall back to tensorflow::error::INTERNAL.
+  *status = ::tensorflow::errors::Internal(kMessage);
 }
 
-Status StatusOrHelper::HandleNullObjectCtorArg() {
-  const char* kMessage =
-      "NULL is not a valid constructor argument to StatusOr<T*>";
-  LOG(ERROR) << kMessage;
-  // In optimized builds, we will fall back to tensorflow::error::INTERNAL.
-  return Status(tensorflow::error::INTERNAL, kMessage);
-}
-
-void StatusOrHelper::Crash(const Status& status) {
+void Helper::Crash(const Status& status) {
   LOG(FATAL) << "Attempting to fetch value instead of handling error "
              << status;
 }
 
-}  // namespace internal
+}  // namespace internal_statusor
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/statusor.h b/tensorflow/compiler/xla/statusor.h
index d8cd736238..92bcfa0f44 100644
--- a/tensorflow/compiler/xla/statusor.h
+++ b/tensorflow/compiler/xla/statusor.h
@@ -72,216 +72,233 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_STATUSOR_H_
 
 #include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/compiler/xla/statusor_internals.h"
 #include "tensorflow/core/platform/macros.h"
 
 namespace xla {
 
 #if defined(__clang__)
 // Only clang supports warn_unused_result as a type annotation.
-template <typename T, bool CopyConstructible>
+template <typename T>
 class TF_MUST_USE_RESULT StatusOr;
 #endif
 
-template <typename T,
-          bool CopyConstructible = std::is_copy_constructible<T>::value>
-class StatusOr {
-  template <typename U, bool UC>
+template <typename T>
+class StatusOr : private internal_statusor::StatusOrData<T>,
+                 private internal_statusor::TraitsBase<
+                     std::is_copy_constructible<T>::value,
+                     std::is_move_constructible<T>::value> {
+  template <typename U>
   friend class StatusOr;
 
+  typedef internal_statusor::StatusOrData<T> Base;
+
  public:
   typedef T element_type;
 
-  // Construct a new StatusOr with Status::UNKNOWN status
-  StatusOr();
+  // Constructs a new StatusOr with Status::UNKNOWN status.  This is marked
+  // 'explicit' to try to catch cases like 'return {};', where people think
+  // StatusOr<std::vector<int>> will be initialized with an empty vector,
+  // instead of a Status::UNKNOWN status.
+  explicit StatusOr();
+
+  // StatusOr<T> will be copy constructuble/assignable if T is copy
+  // constructible.
+  StatusOr(const StatusOr&) = default;
+  StatusOr& operator=(const StatusOr&) = default;
+
+  // StatusOr<T> will be move constructuble/assignable if T is move
+  // constructible.
+  StatusOr(StatusOr&&) = default;
+  StatusOr& operator=(StatusOr&&) = default;
+
+  // Conversion copy/move constructor, T must be convertible from U.
+  // TODO(b/62186717): These should not participate in overload resolution if U
+  // is not convertible to T.
+  template <typename U>
+  StatusOr(const StatusOr<U>& other);
+  template <typename U>
+  StatusOr(StatusOr<U>&& other);
 
-  // Construct a new StatusOr with the given non-ok status. After calling
-  // this constructor, calls to ValueOrDie() will CHECK-fail.
-  //
-  // NOTE: Not explicit - we want to use StatusOr<T> as a return
-  // value, so it is convenient and sensible to be able to do 'return
-  // Status()' when the return type is StatusOr<T>.
-  //
-  // REQUIRES: status != Status::OK. This requirement is DCHECKed.
-  // In optimized builds, passing Status::OK here will have the effect
-  // of passing tensorflow::error::INTERNAL as a fallback.
-  StatusOr(Status status);              // NOLINT
+  // Conversion copy/move assignment operator, T must be convertible from U.
+  template <typename U>
+  StatusOr& operator=(const StatusOr<U>& other);
+  template <typename U>
+  StatusOr& operator=(StatusOr<U>&& other);
 
-  // Construct a new StatusOr with the given value. If T is a plain pointer,
-  // value must not be NULL. After calling this constructor, calls to
-  // ValueOrDie() will succeed, and calls to status() will return OK.
+  // Constructs a new StatusOr with the given value. After calling this
+  // constructor, calls to ValueOrDie() will succeed, and calls to status() will
+  // return OK.
   //
   // NOTE: Not explicit - we want to use StatusOr<T> as a return type
   // so it is convenient and sensible to be able to do 'return T()'
   // when the return type is StatusOr<T>.
   //
-  // REQUIRES: if T is a plain pointer, value != NULL. This requirement is
-  // DCHECKed. In optimized builds, passing a NULL pointer here will have
-  // the effect of passing tensorflow::error::INTERNAL as a fallback.
-  StatusOr(const T& value);  // NOLINT
-
-  // Copy constructor.
-  StatusOr(const StatusOr& other) = default;
-
-  // Conversion copy constructor, T must be copy constructible from U
-  template <typename U>
-  StatusOr(const StatusOr<U>& other);
-
-  // Assignment operator.
-  StatusOr& operator=(const StatusOr& other) = default;
+  // REQUIRES: T is copy constructible.
+  StatusOr(const T& value);
 
-  // Conversion assignment operator, T must be assignable from U
-  template <typename U>
-  StatusOr& operator=(const StatusOr<U>& other);
+  // Constructs a new StatusOr with the given non-ok status. After calling
+  // this constructor, calls to ValueOrDie() will CHECK-fail.
+  //
+  // NOTE: Not explicit - we want to use StatusOr<T> as a return
+  // value, so it is convenient and sensible to be able to do 'return
+  // Status()' when the return type is StatusOr<T>.
+  //
+  // REQUIRES: !status.ok(). This requirement is DCHECKed.
+  // In optimized builds, passing Status::OK() here will have the effect
+  // of passing tensorflow::error::INTERNAL as a fallback.
+  StatusOr(const Status& status);
+  StatusOr& operator=(const Status& status);
 
-  // Move constructor and move-assignment operator.
-  StatusOr(StatusOr&& other) = default;
-  StatusOr& operator=(StatusOr&& other) = default;
+  // TODO(b/62186997): Add operator=(T) overloads.
 
-  // Rvalue-reference overloads of the other constructors and assignment
-  // operators, to support move-only types and avoid unnecessary copying.
+  // Similar to the `const T&` overload.
   //
-  // Implementation note: we could avoid all these rvalue-reference overloads
-  // if the existing lvalue-reference overloads took their arguments by value
-  // instead. I think this would also let us omit the conversion assignment
-  // operator altogether, since we'd get the same functionality for free
-  // from the implicit conversion constructor and ordinary assignment.
-  // However, this could result in extra copy operations unless we use
-  // std::move to avoid them, and we can't use std::move because this code
-  // needs to be portable to C++03.
-  StatusOr(T&& value);  // NOLINT
-  template <typename U>
-  StatusOr(StatusOr<U>&& other);
+  // REQUIRES: T is move constructible.
+  StatusOr(T&& value);
 
-  // Returns a reference to our status. If this contains a T, then
-  // returns Status::OK.
-  const Status& status() const { return status_; }
+  // RValue versions of the operations declared above.
+  StatusOr(Status&& status);
+  StatusOr& operator=(Status&& status);
 
   // Returns this->status().ok()
-  bool ok() const { return status_.ok(); }
+  bool ok() const { return this->status_.ok(); }
+
+  // Returns a reference to our status. If this contains a T, then
+  // returns Status::OK().
+  const Status& status() const &;
+  Status status() &&;
 
   // Returns a reference to our current value, or CHECK-fails if !this->ok().
-  const T& ValueOrDie() const;
-  T& ValueOrDie();
+  //
+  // Note: for value types that are cheap to copy, prefer simple code:
+  //
+  //   T value = statusor.ValueOrDie();
+  //
+  // Otherwise, if the value type is expensive to copy, but can be left
+  // in the StatusOr, simply assign to a reference:
+  //
+  //   T& value = statusor.ValueOrDie();  // or `const T&`
+  //
+  // Otherwise, if the value type supports an efficient move, it can be
+  // used as follows:
+  //
+  //   T value = std::move(statusor).ValueOrDie();
+  //
+  // The std::move on statusor instead of on the whole expression enables
+  // warnings about possible uses of the statusor object after the move.
+  // C++ style guide waiver for ref-qualified overloads granted in cl/143176389
+  // See go/ref-qualifiers for more details on such overloads.
+  const T& ValueOrDie() const &;
+  T& ValueOrDie() &;
+  const T&& ValueOrDie() const &&;
+  T&& ValueOrDie() &&;
 
-  // Moves our current value out of this object and returns it, or CHECK-fails
-  // if !this->ok().
-  // Use of this method is discouraged; prefer std::move(statusor.ValueOrDie())
-  // instead.
   T ConsumeValueOrDie() { return std::move(ValueOrDie()); }
 
- private:
-  Status status_;
-  T value_;
-};
-
-// Partial specialization for when T is not copy-constructible. This uses all
-// methods from the core implementation, but removes copy assignment and copy
-// construction.
-template <typename T>
-class StatusOr<T, false> : public StatusOr<T, true> {
- public:
-  // Remove copies.
-  StatusOr(const StatusOr& other) = delete;
-  StatusOr& operator=(const StatusOr& other) = delete;
-  template <typename U>
-  StatusOr(const StatusOr<U>& other) = delete;
-  StatusOr(const T& value) = delete;
-
-  // Use the superclass version for other constructors and operators.
-  StatusOr() = default;
-  StatusOr(StatusOr&& other) = default;
-  StatusOr& operator=(StatusOr&& other) = default;
-  StatusOr(T&& value)  // NOLINT
-      : StatusOr<T, true>::StatusOr(std::move(value)) {}
-  StatusOr(Status status)  // NOLINT
-      : StatusOr<T, true>::StatusOr(std::move(status)) {}
-  template <typename U>
-  StatusOr(StatusOr<U>&& other)  // NOLINT
-      : StatusOr<T, true>::StatusOr(std::move(other)) {}
+  // Ignores any errors. This method does nothing except potentially suppress
+  // complaints from any tools that are checking that errors are not dropped on
+  // the floor.
+  void IgnoreError() const;
 };
 
 ////////////////////////////////////////////////////////////////////////////////
 // Implementation details for StatusOr<T>
 
-namespace internal {
+template <typename T>
+StatusOr<T>::StatusOr() : Base(Status(tensorflow::error::UNKNOWN, "")) {}
 
-class StatusOrHelper {
- public:
-  // Move type-agnostic error handling to the .cc.
-  static Status HandleInvalidStatusCtorArg();
-  static Status HandleNullObjectCtorArg();
-  static void Crash(const Status& status);
-
-  // Customized behavior for StatusOr<T> vs. StatusOr<T*>
-  template <typename T>
-  struct Specialize;
-};
+template <typename T>
+StatusOr<T>::StatusOr(const T& value) : Base(value) {}
 
 template <typename T>
-struct StatusOrHelper::Specialize {
-  // For non-pointer T, a reference can never be NULL.
-  static inline bool IsValueNull(const T& t) { return false; }
-};
+StatusOr<T>::StatusOr(const Status& status) : Base(status) {}
 
 template <typename T>
-struct StatusOrHelper::Specialize<T*> {
-  static inline bool IsValueNull(const T* t) { return t == NULL; }
-};
+StatusOr<T>& StatusOr<T>::operator=(const Status& status) {
+  this->Assign(status);
+  return *this;
+}
 
-}  // namespace internal
+template <typename T>
+StatusOr<T>::StatusOr(T&& value) : Base(std::move(value)) {}
 
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr()
-    : status_(tensorflow::error::UNKNOWN, "") {}
+template <typename T>
+StatusOr<T>::StatusOr(Status&& status) : Base(std::move(status)) {}
 
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr(Status status)
-    : status_(std::move(status)) {
-  if (status_.ok()) {
-    status_ = internal::StatusOrHelper::HandleInvalidStatusCtorArg();
-  }
+template <typename T>
+StatusOr<T>& StatusOr<T>::operator=(Status&& status) {
+  this->Assign(std::move(status));
+  return *this;
 }
 
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr(const T& value)
-    : value_(value) {
-  if (internal::StatusOrHelper::Specialize<T>::IsValueNull(value)) {
-    status_ = internal::StatusOrHelper::HandleNullObjectCtorArg();
-  }
-}
+template <typename T>
+template <typename U>
+inline StatusOr<T>::StatusOr(const StatusOr<U>& other)
+    : Base(static_cast<const typename StatusOr<U>::Base&>(other)) {}
 
-template <typename T, bool CopyConstructible>
+template <typename T>
 template <typename U>
-inline StatusOr<T, CopyConstructible>::StatusOr(const StatusOr<U>& other)
-    : status_(other.status_), value_(other.value_) {}
-
-template <typename T, bool CopyConstructible>
-inline StatusOr<T, CopyConstructible>::StatusOr(T&& value)
-    : value_(std::move(value)) {
-  if (internal::StatusOrHelper::Specialize<T>::IsValueNull(value_)) {
-    status_ = internal::StatusOrHelper::HandleNullObjectCtorArg();
-  }
+inline StatusOr<T>& StatusOr<T>::operator=(const StatusOr<U>& other) {
+  if (other.ok())
+    this->Assign(other.ValueOrDie());
+  else
+    this->Assign(other.status());
+  return *this;
 }
 
-template <typename T, bool CopyConstructible>
+template <typename T>
 template <typename U>
-inline StatusOr<T, CopyConstructible>::StatusOr(StatusOr<U>&& other)
-    : status_(std::move(other.status_)), value_(std::move(other.value_)) {}
+inline StatusOr<T>::StatusOr(StatusOr<U>&& other)
+    : Base(static_cast<typename StatusOr<U>::Base&&>(other)) {}
 
-template <typename T, bool CopyConstructible>
-inline const T& StatusOr<T, CopyConstructible>::ValueOrDie() const {
-  if (!ok()) {
-    internal::StatusOrHelper::Crash(status());
+template <typename T>
+template <typename U>
+inline StatusOr<T>& StatusOr<T>::operator=(StatusOr<U>&& other) {
+  if (other.ok()) {
+    this->Assign(std::move(other).ValueOrDie());
+  } else {
+    this->Assign(std::move(other).status());
   }
-  return value_;
+  return *this;
 }
 
-template <typename T, bool CopyConstructible>
-inline T& StatusOr<T, CopyConstructible>::ValueOrDie() {
-  if (!status_.ok()) {
-    internal::StatusOrHelper::Crash(status());
-  }
-  return value_;
+template <typename T>
+const Status& StatusOr<T>::status() const & {
+  return this->status_;
+}
+template <typename T>
+Status StatusOr<T>::status() && {
+  return ok() ? Status::OK() : std::move(this->status_);
+}
+
+template <typename T>
+const T& StatusOr<T>::ValueOrDie() const & {
+  this->EnsureOk();
+  return this->data_;
+}
+
+template <typename T>
+T& StatusOr<T>::ValueOrDie() & {
+  this->EnsureOk();
+  return this->data_;
+}
+
+template <typename T>
+const T&& StatusOr<T>::ValueOrDie() const && {
+  this->EnsureOk();
+  return std::move(this->data_);
+}
+
+template <typename T>
+T&& StatusOr<T>::ValueOrDie() && {
+  this->EnsureOk();
+  return std::move(this->data_);
+}
+
+template <typename T>
+void StatusOr<T>::IgnoreError() const {
+  // no-op
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/statusor_internals.h b/tensorflow/compiler/xla/statusor_internals.h
new file mode 100644
index 0000000000..a2fda5bb3c
--- /dev/null
+++ b/tensorflow/compiler/xla/statusor_internals.h
@@ -0,0 +1,245 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_
+#define THIRD_PARTY_TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_
+
+#include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+namespace internal_statusor {
+
+class Helper {
+ public:
+  // Move type-agnostic error handling to the .cc.
+  static void HandleInvalidStatusCtorArg(Status*);
+  TF_ATTRIBUTE_NORETURN static void Crash(const Status& status);
+};
+
+// Construct an instance of T in `p` through placement new, passing Args... to
+// the constructor.
+// This abstraction is here mostly for the gcc performance fix.
+template <typename T, typename... Args>
+void PlacementNew(void* p, Args&&... args) {
+#if defined(__GNUC__) && !defined(__clang__)
+  // Teach gcc that 'p' cannot be null, fixing code size issues.
+  if (p == nullptr) __builtin_unreachable();
+#endif
+  new (p) T(std::forward<Args>(args)...);
+}
+
+// Helper base class to hold the data and all operations.
+// We move all this to a base class to allow mixing with the appropriate
+// TraitsBase specialization.
+template <typename T>
+class StatusOrData {
+  template <typename U>
+  friend class StatusOrData;
+
+ public:
+  StatusOrData() = delete;
+
+  StatusOrData(const StatusOrData& other) {
+    if (other.ok()) {
+      MakeValue(other.data_);
+      MakeStatus();
+    } else {
+      MakeStatus(other.status_);
+    }
+  }
+
+  StatusOrData(StatusOrData&& other) noexcept {
+    if (other.ok()) {
+      MakeValue(std::move(other.data_));
+      MakeStatus();
+    } else {
+      MakeStatus(std::move(other.status_));
+    }
+  }
+
+  template <typename U>
+  StatusOrData(const StatusOrData<U>& other) {
+    if (other.ok()) {
+      MakeValue(other.data_);
+      MakeStatus();
+    } else {
+      MakeStatus(other.status_);
+    }
+  }
+
+  template <typename U>
+  StatusOrData(StatusOrData<U>&& other) {
+    if (other.ok()) {
+      MakeValue(std::move(other.data_));
+      MakeStatus();
+    } else {
+      MakeStatus(std::move(other.status_));
+    }
+  }
+
+  explicit StatusOrData(const T& value) : data_(value) { MakeStatus(); }
+  explicit StatusOrData(T&& value) : data_(std::move(value)) { MakeStatus(); }
+
+  explicit StatusOrData(const Status& status) : status_(status) {
+    EnsureNotOk();
+  }
+  explicit StatusOrData(Status&& status) : status_(std::move(status)) {
+    EnsureNotOk();
+  }
+
+  StatusOrData& operator=(const StatusOrData& other) {
+    if (this == &other) return *this;
+    if (other.ok())
+      Assign(other.data_);
+    else
+      Assign(other.status_);
+    return *this;
+  }
+
+  StatusOrData& operator=(StatusOrData&& other) {
+    if (this == &other) return *this;
+    if (other.ok())
+      Assign(std::move(other.data_));
+    else
+      Assign(std::move(other.status_));
+    return *this;
+  }
+
+  ~StatusOrData() {
+    if (ok()) {
+      status_.~Status();
+      data_.~T();
+    } else {
+      status_.~Status();
+    }
+  }
+
+  void Assign(const T& value) {
+    if (ok()) {
+      data_.~T();
+      MakeValue(value);
+    } else {
+      MakeValue(value);
+      status_ = Status::OK();
+    }
+  }
+
+  void Assign(T&& value) {
+    if (ok()) {
+      data_.~T();
+      MakeValue(std::move(value));
+    } else {
+      MakeValue(std::move(value));
+      status_ = Status::OK();
+    }
+  }
+
+  void Assign(const Status& status) {
+    Clear();
+    status_ = status;
+    EnsureNotOk();
+  }
+
+  void Assign(Status&& status) {
+    Clear();
+    status_ = std::move(status);
+    EnsureNotOk();
+  }
+
+  bool ok() const { return status_.ok(); }
+
+ protected:
+  // status_ will always be active after the constructor.
+  // We make it a union to be able to initialize exactly how we need without
+  // waste.
+  // Eg. in the copy constructor we use the default constructor of Status in
+  // the ok() path to avoid an extra Ref call.
+  union {
+    Status status_;
+  };
+
+  // data_ is active iff status_.ok()==true
+  struct Dummy {};
+  union {
+    // When T is const, we need some non-const object we can cast to void* for
+    // the placement new. dummy_ is that object.
+    Dummy dummy_;
+    T data_;
+  };
+
+  void Clear() {
+    if (ok()) data_.~T();
+  }
+
+  void EnsureOk() const {
+    if (!ok()) Helper::Crash(status_);
+  }
+
+  void EnsureNotOk() {
+    if (ok()) Helper::HandleInvalidStatusCtorArg(&status_);
+  }
+
+  // Construct the value (ie. data_) through placement new with the passed
+  // argument.
+  template <typename Arg>
+  void MakeValue(Arg&& arg) {
+    internal_statusor::PlacementNew<T>(&dummy_, std::forward<Arg>(arg));
+  }
+
+  // Construct the status (ie. status_) through placement new with the passed
+  // argument.
+  template <typename... Args>
+  void MakeStatus(Args&&... args) {
+    internal_statusor::PlacementNew<Status>(&status_,
+                                            std::forward<Args>(args)...);
+  }
+};
+
+// Helper base class to allow implicitly deleted constructors and assignment
+// operations in StatusOr.
+// TraitsBase will explicitly delete what it can't support and StatusOr will
+// inherit that behavior implicitly.
+template <bool Copy, bool Move>
+struct TraitsBase {
+  TraitsBase() = default;
+  TraitsBase(const TraitsBase&) = default;
+  TraitsBase(TraitsBase&&) = default;
+  TraitsBase& operator=(const TraitsBase&) = default;
+  TraitsBase& operator=(TraitsBase&&) = default;
+};
+
+template <>
+struct TraitsBase<false, true> {
+  TraitsBase() = default;
+  TraitsBase(const TraitsBase&) = delete;
+  TraitsBase(TraitsBase&&) = default;
+  TraitsBase& operator=(const TraitsBase&) = delete;
+  TraitsBase& operator=(TraitsBase&&) = default;
+};
+
+template <>
+struct TraitsBase<false, false> {
+  TraitsBase() = default;
+  TraitsBase(const TraitsBase&) = delete;
+  TraitsBase(TraitsBase&&) = delete;
+  TraitsBase& operator=(const TraitsBase&) = delete;
+  TraitsBase& operator=(TraitsBase&&) = delete;
+};
+
+}  // namespace internal_statusor
+}  // namespace xla
+
+#endif  // THIRD_PARTY_TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_
diff --git a/tensorflow/compiler/xla/statusor_test.cc b/tensorflow/compiler/xla/statusor_test.cc
index f8555113f8..5fa2211ac6 100644
--- a/tensorflow/compiler/xla/statusor_test.cc
+++ b/tensorflow/compiler/xla/statusor_test.cc
@@ -29,8 +29,6 @@ limitations under the License.
 namespace xla {
 namespace {
 
-using tensorflow::Status;
-
 class Base1 {
  public:
   virtual ~Base1() {}
@@ -59,6 +57,14 @@ class CopyNoAssign {
   const CopyNoAssign& operator=(const CopyNoAssign&);
 };
 
+class NoDefaultConstructor {
+ public:
+  explicit NoDefaultConstructor(int foo);
+};
+
+static_assert(!std::is_default_constructible<NoDefaultConstructor>(),
+              "Should not be default-constructible.");
+
 StatusOr<std::unique_ptr<int>> ReturnUniquePtr() {
   // Uses implicit constructor from T&&
   return std::unique_ptr<int>(new int(0));
@@ -69,6 +75,18 @@ TEST(StatusOr, ElementType) {
   static_assert(std::is_same<StatusOr<char>::element_type, char>(), "");
 }
 
+TEST(StatusOr, TestNoDefaultConstructorInitialization) {
+  // Explicitly initialize it with an error code.
+  StatusOr<NoDefaultConstructor> statusor(tensorflow::errors::Cancelled(""));
+  EXPECT_FALSE(statusor.ok());
+  EXPECT_EQ(statusor.status().code(), tensorflow::error::CANCELLED);
+
+  // Default construction of StatusOr initializes it with an UNKNOWN error code.
+  StatusOr<NoDefaultConstructor> statusor2;
+  EXPECT_FALSE(statusor2.ok());
+  EXPECT_EQ(statusor2.status().code(), tensorflow::error::UNKNOWN);
+}
+
 TEST(StatusOr, TestMoveOnlyInitialization) {
   StatusOr<std::unique_ptr<int>> thing(ReturnUniquePtr());
   ASSERT_TRUE(thing.ok());
diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
index 074e28cec7..d692a81032 100644
--- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc
+++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc
@@ -308,6 +308,137 @@ XLA_TEST_P(BatchNormTest, DISABLED_ON_GPU(RandomizedTests)) {
       ErrorSpec(0.01, 1));
 }
 
+// TODO(b/62764704): Implement on GPU. Disabled on 2017-06-20.
+XLA_TEST_P(BatchNormTest, DISABLED_ON_CPU_PARALLEL(DISABLED_ON_CPU(
+                              DISABLED_ON_GPU(RandomizedGradTests)))) {
+  float epsilon = 0.001;
+  ComputationBuilder builder(client_, TestName());
+  const std::vector<int64>& bounds = GetParam().bounds;
+  Array4D<float> input_array(bounds[0], bounds[1], bounds[2], bounds[3]);
+  input_array.FillRandom(GetParam().random_value_var,
+                         GetParam().random_value_mean);
+
+  Array4D<float> grad_output_array(bounds[0], bounds[1], bounds[2], bounds[3]);
+  grad_output_array.FillRandom(GetParam().random_value_var,
+                               GetParam().random_value_mean);
+
+  const int64 feature_index = GetParam().feature_index;
+  const int64 num_elements_per_feature =
+      Product(bounds) / bounds[feature_index];
+  const int64 feature_bound = bounds[feature_index];
+  std::vector<float> scale(feature_bound, 2);
+
+  auto input_squared =
+      ReferenceUtil::MapArray4D(input_array, [](float a) { return a * a; });
+  std::vector<int64> reduce_dims;
+  for (int64 i = 0; i < bounds.size(); ++i) {
+    if (i != feature_index) {
+      reduce_dims.push_back(i);
+    }
+  }
+
+  auto sum =
+      ReferenceUtil::Reduce4DTo1D(input_array, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  auto sum_squared =
+      ReferenceUtil::Reduce4DTo1D(*input_squared, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  std::vector<float> mean(feature_bound);
+
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean[i] = sum[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> mean_square(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    mean_square[i] = mean[i] * mean[i];
+  }
+
+  std::vector<float> square_mean(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    square_mean[i] = sum_squared[i] / num_elements_per_feature;
+  }
+
+  std::vector<float> var(feature_bound);
+  for (int64 i = 0; i < feature_bound; ++i) {
+    var[i] = square_mean[i] - mean_square[i];
+  }
+
+  Array4D<float> mean_4D =
+      *ReferenceUtil::Broadcast1DTo4D(mean, bounds, feature_index);
+  auto var_4D = *ReferenceUtil::Broadcast1DTo4D(var, bounds, feature_index);
+  auto scale_4D = *ReferenceUtil::Broadcast1DTo4D(scale, bounds, feature_index);
+
+  auto var_add_epsilon = *ReferenceUtil::MapArray4D(
+      var_4D, [epsilon](float a) { return std::sqrt(a + epsilon); });
+
+  auto grad_output_times_var =
+      *ReferenceUtil::MapArray4D(grad_output_array, var_add_epsilon,
+                                 [](float a, float b) { return a * b; });
+
+  auto grad_activation = *ReferenceUtil::MapArray4D(
+      grad_output_times_var, scale_4D, [](float a, float b) { return a * b; });
+
+  auto activation_shifted = *ReferenceUtil::MapArray4D(
+      input_array, mean_4D, [](float a, float b) { return a - b; });
+
+  auto grad_scale_before_reduction =
+      *ReferenceUtil::MapArray4D(grad_output_times_var, activation_shifted,
+                                 [](float a, float b) { return a * b; });
+
+  auto grad_scale = ReferenceUtil::Reduce4DTo1D(
+      grad_scale_before_reduction, /*init=*/0.0f, reduce_dims,
+      [](float a, float b) { return a + b; });
+
+  auto grad_offset =
+      ReferenceUtil::Reduce4DTo1D(grad_output_array, /*init=*/0.0f, reduce_dims,
+                                  [](float a, float b) { return a + b; });
+
+  auto expected_grad_activation =
+      Literal::CreateR4FromArray4D<float>(grad_activation);
+
+  auto input_literal = Literal::CreateR4FromArray4D<float>(input_array);
+  auto scale_literal = Literal::CreateR1<float>(scale);
+  auto mean_literal = Literal::CreateR1<float>(mean);
+  auto var_literal = Literal::CreateR1<float>(var);
+  auto grad_output_literal =
+      Literal::CreateR4FromArray4D<float>(grad_output_array);
+
+  auto input_parameter = builder.Parameter(0, input_literal->shape(), "input");
+  auto scale_parameter = builder.Parameter(1, scale_literal->shape(), "scale");
+  auto mean_parameter = builder.Parameter(2, mean_literal->shape(), "mean");
+  auto var_parameter = builder.Parameter(3, var_literal->shape(), "variance");
+  auto grad_output_parameter =
+      builder.Parameter(4, grad_output_literal->shape(), "grad_output");
+
+  std::unique_ptr<GlobalData> input_data =
+      client_->TransferToServer(*input_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> scale_data =
+      client_->TransferToServer(*scale_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> mean_data =
+      client_->TransferToServer(*mean_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> var_data =
+      client_->TransferToServer(*var_literal).ConsumeValueOrDie();
+  std::unique_ptr<GlobalData> grad_output_data =
+      client_->TransferToServer(*grad_output_literal).ConsumeValueOrDie();
+
+  auto t = builder.BatchNormGrad(input_parameter, scale_parameter,
+                                 mean_parameter, var_parameter,
+                                 grad_output_parameter, epsilon, feature_index);
+
+  auto expected =
+      *Literal::MakeTuple({expected_grad_activation.get(),
+                           Literal::CreateR1<float>(grad_scale).get(),
+                           Literal::CreateR1<float>(grad_offset).get()});
+
+  ComputeAndCompareTuple(&builder, expected,
+                         {input_data.get(), scale_data.get(), mean_data.get(),
+                          var_data.get(), grad_output_data.get()},
+                         ErrorSpec(0.01, 1));
+}
+
 INSTANTIATE_TEST_CASE_P(
     BatchNormTest_Instantiation, BatchNormTest,
     ::testing::Values(BatchNormTestParam{{2, 2, 2, 2}, 0, 100.2f, 200.0f},
@@ -319,6 +450,7 @@ INSTANTIATE_TEST_CASE_P(
                       BatchNormTestParam{{10, 10, 10, 10}, 1, -666.6f, 777.7f},
                       BatchNormTestParam{{10, 10, 10, 10}, 2, 0.f, 777.7f},
                       BatchNormTestParam{{1, 1, 10, 130}, 2, 0.f, 777.7f},
+                      BatchNormTestParam{{1, 1, 130, 11}, 2, 0.f, 777.7f},
                       BatchNormTestParam{{1, 1, 10, 1}, 3, 888.8f, 9.9f},
 
                       BatchNormTestParam{{24, 129, 1, 2}, 2, 10000, 10000},
@@ -446,6 +578,37 @@ XLA_TEST_F(BatchNormTest, DISABLED_ON_GPU(LargeEpsilonTest)) {
                          ErrorSpec(0.1));
 }
 
+// TODO(b/62764704): Implement on CPU and GPU. Disabled on 2017-07-11.
+XLA_TEST_F(BatchNormTest, DISABLED_ON_CPU_PARALLEL(DISABLED_ON_CPU(
+                              DISABLED_ON_GPU(BatchNormGradBasic)))) {
+  const int kFeatureIndex = 2;
+  ComputationBuilder builder(client_, TestName());
+
+  auto operand =
+      builder.ConstantR4FromArray4D<float>(Array4D<float>(2, 2, 2, 1, 0.0f));
+
+  auto scale = builder.ConstantR1<float>({1.0f, 1.0f});
+
+  auto mean = builder.ConstantR1<float>({0.0f, 0.0f});
+
+  auto var = builder.ConstantR1<float>({1.0f, 1.0f});
+
+  auto grad_output = builder.ConstantR4FromArray4D<float>(
+      {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}});
+
+  builder.BatchNormGrad(operand, scale, mean, var, grad_output,
+                        /*epsilon=*/0.0, kFeatureIndex);
+
+  auto expected = *Literal::MakeTuple(
+      {Literal::CreateR4<float>(
+           {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}})
+           .get(),
+       Literal::CreateR1<float>({0, 0}).get(),
+       Literal::CreateR1<float>({16, 20}).get()});
+
+  ComputeAndCompareTuple(&builder, expected, {}, ErrorSpec(0.1));
+}
+
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
index 576c1c703d..9e85e35707 100644
--- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc
@@ -44,295 +44,310 @@ namespace {
 
 class DynamicSliceTest : public ClientLibraryTestBase {
  protected:
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR1() {
     // Slice at dimension start.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {0}, {5},
-                  {0.0, 1.0, 2.0, 3.0, 4.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {0}, {5}, {0, 1, 2, 3, 4});
     // Slice in the middle.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {2}, {3},
-                  {2.0, 3.0, 4.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {2}, {3}, {2, 3, 4});
     // Slice at dimension boundaries.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {5}, {3},
-                  {5.0, 6.0, 7.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {5}, {3}, {5, 6, 7});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {6}, {4},
-                  {6.0, 7.0, 0.0, 1.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {6}, {4}, {6, 7, 0, 1});
     // Zero element slice.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}, {2}, {0}, {});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {2}, {0}, {});
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR2() {
     // Slice at dimension start.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {0, 0}, {2, 2}, {{1.0f, 2.0f}, {4.0f, 5.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {0, 0}, {2, 2},
+                         {{1, 2}, {4, 5}});
     // Slice in the middle.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {1, 1}, {2, 1}, {{5.0f}, {8.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {1, 1}, {2, 1},
+                         {{5}, {8}});
     // Slice at dimension boundaries.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {1, 1}, {2, 1}, {{5.0f}, {8.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {1, 1}, {2, 1},
+                         {{5}, {8}});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {1, 1}, {3, 3},
-                  {{5.0f, 6.0f, 4.0f}, {8.0f, 9.0f, 7.0f}, {2.0f, 3.0f, 1.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {1, 1}, {3, 3},
+                         {{5, 6, 4}, {8, 9, 7}, {2, 3, 1}});
     // Zero element slice: 2x0.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {0, 0}, {2, 0}, {{}, {}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {0, 0}, {2, 0},
+                         {{}, {}});
     // Zero element slice: 0x2.
-    RunR2<IndexT>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-                  {0, 0}, {0, 2}, Array2D<float>(0, 2));
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {0, 0}, {0, 2},
+                         Array2D<DataT>(0, 2));
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR3() {
     // R3 Shape: [2, 3, 2]
     // clang-format off
 
     // Slice at dimension start.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-        {0, 0, 0}, {2, 1, 2},
-      {{{1.0f, 2.0f}}, {{7.0f, 8.0f}}});
+    RunR3<IndexT, DataT>(
+      {{{1, 2}, {3, 4}, {5, 6}},
+       {{7, 8}, {9, 10}, {11, 12}}},
+      {0, 0, 0}, {2, 1, 2},
+      {{{1, 2}}, {{7, 8}}});
 
     // Slice in the middle.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-        {0, 1, 1}, {2, 2, 1},
-      {{{4.0f}, {6.0f}}, {{10.0f}, {12.0f}}});
+    RunR3<IndexT, DataT>(
+      {{{1, 2}, {3, 4}, {5, 6}},
+       {{7, 8}, {9, 10}, {11, 12}}},
+      {0, 1, 1}, {2, 2, 1},
+      {{{4}, {6}}, {{10}, {12}}});
 
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-        {0, 2, 1}, {2, 1, 2},
-      {{{6.0f, 5.0f}}, {{12.0f, 11.0f}}});
+    RunR3<IndexT, DataT>(
+      {{{1, 2}, {3, 4}, {5, 6}},
+       {{7, 8}, {9, 10}, {11, 12}}},
+      {0, 2, 1}, {2, 1, 2},
+      {{{6, 5}}, {{12, 11}}});
 
     // clang-format on
   }
 
-  template <typename IndexT>
-  void RunR1(const std::vector<float>& input_values,
+  template <typename IndexT, typename DataT>
+  void RunR1(tensorflow::gtl::ArraySlice<DataT> input_values,
              const std::vector<IndexT> slice_starts,
              const std::vector<int64>& slice_sizes,
-             const std::vector<float>& expected_values) {
+             tensorflow::gtl::ArraySlice<DataT> expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR1<float>(input_values);
+    auto input = builder.ConstantR1<DataT>(input_values);
     builder.DynamicSlice(input, starts, slice_sizes);
     // Run computation and compare against expected values.
-    ComputeAndCompareR1<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR1<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR2(const Array2D<float>& input_values,
+  template <typename IndexT, typename DataT>
+  void RunR2(const Array2D<DataT>& input_values,
              const std::vector<IndexT> slice_starts,
              const std::vector<int64>& slice_sizes,
-             const Array2D<float>& expected_values) {
+             const Array2D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR2FromArray2D<float>(input_values);
+    auto input = builder.ConstantR2FromArray2D<DataT>(input_values);
     builder.DynamicSlice(input, starts, slice_sizes);
     // Run computation and compare against expected values.
-    ComputeAndCompareR2<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR2<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR3(const Array3D<float>& input_values,
+  template <typename IndexT, typename DataT>
+  void RunR3(const Array3D<DataT>& input_values,
              const std::vector<IndexT> slice_starts,
              const std::vector<int64>& slice_sizes,
-             const Array3D<float>& expected_values) {
+             const Array3D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR3FromArray3D<float>(input_values);
+    auto input = builder.ConstantR3FromArray3D<DataT>(input_values);
     builder.DynamicSlice(input, starts, slice_sizes);
     // Run computation and compare against expected values.
-    ComputeAndCompareR3<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR3<DataT>(&builder, expected_values, {start_data.get()});
   }
 };
 
-XLA_TEST_F(DynamicSliceTest, Int32R1) { TestR1<int32>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R1) { TestR1<int32, int32>(); }
+
+XLA_TEST_F(DynamicSliceTest, Int64R1) { TestR1<int64, float>(); }
+
+XLA_TEST_F(DynamicSliceTest, UInt64R1) { TestR1<uint64, double>(); }
+
+XLA_TEST_F(DynamicSliceTest, Int32R2) { TestR2<int32, float>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int64R1) { TestR1<int64>(); }
+XLA_TEST_F(DynamicSliceTest, Int64R2) { TestR2<int64, double>(); }
 
-XLA_TEST_F(DynamicSliceTest, UInt64R1) { TestR1<uint64>(); }
+XLA_TEST_F(DynamicSliceTest, UInt64R2) { TestR2<uint64, int32>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int32R2) { TestR2<int32>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R3) { TestR3<int32, int32>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int64R2) { TestR2<int64>(); }
+XLA_TEST_F(DynamicSliceTest, Int64R3) { TestR3<int64, float>(); }
 
-XLA_TEST_F(DynamicSliceTest, UInt64R2) { TestR2<uint64>(); }
+XLA_TEST_F(DynamicSliceTest, UInt64R3) { TestR3<uint64, double>(); }
 
-XLA_TEST_F(DynamicSliceTest, Int32R3) { TestR3<int32>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R1Pred) {
+  // Slice at dimension start.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {0},
+                     {5}, {true, false, false, true, false});
+  // Slice in the middle.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {2},
+                     {3}, {false, true, false});
+  // Slice at dimension boundaries.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {5},
+                     {3}, {true, true, false});
+  // Zero element slice.
+  RunR1<int32, bool>({true, false, false, true, false, true, true, false}, {2},
+                     {0}, {});
+}
 
-XLA_TEST_F(DynamicSliceTest, Int64R3) { TestR3<int64>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R2Pred) {
+  // Slice at dimension start.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {0, 0},
+      {2, 2}, {{true, false}, {false, false}});
+  // Slice in the middle.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {1, 1},
+      {2, 1}, {{false}, {true}});
+  // Slice at dimension boundaries.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {1, 1},
+      {2, 1}, {{false}, {true}});
+  // Zero element slice: 2x0.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {0, 0},
+      {2, 0}, {{}, {}});
+  // Zero element slice: 0x2.
+  RunR2<int32, bool>(
+      {{true, false, true}, {false, false, true}, {true, true, false}}, {0, 0},
+      {0, 2}, Array2D<bool>(0, 2));
+}
 
-XLA_TEST_F(DynamicSliceTest, UInt64R3) { TestR3<uint64>(); }
+XLA_TEST_F(DynamicSliceTest, Int32R3Pred) {
+  // R3 Shape: [2, 3, 2]
+  // clang-format off
+
+  // Slice at dimension start.
+  RunR3<int32, bool>(
+    {{{true, false}, {false, true}, {true, true}},
+     {{false, true}, {true, false}, {false, false}}},
+    {0, 0, 0}, {2, 1, 2},
+    {{{true, false}}, {{false, true}}});
+
+  // Slice in the middle.
+  RunR3<int32, bool>(
+    {{{true, false}, {false, true}, {true, true}},
+     {{false, true}, {true, false}, {false, false}}},
+    {0, 1, 1}, {2, 2, 1},
+    {{{true}, {true}}, {{false}, {false}}});
+
+  // clang-format on
+}
 
 class DynamicUpdateSliceTest : public ClientLibraryTestBase {
  protected:
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR1() {
-    // clang-format off
     // Slice at dimension start.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {0},
-                  {8.0, 9.0, 10.0, 3.0, 4.0, 5.0, 6.0, 7.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {0},
+                         {8, 9, 10, 3, 4, 5, 6, 7});
     // Slice in the middle.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {2},
-                  {0.0, 1.0, 8.0, 9.0, 10.0, 5.0, 6.0, 7.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {2},
+                         {0, 1, 8, 9, 10, 5, 6, 7});
     // Slice at dimension boundaries.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {5},
-                  {0.0, 1.0, 2.0, 3.0, 4.0, 8.0, 9.0, 10.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {5},
+                         {0, 1, 2, 3, 4, 8, 9, 10});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {8.0, 9.0, 10.0}, {6},
-                  {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 8.0, 9.0});
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {8, 9, 10}, {6},
+                         {0, 1, 2, 3, 4, 5, 8, 9});
     // Zero-sized update.
-    RunR1<IndexT>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0},
-                  {}, {2},
-                  {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0});
-    // clang-format on
+    RunR1<IndexT, DataT>({0, 1, 2, 3, 4, 5, 6, 7}, {}, {2},
+                         {0, 1, 2, 3, 4, 5, 6, 7});
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR2() {
-    // clang-format off
     // Slice at dimension start.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {0, 0},
-        {{10.0f, 11.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {0, 0},
+                         {{10, 11, 3}, {4, 5, 6}, {7, 8, 9}});
     // Slice in the middle.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {1, 1},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 10.0f, 11.0f}, {7.0f, 8.0f, 9.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {1, 1},
+                         {{1, 2, 3}, {4, 10, 11}, {7, 8, 9}});
     // Slice at dimension boundaries.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {2, 1},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 10.0f, 11.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {2, 1},
+                         {{1, 2, 3}, {4, 5, 6}, {7, 10, 11}});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{10.0f, 11.0f}}, {2, 2},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 10.0f}});
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{10, 11}}, {2, 2},
+                         {{1, 2, 3}, {4, 5, 6}, {7, 8, 10}});
     // Zero-sized update.
-    RunR2<IndexT>(
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}},
-        {{}}, {2, 1},
-        {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}});
-    // clang-format on
+    RunR2<IndexT, DataT>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, {{}}, {2, 1},
+                         {{1, 2, 3}, {4, 5, 6}, {7, 8, 9}});
   }
 
-  template <typename IndexT>
+  template <typename IndexT, typename DataT>
   void TestR3() {
     // R3 Shape: [2, 3, 2]
-    // clang-format off
     // Slice at dimension start.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-      {{{13.0f, 14.0f}, {15.0f, 16.0f}},
-       {{17.0f, 18.0f}, {19.0f, 20.0f}}},
-        {0, 0, 0},
-      {{{13.0f, 14.0f}, {15.0f, 16.0f}, {5.0f, 6.0f}},
-       {{17.0f, 18.0f}, {19.0f, 20.0f}, {11.0f, 12.0f}}});
+    RunR3<IndexT, DataT>(
+        {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}},
+        {{{13, 14}, {15, 16}}, {{17, 18}, {19, 20}}}, {0, 0, 0},
+        {{{13, 14}, {15, 16}, {5, 6}}, {{17, 18}, {19, 20}, {11, 12}}});
     // Slice in the middle.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-      {{{13.0f}, {15.0f}}},
-        {1, 1, 1},
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-        {{7.0f, 8.0f}, {9.0f, 13.0f}, {11.0f, 15.0f}}});
+    RunR3<IndexT, DataT>(
+        {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}}, {{{13}, {15}}},
+        {1, 1, 1}, {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 13}, {11, 15}}});
     // Slice at dimension boundaries, but with sizes that cause indices to wrap.
-    RunR3<IndexT>(
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-       {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}},
-      {{{13.0f}, {15.0f}}},
-        {1, 2, 1},
-      {{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}},
-        {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 13.0f}}});
-    // clang-format on
+    RunR3<IndexT, DataT>(
+        {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}}, {{{13}, {15}}},
+        {1, 2, 1}, {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 13}}});
   }
 
-  template <typename IndexT>
-  void RunR1(const std::vector<float>& input_values,
-             const std::vector<float>& update_values,
+  template <typename IndexT, typename DataT>
+  void RunR1(tensorflow::gtl::ArraySlice<DataT> input_values,
+             tensorflow::gtl::ArraySlice<DataT> update_values,
              const std::vector<IndexT> slice_starts,
-             const std::vector<float>& expected_values) {
+             tensorflow::gtl::ArraySlice<DataT> expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR1<float>(input_values);
-    auto update = builder.ConstantR1<float>(update_values);
+    auto input = builder.ConstantR1<DataT>(input_values);
+    auto update = builder.ConstantR1<DataT>(update_values);
     builder.DynamicUpdateSlice(input, update, starts);
     // Run computation and compare against expected values.
-    ComputeAndCompareR1<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR1<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR2(const Array2D<float>& input_values,
-             const Array2D<float>& update_values,
+  template <typename IndexT, typename DataT>
+  void RunR2(const Array2D<DataT>& input_values,
+             const Array2D<DataT>& update_values,
              const std::vector<IndexT> slice_starts,
-             const Array2D<float>& expected_values) {
+             const Array2D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR2FromArray2D<float>(input_values);
-    auto update = builder.ConstantR2FromArray2D<float>(update_values);
+    auto input = builder.ConstantR2FromArray2D<DataT>(input_values);
+    auto update = builder.ConstantR2FromArray2D<DataT>(update_values);
     builder.DynamicUpdateSlice(input, update, starts);
     // Run computation and compare against expected values.
-    ComputeAndCompareR2<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR2<DataT>(&builder, expected_values, {start_data.get()});
   }
 
-  template <typename IndexT>
-  void RunR3(const Array3D<float>& input_values,
-             const Array3D<float>& update_values,
+  template <typename IndexT, typename DataT>
+  void RunR3(const Array3D<DataT>& input_values,
+             const Array3D<DataT>& update_values,
              const std::vector<IndexT> slice_starts,
-             const Array3D<float>& expected_values) {
+             const Array3D<DataT>& expected_values) {
     ComputationBuilder builder(client_, TestName());
     // Initialize and transfer dynamic slice start indices parameter.
     ComputationDataHandle starts;
     std::unique_ptr<GlobalData> start_data = CreateR1Parameter<IndexT>(
         slice_starts, 0, "slice_starts", &builder, &starts);
     // Build dynamic slice computation.
-    auto input = builder.ConstantR3FromArray3D<float>(input_values);
-    auto update = builder.ConstantR3FromArray3D<float>(update_values);
+    auto input = builder.ConstantR3FromArray3D<DataT>(input_values);
+    auto update = builder.ConstantR3FromArray3D<DataT>(update_values);
     builder.DynamicUpdateSlice(input, update, starts);
     // Run computation and compare against expected values.
-    ComputeAndCompareR3<float>(&builder, expected_values, {start_data.get()},
-                               ErrorSpec(0.000001));
+    ComputeAndCompareR3<DataT>(&builder, expected_values, {start_data.get()});
   }
 
   void RunR3Contiguous(std::vector<int32> operand_shape, int32 index,
@@ -393,23 +408,81 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase {
   }
 };
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int32R1) { TestR1<int32>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R1) { TestR1<int32, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int64R1) { TestR1<int64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int64R1) { TestR1<int64, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, UInt64R1) { TestR1<uint64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, UInt64R1) { TestR1<uint64, double>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int32R2) { TestR2<int32>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R2) { TestR2<int32, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int64R2) { TestR2<int64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int64R2) { TestR2<int64, int64>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, UInt64R2) { TestR2<uint64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, UInt64R2) { TestR2<uint64, int32>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int32R3) { TestR3<int32>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R3) { TestR3<int32, float>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, Int64R3) { TestR3<int64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, Int64R3) { TestR3<int64, int64>(); }
 
-XLA_TEST_F(DynamicUpdateSliceTest, UInt64R3) { TestR3<uint64>(); }
+XLA_TEST_F(DynamicUpdateSliceTest, UInt64R3) { TestR3<uint64, uint64>(); }
+
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R1Pred) {
+  // Slice at dimension start.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false},
+                     {true, true, false}, {0},
+                     {true, true, false, true, false, true, true, false});
+  // Slice in the middle.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false},
+                     {false, true, true}, {2},
+                     {false, false, false, true, true, true, true, false});
+  // Slice at dimension boundaries.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false},
+                     {false, true, true}, {5},
+                     {false, false, true, true, false, false, true, true});
+  // Zero-sized update.
+  RunR1<int32, bool>({false, false, true, true, false, true, true, false}, {},
+                     {2}, {false, false, true, true, false, true, true, false});
+}
+
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R2Pred) {
+  // Slice at dimension start.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}},
+      {{true, false}}, {0, 0},
+      {{true, false, false}, {true, false, true}, {false, true, true}});
+  // Slice in the middle.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}},
+      {{true, false}}, {1, 1},
+      {{false, true, false}, {true, true, false}, {false, true, true}});
+  // Slice at dimension boundaries.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}},
+      {{true, false}}, {2, 1},
+      {{false, true, false}, {true, false, true}, {false, true, false}});
+  // Zero-sized update.
+  RunR2<int32, bool>(
+      {{false, true, false}, {true, false, true}, {false, true, true}}, {{}},
+      {2, 1}, {{false, true, false}, {true, false, true}, {false, true, true}});
+}
+
+XLA_TEST_F(DynamicUpdateSliceTest, Int32R3Pred) {
+  // R3 Shape: [2, 3, 2]
+  // Slice at dimension start.
+  RunR3<int32, bool>(
+      {{{true, false}, {false, true}, {true, true}},
+       {{false, false}, {false, true}, {true, false}}},
+      {{{false, true}, {true, false}}, {{true, true}, {false, true}}},
+      {0, 0, 0},
+      {{{false, true}, {true, false}, {true, true}},
+       {{true, true}, {false, true}, {true, false}}});
+  // Slice in the middle.
+  RunR3<int32, bool>({{{true, false}, {false, true}, {true, true}},
+                      {{false, false}, {false, true}, {true, false}}},
+                     {{{false}, {true}}}, {1, 1, 1},
+                     {{{true, false}, {false, true}, {true, true}},
+                      {{false, false}, {false, false}, {true, true}}});
+}
 
 // Tests for simple R3 case where the update is contiguous (i.e. the minor
 // two dimensions are not sliced).
diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
index 48212dc7d1..527205bbb0 100644
--- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
@@ -39,8 +40,11 @@ limitations under the License.
 namespace xla {
 namespace {
 
-class ReducePrecisionTest : public ClientLibraryTestBase,
-                            public ::testing::WithParamInterface<int> {};
+// Tests to confirm that the ReducePrecision operation produces the expected
+// numerical values.
+class ReducePrecisionAccuracyTest : public ClientLibraryTestBase,
+                                    public ::testing::WithParamInterface<int> {
+};
 
 // For reduction to IEEE-f16, we want to test the following cases, in both
 // positive and negative variants.  (Note: IEEE-f16 is 5 exponent bits and 10
@@ -201,7 +205,7 @@ static const uint32_t test_values[][4] = {
         FPVAL(11111111, 1111111111, 1111111111111)   // NaN
     }};
 
-XLA_TEST_P(ReducePrecisionTest, ReducePrecisionF32) {
+XLA_TEST_P(ReducePrecisionAccuracyTest, ReducePrecisionF32) {
   int index = GetParam();
   int exponent_bits = exponent_sizes[index];
   int mantissa_bits = mantissa_sizes[index];
@@ -238,9 +242,87 @@ XLA_TEST_P(ReducePrecisionTest, ReducePrecisionF32) {
   ComputeAndCompareR1<float>(&builder, expected_values, {a_data.get()});
 }
 
-INSTANTIATE_TEST_CASE_P(ReducePrecisionTest, ReducePrecisionTest,
+INSTANTIATE_TEST_CASE_P(ReducePrecisionAccuracyTest,
+                        ReducePrecisionAccuracyTest,
                         ::testing::Values(0, 1, 2, 3), TestDataToString);
 
+// Tests to confirm that the compiler optimization functions add the expected
+// ReducePrecisionInsertion passes.
+class ReducePrecisionInsertionTest : public ClientLibraryTestBase {};
+
+XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionBeforeFusion) {
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({1.00001});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  // Abs doesn't affect resolution.
+  auto abs = builder.Abs(a);
+
+  // Near 1.0, Log(x) approximates x - 1; this lets us confirm that the
+  // reduce-precision operation showed up in the correct place in the
+  // graph.
+  auto log = builder.Log(abs);
+
+  // Insert precision-reduction after the Abs(x) operation, rounding that
+  // result to exactly 1.0f.
+  auto reduce_precision_pass = execution_options_.mutable_debug_options()
+                                   ->add_hlo_reduce_precision_options();
+  *reduce_precision_pass = ReducePrecisionInsertion::make_options_proto(
+      HloReducePrecisionOptions::BEFORE_OP_FUSION, 5, 10,
+      [](const HloOpcode opcode) { return opcode == HloOpcode::kAbs; });
+
+  ComputeAndCompareR1<float>(&builder, {0.0f}, {a_data.get()});
+}
+
+XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionSkippedAfterFusion) {
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({1.00001});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  // These two operations should be fused by any reasonable backend.
+  auto abs = builder.Abs(a);
+  auto neg = builder.Neg(abs);
+
+  // Add a pass after operation fusion, suffixing kAbs operations.  This
+  // should not see into the fusion nodes and thus should not affect the
+  // result.
+  auto reduce_precision_pass = execution_options_.mutable_debug_options()
+                                   ->add_hlo_reduce_precision_options();
+  *reduce_precision_pass = ReducePrecisionInsertion::make_options_proto(
+      HloReducePrecisionOptions::AFTER_OP_FUSION, 5, 10,
+      [](const HloOpcode opcode) { return opcode == HloOpcode::kAbs; });
+
+  ComputeAndCompareR1<float>(&builder, {-1.00001f}, {a_data.get()});
+}
+
+XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionAddedAfterFusion) {
+  ComputationBuilder builder(client_, TestName());
+
+  std::unique_ptr<Literal> a_literal = Literal::CreateR1<float>({1.00001});
+  std::unique_ptr<GlobalData> a_data =
+      client_->TransferToServer(*a_literal).ConsumeValueOrDie();
+  auto a = builder.Parameter(0, a_literal->shape(), "a");
+
+  // These two operations should be fused by any reasonable backend.
+  auto abs = builder.Abs(a);
+  auto neg = builder.Neg(abs);
+
+  // Add a pass after operation fusion, suffixing kFusion operations.
+  auto reduce_precision_pass = execution_options_.mutable_debug_options()
+                                   ->add_hlo_reduce_precision_options();
+  *reduce_precision_pass = ReducePrecisionInsertion::make_options_proto(
+      HloReducePrecisionOptions::AFTER_OP_FUSION, 5, 10,
+      [](const HloOpcode opcode) { return opcode == HloOpcode::kFusion; });
+
+  ComputeAndCompareR1<float>(&builder, {-1.0f}, {a_data.get()});
+}
+
 }  // namespace
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto
index 00fb7f12b8..be4e00f63c 100644
--- a/tensorflow/compiler/xla/xla.proto
+++ b/tensorflow/compiler/xla/xla.proto
@@ -20,6 +20,24 @@ import "tensorflow/compiler/xla/service/session.proto";
 
 package xla;
 
+// Options for the HLO insert-reduce-precision-operations pass.
+message HloReducePrecisionOptions {
+  // When to run the pass.
+  enum PassTiming {
+    BEFORE_OP_FUSION = 0;
+    AFTER_OP_FUSION = 1;
+  }
+  PassTiming pass_timing = 1;
+
+  // Exponent and mantissa bit counts for the reduced precision.
+  uint32 exponent_bits = 2;
+  uint32 mantissa_bits = 3;
+
+  // Opcodes for operations that should be suffixed with reduced-precision
+  // operations.
+  repeated uint32 opcodes_to_suffix = 4;
+}
+
 // Debugging options for XLA. These options may change at any time - there are
 // no guarantees about backward or forward compatibility for these fields.
 message DebugOptions {
@@ -112,6 +130,11 @@ message DebugOptions {
   // the generated IR.
   bool xla_llvm_enable_invariant_load_metadata = 72;
 
+  // Options for inserting reduce-precision operations for numerical
+  // experimentation.  This is a repeated field, as we may want to have
+  // multiple passes with different parameters.
+  repeated HloReducePrecisionOptions hlo_reduce_precision_options = 80;
+
   // This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the
   // computation will run n! times with all permunations of layouts for the
   // output shape in rank n. For example, with a 3D shape, all permutations of
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index d3cc61ce29..f1f6144acd 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -199,6 +199,7 @@ tensorflow/core/kernels/aggregate_ops.cc
 tensorflow/core/kernels/depthwise_conv_op.cc
 tensorflow/core/kernels/dequantize_op.cc
 tensorflow/core/kernels/meta_support.cc
+tensorflow/core/kernels/population_count_op.cc
 tensorflow/core/kernels/quantization_utils.cc
 tensorflow/core/kernels/quantize_down_and_shrink_range.cc
 tensorflow/core/kernels/quantize_op.cc
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index 06954f51d8..c14463bdad 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -210,7 +210,7 @@ class RNNCellTest(test.TestCase):
           sess.run([variables_lib.global_variables_initializer()])
           sess.run([g, out_m],
                    {x.name: 1 * np.ones([batch_size, input_size]),
-               m.name: 0.1 * np.ones([batch_size - 1, state_size])})
+                    m.name: 0.1 * np.ones([batch_size - 1, state_size])})
 
   def testBasicLSTMCellStateSizeError(self):
     """Tests that state_size must be num_units * 2."""
@@ -218,7 +218,7 @@ class RNNCellTest(test.TestCase):
       with variable_scope.variable_scope(
           "root", initializer=init_ops.constant_initializer(0.5)):
         num_units = 2
-        state_size = num_units * 3 # state_size must be num_units * 2
+        state_size = num_units * 3  # state_size must be num_units * 2
         batch_size = 3
         input_size = 4
         x = array_ops.zeros([batch_size, input_size])
@@ -406,6 +406,31 @@ class RNNCellTest(test.TestCase):
         # States are left untouched
         self.assertAllClose(res[2], res[3])
 
+  def testResidualWrapperWithSlice(self):
+    with self.test_session() as sess:
+      with variable_scope.variable_scope(
+          "root", initializer=init_ops.constant_initializer(0.5)):
+        x = array_ops.zeros([1, 5])
+        m = array_ops.zeros([1, 3])
+        base_cell = rnn_cell_impl.GRUCell(3)
+        g, m_new = base_cell(x, m)
+        variable_scope.get_variable_scope().reuse_variables()
+        def residual_with_slice_fn(inp, out):
+          inp_sliced = array_ops.slice(inp, [0, 0], [-1, 3])
+          return inp_sliced + out
+        g_res, m_new_res = rnn_cell_impl.ResidualWrapper(
+            base_cell, residual_with_slice_fn)(x, m)
+        sess.run([variables_lib.global_variables_initializer()])
+        res_g, res_g_res, res_m_new, res_m_new_res = sess.run(
+            [g, g_res, m_new, m_new_res], {
+                x: np.array([[1., 1., 1., 1., 1.]]),
+                m: np.array([[0.1, 0.1, 0.1]])
+            })
+        # Residual connections
+        self.assertAllClose(res_g_res, res_g + [1., 1., 1.])
+        # States are left untouched
+        self.assertAllClose(res_m_new, res_m_new_res)
+
   def testDeviceWrapper(self):
     with variable_scope.variable_scope(
         "root", initializer=init_ops.constant_initializer(0.5)):
diff --git a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
index be28184ae6..61b7107a17 100644
--- a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
+++ b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py
@@ -220,15 +220,14 @@ class SpectralOpsTest(test.TestCase):
       # stft_bound, inverse_stft_bound).
       # TODO(rjryan): Investigate why STFT gradient error is so high.
       test_configs = [
-          (512, 64, 32, 64, 2e-3, 3e-5),
-          (512, 64, 64, 64, 2e-3, 3e-5),
-          (512, 64, 25, 64, 2e-3, 3e-5),
-          (512, 25, 15, 36, 2e-3, 3e-5),
-          (123, 23, 5, 42, 2e-3, 4e-5),
+          (64, 16, 8, 16),
+          (64, 16, 16, 16),
+          (64, 16, 7, 16),
+          (64, 7, 4, 9),
+          (29, 5, 1, 10),
       ]
 
-      for (signal_length, frame_length, frame_step, fft_length,
-           stft_bound, inverse_stft_bound) in test_configs:
+      for (signal_length, frame_length, frame_step, fft_length) in test_configs:
         signal_shape = [signal_length]
         signal = random_ops.random_uniform(signal_shape)
         stft_shape = [max(0, 1 + (signal_length - frame_length) // frame_step),
@@ -242,8 +241,8 @@ class SpectralOpsTest(test.TestCase):
                                                  stft, stft_shape)
         inverse_stft_error = test.compute_gradient_error(
             stft, stft_shape, inverse_stft, inverse_stft_shape)
-        self.assertLess(stft_error, stft_bound)
-        self.assertLess(inverse_stft_error, inverse_stft_bound)
+        self.assertLess(stft_error, 2e-3)
+        self.assertLess(inverse_stft_error, 4e-5)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 712871cc04..b6fa185709 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -54,9 +54,12 @@ def _tpu_job(run_config):
   return None if run_config.master in ['', 'local'] else 'tpu_worker'
 
 
-def _per_shard_batch_size(global_batch_size, run_config):
+def _per_shard_batch_size(global_batch_size, run_config, use_tpu):
   """Returns the batch size for each shard."""
-  return global_batch_size // run_config.tpu_config.num_shards
+  if use_tpu:
+    return global_batch_size // run_config.tpu_config.num_shards
+  else:
+    return global_batch_size
 
 
 class _SIGNAL(object):
@@ -470,7 +473,7 @@ class _ModelFnWrapper(object):
     self._train_batch_size = train_batch_size
 
   def call_without_tpu(self, features, labels):
-    return self._call_model_fn(features, labels)
+    return self._call_model_fn(features, labels, False)
 
   def convert_to_single_tpu_train_step(self, dequeue_fn):
     """Converts the `model_fn` as a single train step on TPU."""
@@ -481,8 +484,8 @@ class _ModelFnWrapper(object):
       features, labels = dequeue_fn()
 
       # Makes deep copy with `config` and params` in case user mutates them.
-      estimator_spec = self._verify_estimator_spec(self._call_model_fn(
-          features, labels, add_batch_size_in_params=True))
+      estimator_spec = self._verify_estimator_spec(
+          self._call_model_fn(features, labels, True))
       loss, train_op = estimator_spec.loss, estimator_spec.train_op
       with ops.control_dependencies([train_op]):
         return array_ops.identity(loss)
@@ -492,7 +495,7 @@ class _ModelFnWrapper(object):
   def config(self):
     return self._config
 
-  def _call_model_fn(self, features, labels, add_batch_size_in_params=False):
+  def _call_model_fn(self, features, labels, use_tpu):
     """Calls the model_fn with required parameters."""
     model_fn_args = util.fn_args(self._model_fn)
     kwargs = {}
@@ -513,16 +516,15 @@ class _ModelFnWrapper(object):
     if 'params' in model_fn_args:
       kwargs['params'] = params
 
-    if add_batch_size_in_params:
-      if 'params' not in model_fn_args:
-        raise ValueError(
-            'model_fn ({}) does not include params argument, '
-            'required by TPUEstimator to pass batch size as '
-            'params[\'batch_size\']'.format(self._model_fn))
-      if self._mode == model_fn_lib.ModeKeys.TRAIN:
-        # For TPU training. `params` is never `None`.
-        params[_BATCH_SIZE_KEY] = _per_shard_batch_size(self._train_batch_size,
-                                                        config)
+    if 'params' not in model_fn_args:
+      raise ValueError(
+          'model_fn ({}) does not include params argument, '
+          'required by TPUEstimator to pass batch size as '
+          'params[\'batch_size\']'.format(self._model_fn))
+    if self._mode == model_fn_lib.ModeKeys.TRAIN:
+      # For TPU training. `params` is never `None`.
+      params[_BATCH_SIZE_KEY] = _per_shard_batch_size(
+          self._train_batch_size, config, use_tpu)
 
     return self._model_fn(features=features, **kwargs)
 
@@ -609,16 +611,12 @@ class TPUEstimator(estimator_lib.Estimator):
             'batch size {} must be divisible by number of shards {}'
             .format(train_batch_size, config.tpu_config.num_shards))
 
-    if use_tpu:
-      # Verifies the model_fn signature according to Estimator framework.
-      estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
-      # We cannot store config and params in this constructor as parent
-      # constructor might change them, such as assigning a temp dir for
-      # config.model_dir.
-      model_function = augment_model_fn_with_tpu_support(
-          model_fn, train_batch_size)
-    else:
-      model_function = model_fn
+    # Verifies the model_fn signature according to Estimator framework.
+    estimator_lib._verify_model_fn_args(model_fn, params)  # pylint: disable=protected-access
+    # We cannot store config and params in this constructor as parent
+    # constructor might change them, such as assigning a temp dir for
+    # config.model_dir.
+    model_function = _augment_model_fn(model_fn, train_batch_size, use_tpu)
 
     super(TPUEstimator, self).__init__(
         model_fn=model_function,
@@ -670,9 +668,6 @@ class TPUEstimator(estimator_lib.Estimator):
     Raises:
       ValueError: if input_fn takes invalid arguments or does not have `params`.
     """
-    if not self._use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
-      return super(TPUEstimator, self)._call_input_fn(input_fn, mode)
-
     input_fn_args = util.fn_args(input_fn)
     config = self.config  # a deep copy.
     kwargs = {}
@@ -686,8 +681,13 @@ class TPUEstimator(estimator_lib.Estimator):
       kwargs['config'] = config
 
     # Now for TPU training.
-    per_shard_batch_size = _per_shard_batch_size(self._train_batch_size, config)
-    kwargs['params'][_BATCH_SIZE_KEY] = per_shard_batch_size
+    if mode == model_fn_lib.ModeKeys.TRAIN:
+      kwargs['params'][_BATCH_SIZE_KEY] = (
+          _per_shard_batch_size(self._train_batch_size, config, self._use_tpu))
+
+    if not self._use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
+      with ops.device('/cpu:0'):
+        return input_fn(**kwargs)
 
     job = _tpu_job(config)
     def placement_function(index):
@@ -746,7 +746,7 @@ def _create_infeed_enqueue_ops_and_dequeue_fn(inputs_holder):
   return (dequeue_fn, enqueue_fn)
 
 
-def augment_model_fn_with_tpu_support(model_fn, train_batch_size):
+def _augment_model_fn(model_fn, train_batch_size, use_tpu):
   """Returns a new model_fn, which wraps the TPU support."""
 
   def _model_fn(features, labels, mode, config, params):
@@ -755,7 +755,7 @@ def augment_model_fn_with_tpu_support(model_fn, train_batch_size):
                                        train_batch_size)
 
     # TODO(jhseu): Move to EVAL and PREDICT to TPU.
-    if mode != model_fn_lib.ModeKeys.TRAIN:
+    if not use_tpu or mode != model_fn_lib.ModeKeys.TRAIN:
       return model_fn_wrapper.call_without_tpu(features, labels)
 
     inputs = _InputsHolder(sharded_features=features, sharded_labels=labels)
diff --git a/tensorflow/core/framework/attr_value_util.cc b/tensorflow/core/framework/attr_value_util.cc
index 9fdb3da6a0..95cafa24b1 100644
--- a/tensorflow/core/framework/attr_value_util.cc
+++ b/tensorflow/core/framework/attr_value_util.cc
@@ -15,7 +15,9 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value_util.h"
 
+#include <string>
 #include <vector>
+
 #include "tensorflow/core/framework/attr_value.pb_text.h"
 #include "tensorflow/core/framework/tensor.pb_text.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
@@ -27,7 +29,6 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 
 namespace tensorflow {
-
 namespace {
 
 string SummarizeString(const string& str) {
@@ -460,7 +461,8 @@ bool HasPlaceHolder(const AttrValue& val) {
   return false;
 }
 
-bool SubstitutePlaceholders(SubstituteFunc substitute, AttrValue* value) {
+bool SubstitutePlaceholders(const SubstituteFunc& substitute,
+                            AttrValue* value) {
   switch (value->value_case()) {
     case AttrValue::kList: {
       for (NameAttrList& func : *value->mutable_list()->mutable_func()) {
diff --git a/tensorflow/core/framework/attr_value_util.h b/tensorflow/core/framework/attr_value_util.h
index 08cc3b7158..08d813bb6f 100644
--- a/tensorflow/core/framework/attr_value_util.h
+++ b/tensorflow/core/framework/attr_value_util.h
@@ -16,8 +16,10 @@ limitations under the License.
 #ifndef TENSORFLOW_FRAMEWORK_ATTR_VALUE_UTIL_H_
 #define TENSORFLOW_FRAMEWORK_ATTR_VALUE_UTIL_H_
 
+#include <functional>
 #include <string>
 #include <vector>
+
 #include "tensorflow/core/framework/attr_value.pb.h"  // TODO(62899350): Remove
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -100,8 +102,8 @@ bool HasPlaceHolder(const AttrValue& val);
 // SubstituteFunc is given a placeholder string. If the placeholder is
 // unknown, SubstituteFunc returns false. Otherwise, overwrites the
 // attr value and returns true.
-typedef std::function<bool(const string&, AttrValue*)> SubstituteFunc;
-bool SubstitutePlaceholders(SubstituteFunc substitute, AttrValue* value);
+using SubstituteFunc = std::function<bool(const string&, AttrValue*)>;
+bool SubstitutePlaceholders(const SubstituteFunc& substitute, AttrValue* value);
 
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index db279ae67f..7f845bb9e2 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -104,7 +104,8 @@ ConstantFolding::ConstantFolding() {
   ops_to_preserve_ = std::regex(
       "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|"
       "Enter|RefEnter|Exit|RefExit|NextIteration|RefNextIteration|"
-      ".*Quantized.*");
+      ".*Quantized.*",
+      std::regex_constants::optimize);
 }
 
 string ConstantFolding::AddControlDependency(const string& input_name) {
@@ -240,13 +241,18 @@ Status ConstantFolding::MaterializeShapes(const GrapplerItem& item,
 }
 
 bool ConstantFolding::IsFoldable(const NodeDef& node) const {
+  // Folding not applicable to ops with no inputs.
+  if (node.input().empty()) {
+    return false;
+  }
+
   // Skips nodes that must be preserved, and op_types that don't benefit from
   // folding
   if (nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) {
     return false;
   }
-  std::cmatch match;
-  if (std::regex_match(node.op().c_str(), match, ops_to_preserve_)) {
+  if (std::regex_match(node.op().c_str(), ops_to_preserve_,
+                       std::regex_constants::match_any)) {
     return false;
   }
 
@@ -264,23 +270,6 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
 
-  DeviceTypeVector device_types;
-  status = SupportedDeviceTypesForNode({DeviceType(DEVICE_CPU)}, node,
-                                       &device_types);
-  if (!status.ok()) {
-    return false;
-  }
-  // Only fold ops with a CPU implementation available.
-  if (device_types.empty()) {
-    return false;
-  }
-  DCHECK_EQ(DeviceType(DEVICE_CPU), device_types[0]);
-
-  // Folding not applicable to ops with no inputs.
-  if (node.input().empty()) {
-    return false;
-  }
-
   // No need to (and don't) fold nodes that have no outgoing edges. Such nodes
   // could be introduced by an earlier constant folding pass and are preserved
   // in case users want to fetch their values; re-processing them would
@@ -391,12 +380,15 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
       // Control dependency
       break;
     }
-    // There should be a single output since the input node should be a constant
-    // node.
-    TensorVector output;
-    TF_RETURN_IF_ERROR(
-        EvaluateNode(*node_map_->GetNode(input), TensorVector(), &output));
-    inputs.push_back(output[position]);
+    const NodeDef* input_node = node_map_->GetNode(input);
+    if (!IsConstant(*input_node)) {
+      return Status(error::INVALID_ARGUMENT,
+                    strings::StrCat("Can't fold ", node.name(), ", its ", input,
+                                    " isn't constant"));
+    }
+    Tensor* value = new Tensor(input_node->attr().at("dtype").type());
+    CHECK(value->FromProto(input_node->attr().at("value").tensor()));
+    inputs.emplace_back(value);
   }
 
   TensorVector output_tensors;
@@ -583,24 +575,31 @@ Status ConstantFolding::FoldNode(const NodeDef& node, GraphDef* output) {
 
 Status ConstantFolding::FoldGraph(GraphDef* output) {
   std::unordered_set<string> processed_nodes;
-  int previously_processed = 0;
-  do {
-    previously_processed = processed_nodes.size();
-    for (const auto& node : graph_.node()) {
-      if (IsFoldable(node) &&
-          processed_nodes.find(node.name()) == processed_nodes.end()) {
-        Status s = FoldNode(node, output);
-        if (!s.ok()) {
-          VLOG(1) << "Failed to fold node " << node.name() << ": " << s;
+  std::deque<const NodeDef*> queue;
+  for (const auto& node : graph_.node()) {
+    if (IsFoldable(node)) {
+      queue.push_back(&node);
+    }
+  }
+  while (!queue.empty()) {
+    const NodeDef* node = queue.front();
+    queue.pop_front();
+    if (processed_nodes.count(node->name())) {
+      continue;
+    }
+    Status s = FoldNode(*node, output);
+    processed_nodes.insert(node->name());
+    if (!s.ok()) {
+      VLOG(1) << "Failed to fold node " << node->name() << ": " << s;
+    } else {
+      auto outputs = node_map_->GetOutputs(node->name());
+      for (auto& output : outputs) {
+        if (IsFoldable(*output)) {
+          queue.push_back(output);
         }
-        processed_nodes.insert(node.name());
       }
     }
-    // Try again as long as we find new constants. In most cases, this loop will
-    // only run once since the graph is already in topological order.
-    VLOG(1) << "Folded " << processed_nodes.size() - previously_processed
-            << " nodes in this pass";
-  } while (previously_processed != processed_nodes.size());
+  }
 
   // Build the graph after constant folding. Note that we keep all processed
   // nodes in the graph in case users need to fetch their values.
@@ -740,7 +739,6 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
 Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
                                  GraphDef* output) {
   graph_ = item.graph;
-  LOG(INFO) << "Initial graph size: " << item.graph.node_size();
   node_map_.reset(new NodeMap(&graph_));
   for (const auto& node : item.fetch) {
     nodes_to_preserve_.insert(NodeName(node));
@@ -761,7 +759,6 @@ Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
 
   TF_RETURN_IF_ERROR(FoldGraph(output));
   TF_RETURN_IF_ERROR(SimplifyGraph(output, properties));
-  LOG(INFO) << "Optimized graph size: " << output->node_size();
 
   *output->mutable_library() = item.graph.library();
   *output->mutable_versions() = item.graph.versions();
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index fffcb980db..a493452777 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -701,6 +701,39 @@ tf_kernel_library(
     deps = ARRAY_DEPS,
 )
 
+tf_kernel_library(
+    name = "compare_and_bitpack_op",
+    srcs = ["compare_and_bitpack_op.cc"],
+    hdrs = ["compare_and_bitpack_op.h"],
+    gpu_srcs = [
+        "compare_and_bitpack_op.h",
+        "compare_and_bitpack_op_gpu.cu.cc",
+    ],
+    deps = ARRAY_DEPS,
+)
+
+# TODO(ebrevdo): Add benchmarks once the op is in the autogen array namespace.
+# tf_cuda_cc_test(
+#     name = "compare_and_bitpack_op_test",
+#     srcs = ["compare_and_bitpack_op_test.cc"],
+#     deps = [
+#         ":array",
+#         ":ops_testutil",
+#         ":ops_util",
+#         "//third_party/eigen3",
+#         "//tensorflow/cc:cc_ops",
+#         "//tensorflow/cc:cc_ops_internal",
+#         "//tensorflow/core:core_cpu",
+#         "//tensorflow/core:core_cpu_internal",
+#         "//tensorflow/core:framework",
+#         "//tensorflow/core:lib",
+#         "//tensorflow/core:protos_all_cc",
+#         "//tensorflow/core:test",
+#         "//tensorflow/core:test_main",
+#         "//tensorflow/core:testlib",
+#     ],
+# )
+
 tf_kernel_library(
     name = "reshape_op",
     prefix = "reshape_op",
@@ -2344,10 +2377,12 @@ cc_library(
         ":bucketize_op",
         ":cast_op",
         ":check_numerics_op",
+        ":compare_and_bitpack_op",
         ":cross_op",
         ":cwise_op",
         ":fft_ops",
         ":matmul_op",
+        ":population_count_op",
         ":reduction_ops",
         ":scan_ops",
         ":segment_reduction_ops",
@@ -2409,6 +2444,12 @@ tf_kernel_library(
     deps = MATH_DEPS + ["//tensorflow/core:bitwise_ops_op_lib"],
 )
 
+tf_kernel_library(
+    name = "population_count_op",
+    prefix = "population_count_op",
+    deps = MATH_DEPS,
+)
+
 tf_kernel_library(
     name = "fft_ops",
     prefix = "fft_ops",
@@ -4292,6 +4333,8 @@ filegroup(
         "fake_quant_ops.cc",
         "fifo_queue.cc",
         "fused_batch_norm_op.cc",
+        "population_count_op.cc",
+        "population_count_op.h",
         "winograd_transform.h",
         ":android_extended_ops_headers",
     ] + select({
diff --git a/tensorflow/core/kernels/compare_and_bitpack_op.cc b/tensorflow/core/kernels/compare_and_bitpack_op.cc
new file mode 100644
index 0000000000..9f626a274a
--- /dev/null
+++ b/tensorflow/core/kernels/compare_and_bitpack_op.cc
@@ -0,0 +1,185 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// See docs in ../ops/math_ops.cc
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow/core/kernels/compare_and_bitpack_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+class CompareAndBitpackOp : public OpKernel {
+ public:
+  explicit CompareAndBitpackOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* c) override {
+    const Tensor& input_t = c->input(0);
+    const Tensor& threshold_t = c->input(1);
+    OP_REQUIRES(
+        c, TensorShapeUtils::IsScalar(threshold_t.shape()),
+        errors::InvalidArgument("Compare must be a scalar, but saw shape: ",
+                                threshold_t.shape().DebugString()));
+    const TensorShape& input_shape = input_t.shape();
+    OP_REQUIRES(c, TensorShapeUtils::IsVectorOrHigher(input_shape),
+                errors::InvalidArgument(
+                    "Input should be at least a vector, but saw a scalar."));
+    OP_REQUIRES(c, input_shape.dim_size(input_shape.dims() - 1) % 8 == 0,
+                errors::InvalidArgument(
+                    "Inner dimension of input should be "
+                    "divisible by ",
+                    8, ", but saw shape: ", input_shape.DebugString()));
+
+    TensorShape output_shape = input_shape;
+    int rank = input_shape.dims();
+    output_shape.set_dim(rank - 1, input_shape.dim_size(rank - 1) / 8);
+
+    Tensor* output_t;
+    OP_REQUIRES_OK(c, c->allocate_output(0, output_shape, &output_t));
+
+    auto input = input_t.flat_inner_dims<T>();
+    auto threshold = threshold_t.scalar<T>();
+    auto output = output_t->flat_inner_dims<uint8>();
+
+    functor::CompareAndBitpack<Device, T> func;
+    func(c, input, threshold, output);
+  }
+};
+
+#define REGISTER_COMPARE_AND_BITPACK(type)                                    \
+  REGISTER_KERNEL_BUILDER(                                                    \
+      Name("CompareAndBitpack").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      CompareAndBitpackOp<CPUDevice, type>);
+
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_COMPARE_AND_BITPACK);
+TF_CALL_bool(REGISTER_COMPARE_AND_BITPACK);
+
+#undef REGISTER_COMPARE_AND_BITPACK
+
+namespace functor {
+
+template <typename T, class = void, class = void>
+struct ComputeShard {
+  static EIGEN_STRONG_INLINE void Compute(typename TTypes<T>::ConstMatrix input,
+                                          typename TTypes<uint8>::Matrix output,
+                                          const T& thresh, int64 start,
+                                          int64 limit) {
+    for (int64 i = start; i < limit; ++i) {
+      uint8* out = output.data() + i;
+      const T* block = input.data() + 8 * i;
+      *out = ((((block[0] > thresh) << 7)) | (((block[1] > thresh) << 6)) |
+              (((block[2] > thresh) << 5)) | (((block[3] > thresh) << 4)) |
+              (((block[4] > thresh) << 3)) | (((block[5] > thresh) << 2)) |
+              (((block[6] > thresh) << 1)) | (((block[7] > thresh))));
+    }
+  }
+};
+
+// Specialization for bool on systems where sizeof(bool) == 1.
+template <typename T>
+struct ComputeShard<T,
+                    typename std::enable_if<std::is_same<T, bool>::value>::type,
+                    typename std::enable_if<sizeof(T) == 1>::type> {
+  static EIGEN_STRONG_INLINE void Compute(
+      typename TTypes<bool>::ConstMatrix input,
+      typename TTypes<uint8>::Matrix output, bool /*thresh*/, int64 start,
+      int64 limit) {
+    // NOTE(ebrevdo): This assumes memory is little-endian.
+    for (int64 i = start; i < limit; ++i) {
+      uint8* out = output.data() + i;
+      const int64 block = *reinterpret_cast<const int64*>(input.data() + 8 * i);
+      *out =
+          ((((block & (1LL << (7 * 8))) >> (7 * 8 - 0))) |
+           (((block & (1LL << (6 * 8))) >> (6 * 8 - 1))) |
+           (((block & (1LL << (5 * 8))) >> (5 * 8 - 2))) |
+           (((block & (1LL << (4 * 8))) >> (4 * 8 - 3))) |
+           (((block & (1LL << (3 * 8))) >> (3 * 8 - 4))) |
+           (((block & (1LL << (2 * 8))) >> (2 * 8 - 5))) |
+           (((block & (1LL << 8)) >> (1 * 8 - 6))) | (((block & (1LL)) << 7)));
+    }
+  }
+};
+
+template <typename T>
+struct CompareAndBitpack<CPUDevice, T> {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstMatrix input,
+                  typename TTypes<T>::ConstScalar threshold,
+                  TTypes<uint8>::Matrix output) {
+    const T thresh = threshold();
+    auto shard = [&, thresh](int64 start, int64 limit) {
+      ComputeShard<T>::Compute(input, output, thresh, start, limit);
+    };
+    int64 total_shards = output.size();  // Approximate cmp as an add and
+                                         // bitwise-or + shift as an add.
+    const double total_cost = 8 * (Eigen::TensorOpCost::AddCost<T>() +
+                                   Eigen::TensorOpCost::AddCost<uint8>());
+    const int64 shard_cost = (total_cost >= static_cast<double>(kint64max))
+                                 ? kint64max
+                                 : static_cast<int64>(total_cost);
+
+    auto worker_threads = *(c->device()->tensorflow_cpu_worker_threads());
+    Shard(worker_threads.num_threads, worker_threads.workers, total_shards,
+          shard_cost, shard);
+  }
+};
+
+}  // namespace functor
+
+#if GOOGLE_CUDA
+
+#define REGISTER_COMPARE_AND_BITPACK(type)                                    \
+  REGISTER_KERNEL_BUILDER(                                                    \
+      Name("CompareAndBitpack").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
+      CompareAndBitpackOp<GPUDevice, type>);
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_COMPARE_AND_BITPACK);
+TF_CALL_bool(REGISTER_COMPARE_AND_BITPACK);
+
+#undef REGISTER_COMPARE_AND_BITPACK
+
+namespace functor {
+
+#define DECLARE_GPU_SPEC(T)                                      \
+  template <>                                                    \
+  void CompareAndBitpack<GPUDevice, T>::operator()(              \
+      OpKernelContext* c, typename TTypes<T>::ConstMatrix input, \
+      typename TTypes<T>::ConstScalar threshold,                 \
+      TTypes<uint8>::Matrix output);                             \
+  extern template struct CompareAndBitpack<GPUDevice, T>;
+
+TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC)
+TF_CALL_bool(DECLARE_GPU_SPEC)
+
+#undef DECLARE_GPU_SPEC
+
+}  // namespace functor
+
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/compare_and_bitpack_op.h b/tensorflow/core/kernels/compare_and_bitpack_op.h
new file mode 100644
index 0000000000..8e020249c1
--- /dev/null
+++ b/tensorflow/core/kernels/compare_and_bitpack_op.h
@@ -0,0 +1,42 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_COMPARE_AND_BITPACK_OP_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_COMPARE_AND_BITPACK_OP_H_
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+namespace functor {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+struct CompareAndBitpack {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstMatrix input,
+                  typename TTypes<T>::ConstScalar threshold,
+                  TTypes<uint8>::Matrix output);
+};
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_COMPARE_AND_BITPACK_OP_H_
diff --git a/tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc b/tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc
new file mode 100644
index 0000000000..345405e3fe
--- /dev/null
+++ b/tensorflow/core/kernels/compare_and_bitpack_op_gpu.cu.cc
@@ -0,0 +1,141 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "tensorflow/core/kernels/compare_and_bitpack_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
+namespace tensorflow {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace functor {
+
+template <typename T>
+__global__ void CompareAndBitpackKernel(const int size, const T* threshold,
+                                        const T* input, uint8* output) {
+  // TODO(ebrevdo): Erich said: to get a better memory access pattern
+  // you could have 8 threads load this data and do a comparison, then
+  // use the ballot instruction to combine the values from each thread
+  // in the warp in one instruction (so each thread will have the
+  // result for 4 blocks) followed by an appropriate shift and mask to
+  // get the 8-bits of interest.
+  const T thresh = ldg(threshold);
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const T* block = input + 8 * i;
+    output[i] =
+        ((((ldg(block) > thresh) << 7)) | (((ldg(block + 1) > thresh) << 6)) |
+         (((ldg(block + 2) > thresh) << 5)) |
+         (((ldg(block + 3) > thresh) << 4)) |
+         (((ldg(block + 4) > thresh) << 3)) |
+         (((ldg(block + 5) > thresh) << 2)) |
+         (((ldg(block + 6) > thresh) << 1)) | (((ldg(block + 7) > thresh))));
+  }
+}
+
+template <>
+__global__ void CompareAndBitpackKernel<bool>(const int size,
+                                              const bool* threshold,
+                                              const bool* input,
+                                              uint8* output) {
+  // TODO(ebrevdo): Erich said: I think you could again have multiple
+  // threads work on one block and use the ballot instruction to the
+  // bit packing in one instruction.
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const int64 block = ldg(reinterpret_cast<const int64*>(input + 8 * i));
+    // NOTE(ebrevdo): This assumes memory is little-endian.
+    output[i] =
+        ((((block & (1LL << (7 * 8))) >> (7 * 8 - 0))) |
+         (((block & (1LL << (6 * 8))) >> (6 * 8 - 1))) |
+         (((block & (1LL << (5 * 8))) >> (5 * 8 - 2))) |
+         (((block & (1LL << (4 * 8))) >> (4 * 8 - 3))) |
+         (((block & (1LL << (3 * 8))) >> (3 * 8 - 4))) |
+         (((block & (1LL << (2 * 8))) >> (2 * 8 - 5))) |
+         (((block & (1LL << 8)) >> (1 * 8 - 6))) | (((block & (1LL)) << 7)));
+  }
+}
+
+template <>
+__global__ void CompareAndBitpackKernel<float>(const int size,
+                                               const float* threshold,
+                                               const float* input,
+                                               uint8* output) {
+  const float thresh = ldg(threshold);
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const float4 block0 = ldg(reinterpret_cast<const float4*>(input + 8 * i));
+    const float4 block1 =
+        ldg(reinterpret_cast<const float4*>(input + 8 * i + 4));
+    output[i] = ((((block0.x > thresh) << 7)) | (((block0.y > thresh) << 6)) |
+                 (((block0.z > thresh) << 5)) | (((block0.w > thresh) << 4)) |
+                 (((block1.x > thresh) << 3)) | (((block1.y > thresh) << 2)) |
+                 (((block1.z > thresh) << 1)) | (((block1.w > thresh))));
+  }
+}
+
+template <>
+__global__ void CompareAndBitpackKernel<double>(const int size,
+                                                const double* threshold,
+                                                const double* input,
+                                                uint8* output) {
+  const double thresh = ldg(threshold);
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    const double2 block0 = ldg(reinterpret_cast<const double2*>(input + 8 * i));
+    const double2 block1 =
+        ldg(reinterpret_cast<const double2*>(input + 8 * i + 2));
+    const double2 block2 =
+        ldg(reinterpret_cast<const double2*>(input + 8 * i + 4));
+    const double2 block3 =
+        ldg(reinterpret_cast<const double2*>(input + 8 * i + 6));
+    output[i] = ((((block0.x > thresh) << 7)) | (((block0.y > thresh) << 6)) |
+                 (((block1.x > thresh) << 5)) | (((block1.y > thresh) << 4)) |
+                 (((block2.x > thresh) << 3)) | (((block2.y > thresh) << 2)) |
+                 (((block3.x > thresh) << 1)) | (((block3.y > thresh))));
+  }
+}
+
+#define DEFINE_GPU_SPECS(T)                                               \
+  template <>                                                             \
+  void CompareAndBitpack<GPUDevice, T>::operator()(                       \
+      OpKernelContext* c, typename TTypes<T>::ConstMatrix input,          \
+      typename TTypes<T>::ConstScalar threshold,                          \
+      TTypes<uint8>::Matrix output) {                                     \
+    const GPUDevice& d = c->eigen_device<GPUDevice>();                    \
+    int64 total_count = output.size();                                    \
+    CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);        \
+                                                                          \
+    CompareAndBitpackKernel<T>                                            \
+        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>( \
+            total_count, threshold.data(), input.data(), output.data());  \
+  }
+
+TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS)
+TF_CALL_bool(DEFINE_GPU_SPECS)
+
+#undef DECLARE_GPU_SPECS
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/population_count_op.cc b/tensorflow/core/kernels/population_count_op.cc
new file mode 100644
index 0000000000..12ff6b69f8
--- /dev/null
+++ b/tensorflow/core/kernels/population_count_op.cc
@@ -0,0 +1,163 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// See docs in ../ops/math_ops.cc
+
+#define EIGEN_USE_THREADS
+
+#include <bitset>
+
+#include "tensorflow/core/kernels/population_count_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/util/work_sharder.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+class PopulationCountOp : public OpKernel {
+ public:
+  explicit PopulationCountOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* c) override {
+    const Tensor& input_t = c->input(0);
+    Tensor* output_t;
+    OP_REQUIRES_OK(c, c->allocate_output(0, input_t.shape(), &output_t));
+
+    auto input = input_t.flat<T>();
+    auto output = output_t->flat<uint8>();
+
+    functor::PopulationCount<Device, T> popcnt;
+    popcnt(c, input, output);
+  }
+};
+
+#define REGISTER_POPULATION_COUNT(type)                                     \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("PopulationCount").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      PopulationCountOp<CPUDevice, type>);
+
+TF_CALL_uint8(REGISTER_POPULATION_COUNT);
+TF_CALL_int8(REGISTER_POPULATION_COUNT);
+TF_CALL_uint16(REGISTER_POPULATION_COUNT);
+TF_CALL_int16(REGISTER_POPULATION_COUNT);
+TF_CALL_int32(REGISTER_POPULATION_COUNT);
+TF_CALL_int64(REGISTER_POPULATION_COUNT);
+
+#undef REGISTER_POPULATION_COUNT
+
+namespace functor {
+
+namespace {
+
+template <typename T>
+inline uint8 PopCnt(const T v);
+
+#define POPCNT(T, N)                  \
+  template <>                         \
+  uint8 PopCnt<T>(const T v) {        \
+    return std::bitset<N>(v).count(); \
+  }
+
+POPCNT(int8, 8);
+POPCNT(uint8, 8);
+POPCNT(int16, 16);
+POPCNT(uint16, 16);
+POPCNT(int32, 32);
+POPCNT(int64, 64);
+
+#undef POPCNT
+
+}  // namespace
+
+template <typename T>
+struct PopulationCount<CPUDevice, T> {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstFlat input,
+                  TTypes<uint8>::Flat output) {
+    const T* input_ptr = input.data();
+    uint8* output_ptr = output.data();
+    auto shard = [input_ptr, output_ptr](int64 start, int64 limit) {
+      for (int64 i = start; i < limit; ++i) {
+        output_ptr[i] = PopCnt<T>(input_ptr[i]);
+      }
+    };
+    int64 total_shards = input.size();
+    // Approximating cost of popcnt: convert T to int64
+    // (std::bitset constructor) and convert int64 to uint8
+    // (bitset.count() -> output).  The .count() itself is relatively cheap.
+    const double total_cost = (Eigen::TensorOpCost::CastCost<T, uint8>() +
+                               Eigen::TensorOpCost::CastCost<int64, uint8>());
+    const int64 shard_cost = (total_cost >= static_cast<double>(kint64max))
+                                 ? kint64max
+                                 : static_cast<int64>(total_cost);
+
+    auto worker_threads = *(c->device()->tensorflow_cpu_worker_threads());
+    Shard(worker_threads.num_threads, worker_threads.workers, total_shards,
+          shard_cost, shard);
+  }
+};
+
+}  // namespace functor
+
+#if GOOGLE_CUDA
+
+#define REGISTER_POPULATION_COUNT(type)                                     \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("PopulationCount").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
+      PopulationCountOp<GPUDevice, type>)
+
+TF_CALL_uint8(REGISTER_POPULATION_COUNT);
+TF_CALL_int8(REGISTER_POPULATION_COUNT);
+TF_CALL_uint16(REGISTER_POPULATION_COUNT);
+TF_CALL_int16(REGISTER_POPULATION_COUNT);
+TF_CALL_int32(REGISTER_POPULATION_COUNT);
+TF_CALL_int64(REGISTER_POPULATION_COUNT);
+
+#undef REGISTER_POPULATION_COUNT
+
+namespace functor {
+
+#define DECLARE_GPU_SPEC(T)                                    \
+  template <>                                                  \
+  void PopulationCount<GPUDevice, T>::operator()(              \
+      OpKernelContext* c, typename TTypes<T>::ConstFlat input, \
+      TTypes<uint8>::Flat output);                             \
+  extern template struct PopulationCount<GPUDevice, T>
+
+TF_CALL_uint8(DECLARE_GPU_SPEC);
+TF_CALL_int8(DECLARE_GPU_SPEC);
+TF_CALL_uint16(DECLARE_GPU_SPEC);
+TF_CALL_int16(DECLARE_GPU_SPEC);
+TF_CALL_int32(DECLARE_GPU_SPEC);
+TF_CALL_int64(DECLARE_GPU_SPEC);
+
+#undef DECLARE_GPU_SPEC
+
+}  // namespace functor
+
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/population_count_op.h b/tensorflow/core/kernels/population_count_op.h
new file mode 100644
index 0000000000..de89582e13
--- /dev/null
+++ b/tensorflow/core/kernels/population_count_op.h
@@ -0,0 +1,38 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_POPULATION_COUNT_OP_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_POPULATION_COUNT_OP_H_
+
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+namespace functor {
+
+template <typename Device, typename T>
+struct PopulationCount {
+  void operator()(OpKernelContext* c, typename TTypes<T>::ConstFlat input,
+                  TTypes<uint8>::Flat output);
+};
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_POPULATION_COUNT_OP_H_
diff --git a/tensorflow/core/kernels/population_count_op_gpu.cu.cc b/tensorflow/core/kernels/population_count_op_gpu.cu.cc
new file mode 100644
index 0000000000..27a687ba40
--- /dev/null
+++ b/tensorflow/core/kernels/population_count_op_gpu.cu.cc
@@ -0,0 +1,92 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include "tensorflow/core/kernels/population_count_op.h"
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
+namespace tensorflow {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+namespace functor {
+
+template <typename T>
+__global__ void PopulationCountKernel(const int size, const T* input,
+                                      uint8* output) {
+  CUDA_1D_KERNEL_LOOP(i, size) { output[i] = __popc(ldg(input + i)); }
+}
+
+template <>
+__global__ void PopulationCountKernel(const int size, const int8* input,
+                                      uint8* output) {
+  // For some reason, __popc on a negative int8 gets confused.
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    output[i] = __popc(ldg(reinterpret_cast<const uint8*>(input + i)));
+  }
+}
+
+template <>
+__global__ void PopulationCountKernel(const int size, const int16* input,
+                                      uint8* output) {
+  // For some reason, __popc on a negative int16 gets confused.
+  CUDA_1D_KERNEL_LOOP(i, size) {
+    output[i] = __popc(ldg(reinterpret_cast<const uint16*>(input + i)));
+  }
+}
+
+template <>
+__global__ void PopulationCountKernel<int64>(const int size, const int64* input,
+                                             uint8* output) {
+  CUDA_1D_KERNEL_LOOP(i, size) { output[i] = __popcll(ldg(input + i)); }
+}
+
+#define DEFINE_GPU_SPECS(T)                                               \
+  template <>                                                             \
+  void PopulationCount<GPUDevice, T>::operator()(                         \
+      OpKernelContext* c, typename TTypes<T>::ConstFlat input,            \
+      TTypes<uint8>::Flat output) {                                       \
+    const GPUDevice& d = c->eigen_device<GPUDevice>();                    \
+    int64 total_count = input.size();                                     \
+    CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);        \
+    PopulationCountKernel<T>                                              \
+        <<<config.block_count, config.thread_per_block, 0, d.stream()>>>( \
+            total_count, input.data(), output.data());                    \
+  }
+
+TF_CALL_uint8(DEFINE_GPU_SPECS);
+TF_CALL_int8(DEFINE_GPU_SPECS);
+TF_CALL_uint16(DEFINE_GPU_SPECS);
+TF_CALL_int16(DEFINE_GPU_SPECS);
+TF_CALL_int32(DEFINE_GPU_SPECS);
+TF_CALL_int64(DEFINE_GPU_SPECS);
+
+#undef DEFINE_GPU_SPECS
+
+}  // namespace functor
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/ops/bitwise_ops.cc b/tensorflow/core/ops/bitwise_ops.cc
index 2005d5e102..3ffc4ab74a 100644
--- a/tensorflow/core/ops/bitwise_ops.cc
+++ b/tensorflow/core/ops/bitwise_ops.cc
@@ -40,6 +40,22 @@ computation is performed on the underlying representation of x.
       .Attr("T: {int8, int16, int32, int64, uint8, uint16}") \
       .SetShapeFn(shape_inference::UnchangedShape)
 
+REGISTER_OP("PopulationCount")
+    .Input("x: T")
+    .Output("y: uint8")
+    .Attr("T: {int8, int16, int32, int64, uint8, uint16}")
+    .SetShapeFn(shape_inference::UnchangedShape)
+    .Doc(R"doc(
+Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
+
+For each entry in `x`, calculates the number of `1` (on) bits in the binary
+representation of that entry.
+
+**NOTE**: It is more efficient to first `tf.bitcast` your tensors into
+`int32` or `int64` and perform the bitcount on the result, than to feed in
+8- or 16-bit inputs and then aggregate the resulting counts.
+)doc");
+
 REGISTER_OP("BitwiseAnd").BINARY_BITWISE().Doc(R"doc(
 Elementwise computes the bitwise AND of `x` and `y`.
 
diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 94224f22b9..b82035bfc3 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -4597,6 +4597,37 @@ op {
     }
   }
 }
+op {
+  name: "CompareAndBitpack"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "threshold"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BOOL
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
 op {
   name: "Complex"
   input_arg {
@@ -16267,6 +16298,31 @@ op {
     }
   }
 }
+op {
+  name: "PopulationCount"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+}
 op {
   name: "Pow"
   input_arg {
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index f4e0625c66..36f999ff60 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -2458,6 +2458,64 @@ out_type: The type of the output. Should be a lower bit depth than Tinput.
 
 )doc");
 
+REGISTER_OP("CompareAndBitpack")
+    .Input("input: T")
+    .Input("threshold: T")
+    .Output("output: uint8")
+    .Attr("T: {bool, float16, float32, float64, int8, int16, int32, int64}")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle input;
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &input));
+      ShapeHandle unused;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
+      ShapeHandle output = input;
+      if (c->RankKnown(input)) {
+        int rank = c->Rank(input);
+        auto inner_dim = c->Dim(input, rank - 1);
+        DimensionHandle inferred_dim;
+        TF_RETURN_IF_ERROR(c->Divide(inner_dim, 8,
+                                     /* evenly_divisible */ true,
+                                     &inferred_dim));
+        TF_RETURN_IF_ERROR(
+            c->ReplaceDim(output, rank - 1, inferred_dim, &output));
+      }
+      c->set_output(0, output);
+
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
+
+Each comparison returns a boolean `true` (if `input_value > threshold`)
+or and `false` otherwise.
+
+This operation is useful for Locality-Sensitive-Hashing (LSH) and other
+algorithms that use hashing approximations of cosine and `L2` distances;
+codes can be generated from an input via:
+
+```python
+codebook_size = 50
+codebook_bits = codebook_size * 32
+codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
+                           dtype=x.dtype,
+                           initializer=tf.orthogonal_initializer())
+codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
+codes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32
+# now codes has shape x.shape[:-1] + [codebook_size]
+```
+
+**NOTE**: Currently, the innermost dimension of the tensor must be divisible
+by 8.
+
+Given an `input` shaped `[s0, s1, ..., s_n]`, the output is
+a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`.
+
+input: Values to compare against `threshold` and bitpack.
+threshold: Threshold to compare against.
+T: The type of the input and threshold.
+output: The bitpacked comparisons.
+)doc");
+
 REGISTER_OP("RequantizationRange")
     .Input("input: Tinput")
     .Input("input_min: float")
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 1822fc1133..468434bd28 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -499,7 +499,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -601,7 +601,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -1691,7 +1691,7 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
+    description: "int32 or int64, must be in the range `[-rank(input), rank(input))`.\nDescribes which dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
@@ -1757,7 +1757,7 @@ op {
   }
   input_arg {
     name: "dimension"
-    description: "int32 or int64, 0 <= dimension < rank(input).  Describes\nwhich dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
+    description: "int32 or int64, must be in the range `[-rank(input), rank(input))`.\nDescribes which dimension of the input Tensor to reduce across. For vectors,\nuse dimension = 0."
     type_attr: "Tidx"
   }
   output_arg {
@@ -4406,6 +4406,43 @@ op {
   summary: "Computes the reverse mode backpropagated gradient of the Cholesky algorithm."
   description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by\nIain Murray http://arxiv.org/abs/1602.07527."
 }
+op {
+  name: "CompareAndBitpack"
+  input_arg {
+    name: "input"
+    description: "Values to compare against `threshold` and bitpack."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "threshold"
+    description: "Threshold to compare against."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "The bitpacked comparisons."
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    description: "The type of the input and threshold."
+    allowed_values {
+      list {
+        type: DT_BOOL
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Compare values of `input` to `threshold` and pack resulting bits into a `uint8`."
+  description: "Each comparison returns a boolean `true` (if `input_value > threshold`)\nor and `false` otherwise.\n\nThis operation is useful for Locality-Sensitive-Hashing (LSH) and other\nalgorithms that use hashing approximations of cosine and `L2` distances;\ncodes can be generated from an input via:\n\n```python\ncodebook_size = 50\ncodebook_bits = codebook_size * 32\ncodebook = tf.get_variable(\'codebook\', [x.shape[-1].value, codebook_bits],\n                           dtype=x.dtype,\n                           initializer=tf.orthogonal_initializer())\ncodes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)\ncodes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32\n# now codes has shape x.shape[:-1] + [codebook_size]\n```\n\n**NOTE**: Currently, the innermost dimension of the tensor must be divisible\nby 8.\n\nGiven an `input` shaped `[s0, s1, ..., s_n]`, the output is\na `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`."
+}
 op {
   name: "Complex"
   input_arg {
@@ -5656,10 +5693,12 @@ op {
   name: "Cumprod"
   input_arg {
     name: "x"
+    description: "A `Tensor`. Must be one of the following types: `float32`, `float64`,\n`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n`complex128`, `qint8`, `quint8`, `qint32`, `half`."
     type_attr: "T"
   }
   input_arg {
     name: "axis"
+    description: "A `Tensor` of type `int32` (default: 0). Must be in the range\n`[-rank(x), rank(x))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -5672,6 +5711,7 @@ op {
     default_value {
       b: false
     }
+    description: "If `True`, perform exclusive cumprod."
   }
   attr {
     name: "reverse"
@@ -5679,6 +5719,7 @@ op {
     default_value {
       b: false
     }
+    description: "A `bool` (default: False)."
   }
   attr {
     name: "T"
@@ -5722,10 +5763,12 @@ op {
   name: "Cumsum"
   input_arg {
     name: "x"
+    description: "A `Tensor`. Must be one of the following types: `float32`, `float64`,\n`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n`complex128`, `qint8`, `quint8`, `qint32`, `half`."
     type_attr: "T"
   }
   input_arg {
     name: "axis"
+    description: "A `Tensor` of type `int32` (default: 0). Must be in the range\n`[-rank(x), rank(x))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -5738,6 +5781,7 @@ op {
     default_value {
       b: false
     }
+    description: "If `True`, perform exclusive cumsum."
   }
   attr {
     name: "reverse"
@@ -5745,6 +5789,7 @@ op {
     default_value {
       b: false
     }
+    description: "A `bool` (default: False)."
   }
   attr {
     name: "T"
@@ -7615,7 +7660,7 @@ op {
   }
   input_arg {
     name: "dim"
-    description: "0-D (scalar). Specifies the dimension index at which to\nexpand the shape of `input`."
+    description: "0-D (scalar). Specifies the dimension index at which to\nexpand the shape of `input`. Must be in the range\n`[-rank(input) - 1, rank(input)]`."
     type_attr: "Tdim"
   }
   output_arg {
@@ -12325,7 +12370,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -13102,7 +13147,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -13293,7 +13338,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -15436,6 +15481,33 @@ op {
   summary: "Compute the polygamma function \\\\(\\psi^{(n)}(x)\\\\)."
   description: "The polygamma function is defined as:\n\n\n\\\\(\\psi^{(n)}(x) = \\frac{d^n}{dx^n} \\psi(x)\\\\)\n\nwhere \\\\(\\psi(x)\\\\) is the digamma function."
 }
+op {
+  name: "PopulationCount"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type: DT_UINT8
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Computes element-wise population count (a.k.a. popcount, bitsum, bitcount)."
+  description: "For each entry in `x`, calculates the number of `1` (on) bits in the binary\nrepresentation of that entry.\n\n**NOTE**: It is more efficient to first `tf.bitcast` your tensors into\n`int32` or `int64` and perform the bitcount on the result, than to feed in\n8- or 16-bit inputs and then aggregate the resulting counts."
+}
 op {
   name: "Pow"
   input_arg {
@@ -15662,7 +15734,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -21708,7 +21780,7 @@ op {
   }
   input_arg {
     name: "axis"
-    description: "1-D. The indices of the dimensions to reverse."
+    description: "1-D. The indices of the dimensions to reverse. Must be in the range\n`[-rank(tensor), rank(tensor))`."
     type_attr: "Tidx"
   }
   output_arg {
@@ -27260,7 +27332,7 @@ op {
       list {
       }
     }
-    description: "If specified, only squeezes the dimensions listed. The dimension\nindex starts at 0. It is an error to squeeze a dimension that is not 1."
+    description: "If specified, only squeezes the dimensions listed. The dimension\nindex starts at 0. It is an error to squeeze a dimension that is not 1. Must\nbe in the range `[-rank(input), rank(input))`."
     has_minimum: true
   }
   summary: "Removes dimensions of size 1 from the shape of a tensor."
@@ -28250,7 +28322,7 @@ op {
   }
   input_arg {
     name: "reduction_indices"
-    description: "The dimensions to reduce."
+    description: "The dimensions to reduce. Must be in the range\n`[-rank(input), rank(input))`."
     type_attr: "Tidx"
   }
   output_arg {
diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl
index e1f123a0a0..04bf2aeca6 100644
--- a/tensorflow/core/platform/default/build_config_root.bzl
+++ b/tensorflow/core/platform/default/build_config_root.bzl
@@ -28,7 +28,7 @@ def tf_additional_verbs_deps():
       "//tensorflow:with_verbs_support": [
           "//tensorflow/contrib/verbs:verbs_server_lib",
           "//tensorflow/contrib/verbs:grpc_verbs_client",
-      ],
+      ], 
       "//conditions:default": [],
   })
 
diff --git a/tensorflow/core/platform/macros.h b/tensorflow/core/platform/macros.h
index eaf0171e72..47523c7d2b 100644
--- a/tensorflow/core/platform/macros.h
+++ b/tensorflow/core/platform/macros.h
@@ -20,6 +20,7 @@ limitations under the License.
 #if (defined(__GNUC__) || defined(__APPLE__)) && !defined(SWIG)
 // Compiler supports GCC-style attributes
 #define TF_ATTRIBUTE_NORETURN __attribute__((noreturn))
+#define TF_ATTRIBUTE_ALWAYS_INLINE __attribute__((always_inline))
 #define TF_ATTRIBUTE_NOINLINE __attribute__((noinline))
 #define TF_ATTRIBUTE_UNUSED __attribute__((unused))
 #define TF_ATTRIBUTE_COLD __attribute__((cold))
@@ -33,6 +34,7 @@ limitations under the License.
 #elif defined(COMPILER_MSVC)
 // Non-GCC equivalents
 #define TF_ATTRIBUTE_NORETURN __declspec(noreturn)
+#define TF_ATTRIBUTE_ALWAYS_INLINE
 #define TF_ATTRIBUTE_NOINLINE
 #define TF_ATTRIBUTE_UNUSED
 #define TF_ATTRIBUTE_COLD
@@ -43,6 +45,7 @@ limitations under the License.
 #else
 // Non-GCC equivalents
 #define TF_ATTRIBUTE_NORETURN
+#define TF_ATTRIBUTE_ALWAYS_INLINE
 #define TF_ATTRIBUTE_NOINLINE
 #define TF_ATTRIBUTE_UNUSED
 #define TF_ATTRIBUTE_COLD
diff --git a/tensorflow/core/platform/posix/error.cc b/tensorflow/core/platform/posix/error.cc
index df5c800879..e9baad5422 100644
--- a/tensorflow/core/platform/posix/error.cc
+++ b/tensorflow/core/platform/posix/error.cc
@@ -171,11 +171,7 @@ error::Code ErrnoToCode(int err_number) {
 
 Status IOError(const string& context, int err_number) {
   auto code = ErrnoToCode(err_number);
-  if (code == error::UNKNOWN) {
-    return Status(code, strings::StrCat(context, "; ", strerror(err_number)));
-  } else {
-    return Status(code, context);
-  }
+  return Status(code, strings::StrCat(context, "; ", strerror(err_number)));
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 10388509a9..095cbbe637 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -1130,7 +1130,8 @@ type SqueezeAttr func(optionalAttr)
 // SqueezeSqueezeDims sets the optional squeeze_dims attribute to value.
 //
 // value: If specified, only squeezes the dimensions listed. The dimension
-// index starts at 0. It is an error to squeeze a dimension that is not 1.
+// index starts at 0. It is an error to squeeze a dimension that is not 1. Must
+// be in the range `[-rank(input), rank(input))`.
 // If not specified, defaults to <>
 //
 // REQUIRES: len(value) >= 0
@@ -7069,6 +7070,61 @@ func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_ha
 	return op.Output(0)
 }
 
+// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
+type TextLineReaderV2Attr func(optionalAttr)
+
+// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
+//
+// value: Number of lines to skip from the beginning of every file.
+// If not specified, defaults to 0
+func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["skip_header_lines"] = value
+	}
+}
+
+// TextLineReaderV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this reader is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this reader is named in the given bucket
+// with this shared_name. Otherwise, the node name is used instead.
+// If not specified, defaults to ""
+func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// A Reader that outputs the lines of a file delimited by '\n'.
+//
+// Returns The handle to reference the Reader.
+func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "TextLineReaderV2",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes rectified linear 6: `min(max(features, 0), 6)`.
 func Relu6(scope *Scope, features tf.Output) (activations tf.Output) {
 	if scope.Err() != nil {
@@ -12819,7 +12875,8 @@ func ReciprocalGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 //
 // Arguments:
 //	tensor: Up to 8-D.
-//	axis: 1-D. The indices of the dimensions to reverse.
+//	axis: 1-D. The indices of the dimensions to reverse. Must be in the range
+// `[-rank(tensor), rank(tensor))`.
 //
 // Returns The same shape as `tensor`.
 func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) {
@@ -14493,61 +14550,6 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
-// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
-type TextLineReaderV2Attr func(optionalAttr)
-
-// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
-//
-// value: Number of lines to skip from the beginning of every file.
-// If not specified, defaults to 0
-func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["skip_header_lines"] = value
-	}
-}
-
-// TextLineReaderV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this reader is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this reader is named in the given bucket
-// with this shared_name. Otherwise, the node name is used instead.
-// If not specified, defaults to ""
-func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// A Reader that outputs the lines of a file delimited by '\n'.
-//
-// Returns The handle to reference the Reader.
-func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "TextLineReaderV2",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Component-wise multiplies a SparseTensor by a dense Tensor.
 //
 // The output locations corresponding to the implicitly zero elements in the sparse
@@ -16147,6 +16149,8 @@ func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf
 type CumprodAttr func(optionalAttr)
 
 // CumprodExclusive sets the optional exclusive attribute to value.
+//
+// value: If `True`, perform exclusive cumprod.
 // If not specified, defaults to false
 func CumprodExclusive(value bool) CumprodAttr {
 	return func(m optionalAttr) {
@@ -16155,6 +16159,8 @@ func CumprodExclusive(value bool) CumprodAttr {
 }
 
 // CumprodReverse sets the optional reverse attribute to value.
+//
+// value: A `bool` (default: False).
 // If not specified, defaults to false
 func CumprodReverse(value bool) CumprodAttr {
 	return func(m optionalAttr) {
@@ -16192,6 +16198,13 @@ func CumprodReverse(value bool) CumprodAttr {
 // ```python
 // tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
 // ```
+//
+// Arguments:
+//	x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+//	axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+// `[-rank(x), rank(x))`.
 func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
 	if scope.Err() != nil {
 		return
@@ -16420,6 +16433,8 @@ func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, ma
 type CumsumAttr func(optionalAttr)
 
 // CumsumExclusive sets the optional exclusive attribute to value.
+//
+// value: If `True`, perform exclusive cumsum.
 // If not specified, defaults to false
 func CumsumExclusive(value bool) CumsumAttr {
 	return func(m optionalAttr) {
@@ -16428,6 +16443,8 @@ func CumsumExclusive(value bool) CumsumAttr {
 }
 
 // CumsumReverse sets the optional reverse attribute to value.
+//
+// value: A `bool` (default: False).
 // If not specified, defaults to false
 func CumsumReverse(value bool) CumsumAttr {
 	return func(m optionalAttr) {
@@ -16465,6 +16482,13 @@ func CumsumReverse(value bool) CumsumAttr {
 // ```python
 // tf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]
 // ```
+//
+// Arguments:
+//	x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+//	axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+// `[-rank(x), rank(x))`.
 func Cumsum(scope *Scope, x tf.Output, axis tf.Output, optional ...CumsumAttr) (out tf.Output) {
 	if scope.Err() != nil {
 		return
@@ -17894,6 +17918,28 @@ func Svd(scope *Scope, input tf.Output, optional ...SvdAttr) (s tf.Output, u tf.
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
+//
+// For each entry in `x`, calculates the number of `1` (on) bits in the binary
+// representation of that entry.
+//
+// **NOTE**: It is more efficient to first `tf.bitcast` your tensors into
+// `int32` or `int64` and perform the bitcount on the result, than to feed in
+// 8- or 16-bit inputs and then aggregate the resulting counts.
+func PopulationCount(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "PopulationCount",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // AssertAttr is an optional argument to Assert.
 type AssertAttr func(optionalAttr)
 
@@ -18063,7 +18109,8 @@ func AnyKeepDims(value bool) AnyAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Any(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...AnyAttr) (output tf.Output) {
@@ -19213,7 +19260,8 @@ func ProdKeepDims(value bool) ProdAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Prod(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...ProdAttr) (output tf.Output) {
@@ -20258,7 +20306,8 @@ func MaxKeepDims(value bool) MaxAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Max(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MaxAttr) (output tf.Output) {
@@ -20583,7 +20632,8 @@ func Sqrt(scope *Scope, x tf.Output) (y tf.Output) {
 // Arguments:
 //
 //	dim: 0-D (scalar). Specifies the dimension index at which to
-// expand the shape of `input`.
+// expand the shape of `input`. Must be in the range
+// `[-rank(input) - 1, rank(input)]`.
 //
 // Returns Contains the same data as `input`, but its shape has an additional
 // dimension of size 1 added.
@@ -20623,7 +20673,8 @@ func AllKeepDims(value bool) AllAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func All(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...AllAttr) (output tf.Output) {
@@ -21665,8 +21716,8 @@ func ArgMinOutputType(value tf.DataType) ArgMinAttr {
 //
 // Arguments:
 //
-//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
-// which dimension of the input Tensor to reduce across. For vectors,
+//	dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+// Describes which dimension of the input Tensor to reduce across. For vectors,
 // use dimension = 0.
 func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) {
 	if scope.Err() != nil {
@@ -22716,7 +22767,8 @@ func MeanKeepDims(value bool) MeanAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Mean(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MeanAttr) (output tf.Output) {
@@ -22856,7 +22908,8 @@ func MinKeepDims(value bool) MinAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Min(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MinAttr) (output tf.Output) {
@@ -22914,8 +22967,8 @@ func ArgMaxOutputType(value tf.DataType) ArgMaxAttr {
 //
 // Arguments:
 //
-//	dimension: int32 or int64, 0 <= dimension < rank(input).  Describes
-// which dimension of the input Tensor to reduce across. For vectors,
+//	dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`.
+// Describes which dimension of the input Tensor to reduce across. For vectors,
 // use dimension = 0.
 func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) {
 	if scope.Err() != nil {
@@ -23888,6 +23941,51 @@ func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Outp
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
+//
+// Each comparison returns a boolean `true` (if `input_value > threshold`)
+// or and `false` otherwise.
+//
+// This operation is useful for Locality-Sensitive-Hashing (LSH) and other
+// algorithms that use hashing approximations of cosine and `L2` distances;
+// codes can be generated from an input via:
+//
+// ```python
+// codebook_size = 50
+// codebook_bits = codebook_size * 32
+// codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
+//                            dtype=x.dtype,
+//                            initializer=tf.orthogonal_initializer())
+// codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
+// codes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32
+// # now codes has shape x.shape[:-1] + [codebook_size]
+// ```
+//
+// **NOTE**: Currently, the innermost dimension of the tensor must be divisible
+// by 8.
+//
+// Given an `input` shaped `[s0, s1, ..., s_n]`, the output is
+// a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`.
+//
+// Arguments:
+//	input: Values to compare against `threshold` and bitpack.
+//	threshold: Threshold to compare against.
+//
+// Returns The bitpacked comparisons.
+func CompareAndBitpack(scope *Scope, input tf.Output, threshold tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "CompareAndBitpack",
+		Input: []tf.Input{
+			input, threshold,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
 //
 // Arguments:
@@ -24724,7 +24822,8 @@ func SumKeepDims(value bool) SumAttr {
 //
 // Arguments:
 //	input: The tensor to reduce.
-//	reduction_indices: The dimensions to reduce.
+//	reduction_indices: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
 //
 // Returns The reduced tensor.
 func Sum(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...SumAttr) (output tf.Output) {
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index cac05c372a..896d466c25 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1671,6 +1671,18 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "compare_and_bitpack_op_test",
+    size = "small",
+    srcs = ["compare_and_bitpack_op_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+    ],
+)
+
 cuda_py_test(
     name = "scalar_test",
     size = "small",
diff --git a/tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py b/tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py
new file mode 100644
index 0000000000..56ddd6e428
--- /dev/null
+++ b/tensorflow/python/kernel_tests/compare_and_bitpack_op_test.py
@@ -0,0 +1,83 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow.ops.compare_and_bitpack_op."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+class CompareAndBitpackTest(test.TestCase):
+
+  def _testCompareAndBitpack(self,
+                             x, threshold,
+                             truth,
+                             expected_err_re=None):
+    with self.test_session(use_gpu=True):
+      ans = math_ops.compare_and_bitpack(x, threshold)
+      if expected_err_re is None:
+        tf_ans = ans.eval()
+        self.assertShapeEqual(truth, ans)
+        self.assertAllEqual(tf_ans, truth)
+      else:
+        with self.assertRaisesOpError(expected_err_re):
+          ans.eval()
+
+  def _testBasic(self, dtype):
+    rows = 371
+    cols = 294
+    x = np.random.randn(rows, cols * 8)
+    if dtype == np.bool:
+      x = x > 0
+    else:
+      x = x.astype(dtype)
+    threshold = dtype(0)
+    # np.packbits flattens the tensor, so we reshape it back to the
+    # expected dimensions.
+    truth = np.packbits(x > threshold).reshape(rows, cols)
+    self._testCompareAndBitpack(x, threshold, truth)
+
+  def testBasicFloat32(self):
+    self._testBasic(np.float32)
+
+  def testBasicFloat64(self):
+    self._testBasic(np.float64)
+
+  def testBasicFloat16(self):
+    self._testBasic(np.float16)
+
+  def testBasicBool(self):
+    self._testBasic(np.bool)
+
+  def testBasicInt8(self):
+    self._testBasic(np.int8)
+
+  def testBasicInt16(self):
+    self._testBasic(np.int16)
+
+  def testBasicInt32(self):
+    self._testBasic(np.int32)
+
+  def testBasicInt64(self):
+    self._testBasic(np.int64)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/kernel_tests/gather_op_test.py b/tensorflow/python/kernel_tests/gather_op_test.py
index 04d65b88a1..9a94692569 100644
--- a/tensorflow/python/kernel_tests/gather_op_test.py
+++ b/tensorflow/python/kernel_tests/gather_op_test.py
@@ -88,58 +88,58 @@ class GatherTest(test.TestCase):
 
   def testHigherRank(self):
     # We check that scalar and empty indices shapes work as well
-    for shape in (4, 3, 2), (2, 1, 3, 2):
-      for indices_shape in (), (0,), (3, 0), (3, 5), (5, 2, 3):
-        for dtype in _TEST_TYPES:
-          for axis in range(len(shape)):
-            params = self._buildParams(np.random.randn(*shape), dtype)
-            indices = np.random.randint(shape[axis], size=indices_shape)
-            with self.test_session(use_gpu=True) as sess:
-              tf_params = constant_op.constant(params)
-              tf_indices = constant_op.constant(indices)
-              # Check that both positive and negative indices for axis work.
-              tf_axis = constant_op.constant(axis)
-              tf_negative_axis = constant_op.constant(-len(shape) + axis)
-              gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
-              gather_negative_axis = array_ops.gather(
-                  tf_params, tf_indices, axis=tf_negative_axis)
-              gather_value, gather_negative_axis_value = sess.run(
-                  [gather, gather_negative_axis])
-              gather_np = np.take(params, indices, axis)
-              self.assertAllEqual(gather_np, gather_value)
-              self.assertAllEqual(gather_np, gather_negative_axis_value)
-              expected_shape = (params.shape[:axis] + indices.shape +
-                                params.shape[axis + 1:])
-              self.assertEqual(expected_shape, gather.shape)
-              self.assertEqual(expected_shape, gather_negative_axis.shape)
-
-              # Test gradients
-              gather_grad = np.random.randn(
-                  *gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
-              if dtype.is_complex:
-                gather_grad -= 1j * gather_grad
-              params_grad, indices_grad, axis_grad = gradients_impl.gradients(
-                  gather, [tf_params, tf_indices, tf_axis], gather_grad)
-              self.assertEqual(indices_grad, None)
-              self.assertEqual(axis_grad, None)
-              # For axis 0, we are able to create an efficient IndexedSlices for
-              # the gradient.
-              if axis == 0:
-                self.assertEqual(type(params_grad), ops.IndexedSlices)
-                params_grad = ops.convert_to_tensor(params_grad)
-              correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
-              outer_dims = axis
-              inner_dims = len(shape) - axis - 1
-              gather_grad = gather_grad.reshape(
-                  shape[:axis] + (indices.size,) + shape[axis + 1:])
-              for source_index, dest_index in enumerate(indices.flat):
-                dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
+    shape = (2, 1, 3, 2)
+    for indices_shape in (), (0,), (2, 0), (2, 3):
+      for dtype in _TEST_TYPES:
+        for axis in range(len(shape)):
+          params = self._buildParams(np.random.randn(*shape), dtype)
+          indices = np.random.randint(shape[axis], size=indices_shape)
+          with self.test_session(use_gpu=True) as sess:
+            tf_params = constant_op.constant(params)
+            tf_indices = constant_op.constant(indices)
+            # Check that both positive and negative indices for axis work.
+            tf_axis = constant_op.constant(axis)
+            tf_negative_axis = constant_op.constant(-len(shape) + axis)
+            gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
+            gather_negative_axis = array_ops.gather(
+                tf_params, tf_indices, axis=tf_negative_axis)
+            gather_value, gather_negative_axis_value = sess.run(
+                [gather, gather_negative_axis])
+            gather_np = np.take(params, indices, axis)
+            self.assertAllEqual(gather_np, gather_value)
+            self.assertAllEqual(gather_np, gather_negative_axis_value)
+            expected_shape = (params.shape[:axis] + indices.shape +
+                              params.shape[axis + 1:])
+            self.assertEqual(expected_shape, gather.shape)
+            self.assertEqual(expected_shape, gather_negative_axis.shape)
+
+            # Test gradients
+            gather_grad = np.random.randn(
+                *gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
+            if dtype.is_complex:
+              gather_grad -= 1j * gather_grad
+            params_grad, indices_grad, axis_grad = gradients_impl.gradients(
+                gather, [tf_params, tf_indices, tf_axis], gather_grad)
+            self.assertEqual(indices_grad, None)
+            self.assertEqual(axis_grad, None)
+            # For axis 0, we are able to create an efficient IndexedSlices for
+            # the gradient.
+            if axis == 0:
+              self.assertEqual(type(params_grad), ops.IndexedSlices)
+              params_grad = ops.convert_to_tensor(params_grad)
+            correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
+            outer_dims = axis
+            inner_dims = len(shape) - axis - 1
+            gather_grad = gather_grad.reshape(
+                shape[:axis] + (indices.size,) + shape[axis + 1:])
+            for source_index, dest_index in enumerate(indices.flat):
+              dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
+                            (slice(None),) * inner_dims)
+              source_slice = ((slice(None),) * outer_dims + (source_index,) +
                               (slice(None),) * inner_dims)
-                source_slice = ((slice(None),) * outer_dims + (source_index,) +
-                                (slice(None),) * inner_dims)
-                correct_params_grad[dest_slice] += gather_grad[source_slice]
-              self.assertAllClose(correct_params_grad, params_grad.eval(),
-                                  atol=2e-6, rtol=2e-6)
+              correct_params_grad[dest_slice] += gather_grad[source_slice]
+            self.assertAllClose(correct_params_grad, params_grad.eval(),
+                                atol=2e-6, rtol=2e-6)
 
   def testString(self):
     params = np.array([[b"asdf", b"zxcv"], [b"qwer", b"uiop"]])
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index bb86640eab..f64c89ac5d 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -330,7 +330,7 @@ def rank(input, name=None):
   # pylint: disable=redefined-builtin
   """Returns the rank of a tensor.
 
-  This operation returns an integer representing the rank of `input`.
+  Returns a 0-D `int32` `Tensor` representing the rank of `input`.
 
   For example:
 
diff --git a/tensorflow/python/ops/bitwise_ops.py b/tensorflow/python/ops/bitwise_ops.py
index cbabc3ed9b..44daf13537 100644
--- a/tensorflow/python/ops/bitwise_ops.py
+++ b/tensorflow/python/ops/bitwise_ops.py
@@ -36,5 +36,6 @@ ops.NotDifferentiable("BitwiseAnd")
 ops.NotDifferentiable("BitwiseOr")
 ops.NotDifferentiable("BitwiseXor")
 ops.NotDifferentiable("Invert")
+ops.NotDifferentiable("PopulationCount")
 
 remove_undocumented(__name__)
diff --git a/tensorflow/python/ops/bitwise_ops_test.py b/tensorflow/python/ops/bitwise_ops_test.py
index 904cf99a5a..1d08c8f82d 100644
--- a/tensorflow/python/ops/bitwise_ops_test.py
+++ b/tensorflow/python/ops/bitwise_ops_test.py
@@ -18,10 +18,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+import six
+
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import bitwise_ops
+from tensorflow.python.ops import gen_bitwise_ops
 from tensorflow.python.platform import googletest
 
 
@@ -46,6 +50,25 @@ class BitwiseOpTest(test_util.TensorFlowTestCase):
         self.assertAllEqual(or_result, [5, 5, 7, 15])
         self.assertAllEqual(xor_result, [5, 5, 4, 5])
 
+  def testPopulationCountOp(self):
+    dtype_list = [dtypes.int8, dtypes.int16,
+                  dtypes.int32, dtypes.int64,
+                  dtypes.uint8, dtypes.uint16]
+    raw_inputs = [0, 1, -1, 3, -3, 5, -5, 14, -14,
+                  127, 128, 255, 256, 65535, 65536,
+                  2**31 - 1, 2**31, 2**32 - 1, 2**32, -2**32 + 1, -2**32,
+                  -2**63 + 1, 2**63 - 1]
+    def count_bits(x):
+      return sum([bin(z).count("1") for z in six.iterbytes(x.tobytes())])
+    for dtype in dtype_list:
+      with self.test_session(use_gpu=True) as sess:
+        print("PopulationCount test: ", dtype)
+        inputs = np.array(raw_inputs, dtype=dtype.as_numpy_dtype)
+        truth = [count_bits(x) for x in inputs]
+        input_tensor = constant_op.constant(inputs, dtype=dtype)
+        popcnt_result = sess.run(gen_bitwise_ops.population_count(input_tensor))
+        self.assertAllEqual(truth, popcnt_result)
+
   def testInvertOp(self):
     dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
                   dtypes.uint8, dtypes.uint16]
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 44d6c7e275..4ba812eaf5 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -61,6 +61,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import gen_control_flow_ops
@@ -983,9 +984,16 @@ class GradLoopState(object):
             # the right control flow context.
             real_value = self._grad_context.AddValue(cur_value)
             break
+        elif constant_op.is_constant(cur_value):
+          # If the value to be forwarded is a constant, clone the constant in
+          # the gradient loop rather than using a stack.
+          # TODO(phawkins): consider hoisting the constant out of the loop
+          # instead.
+          real_value = constant_op.constant(
+              tensor_util.constant_value(cur_value), dtype=cur_value.dtype)
+          break
         else:
           # Record the history of this value in forward_ctxt.
-          # TODO(yuanbyu): Avoid recording constants.
           self._grad_context.Exit()
           history_value = cur_grad_state.AddForwardAccumulator(cur_value)
           self._grad_context.Enter()
diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py
index 1e2f999995..42b4f952bb 100644
--- a/tensorflow/python/ops/init_ops.py
+++ b/tensorflow/python/ops/init_ops.py
@@ -41,7 +41,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import math_ops
 
 
 class Initializer(object):
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index 87f6f92a8a..cc8c623947 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -830,7 +830,8 @@ class ReluTest(test_lib.TestCase):
 
 class MomentsTest(test_lib.TestCase):
 
-  def doOutputTest(self, input_shape, moments_axes, tol=1e-4):
+  def doOutputTest(self, input_shape, moments_axes, tol=1e-4,
+                   check_gradients=False):
     for mu in [0.0, 1.0, 1e3]:
       for sigma in [1.0, 0.1]:
         for keep_dims in [True, False]:
@@ -846,6 +847,15 @@ class MomentsTest(test_lib.TestCase):
               mean, variance = nn_impl.moments(
                   inputs, moments_axes, keep_dims=keep_dims)
 
+              if check_gradients:
+                err = gradient_checker.compute_gradient_error(
+                    inputs, input_shape, mean, mean.shape.as_list())
+                self.assertLess(err, 1e-3)
+                err = gradient_checker.compute_gradient_error(
+                    inputs, input_shape, variance, variance.shape.as_list())
+                self.assertLess(err, 1e-3)
+
+              # Evaluate.
               [mean, variance] = sess.run([mean, variance])
               # Make sure that there are no NaNs
               self.assertFalse(np.isnan(mean).any())
@@ -853,6 +863,12 @@ class MomentsTest(test_lib.TestCase):
               self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol)
               self.assertAllClose(variance, expected_var, rtol=tol, atol=tol)
 
+  def testOutputAndGradient2DInput0(self):
+    self.doOutputTest((10, 10), (0,), check_gradients=True)
+
+  def testOutputAndGradient2DInput01(self):
+    self.doOutputTest((10, 10), (0, 1), check_gradients=True)
+
   def testOutput2DInput0(self):
     self.doOutputTest((10, 300), (0,))
 
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index f7854e86c0..304b6ae665 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -786,13 +786,18 @@ class DropoutWrapper(RNNCell):
 class ResidualWrapper(RNNCell):
   """RNNCell wrapper that ensures cell inputs are added to the outputs."""
 
-  def __init__(self, cell):
+  def __init__(self, cell, residual_fn=None):
     """Constructs a `ResidualWrapper` for `cell`.
 
     Args:
       cell: An instance of `RNNCell`.
+      residual_fn: (Optional) The function to map raw cell inputs and raw cell
+        outputs to the actual cell outputs of the residual network.
+        Defaults to calling nest.map_structure on (lambda i, o: i + o), inputs
+        and outputs.
     """
     self._cell = cell
+    self._residual_fn = residual_fn
 
   @property
   def state_size(self):
@@ -807,7 +812,7 @@ class ResidualWrapper(RNNCell):
       return self._cell.zero_state(batch_size, dtype)
 
   def __call__(self, inputs, state, scope=None):
-    """Run the cell and add its inputs to its outputs.
+    """Run the cell and then apply the residual_fn on its inputs to its outputs.
 
     Args:
       inputs: cell inputs.
@@ -822,13 +827,14 @@ class ResidualWrapper(RNNCell):
       ValueError: If cell inputs and outputs have different structure (value).
     """
     outputs, new_state = self._cell(inputs, state, scope=scope)
-    nest.assert_same_structure(inputs, outputs)
     # Ensure shapes match
     def assert_shape_match(inp, out):
       inp.get_shape().assert_is_compatible_with(out.get_shape())
-    nest.map_structure(assert_shape_match, inputs, outputs)
-    res_outputs = nest.map_structure(
-        lambda inp, out: inp + out, inputs, outputs)
+    def default_residual_fn(inputs, outputs):
+      nest.assert_same_structure(inputs, outputs)
+      nest.map_structure(assert_shape_match, inputs, outputs)
+      return nest.map_structure(lambda inp, out: inp + out, inputs, outputs)
+    res_outputs = (self._residual_fn or default_residual_fn)(inputs, outputs)
     return (res_outputs, new_state)
 
 
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
index b21d9a8ee3..a75e9e8080 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
@@ -54,7 +54,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'cell\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'self\', \'cell\', \'residual_fn\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "add_loss"
diff --git a/tensorflow/tools/ci_build/builds/builds_common.sh b/tensorflow/tools/ci_build/builds/builds_common.sh
index 9323a96e74..e3b58d038a 100644
--- a/tensorflow/tools/ci_build/builds/builds_common.sh
+++ b/tensorflow/tools/ci_build/builds/builds_common.sh
@@ -17,6 +17,7 @@
 # Common Bash functions used by build scripts
 
 COLOR_NC='\033[0m'
+COLOR_LIGHT_GRAY='\033[0;37m'
 COLOR_GREEN='\033[0;32m'
 COLOR_RED='\033[0;31m'
 
diff --git a/tensorflow/tools/ci_build/builds/configured b/tensorflow/tools/ci_build/builds/configured
index 25cb51ea7c..563e07e3af 100755
--- a/tensorflow/tools/ci_build/builds/configured
+++ b/tensorflow/tools/ci_build/builds/configured
@@ -56,7 +56,7 @@ else
 fi
 
 pushd "${CI_TENSORFLOW_SUBMODULE_PATH:-.}"
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 popd
 
 # Gather and print build information
diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh
index db011a6bad..112dab3a73 100755
--- a/tensorflow/tools/ci_build/builds/pip.sh
+++ b/tensorflow/tools/ci_build/builds/pip.sh
@@ -73,6 +73,9 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 source "${SCRIPT_DIR}/builds_common.sh"
 
 
+SKIP_RETURN_CODE=112
+
+
 # Get the command line arguments
 CONTAINER_TYPE=$( echo "$1" | tr '[:upper:]' '[:lower:]' )
 shift
@@ -310,6 +313,13 @@ create_activate_virtualenv_and_install_tensorflow() {
 # Smoke test of tensorflow install in clean virtualenv
 ################################################################################
 do_clean_virtualenv_smoke_test() {
+  if [[ -n "${NO_TEST_ON_INSTALL}" ]] &&
+       [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
+    echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
+    echo "  Skipping smoke test of tensorflow install in clean virtualenv"
+    return ${SKIP_RETURN_CODE}
+  fi
+
   CLEAN_VENV_DIR="${PIP_TEST_ROOT}/venv_clean"
   create_activate_virtualenv_and_install_tensorflow --clean \
     "${CLEAN_VENV_DIR}" "${WHL_PATH}"
@@ -361,6 +371,7 @@ do_virtualenv_pip_test() {
      [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
     echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
     echo "  Skipping ALL Python unit tests on install"
+    return ${SKIP_RETURN_CODE}
   else
     # Call run_pip_tests.sh to perform test-on-install
     "${SCRIPT_DIR}/run_pip_tests.sh" --virtualenv ${GPU_FLAG} ${MAC_FLAG}
@@ -379,6 +390,7 @@ do_virtualenv_oss_serial_pip_test() {
      [[ "${NO_TEST_ON_INSTALL}" != "0" ]]; then
     echo "NO_TEST_ON_INSTALL=${NO_TEST_ON_INSTALL}:"
     echo "  Skipping Python unit tests on install tagged with oss_serial"
+    return ${SKIP_RETURN_CODE}
   else
     # Call run_pip_tests.sh to perform test-on-install
     "${SCRIPT_DIR}/run_pip_tests.sh" \
@@ -402,6 +414,7 @@ do_test_user_ops() {
     fi
   else
     echo "Skipping user-op test-on-install due to DO_TEST_USER_OPS = ${DO_TEST_USER_OPS}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -424,6 +437,7 @@ do_test_tfdbg_binaries() {
     popd
   else
     echo "Skipping test of tfdbg binaries due to DO_TEST_TFDBG_BINARIES = ${DO_TEST_TFDBG_BINARIES}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -439,6 +453,7 @@ do_test_tutorials() {
     fi
   else
     echo "Skipping tutorial tests-on-install due to DO_TEST_TUTORIALS = ${DO_TEST_TUTORIALS}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -455,6 +470,7 @@ do_ffmpeg_integration_test() {
     fi
   else
     echo "Skipping ffmpeg integration due to DO_INTEGRATION_TESTS = ${DO_INTEGRATION_TESTS}"
+    return ${SKIP_RETURN_CODE}
   fi
 }
 
@@ -468,6 +484,7 @@ PIP_TASKS_DESC=("Smoke test of pip install in clean virtualenv" "PIP tests in vi
 COUNTER=0
 FAIL_COUNTER=0
 PASS_COUNTER=0
+SKIP_COUNTER=0
 while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
   INDEX=COUNTER
   ((INDEX++))
@@ -480,7 +497,9 @@ while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
   ${PIP_TASKS[COUNTER]}
   RESULT=$?
 
-  if [[ ${RESULT} != "0" ]]; then
+  if [[ ${RESULT} == ${SKIP_RETURN_CODE} ]]; then
+    ((SKIP_COUNTER++))
+  elif [[ ${RESULT} != "0" ]]; then
     ((FAIL_COUNTER++))
   else
     ((PASS_COUNTER++))
@@ -503,7 +522,9 @@ while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
   ((INDEX++))
 
   echo "${INDEX}. ${PIP_TASKS[COUNTER]}: ${PIP_TASKS_DESC[COUNTER]}"
-  if [[ ${STEP_EXIT_CODES[COUNTER]} == "0" ]]; then
+  if [[ ${STEP_EXIT_CODES[COUNTER]} == ${SKIP_RETURN_CODE} ]]; then
+    printf "  ${COLOR_LIGHT_GRAY}SKIP${COLOR_NC}\n"
+  elif [[ ${STEP_EXIT_CODES[COUNTER]} == "0" ]]; then
     printf "  ${COLOR_GREEN}PASS${COLOR_NC}\n"
   else
     printf "  ${COLOR_RED}FAIL${COLOR_NC}\n"
@@ -513,7 +534,7 @@ while [[ ${COUNTER} -lt "${#PIP_TASKS[@]}" ]]; do
 done
 
 echo
-echo "${FAIL_COUNTER} failed; ${PASS_COUNTER} passed."
+echo "${SKIP_COUNTER} skipped; ${FAIL_COUNTER} failed; ${PASS_COUNTER} passed."
 
 echo
 if [[ ${FAIL_COUNTER} == "0" ]]; then
diff --git a/tensorflow/tools/ci_build/builds/run_pip_tests.sh b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
index f66846654d..9a6890401b 100755
--- a/tensorflow/tools/ci_build/builds/run_pip_tests.sh
+++ b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
@@ -120,7 +120,7 @@ else
 fi
 
 export TF_NEED_CUDA=$IS_GPU
-yes "" | ./configure
+${PYTHON_BIN_PATH} configure.py
 
 # Figure out how many concurrent tests we can run and do run the tests.
 BAZEL_PARALLEL_TEST_FLAGS=""
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
index 118e85fee0..ca84079654 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_cc_core.sh
@@ -30,7 +30,7 @@ export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 # Only running cc tests, python version does not matter.
 export PYTHON_BIN_PATH=`which python`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test --test_lang_filters=cc -k \
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
index fa3d27fa41..5c82c9efaf 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py2_core.sh
@@ -29,7 +29,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=`which python2`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test --test_lang_filters=py -k \
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
index 258dec4fec..7155636a53 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
@@ -29,7 +29,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=`which python3`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test -k \
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
index 9c450ab4da..218d2a8991 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_core.sh
@@ -29,7 +29,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=`which python3`
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test --test_lang_filters=py -k \
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
index f2ea8d3c77..dff72c25bf 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_cc_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 export TF_NEED_CUDA=1
 export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test -k \
diff --git a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
index 4e0c3d1d33..a36a8445af 100755
--- a/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
+++ b/tensorflow/tools/ci_build/linux/gpu/run_py3_core.sh
@@ -32,7 +32,7 @@ export PYTHON_BIN_PATH=`which python3`
 export TF_NEED_CUDA=1
 export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test -k \
diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
index 0b8c73993f..0ee894e2c4 100755
--- a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
+++ b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh
@@ -30,7 +30,7 @@ export TF_NEED_GCP=0
 export TF_NEED_HDFS=0
 export TF_NEED_CUDA=0
 export PYTHON_BIN_PATH=$(which python2)
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 which bazel
 bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \
     --test_timeout 300,450,1200,3600 \
diff --git a/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh b/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
index 1106413071..f548adc5ca 100755
--- a/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
+++ b/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
@@ -33,7 +33,7 @@ export TF_NEED_CUDA=1
 export TF_ENABLE_XLA=1
 export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 
-yes "" | ./configure
+$PYTHON_BIN_PATH configure.py
 
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --config=cuda --test_tag_filters=-no_gpu,-benchmark-test -k \
diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD
index 12bacf3c27..2d96406d27 100644
--- a/third_party/llvm/llvm.BUILD
+++ b/third_party/llvm/llvm.BUILD
@@ -899,6 +899,7 @@ cc_library(
         "include/llvm/Target/ARM/InstPrinter/*.h",
         "include/llvm/Target/ARM/InstPrinter/*.def",
         "include/llvm/Target/ARM/InstPrinter/*.inc",
+        "lib/Target/ARM/*.h",
         "lib/Target/ARM/InstPrinter/*.h",
     ]),
     copts = ["-Iexternal/llvm/lib/Target/ARM"],
@@ -1206,6 +1207,7 @@ cc_library(
         "lib/IR/*.h",
     ]),
     hdrs = glob([
+        "include/llvm/Analysis/*.def",
         "include/llvm/IR/*.h",
         "include/llvm/IR/*.def",
         "include/llvm/IR/*.inc",
@@ -2022,6 +2024,8 @@ cc_library(
         "lib/Target/*.h",
     ]),
     hdrs = glob([
+        "include/llvm/CodeGen/*.h",
+        "include/llvm/CodeGen/*.def",
         "include/llvm/Target/*.h",
         "include/llvm/Target/*.def",
         "include/llvm/Target/*.inc",
-- 
GitLab


From 689cbda96444511bd37a01b125791c45a093bec3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 19:17:40 -0700
Subject: [PATCH 1330/1375] Plumbs rendezvous through function calls.

PiperOrigin-RevId: 163157033
---
 tensorflow/core/common_runtime/function.cc    | 13 ++-
 .../core/common_runtime/memory_types.cc       | 33 +++++---
 tensorflow/core/framework/function.h          |  6 +-
 tensorflow/core/graph/graph_partition.cc      | 41 +++++++++-
 tensorflow/core/kernels/function_ops.cc       |  2 +
 tensorflow/core/kernels/sendrecv_ops.cc       | 29 ++++++-
 tensorflow/core/kernels/sendrecv_ops.h        |  2 +
 tensorflow/python/framework/function_test.py  | 81 +++++++++++++------
 8 files changed, 156 insertions(+), 51 deletions(-)

diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index 94b9d33c0c..c9a4b47681 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -260,6 +260,8 @@ class CallOp : public AsyncOpKernel {
                       done);
     FunctionLibraryRuntime::Options opts;
     opts.step_id = ctx->step_id();
+    opts.rendezvous = ctx->rendezvous();
+    opts.cancellation_manager = ctx->cancellation_manager();
     opts.step_container = ctx->step_container();
     opts.stats_collector = ctx->stats_collector();
     opts.runner = ctx->runner();
@@ -545,23 +547,18 @@ void FunctionLibraryRuntimeImpl::Run(const Options& opts, Handle handle,
   Executor::Args exec_args;
   // Inherit the step_id from the caller.
   exec_args.step_id = opts.step_id;
-  exec_args.step_container = opts.step_container;
-
+  exec_args.rendezvous = opts.rendezvous;
   exec_args.stats_collector = opts.stats_collector;
   exec_args.call_frame = frame;
   exec_args.cancellation_manager = opts.cancellation_manager;
+  exec_args.step_container = opts.step_container;
   exec_args.runner = *opts.runner;
-  // TODO(zhifengc): we can avoid creating rendez here if we know
-  // there is no send/recv nodes in the graph.
-  auto* rendez = new IntraProcessRendezvous(device_mgr_);
-  exec_args.rendezvous = rendez;
   item->exec->RunAsync(
       // Executor args
       exec_args,
       // Done callback.
-      [item, frame, rets, rendez, done](const Status& status) {
+      [item, frame, rets, done](const Status& status) {
         item->Unref();
-        rendez->Unref();
         Status s = status;
         if (s.ok()) {
           s = frame->GetRetvals(rets);
diff --git a/tensorflow/core/common_runtime/memory_types.cc b/tensorflow/core/common_runtime/memory_types.cc
index 21ed73df77..76b926ba40 100644
--- a/tensorflow/core/common_runtime/memory_types.cc
+++ b/tensorflow/core/common_runtime/memory_types.cc
@@ -104,10 +104,21 @@ Status ValidateMemoryTypes(const DeviceType& device_type, const Graph* g) {
       });
 }
 
-static Node* Send(Graph* g, const string& device_name, bool host,
-                  const Edge* edge) {
-  const string tensor_name =
-      strings::StrCat("edge_", edge->id(), "_", edge->src()->name());
+// Given an Edge whose two endpoints have different memory types and
+// are gonna to insert a pair of HostSend/Recv or Send/HostRecv nodes,
+// GetTensorName() returns a unique string that we can use as part of
+// the rendezvous key. The return string is guaranteed to be unique
+// within this process. That is sufficient because EnsureMemoryTypes
+// is only used on a TensorFlow graph that is gonna to be executed in
+// a single tf device (hence within a single process).
+static string GetTensorName(const Edge* edge) {
+  static std::atomic<int64> counter(0);
+  return strings::StrCat("memtype_", counter.fetch_add(1), "_",
+                         edge->src()->name());
+}
+
+static Node* Send(Graph* g, const string& tensor_name,
+                  const string& device_name, bool host, const Edge* edge) {
   Node* ret;
   TF_CHECK_OK(NodeBuilder(g->NewName("n"), host ? "_HostSend" : "_Send")
                   .Input(edge->src(), edge->src_output())
@@ -115,14 +126,13 @@ static Node* Send(Graph* g, const string& device_name, bool host,
                   .Attr("send_device", device_name)
                   .Attr("send_device_incarnation", 0)  // Do not care.
                   .Attr("recv_device", device_name)
+                  .Attr("_hostmem_sendrecv", true)
                   .Finalize(g, &ret));
   return ret;
 }
 
-static Node* Recv(Graph* g, const string& device_name, bool host,
-                  const Edge* edge) {
-  const string tensor_name =
-      strings::StrCat("edge_", edge->id(), "_", edge->src()->name());
+static Node* Recv(Graph* g, const string& tensor_name,
+                  const string& device_name, bool host, const Edge* edge) {
   Node* ret;
   TF_CHECK_OK(
       NodeBuilder(g->NewName("n"), host ? "_HostRecv" : "_Recv")
@@ -131,6 +141,7 @@ static Node* Recv(Graph* g, const string& device_name, bool host,
           .Attr("send_device", device_name)
           .Attr("send_device_incarnation", 0)
           .Attr("recv_device", device_name)
+          .Attr("_hostmem_sendrecv", true)
           .Finalize(g, &ret));
   return ret;
 }
@@ -171,8 +182,10 @@ Status EnsureMemoryTypes(const DeviceType& device_type,
       Endpoint key{e->src()->id(), e->src_output()};
       auto iter = recv_nodes.find(key);
       if (iter == recv_nodes.end()) {
-        Node* send = Send(g, device_name, (item.sm == HOST_MEMORY), e);
-        recv = Recv(g, device_name, (item.dm == HOST_MEMORY), e);
+        const string tensor_name = GetTensorName(e);
+        Node* send =
+            Send(g, tensor_name, device_name, (item.sm == HOST_MEMORY), e);
+        recv = Recv(g, tensor_name, device_name, (item.dm == HOST_MEMORY), e);
         if (!has_ref) {
           // We only cache if there is no ref is involved.
           recv_nodes[key] = recv;
diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h
index aeb924a709..d840d2f001 100644
--- a/tensorflow/core/framework/function.h
+++ b/tensorflow/core/framework/function.h
@@ -37,6 +37,7 @@ class CancellationManager;
 class GraphDef;
 class OpKernel;
 class ResourceMgr;
+class Rendezvous;
 class ScopedStepContainer;
 class StepStatsCollector;
 class Node;
@@ -398,11 +399,10 @@ class FunctionLibraryRuntime {
   //
   // Does not take ownership of "rets".
   struct Options {
-    CancellationManager* cancellation_manager = nullptr;
     // The id of the step that is calling this function.
     int64 step_id = 0;
-
-    // Per-step container.
+    Rendezvous* rendezvous = nullptr;
+    CancellationManager* cancellation_manager = nullptr;
     ScopedStepContainer* step_container = nullptr;
     StepStatsCollector* stats_collector = nullptr;
 
diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index 8ef2231aa2..750e18a9ca 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -896,6 +896,36 @@ Status AddControlEdges(const PartitionOptions& opts,
   return Status::OK();
 }
 
+// If 'ndef' is a Send or Recv, fills its attr send_device_incarnation
+// if possible.
+void SetIncarnation(const PartitionOptions& opts, NodeDef* ndef) {
+  StringPiece op(ndef->op());
+  if (op != "_Send" && op != "_Recv") {
+    // Not related to send/recv.
+    return;
+  }
+  string send_device;
+  if (!GetNodeAttr(*ndef, "send_device", &send_device).ok()) {
+    // No known send_device. The runtime will detect it later.
+    return;
+  }
+  int64 incarnation = opts.get_incarnation(send_device);
+  AddNodeAttr("send_device_incarnation", incarnation, ndef);
+}
+
+// Sets attribute send_device_incarnation of all Send/Recv nodes in
+// 'gdef', if possible.
+void SetIncarnation(const PartitionOptions& opts, GraphDef* gdef) {
+  for (NodeDef& ndef : *gdef->mutable_node()) {
+    SetIncarnation(opts, &ndef);
+  }
+  for (FunctionDef& fdef : *gdef->mutable_library()->mutable_function()) {
+    for (NodeDef& ndef : *fdef.mutable_node_def()) {
+      SetIncarnation(opts, &ndef);
+    }
+  }
+}
+
 Status Partition(const PartitionOptions& opts, Graph* g,
                  std::unordered_map<string, GraphDef>* partitions) {
   Status status;
@@ -1130,10 +1160,15 @@ Status Partition(const PartitionOptions& opts, Graph* g,
     }
   }
 
-  // Set versions and function library
+  // Set versions, function library and send/recv incarnation.
   for (auto& it : *partitions) {
-    it.second.mutable_versions()->CopyFrom(g->versions());
-    *it.second.mutable_library() = g->flib_def().ToProto();
+    GraphDef* gdef = &it.second;
+    *gdef->mutable_versions() = g->versions();
+    *gdef->mutable_library() = g->flib_def().ToProto();
+
+    // Traverse the graph to fill every send/recv op's incarnation
+    // information.
+    SetIncarnation(opts, gdef);
   }
 
   // Set the start times for recvs at the very end.
diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc
index 58c4ed37c4..71f1b4e063 100644
--- a/tensorflow/core/kernels/function_ops.cc
+++ b/tensorflow/core/kernels/function_ops.cc
@@ -237,6 +237,8 @@ class SymbolicGradientOp : public AsyncOpKernel {
 
     FunctionLibraryRuntime::Options opts;
     opts.step_id = ctx->step_id();
+    opts.rendezvous = ctx->rendezvous();
+    opts.cancellation_manager = ctx->cancellation_manager();
     opts.runner = ctx->runner();
     std::vector<Tensor> args;
     args.reserve(ctx->num_inputs());
diff --git a/tensorflow/core/kernels/sendrecv_ops.cc b/tensorflow/core/kernels/sendrecv_ops.cc
index 2a98a6530c..9c242052f7 100644
--- a/tensorflow/core/kernels/sendrecv_ops.cc
+++ b/tensorflow/core/kernels/sendrecv_ops.cc
@@ -39,6 +39,19 @@ static void GetRendezvousKey(const string& key_prefix,
                      frame_iter.iter_id);
 }
 
+static FrameAndIter GetFrameAndIter(OpKernelContext* ctx,
+                                    bool hostmem_sendrecv) {
+  if (hostmem_sendrecv && ctx->call_frame() != nullptr) {
+    // Host memory send/recv pairs are added by
+    // common_runtime/memory_types.cc.  When the pair of nodes are
+    // added inside a function, we need to use the function call frame
+    // to formulate the unique rendezvous key.
+    return FrameAndIter(reinterpret_cast<uint64>(ctx->call_frame()), 0);
+  } else {
+    return ctx->frame_iter();
+  }
+}
+
 SendOp::SendOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
   string send_device;
   OP_REQUIRES_OK(ctx, ctx->GetAttr("send_device", &send_device));
@@ -56,6 +69,9 @@ SendOp::SendOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
   // proactively cache the rendezvous key for the top-level.
   GetRendezvousKey(key_prefix_, {0, 0}, &parsed_key_.buf_);
   OP_REQUIRES_OK(ctx, Rendezvous::ParseKey(parsed_key_.buf_, &parsed_key_));
+  if (!ctx->GetAttr("_hostmem_sendrecv", &hostmem_sendrecv_).ok()) {
+    hostmem_sendrecv_ = false;
+  }
 }
 
 void SendOp::Compute(OpKernelContext* ctx) {
@@ -71,7 +87,8 @@ void SendOp::Compute(OpKernelContext* ctx) {
   args.device_context = ctx->op_device_context();
   args.alloc_attrs = ctx->input_alloc_attr(0);
 
-  if (ctx->frame_iter() == FrameAndIter(0, 0)) {
+  FrameAndIter frame_iter = GetFrameAndIter(ctx, hostmem_sendrecv_);
+  if (frame_iter == FrameAndIter(0, 0)) {
     // Use the cached rendezvous key.
     VLOG(2) << "Send " << parsed_key_.buf_;
     OP_REQUIRES_OK(ctx,
@@ -79,7 +96,7 @@ void SendOp::Compute(OpKernelContext* ctx) {
                                            ctx->is_input_dead()));
   } else {
     Rendezvous::ParsedKey in_loop_parsed;
-    GetRendezvousKey(key_prefix_, ctx->frame_iter(), &in_loop_parsed.buf_);
+    GetRendezvousKey(key_prefix_, frame_iter, &in_loop_parsed.buf_);
     VLOG(2) << "Send " << in_loop_parsed.buf_;
     OP_REQUIRES_OK(ctx,
                    Rendezvous::ParseKey(in_loop_parsed.buf_, &in_loop_parsed));
@@ -120,6 +137,9 @@ RecvOp::RecvOp(OpKernelConstruction* ctx) : AsyncOpKernel(ctx) {
   // proactively cache the rendezvous key for the top-level.
   GetRendezvousKey(key_prefix_, {0, 0}, &parsed_key_.buf_);
   OP_REQUIRES_OK(ctx, Rendezvous::ParseKey(parsed_key_.buf_, &parsed_key_));
+  if (!ctx->GetAttr("_hostmem_sendrecv", &hostmem_sendrecv_).ok()) {
+    hostmem_sendrecv_ = false;
+  }
 }
 
 void RecvOp::ComputeAsync(OpKernelContext* ctx, DoneCallback done) {
@@ -151,12 +171,13 @@ void RecvOp::ComputeAsync(OpKernelContext* ctx, DoneCallback done) {
       },
       std::move(done), _1, _2, _3, _4, _5);
 
-  if (ctx->frame_iter() == FrameAndIter(0, 0)) {
+  FrameAndIter frame_iter = GetFrameAndIter(ctx, hostmem_sendrecv_);
+  if (frame_iter == FrameAndIter(0, 0)) {
     VLOG(2) << "Recv " << parsed_key_.buf_;
     ctx->rendezvous()->RecvAsync(parsed_key_, args, std::move(done_cb));
   } else {
     Rendezvous::ParsedKey in_loop_parsed;
-    GetRendezvousKey(key_prefix_, ctx->frame_iter(), &in_loop_parsed.buf_);
+    GetRendezvousKey(key_prefix_, frame_iter, &in_loop_parsed.buf_);
     VLOG(2) << "Recv " << in_loop_parsed.buf_;
     OP_REQUIRES_OK_ASYNC(
         ctx, Rendezvous::ParseKey(in_loop_parsed.buf_, &in_loop_parsed), done);
diff --git a/tensorflow/core/kernels/sendrecv_ops.h b/tensorflow/core/kernels/sendrecv_ops.h
index 67867e3308..1ff8eff13f 100644
--- a/tensorflow/core/kernels/sendrecv_ops.h
+++ b/tensorflow/core/kernels/sendrecv_ops.h
@@ -29,6 +29,7 @@ class SendOp : public OpKernel {
  private:
   string key_prefix_;
   Rendezvous::ParsedKey parsed_key_;
+  bool hostmem_sendrecv_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(SendOp);
 };
@@ -41,6 +42,7 @@ class RecvOp : public AsyncOpKernel {
  private:
   string key_prefix_;
   Rendezvous::ParsedKey parsed_key_;
+  bool hostmem_sendrecv_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(RecvOp);
 };
diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py
index c4e841b81f..abfd0b7660 100644
--- a/tensorflow/python/framework/function_test.py
+++ b/tensorflow/python/framework/function_test.py
@@ -325,6 +325,25 @@ class FunctionTest(test.TestCase):
                                    "assertion"):
         _ = MyFn(100.0).eval()
 
+  def testWhileLoopCallsFunc(self):
+    with self.test_session(use_gpu=True) as sess:
+
+      @function.Defun(dtypes.float32)
+      def Times2(x):
+        constant_two = constant_op.constant(2, dtypes.int32)
+        two_on_gpu = math_ops.cast(constant_two, dtypes.float32)
+        return x * two_on_gpu
+
+      def Body(x):
+        x2 = Times2(x)
+        x2.set_shape([])
+        return x2
+
+      loop = control_flow_ops.while_loop(lambda x: x < 1e5, Body, [1.0])
+
+      ans = sess.run(loop)
+      self.assertAllClose(ans, 131072.)
+
   def testControlFlowStrictness(self):
     """Inlined functions must not execute in a untaken control flow branch."""
 
@@ -588,8 +607,8 @@ class FunctionTest(test.TestCase):
       self.assertAllClose(vals[2], vals[3])
 
   def testDeclare(self):
-    foo = function.Declare("Foo", [("x", dtypes.float32)],
-                           [("y", dtypes.float32)])
+    foo = function.Declare("Foo", [("x", dtypes.float32)], [("y",
+                                                             dtypes.float32)])
 
     @function.Defun(dtypes.float32, func_name="Foo", out_names=["y"])
     def FooImpl(x):
@@ -607,8 +626,8 @@ class FunctionTest(test.TestCase):
       self.assertAllClose(expected, y.eval(feed_dict={x: rand}))
 
   def testDeclareUsedInDefun(self):
-    foo = function.Declare("Foo", [("x", dtypes.float32)],
-                           [("y", dtypes.float32)])
+    foo = function.Declare("Foo", [("x", dtypes.float32)], [("y",
+                                                             dtypes.float32)])
 
     @function.Defun()
     def Bar(x):
@@ -630,8 +649,8 @@ class FunctionTest(test.TestCase):
       self.assertAllClose(expected, y.eval(feed_dict={x: rand}))
 
   def testDeclareTypeMistake(self):
-    foo = function.Declare("Foo", [("x", dtypes.float32)],
-                           [("y", dtypes.float32)])
+    foo = function.Declare("Foo", [("x", dtypes.float32)], [("y",
+                                                             dtypes.float32)])
 
     @function.Defun(dtypes.float32, func_name="Foo", out_names=["y"])
     def Foo(x):
@@ -749,8 +768,9 @@ class FunctionTest(test.TestCase):
       self.assertAllEqual(v1, 20.)
 
   def testShapeFunction(self):
-    @function.Defun(dtypes.float32,
-                    shape_func=lambda op: [op.inputs[0].get_shape()])
+
+    @function.Defun(
+        dtypes.float32, shape_func=lambda op: [op.inputs[0].get_shape()])
     def Foo(x):
       return x + 1.0
 
@@ -767,11 +787,12 @@ class FunctionTest(test.TestCase):
       self.assertAllEqual(y.get_shape().as_list(), [1, 1, 2, 3])
 
   def testVariableReuse(self):
+
     def LinearWithReuse(input_tensor, reuse=None):
       size = input_tensor.shape.dims[1]
       with variable_scope.variable_scope("linear", reuse=reuse):
-        w = variable_scope.get_variable("w", shape=[size, size],
-                                        dtype=input_tensor.dtype)
+        w = variable_scope.get_variable(
+            "w", shape=[size, size], dtype=input_tensor.dtype)
       return math_ops.matmul(input_tensor, w)
 
     @function.Defun(dtypes.float32)
@@ -789,15 +810,19 @@ class FunctionTest(test.TestCase):
 
     with session.Session() as sess:
       sess.run(variables.global_variables_initializer())
-      output_val = sess.run(output_op,
-                            feed_dict={input_op: np.random.rand(32, 100)})
+      output_val = sess.run(
+          output_op, feed_dict={input_op: np.random.rand(32, 100)})
       self.assertEqual(output_val.shape, (32, 100))
 
   def testFunctionCallInDifferentVariableScopes(self):
+
     @function.Defun(dtypes.float32)
     def Foo(inputs):
-      var = variable_scope.get_variable("var", shape=[10], dtype=dtypes.float32,
-                                        initializer=init_ops.ones_initializer())
+      var = variable_scope.get_variable(
+          "var",
+          shape=[10],
+          dtype=dtypes.float32,
+          initializer=init_ops.ones_initializer())
       return inputs + var
 
     input_op = array_ops.placeholder(shape=[10], dtype=dtypes.float32)
@@ -813,8 +838,8 @@ class FunctionTest(test.TestCase):
 
     with session.Session() as sess:
       sess.run(variables.global_variables_initializer())
-      out1, out2 = sess.run([out1_op, out2_op],
-                            feed_dict={input_op: np.linspace(1, 10, 10)})
+      out1, out2 = sess.run(
+          [out1_op, out2_op], feed_dict={input_op: np.linspace(1, 10, 10)})
       self.assertAllEqual(out1, np.linspace(2, 11, 10))
       self.assertAllEqual(out2, np.linspace(2, 11, 10))
 
@@ -852,12 +877,15 @@ class FunctionsFromProtos(test.TestCase):
     self.assertEqual(func.captured_inputs, new_func.captured_inputs)
 
   def testBasic(self):
+
     @function.Defun(dtypes.float32, dtypes.float32)
     def Foo(x, y):
       return x + y
+
     self.expectFunctionsEqual(Foo)
 
   def testGradFunc(self):
+
     @function.Defun(dtypes.float32, dtypes.float32)
     def G(x, dy):
       return x * dy
@@ -865,10 +893,12 @@ class FunctionsFromProtos(test.TestCase):
     @function.Defun(dtypes.float32, grad_func=G)
     def F(x):
       return math_ops.exp(x) - math_ops.exp(-x)
+
     self.expectFunctionsEqual(F, grad_func=G)
 
   def testCapturedInputs(self):
     c = constant_op.constant(10, dtypes.int64)
+
     @function.Defun(dtypes.int64)
     def Foo(x):
       return x + c
@@ -885,6 +915,7 @@ class FunctionsFromProtos(test.TestCase):
     self.assertEqual(len(new_func.captured_inputs), 0)
 
   def testNestedFunctions(self):
+
     @function.Defun(dtypes.float32)
     def Outer(x):
 
@@ -958,6 +989,7 @@ class FunctionsFromProtos(test.TestCase):
     self.assertEqual(len(function._from_library(library)), 0)
 
   def testFromLibraryMissingFuncDef(self):
+
     @function.Defun(dtypes.float32, dtypes.float32)
     def G1(x, dy):
       return x * dy
@@ -989,6 +1021,7 @@ class FunctionsFromProtos(test.TestCase):
       function._from_library(library)
 
   def testFromLibraryCyclicGradFuncs(self):
+
     @function.Defun(dtypes.float32)
     def F1(x):
       return math_ops.exp(x) - math_ops.exp(-x)
@@ -1242,10 +1275,11 @@ class FunctionInlineControlTest(test.TestCase):
       inp = np.random.uniform(-1, 1, [16, 1]).astype(np.float32)
       run_metadata = config_pb2.RunMetadata()
       with session.Session(graph=g, config=cfg) as sess:
-        ans = sess.run([y, dx], {x: inp},
-                       run_metadata=run_metadata,
-                       options=config_pb2.RunOptions(
-                           trace_level=config_pb2.RunOptions.FULL_TRACE))
+        ans = sess.run(
+            [y, dx], {x: inp},
+            run_metadata=run_metadata,
+            options=config_pb2.RunOptions(
+                trace_level=config_pb2.RunOptions.FULL_TRACE))
         print(ans[0], np.sum(ans[1]))
         self.assertAllClose(ans[0], 255.971, rtol=1e-3)
         self.assertAllClose(np.sum(ans[1]), 13.0408, rtol=1e-3)
@@ -1275,8 +1309,7 @@ class ModuleFunctionTest(test.TestCase):
   def testBasic(self):
     with ops.Graph().as_default():
       a, b, c, d, e = [
-          constant_op.constant(
-              [[_]], dtype=dtypes.float32) for _ in range(5)
+          constant_op.constant([[_]], dtype=dtypes.float32) for _ in range(5)
       ]
       y = Linear(a, b, c)
       z = Linear2(a, b, c, d, e)
@@ -1295,7 +1328,8 @@ class VariableHoistingTest(test.TestCase):
           initializer=init_ops.random_uniform_initializer(seed=312),
           use_resource=use_resource)
       b = variable_scope.get_variable(
-          "b", (64), initializer=init_ops.zeros_initializer(),
+          "b", (64),
+          initializer=init_ops.zeros_initializer(),
           use_resource=use_resource),
       return math_ops.sigmoid(math_ops.matmul(x, w) + b)
 
@@ -1354,5 +1388,6 @@ class VariableHoistingTest(test.TestCase):
     self._testSimpleModel(True, use_resource=True)
     self._testSimpleModel(False, use_resource=True)
 
+
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 8b3cbeb507f2f38e7785fb0625f8b0ee9992d72a Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Tue, 25 Jul 2017 19:23:01 -0700
Subject: [PATCH 1331/1375] Automated g4 rollback of changelist 163128673

PiperOrigin-RevId: 163157277
---
 tensorflow/core/platform/env.cc      | 91 ----------------------------
 tensorflow/core/platform/env.h       |  6 --
 tensorflow/core/platform/env_test.cc | 28 ---------
 3 files changed, 125 deletions(-)

diff --git a/tensorflow/core/platform/env.cc b/tensorflow/core/platform/env.cc
index 568a22b295..2fdd989c9b 100644
--- a/tensorflow/core/platform/env.cc
+++ b/tensorflow/core/platform/env.cc
@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include <sys/stat.h>
 #include <deque>
 #include <utility>
 #include <vector>
@@ -31,10 +30,7 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/lib/io/path.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
-#include "tensorflow/core/platform/env_time.h"
-#include "tensorflow/core/platform/host_info.h"
 #include "tensorflow/core/platform/protobuf.h"
 
 namespace tensorflow {
@@ -277,93 +273,6 @@ string Env::GetExecutablePath() {
   return exe_path;
 }
 
-bool Env::LocalTempFilename(string* filename) {
-  std::vector<string> dirs;
-  GetLocalTempDirectories(&dirs);
-
-  // Try each directory, as they might be full, have inappropriate
-  // permissions or have different problems at times.
-  for (const string& dir : dirs) {
-#ifdef __APPLE__
-    uint64_t tid64;
-    pthread_threadid_np(nullptr, &tid64);
-    int32 tid = static_cast<int32>(tid64);
-    int32 pid = static_cast<int32>(getpid());
-#elif defined(PLATFORM_WINDOWS)
-    int32 tid = static_cast<int32>(GetCurrentThreadId());
-    int32 pid = static_cast<int32>(GetCurrentProcessId());
-#else
-    int32 tid = static_cast<int32>(pthread_self());
-    int32 pid = static_cast<int32>(getpid());
-#endif
-    uint64 now_microsec = NowMicros();
-
-    *filename = io::JoinPath(
-        dir, strings::Printf("tempfile-%s-%x-%d-%llx", port::Hostname().c_str(),
-                             tid, pid, now_microsec));
-    if (FileExists(*filename).ok()) {
-      filename->clear();
-    } else {
-      return true;
-    }
-  }
-  return false;
-}
-
-void Env::GetLocalTempDirectories(std::vector<string>* list) {
-  list->clear();
-#ifdef PLATFORM_WINDOWS
-  // On windows we'll try to find a directory in this order:
-  //   C:/Documents & Settings/whomever/TEMP (or whatever GetTempPath() is)
-  //   C:/TMP/
-  //   C:/TEMP/
-  //   C:/WINDOWS/ or C:/WINNT/
-  //   .
-  char tmp[MAX_PATH];
-  // GetTempPath can fail with either 0 or with a space requirement > bufsize.
-  // See http://msdn.microsoft.com/en-us/library/aa364992(v=vs.85).aspx
-  DWORD n = GetTempPathA(MAX_PATH, tmp);
-  if (n > 0 && n <= MAX_PATH) list->push_back(tmp);
-  list->push_back("C:\\tmp\\");
-  list->push_back("C:\\temp\\");
-#else
-  // Directories, in order of preference. If we find a dir that
-  // exists, we stop adding other less-preferred dirs
-  const char* candidates[] = {
-      // Non-null only during unittest/regtest
-      getenv("TEST_TMPDIR"),
-
-      // Explicitly-supplied temp dirs
-      getenv("TMPDIR"),
-      getenv("TMP"),
-
-      // The old classic tmpdir
-      "/export/hda3/tmp",
-
-      // If all else fails
-      "/tmp",
-  };
-
-  for (const char* d : candidates) {
-    if (!d || d[0] == '\0') continue;  // Empty env var
-
-    // Make sure we don't surprise anyone who's expecting a '/'
-    string dstr = d;
-    if (dstr[dstr.size() - 1] != '/') {
-      dstr += "/";
-    }
-
-    struct stat statbuf;
-    if (!stat(d, &statbuf) && S_ISDIR(statbuf.st_mode) &&
-        !access(dstr.c_str(), 0)) {
-      // We found a dir that exists and is accessible - we're done.
-      list->push_back(dstr);
-      return;
-    }
-  }
-#endif
-}
-
 Thread::~Thread() {}
 
 EnvWrapper::~EnvWrapper() {}
diff --git a/tensorflow/core/platform/env.h b/tensorflow/core/platform/env.h
index da8c3e2d7e..1b7e024b0f 100644
--- a/tensorflow/core/platform/env.h
+++ b/tensorflow/core/platform/env.h
@@ -215,9 +215,6 @@ class Env {
   /// symlinks if there is any.
   string GetExecutablePath();
 
-  /// Creates a local unique temporary file name. Returns true if success.
-  bool LocalTempFilename(string* filename);
-
   // TODO(jeff,sanjay): Add back thread/thread-pool support if needed.
   // TODO(jeff,sanjay): if needed, tighten spec so relative to epoch, or
   // provide a routine to get the absolute time.
@@ -282,9 +279,6 @@ class Env {
       const string& version) = 0;
 
  private:
-  // Returns a possible list of local temporary directories.
-  void GetLocalTempDirectories(std::vector<string>* list);
-
   std::unique_ptr<FileSystemRegistry> file_system_registry_;
   TF_DISALLOW_COPY_AND_ASSIGN(Env);
   EnvTime* envTime = EnvTime::Default();
diff --git a/tensorflow/core/platform/env_test.cc b/tensorflow/core/platform/env_test.cc
index 50dd0cd58b..7bc1882c86 100644
--- a/tensorflow/core/platform/env_test.cc
+++ b/tensorflow/core/platform/env_test.cc
@@ -298,32 +298,4 @@ TEST_F(DefaultEnvTest, GetExecutablePath) {
   TF_EXPECT_OK(env->FileExists(env->GetExecutablePath()));
 }
 
-TEST_F(DefaultEnvTest, LocalTempFilename) {
-  Env* env = Env::Default();
-  string filename;
-  EXPECT_TRUE(env->LocalTempFilename(&filename));
-  EXPECT_FALSE(env->FileExists(filename).ok());
-
-  // Write something to the temporary file.
-  std::unique_ptr<WritableFile> file_to_write;
-  TF_CHECK_OK(env->NewWritableFile(filename, &file_to_write));
-  TF_CHECK_OK(file_to_write->Append("Null"));
-  TF_CHECK_OK(file_to_write->Close());
-  TF_CHECK_OK(env->FileExists(filename));
-
-  // Read from the temporary file and check content.
-  std::unique_ptr<RandomAccessFile> file_to_read;
-  TF_CHECK_OK(env->NewRandomAccessFile(filename, &file_to_read));
-  StringPiece content;
-  char scratch[1024];
-  CHECK_EQ(error::OUT_OF_RANGE,
-           file_to_read->Read(0 /* offset */, 1024 /* n */, &content, scratch)
-               .code());
-  EXPECT_EQ("Null", content.ToString());
-
-  // Delete the temporary file.
-  TF_CHECK_OK(env->DeleteFile(filename));
-  EXPECT_FALSE(env->FileExists(filename).ok());
-}
-
 }  // namespace tensorflow
-- 
GitLab


From 19290f0567366bcdd3eb7adfe0f830a053cc1314 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 25 Jul 2017 20:27:22 -0700
Subject: [PATCH 1332/1375] Internal change.

PiperOrigin-RevId: 163160761
---
 tensorflow/compiler/xla/client/client_library.cc | 8 ++++++++
 tensorflow/compiler/xla/client/client_library.h  | 5 +++++
 2 files changed, 13 insertions(+)

diff --git a/tensorflow/compiler/xla/client/client_library.cc b/tensorflow/compiler/xla/client/client_library.cc
index 801596c0ad..b1663bc815 100644
--- a/tensorflow/compiler/xla/client/client_library.cc
+++ b/tensorflow/compiler/xla/client/client_library.cc
@@ -149,4 +149,12 @@ ClientLibrary::GetOrCreateCompileOnlyClient(
   return cl;
 }
 
+/* static */ void ClientLibrary::DestroyLocalInstances() {
+  ClientLibrary& client_library = Singleton();
+  tensorflow::mutex_lock lock(client_library.service_mutex_);
+
+  client_library.local_instances_.clear();
+  client_library.compile_only_instances_.clear();
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/client/client_library.h b/tensorflow/compiler/xla/client/client_library.h
index cff2b5124e..a6f30d82e4 100644
--- a/tensorflow/compiler/xla/client/client_library.h
+++ b/tensorflow/compiler/xla/client/client_library.h
@@ -93,6 +93,11 @@ class ClientLibrary {
   static StatusOr<CompileOnlyClient*> GetOrCreateCompileOnlyClient(
       perftools::gputools::Platform* platform = nullptr);
 
+  // Clears the local instance and compile only instance caches. The client
+  // pointers returned by the previous GetOrCreateLocalClient() or
+  // GetOrCreateCompileOnlyClient() invocations are not valid anymore.
+  static void DestroyLocalInstances();
+
  private:
   // Returns the singleton instance of ClientLibrary.
   static ClientLibrary& Singleton();
-- 
GitLab


From fdeb2d23ddd36cfaa48fc2f39f904c001f4a2284 Mon Sep 17 00:00:00 2001
From: Adam Salvail <adam@elementai.com>
Date: Tue, 25 Jul 2017 23:34:56 -0400
Subject: [PATCH 1333/1375] Minor corrections to tensordot documentation
 (#11764)

Changed some math indices formating and the format of the `axes` argument for Example 3.
---
 tensorflow/python/ops/math_ops.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 8ebffd0bbb..3e91ec0684 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -2338,8 +2338,8 @@ def tensordot(a, b, axes, name=None):
   Example 2: When `a` and `b` are matrices (order 2), the case
   `axes = [[1], [0]]` is equivalent to matrix multiplication.
 
-  Example 3: Suppose that \\(a_ijk\\) and \\(b_lmn\\) represent two
-  tensors of order 3. Then, `contract(a, b, [0], [2])` is the order 4 tensor
+  Example 3: Suppose that \\(a_{ijk}\\) and \\(b_{lmn}\\) represent two
+  tensors of order 3. Then, `contract(a, b, [[0], [2]])` is the order 4 tensor
   \\(c_{jklm}\\) whose entry
   corresponding to the indices \\((j,k,l,m)\\) is given by:
 
-- 
GitLab


From 80d57aeaddf915368313fc07d58092e0a92af0b6 Mon Sep 17 00:00:00 2001
From: resec <resec0109@gmail.com>
Date: Wed, 26 Jul 2017 12:26:38 +0800
Subject: [PATCH 1334/1375] Add less_equal and logical_or to android_core_ops
 (#11739)

Added below two ops to android_core_ops, as similar to #11631
tensorflow/core/kernels/cwise_op_logical_or.cc
tensorflow/core/kernels/cwise_op_less_equal.cc

I think these are very basic ops and should add to the list by default.
---
 tensorflow/core/kernels/BUILD | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index a493452777..692e1c37f0 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -4307,9 +4307,11 @@ filegroup(
         "cwise_op_invert.cc",
         "cwise_op_isfinite.cc",
         "cwise_op_less.cc",
+        "cwise_op_less_equal.cc",
         "cwise_op_log.cc",
         "cwise_op_logical_and.cc",
         "cwise_op_logical_not.cc",
+        "cwise_op_logical_or.cc",
         "cwise_op_maximum.cc",
         "cwise_op_minimum.cc",
         "cwise_op_mul_1.cc",
-- 
GitLab


From dbd0f2e2d46044c90e27290f4aa35ebccba2e557 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Tue, 25 Jul 2017 21:35:55 -0700
Subject: [PATCH 1335/1375] [XLA] Use HloEvaluator for convolution in
 reference_util.

PiperOrigin-RevId: 163164566
---
 tensorflow/compiler/xla/BUILD             |   3 +
 tensorflow/compiler/xla/reference_util.cc | 247 +++++++---------------
 2 files changed, 81 insertions(+), 169 deletions(-)

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index e0a03a78f1..ba90b13b38 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -563,6 +563,9 @@ cc_library(
         ":xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:padding",
+        "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service:hlo_evaluator",
+        "//tensorflow/compiler/xla/service:shape_inference",
         "//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_matmul",
         "//tensorflow/core:lib",
     ],
diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index 7ef5c6d916..c851c38ea4 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -20,6 +20,9 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/shape_inference.h"
 #include "tensorflow/compiler/xla/window_util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/math/math_util.h"
@@ -446,179 +449,85 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
     std::pair<int64, int64> kernel_stride, Padding padding,
     std::pair<int64, int64> lhs_dilation, std::pair<int64, int64> rhs_dilation,
     ConvolutionDimensionNumbers dnums) {
-  std::array<int64, 4> lhs_dimensions{{lhs.n1(), lhs.n2(), lhs.n3(), lhs.n4()}};
-  std::array<int64, 4> rhs_dimensions{{rhs.n1(), rhs.n2(), rhs.n3(), rhs.n4()}};
-
-  const int64 ksy = kernel_stride.first;
-  const int64 ksx = kernel_stride.second;
-  const int64 dy = lhs_dilation.first;
-  const int64 dx = lhs_dilation.second;
-  const int64 dky = rhs_dilation.first;
-  const int64 dkx = rhs_dilation.second;
-  CHECK_GE(dky, 1);
-  CHECK_GE(dkx, 1);
-  CHECK_GE(dy, 1);
-  CHECK_GE(dx, 1);
-
-  // Get all dimension sizes in lhs and rhs based on the given convolution
-  // dimension configuration.
-  const int64 ix = window_util::DilatedBound(
-      lhs_dimensions[dnums.spatial_dimensions(1)], dx);
-  const int64 iy = window_util::DilatedBound(
-      lhs_dimensions[dnums.spatial_dimensions(0)], dy);
-  const int64 iz = lhs_dimensions[dnums.feature_dimension()];
-  const int64 samples = lhs_dimensions[dnums.batch_dimension()];
-  const int64 kx = window_util::DilatedBound(
-      rhs_dimensions[dnums.kernel_spatial_dimensions(1)], dkx);
-  const int64 ky = window_util::DilatedBound(
-      rhs_dimensions[dnums.kernel_spatial_dimensions(0)], dky);
-  const int64 oz = rhs_dimensions[dnums.kernel_output_feature_dimension()];
-  {
-    const int64 kiz = rhs_dimensions[dnums.kernel_input_feature_dimension()];
-    CHECK_EQ(kiz, iz);
+  HloComputation::Builder b("ConvArray4DGeneralDimensionDilated");
+  auto lhs_literal = Literal::CreateR4FromArray4D<float>(lhs);
+  auto rhs_literal = Literal::CreateR4FromArray4D<float>(rhs);
+
+  std::array<int64, 2> ordered_kernel_strides;
+  std::array<int64, 2> ordered_input_dimensions;
+  std::array<int64, 2> ordered_kernel_dimensions;
+  if (dnums.kernel_spatial_dimensions(0) > dnums.kernel_spatial_dimensions(1)) {
+    ordered_kernel_strides[0] = kernel_stride.second;
+    ordered_kernel_strides[1] = kernel_stride.first;
+  } else {
+    ordered_kernel_strides[0] = kernel_stride.first;
+    ordered_kernel_strides[1] = kernel_stride.second;
   }
 
-  if (padding == Padding::kSame) {
-    // We reject same padding with kernel striding, since it's somewhat
-    // nonsensical. We can always follow up to implement this with the desired
-    // semantics if anybody actually uses it.
-    CHECK_EQ(1, ksy);
-    CHECK_EQ(1, ksx);
-  }
-
-  const int64 ox =
-      padding == Padding::kSame ? ix : window_util::StridedBound(ix, kx, ksx);
-  const int64 oy =
-      padding == Padding::kSame ? iy : window_util::StridedBound(iy, ky, ksy);
-  const int64 istartx =
-      padding == Padding::kValid ? 0 : kx % 2 == 0 ? -(kx / 2 - 1) : -kx / 2;
-  const int64 istarty =
-      padding == Padding::kValid ? 0 : ky % 2 == 0 ? -(ky / 2 - 1) : -ky / 2;
-  // Create the output result array and reset the values to 0.
-  std::array<int64, 4> result_dimensions;
-  result_dimensions[dnums.batch_dimension()] = samples;
-  result_dimensions[dnums.feature_dimension()] = oz;
-  result_dimensions[dnums.spatial_dimensions(0)] = oy;
-  result_dimensions[dnums.spatial_dimensions(1)] = ox;
+  ordered_input_dimensions[0] =
+      lhs_literal->shape().dimensions(dnums.spatial_dimensions(0));
+  ordered_input_dimensions[1] =
+      lhs_literal->shape().dimensions(dnums.spatial_dimensions(1));
+  ordered_kernel_dimensions[0] =
+      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(0));
+  ordered_kernel_dimensions[1] =
+      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(1));
+
+  std::vector<std::pair<int64, int64>> paddings =
+      MakePadding(ordered_input_dimensions, ordered_kernel_dimensions,
+                  ordered_kernel_strides, padding);
+  CHECK_EQ(paddings.size(), 2);
+
+  Window window;
+
+  WindowDimension dim;
+  dim.set_size(
+      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(0)));
+  dim.set_stride(kernel_stride.first);
+  dim.set_padding_low(paddings[0].first);
+  dim.set_padding_high(paddings[0].second);
+  dim.set_window_dilation(rhs_dilation.first);
+  dim.set_base_dilation(lhs_dilation.first);
+  *window.add_dimensions() = dim;
+
+  WindowDimension dim2;
+  dim2.set_size(
+      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(1)));
+  dim2.set_stride(kernel_stride.second);
+  dim2.set_padding_low(paddings[1].first);
+  dim2.set_padding_high(paddings[1].second);
+  dim2.set_window_dilation(rhs_dilation.second);
+  dim2.set_base_dilation(lhs_dilation.second);
+  *window.add_dimensions() = dim2;
+
+  const Shape& shape =
+      ShapeInference::InferConvolveShape(lhs_literal->shape(),
+                                         rhs_literal->shape(), window, dnums)
+          .ConsumeValueOrDie();
+
+  HloInstruction* lhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
+  HloInstruction* rhs_instruction =
+      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
+
+  b.AddInstruction(HloInstruction::CreateConvolve(
+      shape, lhs_instruction, rhs_instruction, window, dnums));
+
+  HloEvaluator evaluator;
+  std::unique_ptr<Literal> result_literal =
+      evaluator.Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
+
+  CHECK_EQ(ShapeUtil::Rank(result_literal->shape()), 4);
   auto result =
-      MakeUnique<Array4D<float>>(result_dimensions[0], result_dimensions[1],
-                                 result_dimensions[2], result_dimensions[3]);
-  result->Fill(0.0);
-
-  const auto is_int32 = [](int64 x) {
-    return x >= std::numeric_limits<int32>::min() &&
-           x <= std::numeric_limits<int32>::max();
-  };
-
-  // 64-bit idiv/mod are much more expensive x86-64 than 32-bit idiv/imod (at
-  // least on x86-64), so we avoid them where possible.
-  const auto fast_idiv64 = [&](int64 a, int64 b) {
-    if (is_int32(a) && is_int32(b)) {
-      return static_cast<int64>(static_cast<int32>(a) / static_cast<int32>(b));
-    }
-    return a / b;
-  };
-  const auto fast_imod64 = [&](int64 a, int64 b) {
-    if (is_int32(a) && is_int32(b)) {
-      return static_cast<int64>(static_cast<int32>(a) % static_cast<int32>(b));
-    }
-    return a % b;
-  };
-
-  // Lambda to access the lhs operand at the given 4D index.
-  const auto lhs_element = [&](int64 batch, int64 feature, int64 height,
-                               int64 width) {
-    if (fast_imod64(height, dy) != 0 || fast_imod64(width, dx) != 0) {
-      return 0.0f;
-    }
+      MakeUnique<Array4D<float>>(result_literal->shape().dimensions(0),
+                                 result_literal->shape().dimensions(1),
+                                 result_literal->shape().dimensions(2),
+                                 result_literal->shape().dimensions(3));
+
+  result->Each([&](tensorflow::gtl::ArraySlice<int64> indices, float* value) {
+    *value = result_literal->Get<float>(indices);
+  });
 
-    std::array<int64, 4> index;
-    index[dnums.batch_dimension()] = batch;
-    index[dnums.feature_dimension()] = feature;
-    index[dnums.spatial_dimensions(0)] = fast_idiv64(height, dy);
-    index[dnums.spatial_dimensions(1)] = fast_idiv64(width, dx);
-    return lhs(index[0], index[1], index[2], index[3]);
-  };
-
-  // Lambda to access the rhs operand at the given 4D index.  height_over_dky
-  // should be equal to height / dky, and width_over_dkx should be equal to
-  // width / dkx.  (This is an optimization to avoid doing divisions.)
-  const auto rhs_element =
-      [&](int64 kernel_output_feature, int64 kernel_input_feature, int64 height,
-          int64 width, int64 height_over_dky, int64 width_over_dkx) {
-        DCHECK_EQ(height % dky, 0);
-        DCHECK_EQ(width % dkx, 0);
-        DCHECK_EQ(height / dky, height_over_dky);
-        DCHECK_EQ(width / dkx, width_over_dkx);
-
-        std::array<int64, 4> index;
-        index[dnums.kernel_output_feature_dimension()] = kernel_output_feature;
-        index[dnums.kernel_input_feature_dimension()] = kernel_input_feature;
-        index[dnums.kernel_spatial_dimensions(0)] = height_over_dky;
-        index[dnums.kernel_spatial_dimensions(1)] = width_over_dkx;
-        return rhs(index[0], index[1], index[2], index[3]);
-      };
-
-  // Lambda to access the result data at the given 4D index.
-  const auto result_element = [&](int64 batch, int64 kernel_output_feature,
-                                  int64 height, int64 width) -> float& {
-    std::array<int64, 4> index;
-    index[dnums.batch_dimension()] = batch;
-    index[dnums.feature_dimension()] = kernel_output_feature;
-    index[dnums.spatial_dimensions(0)] = height;
-    index[dnums.spatial_dimensions(1)] = width;
-    return (*result)(index[0], index[1], index[2], index[3]);
-  };
-
-  for (int64 oyi = 0; oyi < oy; ++oyi) {
-    for (int64 oxi = 0; oxi < ox; ++oxi) {
-      for (int64 sample = 0; sample < samples; ++sample) {
-        for (int64 izi = 0; izi < iz; ++izi) {
-          for (int64 ozi = 0; ozi < oz; ++ozi) {
-            for (int64 kyi = 0, kyi_over_dky = 0; kyi < ky;
-                 kyi += dky, kyi_over_dky++) {
-              for (int64 kxi = 0, kxi_over_dkx = 0; kxi < kx;
-                   kxi += dkx, kxi_over_dkx++) {
-                int64 iyi = istarty + ksy * oyi + kyi;
-                int64 ixi = istartx + ksx * oxi + kxi;
-                float input = (iyi >= iy || ixi >= ix || iyi < 0 || ixi < 0)
-                                  ? 0.0
-                                  : lhs_element(sample, izi, iyi, ixi);
-                float gain =
-                    rhs_element(ozi, izi, kyi, kxi, kyi_over_dky, kxi_over_dkx);
-                float addend = input * gain;
-                result_element(sample, ozi, oyi, oxi) += addend;
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-  if (samples == 0 || kx == 0 || ky == 0 || ox == 0 || oy == 0 || oz == 0 ||
-      iz == 0) {
-    LOG(INFO) << "Output will be trivially empty because one of these "
-                 "dimensions is 0: samples: "
-              << samples << " kx: " << kx << " ky: " << ky << " ox: " << ox
-              << " oy: " << oy << " oz: " << oz << " iz: " << iz;
-    return result;
-  }
-  bool trivial = true;
-  auto check_trivial = [&trivial](tensorflow::gtl::ArraySlice<int64> indices,
-                                  float value) {
-    if (value != 0.0) {
-      trivial = false;
-    }
-  };
-  lhs.Each(check_trivial);
-  if (trivial) {
-    LOG(FATAL) << "LHS is all 0.0.";
-  }
-  trivial = true;
-  rhs.Each(check_trivial);
-  if (trivial) {
-    LOG(FATAL) << "RHS is all 0.0.";
-  }
   return result;
 }
 
-- 
GitLab


From 0de74e448cb64409033ea169542ef4d1552bf36b Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Tue, 25 Jul 2017 21:39:11 -0700
Subject: [PATCH 1336/1375] Removing session reset since destroying the session
 object would delete its variables as well. Resetting session might
 unintentionally close other sessions in the same process.

PiperOrigin-RevId: 163164738
---
 tensorflow/core/grappler/clusters/BUILD       |   2 +
 .../core/grappler/clusters/single_machine.cc  |   8 +-
 .../grappler/clusters/single_machine_test.cc  | 123 ++++++++++++++++++
 3 files changed, 127 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index 667023845c..e7230b3754 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -114,7 +114,9 @@ cc_test(
     deps = [
         ":single_machine",
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:resource_variable_ops",
         "//tensorflow/cc:scope",
+        "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:lib_proto_parsing",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index a1531f1cfc..3481b2b158 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -73,8 +73,6 @@ SingleMachine::~SingleMachine() {
   // when we delete the session.
   thread_pool_.reset();
 
-  Reset(options_, {}).IgnoreError();
-
   CHECK(already_created);
   already_created = false;
 }
@@ -277,11 +275,9 @@ Status SingleMachine::ResetSession() {
     // Make sure the session is properly closed
     TF_RETURN_IF_ERROR(Shutdown());
 
-    // We need to Reset the session to ensure that all the variables are
-    // deleted. But first we need to delete the session since Reset()
-    // deletes some of the containers referenced by the session.
+    // Destroying the object deletes all its varibles as well. This is only true
+    // for DirectSession.
     session_.reset();
-    TF_RETURN_IF_ERROR(Reset(options_, {}));
   }
 
   LOG(INFO) << "Starting new session";
diff --git a/tensorflow/core/grappler/clusters/single_machine_test.cc b/tensorflow/core/grappler/clusters/single_machine_test.cc
index b73b084793..d8660e11a2 100644
--- a/tensorflow/core/grappler/clusters/single_machine_test.cc
+++ b/tensorflow/core/grappler/clusters/single_machine_test.cc
@@ -15,7 +15,10 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/clusters/single_machine.h"
 #include "tensorflow/cc/framework/scope.h"
+#include "tensorflow/cc/ops/resource_variable_ops.h"
 #include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/common_runtime/device.h"
+#include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/step_stats.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
@@ -24,6 +27,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/protobuf/queue_runner.pb.h"
 
 namespace tensorflow {
 namespace grappler {
@@ -349,6 +353,7 @@ TEST_F(SingleMachineTest, InitializationMemory) {
 }
 
 namespace {
+
 template <class T>
 inline void SetNodeAttr(const string& key, const T& value, NodeDef* node) {
   AttrValue attr_value;
@@ -463,6 +468,124 @@ TEST_F(SingleMachineTest, PersistentMemory) {
   EXPECT_TRUE(found_hashtable);
 }
 
+#if defined(PLATFORM_GOOGLE)
+namespace {
+
+SessionOptions GetSessionOption(int num_cpu_cores, int num_gpus) {
+  SessionOptions options;
+  // Copied from single_machine.h
+  (*options.config.mutable_device_count())["CPU"] = 1;
+  if (num_gpus > 0) {
+    (*options.config.mutable_device_count())["GPU"] = num_gpus;
+  }
+  CHECK_GE(num_cpu_cores, 1);
+  options.config.set_intra_op_parallelism_threads(num_cpu_cores);
+  options.config.add_session_inter_op_thread_pool()->set_num_threads(
+      num_cpu_cores);
+  return options;
+}
+
+Status GetDeviceMemoryStats(
+    const SessionOptions& session_option,
+    std::unordered_map<string, AllocatorStats>* allocator_stats_by_device) {
+  std::vector<Device*> devices;
+  TF_RETURN_IF_ERROR(DeviceFactory::AddDevices(session_option,
+                                               "" /* name_prefix */, &devices));
+  allocator_stats_by_device->clear();
+  for (Device* device : devices) {
+    AllocatorStats stats;
+    auto* allocator = device->GetAllocator(AllocatorAttributes());
+    if (!allocator->TracksAllocationSizes()) {
+      return Status(error::INVALID_ARGUMENT,
+                    "Tracking allocation is not enabled.");
+    }
+    allocator->GetStats(&stats);
+    (*allocator_stats_by_device)[device->name()] = stats;
+  }
+  return Status::OK();
+}
+
+}  // namespace
+
+TEST_F(SingleMachineTest, ReleaseMemoryAfterDestruction) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+
+  // Add a variable and initializer.
+  Output a = ops::Variable(s.WithOpName("a"), TensorShape({128, 256}),
+                           DataType::DT_FLOAT);
+  Output a_init =
+      ops::RandomNormal(s.WithOpName("a/init"), {128, 256}, DataType::DT_FLOAT);
+  Output a_init_assign = ops::Assign(s.WithOpName("a/init/assign"), a, a_init);
+
+  // Add a resource variable.
+  Output b =
+      ops::VarHandleOp(s.WithOpName("b"), DataType::DT_FLOAT, {256, 512});
+  Output b_read =
+      ops::ReadVariableOp(s.WithOpName("b/read"), b, DataType::DT_FLOAT);
+  Output b_init =
+      ops::RandomNormal(s.WithOpName("b/init"), {256, 512}, DataType::DT_FLOAT);
+  auto b_init_assign =
+      ops::AssignVariableOp(s.WithOpName("b/init/assign"), b, b_init);
+
+  // Add a queue.
+  ops::FIFOQueue queue(s.WithOpName("queue"), {DataType::DT_STRING});
+  Output some_string =
+      ops::Const(s.WithOpName("some_string"), string("nothing"));
+  ops::QueueEnqueue enqueue(s.WithOpName("enqueue"), queue, {some_string});
+  ops::QueueDequeue dequeue(s.WithOpName("dequeue"), queue,
+                            {DataType::DT_STRING});
+
+  // Add a IdentityReader.
+  ops::IdentityReader reader(s.WithOpName("identity_reader"));
+  ops::ReaderRead read(s.WithOpName("read_from_queue"), reader, queue);
+
+  Output var_mul = ops::MatMul(s.WithOpName("var_matmul"), a, b_read);
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  QueueRunnerDef queue_runner;
+  queue_runner.set_queue_name("queue");
+  *queue_runner.add_enqueue_op_name() = "enqueue";
+  item.queue_runners.push_back(queue_runner);
+
+  item.init_ops.push_back("a/init/assign");
+  item.init_ops.push_back("b/init/assign");
+  item.fetch.push_back("var_matmul");
+  item.fetch.push_back("dequeue");
+
+  // Run the graph
+  TF_CHECK_OK(cluster_->Initialize(item));
+  EnableCPUAllocatorStats(true);
+
+  SessionOptions options =
+      GetSessionOption(3 /* cpu cores */, 0 /* num gpus */);
+  std::unordered_map<string, AllocatorStats> device_memory_before;
+  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory_before));
+  EXPECT_EQ(device_memory_before.size(), 1);
+
+  RunMetadata metadata;
+  TF_CHECK_OK(cluster_->Run(item.graph, item.feed, item.fetch, &metadata));
+
+  // Check there is memory that is not released.
+  std::unordered_map<string, AllocatorStats> device_memory;
+  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory));
+  EXPECT_EQ(device_memory.size(), 1);
+  EXPECT_GT(device_memory.begin()->second.bytes_in_use, 0);
+
+  // Reset cluster_ would release all memory.
+  cluster_.reset();
+  std::unordered_map<string, AllocatorStats> device_memory_after;
+  TF_CHECK_OK(GetDeviceMemoryStats(options, &device_memory_after));
+
+  // Check memory used by resources are released after cluster destruction.
+  EXPECT_EQ(device_memory_before.size(), 1);
+  EXPECT_EQ(device_memory_after.size(), 1);
+  EXPECT_EQ(device_memory_before.begin()->second.bytes_in_use, 0);
+  EXPECT_EQ(device_memory_after.begin()->second.bytes_in_use, 0);
+}
+#endif
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From c2ce4f68c744e6d328746b144ff1fcf98ac99e6c Mon Sep 17 00:00:00 2001
From: Lakshay Garg <lakshayg@outlook.in>
Date: Wed, 26 Jul 2017 10:18:01 +0530
Subject: [PATCH 1337/1375] Implemented selu activation #10612 (#10818)

* Implemented selu activation #10612

* fix the error in _SeluGradGrad

* update golden file for api change

* add XLA kernels for Selu and SeluGrad
---
 tensorflow/cc/gradients/nn_grad.cc            |  9 ++
 tensorflow/cc/gradients/nn_grad_test.cc       | 10 ++
 tensorflow/cc/ops/op_gen_overrides.pbtxt      |  1 +
 tensorflow/compiler/tests/binary_ops_test.py  |  8 ++
 tensorflow/compiler/tests/randomized_tests.cc | 17 ++++
 tensorflow/compiler/tests/unary_ops_test.py   |  5 +
 tensorflow/compiler/tf2xla/kernels/elu_op.cc  | 44 +++++++++
 tensorflow/core/kernels/relu_op.cc            | 62 ++++++++++---
 tensorflow/core/kernels/relu_op.h             | 42 +++++++++
 tensorflow/core/kernels/relu_op_functor.h     | 40 ++++++++
 tensorflow/core/kernels/relu_op_gpu.cu.cc     |  4 +-
 tensorflow/core/ops/nn_ops.cc                 | 27 ++++++
 tensorflow/core/ops/nn_ops_test.cc            |  3 +-
 tensorflow/core/ops/ops.pbtxt                 | 54 +++++++++++
 tensorflow/docs_src/api_guides/python/nn.md   |  3 +-
 tensorflow/go/op/wrappers.go                  | 40 ++++++++
 .../python/kernel_tests/relu_op_test.py       | 91 +++++++++++++++++++
 tensorflow/python/ops/hidden_ops.txt          |  1 +
 tensorflow/python/ops/nn.py                   |  1 +
 tensorflow/python/ops/nn_grad.py              | 15 +++
 .../tools/api/golden/tensorflow.nn.pbtxt      |  4 +
 21 files changed, 466 insertions(+), 15 deletions(-)

diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc
index 952b2015ed..f9d69ff896 100644
--- a/tensorflow/cc/gradients/nn_grad.cc
+++ b/tensorflow/cc/gradients/nn_grad.cc
@@ -86,6 +86,15 @@ Status EluGradHelper(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("Elu", EluGradHelper);
 
+Status SeluGradHelper(const Scope& scope, const Operation& op,
+                      const std::vector<Output>& grad_inputs,
+                      std::vector<Output>* grad_outputs) {
+  auto dx = internal::SeluGrad(scope, grad_inputs[0], op.output(0));
+  grad_outputs->push_back(dx);
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("Selu", SeluGradHelper);
+
 }  // anonymous namespace
 }  // namespace ops
 }  // namespace tensorflow
diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc
index daa87546ec..eab5b44626 100644
--- a/tensorflow/cc/gradients/nn_grad_test.cc
+++ b/tensorflow/cc/gradients/nn_grad_test.cc
@@ -103,5 +103,15 @@ TEST_F(NNGradTest, EluGrad) {
   RunTest(x, x_init_value, y, shape);
 }
 
+TEST_F(NNGradTest, SeluGrad) {
+  TensorShape shape({5, 2});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
+  auto y = Selu(scope_, x);
+  Tensor x_init_value = test::AsTensor<float>(
+      {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f, 0.1f, 0.3f, 0.5f, 0.7f, 0.9f},
+      {5, 2});
+  RunTest(x, x_init_value, y, shape);
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/cc/ops/op_gen_overrides.pbtxt b/tensorflow/cc/ops/op_gen_overrides.pbtxt
index a1f79177f7..2252cbb289 100644
--- a/tensorflow/cc/ops/op_gen_overrides.pbtxt
+++ b/tensorflow/cc/ops/op_gen_overrides.pbtxt
@@ -177,6 +177,7 @@ op { name: "MaxPoolGradWithArgmax" hide: true }
 op { name: "ReluGrad" hide: true }
 op { name: "Relu6Grad" hide: true }
 op { name: "EluGrad" hide: true }
+op { name: "SeluGrad" hide: true }
 op { name: "SoftplusGrad" hide: true }
 op { name: "SoftsignGrad" hide: true }
 op { name: "FractionalAvgPoolGrad" hide: true }
diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py
index 9eaede7f40..0bdbf53c39 100644
--- a/tensorflow/compiler/tests/binary_ops_test.py
+++ b/tensorflow/compiler/tests/binary_ops_test.py
@@ -113,6 +113,14 @@ class BinaryOpsTest(XLATestCase):
           np.array([-.6, -.4, -.2, 0, .2, .4], dtype=dtype),
           expected=np.array([0.4, 1.2, 2.4, 4, 5, 6], dtype=dtype))
 
+      self._testBinary(
+          gen_nn_ops._selu_grad,
+          np.array([1, 2, 3, 4, 5, 6], dtype=dtype),
+          np.array([-.6, -.4, -.2, .2, .4, .6], dtype=dtype),
+          expected=np.array(
+              [1.158099340847, 2.7161986816948, 4.67429802254,
+               4.202803949422, 5.2535049367774, 6.30420592413], dtype=dtype))
+
       self._testBinary(
           gen_nn_ops._relu_grad,
           np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtype),
diff --git a/tensorflow/compiler/tests/randomized_tests.cc b/tensorflow/compiler/tests/randomized_tests.cc
index d3821ad02e..825fd9de2e 100644
--- a/tensorflow/compiler/tests/randomized_tests.cc
+++ b/tensorflow/compiler/tests/randomized_tests.cc
@@ -1434,6 +1434,23 @@ TEST_F(OpTest, EluGrad) {
   });
 }
 
+TEST_F(OpTest, Selu) {
+  Repeatedly([this]() {
+    return ExpectTfAndXlaOutputsAreClose(
+        OpTestBuilder("Selu").RandomInput(DT_FLOAT).Attr("T", DT_FLOAT));
+  });
+}
+
+TEST_F(OpTest, SeluGrad) {
+  Repeatedly([this]() {
+    auto dims = RandomDims();
+    return ExpectTfAndXlaOutputsAreClose(OpTestBuilder("SeluGrad")
+                                             .RandomInput(DT_FLOAT, dims)
+                                             .RandomInput(DT_FLOAT, dims)
+                                             .Attr("T", DT_FLOAT));
+  });
+}
+
 TEST_F(OpTest, Equal) {
   Repeatedly([this]() {
     DataType type = Choose<DataType>({DT_INT32, DT_FLOAT});
diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py
index ce35eb9197..81ff18f302 100644
--- a/tensorflow/compiler/tests/unary_ops_test.py
+++ b/tensorflow/compiler/tests/unary_ops_test.py
@@ -229,6 +229,11 @@ class UnaryOpsTest(XLATestCase):
           np.array([[-1, 0, 1]], dtype=dtype),
           expected=np.array([[-0.63212056, 0, 1]], dtype=dtype))
 
+      self._assertOpOutputMatchesExpected(
+          nn_ops.selu,
+          np.array([[-1, 0, 1]], dtype=dtype),
+          expected=np.array([[-1.11133074, 0., 1.05070099]], dtype=dtype))
+
       self._assertOpOutputMatchesExpected(
           nn_ops.relu,
           np.array([[-1, 1]], dtype=dtype),
diff --git a/tensorflow/compiler/tf2xla/kernels/elu_op.cc b/tensorflow/compiler/tf2xla/kernels/elu_op.cc
index 62a5e1bd42..2fd27c5ca7 100644
--- a/tensorflow/compiler/tf2xla/kernels/elu_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/elu_op.cc
@@ -61,5 +61,49 @@ class EluGradOp : public XlaOpKernel {
 REGISTER_XLA_OP(Name("Elu"), EluOp);
 REGISTER_XLA_OP(Name("EluGrad"), EluGradOp);
 
+class SeluOp : public XlaOpKernel {
+ public:
+  explicit SeluOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+  // Computes the max of the scalar input x and 0.
+  void Compile(XlaOpKernelContext* ctx) override {
+    xla::ComputationBuilder* b = ctx->builder();
+    const auto zero = XlaHelpers::Zero(b, input_type(0));
+    const auto one = XlaHelpers::One(b, input_type(0));
+    const auto scale = XlaHelpers::FloatLiteral(b, input_type(0),
+            1.0507009873554804934193349852946);
+    const auto scale_alpha = XlaHelpers::FloatLiteral(b, input_type(0),
+            1.7580993408473768599402175208123);
+    const auto pred = b->Gt(ctx->Input(0), zero);
+    const auto expm1 = b->Sub(b->Exp(ctx->Input(0)), one);
+    ctx->SetOutput(0, b->Select(pred, b->Mul(scale, ctx->Input(0)),
+                                      b->Mul(scale_alpha, expm1)));
+  }
+};
+
+class SeluGradOp : public XlaOpKernel {
+ public:
+  explicit SeluGradOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+  // Return the lhs (incoming gradient) if the rhs (input feature) > 0,
+  // otherwise return lhs * (1 + rhs).
+  void Compile(XlaOpKernelContext* ctx) override {
+    xla::ComputationBuilder* b = ctx->builder();
+    const auto zero = XlaHelpers::Zero(b, input_type(0));
+    const auto one = XlaHelpers::One(b, input_type(0));
+    const auto scale = XlaHelpers::FloatLiteral(b, input_type(0),
+            1.0507009873554804934193349852946);
+    const auto scale_alpha = XlaHelpers::FloatLiteral(b, input_type(0),
+            1.7580993408473768599402175208123);
+    const auto grad = ctx->Input(0);
+    const auto activation = ctx->Input(1);
+    const auto lin_grad = b->Mul(grad, scale);
+    const auto exp_grad = b->Mul(grad, b->Add(activation, scale_alpha));
+    const auto pred = b->Gt(activation, zero);
+    ctx->SetOutput(0, b->Select(pred, lin_grad, exp_grad));
+  }
+};
+
+REGISTER_XLA_OP(Name("Selu"), SeluOp);
+REGISTER_XLA_OP(Name("SeluGrad"), SeluGradOp);
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/relu_op.cc b/tensorflow/core/kernels/relu_op.cc
index d8d30e87e2..afad288cc0 100644
--- a/tensorflow/core/kernels/relu_op.cc
+++ b/tensorflow/core/kernels/relu_op.cc
@@ -50,15 +50,21 @@ typedef Eigen::SyclDevice SYCLDevice;
 TF_CALL_REAL_NUMBER_TYPES(REGISTER_RELU_KERNELS);
 #undef REGISTER_RELU_KERNELS
 
-#define REGISTER_ELU_KERNELS(type)                                  \
-  REGISTER_KERNEL_BUILDER(                                          \
-      Name("Elu").Device(DEVICE_CPU).TypeConstraint<type>("T"),     \
-      EluOp<CPUDevice, type>);                                      \
-  REGISTER_KERNEL_BUILDER(                                          \
-      Name("EluGrad").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
-      EluGradOp<CPUDevice, type>)
-
-// Elu only makes sense with float or double.
+#define REGISTER_ELU_KERNELS(type)                                   \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("Elu").Device(DEVICE_CPU).TypeConstraint<type>("T"),      \
+      EluOp<CPUDevice, type>);                                       \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("EluGrad").Device(DEVICE_CPU).TypeConstraint<type>("T"),  \
+      EluGradOp<CPUDevice, type>);                                   \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("Selu").Device(DEVICE_CPU).TypeConstraint<type>("T"),     \
+      SeluOp<CPUDevice, type>);                                      \
+  REGISTER_KERNEL_BUILDER(                                           \
+      Name("SeluGrad").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      SeluGradOp<CPUDevice, type>)
+
+// Elu and Selu only make sense with float or double.
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_ELU_KERNELS);
 #undef REGISTER_ELU_KERNELS
 
@@ -103,7 +109,23 @@ namespace functor {
       const GPUDevice& d, typename TTypes<T>::ConstTensor gradients,           \
       typename TTypes<T>::ConstTensor activations,                             \
       typename TTypes<T>::Tensor backprops);                                   \
-  extern template struct EluGrad<GPUDevice, T>;
+  extern template struct EluGrad<GPUDevice, T>;                                \
+                                                                               \
+  template <>                                                                  \
+  void Selu<GPUDevice, T>::operator()(                                         \
+      const GPUDevice& d,                                                      \
+      typename TTypes<T>::ConstTensor features,                                \
+      typename TTypes<T>::Tensor activations);                                 \
+  extern template struct Selu<GPUDevice, T>;                                   \
+                                                                               \
+  template <>                                                                  \
+  void SeluGrad<GPUDevice, T>::operator()(                                     \
+      const GPUDevice& d, typename TTypes<T>::ConstTensor gradients,           \
+      typename TTypes<T>::ConstTensor activations,                             \
+      typename TTypes<T>::Tensor backprops);                                   \
+  extern template struct SeluGrad<GPUDevice, T>;
+
+
 
 TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
 }  // namespace functor
@@ -127,7 +149,15 @@ TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
       EluOp<GPUDevice, type>);                                        \
   REGISTER_KERNEL_BUILDER(                                            \
       Name("EluGrad").Device(DEVICE_GPU).TypeConstraint<type>("T"),   \
-      EluGradOp<GPUDevice, type>)
+      EluGradOp<GPUDevice, type>);                                    \
+  REGISTER_KERNEL_BUILDER(                                            \
+      Name("Selu").Device(DEVICE_GPU).TypeConstraint<type>("T"),      \
+      SeluOp<GPUDevice, type>);                                       \
+  REGISTER_KERNEL_BUILDER(                                            \
+      Name("SeluGrad").Device(DEVICE_GPU).TypeConstraint<type>("T"),  \
+      SeluGradOp<GPUDevice, type>)
+
+
 
 TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
 #undef REGISTER_GPU_KERNELS
@@ -154,7 +184,15 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
       EluOp<SYCLDevice, type>);                                        \
   REGISTER_KERNEL_BUILDER(                                             \
       Name("EluGrad").Device(DEVICE_SYCL).TypeConstraint<type>("T"),   \
-      EluGradOp<SYCLDevice, type>)
+      EluGradOp<SYCLDevice, type>);                                    \
+  REGISTER_KERNEL_BUILDER(                                             \
+      Name("Selu").Device(DEVICE_SYCL).TypeConstraint<type>("T"),      \
+      SeluOp<SYCLDevice, type>);                                       \
+  REGISTER_KERNEL_BUILDER(                                             \
+      Name("SeluGrad").Device(DEVICE_SYCL).TypeConstraint<type>("T"),  \
+      SeluGradOp<SYCLDevice, type>)
+
+
 
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS);
 #undef REGISTER_SYCL_KERNELS
diff --git a/tensorflow/core/kernels/relu_op.h b/tensorflow/core/kernels/relu_op.h
index 365c6201a5..e712b02bd7 100644
--- a/tensorflow/core/kernels/relu_op.h
+++ b/tensorflow/core/kernels/relu_op.h
@@ -173,6 +173,48 @@ void EluGradOp<Device, T>::OperateNoTemplate(OpKernelContext* context,
           output->flat<T>());
 }
 
+template <typename Device, typename T>
+class SeluOp : public UnaryElementWiseOp<T, SeluOp<Device, T>> {
+ public:
+  using UnaryElementWiseOp<T, SeluOp<Device, T>>::UnaryElementWiseOp;
+
+  void Operate(OpKernelContext* context, const Tensor& input, Tensor* output) {
+    functor::Selu<Device, T> functor;
+    functor(context->eigen_device<Device>(), input.flat<T>(),
+            output->flat<T>());
+  }
+};
+
+template <typename Device, typename T>
+class SeluGradOp : public BinaryElementWiseOp<T, SeluGradOp<Device, T>> {
+ public:
+  using BinaryElementWiseOp<T, SeluGradOp<Device, T>>::BinaryElementWiseOp;
+
+  void OperateNoTemplate(OpKernelContext* context, const Tensor& g,
+                         const Tensor& a, Tensor* output);
+
+  // INPUTS:
+  //   g (gradients): backpropagated gradients
+  //   a (outputs): outputs of the SeluOp()
+  // OUTPUT:
+  //   gradients to backprop
+  template <int NDIMS>
+  void Operate(OpKernelContext* context, const Tensor& g, const Tensor& a,
+               Tensor* output) {
+    OperateNoTemplate(context, g, a, output);
+  }
+};
+
+template <typename Device, typename T>
+void SeluGradOp<Device, T>::OperateNoTemplate(OpKernelContext* context,
+                                              const Tensor& g, const Tensor& a,
+                                              Tensor* output) {
+  if (!ReluHelpers::ValidateSameSize(context, g, a)) return;
+  functor::SeluGrad<Device, T> functor;
+  functor(context->eigen_device<Device>(), g.flat<T>(), a.flat<T>(),
+          output->flat<T>());
+}
+
 }  // namespace tensorflow
 
 #undef EIGEN_USE_THREADS
diff --git a/tensorflow/core/kernels/relu_op_functor.h b/tensorflow/core/kernels/relu_op_functor.h
index 633522920c..9577b963c6 100644
--- a/tensorflow/core/kernels/relu_op_functor.h
+++ b/tensorflow/core/kernels/relu_op_functor.h
@@ -125,6 +125,46 @@ struct EluGrad {
   }
 };
 
+// Functor used by SeluOp to do the computations.
+template <typename Device, typename T>
+struct Selu {
+  // Computes Selu activation.
+  //
+  // features: any shape.
+  // activations: same shape as "features".
+  void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
+                  typename TTypes<T>::Tensor activations) {
+    // features.constant(?)
+    const auto scale = static_cast<T>(1.0507009873554804934193349852946);
+    const auto scale_alpha = static_cast<T>(1.7580993408473768599402175208123);
+    const auto one = static_cast<T>(1);
+    const auto zero = static_cast<T>(0);
+    activations.device(d) =
+        (features < zero)
+            .select(scale_alpha * (features.exp() - features.constant(one)),
+                    scale * features);
+  }
+};
+
+// Functor used by SeluGradOp to do the computations.
+template <typename Device, typename T>
+struct SeluGrad {
+  // Computes SeluGrad backprops.
+  //
+  // gradients: gradients backpropagated to the Selu op.
+  // activations: outputs of the Selu op.
+  // backprops: gradients to backpropagate to the Selu inputs.
+  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
+                  typename TTypes<T>::ConstTensor activations,
+                  typename TTypes<T>::Tensor backprops) {
+    const auto scale = static_cast<T>(1.0507009873554804934193349852946);
+    const auto scale_alpha = static_cast<T>(1.7580993408473768599402175208123);
+    backprops.device(d) =
+        (activations < static_cast<T>(0)).select(
+            gradients * (activations + scale_alpha), gradients * scale);
+  }
+};
+
 }  // namespace functor
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/relu_op_gpu.cu.cc b/tensorflow/core/kernels/relu_op_gpu.cu.cc
index 30c4a289f7..ec09d8dfea 100644
--- a/tensorflow/core/kernels/relu_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/relu_op_gpu.cu.cc
@@ -35,7 +35,9 @@ typedef Eigen::GpuDevice GPUDevice;
   template struct functor::Relu6<GPUDevice, T>;     \
   template struct functor::Relu6Grad<GPUDevice, T>; \
   template struct functor::Elu<GPUDevice, T>;       \
-  template struct functor::EluGrad<GPUDevice, T>;
+  template struct functor::EluGrad<GPUDevice, T>;   \
+  template struct functor::Selu<GPUDevice, T>;      \
+  template struct functor::SeluGrad<GPUDevice, T>;
 
 TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_KERNELS);
 
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 555b97f53b..1018742521 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -1779,6 +1779,33 @@ backprops: The gradients: `gradients * (outputs + 1)` if outputs < 0,
 `gradients` otherwise.
 )doc");
 
+REGISTER_OP("Selu")
+    .Input("features: T")
+    .Output("activations: T")
+    .Attr("T: {half, float, double}")
+    .SetShapeFn(shape_inference::UnchangedShape)
+    .Doc(R"doc(
+Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
+if < 0, `scale * features` otherwise.
+
+See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+)doc");
+
+REGISTER_OP("SeluGrad")
+    .Input("gradients: T")
+    .Input("outputs: T")
+    .Output("backprops: T")
+    .Attr("T: {half, float, double}")
+    .SetShapeFn(shape_inference::MergeBothInputsShapeFn)
+    .Doc(R"doc(
+Computes gradients for the scaled exponential linear (Selu) operation.
+
+gradients: The backpropagated gradients to the corresponding Selu operation.
+outputs: The outputs of the corresponding Selu operation.
+backprops: The gradients: `gradients * (outputs + scale * alpha)`
+if outputs < 0, `scale * gradients` otherwise.
+)doc");
+
 REGISTER_OP("Softplus")
     .Input("features: T")
     .Output("activations: T")
diff --git a/tensorflow/core/ops/nn_ops_test.cc b/tensorflow/core/ops/nn_ops_test.cc
index a60b1c3788..51e4f8bffe 100644
--- a/tensorflow/core/ops/nn_ops_test.cc
+++ b/tensorflow/core/ops/nn_ops_test.cc
@@ -412,7 +412,8 @@ TEST(NNOpsTest, Dilation2DBackpropFilter_ShapeFn) {
 
 TEST(NNOpsTest, MergeBothInputs_ShapeFn) {
   for (const char* op_name :
-       {"ReluGrad", "Relu6Grad", "EluGrad", "SoftplusGrad", "SoftsignGrad"}) {
+       {"ReluGrad", "Relu6Grad", "EluGrad", "SeluGrad", "SoftplusGrad",
+        "SoftsignGrad"}) {
     ShapeInferenceTestOp op(op_name);
 
     INFER_OK(op, "?;?", "in0|in1");
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 468434bd28..2839575ec7 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -23383,6 +23383,60 @@ op {
   summary: "Computes the eigen decomposition of one or more square self-adjoint matrices."
   description: "Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in\n`input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`.\n\n```python\n# a is a tensor.\n# e is a tensor of eigenvalues.\n# v is a tensor of eigenvectors.\ne, v = self_adjoint_eig(a)\ne = self_adjoint_eig(a, compute_v=False)\n```"
 }
+op {
+  name: "Selu"
+  input_arg {
+    name: "features"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "activations"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  summary: "Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` if < 0, `scale * features` otherwise."
+  description: "See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)"
+}
+op {
+  name: "SeluGrad"
+  input_arg {
+    name: "gradients"
+    description: "The backpropagated gradients to the corresponding Selu operation."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "outputs"
+    description: "The outputs of the corresponding Selu operation."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "backprops"
+    description: "The gradients: `gradients * (outputs + scale * alpha)` if outputs < 0,\n`scale * gradients` otherwise."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  summary: "Computes gradients for the scaled exponential linear (Selu) operation."
+}
 op {
   name: "SerializeManySparse"
   input_arg {
diff --git a/tensorflow/docs_src/api_guides/python/nn.md b/tensorflow/docs_src/api_guides/python/nn.md
index 4f188372a0..75dbb04e7d 100644
--- a/tensorflow/docs_src/api_guides/python/nn.md
+++ b/tensorflow/docs_src/api_guides/python/nn.md
@@ -8,7 +8,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 ## Activation Functions
 
 The activation ops provide different types of nonlinearities for use in neural
-networks.  These include smooth nonlinearities (`sigmoid`, `tanh`, `elu`,
+networks. These include smooth nonlinearities (`sigmoid`, `tanh`, `elu`, `selu`,
 `softplus`, and `softsign`), continuous but not everywhere differentiable
 functions (`relu`, `relu6`, `crelu` and `relu_x`), and random regularization
 (`dropout`).
@@ -20,6 +20,7 @@ shape as the input tensor.
 *   @{tf.nn.relu6}
 *   @{tf.nn.crelu}
 *   @{tf.nn.elu}
+*   @{tf.nn.selu}
 *   @{tf.nn.softplus}
 *   @{tf.nn.softsign}
 *   @{tf.nn.dropout}
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 095cbbe637..43e09c498c 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -16265,6 +16265,28 @@ func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyReso
 	return scope.AddOperation(opspec)
 }
 
+// Computes gradients for the scaled exponential linear (Selu) operation.
+//
+// Arguments:
+//	gradients: The backpropagated gradients to the corresponding Selu operation.
+//	outputs: The outputs of the corresponding Selu operation.
+//
+// Returns The gradients: `gradients * (outputs + scale * alpha)` if outputs < 0,
+// `scale * gradients` otherwise.
+func SeluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SeluGrad",
+		Input: []tf.Input{
+			gradients, outputs,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Converts each string in the input Tensor to its hash mod by a number of buckets.
 //
 // The hash function is deterministic on the content of the string within the
@@ -20541,6 +20563,24 @@ func Elu(scope *Scope, features tf.Output) (activations tf.Output) {
 	return op.Output(0)
 }
 
+// Computes scaled exponential linear: `1.758099 * (exp(features) - 1)` if < 0,
+// `1.050701 * features` otherwise.
+//
+// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+func Selu(scope *Scope, features tf.Output) (activations tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Selu",
+		Input: []tf.Input{
+			features,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes square of x element-wise.
 //
 // I.e., \\(y = x * x = x^2\\).
diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py
index 63ac743843..8cd1f52d80 100644
--- a/tensorflow/python/kernel_tests/relu_op_test.py
+++ b/tensorflow/python/kernel_tests/relu_op_test.py
@@ -320,6 +320,97 @@ class EluTest(test.TestCase):
     self.assertLess(err, 1e-6)
 
 
+class SeluTest(test.TestCase):
+
+  def _npSelu(self, np_features):
+    scale = 1.0507009873554804934193349852946
+    scale_alpha = 1.7580993408473768599402175208123
+    return np.where(np_features < 0, scale_alpha * (np.exp(np_features) - 1),
+                    scale * np_features)
+
+  def testNpSelu(self):
+    self.assertAllClose(
+        np.array([[-1.0433095, 0.73549069, -0.6917582, 0.3152103 , -0.16730527],
+                 [0.1050701 , -0.45566732, 0.5253505, -0.88505305, 0.9456309]]),
+        self._npSelu(
+            np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, 0.9]
+                     ])))
+
+  def _testSelu(self, np_features, use_gpu=False):
+    np_selu = self._npSelu(np_features)
+    with self.test_session(use_gpu=use_gpu):
+      selu = nn_ops.selu(np_features)
+      tf_selu = selu.eval()
+    self.assertAllClose(np_selu, tf_selu)
+    self.assertShapeEqual(np_selu, selu)
+
+  def testNumbers(self):
+    for t in [np.float16, np.float32, np.float64]:
+      self._testSelu(
+          np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
+          use_gpu=False)
+      self._testSelu(
+          np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
+          use_gpu=True)
+
+  def testGradientFloat32(self):
+    with self.test_session():
+      x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
+      x = constant_op.constant(x_val, name="x")
+      y = nn_ops.selu(x, name="selu")
+      x_init = np.asarray(x_val, dtype=np.float32, order="F")
+      err = gradient_checker.compute_gradient_error(
+          x, [2, 5], y, [2, 5], x_init_value=x_init)
+    print("selu (float32) gradient err = ", err)
+    self.assertLess(err, 1e-4)
+
+  def testGradientFloat64(self):
+    with self.test_session():
+      x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
+      x = constant_op.constant(x_val, dtype=dtypes.float64, name="x")
+      y = nn_ops.selu(x, name="selu")
+      x_init = np.asarray(x_val, dtype=np.float64, order="F")
+      err = gradient_checker.compute_gradient_error(
+          x, [2, 5], y, [2, 5], x_init_value=x_init)
+    print("selu (float64) gradient err = ", err)
+    self.assertLess(err, 1e-6)
+
+  def testGradGradFloat32(self):
+    with self.test_session():
+      x = constant_op.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5],
+          name="x")
+      y = nn_ops.selu(x, name="selu")
+      z = gradients_impl.gradients(y, x)
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float32,
+          order="F")
+      err = gradient_checker.compute_gradient_error(
+          x, [2, 5], z[0], [2, 5], x_init_value=x_init)
+    print("selu (float32) gradient of gradient err = ", err)
+    self.assertLess(err, 1e-4)
+
+  def testGradGradFloat64(self):
+    with self.test_session():
+      x = constant_op.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5],
+          dtype=dtypes.float64,
+          name="x")
+      y = nn_ops.selu(x, name="selu")
+      z = gradients_impl.gradients(y, x)
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float64,
+          order="F")
+      err = gradient_checker.compute_gradient_error(
+          x, [2, 5], z[0], [2, 5], x_init_value=x_init)
+    print("selu (float64) gradient of gradient err = ", err)
+    self.assertLess(err, 1e-6)
+
+
 class CreluTest(test.TestCase):
 
   def testCreluShape(self):
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index b45d8987a3..5197316432 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -290,6 +290,7 @@ MaxPool3DGradGrad
 ReluGrad
 Relu6Grad
 EluGrad
+SeluGrad
 SoftplusGrad
 SoftsignGrad
 TopK
diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py
index d05cba2e93..60e9695dcb 100644
--- a/tensorflow/python/ops/nn.py
+++ b/tensorflow/python/ops/nn.py
@@ -22,6 +22,7 @@ See the @{$python/nn} guide.
 @@relu6
 @@crelu
 @@elu
+@@selu
 @@softplus
 @@softsign
 @@dropout
diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py
index f1453f9ef0..50673ed427 100644
--- a/tensorflow/python/ops/nn_grad.py
+++ b/tensorflow/python/ops/nn_grad.py
@@ -335,6 +335,16 @@ def _EluGradGrad(op, grad):
                                           dtype=elu_x.dtype)))
 
 
+@ops.RegisterGradient("SeluGrad")
+def _SeluGradGrad(op, grad):
+  x = op.inputs[1]
+  scale_alpha = 1.7580993408473768599402175208123
+  return (gen_nn_ops._elu_grad(grad, op.outputs[0]),
+          array_ops.where(
+              x < 0., gen_nn_ops._elu_grad(grad, op.outputs[0] + scale_alpha),
+              array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)))
+
+
 @ops.RegisterGradient("Relu6")
 def _Relu6Grad(op, grad):
   return gen_nn_ops._relu6_grad(grad, op.inputs[0])
@@ -345,6 +355,11 @@ def _EluGrad(op, grad):
   return gen_nn_ops._elu_grad(grad, op.outputs[0])
 
 
+@ops.RegisterGradient("Selu")
+def _SeluGrad(op, grad):
+  return gen_nn_ops._selu_grad(grad, op.outputs[0])
+
+
 @ops.RegisterGradient("Softplus")
 def _SoftplusGrad(op, grad):
   return gen_nn_ops._softplus_grad(grad, op.inputs[0])
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt
index 9f817beafd..3beb95d25c 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt
@@ -256,6 +256,10 @@ tf_module {
     name: "sampled_softmax_loss"
     argspec: "args=[\'weights\', \'biases\', \'labels\', \'inputs\', \'num_sampled\', \'num_classes\', \'num_true\', \'sampled_values\', \'remove_accidental_hits\', \'partition_strategy\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'True\', \'mod\', \'sampled_softmax_loss\'], "
   }
+  member_method {
+    name: "selu"
+    argspec: "args=[\'features\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "separable_conv2d"
     argspec: "args=[\'input\', \'depthwise_filter\', \'pointwise_filter\', \'strides\', \'padding\', \'rate\', \'name\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-- 
GitLab


From 16d39e94e3724417fcaed87035434e098e892842 Mon Sep 17 00:00:00 2001
From: James Pruegsanusak <korrawat@mit.edu>
Date: Wed, 26 Jul 2017 13:01:58 +0800
Subject: [PATCH 1338/1375] Fix quantization tutorial (#11285)

* Remove outdated dependencies in quantization tutorial

* Fix quantization tutorial's example
---
 tensorflow/docs_src/performance/quantization.md | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md
index a37748d0c9..d050fc5c56 100644
--- a/tensorflow/docs_src/performance/quantization.md
+++ b/tensorflow/docs_src/performance/quantization.md
@@ -93,7 +93,7 @@ curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.t
 tar xzf /tmp/inceptionv3.tgz -C /tmp/
 bazel build tensorflow/tools/graph_transforms:transform_graph
 bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
-  --in_graph=/tmp/classify_image_graph_def.pb \
+  --inputs="Mul" --in_graph=/tmp/classify_image_graph_def.pb \
   --outputs="softmax" --out_graph=/tmp/quantized_graph.pb \
   --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3")
     remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true)
@@ -108,12 +108,6 @@ versus 91MB). You can still run this model using exactly the same inputs and
 outputs though, and you should get equivalent results. Here's an example:
 
 ```sh
-# Note: You need to add the dependencies of the quantization operation to the
-#       cc_binary in the BUILD file of the label_image program:
-#
-#     //tensorflow/contrib/quantization:cc_ops
-#     //tensorflow/contrib/quantization/kernels:quantized_ops
-
 bazel build tensorflow/examples/label_image:label_image
 bazel-bin/tensorflow/examples/label_image/label_image \
 --image=<input-image> \
-- 
GitLab


From b01346de8b5893a09d50ff4d9c80ca442a327a76 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Tue, 25 Jul 2017 23:16:04 -0700
Subject: [PATCH 1339/1375] Automated g4 rollback of changelist 163164566

PiperOrigin-RevId: 163170549
---
 tensorflow/compiler/xla/BUILD             |   3 -
 tensorflow/compiler/xla/reference_util.cc | 247 +++++++++++++++-------
 2 files changed, 169 insertions(+), 81 deletions(-)

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index ba90b13b38..e0a03a78f1 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -563,9 +563,6 @@ cc_library(
         ":xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:padding",
-        "//tensorflow/compiler/xla/service:hlo",
-        "//tensorflow/compiler/xla/service:hlo_evaluator",
-        "//tensorflow/compiler/xla/service:shape_inference",
         "//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_matmul",
         "//tensorflow/core:lib",
     ],
diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index c851c38ea4..7ef5c6d916 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -20,9 +20,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
-#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
-#include "tensorflow/compiler/xla/service/hlo_instruction.h"
-#include "tensorflow/compiler/xla/service/shape_inference.h"
 #include "tensorflow/compiler/xla/window_util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/math/math_util.h"
@@ -449,85 +446,179 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
     std::pair<int64, int64> kernel_stride, Padding padding,
     std::pair<int64, int64> lhs_dilation, std::pair<int64, int64> rhs_dilation,
     ConvolutionDimensionNumbers dnums) {
-  HloComputation::Builder b("ConvArray4DGeneralDimensionDilated");
-  auto lhs_literal = Literal::CreateR4FromArray4D<float>(lhs);
-  auto rhs_literal = Literal::CreateR4FromArray4D<float>(rhs);
-
-  std::array<int64, 2> ordered_kernel_strides;
-  std::array<int64, 2> ordered_input_dimensions;
-  std::array<int64, 2> ordered_kernel_dimensions;
-  if (dnums.kernel_spatial_dimensions(0) > dnums.kernel_spatial_dimensions(1)) {
-    ordered_kernel_strides[0] = kernel_stride.second;
-    ordered_kernel_strides[1] = kernel_stride.first;
-  } else {
-    ordered_kernel_strides[0] = kernel_stride.first;
-    ordered_kernel_strides[1] = kernel_stride.second;
+  std::array<int64, 4> lhs_dimensions{{lhs.n1(), lhs.n2(), lhs.n3(), lhs.n4()}};
+  std::array<int64, 4> rhs_dimensions{{rhs.n1(), rhs.n2(), rhs.n3(), rhs.n4()}};
+
+  const int64 ksy = kernel_stride.first;
+  const int64 ksx = kernel_stride.second;
+  const int64 dy = lhs_dilation.first;
+  const int64 dx = lhs_dilation.second;
+  const int64 dky = rhs_dilation.first;
+  const int64 dkx = rhs_dilation.second;
+  CHECK_GE(dky, 1);
+  CHECK_GE(dkx, 1);
+  CHECK_GE(dy, 1);
+  CHECK_GE(dx, 1);
+
+  // Get all dimension sizes in lhs and rhs based on the given convolution
+  // dimension configuration.
+  const int64 ix = window_util::DilatedBound(
+      lhs_dimensions[dnums.spatial_dimensions(1)], dx);
+  const int64 iy = window_util::DilatedBound(
+      lhs_dimensions[dnums.spatial_dimensions(0)], dy);
+  const int64 iz = lhs_dimensions[dnums.feature_dimension()];
+  const int64 samples = lhs_dimensions[dnums.batch_dimension()];
+  const int64 kx = window_util::DilatedBound(
+      rhs_dimensions[dnums.kernel_spatial_dimensions(1)], dkx);
+  const int64 ky = window_util::DilatedBound(
+      rhs_dimensions[dnums.kernel_spatial_dimensions(0)], dky);
+  const int64 oz = rhs_dimensions[dnums.kernel_output_feature_dimension()];
+  {
+    const int64 kiz = rhs_dimensions[dnums.kernel_input_feature_dimension()];
+    CHECK_EQ(kiz, iz);
   }
 
-  ordered_input_dimensions[0] =
-      lhs_literal->shape().dimensions(dnums.spatial_dimensions(0));
-  ordered_input_dimensions[1] =
-      lhs_literal->shape().dimensions(dnums.spatial_dimensions(1));
-  ordered_kernel_dimensions[0] =
-      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(0));
-  ordered_kernel_dimensions[1] =
-      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(1));
-
-  std::vector<std::pair<int64, int64>> paddings =
-      MakePadding(ordered_input_dimensions, ordered_kernel_dimensions,
-                  ordered_kernel_strides, padding);
-  CHECK_EQ(paddings.size(), 2);
-
-  Window window;
-
-  WindowDimension dim;
-  dim.set_size(
-      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(0)));
-  dim.set_stride(kernel_stride.first);
-  dim.set_padding_low(paddings[0].first);
-  dim.set_padding_high(paddings[0].second);
-  dim.set_window_dilation(rhs_dilation.first);
-  dim.set_base_dilation(lhs_dilation.first);
-  *window.add_dimensions() = dim;
-
-  WindowDimension dim2;
-  dim2.set_size(
-      rhs_literal->shape().dimensions(dnums.kernel_spatial_dimensions(1)));
-  dim2.set_stride(kernel_stride.second);
-  dim2.set_padding_low(paddings[1].first);
-  dim2.set_padding_high(paddings[1].second);
-  dim2.set_window_dilation(rhs_dilation.second);
-  dim2.set_base_dilation(lhs_dilation.second);
-  *window.add_dimensions() = dim2;
-
-  const Shape& shape =
-      ShapeInference::InferConvolveShape(lhs_literal->shape(),
-                                         rhs_literal->shape(), window, dnums)
-          .ConsumeValueOrDie();
-
-  HloInstruction* lhs_instruction =
-      b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal)));
-  HloInstruction* rhs_instruction =
-      b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal)));
-
-  b.AddInstruction(HloInstruction::CreateConvolve(
-      shape, lhs_instruction, rhs_instruction, window, dnums));
-
-  HloEvaluator evaluator;
-  std::unique_ptr<Literal> result_literal =
-      evaluator.Evaluate(b.Build().get(), {}).ConsumeValueOrDie();
-
-  CHECK_EQ(ShapeUtil::Rank(result_literal->shape()), 4);
-  auto result =
-      MakeUnique<Array4D<float>>(result_literal->shape().dimensions(0),
-                                 result_literal->shape().dimensions(1),
-                                 result_literal->shape().dimensions(2),
-                                 result_literal->shape().dimensions(3));
+  if (padding == Padding::kSame) {
+    // We reject same padding with kernel striding, since it's somewhat
+    // nonsensical. We can always follow up to implement this with the desired
+    // semantics if anybody actually uses it.
+    CHECK_EQ(1, ksy);
+    CHECK_EQ(1, ksx);
+  }
 
-  result->Each([&](tensorflow::gtl::ArraySlice<int64> indices, float* value) {
-    *value = result_literal->Get<float>(indices);
-  });
+  const int64 ox =
+      padding == Padding::kSame ? ix : window_util::StridedBound(ix, kx, ksx);
+  const int64 oy =
+      padding == Padding::kSame ? iy : window_util::StridedBound(iy, ky, ksy);
+  const int64 istartx =
+      padding == Padding::kValid ? 0 : kx % 2 == 0 ? -(kx / 2 - 1) : -kx / 2;
+  const int64 istarty =
+      padding == Padding::kValid ? 0 : ky % 2 == 0 ? -(ky / 2 - 1) : -ky / 2;
+  // Create the output result array and reset the values to 0.
+  std::array<int64, 4> result_dimensions;
+  result_dimensions[dnums.batch_dimension()] = samples;
+  result_dimensions[dnums.feature_dimension()] = oz;
+  result_dimensions[dnums.spatial_dimensions(0)] = oy;
+  result_dimensions[dnums.spatial_dimensions(1)] = ox;
+  auto result =
+      MakeUnique<Array4D<float>>(result_dimensions[0], result_dimensions[1],
+                                 result_dimensions[2], result_dimensions[3]);
+  result->Fill(0.0);
+
+  const auto is_int32 = [](int64 x) {
+    return x >= std::numeric_limits<int32>::min() &&
+           x <= std::numeric_limits<int32>::max();
+  };
+
+  // 64-bit idiv/mod are much more expensive x86-64 than 32-bit idiv/imod (at
+  // least on x86-64), so we avoid them where possible.
+  const auto fast_idiv64 = [&](int64 a, int64 b) {
+    if (is_int32(a) && is_int32(b)) {
+      return static_cast<int64>(static_cast<int32>(a) / static_cast<int32>(b));
+    }
+    return a / b;
+  };
+  const auto fast_imod64 = [&](int64 a, int64 b) {
+    if (is_int32(a) && is_int32(b)) {
+      return static_cast<int64>(static_cast<int32>(a) % static_cast<int32>(b));
+    }
+    return a % b;
+  };
+
+  // Lambda to access the lhs operand at the given 4D index.
+  const auto lhs_element = [&](int64 batch, int64 feature, int64 height,
+                               int64 width) {
+    if (fast_imod64(height, dy) != 0 || fast_imod64(width, dx) != 0) {
+      return 0.0f;
+    }
 
+    std::array<int64, 4> index;
+    index[dnums.batch_dimension()] = batch;
+    index[dnums.feature_dimension()] = feature;
+    index[dnums.spatial_dimensions(0)] = fast_idiv64(height, dy);
+    index[dnums.spatial_dimensions(1)] = fast_idiv64(width, dx);
+    return lhs(index[0], index[1], index[2], index[3]);
+  };
+
+  // Lambda to access the rhs operand at the given 4D index.  height_over_dky
+  // should be equal to height / dky, and width_over_dkx should be equal to
+  // width / dkx.  (This is an optimization to avoid doing divisions.)
+  const auto rhs_element =
+      [&](int64 kernel_output_feature, int64 kernel_input_feature, int64 height,
+          int64 width, int64 height_over_dky, int64 width_over_dkx) {
+        DCHECK_EQ(height % dky, 0);
+        DCHECK_EQ(width % dkx, 0);
+        DCHECK_EQ(height / dky, height_over_dky);
+        DCHECK_EQ(width / dkx, width_over_dkx);
+
+        std::array<int64, 4> index;
+        index[dnums.kernel_output_feature_dimension()] = kernel_output_feature;
+        index[dnums.kernel_input_feature_dimension()] = kernel_input_feature;
+        index[dnums.kernel_spatial_dimensions(0)] = height_over_dky;
+        index[dnums.kernel_spatial_dimensions(1)] = width_over_dkx;
+        return rhs(index[0], index[1], index[2], index[3]);
+      };
+
+  // Lambda to access the result data at the given 4D index.
+  const auto result_element = [&](int64 batch, int64 kernel_output_feature,
+                                  int64 height, int64 width) -> float& {
+    std::array<int64, 4> index;
+    index[dnums.batch_dimension()] = batch;
+    index[dnums.feature_dimension()] = kernel_output_feature;
+    index[dnums.spatial_dimensions(0)] = height;
+    index[dnums.spatial_dimensions(1)] = width;
+    return (*result)(index[0], index[1], index[2], index[3]);
+  };
+
+  for (int64 oyi = 0; oyi < oy; ++oyi) {
+    for (int64 oxi = 0; oxi < ox; ++oxi) {
+      for (int64 sample = 0; sample < samples; ++sample) {
+        for (int64 izi = 0; izi < iz; ++izi) {
+          for (int64 ozi = 0; ozi < oz; ++ozi) {
+            for (int64 kyi = 0, kyi_over_dky = 0; kyi < ky;
+                 kyi += dky, kyi_over_dky++) {
+              for (int64 kxi = 0, kxi_over_dkx = 0; kxi < kx;
+                   kxi += dkx, kxi_over_dkx++) {
+                int64 iyi = istarty + ksy * oyi + kyi;
+                int64 ixi = istartx + ksx * oxi + kxi;
+                float input = (iyi >= iy || ixi >= ix || iyi < 0 || ixi < 0)
+                                  ? 0.0
+                                  : lhs_element(sample, izi, iyi, ixi);
+                float gain =
+                    rhs_element(ozi, izi, kyi, kxi, kyi_over_dky, kxi_over_dkx);
+                float addend = input * gain;
+                result_element(sample, ozi, oyi, oxi) += addend;
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  if (samples == 0 || kx == 0 || ky == 0 || ox == 0 || oy == 0 || oz == 0 ||
+      iz == 0) {
+    LOG(INFO) << "Output will be trivially empty because one of these "
+                 "dimensions is 0: samples: "
+              << samples << " kx: " << kx << " ky: " << ky << " ox: " << ox
+              << " oy: " << oy << " oz: " << oz << " iz: " << iz;
+    return result;
+  }
+  bool trivial = true;
+  auto check_trivial = [&trivial](tensorflow::gtl::ArraySlice<int64> indices,
+                                  float value) {
+    if (value != 0.0) {
+      trivial = false;
+    }
+  };
+  lhs.Each(check_trivial);
+  if (trivial) {
+    LOG(FATAL) << "LHS is all 0.0.";
+  }
+  trivial = true;
+  rhs.Each(check_trivial);
+  if (trivial) {
+    LOG(FATAL) << "RHS is all 0.0.";
+  }
   return result;
 }
 
-- 
GitLab


From 5c41a7cea20e4e8e6b3cbe0824d5d67d56238394 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 26 Jul 2017 00:13:14 -0700
Subject: [PATCH 1340/1375] DirectSession: Add accessor to DeviceMgr.

This will enable some experimentation with keeping TF_Tensor objects
in the C API backed by device memory (avoiding copies to host memory).

PiperOrigin-RevId: 163173902
---
 tensorflow/core/common_runtime/direct_session.h     |  6 ++++--
 .../core/common_runtime/direct_session_test.cc      | 11 +++++++++++
 tensorflow/core/public/session.h                    | 13 +++++++++++++
 3 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session.h b/tensorflow/core/common_runtime/direct_session.h
index c3cc573f2e..cfc029132a 100644
--- a/tensorflow/core/common_runtime/direct_session.h
+++ b/tensorflow/core/common_runtime/direct_session.h
@@ -98,14 +98,16 @@ class DirectSession : public Session {
   ::tensorflow::Status ListDevices(
       std::vector<DeviceAttributes>* response) override;
   ::tensorflow::Status Close() override;
+  ::tensorflow::Status LocalDeviceManager(const DeviceMgr** output) override {
+    *output = device_mgr_.get();
+    return ::tensorflow::Status::OK();
+  }
 
   void ExportCostModels(CostModelManager::CostModelMap* cost_models) {
     cost_model_manager_.ExportCostModels(cost_models);
   }
 
  private:
-  typedef DirectSession ME;
-
   // We create one executor and its dependent library runtime for
   // every partition.
   struct PerPartitionExecutorsAndLib {
diff --git a/tensorflow/core/common_runtime/direct_session_test.cc b/tensorflow/core/common_runtime/direct_session_test.cc
index 8010a58f6c..097dab8406 100644
--- a/tensorflow/core/common_runtime/direct_session_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_test.cc
@@ -22,6 +22,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/common_runtime/device_factory.h"
+#include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/op_kernel.h"
@@ -1248,6 +1249,16 @@ TEST(DirectSessionTest, TestDirectSessionReset) {
   EXPECT_EQ("Cancelled: Session has been closed.", s.ToString());
 }
 
+TEST(DirectSessionTest, LocalDeviceManager) {
+  SessionOptions options;
+  std::unique_ptr<Session> session(NewSession(options));
+
+  const DeviceMgr* mgr = nullptr;
+  TF_ASSERT_OK(session->LocalDeviceManager(&mgr));
+  ASSERT_TRUE(mgr != nullptr);
+  EXPECT_GT(mgr->ListDevices().size(), 0);
+}
+
 // A simple benchmark for the overhead of `DirectSession::Run()` calls
 // with varying numbers of feeds/fetches.
 void FeedFetchBenchmarkHelper(int num_feeds, int iters) {
diff --git a/tensorflow/core/public/session.h b/tensorflow/core/public/session.h
index c1f097c7c6..7446fbdb51 100644
--- a/tensorflow/core/public/session.h
+++ b/tensorflow/core/public/session.h
@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/public/session_options.h"
 
 namespace tensorflow {
+class DeviceMgr;
 
 /// \brief A Session instance lets a caller drive a TensorFlow graph
 /// computation.
@@ -177,12 +178,24 @@ class Session {
   /// *response. This API is optional. If it is unimplemented, Status will
   /// return a corresponding error message, and *response will be unmodified.
   virtual Status ListDevices(std::vector<DeviceAttributes>* response) = 0;
+
   /// \brief Closes this session.
   ///
   /// Closing a session releases the resources used by this session
   /// on the TensorFlow runtime (specified during session creation by
   /// the `SessionOptions::target` field).
   virtual Status Close() = 0;
+
+  // NOTE(ashankar): As of July 2017, this is was a method added to
+  // faciliate some experimentation. Reconsider/re-evaluate after
+  // September 2017.
+  //
+  // Sets `*output` to the `DeviceMgr` that owns accessible devices in the
+  // address-space of the caller.
+  virtual Status LocalDeviceManager(const DeviceMgr** output) {
+    return errors::Unimplemented(
+        "LocalDeviceManager is not supported for this session.");
+  }
 };
 
 /// \brief Create a new session with the given options.
-- 
GitLab


From 02a5c34b1ececbd7e183a0f358e5605323735143 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 03:48:28 -0700
Subject: [PATCH 1341/1375] New "Estimators" unit of Programmer's Guide.

PiperOrigin-RevId: 163189681
---
 .../docs_src/programmers_guide/estimators.md  | 153 ++++++++++++++++++
 1 file changed, 153 insertions(+)
 create mode 100644 tensorflow/docs_src/programmers_guide/estimators.md

diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/programmers_guide/estimators.md
new file mode 100644
index 0000000000..a5724ea294
--- /dev/null
+++ b/tensorflow/docs_src/programmers_guide/estimators.md
@@ -0,0 +1,153 @@
+# Estimators
+
+This document introduces **Estimators**--a high-level TensorFlow API that
+greatly simplifies machine learning programming. Estimators encapsulate
+the following actions:
+
+*   training
+*   evaluation
+*   prediction
+*   export for serving
+
+You may either use the pre-made Estimators we provide or write your
+own custom Estimators.  All Estimators--whether pre-made or custom--are
+classes based on the `tf.estimator.Estimator` class.
+
+Note: TensorFlow also provides an Estimator class at
+`tf.contrib.learn.Estimator`, which you should not use.</aside>
+
+
+## Advantages of Estimators
+
+Estimators provide the following benefits:
+
+*   You can run Estimators-based models on a local host or on a
+    distributed multi-server environment without changing your model.
+    Furthermore, you can run Estimators-based models on CPUs, GPUs,
+    or TPUs without recoding your model.
+*   Estimators simplify sharing implementations between model developers.
+*   You can develop a state of the art model with high-level intuitive code,
+    In short, it is generally much easier to create models with Estimators
+    than with the low-level TensorFlow APIs.
+*   Estimators are themselves built on tf.layers, which
+    simplifies customization.
+*   Estimators build the graph for you.  In other words, you don't have to
+    build the graph.
+*   Estimators provide a safe distributed training loop that controls how and
+    when to:
+    *   build the graph
+    *   initialize variables
+    *   start queues
+    *   handle exceptions
+    *   create checkpoint files and recover from failures
+    *   save summaries for TensorBoard
+
+When writing an application with Estimators, you must separate the data input
+pipeline from the model.  This separation simplifies experiments with
+different data sets.
+
+
+## Pre-made Estimators
+
+Pre-made Estimators enable you to work at a much higher conceptual level
+than the base TensorFlow APIs. You no longer have to worry about creating
+the computational graph or sessions since Estimators handle all
+the "plumbing" for you.  That is, pre-made Estimators create and manage
+`Graph` and `Session` objects for you.  Furthermore, pre-made Estimators
+let you experiment with different model architectures by making only minimal
+code changes.  `DNNClassifier`, for example, is a pre-made Estimator class that
+trains classification models through dense, feed-forward neural networks.
+
+
+### Structure of a pre-made Estimators program
+
+A TensorFlow program relying on a pre-made Estimator typically consists
+of the following four steps:
+
+1.  **Write one or more dataset importing functions.** For example, you might
+    create one function to import the training set and another function to
+    import the test set. Each dataset importing function must return two
+    objects:
+
+    *   a dictionary in which the keys are feature column names and the
+        values are Tensors (or SparseTensors) containing the corresponding
+        feature data
+    *   a Tensor containing one or more labels
+
+    For example, the following code illustrates the basic skeleton for
+    an input function:
+
+        def input_fn(dataset):
+           ...  # manipulate dataset, extracting feature names and the label
+           return feature_dict, label
+
+    See @{$datasets$Using the `Dataset` API for TensorFlow Input Pipelines}
+    for full details.)
+
+2.  **Define the feature columns.** Each @{tf.feature_column}
+    identifies a feature name, its type, and any input pre-processing.
+    For example, the following snippet creates three feature
+    columns that hold integer or floating-point data.  The first two
+    feature columns simply identify the feature's name and type. The
+    third feature column also specifies a lambda the program will invoke
+    to scale the raw data:
+
+        # Define three numeric feature columns.
+        population = tf.feature_column.numeric_column('population')
+        crime_rate = tf.feature_column.numeric_column('crime_rate')
+        median_education = tf.feature_column.numeric_column('median_education',
+                            normalizer_fn='lambda x: x - global_education_mean')
+
+3.  **Instantiate the relevant pre-made Estimator.**  For example, here's
+    a sample instantiation of a pre-made Estimator named `LinearClassifier`:
+
+        # Instantiate an estimator, passing the feature columns.
+        estimator = tf.estimator.Estimator.LinearClassifier(
+            feature_columns=[population, crime_rate, median_education],
+            )
+
+4.  **Call a training, evaluation, or inference method.**
+    For example, all Estimators provide a `train` method, which trains a model.
+
+        # my_training_set is the function created in Step 1
+        estimator.train(input_fn=my_training_set, steps=2000)
+
+
+### Benefits of pre-made Estimators
+
+Pre-made Estimators encode best practices, providing the following benefits:
+
+*   Best practices for determining where different parts of the computational
+    graph should run, implementing strategies on a single machine or on a
+    cluster.
+*   Best practices for event (summary) writing and universally useful
+    summaries.
+
+If you don't use pre-made Estimators, you must implement the preceding
+features yourself.
+
+
+## Custom Estimators
+
+The heart of every Estimator--whether pre-made or custom--is its
+**model function**, which is a method that builds graphs for training,
+evaluation, and prediction. When you are using a pre-made Estimator,
+someone else has already implemented the model function. When relying
+on a custom Estimator, you must write the model function yourself. A
+${$extend/estimators$companion document)
+explains how to write the model function.
+
+
+## Recommended workflow
+
+We recommend the following workflow:
+
+1.  Assuming a suitable pre-made Estimator exists, use it to build your
+    first model and use its results to establish a baseline.
+2.  Build and test your overall pipeline, including the integrity and
+    reliability of your data with this pre-made Estimator.
+3.  If suitable alternative pre-made Estimators are available, run
+    experiments to determine which pre-made Estimator produces the
+    best results.
+4.  Possibly, further improve your model by building your own custom Estimator.
+
-- 
GitLab


From 49495697cddef73a0dd870176dab488bb2a65520 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Wed, 26 Jul 2017 07:37:47 -0700
Subject: [PATCH 1342/1375] Replace local tag with requires-gpu tag for cuda
 and sycl tests

PiperOrigin-RevId: 163205190
---
 tensorflow/core/platform/default/build_config_root.bzl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl
index fa4ac4ba73..e1f123a0a0 100644
--- a/tensorflow/core/platform/default/build_config_root.bzl
+++ b/tensorflow/core/platform/default/build_config_root.bzl
@@ -3,10 +3,10 @@
 # be separate to avoid cyclic references.
 
 def tf_cuda_tests_tags():
-  return ["local"]
+  return ["requires-gpu"]
 
 def tf_sycl_tests_tags():
-  return ["local"]
+  return ["requires-gpu"]
 
 def tf_additional_plugin_deps():
   return select({
@@ -28,7 +28,7 @@ def tf_additional_verbs_deps():
       "//tensorflow:with_verbs_support": [
           "//tensorflow/contrib/verbs:verbs_server_lib",
           "//tensorflow/contrib/verbs:grpc_verbs_client",
-      ], 
+      ],
       "//conditions:default": [],
   })
 
-- 
GitLab


From 78a9b95436f45438abf3e818307f707e9ae92343 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 08:35:19 -0700
Subject: [PATCH 1343/1375] [XLA] Finish normalizing fusion computations into
 standard computations

PiperOrigin-RevId: 163210327
---
 tensorflow/compiler/xla/service/BUILD         |   1 +
 .../xla/service/algebraic_simplifier.cc       |   3 +
 .../xla/service/batchnorm_rewriter.cc         |   3 +
 .../compiler/xla/service/buffer_assignment.cc |   6 +
 .../compiler/xla/service/buffer_liveness.cc   |   3 +
 .../compiler/xla/service/copy_insertion.cc    |   3 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   9 +
 .../cpu/cpu_parallelization_preparation.cc    |   3 +
 .../compiler/xla/service/gpu/fusion_merger.cc |   9 +-
 .../compiler/xla/service/gpu/hlo_schedule.cc  |   3 +-
 .../xla/service/hlo_constant_folding.cc       |   3 +
 .../xla/service/hlo_cost_analysis_test.cc     |  82 +++----
 tensorflow/compiler/xla/service/hlo_cse.cc    |   3 +
 tensorflow/compiler/xla/service/hlo_dce.cc    |   3 +
 .../compiler/xla/service/hlo_instruction.cc   |  69 +++---
 .../compiler/xla/service/hlo_instruction.h    |   4 -
 .../xla/service/hlo_instruction_test.cc       | 215 ++++++++++--------
 .../compiler/xla/service/hlo_ordering.cc      |   3 +
 .../xla/service/hlo_rematerialization.cc      |   3 +
 .../compiler/xla/service/hlo_scheduling.cc    |   4 +
 .../xla/service/instruction_fusion.cc         |  11 +-
 .../compiler/xla/service/layout_assignment.cc |   5 +
 .../compiler/xla/service/name_uniquer.cc      |   4 +
 .../xla/service/reduce_precision_insertion.cc |   3 +
 .../compiler/xla/service/reshape_mover.cc     |  11 +-
 .../xla/service/reshape_mover_test.cc         |  11 +-
 .../compiler/xla/service/transpose_folding.cc |   9 +-
 .../xla/service/tuple_points_to_analysis.cc   |   6 +
 28 files changed, 302 insertions(+), 190 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index a4612bb6c1..8fb0faf026 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1210,6 +1210,7 @@ cc_test(
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/core:lib",
         "//tensorflow/core:test_main",
     ],
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 4837402c15..691f9f2296 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -1586,6 +1586,9 @@ StatusOr<bool> AlgebraicSimplifier::Run(HloModule* module) {
   // module, invalidating iteration.
   std::vector<HloComputation*> computations;
   for (auto& comp : module->computations()) {
+    if (comp->IsFusionComputation()) {
+      continue;
+    }
     computations.push_back(comp.get());
   }
   for (auto& comp : computations) {
diff --git a/tensorflow/compiler/xla/service/batchnorm_rewriter.cc b/tensorflow/compiler/xla/service/batchnorm_rewriter.cc
index 5d5d3caa2f..ca2d413e11 100644
--- a/tensorflow/compiler/xla/service/batchnorm_rewriter.cc
+++ b/tensorflow/compiler/xla/service/batchnorm_rewriter.cc
@@ -268,6 +268,9 @@ StatusOr<bool> BatchNormRewriter::Run(HloModule* module) {
   // module, invalidating iteration.
   std::vector<HloComputation*> computations;
   for (auto& comp : module->computations()) {
+    if (comp->IsFusionComputation()) {
+      continue;
+    }
     computations.push_back(comp.get());
   }
   for (auto& comp : computations) {
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index ddc3d11b7c..ae31135a1a 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -1219,6 +1219,9 @@ void BufferAssigner::BuildColocatedBufferSets(
   const TuplePointsToAnalysis& points_to_analysis =
       buffer_liveness.points_to_analysis();
   for (const HloComputation* computation : module->MakeComputationPostOrder()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     for (const HloInstruction* instruction :
          computation->MakeInstructionPostOrder()) {
       const HloOpcode opcode = instruction->opcode();
@@ -1386,6 +1389,9 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
   // their own BufferAllocation.
   for (auto* computation : thread_local_computations) {
     TF_RET_CHECK(computation != module->entry_computation());
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     TF_RETURN_IF_ERROR(AssignBuffersForComputation(
         computation, module->config().debug_options(),
         /*is_thread_local=*/true, colocated_buffers, colocated_allocations,
diff --git a/tensorflow/compiler/xla/service/buffer_liveness.cc b/tensorflow/compiler/xla/service/buffer_liveness.cc
index 6720a90ef8..f085ffa6bc 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness.cc
@@ -47,6 +47,9 @@ StatusOr<std::unique_ptr<BufferLiveness>> BufferLiveness::Run(
 tensorflow::Status BufferLiveness::Analyze() {
   TF_ASSIGN_OR_RETURN(points_to_analysis_, TuplePointsToAnalysis::Run(module_));
   for (auto& computation : module_->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     // Gather all instructions whose buffers might alias other instructions into
     // the set aliased_buffers_.  This includes those contained as a tuple
     // element in other instruction's output.
diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc
index a3803c34ba..c47abe9c62 100644
--- a/tensorflow/compiler/xla/service/copy_insertion.cc
+++ b/tensorflow/compiler/xla/service/copy_insertion.cc
@@ -551,6 +551,9 @@ StatusOr<bool> CopyInsertion::Run(HloModule* module) {
   // Add copies of computation root instructions, if needed.
   FlatMap<const HloComputation*, ShapeTree<bool>> while_body_read_only_indices;
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     VLOG(2) << "computation " << computation->name();
     InstructionCopier root_copier(computation->root_instruction(),
                                   /*copy_users=*/{});
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index b86342d0b3..59e8c75b91 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -519,6 +519,9 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
         new std::map<HloInstruction*, string>());
     for (auto embedded_computation :
          computation->MakeEmbeddedComputationsList()) {
+      if (embedded_computation->IsFusionComputation()) {
+        continue;
+      }
       auto parallel_computation_iter =
           parallel_computations.find(embedded_computation);
       // All parallel computations are considered to be an entry computation for
@@ -591,6 +594,9 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::Compile(
 
     for (auto embedded_computation :
          computation->MakeEmbeddedComputationsList()) {
+      if (embedded_computation->IsFusionComputation()) {
+        continue;
+      }
       TF_RETURN_IF_ERROR(
           ir_emitter
               .EmitComputation(embedded_computation,
@@ -755,6 +761,9 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
     HloComputation* computation = module->entry_computation();
     for (auto embedded_computation :
          computation->MakeEmbeddedComputationsList()) {
+      if (embedded_computation->IsFusionComputation()) {
+        continue;
+      }
       TF_RETURN_IF_ERROR(
           ir_emitter
               .EmitComputation(embedded_computation,
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc
index af931f7b01..4d0e0f744a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.cc
@@ -125,6 +125,9 @@ StatusOr<bool> ParallelizationPreparation::Run(HloModule* module) {
   TF_ASSIGN_OR_RETURN(auto points_to_analysis,
                       TuplePointsToAnalysis::Run(module));
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     HloInstruction* root = computation->root_instruction();
     // Copy root instruction if it does not define its own top-level buffer.
     // TODO(b/32885001) Remove these copies (at least for the unambiguous case).
diff --git a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc
index e698646d18..a9ef204b46 100644
--- a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc
+++ b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc
@@ -293,12 +293,19 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) {
 StatusOr<bool> FusionMerger::Run(HloModule* module) {
   bool changed = false;
   VLOG(2) << "FusionMerger for module: " << module->name();
+  std::vector<HloComputation*> computations;
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
+    computations.push_back(computation.get());
+  }
+  for (auto& computation : computations) {
     VLOG(1) << "Before running FusionInstructionMerger for computation: "
             << computation->name();
     XLA_VLOG_LINES(3, computation->ToString());
 
-    FusionInstructionMerger fusion_merger(computation.get());
+    FusionInstructionMerger fusion_merger(computation);
     TF_RETURN_IF_ERROR(fusion_merger.Run());
     changed |= fusion_merger.changed();
 
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
index c61e47a93c..81e905a066 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
@@ -120,7 +120,8 @@ GpuHloOrdering::GpuHloOrdering(
   // do that yet since it's hard to ensure that the order here is the order used
   // by IrEmitterNested. And mismatched ordering bugs would be hard to find.
   for (auto& computation : module->computations()) {
-    if (computation.get() != module->entry_computation()) {
+    if (computation.get() != module->entry_computation() &&
+        !computation->IsFusionComputation()) {
       predecessors_.emplace(computation.get(),
                             computation->ComputeReachability());
     }
diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding.cc b/tensorflow/compiler/xla/service/hlo_constant_folding.cc
index 804efdd906..1a2eed5f60 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding.cc
@@ -42,6 +42,9 @@ StatusOr<bool> HloConstantFolding::Run(HloModule* module) {
   bool changed = false;
 
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     for (auto instruction : computation->MakeInstructionPostOrder()) {
       // Skip dead code.
       if (instruction->user_count() == 0 &&
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
index f745683165..0a288a77ad 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
@@ -31,6 +31,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/user_computation.h"
 #include "tensorflow/compiler/xla/service/versioned_computation_handle.h"
 #include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/core/platform/logging.h"
 
 #include "tensorflow/compiler/xla/statusor.h"
@@ -329,7 +330,7 @@ TEST_F(HloCostAnalysisTest, MatmulAndConvolutionCanBeTheSameComputation) {
   EXPECT_EQ(conv_analysis.flop_count(), matmul_analysis.flop_count());
 }
 
-using FusionCostAnalysis = ::testing::Test;
+using FusionCostAnalysis = HloTestBase;
 
 TEST_F(FusionCostAnalysis, LoopFusion) {
   // Do this 4 times with different per-second rates to test the computation of
@@ -345,32 +346,32 @@ TEST_F(FusionCostAnalysis, LoopFusion) {
     //   mul = Mul(exp, C3)
     //   sub = Sub(mul, clamp)
     //   tuple = Tuple({sub, sub, mul, C1})
-    auto c1 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-        /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2));
-    auto c2 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-        /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2));
-    auto c3 = HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
-        /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2));
-
-    auto add = HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, c1.get(),
-                                            c2.get());
-    auto clamp = HloInstruction::CreateTernary(r2f32, HloOpcode::kClamp,
-                                               c2.get(), add.get(), add.get());
-    auto exp = HloInstruction::CreateUnary(r2f32, HloOpcode::kExp, add.get());
-    auto mul = HloInstruction::CreateBinary(r2f32, HloOpcode::kMultiply,
-                                            exp.get(), c3.get());
-    auto sub = HloInstruction::CreateBinary(r2f32, HloOpcode::kSubtract,
-                                            mul.get(), clamp.get());
-    auto tuple = HloInstruction::CreateTuple(
-        {sub.get(), sub.get(), mul.get(), c1.get()});
-
-    auto fusion = HloInstruction::CreateFusion(
-        r2f32, HloInstruction::FusionKind::kLoop, tuple.get());
-    fusion->FuseInstruction(sub.get());
-    fusion->FuseInstruction(mul.get());
-    fusion->FuseInstruction(exp.get());
-    fusion->FuseInstruction(clamp.get());
-    fusion->FuseInstruction(add.get());
+    HloComputation::Builder builder(TestName());
+    auto c1 = builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+            /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2)));
+    auto c2 = builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+            /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2)));
+    auto c3 = builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+            /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2)));
+    auto add = builder.AddInstruction(
+        HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, c1, c2));
+    auto clamp = builder.AddInstruction(
+        HloInstruction::CreateTernary(r2f32, HloOpcode::kClamp, c2, add, add));
+    auto exp = builder.AddInstruction(
+        HloInstruction::CreateUnary(r2f32, HloOpcode::kExp, add));
+    auto mul = builder.AddInstruction(
+        HloInstruction::CreateBinary(r2f32, HloOpcode::kMultiply, exp, c3));
+    auto sub = builder.AddInstruction(
+        HloInstruction::CreateBinary(r2f32, HloOpcode::kSubtract, mul, clamp));
+    auto tuple = HloInstruction::CreateTuple({sub, sub, mul, c1});
+
+    HloModule module(TestName());
+    auto* computation = module.AddEntryComputation(builder.Build());
+    auto* fusion = computation->CreateFusionInstruction(
+        {sub, mul, exp, clamp, add}, HloInstruction::FusionKind::kLoop);
 
     // The time given these rates at i == 0 is exactly even among the properties
     // at 1.0 seconds. For other values, one of the rates is slower so that it
@@ -398,18 +399,21 @@ TEST_F(FusionCostAnalysis, NoLayout) {
   Shape shape_without_layout = shape_with_layout;
   shape_without_layout.clear_layout();
 
-  auto c1 = HloInstruction::CreateConstant(
-      Literal::CreateR4FromArray4D(Array4D<float>(2, 3, 4, 5)));
-  auto c2 = HloInstruction::CreateConstant(Literal::CreateR1<float>({1, 2, 3}));
-
-  auto broadcast =
-      HloInstruction::CreateBroadcast(shape_without_layout, c2.get(), {1});
-  auto add = HloInstruction::CreateBinary(shape_with_layout, HloOpcode::kAdd,
-                                          c1.get(), broadcast.get());
-
-  auto fusion = HloInstruction::CreateFusion(
-      shape_with_layout, HloInstruction::FusionKind::kLoop, add.get());
-  fusion->FuseInstruction(broadcast.get());
+  HloComputation::Builder builder(TestName());
+  auto c1 = builder.AddInstruction(HloInstruction::CreateConstant(
+      Literal::CreateR4FromArray4D(Array4D<float>(2, 3, 4, 5))));
+  auto c2 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<float>({1, 2, 3})));
+
+  auto broadcast = builder.AddInstruction(
+      HloInstruction::CreateBroadcast(shape_without_layout, c2, {1}));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      shape_with_layout, HloOpcode::kAdd, c1, broadcast));
+
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
+  auto* fusion = computation->CreateFusionInstruction(
+      {add, broadcast}, HloInstruction::FusionKind::kLoop);
 
   HloCostAnalysis fusion_analysis(ShapeSize);
   ASSERT_IS_OK(fusion->Accept(&fusion_analysis));
diff --git a/tensorflow/compiler/xla/service/hlo_cse.cc b/tensorflow/compiler/xla/service/hlo_cse.cc
index 0fef89a06d..690c084efb 100644
--- a/tensorflow/compiler/xla/service/hlo_cse.cc
+++ b/tensorflow/compiler/xla/service/hlo_cse.cc
@@ -92,6 +92,9 @@ bool CombineConstants(HloComputation* computation, bool is_layout_sensitive) {
 StatusOr<bool> HloCSE::Run(HloModule* module) {
   bool changed = false;
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     changed |= CombineConstants(computation.get(), is_layout_sensitive_);
 
     std::list<HloInstruction*> post_order =
diff --git a/tensorflow/compiler/xla/service/hlo_dce.cc b/tensorflow/compiler/xla/service/hlo_dce.cc
index 3755b9e4c0..5b2c57da4f 100644
--- a/tensorflow/compiler/xla/service/hlo_dce.cc
+++ b/tensorflow/compiler/xla/service/hlo_dce.cc
@@ -38,6 +38,9 @@ StatusOr<bool> HloDCE::Run(HloModule* module) {
   bool changed = false;
 
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     std::unordered_set<HloInstruction*> live_instructions;
     TF_RETURN_IF_ERROR(computation->root_instruction()->Accept(
         [&live_instructions](HloInstruction* instruction) {
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index f52882cca5..ed8a942d03 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -560,19 +560,20 @@ HloInstruction* HloInstruction::CloneAndFuseInternal(
     HloInstruction* instruction_to_fuse) {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
   CHECK(instruction_to_fuse->IsFusable());
-
+  if (GetModule()) {
+    XLA_VLOG_LINES(1, GetModule()->ToString());
+  }
   HloInstruction* clone = nullptr;
-  if (fused_instructions_computation_ == nullptr) {
+  if (called_computations_.empty()) {
     // New fusion instruction.
     auto builder = HloComputation::Builder("fused_computation", true);
     builder.AddInstruction(instruction_to_fuse->Clone(/*suffix=*/""));
-    fused_instructions_computation_ = builder.Build();
+    called_computations_.push_back(
+        CHECK_NOTNULL(GetModule())->AddEmbeddedComputation(builder.Build()));
     clone = fused_expression_root();
     clone->parent_fusion_instruction_ = this;
   } else {
-    CHECK(fused_instructions_computation_ != nullptr &&
-          fused_instructions_computation_->IsFusionComputation());
-    clone = fused_instructions_computation_->AddInstruction(
+    clone = fused_instructions_computation()->AddInstruction(
         instruction_to_fuse->Clone(/*suffix=*/""));
     clone->parent_fusion_instruction_ = this;
     // instruction_to_fuse is necessarily an operand of the fusion instruction.
@@ -583,7 +584,7 @@ HloInstruction* HloInstruction::CloneAndFuseInternal(
     CHECK(std::find(operands_.begin(), operands_.end(), instruction_to_fuse) !=
           operands_.end());
     const std::vector<HloInstruction*>& fused_parameters_ =
-        fused_instructions_computation_->parameter_instructions();
+        fused_instructions_computation()->parameter_instructions();
     for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) {
       if (instruction_to_fuse == operands_[operand_num]) {
         // replace the fused parameter instruction's uses with the clone.
@@ -593,7 +594,7 @@ HloInstruction* HloInstruction::CloneAndFuseInternal(
         // Remove the corresponding fused parameter and operand from their
         // respective vectors.
         TF_CHECK_OK(
-            fused_instructions_computation_->RemoveParameter(operand_num));
+            fused_instructions_computation()->RemoveParameter(operand_num));
         operands_.erase(operands_.begin() + operand_num);
         break;
       }
@@ -605,7 +606,7 @@ HloInstruction* HloInstruction::CloneAndFuseInternal(
 
   // Reread the parameters in the computation.
   const std::vector<HloInstruction*>& fused_parameters_ =
-      fused_instructions_computation_->parameter_instructions();
+      fused_instructions_computation()->parameter_instructions();
 
   // Add each operand of the clone as an operand of the fusion instruction. A
   // complication is that some clone operands may already be operands of the
@@ -638,7 +639,7 @@ HloInstruction* HloInstruction::CloneAndFuseInternal(
           CreateParameter(param_no, operand->shape(), param_name);
 
       param_instruction->parent_fusion_instruction_ = this;
-      fused_param = fused_instructions_computation_->AddParameter(
+      fused_param = fused_instructions_computation()->AddParameter(
           std::move(param_instruction));
       AppendOperand(operand);
     }
@@ -652,7 +653,6 @@ HloInstruction* HloInstruction::CloneAndFuseInternal(
       called_computations_.push_back(computation);
     }
   }
-
   return clone;
 }
 
@@ -663,17 +663,15 @@ RandomDistribution HloInstruction::random_distribution() const {
 
 void HloInstruction::CheckFusionInstruction() const {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
-  CHECK(fused_instructions_computation_ != nullptr &&
-        fused_instructions_computation_->IsFusionComputation());
 
   const std::list<std::unique_ptr<HloInstruction>>& fused_instructions_ =
-      fused_instructions_computation_->instructions();
+      fused_instructions_computation()->instructions();
   // All instructions owned by this fusion instruction must be fused, and the
   // parent fusion instruction of the fused instructions must be 'this'.
   for (auto& instruction : fused_instructions_) {
     CHECK(instruction->IsFused());
     CHECK_EQ(this, instruction->fusion_instruction());
-    CHECK_EQ(fused_instructions_computation_.get(), instruction->parent())
+    CHECK_EQ(fused_instructions_computation(), instruction->parent())
         << instruction->ToString();
   }
 
@@ -976,8 +974,6 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneFusionWithNewOperands(
     const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands) {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
   CHECK(parent() != nullptr);
-  CHECK(fused_instructions_computation_ != nullptr &&
-        fused_instructions_computation_->IsFusionComputation());
 
   auto new_instruction =
       WrapUnique(new HloInstruction(HloOpcode::kFusion, shape));
@@ -992,9 +988,9 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneFusionWithNewOperands(
   // fused instructions.
   std::vector<HloInstruction*> new_fused_parameters;
   const std::vector<HloInstruction*>& fused_parameters_ =
-      fused_instructions_computation_->parameter_instructions();
+      fused_instructions_computation()->parameter_instructions();
   const std::list<std::unique_ptr<HloInstruction>>& fused_instructions_ =
-      fused_instructions_computation_->instructions();
+      fused_instructions_computation()->instructions();
 
   for (HloInstruction* old_fused_parameter : fused_parameters_) {
     new_fused_instructions.push_back(old_fused_parameter->Clone());
@@ -1028,7 +1024,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneFusionWithNewOperands(
   }
   new_instruction->fusion_kind_ = fusion_kind_;
   auto computation_builder = HloComputation::Builder(
-      fused_instructions_computation_->name() + ".clone", true);
+      fused_instructions_computation()->name() + ".clone", true);
   // We iterated the fusion instructions in reverse post order which means
   // that we must reverse our new list of fusion instructions.
   for (auto new_fused_instruction_iter = new_fused_instructions.rbegin();
@@ -1037,8 +1033,10 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneFusionWithNewOperands(
     computation_builder.AddInstruction(std::move(*new_fused_instruction_iter));
   }
   auto fused_root_ = fused_expression_root();
-  new_instruction->fused_instructions_computation_ =
-      computation_builder.Build(FindOrDie(old_to_new, fused_root_));
+  new_instruction->called_computations_.push_back(
+      CHECK_NOTNULL(GetModule())
+          ->AddEmbeddedComputation(
+              computation_builder.Build(FindOrDie(old_to_new, fused_root_))));
   new_instruction->set_parent(parent());
   new_instruction->CheckFusionInstruction();
   return new_instruction;
@@ -1769,7 +1767,10 @@ bool HloInstruction::IsFusable() const {
 
 HloComputation* HloInstruction::fused_instructions_computation() const {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
-  return fused_instructions_computation_.get();
+  CHECK(!called_computations_.empty());
+  auto* fused_instructions_computation = called_computations_.front();
+  CHECK(fused_instructions_computation->IsFusionComputation());
+  return fused_instructions_computation;
 }
 
 HloInstruction* HloInstruction::fusion_instruction() const {
@@ -1779,32 +1780,24 @@ HloInstruction* HloInstruction::fusion_instruction() const {
 
 HloInstruction* HloInstruction::fused_expression_root() const {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
-  CHECK(fused_instructions_computation_ != nullptr &&
-        fused_instructions_computation_->IsFusionComputation());
-  return fused_instructions_computation_->root_instruction();
+  return fused_instructions_computation()->root_instruction();
 }
 
 HloInstruction* HloInstruction::fused_parameter(int64 parameter_number) const {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
-  CHECK(fused_instructions_computation_ != nullptr &&
-        fused_instructions_computation_->IsFusionComputation());
-  return fused_instructions_computation_->parameter_instruction(
+  return fused_instructions_computation()->parameter_instruction(
       parameter_number);
 }
 
 const std::vector<HloInstruction*>& HloInstruction::fused_parameters() const {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
-  CHECK(fused_instructions_computation_ != nullptr &&
-        fused_instructions_computation_->IsFusionComputation());
-  return fused_instructions_computation_->parameter_instructions();
+  return fused_instructions_computation()->parameter_instructions();
 }
 
 const std::list<std::unique_ptr<HloInstruction>>&
 HloInstruction::fused_instructions() const {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
-  CHECK(fused_instructions_computation_ != nullptr &&
-        fused_instructions_computation_->IsFusionComputation());
-  return fused_instructions_computation_->instructions();
+  return fused_instructions_computation()->instructions();
 }
 
 HloInstruction::HloInstruction(HloOpcode opcode, const Shape& shape)
@@ -2039,7 +2032,7 @@ static Status PostOrderDFS(HloInstruction* root, DfsHloVisitor* visitor,
 
 Status HloInstruction::Accept(DfsHloVisitor* visitor, bool call_finish_visit,
                               bool ignore_control_predecessors) {
-  VLOG(2) << "HloInstruction::Accept(" << name() << ")";
+  VLOG(3) << "HloInstruction::Accept(" << name() << ")";
   TF_RETURN_IF_ERROR(
       PostOrderDFS(this, visitor, nullptr, ignore_control_predecessors));
   if (call_finish_visit) {
@@ -2055,8 +2048,11 @@ Status HloInstruction::AcceptWithOperandOrder(
   TF_RETURN_IF_ERROR(PostOrderDFS(this, visitor, &operand_order,
                                   /*ignore_control_predecessors=*/false));
   if (call_finish_visit) {
+    VLOG(3) << "HloInstruction::AcceptWithOperandOrder BEFORE FINISH VISIT";
     TF_RETURN_IF_ERROR(visitor->FinishVisit(this));
+    VLOG(3) << "HloInstruction::AcceptWithOperandOrder AFTER FINISH VISIT";
   }
+  VLOG(2) << "HloInstruction::AcceptWithOperandOrder EXIT";
   return Status::OK();
 }
 
@@ -2458,6 +2454,7 @@ HloModule* HloInstruction::GetModule() const {
 }
 
 void HloInstruction::UniquifyName(NameUniquer* name_uniquer) {
+  string parent_str = parent() == nullptr ? "noparent" : parent()->name();
   name_ = name_uniquer->GetUniqueName(name_);
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index e2e77e5219..3c188ec83f 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -935,10 +935,6 @@ class HloInstruction {
   // padding of this pad instruction. Only set for pad instructions.
   std::unique_ptr<PaddingConfig> padding_config_;
 
-  // The computation that stores of instructions fused into this fusion
-  // instruction. Only set for fusion instructions.
-  std::unique_ptr<HloComputation> fused_instructions_computation_;
-
   // If this instruction is fused into a fusion instruction, this field points
   // to the fusion instruction.
   HloInstruction* parent_fusion_instruction_ = nullptr;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index bb1b477e13..5951c833db 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -557,78 +557,89 @@ TEST_F(HloInstructionTest, PostProcessAllVisitedNodes) {
 }
 
 TEST_F(HloInstructionTest, SingletonFusionOp) {
+  HloComputation::Builder builder(TestName());
   // Create a fusion instruction containing a single unary operation.
-  auto constant =
-      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
-  auto exp =
-      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant.get());
-
-  auto fusion = HloInstruction::CreateFusion(
-      r0f32_, HloInstruction::FusionKind::kLoop, exp.get());
-
-  EXPECT_THAT(fusion->operands(), ElementsAre(constant.get()));
-  EXPECT_THAT(constant->users(), UnorderedElementsAre(fusion.get(), exp.get()));
+  auto constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f)));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant));
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
+  auto* fusion = computation->CreateFusionInstruction(
+      {exp}, HloInstruction::FusionKind::kLoop);
+
+  EXPECT_THAT(fusion->operands(), ElementsAre(constant));
+  EXPECT_THAT(constant->users(), ElementsAre(fusion));
 }
 
 TEST_F(HloInstructionTest, BinaryFusionOp) {
+  HloComputation::Builder builder(TestName());
   // Create a fusion instruction containing a single binary operation.
-  auto constant1 =
-      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
-  auto constant2 =
-      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.1f));
-  auto add = HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd,
-                                          constant1.get(), constant2.get());
-
-  auto fusion = HloInstruction::CreateFusion(
-      r0f32_, HloInstruction::FusionKind::kLoop, add.get());
-
-  EXPECT_THAT(fusion->operands(),
-              ElementsAre(constant1.get(), constant2.get()));
-  EXPECT_THAT(constant1->users(),
-              UnorderedElementsAre(fusion.get(), add.get()));
-  EXPECT_THAT(constant2->users(),
-              UnorderedElementsAre(fusion.get(), add.get()));
+  auto constant1 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f)));
+  auto constant2 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(42.1f)));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      r0f32_, HloOpcode::kAdd, constant1, constant2));
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
+  auto* fusion = computation->CreateFusionInstruction(
+      {add}, HloInstruction::FusionKind::kLoop);
+
+  EXPECT_THAT(fusion->operands(), ElementsAre(constant1, constant2));
+  EXPECT_THAT(constant1->users(), ElementsAre(fusion));
+  EXPECT_THAT(constant2->users(), ElementsAre(fusion));
 }
 
 TEST_F(HloInstructionTest, ChainFusionOp) {
+  HloComputation::Builder builder(TestName());
   // Create a chain of fused unary ops.
-  auto constant =
-      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
-  auto exp1 =
-      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant.get());
-  auto exp2 = HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp1.get());
-  auto exp3 = HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp2.get());
-
-  auto fusion = HloInstruction::CreateFusion(
-      r0f32_, HloInstruction::FusionKind::kLoop, exp3.get());
-  fusion->FuseInstruction(exp2.get());
-  fusion->FuseInstruction(exp1.get());
-
-  EXPECT_THAT(fusion->operands(), ElementsAre(constant.get()));
-  EXPECT_THAT(constant->users(),
-              UnorderedElementsAre(fusion.get(), exp1.get()));
+  auto constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f)));
+  auto exp1 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant));
+  auto exp2 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp1));
+  auto exp3 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp2));
+
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
+  auto* fusion = computation->CreateFusionInstruction(
+      {exp3, exp2, exp1}, HloInstruction::FusionKind::kLoop);
+
+  EXPECT_THAT(fusion->operands(), ElementsAre(constant));
+  EXPECT_THAT(constant->users(), ElementsAre(fusion));
 }
 
 TEST_F(HloInstructionTest, PreserveMetadataInFusionAndClone) {
+  HloComputation::Builder builder(TestName());
   // Create a chain of fused unary ops.
-  auto constant =
-      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
-  auto exp1 =
-      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant.get());
-  auto exp2 = HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp1.get());
+  auto constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f)));
+  auto exp1 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant));
+  auto exp2 = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp1));
   OpMetadata metadata;
   metadata.set_op_name("tf_op");
   exp1->set_metadata(metadata);
   exp2->set_metadata(metadata);
 
-  auto fusion = HloInstruction::CreateFusion(
-      r0f32_, HloInstruction::FusionKind::kLoop, exp2.get());
-  auto* fused = fusion->FuseInstruction(exp1.get());
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
+  auto* fusion = computation->CreateFusionInstruction(
+      {exp2, exp1}, HloInstruction::FusionKind::kLoop);
+
   EXPECT_TRUE(protobuf_util::ProtobufEquals(metadata, fusion->metadata()));
-  EXPECT_TRUE(protobuf_util::ProtobufEquals(metadata, fused->metadata()));
+  EXPECT_TRUE(protobuf_util::ProtobufEquals(
+      metadata, fusion->fused_expression_root()->metadata()));
+  EXPECT_TRUE(protobuf_util::ProtobufEquals(
+      metadata, fusion->fused_expression_root()->operand(0)->metadata()));
 }
 
 TEST_F(HloInstructionTest, FusionOpWithCalledComputations) {
+  HloComputation::Builder builder(TestName());
   // Create a fusion instruction containing a single unary operation.
   const Shape scalar_shape = ShapeUtil::MakeShape(F32, {});
 
@@ -642,33 +653,36 @@ TEST_F(HloInstructionTest, FusionOpWithCalledComputations) {
   std::unique_ptr<HloComputation> computation_x = make_map_computation();
   std::unique_ptr<HloComputation> computation_y = make_map_computation();
 
-  auto constant =
-      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
-  auto map_1_x =
-      HloInstruction::CreateMap(scalar_shape, {constant.get()},
-                                computation_x.get(), /*static_operands=*/{});
-  auto map_2_x =
-      HloInstruction::CreateMap(scalar_shape, {map_1_x.get()},
-                                computation_x.get(), /*static_operands=*/{});
-  auto map_3_y =
-      HloInstruction::CreateMap(scalar_shape, {map_2_x.get()},
-                                computation_y.get(), /*static_operands=*/{});
-
-  auto fusion = HloInstruction::CreateFusion(
-      scalar_shape, HloInstruction::FusionKind::kLoop, map_3_y.get());
-
-  EXPECT_THAT(fusion->called_computations(), ElementsAre(computation_y.get()));
-
-  fusion->FuseInstruction(map_2_x.get());
+  auto constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f)));
+  auto map_1_x = builder.AddInstruction(HloInstruction::CreateMap(
+      scalar_shape, {constant}, computation_x.get(), /*static_operands=*/{}));
+  auto map_2_x = builder.AddInstruction(HloInstruction::CreateMap(
+      scalar_shape, {map_1_x}, computation_x.get(), /*static_operands=*/{}));
+  auto map_3_y = builder.AddInstruction(HloInstruction::CreateMap(
+      scalar_shape, {map_2_x}, computation_y.get(), /*static_operands=*/{}));
+
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
+  auto* fusion = computation->CreateFusionInstruction(
+      {map_3_y}, HloInstruction::FusionKind::kLoop);
+  auto* fused_computation = fusion->fused_instructions_computation();
   EXPECT_THAT(fusion->called_computations(),
-              ElementsAre(computation_y.get(), computation_x.get()));
+              ElementsAre(fused_computation, computation_y.get()));
 
-  fusion->FuseInstruction(map_1_x.get());
-  EXPECT_THAT(fusion->called_computations(),
-              ElementsAre(computation_y.get(), computation_x.get()));
+  fusion->FuseInstruction(map_2_x);
+  EXPECT_THAT(
+      fusion->called_computations(),
+      ElementsAre(fused_computation, computation_y.get(), computation_x.get()));
+
+  fusion->FuseInstruction(map_1_x);
+  EXPECT_THAT(
+      fusion->called_computations(),
+      ElementsAre(fused_computation, computation_y.get(), computation_x.get()));
 }
 
 TEST_F(HloInstructionTest, ComplexFusionOp) {
+  HloComputation::Builder builder(TestName());
   // Fuse all instructions in complicated expression:
   //
   //   add = Add(C1, C2)
@@ -680,35 +694,35 @@ TEST_F(HloInstructionTest, ComplexFusionOp) {
   //
   // Notable complexities are repeated operands in a same instruction, different
   // shapes, use of value in different expressions.
-  auto c1 = HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f));
-  auto c2 = HloInstruction::CreateConstant(Literal::CreateR0<float>(2.1f));
-  auto c3 = HloInstruction::CreateConstant(Literal::CreateR0<float>(9.0f));
-
-  auto add =
-      HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, c1.get(), c2.get());
-  auto clamp = HloInstruction::CreateTernary(r0f32_, HloOpcode::kClamp,
-                                             c2.get(), add.get(), add.get());
-  auto exp = HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, add.get());
-  auto mul = HloInstruction::CreateBinary(r0f32_, HloOpcode::kMultiply,
-                                          exp.get(), c3.get());
-  auto sub = HloInstruction::CreateBinary(r0f32_, HloOpcode::kSubtract,
-                                          mul.get(), clamp.get());
+  auto c1 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(1.1f)));
+  auto c2 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(2.1f)));
+  auto c3 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<float>(9.0f)));
+
+  auto add = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, c1, c2));
+  auto clamp = builder.AddInstruction(
+      HloInstruction::CreateTernary(r0f32_, HloOpcode::kClamp, c2, add, add));
+  auto exp = builder.AddInstruction(
+      HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, add));
+  auto mul = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32_, HloOpcode::kMultiply, exp, c3));
+  auto sub = builder.AddInstruction(
+      HloInstruction::CreateBinary(r0f32_, HloOpcode::kSubtract, mul, clamp));
   auto tuple =
-      HloInstruction::CreateTuple({sub.get(), sub.get(), mul.get(), c1.get()});
+      builder.AddInstruction(HloInstruction::CreateTuple({sub, sub, mul, c1}));
 
-  auto fusion = HloInstruction::CreateFusion(
-      r0f32_, HloInstruction::FusionKind::kLoop, tuple.get());
-  fusion->FuseInstruction(sub.get());
-  fusion->FuseInstruction(mul.get());
-  fusion->FuseInstruction(exp.get());
-  fusion->FuseInstruction(clamp.get());
-  fusion->FuseInstruction(add.get());
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
+  auto* fusion = computation->CreateFusionInstruction(
+      {tuple, sub, mul, exp, clamp, add}, HloInstruction::FusionKind::kLoop);
 
   // Operands in the fusion instruction's operands() vector should be in the
   // order in which their users were added fused.
-  EXPECT_THAT(fusion->operands(), ElementsAre(c1.get(), c3.get(), c2.get()));
-  EXPECT_THAT(c1->users(),
-              UnorderedElementsAre(add.get(), tuple.get(), fusion.get()));
+  EXPECT_THAT(fusion->operands(), ElementsAre(c1, c3, c2));
+  EXPECT_THAT(c1->users(), ElementsAre(fusion));
 }
 
 // Convenience function for comparing two HloInstructions inside of
@@ -864,7 +878,8 @@ TEST_F(HloInstructionTest, PartiallyElementwise) {
   HloInstruction* max = builder.AddInstruction(
       HloInstruction::CreateBinary(r2f32, HloOpcode::kMaximum, div, broadcast));
 
-  auto computation = builder.Build();
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
   HloInstruction* fusion = computation->CreateFusionInstruction(
       {max, broadcast, div, mul}, HloInstruction::FusionKind::kLoop);
   EXPECT_FALSE(fusion->IsElementwise());
@@ -906,7 +921,8 @@ TEST_F(HloInstructionTest, PartiallyElementwiseWithReuse) {
   HloInstruction* sub = builder.AddInstruction(HloInstruction::CreateBinary(
       r1f32, HloOpcode::kSubtract, min, broadcast));
 
-  auto computation = builder.Build();
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
   HloInstruction* fusion = computation->CreateFusionInstruction(
       {sub, broadcast, min}, HloInstruction::FusionKind::kLoop);
   EXPECT_FALSE(fusion->IsElementwise());
@@ -945,7 +961,8 @@ TEST_F(HloInstructionTest, CloneOfFusionPreservesShape) {
   HloInstruction* dot = builder.AddInstruction(
       HloInstruction::CreateBinary(sout, HloOpcode::kDot, x, reshape));
 
-  auto computation = builder.Build();
+  HloModule module(TestName());
+  auto* computation = module.AddEntryComputation(builder.Build());
   HloInstruction* fusion = computation->CreateFusionInstruction(
       {dot, reshape}, HloInstruction::FusionKind::kTransposeDot);
 
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc
index 7230682d0b..4c3ff3bdaf 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering.cc
@@ -183,6 +183,9 @@ DependencyHloOrdering::DependencyHloOrdering(const HloModule* module)
   // ordering based on dependencies. ExecutesBefore will return true iff there
   // exists a path in the HLO computation graph from 'a' to 'b'.
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     predecessors_.emplace(computation.get(),
                           computation->ComputeReachability());
   }
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
index d19e8034ac..fd08796e50 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
@@ -1202,6 +1202,9 @@ StatusOr<bool> HloRematerialization::Run(
   // After DCE, the module sequence may include instructions which no longer
   // exist.
   for (const auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     if (sequence->at(computation.get()).size() !=
         computation->instruction_count()) {
       // A size mismatch between the computation instruction count and the size
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc
index 17f55f9cfb..922236ee1e 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling.cc
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc
@@ -400,6 +400,9 @@ CreateMemoryMinimizingSequence(
   TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
                       TuplePointsToAnalysis::Run(&module));
   for (const auto& computation : module.computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     TF_ASSIGN_OR_RETURN(sequence[computation.get()],
                         CreateMemoryMinimizingSequence(
                             *computation, *points_to_analysis, size_function));
@@ -410,6 +413,7 @@ CreateMemoryMinimizingSequence(
 StatusOr<std::vector<const HloInstruction*>> CreateMemoryMinimizingSequence(
     const HloComputation& computation,
     const LogicalBuffer::SizeFunction& size_function) {
+  CHECK(!computation.IsFusionComputation());
   TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis,
                       TuplePointsToAnalysis::Run(computation.parent()));
   return CreateMemoryMinimizingSequence(computation, *points_to_analysis,
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 482ab9b94a..24af07bd4b 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -211,8 +211,17 @@ bool InstructionFusion::CanFuseOnAllPaths(
 
 StatusOr<bool> InstructionFusion::Run(HloModule* module) {
   bool changed = false;
+
+  std::vector<HloComputation*> computations;
   for (auto& computation : module->computations()) {
-    computation_ = computation.get();
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
+    computations.push_back(computation.get());
+  }
+  for (auto& computation : computations) {
+    CHECK(!computation->IsFusionComputation());
+    computation_ = computation;
 
     // We want to be able to remove arbitrary instructions from the post order
     // and also compare positions of instructions in the post order. To make
diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index aafface0b9..7d41be94ce 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -611,6 +611,9 @@ Status CheckLayouts(
   TF_ASSIGN_OR_RETURN(auto points_to_analysis,
                       TuplePointsToAnalysis::Run(module));
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     for (auto& instruction : computation->instructions()) {
       // Verify every instruction has a layout and the layout is valid for the
       // shape.
@@ -1356,6 +1359,8 @@ StatusOr<bool> LayoutAssignment::Run(HloModule* module) {
     if (computation == module->entry_computation()) {
       TF_RETURN_IF_ERROR(RunOnComputation(*entry_computation_layout_,
                                           module->entry_computation()));
+    } else if (computation->IsFusionComputation()) {
+      continue;
     } else {
       ComputationLayout computation_layout(computation->ComputeProgramShape());
       // Setting all embedded computations to the default layout is potentially
diff --git a/tensorflow/compiler/xla/service/name_uniquer.cc b/tensorflow/compiler/xla/service/name_uniquer.cc
index 4014856b9b..069f85af72 100644
--- a/tensorflow/compiler/xla/service/name_uniquer.cc
+++ b/tensorflow/compiler/xla/service/name_uniquer.cc
@@ -29,7 +29,11 @@ string NameUniquer::GetUniqueName(tensorflow::StringPiece prefix) {
     return root;
   } else {
     tensorflow::strings::StrAppend(&root, separator_, *count);
+    // Increment lookup under old 'root' name.
     (*count)++;
+    // Initialize count under new 'root' name.
+    count = &(generated_names_[root]);
+    *count = 1;
     return root;
   }
 }
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
index e083226b14..9f12471ffd 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
@@ -26,6 +26,9 @@ StatusOr<bool> ReducePrecisionInsertion::Run(HloModule* module) {
   VLOG(1) << "Running ReducePrecisionInsertion pass on " << module->name();
 
   for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     std::vector<HloInstruction*> instructions_to_suffix;
 
     for (auto& instruction : computation->instructions()) {
diff --git a/tensorflow/compiler/xla/service/reshape_mover.cc b/tensorflow/compiler/xla/service/reshape_mover.cc
index 2d35ba5e54..1c648d58c7 100644
--- a/tensorflow/compiler/xla/service/reshape_mover.cc
+++ b/tensorflow/compiler/xla/service/reshape_mover.cc
@@ -312,10 +312,17 @@ StatusOr<bool> TrySinkReshapeOrTranspose(HloComputation* computation,
 
 StatusOr<bool> ReshapeMover::Run(HloModule* module) {
   bool changed = false;
-  for (const auto& comp : module->computations()) {
+  std::vector<HloComputation*> computations;
+  for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
+    computations.push_back(computation.get());
+  }
+  for (const auto& comp : computations) {
     for (HloInstruction* instruction : comp->MakeInstructionPostOrder()) {
       TF_ASSIGN_OR_RETURN(bool did_change,
-                          TrySinkReshapeOrTranspose(comp.get(), instruction));
+                          TrySinkReshapeOrTranspose(comp, instruction));
       changed |= did_change;
     }
   }
diff --git a/tensorflow/compiler/xla/service/reshape_mover_test.cc b/tensorflow/compiler/xla/service/reshape_mover_test.cc
index 49c1755520..1589d52a25 100644
--- a/tensorflow/compiler/xla/service/reshape_mover_test.cc
+++ b/tensorflow/compiler/xla/service/reshape_mover_test.cc
@@ -351,16 +351,15 @@ TEST_F(ReshapeMoverTest, EquivalentReshapesMovedAcrossFusion) {
   auto add = builder.AddInstruction(HloInstruction::CreateBinary(
       root_shape, HloOpcode::kAdd, reshape0, reshape1));
 
-  auto module = CreateNewModule();
-  auto computation = module->AddEntryComputation(builder.Build());
-  auto fusion = computation->AddInstruction(HloInstruction::CreateFusion(
-      add->shape(), HloInstruction::FusionKind::kLoop, add));
-  TF_CHECK_OK(computation->ReplaceInstruction(add, fusion));
+  HloModule module(TestName());
+  auto computation = module.AddEntryComputation(builder.Build());
+  computation->CreateFusionInstruction({add},
+                                       HloInstruction::FusionKind::kLoop);
 
   EXPECT_THAT(computation->root_instruction(),
               op::Fusion(op::Reshape(param0), op::Reshape(param1)));
 
-  EXPECT_TRUE(ReshapeMover().Run(module.get()).ValueOrDie());
+  EXPECT_TRUE(ReshapeMover().Run(&module).ValueOrDie());
 
   EXPECT_THAT(computation->root_instruction(),
               op::Reshape(op::Fusion(param0, param1)));
diff --git a/tensorflow/compiler/xla/service/transpose_folding.cc b/tensorflow/compiler/xla/service/transpose_folding.cc
index a0c88c6bbc..5858335736 100644
--- a/tensorflow/compiler/xla/service/transpose_folding.cc
+++ b/tensorflow/compiler/xla/service/transpose_folding.cc
@@ -172,7 +172,14 @@ StatusOr<bool> TransposeFolding::Run(HloModule* module) {
     return tensorflow::Status::OK();
   };
 
-  for (auto& comp : module->computations()) {
+  std::vector<HloComputation*> computations;
+  for (auto& computation : module->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
+    computations.push_back(computation.get());
+  }
+  for (auto& comp : computations) {
     TF_RETURN_IF_ERROR(comp->Accept(visit_fn));
   }
 
diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
index 182e99cf1c..3c4dc19aef 100644
--- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
+++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
@@ -135,6 +135,9 @@ TuplePointsToAnalysis::Run(const HloModule* module) {
 Status TuplePointsToAnalysis::Analyze() {
   points_to_.clear();
   for (auto& computation : module_->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     TF_RETURN_IF_ERROR(computation->Accept(this));
     TF_RETURN_IF_ERROR(
         PopulateDefinedBuffersAndAliases(computation->instructions()));
@@ -451,6 +454,9 @@ string TuplePointsToAnalysis::ToString() const {
   string output = tensorflow::strings::Printf(
       "TuplePointsToSet for module %s:\n", module_->name().c_str());
   for (const auto& computation : module_->computations()) {
+    if (computation->IsFusionComputation()) {
+      continue;
+    }
     const char* entry =
         computation.get() == module_->entry_computation() ? "entry " : "";
     tensorflow::strings::StrAppend(&output, entry, "computation ",
-- 
GitLab


From 57e38b03adcac52dedbe6bf350f8fa916f73df6d Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Wed, 26 Jul 2017 09:03:08 -0700
Subject: [PATCH 1344/1375] Create new tf.learn.estimator.head which is based
 on a tf.Distribution instance.

PiperOrigin-RevId: 163213141
---
 tensorflow/contrib/bayesflow/BUILD            |   8 +-
 .../python/kernel_tests/entropy_test.py       |  43 ++--
 tensorflow/contrib/distributions/BUILD        |  25 ++-
 tensorflow/contrib/distributions/__init__.py  |   2 +
 .../python/kernel_tests/estimator_test.py     | 114 +++++++++++
 .../distributions/python/ops/estimator.py     | 185 ++++++++++++++++++
 .../python/ops/mvn_linear_operator.py         |   4 +-
 .../learn/python/learn/estimators/head.py     |   8 +-
 8 files changed, 358 insertions(+), 31 deletions(-)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/estimator_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/estimator.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 1cd6e64b32..d324c7d0d0 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -35,23 +35,20 @@ py_library(
 
 cuda_py_test(
     name = "csiszar_divergence_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/csiszar_divergence_test.py"],
     additional_deps = [
         ":bayesflow_py",
         "//third_party/py/numpy",
         "//tensorflow/contrib/distributions:distributions_py",
-        "//tensorflow/contrib/layers:layers_py",
         "//tensorflow/python/ops/distributions",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:gradients",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
-        "//tensorflow/python:platform_test",
     ],
 )
 
@@ -84,12 +81,11 @@ cuda_py_test(
         "//third_party/py/numpy",
         "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/contrib/layers:layers_py",
+        "//tensorflow/python/ops/distributions",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
-        "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
     ],
 )
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py
index 6cdaa31870..0bd12b84d1 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py
@@ -20,22 +20,24 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib import distributions as distributions_lib
 from tensorflow.contrib import layers as layers_lib
-from tensorflow.contrib.bayesflow.python.ops import entropy_impl as entropy_lib
+from tensorflow.contrib.bayesflow.python.ops import entropy_impl as entropy
+from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib
+from tensorflow.contrib.distributions.python.ops import mvn_tril as mvn_tril_lib
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import variables
+from tensorflow.python.ops.distributions import kullback_leibler as kullback_leibler_lib
+from tensorflow.python.ops.distributions import normal as normal_lib
+from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.platform import test
 
-distributions = distributions_lib
 layers = layers_lib
-entropy = entropy_lib
 
 
-class NormalNoEntropy(distributions.Normal):  # pylint: disable=no-init
+class NormalNoEntropy(normal_lib.Normal):  # pylint: disable=no-init
   """Normal distribution without a `.entropy` method."""
 
   def entropy(self):
@@ -81,10 +83,10 @@ class ElboRatioTest(test.TestCase):
     n_samples = 5000
 
     with self.test_session():
-      q = distributions.MultivariateNormalDiag(
+      q = mvn_diag_lib.MultivariateNormalDiag(
           loc=self._rng.rand(*vector_shape),
           scale_diag=self._rng.rand(*vector_shape))
-      p = distributions.MultivariateNormalDiag(
+      p = mvn_diag_lib.MultivariateNormalDiag(
           loc=self._rng.rand(*vector_shape),
           scale_diag=self._rng.rand(*vector_shape))
 
@@ -95,7 +97,7 @@ class ElboRatioTest(test.TestCase):
           n=n_samples,
           form=entropy.ELBOForms.sample,
           seed=42)
-      actual_kl = distributions.kl_divergence(q, p)
+      actual_kl = kullback_leibler_lib.kl_divergence(q, p)
 
       # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
       # pass.
@@ -109,10 +111,10 @@ class ElboRatioTest(test.TestCase):
 
     vector_shape = (2, 3)
     with self.test_session():
-      q = distributions.MultivariateNormalDiag(
+      q = mvn_diag_lib.MultivariateNormalDiag(
           loc=self._rng.rand(*vector_shape),
           scale_diag=self._rng.rand(*vector_shape))
-      p = distributions.MultivariateNormalDiag(
+      p = mvn_diag_lib.MultivariateNormalDiag(
           loc=self._rng.rand(*vector_shape),
           scale_diag=self._rng.rand(*vector_shape))
 
@@ -123,7 +125,7 @@ class ElboRatioTest(test.TestCase):
           n=n_samples,
           form=entropy.ELBOForms.analytic_entropy,
           seed=42)
-      actual_kl = distributions.kl_divergence(q, p)
+      actual_kl = kullback_leibler_lib.kl_divergence(q, p)
 
       # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
       # pass.
@@ -135,7 +137,7 @@ class ElboRatioTest(test.TestCase):
 
     vector_shape = (2, 3)
     with self.test_session():
-      q = distributions.MultivariateNormalDiag(
+      q = mvn_diag_lib.MultivariateNormalDiag(
           loc=self._rng.rand(*vector_shape),
           scale_diag=self._rng.rand(*vector_shape))
 
@@ -155,7 +157,7 @@ class EntropyShannonTest(test.TestCase):
 
   def test_normal_entropy_default_form_uses_exact_entropy(self):
     with self.test_session():
-      dist = distributions.Normal(loc=1.11, scale=2.22)
+      dist = normal_lib.Normal(loc=1.11, scale=2.22)
       mc_entropy = entropy.entropy_shannon(dist, n=11)
       exact_entropy = dist.entropy()
       self.assertEqual(exact_entropy.get_shape(), mc_entropy.get_shape())
@@ -163,7 +165,7 @@ class EntropyShannonTest(test.TestCase):
 
   def test_normal_entropy_analytic_form_uses_exact_entropy(self):
     with self.test_session():
-      dist = distributions.Normal(loc=1.11, scale=2.22)
+      dist = normal_lib.Normal(loc=1.11, scale=2.22)
       mc_entropy = entropy.entropy_shannon(
           dist, form=entropy.ELBOForms.analytic_entropy)
       exact_entropy = dist.entropy()
@@ -173,7 +175,7 @@ class EntropyShannonTest(test.TestCase):
   def test_normal_entropy_sample_form_gets_approximate_answer(self):
     # Tested by showing we get a good answer that is not exact.
     with self.test_session():
-      dist = distributions.Normal(loc=1.11, scale=2.22)
+      dist = normal_lib.Normal(loc=1.11, scale=2.22)
       mc_entropy = entropy.entropy_shannon(
           dist, n=1000, form=entropy.ELBOForms.sample, seed=0)
       exact_entropy = dist.entropy()
@@ -193,7 +195,7 @@ class EntropyShannonTest(test.TestCase):
       # NormalNoEntropy is like a Normal, but does not have .entropy method, so
       # we are forced to fall back on sample entropy.
       dist_no_entropy = NormalNoEntropy(loc=1.11, scale=2.22)
-      dist_yes_entropy = distributions.Normal(loc=1.11, scale=2.22)
+      dist_yes_entropy = normal_lib.Normal(loc=1.11, scale=2.22)
 
       mc_entropy = entropy.entropy_shannon(
           dist_no_entropy, n=1000, form=entropy.ELBOForms.sample, seed=0)
@@ -222,15 +224,16 @@ class RenyiRatioTest(test.TestCase):
     mu_true = np.array([1.0, -1.0], dtype=np.float64)
     chol_true = np.array([[2.0, 0.0], [0.5, 1.0]], dtype=np.float64)
     with self.test_session() as sess:
-      target = distributions.MultivariateNormalTriL(mu_true, chol_true)
+      target = mvn_tril_lib.MultivariateNormalTriL(mu_true, chol_true)
 
       # Set up q distribution by defining mean/covariance as Variables
       mu = variables.Variable(
           np.zeros(mu_true.shape), dtype=mu_true.dtype, name='mu')
       mat = variables.Variable(
           np.zeros(chol_true.shape), dtype=chol_true.dtype, name='mat')
-      chol = distributions.matrix_diag_transform(mat, transform=nn_ops.softplus)
-      q = distributions.MultivariateNormalTriL(mu, chol)
+      chol = distribution_util.matrix_diag_transform(
+          mat, transform=nn_ops.softplus)
+      q = mvn_tril_lib.MultivariateNormalTriL(mu, chol)
       for alpha in [0.25, 0.75]:
 
         negative_renyi_divergence = entropy.renyi_ratio(
@@ -262,7 +265,7 @@ class RenyiRatioTest(test.TestCase):
     n = 1000
     vector_shape = (2, 3)
     with self.test_session():
-      q = distributions.MultivariateNormalDiag(
+      q = mvn_diag_lib.MultivariateNormalDiag(
           loc=self._rng.rand(*vector_shape),
           scale_diag=self._rng.rand(*vector_shape))
       for alpha in [0.25, 0.75]:
diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index e29456f4e6..de1956750d 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -36,27 +36,48 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":bijectors_py",
+        "//tensorflow/contrib/framework:framework_py",
+        "//tensorflow/contrib/learn",
         "//tensorflow/contrib/linalg:linalg_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:clip_ops",
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:init_ops",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:state_ops",
         "//tensorflow/python:tensor_util",
         "//tensorflow/python:util",
+        "//tensorflow/python:variable_scope",
         "//tensorflow/python/ops/distributions",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],
 )
 
+cuda_py_test(
+    name = "estimator_test",
+    size = "small",
+    srcs = ["python/kernel_tests/estimator_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "@six_archive//:six",
+        "//tensorflow/contrib/learn",
+        "//tensorflow/contrib/learn:head_test",
+        "//tensorflow/python/ops/distributions",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:session",
+    ],
+)
+
 cuda_py_test(
     name = "distribution_test",
     size = "small",
diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py
index f4ebb3cb59..7a2aebddd2 100644
--- a/tensorflow/contrib/distributions/__init__.py
+++ b/tensorflow/contrib/distributions/__init__.py
@@ -30,6 +30,7 @@ from tensorflow.contrib.distributions.python.ops.conditional_transformed_distrib
 from tensorflow.contrib.distributions.python.ops.deterministic import *
 from tensorflow.contrib.distributions.python.ops.distribution_util import matrix_diag_transform
 from tensorflow.contrib.distributions.python.ops.distribution_util import softplus_inverse
+from tensorflow.contrib.distributions.python.ops.estimator import *
 from tensorflow.contrib.distributions.python.ops.geometric import *
 from tensorflow.contrib.distributions.python.ops.inverse_gamma import *
 from tensorflow.contrib.distributions.python.ops.logistic import *
@@ -147,6 +148,7 @@ _allowed_symbols = [
     'percentile',
     'assign_exponential_moving_mean_variance',
     'exponential_moving_mean_variance',
+    'estimator_head_distribution_regression',
 ]
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/estimator_test.py b/tensorflow/contrib/distributions/python/kernel_tests/estimator_test.py
new file mode 100644
index 0000000000..5ff0544c97
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/estimator_test.py
@@ -0,0 +1,114 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for estimator.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import six
+
+from tensorflow.contrib.distributions.python.ops import estimator as estimator_lib
+from tensorflow.contrib.learn.python.learn.estimators import constants
+from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
+from tensorflow.contrib.learn.python.learn.estimators import model_fn
+from tensorflow.contrib.learn.python.learn.estimators.head_test import _assert_metrics
+from tensorflow.contrib.learn.python.learn.estimators.head_test import _assert_no_variables
+from tensorflow.contrib.learn.python.learn.estimators.head_test import _assert_summary_tags
+from tensorflow.python.client import session
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops.distributions import normal as normal_lib
+from tensorflow.python.platform import test
+
+
+class EstimatorHeadDistributionRegressionTest(test.TestCase):
+
+  def _assert_output_alternatives(self, model_fn_ops):
+    self.assertEquals({
+        None: constants.ProblemType.LINEAR_REGRESSION
+    }, {
+        k: v[0] for k, v in six.iteritems(model_fn_ops.output_alternatives)
+    })
+
+  def testNormalLocScaleLogits(self):
+    # We will bias logits[..., 1] so that: logits[..., 1]=0 implies scale=1.
+    scale_bias = np.log(np.expm1(1.))
+
+    def softplus(x):
+      return np.log1p(np.exp(x))
+
+    def actual_loss(logits, labels):
+      mu = actual_mean(logits)
+      sigma = actual_stddev(logits)
+      labels = np.squeeze(labels, -1)
+      z = (labels - mu) / sigma
+      loss = 0.5 * (z**2. + np.log(2. * np.pi)) + np.log(sigma)
+      return loss.mean()
+
+    def actual_mean(logits):
+      return logits[..., 0]
+
+    def actual_stddev(logits):
+      return softplus(logits[..., 1] + scale_bias)
+
+    def make_distribution_fn(logits):
+      return normal_lib.Normal(
+          loc=logits[..., 0],
+          scale=nn_ops.softplus(logits[..., 1] + scale_bias))
+
+    head = estimator_lib.estimator_head_distribution_regression(
+        make_distribution_fn,
+        logits_dimension=2)
+    labels = np.float32([[-1.],
+                         [0.],
+                         [1.]])
+    logits = np.float32([[0., -1],
+                         [1, 0.5],
+                         [-1, 1]])
+    with ops.Graph().as_default(), session.Session():
+      # Convert to tensor so we can index into head.distributions.
+      tflogits = ops.convert_to_tensor(logits, name="logits")
+      model_fn_ops = head.create_model_fn_ops(
+          {},
+          labels=labels,
+          mode=model_fn.ModeKeys.TRAIN,
+          train_op_fn=head_lib.no_op_train_fn,
+          logits=tflogits)
+      self._assert_output_alternatives(model_fn_ops)
+      _assert_summary_tags(self, ["loss"])
+      _assert_no_variables(self)
+      loss = actual_loss(logits, labels)
+      _assert_metrics(self, loss, {"loss": loss}, model_fn_ops)
+
+      # Now we verify the underlying distribution was correctly constructed.
+      expected_mean = logits[..., 0]
+      self.assertAllClose(
+          expected_mean,
+          head.distribution(tflogits).mean().eval(),
+          rtol=1e-6, atol=0.)
+
+      expected_stddev = softplus(logits[..., 1] + scale_bias)
+      self.assertAllClose(
+          expected_stddev,
+          head.distribution(tflogits).stddev().eval(),
+          rtol=1e-6, atol=0.)
+      # Should have created only one distribution.
+      self.assertEqual(1, len(head.distributions))
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/estimator.py b/tensorflow/contrib/distributions/python/ops/estimator.py
new file mode 100644
index 0000000000..6b53338c45
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/estimator.py
@@ -0,0 +1,185 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions to bridge `Distribution`s and `tf.contrib.learn.estimator` APIs."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.learn.python.learn.estimators.head import _compute_weighted_loss
+from tensorflow.contrib.learn.python.learn.estimators.head import _RegressionHead
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+
+
+__all__ = [
+    "estimator_head_distribution_regression",
+]
+
+
+def estimator_head_distribution_regression(make_distribution_fn,
+                                           label_dimension=1,
+                                           logits_dimension=None,
+                                           label_name=None,
+                                           weight_column_name=None,
+                                           enable_centered_bias=False,
+                                           head_name=None):
+  """Creates a `Head` for regression under a generic distribution.
+
+  Args:
+    make_distribution_fn: Python `callable` which returns a `tf.Distribution`
+      instance created using only logits.
+    label_dimension: Number of regression labels per example. This is the size
+      of the last dimension of the labels `Tensor` (typically, this has shape
+      `[batch_size, label_dimension]`).
+    logits_dimension: Number of logits per example. This is the size of the last
+      dimension of the logits `Tensor` (typically, this has shape
+      `[batch_size, logits_dimension]`).
+      Default value: `label_dimension`.
+    label_name: Python `str`, name of the key in label `dict`. Can be `None` if
+      label is a `Tensor` (single headed models).
+    weight_column_name: Python `str` defining feature column name representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example.
+    enable_centered_bias: Python `bool`. If `True`, estimator will learn a
+      centered bias variable for each class. Rest of the model structure learns
+      the residual after centered bias.
+    head_name: Python `str`, name of the head. Predictions, summary and metrics
+      keys are suffixed by `"/" + head_name` and the default variable scope is
+      `head_name`.
+
+  Returns:
+    An instance of `Head` for generic regression.
+  """
+  return _DistributionRegressionHead(
+      make_distribution_fn=make_distribution_fn,
+      label_dimension=label_dimension,
+      logits_dimension=logits_dimension,
+      label_name=label_name,
+      weight_column_name=weight_column_name,
+      enable_centered_bias=enable_centered_bias,
+      head_name=head_name)
+
+
+class _DistributionRegressionHead(_RegressionHead):
+  """Creates a _RegressionHead instance from an arbitray `Distribution`."""
+
+  def __init__(self,
+               make_distribution_fn,
+               label_dimension,
+               logits_dimension=None,
+               label_name=None,
+               weight_column_name=None,
+               enable_centered_bias=False,
+               head_name=None):
+    """`Head` for regression.
+
+    Args:
+      make_distribution_fn: Python `callable` which returns a `tf.Distribution`
+        instance created using only logits.
+      label_dimension: Number of regression labels per example. This is the
+        size of the last dimension of the labels `Tensor` (typically, this has
+        shape `[batch_size, label_dimension]`).
+      logits_dimension: Number of logits per example. This is the size of the
+        last dimension of the logits `Tensor` (typically, this has shape
+        `[batch_size, logits_dimension]`).
+        Default value: `label_dimension`.
+      label_name: Python `str`, name of the key in label `dict`. Can be `None`
+        if label is a tensor (single headed models).
+      weight_column_name: Python `str` defining feature column name representing
+        weights. It is used to down weight or boost examples during training. It
+        will be multiplied by the loss of the example.
+      enable_centered_bias: Python `bool`. If `True`, estimator will learn a
+        centered bias variable for each class. Rest of the model structure
+        learns the residual after centered bias.
+      head_name: Python `str`, name of the head. Predictions, summary and
+        metrics keys are suffixed by `"/" + head_name` and the default variable
+        scope is `head_name`.
+
+    Raises:
+      TypeError: if `make_distribution_fn` is not `callable`.
+    """
+    if not callable(make_distribution_fn):
+      raise TypeError("`make_distribution_fn` must be a callable function.")
+
+    self._distributions = {}
+    self._make_distribution_fn = make_distribution_fn
+
+    def static_value(x):
+      """Returns the static value of a `Tensor` or `None`."""
+      return tensor_util.constant_value(ops.convert_to_tensor(x))
+
+    def concat_vectors(*args):
+      """Concatenates input vectors, statically if possible."""
+      args_ = [static_value(x) for x in args]
+      if any(vec is None for vec in args_):
+        return array_ops.concat(args, axis=0)
+      return [val for vec in args_ for val in vec]
+
+    def loss_fn(labels, logits, weights=None):
+      """Returns the loss of using `logits` to predict `labels`."""
+      d = self.distribution(logits)
+      labels_batch_shape = labels.shape.with_rank_at_least(1)[:-1]
+      labels_batch_shape = (
+          labels_batch_shape.as_list() if labels_batch_shape.is_fully_defined()
+          else array_ops.shape(labels)[:-1])
+      labels = array_ops.reshape(
+          labels,
+          shape=concat_vectors(labels_batch_shape, d.event_shape_tensor()))
+      return _compute_weighted_loss(
+          loss_unweighted=-d.log_prob(labels),
+          weight=weights)
+
+    def link_fn(logits):
+      """Returns the inverse link function at `logits`."""
+      # Note: What the API calls a "link function" is really the inverse-link
+      # function, i.e., the "mean".
+      d = self.distribution(logits)
+      return d.mean()
+
+    super(_DistributionRegressionHead, self).__init__(
+        label_dimension=label_dimension,
+        loss_fn=loss_fn,
+        link_fn=link_fn,
+        logits_dimension=logits_dimension,
+        label_name=label_name,
+        weight_column_name=weight_column_name,
+        enable_centered_bias=enable_centered_bias,
+        head_name=head_name)
+
+  @property
+  def distributions(self):
+    """Returns all distributions created by `DistributionRegressionHead`."""
+    return self._distributions
+
+  def distribution(self, logits, name=None):
+    """Retrieves a distribution instance, parameterized by `logits`.
+
+    Args:
+      logits: `float`-like `Tensor` representing the parameters of the
+        underlying distribution.
+      name: The Python `str` name to given to this op.
+        Default value: "distribution".
+
+    Returns:
+      distribution: `tf.Distribution` instance parameterized by `logits`.
+    """
+    with ops.name_scope(name, "distribution", [logits]):
+      d = self._distributions.get(logits, None)
+      if d is None:
+        d = self._make_distribution_fn(logits)
+        self._distributions[logits] = d
+      return d
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
index b25250d367..50c7ba418b 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
@@ -19,8 +19,8 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib import linalg
-from tensorflow.contrib.distributions.python.ops import bijectors
 from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.distributions.python.ops.bijectors import AffineLinearOperator
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import linalg_ops
@@ -189,7 +189,7 @@ class MultivariateNormalLinearOperator(
         distribution=normal.Normal(
             loc=array_ops.zeros([], dtype=scale.dtype),
             scale=array_ops.ones([], dtype=scale.dtype)),
-        bijector=bijectors.AffineLinearOperator(
+        bijector=AffineLinearOperator(
             shift=loc, scale=scale, validate_args=validate_args),
         batch_shape=batch_shape,
         event_shape=event_shape,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index 699a92d38a..7b49cd475d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -665,6 +665,7 @@ class _RegressionHead(_SingleHead):
                label_dimension,
                loss_fn,
                link_fn,
+               logits_dimension=None,
                label_name=None,
                weight_column_name=None,
                enable_centered_bias=False,
@@ -677,6 +678,10 @@ class _RegressionHead(_SingleHead):
         shape `[batch_size, label_dimension]`).
       loss_fn: Loss function, takes logits and labels and returns loss.
       link_fn: Link function, takes a logits tensor and returns the output.
+      logits_dimension: Number of logits per example. This is the
+        size of the last dimension of the logits `Tensor` (typically, this has
+        shape `[batch_size, label_dimension]`).
+        Default value: `label_dimension`.
       label_name: String, name of the key in label dict. Can be null if label
           is a tensor (single headed models).
       weight_column_name: A string defining feature column name representing
@@ -691,7 +696,8 @@ class _RegressionHead(_SingleHead):
     """
     super(_RegressionHead, self).__init__(
         problem_type=constants.ProblemType.LINEAR_REGRESSION,
-        logits_dimension=label_dimension,
+        logits_dimension=(logits_dimension if logits_dimension is not None
+                          else label_dimension),
         label_name=label_name,
         weight_column_name=weight_column_name,
         head_name=head_name)
-- 
GitLab


From b244912b2f6c19985d6830f751e7ed1e36605adc Mon Sep 17 00:00:00 2001
From: ebrevdo <ebrevdo@users.noreply.github.com>
Date: Wed, 26 Jul 2017 09:39:40 -0700
Subject: [PATCH 1345/1375] Add Raspberry PI build (#11675)

* Add support for Raspberry PI (ARM) builds.

To build:

./configure

bazel build --sandbox_debug -c opt --copt=-march=armv6 --copt=-mfpu=vfp \
  --copt=-funsafe-math-optimizations --copt=-ftree-vectorize \
  --copt=-fomit-frame-pointer --cpu=armeabi \
  --crosstool_top=@local_config_arm_compiler//:toolchain \
  --verbose_failures --build_python_zip ...targets...

* Additional changes to get raspberry pi build working.

update platform.h and crosstool template for define RASPBERRY_PI.

* Add build_raspberry_pi.sh with some documentation.

* Cleanups for build_raspberry_pi.sh

* contrib/makefile/Makefile in PI mode: add -DRASPBERRY_PI.

* Recommend using aptitude instead of apt-get in build_raspberry_pi.sh.

* More comments to build_raspberry_pi.sh

* Switched to tar file for ARM tool chain

* More fixes to arm build scripts; move tools/arm_compiler -> third_party/toolchains/cpus/arm.

* Update comments in build_raspberry_pi: get ubuntu ports for your lsb codename.

* Hacked around cxx_builtin_include_directory problem in CROSSTOOL

* Added an extra system include

* Added Pi Two/Three NEON support, with fix for Eigen version

* Undo pete's Eigen change - it breaks OS X build.  Will be fixed later.
---
 arm_compiler.BUILD                            |  81 ++
 tensorflow/contrib/makefile/Makefile          |   2 +-
 tensorflow/core/platform/platform.h           |   5 +-
 tensorflow/workspace.bzl                      |  23 +-
 third_party/toolchains/cpus/arm/BUILD         |  49 +
 third_party/toolchains/cpus/arm/CROSSTOOL.tpl | 874 ++++++++++++++++++
 .../cpus/arm/arm_compiler_configure.bzl       |  27 +
 .../toolchains/cpus/arm/build_raspberry_pi.sh |  58 ++
 8 files changed, 1114 insertions(+), 5 deletions(-)
 create mode 100644 arm_compiler.BUILD
 create mode 100644 third_party/toolchains/cpus/arm/BUILD
 create mode 100644 third_party/toolchains/cpus/arm/CROSSTOOL.tpl
 create mode 100644 third_party/toolchains/cpus/arm/arm_compiler_configure.bzl
 create mode 100755 third_party/toolchains/cpus/arm/build_raspberry_pi.sh

diff --git a/arm_compiler.BUILD b/arm_compiler.BUILD
new file mode 100644
index 0000000000..b231d0180e
--- /dev/null
+++ b/arm_compiler.BUILD
@@ -0,0 +1,81 @@
+package(default_visibility = ['//visibility:public'])
+
+filegroup(
+  name = 'gcc',
+  srcs = [
+    'bin/arm-linux-gnueabihf-gcc',
+  ],
+)
+
+filegroup(
+  name = 'ar',
+  srcs = [
+    'bin/arm-linux-gnueabihf-ar',
+  ],
+)
+
+filegroup(
+  name = 'ld',
+  srcs = [
+    'bin/arm-linux-gnueabihf-ld',
+  ],
+)
+
+filegroup(
+  name = 'nm',
+  srcs = [
+    'bin/arm-linux-gnueabihf-nm',
+  ],
+)
+
+filegroup(
+  name = 'objcopy',
+  srcs = [
+    'bin/arm-linux-gnueabihf-objcopy',
+  ],
+)
+
+filegroup(
+  name = 'objdump',
+  srcs = [
+    'bin/arm-linux-gnueabihf-objdump',
+  ],
+)
+
+filegroup(
+  name = 'strip',
+  srcs = [
+    'bin/arm-linux-gnueabihf-strip',
+  ],
+)
+
+filegroup(
+  name = 'as',
+  srcs = [
+    'bin/arm-linux-gnueabihf-as',
+  ],
+)
+
+filegroup(
+  name = 'compiler_pieces',
+  srcs = glob([
+    'arm-linux-gnueabihf/**',
+    'libexec/**',
+    'lib/gcc/arm-linux-gnueabihf/**',
+    'include/**',
+  ]),
+)
+
+filegroup(
+  name = 'compiler_components',
+  srcs = [
+    ':gcc',
+    ':ar',
+    ':ld',
+    ':nm',
+    ':objcopy',
+    ':objdump',
+    ':strip',
+    ':as',
+  ],
+)
diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index fc465ab380..2e45ddad54 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -202,7 +202,7 @@ ifeq ($(TARGET),LINUX)
 endif
 # If we're cross-compiling for the Raspberry Pi, use the right gcc.
 ifeq ($(TARGET),PI)
-	CXXFLAGS += -D__ANDROID_TYPES_SLIM__
+	CXXFLAGS += -D__ANDROID_TYPES_SLIM__ -DRASPBERRY_PI
 	LDFLAGS := -Wl,--no-whole-archive
 	LIBS += -ldl -lpthread
 	LIBFLAGS += -Wl,--allow-multiple-definition -Wl,--whole-archive
diff --git a/tensorflow/core/platform/platform.h b/tensorflow/core/platform/platform.h
index 7c44b22eff..12120c4ab9 100644
--- a/tensorflow/core/platform/platform.h
+++ b/tensorflow/core/platform/platform.h
@@ -43,9 +43,10 @@ limitations under the License.
 #elif defined(__arm__)
 #define PLATFORM_POSIX
 
-// Since there's no macro for the Raspberry Pi, assume we're on a mobile
-// platform if we're compiling for the ARM CPU.
+// Require an outside macro to tell us if we're building for Raspberry Pi.
+#if !defined(RASPBERRY_PI)
 #define IS_MOBILE_PLATFORM
+#endif  // !defined(RASPBERRY_PI)
 
 #else
 // If no platform specified, use:
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 71e4b8f5bb..6dd3fc4acd 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -3,9 +3,12 @@
 load("//third_party/gpus:cuda_configure.bzl", "cuda_configure")
 load("//third_party/sycl:sycl_configure.bzl", "sycl_configure")
 load("//third_party/mkl:build_defs.bzl", "mkl_repository")
-load("@io_bazel_rules_closure//closure/private:java_import_external.bzl", "java_import_external")
+load("@io_bazel_rules_closure//closure/private:java_import_external.bzl",
+     "java_import_external")
 load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external")
 load("//third_party/py:python_configure.bzl", "python_configure")
+load("//third_party/toolchains/cpus/arm:arm_compiler_configure.bzl",
+     "arm_compiler_configure")
 
 
 def _is_windows(repository_ctx):
@@ -83,7 +86,6 @@ temp_workaround_http_archive = repository_rule(
     },
 )
 
-
 # Executes specified command with arguments and calls 'fail' if it exited with
 # non-zero code
 def _execute_and_check_ret_code(repo_ctx, cmd_and_args):
@@ -142,6 +144,12 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   sycl_configure(name="local_config_sycl")
   python_configure(name="local_config_python")
 
+  # Point //external/local_config_arm_compiler to //external/arm_compiler
+  arm_compiler_configure(
+      name="local_config_arm_compiler",
+      remote_config_repo="../arm_compiler",
+      build_file = str(Label("//third_party/toolchains/cpus/arm:BUILD")))
+
   mkl_repository(
       name = "mkl",
       urls = [
@@ -169,6 +177,17 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       build_file = str(Label("//third_party:eigen.BUILD")),
   )
 
+  native.new_http_archive(
+    name = "arm_compiler",
+    build_file = str(Label("//:arm_compiler.BUILD")),
+    sha256 = "970285762565c7890c6c087d262b0a18286e7d0384f13a37786d8521773bc969",
+    strip_prefix = "tools-0e906ebc527eab1cdbf7adabff5b474da9562e9f/arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf",
+    urls = [
+        "http://mirror.bazel.build/github.com/raspberrypi/tools/archive/0e906ebc527eab1cdbf7adabff5b474da9562e9f.tar.gz",
+        "https://github.com/raspberrypi/tools/archive/0e906ebc527eab1cdbf7adabff5b474da9562e9f.tar.gz",
+    ],
+  )
+
   native.new_http_archive(
       name = "libxsmm_archive",
       urls = [
diff --git a/third_party/toolchains/cpus/arm/BUILD b/third_party/toolchains/cpus/arm/BUILD
new file mode 100644
index 0000000000..10c7867c23
--- /dev/null
+++ b/third_party/toolchains/cpus/arm/BUILD
@@ -0,0 +1,49 @@
+package(default_visibility = ["//visibility:public"])
+
+cc_toolchain_suite(
+    name = "toolchain",
+    toolchains = {
+        "armeabi|compiler": ":cc-compiler-armeabi",
+        "local|compiler": ":cc-compiler-local",
+    },
+)
+
+filegroup(
+    name = "empty",
+    srcs = [],
+)
+
+filegroup(
+    name = "arm_linux_all_files",
+    srcs = [
+        "@arm_compiler//:compiler_pieces",
+    ],
+)
+
+cc_toolchain(
+    name = "cc-compiler-local",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "local",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 1,
+)
+
+cc_toolchain(
+    name = "cc-compiler-armeabi",
+    all_files = ":arm_linux_all_files",
+    compiler_files = ":arm_linux_all_files",
+    cpu = "armeabi",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":arm_linux_all_files",
+    objcopy_files = "arm_linux_all_files",
+    static_runtime_libs = [":empty"],
+    strip_files = "arm_linux_all_files",
+    supports_param_files = 1,
+)
diff --git a/third_party/toolchains/cpus/arm/CROSSTOOL.tpl b/third_party/toolchains/cpus/arm/CROSSTOOL.tpl
new file mode 100644
index 0000000000..6753476c15
--- /dev/null
+++ b/third_party/toolchains/cpus/arm/CROSSTOOL.tpl
@@ -0,0 +1,874 @@
+major_version: "local"
+minor_version: ""
+default_target_cpu: "same_as_host"
+
+default_toolchain {
+  cpu: "k8"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "piii"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "darwin"
+  toolchain_identifier: "local_darwin"
+}
+default_toolchain {
+  cpu: "freebsd"
+  toolchain_identifier: "local_freebsd"
+}
+default_toolchain {
+  cpu: "armeabi"
+  toolchain_identifier: "arm-linux-gnueabihf"
+}
+default_toolchain {
+  cpu: "arm"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "x64_windows"
+  toolchain_identifier: "local_windows_msys64"
+}
+default_toolchain {
+  cpu: "x64_windows_msvc"
+  toolchain_identifier: "vc_14_0_x64"
+}
+default_toolchain {
+  cpu: "s390x"
+  toolchain_identifier: "local_linux"
+}
+
+toolchain {
+  abi_version: "armeabi"
+  abi_libc_version: "armeabi"
+  builtin_sysroot: ""
+  compiler: "compiler"
+  host_system_name: "armeabi"
+  needsPic: true
+  supports_gold_linker: false
+  supports_incremental_linker: false
+  supports_fission: false
+  supports_interface_shared_objects: false
+  supports_normalizing_ar: false
+  supports_start_end_lib: false
+  target_libc: "armeabi"
+  target_cpu: "armeabi"
+  target_system_name: "armeabi"
+  toolchain_identifier: "arm-linux-gnueabihf"
+
+  tool_path { name: "ar" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-ar" }
+  tool_path { name: "compat-ld" path: "/bin/false" }
+  tool_path { name: "cpp" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-cpp" }
+  tool_path { name: "dwp" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-dwp" }
+  tool_path { name: "gcc" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-gcc" }
+  tool_path { name: "gcov" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-gcov" }
+  tool_path { name: "ld" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-ld" }
+
+  tool_path { name: "nm" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-nm" }
+  tool_path { name: "objcopy" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-objcopy" }
+  tool_path { name: "objdump" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-objdump" }
+  tool_path { name: "strip" path: "%{ARM_COMPILER_PATH}%/bin/arm-linux-gnueabihf-strip" }
+
+  cxx_builtin_include_directory: "%{ARM_COMPILER_PATH}%/arm-linux-gnueabihf/include/c++/4.9.3/"
+  cxx_builtin_include_directory: "%{ARM_COMPILER_PATH}%/arm-linux-gnueabihf/sysroot/usr/include/"
+  cxx_builtin_include_directory: "%{ARM_COMPILER_PATH}%/arm-linux-gnueabihf/libc/usr/include/"
+  cxx_builtin_include_directory: "%{ARM_COMPILER_PATH}%/lib/gcc/arm-linux-gnueabihf/4.9.3/include"
+  cxx_builtin_include_directory: "%{ARM_COMPILER_PATH}%/lib/gcc/arm-linux-gnueabihf/4.9.3/include-fixed"
+  cxx_builtin_include_directory: "%{ARM_COMPILER_PATH}%/local_include"
+  cxx_builtin_include_directory: "/usr/include"
+
+  cxx_flag: "-std=c++11"	
+  # The cxx_builtin_include_directory directives don't seem to be adding these, so
+  # explicitly set them as flags. There's a query to the Bazel team outstanding about
+  # why this is necessary.
+  cxx_flag: "-isystem"
+  cxx_flag: "/usr/include/arm-linux-gnueabihf"
+  cxx_flag: "-isystem"
+  cxx_flag: "/usr/include/python2.7"
+  cxx_flag: "-isystem"
+  cxx_flag: "/usr/include/"
+  linker_flag: "-lstdc++"
+
+  unfiltered_cxx_flag: "-Wno-builtin-macro-redefined"
+  unfiltered_cxx_flag: "-D__DATE__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIMESTAMP__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIME__=\"redacted\""
+
+  unfiltered_cxx_flag: "-no-canonical-prefixes"
+  unfiltered_cxx_flag: "-fno-canonical-system-headers"
+
+  compiler_flag: "-U_FORTIFY_SOURCE"
+  compiler_flag: "-D_FORTIFY_SOURCE=1"
+  compiler_flag: "-fstack-protector"
+  compiler_flag: "-DRASPBERRY_PI"  # To differentiate from mobile builds.
+  linker_flag: "-Wl,-z,relro,-z,now"
+
+  linker_flag: "-no-canonical-prefixes"
+  linker_flag: "-pass-exit-codes"
+
+  linker_flag: "-Wl,--build-id=md5"
+  linker_flag: "-Wl,--hash-style=gnu"
+
+  compilation_mode_flags {
+    mode: DBG
+    # Enable debug symbols.
+    compiler_flag: "-g"
+  }
+  compilation_mode_flags {
+    mode: OPT
+
+    # No debug symbols.
+    # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt or
+    # even generally? However, that can't happen here, as it requires special
+    # handling in Bazel.
+    compiler_flag: "-g0"
+
+    # Conservative choice for -O
+    # -O3 can increase binary size and even slow down the resulting binaries.
+    # Profile first and / or use FDO if you need better performance than this.
+    compiler_flag: "-O2"
+
+    # Disable assertions
+    compiler_flag: "-DNDEBUG"
+
+    # Removal of unused code and data at link time (can this increase binary size in some cases?).
+    compiler_flag: "-ffunction-sections"
+    compiler_flag: "-fdata-sections"
+    linker_flag: "-Wl,--gc-sections"
+  }
+  linking_mode_flags { mode: DYNAMIC }
+
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "compiler"
+  host_system_name: "local"
+  needsPic: true
+  supports_gold_linker: false
+  supports_incremental_linker: false
+  supports_fission: false
+  supports_interface_shared_objects: false
+  supports_normalizing_ar: false
+  supports_start_end_lib: false
+  target_libc: "local"
+  target_cpu: "local"
+  target_system_name: "local"
+  toolchain_identifier: "local_linux"
+
+  tool_path { name: "ar" path: "/usr/bin/ar" }
+  tool_path { name: "compat-ld" path: "/usr/bin/ld" }
+  tool_path { name: "cpp" path: "/usr/bin/cpp" }
+  tool_path { name: "dwp" path: "/usr/bin/dwp" }
+  tool_path { name: "gcc" path: "/usr/bin/gcc" }
+  cxx_flag: "-std=c++0x"
+  linker_flag: "-lstdc++"
+  linker_flag: "-B/usr/bin/"
+
+  # TODO(bazel-team): In theory, the path here ought to exactly match the path
+  # used by gcc. That works because bazel currently doesn't track files at
+  # absolute locations and has no remote execution, yet. However, this will need
+  # to be fixed, maybe with auto-detection?
+  cxx_builtin_include_directory: "/usr/lib/gcc/"
+  cxx_builtin_include_directory: "/usr/local/include"
+  cxx_builtin_include_directory: "/usr/include"
+  tool_path { name: "gcov" path: "/usr/bin/gcov" }
+
+  # C(++) compiles invoke the compiler (as that is the one knowing where
+  # to find libraries), but we provide LD so other rules can invoke the linker.
+  tool_path { name: "ld" path: "/usr/bin/ld" }
+
+  tool_path { name: "nm" path: "/usr/bin/nm" }
+  tool_path { name: "objcopy" path: "/usr/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "/usr/bin/objdump" }
+  tool_path { name: "strip" path: "/usr/bin/strip" }
+
+  # Anticipated future default.
+  unfiltered_cxx_flag: "-no-canonical-prefixes"
+  unfiltered_cxx_flag: "-fno-canonical-system-headers"
+
+  # Make C++ compilation deterministic. Use linkstamping instead of these
+  # compiler symbols.
+  unfiltered_cxx_flag: "-Wno-builtin-macro-redefined"
+  unfiltered_cxx_flag: "-D__DATE__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIMESTAMP__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIME__=\"redacted\""
+
+  # Security hardening on by default.
+  # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases.
+  # We need to undef it before redefining it as some distributions now have
+  # it enabled by default.
+  compiler_flag: "-U_FORTIFY_SOURCE"
+  compiler_flag: "-D_FORTIFY_SOURCE=1"
+  compiler_flag: "-fstack-protector"
+  linker_flag: "-Wl,-z,relro,-z,now"
+
+  # Enable coloring even if there's no attached terminal. Bazel removes the
+  # escape sequences if --nocolor is specified. This isn't supported by gcc
+  # on Ubuntu 14.04.
+  # compiler_flag: "-fcolor-diagnostics"
+
+  # All warnings are enabled. Maybe enable -Werror as well?
+  compiler_flag: "-Wall"
+  # Enable a few more warnings that aren't part of -Wall.
+  compiler_flag: "-Wunused-but-set-parameter"
+  # But disable some that are problematic.
+  compiler_flag: "-Wno-free-nonheap-object" # has false positives
+
+  # Keep stack frames for debugging, even in opt mode.
+  compiler_flag: "-fno-omit-frame-pointer"
+
+  # Anticipated future default.
+  linker_flag: "-no-canonical-prefixes"
+  # Have gcc return the exit code from ld.
+  linker_flag: "-pass-exit-codes"
+  # Stamp the binary with a unique identifier.
+  linker_flag: "-Wl,--build-id=md5"
+  linker_flag: "-Wl,--hash-style=gnu"
+  # Gold linker only? Can we enable this by default?
+  # linker_flag: "-Wl,--warn-execstack"
+  # linker_flag: "-Wl,--detect-odr-violations"
+
+  compilation_mode_flags {
+    mode: DBG
+    # Enable debug symbols.
+    compiler_flag: "-g"
+  }
+  compilation_mode_flags {
+    mode: OPT
+
+    # No debug symbols.
+    # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt or
+    # even generally? However, that can't happen here, as it requires special
+    # handling in Bazel.
+    compiler_flag: "-g0"
+
+    # Conservative choice for -O
+    # -O3 can increase binary size and even slow down the resulting binaries.
+    # Profile first and / or use FDO if you need better performance than this.
+    compiler_flag: "-O2"
+
+    # Disable assertions
+    compiler_flag: "-DNDEBUG"
+
+    # Removal of unused code and data at link time (can this increase binary size in some cases?).
+    compiler_flag: "-ffunction-sections"
+    compiler_flag: "-fdata-sections"
+    linker_flag: "-Wl,--gc-sections"
+  }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "compiler"
+  host_system_name: "local"
+  needsPic: true
+  target_libc: "macosx"
+  target_cpu: "darwin"
+  target_system_name: "local"
+  toolchain_identifier: "local_darwin"
+
+  tool_path { name: "ar" path: "/usr/bin/libtool" }
+  tool_path { name: "compat-ld" path: "/usr/bin/ld" }
+  tool_path { name: "cpp" path: "/usr/bin/cpp" }
+  tool_path { name: "dwp" path: "/usr/bin/dwp" }
+  tool_path { name: "gcc" path: "osx_cc_wrapper.sh" }
+  cxx_flag: "-std=c++0x"
+  ar_flag: "-static"
+  ar_flag: "-s"
+  ar_flag: "-o"
+  linker_flag: "-lstdc++"
+  linker_flag: "-undefined"
+  linker_flag: "dynamic_lookup"
+  linker_flag: "-headerpad_max_install_names"
+  # TODO(ulfjack): This is wrong on so many levels. Figure out a way to auto-detect the proper
+  # setting from the local compiler, and also how to make incremental builds correct.
+  cxx_builtin_include_directory: "/"
+  tool_path { name: "gcov" path: "/usr/bin/gcov" }
+  tool_path { name: "ld" path: "/usr/bin/ld" }
+  tool_path { name: "nm" path: "/usr/bin/nm" }
+  tool_path { name: "objcopy" path: "/usr/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "/usr/bin/objdump" }
+  tool_path { name: "strip" path: "/usr/bin/strip" }
+
+  # Anticipated future default.
+  unfiltered_cxx_flag: "-no-canonical-prefixes"
+
+  # Make C++ compilation deterministic. Use linkstamping instead of these
+  # compiler symbols.
+  unfiltered_cxx_flag: "-Wno-builtin-macro-redefined"
+  unfiltered_cxx_flag: "-D__DATE__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIMESTAMP__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIME__=\"redacted\""
+
+  # Security hardening on by default.
+  # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases.
+  compiler_flag: "-D_FORTIFY_SOURCE=1"
+  compiler_flag: "-fstack-protector"
+
+  # Enable coloring even if there's no attached terminal. Bazel removes the
+  # escape sequences if --nocolor is specified.
+  compiler_flag: "-fcolor-diagnostics"
+
+  # All warnings are enabled. Maybe enable -Werror as well?
+  compiler_flag: "-Wall"
+  # Enable a few more warnings that aren't part of -Wall.
+  compiler_flag: "-Wthread-safety"
+  compiler_flag: "-Wself-assign"
+
+  # Keep stack frames for debugging, even in opt mode.
+  compiler_flag: "-fno-omit-frame-pointer"
+
+  # Anticipated future default.
+  linker_flag: "-no-canonical-prefixes"
+
+  compilation_mode_flags {
+    mode: DBG
+    # Enable debug symbols.
+    compiler_flag: "-g"
+  }
+  compilation_mode_flags {
+    mode: OPT
+    # No debug symbols.
+    # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt or even generally?
+    # However, that can't happen here, as it requires special handling in Bazel.
+    compiler_flag: "-g0"
+
+    # Conservative choice for -O
+    # -O3 can increase binary size and even slow down the resulting binaries.
+    # Profile first and / or use FDO if you need better performance than this.
+    compiler_flag: "-O2"
+
+    # Disable assertions
+    compiler_flag: "-DNDEBUG"
+
+    # Removal of unused code and data at link time (can this increase binary size in some cases?).
+    compiler_flag: "-ffunction-sections"
+    compiler_flag: "-fdata-sections"
+  }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "compiler"
+  host_system_name: "local"
+  needsPic: true
+  supports_gold_linker: false
+  supports_incremental_linker: false
+  supports_fission: false
+  supports_interface_shared_objects: false
+  supports_normalizing_ar: false
+  supports_start_end_lib: false
+  target_libc: "local"
+  target_cpu: "freebsd"
+  target_system_name: "local"
+  toolchain_identifier: "local_freebsd"
+
+  tool_path { name: "ar" path: "/usr/bin/ar" }
+  tool_path { name: "compat-ld" path: "/usr/bin/ld" }
+  tool_path { name: "cpp" path: "/usr/bin/cpp" }
+  tool_path { name: "dwp" path: "/usr/bin/dwp" }
+  tool_path { name: "gcc" path: "/usr/bin/clang" }
+  cxx_flag: "-std=c++0x"
+  linker_flag: "-lstdc++"
+  linker_flag: "-B/usr/bin/"
+
+  # TODO(bazel-team): In theory, the path here ought to exactly match the path
+  # used by gcc. That works because bazel currently doesn't track files at
+  # absolute locations and has no remote execution, yet. However, this will need
+  # to be fixed, maybe with auto-detection?
+  cxx_builtin_include_directory: "/usr/lib/clang"
+  cxx_builtin_include_directory: "/usr/local/include"
+  cxx_builtin_include_directory: "/usr/include"
+  tool_path { name: "gcov" path: "/usr/bin/gcov" }
+
+  # C(++) compiles invoke the compiler (as that is the one knowing where
+  # to find libraries), but we provide LD so other rules can invoke the linker.
+  tool_path { name: "ld" path: "/usr/bin/ld" }
+
+  tool_path { name: "nm" path: "/usr/bin/nm" }
+  tool_path { name: "objcopy" path: "/usr/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "/usr/bin/objdump" }
+  tool_path { name: "strip" path: "/usr/bin/strip" }
+
+  # Anticipated future default.
+  unfiltered_cxx_flag: "-no-canonical-prefixes"
+
+  # Make C++ compilation deterministic. Use linkstamping instead of these
+  # compiler symbols.
+  unfiltered_cxx_flag: "-Wno-builtin-macro-redefined"
+  unfiltered_cxx_flag: "-D__DATE__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIMESTAMP__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIME__=\"redacted\""
+
+  # Security hardening on by default.
+  # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases.
+  # We need to undef it before redefining it as some distributions now have
+  # it enabled by default.
+  compiler_flag: "-U_FORTIFY_SOURCE"
+  compiler_flag: "-D_FORTIFY_SOURCE=1"
+  compiler_flag: "-fstack-protector"
+  linker_flag: "-Wl,-z,relro,-z,now"
+
+  # Enable coloring even if there's no attached terminal. Bazel removes the
+  # escape sequences if --nocolor is specified. This isn't supported by gcc
+  # on Ubuntu 14.04.
+  # compiler_flag: "-fcolor-diagnostics"
+
+  # All warnings are enabled. Maybe enable -Werror as well?
+  compiler_flag: "-Wall"
+  # Enable a few more warnings that aren't part of -Wall.
+  #compiler_flag: "-Wunused-but-set-parameter"
+  # But disable some that are problematic.
+  #compiler_flag: "-Wno-free-nonheap-object" # has false positives
+
+  # Keep stack frames for debugging, even in opt mode.
+  compiler_flag: "-fno-omit-frame-pointer"
+
+  # Anticipated future default.
+  linker_flag: "-no-canonical-prefixes"
+  # Have gcc return the exit code from ld.
+  #linker_flag: "-pass-exit-codes"
+  # Stamp the binary with a unique identifier.
+  #linker_flag: "-Wl,--build-id=md5"
+  linker_flag: "-Wl,--hash-style=gnu"
+  # Gold linker only? Can we enable this by default?
+  # linker_flag: "-Wl,--warn-execstack"
+  # linker_flag: "-Wl,--detect-odr-violations"
+
+  compilation_mode_flags {
+    mode: DBG
+    # Enable debug symbols.
+    compiler_flag: "-g"
+  }
+  compilation_mode_flags {
+    mode: OPT
+
+    # No debug symbols.
+    # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt or
+    # even generally? However, that can't happen here, as it requires special
+    # handling in Bazel.
+    compiler_flag: "-g0"
+
+    # Conservative choice for -O
+    # -O3 can increase binary size and even slow down the resulting binaries.
+    # Profile first and / or use FDO if you need better performance than this.
+    compiler_flag: "-O2"
+
+    # Disable assertions
+    compiler_flag: "-DNDEBUG"
+
+    # Removal of unused code and data at link time (can this increase binary size in some cases?).
+    compiler_flag: "-ffunction-sections"
+    compiler_flag: "-fdata-sections"
+    linker_flag: "-Wl,--gc-sections"
+  }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "windows_mingw"
+  host_system_name: "local"
+  needsPic: false
+  target_libc: "local"
+  target_cpu: "x64_windows"
+  target_system_name: "local"
+  toolchain_identifier: "local_windows_mingw"
+
+  tool_path { name: "ar" path: "C:/mingw/bin/ar" }
+  tool_path { name: "compat-ld" path: "C:/mingw/bin/ld" }
+  tool_path { name: "cpp" path: "C:/mingw/bin/cpp" }
+  tool_path { name: "dwp" path: "C:/mingw/bin/dwp" }
+  tool_path { name: "gcc" path: "C:/mingw/bin/gcc" }
+  cxx_flag: "-std=c++0x"
+  # TODO(bazel-team): In theory, the path here ought to exactly match the path
+  # used by gcc. That works because bazel currently doesn't track files at
+  # absolute locations and has no remote execution, yet. However, this will need
+  # to be fixed, maybe with auto-detection?
+  cxx_builtin_include_directory: "C:/mingw/include"
+  cxx_builtin_include_directory: "C:/mingw/lib/gcc"
+  tool_path { name: "gcov" path: "C:/mingw/bin/gcov" }
+  tool_path { name: "ld" path: "C:/mingw/bin/ld" }
+  tool_path { name: "nm" path: "C:/mingw/bin/nm" }
+  tool_path { name: "objcopy" path: "C:/mingw/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "C:/mingw/bin/objdump" }
+  tool_path { name: "strip" path: "C:/mingw/bin/strip" }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "windows_msys64_mingw64"
+  host_system_name: "local"
+  needsPic: false
+  target_libc: "local"
+  target_cpu: "x64_windows"
+  target_system_name: "local"
+  toolchain_identifier: "local_windows_msys64_mingw64"
+
+  tool_path { name: "ar" path: "C:/tools/msys64/mingw64/bin/ar" }
+  tool_path { name: "compat-ld" path: "C:/tools/msys64/mingw64/bin/ld" }
+  tool_path { name: "cpp" path: "C:/tools/msys64/mingw64/bin/cpp" }
+  tool_path { name: "dwp" path: "C:/tools/msys64/mingw64/bin/dwp" }
+  tool_path { name: "gcc" path: "C:/tools/msys64/mingw64/bin/gcc" }
+  cxx_flag: "-std=c++0x"
+  # TODO(bazel-team): In theory, the path here ought to exactly match the path
+  # used by gcc. That works because bazel currently doesn't track files at
+  # absolute locations and has no remote execution, yet. However, this will need
+  # to be fixed, maybe with auto-detection?
+  cxx_builtin_include_directory: "C:/tools/msys64/mingw64/x86_64-w64-mingw32/include"
+  tool_path { name: "gcov" path: "C:/tools/msys64/mingw64/bin/gcov" }
+  tool_path { name: "ld" path: "C:/tools/msys64/mingw64/bin/ld" }
+  tool_path { name: "nm" path: "C:/tools/msys64/mingw64/bin/nm" }
+  tool_path { name: "objcopy" path: "C:/tools/msys64/mingw64/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "C:/tools/msys64/mingw64/bin/objdump" }
+  tool_path { name: "strip" path: "C:/tools/msys64/mingw64/bin/strip" }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "windows_clang"
+  host_system_name: "local"
+  needsPic: false
+  target_libc: "local"
+  target_cpu: "x64_windows"
+  target_system_name: "local"
+  toolchain_identifier: "local_windows_clang"
+
+  tool_path { name: "ar" path: "C:/mingw/bin/ar" }
+  tool_path { name: "compat-ld" path: "C:/Program Files (x86)/LLVM/bin/ld" }
+  tool_path { name: "cpp" path: "C:/Program Files (x86)/LLVM/bin/cpp" }
+  tool_path { name: "dwp" path: "C:/Program Files (x86)/LLVM/bin/dwp" }
+  tool_path { name: "gcc" path: "C:/Program Files (x86)/LLVM/bin/clang" }
+  cxx_flag: "-std=c++0x"
+  # TODO(bazel-team): In theory, the path here ought to exactly match the path
+  # used by gcc. That works because bazel currently doesn't track files at
+  # absolute locations and has no remote execution, yet. However, this will need
+  # to be fixed, maybe with auto-detection?
+  cxx_builtin_include_directory: "/usr/lib/gcc/"
+  cxx_builtin_include_directory: "/usr/local/include"
+  cxx_builtin_include_directory: "/usr/include"
+  tool_path { name: "gcov" path: "C:/Program Files (x86)/LLVM/bin/gcov" }
+  tool_path { name: "ld" path: "C:/Program Files (x86)/LLVM/bin/ld" }
+  tool_path { name: "nm" path: "C:/Program Files (x86)/LLVM/bin/nm" }
+  tool_path { name: "objcopy" path: "C:/Program Files (x86)/LLVM/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "C:/Program Files (x86)/LLVM/bin/objdump" }
+  tool_path { name: "strip" path: "C:/Program Files (x86)/LLVM/bin/strip" }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+   abi_version: "local"
+   abi_libc_version: "local"
+   builtin_sysroot: ""
+   compiler: "windows_msys64"
+   host_system_name: "local"
+   needsPic: false
+   target_libc: "local"
+   target_cpu: "x64_windows"
+   target_system_name: "local"
+   toolchain_identifier: "local_windows_msys64"
+
+   tool_path { name: "ar" path: "C:/tools/msys64/usr/bin/ar" }
+   tool_path { name: "compat-ld" path: "C:/tools/msys64/usr/bin/ld" }
+   tool_path { name: "cpp" path: "C:/tools/msys64/usr/bin/cpp" }
+   tool_path { name: "dwp" path: "C:/tools/msys64/usr/bin/dwp" }
+   # Use gcc instead of g++ so that C will compile correctly.
+   tool_path { name: "gcc" path: "C:/tools/msys64/usr/bin/gcc" }
+   cxx_flag: "-std=gnu++0x"
+   linker_flag: "-lstdc++"
+   # TODO(bazel-team): In theory, the path here ought to exactly match the path
+   # used by gcc. That works because bazel currently doesn't track files at
+   # absolute locations and has no remote execution, yet. However, this will need
+   # to be fixed, maybe with auto-detection?
+   cxx_builtin_include_directory: "C:/tools/msys64/"
+   cxx_builtin_include_directory: "/usr/"
+   tool_path { name: "gcov" path: "C:/tools/msys64/usr/bin/gcov" }
+   tool_path { name: "ld" path: "C:/tools/msys64/usr/bin/ld" }
+   tool_path { name: "nm" path: "C:/tools/msys64/usr/bin/nm" }
+   tool_path { name: "objcopy" path: "C:/tools/msys64/usr/bin/objcopy" }
+   objcopy_embed_flag: "-I"
+   objcopy_embed_flag: "binary"
+   tool_path { name: "objdump" path: "C:/tools/msys64/usr/bin/objdump" }
+   tool_path { name: "strip" path: "C:/tools/msys64/usr/bin/strip" }
+   linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  toolchain_identifier: "vc_14_0_x64"
+  host_system_name: "local"
+  target_system_name: "local"
+
+  abi_version: "local"
+  abi_libc_version: "local"
+  target_cpu: "x64_windows_msvc"
+  compiler: "cl"
+  target_libc: "msvcrt140"
+  default_python_version: "python2.7"
+  cxx_builtin_include_directory: "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/INCLUDE"
+  cxx_builtin_include_directory: "C:/Program Files (x86)/Windows Kits/10/include/"
+  cxx_builtin_include_directory: "C:/Program Files (x86)/Windows Kits/8.1/include/"
+  cxx_builtin_include_directory: "C:/Program Files (x86)/GnuWin32/include/"
+  cxx_builtin_include_directory: "C:/python_27_amd64/files/include"
+  tool_path {
+    name: "ar"
+    path: "wrapper/bin/msvc_link.bat"
+  }
+  tool_path {
+    name: "cpp"
+    path: "wrapper/bin/msvc_cl.bat"
+  }
+  tool_path {
+    name: "gcc"
+    path: "wrapper/bin/msvc_cl.bat"
+  }
+  tool_path {
+    name: "gcov"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "ld"
+    path: "wrapper/bin/msvc_link.bat"
+  }
+  tool_path {
+    name: "nm"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "objcopy"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "objdump"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "strip"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  supports_gold_linker: false
+  supports_start_end_lib: false
+  supports_interface_shared_objects: false
+  supports_incremental_linker: false
+  supports_normalizing_ar: true
+  needsPic: false
+
+  compiler_flag: "-m64"
+  compiler_flag: "/D__inline__=__inline"
+  # TODO(pcloudy): Review those flags below, they should be defined by cl.exe
+  compiler_flag: "/DOS_WINDOWS=OS_WINDOWS"
+  compiler_flag: "/DCOMPILER_MSVC"
+
+  # Don't pollute with GDI macros in windows.h.
+  compiler_flag: "/DNOGDI"
+  # Don't define min/max macros in windows.h.
+  compiler_flag: "/DNOMINMAX"
+  compiler_flag: "/DPRAGMA_SUPPORTED"
+  # Platform defines.
+  compiler_flag: "/D_WIN32_WINNT=0x0600"
+  # Turn off warning messages.
+  compiler_flag: "/D_CRT_SECURE_NO_DEPRECATE"
+  compiler_flag: "/D_CRT_SECURE_NO_WARNINGS"
+  compiler_flag: "/D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS"
+  # Use math constants (M_PI, etc.) from the math library
+  compiler_flag: "/D_USE_MATH_DEFINES"
+
+  # Useful options to have on for compilation.
+  # Suppress startup banner.
+  compiler_flag: "/nologo"
+  # Increase the capacity of object files to 2^32 sections.
+  compiler_flag: "/bigobj"
+  # Allocate 500MB for precomputed headers.
+  compiler_flag: "/Zm500"
+  # Use unsigned char by default.
+  compiler_flag: "/J"
+  # Use function level linking.
+  compiler_flag: "/Gy"
+  # Use string pooling.
+  compiler_flag: "/GF"
+  # Warning level 3 (could possibly go to 4 in the future).
+  compiler_flag: "/W3"
+  # Catch both asynchronous (structured) and synchronous (C++) exceptions.
+  compiler_flag: "/EHsc"
+
+  # Globally disabled warnings.
+  # Don't warn about elements of array being be default initialized.
+  compiler_flag: "/wd4351"
+  # Don't warn about no matching delete found.
+  compiler_flag: "/wd4291"
+  # Don't warn about diamond inheritance patterns.
+  compiler_flag: "/wd4250"
+  # Don't warn about insecure functions (e.g. non _s functions).
+  compiler_flag: "/wd4996"
+
+  linker_flag: "-m64"
+
+  feature {
+    name: 'include_paths'
+    flag_set {
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-header-preprocessing'
+      action: 'c++-module-compile'
+      flag_group {
+        flag: '/I%{quote_include_paths}'
+      }
+      flag_group {
+        flag: '/I%{include_paths}'
+      }
+      flag_group {
+        flag: '/I%{system_include_paths}'
+      }
+    }
+  }
+
+  feature {
+    name: 'dependency_file'
+    flag_set {
+      action: 'assemble'
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-module-compile'
+      action: 'c++-header-preprocessing'
+      action: 'c++-header-parsing'
+      expand_if_all_available: 'dependency_file'
+      flag_group {
+        flag: '/DEPENDENCY_FILE'
+        flag: '%{dependency_file}'
+      }
+    }
+  }
+
+  # Stop passing -frandom-seed option
+  feature {
+    name: 'random_seed'
+  }
+
+  # This feature is just for enabling flag_set in action_config for -c and -o options during the transitional period
+  feature {
+    name: 'compile_action_flags_in_flag_set'
+  }
+
+  action_config {
+    config_name: 'c-compile'
+    action_name: 'c-compile'
+    tool {
+      tool_path: 'wrapper/bin/msvc_cl.bat'
+    }
+    flag_set {
+      flag_group {
+        flag: '/c'
+        flag: '%{source_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_object_file'
+      flag_group {
+        flag: '/Fo%{output_object_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_assembly_file'
+      flag_group {
+        flag: '/Fa%{output_assembly_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_preprocess_file'
+      flag_group {
+        flag: '/P'
+        flag: '/Fi%{output_preprocess_file}'
+      }
+    }
+  }
+
+  action_config {
+    config_name: 'c++-compile'
+    action_name: 'c++-compile'
+    tool {
+      tool_path: 'wrapper/bin/msvc_cl.bat'
+    }
+    flag_set {
+      flag_group {
+        flag: '/c'
+        flag: '%{source_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_object_file'
+      flag_group {
+        flag: '/Fo%{output_object_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_assembly_file'
+      flag_group {
+        flag: '/Fa%{output_assembly_file}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'output_preprocess_file'
+      flag_group {
+        flag: '/P'
+        flag: '/Fi%{output_preprocess_file}'
+      }
+    }
+  }
+
+  compilation_mode_flags {
+    mode: DBG
+    compiler_flag: "/DDEBUG=1"
+    # This will signal the wrapper that we are doing a debug build, which sets
+    # some internal state of the toolchain wrapper. It is intentionally a "-"
+    # flag to make this very obvious.
+    compiler_flag: "-g"
+    compiler_flag: "/Od"
+    compiler_flag: "-Xcompilation-mode=dbg"
+  }
+
+  compilation_mode_flags {
+    mode: FASTBUILD
+    compiler_flag: "/DNDEBUG"
+    compiler_flag: "/Od"
+    compiler_flag: "-Xcompilation-mode=fastbuild"
+  }
+
+  compilation_mode_flags {
+    mode: OPT
+    compiler_flag: "/DNDEBUG"
+    compiler_flag: "/O2"
+    compiler_flag: "-Xcompilation-mode=opt"
+  }
+}
diff --git a/third_party/toolchains/cpus/arm/arm_compiler_configure.bzl b/third_party/toolchains/cpus/arm/arm_compiler_configure.bzl
new file mode 100644
index 0000000000..5eb3b7bb1c
--- /dev/null
+++ b/third_party/toolchains/cpus/arm/arm_compiler_configure.bzl
@@ -0,0 +1,27 @@
+# -*- Python -*-
+"""Repository rule for arm compiler autoconfiguration."""
+
+def _tpl(repository_ctx, tpl, substitutions={}, out=None):
+  if not out:
+    out = tpl
+  repository_ctx.template(
+      out,
+      Label("//third_party/toolchains/cpus/arm:%s.tpl" % tpl),
+      substitutions)
+
+
+def _arm_compiler_configure_impl(repository_ctx):
+  _tpl(repository_ctx, "CROSSTOOL", {
+      "%{ARM_COMPILER_PATH}%": str(repository_ctx.path(
+          repository_ctx.attr.remote_config_repo)),
+  })
+  repository_ctx.symlink(repository_ctx.attr.build_file, "BUILD")
+
+
+arm_compiler_configure = repository_rule(
+    implementation = _arm_compiler_configure_impl,
+    attrs = {
+        "remote_config_repo": attr.string(mandatory = False, default =""),
+        "build_file": attr.label(),
+    },
+)
diff --git a/third_party/toolchains/cpus/arm/build_raspberry_pi.sh b/third_party/toolchains/cpus/arm/build_raspberry_pi.sh
new file mode 100755
index 0000000000..a5ad0fce0e
--- /dev/null
+++ b/third_party/toolchains/cpus/arm/build_raspberry_pi.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+set -e
+
+# By default this builds packages for the Pi Two and Three only, since the NEON support
+# this allows makes calculations many times faster. To support the Pi One or Zero, pass
+# PI_ONE as the first argument to the script, for example:
+# third_party/toolchains/cpus/arm/build_raspberry_pi.sh PI_ONE
+#
+# To install the cross-compilation support for Python this script needs on Ubuntu Trusty, run
+# something like these steps, after backing up your original /etc/apt/sources.list file:
+#
+# dpkg --add-architecture armhf
+# echo 'deb [arch=armhf] http://ports.ubuntu.com/ trusty main restricted universe multiverse' >> /etc/apt/sources.list.d/armhf.list
+# echo 'deb [arch=armhf] http://ports.ubuntu.com/ trusty-updates main restricted universe multiverse' >> /etc/apt/sources.list.d/armhf.list
+# echo 'deb [arch=armhf] http://ports.ubuntu.com/ trusty-security main restricted universe multiverse' >> /etc/apt/sources.list.d/armhf.list
+# echo 'deb [arch=armhf] http://ports.ubuntu.com/ trusty-backports main restricted universe multiverse' >> /etc/apt/sources.list.d/armhf.list
+# sed -i 's#deb http://archive.ubuntu.com/ubuntu/#deb [arch=amd64] http://archive.ubuntu.com/ubuntu/#g' /etc/apt/sources.list
+# apt-get update
+# apt-get install -y libpython-all-dev:armhf
+#
+# Make sure you have an up to date version of the Bazel build tool installed too.
+
+yes '' | ./configure
+
+if [[ $1 == "PI_ONE" ]]; then
+  PI_COPTS="--copt=-march=armv6 --copt=-mfpu=vfp"
+  echo "Building for the Pi One/Zero, with no NEON support"
+else
+  PI_COPTS='--copt=-march=armv7-a --copt=-mfpu=neon-vfpv4
+  --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_1
+  --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_2
+  --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_8'
+  echo "Building for the Pi Two/Three, with NEON acceleration"
+fi
+
+bazel build -c opt ${PI_COPTS} \
+  --copt=-funsafe-math-optimizations --copt=-ftree-vectorize \
+  --copt=-fomit-frame-pointer --cpu=armeabi \
+  --crosstool_top=@local_config_arm_compiler//:toolchain \
+  --verbose_failures \
+  //tensorflow/tools/benchmark:benchmark_model \
+  //tensorflow/tools/pip_package:build_pip_package
+
+TMPDIR=$(mktemp -d -t tmp.XXXXXXXXXX)
+echo "Final outputs will go to ${TMPDIR}"
+
+# Build a universal wheel.
+BDIST_OPTS="--universal" \
+  bazel-bin/tensorflow/tools/pip_package/build_pip_package "${TMPDIR}"
+
+OLD_FN=$(ls "${TMPDIR}" | grep \.whl)
+SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-any.whl/; print'
+NEW_FN=$(echo "${OLD_FN}" | perl -ne "${SUB}")
+mv "${TMPDIR}/${OLD_FN}" "${TMPDIR}/${NEW_FN}"
+cp bazel-bin/tensorflow/tools/benchmark/benchmark_model "${TMPDIR}"
+
+echo "Output can be found here:"
+find "${TMPDIR}"
-- 
GitLab


From 2b4a0f9a485d47ceb572c4ff4e1ad3100dc1c3e0 Mon Sep 17 00:00:00 2001
From: resec <resec0109@gmail.com>
Date: Thu, 27 Jul 2017 01:51:17 +0800
Subject: [PATCH 1346/1375] Enable Android lib bazel build for mips and mips64
 (#11773)

---
 WORKSPACE                        |  3 +++
 tensorflow/BUILD                 | 18 ++++++++++++++++++
 tensorflow/contrib/android/BUILD |  3 ++-
 tensorflow/core/BUILD            |  5 ++---
 tensorflow/tensorflow.bzl        | 15 +++++++++++++++
 5 files changed, 40 insertions(+), 4 deletions(-)

diff --git a/WORKSPACE b/WORKSPACE
index 74ce13f4e8..6b5d24560c 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -32,6 +32,9 @@ load("//tensorflow:workspace.bzl", "tf_workspace")
 #    name="androidndk",
 #    path="<PATH_TO_NDK>",
 #    # This needs to be 14 or higher to compile TensorFlow.
+#    # Please specify API level to >= 21 to build for 64-bit
+#    # archtectures or the Android NDK will automatically select biggest
+#    # API level that it supports without notice.
 #    # Note that the NDK version is not the API level.
 #    api_level=14)
 
diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 0b5ba805a8..a162bcf452 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -63,6 +63,24 @@ config_setting(
     visibility = ["//visibility:public"],
 )
 
+config_setting(
+    name = "android_mips",
+    values = {
+        "crosstool_top": "//external:android/crosstool",
+        "cpu": "mips",
+    },
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "android_mips64",
+    values = {
+        "crosstool_top": "//external:android/crosstool",
+        "cpu": "mips64",
+    },
+    visibility = ["//visibility:public"],
+)
+
 config_setting(
     name = "darwin",
     values = {"cpu": "darwin"},
diff --git a/tensorflow/contrib/android/BUILD b/tensorflow/contrib/android/BUILD
index 952f24f34b..0833790e99 100644
--- a/tensorflow/contrib/android/BUILD
+++ b/tensorflow/contrib/android/BUILD
@@ -11,6 +11,7 @@ load(
     "//tensorflow:tensorflow.bzl",
     "tf_copts",
     "if_android",
+    "if_android_mips",
 )
 
 exports_files([
@@ -85,7 +86,7 @@ cc_binary(
         "-Wl,--gc-sections",
         "-Wl,--version-script",  # This line must be directly followed by LINKER_SCRIPT.
         LINKER_SCRIPT,
-    ]),
+    ]) + if_android_mips(["-latomic"]),
     linkshared = 1,
     linkstatic = 1,
     tags = [
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 02355a7626..deb2d99feb 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -64,6 +64,7 @@ load(
     "//tensorflow:tensorflow.bzl",
     "full_path",
     "if_android",
+    "if_not_android_mips_and_mips64",
     "if_ios",
     "if_linux_x86_64",
     "if_not_mobile",
@@ -933,9 +934,7 @@ filegroup(
 cc_library(
     name = "android_tensorflow_lib_lite",
     srcs = if_android(["//tensorflow/core:android_srcs"]),
-    copts = tf_copts() + [
-        "-Os",
-    ],
+    copts = tf_copts() + if_not_android_mips_and_mips64(["-Os"]),
     linkopts = ["-lz"],
     tags = [
         "manual",
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index cddf16f147..5fd9fe597d 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -72,6 +72,13 @@ def if_android_arm64(a):
   })
 
 
+def if_android_mips(a):
+  return select({
+      clean_dep("//tensorflow:android_mips"): a,
+      "//conditions:default": [],
+  })
+
+
 def if_not_android(a):
   return select({
       clean_dep("//tensorflow:android"): [],
@@ -79,6 +86,14 @@ def if_not_android(a):
   })
 
 
+def if_not_android_mips_and_mips64(a):
+  return select({
+      clean_dep("//tensorflow:android_mips"): [],
+      clean_dep("//tensorflow:android_mips64"): [],
+      "//conditions:default": a,
+  })
+
+
 def if_android(a):
   return select({
       clean_dep("//tensorflow:android"): a,
-- 
GitLab


From 9eb2fe4c3a8cf3e64dce4603b03d91da76d8b27e Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Wed, 26 Jul 2017 11:17:07 -0700
Subject: [PATCH 1347/1375] Add LocalTempFilename function to Env class which
 creates a local temp file name

PiperOrigin-RevId: 163230810
---
 tensorflow/core/platform/env.cc         | 37 +++++++++++++++++++++++++
 tensorflow/core/platform/env.h          |  6 ++++
 tensorflow/core/platform/env_test.cc    | 28 +++++++++++++++++++
 tensorflow/core/platform/posix/env.cc   | 35 +++++++++++++++++++++++
 tensorflow/core/platform/windows/env.cc | 17 ++++++++++++
 5 files changed, 123 insertions(+)

diff --git a/tensorflow/core/platform/env.cc b/tensorflow/core/platform/env.cc
index 2fdd989c9b..44f11aef96 100644
--- a/tensorflow/core/platform/env.cc
+++ b/tensorflow/core/platform/env.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include <sys/stat.h>
 #include <deque>
 #include <utility>
 #include <vector>
@@ -30,7 +31,10 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/lib/io/path.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/env_time.h"
+#include "tensorflow/core/platform/host_info.h"
 #include "tensorflow/core/platform/protobuf.h"
 
 namespace tensorflow {
@@ -273,6 +277,39 @@ string Env::GetExecutablePath() {
   return exe_path;
 }
 
+bool Env::LocalTempFilename(string* filename) {
+  std::vector<string> dirs;
+  GetLocalTempDirectories(&dirs);
+
+  // Try each directory, as they might be full, have inappropriate
+  // permissions or have different problems at times.
+  for (const string& dir : dirs) {
+#ifdef __APPLE__
+    uint64_t tid64;
+    pthread_threadid_np(nullptr, &tid64);
+    int32 tid = static_cast<int32>(tid64);
+    int32 pid = static_cast<int32>(getpid());
+#elif defined(PLATFORM_WINDOWS)
+    int32 tid = static_cast<int32>(GetCurrentThreadId());
+    int32 pid = static_cast<int32>(GetCurrentProcessId());
+#else
+    int32 tid = static_cast<int32>(pthread_self());
+    int32 pid = static_cast<int32>(getpid());
+#endif
+    uint64 now_microsec = NowMicros();
+
+    *filename = io::JoinPath(
+        dir, strings::Printf("tempfile-%s-%x-%d-%llx", port::Hostname().c_str(),
+                             tid, pid, now_microsec));
+    if (FileExists(*filename).ok()) {
+      filename->clear();
+    } else {
+      return true;
+    }
+  }
+  return false;
+}
+
 Thread::~Thread() {}
 
 EnvWrapper::~EnvWrapper() {}
diff --git a/tensorflow/core/platform/env.h b/tensorflow/core/platform/env.h
index 1b7e024b0f..da8c3e2d7e 100644
--- a/tensorflow/core/platform/env.h
+++ b/tensorflow/core/platform/env.h
@@ -215,6 +215,9 @@ class Env {
   /// symlinks if there is any.
   string GetExecutablePath();
 
+  /// Creates a local unique temporary file name. Returns true if success.
+  bool LocalTempFilename(string* filename);
+
   // TODO(jeff,sanjay): Add back thread/thread-pool support if needed.
   // TODO(jeff,sanjay): if needed, tighten spec so relative to epoch, or
   // provide a routine to get the absolute time.
@@ -279,6 +282,9 @@ class Env {
       const string& version) = 0;
 
  private:
+  // Returns a possible list of local temporary directories.
+  void GetLocalTempDirectories(std::vector<string>* list);
+
   std::unique_ptr<FileSystemRegistry> file_system_registry_;
   TF_DISALLOW_COPY_AND_ASSIGN(Env);
   EnvTime* envTime = EnvTime::Default();
diff --git a/tensorflow/core/platform/env_test.cc b/tensorflow/core/platform/env_test.cc
index 7bc1882c86..50dd0cd58b 100644
--- a/tensorflow/core/platform/env_test.cc
+++ b/tensorflow/core/platform/env_test.cc
@@ -298,4 +298,32 @@ TEST_F(DefaultEnvTest, GetExecutablePath) {
   TF_EXPECT_OK(env->FileExists(env->GetExecutablePath()));
 }
 
+TEST_F(DefaultEnvTest, LocalTempFilename) {
+  Env* env = Env::Default();
+  string filename;
+  EXPECT_TRUE(env->LocalTempFilename(&filename));
+  EXPECT_FALSE(env->FileExists(filename).ok());
+
+  // Write something to the temporary file.
+  std::unique_ptr<WritableFile> file_to_write;
+  TF_CHECK_OK(env->NewWritableFile(filename, &file_to_write));
+  TF_CHECK_OK(file_to_write->Append("Null"));
+  TF_CHECK_OK(file_to_write->Close());
+  TF_CHECK_OK(env->FileExists(filename));
+
+  // Read from the temporary file and check content.
+  std::unique_ptr<RandomAccessFile> file_to_read;
+  TF_CHECK_OK(env->NewRandomAccessFile(filename, &file_to_read));
+  StringPiece content;
+  char scratch[1024];
+  CHECK_EQ(error::OUT_OF_RANGE,
+           file_to_read->Read(0 /* offset */, 1024 /* n */, &content, scratch)
+               .code());
+  EXPECT_EQ("Null", content.ToString());
+
+  // Delete the temporary file.
+  TF_CHECK_OK(env->DeleteFile(filename));
+  EXPECT_FALSE(env->FileExists(filename).ok());
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/posix/env.cc b/tensorflow/core/platform/posix/env.cc
index 8cfb39ae18..ba3c4e7090 100644
--- a/tensorflow/core/platform/posix/env.cc
+++ b/tensorflow/core/platform/posix/env.cc
@@ -131,4 +131,39 @@ Env* Env::Default() {
 }
 #endif
 
+void Env::GetLocalTempDirectories(std::vector<string>* list) {
+  list->clear();
+  // Directories, in order of preference. If we find a dir that
+  // exists, we stop adding other less-preferred dirs
+  const char* candidates[] = {
+      // Non-null only during unittest/regtest
+      getenv("TEST_TMPDIR"),
+
+      // Explicitly-supplied temp dirs
+      getenv("TMPDIR"),
+      getenv("TMP"),
+
+      // If all else fails
+      "/tmp",
+  };
+
+  for (const char* d : candidates) {
+    if (!d || d[0] == '\0') continue;  // Empty env var
+
+    // Make sure we don't surprise anyone who's expecting a '/'
+    string dstr = d;
+    if (dstr[dstr.size() - 1] != '/') {
+      dstr += "/";
+    }
+
+    struct stat statbuf;
+    if (!stat(d, &statbuf) && S_ISDIR(statbuf.st_mode) &&
+        !access(dstr.c_str(), 0)) {
+      // We found a dir that exists and is accessible - we're done.
+      list->push_back(dstr);
+      return;
+    }
+  }
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/windows/env.cc b/tensorflow/core/platform/windows/env.cc
index fa3d6a8301..98fcf927ac 100644
--- a/tensorflow/core/platform/windows/env.cc
+++ b/tensorflow/core/platform/windows/env.cc
@@ -172,4 +172,21 @@ Env* Env::Default() {
   return default_env;
 }
 
+void Env::GetLocalTempDirectories(std::vector<string>* list) {
+  list->clear();
+  // On windows we'll try to find a directory in this order:
+  //   C:/Documents & Settings/whomever/TEMP (or whatever GetTempPath() is)
+  //   C:/TMP/
+  //   C:/TEMP/
+  //   C:/WINDOWS/ or C:/WINNT/
+  //   .
+  char tmp[MAX_PATH];
+  // GetTempPath can fail with either 0 or with a space requirement > bufsize.
+  // See http://msdn.microsoft.com/en-us/library/aa364992(v=vs.85).aspx
+  DWORD n = GetTempPathA(MAX_PATH, tmp);
+  if (n > 0 && n <= MAX_PATH) list->push_back(tmp);
+  list->push_back("C:\\tmp\\");
+  list->push_back("C:\\temp\\");
+}
+
 }  // namespace tensorflow
-- 
GitLab


From 0ca9c29ffef9c7ef6a05bd9d4965a93cded9b1b6 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 26 Jul 2017 11:39:27 -0700
Subject: [PATCH 1348/1375] Setup a resource manager and enable the use of
 multiple threads during folding

PiperOrigin-RevId: 163233960
---
 tensorflow/core/grappler/optimizers/constant_folding.cc | 5 ++++-
 tensorflow/core/grappler/optimizers/constant_folding.h  | 2 ++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 7f845bb9e2..72bbaa78af 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -59,7 +59,7 @@ class EigenThreadPoolWrapper : public Eigen::ThreadPoolInterface {
 class DeviceSimple : public DeviceBase {
  public:
   DeviceSimple() : DeviceBase(Env::Default()) {
-    eigen_worker_threads_.num_threads = 1;
+    eigen_worker_threads_.num_threads = port::NumSchedulableCPUs();
     eigen_worker_threads_.workers = new thread::ThreadPool(
         Env::Default(), "constant_folding", eigen_worker_threads_.num_threads);
     eigen_threadpool_wrapper_.reset(
@@ -101,6 +101,8 @@ string AsControlDependency(const NodeDef& node) {
 }  // namespace
 
 ConstantFolding::ConstantFolding() {
+  resource_mgr_.reset(new ResourceMgr());
+
   ops_to_preserve_ = std::regex(
       "Placeholder.*|Const|.*Save.*|.*Restore.*|.*Reader|"
       "Enter|RefEnter|Exit|RefExit|NextIteration|RefNextIteration|"
@@ -346,6 +348,7 @@ Status ConstantFolding::EvaluateNode(const NodeDef& node,
   params.frame_iter = FrameAndIter(0, 0);
   params.inputs = &inputs;
   params.op_kernel = op_kernel.get();
+  params.resource_manager = resource_mgr_.get();
 
   gtl::InlinedVector<AllocatorAttributes, 4> output_attrs;
   const int num_outputs = op_kernel->num_outputs();
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index 1c20233345..88475e4e75 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include <regex>
 #include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
 #include "tensorflow/core/grappler/utils.h"
@@ -70,6 +71,7 @@ class ConstantFolding : public GraphOptimizer {
   Status SimplifyGraph(GraphDef* output, const GraphProperties& properties);
 
   std::unique_ptr<DeviceBase> device_;
+  std::unique_ptr<ResourceMgr> resource_mgr_;
   GraphDef graph_;
   std::unique_ptr<NodeMap> node_map_;
   std::set<string> nodes_to_preserve_;
-- 
GitLab


From caca1c52e6c42e1b58ffee79e10fa4fde6f287ad Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 26 Jul 2017 11:39:42 -0700
Subject: [PATCH 1349/1375] Introduced a default setting for constant folding,
 currently set to OFF. Will be turned to on later on.

PiperOrigin-RevId: 163233994
---
 tensorflow/core/grappler/clusters/cluster.cc         |  5 ++++-
 .../core/grappler/optimizers/meta_optimizer.cc       |  7 ++++---
 tensorflow/core/protobuf/rewriter_config.proto       | 12 +++++++++++-
 3 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/grappler/clusters/cluster.cc b/tensorflow/core/grappler/clusters/cluster.cc
index 930f7d83be..bc16f6c633 100644
--- a/tensorflow/core/grappler/clusters/cluster.cc
+++ b/tensorflow/core/grappler/clusters/cluster.cc
@@ -55,12 +55,15 @@ void Cluster::DisableOptimizer(bool disable) {
         options_.config.mutable_graph_options()->mutable_rewrite_options();
     rewriter_config->set_optimize_tensor_layout(false);
     rewriter_config->set_disable_model_pruning(true);
-    rewriter_config->set_constant_folding(false);
+    rewriter_config->set_constant_folding(RewriterConfig::OFF);
     rewriter_config->set_memory_optimization(RewriterConfig::NO_MEM_OPT);
     rewriter_config->mutable_auto_parallel()->set_enable(false);
     rewriter_config->clear_optimizers();
   } else {
     options->set_opt_level(OptimizerOptions::L1);
+    auto rewriter_config =
+        options_.config.mutable_graph_options()->mutable_rewrite_options();
+    rewriter_config->set_constant_folding(RewriterConfig::DEFAULT);
   }
 }
 
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index f094b86ca7..9df34a3793 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -58,7 +58,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
     if (!cfg_.disable_model_pruning()) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(new ModelPruner()));
     }
-    if (cfg_.constant_folding()) {
+    if (cfg_.constant_folding() == RewriterConfig::ON) {
       optimizers.push_back(
           std::unique_ptr<GraphOptimizer>(new ConstantFolding()));
     }
@@ -121,8 +121,9 @@ void MetaOptimizer::Feedback(Cluster* cluster, const GrapplerItem& item,
 
 bool MetaOptimizerEnabled(const RewriterConfig& cfg) {
   return !cfg.disable_model_pruning() || cfg.optimize_tensor_layout() ||
-         cfg.constant_folding() || cfg.auto_parallel().enable() ||
-         cfg.memory_optimization() > 0 || !cfg.optimizers().empty();
+         cfg.constant_folding() == RewriterConfig::ON ||
+         cfg.auto_parallel().enable() || cfg.memory_optimization() > 0 ||
+         !cfg.optimizers().empty();
 }
 
 Status RunMetaOptimizer(const GrapplerItem& item, const RewriterConfig& cfg,
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 5480bdaad8..a9a1993bdf 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -19,9 +19,19 @@ message RewriterConfig {
   // configuration options do not apply to explicitly triggered optimization
   // passes in the optimizers field.
 
+  enum Toggle {
+    DEFAULT = 0;
+    ON = 1;
+    OFF = 2;
+  }
+
+  // Optimize tensor layouts
   bool optimize_tensor_layout = 1;
+  // Fold constants (default is OFF)
+  Toggle constant_folding = 3;
+
+  // If true, don't remove unecessary ops from the graph
   bool disable_model_pruning = 2;
-  bool constant_folding = 3;
 
   enum MemOptType {
     // Disabled in the meta-optimizer.
-- 
GitLab


From 51cf7d0954c15e628c35fa3cca6e2ba7587f72ea Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 26 Jul 2017 12:24:33 -0700
Subject: [PATCH 1350/1375] Updated the memory optimization config to introduce
 an explicit default value. This will make it possible change change the
 default behavior in the future by updating the meta optimizer code to
 interpret that default value differently (e.g we could assume default means
 heuristics). The default value remains OFF.

PiperOrigin-RevId: 163239483
---
 tensorflow/core/grappler/clusters/cluster.cc            | 1 +
 tensorflow/core/grappler/optimizers/memory_optimizer.cc | 2 +-
 tensorflow/core/grappler/optimizers/meta_optimizer.cc   | 4 ++--
 tensorflow/core/protobuf/rewriter_config.proto          | 8 +++++---
 4 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/grappler/clusters/cluster.cc b/tensorflow/core/grappler/clusters/cluster.cc
index bc16f6c633..3205d67517 100644
--- a/tensorflow/core/grappler/clusters/cluster.cc
+++ b/tensorflow/core/grappler/clusters/cluster.cc
@@ -64,6 +64,7 @@ void Cluster::DisableOptimizer(bool disable) {
     auto rewriter_config =
         options_.config.mutable_graph_options()->mutable_rewrite_options();
     rewriter_config->set_constant_folding(RewriterConfig::DEFAULT);
+    rewriter_config->set_memory_optimization(RewriterConfig::DEFAULT_MEM_OPT);
   }
 }
 
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 31e525fb4a..a0446bf566 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -447,7 +447,7 @@ void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
                  (cheap_to_recompute_ops.count(node.op()) > 0 ||
                   node.attr().count(kRecomputeHint) > 0);
         });
-  } else {  // optimization_level == RewriterConfig::MANUAL
+  } else if (optimization_level == RewriterConfig::MANUAL) {
     recomputed_subgraphs =
         GetOpGroupsToRecompute(graph, node_map, [&feeds](const NodeDef& node) {
           return !IsTargetOp(node) && feeds.count(node.name()) == 0 &&
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 9df34a3793..5527500af9 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -66,7 +66,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
       optimizers.push_back(
           std::unique_ptr<GraphOptimizer>(new LayoutOptimizer()));
     }
-    if (cfg_.memory_optimization() > 0) {
+    if (cfg_.memory_optimization() > 1) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
           new MemoryOptimizer(cfg_.memory_optimization())));
     }
@@ -122,7 +122,7 @@ void MetaOptimizer::Feedback(Cluster* cluster, const GrapplerItem& item,
 bool MetaOptimizerEnabled(const RewriterConfig& cfg) {
   return !cfg.disable_model_pruning() || cfg.optimize_tensor_layout() ||
          cfg.constant_folding() == RewriterConfig::ON ||
-         cfg.auto_parallel().enable() || cfg.memory_optimization() > 0 ||
+         cfg.auto_parallel().enable() || cfg.memory_optimization() > 1 ||
          !cfg.optimizers().empty();
 }
 
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index a9a1993bdf..1f9e2472a4 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -34,13 +34,15 @@ message RewriterConfig {
   bool disable_model_pruning = 2;
 
   enum MemOptType {
+    // The default setting (currently disabled)
+    DEFAULT_MEM_OPT = 0;
     // Disabled in the meta-optimizer.
-    NO_MEM_OPT = 0;
+    NO_MEM_OPT = 1;
     // Driven by manual op-level annotations.
-    MANUAL = 1;
+    MANUAL = 2;
     // Driven by heuristics. The behavior of these heuristics is subject to
     // change. Currently includes an experimental recomputation heuristic.
-    HEURISTICS = 2;
+    HEURISTICS = 3;
   }
   // Configures memory optimization passes through the meta-optimizer. Has no
   // effect on manually requested memory optimization passes in the optimizers
-- 
GitLab


From b873d8085e6bb05a69e4043fa74468f6014766af Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 26 Jul 2017 13:23:19 -0700
Subject: [PATCH 1351/1375] Fix arg lists for args and kwargs in docs.

PiperOrigin-RevId: 163246117
---
 tensorflow/tools/docs/parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py
index 18c3c98dc2..563e5be814 100644
--- a/tensorflow/tools/docs/parser.py
+++ b/tensorflow/tools/docs/parser.py
@@ -507,7 +507,7 @@ def _parse_function_details(docstring):
   pairs = list(_gen_pairs(parts[1:]))
 
   function_details = []
-  item_re = re.compile(r'^  (\w+):', re.MULTILINE)
+  item_re = re.compile(r'^  (\*?\*?\w+):', re.MULTILINE)
 
   for keyword, content in pairs:
     content = item_re.split(content)
-- 
GitLab


From 0c5858ca972946f218b2098b2d14f332bf801182 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 13:25:03 -0700
Subject: [PATCH 1352/1375] Reflow comments to avoid being too long.

PiperOrigin-RevId: 163246389
---
 tensorflow/python/training/input.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index a602438e0b..396ec11a02 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -1086,8 +1086,8 @@ def maybe_batch_join(tensors_list, keep_input, batch_size, capacity=32,
       added to the queue or not.  If it is a scalar and evaluates `True`, then
       `tensors` are all added to the queue. If it is a vector and `enqueue_many`
       is `True`, then each example is added to the queue only if the
-      corresponding value in `keep_input` is `True`. This tensor essentially acts
-      as a filtering mechanism.
+      corresponding value in `keep_input` is `True`. This tensor essentially
+      acts as a filtering mechanism.
     batch_size: An integer. The new batch size pulled from the queue.
     capacity: An integer. The maximum number of elements in the queue.
     enqueue_many: Whether each tensor in `tensor_list_list` is a single
@@ -1237,8 +1237,8 @@ def maybe_shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
       added to the queue or not.  If it is a scalar and evaluates `True`, then
       `tensors` are all added to the queue. If it is a vector and `enqueue_many`
       is `True`, then each example is added to the queue only if the
-      corresponding value in `keep_input` is `True`. This tensor essentially acts
-      as a filtering mechanism.
+      corresponding value in `keep_input` is `True`. This tensor essentially
+      acts as a filtering mechanism.
     num_threads: The number of threads enqueuing `tensor_list`.
     seed: Seed for the random shuffling within the queue.
     enqueue_many: Whether each tensor in `tensor_list` is a single example.
@@ -1379,8 +1379,8 @@ def maybe_shuffle_batch_join(tensors_list, batch_size, capacity,
       added to the queue or not.  If it is a scalar and evaluates `True`, then
       `tensors` are all added to the queue. If it is a vector and `enqueue_many`
       is `True`, then each example is added to the queue only if the
-      corresponding value in `keep_input` is `True`. This tensor essentially acts
-      as a filtering mechanism.
+      corresponding value in `keep_input` is `True`. This tensor essentially
+      acts as a filtering mechanism.
     seed: Seed for the random shuffling within the queue.
     enqueue_many: Whether each tensor in `tensor_list_list` is a single
       example.
-- 
GitLab


From 067deebb31e3b1213bd041cb4715967e1ecaf8f3 Mon Sep 17 00:00:00 2001
From: Eric Liu <ioeric@google.com>
Date: Wed, 26 Jul 2017 13:38:13 -0700
Subject: [PATCH 1353/1375] [tpu:profiler] Make duration of tracing changeable.

Make the default duration 2 seconds.
Also avoid dumping empty trace to log directory.

PiperOrigin-RevId: 163248374
---
 .../tpu/profiler/capture_tpu_profile.cc       | 30 +++++++++++++++----
 .../contrib/tpu/profiler/tpu_profiler.proto   |  3 +-
 2 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
index bda049f3f4..a510cb5346 100644
--- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
+++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc
@@ -66,8 +66,10 @@ void DumpTraceToLogDirectory(const tensorflow::string& logdir,
   LOG(INFO) << "Dumped trace data to " << path;
 }
 
-ProfileResponse Profile(const tensorflow::string& service_addr) {
+ProfileResponse Profile(const tensorflow::string& service_addr,
+                        int duration_ms) {
   ProfileRequest request;
+  request.set_duration_ms(duration_ms);
   ProfileResponse response;
   ClientContext context;
   ::grpc::ChannelArguments channel_args;
@@ -77,7 +79,7 @@ ProfileResponse Profile(const tensorflow::string& service_addr) {
   std::unique_ptr<TPUProfiler::Stub> stub =
       TPUProfiler::NewStub(::grpc::CreateCustomChannel(
           service_addr, ::grpc::InsecureChannelCredentials(), channel_args));
-  TF_CHECK_OK(FromGrpcStatus(stub->Profile(&context, request, &response)));
+  TF_QCHECK_OK(FromGrpcStatus(stub->Profile(&context, request, &response)));
   return response;
 }
 
@@ -88,12 +90,16 @@ ProfileResponse Profile(const tensorflow::string& service_addr) {
 int main(int argc, char** argv) {
   tensorflow::string FLAGS_service_addr;
   tensorflow::string FLAGS_logdir;
+  int FLAGS_duration_ms = 2000;
   std::vector<tensorflow::Flag> flag_list = {
       tensorflow::Flag("service_addr", &FLAGS_service_addr,
                        "Address of TPU profiler service e.g. localhost:8466"),
       tensorflow::Flag("logdir", &FLAGS_logdir,
                        "Path of TensorBoard log directory e.g. /tmp/tb_log"),
+      tensorflow::Flag("duration_ms", &FLAGS_duration_ms,
+                       "Duration of tracing in ms. Default is 2000ms."),
   };
+
   tensorflow::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
   bool parse_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
   if (!parse_ok || FLAGS_service_addr.empty() || FLAGS_logdir.empty()) {
@@ -101,9 +107,23 @@ int main(int argc, char** argv) {
     return 2;
   }
   tensorflow::port::InitMain(argv[0], &argc, &argv);
+
+  int duration_ms = FLAGS_duration_ms;
   tensorflow::ProfileResponse response =
-      tensorflow::tpu::Profile(FLAGS_service_addr);
+      tensorflow::tpu::Profile(FLAGS_service_addr, duration_ms);
   // Ignore computation_graph for now.
-  tensorflow::tpu::DumpTraceToLogDirectory(FLAGS_logdir,
-                                           response.encoded_trace());
+  if (response.encoded_trace().empty()) {
+    LOG(WARNING) << "No trace event is collected during the " << duration_ms
+                 << "ms interval.";
+  } else {
+    tensorflow::tpu::DumpTraceToLogDirectory(FLAGS_logdir,
+                                             response.encoded_trace());
+  }
+  // Print this at the end so that it's not buried in irrelevant LOG messages.
+  std::cout
+      << "NOTE: using the trace duration " << duration_ms << "ms." << std::endl
+      << "Set an appropriate duration (with --duration_ms) if you "
+         "don't see a full step in your trace or the captured trace is too "
+         "large."
+      << std::endl;
 }
diff --git a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto
index d12eccbf4b..0b24da7f1e 100644
--- a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto
+++ b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto
@@ -13,7 +13,8 @@ service TPUProfiler {
 
 message ProfileRequest {
   // In future, the caller will be able to customize when profiling starts and
-  // stops. For now, it always collects 10 seconds worth of data.
+  // stops. For now, it collects `duration_ms` milliseconds worth of data.
+  uint64 duration_ms = 1;
 
   // In future, the caller will indicate which TF session is being profiled, and
   // only data relating to that program will be returned. For now, we assume
-- 
GitLab


From 1f2cc7ccac64c14676776c23e8313a034cd66e74 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 13:44:38 -0700
Subject: [PATCH 1354/1375] Build fix for Android nightly build

PiperOrigin-RevId: 163249287
---
 tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
index 49a8d095d6..71bc4187b7 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc
@@ -437,7 +437,7 @@ TEST(GraphTransferer,
             << "with quantized input";
   CheckHexagonControllerVersion();
 
-  const IGraphTransferOpsDefinitions* ops_definitions =
+  const IRemoteFusedGraphOpsDefinitions* ops_definitions =
       &HexagonOpsDefinitions::getInstance();
   std::vector<std::pair<string, Tensor>> inputs;
   inputs.emplace_back("Mul", Tensor(DT_QUINT8, {1, WIDTH, HEIGHT, DEPTH}));
-- 
GitLab


From 4eb749185981692e6ed327e241f3f8ca91a5a08f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 13:51:36 -0700
Subject: [PATCH 1355/1375] [XLA] Refactor insert-ReducePrecisionInsertion-pass
 code into a separate function to simplify optimization functions.

PiperOrigin-RevId: 163250247
---
 tensorflow/compiler/xla/service/BUILD         |  1 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 20 +++++-----------
 .../compiler/xla/service/gpu/gpu_compiler.cc  | 23 +++++--------------
 .../xla/service/reduce_precision_insertion.cc | 14 +++++++++++
 .../xla/service/reduce_precision_insertion.h  |  8 +++++++
 5 files changed, 35 insertions(+), 31 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 8fb0faf026..0b85d6febc 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1946,6 +1946,7 @@ cc_library(
         ":buffer_liveness",
         ":hlo",
         ":hlo_pass",
+        ":hlo_pass_pipeline",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/core:lib",
     ],
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 59e8c75b91..f115236ee7 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -254,13 +254,9 @@ Status CpuCompiler::RunHloPasses(HloModule* module) {
   HloPassPipeline pipeline("CPU");
   pipeline.AddInvariantChecker<HloVerifier>();
 
-  for (const auto& reduce_precision_options :
-       module->config().debug_options().hlo_reduce_precision_options()) {
-    if (reduce_precision_options.pass_timing() ==
-        HloReducePrecisionOptions::BEFORE_OP_FUSION) {
-      pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
-    }
-  }
+  ReducePrecisionInsertion::AddPasses(
+      &pipeline, module->config().debug_options(),
+      HloReducePrecisionOptions::BEFORE_OP_FUSION);
 
   // TODO(b/35786417): Re-enable inliner pass after fixing the bug and deciding
   // where we will take this pass in future.
@@ -288,13 +284,9 @@ Status CpuCompiler::RunHloPasses(HloModule* module) {
   pipeline.AddPass<HloCSE>(/*is_layout_sensitive=*/false);
   pipeline.AddPass<CpuInstructionFusion>();
 
-  for (const auto& reduce_precision_options :
-       module->config().debug_options().hlo_reduce_precision_options()) {
-    if (reduce_precision_options.pass_timing() ==
-        HloReducePrecisionOptions::AFTER_OP_FUSION) {
-      pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
-    }
-  }
+  ReducePrecisionInsertion::AddPasses(
+      &pipeline, module->config().debug_options(),
+      HloReducePrecisionOptions::AFTER_OP_FUSION);
 
   pipeline.AddPass<CpuLayoutAssignment>(
       module->mutable_entry_computation_layout());
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 2acf95084a..031ecbd3ae 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -124,15 +124,9 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
   {
     HloPassPipeline pipeline("optimization");
     pipeline.AddInvariantChecker<HloVerifier>();
-
-    for (const auto& reduce_precision_options :
-         hlo_module->config().debug_options().hlo_reduce_precision_options()) {
-      if (reduce_precision_options.pass_timing() ==
-          HloReducePrecisionOptions::BEFORE_OP_FUSION) {
-        pipeline.AddPass<ReducePrecisionInsertion>(reduce_precision_options);
-      }
-    }
-
+    ReducePrecisionInsertion::AddPasses(
+        &pipeline, hlo_module->config().debug_options(),
+        HloReducePrecisionOptions::BEFORE_OP_FUSION);
     {
       auto& pass =
           pipeline.AddPass<HloPassFix<HloPassPipeline>>("simplification");
@@ -162,14 +156,9 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
     TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status());
 
     HloPassPipeline reduce_pipeline("reduce-precision");
-    for (const auto& reduce_precision_options :
-         hlo_module->config().debug_options().hlo_reduce_precision_options()) {
-      if (reduce_precision_options.pass_timing() ==
-          HloReducePrecisionOptions::AFTER_OP_FUSION) {
-        reduce_pipeline.AddPass<ReducePrecisionInsertion>(
-            reduce_precision_options);
-      }
-    }
+    ReducePrecisionInsertion::AddPasses(
+        &reduce_pipeline, hlo_module->config().debug_options(),
+        HloReducePrecisionOptions::AFTER_OP_FUSION);
     StatusOr<bool> reduce_result = reduce_pipeline.Run(hlo_module);
     TF_RETURN_IF_ERROR(reduce_result.status());
 
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
index 9f12471ffd..a3079a0c03 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
@@ -92,4 +92,18 @@ HloReducePrecisionOptions ReducePrecisionInsertion::make_options_proto(
   return options;
 }
 
+bool ReducePrecisionInsertion::AddPasses(
+    HloPassPipeline* pipeline, const DebugOptions& debug_options,
+    const HloReducePrecisionOptions::PassTiming pass_timing) {
+  bool passes_added = false;
+  for (const auto& pass_options :
+       debug_options.hlo_reduce_precision_options()) {
+    if (pass_options.pass_timing() == pass_timing) {
+      pipeline->AddPass<ReducePrecisionInsertion>(pass_options);
+      passes_added = true;
+    }
+  }
+  return passes_added;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.h b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
index 34b865b9ce..a6fcee0039 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.h
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.h
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
 
 namespace xla {
@@ -72,6 +73,13 @@ class ReducePrecisionInsertion : public HloPassInterface {
       const int exponent_bits, const int mantissa_bits,
       const OpcodeFilterFunction& should_reduce_output_precision);
 
+  // Add ReducePrecisionInsertion passes to an HloPassPipeline based on the list
+  // of HloReducePrecisionOptions in a DebugOptions proto.  Returns true if any
+  // passes were added.
+  static bool AddPasses(
+      HloPassPipeline* pipeline, const DebugOptions& debug_options,
+      const HloReducePrecisionOptions::PassTiming pass_timing);
+
  private:
   // Parameters for the precision reduction to be added.
   const int exponent_bits_;
-- 
GitLab


From f1537588d4983dfdabfdd82442264a9d1a702d2f Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Wed, 26 Jul 2017 15:13:16 -0700
Subject: [PATCH 1356/1375] Set device for nodes added by LayoutOptimizer.

PiperOrigin-RevId: 163263257
---
 .../grappler/optimizers/layout_optimizer.cc   | 55 +++++++++++--------
 1 file changed, 32 insertions(+), 23 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 55da0f710d..5a4e1b76e2 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -238,6 +238,7 @@ class NodeProcessor {
     *node->add_input() = input_name;
     *node->add_input() = NHWCToNCHW ? kPermNHWCToNCHW : kPermNCHWToNHWC;
     node->set_op("Transpose");
+    node->set_device(node_->device());
     AttrValue attr_data_type;
     attr_data_type.set_type(data_type);
     node->mutable_attr()->insert({"T", attr_data_type});
@@ -273,11 +274,10 @@ class NodeProcessor {
       int output_pos = NodePosition(node_->input(pos));
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "T"));
       TF_RETURN_IF_ERROR(HasAttribute(*input_node, "_output_shapes"));
-      NodeDef* transpose = AddNodeTranspose(
+      AddNodeTranspose(
           node_name, node_->input(pos), node_->attr().at("T").type(),
           input_node->attr().at("_output_shapes").list().shape(output_pos),
           true);
-      transpose->set_device(node_->device());
       node_map_->UpdateOutput(node_->input(pos), node_->name(), node_name);
       node_map_->AddOutput(node_name, node_->name());
       *node_->mutable_input(pos) = node_name;
@@ -313,10 +313,9 @@ class NodeProcessor {
       }
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "T"));
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "_output_shapes"));
-      NodeDef* transpose = AddNodeTranspose(
-          node_name, node_->name(), node_->attr().at("T").type(),
-          node_->attr().at("_output_shapes").list().shape(0), false);
-      transpose->set_device(node_->device());
+      AddNodeTranspose(node_name, node_->name(), node_->attr().at("T").type(),
+                       node_->attr().at("_output_shapes").list().shape(0),
+                       false);
       *it = node_name;
       node_map_->UpdateOutput(node_->name(), output->name(), node_name);
       node_map_->AddOutput(node_name, output->name());
@@ -604,6 +603,7 @@ class BinaryOpProcessor : public AgnosticNodeProcessor {
     node_map_->AddNode(name, node);
     node->set_name(name);
     node->set_op("Const");
+    node->set_device(node_->device());
     AttrValue attr_data_type;
     attr_data_type.set_type(DT_INT32);
     node->mutable_attr()->insert({"dtype", attr_data_type});
@@ -628,6 +628,7 @@ class BinaryOpProcessor : public AgnosticNodeProcessor {
     *node->add_input() = input_name;
     *node->add_input() = shape_const_node_name;
     node->set_op("Reshape");
+    node->set_device(node_->device());
 
     AttrValue attr_type_indices;
     attr_type_indices.set_type(DT_INT32);
@@ -650,13 +651,10 @@ class BinaryOpProcessor : public AgnosticNodeProcessor {
       TF_RETURN_IF_ERROR(HasAttribute(*input_node, "_output_shapes"));
       int vector_size =
           input_node->attr().at("_output_shapes").list().shape(0).dim(0).size();
-      NodeDef* shp = AddNodeShapeConst(shape_const_node_name, vector_size);
-      shp->set_device("/job:localhost/replica:0/task:0/cpu:0");
+      AddNodeShapeConst(shape_const_node_name, vector_size);
       TF_RETURN_IF_ERROR(HasAttribute(*node_, "T"));
-      NodeDef* reshape =
-          AddNodeReshape(reshape_node_name, node_->input(1),
-                         shape_const_node_name, node_->attr().at("T").type());
-      reshape->set_device(node_->device());
+      AddNodeReshape(reshape_node_name, node_->input(1), shape_const_node_name,
+                     node_->attr().at("T").type());
       node_map_->AddOutput(shape_const_node_name, reshape_node_name);
       node_map_->UpdateOutput(node_->input(1), node_->name(),
                               reshape_node_name);
@@ -953,8 +951,12 @@ struct TuningConfig {
 
 class DataLayoutOptimizer {
  public:
-  explicit DataLayoutOptimizer(GraphDef* graph, TuningConfig config)
-      : graph_(graph), node_map_(graph_), config_(config) {}
+  explicit DataLayoutOptimizer(const string& default_device, GraphDef* graph,
+                               TuningConfig config)
+      : default_device_(default_device),
+        graph_(graph),
+        node_map_(graph_),
+        config_(config) {}
 
   Status Optimize() {
     LOG(INFO) << "Number of nodes for original graph: " << graph_->node_size();
@@ -972,6 +974,7 @@ class DataLayoutOptimizer {
     node_map_.AddNode(name, node);
     node->set_name(name);
     node->set_op("Const");
+    node->set_device(default_device_);
     AttrValue attr_data_type;
     attr_data_type.set_type(DT_INT32);
     node->mutable_attr()->insert({"dtype", attr_data_type});
@@ -990,6 +993,7 @@ class DataLayoutOptimizer {
     node_map_.AddNode(name, node);
     node->set_name(name);
     node->set_op("Const");
+    node->set_device(default_device_);
     AttrValue attr_data_type;
     attr_data_type.set_type(dtype);
     node->mutable_attr()->insert({"dtype", attr_data_type});
@@ -1014,6 +1018,7 @@ class DataLayoutOptimizer {
     node_map_.AddNode(kReductionConst, node);
     node->set_name(kReductionConst);
     node->set_op("Const");
+    node->set_device(default_device_);
     AttrValue attr_data_type;
     attr_data_type.set_type(DT_INT32);
     node->mutable_attr()->insert({"dtype", attr_data_type});
@@ -1072,15 +1077,10 @@ class DataLayoutOptimizer {
     // expanded.
     if (graph_->node_size() > node_size_original) {
       NodeDef* n = AddNodePermConst(kPermNHWCToNCHW, {0, 3, 1, 2});
-      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       n = AddNodePermConst(kPermNCHWToNHWC, {0, 2, 3, 1});
-      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       n = AddNodeConcatConst();
-      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       n = AddGatherAxisConst();
-      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       n = AddNodeReductionConst();
-      n->set_device("/job:localhost/replica:0/task:0/cpu:0");
       std::set<string> ops_format_agnostic = GetOpsFormatAgnostic();
       for (int i = 0; i < graph_->node_size(); i++) {
         if (ops_format_agnostic.find(graph_->node(i).op()) !=
@@ -1169,6 +1169,7 @@ class DataLayoutOptimizer {
     return Status::OK();
   }
 
+  string default_device_;
   GraphDef* graph_;
   NodeMap node_map_;
   TuningConfig config_;
@@ -1221,16 +1222,24 @@ Status LayoutOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
   *output = new_item.graph;
   TuningConfig config;
   config.no_gemm = false;
-  DataLayoutOptimizer layout_optimizer(output, config);
-  status = layout_optimizer.Optimize();
+  string default_device = "/job:localhost/replica:0/task:0/cpu:0";
+  if (cluster) {
+    if (!cluster->GetDevices().empty()) {
+      default_device = cluster->GetDevices().begin()->first;
+    }
+  }
+  std::unique_ptr<DataLayoutOptimizer> layout_optimizer(
+      new DataLayoutOptimizer(default_device, output, config));
+  status = layout_optimizer->Optimize();
   // This is based on an empirical observation that if the introduced Transpose
   // nodes is more than 30, not using GEMM implementation would result in better
   // performance.
   if (status.ok() && GetNumTranspose(*output) > 30) {
     *output = new_item.graph;
     config.no_gemm = true;
-    DataLayoutOptimizer layout_optimizer(output, config);
-    status = layout_optimizer.Optimize();
+    layout_optimizer.reset(
+        new DataLayoutOptimizer(default_device, output, config));
+    status = layout_optimizer->Optimize();
   }
 
   if (!status.ok()) {
-- 
GitLab


From 1bad826d6f94eb2f9b619badf3e9c2d42e370e9c Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Wed, 26 Jul 2017 15:27:52 -0700
Subject: [PATCH 1357/1375] Rollback of GPU kernel implementation of transpose
 for tensors with one small dimension. END_PUBLIC

BEGIN_PUBLIC
Automated g4 rollback of changelist 162525519

PiperOrigin-RevId: 163265445
---
 tensorflow/core/kernels/conv_ops_gpu_3.cu.cc  | 127 +---------------
 tensorflow/python/BUILD                       |  20 ---
 .../python/kernel_tests/transpose_op_test.py  |  20 ---
 tensorflow/python/ops/conv2d_benchmark.py     | 141 ------------------
 tensorflow/python/ops/transpose_benchmark.py  |  60 +++-----
 5 files changed, 23 insertions(+), 345 deletions(-)
 delete mode 100644 tensorflow/python/ops/conv2d_benchmark.py

diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
index bcabda848c..2307c2de0e 100644
--- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
+++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
@@ -272,87 +272,6 @@ __global__ void SwapDimension1And2InTensor3UsingTiles(const T* input,
   }
 }
 
-// Use shared memory tiles to swap dimension-1 and dimension-2 of a 3D tensor
-// when only one of the dimension sizes is smaller than 16,
-// where dimensions are zero-based: output[i][j][k] = input[i][k][j].
-//
-// small_dim = the_smaller_dimension_size
-// large_dim = the_larger_dimension_size
-// tile_num_per_block = blockDim.x
-// kTileLength = small_dim
-//
-// Each thread block operates on a single rectangle tile, where its width is
-// kTileLength (we currently set it to 64) and its height is small_dim,
-// We set the thread block's X dimension to be tile_num_per_block, and its Y
-// and Z to be one.
-template <typename T, bool SmallDim2>
-__global__ void SwapDimension1And2InTensor3SmallDim(const T* input,
-                                                    int batch_per_block,
-                                                    Dimension<3> input_dims,
-                                                    T* output) {
-  // TODO(yangzihao) avoid share memory bank conflict.
-  extern __shared__ __align__(sizeof(T)) unsigned char shmem[];
-  T* shared_memory_tile = reinterpret_cast<T*>(shmem);
-
-  eigen_assert(blockDim.y == 1);
-  eigen_assert(blockDim.z == 1);
-  eigen_assert(gridDim.z == 1);
-
-  int block_offset = blockIdx.x * blockDim.x * batch_per_block;
-
-  int x = threadIdx.x;
-  int tile_height = blockDim.x;
-
-  // Get tile height, width, and thread/block origin indices.
-  int small_dim = SmallDim2 ? input_dims[2] : input_dims[1];
-  int large_dim = SmallDim2 ? input_dims[1] : input_dims[2];
-
-  int global_offset = small_dim * large_dim * (blockIdx.y * batch_per_block) +
-                      (SmallDim2 ? block_offset * small_dim : block_offset) + x;
-  if (global_offset > (input_dims[0] * input_dims[1] * input_dims[2])) return;
-
-  for (int batch = 0; batch < batch_per_block; ++batch) {
-    int block_origin_idx =
-        small_dim * large_dim * (blockIdx.y * batch_per_block + batch);
-    int thread_origin_idx =
-        block_origin_idx +
-        (SmallDim2 ? block_offset * small_dim : block_offset) + x;
-
-    if (block_offset + blockDim.x > large_dim) {
-      tile_height = large_dim - block_offset;
-    }
-
-    // Load a continuous memory region to shared memory tile.
-    if (x < tile_height) {
-      for (int y = 0; y < small_dim; y++) {
-        int shmem_index =
-            SmallDim2 ? (x + y * tile_height) : (x * small_dim + y);
-        shared_memory_tile[shmem_index] =
-            ldg(input + thread_origin_idx +
-                y * (SmallDim2 ? tile_height : large_dim));
-      }
-    }
-
-    __syncthreads();
-
-    // Get block origin index for output array.
-    int output_block_offset = block_origin_idx;
-    int output_block_idx = SmallDim2 ? block_offset : block_offset * small_dim;
-    int output_block_origin_idx = output_block_offset + output_block_idx;
-
-    // Store the tranposed memory region in shared memory to device.
-    if (x < tile_height) {
-      for (int y = 0; y < small_dim; y++) {
-        int output_idx = output_block_origin_idx + x +
-                         y * (SmallDim2 ? large_dim : tile_height);
-        int shmem_index =
-            SmallDim2 ? (x * small_dim + y) : (x + y * tile_height);
-        output[output_idx] = shared_memory_tile[shmem_index];
-      }
-    }
-  }
-}
-
 // A Cuda custom kernel that convert input to output, given proper padding on
 // the left and the top. The padded value is zero.
 template <typename T, int NDIMS>
@@ -501,63 +420,25 @@ template <typename T>
 void RunSwapDimension1And2InTensor3(const GPUDevice& d, const T* input,
                                     const Dimension<3>& input_dims, T* output) {
   // If both dimensions are not trivial, use tiles for the actual swapping.
-  // If one dimension is trivial, use SmallDim kernel for swapping.
   // Otherwise, the trivial swapping relying on the ldg cache is more efficient.
   static const int kMinDimensionToUseTiles = 16;
   bool use_tiles = (input_dims[1] >= kMinDimensionToUseTiles &&
                     input_dims[2] >= kMinDimensionToUseTiles);
-  bool use_small_dim = ((input_dims[1] >= kMinDimensionToUseTiles &&
-                         input_dims[2] < kMinDimensionToUseTiles)) ||
-                       ((input_dims[1] < kMinDimensionToUseTiles &&
-                         input_dims[2] >= kMinDimensionToUseTiles));
-  static const int NumSubTiles = 8;
-
   if (use_tiles) {
+    // We get best performance when TileSize is the number of threads in a warp
+    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
+    // threads.
     static const int TileSize = 32;
+    static const int NumSubTiles = 8;
     Dimension<3> input_dims_in_tiles = {
         input_dims[0], (input_dims[1] + TileSize - 1) / TileSize,
         (input_dims[2] + TileSize - 1) / TileSize,
     };
     int total_tiles_count = input_dims_in_tiles[0] * input_dims_in_tiles[1] *
                             input_dims_in_tiles[2];
-    // We get best performance when TileSize is the number of threads in a warp
-    // (32 on our GPUs) and NumSubTiles is 8, so our block size is 8 * 32 = 256
-    // threads.
     SwapDimension1And2InTensor3UsingTiles<T, TileSize, NumSubTiles><<<
         total_tiles_count, dim3(TileSize, NumSubTiles), 0, d.stream()>>>(
         input, input_dims, output);
-  } else if (use_small_dim) {
-    // When only one of the dimensions is smaller than kMinDimensionToUseTiles,
-    // we use one block to process a rectangle region with the size of
-    // kTileLength * small_dim. We found that when set kTileLength to 64 on
-    // TitanX Maxwell GPU, it achieves the best performance.
-    //              large_dim
-    //            +---------------...--------+
-    //            |            |        |    |
-    // small_dim  |            |  ...   |    |
-    //            |            |        |    |
-    //            +--------------...---------+
-    //            \----- ------/         \- -/
-    //                  V                  V
-    //    kTileLength(tile_height)    tile_height
-    static const int kTileLength = 64;
-    static const int kGridDimY = 2048;
-    int small_dim = std::min(input_dims[2], input_dims[1]);
-    int large_dim = std::max(input_dims[2], input_dims[1]);
-    int tile_num_per_block = (large_dim + kTileLength - 1) / kTileLength;
-    int grid_dim_y = input_dims[0] < kGridDimY ? input_dims[0] : kGridDimY;
-    int batch_per_block = (input_dims[0] + grid_dim_y - 1) / grid_dim_y;
-    if (input_dims[2] < input_dims[1]) {
-      SwapDimension1And2InTensor3SmallDim<T, true>
-          <<<dim3(tile_num_per_block, grid_dim_y), kTileLength,
-             kTileLength * small_dim * sizeof(T), d.stream()>>>(
-              input, batch_per_block, input_dims, output);
-    } else {
-      SwapDimension1And2InTensor3SmallDim<T, false>
-          <<<dim3(tile_num_per_block, grid_dim_y), kTileLength,
-             kTileLength * small_dim * sizeof(T), d.stream()>>>(
-              input, batch_per_block, input_dims, output);
-    }
   } else {
     int total_element_count = input_dims[0] * input_dims[1] * input_dims[2];
     CudaLaunchConfig config = GetCudaLaunchConfig(total_element_count, d);
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 1fb860a9ee..80e1fd5cda 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3799,26 +3799,6 @@ cuda_py_test(
     main = "ops/concat_benchmark.py",
 )
 
-cuda_py_test(
-    name = "conv2d_benchmark",
-    size = "large",
-    srcs = ["ops/conv2d_benchmark.py"],
-    additional_deps = [
-        ":client",
-        ":client_testlib",
-        ":control_flow_ops",
-        ":framework_for_generated_wrappers",
-        ":nn_ops",
-        ":platform",
-        ":platform_benchmark",
-        ":random_ops",
-        ":variables",
-        "//third_party/py/numpy",
-        "//tensorflow/core:protos_all_py",
-    ],
-    main = "ops/conv2d_benchmark.py",
-)
-
 cuda_py_test(
     name = "split_benchmark",
     srcs = ["ops/split_benchmark.py"],
diff --git a/tensorflow/python/kernel_tests/transpose_op_test.py b/tensorflow/python/kernel_tests/transpose_op_test.py
index 67bdb4237d..570fa79944 100644
--- a/tensorflow/python/kernel_tests/transpose_op_test.py
+++ b/tensorflow/python/kernel_tests/transpose_op_test.py
@@ -229,26 +229,6 @@ class TransposeTest(test.TestCase):
         self.assertAllEqual(np_ans, tf_ans)
         self.assertShapeEqual(np_ans, y)
 
-  def testLargeSizeGPU(self):
-    # If no GPU available, skip the test
-    if not test.is_gpu_available(cuda_only=True):
-      return
-
-    large_shapes = [[1000000, 31, 3], [3, 1000000, 31], [3, 31, 1000000],
-                    [2, 1000, 1000], [1000, 2, 1000], [1000, 1000, 2]]
-    perms = [[0, 2, 1]] * 6
-
-    for input_shape, perm in zip(large_shapes, perms):
-      total_size = np.prod(input_shape)
-      inp = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_shape)
-      np_ans = self._np_transpose(inp, perm)
-      with self.test_session(use_gpu=True):
-        inx = ops.convert_to_tensor(inp)
-        y = array_ops.transpose(inx, perm)
-        tf_ans = y.eval()
-      self.assertAllEqual(np_ans, tf_ans)
-      self.assertShapeEqual(np_ans, y)
-
   def testNop(self):
     self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
 
diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py
deleted file mode 100644
index 6992fa57ea..0000000000
--- a/tensorflow/python/ops/conv2d_benchmark.py
+++ /dev/null
@@ -1,141 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Benchmark for Conv2D op."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import itertools
-import time
-
-from tensorflow.python.client import session as session_lib
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import nn_ops
-from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import variables
-from tensorflow.python.platform import test
-
-
-def build_graph(device, input_shape, filter_shape, strides, padding, num_iters):
-  """builds a graph containing a sequence of conv2d operations.
-
-  Args:
-    device: String, the device to run on.
-    input_shape: Shape of the input tensor.
-    filter_shape: Shape of the filter tensor.
-    strides: A list of ints. 1-D of length 4. The stride of sliding
-             window for each dimension of input.
-    padding: A string from: "SAME", "VALID". The type of padding
-             algorithm to use.
-    num_iters: number of iterations to run conv2d.
-
-  Returns:
-    An array of tensors to run()
-  """
-  with ops.device("/%s:0" % device):
-    inp = variables.Variable(random_ops.truncated_normal(input_shape))
-    filt = variables.Variable(random_ops.truncated_normal(filter_shape))
-
-    outputs = []
-    conv2d_op = nn_ops.conv2d(inp, filt, strides, padding, data_format="NHWC")
-    outputs.append(conv2d_op)
-    for _ in range(1, num_iters):
-      with ops.control_dependencies([conv2d_op]):
-        conv2d_op = nn_ops.conv2d(
-            inp, filt, strides, padding, data_format="NHWC")
-        outputs.append(conv2d_op)
-    return control_flow_ops.group(*outputs)
-
-
-class Conv2DBenchmark(test.Benchmark):
-  """Benchmark conv2d!"""
-
-  def _run_graph(self, device, input_shape, filter_shape, strides, padding,
-                 num_iters):
-    """runs the graph and print its execution time.
-
-    Args:
-      device: String, the device to run on.
-      input_shape: Shape of the input tensor.
-      filter_shape: Shape of the filter tensor.
-      strides: A list of ints. 1-D of length 4. The stride of sliding
-               window for each dimension of input.
-      padding: A string from: "SAME", "VALID". The type of padding
-               algorithm to use.  num_iters: Number of iterations to run the
-                 benchmark.
-      num_iters: number of iterations to run conv2d.
-
-    Returns:
-      The duration of the run in seconds.
-    """
-    graph = ops.Graph()
-    with graph.as_default():
-      outputs = build_graph(device, input_shape, filter_shape, strides, padding,
-                            num_iters)
-      with session_lib.Session(graph=graph) as session:
-        variables.global_variables_initializer().run()
-        # warmup runs
-        session.run(outputs)
-
-        start_time = time.time()
-        session.run(outputs)
-        duration = (time.time() - start_time) / num_iters
-
-        print("%s inputshape:%s filtershape:%s strides:%s padding:%s "
-              "%d iters: %.8f sec" %
-              (device, str(input_shape).replace(" ", ""),
-               str(filter_shape).replace(" ", ""),
-               str(strides).replace(" ", ""), padding, num_iters, duration))
-
-    name_template = (
-        "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_"
-        "strides_{strides}_padding_{padding}")
-
-    self.report_benchmark(
-        name=name_template.format(
-            device=device,
-            inputshape=str(input_shape).replace(" ", ""),
-            filtershape=str(filter_shape).replace(" ", ""),
-            strides=str(strides).replace(" ", ""),
-            padding=padding).replace(" ", ""),
-        iters=num_iters,
-        wall_time=duration / num_iters)
-
-    return duration
-
-  def benchmark_conv2d(self):
-    print("conv2d benchmark:")
-
-    h = 500
-    w = 500
-    fh = 3
-    fw = 3
-    input_shapes = []
-    filter_shapes = []
-    for b, c in itertools.product([4, 16, 32], [i for i in range(3, 16)]):
-      input_shapes += [[b, h, w, c]]
-      filter_shapes += [[fh, fw, c, b]]
-    strides = [[1, 2, 2, 1]]
-    paddings = ["VALID", "SAME"]
-    for ishape, fshape in zip(input_shapes, filter_shapes):
-      for stride in strides:
-        for padding in paddings:
-          self._run_graph("gpu", ishape, fshape, stride, padding, 80)
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/python/ops/transpose_benchmark.py b/tensorflow/python/ops/transpose_benchmark.py
index cddefacf2e..6047f4ae93 100644
--- a/tensorflow/python/ops/transpose_benchmark.py
+++ b/tensorflow/python/ops/transpose_benchmark.py
@@ -1,4 +1,4 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -32,7 +32,7 @@ from tensorflow.python.platform import test
 
 
 def build_graph(device, input_shape, perm, datatype, num_iters):
-  """builds a graph containing a sequence of conv2d operations.
+  """Build a graph containing a sequence of conv2d operations.
 
   Args:
     device: String, the device to run on.
@@ -50,12 +50,10 @@ def build_graph(device, input_shape, perm, datatype, num_iters):
     t = constant_op.constant(inp, shape=input_shape)
 
     outputs = []
-    transpose_op = array_ops.transpose(t, perm)
-    outputs.append(transpose_op)
-    for _ in range(1, num_iters):
-      with ops.control_dependencies([transpose_op]):
-        transpose_op = array_ops.transpose(t, perm)
-        outputs.append(transpose_op)
+    outputs.append(array_ops.transpose(t, perm))
+    for i in range(1, num_iters):
+      with ops.control_dependencies([outputs[i - 1]]):
+        outputs.append(array_ops.transpose(t, perm))
     return control_flow_ops.group(*outputs)
 
 
@@ -63,7 +61,7 @@ class TransposeBenchmark(test.Benchmark):
   """Benchmark transpose!"""
 
   def _run_graph(self, device, input_shape, perm, num_iters, datatype):
-    """runs the graph and print its execution time.
+    """Run the graph and print its execution time.
 
     Args:
       device: String, the device to run on.
@@ -84,11 +82,9 @@ class TransposeBenchmark(test.Benchmark):
         session.run(outputs)
         start_time = time.time()
         session.run(outputs)
-
         duration = (time.time() - start_time) / num_iters
         throughput = np.prod(
             np.array(input_shape)) * datatype().itemsize * 2 / duration / 1e9
-
         print("%s %s inputshape:%s perm:%s %d %.6fsec, %.4fGB/s." %
               (device, str(datatype), str(input_shape).replace(" ", ""),
                str(perm).replace(" ", ""), num_iters, duration, throughput))
@@ -112,19 +108,19 @@ class TransposeBenchmark(test.Benchmark):
 
     datatypes = [np.complex128, np.float64, np.float32, np.float16, np.int8]
 
-    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2
-    small_shapes += [[2, 100, 100, 16], [2, 16, 100, 100]] * 2
-    small_shapes += [[2, 5000, 16], [2, 16, 5000]] * 2
-    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
-    small_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
-    small_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2 + [[
+        2, 100, 100, 16
+    ], [2, 16, 100, 100]] * 2 + [[2, 5000, 16], [2, 16, 5000]] * 2
+    small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
+        [0, 3, 1, 2], [0, 2, 3, 1]
+    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
-    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2
-    large_shapes += [[2, 1000, 1000, 32], [2, 1000, 1000, 64]] * 2
-    large_shapes += [[2, 1000000, 32], [2, 1000000, 64]] * 2
-    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
-    large_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
-    large_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
+    large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2 + [[
+        2, 1000, 1000, 32
+    ], [2, 1000, 1000, 64]] * 2 + [[2, 1000000, 32], [2, 1000000, 64]] * 2
+    large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
+        [0, 3, 1, 2], [0, 2, 3, 1]
+    ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
 
     huge_shapes = [[2, 100, 100, 100, 128], [2, 1000, 1000, 128],
                    [2, 1000000, 128]] * 2
@@ -147,23 +143,5 @@ class TransposeBenchmark(test.Benchmark):
             for ishape, perm in zip(huge_shapes, huge_perms):
               self._run_graph("gpu", ishape, perm, num_iters, datatype)
 
-    small_dim_large_shapes = [[2, 1000000, 3], [2, 3, 1000000], [2, 1000000, 8],
-                              [2, 8, 1000000]]
-    small_dim_small_shapes = [[2, 5000, 3], [2, 3, 5000], [2, 5000, 8],
-                              [2, 8, 5000]]
-    small_dim_perms = [[0, 2, 1]] * 4
-
-    num_iters = 320
-    small_dim_large_shape_datatypes = [np.float64, np.float32, np.int8]
-    for datatype in small_dim_large_shape_datatypes:
-      for ishape, perm in zip(small_dim_large_shapes, small_dim_perms):
-        self._run_graph("gpu", ishape, perm, num_iters, datatype)
-
-    small_dim_small_shape_datatypes = [np.complex128, np.float16]
-    for datatype in small_dim_small_shape_datatypes:
-      for ishape, perm in zip(small_dim_small_shapes, small_dim_perms):
-        self._run_graph("gpu", ishape, perm, num_iters, datatype)
-
-
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 08790e73d1d83bbb48d73d6f358466a9c75b06a9 Mon Sep 17 00:00:00 2001
From: Chris Leary <leary@google.com>
Date: Wed, 26 Jul 2017 15:28:52 -0700
Subject: [PATCH 1358/1375] [XLA] Fix a bug in cloning outfeeds, carried the
 wrong shape.

PiperOrigin-RevId: 163265592
---
 .../compiler/xla/service/hlo_instruction.cc   |  2 +-
 .../xla/service/hlo_instruction_test.cc       | 21 +++++++++++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index ed8a942d03..c11fea09d1 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -912,7 +912,7 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       return CreateInfeed(shape, infeed_config());
     case HloOpcode::kOutfeed:
       CHECK_EQ(new_operands.size(), 1);
-      return CreateOutfeed(shape, new_operands[0], outfeed_config());
+      return CreateOutfeed(outfeed_shape_, new_operands[0], outfeed_config());
     case HloOpcode::kBatchNormGrad:
       CHECK_EQ(new_operands.size(), 5);
       return CreateBatchNormGrad(shape, new_operands[0], new_operands[1],
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index 5951c833db..ced8417fce 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -638,6 +638,27 @@ TEST_F(HloInstructionTest, PreserveMetadataInFusionAndClone) {
       metadata, fusion->fused_expression_root()->operand(0)->metadata()));
 }
 
+TEST_F(HloInstructionTest, PreserveOutfeedShapeThroughClone) {
+  HloComputation::Builder builder(TestName());
+  auto constant = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR2<float>({
+          {1, 2},
+          {3, 4},
+      })));
+  auto shape10 = ShapeUtil::MakeShapeWithLayout(F32, {2, 3}, {1, 0});
+  auto shape01 = ShapeUtil::MakeShapeWithLayout(F32, {2, 3}, {0, 1});
+  auto outfeed10 = builder.AddInstruction(
+      HloInstruction::CreateOutfeed(shape10, constant, ""));
+  auto outfeed01 = builder.AddInstruction(
+      HloInstruction::CreateOutfeed(shape01, constant, ""));
+
+  auto clone01 = builder.AddInstruction(outfeed01->Clone());
+  auto clone10 = builder.AddInstruction(outfeed10->Clone());
+
+  EXPECT_TRUE(ShapeUtil::Equal(clone01->outfeed_shape(), shape01));
+  EXPECT_TRUE(ShapeUtil::Equal(clone10->outfeed_shape(), shape10));
+}
+
 TEST_F(HloInstructionTest, FusionOpWithCalledComputations) {
   HloComputation::Builder builder(TestName());
   // Create a fusion instruction containing a single unary operation.
-- 
GitLab


From 69e323cc627600dd71959815d8440c9bae569832 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 26 Jul 2017 15:34:53 -0700
Subject: [PATCH 1359/1375] Fix comment ypo

PiperOrigin-RevId: 163266376
---
 tensorflow/core/public/session.h | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/public/session.h b/tensorflow/core/public/session.h
index 7446fbdb51..bca384e59f 100644
--- a/tensorflow/core/public/session.h
+++ b/tensorflow/core/public/session.h
@@ -186,9 +186,8 @@ class Session {
   /// the `SessionOptions::target` field).
   virtual Status Close() = 0;
 
-  // NOTE(ashankar): As of July 2017, this is was a method added to
-  // faciliate some experimentation. Reconsider/re-evaluate after
-  // September 2017.
+  // NOTE(ashankar): As of July 2017, this method was added to faciliate some
+  // experimentation. Reconsider/re-evaluate after September 2017.
   //
   // Sets `*output` to the `DeviceMgr` that owns accessible devices in the
   // address-space of the caller.
-- 
GitLab


From 1b8458a1c9b34fd999c05542a715efb626fce407 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 16:00:15 -0700
Subject: [PATCH 1360/1375] Shorten docstring line.

PiperOrigin-RevId: 163269709
---
 tensorflow/python/training/moving_averages.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/training/moving_averages.py b/tensorflow/python/training/moving_averages.py
index b31027ca3c..4dbe820e5a 100644
--- a/tensorflow/python/training/moving_averages.py
+++ b/tensorflow/python/training/moving_averages.py
@@ -51,8 +51,8 @@ def assign_moving_average(variable, value, decay, zero_debias=True, name=None):
     variable: A Variable.
     value: A tensor with the same shape as 'variable'.
     decay: A float Tensor or float value.  The moving average decay.
-    zero_debias: A python bool. If true, assume the variable is 0-initialized and
-      unbias it, as in https://arxiv.org/abs/1412.6980. See docstring in
+    zero_debias: A python bool. If true, assume the variable is 0-initialized
+      and unbias it, as in https://arxiv.org/abs/1412.6980. See docstring in
       `_zero_debias` for more details.
     name: Optional name of the returned operation.
 
-- 
GitLab


From ce7a355bd8b63b881aaaa7de216cd198267a596f Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Wed, 26 Jul 2017 16:21:02 -0700
Subject: [PATCH 1361/1375] Update contrib/distributions/estimator_test build
 dependency.

PiperOrigin-RevId: 163272464
---
 tensorflow/contrib/distributions/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index de1956750d..94e5c3785b 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -76,6 +76,7 @@ cuda_py_test(
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:session",
     ],
+    tags = ["no_pip"],  # contrib/learn:head_test is not available in pip.
 )
 
 cuda_py_test(
-- 
GitLab


From 6028c071b592330fabf107df08d880ec443d6844 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Wed, 26 Jul 2017 16:47:29 -0700
Subject: [PATCH 1362/1375] Highlight incoming/outgoing edges on hover in HLO
 graphviz dumps, and other improvements.

Other improvements:

 - Don't show tooltips for nodes and clusters.  Previously we'd show a
   tooltip containing a pointer value expressed as decimal.  Not so
   useful.

 - Show tooltips on edges with the to/from node names.

 - Fix bug wherein if we had

   - a node at the "edge" of the graph (so its operands aren't included
     unless they're referenced by another node),
   - with all of its operands included in the graph save one or more
     constants, and
   - those constants weren't referenced by any nodes not at the edge of
     the graph,

   we would incorrectly draw the node as "grayed out", indicating that
   one of its operands (namely, its constant operand) wasn't present in
   the graph.

   This is wrong because constants are inlined into their users, so they
   should always count as "displayed" for the purposes of determining
   whether a node is grayed out.

PiperOrigin-RevId: 163276108
---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 170 ++++++++++++++----
 tensorflow/core/lib/hash/hash.h               |   7 +
 2 files changed, 139 insertions(+), 38 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index c6202548f1..c6c0665831 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -191,12 +191,16 @@ string NodeColorAttributes(ColorScheme color) {
       case kYellow:
         return make_tuple("filled", "#fff9c4", "#cbc693", "black");
       case kDashedBorder:
-        return make_tuple("dashed", "white", "#757575", "#757575");
+        // "filled,dashed" looks the same as "dashed", since we have a white
+        // background.  But we use "filled,dashed" so that when you hover over
+        // any part of the node (not just the text inside the node), our css
+        // :hover rule is triggered.
+        return make_tuple("filled,dashed", "white", "#757575", "#757575");
     }
   }();
 
   return Printf(
-      R"(style=%s, fontcolor="%s", color="%s", fillcolor="%s")", style,
+      R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", style,
       font_color, stroke_color, fill_color);
 }
 
@@ -304,6 +308,7 @@ optional<string> MatchTrivialComputation(const HloComputation* computation) {
   }
 }
 
+// Encapsulates logic for dumping an HLO module to DOT (i.e. graphviz syntax).
 class HloDotDumper {
  public:
   HloDotDumper(const HloComputation* computation, tensorflow::StringPiece label,
@@ -329,6 +334,9 @@ class HloDotDumper {
     return StrCat("cluster_", reinterpret_cast<uint64>(computation));
   }
 
+  // Generates graph header/footer.  These should be called *after* dumping all
+  // of the instructions and subcomputations for the graph, as they both use
+  // data generated while dumping the graph.
   string Header();
   string Footer();
 
@@ -360,6 +368,24 @@ class HloDotDumper {
   const HloExecutionProfile* profile_;  // may be null
   const NodeFilter filter_;
 
+  // Each HloInstruction dumped gets a monotically-increasing node ID.  This
+  // must start at 1, because that's where graphviz's accounting starts.
+  int64 next_node_id_ = 1;
+  std::unordered_map<const HloInstruction*, int64> node_ids_;
+
+  // Each (from, to) edge gets a monotonically-increasing ID.  This is a
+  // multimap because it's possible for the same edge to appear multiple times
+  // in the graph (e.g. x^2 may be represented as mul(x, x)).
+  int64 next_edge_id_ = 1;
+  std::unordered_multimap<
+      std::pair<const HloInstruction*, const HloInstruction*>, int64,
+      tensorflow::hash<std::pair<const HloInstruction*, const HloInstruction*>>>
+      edge_ids_;
+
+  // Each HloComputation that's emitted gets a monotonically-increasing ID.
+  int64 next_cluster_id_ = 1;
+  std::unordered_map<const HloComputation*, int64> cluster_ids_;
+
   // Edges to print from Footer().  Edges come at the end because graphviz is
   // unhappy if an edge from a subcomputation to a node in the outer computation
   // appears before both the inner computation and the destination node are
@@ -368,25 +394,32 @@ class HloDotDumper {
 };
 
 string HloDotDumper::Dump() {
-  string g = Header();
+  string body;
   for (const auto& kv : SubcomputationsToDump()) {
     const HloComputation* subcomp = kv.first;
     const HloInstruction* parent = kv.second;
-    StrAppend(&g, DumpSubcomputation(subcomp, parent));
+    StrAppend(&body, DumpSubcomputation(subcomp, parent));
   }
-  StrAppend(&g, DumpComputation(computation_));
+  StrAppend(&body, DumpComputation(computation_));
+
+  // By contract, Header() and Footer() have to be called after we've dumped all
+  // our instructions, because they use state generated during that process.
+  string g = Header();
+  StrAppend(&g, body);
   StrAppend(&g, Footer());
   return g;
 }
 
 string HloDotDumper::Header() {
-  // DOT graphs accept a stylesheet as a URI.  So naturally, an inline
-  // stylesheet is a data URI!
   const char* fmt = R"(digraph G {
 rankdir = TB;
 compound = true;
 label = <<b>%s</b>>;
 labelloc = t;
+// Disable the tooltip.  Interestingly, "" doesn't work!
+tooltip = " ";
+// DOT graphs accept a stylesheet as a URI.  So naturally, an inline
+// stylesheet is a data URI!
 stylesheet="
   data:text/css,
   @import url(https://fonts.googleapis.com/css?family=Roboto:400,700);
@@ -394,6 +427,8 @@ stylesheet="
     font-family: 'Roboto';
     font-size: 12px;
   }
+
+  %s
 "
 
 )";
@@ -404,7 +439,59 @@ stylesheet="
     Appendf(&graph_label, "<br/>total cycles = %lld (%s)", cycles,
             tensorflow::strings::HumanReadableNum(cycles));
   }
-  return Printf(fmt, graph_label);
+
+  // Create CSS rules that say, when you hover over the given node or cluster,
+  // turn the given edge the given color.
+  //
+  // We rely on a few properties of how graphviz generates SVGs:
+  //
+  //  - Nodes are named "nodeN", where N corresponds to the 1-based index of
+  //    the node in our DOT (i.e. the first node in the DOT is "node1", etc.).
+  //    Edges are similarly named "edgeN", and clusters are named "clustN".
+  //  - Nodes come before their in- and out-edges in the SVG.  We need this
+  //    because the "X ~ Y" CSS selector finds a sibling of X that *comes
+  //    after X in the DOM* and matches Y.
+  std::vector<string> edge_css_rules;
+  const char* kBlue = "#1976d2";
+  const char* kRed = "#d32f2f";
+  for (const auto& kv : edge_ids_) {
+    const HloInstruction* from_node = kv.first.first;
+    const HloInstruction* to_node = kv.first.second;
+    int64 edge_id = kv.second;
+
+    auto add_hover_css_rule = [&](string elem_type, int64 elem_id,
+                                  const char* color) {
+      // One could imagine other ways of writing this CSS rule that involve less
+      // duplication, but this way seems to be relatively performant.
+      edge_css_rules.push_back(Printf(
+          "  #%s%d:hover ~ #edge%lld text { fill: %s; }\n"
+          "  #%s%d:hover ~ #edge%lld path { stroke: %s; stroke-width: .2em; }\n"
+          "  #%s%d:hover ~ #edge%lld polygon { "
+          "fill: %s; stroke: %s; stroke-width: .2em; }\n",
+          elem_type, elem_id, edge_id, color,  //
+          elem_type, elem_id, edge_id, color,  //
+          elem_type, elem_id, edge_id, color, color));
+    };
+
+    int64 from_node_id = node_ids_.at(from_node);
+    int64 to_node_id = node_ids_.at(to_node);
+    add_hover_css_rule("node", from_node_id, kBlue);
+    add_hover_css_rule("node", to_node_id, kRed);
+
+    // If this edge crosses a fusion cluster boundary, highlight it when the
+    // cluster is hovered over.
+    if (from_node->IsFused() &&
+        from_node->fusion_instruction()->fused_expression_root() == from_node) {
+      int64 cluster_id = cluster_ids_.at(from_node->parent());
+      add_hover_css_rule("clust", cluster_id, kBlue);
+    }
+    if (to_node->IsFused() && to_node->opcode() == HloOpcode::kParameter) {
+      int64 cluster_id = cluster_ids_.at(to_node->parent());
+      add_hover_css_rule("clust", cluster_id, kRed);
+    }
+  }
+
+  return Printf(fmt, graph_label, Join(edge_css_rules, "\n"));
 }
 
 string HloDotDumper::Footer() { return StrCat(Join(edges_, "\n"), "\n}"); }
@@ -440,11 +527,14 @@ string HloDotDumper::DumpSubcomputation(const HloComputation* subcomp,
 %s;
 label = <%s>;
 labelloc = t;
+tooltip = " ";
 %s
 }  // %s
 
 )";
 
+  cluster_ids_[subcomp] = next_cluster_id_++;
+
   string id = SubcomputationId(subcomp);
 
   string subcomp_label, style;
@@ -475,10 +565,14 @@ labelloc = t;
   // belongs to a fusion node, it's drawn in place of the fusion instruction, so
   // there's no need to link those.
   if (parent_instr->opcode() != HloOpcode::kFusion) {
-    const char* edge_fmt = R"(%s -> %s [ltail="%s", style="dashed"];)";
+    edge_ids_.insert(
+        {{subcomp->root_instruction(), parent_instr}, next_edge_id_++});
+    const char* edge_fmt =
+        R"(%s -> %s [ltail="%s", style="dashed" tooltip="%s -> %s"];)";
     edges_.push_back(
         Printf(edge_fmt, InstructionId(subcomp->root_instruction()),
-               InstructionId(parent_instr), SubcomputationId(subcomp)));
+               InstructionId(parent_instr), SubcomputationId(subcomp),
+               subcomp->name(), parent_instr->name()));
   }
 
   return computation;
@@ -508,6 +602,8 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr) {
     return "";
   }
 
+  node_ids_[instr] = next_node_id_++;
+
   ColorScheme color = GetInstructionColor(instr);
   string node_shape = GetInstructionNodeShape(instr);
   string node_label = GetInstructionNodeLabel(instr);
@@ -534,8 +630,10 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr) {
     }
   }
 
-  return Printf("%s [label=<%s>, shape=%s, %s];\n", InstructionId(instr),
-                node_body, node_shape, NodeColorAttributes(color));
+  return Printf(R"(%s [label=<%s>, shape=%s, tooltip=" ", %s];)"
+                "\n",
+                InstructionId(instr), node_body, node_shape,
+                NodeColorAttributes(color));
 }
 
 string HloDotDumper::GetInstructionNodeInlinedConstants(
@@ -776,12 +874,15 @@ void HloDotDumper::AddInstructionIncomingEdges(const HloInstruction* instr) {
     if (!filter_.Show(from) || from->opcode() == HloOpcode::kConstant) {
       return;
     }
-    string edge = Printf("%s -> %s", InstructionId(from), InstructionId(to));
+    edge_ids_.insert({{from, to}, next_edge_id_++});
+
+    string edge_label;
     if (instr->operand_count() > 1) {
-      Appendf(&edge, R"( [headlabel="%lld",labeldistance=2])", operand_num);
+      edge_label = Printf(R"( headlabel="%lld", labeldistance=2)", operand_num);
     }
-    StrAppend(&edge, ";");
-    edges_.push_back(edge);
+    const char* kEdgeFmt = R"(%s -> %s [tooltip="%s -> %s" %s];)";
+    edges_.push_back(Printf(kEdgeFmt, InstructionId(from), InstructionId(to),
+                            from->name(), to->name(), edge_label));
   };
 
   // Add edges from instr's operands to instr.  Parameters within fusion
@@ -945,40 +1046,33 @@ NodeFilter MakeNodeFilter(const HloInstruction* root, int64 radius) {
   }
 
   auto is_displayed = [&](const HloInstruction* instr) {
-    return nodes.count(instr) > 0;
+    // Constants are displayed inline with their users; they're never omitted.
+    return nodes.count(instr) > 0 || instr->opcode() == HloOpcode::kConstant;
   };
 
-  // Mark nodes which don't have all of their operands present as "some operands
-  // omitted".
+  // Make a second pass over 'nodes' to fix up the NodeFilterResults now that we
+  // know which nodes will be included in the graph.
   for (auto& kv : nodes) {
     const HloInstruction* instr = kv.first;
     NodeFilterResult& filter_result = kv.second;
     const auto& operands = instr->operands();
 
-    // Mark nodes with some omitted as "some operands omitted".
     if (std::any_of(operands.begin(), operands.end(), is_displayed) &&
         !std::all_of(operands.begin(), operands.end(), is_displayed)) {
+      // Mark nodes with some operands omitted appropriately.
       filter_result = kSomeOperandsOmitted;
+    } else if (!operands.empty() &&
+               std::none_of(operands.begin(), operands.end(), is_displayed)) {
+      // Mark nodes with *all* operands omitted appropriately.
+      filter_result = kOmitNodeOperands;
     }
-  }
-
-  // Promote nodes with type kSomeUsersOmitted to kNormalNode if all of their
-  // users made it into the graph by other means.
-  for (auto& kv : nodes) {
-    const auto& users = kv.first->users();
-    if (kv.second == kSomeUsersOmitted &&
-        std::all_of(users.begin(), users.end(), is_displayed)) {
-      kv.second = kNormalNode;
-    }
-  }
 
-  // If none of a node's operands appear in nodes, mark it as type
-  // kOmitNodeOperands so it gets styled appropriately.
-  for (auto& kv : nodes) {
-    const auto& operands = kv.first->operands();
-    if (!operands.empty() &&
-        std::none_of(operands.begin(), operands.end(), is_displayed)) {
-      kv.second = kOmitNodeOperands;
+    // Promote nodes with type kSomeUsersOmitted to kNormalNode if all of their
+    // users made it into the graph.
+    if (filter_result == kSomeUsersOmitted &&
+        std::all_of(instr->users().begin(), instr->users().end(),
+                    is_displayed)) {
+      filter_result = kNormalNode;
     }
   }
 
diff --git a/tensorflow/core/lib/hash/hash.h b/tensorflow/core/lib/hash/hash.h
index 73b7c94d1f..77b8031598 100644
--- a/tensorflow/core/lib/hash/hash.h
+++ b/tensorflow/core/lib/hash/hash.h
@@ -77,6 +77,13 @@ struct hash<StringPiece> {
   }
 };
 
+template <typename T, typename U>
+struct hash<std::pair<T, U>> {
+  size_t operator()(const std::pair<T, U>& p) const {
+    return Hash64Combine(hash<T>()(p.first), hash<U>()(p.second));
+  }
+};
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_LIB_HASH_HASH_H_
-- 
GitLab


From 423c1eea0e47eb71d3bf3ec7e99e7a4a63c3e433 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 16:51:45 -0700
Subject: [PATCH 1363/1375] BREAKING CHANGE: Fix semantic error in how
 maybe_batch* handles sparse tensors.

PiperOrigin-RevId: 163276613
---
 tensorflow/python/training/input.py      |  9 ++++-
 tensorflow/python/training/input_test.py | 46 ++++++++++++++++++++++++
 2 files changed, 54 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 396ec11a02..94c5df619f 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -492,8 +492,15 @@ def _store_sparse_tensors(tensor_list, enqueue_many, keep_input,
           lambda: -1 * array_ops.ones(array_ops.shape(t)[0:1], dtypes.int64))
       out_tensor.set_shape([None])  # necessary when t.ndims is unknown
       return out_tensor
+    def _sparse_values_to_keep(t, keep_input):
+      """Convert a per-row `keep_input` vector to a per-value one."""
+      # Get the rows of every value in the sparse Tensor.
+      row_values = array_ops.reshape(
+          t.indices, [array_ops.shape(t.indices)[0], -1])[:, 0]
+      # The value should be kept iff the row should be kept.
+      return array_ops.gather(keep_input, row_values)
     if keep_input.shape.ndims == 1:
-      t = sparse_ops.sparse_retain(t, keep_input)
+      t = sparse_ops.sparse_retain(t, _sparse_values_to_keep(t, keep_input))
       store_f = lambda t, name, _: _store_many_sparse(t, shared_name=name)
     elif enqueue_many:
       store_f = _maybe_store_many_sparse
diff --git a/tensorflow/python/training/input_test.py b/tensorflow/python/training/input_test.py
index d32768f5da..3a25bfe343 100644
--- a/tensorflow/python/training/input_test.py
+++ b/tensorflow/python/training/input_test.py
@@ -903,6 +903,29 @@ class BatchTest(test_lib.TestCase):
         [sparse], keep_input=[True, False], batch_size=2, enqueue_many=True)
     self.assertIs(None, batched.dense_shape.get_shape().num_elements())
 
+  def testMaybeBatchCorrectValues(self):
+    sparse_t = sparse_tensor.SparseTensor(
+        indices=[[0, 1], [0, 2], [1, 0], [1, 3]],
+        dense_shape=[2, 4],
+        values=[5, 4, 7, 2])
+    keep = constant_op.constant([True, False])
+    batched = inp.maybe_batch(
+        [sparse_t], keep_input=keep, batch_size=1, enqueue_many=True)
+
+    with self.test_session():
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(coord=coord)
+
+      batched_np = batched.eval()
+
+      coord.request_stop()
+      for thread in threads:
+        thread.join()
+
+    self.assertAllEqual([[0, 1], [0, 2]], batched_np.indices)
+    self.assertAllEqual([5, 4], batched_np.values)
+    self.assertAllEqual([1, 4], batched_np.dense_shape)
+
 
 class BatchJoinTest(test_lib.TestCase):
 
@@ -1457,6 +1480,29 @@ class BatchJoinTest(test_lib.TestCase):
         [[sparse]], keep_input=[True, False], batch_size=2, enqueue_many=True)
     self.assertIs(None, batched.dense_shape.get_shape().num_elements())
 
+  def testMaybeBatchCorrectValues(self):
+    sparse = sparse_tensor.SparseTensor(
+        indices=[[0, 1], [0, 2], [1, 0], [1, 3]],
+        dense_shape=[2, 4],
+        values=[5, 4, 7, 2])
+    keep = constant_op.constant([True, False])
+    batched = inp.maybe_batch_join(
+        [[sparse]], keep_input=keep, batch_size=1, enqueue_many=True)
+
+    with self.test_session():
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(coord=coord)
+
+      batched_np = batched.eval()
+
+      coord.request_stop()
+      for thread in threads:
+        thread.join()
+
+    self.assertAllEqual([[0, 1], [0, 2]], batched_np.indices)
+    self.assertAllEqual([5, 4], batched_np.values)
+    self.assertAllEqual([1, 4], batched_np.dense_shape)
+
 
 class ShuffleBatchTest(test_lib.TestCase):
 
-- 
GitLab


From 9b30dc3a824fd277fcd622a458b25f26c0db7b72 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 26 Jul 2017 17:46:06 -0700
Subject: [PATCH 1364/1375] Remove final mentions of `get_shape` in docstring.

PiperOrigin-RevId: 163282839
---
 tensorflow/python/training/input.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py
index 94c5df619f..704017c244 100644
--- a/tensorflow/python/training/input.py
+++ b/tensorflow/python/training/input.py
@@ -884,7 +884,7 @@ def batch(tensors, batch_size, num_threads=1, capacity=32,
   `batch_size` is returned when the queue is closed and there are not enough
   elements to fill the batch, otherwise the pending elements are discarded.
   In addition, all output tensors' static shapes, as accessed via the
-  `get_shape` method will have a first `Dimension` value of `None`, and
+  `shape` property will have a first `Dimension` value of `None`, and
   operations that depend on fixed batch_size would fail.
 
   Args:
@@ -1038,7 +1038,7 @@ def batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False,
   `batch_size` is returned when the queue is closed and there are not enough
   elements to fill the batch, otherwise the pending elements are discarded.
   In addition, all output tensors' static shapes, as accessed via the
-  `get_shape` method will have a first `Dimension` value of `None`, and
+  `shape` property will have a first `Dimension` value of `None`, and
   operations that depend on fixed batch_size would fail.
 
   Args:
@@ -1183,7 +1183,7 @@ def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
   `batch_size` is returned when the queue is closed and there are not enough
   elements to fill the batch, otherwise the pending elements are discarded.
   In addition, all output tensors' static shapes, as accessed via the
-  `get_shape` method will have a first `Dimension` value of `None`, and
+  `shape` property will have a first `Dimension` value of `None`, and
   operations that depend on fixed batch_size would fail.
 
   Args:
@@ -1325,7 +1325,7 @@ def shuffle_batch_join(tensors_list, batch_size, capacity,
   `batch_size` is returned when the queue is closed and there are not enough
   elements to fill the batch, otherwise the pending elements are discarded.
   In addition, all output tensors' static shapes, as accessed via the
-  `get_shape` method will have a first `Dimension` value of `None`, and
+  `shape` property will have a first `Dimension` value of `None`, and
   operations that depend on fixed batch_size would fail.
 
   Args:
-- 
GitLab


From 88abddbc30a34855d8cdf44c81eac388fbec955b Mon Sep 17 00:00:00 2001
From: Alan Yee <alyee@ucsd.edu>
Date: Wed, 26 Jul 2017 18:11:02 -0700
Subject: [PATCH 1365/1375] Update README.md (#11793)

Remove bad practices of sudo pip and install use safer pip install commands
---
 tensorflow/examples/learn/README.md | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index 416b809bb1..70d9db85ee 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -7,28 +7,28 @@ See the [Quickstart tutorial](https://www.tensorflow.org/get_started/estimator)
 for an introduction to the API.
 
 To run most of these examples, you need to install the `scikit learn` library
-(`sudo pip install sklearn`). Some examples use the `pandas` library for data
-processing (`sudo pip install pandas`).
+(`pip install -U scikit-learn`). Some examples use the `pandas` library for data
+processing (`pip install -U pandas`).
 
 ## Basics
 
-* [Deep Neural Network Regression with Boston Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/boston.py)
-* [Deep Neural Network Classification with Iris Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris.py)
-* [Building a Custom Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_model.py)
-* [Building a Model Using Different GPU Configurations]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_run_config.py)
+* [Deep Neural Network Regression with Boston Data](https://www.tensorflow.org/code/tensorflow/examples/learn/boston.py)
+* [Deep Neural Network Classification with Iris Data](https://www.tensorflow.org/code/tensorflow/examples/learn/iris.py)
+* [Building a Custom Model](https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_model.py)
+* [Building a Model Using Different GPU Configurations](https://www.tensorflow.org/code/tensorflow/examples/learn/iris_run_config.py)
 
 ## Techniques
 
-* [Deep Neural Network with Customized Decay Function]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
+* [Deep Neural Network with Customized Decay Function](https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
 
 ## Specialized Models
-* [Building a Random Forest Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/random_forest_mnist.py)
-* [Building a Wide & Deep Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py)
-* [Building a Residual Network Model]( https://www.tensorflow.org/code/tensorflow/examples/learn/resnet.py)
+* [Building a Random Forest Model](https://www.tensorflow.org/code/tensorflow/examples/learn/random_forest_mnist.py)
+* [Building a Wide & Deep Model](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py)
+* [Building a Residual Network Model](https://www.tensorflow.org/code/tensorflow/examples/learn/resnet.py)
 
 ## Text classification
 
-* [Text Classification Using Recurrent Neural Networks on Words]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification.py)
-* [Text Classification Using Convolutional Neural Networks on Words]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_cnn.py)
-* [Text Classification Using Recurrent Neural Networks on Characters]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_rnn.py)
-* [Text Classification Using Convolutional Neural Networks on Characters]( https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_cnn.py)
+* [Text Classification Using Recurrent Neural Networks on Words](https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification.py)
+* [Text Classification Using Convolutional Neural Networks on Words](https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_cnn.py)
+* [Text Classification Using Recurrent Neural Networks on Characters](https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_rnn.py)
+* [Text Classification Using Convolutional Neural Networks on Characters](https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_cnn.py)
-- 
GitLab


From 1f7c0f917e3f4163d437ea3aa64d8246c2eba40a Mon Sep 17 00:00:00 2001
From: Kongsea <kongsea@gmail.com>
Date: Thu, 27 Jul 2017 00:59:48 -0500
Subject: [PATCH 1366/1375] Refine docstrings (#11800)

---
 .../contrib/keras/python/keras/layers/convolutional.py       | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/keras/python/keras/layers/convolutional.py b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
index e039959041..73e87f68dd 100644
--- a/tensorflow/contrib/keras/python/keras/layers/convolutional.py
+++ b/tensorflow/contrib/keras/python/keras/layers/convolutional.py
@@ -619,11 +619,12 @@ class Conv3DTranspose(tf_convolutional_layers.Conv3D, Layer):
       filters: Integer, the dimensionality of the output space
           (i.e. the number of output filters in the convolution).
       kernel_size: An integer or tuple/list of 3 integers, specifying the
-          width and height of the 3D convolution window.
+          depth, height and width of the 3D convolution window.
           Can be a single integer to specify the same value for
           all spatial dimensions.
       strides: An integer or tuple/list of 3 integers,
-          specifying the strides of the convolution along the width and height.
+          specifying the strides of the convolution along the depth, height
+            and width.
           Can be a single integer to specify the same value for
           all spatial dimensions.
           Specifying any stride value != 1 is incompatible with specifying
-- 
GitLab


From cea9ef6f548ef38fbc368be23f7262c662be6c67 Mon Sep 17 00:00:00 2001
From: horance <horance-liu@users.noreply.github.com>
Date: Thu, 27 Jul 2017 14:00:05 +0800
Subject: [PATCH 1367/1375] Refactoring device name utils (#11797)

* remove duplicated code for full_name and legacy_name for DeviceNameUtils

* replace tabs

* Real->Device
---
 tensorflow/core/util/device_name_utils.cc | 25 ++++++++++++-----------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/util/device_name_utils.cc b/tensorflow/core/util/device_name_utils.cc
index 1773ac00b7..530a1737e5 100644
--- a/tensorflow/core/util/device_name_utils.cc
+++ b/tensorflow/core/util/device_name_utils.cc
@@ -85,28 +85,29 @@ static bool ConsumeNumber(StringPiece* in, int* val) {
   }
 }
 
-/* static */
-string DeviceNameUtils::FullName(const string& job, int replica, int task,
-                                 const string& type, int id) {
+// Returns a fully qualified device name given the parameters.
+static string DeviceName(const string& job, int replica, int task,
+                         const string& device_prefix, const string& device_type,
+                         int id) {
   CHECK(IsJobName(job)) << job;
   CHECK_LE(0, replica);
   CHECK_LE(0, task);
-  CHECK(!type.empty());
+  CHECK(!device_type.empty());
   CHECK_LE(0, id);
   return strings::StrCat("/job:", job, "/replica:", replica, "/task:", task,
-                         "/device:", type, ":", id);
+                         device_prefix, device_type, ":", id);
+}
+
+/* static */
+string DeviceNameUtils::FullName(const string& job, int replica, int task,
+                                 const string& type, int id) {
+  return DeviceName(job, replica, task, "/device:", type, id);
 }
 
 /* static */
 string DeviceNameUtils::LegacyName(const string& job, int replica, int task,
                                    const string& type, int id) {
-  CHECK(IsJobName(job)) << job;
-  CHECK_LE(0, replica);
-  CHECK_LE(0, task);
-  CHECK(!type.empty());
-  CHECK_LE(0, id);
-  return strings::StrCat("/job:", job, "/replica:", replica, "/task:", task,
-                         "/", str_util::Lowercase(type), ":", id);
+  return DeviceName(job, replica, task, "/", str_util::Lowercase(type), id);
 }
 
 bool DeviceNameUtils::ParseFullName(StringPiece fullname, ParsedName* p) {
-- 
GitLab


From 2381ce5c3368ac1c0d4aec41c6a42ec2e32bbbcf Mon Sep 17 00:00:00 2001
From: Abdullah Alrasheed <a.rasheed@tc-sa.com>
Date: Fri, 28 Jul 2017 00:24:14 +0800
Subject: [PATCH 1368/1375] DOC: Fix typo. (#11813)

you could could be I/O bottlenecked.
TO:
you could be I/O bottlenecked.
---
 tensorflow/docs_src/performance/performance_guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index a5508ac23e..9ac60024a1 100644
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -104,7 +104,7 @@ with tf.device('/cpu:0'):
 Under some circumstances, both the CPU and GPU can be starved for data by the
 I/O system. If you are using many small files to form your input data set, you
 may be limited by the speed of your filesystem. If your training loop runs
-faster when using SSDs vs HDDs for storing your input data, you could could be
+faster when using SSDs vs HDDs for storing your input data, you could be
 I/O bottlenecked.
 
 If this is the case, you should pre-process your input data, creating a few
-- 
GitLab


From b26f9cd44b7f1c3b9f6cdf0af216eee5ef765d3f Mon Sep 17 00:00:00 2001
From: David Norman <DavidNorman@users.noreply.github.com>
Date: Thu, 27 Jul 2017 17:58:23 +0100
Subject: [PATCH 1369/1375] Ensure that the multi-instruction fuse can take
 shared inputs (#11748)

* Ensure that the multi-instruction fuse can take shared inputs

Note that the fuse action only works when the shared input / constant
appears after all of its consumers in the list of instructions.

* Add a comment describing the test
---
 tensorflow/compiler/xla/tests/fusion_test.cc | 36 ++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index 7803d234fd..df52f168a8 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -522,6 +522,42 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceWindow)) {
       *ExecuteAndTransfer(std::move(hlo_module), {}));
 }
 
+// When a constant (or other op) which has multiple users is imported
+// into a fusion, it should remain shared, rather than being duplicated
+// within the fusion.
+XLA_TEST_F(FusionTest, SharedConstant) {
+  auto hlo_module = CreateNewModule();
+
+  auto builder = HloComputation::Builder(TestName());
+  auto const0 = builder.AddInstruction(
+          HloInstruction::CreateConstant(Literal::CreateR1<int32>({0})));
+  auto const1 = builder.AddInstruction(
+          HloInstruction::CreateConstant(Literal::CreateR1<int32>({2})));
+  auto add1 = builder.AddInstruction(HloInstruction::CreateBinary(
+          ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, const0));
+  auto add2 = builder.AddInstruction(HloInstruction::CreateBinary(
+          ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add1));
+  auto add3 = builder.AddInstruction(HloInstruction::CreateBinary(
+          ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add2));
+  auto add4 = builder.AddInstruction(HloInstruction::CreateBinary(
+          ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add3));
+  hlo_module->AddEntryComputation(builder.Build())
+      ->CreateFusionInstruction(
+        {add4, add3, add2, add1, const1},
+        HloInstruction::FusionKind::kLoop);
+
+  HloComputation* entry_comp = hlo_module->entry_computation();
+
+  // entry computation contains the constant(0) and the fusion
+  EXPECT_EQ(entry_comp->instructions().size(), 2);
+
+  // fused instruction contains the constant(2), the parameter, and 4 adds
+  EXPECT_EQ(entry_comp->root_instruction()->fused_instructions().size(), 6);
+
+  LiteralTestUtil::ExpectEqual(*Literal::CreateR1<int32>({8}),
+          *ExecuteAndTransfer(std::move(hlo_module), {}));
+}
+
 XLA_TEST_F(FusionTest, Add2D) { TestElementwise2D<float, 2>(HloOpcode::kAdd); }
 
 XLA_TEST_F(FusionTest, Subtract2D) {
-- 
GitLab


From 3cd828474f50b7a067115e0a38a1d078ff7432c1 Mon Sep 17 00:00:00 2001
From: David Norman <DavidNorman@users.noreply.github.com>
Date: Thu, 27 Jul 2017 18:38:12 +0100
Subject: [PATCH 1370/1375] Fix error with default python path selection
 (#11814)

* Fix error with default python path selection

* Move setting of environment var outside if / else
---
 configure.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configure.py b/configure.py
index fac00d1b74..e39bd53efb 100644
--- a/configure.py
+++ b/configure.py
@@ -175,7 +175,7 @@ def setup_python(environ_cp):
   if not python_lib_path:
     python_lib_paths = get_python_path(environ_cp)
     if environ_cp.get('USE_DEFAULT_PYTHON_LIB_PATH') == '1':
-      environ_cp['PYTHON_LIB_PATH'] = python_lib_paths[0]
+      python_lib_path = python_lib_paths[0]
     else:
       print('Found possible Python library paths:\n%s' %
             '\n'.join(python_lib_paths))
@@ -185,7 +185,7 @@ def setup_python(environ_cp):
           % python_lib_paths[0])
       if not python_lib_path:
         python_lib_path = default_python_lib_path
-      environ_cp['PYTHON_LIB_PATH'] = python_lib_path
+    environ_cp['PYTHON_LIB_PATH'] = python_lib_path
 
   python_major_version = sys.version_info[0]
   # Convert python path to Windows style before writing into bazel.rc
-- 
GitLab


From d09304fca478b8db5c651eec6fb2559844a73373 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <vomjom@vomjom.net>
Date: Thu, 27 Jul 2017 10:40:16 -0700
Subject: [PATCH 1371/1375] Upgrade gRPC (#11768)

* BUILD rule modifications

* More build fixes

* Code changes

* More code fixes

* Working tests

* CMake build

* Fix pprof

* Fix header includes

* CMake fix test

* Bazel clean

* Fix verbs

* More verbs fixes

* bazel clean for XLA

* Windows build fix test

* Add openssl/rand.h

* New cmake build command

* --config Release
---
 tensorflow/compiler/plugin/executor/BUILD     |     2 +-
 tensorflow/compiler/xla/xla.bzl               |     8 +-
 tensorflow/contrib/cmake/external/grpc.cmake  |    13 +-
 .../contrib/cmake/patches/grpc/CMakeLists.txt | 14346 +++++++++++++++-
 tensorflow/contrib/cmake/patches/grpc/rand.h  |    14 +
 .../contrib/factorization/kernels/BUILD       |     8 +-
 tensorflow/contrib/ffmpeg/default/BUILD       |     2 +-
 tensorflow/contrib/image/BUILD                |     2 +-
 .../contrib/input_pipeline/kernels/BUILD      |     2 +-
 tensorflow/contrib/layers/kernels/BUILD       |     2 +-
 tensorflow/contrib/tensor_forest/BUILD        |     4 +-
 tensorflow/contrib/tensor_forest/hybrid/BUILD |     2 +-
 .../contrib/verbs/grpc_verbs_service_impl.cc  |    14 +-
 .../contrib/verbs/grpc_verbs_service_impl.h   |     2 +-
 .../rpc/grpc_master_service_impl.cc           |    35 +-
 .../rpc/grpc_master_service_impl.h            |    32 +-
 .../rpc/grpc_serialization_traits.h           |    69 +-
 .../rpc/grpc_tensor_coding.cc                 |    26 +-
 .../core/distributed_runtime/rpc/grpc_util.cc |     4 +-
 .../distributed_runtime/rpc/grpc_util_test.cc |     2 +-
 .../rpc/grpc_worker_service_impl.cc           |     4 +-
 .../rpc/grpc_worker_service_impl.h            |     2 +-
 .../core/platform/default/build_config.bzl    |    18 +-
 .../platform/default/grpc_response_reader.h   |     2 +-
 .../core/platform/grpc_response_reader.h      |     2 +-
 tensorflow/go/genop/generate.sh               |     2 +-
 tensorflow/python/BUILD                       |     4 +-
 tensorflow/tensorflow.bzl                     |     2 +-
 .../tools/ci_build/ci_parameterized_build.sh  |     4 +
 .../tools/ci_build/xla/linux/gpu/run_py3.sh   |     1 +
 tensorflow/tools/lib_package/BUILD            |     4 +-
 tensorflow/tools/pip_package/BUILD            |     4 +-
 .../tools/pip_package/build_pip_package.sh    |     2 +-
 tensorflow/tools/pip_package/setup.py         |     4 +-
 tensorflow/workspace.bzl                      |    48 +-
 third_party/grpc.BUILD                        |  2478 ---
 third_party/grpc/BUILD                        |     0
 third_party/grpc/grpc.patch                   |    76 +
 third_party/nanopb.BUILD                      |    23 -
 third_party/pprof.BUILD                       |     8 +-
 40 files changed, 14487 insertions(+), 2790 deletions(-)
 create mode 100644 tensorflow/contrib/cmake/patches/grpc/rand.h
 delete mode 100644 third_party/grpc.BUILD
 create mode 100644 third_party/grpc/BUILD
 create mode 100644 third_party/grpc/grpc.patch
 delete mode 100644 third_party/nanopb.BUILD

diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
index 2e5875705f..bc7c25c120 100644
--- a/tensorflow/compiler/plugin/executor/BUILD
+++ b/tensorflow/compiler/plugin/executor/BUILD
@@ -18,7 +18,7 @@ cc_library(
         "//tensorflow/compiler/xla/service",
         "//third_party/eigen3",
         "@local_config_cuda//cuda:cuda_headers",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/xla.bzl b/tensorflow/compiler/xla/xla.bzl
index bdd3dfe82d..f44cbefe22 100644
--- a/tensorflow/compiler/xla/xla.bzl
+++ b/tensorflow/compiler/xla/xla.bzl
@@ -1,15 +1,15 @@
 """Wrapper around cc_proto_library used inside the XLA codebase."""
 
-load("@protobuf//:protobuf.bzl", "cc_proto_library")
+load("@protobuf_archive//:protobuf.bzl", "cc_proto_library")
 
 # xla_proto_library() is a convenience wrapper around cc_proto_library.
 def xla_proto_library(name, srcs=[], deps=[], visibility=None, testonly=0):
   cc_proto_library(name=name,
                    srcs=srcs,
                    deps=deps,
-                   cc_libs = ["@protobuf//:protobuf"],
-                   protoc="@protobuf//:protoc",
-                   default_runtime="@protobuf//:protobuf",
+                   cc_libs = ["@protobuf_archive//:protobuf"],
+                   protoc="@protobuf_archive//:protoc",
+                   default_runtime="@protobuf_archive//:protobuf",
                    testonly=testonly,
                    visibility=visibility,)
 
diff --git a/tensorflow/contrib/cmake/external/grpc.cmake b/tensorflow/contrib/cmake/external/grpc.cmake
index d7201680ce..0740c38dd3 100644
--- a/tensorflow/contrib/cmake/external/grpc.cmake
+++ b/tensorflow/contrib/cmake/external/grpc.cmake
@@ -17,7 +17,7 @@ include (ExternalProject)
 set(GRPC_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/include)
 set(GRPC_URL https://github.com/grpc/grpc.git)
 set(GRPC_BUILD ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc)
-set(GRPC_TAG 3bc78cd0b5bd784a235c01612d634b1ec5f8fb97)
+set(GRPC_TAG 781fd6f6ea03645a520cd5c675da67ab61f87e4b)
 
 if(WIN32)
   set(grpc_STATIC_LIBRARIES
@@ -38,7 +38,10 @@ ExternalProject_Add(grpc
     GIT_TAG ${GRPC_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    # TODO(jhseu): Remove this PATCH_COMMAND once grpc removes the dependency
+    # on "grpc" from the "grpc++_unsecure" rule.
     PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/grpc/CMakeLists.txt ${GRPC_BUILD}
+    BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target grpc++_unsecure
     INSTALL_COMMAND ""
     CMAKE_CACHE_ARGS
         -DCMAKE_BUILD_TYPE:STRING=Release
@@ -46,5 +49,13 @@ ExternalProject_Add(grpc
         -DPROTOBUF_INCLUDE_DIRS:STRING=${PROTOBUF_INCLUDE_DIRS}
         -DPROTOBUF_LIBRARIES:STRING=${protobuf_STATIC_LIBRARIES}
         -DZLIB_ROOT:STRING=${ZLIB_INSTALL}
+	-DgRPC_SSL_PROVIDER:STRING=NONE
 )
 
+# grpc/src/core/ext/census/tracing.c depends on the existence of openssl/rand.h.
+ExternalProject_Add_Step(grpc copy_rand
+    COMMAND ${CMAKE_COMMAND} -E copy
+    ${CMAKE_SOURCE_DIR}/patches/grpc/rand.h ${GRPC_BUILD}/include/openssl/rand.h
+    DEPENDEES patch
+    DEPENDERS build
+)
diff --git a/tensorflow/contrib/cmake/patches/grpc/CMakeLists.txt b/tensorflow/contrib/cmake/patches/grpc/CMakeLists.txt
index ce8a0cb72c..84722c5ca2 100644
--- a/tensorflow/contrib/cmake/patches/grpc/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/patches/grpc/CMakeLists.txt
@@ -1,61 +1,780 @@
-# GRPC global cmake file, modified for the TensorFlow build system.
+# GRPC global cmake file
 # This currently builds C and C++ code.
-
-# This file is based on the CMakeLists.txt available from here:
-# https://github.com/grpc/grpc/blob/3bc78cd0b5bd784a235c01612d634b1ec5f8fb97/CMakeLists.txt
-# with modifications to remove dependencies on SSL, and to reuse
-# previously compiled libprotobuf.
+# This file has been automatically generated from a template file.
+# Please look at the templates directory instead.
+# This file can be regenerated from the template by running
+# tools/buildgen/generate_projects.sh
 #
-# Copyright 2016, Google Inc.
-# All rights reserved.
+# Copyright 2015 gRPC authors.
 #
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are
-# met:
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-#     * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-#     * Redistributions in binary form must reproduce the above
-# copyright notice, this list of conditions and the following disclaimer
-# in the documentation and/or other materials provided with the
-# distribution.
-#     * Neither the name of Google Inc. nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+
 cmake_minimum_required(VERSION 2.8)
 
 set(PACKAGE_NAME      "grpc")
-set(PACKAGE_VERSION   "1.0.0-pre2-tensorflow")
+set(PACKAGE_VERSION   "1.5.0-dev")
 set(PACKAGE_STRING    "${PACKAGE_NAME} ${PACKAGE_VERSION}")
 set(PACKAGE_TARNAME   "${PACKAGE_NAME}-${PACKAGE_VERSION}")
 set(PACKAGE_BUGREPORT "https://github.com/grpc/grpc/issues/")
 project(${PACKAGE_NAME} C CXX)
 
-set(CMAKE_POSITION_INDEPENDENT_CODE ON)
-set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -std=c11")
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+set(gRPC_INSTALL_BINDIR "${CMAKE_INSTALL_PREFIX}/bin" CACHE PATH "Installation directory for executables")
+set(gRPC_INSTALL_LIBDIR "${CMAKE_INSTALL_PREFIX}/lib" CACHE PATH "Installation directory for libraries")
+set(gRPC_INSTALL_INCLUDEDIR "${CMAKE_INSTALL_PREFIX}/include" CACHE PATH "Installation directory for headers")
+set(gRPC_INSTALL_CMAKEDIR "${CMAKE_INSTALL_PREFIX}/lib/cmake/${PACKAGE_NAME}" CACHE PATH "Installation directory for cmake config files")
+
+# Options
+option(gRPC_BUILD_TESTS "Build tests" OFF)
+
+set(gRPC_INSTALL_default ON)
+if (NOT CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
+  # Disable gRPC_INSTALL by default if building as a submodule
+  set(gRPC_INSTALL_default OFF)
+endif()
+set(gRPC_INSTALL ${gRPC_INSTALL_default} CACHE BOOL
+    "Generate installation target: gRPC_ZLIB_PROVIDER, gRPC_CARES_PROVIDER, gRPC_SSL_PROVIDER and gRPC_PROTOBUF_PROVIDER must all be \"package\"")
+
+set(gRPC_ZLIB_PROVIDER "module" CACHE STRING "Provider of zlib library")
+set_property(CACHE gRPC_ZLIB_PROVIDER PROPERTY STRINGS "module" "package")
+
+set(gRPC_CARES_PROVIDER "module" CACHE STRING "Provider of c-ares library")
+set_property(CACHE gRPC_CARES_PROVIDER PROPERTY STRINGS "module" "package")
 
+set(gRPC_SSL_PROVIDER "module" CACHE STRING "Provider of ssl library")
+set_property(CACHE gRPC_SSL_PROVIDER PROPERTY STRINGS "module" "package")
+
+set(gRPC_PROTOBUF_PROVIDER "module" CACHE STRING "Provider of protobuf library")
+set_property(CACHE gRPC_PROTOBUF_PROVIDER PROPERTY STRINGS "module" "package")
+
+set(gRPC_PROTOBUF_PACKAGE_TYPE "" CACHE STRING "Algorithm for searching protobuf package")
+set_property(CACHE gRPC_PROTOBUF_PACKAGE_TYPE PROPERTY STRINGS "CONFIG" "MODULE")
+
+set(gRPC_GFLAGS_PROVIDER "module" CACHE STRING "Provider of gflags library")
+set_property(CACHE gRPC_GFLAGS_PROVIDER PROPERTY STRINGS "module" "package")
+
+set(gRPC_BENCHMARK_PROVIDER "module" CACHE STRING "Provider of benchmark library")
+set_property(CACHE gRPC_BENCHMARK_PROVIDER PROPERTY STRINGS "module" "package")
+
+set(gRPC_USE_PROTO_LITE OFF CACHE BOOL "Use the protobuf-lite library")
+
+if(UNIX)
+  if(${CMAKE_SYSTEM_NAME} MATCHES "Linux")
+    set(_gRPC_PLATFORM_LINUX ON)
+  elseif(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(_gRPC_PLATFORM_MAC ON)
+  else()
+    set(_gRPC_PLATFORM_POSIX ON)
+  endif()
+endif()
 if(WIN32)
-  add_definitions(-D_WIN32_WINNT=0x0A00)
-  find_package(ZLIB REQUIRED)
-endif(WIN32)
+  set(_gRPC_PLATFORM_WINDOWS ON)
+endif()
+
+set(CMAKE_POSITION_INDEPENDENT_CODE TRUE)
+
+if (MSVC)
+  include(cmake/msvc_static_runtime.cmake)
+  add_definitions(-D_WIN32_WINNT=0x600 -D_SCL_SECURE_NO_WARNINGS -D_CRT_SECURE_NO_WARNINGS -D_WINSOCK_DEPRECATED_NO_WARNINGS)
+  # needed to compile protobuf
+  add_definitions(/wd4065 /wd4506)
+  # TODO(jtattermusch): revisit C4267 occurrences throughout the code
+  add_definitions(/wd4267)
+endif()
+
+if (gRPC_USE_PROTO_LITE)
+  set(_gRPC_PROTOBUF_LIBRARY_NAME "libprotobuf-lite")
+  add_definitions("-DGRPC_USE_PROTO_LITE")
+else()
+  set(_gRPC_PROTOBUF_LIBRARY_NAME "libprotobuf")
+endif()
+
+if("${gRPC_ZLIB_PROVIDER}" STREQUAL "module")
+  if(NOT ZLIB_ROOT_DIR)
+    set(ZLIB_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party/zlib)
+  endif()
+  set(ZLIB_INCLUDE_DIR "${ZLIB_ROOT_DIR}")
+  if(EXISTS "${ZLIB_ROOT_DIR}/CMakeLists.txt")
+      # TODO(jtattermusch): workaround for https://github.com/madler/zlib/issues/218
+      include_directories(${ZLIB_INCLUDE_DIR})
+
+      add_subdirectory(${ZLIB_ROOT_DIR} third_party/zlib)
+      if(TARGET zlibstatic)
+          set(_gRPC_ZLIB_LIBRARIES zlibstatic)
+      endif()
+  else()
+      message(WARNING "gRPC_ZLIB_PROVIDER is \"module\" but ZLIB_ROOT_DIR is wrong")
+  endif()
+  if(gRPC_INSTALL)
+    message(WARNING "gRPC_INSTALL will be forced to FALSE because gRPC_ZLIB_PROVIDER is \"module\"")
+    set(gRPC_INSTALL FALSE)
+  endif()
+elseif("${gRPC_ZLIB_PROVIDER}" STREQUAL "package")
+  find_package(ZLIB)
+  if(TARGET ZLIB::ZLIB)
+    set(_gRPC_ZLIB_LIBRARIES ZLIB::ZLIB)
+  endif()
+  set(_gRPC_FIND_ZLIB "if(NOT ZLIB_FOUND)\n  find_package(ZLIB)\nendif()")
+endif()
+
+if("${gRPC_CARES_PROVIDER}" STREQUAL "module")
+  if(NOT CARES_ROOT_DIR)
+    set(CARES_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/c-ares)
+  endif()
+  string(TOLOWER ${CMAKE_SYSTEM_NAME} CARES_SYSTEM_NAME)
+  set(CARES_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/third_party/cares/cares")
+  set(CARES_BUILD_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/third_party/cares")
+  set(CARES_PLATFORM_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/third_party/cares/config_${CARES_SYSTEM_NAME}")
+  if(EXISTS "${CARES_ROOT_DIR}/CMakeLists.txt")
+    if("${CARES_SYSTEM_NAME}" MATCHES "windows")
+      add_definitions(-DCARES_STATICLIB=1)
+      add_definitions(-DWIN32_LEAN_AND_MEAN=1)
+    else()
+      add_definitions(-DHAVE_CONFIG_H=1)
+      add_definitions(-D_GNU_SOURCE=1)
+    endif()
+    add_subdirectory(src/c-ares third_party/cares)
+    if(TARGET cares)
+        set(_gRPC_CARES_LIBRARIES cares)
+    endif()
+  else()
+    message(WARNING "gRPC_CARES_PROVIDER is \"module\" but CARES_ROOT_DIR is wrong")
+  endif()
+  if(gRPC_INSTALL)
+    message(WARNING "gRPC_INSTALL will be forced to FALSE because gRPC_CARES_PROVIDER is \"module\"")
+    set(gRPC_INSTALL FALSE)
+  endif()
+elseif("${gRPC_CARES_PROVIDER}" STREQUAL "package")
+  find_package(c-ares CONFIG)
+  if(TARGET c-ares::cares)
+    set(_gRPC_CARES_LIBRARIES c-ares::cares)
+  endif()
+  set(_gRPC_FIND_CARES "if(NOT c-ares_FOUND)\n  find_package(c-ares CONFIG)\nendif()")
+endif()
+
+if("${gRPC_PROTOBUF_PROVIDER}" STREQUAL "module")
+  # Building the protobuf tests require gmock what is not part of a standard protobuf checkout.
+  # Disable them unless they are explicitly requested from the cmake command line (when we assume
+  # gmock is downloaded to the right location inside protobuf).
+  if(NOT protobuf_BUILD_TESTS)
+    set(protobuf_BUILD_TESTS OFF CACHE BOOL "Build protobuf tests")
+  endif()
+  # Disable building protobuf with zlib. Building protobuf with zlib breaks
+  # the build if zlib is not installed on the system.
+  if(NOT protobuf_WITH_ZLIB)
+    set(protobuf_WITH_ZLIB OFF CACHE BOOL "Build protobuf with zlib.")
+  endif()
+  if(NOT PROTOBUF_ROOT_DIR)
+    set(PROTOBUF_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party/protobuf)
+  endif()
+  set(PROTOBUF_WELLKNOWN_IMPORT_DIR ${PROTOBUF_ROOT_DIR}/src)
+  if(EXISTS "${PROTOBUF_ROOT_DIR}/cmake/CMakeLists.txt")
+    set(protobuf_MSVC_STATIC_RUNTIME OFF CACHE BOOL "Link static runtime libraries")
+    add_subdirectory(${PROTOBUF_ROOT_DIR}/cmake third_party/protobuf)
+    if(TARGET ${_gRPC_PROTOBUF_LIBRARY_NAME})
+      set(_gRPC_PROTOBUF_LIBRARIES ${_gRPC_PROTOBUF_LIBRARY_NAME})
+    endif()
+    if(TARGET libprotoc)
+      set(_gRPC_PROTOBUF_PROTOC_LIBRARIES libprotoc)
+    endif()
+    if(TARGET protoc)
+      set(_gRPC_PROTOBUF_PROTOC protoc)
+    endif()
+  else()
+      message(WARNING "gRPC_PROTOBUF_PROVIDER is \"module\" but PROTOBUF_ROOT_DIR is wrong")
+  endif()
+  if(gRPC_INSTALL)
+    message(WARNING "gRPC_INSTALL will be forced to FALSE because gRPC_PROTOBUF_PROVIDER is \"module\"")
+    set(gRPC_INSTALL FALSE)
+  endif()
+elseif("${gRPC_PROTOBUF_PROVIDER}" STREQUAL "package")
+  find_package(Protobuf ${gRPC_PROTOBUF_PACKAGE_TYPE})
+  if(Protobuf_FOUND OR PROTOBUF_FOUND)
+    if(TARGET protobuf::${_gRPC_PROTOBUF_LIBRARY_NAME})
+      set(_gRPC_PROTOBUF_LIBRARIES protobuf::${_gRPC_PROTOBUF_LIBRARY_NAME})
+    else()
+      set(_gRPC_PROTOBUF_LIBRARIES ${PROTOBUF_LIBRARIES})
+    endif()
+    if(TARGET protobuf::libprotoc)
+      set(_gRPC_PROTOBUF_PROTOC_LIBRARIES protobuf::libprotoc)
+    else()
+      set(_gRPC_PROTOBUF_PROTOC_LIBRARIES ${PROTOBUF_PROTOC_LIBRARIES})
+    endif()
+    if(TARGET protobuf::protoc)
+      set(_gRPC_PROTOBUF_PROTOC protobuf::protoc)
+    else()
+      set(_gRPC_PROTOBUF_PROTOC ${PROTOBUF_PROTOC_EXECUTABLE})
+    endif()
+    set(_gRPC_FIND_PROTOBUF "if(NOT Protobuf_FOUND AND NOT PROTOBUF_FOUND)\n  find_package(Protobuf ${gRPC_PROTOBUF_PACKAGE_TYPE})\nendif()")
+  endif()
+  if(PROTOBUF_FOUND)
+    include_directories(${PROTOBUF_INCLUDE_DIRS})
+  endif()
+  set(PROTOBUF_WELLKNOWN_IMPORT_DIR /usr/local/include)
+endif()
+
+if("${gRPC_SSL_PROVIDER}" STREQUAL "module")
+  if(NOT BORINGSSL_ROOT_DIR)
+    set(BORINGSSL_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party/boringssl)
+  endif()
+  if(EXISTS "${BORINGSSL_ROOT_DIR}/CMakeLists.txt")
+    set(OPENSSL_NO_ASM ON)  # make boringssl buildable with Visual Studio
+    add_subdirectory(${BORINGSSL_ROOT_DIR} third_party/boringssl)
+    if(TARGET ssl)
+      set(_gRPC_SSL_LIBRARIES ssl)
+    endif()
+  else()
+      message(WARNING "gRPC_SSL_PROVIDER is \"module\" but BORINGSSL_ROOT_DIR is wrong")
+  endif()
+  if(gRPC_INSTALL)
+    message(WARNING "gRPC_INSTALL will be forced to FALSE because gRPC_SSL_PROVIDER is \"module\"")
+    set(gRPC_INSTALL FALSE)
+  endif()
+elseif("${gRPC_SSL_PROVIDER}" STREQUAL "package")
+  find_package(OpenSSL)
+  if(TARGET OpenSSL::SSL)
+    set(_gRPC_SSL_LIBRARIES OpenSSL::SSL)
+  endif()
+  set(_gRPC_FIND_SSL "if(NOT OpenSSL_FOUND)\n  find_package(OpenSSL)\nendif()")
+endif()
+
+if("${gRPC_GFLAGS_PROVIDER}" STREQUAL "module")
+  if(NOT GFLAGS_ROOT_DIR)
+    set(GFLAGS_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party/gflags)
+  endif()
+  if(EXISTS "${GFLAGS_ROOT_DIR}/CMakeLists.txt")
+      add_subdirectory(${GFLAGS_ROOT_DIR} third_party/gflags)
+      if(TARGET gflags_static)
+          set(_gRPC_GFLAGS_LIBRARIES gflags_static)
+      endif()
+  else()
+      message(WARNING "gRPC_GFLAGS_PROVIDER is \"module\" but GFLAGS_ROOT_DIR is wrong")
+  endif()
+elseif("${gRPC_GFLAGS_PROVIDER}" STREQUAL "package")
+  find_package(gflags)
+  if(TARGET gflags::gflags)
+    set(_gRPC_GFLAGS_LIBRARIES gflags::gflags)
+  endif()
+  set(_gRPC_FIND_GFLAGS "if(NOT gflags_FOUND)\n  find_package(gflags)\nendif()")
+endif()
+
+if("${gRPC_BENCHMARK_PROVIDER}" STREQUAL "module")
+  if(NOT BENCHMARK_ROOT_DIR)
+    set(BENCHMARK_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party/benchmark)
+  endif()
+  if(EXISTS "${BENCHMARK_ROOT_DIR}/CMakeLists.txt")
+      add_subdirectory(${BENCHMARK_ROOT_DIR} third_party/benchmark)
+      if(TARGET benchmark)
+          set(_gRPC_BENCHMARK_LIBRARIES benchmark)
+      endif()
+  else()
+      message(WARNING "gRPC_BENCHMARK_PROVIDER is \"module\" but BENCHMARK_ROOT_DIR is wrong")
+  endif()
+elseif("${gRPC_BENCHMARK_PROVIDER}" STREQUAL "package")
+  find_package(benchmark)
+  if(TARGET benchmark::benchmark)
+    set(_gRPC_BENCHMARK_LIBRARIES benchmark::benchmark)
+  endif()
+  set(_gRPC_FIND_BENCHMARK "if(NOT benchmark_FOUND)\n  find_package(benchmark)\nendif()")
+endif()
+
+if(NOT MSVC)
+  set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} -std=c99")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+endif()
+
+if(_gRPC_PLATFORM_MAC)
+  set(_gRPC_ALLTARGETS_LIBRARIES ${CMAKE_DL_LIBS} m pthread)
+elseif(UNIX)
+  set(_gRPC_ALLTARGETS_LIBRARIES ${CMAKE_DL_LIBS} rt m pthread)
+endif()
+
+if(WIN32 AND MSVC)
+  set(_gRPC_BASELIB_LIBRARIES wsock32 ws2_32)
+endif()
+
+# Create directory for generated .proto files
+set(_gRPC_PROTO_GENS_DIR ${CMAKE_BINARY_DIR}/gens)
+file(MAKE_DIRECTORY ${_gRPC_PROTO_GENS_DIR})
+
+#  protobuf_generate_grpc_cpp
+#  --------------------------
+#
+#   Add custom commands to process ``.proto`` files to C++ using protoc and
+#   GRPC plugin::
+#
+#     protobuf_generate_grpc_cpp [<ARGN>...]
+#
+#   ``ARGN``
+#     ``.proto`` files
+#
+function(protobuf_generate_grpc_cpp)
+  if(NOT ARGN)
+    message(SEND_ERROR "Error: PROTOBUF_GENERATE_GRPC_CPP() called without any proto files")
+    return()
+  endif()
+
+  set(_protobuf_include_path -I . -I ${PROTOBUF_WELLKNOWN_IMPORT_DIR})
+  foreach(FIL ${ARGN})
+    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
+    get_filename_component(FIL_WE ${FIL} NAME_WE)
+    file(RELATIVE_PATH REL_FIL ${CMAKE_CURRENT_SOURCE_DIR} ${ABS_FIL})
+    get_filename_component(REL_DIR ${REL_FIL} DIRECTORY)
+    set(RELFIL_WE "${REL_DIR}/${FIL_WE}")
+
+    add_custom_command(
+      OUTPUT "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.grpc.pb.cc"
+             "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.grpc.pb.h"
+             "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}_mock.grpc.pb.h"
+             "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.pb.cc"
+             "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.pb.h"
+      COMMAND $<TARGET_FILE:${_gRPC_PROTOBUF_PROTOC}>
+      ARGS --grpc_out=generate_mock_code=true:${_gRPC_PROTO_GENS_DIR}
+           --cpp_out=${_gRPC_PROTO_GENS_DIR}
+           --plugin=protoc-gen-grpc=$<TARGET_FILE:grpc_cpp_plugin>
+           ${_protobuf_include_path}
+           ${REL_FIL}
+      DEPENDS ${ABS_FIL} ${_gRPC_PROTOBUF_PROTOC} grpc_cpp_plugin
+      WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+      COMMENT "Running gRPC C++ protocol buffer compiler on ${FIL}"
+      VERBATIM)
+
+      set_source_files_properties("${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.grpc.pb.cc" "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.grpc.pb.h"  "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}_mock.grpc.pb.h" "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.pb.cc" "${_gRPC_PROTO_GENS_DIR}/${RELFIL_WE}.pb.h" PROPERTIES GENERATED TRUE)
+  endforeach()
+endfunction()
+
+add_custom_target(plugins
+  DEPENDS
+  grpc_cpp_plugin
+  grpc_csharp_plugin
+  grpc_node_plugin
+  grpc_objective_c_plugin
+  grpc_php_plugin
+  grpc_python_plugin
+  grpc_ruby_plugin
+)
+
+add_custom_target(tools_c
+  DEPENDS
+  check_epollexclusive
+  gen_hpack_tables
+  gen_legal_metadata_characters
+  gen_percent_encoding_tables
+  grpc_create_jwt
+  grpc_print_google_default_creds_token
+  grpc_verify_jwt
+)
+
+add_custom_target(tools_cxx
+  DEPENDS
+)
+
+add_custom_target(tools
+  DEPENDS tools_c tools_cxx)
+
+if (gRPC_BUILD_TESTS)
+add_custom_target(buildtests_c)
+add_dependencies(buildtests_c alarm_test)
+add_dependencies(buildtests_c algorithm_test)
+add_dependencies(buildtests_c alloc_test)
+add_dependencies(buildtests_c alpn_test)
+add_dependencies(buildtests_c arena_test)
+add_dependencies(buildtests_c bad_server_response_test)
+add_dependencies(buildtests_c bdp_estimator_test)
+add_dependencies(buildtests_c bin_decoder_test)
+add_dependencies(buildtests_c bin_encoder_test)
+add_dependencies(buildtests_c census_context_test)
+add_dependencies(buildtests_c census_intrusive_hash_map_test)
+add_dependencies(buildtests_c census_resource_test)
+add_dependencies(buildtests_c census_trace_context_test)
+add_dependencies(buildtests_c channel_create_test)
+add_dependencies(buildtests_c chttp2_hpack_encoder_test)
+add_dependencies(buildtests_c chttp2_stream_map_test)
+add_dependencies(buildtests_c chttp2_varint_test)
+add_dependencies(buildtests_c combiner_test)
+add_dependencies(buildtests_c compression_test)
+add_dependencies(buildtests_c concurrent_connectivity_test)
+add_dependencies(buildtests_c connection_refused_test)
+add_dependencies(buildtests_c dns_resolver_connectivity_test)
+add_dependencies(buildtests_c dns_resolver_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c dualstack_socket_test)
+endif()
+add_dependencies(buildtests_c endpoint_pair_test)
+add_dependencies(buildtests_c error_test)
+if(_gRPC_PLATFORM_LINUX)
+add_dependencies(buildtests_c ev_epollsig_linux_test)
+endif()
+add_dependencies(buildtests_c fake_resolver_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c fd_conservation_posix_test)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c fd_posix_test)
+endif()
+add_dependencies(buildtests_c fling_client)
+add_dependencies(buildtests_c fling_server)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c fling_stream_test)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c fling_test)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c goaway_server_test)
+endif()
+add_dependencies(buildtests_c gpr_avl_test)
+add_dependencies(buildtests_c gpr_backoff_test)
+add_dependencies(buildtests_c gpr_cmdline_test)
+add_dependencies(buildtests_c gpr_cpu_test)
+add_dependencies(buildtests_c gpr_env_test)
+add_dependencies(buildtests_c gpr_histogram_test)
+add_dependencies(buildtests_c gpr_host_port_test)
+add_dependencies(buildtests_c gpr_log_test)
+add_dependencies(buildtests_c gpr_mpscq_test)
+add_dependencies(buildtests_c gpr_spinlock_test)
+add_dependencies(buildtests_c gpr_stack_lockfree_test)
+add_dependencies(buildtests_c gpr_string_test)
+add_dependencies(buildtests_c gpr_sync_test)
+add_dependencies(buildtests_c gpr_thd_test)
+add_dependencies(buildtests_c gpr_time_test)
+add_dependencies(buildtests_c gpr_tls_test)
+add_dependencies(buildtests_c gpr_useful_test)
+add_dependencies(buildtests_c grpc_auth_context_test)
+add_dependencies(buildtests_c grpc_b64_test)
+add_dependencies(buildtests_c grpc_byte_buffer_reader_test)
+add_dependencies(buildtests_c grpc_channel_args_test)
+add_dependencies(buildtests_c grpc_channel_stack_test)
+add_dependencies(buildtests_c grpc_completion_queue_test)
+add_dependencies(buildtests_c grpc_completion_queue_threading_test)
+add_dependencies(buildtests_c grpc_credentials_test)
+add_dependencies(buildtests_c grpc_fetch_oauth2)
+add_dependencies(buildtests_c grpc_invalid_channel_args_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c grpc_json_token_test)
+endif()
+add_dependencies(buildtests_c grpc_jwt_verifier_test)
+add_dependencies(buildtests_c grpc_security_connector_test)
+if(_gRPC_PLATFORM_LINUX)
+add_dependencies(buildtests_c handshake_client)
+endif()
+if(_gRPC_PLATFORM_LINUX)
+add_dependencies(buildtests_c handshake_server)
+endif()
+add_dependencies(buildtests_c hpack_parser_test)
+add_dependencies(buildtests_c hpack_table_test)
+add_dependencies(buildtests_c http_parser_test)
+add_dependencies(buildtests_c httpcli_format_request_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c httpcli_test)
+endif()
+if(_gRPC_PLATFORM_LINUX)
+add_dependencies(buildtests_c httpscli_test)
+endif()
+add_dependencies(buildtests_c init_test)
+add_dependencies(buildtests_c invalid_call_argument_test)
+add_dependencies(buildtests_c json_rewrite)
+add_dependencies(buildtests_c json_rewrite_test)
+add_dependencies(buildtests_c json_stream_error_test)
+add_dependencies(buildtests_c json_test)
+add_dependencies(buildtests_c lame_client_test)
+add_dependencies(buildtests_c lb_policies_test)
+add_dependencies(buildtests_c load_file_test)
+add_dependencies(buildtests_c memory_profile_client)
+add_dependencies(buildtests_c memory_profile_server)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c memory_profile_test)
+endif()
+add_dependencies(buildtests_c message_compress_test)
+add_dependencies(buildtests_c minimal_stack_is_minimal_test)
+add_dependencies(buildtests_c mlog_test)
+add_dependencies(buildtests_c multiple_server_queues_test)
+add_dependencies(buildtests_c murmur_hash_test)
+add_dependencies(buildtests_c no_server_test)
+add_dependencies(buildtests_c num_external_connectivity_watchers_test)
+add_dependencies(buildtests_c parse_address_test)
+add_dependencies(buildtests_c percent_encoding_test)
+if(_gRPC_PLATFORM_LINUX)
+add_dependencies(buildtests_c pollset_set_test)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c resolve_address_posix_test)
+endif()
+add_dependencies(buildtests_c resolve_address_test)
+add_dependencies(buildtests_c resource_quota_test)
+add_dependencies(buildtests_c secure_channel_create_test)
+add_dependencies(buildtests_c secure_endpoint_test)
+add_dependencies(buildtests_c sequential_connectivity_test)
+add_dependencies(buildtests_c server_chttp2_test)
+add_dependencies(buildtests_c server_test)
+add_dependencies(buildtests_c slice_buffer_test)
+add_dependencies(buildtests_c slice_hash_table_test)
+add_dependencies(buildtests_c slice_string_helpers_test)
+add_dependencies(buildtests_c slice_test)
+add_dependencies(buildtests_c sockaddr_resolver_test)
+add_dependencies(buildtests_c sockaddr_utils_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c socket_utils_test)
+endif()
+add_dependencies(buildtests_c status_conversion_test)
+add_dependencies(buildtests_c stream_compression_test)
+add_dependencies(buildtests_c stream_owned_slice_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c tcp_client_posix_test)
+endif()
+add_dependencies(buildtests_c tcp_client_uv_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c tcp_posix_test)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c tcp_server_posix_test)
+endif()
+add_dependencies(buildtests_c tcp_server_uv_test)
+add_dependencies(buildtests_c time_averaged_stats_test)
+add_dependencies(buildtests_c timeout_encoding_test)
+add_dependencies(buildtests_c timer_heap_test)
+add_dependencies(buildtests_c timer_list_test)
+add_dependencies(buildtests_c transport_connectivity_state_test)
+add_dependencies(buildtests_c transport_metadata_test)
+add_dependencies(buildtests_c transport_pid_controller_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c transport_security_test)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c udp_server_test)
+endif()
+add_dependencies(buildtests_c uri_parser_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c wakeup_fd_cv_test)
+endif()
+add_dependencies(buildtests_c public_headers_must_be_c89)
+add_dependencies(buildtests_c badreq_bad_client_test)
+add_dependencies(buildtests_c connection_prefix_bad_client_test)
+add_dependencies(buildtests_c head_of_line_blocking_bad_client_test)
+add_dependencies(buildtests_c headers_bad_client_test)
+add_dependencies(buildtests_c initial_settings_frame_bad_client_test)
+add_dependencies(buildtests_c large_metadata_bad_client_test)
+add_dependencies(buildtests_c server_registered_method_bad_client_test)
+add_dependencies(buildtests_c simple_request_bad_client_test)
+add_dependencies(buildtests_c unknown_frame_bad_client_test)
+add_dependencies(buildtests_c window_overflow_bad_client_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c bad_ssl_cert_server)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c bad_ssl_cert_test)
+endif()
+add_dependencies(buildtests_c h2_census_test)
+add_dependencies(buildtests_c h2_compress_test)
+add_dependencies(buildtests_c h2_fakesec_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c h2_fd_test)
+endif()
+add_dependencies(buildtests_c h2_full_test)
+if(_gRPC_PLATFORM_LINUX)
+add_dependencies(buildtests_c h2_full+pipe_test)
+endif()
+add_dependencies(buildtests_c h2_full+trace_test)
+add_dependencies(buildtests_c h2_full+workarounds_test)
+add_dependencies(buildtests_c h2_http_proxy_test)
+add_dependencies(buildtests_c h2_load_reporting_test)
+add_dependencies(buildtests_c h2_oauth2_test)
+add_dependencies(buildtests_c h2_proxy_test)
+add_dependencies(buildtests_c h2_sockpair_test)
+add_dependencies(buildtests_c h2_sockpair+trace_test)
+add_dependencies(buildtests_c h2_sockpair_1byte_test)
+add_dependencies(buildtests_c h2_ssl_test)
+add_dependencies(buildtests_c h2_ssl_cert_test)
+add_dependencies(buildtests_c h2_ssl_proxy_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c h2_uds_test)
+endif()
+add_dependencies(buildtests_c inproc_test)
+add_dependencies(buildtests_c h2_census_nosec_test)
+add_dependencies(buildtests_c h2_compress_nosec_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c h2_fd_nosec_test)
+endif()
+add_dependencies(buildtests_c h2_full_nosec_test)
+if(_gRPC_PLATFORM_LINUX)
+add_dependencies(buildtests_c h2_full+pipe_nosec_test)
+endif()
+add_dependencies(buildtests_c h2_full+trace_nosec_test)
+add_dependencies(buildtests_c h2_full+workarounds_nosec_test)
+add_dependencies(buildtests_c h2_http_proxy_nosec_test)
+add_dependencies(buildtests_c h2_load_reporting_nosec_test)
+add_dependencies(buildtests_c h2_proxy_nosec_test)
+add_dependencies(buildtests_c h2_sockpair_nosec_test)
+add_dependencies(buildtests_c h2_sockpair+trace_nosec_test)
+add_dependencies(buildtests_c h2_sockpair_1byte_nosec_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_c h2_uds_nosec_test)
+endif()
+add_dependencies(buildtests_c inproc_nosec_test)
+add_dependencies(buildtests_c api_fuzzer_one_entry)
+add_dependencies(buildtests_c client_fuzzer_one_entry)
+add_dependencies(buildtests_c hpack_parser_fuzzer_test_one_entry)
+add_dependencies(buildtests_c http_request_fuzzer_test_one_entry)
+add_dependencies(buildtests_c http_response_fuzzer_test_one_entry)
+add_dependencies(buildtests_c json_fuzzer_test_one_entry)
+add_dependencies(buildtests_c nanopb_fuzzer_response_test_one_entry)
+add_dependencies(buildtests_c nanopb_fuzzer_serverlist_test_one_entry)
+add_dependencies(buildtests_c percent_decode_fuzzer_one_entry)
+add_dependencies(buildtests_c percent_encode_fuzzer_one_entry)
+add_dependencies(buildtests_c server_fuzzer_one_entry)
+add_dependencies(buildtests_c ssl_server_fuzzer_one_entry)
+add_dependencies(buildtests_c uri_fuzzer_test_one_entry)
+
+add_custom_target(buildtests_cxx)
+add_dependencies(buildtests_cxx alarm_cpp_test)
+add_dependencies(buildtests_cxx async_end2end_test)
+add_dependencies(buildtests_cxx auth_property_iterator_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_arena)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_call_create)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_chttp2_hpack)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_chttp2_transport)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_closure)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_cq)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_cq_multiple_threads)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_error)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_fullstack_streaming_ping_pong)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_fullstack_streaming_pump)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_fullstack_trickle)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_fullstack_unary_ping_pong)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_metadata)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx bm_pollset)
+endif()
+add_dependencies(buildtests_cxx channel_arguments_test)
+add_dependencies(buildtests_cxx channel_filter_test)
+add_dependencies(buildtests_cxx cli_call_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx client_crash_test)
+endif()
+add_dependencies(buildtests_cxx client_crash_test_server)
+add_dependencies(buildtests_cxx client_lb_end2end_test)
+add_dependencies(buildtests_cxx codegen_test_full)
+add_dependencies(buildtests_cxx codegen_test_minimal)
+add_dependencies(buildtests_cxx credentials_test)
+add_dependencies(buildtests_cxx cxx_byte_buffer_test)
+add_dependencies(buildtests_cxx cxx_slice_test)
+add_dependencies(buildtests_cxx cxx_string_ref_test)
+add_dependencies(buildtests_cxx cxx_time_test)
+add_dependencies(buildtests_cxx end2end_test)
+add_dependencies(buildtests_cxx error_details_test)
+add_dependencies(buildtests_cxx filter_end2end_test)
+add_dependencies(buildtests_cxx generic_end2end_test)
+add_dependencies(buildtests_cxx golden_file_test)
+add_dependencies(buildtests_cxx grpc_cli)
+add_dependencies(buildtests_cxx grpc_tool_test)
+add_dependencies(buildtests_cxx grpclb_api_test)
+add_dependencies(buildtests_cxx grpclb_end2end_test)
+add_dependencies(buildtests_cxx grpclb_test)
+add_dependencies(buildtests_cxx health_service_end2end_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx http2_client)
+endif()
+add_dependencies(buildtests_cxx hybrid_end2end_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx interop_client)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx interop_server)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx interop_test)
+endif()
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx json_run_localhost)
+endif()
+add_dependencies(buildtests_cxx memory_test)
+add_dependencies(buildtests_cxx metrics_client)
+add_dependencies(buildtests_cxx mock_test)
+add_dependencies(buildtests_cxx noop-benchmark)
+add_dependencies(buildtests_cxx proto_server_reflection_test)
+add_dependencies(buildtests_cxx proto_utils_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx qps_interarrival_test)
+endif()
+add_dependencies(buildtests_cxx qps_json_driver)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx qps_openloop_test)
+endif()
+add_dependencies(buildtests_cxx qps_worker)
+add_dependencies(buildtests_cxx reconnect_interop_client)
+add_dependencies(buildtests_cxx reconnect_interop_server)
+add_dependencies(buildtests_cxx secure_auth_context_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx secure_sync_unary_ping_pong_test)
+endif()
+add_dependencies(buildtests_cxx server_builder_plugin_test)
+add_dependencies(buildtests_cxx server_builder_test)
+add_dependencies(buildtests_cxx server_context_test_spouse_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx server_crash_test)
+endif()
+add_dependencies(buildtests_cxx server_crash_test_client)
+add_dependencies(buildtests_cxx server_request_call_test)
+add_dependencies(buildtests_cxx shutdown_test)
+add_dependencies(buildtests_cxx status_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx streaming_throughput_test)
+endif()
+add_dependencies(buildtests_cxx stress_test)
+add_dependencies(buildtests_cxx thread_manager_test)
+add_dependencies(buildtests_cxx thread_stress_test)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+add_dependencies(buildtests_cxx writes_per_rpc_test)
+endif()
+
+add_custom_target(buildtests
+  DEPENDS buildtests_c buildtests_cxx)
+endif (gRPC_BUILD_TESTS)
+
 
 add_library(gpr
   src/core/lib/profiling/basic_timers.c
   src/core/lib/profiling/stap_timers.c
   src/core/lib/support/alloc.c
+  src/core/lib/support/arena.c
+  src/core/lib/support/atm.c
   src/core/lib/support/avl.c
   src/core/lib/support/backoff.c
   src/core/lib/support/cmdline.c
@@ -73,9 +792,8 @@ add_library(gpr
   src/core/lib/support/log_linux.c
   src/core/lib/support/log_posix.c
   src/core/lib/support/log_windows.c
+  src/core/lib/support/mpscq.c
   src/core/lib/support/murmur_hash.c
-  src/core/lib/support/slice.c
-  src/core/lib/support/slice_buffer.c
   src/core/lib/support/stack_lockfree.c
   src/core/lib/support/string.c
   src/core/lib/support/string_posix.c
@@ -100,78 +818,228 @@ add_library(gpr
   src/core/lib/support/wrap_memcpy.c
 )
 
+if(WIN32 AND MSVC)
+  set_target_properties(gpr PROPERTIES COMPILE_PDB_NAME "gpr"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/gpr.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
 target_include_directories(gpr
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
-  PRIVATE ${PROTOBUF_INCLUDE_DIRS}
-  PRIVATE ${ZLIB_INCLUDE_DIRS}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
 )
 
-add_library(grpc_unsecure
+target_link_libraries(gpr
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+)
+
+foreach(_hdr
+  include/grpc/support/alloc.h
+  include/grpc/support/atm.h
+  include/grpc/support/atm_gcc_atomic.h
+  include/grpc/support/atm_gcc_sync.h
+  include/grpc/support/atm_windows.h
+  include/grpc/support/avl.h
+  include/grpc/support/cmdline.h
+  include/grpc/support/cpu.h
+  include/grpc/support/histogram.h
+  include/grpc/support/host_port.h
+  include/grpc/support/log.h
+  include/grpc/support/log_windows.h
+  include/grpc/support/port_platform.h
+  include/grpc/support/string_util.h
+  include/grpc/support/subprocess.h
+  include/grpc/support/sync.h
+  include/grpc/support/sync_generic.h
+  include/grpc/support/sync_posix.h
+  include/grpc/support/sync_windows.h
+  include/grpc/support/thd.h
+  include/grpc/support/time.h
+  include/grpc/support/tls.h
+  include/grpc/support/tls_gcc.h
+  include/grpc/support/tls_msvc.h
+  include/grpc/support/tls_pthread.h
+  include/grpc/support/useful.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS gpr EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(gpr_test_util
+  test/core/util/test_config.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(gpr_test_util PROPERTIES COMPILE_PDB_NAME "gpr_test_util"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/gpr_test_util.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(gpr_test_util
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_test_util
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+
+add_library(grpc
   src/core/lib/surface/init.c
-  src/core/lib/surface/init_unsecure.c
   src/core/lib/channel/channel_args.c
   src/core/lib/channel/channel_stack.c
   src/core/lib/channel/channel_stack_builder.c
-  src/core/lib/channel/compress_filter.c
   src/core/lib/channel/connected_channel.c
-  src/core/lib/channel/http_client_filter.c
-  src/core/lib/channel/http_server_filter.c
+  src/core/lib/channel/handshaker.c
+  src/core/lib/channel/handshaker_factory.c
+  src/core/lib/channel/handshaker_registry.c
   src/core/lib/compression/compression.c
   src/core/lib/compression/message_compress.c
-  src/core/lib/debug/trace.c
+  src/core/lib/compression/stream_compression.c
   src/core/lib/http/format_request.c
   src/core/lib/http/httpcli.c
   src/core/lib/http/parser.c
   src/core/lib/iomgr/closure.c
+  src/core/lib/iomgr/combiner.c
   src/core/lib/iomgr/endpoint.c
   src/core/lib/iomgr/endpoint_pair_posix.c
+  src/core/lib/iomgr/endpoint_pair_uv.c
   src/core/lib/iomgr/endpoint_pair_windows.c
   src/core/lib/iomgr/error.c
-  src/core/lib/iomgr/ev_epoll_linux.c
-  src/core/lib/iomgr/ev_poll_and_epoll_posix.c
+  src/core/lib/iomgr/ev_epoll1_linux.c
+  src/core/lib/iomgr/ev_epoll_limited_pollers_linux.c
+  src/core/lib/iomgr/ev_epoll_thread_pool_linux.c
+  src/core/lib/iomgr/ev_epollex_linux.c
+  src/core/lib/iomgr/ev_epollsig_linux.c
   src/core/lib/iomgr/ev_poll_posix.c
   src/core/lib/iomgr/ev_posix.c
+  src/core/lib/iomgr/ev_windows.c
   src/core/lib/iomgr/exec_ctx.c
   src/core/lib/iomgr/executor.c
   src/core/lib/iomgr/iocp_windows.c
   src/core/lib/iomgr/iomgr.c
   src/core/lib/iomgr/iomgr_posix.c
+  src/core/lib/iomgr/iomgr_uv.c
   src/core/lib/iomgr/iomgr_windows.c
+  src/core/lib/iomgr/is_epollexclusive_available.c
   src/core/lib/iomgr/load_file.c
+  src/core/lib/iomgr/lockfree_event.c
   src/core/lib/iomgr/network_status_tracker.c
   src/core/lib/iomgr/polling_entity.c
+  src/core/lib/iomgr/pollset_set_uv.c
   src/core/lib/iomgr/pollset_set_windows.c
+  src/core/lib/iomgr/pollset_uv.c
   src/core/lib/iomgr/pollset_windows.c
   src/core/lib/iomgr/resolve_address_posix.c
+  src/core/lib/iomgr/resolve_address_uv.c
   src/core/lib/iomgr/resolve_address_windows.c
+  src/core/lib/iomgr/resource_quota.c
   src/core/lib/iomgr/sockaddr_utils.c
+  src/core/lib/iomgr/socket_factory_posix.c
+  src/core/lib/iomgr/socket_mutator.c
   src/core/lib/iomgr/socket_utils_common_posix.c
   src/core/lib/iomgr/socket_utils_linux.c
   src/core/lib/iomgr/socket_utils_posix.c
+  src/core/lib/iomgr/socket_utils_uv.c
+  src/core/lib/iomgr/socket_utils_windows.c
   src/core/lib/iomgr/socket_windows.c
   src/core/lib/iomgr/tcp_client_posix.c
+  src/core/lib/iomgr/tcp_client_uv.c
   src/core/lib/iomgr/tcp_client_windows.c
   src/core/lib/iomgr/tcp_posix.c
   src/core/lib/iomgr/tcp_server_posix.c
+  src/core/lib/iomgr/tcp_server_utils_posix_common.c
+  src/core/lib/iomgr/tcp_server_utils_posix_ifaddrs.c
+  src/core/lib/iomgr/tcp_server_utils_posix_noifaddrs.c
+  src/core/lib/iomgr/tcp_server_uv.c
   src/core/lib/iomgr/tcp_server_windows.c
+  src/core/lib/iomgr/tcp_uv.c
   src/core/lib/iomgr/tcp_windows.c
   src/core/lib/iomgr/time_averaged_stats.c
-  src/core/lib/iomgr/timer.c
+  src/core/lib/iomgr/timer_generic.c
   src/core/lib/iomgr/timer_heap.c
+  src/core/lib/iomgr/timer_manager.c
+  src/core/lib/iomgr/timer_uv.c
   src/core/lib/iomgr/udp_server.c
   src/core/lib/iomgr/unix_sockets_posix.c
   src/core/lib/iomgr/unix_sockets_posix_noop.c
+  src/core/lib/iomgr/wakeup_fd_cv.c
   src/core/lib/iomgr/wakeup_fd_eventfd.c
   src/core/lib/iomgr/wakeup_fd_nospecial.c
   src/core/lib/iomgr/wakeup_fd_pipe.c
   src/core/lib/iomgr/wakeup_fd_posix.c
-  src/core/lib/iomgr/workqueue_posix.c
-  src/core/lib/iomgr/workqueue_windows.c
   src/core/lib/json/json.c
   src/core/lib/json/json_reader.c
   src/core/lib/json/json_string.c
   src/core/lib/json/json_writer.c
+  src/core/lib/slice/b64.c
+  src/core/lib/slice/percent_encoding.c
+  src/core/lib/slice/slice.c
+  src/core/lib/slice/slice_buffer.c
+  src/core/lib/slice/slice_hash_table.c
+  src/core/lib/slice/slice_intern.c
+  src/core/lib/slice/slice_string_helpers.c
   src/core/lib/surface/alarm.c
   src/core/lib/surface/api_trace.c
   src/core/lib/surface/byte_buffer.c
@@ -184,21 +1052,28 @@ add_library(grpc_unsecure
   src/core/lib/surface/channel_ping.c
   src/core/lib/surface/channel_stack_type.c
   src/core/lib/surface/completion_queue.c
+  src/core/lib/surface/completion_queue_factory.c
   src/core/lib/surface/event_string.c
-  src/core/lib/surface/lame_client.c
+  src/core/lib/surface/lame_client.cc
   src/core/lib/surface/metadata_array.c
   src/core/lib/surface/server.c
   src/core/lib/surface/validate_metadata.c
   src/core/lib/surface/version.c
+  src/core/lib/transport/bdp_estimator.c
   src/core/lib/transport/byte_stream.c
   src/core/lib/transport/connectivity_state.c
+  src/core/lib/transport/error_utils.c
   src/core/lib/transport/metadata.c
   src/core/lib/transport/metadata_batch.c
+  src/core/lib/transport/pid_controller.c
+  src/core/lib/transport/service_config.c
   src/core/lib/transport/static_metadata.c
+  src/core/lib/transport/status_conversion.c
+  src/core/lib/transport/timeout_encoding.c
   src/core/lib/transport/transport.c
   src/core/lib/transport/transport_op_string.c
-  src/core/ext/transport/chttp2/server/insecure/server_chttp2.c
-  src/core/ext/transport/chttp2/server/insecure/server_chttp2_posix.c
+  src/core/lib/debug/trace.c
+  src/core/ext/transport/chttp2/server/secure/server_secure_chttp2.c
   src/core/ext/transport/chttp2/transport/bin_decoder.c
   src/core/ext/transport/chttp2/transport/bin_encoder.c
   src/core/ext/transport/chttp2/transport/chttp2_plugin.c
@@ -212,112 +1087,13329 @@ add_library(grpc_unsecure
   src/core/ext/transport/chttp2/transport/hpack_encoder.c
   src/core/ext/transport/chttp2/transport/hpack_parser.c
   src/core/ext/transport/chttp2/transport/hpack_table.c
+  src/core/ext/transport/chttp2/transport/http2_settings.c
   src/core/ext/transport/chttp2/transport/huffsyms.c
   src/core/ext/transport/chttp2/transport/incoming_metadata.c
   src/core/ext/transport/chttp2/transport/parsing.c
-  src/core/ext/transport/chttp2/transport/status_conversion.c
   src/core/ext/transport/chttp2/transport/stream_lists.c
   src/core/ext/transport/chttp2/transport/stream_map.c
-  src/core/ext/transport/chttp2/transport/timeout_encoding.c
   src/core/ext/transport/chttp2/transport/varint.c
   src/core/ext/transport/chttp2/transport/writing.c
   src/core/ext/transport/chttp2/alpn/alpn.c
+  src/core/ext/filters/http/client/http_client_filter.c
+  src/core/ext/filters/http/http_filters_plugin.c
+  src/core/ext/filters/http/message_compress/message_compress_filter.c
+  src/core/ext/filters/http/server/http_server_filter.c
+  src/core/lib/http/httpcli_security_connector.c
+  src/core/lib/security/context/security_context.c
+  src/core/lib/security/credentials/composite/composite_credentials.c
+  src/core/lib/security/credentials/credentials.c
+  src/core/lib/security/credentials/credentials_metadata.c
+  src/core/lib/security/credentials/fake/fake_credentials.c
+  src/core/lib/security/credentials/google_default/credentials_generic.c
+  src/core/lib/security/credentials/google_default/google_default_credentials.c
+  src/core/lib/security/credentials/iam/iam_credentials.c
+  src/core/lib/security/credentials/jwt/json_token.c
+  src/core/lib/security/credentials/jwt/jwt_credentials.c
+  src/core/lib/security/credentials/jwt/jwt_verifier.c
+  src/core/lib/security/credentials/oauth2/oauth2_credentials.c
+  src/core/lib/security/credentials/plugin/plugin_credentials.c
+  src/core/lib/security/credentials/ssl/ssl_credentials.c
+  src/core/lib/security/transport/client_auth_filter.c
+  src/core/lib/security/transport/lb_targets_info.c
+  src/core/lib/security/transport/secure_endpoint.c
+  src/core/lib/security/transport/security_connector.c
+  src/core/lib/security/transport/security_handshaker.c
+  src/core/lib/security/transport/server_auth_filter.c
+  src/core/lib/security/transport/tsi_error.c
+  src/core/lib/security/util/json_util.c
+  src/core/lib/surface/init_secure.c
+  src/core/tsi/fake_transport_security.c
+  src/core/tsi/gts_transport_security.c
+  src/core/tsi/ssl_transport_security.c
+  src/core/tsi/transport_security.c
+  src/core/tsi/transport_security_adapter.c
+  src/core/ext/transport/chttp2/server/chttp2_server.c
+  src/core/ext/transport/chttp2/client/secure/secure_channel_create.c
+  src/core/ext/filters/client_channel/channel_connectivity.c
+  src/core/ext/filters/client_channel/client_channel.c
+  src/core/ext/filters/client_channel/client_channel_factory.c
+  src/core/ext/filters/client_channel/client_channel_plugin.c
+  src/core/ext/filters/client_channel/connector.c
+  src/core/ext/filters/client_channel/http_connect_handshaker.c
+  src/core/ext/filters/client_channel/http_proxy.c
+  src/core/ext/filters/client_channel/lb_policy.c
+  src/core/ext/filters/client_channel/lb_policy_factory.c
+  src/core/ext/filters/client_channel/lb_policy_registry.c
+  src/core/ext/filters/client_channel/parse_address.c
+  src/core/ext/filters/client_channel/proxy_mapper.c
+  src/core/ext/filters/client_channel/proxy_mapper_registry.c
+  src/core/ext/filters/client_channel/resolver.c
+  src/core/ext/filters/client_channel/resolver_factory.c
+  src/core/ext/filters/client_channel/resolver_registry.c
+  src/core/ext/filters/client_channel/retry_throttle.c
+  src/core/ext/filters/client_channel/subchannel.c
+  src/core/ext/filters/client_channel/subchannel_index.c
+  src/core/ext/filters/client_channel/uri_parser.c
+  src/core/ext/filters/deadline/deadline_filter.c
+  src/core/ext/transport/chttp2/client/chttp2_connector.c
+  src/core/ext/transport/chttp2/server/insecure/server_chttp2.c
+  src/core/ext/transport/chttp2/server/insecure/server_chttp2_posix.c
   src/core/ext/transport/chttp2/client/insecure/channel_create.c
   src/core/ext/transport/chttp2/client/insecure/channel_create_posix.c
-  src/core/ext/client_config/channel_connectivity.c
-  src/core/ext/client_config/client_channel.c
-  src/core/ext/client_config/client_channel_factory.c
-  src/core/ext/client_config/client_config.c
-  src/core/ext/client_config/client_config_plugin.c
-  src/core/ext/client_config/connector.c
-  src/core/ext/client_config/default_initial_connect_string.c
-  src/core/ext/client_config/initial_connect_string.c
-  src/core/ext/client_config/lb_policy.c
-  src/core/ext/client_config/lb_policy_factory.c
-  src/core/ext/client_config/lb_policy_registry.c
-  src/core/ext/client_config/parse_address.c
-  src/core/ext/client_config/resolver.c
-  src/core/ext/client_config/resolver_factory.c
-  src/core/ext/client_config/resolver_registry.c
-  src/core/ext/client_config/subchannel.c
-  src/core/ext/client_config/subchannel_call_holder.c
-  src/core/ext/client_config/subchannel_index.c
-  src/core/ext/client_config/uri_parser.c
-  src/core/ext/resolver/dns/native/dns_resolver.c
-  src/core/ext/resolver/sockaddr/sockaddr_resolver.c
-  src/core/ext/load_reporting/load_reporting.c
-  src/core/ext/load_reporting/load_reporting_filter.c
-  src/core/ext/lb_policy/grpclb/load_balancer_api.c
-  src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c
+  src/core/ext/transport/inproc/inproc_plugin.c
+  src/core/ext/transport/inproc/inproc_transport.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/client_load_reporting_filter.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/grpclb.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/grpclb_channel_secure.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/grpclb_client_stats.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/load_balancer_api.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c
   third_party/nanopb/pb_common.c
   third_party/nanopb/pb_decode.c
   third_party/nanopb/pb_encode.c
-  src/core/ext/lb_policy/pick_first/pick_first.c
-  src/core/ext/lb_policy/round_robin/round_robin.c
+  src/core/ext/filters/client_channel/resolver/fake/fake_resolver.c
+  src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.c
+  src/core/ext/filters/client_channel/lb_policy/round_robin/round_robin.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/dns_resolver_ares.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/grpc_ares_ev_driver_posix.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/grpc_ares_wrapper.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/grpc_ares_wrapper_fallback.c
+  src/core/ext/filters/client_channel/resolver/dns/native/dns_resolver.c
+  src/core/ext/filters/client_channel/resolver/sockaddr/sockaddr_resolver.c
+  src/core/ext/filters/load_reporting/load_reporting.c
+  src/core/ext/filters/load_reporting/load_reporting_filter.c
+  src/core/ext/census/base_resources.c
   src/core/ext/census/context.c
   src/core/ext/census/gen/census.pb.c
+  src/core/ext/census/gen/trace_context.pb.c
   src/core/ext/census/grpc_context.c
   src/core/ext/census/grpc_filter.c
   src/core/ext/census/grpc_plugin.c
   src/core/ext/census/initialize.c
+  src/core/ext/census/intrusive_hash_map.c
   src/core/ext/census/mlog.c
   src/core/ext/census/operation.c
   src/core/ext/census/placeholders.c
+  src/core/ext/census/resource.c
+  src/core/ext/census/trace_context.c
   src/core/ext/census/tracing.c
-  src/core/plugin_registry/grpc_unsecure_plugin_registry.c
+  src/core/ext/filters/max_age/max_age_filter.c
+  src/core/ext/filters/message_size/message_size_filter.c
+  src/core/ext/filters/workarounds/workaround_cronet_compression_filter.c
+  src/core/ext/filters/workarounds/workaround_utils.c
+  src/core/plugin_registry/grpc_plugin_registry.c
 )
 
-target_include_directories(grpc_unsecure
+if(WIN32 AND MSVC)
+  set_target_properties(grpc PROPERTIES COMPILE_PDB_NAME "grpc"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
   PRIVATE ${PROTOBUF_ROOT_DIR}/src
-  PRIVATE ${ZLIB_INCLUDE_DIRS}
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
 )
 
-target_link_libraries(grpc_unsecure
+target_link_libraries(grpc
+  ${_gRPC_BASELIB_LIBRARIES}
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ZLIB_LIBRARIES}
+  ${_gRPC_CARES_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
   gpr
 )
 
-add_library(grpc++_unsecure
-  src/cpp/common/insecure_create_auth_context.cc
-  src/cpp/client/channel.cc
-  src/cpp/client/client_context.cc
-  src/cpp/client/create_channel.cc
-  src/cpp/client/create_channel_internal.cc
-  src/cpp/client/create_channel_posix.cc
-  src/cpp/client/credentials.cc
-  src/cpp/client/generic_stub.cc
-  src/cpp/client/insecure_credentials.cc
-  src/cpp/common/channel_arguments.cc
-  src/cpp/common/completion_queue.cc
-  src/cpp/common/core_codegen.cc
-  src/cpp/common/rpc_method.cc
-  src/cpp/server/async_generic_service.cc
-  src/cpp/server/create_default_thread_pool.cc
-  src/cpp/server/dynamic_thread_pool.cc
-  src/cpp/server/insecure_server_credentials.cc
-  src/cpp/server/server.cc
-  src/cpp/server/server_builder.cc
-  src/cpp/server/server_context.cc
-  src/cpp/server/server_credentials.cc
-  src/cpp/server/server_posix.cc
-  src/cpp/util/byte_buffer.cc
-  src/cpp/util/slice.cc
-  src/cpp/util/status.cc
-  src/cpp/util/string_ref.cc
-  src/cpp/util/time.cc
-  src/cpp/codegen/codegen_init.cc
+foreach(_hdr
+  include/grpc/byte_buffer.h
+  include/grpc/byte_buffer_reader.h
+  include/grpc/compression.h
+  include/grpc/grpc.h
+  include/grpc/grpc_posix.h
+  include/grpc/grpc_security_constants.h
+  include/grpc/load_reporting.h
+  include/grpc/slice.h
+  include/grpc/slice_buffer.h
+  include/grpc/status.h
+  include/grpc/support/workaround_list.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+  include/grpc/grpc_security.h
+  include/grpc/census.h
 )
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
 
-target_include_directories(grpc++_unsecure
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_library(grpc_cronet
+  src/core/lib/surface/init.c
+  src/core/lib/channel/channel_args.c
+  src/core/lib/channel/channel_stack.c
+  src/core/lib/channel/channel_stack_builder.c
+  src/core/lib/channel/connected_channel.c
+  src/core/lib/channel/handshaker.c
+  src/core/lib/channel/handshaker_factory.c
+  src/core/lib/channel/handshaker_registry.c
+  src/core/lib/compression/compression.c
+  src/core/lib/compression/message_compress.c
+  src/core/lib/compression/stream_compression.c
+  src/core/lib/http/format_request.c
+  src/core/lib/http/httpcli.c
+  src/core/lib/http/parser.c
+  src/core/lib/iomgr/closure.c
+  src/core/lib/iomgr/combiner.c
+  src/core/lib/iomgr/endpoint.c
+  src/core/lib/iomgr/endpoint_pair_posix.c
+  src/core/lib/iomgr/endpoint_pair_uv.c
+  src/core/lib/iomgr/endpoint_pair_windows.c
+  src/core/lib/iomgr/error.c
+  src/core/lib/iomgr/ev_epoll1_linux.c
+  src/core/lib/iomgr/ev_epoll_limited_pollers_linux.c
+  src/core/lib/iomgr/ev_epoll_thread_pool_linux.c
+  src/core/lib/iomgr/ev_epollex_linux.c
+  src/core/lib/iomgr/ev_epollsig_linux.c
+  src/core/lib/iomgr/ev_poll_posix.c
+  src/core/lib/iomgr/ev_posix.c
+  src/core/lib/iomgr/ev_windows.c
+  src/core/lib/iomgr/exec_ctx.c
+  src/core/lib/iomgr/executor.c
+  src/core/lib/iomgr/iocp_windows.c
+  src/core/lib/iomgr/iomgr.c
+  src/core/lib/iomgr/iomgr_posix.c
+  src/core/lib/iomgr/iomgr_uv.c
+  src/core/lib/iomgr/iomgr_windows.c
+  src/core/lib/iomgr/is_epollexclusive_available.c
+  src/core/lib/iomgr/load_file.c
+  src/core/lib/iomgr/lockfree_event.c
+  src/core/lib/iomgr/network_status_tracker.c
+  src/core/lib/iomgr/polling_entity.c
+  src/core/lib/iomgr/pollset_set_uv.c
+  src/core/lib/iomgr/pollset_set_windows.c
+  src/core/lib/iomgr/pollset_uv.c
+  src/core/lib/iomgr/pollset_windows.c
+  src/core/lib/iomgr/resolve_address_posix.c
+  src/core/lib/iomgr/resolve_address_uv.c
+  src/core/lib/iomgr/resolve_address_windows.c
+  src/core/lib/iomgr/resource_quota.c
+  src/core/lib/iomgr/sockaddr_utils.c
+  src/core/lib/iomgr/socket_factory_posix.c
+  src/core/lib/iomgr/socket_mutator.c
+  src/core/lib/iomgr/socket_utils_common_posix.c
+  src/core/lib/iomgr/socket_utils_linux.c
+  src/core/lib/iomgr/socket_utils_posix.c
+  src/core/lib/iomgr/socket_utils_uv.c
+  src/core/lib/iomgr/socket_utils_windows.c
+  src/core/lib/iomgr/socket_windows.c
+  src/core/lib/iomgr/tcp_client_posix.c
+  src/core/lib/iomgr/tcp_client_uv.c
+  src/core/lib/iomgr/tcp_client_windows.c
+  src/core/lib/iomgr/tcp_posix.c
+  src/core/lib/iomgr/tcp_server_posix.c
+  src/core/lib/iomgr/tcp_server_utils_posix_common.c
+  src/core/lib/iomgr/tcp_server_utils_posix_ifaddrs.c
+  src/core/lib/iomgr/tcp_server_utils_posix_noifaddrs.c
+  src/core/lib/iomgr/tcp_server_uv.c
+  src/core/lib/iomgr/tcp_server_windows.c
+  src/core/lib/iomgr/tcp_uv.c
+  src/core/lib/iomgr/tcp_windows.c
+  src/core/lib/iomgr/time_averaged_stats.c
+  src/core/lib/iomgr/timer_generic.c
+  src/core/lib/iomgr/timer_heap.c
+  src/core/lib/iomgr/timer_manager.c
+  src/core/lib/iomgr/timer_uv.c
+  src/core/lib/iomgr/udp_server.c
+  src/core/lib/iomgr/unix_sockets_posix.c
+  src/core/lib/iomgr/unix_sockets_posix_noop.c
+  src/core/lib/iomgr/wakeup_fd_cv.c
+  src/core/lib/iomgr/wakeup_fd_eventfd.c
+  src/core/lib/iomgr/wakeup_fd_nospecial.c
+  src/core/lib/iomgr/wakeup_fd_pipe.c
+  src/core/lib/iomgr/wakeup_fd_posix.c
+  src/core/lib/json/json.c
+  src/core/lib/json/json_reader.c
+  src/core/lib/json/json_string.c
+  src/core/lib/json/json_writer.c
+  src/core/lib/slice/b64.c
+  src/core/lib/slice/percent_encoding.c
+  src/core/lib/slice/slice.c
+  src/core/lib/slice/slice_buffer.c
+  src/core/lib/slice/slice_hash_table.c
+  src/core/lib/slice/slice_intern.c
+  src/core/lib/slice/slice_string_helpers.c
+  src/core/lib/surface/alarm.c
+  src/core/lib/surface/api_trace.c
+  src/core/lib/surface/byte_buffer.c
+  src/core/lib/surface/byte_buffer_reader.c
+  src/core/lib/surface/call.c
+  src/core/lib/surface/call_details.c
+  src/core/lib/surface/call_log_batch.c
+  src/core/lib/surface/channel.c
+  src/core/lib/surface/channel_init.c
+  src/core/lib/surface/channel_ping.c
+  src/core/lib/surface/channel_stack_type.c
+  src/core/lib/surface/completion_queue.c
+  src/core/lib/surface/completion_queue_factory.c
+  src/core/lib/surface/event_string.c
+  src/core/lib/surface/lame_client.cc
+  src/core/lib/surface/metadata_array.c
+  src/core/lib/surface/server.c
+  src/core/lib/surface/validate_metadata.c
+  src/core/lib/surface/version.c
+  src/core/lib/transport/bdp_estimator.c
+  src/core/lib/transport/byte_stream.c
+  src/core/lib/transport/connectivity_state.c
+  src/core/lib/transport/error_utils.c
+  src/core/lib/transport/metadata.c
+  src/core/lib/transport/metadata_batch.c
+  src/core/lib/transport/pid_controller.c
+  src/core/lib/transport/service_config.c
+  src/core/lib/transport/static_metadata.c
+  src/core/lib/transport/status_conversion.c
+  src/core/lib/transport/timeout_encoding.c
+  src/core/lib/transport/transport.c
+  src/core/lib/transport/transport_op_string.c
+  src/core/lib/debug/trace.c
+  src/core/ext/transport/cronet/client/secure/cronet_channel_create.c
+  src/core/ext/transport/cronet/transport/cronet_api_dummy.c
+  src/core/ext/transport/cronet/transport/cronet_transport.c
+  src/core/ext/transport/chttp2/client/secure/secure_channel_create.c
+  src/core/ext/transport/chttp2/transport/bin_decoder.c
+  src/core/ext/transport/chttp2/transport/bin_encoder.c
+  src/core/ext/transport/chttp2/transport/chttp2_plugin.c
+  src/core/ext/transport/chttp2/transport/chttp2_transport.c
+  src/core/ext/transport/chttp2/transport/frame_data.c
+  src/core/ext/transport/chttp2/transport/frame_goaway.c
+  src/core/ext/transport/chttp2/transport/frame_ping.c
+  src/core/ext/transport/chttp2/transport/frame_rst_stream.c
+  src/core/ext/transport/chttp2/transport/frame_settings.c
+  src/core/ext/transport/chttp2/transport/frame_window_update.c
+  src/core/ext/transport/chttp2/transport/hpack_encoder.c
+  src/core/ext/transport/chttp2/transport/hpack_parser.c
+  src/core/ext/transport/chttp2/transport/hpack_table.c
+  src/core/ext/transport/chttp2/transport/http2_settings.c
+  src/core/ext/transport/chttp2/transport/huffsyms.c
+  src/core/ext/transport/chttp2/transport/incoming_metadata.c
+  src/core/ext/transport/chttp2/transport/parsing.c
+  src/core/ext/transport/chttp2/transport/stream_lists.c
+  src/core/ext/transport/chttp2/transport/stream_map.c
+  src/core/ext/transport/chttp2/transport/varint.c
+  src/core/ext/transport/chttp2/transport/writing.c
+  src/core/ext/transport/chttp2/alpn/alpn.c
+  src/core/ext/filters/http/client/http_client_filter.c
+  src/core/ext/filters/http/http_filters_plugin.c
+  src/core/ext/filters/http/message_compress/message_compress_filter.c
+  src/core/ext/filters/http/server/http_server_filter.c
+  src/core/ext/filters/client_channel/channel_connectivity.c
+  src/core/ext/filters/client_channel/client_channel.c
+  src/core/ext/filters/client_channel/client_channel_factory.c
+  src/core/ext/filters/client_channel/client_channel_plugin.c
+  src/core/ext/filters/client_channel/connector.c
+  src/core/ext/filters/client_channel/http_connect_handshaker.c
+  src/core/ext/filters/client_channel/http_proxy.c
+  src/core/ext/filters/client_channel/lb_policy.c
+  src/core/ext/filters/client_channel/lb_policy_factory.c
+  src/core/ext/filters/client_channel/lb_policy_registry.c
+  src/core/ext/filters/client_channel/parse_address.c
+  src/core/ext/filters/client_channel/proxy_mapper.c
+  src/core/ext/filters/client_channel/proxy_mapper_registry.c
+  src/core/ext/filters/client_channel/resolver.c
+  src/core/ext/filters/client_channel/resolver_factory.c
+  src/core/ext/filters/client_channel/resolver_registry.c
+  src/core/ext/filters/client_channel/retry_throttle.c
+  src/core/ext/filters/client_channel/subchannel.c
+  src/core/ext/filters/client_channel/subchannel_index.c
+  src/core/ext/filters/client_channel/uri_parser.c
+  src/core/ext/filters/deadline/deadline_filter.c
+  src/core/lib/http/httpcli_security_connector.c
+  src/core/lib/security/context/security_context.c
+  src/core/lib/security/credentials/composite/composite_credentials.c
+  src/core/lib/security/credentials/credentials.c
+  src/core/lib/security/credentials/credentials_metadata.c
+  src/core/lib/security/credentials/fake/fake_credentials.c
+  src/core/lib/security/credentials/google_default/credentials_generic.c
+  src/core/lib/security/credentials/google_default/google_default_credentials.c
+  src/core/lib/security/credentials/iam/iam_credentials.c
+  src/core/lib/security/credentials/jwt/json_token.c
+  src/core/lib/security/credentials/jwt/jwt_credentials.c
+  src/core/lib/security/credentials/jwt/jwt_verifier.c
+  src/core/lib/security/credentials/oauth2/oauth2_credentials.c
+  src/core/lib/security/credentials/plugin/plugin_credentials.c
+  src/core/lib/security/credentials/ssl/ssl_credentials.c
+  src/core/lib/security/transport/client_auth_filter.c
+  src/core/lib/security/transport/lb_targets_info.c
+  src/core/lib/security/transport/secure_endpoint.c
+  src/core/lib/security/transport/security_connector.c
+  src/core/lib/security/transport/security_handshaker.c
+  src/core/lib/security/transport/server_auth_filter.c
+  src/core/lib/security/transport/tsi_error.c
+  src/core/lib/security/util/json_util.c
+  src/core/lib/surface/init_secure.c
+  src/core/tsi/fake_transport_security.c
+  src/core/tsi/gts_transport_security.c
+  src/core/tsi/ssl_transport_security.c
+  src/core/tsi/transport_security.c
+  src/core/tsi/transport_security_adapter.c
+  src/core/ext/transport/chttp2/client/chttp2_connector.c
+  src/core/ext/filters/load_reporting/load_reporting.c
+  src/core/ext/filters/load_reporting/load_reporting_filter.c
+  src/core/plugin_registry/grpc_cronet_plugin_registry.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_cronet PROPERTIES COMPILE_PDB_NAME "grpc_cronet"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_cronet.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc_cronet
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
-  PRIVATE ${PROTOBUF_INCLUDE_DIRS}
-  PRIVATE ${ZLIB_INCLUDE_DIRS}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
 )
 
-target_link_libraries(grpc++_unsecure
-  ${PROTOBUF_LIBRARIES}
+target_link_libraries(grpc_cronet
+  ${_gRPC_BASELIB_LIBRARIES}
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ZLIB_LIBRARIES}
+  ${_gRPC_CARES_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
   gpr
-  grpc_unsecure
 )
+
+foreach(_hdr
+  include/grpc/byte_buffer.h
+  include/grpc/byte_buffer_reader.h
+  include/grpc/compression.h
+  include/grpc/grpc.h
+  include/grpc/grpc_posix.h
+  include/grpc/grpc_security_constants.h
+  include/grpc/load_reporting.h
+  include/grpc/slice.h
+  include/grpc/slice_buffer.h
+  include/grpc/status.h
+  include/grpc/support/workaround_list.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+  include/grpc/grpc_cronet.h
+  include/grpc/grpc_security.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_cronet EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(grpc_test_util
+  test/core/end2end/data/client_certs.c
+  test/core/end2end/data/server1_cert.c
+  test/core/end2end/data/server1_key.c
+  test/core/end2end/data/test_root_cert.c
+  test/core/security/oauth2_utils.c
+  src/core/ext/filters/client_channel/resolver/fake/fake_resolver.c
+  test/core/end2end/cq_verifier.c
+  test/core/end2end/fixtures/http_proxy_fixture.c
+  test/core/end2end/fixtures/proxy.c
+  test/core/iomgr/endpoint_tests.c
+  test/core/util/debugger_macros.c
+  test/core/util/grpc_profiler.c
+  test/core/util/memory_counters.c
+  test/core/util/mock_endpoint.c
+  test/core/util/parse_hexstring.c
+  test/core/util/passthru_endpoint.c
+  test/core/util/port.c
+  test/core/util/port_server_client.c
+  test/core/util/slice_splitter.c
+  test/core/util/trickle_endpoint.c
+  src/core/lib/channel/channel_args.c
+  src/core/lib/channel/channel_stack.c
+  src/core/lib/channel/channel_stack_builder.c
+  src/core/lib/channel/connected_channel.c
+  src/core/lib/channel/handshaker.c
+  src/core/lib/channel/handshaker_factory.c
+  src/core/lib/channel/handshaker_registry.c
+  src/core/lib/compression/compression.c
+  src/core/lib/compression/message_compress.c
+  src/core/lib/compression/stream_compression.c
+  src/core/lib/http/format_request.c
+  src/core/lib/http/httpcli.c
+  src/core/lib/http/parser.c
+  src/core/lib/iomgr/closure.c
+  src/core/lib/iomgr/combiner.c
+  src/core/lib/iomgr/endpoint.c
+  src/core/lib/iomgr/endpoint_pair_posix.c
+  src/core/lib/iomgr/endpoint_pair_uv.c
+  src/core/lib/iomgr/endpoint_pair_windows.c
+  src/core/lib/iomgr/error.c
+  src/core/lib/iomgr/ev_epoll1_linux.c
+  src/core/lib/iomgr/ev_epoll_limited_pollers_linux.c
+  src/core/lib/iomgr/ev_epoll_thread_pool_linux.c
+  src/core/lib/iomgr/ev_epollex_linux.c
+  src/core/lib/iomgr/ev_epollsig_linux.c
+  src/core/lib/iomgr/ev_poll_posix.c
+  src/core/lib/iomgr/ev_posix.c
+  src/core/lib/iomgr/ev_windows.c
+  src/core/lib/iomgr/exec_ctx.c
+  src/core/lib/iomgr/executor.c
+  src/core/lib/iomgr/iocp_windows.c
+  src/core/lib/iomgr/iomgr.c
+  src/core/lib/iomgr/iomgr_posix.c
+  src/core/lib/iomgr/iomgr_uv.c
+  src/core/lib/iomgr/iomgr_windows.c
+  src/core/lib/iomgr/is_epollexclusive_available.c
+  src/core/lib/iomgr/load_file.c
+  src/core/lib/iomgr/lockfree_event.c
+  src/core/lib/iomgr/network_status_tracker.c
+  src/core/lib/iomgr/polling_entity.c
+  src/core/lib/iomgr/pollset_set_uv.c
+  src/core/lib/iomgr/pollset_set_windows.c
+  src/core/lib/iomgr/pollset_uv.c
+  src/core/lib/iomgr/pollset_windows.c
+  src/core/lib/iomgr/resolve_address_posix.c
+  src/core/lib/iomgr/resolve_address_uv.c
+  src/core/lib/iomgr/resolve_address_windows.c
+  src/core/lib/iomgr/resource_quota.c
+  src/core/lib/iomgr/sockaddr_utils.c
+  src/core/lib/iomgr/socket_factory_posix.c
+  src/core/lib/iomgr/socket_mutator.c
+  src/core/lib/iomgr/socket_utils_common_posix.c
+  src/core/lib/iomgr/socket_utils_linux.c
+  src/core/lib/iomgr/socket_utils_posix.c
+  src/core/lib/iomgr/socket_utils_uv.c
+  src/core/lib/iomgr/socket_utils_windows.c
+  src/core/lib/iomgr/socket_windows.c
+  src/core/lib/iomgr/tcp_client_posix.c
+  src/core/lib/iomgr/tcp_client_uv.c
+  src/core/lib/iomgr/tcp_client_windows.c
+  src/core/lib/iomgr/tcp_posix.c
+  src/core/lib/iomgr/tcp_server_posix.c
+  src/core/lib/iomgr/tcp_server_utils_posix_common.c
+  src/core/lib/iomgr/tcp_server_utils_posix_ifaddrs.c
+  src/core/lib/iomgr/tcp_server_utils_posix_noifaddrs.c
+  src/core/lib/iomgr/tcp_server_uv.c
+  src/core/lib/iomgr/tcp_server_windows.c
+  src/core/lib/iomgr/tcp_uv.c
+  src/core/lib/iomgr/tcp_windows.c
+  src/core/lib/iomgr/time_averaged_stats.c
+  src/core/lib/iomgr/timer_generic.c
+  src/core/lib/iomgr/timer_heap.c
+  src/core/lib/iomgr/timer_manager.c
+  src/core/lib/iomgr/timer_uv.c
+  src/core/lib/iomgr/udp_server.c
+  src/core/lib/iomgr/unix_sockets_posix.c
+  src/core/lib/iomgr/unix_sockets_posix_noop.c
+  src/core/lib/iomgr/wakeup_fd_cv.c
+  src/core/lib/iomgr/wakeup_fd_eventfd.c
+  src/core/lib/iomgr/wakeup_fd_nospecial.c
+  src/core/lib/iomgr/wakeup_fd_pipe.c
+  src/core/lib/iomgr/wakeup_fd_posix.c
+  src/core/lib/json/json.c
+  src/core/lib/json/json_reader.c
+  src/core/lib/json/json_string.c
+  src/core/lib/json/json_writer.c
+  src/core/lib/slice/b64.c
+  src/core/lib/slice/percent_encoding.c
+  src/core/lib/slice/slice.c
+  src/core/lib/slice/slice_buffer.c
+  src/core/lib/slice/slice_hash_table.c
+  src/core/lib/slice/slice_intern.c
+  src/core/lib/slice/slice_string_helpers.c
+  src/core/lib/surface/alarm.c
+  src/core/lib/surface/api_trace.c
+  src/core/lib/surface/byte_buffer.c
+  src/core/lib/surface/byte_buffer_reader.c
+  src/core/lib/surface/call.c
+  src/core/lib/surface/call_details.c
+  src/core/lib/surface/call_log_batch.c
+  src/core/lib/surface/channel.c
+  src/core/lib/surface/channel_init.c
+  src/core/lib/surface/channel_ping.c
+  src/core/lib/surface/channel_stack_type.c
+  src/core/lib/surface/completion_queue.c
+  src/core/lib/surface/completion_queue_factory.c
+  src/core/lib/surface/event_string.c
+  src/core/lib/surface/lame_client.cc
+  src/core/lib/surface/metadata_array.c
+  src/core/lib/surface/server.c
+  src/core/lib/surface/validate_metadata.c
+  src/core/lib/surface/version.c
+  src/core/lib/transport/bdp_estimator.c
+  src/core/lib/transport/byte_stream.c
+  src/core/lib/transport/connectivity_state.c
+  src/core/lib/transport/error_utils.c
+  src/core/lib/transport/metadata.c
+  src/core/lib/transport/metadata_batch.c
+  src/core/lib/transport/pid_controller.c
+  src/core/lib/transport/service_config.c
+  src/core/lib/transport/static_metadata.c
+  src/core/lib/transport/status_conversion.c
+  src/core/lib/transport/timeout_encoding.c
+  src/core/lib/transport/transport.c
+  src/core/lib/transport/transport_op_string.c
+  src/core/lib/debug/trace.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_test_util PROPERTIES COMPILE_PDB_NAME "grpc_test_util"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_test_util.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc_test_util
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_test_util
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+  grpc
+)
+
+foreach(_hdr
+  include/grpc/byte_buffer.h
+  include/grpc/byte_buffer_reader.h
+  include/grpc/compression.h
+  include/grpc/grpc.h
+  include/grpc/grpc_posix.h
+  include/grpc/grpc_security_constants.h
+  include/grpc/load_reporting.h
+  include/grpc/slice.h
+  include/grpc/slice_buffer.h
+  include/grpc/status.h
+  include/grpc/support/workaround_list.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(grpc_test_util_unsecure
+  src/core/ext/filters/client_channel/resolver/fake/fake_resolver.c
+  test/core/end2end/cq_verifier.c
+  test/core/end2end/fixtures/http_proxy_fixture.c
+  test/core/end2end/fixtures/proxy.c
+  test/core/iomgr/endpoint_tests.c
+  test/core/util/debugger_macros.c
+  test/core/util/grpc_profiler.c
+  test/core/util/memory_counters.c
+  test/core/util/mock_endpoint.c
+  test/core/util/parse_hexstring.c
+  test/core/util/passthru_endpoint.c
+  test/core/util/port.c
+  test/core/util/port_server_client.c
+  test/core/util/slice_splitter.c
+  test/core/util/trickle_endpoint.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_test_util_unsecure PROPERTIES COMPILE_PDB_NAME "grpc_test_util_unsecure"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_test_util_unsecure.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc_test_util_unsecure
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_test_util_unsecure
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr
+  gpr_test_util
+  grpc_unsecure
+  grpc
+)
+
+
+endif (gRPC_BUILD_TESTS)
+
+add_library(grpc_unsecure
+  src/core/lib/surface/init.c
+  src/core/lib/surface/init_unsecure.c
+  src/core/lib/channel/channel_args.c
+  src/core/lib/channel/channel_stack.c
+  src/core/lib/channel/channel_stack_builder.c
+  src/core/lib/channel/connected_channel.c
+  src/core/lib/channel/handshaker.c
+  src/core/lib/channel/handshaker_factory.c
+  src/core/lib/channel/handshaker_registry.c
+  src/core/lib/compression/compression.c
+  src/core/lib/compression/message_compress.c
+  src/core/lib/compression/stream_compression.c
+  src/core/lib/http/format_request.c
+  src/core/lib/http/httpcli.c
+  src/core/lib/http/parser.c
+  src/core/lib/iomgr/closure.c
+  src/core/lib/iomgr/combiner.c
+  src/core/lib/iomgr/endpoint.c
+  src/core/lib/iomgr/endpoint_pair_posix.c
+  src/core/lib/iomgr/endpoint_pair_uv.c
+  src/core/lib/iomgr/endpoint_pair_windows.c
+  src/core/lib/iomgr/error.c
+  src/core/lib/iomgr/ev_epoll1_linux.c
+  src/core/lib/iomgr/ev_epoll_limited_pollers_linux.c
+  src/core/lib/iomgr/ev_epoll_thread_pool_linux.c
+  src/core/lib/iomgr/ev_epollex_linux.c
+  src/core/lib/iomgr/ev_epollsig_linux.c
+  src/core/lib/iomgr/ev_poll_posix.c
+  src/core/lib/iomgr/ev_posix.c
+  src/core/lib/iomgr/ev_windows.c
+  src/core/lib/iomgr/exec_ctx.c
+  src/core/lib/iomgr/executor.c
+  src/core/lib/iomgr/iocp_windows.c
+  src/core/lib/iomgr/iomgr.c
+  src/core/lib/iomgr/iomgr_posix.c
+  src/core/lib/iomgr/iomgr_uv.c
+  src/core/lib/iomgr/iomgr_windows.c
+  src/core/lib/iomgr/is_epollexclusive_available.c
+  src/core/lib/iomgr/load_file.c
+  src/core/lib/iomgr/lockfree_event.c
+  src/core/lib/iomgr/network_status_tracker.c
+  src/core/lib/iomgr/polling_entity.c
+  src/core/lib/iomgr/pollset_set_uv.c
+  src/core/lib/iomgr/pollset_set_windows.c
+  src/core/lib/iomgr/pollset_uv.c
+  src/core/lib/iomgr/pollset_windows.c
+  src/core/lib/iomgr/resolve_address_posix.c
+  src/core/lib/iomgr/resolve_address_uv.c
+  src/core/lib/iomgr/resolve_address_windows.c
+  src/core/lib/iomgr/resource_quota.c
+  src/core/lib/iomgr/sockaddr_utils.c
+  src/core/lib/iomgr/socket_factory_posix.c
+  src/core/lib/iomgr/socket_mutator.c
+  src/core/lib/iomgr/socket_utils_common_posix.c
+  src/core/lib/iomgr/socket_utils_linux.c
+  src/core/lib/iomgr/socket_utils_posix.c
+  src/core/lib/iomgr/socket_utils_uv.c
+  src/core/lib/iomgr/socket_utils_windows.c
+  src/core/lib/iomgr/socket_windows.c
+  src/core/lib/iomgr/tcp_client_posix.c
+  src/core/lib/iomgr/tcp_client_uv.c
+  src/core/lib/iomgr/tcp_client_windows.c
+  src/core/lib/iomgr/tcp_posix.c
+  src/core/lib/iomgr/tcp_server_posix.c
+  src/core/lib/iomgr/tcp_server_utils_posix_common.c
+  src/core/lib/iomgr/tcp_server_utils_posix_ifaddrs.c
+  src/core/lib/iomgr/tcp_server_utils_posix_noifaddrs.c
+  src/core/lib/iomgr/tcp_server_uv.c
+  src/core/lib/iomgr/tcp_server_windows.c
+  src/core/lib/iomgr/tcp_uv.c
+  src/core/lib/iomgr/tcp_windows.c
+  src/core/lib/iomgr/time_averaged_stats.c
+  src/core/lib/iomgr/timer_generic.c
+  src/core/lib/iomgr/timer_heap.c
+  src/core/lib/iomgr/timer_manager.c
+  src/core/lib/iomgr/timer_uv.c
+  src/core/lib/iomgr/udp_server.c
+  src/core/lib/iomgr/unix_sockets_posix.c
+  src/core/lib/iomgr/unix_sockets_posix_noop.c
+  src/core/lib/iomgr/wakeup_fd_cv.c
+  src/core/lib/iomgr/wakeup_fd_eventfd.c
+  src/core/lib/iomgr/wakeup_fd_nospecial.c
+  src/core/lib/iomgr/wakeup_fd_pipe.c
+  src/core/lib/iomgr/wakeup_fd_posix.c
+  src/core/lib/json/json.c
+  src/core/lib/json/json_reader.c
+  src/core/lib/json/json_string.c
+  src/core/lib/json/json_writer.c
+  src/core/lib/slice/b64.c
+  src/core/lib/slice/percent_encoding.c
+  src/core/lib/slice/slice.c
+  src/core/lib/slice/slice_buffer.c
+  src/core/lib/slice/slice_hash_table.c
+  src/core/lib/slice/slice_intern.c
+  src/core/lib/slice/slice_string_helpers.c
+  src/core/lib/surface/alarm.c
+  src/core/lib/surface/api_trace.c
+  src/core/lib/surface/byte_buffer.c
+  src/core/lib/surface/byte_buffer_reader.c
+  src/core/lib/surface/call.c
+  src/core/lib/surface/call_details.c
+  src/core/lib/surface/call_log_batch.c
+  src/core/lib/surface/channel.c
+  src/core/lib/surface/channel_init.c
+  src/core/lib/surface/channel_ping.c
+  src/core/lib/surface/channel_stack_type.c
+  src/core/lib/surface/completion_queue.c
+  src/core/lib/surface/completion_queue_factory.c
+  src/core/lib/surface/event_string.c
+  src/core/lib/surface/lame_client.cc
+  src/core/lib/surface/metadata_array.c
+  src/core/lib/surface/server.c
+  src/core/lib/surface/validate_metadata.c
+  src/core/lib/surface/version.c
+  src/core/lib/transport/bdp_estimator.c
+  src/core/lib/transport/byte_stream.c
+  src/core/lib/transport/connectivity_state.c
+  src/core/lib/transport/error_utils.c
+  src/core/lib/transport/metadata.c
+  src/core/lib/transport/metadata_batch.c
+  src/core/lib/transport/pid_controller.c
+  src/core/lib/transport/service_config.c
+  src/core/lib/transport/static_metadata.c
+  src/core/lib/transport/status_conversion.c
+  src/core/lib/transport/timeout_encoding.c
+  src/core/lib/transport/transport.c
+  src/core/lib/transport/transport_op_string.c
+  src/core/lib/debug/trace.c
+  src/core/ext/transport/chttp2/server/insecure/server_chttp2.c
+  src/core/ext/transport/chttp2/server/insecure/server_chttp2_posix.c
+  src/core/ext/transport/chttp2/transport/bin_decoder.c
+  src/core/ext/transport/chttp2/transport/bin_encoder.c
+  src/core/ext/transport/chttp2/transport/chttp2_plugin.c
+  src/core/ext/transport/chttp2/transport/chttp2_transport.c
+  src/core/ext/transport/chttp2/transport/frame_data.c
+  src/core/ext/transport/chttp2/transport/frame_goaway.c
+  src/core/ext/transport/chttp2/transport/frame_ping.c
+  src/core/ext/transport/chttp2/transport/frame_rst_stream.c
+  src/core/ext/transport/chttp2/transport/frame_settings.c
+  src/core/ext/transport/chttp2/transport/frame_window_update.c
+  src/core/ext/transport/chttp2/transport/hpack_encoder.c
+  src/core/ext/transport/chttp2/transport/hpack_parser.c
+  src/core/ext/transport/chttp2/transport/hpack_table.c
+  src/core/ext/transport/chttp2/transport/http2_settings.c
+  src/core/ext/transport/chttp2/transport/huffsyms.c
+  src/core/ext/transport/chttp2/transport/incoming_metadata.c
+  src/core/ext/transport/chttp2/transport/parsing.c
+  src/core/ext/transport/chttp2/transport/stream_lists.c
+  src/core/ext/transport/chttp2/transport/stream_map.c
+  src/core/ext/transport/chttp2/transport/varint.c
+  src/core/ext/transport/chttp2/transport/writing.c
+  src/core/ext/transport/chttp2/alpn/alpn.c
+  src/core/ext/filters/http/client/http_client_filter.c
+  src/core/ext/filters/http/http_filters_plugin.c
+  src/core/ext/filters/http/message_compress/message_compress_filter.c
+  src/core/ext/filters/http/server/http_server_filter.c
+  src/core/ext/transport/chttp2/server/chttp2_server.c
+  src/core/ext/transport/chttp2/client/insecure/channel_create.c
+  src/core/ext/transport/chttp2/client/insecure/channel_create_posix.c
+  src/core/ext/transport/chttp2/client/chttp2_connector.c
+  src/core/ext/filters/client_channel/channel_connectivity.c
+  src/core/ext/filters/client_channel/client_channel.c
+  src/core/ext/filters/client_channel/client_channel_factory.c
+  src/core/ext/filters/client_channel/client_channel_plugin.c
+  src/core/ext/filters/client_channel/connector.c
+  src/core/ext/filters/client_channel/http_connect_handshaker.c
+  src/core/ext/filters/client_channel/http_proxy.c
+  src/core/ext/filters/client_channel/lb_policy.c
+  src/core/ext/filters/client_channel/lb_policy_factory.c
+  src/core/ext/filters/client_channel/lb_policy_registry.c
+  src/core/ext/filters/client_channel/parse_address.c
+  src/core/ext/filters/client_channel/proxy_mapper.c
+  src/core/ext/filters/client_channel/proxy_mapper_registry.c
+  src/core/ext/filters/client_channel/resolver.c
+  src/core/ext/filters/client_channel/resolver_factory.c
+  src/core/ext/filters/client_channel/resolver_registry.c
+  src/core/ext/filters/client_channel/retry_throttle.c
+  src/core/ext/filters/client_channel/subchannel.c
+  src/core/ext/filters/client_channel/subchannel_index.c
+  src/core/ext/filters/client_channel/uri_parser.c
+  src/core/ext/filters/deadline/deadline_filter.c
+  src/core/ext/transport/inproc/inproc_plugin.c
+  src/core/ext/transport/inproc/inproc_transport.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/dns_resolver_ares.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/grpc_ares_ev_driver_posix.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/grpc_ares_wrapper.c
+  src/core/ext/filters/client_channel/resolver/dns/c_ares/grpc_ares_wrapper_fallback.c
+  src/core/ext/filters/client_channel/resolver/dns/native/dns_resolver.c
+  src/core/ext/filters/client_channel/resolver/sockaddr/sockaddr_resolver.c
+  src/core/ext/filters/client_channel/resolver/fake/fake_resolver.c
+  src/core/ext/filters/load_reporting/load_reporting.c
+  src/core/ext/filters/load_reporting/load_reporting_filter.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/client_load_reporting_filter.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/grpclb.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/grpclb_channel.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/grpclb_client_stats.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/load_balancer_api.c
+  src/core/ext/filters/client_channel/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c
+  third_party/nanopb/pb_common.c
+  third_party/nanopb/pb_decode.c
+  third_party/nanopb/pb_encode.c
+  src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.c
+  src/core/ext/filters/client_channel/lb_policy/round_robin/round_robin.c
+  src/core/ext/census/base_resources.c
+  src/core/ext/census/context.c
+  src/core/ext/census/gen/census.pb.c
+  src/core/ext/census/gen/trace_context.pb.c
+  src/core/ext/census/grpc_context.c
+  src/core/ext/census/grpc_filter.c
+  src/core/ext/census/grpc_plugin.c
+  src/core/ext/census/initialize.c
+  src/core/ext/census/intrusive_hash_map.c
+  src/core/ext/census/mlog.c
+  src/core/ext/census/operation.c
+  src/core/ext/census/placeholders.c
+  src/core/ext/census/resource.c
+  src/core/ext/census/trace_context.c
+  src/core/ext/census/tracing.c
+  src/core/ext/filters/max_age/max_age_filter.c
+  src/core/ext/filters/message_size/message_size_filter.c
+  src/core/ext/filters/workarounds/workaround_cronet_compression_filter.c
+  src/core/ext/filters/workarounds/workaround_utils.c
+  src/core/plugin_registry/grpc_unsecure_plugin_registry.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_unsecure PROPERTIES COMPILE_PDB_NAME "grpc_unsecure"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_unsecure.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc_unsecure
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_unsecure
+  ${_gRPC_BASELIB_LIBRARIES}
+  ${_gRPC_ZLIB_LIBRARIES}
+  ${_gRPC_CARES_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr
+)
+
+foreach(_hdr
+  include/grpc/byte_buffer.h
+  include/grpc/byte_buffer_reader.h
+  include/grpc/compression.h
+  include/grpc/grpc.h
+  include/grpc/grpc_posix.h
+  include/grpc/grpc_security_constants.h
+  include/grpc/load_reporting.h
+  include/grpc/slice.h
+  include/grpc/slice_buffer.h
+  include/grpc/status.h
+  include/grpc/support/workaround_list.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+  include/grpc/census.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_unsecure EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(reconnect_server
+  test/core/util/reconnect_server.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(reconnect_server PROPERTIES COMPILE_PDB_NAME "reconnect_server"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/reconnect_server.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(reconnect_server
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(reconnect_server
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  test_tcp_server
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(test_tcp_server
+  test/core/util/test_tcp_server.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(test_tcp_server PROPERTIES COMPILE_PDB_NAME "test_tcp_server"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/test_tcp_server.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(test_tcp_server
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(test_tcp_server
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+
+add_library(grpc++
+  src/cpp/client/insecure_credentials.cc
+  src/cpp/client/secure_credentials.cc
+  src/cpp/common/auth_property_iterator.cc
+  src/cpp/common/secure_auth_context.cc
+  src/cpp/common/secure_channel_arguments.cc
+  src/cpp/common/secure_create_auth_context.cc
+  src/cpp/server/insecure_server_credentials.cc
+  src/cpp/server/secure_server_credentials.cc
+  src/cpp/client/channel_cc.cc
+  src/cpp/client/client_context.cc
+  src/cpp/client/create_channel.cc
+  src/cpp/client/create_channel_internal.cc
+  src/cpp/client/create_channel_posix.cc
+  src/cpp/client/credentials_cc.cc
+  src/cpp/client/generic_stub.cc
+  src/cpp/common/channel_arguments.cc
+  src/cpp/common/channel_filter.cc
+  src/cpp/common/completion_queue_cc.cc
+  src/cpp/common/core_codegen.cc
+  src/cpp/common/resource_quota_cc.cc
+  src/cpp/common/rpc_method.cc
+  src/cpp/common/version_cc.cc
+  src/cpp/server/async_generic_service.cc
+  src/cpp/server/channel_argument_option.cc
+  src/cpp/server/create_default_thread_pool.cc
+  src/cpp/server/dynamic_thread_pool.cc
+  src/cpp/server/health/default_health_check_service.cc
+  src/cpp/server/health/health.pb.c
+  src/cpp/server/health/health_check_service.cc
+  src/cpp/server/health/health_check_service_server_builder_option.cc
+  src/cpp/server/server_builder.cc
+  src/cpp/server/server_cc.cc
+  src/cpp/server/server_context.cc
+  src/cpp/server/server_credentials.cc
+  src/cpp/server/server_posix.cc
+  src/cpp/thread_manager/thread_manager.cc
+  src/cpp/util/byte_buffer_cc.cc
+  src/cpp/util/slice_cc.cc
+  src/cpp/util/status.cc
+  src/cpp/util/string_ref.cc
+  src/cpp/util/time_cc.cc
+  third_party/nanopb/pb_common.c
+  third_party/nanopb/pb_decode.c
+  third_party/nanopb/pb_encode.c
+  src/cpp/codegen/codegen_init.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++ PROPERTIES COMPILE_PDB_NAME "grpc++"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc++
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++
+  ${_gRPC_BASELIB_LIBRARIES}
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+)
+
+foreach(_hdr
+  include/grpc++/alarm.h
+  include/grpc++/channel.h
+  include/grpc++/client_context.h
+  include/grpc++/completion_queue.h
+  include/grpc++/create_channel.h
+  include/grpc++/create_channel_posix.h
+  include/grpc++/ext/health_check_service_server_builder_option.h
+  include/grpc++/generic/async_generic_service.h
+  include/grpc++/generic/generic_stub.h
+  include/grpc++/grpc++.h
+  include/grpc++/health_check_service_interface.h
+  include/grpc++/impl/call.h
+  include/grpc++/impl/channel_argument_option.h
+  include/grpc++/impl/client_unary_call.h
+  include/grpc++/impl/codegen/core_codegen.h
+  include/grpc++/impl/grpc_library.h
+  include/grpc++/impl/method_handler_impl.h
+  include/grpc++/impl/rpc_method.h
+  include/grpc++/impl/rpc_service_method.h
+  include/grpc++/impl/serialization_traits.h
+  include/grpc++/impl/server_builder_option.h
+  include/grpc++/impl/server_builder_plugin.h
+  include/grpc++/impl/server_initializer.h
+  include/grpc++/impl/service_type.h
+  include/grpc++/resource_quota.h
+  include/grpc++/security/auth_context.h
+  include/grpc++/security/auth_metadata_processor.h
+  include/grpc++/security/credentials.h
+  include/grpc++/security/server_credentials.h
+  include/grpc++/server.h
+  include/grpc++/server_builder.h
+  include/grpc++/server_context.h
+  include/grpc++/server_posix.h
+  include/grpc++/support/async_stream.h
+  include/grpc++/support/async_unary_call.h
+  include/grpc++/support/byte_buffer.h
+  include/grpc++/support/channel_arguments.h
+  include/grpc++/support/config.h
+  include/grpc++/support/slice.h
+  include/grpc++/support/status.h
+  include/grpc++/support/status_code_enum.h
+  include/grpc++/support/string_ref.h
+  include/grpc++/support/stub_options.h
+  include/grpc++/support/sync_stream.h
+  include/grpc++/support/time.h
+  include/grpc++/impl/codegen/async_stream.h
+  include/grpc++/impl/codegen/async_unary_call.h
+  include/grpc++/impl/codegen/call.h
+  include/grpc++/impl/codegen/call_hook.h
+  include/grpc++/impl/codegen/channel_interface.h
+  include/grpc++/impl/codegen/client_context.h
+  include/grpc++/impl/codegen/client_unary_call.h
+  include/grpc++/impl/codegen/completion_queue.h
+  include/grpc++/impl/codegen/completion_queue_tag.h
+  include/grpc++/impl/codegen/config.h
+  include/grpc++/impl/codegen/core_codegen_interface.h
+  include/grpc++/impl/codegen/create_auth_context.h
+  include/grpc++/impl/codegen/grpc_library.h
+  include/grpc++/impl/codegen/metadata_map.h
+  include/grpc++/impl/codegen/method_handler_impl.h
+  include/grpc++/impl/codegen/rpc_method.h
+  include/grpc++/impl/codegen/rpc_service_method.h
+  include/grpc++/impl/codegen/security/auth_context.h
+  include/grpc++/impl/codegen/serialization_traits.h
+  include/grpc++/impl/codegen/server_context.h
+  include/grpc++/impl/codegen/server_interface.h
+  include/grpc++/impl/codegen/service_type.h
+  include/grpc++/impl/codegen/slice.h
+  include/grpc++/impl/codegen/status.h
+  include/grpc++/impl/codegen/status_code_enum.h
+  include/grpc++/impl/codegen/string_ref.h
+  include/grpc++/impl/codegen/stub_options.h
+  include/grpc++/impl/codegen/sync_stream.h
+  include/grpc++/impl/codegen/time.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+  include/grpc++/impl/codegen/proto_utils.h
+  include/grpc++/impl/codegen/config_protobuf.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc++ EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_library(grpc++_cronet
+  src/cpp/client/cronet_credentials.cc
+  src/cpp/client/insecure_credentials.cc
+  src/cpp/common/insecure_create_auth_context.cc
+  src/cpp/server/insecure_server_credentials.cc
+  src/cpp/client/channel_cc.cc
+  src/cpp/client/client_context.cc
+  src/cpp/client/create_channel.cc
+  src/cpp/client/create_channel_internal.cc
+  src/cpp/client/create_channel_posix.cc
+  src/cpp/client/credentials_cc.cc
+  src/cpp/client/generic_stub.cc
+  src/cpp/common/channel_arguments.cc
+  src/cpp/common/channel_filter.cc
+  src/cpp/common/completion_queue_cc.cc
+  src/cpp/common/core_codegen.cc
+  src/cpp/common/resource_quota_cc.cc
+  src/cpp/common/rpc_method.cc
+  src/cpp/common/version_cc.cc
+  src/cpp/server/async_generic_service.cc
+  src/cpp/server/channel_argument_option.cc
+  src/cpp/server/create_default_thread_pool.cc
+  src/cpp/server/dynamic_thread_pool.cc
+  src/cpp/server/health/default_health_check_service.cc
+  src/cpp/server/health/health.pb.c
+  src/cpp/server/health/health_check_service.cc
+  src/cpp/server/health/health_check_service_server_builder_option.cc
+  src/cpp/server/server_builder.cc
+  src/cpp/server/server_cc.cc
+  src/cpp/server/server_context.cc
+  src/cpp/server/server_credentials.cc
+  src/cpp/server/server_posix.cc
+  src/cpp/thread_manager/thread_manager.cc
+  src/cpp/util/byte_buffer_cc.cc
+  src/cpp/util/slice_cc.cc
+  src/cpp/util/status.cc
+  src/cpp/util/string_ref.cc
+  src/cpp/util/time_cc.cc
+  third_party/nanopb/pb_common.c
+  third_party/nanopb/pb_decode.c
+  third_party/nanopb/pb_encode.c
+  src/cpp/codegen/codegen_init.cc
+  src/core/ext/transport/chttp2/client/insecure/channel_create.c
+  src/core/ext/transport/chttp2/client/insecure/channel_create_posix.c
+  src/core/ext/transport/chttp2/client/chttp2_connector.c
+  src/core/ext/transport/chttp2/transport/bin_decoder.c
+  src/core/ext/transport/chttp2/transport/bin_encoder.c
+  src/core/ext/transport/chttp2/transport/chttp2_plugin.c
+  src/core/ext/transport/chttp2/transport/chttp2_transport.c
+  src/core/ext/transport/chttp2/transport/frame_data.c
+  src/core/ext/transport/chttp2/transport/frame_goaway.c
+  src/core/ext/transport/chttp2/transport/frame_ping.c
+  src/core/ext/transport/chttp2/transport/frame_rst_stream.c
+  src/core/ext/transport/chttp2/transport/frame_settings.c
+  src/core/ext/transport/chttp2/transport/frame_window_update.c
+  src/core/ext/transport/chttp2/transport/hpack_encoder.c
+  src/core/ext/transport/chttp2/transport/hpack_parser.c
+  src/core/ext/transport/chttp2/transport/hpack_table.c
+  src/core/ext/transport/chttp2/transport/http2_settings.c
+  src/core/ext/transport/chttp2/transport/huffsyms.c
+  src/core/ext/transport/chttp2/transport/incoming_metadata.c
+  src/core/ext/transport/chttp2/transport/parsing.c
+  src/core/ext/transport/chttp2/transport/stream_lists.c
+  src/core/ext/transport/chttp2/transport/stream_map.c
+  src/core/ext/transport/chttp2/transport/varint.c
+  src/core/ext/transport/chttp2/transport/writing.c
+  src/core/lib/channel/channel_args.c
+  src/core/lib/channel/channel_stack.c
+  src/core/lib/channel/channel_stack_builder.c
+  src/core/lib/channel/connected_channel.c
+  src/core/lib/channel/handshaker.c
+  src/core/lib/channel/handshaker_factory.c
+  src/core/lib/channel/handshaker_registry.c
+  src/core/lib/compression/compression.c
+  src/core/lib/compression/message_compress.c
+  src/core/lib/compression/stream_compression.c
+  src/core/lib/http/format_request.c
+  src/core/lib/http/httpcli.c
+  src/core/lib/http/parser.c
+  src/core/lib/iomgr/closure.c
+  src/core/lib/iomgr/combiner.c
+  src/core/lib/iomgr/endpoint.c
+  src/core/lib/iomgr/endpoint_pair_posix.c
+  src/core/lib/iomgr/endpoint_pair_uv.c
+  src/core/lib/iomgr/endpoint_pair_windows.c
+  src/core/lib/iomgr/error.c
+  src/core/lib/iomgr/ev_epoll1_linux.c
+  src/core/lib/iomgr/ev_epoll_limited_pollers_linux.c
+  src/core/lib/iomgr/ev_epoll_thread_pool_linux.c
+  src/core/lib/iomgr/ev_epollex_linux.c
+  src/core/lib/iomgr/ev_epollsig_linux.c
+  src/core/lib/iomgr/ev_poll_posix.c
+  src/core/lib/iomgr/ev_posix.c
+  src/core/lib/iomgr/ev_windows.c
+  src/core/lib/iomgr/exec_ctx.c
+  src/core/lib/iomgr/executor.c
+  src/core/lib/iomgr/iocp_windows.c
+  src/core/lib/iomgr/iomgr.c
+  src/core/lib/iomgr/iomgr_posix.c
+  src/core/lib/iomgr/iomgr_uv.c
+  src/core/lib/iomgr/iomgr_windows.c
+  src/core/lib/iomgr/is_epollexclusive_available.c
+  src/core/lib/iomgr/load_file.c
+  src/core/lib/iomgr/lockfree_event.c
+  src/core/lib/iomgr/network_status_tracker.c
+  src/core/lib/iomgr/polling_entity.c
+  src/core/lib/iomgr/pollset_set_uv.c
+  src/core/lib/iomgr/pollset_set_windows.c
+  src/core/lib/iomgr/pollset_uv.c
+  src/core/lib/iomgr/pollset_windows.c
+  src/core/lib/iomgr/resolve_address_posix.c
+  src/core/lib/iomgr/resolve_address_uv.c
+  src/core/lib/iomgr/resolve_address_windows.c
+  src/core/lib/iomgr/resource_quota.c
+  src/core/lib/iomgr/sockaddr_utils.c
+  src/core/lib/iomgr/socket_factory_posix.c
+  src/core/lib/iomgr/socket_mutator.c
+  src/core/lib/iomgr/socket_utils_common_posix.c
+  src/core/lib/iomgr/socket_utils_linux.c
+  src/core/lib/iomgr/socket_utils_posix.c
+  src/core/lib/iomgr/socket_utils_uv.c
+  src/core/lib/iomgr/socket_utils_windows.c
+  src/core/lib/iomgr/socket_windows.c
+  src/core/lib/iomgr/tcp_client_posix.c
+  src/core/lib/iomgr/tcp_client_uv.c
+  src/core/lib/iomgr/tcp_client_windows.c
+  src/core/lib/iomgr/tcp_posix.c
+  src/core/lib/iomgr/tcp_server_posix.c
+  src/core/lib/iomgr/tcp_server_utils_posix_common.c
+  src/core/lib/iomgr/tcp_server_utils_posix_ifaddrs.c
+  src/core/lib/iomgr/tcp_server_utils_posix_noifaddrs.c
+  src/core/lib/iomgr/tcp_server_uv.c
+  src/core/lib/iomgr/tcp_server_windows.c
+  src/core/lib/iomgr/tcp_uv.c
+  src/core/lib/iomgr/tcp_windows.c
+  src/core/lib/iomgr/time_averaged_stats.c
+  src/core/lib/iomgr/timer_generic.c
+  src/core/lib/iomgr/timer_heap.c
+  src/core/lib/iomgr/timer_manager.c
+  src/core/lib/iomgr/timer_uv.c
+  src/core/lib/iomgr/udp_server.c
+  src/core/lib/iomgr/unix_sockets_posix.c
+  src/core/lib/iomgr/unix_sockets_posix_noop.c
+  src/core/lib/iomgr/wakeup_fd_cv.c
+  src/core/lib/iomgr/wakeup_fd_eventfd.c
+  src/core/lib/iomgr/wakeup_fd_nospecial.c
+  src/core/lib/iomgr/wakeup_fd_pipe.c
+  src/core/lib/iomgr/wakeup_fd_posix.c
+  src/core/lib/json/json.c
+  src/core/lib/json/json_reader.c
+  src/core/lib/json/json_string.c
+  src/core/lib/json/json_writer.c
+  src/core/lib/slice/b64.c
+  src/core/lib/slice/percent_encoding.c
+  src/core/lib/slice/slice.c
+  src/core/lib/slice/slice_buffer.c
+  src/core/lib/slice/slice_hash_table.c
+  src/core/lib/slice/slice_intern.c
+  src/core/lib/slice/slice_string_helpers.c
+  src/core/lib/surface/alarm.c
+  src/core/lib/surface/api_trace.c
+  src/core/lib/surface/byte_buffer.c
+  src/core/lib/surface/byte_buffer_reader.c
+  src/core/lib/surface/call.c
+  src/core/lib/surface/call_details.c
+  src/core/lib/surface/call_log_batch.c
+  src/core/lib/surface/channel.c
+  src/core/lib/surface/channel_init.c
+  src/core/lib/surface/channel_ping.c
+  src/core/lib/surface/channel_stack_type.c
+  src/core/lib/surface/completion_queue.c
+  src/core/lib/surface/completion_queue_factory.c
+  src/core/lib/surface/event_string.c
+  src/core/lib/surface/lame_client.cc
+  src/core/lib/surface/metadata_array.c
+  src/core/lib/surface/server.c
+  src/core/lib/surface/validate_metadata.c
+  src/core/lib/surface/version.c
+  src/core/lib/transport/bdp_estimator.c
+  src/core/lib/transport/byte_stream.c
+  src/core/lib/transport/connectivity_state.c
+  src/core/lib/transport/error_utils.c
+  src/core/lib/transport/metadata.c
+  src/core/lib/transport/metadata_batch.c
+  src/core/lib/transport/pid_controller.c
+  src/core/lib/transport/service_config.c
+  src/core/lib/transport/static_metadata.c
+  src/core/lib/transport/status_conversion.c
+  src/core/lib/transport/timeout_encoding.c
+  src/core/lib/transport/transport.c
+  src/core/lib/transport/transport_op_string.c
+  src/core/lib/debug/trace.c
+  src/core/ext/transport/chttp2/alpn/alpn.c
+  src/core/ext/filters/http/client/http_client_filter.c
+  src/core/ext/filters/http/http_filters_plugin.c
+  src/core/ext/filters/http/message_compress/message_compress_filter.c
+  src/core/ext/filters/http/server/http_server_filter.c
+  src/core/ext/filters/client_channel/channel_connectivity.c
+  src/core/ext/filters/client_channel/client_channel.c
+  src/core/ext/filters/client_channel/client_channel_factory.c
+  src/core/ext/filters/client_channel/client_channel_plugin.c
+  src/core/ext/filters/client_channel/connector.c
+  src/core/ext/filters/client_channel/http_connect_handshaker.c
+  src/core/ext/filters/client_channel/http_proxy.c
+  src/core/ext/filters/client_channel/lb_policy.c
+  src/core/ext/filters/client_channel/lb_policy_factory.c
+  src/core/ext/filters/client_channel/lb_policy_registry.c
+  src/core/ext/filters/client_channel/parse_address.c
+  src/core/ext/filters/client_channel/proxy_mapper.c
+  src/core/ext/filters/client_channel/proxy_mapper_registry.c
+  src/core/ext/filters/client_channel/resolver.c
+  src/core/ext/filters/client_channel/resolver_factory.c
+  src/core/ext/filters/client_channel/resolver_registry.c
+  src/core/ext/filters/client_channel/retry_throttle.c
+  src/core/ext/filters/client_channel/subchannel.c
+  src/core/ext/filters/client_channel/subchannel_index.c
+  src/core/ext/filters/client_channel/uri_parser.c
+  src/core/ext/filters/deadline/deadline_filter.c
+  src/core/ext/transport/chttp2/server/insecure/server_chttp2.c
+  src/core/ext/transport/chttp2/server/insecure/server_chttp2_posix.c
+  src/core/ext/transport/chttp2/server/chttp2_server.c
+  src/core/ext/census/base_resources.c
+  src/core/ext/census/context.c
+  src/core/ext/census/gen/census.pb.c
+  src/core/ext/census/gen/trace_context.pb.c
+  src/core/ext/census/grpc_context.c
+  src/core/ext/census/grpc_filter.c
+  src/core/ext/census/grpc_plugin.c
+  src/core/ext/census/initialize.c
+  src/core/ext/census/intrusive_hash_map.c
+  src/core/ext/census/mlog.c
+  src/core/ext/census/operation.c
+  src/core/ext/census/placeholders.c
+  src/core/ext/census/resource.c
+  src/core/ext/census/trace_context.c
+  src/core/ext/census/tracing.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++_cronet PROPERTIES COMPILE_PDB_NAME "grpc++_cronet"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++_cronet.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc++_cronet
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++_cronet
+  ${_gRPC_BASELIB_LIBRARIES}
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr
+  grpc_cronet
+  grpc
+)
+
+foreach(_hdr
+  include/grpc++/alarm.h
+  include/grpc++/channel.h
+  include/grpc++/client_context.h
+  include/grpc++/completion_queue.h
+  include/grpc++/create_channel.h
+  include/grpc++/create_channel_posix.h
+  include/grpc++/ext/health_check_service_server_builder_option.h
+  include/grpc++/generic/async_generic_service.h
+  include/grpc++/generic/generic_stub.h
+  include/grpc++/grpc++.h
+  include/grpc++/health_check_service_interface.h
+  include/grpc++/impl/call.h
+  include/grpc++/impl/channel_argument_option.h
+  include/grpc++/impl/client_unary_call.h
+  include/grpc++/impl/codegen/core_codegen.h
+  include/grpc++/impl/grpc_library.h
+  include/grpc++/impl/method_handler_impl.h
+  include/grpc++/impl/rpc_method.h
+  include/grpc++/impl/rpc_service_method.h
+  include/grpc++/impl/serialization_traits.h
+  include/grpc++/impl/server_builder_option.h
+  include/grpc++/impl/server_builder_plugin.h
+  include/grpc++/impl/server_initializer.h
+  include/grpc++/impl/service_type.h
+  include/grpc++/resource_quota.h
+  include/grpc++/security/auth_context.h
+  include/grpc++/security/auth_metadata_processor.h
+  include/grpc++/security/credentials.h
+  include/grpc++/security/server_credentials.h
+  include/grpc++/server.h
+  include/grpc++/server_builder.h
+  include/grpc++/server_context.h
+  include/grpc++/server_posix.h
+  include/grpc++/support/async_stream.h
+  include/grpc++/support/async_unary_call.h
+  include/grpc++/support/byte_buffer.h
+  include/grpc++/support/channel_arguments.h
+  include/grpc++/support/config.h
+  include/grpc++/support/slice.h
+  include/grpc++/support/status.h
+  include/grpc++/support/status_code_enum.h
+  include/grpc++/support/string_ref.h
+  include/grpc++/support/stub_options.h
+  include/grpc++/support/sync_stream.h
+  include/grpc++/support/time.h
+  include/grpc++/impl/codegen/async_stream.h
+  include/grpc++/impl/codegen/async_unary_call.h
+  include/grpc++/impl/codegen/call.h
+  include/grpc++/impl/codegen/call_hook.h
+  include/grpc++/impl/codegen/channel_interface.h
+  include/grpc++/impl/codegen/client_context.h
+  include/grpc++/impl/codegen/client_unary_call.h
+  include/grpc++/impl/codegen/completion_queue.h
+  include/grpc++/impl/codegen/completion_queue_tag.h
+  include/grpc++/impl/codegen/config.h
+  include/grpc++/impl/codegen/core_codegen_interface.h
+  include/grpc++/impl/codegen/create_auth_context.h
+  include/grpc++/impl/codegen/grpc_library.h
+  include/grpc++/impl/codegen/metadata_map.h
+  include/grpc++/impl/codegen/method_handler_impl.h
+  include/grpc++/impl/codegen/rpc_method.h
+  include/grpc++/impl/codegen/rpc_service_method.h
+  include/grpc++/impl/codegen/security/auth_context.h
+  include/grpc++/impl/codegen/serialization_traits.h
+  include/grpc++/impl/codegen/server_context.h
+  include/grpc++/impl/codegen/server_interface.h
+  include/grpc++/impl/codegen/service_type.h
+  include/grpc++/impl/codegen/slice.h
+  include/grpc++/impl/codegen/status.h
+  include/grpc++/impl/codegen/status_code_enum.h
+  include/grpc++/impl/codegen/string_ref.h
+  include/grpc++/impl/codegen/stub_options.h
+  include/grpc++/impl/codegen/sync_stream.h
+  include/grpc++/impl/codegen/time.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+  include/grpc/byte_buffer.h
+  include/grpc/byte_buffer_reader.h
+  include/grpc/compression.h
+  include/grpc/grpc.h
+  include/grpc/grpc_posix.h
+  include/grpc/grpc_security_constants.h
+  include/grpc/load_reporting.h
+  include/grpc/slice.h
+  include/grpc/slice_buffer.h
+  include/grpc/status.h
+  include/grpc/support/workaround_list.h
+  include/grpc/census.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc++_cronet EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_library(grpc++_error_details
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/status/status.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/status/status.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/status/status.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/status/status.grpc.pb.h
+  src/cpp/util/error_details.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++_error_details PROPERTIES COMPILE_PDB_NAME "grpc++_error_details"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++_error_details.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/status/status.proto
+)
+
+target_include_directories(grpc++_error_details
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++_error_details
+  ${_gRPC_BASELIB_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+)
+
+foreach(_hdr
+  include/grpc++/support/error_details.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc++_error_details EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(grpc++_proto_reflection_desc_db
+  test/cpp/util/proto_reflection_descriptor_database.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.grpc.pb.h
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++_proto_reflection_desc_db PROPERTIES COMPILE_PDB_NAME "grpc++_proto_reflection_desc_db"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++_proto_reflection_desc_db.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/reflection/v1alpha/reflection.proto
+)
+
+target_include_directories(grpc++_proto_reflection_desc_db
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++_proto_reflection_desc_db
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+)
+
+foreach(_hdr
+  include/grpc++/impl/codegen/config_protobuf.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+endif (gRPC_BUILD_TESTS)
+
+add_library(grpc++_reflection
+  src/cpp/ext/proto_server_reflection.cc
+  src/cpp/ext/proto_server_reflection_plugin.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.grpc.pb.h
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++_reflection PROPERTIES COMPILE_PDB_NAME "grpc++_reflection"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++_reflection.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/reflection/v1alpha/reflection.proto
+)
+
+target_include_directories(grpc++_reflection
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++_reflection
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+)
+
+foreach(_hdr
+  include/grpc++/ext/proto_server_reflection_plugin.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc++_reflection EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(grpc++_test_config
+  test/cpp/util/test_config_cc.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++_test_config PROPERTIES COMPILE_PDB_NAME "grpc++_test_config"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++_test_config.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc++_test_config
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++_test_config
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(grpc++_test_util
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/health/v1/health.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/health/v1/health.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/health/v1/health.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/health/v1/health.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_mock.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/duplicate/echo_duplicate.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/duplicate/echo_duplicate.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/duplicate/echo_duplicate.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/duplicate/echo_duplicate.grpc.pb.h
+  test/cpp/end2end/test_service_impl.cc
+  test/cpp/util/byte_buffer_proto_helper.cc
+  test/cpp/util/create_test_channel.cc
+  test/cpp/util/string_ref_helper.cc
+  test/cpp/util/subprocess.cc
+  test/cpp/util/test_credentials_provider.cc
+  src/cpp/codegen/codegen_init.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++_test_util PROPERTIES COMPILE_PDB_NAME "grpc++_test_util"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++_test_util.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/health/v1/health.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo_messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/duplicate/echo_duplicate.proto
+)
+
+target_include_directories(grpc++_test_util
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++_test_util
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc_test_util
+  grpc
+)
+
+foreach(_hdr
+  include/grpc++/impl/codegen/async_stream.h
+  include/grpc++/impl/codegen/async_unary_call.h
+  include/grpc++/impl/codegen/call.h
+  include/grpc++/impl/codegen/call_hook.h
+  include/grpc++/impl/codegen/channel_interface.h
+  include/grpc++/impl/codegen/client_context.h
+  include/grpc++/impl/codegen/client_unary_call.h
+  include/grpc++/impl/codegen/completion_queue.h
+  include/grpc++/impl/codegen/completion_queue_tag.h
+  include/grpc++/impl/codegen/config.h
+  include/grpc++/impl/codegen/core_codegen_interface.h
+  include/grpc++/impl/codegen/create_auth_context.h
+  include/grpc++/impl/codegen/grpc_library.h
+  include/grpc++/impl/codegen/metadata_map.h
+  include/grpc++/impl/codegen/method_handler_impl.h
+  include/grpc++/impl/codegen/rpc_method.h
+  include/grpc++/impl/codegen/rpc_service_method.h
+  include/grpc++/impl/codegen/security/auth_context.h
+  include/grpc++/impl/codegen/serialization_traits.h
+  include/grpc++/impl/codegen/server_context.h
+  include/grpc++/impl/codegen/server_interface.h
+  include/grpc++/impl/codegen/service_type.h
+  include/grpc++/impl/codegen/slice.h
+  include/grpc++/impl/codegen/status.h
+  include/grpc++/impl/codegen/status_code_enum.h
+  include/grpc++/impl/codegen/string_ref.h
+  include/grpc++/impl/codegen/stub_options.h
+  include/grpc++/impl/codegen/sync_stream.h
+  include/grpc++/impl/codegen/time.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+  include/grpc++/impl/codegen/proto_utils.h
+  include/grpc++/impl/codegen/config_protobuf.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+endif (gRPC_BUILD_TESTS)
+
+add_library(grpc++_unsecure
+  src/cpp/client/insecure_credentials.cc
+  src/cpp/common/insecure_create_auth_context.cc
+  src/cpp/server/insecure_server_credentials.cc
+  src/cpp/client/channel_cc.cc
+  src/cpp/client/client_context.cc
+  src/cpp/client/create_channel.cc
+  src/cpp/client/create_channel_internal.cc
+  src/cpp/client/create_channel_posix.cc
+  src/cpp/client/credentials_cc.cc
+  src/cpp/client/generic_stub.cc
+  src/cpp/common/channel_arguments.cc
+  src/cpp/common/channel_filter.cc
+  src/cpp/common/completion_queue_cc.cc
+  src/cpp/common/core_codegen.cc
+  src/cpp/common/resource_quota_cc.cc
+  src/cpp/common/rpc_method.cc
+  src/cpp/common/version_cc.cc
+  src/cpp/server/async_generic_service.cc
+  src/cpp/server/channel_argument_option.cc
+  src/cpp/server/create_default_thread_pool.cc
+  src/cpp/server/dynamic_thread_pool.cc
+  src/cpp/server/health/default_health_check_service.cc
+  src/cpp/server/health/health.pb.c
+  src/cpp/server/health/health_check_service.cc
+  src/cpp/server/health/health_check_service_server_builder_option.cc
+  src/cpp/server/server_builder.cc
+  src/cpp/server/server_cc.cc
+  src/cpp/server/server_context.cc
+  src/cpp/server/server_credentials.cc
+  src/cpp/server/server_posix.cc
+  src/cpp/thread_manager/thread_manager.cc
+  src/cpp/util/byte_buffer_cc.cc
+  src/cpp/util/slice_cc.cc
+  src/cpp/util/status.cc
+  src/cpp/util/string_ref.cc
+  src/cpp/util/time_cc.cc
+  third_party/nanopb/pb_common.c
+  third_party/nanopb/pb_decode.c
+  third_party/nanopb/pb_encode.c
+  src/cpp/codegen/codegen_init.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc++_unsecure PROPERTIES COMPILE_PDB_NAME "grpc++_unsecure"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc++_unsecure.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc++_unsecure
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc++_unsecure
+  ${_gRPC_BASELIB_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr
+  grpc_unsecure
+)
+
+foreach(_hdr
+  include/grpc++/alarm.h
+  include/grpc++/channel.h
+  include/grpc++/client_context.h
+  include/grpc++/completion_queue.h
+  include/grpc++/create_channel.h
+  include/grpc++/create_channel_posix.h
+  include/grpc++/ext/health_check_service_server_builder_option.h
+  include/grpc++/generic/async_generic_service.h
+  include/grpc++/generic/generic_stub.h
+  include/grpc++/grpc++.h
+  include/grpc++/health_check_service_interface.h
+  include/grpc++/impl/call.h
+  include/grpc++/impl/channel_argument_option.h
+  include/grpc++/impl/client_unary_call.h
+  include/grpc++/impl/codegen/core_codegen.h
+  include/grpc++/impl/grpc_library.h
+  include/grpc++/impl/method_handler_impl.h
+  include/grpc++/impl/rpc_method.h
+  include/grpc++/impl/rpc_service_method.h
+  include/grpc++/impl/serialization_traits.h
+  include/grpc++/impl/server_builder_option.h
+  include/grpc++/impl/server_builder_plugin.h
+  include/grpc++/impl/server_initializer.h
+  include/grpc++/impl/service_type.h
+  include/grpc++/resource_quota.h
+  include/grpc++/security/auth_context.h
+  include/grpc++/security/auth_metadata_processor.h
+  include/grpc++/security/credentials.h
+  include/grpc++/security/server_credentials.h
+  include/grpc++/server.h
+  include/grpc++/server_builder.h
+  include/grpc++/server_context.h
+  include/grpc++/server_posix.h
+  include/grpc++/support/async_stream.h
+  include/grpc++/support/async_unary_call.h
+  include/grpc++/support/byte_buffer.h
+  include/grpc++/support/channel_arguments.h
+  include/grpc++/support/config.h
+  include/grpc++/support/slice.h
+  include/grpc++/support/status.h
+  include/grpc++/support/status_code_enum.h
+  include/grpc++/support/string_ref.h
+  include/grpc++/support/stub_options.h
+  include/grpc++/support/sync_stream.h
+  include/grpc++/support/time.h
+  include/grpc++/impl/codegen/async_stream.h
+  include/grpc++/impl/codegen/async_unary_call.h
+  include/grpc++/impl/codegen/call.h
+  include/grpc++/impl/codegen/call_hook.h
+  include/grpc++/impl/codegen/channel_interface.h
+  include/grpc++/impl/codegen/client_context.h
+  include/grpc++/impl/codegen/client_unary_call.h
+  include/grpc++/impl/codegen/completion_queue.h
+  include/grpc++/impl/codegen/completion_queue_tag.h
+  include/grpc++/impl/codegen/config.h
+  include/grpc++/impl/codegen/core_codegen_interface.h
+  include/grpc++/impl/codegen/create_auth_context.h
+  include/grpc++/impl/codegen/grpc_library.h
+  include/grpc++/impl/codegen/metadata_map.h
+  include/grpc++/impl/codegen/method_handler_impl.h
+  include/grpc++/impl/codegen/rpc_method.h
+  include/grpc++/impl/codegen/rpc_service_method.h
+  include/grpc++/impl/codegen/security/auth_context.h
+  include/grpc++/impl/codegen/serialization_traits.h
+  include/grpc++/impl/codegen/server_context.h
+  include/grpc++/impl/codegen/server_interface.h
+  include/grpc++/impl/codegen/service_type.h
+  include/grpc++/impl/codegen/slice.h
+  include/grpc++/impl/codegen/status.h
+  include/grpc++/impl/codegen/status_code_enum.h
+  include/grpc++/impl/codegen/string_ref.h
+  include/grpc++/impl/codegen/stub_options.h
+  include/grpc++/impl/codegen/sync_stream.h
+  include/grpc++/impl/codegen/time.h
+  include/grpc/impl/codegen/byte_buffer_reader.h
+  include/grpc/impl/codegen/compression_types.h
+  include/grpc/impl/codegen/connectivity_state.h
+  include/grpc/impl/codegen/exec_ctx_fwd.h
+  include/grpc/impl/codegen/grpc_types.h
+  include/grpc/impl/codegen/propagation_bits.h
+  include/grpc/impl/codegen/slice.h
+  include/grpc/impl/codegen/status.h
+  include/grpc/impl/codegen/atm.h
+  include/grpc/impl/codegen/atm_gcc_atomic.h
+  include/grpc/impl/codegen/atm_gcc_sync.h
+  include/grpc/impl/codegen/atm_windows.h
+  include/grpc/impl/codegen/gpr_slice.h
+  include/grpc/impl/codegen/gpr_types.h
+  include/grpc/impl/codegen/port_platform.h
+  include/grpc/impl/codegen/sync.h
+  include/grpc/impl/codegen/sync_generic.h
+  include/grpc/impl/codegen/sync_posix.h
+  include/grpc/impl/codegen/sync_windows.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc++_unsecure EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(grpc_benchmark
+  test/cpp/microbenchmarks/helpers.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_benchmark PROPERTIES COMPILE_PDB_NAME "grpc_benchmark"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_benchmark.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc_benchmark
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_benchmark
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  benchmark
+  grpc++
+  grpc_test_util
+  grpc
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(grpc_cli_libs
+  test/cpp/util/cli_call.cc
+  test/cpp/util/cli_credentials.cc
+  test/cpp/util/grpc_tool.cc
+  test/cpp/util/proto_file_parser.cc
+  test/cpp/util/service_describer.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/reflection/v1alpha/reflection.grpc.pb.h
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_cli_libs PROPERTIES COMPILE_PDB_NAME "grpc_cli_libs"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_cli_libs.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/reflection/v1alpha/reflection.proto
+)
+
+target_include_directories(grpc_cli_libs
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_cli_libs
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_proto_reflection_desc_db
+  grpc++
+  grpc
+)
+
+foreach(_hdr
+  include/grpc++/impl/codegen/config_protobuf.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+endif (gRPC_BUILD_TESTS)
+
+add_library(grpc_plugin_support
+  src/compiler/cpp_generator.cc
+  src/compiler/csharp_generator.cc
+  src/compiler/node_generator.cc
+  src/compiler/objective_c_generator.cc
+  src/compiler/php_generator.cc
+  src/compiler/python_generator.cc
+  src/compiler/ruby_generator.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_plugin_support PROPERTIES COMPILE_PDB_NAME "grpc_plugin_support"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_plugin_support.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc_plugin_support
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_plugin_support
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+)
+
+foreach(_hdr
+  include/grpc++/impl/codegen/config_protobuf.h
+)
+  string(REPLACE "include/" "" _path ${_hdr})
+  get_filename_component(_path ${_path} PATH)
+  install(FILES ${_hdr}
+    DESTINATION "${gRPC_INSTALL_INCLUDEDIR}/${_path}"
+  )
+endforeach()
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_plugin_support EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(http2_client_main
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.h
+  test/cpp/interop/http2_client.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(http2_client_main PROPERTIES COMPILE_PDB_NAME "http2_client_main"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/http2_client_main.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/empty.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/test.proto
+)
+
+target_include_directories(http2_client_main
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(http2_client_main
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  grpc++_test_config
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(interop_client_helper
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  test/cpp/interop/client_helper.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(interop_client_helper PROPERTIES COMPILE_PDB_NAME "interop_client_helper"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/interop_client_helper.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+
+target_include_directories(interop_client_helper
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_client_helper
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(interop_client_main
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.h
+  test/cpp/interop/client.cc
+  test/cpp/interop/interop_client.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(interop_client_main PROPERTIES COMPILE_PDB_NAME "interop_client_main"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/interop_client_main.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/empty.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/test.proto
+)
+
+target_include_directories(interop_client_main
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_client_main
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  interop_client_helper
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(interop_server_helper
+  test/cpp/interop/server_helper.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(interop_server_helper PROPERTIES COMPILE_PDB_NAME "interop_server_helper"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/interop_server_helper.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(interop_server_helper
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_server_helper
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(interop_server_lib
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.h
+  test/cpp/interop/interop_server.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(interop_server_lib PROPERTIES COMPILE_PDB_NAME "interop_server_lib"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/interop_server_lib.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/empty.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/test.proto
+)
+
+target_include_directories(interop_server_lib
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_server_lib
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  interop_server_helper
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(interop_server_main
+  test/cpp/interop/interop_server_bootstrap.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(interop_server_main PROPERTIES COMPILE_PDB_NAME "interop_server_main"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/interop_server_main.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(interop_server_main
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_server_main
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  interop_server_lib
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(qps
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.grpc.pb.h
+  test/cpp/qps/benchmark_config.cc
+  test/cpp/qps/client_async.cc
+  test/cpp/qps/client_sync.cc
+  test/cpp/qps/driver.cc
+  test/cpp/qps/parse_json.cc
+  test/cpp/qps/qps_worker.cc
+  test/cpp/qps/report.cc
+  test/cpp/qps/server_async.cc
+  test/cpp/qps/server_sync.cc
+  test/cpp/qps/usage_timer.cc
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(qps PROPERTIES COMPILE_PDB_NAME "qps"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/qps.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/payloads.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/stats.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/control.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/services.proto
+)
+
+target_include_directories(qps
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(qps
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc++_test_util
+  grpc++
+  grpc
+)
+
+
+endif (gRPC_BUILD_TESTS)
+
+add_library(grpc_csharp_ext SHARED
+  src/csharp/ext/grpc_csharp_ext.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(grpc_csharp_ext PROPERTIES COMPILE_PDB_NAME "grpc_csharp_ext"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/grpc_csharp_ext.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(grpc_csharp_ext
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_csharp_ext
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+)
+
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_csharp_ext EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_library(ares
+  third_party/cares/cares/ares__close_sockets.c
+  third_party/cares/cares/ares__get_hostent.c
+  third_party/cares/cares/ares__read_line.c
+  third_party/cares/cares/ares__timeval.c
+  third_party/cares/cares/ares_cancel.c
+  third_party/cares/cares/ares_create_query.c
+  third_party/cares/cares/ares_data.c
+  third_party/cares/cares/ares_destroy.c
+  third_party/cares/cares/ares_expand_name.c
+  third_party/cares/cares/ares_expand_string.c
+  third_party/cares/cares/ares_fds.c
+  third_party/cares/cares/ares_free_hostent.c
+  third_party/cares/cares/ares_free_string.c
+  third_party/cares/cares/ares_getenv.c
+  third_party/cares/cares/ares_gethostbyaddr.c
+  third_party/cares/cares/ares_gethostbyname.c
+  third_party/cares/cares/ares_getnameinfo.c
+  third_party/cares/cares/ares_getopt.c
+  third_party/cares/cares/ares_getsock.c
+  third_party/cares/cares/ares_init.c
+  third_party/cares/cares/ares_library_init.c
+  third_party/cares/cares/ares_llist.c
+  third_party/cares/cares/ares_mkquery.c
+  third_party/cares/cares/ares_nowarn.c
+  third_party/cares/cares/ares_options.c
+  third_party/cares/cares/ares_parse_a_reply.c
+  third_party/cares/cares/ares_parse_aaaa_reply.c
+  third_party/cares/cares/ares_parse_mx_reply.c
+  third_party/cares/cares/ares_parse_naptr_reply.c
+  third_party/cares/cares/ares_parse_ns_reply.c
+  third_party/cares/cares/ares_parse_ptr_reply.c
+  third_party/cares/cares/ares_parse_soa_reply.c
+  third_party/cares/cares/ares_parse_srv_reply.c
+  third_party/cares/cares/ares_parse_txt_reply.c
+  third_party/cares/cares/ares_platform.c
+  third_party/cares/cares/ares_process.c
+  third_party/cares/cares/ares_query.c
+  third_party/cares/cares/ares_search.c
+  third_party/cares/cares/ares_send.c
+  third_party/cares/cares/ares_strcasecmp.c
+  third_party/cares/cares/ares_strdup.c
+  third_party/cares/cares/ares_strerror.c
+  third_party/cares/cares/ares_timeout.c
+  third_party/cares/cares/ares_version.c
+  third_party/cares/cares/ares_writev.c
+  third_party/cares/cares/bitncmp.c
+  third_party/cares/cares/inet_net_pton.c
+  third_party/cares/cares/inet_ntop.c
+  third_party/cares/cares/windows_port.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(ares PROPERTIES COMPILE_PDB_NAME "ares"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ares.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(ares
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(ares
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(bad_client_test
+  test/core/bad_client/bad_client.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(bad_client_test PROPERTIES COMPILE_PDB_NAME "bad_client_test"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/bad_client_test.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(bad_client_test
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bad_client_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(bad_ssl_test_server
+  test/core/bad_ssl/server_common.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(bad_ssl_test_server PROPERTIES COMPILE_PDB_NAME "bad_ssl_test_server"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/bad_ssl_test_server.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(bad_ssl_test_server
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bad_ssl_test_server
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(end2end_tests
+  test/core/end2end/end2end_tests.c
+  test/core/end2end/end2end_test_utils.c
+  test/core/end2end/tests/authority_not_supported.c
+  test/core/end2end/tests/bad_hostname.c
+  test/core/end2end/tests/bad_ping.c
+  test/core/end2end/tests/binary_metadata.c
+  test/core/end2end/tests/call_creds.c
+  test/core/end2end/tests/cancel_after_accept.c
+  test/core/end2end/tests/cancel_after_client_done.c
+  test/core/end2end/tests/cancel_after_invoke.c
+  test/core/end2end/tests/cancel_after_round_trip.c
+  test/core/end2end/tests/cancel_before_invoke.c
+  test/core/end2end/tests/cancel_in_a_vacuum.c
+  test/core/end2end/tests/cancel_with_status.c
+  test/core/end2end/tests/compressed_payload.c
+  test/core/end2end/tests/connectivity.c
+  test/core/end2end/tests/default_host.c
+  test/core/end2end/tests/disappearing_server.c
+  test/core/end2end/tests/empty_batch.c
+  test/core/end2end/tests/filter_call_init_fails.c
+  test/core/end2end/tests/filter_causes_close.c
+  test/core/end2end/tests/filter_latency.c
+  test/core/end2end/tests/graceful_server_shutdown.c
+  test/core/end2end/tests/high_initial_seqno.c
+  test/core/end2end/tests/hpack_size.c
+  test/core/end2end/tests/idempotent_request.c
+  test/core/end2end/tests/invoke_large_request.c
+  test/core/end2end/tests/keepalive_timeout.c
+  test/core/end2end/tests/large_metadata.c
+  test/core/end2end/tests/load_reporting_hook.c
+  test/core/end2end/tests/max_concurrent_streams.c
+  test/core/end2end/tests/max_connection_age.c
+  test/core/end2end/tests/max_connection_idle.c
+  test/core/end2end/tests/max_message_length.c
+  test/core/end2end/tests/negative_deadline.c
+  test/core/end2end/tests/network_status_change.c
+  test/core/end2end/tests/no_logging.c
+  test/core/end2end/tests/no_op.c
+  test/core/end2end/tests/payload.c
+  test/core/end2end/tests/ping.c
+  test/core/end2end/tests/ping_pong_streaming.c
+  test/core/end2end/tests/proxy_auth.c
+  test/core/end2end/tests/registered_call.c
+  test/core/end2end/tests/request_with_flags.c
+  test/core/end2end/tests/request_with_payload.c
+  test/core/end2end/tests/resource_quota_server.c
+  test/core/end2end/tests/server_finishes_request.c
+  test/core/end2end/tests/shutdown_finishes_calls.c
+  test/core/end2end/tests/shutdown_finishes_tags.c
+  test/core/end2end/tests/simple_cacheable_request.c
+  test/core/end2end/tests/simple_delayed_request.c
+  test/core/end2end/tests/simple_metadata.c
+  test/core/end2end/tests/simple_request.c
+  test/core/end2end/tests/streaming_error_response.c
+  test/core/end2end/tests/trailing_metadata.c
+  test/core/end2end/tests/workaround_cronet_compression.c
+  test/core/end2end/tests/write_buffering.c
+  test/core/end2end/tests/write_buffering_at_end.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(end2end_tests PROPERTIES COMPILE_PDB_NAME "end2end_tests"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/end2end_tests.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(end2end_tests
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(end2end_tests
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_library(end2end_nosec_tests
+  test/core/end2end/end2end_nosec_tests.c
+  test/core/end2end/end2end_test_utils.c
+  test/core/end2end/tests/authority_not_supported.c
+  test/core/end2end/tests/bad_hostname.c
+  test/core/end2end/tests/bad_ping.c
+  test/core/end2end/tests/binary_metadata.c
+  test/core/end2end/tests/cancel_after_accept.c
+  test/core/end2end/tests/cancel_after_client_done.c
+  test/core/end2end/tests/cancel_after_invoke.c
+  test/core/end2end/tests/cancel_after_round_trip.c
+  test/core/end2end/tests/cancel_before_invoke.c
+  test/core/end2end/tests/cancel_in_a_vacuum.c
+  test/core/end2end/tests/cancel_with_status.c
+  test/core/end2end/tests/compressed_payload.c
+  test/core/end2end/tests/connectivity.c
+  test/core/end2end/tests/default_host.c
+  test/core/end2end/tests/disappearing_server.c
+  test/core/end2end/tests/empty_batch.c
+  test/core/end2end/tests/filter_call_init_fails.c
+  test/core/end2end/tests/filter_causes_close.c
+  test/core/end2end/tests/filter_latency.c
+  test/core/end2end/tests/graceful_server_shutdown.c
+  test/core/end2end/tests/high_initial_seqno.c
+  test/core/end2end/tests/hpack_size.c
+  test/core/end2end/tests/idempotent_request.c
+  test/core/end2end/tests/invoke_large_request.c
+  test/core/end2end/tests/keepalive_timeout.c
+  test/core/end2end/tests/large_metadata.c
+  test/core/end2end/tests/load_reporting_hook.c
+  test/core/end2end/tests/max_concurrent_streams.c
+  test/core/end2end/tests/max_connection_age.c
+  test/core/end2end/tests/max_connection_idle.c
+  test/core/end2end/tests/max_message_length.c
+  test/core/end2end/tests/negative_deadline.c
+  test/core/end2end/tests/network_status_change.c
+  test/core/end2end/tests/no_logging.c
+  test/core/end2end/tests/no_op.c
+  test/core/end2end/tests/payload.c
+  test/core/end2end/tests/ping.c
+  test/core/end2end/tests/ping_pong_streaming.c
+  test/core/end2end/tests/proxy_auth.c
+  test/core/end2end/tests/registered_call.c
+  test/core/end2end/tests/request_with_flags.c
+  test/core/end2end/tests/request_with_payload.c
+  test/core/end2end/tests/resource_quota_server.c
+  test/core/end2end/tests/server_finishes_request.c
+  test/core/end2end/tests/shutdown_finishes_calls.c
+  test/core/end2end/tests/shutdown_finishes_tags.c
+  test/core/end2end/tests/simple_cacheable_request.c
+  test/core/end2end/tests/simple_delayed_request.c
+  test/core/end2end/tests/simple_metadata.c
+  test/core/end2end/tests/simple_request.c
+  test/core/end2end/tests/streaming_error_response.c
+  test/core/end2end/tests/trailing_metadata.c
+  test/core/end2end/tests/workaround_cronet_compression.c
+  test/core/end2end/tests/write_buffering.c
+  test/core/end2end/tests/write_buffering_at_end.c
+)
+
+if(WIN32 AND MSVC)
+  set_target_properties(end2end_nosec_tests PROPERTIES COMPILE_PDB_NAME "end2end_nosec_tests"
+    COMPILE_PDB_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}"
+  )
+  if (gRPC_INSTALL)
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/end2end_nosec_tests.pdb
+      DESTINATION ${gRPC_INSTALL_LIBDIR} OPTIONAL
+    )
+  endif()
+endif()
+
+
+target_include_directories(end2end_nosec_tests
+  PUBLIC $<INSTALL_INTERFACE:include> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${ZLIB_INCLUDE_DIR}
+  PRIVATE ${BENCHMARK}/include
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(end2end_nosec_tests
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+
+endif (gRPC_BUILD_TESTS)
+
+if (gRPC_BUILD_TESTS)
+
+add_executable(alarm_test
+  test/core/surface/alarm_test.c
+)
+
+
+target_include_directories(alarm_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(alarm_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(algorithm_test
+  test/core/compression/algorithm_test.c
+)
+
+
+target_include_directories(algorithm_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(algorithm_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(alloc_test
+  test/core/support/alloc_test.c
+)
+
+
+target_include_directories(alloc_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(alloc_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(alpn_test
+  test/core/transport/chttp2/alpn_test.c
+)
+
+
+target_include_directories(alpn_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(alpn_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(arena_test
+  test/core/support/arena_test.c
+)
+
+
+target_include_directories(arena_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(arena_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(bad_server_response_test
+  test/core/end2end/bad_server_response_test.c
+)
+
+
+target_include_directories(bad_server_response_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bad_server_response_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  test_tcp_server
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(bdp_estimator_test
+  test/core/transport/bdp_estimator_test.c
+)
+
+
+target_include_directories(bdp_estimator_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bdp_estimator_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(bin_decoder_test
+  test/core/transport/chttp2/bin_decoder_test.c
+)
+
+
+target_include_directories(bin_decoder_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bin_decoder_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(bin_encoder_test
+  test/core/transport/chttp2/bin_encoder_test.c
+)
+
+
+target_include_directories(bin_encoder_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bin_encoder_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(census_context_test
+  test/core/census/context_test.c
+)
+
+
+target_include_directories(census_context_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(census_context_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(census_intrusive_hash_map_test
+  test/core/census/intrusive_hash_map_test.c
+)
+
+
+target_include_directories(census_intrusive_hash_map_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(census_intrusive_hash_map_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(census_resource_test
+  test/core/census/resource_test.c
+)
+
+
+target_include_directories(census_resource_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(census_resource_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(census_trace_context_test
+  test/core/census/trace_context_test.c
+)
+
+
+target_include_directories(census_trace_context_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(census_trace_context_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(channel_create_test
+  test/core/surface/channel_create_test.c
+)
+
+
+target_include_directories(channel_create_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(channel_create_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+
+add_executable(check_epollexclusive
+  test/build/check_epollexclusive.c
+)
+
+
+target_include_directories(check_epollexclusive
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(check_epollexclusive
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS check_epollexclusive EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_executable(chttp2_hpack_encoder_test
+  test/core/transport/chttp2/hpack_encoder_test.c
+)
+
+
+target_include_directories(chttp2_hpack_encoder_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(chttp2_hpack_encoder_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(chttp2_stream_map_test
+  test/core/transport/chttp2/stream_map_test.c
+)
+
+
+target_include_directories(chttp2_stream_map_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(chttp2_stream_map_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(chttp2_varint_test
+  test/core/transport/chttp2/varint_test.c
+)
+
+
+target_include_directories(chttp2_varint_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(chttp2_varint_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(combiner_test
+  test/core/iomgr/combiner_test.c
+)
+
+
+target_include_directories(combiner_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(combiner_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(compression_test
+  test/core/compression/compression_test.c
+)
+
+
+target_include_directories(compression_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(compression_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(concurrent_connectivity_test
+  test/core/surface/concurrent_connectivity_test.c
+)
+
+
+target_include_directories(concurrent_connectivity_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(concurrent_connectivity_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(connection_refused_test
+  test/core/end2end/connection_refused_test.c
+)
+
+
+target_include_directories(connection_refused_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(connection_refused_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(dns_resolver_connectivity_test
+  test/core/client_channel/resolvers/dns_resolver_connectivity_test.c
+)
+
+
+target_include_directories(dns_resolver_connectivity_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(dns_resolver_connectivity_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(dns_resolver_test
+  test/core/client_channel/resolvers/dns_resolver_test.c
+)
+
+
+target_include_directories(dns_resolver_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(dns_resolver_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(dualstack_socket_test
+  test/core/end2end/dualstack_socket_test.c
+)
+
+
+target_include_directories(dualstack_socket_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(dualstack_socket_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(endpoint_pair_test
+  test/core/iomgr/endpoint_pair_test.c
+)
+
+
+target_include_directories(endpoint_pair_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(endpoint_pair_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(error_test
+  test/core/iomgr/error_test.c
+)
+
+
+target_include_directories(error_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(error_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX)
+
+add_executable(ev_epollsig_linux_test
+  test/core/iomgr/ev_epollsig_linux_test.c
+)
+
+
+target_include_directories(ev_epollsig_linux_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(ev_epollsig_linux_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(fake_resolver_test
+  test/core/client_channel/resolvers/fake_resolver_test.c
+)
+
+
+target_include_directories(fake_resolver_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(fake_resolver_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(fd_conservation_posix_test
+  test/core/iomgr/fd_conservation_posix_test.c
+)
+
+
+target_include_directories(fd_conservation_posix_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(fd_conservation_posix_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(fd_posix_test
+  test/core/iomgr/fd_posix_test.c
+)
+
+
+target_include_directories(fd_posix_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(fd_posix_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(fling_client
+  test/core/fling/client.c
+)
+
+
+target_include_directories(fling_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(fling_client
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(fling_server
+  test/core/fling/server.c
+)
+
+
+target_include_directories(fling_server
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(fling_server
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(fling_stream_test
+  test/core/fling/fling_stream_test.c
+)
+
+
+target_include_directories(fling_stream_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(fling_stream_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(fling_test
+  test/core/fling/fling_test.c
+)
+
+
+target_include_directories(fling_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(fling_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+
+add_executable(gen_hpack_tables
+  tools/codegen/core/gen_hpack_tables.c
+)
+
+
+target_include_directories(gen_hpack_tables
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gen_hpack_tables
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr
+  grpc
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS gen_hpack_tables EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(gen_legal_metadata_characters
+  tools/codegen/core/gen_legal_metadata_characters.c
+)
+
+
+target_include_directories(gen_legal_metadata_characters
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gen_legal_metadata_characters
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS gen_legal_metadata_characters EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(gen_percent_encoding_tables
+  tools/codegen/core/gen_percent_encoding_tables.c
+)
+
+
+target_include_directories(gen_percent_encoding_tables
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gen_percent_encoding_tables
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS gen_percent_encoding_tables EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(goaway_server_test
+  test/core/end2end/goaway_server_test.c
+)
+
+
+target_include_directories(goaway_server_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(goaway_server_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_avl_test
+  test/core/support/avl_test.c
+)
+
+
+target_include_directories(gpr_avl_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_avl_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_backoff_test
+  test/core/support/backoff_test.c
+)
+
+
+target_include_directories(gpr_backoff_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_backoff_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_cmdline_test
+  test/core/support/cmdline_test.c
+)
+
+
+target_include_directories(gpr_cmdline_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_cmdline_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_cpu_test
+  test/core/support/cpu_test.c
+)
+
+
+target_include_directories(gpr_cpu_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_cpu_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_env_test
+  test/core/support/env_test.c
+)
+
+
+target_include_directories(gpr_env_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_env_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_histogram_test
+  test/core/support/histogram_test.c
+)
+
+
+target_include_directories(gpr_histogram_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_histogram_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_host_port_test
+  test/core/support/host_port_test.c
+)
+
+
+target_include_directories(gpr_host_port_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_host_port_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_log_test
+  test/core/support/log_test.c
+)
+
+
+target_include_directories(gpr_log_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_log_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_mpscq_test
+  test/core/support/mpscq_test.c
+)
+
+
+target_include_directories(gpr_mpscq_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_mpscq_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_spinlock_test
+  test/core/support/spinlock_test.c
+)
+
+
+target_include_directories(gpr_spinlock_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_spinlock_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_stack_lockfree_test
+  test/core/support/stack_lockfree_test.c
+)
+
+
+target_include_directories(gpr_stack_lockfree_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_stack_lockfree_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_string_test
+  test/core/support/string_test.c
+)
+
+
+target_include_directories(gpr_string_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_string_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_sync_test
+  test/core/support/sync_test.c
+)
+
+
+target_include_directories(gpr_sync_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_sync_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_thd_test
+  test/core/support/thd_test.c
+)
+
+
+target_include_directories(gpr_thd_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_thd_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_time_test
+  test/core/support/time_test.c
+)
+
+
+target_include_directories(gpr_time_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_time_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_tls_test
+  test/core/support/tls_test.c
+)
+
+
+target_include_directories(gpr_tls_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_tls_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(gpr_useful_test
+  test/core/support/useful_test.c
+)
+
+
+target_include_directories(gpr_useful_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(gpr_useful_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_auth_context_test
+  test/core/security/auth_context_test.c
+)
+
+
+target_include_directories(grpc_auth_context_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_auth_context_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_b64_test
+  test/core/slice/b64_test.c
+)
+
+
+target_include_directories(grpc_b64_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_b64_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_byte_buffer_reader_test
+  test/core/surface/byte_buffer_reader_test.c
+)
+
+
+target_include_directories(grpc_byte_buffer_reader_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_byte_buffer_reader_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_channel_args_test
+  test/core/channel/channel_args_test.c
+)
+
+
+target_include_directories(grpc_channel_args_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_channel_args_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_channel_stack_test
+  test/core/channel/channel_stack_test.c
+)
+
+
+target_include_directories(grpc_channel_stack_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_channel_stack_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_completion_queue_test
+  test/core/surface/completion_queue_test.c
+)
+
+
+target_include_directories(grpc_completion_queue_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_completion_queue_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_completion_queue_threading_test
+  test/core/surface/completion_queue_threading_test.c
+)
+
+
+target_include_directories(grpc_completion_queue_threading_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_completion_queue_threading_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+
+add_executable(grpc_create_jwt
+  test/core/security/create_jwt.c
+)
+
+
+target_include_directories(grpc_create_jwt
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_create_jwt
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_create_jwt EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_credentials_test
+  test/core/security/credentials_test.c
+)
+
+
+target_include_directories(grpc_credentials_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_credentials_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_fetch_oauth2
+  test/core/security/fetch_oauth2.c
+)
+
+
+target_include_directories(grpc_fetch_oauth2
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_fetch_oauth2
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_invalid_channel_args_test
+  test/core/surface/invalid_channel_args_test.c
+)
+
+
+target_include_directories(grpc_invalid_channel_args_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_invalid_channel_args_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(grpc_json_token_test
+  test/core/security/json_token_test.c
+)
+
+
+target_include_directories(grpc_json_token_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_json_token_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_jwt_verifier_test
+  test/core/security/jwt_verifier_test.c
+)
+
+
+target_include_directories(grpc_jwt_verifier_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_jwt_verifier_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+
+add_executable(grpc_print_google_default_creds_token
+  test/core/security/print_google_default_creds_token.c
+)
+
+
+target_include_directories(grpc_print_google_default_creds_token
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_print_google_default_creds_token
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_print_google_default_creds_token EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_security_connector_test
+  test/core/security/security_connector_test.c
+)
+
+
+target_include_directories(grpc_security_connector_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_security_connector_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+
+add_executable(grpc_verify_jwt
+  test/core/security/verify_jwt.c
+)
+
+
+target_include_directories(grpc_verify_jwt
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(grpc_verify_jwt
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_verify_jwt EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX)
+
+add_executable(handshake_client
+  test/core/handshake/client_ssl.c
+)
+
+
+target_include_directories(handshake_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(handshake_client
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX)
+
+add_executable(handshake_server
+  test/core/handshake/server_ssl.c
+)
+
+
+target_include_directories(handshake_server
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(handshake_server
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(hpack_parser_test
+  test/core/transport/chttp2/hpack_parser_test.c
+)
+
+
+target_include_directories(hpack_parser_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(hpack_parser_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(hpack_table_test
+  test/core/transport/chttp2/hpack_table_test.c
+)
+
+
+target_include_directories(hpack_table_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(hpack_table_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(http_parser_test
+  test/core/http/parser_test.c
+)
+
+
+target_include_directories(http_parser_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(http_parser_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(httpcli_format_request_test
+  test/core/http/format_request_test.c
+)
+
+
+target_include_directories(httpcli_format_request_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(httpcli_format_request_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(httpcli_test
+  test/core/http/httpcli_test.c
+)
+
+
+target_include_directories(httpcli_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(httpcli_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX)
+
+add_executable(httpscli_test
+  test/core/http/httpscli_test.c
+)
+
+
+target_include_directories(httpscli_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(httpscli_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(init_test
+  test/core/surface/init_test.c
+)
+
+
+target_include_directories(init_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(init_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(invalid_call_argument_test
+  test/core/end2end/invalid_call_argument_test.c
+)
+
+
+target_include_directories(invalid_call_argument_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(invalid_call_argument_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(json_rewrite
+  test/core/json/json_rewrite.c
+)
+
+
+target_include_directories(json_rewrite
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(json_rewrite
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(json_rewrite_test
+  test/core/json/json_rewrite_test.c
+)
+
+
+target_include_directories(json_rewrite_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(json_rewrite_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(json_stream_error_test
+  test/core/json/json_stream_error_test.c
+)
+
+
+target_include_directories(json_stream_error_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(json_stream_error_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(json_test
+  test/core/json/json_test.c
+)
+
+
+target_include_directories(json_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(json_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(lame_client_test
+  test/core/surface/lame_client_test.c
+)
+
+
+target_include_directories(lame_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(lame_client_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(lb_policies_test
+  test/core/client_channel/lb_policies_test.c
+)
+
+
+target_include_directories(lb_policies_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(lb_policies_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(load_file_test
+  test/core/iomgr/load_file_test.c
+)
+
+
+target_include_directories(load_file_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(load_file_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(memory_profile_client
+  test/core/memory_usage/client.c
+)
+
+
+target_include_directories(memory_profile_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(memory_profile_client
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(memory_profile_server
+  test/core/memory_usage/server.c
+)
+
+
+target_include_directories(memory_profile_server
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(memory_profile_server
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(memory_profile_test
+  test/core/memory_usage/memory_usage_test.c
+)
+
+
+target_include_directories(memory_profile_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(memory_profile_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(message_compress_test
+  test/core/compression/message_compress_test.c
+)
+
+
+target_include_directories(message_compress_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(message_compress_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(minimal_stack_is_minimal_test
+  test/core/channel/minimal_stack_is_minimal_test.c
+)
+
+
+target_include_directories(minimal_stack_is_minimal_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(minimal_stack_is_minimal_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(mlog_test
+  test/core/census/mlog_test.c
+)
+
+
+target_include_directories(mlog_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(mlog_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(multiple_server_queues_test
+  test/core/end2end/multiple_server_queues_test.c
+)
+
+
+target_include_directories(multiple_server_queues_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(multiple_server_queues_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(murmur_hash_test
+  test/core/support/murmur_hash_test.c
+)
+
+
+target_include_directories(murmur_hash_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(murmur_hash_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(no_server_test
+  test/core/end2end/no_server_test.c
+)
+
+
+target_include_directories(no_server_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(no_server_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(num_external_connectivity_watchers_test
+  test/core/surface/num_external_connectivity_watchers_test.c
+)
+
+
+target_include_directories(num_external_connectivity_watchers_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(num_external_connectivity_watchers_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(parse_address_test
+  test/core/client_channel/parse_address_test.c
+)
+
+
+target_include_directories(parse_address_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(parse_address_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(percent_encoding_test
+  test/core/slice/percent_encoding_test.c
+)
+
+
+target_include_directories(percent_encoding_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(percent_encoding_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX)
+
+add_executable(pollset_set_test
+  test/core/iomgr/pollset_set_test.c
+)
+
+
+target_include_directories(pollset_set_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(pollset_set_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(resolve_address_posix_test
+  test/core/iomgr/resolve_address_posix_test.c
+)
+
+
+target_include_directories(resolve_address_posix_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(resolve_address_posix_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(resolve_address_test
+  test/core/iomgr/resolve_address_test.c
+)
+
+
+target_include_directories(resolve_address_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(resolve_address_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(resource_quota_test
+  test/core/iomgr/resource_quota_test.c
+)
+
+
+target_include_directories(resource_quota_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(resource_quota_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(secure_channel_create_test
+  test/core/surface/secure_channel_create_test.c
+)
+
+
+target_include_directories(secure_channel_create_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(secure_channel_create_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(secure_endpoint_test
+  test/core/security/secure_endpoint_test.c
+)
+
+
+target_include_directories(secure_endpoint_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(secure_endpoint_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(sequential_connectivity_test
+  test/core/surface/sequential_connectivity_test.c
+)
+
+
+target_include_directories(sequential_connectivity_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(sequential_connectivity_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_chttp2_test
+  test/core/surface/server_chttp2_test.c
+)
+
+
+target_include_directories(server_chttp2_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(server_chttp2_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_test
+  test/core/surface/server_test.c
+)
+
+
+target_include_directories(server_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(server_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(slice_buffer_test
+  test/core/slice/slice_buffer_test.c
+)
+
+
+target_include_directories(slice_buffer_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(slice_buffer_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(slice_hash_table_test
+  test/core/slice/slice_hash_table_test.c
+)
+
+
+target_include_directories(slice_hash_table_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(slice_hash_table_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(slice_string_helpers_test
+  test/core/slice/slice_string_helpers_test.c
+)
+
+
+target_include_directories(slice_string_helpers_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(slice_string_helpers_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(slice_test
+  test/core/slice/slice_test.c
+)
+
+
+target_include_directories(slice_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(slice_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(sockaddr_resolver_test
+  test/core/client_channel/resolvers/sockaddr_resolver_test.c
+)
+
+
+target_include_directories(sockaddr_resolver_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(sockaddr_resolver_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(sockaddr_utils_test
+  test/core/iomgr/sockaddr_utils_test.c
+)
+
+
+target_include_directories(sockaddr_utils_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(sockaddr_utils_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(socket_utils_test
+  test/core/iomgr/socket_utils_test.c
+)
+
+
+target_include_directories(socket_utils_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(socket_utils_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(status_conversion_test
+  test/core/transport/status_conversion_test.c
+)
+
+
+target_include_directories(status_conversion_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(status_conversion_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(stream_compression_test
+  test/core/compression/stream_compression_test.c
+)
+
+
+target_include_directories(stream_compression_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(stream_compression_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(stream_owned_slice_test
+  test/core/transport/stream_owned_slice_test.c
+)
+
+
+target_include_directories(stream_owned_slice_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(stream_owned_slice_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(tcp_client_posix_test
+  test/core/iomgr/tcp_client_posix_test.c
+)
+
+
+target_include_directories(tcp_client_posix_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(tcp_client_posix_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(tcp_client_uv_test
+  test/core/iomgr/tcp_client_uv_test.c
+)
+
+
+target_include_directories(tcp_client_uv_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(tcp_client_uv_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(tcp_posix_test
+  test/core/iomgr/tcp_posix_test.c
+)
+
+
+target_include_directories(tcp_posix_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(tcp_posix_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(tcp_server_posix_test
+  test/core/iomgr/tcp_server_posix_test.c
+)
+
+
+target_include_directories(tcp_server_posix_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(tcp_server_posix_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(tcp_server_uv_test
+  test/core/iomgr/tcp_server_uv_test.c
+)
+
+
+target_include_directories(tcp_server_uv_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(tcp_server_uv_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(time_averaged_stats_test
+  test/core/iomgr/time_averaged_stats_test.c
+)
+
+
+target_include_directories(time_averaged_stats_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(time_averaged_stats_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(timeout_encoding_test
+  test/core/transport/timeout_encoding_test.c
+)
+
+
+target_include_directories(timeout_encoding_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(timeout_encoding_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(timer_heap_test
+  test/core/iomgr/timer_heap_test.c
+)
+
+
+target_include_directories(timer_heap_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(timer_heap_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(timer_list_test
+  test/core/iomgr/timer_list_test.c
+)
+
+
+target_include_directories(timer_list_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(timer_list_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(transport_connectivity_state_test
+  test/core/transport/connectivity_state_test.c
+)
+
+
+target_include_directories(transport_connectivity_state_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(transport_connectivity_state_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(transport_metadata_test
+  test/core/transport/metadata_test.c
+)
+
+
+target_include_directories(transport_metadata_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(transport_metadata_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(transport_pid_controller_test
+  test/core/transport/pid_controller_test.c
+)
+
+
+target_include_directories(transport_pid_controller_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(transport_pid_controller_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(transport_security_test
+  test/core/tsi/transport_security_test.c
+)
+
+
+target_include_directories(transport_security_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(transport_security_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(udp_server_test
+  test/core/iomgr/udp_server_test.c
+)
+
+
+target_include_directories(udp_server_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(udp_server_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(uri_parser_test
+  test/core/client_channel/uri_parser_test.c
+)
+
+
+target_include_directories(uri_parser_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(uri_parser_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(wakeup_fd_cv_test
+  test/core/iomgr/wakeup_fd_cv_test.c
+)
+
+
+target_include_directories(wakeup_fd_cv_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(wakeup_fd_cv_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(alarm_cpp_test
+  test/cpp/common/alarm_cpp_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(alarm_cpp_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(alarm_cpp_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(async_end2end_test
+  test/cpp/end2end/async_end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(async_end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(async_end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(auth_property_iterator_test
+  test/cpp/common/auth_property_iterator_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(auth_property_iterator_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(auth_property_iterator_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_arena
+  test/cpp/microbenchmarks/bm_arena.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_arena
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_arena
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_call_create
+  test/cpp/microbenchmarks/bm_call_create.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_call_create
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_call_create
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_chttp2_hpack
+  test/cpp/microbenchmarks/bm_chttp2_hpack.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_chttp2_hpack
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_chttp2_hpack
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_chttp2_transport
+  test/cpp/microbenchmarks/bm_chttp2_transport.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_chttp2_transport
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_chttp2_transport
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_closure
+  test/cpp/microbenchmarks/bm_closure.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_closure
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_closure
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_cq
+  test/cpp/microbenchmarks/bm_cq.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_cq
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_cq
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_cq_multiple_threads
+  test/cpp/microbenchmarks/bm_cq_multiple_threads.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_cq_multiple_threads
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_cq_multiple_threads
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_error
+  test/cpp/microbenchmarks/bm_error.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_error
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_error
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_fullstack_streaming_ping_pong
+  test/cpp/microbenchmarks/bm_fullstack_streaming_ping_pong.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_fullstack_streaming_ping_pong
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_fullstack_streaming_ping_pong
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_fullstack_streaming_pump
+  test/cpp/microbenchmarks/bm_fullstack_streaming_pump.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_fullstack_streaming_pump
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_fullstack_streaming_pump
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_fullstack_trickle
+  test/cpp/microbenchmarks/bm_fullstack_trickle.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_fullstack_trickle
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_fullstack_trickle
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_fullstack_unary_ping_pong
+  test/cpp/microbenchmarks/bm_fullstack_unary_ping_pong.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_fullstack_unary_ping_pong
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_fullstack_unary_ping_pong
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_metadata
+  test/cpp/microbenchmarks/bm_metadata.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_metadata
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_metadata
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bm_pollset
+  test/cpp/microbenchmarks/bm_pollset.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(bm_pollset
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(bm_pollset
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_benchmark
+  benchmark
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(channel_arguments_test
+  test/cpp/common/channel_arguments_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(channel_arguments_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(channel_arguments_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(channel_filter_test
+  test/cpp/common/channel_filter_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(channel_filter_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(channel_filter_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(cli_call_test
+  test/cpp/util/cli_call_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(cli_call_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(cli_call_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_cli_libs
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(client_crash_test
+  test/cpp/end2end/client_crash_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(client_crash_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(client_crash_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(client_crash_test_server
+  test/cpp/end2end/client_crash_test_server.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(client_crash_test_server
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(client_crash_test_server
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(client_lb_end2end_test
+  test/cpp/end2end/client_lb_end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(client_lb_end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(client_lb_end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(codegen_test_full
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.grpc.pb.h
+  test/cpp/codegen/codegen_test_full.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/control.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/payloads.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/services.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/stats.proto
+)
+
+target_include_directories(codegen_test_full
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(codegen_test_full
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(codegen_test_minimal
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/control.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/payloads.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/services.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/stats.grpc.pb.h
+  test/cpp/codegen/codegen_test_minimal.cc
+  src/cpp/codegen/codegen_init.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/control.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/payloads.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/services.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/stats.proto
+)
+
+target_include_directories(codegen_test_minimal
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(codegen_test_minimal
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(credentials_test
+  test/cpp/client/credentials_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(credentials_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(credentials_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(cxx_byte_buffer_test
+  test/cpp/util/byte_buffer_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(cxx_byte_buffer_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(cxx_byte_buffer_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(cxx_slice_test
+  test/cpp/util/slice_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(cxx_slice_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(cxx_slice_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(cxx_string_ref_test
+  test/cpp/util/string_ref_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(cxx_string_ref_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(cxx_string_ref_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(cxx_time_test
+  test/cpp/util/time_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(cxx_time_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(cxx_time_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(end2end_test
+  test/cpp/end2end/end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(error_details_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.h
+  test/cpp/util/error_details_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo_messages.proto
+)
+
+target_include_directories(error_details_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(error_details_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_error_details
+  grpc++
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(filter_end2end_test
+  test/cpp/end2end/filter_end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(filter_end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(filter_end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(generic_end2end_test
+  test/cpp/end2end/generic_end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(generic_end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(generic_end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(golden_file_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/compiler_test.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/compiler_test.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/compiler_test.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/compiler_test.grpc.pb.h
+  test/cpp/codegen/golden_file_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/compiler_test.proto
+)
+
+target_include_directories(golden_file_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(golden_file_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_cli
+  test/cpp/util/grpc_cli.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(grpc_cli
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_cli
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_cli_libs
+  grpc++_proto_reflection_desc_db
+  grpc++
+  grpc
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+
+add_executable(grpc_cpp_plugin
+  src/compiler/cpp_plugin.cc
+)
+
+
+target_include_directories(grpc_cpp_plugin
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_cpp_plugin
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_plugin_support
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_cpp_plugin EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(grpc_csharp_plugin
+  src/compiler/csharp_plugin.cc
+)
+
+
+target_include_directories(grpc_csharp_plugin
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_csharp_plugin
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_plugin_support
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_csharp_plugin EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(grpc_node_plugin
+  src/compiler/node_plugin.cc
+)
+
+
+target_include_directories(grpc_node_plugin
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_node_plugin
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_plugin_support
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_node_plugin EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(grpc_objective_c_plugin
+  src/compiler/objective_c_plugin.cc
+)
+
+
+target_include_directories(grpc_objective_c_plugin
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_objective_c_plugin
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_plugin_support
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_objective_c_plugin EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(grpc_php_plugin
+  src/compiler/php_plugin.cc
+)
+
+
+target_include_directories(grpc_php_plugin
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_php_plugin
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_plugin_support
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_php_plugin EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(grpc_python_plugin
+  src/compiler/python_plugin.cc
+)
+
+
+target_include_directories(grpc_python_plugin
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_python_plugin
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_plugin_support
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_python_plugin EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+
+add_executable(grpc_ruby_plugin
+  src/compiler/ruby_plugin.cc
+)
+
+
+target_include_directories(grpc_ruby_plugin
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_ruby_plugin
+  ${_gRPC_PROTOBUF_PROTOC_LIBRARIES}
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_plugin_support
+)
+
+
+if (gRPC_INSTALL)
+  install(TARGETS grpc_ruby_plugin EXPORT gRPCTargets
+    RUNTIME DESTINATION ${gRPC_INSTALL_BINDIR}
+    LIBRARY DESTINATION ${gRPC_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${gRPC_INSTALL_LIBDIR}
+  )
+endif()
+
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpc_tool_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.h
+  test/cpp/util/grpc_tool_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo_messages.proto
+)
+
+target_include_directories(grpc_tool_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpc_tool_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_cli_libs
+  grpc++_proto_reflection_desc_db
+  grpc++_reflection
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpclb_api_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.grpc.pb.h
+  test/cpp/grpclb/grpclb_api_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/lb/v1/load_balancer.proto
+)
+
+target_include_directories(grpclb_api_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpclb_api_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpclb_end2end_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.grpc.pb.h
+  test/cpp/end2end/grpclb_end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/lb/v1/load_balancer.proto
+)
+
+target_include_directories(grpclb_end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpclb_end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(grpclb_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/lb/v1/load_balancer.grpc.pb.h
+  test/cpp/grpclb/grpclb_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/lb/v1/load_balancer.proto
+)
+
+target_include_directories(grpclb_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(grpclb_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(health_service_end2end_test
+  test/cpp/end2end/health_service_end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(health_service_end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(health_service_end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(http2_client
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(http2_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(http2_client
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  http2_client_main
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(hybrid_end2end_test
+  test/cpp/end2end/hybrid_end2end_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(hybrid_end2end_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(hybrid_end2end_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(interop_client
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(interop_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_client
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  interop_client_main
+  interop_client_helper
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(interop_server
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(interop_server
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_server
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  interop_server_main
+  interop_server_helper
+  interop_server_lib
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(interop_test
+  test/cpp/interop/interop_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(interop_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(interop_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(json_run_localhost
+  test/cpp/qps/json_run_localhost.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(json_run_localhost
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(json_run_localhost
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(memory_test
+  test/core/support/memory_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(memory_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(memory_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(metrics_client
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.grpc.pb.h
+  test/cpp/interop/metrics_client.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/metrics.proto
+)
+
+target_include_directories(metrics_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(metrics_client
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(mock_test
+  test/cpp/end2end/mock_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(mock_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(mock_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(noop-benchmark
+  test/cpp/microbenchmarks/noop-benchmark.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(noop-benchmark
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(noop-benchmark
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  benchmark
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(proto_server_reflection_test
+  test/cpp/end2end/proto_server_reflection_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(proto_server_reflection_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(proto_server_reflection_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_proto_reflection_desc_db
+  grpc++_reflection
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(proto_utils_test
+  test/cpp/codegen/proto_utils_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(proto_utils_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(proto_utils_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(qps_interarrival_test
+  test/cpp/qps/qps_interarrival_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(qps_interarrival_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(qps_interarrival_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  qps
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(qps_json_driver
+  test/cpp/qps/qps_json_driver.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(qps_json_driver
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(qps_json_driver
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  qps
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(qps_openloop_test
+  test/cpp/qps/qps_openloop_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(qps_openloop_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(qps_openloop_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  qps
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(qps_worker
+  test/cpp/qps/worker.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(qps_worker
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(qps_worker
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  qps
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(reconnect_interop_client
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.h
+  test/cpp/interop/reconnect_interop_client.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/empty.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/test.proto
+)
+
+target_include_directories(reconnect_interop_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(reconnect_interop_client
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(reconnect_interop_server
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.h
+  test/cpp/interop/reconnect_interop_server.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/empty.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/test.proto
+)
+
+target_include_directories(reconnect_interop_server
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(reconnect_interop_server
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  reconnect_server
+  test_tcp_server
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(secure_auth_context_test
+  test/cpp/common/secure_auth_context_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(secure_auth_context_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(secure_auth_context_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(secure_sync_unary_ping_pong_test
+  test/cpp/qps/secure_sync_unary_ping_pong_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(secure_sync_unary_ping_pong_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(secure_sync_unary_ping_pong_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  qps
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_builder_plugin_test
+  test/cpp/end2end/server_builder_plugin_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(server_builder_plugin_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(server_builder_plugin_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_builder_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.h
+  test/cpp/server/server_builder_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo_messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo.proto
+)
+
+target_include_directories(server_builder_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(server_builder_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  gpr_test_util
+  grpc++
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_context_test_spouse_test
+  test/cpp/test/server_context_test_spouse_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(server_context_test_spouse_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(server_context_test_spouse_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(server_crash_test
+  test/cpp/end2end/server_crash_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(server_crash_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(server_crash_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_crash_test_client
+  test/cpp/end2end/server_crash_test_client.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(server_crash_test_client
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(server_crash_test_client
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_request_call_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo_messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/echo.grpc.pb.h
+  test/cpp/server/server_request_call_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo_messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/echo.proto
+)
+
+target_include_directories(server_request_call_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(server_request_call_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  gpr_test_util
+  grpc++
+  grpc
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(shutdown_test
+  test/cpp/end2end/shutdown_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(shutdown_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(shutdown_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(status_test
+  test/cpp/util/status_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(status_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(status_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(streaming_throughput_test
+  test/cpp/end2end/streaming_throughput_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(streaming_throughput_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(streaming_throughput_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(stress_test
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/empty.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/messages.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/metrics.grpc.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.cc
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.pb.h
+  ${_gRPC_PROTO_GENS_DIR}/src/proto/grpc/testing/test.grpc.pb.h
+  test/cpp/interop/interop_client.cc
+  test/cpp/interop/stress_interop_client.cc
+  test/cpp/interop/stress_test.cc
+  test/cpp/util/metrics_server.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/empty.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/messages.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/metrics.proto
+)
+protobuf_generate_grpc_cpp(
+  src/proto/grpc/testing/test.proto
+)
+
+target_include_directories(stress_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(stress_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(thread_manager_test
+  test/cpp/thread_manager/thread_manager_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(thread_manager_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(thread_manager_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++
+  grpc
+  gpr
+  grpc++_test_config
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(thread_stress_test
+  test/cpp/end2end/thread_stress_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(thread_stress_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(thread_stress_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(writes_per_rpc_test
+  test/cpp/performance/writes_per_rpc_test.cc
+  third_party/googletest/googletest/src/gtest-all.cc
+  third_party/googletest/googlemock/src/gmock-all.cc
+)
+
+
+target_include_directories(writes_per_rpc_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+  PRIVATE third_party/googletest/googletest/include
+  PRIVATE third_party/googletest/googletest
+  PRIVATE third_party/googletest/googlemock/include
+  PRIVATE third_party/googletest/googlemock
+  PRIVATE ${_gRPC_PROTO_GENS_DIR}
+)
+
+target_link_libraries(writes_per_rpc_test
+  ${_gRPC_PROTOBUF_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc++_test_util
+  grpc_test_util
+  grpc++
+  grpc
+  gpr_test_util
+  gpr
+  ${_gRPC_GFLAGS_LIBRARIES}
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(public_headers_must_be_c89
+  test/core/surface/public_headers_must_be_c89.c
+)
+
+
+target_include_directories(public_headers_must_be_c89
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(public_headers_must_be_c89
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(badreq_bad_client_test
+  test/core/bad_client/tests/badreq.c
+)
+
+
+target_include_directories(badreq_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(badreq_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(connection_prefix_bad_client_test
+  test/core/bad_client/tests/connection_prefix.c
+)
+
+
+target_include_directories(connection_prefix_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(connection_prefix_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(head_of_line_blocking_bad_client_test
+  test/core/bad_client/tests/head_of_line_blocking.c
+)
+
+
+target_include_directories(head_of_line_blocking_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(head_of_line_blocking_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(headers_bad_client_test
+  test/core/bad_client/tests/headers.c
+)
+
+
+target_include_directories(headers_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(headers_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(initial_settings_frame_bad_client_test
+  test/core/bad_client/tests/initial_settings_frame.c
+)
+
+
+target_include_directories(initial_settings_frame_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(initial_settings_frame_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(large_metadata_bad_client_test
+  test/core/bad_client/tests/large_metadata.c
+)
+
+
+target_include_directories(large_metadata_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(large_metadata_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_registered_method_bad_client_test
+  test/core/bad_client/tests/server_registered_method.c
+)
+
+
+target_include_directories(server_registered_method_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(server_registered_method_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(simple_request_bad_client_test
+  test/core/bad_client/tests/simple_request.c
+)
+
+
+target_include_directories(simple_request_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(simple_request_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(unknown_frame_bad_client_test
+  test/core/bad_client/tests/unknown_frame.c
+)
+
+
+target_include_directories(unknown_frame_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(unknown_frame_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(window_overflow_bad_client_test
+  test/core/bad_client/tests/window_overflow.c
+)
+
+
+target_include_directories(window_overflow_bad_client_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(window_overflow_bad_client_test
+  ${_gRPC_SSL_LIBRARIES}
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_client_test
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bad_ssl_cert_server
+  test/core/bad_ssl/servers/cert.c
+)
+
+
+target_include_directories(bad_ssl_cert_server
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bad_ssl_cert_server
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  bad_ssl_test_server
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(bad_ssl_cert_test
+  test/core/bad_ssl/bad_ssl_test.c
+)
+
+
+target_include_directories(bad_ssl_cert_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(bad_ssl_cert_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_census_test
+  test/core/end2end/fixtures/h2_census.c
+)
+
+
+target_include_directories(h2_census_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_census_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_compress_test
+  test/core/end2end/fixtures/h2_compress.c
+)
+
+
+target_include_directories(h2_compress_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_compress_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_fakesec_test
+  test/core/end2end/fixtures/h2_fakesec.c
+)
+
+
+target_include_directories(h2_fakesec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_fakesec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(h2_fd_test
+  test/core/end2end/fixtures/h2_fd.c
+)
+
+
+target_include_directories(h2_fd_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_fd_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_full_test
+  test/core/end2end/fixtures/h2_full.c
+)
+
+
+target_include_directories(h2_full_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX)
+
+add_executable(h2_full+pipe_test
+  test/core/end2end/fixtures/h2_full+pipe.c
+)
+
+
+target_include_directories(h2_full+pipe_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full+pipe_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_full+trace_test
+  test/core/end2end/fixtures/h2_full+trace.c
+)
+
+
+target_include_directories(h2_full+trace_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full+trace_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_full+workarounds_test
+  test/core/end2end/fixtures/h2_full+workarounds.c
+)
+
+
+target_include_directories(h2_full+workarounds_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full+workarounds_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_http_proxy_test
+  test/core/end2end/fixtures/h2_http_proxy.c
+)
+
+
+target_include_directories(h2_http_proxy_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_http_proxy_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_load_reporting_test
+  test/core/end2end/fixtures/h2_load_reporting.c
+)
+
+
+target_include_directories(h2_load_reporting_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_load_reporting_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_oauth2_test
+  test/core/end2end/fixtures/h2_oauth2.c
+)
+
+
+target_include_directories(h2_oauth2_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_oauth2_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_proxy_test
+  test/core/end2end/fixtures/h2_proxy.c
+)
+
+
+target_include_directories(h2_proxy_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_proxy_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_sockpair_test
+  test/core/end2end/fixtures/h2_sockpair.c
+)
+
+
+target_include_directories(h2_sockpair_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_sockpair_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_sockpair+trace_test
+  test/core/end2end/fixtures/h2_sockpair+trace.c
+)
+
+
+target_include_directories(h2_sockpair+trace_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_sockpair+trace_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_sockpair_1byte_test
+  test/core/end2end/fixtures/h2_sockpair_1byte.c
+)
+
+
+target_include_directories(h2_sockpair_1byte_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_sockpair_1byte_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_ssl_test
+  test/core/end2end/fixtures/h2_ssl.c
+)
+
+
+target_include_directories(h2_ssl_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_ssl_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_ssl_cert_test
+  test/core/end2end/fixtures/h2_ssl_cert.c
+)
+
+
+target_include_directories(h2_ssl_cert_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_ssl_cert_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_ssl_proxy_test
+  test/core/end2end/fixtures/h2_ssl_proxy.c
+)
+
+
+target_include_directories(h2_ssl_proxy_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_ssl_proxy_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(h2_uds_test
+  test/core/end2end/fixtures/h2_uds.c
+)
+
+
+target_include_directories(h2_uds_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_uds_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(inproc_test
+  test/core/end2end/fixtures/inproc.c
+)
+
+
+target_include_directories(inproc_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(inproc_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_tests
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_census_nosec_test
+  test/core/end2end/fixtures/h2_census.c
+)
+
+
+target_include_directories(h2_census_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_census_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_compress_nosec_test
+  test/core/end2end/fixtures/h2_compress.c
+)
+
+
+target_include_directories(h2_compress_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_compress_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(h2_fd_nosec_test
+  test/core/end2end/fixtures/h2_fd.c
+)
+
+
+target_include_directories(h2_fd_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_fd_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_full_nosec_test
+  test/core/end2end/fixtures/h2_full.c
+)
+
+
+target_include_directories(h2_full_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX)
+
+add_executable(h2_full+pipe_nosec_test
+  test/core/end2end/fixtures/h2_full+pipe.c
+)
+
+
+target_include_directories(h2_full+pipe_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full+pipe_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_full+trace_nosec_test
+  test/core/end2end/fixtures/h2_full+trace.c
+)
+
+
+target_include_directories(h2_full+trace_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full+trace_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_full+workarounds_nosec_test
+  test/core/end2end/fixtures/h2_full+workarounds.c
+)
+
+
+target_include_directories(h2_full+workarounds_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_full+workarounds_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_http_proxy_nosec_test
+  test/core/end2end/fixtures/h2_http_proxy.c
+)
+
+
+target_include_directories(h2_http_proxy_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_http_proxy_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_load_reporting_nosec_test
+  test/core/end2end/fixtures/h2_load_reporting.c
+)
+
+
+target_include_directories(h2_load_reporting_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_load_reporting_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_proxy_nosec_test
+  test/core/end2end/fixtures/h2_proxy.c
+)
+
+
+target_include_directories(h2_proxy_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_proxy_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_sockpair_nosec_test
+  test/core/end2end/fixtures/h2_sockpair.c
+)
+
+
+target_include_directories(h2_sockpair_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_sockpair_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_sockpair+trace_nosec_test
+  test/core/end2end/fixtures/h2_sockpair+trace.c
+)
+
+
+target_include_directories(h2_sockpair+trace_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_sockpair+trace_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(h2_sockpair_1byte_nosec_test
+  test/core/end2end/fixtures/h2_sockpair_1byte.c
+)
+
+
+target_include_directories(h2_sockpair_1byte_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_sockpair_1byte_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+if(_gRPC_PLATFORM_LINUX OR _gRPC_PLATFORM_MAC OR _gRPC_PLATFORM_POSIX)
+
+add_executable(h2_uds_nosec_test
+  test/core/end2end/fixtures/h2_uds.c
+)
+
+
+target_include_directories(h2_uds_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(h2_uds_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif()
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(inproc_nosec_test
+  test/core/end2end/fixtures/inproc.c
+)
+
+
+target_include_directories(inproc_nosec_test
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(inproc_nosec_test
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  end2end_nosec_tests
+  grpc_test_util_unsecure
+  grpc_unsecure
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(api_fuzzer_one_entry
+  test/core/end2end/fuzzers/api_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(api_fuzzer_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(api_fuzzer_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(client_fuzzer_one_entry
+  test/core/end2end/fuzzers/client_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(client_fuzzer_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(client_fuzzer_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(hpack_parser_fuzzer_test_one_entry
+  test/core/transport/chttp2/hpack_parser_fuzzer_test.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(hpack_parser_fuzzer_test_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(hpack_parser_fuzzer_test_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(http_request_fuzzer_test_one_entry
+  test/core/http/request_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(http_request_fuzzer_test_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(http_request_fuzzer_test_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(http_response_fuzzer_test_one_entry
+  test/core/http/response_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(http_response_fuzzer_test_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(http_response_fuzzer_test_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(json_fuzzer_test_one_entry
+  test/core/json/fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(json_fuzzer_test_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(json_fuzzer_test_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(nanopb_fuzzer_response_test_one_entry
+  test/core/nanopb/fuzzer_response.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(nanopb_fuzzer_response_test_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(nanopb_fuzzer_response_test_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(nanopb_fuzzer_serverlist_test_one_entry
+  test/core/nanopb/fuzzer_serverlist.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(nanopb_fuzzer_serverlist_test_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(nanopb_fuzzer_serverlist_test_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(percent_decode_fuzzer_one_entry
+  test/core/slice/percent_decode_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(percent_decode_fuzzer_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(percent_decode_fuzzer_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(percent_encode_fuzzer_one_entry
+  test/core/slice/percent_encode_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(percent_encode_fuzzer_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(percent_encode_fuzzer_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(server_fuzzer_one_entry
+  test/core/end2end/fuzzers/server_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(server_fuzzer_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(server_fuzzer_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(ssl_server_fuzzer_one_entry
+  test/core/security/ssl_server_fuzzer.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(ssl_server_fuzzer_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(ssl_server_fuzzer_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+if (gRPC_BUILD_TESTS)
+
+add_executable(uri_fuzzer_test_one_entry
+  test/core/client_channel/uri_fuzzer_test.c
+  test/core/util/one_corpus_entry_fuzzer.c
+)
+
+
+target_include_directories(uri_fuzzer_test_one_entry
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${BORINGSSL_ROOT_DIR}/include
+  PRIVATE ${PROTOBUF_ROOT_DIR}/src
+  PRIVATE ${BENCHMARK_ROOT_DIR}/include
+  PRIVATE ${ZLIB_ROOT_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/zlib
+  PRIVATE ${CARES_BUILD_INCLUDE_DIR}
+  PRIVATE ${CARES_INCLUDE_DIR}
+  PRIVATE ${CARES_PLATFORM_INCLUDE_DIR}
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/cares/cares
+  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/third_party/gflags/include
+)
+
+target_link_libraries(uri_fuzzer_test_one_entry
+  ${_gRPC_ALLTARGETS_LIBRARIES}
+  grpc_test_util
+  grpc
+  gpr_test_util
+  gpr
+)
+
+endif (gRPC_BUILD_TESTS)
+
+
+
+
+
+
+
+if (gRPC_INSTALL)
+  install(EXPORT gRPCTargets
+    DESTINATION ${gRPC_INSTALL_CMAKEDIR}
+    NAMESPACE gRPC::
+  )
+endif()
+
+foreach(_config gRPCConfig gRPCConfigVersion)
+  configure_file(tools/cmake/${_config}.cmake.in
+    ${_config}.cmake @ONLY)
+  install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${_config}.cmake
+    DESTINATION ${gRPC_INSTALL_CMAKEDIR}
+  )
+endforeach()
diff --git a/tensorflow/contrib/cmake/patches/grpc/rand.h b/tensorflow/contrib/cmake/patches/grpc/rand.h
new file mode 100644
index 0000000000..194cb683fc
--- /dev/null
+++ b/tensorflow/contrib/cmake/patches/grpc/rand.h
@@ -0,0 +1,14 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
diff --git a/tensorflow/contrib/factorization/kernels/BUILD b/tensorflow/contrib/factorization/kernels/BUILD
index 15c527b0e8..50deb08616 100644
--- a/tensorflow/contrib/factorization/kernels/BUILD
+++ b/tensorflow/contrib/factorization/kernels/BUILD
@@ -12,7 +12,7 @@ cc_library(
         ":clustering_ops",
         ":masked_matmul_ops",
         ":wals_solver_ops",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
 )
 
@@ -22,7 +22,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
     alwayslink = 1,
 )
@@ -33,7 +33,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
     alwayslink = 1,
 )
@@ -45,7 +45,7 @@ cc_library(
         "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core/kernels:bounds_check",
         "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
     alwayslink = 1,
 )
diff --git a/tensorflow/contrib/ffmpeg/default/BUILD b/tensorflow/contrib/ffmpeg/default/BUILD
index bf8419c04a..917488080e 100644
--- a/tensorflow/contrib/ffmpeg/default/BUILD
+++ b/tensorflow/contrib/ffmpeg/default/BUILD
@@ -17,7 +17,7 @@ cc_library(
     ],
     deps = [
         "//tensorflow/core:framework_headers_lib",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
 )
 
diff --git a/tensorflow/contrib/image/BUILD b/tensorflow/contrib/image/BUILD
index bb496a0f87..b2dfd7a0df 100755
--- a/tensorflow/contrib/image/BUILD
+++ b/tensorflow/contrib/image/BUILD
@@ -106,7 +106,7 @@ tf_custom_op_library(
         "ops/single_image_random_dot_stereograms_ops.cc",
     ],
     deps = [
-        "@protobuf//:protobuf",
+        "@protobuf_archive//:protobuf",
     ],
 )
 
diff --git a/tensorflow/contrib/input_pipeline/kernels/BUILD b/tensorflow/contrib/input_pipeline/kernels/BUILD
index 99bf1c8743..f20a6e38d4 100644
--- a/tensorflow/contrib/input_pipeline/kernels/BUILD
+++ b/tensorflow/contrib/input_pipeline/kernels/BUILD
@@ -13,7 +13,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
     alwayslink = 1,
 )
diff --git a/tensorflow/contrib/layers/kernels/BUILD b/tensorflow/contrib/layers/kernels/BUILD
index 15b984f938..e407a9ce01 100644
--- a/tensorflow/contrib/layers/kernels/BUILD
+++ b/tensorflow/contrib/layers/kernels/BUILD
@@ -14,7 +14,7 @@ cc_library(
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
         "@farmhash_archive//:farmhash",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
     alwayslink = 1,
 )
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index e00b1623a0..5f5be7c073 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -53,7 +53,7 @@ cc_library(
         ":tree_utils",
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
     alwayslink = 1,
 )
@@ -356,7 +356,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
 )
 
diff --git a/tensorflow/contrib/tensor_forest/hybrid/BUILD b/tensorflow/contrib/tensor_forest/hybrid/BUILD
index f9b9914341..13b9749756 100644
--- a/tensorflow/contrib/tensor_forest/hybrid/BUILD
+++ b/tensorflow/contrib/tensor_forest/hybrid/BUILD
@@ -88,7 +88,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework_headers_lib",
         "//third_party/eigen3",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
 )
 
diff --git a/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc b/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc
index e0ba78dbfd..a1fbea57dd 100644
--- a/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc
+++ b/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc
@@ -43,20 +43,22 @@ VerbsService::Stub::Stub(
     const std::shared_ptr< ::grpc::ChannelInterface>& channel)
     : channel_(channel),
       rpcmethod_GetRemoteAddress_(grpcVerbsService_method_names[0],
-                                  ::grpc::RpcMethod::NORMAL_RPC, channel) {}
+                                  ::grpc::internal::RpcMethod::NORMAL_RPC,
+                                  channel) {}
 
 ::grpc::Status VerbsService::Stub::GetRemoteAddress(
     ::grpc::ClientContext* context, const GetRemoteAddressRequest& request,
     GetRemoteAddressResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_GetRemoteAddress_,
-                                   context, request, response);
+  return ::grpc::internal::BlockingUnaryCall(
+      channel_.get(), rpcmethod_GetRemoteAddress_, context, request, response);
 }
 
 VerbsService::AsyncService::AsyncService() {
   for (int i = 0; i < 1; ++i) {
-    AddMethod(new ::grpc::RpcServiceMethod(grpcVerbsService_method_names[i],
-                                           ::grpc::RpcMethod::NORMAL_RPC,
-                                           nullptr));
+    AddMethod(new ::grpc::internal::RpcServiceMethod(
+        grpcVerbsService_method_names[i],
+        ::grpc::internal::RpcMethod::NORMAL_RPC,
+        nullptr));
     ::grpc::Service::MarkMethodAsync(i);
   }
 }
diff --git a/tensorflow/contrib/verbs/grpc_verbs_service_impl.h b/tensorflow/contrib/verbs/grpc_verbs_service_impl.h
index f7ea774b66..86431ca030 100644
--- a/tensorflow/contrib/verbs/grpc_verbs_service_impl.h
+++ b/tensorflow/contrib/verbs/grpc_verbs_service_impl.h
@@ -61,7 +61,7 @@ class VerbsService GRPC_FINAL {
 
    private:
     std::shared_ptr< ::grpc::ChannelInterface> channel_;
-    const ::grpc::RpcMethod rpcmethod_GetRemoteAddress_;
+    const ::grpc::internal::RpcMethod rpcmethod_GetRemoteAddress_;
   };
   static std::unique_ptr<Stub> NewStub(
       const std::shared_ptr< ::grpc::ChannelInterface>& channel,
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc
index c42622dd50..17d0047eb2 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc
@@ -49,74 +49,75 @@ MasterService::Stub::Stub(
     const std::shared_ptr< ::grpc::ChannelInterface>& channel)
     : channel_(channel),
       rpcmethod_CreateSession_(grpcMasterService_method_names[0],
-                               ::grpc::RpcMethod::NORMAL_RPC, channel),
+                               ::grpc::internal::RpcMethod::NORMAL_RPC, channel),
       rpcmethod_ExtendSession_(grpcMasterService_method_names[1],
-                               ::grpc::RpcMethod::NORMAL_RPC, channel),
+                               ::grpc::internal::RpcMethod::NORMAL_RPC, channel),
       rpcmethod_PartialRunSetup_(grpcMasterService_method_names[2],
-                                 ::grpc::RpcMethod::NORMAL_RPC, channel),
+                                 ::grpc::internal::RpcMethod::NORMAL_RPC, channel),
       rpcmethod_RunStep_(grpcMasterService_method_names[3],
-                         ::grpc::RpcMethod::NORMAL_RPC, channel),
+                         ::grpc::internal::RpcMethod::NORMAL_RPC, channel),
       rpcmethod_CloseSession_(grpcMasterService_method_names[4],
-                              ::grpc::RpcMethod::NORMAL_RPC, channel),
+                              ::grpc::internal::RpcMethod::NORMAL_RPC, channel),
       rpcmethod_ListDevices_(grpcMasterService_method_names[5],
-                             ::grpc::RpcMethod::NORMAL_RPC, channel),
+                             ::grpc::internal::RpcMethod::NORMAL_RPC, channel),
       rpcmethod_Reset_(grpcMasterService_method_names[6],
-                       ::grpc::RpcMethod::NORMAL_RPC, channel) {}
+                       ::grpc::internal::RpcMethod::NORMAL_RPC, channel) {}
 
 ::grpc::Status MasterService::Stub::CreateSession(
     ::grpc::ClientContext* context, const CreateSessionRequest& request,
     CreateSessionResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_CreateSession_,
+  return ::grpc::internal::BlockingUnaryCall(channel_.get(), rpcmethod_CreateSession_,
                                    context, request, response);
 }
 
 ::grpc::Status MasterService::Stub::ExtendSession(
     ::grpc::ClientContext* context, const ExtendSessionRequest& request,
     ExtendSessionResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_ExtendSession_,
+  return ::grpc::internal::BlockingUnaryCall(channel_.get(), rpcmethod_ExtendSession_,
                                    context, request, response);
 }
 
 ::grpc::Status MasterService::Stub::PartialRunSetup(
     ::grpc::ClientContext* context, const PartialRunSetupRequest& request,
     PartialRunSetupResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_PartialRunSetup_,
+  return ::grpc::internal::BlockingUnaryCall(channel_.get(), rpcmethod_PartialRunSetup_,
                                    context, request, response);
 }
 
 ::grpc::Status MasterService::Stub::RunStep(::grpc::ClientContext* context,
                                             const RunStepRequest& request,
                                             RunStepResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_RunStep_, context,
+  return ::grpc::internal::BlockingUnaryCall(channel_.get(), rpcmethod_RunStep_, context,
                                    request, response);
 }
 
 ::grpc::Status MasterService::Stub::CloseSession(
     ::grpc::ClientContext* context, const CloseSessionRequest& request,
     CloseSessionResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_CloseSession_,
+  return ::grpc::internal::BlockingUnaryCall(channel_.get(), rpcmethod_CloseSession_,
                                    context, request, response);
 }
 
 ::grpc::Status MasterService::Stub::ListDevices(
     ::grpc::ClientContext* context, const ListDevicesRequest& request,
     ListDevicesResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_ListDevices_,
+  return ::grpc::internal::BlockingUnaryCall(channel_.get(), rpcmethod_ListDevices_,
                                    context, request, response);
 }
 
 ::grpc::Status MasterService::Stub::Reset(::grpc::ClientContext* context,
                                           const ResetRequest& request,
                                           ResetResponse* response) {
-  return ::grpc::BlockingUnaryCall(channel_.get(), rpcmethod_Reset_, context,
+  return ::grpc::internal::BlockingUnaryCall(channel_.get(), rpcmethod_Reset_, context,
                                    request, response);
 }
 
 MasterService::AsyncService::AsyncService() {
   for (int i = 0; i < 7; ++i) {
-    AddMethod(new ::grpc::RpcServiceMethod(grpcMasterService_method_names[i],
-                                           ::grpc::RpcMethod::NORMAL_RPC,
-                                           nullptr));
+    AddMethod(new ::grpc::internal::RpcServiceMethod(
+        grpcMasterService_method_names[i],
+        ::grpc::internal::RpcMethod::NORMAL_RPC,
+        nullptr));
     ::grpc::Service::MarkMethodAsync(i);
   }
 }
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h
index a3a2ac8020..412395c526 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h
@@ -53,7 +53,7 @@ namespace grpc {
 // definition in "//tensorflow/core/protobuf/master_service.proto",
 // and the gRPC generated stub and service classes.
 // See that file for the definition of methods and messages.
-class MasterService GRPC_FINAL {
+class MasterService final {
  public:
   class StubInterface {
    public:
@@ -80,40 +80,40 @@ class MasterService GRPC_FINAL {
                                  const ResetRequest& request,
                                  ResetResponse* response) = 0;
   };
-  class Stub GRPC_FINAL : public StubInterface {
+  class Stub final : public StubInterface {
    public:
     Stub(const std::shared_ptr< ::grpc::ChannelInterface>& channel);
     ::grpc::Status CreateSession(::grpc::ClientContext* context,
                                  const CreateSessionRequest& request,
-                                 CreateSessionResponse* response) GRPC_OVERRIDE;
+                                 CreateSessionResponse* response) override;
     ::grpc::Status ExtendSession(::grpc::ClientContext* context,
                                  const ExtendSessionRequest& request,
-                                 ExtendSessionResponse* response) GRPC_OVERRIDE;
+                                 ExtendSessionResponse* response) override;
     ::grpc::Status PartialRunSetup(
         ::grpc::ClientContext* context, const PartialRunSetupRequest& request,
-        PartialRunSetupResponse* response) GRPC_OVERRIDE;
+        PartialRunSetupResponse* response) override;
     ::grpc::Status RunStep(::grpc::ClientContext* context,
                            const RunStepRequest& request,
-                           RunStepResponse* response) GRPC_OVERRIDE;
+                           RunStepResponse* response) override;
     ::grpc::Status CloseSession(::grpc::ClientContext* context,
                                 const CloseSessionRequest& request,
-                                CloseSessionResponse* response) GRPC_OVERRIDE;
+                                CloseSessionResponse* response) override;
     ::grpc::Status ListDevices(::grpc::ClientContext* context,
                                const ListDevicesRequest& request,
-                               ListDevicesResponse* response) GRPC_OVERRIDE;
+                               ListDevicesResponse* response) override;
     ::grpc::Status Reset(::grpc::ClientContext* context,
                          const ResetRequest& request,
-                         ResetResponse* response) GRPC_OVERRIDE;
+                         ResetResponse* response) override;
 
    private:
     std::shared_ptr< ::grpc::ChannelInterface> channel_;
-    const ::grpc::RpcMethod rpcmethod_CreateSession_;
-    const ::grpc::RpcMethod rpcmethod_ExtendSession_;
-    const ::grpc::RpcMethod rpcmethod_PartialRunSetup_;
-    const ::grpc::RpcMethod rpcmethod_RunStep_;
-    const ::grpc::RpcMethod rpcmethod_CloseSession_;
-    const ::grpc::RpcMethod rpcmethod_ListDevices_;
-    const ::grpc::RpcMethod rpcmethod_Reset_;
+    const ::grpc::internal::RpcMethod rpcmethod_CreateSession_;
+    const ::grpc::internal::RpcMethod rpcmethod_ExtendSession_;
+    const ::grpc::internal::RpcMethod rpcmethod_PartialRunSetup_;
+    const ::grpc::internal::RpcMethod rpcmethod_RunStep_;
+    const ::grpc::internal::RpcMethod rpcmethod_CloseSession_;
+    const ::grpc::internal::RpcMethod rpcmethod_ListDevices_;
+    const ::grpc::internal::RpcMethod rpcmethod_Reset_;
   };
   static std::unique_ptr<Stub> NewStub(
       const std::shared_ptr< ::grpc::ChannelInterface>& channel,
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h b/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
index 0616b65af0..b35d4843e8 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RPC_GRPC_SERIALIZATION_TRAITS_H_
 
 #include "grpc++/impl/codegen/proto_utils.h"
+#include "grpc++/support/slice.h"
 
 namespace grpc {
 
@@ -24,7 +25,7 @@ namespace tensorflow_helper {
 
 const int kGrpcBufferWriterMaxBufferLength = 8192;
 
-class GrpcBufferWriter GRPC_FINAL
+class GrpcBufferWriter final
     : public ::grpc::protobuf::io::ZeroCopyOutputStream {
  public:
   explicit GrpcBufferWriter(grpc_byte_buffer** bp, int block_size)
@@ -33,35 +34,35 @@ class GrpcBufferWriter GRPC_FINAL
     slice_buffer_ = &(*bp)->data.raw.slice_buffer;
   }
 
-  ~GrpcBufferWriter() GRPC_OVERRIDE {
+  ~GrpcBufferWriter() override {
     if (have_backup_) {
-      g_core_codegen_interface->gpr_slice_unref(backup_slice_);
+      g_core_codegen_interface->grpc_slice_unref(backup_slice_);
     }
   }
 
-  bool Next(void** data, int* size) GRPC_OVERRIDE {
+  bool Next(void** data, int* size) override {
     if (have_backup_) {
       slice_ = backup_slice_;
       have_backup_ = false;
     } else {
-      slice_ = g_core_codegen_interface->gpr_slice_malloc(block_size_);
+      slice_ = g_core_codegen_interface->grpc_slice_malloc(block_size_);
     }
-    *data = GPR_SLICE_START_PTR(slice_);
+    *data = GRPC_SLICE_START_PTR(slice_);
     // On win x64, int is only 32bit
-    GPR_CODEGEN_ASSERT(GPR_SLICE_LENGTH(slice_) <= INT_MAX);
-    byte_count_ += * size = (int)GPR_SLICE_LENGTH(slice_);
-    g_core_codegen_interface->gpr_slice_buffer_add(slice_buffer_, slice_);
+    GPR_CODEGEN_ASSERT(GRPC_SLICE_LENGTH(slice_) <= INT_MAX);
+    byte_count_ += * size = (int)GRPC_SLICE_LENGTH(slice_);
+    g_core_codegen_interface->grpc_slice_buffer_add(slice_buffer_, slice_);
     return true;
   }
 
-  void BackUp(int count) GRPC_OVERRIDE {
-    g_core_codegen_interface->gpr_slice_buffer_pop(slice_buffer_);
+  void BackUp(int count) override {
+    g_core_codegen_interface->grpc_slice_buffer_pop(slice_buffer_);
     if (count == block_size_) {
       backup_slice_ = slice_;
     } else {
-      backup_slice_ = g_core_codegen_interface->gpr_slice_split_tail(
-          &slice_, GPR_SLICE_LENGTH(slice_) - count);
-      g_core_codegen_interface->gpr_slice_buffer_add(slice_buffer_, slice_);
+      backup_slice_ = g_core_codegen_interface->grpc_slice_split_tail(
+          &slice_, GRPC_SLICE_LENGTH(slice_) - count);
+      g_core_codegen_interface->grpc_slice_buffer_add(slice_buffer_, slice_);
     }
     // It's dangerous to keep an inlined grpc_slice as the backup slice, since
     // on a following Next() call, a reference will be returned to this slice
@@ -71,18 +72,18 @@ class GrpcBufferWriter GRPC_FINAL
     byte_count_ -= count;
   }
 
-  grpc::protobuf::int64 ByteCount() const GRPC_OVERRIDE { return byte_count_; }
+  grpc::protobuf::int64 ByteCount() const override { return byte_count_; }
 
  private:
   const int block_size_;
   int64_t byte_count_;
-  gpr_slice_buffer* slice_buffer_;
+  grpc_slice_buffer* slice_buffer_;
   bool have_backup_;
-  gpr_slice backup_slice_;
-  gpr_slice slice_;
+  grpc_slice backup_slice_;
+  grpc_slice slice_;
 };
 
-class GrpcBufferReader GRPC_FINAL
+class GrpcBufferReader final
     : public ::grpc::protobuf::io::ZeroCopyInputStream {
   typedef void (CoreCodegenInterface::*OldReaderInitAPI)(
       grpc_byte_buffer_reader* reader, grpc_byte_buffer* buffer);
@@ -104,13 +105,13 @@ class GrpcBufferReader GRPC_FINAL
     ReaderInit(&CoreCodegenInterface::grpc_byte_buffer_reader_init, &reader_,
                buffer);
   }
-  ~GrpcBufferReader() GRPC_OVERRIDE {
+  ~GrpcBufferReader() override {
     g_core_codegen_interface->grpc_byte_buffer_reader_destroy(&reader_);
   }
 
-  bool Next(const void** data, int* size) GRPC_OVERRIDE {
+  bool Next(const void** data, int* size) override {
     if (backup_count_ > 0) {
-      *data = GPR_SLICE_START_PTR(slice_) + GPR_SLICE_LENGTH(slice_) -
+      *data = GRPC_SLICE_START_PTR(slice_) + GRPC_SLICE_LENGTH(slice_) -
               backup_count_;
       GPR_CODEGEN_ASSERT(backup_count_ <= INT_MAX);
       *size = (int)backup_count_;
@@ -121,17 +122,17 @@ class GrpcBufferReader GRPC_FINAL
                                                                 &slice_)) {
       return false;
     }
-    g_core_codegen_interface->gpr_slice_unref(slice_);
-    *data = GPR_SLICE_START_PTR(slice_);
+    g_core_codegen_interface->grpc_slice_unref(slice_);
+    *data = GRPC_SLICE_START_PTR(slice_);
     // On win x64, int is only 32bit
-    GPR_CODEGEN_ASSERT(GPR_SLICE_LENGTH(slice_) <= INT_MAX);
-    byte_count_ += * size = (int)GPR_SLICE_LENGTH(slice_);
+    GPR_CODEGEN_ASSERT(GRPC_SLICE_LENGTH(slice_) <= INT_MAX);
+    byte_count_ += * size = (int)GRPC_SLICE_LENGTH(slice_);
     return true;
   }
 
-  void BackUp(int count) GRPC_OVERRIDE { backup_count_ = count; }
+  void BackUp(int count) override { backup_count_ = count; }
 
-  bool Skip(int count) GRPC_OVERRIDE {
+  bool Skip(int count) override {
     const void* data;
     int size;
     while (Next(&data, &size)) {
@@ -146,7 +147,7 @@ class GrpcBufferReader GRPC_FINAL
     return false;
   }
 
-  grpc::protobuf::int64 ByteCount() const GRPC_OVERRIDE {
+  grpc::protobuf::int64 ByteCount() const override {
     return byte_count_ - backup_count_;
   }
 
@@ -154,7 +155,7 @@ class GrpcBufferReader GRPC_FINAL
   int64_t byte_count_;
   int64_t backup_count_;
   grpc_byte_buffer_reader reader_;
-  gpr_slice slice_;
+  grpc_slice slice_;
 };
 
 }  // namespace tensorflow_helper
@@ -175,12 +176,12 @@ class UnlimitedSizeProtoSerializationTraits {
       return Status(StatusCode::INTERNAL, "Message length was negative");
     } else if (byte_size <=
                tensorflow_helper::kGrpcBufferWriterMaxBufferLength) {
-      gpr_slice slice = g_core_codegen_interface->gpr_slice_malloc(byte_size);
+      grpc_slice slice = g_core_codegen_interface->grpc_slice_malloc(byte_size);
       GPR_CODEGEN_ASSERT(
-          GPR_SLICE_END_PTR(slice) ==
-          msg.SerializeWithCachedSizesToArray(GPR_SLICE_START_PTR(slice)));
+          GRPC_SLICE_END_PTR(slice) ==
+          msg.SerializeWithCachedSizesToArray(GRPC_SLICE_START_PTR(slice)));
       *bp = g_core_codegen_interface->grpc_raw_byte_buffer_create(&slice, 1);
-      g_core_codegen_interface->gpr_slice_unref(slice);
+      g_core_codegen_interface->grpc_slice_unref(slice);
       return g_core_codegen_interface->ok();
     } else {
       tensorflow_helper::GrpcBufferWriter writer(
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
index df226a92a5..9121303b0e 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc
@@ -38,9 +38,9 @@ static void unref_tensorbuffer(void* raw) {
 void EncodeRecvTensorResponseToByteBuffer(const RecvTensorResponse& proto,
                                           ::grpc::ByteBuffer* result) {
   size_t len = proto.ByteSizeLong();
-  gpr_slice s = gpr_slice_malloc(len);
+  grpc_slice s = grpc_slice_malloc(len);
   proto.SerializeWithCachedSizesToArray(
-      reinterpret_cast<uint8*>(GPR_SLICE_START_PTR(s)));
+      reinterpret_cast<uint8*>(GRPC_SLICE_START_PTR(s)));
   ::grpc::Slice slice(s, ::grpc::Slice::STEAL_REF);
   *result = ::grpc::ByteBuffer(&slice, 1);
 }
@@ -68,12 +68,12 @@ void EncodeRecvTensorResponseToByteBuffer(const RecvTensorResponse& proto,
 // E:   <actual data for val's representation>
 //
 // If the tensor data is up to "kLargeTensorBytes", then A
-// through E will all be encoded into "*result" in a single gpr_slice.
+// through E will all be encoded into "*result" in a single grpc_slice.
 //
 // If the tensor data is larger than "kLargeTensorBytes", then A through
-// D2 will be encoded in one gpr_slice, and E will be encoded in a second
-// gpr_slice that points to the backing store for the tensor data, to avoid
-// copying the tensor data (and the gpr_slice setup will be arrange so as
+// D2 will be encoded in one grpc_slice, and E will be encoded in a second
+// grpc_slice that points to the backing store for the tensor data, to avoid
+// copying the tensor data (and the grpc_slice setup will be arrange so as
 // to dereference the underlying tensor data buffer when it is no longer
 // needed in the "*result" ByteBuffer).
 static int VarLengthEncodingSize(uint32 tag, size_t bytes) {
@@ -209,11 +209,11 @@ void EncodeTensorToByteBuffer(bool is_dead, const Tensor& val,
     int num_slices = 0;
     {
       size_t slice_len = e.size() + (tensor_data_is_large ? 0 : tdata.size());
-      gpr_slice s0 = gpr_slice_malloc(slice_len);
-      memcpy(GPR_SLICE_START_PTR(s0), e.data(), e.size());
+      grpc_slice s0 = grpc_slice_malloc(slice_len);
+      memcpy(GRPC_SLICE_START_PTR(s0), e.data(), e.size());
       if (!tensor_data_is_large) {
         // (E)
-        memcpy(GPR_SLICE_START_PTR(s0) + e.size(), tdata.data(), tdata.size());
+        memcpy(GRPC_SLICE_START_PTR(s0) + e.size(), tdata.data(), tdata.size());
       }
       slices[0] = ::grpc::Slice(s0, ::grpc::Slice::STEAL_REF);
       num_slices += 1;
@@ -230,7 +230,7 @@ void EncodeTensorToByteBuffer(bool is_dead, const Tensor& val,
       // hypothetical grpc_slice-related changes (e.g. the
       // implementation could decide to destroy 0-length slices
       // eagerly).  In practice, this does not happen with the current
-      // implementation, and the gpr_slice interface at the moment does
+      // implementation, and the grpc_slice interface at the moment does
       // not allow us to do the Tensor-unreferencing in the right way
       // (since the Tensor pointer is different than the backing store
       // array pointer).
@@ -245,13 +245,13 @@ void EncodeTensorToByteBuffer(bool is_dead, const Tensor& val,
 
       const TensorBuffer* buf = DMAHelper::buffer(&val);
       buf->Ref();
-      gpr_slice s1 = gpr_slice_new(
+      grpc_slice s1 = grpc_slice_new(
           const_cast<void*>(static_cast<const void*>(tdata.data())),
           tdata.size(), do_nothing);
       slices[1] = ::grpc::Slice(s1, ::grpc::Slice::STEAL_REF);
 
-      gpr_slice s2 =
-          gpr_slice_new(const_cast<TensorBuffer*>(buf), 0, unref_tensorbuffer);
+      grpc_slice s2 =
+          grpc_slice_new(const_cast<TensorBuffer*>(buf), 0, unref_tensorbuffer);
       slices[2] = ::grpc::Slice(s2, ::grpc::Slice::STEAL_REF);
       num_slices += 2;
     }
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_util.cc b/tensorflow/core/distributed_runtime/rpc/grpc_util.cc
index fe66c7c570..f5d04549b4 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_util.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_util.cc
@@ -80,9 +80,9 @@ grpc::protobuf::int64 GrpcByteBufferSource::ByteCount() const {
 void GrpcUnparseProto(const protobuf::Message& src, grpc::ByteBuffer* dst) {
   // TODO(sanjay): For bigger protos, serialize into a ZeroCopyOutputStream.
   size_t len = src.ByteSizeLong();
-  gpr_slice s = gpr_slice_malloc(len);
+  grpc_slice s = grpc_slice_malloc(len);
   src.SerializeWithCachedSizesToArray(
-      reinterpret_cast<uint8*>(GPR_SLICE_START_PTR(s)));
+      reinterpret_cast<uint8*>(GRPC_SLICE_START_PTR(s)));
   ::grpc::Slice slice(s, ::grpc::Slice::STEAL_REF);
   ::grpc::ByteBuffer buffer(&slice, 1);
   // TODO(sanjay): Use Swap() when grpc version we are using is new enough.
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc b/tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc
index b12ad955d6..d2bc50324d 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_util_test.cc
@@ -38,7 +38,7 @@ grpc::ByteBuffer MakeBuffer(const string& str, int num_slices) {
   const size_t per_slice = (str.size() + num_slices - 1) / num_slices;
   for (size_t pos = 0; pos < str.size();) {
     const size_t n = std::min(str.size() - pos, per_slice);
-    auto slice = gpr_slice_from_copied_buffer(&str[pos], n);
+    auto slice = grpc_slice_from_copied_buffer(&str[pos], n);
     slices.push_back(::grpc::Slice(slice, ::grpc::Slice::STEAL_REF));
     pos += n;
   }
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
index 80a2f89337..348c6dc98b 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
@@ -58,9 +58,9 @@ namespace grpc {
 
 WorkerService::AsyncService::AsyncService() {
   for (int i = 0; i < kGrpcNumWorkerMethods; ++i) {
-    AddMethod(new ::grpc::RpcServiceMethod(
+    AddMethod(new ::grpc::internal::RpcServiceMethod(
         GrpcWorkerMethodName(static_cast<GrpcWorkerMethod>(i)),
-        ::grpc::RpcMethod::NORMAL_RPC, nullptr));
+        ::grpc::internal::RpcMethod::NORMAL_RPC, nullptr));
     ::grpc::Service::MarkMethodAsync(i);
   }
 }
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
index bfdd58d46d..e9862a61a3 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
@@ -130,7 +130,7 @@ namespace grpc {
 // definition in "//tensorflow/core/protobuf/worker_service.proto",
 // and the gRPC generated stub and service classes.
 // See the proto file for the definition of methods and messages.
-class WorkerService GRPC_FINAL {
+class WorkerService final {
  public:
   class AsyncService : public ::grpc::Service {
    public:
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 5db0fe423c..73e8cb8c50 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -1,7 +1,7 @@
 # Platform-specific build configurations.
 
-load("@protobuf//:protobuf.bzl", "cc_proto_library")
-load("@protobuf//:protobuf.bzl", "py_proto_library")
+load("@protobuf_archive//:protobuf.bzl", "cc_proto_library")
+load("@protobuf_archive//:protobuf.bzl", "py_proto_library")
 load("//tensorflow:tensorflow.bzl", "if_not_mobile")
 load("//tensorflow:tensorflow.bzl", "if_not_windows")
 
@@ -44,15 +44,15 @@ def tf_proto_library_cc(name, srcs = [], has_services = None,
   cc_proto_library(
       name = name + "_cc",
       srcs = srcs,
-      deps = tf_deps(protodeps, "_cc") + ["@protobuf//:cc_wkt_protos"],
-      cc_libs = cc_libs + ["@protobuf//:protobuf"],
+      deps = tf_deps(protodeps, "_cc") + ["@protobuf_archive//:cc_wkt_protos"],
+      cc_libs = cc_libs + ["@protobuf_archive//:protobuf"],
       copts = if_not_windows([
           "-Wno-unknown-warning-option",
           "-Wno-unused-but-set-variable",
           "-Wno-sign-compare",
       ]),
-      protoc = "@protobuf//:protoc",
-      default_runtime = "@protobuf//:protobuf",
+      protoc = "@protobuf_archive//:protoc",
+      default_runtime = "@protobuf_archive//:protobuf",
       use_grpc_plugin = use_grpc_plugin,
       testonly = testonly,
       visibility = visibility,
@@ -65,9 +65,9 @@ def tf_proto_library_py(name, srcs=[], protodeps=[], deps=[], visibility=[],
       name = name + "_py",
       srcs = srcs,
       srcs_version = srcs_version,
-      deps = deps + tf_deps(protodeps, "_py") + ["@protobuf//:protobuf_python"],
-      protoc = "@protobuf//:protoc",
-      default_runtime = "@protobuf//:protobuf_python",
+      deps = deps + tf_deps(protodeps, "_py") + ["@protobuf_archive//:protobuf_python"],
+      protoc = "@protobuf_archive//:protoc",
+      default_runtime = "@protobuf_archive//:protobuf_python",
       visibility = visibility,
       testonly = testonly,
   )
diff --git a/tensorflow/core/platform/default/grpc_response_reader.h b/tensorflow/core/platform/default/grpc_response_reader.h
index e2be9d6a65..7c175345fc 100644
--- a/tensorflow/core/platform/default/grpc_response_reader.h
+++ b/tensorflow/core/platform/default/grpc_response_reader.h
@@ -24,7 +24,7 @@ template <class ResponseMessage, class RequestMessage>
 ::grpc::ClientAsyncResponseReader<ResponseMessage>*
 CreateClientAsyncResponseReader(::grpc::ChannelInterface* channel,
                                 ::grpc::CompletionQueue* cq,
-                                const ::grpc::RpcMethod& method,
+                                const ::grpc::internal::RpcMethod& method,
                                 ::grpc::ClientContext* context,
                                 const RequestMessage& request) {
   return new ::grpc::ClientAsyncResponseReader<ResponseMessage>(
diff --git a/tensorflow/core/platform/grpc_response_reader.h b/tensorflow/core/platform/grpc_response_reader.h
index e36eb698f3..5dc78d0dcb 100644
--- a/tensorflow/core/platform/grpc_response_reader.h
+++ b/tensorflow/core/platform/grpc_response_reader.h
@@ -38,7 +38,7 @@ template <class ResponseMessage, class RequestMessage>
 ::grpc::ClientAsyncResponseReader<ResponseMessage>*
 CreateClientAsyncResponseReader(::grpc::ChannelInterface* channel,
                                 ::grpc::CompletionQueue* cq,
-                                const ::grpc::RpcMethod& method,
+                                const ::grpc::internal::RpcMethod& method,
                                 ::grpc::ClientContext* context,
                                 const RequestMessage& request);
 
diff --git a/tensorflow/go/genop/generate.sh b/tensorflow/go/genop/generate.sh
index 15ef3b9525..01fcfb9058 100644
--- a/tensorflow/go/genop/generate.sh
+++ b/tensorflow/go/genop/generate.sh
@@ -36,7 +36,7 @@ then
   then
     echo "Protocol buffer compiler protoc not found in PATH or in ${PROTOC}"
     echo "Perhaps build it using:"
-    echo "bazel build --config opt @protobuf//:protoc"
+    echo "bazel build --config opt @protobuf_archive//:protoc"
     exit 1
   fi
   PROTOC=$PATH_PROTOC
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 80e1fd5cda..2c216300d1 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -277,7 +277,7 @@ cc_binary(
     linkshared = 1,
     deps = [
         "//tensorflow/core:framework_headers_lib",
-        "@protobuf//:protobuf_headers",
+        "@protobuf_archive//:protobuf_headers",
     ],
 )
 
@@ -2518,7 +2518,7 @@ py_library(
     deps = [
         "//third_party/py/numpy",
         "@org_python_pypi_backports_weakref",
-        "@protobuf//:protobuf_python",
+        "@protobuf_archive//:protobuf_python",
         "@six_archive//:six",
     ],
 )
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 5fd9fe597d..d31b9a6e31 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -868,7 +868,7 @@ def cc_header_only_library(name, deps=[], **kwargs):
 
 def tf_custom_op_library_additional_deps():
   return [
-      "@protobuf//:protobuf_headers",
+      "@protobuf_archive//:protobuf_headers",
       clean_dep("//third_party/eigen3"),
       clean_dep("//tensorflow/core:framework_headers_lib"),
   ]
diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index 6c09925454..13cfaad57e 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -95,6 +95,10 @@
 #
 # This script can be used by Jenkins parameterized / matrix builds.
 
+# TODO(jhseu): Temporary for the gRPC pull request due to the
+# protobuf -> protobuf_archive rename. Remove later.
+TF_BUILD_BAZEL_CLEAN=1
+
 # Helper function: Convert to lower case
 to_lower () {
   echo "$1" | tr '[:upper:]' '[:lower:]'
diff --git a/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh b/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
index f548adc5ca..4a2f954dc9 100755
--- a/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
+++ b/tensorflow/tools/ci_build/xla/linux/gpu/run_py3.sh
@@ -35,6 +35,7 @@ export TF_CUDA_COMPUTE_CAPABILITIES=3.7
 
 $PYTHON_BIN_PATH configure.py
 
+bazel clean
 # Run bazel test command. Double test timeouts to avoid flakes.
 bazel test --config=cuda --test_tag_filters=-no_gpu,-benchmark-test -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 \
diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD
index 6b2523d52b..536437df2b 100644
--- a/tensorflow/tools/lib_package/BUILD
+++ b/tensorflow/tools/lib_package/BUILD
@@ -102,7 +102,7 @@ genrule(
         "@lmdb//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
         "@png_archive//:LICENSE",
-        "@protobuf//:LICENSE",
+        "@protobuf_archive//:LICENSE",
         "@snappy//:COPYING",
         "@zlib_archive//:zlib.h",
     ] + if_mkl([
@@ -136,7 +136,7 @@ genrule(
         "@lmdb//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
         "@png_archive//:LICENSE",
-        "@protobuf//:LICENSE",
+        "@protobuf_archive//:LICENSE",
         "@snappy//:COPYING",
         "@zlib_archive//:zlib.h",
     ] + if_mkl([
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index bffa870d32..4cd42d79c0 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -119,9 +119,9 @@ filegroup(
         "@libxsmm_archive//:LICENSE",
         "@lmdb//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
-        "@nanopb_git//:LICENSE.txt",
+        "@grpc//third_party/nanopb:LICENSE.txt",
         "@png_archive//:LICENSE",
-        "@protobuf//:LICENSE",
+        "@protobuf_archive//:LICENSE",
         "@six_archive//:LICENSE",
         "@snappy//:COPYING",
         "@zlib_archive//:zlib.h",
diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh
index 5007ff7164..ff7db52cb0 100755
--- a/tensorflow/tools/pip_package/build_pip_package.sh
+++ b/tensorflow/tools/pip_package/build_pip_package.sh
@@ -130,7 +130,7 @@ function main() {
   mkdir -p ${TMPDIR}/google
   mkdir -p ${TMPDIR}/third_party
   pushd ${RUNFILES%org_tensorflow}
-  for header in $(find protobuf -name \*.h); do
+  for header in $(find protobuf_archive -name \*.h); do
     mkdir -p "${TMPDIR}/google/$(dirname ${header})"
     cp "$header" "${TMPDIR}/google/$(dirname ${header})/"
   done
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 3be2707184..0b0ee4c857 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -113,7 +113,7 @@ class InstallHeaders(Command):
     install_dir = os.path.join(self.install_dir, os.path.dirname(header))
     # Get rid of some extra intervening directories so we can have fewer
     # directories for -I
-    install_dir = re.sub('/google/protobuf/src', '', install_dir)
+    install_dir = re.sub('/google/protobuf_archive/src', '', install_dir)
 
     # Copy eigen code into tensorflow/include.
     # A symlink would do, but the wheel file that gets created ignores
@@ -164,7 +164,7 @@ else:
 
 headers = (list(find_files('*.h', 'tensorflow/core')) +
            list(find_files('*.h', 'tensorflow/stream_executor')) +
-           list(find_files('*.h', 'google/protobuf/src')) +
+           list(find_files('*.h', 'google/protobuf_archive/src')) +
            list(find_files('*', 'third_party/eigen3')) +
            list(find_files('*', 'external/eigen_archive')))
 
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 6dd3fc4acd..c2f42ba0c5 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -358,7 +358,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   )
 
   patched_http_archive(
-      name = "protobuf",
+      name = "protobuf_archive",
       urls = [
           "https://github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
           "http://mirror.bazel.build/github.com/google/protobuf/archive/0b059a3d8a8f8aa40dde7bea55edca4ec5dfea66.tar.gz",
@@ -372,6 +372,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       patch_file = str(Label("//third_party/protobuf:add_noinlines.patch")),
   )
 
+  native.bind(
+      name = "protobuf",
+      actual = "@protobuf_archive//:protobuf",
+  )
+
   # We need to import the protobuf library under the names com_google_protobuf
   # and com_google_protobuf_cc to enable proto_library support in bazel.
   # Unfortunately there is no way to alias http_archives at the moment.
@@ -470,23 +475,30 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   # to point to the protobuf's compiler library.
   native.bind(
       name = "protobuf_clib",
-      actual = "@protobuf//:protoc_lib",
+      actual = "@protobuf_archive//:protoc_lib",
   )
 
   native.bind(
-      name = "protobuf_compiler",
-      actual = "@protobuf//:protoc_lib",
+      name = "libssl",
+      actual = "@boringssl//:ssl",
   )
 
-  native.new_http_archive(
+  # gRPC has includes directly from their third_party path for nanopb, so we
+  # must depend on their version of it.
+  native.bind(
+      name = "nanopb",
+      actual = "@grpc//third_party/nanopb:nanopb",
+  )
+
+  patched_http_archive(
       name = "grpc",
       urls = [
-          "http://mirror.bazel.build/github.com/grpc/grpc/archive/d7ff4ff40071d2b486a052183e3e9f9382afb745.tar.gz",
-          "https://github.com/grpc/grpc/archive/d7ff4ff40071d2b486a052183e3e9f9382afb745.tar.gz",
+          "http://mirror.bazel.build/github.com/grpc/grpc/archive/781fd6f6ea03645a520cd5c675da67ab61f87e4b.tar.gz",
+          "https://github.com/grpc/grpc/archive/781fd6f6ea03645a520cd5c675da67ab61f87e4b.tar.gz",
       ],
-      sha256 = "a15f352436ab92c521b1ac11e729e155ace38d0856380cf25048c5d1d9ba8e31",
-      strip_prefix = "grpc-d7ff4ff40071d2b486a052183e3e9f9382afb745",
-      build_file = str(Label("//third_party:grpc.BUILD")),
+      sha256 = "2004635e6a078acfac8ffa71738397796be4f8fb72f572cc44ecee5d99511d9f",
+      strip_prefix = "grpc-781fd6f6ea03645a520cd5c675da67ab61f87e4b",
+      patch_file = str(Label("//third_party/grpc:grpc.patch")),
   )
 
   # protobuf expects //external:grpc_cpp_plugin to point to grpc's
@@ -566,22 +578,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       patch_file = str(Label("//third_party/boringssl:add_boringssl_s390x.patch")),
   )
 
-  native.new_http_archive(
-      name = "nanopb_git",
-      urls = [
-          "http://mirror.bazel.build/github.com/nanopb/nanopb/archive/1251fa1065afc0d62f635e0f63fec8276e14e13c.tar.gz",
-          "https://github.com/nanopb/nanopb/archive/1251fa1065afc0d62f635e0f63fec8276e14e13c.tar.gz",
-      ],
-      sha256 = "ab1455c8edff855f4f55b68480991559e51c11e7dab060bbab7cffb12dd3af33",
-      strip_prefix = "nanopb-1251fa1065afc0d62f635e0f63fec8276e14e13c",
-      build_file = str(Label("//third_party:nanopb.BUILD")),
-  )
-
-  native.bind(
-      name = "nanopb",
-      actual = "@nanopb_git//:nanopb",
-  )
-
   native.new_http_archive(
       name = "zlib_archive",
       urls = [
diff --git a/third_party/grpc.BUILD b/third_party/grpc.BUILD
deleted file mode 100644
index b79259618f..0000000000
--- a/third_party/grpc.BUILD
+++ /dev/null
@@ -1,2478 +0,0 @@
-# NOTE(mrry): This file is an edited version of the following file:
-# https://raw.githubusercontent.com/grpc/grpc/d7ff4ff40071d2b486a052183e3e9f9382afb745/BUILD
-# ...with small modifications to fix the build rules for :grpc++_unsecure.
-#
-# TODO(mrry): Upstream these fixes back to the gRPC repository.
-# TODO(jart): Fix nanopb's BUILD file. Fix grpc BUILD file.
-
-# GRPC Bazel BUILD file.
-# This currently builds C, C++ and Objective-C code.
-# This file has been automatically generated from a template file.
-# Please look at the templates directory instead.
-# This file can be regenerated from the template by running
-# tools/buildgen/generate_projects.sh
-
-# Copyright 2015, Google Inc.
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are
-# met:
-#
-#     * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-#     * Redistributions in binary form must reproduce the above
-# copyright notice, this list of conditions and the following disclaimer
-# in the documentation and/or other materials provided with the
-# distribution.
-#     * Neither the name of Google Inc. nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-licenses(["notice"])  # 3-clause BSD
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files(["LICENSE"])
-
-genrule(
-    name = "pb_h",
-    outs = ["third_party/nanopb/pb.h"],
-    cmd = "echo '#include <pb.h>' >$@",
-    visibility = ["//visibility:private"],
-)
-
-genrule(
-    name = "pb_decode_h",
-    outs = ["third_party/nanopb/pb_decode.h"],
-    cmd = "echo '#include <pb_decode.h>' >$@",
-    visibility = ["//visibility:private"],
-)
-
-genrule(
-    name = "pb_encode_h",
-    outs = ["third_party/nanopb/pb_encode.h"],
-    cmd = "echo '#include <pb_encode.h>' >$@",
-    visibility = ["//visibility:private"],
-)
-
-cc_library(
-    name = "gpr",
-    srcs = [
-        "src/core/lib/profiling/basic_timers.c",
-        "src/core/lib/profiling/stap_timers.c",
-        "src/core/lib/profiling/timers.h",
-        "src/core/lib/support/alloc.c",
-        "src/core/lib/support/avl.c",
-        "src/core/lib/support/backoff.c",
-        "src/core/lib/support/backoff.h",
-        "src/core/lib/support/block_annotate.h",
-        "src/core/lib/support/cmdline.c",
-        "src/core/lib/support/cpu_iphone.c",
-        "src/core/lib/support/cpu_linux.c",
-        "src/core/lib/support/cpu_posix.c",
-        "src/core/lib/support/cpu_windows.c",
-        "src/core/lib/support/env.h",
-        "src/core/lib/support/env_linux.c",
-        "src/core/lib/support/env_posix.c",
-        "src/core/lib/support/env_windows.c",
-        "src/core/lib/support/histogram.c",
-        "src/core/lib/support/host_port.c",
-        "src/core/lib/support/log.c",
-        "src/core/lib/support/log_android.c",
-        "src/core/lib/support/log_linux.c",
-        "src/core/lib/support/log_posix.c",
-        "src/core/lib/support/log_windows.c",
-        "src/core/lib/support/murmur_hash.c",
-        "src/core/lib/support/murmur_hash.h",
-        "src/core/lib/support/slice.c",
-        "src/core/lib/support/slice_buffer.c",
-        "src/core/lib/support/stack_lockfree.c",
-        "src/core/lib/support/stack_lockfree.h",
-        "src/core/lib/support/string.c",
-        "src/core/lib/support/string.h",
-        "src/core/lib/support/string_posix.c",
-        "src/core/lib/support/string_util_windows.c",
-        "src/core/lib/support/string_windows.c",
-        "src/core/lib/support/string_windows.h",
-        "src/core/lib/support/subprocess_posix.c",
-        "src/core/lib/support/subprocess_windows.c",
-        "src/core/lib/support/sync.c",
-        "src/core/lib/support/sync_posix.c",
-        "src/core/lib/support/sync_windows.c",
-        "src/core/lib/support/thd.c",
-        "src/core/lib/support/thd_internal.h",
-        "src/core/lib/support/thd_posix.c",
-        "src/core/lib/support/thd_windows.c",
-        "src/core/lib/support/time.c",
-        "src/core/lib/support/time_posix.c",
-        "src/core/lib/support/time_precise.c",
-        "src/core/lib/support/time_precise.h",
-        "src/core/lib/support/time_windows.c",
-        "src/core/lib/support/tls_pthread.c",
-        "src/core/lib/support/tmpfile.h",
-        "src/core/lib/support/tmpfile_msys.c",
-        "src/core/lib/support/tmpfile_posix.c",
-        "src/core/lib/support/tmpfile_windows.c",
-        "src/core/lib/support/wrap_memcpy.c",
-    ],
-    hdrs = [
-        "include/grpc/impl/codegen/alloc.h",
-        "include/grpc/impl/codegen/atm.h",
-        "include/grpc/impl/codegen/atm_gcc_atomic.h",
-        "include/grpc/impl/codegen/atm_gcc_sync.h",
-        "include/grpc/impl/codegen/atm_windows.h",
-        "include/grpc/impl/codegen/log.h",
-        "include/grpc/impl/codegen/port_platform.h",
-        "include/grpc/impl/codegen/slice.h",
-        "include/grpc/impl/codegen/slice_buffer.h",
-        "include/grpc/impl/codegen/sync.h",
-        "include/grpc/impl/codegen/sync_generic.h",
-        "include/grpc/impl/codegen/sync_posix.h",
-        "include/grpc/impl/codegen/sync_windows.h",
-        "include/grpc/impl/codegen/time.h",
-        "include/grpc/support/alloc.h",
-        "include/grpc/support/atm.h",
-        "include/grpc/support/atm_gcc_atomic.h",
-        "include/grpc/support/atm_gcc_sync.h",
-        "include/grpc/support/atm_windows.h",
-        "include/grpc/support/avl.h",
-        "include/grpc/support/cmdline.h",
-        "include/grpc/support/cpu.h",
-        "include/grpc/support/histogram.h",
-        "include/grpc/support/host_port.h",
-        "include/grpc/support/log.h",
-        "include/grpc/support/log_windows.h",
-        "include/grpc/support/port_platform.h",
-        "include/grpc/support/slice.h",
-        "include/grpc/support/slice_buffer.h",
-        "include/grpc/support/string_util.h",
-        "include/grpc/support/subprocess.h",
-        "include/grpc/support/sync.h",
-        "include/grpc/support/sync_generic.h",
-        "include/grpc/support/sync_posix.h",
-        "include/grpc/support/sync_windows.h",
-        "include/grpc/support/thd.h",
-        "include/grpc/support/time.h",
-        "include/grpc/support/tls.h",
-        "include/grpc/support/tls_gcc.h",
-        "include/grpc/support/tls_msvc.h",
-        "include/grpc/support/tls_pthread.h",
-        "include/grpc/support/useful.h",
-    ],
-    includes = [
-        ".",
-        "include",
-    ],
-    linkopts = ["-lpthread"],
-)
-
-cc_library(
-    name = "grpc",
-    srcs = [
-        "src/core/ext/census/aggregation.h",
-        "src/core/ext/census/census_interface.h",
-        "src/core/ext/census/census_rpc_stats.h",
-        "src/core/ext/census/context.c",
-        "src/core/ext/census/gen/census.pb.c",
-        "src/core/ext/census/gen/census.pb.h",
-        "src/core/ext/census/grpc_context.c",
-        "src/core/ext/census/grpc_filter.c",
-        "src/core/ext/census/grpc_filter.h",
-        "src/core/ext/census/grpc_plugin.c",
-        "src/core/ext/census/initialize.c",
-        "src/core/ext/census/mlog.c",
-        "src/core/ext/census/mlog.h",
-        "src/core/ext/census/operation.c",
-        "src/core/ext/census/placeholders.c",
-        "src/core/ext/census/rpc_metric_id.h",
-        "src/core/ext/census/tracing.c",
-        "src/core/ext/client_config/channel_connectivity.c",
-        "src/core/ext/client_config/client_channel.c",
-        "src/core/ext/client_config/client_channel.h",
-        "src/core/ext/client_config/client_channel_factory.c",
-        "src/core/ext/client_config/client_channel_factory.h",
-        "src/core/ext/client_config/client_config.c",
-        "src/core/ext/client_config/client_config.h",
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-        "src/core/ext/client_config/initial_connect_string.c",
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-        "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c",
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-        "src/core/ext/lb_policy/pick_first/pick_first.c",
-        "src/core/ext/lb_policy/round_robin/round_robin.c",
-        "src/core/ext/load_reporting/load_reporting.c",
-        "src/core/ext/load_reporting/load_reporting.h",
-        "src/core/ext/load_reporting/load_reporting_filter.c",
-        "src/core/ext/load_reporting/load_reporting_filter.h",
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-        "src/core/ext/resolver/sockaddr/sockaddr_resolver.c",
-        "src/core/ext/transport/chttp2/alpn/alpn.c",
-        "src/core/ext/transport/chttp2/alpn/alpn.h",
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-        "src/core/ext/transport/chttp2/transport/frame_window_update.c",
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-        "src/core/ext/transport/chttp2/transport/hpack_encoder.c",
-        "src/core/ext/transport/chttp2/transport/hpack_encoder.h",
-        "src/core/ext/transport/chttp2/transport/hpack_parser.c",
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-        "src/core/ext/transport/chttp2/transport/huffsyms.c",
-        "src/core/ext/transport/chttp2/transport/huffsyms.h",
-        "src/core/ext/transport/chttp2/transport/incoming_metadata.c",
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-        "src/core/ext/transport/chttp2/transport/internal.h",
-        "src/core/ext/transport/chttp2/transport/parsing.c",
-        "src/core/ext/transport/chttp2/transport/status_conversion.c",
-        "src/core/ext/transport/chttp2/transport/status_conversion.h",
-        "src/core/ext/transport/chttp2/transport/stream_lists.c",
-        "src/core/ext/transport/chttp2/transport/stream_map.c",
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-        "src/core/ext/transport/chttp2/transport/timeout_encoding.h",
-        "src/core/ext/transport/chttp2/transport/varint.c",
-        "src/core/ext/transport/chttp2/transport/varint.h",
-        "src/core/ext/transport/chttp2/transport/writing.c",
-        "src/core/lib/channel/channel_args.c",
-        "src/core/lib/channel/channel_args.h",
-        "src/core/lib/channel/channel_stack.c",
-        "src/core/lib/channel/channel_stack.h",
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-        "src/core/lib/channel/compress_filter.c",
-        "src/core/lib/channel/compress_filter.h",
-        "src/core/lib/channel/connected_channel.c",
-        "src/core/lib/channel/connected_channel.h",
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-        "src/core/lib/debug/trace.c",
-        "src/core/lib/debug/trace.h",
-        "src/core/lib/http/format_request.c",
-        "src/core/lib/http/format_request.h",
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-        "src/core/lib/iomgr/closure.c",
-        "src/core/lib/iomgr/closure.h",
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-        "src/core/lib/iomgr/endpoint_pair_windows.c",
-        "src/core/lib/iomgr/error.c",
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-        "src/core/lib/iomgr/ev_epoll_linux.c",
-        "src/core/lib/iomgr/ev_epoll_linux.h",
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-        "src/core/lib/iomgr/ev_poll_posix.h",
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-        "src/core/lib/iomgr/ev_posix.h",
-        "src/core/lib/iomgr/exec_ctx.c",
-        "src/core/lib/iomgr/exec_ctx.h",
-        "src/core/lib/iomgr/executor.c",
-        "src/core/lib/iomgr/executor.h",
-        "src/core/lib/iomgr/iocp_windows.c",
-        "src/core/lib/iomgr/iocp_windows.h",
-        "src/core/lib/iomgr/iomgr.c",
-        "src/core/lib/iomgr/iomgr.h",
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-        "src/core/lib/iomgr/iomgr_posix.c",
-        "src/core/lib/iomgr/iomgr_posix.h",
-        "src/core/lib/iomgr/iomgr_windows.c",
-        "src/core/lib/iomgr/load_file.c",
-        "src/core/lib/iomgr/load_file.h",
-        "src/core/lib/iomgr/network_status_tracker.c",
-        "src/core/lib/iomgr/network_status_tracker.h",
-        "src/core/lib/iomgr/polling_entity.c",
-        "src/core/lib/iomgr/polling_entity.h",
-        "src/core/lib/iomgr/pollset.h",
-        "src/core/lib/iomgr/pollset_set.h",
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-        "src/core/lib/iomgr/resolve_address.h",
-        "src/core/lib/iomgr/resolve_address_posix.c",
-        "src/core/lib/iomgr/resolve_address_windows.c",
-        "src/core/lib/iomgr/sockaddr.h",
-        "src/core/lib/iomgr/sockaddr_posix.h",
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-        "src/core/lib/iomgr/socket_utils_linux.c",
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-        "src/core/lib/iomgr/socket_utils_posix.h",
-        "src/core/lib/iomgr/socket_windows.c",
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-        "src/core/lib/iomgr/udp_server.c",
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-        "src/core/lib/iomgr/unix_sockets_posix.c",
-        "src/core/lib/iomgr/unix_sockets_posix.h",
-        "src/core/lib/iomgr/unix_sockets_posix_noop.c",
-        "src/core/lib/iomgr/wakeup_fd_eventfd.c",
-        "src/core/lib/iomgr/wakeup_fd_nospecial.c",
-        "src/core/lib/iomgr/wakeup_fd_pipe.c",
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-        "src/core/lib/iomgr/workqueue_posix.c",
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-        "src/core/lib/iomgr/workqueue_windows.c",
-        "src/core/lib/iomgr/workqueue_windows.h",
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-        "src/core/lib/security/credentials/iam/iam_credentials.h",
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-        "src/core/lib/security/credentials/oauth2/oauth2_credentials.c",
-        "src/core/lib/security/credentials/oauth2/oauth2_credentials.h",
-        "src/core/lib/security/credentials/plugin/plugin_credentials.c",
-        "src/core/lib/security/credentials/plugin/plugin_credentials.h",
-        "src/core/lib/security/credentials/ssl/ssl_credentials.c",
-        "src/core/lib/security/credentials/ssl/ssl_credentials.h",
-        "src/core/lib/security/transport/auth_filters.h",
-        "src/core/lib/security/transport/client_auth_filter.c",
-        "src/core/lib/security/transport/handshake.c",
-        "src/core/lib/security/transport/handshake.h",
-        "src/core/lib/security/transport/secure_endpoint.c",
-        "src/core/lib/security/transport/secure_endpoint.h",
-        "src/core/lib/security/transport/security_connector.c",
-        "src/core/lib/security/transport/security_connector.h",
-        "src/core/lib/security/transport/server_auth_filter.c",
-        "src/core/lib/security/transport/tsi_error.c",
-        "src/core/lib/security/transport/tsi_error.h",
-        "src/core/lib/security/util/b64.c",
-        "src/core/lib/security/util/b64.h",
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-        "src/core/lib/surface/alarm.c",
-        "src/core/lib/surface/api_trace.c",
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-        "src/core/lib/transport/metadata_batch.h",
-        "src/core/lib/transport/static_metadata.c",
-        "src/core/lib/transport/static_metadata.h",
-        "src/core/lib/transport/transport.c",
-        "src/core/lib/transport/transport.h",
-        "src/core/lib/transport/transport_impl.h",
-        "src/core/lib/transport/transport_op_string.c",
-        "src/core/lib/tsi/fake_transport_security.c",
-        "src/core/lib/tsi/fake_transport_security.h",
-        "src/core/lib/tsi/ssl_transport_security.c",
-        "src/core/lib/tsi/ssl_transport_security.h",
-        "src/core/lib/tsi/ssl_types.h",
-        "src/core/lib/tsi/transport_security.c",
-        "src/core/lib/tsi/transport_security.h",
-        "src/core/lib/tsi/transport_security_interface.h",
-        "src/core/plugin_registry/grpc_plugin_registry.c",
-        "third_party/nanopb/pb.h",
-        "third_party/nanopb/pb_decode.h",
-        "third_party/nanopb/pb_encode.h",
-    ],
-    hdrs = [
-        "include/grpc/byte_buffer.h",
-        "include/grpc/byte_buffer_reader.h",
-        "include/grpc/census.h",
-        "include/grpc/compression.h",
-        "include/grpc/grpc.h",
-        "include/grpc/grpc_posix.h",
-        "include/grpc/grpc_security.h",
-        "include/grpc/grpc_security_constants.h",
-        "include/grpc/impl/codegen/alloc.h",
-        "include/grpc/impl/codegen/atm.h",
-        "include/grpc/impl/codegen/atm_gcc_atomic.h",
-        "include/grpc/impl/codegen/atm_gcc_sync.h",
-        "include/grpc/impl/codegen/atm_windows.h",
-        "include/grpc/impl/codegen/byte_buffer.h",
-        "include/grpc/impl/codegen/byte_buffer_reader.h",
-        "include/grpc/impl/codegen/compression_types.h",
-        "include/grpc/impl/codegen/connectivity_state.h",
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-        "include/grpc/impl/codegen/slice.h",
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-        "include/grpc/impl/codegen/sync_posix.h",
-        "include/grpc/impl/codegen/sync_windows.h",
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-    includes = [
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-    ],
-    deps = [
-        ":gpr",
-        "//external:libssl",
-        "//external:nanopb",
-        "//external:zlib",
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-)
-
-cc_library(
-    name = "grpc_cronet",
-    srcs = [
-        "src/core/ext/client_config/channel_connectivity.c",
-        "src/core/ext/client_config/client_channel.c",
-        "src/core/ext/client_config/client_channel.h",
-        "src/core/ext/client_config/client_channel_factory.c",
-        "src/core/ext/client_config/client_channel_factory.h",
-        "src/core/ext/client_config/client_config.c",
-        "src/core/ext/client_config/client_config.h",
-        "src/core/ext/client_config/client_config_plugin.c",
-        "src/core/ext/client_config/connector.c",
-        "src/core/ext/client_config/connector.h",
-        "src/core/ext/client_config/default_initial_connect_string.c",
-        "src/core/ext/client_config/initial_connect_string.c",
-        "src/core/ext/client_config/initial_connect_string.h",
-        "src/core/ext/client_config/lb_policy.c",
-        "src/core/ext/client_config/lb_policy.h",
-        "src/core/ext/client_config/lb_policy_factory.c",
-        "src/core/ext/client_config/lb_policy_factory.h",
-        "src/core/ext/client_config/lb_policy_registry.c",
-        "src/core/ext/client_config/lb_policy_registry.h",
-        "src/core/ext/client_config/parse_address.c",
-        "src/core/ext/client_config/parse_address.h",
-        "src/core/ext/client_config/resolver.c",
-        "src/core/ext/client_config/resolver.h",
-        "src/core/ext/client_config/resolver_factory.c",
-        "src/core/ext/client_config/resolver_factory.h",
-        "src/core/ext/client_config/resolver_registry.c",
-        "src/core/ext/client_config/resolver_registry.h",
-        "src/core/ext/client_config/subchannel.c",
-        "src/core/ext/client_config/subchannel.h",
-        "src/core/ext/client_config/subchannel_call_holder.c",
-        "src/core/ext/client_config/subchannel_call_holder.h",
-        "src/core/ext/client_config/subchannel_index.c",
-        "src/core/ext/client_config/subchannel_index.h",
-        "src/core/ext/client_config/uri_parser.c",
-        "src/core/ext/client_config/uri_parser.h",
-        "src/core/ext/transport/chttp2/alpn/alpn.c",
-        "src/core/ext/transport/chttp2/alpn/alpn.h",
-        "src/core/ext/transport/chttp2/client/secure/secure_channel_create.c",
-        "src/core/ext/transport/chttp2/transport/bin_decoder.c",
-        "src/core/ext/transport/chttp2/transport/bin_decoder.h",
-        "src/core/ext/transport/chttp2/transport/bin_encoder.c",
-        "src/core/ext/transport/chttp2/transport/bin_encoder.h",
-        "src/core/ext/transport/chttp2/transport/chttp2_plugin.c",
-        "src/core/ext/transport/chttp2/transport/chttp2_transport.c",
-        "src/core/ext/transport/chttp2/transport/chttp2_transport.h",
-        "src/core/ext/transport/chttp2/transport/frame.h",
-        "src/core/ext/transport/chttp2/transport/frame_data.c",
-        "src/core/ext/transport/chttp2/transport/frame_data.h",
-        "src/core/ext/transport/chttp2/transport/frame_goaway.c",
-        "src/core/ext/transport/chttp2/transport/frame_goaway.h",
-        "src/core/ext/transport/chttp2/transport/frame_ping.c",
-        "src/core/ext/transport/chttp2/transport/frame_ping.h",
-        "src/core/ext/transport/chttp2/transport/frame_rst_stream.c",
-        "src/core/ext/transport/chttp2/transport/frame_rst_stream.h",
-        "src/core/ext/transport/chttp2/transport/frame_settings.c",
-        "src/core/ext/transport/chttp2/transport/frame_settings.h",
-        "src/core/ext/transport/chttp2/transport/frame_window_update.c",
-        "src/core/ext/transport/chttp2/transport/frame_window_update.h",
-        "src/core/ext/transport/chttp2/transport/hpack_encoder.c",
-        "src/core/ext/transport/chttp2/transport/hpack_encoder.h",
-        "src/core/ext/transport/chttp2/transport/hpack_parser.c",
-        "src/core/ext/transport/chttp2/transport/hpack_parser.h",
-        "src/core/ext/transport/chttp2/transport/hpack_table.c",
-        "src/core/ext/transport/chttp2/transport/hpack_table.h",
-        "src/core/ext/transport/chttp2/transport/http2_errors.h",
-        "src/core/ext/transport/chttp2/transport/huffsyms.c",
-        "src/core/ext/transport/chttp2/transport/huffsyms.h",
-        "src/core/ext/transport/chttp2/transport/incoming_metadata.c",
-        "src/core/ext/transport/chttp2/transport/incoming_metadata.h",
-        "src/core/ext/transport/chttp2/transport/internal.h",
-        "src/core/ext/transport/chttp2/transport/parsing.c",
-        "src/core/ext/transport/chttp2/transport/status_conversion.c",
-        "src/core/ext/transport/chttp2/transport/status_conversion.h",
-        "src/core/ext/transport/chttp2/transport/stream_lists.c",
-        "src/core/ext/transport/chttp2/transport/stream_map.c",
-        "src/core/ext/transport/chttp2/transport/stream_map.h",
-        "src/core/ext/transport/chttp2/transport/timeout_encoding.c",
-        "src/core/ext/transport/chttp2/transport/timeout_encoding.h",
-        "src/core/ext/transport/chttp2/transport/varint.c",
-        "src/core/ext/transport/chttp2/transport/varint.h",
-        "src/core/ext/transport/chttp2/transport/writing.c",
-        "src/core/ext/transport/cronet/client/secure/cronet_channel_create.c",
-        "src/core/ext/transport/cronet/transport/cronet_api_dummy.c",
-        "src/core/ext/transport/cronet/transport/cronet_transport.c",
-        "src/core/lib/channel/channel_args.c",
-        "src/core/lib/channel/channel_args.h",
-        "src/core/lib/channel/channel_stack.c",
-        "src/core/lib/channel/channel_stack.h",
-        "src/core/lib/channel/channel_stack_builder.c",
-        "src/core/lib/channel/channel_stack_builder.h",
-        "src/core/lib/channel/compress_filter.c",
-        "src/core/lib/channel/compress_filter.h",
-        "src/core/lib/channel/connected_channel.c",
-        "src/core/lib/channel/connected_channel.h",
-        "src/core/lib/channel/context.h",
-        "src/core/lib/channel/http_client_filter.c",
-        "src/core/lib/channel/http_client_filter.h",
-        "src/core/lib/channel/http_server_filter.c",
-        "src/core/lib/channel/http_server_filter.h",
-        "src/core/lib/compression/algorithm_metadata.h",
-        "src/core/lib/compression/compression.c",
-        "src/core/lib/compression/message_compress.c",
-        "src/core/lib/compression/message_compress.h",
-        "src/core/lib/debug/trace.c",
-        "src/core/lib/debug/trace.h",
-        "src/core/lib/http/format_request.c",
-        "src/core/lib/http/format_request.h",
-        "src/core/lib/http/httpcli.c",
-        "src/core/lib/http/httpcli.h",
-        "src/core/lib/http/httpcli_security_connector.c",
-        "src/core/lib/http/parser.c",
-        "src/core/lib/http/parser.h",
-        "src/core/lib/iomgr/closure.c",
-        "src/core/lib/iomgr/closure.h",
-        "src/core/lib/iomgr/endpoint.c",
-        "src/core/lib/iomgr/endpoint.h",
-        "src/core/lib/iomgr/endpoint_pair.h",
-        "src/core/lib/iomgr/endpoint_pair_posix.c",
-        "src/core/lib/iomgr/endpoint_pair_windows.c",
-        "src/core/lib/iomgr/error.c",
-        "src/core/lib/iomgr/error.h",
-        "src/core/lib/iomgr/ev_epoll_linux.c",
-        "src/core/lib/iomgr/ev_epoll_linux.h",
-        "src/core/lib/iomgr/ev_poll_and_epoll_posix.c",
-        "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
-        "src/core/lib/iomgr/ev_poll_posix.c",
-        "src/core/lib/iomgr/ev_poll_posix.h",
-        "src/core/lib/iomgr/ev_posix.c",
-        "src/core/lib/iomgr/ev_posix.h",
-        "src/core/lib/iomgr/exec_ctx.c",
-        "src/core/lib/iomgr/exec_ctx.h",
-        "src/core/lib/iomgr/executor.c",
-        "src/core/lib/iomgr/executor.h",
-        "src/core/lib/iomgr/iocp_windows.c",
-        "src/core/lib/iomgr/iocp_windows.h",
-        "src/core/lib/iomgr/iomgr.c",
-        "src/core/lib/iomgr/iomgr.h",
-        "src/core/lib/iomgr/iomgr_internal.h",
-        "src/core/lib/iomgr/iomgr_posix.c",
-        "src/core/lib/iomgr/iomgr_posix.h",
-        "src/core/lib/iomgr/iomgr_windows.c",
-        "src/core/lib/iomgr/load_file.c",
-        "src/core/lib/iomgr/load_file.h",
-        "src/core/lib/iomgr/network_status_tracker.c",
-        "src/core/lib/iomgr/network_status_tracker.h",
-        "src/core/lib/iomgr/polling_entity.c",
-        "src/core/lib/iomgr/polling_entity.h",
-        "src/core/lib/iomgr/pollset.h",
-        "src/core/lib/iomgr/pollset_set.h",
-        "src/core/lib/iomgr/pollset_set_windows.c",
-        "src/core/lib/iomgr/pollset_set_windows.h",
-        "src/core/lib/iomgr/pollset_windows.c",
-        "src/core/lib/iomgr/pollset_windows.h",
-        "src/core/lib/iomgr/resolve_address.h",
-        "src/core/lib/iomgr/resolve_address_posix.c",
-        "src/core/lib/iomgr/resolve_address_windows.c",
-        "src/core/lib/iomgr/sockaddr.h",
-        "src/core/lib/iomgr/sockaddr_posix.h",
-        "src/core/lib/iomgr/sockaddr_utils.c",
-        "src/core/lib/iomgr/sockaddr_utils.h",
-        "src/core/lib/iomgr/sockaddr_windows.h",
-        "src/core/lib/iomgr/socket_utils_common_posix.c",
-        "src/core/lib/iomgr/socket_utils_linux.c",
-        "src/core/lib/iomgr/socket_utils_posix.c",
-        "src/core/lib/iomgr/socket_utils_posix.h",
-        "src/core/lib/iomgr/socket_windows.c",
-        "src/core/lib/iomgr/socket_windows.h",
-        "src/core/lib/iomgr/tcp_client.h",
-        "src/core/lib/iomgr/tcp_client_posix.c",
-        "src/core/lib/iomgr/tcp_client_windows.c",
-        "src/core/lib/iomgr/tcp_posix.c",
-        "src/core/lib/iomgr/tcp_posix.h",
-        "src/core/lib/iomgr/tcp_server.h",
-        "src/core/lib/iomgr/tcp_server_posix.c",
-        "src/core/lib/iomgr/tcp_server_windows.c",
-        "src/core/lib/iomgr/tcp_windows.c",
-        "src/core/lib/iomgr/tcp_windows.h",
-        "src/core/lib/iomgr/time_averaged_stats.c",
-        "src/core/lib/iomgr/time_averaged_stats.h",
-        "src/core/lib/iomgr/timer.c",
-        "src/core/lib/iomgr/timer.h",
-        "src/core/lib/iomgr/timer_heap.c",
-        "src/core/lib/iomgr/timer_heap.h",
-        "src/core/lib/iomgr/udp_server.c",
-        "src/core/lib/iomgr/udp_server.h",
-        "src/core/lib/iomgr/unix_sockets_posix.c",
-        "src/core/lib/iomgr/unix_sockets_posix.h",
-        "src/core/lib/iomgr/unix_sockets_posix_noop.c",
-        "src/core/lib/iomgr/wakeup_fd_eventfd.c",
-        "src/core/lib/iomgr/wakeup_fd_nospecial.c",
-        "src/core/lib/iomgr/wakeup_fd_pipe.c",
-        "src/core/lib/iomgr/wakeup_fd_pipe.h",
-        "src/core/lib/iomgr/wakeup_fd_posix.c",
-        "src/core/lib/iomgr/wakeup_fd_posix.h",
-        "src/core/lib/iomgr/workqueue.h",
-        "src/core/lib/iomgr/workqueue_posix.c",
-        "src/core/lib/iomgr/workqueue_posix.h",
-        "src/core/lib/iomgr/workqueue_windows.c",
-        "src/core/lib/iomgr/workqueue_windows.h",
-        "src/core/lib/json/json.c",
-        "src/core/lib/json/json.h",
-        "src/core/lib/json/json_common.h",
-        "src/core/lib/json/json_reader.c",
-        "src/core/lib/json/json_reader.h",
-        "src/core/lib/json/json_string.c",
-        "src/core/lib/json/json_writer.c",
-        "src/core/lib/json/json_writer.h",
-        "src/core/lib/security/context/security_context.c",
-        "src/core/lib/security/context/security_context.h",
-        "src/core/lib/security/credentials/composite/composite_credentials.c",
-        "src/core/lib/security/credentials/composite/composite_credentials.h",
-        "src/core/lib/security/credentials/credentials.c",
-        "src/core/lib/security/credentials/credentials.h",
-        "src/core/lib/security/credentials/credentials_metadata.c",
-        "src/core/lib/security/credentials/fake/fake_credentials.c",
-        "src/core/lib/security/credentials/fake/fake_credentials.h",
-        "src/core/lib/security/credentials/google_default/credentials_posix.c",
-        "src/core/lib/security/credentials/google_default/credentials_windows.c",
-        "src/core/lib/security/credentials/google_default/google_default_credentials.c",
-        "src/core/lib/security/credentials/google_default/google_default_credentials.h",
-        "src/core/lib/security/credentials/iam/iam_credentials.c",
-        "src/core/lib/security/credentials/iam/iam_credentials.h",
-        "src/core/lib/security/credentials/jwt/json_token.c",
-        "src/core/lib/security/credentials/jwt/json_token.h",
-        "src/core/lib/security/credentials/jwt/jwt_credentials.c",
-        "src/core/lib/security/credentials/jwt/jwt_credentials.h",
-        "src/core/lib/security/credentials/jwt/jwt_verifier.c",
-        "src/core/lib/security/credentials/jwt/jwt_verifier.h",
-        "src/core/lib/security/credentials/oauth2/oauth2_credentials.c",
-        "src/core/lib/security/credentials/oauth2/oauth2_credentials.h",
-        "src/core/lib/security/credentials/plugin/plugin_credentials.c",
-        "src/core/lib/security/credentials/plugin/plugin_credentials.h",
-        "src/core/lib/security/credentials/ssl/ssl_credentials.c",
-        "src/core/lib/security/credentials/ssl/ssl_credentials.h",
-        "src/core/lib/security/transport/auth_filters.h",
-        "src/core/lib/security/transport/client_auth_filter.c",
-        "src/core/lib/security/transport/handshake.c",
-        "src/core/lib/security/transport/handshake.h",
-        "src/core/lib/security/transport/secure_endpoint.c",
-        "src/core/lib/security/transport/secure_endpoint.h",
-        "src/core/lib/security/transport/security_connector.c",
-        "src/core/lib/security/transport/security_connector.h",
-        "src/core/lib/security/transport/server_auth_filter.c",
-        "src/core/lib/security/transport/tsi_error.c",
-        "src/core/lib/security/transport/tsi_error.h",
-        "src/core/lib/security/util/b64.c",
-        "src/core/lib/security/util/b64.h",
-        "src/core/lib/security/util/json_util.c",
-        "src/core/lib/security/util/json_util.h",
-        "src/core/lib/surface/alarm.c",
-        "src/core/lib/surface/api_trace.c",
-        "src/core/lib/surface/api_trace.h",
-        "src/core/lib/surface/byte_buffer.c",
-        "src/core/lib/surface/byte_buffer_reader.c",
-        "src/core/lib/surface/call.c",
-        "src/core/lib/surface/call.h",
-        "src/core/lib/surface/call_details.c",
-        "src/core/lib/surface/call_log_batch.c",
-        "src/core/lib/surface/call_test_only.h",
-        "src/core/lib/surface/channel.c",
-        "src/core/lib/surface/channel.h",
-        "src/core/lib/surface/channel_init.c",
-        "src/core/lib/surface/channel_init.h",
-        "src/core/lib/surface/channel_ping.c",
-        "src/core/lib/surface/channel_stack_type.c",
-        "src/core/lib/surface/channel_stack_type.h",
-        "src/core/lib/surface/completion_queue.c",
-        "src/core/lib/surface/completion_queue.h",
-        "src/core/lib/surface/event_string.c",
-        "src/core/lib/surface/event_string.h",
-        "src/core/lib/surface/init.c",
-        "src/core/lib/surface/init.h",
-        "src/core/lib/surface/init_secure.c",
-        "src/core/lib/surface/lame_client.c",
-        "src/core/lib/surface/lame_client.h",
-        "src/core/lib/surface/metadata_array.c",
-        "src/core/lib/surface/server.c",
-        "src/core/lib/surface/server.h",
-        "src/core/lib/surface/validate_metadata.c",
-        "src/core/lib/surface/version.c",
-        "src/core/lib/transport/byte_stream.c",
-        "src/core/lib/transport/byte_stream.h",
-        "src/core/lib/transport/connectivity_state.c",
-        "src/core/lib/transport/connectivity_state.h",
-        "src/core/lib/transport/metadata.c",
-        "src/core/lib/transport/metadata.h",
-        "src/core/lib/transport/metadata_batch.c",
-        "src/core/lib/transport/metadata_batch.h",
-        "src/core/lib/transport/static_metadata.c",
-        "src/core/lib/transport/static_metadata.h",
-        "src/core/lib/transport/transport.c",
-        "src/core/lib/transport/transport.h",
-        "src/core/lib/transport/transport_impl.h",
-        "src/core/lib/transport/transport_op_string.c",
-        "src/core/lib/tsi/fake_transport_security.c",
-        "src/core/lib/tsi/fake_transport_security.h",
-        "src/core/lib/tsi/ssl_transport_security.c",
-        "src/core/lib/tsi/ssl_transport_security.h",
-        "src/core/lib/tsi/ssl_types.h",
-        "src/core/lib/tsi/transport_security.c",
-        "src/core/lib/tsi/transport_security.h",
-        "src/core/lib/tsi/transport_security_interface.h",
-        "src/core/plugin_registry/grpc_cronet_plugin_registry.c",
-        "third_party/nanopb/pb.h",
-        "third_party/nanopb/pb_decode.h",
-        "third_party/nanopb/pb_encode.h",
-        "third_party/objective_c/Cronet/cronet_c_for_grpc.h",
-    ],
-    hdrs = [
-        "include/grpc/byte_buffer.h",
-        "include/grpc/byte_buffer_reader.h",
-        "include/grpc/compression.h",
-        "include/grpc/grpc.h",
-        "include/grpc/grpc_cronet.h",
-        "include/grpc/grpc_posix.h",
-        "include/grpc/grpc_security.h",
-        "include/grpc/grpc_security_constants.h",
-        "include/grpc/impl/codegen/alloc.h",
-        "include/grpc/impl/codegen/atm.h",
-        "include/grpc/impl/codegen/atm_gcc_atomic.h",
-        "include/grpc/impl/codegen/atm_gcc_sync.h",
-        "include/grpc/impl/codegen/atm_windows.h",
-        "include/grpc/impl/codegen/byte_buffer.h",
-        "include/grpc/impl/codegen/byte_buffer_reader.h",
-        "include/grpc/impl/codegen/compression_types.h",
-        "include/grpc/impl/codegen/connectivity_state.h",
-        "include/grpc/impl/codegen/grpc_types.h",
-        "include/grpc/impl/codegen/log.h",
-        "include/grpc/impl/codegen/port_platform.h",
-        "include/grpc/impl/codegen/propagation_bits.h",
-        "include/grpc/impl/codegen/slice.h",
-        "include/grpc/impl/codegen/slice_buffer.h",
-        "include/grpc/impl/codegen/status.h",
-        "include/grpc/impl/codegen/sync.h",
-        "include/grpc/impl/codegen/sync_generic.h",
-        "include/grpc/impl/codegen/sync_posix.h",
-        "include/grpc/impl/codegen/sync_windows.h",
-        "include/grpc/impl/codegen/time.h",
-        "include/grpc/status.h",
-    ],
-    includes = [
-        ".",
-        "include",
-    ],
-    deps = [
-        ":gpr",
-        "//external:libssl",
-    ],
-)
-
-cc_library(
-    name = "grpc_unsecure",
-    srcs = [
-        "src/core/ext/census/aggregation.h",
-        "src/core/ext/census/census_interface.h",
-        "src/core/ext/census/census_rpc_stats.h",
-        "src/core/ext/census/context.c",
-        "src/core/ext/census/gen/census.pb.c",
-        "src/core/ext/census/gen/census.pb.h",
-        "src/core/ext/census/grpc_context.c",
-        "src/core/ext/census/grpc_filter.c",
-        "src/core/ext/census/grpc_filter.h",
-        "src/core/ext/census/grpc_plugin.c",
-        "src/core/ext/census/initialize.c",
-        "src/core/ext/census/mlog.c",
-        "src/core/ext/census/mlog.h",
-        "src/core/ext/census/operation.c",
-        "src/core/ext/census/placeholders.c",
-        "src/core/ext/census/rpc_metric_id.h",
-        "src/core/ext/census/tracing.c",
-        "src/core/ext/client_config/channel_connectivity.c",
-        "src/core/ext/client_config/client_channel.c",
-        "src/core/ext/client_config/client_channel.h",
-        "src/core/ext/client_config/client_channel_factory.c",
-        "src/core/ext/client_config/client_channel_factory.h",
-        "src/core/ext/client_config/client_config.c",
-        "src/core/ext/client_config/client_config.h",
-        "src/core/ext/client_config/client_config_plugin.c",
-        "src/core/ext/client_config/connector.c",
-        "src/core/ext/client_config/connector.h",
-        "src/core/ext/client_config/default_initial_connect_string.c",
-        "src/core/ext/client_config/initial_connect_string.c",
-        "src/core/ext/client_config/initial_connect_string.h",
-        "src/core/ext/client_config/lb_policy.c",
-        "src/core/ext/client_config/lb_policy.h",
-        "src/core/ext/client_config/lb_policy_factory.c",
-        "src/core/ext/client_config/lb_policy_factory.h",
-        "src/core/ext/client_config/lb_policy_registry.c",
-        "src/core/ext/client_config/lb_policy_registry.h",
-        "src/core/ext/client_config/parse_address.c",
-        "src/core/ext/client_config/parse_address.h",
-        "src/core/ext/client_config/resolver.c",
-        "src/core/ext/client_config/resolver.h",
-        "src/core/ext/client_config/resolver_factory.c",
-        "src/core/ext/client_config/resolver_factory.h",
-        "src/core/ext/client_config/resolver_registry.c",
-        "src/core/ext/client_config/resolver_registry.h",
-        "src/core/ext/client_config/subchannel.c",
-        "src/core/ext/client_config/subchannel.h",
-        "src/core/ext/client_config/subchannel_call_holder.c",
-        "src/core/ext/client_config/subchannel_call_holder.h",
-        "src/core/ext/client_config/subchannel_index.c",
-        "src/core/ext/client_config/subchannel_index.h",
-        "src/core/ext/client_config/uri_parser.c",
-        "src/core/ext/client_config/uri_parser.h",
-        "src/core/ext/lb_policy/grpclb/load_balancer_api.c",
-        "src/core/ext/lb_policy/grpclb/load_balancer_api.h",
-        "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c",
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-        "src/core/ext/transport/chttp2/alpn/alpn.c",
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-        "src/core/ext/transport/chttp2/client/insecure/channel_create.c",
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-        "src/core/ext/transport/chttp2/server/insecure/server_chttp2.c",
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-        "src/core/lib/iomgr/network_status_tracker.c",
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-        "src/core/lib/iomgr/udp_server.c",
-        "src/core/lib/iomgr/udp_server.h",
-        "src/core/lib/iomgr/unix_sockets_posix.c",
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-        "src/core/lib/iomgr/unix_sockets_posix_noop.c",
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-        "src/core/lib/iomgr/wakeup_fd_nospecial.c",
-        "src/core/lib/iomgr/wakeup_fd_pipe.c",
-        "src/core/lib/iomgr/wakeup_fd_pipe.h",
-        "src/core/lib/iomgr/wakeup_fd_posix.c",
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-        "src/core/lib/transport/byte_stream.c",
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-        "src/core/lib/transport/metadata_batch.c",
-        "src/core/lib/transport/metadata_batch.h",
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-        "src/core/lib/transport/static_metadata.h",
-        "src/core/lib/transport/transport.c",
-        "src/core/lib/transport/transport.h",
-        "src/core/lib/transport/transport_impl.h",
-        "src/core/lib/transport/transport_op_string.c",
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-    ],
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-        "include/grpc/compression.h",
-        "include/grpc/grpc.h",
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-)
-
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-        "src/core/lib/channel/channel_args.c",
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-        "src/core/lib/channel/compress_filter.c",
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-        "src/core/lib/channel/connected_channel.c",
-        "src/core/lib/channel/connected_channel.h",
-        "src/core/lib/channel/context.h",
-        "src/core/lib/channel/http_client_filter.c",
-        "src/core/lib/channel/http_client_filter.h",
-        "src/core/lib/channel/http_server_filter.c",
-        "src/core/lib/channel/http_server_filter.h",
-        "src/core/lib/compression/algorithm_metadata.h",
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-        "src/core/lib/compression/message_compress.c",
-        "src/core/lib/compression/message_compress.h",
-        "src/core/lib/debug/trace.c",
-        "src/core/lib/debug/trace.h",
-        "src/core/lib/http/format_request.c",
-        "src/core/lib/http/format_request.h",
-        "src/core/lib/http/httpcli.c",
-        "src/core/lib/http/httpcli.h",
-        "src/core/lib/http/parser.c",
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-        "src/core/lib/iomgr/closure.c",
-        "src/core/lib/iomgr/closure.h",
-        "src/core/lib/iomgr/endpoint.c",
-        "src/core/lib/iomgr/endpoint.h",
-        "src/core/lib/iomgr/endpoint_pair.h",
-        "src/core/lib/iomgr/endpoint_pair_posix.c",
-        "src/core/lib/iomgr/endpoint_pair_windows.c",
-        "src/core/lib/iomgr/error.c",
-        "src/core/lib/iomgr/error.h",
-        "src/core/lib/iomgr/ev_epoll_linux.c",
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-        "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
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-        "src/core/lib/iomgr/ev_poll_posix.h",
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-        "src/core/lib/iomgr/exec_ctx.c",
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-        "src/core/lib/iomgr/executor.c",
-        "src/core/lib/iomgr/executor.h",
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-        "src/core/lib/iomgr/iocp_windows.h",
-        "src/core/lib/iomgr/iomgr.c",
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-        "src/core/lib/iomgr/iomgr_windows.c",
-        "src/core/lib/iomgr/load_file.c",
-        "src/core/lib/iomgr/load_file.h",
-        "src/core/lib/iomgr/network_status_tracker.c",
-        "src/core/lib/iomgr/network_status_tracker.h",
-        "src/core/lib/iomgr/polling_entity.c",
-        "src/core/lib/iomgr/polling_entity.h",
-        "src/core/lib/iomgr/pollset.h",
-        "src/core/lib/iomgr/pollset_set.h",
-        "src/core/lib/iomgr/pollset_set_windows.c",
-        "src/core/lib/iomgr/pollset_set_windows.h",
-        "src/core/lib/iomgr/pollset_windows.c",
-        "src/core/lib/iomgr/pollset_windows.h",
-        "src/core/lib/iomgr/resolve_address.h",
-        "src/core/lib/iomgr/resolve_address_posix.c",
-        "src/core/lib/iomgr/resolve_address_windows.c",
-        "src/core/lib/iomgr/sockaddr.h",
-        "src/core/lib/iomgr/sockaddr_posix.h",
-        "src/core/lib/iomgr/sockaddr_utils.c",
-        "src/core/lib/iomgr/sockaddr_utils.h",
-        "src/core/lib/iomgr/sockaddr_windows.h",
-        "src/core/lib/iomgr/socket_utils_common_posix.c",
-        "src/core/lib/iomgr/socket_utils_linux.c",
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-        "src/core/lib/iomgr/socket_windows.c",
-        "src/core/lib/iomgr/socket_windows.h",
-        "src/core/lib/iomgr/tcp_client.h",
-        "src/core/lib/iomgr/tcp_client_posix.c",
-        "src/core/lib/iomgr/tcp_client_windows.c",
-        "src/core/lib/iomgr/tcp_posix.c",
-        "src/core/lib/iomgr/tcp_posix.h",
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-        "src/core/lib/iomgr/tcp_server_posix.c",
-        "src/core/lib/iomgr/tcp_server_windows.c",
-        "src/core/lib/iomgr/tcp_windows.c",
-        "src/core/lib/iomgr/tcp_windows.h",
-        "src/core/lib/iomgr/time_averaged_stats.c",
-        "src/core/lib/iomgr/time_averaged_stats.h",
-        "src/core/lib/iomgr/timer.c",
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-        "src/core/lib/iomgr/timer_heap.h",
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-        "src/core/lib/iomgr/udp_server.h",
-        "src/core/lib/iomgr/unix_sockets_posix.c",
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-        "src/core/lib/iomgr/workqueue.h",
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-        "src/core/lib/json/json.h",
-        "src/core/lib/json/json_common.h",
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-        "src/core/lib/json/json_reader.h",
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-        "src/core/lib/transport/metadata_batch.h",
-        "src/core/lib/transport/static_metadata.c",
-        "src/core/lib/transport/static_metadata.h",
-        "src/core/lib/transport/transport.c",
-        "src/core/lib/transport/transport.h",
-        "src/core/lib/transport/transport_impl.h",
-        "src/core/lib/transport/transport_op_string.c",
-        "src/cpp/client/channel.cc",
-        "src/cpp/client/client_context.cc",
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-        "src/cpp/client/create_channel_internal.cc",
-        "src/cpp/client/create_channel_internal.h",
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-        "src/cpp/client/generic_stub.cc",
-        "src/cpp/client/insecure_credentials.cc",
-        "src/cpp/client/secure_credentials.cc",
-        "src/cpp/client/secure_credentials.h",
-        "src/cpp/codegen/codegen_init.cc",
-        "src/cpp/common/auth_property_iterator.cc",
-        "src/cpp/common/channel_arguments.cc",
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-        "src/cpp/common/core_codegen.cc",
-        "src/cpp/common/rpc_method.cc",
-        "src/cpp/common/secure_auth_context.cc",
-        "src/cpp/common/secure_auth_context.h",
-        "src/cpp/common/secure_channel_arguments.cc",
-        "src/cpp/common/secure_create_auth_context.cc",
-        "src/cpp/server/async_generic_service.cc",
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-        "include/grpc++/create_channel.h",
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-        "include/grpc++/generic/generic_stub.h",
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-        "include/grpc++/impl/client_unary_call.h",
-        "include/grpc++/impl/codegen/async_stream.h",
-        "include/grpc++/impl/codegen/async_unary_call.h",
-        "include/grpc++/impl/codegen/call.h",
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-        "include/grpc++/impl/codegen/client_context.h",
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-        "include/grpc++/impl/codegen/completion_queue.h",
-        "include/grpc++/impl/codegen/completion_queue_tag.h",
-        "include/grpc++/impl/codegen/config.h",
-        "include/grpc++/impl/codegen/core_codegen.h",
-        "include/grpc++/impl/codegen/core_codegen_interface.h",
-        "include/grpc++/impl/codegen/create_auth_context.h",
-        "include/grpc++/impl/codegen/grpc_library.h",
-        "include/grpc++/impl/codegen/method_handler_impl.h",
-        "include/grpc++/impl/codegen/proto_utils.h",
-        "include/grpc++/impl/codegen/rpc_method.h",
-        "include/grpc++/impl/codegen/rpc_service_method.h",
-        "include/grpc++/impl/codegen/security/auth_context.h",
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-        "include/grpc++/impl/codegen/server_context.h",
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-        "include/grpc++/impl/codegen/string_ref.h",
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-        "include/grpc++/impl/codegen/time.h",
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-        "include/grpc++/security/auth_metadata_processor.h",
-        "include/grpc++/security/credentials.h",
-        "include/grpc++/security/server_credentials.h",
-        "include/grpc++/server.h",
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-        "include/grpc++/support/byte_buffer.h",
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-        "include/grpc++/support/status.h",
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-        "include/grpc++/support/string_ref.h",
-        "include/grpc++/support/stub_options.h",
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-        "include/grpc++/support/time.h",
-        "include/grpc/byte_buffer.h",
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-        "include/grpc/compression.h",
-        "include/grpc/grpc.h",
-        "include/grpc/grpc_posix.h",
-        "include/grpc/grpc_security_constants.h",
-        "include/grpc/impl/codegen/alloc.h",
-        "include/grpc/impl/codegen/atm.h",
-        "include/grpc/impl/codegen/atm_gcc_atomic.h",
-        "include/grpc/impl/codegen/atm_gcc_sync.h",
-        "include/grpc/impl/codegen/atm_windows.h",
-        "include/grpc/impl/codegen/byte_buffer.h",
-        "include/grpc/impl/codegen/byte_buffer_reader.h",
-        "include/grpc/impl/codegen/compression_types.h",
-        "include/grpc/impl/codegen/connectivity_state.h",
-        "include/grpc/impl/codegen/grpc_types.h",
-        "include/grpc/impl/codegen/log.h",
-        "include/grpc/impl/codegen/port_platform.h",
-        "include/grpc/impl/codegen/propagation_bits.h",
-        "include/grpc/impl/codegen/slice.h",
-        "include/grpc/impl/codegen/slice_buffer.h",
-        "include/grpc/impl/codegen/status.h",
-        "include/grpc/impl/codegen/sync.h",
-        "include/grpc/impl/codegen/sync_generic.h",
-        "include/grpc/impl/codegen/sync_posix.h",
-        "include/grpc/impl/codegen/sync_windows.h",
-        "include/grpc/impl/codegen/time.h",
-        "include/grpc/status.h",
-    ],
-    copts = [
-        "-std=gnu99",
-    ],
-    includes = [
-        ".",
-        "include",
-    ],
-    deps = [
-        ":gpr",
-        ":grpc",
-        "//external:libssl",
-        "//external:protobuf_clib",
-    ],
-)
-
-cc_library(
-    name = "grpc++_reflection",
-    srcs = [
-        "src/cpp/ext/proto_server_reflection.cc",
-        "src/cpp/ext/proto_server_reflection.h",
-        "src/cpp/ext/proto_server_reflection_plugin.cc",
-        "src/cpp/ext/reflection.grpc.pb.cc",
-        "src/cpp/ext/reflection.pb.cc",
-    ],
-    hdrs = [
-        "include/grpc++/ext/proto_server_reflection_plugin.h",
-        "include/grpc++/ext/reflection.grpc.pb.h",
-        "include/grpc++/ext/reflection.pb.h",
-        "include/grpc++/impl/codegen/async_stream.h",
-        "include/grpc++/impl/codegen/async_unary_call.h",
-        "include/grpc++/impl/codegen/call.h",
-        "include/grpc++/impl/codegen/call_hook.h",
-        "include/grpc++/impl/codegen/channel_interface.h",
-        "include/grpc++/impl/codegen/client_context.h",
-        "include/grpc++/impl/codegen/client_unary_call.h",
-        "include/grpc++/impl/codegen/completion_queue.h",
-        "include/grpc++/impl/codegen/completion_queue_tag.h",
-        "include/grpc++/impl/codegen/config.h",
-        "include/grpc++/impl/codegen/config_protobuf.h",
-        "include/grpc++/impl/codegen/core_codegen_interface.h",
-        "include/grpc++/impl/codegen/create_auth_context.h",
-        "include/grpc++/impl/codegen/grpc_library.h",
-        "include/grpc++/impl/codegen/method_handler_impl.h",
-        "include/grpc++/impl/codegen/proto_utils.h",
-        "include/grpc++/impl/codegen/rpc_method.h",
-        "include/grpc++/impl/codegen/rpc_service_method.h",
-        "include/grpc++/impl/codegen/security/auth_context.h",
-        "include/grpc++/impl/codegen/serialization_traits.h",
-        "include/grpc++/impl/codegen/server_context.h",
-        "include/grpc++/impl/codegen/server_interface.h",
-        "include/grpc++/impl/codegen/service_type.h",
-        "include/grpc++/impl/codegen/status.h",
-        "include/grpc++/impl/codegen/status_code_enum.h",
-        "include/grpc++/impl/codegen/string_ref.h",
-        "include/grpc++/impl/codegen/stub_options.h",
-        "include/grpc++/impl/codegen/sync.h",
-        "include/grpc++/impl/codegen/sync_cxx11.h",
-        "include/grpc++/impl/codegen/sync_no_cxx11.h",
-        "include/grpc++/impl/codegen/sync_stream.h",
-        "include/grpc++/impl/codegen/time.h",
-        "include/grpc/impl/codegen/alloc.h",
-        "include/grpc/impl/codegen/atm.h",
-        "include/grpc/impl/codegen/atm_gcc_atomic.h",
-        "include/grpc/impl/codegen/atm_gcc_sync.h",
-        "include/grpc/impl/codegen/atm_windows.h",
-        "include/grpc/impl/codegen/byte_buffer.h",
-        "include/grpc/impl/codegen/byte_buffer_reader.h",
-        "include/grpc/impl/codegen/compression_types.h",
-        "include/grpc/impl/codegen/connectivity_state.h",
-        "include/grpc/impl/codegen/grpc_types.h",
-        "include/grpc/impl/codegen/log.h",
-        "include/grpc/impl/codegen/port_platform.h",
-        "include/grpc/impl/codegen/propagation_bits.h",
-        "include/grpc/impl/codegen/slice.h",
-        "include/grpc/impl/codegen/slice_buffer.h",
-        "include/grpc/impl/codegen/status.h",
-        "include/grpc/impl/codegen/sync.h",
-        "include/grpc/impl/codegen/sync_generic.h",
-        "include/grpc/impl/codegen/sync_posix.h",
-        "include/grpc/impl/codegen/sync_windows.h",
-        "include/grpc/impl/codegen/time.h",
-    ],
-    includes = [
-        ".",
-        "include",
-    ],
-    deps = [
-        ":grpc++",
-    ],
-)
-
-cc_library(
-    name = "grpc++_unsecure",
-    srcs = [
-        "src/cpp/client/channel.cc",
-        "src/cpp/client/client_context.cc",
-        "src/cpp/client/create_channel.cc",
-        "src/cpp/client/create_channel_internal.cc",
-        "src/cpp/client/create_channel_internal.h",
-        "src/cpp/client/create_channel_posix.cc",
-        "src/cpp/client/credentials.cc",
-        "src/cpp/client/generic_stub.cc",
-        "src/cpp/client/insecure_credentials.cc",
-        "src/cpp/codegen/codegen_init.cc",
-        "src/cpp/common/channel_arguments.cc",
-        "src/cpp/common/completion_queue.cc",
-        "src/cpp/common/core_codegen.cc",
-        "src/cpp/common/insecure_create_auth_context.cc",
-        "src/cpp/common/rpc_method.cc",
-        "src/cpp/server/async_generic_service.cc",
-        "src/cpp/server/create_default_thread_pool.cc",
-        "src/cpp/server/dynamic_thread_pool.cc",
-        "src/cpp/server/dynamic_thread_pool.h",
-        "src/cpp/server/insecure_server_credentials.cc",
-        "src/cpp/server/server.cc",
-        "src/cpp/server/server_builder.cc",
-        "src/cpp/server/server_context.cc",
-        "src/cpp/server/server_credentials.cc",
-        "src/cpp/server/server_posix.cc",
-        "src/cpp/server/thread_pool_interface.h",
-        "src/cpp/util/byte_buffer.cc",
-        "src/cpp/util/slice.cc",
-        "src/cpp/util/status.cc",
-        "src/cpp/util/string_ref.cc",
-        "src/cpp/util/time.cc",
-    ],
-    hdrs = [
-        "include/grpc++/alarm.h",
-        "include/grpc++/channel.h",
-        "include/grpc++/client_context.h",
-        "include/grpc++/completion_queue.h",
-        "include/grpc++/create_channel.h",
-        "include/grpc++/create_channel_posix.h",
-        "include/grpc++/generic/async_generic_service.h",
-        "include/grpc++/generic/generic_stub.h",
-        "include/grpc++/grpc++.h",
-        "include/grpc++/impl/call.h",
-        "include/grpc++/impl/client_unary_call.h",
-        "include/grpc++/impl/codegen/async_stream.h",
-        "include/grpc++/impl/codegen/async_unary_call.h",
-        "include/grpc++/impl/codegen/call.h",
-        "include/grpc++/impl/codegen/call_hook.h",
-        "include/grpc++/impl/codegen/channel_interface.h",
-        "include/grpc++/impl/codegen/client_context.h",
-        "include/grpc++/impl/codegen/client_unary_call.h",
-        "include/grpc++/impl/codegen/completion_queue.h",
-        "include/grpc++/impl/codegen/completion_queue_tag.h",
-        "include/grpc++/impl/codegen/config.h",
-        "include/grpc++/impl/codegen/config_protobuf.h",
-        "include/grpc++/impl/codegen/core_codegen.h",
-        "include/grpc++/impl/codegen/core_codegen_interface.h",
-        "include/grpc++/impl/codegen/create_auth_context.h",
-        "include/grpc++/impl/codegen/grpc_library.h",
-        "include/grpc++/impl/codegen/method_handler_impl.h",
-        "include/grpc++/impl/codegen/proto_utils.h",
-        "include/grpc++/impl/codegen/rpc_method.h",
-        "include/grpc++/impl/codegen/rpc_service_method.h",
-        "include/grpc++/impl/codegen/security/auth_context.h",
-        "include/grpc++/impl/codegen/serialization_traits.h",
-        "include/grpc++/impl/codegen/server_context.h",
-        "include/grpc++/impl/codegen/server_interface.h",
-        "include/grpc++/impl/codegen/service_type.h",
-        "include/grpc++/impl/codegen/status.h",
-        "include/grpc++/impl/codegen/status_code_enum.h",
-        "include/grpc++/impl/codegen/string_ref.h",
-        "include/grpc++/impl/codegen/stub_options.h",
-        "include/grpc++/impl/codegen/sync.h",
-        "include/grpc++/impl/codegen/sync_cxx11.h",
-        "include/grpc++/impl/codegen/sync_no_cxx11.h",
-        "include/grpc++/impl/codegen/sync_stream.h",
-        "include/grpc++/impl/codegen/time.h",
-        "include/grpc++/impl/grpc_library.h",
-        "include/grpc++/impl/method_handler_impl.h",
-        "include/grpc++/impl/rpc_method.h",
-        "include/grpc++/impl/rpc_service_method.h",
-        "include/grpc++/impl/serialization_traits.h",
-        "include/grpc++/impl/server_builder_option.h",
-        "include/grpc++/impl/server_builder_plugin.h",
-        "include/grpc++/impl/server_initializer.h",
-        "include/grpc++/impl/service_type.h",
-        "include/grpc++/impl/sync.h",
-        "include/grpc++/impl/sync_cxx11.h",
-        "include/grpc++/impl/sync_no_cxx11.h",
-        "include/grpc++/impl/thd.h",
-        "include/grpc++/impl/thd_cxx11.h",
-        "include/grpc++/impl/thd_no_cxx11.h",
-        "include/grpc++/security/auth_context.h",
-        "include/grpc++/security/auth_metadata_processor.h",
-        "include/grpc++/security/credentials.h",
-        "include/grpc++/security/server_credentials.h",
-        "include/grpc++/server.h",
-        "include/grpc++/server_builder.h",
-        "include/grpc++/server_context.h",
-        "include/grpc++/server_posix.h",
-        "include/grpc++/support/async_stream.h",
-        "include/grpc++/support/async_unary_call.h",
-        "include/grpc++/support/byte_buffer.h",
-        "include/grpc++/support/channel_arguments.h",
-        "include/grpc++/support/config.h",
-        "include/grpc++/support/slice.h",
-        "include/grpc++/support/status.h",
-        "include/grpc++/support/status_code_enum.h",
-        "include/grpc++/support/string_ref.h",
-        "include/grpc++/support/stub_options.h",
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-    ],
-    includes = [
-        ".",
-        "include",
-    ],
-    linkopts = ["-lpthread"],
-    deps = [
-        ":gpr",
-        ":grpc_unsecure",
-        "//external:protobuf_clib",
-    ],
-)
-
-cc_library(
-    name = "grpc_plugin_support",
-    srcs = [
-        "src/compiler/config.h",
-        "src/compiler/cpp_generator.cc",
-        "src/compiler/cpp_generator.h",
-        "src/compiler/cpp_generator_helpers.h",
-        "src/compiler/csharp_generator.cc",
-        "src/compiler/csharp_generator.h",
-        "src/compiler/csharp_generator_helpers.h",
-        "src/compiler/generator_helpers.h",
-        "src/compiler/node_generator.cc",
-        "src/compiler/node_generator.h",
-        "src/compiler/node_generator_helpers.h",
-        "src/compiler/objective_c_generator.cc",
-        "src/compiler/objective_c_generator.h",
-        "src/compiler/objective_c_generator_helpers.h",
-        "src/compiler/python_generator.cc",
-        "src/compiler/python_generator.h",
-        "src/compiler/ruby_generator.cc",
-        "src/compiler/ruby_generator.h",
-        "src/compiler/ruby_generator_helpers-inl.h",
-        "src/compiler/ruby_generator_map-inl.h",
-        "src/compiler/ruby_generator_string-inl.h",
-    ],
-    hdrs = [
-        "include/grpc++/impl/codegen/config_protobuf.h",
-    ],
-    includes = [
-        ".",
-        "include",
-    ],
-    deps = [
-        "//external:protobuf_compiler",
-    ],
-)
-
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-        "src/core/lib/debug/trace.h",
-        "src/core/lib/http/format_request.h",
-        "src/core/lib/http/httpcli.h",
-        "src/core/lib/http/parser.h",
-        "src/core/lib/iomgr/closure.h",
-        "src/core/lib/iomgr/endpoint.h",
-        "src/core/lib/iomgr/endpoint_pair.h",
-        "src/core/lib/iomgr/error.h",
-        "src/core/lib/iomgr/ev_epoll_linux.h",
-        "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
-        "src/core/lib/iomgr/ev_poll_posix.h",
-        "src/core/lib/iomgr/ev_posix.h",
-        "src/core/lib/iomgr/exec_ctx.h",
-        "src/core/lib/iomgr/executor.h",
-        "src/core/lib/iomgr/iocp_windows.h",
-        "src/core/lib/iomgr/iomgr.h",
-        "src/core/lib/iomgr/iomgr_internal.h",
-        "src/core/lib/iomgr/iomgr_posix.h",
-        "src/core/lib/iomgr/load_file.h",
-        "src/core/lib/iomgr/network_status_tracker.h",
-        "src/core/lib/iomgr/polling_entity.h",
-        "src/core/lib/iomgr/pollset.h",
-        "src/core/lib/iomgr/pollset_set.h",
-        "src/core/lib/iomgr/pollset_set_windows.h",
-        "src/core/lib/iomgr/pollset_windows.h",
-        "src/core/lib/iomgr/resolve_address.h",
-        "src/core/lib/iomgr/sockaddr.h",
-        "src/core/lib/iomgr/sockaddr_posix.h",
-        "src/core/lib/iomgr/sockaddr_utils.h",
-        "src/core/lib/iomgr/sockaddr_windows.h",
-        "src/core/lib/iomgr/socket_utils_posix.h",
-        "src/core/lib/iomgr/socket_windows.h",
-        "src/core/lib/iomgr/tcp_client.h",
-        "src/core/lib/iomgr/tcp_posix.h",
-        "src/core/lib/iomgr/tcp_server.h",
-        "src/core/lib/iomgr/tcp_windows.h",
-        "src/core/lib/iomgr/time_averaged_stats.h",
-        "src/core/lib/iomgr/timer.h",
-        "src/core/lib/iomgr/timer_heap.h",
-        "src/core/lib/iomgr/udp_server.h",
-        "src/core/lib/iomgr/unix_sockets_posix.h",
-        "src/core/lib/iomgr/wakeup_fd_pipe.h",
-        "src/core/lib/iomgr/wakeup_fd_posix.h",
-        "src/core/lib/iomgr/workqueue.h",
-        "src/core/lib/iomgr/workqueue_posix.h",
-        "src/core/lib/iomgr/workqueue_windows.h",
-        "src/core/lib/json/json.h",
-        "src/core/lib/json/json_common.h",
-        "src/core/lib/json/json_reader.h",
-        "src/core/lib/json/json_writer.h",
-        "src/core/lib/security/context/security_context.h",
-        "src/core/lib/security/credentials/composite/composite_credentials.h",
-        "src/core/lib/security/credentials/credentials.h",
-        "src/core/lib/security/credentials/fake/fake_credentials.h",
-        "src/core/lib/security/credentials/google_default/google_default_credentials.h",
-        "src/core/lib/security/credentials/iam/iam_credentials.h",
-        "src/core/lib/security/credentials/jwt/json_token.h",
-        "src/core/lib/security/credentials/jwt/jwt_credentials.h",
-        "src/core/lib/security/credentials/jwt/jwt_verifier.h",
-        "src/core/lib/security/credentials/oauth2/oauth2_credentials.h",
-        "src/core/lib/security/credentials/plugin/plugin_credentials.h",
-        "src/core/lib/security/credentials/ssl/ssl_credentials.h",
-        "src/core/lib/security/transport/auth_filters.h",
-        "src/core/lib/security/transport/handshake.h",
-        "src/core/lib/security/transport/secure_endpoint.h",
-        "src/core/lib/security/transport/security_connector.h",
-        "src/core/lib/security/transport/tsi_error.h",
-        "src/core/lib/security/util/b64.h",
-        "src/core/lib/security/util/json_util.h",
-        "src/core/lib/surface/api_trace.h",
-        "src/core/lib/surface/call.h",
-        "src/core/lib/surface/call_test_only.h",
-        "src/core/lib/surface/channel.h",
-        "src/core/lib/surface/channel_init.h",
-        "src/core/lib/surface/channel_stack_type.h",
-        "src/core/lib/surface/completion_queue.h",
-        "src/core/lib/surface/event_string.h",
-        "src/core/lib/surface/init.h",
-        "src/core/lib/surface/lame_client.h",
-        "src/core/lib/surface/server.h",
-        "src/core/lib/transport/byte_stream.h",
-        "src/core/lib/transport/connectivity_state.h",
-        "src/core/lib/transport/metadata.h",
-        "src/core/lib/transport/metadata_batch.h",
-        "src/core/lib/transport/static_metadata.h",
-        "src/core/lib/transport/transport.h",
-        "src/core/lib/transport/transport_impl.h",
-        "src/core/lib/tsi/fake_transport_security.h",
-        "src/core/lib/tsi/ssl_transport_security.h",
-        "src/core/lib/tsi/ssl_types.h",
-        "src/core/lib/tsi/transport_security.h",
-        "src/core/lib/tsi/transport_security_interface.h",
-        "third_party/nanopb/pb.h",
-        "third_party/nanopb/pb_decode.h",
-        "third_party/nanopb/pb_encode.h",
-    ],
-    includes = [
-        ".",
-        "include",
-    ],
-    sdk_dylibs = ["libz"],
-    deps = [
-        ":gpr_objc",
-        "//external:libssl_objc",
-        "//external:nanopb",
-    ],
-)
-
-cc_binary(
-    name = "grpc_cpp_plugin",
-    srcs = [
-        "src/compiler/cpp_plugin.cc",
-    ],
-    deps = [
-        ":grpc_plugin_support",
-        "//external:protobuf_compiler",
-    ],
-)
-
-cc_binary(
-    name = "grpc_csharp_plugin",
-    srcs = [
-        "src/compiler/csharp_plugin.cc",
-    ],
-    deps = [
-        ":grpc_plugin_support",
-        "//external:protobuf_compiler",
-    ],
-)
-
-cc_binary(
-    name = "grpc_node_plugin",
-    srcs = [
-        "src/compiler/node_plugin.cc",
-    ],
-    deps = [
-        ":grpc_plugin_support",
-        "//external:protobuf_compiler",
-    ],
-)
-
-cc_binary(
-    name = "grpc_objective_c_plugin",
-    srcs = [
-        "src/compiler/objective_c_plugin.cc",
-    ],
-    deps = [
-        ":grpc_plugin_support",
-        "//external:protobuf_compiler",
-    ],
-)
-
-cc_binary(
-    name = "grpc_python_plugin",
-    srcs = [
-        "src/compiler/python_plugin.cc",
-    ],
-    deps = [
-        ":grpc_plugin_support",
-        "//external:protobuf_compiler",
-    ],
-)
-
-cc_binary(
-    name = "grpc_ruby_plugin",
-    srcs = [
-        "src/compiler/ruby_plugin.cc",
-    ],
-    deps = [
-        ":grpc_plugin_support",
-        "//external:protobuf_compiler",
-    ],
-)
-
-objc_path = "src/objective-c"
-
-rx_library_path = objc_path + "/RxLibrary"
-
-objc_library(
-    name = "rx_library",
-    srcs = glob([
-        rx_library_path + "/*.m",
-        rx_library_path + "/transformations/*.m",
-    ]),
-    hdrs = glob([
-        rx_library_path + "/*.h",
-        rx_library_path + "/transformations/*.h",
-    ]),
-    includes = [objc_path],
-    deps = [
-        ":rx_library_private",
-    ],
-)
-
-objc_library(
-    name = "rx_library_private",
-    srcs = glob([rx_library_path + "/private/*.m"]),
-    hdrs = glob([rx_library_path + "/private/*.h"]),
-    visibility = ["//visibility:private"],
-)
-
-objc_client_path = objc_path + "/GRPCClient"
-
-objc_library(
-    name = "grpc_client",
-    srcs = glob([
-        objc_client_path + "/*.m",
-        objc_client_path + "/private/*.m",
-    ]),
-    hdrs = glob([
-        objc_client_path + "/*.h",
-        objc_client_path + "/private/*.h",
-    ]),
-    bundles = [":gRPCCertificates"],
-    includes = [objc_path],
-    deps = [
-        ":grpc_objc",
-        ":rx_library",
-    ],
-)
-
-objc_bundle_library(
-    # The choice of name is signicant here, since it determines the bundle name.
-    name = "gRPCCertificates",
-    resources = ["etc/roots.pem"],
-)
-
-proto_objc_rpc_path = objc_path + "/ProtoRPC"
-
-objc_library(
-    name = "proto_objc_rpc",
-    srcs = glob([
-        proto_objc_rpc_path + "/*.m",
-    ]),
-    hdrs = glob([
-        proto_objc_rpc_path + "/*.h",
-    ]),
-    includes = [objc_path],
-    deps = [
-        ":grpc_client",
-        ":rx_library",
-        "//external:protobuf_objc",
-    ],
-)
diff --git a/third_party/grpc/BUILD b/third_party/grpc/BUILD
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/third_party/grpc/grpc.patch b/third_party/grpc/grpc.patch
new file mode 100644
index 0000000000..6e5b4b02fb
--- /dev/null
+++ b/third_party/grpc/grpc.patch
@@ -0,0 +1,76 @@
+diff --git a/bazel/grpc_build_system.bzl b/bazel/grpc_build_system.bzl
+index f793cae56d..0295adb8ab 100644
+--- a/bazel/grpc_build_system.bzl
++++ b/bazel/grpc_build_system.bzl
+@@ -80,7 +80,7 @@ def grpc_cc_test(name, srcs = [], deps = [], external_deps = [], args = [], data
+     linkopts = ["-pthread"],
+   )
+ 
+-def grpc_cc_binary(name, srcs = [], deps = [], external_deps = [], args = [], data = [], language = "C++", testonly = False, linkshared = False):
++def grpc_cc_binary(name, srcs = [], deps = [], external_deps = [], args = [], data = [], language = "C++", testonly = False, linkshared = False, linkopts = []):
+   copts = []
+   if language.upper() == "C":
+     copts = ["-std=c99"]
+@@ -93,7 +93,7 @@ def grpc_cc_binary(name, srcs = [], deps = [], external_deps = [], args = [], da
+     linkshared = linkshared,
+     deps = deps + ["//external:" + dep for dep in external_deps],
+     copts = copts,
+-    linkopts = ["-pthread"],
++    linkopts = ["-pthread"] + linkopts,
+   )
+ 
+ def grpc_generate_one_off_targets():
+diff --git a/src/core/plugin_registry/grpc_unsecure_plugin_registry.c b/src/core/plugin_registry/grpc_unsecure_plugin_registry.c
+index 7eb599d81a..4cc2e30af4 100644
+--- a/src/core/plugin_registry/grpc_unsecure_plugin_registry.c
++++ b/src/core/plugin_registry/grpc_unsecure_plugin_registry.c
+@@ -28,18 +28,12 @@ extern void grpc_client_channel_init(void);
+ extern void grpc_client_channel_shutdown(void);
+ extern void grpc_inproc_plugin_init(void);
+ extern void grpc_inproc_plugin_shutdown(void);
+-extern void grpc_resolver_dns_ares_init(void);
+-extern void grpc_resolver_dns_ares_shutdown(void);
+ extern void grpc_resolver_dns_native_init(void);
+ extern void grpc_resolver_dns_native_shutdown(void);
+ extern void grpc_resolver_sockaddr_init(void);
+ extern void grpc_resolver_sockaddr_shutdown(void);
+-extern void grpc_resolver_fake_init(void);
+-extern void grpc_resolver_fake_shutdown(void);
+ extern void grpc_load_reporting_plugin_init(void);
+ extern void grpc_load_reporting_plugin_shutdown(void);
+-extern void grpc_lb_policy_grpclb_init(void);
+-extern void grpc_lb_policy_grpclb_shutdown(void);
+ extern void grpc_lb_policy_pick_first_init(void);
+ extern void grpc_lb_policy_pick_first_shutdown(void);
+ extern void grpc_lb_policy_round_robin_init(void);
+@@ -64,18 +58,12 @@ void grpc_register_built_in_plugins(void) {
+                        grpc_client_channel_shutdown);
+   grpc_register_plugin(grpc_inproc_plugin_init,
+                        grpc_inproc_plugin_shutdown);
+-  grpc_register_plugin(grpc_resolver_dns_ares_init,
+-                       grpc_resolver_dns_ares_shutdown);
+   grpc_register_plugin(grpc_resolver_dns_native_init,
+                        grpc_resolver_dns_native_shutdown);
+   grpc_register_plugin(grpc_resolver_sockaddr_init,
+                        grpc_resolver_sockaddr_shutdown);
+-  grpc_register_plugin(grpc_resolver_fake_init,
+-                       grpc_resolver_fake_shutdown);
+   grpc_register_plugin(grpc_load_reporting_plugin_init,
+                        grpc_load_reporting_plugin_shutdown);
+-  grpc_register_plugin(grpc_lb_policy_grpclb_init,
+-                       grpc_lb_policy_grpclb_shutdown);
+   grpc_register_plugin(grpc_lb_policy_pick_first_init,
+                        grpc_lb_policy_pick_first_shutdown);
+   grpc_register_plugin(grpc_lb_policy_round_robin_init,
+diff --git a/test/cpp/util/BUILD b/test/cpp/util/BUILD
+index 33240f6f69..d2e1f67f06 100644
+--- a/test/cpp/util/BUILD
++++ b/test/cpp/util/BUILD
+@@ -29,6 +29,7 @@ package(
+ grpc_cc_binary(
+     name = "testso.so",
+     srcs = [],
++    linkopts = ['-Wl,--no-undefined'],
+     linkshared = 1,
+     deps = ["//:grpc++_unsecure"],
+ )
diff --git a/third_party/nanopb.BUILD b/third_party/nanopb.BUILD
deleted file mode 100644
index d21866911b..0000000000
--- a/third_party/nanopb.BUILD
+++ /dev/null
@@ -1,23 +0,0 @@
-# Description:
-#   Nanopb, a tiny ANSI C protobuf implementation for use on embedded devices.
-
-licenses(["notice"])  # zlib license
-
-exports_files(["LICENSE.txt"])
-
-cc_library(
-    name = "nanopb",
-    srcs = [
-        "pb_common.c",
-        "pb_decode.c",
-        "pb_encode.c",
-    ],
-    hdrs = [
-        "pb.h",
-        "pb_common.h",
-        "pb_decode.h",
-        "pb_encode.h",
-    ],
-    includes = ["."],
-    visibility = ["//visibility:public"],
-)
diff --git a/third_party/pprof.BUILD b/third_party/pprof.BUILD
index edd5209594..8bd5bacaf1 100644
--- a/third_party/pprof.BUILD
+++ b/third_party/pprof.BUILD
@@ -4,15 +4,15 @@ package(
 
 licenses(["notice"])  # MIT
 
-load("@protobuf//:protobuf.bzl", "py_proto_library")
+load("@protobuf_archive//:protobuf.bzl", "py_proto_library")
 
 exports_files(["pprof/LICENSE"])
 
 py_proto_library(
     name = "pprof_proto_py",
     srcs = ["proto/profile.proto"],
-    default_runtime = "@protobuf//:protobuf_python",
-    protoc = "@protobuf//:protoc",
+    default_runtime = "@protobuf_archive//:protobuf_python",
+    protoc = "@protobuf_archive//:protoc",
     srcs_version = "PY2AND3",
-    deps = ["@protobuf//:protobuf_python"],
+    deps = ["@protobuf_archive//:protobuf_python"],
 )
-- 
GitLab


From 599165861e9f8656f80a0162b40575f55fae171a Mon Sep 17 00:00:00 2001
From: KB Sriram <kbsriram@gmail.com>
Date: Thu, 27 Jul 2017 10:53:57 -0700
Subject: [PATCH 1372/1375] Add the Constant operator class (#11559)

Create a custom operator class to create constants in the Graph,
and introduce the Operator marker annotation to identify
operator classes.

Please see #7149 for the master tracking issue.
---
 tensorflow/java/BUILD                         |  15 +-
 .../tensorflow/op/annotation/Operator.java    | 112 ++++++++++++
 .../java/org/tensorflow/op/core/Constant.java | 173 ++++++++++++++++++
 .../org/tensorflow/op/core/ConstantTest.java  | 131 +++++++++++++
 4 files changed, 430 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/annotation/Operator.java
 create mode 100644 tensorflow/java/src/main/java/org/tensorflow/op/core/Constant.java
 create mode 100644 tensorflow/java/src/test/java/org/tensorflow/op/core/ConstantTest.java

diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 9fb4821cb1..64b3767735 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -34,7 +34,7 @@ filegroup(
 
 filegroup(
     name = "java_op_sources",
-    srcs = glob(["src/main/java/org/tensorflow/op/*.java"]),
+    srcs = glob(["src/main/java/org/tensorflow/op/**/*.java"]),
     visibility = [
         "//tensorflow/java:__pkg__",
     ],
@@ -191,6 +191,19 @@ java_test(
     ],
 )
 
+java_test(
+    name = "ConstantTest",
+    size = "small",
+    srcs = ["src/test/java/org/tensorflow/op/core/ConstantTest.java"],
+    javacopts = JAVACOPTS,
+    test_class = "org.tensorflow.op.core.ConstantTest",
+    deps = [
+        ":tensorflow",
+        ":testutil",
+        "@junit",
+    ],
+)
+
 filegroup(
     name = "libtensorflow_jni",
     srcs = select({
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/annotation/Operator.java b/tensorflow/java/src/main/java/org/tensorflow/op/annotation/Operator.java
new file mode 100644
index 0000000000..59476fb43d
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/annotation/Operator.java
@@ -0,0 +1,112 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op.annotation;
+
+import java.lang.annotation.Documented;
+import java.lang.annotation.ElementType;
+import java.lang.annotation.Retention;
+import java.lang.annotation.RetentionPolicy;
+import java.lang.annotation.Target;
+
+/**
+ * Annotation used by classes to make TensorFlow operations conveniently accessible via {@code
+ * org.tensorflow.op.Ops}.
+ *
+ * <p>An annotation processor (TODO: not yet implemented) builds the {@code Ops} class by
+ * aggregating all classes annotated as {@code @Operator}s. Each annotated class <b>must</b> have at
+ * least one public static factory method named {@code create} that accepts a {@link
+ * org.tensorflow.op.Scope} as its first argument. The processor then adds a convenience method in
+ * the {@code Ops} class. For example:
+ *
+ * <pre>{@code
+ * @Operator
+ * public final class MyOp implements Op {
+ *   public static MyOp create(Scope scope, Operand operand) {
+ *     ...
+ *   }
+ * }
+ * }</pre>
+ *
+ * <p>results in a method in the {@code Ops} class
+ *
+ * <pre>{@code
+ * import org.tensorflow.op.Ops;
+ * ...
+ * Ops ops = new Ops(graph);
+ * ...
+ * ops.myOp(operand);
+ * // and has exactly the same effect as calling
+ * // MyOp.create(ops.getScope(), operand);
+ * }</pre>
+ */
+@Documented
+@Target(ElementType.TYPE)
+@Retention(RetentionPolicy.CLASS)
+public @interface Operator {
+  /**
+   * Specify an optional group within the {@code Ops} class.
+   *
+   * <p>By default, an annotation processor will create convenience methods directly in the {@code
+   * Ops} class. An annotated operator may optionally choose to place the method within a group. For
+   * example:
+   *
+   * <pre>{@code
+   * @Operator(group="math")
+   * public final class Add extends PrimitiveOp implements Operand {
+   *   ...
+   * }
+   * }</pre>
+   *
+   * <p>results in the {@code add} method placed within a {@code math} group within the {@code Ops}
+   * class.
+   *
+   * <pre>{@code
+   * ops.math().add(...);
+   * }</pre>
+   *
+   * <p>The group name must be a <a
+   * href="https://docs.oracle.com/javase/specs/jls/se7/html/jls-3.html#jls-3.8">valid Java
+   * identifier</a>.
+   */
+  String group() default "";
+
+  /**
+   * Name for the wrapper method used in the {@code Ops} class.
+   *
+   * <p>By default, a processor derives the method name in the {@code Ops} class from the class name
+   * of the operator. This attribute allow you to provide a different name instead. For example:
+   *
+   * <pre>{@code
+   * @Operator(name="myOperation")
+   * public final class MyRealOperation implements Operand {
+   *   public static MyRealOperation create(...)
+   * }
+   * }</pre>
+   *
+   * <p>results in this method added to the {@code Ops} class
+   *
+   * <pre>{@code
+   * ops.myOperation(...);
+   * // and is the same as calling
+   * // MyRealOperation.create(...)
+   * }</pre>
+   *
+   * <p>The name must be a <a
+   * href="https://docs.oracle.com/javase/specs/jls/se7/html/jls-3.html#jls-3.8">valid Java
+   * identifier</a>.
+   */
+  String name() default "";
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/core/Constant.java b/tensorflow/java/src/main/java/org/tensorflow/op/core/Constant.java
new file mode 100644
index 0000000000..cd7931d3bb
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/op/core/Constant.java
@@ -0,0 +1,173 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op.core;
+
+import java.nio.ByteBuffer;
+import java.nio.DoubleBuffer;
+import java.nio.FloatBuffer;
+import java.nio.IntBuffer;
+import java.nio.LongBuffer;
+import org.tensorflow.DataType;
+import org.tensorflow.Operand;
+import org.tensorflow.Operation;
+import org.tensorflow.Output;
+import org.tensorflow.Tensor;
+import org.tensorflow.op.PrimitiveOp;
+import org.tensorflow.op.Scope;
+import org.tensorflow.op.annotation.Operator;
+
+/** An operator producing a constant value. */
+@Operator
+public final class Constant extends PrimitiveOp implements Operand {
+  /**
+   * Create a constant from a Java object.
+   *
+   * <p>The argument {@code object} is first converted into a Tensor using {@link
+   * org.tensorflow.Tensor#create(Object)}, so only Objects supported by this method must be
+   * provided. For example:
+   *
+   * <pre>{@code
+   * Constant.create(scope, 7); // returns a constant scalar tensor 7
+   * }</pre>
+   *
+   * @param scope is a scope used to add the underlying operation.
+   * @param object a Java object representing the constant.
+   * @see org.tensorflow.Tensor#create(Object) Tensor.create
+   */
+  public static Constant create(Scope scope, Object object) {
+    try (Tensor value = Tensor.create(object)) {
+      return createWithTensor(scope, value);
+    }
+  }
+
+  /**
+   * Create a {@link DataType#INT32} constant with data from the given buffer.
+   *
+   * <p>Creates a constant with the given shape by copying elements from the buffer (starting from
+   * its current position) into the tensor. For example, if {@code shape = {2,3} } (which represents
+   * a 2x3 matrix) then the buffer must have 6 elements remaining, which will be consumed by this
+   * method.
+   *
+   * @param scope is a scope used to add the underlying operation.
+   * @param shape the tensor shape.
+   * @param data a buffer containing the tensor data.
+   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
+   */
+  public static Constant create(Scope scope, long[] shape, IntBuffer data) {
+    try (Tensor value = Tensor.create(shape, data)) {
+      return createWithTensor(scope, value);
+    }
+  }
+
+  /**
+   * Create a {@link DataType#FLOAT} constant with data from the given buffer.
+   *
+   * <p>Creates a constant with the given shape by copying elements from the buffer (starting from
+   * its current position) into the tensor. For example, if {@code shape = {2,3} } (which represents
+   * a 2x3 matrix) then the buffer must have 6 elements remaining, which will be consumed by this
+   * method.
+   *
+   * @param scope is a scope used to add the underlying operation.
+   * @param shape the tensor shape.
+   * @param data a buffer containing the tensor data.
+   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
+   */
+  public static Constant create(Scope scope, long[] shape, FloatBuffer data) {
+    try (Tensor value = Tensor.create(shape, data)) {
+      return createWithTensor(scope, value);
+    }
+  }
+
+  /**
+   * Create a {@link DataType#DOUBLE} constant with data from the given buffer.
+   *
+   * <p>Creates a constant with the given shape by copying elements from the buffer (starting from
+   * its current position) into the tensor. For example, if {@code shape = {2,3} } (which represents
+   * a 2x3 matrix) then the buffer must have 6 elements remaining, which will be consumed by this
+   * method.
+   *
+   * @param scope is a scope used to add the underlying operation.
+   * @param shape the tensor shape.
+   * @param data a buffer containing the tensor data.
+   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
+   */
+  public static Constant create(Scope scope, long[] shape, DoubleBuffer data) {
+    try (Tensor value = Tensor.create(shape, data)) {
+      return createWithTensor(scope, value);
+    }
+  }
+
+  /**
+   * Create a {@link DataType#INT64} constant with data from the given buffer.
+   *
+   * <p>Creates a constant with the given shape by copying elements from the buffer (starting from
+   * its current position) into the tensor. For example, if {@code shape = {2,3} } (which represents
+   * a 2x3 matrix) then the buffer must have 6 elements remaining, which will be consumed by this
+   * method.
+   *
+   * @param scope is a scope used to add the underlying operation.
+   * @param shape the tensor shape.
+   * @param data a buffer containing the tensor data.
+   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
+   */
+  public static Constant create(Scope scope, long[] shape, LongBuffer data) {
+    try (Tensor value = Tensor.create(shape, data)) {
+      return createWithTensor(scope, value);
+    }
+  }
+
+  /**
+   * Create a constant with data from the given buffer.
+   *
+   * <p>Creates a Constant with the provided shape of any type where the constant data has been
+   * encoded into {@code data} as per the specification of the TensorFlow <a
+   * href="https://www.tensorflow.org/code/tensorflow/c/c_api.h">C API</a>.
+   *
+   * @param scope is a scope used to add the underlying operation.
+   * @param dataType the tensor datatype.
+   * @param shape the tensor shape.
+   * @param data a buffer containing the tensor data.
+   * @throws IllegalArgumentException If the tensor datatype or shape is not compatible with the
+   *     buffer
+   */
+  public static Constant create(Scope scope, DataType dataType, long[] shape, ByteBuffer data) {
+    try (Tensor value = Tensor.create(dataType, shape, data)) {
+      return createWithTensor(scope, value);
+    }
+  }
+
+  private static Constant createWithTensor(Scope scope, Tensor value) {
+    return new Constant(
+        scope
+            .graph()
+            .opBuilder("Const", scope.makeOpName("Const"))
+            .setAttr("value", value)
+            .setAttr("dtype", value.dataType())
+            .build());
+  }
+
+  @Override
+  public Output asOutput() {
+    return output;
+  }
+
+  private Constant(Operation operation) {
+    super(operation);
+    output = operation.output(0);
+  }
+
+  private final Output output;
+}
diff --git a/tensorflow/java/src/test/java/org/tensorflow/op/core/ConstantTest.java b/tensorflow/java/src/test/java/org/tensorflow/op/core/ConstantTest.java
new file mode 100644
index 0000000000..ec23792485
--- /dev/null
+++ b/tensorflow/java/src/test/java/org/tensorflow/op/core/ConstantTest.java
@@ -0,0 +1,131 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow.op.core;
+
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertTrue;
+
+import java.io.ByteArrayOutputStream;
+import java.io.DataOutputStream;
+import java.io.IOException;
+import java.nio.ByteBuffer;
+import java.nio.DoubleBuffer;
+import java.nio.FloatBuffer;
+import java.nio.IntBuffer;
+import java.nio.LongBuffer;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.tensorflow.DataType;
+import org.tensorflow.Graph;
+import org.tensorflow.Session;
+import org.tensorflow.Tensor;
+import org.tensorflow.op.Scope;
+
+@RunWith(JUnit4.class)
+public class ConstantTest {
+  private static final float EPSILON = 1e-7f;
+
+  @Test
+  public void createIntBuffer() {
+    int[] ints = {1, 2, 3, 4};
+    long[] shape = {4};
+
+    try (Graph g = new Graph();
+        Session sess = new Session(g)) {
+      Scope scope = new Scope(g);
+      Constant op = Constant.create(scope, shape, IntBuffer.wrap(ints));
+      Tensor result = sess.runner().fetch(op.asOutput()).run().get(0);
+      int[] actual = new int[ints.length];
+      assertArrayEquals(ints, result.copyTo(actual));
+    }
+  }
+
+  @Test
+  public void createFloatBuffer() {
+    float[] floats = {1, 2, 3, 4};
+    long[] shape = {4};
+
+    try (Graph g = new Graph();
+        Session sess = new Session(g)) {
+      Scope scope = new Scope(g);
+      Constant op = Constant.create(scope, shape, FloatBuffer.wrap(floats));
+      Tensor result = sess.runner().fetch(op.asOutput()).run().get(0);
+      float[] actual = new float[floats.length];
+      assertArrayEquals(floats, result.copyTo(actual), EPSILON);
+    }
+  }
+
+  @Test
+  public void createDoubleBuffer() {
+    double[] doubles = {1, 2, 3, 4};
+    long[] shape = {4};
+
+    try (Graph g = new Graph();
+        Session sess = new Session(g)) {
+      Scope scope = new Scope(g);
+      Constant op = Constant.create(scope, shape, DoubleBuffer.wrap(doubles));
+      Tensor result = sess.runner().fetch(op.asOutput()).run().get(0);
+      double[] actual = new double[doubles.length];
+      assertArrayEquals(doubles, result.copyTo(actual), EPSILON);
+    }
+  }
+
+  @Test
+  public void createLongBuffer() {
+    long[] longs = {1, 2, 3, 4};
+    long[] shape = {4};
+
+    try (Graph g = new Graph();
+        Session sess = new Session(g)) {
+      Scope scope = new Scope(g);
+      Constant op = Constant.create(scope, shape, LongBuffer.wrap(longs));
+      Tensor result = sess.runner().fetch(op.asOutput()).run().get(0);
+      long[] actual = new long[longs.length];
+      assertArrayEquals(longs, result.copyTo(actual));
+    }
+  }
+
+  @Test
+  public void createStringBuffer() throws IOException {
+
+    byte[] data = {(byte) 1, (byte) 2, (byte) 3, (byte) 4};
+    long[] shape = {};
+
+    // byte arrays (DataType.STRING in Tensorflow) are encoded as an offset in the data buffer,
+    // followed by a varint encoded size, followed by the data.
+    ByteArrayOutputStream baout = new ByteArrayOutputStream();
+    DataOutputStream out = new DataOutputStream(baout);
+    // Offset in array.
+    out.writeLong(0L);
+    // Varint encoded length of buffer.
+    // For any number < 0x80, the varint encoding is simply the number itself.
+    // https://developers.google.com/protocol-buffers/docs/encoding#varints
+    assertTrue(data.length < 0x80);
+    out.write(data.length);
+    out.write(data);
+    out.close();
+    byte[] content = baout.toByteArray();
+
+    try (Graph g = new Graph();
+        Session sess = new Session(g)) {
+      Scope scope = new Scope(g);
+      Constant op = Constant.create(scope, DataType.STRING, shape, ByteBuffer.wrap(content));
+      Tensor result = sess.runner().fetch(op.asOutput()).run().get(0);
+      assertArrayEquals(data, result.bytesValue());
+    }
+  }
+}
-- 
GitLab


From f40189d262bb41b7b85a95c3ceeac6f269ae9a0a Mon Sep 17 00:00:00 2001
From: qjivy <ji.qiu@spreadtrum.com>
Date: Fri, 28 Jul 2017 01:54:43 +0800
Subject: [PATCH 1373/1375] PR again: Enable building label_image with
 jpeg/gif/png decoder for Android.  (#11475)

* Enable building label_image with jpeg/gif/png decoder for Android.
Add dependency "android_tesnorflow_image_op" to label_image, which
is not overlapped with android_tensorflow_kernels.

* Running buildifier to reformat the BUILD files for
sanity check.
---
 tensorflow/core/BUILD                 | 80 +++++++++++++++++++++++++++
 tensorflow/core/kernels/BUILD         | 26 +++++++++
 tensorflow/core/platform/gif.h        |  2 +-
 tensorflow/core/platform/jpeg.h       |  2 +-
 tensorflow/core/platform/png.h        |  2 +-
 tensorflow/examples/label_image/BUILD |  3 +
 third_party/gif.BUILD                 | 16 ++++++
 7 files changed, 128 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index deb2d99feb..11df834c75 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1429,6 +1429,86 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "android_jpeg_internal",
+    srcs = [
+        "lib/jpeg/jpeg_handle.cc",
+        "lib/jpeg/jpeg_mem.cc",
+        "platform/jpeg.h",
+    ],
+    hdrs = [
+        "lib/core/stringpiece.h",
+        "lib/jpeg/jpeg_handle.h",
+        "lib/jpeg/jpeg_mem.h",
+        "platform/default/dynamic_annotations.h",
+        "platform/default/integral_types.h",
+        "platform/default/logging.h",
+        "platform/dynamic_annotations.h",
+        "platform/logging.h",
+        "platform/macros.h",
+        "platform/mem.h",
+        "platform/platform.h",
+        "platform/types.h",
+    ],
+    copts = tf_copts(),
+    linkopts = ["-ldl"],
+    deps = [
+        "//tensorflow/core/platform/default/build_config:jpeg",
+    ],
+)
+
+cc_library(
+    name = "android_gif_internal",
+    srcs = [
+        "lib/gif/gif_io.cc",
+        "platform/gif.h",
+    ],
+    hdrs = [
+        "lib/core/stringpiece.h",
+        "lib/gif/gif_io.h",
+        "lib/gtl/cleanup.h",
+        "platform/default/dynamic_annotations.h",
+        "platform/default/integral_types.h",
+        "platform/default/logging.h",
+        "platform/dynamic_annotations.h",
+        "platform/logging.h",
+        "platform/macros.h",
+        "platform/mem.h",
+        "platform/platform.h",
+        "platform/types.h",
+    ],
+    copts = tf_copts(),
+    linkopts = ["-ldl"],
+    deps = [
+        "//tensorflow/core/platform/default/build_config:gif",
+    ],
+)
+
+cc_library(
+    name = "android_png_internal",
+    srcs = [
+        "lib/png/png_io.cc",
+        "platform/png.h",
+    ],
+    hdrs = [
+        "lib/core/casts.h",
+        "lib/core/stringpiece.h",
+        "lib/png/png_io.h",
+        "platform/cpu_info.h",
+        "platform/default/integral_types.h",
+        "platform/default/logging.h",
+        "platform/logging.h",
+        "platform/macros.h",
+        "platform/platform.h",
+        "platform/types.h",
+    ],
+    copts = tf_copts(),
+    linkopts = ["-ldl"],
+    deps = [
+        "@png_archive//:png",
+    ],
+)
+
 proto_text_hdrs_and_srcs = tf_generate_proto_text_sources(
     name = "proto_text_srcs_all",
     srcs = CORE_PROTO_SRCS,
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 692e1c37f0..f45bb72c38 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -4536,6 +4536,32 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_library(
+    name = "android_tensorflow_image_op",
+    srcs = [
+        "decode_image_op.cc",
+    ],
+    copts = tf_copts(),
+    linkopts = select({
+        "//tensorflow:android": [
+            "-ldl",
+        ],
+        "//conditions:default": [],
+    }),
+    tags = [
+        "manual",
+        "notap",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/core:android_gif_internal",
+        "//tensorflow/core:android_jpeg_internal",
+        "//tensorflow/core:android_png_internal",
+        "//tensorflow/core:android_tensorflow_lib_lite",
+    ],
+    alwayslink = 1,
+)
+
 #   Quantization-specific OpKernels
 
 tf_kernel_library(
diff --git a/tensorflow/core/platform/gif.h b/tensorflow/core/platform/gif.h
index 8a719a8cf1..9c72d34ff5 100644
--- a/tensorflow/core/platform/gif.h
+++ b/tensorflow/core/platform/gif.h
@@ -20,7 +20,7 @@ limitations under the License.
 
 #if defined(PLATFORM_GOOGLE)
 #include "tensorflow/core/platform/google/build_config/gif.h"
-#elif (defined(PLATFORM_POSIX) && !defined(IS_MOBILE_PLATFORM)) || defined(PLATFORM_WINDOWS)
+#elif defined(PLATFORM_POSIX)|| defined(PLATFORM_WINDOWS) ||defined(PLATFORM_POSIX_ANDROID)
 #include <gif_lib.h>
 #else
 #error Define the appropriate PLATFORM_<foo> macro for this platform
diff --git a/tensorflow/core/platform/jpeg.h b/tensorflow/core/platform/jpeg.h
index f5b4deed55..edbcbd960a 100644
--- a/tensorflow/core/platform/jpeg.h
+++ b/tensorflow/core/platform/jpeg.h
@@ -20,7 +20,7 @@ limitations under the License.
 
 #if defined(PLATFORM_GOOGLE)
 #include "tensorflow/core/platform/google/build_config/jpeg.h"
-#elif (defined(PLATFORM_POSIX) && !defined(IS_MOBILE_PLATFORM)) || defined(PLATFORM_WINDOWS)
+#elif defined(PLATFORM_POSIX)|| defined(PLATFORM_WINDOWS) ||defined(PLATFORM_POSIX_ANDROID)
 #include <stddef.h>
 #include <stdio.h>
 #include <stdlib.h>
diff --git a/tensorflow/core/platform/png.h b/tensorflow/core/platform/png.h
index 4500d9abe2..5b0203c343 100644
--- a/tensorflow/core/platform/png.h
+++ b/tensorflow/core/platform/png.h
@@ -20,7 +20,7 @@ limitations under the License.
 
 #if defined(PLATFORM_GOOGLE)
 #include "tensorflow/core/platform/google/build_config/png.h"
-#elif (defined(PLATFORM_POSIX) && !defined(IS_MOBILE_PLATFORM)) || defined(PLATFORM_WINDOWS)
+#elif defined(PLATFORM_POSIX)|| defined(PLATFORM_WINDOWS) ||defined(PLATFORM_POSIX_ANDROID)
 #include <png.h>
 #else
 #error Define the appropriate PLATFORM_<foo> macro for this platform
diff --git a/tensorflow/examples/label_image/BUILD b/tensorflow/examples/label_image/BUILD
index d677e58ac3..ddbcf68df8 100644
--- a/tensorflow/examples/label_image/BUILD
+++ b/tensorflow/examples/label_image/BUILD
@@ -31,6 +31,9 @@ cc_binary(
             # Jpg, gif, and png related code won't be included
             "//tensorflow/cc:cc_ops",
             "//tensorflow/core:android_tensorflow_lib",
+            # cc:android_tensorflow_image_op is for including jpeg/gif/png
+            # decoder to enable real-image evaluation on Android
+            "//tensorflow/core/kernels:android_tensorflow_image_op",
         ],
         "//conditions:default": [
             "//tensorflow/cc:cc_ops",
diff --git a/third_party/gif.BUILD b/third_party/gif.BUILD
index ad6821af3c..21c5c11a44 100644
--- a/third_party/gif.BUILD
+++ b/third_party/gif.BUILD
@@ -20,6 +20,15 @@ cc_library(
         "lib/quantize.c",
     ],
     hdrs = ["lib/gif_lib.h"],
+    defines = select({
+        #"@%ws%//tensorflow:android": [
+        ":android": [
+            "S_IREAD=S_IRUSR",
+            "S_IWRITE=S_IWUSR",
+            "S_IEXEC=S_IXUSR",
+        ],
+        "//conditions:default": [],
+    }),  
     includes = ["lib/."],
     visibility = ["//visibility:public"],
     deps = select({
@@ -54,3 +63,10 @@ config_setting(
         "cpu": "x64_windows",
     },
 )
+
+config_setting(
+    name = "android",
+    values = {"crosstool_top": "//external:android/crosstool"},
+)
+
+
-- 
GitLab


From a4a3a3335bd273e6224d16a934b59531ef239893 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Thu, 27 Jul 2017 20:09:24 +0200
Subject: [PATCH 1374/1375] Fix ./configure on Windows (#11775)

* Fix ./configure on Windows

* Disable bitwise_ops_test on Windows
---
 configure               | 4 ++--
 configure.py            | 4 ++--
 tensorflow/python/BUILD | 1 +
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/configure b/configure
index c6df6992d9..9c21d2b03a 100755
--- a/configure
+++ b/configure
@@ -8,7 +8,7 @@ if [ -z "$PYTHON_BIN_PATH" ]; then
 fi
 
 # Set all env variables
-$PYTHON_BIN_PATH configure.py
+"$PYTHON_BIN_PATH" configure.py
 
+echo "Configuration finished"
 
-echo "Configuration finished"
\ No newline at end of file
diff --git a/configure.py b/configure.py
index e39bd53efb..edb0a47ee6 100644
--- a/configure.py
+++ b/configure.py
@@ -240,7 +240,7 @@ def run_gen_git_source(environ_cp):
   Args:
     environ_cp: copy of the os.environ.
   """
-  cmd = '%s tensorflow/tools/git/gen_git_source.py --configure %s' % (
+  cmd = '"%s" tensorflow/tools/git/gen_git_source.py --configure %s' % (
       environ_cp.get('PYTHON_BIN_PATH'), os.getcwd())
   os.system(cmd)
 
@@ -379,7 +379,7 @@ def check_bazel_version(min_version):
     min_version: string for minimum bazel version.
   """
   try:
-    curr_version = run_shell('bazel version')
+    curr_version = run_shell('bazel --batch version')
   except subprocess.CalledProcessError:
     print('Cannot find bazel. Please install bazel.')
     sys.exit(0)
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 2c216300d1..adbe53a3e3 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2177,6 +2177,7 @@ cuda_py_test(
         ":dtypes",
         ":framework_test_lib",
     ],
+    tags = ["no_windows"],
 )
 
 cuda_py_test(
-- 
GitLab


From 22241cd42d1fefdec88edbbf16c1f335052ee93b Mon Sep 17 00:00:00 2001
From: joetoth <joetoth@gmail.com>
Date: Thu, 27 Jul 2017 19:13:54 -0400
Subject: [PATCH 1375/1375] External leveldb link changed (#11833)

table_format.txt was renamed to table_format.md
---
 tensorflow/core/lib/io/table_format.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/lib/io/table_format.txt b/tensorflow/core/lib/io/table_format.txt
index e37c627f5b..dd846e0129 100644
--- a/tensorflow/core/lib/io/table_format.txt
+++ b/tensorflow/core/lib/io/table_format.txt
@@ -5,4 +5,4 @@ The table format is similar to the table format for the LevelDB
 open source key/value store, with the exception that our tables
 do not support "filter" meta blocks (Bloom Filters).  See:
 
-https://github.com/google/leveldb/blob/master/doc/table_format.txt
+https://github.com/google/leveldb/blob/master/doc/table_format.md
-- 
GitLab